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Packages produced are intended to be used with AnnotationDbi. 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Package: r-bioc-annotatr Architecture: all Version: 1.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4132 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-bioc-annotationhub, r-cran-dplyr, r-bioc-genomicfeatures, r-bioc-genomicranges, r-bioc-seqinfo, r-cran-ggplot2, r-bioc-iranges, r-cran-readr, r-bioc-regioner, r-cran-reshape2, r-cran-rlang, r-bioc-rtracklayer, r-bioc-s4vectors Suggests: r-bioc-genomeinfodb, r-bioc-biocstyle, r-cran-devtools, r-cran-knitr, r-bioc-org.dm.eg.db, r-bioc-org.gg.eg.db, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-bioc-org.rn.eg.db, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-txdb.dmelanogaster.ucsc.dm6.ensgene, r-bioc-txdb.drerio.ucsc.danrer10.refgene, r-bioc-txdb.drerio.ucsc.danrer11.refgene, r-bioc-txdb.ggallus.ucsc.galgal5.refgene, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-txdb.mmusculus.ucsc.mm9.knowngene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-bioc-txdb.mmusculus.ucsc.mm39.knowngene, r-bioc-txdb.rnorvegicus.ucsc.rn4.ensgene, r-bioc-txdb.rnorvegicus.ucsc.rn5.refgene, r-bioc-txdb.rnorvegicus.ucsc.rn6.refgene, r-bioc-txdb.rnorvegicus.ucsc.rn7.refgene Filename: pool/dists/noble/main/r-bioc-annotatr_1.38.0-1.ca2404.1_all.deb Size: 2975872 MD5sum: f33c2b40ae4eacbc58bc420cd87ea301 SHA1: aaf2caacddf9496e505f2d1c67399b98b7e75601 SHA256: 4222f244b927970c471902feb89d339d4d4667972263a82075a2e34e17e3145d SHA512: 60ae65fd1adc58400494bc877d9e5c6abba29bec3f78fd121b8973cea5d1bb8ce0c14a1ae4cab8a0fa1413bc287cdd4d093fe6c3635c203663af87042bfc2c6a Homepage: https://cran.r-project.org/package=annotatr Description: Bioc Package 'annotatr' (Annotation of Genomic Regions to Genomic Annotations) Given a set of genomic sites/regions (e.g. ChIP-seq peaks, CpGs, differentially methylated CpGs or regions, SNPs, etc.) it is often of interest to investigate the intersecting genomic annotations. Such annotations include those relating to gene models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs (CpG islands, CpG shores, CpG shelves), or regulatory sequences such as enhancers. The annotatr package provides an easy way to summarize and visualize the intersection of genomic sites/regions with genomic annotations. Package: r-bioc-anvil Architecture: all Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4238 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-bioc-anvilbase, r-cran-futile.logger, r-bioc-gcptools, r-cran-jsonlite, r-cran-httr, r-cran-digest, r-cran-keyring, r-cran-rapiclient, r-cran-yaml, r-cran-tibble, r-cran-shiny, r-cran-dt, r-cran-miniui, r-cran-htmltools, r-bioc-biocbaseutils Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-readr, r-bioc-biocstyle, r-cran-devtools, r-bioc-anvilaz, r-bioc-anvilgcp, r-cran-lifecycle Filename: pool/dists/noble/main/r-bioc-anvil_1.24.0-1.ca2404.1_all.deb Size: 551006 MD5sum: e0b3f4c90af3d730d71425488d378a52 SHA1: 07fa50d8db84e298d7ae4eaeafa5d451570b55aa SHA256: e7d2af82c1875e2494ac54904f0d341501e88b81b29851da563c786acef7ada0 SHA512: e743914c3a1e96ef241c7172148d3672c593fd4e457a5b7f75822b13c27c1b61dd5eb3ac8ed61b81ca98872af2de91f6af81e901071d8725a28a25b33b46ec32 Homepage: https://cran.r-project.org/package=AnVIL Description: Bioc Package 'AnVIL' (Bioconductor on the AnVIL compute environment) The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The AnVIL package provides programatic access to the Dockstore, Leonardo, Rawls, TDR, and Terra RESTful programming interfaces. For platform-specific user-level functionality, see either the AnVILGCP or AnVILAz package. 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Packages that use either GCP or Azure in AnVIL are built on top of AnVILBase. Extension packages will provide methods for interacting with other cloud providers. 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These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. 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Package: r-bioc-arrayqualitymetrics Architecture: all Version: 3.68.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1653 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-affy, r-bioc-affyplm, r-bioc-beadarray, r-bioc-biobase, r-bioc-genefilter, r-cran-gridsvg, r-cran-hmisc, r-cran-hwriter, r-cran-jsonlite, r-cran-lattice, r-cran-latticeextra, r-bioc-limma, r-cran-rcolorbrewer, r-cran-setrng, r-bioc-vsn, r-cran-xml, r-cran-svglite Suggests: r-bioc-allmll, r-bioc-ccl4, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-arrayqualitymetrics_3.68.0-1.ca2404.1_all.deb Size: 471004 MD5sum: 2622486121e487d4f0e4783e3c5f8443 SHA1: 4c1b9a858b57739876609103bafc09972bd1ca53 SHA256: 05451900ab6858e123ec9636b16d0d03e4de04e385017b4cf59b3722a0de24fb SHA512: c12be405841da1f432bea3ea0f9932363260f5a58d33cb8f54753cae26f344a4fcb2c275c12a2127d1115c677b30e9501292a3c3be68316930d6ca5acef8bb99 Homepage: https://cran.r-project.org/package=arrayQualityMetrics Description: Bioc Package 'arrayQualityMetrics' (Quality metrics report for microarray data sets) This package generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). One and two color array platforms are supported. Package: r-bioc-assorthead Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13170 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-assorthead_1.6.1-1.ca2404.1_all.deb Size: 1592436 MD5sum: 197c93751003994d7bec67dac5dba910 SHA1: c30d0c7dc3561651f937d47bf55e9b77e2ae5c22 SHA256: e4ef1e5a72c9b1155950194b9bb196bcbe44603ea7de8a794d3f9b76a2cc38c7 SHA512: ef79097444770bfb3ffc82532d9424b8c0c906304fcb83d2b3c8e82fd9397c33fadc658cd2111b5c3a908513e0f163a1ae2c3c07ebe978737fc42b0b0517e6e8 Homepage: https://cran.r-project.org/package=assorthead Description: Bioc Package 'assorthead' (Assorted Header-Only C++ Libraries) Vendors an assortment of useful header-only C++ libraries. Bioconductor packages can use these libraries in their own C++ code by LinkingTo this package without introducing any additional dependencies. The use of a central repository avoids duplicate vendoring of libraries across multiple R packages, and enables better coordination of version updates across cohorts of interdependent C++ libraries. Package: r-bioc-atacseqqc Architecture: all Version: 1.36.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 16468 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-bsgenome, r-bioc-biostrings, r-bioc-chippeakanno, r-bioc-iranges, r-bioc-genomicranges, r-bioc-genomicalignments, r-bioc-genomeinfodb, r-bioc-genomicscores, r-bioc-limma, r-bioc-rsamtools, r-cran-randomforest, r-bioc-rtracklayer, r-bioc-motifstack, r-cran-preseqr, r-cran-kernsmooth, r-bioc-edger, r-bioc-biocparallel Suggests: r-bioc-biocstyle, r-cran-knitr, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-phastcons100way.ucsc.hg19, r-bioc-motifdb, r-bioc-trackviewer, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-atacseqqc_1.36.0-1.ca2404.1_all.deb Size: 14579418 MD5sum: 185e5751d840a572a5816b090b9ca9d2 SHA1: ad48cc44ab7d9b33443833806b924dffe6360881 SHA256: 64d4e0cae558e4e61f0d59e640f69288790b183502a461bf4b367a4160cad7ff SHA512: 03dca39c70e429785dca78df4d4a4cc150ad9bad1de9d197aa9882ffb7caae1c6af76ffbd591a91e1a1efce5454f6b5164443d5a0c43546866054d73f95ed4b5 Homepage: https://cran.r-project.org/package=ATACseqQC Description: Bioc Package 'ATACseqQC' (ATAC-seq Quality Control) ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the other methods, ATAC-seq requires less amount of the biological samples and time to process. In the process of analyzing several ATAC-seq dataset produced in our labs, we learned some of the unique aspects of the quality assessment for ATAC-seq data.To help users to quickly assess whether their ATAC-seq experiment is successful, we developed ATACseqQC package partially following the guideline published in Nature Method 2013 (Greenleaf et al.), including diagnostic plot of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints. Package: r-bioc-aucell Architecture: all Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3565 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-cran-data.table, r-bioc-gseabase, r-cran-matrix, r-cran-mixtools, r-cran-r.utils, r-bioc-summarizedexperiment, r-bioc-biocgenerics Suggests: r-bioc-biobase, r-bioc-biocstyle, r-cran-dosnow, r-cran-dynamictreecut, r-cran-dt, r-bioc-geoquery, r-cran-knitr, r-cran-nmf, r-cran-plyr, r-cran-r2html, r-cran-rmarkdown, r-cran-reshape2, r-cran-plotly, r-cran-rtsne, r-cran-testthat, r-cran-zoo Filename: pool/dists/noble/main/r-bioc-aucell_1.34.0-1.ca2404.1_all.deb Size: 2271340 MD5sum: 1ab7d19fcbf869b08b5cc0c3b91cdaed SHA1: f1f7fa01640ae590cb0147f9fb6fae670e40e0d6 SHA256: 18a3ba0a1f6a43cfb15f5bb9299a13438697381a0f3bc134af0d551d45f942d4 SHA512: 71448081535b0a6a70b6b3f2c6a60c820b377b7d89b8a0c2f0a05af1f64e1f4a458cc9c6895a5a30154b6e19a2b14076648226fde540d5a8b27960ae1c9d7ef9 Homepage: https://cran.r-project.org/package=AUCell Description: Bioc Package 'AUCell' (AUCell: Analysis of 'gene set' activity in single-cell RNA-seqdata (e.g. identify cells with specific gene signatures)) AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed. Package: r-bioc-ballgown Architecture: all Version: 2.43.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4592 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-cran-rcolorbrewer, r-bioc-sva, r-bioc-limma, r-bioc-rtracklayer, r-bioc-biobase, r-bioc-seqinfo Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-bioc-ballgown_2.43.0-1.ca2404.1_all.deb Size: 3000152 MD5sum: f23330d506bce03ab0adc0ed2b321095 SHA1: db47e9f83ca48c1da7b27d425669bd2177504f10 SHA256: bad244e2d2db3fc904bdaf0fc65cffa52dd1506e501a040c16aff7cced7f12ff SHA512: a055ac9664a854ca174593e1194c455fcfffe30acf670d05dfe73f93e3fcf0e2f163e9251cd835df6e11898db5fce26a3067099d078a62946f75d6cfa39bcb79 Homepage: https://cran.r-project.org/package=ballgown Description: Bioc Package 'ballgown' (Flexible, isoform-level differential expression analysis) Tools for statistical analysis of assembled transcriptomes, including flexible differential expression analysis, visualization of transcript structures, and matching of assembled transcripts to annotation. Package: r-bioc-basilisk.utils Architecture: all Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-dir.expiry Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-basilisk.utils_1.24.0-1.ca2404.1_all.deb Size: 246608 MD5sum: a28be831c11d3c492666e321e391ebb3 SHA1: 719e14549bf3e39f3c3f318151d91215ebc65538 SHA256: 49aed75bac4d8673d6c6978bc625e274de351b0c301c3557633a05eb704e3132 SHA512: 97ff81eeda2c03b02e04fedf4edbe48110d9ca94d5b25315c5a720e3bb7527bc7c0a6b32ecb00e99bcf65b3dd0995aa743dc5d5234bf349c2c0e6ecd8012f51f Homepage: https://cran.r-project.org/package=basilisk.utils Description: Bioc Package 'basilisk.utils' (Centralized Conda Installation for Bioconductor Packages) Provides a centralized conda installation for use by other Bioconductor packages. If conda is not already available on the system, it is downloaded and installed from the Miniforge project; otherwise, no action is performed. Historically, this package was used to provide a Python installation for basilisk, hence the name. 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Package: r-bioc-bumphunter Architecture: all Version: 1.54.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4419 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-cran-foreach, r-cran-iterators, r-cran-locfit, r-cran-matrixstats, r-bioc-limma, r-cran-dorng, r-bioc-biocgenerics, r-bioc-genomicfeatures, r-bioc-annotationdbi Suggests: r-cran-testthat, r-cran-runit, r-cran-doparallel, r-bioc-genomeinfodb, r-bioc-txdbmaker, r-bioc-org.hs.eg.db, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene Filename: pool/dists/noble/main/r-bioc-bumphunter_1.54.0-1.ca2404.1_all.deb Size: 4303472 MD5sum: 76af90322624421f8497eb7b989e704e SHA1: 7dd91cd4aa65056c6b41f69feee6b099b64af55d SHA256: 7f65e9bc6558a2446eef08f5f08453b7cebfa94592fc40bb457406bdb7f5f1e1 SHA512: 058b46f325c066306ffc2e770f0d63cd5237bcb545ce7d6ce8d716ea10492d00d52aa242bd7ff06c97b6072ffbb7ad98a443befa08d16c80711ef07649540137 Homepage: https://cran.r-project.org/package=bumphunter Description: Bioc Package 'bumphunter' (Bump Hunter) Tools for finding bumps in genomic data Package: r-bioc-bumpymatrix Architecture: all Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1922 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-s4vectors, r-bioc-iranges Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-bumpymatrix_1.20.0-1.ca2404.1_all.deb Size: 877646 MD5sum: 9921d89e7524293351f22ea7879cd877 SHA1: b8cd01186616bc1398c31aa70606fae313d653fe SHA256: e3edb39df2bbc6004b03735bc60cb11cc4c34a44e2457f6b08e4fe992e61ffbf SHA512: d7953d81e91fbc6cfc8e66fc2c536b475b9e54f344ecbb00530bdad3332802cd3598e33020597017859becdecd6ebc511176fa41968fa1457b8412a1022f61b1 Homepage: https://cran.r-project.org/package=BumpyMatrix Description: Bioc Package 'BumpyMatrix' (Bumpy Matrix of Non-Scalar Objects) Implements the BumpyMatrix class and several subclasses for holding non-scalar objects in each entry of the matrix. This is akin to a ragged array but the raggedness is in the third dimension, much like a bumpy surface - hence the name. Of particular interest is the BumpyDataFrameMatrix, where each entry is a Bioconductor data frame. This allows us to naturally represent multivariate data in a format that is compatible with two-dimensional containers like the SummarizedExperiment and MultiAssayExperiment objects. Package: r-bioc-catalyst Architecture: all Version: 1.36.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15796 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-circlize, r-bioc-complexheatmap, r-bioc-consensusclusterplus, r-cran-cowplot, r-cran-dplyr, r-cran-drc, r-bioc-flowcore, r-bioc-flowsom, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggridges, r-cran-gridextra, r-cran-matrix, r-cran-matrixstats, r-cran-nnls, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rtsne, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-cran-scales, r-bioc-scater Suggests: r-bioc-biocstyle, r-bioc-diffcyt, r-bioc-flowworkspace, r-bioc-ggcyto, r-cran-knitr, r-bioc-opencyto, r-cran-rmarkdown, r-cran-testthat, r-cran-uwot Filename: pool/dists/noble/main/r-bioc-catalyst_1.36.0-1.ca2404.1_all.deb Size: 12187944 MD5sum: 84b072b51862ec3702a5142a5eb40969 SHA1: e3fbd1e391a4e0c493d93a18f02b1cb5bd25be14 SHA256: 6c049ee18c2324ab71e06fdcaa84ad135514e5ec7b302011a69ea1a8d4e9a985 SHA512: 6c2399f0c9b394d3ade195a987c5c775079358346b7fe34d0d3868d7391a7e40b3b8c3e857e65c30058ccb787c96efddcfbbb690195febeb71d43ea62945223b Homepage: https://cran.r-project.org/package=CATALYST Description: Bioc Package 'CATALYST' (Cytometry dATa anALYSis Tools) CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively. Package: r-bioc-category Architecture: all Version: 2.78.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1982 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-annotationdbi, r-bioc-biobase, r-cran-matrix, r-bioc-graph, r-bioc-rbgl, r-bioc-gseabase, r-bioc-genefilter, r-bioc-annotate, r-cran-dbi Suggests: r-bioc-ebarrays, r-bioc-all, r-bioc-rgraphviz, r-cran-rcolorbrewer, r-cran-xtable, r-bioc-hgu95av2.db, r-bioc-keggrest, r-bioc-karyoploter, r-bioc-geneplotter, r-bioc-limma, r-cran-lattice, r-cran-runit, r-bioc-org.sc.sgd.db, r-bioc-gostats, r-bioc-go.db Filename: pool/dists/noble/main/r-bioc-category_2.78.0-1.ca2404.1_all.deb Size: 1357616 MD5sum: 638a841f5e7fecde79e8f90b5186f525 SHA1: f82bbe9272db4b7a85cf3c8d9e91fb2e84118617 SHA256: 542570f3d24a19e53d9e2cfa54dd91a6771cb1c5906f17327c8a44ba3748cb76 SHA512: 9d91a53d0dd076cb608485c104e923ab6392721630d0e7107f9024ce1db4e1452f659f0426f140846a73bceee65f0374995fc3f0024e6fecbac20ab0c402bb1e Homepage: https://cran.r-project.org/package=Category Description: Bioc Package 'Category' (Category Analysis) A collection of tools for performing category (gene set enrichment) analysis. Package: r-bioc-cbioportaldata Architecture: all Version: 2.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3101 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-anvil, r-bioc-multiassayexperiment, r-bioc-biocbaseutils, r-bioc-biocfilecache, r-cran-digest, r-cran-dplyr, r-bioc-seqinfo, r-bioc-genomicranges, r-cran-httr, r-bioc-iranges, r-cran-readr, r-bioc-raggedexperiment, r-bioc-rtcgatoolbox, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-cran-tibble, r-cran-tidyr, r-bioc-tcgautils Suggests: r-bioc-biocstyle, r-cran-jsonlite, r-cran-knitr, r-cran-survival, r-cran-survminer, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-cbioportaldata_2.24.0-1.ca2404.1_all.deb Size: 469794 MD5sum: 7b5877fe21b03862dd4eaaf13ee743dc SHA1: bdb617f954be42ea24d73a53a4e977c98a26bf88 SHA256: 3d331bb3bf1c19c0f13591e6b67204ed10b60c4887bc172ef81a2d64d9946283 SHA512: f6fb849484eaa966113bc824c74a4de75f56cf2c472811f30b6ac1017b4b9a7584c9ecf4f99ed37cdfa495a48e2d874681626c557e2267ba8e5c5152e2bd2279 Homepage: https://cran.r-project.org/package=cBioPortalData Description: Bioc Package 'cBioPortalData' (Exposes and Makes Available Data from the cBioPortal WebResources) The cBioPortalData R package accesses study datasets from the cBio Cancer Genomics Portal. It accesses the data either from the pre-packaged zip / tar files or from the API interface that was recently implemented by the cBioPortal Data Team. The package can provide data in either tabular format or with MultiAssayExperiment object that uses familiar Bioconductor data representations. Package: r-bioc-celldex Architecture: all Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1370 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-cran-matrix, r-bioc-experimenthub, r-bioc-annotationhub, r-bioc-annotationdbi, r-bioc-s4vectors, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-gypsum, r-bioc-alabaster.base, r-bioc-alabaster.matrix, r-bioc-alabaster.se, r-cran-dbi, r-cran-rsqlite, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-dt, r-cran-jsonvalidate, r-cran-biocmanager, r-bioc-ensembldb Filename: pool/dists/noble/main/r-bioc-celldex_1.22.0-1.ca2404.1_all.deb Size: 430144 MD5sum: 88f50dc4994be906151b07c2e562c55e SHA1: e0c56015d1a54b9881feefc76f71cc6764416e93 SHA256: 7ecee8fdcd158e259469b64fda8704f0677154983d252e591164bda84c8726bc SHA512: 650922342e5ca33ad0e658a0ec6bdd5004c9f09015a08193123269f14e13633b65c1715bd9d0353ce5aff0e0e9d7ff0d3ed93cb4b431db087d12631df5c5f206 Homepage: https://cran.r-project.org/package=celldex Description: Bioc Package 'celldex' (Index of Reference Cell Type Datasets) Provides a collection of reference expression datasets with curated cell type labels, for use in procedures like automated annotation of single-cell data or deconvolution of bulk RNA-seq. Package: r-bioc-cghbase Architecture: all Version: 1.72.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1386 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-marray Filename: pool/dists/noble/main/r-bioc-cghbase_1.72.0-1.ca2404.1_all.deb Size: 1105156 MD5sum: e33d71e6970478a15ec8e1d08fab7319 SHA1: 09dfbfa61c93c012c24896d4bc8ab3508b5248b5 SHA256: 92da6f136fd8b00210ad72d1a637fae72e218693feb9b7cbf97b8929909fd22f SHA512: b7233bad22b5c90164aa3a7ccaefe2ad9e29f7b245313b2de9894c0bad1fa49f351320c56d5b197052ed5b436c01dacbdb303a114647bd7a41275633140d13fe Homepage: https://cran.r-project.org/package=CGHbase Description: Bioc Package 'CGHbase' (CGHbase: Base functions and classes for arrayCGH data analysis.) Contains functions and classes that are needed by arrayCGH packages. Package: r-bioc-cghcall Architecture: all Version: 2.74.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 789 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-impute, r-bioc-dnacopy, r-bioc-biobase, r-bioc-cghbase, r-cran-snowfall Filename: pool/dists/noble/main/r-bioc-cghcall_2.74.0-1.ca2404.1_all.deb Size: 540288 MD5sum: 2fa870fb21ebe5c58a882c58a4a44f0c SHA1: 72fca5e57b8da9b475ce2784aa54858b1f950132 SHA256: f3cee503191bf56eb2168ea9f0e28829dd205a8b378a732d4b3c1a77428f5378 SHA512: 07b181c146757a34e01eb06952aa1f5417b6fee81f60e0a2e324eb390b19981ecf82a99ddf4701ff651b7d7507f22caad34241bed7b11744eba19366cf02a958 Homepage: https://cran.r-project.org/package=CGHcall Description: Bioc Package 'CGHcall' (Calling aberrations for array CGH tumor profiles.) Calls aberrations for array CGH data using a six state mixture model as well as several biological concepts that are ignored by existing algorithms. Visualization of profiles is also provided. Package: r-bioc-chippeakanno Architecture: all Version: 3.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 28451 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-iranges, r-bioc-genomicranges, r-bioc-s4vectors, r-bioc-annotationdbi, r-bioc-biocgenerics, r-bioc-biostrings, r-bioc-pwalign, r-cran-dbi, r-cran-dplyr, r-bioc-genomeinfodb, r-bioc-genomicalignments, r-bioc-genomicfeatures, r-bioc-rbgl, r-bioc-rsamtools, r-bioc-summarizedexperiment, r-cran-venndiagram, r-bioc-biomart, r-cran-ggplot2, r-bioc-graph, r-bioc-interactionset, r-bioc-keggrest, r-cran-matrixstats, r-bioc-multtest, r-bioc-regioner, r-bioc-rtracklayer, r-bioc-universalmotif, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-data.table, r-cran-scales, r-bioc-ensembldb Suggests: r-bioc-annotationhub, r-bioc-bsgenome, r-bioc-limma, r-bioc-reactome.db, r-cran-biocmanager, r-bioc-biocstyle, r-bioc-bsgenome.ecoli.ncbi.20080805, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-org.ce.eg.db, r-bioc-org.hs.eg.db, r-bioc-bsgenome.celegans.ucsc.ce10, r-bioc-bsgenome.drerio.ucsc.danrer7, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-bioc-delayedarray, r-cran-idr, r-cran-seqinr, r-bioc-ensdb.hsapiens.v75, r-bioc-ensdb.hsapiens.v79, r-bioc-ensdb.hsapiens.v86, r-bioc-txdb.hsapiens.ucsc.hg18.knowngene, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-go.db, r-cran-gplots, r-cran-upsetr, r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-testthat, r-bioc-trackviewer, r-bioc-motifstack, r-bioc-organismdbi, r-bioc-biocfilecache Filename: pool/dists/noble/main/r-bioc-chippeakanno_3.46.0-1.ca2404.1_all.deb Size: 22862936 MD5sum: 722635069e794697a6ea08b60870f5f0 SHA1: d91317f40e7436611dc5bfe3d769fe453c743fbd SHA256: 66f35cefcc40f21feccaa194661d1297c0cf5f96dbdcfdbad6cb664d87f0f580 SHA512: a5bcd23420becf8229fb92c416bff5e18f7e5afc8d028f8b1024095bfea80e47ce1d596e2b3e6e87db0ec8b6747a835d90df5030740f313f054fbfc5ea898968 Homepage: https://cran.r-project.org/package=ChIPpeakAnno Description: Bioc Package 'ChIPpeakAnno' (Batch annotation of the peaks identified from either ChIP-seq,ChIP-chip experiments, or any experiments that result in largenumber of genomic interval data) The package encompasses a range of functions for identifying the closest gene, exon, miRNA, or custom features—such as highly conserved elements and user-supplied transcription factor binding sites. Additionally, users can retrieve sequences around the peaks and obtain enriched Gene Ontology (GO) or Pathway terms. In version 2.0.5 and beyond, new functionalities have been introduced. These include features for identifying peaks associated with bi-directional promoters along with summary statistics (peaksNearBDP), summarizing motif occurrences in peaks (summarizePatternInPeaks), and associating additional identifiers with annotated peaks or enrichedGO (addGeneIDs). The package integrates with various other packages such as biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest, and stat to enhance its analytical capabilities. Package: r-bioc-chipseeker Architecture: all Version: 1.48.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8082 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-cran-aplot, r-bioc-biocgenerics, r-cran-boot, r-cran-dplyr, r-bioc-enrichplot, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-genomicfeatures, r-cran-ggplot2, r-cran-gplots, r-cran-gtools, r-cran-magrittr, r-cran-plotrix, r-cran-rcolorbrewer, r-cran-rlang, r-bioc-rtracklayer, r-bioc-s4vectors, r-cran-scales, r-cran-tibble, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-yulab.utils Suggests: r-bioc-clusterprofiler, r-cran-ggimage, r-cran-ggplotify, r-cran-ggupset, r-cran-ggvenndiagram, r-cran-knitr, r-bioc-org.hs.eg.db, r-cran-prettydoc, r-bioc-reactomepa, r-cran-rmarkdown, r-cran-testthat, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene Filename: pool/dists/noble/main/r-bioc-chipseeker_1.48.0-1.ca2404.1_all.deb Size: 7479366 MD5sum: 8527e14ee94bbd5527e6a3e114dcf851 SHA1: d85157e864157c2cf4c4c538bb0a6475c64847d1 SHA256: 7b58de1ac394323f8855c699143b2806050e1b3edc5af18de8c40b9be5b92c99 SHA512: 32be5e254b3b2f3824e5f57e6530720768d7401d73c356ecd66de6177439acd3e020cf8aee6ea98a561b4dfeb1f6a28078b01d9051f21142900438f33b156ac8 Homepage: https://cran.r-project.org/package=ChIPseeker Description: Bioc Package 'ChIPseeker' (ChIPseeker for ChIP peak Annotation, Comparison, andVisualization) This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes. Package: r-bioc-cicero Architecture: all Version: 1.30.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1802 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-monocle, r-bioc-gviz, r-cran-assertthat, r-bioc-biobase, r-bioc-biocgenerics, r-cran-data.table, r-cran-dplyr, r-cran-fnn, r-bioc-genomicranges, r-cran-ggplot2, r-cran-glasso, r-cran-igraph, r-bioc-iranges, r-cran-matrix, r-cran-plyr, r-cran-reshape2, r-bioc-s4vectors, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-vgam Suggests: r-bioc-annotationdbi, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-bioc-rtracklayer, r-cran-testthat, r-cran-vdiffr, r-cran-covr Filename: pool/dists/noble/main/r-bioc-cicero_1.30.0-1.ca2404.1_all.deb Size: 1126524 MD5sum: 02a900017b51b10843491bc4bacdb9aa SHA1: 853dc388ee29cb58df138f04027ab2d66b74e7b4 SHA256: b180ee84c8d85ebeca765471368c6ba35739e6ab10028aae1317cdda898726e7 SHA512: 1fdaa3d851a1eb2a26409dc7fbf0e7ad353d038775bd8fd4313cb290c24f571374587863b36b94be7e289f7211c2c618cbac24d32b7a92181cea7b909e340d32 Homepage: https://cran.r-project.org/package=cicero Description: Bioc Package 'cicero' (Predict cis-co-accessibility from single-cell chromatinaccessibility data) Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends monocle 2 for use in chromatin accessibility data. Package: r-bioc-clusterprofiler Architecture: all Version: 4.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1214 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aisdk, r-bioc-annotationdbi, r-cran-dplyr, r-cran-enrichit, r-bioc-enrichplot, r-cran-ggplot2, r-bioc-go.db, r-bioc-gosemsim, r-cran-gson, r-cran-httr, r-cran-igraph, r-cran-jsonlite, r-cran-magrittr, r-cran-plyr, r-bioc-qvalue, r-cran-rlang, r-cran-tidyr, r-cran-yulab.utils Suggests: r-bioc-annotationhub, r-cran-biocmanager, r-bioc-dose, r-cran-ggtangle, r-cran-readr, r-bioc-org.hs.eg.db, r-cran-quarto, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-clusterprofiler_4.20.0-1.ca2404.1_all.deb Size: 789648 MD5sum: cf162accb18bf8f89c3c7b7d81aa1dcf SHA1: 8e02f6b859bfc75a22f7004f9232460cfaa6820e SHA256: ce255a9fac84c15afedc7be803dad33fea43cb5ec959c6d5a0070e0c0992ad46 SHA512: eac0978c6b447b82ee1e322b29c47e394dcdb0ba4870598d95dc899d9d70ecb542c3c3664d847ad4ca24447bc772bc2021288d1578bc6f7f3482778993f4f0f0 Homepage: https://cran.r-project.org/package=clusterProfiler Description: Bioc Package 'clusterProfiler' (A Universal Enrichment Tool for Interpreting Omics Data) A universal tool for interpreting functional characteristics of omics data. It supports Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) for both coding and non-coding genomics data of thousands of species. It provides a unified and tidy interface to access, manipulate, and visualize enrichment results. A key capability is the simultaneous analysis and comparison of datasets from multiple treatments or time points. Furthermore, it integrates Large Language Model (LLM) capabilities to provide automated and insightful interpretation of enrichment results. Package: r-bioc-cmapr Architecture: all Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6090 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-rhdf5, r-cran-data.table, r-bioc-flowcore, r-bioc-summarizedexperiment, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-testthat, r-bioc-biocstyle, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-cmapr_1.24.0-1.ca2404.1_all.deb Size: 3924344 MD5sum: 562fa66ae745afa23f29ed436940c775 SHA1: 858c847566c97c4e989962390c047f38853506b5 SHA256: be8d510f089f95dc54bcf60cc86cd3605ce57d1fdb0312b9f3ceaaf4116db4f9 SHA512: 0b44d378a69edb0f03644b44a1d068bdaea6d3e4230a3cea339867e00e70e90af28f4214b5f08bb6b2a25d0e353a1ff85690e1cb8a464ce23cd1ac144d4590b6 Homepage: https://cran.r-project.org/package=cmapR Description: Bioc Package 'cmapR' (CMap Tools in R) The Connectivity Map (CMap) is a massive resource of perturbational gene expression profiles built by researchers at the Broad Institute and funded by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Please visit https://clue.io for more information. The cmapR package implements methods to parse, manipulate, and write common CMap data objects, such as annotated matrices and collections of gene sets. Package: r-bioc-complexheatmap Architecture: all Version: 2.28.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3559 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circlize, r-cran-getoptlong, r-cran-colorspace, r-cran-clue, r-cran-rcolorbrewer, r-cran-globaloptions, r-cran-png, r-cran-digest, r-bioc-iranges, r-cran-matrixstats, r-cran-foreach, r-cran-doparallel, r-cran-codetools Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-dendsort, r-cran-jpeg, r-cran-tiff, r-cran-fastcluster, r-bioc-enrichedheatmap, r-cran-dendextend, r-cran-grimport, r-cran-grimport2, r-cran-glue, r-bioc-genomicranges, r-cran-gridtext, r-cran-pheatmap, r-cran-gridgraphics, r-cran-gplots, r-cran-rmarkdown, r-cran-cairo, r-cran-magick Filename: pool/dists/noble/main/r-bioc-complexheatmap_2.28.0-1.ca2404.1_all.deb Size: 3032710 MD5sum: 7349d25584a85a21c7581c0a151eaf46 SHA1: 889e2c0855be25edf4d2cd42897bf3b4157de9d0 SHA256: c9499264720471cd31dd226569b34a0ef01b431d6037d9e3878a22a764ba03fd SHA512: 148f64a8cdcc47d8ec5ac22dc96db949a2206eceb6a9c8f65ad55d52d72d4b236765ba29858bfa8ef4a72d188a6570847298ffe47fcabd676fa2b5e10244f959 Homepage: https://cran.r-project.org/package=ComplexHeatmap Description: Bioc Package 'ComplexHeatmap' (Make Complex Heatmaps) Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics. Package: r-bioc-compounddb Architecture: all Version: 1.16.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3234 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationfilter, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-chemminer, r-cran-tibble, r-cran-jsonlite, r-cran-dplyr, r-cran-dbi, r-cran-dbplyr, r-cran-rsqlite, r-bioc-biobase, r-bioc-protgenerics, r-cran-xml2, r-bioc-iranges, r-bioc-spectra, r-bioc-mscoreutils, r-bioc-metabocoreutils, r-bioc-biocparallel, r-cran-stringi, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-biocstyle, r-bioc-msbackendmgf Filename: pool/dists/noble/main/r-bioc-compounddb_1.16.0-1.ca2404.1_all.deb Size: 1137090 MD5sum: 27617509eefd673911cfb8609e33a6c1 SHA1: 130c1804054e80e1331a1e0d2e4554e124deefe5 SHA256: 9c2087159d512e5a6380ecdd08d68040810c5461bd655fcd6f509aa421e13bd6 SHA512: 8021458d51337b6c802a7b99589cae90e8d314bda7206a5f956de2a27862eb68a7eb71fa5b23d49f7bcbb3d73fe1168c0168dbf72f4fe2063185b01f133ff954 Homepage: https://cran.r-project.org/package=CompoundDb Description: Bioc Package 'CompoundDb' (Creating and Using (Chemical) Compound Annotation Databases) CompoundDb provides functionality to create and use (chemical) compound annotation databases from a variety of different sources such as LipidMaps, HMDB, ChEBI or MassBank. The database format allows to store in addition MS/MS spectra along with compound information. The package provides also a backend for Bioconductor's Spectra package and allows thus to match experimetal MS/MS spectra against MS/MS spectra in the database. Databases can be stored in SQLite format and are thus portable. Package: r-bioc-consensusclusterplus Architecture: all Version: 1.76.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-all, r-cran-cluster Filename: pool/dists/noble/main/r-bioc-consensusclusterplus_1.76.0-1.ca2404.1_all.deb Size: 446524 MD5sum: 5d160a8d51e2c167248e86cdf398e564 SHA1: d9de3c01bdc951eaf0a4feeb23506d384e64316b SHA256: fc67f6fd543dfd5a0a5f1fa062b1b185cef0352477cba8c20a073b8fd646f410 SHA512: 7c5c8cdbdc34b84adec95ee53dc6b74bb7eadb19b13e780796937cd38f8ce506c27308cb8b32a4df5239fa99a848e8dc45ad8258fc950b1923eccf4a579b3cd7 Homepage: https://cran.r-project.org/package=ConsensusClusterPlus Description: Bioc Package 'ConsensusClusterPlus' (ConsensusClusterPlus) algorithm for determining cluster count and membership by stability evidence in unsupervised analysis Package: r-bioc-cordon Architecture: all Version: 1.30.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3891 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-bioc-biobase, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-ggplot2, r-cran-data.table Suggests: r-bioc-biocstyle, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-cordon_1.30.0-1.ca2404.1_all.deb Size: 2647460 MD5sum: 5d0a2915841b8aded02140992169061f SHA1: f9a877891411de0a28cdb7d6dd7c8e242efb5f5c SHA256: 99589bc3244cae4f08e2d5372c05a284a86edfca83911288ff44f0b0bddaa016 SHA512: 38daf0655456fa026474c16bd5d62589c8fe9ce3c31bfb0547d7dc7ec61fe3f8e9187aed8f573b7b22285c1508dabe46aacb13acbfda008af6b73aae2a280ae4 Homepage: https://cran.r-project.org/package=coRdon Description: Bioc Package 'coRdon' (Codon Usage Analysis and Prediction of Gene Expressivity) Tool for analysis of codon usage in various unannotated or KEGG/COG annotated DNA sequences. Calculates different measures of CU bias and CU-based predictors of gene expressivity, and performs gene set enrichment analysis for annotated sequences. Implements several methods for visualization of CU and enrichment analysis results. Package: r-bioc-coregx Architecture: all Version: 2.16.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3870 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-summarizedexperiment, r-bioc-biobase, r-bioc-s4vectors, r-bioc-multiassayexperiment, r-bioc-matrixgenerics, r-bioc-piano, r-bioc-biocparallel, r-bioc-bumpymatrix, r-cran-checkmate, r-cran-lsa, r-cran-data.table, r-cran-crayon, r-cran-glue, r-cran-rlang, r-cran-bench Suggests: r-cran-pander, r-cran-markdown, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-knitr, r-cran-formatr, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-coregx_2.16.0-1.ca2404.1_all.deb Size: 2267236 MD5sum: 90ed3d907786f946a82ffb8d6fa66318 SHA1: 3b1fddf988495dc97e4e958951a87d0aaabdc007 SHA256: 9710b46a8a781cc9918534993dbef0aa2524567e4468076a5a87d53150083bcd SHA512: 85b5f9ffc7a6d731cbf8fe732e3d36d6b66324ee9c7054098811474fafacbf804d546c826415dda00b93e8e2a112b4dbe345dbaadc00fc8153d34193ac0364da Homepage: https://cran.r-project.org/package=CoreGx Description: Bioc Package 'CoreGx' (Classes and Functions to Serve as the Basis for Other 'Gx'Packages) A collection of functions and classes which serve as the foundation for our lab's suite of R packages, such as 'PharmacoGx' and 'RadioGx'. This package was created to abstract shared functionality from other lab package releases to increase ease of maintainability and reduce code repetition in current and future 'Gx' suite programs. Major features include a 'CoreSet' class, from which 'RadioSet' and 'PharmacoSet' are derived, along with get and set methods for each respective slot. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, as well as: Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., Freeman, M., Selby, H., Gendoo, D., Grossman, P., Beck, A., Aerts, H., Lupien, M., Goldenberg, A. (2015) . Manem, V., Labie, M., Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M., Abazeed, M., Haibe-Kains, B., Bratman, S. (2018) . Package: r-bioc-cqn Architecture: all Version: 1.58.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1090 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mclust, r-cran-nor1mix, r-cran-quantreg Suggests: r-cran-scales, r-bioc-edger Filename: pool/dists/noble/main/r-bioc-cqn_1.58.0-1.ca2404.1_all.deb Size: 992412 MD5sum: 383ddfaa1bfdb4017331f24b1558f5ea SHA1: 46aa7ca2097928c22971485cf6038a639b7e532e SHA256: a71b93d0c4bec54c36b4da45073c2b5524ece2b9571ef79cd771f1776cbb8101 SHA512: 4f2de7d4fde3070528027cadaf8ccd218095af372876a9cdf3cc265a12c4433403fc6b27f4aaac5595d7cf555028f34fda017a79fc0876c22c97ce3da672005e Homepage: https://cran.r-project.org/package=cqn Description: Bioc Package 'cqn' (Conditional quantile normalization) A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. Package: r-bioc-cytomapper Architecture: all Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7667 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-ebimage, r-bioc-singlecellexperiment, r-bioc-spatialexperiment, r-bioc-s4vectors, r-bioc-biocparallel, r-bioc-hdf5array, r-bioc-delayedarray, r-cran-rcolorbrewer, r-cran-viridis, r-bioc-summarizedexperiment, r-cran-raster, r-cran-ggplot2, r-cran-ggbeeswarm, r-cran-svgpanzoom, r-cran-svglite, r-cran-shiny, r-cran-shinydashboard, r-cran-matrixstats, r-bioc-rhdf5, r-cran-nnls Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-cowplot, r-cran-testthat, r-cran-shinytest Filename: pool/dists/noble/main/r-bioc-cytomapper_1.24.0-1.ca2404.1_all.deb Size: 4170680 MD5sum: 52fc88a0c005f21a45b183d2d27ae870 SHA1: 0a41a634db98f46a1ece02273bcd7318cdb4a305 SHA256: 40015745a243093e69dc8d385f1c727ee2d7726da8cffea3f1e72e8b1d891c3a SHA512: de91a21fc2717f2982edd05d067e9594ff164c3e50cadec939d55299267acfd05cdf63304735b5f05fb3471c9e99dbd4f9dcdd5f6fe37d48fd50ab7c515b7327 Homepage: https://cran.r-project.org/package=cytomapper Description: Bioc Package 'cytomapper' (Visualization of highly multiplexed imaging data in R) Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells. Package: r-bioc-decontam Architecture: all Version: 1.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1353 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-phyloseq Filename: pool/dists/noble/main/r-bioc-decontam_1.32.0-1.ca2404.1_all.deb Size: 733996 MD5sum: 81645363c9eb1eeb68c04a1b774834d4 SHA1: 407b79efab21a9c46fd85dcc810e41f3dfd4f1d6 SHA256: b33fc9952a1b48a782d9e631ec5542de18c3bbd78b4c5ff41508ce6ee38f87e5 SHA512: 77cbebe8f314f3166322515eb45cb632f542e695f31a6719b87af7d751a9fa595e5626555026e1f7d4ab59eb2d580d7f162739439f7fe3b78a29446d461ef740 Homepage: https://cran.r-project.org/package=decontam Description: Bioc Package 'decontam' (Identify Contaminants in Marker-gene and Metagenomics SequencingData) Simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. Works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA quantitation data or sequenced negative control samples. Package: r-bioc-decoupler Architecture: all Version: 2.17.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8526 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocparallel, r-cran-broom, r-cran-dplyr, r-cran-magrittr, r-cran-matrix, r-cran-parallelly, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-withr Suggests: r-cran-glmnet, r-bioc-gsva, r-bioc-viper, r-bioc-fgsea, r-bioc-aucell, r-bioc-summarizedexperiment, r-cran-rpart, r-cran-ranger, r-bioc-biocstyle, r-cran-covr, r-cran-knitr, r-cran-pkgdown, r-cran-refmanager, r-cran-rmarkdown, r-cran-roxygen2, r-cran-sessioninfo, r-cran-pheatmap, r-cran-testthat, r-bioc-omnipathr, r-cran-seurat, r-cran-ggplot2, r-cran-ggrepel, r-cran-patchwork, r-cran-magick Filename: pool/dists/noble/main/r-bioc-decoupler_2.17.0-1.ca2404.1_all.deb Size: 3537916 MD5sum: 3754b1768cd3fab0c26734be72014e06 SHA1: 6e2185720b4bf6a089d4fe2017e01bd3566a02d2 SHA256: 98fa0b2d9abcd9e099251016f4687b84596b35bf13398f0adc6dca8feacdda44 SHA512: d825682896f19dc9d151794cc29a41096e78be9a4d8df39e1302e48de076ed493411c3916de2b8e4b6a05eaf5545d47c187950aeca33981ffd5b545502a11236 Homepage: https://cran.r-project.org/package=decoupleR Description: Bioc Package 'decoupleR' (decoupleR: Ensemble of computational methods to infer biologicalactivities from omics data) Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase. Package: r-bioc-degreport Architecture: all Version: 1.48.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4195 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-cran-broom, r-cran-circlize, r-bioc-complexheatmap, r-cran-cowplot, r-bioc-consensusclusterplus, r-cran-cluster, r-cran-dendextend, r-bioc-deseq2, r-cran-dplyr, r-bioc-edger, r-cran-ggplot2, r-cran-ggdendro, r-cran-ggrepel, r-cran-knitr, r-cran-logging, r-cran-magrittr, r-cran-psych, r-cran-rcolorbrewer, r-cran-reshape, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-stringi, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-cran-tidyr, r-cran-tibble Suggests: r-bioc-biocstyle, r-bioc-annotationdbi, r-bioc-limma, r-cran-pheatmap, r-cran-rmarkdown, r-cran-statmod, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-degreport_1.48.0-1.ca2404.1_all.deb Size: 3253392 MD5sum: 8f658cfb9f97995524023143b5b886fa SHA1: 239a4004b32f5e10c62fc33cd269e07e2978082e SHA256: 3c8906f952a05d4ac39d101b2dca5dac6f6b4d97dc5880f0356b13fd5ef3d816 SHA512: 812cdf29c12eb4af0be3ebac0dd04f3139f75de1039927d83617d98138f913c6dbcec1fef614a469410ab9092694d1b62dd71dea9a5829d57f6666046e5b0081 Homepage: https://cran.r-project.org/package=DEGreport Description: Bioc Package 'DEGreport' (Report of DEG analysis) Creation of ready-to-share figures of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene. Package: r-bioc-delayedarray Architecture: all Version: 0.38.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3692 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-biocgenerics, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-s4arrays, r-bioc-sparsearray Suggests: r-bioc-biocparallel, r-bioc-hdf5array, r-bioc-zarrarray, r-bioc-genefilter, r-bioc-summarizedexperiment, r-bioc-airway, r-cran-lobstr, r-bioc-delayedmatrixstats, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-runit Filename: pool/dists/noble/main/r-bioc-delayedarray_0.38.1-1.ca2404.1_all.deb Size: 2182352 MD5sum: a139374ae71eb2f08c4bc6a1ec37d3c2 SHA1: cc7a68eeea7dce167c45784891059a22ca1252a3 SHA256: e70197dfc8b2fd5b7e0697c57bd2291140ec5477f234abd33cb5edb005b9abbc SHA512: d145a649a1ee4db437e996ec4a625f14ac92b4e11cdac49adfcddd179bf304095e0cf08bee243f1097ac83f680a0c051ec9a024f3ec23bc7fcb66525a4c50fd6 Homepage: https://cran.r-project.org/package=DelayedArray Description: Bioc Package 'DelayedArray' (A unified framework for working transparently with on-disk andin-memory array-like datasets) Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames. Package: r-bioc-delayedmatrixstats Architecture: all Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1440 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-matrixgenerics, r-bioc-delayedarray, r-bioc-sparsematrixstats, r-cran-matrix, r-bioc-s4vectors, r-bioc-iranges, r-bioc-sparsearray Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-microbenchmark, r-cran-profmem, r-bioc-hdf5array, r-cran-matrixstats Filename: pool/dists/noble/main/r-bioc-delayedmatrixstats_1.34.0-1.ca2404.1_all.deb Size: 705058 MD5sum: e5ee4f3fdc1fb82672f07bb8e385318d SHA1: 90691d8726fa3737db4d17baa366b91a87d86163 SHA256: 38c17052ebb6bd444fafd892804b38d893294669fd6dc2a50627526175947a33 SHA512: 86d5eeed82eacc44c43bd6fd151b6da340b7ff148fc8a9a7f2c36e89bcba1998efce93de065d02eb9345ec9955cf988b5ccaa1dec581bc37b09535a393239a01 Homepage: https://cran.r-project.org/package=DelayedMatrixStats Description: Bioc Package 'DelayedMatrixStats' (Functions that Apply to Rows and Columns of 'DelayedMatrix'Objects) A port of the 'matrixStats' API for use with DelayedMatrix objects from the 'DelayedArray' package. High-performing functions operating on rows and columns of DelayedMatrix objects, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. Package: r-bioc-dep Architecture: all Version: 1.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6012 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-tibble, r-cran-tidyr, r-bioc-summarizedexperiment, r-bioc-msnbase, r-bioc-limma, r-bioc-vsn, r-cran-fdrtool, r-cran-ggrepel, r-bioc-complexheatmap, r-cran-rcolorbrewer, r-cran-circlize, r-cran-shiny, r-cran-shinydashboard, r-cran-dt, r-cran-rmarkdown, r-cran-assertthat, r-cran-gridextra, r-cran-imputelcmd, r-cran-cluster Suggests: r-cran-testthat, r-cran-enrichr, r-cran-knitr, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-dep_1.32.0-1.ca2404.1_all.deb Size: 4176580 MD5sum: 2bbf130b56f5f6d2164b008495dcaa09 SHA1: 1cec339252c616d14861c8b8f15c9063fd6517dc SHA256: c5321cc51226cd7871472332691ddf5985cda69e2cfc941877f7950300a1dd6c SHA512: 0cf1c540fb336f6ea45c85738b0dd663baae7f28e3fd01f7ee011c0d83720bc7969b51db24c32a07fd81786280ed7144a18f53cd7a6be4507520f24ad5e33844 Homepage: https://cran.r-project.org/package=DEP Description: Bioc Package 'DEP' (Differential Enrichment analysis of Proteomics data) This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package. Package: r-bioc-depmap Architecture: all Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3796 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-bioc-experimenthub, r-bioc-annotationhub, r-bioc-biocfilecache, r-cran-httr2, r-cran-curl, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-viridis, r-cran-gridextra, r-cran-ggplot2, r-cran-readr, r-cran-stringr, r-cran-tidyverse, r-cran-magick Filename: pool/dists/noble/main/r-bioc-depmap_1.26.0-1.ca2404.1_all.deb Size: 1507910 MD5sum: f02a7700d20cf17d46101441cd876464 SHA1: a7e80a7b57562dbc100b40ac9f6530a3a43624c4 SHA256: 544eb13a32a588e58904435f7d9f99f70705bf2c7f15b98d44a77f53dc427837 SHA512: fa2894ead26ddac52adfad36b132e51640589de29707f7b34fca3f361f49e0dadd48ebdf110daaeadec9f6d017e368f54e5d061bf2e6bc4f5b2e6407cbe125d5 Homepage: https://cran.r-project.org/package=depmap Description: Bioc Package 'depmap' (Cancer Dependency Map Data Package) The depmap package is a data package that accesses datsets from the Broad Institute DepMap cancer dependency study using ExperimentHub. Datasets from the most current release are available, including RNAI and CRISPR-Cas9 gene knockout screens quantifying the genetic dependency for select cancer cell lines. Additional datasets are also available pertaining to the log copy number of genes for select cell lines, protein expression of cell lines as measured by reverse phase protein lysate microarray (RPPA), 'Transcript Per Million' (TPM) data, as well as supplementary datasets which contain metadata and mutation calls for the other datasets found in the current release. The 19Q3 release adds the drug_dependency dataset, that contains cancer cell line dependency data with respect to drug and drug-candidate compounds. The 20Q2 release adds the proteomic dataset that contains quantitative profiling of proteins via mass spectrometry. This package will be updated on a quarterly basis to incorporate the latest Broad Institute DepMap Public cancer dependency datasets. All data made available in this package was generated by the Broad Institute DepMap for research purposes and not intended for clinical use. This data is distributed under the Creative Commons license (Attribution 4.0 International (CC BY 4.0)). Package: r-bioc-derfinder Architecture: all Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4872 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-annotationdbi, r-bioc-biocparallel, r-bioc-bumphunter, r-bioc-derfinderhelper, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-genomicalignments, r-bioc-genomicfeatures, r-bioc-genomicfiles, r-bioc-genomicranges, r-cran-hmisc, r-bioc-iranges, r-bioc-qvalue, r-bioc-rsamtools, r-bioc-rtracklayer, r-bioc-s4vectors Suggests: r-bioc-biocstyle, r-cran-sessioninfo, r-bioc-derfinderdata, r-bioc-derfinderplot, r-bioc-deseq2, r-cran-ggplot2, r-cran-knitr, r-bioc-limma, r-cran-refmanager, r-cran-rmarkdown, r-cran-testthat, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-covr Filename: pool/dists/noble/main/r-bioc-derfinder_1.46.0-1.ca2404.1_all.deb Size: 1934760 MD5sum: d36b5c1407558463b71c7baf0d688375 SHA1: af55f72e67fbe719d397b41a046acf078d729aee SHA256: 9703cefb4f2fc1c39acc02a68d1998c711e9a760120cf9bcf63463c0b8b2e2f0 SHA512: 4304dc4d631f66e1dbf343676d304aa66312587985d1ade03dfcdadb04d011185009293d44ec58bfd153a233b012ad289ac0d4320899e0c0bc85ee0915a06268 Homepage: https://cran.r-project.org/package=derfinder Description: Bioc Package 'derfinder' (Annotation-agnostic differential expression analysis of RNA-seqdata at base-pair resolution via the DER Finder approach) This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package: (1) single base-level F-statistics and (2) DER identification at the expressed regions-level. The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks. Package: r-bioc-derfinderhelper Architecture: all Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1017 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-iranges, r-cran-matrix, r-bioc-s4vectors Suggests: r-cran-sessioninfo, r-cran-knitr, r-bioc-biocstyle, r-cran-refmanager, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-bioc-derfinderhelper_1.46.0-1.ca2404.1_all.deb Size: 304472 MD5sum: a5f4c8d89733e499be58b4735437c68f SHA1: d604c9dcb14ac83eb7fe832170b4473d0c41fa78 SHA256: b3f67aec601a343cd0114aceb4a37df715b5631dd60d5184a76cbaf174ce3132 SHA512: 61ffada6ca745a40b58c99cc577c68af7436e4dc161de6d10bf1ec3e0dc8210b4068a195dd075d8198c3c16b9d9c5b8dfe343b5857b83b0be8a0dbd095212b2d Homepage: https://cran.r-project.org/package=derfinderHelper Description: Bioc Package 'derfinderHelper' (derfinder helper package) Helper package for speeding up the derfinder package when using multiple cores. This package is particularly useful when using BiocParallel and it helps reduce the time spent loading the full derfinder package when running the F-statistics calculation in parallel. Package: r-bioc-dexseq Architecture: all Version: 1.58.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3466 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocparallel, r-bioc-biobase, r-bioc-summarizedexperiment, r-bioc-iranges, r-bioc-genomicranges, r-bioc-deseq2, r-bioc-annotationdbi, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-biomart, r-cran-hwriter, r-cran-stringr, r-bioc-rsamtools, r-cran-statmod, r-bioc-geneplotter, r-bioc-genefilter Suggests: r-bioc-genomeinfodb, r-bioc-genomicfeatures, r-bioc-txdbmaker, r-bioc-pasilla, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-pasillabamsubset, r-bioc-genomicalignments, r-cran-roxygen2, r-bioc-glmgampoi Filename: pool/dists/noble/main/r-bioc-dexseq_1.58.0-1.ca2404.1_all.deb Size: 2051854 MD5sum: 4020f9e68d50ceae7f67fd716c2f2690 SHA1: 339c573baedb0b1a2b3cc41a772b1dd8028aecdd SHA256: 7715c35dbaceec9164aaa98046ddab0dbbf050370d2a3d042fff90788991faa4 SHA512: 0ca470cce9b10c963ba077f98ce4c46c292a00e78f8692380b2540bb1f78862983fcd9866d822e784d621258ccdb18bc5e4ca0448b1d27339f4be56b47831f4a Homepage: https://cran.r-project.org/package=DEXSeq Description: Bioc Package 'DEXSeq' (Inference of differential exon usage in RNA-Seq) The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results. Package: r-bioc-diffcoexp Architecture: all Version: 1.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-wgcna, r-bioc-summarizedexperiment, r-cran-diffcorr, r-cran-psych, r-cran-igraph, r-bioc-biocgenerics Suggests: r-bioc-geoquery, r-cran-runit Filename: pool/dists/noble/main/r-bioc-diffcoexp_1.32.0-1.ca2404.1_all.deb Size: 327606 MD5sum: 54ed8090826de4779f372806d54c460b SHA1: 573120fca442d1297eea2106aebf460c62698172 SHA256: 5f2dbfbd6f5c4db9b3065d8f887595a1a6972da5f12141dc1c0811ca5fc67697 SHA512: e76fc43821b96a0813eb1e743e23d85c2a03bbba513ff0a1b9562d3645a192893cab7a742f2b1f72b5b230c3c37bd337f0820e9d6d4643fba1f65473036a28de Homepage: https://cran.r-project.org/package=diffcoexp Description: Bioc Package 'diffcoexp' (Differential Co-expression Analysis) A tool for the identification of differentially coexpressed links (DCLs) and differentially coexpressed genes (DCGs). DCLs are gene pairs with significantly different correlation coefficients under two conditions. DCGs are genes with significantly more DCLs than by chance. Package: r-bioc-diffcyt Architecture: all Version: 1.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1592 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-flowcore, r-bioc-flowsom, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-bioc-limma, r-bioc-edger, r-cran-lme4, r-cran-multcomp, r-cran-dplyr, r-cran-tidyr, r-cran-reshape2, r-cran-magrittr, r-bioc-complexheatmap, r-cran-circlize Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-hdcytodata, r-bioc-catalyst Filename: pool/dists/noble/main/r-bioc-diffcyt_1.32.0-1.ca2404.1_all.deb Size: 898718 MD5sum: 0682ec4d7671b0958b5de5c507f09f80 SHA1: fa68870083246a2adc2eba78a3dc3507f46bf28e SHA256: 8e047ee613f3a589ddf22e7e92de69e55b9d5302680135eacf96d46d9ff7f21b SHA512: dee83cf7f4f5abd40d7067312c62158b4518bb7eb7b35d13f218394b88c8fd0743c1c71de8ae29f52741af0182d6a19f9e6b69a77ef2f4e2976c63253f981190 Homepage: https://cran.r-project.org/package=diffcyt Description: Bioc Package 'diffcyt' (Differential discovery in high-dimensional cytometry viahigh-resolution clustering) Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. Package: r-bioc-dir.expiry Architecture: all Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-filelock Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-dir.expiry_1.20.0-1.ca2404.1_all.deb Size: 297284 MD5sum: 07f55de9f8ab69024cb41957785741c9 SHA1: cfdd5858c992367ee5d4fc7ca7ffe6f83301cdd2 SHA256: 6ce1acd8c06bb3dfeb25040a2496e1420aeba32bd7e17b94124848d49301aa67 SHA512: f15cf4b62438decdc000d8459c6a8bfe377f6a826243b508364d408afaed7ce019e8472da9523abbbdf04f7ce905bc62f6a677ac74a3f9dffce0e095b9c26795 Homepage: https://cran.r-project.org/package=dir.expiry Description: Bioc Package 'dir.expiry' (Managing Expiration for Cache Directories) Implements an expiration system for access to versioned directories. Directories that have not been accessed by a registered function within a certain time frame are deleted. This aims to reduce disk usage by eliminating obsolete caches generated by old versions of packages. Package: r-bioc-dittoseq Architecture: all Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3421 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-colorspace, r-cran-gridextra, r-cran-cowplot, r-cran-reshape2, r-cran-pheatmap, r-cran-ggrepel, r-cran-ggridges, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-bioc-s4vectors Suggests: r-cran-plotly, r-cran-testthat, r-cran-seurat, r-bioc-deseq2, r-bioc-edger, r-cran-ggplot.multistats, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-scrnaseq, r-cran-ggrastr, r-bioc-complexheatmap, r-bioc-bluster, r-bioc-scater, r-bioc-scran, r-cran-mass Filename: pool/dists/noble/main/r-bioc-dittoseq_1.24.0-1.ca2404.1_all.deb Size: 1947864 MD5sum: 4a445c2f8258ad7109cf9f2170e8cf2f SHA1: 0032b879334ab37e8445f4d1a50d2e6aa94de5c8 SHA256: 73d545a22f2c51566b2bd2bcbec02cd38c64f3cb60809dc96dd49ec8a1e8fce5 SHA512: fbd0e9c74fe9e555bfe20388097dd903138f29d1e5ae3fc646b939a18744a85c9fbeb44d2211cc4790860df165d6c43fd68096519730e7a922ab7d523a64953f Homepage: https://cran.r-project.org/package=dittoSeq Description: Bioc Package 'dittoSeq' (User Friendly Single-Cell and Bulk RNA Sequencing Visualization) A universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors(). Package: r-bioc-dmrcate Architecture: all Version: 3.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1283 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationhub, r-bioc-experimenthub, r-bioc-bsseq, r-bioc-seqinfo, r-bioc-limma, r-bioc-edger, r-bioc-minfi, r-bioc-missmethyl, r-bioc-genomicranges, r-cran-plyr, r-bioc-gviz, r-bioc-iranges, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-bioc-biomart Suggests: r-cran-knitr, r-cran-runit, r-bioc-biocgenerics, r-bioc-genomeinfodb, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-illuminahumanmethylationepicanno.ilm10b4.hg19, r-bioc-illuminahumanmethylationepicv2anno.20a1.hg38, r-bioc-flowsorted.blood.epic, r-bioc-tissuetreg, r-bioc-dmrcatedata, r-bioc-epicv2manifest Filename: pool/dists/noble/main/r-bioc-dmrcate_3.8.0-1.ca2404.1_all.deb Size: 1038748 MD5sum: 87984a011cfa5093bf668afb4f62169c SHA1: 4474418bc9c67095cafb4153b58bc7dbc7eec3f3 SHA256: 5c2a812f63c94567e8a3724134ff6d1ae48ffbf671adeec4c7e2acbb2f203f6e SHA512: 9a1ef0e08318d68b90a67d70549ed8b5e3e75737244986d3131e8c07dc5f7a2f9a1580a1acd43fe34d349f4a6171de3a058c745efcfee113bef4fc2c09700def Homepage: https://cran.r-project.org/package=DMRcate Description: Bioc Package 'DMRcate' (Methylation array and sequencing spatial analysis methods) De novo identification and extraction of differentially methylated regions (DMRs) from the human genome using Whole Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array (450K and EPIC) data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes GRanges generation and plotting functions. Package: r-bioc-dose Architecture: all Version: 4.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5792 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-cran-enrichit, r-cran-ggplot2, r-bioc-gosemsim, r-cran-reshape2, r-cran-yulab.utils Suggests: r-cran-prettydoc, r-bioc-clusterprofiler, r-cran-gson, r-cran-knitr, r-cran-memoise, r-bioc-org.hs.eg.db, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-dose_4.6.0-1.ca2404.1_all.deb Size: 5834202 MD5sum: 3689edd9d01204618a49b9a9b8ded45c SHA1: 3dec864b14ffc4547eff16e36105c73886afb9dd SHA256: 455bb40510e047b8c5a8fefd3d8c3808a76c1956efefba0f15a968c6b883e0fd SHA512: 92928768eff338434fd579b8b21a81dcc63938304afcda3e3a45dbd30126a8cbeee4a41ab754f14ad93b452cba178c827f02f00f960e5f0cfb6cd17b86a2caec Homepage: https://cran.r-project.org/package=DOSE Description: Bioc Package 'DOSE' (Disease Ontology Semantic and Enrichment analysis) This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data. Package: r-bioc-drimseq Architecture: all Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1032 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-biocparallel, r-bioc-limma, r-bioc-edger, r-cran-ggplot2, r-cran-reshape2 Suggests: r-bioc-pasillatranscriptexpr, r-bioc-geuvadistranscriptexpr, r-bioc-biocstyle, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-drimseq_1.40.0-1.ca2404.1_all.deb Size: 786210 MD5sum: 1884ae62397163f44e60a0c006fde062 SHA1: 820dceb1bfe6a7aa8488500ca4ded7fcd39431f6 SHA256: 6cf996786efb21e6dd48db9093416ec5a3b1e2e694a3a734dfb83d9aa3c7ceab SHA512: 4b91fbc8c299114c5e0c9af283ea24887278985a41c35113fc8fc9151b174d476839f196398b9b46eaee0b9272f0e5d5bde437a6b7595e57e615793f734fd097 Homepage: https://cran.r-project.org/package=DRIMSeq Description: Bioc Package 'DRIMSeq' (Differential transcript usage and tuQTL analyses withDirichlet-multinomial model in RNA-seq) The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results. Package: r-bioc-dyndoc Architecture: all Version: 1.90.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-bioc-dyndoc_1.90.0-1.ca2404.1_all.deb Size: 217260 MD5sum: 1a11e06917bfd811f4674bdbf7a1fb3d SHA1: e29bcfb4551d4d17ae728bff71d733dd0a8963dc SHA256: dde026281cf50ac4e39cf44dff5000bc1aa0fddb2ee2284a808ee24fd154f88d SHA512: a195bedbc03546b89a0febe10b06c14eac55a6042bdcc9036d5859f3aa08709f1c6ebffa42cde5b2dea6a1a6fda1d3dbe5603047715678460dda6c0e7f5fd12c Homepage: https://cran.r-project.org/package=DynDoc Description: Bioc Package 'DynDoc' (Dynamic document tools) A set of functions to create and interact with dynamic documents and vignettes. Package: r-bioc-edaseq Architecture: all Version: 2.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2306 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-shortread, r-bioc-biocgenerics, r-bioc-iranges, r-bioc-aroma.light, r-bioc-rsamtools, r-bioc-biomart, r-bioc-biostrings, r-bioc-annotationdbi, r-bioc-genomicfeatures, r-bioc-genomicranges, r-cran-biocmanager Suggests: r-bioc-biocstyle, r-cran-knitr, r-bioc-yeastrnaseq, r-bioc-leebamviews, r-bioc-edger, r-cran-kernsmooth, r-cran-testthat, r-bioc-deseq2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-edaseq_2.46.0-1.ca2404.1_all.deb Size: 1401548 MD5sum: 48ade32f4e565877cc2a9b65da0cad35 SHA1: 71b0afea63cf53c839734763816ecd728b498b3f SHA256: ff46a42d1e415d4fcc2df626cceec2a68ae8a41e450b28c87a01784d7387eb05 SHA512: 78f8f2411363e1bdc107a8f8347a74110e9d653e4dc4f1b4eb1053903dbdf1014ea28cd89b0e632c9b966f3fc6342ebc0d893856eab9c5de5fc45db9e2bd7a70 Homepage: https://cran.r-project.org/package=EDASeq Description: Bioc Package 'EDASeq' (Exploratory Data Analysis and Normalization for RNA-Seq) Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010). Package: r-bioc-empiricalbrownsmethod Architecture: all Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-bioc-biocstyle, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-empiricalbrownsmethod_1.40.0-1.ca2404.1_all.deb Size: 53646 MD5sum: 251a51e9dd5db0191f75a5710f6113c5 SHA1: 681d121aa4b7937c44d55892139fe49979607e45 SHA256: 90d2291daba77dadaaf2d15db1803080ddfa069bb5446e49422add6a2bbad5ff SHA512: 5009f4b469aae27a188e31d3de99449b4bc6484ac02f90d7c75bc8869a31534654203180c0ed0ecc266d1395e5614d2620c1cbcdca0055ba22f766bdc427eae4 Homepage: https://cran.r-project.org/package=EmpiricalBrownsMethod Description: Bioc Package 'EmpiricalBrownsMethod' (Uses Brown's method to combine p-values from dependent tests) Combining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package implements an empirical adaptation of Brown’s Method (an extension of Fisher’s Method) for combining dependent P-values which is appropriate for highly correlated data sets found in high-throughput biological experiments. Package: r-bioc-enhancedvolcano Architecture: all Version: 1.30.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7923 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-scales Suggests: r-cran-runit, r-cran-ggrastr, r-bioc-biocgenerics, r-cran-knitr, r-bioc-deseq2, r-bioc-pasilla, r-bioc-airway, r-bioc-org.hs.eg.db, r-cran-gridextra, r-cran-magrittr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-enhancedvolcano_1.30.0-1.ca2404.1_all.deb Size: 5602658 MD5sum: daac5c3f3c1279992fb38bd7f5a1b3a3 SHA1: 7356a027e152bac99c4de3fe078ffac10c21021c SHA256: 696587a9f9c5ba54fbfb3ad81cbbb113bab51426c1c3e32072ef5e412aefe0ea SHA512: 8b449d1781cad5c0ac5f8927d6f1d759d79e2889d221653e9393b4ad6824604966b0bccce2da06222209d7b4c796501b38db308f2e7bcab6cba441f52c7efe4b Homepage: https://cran.r-project.org/package=EnhancedVolcano Description: Bioc Package 'EnhancedVolcano' (Publication-ready volcano plots with enhanced colouring andlabeling) Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many point labels in the plot window as possible, thus avoiding 'clogging' up the plot with labels that could not otherwise have been read. Other functionality allows the user to identify up to 4 different types of attributes in the same plot space via colour, shape, size, and shade parameter configurations. Package: r-bioc-enrichmentbrowser Architecture: all Version: 2.42.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2739 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-bioc-graph, r-bioc-annotationdbi, r-bioc-biocfilecache, r-cran-biocmanager, r-bioc-gseabase, r-bioc-go.db, r-bioc-keggrest, r-bioc-kegggraph, r-bioc-rgraphviz, r-bioc-s4vectors, r-bioc-spia, r-bioc-edger, r-bioc-graphite, r-cran-hwriter, r-bioc-limma, r-bioc-pathview, r-bioc-safe Suggests: r-bioc-all, r-bioc-biocstyle, r-bioc-complexheatmap, r-bioc-deseq2, r-bioc-reportingtools, r-bioc-airway, r-bioc-biocgraph, r-bioc-hgu95av2.db, r-bioc-geneplotter, r-cran-knitr, r-cran-msigdbr, r-cran-rmarkdown, r-cran-statmod Filename: pool/dists/noble/main/r-bioc-enrichmentbrowser_2.42.0-1.ca2404.1_all.deb Size: 1480632 MD5sum: d1eef12f26813cd2b4e453cd46fa4e78 SHA1: 80b7e78fca6f5254a6636b040e9c1c072f36e617 SHA256: c5072ec66db19f92bf27537a7b367121c86f593a53e5f6f79a4fa64c8f166db7 SHA512: dbadd60389bf5d826736ac2f8ca651fc807277f950fe798519993c4670cb20afacc5ed76f486aa4c36635051955dcb030d253d0e37cfe7a23ac10cebf68a6a02 Homepage: https://cran.r-project.org/package=EnrichmentBrowser Description: Bioc Package 'EnrichmentBrowser' (Seamless navigation through combined results of set-based andnetwork-based enrichment analysis) The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways. Package: r-bioc-enrichplot Architecture: all Version: 1.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aplot, r-bioc-dose, r-cran-dplyr, r-cran-enrichit, r-cran-ggfun, r-cran-ggnewscale, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggtangle, r-bioc-ggtree, r-bioc-gosemsim, r-cran-igraph, r-cran-purrr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rlang, r-cran-scatterpie, r-cran-tidydr, r-cran-yulab.utils Suggests: r-bioc-annotationdbi, r-bioc-clusterprofiler, r-cran-europepmc, r-cran-ggarchery, r-cran-ggforce, r-cran-gghoriplot, r-cran-ggplotify, r-cran-ggridges, r-cran-ggstar, r-bioc-ggtreeextra, r-cran-ggupset, r-cran-glue, r-cran-gridextra, r-cran-gson, r-bioc-org.hs.eg.db, r-cran-quarto, r-cran-scales, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-bioc-enrichplot_1.32.0-1.ca2404.1_all.deb Size: 292318 MD5sum: d4bd5aa8e5a134a269913df448dc5a97 SHA1: 5bcee111a21857fb73003b8102db50ddccf68be2 SHA256: 0fef410277cabdb37368adb517f5223464328196d7cd5ac7ff7d71d3d08b791c SHA512: 9e0fd5641462a4713b6ccea348e60ab09227b8a033e4975b3e3d1b2a248726cbf3b7746b055c1a61e787a69fcd27f7483fd3b7dd050a134ed80f88c78647fbb2 Homepage: https://cran.r-project.org/package=enrichplot Description: Bioc Package 'enrichplot' (Visualization of Functional Enrichment Result) The 'enrichplot' package provides visualization methods for interpreting functional enrichment results from ORA or GSEA analyses. It is designed to work with the 'clusterProfiler' ecosystem and builds on 'ggplot2' for flexible and extensible graphics. Package: r-bioc-ensdb.hsapiens.v75 Architecture: all Version: 2.99.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352493 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-ensembldb Filename: pool/dists/noble/main/r-bioc-ensdb.hsapiens.v75_2.99.0-1.ca2404.1_all.deb Size: 61897766 MD5sum: 5ac3a153ebf87c555fe16d14a8b02673 SHA1: 4fc69037cb26c9e5eab13fdf089e9de366d04c5d SHA256: 22e93d894c629e688a3a1e3f0127174eb123de7eb9e801190dcf7e62058523a6 SHA512: a8e9718b45ee42f62a6733b448d1fefdee907a36610c19b6bd0fa8b069102e37529758c1969153ee2d83b63e77435a78612f7b21e4641fb68832235490da7c05 Homepage: https://cran.r-project.org/package=EnsDb.Hsapiens.v75 Description: Bioc Package 'EnsDb.Hsapiens.v75' (Ensembl based annotation package) Exposes an annotation databases generated from Ensembl. Package: r-bioc-ensembldb Architecture: all Version: 2.36.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8053 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-genomicfeatures, r-bioc-annotationfilter, r-cran-rsqlite, r-cran-dbi, r-bioc-biobase, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-annotationdbi, r-bioc-rtracklayer, r-bioc-s4vectors, r-bioc-rsamtools, r-bioc-iranges, r-bioc-protgenerics, r-bioc-biostrings, r-cran-curl Suggests: r-bioc-biocstyle, r-cran-knitr, r-bioc-ensdb.hsapiens.v86, r-cran-testthat, r-bioc-bsgenome.hsapiens.ncbi.grch38, r-bioc-ggbio, r-bioc-gviz, r-cran-rmarkdown, r-bioc-annotationhub Filename: pool/dists/noble/main/r-bioc-ensembldb_2.36.0-1.ca2404.1_all.deb Size: 2476364 MD5sum: 81d52269852e766fb4c3cbf854d5a57e SHA1: 1ebdda2834afe58acf9cc7127082e9ed57d4848d SHA256: ef9d899c8a321eeef87251f7d0cf1309013b6b2ff9655bf33ee2750e8b7e1c91 SHA512: dbd7e44284de03aa46208f0ac542e85e7d3910d145d9395ca8631b6a3206a8cfb68bf1e8f999243cc29f9c377fa07dea4e398cdfb7d3f7b2b392fd9d90ed553b Homepage: https://cran.r-project.org/package=ensembldb Description: Bioc Package 'ensembldb' (Utilities to create and use Ensembl-based annotation databases) The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. Finally, ensembldb provides functions to map between genomic, transcript and protein coordinates. Package: r-bioc-epidish Architecture: all Version: 2.28.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3859 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-e1071, r-cran-quadprog, r-cran-matrixstats, r-cran-stringr, r-cran-locfdr, r-cran-matrix, r-bioc-genefilter Suggests: r-cran-roxygen2, r-bioc-geoquery, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-biobase, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-epidish_2.28.0-1.ca2404.1_all.deb Size: 2142676 MD5sum: eb38f0b7c5019e16826cdfa9f696d1fc SHA1: 0dfe97e4663921c7e76accea9a4c0846ba0e922c SHA256: 8a56bc3f05cd31b952c84e870a64d42bd83a11c42ca5cb17c5bac8d301fc23cd SHA512: 8bad316093bdb03e2f0cf0e836b9f067cda7efc2eb0bfe9a3a1a81b99c57074d16b6dafce693e8d5c130630564139cc602cd3457822b2770138303044ea66607 Homepage: https://cran.r-project.org/package=EpiDISH Description: Bioc Package 'EpiDISH' (Epigenetic Dissection of Intra-Sample-Heterogeneity) EpiDISH is a R package to infer the proportions of a priori known cell-types present in a sample representing a mixture of such cell-types. Right now, the package can be used on DNAm data of blood-tissue of any age, from birth to old-age, generic epithelial tissue and breast tissue. Besides, the package provides a function that allows the identification of differentially methylated cell-types and their directionality of change in Epigenome-Wide Association Studies. Package: r-bioc-escape Architecture: all Version: 2.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2969 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggdist, r-cran-ggplot2, r-cran-matrix, r-bioc-matrixgenerics, r-bioc-summarizedexperiment Suggests: r-bioc-aucell, r-bioc-biocparallel, r-bioc-biocstyle, r-bioc-delayedmatrixstats, r-cran-dplyr, r-bioc-fgsea, r-bioc-gseabase, r-cran-ggraph, r-cran-ggridges, r-cran-ggpointdensity, r-bioc-gsva, r-cran-hexbin, r-cran-igraph, r-cran-irlba, r-cran-knitr, r-bioc-msigdb, r-cran-patchwork, r-cran-rmarkdown, r-cran-rlang, r-bioc-scran, r-cran-seuratobject, r-cran-seurat, r-bioc-singlecellexperiment, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-bioc-ucell Filename: pool/dists/noble/main/r-bioc-escape_2.8.0-1.ca2404.1_all.deb Size: 1754094 MD5sum: 1fe44b5c0df0d358e28eb3c53ec93e6c SHA1: 01f75f71100962732eef8897451088135366005f SHA256: 5fa0726ebd89f8cb58e0082ad300d6cd67cb0f1dbf498344dd20f14e3bfb22b8 SHA512: adece73bbcc74f2fbd360db79bf7f81186f0195f55f7eb4a33fe429aa0a3f72f2bc43cd53665b208390d964b24238500ecb57597ac1f34c2c12541fa0ebecd2b Homepage: https://cran.r-project.org/package=escape Description: Bioc Package 'escape' (Easy single cell analysis platform for enrichment) A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize ssGSEA, GSVA, AUCell, and UCell-based enrichment calculations across individual cells. Alternatively, escape supports use of rank-based GSEA, such as the use of differential gene expression via fgsea. 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ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated metadata, tags and date of modification. The client creates and manages a local cache of files retrieved enabling quick and reproducible access. 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Package: r-bioc-ggcyto Architecture: all Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7171 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-bioc-flowcore, r-bioc-ncdfflow, r-bioc-flowworkspace, r-cran-plyr, r-cran-scales, r-cran-hexbin, r-cran-data.table, r-cran-rcolorbrewer, r-cran-gridextra, r-cran-rlang Suggests: r-cran-testthat, r-bioc-flowworkspacedata, r-cran-knitr, r-cran-rmarkdown, r-bioc-flowstats, r-bioc-opencyto, r-bioc-flowviz, r-cran-ggridges, r-cran-vdiffr Filename: pool/dists/noble/main/r-bioc-ggcyto_1.40.0-1.ca2404.1_all.deb Size: 3234250 MD5sum: d3a3e2cde9bdd7c7f3dcd9f76ca96e88 SHA1: 3fa0759744ddc0421fbf7540e5501d3062e7bc19 SHA256: a4b285f8606fc6f29fa85b1a58035a60a2f7731c2aa3d34848247654cd39e405 SHA512: 616d2510af6400c6ae20c61a552f3f1e34706d1e6870b127f150c7f01d172ac1de39ff3a6613ac97468ae5cabb6777d46a50dd1f737db73de84f0899ab16339d Homepage: https://cran.r-project.org/package=ggcyto Description: Bioc Package 'ggcyto' (Visualize Cytometry data with ggplot) With the dedicated fortify method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. ggcyto wrapper and some customed layers also make it easy to add gates and population statistics to the plot. Package: r-bioc-ggkegg Architecture: all Version: 1.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5403 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggraph, r-cran-xml, r-cran-igraph, r-cran-tidygraph, r-bioc-biocfilecache, r-cran-data.table, r-cran-dplyr, r-cran-magick, r-cran-patchwork, r-cran-shadowtext, r-cran-stringr, r-cran-tibble, r-cran-gtable Suggests: r-cran-knitr, r-bioc-clusterprofiler, r-cran-bnlearn, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-annotationdbi, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-ggkegg_1.10.0-1.ca2404.1_all.deb Size: 3884064 MD5sum: 2b011f5af16a0451b83a7678f8837f50 SHA1: f909bff51130c56422e6b43662eb4f20050a14f7 SHA256: fa2cccd0988e4fad4a5e4cfadec5b92b44166fe6bc4e4c948769d35fc84aa541 SHA512: 88e68e1f5b2969ff1dd3a7c01f4971c75ff903b9aee392032e3727814ce543085e1beb348b5a9d849bd293caf9a5d7e1022554aa329dec16fc0bd524b2d01a65 Homepage: https://cran.r-project.org/package=ggkegg Description: Bioc Package 'ggkegg' (Analyzing and visualizing KEGG information using the grammar ofgraphics) This package aims to import, parse, and analyze KEGG data such as KEGG PATHWAY and KEGG MODULE. The package supports visualizing KEGG information using ggplot2 and ggraph through using the grammar of graphics. The package enables the direct visualization of the results from various omics analysis packages. 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Supports MSA of DNA, RNA, and protein sequences using 'ggplot2'. Multiple sequence alignment can easily be combined with other 'ggplot2' plots, such as phylogenetic tree Visualized by 'ggtree', boxplot, genome map and so on. More features: visualization of sequence logos, sequence bundles, RNA secondary structures and detection of sequence recombinations. 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The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian". 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Remove reads aligning to these regions prior to peak calling, for cleaner ChIP analysis. 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J Neurosci 28:264-278, 2008; Carrel and Willard, Nature, 434:400-404, 2005; Huang et al. PNAS, 104:9758-9763, 2007; Pickrell et al. Nature, 464:768-722, 2010; Skaletsky et al. Nature, 423:825-837; Verhaak et al. Cancer Cell 17:98-110, 2010; Costa et al. FEBS J, 288:2311-2331, 2021. Package: r-bioc-gviz Architecture: all Version: 1.56.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10684 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomicranges, r-bioc-xvector, r-bioc-rtracklayer, r-cran-lattice, r-cran-rcolorbrewer, r-bioc-biomart, r-bioc-annotationdbi, r-bioc-biobase, r-bioc-genomicfeatures, r-bioc-ensembldb, r-bioc-bsgenome, r-bioc-biostrings, r-bioc-biovizbase, r-bioc-rsamtools, r-cran-latticeextra, r-cran-matrixstats, r-bioc-genomicalignments, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-biocgenerics, r-cran-digest Suggests: r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-xml2, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-gviz_1.56.0-1.ca2404.1_all.deb Size: 7129516 MD5sum: 5e53857f2bcbb00c9fa502f3d5302ca8 SHA1: 540be818162d3d3e15079e136d6a76eb98015bb5 SHA256: df772095972e34c3d43a567038381625a6e80e7727102c9b237b551168d22ee2 SHA512: 33acfe91b5b0cb0f814e7876a3079ee55ce8482843c38d9432fe9cdeafb8061fcc3b8864868442daa114fb02a449ae9a38239b36fc97fab0be6f39174b988d21 Homepage: https://cran.r-project.org/package=Gviz Description: Bioc Package 'Gviz' (Plotting data and annotation information along genomiccoordinates) Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. Package: r-bioc-gwascat Architecture: all Version: 2.44.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 37093 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-genomicfeatures, r-cran-readr, r-bioc-biostrings, r-bioc-annotationdbi, r-bioc-biocfilecache, r-bioc-snpstats, r-bioc-variantannotation, r-bioc-annotationhub, r-cran-data.table, r-cran-tibble Suggests: r-bioc-do.db, r-cran-dt, r-cran-knitr, r-bioc-rbgl, r-cran-testthat, r-cran-rmarkdown, r-cran-dplyr, r-bioc-gviz, r-bioc-rsamtools, r-bioc-rtracklayer, r-bioc-graph, r-bioc-ggbio, r-bioc-delayedarray, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-org.hs.eg.db, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-gwascat_2.44.0-1.ca2404.1_all.deb Size: 35686082 MD5sum: 6c9dca26b84102e72d1848f7fb8bdc7d SHA1: 0a483fc98c4e643b9332060ae2d1d211ff4b349b SHA256: 046199bb6796e7f4ef2be63a4b72d2d56ef0cdaf49833e7d6065e6927f83e475 SHA512: 9d460af35e8c0445d1afed5aee341bd3b742d230e2975c6bcc903bc1a31eadc10c516330b6bab80a74859c6c5238f884d84d1cb43ea7e1504c7aaa7096f64b52 Homepage: https://cran.r-project.org/package=gwascat Description: Bioc Package 'gwascat' (representing and modeling data in the EMBL-EBI GWAS catalog) Represent and model data in the EMBL-EBI GWAS catalog. 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Package: r-bioc-gypsum Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-filelock, r-cran-rappdirs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-biocstyle, r-cran-digest, r-cran-jsonvalidate, r-cran-dbi, r-cran-rsqlite, r-bioc-s4vectors Filename: pool/dists/noble/main/r-bioc-gypsum_1.8.0-1.ca2404.1_all.deb Size: 412576 MD5sum: 20421d17d194e203d56f934e32b546aa SHA1: a4232ffb2e31275ef67c215f4f44bb908b0554e2 SHA256: 72ac7b93b74e7979255492cbc8b233c55bb5d17a970422d35d1edc48f05770db SHA512: b82385bc93e94f0f8ea5c4fb95012723c2562194116e0ce73c682856bdce2d8323f1505fdb1701ac05607caffb6d1388a5be1e02c7c100d4c058c3b96c44533e Homepage: https://cran.r-project.org/package=gypsum Description: Bioc Package 'gypsum' (Interface to the gypsum REST API) Client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. 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It implements the HDF5Array, H5SparseMatrix, H5ADMatrix, and TENxMatrix classes, 4 convenient and memory-efficient array-like containers for representing and manipulating either: (1) a conventional (a.k.a. dense) HDF5 dataset, (2) an HDF5 sparse matrix (stored in CSR/CSC/Yale format), (3) the central matrix of an h5ad file (or any matrix in the /layers group), or (4) a 10x Genomics sparse matrix. All these containers are DelayedArray extensions and thus support all operations (delayed or block-processed) supported by DelayedArray objects. Package: r-bioc-hdo.db Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8933 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-annotationdbi, r-cran-dbi Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-hdo.db_1.0.0-1.ca2404.1_all.deb Size: 2208510 MD5sum: 4f6275b1d0b92201624f9798e062c976 SHA1: 73e88c85e0f8a5cc6e57b82270dc686be2f047ff SHA256: 41b50b43962d5c6cc202bf9bda1d9632210fc2d304ccf7ae663eac18fd7973f6 SHA512: 0cdaf40ed8263471f6d6fe03061a8f4cabbbcee782e086073f97aec86e652c149d72500e5431b14d358cace47ffce225b45051e857e9a5bfc6c90823221dd22f Homepage: https://cran.r-project.org/package=HDO.db Description: Bioc Package 'HDO.db' (A set of annotation maps describing the entire Human DiseaseOntology) A set of annotation maps describing the entire Human Disease Ontology assembled using data from DO. 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By default, both samples (columns) and features (row) of the matrix are sorted according to a hierarchical clustering, and the corresponding dendrogram is plotted. Optionally, panels with additional information about samples and features can be added to the plot. Package: r-bioc-hgu95a.db Architecture: all Version: 3.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1324 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-annotationdbi, r-bioc-org.hs.eg.db Suggests: r-cran-dbi, r-bioc-annotate, r-cran-runit Filename: pool/dists/noble/main/r-bioc-hgu95a.db_3.13.0-1.ca2404.1_all.deb Size: 490860 MD5sum: dc99f949e913f4aa313067111533b2fa SHA1: 0ff78565342bbce18e944fdbd97c20c933aa5f5d SHA256: f3c5b95a9752d205304c333389afe24c2d3bc5adef6ae58d0ac9c34d927c8580 SHA512: 1a3ce451d679268a49513609f477e50a734678661b10ec1c177c40a1b52be247d70dba0642a11428dc03013cff000c58e2bc87006e6ea1a827c78741e76d67bd Homepage: https://cran.r-project.org/package=hgu95a.db Description: Bioc Package 'hgu95a.db' (Affymetrix Affymetrix HG_U95A Array annotation data (chiphgu95a)) Affymetrix Affymetrix HG_U95A Array annotation data (chip hgu95a) assembled using data from public repositories Package: r-bioc-hiccompare Architecture: all Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4116 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-ggplot2, r-cran-gridextra, r-cran-mgcv, r-bioc-interactionset, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-biocparallel, r-cran-kernsmooth, r-cran-pheatmap, r-cran-gtools, r-bioc-rhdf5 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-multihiccompare Filename: pool/dists/noble/main/r-bioc-hiccompare_1.34.0-1.ca2404.1_all.deb Size: 3883202 MD5sum: f9f80e190741f9b23c7bfddbbe9f7b4e SHA1: 32905ff3c188178529320a88f55b72bf2829e56f SHA256: 17692f7d799d2224d57250ae8abfa11bab298557fe20c8401766f947575ba590 SHA512: b895703a46313069de91f02dae2d123fe37115f5d185724b3f986ec4228c8ace43c696dc029e2cc40b13d69f3ecb24a2b33a7afa1f5d45d3b350d4e721e8ee4e Homepage: https://cran.r-project.org/package=HiCcompare Description: Bioc Package 'HiCcompare' (HiCcompare: Joint normalization and comparative analysis ofmultiple Hi-C datasets) HiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. HiCcompare operates on processed Hi-C data in the form of chromosome-specific chromatin interaction matrices. It accepts three-column tab-separated text files storing chromatin interaction matrices in a sparse matrix format which are available from several sources. HiCcompare is designed to give the user the ability to perform a comparative analysis on the 3-Dimensional structure of the genomes of cells in different biological states.`HiCcompare` differs from other packages that attempt to compare Hi-C data in that it works on processed data in chromatin interaction matrix format instead of pre-processed sequencing data. In addition, `HiCcompare` provides a non-parametric method for the joint normalization and removal of biases between two Hi-C datasets for the purpose of comparative analysis. `HiCcompare` also provides a simple yet robust method for detecting differences between Hi-C datasets. 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In this experiment, primary human skeletal muscle myoblasts (HSMM) were expanded under high mitogen conditions (GM) and then differentiated by switching to low-mitogen media (DM). RNA-Seq libraries were sequenced from each of several hundred cells taken over a time-course of serum-induced differentiation. Between 49 and 77 cells were captured at each of four time points (0, 24, 48, 72 hours) following serum switch using the Fluidigm C1 microfluidic system. RNA from each cell was isolated and used to construct mRNA-Seq libraries, which were then sequenced to a depth of ~4 million reads per library, resulting in a complete gene expression profile for each cell. 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The interface supports transmission of selections between plots and tables, code tracking, interactive tours, interactive or programmatic initialization, preservation of app state, and extensibility to new panel types via S4 classes. Special attention is given to single-cell data in a SingleCellExperiment object with visualization of dimensionality reduction results. Package: r-bioc-karyoploter Architecture: all Version: 1.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3657 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-regioner, r-bioc-genomicranges, r-bioc-iranges, r-bioc-rsamtools, r-cran-memoise, r-bioc-rtracklayer, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-s4vectors, r-bioc-biovizbase, r-cran-digest, r-cran-bezier, r-bioc-genomicfeatures, r-bioc-bamsignals, r-bioc-annotationdbi, r-bioc-variantannotation Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat, r-cran-magrittr, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-bsgenome.hsapiens.ucsc.hg19.masked, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-bioc-pasillabamsubset Filename: pool/dists/noble/main/r-bioc-karyoploter_1.38.0-1.ca2404.1_all.deb Size: 2529768 MD5sum: 5f7f6fe4dde452e652dae1a705153e2c SHA1: 21280ece031ce16f9ee524066bf17c125b1825d3 SHA256: 6bacca91c3877cb7f85b501cbe033ab343dac5df92b6b56becbbaf28b86811ab SHA512: 5160b8ccc5b8fe8a1dba17dfb33c6950f825015d194a71b242820a36d2f0d1a7fc200a8d4afb900b5569ee519a8554c1f5ba27b292eb84a03707f6c151e66494 Homepage: https://cran.r-project.org/package=karyoploteR Description: Bioc Package 'karyoploteR' (Plot customizable linear genomes displaying arbitrary data) karyoploteR creates karyotype plots of arbitrary genomes and offers a complete set of functions to plot arbitrary data on them. It mimicks many R base graphics functions coupling them with a coordinate change function automatically mapping the chromosome and data coordinates into the plot coordinates. In addition to the provided data plotting functions, it is easy to add new ones. Package: r-bioc-kegggraph Architecture: all Version: 1.72.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2350 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xml, r-bioc-graph, r-cran-rcurl, r-bioc-rgraphviz Suggests: r-bioc-rbgl, r-cran-testthat, r-cran-rcolorbrewer, r-bioc-org.hs.eg.db, r-bioc-hgu133plus2.db, r-bioc-spia Filename: pool/dists/noble/main/r-bioc-kegggraph_1.72.0-1.ca2404.1_all.deb Size: 1659310 MD5sum: 6fb05846163ef4caf9ad209739f3a600 SHA1: 13d6ed27063a22c3b074a7eb5d0ed8ca3dae59fd SHA256: 9b43dcfc0c6aedc70117a2838182b0e4849632e614809c21b80e5261b1df0e6a SHA512: b7ccc72b8251357c9369370dcef96555ed94099563e323987ba3eb1d320dd5c1066674eabc5e454d1a555f0f9dd1f7bd5712e805967714bd64298e7e71b24b95 Homepage: https://cran.r-project.org/package=KEGGgraph Description: Bioc Package 'KEGGgraph' (KEGGgraph: A graph approach to KEGG PATHWAY in R andBioconductor) KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc. Package: r-bioc-keggrest Architecture: all Version: 1.52.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 867 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-png, r-bioc-biostrings Suggests: r-cran-runit, r-bioc-biocgenerics, r-bioc-biocstyle, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-bioc-keggrest_1.52.0-1.ca2404.1_all.deb Size: 384726 MD5sum: 9d1c17fb6ec0f78e6b4ac6125d7c6913 SHA1: c0237cb0d5bd37402f9776ab3b7a33e4d3d0ffde SHA256: 8050d8842dac148c1191699383c5859537fba75f42f22fd4d22c4ff019a1bcba SHA512: 1c88aa06c8c76969dfb5a6b1578c7415c5a2710f11dfadcbf0427320929dea66e82f178651b8df929ce34f0de8b7e0fc539eaa8ff5a59d5129b56b51abd52d9a Homepage: https://cran.r-project.org/package=KEGGREST Description: Bioc Package 'KEGGREST' (Client-side REST access to the Kyoto Encyclopedia of Genes andGenomes (KEGG)) A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. Only for academic use by academic users belonging to academic institutions (see ). Note that KEGGREST is based on KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG (python package) by Aurelien Mazurie. Package: r-bioc-lbe Architecture: all Version: 1.80.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 933 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-bioc-qvalue Filename: pool/dists/noble/main/r-bioc-lbe_1.80.0-1.ca2404.1_all.deb Size: 615288 MD5sum: d1ec6d2da3049082d7e981c0c3bbc88b SHA1: eb24d3dea72210a3eb573b86537c5d54a16bbbb4 SHA256: 10cca22a64be1069cb7f2584023579f15f1e4f4c10dc7c741d96ae0356640a0e SHA512: e2cb83573b018ae8588e92dc99216f6d33bcd63fdda2e9a831cb599cd80e5cb58796c9f541816aa334d66a6ba0176e6ca3a74eb7d42f389552db57698498cc65 Homepage: https://cran.r-project.org/package=LBE Description: Bioc Package 'LBE' (Estimation of the false discovery rate) LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate (and so the q-values) in the framework of estimating procedures based on the marginal distribution of the p-values without assumption for the alternative hypothesis. Package: r-bioc-lefser Architecture: all Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1461 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-cran-coin, r-cran-mass, r-cran-ggplot2, r-bioc-s4vectors, r-cran-dplyr, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-forcats, r-cran-stringr, r-bioc-ggtree, r-bioc-biocgenerics, r-cran-ape, r-cran-ggrepel, r-bioc-mia, r-cran-purrr, r-cran-tidyselect, r-bioc-treeio Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-curatedmetagenomicdata, r-bioc-biocstyle, r-bioc-phyloseq, r-cran-pkgdown, r-cran-covr, r-cran-withr Filename: pool/dists/noble/main/r-bioc-lefser_1.22.0-1.ca2404.1_all.deb Size: 879722 MD5sum: ff6025c584d0425afedd3c359d25f942 SHA1: 5d45b189f30799764da902490485e7c9e2188fc1 SHA256: 3784570bfa21ef0a80bfa97f7a3f499e4c4fe3938be9466d508c426cf3f956d0 SHA512: d50f3a46db96de26030a155419f3c2ca0b396027d60b8e5a4623ee50958aaea08782216c3394a8564f400cb1325a8e2db311aca4f85722d1516803fdb16ecb12 Homepage: https://cran.r-project.org/package=lefser Description: Bioc Package 'lefser' (R implementation of the LEfSE method for microbiome biomarkerdiscovery) lefser is the R implementation of the popular microbiome biomarker discovery too, LEfSe. It uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to find biomarkers from two-level classes (and optional sub-classes). Package: r-bioc-loomexperiment Architecture: all Version: 1.30.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1730 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-s4vectors, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-rhdf5, r-bioc-biocio, r-bioc-delayedarray, r-bioc-genomicranges, r-bioc-hdf5array, r-cran-matrix, r-cran-stringr Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-reticulate Filename: pool/dists/noble/main/r-bioc-loomexperiment_1.30.0-1.ca2404.1_all.deb Size: 686034 MD5sum: c6e0f27c8e33922ecd202ece83e44f64 SHA1: cff14fb63b51f4da7122483da844b615fb74784b SHA256: 6b6d395e6d746969d6b06b916bad16032144e4604142f856917c649c78cdb67e SHA512: b81f00c9cdd1981322ff39ced93bc8bae0afaadb0ec5e6b8e0272c9e990490592dec46cd5e1720ed57c9c46a53ad85fd921a187952d839ea0e520a50c615a5c4 Homepage: https://cran.r-project.org/package=LoomExperiment Description: Bioc Package 'LoomExperiment' (LoomExperiment container) The LoomExperiment package provide a means to easily convert the Bioconductor "Experiment" classes to loom files and vice versa. Package: r-bioc-lumi Architecture: all Version: 2.64.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5029 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-affy, r-bioc-methylumi, r-bioc-genomicfeatures, r-bioc-genomicranges, r-bioc-annotate, r-cran-lattice, r-cran-mgcv, r-cran-nleqslv, r-cran-kernsmooth, r-bioc-preprocesscore, r-cran-rsqlite, r-cran-dbi, r-bioc-annotationdbi, r-cran-mass Suggests: r-bioc-beadarray, r-bioc-limma, r-bioc-vsn, r-bioc-lumibarnes, r-bioc-lumihumanall.db, r-bioc-lumihumanidmapping, r-bioc-genefilter, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-bioc-lumi_2.64.0-1.ca2404.1_all.deb Size: 4730852 MD5sum: ec54bab4d932cc36ec679f65728416f7 SHA1: 507ae731c27c18c31806c6172e15d06e6b2ad7a0 SHA256: add687726647953f8cc80a392139f5ec5bac747eaf3d75d54c6de8547892875c SHA512: c9e0a77975dba11ce83d41826934f5da57ddc8eaf02dda1afe0f75d13d28216f73a734a1a1bd296452d696d61409ab057dd55f26d1aa5ee14dd23fbf2ecb4cbd Homepage: https://cran.r-project.org/package=lumi Description: Bioc Package 'lumi' (BeadArray Specific Methods for Illumina Methylation andExpression Microarrays) The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays. Package: r-bioc-m3c Architecture: all Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 849 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-dosnow, r-cran-cluster, r-cran-foreach, r-cran-doparallel, r-cran-matrixcalc, r-cran-rtsne, r-cran-corpcor, r-cran-umap Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-m3c_1.34.0-1.ca2404.1_all.deb Size: 775920 MD5sum: cb2636e1bfce856580fc0306d5d642c5 SHA1: 747e0b034c8a2c6f778f4303d6357353b5c9d0e1 SHA256: c58a17078ebcec69c31fa504ba697e5ad8722501e3bcfc7d69a129cf8fed4b8d SHA512: c58eb00ef1059fc2abc05cca2f10dd0a38b70d7d6dca772e5a6cd85385ca00f895178c9ca9df1c16d8bf9e339db4454c5ab942ba8d3ee932aad9847c0fb3ce2d Homepage: https://cran.r-project.org/package=M3C Description: Bioc Package 'M3C' (Monte Carlo Reference-based Consensus Clustering) M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1. Package: r-bioc-m3drop Architecture: all Version: 1.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13311 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-numderiv, r-cran-rcolorbrewer, r-cran-gplots, r-cran-bbmle, r-cran-statmod, r-cran-matrixstats, r-cran-matrix, r-cran-irlba, r-cran-reldist, r-cran-hmisc, r-bioc-scater Suggests: r-cran-rocr, r-cran-knitr, r-bioc-m3dexampledata, r-bioc-singlecellexperiment, r-cran-seurat, r-bioc-biobase Filename: pool/dists/noble/main/r-bioc-m3drop_1.38.0-1.ca2404.1_all.deb Size: 12821776 MD5sum: 03d542b52fd1f96d3de79e8f9c7c9331 SHA1: 409c79f7953b1afea0692f846c4f3c36b42d1e9b SHA256: e30960f18410ab04b7afc5f5b7b493a1f7aafab61f25e511ced407cb303e8827 SHA512: 341ab8fe3d63eae85b977c8df8b0f3b2dc2dca1274c725df9d17d8281d7e1db21dc55400d92af643729551d670819a36ba42e4b2a291b6349010305214f22941 Homepage: https://cran.r-project.org/package=M3Drop Description: Bioc Package 'M3Drop' (Michaelis-Menten Modelling of Dropouts in single-cell RNASeq) This package fits a model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. Also includes an method for calculating exact Pearson residuals in UMI-tagged data using a library-size aware negative binomial model. Package: r-bioc-maaslin2 Architecture: all Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1741 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-robustbase, r-cran-biglm, r-cran-pcapp, r-bioc-edger, r-bioc-metagenomeseq, r-cran-pbapply, r-cran-car, r-cran-dplyr, r-cran-vegan, r-cran-chemometrics, r-cran-ggplot2, r-cran-pheatmap, r-cran-logging, r-cran-data.table, r-cran-lmertest, r-cran-hash, r-cran-optparse, r-cran-glmmtmb, r-cran-mass, r-cran-cplm, r-cran-pscl, r-cran-lme4, r-cran-tibble Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-bioc-maaslin2_1.26.0-1.ca2404.1_all.deb Size: 570400 MD5sum: 7f3281b79e35364892dfc42b8ea45696 SHA1: cb0c83e90160237434fe65c31266be74fdc750d8 SHA256: a5b730d4dcaa2b4c5bbb50a02b2cd2158f57d82c5101555f1bbef554be6dbe39 SHA512: d4158f6f2a309b8865b911fb3a2d8d4c1ac256534ec2eb64246ee41fad1cb7531389624984039493896527cc7769a98c01f94dcd68d5097c97281287985efc4e Homepage: https://cran.r-project.org/package=Maaslin2 Description: Bioc Package 'Maaslin2' ("Multivariable Association Discovery in Population-scaleMeta-omics Studies") MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta'omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods. MaAsLin2 is the next generation of MaAsLin. Package: r-bioc-mageckflute Architecture: all Version: 2.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 27253 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase, r-cran-gridextra, r-cran-ggplot2, r-cran-ggrepel, r-cran-reshape2, r-bioc-dose, r-bioc-clusterprofiler, r-bioc-pathview, r-bioc-enrichplot, r-cran-msigdbr, r-bioc-depmap Suggests: r-bioc-biomart, r-bioc-biocstyle, r-cran-dendextend, r-cran-knitr, r-cran-pheatmap, r-cran-png, r-cran-scales, r-bioc-sva, r-cran-biocmanager Filename: pool/dists/noble/main/r-bioc-mageckflute_2.9.0-1.ca2404.1_all.deb Size: 20085962 MD5sum: a3b08893041bc151ed2ea1bfe013a2f2 SHA1: 5c777dc971aa93fd243716abc8f48e896a44bfe3 SHA256: 874a35ab41c74c5bd5000b330c80d548108f8dcfd5a113df76932c67af9257bf SHA512: 3a211353f14cf383d8c3cbf198d52f488742bfa5180ea05d74a4fea0ad5735ebc1471a7fb02233f6bfe43ea327ef58d7126c96b9f69699c115a9db969c186c71 Homepage: https://cran.r-project.org/package=MAGeCKFlute Description: Bioc Package 'MAGeCKFlute' (Integrative Analysis Pipeline for Pooled CRISPR FunctionalGenetic Screens) CRISPR (clustered regularly interspaced short palindrome repeats) coupled with nuclease Cas9 (CRISPR/Cas9) screens represent a promising technology to systematically evaluate gene functions. Data analysis for CRISPR/Cas9 screens is a critical process that includes identifying screen hits and exploring biological functions for these hits in downstream analysis. We have previously developed two algorithms, MAGeCK and MAGeCK-VISPR, to analyze CRISPR/Cas9 screen data in various scenarios. These two algorithms allow users to perform quality control, read count generation and normalization, and calculate beta score to evaluate gene selection performance. In downstream analysis, the biological functional analysis is required for understanding biological functions of these identified genes with different screening purposes. Here, We developed MAGeCKFlute for supporting downstream analysis. MAGeCKFlute provides several strategies to remove potential biases within sgRNA-level read counts and gene-level beta scores. The downstream analysis with the package includes identifying essential, non-essential, and target-associated genes, and performing biological functional category analysis, pathway enrichment analysis and protein complex enrichment analysis of these genes. The package also visualizes genes in multiple ways to benefit users exploring screening data. Collectively, MAGeCKFlute enables accurate identification of essential, non-essential, and targeted genes, as well as their related biological functions. This vignette explains the use of the package and demonstrates typical workflows. Package: r-bioc-marray Architecture: all Version: 1.90.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9999 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-limma Suggests: r-bioc-tkwidgets Filename: pool/dists/noble/main/r-bioc-marray_1.90.0-1.ca2404.1_all.deb Size: 5645794 MD5sum: 4f95356be437f1581d1695296a6eb2bf SHA1: 0aec19229a6444fb7a9ec5f85738893feecba2ce SHA256: 585d095480f6d37a340966cf62a1c7598e324b98ee3f5d3e3ca064efa42fad43 SHA512: 59552dc93487d8fe33ae4bc450c5e8e2278db0e934072018f39226510a0ff7f0f5ec325677fcf32076ad289a1d5411750d457f1ff63a2bf66c32c4301b259ca0 Homepage: https://cran.r-project.org/package=marray Description: Bioc Package 'marray' (Exploratory analysis for two-color spotted microarray data) Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking. Package: r-bioc-mast Architecture: all Version: 1.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11185 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-s4vectors, r-cran-data.table, r-cran-ggplot2, r-cran-plyr, r-cran-stringr, r-cran-abind, r-cran-reshape2, r-bioc-summarizedexperiment, r-cran-progress, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lme4, r-cran-blme, r-cran-roxygen2, r-cran-numderiv, r-cran-car, r-cran-gdata, r-cran-lattice, r-cran-ggally, r-bioc-gseabase, r-cran-nmf, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-rsvd, r-bioc-limma, r-cran-rcolorbrewer, r-bioc-biocstyle, r-bioc-scater, r-bioc-delayedarray, r-bioc-hdf5array, r-bioc-zinbwave, r-cran-dplyr Filename: pool/dists/noble/main/r-bioc-mast_1.38.0-1.ca2404.1_all.deb Size: 7521172 MD5sum: 6e1729f3461029ff938acfe3c56afa0f SHA1: cea67f9e76e2467d9cd0b71e96f4fa961defa20a SHA256: a1ab20383ed1cc33b7192b09c7b38e05b6c9b87323b0afb5d862af3b135a5870 SHA512: 47b4cd6f51dc7c2eedf16bd8d8f297a5d894ad93e271aaf7fc4c77d585990528ba01a1ab7c9a2b9b65f9ac0c54b8664fcd2810d71357beb79422736e9887c40a Homepage: https://cran.r-project.org/package=MAST Description: Bioc Package 'MAST' (Model-based Analysis of Single Cell Transcriptomics) Methods and models for handling zero-inflated single cell assay data. Package: r-bioc-matrixgenerics Architecture: all Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrixstats Suggests: r-cran-matrix, r-bioc-sparsematrixstats, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-summarizedexperiment, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-matrixgenerics_1.24.0-1.ca2404.1_all.deb Size: 438512 MD5sum: c4dc0d0c3952ad1a582524dfeb79e8b2 SHA1: 4c5e0dba9a279f5e0e3206ee1f83b61c13de0cb2 SHA256: 94c478d3ec3d4a85bc6e18650410b32e6f8fbff438e1a832d060d8d3a7db0826 SHA512: ded8351d38087f0246b1552478989c6302e327770c4aa75720e0b81eefb46b16714e10f1e2bdb5d31a89ddd8177932acee63b7657291c65e677b09efeb4c2289 Homepage: https://cran.r-project.org/package=MatrixGenerics Description: Bioc Package 'MatrixGenerics' (S4 Generic Summary Statistic Functions that Operate onMatrix-Like Objects) S4 generic functions modeled after the 'matrixStats' API for alternative matrix implementations. Packages with alternative matrix implementation can depend on this package and implement the generic functions that are defined here for a useful set of row and column summary statistics. Other package developers can import this package and handle a different matrix implementations without worrying about incompatibilities. Package: r-bioc-metaboannotation Architecture: all Version: 1.16.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4597 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-mscoreutils, r-bioc-metabocoreutils, r-bioc-protgenerics, r-bioc-s4vectors, r-bioc-spectra, r-bioc-biocparallel, r-bioc-summarizedexperiment, r-bioc-qfeatures, r-bioc-annotationhub, r-bioc-compounddb Suggests: r-cran-testthat, r-cran-knitr, r-bioc-msdatahub, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-plotly, r-cran-shiny, r-cran-shinyjs, r-cran-msentropy, r-cran-dt, r-cran-microbenchmark, r-bioc-mzr Filename: pool/dists/noble/main/r-bioc-metaboannotation_1.16.0-1.ca2404.1_all.deb Size: 1443850 MD5sum: 58f190272d53794ee22fa40e72241c75 SHA1: cc5faba05ef8303323aca113b2a8452974120c69 SHA256: 1a5a0dccd35d07d3f66f62609b22f291e9475adffca8cdae93d6a7f60a74f39a SHA512: e180ec099f1977e11ccd86c32a4b510ec0412989e50d7f7fdb9ead02d2a33c223158a55d9b3c67c7a182e92f44bab42cf596d16f4380d5c6876088a9a7539eda Homepage: https://cran.r-project.org/package=MetaboAnnotation Description: Bioc Package 'MetaboAnnotation' (Utilities for Annotation of Metabolomics Data) High level functions to assist in annotation of (metabolomics) data sets. These include functions to perform simple tentative annotations based on mass matching but also functions to consider m/z and retention times for annotation of LC-MS features given that respective reference values are available. In addition, the function provides high-level functions to simplify matching of LC-MS/MS spectra against spectral libraries and objects and functionality to represent and manage such matched data. Package: r-bioc-metabocoreutils Architecture: all Version: 1.20.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2893 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-mscoreutils, r-bioc-biocparallel Suggests: r-bioc-biocstyle, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-robustbase Filename: pool/dists/noble/main/r-bioc-metabocoreutils_1.20.1-1.ca2404.1_all.deb Size: 1357822 MD5sum: 3b2f1d53f604c9597d5baa28c484733a SHA1: f218b443f6017d2bb288f8b9d63f5663e17f269b SHA256: 474077904ac84d9c348832933ace0cd3f3d8eb9811ea651370c66c18c664d715 SHA512: 65773850081c40e2b04d6a52b2d5ef013d0581460b9421fc798dc3cffc4be6af521315264f92341690125c2e85f5b3e92ede2ae7c708634ee48aa03e4b57ca82 Homepage: https://cran.r-project.org/package=MetaboCoreUtils Description: Bioc Package 'MetaboCoreUtils' (Core Utils for Metabolomics Data) MetaboCoreUtils defines metabolomics-related core functionality provided as low-level functions to allow a data structure-independent usage across various R packages. This includes functions to calculate between ion (adduct) and compound mass-to-charge ratios and masses or functions to work with chemical formulas. The package provides also a set of adduct definitions and information on some commercially available internal standard mixes commonly used in MS experiments. Package: r-bioc-metagenomeseq Architecture: all Version: 1.54.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2642 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-limma, r-cran-glmnet, r-cran-rcolorbrewer, r-cran-matrixstats, r-cran-foreach, r-cran-matrix, r-cran-gplots, r-bioc-wrench Suggests: r-bioc-annotate, r-bioc-biocgenerics, r-bioc-biomformat, r-cran-knitr, r-cran-gss, r-cran-testthat, r-cran-vegan, r-bioc-ihw, r-bioc-sparsearray Filename: pool/dists/noble/main/r-bioc-metagenomeseq_1.54.0-1.ca2404.1_all.deb Size: 2139002 MD5sum: e5a647d538a614a465617470c53a9b59 SHA1: 98ca37c91f27c82bc6d974b8f706c2805a664733 SHA256: efc88a5dd1da6880ad4354bcdbed45851f46d22a83a786d9bb09ca94f6833efb SHA512: 53e5eb13e2603bc34dd90fddfa76833fc7fdaf0d9dffe2382308b8f3ed1b79c7f335bf376bcc8f4c60f99e12e86cb5fb439f4e12cb7f05eb098c513ff6e80c3f Homepage: https://cran.r-project.org/package=metagenomeSeq Description: Bioc Package 'metagenomeSeq' (Statistical analysis for sparse high-throughput sequencing) metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations. Package: r-bioc-methylumi Architecture: all Version: 2.58.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12116 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-cran-scales, r-cran-reshape2, r-cran-ggplot2, r-cran-matrixstats, r-bioc-fdb.infiniummethylation.hg19, r-bioc-minfi, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-lattice, r-bioc-annotate, r-bioc-genefilter, r-bioc-annotationdbi, r-bioc-illuminaio, r-bioc-genomicfeatures Suggests: r-bioc-lumi, r-bioc-limma, r-cran-xtable, r-cran-sqn, r-cran-mass, r-bioc-rtracklayer, r-bioc-biostrings, r-bioc-tcgamethylation450k, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-fdb.infiniummethylation.hg18, r-bioc-homo.sapiens, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-methylumi_2.58.0-1.ca2404.1_all.deb Size: 7416250 MD5sum: e9ddcc10bb21362ca6257bf021fb7cd3 SHA1: 07b7e7a9ee9a6ca3ab3317475ce66b06489ef3fb SHA256: 1ffbbd0fb746912ead7fd9241efb6e152fd8fc11c4d5b7219bf58d35c12ea345 SHA512: 82245cd137da0f7f01c0d267fa9503e291caa4114e5a1ccec54128cb3b289c8ea5fe4475714e4a46072fc5d90a6bebbbc1fd446d56a61140b12547e2e55a2f30 Homepage: https://cran.r-project.org/package=methylumi Description: Bioc Package 'methylumi' (Handle Illumina methylation data) This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included. Package: r-bioc-mfuzz Architecture: all Version: 2.72.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1365 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-cran-e1071, r-bioc-tkwidgets Suggests: r-bioc-marray Filename: pool/dists/noble/main/r-bioc-mfuzz_2.72.0-1.ca2404.1_all.deb Size: 777162 MD5sum: ca1573ace3b2e34ea2d2c12138565c40 SHA1: aa53f5c0e47b4ead2090a1f740e2b4e19bd7a3f4 SHA256: 75586bae9b8a92de32f7afc86506d3ba860b59ce83982c25b2b82af21838e895 SHA512: ce602747a3193a601dd389c72f47310fc46cb430d9f368ce230f1cc737694db0a01573b841cd7345d2b32caa1c44bc453ce8fa5f58af0a2b61c4a82bfda6b76a Homepage: https://cran.r-project.org/package=Mfuzz Description: Bioc Package 'Mfuzz' (Soft clustering of omics time series data) The Mfuzz package implements noise-robust soft clustering of omics time-series data, including transcriptomic, proteomic or metabolomic data. It is based on the use of c-means clustering. For convenience, it includes a graphical user interface. Package: r-bioc-microbiome Architecture: all Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1529 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-phyloseq, r-cran-ggplot2, r-bioc-biostrings, r-cran-compositions, r-cran-dplyr, r-cran-reshape2, r-cran-rtsne, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-vegan Suggests: r-bioc-biocgenerics, r-bioc-biocstyle, r-cran-cairo, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-microbiome_1.34.0-1.ca2404.1_all.deb Size: 1016012 MD5sum: dde4a042b48604f247ca1cdd3265b80d SHA1: edefc2b78a155ec6395aaf6d84c02b448f7a243f SHA256: eb150eeac95b60b000dec293251b3b10c18551849a95232acff789388870b656 SHA512: 6294a576f87df3160c55da1f587e43a6485c0d1940246c8333b3a675905a6f5908407f9fe5d178501cca7fbcb3df987aa76a7c0604f7bc308beb9d9bc84cae2b Homepage: https://cran.r-project.org/package=microbiome Description: Bioc Package 'microbiome' (Microbiome Analytics) Utilities for microbiome analysis. Package: r-bioc-microbiomemarker Architecture: all Version: 1.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4464 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-bioc-phyloseq, r-cran-magrittr, r-cran-purrr, r-cran-mass, r-cran-ggplot2, r-cran-tibble, r-cran-rlang, r-cran-coin, r-bioc-ggtree, r-cran-tidytree, r-bioc-iranges, r-cran-tidyr, r-cran-patchwork, r-cran-ggsignif, r-bioc-metagenomeseq, r-bioc-deseq2, r-bioc-edger, r-bioc-biocgenerics, r-bioc-biostrings, r-cran-yaml, r-bioc-biomformat, r-bioc-s4vectors, r-bioc-biobase, r-bioc-complexheatmap, r-bioc-ancombc, r-cran-caret, r-bioc-limma, r-bioc-aldex2, r-bioc-multtest, r-cran-plotroc, r-cran-vegan, r-cran-proc, r-bioc-biocparallel Suggests: r-cran-testthat, r-cran-covr, r-cran-glmnet, r-cran-matrix, r-cran-kernlab, r-cran-e1071, r-cran-ranger, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-withr Filename: pool/dists/noble/main/r-bioc-microbiomemarker_1.10.0-1.ca2404.1_all.deb Size: 3330164 MD5sum: 8bb9dba56130dae12cb3a19481715236 SHA1: 56d1f0fbbc27886ca505e495d32682e4b49b3f7f SHA256: 92290b3ec625ff2a6cb6f7cff36e97a3802dfa31b748e8d70b282d16d3bd18b9 SHA512: ddcc93619578e0eb02173a10e6c2bf8fca1268b6c6e8024b6a4566633de31d822368750af7177f973f08ed4d4ec613fdf39f61efefb1610860b188d89709f8d0 Homepage: https://cran.r-project.org/package=microbiomeMarker Description: Bioc Package 'microbiomeMarker' (microbiome biomarker analysis toolkit) To date, a number of methods have been developed for microbiome marker discovery based on metagenomic profiles, e.g. LEfSe. However, all of these methods have its own advantages and disadvantages, and none of them is considered standard or universal. Moreover, different programs or softwares may be development using different programming languages, even in different operating systems. Here, we have developed an all-in-one R package microbiomeMarker that integrates commonly used differential analysis methods as well as three machine learning-based approaches, including Logistic regression, Random forest, and Support vector machine, to facilitate the identification of microbiome markers. Package: r-bioc-microbiotaprocess Architecture: all Version: 1.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8130 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-tidyr, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-bioc-biostrings, r-cran-ggrepel, r-cran-vegan, r-cran-zoo, r-bioc-ggtree, r-cran-tidytree, r-cran-mass, r-cran-rlang, r-cran-tibble, r-cran-coin, r-cran-ggsignif, r-cran-patchwork, r-cran-ggstar, r-cran-tidyselect, r-bioc-summarizedexperiment, r-cran-foreach, r-bioc-treeio, r-cran-pillar, r-cran-cli, r-cran-plyr, r-cran-dtplyr, r-bioc-ggtreeextra, r-cran-data.table, r-cran-ggfun Suggests: r-cran-rmarkdown, r-cran-prettydoc, r-cran-testthat, r-cran-knitr, r-cran-nlme, r-cran-phangorn, r-bioc-decipher, r-cran-randomforest, r-cran-jsonlite, r-bioc-biomformat, r-cran-scales, r-cran-yaml, r-cran-withr, r-bioc-s4vectors, r-cran-purrr, r-cran-seqmagick, r-cran-glue, r-cran-ggupset, r-cran-ggvenndiagram, r-cran-ggalluvial, r-cran-forcats, r-bioc-phyloseq, r-cran-aplot, r-cran-ggnewscale, r-cran-ggside, r-cran-ggh4x, r-bioc-hopach, r-cran-shadowtext, r-bioc-dirichletmultinomial, r-cran-ggpp, r-cran-biocmanager, r-bioc-rhdf5 Filename: pool/dists/noble/main/r-bioc-microbiotaprocess_1.24.0-1.ca2404.1_all.deb Size: 5383438 MD5sum: 9870b72bb7f112d29e9ddbd4dd3b7909 SHA1: f00a914aeefa36e57edd76ead62a673a47fed3fa SHA256: 2f07a406e148038e0ecaba68e002db15fd43fb79d5a69c3bf61eb636ea557996 SHA512: 4fcc9ae14e22f6942e2fd16839c7d2b3adb250dceab05951da2a5fbac7078e7d13b2b69418bb27f86b3128875e87dd391f64f9855cd5eb81129e1859c004b769 Homepage: https://cran.r-project.org/package=MicrobiotaProcess Description: Bioc Package 'MicrobiotaProcess' (A comprehensive R package for managing and analyzing microbiomeand other ecological data within the tidy framework) MicrobiotaProcess is an R package for analysis, visualization and biomarker discovery of microbial datasets. It introduces MPSE class, this make it more interoperable with the existing computing ecosystem. Moreover, it introduces a tidy microbiome data structure paradigm and analysis grammar. It provides a wide variety of microbiome data analysis procedures under the unified and common framework (tidy-like framework). Package: r-bioc-minfi Architecture: all Version: 1.58.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2581 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-biostrings, r-bioc-bumphunter, r-bioc-s4vectors, r-bioc-seqinfo, r-bioc-biobase, r-bioc-iranges, r-cran-beanplot, r-cran-rcolorbrewer, r-cran-lattice, r-cran-nor1mix, r-bioc-siggenes, r-bioc-limma, r-bioc-preprocesscore, r-bioc-illuminaio, r-bioc-delayedmatrixstats, r-cran-mclust, r-bioc-genefilter, r-cran-nlme, r-cran-reshape, r-cran-mass, r-cran-quadprog, r-cran-data.table, r-bioc-geoquery, r-bioc-delayedarray, r-bioc-hdf5array, r-bioc-biocparallel Suggests: r-bioc-illuminahumanmethylation450kmanifest, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-minfidata, r-bioc-minfidataepic, r-bioc-flowsorted.blood.450k, r-cran-runit, r-cran-digest, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-minfi_1.58.0-1.ca2404.1_all.deb Size: 1341422 MD5sum: 29e38e7c75046f72006742d6b6fe39e0 SHA1: e083333f4fbd0d77ed3d8457cf06d1240128beff SHA256: 470370ee410b7459a38aed175ee5d625fb3fbf17f4f237e3970aa23e0f039428 SHA512: c238aa8b6da091c38f9f500e73f1e059e38936d1705d12e532e6d72f47492b62fa53ad3ef6fec141301f141a2fab046dce8270349abd6b86e00f4eba950aa7f9 Homepage: https://cran.r-project.org/package=minfi Description: Bioc Package 'minfi' (Analyze Illumina Infinium DNA methylation arrays) Tools to analyze & visualize Illumina Infinium methylation arrays. Package: r-bioc-missmethyl Architecture: all Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2556 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-illuminahumanmethylationepicanno.ilm10b4.hg19, r-bioc-illuminahumanmethylationepicv2anno.20a1.hg38, r-bioc-annotationdbi, r-cran-biasedurn, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-go.db, r-bioc-illuminahumanmethylation450kmanifest, r-bioc-illuminahumanmethylationepicmanifest, r-bioc-illuminahumanmethylationepicv2manifest, r-bioc-iranges, r-bioc-limma, r-bioc-methylumi, r-bioc-minfi, r-bioc-org.hs.eg.db, r-cran-ruv, r-bioc-s4vectors, r-cran-statmod, r-cran-stringr, r-bioc-summarizedexperiment Suggests: r-bioc-biocstyle, r-bioc-edger, r-cran-knitr, r-bioc-minfidata, r-cran-rmarkdown, r-bioc-tweedeseqcountdata, r-bioc-dmrcate, r-bioc-experimenthub Filename: pool/dists/noble/main/r-bioc-missmethyl_1.46.0-1.ca2404.1_all.deb Size: 1570718 MD5sum: fb551827c14329f421e30b8b41deb027 SHA1: 1cb6873cb65404013e76d91dd1cae7ac471ae813 SHA256: 9bda58812278b5666c57246a5917b19a50bb9cddc14badd2d4364d3e69513e0e SHA512: 6d22a7112d6f86ef33f789e30613de50f6ca44f08f63baa2968803d0795d95337a0d67e22c8422454ed7faa3963397bd838d3644ddc59600284820080628afa7 Homepage: https://cran.r-project.org/package=missMethyl Description: Bioc Package 'missMethyl' (Analysing Illumina HumanMethylation BeadChip Data) Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes. Package: r-bioc-mixomics Architecture: all Version: 6.36.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 24943 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-lattice, r-cran-ggplot2, r-cran-igraph, r-cran-ellipse, r-cran-corpcor, r-cran-rcolorbrewer, r-cran-dplyr, r-cran-tidyr, r-cran-reshape2, r-cran-matrixstats, r-cran-rarpack, r-cran-gridextra, r-cran-ggrepel, r-bioc-biocparallel, r-cran-rgl, r-cran-rlang Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-mime, r-cran-testthat, r-cran-microbenchmark, r-cran-magick, r-cran-vdiffr, r-cran-kableextra, r-cran-devtools Filename: pool/dists/noble/main/r-bioc-mixomics_6.36.0-1.ca2404.1_all.deb Size: 19438458 MD5sum: b519d3e47979caebac66bc3be2c5a5c4 SHA1: 5ae83c8ea2a1bed3c8ebd702423da426b63e5e92 SHA256: 38ddbd9532485e73fa2112d1d769eaa19b2181dae0a69d20685026912c396771 SHA512: 17eef2404e34d437b367acf447b69ffa74c429ad08e5f60fe96add2fc3f47acf29619b9899ec6d151b7719b374db520e77da782d567426393bce3cfef951751a Homepage: https://cran.r-project.org/package=mixOmics Description: Bioc Package 'mixOmics' (Omics Data Integration Project) Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares. Package: r-bioc-mlinterfaces Architecture: all Version: 1.92.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5097 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-bioc-biocgenerics, r-bioc-biobase, r-bioc-annotate, r-cran-cluster, r-cran-gdata, r-cran-pls, r-cran-sfsmisc, r-cran-mass, r-cran-rpart, r-bioc-genefilter, r-cran-fpc, r-cran-ggvis, r-cran-shiny, r-cran-gbm, r-cran-rcolorbrewer, r-cran-hwriter, r-cran-threejs, r-cran-mlbench, r-cran-magrittr, r-bioc-summarizedexperiment Suggests: r-cran-class, r-cran-e1071, r-cran-ipred, r-cran-randomforest, r-bioc-gpls, r-cran-pamr, r-cran-nnet, r-bioc-all, r-bioc-hgu95av2.db, r-cran-som, r-bioc-hu6800.db, r-cran-lattice, r-cran-caret, r-bioc-golubesets, r-cran-ada, r-bioc-keggorthology, r-cran-kernlab, r-cran-mboost, r-cran-party, r-cran-klar, r-bioc-biocstyle, r-cran-knitr, r-cran-testthat, r-bioc-airway Filename: pool/dists/noble/main/r-bioc-mlinterfaces_1.92.0-1.ca2404.1_all.deb Size: 3025760 MD5sum: 9ec34ae302b8afb1b8ae6f91c7b0a662 SHA1: c979f7d349c0821012036a2c41cdb9dd6d1ed849 SHA256: 1b54f5ba17e475caf2d617455b2c642186053ac678eea6b4ad9563c450e18d1a SHA512: 52d68db53bd89333ae13aa54775cc82cf3b9b648003c28885343a3df07a453c84737f5d02122aa5be331bb2a85bf8fcdfea2c184ea7b18639d029a6ca38eb961 Homepage: https://cran.r-project.org/package=MLInterfaces Description: Bioc Package 'MLInterfaces' (Uniform interfaces to R machine learning procedures for data inBioconductor containers) This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers. 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This encompasses the most widely used steps, from running various enrichment analysis tools with a unified interface to creating plots and beautifying table components linking to external websites and databases. This streamlines the generation of comprehensive analysis reports. 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Package: r-bioc-motifstack Architecture: all Version: 1.56.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4989 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ade4, r-bioc-biostrings, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-xml, r-bioc-tfbstools Suggests: r-cran-cairo, r-cran-grimport, r-cran-grimport2, r-bioc-biocgenerics, r-bioc-motifdb, r-cran-rcolorbrewer, r-bioc-biocstyle, r-cran-knitr, r-cran-runit, r-cran-rmarkdown, r-bioc-jaspar2020 Filename: pool/dists/noble/main/r-bioc-motifstack_1.56.0-1.ca2404.1_all.deb Size: 2718676 MD5sum: ffa88a625e78515727e75fb6bc77aaca SHA1: 7dd97942b4fda442a57399a26de44dc4d2b4b0e1 SHA256: 0d6bdded592c669332c61cccd547fefb310f9123e5457a7b424fac6c76f22177 SHA512: ebfb02890553bcb41a055fb970b5cfd21e621b5bc095f65253f62fa3291cb2def3a00877b3eb647816d6d6adbff8bf9501d4f50567fe6d640b44b6a87230fe82 Homepage: https://cran.r-project.org/package=motifStack Description: Bioc Package 'motifStack' (Plot stacked logos for single or multiple DNA, RNA and aminoacid sequence) The motifStack package is designed for graphic representation of multiple motifs with different similarity scores. It works with both DNA/RNA sequence motif and amino acid sequence motif. In addition, it provides the flexibility for users to customize the graphic parameters such as the font type and symbol colors. 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Objects defined in this package are supposed to be used with the Spectra Bioconductor package. This package thus adds mgf file support to the Spectra package. 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The MsExperiment package provides light-weight and flexible containers for MS experiments building on the new MS infrastructure provided by the Spectra, QFeatures and related packages. Along with raw data representations, links to original data files and sample annotations, additional metadata or annotations can also be stored within the MsExperiment container. To guarantee maximum flexibility only minimal constraints are put on the type and content of the data within the containers. Package: r-bioc-msfeatures Architecture: all Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2640 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-protgenerics, r-bioc-mscoreutils, r-bioc-summarizedexperiment Suggests: r-cran-testthat, r-cran-roxygen2, r-bioc-biocstyle, r-cran-pheatmap, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-msfeatures_1.20.0-1.ca2404.1_all.deb Size: 1511390 MD5sum: e96bc6d8115b7ad45cfc2d2e8027153b SHA1: d2daff7a166c039cd852a66cc84b44f9fed97e0f SHA256: c9c3840189cf6faef21376e515e321967bec6e32c4b40d5b563e9722aeb4dc91 SHA512: 1b67898c4015920eba17f596c1636b4250d40984fa63845c15a677fee763e35c43fa06ee2cb3cdf30db127833995d78e11ef67d412e5a6a1f832321f798a0ca7 Homepage: https://cran.r-project.org/package=MsFeatures Description: Bioc Package 'MsFeatures' (Functionality for Mass Spectrometry Features) The MsFeature package defines functionality for Mass Spectrometry features. This includes functions to group (LC-MS) features based on some of their properties, such as retention time (coeluting features), or correlation of signals across samples. This packge hence allows to group features, and its results can be used as an input for the `QFeatures` package which allows to aggregate abundance levels of features within each group. This package defines concepts and functions for base and common data types, implementations for more specific data types are expected to be implemented in the respective packages (such as e.g. `xcms`). All functionality of this package is implemented in a modular way which allows combination of different grouping approaches and enables its re-use in other R packages. 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Signatures are stored in GeneSet class objects form the GSEABase package and the entire database is stored in a GeneSetCollection object. These data are then hosted on the ExperimentHub. Data used in this package was obtained from the MSigDB of the Broad Institute. Metadata for each gene set is stored along with the gene set in the GeneSet class object. 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It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames. Facilities are provided for reshaping data into wide and long formats for adaptability to graphing and downstream analysis. Package: r-bioc-multidataset Architecture: all Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1942 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-cran-ggplot2, r-cran-ggrepel, r-cran-qqman, r-bioc-limma Suggests: r-bioc-brgedata, r-bioc-minfi, r-bioc-minfidata, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-omicade4, r-bioc-iclusterplus, r-bioc-geoquery, r-bioc-multiassayexperiment, r-bioc-biocstyle, r-bioc-raggedexperiment Filename: pool/dists/noble/main/r-bioc-multidataset_1.40.0-1.ca2404.1_all.deb Size: 607282 MD5sum: f3159fc0e00bb4899e711ddc8657dbb9 SHA1: 35edb24d350c282624fc70b204c99bb9453801db SHA256: 08aee474b3b7a24cc02b7154cbe0408c4b0297add340d5260c04871093cc6a7f SHA512: 62ac8958b3bb84640ad98ea303c8eb0e6c718076c9112fbcc7c5f8ffbab34324cf53fc2c6d55bc6daf4ddec7364bc98e4d342598f02b49bd4fc588d662511604 Homepage: https://cran.r-project.org/package=MultiDataSet Description: Bioc Package 'MultiDataSet' (Implementation of MultiDataSet and ResultSet) Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages. Package: r-bioc-multihiccompare Architecture: all Version: 1.30.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7051 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-bioc-hiccompare, r-bioc-edger, r-bioc-biocparallel, r-cran-qqman, r-cran-pheatmap, r-bioc-genomicranges, r-cran-pbapply, r-bioc-genomeinfodbdata, r-bioc-genomeinfodb, r-cran-aggregation Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-multihiccompare_1.30.0-1.ca2404.1_all.deb Size: 5242290 MD5sum: 5d332ca7e1678d25fd86fbb13c24a68e SHA1: 46faffbf53b1baa55bf5bb7cea7d98923f930cce SHA256: 0e06c9daef32212d33262cd2d80ebb909b3833ebe9f80571f09e062deae2c1af SHA512: 0d47ca0b9fd3d4ab4d4ed52701ad6b72fc48bacd20c83f5af4f775d31f5907b8fbd1b8e3690534a6536fa6083a80058604defa686d52d8cdd37853995e114146 Homepage: https://cran.r-project.org/package=multiHiCcompare Description: Bioc Package 'multiHiCcompare' (Normalize and detect differences between Hi-C datasets whenreplicates of each experimental condition are available) multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner. Package: r-bioc-mungesumstats Architecture: all Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4315 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-r.utils, r-cran-dplyr, r-bioc-genomicranges, r-bioc-genomeinfodb, r-bioc-iranges, r-cran-ieugwasr, r-bioc-bsgenome, r-bioc-biostrings, r-cran-stringr, r-bioc-variantannotation, r-bioc-rtracklayer, r-cran-rcurl Suggests: r-bioc-snplocs.hsapiens.dbsnp144.grch37, r-bioc-snplocs.hsapiens.dbsnp144.grch38, r-bioc-snplocs.hsapiens.dbsnp155.grch37, r-bioc-snplocs.hsapiens.dbsnp155.grch38, r-bioc-bsgenome.hsapiens.1000genomes.hs37d5, r-bioc-bsgenome.hsapiens.ncbi.grch38, r-bioc-biocgenerics, r-bioc-s4vectors, r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat, r-cran-upsetr, r-bioc-biocstyle, r-cran-covr, r-bioc-rsamtools, r-bioc-matrixgenerics, r-cran-badger, r-bioc-biocparallel, r-bioc-genomicfiles Filename: pool/dists/noble/main/r-bioc-mungesumstats_1.20.0-1.ca2404.1_all.deb Size: 2410966 MD5sum: 0563fc55de2db8a86a50beccac5346fa SHA1: acc2f2efe655f374192d7473d1015fce3b6d3715 SHA256: 3eaec72d7e90df632476804afea8b1bbe70d2978fa02223e45bde849e04861e5 SHA512: 96f65e1f986158957d6a3f746a319dba2a1885920c2a3495d11611fb729e5bc5435341af62dd16d3d5053bdc981638d2a41148be92e1c4286268a25ce7e60d64 Homepage: https://cran.r-project.org/package=MungeSumstats Description: Bioc Package 'MungeSumstats' (Standardise summary statistics from GWAS) The *MungeSumstats* package is designed to facilitate the standardisation of GWAS summary statistics. It reformats inputted summary statisitics to include SNP, CHR, BP and can look up these values if any are missing. It also pefrorms dozens of QC and filtering steps to ensure high data quality and minimise inter-study differences. Package: r-bioc-muscat Architecture: all Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11292 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocparallel, r-cran-blme, r-bioc-complexheatmap, r-cran-dplyr, r-bioc-edger, r-cran-ggplot2, r-cran-glmmtmb, r-bioc-limma, r-cran-lmertest, r-cran-lme4, r-cran-matrix, r-bioc-matrixgenerics, r-cran-matrixstats, r-cran-progress, r-cran-rlang, r-bioc-s4vectors, r-cran-scales, r-bioc-scater, r-bioc-scuttle, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-variancepartition Suggests: r-bioc-biocstyle, r-bioc-countsimqc, r-bioc-deseq2, r-bioc-annotationhub, r-bioc-experimenthub, r-bioc-icobra, r-bioc-ihw, r-cran-knitr, r-cran-patchwork, r-cran-phylogram, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rmarkdown, r-cran-sctransform, r-cran-statmod, r-bioc-stager, r-cran-testthat, r-cran-tidyr, r-cran-upsetr Filename: pool/dists/noble/main/r-bioc-muscat_1.26.0-1.ca2404.1_all.deb Size: 7275028 MD5sum: 8303a4fc4da7830501aab56b71055217 SHA1: acce5eb911f13e372259857c81812f2791cf4906 SHA256: 32cb25e21931103fe3e641ad803a24cb559b6f20f0ac621cdfbca6af46a95308 SHA512: 26bc9993c66d130882bf54f74d1b162d2b2102b6cf7c044ad4f3e4a829dc3b63ae649e04206a5852843f39c3cbb2ef27267e238f88d5acbd6ed47e24cb93d0e5 Homepage: https://cran.r-project.org/package=muscat Description: Bioc Package 'muscat' (Multi-sample multi-group scRNA-seq data analysis tools) `muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data. Package: r-bioc-mutationalpatterns Architecture: all Version: 3.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10138 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-cran-nmf, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-bsgenome, r-bioc-variantannotation, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-magrittr, r-cran-ggplot2, r-cran-pracma, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-biostrings, r-cran-ggdendro, r-cran-cowplot, r-cran-ggalluvial, r-cran-rcolorbrewer Suggests: r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-biocstyle, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-biomart, r-cran-gridextra, r-bioc-rtracklayer, r-bioc-ccfindr, r-bioc-genomicfeatures, r-bioc-annotationdbi, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-mutationalpatterns_3.22.0-1.ca2404.1_all.deb Size: 6244194 MD5sum: fe91410c39cfc75c6477ada73d8af5b3 SHA1: df2040668993bba6f48efbb87d1ae38a27113368 SHA256: 56f12763a77be3ee21ef823dfc0dd60b37b0ed3649e8aaef5d606a8c184563c8 SHA512: 404d185c058da3a38496965ab022299f168c91aaf6c8debb2d228617a0a10bde7b4ba34fd6be7d086d9b2cc3daccaa4a8ff7cf05ebd912841f16fb4a87355ee1 Homepage: https://cran.r-project.org/package=MutationalPatterns Description: Bioc Package 'MutationalPatterns' (Comprehensive genome-wide analysis of mutational processes) Mutational processes leave characteristic footprints in genomic DNA. This package provides a comprehensive set of flexible functions that allows researchers to easily evaluate and visualize a multitude of mutational patterns in base substitution catalogues of e.g. healthy samples, tumour samples, or DNA-repair deficient cells. The package covers a wide range of patterns including: mutational signatures, transcriptional and replicative strand bias, lesion segregation, genomic distribution and association with genomic features, which are collectively meaningful for studying the activity of mutational processes. The package works with single nucleotide variants (SNVs), insertions and deletions (Indels), double base substitutions (DBSs) and larger multi base substitutions (MBSs). The package provides functionalities for both extracting mutational signatures de novo and determining the contribution of previously identified mutational signatures on a single sample level. MutationalPatterns integrates with common R genomic analysis workflows and allows easy association with (publicly available) annotation data. Package: r-bioc-mygene Architecture: all Version: 1.48.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicfeatures, r-bioc-txdbmaker, r-cran-httr, r-cran-jsonlite, r-cran-hmisc, r-cran-sqldf, r-cran-plyr, r-bioc-s4vectors Suggests: r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-mygene_1.48.0-1.ca2404.1_all.deb Size: 228182 MD5sum: b096343832935233f70045bbe353927a SHA1: 0378a4bfeed3587f8c4fbd45aa00553d94e55584 SHA256: a89758a3b17789b5c9725974f82e87d55a106e637149a5bd291202f42d06c2d6 SHA512: e578d6f0167f6066c4cffe4d9bb237a8ffb0abaad188850eb26860d808ebb4e6aabd93da891ec8946e3f89e7c3b0766c47fd402a52e87682a10bc79feee21161 Homepage: https://cran.r-project.org/package=mygene Description: Bioc Package 'mygene' (Access MyGene.Info_ services) MyGene.Info_ provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. *mygene*, is an easy-to-use R wrapper to access MyGene.Info_ services. Package: r-bioc-mzid Architecture: all Version: 1.50.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3398 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xml, r-cran-plyr, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-bioc-protgenerics Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-mzid_1.50.0-1.ca2404.1_all.deb Size: 753536 MD5sum: fe60da6afed28563071e4f60d05ba11e SHA1: abbf1f5b50998bbde7d804349595e056742825a7 SHA256: 240e328c74dd553e9e6aa3cd770692d2e20fcf5f01f5b9d415c264748158d3a1 SHA512: 9d402027b2de935a7f7672afe5128e38c7bbf68a8a370ad21aea793ba263897c05f85d408342b8c073679be80ca77a41934adc1f7f77998f9e172337f9aeaed8 Homepage: https://cran.r-project.org/package=mzID Description: Bioc Package 'mzID' (An mzIdentML parser for R) A parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. Please contact the maintainer with any problems and supply an mzIdentML file so the problems can be fixed quickly. Package: r-bioc-nanostringnctools Architecture: all Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10333 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-s4vectors, r-cran-ggplot2, r-bioc-biocgenerics, r-bioc-biostrings, r-cran-ggbeeswarm, r-cran-ggiraph, r-cran-ggthemes, r-bioc-iranges, r-cran-pheatmap, r-cran-rcolorbrewer Suggests: r-bioc-biovizbase, r-bioc-ggbio, r-cran-runit, r-cran-rmarkdown, r-cran-knitr, r-cran-qpdf Filename: pool/dists/noble/main/r-bioc-nanostringnctools_1.20.0-1.ca2404.1_all.deb Size: 5010648 MD5sum: 715e1a49278cee411bf5a11e02cc4a61 SHA1: df2e16181abbaecb4357ef5f8074553916ed9e62 SHA256: 46e6e99cdf6479a44c9320a18319806e6e18201d7b74ca514440676934ba92a5 SHA512: 2f13e2ca591d228e41c9a408a140a7e6efac6384d63cd5f65b759285a2a37dcfa81fd97ac151ef115a5966ad2ca53bfd8c1f9e35c20052be26b19526f0231508 Homepage: https://cran.r-project.org/package=NanoStringNCTools Description: Bioc Package 'NanoStringNCTools' (NanoString nCounter Tools) Tools for NanoString Technologies nCounter Technology. Provides support for reading RCC files into an ExpressionSet derived object. Also includes methods for QC and normalizaztion of NanoString data. Package: r-bioc-nebulosa Architecture: all Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5273 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-patchwork, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-seuratobject, r-cran-ks, r-cran-matrix, r-cran-ggrastr Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-bioc-scater, r-bioc-scran, r-bioc-dropletutils, r-cran-igraph, r-bioc-biocfilecache, r-cran-seurat Filename: pool/dists/noble/main/r-bioc-nebulosa_1.22.0-1.ca2404.1_all.deb Size: 2791160 MD5sum: 9462a7853e6fe1b497edc07ba00dc878 SHA1: baa2e443a5a87a1c3f471e8e5f134592e9a1fa21 SHA256: b97abcb1c3c0b09090b6a16cc50100f76b18946a9555933b6f98324e9daf7503 SHA512: be0c8bf0ada5124a5dab9fd1f3a9443724a365777aa7284787c409f057526a84d09710b938a05862afd3e28f2cb5cf2c6f47796a491b84d366b0143b2319bcc4 Homepage: https://cran.r-project.org/package=Nebulosa Description: Bioc Package 'Nebulosa' (Single-Cell Data Visualisation Using Kernel Gene-WeightedDensity Estimation) This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa. Package: r-bioc-noiseq Architecture: all Version: 2.56.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2718 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-cran-matrix Filename: pool/dists/noble/main/r-bioc-noiseq_2.56.0-1.ca2404.1_all.deb Size: 2507288 MD5sum: 32e415ed9df4cb0a214bd37de6558fa4 SHA1: 062665acf485f7dce5d1de54dc5cfbc21c3365b7 SHA256: 202c2c7c96ffc3b974ce5b91c71f1189f48b652fac3e402e6d668c17388a98c0 SHA512: 289d7070277d3d37bdcfa47c32d69953102887445df4ff2616112f36dc2eb3c91499ecb1d785a67efaffd96e1a09e049f3767ee8cdd1a0a180af78ebe1ccfe9b Homepage: https://cran.r-project.org/package=NOISeq Description: Bioc Package 'NOISeq' (Exploratory analysis and differential expression for RNA-seqdata) Analysis of RNA-seq expression data or other similar kind of data. Exploratory plots to evualuate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. Differential expression between two experimental conditions with no parametric assumptions. Package: r-bioc-oligoclasses Architecture: all Version: 1.74.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1824 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-biobase, r-bioc-iranges, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-biostrings, r-bioc-affyio, r-cran-foreach, r-cran-biocmanager, r-bioc-s4vectors, r-cran-rsqlite, r-cran-dbi, r-cran-ff Suggests: r-bioc-hapmapsnp5, r-bioc-hapmapsnp6, r-bioc-pd.genomewidesnp.6, r-bioc-pd.genomewidesnp.5, r-bioc-pd.mapping50k.hind240, r-bioc-pd.mapping50k.xba240, r-bioc-pd.mapping250k.sty, r-bioc-pd.mapping250k.nsp, r-bioc-genomewidesnp6crlmm, r-bioc-genomewidesnp5crlmm, r-cran-runit, r-bioc-human370v1ccrlmm, r-bioc-vanillaice, r-bioc-crlmm Filename: pool/dists/noble/main/r-bioc-oligoclasses_1.74.0-1.ca2404.1_all.deb Size: 1256796 MD5sum: ade203372452a8da6debeb77bd301865 SHA1: f0d5095a7f1798e10c0e29e8fdeb803a1f2eb107 SHA256: 80e7ffa8e5a8c31dff246805238336519083b7352c9f533c6b28644b680641f2 SHA512: bd179be476eb4625a703c55c0ecb24a97e52c58ca77cda302eb720f90ac0b918be698fa58327e1d1289a9bc3841a0393efa24989aa7584268af0d2cbe5a64aa1 Homepage: https://cran.r-project.org/package=oligoClasses Description: Bioc Package 'oligoClasses' (Classes for high-throughput arrays supported by oligo and crlmm) This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages. Package: r-bioc-omnipathr Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8153 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-crayon, r-cran-curl, r-cran-digest, r-cran-dplyr, r-cran-fs, r-cran-httr2, r-cran-igraph, r-cran-jsonlite, r-cran-later, r-cran-logger, r-cran-lubridate, r-cran-magrittr, r-cran-progress, r-cran-purrr, r-cran-rappdirs, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-rsqlite, r-cran-r.utils, r-cran-rvest, r-cran-sessioninfo, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs, r-cran-withr, r-cran-xml, r-cran-xml2, r-cran-yaml, r-cran-zip Suggests: r-bioc-biocstyle, r-cran-bookdown, r-cran-ggplot2, r-cran-ggraph, r-cran-gprofiler2, r-cran-knitr, r-cran-mlrmbo, r-cran-parallelmap, r-cran-paramhelpers, r-cran-r.matlab, r-bioc-sbmlr, r-cran-sigmajs, r-cran-smoof, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-omnipathr_4.0.0-1.ca2404.1_all.deb Size: 2552002 MD5sum: 8e105acbb287998ef84125779e0f15cd SHA1: 7c538c2f3ba5c177a85e39ba8afb469fc234b984 SHA256: 588e0f168df01f518853b3f3793fb7840782188880f97777293a752d06282310 SHA512: f926c3aa4f152262fa5f4ac6e50e9e07d3b3fe460460b480ee10e069ff33efe8adc7548b01acc72077c09c7fc6c8dfa46df1d337a110b89e701d94a18189ab7e Homepage: https://cran.r-project.org/package=OmnipathR Description: Bioc Package 'OmnipathR' (OmniPath web service client and more) A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github). Package: r-bioc-org.dr.eg.db Architecture: all Version: 3.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144077 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-annotationdbi Suggests: r-cran-dbi, r-bioc-annotate, r-cran-runit Filename: pool/dists/noble/main/r-bioc-org.dr.eg.db_3.22.0-1.ca2404.1_all.deb Size: 25092002 MD5sum: c8ae3461b9a0811719be1b4c72273dcb SHA1: ea8ece2dd5599b9b8ee61654afb1c3ded9fdca81 SHA256: 40c516a854e748cefb8a501119ad8bbccbb564d8cbd1d07ef59942a1337c361e SHA512: c7e100953b815ac8313ea1fe13659051cb9a5d48104ef5cb3b90b97649aff451409a3d4d9c927c1f457763cfab380754d49a3f0a337e915434226e7053dc65cf Homepage: https://cran.r-project.org/package=org.Dr.eg.db Description: Bioc Package 'org.Dr.eg.db' (Genome wide annotation for Zebrafish) Genome wide annotation for Zebrafish, primarily based on mapping using Entrez Gene identifiers. Package: r-bioc-org.hs.eg.db Architecture: all Version: 3.23.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407999 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi Suggests: r-cran-dbi, r-bioc-annotate, r-cran-runit Filename: pool/dists/noble/main/r-bioc-org.hs.eg.db_3.23.1-1.ca2404.1_all.deb Size: 67848116 MD5sum: 52ee45db694af01da759ada514c83d58 SHA1: 8869aba1d1036eb603079f899fa79198d6ba47c4 SHA256: e58097a19057d29c6c20775a79ce079547ccaa3937ae2fe1bde92332ad00c9f4 SHA512: a3b9b08f2b2682d0cec6d4225064ca2bd118e1057084378288a6d9bd0f5f793e709addba3622bd0f09d325badf3e1cff72ff305eceec9f44e49a0245f224f5e0 Homepage: https://cran.r-project.org/package=org.Hs.eg.db Description: Bioc Package 'org.Hs.eg.db' (Genome wide annotation for Human) Genome wide annotation for Human, primarily based on mapping using Entrez Gene identifiers. Package: r-bioc-org.mm.eg.db Architecture: all Version: 3.23.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389774 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi Suggests: r-cran-dbi, r-bioc-annotate, r-cran-runit Filename: pool/dists/noble/main/r-bioc-org.mm.eg.db_3.23.0-1.ca2404.1_all.deb Size: 62154034 MD5sum: 7540ebbf79d8d9c991930451c63d9c37 SHA1: b1e6647d780fd51d462455d4822ee74121f95124 SHA256: 08a34bf0b8704453102b5267eee5b9d5425faed2631ab07632488ea99425de6e SHA512: 6a65204b0e00935a2ba4a914f9518902a599293dbfbeea3584b97c486cef30a93736b3653beea7f104ade0814dd093ba7fb9ec196a6059abe6036817b6f959ba Homepage: https://cran.r-project.org/package=org.Mm.eg.db Description: Bioc Package 'org.Mm.eg.db' (Genome wide annotation for Mouse) Genome wide annotation for Mouse, primarily based on mapping using Entrez Gene identifiers. Package: r-bioc-organism.dplyr Architecture: all Version: 1.37.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-bioc-annotationfilter, r-cran-rsqlite, r-bioc-s4vectors, r-bioc-seqinfo, r-bioc-iranges, r-bioc-genomicranges, r-bioc-genomicfeatures, r-bioc-annotationdbi, r-cran-rlang, r-bioc-biocfilecache, r-cran-dbi, r-cran-dbplyr, r-cran-tibble Suggests: r-bioc-genomeinfodb, r-bioc-org.hs.eg.db, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-org.mm.eg.db, r-bioc-txdb.mmusculus.ucsc.mm10.ensgene, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-magick, r-bioc-biocstyle, r-cran-ggplot2 Filename: pool/dists/noble/main/r-bioc-organism.dplyr_1.37.1-1.ca2404.1_all.deb Size: 796258 MD5sum: 2d3314d0e327a5ae4e87de473e093cb4 SHA1: af69145220d08f6b99a9b9d086cbd5c2e0110c8d SHA256: 114bd5eabb9139eb2d72403b35d2e55967827f98a017bd69b42be06fffdf561e SHA512: ff869027463f30b16575c6451da480f959269bc72144c24f72dfaf78cec83d2a41dc61fc04963594956ae598a70f8fdda11a9856e1ab9942c513c2687c34058d Homepage: https://cran.r-project.org/package=Organism.dplyr Description: Bioc Package 'Organism.dplyr' (dplyr-based Access to Bioconductor Annotation Resources) This package provides an alternative interface to Bioconductor 'annotation' resources, in particular the gene identifier mapping functionality of the 'org' packages (e.g., org.Hs.eg.db) and the genome coordinate functionality of the 'TxDb' packages (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene). Package: r-bioc-organismdbi Architecture: all Version: 1.54.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1378 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-annotationdbi, r-bioc-seqinfo, r-bioc-genomicfeatures, r-cran-dbi, r-cran-biocmanager, r-bioc-biobase, r-bioc-graph, r-bioc-rbgl, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomicranges Suggests: r-bioc-txdbmaker, r-bioc-genomeinfodbdata, r-bioc-homo.sapiens, r-bioc-rattus.norvegicus, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-annotationhub, r-bioc-fdb.ucsc.trnas, r-bioc-rtracklayer, r-bioc-biomart, r-cran-runit, r-cran-rmariadb, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-organismdbi_1.54.0-1.ca2404.1_all.deb Size: 708072 MD5sum: e31136dc3809907893f6e9689da3dece SHA1: bbe1c2845fd666ae40fe21bf34e2fe4c92ae59bc SHA256: df2f998085d441ff8724c08b0c3bb16ef1b157c9a2ce60c41f192119056a3d0a SHA512: 489c4e32e81719d01f37bfc51f33a0a97b3f6ff93a714d507a05d3ca210c3c4ee94420d556fb5fa8beeed455c000ad1f5cfc348d536bab1aa995fa2779045218 Homepage: https://cran.r-project.org/package=OrganismDbi Description: Bioc Package 'OrganismDbi' (Software to enable the smooth interfacing of different databasepackages) The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods. Package: r-bioc-orthogene Architecture: all Version: 1.15.02-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4432 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-matrix, r-cran-jsonlite, r-cran-homologene, r-cran-gprofiler2, r-cran-babelgene, r-cran-data.table, r-cran-ggplot2, r-cran-ggpubr, r-cran-patchwork, r-bioc-delayedarray, r-cran-grr, r-cran-repmis, r-bioc-ggtree Suggests: r-cran-rworkflows, r-cran-remotes, r-cran-knitr, r-bioc-biocstyle, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat, r-cran-piggyback, r-cran-magick, r-bioc-genomeinfodbdata, r-cran-ape, r-cran-phytools, r-cran-rphylopic, r-cran-treetools, r-cran-ggimage, r-bioc-omadb Filename: pool/dists/noble/main/r-bioc-orthogene_1.15.02-1.ca2404.1_all.deb Size: 2491540 MD5sum: 67323e0e9f35ad305d2a9061b9f07c6a SHA1: 1b8892fd621008eefc33075e17abf134c24bb773 SHA256: 9be6d53506262a91da897856da1b038bd21e6476166b1c0af4b1bdacbf4f8f50 SHA512: eee85f52aeab19092febede13bb999daec094f6d7aeab0c11ef6d1f5e600acd86a43e66fdb004f4ba772ebabdad9ef916737f05ac16c8d660ba8f2804b632507 Homepage: https://cran.r-project.org/package=orthogene Description: Bioc Package 'orthogene' (Interspecies gene mapping) `orthogene` is an R package for easy mapping of orthologous genes across hundreds of species. It pulls up-to-date gene ortholog mappings across **700+ organisms**. It also provides various utility functions to aggregate/expand common objects (e.g. data.frames, gene expression matrices, lists) using **1:1**, **many:1**, **1:many** or **many:many** gene mappings, both within- and between-species. Package: r-bioc-pathview Architecture: all Version: 1.52.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3196 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-kegggraph, r-cran-xml, r-bioc-rgraphviz, r-bioc-graph, r-cran-png, r-bioc-annotationdbi, r-bioc-org.hs.eg.db, r-bioc-keggrest Suggests: r-bioc-gage, r-bioc-org.mm.eg.db, r-cran-runit, r-bioc-biocgenerics Filename: pool/dists/noble/main/r-bioc-pathview_1.52.0-1.ca2404.1_all.deb Size: 2932854 MD5sum: 036b595c0f867a6b7dd3d07fbb336c6e SHA1: 574bfe665f6138c81aa1d8914ef1275af6a914ed SHA256: 38b7b0953cf29a4ac5408974eea917fa8f6ba60e5dee43ba12ca3fe7b26ff749 SHA512: 468a8079ab08ba37841350260aa0bfd102984f4ff3391c248aebad5e48f7f6040fe26113ce782be59fd0b9fb2e1e7cc40453c1d357f371264d9fc7dea71fb3a3 Homepage: https://cran.r-project.org/package=pathview Description: Bioc Package 'pathview' (a tool set for pathway based data integration and visualization) Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis. Package: r-bioc-pcaexplorer Architecture: all Version: 3.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13783 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-deseq2, r-bioc-summarizedexperiment, r-bioc-mosdef, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-genefilter, r-cran-ggplot2, r-cran-heatmaply, r-cran-plotly, r-cran-scales, r-cran-nmf, r-cran-plyr, r-bioc-topgo, r-bioc-limma, r-bioc-gostats, r-bioc-go.db, r-bioc-annotationdbi, r-cran-shiny, r-cran-shinydashboard, r-cran-shinybs, r-cran-ggrepel, r-cran-dt, r-cran-shinyace, r-cran-threejs, r-bioc-biomart, r-cran-pheatmap, r-cran-knitr, r-cran-rmarkdown, r-cran-base64enc, r-cran-tidyr Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-markdown, r-bioc-airway, r-bioc-org.hs.eg.db, r-cran-htmltools Filename: pool/dists/noble/main/r-bioc-pcaexplorer_3.6.0-1.ca2404.1_all.deb Size: 6871776 MD5sum: 423aca853279e61e724d9bdadd96eead SHA1: 818aa5d9d24df67806ee60852b041272866b702f SHA256: b5ace0eea59fa85be0d0af62e1e6c22ac02d56ec9e4eb52389850f6a781e4d56 SHA512: 7198c97d65fdb5da063540feeb931cc6284d54e4428d8f466498cc3d0e4df8c3036243056058d7e4ebf316f1279ca13094d3fe7a96affe6fb74bb7569079276e Homepage: https://cran.r-project.org/package=pcaExplorer Description: Bioc Package 'pcaExplorer' (Interactive Visualization of RNA-seq Data Using a PrincipalComponents Approach) This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis. Package: r-bioc-pedixplorer Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12188 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-stringr, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-quadprog, r-cran-matrix, r-bioc-s4vectors, r-cran-shiny, r-cran-readxl, r-cran-dt, r-cran-igraph, r-cran-shinycssloaders, r-cran-shinyhelper, r-cran-shinyjs, r-cran-shinyjqui, r-cran-shinywidgets, r-cran-htmlwidgets, r-cran-plotly, r-cran-colourpicker, r-cran-shinytoastr Suggests: r-cran-diffviewer, r-cran-gridextra, r-cran-testthat, r-cran-vdiffr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-knitr, r-cran-withr, r-cran-qpdf, r-cran-shinytest2, r-cran-devtools, r-cran-r.devices, r-cran-usethis, r-cran-rlang, r-cran-magick, r-cran-cowplot Filename: pool/dists/noble/main/r-bioc-pedixplorer_1.8.0-1.ca2404.1_all.deb Size: 4998648 MD5sum: 05b47f8295044d93fcd0b90584760e37 SHA1: 9bf82ba2312a4cbd337b5358a48af3946abf608f SHA256: 0ce7ba066c487899e52798716ce8620468ed139fe76a0ffa0591c3e0460bbca1 SHA512: fbec379c4f875686e71fcb2696442751126c15306c210815228b4234d1349ee644d67e481a8ea1cfcdfd0c895aa1a3bebe5d1b20bc4b4efc1538a2891bf98ab9 Homepage: https://cran.r-project.org/package=Pedixplorer Description: Bioc Package 'Pedixplorer' (Pedigree Functions) Routines to handle family data with a Pedigree object. The initial purpose was to create correlation structures that describe family relationships such as kinship and identity-by-descent, which can be used to model family data in mixed effects models, such as in the coxme function. Also includes a tool for Pedigree drawing which is focused on producing compact layouts without intervention. Recent additions include utilities to trim the Pedigree object with various criteria, and kinship for the X chromosome. 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There are three main types: (1) data manipulation and exploration—functions useful for converting reads to haplotypes and frequencies, repairing reads, intersecting strand haplotypes, and visualizing haplotype alignments. (2) diversity indices—functions to compute diversity and entropy, in which incidence, abundance, and functional indices are considered. (3) data simulation—functions useful for generating random viral quasispecies data. Package: r-bioc-quantiseqr Architecture: all Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5050 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-cran-limsolve, r-cran-mass, r-bioc-preprocesscore, r-bioc-summarizedexperiment, r-cran-ggplot2, r-cran-tidyr, r-cran-rlang Suggests: r-bioc-annotationdbi, r-bioc-biocstyle, r-cran-dplyr, r-bioc-experimenthub, r-bioc-geoquery, r-cran-knitr, r-bioc-macrophage, r-bioc-org.hs.eg.db, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-bioc-quantiseqr_1.20.0-1.ca2404.1_all.deb Size: 3666140 MD5sum: 9a00c7e45ee9ddc9a085e434ba762be3 SHA1: cd7b619426a1bfd88ef601a0dc186451c19df1b7 SHA256: 496d2c4b214b89a9f415f099f7361d3fb14cdd64651ea1e81380311489780657 SHA512: 0600d2f8c2b04880d04fb4da6709e6ec502497405da34b939dc028e09d53af9e249d7ee8157357935a798597ca10d52c219605c57b853b371d6efb372f7ab386 Homepage: https://cran.r-project.org/package=quantiseqr Description: Bioc Package 'quantiseqr' (Quantification of the Tumor Immune contexture from RNA-seq data) This package provides a streamlined workflow for the quanTIseq method, developed to perform the quantification of the Tumor Immune contexture from RNA-seq data. The quantification is performed against the TIL10 signature (dissecting the contributions of ten immune cell types), carefully crafted from a collection of human RNA-seq samples. The TIL10 signature has been extensively validated using simulated, flow cytometry, and immunohistochemistry data. Package: r-bioc-quantro Architecture: all Version: 1.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4047 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-minfi, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-ggplot2, r-cran-rcolorbrewer Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-runit, r-bioc-biocgenerics, r-bioc-biocstyle Filename: pool/dists/noble/main/r-bioc-quantro_1.46.0-1.ca2404.1_all.deb Size: 3153818 MD5sum: 4580dff872dd4196eeadda1ea91bcd5a SHA1: 35886209572b3957678feb12dd85bfd5f903606a SHA256: bc97c9b3c30f0179e03e0230da6cb3e12bb5f41f2ac79485165b5e79d661d09c SHA512: a3d199265d0041dd754fa5b0e7cacdbd1115ff76d22a1f3f813721996f976ae9694fd6040f18872a7785392d10cebdf59012009a9c6e17273273100cc9ab0b4a Homepage: https://cran.r-project.org/package=quantro Description: Bioc Package 'quantro' (A test for when to use quantile normalization) A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups. Package: r-bioc-quantsmooth Architecture: all Version: 1.78.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quantreg Filename: pool/dists/noble/main/r-bioc-quantsmooth_1.78.0-1.ca2404.1_all.deb Size: 430662 MD5sum: 6a9d6cd8b86f61a5835cc028157e4d10 SHA1: bc99884b4824aa844d5a48f718e14ca74f2ca6df SHA256: 4d77ec32a3bf437840a9d7655583b7e3512aaa7ba9bbf1a28d4bcd5432bc2474 SHA512: b117ade0d3132f5ae75f4dc279606c3bfe71f3ce814d8af49fc0245c7fc9efc5014484e85137a24de19a6c3f9e538bb9d1356a412666258fbec0af6873778796 Homepage: https://cran.r-project.org/package=quantsmooth Description: Bioc Package 'quantsmooth' (Quantile smoothing and genomic visualization of array data) Implements quantile smoothing as introduced in: Quantile smoothing of array CGH data; Eilers PH, de Menezes RX; Bioinformatics. 2005 Apr 1;21(7):1146-53. Package: r-bioc-qusage Architecture: all Version: 2.46.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10270 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-limma, r-bioc-biobase, r-cran-nlme, r-cran-emmeans, r-cran-fftw Filename: pool/dists/noble/main/r-bioc-qusage_2.46.0-1.ca2404.1_all.deb Size: 10238950 MD5sum: 08951d4a61f92e7ab2ce7f9894ec870c SHA1: b2510dbaf2352f3dbb84fa9ce4ac45e7ffbcbbbf SHA256: 2f5b0eb157f922bd03ebb194c6ce498bec7db21948171af672f20b991901efe1 SHA512: 2720ca3275b10db7ce416f153f7b2edb0c579c294baefe382cda84974864faa5375d27dcdd30d2692b12ebcb8f740472cba7cc1bfcac782e9e95df4615e4e20f Homepage: https://cran.r-project.org/package=qusage Description: Bioc Package 'qusage' (qusage: Quantitative Set Analysis for Gene Expression) This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. The QuSAGE package also includes a mixed effects model implementation, as described in (Turner JA et al, BMC Bioinformatics, 2015), and a meta-analysis framework as described in (Meng H, et al. PLoS Comput Biol. 2019). For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu) Package: r-bioc-qvalue Architecture: all Version: 2.44.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2823 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-bioc-qvalue_2.44.0-1.ca2404.1_all.deb Size: 2810588 MD5sum: 005e898b46f68cc2a8763214693ba47f SHA1: 790cf9eadcc66c0c75fb7c09de86e5c31173f64f SHA256: 0637f3b4735edf2aa2f6bb172846117a9479dae8bfddd30a8e8772ea1ae2e54c SHA512: 820f8176bc30b559c1322ecac0512ee783ebb0e406c3d9ac4de953e607206b4437afcf6843e1d7abc4571cee1e89690bd93027394a9755c4e8b88fcd76188aa5 Homepage: https://cran.r-project.org/package=qvalue Description: Bioc Package 'qvalue' (Q-value estimation for false discovery rate control) This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. Package: r-bioc-r4rna Architecture: all Version: 1.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1120 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings Filename: pool/dists/noble/main/r-bioc-r4rna_1.40.0-1.ca2404.1_all.deb Size: 1039304 MD5sum: 528fe1e9dfbc326c1cfb2107eb277e12 SHA1: a790108027edb801fe00844a2cdeb1a80218dae8 SHA256: 66868e9d8c731a20b2a211a00e9db05ff79397f6f38b0456fdb79f2ca17821c7 SHA512: 75038ec8050f2aa5f05f489b28414df426f7f5e0d9eb2b5d43750d4ec1675cf91b317e122d1e16dc3c82273b05920f86e74e287dbd5dc70d5d840bd7b326d7ad Homepage: https://cran.r-project.org/package=R4RNA Description: Bioc Package 'R4RNA' (An R package for RNA visualization and analysis) A package for RNA basepair analysis, including the visualization of basepairs as arc diagrams for easy comparison and annotation of sequence and structure. Arc diagrams can additionally be projected onto multiple sequence alignments to assess basepair conservation and covariation, with numerical methods for computing statistics for each. Package: r-bioc-raggedexperiment Architecture: all Version: 1.36.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2952 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-biocbaseutils, r-bioc-biocgenerics, r-bioc-seqinfo, r-bioc-iranges, r-cran-matrix, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-summarizedexperiment Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-genomeinfodb, r-bioc-multiassayexperiment Filename: pool/dists/noble/main/r-bioc-raggedexperiment_1.36.0-1.ca2404.1_all.deb Size: 776018 MD5sum: 69e6e6768b6377456fe5aba0afbf0780 SHA1: 202ffed388b9e81c2c4b06c6e6985b78ca31a98a SHA256: 450463df9a6fee1aad852b80fdea3a6939ff18ce5cd29fd168119f82ee5d9f71 SHA512: a51d7dee5588c6ff08e4d08a5cd6d3d9eefc0fa6e264ea6236f80dc24eee63538fe5dc6562ecd8dc9ad915f6fded6dd88b3789b6eb904c97cb09077e87066309 Homepage: https://cran.r-project.org/package=RaggedExperiment Description: Bioc Package 'RaggedExperiment' (Representation of Sparse Experiments and Assays Across Samples) This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. The RaggedExperiment class derives from a GRangesList representation and provides a semblance of a rectangular dataset. Package: r-bioc-rankprod Architecture: all Version: 3.38.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 959 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rmpfr, r-cran-gmp Filename: pool/dists/noble/main/r-bioc-rankprod_3.38.0-1.ca2404.1_all.deb Size: 887938 MD5sum: ed8420714b85a2a278b317792c8f7da7 SHA1: dba1ce2338d8b20392b29eb6fad5515128911044 SHA256: d925be418dc91da25b9c0f150a698c0f0bd6c4df907b11949219a92b4ccc8325 SHA512: 5fd3295df8d3b849416e4724aa041664ea9cd8d6d4e538079c916f9abd33e314d9100f43f0cca5344a8c416603c3b39f28775ce1621df1aa7e67bac99a716210 Homepage: https://cran.r-project.org/package=RankProd Description: Bioc Package 'RankProd' (Rank Product method for identifying differentially expressedgenes with application in meta-analysis) Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification. Package: r-bioc-rcistarget Architecture: all Version: 1.29.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15552 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-aucell, r-bioc-biocgenerics, r-cran-data.table, r-bioc-genomeinfodb, r-bioc-genomicranges, r-cran-arrow, r-cran-dplyr, r-cran-tibble, r-bioc-gseabase, r-cran-r.utils, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-cran-zoo Suggests: r-bioc-biobase, r-bioc-biocstyle, r-bioc-biocparallel, r-cran-doparallel, r-cran-dt, r-cran-foreach, r-cran-gplots, r-bioc-rtracklayer, r-cran-igraph, r-cran-knitr, r-bioc-rcistarget.hg19.motifdbs.cisbponly.500bp, r-cran-rmarkdown, r-cran-testthat, r-cran-visnetwork Filename: pool/dists/noble/main/r-bioc-rcistarget_1.29.0-1.ca2404.1_all.deb Size: 12916128 MD5sum: 3ce8a792b71db997866fdc0c3dd57a65 SHA1: 0ea7479c0577ba61cee8fabe26833641635eeb47 SHA256: ab9f2881050f48334a39ac3cad75c79d1487bd402e370dbd2c43410934be0f3e SHA512: 872d3f3a4d58f139da2a052817f46d508eec9d1de6ee590b36368ca0b3f970210c8b85e7303d5d9844df421e6c7ae432ab3f0e65738b29b4172196a0d5b2a124 Homepage: https://cran.r-project.org/package=RcisTarget Description: Bioc Package 'RcisTarget' (RcisTarget Identify transcription factor binding motifs enrichedon a list of genes or genomic regions) RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge). Package: r-bioc-rcy3 Architecture: all Version: 2.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17762 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-rjsonio, r-cran-xml, r-bioc-biocgenerics, r-bioc-graph, r-cran-fs, r-cran-uuid, r-cran-stringi, r-cran-glue, r-cran-rcurl, r-cran-base64url, r-cran-base64enc, r-cran-irkernel, r-cran-irdisplay, r-cran-rcolorbrewer, r-cran-gplots Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-igraph Filename: pool/dists/noble/main/r-bioc-rcy3_2.32.0-1.ca2404.1_all.deb Size: 3742688 MD5sum: 8381bbb9fc7c8649fb715480208d08dd SHA1: 3161ff7980e0540f1de28d449b884b5986cc61a2 SHA256: 7b42f88f8d0af4e91dac0b54b1f4f3baaebff124b0a13f34931ba835fd7503d1 SHA512: 92708dcc1f71d14be85c21e2bf5f777b99556720ff6c050efe3d1a752645083130754a381b13026c4efa820da979298bd44173968e370dd9700a023ec62a0ebe Homepage: https://cran.r-project.org/package=RCy3 Description: Bioc Package 'RCy3' (Functions to Access and Control Cytoscape) Vizualize, analyze and explore networks using Cytoscape via R. Anything you can do using the graphical user interface of Cytoscape, you can now do with a single RCy3 function. Package: r-bioc-reactome.db Architecture: all Version: 1.96.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1398115 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi Suggests: r-cran-rsqlite Filename: pool/dists/noble/main/r-bioc-reactome.db_1.96.0-1.ca2404.1_all.deb Size: 220047100 MD5sum: 0c9f6be79655c673710ced253f65a49c SHA1: b5fa6b50a5833049710c7188ecf8e8063be35782 SHA256: 499ea43ba72284fe2203dadbcac1ab8c93e857d87d42666f84cc4f31a0211032 SHA512: fed96d7de67cac8d0a36b00e50c6ce14ed5c5f57ff230032bf8e5993e599b403c49a7d6b62993548dd828096eb80e7f188c643fe2f076dc89d21460a6fd9f124 Homepage: https://cran.r-project.org/package=reactome.db Description: Bioc Package 'reactome.db' (A set of annotation maps for reactome) A set of annotation maps for reactome assembled using data from reactome. This package has been created by a third-party developer, and is not affiliated with the Reactome team. Package: r-bioc-reactomegsa Architecture: all Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1149 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocsingular, r-cran-dplyr, r-cran-ggplot2, r-cran-gplots, r-cran-httr, r-cran-igraph, r-cran-jsonlite, r-cran-progress, r-cran-rcolorbrewer, r-bioc-summarizedexperiment, r-cran-tidyr Suggests: r-cran-devtools, r-cran-knitr, r-bioc-reactomegsa.data, r-cran-rmarkdown, r-bioc-scater, r-bioc-scran, r-bioc-scrnaseq, r-bioc-scuttle, r-cran-seurat, r-bioc-singlecellexperiment, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-reactomegsa_1.26.0-1.ca2404.1_all.deb Size: 723670 MD5sum: 64650f30e714993c2211e5200b2e555f SHA1: 7d5268299f7f8b200b2d8f51537d8536e7407264 SHA256: dff0b5afc161658e0c38c443df59be89ceec9f2f5e2f78daa8500e22e90b5396 SHA512: e5c8297269933b1f788b062320cfdfa6a21629a0bd9177678381508752a0e8c6fc732a192dfe8aac9d3d841b2059090365def1d9b283cba32e48783d38312202 Homepage: https://cran.r-project.org/package=ReactomeGSA Description: Bioc Package 'ReactomeGSA' (Client for the Reactome Analysis Service for comparativemulti-omics gene set analysis) The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited. Package: r-bioc-reactomepa Architecture: all Version: 1.56.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-bioc-enrichplot, r-cran-enrichit, r-cran-ggplot2, r-cran-ggraph, r-bioc-reactome.db, r-cran-igraph, r-bioc-graphite, r-cran-gson, r-cran-yulab.utils Suggests: r-bioc-clusterprofiler, r-cran-knitr, r-cran-rmarkdown, r-bioc-org.hs.eg.db, r-cran-prettydoc, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-reactomepa_1.56.0-1.ca2404.1_all.deb Size: 85114 MD5sum: f857af8ff551cd96144524be6ae28691 SHA1: a46fe2a57e170cb1967d95d31ef7d67616d85e53 SHA256: b7d3c4b9b0c2eabc7e5cd838ab1ce33f3ecc8f4320f2ba8d8a9e2bdcde62a754 SHA512: 4fc2c8eb9af3ca34aa88ace77ba1f1b97e075f58ec2c698a678a813074140ff2b89f785022cc6f87e98e55d51380610df1d0d98cdc34f1eb158477d12adb09be Homepage: https://cran.r-project.org/package=ReactomePA Description: Bioc Package 'ReactomePA' (Reactome Pathway Analysis) This package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. This package is not affiliated with the Reactome team. Package: r-bioc-recount3 Architecture: all Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1553 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-bioc-biocfilecache, r-cran-data.table, r-bioc-genomicranges, r-cran-httr, r-cran-matrix, r-cran-r.utils, r-bioc-rtracklayer, r-bioc-s4vectors, r-cran-sessioninfo Suggests: r-bioc-biocstyle, r-cran-covr, r-cran-knitcitations, r-cran-knitr, r-cran-lobstr, r-cran-refmanager, r-cran-rmarkdown, r-cran-testthat, r-bioc-recount Filename: pool/dists/noble/main/r-bioc-recount3_1.22.0-1.ca2404.1_all.deb Size: 613028 MD5sum: 83b655005b9cbe300379b6a6dd72aff9 SHA1: b5a6497daf6485f595f1f2cb3df274a9eaf5a766 SHA256: 41f85d62bf9d9019859ee88652dd99b6576f205f82e8df26d7d715c4db3b3c8f SHA512: 3bb80d9b99cb52930cada4fdeee032a2616f946f0a1faea72c390e9c592b845ec962209204d8d7fe1d54da8084274d14c5830e898f1d59d4fd9c6c84e0a57322 Homepage: https://cran.r-project.org/package=recount3 Description: Bioc Package 'recount3' (Explore and download data from the recount3 project) The recount3 package enables access to a large amount of uniformly processed RNA-seq data from human and mouse. You can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level with sample metadata and QC statistics. In addition we provide access to sample coverage BigWig files. 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Graph, node, and edge attributes can be configured using either graphical or command-line methods, following igraph syntax rules. 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Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment). 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It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care. RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients' treatment. Furthermore, RTCGA package transforms TCGA data to tidy form which is convenient to use. 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Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. We developed an open source and extensible R based data client for Firehose pre-processed data and demonstrated its use with sample case studies. Results showed that RTCGAToolbox could improve data management for researchers who are interested with TCGA data. In addition, it can be integrated with other analysis pipelines for following data analysis. 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This includes chip tango addresses, mapping information, performance annotation, and trained predictor for Infinium array data. This package provides user access to essential annotation data for working with many generations of the Infinium DNA methylation array. Currently we support human array (HM27, HM450, EPIC), mouse array (MM285) and the HorvathMethylChip40 (Mammal40) array. 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Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation. Package: r-bioc-siggenes Architecture: all Version: 1.86.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1494 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-multtest, r-cran-scrime Suggests: r-bioc-affy, r-bioc-annotate, r-bioc-genefilter, r-cran-kernsmooth Filename: pool/dists/noble/main/r-bioc-siggenes_1.86.0-1.ca2404.1_all.deb Size: 1275358 MD5sum: e966cf8534cc54855f7c646c3c6753f3 SHA1: 7f67fcf9b033b7d72bb5088f3d7cb48336d2414e SHA256: 07704afd6123a8fc5da8cd636c4fb3cb96af60935588fd4348c9ac6445a08b54 SHA512: 4273b9448f9d0810f614ba4e419a037872142d5f953890c1d3882de8193d7a417f5c74dbde9413a85a7886265fcf020a0e2d757a1240b2c725faddefe9a106c0 Homepage: https://cran.r-project.org/package=siggenes Description: Bioc Package 'siggenes' (Multiple Testing using SAM and Efron's Empirical BayesApproaches) Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM). Package: r-bioc-signaturesearchdata Architecture: all Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2452 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-experimenthub, r-bioc-affy, r-bioc-limma, r-bioc-biobase, r-cran-magrittr, r-cran-dplyr, r-cran-r.utils, r-bioc-rhdf5 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-signaturesearchdata_1.26.0-1.ca2404.1_all.deb Size: 600048 MD5sum: ad0b09a177f9177817d9ae90f3c35e9d SHA1: a952b8f8e5a7f7f419613a27491973c534a3acd1 SHA256: 5d7c7370b7a5dfe40030ac543f0ea79d061c7854178817aa428bf59127159847 SHA512: 11e92686d81feb2d88b3541e8310cc4a2b7507363d95ba967c23ea19ee273f8e937003543e944b84450bb93f153f1ec8895519bf0cc699ae5435eaf71ad9e8f1 Homepage: https://cran.r-project.org/package=signatureSearchData Description: Bioc Package 'signatureSearchData' (Datasets for signatureSearch package) CMAP/LINCS hdf5 databases and other annotations used for signatureSearch software package. Package: r-bioc-simplifyenrichment Architecture: all Version: 2.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1617 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-simona, r-bioc-complexheatmap, r-cran-circlize, r-cran-getoptlong, r-cran-digest, r-cran-tm, r-bioc-go.db, r-bioc-annotationdbi, r-cran-slam, r-cran-clue, r-cran-cluster, r-cran-colorspace, r-cran-globaloptions Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-cowplot, r-cran-mclust, r-cran-apcluster, r-cran-mcl, r-cran-dbscan, r-cran-igraph, r-cran-gridextra, r-cran-dynamictreecut, r-cran-testthat, r-cran-gridgraphics, r-cran-flexclust, r-cran-biocmanager, r-bioc-interactivecomplexheatmap, r-cran-shiny, r-cran-shinydashboard, r-bioc-cola, r-bioc-hu6800.db, r-cran-rmarkdown, r-bioc-genefilter, r-cran-gridtext, r-cran-fpc Filename: pool/dists/noble/main/r-bioc-simplifyenrichment_2.6.0-1.ca2404.1_all.deb Size: 1582224 MD5sum: dbabf68599a97b7f0f1a7965a8043b87 SHA1: 8bcf80be9b2d3950b243936c7e4d7328d91933fa SHA256: f9b8270e8c11e0c196f299da861065030f9a64b2a0b3fc0f10078d12d41a204a SHA512: 694a8345d0b15d64ed8529b8e6212fdbf90031d21076e85f9253b97b629aee120430543255afe74105eb22de27be89f44c210e691d79a1d7425068aff45ccb97 Homepage: https://cran.r-project.org/package=simplifyEnrichment Description: Bioc Package 'simplifyEnrichment' (Simplify Functional Enrichment Results) A new clustering algorithm, "binary cut", for clustering similarity matrices of functional terms is implemeted in this package. It also provides functions for visualizing, summarizing and comparing the clusterings. Package: r-bioc-singlecellexperiment Architecture: all Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4202 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-delayedarray Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-matrix, r-bioc-scrnaseq, r-cran-rtsne Filename: pool/dists/noble/main/r-bioc-singlecellexperiment_1.34.0-1.ca2404.1_all.deb Size: 1110446 MD5sum: afae5a33349c84266650c842044b1ae7 SHA1: 822f8f3c57ac7fc75ddf59a78353b6b21c7f17ec SHA256: c7abda1039f1f144138acd3ccd08a4137993112fd9069a1636d034488b815b4a SHA512: df3b22e0c98df54de7946eadcfa2fbdc54ebbae30d6438231e9febc29fe9a9ac03601d8246efae3e7b34a358dc030105951d1f2094dfd6232857b05c94636818 Homepage: https://cran.r-project.org/package=SingleCellExperiment Description: Bioc Package 'SingleCellExperiment' (S4 Classes for Single Cell Data) Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries. Package: r-bioc-singlecelltk Architecture: all Version: 2.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7336 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-bioc-delayedarray, r-bioc-biobase, r-cran-ape, r-cran-anndata, r-bioc-annotationhub, r-bioc-batchelor, r-bioc-biocparallel, r-bioc-celldex, r-cran-colourpicker, r-cran-colorspace, r-cran-cowplot, r-cran-cluster, r-bioc-complexheatmap, r-cran-data.table, r-bioc-delayedmatrixstats, r-bioc-deseq2, r-cran-dplyr, r-cran-dt, r-bioc-experimenthub, r-bioc-ensembldb, r-cran-fields, r-cran-ggplot2, r-cran-ggplotify, r-cran-ggrepel, r-bioc-ggtree, r-cran-gridextra, r-bioc-gsva, r-bioc-gsvadata, r-cran-igraph, r-cran-kernsmooth, r-bioc-limma, r-bioc-mast, r-cran-matrix, r-cran-matrixstats, r-cran-msigdbr, r-bioc-multtest, r-cran-plotly, r-cran-plyr, r-cran-rocr, r-cran-rtsne, r-bioc-s4vectors, r-bioc-scater, r-bioc-scmerge, r-bioc-scran, r-cran-seurat, r-cran-shiny, r-cran-shinyjs, r-bioc-singler, r-cran-stringr, r-cran-soupx, r-bioc-sva, r-cran-reshape2, r-cran-shinyalert, r-cran-circlize, r-cran-enrichr, r-bioc-celda, r-cran-shinycssloaders, r-bioc-dropletutils, r-bioc-scds, r-cran-reticulate, r-bioc-tximport, r-cran-tidyr, r-bioc-eds, r-cran-withr, r-bioc-gseabase, r-cran-r.utils, r-bioc-zinbwave, r-bioc-scrnaseq, r-bioc-tenxpbmcdata, r-cran-yaml, r-cran-rmarkdown, r-cran-magrittr, r-bioc-scdblfinder, r-cran-metap, r-cran-vam, r-cran-tibble, r-cran-rlang, r-bioc-tscan, r-bioc-trajectoryutils, r-bioc-scuttle, r-bioc-zellkonverter, r-cran-lifecycle Suggests: r-cran-testthat, r-bioc-rsubread, r-bioc-biocstyle, r-cran-knitr, r-cran-lintr, r-cran-spelling, r-bioc-org.mm.eg.db, r-cran-kableextra, r-cran-shinythemes, r-cran-shinybs, r-cran-shinyjqui, r-cran-shinywidgets, r-cran-shinyfiles, r-bioc-biocgenerics, r-cran-rcolorbrewer, r-cran-fastmap, r-cran-harmony, r-cran-seuratobject, r-cran-optparse Filename: pool/dists/noble/main/r-bioc-singlecelltk_2.22.0-1.ca2404.1_all.deb Size: 4488382 MD5sum: 5da53ccdf8eb657c1501d5d2696d3728 SHA1: e077b70fd8fdd5968a26885cc9ea953edcc3ae4e SHA256: 96699dd9e74c556f05663c9ff33a7ab49bea8db6d3e4f1e59bb60c505c2db17e SHA512: 34445b4810bc8cb89585abb5e4793c65d0e58ce288b1a949ec52cd481ca9fff84bfa4530ce6ccd27406fb980d7d37efc6961201829d06cdfa15efcce664b6c5c Homepage: https://cran.r-project.org/package=singleCellTK Description: Bioc Package 'singleCellTK' (Comprehensive and Interactive Analysis of Single Cell RNA-SeqData) The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk. Package: r-bioc-singscore Architecture: all Version: 1.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4620 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-bioc-gseabase, r-cran-plotly, r-cran-tidyr, r-cran-plyr, r-cran-magrittr, r-cran-reshape, r-bioc-edger, r-cran-rcolorbrewer, r-bioc-biobase, r-bioc-biocparallel, r-bioc-summarizedexperiment, r-cran-matrixstats, r-cran-reshape2, r-bioc-s4vectors Suggests: r-cran-pkgdown, r-bioc-biocstyle, r-cran-hexbin, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-bioc-singscore_1.32.0-1.ca2404.1_all.deb Size: 3602552 MD5sum: 8f40968cbdf67efd8be4673bad1f583e SHA1: 322f493027a40064d8d8875482e2246e3ecd34ae SHA256: b187ca683a0bd20cbfe3c6fddcea4557c8f122ac43e95be82808724135a869b3 SHA512: 312aa6f3fc92b20a20a6f300ba35bdb36a35990455a4a8b4a4c0bf263f3f5dcb8197220376c8ac2fc301d980ab58bc76b7358b36829d8e8005560546c484ca50 Homepage: https://cran.r-project.org/package=singscore Description: Bioc Package 'singscore' (Rank-based single-sample gene set scoring method) A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level. Package: r-bioc-slingshot Architecture: all Version: 2.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3673 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-princurve, r-bioc-trajectoryutils, r-cran-igraph, r-cran-matrixstats, r-bioc-s4vectors, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment Suggests: r-bioc-biocgenerics, r-bioc-biocstyle, r-bioc-clusterexperiment, r-bioc-delayedmatrixstats, r-cran-knitr, r-cran-mclust, r-cran-mgcv, r-cran-rcolorbrewer, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat, r-cran-uwot, r-cran-covr Filename: pool/dists/noble/main/r-bioc-slingshot_2.20.0-1.ca2404.1_all.deb Size: 1990460 MD5sum: 8e1d91dc78c5b3fd13b3992716cce7b8 SHA1: 72f296c8dc93f3bd4946ff28fb3a8f10cd7b6f2f SHA256: 12f660e0d640715c6992bb8ed2821c658394f8fc68e9c3ffdfc3a2da9313f2dd SHA512: b8b2ebb645c49df46b0db56a9b24ca5ea79d626c627bdd36622ac5c08b74191185c209adbe42495f048989a08965637b71e22e7e2118dd6e9ac7e7bcd2d305d5 Homepage: https://cran.r-project.org/package=slingshot Description: Bioc Package 'slingshot' (Tools for ordering single-cell sequencing) Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction. Package: r-bioc-soggi Architecture: all Version: 1.40.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3993 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-summarizedexperiment, r-cran-reshape2, r-cran-ggplot2, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-biostrings, r-bioc-rsamtools, r-bioc-genomicalignments, r-bioc-rtracklayer, r-bioc-preprocesscore, r-bioc-chipseq, r-bioc-biocparallel Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-soggi_1.40.1-1.ca2404.1_all.deb Size: 3877340 MD5sum: 65fa197a828beb1b5fcec6a2692accba SHA1: 8b4fbbaee1ffc1d52f91433c4e297b193bacd1aa SHA256: baac675cc37bce3f304458006577da9eb740864aaf2cb8d0dea5aa283185f40c SHA512: 519169a66e9b344fa6f141896cb029c8a40b421f19bd965522aa7fb2e0ec186b614a2b0ff6ee5f3c4ac95ede368fa8b8a440dc0c791bc246d924353783f0c1dc Homepage: https://cran.r-project.org/package=soGGi Description: Bioc Package 'soGGi' (Visualise ChIP-seq, MNase-seq and motif occurrence as aggregateplots Summarised Over Grouped Genomic Intervals) The soGGi package provides a toolset to create genomic interval aggregate/summary plots of signal or motif occurence from BAM and bigWig files as well as PWM, rlelist, GRanges and GAlignments Bioconductor objects. soGGi allows for normalisation, transformation and arithmetic operation on and between summary plot objects as well as grouping and subsetting of plots by GRanges objects and user supplied metadata. Plots are created using the GGplot2 libary to allow user defined manipulation of the returned plot object. Coupled together, soGGi features a broad set of methods to visualise genomics data in the context of groups of genomic intervals such as genes, superenhancers and transcription factor binding events. Package: r-bioc-spatialexperiment Architecture: all Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7716 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-rjson, r-cran-magick, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-bioc-biocgenerics, r-bioc-biocfilecache Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-biocstyle, r-bioc-bumpymatrix, r-bioc-dropletutils, r-bioc-visiumio Filename: pool/dists/noble/main/r-bioc-spatialexperiment_1.22.0-1.ca2404.1_all.deb Size: 5064524 MD5sum: e83214ccfb17647ce9d51bbff80712d2 SHA1: 6a83379c9538fdcbab528f0fe37fcb8afeb39c23 SHA256: a561f6b1fa93ecb93109cfefddea357eb784a2c0d16cf83644e8c8cbdc7bd94b SHA512: 1fd3646b30bf5a6cf6cdc5f8d8f3d3a855c5b657f5581dc04bfcb68043db4ea89dc3a5d98bfcea0e29f1724951d1b15e3cd1ad281102f1de703e353d56f1214f Homepage: https://cran.r-project.org/package=SpatialExperiment Description: Bioc Package 'SpatialExperiment' (S4 Class for Spatially Resolved -omics Data) Defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordinates, images, and image metadata. A specialized constructor function is included for data from the 10x Genomics Visium platform. Package: r-bioc-spectra Architecture: all Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5398 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-s4vectors, r-bioc-biocparallel, r-bioc-protgenerics, r-bioc-iranges, r-bioc-mscoreutils, r-cran-fs, r-bioc-biocgenerics, r-bioc-metabocoreutils, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-bioc-msdatahub, r-cran-roxygen2, r-bioc-biocstyle, r-bioc-mzr, r-bioc-rhdf5, r-cran-rmarkdown, r-cran-vdiffr, r-cran-msentropy, r-cran-patrick Filename: pool/dists/noble/main/r-bioc-spectra_1.22.0-1.ca2404.1_all.deb Size: 1851296 MD5sum: 324f2ed1d02494634cc813c9380ab799 SHA1: e7c42cf60578a0b589c508ac251623115c5ecf87 SHA256: 7b514eb0499183894fac933602af0d18f18638656a488a3f802f5432d960f575 SHA512: 3e28eccfc91c5c2b49df16577a1d4ccabb60c705cc74a9ceb629dddaf53f21131739a9555dcc0e987c39f63bbfc1a26bf94ca056a7ff80612c262854a4dcaadc Homepage: https://cran.r-project.org/package=Spectra Description: Bioc Package 'Spectra' (Spectra Infrastructure for Mass Spectrometry Data) The Spectra package defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data. It provides different implementations (backends) to store mass spectrometry data. These comprise backends tuned for fast data access and processing and backends for very large data sets ensuring a small memory footprint. Package: r-bioc-spia Architecture: all Version: 2.64.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4910 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-kegggraph Suggests: r-bioc-graph, r-bioc-rgraphviz, r-bioc-hgu133plus2.db Filename: pool/dists/noble/main/r-bioc-spia_2.64.0-1.ca2404.1_all.deb Size: 2592410 MD5sum: ca96f191ed0c1d0c83a7cc061fdee349 SHA1: 7789e0eb53f117ef4ccf98555f05074ecbac5132 SHA256: e4c7a68ef45514e3aa2cd63edb7212aa79273e8c2836b5fa94aa7c0e803d524a SHA512: 97a05728e4a12d111ddadf7203095eb44badb9b41bffee56495d0a5ac38fa16dc9f360d5e496a477e7aebe7a6a941b75cbd55810c827dbbea97b3a66493be1ab Homepage: https://cran.r-project.org/package=SPIA Description: Bioc Package 'SPIA' (Signaling Pathway Impact Analysis (SPIA) using combined evidenceof pathway over-representation and unusual signalingperturbations) This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study. Package: r-bioc-splatter Architecture: all Version: 1.36.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10255 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-biocgenerics, r-bioc-biocparallel, r-cran-checkmate, r-cran-crayon, r-bioc-edger, r-cran-fitdistrplus, r-cran-lifecycle, r-cran-locfit, r-cran-matrixstats, r-cran-rlang, r-bioc-s4vectors, r-bioc-scrapper, r-bioc-scuttle, r-bioc-summarizedexperiment, r-cran-withr Suggests: r-bioc-basics, r-cran-biocmanager, r-bioc-biocsingular, r-bioc-biocstyle, r-bioc-biostrings, r-cran-covr, r-cran-cowplot, r-bioc-genomeinfodb, r-bioc-genomicranges, r-cran-ggplot2, r-cran-igraph, r-bioc-iranges, r-cran-knitr, r-cran-limsolve, r-cran-lme4, r-cran-magick, r-bioc-phenopath, r-bioc-preprocesscore, r-cran-progress, r-cran-pscl, r-cran-rmarkdown, r-cran-scales, r-bioc-scater, r-bioc-scdd, r-bioc-scran, r-cran-sparsedc, r-cran-spelling, r-cran-testthat, r-bioc-variantannotation, r-bioc-zinbwave Filename: pool/dists/noble/main/r-bioc-splatter_1.36.0-1.ca2404.1_all.deb Size: 5879094 MD5sum: 0b37ed6c3a105f46220edfeb6daa4dc6 SHA1: 53852f81467e629d8e4bad9a0dd344f3fe0708d5 SHA256: a969ad7900d670fae88e12fb6c9ee8504a1e1112ba36101e4c6a6bb3dd883506 SHA512: 3cbae8d832eee1d9826227453c576f0c4f73d152bb2b27fc3e02f0c942d8f5b5a2b99388d49f0e81c2a7d5294757b5b1c5cfa89c4a83ca2ef7cd37a3e1cc07af Homepage: https://cran.r-project.org/package=splatter Description: Bioc Package 'splatter' (Simple Simulation of Single-cell RNA Sequencing Data) Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets. Package: r-bioc-sradb Architecture: all Version: 1.74.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5067 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rsqlite, r-bioc-graph, r-cran-rcurl, r-cran-r.utils Suggests: r-bioc-rgraphviz Filename: pool/dists/noble/main/r-bioc-sradb_1.74.0-1.ca2404.1_all.deb Size: 720996 MD5sum: 7cb8005827b36be934141c70c9f69361 SHA1: c996ddbdccde4a5d2aef24f7a5903c36d0418238 SHA256: d5ef7f4c8ade8dddccbdfaef8708af9abbccd8bef78d822ec7548d101e286bd9 SHA512: d03de05dbac9bc432652a3132a5dfae6f626a95b8ed3ab7cbf4ceaa9291bb99398057f41d5304496fff9938ec0f8b8133d42bb517c307290292b1784b4cd849c Homepage: https://cran.r-project.org/package=SRAdb Description: Bioc Package 'SRAdb' (A compilation of metadata from NCBI SRA and tools) The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata. Package: r-bioc-stringdb Architecture: all Version: 2.24.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4689 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-png, r-cran-sqldf, r-cran-plyr, r-cran-igraph, r-cran-httr, r-cran-rcolorbrewer, r-cran-gplots, r-cran-hash, r-cran-plotrix Suggests: r-cran-runit, r-bioc-biocgenerics Filename: pool/dists/noble/main/r-bioc-stringdb_2.24.0-1.ca2404.1_all.deb Size: 4653628 MD5sum: 547aa4b2c846eae9e07deedad01981f8 SHA1: 0b0a7af4e0dca7583b416062ccc17f45706ddd12 SHA256: ce62322520865005cc52436b04db9fde274fae834488111f846613644ec482be SHA512: 03ee084fee630e9deda37816ff97efb0595274dd291c9ae569d444cafd96aea98cceb56410c558e707d7ba6b23c22cf01258b91089430dc5085e9f780ad312d2 Homepage: https://cran.r-project.org/package=STRINGdb Description: Bioc Package 'STRINGdb' (STRINGdb - Protein-Protein Interaction Networks and FunctionalEnrichment Analysis) The STRINGdb package provides an R interface to STRING, a protein-protein interaction database and functional enrichment analysis tool (https://string-db.org). Package: r-bioc-summarizedexperiment Architecture: all Version: 1.42.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3977 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-matrixgenerics, r-bioc-genomicranges, r-bioc-biobase, r-cran-matrix, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-s4arrays, r-bioc-delayedarray Suggests: r-bioc-genomeinfodb, r-bioc-rhdf5, r-bioc-hdf5array, r-bioc-annotate, r-bioc-annotationdbi, r-bioc-genomicfeatures, r-bioc-sparsearray, r-bioc-singlecellexperiment, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-hgu95av2.db, r-bioc-airway, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-runit, r-cran-testthat, r-cran-digest Filename: pool/dists/noble/main/r-bioc-summarizedexperiment_1.42.0-1.ca2404.1_all.deb Size: 1438018 MD5sum: 8cef6687754deadec9142df2ecc3224d SHA1: 4020c173f37e80327ea9a790467e285c6db5c83b SHA256: 17ef7821a43961b3f34bad80ec44fc9efbf096bc5ee3111905f2e81ae0a0f5be SHA512: 1d8b180cd124138ba8ca1f9f673cca40cbba74310b1c20886dfe73db6da3aefa97c283c9bb349fca56a61bd4a924bd63b15abfdb23489c9993ec8a9d5c5483d7 Homepage: https://cran.r-project.org/package=SummarizedExperiment Description: Bioc Package 'SummarizedExperiment' (A container (S4 class) for matrix-like assays) The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples. Package: r-bioc-suprahex Architecture: all Version: 1.42.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3861 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hexbin, r-cran-ape, r-cran-mass, r-cran-readr, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-magrittr, r-cran-igraph Filename: pool/dists/noble/main/r-bioc-suprahex_1.42.0-1.ca2404.1_all.deb Size: 3438146 MD5sum: 13ef40c0b890c52a4fbbb3e7bfd90b2a SHA1: dcbc6593fa1d61dd29332deb899f7df5a66d679c SHA256: 7c25ef2ad4d922371c9ba181db299a1d009e6ae16389d23b4518a1879e5bae20 SHA512: ab5f4d60bbf56446d40341ac856155f9f0a31df303889c1e026b1e25dd35ec078e62c2f330755d8771bb68e91aeed0f66236b36ed436ae383ccc548b6cee7fc8 Homepage: https://cran.r-project.org/package=supraHex Description: Bioc Package 'supraHex' (supraHex: a supra-hexagonal map for analysing tabular omics data) A supra-hexagonal map is a giant hexagon on a 2-dimensional grid seamlessly consisting of smaller hexagons. It is supposed to train, analyse and visualise a high-dimensional omics input data. The supraHex is able to carry out gene clustering/meta-clustering and sample correlation, plus intuitive visualisations to facilitate exploratory analysis. More importantly, it allows for overlaying additional data onto the trained map to explore relations between input and additional data. So with supraHex, it is also possible to carry out multilayer omics data comparisons. Newly added utilities are advanced heatmap visualisation and tree-based analysis of sample relationships. Uniquely to this package, users can ultrafastly understand any tabular omics data, both scientifically and artistically, especially in a sample-specific fashion but without loss of information on large genes. Package: r-bioc-systempiper Architecture: all Version: 2.18.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13515 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-rsamtools, r-bioc-biostrings, r-bioc-shortread, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-ggplot2, r-cran-yaml, r-cran-stringr, r-cran-magrittr, r-bioc-s4vectors, r-cran-crayon, r-bioc-biocgenerics, r-cran-htmlwidgets Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-systempiperdata, r-bioc-genomicalignments, r-cran-dplyr, r-cran-testthat, r-cran-rjson, r-bioc-annotate, r-bioc-annotationdbi, r-cran-kableextra, r-bioc-go.db, r-bioc-genomeinfodb, r-cran-dt, r-bioc-rtracklayer, r-bioc-limma, r-bioc-edger, r-bioc-deseq2, r-bioc-iranges, r-cran-batchtools, r-bioc-genomicfeatures, r-bioc-txdbmaker, r-bioc-genomeinfodbdata, r-bioc-variantannotation Filename: pool/dists/noble/main/r-bioc-systempiper_2.18.0-1.ca2404.1_all.deb Size: 6363014 MD5sum: 2807dd6296205d4f3de2f658c2485370 SHA1: 7e43eb3f0a816dd576a111ddb73b017fa3acdcb8 SHA256: 4727868f7285f497b7217241eaaab4c1bf5fb8a9844f1e6c5de4cec0e3003131 SHA512: 5cdb94af6de5ffcde187cc278995037e7b3677bc84a7254e713a3cefedd640e61ff94a8487b3c5b02d1c5e29395d8928a24e923c30bc3df9f97a562cb051e108 Homepage: https://cran.r-project.org/package=systemPipeR Description: Bioc Package 'systemPipeR' (systemPipeR: A Multipurpose Workflow Management System forReproducible Data Analysis) systemPipeR is a workflow management environment for reproducible data analysis that integrates R with command-line software. It enables researchers to design, execute, and report complex workflows on local machines and HPC systems. The framework combines R-based analysis with external tools through a Common Workflow Language (CWL) interface, manages workflow dependencies and restart capabilities, and automatically generates reproducible scientific analysis reports. The companion package systemPipeRdata provides ready-to-use workflow templates that simplify workflow setup and customization. Alternatively, workflow templates can be loaded from dedicated GitHub repositories. Package: r-bioc-tcgabiolinks Architecture: all Version: 2.40.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110004 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-downloader, r-bioc-biomart, r-cran-dplyr, r-cran-tibble, r-bioc-genomicranges, r-cran-xml, r-cran-data.table, r-cran-jsonlite, r-cran-plyr, r-cran-knitr, r-cran-ggplot2, r-cran-stringr, r-bioc-iranges, r-cran-rvest, r-bioc-s4vectors, r-cran-r.utils, r-bioc-summarizedexperiment, r-bioc-tcgabiolinksgui.data, r-cran-readr, r-cran-tidyr, r-cran-purrr, r-cran-xml2, r-cran-httr Suggests: r-cran-jpeg, r-cran-png, r-bioc-biocstyle, r-cran-rmarkdown, r-cran-devtools, r-bioc-maftools, r-cran-parmigene, r-cran-c3net, r-bioc-minet, r-bioc-biobase, r-bioc-affy, r-cran-testthat, r-bioc-sesame, r-bioc-annotationhub, r-bioc-experimenthub, r-bioc-pathview, r-bioc-clusterprofiler, r-cran-seurat, r-bioc-complexheatmap, r-cran-circlize, r-bioc-consensusclusterplus, r-cran-igraph, r-bioc-limma, r-bioc-edger, r-bioc-sva, r-bioc-edaseq, r-cran-survminer, r-bioc-genefilter, r-cran-gridextra, r-cran-survival, r-cran-doparallel, r-cran-ggrepel, r-cran-scales, r-cran-dt Filename: pool/dists/noble/main/r-bioc-tcgabiolinks_2.40.0-1.ca2404.1_all.deb Size: 34633672 MD5sum: fb23120d3f7a3e12b77452e656957b15 SHA1: add99faa4e0aa62988a9b17470c5ca650c2b7d37 SHA256: cb173d3cee52ca45c7aa3b5bce7c22c3918a15f563eeb9338338145b181449fc SHA512: 414e14277f4db6effd7e559bc069f86f1655ee461195670e9a54b1e9c33f189333613854a23aaa7c6070131305beec19dbecbb50add3fa5f48442e3a289cd055 Homepage: https://cran.r-project.org/package=TCGAbiolinks Description: Bioc Package 'TCGAbiolinks' (TCGAbiolinks: An R/Bioconductor package for integrative analysiswith GDC data) The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines. Package: r-bioc-tcgabiolinksgui.data Architecture: all Version: 1.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 23171 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-readr, r-cran-dt Filename: pool/dists/noble/main/r-bioc-tcgabiolinksgui.data_1.32.0-1.ca2404.1_all.deb Size: 22789552 MD5sum: 6cbfd5eb59654378746ca40e5d77f1c0 SHA1: c295161dc7eab9fdfd8f54f179289751b018f63d SHA256: f539afc06a8999fd3627ab27b45ce009984bd7b2fcafb041d5d079111b34effc SHA512: bad7ad0396373c995a155068f93626a5f67e90e93a689c2c5aec4b775bb401e28b36e74e4765e01b76b9afd573357e280b51cafca458fbc6f6d59cc90f0a5fe2 Homepage: https://cran.r-project.org/package=TCGAbiolinksGUI.data Description: Bioc Package 'TCGAbiolinksGUI.data' (Data for the TCGAbiolinksGUI package) Supporting data for the TCGAbiolinksGUI package. Package: r-bioc-tcgautils Architecture: all Version: 1.32.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1120 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-annotationdbi, r-bioc-biocgenerics, r-bioc-biocbaseutils, r-bioc-genomeinfodb, r-bioc-genomicfeatures, r-bioc-genomicranges, r-bioc-genomicdatacommons, r-cran-glue, r-bioc-iranges, r-bioc-multiassayexperiment, r-bioc-raggedexperiment, r-cran-rvest, r-bioc-s4vectors, r-bioc-seqinfo, r-cran-stringr, r-bioc-summarizedexperiment, r-cran-xml2 Suggests: r-bioc-annotationhub, r-bioc-bioc.gff, r-bioc-biocfilecache, r-bioc-biocstyle, r-bioc-curatedtcgadata, r-bioc-complexheatmap, r-cran-devtools, r-cran-dplyr, r-cran-httr, r-bioc-illuminahumanmethylation450kanno.ilmn12.hg19, r-bioc-impute, r-cran-knitr, r-cran-magrittr, r-bioc-mirnameconverter, r-bioc-org.hs.eg.db, r-cran-rcolorbrewer, r-cran-readr, r-cran-rmarkdown, r-bioc-rtcgatoolbox, r-bioc-rtracklayer, r-cran-r.utils, r-cran-testthat, r-bioc-txdb.hsapiens.ucsc.hg18.knowngene, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene Filename: pool/dists/noble/main/r-bioc-tcgautils_1.32.0-1.ca2404.1_all.deb Size: 518846 MD5sum: e7a8fcd5d2a3d5c46919b3dbc2ae3694 SHA1: b1dc6fa44c4c319a515dcb54c97f9f9f510b25cf SHA256: 0e048b79178b6f82330e741c3d5fa78c0e5cd092e01da317a92c60ee2eb77e32 SHA512: ae405b09c52a3af295bc5695fb597f516935df6036c257d805880096824ce495af61626325de4830711e1aa53141c6969f5172a97603e507f89c88c93adbd20c Homepage: https://cran.r-project.org/package=TCGAutils Description: Bioc Package 'TCGAutils' (TCGA utility functions for data management) A suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. Exported functions include those that import data from flat files into Bioconductor objects, convert row annotations, and identifier translation via the GDC API. Package: r-bioc-tcseq Architecture: all Version: 1.36.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 913 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-edger, r-bioc-biocgenerics, r-cran-reshape2, r-bioc-genomicranges, r-bioc-iranges, r-bioc-summarizedexperiment, r-bioc-genomicalignments, r-bioc-rsamtools, r-cran-e1071, r-cran-cluster, r-cran-ggplot2, r-cran-locfit Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-bioc-tcseq_1.36.0-1.ca2404.1_all.deb Size: 843202 MD5sum: 22c67366a9b9c48f8d942c274398501c SHA1: a220c0b6143ccb7a073caabebecbda751b962616 SHA256: 53561f6d6e8dcb58e8192f2f1fa90286af916600b10e180936f7c7ef288c4ad8 SHA512: 7c58db23319219a838a2477e51ac01b666215c7fb8566cdaeec8f56c58706c0276e46ae37c126da9177cea61252219c04eba98cfb7259936a97178b72ababf68 Homepage: https://cran.r-project.org/package=TCseq Description: Bioc Package 'TCseq' (Time course sequencing data analysis) Quantitative and differential analysis of epigenomic and transcriptomic time course sequencing data, clustering analysis and visualization of the temporal patterns of time course data. Package: r-bioc-tenxpbmcdata Architecture: all Version: 1.30.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1003 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-hdf5array, r-bioc-annotationhub, r-bioc-experimenthub Suggests: r-cran-rmarkdown, r-cran-knitr, r-bioc-biocstyle, r-cran-snow, r-bioc-biocfilecache, r-bioc-biocparallel Filename: pool/dists/noble/main/r-bioc-tenxpbmcdata_1.30.0-1.ca2404.1_all.deb Size: 288654 MD5sum: 8b0458b42fe18ea2cbce5c6b78b94677 SHA1: abab2be7417894df3fa1f6f856fd3364699301e5 SHA256: e44f6d1739f3d95acc120c75154aac06e2b84e26cbea631cff74d9672c54461a SHA512: 1aeba85a81f77744f8148982816a47db463859076ebd33434bf8593905b11a6b8098b9843158cf544ae1c3bd310db81aad7f1b7215ff0c9169769848e2ce5d3e Homepage: https://cran.r-project.org/package=TENxPBMCData Description: Bioc Package 'TENxPBMCData' (PBMC data from 10X Genomics) Single-cell RNA-seq data for on PBMC cells, generated by 10X Genomics. 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SummarizedExperiment is a widely used data structure in bioinformatics for storing high-throughput genomic data, such as gene expression or DNA sequencing data. The tidySummarizedExperiment package introduces a tidy framework for working with SummarizedExperiment objects. It allows users to convert their data into a tidy format, where each observation is a row and each variable is a column. This tidy representation simplifies data manipulation, integration with other tidyverse packages, and enables seamless integration with the broader ecosystem of tidy tools for data analysis. Package: r-bioc-tkwidgets Architecture: all Version: 1.90.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 751 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-widgettools, r-bioc-dyndoc Suggests: r-bioc-biobase, r-bioc-hgu95av2 Filename: pool/dists/noble/main/r-bioc-tkwidgets_1.90.0-1.ca2404.1_all.deb Size: 654106 MD5sum: a6558bf51c8c0e89cc347f6e9d8075bd SHA1: bf17be594188852181b85a327de265b199ebed60 SHA256: cec851c57b7dc44898e46c55ecaaaf12f437f500485a3f034c0a0309b3010df3 SHA512: f1aa1c06101afdff4ddaaf95b5b9b146051cded95eed311b9989bfc926b1621da122c93d10d6086efa1cac901089fde1bb5a5d2cae11adc4abcb416df391f60e Homepage: https://cran.r-project.org/package=tkWidgets Description: Bioc Package 'tkWidgets' (R based tk widgets) Widgets to provide user interfaces. tcltk should have been installed for the widgets to run. Package: r-bioc-toast Architecture: all Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4593 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-epidish, r-bioc-limma, r-cran-nnls, r-cran-quadprog, r-bioc-summarizedexperiment, r-cran-corpcor, r-cran-doparallel, r-cran-ggplot2, r-cran-tidyr, r-cran-ggally Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-gplots, r-cran-matrixstats, r-cran-matrix Filename: pool/dists/noble/main/r-bioc-toast_1.26.0-1.ca2404.1_all.deb Size: 3931832 MD5sum: 2a4db74413c65667c3d3ccd66edd4088 SHA1: e93be0eefbccd7debbd2142f4e1a5ba71d6f2583 SHA256: 29b306348bbbcf0368bb967d0ccbd9bb346519a1b2bd07c8295c0d87823230fc SHA512: 731c93e5d2ef02e1f85602ea926d7ad7ced63bad983f0d21b6572886a041f420949d8ae9829cbbdc76833a39289469819bc5b5e9ba4a458e94bf3fa3b296e7af Homepage: https://cran.r-project.org/package=TOAST Description: Bioc Package 'TOAST' (Tools for the analysis of heterogeneous tissues) This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. tree-based differential analysis 3. improve variable selection in reference-free deconvolution 4. partial reference-free deconvolution with prior knowledge. Package: r-bioc-topgo Architecture: all Version: 2.64.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4217 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-graph, r-bioc-biobase, r-bioc-go.db, r-bioc-annotationdbi, r-cran-sparsem, r-cran-lattice, r-cran-matrixstats, r-cran-dbi Suggests: r-bioc-all, r-bioc-hgu95av2.db, r-bioc-hgu133a.db, r-bioc-genefilter, r-bioc-multtest, r-bioc-rgraphviz, r-bioc-globaltest, r-cran-knitr, r-bioc-biocstyle, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-topgo_2.64.0-1.ca2404.1_all.deb Size: 2571880 MD5sum: ee806c672ddf0f65d6ae2467d38c9118 SHA1: 81c83178791b79d3289539bab7a3c25901a3bb20 SHA256: 914b596c8ed3292888c9502bed0b4f14cee55d91b42127e3fc3999ec556a277a SHA512: e3a13693157c3dfd0bb6d5a7f6bd54eccabbb768dd0fd7b9d9557a5cbf7ae7e38579126d4e10df43d1d0cfbfcc85d7594caa1e7a02f92e649d7fa02603916e0a Homepage: https://cran.r-project.org/package=topGO Description: Bioc Package 'topGO' (Enrichment Analysis for Gene Ontology) topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied. Package: r-bioc-trackviewer Architecture: all Version: 1.48.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 20480 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-seqinfo, r-bioc-genomeinfodb, r-bioc-genomicalignments, r-bioc-genomicfeatures, r-bioc-gviz, r-bioc-rsamtools, r-bioc-s4vectors, r-bioc-rtracklayer, r-bioc-biocgenerics, r-cran-scales, r-bioc-iranges, r-bioc-annotationdbi, r-cran-grimport, r-cran-htmlwidgets, r-bioc-interactionset, r-bioc-rhdf5, r-cran-strawr, r-bioc-txdbmaker Suggests: r-bioc-biomart, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-runit, r-bioc-org.hs.eg.db, r-bioc-biocstyle, r-cran-knitr, r-bioc-variantannotation, r-cran-httr, r-cran-htmltools, r-cran-rmarkdown, r-bioc-motifstack Filename: pool/dists/noble/main/r-bioc-trackviewer_1.48.0-1.ca2404.1_all.deb Size: 9748190 MD5sum: a0f129df9a35372225609f0cc91bf0d8 SHA1: e56e510ffecf68379a8754c1ee3377225ac77b76 SHA256: f591ebf5601a0786e1a127b1c89b2be891cd11f24c89c6a6e504d36e1e621919 SHA512: 4dcb4862f91e5414f0b99c030878e4a0051db3d0938d9415b87136b23184949f1aee92c3bd7990b03b599d9380c9893f80a0d2b962c41fa19a906a70f65d07c3 Homepage: https://cran.r-project.org/package=trackViewer Description: Bioc Package 'trackViewer' (A R/Bioconductor package with web interface for drawing elegantinteractive tracks or lollipop plot to facilitate integratedanalysis of multi-omics data) Visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data. Package: r-bioc-tradeseq Architecture: all Version: 1.26.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7692 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mgcv, r-bioc-edger, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-slingshot, r-cran-magrittr, r-cran-rcolorbrewer, r-bioc-biocparallel, r-bioc-biobase, r-cran-pbapply, r-cran-igraph, r-cran-ggplot2, r-cran-princurve, r-bioc-s4vectors, r-cran-tibble, r-cran-matrix, r-bioc-trajectoryutils, r-cran-viridis, r-cran-matrixstats, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-bioc-clusterexperiment, r-bioc-delayedmatrixstats Filename: pool/dists/noble/main/r-bioc-tradeseq_1.26.0-1.ca2404.1_all.deb Size: 4257412 MD5sum: fa6155d82fb3b2197e664319b1597381 SHA1: 0da4bd59ff893f010649b8c0f17716b526f41d9a SHA256: 396e94936ee5fc50c7451221c2504d15cdf5d5b5d240212a1af9dac41f325a0b SHA512: 300fa93f36f47f422dca65824d5d493851a0c514627a75d1f4b64235e34f4d3eccbc2f5380a02024a5bad504f5e9a4145cc2d0932a18cc5f8778087d6b1ba1f7 Homepage: https://cran.r-project.org/package=tradeSeq Description: Bioc Package 'tradeSeq' (trajectory-based differential expression analysis for sequencingdata) tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM. Package: r-bioc-trajectoryutils Architecture: all Version: 1.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-matrix, r-cran-igraph, r-bioc-s4vectors, r-bioc-summarizedexperiment Suggests: r-bioc-biocneighbors, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-biocparallel, r-cran-testthat, r-cran-knitr, r-bioc-biocstyle, r-cran-rmarkdown Filename: pool/dists/noble/main/r-bioc-trajectoryutils_1.20.0-1.ca2404.1_all.deb Size: 510974 MD5sum: e7bf645bfee977c8f4b361e60613850f SHA1: 13766da93f1bcf59e9a251709daedf5d2d0c20cb SHA256: 7aa22bbde2996201c8d9337b841f255100e62ef6dd2a27583d19f60c09721412 SHA512: 6974265c12fb18862feb23c0739584c3bb405bf8c8567fba6c176a15ab6f2f9556febc18426c10dcedbca2ef922a032a63ea89788f3d4b2219350d84fb45285c Homepage: https://cran.r-project.org/package=TrajectoryUtils Description: Bioc Package 'TrajectoryUtils' (Single-Cell Trajectory Analysis Utilities) Implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. Include a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime inference results. Package: r-bioc-treeio Architecture: all Version: 1.36.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3829 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-jsonlite, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-tidytree, r-cran-yulab.utils Suggests: r-bioc-biostrings, r-cran-cli, r-cran-ggplot2, r-bioc-ggtree, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-phangorn, r-cran-prettydoc, r-cran-purrr, r-cran-testthat, r-cran-tidyr, r-cran-vroom, r-cran-xml2, r-cran-yaml Filename: pool/dists/noble/main/r-bioc-treeio_1.36.1-1.ca2404.1_all.deb Size: 735988 MD5sum: 9af4a1fbc6750d2d3f03a47aec5376f1 SHA1: 507251443b01b540e9094d566bb55a89206beb5b SHA256: 17cce1f480740520ad57d9b1726535498cb092d024185283c5d80ff4f730a627 SHA512: 77037001c548c47980e7a9258ad90a591a1eae5030e4428c40ca920a57ac6b0b2208b755dd52730c4557fa914350a6ea10bdc94a08cf13bbf85b33a3683c2f71 Homepage: https://cran.r-project.org/package=treeio Description: Bioc Package 'treeio' (Base Classes and Functions for Phylogenetic Tree Input andOutput) 'treeio' is an R package to make it easier to import and store phylogenetic tree with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic tree with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats. Package: r-bioc-treesummarizedexperiment Architecture: all Version: 2.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3118 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-s4vectors, r-bioc-biostrings, r-bioc-biocgenerics, r-cran-ape, r-cran-rlang, r-cran-dplyr, r-bioc-summarizedexperiment, r-bioc-biocparallel, r-bioc-iranges, r-bioc-treeio Suggests: r-bioc-ggtree, r-cran-ggplot2, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-bioc-treesummarizedexperiment_2.20.0-1.ca2404.1_all.deb Size: 1360950 MD5sum: fb2fa118a3c7ff833215faffee26e884 SHA1: b3de20ca2a039d2e41f47a0180f6b38ce2d56db1 SHA256: 8d086ddf48a18199ab81466a7acbf0847989f145c48ad4f7ff6bd740e58c9607 SHA512: eab617ca10f20f736d402ce1c1ceecbe6d52bfd65ba5804554ff8bf5a0089a39db520f18ab36aff199d04547036d429be7efa5df6fb8cdd4e59e4e6d56da6d9c Homepage: https://cran.r-project.org/package=TreeSummarizedExperiment Description: Bioc Package 'TreeSummarizedExperiment' (TreeSummarizedExperiment: a S4 Class for Data with TreeStructures) TreeSummarizedExperiment has extended SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data. Package: r-bioc-tscan Architecture: all Version: 1.50.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3034 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-singlecellexperiment, r-bioc-trajectoryutils, r-cran-ggplot2, r-cran-shiny, r-cran-plyr, r-cran-fastica, r-cran-igraph, r-cran-combinat, r-cran-mgcv, r-cran-mclust, r-cran-gplots, r-cran-matrix, r-bioc-summarizedexperiment, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-s4vectors Suggests: r-cran-knitr, r-cran-testthat, r-bioc-scuttle, r-bioc-scran, r-bioc-metapod, r-bioc-biocparallel, r-bioc-biocneighbors, r-bioc-batchelor Filename: pool/dists/noble/main/r-bioc-tscan_1.50.0-1.ca2404.1_all.deb Size: 2893142 MD5sum: 11722700c2525b8add5c622b542f5d48 SHA1: e093de7ce0ea1cedb03dac9f077590388aefb35e SHA256: d51728c27e51e390ac7de672d6e2e5564d02aad9b0683cc91fd7eb604badccbd SHA512: 34ca04494729696fd0213b3aa63da22b60e2d834c91f61a426395372d10aa1d6608fe6bba4a9f45c0966e08525638a92db7a33ac106ff2436d93511c848192c5 Homepage: https://cran.r-project.org/package=TSCAN Description: Bioc Package 'TSCAN' (Tools for Single-Cell Analysis) Provides methods to perform trajectory analysis based on a minimum spanning tree constructed from cluster centroids. Computes pseudotemporal cell orderings by mapping cells in each cluster (or new cells) to the closest edge in the tree. Uses linear modelling to identify differentially expressed genes along each path through the tree. Several plotting and interactive visualization functions are also implemented. Package: r-bioc-txdb.hsapiens.ucsc.hg19.knowngene Architecture: all Version: 3.22.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136036 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-genomicfeatures, r-bioc-annotationdbi Filename: pool/dists/noble/main/r-bioc-txdb.hsapiens.ucsc.hg19.knowngene_3.22.1-1.ca2404.1_all.deb Size: 37285808 MD5sum: 3aaba29fab75831f127abaebe5f64eca SHA1: 7d617d26f5dd14c49596868eece0465dd7eafb92 SHA256: 7101859a3c169eb4189c96c7748b2389516990ef64c58b7a3b191b9752c847df SHA512: b77ba4c8cefd4d8d5cda5c9f4c4d9ee5144420a38c7c2104cf59e4d0181ab7cb98fb204c932333dea1b6da2227e3c4c5b2641b12557f895265a589c7d2426fcc Homepage: https://cran.r-project.org/package=TxDb.Hsapiens.UCSC.hg19.knownGene Description: Bioc Package 'TxDb.Hsapiens.UCSC.hg19.knownGene' (Annotation package for TxDb object(s)) Exposes an annotation databases generated from UCSC by exposing these as TxDb objects Package: r-bioc-txdb.hsapiens.ucsc.hg38.knowngene Architecture: all Version: 3.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150592 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-genomicfeatures, r-bioc-annotationdbi Filename: pool/dists/noble/main/r-bioc-txdb.hsapiens.ucsc.hg38.knowngene_3.22.0-1.ca2404.1_all.deb Size: 40269750 MD5sum: d8c6939aa4f6f33ba51c5551416bc289 SHA1: 7e7351d0128038853f8192be01a0c4b4346f9fce SHA256: a8d02e637dc68d0193fbd4dfce021538b35daf67d3dadaf2a57c5853c151edd1 SHA512: 999fc32439a3ee5e9202bb3c0641b107345493374cf204d1e350cd3585f148d57c7d5a2a96702333b4a2fe9a1f57d3c75c1ad9f40dc6572162c24fc676fdf14d Homepage: https://cran.r-project.org/package=TxDb.Hsapiens.UCSC.hg38.knownGene Description: Bioc Package 'TxDb.Hsapiens.UCSC.hg38.knownGene' (Annotation package for TxDb object(s)) Exposes an annotation databases generated from UCSC by exposing these as TxDb objects Package: r-bioc-txdb.mmusculus.ucsc.mm10.knowngene Architecture: all Version: 3.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59884 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-genomicfeatures, r-bioc-annotationdbi Filename: pool/dists/noble/main/r-bioc-txdb.mmusculus.ucsc.mm10.knowngene_3.10.0-1.ca2404.1_all.deb Size: 17346986 MD5sum: 091d02a5ea88bdc8a86f560f66743815 SHA1: d06c318e5bf776030bfc7b084a0500608c0d34dc SHA256: 3494bca46f8d54a58664e2792fd99bdb89cb7fb9b1b10d62cf16a96abe69abe2 SHA512: 988c81d3d435e6ddc51cf145e6580e3d74dc83a34071dec447afa5cd4ec831726ae27a6aa1104e22cbcb4c1a74818838294f440c124a840a5aec2e71bf94ca86 Homepage: https://cran.r-project.org/package=TxDb.Mmusculus.UCSC.mm10.knownGene Description: Bioc Package 'TxDb.Mmusculus.UCSC.mm10.knownGene' (Annotation package for TxDb object(s)) Exposes an annotation databases generated from UCSC by exposing these as TxDb objects Package: r-bioc-txdbmaker Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3670 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-genomicfeatures, r-cran-httr, r-cran-rjson, r-cran-dbi, r-cran-rsqlite, r-bioc-iranges, r-bioc-ucsc.utils, r-bioc-genomeinfodb, r-bioc-annotationdbi, r-bioc-biobase, r-bioc-biocio, r-bioc-rtracklayer, r-bioc-biomart Suggests: r-cran-rmariadb, r-bioc-ensembldb, r-bioc-genomeinfodbdata, r-cran-runit, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/noble/main/r-bioc-txdbmaker_1.8.0-1.ca2404.1_all.deb Size: 1067234 MD5sum: c8ee7b212ef784e75978d6d247cdfb0f SHA1: bb6451e97d774a55bd4307bebfc93d4a613ede57 SHA256: 9c8cafba3de6c8653899d16e353c2f3c2061a053bf1a7f7653a4222f5661ba6a SHA512: 28ab884596254feaf3e9bf57c68005aa6a3841921bfe5aa56b2b95aa9180c3c8e87d31e8173d2fd8cb744ad846c8c728e374970c881cc90c87f64555b68322f0 Homepage: https://cran.r-project.org/package=txdbmaker Description: Bioc Package 'txdbmaker' (Tools for making TxDb objects from genomic annotations) A set of tools for making TxDb objects from genomic annotations from various sources (e.g. UCSC, Ensembl, and GFF files). These tools allow the user to download the genomic locations of transcripts, exons, and CDS, for a given assembly, and to import them in a TxDb object. TxDb objects are implemented in the GenomicFeatures package, together with flexible methods for extracting the desired features in convenient formats. 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De novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for computational reproducibility. 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Covers action buttons, text and select inputs, fluid page layouts with HTML landmarks and skip links, 'DT' data tables, and bar and line graphs from 'ggplot2'. Components validate label presence, expose keyboard-accessible ARIA states, and provide a high-contrast toggle. This package was developed by d-fine GmbH on behalf of the German Federal Ministry of Research, Technology and Space (BMFTR). 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Package: r-cran-abcrlda Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-abcrlda_1.0.3-1.ca2404.1_all.deb Size: 206042 MD5sum: 64b1513398105726550d6d041027bb61 SHA1: 5489289c51865a9734f29489e84fff6d683468d4 SHA256: b092721d077e21b05ac8409b4fc1e4e48dca66e9b6364d417660c797976ec22a SHA512: dbccdd3caf00537e0002dde224b7eb37c55cb67d2e8c79661270ec2dbead329469cd0d26329ef1a64f3467992bf8b0153a5e194f0502cab686a2c79387894ce5 Homepage: https://cran.r-project.org/package=abcrlda Description: CRAN Package 'abcrlda' (Asymptotically Bias-Corrected Regularized Linear DiscriminantAnalysis) Offers methods to perform asymptotically bias-corrected regularized linear discriminant analysis (ABC_RLDA) for cost-sensitive binary classification. 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Package: r-cran-abjdata Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3913 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-abjdata_1.1.2-1.ca2404.1_all.deb Size: 3966204 MD5sum: 055cf74cde95c18af0b00fb8c63e7850 SHA1: 141dd25dbfb2fde4764fbde96df4c640c2d4fe07 SHA256: 869cd9a30caac30edf81ad89dd93b2c58935f434e9a8a3f47f4be7e63701a84c SHA512: 35f1777cb9d0bb46374bce3248a9948968d5b79e41ceda19af7c1ddc1a551a1290b52b2a57087ab27d01346f054640c0cd3aa6518b8b4386995481c2b6d1cca4 Homepage: https://cran.r-project.org/package=abjData Description: CRAN Package 'abjData' (Databases Used Routinely by the Brazilian JurimetricsAssociation) The Brazilian Jurimetrics Association (ABJ in Portuguese, see for more information) is a non-profit organization which aims to investigate and promote the use of statistics and probability in the study of Law and its institutions. This package has a set of datasets commonly used in our book. 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This package implements general purpose tools used by ABJ, such as functions for sampling and basic manipulation of Brazilian lawsuits identification number. It also implements functions for text cleaning, such as accentuation removal. 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(2022) . The package contains two movement functions, each of which is based on the Ornstein-Uhlenbeck (OU) model (Ornstein & Uhlenbeck, 1930) . It also contains several visualization and data summarization functions to facilitate the presentation of simulation results. 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As of 2/27/2018 this paper has been submitted and is under scientific review. Using high-dimensional datasets to measure a subject’s overall level of abnormality as compared to a reference population is often needed in outcomes research. Utilizing applications in instrumented gait analysis, that article demonstrates how using data that is inherently non-independent to measure overall abnormality may bias results. A methodology is introduced to address this bias to accurately measure overall abnormality in high dimensional spaces. While this methodology is in line with previous literature, it differs in two major ways. Advantageously, it can be applied to datasets in which the number of observations is less than the number of features/variables, and it can be abstracted to practically any number of domains or dimensions. After applying the proposed methodology to the original data, the researcher is left with a set of uncorrelated variables (i.e. principal components) with which overall abnormality can be measured without bias. Different considerations are discussed in that article in deciding the appropriate number of principal components to keep and the aggregate distance measure to utilize. Package: r-cran-abodoutlier Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster Filename: pool/dists/noble/main/r-cran-abodoutlier_0.1-1.ca2404.1_all.deb Size: 15522 MD5sum: 868b0ce9521f991cc35beacc3bedf994 SHA1: bec5e028b7428afb484236c2e18f4eef02e8a72a SHA256: b208931152bb0bd6753c28071b879d7ec67f2b4512951371e03c1a566bcfc742 SHA512: 9f43ba8a82be308ab3ae8a40e899cc1cc4f453b8dc529257b61afef3ace31ec334fa5f7bba267171adee177a66edd7b6d54d80154c8d5f1681b23fecb9b3ed8d Homepage: https://cran.r-project.org/package=abodOutlier Description: CRAN Package 'abodOutlier' (Angle-Based Outlier Detection) Performs angle-based outlier detection on a given dataframe. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection. 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Unlike the traditional Rosen-Roback measure, this measure accounts for mobility frictions—generated by idiosyncratic tastes and local ties — and trade frictions — generated by trade costs and non-tradable services, thereby reducing non-classical measurement error. The QoL measure is based on Ahlfeldt, Bald, Roth, Seidel (2024) "Measuring Quality of Life under Spatial Frictions". When using this programme or the toolkit in your work, please cite the paper. 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It enables large-scale computation and integrated analysis of sequence-derived features, including physicochemical properties, amino acid descriptor sets, sequence motifs, compositional patterns, and somatic hypermutation metrics. The application supports multiscale analysis across sequences, clones, and repertoires, with interactive visualizations and statistical feature selection. 'AbSolution' also facilitates reproducible research by enabling structured export of data, code, parameters, and computational environments. See for more details. 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For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2024), . 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A variety of user defined options and formatting are included. Package: r-cran-absurvtdc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-readxl Filename: pool/dists/noble/main/r-cran-absurvtdc_0.1.0-1.ca2404.1_all.deb Size: 51102 MD5sum: d85f3e1199d9489382b43586bd6a76d1 SHA1: 37efda88b764c65d6ff47753151bc47100356093 SHA256: 2239f1ea89b1d1b805cb40dcd82c095f55d1aef5a29a9d9441b78d6b30c060dd SHA512: f63df60ca8a7ae060ebd86ce9b5e4f2f5de2320fd2b3dec02839f2adf3dfca35a4ca98e5466994c2a47b19b3f9d62ee025155333584a681c6d531b1225c2f7de Homepage: https://cran.r-project.org/package=ABSurvTDC Description: CRAN Package 'ABSurvTDC' (Survival Analysis using Time Dependent Covariate for AnimalBreeding) Survival analysis is employed to model the time it takes for events to occur. Survival model examines the relationship between survival and one or more predictors, usually termed covariates in the survival-analysis literature. To this end, Cox-proportional (Cox-PH) hazard rate model introduced in a seminal paper by Cox (1972) , is a broadly applicable and the most widely used method of survival analysis. This package can be used to estimate the effect of fixed and time-dependent covariates and also to compute the survival probabilities of the lactation of dairy animal. This package has been developed using algorithm of Klein and Moeschberger (2003) . 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A closed-form (analytic) solution to the degradation model is implemented as a non-linear fit, allowing for the extrapolation of the degradation of a drug product - both in time and temperature. Parametric bootstrap, with kinetic parameters drawn from the multivariate t-distribution, and analytical formulae (the delta method) are available options to calculate the confidence and prediction intervals. The results (modelling, extrapolations and statistical intervals) can be visualised with multiple plots. The examples illustrate the accelerated stability modelling in drugs and vaccines development. 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(2020) and Safari et al. (2022) . Package: r-cran-acceptancesampling Architecture: all Version: 1.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-acceptancesampling_1.0.11-1.ca2404.1_all.deb Size: 418190 MD5sum: db5b5058b1723a50540abf848cee0429 SHA1: b35bc61d795d298736192a6fcd4b4af7adef6e12 SHA256: 5e9574a95b931f052f5be82c66655bc34bf6a4d08e906c72d34c5d42c2fe4589 SHA512: 815228bd9c773fbefa8f5bca8c04a681f8bdbb2d341ddc1d4b24df29f72e34540397972833d3183e02ef3c1206e88718aa298e44f4df6bd543dd2fe2ec2f90b3 Homepage: https://cran.r-project.org/package=AcceptanceSampling Description: CRAN Package 'AcceptanceSampling' (Creation and Evaluation of Acceptance Sampling Plans) Provides functionality for creating and evaluating acceptance sampling plans. Sampling plans can be single, double or multiple. Package: r-cran-accessibility Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3225 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-hmisc, r-cran-rdpack Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-accessibility_1.5.0-1.ca2404.1_all.deb Size: 2752592 MD5sum: 9c76e59cf06c5410b7074d4a04b4774d SHA1: c57d6da16e1f3beb78d55f16cf07cdd3f703f517 SHA256: 43c8ac2fa3a67423edeb2b104e30eac4f2f79f3ed5ce52e737543c823a68b0b5 SHA512: ceab80147944910b6196b46aafbfc9af820b583631381788663f58331f2c7382faf4c85ed79636f527388ed5b01eecc1430b14af4c518e0c486b2a0d51bc475a Homepage: https://cran.r-project.org/package=accessibility Description: CRAN Package 'accessibility' (Transport Accessibility Measures) A set of fast and convenient functions to help conducting accessibility analyses. Given a pre-computed travel cost matrix and a land use dataset (containing for example the location of jobs, healthcare and population), the package allows one to calculate accessibility levels, and accessibility poverty and inequality. The package covers the majority of the most commonly used accessibility measures (such as cumulative opportunities, gravity-based and floating catchment areas methods), some cutting edge measures proposed in the literature (e.g. balancing cost and constrained accessibility) as well as the most frequently used inequality and poverty metrics (such as the Palma ratio, the concentration and Theil indices and the FGT family of measures). 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Accessible 'PDF' files are produced only on a 'Windows' Operating System. One aspect of accessibility is providing a headings structure that is recognised by a screen reader, providing a navigational tool for a blind or partially-sighted person. A key aim is to produce documents of different formats easily from each of a collection of 'R markdown' source files. Input 'R markdown' files are rendered using the render() function from the 'rmarkdown' package . A 'zip' file containing multiple output files can be produced from one function call. A user-supplied template 'Word' document can be used to determine the formatting of an output 'Word' document. Accessible 'PDF' files are produced from 'Word' documents using 'OfficeToPDF' . A convenience function, install_otp() is provided to install this software. The option to print 'HTML' output to (non-accessible) 'PDF' files is also available. 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The package provides functions for creating or modifying 'rmarkdown' documents, resolving known errors and alerts that result in accessibility issues for screen reader users. Package: r-cran-accrual Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tcltk2, r-cran-fgui, r-cran-smpracticals Filename: pool/dists/noble/main/r-cran-accrual_1.4-1.ca2404.1_all.deb Size: 60256 MD5sum: 38daaf18ac3439dd6cb5565c5d0039b2 SHA1: d132906ab0fc4138aa88f6557642bcb06f9bffe5 SHA256: 09807acc54780c07f5e70fc5578de977d564ce3d86a4cd10bccf65fd6920af47 SHA512: f86e34362daac0571d157d4941418761800dc8c65038dc8b0fdb14b4285f7e9f5491993a6ca95668e5283b9740ae0578306fe962695b1f3a2c970a45c0ff4b16 Homepage: https://cran.r-project.org/package=accrual Description: CRAN Package 'accrual' (Bayesian Accrual Prediction) Participant recruitment for medical research is challenging. Slow accrual leads to delays in research. Accrual monitoring during the process of recruitment is critical. Researchers need reliable tools to manage the accrual rate. We developed a Bayesian method that integrates the researcher's experience with previous trials and data from the current study, providing reliable predictions on accrual rate for clinical studies. For more details and background on these methodologies, see the publications of Byron, Stephen and Susan (2008) , and Yu et al. (2015) . In this R package, Bayesian accrual prediction functions are presented, which can be easily used by statisticians and clinical researchers. 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Package: r-cran-aceeditor Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11922 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-reactr, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-aceeditor_1.0.1-1.ca2404.1_all.deb Size: 1660064 MD5sum: 483eb8c3f108bcb373aa730e08995319 SHA1: 416688b21bba5757e8a2635282d04fc564878a4e SHA256: 7da4c5009922717e06ddeb4cb45d11822884f19a40bb98e9b6404eaaeb826f23 SHA512: 1b99d6c12ce66aa97a7c3c6d984a489d5c38013decbf45ae46eabff89f2f0301e1667c82b5eb4c53bb3894d7cf324259b31d85068f1b732855a88e2c2c0d341f Homepage: https://cran.r-project.org/package=aceEditor Description: CRAN Package 'aceEditor' (The 'Ace' Editor as a HTML Widget) Wraps the 'Ace' editor in a HTML widget. The 'Ace' editor has support for many languages. It can be opened in the viewer pane of 'RStudio', and this provides a second source editor. Package: r-cran-acep Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4062 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-stringr, r-cran-magrittr Suggests: r-cran-furrr, r-cran-ollamar, r-cran-future, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-reticulate, r-cran-rsyntax, r-cran-spacyr, r-cran-tidygeocoder, r-cran-udpipe, r-cran-testthat Filename: pool/dists/noble/main/r-cran-acep_0.1.1-1.ca2404.1_all.deb Size: 3479254 MD5sum: 4e09fc48469f9fc390e18d70acabae58 SHA1: feff2543f25a8634b3b94a2ff5931e912d308860 SHA256: 7966f8cc018af63e7352b2dc32cc08a4624ec1ad92c9d7f88461920670a61c8d SHA512: 66a833afd6041ad31d5ebcab14c226f00217541d5ffc752d3a94f5085743b5ebb9d7ceddd20b8c0b60a721b10d68358ba9e8b5912964d0d3862a573890b48b85 Homepage: https://cran.r-project.org/package=ACEP Description: CRAN Package 'ACEP' (Análisis Computacional de Eventos de Protesta) La librería 'ACEP' contiene funciones específicas para desarrollar análisis computacional de eventos de protesta. Asimismo, contiene bases de datos con colecciones de notas sobre protestas y diccionarios de palabras conflictivas. La colección de diccionarios reune diccionarios de diferentes orígenes. The 'ACEP' library contains specific functions to perform computational analysis of protest events. It also contains a database with collections of notes on protests and dictionaries of conflicting words. Collection of dictionaries that brings together dictionaries from different sources. Package: r-cran-acesearch Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-acesearch_1.0.0-1.ca2404.1_all.deb Size: 26526 MD5sum: 6682fe02cc14d2a67b6919990c570eea SHA1: 39fad9c238239c906b953e6dd1c9e4ab29307a26 SHA256: a7b2db1653555106bfb69662a2e7a73dc1ad8743bb92ea34d039b259d299cdf9 SHA512: cd7457e3397ab100792de1d0099c7357f6188d89722e7357a6e350405f075b547bc9ba0c26a655f5aca8d8d7d4e6ddc3da69230d9eb235e3ed3f7b476383df30 Homepage: https://cran.r-project.org/package=ACEsearch Description: CRAN Package 'ACEsearch' ('ACE' Search Engine API) 'ACE' (Advanced Cohort Engine) is a powerful tool that allows constructing cohorts of patients extremely quickly and efficiently. This package is designed to interface directly with an instance of 'ACE' search engine and facilitates API queries and data dumps. Prerequisite is a good knowledge of the temporal language to be able to efficiently construct a query. More information available at . Package: r-cran-acesimfit Architecture: all Version: 0.0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openmx Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-acesimfit_0.0.0.9-1.ca2404.1_all.deb Size: 53518 MD5sum: f57627dd1d1b62b15ee626c0745b27fc SHA1: cd2dd32b8e43ac5654292c27ac6ba2b6cea2295c SHA256: 870760149982aa2037e6dc520da1851e01f64aa9f59430d9faf286372eed939d SHA512: 3b8d8c61bcdd3bd755956ba497ed379c2f0855036d90493e205b240a0c72bd7b027862a0cecd693e311a53e94b5a03f2650b95d4dbb858a719d1876621ec5ddb Homepage: https://cran.r-project.org/package=ACEsimFit Description: CRAN Package 'ACEsimFit' (ACE Kin Pair Data Simulations and Model Fitting) A few functions aim to provide a statistic tool for three purposes. First, simulate kin pairs data based on the assumption that every trait is affected by genetic effects (A), common environmental effects (C) and unique environmental effects (E).Second, use kin pairs data to fit an ACE model and get model fit output.Third, calculate power of A estimate given a specific condition. For the mechanisms of power calculation, we suggest to check Visscher(2004). Package: r-cran-acfmperiod Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 838 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-acfmperiod_1.1.0-1.ca2404.1_all.deb Size: 432482 MD5sum: 0c19a30e8ba6900948bf5b4a314e5e5a SHA1: a73f2a760b3d38b70dabdbaad487f39f7d96345c SHA256: 87d9ec5183048788368a6e8b81d53bdb99954ad065efb0f5bfe97606a72902ee SHA512: 76d28c98c8fb0928367495f8d02af33e1d450f6db06c26d5b98d80c6f500d067ee7910022902b236e085b675dd256a266a3f02d23640a6dc17683c2e5bd2df45 Homepage: https://cran.r-project.org/package=acfMPeriod Description: CRAN Package 'acfMPeriod' (Robust Estimation of the ACF from the M-Periodogram) Non-robust and robust computations of the sample autocovariance (ACOVF) and sample autocorrelation functions (ACF) of univariate and multivariate processes. The methodology consists in reversing the diagonalization procedure involving the periodogram or the cross-periodogram and the Fourier transform vectors, and, thus, obtaining the ACOVF or the ACF as discussed in Fuller (1995) . The robust version is obtained by fitting robust M-regressors to obtain the M-periodogram or M-cross-periodogram as discussed in Reisen et al. (2017) . Package: r-cran-achievegap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-lme4, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-achievegap_0.1.0-1.ca2404.1_all.deb Size: 142192 MD5sum: 20af0e42b688013ddbb1019754885680 SHA1: b06da4fd6d8d265bb5b37ada38c3136f96805a79 SHA256: cd2f5556cb7da1993a783f3ca0fb047839d01ba90366c6d620a3fab8b6d6e99c SHA512: f90ef044068d49712bd1f4310e80d182fe3c5fd8adfc084d6046a2d9cc022f36ad2e35cb2a7c60d0a160457d83340567b88d37a72aeca9c8cf007adeba5632c8 Homepage: https://cran.r-project.org/package=achieveGap Description: CRAN Package 'achieveGap' (Modeling Achievement Gap Trajectories with HierarchicalPenalized Splines) Implements a hierarchical penalized spline framework for estimating achievement gap trajectories in longitudinal educational data. The achievement gap between two groups (e.g., low versus high socioeconomic status) is modeled directly as a smooth function of grade while the baseline trajectory is estimated simultaneously within a mixed-effects model. Smoothing parameters are selected using restricted maximum likelihood (REML), and simultaneous confidence bands with correct joint coverage are constructed using posterior simulation. The package also includes functions for simulation-based benchmarking, visualization of gap trajectories, and hypothesis testing for global and grade-specific differences. The modeling framework builds on penalized spline methods (Eilers and Marx, 1996, ) and generalized additive modeling approaches (Wood, 2017, ), with uncertainty quantification following Marra and Wood (2012, ). Package: r-cran-achilles Architecture: all Version: 1.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1564 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-databaseconnector, r-cran-sqlrender, r-cran-dplyr, r-cran-jsonlite, r-cran-parallellogger, r-cran-readr, r-cran-data.table, r-cran-lubridate, r-cran-tseries, r-cran-rlang Suggests: r-cran-dt, r-cran-magrittr, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-achilles_1.7.2-1.ca2404.1_all.deb Size: 703508 MD5sum: 76d13457f59c5cb679aa811537412536 SHA1: abb4c5f9e8ad57c554dddc482e4bb4bb167fe9d4 SHA256: 63ce1fd3f5f8d8c32e44621b09bab3d1711bb4db79f5ee4b4179c59f3485f760 SHA512: 072e62959d621e40fd12e92e03d44275eeffaf7f28880d8077cc803e00c09cc25dc22fcecbef82ff50c5a55998e3171a2c18079bdda75188810085b07bbd7e47 Homepage: https://cran.r-project.org/package=Achilles Description: CRAN Package 'Achilles' (Achilles Data Source Characterization) Automated Characterization of Health Information at Large-Scale Longitudinal Evidence Systems. Creates a descriptive statistics summary for an Observational Medical Outcomes Partnership Common Data Model standardized data source. This package includes functions for executing summary queries on the specified data source and exporting reporting content for use across a variety of Observational Health Data Sciences and Informatics community applications. Package: r-cran-acid Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist, r-cran-hmisc Suggests: r-cran-ineq Filename: pool/dists/noble/main/r-cran-acid_1.1-1.ca2404.1_all.deb Size: 349846 MD5sum: 48225c3507a8aa53ea6ef5ca70967adb SHA1: 9b26b2df320024ac758f24b364dfda834732fb0c SHA256: e3fd39ce43e972350bb28975301534f5b4a35779907a4591abf13ed63331ce13 SHA512: 38fe09000afbb07efac0e2cc0ab821105861119be2bca16e0e61e95ddbdf9cd5015e154d8eab8616fd0fbf5126896667d0587813dc43489adcc84ad2697bcee5 Homepage: https://cran.r-project.org/package=acid Description: CRAN Package 'acid' (Analysing Conditional Income Distributions) Functions for the analysis of income distributions for subgroups of the population as defined by a set of variables like age, gender, region, etc. This entails a Kolmogorov-Smirnov test for a mixture distribution as well as functions for moments, inequality measures, entropy measures and polarisation measures of income distributions. This package thus aides the analysis of income inequality by offering tools for the exploratory analysis of income distributions at the disaggregated level. Package: r-cran-acled.api Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-acled.api_1.1.8-1.ca2404.1_all.deb Size: 34064 MD5sum: ec7c674e0f537a6a31498e68bc9d4fb6 SHA1: 150f24f5b9c77790040482327655def55688bf73 SHA256: 37e69ec5bc2c2630d054480e37e01367673517ae25a4206eb4cd3fc86b36013b SHA512: 50526b0f931ee03631f67215a271fb90eaa8f1291f79f1513789a3c1272dd5ac0a73cfe9a5f25b565a3d388b5f8b3f84aa7e668c329ad0c5ac2cefe017cddd01 Homepage: https://cran.r-project.org/package=acled.api Description: CRAN Package 'acled.api' (Automated Retrieval of ACLED Conflict Event Data) Access and manage the application programming interface (API) of the Armed Conflict Location & Event Data Project (ACLED) at . The package makes it easy to retrieve a user-defined sample (or all of the available data) of ACLED, enabling a seamless integration of regular data updates into the research work flow. It requires a minimal number of dependencies. See the package's README file for a note on replicability when drawing on ACLED data. When using this package, you acknowledge that you have read ACLED's terms and conditions of use, and that you agree with their attribution requirements. Package: r-cran-acledr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1726 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-httr2, r-cran-lubridate, r-cran-stringr, r-cran-tidyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-janitor, r-cran-rmarkdown, r-cran-readr, r-cran-kableextra, r-cran-ggplot2, r-cran-covr, r-cran-here, r-cran-secret, r-cran-sf, r-cran-raster, r-cran-forcats, r-cran-igraph, r-cran-sjmisc, r-cran-testthat Filename: pool/dists/noble/main/r-cran-acledr_1.0.1-1.ca2404.1_all.deb Size: 1691358 MD5sum: 61e280bbe093bfabe56f775d6f8ff390 SHA1: 44d51a7def69aa2b3b9cdcbf171f9d85e9ed3989 SHA256: ba6277c6d4e5ef8c3c9a098731e8e609fa988c6ce59d8cf93b91e9316a86dc05 SHA512: 0b582798fb4a8d3d71f4e6ac8390accc41f040412009294ef9d6eaa7dd1fe8dedded31010c07847663bb09194e03cc577b53c4157050faddea3deefe04e2c047 Homepage: https://cran.r-project.org/package=acledR Description: CRAN Package 'acledR' (Manipulate ACLED Data) Tools working with data from ACLED (Armed Conflict Location and Event Data). Functions include simplified access to ACLED's API (), methods for keeping local versions of ACLED data up-to-date, and functions for common ACLED data transformations. Package: r-cran-aclhs Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deoptim, r-cran-geor Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aclhs_1.0.1-1.ca2404.1_all.deb Size: 68960 MD5sum: 7abf7a59191119a86d6ed3cc3e93a294 SHA1: 10856b38a337196332f6e58b9125759331fb0c14 SHA256: 22a010167d5b14001a1a08509fd23b7d823a621e16662ce17cbb5bafd9dddda7 SHA512: 0909c8ee4e15e365b5d57d43049516e907abd37b87f8fce4cea79e815f98d6674af83605d5729c16f088a9149912a7e5b3c3a1d63234d458edea6e01167635a0 Homepage: https://cran.r-project.org/package=aclhs Description: CRAN Package 'aclhs' (Autocorrelated Conditioned Latin Hypercube Sampling) Implementation of the autocorrelated conditioned Latin Hypercube Sampling (acLHS) algorithm for 1D (time-series) and 2D (spatial) data. The acLHS algorithm is an extension of the conditioned Latin Hypercube Sampling (cLHS) algorithm that allows sampled data to have similar correlative and statistical features of the original data. Only a properly formatted dataframe needs to be provided to yield subsample indices from the primary function. For more details about the cLHS algorithm, see Minasny and McBratney (2006), . For acLHS, see Le and Vargas (2024) . Package: r-cran-acmgscaler Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-acmgscaler_1.0.0-1.ca2404.1_all.deb Size: 55194 MD5sum: e13c6ffa4498b3aef919156a15f9f9f3 SHA1: 6e358affb8753672bdc43205b650b94a99d9c9b1 SHA256: 620b561b8f13330f65f3ea7bab0bf73fd3d82fd816e772d53f49ba2b8d070f71 SHA512: ae2c9dd1bdea4e977cb292575605aae91e734faa8f70eeaa073e60aba9ab2b81cec9e42bc49fa6eaa7fb137431dc828fadae95e6800e33a704e400bf4bb77aac Homepage: https://cran.r-project.org/package=acmgscaler Description: CRAN Package 'acmgscaler' (Variant Effect Calibration to ACMG/AMP Evidence Strength) Provides a function to calibrate variant effect scores against evidence strength categories defined by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) guidelines. The method computes likelihood ratios of pathogenicity via kernel density estimation of pathogenic and benign score distributions, and derives score intervals corresponding to ACMG/AMP evidence levels. This enables researchers and clinical geneticists to interpret functional and computational variant scores in a reproducible and standardised manner. For details, see Badonyi and Marsh (2025) . Package: r-cran-acne Architecture: all Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aroma.affymetrix, r-cran-mass, r-cran-r.methodss3, r-cran-r.oo, r-cran-r.utils, r-cran-matrixstats, r-cran-r.filesets, r-cran-aroma.core Suggests: r-bioc-dnacopy Filename: pool/dists/noble/main/r-cran-acne_0.9.2-1.ca2404.1_all.deb Size: 131508 MD5sum: d6e0fb79199e13818c694b3c27b15ad4 SHA1: 1e697e8ff86d69ea096a2ac766ae9ee9adb19e8d SHA256: 3c5c64a714e7cc126bb97c0bf4eddc00e3a385e48eb9be108e8cff0158f9e094 SHA512: c72cdfca5007548c80cd3fe7918c6f1967606b02274fb9de1e31f05251c85b40ea9eeaec16ff775f931ded265f1200fac03996385ba287bdd2d11d55c101d302 Homepage: https://cran.r-project.org/package=ACNE Description: CRAN Package 'ACNE' (Affymetrix SNP Probe-Summarization using Non-Negative MatrixFactorization) A summarization method to estimate allele-specific copy number signals for Affymetrix SNP microarrays using non-negative matrix factorization (NMF). Package: r-cran-acnr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3037 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r.utils, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-acnr_1.0.0-1.ca2404.1_all.deb Size: 2829770 MD5sum: 67ef5db0e0c2aa8509da0da19221a6c2 SHA1: 834ea631b6bc2a71cfd5155223f529a28c6c42ba SHA256: ed4f7c6394b03eb101523ac21ee826ea4c78fafcf962d88108686c39b2b8dd6a SHA512: 317579a90061bee869dd8390743998a58a53429087822b6cd4a242b31e2ecb826156a0585da0a38c52bf282261842697b30f86585529c7c416a373938e877980 Homepage: https://cran.r-project.org/package=acnr Description: CRAN Package 'acnr' (Annotated Copy-Number Regions) Provides SNP array data from different types of copy-number regions. These regions were identified manually by the authors of the package and may be used to generate realistic data sets with known truth. Package: r-cran-acopula Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 682 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-acopula_0.9.4-1.ca2404.1_all.deb Size: 620676 MD5sum: ca24bf6a11167386264dbbecf6a5c841 SHA1: fe0a65b0042dbd76707b65c6e46f0b34b6e77d79 SHA256: c203a639b909cdd5fa0a2f714b3d86ce85e63ff4d9a885fd9298c4ea470243da SHA512: 7262a55f5472f21ba735aa89fd84b943dd54f65294e5186b86e3e672711597d22fb2a239001fc34653723b694970cf1ab503e78231000d8c341a1f27a6f12ea9 Homepage: https://cran.r-project.org/package=acopula Description: CRAN Package 'acopula' (Modelling Dependence with Multivariate Archimax (or anyUser-Defined Continuous) Copulas) Archimax copulas are mixture of Archimedean and EV copulas. The package provides definitions of several parametric families of generator and dependence function, computes CDF and PDF, estimates parameters, tests for goodness of fit, generates random sample and checks copula properties for custom constructs. In 2-dimensional case explicit formulas for density are used, contrary to higher dimensions when all derivatives are linearly approximated. Several non-archimax families (normal, FGM, Plackett) are provided as well. Package: r-cran-acorn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-stringi Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-acorn_0.1.0-1.ca2404.1_all.deb Size: 110290 MD5sum: fc2bc16b2ff0e45b4f2c0e84f9cd84be SHA1: 7f8bc79dadc4ff89d2f8e3e13ae30cb0831adac4 SHA256: db1ed988c8cc437af369127c10fbcb78276ff7d59a5d68ed30d1bd49020d58c4 SHA512: 6981376bad5625dd1a6a0f9c635a396c92156506d6ed5f4147424b529f033ec1f3c8a465336addd5a09c00bb936125046e09d9dfad82fabc06121bd84bbd31e7 Homepage: https://cran.r-project.org/package=acoRn Description: CRAN Package 'acoRn' (Exclusion-Based Parentage Assignment Using Multilocus GenotypeData) Exclusion-based parentage assignment is essential for studies in biodiversity conservation and breeding programs - Kang Huang, Rui Mi, Derek W Dunn, Tongcheng Wang, Baoguo Li, (2018), . The tool compares multilocus genotype data of potential parents and offspring, identifying likely parentage relationships while accounting for genotyping errors, missing data, and duplicate genotypes. 'acoRn' includes two algorithms: one generates synthetic genotype data based on user-defined parameters, while the other analyzes existing genotype data to identify parentage patterns. The package is versatile, applicable to diverse organisms, and offers clear visual outputs, making it a valuable resource for researchers. Package: r-cran-acousticndlcoder Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 655 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tuner, r-cran-zoo, r-cran-seewave Filename: pool/dists/noble/main/r-cran-acousticndlcoder_1.0.2-1.ca2404.1_all.deb Size: 600752 MD5sum: 925daaa840901b211c822769e8a11308 SHA1: 86257232832f64cdbb5555a9a03e9c60c43a6beb SHA256: 7cad60d29720a6816938b8e8105a249bdc03169de80f51a11530176ecb09941b SHA512: 7ccb9aaa682068e2c2e05c9803391a858a9067441e2060c90f3482c46e84e3d9dec5d14f9269fe64a6aa25db8a43c850d7c8909dc2ac12d573f184eafbe021f8 Homepage: https://cran.r-project.org/package=AcousticNDLCodeR Description: CRAN Package 'AcousticNDLCodeR' (Coding Sound Files for Use with NDL) Make acoustic cues to use with the R packages 'ndl' or 'ndl2'. The package implements functions used in the PLoS ONE paper: Denis Arnold, Fabian Tomaschek, Konstantin Sering, Florence Lopez, and R. Harald Baayen (2017). Words from spontaneous conversational speech can be recognized with human-like accuracy by an error-driven learning algorithm that discriminates between meanings straight from smart acoustic features, bypassing the phoneme as recognition unit. PLoS ONE 12(4):e0174623 More details can be found in the paper and the supplement. 'ndl' is available on CRAN. 'ndl2' is available by request from . Package: r-cran-acp Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tseries, r-cran-quantmod Filename: pool/dists/noble/main/r-cran-acp_2.1-1.ca2404.1_all.deb Size: 59500 MD5sum: d546b52474eb34bb790a4eac8df2138e SHA1: 7c00742c39e4215f02e7eb6a888ee0a32b5a3adb SHA256: 784d46f9176dfc5229cb88d5af4c2df1592da198c5a283d5bd16d89b19816078 SHA512: 12cea02c8af902e68c1dda6c92312535c9a2a07ec339f98d65169c00b0c50b399a52e2de0750c1b3076b01264af9c57d324dc25a58f363bf752492b2d7a6bdf7 Homepage: https://cran.r-project.org/package=acp Description: CRAN Package 'acp' (Autoregressive Conditional Poisson) Analysis of count data exhibiting autoregressive properties, using the Autoregressive Conditional Poisson model (ACP(p,q)) proposed by Heinen (2003). Package: r-cran-acro Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-admiraldev, r-cran-png Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-acro_0.1.7-1.ca2404.1_all.deb Size: 389628 MD5sum: 31ecf71a0e199be46674f881e62cfb4c SHA1: b780544dd3a28e8db33a1adef66db1cae0719d04 SHA256: 9a3f6c99792a032c04a1bad2b68f4e956491d06e2afdea1ad35a2aa8f30d82ff SHA512: 827114010f7573a3170e4da1ee71afad7b9c58d83859f11477306e40de2a0c4b21b7cf20dc5794df28a87620d9902d7483174f4ba0e94033828e356369d10472 Homepage: https://cran.r-project.org/package=acro Description: CRAN Package 'acro' (A Tool for Semi-Automating the Statistical Disclosure Control ofResearch Outputs) A Tool for Semi-Automating the Statistical Disclosure Control of Research Outputs. Package: r-cran-acroname Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-r.utils, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-hunspell Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-acroname_0.1.0-1.ca2404.1_all.deb Size: 29792 MD5sum: 06ead9276d5f1e761cad2eb7826206c9 SHA1: cc430a784f8cf25687c0899dbf34431e381a535e SHA256: dfcec3397e3f90715c30581928aaf3e2b6067bb3b330304014d22eaa1669915f SHA512: 0bf163db70b2823d67f30feddfff7147d9f456be2dfba1b5bf1e234911265fea3c2c7298792fd8407f1c475c9e64818979804b6288cd327eaa76eb689b30b262 Homepage: https://cran.r-project.org/package=acroname Description: CRAN Package 'acroname' (Engine for Acronyms and Initialisms) A tool for generating acronyms and initialisms from arbitrary text input. Package: r-cran-acrosstic Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-treeclust, r-cran-lpsolve Filename: pool/dists/noble/main/r-cran-acrosstic_1.0-3-1.ca2404.1_all.deb Size: 39090 MD5sum: 5c4fb8e62ebbf484dc9caa30edf22d7d SHA1: 809b67cd78a84348465e83f9121c3a0452677f1d SHA256: 6ed084dc75f43ae662fb36b2e36a9d577388fd459459e1be6e2be6f77d5aad63 SHA512: 59b25ecc945ff74a7a12e26c2a4a88817c9106439a449691c3c9c7b1351da792b6dcc11d0c1c26b3e883cf270913ac9427f9cd7a6ca303cd94a819bcb986b8a9 Homepage: https://cran.r-project.org/package=AcrossTic Description: CRAN Package 'AcrossTic' (A Cost-Minimal Regular Spanning Subgraph with TreeClust) Construct minimum-cost regular spanning subgraph as part of a non-parametric two-sample test for equality of distribution. Package: r-cran-acs Architecture: all Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1487 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-xml, r-cran-plyr, r-cran-httr Filename: pool/dists/noble/main/r-cran-acs_2.1.4-1.ca2404.1_all.deb Size: 1346678 MD5sum: 9a07681a35d19e636d8a0b8a7dc8a93a SHA1: 98374158333c82304f23d940924747f6f7e1e8f4 SHA256: 2f0af353c63256921eae1b47388c0f436b4e1e2eac051be8de221ae9f462c0c5 SHA512: e6e5a0970acd923a34fd39fa6be76fae75338719ef8764a9aa70e8728666231b511f70d46c882c7ea4fa01340f2cdd19639ca86693d5fcff52166571c0f1297d Homepage: https://cran.r-project.org/package=acs Description: CRAN Package 'acs' (Download, Manipulate, and Present American Community Survey andDecennial Data from the US Census) Provides a general toolkit for downloading, managing, analyzing, and presenting data from the U.S. Census (), including SF1 (Decennial short-form), SF3 (Decennial long-form), and the American Community Survey (ACS). Confidence intervals provided with ACS data are converted to standard errors to be bundled with estimates in complex acs objects. Package provides new methods to conduct standard operations on acs objects and present/plot data in statistically appropriate ways. Package: r-cran-acss.data Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13926 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-acss.data_1.2-1.ca2404.1_all.deb Size: 14223878 MD5sum: ac1e5fe04bf52978380dfc4c42157a55 SHA1: dadbe2d2af4e243ada2fdaf386d4ea6002ee82f4 SHA256: e6cca75072f13dc71fd45bb58c39a96603f27ac8ac1e17e0567e62489382cc9b SHA512: 07dbd31bd8cc6602d6840d41cffc141660b9747b2fef23a25b95560f9390891ec4eb6ea04e731b28610b896ea40d3af608a2ef6a1764b5fa9e93ba42f7879b0e Homepage: https://cran.r-project.org/package=acss.data Description: CRAN Package 'acss.data' (Data Only: Algorithmic Complexity of Short Strings (Computed viaCoding Theorem Method)) Data only package providing the algorithmic complexity of short strings, computed using the coding theorem method. For a given set of symbols in a string, all possible or a large number of random samples of Turing machines (TM) with a given number of states (e.g., 5) and number of symbols corresponding to the number of symbols in the strings were simulated until they reached a halting state or failed to end. This package contains data on 4.5 million strings from length 1 to 12 simulated on TMs with 2, 4, 5, 6, and 9 symbols. The complexity of the string corresponds to the distribution of the halting states of the TMs. Package: r-cran-acss Architecture: all Version: 0.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1312 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-acss.data, r-cran-zoo Suggests: r-cran-effects, r-cran-lattice, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-acss_0.3-2-1.ca2404.1_all.deb Size: 1208174 MD5sum: 6c506f938bc128defe1c2ac2737ef0c7 SHA1: 0797f311d56f89f83208294005292834f04d06b8 SHA256: 2558ad24f8debc88c7126374ae5862eddb3d3443af1b684d89d1f8b34abc89b6 SHA512: 7e145e9702899deeccacfb643115b70f16457315e49838cd60883aede40e7c937d82ca3653a538db2b30b201a788feafb2f8cfd9189b55f30669f8e94872f3a9 Homepage: https://cran.r-project.org/package=acss Description: CRAN Package 'acss' (Algorithmic Complexity for Short Strings) Main functionality is to provide the algorithmic complexity for short strings, an approximation of the Kolmogorov Complexity of a short string using the coding theorem method (see ?acss). The database containing the complexity is provided in the data only package acss.data, this package provides functions accessing the data such as prob_random returning the posterior probability that a given string was produced by a random process. In addition, two traditional (but problematic) measures of complexity are also provided: entropy and change complexity. Package: r-cran-acswr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-acswr_1.0-1.ca2404.1_all.deb Size: 249648 MD5sum: 0aab7e4fdf2c10cf05b4cc21e5c2c6a5 SHA1: 8b947aa845ac5c3b2649c753b3b8bbf3d4c7fd10 SHA256: b8652615096904f9ae481ef14781cecf755508cd189134ac44b01c9209239d45 SHA512: 465037910b88cc491305d6cf594e07338cab67eb06daf089d88fd53e2b44dd19b47063a71cbf980f9eaee417fd232663932eb45f683d0899487471a9438d4425 Homepage: https://cran.r-project.org/package=ACSWR Description: CRAN Package 'ACSWR' (A Companion Package for the Book "A Course in Statistics with R") A book designed to meet the requirements of masters students. Tattar, P.N., Suresh, R., and Manjunath, B.G. "A Course in Statistics with R", J. Wiley, ISBN 978-1-119-15272-9. Package: r-cran-act Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4412 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-stringi, r-cran-textutils, r-cran-progress, r-cran-xml, r-cran-xml2, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-act_1.3.1-1.ca2404.1_all.deb Size: 2061384 MD5sum: 28a14a2a6b3dacde588ec67d8f0e56f0 SHA1: 1dce025a0dc1c03d5d0265eb8de948cbf08e64bc SHA256: 53868e7792cf808d3c53036fdbd054898cc1c3be131b726c6a2246153ed8a63a SHA512: 326ca16facb395e9720bbc9a9069177345e8134803261b001b7664c74ae030efbe80f9608bbe7e14f8cd6c14061a533c177d28e1b8a657565f13620e28d2e6f3 Homepage: https://cran.r-project.org/package=act Description: CRAN Package 'act' (Aligned Corpus Toolkit) The Aligned Corpus Toolkit (act) is designed for linguists that work with time aligned transcription data. It offers functions to import and export various annotation file formats ('ELAN' .eaf, 'EXMARaLDA .exb and 'Praat' .TextGrid files), create print transcripts in the style of conversation analysis, search transcripts (span searches across multiple annotations, search in normalized annotations, make concordances etc.), export and re-import search results (.csv and 'Excel' .xlsx format), create cuts for the search results (print transcripts, audio/video cuts using 'FFmpeg' and video sub titles in 'Subrib title' .srt format), modify the data in a corpus (search/replace, delete, filter etc.), interact with 'Praat' using 'Praat'-scripts, and exchange data with the 'rPraat' package. The package is itself written in R and may be expanded by other users. Package: r-cran-actcr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-cosinor, r-cran-cosinor2, r-cran-dplyr, r-cran-minpack.lm Filename: pool/dists/noble/main/r-cran-actcr_0.4.0-1.ca2404.1_all.deb Size: 420716 MD5sum: 93bc8c174ead878144bf52e36f142e23 SHA1: d863a7c2aa6a55e8f09648619401f3de08306c95 SHA256: 9558e0ea9e7922c48a35fbb32b9356cbd9d02a1402461a9cda4dfc3baac63cfa SHA512: 83c712f72e4142c8e55073d6ca99779b397ee0c1434e4a544783a1d03ddaaa9adae217471a45bb09a682a6b3539a58e02afd7862055e0f678e7ac93caf82e899 Homepage: https://cran.r-project.org/package=ActCR Description: CRAN Package 'ActCR' (Extract Circadian Rhythms Metrics from Actigraphy Data) Circadian rhythms are rhythms that oscillate about every 24 h, which has been observed in multiple physiological processes including core body temperature, hormone secretion, heart rate, blood pressure, and many others. Measuring circadian rhythm with wearables is based on a principle that there is increased movement during wake periods and reduced movement during sleep periods, and has been shown to be reliable and valid. This package can be used to extract nonparametric circadian metrics like intradaily variability (IV), interdaily stability (IS), and relative amplitude (RA); and parametric cosinor model and extended cosinor model coefficient. Details can be found in Junrui Di et al (2019) . Package: r-cran-actel Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular, r-cran-data.table, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-fasttime, r-cran-ggplot2, r-cran-knitr, r-cran-readr, r-cran-reshape2, r-cran-rmarkdown, r-cran-rsvg, r-cran-scales, r-cran-stringi, r-cran-stringr, r-cran-svglite Suggests: r-cran-gdistance, r-cran-gwidgets2, r-cran-gwidgets2tcltk, r-cran-raster, r-cran-sp, r-cran-sf, r-cran-terra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-actel_1.4.0-1.ca2404.1_all.deb Size: 1475978 MD5sum: 98d696f412e82c06afaaa6be3cb21141 SHA1: 2f27377be0a31fee0dd916f298586de367c48c63 SHA256: cd46547179c741050ed6bdd12531046fec44e28be84f3881e58b2ecf26ffb518 SHA512: 10fbe8304f44eb8dd701fd768336538f6901e9c3d5d2ebefa517d3c672b55fcbeb19b59a2e89bd628739d3e9ae07a60862c9a5429bf61923aea3ea1d2de5613e Homepage: https://cran.r-project.org/package=actel Description: CRAN Package 'actel' (Acoustic Telemetry Data Analysis) Designed for studies where animals tagged with acoustic tags are expected to move through receiver arrays. This package combines the advantages of automatic sorting and checking of animal movements with the possibility for user intervention on tags that deviate from expected behaviour. The three analysis functions (explore(), migration() and residency()) allow the users to analyse their data in a systematic way, making it easy to compare results from different studies. CJS calculations are based on Perry et al. (2012) . Package: r-cran-actfrag Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-accelerometry, r-cran-dplyr, r-cran-ineq, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-actfrag_0.1.2-1.ca2404.1_all.deb Size: 399262 MD5sum: 32a28540cab02c8d82db59a18a84e964 SHA1: 1e7327eb7298e6ec3ab0d7715650935366911735 SHA256: 352ea90cc789884b490ce6ce39390b11dc346061a4add3e2ae14cf3fb8993f79 SHA512: 264dd7e3cd28f4e9b181a0bbc8dcf86013a909337ae56e9e75dd79663e9624d4571e71c7f5d38820f442455b6c487145a19338f610376a8eddaf7ad2a7087701 Homepage: https://cran.r-project.org/package=ActFrag Description: CRAN Package 'ActFrag' (Activity Fragmentation Metrics Extracted from Minute LevelActivity Data) Recent studies haven shown that, on top of total daily active/sedentary volumes, the time accumulation strategies provide more sensitive information. This package provides functions to extract commonly used fragmentation metrics to quantify such time accumulation strategies based on minute level actigraphy-measured activity counts data. Package: r-cran-actfts Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4033 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openxlsx, r-cran-plotly, r-cran-reactable, r-cran-tseries, r-cran-xts, r-cran-forecast, r-cran-lifecycle Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-actfts_0.3.0-1.ca2404.1_all.deb Size: 1052834 MD5sum: 03e9e213ab54a6501777cdcac0bd0418 SHA1: 13375e6e7324112295f36188fb0047cbaf127837 SHA256: 046cae6d5eee7b668faf8191001b074cf4d1a91b60ad20230126bae8470c17f2 SHA512: 811f32c832dffd2dad554d11d99154b6c5852011b834b177318b45254a3f6013be34631b0adfe3df2fc18adf5d93bf1ab789f54ee24e9491476f6c4b417b7200 Homepage: https://cran.r-project.org/package=actfts Description: CRAN Package 'actfts' (Autocorrelation Tools Featured for Time Series) The 'actfts' package provides tools for performing autocorrelation analysis of time series data. It includes functions to compute and visualize the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Additionally, it performs the Dickey-Fuller, KPSS, and Phillips-Perron unit root tests to assess the stationarity of time series. Theoretical foundations are based on Box and Cox (1964) , Box and Jenkins (1976) , and Box and Pierce (1970) . Statistical methods are also drawn from Kolmogorov (1933) , Kwiatkowski et al. (1992) , and Ljung and Box (1978) . The package integrates functions from 'forecast' (Hyndman & Khandakar, 2008) , 'tseries' (Trapletti & Hornik, 2020) , 'xts' (Ryan & Ulrich, 2020) , and 'stats' (R Core Team, 2023) . Additionally, it provides visualization tools via 'plotly' (Sievert, 2020) and 'reactable' (Glaz, 2023) . The package also incorporates macroeconomic datasets from the U.S. Bureau of Economic Analysis: Disposable Personal Income (DPI) , Gross Domestic Product (GDP) , and Personal Consumption Expenditures (PCEC) . Package: r-cran-actilifecounts Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4860 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gsignal, r-cran-pracma, r-cran-ggirread Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-actilifecounts_1.1.1-1.ca2404.1_all.deb Size: 561298 MD5sum: 557e617a24ec5d426c4af3cc5b54d5ec SHA1: da7e703bf52ca936526ce5641a09d783e49ea0b5 SHA256: 113efbd8a91e9c7876b6ffcb925d740d6195d2093b48548d247c12f69fb924a3 SHA512: 41e06cc31c5d6ef06302c432ca231c72b54d7918461f36bd8ab128dc40845399d781defd2e1199da766a461ac5cb3e492940f3d0ce0bf96640987227058deebd Homepage: https://cran.r-project.org/package=actilifecounts Description: CRAN Package 'actilifecounts' (Generate Activity Counts from Raw Accelerometer Data) A tool to obtain activity counts, originally a translation of the 'python' package 'agcounts' . This tool allows the processing of data from any accelerometer brand, with a more flexible approach to handle different sampling frequencies. Package: r-cran-activanalyzer Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4754 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-dbplyr, r-cran-dplyr, r-cran-flextable, r-cran-forcats, r-cran-ggplot2, r-cran-golem, r-cran-hms, r-cran-lubridate, r-cran-magrittr, r-cran-modelr, r-cran-patchwork, r-cran-physicalactivity, r-cran-plyr, r-cran-reactable, r-cran-rmarkdown, r-cran-rsqlite, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyfeedback, r-cran-shinyjs, r-cran-stringr, r-cran-tidyr, r-cran-zoo Suggests: r-cran-covr, r-cran-knitr, r-cran-spelling, r-cran-testthat, r-cran-processx, r-cran-globals, r-cran-config, r-cran-tidyselect, r-cran-dbi, r-cran-htmltools, r-cran-officer, r-cran-pkgload, r-cran-scales, r-cran-tibble, r-cran-rlang, r-cran-tinytex, r-cran-shinytest2, r-cran-pkgdown, r-cran-callr Filename: pool/dists/noble/main/r-cran-activanalyzer_2.1.2-1.ca2404.1_all.deb Size: 2159340 MD5sum: aa293b501b09890aa128f79ca4a81065 SHA1: a3299ae90e36a2d809cf95a2f46a6434f4d802c3 SHA256: 0638f65c6e2d6390d6aa9a842a194d6d8f165b6aa085361b5ff1368066c010e7 SHA512: b04342327003bba02e9a313fff135ffaeb2da67d1b0c477c42eb5cec2b4f4b7740d22f05da15c501294e6b517bda555ed6264a731e217197d73a851adda4b472 Homepage: https://cran.r-project.org/package=activAnalyzer Description: CRAN Package 'activAnalyzer' (A 'Shiny' App to Analyze Accelerometer-Measured Daily PhysicalBehavior Data) A tool to analyse 'ActiGraph' accelerometer data and to implement the use of the PROactive Physical Activity in COPD (chronic obstructive pulmonary disease) instruments. Once analysis is completed, the app allows to export results to .csv files and to generate a report of the measurement. All the configured inputs relevant for interpreting the results are recorded in the report. In addition to the existing 'R' packages that are fully integrated with the app, the app uses some functions from the 'actigraph.sleepr' package developed by Petkova (2021) . Package: r-cran-activatr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3016 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-geosphere, r-cran-ggmap, r-cran-glue, r-cran-httr, r-cran-lubridate, r-cran-rlang, r-cran-tibble, r-cran-slider, r-cran-xml2 Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-roxygen2, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-activatr_0.2.1-1.ca2404.1_all.deb Size: 2791112 MD5sum: cae8bd316945647af2a1593ecfedc53a SHA1: 7abb3a0154cd53bcb073e862557751db60e4ea3f SHA256: 867a1a48657757568d45a4c7c0905c58117caab3aaf77ffbde77dfc3bacf3780 SHA512: 89ae8832798d048d7e11fc42e4f09ff310d907790ab10f1220c55c00c2c47c011e55da95bf7fc7097e97fc32bc2c34766ad04987e1960b9d74fadbb5d8fae464 Homepage: https://cran.r-project.org/package=activatr Description: CRAN Package 'activatr' (Utilities for Parsing and Plotting Activities) This contains helpful functions for parsing, managing, plotting, and visualizing activities, most often from GPX (GPS Exchange Format) files recorded by GPS devices. It allows easy parsing of the source files into standard R data formats, along with functions to compute derived data for the activity, and to plot the activity in a variety of ways. Package: r-cran-activedriver Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-activedriver_1.0.0-1.ca2404.1_all.deb Size: 180012 MD5sum: ff42e39ec4d74b36cafa04083f96abb5 SHA1: df2475e563303762264869a976e30c5844551ecc SHA256: bdcdd209e68b8ec26530d6f5a253a85d580934fec7a815662671ea9d03e5aa0c SHA512: 53f5783cff285ff11e9cae991bbf173fc718026f73645bcabfc27be28fd3bdb86b908607fce57cce3ee26245a4ab9cbd79fc89890cfec0cb5a82debf124f0f8d Homepage: https://cran.r-project.org/package=ActiveDriver Description: CRAN Package 'ActiveDriver' (Finding Cancer Driver Proteins with Enriched Mutations inPost-Translational Modification Sites) A mutation analysis tool that discovers cancer driver genes with frequent mutations in protein signalling sites such as post-translational modifications (phosphorylation, ubiquitination, etc). The Poisson generalised linear regression model identifies genes where cancer mutations in signalling sites are more frequent than expected from the sequence of the entire gene. Integration of mutations with signalling information helps find new driver genes and propose candidate mechanisms to known drivers. Reference: Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Juri Reimand and Gary D Bader. Molecular Systems Biology (2013) 9:637 . Package: r-cran-activedriverwgs Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1369 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-bsgenome, r-bioc-biostrings, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors Suggests: r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-bioc-bsgenome.mmusculus.ucsc.mm9, r-bioc-bsgenome.mmusculus.ucsc.mm10, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-activedriverwgs_1.2.1-1.ca2404.1_all.deb Size: 858896 MD5sum: 1aa81ac956ee27b606b84e35a6ac2b68 SHA1: 209cb6c93ca2d02172c826dac6864250533bb544 SHA256: e688f4828cca5246a1d5c4a553e887c60fdd78be2cce75bed90fb67d230fc49f SHA512: dadc921c46a624845ea091435171715f8e7656c64493603de0a90528ffdabf64a7f50c105519ce549de93c43d3ca89c838af6eb1ea4b5cb68575bb904a0f01b7 Homepage: https://cran.r-project.org/package=ActiveDriverWGS Description: CRAN Package 'ActiveDriverWGS' (A Driver Discovery Tool for Cancer Whole Genomes) A method for finding enrichments of somatic single nucleotide variants (SNVs) and small insertions-deletions (Indels) in functional elements in the human genome. 'ActiveDriverWGS' detects coding and noncoding cancer driver elements using whole genome sequencing data. The method is part of the publication H. Zhu et al. (2020) "Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks" in Molecular Cell. Package: r-cran-activepathways Architecture: all Version: 2.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3045 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-activepathways_2.0.6-1.ca2404.1_all.deb Size: 1747290 MD5sum: 6c37c2bd6eed181bf554c05cba0371cb SHA1: ffd993847464d1501e6c9f424ac2e74d535c2547 SHA256: 8657eecf4b3fd56cbeda1b36fdffa5cf3131e03962d5e152a654e7897e4907a1 SHA512: 993f9e9144b9ea25d76df882915e6d441cdd2d21a8f46106c3b66bf48aa4e3121a7e49290b85178921adb6fe50b0ca913eb2de00368d9fc06fb18b8ff6bda496 Homepage: https://cran.r-project.org/package=ActivePathways Description: CRAN Package 'ActivePathways' (Integrative Pathway Enrichment Analysis of Multivariate OmicsData) Framework for analysing multiple omics datasets in the context of molecular pathways, biological processes and other types of gene sets. The package uses p-value merging to combine gene- or protein-level signals, followed by ranked hypergeometric tests to determine enriched pathways and processes. Genes can be integrated using directional constraints that reflect how the input datasets are expected interact with one another. This approach allows researchers to interpret a series of omics datasets in the context of known biology and gene function, and discover associations that are only apparent when several datasets are combined. The recent version of the package is part of the following publication: Directional integration and pathway enrichment analysis for multi-omics data. Slobodyanyuk M^, Bahcheli AT^, Klein ZP, Bayati M, Strug LJ, Reimand J. Nature Communications (2024) . Package: r-cran-activity Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-activity_1.3.4-1.ca2404.1_all.deb Size: 259040 MD5sum: c242571e8839fa0925be153069338137 SHA1: d2d8b505869df64d0262fc9ffbfab813203c9bfc SHA256: d30c104f624a426ea68d7f5c43b66c5c1266aa974492a83106463b7cb108ea1b SHA512: 13fb7a0d05d0531f59d3706dbeb1dfb095f36052aaa7f29a25fad5feff0abd94666b54ea48661388e49de35a6ce04193ba2ba52197e47c1adaf6340710332a62 Homepage: https://cran.r-project.org/package=activity Description: CRAN Package 'activity' (Animal Activity Statistics) Provides functions to express clock time data relative to anchor points (typically solar); fit kernel density functions to animal activity time data; plot activity distributions; quantify overall levels of activity; statistically compare activity metrics through bootstrapping; evaluate variation in linear variables with time (or other circular variables). Package: r-cran-activitycounts Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1626 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seewave, r-cran-signal, r-cran-tibble, r-cran-lubridate, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-activitycounts_0.2.1-1.ca2404.1_all.deb Size: 1011566 MD5sum: e1dec28cad6093050f5465f1e491b502 SHA1: 4958a5aba0b6b4f7ae183f97c5c449fd6c9b1e1b SHA256: 5404f54b64db9d6cf5023af59f979959a964d396c3b3d5b87636b10d92297f69 SHA512: 97c27800a4d8732a28dd950f4f9712e15dc86999b5f4a0522df1e69f27b1bd583d3f93fbebeee14fe11cdcdfac45c1f5985a35af3c54b0f1d1ca8f2f5d8a1d3c Homepage: https://cran.r-project.org/package=activityCounts Description: CRAN Package 'activityCounts' (Generate ActiLife Counts) ActiLife software generates activity counts from data collected by Actigraph accelerometers . Actigraph is one of the most common research-grade accelerometers. There is considerable research validating and developing algorithms for human activity using ActiLife counts. Unfortunately, ActiLife counts are proprietary and difficult to implement if researchers use different accelerometer brands. The code creates ActiLife counts from raw acceleration data for different accelerometer brands and it is developed based on the study done by Brond and others (2017) . Package: r-cran-activityindex Architecture: all Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4462 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-data.table, r-cran-r.utils Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-activityindex_0.3.7-1.ca2404.1_all.deb Size: 4085284 MD5sum: 29f426c290d8daf305d7389fe19087ff SHA1: 529dd74bc3cd9b75da475795a7752f758a28b331 SHA256: 0007d519b6835f8203d2931a5b567aa2699b6f250d73b8fc8a11d4889409e97f SHA512: df3e7425bde6d134119ac165060f2003e2ffd37fa5b67c96f5ba2eebc5c63dfda2fbf26e8ac29ee6608256baa5e68b9588ea301a2190502682da789ba7ce4237 Homepage: https://cran.r-project.org/package=ActivityIndex Description: CRAN Package 'ActivityIndex' (Activity Index Calculation using Raw 'Accelerometry' Data) Reads raw 'accelerometry' from 'GT3X+' data and plain table data to calculate Activity Index from 'Bai et al.' (2016) . The Activity Index refers to the square root of the second-level average variance of the three 'accelerometry' axes. Package: r-cran-activpal Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-lubridate, r-cran-magrittr, r-cran-devtools Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-activpal_0.1.4-1.ca2404.1_all.deb Size: 339644 MD5sum: 65eeb941673b7a449129a35078a7883b SHA1: ad10e857fe22cf3c33e3f0329fa07829a73ee367 SHA256: 267ca56afe96d408ca1afab69fd1f31527349d47287126a1937d3ad68212bc71 SHA512: 1110ba651d48308fb10904314146019ad7cec3b8ce72b1d9d2a90657f8d13858f7c26e777ab908b8260d8648af07acabea51c1c23784b90b53b354cbc886bf2c Homepage: https://cran.r-project.org/package=activPAL Description: CRAN Package 'activPAL' (Advanced Processing and Chart Generation from activPAL EventsFiles) Contains functions to generate pre-defined summary statistics from activPAL events files . The package also contains functions to produce informative graphics that visualise physical activity behaviour and trends. This includes generating graphs that align physical activity behaviour with additional time based observations described by other data sets, such as sleep diaries and continuous glucose monitoring data. Package: r-cran-actlifer Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-htmltools Filename: pool/dists/noble/main/r-cran-actlifer_1.0.0-1.ca2404.1_all.deb Size: 459966 MD5sum: fdb68cbd643bc7742d44b6cae74775b1 SHA1: a3ebefe66ecaf74a73fa9c879a6d252401c53a06 SHA256: 285bc1fbe195892a02e4d2db7134e70cfd2377e1b17142e0a923e025269d6ebc SHA512: 9ecc7f6698c4f559f78cc5c61b80594d2a753898d8569c3a7b2f51a4f5e060aec22923f744f3a7352b8beccc1c6491491310ece7e2c5101609d881f0da62db6a Homepage: https://cran.r-project.org/package=actLifer Description: CRAN Package 'actLifer' (Creating Actuarial Life Tables) Contains data and functions that can be used to make actuarial life tables. Each function adds a column to the inputted dataset for each intermediate calculation between mortality rate and life expectancy. Users can run any of our functions to complete the life table until that step, or run lifetable() to output a full life table that can be customized to remove optional columns. Methods for creating lifetables are as described in Zedstatistics (2021) . Package: r-cran-actogrammr Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1590 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-readr, r-cran-tidyr Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-actogrammr_0.2.3-1.ca2404.1_all.deb Size: 174096 MD5sum: eb687a585205fe7cfb48d0ee8ea337f2 SHA1: 19bd1c05b0b537eb48cfa11bb6c38b5933f854eb SHA256: 23e7d96513d9fe9ce352053973fe94660dffec7a0156e3430db20f2e86b9932d SHA512: fc5dc090351898c71b289747c9304293c45cd8cb13d8537621307087a6519851c250985635aff5ec4513a88aed28bee537a5cde13ab4da68995446110bc5519a Homepage: https://cran.r-project.org/package=actogrammr Description: CRAN Package 'actogrammr' (Read in Activity Data and Plot Actograms) Read in activity measurements from standard file formats used by circadian rhythm researchers, currently only 'ClockLab' format, and process and plot the data. The central type of plot is the actogram, as first described by in "Activity and distribution of certain wild mice in relation to biotic communities" by MS Johnson (1926) . Package: r-cran-actuare Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2616 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cplm, r-cran-statmod, r-cran-nlme, r-cran-lme4, r-cran-magrittr, r-cran-data.table, r-cran-ggplot2, r-cran-reformulas Suggests: r-cran-plyr, r-cran-knitr, r-cran-bookdown, r-cran-insurancedata, r-cran-actuar, r-cran-lattice, r-cran-minqa, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-actuare_1.0.0-1.ca2404.1_all.deb Size: 1486232 MD5sum: 4468b0986fd7b127054b738cc340e307 SHA1: 8165927bb5e2660af413e7a9f192deaa388093f1 SHA256: 317d620c4c1801c0a3b5b1e5689e09047903479e8ff5f1742edf289c5c03d278 SHA512: 5c6657253c7400547bceda76747df92fbd12ec2f68498809f6f80e800bd1564e31ea3137bcffbdee90882264493956f118b3f3d016181fb156f19717ca8fc8c8 Homepage: https://cran.r-project.org/package=actuaRE Description: CRAN Package 'actuaRE' (Handling Single-Level and Hierarchically Structured Risk Factorsusing Credibility and Random Effects Models) Fits random effects models for multi-level/high-cardinality factors using credibility theory (Buhlmann-Straub for single-level, Jewell for hierarchical structures), GLM extensions following Ohlsson (2008) , or Tweedie generalized linear mixed models. Provides functions for model fitting, visualization, and prediction. See Campo, B.D.C. and Antonio, K. (2023) . Package: r-cran-actuarialm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-actuarialm_0.1.0-1.ca2404.1_all.deb Size: 51334 MD5sum: 1a696a8afcdcd56b96bf1965a6f83af0 SHA1: 7c02a1a697eb8bcfb243384532727d43be8cc7fb SHA256: da5dea203a8a0e93e972c456a9b297192778a789f8ce7e9a1c3767a77d4c9879 SHA512: f71eada39acc731ba98703b2dfd27db876847eb635f134dd8123b4b7158af39ee376143464e155a0203cf95986621d5c931507304b1fbab0245a2835e7a4f1ea Homepage: https://cran.r-project.org/package=ActuarialM Description: CRAN Package 'ActuarialM' (Computation of Actuarial Measures Using Bell G Family) It computes two frequently applied actuarial measures, the expected shortfall and the value at risk. Seven well-known classical distributions in connection to the Bell generalized family are used as follows: Bell-exponential distribution, Bell-extended exponential distribution, Bell-Weibull distribution, Bell-extended Weibull distribution, Bell-Lomax distribution, Bell-Burr-12 distribution, and Bell-Burr-X distribution. Related works include: a) Fayomi, A., Tahir, M. H., Algarni, A., Imran, M., & Jamal, F. (2022). "A new useful exponential model with applications to quality control and actuarial data". Computational Intelligence and Neuroscience, 2022. . b) Alsadat, N., Imran, M., Tahir, M. H., Jamal, F., Ahmad, H., & Elgarhy, M. (2023). "Compounded Bell-G class of statistical models with applications to COVID-19 and actuarial data". Open Physics, 21(1), 20220242. . Package: r-cran-actuary Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 994 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dtplyr, r-cran-forcats, r-cran-furrr, r-cran-future, r-cran-gamlss.dist, r-cran-ggplot2, r-cran-ggtext, r-cran-mass, r-cran-pracma, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-yardstick Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-actuary_0.1.2-1.ca2404.1_all.deb Size: 971790 MD5sum: 69abe87c325f0752bc8d4347cb332792 SHA1: c2aa62820677e88816a325e665e79426bb2e3586 SHA256: 5060098abb7f29aed6ba06944caca3287ffe6eb27d60ccccd177fd2eed3541bd SHA512: 3f935ab8f3f3a91312a97d7450ba59295e20b48b15570224e3c8473459f4fe846d32ec2628e49a18c46501bb32bff7d64641cf8dac1e528ed73d8fc17cfe5153 Homepage: https://cran.r-project.org/package=actuary Description: CRAN Package 'actuary' (Actuarial Functions and Utilities) Provides actuarial modeling tools for Monte Carlo loss simulations, loss reserving, and reinsurance layer loss calculations. It enables users to generate stochastic loss datasets with customisable frequency and severity distributions, fit development patterns to claim triangles, and calculate reinsurance losses for occurrence and aggregate layers with user-defined retentions, limits, and reinstatements. For development pattern selection, the package includes a machine learning approach that evaluates multiple reserving models using holdout validation to identify the best-fitting pattern based on predictive accuracy, this is based on the algorithm described in Richman, R and Balona, C (2020). Package: r-cran-actuaryr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 723 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-dplyr, r-cran-magrittr, r-cran-crayon, r-cran-purrr, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-actuaryr_1.1.1-1.ca2404.1_all.deb Size: 464630 MD5sum: a0cc3f181b124c762edf2bd7aa8e4a56 SHA1: c6343fbcf5329de0902f2d864d05d18829febdd9 SHA256: 3e55fcbf6b6c0227ec08967aa0fd1184f4c1b68c3c143701f9ef7baafbdaa4bf SHA512: 1c6f4cffdfc6e14ccd9e0cd776fee23305c025893b86552e0792138326008be876088c78c5f846d5aa104c1bef87c4ff2a4e8fc0dd214b404f3b6a5a9fa2e8ef Homepage: https://cran.r-project.org/package=actuaryr Description: CRAN Package 'actuaryr' (Develop Actuarial Models) Actuarial reports are prepared for the last day of a specific period, such as a month, a quarter or a year. Actuarial models assume that certain events happen at the beginning or end of periods. The package contains functions to easily refer to the first or last (working) day within a specific period relative to a base date to facilitate actuarial reporting and to compare results. Package: r-cran-actxps Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tibble, r-cran-rlang, r-cran-glue, r-cran-purrr, r-cran-scales, r-cran-gt, r-cran-paletteer, r-cran-recipes, r-cran-generics, r-cran-readr, r-cran-tidyr, r-cran-vctrs, r-cran-clock, r-cran-cli Suggests: r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-testthat, r-cran-shiny, r-cran-bslib, r-cran-thematic Filename: pool/dists/noble/main/r-cran-actxps_1.6.1-1.ca2404.1_all.deb Size: 2606614 MD5sum: f14771661e38b1a17b6cde8b04f89ae6 SHA1: 9fbb487de175119c4461b0a6a0ad0b0ef9137bd8 SHA256: d5b525a41bdb5a7528e867f00167f76ee49556f2dcd5746c9fb1ed953a8eb4be SHA512: 6d5ad2cc191d52bc8f9c4d98601f8ea51818e420e033ca06c29b2e4b91437d81be9a6a958606ec2c1c4115a4b73c8f24ab5b2ec473027b72112106195153c6a1 Homepage: https://cran.r-project.org/package=actxps Description: CRAN Package 'actxps' (Create Actuarial Experience Studies: Prepare Data, SummarizeResults, and Create Reports) Experience studies are used by actuaries to explore historical experience across blocks of business and to inform assumption setting activities. This package provides functions for preparing data, creating studies, visualizing results, and beginning assumption development. Experience study methods, including exposure calculations, are described in: Atkinson & McGarry (2016) "Experience Study Calculations" . The limited fluctuation credibility method used by the 'exp_stats()' function is described in: Herzog (1999, ISBN:1-56698-374-6) "Introduction to Credibility Theory". Package: r-cran-acuityview Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-imager, r-cran-fftwtools, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-acuityview_1.1.1-1.ca2404.1_all.deb Size: 167294 MD5sum: 2ab0a333fc462906c66d1820eb805792 SHA1: 3842ebf724cf77735767f308ac8634426f62367a SHA256: b10afdd3b0fc71647ea6bb490002767ca8757d98047859c2e4220d7bf46200de SHA512: 29aa284e617f3468891e3871065af110ad7ea9abb5e500d9b36b9cbd454fc32bb8f54047724192aa4dfeecceb815310473739213ee48fd5eff95ff6684809616 Homepage: https://cran.r-project.org/package=AcuityView Description: CRAN Package 'AcuityView' (A Package for Displaying Visual Scenes as They May Appear to anAnimal with Lower Acuity) This code provides a simple method for representing a visual scene as it may be seen by an animal with less acute vision. When using (or for more information), please cite the original publication. Package: r-cran-acumos Architecture: all Version: 0.4-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-rprotobuf, r-cran-rserve, r-cran-restrserve, r-cran-yaml Suggests: r-cran-randomforest, r-cran-testthat, r-cran-callr Filename: pool/dists/noble/main/r-cran-acumos_0.4-4-1.ca2404.1_all.deb Size: 70630 MD5sum: d021cbf024d5c49f18d54f464b281439 SHA1: 191cc6d34eedb3e012d1e1e157fe73766aa42cfb SHA256: 7e13046e00496b35a5bca6c0727234a65c6ff36e574c0a33a2e880ba5fc4b727 SHA512: f634589547ce285b7247f6d7c8c922bcfa4b298e9a45b89d02a29200f7e96eaecfc6719aecdeb50ba29bb392ab3df953c12ad6c5c2dfb822c858367482ef1896 Homepage: https://cran.r-project.org/package=acumos Description: CRAN Package 'acumos' ('Acumos' R Interface) Create, upload and run 'Acumos' R models. 'Acumos' () is a platform and open source framework intended to make it easy to build, share, and deploy AI apps. 'Acumos' is part of the 'LF AI Foundation', an umbrella organization within 'The Linux Foundation'. With this package, user can create a component, and push it to an 'Acumos' platform. Package: r-cran-acv Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-acv_1.0.2-1.ca2404.1_all.deb Size: 51380 MD5sum: 7d1b5b03408942756e81788039aec81b SHA1: e272cb54a8f055c482336646e49889ab7560cf57 SHA256: e4a3b7faca036035accb6c8a905b796993944c669f85aa2e2000c1beb14d2938 SHA512: 91ee0e9c0f70cf54864421b977aaa58ebf3fc1193a7e59824edca92b55acc211659f8b798bb0a1eaedd15195e3fbc379104f8ee130dfdf44852a1d2afd3151fb Homepage: https://cran.r-project.org/package=ACV Description: CRAN Package 'ACV' (Optimal Out-of-Sample Forecast Evaluation and Testing underStationarity) Package 'ACV' (short for Affine Cross-Validation) offers an improved time-series cross-validation loss estimator which utilizes both in-sample and out-of-sample forecasting performance via a carefully constructed affine weighting scheme. Under the assumption of stationarity, the estimator is the best linear unbiased estimator of the out-of-sample loss. Besides that, the package also offers improved versions of Diebold-Mariano and Ibragimov-Muller tests of equal predictive ability which deliver more power relative to their conventional counterparts. For more information, see the accompanying article Stanek (2021) . Package: r-cran-acwr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2d3 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-acwr_0.1.0-1.ca2404.1_all.deb Size: 54194 MD5sum: e154b6ed2fb8bf777f6205bc3f6d52fd SHA1: 1e819763e07a85fcb4409b6153ac003a58ff3616 SHA256: fd89870642198eb495c9f4e9c4c3ccd0f1b328bf17c17ff367cf06355c419e14 SHA512: 42f2cbb72c340c7f93044b6b218556d349fb87e1ce8323387c843834eaf7c4d0f9490b44f360f5ea69e0ed26324d529e9b69215e77d070ccd66835ab672369ad Homepage: https://cran.r-project.org/package=ACWR Description: CRAN Package 'ACWR' (Acute Chronic Workload Ratio Calculation) Functions for calculating the acute chronic workload ratio using three different methods: exponentially weighted moving average (EWMA), rolling average coupled (RAC) and rolling averaged uncoupled (RAU). Examples of this methods can be found in Williams et al. (2017) for EWMA and Windt & Gabbet (2018) for RAC and RAU . Package: r-cran-ada Architecture: all Version: 2.0-5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpart Filename: pool/dists/noble/main/r-cran-ada_2.0-5.1-1.ca2404.1_all.deb Size: 735082 MD5sum: 616e20befae4abfa6cd511e60296e374 SHA1: 073c2dfa602e3bac0107c8d500fa5a13976c7f1e SHA256: 3c5ed5af68aff3a6bfb1988740204ed7a0ae0440c16ef1382c5b7e9c2e69fc5d SHA512: 017b15ea6c2c80371124dd5de2966937b34197944dcb7f54b2358c0b72fc9ffa7b0d84fd2fab91e43c310555360c3c7ffcb797468a4826ec1a62afacf27d63b9 Homepage: https://cran.r-project.org/package=ada Description: CRAN Package 'ada' (The R Package Ada for Stochastic Boosting) Performs discrete, real, and gentle boost under both exponential and logistic loss on a given data set. 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Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done. Since version 2.0 the function margins() is available to calculate the margins for these classifiers. Also a higher flexibility is achieved giving access to the rpart.control() argument of 'rpart'. Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu et al., 2009) is implemented and a new function errorevol() shows the error of the ensembles as a function of the number of iterations. In addition, the ensembles can be pruned using the option 'newmfinal' in the predict.bagging() and predict.boosting() functions and the posterior probability of each class for observations can be obtained. Version 3.1 modifies the relative importance measure to take into account the gain of the Gini index given by a variable in each tree and the weights of these trees. 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Package: r-cran-adace Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-pracma Suggests: r-cran-testthat, r-cran-cubature, r-cran-mass Filename: pool/dists/noble/main/r-cran-adace_1.0.2-1.ca2404.1_all.deb Size: 116922 MD5sum: a2d333e7ee0b574e107e231d07652083 SHA1: 56c92862bb5e982607420f885ef68eaf1b452c7d SHA256: 22e7e585f402eeda04f16b818d7e4c7e3194d5eb7d0f5534e5f848834f7444c4 SHA512: dfc0407fa1af25ed5f1784f72bcce4a058efdd7a0205aba9490d2aa56239bbb33b5394f2856008ebb9bd7727aa9b7eb131c1f2bccf402765e7fd0adf630ce909 Homepage: https://cran.r-project.org/package=adace Description: CRAN Package 'adace' (Estimator of the Adherer Average Causal Effect) Estimate the causal treatment effect for subjects that can adhere to one or both of the treatments. Given longitudinal data with missing observations, consistent causal effects are calculated. 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It uses the biologically inspired operators such as mutation, crossover, selection and replacement.Because of their global search and robustness abilities, GAs have been widely utilized in machine learning, expert systems, data science, engineering, life sciences and many other areas of research and business. However, the regular GAs need the techniques to improve their efficiency in computing time and performance in finding global optimum using some adaptation and hybridization strategies. The adaptive GAs (AGA) increase the convergence speed and success of regular GAs by setting the parameters crossover and mutation probabilities dynamically. 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Package: r-cran-adapdiscom Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-softimpute, r-cran-matrix, r-cran-scout, r-cran-robustbase Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/noble/main/r-cran-adapdiscom_1.0.0-1.ca2404.1_all.deb Size: 95320 MD5sum: 1c8491158051164cb8c634ef4aaf0bf8 SHA1: 4960c646701483df553d3e7d44ad9028091fec26 SHA256: 6ee46a9f191cd1a8dfe9de21236f2af31d2ce3c2f606de1409acb3a7b2804aa8 SHA512: a14e41e69693af3b223ae0f2d2e55b27cf2a9f5003a28c417b150f2d76287600c70a79dc8b23a6b21a518526aa794bd76f170cb2ac22ad1e2fbf450fd7caf473 Homepage: https://cran.r-project.org/package=AdapDiscom Description: CRAN Package 'AdapDiscom' (Adaptive Sparse Regression for Block Missing Multimodal Data) Provides adaptive direct sparse regression for high-dimensional multimodal data with heterogeneous missing patterns and measurement errors. 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Analogous to Ad-plot, Ada-plot can identify symmetry, skewness, and outliers of the data distribution. The Uda-plot is as exceptional as Ud-plot in assessing normality. The d-value that quantifies the degree of proximity between the Uda-plot and the graph of the estimated normal density function helps guide to make decisions on confirmation of normality. Extreme values in the data can be eliminated using the 1.5IQR rule to create its robust version if user demands. Full description of the methodology can be found in the article by Wijesuriya (2025a) . Further, the development of Ad-plot and Ud-plot is contained in both article and the 'adplots' R package by Wijesuriya (2025b & 2025c) and . Package: r-cran-adapsamp Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-adapsamp_1.1.1-1.ca2404.1_all.deb Size: 53228 MD5sum: 5e0058f27b9ec6f229f70f320f524476 SHA1: 0b59315f7ab90bb3e04e6abb463e195cec641c3c SHA256: adf7adcffc7c153096fe8099dcdfbf7daffb0b1f50624c25aac570c503573a39 SHA512: e91a351ecf78ee0cf517b1789c5aae337a7d1314c55f1436ddaf19d24708a6bc7df0ebb73ee0c51e0b14d284b7185b6d36447e84ea3567382e5352bdf4b4193b Homepage: https://cran.r-project.org/package=AdapSamp Description: CRAN Package 'AdapSamp' (Adaptive Sampling Algorithms) For distributions whose probability density functions are log-concave, the adaptive rejection sampling algorithm can be used to build envelope functions for sampling. For others, we can use the modified adaptive rejection sampling algorithm, the concave-convex adaptive rejection sampling algorithm and the adaptive slice sampling algorithm. So we designed an R package mainly including 4 functions: rARS(), rMARS(), rCCARS() and rASS(). These functions can realize sampling based on the algorithms above. 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Additional lasso-based and propensity score-based signal detection approaches are also supplied. See Courtois et al . 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This makes it easier to find and correct the most important problems first. 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Package: r-cran-adaptiveboxplot Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-adaptiveboxplot_0.1.1-1.ca2404.1_all.deb Size: 25818 MD5sum: 38563bef62253e911d6dc3e002939d5a SHA1: e6bc8f33685495111e6ac6e9706e7ebbcbf6d3d2 SHA256: 6f99f1e14b79deadc45311c04f52dea81e85d4bac912a9a7cac283c1d446a30d SHA512: 61b3a452537e7dfdbb732371c346b1db4ffe836889015d2277f357f928b2208bb742f629baebbae83807e1a51c39d29e137e7ac7f8e200f70537177bfa1c18a8 Homepage: https://cran.r-project.org/package=AdaptiveBoxplot Description: CRAN Package 'AdaptiveBoxplot' (FDR(BH) Boxplot and FWER(Holm) Boxplot) Implements a framework for creating boxplots where the whisker lengths are determined by formal multiple testing procedures, making them adaptive to sample size and data characteristics. The function bh_boxplot() generates boxplots that control the False Discovery Rate (FDR) via the Benjamini-Hochberg procedure, and the function holm_boxplot() generates boxplots that control the Family-Wise Error Rate (FWER) via the Holm procedure. The methods are based on the framework in Gang, Lin, and Tong (2025) . 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The package also includes functionality for applying the method to 'phyloseq' objects so that the method can be easily applied to microbiome data and a 'shiny' app for interactive visualization. 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The implemented sampling algorithm was proposed by Vihola (2012) and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate. Package: r-cran-adaptmt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1404 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-glmnet, r-cran-hdtweedie, r-cran-mgcv, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-adaptmt_1.0.0-1.ca2404.1_all.deb Size: 1014726 MD5sum: 8bbede3ed8fc7f74aa755baf89039ff9 SHA1: 7dfcb53ff7289ea0e8b770872617ce8189df2a4c SHA256: cde748f202f102eeaa2584fd0fb7a6003e774ae7fa021b56aa311f2a6870e250 SHA512: 0c9cba7159c0fdf9508a8c2cacc2588edbb0ea1342ea3999c04a59fa90302188ba053d3ab7c2400d421cc0e1dce94ff547aba6e6cb1b2e753a75db6b1c534286 Homepage: https://cran.r-project.org/package=adaptMT Description: CRAN Package 'adaptMT' (Adaptive P-Value Thresholding for Multiple Hypothesis Testingwith Side Information) Implementation of adaptive p-value thresholding (AdaPT), including both a framework that allows the user to specify any algorithm to learn local false discovery rate and a pool of convenient functions that implement specific algorithms. See Lei, Lihua and Fithian, William (2016) . Package: r-cran-adaptr Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4201 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-covr, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-adaptr_1.5.0-1.ca2404.1_all.deb Size: 3795396 MD5sum: fac873b3247c43dc0701d5cb3ac2c311 SHA1: 04808950487f78c979d9363da45e2f7bb1007649 SHA256: 28c26b4e5d4b5f739d032135dce5630486e8c70fa66fd823e3f45a973ff37956 SHA512: 02245ff8a11546048ea728df282425acb9aed02c79311bc6fd52f162b073c56a78185c069a234455780c7c64b0bfe990848247f8341477b2b5196bd95b68cea3 Homepage: https://cran.r-project.org/package=adaptr Description: CRAN Package 'adaptr' (Adaptive Trial Simulator) Package that simulates adaptive (multi-arm, multi-stage) clinical trials using adaptive stopping, adaptive arm dropping, and/or adaptive randomisation. Developed as part of the INCEPT (Intensive Care Platform Trial) project (), primarily supported by a grant from Sygeforsikringen "danmark" (). 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Useful for assessing the quality of single cell RNAseq experiments, estimating the accuracy of signature matrices, and determining cell-type spillover. Please cite: Danziger SA et al. (2019) ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells . Package: r-cran-adaptsmofmri Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1350 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.geom, r-cran-matrix, r-cran-coda, r-cran-mvtnorm, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-adaptsmofmri_1.2-1.ca2404.1_all.deb Size: 1346578 MD5sum: 4d0b2d4706915d308399e31b1424a6ae SHA1: e33dec9354ba163c109edfce516d3e333efbce07 SHA256: 1929be0c7d22eb6f89f4a6fa8019dc1d46212fd7604a6f4b13cf9ff4af3cb696 SHA512: a4e802a0c5a91e17bbf50829471b3e5ef5a2d5fc45e7474532b0143c1690775986fe75b80fb252b8fcfc2c2bdaaca6db1b263abc57258367303ccc5a625471a0 Homepage: https://cran.r-project.org/package=adaptsmoFMRI Description: CRAN Package 'adaptsmoFMRI' (Adaptive Smoothing of FMRI Data) Adaptive smoothing functions for estimating the blood oxygenation level dependent (BOLD) effect by using functional Magnetic Resonance Imaging (fMRI) data, based on adaptive Gauss Markov random fields, for real as well as simulated data. The implemented models make use of efficient Markov Chain Monte Carlo methods. Implemented methods are based on the research developed by A. Brezger, L. Fahrmeir, A. Hennerfeind (2007) . Package: r-cran-adapttest Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-adapttest_1.2-1.ca2404.1_all.deb Size: 131804 MD5sum: 0e971af3050ed0174afc2f05a0110bac SHA1: 9432a8965072a2d37ddc13b2ce6f06e472586923 SHA256: db9ae56344822874619c9167458bf102e2ae9b64d1c5ae71306f2468e7a5d611 SHA512: 5acac0875943b396535b85b3fdca0a70c828f5b247d038b8b7ce5ffac77f2b9c2ae005e7b1749b6c9a0c60929caa19deeb1ecb82121d8aa1a4277de8cf38a16a Homepage: https://cran.r-project.org/package=adaptTest Description: CRAN Package 'adaptTest' (Adaptive Two-Stage Tests) The functions defined in this program serve for implementing adaptive two-stage tests. Currently, four tests are included: Bauer and Koehne (1994), Lehmacher and Wassmer (1999), Vandemeulebroecke (2006), and the horizontal conditional error function. User-defined tests can also be implemented. Reference: Vandemeulebroecke, An investigation of two-stage tests, Statistica Sinica 2006. 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DoE approach follows the approach in "Design and Analysis of Experiments" by Douglas C. Montgomery (2019, ISBN:978-1-119-49244-3). The package also provides utilities used in the course "Analysis of Data and Statistics" at the University of Trento, Italy. 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Yang, P., Ormerod, J., Liu, W., Ma, C., Zomaya, A., Yang, J. (2018) . 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(2023) . 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This package is devoted to provide the alternative method of DEA described in the paper entitled "Stepwise Selection of Variables in DEA Using Contribution Load", by F. Fernandez-Palacin, M. A. Lopez-Sanchez and M. Munoz-Marquez. Pesquisa Operacional 38 (1), pg. 1-24, 2018. . A full functional on-line and interactive version is available at . 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Includes a wide variety of physical, chemical, and biological parameters from 28 lakes. Data are from multiple collection organizations and have been harmonized in both time and space for ease of reuse. Package: r-cran-adlp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1193 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-tidyverse, r-cran-synthetic Filename: pool/dists/noble/main/r-cran-adlp_0.1.0-1.ca2404.1_all.deb Size: 540912 MD5sum: dd84908d0100c1fda5876c6c08d7e081 SHA1: 739d59cfb470fdd4a38c1a03b783349281bf4b16 SHA256: 5e889ec7d9cd67198b85a968accedce1001adc69d5546dc56a167dfbabc92da8 SHA512: 750bc26df42a3396f71c1b534e4b53ce1b11dc9c64ee7026fcfc04d48d82d3d854e610348175263e6d498c50d6ffd8b4514c5dcfc02f225dfb46c457d1a83252 Homepage: https://cran.r-project.org/package=ADLP Description: CRAN Package 'ADLP' (Accident and Development Period Adjusted Linear Pools forActuarial Stochastic Reserving) Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. This package allows for the use of a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework is developed in Avanzi et al. (2023). Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensemble techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). Reference: Avanzi B, Li Y, Wong B, Xian A (2023) "Ensemble distributional forecasting for insurance loss reserving" . Package: r-cran-admiral.test Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3768 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-lintr, r-cran-pkgdown, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-usethis, r-cran-covr Filename: pool/dists/noble/main/r-cran-admiral.test_0.7.0-1.ca2404.1_all.deb Size: 3270638 MD5sum: 4b59363fde7bd28fb91d7600152c9bf1 SHA1: 581a26483e48df76a217da464229fa0a1a64f3bf SHA256: 2007abcc47e528b0319d3c94ac1c5b7f714706c720f5293c2dc87c4184614071 SHA512: 3de6eb39b21c4a9cc403a186a0fd815367dc7efb8da0b7fc3097011e39d1571c667a66bd4d4755950a659b9a97fe839d14be29094c4de2b694e3f179bcfdf703 Homepage: https://cran.r-project.org/package=admiral.test Description: CRAN Package 'admiral.test' (Test Data for the 'admiral' Package) A set of Study Data Tabulation Model (SDTM) datasets from the Clinical Data Interchange Standards Consortium (CDISC) pilot project used for testing and developing Analysis Data Model (ADaM) derivations inside the 'admiral' package. Package: r-cran-admiral Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4793 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-diffdf, r-cran-dt, r-cran-here, r-cran-htmltools, r-cran-knitr, r-cran-pharmaversesdtm, r-cran-reactable, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-admiral_1.4.1-1.ca2404.1_all.deb Size: 2183958 MD5sum: 1c911feb2b877efc5cd50ec2d6989705 SHA1: 21c9831c862c304843a51b6dc810853c1384eba2 SHA256: 66afa4509433c9c2fbc30fb3946279451aa5ac872b89c31af8ab5c2e9ab6bedd SHA512: 92e52b30508e7c8a0c945de0131e5b6baeb338b8e24189f2a139733462d2cef4945735608b673395e05f69068a291bdf030e90388381ccd05fa1f7407a52dfd0 Homepage: https://cran.r-project.org/package=admiral Description: CRAN Package 'admiral' (ADaM in R Asset Library) A toolbox for programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). Package: r-cran-admiraldev Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1648 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-roxygen2, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-withr Suggests: r-cran-diffdf, r-cran-dt, r-cran-htmltools, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-admiraldev_1.4.0-1.ca2404.1_all.deb Size: 1217100 MD5sum: 86e3d79b7a2be51d6158cdd218af9083 SHA1: 8b4bbf31dcca6210cd0c59c4874c133e3272063b SHA256: d920a0fd0c159141418318372938d75a248d93fa8564f37e08f5ff35bf354c23 SHA512: 42e59aef6760281cc7ec4bb093387d4e536a441410d33b25bf7ce66c34e5d41351fef5a6e525dd5c74674e27cae5b969869a09b4da2c46f91abbb4c5edc29563 Homepage: https://cran.r-project.org/package=admiraldev Description: CRAN Package 'admiraldev' (Utility Functions and Development Tools for the Admiral PackageFamily) Utility functions to check data, variables and conditions for functions used in 'admiral' and 'admiral' extension packages. Additional utility helper functions to assist developers with maintaining documentation, testing and general upkeep of 'admiral' and 'admiral' extension packages. Package: r-cran-admiralmetabolic Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-diffdf, r-cran-dt, r-cran-htmltools, r-cran-knitr, r-cran-pharmaversesdtm, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-usethis Filename: pool/dists/noble/main/r-cran-admiralmetabolic_0.3.0-1.ca2404.1_all.deb Size: 225964 MD5sum: d7d73b53a0472fb7f5814cd76d57fcc5 SHA1: 8cf3280fac2212bc33b5f5e101cb8656cc8bb1d1 SHA256: 3ec55c7197876e6053eafc0269d018197de3ed0c9c524c8ce1194985f0d49b2b SHA512: b836020b3741d1bd58bdc005e6db7bf522c724ca57cb0f38ce462fa7418ffa1d9eba6ec1addcf963bd8138b03aa16681ebd0ab2aef50657db225f92ccc616c86 Homepage: https://cran.r-project.org/package=admiralmetabolic Description: CRAN Package 'admiralmetabolic' (Metabolism Extension Package for ADaM in 'R' Asset Library) A toolbox for programming Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). The package is an extension package of the 'admiral' package focusing on the metabolism therapeutic area. Package: r-cran-admiralneuro Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-diffdf, r-cran-dt, r-cran-htmltools, r-cran-knitr, r-cran-metatools, r-cran-pharmaversesdtm, r-cran-reactable, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-admiralneuro_0.2.1-1.ca2404.1_all.deb Size: 211232 MD5sum: 40160afebbb4c434343cb48b70d04dd1 SHA1: 4082c6f8979c9b246af031b0f93593638b041fad SHA256: 9f1278c123baa4cd4616c46bde2a0e5fb2cf802112897f7ab2cc95eb58aa6f5f SHA512: 6ad5a75160a860a5e03fecd4fc9242563000964b9d856faf35432453799b539ca738a3179449488b9ef0ed16ba671447b89cc72c9edbfb111175a3ef12a56c02 Homepage: https://cran.r-project.org/package=admiralneuro Description: CRAN Package 'admiralneuro' (Neuroscience Extension Package for ADaM in 'R' Asset Library) Programming neuroscience specific Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). This package extends the 'admiral' package. Package: r-cran-admiralonco Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1686 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-covr, r-cran-devtools, r-cran-diffdf, r-cran-dt, r-cran-gt, r-cran-here, r-cran-knitr, r-cran-lintr, r-cran-metatools, r-cran-miniui, r-cran-pharmaverseadam, r-cran-pharmaversesdtm, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-usethis, r-cran-withr Filename: pool/dists/noble/main/r-cran-admiralonco_1.4.1-1.ca2404.1_all.deb Size: 346472 MD5sum: d0029add668006490f71b00462db7eca SHA1: ec227998518239a83f1f6e400b319e85f310dad8 SHA256: 1500c748f962bf7f55bafae2741122c8cb3ee83808c6af2fa33dd53d0fc4535c SHA512: 1a7a6c7e2fb3c74d849daf1f6a44a8727c4f86116e68cd00681642eaffada1da3afe04ef28668cfffcfc83d53e0e27233e9037bd400ec846bc8cc2b68cfb82f3 Homepage: https://cran.r-project.org/package=admiralonco Description: CRAN Package 'admiralonco' (Oncology Extension Package for ADaM in 'R' Asset Library) Programming oncology specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team (2021), ). The package is an extension package of the 'admiral' package. Package: r-cran-admiralophtha Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-devtools, r-cran-diffdf, r-cran-knitr, r-cran-lintr, r-cran-miniui, r-cran-pharmaversesdtm, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-usethis, r-cran-withr Filename: pool/dists/noble/main/r-cran-admiralophtha_1.4.0-1.ca2404.1_all.deb Size: 756536 MD5sum: 84f6a5a512f3a48b12fbd3433bfb5ceb SHA1: 7040c2421fd2d818b9b014f88e01c94cf2708ef8 SHA256: 2c32fe4bedf50930e41b174ea86effbc0999c675c25a8b2d6ae2f07591091f28 SHA512: a140b8c72937fc37a8667ceb1a16ea91140b6a9aa003569601ea6bc3fc6d5f941592b4b5d34dc6c9273e8e4249606aae7febb8c3168a8413cf88285a7db7a00b Homepage: https://cran.r-project.org/package=admiralophtha Description: CRAN Package 'admiralophtha' (ADaM in R Asset Library - Ophthalmology) Aids the programming of Clinical Data Standards Interchange Consortium (CDISC) compliant Ophthalmology Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). Package: r-cran-admiralpeds Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 587 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-cli, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tidyselect, r-cran-zoo Suggests: r-cran-knitr, r-cran-lubridate, r-cran-pharmaversesdtm, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-admiralpeds_0.3.0-1.ca2404.1_all.deb Size: 444406 MD5sum: 53089942b6c371d30c244ddbddc3fb1e SHA1: 05c5983bb2b1328d85b172eb11a854cb8323ea2a SHA256: e05657d7c01236960b861e86eade822e8495ca74e3714b9178cd83fc763a8821 SHA512: 3664da814771a177fcbd6de9baf778b4da3dee5b2cef3cd733db995bf1ca4412c40b813726d89d208336bb0474a5c6abcd4802fefc2f47ad8efa4b9a05d69a8e Homepage: https://cran.r-project.org/package=admiralpeds Description: CRAN Package 'admiralpeds' (Pediatrics Extension Package for ADaM in 'R' Asset Library) A toolbox for programming Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). The package is an extension package of the 'admiral' package for pediatric clinical trials. Package: r-cran-admiralvaccine Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admiral, r-cran-admiraldev, r-cran-assertthat, r-cran-cli, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-covr, r-cran-devtools, r-cran-diffdf, r-cran-dt, r-cran-knitr, r-cran-lintr, r-cran-metatools, r-cran-miniui, r-cran-pharmaversesdtm, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-usethis Filename: pool/dists/noble/main/r-cran-admiralvaccine_0.6.0-1.ca2404.1_all.deb Size: 205078 MD5sum: 25e4730f00e30613126e260251fd15f5 SHA1: dcfc817709311ef967c894b69c51d4f892e299ee SHA256: 251562720deaf5c1737de334c61fcebfd8876101b95cc0ce641dde6587005a13 SHA512: 60292264174c1d45086c1fad13b86f49a781d4a6bad34e00cafcf5b0943ecb2e91dfeb95571e057a800be872bccdc6c42757a9fb0ca1a01ff6b07d2627a3e682 Homepage: https://cran.r-project.org/package=admiralvaccine Description: CRAN Package 'admiralvaccine' (Vaccine Extension Package for ADaM in 'R' Asset Library) Programming vaccine specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. Flat model is followed as per Center for Biologics Evaluation and Research (CBER) guidelines for creating vaccine specific domains. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team (2021), ). The package is an extension package of the 'admiral' package. Package: r-cran-admixr Architecture: all Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-rlang Suggests: r-cran-covr, r-cran-glue, r-cran-testthat, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-admixr_0.9.2-1.ca2404.1_all.deb Size: 562394 MD5sum: eea457dbf2e22c8f5cf4c022505517be SHA1: 4eab1c24ee00ee908a650bd92ef27983ae50d85d SHA256: d034c769fdaeee7e9cc4913d38a1de3db4cdf62567f4f4069e80917f6a01e49d SHA512: 2e5d5e1503cacba49068573240b965a7c17f408e3fdb0fb8fe2cb18f74d5113b9a804df60a9788fd210da754393fe14cccdb0e59f616b3d8372af9832feeccfe Homepage: https://cran.r-project.org/package=admixr Description: CRAN Package 'admixr' (An Interface for Running 'ADMIXTOOLS' Analyses) An interface for performing all stages of 'ADMIXTOOLS' analyses () entirely from R. Wrapper functions (D, f4, f3, etc.) completely automate the generation of intermediate configuration files, run 'ADMIXTOOLS' programs on the command-line, and parse output files to extract values of interest. This allows users to focus on the analysis itself instead of worrying about low-level technical details. A set of complementary functions for processing and filtering of data in the 'EIGENSTRAT' format is also provided. Package: r-cran-admmdensestsubmatrix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-admmdensestsubmatrix_0.1.0-1.ca2404.1_all.deb Size: 497136 MD5sum: 31325ca34071bf2e5708740ae1d53478 SHA1: c91798186c75dba1e8d7d6c5e1bf2196a87d0499 SHA256: 744f9094f88d26381651d9afcfb1996ce21464b3091a682749afde0be67eeae0 SHA512: ee84c27d9bcae36766246aec123107ce3f49284394a722d9efab846523894d8d79c0ca23d5b56cbf8aa63546cfff7fcdedbc712e30f368b5eb6a6da2db53bd24 Homepage: https://cran.r-project.org/package=admmDensestSubmatrix Description: CRAN Package 'admmDensestSubmatrix' (Alternating Direction Method of Multipliers to Solve DenseDubmatrix Problem) Solves the problem of identifying the densest submatrix in a given or sampled binary matrix, Bombina et al. (2019) . Package: r-cran-admtools Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 802 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape Suggests: r-cran-fossilsim, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-admtools_0.6.0-1.ca2404.1_all.deb Size: 443424 MD5sum: fb0edca7cea6be97c85e2d72458e985f SHA1: 8e4b18e14d3db38c256930183dac35d1f3b5215c SHA256: f023ff7bb03e7625324a1576c2269a238be5bada92fd6b36633b940241321060 SHA512: e1d8d8d62ecbc983d5d9d0ef9327b1cfb5ba0cee31863b8725cd4d1c1a035899eb39d0d777ed7a616cc9996f561ea8d2b911dca7a5f86e88a311f36f312d1ecd Homepage: https://cran.r-project.org/package=admtools Description: CRAN Package 'admtools' (Estimate and Manipulate Age-Depth Models) Estimate age-depth models from stratigraphic and sedimentological data, and transform data between the time and stratigraphic domain. Package: r-cran-admur Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mathjaxr, r-cran-scales, r-cran-zoo Suggests: r-cran-deoptimr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-admur_1.0.3-1.ca2404.1_all.deb Size: 1807512 MD5sum: 01384336e0b60fd864538876c613a613 SHA1: 7caa6c718505862eb8e058f52a705d13c969c193 SHA256: bac79a2eeadd4f1cb6c308fa38e57274d51c78b946647b55e15d55f97dcabe05 SHA512: ace88420b4f9220aa3f3a1217ad3c4ffb8e818862837c18d271429a172759d5aa6b57d5e5fb4650b76ad36931d6dcdf153291b3352eb2e3308a2a786230cfe7f Homepage: https://cran.r-project.org/package=ADMUR Description: CRAN Package 'ADMUR' (Ancient Demographic Modelling Using Radiocarbon) Provides tools to directly model underlying population dynamics using date datasets (radiocarbon and other) with a Continuous Piecewise Linear (CPL) model framework. Various other model types included. Taphonomic loss included optionally as a power function. Model comparison framework using BIC. Package also calibrates 14C samples, generates Summed Probability Distributions (SPD), and performs SPD simulation analysis to generate a Goodness-of-fit test for the best selected model. Details about the method can be found in Timpson A., Barberena R., Thomas M. G., Mendez C., Manning K. (2020) . Package: r-cran-adnuts Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1038 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-snowfall, r-cran-ellipse, r-cran-rstan, r-cran-r2admb, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-shinystan, r-cran-matrixcalc, r-cran-knitr, r-cran-tmb, r-cran-rmarkdown, r-cran-withr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-adnuts_1.1.2-1.ca2404.1_all.deb Size: 779362 MD5sum: 8b07124dedf72c440b0ad43c64829b4a SHA1: 3483d177e59627c40f867def9a3a67802d31bcbe SHA256: f7ec22c9a13ac6b4fb3d068be00d3af276ec65e03fd585b0c4a2eba6b6dd1928 SHA512: f22df4c9d3a00d30c18896deaac3143d217166d6ce020f964a8b172f45a4f598e79340feae91105ab62dea8e83527766279f92feea6b4d9d6355e326b18c0553 Homepage: https://cran.r-project.org/package=adnuts Description: CRAN Package 'adnuts' (No-U-Turn MCMC Sampling for 'ADMB' Models) Bayesian inference using the no-U-turn (NUTS) algorithm by Hoffman and Gelman (2014) . Designed for 'AD Model Builder' ('ADMB') models, or when R functions for log-density and log-density gradient are available, such as 'Template Model Builder' models and other special cases. Functionality is similar to 'Stan', and the 'rstan' and 'shinystan' packages are used for diagnostics and inference. Package: r-cran-adobeanalyticsr Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1509 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-jsonlite, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-httr, r-cran-tidyr, r-cran-rlang, r-cran-lubridate, r-cran-ggplot2, r-cran-scales, r-cran-r6, r-cran-jose, r-cran-openssl, r-cran-lifecycle, r-cran-glue, r-cran-vctrs, r-cran-progress, r-cran-memoise, r-cran-httr2 Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-adobeanalyticsr_0.5.1-1.ca2404.1_all.deb Size: 1235108 MD5sum: 772bf2ba715424d58d496d5f44240aca SHA1: 9e2849c93cd897f4eb84d2c310cc2a4c2bdb6161 SHA256: 6540ff87eb96d2298a1fafd4973ae375feaf235d36e1225184a5d84039c6d056 SHA512: d5274dab61c69120a9369ad6b0533acd8dc039648316730860a0ae953265027adcc87aa360c94400fc7589457e765018b4897c3cbf33529d9b298afc60916e4b Homepage: https://cran.r-project.org/package=adobeanalyticsr Description: CRAN Package 'adobeanalyticsr' (R Client for 'Adobe Analytics' API 2.0) Connect to the 'Adobe Analytics' API v2.0 which powers 'Analysis Workspace'. The package was developed with the analyst in mind, and it will continue to be developed with the guiding principles of iterative, repeatable, timely analysis. Package: r-cran-adoptr Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1854 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nloptr, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-rpact, r-cran-vdiffr, r-cran-pwr, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-gridextra, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-adoptr_1.1.2-1.ca2404.1_all.deb Size: 906008 MD5sum: 61df0d779111ab399c3a04019639ec8d SHA1: 892bce35c40ad1b64cffb0de6462d19c767c8e84 SHA256: f96ad2b52501eda9e75f4e9e5bf6f084b7cd86fcb444465d13a38c6e73f29fad SHA512: 8db720c05d3a55f46bdb321fa237e3f0cf2b8d1bdb2dba29f5a9af6e0c7670b660687c1e4f230993cad62b57de2bfdf061d4d3fe94e6c3d85e9039d27722d233 Homepage: https://cran.r-project.org/package=adoptr Description: CRAN Package 'adoptr' (Adaptive Optimal Two-Stage Designs) Optimize one or two-arm, two-stage designs for clinical trials with respect to several implemented objective criteria or custom objectives. Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported. See Pilz et al. (2019) and Kunzmann et al. (2021) for details. Package: r-cran-adp Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-adp_0.1.6-1.ca2404.1_all.deb Size: 25504 MD5sum: 6e36a0ac65148f11d41cb084e3c71a8a SHA1: b8686e2156575775e956643f40ab2a9a03471d70 SHA256: c5cd6f8f7b321eee8f3e300596788f31046fce71aa774f702b0d5e930e7a45ba SHA512: cd1c195b2e62250ebb2c975e8448d4a67138ee40f25a6ea1563bb95f78c5381a8b21b56a3dd1f7daec145bbc3dfac7b637fb3c6565f8d4aeabd8a899be937258 Homepage: https://cran.r-project.org/package=ADP Description: CRAN Package 'ADP' (Adoption Probability, Triers and Users Rate of a New Product) Calculate users prevalence of a product based on the prevalence of triers in the population. The measurement of triers is relatively easy. It is just a question of whether a person tried a product even once in his life or not. On the other hand, The measurement of people who also adopt it as part of their life is more complicated since adopting an innovative product is a subjective view of the individual. Mickey Kislev and Shira Kislev developed a formula to calculate the prevalence of a product's users to overcome this difficulty. The current package assists in calculating the users prevalence of a product based on the prevalence of triers in the population. See for: Kislev, M. M., and S. Kislev (2020) . Package: r-cran-adpclust Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1572 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-cluster, r-cran-fields, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-adpclust_0.7-1.ca2404.1_all.deb Size: 1154924 MD5sum: d857c404a8865dcfcc948e52500891ab SHA1: c7d3ceed5a72d92e33d778de31f8975d98f81d22 SHA256: 827997678772a6d793acdbfd3c8c6462a65a426136776dd28ea28e02618c6cc8 SHA512: 07ac22e13006ab9c77e71a114d2d6b91daa5d041872e3a30614c89cf0e133da7b6e135240f368b67ed3d45a6864af48f1eece9c4838417656ac15aa82bf9de91 Homepage: https://cran.r-project.org/package=ADPclust Description: CRAN Package 'ADPclust' (Fast Clustering Using Adaptive Density Peak Detection) An implementation of ADPclust clustering procedures (Fast Clustering Using Adaptive Density Peak Detection). The work is built and improved upon the idea of Rodriguez and Laio (2014). ADPclust clusters data by finding density peaks in a density-distance plot generated from local multivariate Gaussian density estimation. It includes an automatic centroids selection and parameter optimization algorithm, which finds the number of clusters and cluster centroids by comparing average silhouettes on a grid of testing clustering results; It also includes a user interactive algorithm that allows the user to manually selects cluster centroids from a two dimensional "density-distance plot". Here is the research article associated with this package: "Wang, Xiao-Feng, and Yifan Xu (2015) Fast clustering using adaptive density peak detection." Statistical methods in medical research". url: http://smm.sagepub.com/content/early/2015/10/15/0962280215609948.abstract. Package: r-cran-adpf Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-adpf_0.0.1-1.ca2404.1_all.deb Size: 37206 MD5sum: b39b30ff874c08fac11e31b920c5f159 SHA1: 12344b2da294a70fe160094ffc0cb55a9f0c20ae SHA256: 9db96696ca5da683dd04d3b8fe77a9ba30284532cff64aee792fd2aee28c815b SHA512: 58b5aa661711fe757fb00ca920822229dcea7352a186bf977a5b602b72ff4d04bb0c56211bf08e9dbeaa82566d48d0d0742d959beaa3b317c97c3f138c56c62e Homepage: https://cran.r-project.org/package=ADPF Description: CRAN Package 'ADPF' (Use Least Squares Polynomial Regression and Statistical Testingto Improve Savitzky-Golay) This function takes a vector or matrix of data and smooths the data with an improved Savitzky Golay transform. The Savitzky-Golay method for data smoothing and differentiation calculates convolution weights using Gram polynomials that exactly reproduce the results of least-squares polynomial regression. Use of the Savitzky-Golay method requires specification of both filter length and polynomial degree to calculate convolution weights. For maximum smoothing of statistical noise in data, polynomials with low degrees are desirable, while a high polynomial degree is necessary for accurate reproduction of peaks in the data. Extension of the least-squares regression formalism with statistical testing of additional terms of polynomial degree to a heuristically chosen minimum for each data window leads to an adaptive-degree polynomial filter (ADPF). Based on noise reduction for data that consist of pure noise and on signal reproduction for data that is purely signal, ADPF performed nearly as well as the optimally chosen fixed-degree Savitzky-Golay filter and outperformed sub-optimally chosen Savitzky-Golay filters. For synthetic data consisting of noise and signal, ADPF outperformed both optimally chosen and sub-optimally chosen fixed-degree Savitzky-Golay filters. See Barak, P. (1995) for more information. Package: r-cran-adplots Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-adplots_0.1.0-1.ca2404.1_all.deb Size: 69678 MD5sum: 7c04567674838d10cae79891c9e9742f SHA1: 60a8bdbd5235bec271aa39c7875bfb2c8123320b SHA256: 1d2db7e3ca9be32deae4883aa62852eeb8d6cc62506b84b98c38522a0b468606 SHA512: df6814439ee953a604f6980a556cccbdd1ae3534810cf542d78d3ec1b64932807fcaca54ce1759e6c6a2ba2ae602a12d639c76262892367e5627fa4a590269e1 Homepage: https://cran.r-project.org/package=adplots Description: CRAN Package 'adplots' (Ad-Plot and Ud-Plot for Visualizing Distributional Propertiesand Normality) The empirical cumulative average deviation function introduced by the author is utilized to develop both Ad- and Ud-plots. The Ad-plot can identify symmetry, skewness, and outliers of the data distribution, including anomalies. The Ud-plot created by slightly modifying Ad-plot is exceptional in assessing normality, outperforming normal QQ-plot, normal PP-plot, and their derivations. The d-value that quantifies the degree of proximity between the Ud-plot and the graph of the estimated normal density function helps guide to make decisions on confirmation of normality. Full description of this methodology can be found in the article by Wijesuriya (2025) . Package: r-cran-adproclus Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-corrplot, r-cran-ggplot2, r-cran-ggrepel, r-cran-gtools, r-cran-igraph, r-cran-matrixstats, r-cran-multichull, r-cran-nmfn, r-cran-qgraph, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-withr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-adproclus_2.0.1-1.ca2404.1_all.deb Size: 181588 MD5sum: 9e821c9b5f8d0667ff77677850206d0c SHA1: 2d30c45744f2666b30936283ef94daf0b57e7a2d SHA256: 1419376919b7121718a5167689c2496d960300cc3275a35d19d424ead30f0ecd SHA512: b33b9cd23ea2de029b286c524546d14488fe3865007e8f25013d039f52c549cdd9e60bda6eac669e6576b432c5e1f535357440495aa8aaf28727fafe1294a3a2 Homepage: https://cran.r-project.org/package=adproclus Description: CRAN Package 'adproclus' (Additive Profile Clustering Algorithms) Obtain overlapping clustering models for object-by-variable data matrices using the Additive Profile Clustering (ADPROCLUS) method. Also contains the low dimensional ADPROCLUS method for simultaneous dimension reduction and overlapping clustering. For reference see Depril, Van Mechelen, Mirkin (2008) and Depril, Van Mechelen, Wilderjans (2012) . Package: r-cran-adrftools Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1796 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-collapse, r-cran-insight, r-cran-ggplot2, r-cran-marginaleffects, r-cran-mvtnorm, r-cran-rlang, r-cran-sandwich Suggests: r-cran-compquadform, r-cran-fwb, r-cran-survey, r-cran-weightit, r-cran-matchthem, r-cran-mice, r-cran-generics, r-cran-dbarts, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-adrftools_0.1.0-1.ca2404.1_all.deb Size: 1278160 MD5sum: 1d36557ce05f6de720b4d33a2389c89c SHA1: 6a624861873816755b2aade4fdd953f2b92aa631 SHA256: 4e83cec5647b02c67a46a0e55bb639ccb645212848edcd132d56f0be69feafe5 SHA512: 9bb3b65ada10f64e27c6ce13f06dc82feb632bdf434a267e6208a69ab36cb7c0c915b7e54edffc44b3b3fa2bc42f5e0429a0d32bbcec5d7ad3bf0f1b7cfb744e Homepage: https://cran.r-project.org/package=adrftools Description: CRAN Package 'adrftools' (Estimating, Visualizing, and Testing Average Dose-ResponseFunctions) Facilitates estimating, visualizing, and testing average dose-response functions (ADRFs) for characterizing the causal effect of a continuous (i.e., non-discrete) treatment or exposure. Includes support for frequentist and Bayesian regression models, analytical and bootstrap inference, and characterization of subgroup effects. Package: r-cran-adsasi Architecture: all Version: 0.9.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind Filename: pool/dists/noble/main/r-cran-adsasi_0.9.0.2-1.ca2404.1_all.deb Size: 60102 MD5sum: 2029289e7233c79f5173128a29be61bd SHA1: 097d8e84cc7ce6f3c3a698afb0489dee0aa197bd SHA256: dc26d69e3bb12129cb5ecb191a193d0d5c5bbc015689287501f6f64b9efc3e13 SHA512: 3283c16e020a2f1b8c762659f2a54a1cf690a116d701b8be25c49edbd4a4016069915b2f1c8162f6c7ded0f16b3ca10b9786cc01f580a7ca67de85decb1ff3e4 Homepage: https://cran.r-project.org/package=adsasi Description: CRAN Package 'adsasi' (Adaptive Sample Size Simulator) A simulations-first sample size determination package that aims at making sample size formulae obsolete for most easily computable statistical experiments ; the main envisioned use case is clinical trials. The proposed clinical trial must be written by the user in the form of a function that takes as argument a sample size and returns a boolean (for whether or not the trial is a success). The 'adsasi' functions will then use it to find the correct sample size empirically. The unavoidable mis-specification is obviated by trying sample size values close to the right value, the latter being understood as the value that gives the probability of success the user wants (usually 80 or 90% in biostatistics, corresponding to 20 or 10% type II error). Package: r-cran-adsdatahubr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-adsdatahubr_0.1.1-1.ca2404.1_all.deb Size: 29026 MD5sum: 1b926e3acda3665ba1f5d03ec1ac2c83 SHA1: 5c992f848733110dbf209bd0fb4bad6633256898 SHA256: 5655e99fdabdedb69dbac5138f322cba8495c822d976bce258a681498ba259d1 SHA512: d87ef69ff32796f2eb73326b88f34ebf98d8ae271627dbee3dab9b13a6438cfe751cda42555d5a515906cb744634834e2e8f625072f599ad201b2c82e04f25e4 Homepage: https://cran.r-project.org/package=adsDataHubR Description: CRAN Package 'adsDataHubR' (Google Ads Data Hub API Client) Interact with Google Ads Data Hub API . The functionality allows to fetch customer details, submit queries to ADH. Package: r-cran-adsorpr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-adsorpr_0.1.0-1.ca2404.1_all.deb Size: 218138 MD5sum: 41ed93b6861850e9b04a8c564857503c SHA1: edf0078d8e89300035b4c61ae421ca10cc375fe9 SHA256: ba5a9179336fba7794c9e89bb6e437b0cbd002563161430cd621b032c61e68c3 SHA512: 0626733ca4baa5cd003da26ce69d09054d018c6fb9eef4aa77bb2dff0827da8fa7fce24dea7e07f8027e9f30186ae88188ee14c29fc7375f26dd0cdbb7bd762b Homepage: https://cran.r-project.org/package=AdsorpR Description: CRAN Package 'AdsorpR' (Adsorption Isotherm Models) Model adsorption behavior using classical isotherms, including Langmuir, Freundlich, Brunauer–Emmett–Teller (BET), and Temkin models. The package supports parameter estimation through both linearized and non-linear fitting techniques and generates high-quality plots for model diagnostics. It is intended for environmental scientists, chemists, and researchers working on adsorption phenomena in soils, water treatment, and material sciences. Functions are compatible with base 'R' and 'ggplot2' for visualization. Package: r-cran-adsorptioncmf Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nls2, r-cran-metrics, r-cran-ggplot2, r-cran-boot Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-adsorptioncmf_0.1.1-1.ca2404.1_all.deb Size: 105498 MD5sum: f6c77e48bd5219e3b3765c6074bb60f9 SHA1: 6c1c4336f0ad36c82265aa1f7cce28c6cbac2beb SHA256: 5e0fdb85ee5a2678bd11aefaed0eb27c351b1db80b81d2c484915763f29b4efc SHA512: a6b714f04eeb95de2667594b42ec02ad62dd144141a1f2bce806f561e445c3f073efe4dfc424f2cd05d1c6bd7a55c9d6cfc0ef9e45fee0811f9be99de5cbce62 Homepage: https://cran.r-project.org/package=adsoRptionCMF Description: CRAN Package 'adsoRptionCMF' (Classical Model Fitting of Adsorption Isotherms) Provides tools for classical parameter estimation of adsorption isotherm models, including both linear and nonlinear forms of the Freundlich, Langmuir, and Temkin isotherms. This package allows users to fit these models to experimental data, providing parameter estimates along with fit statistics such as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Error metrics are computed to evaluate model performance, and the package produces model fit plots with bootstrapped 95% confidence intervals. Additionally, it generates residual plots for diagnostic assessment of the models. Researchers and engineers in material science, environmental engineering, and chemical engineering can rigorously analyze adsorption behavior in their systems using this straightforward, non-Bayesian approach. For more details, see Harding (1907) . Package: r-cran-adsorptioncv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nls2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-adsorptioncv_0.1.0-1.ca2404.1_all.deb Size: 68606 MD5sum: 594b8a8d2fdb48cae1802e07a147321a SHA1: 860c5e8f8c831fd84135f211b312455d8efff185 SHA256: 63d3859c6ac647513ae4eb9f0970632db7b32223a561557c81820588324cf6d9 SHA512: 19183348abb410d7b6d96b04d6bad4eff4530c0b6ca5dfbadd279e062bbe058d49eb25d1d9e6d7b3b6f2248752b74237dd0f27f450e381236c80dc9a9b59ff01 Homepage: https://cran.r-project.org/package=adsoRptionCV Description: CRAN Package 'adsoRptionCV' (Cross-Validation Methods for Adsorption Isotherm Models) Provides cross-validation tools for adsorption isotherm models, supporting both linear and non-linear forms. Current methods cover commonly used isotherms including the Freundlich, Langmuir, and Temkin models. This package implements K-fold and leave-one-out cross-validation (LOOCV) with optional clustering-based fold assignment to preserve underlying data structures during validation. Model predictive performance is assessed using mean squared error (MSE), with optional graphical visualization of fold-wise MSEs to support intuitive evaluation of model accuracy. This package is intended to facilitate rigorous model validation in adsorption studies and aid researchers in selecting robust isotherm models. For more details, see Montgomery et al. (2012) , Lumumba et al. (2024) , and Yates et al. (2022) . Package: r-cran-adsorptionmcmc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-coda Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-adsorptionmcmc_0.1.0-1.ca2404.1_all.deb Size: 87078 MD5sum: e2626fafbbe53fad55501266a426264b SHA1: 6b69a0f8c81eb243c010e4b60699464b2906656e SHA256: 01ae11ab7d3c8ab9b277cba09cf4ad3bcff3cfa3813a5b1546550cc50922bca4 SHA512: db948a313243935d2141850ebfb09fc585db1b18d7d27bb2effd473566881847351a4ff1f3f097491ce947afcd2499abdf84313c85e9cc0e96a2577c27cfcdda Homepage: https://cran.r-project.org/package=adsoRptionMCMC Description: CRAN Package 'adsoRptionMCMC' (Bayesian Estimation of Adsorption Isotherms via MCMC) Provides tools for Bayesian parameter estimation of adsorption isotherm models using Markov Chain Monte Carlo (MCMC) methods. This package enables users to fit non-linear and linear adsorption isotherm models—Freundlich, Langmuir, and Temkin—within a probabilistic framework, capturing uncertainty and parameter correlations. It provides posterior summaries, 95% credible intervals, convergence diagnostics (Gelman-Rubin), and visualizations through trace and density plots. With this R package, researchers can rigorously analyze adsorption behavior in environmental and chemical systems using robust Bayesian inference. For more details, see Gilks et al. (1995) , and Gamerman & Lopes (2006) . Package: r-cran-adtsa Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-adtsa_1.0.1-1.ca2404.1_all.deb Size: 54968 MD5sum: 4fe9137f7040d89932d893ffe03c1ab2 SHA1: 1cdd08607a8519138e3a97c1919e8462831eb9db SHA256: 3b696ab53c2ac812f7ebddb872502fa7acc9edba47c9cf9cf0e1fdaaf69acd4c SHA512: 2b97bc4eb9e449ebaa669a9db04ce7eb7c76de76090a6e97f7fb1d42c982e0e2b63865c87db7485154635d57d056c1c332a88a14718e011184795ff3ce3a1c61 Homepage: https://cran.r-project.org/package=ADTSA Description: CRAN Package 'ADTSA' (Time Series Analysis) Analyzes autocorrelation and partial autocorrelation using surrogate methods and bootstrapping, and computes the acceleration constants for the vectorized moving block bootstrap provided by this package. 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'aeddo' employs advanced statistical methods, including hierarchical models, in an innovative manner to effectively characterize outbreak signals. It is particularly useful for epidemiologists, public health professionals, and researchers seeking to identify and respond to disease outbreaks in a timely fashion. For a detailed reference on hierarchical models, consult Henrik Madsen and Poul Thyregod's book (2011), ISBN: 9781420091557. Package: r-cran-aedforecasting Architecture: all Version: 0.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-forecast, r-cran-signal Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-aedforecasting_0.20.0-1.ca2404.1_all.deb Size: 194972 MD5sum: 2abeaf1f7c89cd2deeceb524d66d4bfb SHA1: 4bc3253f274eb363e683d1b3c2d2da6dc8382b86 SHA256: aef64ee96d6fd0badddeda5a11ff273d3d39e887e75fe051ea5455d289bbe6f5 SHA512: fd57cda4c051a5d0b4b3a79cb1b6f05a2e40bf9b5635552be4447581ab0cbef6fc7daf8f3b862441781fe6d078193b55969c8c8db28650059d124262fe08151b Homepage: https://cran.r-project.org/package=AEDForecasting Description: CRAN Package 'AEDForecasting' (Change Point Analysis in ARIMA Forecasting) Package to incorporate change point analysis in ARIMA forecasting. Package: r-cran-aedseo Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-lubridate, r-cran-plyr, r-cran-purrr, r-cran-pracma, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-isoweek, r-cran-kableextra, r-cran-knitr, r-cran-mem, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-aedseo_1.1.0-1.ca2404.1_all.deb Size: 864690 MD5sum: 8bf0f730a51fdc33db8fefc56701c263 SHA1: c90a6155310c83b60ddecbe9137c1e2a35eb61dc SHA256: c5bfdc687ca64f94506c1992fa65a4ea3d36fe58b78010c8a64356b8d5be917e SHA512: 61e4c7fd6816f2f78106928374045beb62ba919bb0454af35cb4697d0b045384547479fecc0f51653a6646e902fb0ad57e7a51a069c55aef44c1e20a4a4c6ba1 Homepage: https://cran.r-project.org/package=aedseo Description: CRAN Package 'aedseo' (Automated and Early Detection of Seasonal Epidemic Onset andBurden Levels) A powerful tool for automating the early detection of seasonal epidemic onsets in time series data. It offers the ability to estimate growth rates across consecutive time intervals, calculate the sum of cases (SoC) within those intervals, and estimate seasonal onsets within user defined seasons. With use of a disease-specific threshold it also offers the possibility to estimate seasonal onset of epidemics. Additionally it offers the ability to estimate burden levels for seasons based on historical data. It is aimed towards epidemiologists, public health professionals, and researchers seeking to identify and respond to seasonal epidemics in a timely fashion. Package: r-cran-aeenrich Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-bioc-qvalue, r-cran-doparallel, r-cran-tidyr, r-cran-modelr, r-cran-foreach, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aeenrich_1.1.1-1.ca2404.1_all.deb Size: 475976 MD5sum: 0ae85c2d539d47e42e09ef30e4aab196 SHA1: f70ed83422f1656a2954a8af01a0c8e28d0589f1 SHA256: 89f1d2e82094fa9d945337deefbcaac73fd82409c695550da4438a4f1aca669f SHA512: f4e563a8d1d01bc8b90906172c36b5e0a38ceed1ea4f8839930e667403a718abbbc069bdb7835c64f973ef60737c2a995ff989d29afb69dbad70359e51978d61 Homepage: https://cran.r-project.org/package=AEenrich Description: CRAN Package 'AEenrich' (Adverse Event Enrichment Tests) We extend existing gene enrichment tests to perform adverse event enrichment analysis. Unlike the continuous gene expression data, adverse event data are counts. Therefore, adverse event data has many zeros and ties. We propose two enrichment tests. One is a modified Fisher's exact test based on pre-selected significant adverse events, while the other is based on a modified Kolmogorov-Smirnov statistic. We add Covariate adjustment to improve the analysis."Adverse event enrichment tests using VAERS" Shuoran Li, Lili Zhao (2020) . 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Currently, the package allows the below analysis. (i) the calculation of greenhouse gas flux based on data obtained from trace gas analyzer using the method described in Lin et al. (2024). (ii) the calculation of Dissolved Oxygen (DO) metabolism based on data obtained from dissolved oxygen data logger using the method described in Staehr et al. (2010). Yong et al. (2024) . Staehr et al. (2010) . Package: r-cran-aemo Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-openssl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-aemo_0.4.0-1.ca2404.1_all.deb Size: 236020 MD5sum: d846a6039aef8112b081c12455c81f4d SHA1: 1a6d9ba7149feb02baad49f51796a20f3396dad8 SHA256: 3c10f689b9793b4e3d206255a06fa832585780a2683c187f63ad9465f474f5b9 SHA512: c0191510cde91802cedd2d28e67f1c0101af2c1303aa1da0ae9046d63d06ec61f9a4fe9ba8ef22587c1d7b2c3d6fd65fcda513baa16685804cb90471c58bf2dd Homepage: https://cran.r-project.org/package=aemo Description: CRAN Package 'aemo' (Download Australian Energy Market Operator Data) Fetch Australian Energy Market Operator ('AEMO') public data from 'NEMweb' and the Market Management System Data Model ('MMSDM') historical archive. Provides tidy access to 5-minute and 30-minute wholesale electricity prices, regional demand, dispatch-unit output, interconnector flows, rooftop photovoltaic generation, generator bids, predispatch forecasts, frequency control ancillary services markets, and gas market data across the National Electricity Market ('NEM') regions. Data is published by AEMO under its Copyright Permissions Notice . Package: r-cran-aep Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-aep_0.1.4-1.ca2404.1_all.deb Size: 56434 MD5sum: a510813e10928d8efac03844d15fe094 SHA1: 059590178e51a5efc69550ba3881699fe180d15d SHA256: 589de8b629f2cdeaeedab6139a37528261a753e68d33b76e440a35afcaf971c0 SHA512: 615235676180981d503a6e4955b19468860940310e32c62506fdfcc0feda906864091c5c787d1c6e1de47b479f9832699dcbd117c12d5af48773376d1e991ffa Homepage: https://cran.r-project.org/package=AEP Description: CRAN Package 'AEP' (Statistical Modelling for Asymmetric Exponential PowerDistribution) Developed for Computing the probability density function, cumulative distribution function, random generation, estimating the parameters of asymmetric exponential power distribution, and robust regression analysis with error term that follows asymmetric exponential power distribution. The asymmetric exponential power distribution studied here is a special case of that introduced by Dongming and Zinde-Walsh (2009) . Package: r-cran-aer Architecture: all Version: 1.2-16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2779 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-lmtest, r-cran-sandwich, r-cran-survival, r-cran-zoo, r-cran-formula Suggests: r-cran-boot, r-cran-dynlm, r-cran-effects, r-cran-fgarch, r-cran-forecast, r-cran-foreign, r-cran-ineq, r-cran-kernsmooth, r-cran-lattice, r-cran-longmemo, r-cran-mass, r-cran-mlogit, r-cran-nlme, r-cran-nnet, r-cran-np, r-cran-plm, r-cran-pscl, r-cran-quantreg, r-cran-rgl, r-cran-rocr, r-cran-rugarch, r-cran-sampleselection, r-cran-scatterplot3d, r-cran-strucchange, r-cran-systemfit, r-cran-truncreg, r-cran-tseries, r-cran-urca, r-cran-vars Filename: pool/dists/noble/main/r-cran-aer_1.2-16-1.ca2404.1_all.deb Size: 2554524 MD5sum: 7815b44bfd2b81f700ba5f83d1d0522a SHA1: e242f1f0591dacd8c2e111e34b7f719f4ab49995 SHA256: 233e1094dc3d1f1ea76486ade295ec3a2713af7264c05c93749fea7e84d69c4f SHA512: 85da4c052651d672227f6a5b07c2d0b960cc112892fcaf8dff86b1ff85c78911509a6bbb7d34b55bb0ef249e2d2a52df9950205863eaa2e0c74390cd196b699a Homepage: https://cran.r-project.org/package=AER Description: CRAN Package 'AER' (Applied Econometrics with R) Functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. 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Package: r-cran-aerobiology Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1090 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-writexl, r-cran-ggvis, r-cran-lubridate, r-cran-plotly, r-cran-ggplot2, r-cran-tidyr, r-cran-circular, r-cran-scales, r-cran-zoo, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aerobiology_2.0.2-1.ca2404.1_all.deb Size: 677504 MD5sum: c8ab9246024c3717e75fa555ca83f5fd SHA1: 125d92d6ecb9ac8b4d753f3ab15c0f7368546b16 SHA256: 6c74a5987e8bcb7a0093e95aead29be9bbb30ff373cc4f4466ff36b65f407113 SHA512: ddc7c901ebbd49bc68cb5be82c0273fd1dcf9103dd5c900273e0286e9b08769a37b214fd3a7fc31bc4f13d899120011f83a32485be81185bef21215cf0ad465f Homepage: https://cran.r-project.org/package=AeRobiology Description: CRAN Package 'AeRobiology' (A Computational Tool for Aerobiological Data) Different tools for managing databases of airborne particles, elaborating the main calculations and visualization of results. In a first step, data are checked using tools for quality control and all missing gaps are completed. Then, the main parameters of the pollen season are calculated and represented graphically. Multiple graphical tools are available: pollen calendars, phenological plots, time series, tendencies, interactive plots, abundance plots... Package: r-cran-aeroevapr Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-data.table, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-aeroevapr_0.1.6-1.ca2404.1_all.deb Size: 83156 MD5sum: 13b24fca74d513bf18639b9b7156a2a1 SHA1: c23f0262fcb544f3bcfdee33c3662fe60e93397a SHA256: 2ee66d5aac59da37dd5f0d772103ed34fcd078f657e3290b8d379d8ba543aa89 SHA512: a9bc8e2dda2d47f3a028fecf88ca36486b64e0c08d3cff76d80ad10568b90511caf64d5f7cc7c13855b15f6805ed0576dc835c375bb70bcdadef86ab4f3d2659 Homepage: https://cran.r-project.org/package=AeroEvapR Description: CRAN Package 'AeroEvapR' (Estimating Reservoir Evaporation via Aerodynamic Approach) Developed as an 'R' alternative to the 'AeroEvap' model developed by the Desert Research Institute (DRI) in 'python' which estimates open water evaporation using the aerodynamic mass transfer approach. 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Functions were developed consistent with the approach described in Hogue, Mark; Thompson, Martha; Farfan, Eduardo; Hadlock, Dennis, (2014), "Hand Calculations for Transport of Radioactive Aerosols through Sampling Systems" Health Phys 106, 5, S78-S87, . Package: r-cran-aesopr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-aesopr_0.1.0-1.ca2404.1_all.deb Size: 170028 MD5sum: 6a0d033e73a2313ec289e1167da5619e SHA1: c8a6b204da72cd3d1b26eef3cc2d88fb1cc40fb8 SHA256: 04a918a0a3958aa4f1ea490267f308fa2e802f8ce07fa4a81cd8fc49f17a7849 SHA512: a34b1ec3dd3f02412fae487411fcdbd1eecf7e12b8e4d0707f412558dc4dc79996a862e916a1fe8a7dbf27031bf351853447503fb1ae2d2b8e8e46a9f7ab7955 Homepage: https://cran.r-project.org/package=aesopR Description: CRAN Package 'aesopR' (Tools for Text Analysis of Aesop's Fables) Provides a tidy text corpus of Aesop's Fables sourced from the Library of Congress, along with analysis-ready datasets for sentiment, emotion, and linguistic analysis of moral storytelling. 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An exploration of the AF with a genetic exposure can be found in the package 'AFheritability' Dahlqwist E et al. (2019) . 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Package: r-cran-afdx Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-desctools, r-cran-kableextra, r-cran-coda, r-cran-rjags, r-cran-ggmcmc, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-afdx_1.1.2-1.ca2404.1_all.deb Size: 323126 MD5sum: c5523af8e3d7d5ba9c70ed5b85eb3207 SHA1: a6e67090be04d8aaf1e6d19353571fbc824db20f SHA256: e33e15785b83b6ee386f80fcbc52d95c8d77b36a555b367daeeb2e8469aae1b3 SHA512: 7266fb22dc62383a9f9ae93829d3ac606f81757382edd3c8e57a0c862d13c503ea066bc69a6d72a70325fe69ed6c373db2c03071313c980bef98090e8b59aae1 Homepage: https://cran.r-project.org/package=afdx Description: CRAN Package 'afdx' (Diagnosis Performance Using Attributable Fraction) Estimate diagnosis performance (Sensitivity, Specificity, Positive predictive value, Negative predicted value) of a diagnostic test where can not measure the golden standard but can estimate it using the attributable fraction. Package: r-cran-afex Architecture: all Version: 1.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4740 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-pbkrtest, r-cran-lmertest, r-cran-car, r-cran-reshape2, r-cran-reformulas, r-cran-rlang Suggests: r-cran-emmeans, r-cran-coin, r-cran-xtable, r-cran-plyr, r-cran-optimx, r-cran-nloptr, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-lattice, r-cran-latticeextra, r-cran-multcomp, r-cran-testthat, r-cran-mlmrev, r-cran-dplyr, r-cran-tidyr, r-cran-dfoptim, r-cran-matrix, r-cran-psychtools, r-cran-ggplot2, r-cran-memss, r-cran-effects, r-cran-cardata, r-cran-ggbeeswarm, r-cran-nlme, r-cran-cowplot, r-cran-jtools, r-cran-ggpubr, r-cran-mass, r-cran-glmmtmb, r-cran-brms, r-cran-rstanarm, r-cran-statmod, r-cran-performance, r-cran-see, r-cran-ez, r-cran-ggresidpanel, r-cran-vdiffr, r-cran-glmmadaptive, r-cran-ggthemes Filename: pool/dists/noble/main/r-cran-afex_1.5-1-1.ca2404.1_all.deb Size: 3582408 MD5sum: eeea4dffb024677cf74407908a10cc54 SHA1: 291b2d218caf4f1aba80c3e732a6ef5855931d95 SHA256: a01ec0915a398ea954af8b944eed15ccb348684fcfc333bcc7d169cc31c4fab0 SHA512: fd64d70f2f5bc3ee05d283d2af3d90f16d599b1525c1e5b03f0b88c9f25da9dc9e25fe7dfea4c352ec7c66ccd915f4d3743948c0c33fc9f6dbffd2b7577d4fae Homepage: https://cran.r-project.org/package=afex Description: CRAN Package 'afex' (Analysis of Factorial Experiments) Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex_plot() provides a high-level interface for interaction or one-way plots using ggplot2, combining raw data and model estimates. afex uses type 3 sums of squares as default (imitating commercial statistical software). Package: r-cran-affect Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-affect_0.1.2-1.ca2404.1_all.deb Size: 41740 MD5sum: ad004575bc0e0bd8f49e320382a95b96 SHA1: 1fbf7f43fe2911baa788ea6b2dff79c706dad757 SHA256: 98831161a97c6c7eb02994e6ca51dbe593e102edcdbd2356ebb8fa0b9d77e1cf SHA512: 1dc7d66fc1e2546c95b47b06724221fb2083deafa60256e47778e71a81f73d25ec92d623d5c1225fef9d5156803fbdfc24d16dd2ce8270d73b5b79cd6f3c4567 Homepage: https://cran.r-project.org/package=AFFECT Description: CRAN Package 'AFFECT' (Accelerated Functional Failure Time Model withError-Contaminated Survival Times) We aim to deal with data with measurement error in the response and misclassification censoring status under an AFT model. This package primarily contains three functions, which are used to generate artificial data, correction for error-prone data and estimate the functional covariates for an AFT model. Package: r-cran-affiner Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1922 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6 Suggests: r-cran-artsy, r-cran-ggplot2, r-cran-gridpattern, r-cran-gtable, r-cran-knitr, r-cran-ragg, r-cran-rgl, r-cran-rlang, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-affiner_0.3.1-1.ca2404.1_all.deb Size: 1585576 MD5sum: b16e74255f02ee1050a2df818dc7aac2 SHA1: e32078e799808578996eda57247b6c1baccf3ee6 SHA256: cecdcaa40d96f9a1b7e12dd5435d9c5f34e0ef9202bed984b9133f1ef7ac50fd SHA512: 852b01bd82673fcf1a1e3a02afbe838218b543a56213c2b3313d96b3e138b8ac7df8448f7d08fa9793ddc9201458c74737927482ae1dacffc1a1e7c43d784978 Homepage: https://cran.r-project.org/package=affiner Description: CRAN Package 'affiner' (A Finer Way to Render 3D Illustrated Objects in 'grid' UsingAffine Transformations) Dilate, permute, project, reflect, rotate, shear, and translate 2D and 3D points. Supports parallel projections including oblique projections such as the cabinet projection as well as axonometric projections such as the isometric projection. Use 'grid's "affine transformation" feature to render illustrated flat surfaces. Package: r-cran-affinity Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 678 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-reproj Suggests: r-cran-rmarkdown, r-cran-covr, r-cran-knitr Filename: pool/dists/noble/main/r-cran-affinity_0.2.5-1.ca2404.1_all.deb Size: 622396 MD5sum: e7cd1494029958185dc189bf0434b4d8 SHA1: d432e989eebe6134c1babae0a3948e98a9c87c4e SHA256: 16df1a0b9f4aa51d1b5ffce91ca7de01ee922a7e97663209302158b792592f47 SHA512: e5d7b80fc4261a0bf137a7f7dd1f5c0e47f9acc9d286aea04639c25782da64e948029d46e8fe25cc1a957d63f97788615639ea68e99c69634822ac23f63dd6fc Homepage: https://cran.r-project.org/package=affinity Description: CRAN Package 'affinity' (Raster Georeferencing, Grid Affine Transforms, Cell Abstraction) Tools for raster georeferencing, grid affine transforms, and general raster logic. These functions provide converters between raster specifications, world vector, geotransform, 'RasterIO' window, and 'RasterIO window' in 'sf' package list format. There are functions to offset a matrix by padding any of four corners (useful for vectorizing neighbourhood operations), and helper functions to harvesting user clicks on a graphics device to use for simple georeferencing of images. Methods used are available from and . Package: r-cran-affinitymatrix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-expm, r-cran-mass, r-cran-hmisc, r-cran-ggplot2, r-cran-ggrepel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-affinitymatrix_0.1.0-1.ca2404.1_all.deb Size: 125902 MD5sum: c30384bff00c0f183420b5fa9a9ed46c SHA1: c1ecb252e38cf2d42dd2f9d30396770986914d4b SHA256: 1e6ce274bf77a826a1faebdb5782f386853737a7a6319b0a108f574fc2d54d84 SHA512: 93fe70b428d04e3c4ff1a212f627d8b11f27cbf0ee8a8600518aece1fb8ce067a079b2112842d459932a63795df59557996aa19d89f7754c86f644610286004b Homepage: https://cran.r-project.org/package=affinitymatrix Description: CRAN Package 'affinitymatrix' (Estimation of Affinity Matrix) Tools to study sorting patterns in matching markets and to estimate the affinity matrix of both the bipartite one-to-one matching model without frictions and with Transferable Utility by 'Dupuy' and 'Galichon' (2014) and its 'unipartite' variant by 'Ciscato', 'Galichon' and 'Gousse' (2020) . It also contains all the necessary tools to implement the 'saliency' analysis, to run rank tests of the affinity matrix and to build tables and plots summarizing the findings. Package: r-cran-affluenceindex Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat.univar Filename: pool/dists/noble/main/r-cran-affluenceindex_2.2-1.ca2404.1_all.deb Size: 88984 MD5sum: 8321419074f4ed6b5e65a85c98914af5 SHA1: a64cb8c5f7364759bb3034b2613f0529680221ae SHA256: c80d9f3f4cea7b13c878ad44c36782f2f4950873ec1fc4e1a0eabc9e8f807959 SHA512: 8975e4efdd8386e3ec9553da9ed36070fd84c80c834761b9dd0ae6af9ed2ead3501af97b7261d087b9292223035b53cae13af7d16a29c3efd7e2bb4629997d6d Homepage: https://cran.r-project.org/package=affluenceIndex Description: CRAN Package 'affluenceIndex' (Affluence (Richness) Indices) Enables to compute the statistical indices of affluence (richness) with bootstrap errors, and inequality and polarization indices. Moreover, gives the possibility of calculation of affluence line. Some simple errors are fixed and it works with new version of Spatial Statistics packaged. Package: r-cran-afheritability Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-mvtnorm, r-cran-ggplot2, r-cran-shiny Filename: pool/dists/noble/main/r-cran-afheritability_0.1.0-1.ca2404.1_all.deb Size: 65174 MD5sum: 84528bf3dcef2f22b00b737e215eff70 SHA1: 3daab71d6cb844e55824b1613a1099c54fff057b SHA256: 6aaf82d1aaa697a58ce5c72b5caa9da988b7518f904ed5c743c1f34a06826e17 SHA512: d776dc9bd0365aa53872b2c70eeab8aca1931bb5497edf6d1f2df4b91cfaa5b925d856e18860b733c4d9e16191712a0a1a825c0060ac0b08243502cf3059e721 Homepage: https://cran.r-project.org/package=AFheritability Description: CRAN Package 'AFheritability' (The Attributable Fraction (AF) Described as a Function ofDisease Heritability, Prevalence and Intervention SpecificFactors) The AFfunction() is a function which returns an estimate of the Attributable Fraction (AF) and a plot of the AF as a function of heritability, disease prevalence, size of target group and intervention effect. Since the AF is a function of several factors, a shiny app is used to better illustrate how the relationship between the AF and heritability depends on several other factors. The app is ran by the function runShinyApp(). For more information see Dahlqwist E et al. (2019) . Package: r-cran-afm Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-gstat, r-cran-fractaldim, r-cran-rgl, r-cran-pracma, r-cran-gridextra, r-cran-moments, r-cran-ggplot2, r-cran-sp, r-cran-png, r-cran-plyr, r-cran-igraph, r-cran-shiny, r-cran-shinyjs, r-cran-scales, r-cran-dbscan, r-cran-mixtools, r-cran-fftwtools Filename: pool/dists/noble/main/r-cran-afm_2.0-1.ca2404.1_all.deb Size: 1889856 MD5sum: 5d6869c60145bc035e55e3dd84b0ed85 SHA1: 2aa875b4a151639206ae5b0e458e7c608e0496fb SHA256: 1ab28c96ae27062671fcbbfc5c90f95e5007ac5ced3927408ba0737d72fdcb36 SHA512: 757ddc34f584480d400815d0acb056862ae6ef46fd561b2edc6ac1952881f7cb3b9d577b65d99673f38cebe3c7b8ace0edc58bbc2d82e1d3b413e8ead7ce8214 Homepage: https://cran.r-project.org/package=AFM Description: CRAN Package 'AFM' (Atomic Force Microscope Image Analysis) Provides Atomic Force Microscope images analysis such as Gaussian mixes identification, Power Spectral Density, roughness against lengthscale, experimental variogram and variogram models, fractal dimension and scale, 2D network analysis. The AFM images can be exported to STL format for 3D printing. Package: r-cran-afmpar Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-afmpar_0.2.0-1.ca2404.1_all.deb Size: 16542 MD5sum: 3c6c84a84080c6f3397ac7749e1cb3df SHA1: d039c24c9813839db3da5476d7571507a1fe2ecf SHA256: 8007b68f01a3a4eaeaeba3371c02b14e6918a12646391784959cb342ed7a7721 SHA512: 04a0093ea953bb1c90e70f6eac6f095d9504b60e49f5043c49a5a6ffe9581254d786eb8b1790667cd1beae0c2d12240ae34e7608005b9894baf12eb32f8162a3 Homepage: https://cran.r-project.org/package=afmpar Description: CRAN Package 'afmpar' (Check Validity of Greek AFM and PA Numbers) With the functions in this package you can check the validity of the Greek Tax Identification Number (AFM) and the Greek Personal Number (PA) . The PA is a new universal ID for Greek citizens across all public services and it is to replace older numbers issued by various Greek state agencies. Its format is a 12-character ID consisting of three alphanumeric characters followed by the nine numerical digits of the AFM. Package: r-cran-afmtoolkit Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4824 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-gridextra, r-cran-scales, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-afmtoolkit_1.0.0-1.ca2404.1_all.deb Size: 4635628 MD5sum: be8a1106610a96dea5159979908638dc SHA1: 5405006883e60805f02023b69399501679d856e2 SHA256: b522f0dc296c45341c27ba2967e0982bc9b47b90ddc9a8ad8dbfeb9ba65326ad SHA512: a22225d1428549007c4391f716778f79a0a629aa6deedd25587b8c827b8f599c8e3886aa3a6db228b70ae7629e435dc3f735d30a34ea1ed56672e878e8a1f455 Homepage: https://cran.r-project.org/package=afmToolkit Description: CRAN Package 'afmToolkit' (Functions for Atomic Force Microscope Force-Distance CurvesAnalysis) Set of functions for analyzing Atomic Force Microscope (AFM) force-distance curves. It allows to obtain the contact and unbinding points, perform the baseline correction, estimate the Young's modulus, fit up to two exponential decay function to a stress-relaxation / creep experiment, obtain adhesion energies. These operations can be done either over a single F-d curve or over a set of F-d curves in batch mode. Package: r-cran-afpt Architecture: all Version: 1.1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-afpt_1.1.0.4-1.ca2404.1_all.deb Size: 428468 MD5sum: 71b3297c0a7835c3dd553c49e07e5749 SHA1: 8379935d8b8cf442fd637ad6d2d3d9d3e651f150 SHA256: b04624f38ae8f0d5e4b19577f832daff5fbc40477dcc2e68ca3887643582c62b SHA512: 1ad76ebb21276bb72d0b4f0405abaab5357c619b30e1622505bd8d0ef519b8226a6c6d6be17a82c9ecce44c0384991bca799b04e77146c0f0ef33dc0dd8e437e Homepage: https://cran.r-project.org/package=afpt Description: CRAN Package 'afpt' (Tools for Modelling of Animal Flight Performance) Allows estimation and modelling of flight costs in animal (vertebrate) flight, implementing the aerodynamic power model described in Klein Heerenbrink et al. (2015) . Taking inspiration from the program 'Flight', developed by Colin Pennycuick (Pennycuick (2008) "Modelling the flying bird". Amsterdam: Elsevier. ISBN 0-19-857721-4), flight performance is estimated based on basic morphological measurements such as body mass, wingspan and wing area. 'afpt' can be used to make predictions on how animals should adjust their flight behaviour and wingbeat kinematics to varying flight conditions. Package: r-cran-afr Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-forecast, r-cran-zoo, r-cran-olsrr, r-cran-lmtest, r-cran-nlme, r-cran-ggplot2, r-cran-tseries, r-cran-gridextra, r-cran-rlang, r-cran-xts, r-cran-nortest, r-cran-goftest, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-afr_0.3.8-1.ca2404.1_all.deb Size: 200306 MD5sum: c906d067c254676be72a9871d7b4c13d SHA1: ba8c0d6f223afde63c16b14bf73388ae84c8b0ca SHA256: 8d0c5f1d4469f16c22b83268200a542ea6ddb36e7c270810cb455d1516a5f8d5 SHA512: 91585d7dd35ce1c04562c369dc37ad66c73d983bda704276445c34480225519f12405a7cc7c1245682c32f1db4dc1c6d62bd0aac39fbeade1b815b468a15f24f Homepage: https://cran.r-project.org/package=AFR Description: CRAN Package 'AFR' (Toolkit for Regression Analysis of Kazakhstan Banking SectorData) Tool is created for regression, prediction and forecast analysis of macroeconomic and credit data. The package includes functions from existing R packages adapted for banking sector of Kazakhstan. The purpose of the package is to optimize statistical functions for easier interpretation for bank analysts and non-statisticians. Package: r-cran-africamonitor Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-rmysql, r-cran-data.table, r-cran-collapse Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-africamonitor_0.2.4-1.ca2404.1_all.deb Size: 75376 MD5sum: 849624c13dc2fec92bb08c86b61af6ae SHA1: 23c505fb90f812fbcbd187a6488ab2a878394f50 SHA256: ffd4841804528d3938decb6b89c2d1124f05650c3a75fcc2b83f1d3c3829b45e SHA512: e3db8976417c0f3a705e9a12bf0df69e880522f11998b133d7335e55a37b47948bd44033e36ddd7c494ca863bed0bff1fe140b53ce8099f0e561c1d320276139 Homepage: https://cran.r-project.org/package=africamonitor Description: CRAN Package 'africamonitor' (Africa Macroeconomic Monitor Database API) An R API providing access to a relational database with macroeconomic data for Africa. The database contains >700 macroeconomic time series from mostly international sources, grouped into 50 macroeconomic and development-related topics. Series are carefully selected on the basis of data coverage for Africa, frequency, and relevance to the macro-development context. The project is part of the 'Kiel Institute Africa Initiative' , which, amongst other things, aims to develop a parsimonious database with highly relevant indicators to monitor macroeconomic developments in Africa, accessible through a fast API and a web-based platform at . The database is maintained at the Kiel Institute for the World Economy . 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See also the 'Python' package 'gptables'. Package: r-cran-afthd Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-photobiology, r-cran-r2jags, r-cran-rstpm2, r-cran-survival Filename: pool/dists/noble/main/r-cran-afthd_1.1.0-1.ca2404.1_all.deb Size: 531424 MD5sum: 170effed57354faa37a99d9dd3cb61a6 SHA1: e120ed5011042ca13b3b13960ea3bc4b80776692 SHA256: d085dea2465ff4340027d51cabde841ce506f7b4c9cd9e9defec288d038ad09f SHA512: 9cdfc861a92b7655174efd3d83571b968753a68c34f7d3cdce3db320409eade5f31d4b9fccdf18f3536874ed693c7c7a6392ac971fee53805d19edeba7676cdb Homepage: https://cran.r-project.org/package=afthd Description: CRAN Package 'afthd' (Accelerated Failure Time for High Dimensional Data with MCMC) Functions for Posterior estimates of Accelerated Failure Time(AFT) model with MCMC and Maximum likelihood estimates of AFT model without MCMC for univariate and multivariate analysis in high dimensional gene expression data are available in this 'afthd' package. AFT model with Bayesian framework for multivariate in high dimensional data has been proposed by Prabhash et al.(2016) . Package: r-cran-aftr2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-aftr2_0.1.0-1.ca2404.1_all.deb Size: 10428 MD5sum: 9ec7fc034ed0a938b81e418590225299 SHA1: 96cc0a12f5ef633b05328a3b987308d9d909fe2b SHA256: 33198ced836655c2debf94e4870f9fc11b1fd870ed001828a98a6b668fe9df5b SHA512: 75cf3e8523abd0ab7fc4030a2e9332299729e16c43bbab3240bb7aad7d5800b6f0d2fa2d311c1ed67b8a81d3358cf80af90add12e577bd1e6c7deb311be7811a Homepage: https://cran.r-project.org/package=aftR2 Description: CRAN Package 'aftR2' (R-Squared Measure under Accelerated Failure Time (AFT) Models) Compute the R-squared measure under the accelerated failure time (AFT) models proposed in Chan et. al. (2018) . Package: r-cran-ag5tools Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-reticulate, r-cran-fs, r-cran-doparallel, r-cran-foreach Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ag5tools_0.0.3-1.ca2404.1_all.deb Size: 531296 MD5sum: d7a54ed3705057d7a21e45f287bb5963 SHA1: 18be38c57090183e08fcbfc2dbb1db12f0e12c87 SHA256: 237f92d2bd45004e3771baf72835dedda75ed9f4f1e445dd88d4a15a646cd07a SHA512: 0ff6581c000fc9a8dc6c4ce276377b43858dfbb8c2b81f6b2b43bfc48ae29a0b9f66d97a17be1a2a4cb7d573aa26971454042586b4b23cc41153253b3035afe5 Homepage: https://cran.r-project.org/package=ag5Tools Description: CRAN Package 'ag5Tools' (Toolbox for Downloading and Extracting Copernicus AgERA5 Data) Tools for downloading and extracting data from the Copernicus "Agrometeorological indicators from 1979 to present derived from reanalysis" (AgERA5). Package: r-cran-agd Architecture: all Version: 0.45.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist Filename: pool/dists/noble/main/r-cran-agd_0.45.0-1.ca2404.1_all.deb Size: 449140 MD5sum: d7a4995961ab998edaacdc1eabbf1300 SHA1: 16e8b22386f6a9304cd1e9c5874aef0725bef5c2 SHA256: bb2e04ceb2e82bad90d140aeb6466c80a3c6bcfde7c670ee7071cad1d2716951 SHA512: d680772bae401c6b714d06f1bf99cdbb65d9e30b4c02ff3947a59a0ac64044aaf600e6ab6be86820a54ac1c64b1231b980f2525b8b5429b02fdcd0cf16949e4d Homepage: https://cran.r-project.org/package=AGD Description: CRAN Package 'AGD' (Analysis of Growth Data) Tools for the analysis of growth data: to extract an LMS table from a gamlss object, to calculate the standard deviation scores and its inverse, and to superpose two wormplots from different models. The package contains a some varieties of reference tables, especially for The Netherlands. Package: r-cran-agebanddecomposition Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3085 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-readxl, r-cran-ggplot2, r-cran-dplr, r-cran-patchwork Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-agebanddecomposition_2.0.1-1.ca2404.1_all.deb Size: 1958496 MD5sum: dda57e106121ef37e913fd7e8acbf2ee SHA1: d1095b61a09ad48e531cd389fbca781ecdb3cc2f SHA256: 09a128af81eba285cdc5d029a0b25cdd816be50dfd759cbdfcbae81674398b8f SHA512: e486762ec7fd6918a20d2eb1d9ff7927ddac6a93f00e531d6c37fd86b7c58acb9bedff2bd379e2eb705cf6a0e4852c3f7d82d5006a6ddc37e7a00874b8dfd723 Homepage: https://cran.r-project.org/package=AgeBandDecomposition Description: CRAN Package 'AgeBandDecomposition' (Age Band Decomposition Method for Tree Ring Standardization) Implements the Age Band Decomposition (ABD) method for standardizing tree ring width data while preserving both low and high frequency variability. Unlike traditional detrending approaches that can distort long term growth trends, ABD decomposes ring width series into multiple age classes, detrends each class separately, and then recombines them to create standardized chronologies. This approach improves the detection of growth signals linked to past climatic and environmental factors, making it particularly valuable for dendroecological and dendroclimatological studies. The package provides functions to perform ABD-based standardization, compare results with other common methods (e.g., BAI, C method, RCS), and facilitate the interpretation of growth patterns under current and future climate variability. Package: r-cran-ageg Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ageg_1.0.0-1.ca2404.1_all.deb Size: 14608 MD5sum: b24ec35fba699da56651a6db7767b123 SHA1: 1bcfec8ffa2efa72e0d8f412bc5b1d0179ccaeeb SHA256: d053b1c4068e6490bba0ccea5ad219b9ae73cac4047f71c11711a57b529bc1fd SHA512: 07ceeadd16cf7b81401ff21eedda394e3ee9b8398eab8e04ffdf02f5ae690662a36bf56d218c0094d3ae15fc858b794fca25696e38ac46c18629ecba1bc6584d Homepage: https://cran.r-project.org/package=ageg Description: CRAN Package 'ageg' (Age Grouping Functions) Pair of simple convenience functions to convert a vector of birth dates to age and age distributions. These functions may be helpful when related age and custom age distributions are desired given a vector of birth dates. 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Users can create Bayesian network models from scratch or import existing models in R and export to 'agena.ai' cloud or local API for calculations. Note: running calculations requires a valid 'agena.ai' API license (past the initial trial period of the local API). Package: r-cran-agepopdenom Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1436 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-cli, r-cran-glue, r-cran-terra, r-cran-sf, r-cran-here, r-cran-ggplot2, r-cran-stringr, r-cran-rlang, r-cran-tidyr, r-cran-rdhs, r-cran-tmb, r-cran-curl, r-cran-gstat, r-cran-haven, r-cran-crayon, r-cran-purrr, r-cran-countrycode Suggests: r-cran-sp, r-cran-tibble, r-cran-rstudioapi, r-cran-future, r-cran-future.apply, r-cran-pbmcapply, r-cran-knitr, r-cran-httr, r-cran-scales, r-cran-rmarkdown, r-cran-exactextractr, r-cran-pdist, r-cran-numderiv, r-cran-openxlsx2, r-cran-matrixstats, r-cran-testthat, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-agepopdenom_1.2.3-1.ca2404.1_all.deb Size: 684252 MD5sum: edd1d0b4f19859463a8dcbebdcc4b3bb SHA1: 4b0ba752b4c69a3b9e84e07323188745726fa60c SHA256: a9a713b4c4ccbb6b18b6c7cba7bda4ee122044530a22be5e19435e2ea6da00ec SHA512: 799986cf31bd5ec5a6184259e8fc797e174391972d611235f5d70c5027dacf9dea704ab74d6c42cdac3b8852b4315c3727e86ff9365b0ca26638fbae60f75476 Homepage: https://cran.r-project.org/package=AgePopDenom Description: CRAN Package 'AgePopDenom' (Model Fine-Scale Age-Structured Population Data usingOpen-Source Data) Automate the modelling of age-structured population data using survey data, grid population estimates and urban-rural extents. Package: r-cran-agetopicmodels Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5041 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-gtools, r-cran-magrittr, r-cran-proc, r-cran-reshape2, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-agetopicmodels_0.3.0-1.ca2404.1_all.deb Size: 5064672 MD5sum: 4fbfcafb2a1ba45038bf40080e1a0b2a SHA1: 49c2d16b3909d20475af2236c9bbba30093a381a SHA256: 9504ea56b6e0682f784be6980c3d9d1b1c40a2bf9614e5699a1826d0c78b5ec0 SHA512: 33557e5a39f853eb99ce72b1bd60b603272add38f0ac983582dfaaf0d62eb767720917b46d0d8825c9be85cbf54f5daa8cdbe8ed5199d09f6426c658b6f5d1e5 Homepage: https://cran.r-project.org/package=AgeTopicModels Description: CRAN Package 'AgeTopicModels' (Inferring Age-Dependent Disease Topic from Diagnosis Data) We propose an age-dependent topic modelling (ATM) model, providing a low-rank representation of longitudinal records of hundreds of distinct diseases in large electronic health record data sets. The model assigns to each individual topic weights for several disease topics; each disease topic reflects a set of diseases that tend to co-occur as a function of age, quantified by age-dependent topic loadings for each disease. The model assumes that for each disease diagnosis, a topic is sampled based on the individual’s topic weights (which sum to 1 across topics, for a given individual), and a disease is sampled based on the individual’s age and the age-dependent topic loadings (which sum to 1 across diseases, for a given topic at a given age). The model generalises the Latent Dirichlet Allocation (LDA) model by allowing topic loadings for each topic to vary with age. References: Jiang (2023) . Package: r-cran-ageutils Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-tibble, r-cran-vctrs Suggests: r-cran-dplyr, r-cran-litedown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ageutils_0.1.2-1.ca2404.1_all.deb Size: 54096 MD5sum: 9dcdd4d37a501af3456e9ef50c558fbd SHA1: 6362c7752e1ff6866048a7e37cd327fb85b0f839 SHA256: cbaf8c07dbe8cbde4e623e591cf7d12d79ccd4edde8956e82490f29ea258464e SHA512: acfb0086aa929d8b9eb24db722b20f9e5a724700c2e4a2e784a37fb0cb484ceb59b00d7a13dbc8070e50af258df4f50f79059c752b435f747cc4e0af4844b130 Homepage: https://cran.r-project.org/package=ageutils Description: CRAN Package 'ageutils' (Collection of Functions for Working with Age Intervals) Provides a collection of efficient functions for working with individual ages and corresponding intervals. These include functions for conversion from an age to an interval, aggregation of ages with associated counts in to intervals and the splitting of interval counts based on specified age distributions. Package: r-cran-agfh Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-goftest, r-cran-ks, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-agfh_0.2.1-1.ca2404.1_all.deb Size: 213886 MD5sum: 64a113abe76048e3b953ab2d8168b600 SHA1: 8bf47e03bd9e63192fd7042770155a8f45819e39 SHA256: 2349718046ee15d31681e70818cb932fabb99ad929406c86b7d1f4fd336572bc SHA512: 54925b20e0042d22bd957844b7748da4dd5da316caaf5f65cfda50e97b1cdff25a961c71a7594063e61c5740395e7ed3dde3ab1524044aded69f7ea1e1d33804 Homepage: https://cran.r-project.org/package=agfh Description: CRAN Package 'agfh' (Agnostic Fay-Herriot Model for Small Area Statistics) Implements the Agnostic Fay-Herriot model, an extension of the traditional small area model. In place of normal sampling errors, the sampling error distribution is estimated with a Gaussian process to accommodate a broader class of distributions. This flexibility is most useful in the presence of bounded, multi-modal, or heavily skewed sampling errors. Package: r-cran-agghoo Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-class, r-cran-r6, r-cran-rpart, r-cran-fnn Suggests: r-cran-roxygen2, r-cran-mlbench Filename: pool/dists/noble/main/r-cran-agghoo_0.1-0-1.ca2404.1_all.deb Size: 110910 MD5sum: b70ff252fbdeb5cbe317be50b8a60a15 SHA1: b2f06b3295257891838d3fae44b8dee640b43542 SHA256: 0ae6a7ad6785acfe613726c32f75746e56e0d7d8857d46f8b4c29f7d7aa6840a SHA512: 9b3134251e19c537f36fca08d2e739cb1c8583779072d9a80fad8d4f2286cd053f28268e56b50c92c299b39014f07bfd3126ed0a98447ee425feffd6b194ad12 Homepage: https://cran.r-project.org/package=agghoo Description: CRAN Package 'agghoo' (Aggregated Hold-Out Cross Validation) The 'agghoo' procedure is an alternative to usual cross-validation. Instead of choosing the best model trained on V subsamples, it determines a winner model for each subsample, and then aggregates the V outputs. For the details, see "Aggregated hold-out" by Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle (2021) published in Journal of Machine Learning Research 22(20):1--55. Package: r-cran-aggrecat Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3084 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-gofkernel, r-cran-purrr, r-cran-r2jags, r-cran-coda, r-cran-precrec, r-cran-mathjaxr, r-cran-cli, r-cran-vgam, r-cran-crayon, r-cran-dplyr, r-cran-stringr, r-cran-tidyr, r-cran-tibble, r-cran-ggplot2, r-cran-insight, r-cran-desctools, r-cran-mlmetrics Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-pointblank, r-cran-janitor, r-cran-qualtrics, r-cran-here, r-cran-readxl, r-cran-readr, r-cran-lubridate, r-cran-forcats, r-cran-ggforce, r-cran-ggpubr, r-cran-ggridges, r-cran-rjags, r-cran-tidybayes, r-cran-tidyverse, r-cran-usethis, r-cran-nlme, r-cran-gt, r-cran-gtextras, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-aggrecat_1.1.0-1.ca2404.1_all.deb Size: 1911834 MD5sum: 99abc9beb4316528e107224b292aa01f SHA1: 99389e4d8616db3654111cdaa170821e8b8e37d6 SHA256: 30a990b1ff71cd5f0e30f98e4af009d345bff70430416968b5747079272772eb SHA512: eca589235501116b69d231867b99d1948b113052c63ab3f3c5f9c926b11b52afe2c5f8c887749b91d65b5c15ddb066d55270914bf52459a62eb5e9d18ae7ced8 Homepage: https://cran.r-project.org/package=aggreCAT Description: CRAN Package 'aggreCAT' (Mathematically Aggregating Expert Judgments) The use of structured elicitation to inform decision making has grown dramatically in recent decades, however, judgements from multiple experts must be aggregated into a single estimate. Empirical evidence suggests that mathematical aggregation provides more reliable estimates than enforcing behavioural consensus on group estimates. 'aggreCAT' provides state-of-the-art mathematical aggregation methods for elicitation data including those defined in Hanea, A. et al. (2021) . The package also provides functions to visualise and evaluate the performance of your aggregated estimates on validation data. Package: r-cran-aggregater Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-tibble, r-cran-ncmisc Filename: pool/dists/noble/main/r-cran-aggregater_0.1.1-1.ca2404.1_all.deb Size: 36648 MD5sum: 0f01ff7a09a6375f1dc2220231f6be72 SHA1: c2c4e1a7d31fa6a8e73babe794aff207e9213a5f SHA256: 9eee985a764dd0d0e19befa885d39580c3c3418e13aee2e3ccee16c0b2aea34b SHA512: 1c464b436fe2f518811e09455a8f8c99de88f47537b2e4597ec4cf6537083e924023fd7c9a4ab5405d0b5747756b98edf9f60e5a5ec0622dce6e6cab10dfdffb Homepage: https://cran.r-project.org/package=AggregateR Description: CRAN Package 'AggregateR' (Aggregate Numeric, Date and Categorical Variables) Convenience functions for aggregating a data frame or data table. Currently mean, sum and variance are supported. For Date variables, the recency and duration are supported. There is also support for dummy variables in predictive contexts. Code has been completely re-written in data.table for computational speed. Package: r-cran-aggregation Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-aggregation_1.0.1-1.ca2404.1_all.deb Size: 15716 MD5sum: 1d6860376c24b2a66192d4188e6f4490 SHA1: 8b7dd55161ece278166051b0ffd2a61afc3abb3d SHA256: a23fd0a44782acba47e34d0be8c02c5675cb61d862b84a4dfff81d83eeb69bca SHA512: 86e1490a3499b26ba93304ee7c2a32535fa4e30b1334a1f51079ce42db45e0194dc79edc9812ba6c998fa953b4a5fd68447de2fc8f2f82fe9156fd1f909dd123 Homepage: https://cran.r-project.org/package=aggregation Description: CRAN Package 'aggregation' (p-Value Aggregation Methods) Contains functionality for performing the following methods of p-value aggregation: Fisher's method [Fisher, RA (1932, ISBN: 9780028447308)], the Lancaster method (weighted Fisher's method) [Lancaster, HO (1961, )], and Sidak correction [Sidak, Z (1967, )]. Please cite Yi et al., the manuscript corresponding to this package [Yi, L et al., (2017), ]. Package: r-cran-aggtrees Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 895 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-broom, r-cran-car, r-cran-caret, r-cran-estimatr, r-cran-grf, r-cran-rpart, r-cran-rpart.plot, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aggtrees_2.1.0-1.ca2404.1_all.deb Size: 663444 MD5sum: ea782304ba2846b4641130a5877987e4 SHA1: 59d517c3a11960c3376ca246bdc7daeeee25daf7 SHA256: 8756b0a4a50c7b47fb1f66e4e38b4a4d6c9641164a701eb91826c2cdb8b5147e SHA512: 6a76a7c250b07f137ad772bf51ce766389b8f514b83c35e1ad30b46b69a4b830c2cc0503e8d02b026e45818cb5111cee0af1f38d3248e64fcd9fc4cf270a2a94 Homepage: https://cran.r-project.org/package=aggTrees Description: CRAN Package 'aggTrees' (Aggregation Trees) Nonparametric data-driven approach to discovering heterogeneous subgroups in a selection-on-observables framework. 'aggTrees' allows researchers to assess whether there exists relevant heterogeneity in treatment effects by generating a sequence of optimal groupings, one for each level of granularity. For each grouping, we obtain point estimation and inference about the group average treatment effects. Please reference the use as Di Francesco (2022) . Package: r-cran-aggutils Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-docstring Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aggutils_1.0.2-1.ca2404.1_all.deb Size: 25576 MD5sum: 362214a369e12fd73e617069a057218c SHA1: ac3f203bc8792b6b23069ff40420c2319ca8235c SHA256: e562699972f9ebe3f0cbc5a5a9e4e87ca42ace21c8dab74e75d180abfe7eee6d SHA512: 7e38992e0f2ba0cb3d427a6b8e458129f4b1ab1a408f28aed5668007ec0422d2aeb4635b38c704cb914b99c3a99cbbf3b9820f373ae45ddae1c0810201b5fd16 Homepage: https://cran.r-project.org/package=aggutils Description: CRAN Package 'aggutils' (Utilities for Aggregating Probabilistic Forecasts) Provides several methods for aggregating probabilistic forecasts. You have a group of people who have made probabilistic forecasts for the same event. You want to take advantage of the "wisdom of the crowd" and combine these forecasts in some sensible way. This package provides implementations of several strategies, including geometric mean of odds, an extremized aggregate (Neyman, Roughgarden (2021) ), and "high-density trimmed mean" (Powell et al. (2022) ). 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Several methods are implemented considering additive and non-additive models. Package: r-cran-aghq Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvquad, r-cran-matrix, r-cran-rlang, r-cran-polynom, r-cran-numderiv Suggests: r-cran-trustoptim, r-cran-trust, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aghq_0.4.1-1.ca2404.1_all.deb Size: 330340 MD5sum: d8a223105540e54409957160081722ac SHA1: 41ff051b6af09bb86db9d517f2140d7dbe5c0634 SHA256: 7acd012dee1e8c0075d525517e634cf4915c97a586d53a6c73d13afebe1c78ae SHA512: 66107f73f4055de95ff48f19d70d533d638a654c5e6f6daac46cc507985449d2fc3e0273c653f2c3afb5c7d3c46edadc252a47b1181c1463dae72565146fa127 Homepage: https://cran.r-project.org/package=aghq Description: CRAN Package 'aghq' (Adaptive Gauss Hermite Quadrature for Bayesian Inference) Adaptive Gauss Hermite Quadrature for Bayesian inference. The AGHQ method for normalizing posterior distributions and making Bayesian inferences based on them. Functions are provided for doing quadrature and marginal Laplace approximations, and summary methods are provided for making inferences based on the results. See Stringer (2021). "Implementing Adaptive Quadrature for Bayesian Inference: the aghq Package" . 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The equations developed in Ingel and Jahn-Eimermacher (2014) and their consequences are employed. Package: r-cran-agpris Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-plyr, r-cran-sp, r-cran-spdep, r-cran-spacetime, r-cran-matrixcalc, r-cran-maxlik Suggests: r-cran-terra Filename: pool/dists/noble/main/r-cran-agpris_2.0-1.ca2404.1_all.deb Size: 1168960 MD5sum: e68fb3281f04e8ceb8687694d417aaa8 SHA1: 05e8b41fc70ec92d0732c4a003a310ff4c11f340 SHA256: b1cb2f8726f5d2e274c3d5e9bc0f69d5ac79f7acfd38a7eeea9cbe8e3cee3adc SHA512: 65ec5a741f4a2d8c88d6de1d6740ca7c36b4c61415f5e562a56eeb190d318009097da8d53889bfebc8abc145ea90412a74fde642e5ccf26722c33ee70a350d5f Homepage: https://cran.r-project.org/package=AGPRIS Description: CRAN Package 'AGPRIS' (AGricultural PRoductivity in Space) Functionalities to simulate space-time data and to estimate dynamic-spatial panel data models. Estimators implemented are the BCML (Elhorst (2010), ), the MML (Elhorst (2010) ) and the INLA Bayesian estimator (Lindgren and Rue, (2015) ; Bivand, Gomez-Rubio and Rue, (2015) ) adapted to panel data. The package contains functions to replicate the analyses of the scientific article entitled "Agricultural Productivity in Space" (Baldoni and Esposti (2021), )). Package: r-cran-agree Architecture: all Version: 0.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-miscf, r-cran-lme4, r-cran-r2jags, r-cran-coda Filename: pool/dists/noble/main/r-cran-agree_0.5-3-1.ca2404.1_all.deb Size: 72058 MD5sum: c07282dd726dc2ddfbfc92a3a0da5c99 SHA1: 6d5875336ef6c0c97ae944d261b644c55df98161 SHA256: 66c034d84559b36c37a10a4aa028e228ecf66d407582f8e026f80128ea089776 SHA512: 4dcf58a4193a70d52133ea4be7baf12e7bfbe37a6ad4d4ff3b5e02644ff2671abd03c1b4b5997d62d678f48a1991467df4b6e36e7e2fd919c95f1de3483e35b4 Homepage: https://cran.r-project.org/package=agRee Description: CRAN Package 'agRee' (Various Methods for Measuring Agreement) Bland-Altman plot and scatter plot with identity line for visualization and point and interval estimates for different metrics related to reproducibility/repeatability/agreement including the concordance correlation coefficient, intraclass correlation coefficient, within-subject coefficient of variation, smallest detectable difference, and mean normalized smallest detectable difference. 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Package: r-cran-agricolae Architecture: all Version: 1.3-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1480 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-nlme, r-cran-cluster, r-cran-algdesign Filename: pool/dists/noble/main/r-cran-agricolae_1.3-7-1.ca2404.1_all.deb Size: 1177266 MD5sum: e3ca3636f3cdbb4c74a55d96727a5f02 SHA1: a949d8460219965fa3a8979bb6f5a170282ca6bf SHA256: 7871db9b20184ade23b21bd70ab343f750e88743dc860da3c255acf8d487b556 SHA512: e186d5aa1d00a5152f2059daf7b5439ae3a8cb70935b7e3c2bc5a113dccde11dc4669cb23ae7c3114dd4c77f77e9c4ef9dc8f1db7f2b625b51cc5dbfe0bf33e9 Homepage: https://cran.r-project.org/package=agricolae Description: CRAN Package 'agricolae' (Statistical Procedures for Agricultural Research) Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster. Package: r-cran-agricolaeplotr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3604 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-agricolae, r-cran-raster, r-cran-sp, r-cran-fieldhub, r-cran-tibble, r-cran-sf, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-stplanr, r-cran-ggspatial Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-leaflet Filename: pool/dists/noble/main/r-cran-agricolaeplotr_1.0.0-1.ca2404.1_all.deb Size: 2298578 MD5sum: 7969d71df8f68c9c7150445467ed0400 SHA1: 9a34259074546bec237f323dcb40f501759c5581 SHA256: a3648704a091eed0022b5cb716e9a456f14a529e2a8b0e919faae8bbff6eab68 SHA512: dff2af1c5d48217635981714fc56ed102e975453ee45e8e6fbd4d464f6b010832341d81c89f177d6c53a09c56d33d6b07eb491c48cd8f4a9118de2bf2e03db79 Homepage: https://cran.r-project.org/package=agricolaeplotr Description: CRAN Package 'agricolaeplotr' (Visualization of Design of Experiments from the 'agricolae'Package) Visualization of Design of Experiments from the 'agricolae' package with 'ggplot2' framework The user provides an experiment design from the 'agricolae' package, calls the corresponding function and will receive a visualization with 'ggplot2' based functions that are specific for each design. As there are many different designs, each design is tested on its type. The output can be modified with standard 'ggplot2' commands or with other packages with 'ggplot2' function extensions. 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Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more. 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The package implements widely used diversification and concentration measures including Herfindahl Index,Simpson Index, Entropy Index, Ogive Index, and Maximum Proportion Index. Package: r-cran-agridq Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nortest, r-cran-car, r-cran-lmtest, r-cran-tseries, r-cran-stringdist Suggests: r-cran-testthat, r-cran-covr, r-cran-mass Filename: pool/dists/noble/main/r-cran-agridq_0.1.3-1.ca2404.1_all.deb Size: 144990 MD5sum: 8c1e9eaad4e6077fb70862bcd27eb517 SHA1: 4ff82d105f1072279f33e62b0a3e7eac2f6c622f SHA256: 3bb769b04c707da9b043a2b973bb6d96e9e0bf2bda0ed0ea1e1ddf521a04b95d SHA512: e4e9fe4bacd36f5b26c4c4bbf87bde531e0a8e3a3997d04b91fdcaba56d470cbfbdd7bb447a85f35dc38f6e516dac6276fa27645a51d02d07ec1b34daaca6cbc Homepage: https://cran.r-project.org/package=agriDQ Description: CRAN Package 'agriDQ' (Data Quality Checks and Statistical Assumption Testing forAgricultural Experiments) Provides a comprehensive pipeline for data quality checks and statistical assumption diagnostics in agricultural experimental data. Functions cover outlier detection using Interquartile Range (IQR) fence, Z-score, modified Z-score (Hampel identifier), Grubbs test and Dixon Q-test with consensus flagging; missing data pattern analysis and mechanism classification (Missing Completely At Random/Missing At Random/Missing Not At Random (MCAR/MAR/MNAR)) via Little's test; normality testing using Shapiro-Wilk, Anderson-Darling, Kolmogorov-Smirnov, Lilliefors, Pearson chi-square and Jarque-Bera tests; homogeneity of variance via Bartlett, Levene and Fligner-Killeen tests; independence of errors via Durbin-Watson, Breusch-Godfrey and Wald-Wolfowitz runs tests; experimental design validation for Completely Randomised Design (CRD), Randomised Complete Block Design (RCBD), Latin Square Design (LSD) and factorial designs; qualitative variable consistency checks; and automated HyperText Markup Language (HTML) report generation. Designed to align with Findable, Accessible, Interoperable and Reusable (FAIR) data principles. Methods follow Gomez and Gomez (1984, ISBN:978-0471870920) and Montgomery (2017, ISBN:978-1119492443). Package: r-cran-agrifeature Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-agrifeature_1.0.3-1.ca2404.1_all.deb Size: 24816 MD5sum: 6907e0f930e886487a12e1fb3983edcb SHA1: bfe3e66fca402b9889bbb1578b4def0447c1babb SHA256: 09742b75398095c8cafc5de698e4a26c8eb481cd2c0d2385f77dc3a26a0e2511 SHA512: 8dee54f771e8abd146b0a6a9502fb91e43d6384866b149167eb4e4108901c845d16b6a9757865e74501d4067841626624e07e6a39b60952bc84b3f9fd5e8c770 Homepage: https://cran.r-project.org/package=agrifeature Description: CRAN Package 'agrifeature' (Agriculture Image Feature) Functions to calculate Gray Level Co-occurrence Matrix(GLCM), RGB-based Vegetative Index(RGB VI) and Normalized Difference Vegetation Index(NDVI) family image features. GLCM calculations are based on Haralick (1973) . Package: r-cran-agrireg Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-drc, r-cran-ggplot2, r-cran-patchwork Suggests: r-cran-broom, r-cran-broom.mixed, r-cran-emmeans, r-cran-nlme, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-lmertest, r-cran-car, r-cran-multcomp Filename: pool/dists/noble/main/r-cran-agrireg_0.1.0-1.ca2404.1_all.deb Size: 197586 MD5sum: 4e0dc7972e8cd4a2bafa4f107ec1143d SHA1: 0872b2f37607bb94add066c9b39ebbef77e5e33d SHA256: de478e6692b42e3e637185cb231dc0099e7bf000c4787dc9ffd9e50e14ae4af1 SHA512: ead3e1b8e9d4f0fa4847c5171a95a3e7c07faf9eaccb82ebf7c17fb85ba464d66f9773ab1b68e3f1b010d203752343535676e89470510e712b8294f1a13599a1 Homepage: https://cran.r-project.org/package=agriReg Description: CRAN Package 'agriReg' (Linear and Nonlinear Regression for Agricultural Data) Fit, compare, and visualise linear and nonlinear regression models tailored to field-trial and dose-response agricultural data. Provides S3 classes for mixed-effects models (via 'lme4'), nonlinear growth curves (logistic, 'Gompertz', asymptotic, linear-plateau, quadratic), and four/five-parameter log-logistic dose-response models (via 'drc'). Includes automated starting-value heuristics, goodness-of-fit statistics, residual diagnostics, and 'ggplot2'-based visualisation. Methods are based on Bates and Watts (1988, ISBN:9780471816430), Ritz and others (2015) , and Bates and others (2015) . Package: r-cran-agritutorial Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmertest, r-cran-emmeans, r-cran-pbkrtest, r-cran-lattice, r-cran-nlme, r-cran-ggplot2 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-agritutorial_0.1.5-1.ca2404.1_all.deb Size: 294800 MD5sum: e2d0d0b25107eb17a67aad7d792d936c SHA1: 043a7f79961c6477cbdfe55a2bd0c1119bdc0c28 SHA256: 0c3a7a64d3417a17121de88f67a7137a08a377b75426a9641eeeeab2b9eea116 SHA512: 0567152e7931e0c0d4c03e2c2519674e509e23f166f6070ba518136ac0cb2aba14fca5334e2fef00d8b0dc6f7a19cf2b2307065d8c9935177fdb5b90ab4a04e6 Homepage: https://cran.r-project.org/package=agriTutorial Description: CRAN Package 'agriTutorial' (Tutorial Analysis of Some Agricultural Experiments) Example software for the analysis of data from designed experiments, especially agricultural crop experiments. The basics of the analysis of designed experiments are discussed using real examples from agricultural field trials. A range of statistical methods using a range of R statistical packages are exemplified . The experimental data is made available as separate data sets for each example and the R analysis code is made available as example code. The example code can be readily extended, as required. Package: r-cran-agriutilities Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2365 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-psych, r-cran-dplyr, r-cran-tidyr, r-cran-lme4, r-cran-matrix, r-cran-ggpubr, r-cran-lmertest, r-cran-data.table, r-cran-magrittr, r-cran-emmeans, r-cran-ggrepel, r-cran-tibble, r-cran-rlang, r-cran-statgensta, r-cran-spats Suggests: r-cran-knitr, r-cran-lattice, r-cran-cluster, r-cran-rmarkdown, r-cran-agridat, r-cran-gt Filename: pool/dists/noble/main/r-cran-agriutilities_1.2.3-1.ca2404.1_all.deb Size: 1699536 MD5sum: dcc3f0cd8864e68b0a6bae4839d065a3 SHA1: 648b30ec12d17effd9b169f911db340e65cb2a72 SHA256: c01e3fc978ca5c0299c5b4d6374387c62f192ab6636888f3a9b5796cef876855 SHA512: eea2fe6bbe2cfca6cb3f1c321fa20dad5aa3b1d9a5fb065139d1a4a7b60d43cf813aec161fb04510a61c1cb6a54adb2c860d46bf30febc855b286f624474bcce Homepage: https://cran.r-project.org/package=agriutilities Description: CRAN Package 'agriutilities' (Utilities for Data Analysis in Agriculture) Utilities designed to make the analysis of field trials easier and more accessible for everyone working in plant breeding. It provides a simple and intuitive interface for conducting single and multi-environmental trial analysis, with minimal coding required. Whether you're a beginner or an experienced user, 'agriutilities' will help you quickly and easily carry out complex analyses with confidence. With built-in functions for fitting Linear Mixed Models, 'agriutilities' is the ideal choice for anyone who wants to save time and focus on interpreting their results. Some of the functions require the R package 'asreml' for the 'ASReml' software, this can be obtained upon purchase from 'VSN' international . Package: r-cran-agriwater Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-agriwater_1.0.2-1.ca2404.1_all.deb Size: 310026 MD5sum: d19401aaee76b006d6609bf934190063 SHA1: 149b63fc1780dd3b25f3a392d2308461c0c48c73 SHA256: 5d43848e7d1236d4802b21e9ad93b1751099dfc92b4d38800e169d656863afa7 SHA512: c6851531ec81b92d5e8b42b87002d9bf68cff4569d238ae2011d62885fa3c6a878cc8fd3b9d0f5d6ee1a1be93d7a765ad1c446ea60d2595a48af91f93469ffcb Homepage: https://cran.r-project.org/package=agriwater Description: CRAN Package 'agriwater' (Evapotranspiration and Energy Fluxes Spatial Analysis) Spatial modeling of energy balance and actual evapotranspiration using satellite images and meteorological data. Options of satellite are: Landsat-8 (with and without thermal bands), Sentinel-2 and MODIS. Respectively spatial resolutions are 30, 100, 10 and 250 meters. User can use data from a single meteorological station or a grid of meteorological stations (using any spatial interpolation method). Silva, Teixeira, and Manzione (2019) . Package: r-cran-agrmt Architecture: all Version: 1.42.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-agrmt_1.42.19-1.ca2404.1_all.deb Size: 277690 MD5sum: 0cba608b9b5fba6859a07e6322f7bd72 SHA1: b44a2b8662b95f39052025c37aebe0a9974070b5 SHA256: 82a62d93392902f3c589920618992d7bbef055b84b7c6d553c81bb336f565855 SHA512: 31f21712c69a52ca4854d03563628e7df79ac2ecf34c2e713ab80b59fd9fe31fb6dda964164a2fe44c0ff4808e32e31e53b761b9d1300e4f9cc1b1eb9b9d1e52 Homepage: https://cran.r-project.org/package=agrmt Description: CRAN Package 'agrmt' (Calculate Concentration and Dispersion in Ordered Rating Scales) Calculates concentration and dispersion in ordered rating scales. It implements various measures of concentration and dispersion to describe what researchers variably call agreement, concentration, consensus, dispersion, or polarization among respondents in ordered data. It also implements other related measures to classify distributions. In addition to a generic city-block based concentration measure and a generic dispersion measure, the package implements various measures, including van der Eijk's (2001) measure of agreement A, measures of concentration by Leik, Tatsle and Wierman, Blair and Lacy, Kvalseth, Berry and Mielke, Reardon, and Garcia-Montalvo and Reynal-Querol. Furthermore, the package provides an implementation of Galtungs AJUS-system to classify distributions, as well as a function to identify the position of multiple modes. Package: r-cran-agrobox Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-stringr, r-cran-agricolae, r-cran-pwr, r-cran-rlang, r-cran-openxlsx, r-cran-rstatix, r-cran-multcompview Suggests: r-cran-kableextra, r-cran-magick, r-cran-tinytex, r-cran-cli, r-cran-testthat Filename: pool/dists/noble/main/r-cran-agrobox_0.3.0-1.ca2404.1_all.deb Size: 102430 MD5sum: 9e9de8323ad35900920fb95a7e88a89f SHA1: d84661afbb28da7932c6e331c9547d6cfe10ec9a SHA256: 3a9ebad215cfcdc476dc7fe6b2bb1568f012833ae8c5e7d38cecdf99bed4da34 SHA512: 12d611cc5d4873c6ea43d502a9b29e527738ce895ed12f5b70383e10916ea4f798ff16cc2ffe07bd842f7fff223c7d550998ab328bb3f6a421c40eca0282a957 Homepage: https://cran.r-project.org/package=agrobox Description: CRAN Package 'agrobox' (Data Visualization and Statistical Tools for AgroindustrialExperiments) Set of tools for statistical analysis, visualization, and reporting of agroindustrial and agricultural experiments. The package provides functions to perform one-way and two-way ANOVA with post-hoc tests (Tukey HSD and Duncan MRT), Welch ANOVA for heteroscedastic data, and the Games-Howell post-hoc test as a robust alternative when variance homogeneity fails. Normality of residuals is assessed with the Shapiro-Wilk test and homoscedasticity with the Fligner-Killeen test; the appropriate statistical path is selected automatically based on these diagnostics. Coefficients of variation and statistical power (via one-way ANOVA power analysis) are reported alongside the post-hoc letter display. High-level wrappers allow automated multi-variable analysis with optional clustering by one or two experimental factors, with support for custom level ordering and relabeling. Results are returned as 'ggplot2' boxplots with mean and letter annotations, wide-format summary tables ready for publication or LaTeX rendering, and structured decision summaries for rapid agronomic interpretation. Direct export to Excel spreadsheets and high-resolution image tables is also supported. Functions follow methods widely used in agronomy, field trials, and plant breeding. Key references: Tukey (1949) ; Duncan (1955) ; Welch (1951) ; Games and Howell (1976) ; Shapiro and Wilk (1965) ; Fligner and Killeen (1976) ; Cohen (1988, ISBN:9781138892899); Wickham (2016, ISBN:9783319242750) for 'ggplot2'; see also 'agricolae' and 'rstatix' . Version en espanol: Conjunto de herramientas para el analisis estadistico, visualizacion y generacion de reportes en ensayos agroindustriales y agricolas. Incluye ANOVA univariado y bifactorial con pruebas post-hoc (Tukey HSD y Duncan MRT), ANOVA de Welch para datos heterocedasticos y la prueba post-hoc de Games-Howell como alternativa robusta cuando falla la homogeneidad de varianzas. La normalidad de residuos se evalua con la prueba de Shapiro-Wilk y la homogeneidad de varianzas con la prueba de Fligner-Killeen; la ruta estadistica apropiada se selecciona automaticamente segun estos diagnosticos. Se reportan coeficientes de variacion y potencia estadistica junto con las letras de separacion de medias. Los envoltorios de alto nivel permiten analisis multivariable automatizado con agrupamiento opcional por uno o dos factores experimentales, con soporte para orden y etiquetado personalizado de niveles. Los resultados se devuelven como boxplots con anotaciones de medias y letras, tablas resumen en formato ancho listas para publicacion o renderizado en LaTeX, y resumenes de decision para interpretacion agronomica rapida. Tambien se soporta exportacion directa a Excel e imagenes de alta resolucion para informes tecnicos. Package: r-cran-agror Architecture: all Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1648 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-ggplot2, r-cran-nortest, r-cran-lme4, r-cran-crayon, r-cran-lmtest, r-cran-emmeans, r-cran-multcomp, r-cran-ggrepel, r-cran-mass, r-cran-cowplot, r-cran-multcompview, r-cran-rcolorbrewer, r-cran-drc, r-cran-dunn.test, r-cran-gtools, r-cran-gridextra Suggests: r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-agror_1.3.7-1.ca2404.1_all.deb Size: 1605176 MD5sum: f2c4aeb2dfa92bb3286a2a2afff5002b SHA1: f5a73249f1db4c5280e672a540f1e1a8ed446d81 SHA256: b021253d0dee589f1fa809e42fbe23484b19f3c9f7e239b54150b98499307a18 SHA512: 115257a3084e3778f3937d79b8b01139a2d2c32fc72dc015db111d9c1f10a265a13a754557538d36766581d3c0df3072bc2b75428c727aac1bd5a0873e8b08f3 Homepage: https://cran.r-project.org/package=AgroR Description: CRAN Package 'AgroR' (Experimental Statistics and Graphics for Agricultural Sciences) Performs the analysis of completely randomized experimental designs (CRD), randomized blocks (RBD) and Latin square (LSD), experiments in double and triple factorial scheme (in CRD and RBD), experiments in subdivided plot scheme (in CRD and RBD), subdivided and joint analysis of experiments in CRD and RBD, linear regression analysis, test for two samples. The package performs analysis of variance, ANOVA assumptions and multiple comparison test of means or regression, according to Pimentel-Gomes (2009, ISBN: 978-85-7133-055-9), nonparametric test (Conover, 1999, ISBN: 0471160687), test for two samples, joint analysis of experiments according to Ferreira (2018, ISBN: 978-85-7269-566-4) and generalized linear model (glm) for binomial and Poisson family in CRD and RBD (Carvalho, FJ (2019), ). It can also be used to obtain descriptive measures and graphics, in addition to correlations and creative graphics used in agricultural sciences (Agronomy, Zootechnics, Food Science and related areas). Shimizu, G. D., Marubayashi, R. Y. P., Goncalves, L. S. A. (2025) . Package: r-cran-agroreg Architecture: all Version: 1.2.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-drc, r-cran-ggplot2, r-cran-boot, r-cran-minpack.lm, r-cran-dplyr, r-cran-rcompanion, r-cran-broom, r-cran-egg, r-cran-purrr Filename: pool/dists/noble/main/r-cran-agroreg_1.2.11-1.ca2404.1_all.deb Size: 673706 MD5sum: 80424aafb4cd98e2388f19bc29b8499c SHA1: 42ccdcdb4951d523bbd3a89ab82b3b605993b300 SHA256: 2efb46976666ce38efae44a2fdf5e6df8d40f78392af628a867b9e3029c224b2 SHA512: 08ee78ead9f35445f2b313b0e4a7bcb204eeb54d19ae66564698f35959b535c99efcd3d76d25ed0aa1dfd286ddc2df12af99ff4d4b681532444a90b8e671b776 Homepage: https://cran.r-project.org/package=AgroReg Description: CRAN Package 'AgroReg' (Regression Analysis Linear and Nonlinear for Agriculture) Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). ). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically. Package: r-cran-agrostab Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-agrostab_0.1.0-1.ca2404.1_all.deb Size: 92354 MD5sum: 1a28b9ab32728342d01f3c18e7384736 SHA1: 531ad29a14f583ed6b1f873eec539f37f959ac9d SHA256: 3d3f8b63612af9fdd2a32c4828075aeef1cc471f858a6ce9bb9268450193f982 SHA512: 129b7bae662a613a51a5756d71f9dd8ae8dd71babc180cb65305a25cad8514a41637d38cd41f6a1ba5b926fc4924250dc9eb29b7ca8cc2f95a172f94329f2993 Homepage: https://cran.r-project.org/package=agrostab Description: CRAN Package 'agrostab' (Stability Analysis for Agricultural Research) Statistical procedures to perform stability analysis in plant breeding and to identify stable genotypes under diverse environments. It is possible to calculate coefficient of homeostaticity by Khangildin et al. (1979), variance of specific adaptive ability by Kilchevsky&Khotyleva (1989), weighted homeostaticity index by Martynov (1990), steadiness of stability index by Udachin (1990), superiority measure by Lin&Binn (1988) , regression on environmental index by Erberhart&Rassel (1966) , Tai's (1971) stability parameters , stability variance by Shukla (1972) , ecovalence by Wricke (1962), nonparametric stability parameters by Nassar&Huehn (1987) , Francis&Kannenberg's parameters of stability (1978) . Package: r-cran-agrotech Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-xlsx, r-cran-readxl, r-cran-ggrepel, r-cran-nortest, r-cran-lmtest, r-cran-crayon Filename: pool/dists/noble/main/r-cran-agrotech_1.0.2-1.ca2404.1_all.deb Size: 257430 MD5sum: e5520168c9e3771a7177f1abcbd09881 SHA1: 9f2bb5dd1b1fdc13ee5809dd8896fbc02dd961f4 SHA256: daeb01848e176eaa09198f61d6ff14f92444e0328ef3e957ad3d6648637203b3 SHA512: 45616959c077184fe2b0cd5a26b7fda612bcd86a14f0a9595410141395003fc97e56e0836cf0f568eb9119ab9b268b1a8c2944bf5ea9e8ec2d8f9aa071795d96 Homepage: https://cran.r-project.org/package=AgroTech Description: CRAN Package 'AgroTech' (Data Analysis of Pesticide Application Technology) In total it has 7 functions, three for calculating machine calibration, which determine application rate (L/ha), nozzle flow (L/min) and amount of product (L or kg) to be added. to the tank with each sprayer filling. Two functions for graphs of the flow distribution of the nozzles (L/min) in the application bar and, of the temporal variability of the meteorological conditions (air temperature, relative humidity of the air and wind speed). Two functions to determine the spray deposit (uL/cm2), through the methodology called spectrophotometry, with the aid of bright blue (Palladini, L.A., Raetano, C.G., Velini, E.D. (2005), ) or metallic markers (Chaim, A., Castro, V.L.S.S., Correles, F.M., Galvão, J.A.H., Cabral, O.M.R., Nicolella, G. (1999), ). The package supports the analysis and representation of information, using a single free software that meets the most diverse areas of activity in application technology. 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Package: r-cran-aic Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3685 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixcalc, r-cran-zcompositions, r-cran-shiny, r-bioc-edger, r-bioc-aldex2, r-cran-vegan Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aic_1.0-1.ca2404.1_all.deb Size: 2797366 MD5sum: aceba70d64a3c48a6fb22525165154ac SHA1: 2924636542aa044161cbf011c5ee8277ffca0a9e SHA256: 7953906ae5f6cc401a5130904c571ee26164e4e896c822cbd92c9a1c6664805f SHA512: 5d6e4db3d3ef47c4934ac4f733991c409f8d28c51360dc72eb69af8afd807ce23abc3d889cdac73555aad5a1df5dce099ab18cf47371fda6754bc1da53b10d72 Homepage: https://cran.r-project.org/package=aIc Description: CRAN Package 'aIc' (Testing for Compositional Pathologies in Datasets) A set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) ), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) ) and singularity of the covariation matrix with aIc.singular(). Currently tests five data transformations: prop, clr, TMM, TMMwsp, and RLE from the R packages 'ALDEx2', 'edgeR' and 'DESeq2' (Fernandes et al (2014) , Anders et al. (2013)). Package: r-cran-aiccmodavg Architecture: all Version: 2.3-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4045 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-mass, r-cran-matrix, r-cran-nlme, r-cran-survival, r-cran-unmarked, r-cran-vgam, r-cran-xtable Suggests: r-cran-betareg, r-cran-coxme, r-cran-fitdistrplus, r-cran-glmmtmb, r-cran-lavaan, r-cran-lme4, r-cran-maxlike, r-cran-nnet, r-cran-ordinal, r-cran-pscl, r-cran-r2jags, r-cran-r2openbugs, r-cran-r2winbugs, r-cran-jagsui, r-cran-lmertest Filename: pool/dists/noble/main/r-cran-aiccmodavg_2.3-4-1.ca2404.1_all.deb Size: 2936546 MD5sum: 34b42c22a1c0f5bfe8662ff68fedbc02 SHA1: 9fab6f85e948293db5fc5b0674686e2ec30b267a SHA256: 0f40284afb5ffe926ed087f31874f27625371188cc66a367868402d3770a8d91 SHA512: c552c3dd2f09463aad53b6f42aa6dc1e86424528e309d938331b1463607d1ef559869aa9aa05aad8543fdc510c18b4581ad8aa2a36e9f0ed1ffcbfd7ac1a125d Homepage: https://cran.r-project.org/package=AICcmodavg Description: CRAN Package 'AICcmodavg' (Model Selection and Multimodel Inference Based on (Q)AIC(c)) Functions to implement model selection and multimodel inference based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from various model object classes. The package implements classic model averaging for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates or effect sizes. The package includes diagnostics and goodness-of-fit statistics for certain model types including those of 'unmarkedFit' classes estimating demographic parameters after accounting for imperfect detection probabilities. Some functions also allow the creation of model selection tables for Bayesian models of the 'bugs', 'rjags', and 'jagsUI' classes. Functions also implement model selection using BIC. Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package. Package: r-cran-aiccpermanova Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-car, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-furrr, r-cran-future, r-cran-stringr, r-cran-tidyr, r-cran-vegan Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aiccpermanova_0.0.2-1.ca2404.1_all.deb Size: 42260 MD5sum: 6f233e4a117c9d198d8ca690e25a1d7d SHA1: 7287e96ac921b291ad208e96eda95f05d50e1b5e SHA256: 5e391e9f9669c8219670383e321bedd9f492a53fae367108ff7807a1690956e0 SHA512: 6ef0599454fd8d1af494a43890de51f579105158c49b24fafc4fc25403c1d292c5701dc2f7fe236b38f741f3676c0bdf397f39714d7e0d1c7a38321132d5c41f Homepage: https://cran.r-project.org/package=AICcPermanova Description: CRAN Package 'AICcPermanova' (Model Selection of PERMANOVA Models Using AICc) Provides tools for model selection and model averaging of PerMANOVA models using Akaike Information Criterion corrected for small sample sizes (AICc) and Information Theoretic criteria principles. The package is built around the PERMANOVA analysis from the 'vegan' package and provides a streamlined workflow for generating and comparing models, obtaining model weights, and summarizing results using model averaging approaches. The methods implemented in this package are based on the practical information- theoretic approach described by Burnham, K. P. and Anderson, D. R. (2002) (). Package: r-cran-aid Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-tseries, r-cran-nortest, r-cran-ggplot2, r-cran-psych, r-cran-meta, r-cran-stringr Suggests: r-cran-onewaytests Filename: pool/dists/noble/main/r-cran-aid_3.1-1.ca2404.1_all.deb Size: 83234 MD5sum: 7a7da2d1269cfa419d364ef838bc671f SHA1: f7b4441b06c6f9e53a81c7265c5b77efe9358e17 SHA256: 6da2f0433b8de1862ec3304f55f8778f24d0b6056f64398759ef5ee57c3f7638 SHA512: a7dad69e1ef3af7e431021029b28462940b6a20b8505c54f68382cbed5d3961c8707061ec880cd1c72d14ca100c5b85a1b86e5824105adb5865b3978e30cb521 Homepage: https://cran.r-project.org/package=AID Description: CRAN Package 'AID' (Box-Cox Power Transformation) Performs Box-Cox power transformation for different purposes, graphical approaches, assesses the success of the transformation via tests and plots, computes mean and confidence interval for back transformed data. 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The package provides the 'intData' class for representing interval-valued data, along with functions to aggregate microdata and to estimate parameters of latent distributions. Barycenter and covariance matrix estimation is implemented based on the Mallows distance (Oliveira et al. (2025) ). Robust estimation of the symbolic covariance matrix is implemented via the Interval Minimum Covariance Determinant (IMCD) estimator, enabling outlier detection based on the robust squared Interval-Mahalanobis distance, as proposed by Loureiro et al. (2026) . Package: r-cran-aidar Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aidar_1.0.5-1.ca2404.1_all.deb Size: 76182 MD5sum: e51d8770e7911f1a59c9967b17fdc54e SHA1: f20a18ccf08917f7f7bce818e3678595c2587aca SHA256: b74e2770f19bb82fe190514b22f3fcb154b3de43d4fcf7286d97ec3a6e203d00 SHA512: 6b305e419d9a2558e7a3fd37326afd386cf89c9b68767e2ab9a15c22fca041e05ce71be3af0ae9796cd440c5e034a5913ee91b94956bb756aef705be499d8ce9 Homepage: https://cran.r-project.org/package=aidar Description: CRAN Package 'aidar' (Tools for Reading AIDA Files) Read objects from the AIDA () file and make them available as dataframes in R. 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The current version supports users to test assumptions before relevant analysis of bias from study size and sequential analysis such as mentioned by Wetterslev, J., Jakobsen, J. C., & Gluud, C. (2017) . Package: r-cran-aidif Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-mirt, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aidif_0.1.0-1.ca2404.1_all.deb Size: 110438 MD5sum: ac28cc3609ab8119c8600c3bc490ae84 SHA1: e0ddfb92779d328a9ec4441cd24a5aa25d5516e4 SHA256: c85f87da1bf7d06a84564d086027481c3e82205355683b9deaf982b53ba56828 SHA512: 0b732be404f092342f6a344dccfc315a817f526a7cf7823efdb4b9ef03fdb85c2d72aaf4989d79c10b865a14b75bfeaf6a34374d3251f5fe49d44dc46cd945d6 Homepage: https://cran.r-project.org/package=aiDIF Description: CRAN Package 'aiDIF' (Differential Item Functioning for AI-Scored Assessments) Detects and quantifies differential item functioning (DIF) in AI-scored educational and psychological assessments. 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Package: r-cran-aieconindex Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-aieconindex_0.1.0-1.ca2404.1_all.deb Size: 124336 MD5sum: 3aec4bad818d17abe88a05021645521e SHA1: 9c08da72eb3243cf7434ae0a42fe486f2bb2d02a SHA256: 2601da1035eff0332ec00bf87f75dadb10ee0ad3991a2524898643aa37cfc222 SHA512: 999255ee598c367378415385bdd1b3630e68402cc4e361b2739b74c59d88adb80117c83932cd6702e307aac985b3b5650279b318ab801b2625b630cc26f49779 Homepage: https://cran.r-project.org/package=aieconindex Description: CRAN Package 'aieconindex' (Access the 'Anthropic Economic Index' Dataset) Provides clean, tidy access to the 'Anthropic Economic Index' (AEI) dataset hosted on 'Hugging Face' . The AEI is a recurring release from 'Anthropic' that maps usage of the 'Claude' family of large language models to occupations and tasks using the 'O*NET' taxonomy and the 'Standard Occupational Classification' system, following the methodology of Handa et al. (2025) and the privacy-preserving system 'Clio' of Tamkin et al. (2024) . Functions list available releases, fetch raw and enriched usage tables, retrieve task statements, request hierarchies, and country-level breakdowns, compare two releases, join the index to user-supplied data on a shared key, and compute usage-concentration metrics (Herfindahl-Hirschman Index, top-N concentration ratios, Shannon entropy). Data is cached locally for subsequent calls. Reproducibility helpers produce 'BibTeX' or plain-text citations that include the methodological source paper. This product uses the 'Anthropic Economic Index' data (released under CC-BY by 'Anthropic') but is not endorsed or certified by 'Anthropic'. Package: r-cran-aies Architecture: all Version: 0.99.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1288 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-ebimage, r-cran-e1071, r-cran-fs, r-cran-data.table Suggests: r-cran-stringr, r-cran-colorspace, r-cran-dplyr, r-cran-knitr, r-cran-rstudioapi, r-cran-svdialogs Filename: pool/dists/noble/main/r-cran-aies_0.99.6-1.ca2404.1_all.deb Size: 1072658 MD5sum: 13dc278c9e2dba07279b30915d05cc53 SHA1: e627c6c3a6afaaa173613c792c4f0d2289d2f960 SHA256: e01db51548e20cc3f57a5af6a936c7092396b9e677510c1f760949282056e325 SHA512: 3384ba36f6d064c424412437a2101ec685147a6588efdfa6823242004f6557d43a4349374843c3e65b4baa6446a0013ffdd0fc00ddda51d82769084a87539ca3 Homepage: https://cran.r-project.org/package=AiES Description: CRAN Package 'AiES' (Axon Integrity Evaluation System for Microscopy Images) Provides tools for the quantitative analysis of axon integrity in microscopy images. It implements image pre-processing, adaptive thresholding, feature extraction, and support vector machine-based classification to compute indices such as the Axon Integrity Index (AII) and Degeneration Index (DI). The package is designed for reproducible and automated analysis in neuroscience research. 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The AIFF file format is defined in and Package: r-cran-aimplot Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-gridextra Filename: pool/dists/noble/main/r-cran-aimplot_1.0.0-1.ca2404.1_all.deb Size: 16950 MD5sum: d5401c09f7167890a9aeed7b8bf46209 SHA1: fa5bccac1a5c053d96bd460efca671dba32d339c SHA256: 384dd73be547ef0c40473cefb22b356f7e7b4206718e7a16a5f08754837192bf SHA512: c79fac79f5299987e7cd05b2d342013e493238f59485e5f4610717b5070bdf8503831751cefb450b17c4a875e78717de48a7683436ecffc1232d48106dff0258 Homepage: https://cran.r-project.org/package=aimPlot Description: CRAN Package 'aimPlot' (Create Pie Like Plot for Completeness) Create a pie like plot to visualise if the aim or several aims of a project is achieved or close to be achieved i.e the aim is achieved when the point is at the center of the pie plot. 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Package: r-cran-aimsir17 Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2905 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-aimsir17_0.0.2-1.ca2404.1_all.deb Size: 2503608 MD5sum: d947e0bf847a248980d071ec5744bc2e SHA1: dd4afb277d6148529660d2b18d7af93a3ef29867 SHA256: 6f5b4b77a2e0141b7259a3947a855ae6c426f67ecf3ae42d9464fb0c7b5d6f88 SHA512: f70878085ab6b1bda50a817373386b660a885b659ba8795cc3f344e4557f116a223d09d02c4cc38d740d2cbd5b8b6f54b24539c12e1f810a015bebf438ed4bd2 Homepage: https://cran.r-project.org/package=aimsir17 Description: CRAN Package 'aimsir17' (Irish Weather Observing Stations Hourly Records for 2017) Named after the Irish name for weather, this package contains tidied data from the Irish Meteorological Service's hourly observations for 2017. 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Package: r-cran-aion Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1197 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arkhe Suggests: r-cran-folio, r-cran-fontquiver, r-cran-igraph, r-cran-knitr, r-cran-markdown, r-cran-relations, r-cran-rsvg, r-cran-svglite, r-cran-tinysnapshot, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-aion_1.7.0-1.ca2404.1_all.deb Size: 723776 MD5sum: 01b72bf2e3e8811e951290139d290282 SHA1: 8a1c64794145209cd32543d3df350579f855dcee SHA256: c607978d9d29c2f5b19b8c0365d42d5078257db904ce9557a530c5ac6be82f14 SHA512: 766c879fb6f6e8dabf6e56f9bd4e9d9622e80374b3fff2a5027aca0d009a75102cf73bdd46dd91db32851d384779250eba7d3e2f67158158aa36af15457c87b2 Homepage: https://cran.r-project.org/package=aion Description: CRAN Package 'aion' (Archaeological Time Series) A toolkit for archaeological time series and time intervals. This package provides a system of classes and methods to represent and work with archaeological time series and time intervals. Dates are represented as "rata die" and can be converted to (virtually) any calendar defined by Reingold and Dershowitz (2018) . This packages offers a simple API that can be used by other specialized packages. Package: r-cran-aipw Architecture: all Version: 0.6.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 639 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-superlearner, r-cran-ggplot2, r-cran-future.apply, r-cran-progressr, r-cran-rsolnp Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-tmle Filename: pool/dists/noble/main/r-cran-aipw_0.6.9.3-1.ca2404.1_all.deb Size: 470016 MD5sum: b8d7fbe662ca99413060847ab69a6952 SHA1: 4151e8ff3e7dfd76c3caf406dd774644e5c969ba SHA256: aff83dce3988b344fa77647b72003581fc9e2145e3bf5a4c18696060e0eea10a SHA512: b1ab07d7de1f88f53dcf90c76cf6baaa6c7b8791383d572c6d014e75e9b3458a3ae80f4a8c89d04b57fd82bb0e636a3460d87e0730f09e7a0a76f7aa896f4df5 Homepage: https://cran.r-project.org/package=AIPW Description: CRAN Package 'AIPW' (Augmented Inverse Probability Weighting) The 'AIPW' package implements the augmented inverse probability weighting, a doubly robust estimator, for average causal effect estimation with user-defined stacked machine learning algorithms. To cite the 'AIPW' package, please use: "Yongqi Zhong, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi (2021). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology. ". Visit: for more information. Package: r-cran-air Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-keyring, r-cran-result, r-cran-rjson Suggests: r-cran-testthat, r-cran-vcr Filename: pool/dists/noble/main/r-cran-air_0.2.3-1.ca2404.1_all.deb Size: 27592 MD5sum: a665c0b6001104f0936f1ea061887acd SHA1: d89c07852302bad655f0874e2f3c149963a069e2 SHA256: fb65ce6c7ee7bbf1fef4ef665ec875839006b8eddc7075da545d5b07f4274bfb SHA512: 10109344206bf6c95aec869e2c93f9ad3f0b335740bf7de33c7b5abdb2fb01ae3cfed78a897876c85a8cc1166b26eccdf7bcceb4ec41b795ed691b79620994a9 Homepage: https://cran.r-project.org/package=air Description: CRAN Package 'air' (AI Assistant to Write and Understand R Code) An R console utility that lets you ask R related questions to the 'OpenAI' large language model. It can answer how-to questions by providing code, and what-is questions by explaining what given code does. You must provision your own key for the 'OpenAI' API . Package: r-cran-aire.zmvm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-lubridate, r-cran-progress, r-cran-readr, r-cran-readxl, r-cran-rvest, r-cran-sp, r-cran-stringr, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aire.zmvm_1.0.1-1.ca2404.1_all.deb Size: 463264 MD5sum: 0e26ce8b10352c02b93e9e83dc544e5c SHA1: a16e1ef38d1f1db2bd3c9247a2672154c72f51b9 SHA256: 7c0477f368d01b04b357127f8ef8e81ff41766742599e706d829b70413f82b67 SHA512: 67b8deb8fa966439f41d59d8764c947e1aa5e93aca9947e747f71425f215e8c558d9af7c966309c64077c5af14753d6db87ed1cc641a9c93c501e531fcd0bcad Homepage: https://cran.r-project.org/package=aire.zmvm Description: CRAN Package 'aire.zmvm' (Download Mexico City Pollution, Wind, and Temperature Data) Tools for downloading hourly averages, daily maximums and minimums from each of the pollution, wind, and temperature measuring stations or geographic zones in the Mexico City metro area. The package also includes the locations of each of the stations and zones. See for more information. Package: r-cran-airexposure Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-htmltools, r-cran-httr, r-cran-jsonlite, r-cran-leaflet, r-cran-htmlwidgets, r-cran-lubridate, r-cran-sf Filename: pool/dists/noble/main/r-cran-airexposure_1.0-1.ca2404.1_all.deb Size: 79202 MD5sum: b40d3b5c04e3f70dce977597f91c76ed SHA1: dc63a5280d67da13a7a6c6c02f3fe92935254e9d SHA256: d96b4dd4cbabd49a065cc0b46a3743a2236bf44f969c213cd9cf6c82a5097cdc SHA512: 7d8afe99a043a98aca65765edc35b51f622fd6d91520a5186728e835fc93749e3ec351add59bc34bff5643dc86ce6ea61b712fe6ca9bf508846cfb00b9307185 Homepage: https://cran.r-project.org/package=AirExposure Description: CRAN Package 'AirExposure' (Exposure Model to Air Pollutants Based on Mobility and DailyActivities) Model that assesses daily exposure to air pollution, which considers daily population mobility on a geographical scale and the spatial and temporal variability of pollutant concentrations, in addition to traditional parameters such as exposure time and pollutant concentration. Package: r-cran-airgrdatasets Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1878 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-airgrdatasets_0.2.3-1.ca2404.1_all.deb Size: 1468554 MD5sum: c245f9b7f92e3a615b8b329bba3cacf8 SHA1: 1a11b0b81ef64262642f869ec9db14171f08895f SHA256: ea447a7320726624701e133df7a70b9b7d8d8036918a8dd0f39e9c2ca6730dc7 SHA512: d74f49403872610af9e745042703b58114cd26bdcf82a66505c5b847dfb54c113323b10241081ef77433d461255349d274626c42456677c406b9b1d55de04c72 Homepage: https://cran.r-project.org/package=airGRdatasets Description: CRAN Package 'airGRdatasets' (Hydro-Meteorological Catchments Datasets for the 'airGR'Packages) Sample of hydro-meteorological datasets extracted from the 'CAMELS-FR' French database . It provides metadata and catchment-scale aggregated hydro-meteorological time series on a pool of French catchments for use by the 'airGR' packages. Package: r-cran-airgrdatassim Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-airgrdatassim_0.1.4-1.ca2404.1_all.deb Size: 102724 MD5sum: 525f7831c254a785a2b3e976c15b7019 SHA1: 8319d0b4f73b8331f34b0113fac7845676595969 SHA256: 9d52e56c59ed7bfb6b0742b34e4033c70939d9d723cac44c8546f870f93ff3fc SHA512: 16251f09cd01c20036d8bb7f433fddb639f2e58f9d352db8229e24d5a0f5bd05af4572caf45684c42e6c73ffa51a9718aeb7b6ca3cf3eb08af96bda5a95c9263 Homepage: https://cran.r-project.org/package=airGRdatassim Description: CRAN Package 'airGRdatassim' (Ensemble-Based Data Assimilation with GR Hydrological Models) Add-on to the 'airGR' package which provides the tools to assimilate observed discharges in daily GR hydrological models. The package consists in two functions allowing to perform the assimilation of observed discharges via the Ensemble Kalman filter or the Particle filter as described in Piazzi et al. (2021) . Package: r-cran-airgriwrm Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1897 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-jsonlite, r-cran-png, r-cran-rlang, r-cran-zlib Suggests: r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-waldo Filename: pool/dists/noble/main/r-cran-airgriwrm_0.7.0-1.ca2404.1_all.deb Size: 1338024 MD5sum: b95066717abaae2ad344e0f723201ab0 SHA1: 24ab3e6ac1466ee89a021ad488fc8321768c01ad SHA256: 1522bb93c00177d63a50cd94ddebaacb48fede4298b9ec49f143aa44b95d0c3a SHA512: 6966e57e922e9d22674a8c71b35298018c8b91762c8df399ca58c5ce8798bb057d8be994b915ce799031837c44df9f482bb5659c93d10e6f851057e40c6a5ae3 Homepage: https://cran.r-project.org/package=airGRiwrm Description: CRAN Package 'airGRiwrm' ('airGR' Integrated Water Resource Management) Semi-distributed Precipitation-Runoff Modeling based on 'airGR' package models integrating human infrastructures and their managements. Package: r-cran-airgrteaching Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7094 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dygraphs, r-cran-markdown, r-cran-plotrix, r-cran-shiny, r-cran-shinyjs, r-cran-xts Suggests: r-cran-airgrdatasets, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-airgrteaching_0.3.5-1.ca2404.1_all.deb Size: 3512284 MD5sum: 3a9c42301509d961bf1f0a830796441c SHA1: 6ea0a3d660b89a375933d5833ed64f5ecb772f3d SHA256: b7dea1e81c2d45e24d31fd7889ebf885d56c15838a7ac3547659097fb6a35bfc SHA512: 4dd707db4857755d8f2847d9f9b6c772b65e1b7d461eb7ac2352d2f177b22e40c1ac6d87a7c99219574e658a3eb165a5adbca2fbdc243a9d0fc05d451724ef23 Homepage: https://cran.r-project.org/package=airGRteaching Description: CRAN Package 'airGRteaching' (Teaching Hydrological Modelling with the GR Rainfall-RunoffModels ('Shiny' Interface Included)) Add-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables. Package: r-cran-airly Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-reshape2, r-cran-tibble Suggests: r-cran-testthat, r-cran-httptest, r-cran-covr Filename: pool/dists/noble/main/r-cran-airly_0.1.0-1.ca2404.1_all.deb Size: 118852 MD5sum: c0aa4bed1382347f7002477e0ab5971b SHA1: cd8439bde9979d3fdde4aa606cf9ac4c4aaad533 SHA256: 6fa026abb33405d469ba9a704b6de5cfb125c88f8312cf42a1798257cfa96c15 SHA512: 703dbac7bae1df27e25c181886f471937970a6580b6d4ab3e9fc99809ea529ee7981b983046dc0bb1545573c0f57022fa4f73075262fdad91f64e0e49546fda3 Homepage: https://cran.r-project.org/package=aiRly Description: CRAN Package 'aiRly' (R Wrapper for 'Airly' API) Get information about air quality using 'Airly' API through R. Package: r-cran-airmonitor Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dygraphs, r-cran-leaflet, r-cran-lubridate, r-cran-magrittr, r-cran-mazamacoreutils, r-cran-mazamarollutils, r-cran-mazamatimeseries, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tidyselect, r-cran-xts Suggests: r-cran-knitr, r-cran-markdown, r-cran-testthat, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-airmonitor_0.4.3-1.ca2404.1_all.deb Size: 992878 MD5sum: d4a8e56d8453db5476e12105fb22c296 SHA1: 47c174c62f2bd119cf799656086b5b110fc46df5 SHA256: a9520416a1a785f655538c40ecc2c6341fdcef15e3f426cecea6492d28d62c34 SHA512: 4094474add0cd43924dc75749c8ea838f2fe28ae8ebd52300d29723fa97e62fb5a4fa8ae48dcaa645ae171f6311e43a0396d9ce4035503edd4766c35c22cace9 Homepage: https://cran.r-project.org/package=AirMonitor Description: CRAN Package 'AirMonitor' (Air Quality Data Analysis) Utilities for working with hourly air quality monitoring data with a focus on small particulates (PM2.5). A compact data model is structured as a list with two dataframes. A 'meta' dataframe contains spatial and measuring device metadata associated with deployments at known locations. A 'data' dataframe contains a 'datetime' column followed by columns of measurements associated with each "device-deployment". Algorithms to calculate NowCast and the associated Air Quality Index (AQI) are defined at the US Environmental Projection Agency AirNow program: . Package: r-cran-airnow Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-lintr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-airnow_0.1.1-1.ca2404.1_all.deb Size: 59526 MD5sum: 9d6b335c6aefd5cef5f69621cd7d5847 SHA1: 83738ed9d4a29f6a183933e6b9a6517920ce27da SHA256: 77b78787a4d59adfa900cba2876d30678bc04a368e7a9398f23ea53e5c1d0293 SHA512: c2fc5b85becdfcb2aa19a349c79071c9b9239a7a56d091d4ee0a40f935a68cb159b3e184bd27c07ef703c9a355109fcb9c318fc0f188074b1dedc7a6fc8541d5 Homepage: https://cran.r-project.org/package=airnow Description: CRAN Package 'airnow' (Retrieve 'AirNow' Air Quality Observations and Forecasts) Retrieve air quality data via the 'AirNow' API. Package: r-cran-airportproblems Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-plotly Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-airportproblems_0.1.0-1.ca2404.1_all.deb Size: 335508 MD5sum: 29d8c4f3873618d71ca30181e619a032 SHA1: d7c3303cc02713f2ab2e89964dfa42a37c4e95f4 SHA256: dece73732c5a5f3e233b48b53caef8488121ecba57a06cf22031f2555a1a7c2a SHA512: 38b04d43fafa124cb76b12395c33159e068fae1ff5541155f293f1334f55a12cb42ddfe385da01b22e7733b2282c0a03ed7fe3b59d199c688fbba3e8b1e27b3d Homepage: https://cran.r-project.org/package=AirportProblems Description: CRAN Package 'AirportProblems' (Analysis of Cost Allocation for Airport Problems) Airport problems, introduced by Littlechild and Owen (1973) , are cost allocation problems where agents share the cost of a facility (or service) based on their ordered needs. Valid allocations must satisfy no-subsidy constraints, meaning that no group of agents contributes more than the highest cost of its members (i.e., no agent is allowed to subsidize another). A rule is a mechanism that selects an allocation vector for a given problem. This package computes several rules proposed in the literature, including both standard rules and their variants, such as weighted versions, rules for clones, and rules based on the agents’ hierarchy order. These rules can be applied to various problems of interest, including the allocation of liabilities and the maintenance of irrigation systems, among others. Moreover, the package provides functions for graphical representation, enabling users to visually compare the outcomes produced by each rule, or to display the no-subsidy set. In addition, it includes four datasets illustrating different applications and examples of airport problems. For a more detailed explanation of all concepts, see Thomson (2024) . Package: r-cran-airportr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 757 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-airportr_0.1.3-1.ca2404.1_all.deb Size: 371240 MD5sum: bf557f4e1a9d3d315880088b38935ea7 SHA1: 62d48218f693822c4296439bd66ccefd27524abd SHA256: d31caffaf33888b9185043d3c091c8cd048543cba4cea5e5d601325e75b6349b SHA512: f7491f3a56ade5626034c4851f19151019a93c17cac89de1e89118ffe5a5877383555d21e66e55e8206df34cc69f9c23c9c88082777671bc065f83a7459607ba Homepage: https://cran.r-project.org/package=airportr Description: CRAN Package 'airportr' (Convenience Tools for Working with Airport Data) Retrieves open source airport data and provides tools to look up information, translate names into codes and vice-verse, as well as some basic calculation functions for measuring distances. Data is licensed under the Open Database License. Package: r-cran-airports Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 817 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-airports_0.1.0-1.ca2404.1_all.deb Size: 791168 MD5sum: 8ec799209e438ced1c283f47ef41d244 SHA1: 14955ab5d71067c24b768a13f837fee96d04b6f0 SHA256: bac690e87571a8e596b812e8189221c762c66f549c80f6b87de69ace0251c320 SHA512: ff806cb296c116170da9d34f100ae978eb0a593e40c027314d2a4908db51d6f6e25dc00b46c0852e63a7ec3e30f36316801d7cc985b21d86cd1ca55ea3829212 Homepage: https://cran.r-project.org/package=airports Description: CRAN Package 'airports' (Data on Airports) Geographic, use, and property related data on airports. Package: r-cran-airqualityes Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3808 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-airqualityes_1.0.0-1.ca2404.1_all.deb Size: 3810674 MD5sum: 8f0c4432f21c2a53ee918422c3183158 SHA1: 9c9069499bd3ccb25f0febc859447c4fb0d9a196 SHA256: 6ece0231238cc717320bb7b30e2f41b5e3eb3757984c4dec17bd12f1a6f5547c SHA512: a57cd61e6a3adacd31e4f34cc72f34c2d6054d67bf52965aabe4adcd381a0fd3000a87a7759c4a4458dbf99e2145610095fece7f22079a816966e50ed342c849 Homepage: https://cran.r-project.org/package=airqualityES Description: CRAN Package 'airqualityES' (Air Quality Measurements in Spain from 2011 to 2018) These dataset contains daily quality air measurements in Spain over a period of 18 years (from 2001 to 2018). The measurements refer to several pollutants. These data are openly published by the Government of Spain. The datasets were originally spread over a number of files and formats. Here, the same information is contained in simple dataframe for convenience of researches, journalists or general public. See the Spanish Government website for more information. Package: r-cran-airr Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-readr, r-cran-stringi, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-testthat Filename: pool/dists/noble/main/r-cran-airr_1.6.1-1.ca2404.1_all.deb Size: 566396 MD5sum: 0f759f72cd2a94d2007d65c2643915f9 SHA1: d3cadf9849356548688da8bfc036efe6fdc98b0e SHA256: df2433f3f6f739aea02aa87bb867da54b8536ad77311216c7f2e711b601bd29d SHA512: 629ab3701d263b4bde9b974a6f6d3d11eb9079a3c2896156b52502c4fddf19b885d92dca1213b7afa5748083a4ed5ecb9d1c16cc2503565424506b3eaffaca96 Homepage: https://cran.r-project.org/package=airr Description: CRAN Package 'airr' (AIRR Data Representation Reference Library) Schema definitions and read, write and validation tools for data formatted in accordance with the AIRR Data Representation schemas defined by the AIRR Community . Package: r-cran-airscreen Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-airscreen_0.1.0-1.ca2404.1_all.deb Size: 62210 MD5sum: 7e3639bae3e0068b33d31c44bc6ad7ae SHA1: 150705135294cd0c8f5b08e082d8b40b2afa1e57 SHA256: 43ae6737dd0aaee67e337f2c3aded8806ac10e08a19efb523131ca78597d699d SHA512: c251480c37bca458e9d28f8a7dd0356906e4d7bef86d2c80e6c8900c8e5053cdfbcd33abbf628540269f41c3176b2de3d770971de01c59cdc4ae8fe00797774b Homepage: https://cran.r-project.org/package=AirScreen Description: CRAN Package 'AirScreen' (Feature Screening via Adaptive Iterative Ridge (Air-HOLP andAir-OLS)) Implements two complementary high-dimensional feature screening methods, Adaptive Iterative Ridge High-dimensional Ordinary Least-squares Projection (Air-HOLP, suitable when the number of predictors p is greater than or equal to the sample size n) and Adaptive Iterative Ridge Ordinary Least Squares (Air-OLS, for n greater than p). Also provides helper functions to generate compound-symmetry and AR(1) correlated data, plus a unified Air() front end and a summary method. For methodological details see Joudah, Muller and Zhu (2025) . Package: r-cran-airship Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2234 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinybs, r-cran-dt, r-cran-shinybusy, r-cran-plotly, r-cran-dplyr, r-cran-tidyselect, r-cran-tidyr, r-cran-stringr, r-cran-colourpicker, r-cran-shinywidgets, r-cran-shinydashboard, r-cran-scales, r-cran-cairo, r-cran-ggplot2, r-cran-rlang, r-cran-magrittr, r-cran-shinyjs, r-cran-data.table, r-cran-shinyalert, r-cran-vctrs, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggpubr Filename: pool/dists/noble/main/r-cran-airship_1.4.3-1.ca2404.1_all.deb Size: 1901178 MD5sum: c5cafeb37cf7b9e5b8764916396f6b5c SHA1: 01e1cbea03fd626471a4d0d3e64ec99dee32268e SHA256: 0f33b2b10bb1fc2dc87eb54ffeb6b8616ac88b5f69ddecad6d8ae78378987fa3 SHA512: 4c9b6fad3b86abb8ad48fc5e3e1de5150ebc48d754cb06825a4a9d4525eb9acaf1977dc1baf89c65f10936798b4b65b622b8bae500db4fbc2630b4ea6744bc37 Homepage: https://cran.r-project.org/package=airship Description: CRAN Package 'airship' (Visualization of Simulated Datasets with Multiple SimulationInput Dimensions) Plots simulation results of clinical trials. Its main feature is allowing users to simultaneously investigate the impact of several simulation input dimensions through dynamic filtering of the simulation results. A more detailed description of the app can be found in Meyer et al. or the vignettes on 'GitHub'. 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These functions can enhance the quality of title and abstract screenings while reducing the total screening time significantly. In addition, the package includes tools for quality assessment of title and abstract screenings, as described in Vembye, Christensen, Mølgaard, and Schytt (2025) . Package: r-cran-aisdk Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4069 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-httr2, r-cran-jsonlite, r-cran-rlang, r-cran-yaml, r-cran-callr, r-cran-processx, r-cran-memoise, r-cran-digest, r-cran-base64enc, r-cran-curl, r-cran-openssl Suggests: r-cran-testthat, r-cran-httptest2, r-cran-cli, r-cran-knitr, r-cran-rmarkdown, r-cran-skimr, r-cran-evaluate, r-cran-shiny, r-cran-shinyjs, r-cran-bslib, r-cran-commonmark, r-cran-ggplot2, r-cran-dplyr, r-cran-readr, r-cran-readxl, r-cran-dbi, r-cran-rsqlite, r-cran-torch, r-cran-onnx, r-cran-rstudioapi, r-cran-dt, r-cran-withr, r-cran-httpuv, r-cran-devtools, r-cran-fs, r-cran-dotenv, r-cran-quarto, r-cran-pdftools Filename: pool/dists/noble/main/r-cran-aisdk_1.1.0-1.ca2404.1_all.deb Size: 3044674 MD5sum: c7a5b08093a8410e3cfa998c399fd508 SHA1: 3b5f9a7fabaff161cc6ad5a14e3bd5f0333d162c SHA256: a640832d25493dc1c600dc092cba6f9587b56dcb14d09691100c2001740bd179 SHA512: ed379a3a62cc16ebcd2e0a0ad8e94d6996ad1c5ed6fdd04d7c9d44697d7546ec664a28113585fe90bda256e5565c4117d41055fea51612ebcc6d9194b7af1dbe Homepage: https://cran.r-project.org/package=aisdk Description: CRAN Package 'aisdk' (Unified Interface for AI Model Providers) A production-grade AI toolkit for R featuring a layered architecture (Specification, Utilities, Providers, Core), request interception support, robust error handling with exponential retry delays, support for multiple AI model providers ('OpenAI', 'Anthropic', etc.), local small language model inference, distributed 'MCP' ecosystem, multi-agent orchestration, progressive knowledge loading through skills, and a global skill store for sharing AI capabilities. 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The extractor utility implements code from 'Matrix.utils', Varrichio C (2020), . Package: r-cran-akmbiclust Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-akmbiclust_0.1.0-1.ca2404.1_all.deb Size: 18770 MD5sum: a2eb66473cfc7da3c5855b2156c35b04 SHA1: f0ed6d6d0769a0c75951d9f0a4ef363467bbf3ef SHA256: c1f9ef293590c27afc3817f9a8c3c5be6a9ba2d6042c224d6e11cec660553b8a SHA512: 0225b1b2051a03d11cfb3b3b8b14d3f88d8c7d48d66fdfa251974c0f70fcabde9010ef9c897e51e92e2a5d293f4b8492f5fc03630d90be21674b5a00f0d0d035 Homepage: https://cran.r-project.org/package=akmbiclust Description: CRAN Package 'akmbiclust' (Alternating K-Means Biclustering) Implements the alternating k-means biclustering algorithm in Fraiman and Li (2020) . 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Package: r-cran-albatross Architecture: all Version: 0.3-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 821 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multiway, r-cran-cmls, r-cran-pracma, r-cran-lattice, r-cran-matrix Filename: pool/dists/noble/main/r-cran-albatross_0.3-9-1.ca2404.1_all.deb Size: 524976 MD5sum: 0a0acd6200c5a12a2429b04378cec505 SHA1: 93eb3ab4536ffb48995a3654f834e4472ab46917 SHA256: a588c115ad8816c477e5cbf6bae5b494eecda0577a2ee67b68496b149b7843df SHA512: 0ec3b5ddc9f669c87071b3699f3975be44f976747e51f39a09da41bd16d14ea96a9a9b1fd416c1697fa0cdd7f73844cc74f0905a8f5296118148f71270ec9545 Homepage: https://cran.r-project.org/package=albatross Description: CRAN Package 'albatross' (PARAFAC Analysis of Fluorescence Excitation-Emission Matrices) Perform parallel factor analysis (PARAFAC: Hitchcock, 1927) on fluorescence excitation-emission matrices: handle scattering signal and inner filter effect, scale the dataset, fit the model; perform split-half validation or jack-knifing. Modified approaches such as Whittaker interpolation, randomised split-half, and fluorescence and scattering model estimation are also available. The package has a low dependency footprint and has been tested on a wide range of R versions. Package: r-cran-albersdown Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-gt, r-cran-bslib, r-cran-usethis, r-cran-yaml, r-cran-cli, r-cran-testthat, r-cran-viridislite, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-albersdown_1.0.0-1.ca2404.1_all.deb Size: 787862 MD5sum: 244fb11213b0af395b35ecc80cb69d12 SHA1: 3fb6b7042458d272d30375768c2ea71f99f02c62 SHA256: 4beaee0f1b23ad92ecb0711865ab5aa4bd315c0e0c70317d981f4fb0c00c2ebd SHA512: 5e8b418a0625e68b2efb69f67ee016c25f4b9fada7c90f21d21e0e6b2b28deb398916f7507e70b9ebba03bc7da2eb4138e60496c70fb30a533409c83ebd67cb6 Homepage: https://cran.r-project.org/package=albersdown Description: CRAN Package 'albersdown' (Minimalist Theme and Vignette Kit for 'pkgdown' and R Markdown) Provides a minimalist 'ggplot2' theme, colour scales, and 'pkgdown' template built around a curated colour palette system inspired by Josef Albers' colour theory (Albers (1963, ISBN:978-0-300-17935-4) "Interaction of Color"). Includes helpers to apply consistent theming to 'ggplot2' plots, 'gt' tables, and 'bslib' Bootstrap 5 sites, along with one-command setup functions for adopting the style across an R package. Package: r-cran-albi Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-openxlsx, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-rlang, r-cran-readxl Filename: pool/dists/noble/main/r-cran-albi_0.1.9-1.ca2404.1_all.deb Size: 189946 MD5sum: 0d130d200dfaf965b336ff136af5b9ae SHA1: 0f3f425cab858a25b45fea13a00e4027c0ea6426 SHA256: bec17744880af53d1959a6d9c56f677e5081c8f0d40e67fee92d022bb7afb780 SHA512: 54d0c3207704a849cc66039fa7b16d1559be64d54b5826efe09efce4de416ed2f16d8a600e586e4b13ba816af968866e2ca023184dbb084bf50b99c55fe8dd10 Homepage: https://cran.r-project.org/package=aLBI Description: CRAN Package 'aLBI' (Estimating Length-Based Indicators for Fish Stock) Provides tools for estimating length-based indicators from length frequency data to assess fish stock status and manage fisheries sustainably. Implements methods from Cope and Punt (2009) for data-limited stock assessment and Froese (2004) for detecting overfishing using simple indicators. Key functions include: FrequencyTable(): Calculate the frequency table from the collected and also the extract the length frequency data from the frequency table with the upper length_range. A numeric value specifying the bin width for class intervals. If not provided, the bin width is automatically calculated using Wang (2020) formula. FreqTM(): Creates a frequency distribution table for fish length data across multiple months using a consistent length class structure. The bin width is determined by either a custom value or Wang's formula, applied uniformly across all months. The function dynamically detects and renames columns to 'Month' and 'Length' from the input dataframe. The maximum observed length is included as part of the last class, with the upper bound set to the smallest multiple of the bin width greater than or equal to the maximum length. Months can be converted to dates using a configurable day and year, with dates assigned sequentially in 'day.month.year' format (e.g., 15.01.26). FishPar(): Calculates length-based indicators (LBIs) proposed by Froese (2004) such as the percentage of mature fish (Pmat), percentage of optimal length fish (Popt), percentage of mega spawners (Pmega), and the sum of these as Pobj. This function also estimates confidence intervals for different lengths, visualizes length frequency distributions, and provides data frames containing calculated values. FishSS(): Makes decisions based on input from Cope and Punt (2009) and parameters calculated by FishPar() (e.g., Pobj, Pmat, Popt, LM_ratio) to determine stock status as target spawning biomass (TSB40) and limit spawning biomass (LSB25), and selectivity. LWR(): Fits and visualizes length-weight relationships using linear regression, with options for log-transformation and customizable plotting. Package: r-cran-albopictus Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-albopictus_0.5-1.ca2404.1_all.deb Size: 124862 MD5sum: 968c1e0b978480d0ab327e924f9468a7 SHA1: 3de80dd445aec2ea7b4adab1b19157b59a2c5e72 SHA256: 2bd2a4b1037af1880b4f555f2a6ae60b3e4900127abb7bf3e4dcbf2048d36e91 SHA512: e61dae6372893c6f3ad68814770584a692499c6a0a435e2189b928c39622a8b7eb8ba35942ef10ca52f0b7810b44c433cc462d01d7bb114c1798d90ff4a56fc0 Homepage: https://cran.r-project.org/package=albopictus Description: CRAN Package 'albopictus' (Age-Structured Population Dynamics Model) Implements discrete time deterministic and stochastic age-structured population dynamics models described in Erguler and others (2016) and Erguler and others (2017) . Package: r-cran-alcoholsurv Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sensitivitymv Suggests: r-cran-survival, r-cran-itos, r-cran-coin Filename: pool/dists/noble/main/r-cran-alcoholsurv_0.7.2-1.ca2404.1_all.deb Size: 122178 MD5sum: f165fdafbf706896ce9ac0be3a61c4a4 SHA1: e48c4de192a5c1d5366cae1c082f8d3db9c4bb8c SHA256: e7de63cc917abde77b3eafdce219faa9edd5d6867e9475af5ef7cae1749b581c SHA512: 6271a2311d738217707b51e2485412eb86a6886630c496c7923159acdfa9e1602aae2a5608eb1c3ec9ee4f2b86464362025bcec80de7b83dd860edf1baaea4ee Homepage: https://cran.r-project.org/package=alcoholSurv Description: CRAN Package 'alcoholSurv' (Light Daily Alcohol and Longevity) Contains data from an observational study concerning possible effects of light daily alcohol consumption on survival and on HDL cholesterol. It also replicates various simple analyses in Rosenbaum (2025) . Finally, it includes new R code in wgtRankCef() that implements and replicates a new method for constructing evidence factors in observational block designs in Rosenbaum (2026) ). Package: r-cran-ald Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ald_1.3.1-1.ca2404.1_all.deb Size: 44386 MD5sum: 08767f2f48746bcd5df5f7a6b0cce6d1 SHA1: e6d8492c86502f19a28dcdb0cc41a712b36c5bde SHA256: f1d91b94e49ad54a95537ff71157f6268174f7b33d0653ad0bafc82aad993164 SHA512: c38e24a92285812460ba7d58f1a18577217d748cac3e56a8b2c83b83ce59e6ebfccea1922c5c07dd730362a3bff5d9968946fb3e7658572eaaed42b8141ac0fc Homepage: https://cran.r-project.org/package=ald Description: CRAN Package 'ald' (The Asymmetric Laplace Distribution) It provides the density, distribution function, quantile function, random number generator, likelihood function, moments and Maximum Likelihood estimators for a given sample, all this for the three parameter Asymmetric Laplace Distribution defined in Koenker and Machado (1999). This is a special case of the skewed family of distributions available in Galarza et.al. (2017) useful for quantile regression. Package: r-cran-aldex3 Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-lme4, r-cran-lmertest, r-cran-mass, r-cran-nlme, r-cran-abind, r-cran-matrixstats Suggests: r-cran-rbeta2009, r-cran-testthat, r-cran-lmtest, r-cran-sandwich, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aldex3_1.0.2-1.ca2404.1_all.deb Size: 137778 MD5sum: 69eb8819a0900735f3d9ed178d83951c SHA1: 1d1d38220b4ad7199008a5d3a545eb994e3b1eae SHA256: 88d49e51cf3cbed84bae20b20f9816e7888b7745a5326e52edfd5352a8815e05 SHA512: 2cff4ec6f47a2f7a6b2e043e748a1716ab34a123d30957f52992f29c9d270e2be55b991ea539b51c760c34bff4967b2f8d79f4f7f126aa992f3756e494f576ba Homepage: https://cran.r-project.org/package=ALDEx3 Description: CRAN Package 'ALDEx3' (Linear Models for Sequence Count Data) Provides scalable generalized linear and mixed effects models tailored for sequence count data analysis (e.g., analysis of 16S or RNA-seq data). Uses Dirichlet-multinomial sampling to quantify uncertainty in relative abundance or relative expression conditioned on observed count data. Implements scale models as a generalization of normalizations which account for uncertainty in scale (e.g., total abundances) as described in Nixon et al. (2025) and McGovern et al. (2025) . Package: r-cran-aldqr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hyperbolicdist, r-cran-sn Filename: pool/dists/noble/main/r-cran-aldqr_1.0-1.ca2404.1_all.deb Size: 40382 MD5sum: 81440d52e5b968eacaedf05f763d9382 SHA1: c3e4dd25ae632856254ce40e4be49d92d17f7ff5 SHA256: 811b723dcffc72e82919c835542d0737793cb44ac7f2a8476b67b212f798ab47 SHA512: d8db97e976e375fdc7b2ab731a4df6a42efdf934c8921ceb23e0954c88457719c1d08485db40e040659be43f23e920a9026a41dd9c405da79135d2029edf392c Homepage: https://cran.r-project.org/package=ALDqr Description: CRAN Package 'ALDqr' (Quantile Regression Using Asymmetric Laplace Distribution) EM algorithm for estimation of parameters and other methods in a quantile regression. Package: r-cran-aldvmm Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 781 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-checkmate, r-cran-optimx, r-cran-formula, r-cran-sandwich, r-cran-lmtest Suggests: r-cran-knitr, r-cran-kableextra, r-cran-markdown, r-cran-tinytex, r-cran-testthat, r-cran-covr, r-cran-rmarkdown, r-cran-bookdown, r-cran-xtable, r-cran-ggplot2, r-cran-scales, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-aldvmm_0.9.0-1.ca2404.1_all.deb Size: 419982 MD5sum: 59739319ece1c0a3768e7adec3c5b492 SHA1: 2e14a6024d3e1efd42564afa361ed245d447fda9 SHA256: 3840edffd7d811615c69f7199faecb85c953fb7f6d46b99f19d0c81c95b1ca09 SHA512: c1aa47bafe08d4402f4db05d8006ee270c4435a30dbea40ddcf17da1d695a57fd785cf165d70015f2aaa1ef547420e9d80e7287b2ac55fd8c3984647851e20cb Homepage: https://cran.r-project.org/package=aldvmm Description: CRAN Package 'aldvmm' (Adjusted Limited Dependent Variable Mixture Models) The goal of the package 'aldvmm' is to fit adjusted limited dependent variable mixture models of health state utilities. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package 'aldvmm' uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) using normal component distributions and a multinomial logit model of probabilities of component membership. Package: r-cran-ale Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2091 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-insight, r-cran-patchwork, r-cran-progressr, r-cran-purrr, r-cran-rlang, r-cran-s7, r-cran-staccuracy, r-cran-stringr, r-cran-tidyr, r-cran-univariateml Suggests: r-cran-knitr, r-cran-mgcv, r-cran-nnet, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-yaimpute Filename: pool/dists/noble/main/r-cran-ale_0.5.3-1.ca2404.1_all.deb Size: 1560486 MD5sum: 74d526aabf12d0399f3a32ca46cc72e4 SHA1: d47a1f85b1f5bf3bc1fadb7ad5aa61ecb163af0e SHA256: 8fbec4a28c2103a6087d30531f89351a42a531645026270c02652d9516509228 SHA512: 1b81ad91650d0150f3cc90b8ab73e606a43cfed8ac248d3aa2766920a7ed66b2745fa9e3b3dfc1c63dd9cd6643e0ed81a8e5861d7e7481bca50a84f8af8f6fa6 Homepage: https://cran.r-project.org/package=ale Description: CRAN Package 'ale' (Interpretable Machine Learning and Statistical Inference withAccumulated Local Effects (ALE)) Accumulated Local Effects (ALE) were initially developed as a model-agnostic approach for global explanations of the results of black-box machine learning algorithms. ALE has a key advantage over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values represent a clean functional decomposition of the model. As such, ALE values are not affected by the presence or absence of interactions among variables in a mode. Moreover, its computation is relatively rapid. This package reimplements the algorithms for calculating ALE data and develops highly interpretable visualizations for plotting these ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference. For more details, see Okoli, Chitu. 2023. “Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).” arXiv. . Package: r-cran-aleplot Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 824 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-yaimpute Suggests: r-cran-r.rsp, r-cran-nnet Filename: pool/dists/noble/main/r-cran-aleplot_1.1-1.ca2404.1_all.deb Size: 637314 MD5sum: b81f7c22ca8773df668ca07cc7a0b150 SHA1: 9e6afe2398837ac51bff639aec911f2135055ae4 SHA256: 037eff678f793064af88f362207709871335ec2c480ae2d812b982ceb9c2dd33 SHA512: 27102ac9e85a3585ac79c8c004552769076c8ae2a2aa304489fe8286eb7f07ab41672c3f3ac0b33f17f8828ae1eef6bb86a8a57a83c1bfec4c7d7dec61a16da9 Homepage: https://cran.r-project.org/package=ALEPlot Description: CRAN Package 'ALEPlot' (Accumulated Local Effects (ALE) Plots and Partial Dependence(PD) Plots) Visualizes the main effects of individual predictor variables and their second-order interaction effects in black-box supervised learning models. The package creates either Accumulated Local Effects (ALE) plots and/or Partial Dependence (PD) plots, given a fitted supervised learning model. Package: r-cran-alfq Architecture: all Version: 1.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-plyr, r-cran-caret, r-cran-seqinr, r-cran-lattice, r-cran-randomforest, r-cran-rocr, r-cran-reshape2, r-cran-bio3d Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-alfq_1.3.6-1.ca2404.1_all.deb Size: 264894 MD5sum: e89cc0a3be44c872fb47addf71ba800d SHA1: 0bd426dc89c2193402d7587c163f2a012f3c0e1c SHA256: 1b01a534926e1cb00e93a788200ee5d5084bf0128c0b52aa7eb7cd6f65171a86 SHA512: 458d6b7bf298f06f7ffc2675c6d9e93540a66b4f75c41eabf55ec1bbaa25970f5e057728e7973dc685a5f5c70629b37249cda6d8647ccec6c039f844a7e19b3b Homepage: https://cran.r-project.org/package=aLFQ Description: CRAN Package 'aLFQ' (Estimating Absolute Protein Quantities from Label-Free LC-MS/MSProteomics Data) Determination of absolute protein quantities is necessary for multiple applications, such as mechanistic modeling of biological systems. Quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. To estimate absolute quantitative information using these relative abundance measurements requires additional information such as heavy-labeled references of known concentration. Multiple methods have been using different references and strategies; some are easily available whereas others require more effort on the users end. Hence, we believe the field might benefit from making some of these methods available under an automated framework, which also facilitates validation of the chosen strategy. We have implemented the most commonly used absolute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on either MS1-, MS2-levels or spectral counts together with validation algorithms to enable automated data analysis and error estimation. Specifically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputation of proteome-wide absolute protein quantity estimation. Our open-source software is written in the statistical programming language R and validated and demonstrated on a synthetic sample. Package: r-cran-alfr Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-stringr Suggests: r-cran-devtools, r-cran-httptest, r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-remotes, r-cran-spelling, r-cran-fs Filename: pool/dists/noble/main/r-cran-alfr_1.2.1-1.ca2404.1_all.deb Size: 52416 MD5sum: f7e52355e1e3f88d5c489ebb881eebcf SHA1: a3a7139336123de2b96539309b5372e04e18da9a SHA256: f92c9fd52dd47f397c43cb06c5b424555c9f88d98dd6aa3da664a237c9f802c3 SHA512: 810d8578a94923869cb544c5956fe27f1d052d4fc2b9b92f76a0fe16da412272cf368b58644d986ce6f09ed80a9fa0bc3d94a81f3be68e43f89d81252cc09d26 Homepage: https://cran.r-project.org/package=alfr Description: CRAN Package 'alfr' (Connectivity to 'Alfresco' Content Management Repositories) Allows you to connect to an 'Alfresco' content management repository and interact with its contents using simple and intuitive functions. You will be able to establish a connection session to the 'Alfresco' repository, read and upload content and manage folder hierarchies. For more details on the 'Alfresco' content management repository see . Package: r-cran-alfred Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyselect, r-cran-tidyr, r-cran-tibble, r-cran-magrittr, r-cran-lubridate, r-cran-jsonlite, r-cran-rlang, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-covr Filename: pool/dists/noble/main/r-cran-alfred_0.2.1-1.ca2404.1_all.deb Size: 19872 MD5sum: 6be84bf4da1e14ffc2a9add995b18f3a SHA1: 5f199ce0411f22f4d4ec17f38c04e37df137837b SHA256: c3c061bc3d872aee24f32844882cee9d7323ab8bc10263d08f14535397e0c31b SHA512: b7ac4f9a02ee3cab9f93e018712054250ff1f0f8bfbdce4c4ad79dc44efac103e50fa0e4e44d55564f4ec98b4850ca37c041134be81994d927b478c5c8bb0c0a Homepage: https://cran.r-project.org/package=alfred Description: CRAN Package 'alfred' (Downloading Time Series from ALFRED Database for VariousVintages) Provides direct access to the ALFRED () and FRED () databases. Its functions return tidy data frames for different releases of the specified time series. Note that this product uses the FRED© API but is not endorsed or certified by the Federal Reserve Bank of St. Louis. Package: r-cran-algaeclassify Architecture: all Version: 2.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lubridate, r-cran-curl, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-algaeclassify_2.0.6-1.ca2404.1_all.deb Size: 238646 MD5sum: 64bd544211aa5231f37af93e9e87ec3c SHA1: 961ea8d73b8c6fc46f1e2d47aa44aed496417a75 SHA256: 5bdf581d9e554dc1e7c3dbf591bad7ef61094e398ac04ba6bd6f156fe88fe63a SHA512: 42e79bf288697278c7b9d7785834cf633fbab10d134398ca06bdddce5529e535a5df133037a922044de644be205bd268d39329ed434c4f2411f22b792b84446f Homepage: https://cran.r-project.org/package=algaeClassify Description: CRAN Package 'algaeClassify' (Tools to Query the 'Algaebase' Online Database, StandardizePhytoplankton Taxonomic Data, and Perform Functional GroupClassifications) Functions that facilitate the use of accepted taxonomic nomenclature, collection of functional trait data, and assignment of functional group classifications to phytoplankton species. Possible classifications include Morpho-functional group (MFG; Salmaso et al. 2015 ) and CSR (Reynolds 1988; Functional morphology and the adaptive strategies of phytoplankton. In C.D. Sandgren (ed). Growth and reproductive strategies of freshwater phytoplankton, 388-433. Cambridge University Press, New York). Versions 2.0.0 and later includes new functions for querying the 'algaebase' online taxonomic database (www.algaebase.org), however these functions require a valid API key that must be acquired from the 'algaebase' administrators. Note that none of the 'algaeClassify' authors are affiliated with 'algaebase' in any way. Taxonomic names can also be checked against a variety of taxonomic databases using the 'Global Names Resolver' service via its API (). In addition, currently accepted and outdated synonyms, and higher taxonomy, can be extracted for lists of species from the 'ITIS' database via its JSON web service API. The 'algaeClassify' package is a product of the GEISHA (Global Evaluation of the Impacts of Storms on freshwater Habitat and Structure of phytoplankton Assemblages), funded by CESAB (Centre for Synthesis and Analysis of Biodiversity) and the U.S. Geological Survey John Wesley Powell Center for Synthesis and Analysis, with data and other support provided by members of GLEON (Global Lake Ecology Observation Network). DISCLAIMER: This software has been approved for release by the U.S. Geological Survey (USGS). Although the software has been subjected to rigorous review, the USGS reserves the right to update the software as needed pursuant to further analysis and review. No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. Furthermore, the software is released on condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use. Package: r-cran-algebraic.dist Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-algebraic.dist_1.0.0-1.ca2404.1_all.deb Size: 863086 MD5sum: db3d4690178c2fb993ee0aaca769f256 SHA1: 535a41ea948917308bb955b80972283a349e2a19 SHA256: 8a537e2caa5c88986e1186cb8ba2d3196815a8b50104f7b3783980b113a14d46 SHA512: b6fe615db410b9e11b01a433c9c4680af03fa798a9f190adeec450acc783694d91bee2eec5c2c3ed6c1d19c4b8d3bda6d1729f1615fb0b5e2b7d61694335359f Homepage: https://cran.r-project.org/package=algebraic.dist Description: CRAN Package 'algebraic.dist' (Algebra over Probability Distributions) Provides an algebra over probability distributions enabling composition, sampling, and automatic simplification to closed forms. Supports normal, exponential, gamma, Weibull, chi-squared, uniform, beta, log-normal, Poisson, multivariate normal, empirical, and mixture distributions with algebraic operators (addition, subtraction, multiplication, division, power, exp, log, min, max) that automatically simplify when mathematical identities apply. Includes closed-form MVN conditioning (Schur complement), affine transformations, mixture marginals/conditionals (Bayes rule), and limiting distribution builders (CLT, LLN, delta method). Uses S3 classes for distributions and R6 for support objects. Package: r-cran-algebraic.mle Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 923 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-algebraic.dist, r-cran-boot, r-cran-mvtnorm, r-cran-mass, r-cran-numderiv Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-tibble, r-cran-cdft Filename: pool/dists/noble/main/r-cran-algebraic.mle_2.0.2-1.ca2404.1_all.deb Size: 480302 MD5sum: 71faf9b5970b66915ff6882493efaadb SHA1: a5a3d53b9991697f38f0f71b1b00891724ac1807 SHA256: c2f312cefde8cefaa95666f47c5d09f4a39f9521a853635138462d46d6ce9d7d SHA512: 094a811cba7fde3da2b0da2ef00d94cf38a92a554f722607f3e8337e13fc93e22b90c39fde7dcddadba70ef569de9ad4f7e407a84629d5292ed614ad18e55fac Homepage: https://cran.r-project.org/package=algebraic.mle Description: CRAN Package 'algebraic.mle' (Algebraic Maximum Likelihood Estimators) The maximum likelihood estimator (MLE) is a technology: under regularity conditions, any MLE is asymptotically normal with variance given by the inverse Fisher information. This package exploits that structure by defining an algebra over MLEs. Compose independent estimators into joint MLEs via block-diagonal covariance ('joint'), optimally combine repeated estimates via inverse-variance weighting ('combine'), propagate transformations via the delta method ('rmap'), and bridge to distribution algebra via conversion to normal or multivariate normal objects ('as_dist'). Supports asymptotic ('mle', 'mle_numerical') and bootstrap ('mle_boot') estimators with a unified interface for inference: confidence intervals, standard errors, AIC, Fisher information, and predictive intervals. For background on maximum likelihood estimation, see Casella and Berger (2002, ISBN:978-0534243128). For the delta method and variance estimation, see Lehmann and Casella (1998, ISBN:978-0387985022). Package: r-cran-algebraichaplopackage Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-algebraichaplopackage_1.2-1.ca2404.1_all.deb Size: 89112 MD5sum: 9bb600bd4bd02d45f32327b0776745bb SHA1: 0cd719690c7149709942e01bd12f4794d41088bb SHA256: 2622c4333cff144161d3ca310fa2e3316e6e631a74053ddd08ee6e01b6ea9e08 SHA512: 31e7e174e674a8acce8efc2160f575b83d740f4faf2a4106f25ca628c79e28201046f97a0dfe4de7d79d47fd174c0a3e7d2442a82c55a9c4b2ba9eb6a311fea2 Homepage: https://cran.r-project.org/package=AlgebraicHaploPackage Description: CRAN Package 'AlgebraicHaploPackage' (Haplotype Two Snips Out of a Paired Group of Patients) Two unordered pairs of data of two different snips positions is haplotyped by resolving a small number ob closed equations. Package: r-cran-algeriapis Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-algeriapis_0.1.0-1.ca2404.1_all.deb Size: 304376 MD5sum: 126bd1a7fcfd793bf765b19283adc742 SHA1: e1d33723f5ef08bf8f766f7a468a9a382b0a9609 SHA256: 22b7f28e1c87f482388be8f9bd6373dfe72fdce1510e0239e0463a22fc71ab61 SHA512: e93b3f22a6b0e3e71515f04e63659fd5035069c548eb1b639cd34253b245cb4a4de895d248b003d64764e686871333e3c0e4dc1f2ee3b48cac1c0098fcb5a1e1 Homepage: https://cran.r-project.org/package=AlgeriAPIs Description: CRAN Package 'AlgeriAPIs' (Access Algerian Data via Public APIs) Provides functions to access data from public RESTful APIs including 'World Bank API' and 'REST Countries API', retrieving real-time or historical information related to Algeria. The package enables users to query economic indicators and international demographic and geopolitical statistics in a reproducible way. It is designed for researchers, analysts, and developers who require reliable and programmatic access to Algerian data through established APIs. For more information on the APIs, see: 'World Bank API' and 'REST Countries API' . Package: r-cran-algo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-htmltools, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-algo_0.1.0-1.ca2404.1_all.deb Size: 17912 MD5sum: 6ad99d6e1d9c999d0c944317f7ac92c4 SHA1: 1dc60d9d67a3edfcf38399ee053712e758e6bf25 SHA256: 005bca3156301e63a58b42a9824b8c4753d56aac0b4de43b8a88a38d849dbb2a SHA512: 738fc2691504763c7bc77776aa8ef2b4d30ace4f6eeb57b3b5b35ad2bfb7947f515c2fa10af942046ede2c3155fec1384e2191799a95bbec368524d43e4908b2 Homepage: https://cran.r-project.org/package=algo Description: CRAN Package 'algo' (Implement an Address Search Auto Completion Menu on 'Shiny' TextInputs Using the 'Algolia Places' 'Javascript' Library) Allows the user to implement an address search auto completion menu on 'shiny' text inputs. This is done using the 'Algolia Places' 'JavaScript' library. See . Package: r-cran-algorithmia Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64enc, r-cran-httr, r-cran-rjson Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-runit Filename: pool/dists/noble/main/r-cran-algorithmia_0.3.0-1.ca2404.1_all.deb Size: 224148 MD5sum: ed4732808bcb8de87f97061c7ba30ce4 SHA1: 5f1ca08a846ed936a8156a8d6f723fa49617a054 SHA256: 67e18f6f26c9b3c6071448a1853d9887ed8399c8ac6c44c7c23ac9783286ebfe SHA512: d9f61141fea1bb586cad2c2784a730e36842e5141b69eb802d0e515d89591865994516df86a4fd18e81e2c693d50072a02250a378371a6c1f697d5ff8eca9bc4 Homepage: https://cran.r-project.org/package=algorithmia Description: CRAN Package 'algorithmia' (Allows you to Easily Interact with the Algorithmia Platform) The company, Algorithmia, houses the largest marketplace of online algorithms. This package essentially holds a bunch of REST wrappers that make it very easy to call algorithms in the Algorithmia platform and access files and directories in the Algorithmia data API. To learn more about the services they offer and the algorithms in the platform visit . More information for developers can be found at . Package: r-cran-aliases2entrez Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2011 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-bioc-limma, r-bioc-org.hs.eg.db, r-bioc-annotationdbi, r-cran-foreach, r-cran-readr, r-cran-rcurl Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-aliases2entrez_0.1.2-1.ca2404.1_all.deb Size: 610336 MD5sum: c5a9c3e90c23cc7c23779c228cfeed20 SHA1: 740df57250b539741ac8270a32d9be7f7aa55036 SHA256: 0d2678eef85d6a947017a4d55c772d8829f69bcfc76f956455235c2b0f041850 SHA512: 85ed4f7c63e7d469ea07f85d61af48bc95c98edef9755ae5d1439a6fe0d03b583b5e180e1e2a7bd5b0d77541207828b2c17e2c120b1dd765aff4327ccad00602 Homepage: https://cran.r-project.org/package=aliases2entrez Description: CRAN Package 'aliases2entrez' (Converts Human gene symbols to entrez IDs) Queries multiple resources authors HGNC (2019) , authors limma (2015) to find the correspondence between evolving nomenclature of human gene symbols, aliases, previous symbols or synonyms with stable, curated gene entrezID from NCBI database. This allows fast, accurate and up-to-date correspondence between human gene expression datasets from various date and platform (e.g: gene symbol: BRCA1 - ID: 672). Package: r-cran-alien Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-helpersmg, r-cran-rlang, r-cran-tidyr Suggests: r-cran-bh, r-cran-knitr, r-cran-purrr, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppparallel, r-cran-rmarkdown, r-cran-rstan, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-alien_1.0.2-1.ca2404.1_all.deb Size: 138984 MD5sum: a064f50235cc1ee77a6e767993e42290 SHA1: 702ba2b109b73a4ba3caf2e23b401ec0b0397a28 SHA256: f574decbd8702312d0497c0640ee9d7994b7d4c0875c40b09b3312c332855c4f SHA512: aed5566a597434c0935686499dfc7d61e056d7b63a16459308ee6dcb1248847d1f434e711c56e673477b01180ab846dfab3368fa044d3fe32c7b16d90a2c56c8 Homepage: https://cran.r-project.org/package=alien Description: CRAN Package 'alien' (Estimate Invasive and Alien Species (IAS) Introduction Rates) Easily estimate the introduction rates of alien species given first records data. It specializes in addressing the role of sampling on the pattern of discoveries, thus providing better estimates than using Generalized Linear Models which assume perfect immediate detection of newly introduced species. Package: r-cran-align Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matlab Filename: pool/dists/noble/main/r-cran-align_0.1.0-1.ca2404.1_all.deb Size: 30030 MD5sum: 2f18d270f72a472c3ec50c0fc6f5e528 SHA1: 9b5c19c33d20e9fcb2b6695c46e93948e23ed7f3 SHA256: 66b151a854ca5ee547c0c451937d45902430212dbb86376ad308f9acef2ba826 SHA512: c94a2407d052e8f1a327168eba2f9f0f88144d35198f7e5a36d3b9e007ff8be6f1afb21a38b5299968fae8bc57b81f576d6911a5a93346ba321f47ce79f17438 Homepage: https://cran.r-project.org/package=align Description: CRAN Package 'align' (A Modified DTW Algorithm for Stratigraphic Time Series Alignment) A dynamic time warping (DTW) algorithm for stratigraphic alignment, translated into R from the original published 'MATLAB' code by Hay et al. (2019) . The DTW algorithm incorporates two geologically relevant parameters (g and edge) for augmenting the typical DTW cost matrix, allowing for a range of sedimentologic and chronologic conditions to be explored, as well as the generation of an alignment library (as opposed to a single alignment solution). The g parameter relates to the relative sediment accumulation rate between the two time series records, while the edge parameter relates to the amount of total shared time between the records. Note that this algorithm is used for all DTW alignments in the Align Shiny application, detailed in Hagen et al. (in review). Package: r-cran-alignlv Architecture: all Version: 0.1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-mirt, r-cran-lavaan, r-cran-magrittr, r-cran-purrr, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-rlang Suggests: r-cran-dorng, r-cran-doparallel, r-cran-foreach, r-cran-testthat Filename: pool/dists/noble/main/r-cran-alignlv_0.1.0.0-1.ca2404.1_all.deb Size: 91606 MD5sum: 41343bdbd5220c04706a2782e327cb3b SHA1: 6f81aa631ac7f7a35a7cae9509889b4cc5803c86 SHA256: 49acca06c3d4f095bcbe648cd3fdec83aa97959ee7eeb88e1355e40679dded1e SHA512: ab6ee2060af0c6bf91cf1c24e9a5f6ebe9d97bf95fd7e53145586d2580fb7627a1369f03ad89dfb2661d75800467e84ccef7b40e54841b949fe76a7dc7eeabf1 Homepage: https://cran.r-project.org/package=AlignLV Description: CRAN Package 'AlignLV' (Multiple Group Item Response Theory Alignment Helpers for'lavaan' and 'mirt') Allows for multiple group item response theory alignment a la 'Mplus' to be applied to lists of single-group models estimated in 'lavaan' or 'mirt'. Allows item sets that are overlapping but not identical, facilitating alignment in secondary data analysis where not all items may be shared across assessments. Package: r-cran-alkahest Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1324 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-markdown, r-cran-matrix, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-alkahest_1.3.0-1.ca2404.1_all.deb Size: 1067052 MD5sum: 1c4189a6c565fcf83127dafa7ca2069d SHA1: c7875e1134a9309421ae2326b2f297a044f46627 SHA256: f6e7427b2d206b6c580fceb79136bed72435b35e3e9e9aa5642fc44bbf5085c9 SHA512: 7ed658fec657fba209e4676ddd00b45bb60abd5c7f5ac50c75494c42198ba83e3d66b676fce361a5ca9155dbbd1137fae794645b0b711f0ee0f4d7e13a09fea1 Homepage: https://cran.r-project.org/package=alkahest Description: CRAN Package 'alkahest' (Pre-Processing XY Data from Experimental Methods) A lightweight, dependency-free toolbox for pre-processing XY data from experimental methods (i.e. any signal that can be measured along a continuous variable). This package provides methods for baseline estimation and correction, smoothing, normalization, integration and peaks detection. Baseline correction methods includes polynomial fitting as described in Lieber and Mahadevan-Jansen (2003) , Rolling Ball algorithm after Kneen and Annegarn (1996) , SNIP algorithm after Ryan et al. (1988) , 4S Peak Filling after Liland (2015) and more. Package: r-cran-allcontributors Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1534 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clipr, r-cran-curl, r-cran-gert, r-cran-gh, r-cran-gitcreds, r-cran-magrittr Suggests: r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-allcontributors_0.2.3-1.ca2404.1_all.deb Size: 482140 MD5sum: 00580bbf8baf91c6ef98555358e961ed SHA1: f25a41bf7cf7db8677ecbea5140b9c5cbac51ad4 SHA256: a6e7dd9efe131b0e61d202f7405a6d62aeaed6e28f9a477309a9e06baf26d801 SHA512: e8578c8f15021596c217b2aa2fe995d0642e3237a0121211cd11883bdbd1cce21cb0c4fec6cfc8c9b9e9eb2d3c1e6137674d0620de662400018047e32b0efdd7 Homepage: https://cran.r-project.org/package=allcontributors Description: CRAN Package 'allcontributors' (Acknowledge all Contributors to a Project) Acknowledge all contributors to a project via a single function call. The function appends to a 'README' or other specified file(s) a table with names of all individuals who contributed via code or repository issues. The package also includes several additional functions to extract and quantify contributions to any repository. Package: r-cran-allehap Architecture: all Version: 0.9.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-allehap_0.9.9-1.ca2404.1_all.deb Size: 580246 MD5sum: 6db25b72a2ac85e29558831c0a208ee6 SHA1: 656da90f908cec3108a0e6350e09955aef7a4ad4 SHA256: 96e816176793d68afeed7d4de66dc054e24e931aee2127a5c164d6ac559b60d6 SHA512: d9f008dbe21b4083f591a1454d00656c77e5560e61bf4f55d24f6fc0c76c4c1e3f491295a9bb69d0097c8faeacecc27a6759dd867c5f0ab20ec0f642f31ad817 Homepage: https://cran.r-project.org/package=alleHap Description: CRAN Package 'alleHap' (Allele Imputation and Haplotype Reconstruction from PedigreeDatabases) Tools to simulate alphanumeric alleles, impute genetic missing data and reconstruct non-recombinant haplotypes from pedigree databases in a deterministic way. Allelic simulations can be implemented taking into account many factors (such as number of families, markers, alleles per marker, probability and proportion of missing genotypes, recombination rate, etc). Genotype imputation can be used with simulated datasets or real databases (previously loaded in .ped format). Haplotype reconstruction can be carried out even with missing data, since the program firstly imputes each family genotype (without a reference panel), to later reconstruct the corresponding haplotypes for each family member. All this considering that each individual (due to meiosis) should unequivocally have two alleles per marker (one inherited from each parent) and thus imputation and reconstruction results can be deterministically calculated. Package: r-cran-allelematch Architecture: all Version: 2.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dynamictreecut Filename: pool/dists/noble/main/r-cran-allelematch_2.5.5-1.ca2404.1_all.deb Size: 212324 MD5sum: ef99add5a03ebe4be800013dc392909d SHA1: 8b0ce7f2ead1aea410deba56d9e8741554fbc0ab SHA256: 19d50b0a9d8062c4e1b2d53e01336fc993a24f511f09d14400a5e97642c0b357 SHA512: 2fc402240758184b1815d0b195801ff172c92117753ecf9e9a9dee54b82a9ebe6c35e681c4f86d29d6bcfdbf78d39f0ce38923bb3169734ffdbf5808b0880c87 Homepage: https://cran.r-project.org/package=allelematch Description: CRAN Package 'allelematch' (Identifying Unique Multilocus Genotypes where Genotyping Errorand Missing Data may be Present) Tools for the identification of unique of multilocus genotypes when both genotyping error and missing data may be present; targeted for use with large datasets and databases containing multiple samples of each individual (a common situation in conservation genetics, particularly in non-invasive wildlife sampling applications). Functions explicitly incorporate missing data and can tolerate allele mismatches created by genotyping error. If you use this package, please cite the original publication in Molecular Ecology Resources (Galpern et al., 2012), the details for which can be generated using citation('allelematch'). For a complete vignette, please access via the Data S1 Supplementary documentation and tutorials (PDF) located at . Package: r-cran-alleleretain Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-pedigree Filename: pool/dists/noble/main/r-cran-alleleretain_2.0.2-1.ca2404.1_all.deb Size: 350192 MD5sum: e3e5804ded9f7eaa1221a6374e96933d SHA1: f158af77fcb7e78be6fc1e23d61126fab79d2662 SHA256: 34e3504ba27ffb0eaa45404bd87bdd01fead8628e1cccc8722ce871076f2ea60 SHA512: dc45c467227d494e5130a0e3dc30bf8766f8a1b4b7c2ecb836d90bcd76a8e1cfdc1e1c466fa4fb225251c853a861238543bb385da7795ca5b8b74b62aeff68bf Homepage: https://cran.r-project.org/package=AlleleRetain Description: CRAN Package 'AlleleRetain' (Allele Retention, Inbreeding, and Demography) Simulate the effect of management or demography on allele retention and inbreeding accumulation in bottlenecked populations of animals with overlapping generations. Package: r-cran-alleleshift Architecture: all Version: 1.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 690 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-biodiversityr, r-cran-adegenet Suggests: r-cran-poppr, r-cran-mgcv, r-cran-dplyr, r-cran-ggplot2, r-cran-ggally, r-cran-ggforce, r-cran-ggrepel, r-cran-ggsci, r-cran-gggibbous, r-cran-gganimate, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-alleleshift_1.1-3-1.ca2404.1_all.deb Size: 573126 MD5sum: b8b2c717cc3cc56fb625b97f10dfad10 SHA1: 2c46d7b383bbf2f2c07bf9dc576b39f1602c62f6 SHA256: fdafba92e548f5c42df22ca107ccf9cc0d674b2db6c0c029969a2fe894fdeed8 SHA512: 700c8becae3527731626830b0271a7dba266656ebe2d2cb3eb1215fcb27d44db5bf1076322fd1fdd7085237df3ae757278522c1b4f4d237e46dee0c29068937f Homepage: https://cran.r-project.org/package=AlleleShift Description: CRAN Package 'AlleleShift' (Predict and Visualize Population-Level Changes in AlleleFrequencies in Response to Climate Change) Methods () are provided of calibrating and predicting shifts in allele frequencies through redundancy analysis ('vegan::rda()') and generalized additive models ('mgcv::gam()'). Visualization functions for predicted changes in allele frequencies include 'shift.dot.ggplot()', 'shift.pie.ggplot()', 'shift.moon.ggplot()', 'shift.waffle.ggplot()' and 'shift.surf.ggplot()' that are made with input data sets that are prepared by helper functions for each visualization method. Examples in the documentation show how to prepare animated climate change graphics through a time series with the 'gganimate' package. Function 'amova.rda()' shows how Analysis of Molecular Variance can be directly conducted with the results from redundancy analysis. Package: r-cran-allestimates Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-ggplot2, r-cran-survival, r-cran-tidyr, r-cran-stringr, r-cran-dplyr Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-allestimates_0.2.3-1.ca2404.1_all.deb Size: 272868 MD5sum: 5fba0ea9a71a2cf84220d0ff682c815b SHA1: 3dcb344a1b49d0e15d4180ad2c9b093f90149c46 SHA256: 99af8fea7e960b22c5618c8e1498027f00e02757c538c6177925f2c6552c7f25 SHA512: 354f40a0f2a1c976bd9b4f03bc7cf4df5acee74b2d8fc384d698a2e137baadb251c57ef951ae83469ab66f7d4475a75fd4777be89b8545e1be09aeba00a49f8e Homepage: https://cran.r-project.org/package=allestimates Description: CRAN Package 'allestimates' (Effect Estimates from All Models) Estimates and plots effect estimates from models with all possible combinations of a list of variables. It can be used for assessing treatment effects in clinical trials or risk factors in bio-medical and epidemiological research. Like Stata command 'confall' (Wang Z (2007) ), 'allestimates' calculates and stores all effect estimates, and plots them against p values or Akaike information criterion (AIC) values. It currently has functions for linear regression: all_lm(), logistic and Poisson regression: all_glm(), and Cox proportional hazards regression: all_cox(). Package: r-cran-allmetrics Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-allmetrics_0.2.1-1.ca2404.1_all.deb Size: 12868 MD5sum: b284699626eac9995ba60537c964fff7 SHA1: 78146cd05593347a15fbdf9a977311d7525fb0a2 SHA256: fd5623bfa487d174cb84c3a7f75826a548cf7fa151ecae17f12c2830d3b7fb0b SHA512: 27f2094709816162f1b895819e3ba552b010a6125389547492088c40925e69e4783f0d5381f806f824a528aa657819062dff7996c7955dcf96b2774f007efd00 Homepage: https://cran.r-project.org/package=AllMetrics Description: CRAN Package 'AllMetrics' (Calculating Multiple Performance Metrics of a Prediction Model) Provides a function to calculate multiple performance metrics for actual and predicted values. In total eight metrics will be calculated for particular actual and predicted series. Helps to describe a Statistical model's performance in predicting a data. Also helps to compare various models' performance. The metrics are Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), Mean absolute Error (MAE), Mean absolute percentage error (MAPE), Mean Absolute Scaled Error (MASE), Nash-Sutcliffe Efficiency (NSE), Willmott’s Index (WI), and Legates and McCabe Index (LME). Among them, first five are expected to be lesser whereas, the last three are greater the better. More details can be found from Garai and Paul (2023) and Garai et al. (2024) . Package: r-cran-allmt Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1588 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-htmltable, r-cran-plyr, r-cran-readxl, r-cran-reshape2, r-cran-rio, r-cran-scales, r-cran-stringr, r-cran-survival, r-cran-survminer, r-cran-tibble Suggests: r-cran-bibtex, r-cran-bookdown, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-allmt_0.1.1-1.ca2404.1_all.deb Size: 480034 MD5sum: cf6b8766eb59f3b0521a1be86124dd19 SHA1: a4e782bc4683479897df4c9097c90c3af7620b5f SHA256: a4913704e769b51cf5d16fb13d30e5987e430f70b05f66d613251ae587f94a02 SHA512: 67b566f08579b8555568cbf342e5d702f7dcfeba2c4682196b5ce9ee94f4c56c19cbbec14d65a54d84cb90ac8684cdb591f03ae76874eb2d3b8cb6401cbbcfa3 Homepage: https://cran.r-project.org/package=allMT Description: CRAN Package 'allMT' (Acute Lymphoblastic Leukemia Maintenance Therapy Analysis) Evaluates acute lymphoblastic leukemia maintenance therapy practice at patient and cohort level. Package: r-cran-allocation Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmpfr Suggests: r-cran-quarto Filename: pool/dists/noble/main/r-cran-allocation_0.1.0-1.ca2404.1_all.deb Size: 107228 MD5sum: f864f9b6f67f7e11db540706782bc3b6 SHA1: 8c2fc696b9115795e3aaa5bf945b389ff43c9559 SHA256: a4015d4155f279366c715f39fd812c2ba444d72b20200bee118a2fec8a129b52 SHA512: ada0f232d7a78defd763055f3972577b22501be149447b75265c52e36f60247da9acd5cadd014f9969c7eddcba572221942436073cad9b396e95c48afce020af Homepage: https://cran.r-project.org/package=allocation Description: CRAN Package 'allocation' (Exact Optimal Allocation Algorithms for Stratified Sampling) Implements several exact methods for allocating optimal sample sizes when designing stratified samples. These methods are discussed in Wright (2012) and Wright (2017) . Package: r-cran-allofus Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1575 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-tidyr, r-cran-magrittr, r-cran-dplyr, r-cran-glue, r-cran-bigrquery, r-cran-purrr, r-cran-dbplyr, r-cran-sessioninfo, r-cran-rlang, r-cran-stringr, r-cran-dbi, r-cran-lifecycle, r-cran-bit64 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-kableextra, r-cran-dt, r-cran-googlesheets4, r-cran-tibble, r-cran-forcats, r-cran-gh, r-cran-sqlrender Filename: pool/dists/noble/main/r-cran-allofus_1.2.0-1.ca2404.1_all.deb Size: 1004762 MD5sum: 0efb7d8377fcbf76017a4b3569866d12 SHA1: 5b0b192fe3fea250c631678d5821be6654a1a93f SHA256: f182ba2ce6da22b2b230626ca8195f43b293f1ec757d82be7230ab08ecc1786d SHA512: 34fd645e1ac1df3f3530adb2a733c6e5451b7cdd8b7efa82e7faf3bfb3fa0bd1f0b9f5fb0e1f3043a6f7f3186650f119f57a1e82975df975442e6c2646b35577 Homepage: https://cran.r-project.org/package=allofus Description: CRAN Package 'allofus' (Interface for 'All of Us' Researcher Workbench) Streamline use of the 'All of Us' Researcher Workbench ()with tools to extract and manipulate data from the 'All of Us' database. Increase interoperability with the Observational Health Data Science and Informatics ('OHDSI') tool stack by decreasing reliance of 'All of Us' tools and allowing for cohort creation via 'Atlas'. Improve reproducible and transparent research using 'All of Us'. Package: r-cran-allometric Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-units, r-cran-refmanager, r-cran-magrittr, r-cran-purrr, r-cran-isocodes, r-cran-tidyr, r-cran-progress, r-cran-vctrs, r-cran-openssl, r-cran-curl, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-allometric_2.3.0-1.ca2404.1_all.deb Size: 498448 MD5sum: 870ba4a420ee3da204c47186a984fd65 SHA1: 3ea4895618d3cd316ca4653d86965d54d0578400 SHA256: 844bb8eb4d671f44e68fc9affbfdaf2c72110e91dc7948e1f2a45c3b7c71c403 SHA512: f0e381082da69760cad0f158b4b8f64414b1283648bf0b50484d5452379905117d2e5d9569660b64a5ad81fc990e15264ede7a4359977f61fc69a6edbbc478da Homepage: https://cran.r-project.org/package=allometric Description: CRAN Package 'allometric' (Structured Allometric Models for Trees) Access allometric models used in forest resource analysis, such as volume equations, taper equations, biomass models, among many others. Users are able to efficiently find and select allometric models suitable for their project area and use them in analysis. Additionally, 'allometric' provides a structured framework for adding new models to an open-source models repository. Package: r-cran-allometry Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-allometry_0.1.1-1.ca2404.1_all.deb Size: 15706 MD5sum: 2397d0bb911fd9b402303dfa02a8e498 SHA1: 775db594549db0397e4c4392ec05356c88dbfab7 SHA256: 9ee668bc96ef5c49bbf458c0396e7bb6b486f0781a71f3b400a18289b77c834b SHA512: ec77df0b75bed6baf66741b1bd7e80cc5277ef435ace4483363283350646032c70ca759afeb9392406a795e6bfb79005c31fa29c388400df2e265dc8a7554e25 Homepage: https://cran.r-project.org/package=allometry Description: CRAN Package 'allometry' (Examples of Datasets on Allometry) Examples of datasets on allometry, the study of the relationship of biological traits to body size. This package contains the dataset of morphological measurement taken from 113 maritime earwigs (Anisolabis maritima) by Matsuzawa and Konuma (2025) . Package: r-cran-allomr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-allomr_0.3.0-1.ca2404.1_all.deb Size: 15460 MD5sum: 8fc0205e201a3a466ed02a2ec4788033 SHA1: 0a382d2cea1545d39bf7c1777d3f1f3ef699aed3 SHA256: 45bfb6c35501d6a918dff6169ae7014df4f1ac7f896f66b6ffdd2610c371079e SHA512: e1ea6db7af6b93bbe96be6881168325e39ce85684999e6db96c4a645df61166f87f5168780c2544124eea771448abad0bd73841c8fa9e88282ea9ba06edd6788 Homepage: https://cran.r-project.org/package=allomr Description: CRAN Package 'allomr' (Removing Allometric Effects of Body Size in MorphologicalAnalysis) Implementation of the technique of Lleonart et al. (2000) to scale body measurements that exhibit an allometric growth. This procedure is a theoretical generalization of the technique used by Thorpe (1975) and Thorpe (1976) . Package: r-cran-allspice Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4373 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-allspice_1.0.7-1.ca2404.1_all.deb Size: 1526424 MD5sum: e861c0ca6cadedeae92c6fb21598b2a0 SHA1: 27df6e240e76509aee6fe5e7cc9c868952847f06 SHA256: 910a3ea0d8fdcd508a5f362c766b05a23b5e23ef37fcec351f6915db95f08a2e SHA512: bce92fdfb7a7462b12b893098a85c5bee328be509a939b9f206a374735eb597007e2aef4d5424857a1cea6dd165d63b3f8abf9ed2d2f2f08992f53cf55c18155 Homepage: https://cran.r-project.org/package=Allspice Description: CRAN Package 'Allspice' (RNA-Seq Profile Classifier) We developed a lightweight machine learning tool for RNA profiling of acute lymphoblastic leukemia (ALL), however, it can be used for any problem where multiple classes need to be identified from multi-dimensional data. The methodology is described in Makinen V-P, Rehn J, Breen J, Yeung D, White DL (2022) Multi-cohort transcriptomic subtyping of B-cell acute lymphoblastic leukemia, International Journal of Molecular Sciences 23:4574, . The classifier contains optimized mean profiles of the classes (centroids) as observed in the training data, and new samples are matched to these centroids using the shortest Euclidean distance. Centroids derived from a dataset of 1,598 ALL patients are included, but users can train the models with their own data as well. The output includes both numerical and visual presentations of the classification results. Samples with mixed features from multiple classes or atypical values are also identified. Package: r-cran-allspicer Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-readr, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-allspicer_0.1.9-1.ca2404.1_all.deb Size: 43578 MD5sum: 3309225c758d64778c6baf4464323829 SHA1: 6d812391d3c98d054ac57a7029b23804e9cea36b SHA256: 0c301c650768b25927c7ea597af7f9c68ccebf77312819ec7280041013cdd55a SHA512: 2c0f407f9c2735ab2a83a35cfacb3fc4d87d25f391e88dbcc9c168140d03c99c9c20ef166b56e3087741cfc66eb69faa1f1a058a5eed1609b48df8d4d26aaa83 Homepage: https://cran.r-project.org/package=ALLSPICER Description: CRAN Package 'ALLSPICER' (ALLelic Spectrum of Pleiotropy Informed Correlated Effects) Provides statistical tools to analyze heterogeneous effects of rare variants within genes that are associated with multiple traits. The package implements methods for assessing pleiotropic effects and identifying allelic heterogeneity, which can be useful in large-scale genetic studies. Methods include likelihood-based statistical tests to assess these effects. For more details, see Lu et al. (2024) . Package: r-cran-alluvial Architecture: all Version: 0.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-testthat, r-cran-reshape2, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-alluvial_0.1-2-1.ca2404.1_all.deb Size: 537604 MD5sum: 3617c8c1af4f362aa597bee054835856 SHA1: 70c506746b9963126706a84aec40bc433b9b425e SHA256: 9df34dc3c67e35b168715e49140ee0abd4662d164079152ce30a3c2489a750e8 SHA512: 31884d49e597a1fe05635c75c4cee1779303ef2f40651d0d43db332f8b2306278b2f0504874fdfb7b1f83a9444ce9cd9e6704eb7b9412c90f04e64caa2c55cce Homepage: https://cran.r-project.org/package=alluvial Description: CRAN Package 'alluvial' (Alluvial Diagrams) Creating alluvial diagrams (also known as parallel sets plots) for multivariate and time series-like data. Package: r-cran-alone Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-forcats Filename: pool/dists/noble/main/r-cran-alone_0.7-1.ca2404.1_all.deb Size: 55386 MD5sum: 9d8f73b77d458011067403b3515adc58 SHA1: 30b737d4f2328f934d93d8d7f2b9220b47a0c867 SHA256: 6047cb24ea325f1112e1956842e0a31f96946643a34779f51a44486c1f89f32d SHA512: d36602aac7c276c3452662a8a1318f035edc93936c8c8e1ecc1860f89366423698560e3746ae633cd1910463b3a622cca08e67216aac745912bac0381aef6166 Homepage: https://cran.r-project.org/package=alone Description: CRAN Package 'alone' (Datasets from the Survival TV Series Alone) A collection of datasets on the Alone survival TV series in tidy format. Included in the package are 4 datasets detailing the survivors, their loadouts, episode details and season information. 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A typical function is to split a dataset into a training dataset and a test dataset. Then compare the data distribution of the two datasets. Another feature is to support the development of predictive models and to compare the performance of several predictive models, helping to select the best model. 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It is a lightweight and flexible alternative to 'pkgdown', with support for many documentation generators, including 'Quarto', 'Docute', 'Docsify', and 'MkDocs'. Package: r-cran-alteredpqr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-alteredpqr_0.1.0-1.ca2404.1_all.deb Size: 451180 MD5sum: 2f2825238009202ed9f44a7716a1996c SHA1: 1a4531828545781532ee019f563f393b923dd0e9 SHA256: fc617815875643387ec8f2b9cc5ade4d26562dd1e543296ac10d592645a6ac20 SHA512: cfba8259772c1049444675df817254788c3fbc1f14032165c4bea8cae5482c76784ae3351880c5d665022bad395514bede9eb12256227b90502e96191f03001b Homepage: https://cran.r-project.org/package=AlteredPQR Description: CRAN Package 'AlteredPQR' (Detection of Altered Protein Quantitative Relationships) Inference of protein complex states from quantitative proteomics data. 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Package: r-cran-altfuelr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2434 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-lubridate, r-cran-dplyr, r-cran-magrittr, r-cran-stringr Filename: pool/dists/noble/main/r-cran-altfuelr_0.1.0-1.ca2404.1_all.deb Size: 2410708 MD5sum: c230663bab582d734c893d2b47197e1d SHA1: 9ea647a8ff6f5b91ec27a2ffaf08a4c4dcd20ff0 SHA256: 1394f5fe8a518a3aeee72cfe370541c62ad7fb3ea2156301554b7e0d74b7d213 SHA512: 6bdd35e8caed62f40a70b17fe639464975f3ef9c2248350c79dd09b262eddb6c3e42019b027f180be269798922c1e6f41e38b419a826c7f8f750edab7a8334a5 Homepage: https://cran.r-project.org/package=altfuelr Description: CRAN Package 'altfuelr' (Provides an Interface to the NREL Alternate Fuels Locator) Provides a number of functions to access the National Energy Research Laboratory Alternate Fuel Locator API . 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Package: r-cran-altmeta Architecture: all Version: 4.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 608 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-lme4, r-cran-matrix, r-cran-metafor Filename: pool/dists/noble/main/r-cran-altmeta_4.3.1-1.ca2404.1_all.deb Size: 554544 MD5sum: a645ec5ef94d1b3d43eb8b14ab9b08f7 SHA1: efe0779fa18235fd93492ac5d9cf04f9a531dde9 SHA256: b73dd6114b5c2deb2423bb3d47e37ab5092f758926ba8dedc9feddb871bcd9ec SHA512: 9f96ea2ca7032f7a20ae8a6f23f8675eab4ebe8d348086fe2eec4ec1b07fa8b3add0f7d96250dfab2acf97bce95198992253a40788e3367fa3e224fc98bb422a Homepage: https://cran.r-project.org/package=altmeta Description: CRAN Package 'altmeta' (Alternative Meta-Analysis Methods) Provides alternative statistical methods for meta-analysis, including: - bivariate generalized linear mixed models for synthesizing odds ratios, relative risks, and risk differences (Chu et al., 2012 ) - tests and measures for between-study heterogeneity (Lin et al., 2017 ; Wang et al., 2022 ; Yu et al., 2025 ); - measures, tests, and visualization tools for publication bias or small-study effects (Lin and Chu, 2018 ; Lin, 2019 ; Lin, 2020 ; Shi et al., 2020 ); - meta-analysis of combining standardized mean differences and odds ratios (Jing et al., 2023 ); - meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc. (Reitsma et al., 2005 ; Chu and Cole, 2006 ); - meta-analysis methods for synthesizing proportions (Lin and Chu, 2020 ); - models for multivariate meta-analysis, measures of inconsistency degrees of freedom in Bayesian network meta-analysis, and predictive P-score (Lin and Chu, 2018 ; Lin, 2020 ; Rosenberger et al., 2021 ). Package: r-cran-altopt Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature, r-cran-lattice Filename: pool/dists/noble/main/r-cran-altopt_0.1.2-1.ca2404.1_all.deb Size: 116124 MD5sum: 8c56892f24695434217978dd58c206e5 SHA1: 051b9e06d1106ee8cc9228c2cb49afa090d327e7 SHA256: aa8928abe7dcfd9b6d9169a99ed0149acf35343350526e3a0e9904a81309627e SHA512: 21a876b0f72e0664aece56091b0d14f87bd54741836ccb4170bc3c3c7b1ca79771bab5ea648d1a7cbaaf2446df6a02a329d17b0af7c5258a45cb09f3edcff4ba Homepage: https://cran.r-project.org/package=ALTopt Description: CRAN Package 'ALTopt' (Optimal Experimental Designs for Accelerated Life Testing) Creates the optimal (D, U and I) designs for the accelerated life testing with right censoring or interval censoring. It uses generalized linear model (GLM) approach to derive the asymptotic variance-covariance matrix of regression coefficients. The failure time distribution is assumed to follow Weibull distribution with a known shape parameter and log-linear link functions are used to model the relationship between failure time parameters and stress variables. The acceleration model may have multiple stress factors, although most ALTs involve only two or less stress factors. ALTopt package also provides several plotting functions including contour plot, Fraction of Use Space (FUS) plot and Variance Dispersion graphs of Use Space (VDUS) plot. For more details, see Seo and Pan (2015) . 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Package: r-cran-amadeus Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1941 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-sftime, r-cran-terra, r-cran-data.table, r-cran-httr2, r-cran-exactextractr, r-cran-stringi, r-cran-stars, r-cran-tidyr, r-cran-rlang, r-cran-archive, r-cran-collapse, r-cran-rdpack Suggests: r-cran-covr, r-cran-devtools, r-cran-dorng, r-cran-fnn, r-cran-furrr, r-cran-ggplot2, r-cran-knitr, r-cran-lwgeom, r-cran-maps, r-cran-mirai, r-cran-nhdplustools, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr, r-cran-targets, r-cran-testthat, r-cran-tigris, r-cran-withr Filename: pool/dists/noble/main/r-cran-amadeus_2.0.0-1.ca2404.1_all.deb Size: 1487544 MD5sum: 18afa45ecc72cea9f76ebfb9bb3b0267 SHA1: 3d70a07de25073e7d62a71646ff63ca8be9eab6c SHA256: b2fff86d85983025fcae05cbc61d08bfb88966605a0572085686e5990da99e94 SHA512: 555d8c926bd5ea4e8148c3aa6ac57c94e0c092b330fe1e665b9e9d129f601b00cbc2060d360087308336cde4cb77f8893f4e939813f4fbe690d68a980332d446 Homepage: https://cran.r-project.org/package=amadeus Description: CRAN Package 'amadeus' (Accessing and Analyzing Large-Scale Environmental Data) Functions are designed to facilitate access to and utility with large scale, publicly available environmental data in R. The package contains functions for downloading raw data files from web URLs (download_data()), processing the raw data files into clean spatial objects (process_covariates()), and extracting values from the spatial data objects at point and polygon locations (calculate_covariates()). These functions call a series of source-specific functions which are tailored to each data sources/datasets particular URL structure, data format, and spatial/temporal resolution. The functions are tested, versioned, and open source and open access. For sum_edc() method details, see Messier, Akita, and Serre (2012) . Package: r-cran-amanida Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 573 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-readr, r-cran-readxl, r-cran-rmarkdown, r-cran-tibble, r-cran-tidyr, r-cran-tidyverse, r-cran-webchem Suggests: r-cran-markdown, r-bioc-metaboliteidmapping, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-amanida_0.3.0-1.ca2404.1_all.deb Size: 377166 MD5sum: a728a61c6fe1a4664f45056dc2a128d6 SHA1: 3c5bf66fa22f0edd1f591bbd930392867385b72e SHA256: aa4513e69fbcce66563f370f35a6ac8f4f0dadcd41b613de833699459d75a99e SHA512: ab5824420e57ce306cabc725cecf1e3473dc8349d91454729dfb5c370b270a470f163ac0a688fe9e0517c1735ef3e04c0b0d6ac52094d454c1e054d4426ce879 Homepage: https://cran.r-project.org/package=amanida Description: CRAN Package 'amanida' (Meta-Analysis for Non-Integral Data) Combination of results for meta-analysis using significance and effect size only. 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Package: r-cran-amanpg Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4164 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-amanpg_0.3.4-1.ca2404.1_all.deb Size: 4192794 MD5sum: 345704041a856b40a3fa6871758ef4da SHA1: 6873442c43b669b3df374e880dcd83bf3e1d503b SHA256: a8dcd953abb82fc24cc1abb92d061da5bbc162b7763b4a5cb4108302ecfd656e SHA512: 0d0dc46c880de1922c40580625fd392c9370ae15880fcd375f5115a25bad4ef58fd44cd4b4787bad4b837cf40621a256f5601a8c003424f9b0d42064ba51b3ce Homepage: https://cran.r-project.org/package=amanpg Description: CRAN Package 'amanpg' (Alternating Manifold Proximal Gradient Method for Sparse PCA) Alternating Manifold Proximal Gradient Method for Sparse PCA uses the Alternating Manifold Proximal Gradient (AManPG) method to find sparse principal components from a data or covariance matrix. 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Package: r-cran-amapgeocode Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-rlang, r-cran-digest, r-cran-xml2, r-cran-tibble, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httptest2, r-cran-vcr, r-cran-spelling, r-cran-covr, r-cran-shiny, r-cran-bslib, r-cran-dt, r-cran-readr Filename: pool/dists/noble/main/r-cran-amapgeocode_1.0.0-1.ca2404.1_all.deb Size: 215632 MD5sum: 92541c8e988c7dcbc49ca88a91b5640d SHA1: fe5cbb04720ba3a269392a967f326ecadeffd2ae SHA256: 5fef6bedf9853df30cdc83aadfd6c2626deba7dc7716f92817f28f33f0ed72ff SHA512: 81a490c5d07b67628d6959e353e7ae5761359dc5dde47fdca635b1ab27961dd6d967dbd8b45994c6c215626447de3c0806d9e483aeedc8449636778416bdbe5c Homepage: https://cran.r-project.org/package=amapGeocode Description: CRAN Package 'amapGeocode' (An Interface to the 'AutoNavi Maps' API Geocoding Services) Getting and parsing data of location geocode/reverse-geocode and administrative regions from 'AutoNavi Maps' API. 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The included list of benthic fauna species according to their sensitivity to pollution. Matching species in sample data to the list allows the calculation of fractions of individuals in the different sensitivity categories and thereafter the AMBI index. The Shannon Diversity Index H' and the Danish benthic fauna quality index DKI (Dansk Kvalitetsindeks) can also be calculated, as well as the multivariate M-AMBI index. Borja, A., Franco, J. ,Pérez, V. (2000) "A marine biotic index to establish the ecological quality of soft bottom benthos within European estuarine and coastal environments" . Package: r-cran-amcp Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-amcp_1.0.2-1.ca2404.1_all.deb Size: 370542 MD5sum: 3fcef8c6c88e6820227e918897cd4fcd SHA1: 8e1a90d1219f8cd4aa364acdd7466e17fe63cb61 SHA256: 55d24e318cd770373d29f60741e95358de2d10a5536dca8db85a9f9196d9d816 SHA512: 449d6ae9043d6943e22189b75acd3a512c586c28cd559365611709c5af6154a532ce8f52093b821bb067a0f8012fbc7d6ba025a105d2f00a8c697a34e4d936a7 Homepage: https://cran.r-project.org/package=AMCP Description: CRAN Package 'AMCP' (A Model Comparison Perspective) Accompanies the book "Designing experiments and analyzing data: A model comparison perspective" (3rd ed.) by Maxwell, Delaney, & Kelley (2018; Routledge). Contains all of the data sets in the book's chapters and end-of-chapter exercises. Information about the book is available at . 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Package: r-cran-ammistability Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-agricolae, r-cran-ggcorrplot, r-cran-ggplot2, r-cran-reshape2, r-cran-rdpack, r-cran-mathjaxr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pander, r-cran-xml, r-cran-httr, r-cran-rcurl Filename: pool/dists/noble/main/r-cran-ammistability_0.1.4-1.ca2404.1_all.deb Size: 472732 MD5sum: 6097ce883dde46f981668ae15bc39d22 SHA1: eb1f2fe29f9fe68e12b943112177725a830125c1 SHA256: b9479936de8fe2c5875b88331b76c9ad67a5efc50a55076d5b38033b7d79c218 SHA512: 48ef201bc1fbc26a56bf745898fd4d46999662f65c81c6dfd262f43eea427040dadfb8955548226e59df787fb82e7a2672c6f0b3f87fe16e54a23eb7b6e0da77 Homepage: https://cran.r-project.org/package=ammistability Description: CRAN Package 'ammistability' (Additive Main Effects and Multiplicative Interaction ModelStability Parameters) Computes various stability parameters from Additive Main Effects and Multiplicative Interaction (AMMI) analysis results such as Modified AMMI Stability Value (MASV), Sums of the Absolute Value of the Interaction Principal Component Scores (SIPC), Sum Across Environments of Genotype-Environment Interaction Modelled by AMMI (AMGE), Sum Across Environments of Absolute Value of Genotype-Environment Interaction Modelled by AMMI (AV_(AMGE)), AMMI Stability Index (ASI), Modified ASI (MASI), AMMI Based Stability Parameter (ASTAB), Annicchiarico's D Parameter (DA), Zhang's D Parameter (DZ), Averages of the Squared Eigenvector Values (EV), Stability Measure Based on Fitted AMMI Model (FA), Absolute Value of the Relative Contribution of IPCs to the Interaction (Za). Further calculates the Simultaneous Selection Index for Yield and Stability from the computed stability parameters. See the vignette for complete list of citations for the methods implemented. Package: r-cran-ammodels Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1661 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-unmarked Suggests: r-cran-r.rsp, r-cran-shiny, r-cran-shinybs, r-cran-knitr, r-cran-aiccmodavg Filename: pool/dists/noble/main/r-cran-ammodels_0.1.4-1.ca2404.1_all.deb Size: 1222132 MD5sum: 45f3093ec8e76774324b4aad2bef64c6 SHA1: 788cc406583c18f2a8aa5ac9b8b0292f5971015b SHA256: 98d45cafd590120b5fdc405a24fc7e82f615f9d9dcc04a6d7c1637f1f3e178e9 SHA512: 15b419d83c73f7865237f2af221be508ad53ab6fbf2f3588c2c374be54edf570b7cd025936fef6037671e212da9a4ccb6977a469248132f868bafe061866a508 Homepage: https://cran.r-project.org/package=AMModels Description: CRAN Package 'AMModels' (Adaptive Management Model Manager) Helps enable adaptive management by codifying knowledge in the form of models generated from numerous analyses and data sets. Facilitates this process by storing all models and data sets in a single object that can be updated and saved, thus tracking changes in knowledge through time. A shiny application called AM Model Manager (modelMgr()) enables the use of these functions via a GUI. Package: r-cran-ammoniaconcentration Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ammoniaconcentration_0.1-1.ca2404.1_all.deb Size: 11224 MD5sum: b92ccaab8506025e4c1f0e3bd4d943cf SHA1: 96e56adcf9a0abe69e845b8863c8d340c708700d SHA256: 7b8cc40dd94093a017f93a2eeeef52336ce6621c582589c4623c10c389a0e272 SHA512: 19bb03cdf7688278bc8509a04419502321b720422104fef5be55aa71f9721340752fdd153e5e8a62fe6cda3a207baf66436d4ba38d0ab581317ec1c5508056b5 Homepage: https://cran.r-project.org/package=AmmoniaConcentration Description: CRAN Package 'AmmoniaConcentration' (Un-Ionized Ammonia Concentration) Provides a function to calculate the concentration of un-ionized ammonia in the total ammonia in aqueous solution using the pH and temperature values. Package: r-cran-amnlfa Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 858 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mplusautomation, r-cran-reshape2, r-cran-gridextra, r-cran-stringr, r-cran-plyr, r-cran-devtools, r-cran-dplyr, r-cran-stringi Filename: pool/dists/noble/main/r-cran-amnlfa_1.1.2-1.ca2404.1_all.deb Size: 235436 MD5sum: 448a83a400a50536aadd0565756d49ec SHA1: 1fb1ce8991a80ca5d2e676ee9251d41221510567 SHA256: 29499c5c773e823478788bf5fababb9cfa4ccd0d4b9ec0f37bfcaf26e540cab5 SHA512: dfbfc30a67c3f92043bf544333ce02f00b22cfa549d2bd612ea82a81fb6c78efdbebe45ebc7f5055f4bbf66fd3e40df9579e66b40e1b5940705eb54f74557e49 Homepage: https://cran.r-project.org/package=aMNLFA Description: CRAN Package 'aMNLFA' (Automated Moderated Nonlinear Factor Analysis Using 'M-plus') Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, using the method described by Gottfredson and colleagues (2019) . This package creates M-plus input files which may be run iteratively to test two different types of covariate effects on items: (1) latent variable impact (both mean and variance); and (2) differential item functioning. After sequentially testing for all effects, it also creates a final model by including all significant effects after adjusting for multiple comparisons. Finally, the package creates a scoring model which uses the final values of parameter estimates to generate latent variable scores. \n\n This package generates TEMPLATES for M-plus inputs, which can and should be inspected, altered, and run by the user. In addition to being presented without warranty of any kind, the package is provided under the assumption that everyone who uses it is reading, interpreting, understanding, and altering every M-plus input and output file. There is no one right way to implement moderated nonlinear factor analysis, and this package exists solely to save users time as they generate M-plus syntax according to their own judgment. Package: r-cran-amoudsurv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ahsurv, r-cran-flexsurv, r-cran-pracma Filename: pool/dists/noble/main/r-cran-amoudsurv_0.1.0-1.ca2404.1_all.deb Size: 196380 MD5sum: 7aaba500fe5da7ddba65b3ba36b913e7 SHA1: f9544352631c2eb96ad1de632f09fc598a54e57e SHA256: 21735cfa885c71624b6958f85f48812547fe16e7f005bd2c1a9b87db6f27a697 SHA512: caeb9c740df3679ab8b94e51051f75702d95abe357a5fb58f2882632d2db736f20d84a1f594fc4654e39d15cfda6b604ea54ae1251bf224e6dd969ec5b5767df Homepage: https://cran.r-project.org/package=AmoudSurv Description: CRAN Package 'AmoudSurv' (Tractable Parametric Odds-Based Regression Models) Fits tractable fully parametric odds-based regression models for survival data, including proportional odds (PO), accelerated failure time (AFT), accelerated odds (AO), and General Odds (GO) models in overall survival frameworks. Given at least an R function specifying the survivor, hazard rate and cumulative distribution functions, any user-defined parametric distribution can be fitted. We applied and evaluated a minimum of seventeen (17) various baseline distributions that can handle different failure rate shapes for each of the four different proposed odds-based regression models. For more information see Bennet et al., (1983) , and Muse et al., (2022) . Package: r-cran-amp.dm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 753 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-dplyr, r-cran-vctrs, r-cran-forcats, r-cran-readxl, r-cran-haven, r-cran-fs, r-cran-xtable Suggests: r-cran-ggplot2, r-cran-patchwork, r-cran-rstudioapi, r-cran-knitr, r-cran-r3port, r-cran-testthat, r-cran-quarto, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-amp.dm_0.2.1-1.ca2404.1_all.deb Size: 654064 MD5sum: 7d422b60997343b998d8ae2b3002cdc1 SHA1: 8715d6a438ac929e16f235040dc655c57f48e5e7 SHA256: 8adb9853596276762a3820491b91b339cacbcd8f0b03b8f4a8f6e62914b97da1 SHA512: f618d750cbee6d32badff6eb9980ba0fb7b2b6e07cbcd9177440b5ff2d48c9003e38917db614dca753b901b54a81e8cb37bbbcfe41f0ecac1d86f1a758d0211b Homepage: https://cran.r-project.org/package=amp.dm Description: CRAN Package 'amp.dm' (Data Management Tools for Pharmacometrics) Tools and functions to efficiently create datasets used in pharmacometric analysis. Additional functionality is added to create documentation and prepare files for submission and quality control purposes. Package: r-cran-amp.sim Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1286 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-whisker, r-cran-rlang, r-cran-cli, r-cran-lifecycle, r-cran-nmdata Suggests: r-cran-nonmem2rx, r-cran-tidyr, r-cran-shiny, r-cran-shinyjs, r-cran-bslib, r-cran-rstudioapi, r-cran-desolve, r-cran-rxode2, r-cran-mrgsolve, r-cran-r3port, r-cran-ggplot2, r-cran-dplyr, r-cran-testthat, r-cran-quarto Filename: pool/dists/noble/main/r-cran-amp.sim_0.1.1-1.ca2404.1_all.deb Size: 862726 MD5sum: bfcd71dcddcb5a9a2e874767454f4c38 SHA1: b795a689cb66816800523deb7f1e59877e6c3e01 SHA256: 9248603742084f78f6f1f48c40ef874d430e3a4bff8bddf699adfe6e853617d0 SHA512: 1418ec66b9ef7b5cd21196a8f1ab30ef816ccc093d5b60552ca4be9eea097c631769cd2a80c806fb2471aea1bebea765f7abf515b9e8118c2b119e9b039140e6 Homepage: https://cran.r-project.org/package=amp.sim Description: CRAN Package 'amp.sim' (Flexible Simulation Utilities for Pharmacometric Modeling) The goal of 'amp.sim' is to transform 'NONMEM' models into R syntax so they can be used for simulations using the 'deSolve', 'nlmixr2' or 'mrgsolve' package. Additionally, functionality is included to aid simulations performed directly in 'NONMEM' and to automatically create shiny apps for simulation models. Package: r-cran-amp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-superlearner, r-cran-glmnet, r-cran-dplyr, r-cran-rlang, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-amp_1.0.0-1.ca2404.1_all.deb Size: 237256 MD5sum: cbb2bf7573eb56fe319e36f326ceb0d0 SHA1: c39369ffb8832b8a0797c2c4b5c442b5fd6eb1de SHA256: 8ae4fa9b4ecb0935b244bb7df68bed27879a1ea5e016e7dfd414f81509b1ed0b SHA512: ac4b7331144406b167a5b6e8ee836c34f07af7025b5ff147f31590541417f45e36d035a4395408ef2c5559bb5e24a40f5c6dff6feeed9fec2c831a24d380330d Homepage: https://cran.r-project.org/package=amp Description: CRAN Package 'amp' (Statistical Test for the Multivariate Point Null Hypotheses) A testing framework for testing the multivariate point null hypothesis. A testing framework described in Elder et al. (2022) to test the multivariate point null hypothesis. After the user selects a parameter of interest and defines the assumed data generating mechanism, this information should be encoded in functions for the parameter estimator and its corresponding influence curve. Some parameter and data generating mechanism combinations have codings in this package, and are explained in detail in the article. Package: r-cran-ampd Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ampd_0.2-1.ca2404.1_all.deb Size: 16912 MD5sum: 3a6ff9e5eeb33cd460493fa67119b0d3 SHA1: 5995ff6a2941ec9203a52537321c649ca41731eb SHA256: 693fe48702a439d539c47de51ba087be6580fff7ab7ce44f8618aa5fd327c92b SHA512: dc8a4fd243dc6996e5902108f11f987ca722e5c166f09d4d79b5415580f8929572c59b80f7362df99c25b9d5a46ea25e97a73f7d704b0898e5b54bb74c4dad4e Homepage: https://cran.r-project.org/package=ampd Description: CRAN Package 'ampd' (An Algorithm for Automatic Peak Detection in Noisy Periodic andQuasi-Periodic Signals) A method for automatic detection of peaks in noisy periodic and quasi-periodic signals. This method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima. For further information see . Package: r-cran-ampgram Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biogram, r-cran-devtools, r-cran-pbapply, r-cran-ranger, r-cran-shiny, r-cran-stringi Suggests: r-cran-dt, r-cran-ggplot2, r-cran-pander, r-cran-rmarkdown, r-cran-shinythemes, r-cran-spelling Filename: pool/dists/noble/main/r-cran-ampgram_1.0-1.ca2404.1_all.deb Size: 86746 MD5sum: 843ace5073865f3858273570d983b917 SHA1: 14794385a4fffee623340115e7d6804e82d6f74a SHA256: fbff42459ef0d262afab28baf123f5c978ffcabd6c707671f40aa932db11987c SHA512: 74c1cc8cf848ee5f28c9ab4875cfe2cdea2cc165f80f73447855f5993787dc84c05e51644681cd1356cc17f349749ca34161cf318151b660964848810439a795 Homepage: https://cran.r-project.org/package=AmpGram Description: CRAN Package 'AmpGram' (Prediction of Antimicrobial Peptides) Predicts antimicrobial peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. The AmpGram model is too large for CRAN and it has to be downloaded separately from the repository: . Package: r-cran-ample Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3942 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-markdown, r-cran-rcolorbrewer, r-cran-r6, r-cran-scales, r-cran-shinyjs, r-cran-ggplot2, r-cran-shinyscreenshot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ample_1.0.2-1.ca2404.1_all.deb Size: 1511168 MD5sum: 82d57de008981ab8ad28a4e19458d60d SHA1: c7f6d8ff4694fc9d64e3f190496d30feb26a1519 SHA256: 98d18c7b07f3115e90ab584f52ce9926ca3228c508888fe2660ce779c2ac176d SHA512: b25d3cdd876baade6f11da53b38d15974ae603ce6823809671a1e73b9ab88e0777ba030e506d46bbcb091180c6602a3a61be46428d4c602d77db3ca9d1b22b5a Homepage: https://cran.r-project.org/package=AMPLE Description: CRAN Package 'AMPLE' (Shiny Apps to Support Capacity Building on Harvest Control Rules) Three Shiny apps are provided that introduce Harvest Control Rules (HCR) for fisheries management. 'Introduction to HCRs' provides a simple overview to how HCRs work. Users are able to select their own HCR and step through its performance, year by year. Biological variability and estimation uncertainty are introduced. 'Measuring performance' builds on the previous app and introduces the idea of using performance indicators to measure HCR performance. 'Comparing performance' allows multiple HCRs to be created and tested, and their performance compared so that the preferred HCR can be selected. Package: r-cran-ampliconduo Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-xtable Filename: pool/dists/noble/main/r-cran-ampliconduo_1.1.1-1.ca2404.1_all.deb Size: 564934 MD5sum: 95ea3f366f3a244a93f7809444cce898 SHA1: a0595788678f8ec6bf87b2d7a47221bcf4813ca6 SHA256: 83739a1f558caffa0e0429606a2afe91135665658aa6be180b4529334264a0fa SHA512: 5c94857d47eda4d69f3eb3e095e9d80b6eed93706b235c942676412fafe27c65c0f482e72427b12ab9063215ddac75ea6de4e18dbec3c105df6c0b036e000822 Homepage: https://cran.r-project.org/package=AmpliconDuo Description: CRAN Package 'AmpliconDuo' (Statistical Analysis of Amplicon Data of the Same Sample toIdentify Artefacts) Increasingly powerful techniques for high-throughput sequencing open the possibility to comprehensively characterize microbial communities, including rare species. However, a still unresolved issue are the substantial error rates in the experimental process generating these sequences. To overcome these limitations we propose an approach, where each sample is split and the same amplification and sequencing protocol is applied to both halves. This procedure should allow to detect likely PCR and sequencing artifacts, and true rare species by comparison of the results of both parts. The AmpliconDuo package, whereas amplicon duo from here on refers to the two amplicon data sets of a split sample, is intended to help interpret the obtained read frequency distribution across split samples, and to filter the false positive reads. Package: r-cran-amr Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5001 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-cleaner, r-cran-cli, r-cran-crayon, r-cran-curl, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-openxlsx, r-cran-parallelly, r-cran-pillar, r-cran-progress, r-cran-readxl, r-cran-recipes, r-cran-rlang, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-rvest, r-cran-skimr, r-cran-testthat, r-cran-tibble, r-cran-tidymodels, r-cran-tidyselect, r-cran-tinytest, r-cran-vctrs, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-amr_3.0.1-1.ca2404.1_all.deb Size: 4799708 MD5sum: 92a902f03d72bbbf17bab3027899e128 SHA1: 4d069f1f4aad493c47080b996b3624afeb15ff40 SHA256: 384e4f3717bedcf1cea8620bc1d632ae612787185353902ac560f524fbf49ead SHA512: cc066e8a905218e678b8675e8b84ad4246eac16d1e75ab427bb028c717b57594a1bdff898e1710f57e92f8f9adc1d4f984397d49051bb1567c0eeb06b3c3d56c Homepage: https://cran.r-project.org/package=AMR Description: CRAN Package 'AMR' (Antimicrobial Resistance Data Analysis) Functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by using evidence-based methods, as described in . Package: r-cran-amregtest Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-allelematch, r-cran-digest, r-cran-remotes, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-amregtest_1.0.5-1.ca2404.1_all.deb Size: 134350 MD5sum: 24634e0b651e7fb564d25bf9704a4313 SHA1: e026eb96532d03bc1af77a3def284669499f3dcf SHA256: 1707b9096cc0cf300c2b2b18dcc46f47aedcbc317e3c03d046ef97ff98b72f52 SHA512: cf8512df28c51a3128993addc6f671b156231eb606d7392c6e6797b70f06cc121a9293e3dc9c6c568da657b2c368f2e54651843ce6e3339145088de35ba90ec4 Homepage: https://cran.r-project.org/package=amregtest Description: CRAN Package 'amregtest' (Runs Allelematch Regression Tests) Automates regression testing of package 'allelematch'. Over 2500 tests covers all functions in 'allelematch', reproduces the examples from the documentation and includes negative tests. The implementation is based on 'testthat'. Package: r-cran-amscorer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-amscorer_0.1.0-1.ca2404.1_all.deb Size: 69346 MD5sum: 7962b3f56fce1e5bf92e063bb427ffcc SHA1: a74e1c294405ec56f358f17ba63dcc0d0de5e740 SHA256: e37e91d984d43eb86bbc129bdabc3164c3a0cb7956bfdbbd579876e8ff880c8e SHA512: aa99aace4565f100a221ab69a0f0a8b76f7fba8d0d4dbafd937c571c6c57e8b787f025a9e764883f5e02e03aa865b5d97d73b4a728863abb8de77ca7ce45ee88 Homepage: https://cran.r-project.org/package=amscorer Description: CRAN Package 'amscorer' (Clinical Scores Calculator for Healthcare) Provides functions to compute various clinical scores used in healthcare. These include the Charlson Comorbidity Index (CCI), predicting 10-year survival in patients with multiple comorbidities; the EPICES score, an individual indicator of precariousness considering its multidimensional nature; the MELD score for chronic liver disease severity; the Alternative Fistula Risk Score (a-FRS) for postoperative pancreatic fistula risk; and the Distal Pancreatectomy Fistula Risk Score (D-FRS) for risk following distal pancreatectomy. For detailed methodology, refer to Charlson et al. (1987) , Sass et al. (2006) , Kamath et al. (2001) , Kim et al. (2008) Kim et al. (2021) , Mungroop et al. (2019) , and de Pastena et al. (2023) .. Package: r-cran-amt Architecture: all Version: 0.3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4765 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-circular, r-cran-ctmm, r-cran-data.table, r-cran-dplyr, r-cran-fitdistrplus, r-cran-fnn, r-cran-kernsmooth, r-cran-lubridate, r-cran-mass, r-cran-purrr, r-cran-rdpack, r-cran-rlang, r-cran-sf, r-cran-sfheaders, r-cran-survival, r-cran-terra, r-cran-tibble, r-cran-tidyr Suggests: r-cran-adehabitatlt, r-cran-broom, r-cran-ggplot2, r-cran-ggraph, r-cran-geosphere, r-cran-knitr, r-cran-leaflet, r-cran-movehmm, r-cran-rmarkdown, r-cran-sessioninfo, r-cran-suncalc, r-cran-tidygraph, r-cran-tinytest, r-cran-units Filename: pool/dists/noble/main/r-cran-amt_0.3.1.0-1.ca2404.1_all.deb Size: 3505264 MD5sum: 72a376ca3035af8803fc1b5763138b58 SHA1: ca6088b39e25b526be941fe4a24aed56386595b1 SHA256: 696c920fa6aa0cf1d022bee2337e2a4eceb84fa8752bd7f0130f4ec2d7d72065 SHA512: a3bc776376dcb6d2a72286300e39fb2ab94ca7c05e5a1e55a5237e2f1fb8d464382c63021ea1d1a684fc0674bbead02a6a9254c647282e320dbe8d68c7c43f25 Homepage: https://cran.r-project.org/package=amt Description: CRAN Package 'amt' (Animal Movement Tools) Manage and analyze animal movement data. The functionality of 'amt' includes methods to calculate home ranges, track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses, and simulation of space-use from fitted step-selection functions. Package: r-cran-amvenndiagram5 Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1628 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-partitions, r-cran-venn Suggests: r-cran-shiny Filename: pool/dists/noble/main/r-cran-amvenndiagram5_1.0.0-1.ca2404.1_all.deb Size: 1119970 MD5sum: 57ca6785fd173deb2eb77fc3201f9bec SHA1: 9e47ee4895b16d228e0089a336a7e8283a3dc62a SHA256: 68b5fddddd39a8fbd917f5e34be708e7e14c525e4476ff8ddedfa265a81d6f9e SHA512: e558bd61657ded76e476c98e8ae45ecf16a09002bdb99feadbb1f4849e080cc5960d6362d7456109e22dad883249abec07835c23e53fe1aeaaf5ef6d6cb5555b Homepage: https://cran.r-project.org/package=amVennDiagram5 Description: CRAN Package 'amVennDiagram5' (Interactive Venn Diagrams) Creates interactive Venn diagrams using the 'amCharts5' library for 'JavaScript'. They can be used directly from the R console, from 'RStudio', in 'shiny' applications, and in 'rmarkdown' documents. Package: r-cran-amylogram Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biogram, r-cran-ranger, r-cran-seqinr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-amylogram_1.1-1.ca2404.1_all.deb Size: 644594 MD5sum: ae85acddecbf454d3817c88545746989 SHA1: fb82e4c89314cd532066e0e68067be9202025e53 SHA256: 712ee3ae211b6c3044bbab2f0042e2e19c2afd73a404ffd9a1a7fcee8e3944a7 SHA512: bd3c523d80f38741a52d4609b1d46170eac0342a9b6a1fc697602864ee5a71b31bcfa29b693caa949eb0546601e65629d3a38cead1cbc9d5e93c301ef90cd146 Homepage: https://cran.r-project.org/package=AmyloGram Description: CRAN Package 'AmyloGram' (Prediction of Amyloid Proteins) Predicts amyloid proteins using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. Package: r-cran-anabel Architecture: all Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-kableextra, r-cran-minpack.lm, r-cran-openxlsx, r-cran-progress, r-cran-purrr, r-cran-qpdf, r-cran-reshape2, r-cran-rlang, r-cran-tidyr Suggests: r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-anabel_3.0.2-1.ca2404.1_all.deb Size: 797424 MD5sum: 1c6918cd9b90d0bbf2b63aac8030c3bd SHA1: 430dacc9b555c4363ce56e1473eb5745ee119412 SHA256: 55923d14ceee86d4191a9b3729c41d665f3d7f300f04fb11c73290a570915070 SHA512: 7e64d43cf9372186ccc7fb29e009c4430ae08844884cc8379e0f5b015d8676b5f686e11e83696af7c38ed4f9a50d339eed3b397bebbd20d12c26e1070982adda Homepage: https://cran.r-project.org/package=anabel Description: CRAN Package 'anabel' (Analysis of Binding Events + l) A free software for a fast and easy analysis of 1:1 molecular interaction studies. This package is suitable for a high-throughput data analysis. Both the online app and the package are completely open source. You provide a table of sensogram, tell 'anabel' which method to use, and it takes care of all fitting details. The first two releases of 'anabel' were created and implemented as in (, ). Package: r-cran-anaconda Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-bioc-deseq2, r-cran-ggplot2, r-cran-ggrepel, r-cran-pheatmap, r-cran-lookup, r-cran-plyr, r-cran-data.table, r-cran-rafalib, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-anaconda_0.1.5-1.ca2404.1_all.deb Size: 176224 MD5sum: 9fb4432663ec4a36481b613a1ba06159 SHA1: d0502f2d32f0add548f4ce9a4bdb53d4e3f4ddb3 SHA256: 1bcddfeb1534e09e7f72c7ff9156d38f5348aad52c046094c3318b4b9e60eeff SHA512: b77d2bf5627ec78d72524ff953c7038dc34568e181871050858563795e3a83316b376dc5f4206453b2d35859bc1f99b98d98c21cd24646145085028c22c61374 Homepage: https://cran.r-project.org/package=Anaconda Description: CRAN Package 'Anaconda' (Targeted Differential and Global Enrichment Analysis ofTaxonomic Rank by Shared Asvs) Targeted differential and global enrichment analysis of taxonomic rank by shared ASVs (Amplicon Sequence Variant), for high-throughput eDNA sequencing of fungi, bacteria, and metazoan. Actually works in two steps: I) Targeted differential analysis from QIIME2 data and II) Global analysis by Taxon Mann-Whitney U test analysis from targeted analysis (I) (I) Estimate variance-mean dependence in count/abundance ASVs data from high-throughput sequencing assays and test for differential represented ASVs based on a model using the negative binomial distribution. (II) NCBITaxon_MWU uses continuous measure of significance (such as fold-change or -log(p-value)) to identify NCBITaxon that are significantly enriches with either up- or down-represented ASVs. If the measure is binary (0 or 1) the script will perform a typical 'NCBITaxon enrichment' analysis based Fisher's exact test: it will show NCBITaxon over-represented among the ASVs that have 1 as their measure. On the plot, different fonts are used to indicate significance and color indicates enrichment with either up (red) or down (blue) regulated ASVs. No colors are shown for binary measure analysis. The tree on the plot is hierarchical clustering of NCBITaxon based on shared ASVs. Categories with no branch length between them are subsets of each other. The fraction next to the category name indicates the fraction of 'good' ASVs in it; 'good' ASVs are the ones exceeding the arbitrary absValue cutoff (option in taxon_mwuPlot()). For Fisher's based test, specify absValue=0.5. This value does not affect statistics and is used for plotting only. The original idea was for genes differential expression analysis from Wright et al (2015) ; adapted here for taxonomic analysis. The 'Anaconda' package makes it possible to carry out these analyses by automatically creating several graphs and tables and storing them in specially created subfolders. You will need your QIIME2 pipeline output for each kingdom (eg; Fungi and/or Bacteria and/or Metazoan): i) taxonomy.tsv, ii) taxonomy_RepSeq.tsv, iii) ASV.tsv and iv) SampleSheet_comparison.txt (the latter being created by you). Package: r-cran-anacor Architecture: all Version: 1.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-colorspace, r-cran-fda Filename: pool/dists/noble/main/r-cran-anacor_1.1-5-1.ca2404.1_all.deb Size: 339140 MD5sum: 09d9a8ce1df211e6b1196e29b781ed06 SHA1: 44f66a95ba0d35f505a2dd0ebceb7eec1a00618c SHA256: 2f88c3e73bd4d46532d6a268404e39bee44a8553fdcd24d1827eecc9c02ebde6 SHA512: 9eafe158f7893d33bcdf9f4e2b89fdf72c5e1a9109e3339d5c3476dc0f79a2ef16fd6db603c08c05380fb9d582c3e09dcd44d50a446287726ee4a71e877b0b7d Homepage: https://cran.r-project.org/package=anacor Description: CRAN Package 'anacor' (Simple and Canonical Correspondence Analysis) Performs simple and canonical CA (covariates on rows/columns) on a two-way frequency table (with missings) by means of SVD. 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Package: r-cran-analogsea Architecture: all Version: 1.0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-yaml Suggests: r-cran-testthat, r-cran-knitr, r-cran-ssh, r-cran-aws.s3, r-cran-arrow Filename: pool/dists/noble/main/r-cran-analogsea_1.0.7.2-1.ca2404.1_all.deb Size: 373864 MD5sum: bd34d7319861e6921f7cd2395da18bc5 SHA1: d36b3f2797389d0a4d91beb61ece164266aa78d6 SHA256: b5703700988fc778f12ef013aedd0d39baa92edfbb33a37b10eb7f74d5bcb0cf SHA512: 965b155f46738c67f584b016cfdc98dec31e055879f04f8572a02ba7796450c15521b4cfaf6c95e46e00d1f9e8bf87ab7fbaad49112d0b1f67d7b64313bbe878 Homepage: https://cran.r-project.org/package=analogsea Description: CRAN Package 'analogsea' (Interface to 'DigitalOcean') Provides a set of functions for interacting with the 'DigitalOcean' API , including creating images, destroying them, rebooting, getting details on regions, and available images. 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Package: r-cran-ananseseurat Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-magrittr, r-cran-patchwork, r-cran-png, r-cran-purrr, r-cran-rlang, r-cran-seurat, r-cran-stringr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-signac, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ananseseurat_1.2.0-1.ca2404.1_all.deb Size: 380512 MD5sum: ad23d98a5986bc00978e695412f2f9bb SHA1: 5716d0ec0b98f7b7f2e8a16d4c0cca61e01b4945 SHA256: 9bc1ee9cbe6e700cf6e5ec3df03aaec67b168b45ec39f877e9fc690e24dc47ca SHA512: 158704390420b355ef590164f955c4b1008d8cc7401493acb40e10d83ff690dbb5a0404117760a0767275ea9582fba447f42f7a4323449664588553b2a5b603f Homepage: https://cran.r-project.org/package=AnanseSeurat Description: CRAN Package 'AnanseSeurat' (Construct ANANSE GRN-Analysis Seurat) Enables gene regulatory network (GRN) analysis on single cell clusters, using the GRN analysis software 'ANANSE', Xu et al.(2021) . 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See Ackerman (2018) . 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The network generating algorithm first determines the X and Y coordinates of N nodes within a rectangle with a side length of L and an area of A. Then it computes the pair-wise Euclidean distance Dij between node i and j, and then a complete network with 1/Dij as link weights is constructed. Then, the algorithm removes links from the complete network with the probability as shown in the function ahn_prob(). Such link removals can make the network disconnected whereas a connected network is wanted. In such cases, the algorithm rewires one network component to its spatially nearest neighbouring component and repeat doing this until the network is connected again. Finally, it outputs an undirected network (weighted or unweighted, connected or disconnected). This package came with a manuscript on modelling the physical configurations of animal habitats using networks (in preparation). 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Package: r-cran-animation Architecture: all Version: 2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 880 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magick Suggests: r-cran-mass, r-cran-class, r-cran-cairo, r-cran-fimport, r-cran-rgl, r-cran-zoo, r-cran-testit Filename: pool/dists/noble/main/r-cran-animation_2.8-1.ca2404.1_all.deb Size: 533918 MD5sum: dd7dc69ccdd2ae28c69c19a54e2a342b SHA1: 49e8bd670a8acf384b3f6cbb61e38f20be3dcc7d SHA256: bdb62cab3d702d86da16e96ca83e5487316019aeea2ba57668c146d4172d57f3 SHA512: 391974a470138f7fbf91f65129c7da44407586ec3b2b077cffa64e21990b13fb14ab30aa8b81523ac33f609e82a28c1ecba2d37ac901efbb21426c2de8425e10 Homepage: https://cran.r-project.org/package=animation Description: CRAN Package 'animation' (A Gallery of Animations in Statistics and Utilities to CreateAnimations) Provides functions for animations in statistics, covering topics in probability theory, mathematical statistics, multivariate statistics, non-parametric statistics, sampling survey, linear models, time series, computational statistics, data mining and machine learning. These functions may be helpful in teaching statistics and data analysis. Also provided in this package are a series of functions to save animations to various formats, e.g. Flash, 'GIF', HTML pages, 'PDF' and videos. 'PDF' animations can be inserted into 'Sweave' / 'knitr' easily. 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Providing the function for preparing, plotting, and animating the data. Krisanat Anukarnsakulchularp (2023) . 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The 2018 Journal of Computational and Graphical Statistics paper, describes the concepts implemented. Package: r-cran-animl Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-reticulate Filename: pool/dists/noble/main/r-cran-animl_3.2.0-1.ca2404.1_all.deb Size: 126142 MD5sum: 7d47629b6cda8e8bc7ddb4de5d81dbc5 SHA1: 15a317997544d21d4c99f1d2f34ce3c13c2b2433 SHA256: 016ca79bc72f40c6553442ae415b3a82e20c962551271f175f86c29f7aaeb6e5 SHA512: 4b59610b1acde7284a3a76d72318e0aea3c0d1662754acacc0bb8827c41095504d7fa96490db83bc452e1a08a52a5ba46a805e6e11931ebd4550010783a9cc02 Homepage: https://cran.r-project.org/package=animl Description: CRAN Package 'animl' (A Collection of ML Tools for Conservation Research) Functions required to classify subjects within camera trap field data. The package can handle both images and videos. The authors recommend a two-step approach using Microsoft's 'MegaDector' model and then a second model trained on the classes of interest. 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Methods used include g:Profiler (Raudvere et al. (2019) ), biomaRt (Durinck et al. (2009) ), and the Open Targets Platform (Koscielny et al. (2017) ). 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This package provides functions allowing virulence to be estimated by maximum likelihood techniques. The approach is based on the analysis of relative survival comparing survival in matching cohorts of infected vs. uninfected hosts (Agnew 2019) . 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This becomes apparent when the data are plotted, which illustrates the importance of using graphical displays in Statistics. This package enables the creation of datasets that have identical marginal sample means and sample variances, sample correlation, least squares regression coefficients and coefficient of determination. The user supplies an initial dataset, which is shifted, scaled and rotated in order to achieve target summary statistics. The general shape of the initial dataset is retained. The target statistics can be supplied directly or calculated based on a user-supplied dataset. The 'datasauRus' package provides further examples of datasets that have markedly different scatter plots but share many sample summary statistics. Package: r-cran-ansm5 Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ansm5_1.1.1-1.ca2404.1_all.deb Size: 568426 MD5sum: 008bf7d500594ff390e88c96203129de SHA1: 8893f8c1fce2a690f05f72a26ce732d9a0d4513a SHA256: b9f4906c4c6a722d3c5ba810140e5cc0bd752399acccca41de42ea1529db5f6b SHA512: 924cb4e91cc7a7e9a1a8f5aed3be9450cf9536ba856a105b4c65a2ca68f56b12c0bcecb78fd882ba61f4df7706525fd43d6314effac26289ed0bab31fa1e8973 Homepage: https://cran.r-project.org/package=ANSM5 Description: CRAN Package 'ANSM5' (Functions and Data for the Book "Applied NonparametricStatistical Methods", 5th Edition) Functions and data to accompany the 5th edition of the book "Applied Nonparametric Statistical Methods" (4th edition: Sprent & Smeeton, 2024, ISBN:158488701X), the revisions from the 4th edition including a move from describing the output from a miscellany of statistical software packages to using R. While the output from many of the functions can also be obtained using a range of other R functions, this package provides functions in a unified setting and give output using both p-values and confidence intervals, exemplifying the book's approach of treating p-values as a guide to statistical importance and not an end product in their own right. Please note that in creating the ANSM5 package we do not claim to have produced software which is necessarily the most computationally efficient nor the most comprehensive. Package: r-cran-antangiocool Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 35286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-rjava, r-cran-rweka, r-cran-rpart Filename: pool/dists/noble/main/r-cran-antangiocool_1.2-1.ca2404.1_all.deb Size: 16819556 MD5sum: fd82424fb7585fa220f981f47d84b250 SHA1: 0125bd1832dc849e9adebd33d629f3d763305135 SHA256: e1be0cdbd313cab5466d3f7365899f4c30da9f12f9d4b18338b3f8037eda4caa SHA512: 7d31f30f9f5ab94fdfd9b74cd1e12f110296b84b42cf9d7bc5783dc3db16698ce32d2c389d32cbaf1e0e751fe5c4319cbd75d5b4024d6a784bc4af72df6cc18c Homepage: https://cran.r-project.org/package=AntAngioCOOL Description: CRAN Package 'AntAngioCOOL' (Anti-Angiogenic Peptide Prediction) Machine learning based package to predict anti-angiogenic peptides using heterogeneous sequence descriptors. 'AntAngioCOOL' exploits five descriptor types of a peptide of interest to do prediction including: pseudo amino acid composition, k-mer composition, k-mer composition (reduced alphabet), physico-chemical profile and atomic profile. According to the obtained results, 'AntAngioCOOL' reached to a satisfactory performance in anti-angiogenic peptide prediction on a benchmark non-redundant independent test dataset. Package: r-cran-antareseditobject Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1835 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-antaresread, r-cran-assertthat, r-cran-cli, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-whisker, r-cran-doparallel, r-cran-dofuture, r-cran-memuse, r-cran-progressr, r-cran-pbapply, r-cran-future, r-cran-plyr, r-cran-yaml, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-data.tree Filename: pool/dists/noble/main/r-cran-antareseditobject_1.0.0-1.ca2404.1_all.deb Size: 927844 MD5sum: d52211814373fac87b0761d67667fcec SHA1: 4dda37a969e3a39fa5f9a32d3865b470d61b33fa SHA256: 6ded7a331e8bac8981a6bb699fa9533311c1a75a1bdfa17a1f4080524a50cb12 SHA512: 7febc441285e05ed79f471d696285305997b8bc9df805d028caa0c827563607feab3cc3ad327073b1a713d1143697d5f4b79ff28617ff76cdf873994c9714dde Homepage: https://cran.r-project.org/package=antaresEditObject Description: CRAN Package 'antaresEditObject' (Edit an 'Antares' Simulation) Edit an 'Antares' simulation before running it : create new areas, links, thermal clusters or binding constraints or edit existing ones. Update 'Antares' general & optimization settings. 'Antares' is an open source power system generator, more information available here : . 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This package provides functions to create new columns like net load, load factors, upward and downward margins or to compute aggregated statistics like economic surpluses of consumers, producers and sectors. Package: r-cran-antaresread Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4628 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-bit64, r-cran-lubridate, r-cran-plyr, r-cran-stringr, r-cran-stringi, r-cran-shiny, r-cran-pbapply, r-cran-doparallel, r-cran-jsonlite, r-cran-httr, r-cran-memuse, r-cran-purrr, r-cran-lifecycle, r-cran-assertthat, r-cran-arrow Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-foreach, r-cran-htmltools Filename: pool/dists/noble/main/r-cran-antaresread_3.0.0-1.ca2404.1_all.deb Size: 3436304 MD5sum: 0d07cf16a12a6b069f22e12f149629a5 SHA1: bd7376afc959b36edb3f33188efef998986b8179 SHA256: 3ceab93ada7b5657efb2eb546a440ce31f9baf2ee4da9fa392dbd348de4114dc SHA512: fac6c16b9a0e5d640b8aab67374136e3a1056e42fd9abccfb9e853dcdebf4c666ce565817712301c299f5a444d43b6b155454d3ea2364bc9f9e0cc83fb4c1b2e Homepage: https://cran.r-project.org/package=antaresRead Description: CRAN Package 'antaresRead' (Import, Manipulate and Explore the Results of an 'Antares'Simulation) Import, manipulate and explore results generated by 'Antares', a powerful open source software developed by RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems (more information about 'Antares' here : ). 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Package: r-cran-antclassify Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-jsonlite, r-cran-magrittr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-antclassify_0.2.1-1.ca2404.1_all.deb Size: 106354 MD5sum: a998967c7c35d407f2bbabcbed501272 SHA1: 3295dbbf1cda85063a87a23e5cbf03c28a3dd112 SHA256: 15e17333e4c5255fe0f20567e7942d5449179aa3ae566e949a2535e1cfec2a62 SHA512: 4319fb6fc30934e8cea5dce29ad3fb9d0a412070586d0e7acc9c7f5270ae8c5a98a8780311dded6529a21970a6d5e3510e0fb4a67fc37b5fcbd9de61c7f076fc Homepage: https://cran.r-project.org/package=AntClassify Description: CRAN Package 'AntClassify' (Functional Guilds, Invasion Status, Endemism, and Rarity of Ants) Provides functions for the analysis of ant communities, aiming to standardize workflows in myrmecology. The package automates the assignment of species to functional guilds based on trophic strategies, feeding habits, and foraging behavior, using established classification frameworks (Silva et al., 2015 ; Silvestre et al., 2003 ; Delabie et al., 2000 ), and also includes a novel classification system implemented within the package, developed from ant species occurring in urban environments. It also includes routines to flag exotic species of Brazil (Vieira, 2025, unpublished master's thesis), identify endemic species (Silva et al., 2025 ), and classify species rarity and rarity forms of the Atlantic Forest (Silva et al., 2024 ). The package reduces manual effort and improves reproducibility, supporting research and biodiversity management of Neotropical ant communities. Package: r-cran-antedep Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1073 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nloptr, r-cran-ggplot2, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-antedep_0.2.0-1.ca2404.1_all.deb Size: 955544 MD5sum: 6a9a721454d88a89850c5c80e72bad0a SHA1: 1afd112201af7de492267b142657705a52895e64 SHA256: 5d117d4d374bc7d7381637c7af600511fe1cef793d1f9f9b6b145eb4e486e249 SHA512: b72111654f4447dd8e74e45dc6a52df9b6a8b466009c9ea694daf3751ca76ca5b5f0e6e23fec143b164d153adfa68e52f44f30e5ad241bf5db1e36c3fc56c16e Homepage: https://cran.r-project.org/package=antedep Description: CRAN Package 'antedep' (Antedependence Models for Longitudinal Data) Fitting, simulation, and inference for antedependence models for longitudinal data, as described in Zimmerman and Nunez-Anton (2009, ISBN:9781420011074). Supports integer-valued antedependence (INAD) models for count data with thinning operators (binomial, Poisson, negative binomial) and flexible innovation distributions (Poisson, Bell, negative binomial), categorical antedependence models for discrete-state longitudinal outcomes, and Gaussian antedependence (AD) models for continuous data. Implements maximum likelihood estimation via time-separable optimization and block coordinate descent, with confidence intervals based on Louis' identity and profile likelihood. Package: r-cran-anthro Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-anthro_1.1.0-1.ca2404.1_all.deb Size: 367280 MD5sum: aa8438e06bdc47848595ff8fd6cc2d47 SHA1: 186c0bbff5bcee0b80a1ccfcb0de178a3937d8de SHA256: 55be958b4a97752300698be817ac654eb3a4034f70c9529155342fd44f97e0af SHA512: 2e7c088a39ed705c5a5eafea4d1cfa12d3d4bb1c957b120f21a405b7cfc3c20c134192a0c651f00d47936daf643bb27a10c5a1c5a3c3c297ed0f7612bf3d070f Homepage: https://cran.r-project.org/package=anthro Description: CRAN Package 'anthro' (Computation of the WHO Child Growth Standards) Provides WHO Child Growth Standards (z-scores) with confidence intervals and standard errors around the prevalence estimates, taking into account complex sample designs. More information on the methods is available online: . 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More information on the methods is available online: . 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Package: r-cran-antibodyforests Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2744 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-bioc-biostrings, r-cran-dplyr, r-cran-gtools, r-cran-igraph, r-cran-magrittr, r-bioc-pwalign, r-cran-rlang, r-cran-scales, r-cran-seqinr, r-cran-stringdist, r-cran-stringr, r-cran-tidyr, r-cran-viridis Suggests: r-cran-alakazam, r-cran-base64enc, r-cran-bio3d, r-cran-combinat, r-cran-devtools, r-cran-dt, r-cran-fpc, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggsignif, r-cran-htmltools, r-cran-knitr, r-bioc-msa, r-cran-phangorn, r-cran-pheatmap, r-cran-philentropy, r-cran-peptides, r-cran-rcompadre, r-cran-rmarkdown, r-cran-rpanda, r-cran-swiper Filename: pool/dists/noble/main/r-cran-antibodyforests_1.1.0-1.ca2404.1_all.deb Size: 1791990 MD5sum: 5e9e5a92d82afdad386cac1ae2820da4 SHA1: 7dffa94f9c39b89009af9a3ac8f265d4c095f70a SHA256: 7219b1620f00c8f6235af81c41fceb23dda3fbbe3d08bd7903a51b6e3eff6cbb SHA512: 53d75e581eb46be886713211d333cde01a16171b718a5af889f3ed4a9210ae2f9b5ac6dff4ced700d2b9052286a5159b946673477bab55e6448841d58cc4a6ec Homepage: https://cran.r-project.org/package=AntibodyForests Description: CRAN Package 'AntibodyForests' (Delineating Inter- And Intra-Antibody Repertoire Evolution) The generated wealth of immune repertoire sequencing data requires software to investigate and quantify inter- and intra-antibody repertoire evolution to uncover how B cells evolve during immune responses. Here, we present 'AntibodyForests', a software to investigate and quantify inter- and intra-antibody repertoire evolution. Package: r-cran-antibodytiters Architecture: all Version: 0.1.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openxlsx, r-cran-desctools Filename: pool/dists/noble/main/r-cran-antibodytiters_0.1.24-1.ca2404.1_all.deb Size: 216792 MD5sum: bd6f19bdcd85bf0a87ba2f4b15b2e37c SHA1: f0122172ac58645dc454e0834c406d0f8fb6ec9c SHA256: f9d195bb04af2466f624098dfcf1afe26f737fa76313041dde146711820ac13a SHA512: d87cabb77fe1961059ec47d5f83c8f6de4c973d530f3c6fa5f320ab7e2f7ea8010060ea2feeb524e97c427d1bd8c3768fe87f6fbf518baa419591b581368a3b0 Homepage: https://cran.r-project.org/package=AntibodyTiters Description: CRAN Package 'AntibodyTiters' (Antibody Titer Analysis of Vaccinated Patients) Visualization of antibody titer scores is valuable for examination of vaccination effects. 'AntibodyTiters' visualizes antibody titers of all or selected patients. This package also produces empty excel files in a specified format, in which users can fill in experimental data for visualization. Excel files with toy data can also be produced, so that users can see how it is visualized before obtaining real data. The data should contain titer scores at pre-vaccination, after-1st shot, after-2nd shot, and at least one additional sampling points. Patients with missing values can be included. The first two sampling points (pre-vaccination and after-1st shot) will be plotted discretely, whereas those following will be plotted on a continuous time scale that starts from the day of second shot. Half-life of titer can also be calculated for each pair of sampling points. Package: r-cran-antitrust Architecture: all Version: 0.99.30-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2438 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb, r-cran-numderiv Suggests: r-cran-mass, r-cran-ggplot2, r-cran-knitr, r-cran-bookdown, r-cran-rmarkdown, r-cran-competitiontoolbox Filename: pool/dists/noble/main/r-cran-antitrust_0.99.30-1.ca2404.1_all.deb Size: 1130872 MD5sum: 91188663fed7044bb5718db6b7fb42ab SHA1: b1690707902e9f15f591fe0710d6e992b7239f5b SHA256: 5b5703a03bac2adc68452c82979755cdac64c0a34c62323e0126546fbb05b1b8 SHA512: e9ec6eabad1766d5fc6535c861091bdf976bf6e63b8eca7a8408008525901bcca28cd1b210f3c2149d9dd524c9877e17bd09df6c66a11959d8c75de8b24bafbc Homepage: https://cran.r-project.org/package=antitrust Description: CRAN Package 'antitrust' (Tools for Antitrust Practitioners) A collection of tools for antitrust practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of mergers under different competitive regimes. Package: r-cran-antsnet Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ranger, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-patchwork, r-cran-viridis, r-cran-ggpubr Suggests: r-cran-shiny, r-cran-cowplot, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-antsnet_1.0.0-1.ca2404.1_all.deb Size: 248646 MD5sum: c55d7ea332932a7daa049da0b675ebd8 SHA1: 522be5a19629714014edd2a05c222c58928a1676 SHA256: 13cecc4a4fe1ca2c9c03d3f845adb5241ec3533d1c712ee1c56709861e91342f SHA512: 5d627e628de31b0a90b44037dfb60acea70e293446bdbe59f73657a662dcd539531de6e9b1d868f18e6ce2dd2dd9172726ed68276d25a4f844d56c97c0e084f8 Homepage: https://cran.r-project.org/package=AntsNet Description: CRAN Package 'AntsNet' (Unified Simulation of Isomorphisms Between Ant ColonyIntelligence and Machine Learning) Implements the full suite of simulation, visualization, and analysis tools for exploring the mathematical isomorphisms between ant colony decision-making and three major paradigms of machine learning: random forests (Part I: variance reduction through decorrelation), boosting (Part II: bias reduction through adaptive recruitment), and neural networks (Part III: gradient-based generational learning). 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Package: r-cran-aod Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass, r-cran-boot, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-aod_1.3.3-1.ca2404.1_all.deb Size: 392010 MD5sum: eb3fa4edacf6c3b55984147b509acce5 SHA1: 0895d13a36e8efc790e3dc1561eaed1050e9fbca SHA256: d7d35c9028cd43a5e8531e125cfc038441d38d146437800aca54ee971699fd6a SHA512: 4357dfc81947e7a11cbd74232b7390567cf04de488184a424a4faf4b824937d8bcf82a7fa1dcc51f501883f8f9d86d03d7d91765fee751ff5d36025e2a8d723a Homepage: https://cran.r-project.org/package=aod Description: CRAN Package 'aod' (Analysis of Overdispersed Data) Provides a set of functions to analyse overdispersed counts or proportions. Most of the methods are already available elsewhere but are scattered in different packages. 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These functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM). aods3 is an S3 re-implementation of the deprecated S4 package aod. Package: r-cran-aoos Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-roxygen2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rbenchmark, r-cran-r6, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aoos_0.5.0-1.ca2404.1_all.deb Size: 258320 MD5sum: e83302aacd76ccd99f5569875c0a2dd2 SHA1: 26bebefc7d90a4e213758f3d86bcfdaaba44bab4 SHA256: 2400c34786b1a8289515e6db9a8682ffab9fd511236d2bfb8ac8fcf5fa825afc SHA512: 71782ae39282d4a55403db51256ca3f4ca567d06032d2c787f609b527e95fda4003dd032d5dca72df29d7cef73e5e00764f3491ae31f75f1f75ffe8cb285fbf8 Homepage: https://cran.r-project.org/package=aoos Description: CRAN Package 'aoos' (Another Object Orientation System) Another implementation of object-orientation in R. It provides syntactic sugar for the S4 class system and two alternative new implementations. One is an experimental version built around S4 and the other one makes it more convenient to work with lists as objects. Package: r-cran-aopdata Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-checkmate, r-cran-curl, r-cran-data.table, r-cran-rlang, r-cran-sf Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-units Filename: pool/dists/noble/main/r-cran-aopdata_1.1.2-1.ca2404.1_all.deb Size: 400018 MD5sum: c5bd4270875a34a14a7af97aad195766 SHA1: eeaf2c906024f966acd9dc3f1290f5b4b5dccb9f SHA256: ad79cc5f34f8b993cd9ecb547143f49b0633582062dc42b9f9eda19b60f86618 SHA512: 8a2b75bee70fedb7efb024b5da4e6026692b994f9e38a36bdffae691162372a942952edc6634036209678ece9d89e2af513a5d04f06977e7df24104b7fcfbc3a Homepage: https://cran.r-project.org/package=aopdata Description: CRAN Package 'aopdata' (Data from the 'Access to Opportunities Project (AOP)') Download data from the 'Access to Opportunities Project (AOP)'. The 'aopdata' package brings annual estimates of access to employment, health, education and social assistance services by transport mode, as well as data on the spatial distribution of population, jobs, health care, schools and social assistance facilities at a fine spatial resolution for all cities included in the project. More info on the 'AOP' website . Package: r-cran-aoptbdtvc Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-mass Filename: pool/dists/noble/main/r-cran-aoptbdtvc_0.0.3-1.ca2404.1_all.deb Size: 89076 MD5sum: b2b7fe8597bfe3a791f8084fdc12b209 SHA1: 696597befdd5fe948231a2be0623eef16631361c SHA256: 513a7af3b802e17de6a18174f016a1616505c48c0d3032fafe88428465f80c05 SHA512: af906faaff5cb0c2e4a93696cd6fb3394d3e3297d970ceb061a51ba2199017f70f419400d2001319b09e6454dd0e2c40f5422afa66637f041fb37b6ba8eec39b Homepage: https://cran.r-project.org/package=Aoptbdtvc Description: CRAN Package 'Aoptbdtvc' (A-Optimal Block Designs for Comparing Test Treatments withControls) A collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737. Package: r-cran-aoristic Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openxlsx, r-cran-ggplot2, r-cran-lubridate, r-cran-plyr, r-cran-scales, r-cran-tidyr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-aoristic_1.1.1-1.ca2404.1_all.deb Size: 123070 MD5sum: ec4703eb3d4d6002d826eae02f197e00 SHA1: 1b17e4be099cf3568351046ad1c114d195d84b68 SHA256: 1191b20377baf95fa2a7c6271b92456ae57a92fde4b2d74da364ee53f753b6d6 SHA512: eaa97b51d73e7255dbc8aaf2a12e35c10e6dc55e300fc394dbe668ddf8092bb5bbf53b88a7c3a365389423052d6049150e785cbd3d53dfb99396def1182835fd Homepage: https://cran.r-project.org/package=aoristic Description: CRAN Package 'aoristic' (Generates Aoristic Probability Distributions) It can sometimes be difficult to ascertain when some events (such as property crime) occur because the victim is not present when the crime happens. As a result, police databases often record a 'start' (or 'from') date and time, and an 'end' (or 'to') date and time. The time span between these date/times can be minutes, hours, or sometimes days, hence the term 'Aoristic'. Aoristic is one of the past tenses in Greek and represents an uncertain occurrence in time. For events with a location describes with either a latitude/longitude, or X,Y coordinate pair, and a start and end date/time, this package generates an aoristic data frame with aoristic weighted probability values for each hour of the week, for each observation. The coordinates are not necessary for the program to calculate aoristic weights; however, they are part of this package because a spatial component has been integral to aoristic analysis from the start. Dummy coordinates can be introduced if the user only has temporal data. Outputs include an aoristic data frame, as well as summary graphs and displays. For more information see: Ratcliffe, JH (2002) Aoristic signatures and the temporal analysis of high volume crime patterns, Journal of Quantitative Criminology. 18 (1): 23-43. Note: This package replaces an original 'aoristic' package (version 0.6) by George Kikuchi that has been discontinued with his permission. Package: r-cran-aos Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-htmltools Suggests: r-cran-shiny Filename: pool/dists/noble/main/r-cran-aos_0.1.0-1.ca2404.1_all.deb Size: 67906 MD5sum: 5c8f17924bfb6702d65a6dc003ca122b SHA1: feed655118c07e2c62f917b2c78afdc9e6841687 SHA256: b59e012a8fccdd967b3cb5baaa7b1e549a66024464b6542896d6a048a07b30ee SHA512: dbeff12cb93af20fec8859de8973a422aac79ca09f39abdc63dc40c4aa668059e8d2ec15b03fcccb2303a0c939235369b88dd60bd47593e9c6066ca05da95f71 Homepage: https://cran.r-project.org/package=aos Description: CRAN Package 'aos' (Animate on Scroll Library for 'shiny') Trigger animation effects on scroll on any HTML element of 'shiny' and 'rmarkdown', such as any text or plot, thanks to the 'AOS' Animate On Scroll jQuery library. 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For more details see - Koleck TA, Dreisbach C, Zhang C, Grayson S, Lor M, Deng Z, Conway A, Higgins PDR, Bakken S (2024) . 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References: Burdick & Graybill (1992, ISBN-13: 978-0824786441); Weerahandi (1995) ; Lin & Liao (2008) . 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The package includes classical and modern methods: Singular Value Decomposition (SVD) based on Eckart and Young (1936) , Power Iteration based on Hotelling (1933) , QR Algorithm based on Francis (1961) , Jacobi Algorithm based on Jacobi (1846) , Arnoldi Iteration based on Arnoldi (1951) , 'NIPALS' based on Wold (1975) , Alternating Least Squares (ALS) based on Kolda and Bader (2009) , Probabilistic PCA (PPCA) with EM Algorithm based on Tipping and Bishop (1999) , and Generalized Hebbian Algorithm (GHA) based on Sanger (1989) . 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Hodges in 2019. A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics. Package: r-cran-apcinteraction Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-pbapply, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-apcinteraction_0.1.0-1.ca2404.1_all.deb Size: 519588 MD5sum: d2ce0dbe688e8e5a7041cab1fa5f2cb1 SHA1: 9fbace35484b963a11b6afaee987145cc47dc0f8 SHA256: e57bf0dfba5ffd100b45ac6b1a4ea0e03bbb280c095221d3cc847593df161e2c SHA512: c1a2ad6f1fb3a5953350040d2cee41c3723b103174c771b3e16301b4fc20dfc2b4ce14e17c864b05a5d05225711910ddb28e7cf2f0066d0896d450e220a44dac Homepage: https://cran.r-project.org/package=APCinteraction Description: CRAN Package 'APCinteraction' (Nonparametric Interaction Tests in Balanced Two-Way ANOVA Models) Provides novel nonparametric tests, 'APCSSA' and 'APCSSM', for interaction in two-way ANOVA designs with balanced replications using all possible comparisons. These statistics extend previous methods, allow greater flexibility, and demonstrate higher power in detecting interactions for non-normal data. The package includes optimized functions for computing these test statistics, generating interaction plots, and simulating their null distributions. The companion package 'APCinteractionData' is available on 'GitHub' . Methods are described and compared empirically in Tran, Wagaman, Nguyen, Jacobson, and Hartlaub (2024) . Package: r-cran-apcoa Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan, r-cran-randomcolor, r-cran-ape, r-cran-car, r-cran-cluster Filename: pool/dists/noble/main/r-cran-apcoa_1.3-1.ca2404.1_all.deb Size: 29452 MD5sum: e04de5c0e1ea7cac1d0a82d15f80fda0 SHA1: 63c027c2d97abc687466c638dba814800dc5c27a SHA256: 66fa830183ed1499df4fd7dcddff33b547a3bbb2717bbc2412d9390267ac7991 SHA512: f7747b558dd4ba4e3503c34e645685d9bc1b0a42a6b8eed61b36bf038f63c3cbb889ab5535604889f7faa8deb1514e7c261d54de1fe9090a7542d2eb2cab4b34 Homepage: https://cran.r-project.org/package=aPCoA Description: CRAN Package 'aPCoA' (Covariate Adjusted PCoA Plot) In fields such as ecology, microbiology, and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis (PCoA) is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide 'aPCoA' as an easy-to-use tool to improve data visualization in this context, enabling enhanced presentation of the effects of interest. Details are described in Yushu Shi, Liangliang Zhang, Kim-Anh Do, Christine Peterson and Robert Jenq (2020) Bioinformatics, Volume 36, Issue 13, 4099-4101. Package: r-cran-apctools Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6011 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggpubr, r-cran-checkmate, r-cran-knitr, r-cran-ggplot2, r-cran-colorspace, r-cran-dplyr, r-cran-mgcv, r-cran-scales, r-cran-tidyr, r-cran-stringr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-apctools_1.0.8-1.ca2404.1_all.deb Size: 4072214 MD5sum: eb30767d9569efed28cfa54683b0f4d0 SHA1: 8199dfca38652a419f0739692b4db7129cf71503 SHA256: cb52e1ec750b0713a1045aeb0281118e511e243b566f61b60809b0ae9937e614 SHA512: fda39aae1bb32aa46f8db0d5be84d2051b09834ec6be2bbbe661edd04e4c7ffa44db3e1210ef2f734a6245982592cc150a16ad8bfce28c33e2a749051df43da0 Homepage: https://cran.r-project.org/package=APCtools Description: CRAN Package 'APCtools' (Routines for Descriptive and Model-Based APC Analysis) Age-Period-Cohort (APC) analyses are used to differentiate relevant drivers for long-term developments. The 'APCtools' package offers visualization techniques and general routines to simplify the workflow of an APC analysis. Sophisticated functions are available both for descriptive and regression model-based analyses. For the former, we use density (or ridgeline) matrices and (hexagonally binned) heatmaps as innovative visualization techniques building on the concept of Lexis diagrams. Model-based analyses build on the separation of the temporal dimensions based on generalized additive models, where a tensor product interaction surface (usually between age and period) is utilized to represent the third dimension (usually cohort) on its diagonal. Such tensor product surfaces can also be estimated while accounting for further covariates in the regression model. See Weigert et al. (2021) for methodological details. Package: r-cran-apdesign Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-apdesign_1.0.0-1.ca2404.1_all.deb Size: 22848 MD5sum: 98898ff7c5555ba31d2c6f272725ab9c SHA1: 053c6f319c4141fdbfa04cb8fa0e2da9df6028cb SHA256: a78a675863380ae5b2c29c395710bb011afd428280358ebb43afac8f9a66e0a4 SHA512: 30c17201d53f03bdb2e794e71d1346b0001ff68a7100ce721c6413048616ff018b341c4aac6d099ade4501cf2e39b09cce17643aba6d266815a2f380ab6f1793 Homepage: https://cran.r-project.org/package=apdesign Description: CRAN Package 'apdesign' (An Implementation of the Additive Polynomial Design Matrix) An implementation of the additive polynomial (AP) design matrix. It constructs and appends an AP design matrix to a data frame for use with longitudinal data subject to seasonality. 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This reduces the redundancy of the overlapping pathways and helps to notice the most important biological themes in the data (Kerseviciute and Gordevicius (2023) ). Package: r-cran-apercu Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pls Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-apercu_0.2.5-1.ca2404.1_all.deb Size: 27850 MD5sum: 2384fc3da5aa6ba63b293498149198e8 SHA1: f5125168eb76851fe0bbd5d0e650c78e8a7cf94b SHA256: fe27e642c934da6185c20cc93afd3c9b0465fd653bf99ea3e78636c22f7bf5e7 SHA512: ebe24ee33ec2f55b2a5bfbbdecf7def2589eb502947f65459fa03a3c01074449646fcc00ece42a781d06afe146f91f46235e0c22c1ac34a19b676bf4d3074424 Homepage: https://cran.r-project.org/package=apercu Description: CRAN Package 'apercu' (Quick Look at your Data) The goal is to print an "aperçu", a short view of a vector, a matrix, a data.frame, a list or an array. By default, it prints the first 5 elements of each dimension. By default, the number of columns is equal to the number of lines. If you want to control the selection of the elements, you can pass a list, with each element being a vector giving the selection for each dimension. 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'apex' implements the new S4 classes 'multidna', 'multiphyDat' and associated methods to handle aligned DNA sequences from multiple genes. Package: r-cran-apexcharter Architecture: all Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-magrittr, r-cran-rlang, r-cran-ggplot2, r-cran-jsonlite, r-cran-shiny Suggests: r-cran-testthat, r-cran-knitr, r-cran-scales, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-apexcharter_0.4.5-1.ca2404.1_all.deb Size: 635794 MD5sum: 7464082f40af1cc31cb7de958ecfec28 SHA1: 5b6e753027ccded44d3cd883cfc31e526534aaf8 SHA256: 090bab398cb788599ee25c8f6453382db969eb8c9ef069cfa5bb3faffe4208bc SHA512: c78275000b9d886fb341fc0bada4aba1bfbf417e6c733aa330fe055c78e85b17e0fe57e2e55aa08cb75f2fec5614cb5609ac14293e9acdb1810ea7af054167f1 Homepage: https://cran.r-project.org/package=apexcharter Description: CRAN Package 'apexcharter' (Create Interactive Chart with the JavaScript 'ApexCharts'Library) Provides an 'htmlwidgets' interface to 'apexcharts.js'. 'Apexcharts' is a modern JavaScript charting library to build interactive charts and visualizations with simple API. 'Apexcharts' examples and documentation are available here: . Package: r-cran-apfr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-apfr_1.0.2-1.ca2404.1_all.deb Size: 63108 MD5sum: c6378eb7cedf55b61f2bd1bf57574446 SHA1: b3a721366e914f6ff22aa973fac93b639c97d8f9 SHA256: 0b21c4f0250308da5643df080b3ae2d150da7b6f57d962103b4392fe6b2fb523 SHA512: b233b53ffe3e4efb6dd6b77a6a5a5eac52041e3d3c1da7fa98137acea6f79bded254fe2812055011bb620c637b951c5ebcaffc0aded645a9980f5dc77da9a0ce Homepage: https://cran.r-project.org/package=APFr Description: CRAN Package 'APFr' (Multiple Testing Approach using Average Power Function (APF) andBayes FDR Robust Estimation) Implements a multiple testing approach to the choice of a threshold gamma on the p-values using the Average Power Function (APF) and Bayes False Discovery Rate (FDR) robust estimation. Function apf_fdr() estimates both quantities from either raw data or p-values. Function apf_plot() produces smooth graphs and tables of the relevant results. Details of the methods can be found in Quatto P, Margaritella N, et al. (2019) . Package: r-cran-api2lm Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-api2lm_0.2-1.ca2404.1_all.deb Size: 293748 MD5sum: 12cd3586f4bb55edf792340bda6efe0a SHA1: 1ae51ad65c49beea58443e8771b6e7fd43889bc9 SHA256: e5f6eb5e89958f84e4b0d7619348a11d46b5e10e8edae95e68b9c29db815d01b SHA512: 42d4ef7c8b5ab2eec5ec93e3f9ecc89e190a994c6b55a5c902e128d3a516f47828f98b7f70ee317872daa1f598f451b671898a1124478e72930cca0206ee804d Homepage: https://cran.r-project.org/package=api2lm Description: CRAN Package 'api2lm' (Functions and Data Sets for the Book "A Progressive Introductionto Linear Models") Simplifies aspects of linear regression analysis, particularly simultaneous inference. Additionally, supports "A Progressive Introduction to Linear Models" by Joshua French (). 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See . Package: r-cran-aplms Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-mass, r-cran-matrix, r-cran-rlist, r-cran-psych, r-cran-rmutil Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aplms_0.1.0-1.ca2404.1_all.deb Size: 265838 MD5sum: 356b7c127165887c1e873e7ce14a1b3e SHA1: e5bb397b82696900c03a99c78c3220a668777f09 SHA256: ad763e09f97149fe0da38bbc9941815dcb3b9c81142807b909c2fb5aea8575ec SHA512: 8f6932744816bb4b57f6301e18f0cb12781e85b39fcda379fa89ffa873126e248045232a45cde8a62262e73499396806f24270af36bb7e462eb8f2c7c69d79eb Homepage: https://cran.r-project.org/package=aplms Description: CRAN Package 'aplms' (Additive Partial Linear Models with Symmetric AutoregressiveErrors) Set of tools for fitting the additive partial linear models with symmetric autoregressive errors of order p, or APLMS-AR(p). This setup enables the modeling of a time series response variable using linear and nonlinear structures of a set of explanatory variables, with nonparametric components approximated by natural cubic splines or P-splines. It also accounts for autoregressive error terms with distributions that have lighter or heavier tails than the normal distribution. The package includes various error distributions, such as normal, generalized normal, Student's t, generalized Student's t, power-exponential, and Cauchy distributions. Chou-Chen, S.W., Oliveira, R.A., Raicher, I., Gilberto A. Paula (2024) . Package: r-cran-aplore3 Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-mass, r-cran-vcdextra, r-cran-nnet, r-cran-survival, r-cran-proc Filename: pool/dists/noble/main/r-cran-aplore3_0.9-1.ca2404.1_all.deb Size: 444194 MD5sum: 4e334569bff836fc480e7cede89ff3c1 SHA1: 54883f101cabcd298e3cf3049887b6642a98be13 SHA256: 3db968f1eee0d6efb5d56417efb2e95ccf5c7a622f9d365fca91c2d9f51b47d0 SHA512: a7d83bfb8073802eb504de55a6b6af703a8c8e4b97c434fcbf4c25db0d9f3c00fe90d477a418eb4ed31bd8eec4ca390ab4fe10f60ad6392776fbd1b7a9cb3c39 Homepage: https://cran.r-project.org/package=aplore3 Description: CRAN Package 'aplore3' (Datasets from Hosmer, Lemeshow and Sturdivant, "Applied LogisticRegression" (3rd Ed., 2013)) An unofficial companion to "Applied Logistic Regression" by D.W. Hosmer, S. Lemeshow and R.X. Sturdivant (3rd ed., 2013) containing the dataset used in the book. Package: r-cran-aplosnca Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azureauth, r-cran-httr, r-cran-jsonlite, r-cran-stringr Suggests: r-cran-httptest2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-downloader Filename: pool/dists/noble/main/r-cran-aplosnca_1.0.1-1.ca2404.1_all.deb Size: 51026 MD5sum: 83aa1e82384bfe065463625821f1e9fa SHA1: 73f025b69cf9401eb3074606c61eca2cc4457ce4 SHA256: 9cecdda379b48071944618843ea5dc00e36626eca032b0ba2043d89203d5442e SHA512: 7ff95669a472ba3b9d6a5fda781ccb6fa3cd71688c720cebfa8edfbb779e4ab32ccfcb0068b7da36c41cdc0a68ba45162aa628a94c88d22bac44d9395a623c8f Homepage: https://cran.r-project.org/package=AplosNCA Description: CRAN Package 'AplosNCA' (Use 'Aplos NCA API' for Pharmacokinetic Analysis) Using this package you can interact with the 'Aplos NCA API' using standard R functions. This will allow you to authenticate with your 'Aplos NCA' account, upload input datasets, initiate analyses, and download results. 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Matches are approximately optimal in the sense that the cost of the solution is at most twice the optimal cost, Crama and Spieksma (1992) , Karmakar, Small and Rosenbaum (2019) . Package: r-cran-appsflyer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-appsflyer_0.1.0-1.ca2404.1_all.deb Size: 22946 MD5sum: 0fdf2526e0d041687ef9d29a935a06a5 SHA1: d7e762750ded41f5a0fee05a2de2c0446de08e03 SHA256: db6bbb9f027d8b68933b0d9d5cc4bc664a50e73301814d8904cb95b329f87f3a SHA512: 91bda5ffd32d350697eebc3823d0da3639645c7813c97806f311c2cc5b434b2a3d5dd14c85c6519b455cf2011a39f74db498f2b9cf088e55092c573c080fdafa Homepage: https://cran.r-project.org/package=appsflyeR Description: CRAN Package 'appsflyeR' (Get Data from 'Appsflyer' via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from 'Appsflyer' using the 'Windsor.ai' API . 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Package: r-cran-aprean3 Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-aprean3_1.0.2-1.ca2404.1_all.deb Size: 204506 MD5sum: 5eba5e7075323e5e71b2014687cbd4c3 SHA1: 54af436b8562a3bfc824c9fe30bdbe385c48ac65 SHA256: 034b340dbb539bc7bf84c401411ce8b5b5236d33ebb805d84238447aa30d276f SHA512: 2fb0b0b384331fa02c63adf4c3d3bc5c2525580fcdf9a7c96ad42eb3710340161868d9c5147dc085e0c3f010f194e973ed0823fe1aba9768c67762c873249021 Homepage: https://cran.r-project.org/package=aprean3 Description: CRAN Package 'aprean3' (Datasets from Draper and Smith "Applied Regression Analysis"(3rd Ed., 1998)) An unofficial companion to the textbook "Applied Regression Analysis" by N.R. Draper and H. Smith (3rd Ed., 1998) including all the accompanying datasets. Package: r-cran-aprof Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aprof_0.4.1-1.ca2404.1_all.deb Size: 59498 MD5sum: 270130ca285511668356c63b4b42daf2 SHA1: 3661046aad637455197eadc6c4217d5eb69481e8 SHA256: 1f116f0257cf233211deb53553b5499e8cfaf41e4e8d837e17ca9ed1cb5bebe9 SHA512: 6f4ff9ddc8b7caa6f3ef894a351b8b24bd028a353a5f004585cfa9ca92b39e2ee3b49c46861bfd4b0cf1d0d9c066f478bf67c34a5979422d4eae9ba7b6609989 Homepage: https://cran.r-project.org/package=aprof Description: CRAN Package 'aprof' (Amdahl's Profiler, Directed Optimization Made Easy) Assists the evaluation of whether and where to focus code optimization, using Amdahl's law and visual aids based on line profiling. Amdahl's profiler organizes profiling output files (including memory profiling) in a visually appealing way. It is meant to help to balance development vs. execution time by helping to identify the most promising sections of code to optimize and projecting potential gains. The package is an addition to R's standard profiling tools and is not a wrapper for them. Package: r-cran-aps Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ranger, r-cran-covr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aps_1.0.1-1.ca2404.1_all.deb Size: 486536 MD5sum: b0f439babd4ee001e6e13fcf469812ce SHA1: 153094ecf308e5135b16cdb1ad3a1b00fd4e9dab SHA256: 61d6d4397212909dfd5209d83f2244e3b673fd987f7bada572bd35879e6f21e8 SHA512: da6404fd6e6783b198b70ed002c1dc9f320a50f2b98f68719194913a9770def8de1e0d5e416d9f9a605add7ef6cc911535dc79abbfae5fde88a4f0f794673c72 Homepage: https://cran.r-project.org/package=APS Description: CRAN Package 'APS' (Analysing Prediction Stability of Non-Deterministic PredictionModels) Provides methods to analyse the stability of non-deterministic prediction models. Prediction stability is quantified either as data-based prediction stability (phi) or as model-based prediction stability (psi). The package implements measures for categorical, ordinal, and metric predictions based on repeated model fitting and corresponding predictions. Methods are based on Lange et al. (2025) . 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The files with an 'apsim' extension correspond to 'APSIM' Classic (7.x) - Windows only - and the ones with an 'apsimx' extension correspond to 'APSIM' "Next Generation". For more information about 'APSIM' see () and for 'APSIM' next generation (). Package: r-cran-apt Architecture: all Version: 4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-erer, r-cran-car, r-cran-urca Filename: pool/dists/noble/main/r-cran-apt_4.0-1.ca2404.1_all.deb Size: 94462 MD5sum: c17527b9ae88e74001850bca497970c1 SHA1: b3747ec9dc86af7c36b8abee307dae34a519091a SHA256: 6f6acb90855328d9fe89817ada7904d9284954156d6855e1c0ceb4120b8693f1 SHA512: eecbef6e6c87b3555c2e258b7f4e957e398ea155ad5a5997cdb3dd92baef18710c0f6e2327ad1983844fb77a61d9b37b8b03195080a570e07bf55ea36da239b6 Homepage: https://cran.r-project.org/package=apt Description: CRAN Package 'apt' (Asymmetric Price Transmission) The transmission between two time-series prices is assessed. It contains several functions for linear and nonlinear threshold co-integration, and furthermore, symmetric and asymmetric error correction models. Package: r-cran-apticalc Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2 Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-apticalc_0.1.1-1.ca2404.1_all.deb Size: 30038 MD5sum: 1818cc0acb75d02413a07e3516aead5e SHA1: 6daac27f460e11b0e4be6f4648bdf87ba333b4de SHA256: dabbbb4069d94e5a38f6ff76d71573a2c2605e9655600938c6a8b7ad52661886 SHA512: 426ef6ff833e1f131b3993ce4e879360a67c01eadce01a7a7a7977416e7c9f60371b925f744140923d9c093e74659df1219f80b9a61898dc678b1e719769cae1 Homepage: https://cran.r-project.org/package=APTIcalc Description: CRAN Package 'APTIcalc' (Air Pollution Tolerance Index (APTI) Calculator) It calculates the Air Pollution Tolerance Index (APTI) of plant species using biochemical parameters such as chlorophyll content, leaf extract pH, relative water content, and ascorbic acid content. It helps in identifying tolerant species for greenbelt development and pollution mitigation studies. It includes a 'shiny' app for interactive APTI calculation and visualisation. For method details see, Sahu et al. (2020).. Package: r-cran-aptools Architecture: all Version: 6.8.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-cmprsk Filename: pool/dists/noble/main/r-cran-aptools_6.8.8-1.ca2404.1_all.deb Size: 75240 MD5sum: 5315421ac212df458ec238e3180f8aa6 SHA1: 1f95ef43115a647ef3bb026e1996fe6d98dacccc SHA256: 0679524f2fee1f747bf5662c15b322f61696e0a116076d626c5731c220765b5b SHA512: 7fde0a138745970e1ac3a0a0cd8db299a6a841bf9e9b847c1fdb99a552ca90c06f5fd312bb6f791f9d6218b897091fc2df34ee03011e33ae6a2b239b4f9ce90b Homepage: https://cran.r-project.org/package=APtools Description: CRAN Package 'APtools' (Average Positive Predictive Values (AP) for Binary Outcomes andCensored Event Times) We provide tools to estimate two prediction accuracy metrics, the average positive predictive values (AP) as well as the well-known AUC (the area under the receiver operator characteristic curve) for risk scores. The outcome of interest is either binary or censored event time. Note that for censored event time, our functions' estimates, the AP and the AUC, are time-dependent for pre-specified time interval(s). A function that compares the APs of two risk scores/markers is also included. Optional outputs include positive predictive values and true positive fractions at the specified marker cut-off values, and a plot of the time-dependent AP versus time (available for event time data). Package: r-cran-apyramid Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1081 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyselect, r-cran-rlang, r-cran-forcats, r-cran-dplyr, r-cran-scales, r-cran-glue Suggests: r-cran-testthat, r-cran-survey, r-cran-srvyr, r-cran-vdiffr, r-cran-covr, r-cran-outbreaks, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-apyramid_0.1.3-1.ca2404.1_all.deb Size: 808762 MD5sum: dc4f5ec57115cb34afef0c250e15123c SHA1: fcc8421de17724d4a9fa122a2c8a51187ee48fe6 SHA256: 35317640625ff92e0b4190f1ce884850ab0e427ccffa8b6d79892f39c3640f70 SHA512: d313b11603175e5d7484a70b3a53f08ec2b5d659ff5187a539bd4b8e7ff9671f7d1d9eaa53fd7614fcc0f73f202c96c629d68829974e5fe50e1ffb097a81015e Homepage: https://cran.r-project.org/package=apyramid Description: CRAN Package 'apyramid' (Visualize Population Pyramids Aggregated by Age) Provides a quick method for visualizing non-aggregated line-list or aggregated census data stratified by age and one or two categorical variables (e.g. gender and health status) with any number of values. It returns a 'ggplot' object, allowing the user to further customize the output. This package is part of the 'R4Epis' project . 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The main functions use signal isolation then break-point/segment (BP/S) methods based on 'strucchange' and 'segmented' methods to detect and quantify change events (Ropkins & Tate, 2021, ; Ropkins et al., 2026, ). Package: r-cran-aqfig Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geor Suggests: r-cran-maps Filename: pool/dists/noble/main/r-cran-aqfig_0.9-1.ca2404.1_all.deb Size: 54016 MD5sum: dc904bca663a27661e6a33c1080cacbb SHA1: bd248aa3338b698b3bceb614db6aa6fa1facd8fc SHA256: 9969d081231435785faecc77b8663107d236e9e0a130e586d126cdd2f8f8118c SHA512: 666fc64e16a3c499ce35662ca76160b160454bf4efad3c2d957dffdb2ea8fd98e0cb31a8cb3a7340ab10980df669b7d712463520f83e3679b29fb3eee88398ea Homepage: https://cran.r-project.org/package=aqfig Description: CRAN Package 'aqfig' (Display Air Quality Model Output and Monitoring Data) Display air quality model output and monitoring data using scatterplots, grids, and legends. Package: r-cran-aqlschemes Architecture: all Version: 1.7-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-aqlschemes_1.7-2-1.ca2404.1_all.deb Size: 353390 MD5sum: fabe9cee756434b901b5ff57b13222cd SHA1: 394c849af239e79cab3960ef5a69c735a21e3f43 SHA256: f11cc100e6d752e90a89feea93c6f835bd3a3b1f0e57dbf38beecc6f934f8068 SHA512: 51cc9fd9eda390d4bd97feecc620e73b52c674e7d32922afe9917abe5e19b175b72db4b7b92e3d283e9ff6ef221cbb6c966dadac2d76fa5788a2ec94895625ec Homepage: https://cran.r-project.org/package=AQLSchemes Description: CRAN Package 'AQLSchemes' (Retrieving Acceptance Sampling Schemes) Functions are included for recalling AQL (Acceptable Quality Level or Acceptance Quality Level) Based single, double, and multiple attribute sampling plans from the Military Standard (MIL-STD-105E) - American National Standards Institute/American Society for Quality (ANSI/ASQ Z1.4) tables and for retrieving variable sampling plans from Military Standard (MIL-STD-414) - American National Standards Institute/American Society for Quality (ANSI/ASQ Z1.9) tables. The sources for these tables are listed in the URL: field. Also included are functions for computing the OC (Operating Characteristic) and ASN (Average Sample Number) coordinates for the attribute plans it recalls, and functions for computing the estimated proportion nonconforming and the maximum allowable proportion nonconforming for variable sampling plans. The MIL-STD AQL Sampling schemes were the most used and copied set of standards in the world. They are intended to be used for sampling a stream of lots, and were used in contract agreements between supplier and customer companies. When the US military dropped support of MIL-STD 105E and 414, The American National Standards Institute (ANSI) and the International Standards Organization (ISO) adopted the standard with few changes or no changes to the central tables. This package is useful because its computer implementation of these tables duplicates that available in other commercial software and subscription online calculators. Package: r-cran-aqp Architecture: all Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-cluster, r-cran-data.table, r-cran-farver, r-cran-digest, r-cran-colorspace, r-cran-ape Suggests: r-cran-mvtnorm, r-cran-soildb, r-cran-sp, r-cran-sf, r-cran-latticeextra, r-cran-tactile, r-cran-compositions, r-cran-markovchain, r-cran-xtable, r-cran-testthat, r-cran-gmedian, r-cran-hmisc, r-cran-tibble, r-cran-rcolorbrewer, r-cran-scales, r-cran-mpspline2, r-cran-soiltexture, r-cran-gower, r-cran-knitr, r-cran-rmarkdown, r-cran-dendextend Filename: pool/dists/noble/main/r-cran-aqp_2.3.2-1.ca2404.1_all.deb Size: 4928584 MD5sum: 681143b62374d3db0ddcb9a88a24f237 SHA1: 1623b154d7fa02b5798f0f9db7da1936a1f18488 SHA256: c849af985377d31fa58e99b2fb9bdd0417c366f3245fff8fc78d440e9ffb331c SHA512: aed7f07c5c3db27052f46894f1b3ec2d2eb788474485ef54d16a1b5932afef88aee6dd8af040bc26b275c42fbde39a73cb9b7a2ed480b50b2e19407061a8b447 Homepage: https://cran.r-project.org/package=aqp Description: CRAN Package 'aqp' (Algorithms for Quantitative Pedology) The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks . These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb . Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice. Package: r-cran-aquaanalytix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-aquaanalytix_0.1.0-1.ca2404.1_all.deb Size: 36038 MD5sum: 7aa63969691d3d1cf742252700b24b70 SHA1: 0267310a120fb9321df972d15affe8617d3d7c02 SHA256: 1cc2f5a2e938bd11a071b1847f11405bb52cc618d939d4083d2cc510dd227202 SHA512: 303ce9a8bbb39aa80915f0278d1245e60a53258e6b343b7f4824592b3e3bd57d20c817285d503e7c9da102a37d9dbb7eacafa4c6a3475a11d429d78da6de59f3 Homepage: https://cran.r-project.org/package=AquaAnalytix Description: CRAN Package 'AquaAnalytix' (Water Quality Analysis) A varied array of mathematical derivations from various titrimetric and colorimetric methods for analyzing water quality parameters were condensed and integrated for the better physicochemical analysis. It is indispensable for managing any aquatic ecosystem, including aquaculture facilities. By substituting titrant and spectrophotometric absorbance readings, accurate determination of the concentrations of critical parameters such as Dissolved Oxygen, Free Carbon Dioxide, Total Alkalinity, Water Hardness, Hydrogen Sulfide, Total Ammonia Nitrogen, Nitrite, Nitrate, Chlorinity, Salinity, Inorganic Phosphate, and Transparency can be facilitated APHA(2017,ISBN:9780875532875). Package: r-cran-aquabeher Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4220 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-rlang, r-cran-sp, r-cran-terra, r-cran-zoo Suggests: r-cran-ggplot2, r-cran-ggrepel, r-cran-knitr, r-cran-learnr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-scales, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aquabeher_1.4.0-1.ca2404.1_all.deb Size: 3230258 MD5sum: bf7ef18b777d42db690fae3985b78c60 SHA1: c1a803326338ca6ca34c2464b4607211fb741214 SHA256: 52fd6327eaf4bd3e7a6fa87b0e371b9de7e95fbad9527340f22f9f1e2169f85c SHA512: ab9e51c8bb064c5f72e96c93f61f3a67c1ba83291210065642d636058df2d105b6dbaee5d6a0ff162415ebf927638eaaeb3a0ef34ff338455cca9948365f9c29 Homepage: https://cran.r-project.org/package=AquaBEHER Description: CRAN Package 'AquaBEHER' (Estimation and Prediction of Wet Season Calendar and Soil WaterBalance for Agriculture) Computes and integrates daily potential evapotranspiration (PET) and a soil water balance model. It allows users to estimate and predict the wet season calendar, including onset, cessation, and duration, based on an agroclimatic approach for a specified period. This functionality helps in managing agricultural water resources more effectively. For detailed methodologies, users can refer to Allen et al. (1998, ISBN:92-5-104219-5); Allen (2005, ISBN:9780784408056); Doorenbos and Pruitt (1975, ISBN:9251002797); Guo et al. (2016) ; Hargreaves and Samani (1985) ; Priestley and Taylor (1972) . Package: r-cran-aquacultur Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2942 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-lubridate, r-cran-tidyr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aquacultur_1.1.1-1.ca2404.1_all.deb Size: 419038 MD5sum: 10bee02e7ff24eb606b45e04573b9b8d SHA1: 1101c9588ae7558ef4b6d5be50addd04cc92cc6b SHA256: 21f04cc0a36e21149b3aa1c2bfcb4177afecb97120747fbcf6ae46e3051c55eb SHA512: 3d8457b7e3556cc568789ba3d290b58ea5814b4e104b1634651c8425911fe682f8664b28711645a28caec4132c36c1f4948eba4e9838828ac375475b529d029f Homepage: https://cran.r-project.org/package=aquacultuR Description: CRAN Package 'aquacultuR' (A Comprehensive R Tool for Zootechnical Metrics) A collection of functions to compute frequently used metrics for nutrition trials in aquaculture. Implementations include metrics to calculate growth, feed conversion, nutrient use efficiency, and feed digestibility. The package supports reproducible workflows for summarising experimental results and reduces manual calculation errors. For additional information see Machado e Silva, Karthikeyan and Tellbüscher (2025) . Package: r-cran-aquadtree Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4629 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp, r-cran-dplyr, r-cran-rlang Suggests: r-cran-sf, r-cran-knitr, r-cran-devtools, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aquadtree_1.0.6-1.ca2404.1_all.deb Size: 4495794 MD5sum: 8e7f05e6748e5f12c40f64a19ae803cf SHA1: cbcd18f5cb8a4e386eaa82aa58758becdaf0bb3f SHA256: 39a0b0091f6239de01419d575187c91575dd702e1ea3728a9495f9e2b70ff16a SHA512: 14ac1f9b9dd90007b0610b67e3792be56374d617025aa637b56b09d1abafd9414c81a6edaced46e69dceb107b65bc8e5f6fa2e8a8af8ce039e0d05baaf53669b Homepage: https://cran.r-project.org/package=AQuadtree Description: CRAN Package 'AQuadtree' (Confidentiality of Spatial Point Data) Provides an automatic aggregation tool to manage point data privacy, intended to be helpful for the production of official spatial data and for researchers. The package pursues the data accuracy at the smallest possible areas preventing individual information disclosure. The methodology, based on hierarchical geographic data structures performs aggregation and local suppression of point data to ensure privacy as described in Lagonigro, R., Oller, R., Martori J.C. (2017) . The data structures are created following the guidelines for grid datasets from the European Forum for Geography and Statistics. Package: r-cran-aquaenv Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm Suggests: r-cran-desolve Filename: pool/dists/noble/main/r-cran-aquaenv_1.0-5-1.ca2404.1_all.deb Size: 724926 MD5sum: 8739595ef0eb73551b3354acbd5c30be SHA1: 9f2fa67c1e27d2abb12d35fd735a5b753b9d1db2 SHA256: de2d9f06ebab4204adffa0f62177a8ed64b561f77d12814d1185b15af42a1ab6 SHA512: 121eeedd352ad170776322c9e40fd2a251294e4bcae862a27d9af66b1e90f68e6b9d66924ba78f1a364c6b2269c7ea1df696d08dc5b694b19af42de8788ff225 Homepage: https://cran.r-project.org/package=AquaEnv Description: CRAN Package 'AquaEnv' (Integrated Development Toolbox for Aquatic Chemical ModelGeneration) Toolbox for the experimental aquatic chemist, focused on acidification and CO2 air-water exchange. It contains all elements to model the pH, the related CO2 air-water exchange, and aquatic acid-base chemistry for an arbitrary marine, estuarine or freshwater system. It contains a suite of tools for sensitivity analysis, visualisation, modelling of chemical batches, and can be used to build dynamic models of aquatic systems. As from version 1.0-4, it also contains functions to calculate the buffer factors. Package: r-cran-aquality Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-aquality_1.4-1.ca2404.1_all.deb Size: 122572 MD5sum: 534025423f3044ed292077894e27bc40 SHA1: 5f2859882e12cc037e550feed9e099181fddab42 SHA256: b8207de1467aac76c9d48f1607b32f35a8a1acc9192c28c0ee888d4516c89362 SHA512: 1e08957b7be6ddcdc6e25feb19373861555eefe98248044ae2f031da653d65513069ece98fe1e2b1c84f43a511964cbfdc3dd5e87e2d258c337c23f5868f3d5e Homepage: https://cran.r-project.org/package=AQuality Description: CRAN Package 'AQuality' (Water and Measurements Quality) The functions proposed in this package allows to evaluate the process of measurement of the chemical components of water numerically or graphically. TSSS(), ICHS and datacheck() functions are useful to control the quality of measurements of chemical components of a sample of water. If one or more measurements include an error, the generated graph will indicate it with a position of the point that represents the sample outside the confidence interval. The function CI() allows to evaluate the possibility of contamination of a water sample after being obtained. Validation() is a function that allows to calculate the quality parameters of a technique for the measurement of a chemical component. Package: r-cran-aquaticlifehistory Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1426 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-mass, r-cran-readr, r-cran-broom, r-cran-mumin, r-cran-magrittr, r-cran-rlist, r-cran-minpack.lm, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aquaticlifehistory_1.0.6-1.ca2404.1_all.deb Size: 964096 MD5sum: ca770b4b5aac638eb380e9693446286d SHA1: f789ef8500025864cc2ea7219bcad2eedb745ab3 SHA256: d1ca0bf11b84486e1c3880a4d8bd33b74ba7acdd8d93b12d9201e2ec8a52ab63 SHA512: 5644f1900185b4bc3bf9dd7f028d472dea64b088fe4fff401b86ac8a36d3941c479ee8c5fead9b2dc8b39579249e2f0ab5c2c938bf26eab917d0fbe7ce4336f1 Homepage: https://cran.r-project.org/package=AquaticLifeHistory Description: CRAN Package 'AquaticLifeHistory' (Life History Analysis Tools) Estimate aquatic species life history using robust techniques. This package supports users undertaking two types of analysis: 1) Growth from length-at-age data, and 2) maturity analyses for length and/or age data. Maturity analyses are performed using generalised linear model approaches incorporating either a binomial or quasibinomial distribution. Growth modelling is performed using the multimodel approach presented by Smart et al. (2016) "Multimodel approaches in shark and ray growth studies: strengths, weaknesses and the future" . Package: r-cran-aquodom Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-httr, r-cran-memoise, r-cran-cachem, r-cran-magrittr, r-cran-dplyr, r-cran-stringr, r-cran-lubridate, r-cran-readr, r-cran-glue, r-cran-rlang, r-cran-openxlsx Suggests: r-cran-testthat, r-cran-readxl Filename: pool/dists/noble/main/r-cran-aquodom_0.1.1-1.ca2404.1_all.deb Size: 33630 MD5sum: 72215abc335aed8604fffd01d6b37565 SHA1: e49ffe7974012e9707d626a5d76cd02bbc527ebe SHA256: 0a40ad6d2a04a57a78d6beebaf0d864cdb379df668dd02ddc5ebf4ffea748613 SHA512: d295961931d8b44e6d16ff295a08b3ba72b2ec4f828e31d1293daa6dfb1bb6edee79b8a648a6f4d94bf14a0737a71dcd1a8252bce7ef9cd3071430c50bd7d22a Homepage: https://cran.r-project.org/package=aquodom Description: CRAN Package 'aquodom' (Access to Aquo domaintables from R (Dutch)) The Aquo Standard is the Dutch Standard for the exchange of data in water management. With *aquodom* (short for aquo domaintables) it is easy to exploit the API () to download domaintables of the Aquo Standard and use them in R. Package: r-cran-ar.matrix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-sparsemvn, r-cran-sp Suggests: r-cran-ggplot2, r-cran-leaflet Filename: pool/dists/noble/main/r-cran-ar.matrix_0.1.0-1.ca2404.1_all.deb Size: 441818 MD5sum: fe44e78b526c25346c6c1343d87802dc SHA1: 643d548fa259a4ea446eca066b4bac47ef2d17d4 SHA256: 8ab8f1a9242792c80ce6f13ff36ae32dabc21a51f2ac134bfa15699e41e8b891 SHA512: dd53002014c003593a502d835805b5ae0113f00aaac19f4824da62462210aa07f672fc1b16bca0dbba1ba3dda10752c12902eb38eb2eecd44c912ddc3189f5f2 Homepage: https://cran.r-project.org/package=ar.matrix Description: CRAN Package 'ar.matrix' (Simulate Auto Regressive Data from Precision Matricies) Using sparse precision matricies and Choleski factorization simulates data that is auto-regressive. Package: r-cran-ar Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-distrib Filename: pool/dists/noble/main/r-cran-ar_1.1-1.ca2404.1_all.deb Size: 26862 MD5sum: 49a077cf44d4299aab343ee7e31df738 SHA1: d16c7ab68be51ec3317062d3445937609696a263 SHA256: c4964649590ffc7e4327b953ed3257349071d9ffcac83a28091fe259379cfdaa SHA512: 0d8bb227614a57ca8724c5a6e8ef7a611903a51059106f6cbbd35ef59699bceb85257d7a9f7a96b060a7821d0a996586e42f5351f0fd621a50654823d0ac149f Homepage: https://cran.r-project.org/package=AR Description: CRAN Package 'AR' (Another Look at the Acceptance-Rejection Method) In mathematics, 'rejection sampling' is a basic technique used to generate observations from a distribution. It is also commonly called 'the Acceptance-Rejection method' or 'Accept-Reject algorithm' and is a type of Monte Carlo method. 'Acceptance-Rejection method' is based on the observation that to sample a random variable one can perform a uniformly random sampling of the 2D cartesian graph, and keep the samples in the region under the graph of its density function. Package 'AR' is able to generate/simulate random data from a probability density function by Acceptance-Rejection method. Moreover, this package is a useful teaching resource for graphical presentation of Acceptance-Rejection method. From the practical point of view, the user needs to calculate a constant in Acceptance-Rejection method, which package 'AR' is able to compute this constant by optimization tools. Several numerical examples are provided to illustrate the graphical presentation for the Acceptance-Rejection Method. Package: r-cran-arabic2kansuji Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-stringr Filename: pool/dists/noble/main/r-cran-arabic2kansuji_0.1.3-1.ca2404.1_all.deb Size: 20886 MD5sum: 1d3c4f75540f50feefe0c6c071c2ff30 SHA1: b148b783d33ec3c9dd302cc76be5f8e4bd7db451 SHA256: 2bab3db483d2c11b8bcd7017889bca3fbf42a27b10c5c6c24a286b09a74d4215 SHA512: e4dd5215d3dde6f6e1df856e91b8747dcf254bff0b4148495cbb9843129ddcffd4b30377eb1c388d5edb49942ef9210e51028489e99e994a4f8d1ecaf51f3037 Homepage: https://cran.r-project.org/package=arabic2kansuji Description: CRAN Package 'arabic2kansuji' (Convert Arabic Numerals to Kansuji) Simple functions to convert given Arabic numerals to Kansuji numerical figures that represent numbers written in Chinese characters. Package: r-cran-arabicstemr Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-arabicstemr_1.3-1.ca2404.1_all.deb Size: 78564 MD5sum: ebc0b91bb6007b5c3d7c8a89d3ddfc98 SHA1: 4d329ed589bd092eedb38c30dc8d60dcaad0fde7 SHA256: be124f64c879b6a1d5ac9e31a251643a6450a3c38f6dfb9a9dbd8763f44e26f9 SHA512: c0342652fd83e361013e60c4e900d22aa2cd8b862ba8367daeb89ba67025cda77b5f28b3b886b50ec2ee61946b25c22ca84c271d25af70aeb3ee3b73f963e1ad Homepage: https://cran.r-project.org/package=arabicStemR Description: CRAN Package 'arabicStemR' (Arabic Stemmer for Text Analysis) Allows users to stem Arabic texts for text analysis. Package: r-cran-arakno Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-httr, r-cran-jsonlite, r-cran-phytools, r-cran-rgbif, r-cran-rworldmap, r-cran-rworldxtra Filename: pool/dists/noble/main/r-cran-arakno_1.3.2-1.ca2404.1_all.deb Size: 139250 MD5sum: 843ee9186247771d8e99b741d58fbbeb SHA1: 3808163d96752468e76eae6dd0f4d4b35760f900 SHA256: 490e8ba8cd2862a0d4e1bed09eff8ae94694e25daff67a131ec52f531591413b SHA512: c6ebcc400ceaa03506e17cf0926d791d365abd52a7bcaad84571932c2f9bceeccd36daa2b95ae55d182b715d8ecfe88831879dd22e59810687642ce019d56cba Homepage: https://cran.r-project.org/package=arakno Description: CRAN Package 'arakno' (ARAchnid KNowledge Online) Allows the user to connect with the World Spider Catalogue (WSC; ) and the World Spider Trait (WST; ) databases. Also performs several basic functions such as checking names validity, retrieving coordinate data from the Global Biodiversity Information Facility (GBIF; ), and mapping. Package: r-cran-aramappings Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clarabel, r-cran-cvxr, r-cran-matrix, r-cran-pracma, r-cran-rglpk, r-cran-slam, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-parallelly, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-aramappings_0.2.0-1.ca2404.1_all.deb Size: 1108658 MD5sum: d2de1fba05842f823d3cc8e52a19c7bd SHA1: b260c9975ab809ad52fbed0ca97f485f536a9565 SHA256: 232f7dbc5b2e540bf9c272bfdd53d7e7756455e6c114e8bc354ed05a29cb16e5 SHA512: 753d92fd5faf0113c6a2114e31b84264d7c20e2e2cbf1fbf7f625b0e174edfe478e79cca74dd74b59be44153956961a6b3a23435b5dc4148637e93ef7a30d375 Homepage: https://cran.r-project.org/package=aramappings Description: CRAN Package 'aramappings' (Computes Adaptable Radial Axes Mappings) Computes low-dimensional point representations of high-dimensional numerical data according to the data visualization method Adaptable Radial Axes described in: Manuel Rubio-Sánchez, Alberto Sanchez, and Dirk J. Lehmann (2017) "Adaptable radial axes plots for improved multivariate data visualization" . Package: r-cran-ararredux Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ararredux_1.0-1.ca2404.1_all.deb Size: 276420 MD5sum: 2458e50072671ab4d0766c5a6ebbd159 SHA1: d4a980975ae385b6805ba2df86b096d19e074767 SHA256: 4409c737f9e6dbd2467352da16e8a2b1fd0f933e501112ae3aca6358abcfe180 SHA512: e753744f19dbf5314debcf2b047010618b4d9878951530ef555092acc2a5d2ce1478ede9c1006465fecfe3ef7a5e9d315285e57c5b66e0d2741a1a29ed154953 Homepage: https://cran.r-project.org/package=ArArRedux Description: CRAN Package 'ArArRedux' (Rigorous Data Reduction and Error Propagation of Ar40 / Ar39Data) Processes noble gas mass spectrometer data to determine the isotopic composition of argon (comprised of Ar36, Ar37, Ar38, Ar39 and Ar40) released from neutron-irradiated potassium-bearing minerals. Then uses these compositions to calculate precise and accurate geochronological ages for multiple samples as well as the covariances between them. Error propagation is done in matrix form, which jointly treats all samples and all isotopes simultaneously at every step of the data reduction process. Includes methods for regression of the time-resolved mass spectrometer signals to t=0 ('time zero') for both single- and multi-collector instruments, blank correction, mass fractionation correction, detector intercalibration, decay corrections, interference corrections, interpolation of the irradiation parameter between neutron fluence monitors, and (weighted mean) age calculation. All operations are performed on the logs of the ratios between the different argon isotopes so as to properly treat them as 'compositional data', sensu Aitchison [1986, The Statistics of Compositional Data, Chapman and Hall]. Package: r-cran-arc Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arules, r-cran-r.utils, r-cran-discretization, r-cran-matrix Suggests: r-cran-qcba Filename: pool/dists/noble/main/r-cran-arc_1.4.2-1.ca2404.1_all.deb Size: 224142 MD5sum: c1324e2d42c3000e3071869283088f44 SHA1: 04a547cd2109adb94cec62a8fb4704a4a7998438 SHA256: 4a987402ac6ae4192c25d79004270cc0887a9af351deaa4c9ac87c9c3e2d51a4 SHA512: 00be3f075401a820fde49fa391f5bcc2cc198b92497cbf454884a5c3983bf83b461edb45919ec8ef8fffba175f798fc4c2a5ee47ab782e0cdfc2fa1b3a89a74d Homepage: https://cran.r-project.org/package=arc Description: CRAN Package 'arc' (Association Rule Classification) Implements the Classification-based on Association Rules (CBA) algorithm for association rule classification. The package, also described in Hahsler et al. (2019) , contains several convenience methods that allow to automatically set CBA parameters (minimum confidence, minimum support) and it also natively handles numeric attributes by integrating a pre-discretization step. The rule generation phase is handled by the 'arules' package. To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the 'qCBA' package is suggested. Package: r-cran-arcgeocoder Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 646 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-jsonlite Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-quarto, r-cran-sf, r-cran-testthat, r-cran-tibble, r-cran-tidygeocoder Filename: pool/dists/noble/main/r-cran-arcgeocoder_0.4.0-1.ca2404.1_all.deb Size: 528358 MD5sum: 5ed95f719d3ebf65c1bd91f1a327bbbe SHA1: 0014b2176836228a3b768a5a6bd9642bdd6e4049 SHA256: 3ab0d0e70cb871ec2ef5da09b802323a59b3d3688f051797543cde1494843d7b SHA512: 6cb86dc69f889c165749f66107e8609125764e9ead733414c2ad66d3f6e07ea8f0cae91ddb1728ef4bc8fa28df9a960a06360293b6c12b9a149263a4dd48bf98 Homepage: https://cran.r-project.org/package=arcgeocoder Description: CRAN Package 'arcgeocoder' (Geocoding with the 'ArcGIS' REST API Service) Lite interface for finding locations of addresses or businesses around the world using the 'ArcGIS' REST API service . Address text can be converted to location candidates and a location can be converted into an address. No API key required. Package: r-cran-arcgis Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arcgisgeocode, r-cran-arcgislayers, r-cran-arcgisplaces, r-cran-arcgisutils, r-cran-cli, r-cran-httr2 Suggests: r-cran-arcpbf, r-cran-calcite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-arcgis_0.2.0-1.ca2404.1_all.deb Size: 15246 MD5sum: 849a857dde230dcafb3f7124aaae6e30 SHA1: 5b78cdfa3ff397eb63ef621d3eaaee6c86d38dc6 SHA256: b05b1e47c0263d303476e9a9a6a52e125587ef48d2191e6d5399dac61153e771 SHA512: f7a93a860223624225381d6ae5f41a6a01706f2978240d3c2372cdb7d133f67075d718f8c76e48c508c5a954ad93047289774eba8fbc2e0e88c51191830a9066 Homepage: https://cran.r-project.org/package=arcgis Description: CRAN Package 'arcgis' (ArcGIS Location Services Meta-Package) Provides easy installation and loading of core ArcGIS location services packages 'arcgislayers', 'arcgisutils', 'arcgisgeocode', and 'arcgisplaces'. Enabling developers to interact with spatial data and services from 'ArcGIS Online', 'ArcGIS Enterprise', and 'ArcGIS Platform'. Learn more about the 'arcgis' meta-package at . Package: r-cran-arcgislayers Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arcgisutils, r-cran-arcpbf, r-cran-cli, r-cran-httr2, r-cran-jsonify, r-cran-lifecycle, r-cran-rcppsimdjson, r-cran-rlang, r-cran-sf, r-cran-terra Suggests: r-cran-testthat, r-cran-vctrs, r-cran-curl, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-arcgislayers_0.6.0-1.ca2404.1_all.deb Size: 272060 MD5sum: 2f8286696455c8db7efa6aea4c248ad1 SHA1: 1c523fab6170a5613550df4c4c0c674b6688caac SHA256: 71aec6bd05c0f5cc1626c40318a397848100109b7b9ac58b707ca3804b9e01b0 SHA512: 726665d538b27ad38f994e102921427e083ff2ff0b324bb7321e961637de7232897b78a4487e9c5abb23362b086264bfc66539b90c0f7f36172d2eb2f7291fc4 Homepage: https://cran.r-project.org/package=arcgislayers Description: CRAN Package 'arcgislayers' (Harness ArcGIS Data Services) Enables users of 'ArcGIS Enterprise', 'ArcGIS Online', or 'ArcGIS Platform' to read, write, publish, or manage vector and raster data via ArcGIS location services REST API endpoints . Package: r-cran-archaeophases.dataset Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3488 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-archaeophases.dataset_0.2.0-1.ca2404.1_all.deb Size: 3398934 MD5sum: e42eb5147a95739733a90e938354be24 SHA1: 6c5158c8050cd7ae8434de52804ae88769ba6e49 SHA256: 67e3714bcd3ecd13746a927ea71ab8713d7da9e5a7994aeba75455338b2da908 SHA512: b475c798b5080028a7a8d11f683f5368dfcdb56d302f34836f9381f25fe189a61c8e1aeb31518aed1a5cce9b2896bc92940e32d680fbc68732c17cdaca63036b Homepage: https://cran.r-project.org/package=ArchaeoPhases.dataset Description: CRAN Package 'ArchaeoPhases.dataset' (Data Sets for 'ArchaeoPhases' Vignettes) Provides the data sets used to build the 'ArchaeoPhases' vignettes. The data sets were formerly distributed with 'ArchaeoPhases', however they exceed current CRAN policy for package size. Package: r-cran-archaeophases Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aion, r-cran-arkhe Suggests: r-cran-coda, r-cran-fontquiver, r-cran-knitr, r-cran-rmarkdown, r-cran-rsvg, r-cran-svglite, r-cran-tinysnapshot, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-archaeophases_2.1.0-1.ca2404.1_all.deb Size: 1925088 MD5sum: 5d56ddb76b852b802498fb2cd8f344e7 SHA1: 38b1e5bb7ad7030f74a743263852117d97396ae0 SHA256: 9b67698d70fd8137f0b902e2bf5c18b0639c32be6dd27c6c26364c707ccdea8d SHA512: 09c31ab6f348e96eb2f59332a25e7917292cb24ab9ae5047ccc76f09405c763dc8a0b2a69ce0fdb91574649ecd20331dfc8b26d0f7e2d24ce6210917fa5fe6e0 Homepage: https://cran.r-project.org/package=ArchaeoPhases Description: CRAN Package 'ArchaeoPhases' (Post-Processing of Markov Chain Monte Carlo Simulations forChronological Modelling) Statistical analysis of archaeological dates and groups of dates. This package allows to post-process Markov Chain Monte Carlo (MCMC) simulations from 'ChronoModel' , 'Oxcal' or 'BCal' . It provides functions for the study of rhythms of the long term from the posterior distribution of a series of dates (tempo and activity plot). It also allows the estimation and visualization of time ranges from the posterior distribution of groups of dates (e.g. duration, transition and hiatus between successive phases) as described in Philippe and Vibet (2020) . Package: r-cran-archdata Architecture: all Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ca, r-cran-circular, r-cran-plotrix, r-cran-mass, r-cran-spatstat Filename: pool/dists/noble/main/r-cran-archdata_1.2-1-1.ca2404.1_all.deb Size: 160340 MD5sum: cecf643208e5ed6e01171a546275650d SHA1: 1315620eb251a4f62f48dfb42523220968b2aaa0 SHA256: 0decd2c1e54c126ffa7eabe7b75abdbdcdf2cdcd70d8625a851ca40821e62da6 SHA512: a720a840900b83b0b6352ffac3701f3cfa9ab0714da07bdb09dd3d4ca00e01161db15a06cf42180a3271efa5a54b7fb5cc93f379e3d9fe6e51832026d7de56f5 Homepage: https://cran.r-project.org/package=archdata Description: CRAN Package 'archdata' (Example Datasets from Archaeological Research) The archdata package provides several types of data that are typically used in archaeological research. It provides all of the data sets used in "Quantitative Methods in Archaeology Using R" by David L Carlson, one of the Cambridge Manuals in Archaeology. Package: r-cran-archeofrag.gui Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 659 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-archeofrag, r-cran-dendextend, r-cran-doparallel, r-cran-dorng, r-cran-dt, r-cran-foreach, r-cran-ggplot2, r-cran-igraph, r-cran-shiny, r-cran-shinythemes Suggests: r-bioc-rbgl Filename: pool/dists/noble/main/r-cran-archeofrag.gui_1.1.4-1.ca2404.1_all.deb Size: 388276 MD5sum: a6114d12f4269f9c297bd8a7502d73dd SHA1: 47ca91491656623012222e6ade944f38e19b662a SHA256: 87ea076ad012e0abdc2b57ba7eb210bbc3b430a191c9f8ce168d7f9c4766d931 SHA512: fb4204ec466fbb473d19d90fea82c38577dd83f835466d3f72a5be01767ef40639826f4c68daca4935fa8b0fb57c97d7546e79d04f17043642ef224ac6b0ecf9 Homepage: https://cran.r-project.org/package=archeofrag.gui Description: CRAN Package 'archeofrag.gui' (Spatial Analysis in Archaeology from Refitting Fragments (GUI)) A 'Shiny' application to access the functionalities and datasets of the 'archeofrag' package for spatial analysis in archaeology from refitting data. Quick and seamless exploration of archaeological refitting datasets, focusing on physical refits only. Features include: built-in documentation and convenient workflow, plot generation and exports, anomaly detection in the spatial distribution of refitting connection, exploration of spatial units merging solutions, simulation of archaeological site formation processes, support for parallel computing, R code generation to re-execute simulations and ensure reproducibility, code generation for the 'openMOLE' model exploration software. A demonstration of the app is available at . Package: r-cran-archeofrag Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 800 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-bioc-rbgl, r-cran-knitr, r-cran-covr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-archeofrag_1.2.4-1.ca2404.1_all.deb Size: 610308 MD5sum: cce0d7f6404b90cdbeccea8b3561bcf7 SHA1: 605a144bd71d75de9714328f2de357891385a916 SHA256: eb57f0fa964eae6e4bc6b008a4360203bbd89489f19c4ce42a62bd9d6a095e90 SHA512: 868fb1d52fdb8cf05dfaa8926189f26ab021f9c2eb8ccee80ca45d660f91daa5a1186452a643772713edf85aa3932c3706fd5c03232d46e05be6c0d236d84995 Homepage: https://cran.r-project.org/package=archeofrag Description: CRAN Package 'archeofrag' (Spatial Analysis in Archaeology from Refitting Fragments) Methods to analyse spatial units in archaeology from the refitting relationships between fragments of objects scattered in these units (e.g. stratigraphic layers). Graphs are used to model archaeological observations. The package is mainly based on the 'igraph' package for graph analysis. Functions can: 1) create, manipulate, visualise, and simulate fragmentation graphs, 2) measure the cohesion and admixture of archaeological spatial units, and 3) characterise the topology of a specific set of refitting relationships. A series of published empirical datasets is included. Documentation about 'archeofrag' is provided by a vignette and by the accompanying scientific papers: Plutniak (2021, Journal of Archaeological Science, ) and Plutniak (2022, Journal of Open Source Software, ). This package is complemented by the 'archeofrag.gui' R package, a companion GUI application available at . Package: r-cran-archeoviz Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-mgcv, r-cran-geometry, r-cran-reshape2, r-cran-svglite, r-cran-htmlwidgets, r-cran-shiny, r-cran-shinythemes, r-cran-knitr Suggests: r-cran-covr, r-cran-seahors, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-archeoviz_1.4.1-1.ca2404.1_all.deb Size: 322620 MD5sum: 3fe6a6fe036a60bd837e4691b6cc3ca3 SHA1: 83962ebdba4ecb675315385a71f904ed057c8fea SHA256: 6b355dfff991fbc8bc36bb724c561bff0f86958dad0b53cf1887ae25114f32ef SHA512: 986df19dfa50e7a1af16e9d46fc55ed6de13d4c5cdf23717cf826942bb7908da004bd703fdf6acf5af6daac65ee7a6d33dd97a2aacc5f4860434f1ee85dd2ef0 Homepage: https://cran.r-project.org/package=archeoViz Description: CRAN Package 'archeoViz' (Visualisation, Exploration, and Web Communication ofArchaeological Spatial Data) An R 'Shiny' application for visual and statistical exploration and web communication of archaeological spatial data, either remains or sites. It offers interactive 3D and 2D visualisations (cross sections and maps of remains, timeline of the work made in a site) which can be exported in SVG and HTML formats. It performs simple spatial statistics (convex hull, regression surfaces, 2D kernel density estimation) and allows exporting data to other online applications for more complex methods. 'archeoViz' can be used offline locally or deployed on a server, either with interactive input of data or with a static data set. Example is provided at . Package: r-cran-archetypal Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-geometry, r-cran-inflection, r-cran-doparallel, r-cran-lpsolve, r-cran-plot3d, r-cran-entropy Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-archetypal_1.3.1-1.ca2404.1_all.deb Size: 3970238 MD5sum: 54c575ac49341a09454458ab74bddeb0 SHA1: 2ad801dcbab0d6723b290ee50c585ab1e0d31e2e SHA256: da7fc85360525c01a20ea5314c636527bbeb962f6817d9fb834296ec7fb3f5f0 SHA512: 40d105946fa26cf4f0dc25886d2e5dda2f5a34c935b77ee8fa63bee7250983dca69a7f20815c648aa703c0563190e7f46a8e54fb149fd7e1a99fdae240c10f90 Homepage: https://cran.r-project.org/package=archetypal Description: CRAN Package 'archetypal' (Finds the Archetypal Analysis of a Data Frame) Performs archetypal analysis by using Principal Convex Hull Analysis under a full control of all algorithmic parameters. It contains a set of functions for determining the initial solution, the optimal algorithmic parameters and the optimal number of archetypes. Post run tools are also available for the assessment of the derived solution. Morup, M., Hansen, LK (2012) . Hochbaum, DS, Shmoys, DB (1985) . Eddy, WF (1977) . Barber, CB, Dobkin, DP, Huhdanpaa, HT (1996) . Christopoulos, DT (2016) . Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., Sunde, U. (2018), . Christopoulos, DT (2015) . Murari, A., Peluso, E., Cianfrani, Gaudio, F., Lungaroni, M., (2019), . Package: r-cran-archetyper Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-here, r-cran-knitr, r-cran-log4r, r-cran-config, r-cran-tidyverse, r-cran-rmarkdown, r-cran-skimr, r-cran-stringr, r-cran-snakecase, r-cran-testthat, r-cran-bannercommenter, r-cran-feather, r-cran-readr, r-cran-ps Suggests: r-cran-rcurl, r-cran-mass, r-cran-broom, r-cran-caret, r-cran-covr, r-cran-gtsummary, r-cran-performance, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-archetyper_0.1.0-1.ca2404.1_all.deb Size: 1047428 MD5sum: ed5273d6f569ac67e5abd363be09e56f SHA1: 451f3957f48a700d98eecd40201cf00eef4bfa91 SHA256: 6bf4058122199802e9015942c35b1bb57df1a92c4041526fda34ba1c5ac16015 SHA512: 1ab3e4474058f2ef522a16c52aa634344c3a9589ee4939045e826f05a85128e03a80b2c8ceeec0a4f03d87571dd360f4ed907093bdf6f5d6c55af02bfb209ff9 Homepage: https://cran.r-project.org/package=archetyper Description: CRAN Package 'archetyper' (An Archetype for Data Mining and Data Science Projects) A project template to support the data science workflow. Package: r-cran-archetypes Architecture: all Version: 2.2-0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1416 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-modeltools, r-cran-nnls Suggests: r-cran-mass, r-cran-vcd, r-cran-mlbench, r-cran-ggplot2, r-cran-tsp, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-archetypes_2.2-0.2-1.ca2404.1_all.deb Size: 1190804 MD5sum: ad8e20198e17b40cf206f47736977a69 SHA1: 4a857f24bbad4697fc6f4f1b1c4b90c1b37d5cf1 SHA256: 62625f877fcdd285bbac3f26e33ab09d8a983a4e47e3f1f2c09437676de90823 SHA512: 9ce3faae398e95205382bb1d9b408cd21f1e0687570af74edae5f829c8e490c4030751f65ab332e5c07dd2ac7c8e8b9ee75456636637a3050e3574e61fa6a2aa Homepage: https://cran.r-project.org/package=archetypes Description: CRAN Package 'archetypes' (Archetypal Analysis) The main function archetypes implements a framework for archetypal analysis supporting arbitrary problem solving mechanisms for the different conceptual parts of the algorithm. Package: r-cran-archidart Architecture: all Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2055 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-xml, r-cran-geometry, r-cran-sp Suggests: r-cran-rgl, r-cran-tda Filename: pool/dists/noble/main/r-cran-archidart_3.4-1.ca2404.1_all.deb Size: 1671690 MD5sum: 8d0efee9df9d84e7b8e0741e608e3358 SHA1: fc4ca5574ec65833d74890d031b45b4078a447b1 SHA256: 405995f3e5fba123e1d10c817a5a5108b7d8c93716d1dfa8938eb86ce536c162 SHA512: 9d46a16eaf3c2cc51aa9a3c936e603ed80844b863a373b1df1d33bb2eca5a742ea47c15c328b2d52bf59a5229de93965640fbfdbb2f72a0908599f697fb09ef0 Homepage: https://cran.r-project.org/package=archiDART Description: CRAN Package 'archiDART' (Plant Root System Architecture Analysis Using DART and RSMLFiles) Analysis of complex plant root system architectures (RSA) using the output files created by Data Analysis of Root Tracings (DART), an open-access software dedicated to the study of plant root architecture and development across time series (Le Bot et al (2010) "DART: a software to analyse root system architecture and development from captured images", Plant and Soil, ), and RSA data encoded with the Root System Markup Language (RSML) (Lobet et al (2015) "Root System Markup Language: toward a unified root architecture description language", Plant Physiology, ). More information can be found in Delory et al (2016) "archiDART: an R package for the automated computation of plant root architectural traits", Plant and Soil, . Package: r-cran-archipelago Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-archipelago_0.1.0-1.ca2404.1_all.deb Size: 967744 MD5sum: e9d173a472582e8b976251c55e37fa18 SHA1: 0c2ecc9bc284046b1e46acabf568dacd8cce609c SHA256: 624573988eb3f7d5dbcc1688d4bd3e11a820db84bf11449beace8d3d4caa8e07 SHA512: c7192973d8618cadead79f9a43c514b53077e2467e17e94aa2697b497fad46e5ea2c4c120e4965710ea381e8669c0f72d3b92f5244ab75435f0be5a2bed08d18 Homepage: https://cran.r-project.org/package=archipelago Description: CRAN Package 'archipelago' (Visualising Variant Set Association Test Results) Provides a graphical method for joint visualisation of Variant Set Association Test (VSAT) results and individual variant association statistics. The Archipelago method assigns genomic coordinates to variant set statistics, allowing simultaneous display of variant-level and set-level signals in a unified plot. This supports interpretation of both collective and individual variant contributions in genetic association studies using variant aggregation approaches. For more see Lawless et al. (2026) . Package: r-cran-archipelagoengine Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-spdep, r-cran-magrittr Suggests: r-cran-splm, r-cran-spatialreg, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-archipelagoengine_0.1.1-1.ca2404.1_all.deb Size: 406374 MD5sum: a12be4c18951afe279aa485d3430511b SHA1: b6797a68f9d5feca1010a3daf5a6fd0c4af8f565 SHA256: 15e25093a89fa1ce3bc56808c9c7a5c3d07cdda70a5e1179f546be2ed3494aee SHA512: b6a23d974430164a5c7b8dcc78353a86f54ab15571959ddf6cba8e1188ad063eaf0db2c253d27360415e8dbf32210e1d5f6b7d0da669a47e9645d04393abd09a Homepage: https://cran.r-project.org/package=ArchipelagoEngine Description: CRAN Package 'ArchipelagoEngine' (Spatial Weight Construction for Archipelagic Geographies) Implements specialized K-Nearest Neighbor (KNN) logic to address the unique challenges of spatial modeling in archipelagic environments. Standard contiguity models often leave significant portions of island nations (e.g., 20% of the Philippines) mathematically isolated. This package provides tools to ensure 100% network connectivity, neutralizing spatial bias and enabling robust econometric inference. Methodology follows Anselin (1988, ISBN:9024737354) and LeSage and Pace (2009) . Package: r-cran-archissur Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gpcsign, r-cran-dicekriging, r-cran-kriginv, r-cran-future.apply, r-cran-truncatednormal, r-cran-randtoolbox, r-cran-rgenoud Suggests: r-cran-future, r-cran-dicedesign, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-archissur_0.0.1-1.ca2404.1_all.deb Size: 91040 MD5sum: 13c0ae45ae87500b409632c0773458f6 SHA1: d081e468deef8600905d2958c74cc51ad0f825e2 SHA256: 1acc55bdd75d7003ef89fcd8bc2f1b5564e7c577c95b5fd3b3f102f25d8116ef SHA512: 7b2be215ef90959d826fae5bce8fbe3d6d7c14886e0f8ae45bcd4b82b953e41dcd5509cc01facf8cf3702e557929c3557516bb0731ff5e59ff45b49d5b804941 Homepage: https://cran.r-project.org/package=ARCHISSUR Description: CRAN Package 'ARCHISSUR' (Active Recovery of a Constrained and Hidden Set by StepwiseUncertainty Reduction Strategy) Stepwise Uncertainty Reduction criterion and algorithm for sequentially learning a Gaussian Process Classifier as described in Menz et al. (2025). Package: r-cran-archiveretriever Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-anytime, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-rvest, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-vcr, r-cran-testthat, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-archiveretriever_0.4.1-1.ca2404.1_all.deb Size: 282966 MD5sum: ec77813fffc6ad5d3e8fe82fe3cd044e SHA1: 33b6618866fe518e02d8ff0ebd5eadcdc2ead375 SHA256: f6786cc2dcd7e25819c4463eadac717dcb10caa9acd747887d7069912d3d4bd7 SHA512: 7a605c5367e796f4e547341c761edec8daeabc369f1625e24a355cfe894fc17bc7c38ca4974e6341520f562eb068deafec43cd544e593dd60556f7dd3fe5599a Homepage: https://cran.r-project.org/package=archiveRetriever Description: CRAN Package 'archiveRetriever' (Retrieve Archived Web Pages from the 'Internet Archive') Scraping content from archived web pages stored in the 'Internet Archive' () using a systematic workflow. Get an overview of the mementos available from the respective homepage, retrieve the Urls and links of the page and finally scrape the content. The final output is stored in tibbles, which can be then easily used for further analysis. Package: r-cran-archivist.github Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1769 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-archivist, r-cran-httr, r-cran-git2r, r-cran-jsonlite, r-cran-digest Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-archivist.github_0.2.6-1.ca2404.1_all.deb Size: 996944 MD5sum: 5fa7c86ac639ffea6cca35118bbd198b SHA1: 112cd678f8d3cfbcac589f9836502219ee7133f3 SHA256: cd4b7b648d4e92e0913043fd6b04bb57e217cd9f4f201d3503b20909d98a2e47 SHA512: 27d957c6dfe869008d0229aef1f94389ad644ace28ab2d6c1bdc475826c86857c775e073bd97cd6c50c1408f81389d4911b9706fe0a0fe869185d7c4a17f97d4 Homepage: https://cran.r-project.org/package=archivist.github Description: CRAN Package 'archivist.github' (Tools for Archiving, Managing and Sharing R Objects via GitHub) The extension of the 'archivist' package integrating the archivist with GitHub via GitHub API, 'git2r' packages and 'httr' package. 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Artifacts like: subsets, data aggregates, plots, statistical models, different versions of data sets and different versions of results. The more projects we work with the more artifacts are produced and the harder it is to manage these artifacts. Archivist helps to store and manage artifacts created in R. Archivist allows you to store selected artifacts as a binary files together with their metadata and relations. Archivist allows to share artifacts with others, either through shared folder or github. Archivist allows to look for already created artifacts by using it's class, name, date of the creation or other properties. Makes it easy to restore such artifacts. Archivist allows to check if new artifact is the exact copy that was produced some time ago. That might be useful either for testing or caching. Package: r-cran-arco Architecture: all Version: 0.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-glmnet, r-cran-boot Filename: pool/dists/noble/main/r-cran-arco_0.3-1-1.ca2404.1_all.deb Size: 92890 MD5sum: a0773e08ef1514dd47a4c69e663d53ef SHA1: bf60ee7b555ec738ca5e8df4e42e043ceb70a672 SHA256: c359a003ae71382805aa099c6263cac2b183866f5b07287d7ebc3a45f05dff55 SHA512: f1ed484058fd6d3e5af7edd68ddd5a97d4f7641ebfb2f5dace3f1e06d8d85663d1db4b7cbcf63e0550a17d3941224a8c82f011ae12ef97d45db4c393f6d0b94a Homepage: https://cran.r-project.org/package=ArCo Description: CRAN Package 'ArCo' (Artificial Counterfactual Package) Set of functions to analyse and estimate Artificial Counterfactual models from Carvalho, Masini and Medeiros (2016) . 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Package: r-cran-ard Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixstats, r-cran-osqp, r-cran-quadprog, r-cran-boot, r-cran-checkmate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ard_0.1.0-1.ca2404.1_all.deb Size: 90542 MD5sum: 21418029250e425969112a6eb1301724 SHA1: 91eb6530201a24ff07177ddab659b89230441e30 SHA256: 3ae0ae1b509cc217565d1b976fbf9f3a4e3fa4dd54cd2bc840d5c5e97d73f0bd SHA512: 223b159b9b2ecff73a95d05c3042b140609f9a405810f0f513b703e84bb5bc239602e4d6299545fd597efb8b06261513d449f38f48ca627b611c02b5cfe08613 Homepage: https://cran.r-project.org/package=aRD Description: CRAN Package 'aRD' (Adjusted Risk Differences via Specifically Penalized Likelihood) Fits a linear-binomial model using a modified Newton-type algorithm for solving the maximum likelihood estimation problem under linear box constraints. 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Package: r-cran-ardec Architecture: all Version: 2.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ardec_2.1-1-1.ca2404.1_all.deb Size: 407628 MD5sum: 9e395e4eb1215b8a539e1666d9511afb SHA1: 47b1061cd7cc9a6acf5d71fe3e758be3524f7242 SHA256: 4e81b6982974e8b529015c76df2cc162b12da84351cfa01cdd244eb0e6ce508c SHA512: 11a08e551a3c18574dd6b6c0545f3b129653042604833427a87b4e142fbd4d2b545c12a5da60c4aca05423f259f1688003ed571b7dfe223ec9cfb47ced023fc1 Homepage: https://cran.r-project.org/package=ArDec Description: CRAN Package 'ArDec' (Time Series Autoregressive-Based Decomposition) Autoregressive-based decomposition of a time series based on the approach in West (1997). Particular cases include the extraction of trend and seasonal components. Package: r-cran-ardeco Architecture: all Version: 2.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-ghql, r-cran-jsonlite, r-cran-stringr, r-cran-dplyr, r-cran-arrow, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-httptest2 Filename: pool/dists/noble/main/r-cran-ardeco_2.2.3-1.ca2404.1_all.deb Size: 74200 MD5sum: 85b6c187aca331ac3a51b7035a6df111 SHA1: f00d485ff21d12b46d80dabaa7c3efff1e4afeb2 SHA256: a986224861a8f0e78d0fc25323034179ad96e0a9b51eb678acee270d8610ff07 SHA512: fc61081e0cb580617599c4cd08c961e8dd4dff47358167fbd71bec9c47400a06cb3e940cbd9d04dc6d7ae52bac8c2c4d2fa09dfb37ee2141002bc221fcda5e72 Homepage: https://cran.r-project.org/package=ARDECO Description: CRAN Package 'ARDECO' (Annual Regional Database of the European Commission (ARDECO)) A set of functions to access the 'ARDECO' (Annual Regional Database of the European Commission) data directly from the official ARDECO public repository through the exploitation of the 'ARDECO' APIs. The APIs are completely transparent to the user and the provided functions provide a direct access to the 'ARDECO' data. The 'ARDECO' database is a collection of variables related to demography, employment, labour market, domestic product, capital formation. Each variable can be exposed in one or more units of measure as well as refers to total values plus additional dimensions like economic sectors, gender, age classes. Data can be also aggregated at country level according to the tercet classes as defined by EUROSTAT. The description of the 'ARDECO' database can be found at the following URL . Package: r-cran-ardl.nardl Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gets, r-cran-plyr, r-cran-dplyr, r-cran-rlist, r-cran-nardl, r-cran-car, r-cran-lmtest, r-cran-texreg, r-cran-stringr, r-cran-tseries, r-cran-sandwich, r-cran-purrr, r-cran-tidyselect Suggests: r-cran-dynamac Filename: pool/dists/noble/main/r-cran-ardl.nardl_1.3.0-1.ca2404.1_all.deb Size: 297998 MD5sum: c322aabf9e50df829d042b3a124ce4f8 SHA1: e2336eaad8088e8769a097c6447ee9eda40b94ec SHA256: 3caa450ff2fae866a7327d543e8f3b85573f8bb57dbbfb5e092799c8001933d2 SHA512: b2546c9b1f7f9405dd6e68142228b1572ab6c650d8e561ec32a9f966834ac873caff41837cac94a7e57fe846eb853530e1ea07e42d581a7b4ed41ed25c3a0c89 Homepage: https://cran.r-project.org/package=ardl.nardl Description: CRAN Package 'ardl.nardl' (Linear and Nonlinear Autoregressive Distributed Lag Models:General-to-Specific Approach) Estimate the linear and nonlinear autoregressive distributed lag (ARDL & NARDL) models and the corresponding error correction models, and test for longrun and short-run asymmetric. The general-to-specific approach is also available in estimating the ARDL and NARDL models. The Pesaran, Shin & Smith (2001) () bounds test for level relationships is also provided. The 'ardl.nardl' package also performs short-run and longrun symmetric restrictions available at Shin et al. (2014) and their corresponding tests. Package: r-cran-ardl Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aod, r-cran-dplyr, r-cran-dynlm, r-cran-gridextra, r-cran-ggplot2, r-cran-lmtest, r-cran-msm, r-cran-stringr, r-cran-zoo Suggests: r-cran-strucchange, r-cran-tseries, r-cran-aiccmodavg, r-cran-sandwich, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ardl_0.2.5-1.ca2404.1_all.deb Size: 566982 MD5sum: a8d5fd4d14dc22087d3e98d16f84cbe8 SHA1: 8bf19dfaf5585d741cb74675827d511908e150f1 SHA256: f2be77098d0c77738b4a70c9dd6eb257f23f94cf0442e4bb01ce2a16c28c933b SHA512: 2e4cff0bb138eeadd4e4dd0a5a64952c3e98cb5b87e1246cbe680d544fd768843c853713e969c7bbc3fdc062890284769f6d2f18f631a1f46b096b95659e0730 Homepage: https://cran.r-project.org/package=ARDL Description: CRAN Package 'ARDL' (ARDL, ECM and Bounds-Test for Cointegration) Creates complex autoregressive distributed lag (ARDL) models and constructs the underlying unrestricted and restricted error correction model (ECM) automatically, just by providing the order. It also performs the bounds-test for cointegration as described in Pesaran et al. (2001) and provides the multipliers and the cointegrating equation. The validity and the accuracy of this package have been verified by successfully replicating the results of Pesaran et al. (2001) in Natsiopoulos and Tzeremes (2022) . Package: r-cran-ardlverse Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-quantreg, r-cran-lmtest, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-ardlverse_1.1.3-1.ca2404.1_all.deb Size: 376362 MD5sum: d07ee5411587ab171cf0d94a49df45be SHA1: 8b7eb4c4d30af109d715099b4e472b16ef7bd0ae SHA256: c2e045f1ded3a88103367f767782737547d07ac6b412937c2c1ef141b246dd13 SHA512: 05d4b52892803f51644a1ccdc705e66369701ac742d05f9e9266f3ffbdd722ab6f4b6fc6a835c9dd4e4098188e48bf5d53ff6273148c2d577468e8720ca92e62 Homepage: https://cran.r-project.org/package=ardlverse Description: CRAN Package 'ardlverse' (Comprehensive ARDL: Panel, Bootstrap and Fourier Methods) A unified framework for Autoregressive Distributed Lag (ARDL) modeling and cointegration analysis. Implements Panel ARDL with Pooled Mean Group (PMG), Mean Group (MG), and Dynamic Fixed Effects (DFE) estimators following Pesaran, Shin & Smith (1999) . Provides bootstrap-based bounds testing per Pesaran, Shin & Smith (2001) . Includes Quantile Nonlinear ARDL (QNARDL) combining distributional and asymmetric effects based on Shin, Yu & Greenwood-Nimmo (2014) , and Fourier ARDL for modeling smooth structural breaks following Enders & Lee (2012) . Features include Augmented ARDL (AARDL) with deferred t and F tests, Multiple-Threshold NARDL for complex asymmetries, Rolling/Recursive ARDL for time-varying relationships, and Panel NARDL for nonlinear panel cointegration. All methods include comprehensive diagnostics, publication-ready outputs, and visualization tools. Package: r-cran-ards Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ards_0.1.3-1.ca2404.1_all.deb Size: 250224 MD5sum: b5fad7629a58a3f68443d2954cedd687 SHA1: 9be9ad586452ec1cd2b7de96ff6dd7df077c01f8 SHA256: e6c4ae02fbe91502fe16d3f5bdcf4111dfcbd9ceb02d2406b60161257467b22e SHA512: 0b44018eecf65640cbf9eef98ca13e758934633befbd2db28c3b795060d88c69037825a4663738fa9dedb9c2a7f25e45bc241b76c0460978ed06cb95afa7a1b7 Homepage: https://cran.r-project.org/package=ards Description: CRAN Package 'ards' (Creates Analysis Results Datasets) Contains functions to help create an Analysis Results Dataset. The dataset follows industry recommended structure. The dataset can be created in multiple passes, using different data frames as input. Analysis Results Datasets are used in the pharmaceutical and biotech industries to capture analysis in a common tabular data structure. Package: r-cran-areabiplot Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nipals Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-areabiplot_1.0.0-1.ca2404.1_all.deb Size: 22530 MD5sum: 5a2c4e0e64551fb4939363bbbb70f1f5 SHA1: 1d71dbe3ef3f67b4f221056b3e7feb7c8b5639b5 SHA256: 682c9ade4b8aa4ed872d2d4d6533423f73d585b31a7058559a3ac477bfb8c64e SHA512: 665467267c576860dc4eaba822ce1a06efa491eeedbffdde1e388f0b83863263f5beebaddc86292c305e561fa0bf07e2c84bd24fee54c29ab44124f2d79c91f8 Homepage: https://cran.r-project.org/package=areabiplot Description: CRAN Package 'areabiplot' (Area Biplot) Considering an (n x m) data matrix X, this package is based on the method proposed by Gower, Groener, and Velden (2010) , and utilize the resulting matrices from the extended version of the NIPALS decomposition to determine n triangles whose areas are used to visually estimate the elements of a specific column of X. After a 90-degree rotation of the sample points, the triangles are drawn regarding the following points: 1.the origin of the axes; 2.the sample points; 3. the vector endpoint representing some variable. Package: r-cran-areal Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1916 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-areal_0.1.8-1.ca2404.1_all.deb Size: 1399902 MD5sum: 1fae658346fc1cacad9c2c82de7bf805 SHA1: 01cee74806e1e803286bde530fc541875f6d3b83 SHA256: 98abba4e12480d0f6d429d711edd8fd8e51e3711ede71a8dc505c191f7793e69 SHA512: dc274717c265784c497747a3d4cbc9c2cce33a1ff650ec408fbcb26bc4ab7cb4a91cd9aa88db9cd71f3cef5e2b48d94cb9b3818b1361c59e80a18ede663bda84 Homepage: https://cran.r-project.org/package=areal Description: CRAN Package 'areal' (Areal Weighted Interpolation) A pipeable, transparent implementation of areal weighted interpolation with support for interpolating multiple variables in a single function call. These tools provide a full-featured workflow for validation and estimation that fits into both modern data management (e.g. tidyverse) and spatial data (e.g. sf) frameworks. 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For applications that surpass the spatial, temporal or thematic scope of any single data source, data must be integrated from several heterogeneous sources. Inconsistent concepts, definitions, or messy data tables make this a tedious and error-prone process. 'arealDB' tackles those problems and helps the user to integrate a harmonised databases of areal data. Read the paper at Ehrmann, Seppelt & Meyer (2020) . Package: r-cran-areaofeffect Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3748 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf Suggests: r-cran-lwgeom, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-svglite Filename: pool/dists/noble/main/r-cran-areaofeffect_0.2.4-1.ca2404.1_all.deb Size: 3559296 MD5sum: bc73e76404f1f44d7327dae524ba0755 SHA1: e32860914b4aa6c85edad04b8719bc4d58490580 SHA256: 73598734f3de29f9181b9e27e45cc147d3a634228e1685a4e10681809eb14551 SHA512: 095cde0cc985bb51db9e2d5f82a9367ea76806e259f40ab2ed8d83851312bd52be959e024964256c422fcc0683a4aa54eb4f65955763a9c1bca180899d822ddf Homepage: https://cran.r-project.org/package=areaOfEffect Description: CRAN Package 'areaOfEffect' (Spatial Support at Scale) Formalizes spatial support at scale for ecological and geographical analysis. Given points and support polygons, classifies points as "core" (inside original support) or "halo" (inside scaled support but outside original), pruning all others. The default scale produces equal core and halo areas - a geometrically derived choice requiring no tuning. An optional mask enforces hard boundaries such as coastlines. Political borders are treated as soft boundaries with no ecological meaning. 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A variety of input formats are supported, including vectors, matrices, data frames, formulas, etc. Package: r-cran-arena2r Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1826 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-tidyr, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-shiny, r-cran-shinydashboard, r-cran-shinybs, r-cran-shinyjs, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-arena2r_1.0.0-1.ca2404.1_all.deb Size: 399538 MD5sum: ae0985b8684db92e21f64b991a6ca960 SHA1: fb343fac241a7b99a2d8cd90cfa184a55b16556f SHA256: eed822dcc5dcd33cdb96aa974862d4a487d78666ead9060cc667ea35783a9695 SHA512: 33cbf6db2027824428e1807d1b64d7cc58a7561e3df915f5854293f02728887d2fcbe62ac2bc5525a18092a632c63b119dacd14764e418b243f111ae6696ac12 Homepage: https://cran.r-project.org/package=arena2r Description: CRAN Package 'arena2r' (Plots, Summary Statistics and Tools for Arena Simulation Users) Reads Arena CSV output files and generates nice tables and plots. The package contains a Shiny App that can be used to interactively visualize Arena's results. 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You can start the server with XAI (Explainable Artificial Intelligence) plots to be generated on-demand or precalculate and auto-upload data file beside shareable 'Arena' URL. 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Includes validation tools designed to handle model hallucinations for a scientific, rigorous use of LLM. Currently supports usage of GPT with more planned, including local and non-proprietary models. For more details on the methodology used please consult the references listed under each function, such as Kent, A. et al. (1995) , van Rijsbergen, C.J. (1979, ISBN:978-0408709293, Levenshtein, V.I. (1966) and Klaus Krippendorff (2011) . Package: r-cran-arf Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 656 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ranger, r-cran-foreach, r-cran-stringr, r-cran-truncnorm Suggests: r-cran-ggplot2, r-cran-doparallel, r-cran-dofuture, r-cran-mlbench, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-palmerpenguins, r-cran-testthat Filename: pool/dists/noble/main/r-cran-arf_0.2.4-1.ca2404.1_all.deb Size: 497318 MD5sum: 1c132c6a38675189612b4bd2af1942b6 SHA1: b90e66a9e3712bf4768cf9292a45a2bfe6bdef36 SHA256: 19ea7412ddc882ac24a22b87a8e78039fc30cbd467fa8514e964dfe23a59ba1b SHA512: 5c785983c8ac9184dc72bd4d49c32b6362d4d67832c928671642fbba2d86b9f3b369a175e9d0a1182e429b2bc84e0791e382aab2740b5300cb16305f1867d293 Homepage: https://cran.r-project.org/package=arf Description: CRAN Package 'arf' (Adversarial Random Forests) Adversarial random forests (ARFs) recursively partition data into fully factorized leaves, where features are jointly independent. The procedure is iterative, with alternating rounds of generation and discrimination. Data becomes increasingly realistic at each round, until original and synthetic samples can no longer be reliably distinguished. This is useful for several unsupervised learning tasks, such as density estimation and data synthesis. Methods for both are implemented in this package. ARFs naturally handle unstructured data with mixed continuous and categorical covariates. They inherit many of the benefits of random forests, including speed, flexibility, and solid performance with default parameters. For details, see Watson et al. (2023) . 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Package: r-cran-argentinapi Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1051 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-lubridate, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-argentinapi_0.2.1-1.ca2404.1_all.deb Size: 962162 MD5sum: 9ab6c7d1e41629f372c8991e2deb439b SHA1: 0c0ec61996ef3d18f4fded209de3b47edc46fa0b SHA256: ba68e62340b79323e303f35a233c65d02adb781bdf43c342dd6757b9763782f1 SHA512: c1bffd828dbccd326508b35a14dfd4cb95a73ee63f6f9e451426fc901fb705ec923032b93407826bd4f9cd9cdacd77cc27c8317500df70216cc95896f0c6625d Homepage: https://cran.r-project.org/package=ArgentinAPI Description: CRAN Package 'ArgentinAPI' (Access Argentinian Data via APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including the 'ArgentinaDatos API', 'REST Countries API', and 'World Bank API' related to Argentina's exchange rates, inflation, political figures, holidays, economic indicators, and general country-level statistics. Additionally, the package includes curated datasets related to Argentina, covering topics such as economic indicators, biodiversity, agriculture, human rights, genetic data, and consumer prices. The package supports research and analysis focused on Argentina by integrating open APIs with high-quality datasets from various domains. For more details on the APIs, see: 'ArgentinaDatos API' , 'REST Countries API' , and 'World Bank API' . Package: r-cran-argentum Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-xml2, r-cran-sf, r-cran-readr Suggests: r-cran-testthat, r-cran-mockery, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-argentum_1.0.0-1.ca2404.1_all.deb Size: 52952 MD5sum: e784268f1d6b63bae1109c4d1feef850 SHA1: 7e5edc6feb56cb791b7803dfdaae0bf95208eb9b SHA256: f8e4df7d46d81465689730ce5bb73afbc7aff728dfb0c4b44ebe75d84ce80a0c SHA512: 8bd19cd128c9c3bfe1b3b3e314b188205abb8c04e699f5534b80e540eeb719562bda0d5715deb67a7301bbbcffd80dcfce18c6a7e334d48f1fd9804fedb1760c Homepage: https://cran.r-project.org/package=Argentum Description: CRAN Package 'Argentum' (Access and Import WMS and WFS Data from Argentine Organizations) Provides functions to retrieve information from Web Feature Service (WFS) and Web Map Service (WMS) layers from various Argentine organizations and import them into R for further analysis. WFS and WMS are standardized protocols for serving georeferenced map data over the internet. For more information on these services, see and . Package: r-cran-argmincs Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bsda, r-cran-glue, r-cran-ldats, r-cran-mass, r-cran-rdpack, r-cran-withr Filename: pool/dists/noble/main/r-cran-argmincs_1.1.0-1.ca2404.1_all.deb Size: 99368 MD5sum: 60b42c96f3de57ca47c6652963a6db2d SHA1: 0a7cc372fc67369127fa3252f1fd622a4531a66c SHA256: 517ff327a86b0871675952c257b19da7a176b5524238c25cf0b0766c82139705 SHA512: 7555b4e682ad25020f6579a1f47dc136b6e4687d0901dc20237ed4faa21858ae26758221e1a77aec1780cb2e987472136efe24dad391c813fb8dbbac5e0ce6f9 Homepage: https://cran.r-project.org/package=argminCS Description: CRAN Package 'argminCS' (Argmin Inference over a Discrete Candidate Set) Provides methods to construct frequentist confidence sets with valid marginal coverage for identifying the population-level argmin or argmax based on IID data. For instance, given an n by p loss matrix—where n is the sample size and p is the number of models—the CS.argmin() method produces a discrete confidence set that contains the model with the minimal (best) expected risk with desired probability. The argmin.HT() method helps check if a specific model should be included in such a confidence set. The main implemented method is proposed by Tianyu Zhang, Hao Lee and Jing Lei (2024) "Winners with confidence: Discrete argmin inference with an application to model selection". Package: r-cran-argo Architecture: all Version: 3.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2599 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-glmnet, r-cran-zoo, r-cran-xml, r-cran-xtable, r-cran-matrix, r-cran-boot Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-argo_3.0.3-1.ca2404.1_all.deb Size: 880002 MD5sum: 95ac6e73bc8c04eb80d81229ca7a7310 SHA1: 070951277cb80bdeeb26d043ae858e4f2bd628af SHA256: b04710717bef4fbb62b6aa0b56ec15f2d7288fa72c2dde3a402f8145c4359451 SHA512: 544d451a384511185d38601d737da7eb2f18128c6dbe4e88a1ec463bace485254a7a384d30628f0feaa23c3e8592bc9cbb2bce0b2c962f1b334be77628c0945b Homepage: https://cran.r-project.org/package=argo Description: CRAN Package 'argo' (Accurate Estimation of Influenza Epidemics using Google SearchData) Augmented Regression with General Online data (ARGO) for accurate estimation of influenza epidemics in United States on national level, regional level and state level. 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Functions are provided to (a) download and cache data files, (b) subset data in various ways, (c) handle quality-control flags and (d) plot the results according to oceanographic conventions. A shiny app is provided for easy exploration of datasets. The package is designed to work well with the 'oce' package, providing a wide range of processing capabilities that are particular to oceanographic analysis. See Kelley, Harbin, and Richards (2021) for more on the scientific context and applications. Package: r-cran-argondash Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4377 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-htmltools, r-cran-argonr Suggests: r-cran-magrittr Filename: pool/dists/noble/main/r-cran-argondash_0.2.5-1.ca2404.1_all.deb Size: 1844990 MD5sum: ad65a2e509e93319ee4c50e83810a392 SHA1: 9743b32aa921cfa364fa483b9e4f45377fd4195e SHA256: 78d1fae3513ca0461573e57fdab479c932804cf70c541025c97651b1378ed078 SHA512: d5799cf0bb6893964c56b868e43dd80de1ee0ddf082e22519658198b962fe7165a9a631f01da4ea73cf83b66227caac5492a251231fcb40cafc193fd5cb0b490 Homepage: https://cran.r-project.org/package=argonDash Description: CRAN Package 'argonDash' (Argon Shiny Dashboard Template) Create awesome 'Bootstrap 4' dashboards powered by 'Argon'. 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Package: r-cran-argosfilter Architecture: all Version: 0.71-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-argosfilter_0.71-1.ca2404.1_all.deb Size: 56728 MD5sum: b75a877e8b27116a352895fa1d2f65db SHA1: a0101d681302a78f0722d148563f288bf74c8b2f SHA256: a605d16f24e1676246bf4a721513e306812c507f2923d2ba82da073997f0fd17 SHA512: f856898ab7c631048232fd1c038f3f41350f3f9253dd8ac9d642150c435427a00a416b2ffb6766569bed40fab49762e339621d3e46fab23ea79bfd92b731a5b9 Homepage: https://cran.r-project.org/package=argosfilter Description: CRAN Package 'argosfilter' (Argos Locations Filter) Filters animal satellite tracking data obtained from the Argos system(), following the algorithm described in Freitas et al (2008) . It is especially indicated for telemetry studies of marine animals, where Argos locations are predominantly of low-quality. 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Package: r-cran-ari Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-text2speech, r-cran-tuner, r-cran-webshot, r-cran-purrr, r-cran-rmarkdown, r-cran-xml2, r-cran-rvest, r-cran-progress, r-cran-hms Suggests: r-cran-testthat, r-cran-xaringan, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ari_0.3.5-1.ca2404.1_all.deb Size: 235946 MD5sum: d80e0a9de550952e74e5a8e3987f16d2 SHA1: 485e415acf475f38273437357ee48d6c97c25910 SHA256: 340bc9b9b6da3947309353cadb360e9e28738ab6814ea9ca9110a173db16e023 SHA512: aa210d1c66f765fb8a59cda21a12ff38310a4ed5c377819e84d9397a4df86b4de3636c3366fc5645d96b84e89e5687f6075f66aba9f09ba879b4cd766feeadf8 Homepage: https://cran.r-project.org/package=ari Description: CRAN Package 'ari' (Automated R Instructor) Create videos from 'R Markdown' documents, or images and audio files. 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Package: r-cran-aridagri Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-emmeans, r-cran-multcomp, r-cran-corrplot, r-cran-factoextra, r-cran-factominer, r-cran-lavaan, r-cran-semplot, r-cran-agricolae, r-cran-car, r-cran-writexl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aridagri_2.0.3-1.ca2404.1_all.deb Size: 221330 MD5sum: 6880e14c73c870749a860c0f45b7786a SHA1: 6ff8d22b8d7ac772fd2e95a90d8fd7bd1241081f SHA256: ad489aaee0da92ca0d27853cc81500453cced48300d67fa14a49bacee6d044a9 SHA512: 60d1603b00bfd970e52ac73089d460300bf1250615c76a99da7ab895c82cb6f22a41685e8d0728062f749a0da5ed1010db3560e492e8d2c3173030dfc4969a5c Homepage: https://cran.r-project.org/package=aridagri Description: CRAN Package 'aridagri' (Comprehensive Statistical Tools for Agricultural Research) A comprehensive suite of statistical and analytical tools for agricultural research. Includes complete analysis of variance (ANOVA) functions for all experimental designs: Completely Randomized Design (CRD), Randomized Block Design (RBD), Pooled RBD, Split Plot with all variations, Split-Split Plot, Strip Plot, Latin Square, Factorial, Augmented, and Alpha Lattice, with proper error terms and comprehensive Standard Error (SE) and Critical Difference (CD) calculations. Features multiple post-hoc tests: Least Significant Difference (LSD), Duncan Multiple Range Test (DMRT), Tukey Honestly Significant Difference (HSD), Student-Newman-Keuls (SNK), Scheffe, Bonferroni, and Dunnett, along with assumption checking and publication-ready output. Advanced methods include stability analysis using Eberhart-Russell regression, Additive Main Effects and Multiplicative Interaction (AMMI), Finlay-Wilkinson regression, Shukla stability variance, Wricke ecovalence, Coefficient of Variation (CV), and Cultivar Superiority Index as described in Eberhart and Russell (1966) . Thermal indices include Growing Degree Days (GDD), Heliothermal Units (HTU), Photothermal Units (PTU), and Heat Use Efficiency (HUE). Crop growth analysis covers Crop Growth Rate (CGR), Relative Growth Rate (RGR), Net Assimilation Rate (NAR), and Leaf Area Index (LAI). Also provides harvest index, yield gap analysis, economic efficiency indices (Benefit-Cost ratio), nutrient use efficiency calculations, correlation matrix, Principal Component Analysis (PCA), path analysis, and Structural Equation Modeling (SEM). Statistical methods follow Gomez and Gomez (1984, ISBN:0471870927) and Panse and Sukhatme (1985, ISBN:8170271169). 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After that does time series analysis using one hybrid model and two specially structured Machine Learning (ML) (Artificial Neural Network or ANN and Support Vector Regression or SVR) models. More information can be obtained from Paul and Garai (2022) . 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Package: r-cran-armadillo4r Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6016 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cpp4r Suggests: r-cran-desc, r-cran-matrix, r-cran-mockery, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-armadillo4r_0.9.0-1.ca2404.1_all.deb Size: 477998 MD5sum: ded599c049ab84656e23ea8f0e086263 SHA1: 44fd58a7666fdc6254db3bae80abd0456da4cac8 SHA256: 110db7f7e26f6918c91ab069a388d2173b9b5a66179f76ffa096023ff3920c85 SHA512: 5e3681ea35ac407c67c5bfe13cd2af3eb82e84290653195d141f220a3f2fbba3360c33dfab731be677f319a6f721378aa64afadaefc3f7eabc2bcbd073849e3f Homepage: https://cran.r-project.org/package=armadillo4r Description: CRAN Package 'armadillo4r' (An 'Armadillo' Interface) Provides function declarations and inline function definitions that facilitate communication between R and the 'Armadillo' 'C++' library for linear algebra and scientific computing. 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Merging a linear time series model like the autoregressive moving average (ARMA) model with a nonlinear neural network model such as the Long Short-Term Memory (LSTM) model can be used as a hybrid model for more accurate modeling purposes. Both the autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models can be implemented. Details can be found in Box et al. (2015, ISBN: 978-1-118-67502-1) and Hochreiter and Schmidhuber (1997) . 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Package: r-cran-arnie Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-arnie_0.1.2-1.ca2404.1_all.deb Size: 16302 MD5sum: 8c9730b16185db054e41a578e55eab9e SHA1: cfe9aac89eec900cf54e5608f223526c02d05059 SHA256: 05d5113233c5429b3e5bb8194e29cf3148ee1d37a30fe5d49ad41db30315fc09 SHA512: e8894a5473717e06ac589c10527504f1067ace39729add0a96b162d1167d62288daf0426a64fc1f2178487095b6917032e6e2665336463cba0ace4e68a2612ed Homepage: https://cran.r-project.org/package=arnie Description: CRAN Package 'arnie' ("Arnie" box office records 1982-2014) Arnold Schwarzenegger movie weekend box office records from 1982-2014 Package: r-cran-aroc Architecture: all Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-np, r-cran-matrix, r-cran-hmisc, r-cran-mass, r-cran-moments, r-cran-nor1mix, r-cran-spatstat.geom Filename: pool/dists/noble/main/r-cran-aroc_1.0-4-1.ca2404.1_all.deb Size: 203696 MD5sum: a75d0987178e9814fdff8d595baa1e8b SHA1: f030da67e52e375e57f8d88838c03baa52a85125 SHA256: b13b354d21d684567a8849b89ffd1cf6f7717b0cdcf4cae74c548023851d8017 SHA512: f6dcc0cf3eacb163ae0e223771a6c03e65e3299e0da0004d0c4bba6f77005bf043ed8e03e82cdef3efb1e216abc1e9586b4d9a1229aa733ebd4c6d7eda2bd509 Homepage: https://cran.r-project.org/package=AROC Description: CRAN Package 'AROC' (Covariate-Adjusted Receiver Operating Characteristic CurveInference) Estimates the covariate-adjusted Receiver Operating Characteristic (AROC) curve and pooled (unadjusted) ROC curve by different methods. Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018) . NOTE: We have created a new package, 'ROCnReg', with more functionalities. It also implements all the methods included in 'AROC'. We, therefore, recommend using 'ROCnReg' ('AROC' will no longer be maintained). 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The only parameter that limits the number of chips that can be processed is the amount of available disk space. The Aroma Framework has successfully been used in studies to process tens of thousands of arrays. This package has actively been used since 2006. Package: r-cran-aroma.apd Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r.methodss3, r-cran-r.oo, r-cran-r.utils, r-cran-r.huge Suggests: r-bioc-affxparser Filename: pool/dists/noble/main/r-cran-aroma.apd_0.7.1-1.ca2404.1_all.deb Size: 118926 MD5sum: 9ea62f7994bfb854a272eba1ac4f2e79 SHA1: d75305105e1173f35a8337ea21ba6916970dc0c3 SHA256: 5793506e56f3eea2d530d308a580a3c1561f3a1786b6a10bb51d9b202b2e57bf SHA512: f1ca04469098d8b339be02c9c7f27e42caa5c54cb7e65458119c7ec74c22d2e74a68c1ee42d5f51a43baffeb503e0c37e3c963e268bcd352bcb244bd50b26e32 Homepage: https://cran.r-project.org/package=aroma.apd Description: CRAN Package 'aroma.apd' (A Probe-Level Data File Format Used by 'aroma.affymetrix'[deprecated]) DEPRECATED. Do not start building new projects based on this package. (The (in-house) APD file format was initially developed to store Affymetrix probe-level data, e.g. normalized CEL intensities. Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets). In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit. This speeds up the read substantially. This package is supporting the Aroma framework and should not be used elsewhere.) 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Package: r-cran-art Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car Filename: pool/dists/noble/main/r-cran-art_1.0-1.ca2404.1_all.deb Size: 26120 MD5sum: dccf264ef0361e5786001c2454b7459b SHA1: 0484ce75ac76ddf643a2f14e5b99a8aee291f89b SHA256: 0392da4312b2927d0f97ad00623a7082b94b4cbab6e175961b70fe6888f52d2b SHA512: e1f22fd441982db7f37954b1a6402b5e08f57e7beaff38f00b6c78564ec4937c299e84eb0ffbd6a3b0320e76468787e9630534cbdec25efd3460ca04e0a143e3 Homepage: https://cran.r-project.org/package=ART Description: CRAN Package 'ART' (Aligned Rank Transform for Nonparametric Factorial Analysis) An implementation of the Aligned Rank Transform technique for factorial analysis (see references below for details) including models with missing terms (unsaturated factorial models). The function first computes a separate aligned ranked response variable for each effect of the user-specified model, and then runs a classic ANOVA on each of the aligned ranked responses. For further details, see Higgins, J. J. and Tashtoush, S. (1994). An aligned rank transform test for interaction. Nonlinear World 1 (2), pp. 201-211. Wobbrock, J.O., Findlater, L., Gergle, D. and Higgins,J.J. (2011). The Aligned Rank Transform for nonparametric factorial analyses using only ANOVA procedures. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '11). New York: ACM Press, pp. 143-146. . 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Package: r-cran-arthistory Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-arthistory_0.1.0-1.ca2404.1_all.deb Size: 254766 MD5sum: 474f0570ff2685c7270cdc894e152689 SHA1: 26bc89a33eb972a3bfaa731eaebc77f02b642661 SHA256: f54d0e2fb53c5fd8f7bc1df0d25ad964599b75afb80b0c9a91b910f0a0640a03 SHA512: f0cfd3ffdf5eaf37e3f3e38a191c2a4b75abee24d59f910149453f5de0687996964ce40d87a2a6c71a01a87d1190fb92a6ae82596adedcb9bb9c13f012d82065 Homepage: https://cran.r-project.org/package=arthistory Description: CRAN Package 'arthistory' (Art History Textbook Data) Data from Gardner and Janson art history textbooks about both the artists featured in these books as well as their works. See Helen Gardner ("Art through the ages; an introduction to its history and significance," 1926, . Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardner’s Art through the ages," 1980, ISBN: 0155037587). Fred S. Kleiner ("Gardner’s art through the ages: a global history," 2020, ISBN: 9781337630702). Horst de la Croix and Richard G. Tansey ("Gardner's art through the ages," 1986, ISBN: 0155037633). Helen Gardner ("Art through the ages; an introduction to its history and significance," 1936, ). Helen Gardner ("Art through the ages," 1948, ). Helen Gardner, revised under the editorship of Sumner M. Crosby ("Art through the ages," 1959, ). Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardner’s Art through the ages," 1975, ISBN: 0155037560). Fred S. Kleiner ("Gardner’s Art through the ages: a global history," 2013, ISBN: 9780495915423. Fred S. Kleiner, Christin J. Mamiya, Richard G. Tansey ("Gardner’s art through the ages," 2001, ISBN: 0155083155). Fred S. Kleiner ("Gardner’s Art through the ages: a global history," 2016, ISBN: 9781285837840). Fred S. Kleiner, Christin J. Mamiya ("Gardner’s art through the ages," 2005, ISBN: 0534640958). Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardner’s Art through the ages," 1970, ISBN: 0155037528). Helen Gardner, Richard G. Tansey, Fred S. Kleiner ("Gardner’s Art through the ages," 1996, ISBN: 0155011413). Helen Gardner, Horst de la Croix, Richard G. Tansey, Diane Kirkpatrick ("Gardner’s Art through the ages," 1991, ISBN: 0155037692). Helen Gardner, Fred S. Kleiner ("Gardner’s Art through the ages: a global history," 2009, ISBN: 9780495093077). Davies, Penelope J.E., Walter B. Denny, Frima Fox Hofrichter, Joseph F. Jacobs, Ann S. Roberts, David L. Simon ("Janson’s history of art: the western tradition," 2007, ISBN: 0131934554). Davies, Penelope J.E., Walter B. Denny, Frima Fox Hofrichter, Joseph F. Jacobs, Ann S. Roberts, David L. Simon ("Janson’s history of art: the western tradition," 2011, ISBN: 9780205685172). H. W. Janson, Anthony F. Janson ("History of Art," 2001, ISBN: 0810934469). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1986, ISBN: 013389388). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1977, ISBN: 0810910527). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1969, ). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1963, ). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1991, ISBN: 0810934019). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1995, ISBN: 0810934213). 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Package: r-cran-artofr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bannercommenter, r-cran-clipr, r-cran-rstudioapi, r-cran-shiny Suggests: r-cran-rmarkdown, r-cran-miniui, r-cran-knitr Filename: pool/dists/noble/main/r-cran-artofr_0.4.1-1.ca2404.1_all.deb Size: 49004 MD5sum: db50b71b761286a432c31b4420f2b1bd SHA1: fcc1cadefec6fc7f7312081e24688d7e35b4f84c SHA256: c7d64153cf69f1a3381610452208abb1c47f997d2bf885ba934c09bbecfcedea SHA512: 6f3d5f15678b2229555360cfab05470399818a511936ccd3a1cf2d3f489bfc85671ad475a5600fc722ba51310c34bdc35a99936cba7b58e16f3d39178ba5dde0 Homepage: https://cran.r-project.org/package=ARTofR Description: CRAN Package 'ARTofR' (To Insert Title, Divider, and Block of Comments) For instructions, check . This is a wrapper of 'bannerCommenter', for inserting neat comments, headers and dividers. 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Package: r-cran-arttransfer Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gbm, r-cran-glmnet, r-cran-nnet, r-cran-randomforest Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-arttransfer_1.0.0-1.ca2404.1_all.deb Size: 78248 MD5sum: 6a7594fd233cd3576a5e37b589be9e33 SHA1: 6687291b2bc14ea622e972c1f07fe735118dbc19 SHA256: 153b5656005f252d3ec46825074bb8d881017580377b804c7c4996cd099c6c2f SHA512: 405737076fe50c738420885a3e86bec5e371b2215012006d0b4e0250921c5ba5f2c4ecd67c119bc95826ab756ad8df404beecd4e259058580894888f32e0ac49 Homepage: https://cran.r-project.org/package=ARTtransfer Description: CRAN Package 'ARTtransfer' (Adaptive and Robust Pipeline for Transfer Learning) Adaptive and Robust Transfer Learning (ART) is a flexible framework for transfer learning that integrates information from auxiliary data sources to improve model performance on primary tasks. 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Extract Metadata from your recordings, select a subset of recordings for interpretation, and prepare files for processing on the 'WildTrax' platform. Read and process metadata from recordings collected using the SongMeter and BAR-LT types of ARUs. Package: r-cran-arvindst Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-forecast, r-cran-tvreg, r-cran-lme4, r-cran-depmixs4, r-cran-reshape2, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-arvindst_1.0.0-1.ca2404.1_all.deb Size: 126130 MD5sum: 1156aacee22ee75b979a6a7046725325 SHA1: cf862162e15c4e98439dc73ca8ab938c32ddcdc9 SHA256: 170c4921694369dbb11c906211f54aba25621bb445ab357f43bc627ba8ff10cf SHA512: 0f8065d367be04f86728a0260085aa6a36d573e1dd4d30fe402eca529322613400b97d61028abf87a0b9592c884b2c8532b3a8489d67e420eeaecdc46366b4b2 Homepage: https://cran.r-project.org/package=ArvindSt Description: CRAN Package 'ArvindSt' (Five Novel Stochastic Regression Models with Arvind-DistributedErrors and Effects) Implements the 'Arvind' distribution and five novel stochastic regression models that replace the traditional Gaussian error assumption with 'Arvind'-distributed errors. The 'Arvind' distribution is a flexible single-parameter continuous distribution on the positive real line characterised by a polynomial numerator with Gaussian-type decay. The package provides complete distribution functions (darvind(), parvind(), qarvind(), rarvind()), maximum likelihood estimation via fit_arvind_mle(), and five model-fitting routines: Random Walk on Coefficients via fit_rw1(), Time-Varying Coefficient Linear Model via fit_tvlm(), Simulation-Extrapolation via fit_simex(), Mixed-Effects Regression via fit_mixed(), and Regime-Switching Hidden Markov Model via fit_hmm(). Additionally provides Monte Carlo forecasting with prediction intervals via forecast_arvind(), comprehensive goodness-of-fit diagnostics (21 metrics and 25 plots) via diagnostics_arvind() and plot_arvind(), k-fold and rolling-window cross-validation via cv_arvind(), and unified model comparison via summary_arvind(). For more details see Pandey, Singh, Tyagi, and Tyagi (2024) "Modelling climate, COVID-19, and reliability data: A new continuous lifetime model under different methods of estimation", Statistics and Applications, 22(2), . Package: r-cran-arxiv Architecture: all Version: 0.18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-xml Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-arxiv_0.18-1.ca2404.1_all.deb Size: 80248 MD5sum: 44066e95fcf86c7c67ba120635671f5d SHA1: 95dba4c9014eee46aa31b923d4e72cc3281217e4 SHA256: a5576580735d7c579f91ffbbc83c5577b1fbdc802aa63d925bfe24b6c0e13a75 SHA512: 5646c6e707ea428dc55d209b8eb92d382943532546134a46ffebd282466632d5be2da89dcb3413efa915f2a82128fa1112b3a7b7b676d4e8f9d5a0ef92cc7bbe Homepage: https://cran.r-project.org/package=aRxiv Description: CRAN Package 'aRxiv' (Interface to the arXiv API) An interface to the API for 'arXiv', a repository of electronic preprints for computer science, mathematics, physics, quantitative biology, quantitative finance, and statistics. 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Package: r-cran-ascenttraining Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1971 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ascenttraining_1.0.0-1.ca2404.1_all.deb Size: 1890622 MD5sum: 5aeecf3b0dd68ae44bebeee530f6529e SHA1: 2ba580df659109ed6dfbb8d935a5633758a4ab71 SHA256: c34bfb85a52064c8fd6b5904e3c71064f6fd20df0224cd1a438af6380ea726ab SHA512: 7870e00ecb7e8877bfe02fede0001f4f091230c9cb872c28d44d5fa24110446cbf38c8b78233bb554fd83ae2b05018d716d4c1cc4a753cad35db698dc1249998 Homepage: https://cran.r-project.org/package=ascentTraining Description: CRAN Package 'ascentTraining' (Ascent Training Datasets) Datasets to be used primarily in conjunction with Ascent training materials but also for the book 'SAMS Teach Yourself R in 24 Hours' (ISBN: 978-0-672-33848-9). Version 1.0-7 is largely for use with the book; however, version 1.1 has a much greater focus on use with training materials, whilst retaining compatibility with the book. 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Package: r-cran-asciichartr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asciichartr_0.1.0-1.ca2404.1_all.deb Size: 15402 MD5sum: c234d2d506483b4dedb4bd60901ab50e SHA1: eabc3b3ced7837a643111ae9e9cb4736a407f0fb SHA256: d44b6f9dbe67965be3d2b34c90b5791351557e075bd83a6d0decf836ca5e2a61 SHA512: d60a83ba7849d9d5ebd7e8c89d8f2b7cff6f7ecd6856d1ae8b6e57797c05e38b33a000f827f71391c6052c57eca7bb3b1fc535ffe41c976320ab9d28b5668ee0 Homepage: https://cran.r-project.org/package=asciichartr Description: CRAN Package 'asciichartr' (Lightweight ASCII Line Graphs) Create ASCII line graphs of a time series directly on your terminal in an easy way. There are some configurations you can add to make the plot the way you like. This project was inspired by the original 'asciichart' package by Igor Kroitor. Package: r-cran-asciiruler Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-asciiruler_0.2-1.ca2404.1_all.deb Size: 40000 MD5sum: 40420f67800073decf8c3349f49e0800 SHA1: e0846fdbc3e908614b88e3f8950d30bb92f3d80c SHA256: b1ce7805197bd22a362dbd92243081e6eec1dbf4a6fed9591067298b893a3be2 SHA512: d4916cd8719ef6c5c469084ee87a73b54a99e67f6217eeb14bbc99435acfb6f8626c8a31086fddc3603d463a3c1410ac59990ce2211be3ff2635d7c1a25d85e8 Homepage: https://cran.r-project.org/package=asciiruler Description: CRAN Package 'asciiruler' (Render an ASCII Ruler) An ASCII ruler is for measuring text and is especially useful for sequence analysis. Included in this package are methods to create ASCII rulers and associated GenBank sequence blocks, multi-column text displays that make it easy for viewers to locate nucleotides by position. Package: r-cran-asciisetupreader Architecture: all Version: 2.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4558 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-haven, r-cran-readr, r-cran-vroom, r-cran-stringr, r-cran-zoo, r-cran-shiny, r-cran-miniui, r-cran-rstudioapi Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-asciisetupreader_2.5.3-1.ca2404.1_all.deb Size: 3437166 MD5sum: 333fd4f0cd020f99ad2cee80f32302c6 SHA1: a6d49ce46c19585a249afe8413f0e912fde2f441 SHA256: ed01eb79620902cae5043fba131ce3f76cacf2a5d3c9e8d8a65aae4c3bc43b0c SHA512: e7d00055baec31b7eb0c520dbc3aed2cd10a69d74d074cddc7c4fc101f626f14ea7b5c7f247a992363fc8da54910daba78cce342a49e7ffb9c4f9704e928259d Homepage: https://cran.r-project.org/package=asciiSetupReader Description: CRAN Package 'asciiSetupReader' (Reads Fixed-Width ASCII Data Files (.txt or .dat) that HaveAccompanying Setup Files (.sps or .sas)) Lets you open a fixed-width ASCII file (.txt or .dat) that has an accompanying setup file (.sps or .sas). These file combinations are sometimes referred to as .txt+.sps, .txt+.sas, .dat+.sps, or .dat+.sas. This will only run in a txt-sps or txt-sas pair in which the setup file contains instructions to open that text file. It will NOT open other text files, .sav, .sas, or .por data files. Fixed-width ASCII files with setup files are common in older (pre-2000) government data. Package: r-cran-ascotracer Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2280 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-lubridate, r-cran-lutz, r-cran-circular, r-cran-purrr, r-cran-sf, r-cran-terra Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ascotracer_0.0.1-1.ca2404.1_all.deb Size: 598046 MD5sum: 0b91e62d3a776994a413885d18cbc41d SHA1: 2521135044d46255c846577d45c032fb9c02a505 SHA256: 697778dcf8705bc4fbec90f41462404072983985502c90ca0da9d903936c2052 SHA512: ccf2943a87ff1ca032d8082d609b99ca5146d1986c1679abe10d87900fd161dc97997a330d246b69bc57edfe8534a5ba4b005f660cae1c4076c0b5c8cf01d490 Homepage: https://cran.r-project.org/package=ascotraceR Description: CRAN Package 'ascotraceR' (Simulate the Spread of Ascochyta Blight in Chickpea) A spatiotemporal model that simulates the spread of Ascochyta blight in chickpea fields based on location-specific weather conditions. This model is adapted from a model developed by Diggle et al. (2002) for simulating the spread of anthracnose in a lupin field. Package: r-cran-asd Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-asd_2.2-1.ca2404.1_all.deb Size: 121900 MD5sum: 187f2df0e13fe2457275695ca7e35f16 SHA1: 79c4f1c2c797c3e93fdb62bc6be5b06fbe077b21 SHA256: 51ced501070cd26c1fedf1d71f2ce4c370f7e62cd3bc341636aeab02ec4b41cb SHA512: f5adf9389660520c877b938ef5a2b2b5b155aeb740431ddc66ba7a1d5e19aa2dd8c37ff4700267406662d084d72c8af9ba0f8cebad3b6b7c132afa946142c8e9 Homepage: https://cran.r-project.org/package=asd Description: CRAN Package 'asd' (Simulations for Adaptive Seamless Designs) Package runs simulations for adaptive seamless designs with and without early outcomes for treatment selection and subpopulation type designs. Package: r-cran-asdreader Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asdreader_0.1-3-1.ca2404.1_all.deb Size: 67146 MD5sum: 388e3eb32dd1e7ee3b5158147827460b SHA1: aef4caa2d3b54b7617369f3a00fa977dedb359f1 SHA256: 0317650ea403cde0afc59efbc7fc91707905b4996eb40fb12addc8bbe32c2548 SHA512: f154c4d75dd916efa96ebe8e895779a23bed8521e115a95ad884234c975354e2d535613607771a9bf8c1cee644a43502460e35129cea107af3974f8c887a6843 Homepage: https://cran.r-project.org/package=asdreader Description: CRAN Package 'asdreader' (Reading ASD Binary Files in R) A simple driver that reads binary data created by the ASD Inc. portable spectrometer instruments, such as the FieldSpec (for more information, see ). Spectral data can be extracted from the ASD files as raw (DN), white reference, radiance, or reflectance. Additionally, the metadata information contained in the ASD file header can also be accessed. Package: r-cran-asgs.foyer Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2664 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp Suggests: r-cran-testthat, r-cran-spdep, r-cran-codetools Filename: pool/dists/noble/main/r-cran-asgs.foyer_0.3.3-1.ca2404.1_all.deb Size: 2642404 MD5sum: 6714557df3cd2681edbe620da1757c24 SHA1: 82882dae4c1c1b77b1d943f154af020260c7bea2 SHA256: 56fdccf2170f0d2216cf2e6d8abaa82ebc0f65a0af5a1764e9e394e87e6d91f0 SHA512: d7c7d8e563d006080d1bc197a994481da28b95ec60ee1d99d4ccdbeea216c1f49e92e94b09be6fd97a5c344bfa4c93a17b6f468daa9955ecb8298b91e5b2f7f1 Homepage: https://cran.r-project.org/package=ASGS.foyer Description: CRAN Package 'ASGS.foyer' (Interface to the Australian Statistical Geography Standard) The Australian Statistical Geography Standard ('ASGS') is a set of shapefiles by the Australian Bureau of Statistics. This package provides an interface to those shapefiles, as well as methods for converting coordinates to shapefiles. Package: r-cran-ashapesampler Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2500 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-alphahull, r-cran-alphashape3d, r-cran-truncnorm, r-cran-rvcg, r-cran-tda, r-cran-doparallel, r-cran-foreach, r-cran-dplyr Suggests: r-cran-knitr, r-cran-testthat, r-cran-rgl, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ashapesampler_1.0.0-1.ca2404.1_all.deb Size: 805392 MD5sum: 71fca92d3b1341936f5002053aaa1d6b SHA1: 9185f2a4c144a23a8f343ced938be7c7254017f9 SHA256: 5a76b203d9227d10d1a66ab5e48b1c9c504c8269bffbe79e356f201e3d004a43 SHA512: ef1ed9db88a45407bf7f18e67aa0986a1bf5e47a09e4bc2e03224e7b688b8cd1ebe5d56258ec6f7c721fdb3ec686a463cc43adb7d99f752714a38e4c934adfe0 Homepage: https://cran.r-project.org/package=ashapesampler Description: CRAN Package 'ashapesampler' (Generating Alpha Shapes) Understanding morphological variation is an important task in many applications. Recent studies in computational biology have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the landscape of current shape simulation methods has been mostly limited to approaches that use black-box inference---making it difficult to systematically assess the power and calibration of sub-image models. In this R package, we introduce the alpha-shape sampler: a probabilistic framework for simulating realistic 2D and 3D shapes based on probability distributions which can be learned from real data or explicitly stated by the user. The 'ashapesampler' package supports two mechanisms for sampling shapes in two and three dimensions. The first, empirically sampling based on an existing data set, was highlighted in the original main text of the paper. The second, probabilistic sampling from a known distribution, is the computational implementation of the theory derived in that paper. Work based on Winn-Nunez et al. (2024) . Package: r-cran-asht Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-exact2x2, r-cran-exactci, r-cran-bpcp, r-cran-coin, r-cran-perm, r-cran-ssanv Suggests: r-cran-bootstrap Filename: pool/dists/noble/main/r-cran-asht_1.0.3-1.ca2404.1_all.deb Size: 263842 MD5sum: 3da459e63720b72f58e1b61b1da48e11 SHA1: bfa80165a93d424d6bfaf61e906b5b213ef40e01 SHA256: cde659746e446144f40362d3869c3ab01e2ea346db8ad741c09c97b5460f0f44 SHA512: 3e317bd0265d43bdbc6e6708a80763ed4e18102ebb554b65e2a98aec60e2f42fe1c647a2d338033bf535b2c4c0d14975aaea571c2b3302e0a94ec628509bbd51 Homepage: https://cran.r-project.org/package=asht Description: CRAN Package 'asht' (Applied Statistical Hypothesis Tests) Gives some hypothesis test functions (sign test, median and other quantile tests, Wilcoxon signed rank test, coefficient of variation test, test of normal variance, test on weighted sums of Poisson [see Fay and Kim ], sample size for t-tests with different variances and non-equal n per arm, Behrens-Fisher test, nonparametric ABC intervals, Wilcoxon-Mann-Whitney test [with effect estimates and confidence intervals, see Fay and Malinovsky ], two-sample melding tests [see Fay, Proschan, and Brittain ], one-way ANOVA allowing var.equal=FALSE [see Brown and Forsythe, 1974, Biometrics]), prevalence confidence intervals that adjust for sensitivity and specificity [see Lang and Reiczigel, 2014 ] or Bayer, Fay, and Graubard, 2023 ). The focus is on hypothesis tests that have compatible confidence intervals, but some functions only have confidence intervals (e.g., prevSeSp). Package: r-cran-asiaverse Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chinapis, r-cran-japanapis, r-cran-southkoreapis, r-cran-indiapis, r-cran-indonesiapis, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-asiaverse_0.1.0-1.ca2404.1_all.deb Size: 104742 MD5sum: 2ef51b51c88e65de44899afe7d4463e1 SHA1: ff3a81f9aefa42419fdc596a9079b1dd78ab1509 SHA256: 013de8567cc7ac85790a0eb93274f36769c2b889cfab6dad1cfc4acd106b19a2 SHA512: 84d181a5abe8c6815170a94b6fb4e20a7c5f477dbe5a9f5cf14523957184ced4158fbb17033179be3de08a4c27f38949d45d407bf596e99d0684449793d23615 Homepage: https://cran.r-project.org/package=Asiaverse Description: CRAN Package 'Asiaverse' (A Metapackage for Asian Countries RESTful APIs and CuratedDatasets) A metapackage that brings together a comprehensive collection of R packages providing access to APIs functions and curated datasets from China, Japan, South Korea, India, and Indonesia. It includes real-time and historical data through public RESTful APIs (Nager.Date, World Bank API, REST Countries API) and extensive curated collections of open datasets covering economics, demographics, public health, environmental data, natural disasters, political indicators, and social metrics. Designed to provide researchers, analysts, educators, and data scientists with centralized access to Asian data sources, this metapackage facilitates reproducible research, comparative analysis, and teaching applications focused on these five major Asian countries. Included packages: - 'ChinAPIs': APIs functions and curated datasets for China and Hong Kong covering air quality, demographics, input-output tables, epidemiology, political structure, and social indicators. - 'JapanAPIs': APIs functions and curated datasets for Japan including natural disasters, economic production, vehicle industry, air quality, demographics, and administrative divisions. - 'SouthKoreAPIs': APIs functions and curated datasets for South Korea covering public health outbreaks, social surveys, elections, economic indicators, natural disasters, climate data, energy consumption, cultural information, and financial markets. - 'IndiAPIs': APIs functions and curated datasets for India with comprehensive collections and real-time access to economic, demographic, and geopolitical indicators. - 'IndonesiAPIs': APIs functions and curated datasets for Indonesia covering holidays, economic indicators, consumer prices, poverty probability, food prices by region, tourism destinations, and minimum wage statistics. For more information on the APIs, see: 'Nager.Date' , 'World Bank API' , and 'REST Countries API' . Package: r-cran-asioheaders Architecture: all Version: 1.30.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5320 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asioheaders_1.30.2-1-1.ca2404.1_all.deb Size: 411764 MD5sum: 65b6f19b9b27ab2ac5dafe8b1c0e1ac8 SHA1: c7d77bd6eebce1cec279ce4e6c3b602764548bf4 SHA256: a32ba59fbfee6b37fb3394378898e04ba76b0f0c1ec7e960c7ea871fe25a1957 SHA512: e87fe74452facc1e30caebe408a33427d25777c6bdc4b30dcdf80b3ee98c8820d1d7f129359827062b8f25a2cbe160b6c24516260ba8c75498d9bfc3ef47d8ac Homepage: https://cran.r-project.org/package=AsioHeaders Description: CRAN Package 'AsioHeaders' ('Asio' C++ Header Files) 'Asio' is a cross-platform C++ library for network and low-level I/O programming that provides developers with a consistent asynchronous model using a modern C++ approach. It is also included in Boost but requires linking when used with Boost. Standalone it can be used header-only (provided a recent compiler). 'Asio' is written and maintained by Christopher M. Kohlhoff, and released under the 'Boost Software License', Version 1.0. Package: r-cran-askgpt Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3538 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-callr, r-cran-dplyr, r-cran-glue, r-cran-rlang, r-cran-httr2, r-cran-rappdirs, r-cran-jsonlite Suggests: r-cran-covr, r-cran-knitr, r-cran-miniui, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shiny, r-cran-shinycssloaders, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-askgpt_0.1.3-1.ca2404.1_all.deb Size: 2393660 MD5sum: 470bf0b6bd7b4d6dad2e484ef87d2270 SHA1: 5e361c367760928e54e1571a172cfe27a2a3867e SHA256: 793bde0de10bd6801dd9f4e1a65eb0626caf477f252f8420b2315c98346f949b SHA512: 072318f3c87fd3989481e117695bf87b4071b0530f9d4f4dbdb3fe9faff6cd3b5e26c15a1014f510b3f343ae18639eb15a7c8270d74a48d5fdbf53ce90f11eaa Homepage: https://cran.r-project.org/package=askgpt Description: CRAN Package 'askgpt' (Asking GPT About R Stuff) A chat package connecting to API endpoints by 'OpenAI' () to answer questions (about R). Package: r-cran-aslib Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-batchtools, r-cran-data.table, r-cran-bbmisc, r-cran-checkmate, r-cran-corrplot, r-cran-ggplot2, r-cran-llama, r-cran-mlr, r-cran-parallelmap, r-cran-paramhelpers, r-cran-plyr, r-cran-reshape2, r-cran-rweka, r-cran-stringr, r-cran-yaml Suggests: r-cran-testthat, r-cran-rpart Filename: pool/dists/noble/main/r-cran-aslib_0.1.3-1.ca2404.1_all.deb Size: 170960 MD5sum: 6ef6d2d2274eed042cb0e358a7204bed SHA1: c81fa168062dd1a917f1c6d781f735654980cedc SHA256: 23b4412d386aee5ad515f66eff70f8e1a0a697ff8d12c5017b9ce74332d9928b SHA512: e0c05f21c33d7c34ad31204024022aa025f2295d1b4c93e5bf7b4511fe4f4abca45c22c7801fc1f020c0fe60586d97b603769110f3b28d35a50a5c170cb0a98b Homepage: https://cran.r-project.org/package=aslib Description: CRAN Package 'aslib' (Interface to the Algorithm Selection Benchmark Library) Provides an interface to the algorithm selection benchmark library at and the 'LLAMA' package () for building algorithm selection models; see Bischl et al. (2016) . Package: r-cran-asm Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fdrtool, r-cran-pracma, r-cran-iso, r-cran-mass, r-cran-quantreg Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-asm_0.2.4-1.ca2404.1_all.deb Size: 72692 MD5sum: f12596d8b147194ff30a158e6dbda461 SHA1: 1ff2c92e8e4500a28be6405e316eb56a1160b550 SHA256: b684858dedd57ce769e165f94dec7af4cfd8ca9398fc1f4019a12651a12ec73f SHA512: 40847bf9f4f57fbfc94bf8bb8e46e18ceb6f5dfe2c025f72cadc85da27e42001f1e5765438f596869c28e4eb63c5ed77d5d76e956857c44a8c542fff63973a31 Homepage: https://cran.r-project.org/package=asm Description: CRAN Package 'asm' (Optimal Convex M-Estimation for Linear Regression via AntitonicScore Matching) Performs linear regression with respect to a data-driven convex loss function that is chosen to minimize the asymptotic covariance of the resulting M-estimator. The convex loss function is estimated in 5 steps: (1) form an initial OLS (ordinary least squares) or LAD (least absolute deviation) estimate of the regression coefficients; (2) use the resulting residuals to obtain a kernel estimator of the error density; (3) estimate the score function of the errors by differentiating the logarithm of the kernel density estimate; (4) compute the L2 projection of the estimated score function onto the set of decreasing functions; (5) take a negative antiderivative of the projected score function estimate. Newton's method (with Hessian modification) is then used to minimize the convex empirical risk function. Further details of the method are given in Feng et al. (2024) . Package: r-cran-asmbook Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-lattice, r-cran-mass, r-cran-tmb Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-asmbook_1.0.2-1.ca2404.1_all.deb Size: 130966 MD5sum: 3ac5a67506944edc547291efe55e218f SHA1: 95d9012365e81bb44f2ee1b791eb2842d2a1c201 SHA256: 7d8de89f1f3dc9f500ae2ad91d006e06ec6508483dbbc079f8f262bfd093c055 SHA512: 4fcaf2bb8e7ca6b3e6e3606983678dfb253a3e024e6cb986f7721d61e128f91208fc6e05978aa4a90cbb954e5bfe67bc705ba093d756f9943d7eb9c84719a7dd Homepage: https://cran.r-project.org/package=ASMbook Description: CRAN Package 'ASMbook' (Functions for the Book "Applied Statistical Modeling forEcologists") Provides functions to accompany the book "Applied Statistical Modeling for Ecologists" by Marc Kéry and Kenneth F. Kellner (2024, ISBN: 9780443137150). Included are functions for simulating and customizing the datasets used for the example models in each chapter, summarizing output from model fitting engines, and running custom Markov Chain Monte Carlo. Package: r-cran-asml Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-ggplot2, r-cran-dalex, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-tidyr, r-cran-reshape2, r-cran-polychrome, r-cran-scales, r-cran-rlang Suggests: r-cran-snow Filename: pool/dists/noble/main/r-cran-asml_1.1.0-1.ca2404.1_all.deb Size: 661558 MD5sum: 9dcb76b2c952422c361ce595b1ef9960 SHA1: 6ab28b13b304f385f9f7fd37276ca30790ca1db3 SHA256: d853a863aaaa145952c163786d1e64007f0f73531ba31617b444e6602fe26d8a SHA512: 043c2feac9721053d9f1a8cac325a427586f7805dd4bc4c4507ebc398bb3803e70d27c8efb014d63845fcf4daeb3e55bc8b154b39bd6fa85333697fb8622eb18 Homepage: https://cran.r-project.org/package=ASML Description: CRAN Package 'ASML' (Algorithm Portfolio Selection with Machine Learning) A wrapper for machine learning (ML) methods to select among a portfolio of algorithms based on the value of a key performance indicator (KPI). A number of features is used to adjust a model to predict the value of the KPI for each algorithm, then, for a new value of the features the KPI is estimated and the algorithm with the best one is chosen. To learn it can use the regression methods in 'caret' package or a custom function defined by the user. Several graphics available to analyze the results obtained. This library has been used in Ghaddar et al. (2023) ). Package: r-cran-asnipe Architecture: all Version: 1.1.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix Suggests: r-cran-ape, r-cran-raster, r-cran-sna Filename: pool/dists/noble/main/r-cran-asnipe_1.1.17-1.ca2404.1_all.deb Size: 440152 MD5sum: 7374f976c4608dcaa4a3e1b9a41a5a81 SHA1: 07c99309cc1f2c8985185b0a816e2a6d226d9dd8 SHA256: 4862df393074b03914e999dbe208c046f4553f253edd9ee21caf7769d263a20a SHA512: 3025abd41dd0edc99cdf36426d32d5d40c25494561ff6977e8edad89b78270a06fa44c7c68048d9f4c0b68d6f3f96452df52a94b0e1b5e5cd21e9c99d2ddc89c Homepage: https://cran.r-project.org/package=asnipe Description: CRAN Package 'asnipe' (Animal Social Network Inference and Permutations for Ecologists) Implements several tools that are used in animal social network analysis, as described in Whitehead (2007) Analyzing Animal Societies and Farine & Whitehead (2015) . In particular, this package provides the tools to infer groups and generate networks from observation data, perform permutation tests on the data, calculate lagged association rates, and performed multiple regression analysis on social network data. Package: r-cran-aspace Architecture: all Version: 4.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-splancs, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-aspace_4.1.2-1.ca2404.1_all.deb Size: 124792 MD5sum: fd34639bc9fc6604206a9ba634671162 SHA1: 7416637179bdfb38bd7c19e3de59be256d89a208 SHA256: 6cdb2ae1de876150180779928efd5be55e486bf1a87f3a92dfab580ce498d659 SHA512: 85b0c3befc6600d2c468a675327cae243c664617a3d6b53b03de3b8dc27a5a4fdabe0220d888e28e6d67f74f15f7ca5f4cf4b7a78eedcc4dc1e5f392c137d3c7 Homepage: https://cran.r-project.org/package=aspace Description: CRAN Package 'aspace' (Functions for Estimating Centrographic Statistics) A collection of functions for computing centrographic statistics (e.g., standard distance, standard deviation ellipse, standard deviation box) for observations taken at point locations. Separate plotting functions have been developed for each measure. Users interested in writing results to ESRI shapefiles can do so by using results from 'aspace' functions as inputs to the convert.to.shapefile() and write.shapefile() functions in the 'shapefiles' library. We intend to provide 'terra' integration for geographic data in a future release. The 'aspace' package was originally conceived to aid in the analysis of spatial patterns of travel behaviour (see Buliung and Remmel 2008 ). Package: r-cran-aspect Architecture: all Version: 1.0-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-sem, r-cran-polycor Filename: pool/dists/noble/main/r-cran-aspect_1.0-7-1.ca2404.1_all.deb Size: 94532 MD5sum: 4e0c382a8c08ac0156858c6bc3496d45 SHA1: 531222c447d962ddcb9850912ae7b73f4ef88a8a SHA256: 068da97125073a2e1efe74ea3fc6904371109d45c66996287871a18c4e851c9e SHA512: 42d520b5eb5f9cc0d96f25bc907b62d9e2b3de356f5e77cfcb3455a42651a4dde2351651e5c014bbcd04ff34519ad348ed95aaa842fa5990f0a2bd93b49d5753 Homepage: https://cran.r-project.org/package=aspect Description: CRAN Package 'aspect' (A General Framework for Multivariate Analysis with OptimalScaling) Contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models. Package: r-cran-aspi Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aspi_0.2.0-1.ca2404.1_all.deb Size: 76440 MD5sum: 6ab25f8cc27d7ef88aed13fcab631ab0 SHA1: 53e766a770b8aed170e7ece46b5299ed7055531a SHA256: 6827546ba515ebb7d80ab55045dcef7fdf49f873c60347d8b6343abbc73df71b SHA512: 135f27eb3a2ee710a532a7727f94fc662e43a5173fc663c54baac9c72dd5cc0e00f1760f6c08d00e380cf7cc1c708bfd65fb87a2500e25abbe0f454de8e0753a Homepage: https://cran.r-project.org/package=aspi Description: CRAN Package 'aspi' (Analysis of Symmetry of Parasitic Infections) Tools for the analysis and visualization of bilateral asymmetry in parasitic infections. Package: r-cran-aspu Architecture: all Version: 1.50-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 875 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gee, r-cran-mass, r-cran-mvtnorm, r-cran-fields, r-cran-matrixstats Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-aspu_1.50-1.ca2404.1_all.deb Size: 747724 MD5sum: 6bafce5eec1b9c29743e0afc3abbc516 SHA1: 794a2f74a692f6e98871e9576915b23449431fae SHA256: 76c4ec2a6aa2cd4e0227a45a2555de7463185c0f52d02823c6f861f19917c549 SHA512: a878d6e95bf3969aed907d1adbf8b83b8fb3246f0339bcd5de35b462bc60ace0c498ab1f2406997e5b389b3953b8dd0175196f280c6a43b6cb001f5c531d1e1e Homepage: https://cran.r-project.org/package=aSPU Description: CRAN Package 'aSPU' (Adaptive Sum of Powered Score Test) R codes for the (adaptive) Sum of Powered Score ('SPU' and 'aSPU') tests, inverse variance weighted Sum of Powered score ('SPUw' and 'aSPUw') tests and gene-based and some pathway based association tests (Pathway based Sum of Powered Score tests ('SPUpath'), adaptive 'SPUpath' ('aSPUpath') test, 'GEEaSPU' test for multiple traits - single 'SNP' (single nucleotide polymorphism) association in generalized estimation equations, 'MTaSPUs' test for multiple traits - single 'SNP' association with Genome Wide Association Studies ('GWAS') summary statistics, Gene-based Association Test that uses an extended 'Simes' procedure ('GATES'), Hybrid Set-based Test ('HYST') and extended version of 'GATES' test for pathway-based association testing ('GATES-Simes'). ). The tests can be used with genetic and other data sets with covariates. The response variable is binary or quantitative. Summary; (1) Single trait-'SNP' set association with individual-level data ('aSPU', 'aSPUw', 'aSPUr'), (2) Single trait-'SNP' set association with summary statistics ('aSPUs'), (3) Single trait-pathway association with individual-level data ('aSPUpath'), (4) Single trait-pathway association with summary statistics ('aSPUsPath'), (5) Multiple traits-single 'SNP' association with individual-level data ('GEEaSPU'), (6) Multiple traits- single 'SNP' association with summary statistics ('MTaSPUs'), (7) Multiple traits-'SNP' set association with summary statistics('MTaSPUsSet'), (8) Multiple traits-pathway association with summary statistics('MTaSPUsSetPath'). Package: r-cran-asremlplus Architecture: all Version: 4.4.58-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dae, r-cran-devtools, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-nloptr, r-cran-qqplotr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rlang, r-cran-sticky, r-cran-stringr, r-cran-trycatchlog Suggests: r-cran-emmeans, r-cran-lattice, r-cran-lmertest, r-cran-pbkrtest, r-cran-r.rsp, r-cran-testthat, r-cran-tictoc Filename: pool/dists/noble/main/r-cran-asremlplus_4.4.58-1.ca2404.1_all.deb Size: 3107500 MD5sum: 80f46782a5f3de1ea2097d4d8980c923 SHA1: ba444d4c00d236c6b80be0dd4114b527f312fb55 SHA256: f68a7119c179f72866979be03d886f6f5002299e11cd70dc055327f8e353c75b SHA512: 13a106b21e475474b18223e1f38db53a9827c8d82da8ac31e8f7c7ec213a72576901d561029f654b1921cfe8328427419667aad46680e47b0a76fa56a814b613 Homepage: https://cran.r-project.org/package=asremlPlus Description: CRAN Package 'asremlPlus' (Augments 'ASReml-R' in Fitting Mixed Models and PackagesGenerally in Exploring Prediction Differences) Assists in automating the selection of terms to include in mixed models when 'asreml' is used to fit the models. Procedures are available for choosing models that conform to the hierarchy or marginality principle, for fitting and choosing between two-dimensional spatial models using correlation, natural cubic smoothing spline and P-spline models. A history of the fitting of a sequence of models is kept in a data frame. Also used to compute functions and contrasts of, to investigate differences between and to plot predictions obtained using any model fitting function. The content falls into the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model selection and description functions, (iv) Model diagnostics and simulation functions, (v) Prediction production and presentation functions, (vi) Response transformation functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions (for further details see 'asremlPlus-package' in help). The 'asreml' package provides a computationally efficient algorithm for fitting a wide range of linear mixed models using Residual Maximum Likelihood. It is a commercial package and a license for it can be purchased from 'VSNi' as 'asreml-R', who will supply a zip file for local installation/updating (see ). It is not needed for functions that are methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be installed from . Package: r-cran-asrgenomics Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4918 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aghmatrix, r-cran-cowplot, r-cran-crayon, r-cran-data.table, r-cran-ellipse, r-cran-factoextra, r-cran-ggplot2, r-cran-matrix, r-cran-scattermore Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-v8, r-cran-testthat Filename: pool/dists/noble/main/r-cran-asrgenomics_1.1.6-1.ca2404.1_all.deb Size: 4841278 MD5sum: 37949451fd2de3f0d5045ddccd2fbabe SHA1: 48d773a25a08ed4d769cfd249d53d6b19beefa2f SHA256: 993ba72467af42c31436f2a6a0700bea1cab12cd1f9a036c5c10b07c377840bd SHA512: c0bf4b375c95b72b19ee09a793b5380bae2308296b1d01414f507d8e4b15aa8ea4cafaa2f2b5ddcaa9fc2420a571925a44aca10f0a1c93103e686a73249f2f6d Homepage: https://cran.r-project.org/package=ASRgenomics Description: CRAN Package 'ASRgenomics' (Complementary Genomic Functions) Presents a series of molecular and genetic routines in the R environment with the aim of assisting in analytical pipelines before and after the use of 'asreml' or another library to perform analyses such as Genomic Selection or Genome-Wide Association Analyses. Methods and examples are described in Gezan, Oliveira, Galli, and Murray (2022) . Package: r-cran-assemblykor Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6526 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-arrow, r-cran-broom, r-cran-dplyr, r-cran-fixest, r-cran-ggplot2, r-cran-htmltools, r-cran-igraph, r-cran-knitr, r-cran-learnr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-scales, r-cran-stringr, r-cran-systemfonts, r-cran-testthat, r-cran-tidyr, r-cran-tidytext Filename: pool/dists/noble/main/r-cran-assemblykor_0.1.2-1.ca2404.1_all.deb Size: 4877604 MD5sum: b2ee5cb399e8f11e28dc08c9013875f4 SHA1: 9a0508595a6c003730d55397a382b0ae59270bec SHA256: a00e855b8364ae4a41acc07ccb9b6e1597b6e9b52797074eb2e58946f5f5d3d3 SHA512: 5975816f1a764e7fdb27407c3c3d63827f1ecdb748744b97d956c134c893c9ac6b4ffb8f3eee44c0f42d2b0710eafe42633d9e4d3433513b03fa5449a6adec5e Homepage: https://cran.r-project.org/package=assemblykor Description: CRAN Package 'assemblykor' (Korean National Assembly Data for Political Science Education) Provides ready-to-use datasets from the Korean National Assembly (assemblies 20 through 22, 2016-2026) for teaching quantitative methods in political science. Includes legislator metadata, bill proposals, roll call votes, asset declarations, and policy seminar records. Designed as a Korean politics counterpart to packages like 'palmerpenguins', enabling students to practice regression, panel data analysis, text analysis, and network analysis with real legislative data. Roll call vote data and spatial voting models are described in Poole and Rosenthal (1985) . Legislative data is sourced from the Korean National Assembly Open API. Package: r-cran-assert Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-assert_1.0.1-1.ca2404.1_all.deb Size: 11588 MD5sum: ec8c301939bbf6bc6c40b6fa37dbd2c0 SHA1: e66794c15b8ae85531740373a3d7dd67ec175da7 SHA256: d506506f29cc4db724128a3b992ffbcc2b42541a3d64fe8fd95e361643953547 SHA512: b4865cb652bc55a69ec231727222cb078b27cd1a919408375303c88e2ddd7488dfed7735ace39fa7b73a38527b57035a8dc7944ea40b56ebd37a2f8c4ea8de21 Homepage: https://cran.r-project.org/package=assert Description: CRAN Package 'assert' (Validate Function Arguments) Lightweight validation tool for checking function arguments and validating data analysis scripts. This is an alternative to stopifnot() from the 'base' package and to assert_that() from the 'assertthat' package. It provides more informative error messages and facilitates debugging. Package: r-cran-assertable Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-assertable_0.2.8-1.ca2404.1_all.deb Size: 58446 MD5sum: b73b2339ab33a4319466e4288e58a2e5 SHA1: 742ad6224aca3507d10ad2b97ffabad95ff6e514 SHA256: 09682fcced9d461353def0b3c9134aea8f2340eebe7abf9bb97c237f764fbb06 SHA512: 38915fc8d1bea763d2ea656647577154c454f654fe31a5ef5a9aba0e88d9e657787379eec74baa1a3957c122dc581a3c12ba9c24ab8fe0aeb77fe1f117cfbb58 Homepage: https://cran.r-project.org/package=assertable Description: CRAN Package 'assertable' (Verbose Assertions for Tabular Data (Data.frames andData.tables)) Simple, flexible, assertions on data.frame or data.table objects with verbose output for vetting. While other assertion packages apply towards more general use-cases, assertable is tailored towards tabular data. It includes functions to check variable names and values, whether the dataset contains all combinations of a given set of unique identifiers, and whether it is a certain length. In addition, assertable includes utility functions to check the existence of target files and to efficiently import multiple tabular data files into one data.table. Package: r-cran-asserthe Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-ggplot2, r-cran-dplyr, r-cran-visnetwork, r-cran-covr, r-cran-htmltools, r-cran-officer, r-cran-flextable, r-cran-knitr, r-cran-shiny, r-cran-shinyjs, r-cran-rstudioapi, r-cran-roxygen2, r-cran-waiter, r-cran-igraph, r-cran-httr Suggests: r-cran-testthat, r-cran-colourpicker, r-cran-clipr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-asserthe_1.0.1-1.ca2404.1_all.deb Size: 186546 MD5sum: 14659964334cd5b062927cc16bd7afad SHA1: 663fa972f7ca0097e144d824ddb2ca3d323e5496 SHA256: 08e07838c95ff1683e02f96e09e2e5ca87138ec11270368b74142a7d1adefbea SHA512: 918cb014141ccd828227927289d305ce820b0d2125e4d374d8f05d855ed07e34a7f9a651ac3e693382085713638bbe5e7e3312b7eff97b441d0964e2c1a871e1 Homepage: https://cran.r-project.org/package=assertHE Description: CRAN Package 'assertHE' (Visualisation and Verification of Health Economic DecisionModels) Designed to help health economic modellers when building and reviewing models. The visualisation functions allow users to more easily review the network of functions in a project, and get lay summaries of them. The asserts included are intended to check for common errors, thereby freeing up time for modellers to focus on tests specific to the individual model in development or review. For more details see Smith and colleagues (2024). Package: r-cran-assertions Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 769 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-rlang Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-assertions_0.3.0-1.ca2404.1_all.deb Size: 572542 MD5sum: bcf08d9e345d946986789bcb8b68732c SHA1: f2a0ffadece364840f23ac1807ebcc1bf512afa9 SHA256: 551149ba5aa0c926f851693070358bbc813bc9b074e797fc2c5d1b82df9a582c SHA512: c1323e8b53f5f848c6d42913314da2b9600d2dee16d40dbc38c919e4a6489e7a6dad5199c88d477306c91f1053b2c42ec5894c5c898a61b1c3faf6d866c13e2c Homepage: https://cran.r-project.org/package=assertions Description: CRAN Package 'assertions' (Simple Assertions for Beautiful and Customisable Error Messages) Provides simple assertions with sensible defaults and customisable error messages. It offers convenient assertion call wrappers and a general assert function that can handle any condition. Default error messages are user friendly and easily customized with inline code evaluation and styling powered by the 'cli' package. 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Package: r-cran-assessor Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 828 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tweedie, r-cran-mass, r-cran-vgam, r-cran-np, r-cran-pscl Suggests: r-cran-statmod, r-cran-rmarkdown, r-cran-knitr, r-cran-aer, r-cran-testthat, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-assessor_1.3.1-1.ca2404.1_all.deb Size: 753008 MD5sum: 8c0464e1427e1458852f3e1dbccd1a05 SHA1: 5057d2074478d544d4f36e3073ce8bdc55bed762 SHA256: bc0e6f10d95e2541e9af2afa6957c9ddd6f1439dd0bbc241d89cdaf3f5ca1cf1 SHA512: 66bf59efd6b388de2e880918d8e1fe756df758291cb8bf938d68419e1b67b2730bd8293d8b78d2df139321dda0754450cccaa4a3e65ee057e1fde40eada916ab Homepage: https://cran.r-project.org/package=assessor Description: CRAN Package 'assessor' (Assessment Tools for Regression Models with Discrete andSemicontinuous Outcomes) Provides assessment tools for regression models with discrete and semicontinuous outcomes proposed in Yang (2021) , Yang (2024) , Yang (2024) , and Yang (2026) . It calculates the double probability integral transform (DPIT) residuals. It also constructs QQ plots of residuals the ordered curve for assessing mean structures, quasi-empirical distribution function for overall assessment, and a formal goodness-of-fit test. 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Users can rely on their own data, or have the package automatically download data from Yahoo Finance (). Several pre-loaded portfolios with data are available, including some which are discussed in Faber (2015, ISBN:9780988679924). 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Package: r-cran-assetpricing Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-polynom, r-cran-desolve Filename: pool/dists/noble/main/r-cran-assetpricing_1.0-3-1.ca2404.1_all.deb Size: 147448 MD5sum: 4be6e717856b69d797884ea58b536a5e SHA1: 0e3f5d248b799fd5da1bc757e26f9cbbc7a03c86 SHA256: 81808a2ba729a7655895f2e44f966b88f134458a2f68b97875c010b23e4602cf SHA512: 9c921215cbbfec784a6d6e396ec62685f818fd022a5b79826bb60ad1b0caeaec310dee4bc92e60421ebd409aeccdd98da1b0ffd3153866843806a95712e459ac Homepage: https://cran.r-project.org/package=AssetPricing Description: CRAN Package 'AssetPricing' (Optimal Pricing of Assets with Fixed Expiry Date) Calculates the optimal price of assets (such as airline flight seats, hotel room bookings) whose value becomes zero after a fixed ``expiry date''. Assumes potential customers arrive (possibly in groups) according to a known inhomogeneous Poisson process. Also assumes a known time-varying elasticity of demand (price sensitivity) function. Uses elementary techniques based on ordinary differential equations. Uses the package deSolve to effect the solution of these differential equations. 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Package: r-cran-assignr Architecture: all Version: 2.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvnfast, r-cran-rlang, r-cran-geosphere, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-assignr_2.4.3-1.ca2404.1_all.deb Size: 795986 MD5sum: a541623ce1914c46f93121f6235af77e SHA1: a5432ee6bc19cc7c9e54a2fd06b2c482ddd3c3d2 SHA256: a9109df4feb54ac2030f98bef397b583c675f78a684726bc5c4da22f7468b5dd SHA512: 0e3160c409d442f012d6d00a33d74bd7e9d6b992ad22ac7583b97a7653afd5a5c850190f00d8ab4c95fabe223b1fc143238eda9f62d52f4535143e3db3058de8 Homepage: https://cran.r-project.org/package=assignR Description: CRAN Package 'assignR' (Infer Geographic Origin from Isotopic Data) Routines for re-scaling isotope maps using known-origin tissue isotope data, assigning origin of unknown samples, and summarizing and assessing assignment results. Methods are adapted from Wunder (2010, in ISBN:9789048133536) and Vander Zanden, H. B. et al. (2014) as described in Ma, C. et al. (2020) . Package: r-cran-assistant Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4670 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-mvtnorm, r-cran-knitr, r-cran-magrittr, r-cran-dplyr Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-assistant_1.4.3-1.ca2404.1_all.deb Size: 605314 MD5sum: ff802729fa7314c3b63304b894d7a1f6 SHA1: 9b91c739449c406f039bdb81d8b784efe5bdf4ba SHA256: c0e9c7d1c40eb4f8771beaacb95ccf11447d4f0b5479e842f29f8ddd7cfce570 SHA512: a538dfb124b6fdcb49f309dea012aee73a6085a2920f3250b3a780ff254e978e48170435dec25b715fdbbe73b5b25b71d9d9f446016602ce9b43c1b5b30e617d Homepage: https://cran.r-project.org/package=ASSISTant Description: CRAN Package 'ASSISTant' (Adaptive Subgroup Selection in Group Sequential Trials) Clinical trial design for subgroup selection in three-stage group sequential trial as described in Lai, Lavori and Liao (2014, ). Includes facilities for design, exploration and analysis of such trials. An implementation of the initial DEFUSE-3 trial is also provided as a vignette. Package: r-cran-assocafc Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 836 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform Filename: pool/dists/noble/main/r-cran-assocafc_1.0.2-1.ca2404.1_all.deb Size: 127120 MD5sum: 405c147af9fe54da700bc9e4eefd39af SHA1: b159232ec93e643c9acc743dea4106e2ba53c1cb SHA256: cdba87568faad4abb3ed27f099fe59df660dd9c248820dce643338aadb67e7d0 SHA512: 77c03d06cba6efd32844bbec46a3ec9676782c7aa9a236184e2d95949fc4740f98c7a3f7683f1b59b03c9e7c8f7f14d9931b24ae013417b2ca26199ce07fc119 Homepage: https://cran.r-project.org/package=AssocAFC Description: CRAN Package 'AssocAFC' (Allele Frequency Comparison) When doing association analysis one does not always have the genotypes for the control population. In such cases it may be necessary to fall back on frequency based tests using well known sources for the frequencies in the control population, for instance, from the 1000 Genomes Project. The Allele Frequency Comparison ('AssocAFC') package performs multiple rare variant association analyses in both population and family-based GWAS (Genome-Wide Association Study) designs. It includes three score tests that are based on the difference of the sum of allele frequencies between cases and controls. Two of these tests, Wcorrected() and Wqls(), are collapsing-based tests and suffer from having protective and risk variants. The third test, afcSKAT(), is a score test that overcomes the mix of SNP (Single-Nucleotide Polymorphism) effect directions. For more details see Saad M and Wijsman EM (2017) . Package: r-cran-assocbin Architecture: all Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1173 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-assocbin_1.1-2-1.ca2404.1_all.deb Size: 946106 MD5sum: 28aad0896c1273b31d1015e950af217e SHA1: 28d59848962cfb501213525015beac6ba1b6aca8 SHA256: f59c866ea73439cbd10faffe22d98be44f513839ece6ea109b63d474a093a936 SHA512: a394313a55f04a08e2db030b910996c1cfa662ad07c6d11d41dcd5a1a19999b740bb381723d37fcc67e299d23afbef9e1344e9a4729304f9c547bcc4d51d056a Homepage: https://cran.r-project.org/package=AssocBin Description: CRAN Package 'AssocBin' (Measuring Association with Recursive Binning) An iterative implementation of a recursive binary partitioning algorithm to measure pairwise dependence with a modular design that allows user specification of the splitting logic and stop criteria. Helper functions provide suggested versions of both and support visualization and the computation of summary statistics on final binnings. For a thorough discussion and demonstration of the algorithm, see Salahub and Oldford (2025) . 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The app provides interactive correlation networks, bivariate plots, and summary tables for different types of variables (numeric and categorical). It also supports optional survey weights and range-based filters on association strengths, making it suitable for the exploration of survey and public data by non-technical users, journalists, educators, and researchers. For background and methodological details, see Soetewey et al. (2025) . Package: r-cran-assocind Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-assocind_1.0.1-1.ca2404.1_all.deb Size: 115486 MD5sum: bb64c2022cd4fb1ca16f76befe6bb1ca SHA1: c971623e1902eddf2e8b4d18a701b355975304a8 SHA256: 1b0c409d546917a63833ee660a757f62afd8d77b20be9b46fbc1205a59065b9c SHA512: c60daea240babfe2098c0f7afb648ab3924a5e7835ff59cec684d57b4fdeb5e05b062a7836cd53ffe0a8c552726a68dee1b836c9d72ce4c920b79d4020b88589 Homepage: https://cran.r-project.org/package=assocInd Description: CRAN Package 'assocInd' (Implements New and Existing Association Indices for ConstructingAnimal Social Networks) Implements several new association indices that can control for various types of errors. Also includes existing association indices and functions for simulating the effects of different rates of error on estimates of association strength between individuals using each method. Package: r-cran-assoctests Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 908 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-mvtnorm, r-cran-combinat, r-cran-fextremes Filename: pool/dists/noble/main/r-cran-assoctests_1.0-1-1.ca2404.1_all.deb Size: 895316 MD5sum: c30df526eec070730af51e7c93718c69 SHA1: b8e41fe8c4fa2d9629bb8756a83166641debd172 SHA256: ddaca6813a2b3c60b1aed4537921a25172dfd6341bffb515540b8b3fd56a6d22 SHA512: f47ad56fa6b02606fb9594cb9116305bcd5ca14968a0ea88a5ba9395e76c439ebaa01aac7de0eb5d83f8e99ea41e8495e010ffd9c05de4e97b9aa47059ecce12 Homepage: https://cran.r-project.org/package=AssocTests Description: CRAN Package 'AssocTests' (Genetic Association Studies) Some procedures including EIGENSTRAT (a procedure for detecting and correcting for population stratification through searching for the eigenvectors in genetic association studies), PCoC (a procedure for correcting for population stratification through calculating the principal coordinates and the clustering of the subjects), Tracy-Widom test (a procedure for detecting the significant eigenvalues of a matrix), distance regression (a procedure for detecting the association between a distance matrix and some independent variants of interest), single-marker test (a procedure for identifying the association between the genotype at a biallelic marker and a trait using the Wald test or the Fisher's exact test), MAX3 (a procedure for testing for the association between a single nucleotide polymorphism and a binary phenotype using the maximum value of the three test statistics derived for the recessive, additive, and dominant models), nonparametric trend test (a procedure for testing for the association between a genetic variant and a non-normal distributed quantitative trait based on the nonparametric risk), and nonparametric MAX3 (a procedure for testing for the association between a biallelic single nucleotide polymorphism and a quantitative trait using the maximum value of the three nonparametric trend tests derived for the recessive, additive, and dominant models), which are commonly used in genetic association studies. To cite this package in publications use: Lin Wang, Wei Zhang, and Qizhai Li. AssocTests: An R Package for Genetic Association Studies. Journal of Statistical Software. 2020; 94(5): 1-26. Package: r-cran-assortnet Architecture: all Version: 0.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-assortnet_0.20-1.ca2404.1_all.deb Size: 23244 MD5sum: 8c352fdb9c38e24578588b9f0895fb62 SHA1: 8ff88124b1655a652dde83959ec741fe3fa40519 SHA256: f8943eb0891101021c3615ce0c1af558cd07ff25fa911157b193fb64b3732277 SHA512: d60da0424b87e4d3527a02f6dc8bb4ca988bd061b28adc81c07e963ddc480fafd12d36bedea6f6fdf277ccd192652cc3d96a33fcde184431e8a08a1510bd06d1 Homepage: https://cran.r-project.org/package=assortnet Description: CRAN Package 'assortnet' (Calculate the Assortativity Coefficient of Weighted and BinaryNetworks) Functions to calculate the assortment of vertices in social networks. This can be measured on both weighted and binary networks, with discrete or continuous vertex values. 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The app is designed for researchers with minimal statistical training and provides diagnostics, plots, and test recommendations for a wide range of analyses. Many statistical assumptions are implemented using the package 'rstatix' (Kassambara, 2019) and 'performance' (Lüdecke et al., 2021) . 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Jogesh Babu (Cambridge University Press, 2012, Appendix C) or on the website of Penn State's Center for Astrostatistics (http://astrostatistics.psu.edu/datasets). These datasets can be used to exercise methodology involving: density estimation; heteroscedastic measurement errors; contingency tables; two-sample hypothesis tests; spatial point processes; nonlinear regression; mixture models; censoring and truncation; multivariate analysis; classification and clustering; inhomogeneous Poisson processes; periodic and stochastic time series analysis. 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Package: r-cran-astsa Architecture: all Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1409 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-astsa_2.5-1.ca2404.1_all.deb Size: 1370878 MD5sum: f1cc38a4734a8ed94b4e72c130e9f5df SHA1: 2f4d2c512100f40e31b70c495cae6da9d789a7ae SHA256: 577a9bd011190c23bed201f0ecfdba6ad095a55e41cf33522127d114ddf59a8d SHA512: e28267bc7a08611a154b76abf8420e2413aa7274eae3ca1cc41c87e82777e22130fb0d382a9076dded876cfc2477f38cad69132e8efebbdce3b3235fce5c1790 Homepage: https://cran.r-project.org/package=astsa Description: CRAN Package 'astsa' (Applied Statistical Time Series Analysis) Contains data sets and scripts for analyzing time series in both the frequency and time domains including state space modeling as well as supporting the texts Time Series Analysis and Its Applications: With R Examples (5th ed), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2025, , and Time Series: A Data Analysis Approach Using R (2nd ed). Chapman-Hall, 2026, . Most scripts are designed to require minimal input to produce aesthetically pleasing output for ease of use in live demonstrations and course work. 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Package: r-cran-asymld Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-haplo.stats, r-cran-fields Filename: pool/dists/noble/main/r-cran-asymld_0.1-1.ca2404.1_all.deb Size: 53218 MD5sum: a9baa5907003198e43ad469754647a7c SHA1: 92becf2e60b4c8a2d38263e654378997162126e2 SHA256: 9320e9675bab03a1f959b7f581c21e98c75f21b81b7b4b099507fad402d0b148 SHA512: 6a4ecd6f6b7a6f69828b0a0233878f38f198c0cb5990883787aef51c8bcb7d6bd9321b8433ff48a5d3ec8b49865c9c5c7173346d22e7e267f37b0a817001e029 Homepage: https://cran.r-project.org/package=asymLD Description: CRAN Package 'asymLD' (Asymmetric Linkage Disequilibrium (ALD) for Polymorphic GeneticData) Computes asymmetric LD measures (ALD) for multi-allelic genetic data. These measures are identical to the correlation measure (r) for bi-allelic data. Package: r-cran-asymmetricsords Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asymmetricsords_1.0.0-1.ca2404.1_all.deb Size: 161212 MD5sum: b8b116534ec147f7ea57181d762d03a1 SHA1: b88be9d38f9a5d3ac29ad180af55ba4cc801b050 SHA256: 46d66d1b11e692d2515fbf6c16f46db4f62922693b98c7d62163d8cad4b6b715 SHA512: bc6a83b4276d484561df33417ddec18b0d3930962de084b01191fa7d26c11591f0bc9466f9c1696fa6f33c16e4c8c638e4147b7c1cf1e1618a6f377461b5ec91 Homepage: https://cran.r-project.org/package=AsymmetricSORDs Description: CRAN Package 'AsymmetricSORDs' (Asymmetric Second Order Rotatable Designs (AsymmetricSORDs)) Response surface designs (RSDs) are widely used for Response Surface Methodology (RSM) based optimization studies, which aid in exploring the relationship between a group of explanatory variables and one or more response variable(s) (G.E.P. Box and K.B. Wilson (1951), "On the experimental attainment of optimum conditions" ; M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) ."Theoretical developments in response surface designs: an informative review and further thoughts".). Second order rotatable designs are the most prominent and popular class of designs used for process and product optimization trials but it is suitable for situations when all the number of levels for each factor is the same. In many practical situations, RSDs with asymmetric levels (J.S. Mehta and M.N. Das (1968). "Asymmetric rotatable designs and orthogonal transformations" ; M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2020) . "Sequential asymmetric third order rotatable designs (SATORDs)" .) are more suitable as these designs explore more regions in the design space.This package contains functions named Asords() ,CCD_coded(), CCD_original(), SORD_coded() and SORD_original() for generating asymmetric/symmetric RSDs along with the randomized layout. It also contains another function named Pred.var() for generating the variance of predicted response as well as the moment matrix based on a second order model. Package: r-cran-asymmetry.measures Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sn, r-cran-skewt, r-cran-gamlss.dist Filename: pool/dists/noble/main/r-cran-asymmetry.measures_0.2-1.ca2404.1_all.deb Size: 145220 MD5sum: b70939b7f53f8065aa8b518b4827cec1 SHA1: 4c1ec52d4aba05bf16292d6f51ae50043d59113a SHA256: 84e36130f9ba2df6e2da8732058d193ea5481bb4c752bb9c4ce618dc0a991aba SHA512: ce348f521a8fc34e0a1ee13aec042645cd99cd4544d63ab64a30a065a29497f9c68ee819c4d4cee36932905ab74aca4d74775493bc56a774f2997eea254cfe85 Homepage: https://cran.r-project.org/package=asymmetry.measures Description: CRAN Package 'asymmetry.measures' (Asymmetry Measures for Probability Density Functions) Provides functions and examples for the weak and strong density asymmetry measures in the articles: "A measure of asymmetry", Patil, Patil and Bagkavos (2012) and "A measure of asymmetry based on a new necessary and sufficient condition for symmetry", Patil, Bagkavos and Wood (2014) . The measures provided here are useful for quantifying the asymmetry of the shape of a density of a random variable. The package facilitates implementation of the measures which are applicable in a variety of fields including e.g. probability theory, statistics and economics. 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An asymmetric data matrix has the same number of rows and columns, and these rows and columns refer to the same set of objects. At least some elements in the upper-triangle are different from the corresponding elements in the lower triangle. An example of an asymmetric matrix is a student migration table, where the rows correspond to the countries of origin of the students and the columns to the destination countries. This package provides algorithms for three multidimensional scaling models, the slide-vector model, a scaling model with unique dimensions and the asymscal model. Furthermore, some other procedures, such as a heat map for skew-symmetric data, and the decomposition of asymmetry are also provided for the exploratory analysis of asymmetric tables. 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It implements a parametric bootstrap framework to compute statistics, generates diagnostic envelopes to assess goodness-of-fit, and evaluates type I error control for Wald tests. By simulating data under the assumption that the model is true, it helps to identify model mis-specifications and enhances the reliability of the model inferences. Package: r-cran-asymptest Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asymptest_0.1.4-1.ca2404.1_all.deb Size: 345730 MD5sum: 46301bc01dddea34b0ecb88ac5ca2e97 SHA1: b2521df790518560e9d351e0ce429bc11ffdb0cf SHA256: 042fd83774c88c58dae276bfaaaae2932216623e78fc052a8785ba0cec66f48b SHA512: 873835691f86f0a55a0292ba654aee0e36343b4208a3bcb81b6fadacac957368d5bf8e66047d0198bbceb84c9d166304c51c012622ee2be34e783eeb2721d3e9 Homepage: https://cran.r-project.org/package=asympTest Description: CRAN Package 'asympTest' (A Simple R Package for Classical Parametric Statistical Testsand Confidence Intervals in Large Samples) One and two sample mean and variance tests (differences and ratios) are considered. The test statistics are all expressed in the same form as the Student t-test, which facilitates their presentation in the classroom. This contribution also fills the gap of a robust (to non-normality) alternative to the chi-square single variance test for large samples, since no such procedure is implemented in standard statistical software. Package: r-cran-asymptor Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-asymptor_1.1.0-1.ca2404.1_all.deb Size: 136318 MD5sum: d0c6e5d6c34095351b3413261b345de0 SHA1: e8be8f41fdf5184e3593b4ae80de76bfb06bc273 SHA256: f5a507cabb60d487fa2da58411786a1fe2a966aab3274edc14bbf26ad18b88f5 SHA512: 8462c4dc5edc65293f2b09804eae88831fde3934934d75e115c62dd73951e191a1b3bf66bdb13f85efcf251a6038e688f3e26edd4bae9a8ee8f735ad5b6a7204 Homepage: https://cran.r-project.org/package=asymptor Description: CRAN Package 'asymptor' (Estimate Asymptomatic Cases via Capture/Recapture Methods) Estimate the lower and upper bound of asymptomatic cases in an epidemic using the capture/recapture methods from Böhning et al. (2020) and Rocchetti et al. (2020) . Note there is currently some discussion about the validity of the methods implemented in this package. You should read carefully the original articles, alongside this answer from Li et al. (2022) before using this package in your project. 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Package: r-cran-asynchlong Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-asynchlong_2.4-1.ca2404.1_all.deb Size: 257838 MD5sum: 564bfccd7b968e09a9141ba53546abca SHA1: ee132d9c3f70cfc2157c66e50481934a0a547db7 SHA256: 3f1815fd02a15673803b290d61d955f16b7eab5872c8947fe30ef91474c9611b SHA512: 73544448743be95d07c62ca421f44e318f9a195069c6c4e8d9565721f17b8db0f047d0468b58021f703a59576a3d736e1878e4ccc26c7ea52c832b468e7f49ae Homepage: https://cran.r-project.org/package=AsynchLong Description: CRAN Package 'AsynchLong' (Regression Analysis of Sparse Asynchronous Longitudinal Data) Estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent response and covariates are mismatched and observed intermittently within subjects. Kernel weighted estimating equations are used for generalized linear models with either time-invariant or time-dependent coefficients. Cao, H., Li, J., and Fine, J. P. (2016) . Cao, H., Zeng, D., and Fine, J. P. (2015) . 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Package: r-cran-ate.error Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-mvtnorm, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ate.error_1.0.0-1.ca2404.1_all.deb Size: 232392 MD5sum: a5f82d9e813bb708fc20e280b0e9bfe1 SHA1: 878dda034a544a9743a9a99d778be04cf8f5a6d3 SHA256: 610c69264b05569a50d0d11d0da5ed0b671048a148c0c4d0aef62911714f38e0 SHA512: e29565a10cf7f15cd4a24980664d140e815e5d7f324c4a404c31923ab8d8a16f6266f76e21c0dd9763b1b61ca2f06a49f42fc01d473dc840abe4c592cabe3c2b Homepage: https://cran.r-project.org/package=ATE.ERROR Description: CRAN Package 'ATE.ERROR' (Estimating ATE with Misclassified Outcomes and MismeasuredCovariates) Addressing measurement error in covariates and misclassification in binary outcome variables within causal inference, the 'ATE.ERROR' package implements inverse probability weighted estimation methods proposed by Shu and Yi (2017, ; 2019, ). 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This work was supported by USARO grant W911NF-17-1-0007. 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Provides tidy access to individual, company, superannuation, goods and services tax ('GST'), fringe benefits tax ('FBT'), Voluntary Tax Transparency Code ('VTTC'), Pay As You Go ('PAYG') withholding, charity, excise, and Corporate Tax Transparency data, plus Division 293, Petroleum Resource Rent Tax, Medicare Levy Surcharge, fuel tax credits, compliance, and Working Holiday Maker aggregates. Includes reproducibility helpers (snapshot pinning, SHA-256 cache integrity, session manifest, optional 'Zenodo' deposit), classification crosswalks ('ANZSIC' 2006 to 2020, 'ANZSCO' 2013 to 2021), panel harmonisation, reconciliation against Final Budget Outcome totals, and real-terms and per-capita helpers backed by bundled Australian Bureau of Statistics ('ABS') Consumer Price Index and Estimated Resident Population series. Bridges to the 'taxstats' 2 per cent microdata sample via column-schema mapping. 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Package: r-cran-auto.pca Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych, r-cran-plyr Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-auto.pca_0.3-1.ca2404.1_all.deb Size: 15528 MD5sum: 36c236cc94f333e3d9062f0cafa924db SHA1: 5b0657af6259f77e6d27768aec77a9c1676acb40 SHA256: 63ca9458f8947ea48525f79ad231169c440ab63ec022aa419b0ec02ac8f51ffa SHA512: db9866f1964269434710f84440d26a0e338b620369b6a07cf048405408d8c423a4b0808995986b3bf76a29b5d3cf46e7cdec07f9cb6345790e383d37ac6f5b36 Homepage: https://cran.r-project.org/package=auto.pca Description: CRAN Package 'auto.pca' (Automatic Variable Reduction Using Principal Component Analysis) PCA done by eigenvalue decomposition of a data correlation matrix, here it automatically determines the number of factors by eigenvalue greater than 1 and it gives the uncorrelated variables based on the rotated component scores, Such that in each principal component variable which has the high variance are selected. It will be useful for non-statisticians in selection of variables. For more information, see the web page. Package: r-cran-autoads Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-ggplot2, r-cran-rlang Filename: pool/dists/noble/main/r-cran-autoads_0.1.0-1.ca2404.1_all.deb Size: 36788 MD5sum: b56af7a2443b45f328b729f9b038d4b1 SHA1: 8624bc9c8d8ac0ce372d443037380176aa2e39ff SHA256: 744c651045aed791b9559d398d71cdec728ad7708e2846a9cd2df1fc176c2186 SHA512: 06d6b8241b219b55982cc542d14e6eef323c1795ba2b2e94c539379cb1cc283dfdbe8b92455ebdd8cdae34de9f498768f56ce0785d9431ec802c348ce40be87d Homepage: https://cran.r-project.org/package=AutoAds Description: CRAN Package 'AutoAds' (Advertisement Metrics Calculation) Calculations of the most common metrics of automated advertisement and plotting of them with trend and forecast. Calculations and description of metrics is taken from different RTB platforms support documentation. Plotting and forecasting is based on packages 'forecast', described in Rob J Hyndman and George Athanasopoulos (2021) "Forecasting: Principles and Practice" and Rob J Hyndman et al "Documentation for 'forecast'" (2003) , and 'ggplot2', described in Hadley Wickham et al "Documentation for 'ggplot2'" (2015) , and Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen (2015) "ggplot2: Elegant Graphics for Data Analysis" . Package: r-cran-autoann Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet Filename: pool/dists/noble/main/r-cran-autoann_0.1.0-1.ca2404.1_all.deb Size: 16428 MD5sum: eb0186ba6f1231e8dfc756d4f1595bde SHA1: ccaa9c19bd9ca8e2c63bb8d70a44d65a95a83d47 SHA256: f9c3d333149e451d7e15c6da664c467c41b8fe1de9e7ed01186c23637aed1051 SHA512: 2d48bd6ee9981bcb9f09c9ba58e888cedb78e63e4ebff391703450b76c7d7a4bf971b0d795170d3df1e90279a5525c34ef26c7c879e2d6ce691c44ae48d42c7c Homepage: https://cran.r-project.org/package=autoann Description: CRAN Package 'autoann' (Neural Network–Based Model Selection and Forecasting) Provides a systematic framework for neural network–based model selection and forecasting using single hidden layer feed-forward networks. It evaluates all possible combinations of predictor variables and hidden layer configurations, selecting the optimal model based on predictive accuracy criteria such as root mean squared error (RMSE) and mean absolute percentage error (MAPE). Predictors are automatically standardized, and model performance is assessed using out-of-sample validation. The package is designed for empirical modelling and forecasting in economics, agriculture, trade, climate, and related applied research domains where nonlinear relationships and robust predictive performance are of primary interest. Package: r-cran-autobagging Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2038 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-xgboost, r-cran-e1071, r-cran-rpart, r-cran-abind, r-cran-caret, r-cran-mass, r-cran-entropy, r-cran-lsr, r-cran-corelearn, r-cran-infotheo, r-cran-minerva, r-cran-party Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-autobagging_0.1.0-1.ca2404.1_all.deb Size: 1970984 MD5sum: 13bf13517a1f429f28c8305391b7bd86 SHA1: f512b0897251caecaad6291b181512f965965c84 SHA256: b7f78c1248fa55501031db315f15cd6b62396889c32632e2efa360887537b936 SHA512: 00b76caf7bfb0ad80e11d68d25088bdb809671f1206c641a3dca7c8173c93081d5c4efaa2befdc40b60fa13c2257061e80fc83c7a543827b8751b6ee736ab190 Homepage: https://cran.r-project.org/package=autoBagging Description: CRAN Package 'autoBagging' (Learning to Rank Bagging Workflows with Metalearning) A framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367. Package: r-cran-autocogs Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 886 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-checkmate, r-cran-diptest, r-cran-dplyr, r-cran-ggplot2, r-cran-hexbin, r-cran-mass, r-cran-mclust, r-cran-moments, r-cran-progress, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autocogs_0.1.5-1.ca2404.1_all.deb Size: 822710 MD5sum: 3747630c9ff75f9ec0bb493cb4e53223 SHA1: 407d09203d35e3a00ddf4b5adab41ceea06d9be0 SHA256: 2fb02ae47b3e4481332284b6d77f35f9098d9f821cc8891c599cbb05b0c3f4d4 SHA512: b3465f18ae27e688b5249138e530458d1304ba32a05879f3bb0b4956ec2a485f1ca86b3f5181c354bc8dc37f89780c6bdb2e8dc5c267086808475d2546eadeac Homepage: https://cran.r-project.org/package=autocogs Description: CRAN Package 'autocogs' (Automatic Cognostic Summaries) Automatically calculates cognostic groups for plot objects and list column plot objects. Results are returned in a nested data frame. Package: r-cran-autocovariateselection Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-autocovariateselection_1.0.0-1.ca2404.1_all.deb Size: 544492 MD5sum: e2b7d2af9e50a0588853d719b8706819 SHA1: dbd2d602d8d9d35bd146160a9e1fc52068375672 SHA256: dfe92200912dbc36a8d63e1e95ccd8c93e3a0f90063a9e5569b312e2cb2a2d3a SHA512: a0477f032f9f9fa7429dab4b7282b3a77d1f258f70220bc289082441318ec45e585cb083e2fadff74d99d63b1de828c7e2e639a49a4151958801fbcb62b95eb9 Homepage: https://cran.r-project.org/package=autoCovariateSelection Description: CRAN Package 'autoCovariateSelection' (Automated Covariate Selection Using HDPS Algorithm) Contains functions to implement automated covariate selection using methods described in the high-dimensional propensity score (HDPS) algorithm by Schneeweiss et.al. Covariate adjustment in real-world-observational-data (RWD) is important for for estimating adjusted outcomes and this can be done by using methods such as, but not limited to, propensity score matching, propensity score weighting and regression analysis. While these methods strive to statistically adjust for confounding, the major challenge is in selecting the potential covariates that can bias the outcomes comparison estimates in observational RWD (Real-World-Data). This is where the utility of automated covariate selection comes in. The functions in this package help to implement the three major steps of automated covariate selection as described by Schneeweiss et. al elsewhere. These three functions, in order of the steps required to execute automated covariate selection are, get_candidate_covariates(), get_recurrence_covariates() and get_prioritised_covariates(). In addition to these functions, a sample real-world-data from publicly available de-identified medical claims data is also available for running examples and also for further exploration. The original article where the algorithm is described by Schneeweiss et.al. (2009) . Package: r-cran-autodb Architecture: all Version: 3.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14582 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rlang, r-cran-spelling, r-cran-diagrammer, r-cran-testthat, r-cran-r.utils, r-cran-hedgehog, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autodb_3.2.4-1.ca2404.1_all.deb Size: 1267366 MD5sum: 68b9c4c2f2b1d109ec05ae621017a13a SHA1: 6b00851db439354b91a65746f32921642764713e SHA256: 32ba32a37c7cda66066edc5e7037967f401d89061f702dc41500972b0c7a8a74 SHA512: 5de4212a7223577e9d525b7ad2b90fcf6b966256dc04d052833502745246d312b1bc210c41df125ab5d159462ae2bb14348bcf934848b599cb2b8804b495316d Homepage: https://cran.r-project.org/package=autodb Description: CRAN Package 'autodb' (Automatic Database Normalisation for Data Frames) Automatic normalisation of a data frame to third normal form, with the intention of easing the process of data cleaning. (Usage to design your actual database for you is not advised.) Originally inspired by the 'AutoNormalize' library for 'Python' by 'Alteryx' (), with various changes and improvements. Automatic discovery of functional or approximate dependencies, normalisation based on those, and plotting of the resulting "database" via 'Graphviz', with options to exclude some attributes at discovery time, or remove discovered dependencies at normalisation time. Package: r-cran-autodeskr Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3509 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autodeskr_0.1.5-1.ca2404.1_all.deb Size: 2389472 MD5sum: 60979c8d5858c27fd6bef8340d6d6baa SHA1: 07b0a5e4bb2b8c4a195ca3d123bd0332e24bd587 SHA256: 31384892128b8ea0d073626c03ffb5342851f6d38ce3cbee2e73dd0e89ae9dde SHA512: 28cf8b46ba1c4d62cd87a666c7ad0414c8fd3149548c5f380780a8f2ac367e56627c502d6592f4c9bba34cb62b979f6d3cacc038c5f3da478975c7aa1e777e87 Homepage: https://cran.r-project.org/package=AutoDeskR Description: CRAN Package 'AutoDeskR' (An Interface to the 'AutoDesk' 'API' Platform) An interface to the 'AutoDesk' 'API' Platform including the Authentication 'API' for obtaining authentication to the 'AutoDesk' Forge Platform, Data Management 'API' for managing data across the platform's cloud services, Design Automation 'API' for performing automated tasks on design files in the cloud, Model Derivative 'API' for translating design files into different formats, sending them to the viewer app, and extracting design data, and Viewer for rendering 2D and 3D models. Package: r-cran-autoensemble Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-h2o, r-cran-h2otools, r-cran-curl Filename: pool/dists/noble/main/r-cran-autoensemble_0.3-1.ca2404.1_all.deb Size: 258358 MD5sum: ec65dcd877ceef9f7114aceefa9b9cad SHA1: 1682d2ea332f929abda5b0079ed5fd535da717dc SHA256: 744255f4923e19ad170c76a2075b384716aa3e6acde2b3d0a5d9f2085bb78071 SHA512: 49aacc94bfa7d9897a59ec203a14cbd6494292129885ee1881ec617a5ce04b2642151ad62a38ed94f94f6b7aebe12d089843979555da430fcc514f5c187a4018 Homepage: https://cran.r-project.org/package=autoEnsemble Description: CRAN Package 'autoEnsemble' (Automated Stacked Ensemble Classifier for Severe Class Imbalance) A stacking solution for modeling imbalanced and severely skewed data. It automates the process of building homogeneous or heterogeneous stacked ensemble models by selecting "best" models according to different criteria. In doing so, it strategically searches for and selects diverse, high-performing base-learners to construct ensemble models optimized for skewed data. This package is particularly useful for addressing class imbalance in datasets, ensuring robust and effective model outcomes through advanced ensemble strategies which aim to stabilize the model, reduce its overfitting, and further improve its generalizability. Package: r-cran-autofc Architecture: all Version: 0.2.0.1010-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lavaan, r-cran-mass, r-cran-simdesign, r-cran-thurstonianirt, r-cran-mplusautomation, r-cran-glue, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autofc_0.2.0.1010-1.ca2404.1_all.deb Size: 168148 MD5sum: 81eb3e5de298fb80ddeea785a2acd9a1 SHA1: c0e8d75bd05ded9bc3083bf3353b6a3c24670a8d SHA256: 03d17b395dfc387514ccf701eff124e6f7041ea6c3b60fee9bbdb4b1c85da689 SHA512: adbf1e1aebbd34fc08a0b30c9d2df1fb844bb9f13cbb541eedf4045f6bf9a2012c1325d1d709bfd8944a41e220a02670fbcd29c381d11a3b9db709602125b3d8 Homepage: https://cran.r-project.org/package=autoFC Description: CRAN Package 'autoFC' (Automatic Construction of Forced-Choice Tests) Forced-choice (FC) response has gained increasing popularity and interest for its resistance to faking when well-designed (Cao & Drasgow, 2019 ). To established well-designed FC scales, typically each item within a block should measure different trait and have similar level of social desirability (Zhang et al., 2020 ). Recent study also suggests the importance of high inter-item agreement of social desirability between items within a block (Pavlov et al., 2021 ). In addition to this, FC developers may also need to maximize factor loading differences (Brown & Maydeu-Olivares, 2011 ) or minimize item location differences (Cao & Drasgow, 2019 ) depending on scoring models. Decision of which items should be assigned to the same block, termed item pairing, is thus critical to the quality of an FC test. This pairing process is essentially an optimization process which is currently carried out manually. However, given that we often need to simultaneously meet multiple objectives, manual pairing becomes impractical or even not feasible once the number of latent traits and/or number of items per trait are relatively large. To address these problems, autoFC is developed as a practical tool for facilitating the automatic construction of FC tests (Li et al., 2022 ), essentially exempting users from the burden of manual item pairing and reducing the computational costs and biases induced by simple ranking methods. Given characteristics of each item (and item responses), FC measures can be constructed either automatically based on user-defined pairing criteria and weights, or based on exact specifications of each block (i.e., blueprint; see Li et al., 2024 ). Users can also generate simulated responses based on the Thurstonian Item Response Theory model (Brown & Maydeu-Olivares, 2011 ) and predict trait scores of simulated/actual respondents based on an estimated model. Package: r-cran-autoflagr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 998 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-isotree, r-cran-dbscan, r-cran-dplyr, r-cran-ggplot2, r-cran-proc, r-cran-prroc, r-cran-knitr, r-cran-gt, r-cran-scales, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-pkgdown, r-cran-ggnewscale Filename: pool/dists/noble/main/r-cran-autoflagr_1.0.0-1.ca2404.1_all.deb Size: 702328 MD5sum: e83606dc94af8eee410c265611ae0e3e SHA1: 7449375ae5131a846c478240f6064588b1c3a03a SHA256: e5659568024e76bb8e1373d8b0d442c87f458fad0e3f0ab69c4209734a3d844b SHA512: a1983b04e5d58f6d4ca932e3136b09e6c58fe1ff4c58bb42f9a2bcbc442c7a42598de3be138453c57055e7f996e22a0fbb75d4bd3a3cbf4b8217e018f8ab8da5 Homepage: https://cran.r-project.org/package=autoFlagR Description: CRAN Package 'autoFlagR' (AI-Driven Anomaly Detection for Data Quality) Automated data quality auditing using unsupervised machine learning. Provides AI-driven anomaly detection for data quality assessment, primarily designed for Electronic Health Records (EHR) data, with benchmarking capabilities for validation and publication. Methods based on: Liu et al. (2008) , Breunig et al. (2000) . Package: r-cran-autogam Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-mgcv, r-cran-purrr, r-cran-rlang, r-cran-staccuracy, r-cran-stringr, r-cran-univariateml Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-autogam_0.1.0-1.ca2404.1_all.deb Size: 111976 MD5sum: 67c3b558cfe03458f9c550294bdb352a SHA1: 1cffdc1eb7a3f3982a1f2d23804b4181f4c6dd8a SHA256: 60550b82d731f389fbca79f36ac05717319c92b021de4202259d7faf886dbad5 SHA512: a04380f679a1c55d740d7e548cd4a959c460ac972c8e34f9830cbaacb01d1d113d4e8f428e1fbabf0d08fa6baf195b6ffddfa8dbb666a851ba7638cb35390726 Homepage: https://cran.r-project.org/package=autogam Description: CRAN Package 'autogam' (Automate the Creation of Generalized Additive Models (GAMs)) This wrapper package for 'mgcv' makes it easier to create high-performing Generalized Additive Models (GAMs). With its central function autogam(), by entering just a dataset and the name of the outcome column as inputs, 'AutoGAM' tries to automate the procedure of configuring a highly accurate GAM which performs at reasonably high speed, even for large datasets. Package: r-cran-autogo Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4076 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readr, r-cran-dplyr, r-cran-ape, r-bioc-complexheatmap, r-bioc-deseq2, r-cran-dichromat, r-cran-ggplot2, r-cran-ggrepel, r-bioc-gsva, r-cran-msigdbr, r-cran-openxlsx, r-cran-purrr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-stringr, r-bioc-summarizedexperiment, r-cran-textshape, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rlang, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autogo_1.0.3-1.ca2404.1_all.deb Size: 3310090 MD5sum: f024fe6f02fa5357186c2efc728cf0ad SHA1: fc690f71ccc6f5362e80f6d2474122a735cc6f65 SHA256: af1ef07c71eb4587a8266020d74f5c8418876f1915541d5d972e7fc34db7022d SHA512: 99db5dfab2430099155007e581312d28af6e9e4c9a2992ecd739ce61a0cfa1e584e6324971b3a9fbb972880be8ee3357e9e4fda7d173973f4f7caa43dc4a49e3 Homepage: https://cran.r-project.org/package=autoGO Description: CRAN Package 'autoGO' (Auto-GO: Reproducible, Robust and High Quality OntologyEnrichment Visualizations) Auto-GO is a framework that enables automated, high quality Gene Ontology enrichment analysis visualizations. It also features a handy wrapper for Differential Expression analysis around the 'DESeq2' package described in Love et al. (2014) . The whole framework is structured in different, independent functions, in order to let the user decide which steps of the analysis to perform and which plot to produce. Package: r-cran-autograph Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3973 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-manynet, r-cran-dplyr, r-cran-ggdendro, r-cran-ggraph, r-cran-ggplot2, r-cran-igraph, r-cran-patchwork, r-cran-tidygraph Suggests: r-cran-gganimate, r-cran-ggforce, r-cran-gifski, r-cran-graphlayouts, r-cran-netrics, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autograph_1.0.3-1.ca2404.1_all.deb Size: 3199926 MD5sum: c3d33c1538c43d74123449a4b51bcabe SHA1: 9accf9aff035d5c21214ff1d161112d63ad09bc1 SHA256: 2e75c971ce3b7fa6a1feb45d8232edfd937550c7bfccd11e083ee22594f4573e SHA512: 330cc7f7b35b8e6e92b569039919cb6afcd965abfabb9d320bf4de7bf034d2d0fb3312765a0f1e7c77c53505dcd06cfadedf80841f6fe58e4f1e74ecf22c1ad6 Homepage: https://cran.r-project.org/package=autograph Description: CRAN Package 'autograph' (Automatic Plotting and Theming of Many Graphs) Visual exploration and presentation of networks should not be difficult. This package includes functions for plotting networks and network-related metrics with sensible and pretty defaults. It includes 'ggplot2'-based plot methods for many popular network package classes. It also includes some novel layout algorithms, and options for straightforward, consistent themes. Package: r-cran-autoharp Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4870 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-stringr, r-cran-rlang, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-lintr, r-cran-igraph, r-cran-xfun Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-autoharp_0.3.2-1.ca2404.1_all.deb Size: 3321254 MD5sum: 198c06929b64e27c6d55e3b575a409fc SHA1: cfb8e4c3ce17aa519d40b2204f644e24e88d7ba9 SHA256: 4411a89b2207e9357115642dd3f58d3d31765aae4e104d9b6d6065b8a74fdaf0 SHA512: 060aae878408135ef38857bf78e4d44871a151cd21795a9c43ad6835e18614afa3694e0afc9d72ce44883272c8ff6f4ebe965c0ec0e83ad89abb54ffb208c78e Homepage: https://cran.r-project.org/package=autoharp Description: CRAN Package 'autoharp' (Semi-Automatic Grading of R and Rmd Scripts) A customisable set of tools for assessing and grading R or R-markdown scripts from students. It allows for checking correctness of code output, runtime statistics and static code analysis. The latter feature is made possible by representing R expressions using a tree structure. Package: r-cran-autohrf Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2899 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-gtools, r-cran-lubridate, r-cran-magrittr, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autohrf_1.1.3-1.ca2404.1_all.deb Size: 1894168 MD5sum: 63c083102bfc8f2300a0e33cfa46e635 SHA1: ebf139648ebb515e301dee756df4aa68f8fe4dfa SHA256: c02afb61cee56dbb32a24f1de66f677c723bb52db0337df04600e3ccb4529c97 SHA512: 3d0c4e9af33d8e5bea641641eacf027df9f533da9b33d0ec99ff218c447479ef98a2c2a29757a734dd5d09d74b06318598e71e9a55f8c20adc64cb296985e863 Homepage: https://cran.r-project.org/package=autohrf Description: CRAN Package 'autohrf' (Automated Generation of Data-Informed GLM Models in Task-BasedfMRI Data Analysis) Analysis of task-related functional magnetic resonance imaging (fMRI) activity at the level of individual participants is commonly based on general linear modelling (GLM) that allows us to estimate to what extent the blood oxygenation level dependent (BOLD) signal can be explained by task response predictors specified in the GLM model. The predictors are constructed by convolving the hypothesised timecourse of neural activity with an assumed hemodynamic response function (HRF). To get valid and precise estimates of task response, it is important to construct a model of neural activity that best matches actual neuronal activity. The construction of models is most often driven by predefined assumptions on the components of brain activity and their duration based on the task design and specific aims of the study. However, our assumptions about the onset and duration of component processes might be wrong and can also differ across brain regions. This can result in inappropriate or suboptimal models, bad fitting of the model to the actual data and invalid estimations of brain activity. Here we present an approach in which theoretically driven models of task response are used to define constraints based on which the final model is derived computationally using the actual data. Specifically, we developed 'autohrf' — a package for the 'R' programming language that allows for data-driven estimation of HRF models. The package uses genetic algorithms to efficiently search for models that fit the underlying data well. The package uses automated parameter search to find the onset and duration of task predictors which result in the highest fitness of the resulting GLM based on the fMRI signal under predefined restrictions. We evaluate the usefulness of the 'autohrf' package on publicly available datasets of task-related fMRI activity. Our results suggest that by using 'autohrf' users can find better task related brain activity models in a quick and efficient manner. Package: r-cran-autoimage Architecture: all Version: 2.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2722 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace, r-cran-fields, r-cran-mapproj, r-cran-ggplot2, r-cran-maps, r-cran-mba Suggests: r-cran-abind, r-cran-testthat, r-cran-spatstat.geom, r-cran-gear, r-cran-broom, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autoimage_2.2.3-1.ca2404.1_all.deb Size: 2165942 MD5sum: fdf6b1ed60c84ec5e3a887dce7859d6a SHA1: 8d115b4fa3f5c5c2a4588c5ce4e8aa52558b548d SHA256: 4f871115e4ef027b492364e6251acd46a3ae7e9c39457ce5a7cd39794326fb57 SHA512: c6dad8f45bf5c37e6f59ac0d3959ed82f2386a7f02ed2ef04405e80f78047d626e9b880650be553877bb5e8d2037cd232e1636e83158a65facdf9fc5da78dabd Homepage: https://cran.r-project.org/package=autoimage Description: CRAN Package 'autoimage' (Multiple Heat Maps for Projected Coordinates) Functions for displaying multiple images or scatterplots with a color scale, i.e., heat maps, possibly with projected coordinates. The package relies on the base graphics system, so graphics are rendered rapidly. Package: r-cran-autoimport Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-desc, r-cran-diffviewer, r-cran-digest, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-shiny, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-callr, r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-pkgload, r-cran-rstudioapi, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-autoimport_0.1.1-1.ca2404.1_all.deb Size: 235362 MD5sum: d40bf8dd05afb07044edf4970e21bae3 SHA1: 87639a794b4d81a013d98f62a86db7efb3e2aa23 SHA256: 21aa595fc1be425672006451e113b37adb499a827befa2aa9e3870f6e10ef05a SHA512: 71d212f460084150321f227ba8878c9218599d3b1ea4308885f3e985b8797e6adf23f181312f4c278ee7fd4b7df13e2342b624fae98439b33e71f344861cbc71 Homepage: https://cran.r-project.org/package=autoimport Description: CRAN Package 'autoimport' (Automatic Generation of @importFrom Tags) A toolbox to read all R files inside a package and automatically generate @importFrom 'roxygen2' tags in the right place. Includes a 'shiny' application to review the changes before applying them. Package: r-cran-autokeras Architecture: all Version: 1.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-keras, r-cran-reticulate Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-autokeras_1.0.12-1.ca2404.1_all.deb Size: 63526 MD5sum: b06fb84080fbc41e3af6a9ff7ac6ef05 SHA1: 32650cf63828076c7ef7e317fa7f38518bfec9e6 SHA256: 8de4c7728d2001a95891d19c9fdd2af6a4653e8baac08bab6473581f4ec00b60 SHA512: 3b3e8453ebc43ced8bfb60cb64c3c440f68acbd294226f25ecf65fe60bcb236a506f19f3514d3716e4b28e0d6251091e8275b22e04285a8917fc9d7211d24947 Homepage: https://cran.r-project.org/package=autokeras Description: CRAN Package 'autokeras' (R Interface to 'AutoKeras') R Interface to 'AutoKeras' . 'AutoKeras' is an open source software library for Automated Machine Learning (AutoML). The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. 'AutoKeras' provides functions to automatically search for architecture and hyperparameters of deep learning models. Package: r-cran-automagic Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-formatr, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-remotes, r-cran-yaml Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-automagic_0.5.1-1.ca2404.1_all.deb Size: 30944 MD5sum: bdb6272aba0fe08a1b1fc5be3dd99441 SHA1: be2c006e0064f35acb019e8a045bf11afd90589f SHA256: da73d031197766725fe60c0c24988f8cb7a22211b16b7455568822d88450c820 SHA512: 05666f2a4b59c7b362a3e7cb71503aebf744030e911dc2c0906a4eedebd1ad6d3ed6fa6faae661c4648be3ce3c6babf15aceaefacf674b5c421a660acea1446e Homepage: https://cran.r-project.org/package=automagic Description: CRAN Package 'automagic' (Automagically Document and Install Packages Necessary to Run RCode) Parse R code in a given directory for R packages and attempt to install them from CRAN or GitHub. Optionally use a dependencies file for tighter control over which package versions to install. Package: r-cran-automap Architecture: all Version: 1.1-20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gstat, r-cran-lattice, r-cran-reshape, r-cran-ggplot2, r-cran-sp, r-cran-sf, r-cran-stars Filename: pool/dists/noble/main/r-cran-automap_1.1-20-1.ca2404.1_all.deb Size: 91108 MD5sum: db2659b08ae75a74228c476be88ab84c SHA1: 51894967c461e83913826774a3cd18d768855d5f SHA256: 565f4d012a5018e15fd9fe6b228f2471f0eb6e9c18c5ad234d1522760ee25b66 SHA512: 8fc70720d6c2b2005ad16ab0d99644005186e0428b5925db655233206b68767fa5a2c9913eb9a5dcae34b509e361a6717ad405997f0a53dbf2b3a65b589e6b12 Homepage: https://cran.r-project.org/package=automap Description: CRAN Package 'automap' (Automatic Interpolation Package) An automatic interpolation is done by automatically estimating the variogram and then calling gstat. An overview is given by Hiemstra et al (2008) . Package: r-cran-automatedreclin Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1258 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-blocking, r-cran-data.table, r-cran-densityratio, r-cran-fixedpoint, r-cran-nleqslv, r-cran-purrr, r-cran-reclin2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-automatedreclin_1.1.1-1.ca2404.1_all.deb Size: 1152804 MD5sum: fe3aebe528e22978f32a6918746936e6 SHA1: 4e50d624c8b02d7f46cd51b4b56a663d7c2e51e6 SHA256: 0041d1d0baafc8eed8d2f45ca07ba4960d8a06f200d3128db213c032a88c6e30 SHA512: 9fe260d191c0b785fcfc221a91e5c82aabf8c48c6dde09f976a05fb6b23fce96031b21398ac183fda760728df12738789e867f32273a3cc40b61570e06144476 Homepage: https://cran.r-project.org/package=automatedRecLin Description: CRAN Package 'automatedRecLin' (Record Linkage Based on an Entropy-Maximizing Classifier) The goal of 'automatedRecLin' is to perform record linkage (also known as entity resolution) in unsupervised or supervised settings. It compares pairs of records from two datasets using selected comparison functions to estimate the probability or density ratio between matched and non-matched records. Based on these estimates, it predicts a set of matches that maximizes entropy. For details see: Lee et al. (2022) , Vo et al. (2023) , Sugiyama et al. (2008) . Package: r-cran-automatedtests Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1844 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-nnet, r-cran-nortest, r-cran-desctools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-automatedtests_0.1.2-1.ca2404.1_all.deb Size: 1195782 MD5sum: 137a2c8a9022c954b5533d36351910f6 SHA1: d2e4b49f35109033a367f77600d910f5cb2d400c SHA256: 675cf4cec028934851b049497be9ab7dc7e4c9613d2733bb41455fdd8637778e SHA512: 084755efd31c97c475bb3471c9f5ddf68ed618ac43a19f588bac0b8c89e998b38a5e33220985cf17ea56fecbb0ba12022222f0627c975221528095c47aa0c301 Homepage: https://cran.r-project.org/package=automatedtests Description: CRAN Package 'automatedtests' (Automating Choosing Statistical Tests) Automatically selects and runs the most appropriate statistical test for your data, returning clear, easy-to-read results. Ideal for all experience levels. Package: r-cran-automfa Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-mass, r-cran-matrix, r-cran-rfast, r-cran-expm, r-cran-rdpack, r-cran-pracma, r-cran-usethis Filename: pool/dists/noble/main/r-cran-automfa_1.0.0-1.ca2404.1_all.deb Size: 267262 MD5sum: bfecbebafaf355cdcd2f26daeac0a76d SHA1: cccd52ceb31c935bb6f3d8eed924eb66488362f9 SHA256: 6d9ca821ba5ede38a9b688973c077fdde12a65059b280c033464d28918c1ba59 SHA512: 60d84121bf820fbd63189abb76184fb08fcceb30ab4039f9680f06d90a80a7b781be27498af86ed3b09dc0864f0e7b23b9eead30fd0fa8bf2bfef747f33af5c9 Homepage: https://cran.r-project.org/package=autoMFA Description: CRAN Package 'autoMFA' (Algorithms for Automatically Fitting MFA Models) Provides methods for fitting the Mixture of Factor Analyzers (MFA) model automatically. The MFA model is a mixture model where each sub-population is assumed to follow the Factor Analysis model. The Factor Analysis (FA) model is a latent variable model which assumes that observations are normally distributed, but imposes constraints on their covariance matrix. The MFA model contains two hyperparameters; g (the number of components in the mixture) and q (the number of factors in each component Factor Analysis model). Usually, the Expectation-Maximisation algorithm would be used to fit the MFA model, but this requires g and q to be known. This package treats g and q as unknowns and provides several methods which infer these values with as little input from the user as possible. Package: r-cran-automl Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-automl_1.3.2-1.ca2404.1_all.deb Size: 419100 MD5sum: de13a70313eaf889fad283b0ee732511 SHA1: 4be53e71c8022454845b5c09b8e2d704c54f43fa SHA256: 29bfe60c4125c77c6f78006f307f2fcd26959e9aeaf45f5d8f34deaf9d0c72f4 SHA512: ffdc523c25bf16955eb7e99496b61d7b23cd7a496a3220ec8a31fdcfe746d4b602c356ce0a22c35ba293d5e4d6ea6a4ebf2b8af31546291ec7e835cf3234b189 Homepage: https://cran.r-project.org/package=automl Description: CRAN Package 'automl' (Deep Learning with Metaheuristic) Fits from simple regression to highly customizable deep neural networks either with gradient descent or metaheuristic, using automatic hyper parameters tuning and custom cost function. A mix inspired by the common tricks on Deep Learning and Particle Swarm Optimization. Package: r-cran-automr Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-mendelianrandomization, r-cran-r2jags, r-cran-coda, r-cran-nortest Filename: pool/dists/noble/main/r-cran-automr_1.1.2-1.ca2404.1_all.deb Size: 191004 MD5sum: 70a0e964a945746977cc50ebb2602116 SHA1: f4268d6ef466355ddf384d47820975c98e48a3a9 SHA256: 33f587c41d85562c2841c73bb787204210ff99ae3146cf30341f0367ca3dd7cd SHA512: 6c0dda58e0f9246dca4f3fc036d7f699c6c18bd6126754a3bc27ad32e3824bb374b48529260fcafcf1dccb2600900fee7e96698c1d9e13a45bfd4d6aa2217798 Homepage: https://cran.r-project.org/package=autoMR Description: CRAN Package 'autoMR' (Automated Mendelian Randomization Workflows and Visualizations) Provides tools to summarize, analyze, and visualize results from Mendelian randomization studies using summarized genetic association data. The package includes functions for generating forest plots and scatter plots at the single-nucleotide polymorphism and Mendelian randomization method levels, and for fitting multiple estimators in a unified workflow, including inverse-variance weighted estimation, Mendelian randomization Egger regression, the weighted median estimator, the robust adjusted profile score, Mendelian randomization pleiotropy residual sum and outlier, Mendelian randomization with the genotype recoding invariance property, and a Bayesian horseshoe method. Related methods are described by Burgess (2013) , Bowden (2015) , Bowden (2016) , Zhao (2020) , Verbanck (2018) , Dudbridge (2025) , and Grant and Burgess (2024) . Related open-source software includes 'TwoSampleMR' , 'mr.raps' , 'MR-PRESSO' , and 'MR-Horse' . Package: r-cran-automrp Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 715 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-lme4, r-cran-gbm, r-cran-e1071, r-cran-tibble, r-cran-glmmlasso, r-cran-ebmaforecast, r-cran-foreach, r-cran-doparallel, r-cran-dorng, r-cran-ggplot2, r-cran-knitr, r-cran-tidyr, r-cran-purrr, r-cran-forcats, r-cran-vglmer, r-cran-stringr Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-automrp_1.0.6-1.ca2404.1_all.deb Size: 676498 MD5sum: b269e2b7e95dda3c6e1e043822fa2c93 SHA1: fa13e6ce7fe76be091ddc2ca9498a4f8e43f359a SHA256: fa75bc9f27d03e7197888095b3aa05d26b3c80234591ee6771b3fe6c9f839429 SHA512: e235b046faa5b5330f4d4213909c1ec26388da93b54702e54f26e5eba19ef1cdb3126c52804940d63cc80ada2d4d67a2ee6f6b99be95ee2dd1b74ad183236291 Homepage: https://cran.r-project.org/package=autoMrP Description: CRAN Package 'autoMrP' (Improving MrP with Ensemble Learning) A tool that improves the prediction performance of multilevel regression with post-stratification (MrP) by combining a number of machine learning methods. For information on the method, please refer to Broniecki, Wüest, Leemann (2020) ''Improving Multilevel Regression with Post-Stratification Through Machine Learning (autoMrP)'' in the 'Journal of Politics'. Final pre-print version: . Package: r-cran-autonewsmd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-git2r, r-cran-quarto, r-cran-r6 Suggests: r-cran-lintr, r-cran-precommit, r-cran-testthat Filename: pool/dists/noble/main/r-cran-autonewsmd_0.1.0-1.ca2404.1_all.deb Size: 79184 MD5sum: f4645c56fae0cd3156ab23c8c83c9edd SHA1: f034533e50350c209edbb04686896c0b9587818c SHA256: 8709f11a0e043328ab46ea8b1bd5635e67f02938c89f64fca8cb73e64d6a0e98 SHA512: 9c0c8892328858bae62530bf2e8b7b08c7160ce1f98fafde5268a24438c32aebe815d68de12aa9efa36560dadda36d1655725b22b2847b55b79a8d3925644dcf Homepage: https://cran.r-project.org/package=autonewsmd Description: CRAN Package 'autonewsmd' (Auto-Generate Changelog using Conventional Commits) Automatically generate a changelog file (NEWS.md / CHANGELOG.md) from the git history using conventional commit messages (). Package: r-cran-autonn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-forecast, r-cran-mlmetrics Filename: pool/dists/noble/main/r-cran-autonn_0.1.0-1.ca2404.1_all.deb Size: 12968 MD5sum: c14620d8e3a19b4777147eb995e3e7a2 SHA1: 86ca1ea0afd94568e4eb9c8a0d3de80e7ababd41 SHA256: 03a42b1f50087bc39d22487b279e1f05c8121bb37fe6b4089d3d97598674db20 SHA512: 488ed1e35324fc350a0dc4890c0c699f15c78aad7d0f30d41022e648594edd5f46a7afa4d1584f0c70a77d46c2d99e8b9d827b8048d12d83d2f11c60122d8188 Homepage: https://cran.r-project.org/package=AutoNN Description: CRAN Package 'AutoNN' (Automatic Neural Network Modeling for Time Series Forecasting) Provides optimal combinations of input nodes and hidden neurons for fitting feedforward single-layer artificial neural networks in time series forecasting. Models are evaluated using root mean square error, mean absolute percentage error, and mean absolute error measures. Package: r-cran-autopipe Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-org.hs.eg.db, r-bioc-clusterprofiler, r-cran-msigdbr Filename: pool/dists/noble/main/r-cran-autopipe_0.1.6-1.ca2404.1_all.deb Size: 157134 MD5sum: de5c79e7c3746673e362d93aa1cfecfe SHA1: 9d31e73bc44dce1e8e3ee2dcf1fd217f34f14966 SHA256: af38b28445bad0536de63a5a6dc87d3fbc3d4a20a50de7f026754c2e76a881c1 SHA512: 284d2bbdfb44920cb95b9313b15a67ad396a94ce5adafcdfabcbc525184e61216868dcdaa4edc79716b19a54c2bbd095f86b65b64072614f4377e455bdc02acc Homepage: https://cran.r-project.org/package=AutoPipe Description: CRAN Package 'AutoPipe' (Automated Transcriptome Classifier Pipeline: ComprehensiveTranscriptome Analysis) An unsupervised fully-automated pipeline for transcriptome analysis or a supervised option to identify characteristic genes from predefined subclasses. We rely on the 'pamr' clustering algorithm to cluster the Data and then draw a heatmap of the clusters with the most significant genes and the least significant genes according to the 'pamr' algorithm. This way we get easy to grasp heatmaps that show us for each cluster which are the clusters most defining genes. Package: r-cran-autoplotly Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-ggfortify Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-autoplotly_0.1.4-1.ca2404.1_all.deb Size: 26128 MD5sum: f0ae061e35be259d958d6477cf060c45 SHA1: e32c5b8864573725eb07fdc0af177ba72d333ba1 SHA256: 12cce843eb0e90af31a7701f112180b93fb1bcc9349878ff0ee41d92530857ff SHA512: c0deea14446984e73a16f7b3b6b646bfe1995fd4df2bc5ce440ed2039841d64eda8ad1f59292f2619ab031c7d6a04f2e79374a5db454abe2e3a2e2de76709201 Homepage: https://cran.r-project.org/package=autoplotly Description: CRAN Package 'autoplotly' (Automatic Generation of Interactive Visualizations forStatistical Results) Functionalities to automatically generate interactive visualizations for statistical results supported by 'ggfortify', such as time series, PCA, clustering and survival analysis, with 'plotly.js' and 'ggplot2' style. 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Package: r-cran-autoplotprotein Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml, r-cran-plyr, r-cran-plotrix, r-cran-seqinr, r-cran-ade4 Filename: pool/dists/noble/main/r-cran-autoplotprotein_1.1-1.ca2404.1_all.deb Size: 83824 MD5sum: 10e4b30464c534b297c785ac943093e7 SHA1: 3fbd0cea8ed8c608fb4cdf2123d03c36af0d1eb9 SHA256: ca4b46c27e3a6a40ffb7ad14e16175ea8bcdf01fc4fd6d744c98b2d6fd9ccbbe SHA512: 1200f8b410170de80be25213bc842d7f9b26823738c96ad91b6ee7ba65df810a6dfce2c33efeca7bc66cfb00d12425f5c47130397469839ae30223207269cd9e Homepage: https://cran.r-project.org/package=Autoplotprotein Description: CRAN Package 'Autoplotprotein' (Development of Visualization Tools for Protein Sequence) The image of the amino acid transform on the protein level is drawn, and the automatic routing of the functional elements such as the domain and the mutation site is completed. Package: r-cran-autoplots Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1853 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-combinat, r-cran-data.table, r-cran-dplyr, r-cran-e1071, r-cran-echarts4r, r-cran-lubridate, r-cran-nortest, r-cran-quanteda, r-cran-quanteda.textstats, r-cran-scales, r-cran-htmltools, r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-autoplots_1.5.0-1.ca2404.1_all.deb Size: 1788646 MD5sum: 9bc3f563ed97f7521c5b8f0a31ea9944 SHA1: fc57f1faf10d5f418b9159de3f7084004302058d SHA256: 5bbc21d26e3975bf88120f7bc21363e247acf98195b4e912f9ee6bf1fc45ae50 SHA512: e8a17d246c5896ce2fd8c9de7694904a5bca231f2f152c95927c7acfc5ff9940856bcfb83681cec41b81346ba990af281041eb51d8b3c3add33191b4db331469 Homepage: https://cran.r-project.org/package=AutoPlots Description: CRAN Package 'AutoPlots' (Creating Echarts Visualizations as Easy as Possible) Create beautiful and interactive visualizations in a single function call. The 'data.table' package is utilized to perform the data wrangling necessary to prepare your data for the plot types you wish to build, along with allowing fast processing for big data. There are two broad classes of plots available: standard plots and machine learning evaluation plots. There are lots of parameters available in each plot type function for customizing the plots (such as faceting) and data wrangling (such as variable transformations and aggregation). Package: r-cran-autoreg Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3671 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-moonbook, r-cran-nortest, r-cran-dplyr, r-cran-crayon, r-cran-stringr, r-cran-tidyr, r-cran-purrr, r-cran-survival, r-cran-mice, r-cran-officer, r-cran-flextable, r-cran-rlang, r-cran-patchwork, r-cran-ggplot2, r-cran-boot, r-cran-broom, r-cran-tidycmprsk, r-cran-scales, r-cran-maxstat, r-cran-pammtools Suggests: r-cran-knitr, r-cran-finalfit, r-cran-lme4, r-cran-th.data, r-cran-rmarkdown, r-cran-survminer, r-cran-asaur, r-cran-cmprsk, r-cran-paireddata Filename: pool/dists/noble/main/r-cran-autoreg_0.3.3-1.ca2404.1_all.deb Size: 2450512 MD5sum: 28ca16fb0c6e331594d568eb08e93557 SHA1: 783f185f8464b1fc351c1bcb6293eee2e72961d0 SHA256: 2d71e9344bf753f55c2566f961cefb6c37ab0d4c05bdca2039f27cdc990f3f07 SHA512: 9d01a705bcca7a615caa889c2555c32013dc14e2c7093f9339c1252445948ac07c16ed58ca1c706a9fcdf2ae9d34d240b69553340f113d45032b63ed2690bfd8 Homepage: https://cran.r-project.org/package=autoReg Description: CRAN Package 'autoReg' (Automatic Linear and Logistic Regression and Survival Analysis) Make summary tables for descriptive statistics and select explanatory variables automatically in various regression models. Support linear models, generalized linear models and cox-proportional hazard models. Generate publication-ready tables summarizing result of regression analysis and plots. The tables and plots can be exported in "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents. Package: r-cran-autoregressionmde Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-autoregressionmde_1.0-1.ca2404.1_all.deb Size: 19168 MD5sum: 845bc0ccf034a14b0e3f6c4fcd596325 SHA1: a486dd760c659893c651b1df7797a2071f74e948 SHA256: 43ca1158b829a5af3ca7445ee283f35509ffc79ce9b0884305ca674eafe5018c SHA512: b49d684b86ce8e20464fe5320d048bbcac67ace8fb440dcced3da36652e5ca56348cb21df2dfde577ebd1ef69b92151e3490d27f5b82f749d07ec9bc294b2414 Homepage: https://cran.r-project.org/package=AutoregressionMDE Description: CRAN Package 'AutoregressionMDE' (Minimum Distance Estimation in Autoregressive Model) Consider autoregressive model of order p where the distribution function of innovation is unknown, but innovations are independent and symmetrically distributed. The package contains a function named ARMDE which takes X (vector of n observations) and p (order of the model) as input argument and returns minimum distance estimator of the parameters in the model. Package: r-cran-autoscore Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2475 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tableone, r-cran-proc, r-cran-randomforest, r-cran-ggplot2, r-cran-knitr, r-cran-hmisc, r-cran-car, r-cran-dplyr, r-cran-ordinal, r-cran-survival, r-cran-tidyr, r-cran-plotly, r-cran-magrittr, r-cran-randomforestsrc, r-cran-rlang, r-cran-survauc, r-cran-survminer Suggests: r-cran-rpart, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autoscore_1.1.0-1.ca2404.1_all.deb Size: 1923698 MD5sum: 09dc285642e40a1e0c8fa5d1fb231dc7 SHA1: b782192ea4eded9c07abeff18129d3d22e9b0ca3 SHA256: 1e03e8dd01a1050a1403b3b6571b50b6bdead3129b759e390cafeab4cf1bf35a SHA512: 336e58b34055bd7563f753b0650c068134a291c7b13c2e9de0a82ecc5ceff7df72d36d4b2d3b430a4f5e1d98e2e0f12526aca66495a51faea3ec4f7ffcb0a9bb Homepage: https://cran.r-project.org/package=AutoScore Description: CRAN Package 'AutoScore' (An Interpretable Machine Learning-Based Automatic Clinical ScoreGenerator) A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The details are described in our research paper. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies. Package: r-cran-autoscorecard Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1380 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-infotheo, r-cran-rocr, r-cran-rpart, r-cran-discretization, r-cran-corrplot, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-autoscorecard_0.3.0-1.ca2404.1_all.deb Size: 709720 MD5sum: 9a00745e8580b8bcb5db98963a7199ac SHA1: cf25499d89096297f319bc6c1ce47da6f5d717a7 SHA256: 8ebdf006673f082a2e2d3fb83ffdd3fe1ad5037196d561c226f5d42e7dc60138 SHA512: bf4184aebdfba5f32ef21e2aa98f83458a90f75388ef0e188b78abb9bd75e50af36771f14fdd55ce267b66b321d09f6e06ea9cf1f358556414f5458c6aa80476 Homepage: https://cran.r-project.org/package=autoScorecard Description: CRAN Package 'autoScorecard' (Fully Automatic Generation of Scorecards) Provides an efficient suite of R tools for scorecard modeling, analysis, and visualization. Including equal frequency binning, equidistant binning, K-means binning, chi-square binning, decision tree binning, data screening, manual parameter modeling, fully automatic generation of scorecards, etc. This package is designed to make scorecard development easier and faster. References include: 1. . 2. Dong-feng Li(Peking University),Class PPT. 3. . 4. . Package: r-cran-autoseed Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-autoseed_0.1.0-1.ca2404.1_all.deb Size: 1505692 MD5sum: 612993c3e1a09be76006715d8c899439 SHA1: 060d1e45a27854c5cc689fb8bef470330d15373b SHA256: 8e92de6330d933fbc52a67f08381772163288f2850556f2f70ab7f1212120258 SHA512: 06b90c35a88280990d7f163de7479543add013d12bfba9b311e2c12469ddcafe3109f77d04a2d90eaac33941d4d58ff7b41420c1b99110b2dea4dbacd9c21873 Homepage: https://cran.r-project.org/package=Autoseed Description: CRAN Package 'Autoseed' (Retrieve Disease-Related Genes from Public Sources) For researchers to quickly and comprehensively acquire disease genes, so as to understand the mechanism of disease, we developed this program to acquire disease-related genes. The data is integrated from three public databases. The three databases are 'eDGAR', 'DrugBank' and 'MalaCards'. The 'eDGAR' is a comprehensive database, containing data on the relationship between disease and genes. 'DrugBank' contains information on 13443 drugs and 5157 targets. 'MalaCards' integrates human disease information, including disease-related genes. Package: r-cran-autoshiny Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Suggests: r-cran-roxygen2, r-cran-magrittr, r-cran-webshot Filename: pool/dists/noble/main/r-cran-autoshiny_0.0.3-1.ca2404.1_all.deb Size: 40340 MD5sum: 75fec4e546d3fd0b9712909ca76a4e01 SHA1: a4d9f2cb060aee357201aa2f8f300fc91c5a9eee SHA256: 542d1a28738989efd2e897cb3d5dd60b92b1c6af2f390c0590fd19e61c05eab0 SHA512: 7901dce24fd650bb0bf1c4ec8cfc29f6cc36674100b78eacd2a3747757e063b96c4292f5bd785eddd58ed84bec80dd67f3550287963239fc7c9a436ba3d77a67 Homepage: https://cran.r-project.org/package=autoshiny Description: CRAN Package 'autoshiny' (Automatic Transformation of an 'R' Function into a 'shiny' App) Static code compilation of a 'shiny' app given an R function (into 'ui.R' and 'server.R' files or into a 'shiny' app object). See examples at . Package: r-cran-autoslider.core Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10460 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-flextable, r-cran-forcats, r-cran-formatters, r-cran-ggplot2, r-cran-ggpubr, r-cran-gridextra, r-cran-gtsummary, r-cran-officer, r-cran-rlang, r-cran-rlistings, r-cran-rtables, r-cran-rvg, r-cran-stringr, r-cran-survival, r-cran-tern, r-cran-tidyr, r-cran-yaml Suggests: r-cran-data.table, r-cran-devtools, r-cran-ellmer, r-cran-filters, r-cran-glue, r-cran-htmltools, r-cran-httr, r-cran-knitr, r-cran-lubridate, r-cran-mime, r-cran-nestcolor, r-cran-purrr, r-cran-r.utils, r-cran-reticulate, r-cran-rmarkdown, r-cran-rsvg, r-cran-svglite, r-cran-testthat, r-cran-tmb, r-cran-withr Filename: pool/dists/noble/main/r-cran-autoslider.core_0.3.2-1.ca2404.1_all.deb Size: 2378220 MD5sum: 4967eb29e323b0cec099bf09d351c5e6 SHA1: 7d9edaac4c990223f3a3c02313209d6f46900685 SHA256: 73f1e99b31afd53128ed2edd8033347219fd0a45361d1befb25cdce9fc77a23a SHA512: 585996ffdfa7acf870bd96f80e5e6063521debe06d48e8d476cfa636e8da6e3a674eb5296304fa5eaf6514587fb80d9d39388afd13e264b1bd29addee4a901e5 Homepage: https://cran.r-project.org/package=autoslider.core Description: CRAN Package 'autoslider.core' (Slide Automation for Tables, Listings and Figures) The normal process of creating clinical study slides is that a statistician manually type in the numbers from outputs and a separate statistician to double check the typed in numbers. This process is time consuming, resource intensive, and error prone. Automatic slide generation is a solution to address these issues. It reduces the amount of work and the required time when creating slides, and reduces the risk of errors from manually typing or copying numbers from the output to slides. It also helps users to avoid unnecessary stress when creating large amounts of slide decks in a short time window. Package: r-cran-autostats Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-tidyselect, r-cran-purrr, r-cran-janitor, r-cran-tibble, r-cran-rlang, r-cran-rlist, r-cran-broom, r-cran-magrittr, r-cran-ggeasy, r-cran-ggplot2, r-cran-jtools, r-cran-gtools, r-cran-ggthemes, r-cran-patchwork, r-cran-tidyr, r-cran-xgboost, r-cran-parsnip, r-cran-recipes, r-cran-rsample, r-cran-tune, r-cran-workflows, r-cran-framecleaner, r-cran-presenter, r-cran-yardstick, r-cran-dials, r-cran-party, r-cran-data.table, r-cran-nnet, r-cran-recosystem, r-cran-ckmeans.1d.dp, r-cran-broom.mixed, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-forcats, r-cran-doparallel, r-cran-hardhat, r-cran-flextable, r-cran-glmnet, r-cran-ggstance, r-cran-matrix, r-cran-bbmisc, r-cran-readr, r-cran-lubridate, r-cran-ranger, r-cran-xicor Filename: pool/dists/noble/main/r-cran-autostats_0.4.2-1.ca2404.1_all.deb Size: 332886 MD5sum: 2585a9c344d770401236629af9d052d4 SHA1: 8b39446f949c7721322e084df2df406cf49f5ed6 SHA256: a9a49d7212976997a3280651ced16ffa90ade872e21e5e93ed30caaecdc70c86 SHA512: f8393a044c4e05f0900908c4f84e25e0a79155e6f68400e44da5a743daa92178300b02b4ad3b0b19595c8e96dfaf88a465e0681d6cfc2ec4eb4be1bccd0f5893 Homepage: https://cran.r-project.org/package=autostats Description: CRAN Package 'autostats' (Auto Stats) Automatically do statistical exploration. Create formulas using 'tidyselect' syntax, and then determine cross-validated model accuracy and variable contributions using 'glm' and 'xgboost'. Contains additional helper functions to create and modify formulas. Has a flagship function to quickly determine relationships between categorical and continuous variables in the data set. Package: r-cran-autostepwiseglm Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-formula.tools Filename: pool/dists/noble/main/r-cran-autostepwiseglm_0.2.0-1.ca2404.1_all.deb Size: 29000 MD5sum: 91c7c33e135980b66f92e3c93bdb79e3 SHA1: b4c9cd2f697230ca16c59b414bf92842c332b4df SHA256: 2dcd48335c5de1f46a8e4c2fbe0d3b15d73713ee57b0a1b79790035f11f162ca SHA512: 02863e38d3081a39317f9b349b8f83258a239bcb03b5279bb63c2e1902da19f1a517c295530dae1c97c1db0f2ddd2260fdd780f3f7cd9aa44dccbd2cfaab3dbc Homepage: https://cran.r-project.org/package=AutoStepwiseGLM Description: CRAN Package 'AutoStepwiseGLM' (Builds Stepwise GLMs via Train and Test Approach) Randomly splits data into testing and training sets. Then, uses stepwise selection to fit numerous multiple regression models on the training data, and tests them on the test data. Returned for each model are plots comparing model Akaike Information Criterion (AIC), Pearson correlation coefficient (r) between the predicted and actual values, Mean Absolute Error (MAE), and R-Squared among the models. Each model is ranked relative to the other models by the model evaluation metrics (i.e., AIC, r, MAE, and R-Squared) and the model with the best mean ranking among the model evaluation metrics is returned. Model evaluation metric weights for AIC, r, MAE, and R-Squared are taken in as arguments as aic_wt, r_wt, mae_wt, and r_squ_wt, respectively. They are equally weighted as default but may be adjusted relative to each other if the user prefers one or more metrics to the others, Field, A. (2013, ISBN:978-1-4462-4918-5). 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Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" . Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" . 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Package: r-cran-aws.ecx Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2614 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjson, r-cran-aws.signature, r-cran-httr, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aws.ecx_1.0.6-1.ca2404.1_all.deb Size: 2268920 MD5sum: e4d99d90951bef65064cf2ac9609f605 SHA1: 4d4e96ba8422904191ae8ec551f90c2820f52f74 SHA256: 109f31347e3553ba51722a99633c07bc3af817619df06622db9366cc5d7e0ff1 SHA512: bebf0744635f24660e067361226153d78480917bda60b3d8b386b9222012ba89df3357c747a4cbb0176f6d968319ddcd9fa5f97a13016fd814773ed1148cc1f7 Homepage: https://cran.r-project.org/package=aws.ecx Description: CRAN Package 'aws.ecx' (Communicating with AWS EC2 and ECS using AWS REST APIs) Providing the functions for communicating with Amazon Web Services(AWS) Elastic Compute Cloud(EC2) and Elastic Container Service(ECS). The functions will have the prefix 'ecs_' or 'ec2_' depending on the class of the API. The request will be sent via the REST API and the parameters are given by the function argument. The credentials can be set via 'aws_set_credentials'. The EC2 documentation can be found at and ECS can be found at . Package: r-cran-aws.iam Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-xml2, r-cran-jsonlite, r-cran-aws.signature Filename: pool/dists/noble/main/r-cran-aws.iam_0.1.8-1.ca2404.1_all.deb Size: 131010 MD5sum: 65a14f871ccad1b3d7aec0426c040fd4 SHA1: 60770e489550ab1a5390debeb865b6fda0758c3f SHA256: eeb3ad95cc5e19dc87f83464092d1eb9f8ef3eead6d0f403c0e7972f46cfda31 SHA512: 7154306c0bdaaeaf3b5b34016e65f6a970acd74951f03cf54caebebcd1255ce4a3b9fbe2455d93c3c7e4166731b48d85f3bd67fca13ec5f946cd2d00e60c156b Homepage: https://cran.r-project.org/package=aws.iam Description: CRAN Package 'aws.iam' (AWS IAM Client Package) A simple client for the Amazon Web Services ('AWS') Identity and Access Management ('IAM') 'API' . Package: r-cran-aws.kms Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-base64enc, r-cran-aws.signature Filename: pool/dists/noble/main/r-cran-aws.kms_0.1.4-1.ca2404.1_all.deb Size: 57218 MD5sum: ad7ec274fb050811f182009a3c6061f3 SHA1: d567ab3758ed44607a5e517e78fa9006f631d8e2 SHA256: 1f82d6b5cf3e6f195939c881e0989fdd7cb66ad706a2a434de135f97e854aef7 SHA512: 27dd6dd6acaf6f249b6c0e3f8f42f08a02df0a1abea13ade88fd7e5dbacb8a5e24b3f3eba9b1f2cc610537ffbc3f8442406fae75c127ecf6e88226fba74a3b06 Homepage: https://cran.r-project.org/package=aws.kms Description: CRAN Package 'aws.kms' ('AWS Key Management Service' Client Package) Client package for the 'AWS Key Management Service' , a cloud service for managing encryption keys. Package: r-cran-aws.lambda Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-aws.signature, r-cran-base64enc Suggests: r-cran-testthat, r-cran-aws.s3, r-cran-aws.iam Filename: pool/dists/noble/main/r-cran-aws.lambda_0.2.0-1.ca2404.1_all.deb Size: 59628 MD5sum: 660f0ecea591f37d729a25db64c38e0a SHA1: 6fe123e80f1bfe5d8175f12cff604c00334ceb40 SHA256: a597feea23b61dd213bfe5f1989f45941fdfdd31bab5d4f935b5220df52c21bd SHA512: 0ce32be2fa5b14b6e60c83a9aeb6234325a4212e52ac1ecbd0bf8f94262d32ab0bd2f268da3434813d90301aea55ab060c6a6f2ab81b1d9819533a74d89c5623 Homepage: https://cran.r-project.org/package=aws.lambda Description: CRAN Package 'aws.lambda' (AWS Lambda Client Package) A simple client package for the Amazon Web Services ('AWS') Lambda API . Package: r-cran-aws.polly Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-aws.signature, r-cran-tuner Filename: pool/dists/noble/main/r-cran-aws.polly_0.1.5-1.ca2404.1_all.deb Size: 30142 MD5sum: 81301dff18d962f37b5b9a2e2578bcf9 SHA1: 09125813107bfaca88235b7a0b9439dfb2ca6c89 SHA256: c9265fd564a6d791413585ebfc648200f7c4cd64a495ccb71ef1f602d58f068a SHA512: 7f98e762c8ff955db8e57549c772ad57322a473b17a2c86f833fc9188784a4605f05fec25dfd182725c5dacfaca107d8f18dd5702fe30fda9d6ea60b2f4500d2 Homepage: https://cran.r-project.org/package=aws.polly Description: CRAN Package 'aws.polly' (Client for AWS Polly) A client for AWS Polly , a speech synthesis service. Package: r-cran-aws.s3 Architecture: all Version: 0.3.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httr, r-cran-xml2, r-cran-base64enc, r-cran-digest, r-cran-aws.signature Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aws.s3_0.3.22-1.ca2404.1_all.deb Size: 205296 MD5sum: fb1183fc14f259cbe6916254430c97f6 SHA1: f5ebb4633b53b3d353af1b1b663e809fa49dd3a4 SHA256: e6c02146ab7c5265c58af166a310974abcc73cb609770aa8283654a328fedd96 SHA512: 800d06e365c324e6eafcdba375db0194b4693af52f7d4bf846e164ef2c4a032805b992143bd1dce5fee5e712fb81dce65f1028e4c2cfe47d86d56a406fe75e5c Homepage: https://cran.r-project.org/package=aws.s3 Description: CRAN Package 'aws.s3' ('AWS S3' Client Package) A simple client package for the Amazon Web Services ('AWS') Simple Storage Service ('S3') 'REST' 'API' . Package: r-cran-aws.signature Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-base64enc Suggests: r-cran-testthat, r-cran-aws.ec2metadata Filename: pool/dists/noble/main/r-cran-aws.signature_0.6.0-1.ca2404.1_all.deb Size: 78892 MD5sum: 87fc8c3be8bcdb7a8beede8b6612360e SHA1: ac11b3a88d2499ee30530c4c60bb0e976d9eab61 SHA256: 873230beb9a1971a480b6b14fb79c402cbe40f7f50cafd008c7de80fb7c6ebae SHA512: 749e320a540521d8a63f4dde8719ca1c12db0da4be4acad4725c836c10cf4d07d64f92063dcf74478f1a74bd8a719a74132626f93ed93f9fb4bdf43aa182be89 Homepage: https://cran.r-project.org/package=aws.signature Description: CRAN Package 'aws.signature' (Amazon Web Services Request Signatures) Generates version 2 and version 4 request signatures for Amazon Web Services ('AWS') Application Programming Interfaces ('APIs') and provides a mechanism for retrieving credentials from environment variables, 'AWS' credentials files, and 'EC2' instance metadata. For use on 'EC2' instances, users will need to install the suggested package 'aws.ec2metadata' . Package: r-cran-aws.transcribe Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-aws.signature Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aws.transcribe_0.1.3-1.ca2404.1_all.deb Size: 28576 MD5sum: 8126864c2c49dcd169638c106efad63e SHA1: 4c0bd60d4396f3a855dd236701da5efe18505537 SHA256: 80bf0faa9f4411a7caf4988b92921ee22eb711d20467bae5d86e44ecc6f5e191 SHA512: e8b4bfa1798a05b50d0a0db75d58ba19678abd7d744380d6c48876ac3bb656053010853a73fe1cdc695a7afd9eed35a426064b99c2cf3896487d8eb206fbaf03 Homepage: https://cran.r-project.org/package=aws.transcribe Description: CRAN Package 'aws.transcribe' (Client for 'AWS Transcribe') Client for 'AWS Transcribe' , a cloud transcription service that can convert an audio media file in English and other languages into a text transcript. Package: r-cran-aws.translate Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-aws.signature Filename: pool/dists/noble/main/r-cran-aws.translate_0.1.4-1.ca2404.1_all.deb Size: 19640 MD5sum: d4948ead312793c0454ef73344bb6236 SHA1: 2c9ea4c5315bd87c714aac37928acb4924bff52a SHA256: 82cafc0fa0d684eb6cc87004e54d650bd260008bedd3987c903cf550f20607cb SHA512: b37eaa9390e862057285c74cd8ebab207b4a07996916e1099bc7bf8b650aed97db29595b1f8326e9f65e47377eb96f5c62244377d3afd927764c5bd0e052f6f8 Homepage: https://cran.r-project.org/package=aws.translate Description: CRAN Package 'aws.translate' (Client for 'AWS Translate') A client for 'AWS Translate' , a machine translation service that will convert a text input in one language into a text output in another language. Package: r-cran-aws.wrfsmn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aws.s3, r-cran-lubridate, r-cran-terra, r-cran-dplyr, r-cran-ggplot2, r-cran-hydrogof, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-aws.wrfsmn_0.1.0-1.ca2404.1_all.deb Size: 129006 MD5sum: ba0843f3b55e0062d18e3bc559a7da7a SHA1: be1a9943cf33b7822f3ca638f34d9c48f3461f8f SHA256: 591930a67719517184214a466a8745f8946085f1dc70cb3fb72fd5277f6f96a0 SHA512: 6526abc44fbf09ccb3a520a744710207c5382685e1fd93c2e9b170fd55d149161c2f7e0322eaf5a6ef2d8b17752684f7df0ab792e3eda3d3a2e40b57d2b4d9db Homepage: https://cran.r-project.org/package=aws.wrfsmn Description: CRAN Package 'aws.wrfsmn' (Data Processing of SMN Hi-Res Weather Forecast from 'AWS') Exploration of Weather Research & Forecasting ('WRF') Model data of Servicio Meteorologico Nacional (SMN) from Amazon Web Services () cloud. The package provides the possibility of data downloading, processing and correction methods. It also has map management and series exploration of available meteorological variables of 'WRF' forecast. Package: r-cran-axisandallies Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-axisandallies_0.1.1-1.ca2404.1_all.deb Size: 38936 MD5sum: 72c49d15b12750d75d3fdf2ea8c6fcec SHA1: eeb53383237a1178bb96d4da27267e98595bea0a SHA256: dc84da2087f6c170db2eea1bcb7668c7c6683fc217272a76b948f7acd623f0b1 SHA512: c138e068a1adaf2f256a8f1fbccd72f7b77a18c7c7d1d65cf556ba859e17ccb1d2c93fe3981f00e5c2fba911f3c16754c7f99b8156cba817343ed116126e1284 Homepage: https://cran.r-project.org/package=axisandallies Description: CRAN Package 'axisandallies' (Axis and Allies Spring) Simulates battles in the board game Axis and Allies Spring 1942, and calculates your probability of winning a battle. This speeds the game up significantly. Package: r-cran-aziad Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-extradistr, r-cran-foreach, r-cran-doparallel, r-cran-qrm, r-cran-corpcor, r-cran-envstats Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-aziad_0.0.3-1.ca2404.1_all.deb Size: 216196 MD5sum: 277e2761e6ef9a2efc800252682ef88d SHA1: bbf44a358f586c9ec99f9f35f290155aada7d072 SHA256: 0fa2bd15ba0709d2c02326df225dff00caaff823560434557661ed0ccb412dbd SHA512: 9c0f6aa29d66260ffe8b3d88a57fc9039e669ac8bd9351d9327e31e1f6d11f1e9ceb0b8da5d88dc8e0ef89c3e2fd2781577e50296f64ec8bbb8934591a4844a0 Homepage: https://cran.r-project.org/package=AZIAD Description: CRAN Package 'AZIAD' (Analyzing Zero-Inflated and Zero-Altered Data) Description: Computes maximum likelihood estimates of general, zero-inflated, and zero-altered models for discrete and continuous distributions. It also performs Kolmogorov-Smirnov (KS) tests and likelihood ratio tests for general, zero-inflated, and zero-altered data. Additionally, it obtains the inverse of the Fisher information matrix and confidence intervals for the parameters of general, zero-inflated, and zero-altered models. The package simulates random deviates from zero-inflated or hurdle models to obtain maximum likelihood estimates. Based on the work of Aldirawi et al. (2022) and Dousti Mousavi et al. (2023) . Package: r-cran-azlogr Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-catools, r-cran-digest, r-cran-httr, r-cran-jsonlite, r-cran-logger Suggests: r-cran-covr, r-cran-knitr, r-cran-mockery, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-azlogr_0.0.6-1.ca2404.1_all.deb Size: 52672 MD5sum: 4fec2bbd2a4564263ff8c11d4141143f SHA1: 2eb225912c8961975588c3b270d4eec64a1e90a6 SHA256: 311884aa90d9421ba7225ce89823a0d0e359b0b36aeb84dcbc36532783821a7b SHA512: 134cca0e0a57b553028706f8b09852a67c8feddce901138f9e02bde4abe604ea6cac15037492386916c5059a2bac4604f1a026c98d393df428c8ffff2f5564c6 Homepage: https://cran.r-project.org/package=azlogr Description: CRAN Package 'azlogr' (Logging in 'R' and Post to 'Azure Log Analytics' Workspace) It extends the functionality of 'logger' package. 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Package: r-cran-azr Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-data.table, r-cran-httpuv, r-cran-clipr, r-cran-processx, r-cran-testthat, r-cran-vcr Filename: pool/dists/noble/main/r-cran-azr_0.3.4-1.ca2404.1_all.deb Size: 646700 MD5sum: 5f3318ebd689559f27700dc02a8fcfdc SHA1: b7e14ea4f8203a27bbba2dfab42121677d69cfd4 SHA256: 1e1bbedc0f3be3b57e0dcda31f0ae60a665f903c52fefe3a610d590da85a3e2d SHA512: e5bd4beb6489966d0a3128655495479da1916b44395d79ae4e5946070a832cbe0b48ad5e45489886d7cc101f8f6e475e4d0957205e7b46544eaa59c3d2caf3b1 Homepage: https://cran.r-project.org/package=azr Description: CRAN Package 'azr' (Credential Chain for Seamless 'OAuth 2.0' Authentication to'Azure Services') Implements a credential chain for 'Azure OAuth 2.0' authentication based on the package 'httr2''s 'OAuth' framework. Sequentially attempts authentication methods until one succeeds. During development allows interactive browser-based flows ('Device Code' and 'Auth Code' flows) and non-interactive flow ('Client Secret') in batch mode. Package: r-cran-azureappinsights Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1335 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rlang, r-cran-assertthat, r-cran-jsonlite, r-cran-lubridate Suggests: r-cran-testthat, r-cran-here Filename: pool/dists/noble/main/r-cran-azureappinsights_0.3.1-1.ca2404.1_all.deb Size: 302794 MD5sum: fdd65592ffd1209bb84d2e538bd72ad1 SHA1: f41199f3b4da40769b50423799f0a7b6f90bed33 SHA256: 0dd5035e1d5a907f6f22c01693fed2b39ad5c8cca52e0e831b5abe66c697a58f SHA512: d7d28cecf70595b3a5315a7abb202bbd463644a63ac133079ba288d955f05065ccf32a414b7e0b1a0c6973e8484a561b900f6d7bb39e93fbb467d6c7e4f0b781 Homepage: https://cran.r-project.org/package=AzureAppInsights Description: CRAN Package 'AzureAppInsights' (Include Azure Application Insights in Shiny Apps) Imports Azure Application Insights for web pages into Shiny apps via Microsoft's JavaScript snippet. 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Package: r-cran-azureauth Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 647 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-openssl, r-cran-jsonlite, r-cran-jose, r-cran-r6, r-cran-rappdirs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httpuv, r-cran-shiny, r-cran-shinyjs, r-cran-azurermr, r-cran-azuregraph Filename: pool/dists/noble/main/r-cran-azureauth_1.3.4-1.ca2404.1_all.deb Size: 460376 MD5sum: b17a5b39376452130b13d279a45ad93b SHA1: 9a2023c127e54bfee354eb9a2bdea924eb5bbcd5 SHA256: 6fc824d975061a43a5c15e94c70bf198708ff4b9c2b397a2b2ab8491d5d217f5 SHA512: a751189b73f369882a5275918d6749621bd69df29d441642004d99bf99aeabf9d1e64405dc5f597b22791ea0ffef163dedaeb986eba3638544cd0ecd2339f1d4 Homepage: https://cran.r-project.org/package=AzureAuth Description: CRAN Package 'AzureAuth' (Authentication Services for Azure Active Directory) Provides Azure Active Directory (AAD) authentication functionality for R users of Microsoft's 'Azure' cloud . Use this package to obtain 'OAuth' 2.0 tokens for services including Azure Resource Manager, Azure Storage and others. It supports both AAD v1.0 and v2.0, as well as multiple authentication methods, including device code and resource owner grant. Tokens are cached in a user-specific directory obtained using the 'rappdirs' package. The interface is based on the 'OAuth' framework in the 'httr' package, but customised and streamlined for Azure. Part of the 'AzureR' family of packages. Package: r-cran-azurecognitive Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-azureauth, r-cran-azurermr, r-cran-jsonlite, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-azurecognitive_1.0.2-1.ca2404.1_all.deb Size: 147172 MD5sum: cba2a71961f00ab9d637e0afcd7f1434 SHA1: c739bada73ede09686d8f073f20fc5b35293d0d2 SHA256: 871ff238f9271ca073caa59e5d1e2f5d2f80c6b9cd7e97e8bd693a0c5971b83b SHA512: b9f2a73416de423211322a4d846ee038647bb13af6f8504a55453883b11f4a3f7d2e9afb5cd5e7d1752ee7bb7f480ce8b3e633c9343f849baca585379809c9c0 Homepage: https://cran.r-project.org/package=AzureCognitive Description: CRAN Package 'AzureCognitive' (Interface to Azure Cognitive Services) An interface to Azure Cognitive Services . Both an 'Azure Resource Manager' interface, for deploying Cognitive Services resources, and a client framework are supplied. While 'AzureCognitive' can be called by the end-user, it is meant to provide a foundation for other packages that will support specific services, like Computer Vision, Custom Vision, language translation, and so on. Part of the 'AzureR' family of packages. Package: r-cran-azurecontainers Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-azurermr, r-cran-azuregraph, r-cran-openssl, r-cran-httr, r-cran-r6, r-cran-processx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-uuid, r-cran-mass, r-cran-bcrypt, r-cran-randomforest, r-cran-plumber, r-cran-restrserve, r-cran-azurekeyvault Filename: pool/dists/noble/main/r-cran-azurecontainers_1.3.3-1.ca2404.1_all.deb Size: 474564 MD5sum: be9e7d1d003b86de6f4fb13cd5060bf6 SHA1: 0abff3c1ee631a27fcd659d77ef0e33186791fbf SHA256: b331ad1f4bc0d4505ec1bb78efe3bd2da5b181d1f2a7e3c842b31d01762b84a4 SHA512: bb6fb7c29e3bba5ba85188d71e3615b08e31abcdd4dbbac7ed33f09eb810d14c39b5fbe7839857d8db06919edfe9d0389301486b8da3365103aab4300497ba93 Homepage: https://cran.r-project.org/package=AzureContainers Description: CRAN Package 'AzureContainers' (Interface to 'Container Instances', 'Docker Registry' and'Kubernetes' in 'Azure') An interface to container functionality in Microsoft's 'Azure' cloud: . Manage 'Azure Container Instance' (ACI), 'Azure Container Registry' (ACR) and 'Azure Kubernetes Service' (AKS) resources, push and pull images, and deploy services. On the client side, lightweight shells to the 'docker', 'docker-compose', 'kubectl' and 'helm' commandline tools are provided. Part of the 'AzureR' family of packages. Package: r-cran-azurecosmosr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-azurermr, r-cran-curl, r-cran-openssl, r-cran-jsonlite, r-cran-httr, r-cran-uuid, r-cran-vctrs Suggests: r-cran-azuretablestor, r-cran-mongolite, r-cran-dbi, r-cran-odbc, r-cran-dplyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-azurecosmosr_1.0.0-1.ca2404.1_all.deb Size: 222752 MD5sum: ca6f25d4737d736de4dd7e737ffea7c4 SHA1: 309cc691a7d321b6cf887a3be4cf6a42d9d42968 SHA256: 693470711ff4df9aa77145638fae34be07ee6cfb70a931bbfe42feb254a000a5 SHA512: 90b9c9295550cce92dd82831c1fdbc2f34c99243315603888db4dd778c197544df96b52b550a4a0db44495495f9eb98c90efb9747ec555c4463d55b03eb65d5d Homepage: https://cran.r-project.org/package=AzureCosmosR Description: CRAN Package 'AzureCosmosR' (Interface to the 'Azure Cosmos DB' 'NoSQL' Database Service) An interface to 'Azure CosmosDB': . On the admin side, 'AzureCosmosR' provides functionality to create and manage 'Cosmos DB' instances in Microsoft's 'Azure' cloud. On the client side, it provides an interface to the 'Cosmos DB' SQL API, letting the user store and query documents and attachments in 'Cosmos DB'. Part of the 'AzureR' family of packages. Package: r-cran-azuregraph Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azureauth, r-cran-httr, r-cran-jsonlite, r-cran-openssl, r-cran-curl, r-cran-r6 Suggests: r-cran-azurermr, r-cran-vctrs, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-azuregraph_1.3.5-1.ca2404.1_all.deb Size: 540924 MD5sum: cfbe2cdcb46aaccd6d54710f04206ec0 SHA1: f8c623612b03d6fe99f0f03b81e41a4a062f585e SHA256: b2fcc6b05457a2907fc3bb2edf7f95506f4dc7fb1d239ce5cda42af986878d01 SHA512: cb5318512b2a84f2643142a4c15c9e73cdbf45b93e292f8fabbfb68ded76289718a00f3aefe3b7a76b22e2d19c7a684e17ab4c6a3cd064dbbb6a0a1c36ae753d Homepage: https://cran.r-project.org/package=AzureGraph Description: CRAN Package 'AzureGraph' (Simple Interface to 'Microsoft Graph') A simple interface to the 'Microsoft Graph' API . 'Graph' is a comprehensive framework for accessing data in various online Microsoft services. This package was originally intended to provide an R interface only to the 'Azure Active Directory' part, with a view to supporting interoperability of R and 'Azure': users, groups, registered apps and service principals. However it has since been expanded into a more general tool for interacting with Graph. Part of the 'AzureR' family of packages. Package: r-cran-azurekeyvault Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-jsonlite, r-cran-openssl, r-cran-jose, r-cran-azurermr, r-cran-azuregraph, r-cran-azureauth Suggests: r-cran-azurestor, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-azurekeyvault_1.0.6-1.ca2404.1_all.deb Size: 511898 MD5sum: c923b2b178eed64546233a66fac8560c SHA1: 877e0d0161e0819a3cb4908e2a58878c7854108a SHA256: 17db0a58b01ccdea94e39a6272d991a511ae2723057c476291a887eaa6e2adce SHA512: d314c28cebb106688349b60ec5f6e83ee35db1681c73af5537cbc6965c991a32229b03a51388f58e3199b7ffc860e5836b888285c1cfdee85eb69560d899e9e6 Homepage: https://cran.r-project.org/package=AzureKeyVault Description: CRAN Package 'AzureKeyVault' (Key and Secret Management in 'Azure') Manage keys, certificates, secrets, and storage accounts in Microsoft's 'Key Vault' service: . Provides facilities to store and retrieve secrets, use keys to encrypt, decrypt, sign and verify data, and manage certificates. Integrates with the 'AzureAuth' package to enable authentication with a certificate, and with the 'openssl' package for importing and exporting cryptographic objects. Part of the 'AzureR' family of packages. Package: r-cran-azurekusto Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 724 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-httr, r-cran-jsonlite, r-cran-r6, r-cran-openssl, r-cran-azureauth, r-cran-azurermr, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-dbi Suggests: r-cran-bit64, r-cran-knitr, r-cran-testthat, r-cran-azuregraph, r-cran-azurestor, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-azurekusto_1.1.4-1.ca2404.1_all.deb Size: 576514 MD5sum: b2fbda375b6da704bc25a8d386a274ec SHA1: 43f390aa321f3f9d636808c1e2570efe411e2651 SHA256: 0eab94d67a1487d059021e1d71ae668dd39c10433c3f00427a2f4a2f76889ae7 SHA512: 383d7bea4b681f211aca8f21c3a9b2cb7bc492ec5754dab6fce3ffa0b15d1a2500db804bfbd506f5e6bc64a778bf5eb5b04282ca452913c2247f32e5429a17e9 Homepage: https://cran.r-project.org/package=AzureKusto Description: CRAN Package 'AzureKusto' (Interface to 'Kusto'/'Azure Data Explorer') An interface to 'Azure Data Explorer', also known as 'Kusto', a fast, distributed data exploration service from Microsoft: . Includes 'DBI' and 'dplyr' interfaces, with the latter modelled after the 'dbplyr' package, whereby queries are translated from R into the native 'KQL' query language and executed lazily. On the admin side, the package extends the object framework provided by 'AzureRMR' to support creation and deletion of databases, and management of database principals. Part of the 'AzureR' family of packages. Package: r-cran-azuremapsr Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geojsonsf, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-purrr, r-cran-rlist, r-cran-sf, r-cran-stringr Filename: pool/dists/noble/main/r-cran-azuremapsr_0.0.2-1.ca2404.1_all.deb Size: 50458 MD5sum: 4b6f7e13685e90d6e57e116d0eaff5c5 SHA1: eb3bd856ab4a5271408d8a1f1c16c3db717d5f79 SHA256: 119a951a4708203f93c7ba4f2d0cf3c5a77c6197ea64072d4242ecc08e1074ad SHA512: 6e1dce62fd423e3f6cb3bc88331bd8c37ccb7b282a8d4a623ffd0856937bf9161fcb9d750b581ff130f60fef4ffece63471a3643b16950963e730a84ec7928a2 Homepage: https://cran.r-project.org/package=azuremapsr Description: CRAN Package 'azuremapsr' (Interface to the 'Azure Maps' API) Provides a wrapper for the Microsoft 'Azure Maps' REST APIs , enabling users to access mapping and geospatial services directly from R. This package simplifies authenticating, building, and sending requests for services like route directions. It handles conversions between R objects (such as 'sf' objects) and the GeoJSON+JSON format required by the API, making it easier to integrate 'Azure Maps' into R-based data analysis workflows. Package: r-cran-azuremlsdk Architecture: all Version: 1.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reticulate, r-cran-plyr, r-cran-dt, r-cran-rstudioapi, r-cran-htmltools, r-cran-servr, r-cran-shiny, r-cran-shinycssloaders Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-dplyr, r-cran-jsonlite, r-cran-foreach, r-cran-iterators Filename: pool/dists/noble/main/r-cran-azuremlsdk_1.10.0-1.ca2404.1_all.deb Size: 501382 MD5sum: 63589eb168844bc1ddce127e019c30a2 SHA1: 4cd170af23bc3c1a825a26238f3924897a410635 SHA256: fec9bf3b40a6dbc0d175400bbd0893d57045a46b1d6941e7fe609feddd6d5399 SHA512: 0468fe523c9ad1fa4963f26fedf4534ba0d4b7391a8aa4c4388a394f0b403a51450811196354b697c84a97dfcaae618b97f1b4207ec13fadc64b2b14a9a02602 Homepage: https://cran.r-project.org/package=azuremlsdk Description: CRAN Package 'azuremlsdk' (Interface to the 'Azure Machine Learning' 'SDK') Interface to the 'Azure Machine Learning' Software Development Kit ('SDK'). Data scientists can use the 'SDK' to train, deploy, automate, and manage machine learning models on the 'Azure Machine Learning' service. To learn more about 'Azure Machine Learning' visit the website: . Package: r-cran-azureqstor Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-azurermr, r-cran-azurestor, r-cran-openssl, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-azureqstor_1.0.2-1.ca2404.1_all.deb Size: 165342 MD5sum: 28d52fde867de1c7b036f3468886bf68 SHA1: 4fecdd14833426b3578891bcd7c0ff7c4411efd2 SHA256: 06c1550fc476d7abef5a87306311e629b1f795c19cc83fb109af300ffda446da SHA512: 6b18457298063e90729e7abcce6f5ec361ad7adb82f226d93851a208032b6757bf8d331669a9f9e5e05c8561bd2d9765f87790375a6eeddc1ba67ef388ac0bbf Homepage: https://cran.r-project.org/package=AzureQstor Description: CRAN Package 'AzureQstor' (Interface to 'Azure Queue Storage') An interface to 'Azure Queue Storage'. This is a cloud service for storing large numbers of messages, for example from automated sensors, that can be accessed remotely via authenticated calls using HTTP or HTTPS. Queue storage is often used to create a backlog of work to process asynchronously. Part of the 'AzureR' family of packages. Package: r-cran-azurermr Architecture: all Version: 2.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azuregraph, r-cran-azureauth, r-cran-httr, r-cran-jsonlite, r-cran-r6, r-cran-uuid Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httpuv, r-cran-azurestor Filename: pool/dists/noble/main/r-cran-azurermr_2.4.5-1.ca2404.1_all.deb Size: 466178 MD5sum: 025db181557925abbd8ea71fb8b65ee7 SHA1: b5fae076166c9b3bebbcb03e6b3ea9765109f502 SHA256: b3f3864cf733424779c6d09387986a411f140b913aa208aed795addda01a31f3 SHA512: cdf4c58b0ac35fdaa4b111b3d01bc37a4e3760bf10eb0f584ef72bfd2d289b5ecb65039afe2dc134cfe312fdbb17c0e0a8ebf816f34b9b26f6fa43181061a728 Homepage: https://cran.r-project.org/package=AzureRMR Description: CRAN Package 'AzureRMR' (Interface to 'Azure Resource Manager') A lightweight but powerful R interface to the 'Azure Resource Manager' REST API. The package exposes a comprehensive class framework and related tools for creating, updating and deleting 'Azure' resource groups, resources and templates. While 'AzureRMR' can be used to manage any 'Azure' service, it can also be extended by other packages to provide extra functionality for specific services. Part of the 'AzureR' family of packages. Package: r-cran-azurestor Architecture: all Version: 3.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 652 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-mime, r-cran-openssl, r-cran-xml2, r-cran-vctrs, r-cran-azurermr Suggests: r-cran-azureauth, r-cran-readr, r-cran-knitr, r-cran-rmarkdown, r-cran-jsonlite, r-cran-testthat, r-cran-processx, r-cran-uuid Filename: pool/dists/noble/main/r-cran-azurestor_3.7.1-1.ca2404.1_all.deb Size: 469708 MD5sum: c4f005719d34986d37a3320813601704 SHA1: cb1e9c273d69dc41839e00f33e6aef193568c252 SHA256: add25bd90a93e43a0dadfb40e7bba2358698792994dabe4a69c195bb7c1ba767 SHA512: 4961c0f286a887afb6ffacfd3f42ae799c7a6ca4112e6ffc066f7dcf500d2d4348f77e27eb0c1b52e9e1f988066d7dacdf5550814f406d94e8242c3906555034 Homepage: https://cran.r-project.org/package=AzureStor Description: CRAN Package 'AzureStor' (Storage Management in 'Azure') Manage storage in Microsoft's 'Azure' cloud: . On the admin side, 'AzureStor' includes features to create, modify and delete storage accounts. On the client side, it includes an interface to blob storage, file storage, and 'Azure Data Lake Storage Gen2': upload and download files and blobs; list containers and files/blobs; create containers; and so on. Authenticated access to storage is supported, via either a shared access key or a shared access signature (SAS). Part of the 'AzureR' family of packages. Package: r-cran-azuretablestor Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-azurermr, r-cran-azurestor, r-cran-jsonlite, r-cran-openssl, r-cran-httr, r-cran-uuid, r-cran-vctrs Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-azuretablestor_1.0.0-1.ca2404.1_all.deb Size: 113820 MD5sum: e0606bca629fba5bec95b29759e7d243 SHA1: 70ceba687b0e0be28da8877660734b26ee15d320 SHA256: b2f430cb362c021963d5fe72013003f8bfbb1d53ac2aa7a29637bb24167ff2f4 SHA512: 5e1d732f7cf6251783b2779ddc2ed64714ff3abaf2045b1e4a0f915b1ea3063b2c51a83d6c3fe85d6080f3ae861eba1f74120800d2f114cff06f8435700bb388 Homepage: https://cran.r-project.org/package=AzureTableStor Description: CRAN Package 'AzureTableStor' (Interface to the Table Storage Service in 'Azure') An interface to the table storage service in 'Azure': . Supplies functionality for reading and writing data stored in tables, both as part of a storage account and from a 'CosmosDB' database with the table service API. Part of the 'AzureR' family of packages. Package: r-cran-azurevision Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1851 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-azurermr, r-cran-azurecognitive, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-azureauth, r-cran-testthat Filename: pool/dists/noble/main/r-cran-azurevision_1.0.2-1.ca2404.1_all.deb Size: 1548542 MD5sum: 862548ab72d933694d1b9891d29f56d0 SHA1: 4b9244591d7a85f2206eb8962f82fe1413b9afa1 SHA256: e8f832e0e7cb92ee6202cff5a97781c1d6b1fc0f76c83c9099551dcae05a24c6 SHA512: 20143538dc3eaacbf0a22088e9745bafd1a9da54b0c85becbb07eeb7e035671fc2e2b2222290e6d0f84b4401efb1d092a602088d5cc71e7016609fcca63d1230 Homepage: https://cran.r-project.org/package=AzureVision Description: CRAN Package 'AzureVision' (Interface to Azure Computer Vision Services) An interface to 'Azure Computer Vision' and 'Azure Custom Vision' , building on the low-level functionality provided by the 'AzureCognitive' package. These services allow users to leverage the cloud to carry out visual recognition tasks using advanced image processing models, without needing powerful hardware of their own. Part of the 'AzureR' family of packages. Package: r-cran-azurevm Architecture: all Version: 2.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-azurermr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-azurekeyvault, r-cran-azurevmmetadata Filename: pool/dists/noble/main/r-cran-azurevm_2.2.2-1.ca2404.1_all.deb Size: 424396 MD5sum: 1cc28bce7cd54621c30d19938a88585e SHA1: 853bc2113b96ca149aacd425751b9bc704275e04 SHA256: 06b9ab7fad5a4ff8e47c340d84fc249b76a187ad72959223bb73f41469b78ec7 SHA512: 119a74e72903697abe0839ad65cf7c6a0ae865f68f669397bec64f7c79ab9cfad6a8dc8bd0d3b2e033521a9034e11d987a9d96479e1f25b9e50e0acfcc226b98 Homepage: https://cran.r-project.org/package=AzureVM Description: CRAN Package 'AzureVM' (Virtual Machines in 'Azure') Functionality for working with virtual machines (VMs) in Microsoft's 'Azure' cloud: . Includes facilities to deploy, startup, shutdown, and cleanly delete VMs and VM clusters. Deployment configurations can be highly customised, and can make use of existing resources as well as creating new ones. A selection of predefined configurations is provided to allow easy deployment of commonly used Linux and Windows images, including Data Science Virtual Machines. With a running VM, execute scripts and install optional extensions. Part of the 'AzureR' family of packages. 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Package: r-cran-babebi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-babebi_0.1.0-1.ca2404.1_all.deb Size: 72190 MD5sum: 3ade7d19c2d25a29eb322b386c80b0f1 SHA1: d4bb09e823d2f656e90a845c4539ebf081c7ce0f SHA256: f4e8da65dafbbebaa8948148b20b82f058cf031d606e35846c562df39f10ff34 SHA512: 7b31b2dfe62015f0b0a55dcb4cdf416852f823a29a20d130568f841b911c5cfab58b3fbba953291d3ce4b04c560d41271f43caf085feef12e211e88587e62270 Homepage: https://cran.r-project.org/package=babebi Description: CRAN Package 'babebi' (Bayesian Estimation and Validation for Small-N Designs withRater Bias) Approximate Bayesian inference and Monte Carlo validation for small-N repeated-measures designs with two time points and two raters. The package is intended for applications in which sample size is limited and the observed outcome may be affected by rater-specific bias. User-supplied data are standardised into a common long-format structure. Pre-post effects are analysed using difference scores in a linear model with a rater indicator as covariate. Posterior summaries for the regression coefficients are obtained from a large-sample normal approximation centred at the least-squares estimate with plug-in covariance under a flat improper prior. Evidence for a non-zero pre-post effect, adjusted for rater differences, is summarised using a BIC-based approximation to the Bayes factor for comparison between models with and without the pre-post effect. Monte Carlo validation uses design quantities estimated from the observed data, including sample size, mean pre-post change, and second-rater additive discrepancy, and summarises inferential performance in terms of bias, root mean squared error, credible interval coverage, posterior tail probabilities, and mean Bayes factor values. For background on the BIC approximation and Bayes factors, see Schwarz (1978) and Kass and Raftery (1995) . 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Package: r-cran-babynames Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5437 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-babynames_1.0.1-1.ca2404.1_all.deb Size: 5533092 MD5sum: aebb3d9eea52b78b79b90eeed5a19810 SHA1: 362b48f6ca01e24b501cf4a3c7d6c5baf086c34d SHA256: 56ac68ce4fe232c2dff42f210f3f2bb72e2a91b03bbb452f95bc1651daf58512 SHA512: 449e6880162c46b72c4569e413d50862964abf115a00a14f4ed954fe8b6773583483b97c77975ed458e92f524fd77b22cc62452f23d7610d94bee27fdc522712 Homepage: https://cran.r-project.org/package=babynames Description: CRAN Package 'babynames' (US Baby Names 1880-2017) US baby names provided by the SSA. This package contains all names used for at least 5 children of either sex. Package: r-cran-babynamesil Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 785 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-babynamesil_0.2.3-1.ca2404.1_all.deb Size: 764446 MD5sum: d66b4d126440497860c24219c8b58e3d SHA1: 1aaf82757914385bc4da0a3363577daefe039420 SHA256: c4a4a0e2127f747719a434f0ae99dd6dfa4568110e22e9992df8e670760cc15e SHA512: bcc0de789e86200193fab0873d3faf2be8bbb8553265e70de324b3ff3655fa84e641ede07e2c5e6629985be781b0fd0eb3a2dd24218d6e8a84a3e5cb191f13ce Homepage: https://cran.r-project.org/package=babynamesIL Description: CRAN Package 'babynamesIL' (Israel Baby Names 1949-2024) Israeli baby names provided by Israel's Central Bureau of Statistics (CBS/LAMAS). Contains names used for at least 5 children in a given year, covering sectors "Jewish", "Muslim", "Christian-Arab", and "Druze" from 1949-2024. Legacy 1948 data and archived "Other" sector data are provided as separate datasets. Primary data source: CBS Release 391/2025 . Package: r-cran-babytimer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-janitor, r-cran-lubridate, r-cran-readr, r-cran-snakecase, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-babytimer_0.1.0-1.ca2404.1_all.deb Size: 31060 MD5sum: c703d0c3c98e896a4ed86d21a2d18e60 SHA1: 9497af4c22c4ddf26bab6baed8833fef8b9009f6 SHA256: 47a96318e4267c72edda9ed240e7f316de7e3731f0656146c8dd88c5b2570bd9 SHA512: 228cce1380dbbeb204f741a6ee1105a0ab40cc0118147f870deb191e7b6c4ff8530b3d9aa4575c13db40e79e9b4b766d1133b7b10b54d02ec69e4baf2a7aea3b Homepage: https://cran.r-project.org/package=babyTimeR Description: CRAN Package 'babyTimeR' (Parse Output from 'BabyTime' Application) 'BabyTime' is an application for tracking infant and toddler care activities like sleeping, eating, etc. This package will take the outputted .zip files and parse it into a usable list object with cleaned data. It handles malformed and incomplete data gracefully and is designed to parse one directory at a time. Package: r-cran-bacco Architecture: all Version: 2.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-emulator, r-cran-calibrator, r-cran-approximator Filename: pool/dists/noble/main/r-cran-bacco_2.1-0-1.ca2404.1_all.deb Size: 305998 MD5sum: d6c83e5c9c148a412ab3dbecc8835457 SHA1: b9ae85ef4135a4ef0729041a6a72df297bbcc7b3 SHA256: 73527ab5d4268dcaad24f660979ea4bdbdfcfc758eff2e8e7aefe4980a82f392 SHA512: 0be3cd7c8f14a7316949913828393d171245206f860b6748fa32a1e6c581923deaf8438056dfdf6bdc45bb7ba9e9ae49e530a4b060c75212ce6fb30cc328cc70 Homepage: https://cran.r-project.org/package=BACCO Description: CRAN Package 'BACCO' (Bayesian Analysis of Computer Code Output (BACCO)) The BACCO bundle of packages is replaced by the BACCO package, which provides a vignette that illustrates the constituent packages (emulator, approximator, calibrator) in use. Package: r-cran-bacct Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-bacct_1.0-1.ca2404.1_all.deb Size: 43652 MD5sum: c315452ca942a3db9af304b05a99c678 SHA1: 670f282acb88131a2704501dbc9fae77a8d6332c SHA256: 643fb939c3a6056a875e5b098af058112add2e054f8e2bf86f798911aeb50a97 SHA512: fd4efedfb20a38faf92313ac2ae2a1ca475396321a23278ca2de48cd92e211e87cd814f5d3c7955fc7c2241f8e850932c68ad7f67fc4467d9fef811acb54cd53 Homepage: https://cran.r-project.org/package=BACCT Description: CRAN Package 'BACCT' (Bayesian Augmented Control for Clinical Trials) Implements the Bayesian Augmented Control (BAC, a.k.a. Bayesian historical data borrowing) method under clinical trial setting by calling 'Just Another Gibbs Sampler' ('JAGS') software. 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Package: r-cran-bacenapi Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-bacenapi_0.3.1-1.ca2404.1_all.deb Size: 185068 MD5sum: c86e6c02d8a179885cb0fb25b2882ef4 SHA1: 0ff7e89d24ef88ccf5a78f450cdb257bb825c597 SHA256: 91a02214d889ba620e31c15c460996905d960f43a99f486b899e4f33e0f67f80 SHA512: 2cd4599aeeb61288e80082cae2e4544b6623f63cfb2b6c8c3f4682c95232b541b410829b17518907d7440cdbb9d4325fdcd32503964b775f419a7908becd9aac Homepage: https://cran.r-project.org/package=BacenAPI Description: CRAN Package 'BacenAPI' (Data Collection from the Central Bank of Brazil) Provides tools to facilitate the access and processing of data from the Central Bank of Brazil API. 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Active institutions information, balance sheets and normative acts. Package: r-cran-bacistool Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags Filename: pool/dists/noble/main/r-cran-bacistool_1.0.0-1.ca2404.1_all.deb Size: 59824 MD5sum: 1b29551e815dcf5c25457219cd1229d5 SHA1: 3098e9880bc03a314418e4a44e4e3a5022371403 SHA256: d57497887ace376b74ccccde4729104f2c0c185038f87f111628e94f361263c8 SHA512: f92a31090994517875157eeb669c3e3c33a9f84cb2bd9a171c4ffba7900aea5aa0df4c016f9369e69ed2f932167a63b2e481792f57f1b6bc16bf1119525bb461 Homepage: https://cran.r-project.org/package=bacistool Description: CRAN Package 'bacistool' (Bayesian Classification and Information Sharing (BaCIS) Tool forthe Design of Multi-Group Phase II Clinical Trials) Provides the design of multi-group phase II clinical trials with binary outcomes using the hierarchical Bayesian classification and information sharing (BaCIS) model. Subgroups are classified into two clusters on the basis of their outcomes mimicking the hypothesis testing framework. Subsequently, information sharing takes place within subgroups in the same cluster, rather than across all subgroups. This method can be applied to the design and analysis of multi-group clinical trials with binary outcomes. Reference: Nan Chen and J. Jack Lee (2019) . Package: r-cran-backpipe Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-magrittr, r-cran-piper, r-cran-r6, r-cran-testthat, r-cran-shiny, r-cran-knitr, r-cran-rstudioapi, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-backpipe_0.2.3-1.ca2404.1_all.deb Size: 24208 MD5sum: ab212943b9b5799cdbd7ab5cc01d6618 SHA1: 1a9ee3632c7d762d279871fc90d28688f1c29264 SHA256: 8aef9314c72ad2478044961d92578cecaef08669e66031e6e5c12150373cfb02 SHA512: 9d74eaf199482fb09d1787e8a5ce27d3f5f441faad53d678b847b15db25d462ab7b3e95b204ad818274b8d187000749d5c862eba28ea8c7616251ff706a0d1ed Homepage: https://cran.r-project.org/package=backpipe Description: CRAN Package 'backpipe' (Backward Pipe (Right-to-Left) Operator) Provides a backward-pipe operator for 'magrittr' (%<%) or 'pipeR' (%<<%) that allows for a performing operations from right-to-left. 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Compared with analyzing the associations of environmental mixtures with each Taxa individually, 'BaHZING' controls Type 1 error rates and provides more stable effect estimates when dealing with small sample sizes. 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'baizer' provides data processing functions frequently used by the author. Hope this package also knows what you want! Package: r-cran-bakeoff Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-tibble, r-cran-scales Filename: pool/dists/noble/main/r-cran-bakeoff_0.2.0-1.ca2404.1_all.deb Size: 236622 MD5sum: 6389acd1fd528f5a05db3103aa95bcae SHA1: e889d5f258b419f4faf5010de5e449e6a182fe75 SHA256: 120b25aece5a7d00d8a35a7c7a39a6f837c3072357261c9485aa015c0f54a4dc SHA512: 19be0cd2b61e837f164faf0f4c3f53015a2453f2bd4908415719011f6c3b758bda7c2932f6793494055306ad96c02f4483e677da16e66657c5382f54421f1ea0 Homepage: https://cran.r-project.org/package=bakeoff Description: CRAN Package 'bakeoff' (Data from "The Great British Bake Off") Data about the bakers, challenges, and ratings for "The Great British Bake Off", from Wikipedia . 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In the motivating Pneumonia Etiology Research for Child Health (PERCH) study, because both quantities of interest sum to one hundred percent, the PERCH scientists frequently refer to them as population etiology pie and individual etiology pie, hence the name of the package. 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Package: r-cran-balli Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-edger, r-bioc-limma, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-balli_0.2.0-1.ca2404.1_all.deb Size: 151990 MD5sum: d149e62c116a2806c3962f6052de1518 SHA1: d919ad45cf5ff71984af8c721561da031cf76262 SHA256: cc42df14251aa13377fa931be139d1e75f3f2ee7d33c0d2c2d0a8b9fa250037c SHA512: d35d713fa18b9f0c31e1862a6fb0855dceb879daf26c3a52c593fe23fd3cbde633af623edcadb8ddd7c78a4814717c2605f415807e709db699c3a57a9f15067e Homepage: https://cran.r-project.org/package=BALLI Description: CRAN Package 'BALLI' (Expression RNA-Seq Data Analysis Based on Linear Mixed Model) Analysis of gene expression RNA-seq data using Bartlett-Adjusted Likelihood-based LInear model (BALLI). Based on likelihood ratio test, it provides comparisons for effect of one or more variables. See Kyungtaek Park (2018) for more information. Package: r-cran-ballmapper Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-scales, r-cran-networkd3, r-cran-testthat, r-cran-fields, r-cran-stringr Filename: pool/dists/noble/main/r-cran-ballmapper_0.2.0-1.ca2404.1_all.deb Size: 77502 MD5sum: 4630fad7b237c4d655d1993571c7bfa5 SHA1: a602d0a2e51af8375f4817d48c93f5f30cc2677d SHA256: 5598e1d224495d63547ffce6f3cd0a0cb0af9be4ff9508f825c469cf292ae34a SHA512: c4f56b25fad44823bd471764d9ba47342111546add64c9bdee1a0c5f8c906e129a7c00efe5d429ce3351d73ad1ee218c97cf8982b3812734d09138c448b087bd Homepage: https://cran.r-project.org/package=BallMapper Description: CRAN Package 'BallMapper' (The Ball Mapper Algorithm) The core algorithm is described in "Ball mapper: a shape summary for topological data analysis" by Pawel Dlotko, (2019) . Please consult the following youtube video the idea of functionality. Ball Mapper provide a topologically accurate summary of a data in a form of an abstract graph. To create it, please provide the coordinates of points (in the points array), values of a function of interest at those points (can be initialized randomly if you do not have it) and the value epsilon which is the radius of the ball in the Ball Mapper construction. It can be understood as the minimal resolution on which we use to create the model of the data. Package: r-cran-bam Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mice Suggests: r-cran-coda, r-cran-nnet Filename: pool/dists/noble/main/r-cran-bam_1.0.3-1.ca2404.1_all.deb Size: 216028 MD5sum: 7bb6e7aef6ebe16761f41db01acee062 SHA1: 5036b7877832d0985b50eec521154cd0a1f576cc SHA256: 65385ca3125f04de733537c26433ff35f30b7b9eb7284f719c7395d9d57d3750 SHA512: cf21f7ff9344da4276d50cdc3ba7b0a77646effa0a565d17f56ace801c68a3801ae1c8e6a9d2387d3c8bca1b19aa559893e2e8fefc95c62ccd5fa283c0578c0d Homepage: https://cran.r-project.org/package=BaM Description: CRAN Package 'BaM' (Functions and Datasets for "Bayesian Methods: A Social andBehavioral Sciences Approach") Functions and datasets for Jeff Gill: "Bayesian Methods: A Social and Behavioral Sciences Approach". First, Second, and Third Edition. Published by Chapman and Hall/CRC (2002, 2007, 2014) . 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The actual URL of the API will depend on your company domain, and will be handled by the package automatically once you setup the config file. The API documentation can be found here . 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In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS). 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The degree of social influence is quantified via the network autocorrelation parameters. In case of a single network, the Bayesian methods of Dittrich, Leenders, and Mulder (2017) and Dittrich, Leenders, and Mulder (2019) are implemented using a normal, flat, or independence Jeffreys prior for the network autocorrelation. In the case of multiple networks, the Bayesian methods of Dittrich, Leenders, and Mulder (2020) are implemented using a multivariate normal prior for the network autocorrelation parameters. Flat priors are implemented for estimating the coefficients. For Bayesian testing of equality and order-constrained hypotheses, the default Bayes factor of Gu, Mulder, and Hoijtink, (2018) is used with the posterior mean and posterior covariance matrix of the NAM parameters based on flat priors as input. 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Package: r-cran-bandit Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-gam Filename: pool/dists/noble/main/r-cran-bandit_0.5.1-1.ca2404.1_all.deb Size: 46240 MD5sum: f555fdd2405b30d473080c1d05369326 SHA1: 1ae39e731b2cf61c1fde438f61ba2d3ecbab369d SHA256: 5dbd3e37bf25d3e11b72a4c5e9aa0b20affec9f3517abb319e76f2fd996fb54e SHA512: bc34c00dee61d5306c3c489ec16f22b7f2e0acfa95088a7e5732fad4862137f7a28a7be34c75aa8bc33fa86ed42ddf57003532357540f9a3cb6d2c0628b58d6e Homepage: https://cran.r-project.org/package=bandit Description: CRAN Package 'bandit' (Functions for Simple a/B Split Test and Multi-Armed BanditAnalysis) A set of functions for doing analysis of A/B split test data and web metrics in general. 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For illustration, several functions for simulating non-contextual and contextual adaptive experiments using Thompson sampling are also supplied. Package: r-cran-bandsfdp Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bandsfdp_1.1.0-1.ca2404.1_all.deb Size: 54598 MD5sum: fa103b707e86f1ff92cf5aa10e04a36a SHA1: fedad73ab9d6e0af98d6db2053267fdb3a6c7812 SHA256: 22d4f663e25c62a095c1fb897540bbdb687ad4900948289d2230e29882923f3e SHA512: 1bed9eba3448c279be581428a05e4ccc60c510247e2a5c0d023ddebba2732b7b39cffebb1d2c0640e94c070719abd9f3bb28a6d08ccf423c607b4017985ef7fa Homepage: https://cran.r-project.org/package=bandsfdp Description: CRAN Package 'bandsfdp' (Compute Upper Prediction Bounds on the FDP in Competition-BasedSetups) Implements functions that calculate upper prediction bounds on the false discovery proportion (FDP) in the list of discoveries returned by competition-based setups, implementing Ebadi et al. (2022) . Such setups include target-decoy competition (TDC) in computational mass spectrometry and the knockoff construction in linear regression (note this package typically uses the terminology of TDC). Included is the standardized (TDC-SB) and uniform (TDC-UB) bound on TDC's FDP, and the simultaneous standardized and uniform bands. Requires pre-computed Monte Carlo statistics available at . This data can be downloaded by running the command 'devtools::install_github("uni-Arya/fdpbandsdata")' in R and restarting R after installation. The size of this data is roughly 81Mb. Package: r-cran-banffit Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-crayon, r-cran-stringr, r-cran-fs, r-cran-lubridate, r-cran-tidyr, r-cran-fabr, r-cran-madshapr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-usethis Filename: pool/dists/noble/main/r-cran-banffit_2.0.0-1.ca2404.1_all.deb Size: 263974 MD5sum: d22f4c1808df91961cc52c9651db9a28 SHA1: 8dd281e737e400d3e8a688c5284c631da6e21e08 SHA256: 6901e2ea6783171e5734055d59ab2dc5db3daed6e6b0b1111df0fa96de692f73 SHA512: 2ae1e00e53c259daf5c40a6fbeca1f0b1569aba3bd2a10941b675768e936cce96f173cccf8adc629e872037d0da3908ca05d1f47a0dad977a7d0a401d866632a Homepage: https://cran.r-project.org/package=banffIT Description: CRAN Package 'banffIT' (Automated Standardized Assignment of the Banff Classification) Assigns standardized diagnoses using the Banff Classification (Category 1 to 6 diagnoses, including Acute and Chronic active T-cell mediated rejection as well as Active, Chronic active, and Chronic antibody mediated rejection). The main function considers a minimal dataset containing biopsies information in a specific format (described by a data dictionary), verifies its content and format (based on the data dictionary), assigns diagnoses, and creates a summary report. The package is developed on the reference guide to the Banff classification of renal allograft pathology Roufosse C, Simmonds N, Clahsen-van Groningen M, et al. A (2018) . The full description of the Banff classification is available at . 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For more information about the 'BAN' and its API, please see . 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Package: r-cran-banxicor Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rvest, r-cran-stringr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-banxicor_0.9.0-1.ca2404.1_all.deb Size: 24702 MD5sum: 801cc0759ac4bb393a5ee0307b330784 SHA1: 1339926d44dc12053eefbcba0a33737a18d1b236 SHA256: 700e67700665698bcf96ea053d54703165c52fd78f78faf8e031aa64dcbae592 SHA512: ce9830884d161eb4af6cea7d1ad7f9508399241e66a9549b1c83d2684353a54b693d90aadddec35453d432694b7344b1c270dfc84de976890ded1c55b796d036 Homepage: https://cran.r-project.org/package=banxicoR Description: CRAN Package 'banxicoR' (Download Data from the Bank of Mexico) Provides functions to scrape IQY calls to Bank of Mexico, downloading and ordering the data conveniently. Package: r-cran-baorista Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nimble, r-cran-coda Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-baorista_0.2.1-1.ca2404.1_all.deb Size: 186866 MD5sum: fb7ff09aaa99b02fc9bae0b5d6e90341 SHA1: 7101a5ebc273d5d04e443a55894ca32621007715 SHA256: f53a6eba84a17f1705d82017e0a0836bf0e4af63d6ef35e0f6faef2e11622bab SHA512: fc70624ef7f7c9b519917fb09153f8ec2d45c87959428371956e47a27bebce345df3aa6753f0388542fd64c07860b4b596374e5b50404141c371d1904c6dd3b8 Homepage: https://cran.r-project.org/package=baorista Description: CRAN Package 'baorista' (Bayesian Aoristic Analyses) Provides an alternative approach to aoristic analyses for archaeological datasets by fitting Bayesian parametric growth models and non-parametric random-walk Intrinsic Conditional Autoregressive (ICAR) models on time frequency data (Crema (2024)). 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Package: r-cran-barrel Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-ggplot2, r-cran-ggrepel, r-cran-vegan, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-barrel_0.1.0-1.ca2404.1_all.deb Size: 2351294 MD5sum: a137e9af9d6b5e16af1424964fff284b SHA1: 89f0ac6b2d13bc548c6a30ecc1893ffc727cc93c SHA256: d4553718ceab1c291928c82071abb71295f11b1baeed82534af359d6f6b648e5 SHA512: 8b8f32eed62b2980f1b01e99ee3c87b25fc63120f6fa1572fcc99c2f1240791be84b5e480c0826c2aa098c3b837f0f5315a759047dab626e780544254efc7fda Homepage: https://cran.r-project.org/package=barrel Description: CRAN Package 'barrel' (Covariance-Based Ellipses and Annotation Tools for OrdinationPlots) Provides tools to visualize ordination results in 'R' by adding covariance-based ellipses, centroids, vectors, and confidence regions to plots created with 'ggplot2'. The package extends the 'vegan' framework and supports Principal Component Analysis (PCA), Redundancy Analysis (RDA), and Non-metric Multidimensional Scaling (NMDS). Ellipses can represent either group dispersion (standard deviation, SD) or centroid precision (standard error, SE), following Wang et al. (2015) . Robust estimators of covariance are implemented, including the Minimum Covariance Determinant (MCD) method of Hubert et al. (2018) . This approach reduces the influence of outliers. barrel is particularly useful for multivariate ecological datasets, promoting reproducible, publication-quality ordination graphics with minimal effort. Package: r-cran-barrks Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-terra, r-cran-rdpack, r-cran-lubridate, r-cran-readr, r-cran-stringr Suggests: r-cran-devtools, r-cran-geosphere, r-cran-knitr, r-cran-mnormt, r-cran-ncdf4, r-cran-rlang, r-cran-rmarkdown, r-cran-suncalc, r-cran-tidyverse, r-cran-tinytest, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-barrks_1.1.2-1.ca2404.1_all.deb Size: 2038170 MD5sum: 0f8a948ad2da3ead202f1f1d247711f0 SHA1: d6d5849ccc0f6e5792a7e90104eef9d9c6aebecc SHA256: fba99154f3f362da8f0b52794348696bf1ffb8d3ad2ab9d0c5329d47964aadb8 SHA512: 79cb4e7298eddf50bd5094c20073d70f666b0adedcffed6aaeb71cbb1a50523dd1b2c47d8273f2db62d2d5e6024d59f216265d0873bbba5c02e6b0e20d7af1ff Homepage: https://cran.r-project.org/package=barrks Description: CRAN Package 'barrks' (Calculate Bark Beetle Phenology Using Different Models) Calculate the bark beetle phenology based on raster data or point-related data. There are multiple models implemented for two bark beetle species. The models can be customized and their submodels (onset of infestation, beetle development, diapause initiation, mortality) can be combined. The following models are available in the package: PHENIPS-Clim (first-time release in this package), PHENIPS (Baier et al. 2007) , RITY (Ogris et al. 2019) , CHAPY (Ogris et al. 2020) , BSO (Jakoby et al. 2019) , Lange et al. (2008) , Jönsson et al. (2011) . The package may be expanded by models for other bark beetle species in the future. Package: r-cran-barry Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-barry_0.2.1-1.ca2404.1_all.deb Size: 93434 MD5sum: a7b48d9c62a80c7b2808e3600627acca SHA1: 166cb3e339f8ef6295440ce769f621c07290f497 SHA256: 52461eafcb47cbe770ec099b9bb9782b8a6cb5dfe33bf5acec40ba0d23f26faf SHA512: 462189bcfb2a86c5c0d310f8b7f589e367b8db03f0585300c3e2eeefd543d08c123787acb306026f3f377e0fb091ca0825084d4109f2f3228be7b6924c4694e7 Homepage: https://cran.r-project.org/package=barry Description: CRAN Package 'barry' (Your Go-to Motif Accountant) Provides the 'C++' header-only library 'barry' for use in R packages. 'barry' is a 'C++' template library for counting sufficient statistics on binary arrays and building discrete exponential-family models. It provides tools for sparse arrays, user-defined count statistics, support set constraints, power set generation, and includes modules for Discrete Exponential Family Models (DEFMs) and network statistics. By placing these headers in this package, we offer an efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided. This package follows the same approach as the 'BH' package which provides 'Boost' headers for R packages. Package: r-cran-bartcause Architecture: all Version: 1.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbarts Suggests: r-cran-testthat, r-cran-lme4, r-cran-rpart, r-cran-tmle, r-cran-stan4bart Filename: pool/dists/noble/main/r-cran-bartcause_1.0-10-1.ca2404.1_all.deb Size: 273408 MD5sum: d3501ddcad19f0c31f7c7ffe0e6067a3 SHA1: c6a551f5fb5ceb077cd7d48ad42f65e1e06ecb22 SHA256: 7d28830dd6f8742d243cc66ba9c06588d17bc31f98ffd9a018691522e89d2117 SHA512: a462c3002d1f581f87e6408747f72225f7a685602614d3f1f715d5c70d20c71dbf92a2eb27f449139318e5a718788c4b31042ae17068274921ccc682eddd7362 Homepage: https://cran.r-project.org/package=bartCause Description: CRAN Package 'bartCause' (Causal Inference using Bayesian Additive Regression Trees) Contains a variety of methods to generate typical causal inference estimates using Bayesian Additive Regression Trees (BART) as the underlying regression model (Hill (2012) ). Package: r-cran-bartmachine Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1549 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bartmachinejars, r-cran-rjava, r-cran-randomforest, r-cran-missforest, r-cran-ggplot2, r-cran-checkmate, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-bartmachine_1.4.2-1.ca2404.1_all.deb Size: 1366312 MD5sum: b798a78b73a9b5aafe035ec9a987efab SHA1: f042ce9ed687c279fe1e45b39528d5b7b37e65ee SHA256: 9e880afaa1240be4092ed5bea3cff92f81d5db38e310a52129093a201bc2409f SHA512: d10efcae1500365ac017edfa81c86760ee7c90c3e005d76d06b028a4985f616be47a28bd525f5d0e550c73132cc0a9af390b0ed7c08e3f6db7b39cd0c9418df1 Homepage: https://cran.r-project.org/package=bartMachine Description: CRAN Package 'bartMachine' (Bayesian Additive Regression Trees) An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization. Package: r-cran-bartmachinejars Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9804 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava Filename: pool/dists/noble/main/r-cran-bartmachinejars_1.2.2-1.ca2404.1_all.deb Size: 8725940 MD5sum: 1f83bd2fd0779e0473ef66056e040a36 SHA1: f9b6c615fc6c1474eca2acdd30b65311217d913a SHA256: 66835b6eb9e509b037a03712184200b82bce5a3896a2aac052ecfa73ca6f9763 SHA512: 2ad8966b9dee86585848d2b1f98d3689ec5e7f98ef6873e57c7b09a4124a678223aa3f5748c90a9d6347b4da6f77e896d5859f9fd7f78938085ba24736553d6f Homepage: https://cran.r-project.org/package=bartMachineJARs Description: CRAN Package 'bartMachineJARs' (bartMachine JARs) These are bartMachine's Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without bartMachine. Package: r-cran-bartman Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorspace, r-cran-cowplot, r-cran-dendser, r-cran-dplyr, r-cran-ggiraph, r-cran-ggnewscale, r-cran-ggplot2, r-cran-ggraph, r-cran-gtable, r-cran-patchwork, r-cran-purrr, r-cran-rlang, r-cran-rrapply, r-cran-scales, r-cran-tidygraph, r-cran-tidyr, r-cran-bart, r-cran-bartmachine, r-cran-dbarts, r-cran-rjava Suggests: r-cran-rocr, r-cran-ggridges, r-cran-ggforce, r-cran-lvplot Filename: pool/dists/noble/main/r-cran-bartman_0.2.1-1.ca2404.1_all.deb Size: 410786 MD5sum: 123c3d87f61adaf6385ffa8d852b8215 SHA1: 1d6018d8e1afee059dfc0d56be27b258ccfb0b2a SHA256: 6191d0b0204d2590a31bc6d547077f9a095510b2f8ab0bf497d9e3920fe4d1ce SHA512: 494978167103b117f58eb17e3903aebe255fdb8a2346ba65677ed8797eaff68957c6f626d020e40ba4e8fed96ea41b8e89a4a7f023f469fa589366f1c296cb60 Homepage: https://cran.r-project.org/package=bartMan Description: CRAN Package 'bartMan' (Create Visualisations for BART Models) Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) model fits. We construct conventional plots to analyze a model’s performance and stability as well as create new tree-based plots to analyze variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our visualisations are designed to work with the most popular BART R packages available, namely 'BART' Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 , 'dbarts' (Vincent Dorie 2023) , and 'bartMachine' (Adam Kapelner and Justin Bleich 2016) . Package: r-cran-barulho Architecture: all Version: 2.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7786 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seewave, r-cran-tuner, r-cran-fftw, r-cran-viridis, r-cran-sim.diffproc, r-cran-png, r-cran-checkmate, r-cran-cli, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-ggplot2, r-cran-knitr, r-cran-kableextra, r-cran-testthat, r-cran-covr, r-cran-formatr, r-cran-rraven, r-cran-monitor Filename: pool/dists/noble/main/r-cran-barulho_2.1.6-1.ca2404.1_all.deb Size: 3971408 MD5sum: 6b6a18d2e3e178db5bb9653e417a0322 SHA1: cebf7e205fac0d08704026f05e6a2f293a7652f4 SHA256: 73d68733e82185455f6924dce193534d96c0360bbfec6e221083b24a6a8fc7b6 SHA512: d3443c10ea2d8f10febc94ad447c810b916acab5398bc3ce7f5e2ebe07968af4fc8812fb3d3c315afe7e5b39d8645a45f1f452ba51afd9d582fdb9dddf8498a8 Homepage: https://cran.r-project.org/package=baRulho Description: CRAN Package 'baRulho' (Quantifying (Animal) Sound Degradation) Intended to facilitate acoustic analysis of (animal) sound propagation experiments, which typically aim to quantify changes in signal structure when transmitted in a given habitat by broadcasting and re-recording animal sounds at increasing distances. The package offers a workflow with functions to prepare the data set for analysis as well as to calculate and visualize several degradation metrics, including blur ratio, signal-to-noise ratio, excess attenuation and envelope correlation among others (Dabelsteen et al 1993 ). 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Likewise, we can use ols(), lrm() and cph() from the 'rms' package for the same functionality. Each of these two sets of commands has a different focus. In many cases, we need to use both sets of commands in the same situation, e.g. we need to filter the full subset model using AIC, and we need to build a visualization graph for the final model. 'base.rms' package can help you to switch between the two sets of commands easily. 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Manipulating imputed datasets and fitting models on them. Summarizing models. 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Authenticate your project, query our tables, save data to disk and memory, all from R. 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Typical use of the package is for removing background effects from spectra originating from various types of spectroscopy and spectrometry, possibly optimizing this with regard to regression or classification results. Correction methods include polynomial fitting, weighted local smoothers and many more. Package: r-cran-baselinenowcast Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4334 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-checkmate, r-cran-rlang, r-cran-purrr Suggests: r-cran-bookdown, r-cran-chainladder, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-lubridate, r-cran-readr, r-cran-ggplot2, r-cran-spelling, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis, r-cran-withr, r-cran-knitr, r-cran-zoo, r-cran-glue Filename: pool/dists/noble/main/r-cran-baselinenowcast_0.2.0-1.ca2404.1_all.deb Size: 2410438 MD5sum: 5c1d8e703fa86efc47641a211d5db56e SHA1: d01c39e4356c02d29ec72cbe97e03a3651869ff0 SHA256: 7a59007d1f1384eda8ade689cdfcc6845c3ed29e41a6c0442ec34b3c66eb5edb SHA512: a61447bc97a4a13954a80f92c29d34c047d541158ced1ad36b2668b22cdb1c8586ba2ab561532198b2ace12d2009d2f6e802d2daf085877942e1f6e958e05208 Homepage: https://cran.r-project.org/package=baselinenowcast Description: CRAN Package 'baselinenowcast' (Baseline Nowcasting for Right-Truncated Epidemiological Data) Nowcasting right-truncated epidemiological data is critical for timely public health decision-making, as reporting delays can create misleading impressions of declining trends in recent data. This package provides nowcasting methods based on using empirical delay distributions and uncertainty from past performance. It is also designed to be used as a baseline method for developers of new nowcasting methods. For more details on the performance of the method(s) in this package applied to case studies of COVID-19 and norovirus, see our recent paper at . The package supports standard data frame inputs with reference date, report date, and count columns, as well as the direct use of reporting triangles, and is compatible with 'epinowcast' objects. Alongside an opinionated default workflow, it has a low-level pipe-friendly modular interface, allowing context-specific workflows. It can accommodate a wide spectrum of reporting schedules, including mixed patterns of reference and reporting (daily-weekly, weekly-daily). It also supports sharing delay distributions and uncertainty estimates between strata, as well as custom uncertainty models and delay estimation methods. 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Package: r-cran-basemodels Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-caret, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-basemodels_1.1.0-1.ca2404.1_all.deb Size: 44382 MD5sum: ae813866925b25a548472ad571f0bddc SHA1: 60fad1f283a9b6913dd1c740fe6f8c2ec7b52a32 SHA256: 945dbbef289342a9359c8e2ad5fcfc153b6441127cf424f8c507af09f2c0e6af SHA512: 85be8766a3c4043b8e38173a3e93c745fecc9ad0947d304ac5d84fc3f375a72eac579f1247b816f52f039c958fb7a82ddca33db6af858482eed91269ffec568c Homepage: https://cran.r-project.org/package=basemodels Description: CRAN Package 'basemodels' (Baseline Models for Classification and Regression) Providing equivalent functions for the dummy classifier and regressor used in 'Python' 'scikit-learn' library. Our goal is to allow R users to easily identify baseline performance for their classification and regression problems. Our baseline models use no predictors, and are useful in cases of class imbalance, multiclass classification, and when users want to quickly identify how much improvement their statistical and machine learning models are over several baseline models. We use a "better" default (proportional guessing) for the dummy classifier than the 'Python' implementation ("prior", which is the most frequent class in the training set). The functions in the package can be used on their own, or introduce methods named 'dummy_regressor' or 'dummy_classifier' that can be used within the caret package pipeline. Package: r-cran-basepenguins Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-basepenguins_0.1.0-1.ca2404.1_all.deb Size: 52100 MD5sum: d266e61f67c217b35cbd0bf27e6651f1 SHA1: 8d0a2fe963759421df1186ecd46fa81a1dac208c SHA256: 2cda2eaef8a6158f1c5572c359b0b985576724f7589b14621d84cd4692148d4e SHA512: fb42d01633de1156e1ae2cfbfcfcae9074a87c476f4d9e95d801bca717566c51a1ee8262a0668b21a6158a04ea5f4a46fe47a5e5ec5d9e629efe469982840967 Homepage: https://cran.r-project.org/package=basepenguins Description: CRAN Package 'basepenguins' (Convert Files that Use 'palmerpenguins' to Work with 'datasets') From 'R' 4.5.0, the 'datasets' package includes the penguins and penguins_raw data sets popularised in the 'palmerpenguins' package. 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Seeds germinate by following accumulation of thermal time in degree days/hours, quantified by multiplying the time of germination with excess of base temperature required by each seed for its germination, which follows log-normal distribution. The theoretical germination course can be obtained by regressing the rate of germination at various fractions against temperature (Garcia et al., 1982), where the fraction-wise regression lines intersect the temperature axis at base temperature and the methodology of determining optimum base temperature has been described by Ellis et al. (1987). This package helps to find the base temperature of seed germination using algorithms of Garcia et al. (1982) and Ellis et al. (1982) . 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Package: r-cran-basinet Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 449 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-bioc-biostrings, r-cran-rweka, r-cran-randomforest, r-cran-rmcfs, r-cran-rjava Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-basinet_0.0.5-1.ca2404.1_all.deb Size: 263932 MD5sum: 22391006592f937ec2c7bf69f5d1d031 SHA1: 6d8f93b5cc9e8e40430c4f7de0feb98904d41cfd SHA256: dbd9303923fee282292585f2d74088c03e2ae2f66340620aa9f2fad525a9e855 SHA512: 2ebae75b291e9fd1db0c64942e0851a10b9633a343b95af21f9efafcf0e630cf1d88e01e8f0548fbc60a7b68041ef74f93fc1099e2d2ced7f340ad5f87373cc2 Homepage: https://cran.r-project.org/package=BASiNET Description: CRAN Package 'BASiNET' (Classification of RNA Sequences using Complex Network Theory) It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of threshold for the purpose of making a classification between the classes of the sequences. There are four data present in the 'BASiNET' package, "sequences", "sequences2", "sequences-predict" and "sequences2-predict" with 11, 10, 11 and 11 sequences respectively. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) , these sequences are used to run examples. The BASiNET was published on Nucleic Acids Research, (ITO, Eric; KATAHIRA, Isaque; VICENTE, Fábio; PEREIRA, Felipe; LOPES, Fabrício, 2018) . Package: r-cran-basinetentropy Architecture: all Version: 0.99.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-bioc-biostrings, r-cran-randomforest Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-basinetentropy_0.99.6-1.ca2404.1_all.deb Size: 152836 MD5sum: d4d6f890802fbb590b50cb5d2aee54bc SHA1: 6c06d52a90de40517825f87c2394ade24d461f99 SHA256: c53a9eacd207be061cd81191b9207f8b3044e1644806064d7c01eb69232b13a0 SHA512: 65f5743296f7eeaa80c8b77638e1da59eae6fecb91b2b6054f92dfd2680444d216f5f376c5b3ea5727439b1bc09a5126fcafeb4e98f248b2dfb237dbf32f8abd Homepage: https://cran.r-project.org/package=BASiNETEntropy Description: CRAN Package 'BASiNETEntropy' (Classification of RNA Sequences using Complex Network andInformation Theory) It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of maximum entropy for the purpose of making a classification between the classes of the sequences. There are two data present in the 'BASiNET' package, "mRNA", and "ncRNA" with 10 sequences. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) , these sequences are used to run examples. Package: r-cran-baskepro Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-baskepro_1.1.1-1.ca2404.1_all.deb Size: 22016 MD5sum: bbe1168d3c01474b3f1913ae90a2b09a SHA1: cbb0a0d63c00e8d411054ebde0e22b14fc94fe5c SHA256: bb096ae19afe7d1b6f43549f2681fdb483f058ab11c0c27bc8b07ffa69cc5b85 SHA512: 06c3e6b38858bffccb77b9064a4097d1f09f8fcb9740d89c73b4149fb2182b653d7884178252f0eabe29b589ec55c6e64114a299dc232395ece2942a9c03c834 Homepage: https://cran.r-project.org/package=BaSkePro Description: CRAN Package 'BaSkePro' (Bayesian Model to Archaeological Faunal Skeletal Profiles) Tool to perform Bayesian inference of carcass processing/transport strategy and bone attrition from archaeofaunal skeletal profiles characterized by percentages of MAU (Minimum Anatomical Units). The approach is based on a generative model for skeletal profiles that replicates the two phases of formation of any faunal assemblage: initial accumulation as a function of human transport strategies and subsequent attrition.Two parameters define this model: 1) the transport preference (alpha), which can take any value between - 1 (mostly axial contribution) and 1 (mostly appendicular contribution) following strategies constructed as a function of butchering efficiency of different anatomical elements and the results of ethnographic studies, and 2) degree of attrition (beta), which can vary between 0 (no attrition) and 10 (maximum attrition) and relates the survivorship of bone elements to their maximum bone density. Starting from uniform prior probability distribution functions of alpha and beta, a Monte Carlo Markov Chain sampling based on a random walk Metropolis-Hasting algorithm is adopted to derive the posterior probability distribution functions, which are then available for interpretation. During this process, the likelihood of obtaining the observed percentages of MAU given a pair of parameter values is estimated by the inverse of the Chi2 statistic, multiplied by the proportion of elements within a 1 percent of the observed value. See Ana B. Marin-Arroyo, David Ocio (2018).. Package: r-cran-basket Architecture: all Version: 0.10.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1237 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gensa, r-cran-foreach, r-cran-ggplot2, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-igraph, r-cran-gridextra, r-cran-itertools, r-cran-crayon, r-cran-cli, r-cran-rcolorbrewer, r-cran-tidygraph, r-cran-ggraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-basket_0.10.11-1.ca2404.1_all.deb Size: 577698 MD5sum: 90b93301453b51ef7b2c3006f8972a0a SHA1: eda731441df47c4776ed1c511aa7a4694f0c144f SHA256: af3eb4404ce361a9ce3611b716d3fec145b8f9b82778fe0ce8f613f73218caf6 SHA512: 08b52dcf9555539d32a17244331d856b19c58d7f63df8f070a972c04c53a34a1bfc3b0ef2457084284ddd0d0caf6b1ba8ce9f2bb1b7c5980fb7c58aa09280cd6 Homepage: https://cran.r-project.org/package=basket Description: CRAN Package 'basket' (Basket Trial Analysis) Implementation of multisource exchangeability models for Bayesian analyses of prespecified subgroups arising in the context of basket trial design and monitoring. The R 'basket' package facilitates implementation of the binary, symmetric multi-source exchangeability model (MEM) with posterior inference arising from both exact computation and Markov chain Monte Carlo sampling. Analysis output includes full posterior samples as well as posterior probabilities, highest posterior density (HPD) interval boundaries, effective sample sizes (ESS), mean and median estimations, posterior exchangeability probability matrices, and maximum a posteriori MEMs. In addition to providing "basketwise" analyses, the package includes similar calculations for "clusterwise" analyses for which subgroups are combined into meta-baskets, or clusters, using graphical clustering algorithms that treat the posterior exchangeability probabilities as edge weights. In addition plotting tools are provided to visualize basket and cluster densities as well as their exchangeability. References include Hyman, D.M., Puzanov, I., Subbiah, V., Faris, J.E., Chau, I., Blay, J.Y., Wolf, J., Raje, N.S., Diamond, E.L., Hollebecque, A. and Gervais, R (2015) ; Hobbs, B.P. and Landin, R. (2018) ; Hobbs, B.P., Kane, M.J., Hong, D.S. and Landin, R. (2018) ; and Kaizer, A.M., Koopmeiners, J.S. and Hobbs, B.P. (2017) . Package: r-cran-basketballanalyzer Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2838 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-magrittr, r-cran-ggrepel, r-cran-gridextra, r-cran-mass, r-cran-directlabels, r-cran-corrplot, r-cran-pbsmapping, r-cran-sp, r-cran-operators, r-cran-stringr, r-cran-ggally, r-cran-statnet.common, r-cran-readr, r-cran-gtools, r-cran-data.table, r-cran-mathjaxr Suggests: r-cran-dendextend, r-cran-ggnetwork, r-cran-ggplotify, r-cran-network Filename: pool/dists/noble/main/r-cran-basketballanalyzer_0.8.1-1.ca2404.1_all.deb Size: 2797292 MD5sum: acc0c7bf75502b4f6f4baab4fbf42a38 SHA1: 32f6a44f2c037ae5887d7b4f2c629fc7441947f8 SHA256: 342b19c1c63867e114123adf613e69b3c2f5c6ed26e05ecc6f393061cab3e8c7 SHA512: 2d8c6f377f5557c5f7fc4a474fc0fa9ebf132dc9e9bac8779761e6660d8c4512be2ff3583d27d53b22c9ff9eb31558e8f2051b197528c2e861ad718667e79da0 Homepage: https://cran.r-project.org/package=BasketballAnalyzeR Description: CRAN Package 'BasketballAnalyzeR' (Analysis and Visualization of Basketball Data) Contains data and code to accompany the book P. Zuccolotto and M. Manisera (2020) Basketball Data Science. Applications with R. CRC Press. ISBN 9781138600799. Package: r-cran-baskettrial Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-baskettrial_0.1.0-1.ca2404.1_all.deb Size: 46618 MD5sum: 1f42d5720a71371a5b3c6bd655d10c1a SHA1: c3117829825ac0262a40bd470c6eb5d853ec6600 SHA256: be4a34ce58a886ff190ded9fb4d3537d9ffd717a1f86a512f410b3e1daaf972f SHA512: d50d68f7ca1723c730819129490baa820f0c69c265101d3c34413677dd2fdba89c2182c2efc2639d673967017ff57270b18791176eb8b85836aeb54f33776bfb Homepage: https://cran.r-project.org/package=BasketTrial Description: CRAN Package 'BasketTrial' (Bayesian Basket Trial Design and Analysis) Provides tools for Bayesian basket trial design and analysis using a novel three-component local power prior framework with global borrowing control, pairwise similarity assessment and a borrowing threshold. Supports simulation-based evaluation of operating characteristics and comparison with other methods. Applicable to both equal and unequal sample size settings in early-phase oncology trials. For more details see Zhou et al. (2023) . Package: r-cran-baskoptr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-baskwrap, r-cran-future.apply Suggests: r-cran-optimizr, r-cran-baskexact, r-cran-testthat, r-cran-progressr, r-cran-basksim Filename: pool/dists/noble/main/r-cran-baskoptr_1.0.4-1.ca2404.1_all.deb Size: 179134 MD5sum: 808ecbac8cb4280dd7344dfedf879dd7 SHA1: 3036a821bf2777e4f6f318d32336331d4f2c6f86 SHA256: 308cfe08869e503146409b79d9e58f1e42c5a54674ad3954b2c0fcd3a84acc34 SHA512: 9e56c338d73bc1acdc999a4c089c3baa5d9fdf0942392736d046871a00a4fc7541fbbf5ffc26bd430216afc8fa9dcf52f9b7ead003f543f216b8e7c47001c6f9 Homepage: https://cran.r-project.org/package=baskoptr Description: CRAN Package 'baskoptr' (Utility-Based Optimization for Basket Trial Designs) A unified framework for optimizing basket trial designs. To this end, the package supplies several utility functions and also a function for executing optimization algorithms on basket trial designs. The considered utility functions are discussed in Sauer et al. (2025) . Package: r-cran-basksim Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrangements, r-cran-bhmbasket, r-cran-dofuture, r-cran-extradistr, r-cran-foreach, r-cran-hdinterval, r-cran-progressr, r-cran-purrr Suggests: r-cran-covr, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-basksim_2.2.0-1.ca2404.1_all.deb Size: 281016 MD5sum: 332ff039fe6f7f4726fc6cbf3b3485db SHA1: 53350a2fa9472e4fd7b110c9676c4e5c416a4dc2 SHA256: 319eb9b9ce0b3b8a92f673a6b9af342976f2e023f3343119a155006a1267d91c SHA512: 71c66344f5dbfd43ccbaac19d50d4e4259618b35d016c08a6b17c3207bf0b256d6bf4ce777c44d287127273f4de8526116cf21697c25641f4f58caa6f0dda913 Homepage: https://cran.r-project.org/package=basksim Description: CRAN Package 'basksim' (Simulation-Based Calculation of Basket Trial OperatingCharacteristics) Provides a unified syntax for the simulation-based comparison of different single-stage basket trial designs with a binary endpoint and equal sample sizes in all baskets. Methods include the designs by Baumann et al. (2025) , Schmitt and Baumann (2025) , Fujikawa et al. (2020) , Berry et al. (2020) , and Neuenschwander et al. (2016) . For the latter two designs, the functions are mostly wrappers for functions provided by the package 'bhmbasket'. Package: r-cran-baskwrap Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-basksim, r-cran-baskexact Suggests: r-cran-testthat, r-cran-here, r-cran-reticulate, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-baskwrap_1.0.3-1.ca2404.1_all.deb Size: 241670 MD5sum: 72e9b6f55f31c20a37de1bc80a17ec2a SHA1: 6436bcf165216ad7e73c87926dd9e56908daf888 SHA256: d9f058b4d916a821aa008df3755dbc2b179e8be0a237bb1a23cacd1fbde942b2 SHA512: 15efa511d223596b00e5ba67698c38619533dbce76a73b58acfe48f2f1ad519fb526d2814b10e97de59da77d81bf0dfd2dba90654e1fd184d84681aad075b536 Homepage: https://cran.r-project.org/package=baskwrap Description: CRAN Package 'baskwrap' (Wrapper Package for Several Basket Trial R Packages) A simple interface to switch between two methods for calculating basket trial characteristics, numerical integration ("exact") and Monte Carlo simulation ("simulated") for the basket trial design by Fujikawa et al. 2020 . The exact implementation is from the 'baskexact' package, see Baumann (2024) . The simulated implementation is from the 'basksim' package, which was developed for Baumann et al. (2024) . The package's syntax is compatible with the 'basksim' syntax and easily extendable. Package: r-cran-bass Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1781 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-truncdist, r-cran-hypergeo Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bass_1.3.1-1.ca2404.1_all.deb Size: 1734816 MD5sum: c794eec6205fcf138e9caf570dda486a SHA1: a3e07f30a551b86d2974e8c9e8ba53fbb30808ad SHA256: d128fb301e833c6163964a6330170ec6cd82200f973451e610c9877e0f28e85a SHA512: e59c3ef4e2b30bcb67022a9a17562a47464ba0ad98b40e63539e5babbdc574b894a2496da6ab15ab7ae690a5edb773e3ca5fad733571d336dce00ca271e415c1 Homepage: https://cran.r-project.org/package=BASS Description: CRAN Package 'BASS' (Bayesian Adaptive Spline Surfaces) Bayesian fitting and sensitivity analysis methods for adaptive spline surfaces described in . Built to handle continuous and categorical inputs as well as functional or scalar output. An extension of the methodology in Denison, Mallick and Smith (1998) . Package: r-cran-basta Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-snowfall Filename: pool/dists/noble/main/r-cran-basta_2.0.2-1.ca2404.1_all.deb Size: 2127456 MD5sum: 9d3af9628701a4fabd612bf8f1b6469f SHA1: 0043706c909bac303a5077cc1ae4288b53b876c5 SHA256: 01afefea94fc925e62c360961e70cdc8caa2f870648c6a892342482dbc7eeac0 SHA512: 94fc31814b3ca65c260cef156eb4cfd84ae752c304f2c6c7f7df2130766614be658bb5a2b9679ae92c09477716f009c1a82c230f284c3492a668e89d8f19c6fc Homepage: https://cran.r-project.org/package=BaSTA Description: CRAN Package 'BaSTA' (Age-Specific Bayesian Survival Trajectory Analysis fromIncomplete Census or Capture-Recapture/Recovery Data) Estimates survival and mortality with covariates from census or capture-recapture/recovery data in a Bayesian framework when many individuals are of unknown age. It includes tools for data checking, model diagnostics and outputs such as life-tables and plots, as described in Colchero, Jones, and Rebke (2012) and Colchero et al. (2021) . Package: r-cran-bat Architecture: all Version: 2.11.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-boot, r-cran-geometry, r-cran-hypervolume, r-cran-mass, r-cran-nls2, r-cran-phytools, r-cran-terra, r-cran-treetools, r-cran-vegan Filename: pool/dists/noble/main/r-cran-bat_2.11.1-1.ca2404.1_all.deb Size: 485986 MD5sum: c94b2eecff640d908eae9f917632000c SHA1: bd9e658e7226c2239774c10c44ed424d67dc89d5 SHA256: cf520d603996413532e2eb98024fa82e147d7b1f24e9eb4633454065de4eb436 SHA512: 4f921efebfd70e216c9b9a4e030ae6fb4599f0e974d6d91435d3e6ebb9f7077fa79a18603839cb1f64bd45409f4b02513105f3bf6c1e744fc6015cb763c4a089 Homepage: https://cran.r-project.org/package=BAT Description: CRAN Package 'BAT' (Biodiversity Assessment Tools) Includes algorithms to assess alpha and beta diversity in all their dimensions (taxonomic, phylogenetic and functional). It allows performing a number of analyses based on species identities/abundances, phylogenetic/functional distances, trees, convex-hulls or kernel density n-dimensional hypervolumes depicting species relationships. Cardoso et al. (2015) . Package: r-cran-batata Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fs, r-cran-glue, r-cran-lubridate, r-cran-jsonlite, r-cran-remotes, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-batata_0.2.1-1.ca2404.1_all.deb Size: 236426 MD5sum: fe2f4c0664d16289bca8750247eb61b5 SHA1: 6011bcf4c4c5d096c74f5d3f72d89f0ffbebb5c5 SHA256: ac6f427ef04507f44b4b202f560e2d5eef9b40b4b83dd6f046cc099923575246 SHA512: e58bc4337a858c903f182cc08cde2fc180929527001134a585d672d3e857829ddea69cb9932cb3a13996fa1fed9317bcbac68829aa2ba4e85777dacde861bd26 Homepage: https://cran.r-project.org/package=batata Description: CRAN Package 'batata' (Managing Packages Removal and Installation) Allows the user to manage easily R packages removal and installation. It offers many functions to display installed packages according to specific dates and removes them if needed. The user is always prompted when running the removal functions in order to confirm the required action. It also provides functions that will install 'Github' starred R packages whether available on 'CRAN' or not. Package: r-cran-batch Architecture: all Version: 1.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-batch_1.1-5-1.ca2404.1_all.deb Size: 42002 MD5sum: eda84a72ffbd9ab01b26d63b113606d5 SHA1: c9634d5fadfe967da42a6e835c4c5a3e2b64dcd2 SHA256: 59a257fbe72f8e90ee6c0cec05f092099114d59bcff2a4fab3f79ecf696e845c SHA512: 52a94584eb77b96171e42e2b6d7d2bfaee927ea4dc5a5938ea2be9f386c884dc9c8cac3dc4e70bceb0e9cb2834468587319d00dbc8378e4b6ce0fc7e50e1a554 Homepage: https://cran.r-project.org/package=batch Description: CRAN Package 'batch' (Batching Routines in Parallel and Passing Command-Line Argumentsto R) Functions to allow you to easily pass command-line arguments into R, and functions to aid in submitting your R code in parallel on a cluster and joining the results afterward (e.g. multiple parameter values for simulations running in parallel, splitting up a permutation test in parallel, etc.). See `parseCommandArgs(...)' for the main example of how to use this package. 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For details about the Bayesian ANOVA based on Gaussian mixtures, see Kelter (2019) . Package: r-cran-bayesarimax Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-forecast Filename: pool/dists/noble/main/r-cran-bayesarimax_0.1.1-1.ca2404.1_all.deb Size: 16210 MD5sum: 2b128de0cf5217191f0a6cd0c72baf62 SHA1: abcac6ac049dab0694620e94dc30e3e4a54397fa SHA256: a92b34dd4bac622e27c618e1018881ee130b70fda08757709ba7c1097177fd1d SHA512: 2006f2fa74121986260d24c269a02f31d68be78138de0d0a08b4b866eac492b4489956e322d73b0ce874dd6c4026fb2a0abbcb064ef9f1c824aaad816153100e Homepage: https://cran.r-project.org/package=BayesARIMAX Description: CRAN Package 'BayesARIMAX' (Bayesian Estimation of ARIMAX Model) The Autoregressive Integrated Moving Average (ARIMA) model is very popular univariate time series model. 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Package: r-cran-bayesbrainmap Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-fmritools, r-cran-fmriscrub, r-cran-foreach, r-cran-matrix, r-cran-matrixstats, r-cran-pesel, r-cran-squarem Suggests: r-cran-ciftitools, r-cran-excursions, r-cran-rnifti, r-cran-oro.nifti, r-cran-gifti, r-cran-ggplot2, r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesbrainmap_0.2.0-1.ca2404.1_all.deb Size: 503828 MD5sum: be76a724ab91bddf152ae60d660fc194 SHA1: 5c254aa27a9a8b88baf80db7c46e7865b1225940 SHA256: 75fdae1a4b385c7f656a89af6495b6de5c382b30128033f1b32ee6ebb2ba95fb SHA512: d69bc943e29eca3ed469b3410e4c5d5be43a068d9c10ba4d90f98160f6e7c23f5f23fb641b182554ef380180039aad27c23ff8268d1e66c2ac01f32606ba6fac Homepage: https://cran.r-project.org/package=BayesBrainMap Description: CRAN Package 'BayesBrainMap' (Estimate Brain Networks and Connectivity with Population-DerivedPriors) Implements Bayesian brain mapping with population-derived priors, including the original model described in Mejia et al. (2020) , the model with spatial priors described in Mejia et al. (2022) , and the model with population-derived priors on functional connectivity described in Mejia et al. (2025) . Population-derived priors are based on templates representing established brain network maps, for example derived from independent component analysis (ICA), parcellations, or other methods.  Model estimation is based on expectation-maximization or variational Bayes algorithms. Includes direct support for 'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats. Package: r-cran-bayescace Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1888 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-rdpack, r-cran-forestplot, r-cran-metafor, r-cran-lme4 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-bayescace_1.2.3-1.ca2404.1_all.deb Size: 1817984 MD5sum: c0f2cdfd31f62b7d11d494a01a4ac0da SHA1: 3b2889a6cd79d73ca21f9a7f9dd89264b48b1245 SHA256: 9d052d5fba6693ebf0723908aa8a5e7cfad82667dc9cabb35035f2fd08a32dcd SHA512: d79c0cabed630ec0002bec0380c15a1ea165f76b80237688022bab3a849d31a1ca721be34c48005d406954f5041ffb6c81ccf6e7058166bed70ad58d22737637 Homepage: https://cran.r-project.org/package=BayesCACE Description: CRAN Package 'BayesCACE' (Bayesian Model for CACE Analysis) Performs CACE (Complier Average Causal Effect analysis) on either a single study or meta-analysis of datasets with binary outcomes, using either complete or incomplete noncompliance information. Our package implements the Bayesian methods proposed in Zhou et al. (2019) , which introduces a Bayesian hierarchical model for estimating CACE in meta-analysis of clinical trials with noncompliance, and Zhou et al. (2021) , with an application example on Epidural Analgesia. 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Package: r-cran-bayescpclust Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-extradistr, r-cran-rcppalgos Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayescpclust_0.1.0-1.ca2404.1_all.deb Size: 97466 MD5sum: 2ad5e41dcb1136fce854d1f9de174ee9 SHA1: e5e06ea498039af764af74ddf38d8586ec94fe41 SHA256: 86ce5e6c62eca976b1fccfda7508d781e979a8c4683b5f77859a934403069d79 SHA512: 118ad7cccdd3559598ff5c7951a1220d09ae53f18fa6b2d77561e3333410877d15b4a45eddc18cfd93be3d3d20c415825c8fab0d598cfaf32a87998ff72d25c8 Homepage: https://cran.r-project.org/package=BayesCPclust Description: CRAN Package 'BayesCPclust' (A Bayesian Approach for Clustering Constant-Wise Change-PointData) A Gibbs sampler algorithm was developed to estimate change points in constant-wise data sequences while performing clustering simultaneously. The algorithm is described in da Cruz, A. C. and de Souza, C. P. E "A Bayesian Approach for Clustering Constant-wise Change-point Data" . Package: r-cran-bayescr Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rootsolve, r-cran-truncdist, r-cran-mvtnorm, r-cran-mnormt Filename: pool/dists/noble/main/r-cran-bayescr_2.1-1.ca2404.1_all.deb Size: 97604 MD5sum: cf0b0580fdd48bb8cc9d10beff86c122 SHA1: 197df41bb97f9dd81adfdb4264b318586916be4d SHA256: 9a8d05f86cb655b0dccb1ecb1095b5c5cf5034c56fdf23a159d43612be8bcfd0 SHA512: 503c56331b72c80c3c32ae93713c7a6f60a4dbe558db0bf975b8b21e1d2f8da8800c0fa1373dc54cf8f8a35bd04037e66235a9173c3e1ab909b32f4edc2d38f5 Homepage: https://cran.r-project.org/package=BayesCR Description: CRAN Package 'BayesCR' (Bayesian Analysis of Censored Regression Models Under ScaleMixture of Skew Normal Distributions) Propose a parametric fit for censored linear regression models based on SMSN distributions, from a Bayesian perspective. Also, generates SMSN random variables. Package: r-cran-bayesct Architecture: all Version: 0.99.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesdp, r-cran-dplyr, r-cran-purrr, r-cran-survival, r-cran-magrittr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-pkgdown, r-cran-devtools, r-cran-knitr Filename: pool/dists/noble/main/r-cran-bayesct_0.99.3-1.ca2404.1_all.deb Size: 270108 MD5sum: 6710ddc9341bc826bfaa60bc0c792b56 SHA1: 301b3e45402cc0a8918d419e7e622f76cde1d427 SHA256: 31d234691cc668555666dade76bb819e6a3fc805d8c566a61c7c804c326a61c6 SHA512: 8545e70d9c67c021c0991d9b721c8b021c87be7debda59a0c289b9e4fe77fc1c1538760a403a7fa7252061921f573e1bbeb49bc84442277da0bd465466641b33 Homepage: https://cran.r-project.org/package=bayesCT Description: CRAN Package 'bayesCT' (Simulation and Analysis of Adaptive Bayesian Clinical Trials) Simulation and analysis of Bayesian adaptive clinical trials for binomial, Gaussian, and time-to-event data types, incorporates historical data and allows early stopping for futility or early success. The package uses novel and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up, and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data into the analysis via the power prior or non-informative priors. Package: r-cran-bayesctdesign Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-eha, r-cran-ggplot2, r-cran-survival, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-bayesctdesign_0.6.1-1.ca2404.1_all.deb Size: 313450 MD5sum: 7636340da2ca071bb4d018d4e682d15c SHA1: 13c9007b075a0116bad2a310a25bd2615f19536d SHA256: e12e7c145da6fda95475acb35f094fa6c0219f11c49c36fbbe75dedc1c6617c3 SHA512: 18f0c692c8310a78e7e627e88771329dcc0f7475e8d233a248cff35756729f4509a6eddbd8b59d061e6886cc5817de214d1e15b9cd310793c698906f4013f2da Homepage: https://cran.r-project.org/package=BayesCTDesign Description: CRAN Package 'BayesCTDesign' (Two Arm Bayesian Clinical Trial Design with and WithoutHistorical Control Data) A set of functions to help clinical trial researchers calculate power and sample size for two-arm Bayesian randomized clinical trials that do or do not incorporate historical control data. At some point during the design process, a clinical trial researcher who is designing a basic two-arm Bayesian randomized clinical trial needs to make decisions about power and sample size within the context of hypothesized treatment effects. Through simulation, the simple_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about treatment effect,control group characteristics, and outcome. If the clinical trial researcher has access to historical control data, then the researcher can design a two-arm Bayesian randomized clinical trial that incorporates the historical data. In such a case, the researcher needs to work through the potential consequences of historical and randomized control differences on trial characteristics, in addition to working through issues regarding power in the context of sample size, treatment effect size, and outcome. If a researcher designs a clinical trial that will incorporate historical control data, the researcher needs the randomized controls to be from the same population as the historical controls. What if this is not the case when the designed trial is implemented? During the design phase, the researcher needs to investigate the negative effects of possible historic/randomized control differences on power, type one error, and other trial characteristics. Using this information, the researcher should design the trial to mitigate these negative effects. Through simulation, the historic_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about historical and randomized control differences as well as treatment effects and outcomes. The results from historic_sim() and simple_sim() can be printed with print_table() and graphed with plot_table() methods. Outcomes considered are Gaussian, Poisson, Bernoulli, Lognormal, Weibull, and Piecewise Exponential. The methods are described in Eggleston et al. (2021) . Package: r-cran-bayescvi Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-mclust, r-cran-ggplot2, r-cran-universalcvi Filename: pool/dists/noble/main/r-cran-bayescvi_1.0.2-1.ca2404.1_all.deb Size: 458992 MD5sum: 5f7e84c32acbf9a1fe51f3680ca91c55 SHA1: 0277206412db9ade86a7cb18d7043c77f1fe93d4 SHA256: d91f9867aee118401e6ff2fac62462e6b2932f2f1fb83453e1e6eca0611b0852 SHA512: 8b29bf3312105b8ed8375a3286752bf8ae0cf1a711ac77fc453b88d8b094c6055fb949eba400553d1a9b0c7202375dac5411aeb455fabc9deb0d8b0dee4b3be7 Homepage: https://cran.r-project.org/package=BayesCVI Description: CRAN Package 'BayesCVI' (Bayesian Cluster Validity Index) Algorithms for computing and generating plots with and without error bars for Bayesian cluster validity index (BCVI) (O. Preedasawakul, and N. Wiroonsri, A Bayesian Cluster Validity Index, Computational Statistics & Data Analysis, 202, 108053, 2025. ) based on several underlying cluster validity indexes (CVIs) including Calinski-Harabasz, Chou-Su-Lai, Davies-Bouldin, Dunn, Pakhira-Bandyopadhyay-Maulik, Point biserial correlation, the score function, Starczewski, and Wiroonsri indices for hard clustering, and Correlation Cluster Validity, the generalized C, HF, KWON, KWON2, Modified Pakhira-Bandyopadhyay-Maulik, Pakhira-Bandyopadhyay-Maulik, Tang, Wiroonsri-Preedasawakul, Wu-Li, and Xie-Beni indices for soft clustering. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). Though BCVI is compatible with any underlying existing CVIs, we recommend users to use either WI or WP as the underlying CVI. Package: r-cran-bayesda Architecture: all Version: 2012.04-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-bayesda_2012.04-1-1.ca2404.1_all.deb Size: 69366 MD5sum: e9ba82a52bb319621b2f6240700fab1e SHA1: 3829b891eb663c884235b531d38d2a56f5a9b013 SHA256: 3ef4c6040f7e5666097ad44b58389bc2aaeaf21e718c18cdaae730d486bf628f SHA512: c65fb05669ac3e2ebafa2d94114f92792770ad9fa8f9ca1f07678947cef53f9c6216d1b40b6d4793c375cd3abab7ec23b6038c2a91ef5b77ec8573e74917d7c5 Homepage: https://cran.r-project.org/package=BayesDA Description: CRAN Package 'BayesDA' (Functions and Datasets for the book "Bayesian Data Analysis") Functions for Bayesian Data Analysis, with datasets from the book "Bayesian data Analysis (second edition)" by Gelman, Carlin, Stern and Rubin. Not all datasets yet, hopefully completed soon. Package: r-cran-bayesdesign Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bayesdesign_0.1.1-1.ca2404.1_all.deb Size: 35424 MD5sum: 2abf12bd3c0c060037d0e8741c13cc8e SHA1: 6aa81748ffc96978c59d2bb1e7e5867a877582d9 SHA256: e4b467e639b887dbaa8910c39c8aba06c202b3a9733490ffeb89e65eade6ca22 SHA512: ba13b8aa5835a8e5920cee29b4446893c55e9fc33ff06659d093a945419ba1b20be755e19052daa0f581d72d8fc272c93d6efd4459cb56448c03d7b5b7efaa2d Homepage: https://cran.r-project.org/package=BayesDesign Description: CRAN Package 'BayesDesign' (Bayesian Single-Arm Design with Survival Endpoints) The proposed event-driven approach for Bayesian two-stage single-arm phase II trial design is a novel clinical trial design and can be regarded as an extension of the Simon’s two-stage design with the time-to-event endpoint. This design is motivated by cancer clinical trials with immunotherapy and molecularly targeted therapy, in which time-to-event endpoint is often a desired endpoint. Package: r-cran-bayesdiagnostics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-checkmate, r-cran-ggplot2, r-cran-gridextra, r-cran-loo, r-cran-posterior, r-cran-matrixstats, r-cran-bridgesampling, r-cran-rstan, r-cran-tidyr Suggests: r-cran-bayesplot, r-cran-covr, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-rmarkdown, r-cran-rstanarm, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-bayesdiagnostics_0.1.0-1.ca2404.1_all.deb Size: 255986 MD5sum: bdda6b697f0eb4efd24449260bbde5e5 SHA1: aa76655514f79e4d8fb54ceb1b9cb60ba02d48af SHA256: 989c71b6bcc9df17479ed5facd30d3197ca668ce9aca639140182c1d8fd43eef SHA512: 3f5f69c0887de70dc24dfb2a8c82175e8aed23e292b8d6bd567476da23854a532b1f991bd2de4a3d0b1f72fad8e08775464c4f69f2bf1cff51077193e2ca8bf0 Homepage: https://cran.r-project.org/package=bayesDiagnostics Description: CRAN Package 'bayesDiagnostics' (Comprehensive Bayesian Model Diagnostics and Comparison Tools) Provides comprehensive tools for Bayesian model diagnostics and comparison. Includes prior sensitivity analysis, posterior predictive checks (Gelman et al. (2013) ), advanced model comparison using Pareto-smoothed importance sampling leave-one-out cross-validation (Vehtari et al. (2017) ), convergence diagnostics, and prior elicitation tools. Integrates with 'brms' (Burkner (2017) ), 'rstan', and 'rstanarm' packages for comprehensive Bayesian workflow diagnostics. Package: r-cran-bayesdip Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bayesdip_0.1.1-1.ca2404.1_all.deb Size: 124994 MD5sum: dcc7d7d95ba77f0f22f1474255d520ef SHA1: 734e176913d49cd3fa9d8568e35fe16d292b358b SHA256: 5eec5a121f228d07d39496631cf5ce9859f9ff9cbb760e5b2e098d28f29862ef SHA512: b62a58bcc8f7618e01c62128d1791f3b4031487e2103d7f0532d363c3b6d22a755501b0370ddcbda16bc7a464bbf08cb44d54939a376bf8bfa9c5e17d500d0c9 Homepage: https://cran.r-project.org/package=BayesDIP Description: CRAN Package 'BayesDIP' (Bayesian Decreasingly Informative Priors for Early TerminationPhase II Trials) Provide early termination phase II trial designs with a decreasingly informative prior (DIP) or a regular Bayesian prior chosen by the user. The program can determine the minimum planned sample size necessary to achieve the user-specified admissible designs. The program can also perform power and expected sample size calculations for the tests in early termination Phase II trials. See Wang C and Sabo RT (2022) ; Sabo RT (2014) . Package: r-cran-bayesdissolution Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-geor, r-cran-mcmcpack, r-cran-mnormt, r-cran-pscl, r-cran-shiny Suggests: r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-bayesdissolution_0.2.1-1.ca2404.1_all.deb Size: 107524 MD5sum: 463abcfb9bc3c193723fd30175be66a9 SHA1: 12e53868865955654e9d010009d71087d038fb43 SHA256: 7138c3f669d45c40e4bb8d90ddb59c09fdf942eee4125a22312147b6ea5b293c SHA512: 08c83c4ddc13d120aee436d31429f9a7ae18b35dc935a53ae4bfe88ef3fc64968be8e2d87c00e3f5083bcf25f5e87f7465adcde84637ed381a38ae1d99d0fa70 Homepage: https://cran.r-project.org/package=BayesDissolution Description: CRAN Package 'BayesDissolution' (Bayesian Models for Dissolution Testing) Fits Bayesian models (amongst others) to dissolution data sets that can be used for dissolution testing. The package was originally constructed to include only the Bayesian models outlined in Pourmohamad et al. (2022) . However, additional Bayesian and non-Bayesian models (based on bootstrapping and generalized pivotal quanties) have also been added. More models may be added over time. Package: r-cran-bayesdistreg Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-bayesdistreg_0.1.0-1.ca2404.1_all.deb Size: 86750 MD5sum: 1239e7570cb892de4525b878359c8d0d SHA1: 0d6c624e88a3dab16c902c3c36549c3a89f7f66f SHA256: 17b10901a79a99576681eec9b5c5733930979355d71ca2fce5b4b3db54e3b45f SHA512: 7a7e115d4b98e0b13f18021968a27d1a232e60468f3eb315aac2c0299adf5e0c3534dd383630c781de81cf2170a0426380dc238de22de00384ae6254e298c995 Homepage: https://cran.r-project.org/package=bayesdistreg Description: CRAN Package 'bayesdistreg' (Bayesian Distribution Regression) Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference. Package: r-cran-bayesertools Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1538 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-ggplot2, r-cran-gt, r-cran-cli, r-cran-rlang, r-cran-rstanarm, r-cran-rstanemax, r-cran-loo, r-cran-tidybayes, r-cran-posterior Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-rstan, r-cran-htmltools, r-cran-digest, r-cran-ggforce, r-cran-xgxr, r-cran-scales, r-cran-readr, r-cran-bayestestr, r-cran-patchwork, r-cran-projpred, r-cran-rsample, r-cran-yardstick Filename: pool/dists/noble/main/r-cran-bayesertools_0.2.5-1.ca2404.1_all.deb Size: 1235434 MD5sum: 3133bad30b001ee85593431c912df2ab SHA1: bcf9b18c91c14397fb9ccca5bed08cccd9e8b01b SHA256: e17417823f364fa39ff8902b74279fab418f64bde32ef6a2549462989f6a3183 SHA512: c975f3b4ab028374fc4349f1964f83a0fa5de59f188b086cc70c0802cfbb7f1ae85da0512bf8d8414b006813e0f48a5cce53931cae421bf9208ef06b7d23b1cc Homepage: https://cran.r-project.org/package=BayesERtools Description: CRAN Package 'BayesERtools' (Bayesian Exposure-Response Analysis Tools) Suite of tools that facilitate exposure-response analysis using Bayesian methods. The package provides a streamlined workflow for fitting types of models that are commonly used in exposure-response analysis - linear and Emax for continuous endpoints, logistic linear and logistic Emax for binary endpoints, as well as performing simulation and visualization. Learn more about the workflow at . Package: r-cran-bayesestdft Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 951 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-bayesestdft_1.0.0-1.ca2404.1_all.deb Size: 877454 MD5sum: bfb1c840eee716bf75a2a684959822f7 SHA1: 1f83c28fa7b9c5a14f4110f62a4959c5bb170bd8 SHA256: 716fa117f82a3d4aaaf56c03263e6d411268184069e07b4d2ecd5d07f7ea410a SHA512: 6d3cd311ebe0c7ace144d85f0b5456b98d480e5cf83771e157accecb6d0f96a10a7ed1466d7f9f2c0190bd4611e13b1f03fc216e67732264e8fb0fef7f56c5a2 Homepage: https://cran.r-project.org/package=bayesestdft Description: CRAN Package 'bayesestdft' (Estimating the Degrees of Freedom of the Student'st-Distribution under a Bayesian Framework) A Bayesian framework to estimate the Student's t-distribution's degrees of freedom is developed. Markov Chain Monte Carlo sampling routines are developed as in to sample from the posterior distribution of the degrees of freedom. A random walk Metropolis algorithm is used for sampling when Jeffrey's and Gamma priors are endowed upon the degrees of freedom. In addition, the Metropolis-adjusted Langevin algorithm for sampling is used under the Jeffrey's prior specification. The Log-normal prior over the degrees of freedom is posed as a viable choice with comparable performance in simulations and real-data application, against other prior choices, where an Elliptical Slice Sampler is used to sample from the concerned posterior. Package: r-cran-bayesfbhborrow Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-survival, r-cran-invgamma, r-cran-mvtnorm, r-cran-checkmate, r-cran-magrittr, r-cran-ggplot2 Suggests: r-cran-tibble, r-cran-readxl, r-cran-testthat, r-cran-rmarkdown, r-cran-ggfortify, r-cran-condsurv Filename: pool/dists/noble/main/r-cran-bayesfbhborrow_2.0.2-1.ca2404.1_all.deb Size: 249928 MD5sum: 34c9ae04a4b0aaa64cfd1f011218dd74 SHA1: 17639e6332a9df465356d2b04994c585a0fa6b54 SHA256: 3d6c1d59ff44cbfd792089794e1fdab4a3c42deb45445ebaa1432e62b1d581d1 SHA512: a2e75a6dde9d4ec27b98305b384e329ea0bf1d863fca6c9999a4d310932a51e4658999ab7483f7646741aeaa2395dc54a7fd92a49b0436b82b06604d33abaa3e Homepage: https://cran.r-project.org/package=BayesFBHborrow Description: CRAN Package 'BayesFBHborrow' (Bayesian Dynamic Borrowing with Flexible Baseline HazardFunction) Allows Bayesian borrowing from a historical dataset for time-to- event data. A flexible baseline hazard function is achieved via a piecewise exponential likelihood with time varying split points and smoothing prior on the historic baseline hazards. The method is described in Scott and Lewin (2024) , and the software paper is in Axillus et al. (2024) . Package: r-cran-bayesfluxr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1179 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-juliacall Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesfluxr_0.1.3-1.ca2404.1_all.deb Size: 1067136 MD5sum: 1fae3e1037e47e7577d6e5bf855dd58c SHA1: 187be390025df8f8ff1488db0f3c5ef6af14a3b6 SHA256: 89688136c760f2fd370fddb053a4550e20e3e2fa5a34a9d435669634ab2c4453 SHA512: a137590af0b4aaa18902b425a4a4782506383f90802834dfa1b61619477f81889d6d02ef097cc661e81deb5eaeceaf0dc1f7c76213b445995a504da6c630b8eb Homepage: https://cran.r-project.org/package=BayesFluxR Description: CRAN Package 'BayesFluxR' (Implementation of Bayesian Neural Networks) Implementation of 'BayesFlux.jl' for R; It extends the famous 'Flux.jl' machine learning library to Bayesian Neural Networks. The goal is not to have the fastest production ready library, but rather to allow more people to be able to use and research on Bayesian Neural Networks. Package: r-cran-bayesgof Architecture: all Version: 5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-orthopolynom, r-cran-vgam, r-cran-bolstad2, r-cran-nleqslv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayesgof_5.2-1.ca2404.1_all.deb Size: 312652 MD5sum: 204d4a842cab2f08cdbff71293c37a2c SHA1: 1c63905405194fd432071474a9916557e0dc68cb SHA256: 9f56860ef80ae1a57c718fcf6d0ba04c1d2e483306b15c7d7cff5420b4464fd3 SHA512: 7761dc3c584964ee229bb371ed5f2f176357c3335ec3285ae34fdd23af0fb22c7a6a714ea3eb36607574c8754c332ab03b3c6599ff5d5812b9e62ff70abe014e Homepage: https://cran.r-project.org/package=BayesGOF Description: CRAN Package 'BayesGOF' (Bayesian Modeling via Frequentist Goodness-of-Fit) A Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. 2018 paper "Generalized Empirical Bayes via Frequentist Goodness of Fit" (). Package: r-cran-bayesgwqs Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-rjags, r-cran-stringr, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesgwqs_0.1.1-1.ca2404.1_all.deb Size: 146250 MD5sum: fc9c2585add8a9da952f8bbe44053ee1 SHA1: 5179e72bc376aee0f64cb78d4fcb7f9812abc70b SHA256: 37247e7f6ef3ea344ba6e30f38b04d0bf25aebbcff066f018aaaa42bbe49bce7 SHA512: 50964dec5d7fb500280c41125c9fe572bc296b1b7573160e6df216c42bf2b4b85bf18fcd526f608f05ae2aa9614f58bec1fc305dd034e14ee2125ba5c4c255ee Homepage: https://cran.r-project.org/package=BayesGWQS Description: CRAN Package 'BayesGWQS' (Bayesian Grouped Weighted Quantile Sum Regression) Fits Bayesian grouped weighted quantile sum (BGWQS) regressions for one or more chemical groups with binary outcomes. Wheeler DC et al. (2019) . Package: r-cran-bayesian Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brms, r-cran-parsnip, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-devtools, r-cran-future, r-cran-knitr, r-cran-recipes, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rstan, r-cran-spelling, r-cran-testthat, r-cran-workflows Filename: pool/dists/noble/main/r-cran-bayesian_1.0.1-1.ca2404.1_all.deb Size: 127688 MD5sum: bd3c38a8bd32c2a4ba5bdd693c3859d8 SHA1: 84383e3ba78fb794334a8a2f47e268669f438ae4 SHA256: 9baae52300860966447ca512d1218bac4a96fe22c1b1185193f5f7ad1d63695c SHA512: d782ee19eeae272fb8256aeda59aa494d532aae487b3c6158e24986c825bf1f1d89f2689551cb927fe5c42c3355fd5f75cfaf40d94845a10372ae76f2fc9340d Homepage: https://cran.r-project.org/package=bayesian Description: CRAN Package 'bayesian' (Bindings for Bayesian TidyModels) Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels' via 'bayesian' . 'tidymodels' is a collection of packages for machine learning; see Kuhn and Wickham (2020) ). The technical details of 'brms' and 'Stan' are described in Bürkner (2017) , Bürkner (2018) , and Carpenter et al. (2017) . Package: r-cran-bayesiandeb Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-posterior, r-cran-rlang, r-cran-cli, r-cran-ggplot2, r-cran-bayesplot, r-cran-desolve Suggests: r-cran-testthat, r-cran-loo, r-cran-digest, r-cran-dplyr, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-bayesiandeb_0.1.4-1.ca2404.1_all.deb Size: 316250 MD5sum: e5717067b6a140cebb563bef15c5d2f1 SHA1: afd9e16277ab2c61d838764f5b065587fd5bc08c SHA256: a355f0af14bb761fc00cad1bf3c86a54550bf1f5b76b24ec5103e001c9fc5254 SHA512: e463fb8d2f67e13fb66342dbfa196818544f3e21a649ce19d91625dcd5ce88e104adc881e727a6b5221208194a910196ca5b289419b74687bdb8df27f6e4ad72 Homepage: https://cran.r-project.org/package=BayesianDEB Description: CRAN Package 'BayesianDEB' (Bayesian Dynamic Energy Budget Modelling) Provides a Bayesian framework for Dynamic Energy Budget (DEB) modelling via 'Stan'. Implements the standard DEB model of Kooijman (2010, ) as a state-space model with Hamiltonian Monte Carlo inference (Carpenter et al., 2017, ). Includes individual-level growth models, growth-reproduction models, hierarchical multi-individual models with partial pooling, and toxicokinetic-toxicodynamic (TKTD) models for ecotoxicology following the DEBtox framework (Jager et al., 2006, ). Supports prior specification from biological knowledge, convergence diagnostics (Vehtari et al., 2021, ), posterior predictive checks, derived quantity estimation, and visualisation via 'ggplot2'. Package: r-cran-bayesiandisaggregation Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-foreach, r-cran-doparallel, r-cran-openxlsx, r-cran-rlang, r-cran-tibble, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-readr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesiandisaggregation_0.1.2-1.ca2404.1_all.deb Size: 127430 MD5sum: 29a9bef5241a9f9b9ba36d7140ffa3bb SHA1: de9f0c69218043fcd3bde2c107f8f56c28796fca SHA256: 80152942585875331d669d3acacbf9a6e1abbe7aaa95b17bff8bf7bc1a345a01 SHA512: 5e0e876f9b0e2b72abd2c74fde0543b5d4846c0ff0220cb422be4da1f265ac93671195ed35d5cdda8aa0920fef3c4786b5e85f608d5c2d85b206238d7879b3b3 Homepage: https://cran.r-project.org/package=BayesianDisaggregation Description: CRAN Package 'BayesianDisaggregation' (Bayesian Methods for Economic Data Disaggregation) Implements a novel Bayesian disaggregation framework that combines Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) dimension reduction of prior weight matrices with deterministic Bayesian updating rules. The method provides Markov Chain Monte Carlo (MCMC) free posterior estimation with built-in diagnostic metrics. While based on established PCA (Jolliffe, 2002) and Bayesian principles (Gelman et al., 2013) , the specific integration for economic disaggregation represents an original methodological contribution. Package: r-cran-bayesianfactorzoo Architecture: all Version: 0.0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-reshape2, r-cran-mass, r-cran-timeseries, r-cran-coda, r-cran-mvtnorm, r-cran-matrixcalc, r-cran-ggplot2, r-cran-nse, r-cran-rdpack, r-cran-matrix Filename: pool/dists/noble/main/r-cran-bayesianfactorzoo_0.0.0.3-1.ca2404.1_all.deb Size: 333964 MD5sum: 698e5b39e1c4b407fcc83b50c5a8762f SHA1: ce70a61e5804d96a4a6ee81de6b1345fa1f35a9c SHA256: 89f5cd34198cb83617bfe53ec38dae52e2142e1e062a7a366f694bd5230bbdb7 SHA512: 8d92c1d10a4927b7164c3fd5347fa0a85fdbd5c1a6b31a8074801bfe47d70086e4ed8ad282f8c07c7bab7b00739635624da8bfd4a7f97d99f9c3da85362a7924 Homepage: https://cran.r-project.org/package=BayesianFactorZoo Description: CRAN Package 'BayesianFactorZoo' (Bayesian Solutions for the Factor Zoo: We Just Ran TwoQuadrillion Models) Contains the functions to use the econometric methods in the paper Bryzgalova, Huang, and Julliard (2023) . In this package, we provide a novel Bayesian framework for analyzing linear asset pricing models: simple, robust, and applicable to high-dimensional problems. For a stand-alone model, we provide functions including BayesianFM() and BayesianSDF() to deliver reliable price of risk estimates for both tradable and nontradable factors. For competing factors and possibly nonnested models, we provide functions including continuous_ss_sdf(), continuous_ss_sdf_v2(), and dirac_ss_sdf_pvalue() to analyze high-dimensional models. If you use this package, please cite the paper. We are thankful to Yunan Ding and Jingtong Zhang for their research assistance. Any errors or omissions are the responsibility of the authors. Package: r-cran-bayesianfitforecast Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesplot, r-cran-readxl, r-cran-xlsx, r-cran-loo, r-cran-openxlsx, r-cran-rstan, r-cran-ggplot2, r-cran-stringr, r-cran-gridextra, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesianfitforecast_1.1.0-1.ca2404.1_all.deb Size: 3059872 MD5sum: 14e2939324049d4a4c4acf590b969fed SHA1: d2a5d4cc9c12994195f3bcd187a79e37e5986665 SHA256: d80092fcb920c4f1c5f1d6b4599caed98c2a6d82c9f4ab8e4dd89e3c9a59a32a SHA512: 87db2dc1e6a3156b00a7a872ae9eda516257064ba4a0f148708c7c252377060c5a7a96e587772fde9356e5f2b5779253e5984fe736c0a3e3da99b2339a096420 Homepage: https://cran.r-project.org/package=BayesianFitForecast Description: CRAN Package 'BayesianFitForecast' (Bayesian Parameter Estimation and Forecasting forEpidemiological Models) Methods for Bayesian parameter estimation and forecasting in epidemiological models. Functions enable model fitting using Bayesian methods and generate forecasts with uncertainty quantification. Implements approaches described in and . Package: r-cran-bayesiangammareg Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-bayesiangammareg_0.1.1-1.ca2404.1_all.deb Size: 66618 MD5sum: cd675bcb8fc4e092ddfbbb5f4fd3eecb SHA1: 3913f965bc2cc85c5d056f3bc71024627adc6abc SHA256: 2a9364d8fd99a66ee5a64a58a4cf08c297ec9f8b3fe2337aa794977b4d2a9fef SHA512: 52b02c30543af4770c714f98d11c060e024d4b6b20c1ccebb5e1991bbc275f955e87297e4f90618843662169add6aab2044060e8a3c55556a8d9a6c631b04bec Homepage: https://cran.r-project.org/package=Bayesiangammareg Description: CRAN Package 'Bayesiangammareg' (Double Generalized Gamma Regression Models) Fits double generalized Gamma regression models from a Bayesian perspective, where both the mean and shape parameters are modeled simultaneously using flexible link functions. The methodology is based on Cepeda-Cuervo and Urdinola (2012) and extended in Cepeda-Cuervo (2026), 'Double Generalized Linear Models: Likelihood and Bayesian Methods' (ISBN: 9781041169970). The package provides parameter estimation, model fitting, and model comparison tools, including Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Package: r-cran-bayesianglasso Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod, r-cran-mass Filename: pool/dists/noble/main/r-cran-bayesianglasso_0.2.0-1.ca2404.1_all.deb Size: 16124 MD5sum: 9ac290ef06e9a06e4f969e25a0cb53c1 SHA1: 5f91694f509bf069fa1ee744840e4de8136d43cf SHA256: c640e6ff814de1bb4679f833b1d2dfa0a478f68ea70c2b402e1f21f3c8142345 SHA512: f5f006d2f7bdbf6b426bf82ebdae1bb4dcf7601e1c33851f836437fe3c3dc91f78d3cf21dba7dec9d0a68b9a6fded3b0dfc19a5a123ce0a453e6dfb571e78fc3 Homepage: https://cran.r-project.org/package=BayesianGLasso Description: CRAN Package 'BayesianGLasso' (Bayesian Graphical Lasso) Implements a data-augmented block Gibbs sampler for simulating the posterior distribution of concentration matrices for specifying the topology and parameterization of a Gaussian Graphical Model (GGM). 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Package: r-cran-bayesianhybriddesign Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-checkmate, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-metrics, r-cran-rbest Suggests: r-cran-broom, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-rstanarm, r-cran-scales, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-bayesianhybriddesign_0.1.0-1.ca2404.1_all.deb Size: 187038 MD5sum: 1a743a0f089d989960b912b239500e60 SHA1: ecde785866b92ae4f8c427645418f4ed7f67dd4f SHA256: 854ff8d6c5c02fee4c1725bb90abeb1156b8a53329e9f8ea0afbeb81330436a7 SHA512: 37d283bafc0af3762c109463ae52a3ce725ccb3723caf6a60efbe29693ab8c0f0973e708d469e8e82227295a651fb7cc1a8014b45bbf58833d176df2eecd5aa0 Homepage: https://cran.r-project.org/package=BayesianHybridDesign Description: CRAN Package 'BayesianHybridDesign' (Bayesian Hybrid Design and Analysis) Implements Bayesian hybrid designs that incorporate historical control data into a current clinical trial. The package uses a dynamic power prior method to determine the degree of borrowing from the historical data, creating a 'hybrid' control arm. This approach is primarily designed for studies with a binary primary endpoint, such as the overall response rate (ORR). Functions are provided for design calibration, sample size calculation, power evaluation, and final analysis. Additionally, it includes functions adapted from the 'SAMprior' package (v1.1.1) by Yang et al. (2023) to support the Self-Adapting Mixture (SAM) prior framework for comparison. Package: r-cran-bayesianinference Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesianinference_0.0.1-1.ca2404.1_all.deb Size: 410230 MD5sum: 4848cd9b9d70872c87fa72a3793b7519 SHA1: 7075c19ef10834b198137e7cf1b3ad48898389b4 SHA256: c0c640bdf9206be2e60c87304fe66c3e3495d26936b84f4a2b95bb193b6f8e28 SHA512: c62d3a9fef9b56ccd5a28aa9710a2494e6ba2d771fa9c4fc253b2bf835199f355956c5f009d3b2d99af99fb4a2523049478b6e9a44768c7bed8dceb38ad84ae3 Homepage: https://cran.r-project.org/package=BayesianInference Description: CRAN Package 'BayesianInference' (Bayesian Inference) Beta version of 'Bayesian Inference' (BI) using 'python' and BI. It aims to unify the modeling experience by providing an intuitive model-building syntax together with the flexibility of low-level abstraction coding. 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Package: r-cran-bayesianlaterality Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tmvtnorm, r-cran-rdpack Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesianlaterality_0.1.2-1.ca2404.1_all.deb Size: 29798 MD5sum: 63562865ecc3783d96756957ee369cd9 SHA1: a67418f809cdccf08faa8c21cf74637293484eb9 SHA256: b6f19ca4eb652e70e71ad8f463e1911a7d6e863fe4c60e05541e76ca521e4772 SHA512: 046d663a3141d1eb1ed9471c81e3547b48d057bc333e876fd0380abc577bfc52ba55b79ece566509bb6dc5570421988a8b473769ae54c2cad8381428c0049c24 Homepage: https://cran.r-project.org/package=BayesianLaterality Description: CRAN Package 'BayesianLaterality' (Predict Brain Asymmetry Based on Handedness and DichoticListening) Functional differences between the cerebral hemispheres are a fundamental characteristic of the human brain. Researchers interested in studying these differences often infer underlying hemispheric dominance for a certain function (e.g., language) from laterality indices calculated from observed performance or brain activation measures . However, any inference from observed measures to latent (unobserved) classes has to consider the prior probability of class membership in the population. The provided functions implement a Bayesian model for predicting hemispheric dominance from observed laterality indices (Sorensen and Westerhausen, Laterality: Asymmetries of Body, Brain and Cognition, 2020, ). Package: r-cran-bayesianmcpmod Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2321 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-dosefinding, r-cran-dplyr, r-cran-ggplot2, r-cran-logistf, r-cran-nloptr, r-cran-rbest, r-cran-tidyr Suggests: r-cran-clindr, r-cran-dofuture, r-cran-future.apply, r-cran-kableextra, r-cran-knitr, r-cran-mcpmodpack, r-cran-reactable, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-bayesianmcpmod_1.3.2-1.ca2404.1_all.deb Size: 1103696 MD5sum: 43199d22c05adfa15e6692a71ff11624 SHA1: c693ff2c4e66af3e235887c41953aba20e2aad12 SHA256: fc0baa7a0df76c2701bcb2b53d22d4d29d12eabbefda34bcfed2212ff4f4b9c5 SHA512: f18483dd916df94677ef3bb7d87b438d5a016d893a0344185e4f48b466a5ba414b94bc75df9aedcb710aa04f93352fbf5ad9d342f5fb26444898ab0a407f1bf7 Homepage: https://cran.r-project.org/package=BayesianMCPMod Description: CRAN Package 'BayesianMCPMod' (Simulate, Evaluate, and Analyze Dose Finding Trials withBayesian MCPMod) Bayesian MCPMod (Fleischer et al. (2022) ) is an innovative method that improves the traditional MCPMod by systematically incorporating historical data, such as previous placebo group data. This package offers functions for simulating, analyzing, and evaluating Bayesian MCPMod trials with normally and binary distributed endpoints. It enables the assessment of trial designs incorporating historical data across various true dose-response relationships and sample sizes. Robust mixture prior distributions, such as those derived with the Meta-Analytic-Predictive approach (Schmidli et al. (2014) ), can be specified for each dose group. Resulting mixture posterior distributions are used in the Bayesian Multiple Comparison Procedure and modeling steps. The modeling step also includes a weighted model averaging approach (Pinheiro et al. (2014) ). Estimated dose-response relationships can be bootstrapped and visualized. Package: r-cran-bayesianmediationa Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-r2jags, r-cran-car, r-cran-gplots, r-cran-lattice Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayesianmediationa_1.0.1-1.ca2404.1_all.deb Size: 212868 MD5sum: d3664ca2fabe5d3b1a77b1048084a6fd SHA1: e9e5538627a119d1d4f909b088802d66b27025e8 SHA256: 9c78af9239bf8f377c3edb25840b8f4a84963789378ceb828e18161b7a74ad12 SHA512: 3993d66e92a7cce55db3775b5f93e34b0b44d748730b796c41481ada4b71e31c934d3ef2ad9bf5f2200fc0bfbfa2c2cda843b8027a18ee002a7a09ce8897e88e Homepage: https://cran.r-project.org/package=BayesianMediationA Description: CRAN Package 'BayesianMediationA' (Bayesian Mediation Analysis) We perform general mediation analysis in the Bayesian setting using the methods described in Yu and Li (2022, ISBN:9780367365479). With the package, the mediation analysis can be performed on different types of outcomes (e.g., continuous, binary, categorical, or time-to-event), with default or user-defined priors and predictive models. The Bayesian estimates and credible sets of mediation effects are reported as analytic results. Package: r-cran-bayesiannetwork Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4810 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-heatmaply, r-cran-lattice, r-cran-networkd3, r-cran-plotly, r-cran-rintrojs, r-cran-shiny, r-cran-shinyace, r-cran-shinydashboard, r-cran-shinywidgets Suggests: r-cran-chromote, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-shinytest2 Filename: pool/dists/noble/main/r-cran-bayesiannetwork_0.4-1.ca2404.1_all.deb Size: 2797410 MD5sum: a14068b752e59e552eb9b482a7f7f771 SHA1: c31f05f73ab6c6d66fd4be6a3fa5f90f1c581b2c SHA256: befb2720d0c216277baaedcd3c668361cc4fffb2ddc4d0aad0141b66e96826e5 SHA512: 04f0af0d5bab0423c958c3aed26336a95123d11a26e8bc19f534a7716abb366db9038abbf77d8c508624680c861ab432a5f0cf71cc609a8dbd41d2ed1003eb5d Homepage: https://cran.r-project.org/package=BayesianNetwork Description: CRAN Package 'BayesianNetwork' (Bayesian Network Modeling and Analysis) A "Shiny"" web application for creating interactive Bayesian Network models, learning the structure and parameters of Bayesian networks, and utilities for classic network analysis. Package: r-cran-bayesianou Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rstan, r-cran-loo, r-cran-posterior, r-cran-ggplot2, r-cran-tidyr, r-cran-openxlsx, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesianou_0.1.3-1.ca2404.1_all.deb Size: 116200 MD5sum: fccf2e4089d3b18741d431a85fa1e968 SHA1: 510d6d1bf02fc03cf3354fe37e24473c2b7aed77 SHA256: 6f03f5529df66a17ef7ebcd5b681f52220df158b100873817168ed6825b53f0e SHA512: 67ce614e3c6575b1c9367a9d92ffcf8957ba3628749cb78daeb62ddc4d9dfe65ec341591e8cf73f47e9a7267b7180a595fae501009f9aa4adcf353e4e35b1aeb Homepage: https://cran.r-project.org/package=bayesianOU Description: CRAN Package 'bayesianOU' (Bayesian Nonlinear Ornstein-Uhlenbeck Models with StochasticVolatility) Fits Bayesian nonlinear Ornstein-Uhlenbeck models with cubic drift, stochastic volatility, and Student-t innovations. The package implements hierarchical priors for sector-specific parameters and supports parallel MCMC sampling via 'Stan'. Model comparison is performed using Pareto Smoothed Importance Sampling Leave-One-Out (PSIS-LOO) cross-validation following Vehtari, Gelman, and Gabry (2017) . Prior specifications follow recommendations from Gelman (2006) for scale parameters. Package: r-cran-bayesianpower Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesianpower_0.2.3-1.ca2404.1_all.deb Size: 46538 MD5sum: f37529217000818daf2d1373cca7b1bb SHA1: f810d41eb62b32124a0b3e8bfe56c0638623a9e0 SHA256: bc3ea3495012f20adc841ef14e1f7b47d90617fa35f2127f23b03436894c60f7 SHA512: a398d0ab2d006b05cc14e6eeb464819a84aa2c26ca3f238124855e994bb222f5a2cc4cd5bde731cade1d7521c3e7f3ae7a4933ea2debdbf80b829af8d3b6d949 Homepage: https://cran.r-project.org/package=BayesianPower Description: CRAN Package 'BayesianPower' (Sample Size and Power for Comparing Inequality ConstrainedHypotheses) A collection of methods to determine the required sample size for the evaluation of inequality constrained hypotheses by means of a Bayes factor. Alternatively, for a given sample size, the unconditional error probabilities or the expected conditional error probabilities can be determined. Additional material on the methods in this package is available in Klaassen, F., Hoijtink, H. & Gu, X. (2019) . Package: r-cran-bayesianqdm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-bayesianqdm_0.1.0-1.ca2404.1_all.deb Size: 745544 MD5sum: e85506a1cbb8f1590c0f5969d5271fe3 SHA1: e77ea2273afcea0cdd36872cbffee41edb37df69 SHA256: d18550a09d4fe532a6922bab757cfaac348da4bec57619f1c1ddc8432268c20d SHA512: 4e63312ee1c80023ddbe29329c12d2f770431db7dba88c7c03ac0f6162548c3b0d9826349c852d332203b708fc9b2a8f8254b03f7fd263a8838b812d83aa53cb Homepage: https://cran.r-project.org/package=BayesianQDM Description: CRAN Package 'BayesianQDM' (Bayesian Quantitative Decision-Making Framework for Binary andContinuous Endpoints) Provides comprehensive methods to calculate posterior probabilities, posterior predictive probabilities, and Go/NoGo/Gray decision probabilities for quantitative decision-making under a Bayesian paradigm in clinical trials. The package supports both single and two-endpoint analyses for binary and continuous outcomes, with controlled, uncontrolled, and external designs. For single continuous endpoints, three calculation methods are available: numerical integration (NI), Monte Carlo simulation (MC), and Moment-Matching approximation (MM). For two continuous endpoints, a bivariate Normal-Inverse-Wishart conjugate model is implemented with MC and MM methods. For two binary endpoints, a Dirichlet-multinomial model is implemented. External designs incorporate historical data through power priors using exact conjugate representations (Normal-Inverse-Chi-squared for single continuous, Normal-Inverse-Wishart for two continuous, and Dirichlet for binary endpoints), enabling closed-form posterior computation without Markov chain Monte Carlo (MCMC) sampling. This approach significantly reduces computational burden while preserving complete Bayesian rigor. The package also provides grid-search functions to find optimal Go and NoGo thresholds that satisfy user-specified operating characteristic criteria for all supported endpoint types and study designs. S3 print() and plot() methods are provided for all decision probability classes, enabling formatted display and visualisation of Go/NoGo/Gray operating characteristics across treatment scenarios. See Kang, Yamaguchi, and Han (2026) for the methodological framework. Package: r-cran-bayesianreasoning Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2009 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-ggtext, r-cran-gt, r-cran-magrittr, r-cran-png, r-cran-reshape2, r-cran-scales, r-cran-tibble, r-cran-tidyr Suggests: r-cran-curl, r-cran-httr, r-cran-knitr, r-cran-patchwork, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-webshot2 Filename: pool/dists/noble/main/r-cran-bayesianreasoning_0.4.3-1.ca2404.1_all.deb Size: 1457752 MD5sum: d4766373ee1c0f2c10d620259f3fbe9f SHA1: 9b2cadd8f27bd986c9ccf6403ba836a24fdb7aad SHA256: 8119ac53b545e1d9fb68d374589171f788b27129fd2b7d001bcaeb465f75a78c SHA512: d6a517ee514b67f9af56a3d6ebf7c2b1d14bc7ef4c112787db070b141a1357a8a28d969f8b623097109dda52a1ce15f39ae42b64148f59943a70f760825cb905 Homepage: https://cran.r-project.org/package=BayesianReasoning Description: CRAN Package 'BayesianReasoning' (Plot Positive and Negative Predictive Values for Medical Tests) Functions to plot and help understand positive and negative predictive values (PPV and NPV), and their relationship with sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007) for a theoretical overview of the technical concepts and Navarrete et al. (2015) for a practical explanation about the importance of their understanding . Package: r-cran-bayesiansurpriser Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-sf, r-cran-scales, r-cran-rlang, r-cran-cli, r-cran-mass, r-cran-rcolorbrewer Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tibble, r-cran-vdiffr, r-cran-tidycensus, r-cran-tigris, r-cran-cancensus, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-bayesiansurpriser_0.1.0-1.ca2404.1_all.deb Size: 2342188 MD5sum: 322f2767235704d220367fa74559b101 SHA1: 229312149080ac4ca76e6fdca73a245beb78f194 SHA256: 52d866ed4ebcea7eda944ecc0cd73b518fc7f4c8d3bd5ac447e061eea2b1e478 SHA512: ee7337924850e6524d6aca575f127f8497f169d500da0b667edc25b84c5be7635fb90e8b246ea8c35eb5c04ebe55c5a4f54fa43f6496b6cf2b9dcb0399a1755e Homepage: https://cran.r-project.org/package=bayesiansurpriser Description: CRAN Package 'bayesiansurpriser' (Bayesian Surprise for De-Biasing Thematic Maps) Implements Bayesian Surprise methodology for data visualization, based on Correll and Heer (2017) "Surprise! Bayesian Weighting for De-Biasing Thematic Maps". Provides tools to weight event data relative to spatio-temporal models, highlighting unexpected patterns while de-biasing against known factors like population density or sampling variation. Integrates seamlessly with 'sf' for spatial data and 'ggplot2' for visualization. Supports temporal/streaming data analysis. Package: r-cran-bayesiantreg Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-bayesiantreg_1.0.1-1.ca2404.1_all.deb Size: 103074 MD5sum: 9365146eff89a8f206cd26e989c5e756 SHA1: c017405291510dccf73dc9d387e179dc94937dd4 SHA256: 95d0de4fba853b8a49496a3ae81207883dd5edff8ff6d8cf484f08c298c5fc0e SHA512: ac85d27aec7e9ea987e32b5fa7aeb585676392944e79d2d347a7a9d4b5608da048fabb07be7bf2a951517bf9ea94c081b239d3381add757522c254ad3fd89518 Homepage: https://cran.r-project.org/package=Bayesiantreg Description: CRAN Package 'Bayesiantreg' (Bayesian t Regression for Modeling Mean and Scale Parameters) Performs Bayesian t Regression where mean and scale parameters are modeling by lineal regression structures, and the degrees of freedom parameters are estimated. Package: r-cran-bayesics Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-rlang, r-cran-janitor, r-cran-extradistr, r-cran-mvtnorm, r-cran-matrix, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-patchwork, r-cran-bms, r-cran-cluster, r-cran-dfba, r-cran-tibble, r-cran-survival Suggests: r-cran-rstanarm, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesics_2.1.1-1.ca2404.1_all.deb Size: 557046 MD5sum: 91e0fb40f0db499c2f46ca091a488bf5 SHA1: 09fbcd1ebd99c3defedf8aad224195ce2ce38016 SHA256: a9f745fea232d03d7035dc6cf3b1492c4c2091fd3a844b0fab56b19ae0eaebd6 SHA512: cc0337bfdab21a3f02003bebc6b84f496a27c5a046fb1b203598c3ac44072aaf27da9ee20f17fe26ce8342e20a4abbfeded5852d1cd4c3519836c4fd0e0496f5 Homepage: https://cran.r-project.org/package=bayesics Description: CRAN Package 'bayesics' (Bayesian Analyses for One- and Two-Sample Inference andRegression Methods) Perform fundamental analyses using Bayesian parametric and non-parametric inference (regression, anova, 1 and 2 sample inference, non-parametric tests, etc.). (Practically) no Markov chain Monte Carlo (MCMC) is used; all exact finite sample inference is completed via closed form solutions or else through posterior sampling automated to ensure precision in interval estimate bounds. Diagnostic plots for model assessment, and key inferential quantities (point and interval estimates, probability of direction, region of practical equivalence, and Bayes factors) and model visualizations are provided. Bayes factors are computed either by the Savage Dickey ratio given in Dickey (1971) or by Chib's method as given in xxx. Interpretations are from Kass and Raftery (1995) . ROPE bounds are based on discussions in Kruschke (2018) . Methods for determining the number of posterior samples required are described in Doss et al. (2014) . Bayesian model averaging is done in part by Feldkircher and Zeugner (2015) . Methods for contingency table analysis is described in Gunel et al. (1974) . Variational Bayes (VB) methods are described in Salimans and Knowles (2013) . Mediation analysis uses the framework described in Imai et al. (2010) . The loss-likelihood bootstrap used in the non-parametric regression modeling is described in Lyddon et al. (2019) . Non-parametric survival methods are described in Qing et al. (2023) . Methods used for the Bayesian Wilcoxon signed-rank analysis is given in Chechile (2018) and for the Bayesian Wilcoxon rank sum analysis in Chechile (2020) . Correlation analysis methods are carried out by Barch and Chechile (2023) , and described in Lindley and Phillips (1976) and Chechile and Barch (2021) . See also Chechile (2020, ISBN: 9780262044585). Package: r-cran-bayeslca Architecture: all Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-coda, r-cran-fields, r-cran-nlme, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-bayeslca_1.9-1.ca2404.1_all.deb Size: 168762 MD5sum: 33bdce7ad08497aeaab30fd469d114e7 SHA1: 3802a8acf0704a005147dd8069f25b4309a0adc8 SHA256: fc32e469b5f2609b724587ef38894711d0d70ef928563fb2b609105b23d144db SHA512: 7c5d1bb7ce5078bc1e117f93aad7559b9f1879368e5f2bedf4c8769760727b61afd33a20a325d8497fd14e0ed6f9ca84a28a21007c48e5cf5a666d7f6d060996 Homepage: https://cran.r-project.org/package=BayesLCA Description: CRAN Package 'BayesLCA' (Bayesian Latent Class Analysis) Bayesian Latent Class Analysis using several different methods. Package: r-cran-bayeslongitudinal Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-learnbayes, r-cran-mvtnorm, r-cran-mass Filename: pool/dists/noble/main/r-cran-bayeslongitudinal_0.1.0-1.ca2404.1_all.deb Size: 53370 MD5sum: df1806e1359a03071ec48318ee4c48f3 SHA1: a7674b04ea2808b72f64a345b0d08fa4c5fdaa9d SHA256: 1920a36e019d7d269ef047c30656ee1662fe94bb4f94b7a28d0fb257e6c32627 SHA512: a91be2a9f997e5f07c7c0533796584175cddf6423b3c7aac40d882440631377303e3ea7f4632dcbbb87658bbd8de0cbc545332ea807f76545b73a35764a78865 Homepage: https://cran.r-project.org/package=bayeslongitudinal Description: CRAN Package 'bayeslongitudinal' (Adjust Longitudinal Regression Models Using Bayesian Methodology) Adjusts longitudinal regression models using Bayesian methodology for covariance structures of composite symmetry (SC), autoregressive ones of order 1 AR (1) and autoregressive moving average of order (1,1) ARMA (1,1). Package: r-cran-bayesmassbal Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-matrix, r-cran-pracma, r-cran-tmvtnorm, r-cran-laplacesdemon, r-cran-hdinterval, r-cran-coda Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-spelling, r-cran-tgp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesmassbal_1.1.0-1.ca2404.1_all.deb Size: 329278 MD5sum: e47d85f7fa3af7a5d40216c87a5b9a0f SHA1: 68d8af41448f5c2d9e3632297020a1e09fba0150 SHA256: 3ff6bb922dbc42dd2327d3aa650b9eef772b257f0605a5fe7ce4102914920894 SHA512: 996bf7ffb98aaac4f2d8de41dc9da1394db82099b09abc566626e5a0d998c7e4211734f78f3c2e47ccdf2ea8c0d7517cdfc9fff2a6c03b65c5fd4efc12e3a166 Homepage: https://cran.r-project.org/package=BayesMassBal Description: CRAN Package 'BayesMassBal' (Bayesian Data Reconciliation of Separation Processes) Bayesian tools that can be used to reconcile, or mass balance, mass flow rate data collected from chemical or particulate separation processes aided by constraints governed by the conservation of mass. Functions included in the package aid the user in organizing and constraining data, using Markov chain Monte Carlo methods to obtain samples from Bayesian models, and in computation of the marginal likelihood of the data, given a particular model, for model selection. Marginal likelihood is approximated by methods in Chib S (1995) . Package: r-cran-bayesmeanscale Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayestestr, r-cran-data.table, r-cran-magrittr, r-cran-posterior Suggests: r-cran-flextable, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-rstanarm, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesmeanscale_0.2.2-1.ca2404.1_all.deb Size: 408872 MD5sum: 64250deb476945666362d64cb5ace4de SHA1: cc613b726bfbe1f8e77bdb21a08f962bcba74ae8 SHA256: 78cd42d02ccf2fd8dad0b8583c49506be0226871e3510e5e4a07d76568527d0a SHA512: bf634d1a80b8c66372750f8d86fa7309badbb78faa5b80957a40806856bda2cf0e05e880ef9de0411a443c17f14b9f7eab3ccd127663704250d632bf7483a5db Homepage: https://cran.r-project.org/package=bayesMeanScale Description: CRAN Package 'bayesMeanScale' (Bayesian Post-Estimation on the Mean Scale) Computes Bayesian posterior distributions of predictions, marginal effects, and differences of marginal effects for various generalized linear models. Importantly, the posteriors are on the mean (response) scale, allowing for more natural interpretation than summaries on the link scale. Also, predictions and marginal effects of the count probabilities for Poisson and negative binomial models can be computed. Package: r-cran-bayesmeta Architecture: all Version: 3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4436 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forestplot, r-cran-metafor, r-cran-mvtnorm, r-cran-numderiv Suggests: r-cran-compute.es, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-bayesmeta_3.5-1.ca2404.1_all.deb Size: 4315656 MD5sum: e168c52a5817a615be7e74d618e2af86 SHA1: 3fe0489b77cf9952afd2937f3227a042dbba1244 SHA256: 2f481c526872f28559639de61b6b6727c5fa105571f391ebc88a1c2fe04d6603 SHA512: 751b1f480bfd4911c8512b00d491dec647d5fa04fe131da810df54f588e90a18a667775ad787f787638a37b5cfc58be861f9e2507752ea20d5b34ce867bc9555 Homepage: https://cran.r-project.org/package=bayesmeta Description: CRAN Package 'bayesmeta' (Bayesian Random-Effects Meta-Analysis and Meta-Regression) A collection of functions allowing to derive the posterior distribution of the model parameters in random-effects meta-analysis or meta-regression, and providing functionality to evaluate joint and marginal posterior probability distributions, predictive distributions, shrinkage effects, posterior predictive p-values, etc.; For more details, see also Roever C (2020) , or Roever C and Friede T (2022) . 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As a Bayesian dynamic survival model, it relaxes the proportional-hazard assumption. Lasso shrinkage controls overfitting, given the increase in the number of free parameters in the model due to presence of two Weibull components in the hazard function. Package: r-cran-bayesmlogit Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1471 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-bayesmlogit_1.0.1-1.ca2404.1_all.deb Size: 554822 MD5sum: 21cdc7acb0fae914345f657fe7292117 SHA1: c0144a5fd6d7d5da19ad133204c74688eb474501 SHA256: 889ed3ad3fafa8ff539a0ab8c1e6045834d474e4f5b7c011fe28ac192f225ba6 SHA512: d9fbf1844dce3e143c24caa25d3b1d306b669438eda952166d42b54232771c38883204482f12356891d2abfe054aaa4403f0dc3737494b0cad2010dfd1586184 Homepage: https://cran.r-project.org/package=bayesmlogit Description: CRAN Package 'bayesmlogit' (A Multistate Life Table (MSLT) Methodology Based on BayesianApproach) Create life tables with a Bayesian approach, which can be very useful for modelling a complex health process when considering multiple predisposing factors and multiple coexisting health conditions. Details for this method can be found in: Lynch, Scott, et al., (2022) ; Zang, Emma, et al., (2022) . Package: r-cran-bayesmofo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3303 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-insight, r-cran-coda, r-cran-tidyverse, r-cran-dplyr, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayesmofo_0.1.0-1.ca2404.1_all.deb Size: 1884202 MD5sum: 69177489fe2767207744367f83850a9b SHA1: 8bfad4b2fbeb60a0464a5a342cab5d9c39ddc394 SHA256: b74f2b260b5bd4cd7845211d5baa20ac89870733f7bd8cf59e04e27ae261858c SHA512: 326fa60ffdeb29020a852fe841a3053ef1e43c672c92add227e52a4f0636a98eadd0b9016ab602c64acd534c4f30df23f093b4e0bbb30e0be574703f9873cb4a Homepage: https://cran.r-project.org/package=BayesMoFo Description: CRAN Package 'BayesMoFo' (Bayesian Mortality Forecasting) Carry out Bayesian estimation and forecasting for a variety of stochastic mortality models using vague prior distributions. Models supported include numerous well-established approaches introduced in the actuarial and demographic literature, such as the Lee-Carter (1992) , the Cairns-Blake-Dowd (2009) , the Li-Lee (2005) , and the Plat (2009) models. The package is designed to analyse stratified mortality data structured as a 3-dimensional array of dimensions p × A × T (strata × age × year). Stratification can represent factors such as cause of death, country, deprivation level, sex, geographic region, insurance product, marital status, socioeconomic group, or smoking behavior. While the primary focus is on analysing stratified data (p > 1), the package can also handle mortality data that are not stratified (p = 1). Model selection via the Deviance Information Criterion (DIC) is supported. Package: r-cran-bayesmortalityplus Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-progress, r-cran-tidyr, r-cran-scales, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-bayesmortalityplus_1.0.0-1.ca2404.1_all.deb Size: 683052 MD5sum: 2e408cda2d4ca092cec89326edc09dba SHA1: 12b197aec5f71d37ba2409fd4376e39ae2e364fc SHA256: c3b334d322a6b93be876843e7f5a2534fa87b17b147567211d138c54cdce1b3f SHA512: 6f8b4921cc5bf61c64a4901817f5ff6274b84e163b98d6ee19e1a9a4a2fd047a9c2b5fe3c28d474235a17fedad09024ff55d8eeaf99f804a1db47bce00b4fddc Homepage: https://cran.r-project.org/package=BayesMortalityPlus Description: CRAN Package 'BayesMortalityPlus' (Bayesian Mortality Modelling) Fit Bayesian graduation mortality using the Heligman-Pollard model, as seen in Heligman, L., & Pollard, J. H. (1980) and Dellaportas, Petros, et al. (2001) , and dynamic linear model (Campagnoli, P., Petris, G., and Petrone, S. (2009) ). While Heligman-Pollard has parameters with a straightforward interpretation yielding some rich analysis, the dynamic linear model provides a very flexible adjustment of the mortality curves by controlling the discount factor value. Closing methods for both Heligman-Pollard and dynamic linear model were also implemented according to Dodd, Erengul, et al. (2018) . The Bayesian Lee-Carter model is also implemented to fit historical mortality tables time series to predict the mortality in the following years and to do improvement analysis, as seen in Lee, R. D., & Carter, L. R. (1992) and Pedroza, C. (2006) . Journal publication available at . Package: r-cran-bayesmrm Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-ggplot2, r-cran-gridextra, r-cran-rgl, r-cran-shiny, r-cran-shinythemes Filename: pool/dists/noble/main/r-cran-bayesmrm_2.4.0-1.ca2404.1_all.deb Size: 92154 MD5sum: f0f4f67410babc4f5281242af8a32509 SHA1: bc7a67abba5eb17a99cb7cd60f662d68cd6b91f7 SHA256: ba24641ea0bf4c587d281e69e8ba1a4b8241d0d4ea4b32b0fdec509df5d8efbc SHA512: 5381b441d26ccfd9e3e330a2d27a38f7432abbcae4e91cba56b775e5feeba53541fcf4790c4d7728074efdd1502b4ba1e188411714580c5b527f585111cd82fd Homepage: https://cran.r-project.org/package=bayesMRM Description: CRAN Package 'bayesMRM' (Bayesian Multivariate Receptor Modeling) Bayesian analysis of multivariate receptor modeling. The package consists of implementations of the methods of Park and Oh (2015) .The package uses 'JAGS'(Just Another Gibbs Sampler) to generate Markov chain Monte Carlo samples of parameters. Package: r-cran-bayesmsm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-mcmcpack, r-cran-r2jags Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesmsm_1.0.0-1.ca2404.1_all.deb Size: 210116 MD5sum: f2ccc2e7596c1e0d3d3c7bef6b02ed0e SHA1: ae2142c47f9c3f12825ac8e89167d349ce9a5373 SHA256: 518fa7d8b293a486f0e8b249143535134eb2d6dda71c65cdefbba254bb1716de SHA512: 18fad5fc15a8658b7cd760e62144fa4978af9e1c63199521d715eb83815a3e64e3509962e146ec95f3733685361702e02f30cd8736fd76f600b31a0987c2cb4d Homepage: https://cran.r-project.org/package=bayesmsm Description: CRAN Package 'bayesmsm' (Fitting Bayesian Marginal Structural Models for LongitudinalObservational Data) Implements Bayesian marginal structural models for causal effect estimation with time-varying treatment and confounding. It includes an extension to handle informative right censoring. The Bayesian importance sampling weights are estimated using JAGS. See Saarela (2015) for methodological details. Package: r-cran-bayesmultimode Architecture: all Version: 0.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-bayesplot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-gtools, r-cran-magrittr, r-cran-mcmcglmm, r-cran-mvtnorm, r-cran-posterior, r-cran-sn, r-cran-stringr, r-cran-tidyr, r-cran-rdpack Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesmultimode_0.7.5-1.ca2404.1_all.deb Size: 5052714 MD5sum: 00c938f3d69a4ed11e6b9c2e0c9e9366 SHA1: b700a44553a308de2ad6286d84e94f9451dc1282 SHA256: 88a9066464bd958831c7b512cb221fbd1402b4eca9abc223e9c711c583f3475a SHA512: 9546640f53996472dcf78ec5e74e488321a71852aff0c996163bb34314f328991722ff34be0b2a894e031ba25f974167f3033afb31d06564fac31fe86df3d857 Homepage: https://cran.r-project.org/package=BayesMultiMode Description: CRAN Package 'BayesMultiMode' (Bayesian Mode Inference) A two-step Bayesian approach for mode inference following Cross, Hoogerheide, Labonne and van Dijk (2024) ). First, a mixture distribution is fitted on the data using a sparse finite mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of mixture components does not have to be known; the size of the mixture is estimated endogenously through the SFM approach. Second, the modes of the estimated mixture at each MCMC draw are retrieved using algorithms specifically tailored for mode detection. These estimates are then used to construct posterior probabilities for the number of modes, their locations and uncertainties, providing a powerful tool for mode inference. Package: r-cran-bayesmultmeta Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-rdpack Suggests: r-cran-mvmeta, r-cran-gplots, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesmultmeta_0.1.1-1.ca2404.1_all.deb Size: 91646 MD5sum: 9b4cb22530ec25e93ae3ae03e5a88227 SHA1: dbf7cb8a2ad74920835b6787c49a75ba76a4e749 SHA256: 58f9a44d7e7cfdde618784cf92d664173cfac4c20cccf5b45be3269d5cc51072 SHA512: 6ecc0a9a6dbe83bdbe3ffb679a59a7b488905825c9773d1554dd65953b7a3a353d22952d775464f362da0959f3cbdea90a9335af6681951477094c1ae643602c Homepage: https://cran.r-project.org/package=BayesMultMeta Description: CRAN Package 'BayesMultMeta' (Bayesian Multivariate Meta-Analysis) Objective Bayesian inference procedures for the parameters of the multivariate random effects model with application to multivariate meta-analysis. The posterior for the model parameters, namely the overall mean vector and the between-study covariance matrix, are assessed by constructing Markov chains based on the Metropolis-Hastings algorithms as developed in Bodnar and Bodnar (2021) (). The Metropolis-Hastings algorithm is designed under the assumption of the normal distribution and the t-distribution when the Berger and Bernardo reference prior and the Jeffreys prior are assigned to the model parameters. Convergence properties of the generated Markov chains are investigated by the rank plots and the split hat-R estimate based on the rank normalization, which are proposed in Vehtari et al. (2021) (). Package: r-cran-bayesnec Architecture: all Version: 2.1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-ggplot2, r-cran-formula.tools, r-cran-loo, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-tidyselect, r-cran-evaluate, r-cran-rlang, r-cran-chk Suggests: r-cran-rstan, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesnec_2.1.3.1-1.ca2404.1_all.deb Size: 4644214 MD5sum: f3822e86bf34dc0ea61f54cad36689b2 SHA1: c6b9cd684437d222e62aacdf2d405f4388b447d2 SHA256: 80ea7dbc4f46acf34a7723c4800a4025722736d036a96a911331aa2114c01ebe SHA512: f75eb7dcc76eb4c973da3dfc05a4a870cc422c4421379e9023b7a09df6fcb2de88b5d6de4ab7b71de28a4c88ccfb75aa66859042083e81eab0be225587819a15 Homepage: https://cran.r-project.org/package=bayesnec Description: CRAN Package 'bayesnec' (A Bayesian No-Effect- Concentration (NEC) Algorithm) Implementation of No-Effect-Concentration estimation that uses 'brms' (see Burkner (2017); Burkner (2018); Carpenter 'et al.' (2017) to fit concentration(dose)-response data using Bayesian methods for the purpose of estimating 'ECx' values, but more particularly 'NEC' (see Fox (2010)), 'NSEC' (see Fisher and Fox (2023)), and 'N(S)EC (see Fisher et al. 2023). A full description of this package can be found in Fisher 'et al.' (2024). This package expands and supersedes an original version implemented in 'R2jags' (see Su and Yajima (2020); Fisher et al. (2020)). Package: r-cran-bayesnetbp Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-rcolorbrewer, r-cran-fields, r-cran-doby, r-bioc-graph, r-cran-bnlearn Suggests: r-bioc-rgraphviz, r-cran-shiny, r-cran-googlevis, r-cran-cyjshiny, r-cran-qtl, r-cran-qtlnet Filename: pool/dists/noble/main/r-cran-bayesnetbp_1.6.1-1.ca2404.1_all.deb Size: 285280 MD5sum: 985fe28e477a5a034d0c5b4017f4324c SHA1: c43cbf3252f72250ab3fbb6794d7c81cd0e94b34 SHA256: d86ef2fcbd21c05397262bc9d2c5e052749ff6a7b7b6f0b3530094a577439fb5 SHA512: 5a7aa6bd87bf434e6c9022ec444eb7cf3c26ae9a77b1e2b609b5ad7a96c7357f800d37aef23c3bc23c5580dbabd2895032078701ce89eba6a307bad46c8ab25b Homepage: https://cran.r-project.org/package=BayesNetBP Description: CRAN Package 'BayesNetBP' (Bayesian Network Belief Propagation) Belief propagation methods in Bayesian Networks to propagate evidence through the network. The implementation of these methods are based on the article: Cowell, RG (2005). Local Propagation in Conditional Gaussian Bayesian Networks . For details please see Yu et. al. (2020) BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks . The optional 'cyjShiny' package for running the Shiny app is available at . Please see the example in the documentation of 'runBayesNetApp' function for installing 'cyjShiny' package from GitHub. Package: r-cran-bayesnsgp Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nimble, r-cran-fnn, r-cran-matrix, r-cran-statmatch Filename: pool/dists/noble/main/r-cran-bayesnsgp_0.2.0-1.ca2404.1_all.deb Size: 470100 MD5sum: 16e7f90a34b7fd11b597d4b79e0a0b5e SHA1: 3d7f19689e7166e7715ac9fae5fcd16e73905a8f SHA256: 6b79f809e2fb7dfb1dc92b995ed77e28eecdf1e104e3378cccd0fed4624fd3f3 SHA512: 620a2aaf45d6e3233effd7de45d94e63313db2613598cec7e542e65608127a7286525841a51546dc96217a4de0abf84dc6f1fcc2a569b39b83c3184deaa5b47a Homepage: https://cran.r-project.org/package=BayesNSGP Description: CRAN Package 'BayesNSGP' (Bayesian Analysis of Non-Stationary Gaussian Process Models) Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) ). Bayesian inference is carried out using Markov chain Monte Carlo methods via the "nimble" package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step. Package: r-cran-bayesorddesign Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ordinal, r-cran-schoolmath, r-cran-coda, r-cran-gsdesign, r-cran-superdiag, r-cran-ggplot2, r-cran-madness, r-cran-rjmcmc, r-cran-r2jags, r-cran-rjags Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesorddesign_0.1.2-1.ca2404.1_all.deb Size: 111278 MD5sum: e844c0e411590c7c8b8901e5bc87f464 SHA1: 5e3db9c07cfe5cc76b7a65255488723bef5dff91 SHA256: 32720915144b40865cdc5560bc49cef832eab061f2bcdc1ee8d8d05d68a8c131 SHA512: 1c5342f31dc9c09326eaabed4afe8841ec07395b8e27db8b5cb294e2fbd2e9571f9e5ba286c7e706b1a8b233c44f46a8ff59c3d668a50485cb97fc0816fd8d80 Homepage: https://cran.r-project.org/package=BayesOrdDesign Description: CRAN Package 'BayesOrdDesign' (Bayesian Group Sequential Design for Ordinal Data) The proposed group-sequential trial design is based on Bayesian methods for ordinal endpoints, including three methods, the proportional-odds-model (PO)-based, non-proportional-odds-model (NPO)-based, and PO/NPO switch-model-based designs, which makes our proposed methods generic to be able to deal with various scenarios. Richard J. Barker, William A. Link (2013) . Thomas A. Murray, Ying Yuan, Peter F. Thall, Joan H. Elizondo, Wayne L.Hofstetter (2018) . Chengxue Zhong, Haitao Pan, Hongyu Miao (2021) . Package: r-cran-bayespiecehazselect Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-bayespiecehazselect_1.1.0-1.ca2404.1_all.deb Size: 90230 MD5sum: d76bb7013103c75d4f31bac40db00a1a SHA1: 15eefa1ed933ecaae9c5ca02ad948745b4acefc1 SHA256: 5850f12f00a0bee66af13370b16874618ac5d1330d9461204a842c4813ddb784 SHA512: 1f6181342b766f236415dd0c586a077138c1f2740556d4d5e269e89a2b1ab10fe8f3aa2d1eb819fbc5d26cb9eef341f4c0b4eb8f41595ceb797798034a499e48 Homepage: https://cran.r-project.org/package=BayesPieceHazSelect Description: CRAN Package 'BayesPieceHazSelect' (Variable Selection in a Hierarchical Bayesian Model for a HazardFunction) Fits a piecewise exponential hazard to survival data using a Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive formulation for the spatial dependency in the hazard rates for each piece. This function uses Metropolis- Hastings-Green MCMC to allow the number of split points to vary and also uses Stochastic Search Variable Selection to determine what covariates drive the risk of the event. This function outputs trace plots depicting the number of split points in the hazard and the number of variables included in the hazard. The function saves all posterior quantities to the desired path. Package: r-cran-bayesplay Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1484 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gginnards Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-vdiffr, r-cran-ggplot2, r-cran-patrick Filename: pool/dists/noble/main/r-cran-bayesplay_0.9.3-1.ca2404.1_all.deb Size: 928318 MD5sum: 5cf2c7ccbbf681d94af03da9c080003f SHA1: 4290b8eb9d2a987f8725b55389bb2eb1e666a667 SHA256: a2bc9db8384d05ef59d53966fb5b990caa6b8504327738506ea641562a328fa7 SHA512: 6dadaa3f37841a714054055998cdc29c0b340ff02bab2cc6a23257355efff88f0290cee7ee397793f8f24b6486918885f9fb11f2e33796e082aef9e9b4dceb6c Homepage: https://cran.r-project.org/package=bayesplay Description: CRAN Package 'bayesplay' (The Bayes Factor Playground) A lightweight modelling syntax for defining likelihoods and priors and for computing Bayes factors for simple one parameter models. It includes functionality for computing and plotting priors, likelihoods, and model predictions. Additional functionality is included for computing and plotting posteriors. Package: r-cran-bayesplot Architecture: all Version: 1.15.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6998 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggridges, r-cran-glue, r-cran-posterior, r-cran-reshape2, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-ggdist, r-cran-ggfortify, r-cran-gridextra, r-cran-hexbin, r-cran-knitr, r-cran-loo, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-rstan, r-cran-rstanarm, r-cran-rstantools, r-cran-scales, r-cran-shinystan, r-cran-survival, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-bayesplot_1.15.0-1.ca2404.1_all.deb Size: 5646704 MD5sum: 6e9299fcc05334a1a26d66a973fa75ae SHA1: 5884ff05970f657207b6225a18f788c8e32d27aa SHA256: 4056f49b4b7af02a062658f75f9880d053a41d38feff317a06ca55e5ac7007ca SHA512: cfabb6d91c0c8221a642f265ea4ad205dec300d4bf22ddc0b5cb27b3b6e9ba8a2779f58d1a767e496933c2864f737f04d9045e0d9b949117dd0fc2c5cb397208 Homepage: https://cran.r-project.org/package=bayesplot Description: CRAN Package 'bayesplot' (Plotting for Bayesian Models) Plotting functions for posterior analysis, MCMC diagnostics, prior and posterior predictive checks, and other visualizations to support the applied Bayesian workflow advocated in Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) . The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'. Package: r-cran-bayespm Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 477 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-extradistr, r-cran-rmutil, r-cran-invgamma Filename: pool/dists/noble/main/r-cran-bayespm_0.2.0-1.ca2404.1_all.deb Size: 447052 MD5sum: 7238f31a5e8c771287e6f9afaff1eba9 SHA1: 9cb6fab2c86a7bef0b7bd7e0fd0a37e624c63276 SHA256: 7899688f93729990f4e3077617290f092e14bdb58aed066b0a0659b976b0385d SHA512: 120aa1b701b395df10c7d2ecb2d87aee024a30ff029139363ecdf378a68915f6352704a11cc87f72662a7a7c9ad72dcef5d4a732485ff9181c3c4a2a6606ba83 Homepage: https://cran.r-project.org/package=bayespm Description: CRAN Package 'bayespm' (Bayesian Statistical Process Monitoring) The R-package bayespm implements Bayesian Statistical Process Control and Monitoring (SPC/M) methodology. These methods utilize available prior information and/or historical data, providing efficient online quality monitoring of a process, in terms of identifying moderate/large transient shifts (i.e., outliers) or persistent shifts of medium/small size in the process. These self-starting, sequentially updated tools can also run under complete absence of any prior information. The Predictive Control Charts (PCC) are introduced for the quality monitoring of data from any discrete or continuous distribution that is a member of the regular exponential family. The Predictive Ratio CUSUMs (PRC) are introduced for the Binomial, Poisson and Normal data (a later version of the library will cover all the remaining distributions from the regular exponential family). The PCC targets transient process shifts of typically large size (a.k.a. outliers), while PRC is focused in detecting persistent (structural) shifts that might be of medium or even small size. Apart from monitoring, both PCC and PRC provide the sequentially updated posterior inference for the monitored parameter. Bourazas K., Kiagias D. and Tsiamyrtzis P. (2022) "Predictive Control Charts (PCC): A Bayesian approach in online monitoring of short runs" , Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs" , Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Design and properties of the predictive ratio cusum (PRC) control charts" . Package: r-cran-bayespmtools Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastlogisticregressionwrap, r-cran-logitnorm, r-cran-mc2d, r-cran-mcmapper, r-cran-proc, r-cran-cobs, r-cran-oor, r-cran-quantreg Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-bayespmtools_0.0.1-1.ca2404.1_all.deb Size: 201364 MD5sum: d80d3fe5f1b461661abe20997516de0e SHA1: 2ab31e868e5bdd33c0ee3d42cea12aa1d11a261c SHA256: 27b2393e173ed9a0b35566485f6735d7c2454f6014d2db020ec7aa67d6b7e43a SHA512: cf435418dec8509379ed4443b2f462ce538938a6fceb37d3ed58fa86f131134e450ce6b70787dfcb567bde96ef4383f6886f8a827c06c4ead93721d01bfceb83 Homepage: https://cran.r-project.org/package=bayespmtools Description: CRAN Package 'bayespmtools' (Bayesian Sample Size and Precision Considerations for RiskPrediction Models) Performs Bayesian sample size, precision, and value-of-information analysis for external validation of existing multi-variable prediction models using the approach proposed by Sadatsafavi and colleagues (2025) . Package: r-cran-bayespocket Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gigrvg, r-cran-truncnorm, r-cran-progress, r-cran-stochtree, r-cran-softbart, r-cran-pbmcapply Filename: pool/dists/noble/main/r-cran-bayespocket_0.1.0-1.ca2404.1_all.deb Size: 63934 MD5sum: fe413e3c9535fea7ccdcbc1ef975ea0e SHA1: 98e9166dd417371e0f80394fb871a191fa655eb1 SHA256: 86ceb7e75626003cdc54a1a4636b034f57d846efbd59ef49ae5191698710c6cc SHA512: 3159e321f55dda8b6a756980b60b504348040986ca32e98355dfccc150ace80bea9f6c88cb5394fb06304e3c17cab44a0c64d46122cb0698e7e2161bb33a27b1 Homepage: https://cran.r-project.org/package=BayesPocket Description: CRAN Package 'BayesPocket' (Bayesian Causal Inference for Periodontal Diseases inLongitudinal Studies) Implements the Mixed Treatment-State Causal Model (MTSCM), a Bayesian framework for estimating causal effects of clinical interventions on bounded continuous outcomes in longitudinal observational studies with irregular visits. The methodology is specifically designed for periodontal disease research, where discrete treatments and continuous disease states (e.g., proportion of periodontal pockets exceeding 3 mm) reciprocally influence one another under dynamic feedback. The package integrates a double-censored Tobit likelihood to handle boundary mass at zero and one, subject-specific random effects to capture within-subject correlation, and flexible tree-based ensemble priors (standard BART and Soft BART) to model complex nonlinear interactions without parametric restrictions. Causal identification is established under the potential outcomes framework via the G-computation formula, with key estimands including the Mixed Average Potential Outcome (MAPO) and the Mixed Probability of Disease Resolution (MPDR). The package provides functions for model fitting, posterior inference, and causal estimand estimation. Package: r-cran-bayespostest Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1329 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cardata, r-cran-catools, r-cran-coda, r-cran-dplyr, r-cran-ggplot2, r-cran-ggridges, r-cran-reshape2, r-cran-rlang, r-cran-texreg, r-cran-tidyr, r-cran-hdinterval, r-cran-rocr, r-cran-r2jags, r-cran-rjags Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rstan, r-cran-testthat, r-cran-covr, r-cran-runjags, r-cran-rstanarm, r-cran-mcmcpack, r-cran-r2winbugs, r-cran-brms Filename: pool/dists/noble/main/r-cran-bayespostest_0.4.0-1.ca2404.1_all.deb Size: 878716 MD5sum: a457c529fc012f93aaefc0c719b26921 SHA1: 4644cb9c7f3fa5c3bb7ffd7b2203137cf3f82d0a SHA256: 9bb8a9872174c834754318f1dee76c44d7d84a54859331f860f4c9cac5750b67 SHA512: c21043b44c2c0c71561134bdc67479f1e2ea7087e40a28a5f2feef93e00831d738ec13b5b27ae7d5a9ce68a4267de966c17afe4dcf1febad790c58141aa01a33 Homepage: https://cran.r-project.org/package=BayesPostEst Description: CRAN Package 'BayesPostEst' (Generate Postestimation Quantities for Bayesian MCMC Estimation) An implementation of functions to generate and plot postestimation quantities after estimating Bayesian regression models using Markov chain Monte Carlo (MCMC). Functionality includes the estimation of the Precision-Recall curves (see Beger, 2016 ), the implementation of the observed values method of calculating predicted probabilities by Hanmer and Kalkan (2013) , the implementation of the average value method of calculating predicted probabilities (see King, Tomz, and Wittenberg, 2000 ), and the generation and plotting of first differences to summarize typical effects across covariates (see Long 1997, ISBN:9780803973749; King, Tomz, and Wittenberg, 2000 ). This package can be used with MCMC output generated by any Bayesian estimation tool including 'JAGS', 'BUGS', 'MCMCpack', and 'Stan'. Package: r-cran-bayesppr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesppr_0.1.0-1.ca2404.1_all.deb Size: 96600 MD5sum: 5d0015da8cb63c1f7842944a8a40fb50 SHA1: 7ac847a501779b2806560928aeb7369976cd1971 SHA256: 71fef45789de01dd3471fde6143ed5e4741f86a12f12eae4cb29d0585783d334 SHA512: ead43ddf22235ca61329acdec8430094223ca5bbe29d1feeefa261e7cc4f90743eac59ed259f78f09625ac67ed1f68e5e49505a9454a75756f254880ce188b98 Homepage: https://cran.r-project.org/package=BayesPPR Description: CRAN Package 'BayesPPR' (Bayesian Projection Pursuit Regression) Bayesian fitting of projection pursuit regression model. Built to handle continuous and categorical inputs and scalar output (Collins et al., 2023 ). Package: r-cran-bayesrecon Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4352 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-nloptr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-forecast, r-cran-glarma, r-cran-scoringrules, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesrecon_1.0.1-1.ca2404.1_all.deb Size: 2852734 MD5sum: 42fb8563df849b9ef98f86ac76ba267b SHA1: 1dcab78c41e8874bf98123265f8bf7b0f213727a SHA256: 5390c5972efca718097bcb915cc9aaae1ea46caed67fae88df4430c62658bb48 SHA512: e13d6a416467ce10512ce5edca15e9dbd9c9b0848a03793845fb63d5431b85610b17f930f506e17dc5488c9c567126d7b9777482a7f723e159922cde407ac0fe Homepage: https://cran.r-project.org/package=bayesRecon Description: CRAN Package 'bayesRecon' (Probabilistic Reconciliation via Conditioning) Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) , MCMC reconciliation of count time series (Corani et al., 2024) , Bottom-Up Importance Sampling (Zambon et al., 2024) , methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024) , analytical reconciliation with Bayesian treatment of the covariance matrix (Carrara et al., 2025) . Package: r-cran-bayesreg Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pgdraw, r-cran-doparallel, r-cran-foreach Filename: pool/dists/noble/main/r-cran-bayesreg_1.3-1.ca2404.1_all.deb Size: 310612 MD5sum: 916a19d7f5c792185570f4086f4d31ea SHA1: d5ff6b8563d75d743f80b373526070167d58f786 SHA256: edad5825147cf71c04652a6f6cc124f904dbcb2163df2e31138b6a791b87d2b3 SHA512: ee105a908001adbc8d43ab070f86fd2c05059ee460f1333e4a2d7174893437fc818c9828671ab5fd306ac445749537581fe64ab1045d427d6c7488a84698f913 Homepage: https://cran.r-project.org/package=bayesreg Description: CRAN Package 'bayesreg' (Bayesian Regression Models with Global-Local Shrinkage Priors) Fits linear or generalized linear regression models using Bayesian global-local shrinkage prior hierarchies as described in Polson and Scott (2010) . Provides an efficient implementation of ridge, lasso, horseshoe and horseshoe+ regression with logistic, Gaussian, Laplace, Student-t, Poisson or geometric distributed targets using the algorithms summarized in Makalic and Schmidt (2016) . Package: r-cran-bayesrep Architecture: all Version: 0.42.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lamw, r-cran-hypergeo Suggests: r-cran-roxygen2, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-bayesrep_0.42.2-1.ca2404.1_all.deb Size: 127236 MD5sum: ed8cea937ee3f2c24191e90785fb741c SHA1: 09e75a1a6845a8b16987c9199d867aad5360583f SHA256: e780970182ea0ce650070a50097308571b80dc85ef7dd004931faa8135c1a099 SHA512: 7a92f8c7aaed62e33fb42a1532cb311c6b73ecc76e791f212dc9aca6471b4c9f5963c1d2bfbb5b95576b5395d06725846228b0aa8c584dcb26411e2aa86b9d0b Homepage: https://cran.r-project.org/package=BayesRep Description: CRAN Package 'BayesRep' (Bayesian Analysis of Replication Studies) Provides tools for the analysis of replication studies using Bayes factors (Pawel and Held, 2022) . 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Package: r-cran-bayesroe Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colourpicker, r-cran-config, r-cran-ggplot2, r-cran-golem, r-cran-scales, r-cran-shiny, r-cran-shinybs Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesroe_0.2-1.ca2404.1_all.deb Size: 76218 MD5sum: aa936be579a5545d2a3a35a14b584549 SHA1: 3b68f6dd1710acf9bd987c6d88b054e0d9537642 SHA256: d91d43d5d3ba9cc95be864c292ffa3a40b2ce171aaa3098677ee3d8e49659b6b SHA512: 56f88bef142ae676eddb0d6115879a26a5cb5b173815b1ae1123ffcebe65fa004fd8247c59988f1661e180bb8151a1bd6fe3642014ff9728f945838d7e441709 Homepage: https://cran.r-project.org/package=bayesROE Description: CRAN Package 'bayesROE' (Bayesian Regions of Evidence) Computation and visualization of Bayesian Regions of Evidence to systematically evaluate the sensitivity of a superiority or non-inferiority claim against any prior assumption of its assessors. Methodological details are elaborated by Hoefler and Miller (). Besides generic functions, the package also provides an intuitive 'Shiny' application, that can be run in local R environments. Package: r-cran-bayesrs Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 854 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-ggplot2, r-cran-metrology, r-cran-reshape, r-cran-coda Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesrs_0.1.3-1.ca2404.1_all.deb Size: 785322 MD5sum: 88678c0c71fc42955bcb52bd46e69fad SHA1: 7987b24e0411e8661d9c33b65d92f6317134f09d SHA256: 126bc421c05354a246b98e06904e5f2bee408aeda4905ab2b3ff0a59bb05f0ce SHA512: 151900bfc30961052dbc4778860f5268cc9c45dabd36e37f8bc6aaa20728b2bdad38480e173e7bdc1e72b11ed0bf9c22ed702db0ea8ab2eb57fad983ca005717 Homepage: https://cran.r-project.org/package=BayesRS Description: CRAN Package 'BayesRS' (Bayes Factors for Hierarchical Linear Models with ContinuousPredictors) Runs hierarchical linear Bayesian models. Samples from the posterior distributions of model parameters in JAGS (Just Another Gibbs Sampler; Plummer, 2017, ). Computes Bayes factors for group parameters of interest with the Savage-Dickey density ratio (Wetzels, Raaijmakers, Jakab, Wagenmakers, 2009, ). Package: r-cran-bayesrules Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2268 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-janitor, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-rstanarm, r-cran-e1071, r-cran-groupdata2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayesrules_0.0.3-1.ca2404.1_all.deb Size: 2146484 MD5sum: e8b0348798eb1f64eb20dd7771fe8a59 SHA1: d72159648d195f395256a024554a40ee2de76ee7 SHA256: f9896d79fc5ee4c442b58c4ed7153daa822a078384a402110569a43d893882ec SHA512: 4171fb01d3f5c9718c99175a2d066797f0fe154888c404244d02ec026c598461fa1de0416c1e09a369301eef9ebe009e693caa3dc796ebbea5ec73d0bb067d5a Homepage: https://cran.r-project.org/package=bayesrules Description: CRAN Package 'bayesrules' (Datasets and Supplemental Functions from Bayes Rules! Book) Provides datasets and functions used for analysis and visualizations in the Bayes Rules! book (). The package contains a set of functions that summarize and plot Bayesian models from some conjugate families and another set of functions for evaluation of some Bayesian models. Package: r-cran-bayess5 Architecture: all Version: 1.41-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-snowfall, r-cran-abind, r-cran-splines2 Filename: pool/dists/noble/main/r-cran-bayess5_1.41-1.ca2404.1_all.deb Size: 158772 MD5sum: a7a0928cdbd60089ce6caa98dbe6aba5 SHA1: dde72ae7dae1f7b6329363abe9a1e93f9b01d493 SHA256: 96f1718ea1b4d1959f7ca608f92954567ab905913245fa37c367506fe65383d3 SHA512: 53a4c824eb6db5eb46f67c2288a014b404cd0d63cf618fba87a347c65b5f5bdcf8a79237cf66b2ebcc996ab7d5f347c173fd43a340d01896ba04659cdd744f83 Homepage: https://cran.r-project.org/package=BayesS5 Description: CRAN Package 'BayesS5' (Bayesian Variable Selection Using Simplified Shotgun StochasticSearch with Screening (S5)) In p >> n settings, full posterior sampling using existing Markov chain Monte Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled "Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya. Package: r-cran-bayess Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mnormt, r-cran-gplots, r-cran-combinat Filename: pool/dists/noble/main/r-cran-bayess_1.6-1.ca2404.1_all.deb Size: 322164 MD5sum: 3012d14bef2fa84117f5f7d15f1daecd SHA1: 73952e38852fd60ceb1df63edd23b5688902487d SHA256: 14e6f7f901ace6d5956165e8dd087e202eeecf89995f9b2dcdf39ce77f809492 SHA512: 54170a96bb2c1f9efa6a77bdb0aff5166f9ead5f95617501bd34c204aecc619677894556d57effe185b6689968e31d6d1ee2d788188c3bc44fbd164580c53e7a Homepage: https://cran.r-project.org/package=bayess Description: CRAN Package 'bayess' (Bayesian Essentials with R) Allows the reenactment of the R programs used in the book Bayesian Essentials with R without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations. Marin J.-M. and Robert C. P. (2014) . Package: r-cran-bayessampling Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 883 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-matrixcalc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-teachingsampling Filename: pool/dists/noble/main/r-cran-bayessampling_1.1.0-1.ca2404.1_all.deb Size: 639806 MD5sum: 646ef2bf2d14cec32c5204e77d4c3934 SHA1: af61d93b3c5d80603d0b11452ad2ba7861905556 SHA256: 71c28b67d9ecaa75dc6268bc945dc02524b4889c30fc27332d3defdc1cac4b5b SHA512: 13c131229e8356208c979c74d0661487528b178e961cbc303d07f248746b0200479bee23b187da906e96451f438e1db9c1d177d3f321cff0c9dceda872642817 Homepage: https://cran.r-project.org/package=BayesSampling Description: CRAN Package 'BayesSampling' (Bayes Linear Estimators for Finite Population) Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement), to the Ratio estimator (using auxiliary information) - BLE_Ratio() - and to categorical data - BLE_Categorical(). The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg() and it is also possible to achieve the design based estimators using vague prior distributions. Based on Gonçalves, K.C.M, Moura, F.A.S and Migon, H.S.(2014) . Package: r-cran-bayessim Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1930 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nimble, r-cran-coda, r-cran-magrittr, r-cran-mass, r-cran-mvtnorm, r-cran-patchwork, r-cran-tidyr, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayessim_1.0.4-1.ca2404.1_all.deb Size: 1539468 MD5sum: 430c18c032fea1ec9c327f8db5a70d2a SHA1: 2b9d6286a25fd16e8c78ebbdfa6bdb2971cace21 SHA256: dcde350d5cc367b75545ea765c125b153c998e4b6a84e767bc1970fd3630fac5 SHA512: cfb0700f0a695c22c73b392611815b7b5732f885d3f0af0689f25fda2ddb3b88264fe0e38939dc1d65cc3dfc4eb19235069dc024c71bee8afc588871b8b445c7 Homepage: https://cran.r-project.org/package=BayesSIM Description: CRAN Package 'BayesSIM' (Integrated Interface of Bayesian Single Index Models using'nimble') Provides tools for fitting Bayesian single index models with flexible choices of priors for both the index and the link function. The package implements model estimation and posterior inference using efficient MCMC algorithms built on the 'nimble' framework, allowing users to specify, extend, and simulate models in a unified and reproducible manner. The following methods are implemented in the package: Antoniadis et al. (2004) , Wang (2009) , Choi et al. (2011) , Dhara et al. (2019) , McGee et al. (2023) . Package: r-cran-bayesssm Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-dplyr, r-cran-future, r-cran-future.apply, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-tidyr, r-cran-extradistr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-bayesssm_0.5.0-1.ca2404.1_all.deb Size: 160540 MD5sum: de0411e01c2f1521ee51c9a50766c736 SHA1: 08173073ce0a17913120e0effa7cb04c431927c8 SHA256: 333c9ff06e3efe3425a857fb4fa773f0b47d3886fb418b4fe2176e683cbb66ba SHA512: 95ff03b28967935be234e815cd84e36f17530db96e99114275b102165509d4df46587c5ee8c67f8ed85af194aeb9ed3ad01e4b26c7fb4a17dbd494ccba8bbaa9 Homepage: https://cran.r-project.org/package=bayesSSM Description: CRAN Package 'bayesSSM' (Bayesian Methods for State Space Models) Implements methods for Bayesian analysis of State Space Models. Includes implementations of the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) and automatic tuning inspired by Pitt et al. (2012) and J. Dahlin and T. B. Schön (2019) . Package: r-cran-bayessurvival Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-ggplot2 Suggests: r-cran-simsurv, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayessurvival_0.2.0-1.ca2404.1_all.deb Size: 339110 MD5sum: c9c0268e7b1836d27b452b022287a842 SHA1: b812df7dc97bcd8c0ecabb53b58828ac76457a3a SHA256: ccae2c842ade89f74764408ede7cae22ae3482d25c353e712bcfe9efaccd4f4e SHA512: d73478285577355f0f7a2798d03e017be0f8125eae04a05440185dc0d1b08d30cd7138c9fe7c60b8444a255c93ce374361c5f28ac18f5eee219d76434c9e89bf Homepage: https://cran.r-project.org/package=BayesSurvival Description: CRAN Package 'BayesSurvival' (Bayesian Survival Analysis for Right Censored Data) Performs unadjusted Bayesian survival analysis for right censored time-to-event data. The main function, BayesSurv(), computes the posterior mean and a credible band for the survival function and for the cumulative hazard, as well as the posterior mean for the hazard, starting from a piecewise exponential (histogram) prior with Gamma distributed heights that are either independent, or have a Markovian dependence structure. A function, PlotBayesSurv(), is provided to easily create plots of the posterior means of the hazard, cumulative hazard and survival function, with a credible band accompanying the latter two. The priors and samplers are described in more detail in Castillo and Van der Pas (2020) "Multiscale Bayesian survival analysis" . In that paper it is also shown that the credible bands for the survival function and the cumulative hazard can be considered confidence bands (under mild conditions) and thus offer reliable uncertainty quantification. Package: r-cran-bayest Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack Suggests: r-cran-coda, r-cran-mass Filename: pool/dists/noble/main/r-cran-bayest_1.5-1.ca2404.1_all.deb Size: 35954 MD5sum: b6fa5d270cb22de04af04f694dbc6650 SHA1: 6d5ffabbdfaf170b859524154e2b199cf3d4917b SHA256: 830981b88216640c2d98caf731a44bb4ce86551b42f2b2e788e505ba784b40b4 SHA512: 2cf4aa466c0916a3dc7732dff245da7282b5f830cff2982941bc1ed9c4a8eb7834cedb9ab21ec29326084533e408a9c85de483481091afbf810a79ab91a115e8 Homepage: https://cran.r-project.org/package=bayest Description: CRAN Package 'bayest' (Effect Size Targeted Bayesian Two-Sample t-Tests via MarkovChain Monte Carlo in Gaussian Mixture Models) Provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) . Package: r-cran-bayestestr Architecture: all Version: 0.18.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1740 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-insight, r-cran-datawizard Suggests: r-cran-bayesfactor, r-cran-bayesqr, r-cran-bayesplot, r-cran-betareg, r-cran-bh, r-cran-blavaan, r-cran-bridgesampling, r-cran-brms, r-cran-collapse, r-cran-curl, r-cran-effectsize, r-cran-emmeans, r-cran-gamm4, r-cran-ggdist, r-cran-ggplot2, r-cran-glmmtmb, r-cran-httr2, r-cran-kernsmooth, r-cran-knitr, r-cran-lavaan, r-cran-lme4, r-cran-logspline, r-cran-marginaleffects, r-cran-mass, r-cran-mclust, r-cran-mediation, r-cran-modelbased, r-cran-ordbetareg, r-cran-parameters, r-cran-patchwork, r-cran-performance, r-cran-posterior, r-cran-quadprog, r-cran-rcppeigen, r-cran-rmarkdown, r-cran-rstan, r-cran-rstanarm, r-cran-see, r-cran-testthat, r-cran-tinytable, r-cran-tweedie, r-cran-withr Filename: pool/dists/noble/main/r-cran-bayestestr_0.18.0-1.ca2404.1_all.deb Size: 1281066 MD5sum: 7eb80e666aa6d6f35b370f650c95159f SHA1: 820f69f1090a980ebd8bac7a249af08daaf12e62 SHA256: 89b292106cea3eea2e730287ef4c8badcd850c24c75789447fc91d83789563d2 SHA512: 75a80f1f340dbfd7187d647eb842d0ab40bb53d59e1664ca65ea2c4298cc1c94f55e65f5e2a9bf94631f477397c4c77cba3c899c72ad8d699e6c03256788a6b0 Homepage: https://cran.r-project.org/package=bayestestR Description: CRAN Package 'bayestestR' (Understand and Describe Bayesian Models and PosteriorDistributions) Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Kruschke, 2015 ) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors). References: Makowski et al. (2021) . Package: r-cran-bayestools Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2173 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-extradistr, r-cran-mvtnorm, r-cran-coda, r-cran-bridgesampling, r-cran-ggplot2, r-cran-rdpack, r-cran-rlang Suggests: r-cran-scales, r-cran-testthat, r-cran-vdiffr, r-cran-covr, r-cran-knitr, r-cran-rstan, r-cran-rjags, r-cran-runjags, r-cran-lme4, r-cran-bayesfactor, r-cran-robma, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayestools_0.3.0-1.ca2404.1_all.deb Size: 1806568 MD5sum: 678456f8c5dc36ac78a52f330e2f0b5d SHA1: c6acabfeeae0bdbb3b947ef77ff9879e0a54d3d9 SHA256: ca29bde4f65275b6b4b462065b48af4508d57e3e8540614482cc642c47af0d81 SHA512: 1b318368933715f81b692d3c2fa5ad64fe07a582d4739ea12615cb08069fd36295f0db9e2271bfdd68cc8c78e9111cff4a268f8d5b67533f7519bd0db42c20a0 Homepage: https://cran.r-project.org/package=BayesTools Description: CRAN Package 'BayesTools' (Tools for Bayesian Analyses) Provides tools for conducting Bayesian analyses and Bayesian model averaging (Kass and Raftery, 1995, , Hoeting et al., 1999, ). The package contains functions for creating a wide range of prior distribution objects, mixing posterior samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc... The tools for working with prior distribution span from visualization, generating 'JAGS' and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions. Package: r-cran-bayestreeprior Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tgp, r-cran-bayestree, r-cran-bartmachine, r-cran-mass Filename: pool/dists/noble/main/r-cran-bayestreeprior_1.0.1-1.ca2404.1_all.deb Size: 58694 MD5sum: a3336adf5afa3601536bd366c4b91756 SHA1: 67f85673f22a5bc9d7e6b287d291d69299f3d9ce SHA256: 2bad3d7829c36a7f3a0db4f817bae971fa94a15c84f47f7fedbd2060b70184ad SHA512: 4b135e3aea3c2f21e6444672284b21afa02a92374677f12c77bec352214a3cc97b745769bbf6d665e940ceecab15e1ea0d3eb85629d7efdbee4c5fb8380a4202 Homepage: https://cran.r-project.org/package=BayesTreePrior Description: CRAN Package 'BayesTreePrior' (Bayesian Tree Prior Simulation) Provides a way to simulate from the prior distribution of Bayesian trees by Chipman et al. (1998) . The prior distribution of Bayesian trees is highly dependent on the design matrix X, therefore using the suggested hyperparameters by Chipman et al. (1998) is not recommended and could lead to unexpected prior distribution. This work is part of my master thesis (expected 2016). Package: r-cran-bayestwin Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreign, r-cran-coda, r-cran-matrixstats, r-cran-rjags Filename: pool/dists/noble/main/r-cran-bayestwin_1.0-1.ca2404.1_all.deb Size: 524804 MD5sum: 177a66707c6968f35c9d4f6acd8fb355 SHA1: e095991107b4d58db01b455f29e3ff94b656347a SHA256: a6c2226ffb37aa6cc7664d9c24bbc3f0acda9c31e9d5c782d19d023c4273ecd2 SHA512: 8ea3a787e8b06c3ec8a72a7e5e6e91b5afad37a9bde0e04525bceb766336ea832e37b563317f610b83b62e229b7ce4d7a2dac0d9c069333a1a54cc8d93ae4e96 Homepage: https://cran.r-project.org/package=BayesTwin Description: CRAN Package 'BayesTwin' (Bayesian Analysis of Item-Level Twin Data) Bayesian analysis of item-level hierarchical twin data using an integrated item response theory model. Analyses are based on Schwabe & van den Berg (2014) , Molenaar & Dolan (2014) , Schwabe, Jonker & van den Berg (2016) and Schwabe, Boomsma & van den Berg (2016) . Package: r-cran-bayesvl Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-stanheaders, r-cran-coda, r-cran-bnlearn, r-cran-ggplot2, r-cran-bayesplot, r-cran-viridis, r-cran-reshape2 Suggests: r-cran-loo Filename: pool/dists/noble/main/r-cran-bayesvl_1.0.0-1.ca2404.1_all.deb Size: 562720 MD5sum: edbad0b39406bd700aaa3a1b42630102 SHA1: ed0a52873e3e3c87e34ba7efa4559f372ce56d3b SHA256: 52bd0d5ac1729338c336e09b3178faed7fd9d8d412dd6e9aef8d39391dbae971 SHA512: f26f9eef4221d2634fe27d0e14711d98a8336e2836767a3e577fd6aadd3a5575cd49f59450553c157462ee39a185acd2de8f5057bdf474862b369f57bd00331b Homepage: https://cran.r-project.org/package=bayesvl Description: CRAN Package 'bayesvl' (Visually Learning the Graphical Structure of Bayesian Networksand Performing MCMC with 'Stan') Provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations. It enables users to: a) Create and examine the (starting) graphical structure of Bayesian networks; b) Create random Bayesian networks using a dataset with customized constraints; c) Generate Stan code for structures of Bayesian networks for sampling the data and learning parameters; d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and produce graphs for posteriors checks. The package refers to one reference item, which describes the methods and algorithms: Vuong, Quan-Hoang and La, Viet-Phuong (2019) The 'bayesvl' R package. Open Science Framework (May 18). Package: r-cran-bayesvolcano Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1549 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-hdinterval, r-cran-purrr, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr Suggests: r-cran-brms, r-cran-rstan, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bayesvolcano_1.0.1-1.ca2404.1_all.deb Size: 1359980 MD5sum: 332d32c8e8fd43bd7ee117b91efdb0bf SHA1: c2f7ace79217211f2e81ed44245e489fb2d0281e SHA256: 9a72f4819cb551cad2ca7bab1c6d952a0c905eea5c0911e5b57c3f2208087083 SHA512: 2d2a8f478129959f1e45f7648caefddcb7ac82df152d080f292f47f3b6ee5ad5ad2de6b95f800363c614d9851c4b26ebfe8c25a0bf9712fcb4543c16392614eb Homepage: https://cran.r-project.org/package=BayesVolcano Description: CRAN Package 'BayesVolcano' (Creating Volcano Plots from Bayesian Model Posteriors) Bayesian models are used to estimate effect sizes (e.g., gene expression changes, protein abundance differences, drug response effects) while accounting for uncertainty, small sample sizes, and complex experimental designs. However, Bayesian posteriors of models with many parameters are often difficult to interpret at a glance. One way to quickly identify important biological changes based on frequentist analysis are volcano plots (using fold-changes and p-values). Bayesian volcano plots bring together the explicit treatment of uncertainty in Bayesian models and the familiar visualization of volcano plots. Package: r-cran-bayesx Architecture: all Version: 0.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3842 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shapefiles, r-cran-sp, r-cran-sf, r-cran-colorspace, r-cran-coda, r-cran-interp Suggests: r-cran-spdep Filename: pool/dists/noble/main/r-cran-bayesx_0.3-3-1.ca2404.1_all.deb Size: 1298822 MD5sum: fc67b96b16502a3c3bedaec780418762 SHA1: 0068ea9f86c206f923e711b16cfb220aefc98c3a SHA256: 3246be979e414cd67c008890b0fdb32f58f31ceb61bbad15c5334cb3c3b4274e SHA512: e10d175532a96c4292f591409b01f639bce97fcb5bbbe39f6c0d051829329cdad82fc29ae9392ef7b0a7a3674f7544fd812d7a8de65147ee595d79d2867ead2e Homepage: https://cran.r-project.org/package=BayesX Description: CRAN Package 'BayesX' (R Utilities Accompanying the Software Package BayesX) Functions for exploring and visualising estimation results obtained with BayesX, a free software for estimating structured additive regression models (). In addition, functions that allow to read, write and manipulate map objects that are required in spatial analyses performed with BayesX. Package: r-cran-bayfoxr Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3059 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bayfoxr_0.0.1-1.ca2404.1_all.deb Size: 3065020 MD5sum: 9ab61b44cc78309385b36f8b9240d424 SHA1: 4eab4ba731f27af026de074f096b14ffe58510fb SHA256: 9596f77db578844df5b049b319f5a67b19310c65073f9184ea8c43928d9f3047 SHA512: d40a31ba94af2f79ca8a9bde5668ecc74c6abbbd398f3977d677e1eb6750dc5d989bfac41cb451479bcab1e85a2096a68146af85ace2cb59b60b2613dcab36f9 Homepage: https://cran.r-project.org/package=bayfoxr Description: CRAN Package 'bayfoxr' (Global Bayesian Foraminifera Core Top Calibration) A Bayesian, global planktic foraminifera core top calibration to modern sea-surface temperatures. Includes four calibration models, considering species-specific calibration parameters and seasonality. Package: r-cran-baylum Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3582 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-cli, r-cran-hexbin, r-cran-kernsmooth, r-cran-luminescence, r-cran-rjags, r-cran-runjags, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-baylum_0.3.3-1.ca2404.1_all.deb Size: 2152858 MD5sum: 8217b4459d950f422cee43728dce909c SHA1: 58c245eae4ab23f0fcf2a5716cf51eda31e02e62 SHA256: 1ab7a71bc8f7867c8514a76c79899dda7e029a3d536d7de11925075492095125 SHA512: 534e0e0a0dbcebe8f78ed55bcc5687f80c205745f38b0165300be1d53b9d1a54990754a2501caf1701790f6ef7bbdea4efb82508d4b4631e8a9b6a61ea8eaa05 Homepage: https://cran.r-project.org/package=BayLum Description: CRAN Package 'BayLum' (Chronological Bayesian Models Integrating Optically StimulatedLuminescence and Radiocarbon Age Dating) Bayesian analysis of luminescence data and C-14 age estimates. Bayesian models are based on the following publications: Combes, B. & Philippe, A. (2017) and Combes et al. (2015) . This includes, amongst others, data import, export, application of age models and palaeodose model. Package: r-cran-baymedr Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-pso, r-cran-rms, r-cran-stringr, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-baymedr_0.2-1.ca2404.1_all.deb Size: 233290 MD5sum: 08832572c12848f949bf3afbf2049f44 SHA1: 8b17cebdde82528dbfb4767e9686a5b4d5a5fb1a SHA256: 3bc74338433d3068f3902667b49228ea4979d307a86634fad9552e53949c252f SHA512: 1089ed8f3bdac4f600f5d73f32c0e1d77f9a779d7984bb031303bf1a5686442e809602033fedb72a4d7d7c33604155655b5de933bdc6bc031b5ebf8b5b756e8a Homepage: https://cran.r-project.org/package=baymedr Description: CRAN Package 'baymedr' (Computation of Bayes Factors for Common Biomedical Designs) BAYesian inference for MEDical designs in R. Functions for the computation of Bayes factors for common biomedical research designs. Implemented are functions to test the equivalence (equiv_bf), non-inferiority (infer_bf), and superiority (super_bf) of an experimental group compared to a control group on a continuous outcome measure, as well as functions for simulating survival data and calculating a Bayes factor for Cox proportional hazards models. Bayes factors for these tests can be computed based on raw data or summary statistics. Package: r-cran-baystability Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-mass, r-cran-rstiefel, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-baystability_0.2.0-1.ca2404.1_all.deb Size: 91436 MD5sum: dc7dfaeec10320c1ce61f00a99d80f60 SHA1: 495de2f5a3a9fa38027fcf65907f04226b5d8cc1 SHA256: 07300d9a7440e122990ef824e3a3d3729ccbefb2cb484ace72395f0f15e18ca6 SHA512: 2863fc8c13422c9c7aebfa85683b9ee6909e8b7dd0691229ba8b1be44547ff648c1b999cec3a014450982e7234d1f39911f2f46d5230638f10e8a62795c34280 Homepage: https://cran.r-project.org/package=baystability Description: CRAN Package 'baystability' (Bayesian Stability Analysis of Genotype by EnvironmentInteraction (GEI)) Performs general Bayesian estimation method of linear–bilinear models for genotype × environment interaction. The method is explained in Perez-Elizalde, S., Jarquin, D., and Crossa, J. (2011) (). Package: r-cran-baystar Architecture: all Version: 0.2-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-coda Filename: pool/dists/noble/main/r-cran-baystar_0.2-10-1.ca2404.1_all.deb Size: 68808 MD5sum: 5928a3946e83b7e4e47a069cebf5328c SHA1: 0cf6e047c1217fffbbc9e86b2cd7f3113bf7340b SHA256: 556cf66e1d392f1f939cc75c5fc457a65fc59e3ded50acb66ffe1ae7bcbbfd2e SHA512: 08facc41cdc9e2155f91ed93e0bc11a91ff28ed73b85d1c064d67e946349dcf7b883ba7bb15b8bfcafb8fa5189bd1616496365faf3936dccfb311d5c024f0832 Homepage: https://cran.r-project.org/package=BAYSTAR Description: CRAN Package 'BAYSTAR' (On Bayesian Analysis of Threshold Autoregressive Models) Fit two-regime threshold autoregressive (TAR) models by Markov chain Monte Carlo methods. 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Package: r-cran-beamr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggmosaic, r-cran-ggplot2, r-cran-ggpubr, r-cran-logistf, r-cran-magrittr, r-cran-mass, r-cran-purrr, r-cran-rlist, r-cran-stringr, r-cran-survival, r-cran-survminer Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-beamr_1.1.0-1.ca2404.1_all.deb Size: 334458 MD5sum: 9282b89c1e46f9f3b87567c9d206290d SHA1: 6edf7f5d058fb85b298bbc62b235e1da9548dc2d SHA256: 814e1c8432dd26d1d3bedf3f9921b6cfe24cc2c8fa3ed1e7c9bad228f50b4e68 SHA512: 2a5c37d843ed11f965843f528af1e549166d72a61fe05d6759936243dd89d6f0c1d4c0c09681d977a7db91b0f6d84679958508b2b8fe68a3ce7c6767f167666a Homepage: https://cran.r-project.org/package=BEAMR Description: CRAN Package 'BEAMR' (Bootstrap Evaluation of Association Matrices) A bootstrap-based approach to integrate multiple forms of high dimensional genomic data with multiple clinical endpoints. This method is used to find clinically meaningful groups of genomic features, such as genes or pathways. A manuscript describing this method is in preparation. Package: r-cran-beanplot Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-vioplot, r-cran-lattice Filename: pool/dists/noble/main/r-cran-beanplot_1.3.1-1.ca2404.1_all.deb Size: 325938 MD5sum: 7c75b70cd89b960c511e9e88a08a367b SHA1: 35ab85daac87fd91d5239ee258ef8b82e18fbeae SHA256: d7b60b451ce83e844cb37bd66bb2c257ea148c0a357d9fc7765d14dfc731b8a3 SHA512: bb18b700556ff2fcc78f9481081ad15ed2441e4e36fd0ce7e5d3d691cd14d977bffe1c156fa3dc6fa1cd466dd19db12019e4d596047dacb57e90f5a8b1203144 Homepage: https://cran.r-project.org/package=beanplot Description: CRAN Package 'beanplot' (Visualization via Beanplots (Like Boxplot/Stripchart/ViolinPlot)) Plots univariate comparison graphs, an alternative to boxplot/stripchart/violin plot. Package: r-cran-beans Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1398 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-beans_0.1.0-1.ca2404.1_all.deb Size: 1394462 MD5sum: 0c3e9d8a6e4d88ddd7a2552def937612 SHA1: 8c03f20ff69b6db30cd3592da3e0828abf6067e3 SHA256: c815d9801570c9adc5de8e5f5bf47fc346cc935643bd4f0314789c1d46668d6c SHA512: 220a8d9569fd29680cf7ea6836f25aef4deb90390629950cddd03a2729cc7905b3c6eb823cbfc88e983f5f5f1debcda0b999b380b48f32416c1069348e71a4ca Homepage: https://cran.r-project.org/package=beans Description: CRAN Package 'beans' (Data on Dried Beans) These data contain morphological image measurements for dried beans from Koklu and Ozkan (2020) . Package: r-cran-bearishtrader Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bearishtrader_1.0.2-1.ca2404.1_all.deb Size: 188824 MD5sum: 31a81be5d40bb7665c17ba4ee04304d7 SHA1: 9dea67b854787fca0f96fbdd6f7a7b80ffbba34d SHA256: 5c73ae3f187c89af54afc79cc4d8108012789c23915e6f118f76cc2a7bdf16a8 SHA512: 5df1a713a5c0b6d66c940aa4fb8c764435ea53ae89d5d2ac997e3b4a3fd57ed0699c54198578d95914e11361cc764fb009080ee02058e7a4a05359f10c7ad57f Homepage: https://cran.r-project.org/package=bearishTrader Description: CRAN Package 'bearishTrader' (Trading Strategies for Bearish Outlook) Stock, Options and Futures Trading Strategies for Traders and Investors with Bearish Outlook. The indicators, strategies, calculations, functions and all other discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Juan A. Serur, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 385-453)", 2019, ISBN: 9781119593577). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). Package: r-cran-beast Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-beast_1.2-1.ca2404.1_all.deb Size: 307010 MD5sum: 1d60909f6af60fb2ccff81b99ea62d48 SHA1: a482f098e214c54c1e38026a3620321cd86a9831 SHA256: 448dc9a901473787ea82bbc1fd2494166656de9774a861ea8769957634c764cc SHA512: f000ce8d77a6e010f558559b11c52048e02090121912c39543730fec57b8641c042027488d02b07018c14178854a85db51cd50e585f431a5ea3650460166d3d0 Homepage: https://cran.r-project.org/package=beast Description: CRAN Package 'beast' (Bayesian Estimation of Change-Points in the Slope ofMultivariate Time-Series) Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2019) for a detailed presentation of the method. Package: r-cran-beastier Architecture: all Version: 2.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-beautier, r-cran-phangorn, r-cran-rappdirs, r-cran-readr, r-cran-rjava, r-cran-rlang, r-cran-sessioninfo, r-cran-stringr, r-cran-tibble, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tracerer Filename: pool/dists/noble/main/r-cran-beastier_2.5.2-1.ca2404.1_all.deb Size: 524986 MD5sum: d43be918f65e75aaf5ccdf8ff7cb5f93 SHA1: 5a2a3cea3d059c07631cb0b0cb9404b1737140e8 SHA256: 2bfcc65af2c941f4a0d538ac695f885b425009d4ba750629e48ef2a52fbc33ef SHA512: 1dec7bc043091a3e52121646564d73ec6b99c18b262a90e406b306627eed9b64e9a12c2d602706f894226f2da7cce71058deab2990d2a3b0fe6cc549e194ff66 Homepage: https://cran.r-project.org/package=beastier Description: CRAN Package 'beastier' (Call 'BEAST2') 'BEAST2' () is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAST2' is a command-line tool. This package provides a way to call 'BEAST2' from an 'R' function call. Package: r-cran-beastjar Architecture: all Version: 10.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8814 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-beastjar_10.5.1-1.ca2404.1_all.deb Size: 7957286 MD5sum: 97dbd25899ee3bd6f6f88d8cc87fa6e1 SHA1: ae7304f638c3d18ca4f5a2e36c323ce3ccb1a29a SHA256: ea269bbb69d0a86c2e04664572bdc8128b27e07325e8a7898014f69f88b7af5b SHA512: add5516ef5e2c31f3040390bc3cb4de7b912dc075e4ae1874326e1951f3e8ca84fa52b8d0385dbe0086a1f75dbd8a693be29eb78cf2ec0351d7cf261fd80adb4 Homepage: https://cran.r-project.org/package=BeastJar Description: CRAN Package 'BeastJar' (JAR Dependency for MCMC Using 'BEAST') Provides JAR to perform Markov chain Monte Carlo (MCMC) inference using the popular Bayesian Evolutionary Analysis by Sampling Trees 'BEAST X' software library of Baele et al (2025) . 'BEAST X' supports auto-tuning Metropolis-Hastings, slice, Hamiltonian Monte Carlo and Sequential Monte Carlo sampling for a large variety of composable standard and phylogenetic statistical models using high performance computing. By placing the 'BEAST X' JAR in this package, we offer an efficient distribution system for 'BEAST X' use by other R packages using CRAN. Package: r-cran-beastt Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-cobalt, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-distributional, r-cran-tidyr, r-cran-ggdist, r-cran-mixtools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-beastt_0.0.1-1.ca2404.1_all.deb Size: 353840 MD5sum: 2ade46faa0657fa17cc04021c0ee832a SHA1: 1814368313770a224c99ac9a697c84677e473556 SHA256: b77ed7521371400aa038b921c24d427b4160a083aec00036c85e16cc1566b83f SHA512: 78c8dac4daee929225b37d6120df9ed80dd74a3a7876e1df77fb03b7b3699714f488be42604e8a821c6384ed87b2239b73473fbbf8723af9492c377d0d54c4c3 Homepage: https://cran.r-project.org/package=beastt Description: CRAN Package 'beastt' (Bayesian Evaluation, Analysis, and Simulation Software Tools forTrials) Bayesian dynamic borrowing with covariate adjustment via inverse probability weighting for simulations and data analyses in clinical trials. This makes it easy to use propensity score methods to balance covariate distributions between external and internal data. Package: r-cran-beautier Architecture: all Version: 2.6.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4791 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-rappdirs, r-cran-purrr, r-cran-rlang, r-cran-seqinr, r-cran-stringr Suggests: r-cran-knitr, r-cran-markdown, r-cran-readr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-beautier_2.6.12-1.ca2404.1_all.deb Size: 1718858 MD5sum: 4bb7cf15b7be1a57b646408e7dae5435 SHA1: 0b917b7d5a177b73ce9fba15f5ca7b63450a195e SHA256: 0f8ae192921b43f661bf4a0748142ff8ad4b9a72d4bece2a910db19d01bd9d4e SHA512: d036fde73e9df7daeee2b1c2b5530aa1d4a347019c57148c0770f3427b7ee44c716621a74d3b032723f4c0330614218737aa3d4b201823df9c6b18f4f57abaee Homepage: https://cran.r-project.org/package=beautier Description: CRAN Package 'beautier' ('BEAUti' from R) 'BEAST2' () is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAUti 2' (which is part of 'BEAST2') is a GUI tool that allows users to specify the many possible setups and generates the XML file 'BEAST2' needs to run. This package provides a way to create 'BEAST2' input files without active user input, but using R function calls instead. Package: r-cran-beaver Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-ellipsis, r-cran-fs, r-cran-ggplot2, r-cran-purrr, r-cran-rjags, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-yodel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-beaver_1.0.0-1.ca2404.1_all.deb Size: 214208 MD5sum: 8a261a85e61cc27c003081977216f808 SHA1: 08a89b5eaedd471a5d55e47365c19f6f2640cb1b SHA256: 54b63640244bf1d71d2f289d9ce5e5d7002413968457431e8bf0dfda2991ac39 SHA512: 6fd71de265899d81e464447b422d7fb5409c9feeda59ab0e3069073e654507b3cb08dc053f3c07a679d82d5a4c2efc75aae2ca95554c7f608f841a4391eca258 Homepage: https://cran.r-project.org/package=beaver Description: CRAN Package 'beaver' (Bayesian Model Averaging of Covariate Adjusted Negative-BinomialDose-Response) Dose-response modeling for negative-binomial distributed data with a variety of dose-response models. Covariate adjustment and Bayesian model averaging is supported. Functions are provided to easily obtain inference on the dose-response relationship and plot the dose-response curve. Package: r-cran-bed Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-stringr, r-cran-shiny, r-cran-dt, r-cran-miniui, r-cran-rstudioapi Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biomart, r-bioc-geoquery, r-cran-base64enc, r-cran-htmltools, r-cran-webshot2, r-cran-rcurl Filename: pool/dists/noble/main/r-cran-bed_1.6.0-1.ca2404.1_all.deb Size: 1607922 MD5sum: 73a7fbf732ca7688138088c25f3d46d9 SHA1: 4e992bdba53eeff82ed88aa17006150425ad7744 SHA256: 6fb5f64df2b5420ee939684876a251aec427c239066363c087353ae93202b411 SHA512: c8509633297c40c5823425a7052e353746ab31c499717aa1ab028ce09072bd73714dc4da33c73fe846f1e42088a721698116c52dc8ede83b832897537d005257 Homepage: https://cran.r-project.org/package=BED Description: CRAN Package 'BED' (Biological Entity Dictionary (BED)) An interface for the 'Neo4j' database providing mapping between different identifiers of biological entities. This Biological Entity Dictionary (BED) has been developed to address three main challenges. The first one is related to the completeness of identifier mappings. Indeed, direct mapping information provided by the different systems are not always complete and can be enriched by mappings provided by other resources. More interestingly, direct mappings not identified by any of these resources can be indirectly inferred by using mappings to a third reference. For example, many human Ensembl gene ID are not directly mapped to any Entrez gene ID but such mappings can be inferred using respective mappings to HGNC ID. The second challenge is related to the mapping of deprecated identifiers. Indeed, entity identifiers can change from one resource release to another. The identifier history is provided by some resources, such as Ensembl or the NCBI, but it is generally not used by mapping tools. The third challenge is related to the automation of the mapping process according to the relationships between the biological entities of interest. Indeed, mapping between gene and protein ID scopes should not be done the same way than between two scopes regarding gene ID. Also, converting identifiers from different organisms should be possible using gene orthologs information. The method has been published by Godard and van Eyll (2018) . Package: r-cran-bedassle Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrixcalc, r-cran-emdbook Filename: pool/dists/noble/main/r-cran-bedassle_1.6.1-1.ca2404.1_all.deb Size: 235284 MD5sum: 05922cab4b2486a9117f3fbdea6634c0 SHA1: b568f74ad1803110458f6c2d3d66b262630f3adc SHA256: a839d44600fa232ab6913f263a9e2217703233d33a67c9bd83698b95a9d11f2b SHA512: 28d9f03ffbe4f4cb18f76b5ceea385373c27dd1b6259f5529db478e0724c116a2d631c75316cb7f2220c21229997b13b8d31849030542682ebfc454767062f30 Homepage: https://cran.r-project.org/package=BEDASSLE Description: CRAN Package 'BEDASSLE' (Quantifies Effects of Geo/Eco Distance on GeneticDifferentiation) Provides functions that allow users to quantify the relative contributions of geographic and ecological distances to empirical patterns of genetic differentiation on a landscape. Specifically, we use a custom Markov chain Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the inference model, as well as functions for performing MCMC diagnosis and assessing model adequacy. Package: r-cran-bedr Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testthat, r-cran-venndiagram, r-cran-data.table, r-cran-r.utils, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bedr_1.1.5-1.ca2404.1_all.deb Size: 967986 MD5sum: ac55096ea881cd4f5563b42524a68393 SHA1: d97bd7d0691e32d61cb71ea96eecd5a9e4725133 SHA256: 0f5eeaf9f81ca6ea180f770cc029225616bf70e819bacdc9a7a0cc337cf061f8 SHA512: f425f21dd280ea4090d4ca0c5e6d6d62104fa18aa19d4e7aa819661dbf92adb564144ca7762de3356945e98d89c44e52582c7c19f0ae5d33b26290749d8a82b3 Homepage: https://cran.r-project.org/package=bedr Description: CRAN Package 'bedr' (Genomic Region Processing using Tools Such as 'BEDTools','BEDOPS' and 'Tabix') Genomic regions processing using open-source command line tools such as 'BEDTools', 'BEDOPS' and 'Tabix'. These tools offer scalable and efficient utilities to perform genome arithmetic e.g indexing, formatting and merging. bedr API enhances access to these tools as well as offers additional utilities for genomic regions processing. Package: r-cran-bedrockbio Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bedrockbio_1.3.1-1.ca2404.1_all.deb Size: 23794 MD5sum: 144a0bf9fffa6ab6e1a1c98cb8060a18 SHA1: f33c209a39bd2335c05a7f601af7dfdaf564046f SHA256: be7d8fd25bba5dbb376eb897b30019d0f26e639d3bfef9250f0059123385359e SHA512: 0f2058271780fab574801cb5b5c8f13f23079127fa35837d0da3bd4e59f490a7649a2462c4a6cbf3d70ddbb7bc9fe9b32977434d29ae4bc0bb0c6fe91225e390 Homepage: https://cran.r-project.org/package=bedrockbio Description: CRAN Package 'bedrockbio' (Open-Access Computational Biology Datasets) Efficiently access the 'Bedrock Bio' library of open-access computational biology datasets. Lazily query datasets backed by 'DuckDB' and 'Apache Iceberg', with support for predicate pushdown and column projection to the cloud storage backend. This enables quick, iterative access to otherwise massive, unwieldy datasets without downloading them in full. See for available datasets and documentation. Package: r-cran-beebdc Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1146 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circlize, r-cran-coordinatecleaner, r-cran-cowplot, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggspatial, r-cran-here, r-cran-igraph, r-cran-lubridate, r-cran-mgsub, r-cran-openxlsx, r-cran-paletteer, r-cran-readr, r-cran-rnaturalearth, r-cran-sf, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-bdc, r-cran-biocmanager, r-cran-classint, r-bioc-complexheatmap, r-cran-countrycode, r-cran-curl, r-cran-devtools, r-cran-emld, r-cran-formatr, r-cran-galah, r-cran-hexbin, r-cran-htmltools, r-cran-htmlwidgets, r-cran-httr, r-cran-inext, r-cran-janitor, r-cran-kableextra, r-cran-knitr, r-cran-leaflet, r-cran-magrittr, r-cran-mosaic, r-cran-pkgdown, r-cran-plotly, r-cran-prettydoc, r-cran-purrr, r-cran-r.utils, r-cran-renv, r-cran-rgnparser, r-cran-rlang, r-cran-rmarkdown, r-cran-rmdformats, r-cran-rnaturalearthdata, r-cran-rvest, r-cran-spader, r-cran-taxadb, r-cran-terra, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-beebdc_1.3.4-1.ca2404.1_all.deb Size: 1024248 MD5sum: 3b1154a40af21f3c00528f67b555387a SHA1: 178413382fe50260e2649c84473c131b6991083d SHA256: 4f48cca79cc73f1ffbe258bf47d9fc71b721fcaf18199ce460384a3565e03c00 SHA512: ff919a381629ca027c3624514823f12a845e96a4dff0f922f385cff37173b07cb2d33018bfe2e3883e597ce6637ec425d5f51b3ef4d20044dfa1b884f53952ba Homepage: https://cran.r-project.org/package=BeeBDC Description: CRAN Package 'BeeBDC' (Occurrence Data Cleaning) Flags and checks occurrence data that are in Darwin Core format. The package includes generic functions and data as well as some that are specific to bees. This package is meant to build upon and be complimentary to other excellent occurrence cleaning packages, including 'bdc' and 'CoordinateCleaner'. This package uses datasets from several sources and particularly from the Discover Life Website, created by Ascher and Pickering (2020). For further information, please see the original publication and package website. Publication - Dorey et al. (2023) and package website - Dorey et al. (2023) . Package: r-cran-beeca Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lifecycle, r-cran-sandwich Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-marginaleffects, r-cran-margins, r-cran-robincar Filename: pool/dists/noble/main/r-cran-beeca_0.2.0-1.ca2404.1_all.deb Size: 120910 MD5sum: bd5d11efc7f113a204e869a4630b4ea0 SHA1: 906bf6531268df7069f19892ddb01ebb8c4ec28c SHA256: 726d577afbdf316ec2d710efb5e82979887893c102cee979e711f47f1ec40515 SHA512: 65a082a11fd4bd6fb0752baa6b53c35343c89735b76fad80e1601d590948ff4a69f2fea8ed403fd0d3b82035ecd3d994f1720025bd6f781528e95332f532339b Homepage: https://cran.r-project.org/package=beeca Description: CRAN Package 'beeca' (Binary Endpoint Estimation with Covariate Adjustment) Performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) ). 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Package: r-cran-beezdemand Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlsr, r-cran-nlstools, r-cran-nls2, r-cran-ggplot2, r-cran-reshape2, r-cran-optimx Suggests: r-cran-openxlsx, r-cran-knitr, r-cran-dplyr, r-cran-tidyr, r-cran-tidyverse, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-beezdemand_0.1.2-1.ca2404.1_all.deb Size: 755066 MD5sum: a4c12179c1f6f01ced39f978edec4009 SHA1: a971cc0538e523a60007eef07cbdd2b393dbdde1 SHA256: 5d69af8477c253a3de8df60dadfa58dbacf2b9497cdfe0849101012928ab6834 SHA512: dcab695064a8400d2f3bd3f2da219cf67c261ae796d2055f9b15fcf9afa8368b1496bdbaa584c9a107102a982f56fced0756eef2253253c333c1799634bbfc91 Homepage: https://cran.r-project.org/package=beezdemand Description: CRAN Package 'beezdemand' (Behavioral Economic Easy Demand) Facilitates many of the analyses performed in studies of behavioral economic demand. The package supports commonly-used options for modeling operant demand including (1) data screening proposed by Stein, Koffarnus, Snider, Quisenberry, & Bickel (2015; ), (2) fitting models of demand such as linear (Hursh, Raslear, Bauman, & Black, 1989, ), exponential (Hursh & Silberberg, 2008, ) and modified exponential (Koffarnus, Franck, Stein, & Bickel, 2015, ), and (3) calculating numerous measures relevant to applied behavioral economists (Intensity, Pmax, Omax). Also supports plotting and comparing data. 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The package supports scoring of the 27-Item Monetary Choice Questionnaire (see Kaplan et al., 2016; ), calculating k values (Mazur's simple hyperbolic and exponential) using nonlinear regression, calculating various Area Under the Curve (AUC) measures, plotting regression curves for both fit-to-group and two-stage approaches, checking for unsystematic discounting (Johnson & Bickel, 2008; ) and scoring of the minute discounting task (see Koffarnus & Bickel, 2014; ) using the Qualtrics 5-trial discounting template (see the Qualtrics Minute Discounting User Guide; ), which is also available as a .qsf file in this package. 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Package: r-cran-behaviorchange Architecture: all Version: 25.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4032 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biasedurn, r-cran-data.tree, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-ggplot2, r-cran-googlesheets4, r-cran-gridextra, r-cran-gtable, r-cran-knitr, r-cran-rmdpartials, r-cran-ufs, r-cran-viridis, r-cran-yum Suggests: r-cran-htmltools, r-cran-kableextra, r-cran-openxlsx, r-cran-patchwork, r-cran-png, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-rsvg, r-cran-webshot Filename: pool/dists/noble/main/r-cran-behaviorchange_25.8.0-1.ca2404.1_all.deb Size: 1797056 MD5sum: 4998022a985342b0fe39b2b6ba5ddb5c SHA1: 6d40859f8ecc720b0fd6dcb3fd55d815007fb44b SHA256: 42d678eb5fbde76adba9c4d92426578eed92e50b39845f00615df0a0acd90793 SHA512: 14219471710e41ffe2826a5ec114ac9d3938551d24ef9e969b95b61b8fb98ba533e873f8451f4e08391adc0dfa078e2011b548ec0dd68241cd8a61be2f2c6447 Homepage: https://cran.r-project.org/package=behaviorchange Description: CRAN Package 'behaviorchange' (Tools for Behavior Change Researchers and Professionals) Contains specialised analyses and visualisation tools for behavior change science. These facilitate conducting determinant studies (for example, using confidence interval-based estimation of relevance, CIBER, or CIBERlite plots, see Crutzen, Noijen & Peters (2017) ), systematically developing, reporting, and analysing interventions (for example, using Acyclic Behavior Change Diagrams), and reporting about intervention effectiveness (for example, using the Numbers Needed for Change, see Gruijters & Peters (2017) ), and computing the required sample size (using the Meaningful Change Definition, see Gruijters & Peters (2020) ). This package is especially useful for researchers in the field of behavior change or health psychology and to behavior change professionals such as intervention developers and prevention workers. 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Package: r-cran-belikelihood Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-nlme, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-belikelihood_1.1-1.ca2404.1_all.deb Size: 328352 MD5sum: 1fc6cfac0226659d392f4fcde311d1e4 SHA1: 673b7c552413646ec0c0252bd2c0749f88ce762e SHA256: 3fba28d999aaabf8f4ca80c7731310d1d4665c6da58e59b434efd6177b0a12f7 SHA512: c6cbd4304a567664dd76374fc80b89fce300ddfe69bbe714cba45ffc9bfe9ab58b90938631b167b06d3bd47154175f49c5c0a2538bb5d94dfa977807ff19ab7d Homepage: https://cran.r-project.org/package=BElikelihood Description: CRAN Package 'BElikelihood' (Likelihood Method for Evaluating Bioequivalence) A likelihood method is implemented to present evidence for evaluating bioequivalence (BE). The functions use bioequivalence data [area under the blood concentration-time curve (AUC) and peak concentration (Cmax)] from various crossover designs commonly used in BE studies including a fully replicated, a partially replicated design, and a conventional 2x2 crossover design. They will calculate the profile likelihoods for the mean difference, total standard deviation ratio, and within subject standard deviation ratio for a test and a reference drug. A plot of a standardized profile likelihood can be generated along with the maximum likelihood estimate and likelihood intervals, which present evidence for bioequivalence. See Liping Du and Leena Choi (2015) . 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These models include the: 1) Bayesian Piecewise Random Effects Model (Bayes_PREM()) which estimates a piecewise random effects (mixture) model for a given number of latent classes and a latent number of possible changepoints in each class, and can incorporate class and outcome predictive covariates (see Lamm (2022) and Lock et al., (2018) ), 2) Bayesian Crossed Random Effects Model (Bayes_CREM()) which estimates a linear, quadratic, exponential, or piecewise crossed random effects models where individuals are changing groups over time (e.g., students and schools; see Rohloff et al., (2024) ), and 3) Bayesian Bivariate Piecewise Random Effects Model (Bayes_BPREM()) which estimates a bivariate piecewise random effects model to jointly model two related outcomes (e.g., reading and math achievement; see Peralta et al., (2022) ). 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Some components in the package can also be used as stand-alone functions. The bent cable (linear-quadratic-linear) generalizes the broken stick (linear-linear), which is also handled by this package. Version 0.2 corrected a glitch in the computation of confidence intervals for the CTP. References that were updated from Versions 0.2.1 and 0.2.2 appear in Version 0.2.3 and up. Version 0.3.0 improved robustness of the error-message producing mechanism. Version 0.3.1 improves the NAMESPACE file of the package. It is the author's intention to distribute any future updates via GitHub. 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Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server. In proton you have to solve four data-based puzzles to find the login and password. There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools. Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try. In frequon you will help to perform statistical cryptanalytic attack on a corpus of ciphered messages. This time seven sub-tasks are pushing the bar much higher. Do you accept the challenge? In regression you will test your modeling skills in a series of eight sub-tasks. Try only if ANOVA is your close friend. It's a part of Beta and Bit project. You will find more about the Beta and Bit project at . Package: r-cran-betacal Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-betacal_0.1.0-1.ca2404.1_all.deb Size: 15198 MD5sum: 329662d86abc23166721750e1a8cebcc SHA1: eaedb84d196806696e6016dec3cc5c0bac4504b8 SHA256: 303ef2760a6e48c42952c8bc80bd7b78be037b329a06de619292c57013cc10dd SHA512: cbdeaaf4ed012a57ff8abdd21b6b3dfc7310403107e165b67c526d463194c5bd551bc316baf3956ba7fdc89ff861598f0bff6c6838d60c4caa5c8a9656911ab4 Homepage: https://cran.r-project.org/package=betacal Description: CRAN Package 'betacal' (Beta Calibration) Fit beta calibration models and obtain calibrated probabilities from them. 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(2022) to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of beta mixture models employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility. Package: r-cran-betadanish Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-maxlik, r-cran-survival Suggests: r-cran-readxl, r-cran-flexsurv, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-betadanish_0.1.0-1.ca2404.1_all.deb Size: 159626 MD5sum: b3f0e6f3e5eac3f1377376e5b72df6c6 SHA1: da2666dbee557a94144be433a8beec42fb5e95ba SHA256: 5158107e44b5eb845ef190eb373e04f4150196971eaf1378821956f063f716d1 SHA512: bfe78cf3caaf7d479fdb881ba30b0c820810f589743868b9bbef614640fb185d37b435ca20c1ea1cd68322af71f135087866e4f46e6312a41fab8385bd8bcc43 Homepage: https://cran.r-project.org/package=BetaDanish Description: CRAN Package 'BetaDanish' (The Beta-Danish Distribution for Lifetime Data Analysis) Implements the four-parameter Beta-Danish distribution and its three-parameter submodel for survival and reliability analysis, based on Ahmad and Danish (2025) . Provides functions for density, distribution, quantile, hazard, and random generation. Includes maximum likelihood estimation for complete and right-censored data, goodness-of-fit assessment, comparison with standard lifetime distributions, and publication-quality visualizations. Advanced modules support Accelerated Failure Time (AFT) regression, mixture and promotion-time cure models, and competing risks analysis. 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Includes, among other things: - d/p/q/r functions for Four-Parameter Beta distributions and Generalized "Binomial" (continuous) distributions, and d/p/r- functions for Beta- Binomial distributions. - d/p/q/r functions for Two- and Four-Parameter Beta distributions parameterized in terms of their means and variances rather than their shape-parameters. - Moment generating functions for Binomial distributions, Beta-Binomial distributions, and observed value distributions. - Functions for estimating classification accuracy and consistency, making use of the Classical Test-Theory based 'Livingston and Lewis' (L&L) and 'Hanson and Brennan' approaches. A shiny app is available, providing a GUI for the L&L approach when used for binary classifications. For url to the app, see documentation for the LL.CA() function. Livingston and Lewis (1995) . Lord (1965) . Hanson (1991) . 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'betaMC' combines ideas from Monte Carlo confidence intervals for the indirect effect (Pesigan and Cheung, 2024 ) and the sampling covariance matrix of regression coefficients (Dudgeon, 2017 ) to generate confidence intervals effect sizes in regression. 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The package contains a dataset of the trajectory of confirmed cases during the coronavirus disease (COVID-19) early outbreak. More detail of the statistical methods can be found in Zhao et al. (2020) . 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This new generalization of the well-known GEV (Generalized Extreme Value) distribution is useful for modeling heterogeneous bimodal data from different areas. 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Package: r-cran-bgfd Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-adequacymodel Filename: pool/dists/noble/main/r-cran-bgfd_0.1-1.ca2404.1_all.deb Size: 242108 MD5sum: 2ab6067820c334c0d8f3577c47b6d4ce SHA1: 884c3f40f0c195c31b0f921441224c7e6ce01031 SHA256: 21dd461644d15a7d71cf8a3e9675d8f62453f70e82be0d0bb0a5cbf3e8f1b8f2 SHA512: c035a8a75ad711bf05fcf4bcf7d9d0857ea2db589bf160a99464a097594e76f3bf83cbc9b2fedf2b8be2bc55f719275168cd19a6d6c4d758c5a30e555d4fd3c4 Homepage: https://cran.r-project.org/package=BGFD Description: CRAN Package 'BGFD' (Bell-G and Complementary Bell-G Family of Distributions) Evaluates the probability density function, cumulative distribution function, quantile function, random numbers, survival function, hazard rate function, and maximum likelihood estimates for the following distributions: Bell exponential, Bell extended exponential, Bell Weibull, Bell extended Weibull, Bell-Fisk, Bell-Lomax, Bell Burr-XII, Bell Burr-X, complementary Bell exponential, complementary Bell extended exponential, complementary Bell Weibull, complementary Bell extended Weibull, complementary Bell-Fisk, complementary Bell-Lomax, complementary Bell Burr-XII and complementary Bell Burr-X distribution. Related work includes: a) Fayomi A., Tahir M. H., Algarni A., Imran M. and Jamal F. (2022). "A new useful exponential model with applications to quality control and actuarial data". Computational Intelligence and Neuroscience, 2022. . b) Alanzi, A. R., Imran M., Tahir M. H., Chesneau C., Jamal F. Shakoor S. and Sami, W. (2023). "Simulation analysis, properties and applications on a new Burr XII model based on the Bell-X functionalities". AIMS Mathematics, 8(3): 6970-7004. . c) Algarni A. (2022). "Group Acceptance Sampling Plan Based on New Compounded Three-Parameter Weibull Model". Axioms, 11(9): 438. . Package: r-cran-bgganalytics Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-r6, r-cran-checkmate, r-cran-pryr, r-cran-stringr, r-cran-xml2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bgganalytics_0.2.1-1.ca2404.1_all.deb Size: 241046 MD5sum: 5477e903cd29d141369085566bf856c8 SHA1: b66691b544c6bdee8f1d387282a619173c120a01 SHA256: 49e811c28cde5d390d9050fb30c93f14f88e31db0a48986f51e412ef04be5159 SHA512: 8e9b0d9a98e2d69ff88925559be8c5c6383cc7584d97e68ceb0a9bb6fb1195d9cc14c0a6ada65d3c3a19caed7f4b8bb7f7c621769049801a8c8c8831e2bd44f0 Homepage: https://cran.r-project.org/package=bggAnalytics Description: CRAN Package 'bggAnalytics' (BoardGameGeek's Board Game Data Analysis Tools) Tools for analysing board game data. Mainly focused on providing an interface for BoardGameGeek's XML API2 through R6 class system objects. 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(2021) ], calculation of relatedness coefficients using path-tracing methods [Wright (1922) ; McArdle & McDonald (1984) ], inference of relatedness, pedigree conversion, and simulation of multi-generational family data [Lyu et al. (2025) ]. For a full overview, see [Garrison et al. (2024) ]. For a big data application see [Burt et al. (2025) . 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Package: r-cran-bhm Architecture: all Version: 1.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-ggplot2, r-cran-gridextra, r-cran-lpl, r-cran-mass, r-cran-survival Filename: pool/dists/noble/main/r-cran-bhm_1.19-1.ca2404.1_all.deb Size: 307650 MD5sum: 084b9035c8b197903b1f27fcef136953 SHA1: 004cc6b78ff8f470f47e6fa9511e21b041817449 SHA256: cfb4d46ceb63fe9a7a4cf57e1192283036360df285b49dfd68d37e7c9ff01a03 SHA512: bf560f070245249761e4c49996f247246c92fcb8e58a03cf1b0c019f4f575ec429686892d14be6c96deee28cf2608c5d0b1e9fd4a4eaf4cb1bb7a38b30cbb807 Homepage: https://cran.r-project.org/package=bhm Description: CRAN Package 'bhm' (Biomarker Threshold Models) Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models and continuous threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method. Package: r-cran-bhmbasket Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-dorng, r-cran-rjags, r-cran-checkmate Suggests: r-cran-dofuture, r-cran-future, r-cran-knitr, r-cran-rmarkdown, r-cran-rbest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bhmbasket_1.1.0-1.ca2404.1_all.deb Size: 252652 MD5sum: 24138ae88c79f2456c9939120cbf1550 SHA1: 9bbb84bc9f32316dbbd5016b8fd6b14f37eca894 SHA256: 9e2995444c3b048bf5675257765815dc2d6129bd019882aec77742ba190fb6ef SHA512: 0b3ea508d79322e1c5c23f52fd3aba46333255885f9c4c90b321783be6d9c7ad844b4f6ab7df82a659dbb39f7287383a383a3ed12d0c1dcc082599e3ceb41da3 Homepage: https://cran.r-project.org/package=bhmbasket Description: CRAN Package 'bhmbasket' (Bayesian Hierarchical Models for Basket Trials) Provides functions for the evaluation of basket trial designs with binary endpoints. Operating characteristics of a basket trial design are assessed by simulating trial data according to scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and assessing decision probabilities on stratum and trial-level based on Go / No-go decision making. The package is build for high flexibility regarding decision rules, number of interim analyses, number of strata, and recruitment. The BHMs proposed by Berry et al. (2013) and Neuenschwander et al. (2016) , as well as a model that combines both approaches are implemented. Functions are provided to implement Bayesian decision rules as for example proposed by Fisch et al. (2015) . In addition, posterior point estimates (mean/median) and credible intervals for response rates and some model parameters can be calculated. For simulated trial data, bias and mean squared errors of posterior point estimates for response rates can be provided. Package: r-cran-bi Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bi_1.2.0-1.ca2404.1_all.deb Size: 22586 MD5sum: f9090063f0cd99e24f7d529e2740e5a1 SHA1: 8ab309c1e789090ff1f0cc6e311233552fe5a59a SHA256: 012ade49987dc46388f2e01833aa02e3b8d9c1442e19a5138cee163e103a7dc1 SHA512: 0527b743cd4ed10afbea35f0b4f1dbcf6964410b6529aeb3d2a337668ee67d0d066e0247fb82562c9dbb42dc36fe1936120e0b1e2d0aa0c05d27dd260dd03f90 Homepage: https://cran.r-project.org/package=BI Description: CRAN Package 'BI' (Blinding Assessment Indexes for Randomized, Controlled, ClinicalTrials) Generate the James Blinding Index, as described in James et al (1996) and the Bang Blinding Index, as described in Bang et al (2004) . These are measures to assess whether or not satisfactory blinding has been maintained in a randomized, controlled, clinical trial. These can be generated for trial subjects, research coordinators and principal investigators, based upon standardized questionnaires that have been administered, to assess whether or not they can correctly guess to which treatment arm (e.g. placebo or treatment) subjects were assigned at randomization. Package: r-cran-biascorrector Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dt, r-cran-magrittr, r-cran-rbiascorrection, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs Suggests: r-cran-lintr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-biascorrector_0.2.3-1.ca2404.1_all.deb Size: 165590 MD5sum: c046ef48246dbc2e41f406e9f7372f69 SHA1: c1d91e87006ccda745755dfec408dde195b4d2c9 SHA256: 0c43bd5583a5823145a51aa55b3b4f606358ab5770ff0fe0235f49fcd5800274 SHA512: 698da1950f3514675ee0fda62045c3d4bd47779623fd24dd3d04316569db1cf6773f2dcec94ffe68ee65c169796ad909eb60e2a29afc89d1899c8f70428f617f Homepage: https://cran.r-project.org/package=BiasCorrector Description: CRAN Package 'BiasCorrector' (A GUI to Correct Measurement Bias in DNA Methylation Analyses) A GUI to correct measurement bias in DNA methylation analyses. The 'BiasCorrector' package just wraps the functions implemented in the 'R' package 'rBiasCorrection' into a shiny web application in order to make them more easily accessible. Publication: Kapsner et al. (2021) . Package: r-cran-bib2df Architecture: all Version: 1.1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-humaniformat, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-bib2df_1.1.2.0-1.ca2404.1_all.deb Size: 66062 MD5sum: 7d3b55813bcbb9796ae3d86edfdf4894 SHA1: 9bb392992d9f0b92e2b4f84a59815eebb4a9d0a6 SHA256: 0133da2f263092b45530f4eb9e376e2f25279990c8e26ab8641fc8d3126c88b0 SHA512: 7ea71d45462756b16fd0a0fa4c8b37b12117f6656d977ce04da85d18490e061b89ab3cf3b72c4ea0c367c3ac4e873b053d4ca4da5162a5a513139474e91092b6 Homepage: https://cran.r-project.org/package=bib2df Description: CRAN Package 'bib2df' (Parse a BibTeX File to a Data Frame) Parse a BibTeX file to a data.frame to make it accessible for further analysis and visualization. Package: r-cran-bibitr Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 836 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreign, r-cran-viridis, r-cran-cluster, r-cran-dendextend, r-cran-lattice, r-cran-randomcolor, r-cran-biclust Filename: pool/dists/noble/main/r-cran-bibitr_0.3.1-1.ca2404.1_all.deb Size: 449674 MD5sum: ed564e8014e49d0422924e733a52e323 SHA1: 9c3af0cc45af4b5090e1f670c7fab2dbcd80ca66 SHA256: bbda9775347395b90c718a201c11b919d143150e19e550f14fd020470712764c SHA512: e31fd27ce5fd8a8e9449e17b0b8237258e908eba21ffb66f9d58bc96b44ef435b75e3d5ef3bf54f2d0f443b2e32065c0c38fb53c1c69adfb3e07428ea74eab4f Homepage: https://cran.r-project.org/package=BiBitR Description: CRAN Package 'BiBitR' (R Wrapper for Java Implementation of BiBit) A simple R wrapper for the Java BiBit algorithm from "A biclustering algorithm for extracting bit-patterns from binary datasets" from Domingo et al. (2011) . An simple adaption for the BiBit algorithm which allows noise in the biclusters is also introduced as well as a function to guide the algorithm towards given (sub)patterns. Further, a workflow to derive noisy biclusters from discoverd larger column patterns is included as well. Package: r-cran-biblio Architecture: all Version: 0.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 676 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcrossref, r-cran-stringr, r-cran-yamlme Suggests: r-cran-covr, r-cran-devtools, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-biblio_0.0.12-1.ca2404.1_all.deb Size: 273134 MD5sum: c205a0309cd3246d4f7212221581d943 SHA1: a70bf2f3aee1eb576833e40cc0f5ce6fa5aa3c98 SHA256: ddc83c629a7405a6df2c598ae878bd0c6e1168453179d44dfc3f1344e336a5a9 SHA512: e49a1aaef643b4c45d142c7028e09177f99eeac8784f8ddb6761f0a7ae85294949b832eca6fc296e55463e8846a1fd7fe6b53dbb74e4c95e7f9c3f5615b4b5a2 Homepage: https://cran.r-project.org/package=biblio Description: CRAN Package 'biblio' (Interacting with BibTeX Databases) Reading and writing BibTeX files using data frames in R sessions. Package: r-cran-bibliometrix Architecture: all Version: 5.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4586 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bibliometrixdata, r-cran-contentanalysis, r-cran-dimensionsr, r-cran-dplyr, r-cran-ca, r-cran-forcats, r-cran-ggplot2, r-cran-ggrepel, r-cran-httr2, r-cran-igraph, r-cran-jsonlite, r-cran-matrix, r-cran-plotly, r-cran-openalexr, r-cran-openxlsx, r-cran-pubmedr, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rscopus, r-cran-shiny, r-cran-shinycssloaders, r-cran-snowballc, r-cran-stringdist, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidytext, r-cran-visnetwork, r-cran-xml2 Suggests: r-cran-knitr, r-cran-ragg, r-cran-rmarkdown, r-cran-testthat, r-cran-wordcloud2 Filename: pool/dists/noble/main/r-cran-bibliometrix_5.4.0-1.ca2404.1_all.deb Size: 2973294 MD5sum: 40646d109d29cc05f99f8eca565e7fe2 SHA1: 9e08c53a84d2b76cffb1662cdefceeef75f68bfb SHA256: 101d7bd69c6adf6bfb049290bea1c6720e43b01754a67a6cb7a0b907ef085348 SHA512: 524f0b639c6d92e8e0d030c8d9c893e6e33b916e4983991f5d0ea111c0b87079362b93d4f7ddcef611525efc2ba666770cf1d855b3b87301a2fec394b8b6f0d9 Homepage: https://cran.r-project.org/package=bibliometrix Description: CRAN Package 'bibliometrix' (Comprehensive Science Mapping Analysis) Tool for quantitative research in scientometrics and bibliometrics. It implements the comprehensive workflow for science mapping analysis proposed in Aria M. and Cuccurullo C. (2017) . 'bibliometrix' provides various routines for importing bibliographic data from 'SCOPUS', 'Clarivate Analytics Web of Science' (), 'Digital Science Dimensions' (), 'OpenAlex' (), 'Cochrane Library' (), 'Lens' (), and 'PubMed' () databases, performing bibliometric analysis and building networks for co-citation, coupling, scientific collaboration and co-word analysis. Package: r-cran-bibliometrixdata Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3892 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bibliometrix Filename: pool/dists/noble/main/r-cran-bibliometrixdata_0.3.0-1.ca2404.1_all.deb Size: 3946554 MD5sum: fea431e2b75c7060b166c9ea27b58318 SHA1: a6c9f33cca14eface965de5702291955eccc5b58 SHA256: d1a5a193ca078d560039e2b82b7db56d25079f2e394efa3d7aafc63da050f90b SHA512: 564035321b1ab23534a26c36b954d6243974d220cacb6a654ab1b825cf309303ef89b4c506fe7ae3722432dda5c4017610e1ba1ca0d7709ffc4776445bcbcb1d Homepage: https://cran.r-project.org/package=bibliometrixData Description: CRAN Package 'bibliometrixData' (Bibliometrix Example Datasets) It contains some example datasets used in 'bibliometrix'. The data are bibliographic datasets exported from the 'SCOPUS' () and 'Clarivate Analytics Web of Science' () databases. They can be used to test the different features of the package 'bibliometrix' (). Package: r-cran-biblionetwork Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-biblionetwork_0.1.0-1.ca2404.1_all.deb Size: 206308 MD5sum: b8866b7f8f0ea8457b6f2655f7653d19 SHA1: e5044dd80c453d40591b45ff9fbcd903fece1c7d SHA256: 7d017992e402bea8793508ae74ac43de74ae3e9bb9d594786d2a64fc26df090d SHA512: e75929a662738741c13456c7e3763475e1224549a2f7323574a23d8cc0208db77cf1e56b3efa029e35de4aae0fc90af6ff35ab366a3ca1d0dca0673794de7cb0 Homepage: https://cran.r-project.org/package=biblionetwork Description: CRAN Package 'biblionetwork' (Create Different Types of Bibliometric Networks) Functions to find edges for bibliometric networks like bibliographic coupling network, co-citation network and co-authorship network. The weights of network edges can be calculated according to different methods, depending on the type of networks, the type of nodes, and what you want to analyse. These functions are optimized to be be used on large dataset. The package contains functions inspired by: Leydesdorff, Loet and Park, Han Woo (2017) ; Perianes-Rodriguez, Antonio, Ludo Waltman, and Nees Jan Van Eck (2016) ; Sen, Subir K. and Shymal K. Gan (1983) ; Shen, Si, Zhu, Danhao, Rousseau, Ronald, Su, Xinning and Wang, Dongbo (2019) ; Zhao, Dangzhi and Strotmann, Andreas (2008) . Package: r-cran-bibliorefer Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bibliorefer_0.1.3-1.ca2404.1_all.deb Size: 402240 MD5sum: 1895875bf02f2ec4e14fed93d8a243f3 SHA1: ea7ef4c6e2fd7ccd636f2d7ae92f12034328d21b SHA256: d513b636455fa011ec310590103e579f42d3f09ae25b69172e0258069dd2c572 SHA512: 73ebc90ef8e3e71a997ce76cf7aeef4ba5230c554f24993b374643babb9cd90921f742a5a5656d3ad9c0d0804dad16c062046c5415ddff653d7962087a17387b Homepage: https://cran.r-project.org/package=bibliorefer Description: CRAN Package 'bibliorefer' (Generator of Main Scientific References) Generates a list, with a size defined by the user, containing the main scientific references and the frequency distribution of authors and journals in the list obtained. The database is a dataframe with academic production metadata made available by bibliographic collections such as Scopus, Web of Science, etc. The temporal evolution of scientific production on a given topic is presented and ordered lists of articles are constructed by number of citations and of authors and journals by level of productivity. Massimo Aria, Corrado Cuccurullo. (2017) . Caibo Zhou, Wenyan Song. (2021) . Package: r-cran-biblioverlap Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 611 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggvenndiagram, r-cran-magrittr, r-cran-matrix, r-cran-rlang, r-cran-shiny, r-cran-stringdist, r-cran-upsetr, r-cran-uuid Suggests: r-cran-dt, r-cran-testthat Filename: pool/dists/noble/main/r-cran-biblioverlap_1.0.2-1.ca2404.1_all.deb Size: 527844 MD5sum: 8fc724df379c1aa4490acfdb4c572319 SHA1: 44bcc9901d7c7b5c1593bd467fde9ad071bd62f0 SHA256: 0b33d6f4e92bf695abc7f437ed0f494b80e21339fae28892f6e02fd79a0aa536 SHA512: b825dfa1d74d7d163e7ab8ad895afb99bf78cf29ee7980fe7a5cd8ee52e3e2c25e58ac7a05b4e63885c6d0d38049b167e41ddd3759b4414bbf0898a9709f9b4a Homepage: https://cran.r-project.org/package=biblioverlap Description: CRAN Package 'biblioverlap' (Document-Level Matching Between Bibliographic Datasets) Identifies and visualizes document overlap in any number of bibliographic datasets. This package implements the identification of overlapping documents through the exact match of a unique identifier (e.g. Digital Object Identifier - DOI) and, for records where the identifier is absent, through a score calculated from a set of fields commonly found in bibliographic datasets (Title, Source, Authors and Publication Year). Additionally, it provides functions to visualize the results of the document matching through a Venn diagram and/or UpSet plot, as well as a summary of the matching procedure. Package: r-cran-bibnets Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1536 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Suggests: r-cran-cograph, r-cran-igraph, r-cran-knitr, r-cran-openalexr, r-cran-rcrossref, r-cran-rmarkdown, r-cran-testthat, r-cran-tidygraph Filename: pool/dists/noble/main/r-cran-bibnets_0.4.4-1.ca2404.1_all.deb Size: 1324576 MD5sum: 98cc0276f0d804a88afad7de6161f353 SHA1: c0e28a288d81ca179dcb13b04d739fa53b2fd7fb SHA256: 920594c8af09fe5b16969e3dfb4077a8f53c35e9064c2261ab47b95cc9b8b1af SHA512: e62685ca19ac3cc3d1c08732b58dce768ea42507c58e21e67c226beda314eb18d2ac5ea0abd4536b246bb12aa9d2ab78971dbc6d65e8700ab31b95f508274a85 Homepage: https://cran.r-project.org/package=bibnets Description: CRAN Package 'bibnets' (Importing, Constructing, and Exporting Bibliometric Networks) Imports, constructs, and exports bibliometric networks from scholarly metadata. Reads 'Scopus', 'Web of Science', 'BibTeX', 'RIS', 'OpenAlex', 'Lens.org', 'Dimensions', and 'Crossref' exports. Goes beyond standard co-networks with attention-weighted networks (lead, last, proximity, circular position weights), position-aware counting (harmonic, arithmetic, geometric, golden-ratio), similarity and dissimilarity normalisations, temporal networks with fixed, sliding, and cumulative windows, disparity-filter backbone extraction, historiograph construction, and local citation scoring. Methods described in López-Pernas, Saqr & Apiola (2023) . Package: r-cran-bibplots Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bibplots_0.0.8-1.ca2404.1_all.deb Size: 75476 MD5sum: bf521953facd125ad75d2eeafa495530 SHA1: d8ff849d472665e585335c63ef75c58f8ccfa4a0 SHA256: 2469c0d9307f668bd95613ec18ce367303f4a94433cce86fbf94f1f7c750d143 SHA512: 0ba0dd1a247530c79b2b773c3e94a2782f4bd92c76448d5f05f9ede532f06fe00bc8ca3c83b5c3d665e434f2c3cae5730e2d272dbefd6626f287fc0474688fbc Homepage: https://cran.r-project.org/package=BibPlots Description: CRAN Package 'BibPlots' (Plot Functions for Use in Bibliometrics) Currently, the package provides several functions for plotting and analyzing bibliometric data (JIF, Journal Impact Factor, and paper percentile values), beamplots with citations and percentiles, and three plot functions to visualize the result of a reference publication year spectroscopy (RPYS) analysis performed in the free software 'CRExplorer' (see ). Further extension to more plot variants is planned. Package: r-cran-bibs Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gigrvg Filename: pool/dists/noble/main/r-cran-bibs_1.1.1-1.ca2404.1_all.deb Size: 74928 MD5sum: 7c0a62efadf7d56698a7a012f0812d17 SHA1: ba0d4f8f33d99225d60124a93e3fb1e9d1a2fdcd SHA256: 94692f4ab2cb001e314362843183b4593d2cb645436483d076f5b0b403720030 SHA512: 0fb730c647c54d9e30fa0f26759fe66d250914aadfd42b3a4632964ea5243f4aa91aed903f89069f9bb301e7ddec9a011519911e51cd8e774e76f3dedde637b8 Homepage: https://cran.r-project.org/package=bibs Description: CRAN Package 'bibs' (Bayesian Inference for the Birnbaum-Saunders Distribution) Developed for the following tasks. 1- Simulating and computing the maximum likelihood estimator for the Birnbaum-Saunders (BS) distribution, 2- Computing the Bayesian estimator for the parameters of the BS distribution based on reference prior proposed by Xu and Tang (2010) and conjugate prior. 3- Computing the Bayesian estimator for the BS distribution based on conjugate prior. 4- Computing the Bayesian estimator for the BS distribution based on Jeffrey prior given by Achcar (1993) 5- Computing the Bayesian estimator for the BS distribution under progressive type-II censoring scheme. Package: r-cran-bibtex Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bibtex_0.5.2-1.ca2404.1_all.deb Size: 70928 MD5sum: 24b1b6a40702a42a28c5896585a2032b SHA1: 5dbacd4b15217d4008690ea03d76731d693be35a SHA256: ac8022ca96eb21a01333b8ff36704dbed7c19edf1fa5756afd4cb3c735324646 SHA512: 9eb203f0f582fb85a88297526da45c1df64d378080c02d8fc92029c5fe0f505e8ff3ae802d7752186ba0a9fbb4717948e3539c5e901e70d56445af8ca45af901 Homepage: https://cran.r-project.org/package=bibtex Description: CRAN Package 'bibtex' (Bibtex Parser) Utility to parse a bibtex file. Package: r-cran-bicausality Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-igraph Filename: pool/dists/noble/main/r-cran-bicausality_0.1.4-1.ca2404.1_all.deb Size: 141570 MD5sum: d86f9436702004c6ba48a2eec5833faf SHA1: 276f4c33157b66efd1a58b733776182948e35696 SHA256: d0a2973e87c87d4aee6b9b294dfc0b247ad08f8a17f6e830c2fbace2614c9db0 SHA512: 75439a3a3a947a230925524f31394d223a4182aec804e03a62abb04328560b94e308692b7520e4d4b32243f58b8c541e8cbe364f9bf8f08d2edc053fccca1290 Homepage: https://cran.r-project.org/package=BiCausality Description: CRAN Package 'BiCausality' (Binary Causality Inference Framework) A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E) where V is a set of nodes representing binary variables and there is an edge from i to j in E if the variable i causes j. The framework determines dependency among variables as well as analyzing confounding factors before deciding whether i causes j. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2023) . Package: r-cran-biclustermd Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-biclust, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-magrittr, r-cran-nycflights13, r-cran-phyclust Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-biclustermd_0.2.4-1.ca2404.1_all.deb Size: 227752 MD5sum: 257654cbbadee2bdd20ec1c02931b4e0 SHA1: 6681cfd4f66f3a8e2498e671972cdf4820200028 SHA256: 04a99c3677eb353780dc38b8ef4721e4df13f0f49e4cd211a343f48c2cac83d3 SHA512: 6a7982ec37c36c4e0941b76b2db24e24184192111701d9bfa0a563fef4537e1eea9c8b7ce5eb236a10ecbe2b13b2909a1fc2fadb400e23e9152d72b5d135600c Homepage: https://cran.r-project.org/package=biclustermd Description: CRAN Package 'biclustermd' (Biclustering with Missing Data) Biclustering is a statistical learning technique that simultaneously partitions and clusters rows and columns of a data matrix. Since the solution space of biclustering is in infeasible to completely search with current computational mechanisms, this package uses a greedy heuristic. The algorithm featured in this package is, to the best our knowledge, the first biclustering algorithm to work on data with missing values. Li, J., Reisner, J., Pham, H., Olafsson, S., and Vardeman, S. (2020) Biclustering with Missing Data. Information Sciences, 510, 304–316. Package: r-cran-bicorn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bicorn_0.1.0-1.ca2404.1_all.deb Size: 78830 MD5sum: 000a18aca33bbe3e7147fb8f7847c6a8 SHA1: a05de6e1bca844ee9002cc79354edfff1438697a SHA256: 72cba360ee23392d38c3f9cdc5258fb18f4042eeb0bcccae6c850c3167d80e17 SHA512: 7d80f7a942e736fa1f926017299f6bbd2ac05447660e123ad49293f2c81dc86a96694035f0710de6298e894b11c59fa3a87e3dc12edd7a8add55e911fd69e3d1 Homepage: https://cran.r-project.org/package=BICORN Description: CRAN Package 'BICORN' (Integrative Inference of De Novo Cis-Regulatory Modules) Prior transcription factor binding knowledge and target gene expression data are integrated in a Bayesian framework for functional cis-regulatory module inference. Using Gibbs sampling, we iteratively estimate transcription factor associations for each gene, regulation strength for each binding event and the hidden activity for each transcription factor. 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It also provides an implementation of the estimators described in Roll (JF, 1984) , Corwin & Schultz (JF, 2012) , and Abdi & Ranaldo (RFS, 2017) . Package: r-cran-bidimregression Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-bidimregression_2.0.1-1.ca2404.1_all.deb Size: 129508 MD5sum: 2ce1e121d6ed51d5674052b3b807106b SHA1: 430889e04c004367df9779a900b52571a115213b SHA256: 93919a3edd961ac79c66970eaeda1994f1cf288301a4170e260e63d7770e3ebf SHA512: 86f3fd698209300edc284dd53cec3c7eecb6d34a9454a670d60bd2b43358a94eae37b3eba1445db3f2d45fbc7b9fced45da99b64728cdb8817a5c5c39d5bb933 Homepage: https://cran.r-project.org/package=BiDimRegression Description: CRAN Package 'BiDimRegression' (Calculates the Bidimensional Regression Between Two 2DConfigurations) Calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965). Package: r-cran-bidser Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-data.tree, r-cran-neuroim2, r-cran-tidyselect, r-cran-dplyr, r-cran-assertthat, r-cran-crayon, r-cran-fs, r-cran-jsonlite, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringdist, r-cran-tibble, r-cran-tidyr, r-cran-httr, r-cran-rio Suggests: r-cran-ggplot2, r-cran-plotly, r-cran-patchwork, r-cran-viridis, r-cran-scales, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-lintr, r-cran-gluedown, r-cran-rnifti, r-cran-future, r-cran-future.apply Filename: pool/dists/noble/main/r-cran-bidser_0.2.0-1.ca2404.1_all.deb Size: 514348 MD5sum: a5a4b06511b934a4f39669990e36675d SHA1: fe695f1798aa884c5c19539ba2d6071f6d11c37b SHA256: 3356257c2839857513c7272e7e1d3a58a604c687f5b39b95124f72f140475587 SHA512: e24015c7e29b644208fa32d259a630fddaf1d62f8dc3faebfbb1f78f56adeefdc81ad9909714b9a95ee8df71cff84731c63762ddfa89b184064f972529139b48 Homepage: https://cran.r-project.org/package=bidser Description: CRAN Package 'bidser' (Work with 'BIDS' (Brain Imaging Data Structure) Projects) Tools for working with 'BIDS' (Brain Imaging Data Structure) formatted neuroimaging datasets. The package provides functionality for reading and querying 'BIDS'-compliant projects, creating mock 'BIDS' datasets for testing, and extracting preprocessed data from 'fMRIPrep' derivatives. It supports searching and filtering 'BIDS' files by various entities such as subject, session, task, and run to streamline neuroimaging data workflows. See Gorgolewski et al. (2016) for the 'BIDS' specification. Package: r-cran-bidsr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2509 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-fastmap, r-cran-fs, r-cran-jsonlite, r-cran-nanotime, r-cran-s7, r-cran-uuid Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bidsr_0.1.1-1.ca2404.1_all.deb Size: 698672 MD5sum: 1ff8dd0cdb9bf563ced6865b7830cc0e SHA1: a7513f8fc8de378aef1c92a47436fb26b7898ddd SHA256: 9a32b86de2ce4f45d510883449b8586bc43d8d6b1ded4ce2993623a513f1a1e7 SHA512: 4e9aff55c404d93f14f49f7f34091e6a436db9d6aead7ea940649706795f840f9b3b957b15ae135bbe50294ddf22572a9c98e464d7e68b210123323e37b087f4 Homepage: https://cran.r-project.org/package=bidsr Description: CRAN Package 'bidsr' (A Brain Imaging Data Structure ('BIDS') Parser) Parse and read the files that comply with the brain imaging data structure, or 'BIDS' format, see the publication from Gorgolewski, K., Auer, T., Calhoun, V. et al. (2016) . Provides query functions to extract and check the 'BIDS' entity information (such as subject, session, task, etc.) from the file paths and suffixes according to the specification. The package is developed and used in the reproducible analysis and visualization of intracranial electroencephalography, or 'RAVE', see Magnotti, J. F., Wang, Z., and Beauchamp, M. S. (2020) ; see 'citation("bidsr")' for details and attributions. Package: r-cran-bidux Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2376 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dplyr, r-cran-glue, r-cran-janitor, r-cran-jsonlite, r-cran-memoise, r-cran-readr, r-cran-rlang, r-cran-rsqlite, r-cran-stringdist, r-cran-tibble Suggests: r-cran-diagrammer, r-cran-knitr, r-cran-otel, r-cran-otelsdk, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-bidux_0.4.0-1.ca2404.1_all.deb Size: 1198886 MD5sum: 5b8075526bc04d756227295083be38e0 SHA1: 7a4ef0139694c0b31bf8280a4f11ffddfe7b1629 SHA256: e9288862a833edfae5ff25e7aa53a86038deccf14e47d248cb69af6c5878ad87 SHA512: 076479ec5dd6ca713e7b46a88c794544e750a22f7e1bc0d3b9089f34e5b4f8b4494227bc29e930192da57ab3a84357dde21f99a7245c8d46f8f6dae4a776e512 Homepage: https://cran.r-project.org/package=bidux Description: CRAN Package 'bidux' (Behavioral Insight Design: A Toolkit for Integrating BehavioralScience in UI/UX Design) Provides a framework and toolkit to guide R dashboard developers in implementing the Behavioral Insight Design (BID) framework. The package offers functions for documenting each of the five stages (Interpret, Notice, Anticipate, Structure, and Validate), along with a comprehensive concept dictionary. Works with both 'shiny' applications and 'Quarto' dashboards. Package: r-cran-bien Architecture: all Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpostgresql, r-cran-dbi, r-cran-ape, r-cran-sf, r-cran-terra, r-cran-doparallel, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-curl, r-cran-maps Filename: pool/dists/noble/main/r-cran-bien_1.2.8-1.ca2404.1_all.deb Size: 380994 MD5sum: a621985212426dfd20ccfcd61b046a98 SHA1: d5ec19acc51bcee1423b3db4843512c23ece9c4d SHA256: e8212616e4fbfef23b9be7664fe87d96653befff4fc06db444196fa02182381b SHA512: 9189dc53bc495b4f733f1ba655af7dfa98e54645ffaf4fbf52d8adf0938a282a4f09448293f784e31e6564c42ff1c9e9b13ebc31b80b0256f2e66bcd6af293c7 Homepage: https://cran.r-project.org/package=BIEN Description: CRAN Package 'BIEN' (Tools for Accessing the Botanical Information and EcologyNetwork Database) Provides Tools for Accessing the Botanical Information and Ecology Network Database. The BIEN database contains cleaned and standardized botanical data including occurrence, trait, plot and taxonomic data (See for more Information). This package provides functions that query the BIEN database by constructing and executing optimized SQL queries. Package: r-cran-bifactorindicescalculator Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-lavaan, r-cran-mirt, r-cran-mplusautomation, r-cran-mnormt Suggests: r-cran-testthat, r-cran-psych Filename: pool/dists/noble/main/r-cran-bifactorindicescalculator_0.2.2-1.ca2404.1_all.deb Size: 227212 MD5sum: 2c580396d8269f22fcbe6af88a9ee07a SHA1: 5a1eb6c7ef35b87166bbe4a401d05e46b593238d SHA256: d3ad0996c3d44fefcce195475994a05eee38f7b523bae1dc72b5dce0d457d488 SHA512: e303c182e28bf9eebc5871bb08ce16910ad89d71b554c84e9810e4c63fce6783d2a1e07d88b9e27404d7c42b3567008e2b28dac5d7a8578dd490f299b0e7ccfa Homepage: https://cran.r-project.org/package=BifactorIndicesCalculator Description: CRAN Package 'BifactorIndicesCalculator' (Bifactor Indices Calculator) The calculator computes bifactor indices such as explained common variance (ECV), hierarchical Omega (OmegaH), percentage of uncontaminated correlations (PUC), item explained common variance (I-ECV), and more. This package is an R version of the 'Excel' based 'Bifactor Indices Calculator' (Dueber, 2017) with added convenience features for directly utilizing output from several programs that can fit confirmatory factor analysis or item response models. 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Implements penalized-likelihood multivariate generalized least squares models, enabling analyses of high-dimensional trait datasets and large trees via searchOptimalConfiguration(). Includes a greedy step-wise shift-search algorithm following approaches developed in Smith et al. (2023) and Berv et al. (2024) . Methods build on multivariate GLS approaches described in Clavel et al. (2019) and implemented in the mvgls() function from the 'mvMORPH' package. Documentation and vignettes are available at , including worked examples for the jaw-shape dataset. Package: r-cran-bifurcatingr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1441 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fmultivar Suggests: r-cran-igraph, r-cran-mass, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bifurcatingr_2.1.0-1.ca2404.1_all.deb Size: 861278 MD5sum: a3996f06c80cf186b5f39fc9f3efd611 SHA1: f05712fcff5a7340c891bec72c5d14466c51c453 SHA256: 2def151eb76cc3666865a24b1af728fda434dc670b96675fdcf495a34867242b SHA512: c86ed661b857cccf59ed34361689b42e354f77d348c8ca3effa84bc9ade60658b8a70179f8f1dd93548b3f5363a3168e7bd865592dd68f5e1955f2931d6e0310 Homepage: https://cran.r-project.org/package=bifurcatingr Description: CRAN Package 'bifurcatingr' (Bifurcating Autoregressive Models) Estimation of bifurcating autoregressive models of any order, p, BAR(p) as well as several types of bias correction for the least squares estimators of the autoregressive parameters as described in Zhou and Basawa (2005) and Elbayoumi and Mostafa (2020) . Currently, the bias correction methods supported include bootstrap (single, double and fast-double) bias correction and linear-bias-function-based bias correction. Functions for generating and plotting bifurcating autoregressive data from any BAR(p) model are also included. This new version includes calculating several type of bias-corrected and -uncorrected confidence intervals for the least squares estimators of the autoregressive parameters as described in Elbayoumi and Mostafa (2023) . Package: r-cran-bigassertr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-bigassertr_0.2.0-1.ca2404.1_all.deb Size: 34328 MD5sum: 3e9c2129ed3066c95404d7b7b08a153b SHA1: 5e4130c17009f3ed5f283a693755397ee687a02a SHA256: 6a8f1d6cdbf2eed0f3ce8ca827e245161e87e70584da4348934dccbd930ee431 SHA512: 41578d1a5be63a9424992a7132ec52bd5fa8e5287606925831912d1cad5bdbb18de9d8fcc150b9a295caf6191c4383aebbde7d05981e5d3f774a28dc671938ef Homepage: https://cran.r-project.org/package=bigassertr Description: CRAN Package 'bigassertr' (Assertion and Message Functions) Enhanced message functions (cat() / message() / warning() / error()) using wrappers around sprintf(). Also, multiple assertion functions (e.g. to check class, length, values, files, arguments, etc.). Package: r-cran-bigbits Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmpfr, r-cran-gmp Filename: pool/dists/noble/main/r-cran-bigbits_1.4-1.ca2404.1_all.deb Size: 63914 MD5sum: 10d4437dde6d9d75af6b25096277fc15 SHA1: ab3863d4d9197bd143341c70394bb0b00d7d54b8 SHA256: 7df4e40bdec4b5e17639060db05377e08216eb193310185754710f2e21061fd4 SHA512: 1a81c33db265b278f1780570fc88be7dadaeb2665572cdd27eb5d2935d2d487b442d768ce08f795628e2b67f5cd457b7ef3503fe82347bbd072380e7ed0b95e0 Homepage: https://cran.r-project.org/package=bigBits Description: CRAN Package 'bigBits' (Perform Boolean Operations on Large Numbers) A set of Boolean operators which accept integers of any size, in any base from 2 to 36, including 2's complement format, and perform actions like "AND," "OR", "NOT", "SHIFTR/L" etc. The output can be in any base specified. 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Package: r-cran-bigchess Architecture: all Version: 1.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-processx Suggests: r-cran-ff, r-cran-rsqlite, r-cran-rjson, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-bigchess_1.9.1-1.ca2404.1_all.deb Size: 1133114 MD5sum: f03f430d0d06a1849663bc01d87e63e3 SHA1: 1f5a16c5fc0ce0137fcf4a331b7815c17c620327 SHA256: 3c0e03d6ab470fc13277a057b060cf5e6b3d783d08c72dc518e677fcd9a5ea0e SHA512: 3e74a176a782e5c8a82b01b52e354d67c7a1814d504d1ee476c5844f0952c5b98b2fcb3b343367a3b5944576882e1e593c1d4e2e5be467ed17915ae66b274b74 Homepage: https://cran.r-project.org/package=bigchess Description: CRAN Package 'bigchess' (Read, Write, Manipulate, Explore Chess PGN Files and R API toUCI Chess Engines) Provides functions for reading *.PGN files with more than one game, including large files without copying it into RAM (using 'ff' package or 'RSQLite' package). Handle chess data and chess aggregated data, count figure moves statistics, create player profile, plot winning chances, browse openings. Set of functions of R API to communicate with UCI-protocol based chess engines. Package: r-cran-bigd Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1187 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-vctrs Filename: pool/dists/noble/main/r-cran-bigd_0.3.1-1.ca2404.1_all.deb Size: 1165086 MD5sum: 5094d7d2aa8c781ed66686ded5296cea SHA1: 84c765bb4972dd2f04c8188669ed8a51eb6b2ce9 SHA256: df6642e009ba3afe9f1a71db2aa45a990a66ca61d9e7439668a5aea99d3fc0a6 SHA512: f2829d768d4b1cadd695b1f346150e36ae31a52c2c8ec8d81c055250a48a750051a69ca0ecc76453b74dd2dfc046d0dd16a5fa026cf2d5bc088573884cfe4232 Homepage: https://cran.r-project.org/package=bigD Description: CRAN Package 'bigD' (Flexibly Format Dates and Times to a Given Locale) Format dates and times flexibly and to whichever locales make sense. Parses dates, times, and date-times in various formats (including string-based ISO 8601 constructions). The formatting syntax gives the user many options for formatting the date and time output in a precise manner. Time zones in the input can be expressed in multiple ways and there are many options for formatting time zones in the output as well. Several of the provided helper functions allow for automatic generation of locale-aware formatting patterns based on date/time skeleton formats and standardized date/time formats with varying specificity. Package: r-cran-bigdatape Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2370 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-tibble, r-cran-httr2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-bigdatape_0.1.0-1.ca2404.1_all.deb Size: 104572 MD5sum: 547aa910825aad12251f79682af0f6ac SHA1: 39e6efe94e6dc38e3d4b19daf7358620f0378785 SHA256: 2ee913639b08e4def061727a5ee49c776b73b0c28ac126a13651569e54316e65 SHA512: fc7bd480666f1ab699274b674c21a746648ec53e75e9899d8aa7445bd85f3e1b8b15055b6f1e5b36968ca7efdc46dfd4c90dd6ef9b0c9549d30fc372d9a746a9 Homepage: https://cran.r-project.org/package=BigDataPE Description: CRAN Package 'BigDataPE' (Secure and Intuitive Access to 'BigDataPE' 'API' Datasets) Designed to simplify the process of retrieving datasets from the 'Big Data PE' platform using secure token-based authentication. It provides functions for securely storing, retrieving, and managing tokens associated with specific datasets, as well as fetching and processing data using the 'httr2' package. Package: r-cran-bigdawg Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1265 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-haplo.stats Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bigdawg_3.1.0-1.ca2404.1_all.deb Size: 1164846 MD5sum: 11a1e61d3c40be3025d49d0357bb4f41 SHA1: 9923c30ea3e09cb6b7ac949379e69911a488453e SHA256: 34c2def92fad0cefd2b44785e17dd2596f1c04625d22bddb0250f7412d3d79fd SHA512: 930508447608507c081cb1d43d695e461b5e6e6f4c9fc091c2453466648012fd2607fd4eef2a1d1182e6d9bb2c4fd50ebd5740219aa2ba9ea72cd566d47b7bcd Homepage: https://cran.r-project.org/package=BIGDAWG Description: CRAN Package 'BIGDAWG' (Case-Cotrol Analysis of Multi-Allelic Loci) Data sets and functions for chi-squared Hardy-Weinberg and case-control association tests of highly polymorphic genetic data [e.g., human leukocyte antigen (HLA) data]. Performs association tests at multiple levels of polymorphism (haplotype, locus and HLA amino-acids) as described in Pappas DJ, Marin W, Hollenbach JA, Mack SJ (2016) . Combines rare variants to a common class to account for sparse cells in tables as described by Hollenbach JA, Mack SJ, Thomson G, Gourraud PA (2012) . Package: r-cran-bigdm Architecture: all Version: 0.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4927 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-doparallel, r-cran-fastdummies, r-cran-foreach, r-cran-future, r-cran-future.apply, r-cran-geos, r-cran-mass, r-cran-matrix, r-cran-parallelly, r-cran-rcolorbrewer, r-cran-rdpack, r-cran-sf, r-cran-spatialreg, r-cran-spdep, r-cran-rlist Suggests: r-cran-bookdown, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tmap Filename: pool/dists/noble/main/r-cran-bigdm_0.5.7-1.ca2404.1_all.deb Size: 4996748 MD5sum: 36bd24aeba056baca5b39522a5a315bf SHA1: 9c9fba90a8701574116d2e03b23de327597086a4 SHA256: 05e0bfa5e8af43482e2c456b94dc028b473cecc252d534176a75a1529cc6ffdc SHA512: 830a83ddb71e7242d579fbd7c0b45a141acfd94f7c6a5cbad6ac6925c8ea6e1063399e81cfa524e5638dd9c76c2607904d3ba5b5db074bf6386e7bd73309fb74 Homepage: https://cran.r-project.org/package=bigDM Description: CRAN Package 'bigDM' (Scalable Bayesian Disease Mapping Models for High-DimensionalData) Implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 ; Orozco-Acosta et al., 2023 and Vicente et al., 2023 ). The creation and develpment of this package has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001). Package: r-cran-bigl Architecture: all Version: 1.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4797 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-minpack.lm, r-cran-numderiv, r-cran-progress, r-cran-plotly, r-cran-robustbase, r-cran-scales, r-cran-nleqslv, r-cran-data.table, r-cran-lifecycle, r-cran-htmlwidgets, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-shiny, r-cran-dt Filename: pool/dists/noble/main/r-cran-bigl_1.9.3-1.ca2404.1_all.deb Size: 1448758 MD5sum: 8603ab69e845c525b9215d637b5d9373 SHA1: 308a68be7f51b815b4262cffb0bfae8b1199071c SHA256: 102aa1a225b4e83a11663466c3baa459edca51054077bdd16c72fcdc5f195ebc SHA512: d2f2e01596f83f1d147b330ee42e0b4380644959e2e3b6c510dd6474a0c35e439d83f93d5a883e6ba912d864f1080c9a7e5478997b494f879b8550aacb24019d Homepage: https://cran.r-project.org/package=BIGL Description: CRAN Package 'BIGL' (Biochemically Intuitive Generalized Loewe Model) Response surface methods for drug synergy analysis. Available methods include generalized and classical Loewe formulations as well as Highest Single Agent methodology. Response surfaces can be plotted in an interactive 3-D plot and formal statistical tests for presence of synergistic effects are available. Implemented methods and tests are described in the article "BIGL: Biochemically Intuitive Generalized Loewe null model for prediction of the expected combined effect compatible with partial agonism and antagonism" by Koen Van der Borght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas, Maxim Nazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017) . Package: r-cran-bigleaf Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-robustbase, r-cran-solartime Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigleaf_0.8.2-1.ca2404.1_all.deb Size: 1028224 MD5sum: dbc73ffb748f69bc6e65e337bdc01443 SHA1: 2c8a1724fed7053acc000055792aa6f68e751d20 SHA256: 0f7bad47d99720db6807363ebac3bc04619f9db19a3127e8a11f98cb43f07123 SHA512: 5f13f479ebe1cd187dbd713cfb6be0a243b3061044ae446986384ef493656243cb86cc11f512a3192881414927293f00e0c981068772f3b2a0072ca304165e2d Homepage: https://cran.r-project.org/package=bigleaf Description: CRAN Package 'bigleaf' (Physical and Physiological Ecosystem Properties from EddyCovariance Data) Calculation of physical (e.g. aerodynamic conductance, surface temperature), and physiological (e.g. canopy conductance, water-use efficiency) ecosystem properties from eddy covariance data and accompanying meteorological measurements. Calculations assume the land surface to behave like a 'big-leaf' and return bulk ecosystem/canopy variables. Package: r-cran-bigmatch Architecture: all Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcbalance, r-cran-liqueuer, r-cran-plyr, r-cran-mvnfast Suggests: r-cran-optmatch Filename: pool/dists/noble/main/r-cran-bigmatch_0.6.4-1.ca2404.1_all.deb Size: 200288 MD5sum: 5a7eb80aa7a156158ab2f7c2a0095150 SHA1: 79ff717aa0c0917aae98a3e889a7bd24c6809de3 SHA256: aa9a03ffaf325561c39209467b568d6efee3a1aa6a90554e2d533573d1f05fd4 SHA512: 925c43e7fe45d66a430456de75815931ae3ce8dcd1471fd8317d409eb9573dd99938e4521669f61f096c0524f6bf597b7d196079a1db3b114a51a4aecf423f3c Homepage: https://cran.r-project.org/package=bigmatch Description: CRAN Package 'bigmatch' (Making Optimal Matching Size-Scalable Using Optimal Calipers) Implements optimal matching with near-fine balance in large observational studies with the use of optimal calipers to get a sparse network. The caliper is optimal in the sense that it is as small as possible such that a matching exists. The main functions in the 'bigmatch' package are optcal() to find the optimal caliper, optconstant() to find the optimal number of nearest neighbors, and nfmatch() to find a near-fine balance match with a caliper and a restriction on the number of nearest neighbors. Yu, R., Silber, J. H., and Rosenbaum, P. R. (2020). . Package: r-cran-bigmds Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-svd, r-cran-corpcor Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigmds_3.0.0-1.ca2404.1_all.deb Size: 70342 MD5sum: c78cc4fa6c4d1fcd167547ced27ac5c5 SHA1: a2eb0b9562b1d9b73456d342cbedc5d90a4e948c SHA256: f21bea3720207f43a69bbdb08d3174126d1672895e749192bd354e24499cd7d7 SHA512: 56c59e463785a9234448446a62e75eb09ca1ebc4cd40da34919d1ebe791124d50c0ec216e531095b7c50712585060242290bca675a14be62f6d825b911ec2a38 Homepage: https://cran.r-project.org/package=bigmds Description: CRAN Package 'bigmds' (Multidimensional Scaling for Big Data) MDS is a statistic tool for reduction of dimensionality, using as input a distance matrix of dimensions n × n. When n is large, classical algorithms suffer from computational problems and MDS configuration can not be obtained. With this package, we address these problems by means of six algorithms, being two of them original proposals: - Landmark MDS proposed by De Silva V. and JB. Tenenbaum (2004). - Interpolation MDS proposed by Delicado P. and C. Pachón-García (2021) (original proposal). - Reduced MDS proposed by Paradis E (2018). - Pivot MDS proposed by Brandes U. and C. Pich (2007) - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-García (2021) (original proposal). - Fast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang (2006). Package: r-cran-bigmemory.sri Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bigmemory.sri_0.1.8-1.ca2404.1_all.deb Size: 12746 MD5sum: e73135fcb6f3f65bd0a7279c858ae399 SHA1: 5e6a37d2a6d7602045716793db7817cbae13bbaa SHA256: 122dbc596d694333d1c0e7017975a0e31d514947cfe58767d6807ba7886ad7c5 SHA512: 358e0d0547a9a16d59681ce4efd4ad2e6081d718dd7cd7f8221f9dc150863ce5be3f4793679513a0e268af5b00edc4f02135a3669aa809b27f56a4ba567f3e94 Homepage: https://cran.r-project.org/package=bigmemory.sri Description: CRAN Package 'bigmemory.sri' (A Shared Resource Interface for Bigmemory Project Packages) A shared resource interface for the bigmemory and synchronicity packages. Package: r-cran-bigmice Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyselect, r-cran-rlang, r-cran-sparklyr, r-cran-data.table, r-cran-matrix Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bigmice_1.0.0-1.ca2404.1_all.deb Size: 163246 MD5sum: c6695d97028172d2fc6a996c9e5a3216 SHA1: b145d376777f9138765c6001733e162e9bea95ec SHA256: 894dc5ad1a1c1675159d89f2701ab59c5b50922a9c1b426fb8fcfbc97760211d SHA512: e0d9ddedb4fe8d8a64d1addf0cb8766f1b894f0aa362e44425b06b5357e60a674e8749c590dfb0d78df0caa995d55c8e16054d4172b1cd846a6407eb03710d26 Homepage: https://cran.r-project.org/package=bigMICE Description: CRAN Package 'bigMICE' (Multiple Imputation of Big Data) A computational toolbox designed for handling missing values in large datasets with the Multiple Imputation by Chained Equations (MICE) by using 'Apache Spark'. 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Include some reexports from other packages, utility functions for splitting and parallelizing over blocks, and choosing and setting the number of cores used. 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'BIGr' provides the ability to filter variant call format (VCF) files, extract single nucleotide polymorphisms (SNPs) from diversity arrays technology missing allele discovery count (DArT MADC) files, and manipulate genotype data for both diploid and polyploid species. It also serves as the core dependency for the 'BIGapp' 'Shiny' app, which provides a user-friendly interface for performing routine genotype analysis tasks such as dosage calling, filtering, principal component analysis (PCA), genome-wide association studies (GWAS), and genomic prediction. For more details about the included 'breedTools' functions, see Funkhouser et al. (2017) , and the 'updog' output format, see Gerard et al. (2018) . Package: r-cran-bigsimr Architecture: all Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-juliacall Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigsimr_0.12.0-1.ca2404.1_all.deb Size: 22416 MD5sum: e6245ae973a7ce51eb759a34b721de35 SHA1: d44ea70ab6a640b63c2142310eb4c9d38022fa69 SHA256: e58eb22657836585f8cc76179c50e6f9c0a9c5d5a552a4e2a53a47689ebddaeb SHA512: dd4a61c9ec79ee37d32e5877bfe0e36fdae7b73bfc4075e5ad80971e3ba1751aa975cc3d89c5b75c7c2bf529da2ac8327f028c53fffe76dc9803c7dc54e5a5c6 Homepage: https://cran.r-project.org/package=bigsimr Description: CRAN Package 'bigsimr' (Fast Generation of High-Dimensional Random Vectors) Simulate multivariate data with arbitrary marginal distributions. 'bigsimr' is a package for simulating high-dimensional multivariate data with a target correlation and arbitrary marginal distributions via Gaussian copula. It utilizes the Julia package 'Bigsimr.jl' for its core routines. Package: r-cran-bigstep Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigmemory, r-cran-magrittr, r-cran-matrixstats, r-cran-r.utils, r-cran-rcppeigen, r-cran-speedglm Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bigstep_1.1.2-1.ca2404.1_all.deb Size: 88636 MD5sum: 0cbf49b58e73f89ee00cc2bb57e57e7d SHA1: f58f4d8b498447a73cdbc2131991e096784c5370 SHA256: 6a5cbb998e6758a29ed46b6a1ec3655ed3428ca4c1db25b8d2ed58381bedadd9 SHA512: 9890d41393c3a9c9db8fd928b7943eb0a4a36271efd219430c00ff9a23a528dfa095270f2833f5525222bab83c41b872f032f9e5b798308f2a3f0c5bd3d416e9 Homepage: https://cran.r-project.org/package=bigstep Description: CRAN Package 'bigstep' (Stepwise Selection for Large Data Sets) Selecting linear and generalized linear models for large data sets using modified stepwise procedure and modern selection criteria (like modifications of Bayesian Information Criterion). Selection can be performed on data which exceed RAM capacity. Bogdan et al., (2004) . Package: r-cran-bikeshare14 Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3524 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-data.table Filename: pool/dists/noble/main/r-cran-bikeshare14_0.1.4-1.ca2404.1_all.deb Size: 3577032 MD5sum: f16aaa7c01b6216638c3a45cdeebc196 SHA1: 0439c0c2a8defe932be09c0dafc1334236817ee0 SHA256: d13922feafe46a8ff9041086e7f631ff4645708dfc495f804b465f36b520e651 SHA512: 6b2f115a114bcf3c08660eb65fa4807ffb3d4290bc7342fae2255eec5adf8eace66dd3c3c72f944a75aa4dc29c4506d79274b513243bfcab8d1faddb737d7973 Homepage: https://cran.r-project.org/package=bikeshare14 Description: CRAN Package 'bikeshare14' (Bay Area Bike Share Trips in 2014) Anonymised Bay Area bike share trip data for the year 2014. Also contains additional metadata on stations and weather. Package: r-cran-bikm1 Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-ade4, r-cran-pracma, r-cran-ggplot2, r-cran-reshape2, r-cran-lpsolve Filename: pool/dists/noble/main/r-cran-bikm1_1.1.0-1.ca2404.1_all.deb Size: 449228 MD5sum: b5b5ab71b278c8408b0323975c62f521 SHA1: bc45f6affff7cc7e32cfa476f2d63f1eb59d74c9 SHA256: ac1a4161469f9b254602dae670453d38aa18a6a33434e79a60acd3091dd03391 SHA512: 0352538fb1e8d23617784fbd1c0918b15730e42eaa10da6414a85c2f2a258d07982e63833166efb08494291d7bfb5555b54f00be54fad1378aadf3a2f9ab4fe3 Homepage: https://cran.r-project.org/package=bikm1 Description: CRAN Package 'bikm1' (Co-Clustering Adjusted Rand Index and Bikm1 Procedure forContingency and Binary Data-Sets) Co-clustering of the rows and columns of a contingency or binary matrix, or double binary matrices and model selection for the number of row and column clusters. Three models are considered: the Poisson latent block model for contingency matrix, the binary latent block model for binary matrix and a new model we develop: the multiple latent block model for double binary matrices. A new procedure named bikm1 is implemented to investigate more efficiently the grid of numbers of clusters. Then, the studied model selection criteria are the integrated completed likelihood (ICL) and the Bayesian integrated likelihood (BIC). Finally, the co-clustering adjusted Rand index (CARI) to measure agreement between co-clustering partitions is implemented. Robert Valerie, Vasseur Yann, Brault Vincent (2021) . Package: r-cran-billboard Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2648 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-billboard_0.1.0-1.ca2404.1_all.deb Size: 2632988 MD5sum: 7e0bf5aa2d0a4bfa87321af95a5f29c4 SHA1: 7c68eb0aede39c2a67a69c75b6e08ecb22b8a1e3 SHA256: 421f2d64df0b7a9dc923dc20ca3ed3379e368614a890005803e4766742ae1202 SHA512: 4318f58bb23c5604d5a8fa92ba5f0435c11f08b78044a5ca0d09e80663878f2fa84a4e701c3c79a82ff31e84ef89f9fd44b7ef7985e50ae729bf3795a1ba3053 Homepage: https://cran.r-project.org/package=billboard Description: CRAN Package 'billboard' (Contains Data of Billboard Hot 100 Songs) Contains data sets regarding songs on the Billboard Hot 100 list from 1960 to 2016. The data sets include the ranks for the given year, musical features of a lot of the songs and lyrics for several of the songs as well. 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Simultaneous equation models definition, estimation, simulation and forecasting with coefficient restrictions, error autocorrelation, exogenization, add-factors, impact and interim multipliers analysis, conditional equation evaluation, rational expectations, endogenous targeting and model renormalization, structural stability, stochastic simulation and forecast, optimal control, by A. Luciani (2022) . Package: r-cran-bimodalindex Architecture: all Version: 1.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-oompabase, r-cran-mclust Suggests: r-cran-oompadata Filename: pool/dists/noble/main/r-cran-bimodalindex_1.1.11-1.ca2404.1_all.deb Size: 231172 MD5sum: cf77e29517d73101b86a945d8117f8e8 SHA1: a09de05a6f5e33f5b350f1c573f6c9452da2dd65 SHA256: 26015fb413a297098bb324206b4f8632fabaf23c79513e765b4893b9dc6f2281 SHA512: 73025c8c57a2dbfd691e5cc74fb03a2a486143d54c3922b80c7fd81d8d22ee5228e5a2a2fba0d114617d6c9ebc85805451f51eaaee13c30326853a49e5378d7a Homepage: https://cran.r-project.org/package=BimodalIndex Description: CRAN Package 'BimodalIndex' (The Bimodality Index) Defines the functions used to compute the bimodal index as defined by Wang et al. (2009) , . 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Package: r-cran-binarybalancedcut Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-binarybalancedcut_0.2-1.ca2404.1_all.deb Size: 14878 MD5sum: 0368c4f8206681fd254cb35019b9260a SHA1: ad8b6d94233eeeb11afecb332527af6e60149a69 SHA256: 22335916d4f70f96313d589136ae6157a60a632d875d6fc0fe345d51ef4ba8af SHA512: 3b8133b335b2dcba508a8a1fa63b156322d802a972e804fbefdfa6111d87301c857567c32939b54d9fcf4a8626d021f98252d68723ecc61aa15c326a9dcd38a4 Homepage: https://cran.r-project.org/package=BinarybalancedCut Description: CRAN Package 'BinarybalancedCut' (Threshold Cut Point of Probability for a Binary Classifier Model) Allows to view the optimal probability cut-off point at which the Sensitivity and Specificity meets and its a best way to minimize both Type-1 and Type-2 error for a binary Classifier in determining the Probability threshold. Package: r-cran-binaryemvs Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-binaryemvs_0.1-1.ca2404.1_all.deb Size: 32156 MD5sum: 9dca19551863c353255d25bbe84d3b1a SHA1: 6bb87a2f4fac8ae42b4882e09cebe7fe419f3c6b SHA256: 2ba8325fdf5d317c68073818013c1fb3e9a750391a08e6d47ac4c5f641636274 SHA512: da1468f6f651afe608774f1a94d02ba51aec94a09eb0cd10eb24bf876d52e39119987db90149b479efa5ce7d8f051efb1f728e3b585144d7d9e5dac1f7454f52 Homepage: https://cran.r-project.org/package=BinaryEMVS Description: CRAN Package 'BinaryEMVS' (Variable Selection for Binary Data Using the EM Algorithm) Implements variable selection for high dimensional datasets with a binary response variable using the EM algorithm. Both probit and logit models are supported. Also included is a useful function to generate high dimensional data with correlated variables. Package: r-cran-binaryeppm Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 486 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-expm, r-cran-numderiv, r-cran-lmtest Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-binaryeppm_3.0-1.ca2404.1_all.deb Size: 430004 MD5sum: 777e3673902a0428c4fecc083c045f6d SHA1: e2bb12ef3114a12dcbe2cf5b5a238329f2c4f766 SHA256: ab621e18a8aca85dbe29fcf97b0692d9d5dbe4586af46d2d3315dee075592902 SHA512: 8b52fed37315d87e75c892096465f82f7c76346177b88547bb9ca1d5ecd938d7bd382851def9076f29e51e7a6edf5875b18f2d0589619ee700805c74e5d7bdd4 Homepage: https://cran.r-project.org/package=BinaryEPPM Description: CRAN Package 'BinaryEPPM' (Mean and Scale-Factor Modeling of Under- And Over-DispersedBinary Data) Under- and over-dispersed binary data are modeled using an extended Poisson process model (EPPM) appropriate for binary data. A feature of the model is that the under-dispersion relative to the binomial distribution only needs to be greater than zero, but the over-dispersion is restricted compared to other distributional models such as the beta and correlated binomials. Because of this, the examples focus on under-dispersed data and how, in combination with the beta or correlated distributions, flexible models can be fitted to data displaying both under- and over-dispersion. Using Generalized Linear Model (GLM) terminology, the functions utilize linear predictors for the probability of success and scale-factor with various link functions for p, and log link for scale-factor, to fit a variety of models relevant to areas such as bioassay. Details of the EPPM are in Faddy and Smith (2012) and Smith and Faddy (2019) . Package: r-cran-binaryrl Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-dofuture, r-cran-foreach, r-cran-dorng, r-cran-progressr Suggests: r-cran-gensa, r-cran-ga, r-cran-deoptim, r-cran-pso, r-cran-mlrmbo, r-cran-mlr, r-cran-paramhelpers, r-cran-smoof, r-cran-lhs, r-cran-dicekriging, r-cran-rgenoud, r-cran-cmaes, r-cran-nloptr Filename: pool/dists/noble/main/r-cran-binaryrl_0.9.0-1.ca2404.1_all.deb Size: 694956 MD5sum: 5405a0185b10ab6167a6dd161c895076 SHA1: 4fed9809358d22ca674d8f490476d8f6eb2cc672 SHA256: 0adee810e0d9df426871b462b06e2555a40242227f47c48166185b3c636adb62 SHA512: edb5806d1baec8084422be23b54aa38bc18c17ac1598a63ff1717245a53aa465810d104322a85357d1e66dced522e44fc4d09e8ed2fc2f3343e242980f24acb0 Homepage: https://cran.r-project.org/package=binaryRL Description: CRAN Package 'binaryRL' (Reinforcement Learning Tools for Two-Alternative Forced ChoiceTasks) Tools for building reinforcement learning (RL) models specifically tailored for Two-Alternative Forced Choice (TAFC) tasks, commonly employed in psychological research. These models build upon the foundational principles of model-free reinforcement learning detailed in Sutton and Barto (2018) . The package allows for the intuitive definition of RL models using simple if-else statements. Our approach to constructing and evaluating these computational models is informed by the guidelines proposed in Wilson & Collins (2019) . Example datasets included with the package are sourced from the work of Mason et al. (2024) . Package: r-cran-binb Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3471 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmarkdown, r-cran-knitr, r-cran-codetools Filename: pool/dists/noble/main/r-cran-binb_0.0.8-1.ca2404.1_all.deb Size: 1813216 MD5sum: db8dfcb0ab26f9145d853a585e9a35b4 SHA1: 5110166a93ad1e0ec63ed450ac4d566234e93813 SHA256: f0039cae2b5cda0ec26125ec8ffb8b40f4ef8130e37a31289a9e16a6e2eaaf70 SHA512: 2f46b38bddffadef2db21e70c286447ed956df53f6be31101e65899a90c63a0bd2ffa810bc6ec26783266f8c9560f109d27fb9189a43792ed81cd71ce4a8c5fd Homepage: https://cran.r-project.org/package=binb Description: CRAN Package 'binb' ('binb' is not 'Beamer') A collection of 'LaTeX' styles using 'Beamer' customization for pdf-based presentation slides in 'RMarkdown'. At present it contains 'RMarkdown' adaptations of the LaTeX themes 'Metropolis' (formerly 'mtheme') theme by Matthias Vogelgesang and others (now included in 'TeXLive'), the 'IQSS' them by Ista Zahn (which is included here), and the 'Monash' theme by Rob J Hyndman. Additional (free) fonts may be needed: 'Metropolis' prefers 'Fira', and 'IQSS' requires 'Libertinus'. 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'BINCOR' is based on a novel estimation approach proposed by Mudelsee (2010, 2014) to estimate the correlation between two climate time series with different timescales. The idea is that autocorrelation (AR1 process) allows to correlate values obtained on different time points. 'BINCOR' contains four functions: bin_cor() (the main function to build the binned time series), plot_ts() (to plot and compare the irregular and binned time series, cor_ts() (to estimate the correlation between the binned time series) and ccf_ts() (to estimate the cross-correlation between the binned time series). A description of the method and package is provided in Polanco-Martínez et al. (2019), . Package: r-cran-binda Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-entropy Suggests: r-cran-crossval Filename: pool/dists/noble/main/r-cran-binda_1.0.4-1.ca2404.1_all.deb Size: 54614 MD5sum: 81f9eebc37d7a8515a463a88dd6e4946 SHA1: dcdce579edc1de12e8395b646c4f9594cc689d1f SHA256: f0e76103d17e31da890e70416be2f3f99100d4a80a579601382900a23ea45005 SHA512: 195c3177231bedc6a5526625042f1f0a02816aed8cc7b61828bef3baee12ae1ef96300dd84738b829b174dfef8428d04b3e50c87f1e605e877c86e32a9375bbd Homepage: https://cran.r-project.org/package=binda Description: CRAN Package 'binda' (Multi-Class Discriminant Analysis using Binary Predictors) Implements functions for multi-class discriminant analysis using binary predictors, for corresponding variable selection, and for dichotomizing continuous data. 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Package: r-cran-binequality Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.cens, r-cran-gamlss.dist, r-cran-survival, r-cran-ineq Filename: pool/dists/noble/main/r-cran-binequality_1.0.4-1.ca2404.1_all.deb Size: 2508932 MD5sum: f3cc39a5b06284df6067619a6b9a1e06 SHA1: 7de6759da8af14c462752a253bbe25aa85caa9a4 SHA256: 5a2980e0183b99f5f1c485b661028096ba8c9dbc8e9858e974a04f8eea89a48c SHA512: 2dbb2a3f9480d57c2a049a6d235202230dfce3cf8c99253d9d093cf1c83adcb403d346def393faf67efd2afbb0f3e7f1cdf86037b6189dc06ec85021fdab2e1b Homepage: https://cran.r-project.org/package=binequality Description: CRAN Package 'binequality' (Methods for Analyzing Binned Income Data) Methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored. We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results. We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states. 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High performance communications systems typically have inherent noise sources and other performance limitations that need to be estimated. Measurements made at high signal to noise ratios typically result in zero errors due to limitation in available measurement time. Package includes theoretical performance functions for common modulation schemes (Proakis, "Digital Communications" (1995, )), polarization shifted QPSK (Agrell & Karlsson (2009, )), and utility functions to work with the performance functions. 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For estimating one proportion or the difference of proportions, a number of confidence interval methods are included, which can deal with various different pool sizes. Further, regression methods are implemented for simple pooling and matrix pooling designs. Methods for identification of positive items in group testing designs: Optimal testing configurations can be found for hierarchical and array-based algorithms. Operating characteristics can be calculated for testing configurations across a wide variety of situations. 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This package supports the computation of boundaries and conditional power for single-arm group sequential test with binary endpoint, via either asymptotic or exact test. The package also provides functions to obtain boundary crossing probabilities given the design. 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It consolidates replicate sample pairs, outputs summary statistics, and produces hierarchical clustering trees and nMDS plots. This package was developed from the publication available here: . The GUI version of this package is available on the R Shiny online server at: or it is accessible via GitHub by typing: shiny::runGitHub("BinMat", "clarkevansteenderen") into the console in R. Two real-world datasets accompany the package: an AFLP dataset of Bunias orientalis samples from Tewes et. al. (2017) , and an ISSR dataset of Nymphaea specimens from Reid et. al. (2021) . The authors of these publications are thanked for allowing the use of their data. 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The confidence intervals are: Jeffreys, Wald, Wald corrected, Wald, Blyth and Still, Agresti and Coull, Wilson, Score, Score corrected, Wald logit, Wald logit corrected, Arcsine and Exact binomial. References include, among others: Vollset, S. E. (1993). "Confidence intervals for a binomial proportion". Statistics in Medicine, 12(9): 809-824. . 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A book on biodiversity and community ecology analysis is available for free download from the website. In 2012, methods for (ensemble) suitability modelling and mapping were expanded in the package. 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Raw laboratory measurements for diverse methods (volumetric, manometric, gravimetric, gas density) can be processed to calculate BMP. Theoretical maximum BMP or methane or biogas yield can be predicted from various measures of substrate composition. Molar mass and calculated oxygen demand (COD') can be determined from a chemical formula. Measured gas volume can be corrected for water vapor and to standard (or user-defined) temperature and pressure. Gas quantity can be converted between volume, mass, and moles. A function for planning BMP experiments can consider multiple constraints in suggesting substrate or inoculum quantities, and check for problems. Inoculum and substrate mass can be determined for planning BMP experiments. Finally, a set of first-order models can be fit to measured methane production rate or cumulative yield in order to extract estimates of ultimate yield and kinetic constants. See Hafner et al. (2018) for details. 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Provides leakage-resistant split policies (subject-grouped, batch-blocked, study leave-out, time-ordered), guarded preprocessing (train-only imputation, normalization, filtering, feature selection), cross-validated fitting with common learners, permutation-gap auditing, batch and fold association tests, and duplicate detection. Package: r-cran-biolink Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rentrez, r-cran-xml2, r-cran-dbi, r-cran-rmysql, r-cran-glue, r-cran-memoise Suggests: r-cran-testthat, r-cran-lintr, r-cran-httr, r-cran-covr Filename: pool/dists/noble/main/r-cran-biolink_0.1.8-1.ca2404.1_all.deb Size: 72334 MD5sum: 683f9fcc05e02d26865cb4ecc1669a8e SHA1: 932f9e8f9599dbfc32494fb8d79a6e31db49cd13 SHA256: fff0bc497f904df263bccb34481eef43ff200d9eeff0f79c9594a999e6cffbe8 SHA512: 83036bfbe9b460f8aa518419c6c8734c724219f81237f1101b63f9d4b194324a21c8d5f82c43bd2b174f8bc87ca51e4a4d8e4a269e7ead6338d166ce66571009 Homepage: https://cran.r-project.org/package=biolink Description: CRAN Package 'biolink' (Create Hyperlinks to Biological Databases and Resources) Generate urls and hyperlinks to commonly used biological databases and resources based on standard identifiers. This is primarily useful when writing dynamic reports that reference things like gene symbols in text or tables, allowing you to, for example, convert gene identifiers to hyperlinks pointing to their entry in the 'NCBI' Gene database. Currently supports 'NCBI' Gene, 'PubMed', Gene Ontology, 'KEGG', CRAN and Bioconductor. Package: r-cran-biologicalactivityindices Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-biologicalactivityindices_0.1.0-1.ca2404.1_all.deb Size: 12322 MD5sum: f8146ecdb4ce3e8ad905dfe1699da323 SHA1: 473f49cbde7ee39a9c540865c3383df848041b7d SHA256: 7dc8db93d1aa4a2800423f477f0bba219dcbbfd98e607a4414f9f18181b215eb SHA512: 7bbef4a1450879519eaef163a82696f38e168c28ecb930cc545fb3846cc5d6e8ec0959b5b9cd02965539d5e79a12d61b908cefbcbf99bc0384748b0d3eabfd87 Homepage: https://cran.r-project.org/package=biologicalActivityIndices Description: CRAN Package 'biologicalActivityIndices' (Biological Activity Indices) Ecological alteration of degraded lands can improve their sustainability by addition of large amount of biomass to soil resulting in improved soil health. Soil biological parameters (such as carbon, nitrogen and phosphorus cycling enzyme activity) are reactive to minute variations in soils [Ghosh et al. (2021) ]. Hence, biological activity index combining Urease, Alkaline Phosphatase, Dehydrogenase (DHA) & Beta-Glucosidase activity will assist in detecting early changes in restored land use systems [Patidar et al. (2023) ]. This package helps to calculate Biological Activity Index (BAI) based on vectors of Land Use System/treatment and control/reference Land Use System containing four values of Urease, Alkaline Phosphatase, DHA & Beta-Glucosidase. (DHA), urease (URE), fluorescein diacetate hydrolysis (FDA) and alkaline phosphatase (ALP) activities are measured in soil samples using triphenyl tetrazolium chloride, urea, fluorescein diacetate and p-nitro phenyl-phosphate as substrates, respectively. Package: r-cran-biom2 Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4349 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wgcna, r-cran-mlr3, r-cran-cmplot, r-cran-ggsci, r-cran-rocr, r-cran-caret, r-cran-ggplot2, r-cran-ggpubr, r-cran-viridis, r-cran-ggthemes, r-cran-ggstatsplot, r-cran-htmlwidgets, r-cran-mlr3verse, r-cran-uwot, r-cran-webshot, r-cran-wordcloud2, r-cran-ggforce, r-cran-igraph, r-cran-ggnetwork Filename: pool/dists/noble/main/r-cran-biom2_1.1.3-1.ca2404.1_all.deb Size: 4170190 MD5sum: 8c98c331a223465129666723275ddfc4 SHA1: 531a5bd2c33154a83c96d1ab0fb700a357c959a2 SHA256: 5551e5b72f1b3c9eee511a7e22e37f258d3b744c685b5306f9ba5e4851f3bae2 SHA512: c766f2d1b2d02de88d622aae930591603427351bb3c77b2e625ad03a10948d5611a84d878626cc5d41c0ae7c40ec6b9a2a3ae7cb1244d2f89243c277f65fafcf Homepage: https://cran.r-project.org/package=BioM2 Description: CRAN Package 'BioM2' (Biologically Explainable Machine Learning Framework) Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) .Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA (Described in Horvath and Zhang (2005) and Langfelder and Horvath (2008) ) methods. Package: r-cran-biomark Architecture: all Version: 0.4.5-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1031 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pls, r-cran-glmnet, r-cran-mass, r-cran-st Filename: pool/dists/noble/main/r-cran-biomark_0.4.5-1.ca2404.2_all.deb Size: 1017102 MD5sum: c37793e493aa553e16281af85dfaa2fd SHA1: 9822971c009ebbc76d742250bc51b31193d7680b SHA256: f816e18e705e4d26e8492752b32739b00cdd0419274249420374a4764adefad7 SHA512: 86b3a6134fe96ae48b7452a85f87a7007043bc08b7ce1b802f2655575538b0d6b8cc7e15a7fc9cd9f7613330407e9f790f7b3887391fe6b74f2b958ff2d6f74d Homepage: https://cran.r-project.org/package=BioMark Description: CRAN Package 'BioMark' (Find Biomarkers in Two-Class Discrimination Problems) Variable selection methods are provided for several classification methods: the lasso/elastic net, PCLDA, PLSDA, and several t-tests. Two approaches for selecting cutoffs can be used, one based on the stability of model coefficients under perturbation, and the other on higher criticism. Package: r-cran-biomass Architecture: all Version: 2.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4714 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm, r-cran-jsonlite, r-cran-proj4, r-cran-data.table, r-cran-rappdirs, r-cran-sf, r-cran-terra, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-testthat, r-cran-vdiffr, r-cran-curl, r-cran-geodata, r-cran-httr2, r-cran-pkgdown, r-cran-dplyr, r-cran-brms, r-cran-bh, r-cran-rcppeigen Filename: pool/dists/noble/main/r-cran-biomass_2.2.7-1.ca2404.1_all.deb Size: 3605262 MD5sum: e6faab1b755af2d4ad6b9cd87627d679 SHA1: ef987544e4f4b3331d43c5de1b802a83fc5e65ec SHA256: 881bb4feea10af6498770d3a1b303d9dec9eea031ca42c389fb163002379451b SHA512: 03243471aa6e0a203710d93f1aea33f12f5a5a05bb6092713e18fa660a9d26063e8b05e19ef7311ac535fd0212f8c8f9db2d251ba2f956c90d911082cffb36c4 Homepage: https://cran.r-project.org/package=BIOMASS Description: CRAN Package 'BIOMASS' (Estimating Aboveground Biomass and Its Uncertainty in TropicalForests) Contains functions for estimating above-ground biomass/carbon and its uncertainty in tropical forests. These functions allow to (1) retrieve and correct taxonomy, (2) estimate wood density and its uncertainty, (3) build height-diameter models, (4) manage tree and plot coordinates, (5) estimate above-ground biomass/carbon at stand level with associated uncertainty. To cite ‘BIOMASS’, please use citation(‘BIOMASS’). For more information, see Réjou-Méchain et al. (2017) . Package: r-cran-biometrics Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3908 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-nlme Suggests: r-cran-datana, r-cran-lattice Filename: pool/dists/noble/main/r-cran-biometrics_1.0.4-1.ca2404.1_all.deb Size: 3644776 MD5sum: 4ffd81bffa83e9cc52e109ef15b1de96 SHA1: 8724056659b109dbefcebd20cc374d659b741438 SHA256: 6d1f9f4ba7c0db4d355b6f447c8fc115f7991a72f816f45fd3441d9d75b08b37 SHA512: ef2fc9fc470e84fbb21582926a1bffc60fc5feac94e2ed56cff0c9f436cb4560597f01920b3c2de0472d9b0fdbc98ebf12b262bd8100f4494b9911ab013114c6 Homepage: https://cran.r-project.org/package=biometrics Description: CRAN Package 'biometrics' (A Package for Biometrics and Modelling) A system of functions and datasets to carry out quantitative analyses in the biological sciences. The package facilitates data management, exploratory analyses, and model assessment. Although it currently focuses on forest ecology, silviculture and decision-making, most of the package functions are applicable across several disciplines, including economics, environmental science, and healthcare. Package: r-cran-biometryassist Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-agricolae, r-cran-askpass, r-cran-curl, r-cran-emmeans, r-cran-ggplot2, r-cran-lattice, r-cran-multcompview, r-cran-pracma, r-cran-patchwork, r-cran-rlang, r-cran-scales, r-cran-stringi, r-cran-xml2 Suggests: r-cran-covr, r-cran-crayon, r-cran-ggspatial, r-cran-knitr, r-cran-matrix, r-cran-mockery, r-cran-openxlsx2, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-biometryassist_1.4.0-1.ca2404.1_all.deb Size: 377040 MD5sum: 613f900f3820aa09231b434552fe8201 SHA1: c6383f68ab362d795ecbaac681e23845b99ca424 SHA256: 907b7846d1848d3e9a7b4572ca2e82fdbdd9a5c9ec823bbd2a8b73cc9600562e SHA512: 54aa518799084f8565f5c4e69181791d30411d1c1ae734430742c51ce25b87715b17ecd2a806faffc226e4a8beca0cb5e67be9fc38ead26ae05a9b55961f378a Homepage: https://cran.r-project.org/package=biometryassist Description: CRAN Package 'biometryassist' (Functions to Assist Design and Analysis of Agronomic Experiments) Provides functions to aid in the design and analysis of agronomic and agricultural experiments through easy access to documentation and helper functions, especially for users who are learning these concepts. While not required for most functionality, this package enhances the `asreml` package which provides a computationally efficient algorithm for fitting mixed models using Residual Maximum Likelihood. It is a commercial package that can be purchased as 'asreml-R' from 'VSNi' , who will supply a zip file for local installation/updating (see ). Package: r-cran-biomod2 Architecture: all Version: 4.3-4-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3035 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-sp, r-cran-reshape, r-cran-reshape2, r-cran-abind, r-cran-foreach, r-cran-ggplot2, r-cran-gbm, r-cran-rpart, r-cran-mass, r-cran-proc, r-cran-presenceabsence, r-cran-dplyr, r-cran-rlang, r-cran-scales Suggests: r-cran-hmisc, r-cran-gam, r-cran-mgcv, r-cran-earth, r-cran-maxnet, r-cran-mda, r-cran-nnet, r-cran-randomforest, r-cran-xgboost, r-cran-cito, r-cran-torch, r-cran-car, r-cran-caret, r-cran-dismo, r-cran-enmeval, r-cran-doparallel, r-cran-raster, r-cran-ggpubr, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-tidyterra, r-cran-ggtext, r-cran-patchwork, r-cran-kableextra, r-cran-dt, r-cran-r.utils, r-cran-paletteer, r-cran-viridis Filename: pool/dists/noble/main/r-cran-biomod2_4.3-4-5-1.ca2404.1_all.deb Size: 1937266 MD5sum: 551b8f404b6aeda395f34d0cec14e1d4 SHA1: 55e6a3c4bcd13f8d1905e2541c040df7fe87ffdd SHA256: cf7eaffde16c2c7f264c74c7e5ed0fc24c6b4ccce35a4b0851501563e31ff679 SHA512: bf4e94ef0968431a9cfdcf2c85de707100ed0459dcc612b40d0fef00935887bf102bfdd9698d21c0b1453c8d910f3a65861ec14ba7eba70eb832aa83ae468906 Homepage: https://cran.r-project.org/package=biomod2 Description: CRAN Package 'biomod2' (Ensemble Platform for Species Distribution Modeling) Functions for species distribution modeling, calibration and evaluation, ensemble of models, ensemble forecasting and visualization. The package permits to run consistently up to 10 single models on a presence/absences (resp presences/pseudo-absences) dataset and to combine them in ensemble models and ensemble projections. Some bench of other evaluation and visualisation tools are also available within the package. Package: r-cran-biomontools Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2996 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-maps, r-cran-rlang, r-cran-tidyselect, r-cran-tidyr Suggests: r-cran-dataexplorer, r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-lazyeval, r-cran-readxl, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat, r-cran-shiny, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyjs, r-cran-shinywidgets, r-cran-writexl, r-cran-shinyalert Filename: pool/dists/noble/main/r-cran-biomontools_1.2.4-1.ca2404.1_all.deb Size: 1682112 MD5sum: 6edbc49413c17719f8ecbf10a4845e9e SHA1: 45ca802353441399c114d0e8c8456896404b5e56 SHA256: b9348330c3b3d61017fb9dc2f9a2ff6504a8771e8c9f759442e08f6c678505af SHA512: 64b7812bfc01e607720c05353b41338152c09b135e26d0bd305148c138f06f7f636633644dcfb52f24bcc4e8cebbda884e315745cc97acf0331f09d2987e2e69 Homepage: https://cran.r-project.org/package=BioMonTools Description: CRAN Package 'BioMonTools' (Biomonitoring and Bioassessment Calculations) An aid for manipulating data associated with biomonitoring and bioassessment. Calculations include metric calculation, marking of excluded taxa, subsampling, and multimetric index calculation. Targeted communities are benthic macroinvertebrates, fish, periphyton, and coral. As described in the Revised Rapid Bioassessment Protocols (Barbour et al. 1999) . Package: r-cran-biomor Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-recipes, r-cran-themis, r-cran-xgboost, r-cran-magrittr, r-cran-dplyr, r-cran-proc Suggests: r-cran-randomforest, r-cran-testthat, r-cran-prroc, r-cran-ggplot2, r-cran-purrr, r-cran-tibble, r-cran-yardstick, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-biomor_0.1.1-1.ca2404.1_all.deb Size: 48828 MD5sum: 8aee9f9deafc113212bc64a1cf0e86e8 SHA1: 8e4369cf38822aae3c081b7ed8b51f0fd99fb28c SHA256: 9817217df9f44628f6d7abbdb8f8dc738448504dbb8a29d56034a1d697205007 SHA512: bb2c83404c91084f850e1fee53247e8f060db7f23c5da23f7da8170f4519118a8b6625588405c7917ae39aca4244bdca66bc79639c1e151b515c7eedb1d6a818 Homepage: https://cran.r-project.org/package=BioMoR Description: CRAN Package 'BioMoR' (Bioinformatics Modeling with Recursion and Autoencoder-BasedEnsemble) Tools for bioinformatics modeling using recursive transformer-inspired architectures, autoencoders, random forests, XGBoost, and stacked ensemble models. Includes utilities for cross-validation, calibration, benchmarking, and threshold optimization in predictive modeling workflows. The methodology builds on ensemble learning (Breiman 2001 ), gradient boosting (Chen and Guestrin 2016 ), autoencoders (Hinton and Salakhutdinov 2006 ), and recursive transformer efficiency approaches such as Mixture-of-Recursions (Bae et al. 2025 ). Package: r-cran-bionetdata Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3273 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bionetdata_1.1-1.ca2404.1_all.deb Size: 3313434 MD5sum: 83a0bf29c9ee30c5aa8fac43e5f9e831 SHA1: 7b83443d489404a823a2c1f07bee1590b0aa3995 SHA256: 41a5a9c2fe9a45a78ecd74e5c265967313e144559c9647f3672ab56461a52e85 SHA512: 72d6d7de7efe075f60dbdd4d1ee04456d221b6173d1b757d62884e4553de1c943a81f03385396405445a1f8ac5ada13855516de96b21d40e9e6e43fd6ff8b1fe Homepage: https://cran.r-project.org/package=bionetdata Description: CRAN Package 'bionetdata' (Biological and Chemical Data Networks) Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured. Package: r-cran-biopet Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-proc, r-cran-vgam Filename: pool/dists/noble/main/r-cran-biopet_0.2.2-1.ca2404.1_all.deb Size: 78202 MD5sum: 890415bf3382cc83bdfa4a88c7e786fe SHA1: 0aca2da4a5439fcba6370341a508b5dbe1436ce2 SHA256: ef80b0b3d19f718a15d36a96a2ccaf19af6a36942a60acc5225b9a7390762083 SHA512: b713cb0a8d1836dd3c07b02c65badbbbd5eb95a719e6414a168ea3264b6a8edf3986046e53acd5de5424b8737a3463293ef476de8c815b222327c71f896ce4f7 Homepage: https://cran.r-project.org/package=BioPET Description: CRAN Package 'BioPET' (Biomarker Prognostic Enrichment Tool) Prognostic Enrichment is a clinical trial strategy of evaluating an intervention in a patient population with a higher rate of the unwanted event than the broader patient population (R. Temple (2010) ). A higher event rate translates to a lower sample size for the clinical trial, which can have both practical and ethical advantages. This package is a tool to help evaluate biomarkers for prognostic enrichment of clinical trials. Package: r-cran-biopetsurv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-biopetsurv_0.1.0-1.ca2404.1_all.deb Size: 112370 MD5sum: 76ff1111978f8b9b3e413764c57fcc0d SHA1: 892bb6b205017ff34ff6a14004936320d4582d15 SHA256: 32947b8f3dbfedda48068e66e2acd7f685fb98f85e840b9bd2b4c5e44be0c530 SHA512: 72ba63afe90aec6c0b4b4d765b76ad1aff146f5f2da9cc13233f595297613fab1b76c8f1cc6e31e6e3c5037e2a4eb76e54b94a3e901cd1fed2673499bb0a6667 Homepage: https://cran.r-project.org/package=BioPETsurv Description: CRAN Package 'BioPETsurv' (Biomarker Prognostic Enrichment Tool for Time-to-Event Trial) Prognostic Enrichment is a strategy of enriching a clinical trial for testing an intervention intended to prevent or delay an unwanted clinical event. A prognostically enriched trial enrolls only patients who are more likely to experience the unwanted clinical event than the broader patient population (R. Temple (2010) ). By testing the intervention in an enriched study population, the trial may be adequately powered with a smaller sample size, which can have both practical and ethical advantages. This package provides tools to evaluate biomarkers for prognostic enrichment of clinical trials with survival/time-to-event outcomes. 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By providing high throughput pipelines and clustering capabilities, 'biopixR' facilitates efficient insight generation for researchers (Schneider J. et al. (2019) ). 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For further details, see Liu et al. (2024) . Package: r-cran-bioprobability Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bioprobability_1.0-1.ca2404.1_all.deb Size: 34932 MD5sum: 426b1958bff481b2f5be67aad7af4a8d SHA1: 854c474d25432e3c308c91488fdf350c72033c53 SHA256: 518dac6595c2fa4e739fca12e027381295de34c40816da8a52acd07a123beade SHA512: ae82747fd6b7314e582102304be4d0c1c03e9690964c855162242ac67001cd8100ebb019b23c27a1ef44c76e8eec6b0b3cfe93be693d8d02b0f1ff38f14813e0 Homepage: https://cran.r-project.org/package=BioProbability Description: CRAN Package 'BioProbability' (Probability in Biostatistics) Several tools for analyzing diagnostic tests and 2x2 contingency tables are provided. In particular, positive and negative predictive values for a diagnostic tests can be calculated from prevalence, sensitivity and specificity values. For contingency tables, relative risk and odds ratio measures are estimated. Furthermore, confidence intervals are provided. Package: r-cran-biorad Architecture: all Version: 0.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5535 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-curl, r-cran-dplyr, r-cran-fields, r-cran-ggplot2, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-lutz, r-cran-raster, r-cran-readr, r-bioc-rhdf5, r-cran-rlang, r-cran-sf, r-cran-sp, r-cran-stringr, r-cran-suntools, r-cran-tidyr, r-cran-tidyselect, r-cran-viridis, r-cran-viridislite, r-cran-xml2 Suggests: r-cran-getrad, r-cran-ggspatial, r-cran-knitr, r-cran-prettymapr, r-cran-rmarkdown, r-cran-rosm, r-cran-testthat, r-cran-vdiffr, r-cran-vol2birdr, r-cran-withr Filename: pool/dists/noble/main/r-cran-biorad_0.11.0-1.ca2404.1_all.deb Size: 4798426 MD5sum: ff913280a66d100f41579830b444529d SHA1: b051afb6353f84f424735860fdb826ced8c9d5a3 SHA256: b6a6a00aa91ccf03d9447027abcfebbac786e84e4c675d533bc6e7f25e067eb0 SHA512: 720b4b8ee3a01f77683681079a2279d8b8094f5ad6561c654fdb2c3c24b1eb5f65754687955e111a17f4713f1e042ad4142bd2d46eeed481f622ffb6e1973251 Homepage: https://cran.r-project.org/package=bioRad Description: CRAN Package 'bioRad' (Biological Analysis and Visualization of Weather Radar Data) Extract, visualize and summarize aerial movements of birds and insects from weather radar data. See Dokter, A. M. et al. (2018) "bioRad: biological analysis and visualization of weather radar data" for a software paper describing package and methodologies. Package: r-cran-biorssay Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace Suggests: r-cran-markdown, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-biorssay_1.1.0-1.ca2404.1_all.deb Size: 239748 MD5sum: 86a196cb49e5c0ae88b71e1714936409 SHA1: 5ba9ccf7c33b6303cb408a5971bfea37f2d32925 SHA256: abf6594dd0928f015689322aee01b1e0e311cffa7011008f65372f2e3baf7ce4 SHA512: e8f6d2d4e3ac18602782b5d556f33b69ef5935c93ec95eebc515d86ade574b6e94608ea44f843f466ef873b5f45ee08fd3504d35cbf97baa95b0758dedf82b81 Homepage: https://cran.r-project.org/package=BioRssay Description: CRAN Package 'BioRssay' (Analyze Bioassays and Probit Graphs) A robust framework for analyzing mortality data from bioassays for one or several strains/lines/populations. Package: r-cran-bios2cor Architecture: all Version: 2.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1404 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bio3d, r-cran-circular, r-cran-bigmemory, r-cran-igraph Filename: pool/dists/noble/main/r-cran-bios2cor_2.2.2-1.ca2404.1_all.deb Size: 774476 MD5sum: 8e8f0df34a4e6b9789dd5000b75c9bca SHA1: f778d0105eb9563eef8fd34584bb510087fa1ff1 SHA256: cd531bb64af6b187a1700a7268f5f1331f7404ad750786269e48b68e908196fb SHA512: f83bdb3252af6266c587089001b119c48d908988bfe973484249971a4234469a806b4e9c23b3244a505f217e46849e776180a49adb4e458fe82080769e654d42 Homepage: https://cran.r-project.org/package=Bios2cor Description: CRAN Package 'Bios2cor' (From Biological Sequences and Simulations to CorrelationAnalysis) Utilities for computation and analysis of correlation/covariation in multiple sequence alignments and in side chain motions during molecular dynamics simulations. Features include the computation of correlation/covariation scores using a variety of scoring functions between either sequence positions in alignments or side chain dihedral angles in molecular dynamics simulations and utilities to analyze the correlation/covariation matrix through a variety of tools including network representation and principal components analysis. In addition, several utility functions are based on the R graphical environment to provide friendly tools for help in data interpretation. Examples of sequence covariation analysis are provided in: (1) Pele J, Moreau M, Abdi H, Rodien P, Castel H, Chabbert M (2014) and (2) Taddese B, Deniaud M, Garnier A, Tiss A, Guissouma H, Abdi H, Henrion D, Chabbert M (2018) . An example of side chain correlated motion analysis is provided in: Taddese B, Garnier A, Abdi H, Henrion D, Chabbert M (2020) . This work was supported by the French National Research Agency (Grant number: ANR-11-BSV2-026) and by GENCI (Grant number: 100567). Package: r-cran-bios2mds Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3033 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-amap, r-cran-e1071, r-cran-scales, r-cran-cluster, r-cran-rgl Filename: pool/dists/noble/main/r-cran-bios2mds_1.2.3-1.ca2404.1_all.deb Size: 2803800 MD5sum: 767c80252ffce8b971191941c29c0c43 SHA1: fcac4c7cffeba91c7f6ebb8c337c2040658da1c0 SHA256: 6640da98364c19851bea2da88abaf0de9f5befeedc4038beb26e9b76153d4c69 SHA512: 170b831137d005d3d1e867516ce9230945d07c4b175e8594d4aa5fde16dc563b3947ca20c34381a84efca60db12d706878100d197e15701cf2156a3266ab7602 Homepage: https://cran.r-project.org/package=bios2mds Description: CRAN Package 'bios2mds' (From Biological Sequences to Multidimensional Scaling) Utilities dedicated to the analysis of biological sequences by metric MultiDimensional Scaling with projection of supplementary data. It contains functions for reading multiple sequence alignment files, calculating distance matrices, performing metric multidimensional scaling and visualizing results. Package: r-cran-biosampler Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vegan Filename: pool/dists/noble/main/r-cran-biosampler_1.0.4-1.ca2404.1_all.deb Size: 56944 MD5sum: 6d8d3fc2cdbc4fc2cf448b116de39774 SHA1: 338afda21e812b3451658ceff6af0c869854e4e9 SHA256: 9b8d123c2e0694a859711300c092fcdab773b09bc5a1d4b8ec634b2e3300cb43 SHA512: 1df2abc71c78cb69f335277f62bd355391deea142ea77efd01fc386e17cce20215d8ae03696afc738d1c727e2e1cacb9f98dd55e5e05f3bd959980efaf7d6175 Homepage: https://cran.r-project.org/package=biosampleR Description: CRAN Package 'biosampleR' (Biodiversity Index Calculation and Bootstrap Confidence IntervalEstimation) Provides tools for the calculation of common biodiversity indices from count data. 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Package: r-cran-bioseq Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1411 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs, r-cran-tibble, r-cran-ape, r-cran-crayon, r-cran-dplyr, r-cran-pillar, r-cran-stringi, r-cran-stringr, r-cran-stringdist, r-cran-readr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-bioseq_0.1.5-1.ca2404.1_all.deb Size: 750272 MD5sum: c6387bc96db98044a75467b34b9110fe SHA1: cc52cdb7d2e6324fbb93cf2a901bcef97bb16cca SHA256: 9fb4770b2b27299912aca1f57129c33e667065cda3ca0a25afb085ed12ae8ba4 SHA512: 516d67aa52bc6ab6fce615e9ede1db7c09aee0ec6331884dd6fb3d34a16391c72981b56ae2d2e1280691bffaae31964b61c59b0acdefb69e744f85b181cb55ed Homepage: https://cran.r-project.org/package=bioseq Description: CRAN Package 'bioseq' (A Toolbox for Manipulating Biological Sequences) Classes and functions to work with biological sequences (DNA, RNA and amino acid sequences). 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Package: r-cran-biosnr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-pracma, r-cran-scales Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-biosnr_1.0-1.ca2404.1_all.deb Size: 125700 MD5sum: 9dced98060400fde4d7f1a69b9fc113f SHA1: ccbf3328bfd864967e299509b517d3c54f9220aa SHA256: e5a76287e14d14272ba04071b048c951c960e197d0c4991960ced8afd7052385 SHA512: d9980fc8df7d3b643f5eea1b5d754d5a78b52396515508a989a404bf12314d0d0b66f0d40e2113bf055a3f33279e8e35103cf59cc0470aaabd35f3682c61bfba Homepage: https://cran.r-project.org/package=bioSNR Description: CRAN Package 'bioSNR' (Bioacoustic Basic Operations with Decibels and the Passive SonarEquation) A beginners toolbox to help those in ecology who want to deepen their understanding or utilize Bioacoustics in their work. 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Package: r-cran-biospear Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3306 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pkgconfig, r-cran-cobs, r-cran-corpcor, r-cran-devtools, r-cran-glmnet, r-cran-grplasso, r-cran-mass, r-cran-matrix, r-cran-mboost, r-cran-plsrcox, r-cran-proc, r-cran-prroc, r-cran-rcurl, r-cran-survauc, r-cran-survival Filename: pool/dists/noble/main/r-cran-biospear_1.0.2-1.ca2404.1_all.deb Size: 3294676 MD5sum: 92bb95e1465c9cc4cbeb70cdc9d2b766 SHA1: c834599e8025d04ba086c97c8bbdc3e763d9d460 SHA256: 9bb469efdefd5f9749a648c87b8a3776cc24802c646eb72118317043ff94fe40 SHA512: 66ec8455f2764f38d7027631b698c40883e6212a4f5a2b49f6c4646108c3c8f803508e5e3bcd296faafdd81adb2a76905850ec84487e94c8d3db0c76aa1b6814 Homepage: https://cran.r-project.org/package=biospear Description: CRAN Package 'biospear' (Biomarker Selection in Penalized Regression Models) Provides some tools for developing and validating prediction models, estimate expected survival of patients and visualize them graphically. Most of the implemented methods are based on penalized regressions such as: the lasso (Tibshirani R (1996)), the elastic net (Zou H et al. (2005) ), the adaptive lasso (Zou H (2006) ), the stability selection (Meinshausen N et al. (2010) ), some extensions of the lasso (Ternes et al. (2016) ), some methods for the interaction setting (Ternes N et al. (2016) ), or others. A function generating simulated survival data set is also provided. 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The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) . This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages. 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Furthermore, the traditional biplot view is enhanced by translated axes with inter-class kernel densities superimposed. For more information on biplots, see Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0). 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This GUI is also aimed for estimate any numerical data matrix using the Clustering and Disjoint Principal component (CDPCA) methodology. 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The logistic biplot represents individuals as points and binary variables as directed vectors in a low-dimensional subspace; the orthogonal projection of each individual onto a variable vector approximates the expected probability that the corresponding characteristic is present. Available fitting methods include conjugate gradient algorithms, a coordinate descent Majorization-Minimization (MM) algorithm, and a block coordinate descent algorithm based on data projection that supports matrices with missing values and allows new individuals to be projected as supplementary rows without refitting the model. A cross-validation procedure is provided to select the number of latent dimensions k. References: Babativa-Marquez and Vicente-Villardon (2021) ; Vicente-Villardon and Galindo (2006, ISBN:9780470973196). 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The package combines citation network analysis with community detection and attribute extraction, also applying natural language processing (NLP) and structural topic modeling (STM) to uncover the contents of research communities. It implements metrics and visualizations of community trajectories, including novelty indicators, citation cycle time, and main path analysis, allowing researchers to map and interpret the dynamics of emerging knowledge fields. Applications of the method include: Souza et al. (2022) , Souza et al. (2022) , Matos et al. (2023) , Maria et al. (2023) , Biazatti et al. (2024) , Felizardo et al. (2025) , and Miranda et al. (2025) . Package: r-cran-birdnetr Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-arrow, r-cran-curl, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-birdnetr_0.3.2-1.ca2404.1_all.deb Size: 574390 MD5sum: f1d81a7efe5a355d1742109c4b59b4d7 SHA1: d863ba353b09b49b2f8546ca2ecc7d8bd358f64c SHA256: 60dfe1c2fea00f9b587591a6ccd69378626039d5feee154cf37d9d6340a315d4 SHA512: fca0363ccc1f7126cc69046d64176881a52e9f43f6e025e751d72d8bc1fbeea65769ccd294d0c28b7854370127e206bcd93a2342437756caa19e1d6f787100a7 Homepage: https://cran.r-project.org/package=birdnetR Description: CRAN Package 'birdnetR' (Deep Learning for Automated (Bird) Sound Identification) Use 'BirdNET', a state-of-the-art deep learning classifier, to automatically identify (bird) sounds. Analyze bioacoustic datasets without any computer science background using a pre-trained model or a custom trained classifier. Predict bird species occurrence based on location and week of the year. Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021) . 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Package: r-cran-birdscanr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 891 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-suntools, r-cran-modi, r-cran-reshape2, r-cran-rodbc, r-cran-rpostgresql, r-cran-rlang, r-cran-rstudioapi, r-cran-sp, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-birdscanr_0.3.0-1.ca2404.1_all.deb Size: 694028 MD5sum: daf21c43732634724541a8902154165a SHA1: b3d93a9ad52080811c68ebd3655e0bdb15f3763e SHA256: e726c62c7d0a2021bc6cfdb87b69b89e91405e1f04bb9861d5648d6a631f9b0b SHA512: 8388dd446c4930b6679e319419cee032540802125c94e9cea09286956cd7b66ac15cb0dbec2a161e12c92d8346fcc9c95537a189d1adda0f84bfc69becbd62ea Homepage: https://cran.r-project.org/package=birdscanR Description: CRAN Package 'birdscanR' (Migration Traffic Rate Calculation Package for 'Birdscan MR1'Radars) Extract data from 'Birdscan MR1' 'SQL' vertical-looking radar databases, filter, and process them to Migration Traffic Rates (#objects per hour and km) or density (#objects per km3) of, for example birds, and insects. Object classifications in the 'Birdscan MR1' databases are based on the dataset of Haest et al. (2021) ). Migration Traffic Rates and densities can be calculated separately for different height bins (with a height resolution of choice) as well as over time periods of choice (e.g., 1/2 hour, 1 hour, 1 day, day/night, the full time period of observation, and anything in between). Two plotting functions are also included to explore the data in the 'SQL' databases and the resulting Migration Traffic Rate results. For details on the Migration Traffic Rate calculation procedures, see Schmid et al. (2019) . Package: r-cran-birk Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-birk_2.1.2-1.ca2404.1_all.deb Size: 47274 MD5sum: 9926e401b3863c33deff6b3d6f1c60ca SHA1: ba87f3cdc8381f211f0f45e8306aad8773f8b744 SHA256: dfb88e50c9840b36945c8505a54d5a7859c34544bf9e1487f714da13fe9c5ac7 SHA512: 25c28c26ad3440777e7c551749d48eda66d9de6b082093c1c6b0c295f4b25a1cfe19caea9f2d392cfc73ddf04edd5df66464f45063670a1f12cfc84317bbf2a4 Homepage: https://cran.r-project.org/package=birk Description: CRAN Package 'birk' (MA Birk's Functions) Collection of tools to make R more convenient. Includes tools to summarize data using statistics not available with base R and manipulate objects for analyses. 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For example, iccplot() plots an item characteristic curve under the two-parameter logistic model. 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Package: r-cran-bisep Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-annotationdbi, r-cran-mclust, r-bioc-go.db, r-bioc-org.hs.eg.db, r-bioc-gosemsim Filename: pool/dists/noble/main/r-cran-bisep_2.3-1.ca2404.1_all.deb Size: 323896 MD5sum: 4c33b77ba460d5f6352844c1024f7fd6 SHA1: c0a20cba72d598a7d0a66a2075363216fcc958a7 SHA256: 7e4883759b0542e783212646782aaf3178c0ef4f58dd97c945f5c010bd4ac6ec SHA512: 7153b163aa6b06132d19371a667bbc17763aacabfea6c3619d5176b4dec9aab23e7c00e7b3602f19dd16f7d8913c810fe820fef7132aea5771ff58cee283fd12 Homepage: https://cran.r-project.org/package=BiSEp Description: CRAN Package 'BiSEp' (Toolkit to Identify Candidate Synthetic Lethality) Enables the user to infer potential synthetic lethal relationships by analysing relationships between bimodally distributed gene pairs in big gene expression datasets. Enables the user to visualise these candidate synthetic lethal relationships. Package: r-cran-bispdep Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spdata, r-cran-sf, r-cran-spdep, r-cran-spatialreg, r-cran-combinat, r-cran-boot, r-cran-ggplot2, r-cran-sp, r-cran-rcolorbrewer Suggests: r-cran-spam Filename: pool/dists/noble/main/r-cran-bispdep_1.0-2-1.ca2404.1_all.deb Size: 184470 MD5sum: fe10f9af04395ef3c4225d6ab227f5b7 SHA1: 81fbe8e996fba9e38580a20f23e2ecd97279986b SHA256: c7e1c70f0072e263f871a964060df69624b875edb159f9df9070a3e46ca691ac SHA512: 0dda1b04281f504540934c17d22e2628064e55201082b196f4d5c7e9e0cf68681db301c7dfa1075c7db5b503f353434578cabbbd7b545a632895a6e320fcd3b4 Homepage: https://cran.r-project.org/package=bispdep Description: CRAN Package 'bispdep' (Statistical Tools for Bivariate Spatial Dependence Analysis) A collection of functions to test spatial autocorrelation between variables, including Moran I, Geary C and Getis G together with scatter plots, functions for mapping and identifying clusters and outliers, functions associated with the moments of the previous statistics that will allow testing whether there is bivariate spatial autocorrelation, and a function that allows identifying (visualizing neighbours) on the map, the neighbors of any region once the scheme of the spatial weights matrix has been established. Package: r-cran-bisquerna Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase, r-cran-limsolve Suggests: r-cran-seurat, r-cran-plyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bisquerna_1.0.5-1.ca2404.1_all.deb Size: 100346 MD5sum: 6a219a187d5ca3416c01a34f52666822 SHA1: 4a66aa567dd4bd78b19a04fbba147f69e5b5d5ff SHA256: fcb0b93ca3b8b1e98b716ce414deaa8f201c0ad81cbc1a4b91c245d7e27d8ebe SHA512: 887920318ed482eb51b82dae53b793d93a5e4e5b8755426b9876d04a9c12e9387906c18867d1f2fd1f3e9265149ed05803fe419b337f4f9245d5333c5791ce3d Homepage: https://cran.r-project.org/package=BisqueRNA Description: CRAN Package 'BisqueRNA' (Decomposition of Bulk Expression with Single-Cell Sequencing) Provides tools to accurately estimate cell type abundances from heterogeneous bulk expression. A reference-based method utilizes single-cell information to generate a signature matrix and transformation of bulk expression for accurate regression based estimates. A marker-based method utilizes known cell-specific marker genes to measure relative abundances across samples. For more details, see Jew and Alvarez et al (2019) . Package: r-cran-bisrna Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-bioc-ihw, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bisrna_0.2.2-1.ca2404.1_all.deb Size: 54802 MD5sum: 2bf5e696d4e240dda6e9a4ac3dddb5ec SHA1: 676a9f0526ef83cf583851bacf8c5a8309572478 SHA256: cac93c6ed12966f732b9df446f7a2f28743132d8fc5faa7f069d12a102f552fa SHA512: 4de6ac9350db18f715cddaa4188652e905ce9d809f0e80ba32250a65c1590da691477251ba7f025515648982cd3cfa1a48ea0d752dab13a3d26946a859957dbd Homepage: https://cran.r-project.org/package=BisRNA Description: CRAN Package 'BisRNA' (Analysis of RNA Cytosine-5 Methylation) Bisulfite-treated RNA non-conversion in a set of samples is analysed as follows : each sample's non-conversion distribution is identified to a Poisson distribution. P-values adjusted for multiple testing are calculated in each sample. Combined non-conversion P-values and standard errors are calculated on the intersection of the set of samples. For further details, see C Legrand, F Tuorto, M Hartmann, R Liebers, D Jakob, M Helm and F Lyko (2017) . Package: r-cran-bite Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-mass, r-cran-phytools, r-cran-coda, r-cran-sm, r-cran-vioplot, r-cran-xml2 Suggests: r-cran-mvmorph Filename: pool/dists/noble/main/r-cran-bite_0.3-1.ca2404.1_all.deb Size: 360898 MD5sum: 5d3214ae0178bc94a55eec3b2651f7d4 SHA1: dc4fd61dddc0104168acd25d2cb922523b0b659c SHA256: dcf7b0d8d2782ce20760ff64b01551aece447a99d1ab53c70325eb50273e2073 SHA512: 9fa7afc72f414ce16cde256a24591600a0ceb724d32ba5b00489f7419101c66260a6faf835263b26ae0298d54267a909886d1a277bcb9b0641b1c92ebfae7d1d Homepage: https://cran.r-project.org/package=bite Description: CRAN Package 'bite' (Bayesian Integrative Models of Trait Evolution) Contains the JIVE (joint inter and intra-specific model of variance evolution) model and other Bayesian models aimed at understanding trait evolution. The goal of the package is to join phylogenetic comparative models (PCM) that tend to integrate various type of data (individual observations, environmental data, fossil data) into a hierarchical Bayesian framework. It contains various PCMs as well as functions to join those models into a hierarchical Bayesian framework in a flexible and user friendly way. It contains various Markov chain Monte-Carlo (MCMC) algorithms, methods for model comparison and many plotting function for pre- and post-processing data visualization. Finally, this package integrates functions allowing bridges between 'R' and the 'BEAST2' implementations of PCMs. Kostikova A, Silvestro D, Pearman PB, Salamin N (2016) . Gaboriau T, Mendes FK, Joly S, Silvestro D, Salamin N (in prep). 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Additional functionalities include the calculation of the correlation coefficient, covariance, and cross-factorial moments, as well as the generation of random variates. The package also implements parameter estimation based on the method of moments. Package: r-cran-bivgeom Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmle, r-cran-copula Filename: pool/dists/noble/main/r-cran-bivgeom_1.0-1.ca2404.1_all.deb Size: 69100 MD5sum: cbfe17bc91a9f8affa5dc831699fca49 SHA1: 47d088bc763a75c764f45b310ab7b0d8f5cc6d28 SHA256: 9879e784e51cba47f9dfb90b7ca3caa278929bac3e3604099640121cadad1323 SHA512: 2a63ed0a083e16b4333d77f49b9395507223193f78bff1bd955722f5c6ff16b3fb68381e405865164c93de10cef7d2b42dae02f9d41aa84ba7273b4cd922b4a8 Homepage: https://cran.r-project.org/package=bivgeom Description: CRAN Package 'bivgeom' (Roy's Bivariate Geometric Distribution) Implements Roy's bivariate geometric model (Roy (1993) ): joint probability mass function, distribution function, survival function, random generation, parameter estimation, and more. Package: r-cran-bivpois Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-bivpois_1.2-1.ca2404.1_all.deb Size: 55942 MD5sum: 2befcdc6a0594b4760a1c41d3bddbc99 SHA1: cf1100692c512a2ff79aa58adad0d4420c1288b6 SHA256: 67c9bd6c4730457ae6023c72ed8bd6bda30da768c2e191b1cd4a66ac684eb209 SHA512: 4011789dc4707b74128ae8b7d4c42ca05ab6eff4691183b59023003eeb4f581ebed6b5fc2b8a2a73d24d01a12cf8c34cdc2ca5d561df8b576c13e77c331cf572 Homepage: https://cran.r-project.org/package=bivpois Description: CRAN Package 'bivpois' (Bivariate Poisson Distribution) Maximum likelihood estimation, random values generation, density computation and other functions for the bivariate Poisson distribution. References include: Kawamura K. (1984). "Direct calculation of maximum likelihood estimator for the bivariate Poisson distribution". Kodai Mathematical Journal, 7(2): 211--221. . Kocherlakota S. and Kocherlakota K. (1992). "Bivariate discrete distributions". CRC Press. . Karlis D. and Ntzoufras I. (2003). "Analysis of sports data by using bivariate Poisson models". Journal of the Royal Statistical Society: Series D (The Statistician), 52(3): 381--393. . Package: r-cran-bivregbls Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ellipse Filename: pool/dists/noble/main/r-cran-bivregbls_1.1.1-1.ca2404.1_all.deb Size: 356538 MD5sum: 57603afccee7c064300021f535c5f576 SHA1: 727d386f99ad9fa069794afcb4ae22844a5794bd SHA256: 6deec6c9cc003b82bfc636191fbb7c681ba5c1d0aefb2286f11595205bc9a27b SHA512: e36e0b0286e7788fe8029a87696b56ec015dd32e8af045d9aab40e6752d6bdee49e75c09b232eae05a34eeba2bfa85df11f9d3ad4f0b126c7eeccdc421ee84e6 Homepage: https://cran.r-project.org/package=BivRegBLS Description: CRAN Package 'BivRegBLS' (Tolerance Interval and EIV Regression - Method ComparisonStudies) Assess the agreement in method comparison studies by tolerance intervals and errors-in-variables (EIV) regressions. The Ordinary Least Square regressions (OLSv and OLSh), the Deming Regression (DR), and the (Correlated)-Bivariate Least Square regressions (BLS and CBLS) can be used with unreplicated or replicated data. The BLS() and CBLS() are the two main functions to estimate a regression line, while XY.plot() and MD.plot() are the two main graphical functions to display, respectively an (X,Y) plot or (M,D) plot with the BLS or CBLS results. Four hyperbolic statistical intervals are provided: the Confidence Interval (CI), the Confidence Bands (CB), the Prediction Interval and the Generalized prediction Interval. Assuming no proportional bias, the (M,D) plot (Band-Altman plot) may be simplified by calculating univariate tolerance intervals (beta-expectation (type I) or beta-gamma content (type II)). Major updates from last version 1.0.0 are: title shortened, include the new functions BLS.fit() and CBLS.fit() as shortcut of the, respectively, functions BLS() and CBLS(). References: B.G. Francq, B. Govaerts (2016) , B.G. Francq, B. Govaerts (2014) , B.G. Francq, B. Govaerts (2014) , B.G. Francq (2013), PhD Thesis, UCLouvain, Errors-in-variables regressions to assess equivalence in method comparison studies, . 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Supports Frank and Gaussian copulas. Allows for mixed margins (e.g., one margin Poisson, the other zero-inflated negative binomial), and several marginal link functions. Built-in methods for publication-quality tables using 'texreg', post-estimation diagnostics using 'DHARMa', and testing for marginal zero-modification via . For information on copula regression for count data, see Genest and Nešlehová (2007) as well as Nikoloulopoulos (2013) . For information on zero-inflated count regression generally, see Lambert (1992) . The author acknowledges support by NSF DMS-1925119 and DMS-212324. Package: r-cran-bkmr Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-nlme, r-cran-fields, r-cran-truncnorm, r-cran-tidyr, r-cran-mass, r-cran-tmvtnorm, r-cran-tibble Filename: pool/dists/noble/main/r-cran-bkmr_0.2.2-1.ca2404.1_all.deb Size: 218022 MD5sum: 92f719d00bac3b597ec301e3fa0255a7 SHA1: 67906c22317646969d07e1a5c669b906e8b9d6ca SHA256: f78fd558b3c9a4b2e2b255df41a7d925416bdab1353ca608373b9a0384365750 SHA512: 3ba006976714bae9619b7fa8b953bf8ae22a9f4a098dfdcecbe2ba612f570f24bb6b50b9147822e89bf5aa2c937673de353805434e1d8d728eb2df41dfc81c89 Homepage: https://cran.r-project.org/package=bkmr Description: CRAN Package 'bkmr' (Bayesian Kernel Machine Regression) Implementation of a statistical approach for estimating the joint health effects of multiple concurrent exposures, as described in Bobb et al (2015) . 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With 'blit', users can easily integrate a wide array of bioinformatics command line tools into their workflows, leveraging the power of R for sophisticated data manipulation and graphical representation. Package: r-cran-blm Architecture: all Version: 2022.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-blm_2022.0.0.1-1.ca2404.1_all.deb Size: 299030 MD5sum: 0c38e739f34c9a57b6fcf3ef4bd211cc SHA1: 517bf299defe48259c755df90c157073fea3074e SHA256: 76b3d0164206726aab95a5526a4253f33b0ab7b5a14fe7a2f73a385cbb06698d SHA512: 60e3ca50d9c60d54b678a4bae4239294213b4e2694eb945c303f274a4caad470c6ccd26400271e48a429b03cccb540737af407f27e29ce46841576e2392c5ffc Homepage: https://cran.r-project.org/package=blm Description: CRAN Package 'blm' (Binomial Linear Regression) Implements regression models for binary data on the absolute risk scale. These models are applicable to cohort and population-based case-control data. Package: r-cran-blme Architecture: all Version: 1.0-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 538 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4 Suggests: r-cran-expint, r-cran-testthat Filename: pool/dists/noble/main/r-cran-blme_1.0-7-1.ca2404.1_all.deb Size: 444132 MD5sum: ff58dc7e25a517f0d614f49f9cf83b57 SHA1: 80208819467b8e640b54a599033c6931e7f8e989 SHA256: 6fe1777c01147dd363ca9a1d816855aaff6e0a674cf3983ca6a05af88bbc3f91 SHA512: 13c9d873a735beb0319b0bb08063338e2970ca321e0db71115581e5ff575db938b9a7583ce6ecd13168bbecbea4f5846e8637db10a171b3cc008e5aca8fae8ac Homepage: https://cran.r-project.org/package=blme Description: CRAN Package 'blme' (Bayesian Linear Mixed-Effects Models) Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting, implementing the methods of Chung, et al. (2013) . 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Package: r-cran-blockcov Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rdpack, r-cran-bbmisc, r-cran-dplyr, r-cran-tibble, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-blockcov_0.1.1-1.ca2404.1_all.deb Size: 163080 MD5sum: d70839c3c330ee2535b78e28400b89b9 SHA1: a65b0e9d5670866f447ef2d28a243e95de4d69b5 SHA256: 4697ee4701aa351872df0aee43f5b2b033407018fa7a616dee81b11f216a4580 SHA512: 411990d0a4f6aba62c146a800058b8fc18f8d8d369dae6d797b958467460367b6516fec77ad337b27e1fd7e5bc01011e7d55db9d065f21a8d7a5d5e6ee24c0c1 Homepage: https://cran.r-project.org/package=BlockCov Description: CRAN Package 'BlockCov' (Estimation of Large Block Covariance Matrices) Computation of large covariance matrices having a block structure up to a permutation of their columns and rows from a small number of samples with respect to the dimension of the matrix. The method is described in the paper Perrot-Dockès et al. (2019) . Package: r-cran-blockedff Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-blockedff_0.1.0-1.ca2404.1_all.deb Size: 70994 MD5sum: 96661710c33a5c240b539c71c98061ac SHA1: 794f754d9741dccd087fc7da171dcc25b6c1495f SHA256: 8fe874285db73a70ee6fedb2a00d3f61590ba1e81686b9eed183788c0ca79bc8 SHA512: 115149705a9a4edd5e4c15816829c002a32d1cbe7c12cd2c8ef9af3f33533be2c2c9e1786f79e1b7378177e07ab13d8c42a34012238c41e9e45f40521fe36aa4 Homepage: https://cran.r-project.org/package=blockedFF Description: CRAN Package 'blockedFF' (Generation of Blocked Fractional Factorial Designs (Two-Leveland Three-Level)) Provides computational tools to generate efficient blocked and unblocked fractional factorial designs for two-level and three-level factors using the generalized Minimum Aberration (MA) criterion and related optimization algorithms. Methodological foundations include the general theory of minimum aberration as described by Cheng and Tang (2005) , and the catalogue of three-level regular fractional factorial designs developed by Xu (2005) . The main functions dol2() and dol3() generate blocked two-level and three-level fractional factorial designs, respectively, using beam search, optimization-based ranking, confounding assessment, and structured output suitable for complete factorial situations. Package: r-cran-blocking Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2596 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-text2vec, r-cran-tokenizers, r-cran-rcpphnsw, r-cran-rcppannoy, r-cran-mlpack, r-cran-rnndescent, r-cran-igraph, r-cran-data.table, r-cran-readr, r-cran-matrix Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown, r-cran-reclin2 Filename: pool/dists/noble/main/r-cran-blocking_1.0.2-1.ca2404.1_all.deb Size: 2327034 MD5sum: 805483307f536592d3e11c75f3996024 SHA1: 28195294482b449a277008b36176faad433850ad SHA256: ea3c1cb9afbbf2ca720a200bbea3f1fe58cbc9444e918aaf73de7a00d8613dd3 SHA512: 250042ab03efc576df2b76bfc3146cdee62e72c2cf66beef3ba2ccf177f2f9416a96c6a550734298d5f43af4be848a3676d4589ce620cc46cdee9e5b76c35a07 Homepage: https://cran.r-project.org/package=blocking Description: CRAN Package 'blocking' (Various Blocking Methods for Entity Resolution) The goal of 'blocking' is to provide blocking methods for record linkage and deduplication using approximate nearest neighbour (ANN) algorithms and graph techniques. It supports multiple ANN implementations via 'rnndescent', 'RcppHNSW', 'RcppAnnoy', and 'mlpack' packages, and provides integration with the 'reclin2' package. The package generates shingles from character strings and similarity vectors for record comparison, and includes evaluation metrics for assessing blocking performance including false positive rate (FPR) and false negative rate (FNR) estimates. For details see: Papadakis et al. (2020) , Steorts et al. (2014) , Dasylva and Goussanou (2021) , Dasylva and Goussanou (2022) . Package: r-cran-blocklength Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 906 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tseries Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-blocklength_0.2.2-1.ca2404.1_all.deb Size: 126652 MD5sum: cfb426e92c553282acb354aadcc030c0 SHA1: 82249da89b54b6cfc544cdfb68b5a6758ed5226b SHA256: b82c1a88f5646c740fd3f14d57283361211f692c0c53ad62e0c058c777c9395f SHA512: 2401bff4236008e7238cb72a578957515b3fb72c2a10d95e4cbef5f3ab29374e04d777a7a1a42ea89a9dc56c3a151a3d1e74867b54364345f20592696f4568ea Homepage: https://cran.r-project.org/package=blocklength Description: CRAN Package 'blocklength' (Select an Optimal Block-Length to Bootstrap Dependent Data(Block Bootstrap)) A set of functions to select the optimal block-length for a dependent bootstrap (block-bootstrap). Includes the Hall, Horowitz, and Jing (1995) subsampling-based cross-validation method, the Politis and White (2004) Spectral Density Plug-in method, including the Patton, Politis, and White (2009) correction, and the Lahiri, Furukawa, and Lee (2007) nonparametric plug-in method, with a corresponding set of S3 plot methods. Package: r-cran-blockmatrix Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-blockmatrix_1.0-1.ca2404.1_all.deb Size: 65676 MD5sum: 8aa2e67b2eb905860236487de7dfccff SHA1: 7e4693d1f13245f188b32cb640d9d6b11e2f6670 SHA256: 987a35098fe30e4cde6edc3019d0e16a8dbe8589b928ef8645eee566913cdc5b SHA512: bd7b2ab2817dea521395d92d71bbb5e7ff144ef0b9100d1c31d971ea03bf71fe86640cf308668090c0914274dfe194fe306d8300b3948879e09c65b386ce0a73 Homepage: https://cran.r-project.org/package=blockmatrix Description: CRAN Package 'blockmatrix' (blockmatrix: Tools to solve algebraic systems with partitionedmatrices) Some elementary matrix algebra tools are implemented to manage block matrices or partitioned matrix, i.e. "matrix of matrices" (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here defined as a new S3 object. In this package, some methods for "matrix" object are rewritten for "blockmatrix" object. New methods are implemented. This package was created to solve equation systems with block matrices for the analysis of environmental vector time series . Bugs/comments/questions/collaboration of any kind are warmly welcomed. Package: r-cran-blockmissingdata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-glmnet, r-cran-glmnetcr, r-cran-mass, r-cran-matrix, r-cran-pryr Filename: pool/dists/noble/main/r-cran-blockmissingdata_0.1.0-1.ca2404.1_all.deb Size: 79004 MD5sum: ef18a481761d380e9eca6a01c4105d63 SHA1: 1cdfd727f8f52d7a3d41ffec7bec063b0b5ef38a SHA256: d4b11c481f42af68211f44465ab4fe324dfdd892541b6a430363e5d8155ec7cb SHA512: 8c1c9dfd5622c16b0e32e2c72b29e0cc3c49a1aa4387c2569b29d46b3c9b810cd8ff800fe74c1dd028b1b28c0313a7c76cd411e745e3b17d836bf2606b224604 Homepage: https://cran.r-project.org/package=BlockMissingData Description: CRAN Package 'BlockMissingData' (Integrating Multi-Source Block-Wise Missing Data in ModelSelection) Model selection method with multiple block-wise imputation for block-wise missing data; see Xue, F., and Qu, A. (2021) . Package: r-cran-blockmodelinggui Architecture: all Version: 1.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-blockmodeling, r-cran-shiny, r-cran-dt, r-cran-htmlwidgets, r-cran-igraph, r-cran-intergraph, r-cran-network, r-cran-shinybusy, r-cran-shinyjs, r-cran-shinythemes, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-blockmodelinggui_1.8.4-1.ca2404.1_all.deb Size: 46648 MD5sum: 4d62738d988fbbf28b173d45c3861416 SHA1: 6f05dc74f6650bb6379572e3a4308e6e6071631f SHA256: 9881e1f36ee6ce9dfc00c3d496acbb5a143609527f4011b6a2a2104ebf1fd4fa SHA512: eb77be7f4a473834700f76ed238624016ece39b32165ef85139bcb69417f36f3916c3394cee4d8444edf4563e652891ad4e7eec3b9c7003e5692d97c70175f12 Homepage: https://cran.r-project.org/package=BlockmodelingGUI Description: CRAN Package 'BlockmodelingGUI' (GUI for the Generalised Blockmodeling of Valued Networks) This app provides some useful tools for Offering an accessible GUI for generalised blockmodeling of single-relation, one-mode networks. The user can execute blockmodeling without having to write a line code by using the app's visual helps. Moreover, there are several ways to visualisations networks and their partitions. Finally, the results can be exported as if they were produced by writing code. The development of this package is financially supported by the Slovenian Research Agency (www.arrs.gov.si) within the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks). Package: r-cran-blockr.core Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1087 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-bslib, r-cran-bsicons, r-cran-jsonlite, r-cran-vctrs, r-cran-generics, r-cran-rlang, r-cran-htmltools, r-cran-evaluate, r-cran-shinyfiles, r-cran-digest, r-cran-cli, r-cran-glue Suggests: r-cran-testthat, r-cran-memuse, r-cran-withr, r-cran-shinytest2, r-cran-roxy.shinylive, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-scoutbar, r-cran-thematic, r-cran-ids, r-cran-ellmer Filename: pool/dists/noble/main/r-cran-blockr.core_0.1.2-1.ca2404.1_all.deb Size: 686884 MD5sum: d29a19ef1d627aba1cd91a810a7aa3fb SHA1: b6bdd84addfe07d89d928ab6453974d5e2a7714c SHA256: e8b350523cb6fed4c611e07970e460741c4cbe859d842c77afe113e7c1893d91 SHA512: 57e18588d35e06277932638c561beedca1b0ac944550d7d64fc0f4fa052fede81d1d842e3811af7c1faaa1a86ab3ecca0d0c32dce5c9c75f802209959d79ecb5 Homepage: https://cran.r-project.org/package=blockr.core Description: CRAN Package 'blockr.core' (Graphical Web-Framework for Data Manipulation and Visualization) A framework for data manipulation and visualization using a web-based point and click user interface where analysis pipelines are decomposed into re-usable and parameterizable blocks. 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Users can build charts through a graphical interface without writing code directly. Includes common chart types (bar charts, line charts, pie charts, scatter plots) as well as statistical plots (boxplots, histograms, density plots, violin plots) with rich customization options and intuitive user interfaces. 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Underlying data manipulation and visualization is possible using a web-based point and click user interface. Package: r-cran-blockrand Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-blockrand_1.5-1.ca2404.1_all.deb Size: 34222 MD5sum: cbf4bf01e46408e834f7c1dd3284eb19 SHA1: 1811cf36ef26b3d584d691f650669391c04a0ada SHA256: 6c47da75612434ac8a19aaa5653ccc531ab4d12711a1a92bf64843f3dbdd690a SHA512: 4eefd163d419daa69c3335aba584f32309227895642af119b3469eb2fd2de24685457ff4f8e686bd78ad049edd999919292c42b2306a841f0f297427c18fb0fc Homepage: https://cran.r-project.org/package=blockrand Description: CRAN Package 'blockrand' (Randomization for Block Random Clinical Trials) Create randomizations for block random clinical trials. Can also produce a pdf file of randomization cards. Package: r-cran-blocksdesign Architecture: all Version: 4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 547 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-polynomf Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-blocksdesign_4.9-1.ca2404.1_all.deb Size: 485388 MD5sum: ab163f0249d97ce450446ac8af4aba17 SHA1: 59a6ad84ed3de9d32c26db3695991ecf7c654d95 SHA256: 867e8b78101ec4cdafb531321be16b11b04d1120d65ab59895ab23a725645cad SHA512: 95ae98696663511e97dbe6b8c437607f56357e592e7b7cad889df9ff169201c5026758da45419873b33d0bccdf62ad8d0ba63dacda73a6fa0550d90631f8ed15 Homepage: https://cran.r-project.org/package=blocksdesign Description: CRAN Package 'blocksdesign' (Nested and Crossed Block Designs for Factorial and UnstructuredTreatment Sets) Constructs treatment and block designs for linear treatment models with crossed or nested block factors. The treatment design can be any feasible linear model and the block design can be any feasible combination of crossed or nested block factors. The block design is a sum of one or more block factors and the block design is optimized sequentially with the levels of each successive block factor optimized conditional on all previously optimized block factors. D-optimality is used throughout except for square or rectangular lattice block designs which are constructed algebraically using mutually orthogonal Latin squares. Crossed block designs with interaction effects are optimized using a weighting scheme which allows for differential weighting of first and second-order block effects. Outputs include a table showing the allocation of treatments to blocks and tables showing the achieved D-efficiency factors for each block and treatment design. Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. JABES 25, 500–522 (2020) . Package: r-cran-blockstrap Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-cli, r-cran-rlang, r-cran-vctrs Suggests: r-cran-hospitalnetwork, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-blockstrap_1.0.0-1.ca2404.1_all.deb Size: 19818 MD5sum: bb3b897149588b0951022b0ece6d9f5f SHA1: 2a5638f9b7a18b29206ad74a80d98a457161ed0c SHA256: 348b2f46010f9c1c11535c8da51d78170d1982234d703c896ec1adca2a02155d SHA512: 618a2633fd74ccd85dd7e46ea9cbcd58b260b26b379ed2e2bb203c0046dce3ce72ea08040a5cf0fe34d28d4c7bc912fedd0460cd1d9c970f41b0c2eb319ed78f Homepage: https://cran.r-project.org/package=blockstrap Description: CRAN Package 'blockstrap' (Sample Dataframes by a Group) Sample dataframes by group, in the form of a 'block bootstrap'. Entire groups are returned allowing for a single 'observation' to span multiple rows of the dataframe. Package: r-cran-blockwiseranktest Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-blockwiseranktest_0.1.0-1.ca2404.1_all.deb Size: 64612 MD5sum: 564f8636bc225e4c4b73c828eb0630b7 SHA1: af0df6fc25c12b78ea8163d2f5e68925a476b3bf SHA256: fe2386bda9358cd514f64c5188ef83dd33bc73a4d8a3ec9a73cd237ade2e6b4c SHA512: 2fc150053b3c9d59564a8118fee177dfe720d8bf9004b0e3c8ad87e473012ed43689ff4d23bc41af5fd7fca9a3efcb982e4e898a3ed3423414541cdd54b461c5 Homepage: https://cran.r-project.org/package=BlockwiseRankTest Description: CRAN Package 'BlockwiseRankTest' (Block-Wise Rank in Similarity Graph Edge-Count Two-Sample Test(BRISE)) Implements the Block-wise Rank in Similarity Graph Edge-count test (BRISE), a rank-based two-sample test designed for block-wise missing data. 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Runtime examples are provided in the package function as well as at . 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The goal of 'bmass' is to comprehensively test all possible multivariate models given the phenotypes and datasets provided. Multivariate models are determined by assigning each phenotype to being either Unassociated (U), Directly associated (D) or Indirectly associated (I) with the genetic variant of interest. Test results for each model are presented in the form of Bayes factors, thereby allowing direct comparisons between models. The underlying framework implemented here is based on the modeling developed in "A Unified Framework for Association Analysis with Multiple Related Phenotypes", M. Stephens (2013) . 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Includes basic tests using multivariate Bernoulli distributions, subgroup analysis via generalized linear models, and multilevel models for clustered data. For statistical underpinnings, see Kavelaars, Mulder, and Kaptein (2020) , Kavelaars, Mulder, and Kaptein (2024) , and Kavelaars, Mulder, and Kaptein (2023) . An interactive shiny app to perform sample size computations is available. Package: r-cran-bmconcor Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4229 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bmconcor_2.0.0-1.ca2404.1_all.deb Size: 659578 MD5sum: 6ba6a900838a7f6b59ea4eea796d49fd SHA1: c93eb07b755c1c25ba5623296ef1dbf82d1f72ee SHA256: 67a4ef820aac766e4a4c091a879eab4316f365b76d74e27f5db6d4f264ee8a5f SHA512: e784d6168caa2efa22a4f83cc40a7641d8d03081727d6e145df35d4c98745f16780fa6b73353b751e78546349eb1ee4aa7c75a6e91a2df1823224ac31d8c4aee Homepage: https://cran.r-project.org/package=BMconcor Description: CRAN Package 'BMconcor' (CONCOR for Structural- And Regular-Equivalence Blockmodeling) The four functions svdcp() ('cp' for column partitioned), svdbip() or svdbip2() ('bip' for bipartitioned), and svdbips() ('s' for a simultaneous optimization of a set of 'r' solutions), correspond to a singular value decomposition (SVD) by blocks notion, by supposing each block depending on relative subspaces, rather than on two whole spaces as usual SVD does. The other functions, based on this notion, are relative to two column partitioned data matrices x and y defining two sets of subsets x_i and y_j of variables and amount to estimate a link between x_i and y_j for the pair (x_i, y_j) relatively to the links associated to all the other pairs. These methods were first presented in: Lafosse R. & Hanafi M.,(1997) and Hanafi M. & Lafosse, R. (2001) . Package: r-cran-bmem Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1056 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-amelia, r-cran-mass, r-cran-snowfall, r-cran-lavaan, r-cran-sem Filename: pool/dists/noble/main/r-cran-bmem_2.2-1.ca2404.1_all.deb Size: 952398 MD5sum: 0a6036dab2a8708646283e8a94ae2c4a SHA1: fac95922534ea8e7088af6be9d057c23b362fa57 SHA256: 66dc51f1c367b35bf9e782a22d54cb946af3451ea71377617da8663aa0f64ee7 SHA512: 54081ffb1a1cb97b20edba514dfadb1023a9fb9452a4aeb4f1222c7a0aaf3475e98a188f0c76ba620a097e86143d49672c333b1695e00aa421d38aca21e275e5 Homepage: https://cran.r-project.org/package=bmem Description: CRAN Package 'bmem' (Mediation Analysis with Missing Data Using Bootstrap) Four methods for mediation analysis with missing data: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included. Bootstrap confidence intervals for mediation effects are obtained. The robust method is also implemented for TS-ML. Since version 1.4, bmem adds the capability to conduct power analysis for mediation models. Details about the methods used can be found in these articles. Zhang and Wang (2003) . Zhang (2014) . 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For missing data, four methods are available: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included to handle missing data. For handling non-normal data, bootstrap and two-stage robust methods can be used. Technical details of the methods can be found in Zhang and Wang (2013, ), Zhang (2014, ), and Yuan and Zhang (2012, ). Package: r-cran-bmet Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-bmet_0.1.0-1.ca2404.1_all.deb Size: 23252 MD5sum: 85040e7d22d9d9a55eb0f5a738e2e9b4 SHA1: a8553860343ea9c823024ed93e415758dfcd6601 SHA256: 78362db188d4e2378d54d7902810aa77fb911682eca32295cfdc568b26d8731f SHA512: 2630514f507d58a576a4e4869796648d98eefa521865ef9f8de927e14235b4f18198bddbcd4b1afe19e44fe4225da34d57e2d518cb35c882ce5fa68f101d8af4 Homepage: https://cran.r-project.org/package=bmet Description: CRAN Package 'bmet' (Bayesian Multigroup Equivalence Testing) Calculates the necessary quantities to perform Bayesian multigroup equivalence testing. Currently the package includes the Bayesian models and equivalence criteria outlined in Pourmohamad and Lee (2023) , but more models and equivalence testing features may be added over time. Package: r-cran-bmiselect Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-mvnfast, r-cran-gigrvg, r-cran-mass, r-cran-rfast, r-cran-foreach, r-cran-doparallel, r-cran-arm, r-cran-mice, r-cran-abind, r-cran-stringr, r-cran-posterior Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bmiselect_1.0.3-1.ca2404.1_all.deb Size: 193610 MD5sum: 341cce58d50476de8992d96479a5d1ae SHA1: bcbbc50e5ceba5d48861a1125a6806e49e820a13 SHA256: 0ed101b4dd42f10ae713817d25e6c5b8ed83f232ddafd2e65708548945a74504 SHA512: 1e2839a6d06316d466a21d02ba3df27d3634a082835c66dd2b92832b5c02e812f2103903bca820a595ec968818dc67d040b187dcf8d341b747e6411c33f03d4f Homepage: https://cran.r-project.org/package=BMIselect Description: CRAN Package 'BMIselect' (Bayesian MI-LASSO for Variable Selection on Multiply-ImputedDatasets) Provides a suite of Bayesian MI-LASSO for variable selection methods for multiply-imputed datasets. The package includes four Bayesian MI-LASSO models using shrinkage (Multi-Laplace, Horseshoe, ARD) and Spike-and-Slab (Spike-and-Laplace) priors, along with tools for model fitting via MCMC, four-step projection predictive variable selection, and hyperparameter calibration. Methods are suitable for both continuous and binary covariates under missing-at-random or missing-completely-at-random assumptions. See Zou, J., Wang, S. and Chen, Q. (2025), Bayesian MI-LASSO for Variable Selection on Multiply-Imputed Data. ArXiv, 2211.00114. for more details. We also provide the frequentist`s MI-LASSO function. Package: r-cran-bml Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 747 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-tidyr, r-cran-stringr, r-cran-r2jags, r-cran-ggplot2, r-cran-ggmcmc, r-cran-coda, r-cran-patchwork, r-cran-tibble, r-cran-purrr, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-rjags Filename: pool/dists/noble/main/r-cran-bml_0.9.0-1.ca2404.1_all.deb Size: 575942 MD5sum: de2c6890ee53167afffba2c0a6291191 SHA1: 882d9ab874f4e4304fd75a3e37fc5ffd5f04eeec SHA256: 6bb59c6b6f8b1ae4199a4869c141366600b930fc0a690a473aaf7067635f947a SHA512: 776c4f7b81e4d1b72ae2676e646ff4c9737e802e1a8233c8dd1004a8689f31349cf29b07548ddeb3342ba59b24e679a176eb62ad6efa963abe2fc4dd3a38d6c5 Homepage: https://cran.r-project.org/package=bml Description: CRAN Package 'bml' (Bayesian Multiple-Membership Multilevel Models withParameterizable Weight Functions) Implements Bayesian multiple-membership multilevel models with parameterizable weight functions via 'JAGS' to model how lower-level units jointly shape higher-level outcomes (micro-macro link) across a range of outcome types (e.g., linear, logit, and survival models). Supports estimation and comparison of alternative aggregation mechanisms, allows weight matrices to be endogenized through parameters and covariates, and accommodates complex dependence structures that extend beyond traditional multilevel frameworks. For details, see Rosche (2026) "A Multilevel Model for Coalition Governments. Uncovering Party-Level Dependencies Within and Between Governments" . Package: r-cran-bmm Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1474 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesplot, r-cran-brms, r-cran-crayon, r-cran-fs, r-cran-glue, r-cran-matrixstats, r-cran-rtdists, r-cran-rlang, r-cran-withr Suggests: r-cran-bookdown, r-cran-cowplot, r-cran-dplyr, r-cran-emmeans, r-cran-fansi, r-cran-ggplot2, r-cran-ggthemes, r-cran-knitr, r-cran-mixtur, r-cran-remotes, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tidybayes, r-cran-tidyr, r-cran-usethis, r-cran-waldo, r-cran-yaml Filename: pool/dists/noble/main/r-cran-bmm_1.3.0-1.ca2404.1_all.deb Size: 1323716 MD5sum: 9abd6485523383c584e7e383946e5619 SHA1: 89e672d0effce72e9a401f26b30d28fd6edd1f02 SHA256: d08e8868297c046e8f8706f4b4a992dac07197754cffe0386c10110107386adf SHA512: d881b1840e9969b2cd640bfc9fd5fd7abc4faa3cdf0fa6e3a251acb9a901773515e8895d515588c26768f262ff69dc994403eb6f3473edd67dee62ae8ecf48b1 Homepage: https://cran.r-project.org/package=bmm Description: CRAN Package 'bmm' (Easy and Accessible Bayesian Measurement Models Using 'brms') Fit computational and measurement models using full Bayesian inference. The package provides a simple and accessible interface by translating complex domain-specific models into 'brms' syntax, a powerful and flexible framework for fitting Bayesian regression models using 'Stan'. The package is designed so that users can easily apply state-of-the-art models in various research fields, and so that researchers can use it as a new model development framework. References: Frischkorn and Popov (2023) . Package: r-cran-bmp Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-pixmap, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bmp_0.3.1-1.ca2404.1_all.deb Size: 108650 MD5sum: 5db2c89b4bcf21332787ff6efb404978 SHA1: 3c0629869fdfdada5999ebf920b493120ef9973b SHA256: d5595a3bb7567c1c373a381c4b8314743600ff7d47d1a7ca2bdce796cbc1e876 SHA512: 1ee0f9d37416c62ccdbc9b4bb2b56b25936680cbd5ee4008da87a9c07cfc5f2259c9ebb7791a369cc55bec2bb6b29a52436551aafd0647142b8275e91e31fc0b Homepage: https://cran.r-project.org/package=bmp Description: CRAN Package 'bmp' (Read Windows Bitmap (BMP) Images) Reads Windows BMP format images. Currently limited to 8 bit greyscale images and 24,32 bit (A)RGB images. Pure R implementation without external dependencies. Package: r-cran-bmrbr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-rvest Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bmrbr_0.2.0-1.ca2404.1_all.deb Size: 23206 MD5sum: 6d0c957736372a03456ee8a5a0cd70ab SHA1: ebcd5db799b43fce8aaba698e326b4013eafa9e2 SHA256: 905c710b328d467eb389768ceacea1ee0a869992c2e3ed1fd5fbc88057a4c869 SHA512: c640b46a4f15e2a46e2041f9a247c039de7dd9b868ded3a8770220ca60ead2d62fdb8c522b8f7667dc6889f4b3e3d233f6a84e0a5b7f4dba9c251088a5b899e8 Homepage: https://cran.r-project.org/package=BMRBr Description: CRAN Package 'BMRBr' ('BMRB' File Downloader) Nuclear magnetic resonance (NMR) is a highly versatile analytical technique for studying molecular configuration, conformation, and dynamics, especially those of biomacromolecules such as proteins. Biological Magnetic Resonance Data Bank ('BMRB') is a repository for Data from NMR Spectroscopy on Proteins, Peptides, Nucleic Acids, and other Biomolecules. Currently, 'BMRB' offers an R package 'RBMRB' to fetch data, however, it doesn't easily offer individual data file downloading and storing in a local directory. When using 'RBMRB', the data will stored as an R object, which fundamentally hinders the NMR researches to access the rich information from raw data, for example, the metadata. Here, 'BMRBr' File Downloader ('BMRBr') offers a more fundamental, low level downloader, which will download original deposited .str format file. This type of file contains information such as entry title, authors, citation, protein sequences, and so on. Many factors affect NMR experiment outputs, such as temperature, resonance sensitivity and etc., approximately 40% of the entries in the 'BMRB' have chemical shift accuracy problems [1,2] Unfortunately, current reference correction methods are heavily dependent on the availability of assigned protein chemical shifts or protein structure. This is my current research project is going to solve, which will be included in the future release of the package. The current version of the package is sufficient and robust enough for downloading individual 'BMRB' data file from the 'BMRB' database . The functionalities of this package includes but not limited: * To simplifies NMR researches by combine data downloading and results analysis together. * To allows NMR data reaches a broader audience that could utilize more than just chemical shifts but also metadata. * To offer reference corrected data for entries without assignment or structure information (future release). Reference: [1] E.L. Ulrich, H. Akutsu, J.F. Doreleijers, Y. Harano, Y.E. Ioannidis, J. Lin, et al., BioMagResBank, Nucl. Acids Res. 36 (2008) D402–8. . [2] L. Wang, H.R. Eghbalnia, A. Bahrami, J.L. Markley, Linear analysis of carbon-13 chemical shift differences and its application to the detection and correction of errors in referencing and spin system identifications, J. Biomol. NMR. 32 (2005) 13–22. . Package: r-cran-bmrmm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-logofgamma, r-cran-mcmcpack, r-cran-multicool, r-cran-pracma Filename: pool/dists/noble/main/r-cran-bmrmm_1.0.1-1.ca2404.1_all.deb Size: 233890 MD5sum: 93c631ee2c67036fdaf6e6d68a11e8d3 SHA1: 92dfd7565fad27fbc9b87808f004fe4634231c84 SHA256: 339c97d2d525f723363c2184065df8c1bc52552f331831eda8acdaa759feafd3 SHA512: 020a4fd9deecc48d5f721b6e45c90519a50b5b5fe60edb8cafa6c4eeee702c42e601d69cec42cdc54d85f5daf5b78f96761184803dc426cf94fe686d200b9ff1 Homepage: https://cran.r-project.org/package=BMRMM Description: CRAN Package 'BMRMM' (An Implementation of the Bayesian Markov (Renewal) Mixed Models) The Bayesian Markov renewal mixed models take sequentially observed categorical data with continuous duration times, being either state duration or inter-state duration. These models comprehensively analyze the stochastic dynamics of both state transitions and duration times under the influence of multiple exogenous factors and random individual effect. The default setting flexibly models the transition probabilities using Dirichlet mixtures and the duration times using gamma mixtures. It also provides the flexibility of modeling the categorical sequences using Bayesian Markov mixed models alone, either ignoring the duration times altogether or dividing duration time into multiples of an additional category in the sequence by a user-specific unit. The package allows extensive inference of the state transition probabilities and the duration times as well as relevant plots and graphs. It also includes a synthetic data set to demonstrate the desired format of input data set and the utility of various functions. Methods for Bayesian Markov renewal mixed models are as described in: Abhra Sarkar et al., (2018) and Yutong Wu et al., (2022) . Package: r-cran-bms Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3105 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bms_0.3.5-1.ca2404.1_all.deb Size: 2914762 MD5sum: 6df92185025fa7a19d4ac2796ac35258 SHA1: 3439c33e7d29926e801c674915c3d085222ae373 SHA256: 36934f83c5d0dd7796fccb4a2a6b72b39f732f820168c0be77f2c4cf39e7271c SHA512: d0f320c751f31c8df2f252897c6eeb350c7e0178ad0a6b4c6ad2e5832ecfdbb405c459d8b8ee962d973f29e7819e78b2b27346179361c9cfb0f89c32164e162d Homepage: https://cran.r-project.org/package=BMS Description: CRAN Package 'BMS' (Bayesian Model Averaging Library) Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, hyper-g and empirical priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison. Also includes Bayesian normal-conjugate linear model with Zellner's g prior, and assorted methods. Package: r-cran-bmscstan Architecture: all Version: 1.2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-ggplot2, r-cran-bayesplot, r-cran-loo, r-cran-logspline, r-cran-laplacesdemon Suggests: r-cran-reshape2, r-cran-gridextra, r-cran-bridgesampling, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-bmscstan_1.2.1.0-1.ca2404.1_all.deb Size: 306334 MD5sum: a53dc51c3e18cbc94f401d26f1aceb52 SHA1: c7b86f30fa417617f0a1bd47e4bb656e0d1e3e1e SHA256: 21df837d545dec153ae354e73ff58db5e94db5634aeba6095ee99f85a74be2be SHA512: 3ab2759b70fb899113ee476d41501eb59ade28108a360c04ccdd7b5fd1afe80d9f01c100b95c68eaa8705bceba2d3543f6ab6d521210544106a50d7b266bb03f Homepage: https://cran.r-project.org/package=bmscstan Description: CRAN Package 'bmscstan' (Bayesian Multilevel Single Case Models using 'Stan') Analyse single case analyses against a control group. Its purpose is to provide a flexible, with good power and low first type error approach that can manage at the same time controls' and patient's data. The use of Bayesian statistics allows to test both the alternative and null hypothesis. Scandola, M., & Romano, D. (2020, August 3). Scandola, M., & Romano, D. (2021). . Package: r-cran-bmt Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partitions, r-cran-fitdistrplus Filename: pool/dists/noble/main/r-cran-bmt_0.1.3-1.ca2404.1_all.deb Size: 338376 MD5sum: 1d1f27b36fa5bea53201490448b32383 SHA1: b82569ef00b7a592f9255a0b276ed55e094e3e42 SHA256: 6f2e3f4a920f211fd842708109dd5da0e4a49089aa486989146d7898bf3a6abb SHA512: b39809dece07160625271067658aa50fe061ef67570978a98f069ab0d0089dab9789af45f748cc6353323cae42615e560b4dce35f9d37d7688d21c43c5973783 Homepage: https://cran.r-project.org/package=BMT Description: CRAN Package 'BMT' (The BMT Distribution) Density, distribution, quantile function, random number generation for the BMT (Bezier-Montenegro-Torres) distribution. Torres-Jimenez C.J. and Montenegro-Diaz A.M. (2017) . Moments, descriptive measures and parameter conversion for different parameterizations of the BMT distribution. Fit of the BMT distribution to non-censored data by maximum likelihood, moment matching, quantile matching, maximum goodness-of-fit, also known as minimum distance, maximum product of spacing, also called maximum spacing, and minimum quantile distance, which can also be called maximum quantile goodness-of-fit. Fit of univariate distributions for non-censored data using maximum product of spacing estimation and minimum quantile distance estimation is also included. Package: r-cran-bmtar Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 564 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brobdingnag, r-cran-mass, r-cran-mcmcpack, r-cran-expm, r-cran-ks, r-cran-mvtnorm, r-cran-doparallel, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-bmtar_0.1.1-1.ca2404.1_all.deb Size: 533604 MD5sum: 7926465158485364c954a6ff2b6ce8c8 SHA1: ec315df4cb5291847563cd1bfac0035a98171ad3 SHA256: b7962fcc1decf10e2ca4e7d0d97accd420e0626ddcec9098de91756208f84923 SHA512: 6fe980dc21bd6432f3f14a621e490c8f6c1be80613a56ad2a07d74d952376eb045f9c9b36270a77e2247fe483215860b685080fcb7e746180d48934769a3d000 Homepage: https://cran.r-project.org/package=BMTAR Description: CRAN Package 'BMTAR' (Bayesian Approach for MTAR Models with Missing Data) Implements parameter estimation using a Bayesian approach for Multivariate Threshold Autoregressive (MTAR) models with missing data using Markov Chain Monte Carlo methods. Performs the simulation of MTAR processes (mtarsim()), estimation of matrix parameters and the threshold values (mtarns()), identification of the autoregressive orders using Bayesian variable selection (mtarstr()), identification of the number of regimes using Metropolised Carlin and Chib (mtarnumreg()) and estimate missing data, coefficients and covariance matrices conditional on the autoregressive orders, the threshold values and the number of regimes (mtarmissing()). Calderon and Nieto (2017) . Package: r-cran-bndesr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-janitor, r-cran-lubridate, r-cran-rcurl, r-cran-readr, r-cran-readxl, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bndesr_1.0.4-1.ca2404.1_all.deb Size: 40408 MD5sum: 68a77d9850cf1dcd2737b9a05a6dc8de SHA1: a8ab98a979a0a38937c8c7bee027ec3492ea2f5d SHA256: 561ce8603da41fc8e6c0325216cd85317c2fa1d0f9c6e1add0008e76a4aa386f SHA512: 346e8dbbead3983aa97a84ed4837b5795bf7d5093bec2f758376ea38cbbc29267ae9719f3af0f0ed828d569b428c9f778cc7364726f42c55a8d568b8b6d02e96 Homepage: https://cran.r-project.org/package=bndesr Description: CRAN Package 'bndesr' (Access Data from the Brazilian Development Bank (BNDES)) Allows access to data on BNDES disbursements and contracts since 1995. The package makes it easy to import data from the bank into R.. Package: r-cran-bndovb Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 650 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-np, r-cran-pracma, r-cran-mass, r-cran-dplyr, r-cran-factormodel, r-cran-nnet Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bndovb_1.1-1.ca2404.1_all.deb Size: 603860 MD5sum: a28e40a950bff1a87d415a2563923bf4 SHA1: c5f382f38b12db0b4bfdff2f0ec02d86a6439715 SHA256: 9b218fb0bd3f8b8dae4851d0b660831d7d1ea8e534e1f4f6ef7ba500e1fe1c9a SHA512: 0f3a0312b2d997772bcb2889f2803a30f6ad9420f247453cfddd25dfda8a449de7831b9f5ff729b387beef7afdf69c4fc896ffe88cf13ec810072ad5a0939482 Homepage: https://cran.r-project.org/package=bndovb Description: CRAN Package 'bndovb' (Bounding Omitted Variable Bias Using Auxiliary Data) Functions to implement a Hwang(2021) estimator, which bounds an omitted variable bias using auxiliary data. Package: r-cran-bnma Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 545 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-ggplot2, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bnma_1.6.1-1.ca2404.1_all.deb Size: 382270 MD5sum: 250887192343361321b72e826097c9f9 SHA1: 7bfe4753017130ccd01f7f6c13c30ad1f73a0ac1 SHA256: f870610394acf239120017c3ec2e7380b2662455bad380114b7528ac63261969 SHA512: 5e92616ec72ac8ecdebf2438a01e4ff51e2e01c8e2fe2c287b0bac5d5f42633d982f266159aecf0783c92e9b54aa795cf928b4357126038b7e03f2db13516237 Homepage: https://cran.r-project.org/package=bnma Description: CRAN Package 'bnma' (Bayesian Network Meta-Analysis using 'JAGS') Network meta-analyses using Bayesian framework following Dias et al. (2013) . Based on the data input, creates prior, model file, and initial values needed to run models in 'rjags'. Able to handle binomial, normal and multinomial arm-level data. Can handle multi-arm trials and includes methods to incorporate covariate and baseline risk effects. Includes standard diagnostics and visualization tools to evaluate the results. Package: r-cran-bnmonitor Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bnlearn, r-cran-dplyr, r-cran-ggplot2, r-cran-grain, r-cran-grbase, r-cran-igraph, r-cran-purrr, r-cran-qgraph, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rlang, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bnmonitor_0.2.2-1.ca2404.1_all.deb Size: 470242 MD5sum: ff56fa17155a4a050bc4a5aa3e93c13e SHA1: a0c8d8e4802393d269c0d7bf3fbc4d4371c6b9ad SHA256: 14a9be5ef49c756e4e1a7969598ccf84314901aa11523b6038b293593e7134e8 SHA512: c57126c8e157865f09e6873ff54ea51b804dede7c5e4432ad519eb024ad09a202eca5ce7c0f9402a8ab2d54d204febb16c190405df4eefc1d3bf96dc195a3abc Homepage: https://cran.r-project.org/package=bnmonitor Description: CRAN Package 'bnmonitor' (An Implementation of Sensitivity Analysis in Bayesian Networks) An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) . 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The package supports modeling complex relationships while providing rigorous uncertainty quantification via posterior distributions. With features like user chosen priors, clear predictions, and support for regression, binary, and multi-class classification, it is well-suited for applications in clinical trials, finance, and other fields requiring robust Bayesian inference and decision-making. References: Neal(1996) . Package: r-cran-bnpa Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bnlearn, r-cran-fastdummies, r-cran-lavaan, r-bioc-rgraphviz, r-cran-semplot, r-cran-xlsx Filename: pool/dists/noble/main/r-cran-bnpa_0.3.0-1.ca2404.1_all.deb Size: 128260 MD5sum: 8242b493740d66fe687c846cf51876fb SHA1: 66a877289ae4486dcce03c8bd85f41960043ce07 SHA256: 063ced6a49448307ed4b1450a2b70f02d8d252b4b849a530df80c73939311bfc SHA512: a5625c12592f1a82a45c7d2497ca737b02ee19a0ef9efd4dc505901af60a087694d919a22f6b6cdd021d0b1a4a69a0ddbdef5209c7f24441e6547a9f7b7d6289 Homepage: https://cran.r-project.org/package=bnpa Description: CRAN Package 'bnpa' (Bayesian Networks & Path Analysis) This project aims to enable the method of Path Analysis to infer causalities from data. For this we propose a hybrid approach, which uses Bayesian network structure learning algorithms from data to create the input file for creation of a PA model. The process is performed in a semi-automatic way by our intermediate algorithm, allowing novice researchers to create and evaluate their own PA models from a data set. The references used for this project are: Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press. . Nagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian networks in r. Springer, 122, 125-127. Scutari, M., & Denis, J. B. . Scutari M (2010). Bayesian networks: with examples in R. Chapman and Hall/CRC. . Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1 - 36. . Package: r-cran-bnpdensity Architecture: all Version: 2025.7.29-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4535 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-survival, r-cran-coda, r-cran-dplyr, r-cran-tidyr, r-cran-viridis Suggests: r-cran-greedyepl, r-cran-rmpfr, r-cran-gmp, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bnpmix, r-cran-salso Filename: pool/dists/noble/main/r-cran-bnpdensity_2025.7.29-1.ca2404.1_all.deb Size: 4540194 MD5sum: 2d5db4b5e192abaf103724c7bfb499d1 SHA1: e9437113b8a5a2a4b3e2f19df764fdcf4e2aa09b SHA256: 8131c878514ac24ba5871d55b06af6ff42b2b85f3733f7eb59d9edf121a6ab30 SHA512: 8126fd49c70bdaa6a634e01c1db7dfbdcb6a4b9df7de45e8211e55db4b49140ddf32e5409619de58dbb5f9f0724b7839cad4b749474f0834a044f710bd97cdcd Homepage: https://cran.r-project.org/package=BNPdensity Description: CRAN Package 'BNPdensity' (Ferguson-Klass Type Algorithm for Posterior Normalized RandomMeasures) Bayesian nonparametric density estimation modeling mixtures by a Ferguson-Klass type algorithm for posterior normalized random measures. 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Package: r-cran-bnpsd Architecture: all Version: 1.3.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 649 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-nnls Suggests: r-cran-popkin, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-bnpsd_1.3.13-1.ca2404.1_all.deb Size: 408362 MD5sum: 830a31b9d569778f089fde2606270bed SHA1: 8169a2a69f4883ce927cb311381fc5e56188811d SHA256: 70f821a9986fadf2e963e6ef5e00bf09e355c969fc79e1e4e4d2a93e78b4a35f SHA512: 02ff96ac5973d46187889b80b9cc54fd8c243593d935cb125a708f886dc19f25ef64f81c820671bb8d2b1b76dd16a6388fc0ad4abe669621b7f5c9c171248902 Homepage: https://cran.r-project.org/package=bnpsd Description: CRAN Package 'bnpsd' (Simulate Genotypes from the BN-PSD Admixture Model) The Pritchard-Stephens-Donnelly (PSD) admixture model has k intermediate subpopulations from which n individuals draw their alleles dictated by their individual-specific admixture proportions. The BN-PSD model additionally imposes the Balding-Nichols (BN) allele frequency model to the intermediate populations, which therefore evolved independently from a common ancestral population T with subpopulation-specific FST (Wright's fixation index) parameters. The BN-PSD model can be used to yield complex population structures. This simulation approach is now extended to subpopulations related by a tree. Method described in Ochoa and Storey (2021) . 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A novel pathway enrichment analysis package based on Bayesian network to investigate the topology features of the pathways. firstly, 187 kyoto encyclopedia of genes and genomes (KEGG) human non-metabolic pathways which their cycles were eliminated by biological approach, enter in analysis as Bayesian network structures. The constructed Bayesian network were optimized by the Least Absolute Shrinkage Selector Operator (lasso) and the parameters were learned based on gene expression data. Finally, the impacted pathways were enriched by Fisher’s Exact Test on significant parameters. 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This includes timetable and fare metadata (including links for full datasets), timetable data at line level, and real-time location data. Package: r-cran-bodycomp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bodycomp_1.0.0-1.ca2404.1_all.deb Size: 70894 MD5sum: e36d39d667270ce2721748cb0fea44de SHA1: 8288f2e72fc3fd1c5cc42f8015ec5d22903ed709 SHA256: 14436ee4cc5235b81f0f384af86577637b10eb31c757912db2ca182abcad74ec SHA512: 16e8b90d2959a63f2b345d10cd9d63f2b8b7c868dc71e4253499b54a17d7c75f1daf0489bccef4500b6bdecccc9a5536f8455003a035ff9f5ee6373a20b79e23 Homepage: https://cran.r-project.org/package=bodycomp Description: CRAN Package 'bodycomp' (Percent Body Fat Values Using Anthropometric PredictionEquations) Skinfold measurements is one of the most popular and practical methods for estimating percent body fat. Body composition is a term that describes the relative proportions of fat, bone, and muscle mass in the human body. Following the collection of skinfold measurements, regression analysis (a statistical procedure used to predict a dependent variable based on one or more independent or predictor variables) is used to estimate total percent body fat in humans. . 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Functionality requires installing the data packages 'adiposerefdata' and 'musclerefdata'. For more information on the underlying research, please visit our website which also includes a graphical interface. The models and underlying data are described in Marquardt JP et al.(planned publication 2025; reserved doi 10.1097/RLI.0000000000001104), "Subcutaneous and Visceral adipose tissue Reference Values from Framingham Heart Study Thoracic and Abdominal CT", *Investigative Radiology* and Tonnesen PE et al. (2023), "Muscle Reference Values from Thoracic and Abdominal CT for Sarcopenia Assessment [column] The Framingham Heart Study", *Investigative Radiology*, . 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(2010)) Real-time quantitative polymerase chain reaction (qPCR) data sets by Boggy et al. (2008) . This package provides a dilution series for one PCR target: a random sequence that minimizes secondary structure and off-target primer binding. The data set is a six-point, ten-fold dilution series. For each concentration there are two replicates. Each amplification curve is 40 cycles long. Original raw data file: . 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Package: r-cran-boin Architecture: all Version: 2.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-iso Filename: pool/dists/noble/main/r-cran-boin_2.7.2-1.ca2404.1_all.deb Size: 202186 MD5sum: b48bc4ed21ead171cfd6149f68dc7433 SHA1: 45fc0b170b7d6f095d7dc63be3685ba1ce052d13 SHA256: 74332aff2e990f7ed9966aebafbaad5e395df29cf3a5f04a180e68f9fd2c7ac2 SHA512: b2127ed95467be7fd7f64cbd7df68540c08216778383040dfc3ff513550bb9eb8644f37901a4bfceb5964ca02d70ff71141a82d9d702baa304624ae5a156ca55 Homepage: https://cran.r-project.org/package=BOIN Description: CRAN Package 'BOIN' (Bayesian Optimal INterval (BOIN) Design for Single-Agent andDrug- Combination Phase I Clinical Trials) The Bayesian optimal interval (BOIN) design is a novel phase I clinical trial design for finding the maximum tolerated dose (MTD). It can be used to design both single-agent and drug-combination trials. The BOIN design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. The prominent advantage of the BOIN design is that it achieves simplicity and superior performance at the same time. The BOIN design is algorithm-based and can be implemented in a simple way similar to the traditional 3+3 design. The BOIN design yields an average performance that is comparable to that of the continual reassessment method (CRM, one of the best model-based designs) in terms of selecting the MTD, but has a substantially lower risk of assigning patients to subtherapeutic or overly toxic doses. For tutorial, please check Yan et al. (2020) . 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Some extensions of BOIN-ET design are also available to allow for time-to-event efficacy and toxicity outcomes based on cumulative and pending data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity outcomes (generalized BOIN-ET: gBOIN-ET), and their combination (TITE-gBOIN-ET). 'boinet' is a package to implement the BOIN-ET design family and supports the conduct of simulation studies to assess operating characteristics of BOIN-ET, TITE-BOIN-ET, gBOIN-ET, and TITE-gBOIN-ET, where users can choose design parameters in flexible and straightforward ways depending on their own application. 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Package: r-cran-bolt4jr Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-glue, r-cran-purrr, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bolt4jr_1.4.0-1.ca2404.1_all.deb Size: 28970 MD5sum: 3b0f79c78ca642a7f2f36197f8bbafd3 SHA1: f8403c688566e286d562765346b2ac68633b1437 SHA256: 5dce9d9910e32f6f4b269f8d5c9e60acb709ad09ceca489bad06d36459bfd17c SHA512: 1d050e9c6b20bf394ff4f765f25916721bea28ad6cb1ecd03763f96954a6192e0093df360ff6eaadc4051b6e3784295aae45b6ffc5c5a9c6cdaf4ce1cb048472 Homepage: https://cran.r-project.org/package=bolt4jr Description: CRAN Package 'bolt4jr' (Interface for the 'Neo4j Bolt' Protocol) Querying, extracting, and processing large-scale network data from Neo4j databases using the 'Neo4j Bolt' protocol. 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Package: r-cran-bondanalyst Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-bondanalyst_1.0.1-1.ca2404.1_all.deb Size: 212488 MD5sum: 398e1bfeb6eeff92f4092604119780d0 SHA1: f190038f3127534c1397ef2428f835ae5f05bebe SHA256: 77eb98be3fdbfcdb47af331561d5d69238285d90b9d9f8c3b0a56406263054e5 SHA512: 026b89c9665aebe1c8a7c0d802e45dd199ff0c38b2b4dc612d0f42adabcbfc33c8e5376cf340b75b22c7f592ee24d340926c8d0172550fd4dc4d3fed3166affb Homepage: https://cran.r-project.org/package=bondAnalyst Description: CRAN Package 'bondAnalyst' (Methods for Fixed-Income Valuation, Risk and Return) Bond Pricing and Fixed-Income Valuation of Selected Securities included here serve as a quick reference of Quantitative Methods for undergraduate courses on Fixed-Income and CFA Level I Readings on Fixed-Income Valuation, Risk and Return. CFA Institute ("CFA Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 107-151, pp. 237-299)", 2019, ISBN: 9781119593577). Barbara S. Petitt ("Fixed Income Analysis", 2019, ISBN: 9781119628132). Frank J. Fabozzi ("Handbook of Finance: Financial Markets and Instruments", 2008, ISBN: 9780470078143). Frank J. Fabozzi ("Fixed Income Analysis", 2007, ISBN: 9780470052211). Package: r-cran-bonedensitymapping Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-ggplot2, r-cran-ggpubr, r-cran-oro.nifti, r-cran-ptinpoly, r-cran-rdist, r-cran-rjson, r-cran-concaveman, r-cran-geometry, r-cran-sp, r-cran-rgl, r-cran-rnifti, r-cran-rvcg, r-cran-fnn, r-cran-nat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-curl Filename: pool/dists/noble/main/r-cran-bonedensitymapping_0.1.4-1.ca2404.1_all.deb Size: 116526 MD5sum: 48ab1281a71e19c60c08416cc2dc405f SHA1: 60f6222f891c5ead30ebc23567efddd5cf09e9f6 SHA256: 6d8c7979486524239263e79076343c7399567bd9f8436f03d4bf84b599a9e8cf SHA512: 13dfcf4c4b9b8decf928343142df3af371f373b8e4729c2ce903d8aebca3db4ca3fdd89199ecb93a4601790134c910e66ad62a61ed975cbe886b3a33a32da375 Homepage: https://cran.r-project.org/package=BoneDensityMapping Description: CRAN Package 'BoneDensityMapping' (Maps Bone Densities from CT Scans to Surface Models) Allows local bone density estimates to be derived from CT data and mapped to 3D bone models in a reproducible manner. 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Package: r-cran-bonev Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-qvalue Filename: pool/dists/noble/main/r-cran-bonev_1.0-1.ca2404.1_all.deb Size: 110616 MD5sum: 04a19b0b9d7c19fc367d0a5d2d385031 SHA1: 5d90f601588f5fc35f333b0f15b96a1e91bca828 SHA256: 1f3afbe1711bdc52795bb040deb46dd3cc804f76a71e7b211963200d542b3939 SHA512: 58ab7f7ea7071bb5a545f0c343378d774f6a1d374ddb2052b89d92a3d38f0076ecd015940398f0763bd5c36978a4f8421a23559f046387f546909aabbc8d5358 Homepage: https://cran.r-project.org/package=BonEV Description: CRAN Package 'BonEV' (An Improved Multiple Testing Procedure for Controlling FalseDiscovery Rates) An improved multiple testing procedure for controlling false discovery rates which is developed based on the Bonferroni procedure with integrated estimates from the Benjamini-Hochberg procedure and the Storey's q-value procedure. 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Package: r-cran-bonn Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-bonn_1.0.3-1.ca2404.1_all.deb Size: 30304 MD5sum: 53d0eeff92740c11d370ca0cacde15c0 SHA1: e778e013e082a5ce7f59f812fb80b2c51946381b SHA256: 40d41236f3d4aab3589516db432a7a701b4105ed8e0e8a8b724ca7e7757babee SHA512: c3e4c6e20a8085ce1538fd38005d47801e153ef884f89589029c2eeef8782f33b8d34c6a603cbd37964f26119345c41bd3268699f4d61de367927c7e6ac833c6 Homepage: https://cran.r-project.org/package=bonn Description: CRAN Package 'bonn' (Access INKAR Database) Retrieve and import data from the INKAR database (Indikatoren und Karten zur Raum- und Stadtentwicklung Datenbank, ) of the Federal Office for Building and Regional Planning (BBSR) in Bonn using their JSON API. 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Package: r-cran-booami Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-withr Suggests: r-cran-mice, r-cran-miceadds, r-cran-matrix, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-booami_0.1.3-1.ca2404.1_all.deb Size: 167564 MD5sum: 916918f65aeff8fbd48663621edfd6d9 SHA1: 376a7240c8a104665a31ca3a8209e0fa2e16c051 SHA256: 385ac6b410be291a7251eecb0487da9e233bda9fe12548e6a11f00adf33ecc6c SHA512: 0f74b10b24bc728f1c5815bed6683aad21e77ce8a4aee9a0c4078395060a087d36b85da0926bea25b8df868490eaeb5d6be67af6fb740ac75a0ca22a0fd7cb41 Homepage: https://cran.r-project.org/package=booami Description: CRAN Package 'booami' (Component-Wise Gradient Boosting after Multiple Imputation) Component-wise gradient boosting for analysis of multiply imputed datasets. 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Package: r-cran-boot.heterogeneity Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 865 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metafor, r-cran-pbmcapply, r-cran-hsaur3, r-cran-knitr, r-cran-rmarkdown Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-boot.heterogeneity_1.1.5-1.ca2404.1_all.deb Size: 301926 MD5sum: 9a448f1458fc874be0c8f8eac43e66a9 SHA1: 92f5ee849a6f02c527289db61df68e4c66c0620d SHA256: e8ca4e36d10e92ef893c352046c36bf27798c90d3934463acea61773c6a69f6b SHA512: ca465e5922c04ee9a09d82bd70e28b5ddc71d405241068c35a9cc28dacb0f36e1cd431429ef16f9f031ca81b6bccfcacc7e6dae292710121c53274f4381d4909 Homepage: https://cran.r-project.org/package=boot.heterogeneity Description: CRAN Package 'boot.heterogeneity' (A Bootstrap-Based Heterogeneity Test for Meta-Analysis) Implements a bootstrap-based heterogeneity test for standardized mean differences (d), Fisher-transformed Pearson's correlations (r), and natural-logarithm-transformed odds ratio (or) in meta-analysis studies. Depending on the presence of moderators, this Monte Carlo based test can be implemented in the random- or mixed-effects model. This package uses rma() function from the R package 'metafor' to obtain parameter estimates and likelihoods, so installation of R package 'metafor' is required. This approach refers to the studies of Anscombe (1956) , Haldane (1940) , Hedges (1981) , Hedges & Olkin (1985, ISBN:978-0123363800), Silagy, Lancaster, Stead, Mant, & Fowler (2004) , Viechtbauer (2010) , and Zuckerman (1994, ISBN:978-0521432009). Package: r-cran-boot.pval Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-rdpack, r-cran-car, r-cran-lme4, r-cran-survival, r-cran-rms, r-cran-gt, r-cran-flextable Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-boot.pval_0.7.0-1.ca2404.1_all.deb Size: 320194 MD5sum: c6901c40f7c0a05202000a6cc4e1db58 SHA1: 819c058d4723ddfb2c8b99387b5fcb7681755807 SHA256: fe131a146b83f61e97ff4ea6889cac539eefd5fa70312594c062816075275d5f SHA512: 13b13c64a914d719214a6271e662e43895e35bb031f50bbeefb9f8845b27e931c7e632b4eac22f5ff5de19785a8004706b789b5c5dadbcf62024f36e9406c6a4 Homepage: https://cran.r-project.org/package=boot.pval Description: CRAN Package 'boot.pval' (Bootstrap p-Values) Computation of bootstrap p-values through inversion of confidence intervals, including convenience functions for regression models and tests of location. Package: r-cran-boot Architecture: all Version: 1.3-32-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 761 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-survival Filename: pool/dists/noble/main/r-cran-boot_1.3-32-1.ca2404.1_all.deb Size: 636236 MD5sum: c719a40019dd5cafcd1a411f5880f4fa SHA1: 63d4d68e95e54f689f73d9d45d7a8fd7d8ad63b1 SHA256: 5f24470dacc742f1b822ee7130f1cf65654e849574932e0f4bbb85c5e0cb697b SHA512: 0173c5487d341d82f7b05dfd17bc9f09b2f221c2b668cf5c820e778a16b32f349838fb7e2b87c495c4c9af169876db27b6c8005befbcdd1d58afd92711d60544 Homepage: https://cran.r-project.org/package=boot Description: CRAN Package 'boot' (Bootstrap Functions) Functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Application" by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S. Package: r-cran-bootcluster Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-mclust, r-cran-flexclust, r-cran-fpc, r-cran-dplyr, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-cran-ggplot2, r-cran-gridextra, r-cran-intergraph, r-cran-ggally, r-cran-network, r-cran-kernlab, r-cran-sna, r-cran-progress Filename: pool/dists/noble/main/r-cran-bootcluster_0.4.3-1.ca2404.1_all.deb Size: 195112 MD5sum: 8a699f71be87726a593f23cbd21ee128 SHA1: c18cd5084abbd2d5e1ecc35d73dfe3aab84be5ea SHA256: 1621dc5e58414c5a913a0282282e1d267126537fdc77944c910be3dcdf83d244 SHA512: dc0ed629b9faa3fe3e0611a1e4a5757b62551d3c8b633c4190e7eefef423aebdb7d65522af6c29526591cf38aefa478860240e963497b15eccb7770beb6e1ea1 Homepage: https://cran.r-project.org/package=bootcluster Description: CRAN Package 'bootcluster' (Bootstrapping Estimates of Clustering Stability) Implementation of the bootstrapping approach for the estimation of clustering stability and its application in estimating the number of clusters, as introduced by Yu et al (2016). Implementation of the non-parametric bootstrap approach to assessing the stability of module detection in a graph, the extension for the selection of a parameter set that defines a graph from data in a way that optimizes stability and the corresponding visualization functions, as introduced by Tian et al (2021) . Implemented out-of-bag stability estimation function and k-select Smin-based k-selection function as introduced by Liu et al (2022) . Implemented ensemble clustering method based-on k-means clustering method, spectral clustering method and hierarchical clustering method. Package: r-cran-bootcomb Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-hdinterval Filename: pool/dists/noble/main/r-cran-bootcomb_1.1.2-1.ca2404.1_all.deb Size: 102552 MD5sum: 390e9f0eb2de1765c0c56f32c5a0bce4 SHA1: 1cc5b289161bb921248199146c4c1a79a6957db4 SHA256: 69be787b3bd802bebde54db8be79b75bf9d7f0f3aaa4772aadfd4cd327811f23 SHA512: b2b433675b1430b60df9c6b8f8a6af7c8546d2da8ce08b8ba581031df7efd21d9fc27828477ec46b88a1efe75dfa8582c8c1ee5fbd3c06a7bf2b4d79dfd26f84 Homepage: https://cran.r-project.org/package=bootComb Description: CRAN Package 'bootComb' (Combine Parameter Estimates via Parametric Bootstrap) Propagate uncertainty from several estimates when combining these estimates via a function. This is done by using the parametric bootstrap to simulate values from the distribution of each estimate to build up an empirical distribution of the combined parameter. Finally either the percentile method is used or the highest density interval is chosen to derive a confidence interval for the combined parameter with the desired coverage. Gaussian copulas are used for when parameters are assumed to be dependent / correlated. References: Davison and Hinkley (1997,ISBN:0-521-57471-4) for the parametric bootstrap and percentile method, Gelman et al. (2014,ISBN:978-1-4398-4095-5) for the highest density interval, Stockdale et al. (2020) for an example of combining conditional prevalences. Package: r-cran-bootes Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bootes_1.3.1-1.ca2404.1_all.deb Size: 55406 MD5sum: 0e6e9d294b971f23419b7fc93417ffb3 SHA1: f97d053a372cdd7e0ec2094477d4fa6e7cd276b4 SHA256: 0ee873c96efbcb11356856405e5e5df441ba17012fcdfc215dd2b17993b9d936 SHA512: ffcf520e0357b02b3673ae28683f471185b91742516ebedf6fdee83a0b39c707104a0b413d8656df03b2e08a5b103f42cf0201fe69470a8237a3c39880a9c9eb Homepage: https://cran.r-project.org/package=bootES Description: CRAN Package 'bootES' (Bootstrap Confidence Intervals on Effect Sizes) Calculate robust measures of effect sizes using the bootstrap. Package: r-cran-bootf2 Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1577 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-mass, r-cran-readxl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bootf2_0.4.1-1.ca2404.1_all.deb Size: 1075388 MD5sum: e7d76f95c13a39a0d4475d981531a733 SHA1: 24eed98df94661586d64de2cb262e10b042a8883 SHA256: 513976888098cd80e0f636ecaf4fd0056eefe0c63d87ce369659a726d3a01a26 SHA512: 1e439830f692f559e8923fdd0afa80f64e1b7677fcae77facd2b53d4fef7fcc0179b30974e569f770e0db010ce7d9608ca59b8390b5678be65223604777a1a6c Homepage: https://cran.r-project.org/package=bootf2 Description: CRAN Package 'bootf2' (Simulation and Comparison of Dissolution Profiles) Compare dissolution profiles with confidence interval of similarity factor f2 using bootstrap methodology as described in the literature, such as Efron and Tibshirani (1993, ISBN:9780412042317), Davison and Hinkley (1997, ISBN:9780521573917), and Shah et al. (1998) . The package can also be used to simulate dissolution profiles based on mathematical modelling and multivariate normal distribution. Package: r-cran-bootgof Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 688 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-r6 Suggests: r-cran-covr, r-cran-roxygen2, r-cran-pkgdown, r-cran-tinytest, r-cran-mockery, r-cran-knitr, r-cran-rmarkdown, r-cran-minpack.lm, r-cran-mass Filename: pool/dists/noble/main/r-cran-bootgof_0.1.1-1.ca2404.1_all.deb Size: 432368 MD5sum: 8c983084c60d03682f250effdfeb72c3 SHA1: ca6bef30ba379195377af0831b272ee1c1af8178 SHA256: 7e11c13d2c9b43d7f7eb0cd1969b386fd8200b3062f9e72103877b98826b3c72 SHA512: a79d2bb2d1d19502f0d2a0fab53c850709c86e9464af2544b5ab2ed07c634776411586cc7051a59c3e183d22d3ea574d17d5590c990e4f4845532e976dda242d Homepage: https://cran.r-project.org/package=bootGOF Description: CRAN Package 'bootGOF' (Bootstrap Based Goodness-of-Fit Tests) Bootstrap based goodness-of-fit tests. It allows to perform rigorous statistical tests to check if a chosen model family is correct based on the marked empirical process. The implemented algorithms are described in (Dikta and Scheer (2021) ) and can be applied to generalized linear models without any further implementation effort. As far as certain linearity conditions are fulfilled the resampling scheme are also applicable beyond generalized linear models. This is reflected in the software architecture which allows to reuse the resampling scheme by implementing only certain interfaces for models that are not supported natively by the package. Package: r-cran-bootimpute Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-smcfcs Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bootimpute_1.3.0-1.ca2404.1_all.deb Size: 66882 MD5sum: b31a2b4bab92fe3b0b7216e3336b34f8 SHA1: 45ed914fc9856a874de4da15de80d94ee5023db7 SHA256: 0a104afb563827fb24ab0c13cda1b9761e4622f2a19716958b38fccd1b5bbda5 SHA512: 4d005554f145b0181332f74613a06d31c785d3c29227fdd26bb8de5198e60ee5646088d1df232f4ebfa73fbf15cb238428f3942530f4eb9f5210b138c80f9b56 Homepage: https://cran.r-project.org/package=bootImpute Description: CRAN Package 'bootImpute' (Bootstrap Inference for Multiple Imputation) Bootstraps and imputes incomplete datasets. Then performs inference on estimates obtained from analysing the imputed datasets as proposed by von Hippel and Bartlett (2021) . Package: r-cran-bootkmeans Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmtest, r-cran-abind, r-cran-mass, r-cran-fclust, r-cran-thresher, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-markdown, r-cran-ggplot2, r-cran-patchwork, r-cran-scales, r-cran-spelling Filename: pool/dists/noble/main/r-cran-bootkmeans_1.0.0-1.ca2404.1_all.deb Size: 460362 MD5sum: 3cbda067ec088e80cc69479a1b0edf14 SHA1: 11f22fa30b4434a739ad9dfacfe9ac5199ecabba SHA256: ce885fcb4675ea116657ef54a5d022b380e8883c10f674d3480477e6b8c37f8f SHA512: e46095438ef0c5eecc3a3788f799670e3df891ec51e821c84677ee48cba6bc26e676149f3fee00d40a366b323277c6455d49f65487d2fa260fe54a2391e73080 Homepage: https://cran.r-project.org/package=bootkmeans Description: CRAN Package 'bootkmeans' (A Bootstrap Augmented k-Means Algorithm for Fuzzy Partitions) Implementation of the bootkmeans algorithm, a bootstrap augmented k-means algorithm that returns probabilistic cluster assignments. From paper by Ghashti, J.S., Andrews, J.L. Thompson, J.R.J., Epp, J. and H.S. Kochar (2025), "A bootstrap augmented k-means algorithm for fuzzy partitions" (Submitted). Package: r-cran-bootlr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-binom Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bootlr_1.0.2-1.ca2404.1_all.deb Size: 57664 MD5sum: bb1dea4a02265096835d1ffe5fe2e12d SHA1: f5fac5f659be28a755a8a84768f1bb2c0c5ad2ab SHA256: e99ea849cc3dc4a38d13343973dd9fb5f38383d46ac54baa66961c23c98b121a SHA512: 972a1685d3c5e4318f21efb33223cdfa14dad0bb66133b34b7d72191fca6f54e968edac1eead3e3654c94d86f973ffe31eaf52c01375fd77ef4c4f890e3af82d Homepage: https://cran.r-project.org/package=bootLR Description: CRAN Package 'bootLR' (Bootstrapped Confidence Intervals for (Negative) LikelihoodRatio Tests) Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology, using the method of Marill et al. (2015) . It is particularly useful when the sensitivity or specificity in the sample is 100%. Note that this does not perform the test on nested models--for that, see 'epicalc::lrtest'. Package: r-cran-bootlrtpairwise Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bootlrtpairwise_0.2.0-1.ca2404.1_all.deb Size: 25226 MD5sum: 1c89f08d62c7662f02f6661d1e11b540 SHA1: 5ffe8d0e6ae3950ae59f0beb945a9bbed349f4cb SHA256: f6d7b0668fdade975923df0e1c1fb67555aab73b4bf87b0dddce93de27d0aa70 SHA512: fed1c9e75e71dd36bbb69f91d798930fa1ad8ee1066e94a866181167caabba2b25f08252a905e465109b409e2843cf87cf8fa69bed0efbf4824db4c583100889 Homepage: https://cran.r-project.org/package=bootLRTpairwise Description: CRAN Package 'bootLRTpairwise' (Bootstrap Hypothesis Tests for Treatment Effects in One-WayANOVA with Unequal Variances) Implements three test procedures using bootstrap resampling techniques for assessing treatment effects in one-way ANOVA models with unequal variances (heteroscedasticity). It includes a parametric bootstrap likelihood ratio test (PB_LRT()), a pairwise parametric bootstrap mean test (PPBMT()), and a Rademacher wild pairwise non-parametric bootstrap test (RWPNPBT()). These methods provide robust alternatives to classical ANOVA and standard pairwise comparisons when the assumption of homogeneity of variances is violated. Package: r-cran-bootmlm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-lme4, r-cran-matrix, r-cran-numderiv Suggests: r-cran-nlme, r-cran-testthat, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-haven, r-cran-msm, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-bootmlm_0.1.1-1.ca2404.1_all.deb Size: 140918 MD5sum: 2ed9993ac11060c0f795b7b1fa11e4b2 SHA1: 1633efb36cb53146133dad3ff85c286bb2b71276 SHA256: 848dec8a6291c138e08f17076bf89fee6b80de2d8b49ef5eb61672e0782e18f6 SHA512: 0f894c36755d6f8162d81d3dbca3563d76f6c194cdee26c8d703e3a81b9d67b3477297f73bb9f7acf7a312fb0441d5dd96369ce43fb1df34d8c3407910b51032 Homepage: https://cran.r-project.org/package=bootmlm Description: CRAN Package 'bootmlm' (Bootstrap Resampling for Multilevel Models) Functions for bootstrapping with multilevel data and models (and mixed-effect models). It implements multiple bootstrap methods under the parametric, residual, and case bootstrap categories, as discussed in Van der Leeden, Meijer, and Busing (2008) and Carpenter, Goldstein, and Rasbash (2003) . Currently it supports fitted objects from the 'lme4' package. Package: r-cran-bootmrmr Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bootmrmr_0.1-1.ca2404.1_all.deb Size: 130866 MD5sum: 5cf39239f947173922344e6be243fdd9 SHA1: 93f4c85a1483d549d37d720ea69fd06148d3d38b SHA256: b8cb591ac71d10b3d021ec748866dffc782fb8218ee498edbfb23db1bef230ca SHA512: d5c6ecc43acf6293a74ff725c1174f7cc7e8a37ba6d98b5edacd510218fe9cd6cd1088e0adf4cda66021fedbcfdc74392c048844ef22a505895839bec1826f1e Homepage: https://cran.r-project.org/package=BootMRMR Description: CRAN Package 'BootMRMR' (Bootstrap-MRMR Technique for Informative Gene Selection) Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications). Package: r-cran-bootnet Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-isingfit, r-cran-qgraph, r-cran-mantar, r-cran-dplyr, r-cran-tidyr, r-cran-gtools, r-cran-corpcor, r-cran-isingsampler, r-cran-mvtnorm, r-cran-abind, r-cran-matrix, r-cran-snow, r-cran-mgm, r-cran-networktoolbox, r-cran-pbapply, r-cran-networktools, r-cran-rlang, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-glasso, r-cran-bdgraph, r-cran-graphicalvar, r-cran-relaimpo, r-cran-lavaan, r-cran-psychtools Filename: pool/dists/noble/main/r-cran-bootnet_1.8-1.ca2404.1_all.deb Size: 334346 MD5sum: 232406adeaa801ab65f658c391c5542c SHA1: 4f6dc9315a21453b8339665ba115a9225ca44d56 SHA256: 4f73ec9f0515de74dbcdcb7077adce3e0f2920e27134ed7dfb6bf400bcac7469 SHA512: 7228f264d1c5522dadc67ff086db07a41547238fc58911dd18685227b022fc995a6830720da212628250d6e3f259eabb58a151150d2fc8ab7281fa1f961c14cb Homepage: https://cran.r-project.org/package=bootnet Description: CRAN Package 'bootnet' (Bootstrap Methods for Various Network Estimation Routines) Bootstrap methods to assess accuracy and stability of estimated network structures and centrality indices . Allows for flexible specification of any undirected network estimation procedure in R, and offers default sets for various estimation routines. Package: r-cran-bootpls Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-doparallel, r-cran-foreach, r-cran-plsrglm, r-cran-pls, r-cran-spls, r-cran-bipartite, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-markdown, r-cran-plsdof, r-cran-prettydoc, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-bootpls_1.1.0-1.ca2404.1_all.deb Size: 217726 MD5sum: f66c1c88c45f04ab0d6993246aafac06 SHA1: f19296491df927a8c6779e33a9b49fd13b530f1b SHA256: 0bdcec14b00bee6b372c117e3b739e5f6d3a0ed305e3f3bdc5c1685798f26d6b SHA512: f479739e64dc5fb9866345bdcc0b5dbdee1bac96b1c3bfeacf25de261fc9c2e28258893bd82e5ec41f3c620780841a4620b32624d53365fe87b545d08ab59685 Homepage: https://cran.r-project.org/package=bootPLS Description: CRAN Package 'bootPLS' (Bootstrap Hyperparameter Selection for PLS Models and Extensions) Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, 'The Multiple Facets of Partial Least Squares and Related Methods', ) and two articles (Magnanensi et al. 2017, 'Statistics and Computing', ) and (Magnanensi et al. 2021, 'Frontiers in Applied Mathematics and Statistics', ). Package: r-cran-bootpr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bootpr_1.0-1.ca2404.1_all.deb Size: 153782 MD5sum: faa1c248493862b3769ad7a8544a789f SHA1: f9eda2abc999f7a5e7ec5ee165158b6f12b44b52 SHA256: d908eb5d34b90bab8fb7c1f7ca40d82f1ef0f0e6b25ae0ff71fd4bc80a1dd2bc SHA512: 98f8d816044bc307e3173965ffd7f3be062141dabdbeeff36e468144db6af8115b7811f437fa7c3a857e66f68e44a57014d74f9e820dd2af8e5b795d8f430643 Homepage: https://cran.r-project.org/package=BootPR Description: CRAN Package 'BootPR' (Bootstrap Prediction Intervals and Bias-Corrected Forecasting) Contains functions for bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series. Package: r-cran-bootstatespace Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-simstatespace, r-cran-dynr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bootstatespace_1.0.3-1.ca2404.1_all.deb Size: 168388 MD5sum: e4c6db3bf8e50badaa2a1fccf07dedd4 SHA1: 54a471016ab5e0b4d423565303b7aa6d70e820aa SHA256: 35cad7ba2337d58c138091249f4274e74aeb97e14a70eae02df72ef1bd35327a SHA512: bf3666eb015f10191e61facd6040755eeb24f6dd3f6bea75fab97cef1391694daf38ccad15467e21ab443748790146fa3c7371dba01cdf878b21ac04d2173306 Homepage: https://cran.r-project.org/package=bootStateSpace Description: CRAN Package 'bootStateSpace' (Bootstrap for State Space Models) Provides a streamlined and user-friendly framework for bootstrapping in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. The parametric bootstrap implemented here was developed and applied in Pesigan, Russell, and Chow (2025) . Package: r-cran-bootstepaic Architecture: all Version: 1.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-bootstepaic_1.4-0-1.ca2404.1_all.deb Size: 23070 MD5sum: 1635c46e1a658825109cdd871e8ed4e0 SHA1: 23c7272953ec1bfa01c7621a6e68cdf8c13a7cb2 SHA256: 9df3017952cf672e8c268582226f13d3cc2e930b1ac8d5a64860901443fd62d0 SHA512: 9bb785182439d264605957852dc9324b53adb18d8ff3fbaa0f8eb46ee1ada208032c724bf1adf16d60f2a03ffc5d66f07a9f90a976bacb765a767c2a73a23029 Homepage: https://cran.r-project.org/package=bootStepAIC Description: CRAN Package 'bootStepAIC' (Bootstrap stepAIC) Model selection by bootstrapping the stepAIC() procedure. Package: r-cran-bootstrapfp Architecture: all Version: 0.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sampling Filename: pool/dists/noble/main/r-cran-bootstrapfp_0.4.6-1.ca2404.1_all.deb Size: 67214 MD5sum: c3b92d2ff56c3f34ffa6456ca0068801 SHA1: 5214df0d6a1047192fd9de0bbdc9cabf288100f9 SHA256: 3e183167684fb9b86f84c4cd1aaa5d2a597cdb410fad1e64ceae2cf468846900 SHA512: b71ea6ec70638a973471992aea945f551ae3a7721af05cd6d6daf8b50ef18058ca3b52f419385463be16964bc5a0ce6be5f1f38be57dffa1754d0bf018a863ca Homepage: https://cran.r-project.org/package=bootstrapFP Description: CRAN Package 'bootstrapFP' (Bootstrap Algorithms for Finite Population Inference) Finite Population bootstrap algorithms to estimate the variance of the Horvitz-Thompson estimator for single-stage sampling. For a survey of bootstrap methods for finite populations, see Mashreghi et Al. (2016) . Package: r-cran-bootstrapqtl Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixeqtl, r-cran-foreach, r-cran-data.table Suggests: r-cran-domc, r-cran-doparallel, r-bioc-qvalue, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bootstrapqtl_1.0.5-1.ca2404.1_all.deb Size: 49406 MD5sum: 545e6fe34b66c4aab384e6abe68bb83d SHA1: 4fd278cee2d7b5e5b15a5c3120547d2a86e5bdfb SHA256: 2391df6987e1df2a78270ad78071a60d2b7eb22292d128b1aad4c31e650606a7 SHA512: 25d7e03f8ae542683f148dd8fce54e2862960585c3cd7a90bd1296608e57ec12fd4a886c4f4c09dd264c9484123bc5748194cf68a7bf37e2a3820023e5e783a9 Homepage: https://cran.r-project.org/package=BootstrapQTL Description: CRAN Package 'BootstrapQTL' (Bootstrap cis-QTL Method that Corrects for the Winner's Curse) Identifies genome-related molecular traits with significant evidence of genetic regulation and performs a bootstrap procedure to correct estimated effect sizes for over-estimation present in cis-QTL mapping studies (The "Winner's Curse"), described in Huang QQ *et al.* 2018 . Package: r-cran-bootstraptests Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bootstraptests_0.1.0-1.ca2404.1_all.deb Size: 136368 MD5sum: 5da24057608a308463cf314ee0c3f1fb SHA1: 06d37d1798ff556a0a54940b1d6e22512febd44a SHA256: f374206262370bcd637c7beb3ec4c6c131fc11abe6a479db07f4b9bf87368068 SHA512: cd3307b98fdc025efa3ce4fc23cb758af66c3f66c90590fddbbabe8b8172702f69bb2a6f2ec87cec1caa03bd5f70417c2faf86bd6aae3ed0b8eed48fa233d655 Homepage: https://cran.r-project.org/package=BootstrapTests Description: CRAN Package 'BootstrapTests' (Bootstrap-Based Hypothesis Testing using Different ResamplingSchemes) Perform bootstrap-based hypothesis testing procedures on three statistical problems. In particular, it covers independence testing, testing the slope in a linear regression setting, and goodness-of-fit testing, following (Derumigny, Galanis, Schipper and Van der Vaart, 2025) . Package: r-cran-bootsurv Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-bootsurv_0.0.1-1.ca2404.1_all.deb Size: 552334 MD5sum: b5aa7e16462c774011c490a8b7bedbac SHA1: 09c1d4c33ec332e36a689464353fa3ffff78c7c2 SHA256: 176fbf9bb9bbd84af4749a5479120948eb21bea2c93628998206a4104b953ba2 SHA512: fac9e3218da3fcd2e42d9fa6b53d7691f133f6ff2a9b476cf2ffe66c68733699251b8b5781853252344bc043c81465910ccb75c82ae2569c90c14c5ba2690a67 Homepage: https://cran.r-project.org/package=bootsurv Description: CRAN Package 'bootsurv' (Bootstrap Methods for Complete Survey Data) Bootstrap resampling methods have been widely studied in the context of survey data. This package implements various bootstrap resampling techniques tailored for survey data, with a focus on stratified simple random sampling and stratified two-stage cluster sampling. It provides tools for precise and consistent bootstrap variance estimation for population totals, means, and quartiles. Additionally, it enables easy generation of bootstrap samples for in-depth analysis. Package: r-cran-bootsvd Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ff Filename: pool/dists/noble/main/r-cran-bootsvd_1.2-1.ca2404.1_all.deb Size: 150278 MD5sum: e033ac91be52791806031e82d0dc168a SHA1: bbef1e1a8d4b4526b88198d22e22a31869ae2105 SHA256: 8a321d902e5588df07c15ee3879514ac0deab3f7831faf4ea7db97ec05f7efa0 SHA512: 8e2e8fd450fda3b3b2c45590a6fb06ec3b3694b461dfd773049a3a4841776b1b8d93a86361ed949e1f5ce5481232b7df6057e49ad46e77ad41299ba8f0a2bed4 Homepage: https://cran.r-project.org/package=bootSVD Description: CRAN Package 'bootSVD' (Fast, Exact Bootstrap Principal Component Analysis for HighDimensional Data) Implements fast, exact bootstrap Principal Component Analysis and Singular Value Decompositions for high dimensional data, as described in (see also ). For data matrices that are too large to operate on in memory, users can input objects with class 'ff' (see the 'ff' package), where the actual data is stored on disk. In response, this package will implement a block matrix algebra procedure for calculating the principal components (PCs) and bootstrap PCs. Depending on options set by the user, the 'parallel' package can be used to parallelize the calculation of the bootstrap PCs. Package: r-cran-bootwar Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2044 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mmcards, r-cran-npboottprm, r-cran-shiny, r-cran-shinyjs, r-cran-shinythemes Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bootwar_0.2.1-1.ca2404.1_all.deb Size: 1767980 MD5sum: f9e25668e2592c9d820927659d9edc83 SHA1: 700ffc579e2db2cceeb7ab655080d7bf05e2de44 SHA256: b8fe5d6bc26bf0d0ab15678c8604361d27427881b99f978e477de9a3aab24261 SHA512: 75c39f6bc72727be949dfff07467942001b7d4d452e4148facb8953a5bd4864dac13ca882520d3b36686a38800d113b4593dad0fbb6570b9aa01dd7c069812a1 Homepage: https://cran.r-project.org/package=bootwar Description: CRAN Package 'bootwar' (Nonparametric Bootstrap Test with Pooled Resampling Card Game) The card game War is simple in its rules but can be lengthy. In another domain, the nonparametric bootstrap test with pooled resampling (nbpr) methods, as outlined in Dwivedi, Mallawaarachchi, and Alvarado (2017) , is optimal for comparing paired or unpaired means in non-normal data, especially for small sample size studies. However, many researchers are unfamiliar with these methods. The 'bootwar' package bridges this gap by enabling users to grasp the concepts of nbpr via Boot War, a variation of the card game War designed for small samples. The package provides functions like score_keeper() and play_round() to streamline gameplay and scoring. Once a predetermined number of rounds concludes, users can employ the analyze_game() function to derive game results. This function leverages the 'npboottprm' package's nonparboot() to report nbpr results and, for comparative analysis, also reports results from the 'stats' package's t.test() function. Additionally, 'bootwar' features an interactive 'shiny' web application, bootwar(). This offers a user-centric interface to experience Boot War, enhancing understanding of nbpr methods across various distributions, sample sizes, number of bootstrap resamples, and confidence intervals. Package: r-cran-bootwptos Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavethresh, r-cran-tseries Filename: pool/dists/noble/main/r-cran-bootwptos_1.2.1-1.ca2404.1_all.deb Size: 56794 MD5sum: 613a4a69a776eeb51188a178da94138b SHA1: e9e0188f32eb58fa460435c064caa4a25de56353 SHA256: dcb21bbf6e41960369ff0e5eee94001e5e521c731c611687b0f10a0c8c803903 SHA512: 3f3e1ea616fd53f381c6165d99337913042ed9d65f82f28745c4c32e50627e1dd5f7858837ce4159715f4b684ed47bb239dd26b5a6bb9f69195fab6757c55e74 Homepage: https://cran.r-project.org/package=BootWPTOS Description: CRAN Package 'BootWPTOS' (Test Stationarity using Bootstrap Wavelet Packet Tests) Provides significance tests for second-order stationarity for time series using bootstrap wavelet packet tests. Provides functionality to visualize the time series with the results of the hypothesis tests superimposed. The methodology is described in Cardinali, A and Nason, G P (2016) "Practical powerful wavelet packet tests for second-order stationarity." Applied and Computational Harmonic Analysis, 44, 558-585 . Package: r-cran-bop2fe Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 648 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gridextra, r-cran-patchwork, r-cran-ggplot2 Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-bop2fe_1.0.3-1.ca2404.1_all.deb Size: 606916 MD5sum: 255b0b46a90a26691a3bbb1efcfdf047 SHA1: 40e1c8624be00b986a6bc1c6f9bfe0fb1d056043 SHA256: 171dec61452db75505c1d284088d82fddae60816460440df2bbfcb0446263287 SHA512: c6ffdc4a6ce8aab1965811ce989355c0cc5f1e3e3a7a15006438d0c392dfeb66206b06d358e2c29e8f1f8c2bf37352cf29861b15a56b6fb7bc91082033c7e2ec Homepage: https://cran.r-project.org/package=BOP2FE Description: CRAN Package 'BOP2FE' (Bayesian Optimal Phase II Design with Futility and EfficacyStopping Boundaries) Bayesian optimal design with futility and efficacy stopping boundaries (BOP2-FE) is a novel statistical framework for single-arm Phase II clinical trials. It enables early termination for efficacy when interim data are promising, while explicitly controlling Type I and Type II error rates. The design supports a variety of endpoint structures, including single binary endpoints, nested endpoints, co-primary endpoints, and joint monitoring of efficacy and toxicity. The package provides tools for enumerating stopping boundaries prior to trial initiation and for conducting simulation studies to evaluate the design’s operating characteristics. Users can flexibly specify design parameters to suit their specific applications. For methodological details, refer to Xu et al. (2025) . Package: r-cran-boptbd Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-igraph Filename: pool/dists/noble/main/r-cran-boptbd_1.0.7-1.ca2404.1_all.deb Size: 84248 MD5sum: 786a227a52d7d5e03cb88ea5419df4ad SHA1: 1191c94d524dd8a97b3f412bfc1d7f23651ddd20 SHA256: 14a0296004a6f489e0dc7620c99c6d6f8b3ffabc62712ebcaafc37682e51dbce SHA512: 738c8a28c14bbc29949dbf10cb87e76f68f15b723e13586ddbcea6adc63eb7d25e494365ea52b41377c6a0e7de6ed2f52fcf6bd651018a7a9155a79ed8a1950e Homepage: https://cran.r-project.org/package=Boptbd Description: CRAN Package 'Boptbd' (Bayesian Optimal Block Designs) Computes Bayesian A- and D-optimal block designs under the linear mixed effects model settings using block/array exchange algorithm of Debusho, Gemechu and Haines (2018) and Gemechu, Debusho and Haines (2025) where the interest is in a comparison of all possible elementary treatment contrasts. The package also provides an optional method of using the graphical user interface (GUI) R package 'tcltk' to ensure that it is user friendly. Package: r-cran-bor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bor_0.1.0-1.ca2404.1_all.deb Size: 22154 MD5sum: b047e18688e4d76ce3889cf777c36ddd SHA1: e9aa7a554f6a9e23273bede2df9ffa966d957436 SHA256: f82b000fcaf1f14ff03a3ee7aed77419e2d2b942ad547149cad73ed4ddff7102 SHA512: 76af7ff894c9000b6c67755a07ab8a37bb597dc7c817261383e5e29313e2b100f0c1a7d81c8961425ef634b5f30b3ce7c4ef643958c0ce9a5051fdfdd94d3674 Homepage: https://cran.r-project.org/package=bor Description: CRAN Package 'bor' (Transforming Behavioral Observation Records into Data Matrices) Transforms focal observations' data, where different types of social interactions can be recorded by multiple observers, into asymmetric data matrices. 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Suitable only for distinct, univariate data where no ties is allowed. 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The main contribution is the use a bilevel optimization problem to select the variables in the training problem that minimize the error in the validation set. Preprint available: [Valcarcel, L. V., San Jose-Eneriz, E., Cendoya, X., Rubio, A., Agirre, X., Prosper, F., & Planes, F. J. (2020). "BOSO: a novel feature selection algorithm for linear regression with high-dimensional data." bioRxiv. ]. In order to run the vignette, it is recommended to install the 'bestsubset' package, using the following command: devtools::install_github(repo="ryantibs/best-subset", subdir="bestsubset"). If you do not have gurobi, run devtools::install_github(repo="lvalcarcel/best-subset", subdir="bestsubset"). Moreover, to install cplexAPI you can check . 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Package: r-cran-botor Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-checkmate, r-cran-logger, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-covr, r-cran-digest Filename: pool/dists/noble/main/r-cran-botor_0.4.1-1.ca2404.1_all.deb Size: 142710 MD5sum: 815f34762499b9e677c6d72049bd7e13 SHA1: d6db86ab643d1bc67c9d6ae6ead44cebd82f8ae4 SHA256: d35092def54e962c5152b32de011e46e42b9e80eff8d44c5197405bd6477724b SHA512: 1acb5f8d87beb5fd7fd64a50d7c3e26794e4399dabe4840476aa00e7dabfa0ef32926c044378e6df483097cd2d79b36d0ce580b21a79e6c5a51ceec1381c4fa5 Homepage: https://cran.r-project.org/package=botor Description: CRAN Package 'botor' ('AWS Python SDK' ('boto3') for R) Fork-safe, raw access to the 'Amazon Web Services' ('AWS') 'SDK' via the 'boto3' 'Python' module, and convenient helper functions to query the 'Simple Storage Service' ('S3') and 'Key Management Service' ('KMS'), partial support for 'IAM', the 'Systems Manager Parameter Store' and 'Secrets Manager'. 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If data are trait values, trait data are transformed to boundary intensities based on approximate first derivatives across latitude and longitude. The package includes functions to create custom null models based on the input data. The boundary statistics are described in: Fortin, Drapeau, and Jacquez (1996) . Package: r-cran-boundedgeworth Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-expint Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-boundedgeworth_0.1.3-1.ca2404.1_all.deb Size: 79718 MD5sum: e4ccd9a8fe93f9579edb6ef92e96d372 SHA1: 93a6df9cfe1e2a14dd1b700d6457b7c8c89e665c SHA256: ebe3c18b9f6a8ab0cc394ace0e3557e6a4648a480f5ec8a422251c4e35d0aee8 SHA512: 3483c7b10ebbc421d5947de06bcd3fa273dcf59f86bbdfce37fe769ecf8780851ac82f9f402dfe182abd0919597315fd6b0e216bff4f19d72238092d2c762150 Homepage: https://cran.r-project.org/package=BoundEdgeworth Description: CRAN Package 'BoundEdgeworth' (Bound on the Error of the First-Order Edgeworth Expansion) Computes uniform bounds on the distance between the cumulative distribution function of a standardized sum of random variables and its first-order Edgeworth expansion, following the article Derumigny, Girard, Guyonvarch (2023) . 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This package provides modified ADF and M-type tests (MZ-alpha, MZ-t, MSB) with p-values computed via Monte Carlo simulation of bounded Brownian motion. Supports one-sided (lower bound only) and two-sided bounds, with automatic lag selection using the MAIC criterion of Ng and Perron (2001) . Package: r-cran-boundingbox Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 757 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-imager, r-cran-gplots Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-boundingbox_1.0.1-1.ca2404.1_all.deb Size: 604728 MD5sum: bc2f12710f240d624f2162cd58263235 SHA1: a90dae5cf64b9f73c892d568c26f9ba005864408 SHA256: cc1fd3c48c8c9b7202412ef9a34c91d1fe1acb3eccc9a7e171406a83f62d42bb SHA512: f99f4b8144ab5a6486ecf23a1ac1376a449af61dc11723690d70199774872673e335db485ce71ff957d941d63a245d1b16799d1c183043a6bdf3c72faaad88ad Homepage: https://cran.r-project.org/package=boundingbox Description: CRAN Package 'boundingbox' (Create a Bounding Box in an Image) Generate ground truth cases for object localization algorithms. Cycle through a list of images, select points around which to generate bounding boxes and assign classifiers. Output the coordinates, and images annotated with boxes and labels. For an example study that uses bounding boxes for image localization and classification see Ibrahim, Badr, Abdallah, and Eissa (2012) "Bounding Box Object Localization Based on Image Superpixelization" . Package: r-cran-boussinesq Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-boussinesq_1.0.6-1.ca2404.1_all.deb Size: 29972 MD5sum: 133a34d5bed2e291bcb885fbbb8dfa40 SHA1: 9b4ceede20f2cdf32c7774bb53ff06de8bff3ac7 SHA256: bb78f2f8ef51cb1a2f9c14a7fb27573d4978cf95dca1e5a439e839f3982610ea SHA512: cf0601cc70d5a7f90868a26471f815af7da62f9851c488e74b7dc5cb85081f3cb4dd8cc4f6e8bd6e788da7cf9ef402617f288de1d66964031da07fd2b5baeb23 Homepage: https://cran.r-project.org/package=boussinesq Description: CRAN Package 'boussinesq' (Analytic Solutions for (Ground-Water) Boussinesq Equation) A collection of R functions were implemented from published and available analytic solutions for the One-Dimensional Boussinesq Equation (ground-water). 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The distance measure gives more weight to differences between quartiles than to differences between extremes, making it less sensitive to outliers. Further,the function calculates the silhouette width (Rousseeuw 1987) for different numbers of clusters and selects the number of clusters that maximizes the average silhouette width, unless a specific number of clusters is provided by the user. The approach implemented in this package is based on the following publications: Rousseeuw (1987) ; Ichino-Yaguchi (1994) ; Arroyo-Maté-Roque (2006) . 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Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time, and differences in medians [Fay, Proschan, and Brittain ]. Basically, the package gives exact inference methods for one- and two-sample exact inferences for Kaplan-Meier curves (e.g., generalizing Fisher's exact test to allow for right censoring), which are especially important for latter parts of the survival curve, small sample sizes or heavily censored data. Includes mid-p options. 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Package: r-cran-bplsr Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1859 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-progress, r-cran-statmod Filename: pool/dists/noble/main/r-cran-bplsr_1.0.5-1.ca2404.1_all.deb Size: 1860736 MD5sum: aa815c153cdeb25fd6c3a3c54ce5b873 SHA1: 6bec047a230654f2b18e3bef446f164093f818df SHA256: 29af790afb5dd32a6f914cac004afab92af420436a10c00e71fea1da81861670 SHA512: b70d70a6a035c9735edf114d7dd02bd7ba77b89ab604df146f9c0c43fba6d93f2be6c28793d585dff128e4d7bafba0672c198f5454e6d55f763cb22c71cceae1 Homepage: https://cran.r-project.org/package=bplsr Description: CRAN Package 'bplsr' (Bayesian partial least squares regression) Fits the Bayesian partial least squares regression model introduced in Urbas et al. (2024) . Suitable for univariate and multivariate regression with high-dimensional data. 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Functionalities can be used to visualize and export BPMN diagrams created using the 'pm4py' and 'bupaRminer' packages. Part of the 'bupaR' ecosystem. 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Package: r-cran-bpp Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bpp_1.0.6-1.ca2404.1_all.deb Size: 160262 MD5sum: 36bbbd5b4668653050d7de89222b744e SHA1: c1f2916a3c699139ea18ba3cbe48ffd6ef02680c SHA256: 4e19c368de4903e647d05dad2c1d87ece012bb6f6aa3597e6f32ce29c8341d03 SHA512: 9cce0fad381ec13e56ea2e0a822a89c6d5792d9e9735b702dafbd54b4688ee274aaf43d59d588d13dce4bbec29add375ee2b94af0494631a9b37b0c67d8b9e96 Homepage: https://cran.r-project.org/package=bpp Description: CRAN Package 'bpp' (Computations Around Bayesian Predictive Power) Implements functions to update Bayesian Predictive Power Computations after not stopping a clinical trial at an interim analysis. Such an interim analysis can either be blinded or unblinded. Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio. Package: r-cran-bqror Architecture: all Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-pracma, r-cran-gigrvg, r-cran-truncnorm, r-cran-npflow, r-cran-invgamma, r-cran-progress Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bqror_1.7.1-1.ca2404.1_all.deb Size: 291870 MD5sum: 7d6b9d2dbae4b290297727d1fc0a4be9 SHA1: 3c23f806723c34ce0218fa15438f9593cb408473 SHA256: 6e57d5cc8d0e31f07b418db60295a86d737af5ef0bab4b3d0c018818a6327aaa SHA512: 5edd95c6e622406f377e3e6b9ebe806b93e5c5c8532c98b5e5924f37bda6e127ef6240e4f818126a9f55d7e9945f8b0bada80b74387c1411fede847337b86495 Homepage: https://cran.r-project.org/package=bqror Description: CRAN Package 'bqror' (Bayesian Quantile Regression for Ordinal Models) Package provides functions for estimation and inference in Bayesian quantile regression with ordinal outcomes. An ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings (MH) algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using a Gibbs sampling algorithm. The summary output presents the posterior mean, posterior standard deviation, 95% credible intervals, and the inefficiency factors along with the two model comparison measures – logarithm of marginal likelihood and the deviance information criterion (DIC). The package also provides functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models. Rahman, M. A. (2016).“Bayesian Quantile Regression for Ordinal Models.” Bayesian Analysis, 11(1): 1-24 . Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and Probability Letters, 54(4): 437–447 . Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica, 46(1): 33-50 . Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of the American Statistical Association, 90(432):1313–1321, 1995. . Chib, S., and Jeliazkov, I. (2001). “Marginal likelihood from the Metropolis-Hastings output.” Journal of the American Statistical Association, 96(453):270–281, 2001. . Package: r-cran-bracatus Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 798 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-geojsonio, r-cran-jsonlite, r-cran-plotfunctions, r-cran-raster, r-cran-rgbif, r-cran-rnaturalearth, r-cran-sf, r-cran-sp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bracatus_2.0.0-1.ca2404.1_all.deb Size: 743398 MD5sum: 64441233c794236c436d54b4144f24dd SHA1: de5cb427814cefa0338c691becd3f718a17e0ffa SHA256: 609a6f37f1afad16335f6f28c71af160c46b5c365ceb7e78d9a48f3d96a41b61 SHA512: 02ca7be9e3b550fe33c00da8213e49d9d50d4e005039a7ea4e2ce8f8395e862e146792bb589ee8bdb79658b755a611a68851799634ae77324ea307f972945cfa Homepage: https://cran.r-project.org/package=bRacatus Description: CRAN Package 'bRacatus' (A Method to Estimate the Accuracy and Biogeographical Status ofGeoreferenced Biological Data) Automated assessment of accuracy and geographical status of georeferenced biological data. The methods rely on reference regions, namely checklists and range maps. Includes functions to obtain data from the Global Biodiversity Information Facility and from the Global Inventory of Floras and Traits . Alternatively, the user can input their own data. Furthermore, provides easy visualisation of the data and the results through the plotting functions. Especially suited for large datasets. The reference for the methodology is: Arlé et al. (under review). Package: r-cran-brace Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-survminer Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-brace_0.1.0-1.ca2404.1_all.deb Size: 61398 MD5sum: e95b65160f562994310116c27a4b1c5f SHA1: 486d68b6aa5ef64cec99b38856a17f3fdbbeba2a SHA256: fbb96c55b29feb1aa89fef0750afd5fe1d222ea3a9663f1757f11a3294dc302f SHA512: 574bebf033877ec90e8744adaf012758600502378ee9f7c7e36b1c8225461747349618a52c56b1651a035c3297d6f26654158932aea10ee20e5ba959816bef8b Homepage: https://cran.r-project.org/package=BRACE Description: CRAN Package 'BRACE' (Bias Reduction Through Analysis of Competing Events (BRACE)) Adjusting the bias due to residual confounding (often called treatment selection bias) in estimating the treatment effect in a proportional hazard model, as described in Williamson et al. (2022) . 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Provides tools for seeding, scheduling, recording results, and tracking standings. Package: r-cran-bracod.r Architecture: all Version: 0.0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bracod.r_0.0.2.0-1.ca2404.1_all.deb Size: 95518 MD5sum: d714b87190ae3d501765cd8d2ba9b090 SHA1: bcc82a0669048e85cc910803ecd594466849358a SHA256: dafb36c1062b7f678bc8df1a8d711421eafbabe64a2c72763199e5cb434964fa SHA512: d831fcb61325474a9f78e4a6efc0125bba0e2b5fac4eaa8cef9dadba0fe711a15d2575dee2f5b4175b3ffa6136fc6c63444beefb1efac1774153cce067e67fde Homepage: https://cran.r-project.org/package=BRACoD.R Description: CRAN Package 'BRACoD.R' (BRACoD: Bayesian Regression Analysis of Compositional Data) The goal of this method is to identify associations between bacteria and an environmental variable in 16S or other compositional data. The environmental variable is any variable which is measure for each microbiome sample, for example, a butyrate measurement paired with every sample in the data. Microbiome data is compositional, meaning that the total abundance of each sample sums to 1, and this introduces severe statistical distortions. This method takes a Bayesian approach to correcting for these statistical distortions, in which the total abundance is treated as an unknown variable. This package runs the python implementation using reticulate. Package: r-cran-brada Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1437 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fbst, r-cran-extradistr, r-cran-doparallel, r-cran-foreach, r-cran-dosnow, r-cran-progress, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dt Filename: pool/dists/noble/main/r-cran-brada_1.0-1.ca2404.1_all.deb Size: 850730 MD5sum: 80311614eee91da5a5b9e89ab3cd485a SHA1: 25700b02d20dd14ef1e4970fea5b27c3e1372d4f SHA256: 16b6241a5bcfc6cb4f9e427dcce5a312ecf6d7ae502e63dda4237f2691aeb710 SHA512: 00da31620651e098c4ee0db804b386866a4b5644ed1b12685697007a194f3c69b8587d839d18b00282b89527d938775287c408acbefd07055869b91e972e8240 Homepage: https://cran.r-project.org/package=brada Description: CRAN Package 'brada' (Bayesian Response-Adaptive Design Analysis) Provides access to a range of functions for analyzing, applying and visualizing Bayesian response-adaptive trial designs for a binary endpoint. 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Package: r-cran-braids Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maybe Filename: pool/dists/noble/main/r-cran-braids_1.0.0-1.ca2404.1_all.deb Size: 75228 MD5sum: 52cbf16b78012d7ae086d57e9905a009 SHA1: 8de6b6aedfff82968f7b530e57fde93f0c593c2f SHA256: 9ef4f072ab0d5ac4d563be871413f5b8292f9daf4bfae777629a336cb4b8d0ec SHA512: c84e7337ce5701bc94c81da5d7ab2a1fa430882e11866a66c10ab97cecc2019993613a1baf994bf300936b2ed2a8f5f73514fb6641b834087cc75e32f7300d3a Homepage: https://cran.r-project.org/package=braids Description: CRAN Package 'braids' (The Braid Groups) Deals with the braid groups. Includes creation of some specific braids, group operations, free reduction, and Bronfman polynomials. Braid theory has applications in fluid mechanics and quantum physics. The code is adapted from the 'Haskell' library 'combinat', and is based on Birman and Brendle (2005) . Package: r-cran-brailler Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3662 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-dplyr, r-cran-extrafont, r-cran-ggplot2, r-cran-gridgraphics, r-cran-gridsvg, r-cran-hunspell, r-cran-knitr, r-cran-mathjaxr, r-cran-moments, r-cran-quarto, r-cran-rdpack, r-cran-rmarkdown, r-cran-roloc, r-cran-rolocisccnbs, r-cran-tidyr, r-cran-whisker, r-cran-xml, r-cran-xtable Suggests: r-cran-broom, r-cran-emmeans, r-cran-ggfortify, r-cran-installr, r-cran-lmtest, r-cran-markdown, r-cran-multcomp, r-cran-nortest, r-cran-rstatix, r-cran-spelling, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-brailler_1.0.2-1.ca2404.1_all.deb Size: 1258332 MD5sum: f38429c502bd598f461ecae2ad38de00 SHA1: 1bd1233d72cdc7ccf70e661a4134c5a6166d9aec SHA256: 38929306a360b23b9ef99ffed74b6d8c241a5832594e8c01a73ca68b726df835 SHA512: bec5a2e82cab601b6688ef0b290b61da7f3ab825f77f2389e664fc1f6662b90af1691d2d0825fff629c88e143439fe9185b26d1faf8ad329985de5d866a8bd63 Homepage: https://cran.r-project.org/package=BrailleR Description: CRAN Package 'BrailleR' (Improved Access for Blind Users) Blind users do not have access to the graphical output from R without printing the content of graphics windows to an embosser of some kind. This is not as immediate as is required for efficient access to statistical output. The functions here are created so that blind people can make even better use of R. This includes the text descriptions of graphs, convenience functions to replace the functionality offered in many GUI front ends, and experimental functionality for optimising graphical content to prepare it for embossing as tactile images. Package: r-cran-braincon Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mass Filename: pool/dists/noble/main/r-cran-braincon_0.3.0-1.ca2404.1_all.deb Size: 197234 MD5sum: f85b2f37165a9da0357c3e6e4309b3f1 SHA1: f6a6e2872277ec6ffb177c6683fcb8c033130bf5 SHA256: 99ca26c949bf411cae8298976aad20f1b8de7881d4bf1bd6d2d964e190f963e0 SHA512: 696520f32f620f8860d8a01a0c1f6d751033c68318abf7cb6e10b4d52f94011c796f47c5495e54d8a39bfa11e7d442513f9f0117f1a32616f016c181e1967c4a Homepage: https://cran.r-project.org/package=BrainCon Description: CRAN Package 'BrainCon' (Inference the Partial Correlations Based on Time Series Data) A statistical tool to inference the multi-level partial correlations based on multi-subject time series data, especially for brain functional connectivity. 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Package: r-cran-braingraph Architecture: all Version: 3.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2346 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-abind, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-lattice, r-cran-mass, r-cran-matrix, r-cran-permute Suggests: r-cran-hmisc, r-cran-ade4, r-cran-boot, r-cran-car, r-cran-expm, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-mediation, r-cran-oro.nifti, r-cran-scales Filename: pool/dists/noble/main/r-cran-braingraph_3.1.2-1.ca2404.1_all.deb Size: 2278466 MD5sum: 7843c47b53a85dbf8da05e36324348bc SHA1: 267df24e5d1b534efd85683eaf7f58f0bd83cf62 SHA256: b36b23ced165712201882c55c29199b20d51f48553799cf34c31341ea8ee8450 SHA512: ca0451e275b6d3cc403a3d87252d86c0f10daad432ed43cb8f19dd6469a73cce520b490d4af683359e032680cdebf6b6a2973ff3b769a6e1513f11ec435b2a06 Homepage: https://cran.r-project.org/package=brainGraph Description: CRAN Package 'brainGraph' (Graph Theory Analysis of Brain MRI Data) A set of tools for performing graph theory analysis of brain MRI data. It works with data from a Freesurfer analysis (cortical thickness, volumes, local gyrification index, surface area), diffusion tensor tractography data (e.g., from FSL) and resting-state fMRI data (e.g., from DPABI). It contains a graphical user interface for graph visualization and data exploration, along with several functions for generating useful figures. Package: r-cran-brainkcca Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2650 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cca, r-cran-kernlab, r-cran-elasticnet, r-cran-rgl, r-cran-brainr, r-cran-misc3d, r-cran-oro.nifti, r-cran-knitr Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-brainkcca_0.1.0-1.ca2404.1_all.deb Size: 2466588 MD5sum: 1af31499e8dcd1a2842dc0625d30f47d SHA1: c057fe2ec1776abe722a866afb11c711781afbda SHA256: ce83350f2c5edb86987a227bae7cb555434f61a3491d32ae9b9fe489ee08d202 SHA512: c2bcf2b1331afa6df34c4cb0c318bb3eed4c38e0ad9cc01ce41edea107aca7cc3210cb4bf4457c38f4ca22bae908a976879dc6515b037e4d0dfe4b0aca6ab92e Homepage: https://cran.r-project.org/package=brainKCCA Description: CRAN Package 'brainKCCA' (Region-Level Connectivity Network Construction via KernelCanonical Correlation Analysis) It is designed to calculate connection between (among) brain regions and plot connection lines. Also, the summary function is included to summarize group-level connectivity network. Kang, Jian (2016) . Package: r-cran-brainnettest Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-brainnettest_0.2.0-1.ca2404.1_all.deb Size: 123004 MD5sum: 60bd96f557b776c0a0bf1b15b8f5dafa SHA1: 68ba6886111ab24e75e841546d568531aa3b9877 SHA256: 016a2efb96fa1e9488f72a6a0f599a935d54e7ad44dde9b436c0cb6fc4b9042f SHA512: 1b1cfd1f9fc91287fddd87e4a22173f365f912f128e9498595d2864ddb77fe63b9581a9fe09e3e52ea10673437ef67807f2ec13afdbe39e16e3625b64835bb22 Homepage: https://cran.r-project.org/package=BrainNetTest Description: CRAN Package 'BrainNetTest' (Hypothesis Testing for Populations of Brain Networks) Non-parametric hypothesis testing for populations of brain networks represented as graphs, following the L1-distance ANOVA framework of Fraiman and Fraiman (2018) . The package builds on this nonparametric graph-comparison framework, extending it with procedures for edge-level inference and identification of the specific connections driving group differences. In particular, it provides utilities to compute central (mean) graphs, pairwise Manhattan distances between adjacency matrices, the group test statistic T, and a fast permutation procedure to identify the critical edges that drive between-group differences. Helper functions to generate synthetic community-structured graphs and to visualise brain networks with communities are also included. Package: r-cran-brainr Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4247 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-misc3d, r-cran-oro.nifti Suggests: r-cran-servr Filename: pool/dists/noble/main/r-cran-brainr_1.7.0-1.ca2404.1_all.deb Size: 3924046 MD5sum: 475b2e908708ef7c64ef3a0f8b35f072 SHA1: e213331852702267268f445b501ceecd0a01b395 SHA256: bc257dd6fc25026ea117aab88b11fe475d1dc0dda3c1cc3ac98dde898b24a86a SHA512: e5dae278abd47450c4f7022522d2f3c40b4c246c4b2af33fa4927311297091a5f01cfc2170a98c7cde533d2e8163fb938af4aef2955ba7a13b635e5155e4122b Homepage: https://cran.r-project.org/package=brainR Description: CRAN Package 'brainR' (Helper Functions to 'misc3d' and 'rgl' Packages for BrainImaging) This includes functions for creating 3D and 4D images using 'WebGL', 'rgl', and 'JavaScript' commands. This package relies on the X toolkit ('XTK', ). Package: r-cran-branchingprocess Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-branchingprocess_0.1.0-1.ca2404.1_all.deb Size: 52828 MD5sum: c9923826df3f2afe37d8e8374729b25c SHA1: f22c5221de6bd596266e47e2f3d74a756c68650a SHA256: bd22154cc1c20ba9761227bc32db5418a6cad17885fcf05ef697aac5d8cf0aa0 SHA512: c1784e375f7047ea54c08439e975f7d09c294844bdf50de263c9e08c5204a8f702ef0140d86854744d634eda8123ecd9c16796c7ca1401c50aa982b452c16446 Homepage: https://cran.r-project.org/package=branchingprocess Description: CRAN Package 'branchingprocess' (Calculate Outbreak Probabilities for a Branching Process Model) Quantify outbreak risk posed by individual importers of a transmissible pathogen. Input parameters of negative binomial offspring distributions for the number of transmissions from each infected individual and initial number of infected. Calculate probabilities of final outbreak size and generations of transmission, as described in Toth et al. (2015) and Toth et al. (2016) . 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'brand.yml' is a simple, portable 'YAML' file that codifies your company's brand guidelines into a format that can be used by 'Quarto', 'Shiny' and 'R' tooling to create branded outputs. Maintain unified, branded theming for web applications to printed reports to dashboards and presentations with a consistent look and feel. 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Package: r-cran-brandwatchr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-brandwatchr_0.3.0-1.ca2404.1_all.deb Size: 106434 MD5sum: d81ed7599bdd09c0396b46e5016d7b42 SHA1: febc6d186d2f8c40ceb31bbcab9cea8f815374dd SHA256: 7c7ad26c1772dcbf2b4b2e0af8a072d62602597d2fb7c3110a444be8b5ff350e SHA512: b250a177bc1e90bbf07c2b15b205327ac4cb6c5c35b852a9bec2ac357de7a6f79758c46a631d4cc0c8c94954cd3f79cb986280315e01ddd38fcefe9c18d62845 Homepage: https://cran.r-project.org/package=brandwatchR Description: CRAN Package 'brandwatchR' ('Brandwatch' API to R) Interact with the 'Brandwatch' API . Allows you to authenticate to the API and obtain data for projects, queries, query groups tags and categories. Also allows you to directly obtain mentions and aggregate data for a specified query or query group. Package: r-cran-brant Architecture: all Version: 0.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/noble/main/r-cran-brant_0.3-0-1.ca2404.1_all.deb Size: 26176 MD5sum: b3ed361cf91486f83683618a5080d52f SHA1: 13e4b3acc429d1c9f2e134bb1c4b23d8eae1ed90 SHA256: 8c71bd8b808aa30d4b9acb5f17368b311452f2a19077437e92168c98cc520036 SHA512: 4e05bc93010388422528b15560100950ba44f387839d8082341959da10bf390cb8f3022644f3431600b9508a4eae321e1c0e45c7cdebf768910c635da791729f Homepage: https://cran.r-project.org/package=brant Description: CRAN Package 'brant' (Test for Parallel Regression Assumption) Tests the parallel regression assumption wit the brant test by Brant (1990) for ordinal logit models generated with the function polr() from the package 'MASS'. 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Package: r-cran-bratteli Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-diagram, r-cran-gmp, r-cran-kantorovich Filename: pool/dists/noble/main/r-cran-bratteli_1.0.0-1.ca2404.1_all.deb Size: 36598 MD5sum: ff35bdf96061f217bd520d3226b68fe5 SHA1: ae39c7bc8d779de18e6cebbef2ff4e6bd894d902 SHA256: b5635d4949bdf71c575adf686d1ac4d152ede2bab4ea9d5eb965ffff41deb7ce SHA512: bf2a7b07f99b9a3af566d5a210b01d2ca22256c2217076930f526edc69b705bcd05a97438d343201ec1d215fc8c406a588a0be6e68aa2c7c9926381ac19a2670 Homepage: https://cran.r-project.org/package=bratteli Description: CRAN Package 'bratteli' (Deal with Bratteli Graphs) Utilities for Bratteli graphs. A tree is an example of a Bratteli graph. The package provides a function which generates a 'LaTeX' file that renders the given Bratteli graph. 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Package: r-cran-brazilcrime Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3356 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forecast, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-kableextra, r-cran-tidyr, r-cran-stringr, r-cran-ggcorrplot, r-cran-formatr, r-cran-lubridate, r-cran-testthat Filename: pool/dists/noble/main/r-cran-brazilcrime_0.3.0-1.ca2404.1_all.deb Size: 3249722 MD5sum: 05e4eb862f4b5e08e39d1c844d85f46d SHA1: 3e3bb3ac1fa6d18f78be0e502331ffe87e34b46a SHA256: 844b7268e450581a4ff3c8944672f6b487cd8d24fa58b2720df7c51cd85ce5fb SHA512: 10503f5dee8f8c7ce43c90352a24aa8f04d542d2d78f41ca6eb283dae809bd901330db5a99366d635130e5d55ed89d54935276217097a16480987934b7e9d936 Homepage: https://cran.r-project.org/package=BrazilCrime Description: CRAN Package 'BrazilCrime' (Accesses Brazilian Public Security Data from SINESP Since 2015) Allows access to data from the Brazilian Public Security Information System (SINESP) by state and municipality. It should be emphasized that the package only extracts the data and facilitates its manipulation in R. Therefore, its sole purpose is to support empirical research. All data credits belong to SINESP, an integrated information platform developed and maintained by the National Secretariat of Public Security (SENASP) of the Ministry of Justice and Public Security. . Package: r-cran-brazildataapi Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-brazildataapi_0.2.0-1.ca2404.1_all.deb Size: 244150 MD5sum: 382e6c102c4a8d3119f288b2da2feded SHA1: e86356f341150f41ea7a8a68e965982b60125ddb SHA256: 4b12f50cd045aad682e2330dca4b495c9cf205617ac0a7529916ef3358c27ca1 SHA512: a15662a875cf2d787aa6b36ae028051a4e8822c1f123e9d989b0cc6b59779121146864688c39152e1b6fd68e812e1d3a84aff94d5646fd158d966e884de0f43a Homepage: https://cran.r-project.org/package=BrazilDataAPI Description: CRAN Package 'BrazilDataAPI' (Access Brazilian Data via APIs and Curated Datasets) Provides functions to access data from the 'BrasilAPI', 'REST Countries API', 'Nager.Date API', and 'World Bank API', related to Brazil's postal codes, banks, holidays, company registrations, international country indicators, public holidays information, and economic development data. Additionally, the package includes curated datasets related to Brazil, covering topics such as demographic data (males and females by state and year), river levels, environmental emission factors, film festivals, and yellow fever outbreak records. The package supports research and analysis focused on Brazil by integrating open APIs with high-quality datasets from multiple domains. For more information on the APIs, see: 'BrasilAPI' , 'Nager.Date' , 'World Bank API' , and 'REST Countries API' . Package: r-cran-brazilmet Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-readxl, r-cran-dplyr, r-cran-tibble, r-cran-lubridate, r-cran-sf, r-cran-stringi, r-cran-terra Filename: pool/dists/noble/main/r-cran-brazilmet_0.4.0-1.ca2404.1_all.deb Size: 198470 MD5sum: 970e1fdf4c7fc42f9957cf70d3a63b60 SHA1: f79ac393ea247bdb9a015fde9d88615b2a027445 SHA256: 9e669b15ed8588629d2a6a19fba76db7218f71a8e219b0cce45fd834fb726b42 SHA512: b43d0505923934f749bd84312d3f61e035e55de2b2d48295692f8abf84f29bfab571af53670a8eb45d3d0597d0776cc0339ab0174a7d52222eb50ba3fb7adeab Homepage: https://cran.r-project.org/package=BrazilMet Description: CRAN Package 'BrazilMet' (Download and Processing of Automatic Weather Stations (AWS) Dataof INMET-Brazil) A collection of functions for downloading and processing automatic weather station (AWS) data from INMET (Brazil’s National Institute of Meteorology), designed to support the estimation of reference evapotranspiration (ETo). The package facilitates streamlined access to meteorological data and aims to simplify analyses in agricultural and environmental contexts. Package: r-cran-brbvs Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copent, r-cran-ggplot2, r-cran-gjrm, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-brbvs_0.2.1-1.ca2404.1_all.deb Size: 86358 MD5sum: 3087263ccf69d3e9461cc25526147b7e SHA1: 15c0d97c76b2eda5956843dcd610530076106a5a SHA256: f971bf278ac6eed8b9196bb6ee30421c850718e0155b6a2397885d4158799356 SHA512: 141c068dadb28bc3e3aef495edf7504fa05da1330d3480ad29c5f285418e791223f9609671adc743022b25e9c12e56018c0b957ba5a725121283bda8fb98c9a8 Homepage: https://cran.r-project.org/package=BRBVS Description: CRAN Package 'BRBVS' (Variable Selection and Ranking in Copula Survival ModelsAffected by General Censoring Scheme) Performs variable selection and ranking based on several measures for the class of copula survival model(s) in high dimensional domain. The package is based on the class of copula survival model(s) implemented in the 'GJRM' package. Package: r-cran-brcal Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1678 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nloptr, r-cran-fields, r-cran-ggplot2, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-xfun, r-cran-gridextra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-brcal_1.0.1-1.ca2404.1_all.deb Size: 1010284 MD5sum: 0c3932ca52af8cc1eea437076afe10c1 SHA1: dd005dd50a25e74d34d51246ad7fc796af7cf81a SHA256: dbb37e1b2d9882f6a0cc6026501b05afdecc0f42f4d61092270638f40fd36986 SHA512: da69e89da3480a7760add9ca53fd81c305945053bd995082611716066b83c3f21859d1f30d8163012bf7356ca640a54ade8eea1276624dd3aa7da1cc625ebf72 Homepage: https://cran.r-project.org/package=BRcal Description: CRAN Package 'BRcal' (Boldness-Recalibration of Binary Events) Boldness-recalibration maximally spreads out probability predictions while maintaining a user specified level of calibration, facilitated the brcal() function. 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Package: r-cran-brclimr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-rlang, r-cran-lobstr, r-cran-magrittr, r-cran-dbi, r-cran-duckdb, r-cran-glue Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-brclimr_0.2.0-1.ca2404.1_all.deb Size: 543216 MD5sum: fe8a723bc902bf180b3bc644bd95aaa3 SHA1: 027b10b403368179e37889a678660be00b4458ec SHA256: 9c41e7b9b69cf4df8b8790fafc0710e3701db601c3e391744929688cb389ce68 SHA512: 5f0f2f035c7a21aaf5e74907655600f9406acd101fe151bcdc67fff0d3e0b3c1991224e5ad80d7f51dd83f3e344c1b4a49c515f74492efdb314eb4d8d56bf93e Homepage: https://cran.r-project.org/package=brclimr Description: CRAN Package 'brclimr' (Fetch Zonal Statistics of Weather Indicators for BrazilianMunicipalities) Fetches zonal statistics from weather indicators that were calculated for each municipality in Brazil using data from the BR-DWGD and TerraClimate projects. Zonal statistics such as mean, maximum, minimum, standard deviation, and sum were computed by taking into account the data cells that intersect the boundaries of each municipality and stored in Parquet files. This procedure was carried out for all Brazilian municipalities, and for all available dates, for every indicator available in the weather products (BR-DWGD and TerraClimate projects). This package queries on-line the already calculated statistics on the Parquet files and returns easy-to-use data.frames. 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'BRCore' implements the workflow proposed by Shade and Stopnisek (2019) and incorporates additional rarefaction steps. The proposed workflow aims to identify persistent microbiomes using abundance-occupancy distributions and neutral community model fitting. For more details on abundance-occupancy distributions see Shade A, Stopnisek N (2019) , for neutral models, see Sloan et al. (2006) and Burns et al. (2015) . Package: r-cran-brdt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tidyverse, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-brdt_0.1.0-1.ca2404.1_all.deb Size: 46648 MD5sum: 13d7748317e62f46b91e09688dc644a3 SHA1: ed39b4c012ac79587616671ce8d691424f5a582f SHA256: 79f1b9e3ef60f4a884c75a0d85277da5abde2c50a5608568b52a70c93a797755 SHA512: e09a1d5ac76aa8f9e40be4f5e7e8b8d9a9c22ccd7a6b77eba882810aa1c10771f4966d29ebb8a289808245826285f9f08b60b2deb373838e2af97c2357f56fb7 Homepage: https://cran.r-project.org/package=BRDT Description: CRAN Package 'BRDT' (Binomial Reliability Demonstration Tests) This is an implementation of design methods for binomial reliability demonstration tests (BRDTs) with failure count data. The acceptance decision uncertainty of BRDT has been quantified and the impacts of the uncertainty on related reliability assurance activities such as reliability growth (RG) and warranty services (WS) are evaluated. This package is associated with the work from the published paper "Optimal Binomial Reliability Demonstration Tests Design under Acceptance Decision Uncertainty" by Suiyao Chen et al. (2020) . Package: r-cran-brea Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-coda Filename: pool/dists/noble/main/r-cran-brea_0.4.2-1.ca2404.1_all.deb Size: 233450 MD5sum: ef74ed93e08873831ac812b314908c66 SHA1: fddaf6ce3bae1e595949d19d370604faf6436f9a SHA256: b94ea6d9e551b2ca1d64f567caa3e9d61dc084cae55939700febb07663f35776 SHA512: 3f851fd24d6beb1569b738b0f8d527e65fb49e24a1ce9afc50b2bfa67b6d4ca60aa512215644a25e80e1cb11713ab8ceab9e5769efd9468e442a6b313278b52d Homepage: https://cran.r-project.org/package=brea Description: CRAN Package 'brea' (Bayesian Recurrent Events Analysis) Functions to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models and multistate models. Package: r-cran-bread Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-bread_0.4.1-1.ca2404.1_all.deb Size: 71228 MD5sum: 0d50d570d4c802e2216adf0e0ac0c09a SHA1: d3500f9c1a1f274a8600376c8a4475ec5e1e2b4b SHA256: 76e44b6e478d9328342be8c4d8a020e413ec28fff006038be184e6a25977a905 SHA512: 1da1594a45e654d896fa166464050105e30256f1aefaadc5e698193c35406e1918e9b8d7fe2e3de8da280cc36d6fee25d75a289498b623e7a019cff24c446f6a Homepage: https://cran.r-project.org/package=bread Description: CRAN Package 'bread' (Analyze Big Files Without Loading Them in Memory) A simple set of wrapper functions for data.table::fread() that allows subsetting or filtering rows and selecting columns of table-formatted files too large for the available RAM. 'b stands for 'big files'. bread makes heavy use of Unix commands like 'grep', 'sed', 'wc', 'awk' and 'cut'. They are available by default in all Unix environments. For Windows, you need to install those commands externally in order to simulate a Unix environment and make sure that the executables are in the Windows PATH variable. To my knowledge, the simplest ways are to install 'RTools', 'Git' or 'Cygwin'. If they have been correctly installed (with the expected registry entries), they should be detected on loading the package and the correct directories will be added automatically to the PATH. 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The package also allows users to quantify and visualise the level of confidence in the estimated degrees of relatedness. 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'breakaway' is the premier package for statistical analysis of microbial diversity. 'breakaway' implements the latest and greatest estimates of species richness, described in Willis and Bunge (2015) , Willis et al. (2017) , and Willis (2016) , as well as the most commonly used estimates, including the objective Bayes approach described in Barger and Bunge (2010) . 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Break Down Table shows contributions of every variable to a final prediction. Break Down Plot presents variable contributions in a concise graphical way. This package work for binary classifiers and general regression models. 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Package: r-cran-breakpoints Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-zoo Filename: pool/dists/noble/main/r-cran-breakpoints_1.2-1.ca2404.1_all.deb Size: 48024 MD5sum: 57356a5e41cd3186f105f6b0be61c8a7 SHA1: 6969d076e2d70619441445031d12abd8b9a9f75d SHA256: 2c961aed87dba360f41071d59cc13bcf0efefc179dbfa92ed68adc2ef6716192 SHA512: a3f4d6573f8115e0603fe551588a8df75d425a38277a3c1cd61a7f20694b2493ac48d7cd5ab116fdf03a583cb6d476ad5b3c2553814f86e45a9cf9f63d6d6864 Homepage: https://cran.r-project.org/package=BreakPoints Description: CRAN Package 'BreakPoints' (Identify Breakpoints in Series of Data) Compute Buishand Range Test, Pettit Test, SNHT, Student t-test, and Mann-Whitney Rank Test, to identify breakpoints in series. For all functions NA is allowed. Since all of the mention methods identify only one breakpoint in a series, a general function to look for N breakpoint is given. Also, the Yamamoto test for climate jump is available. Alexandersson, H. (1986) , Buishand, T. (1982) , Hurtado, S. I., Zaninelli, P. G., & Agosta, E. A. (2020) , Mann, H. B., Whitney, D. R. (1947) , Pettitt, A. N. (1979) , Ruxton, G. D., jul (2006) , Yamamoto, R., Iwashima, T., Kazadi, S. N., & Hoshiai, M. (1985) . 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Creates artificial sample data for testing. Fits Maes/Ghoos, Bluck-Coward self-correcting formula using 'nls', 'nlme'. Methods to fit breath test curves with Bayesian Stan methods are refactored to package 'breathteststan'. For a Shiny GUI, see package 'dmenne/breathtestshiny' on github. 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Package: r-cran-bregr Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2398 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-broom.helpers, r-cran-cli, r-cran-dplyr, r-cran-forestploter, r-cran-ggplot2, r-cran-glue, r-cran-insight, r-cran-lifecycle, r-cran-mirai, r-cran-purrr, r-cran-rlang, r-cran-s7, r-cran-survival, r-cran-tibble, r-cran-vctrs Suggests: r-cran-broom.mixed, r-cran-fs, r-cran-ggalign, r-cran-ggnewscale, r-cran-ggstats, r-cran-ggstatsplot, r-cran-gtsummary, r-cran-ids, r-cran-knitr, r-cran-lme4, r-cran-merderiv, r-cran-qs2, r-cran-rmarkdown, r-cran-testthat, r-cran-ucscxenashiny, r-cran-visreg Filename: pool/dists/noble/main/r-cran-bregr_1.4.0-1.ca2404.1_all.deb Size: 1897442 MD5sum: 2728ec2d65023701cbdfe9990687345b SHA1: bb18bb7bf1bcd2ffc439626dcb6510f1e9a9e40b SHA256: 4f7db6d5007828ee4e73c70dfbb25908dba15419f56d69a514ad8c29831fc524 SHA512: 3d5be97d1dd58ef9262168703a6140ae7ae82a2a82f9e58bdcc73564b07691827d15d462ccc4d7fbcb7147e7b9e6d5893a02e2f9d465535ea94691ed63a05708 Homepage: https://cran.r-project.org/package=bregr Description: CRAN Package 'bregr' (Easy and Efficient Batch Processing of Regression Models) Easily processes batches of univariate or multivariate regression models. 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Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) . For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the 'CellCODE' R package described in Chikina (2015) . The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses. 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The package enables users to quickly retrieve and visualize indicators such as the unemployment rate and the Selic interest rate using a standardized data structure. It is designed for data access and visualization purposes, without performing forecasts or statistical modeling. For more information, see the official API: . Package: r-cran-brickset Architecture: all Version: 2026.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1300 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-piggyback Suggests: r-cran-bookdown, r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-shiny Filename: pool/dists/noble/main/r-cran-brickset_2026.0.0-1.ca2404.1_all.deb Size: 1105070 MD5sum: 8b458adefc367257724a4dd22b3664de SHA1: 5cf3908ef8d4bed991bdca8864256c741484d1f0 SHA256: 501107305f67bd7d6743a4f6d6846c5aec92da826bf97da12e180831e9e9efd2 SHA512: 50ab616dca11319e4be55924f1db2ff83494b9313867134ebe89b4d1e2a62478d3a25aec9480d7143e23b7e7566e58272018b5c0a260cae5d352959b988f83db Homepage: https://cran.r-project.org/package=brickset Description: CRAN Package 'brickset' (Interface with the Brickset API for Getting Data About LEGO Sets) Interface with the 'Brickset' API for getting data about LEGO sets. Data sets that can be used for teaching and learning without the need of a 'Brickset' account and API key are also included. Includes all LEGO since through the end of 2025. Package: r-cran-brickster Architecture: all Version: 0.2.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2235 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-cli, r-cran-curl, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-ini, r-cran-jsonlite, r-cran-lifecycle, r-cran-nanoarrow, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-tibble Suggests: r-cran-arrow, r-cran-htmltools, r-cran-huxtable, r-cran-knitr, r-cran-magick, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-brickster_0.2.13-1.ca2404.1_all.deb Size: 1831868 MD5sum: 0e26c9a594b12a7ad1033d36d29dc804 SHA1: 3dbb73e687d54a91763a729f59659eee9590efef SHA256: a35dcf320c871209073586812e22e0709628a5515b5d8bc89e643ff1a16e1d56 SHA512: 8760bd9ef922a01eca88e3d9c4706d0f80f1af7c467d1a9396fd9a1e4871a5518a47921e62e95f3b2b1c68af1a39d3bddd57f4b7e0061c67131d02beef0f8cd1 Homepage: https://cran.r-project.org/package=brickster Description: CRAN Package 'brickster' (R Toolkit for 'Databricks') Collection of utilities that improve using 'Databricks' from R. Primarily functions that wrap specific 'Databricks' APIs (), 'RStudio' connection pane support, quality of life functions to make 'Databricks' simpler to use. Package: r-cran-bridgedist Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bridgedist_0.1.3-1.ca2404.1_all.deb Size: 79582 MD5sum: ef6c276c94e1b56188c8574c45927252 SHA1: dc841af7d2e06e810f825b8d651b5e55df15cf0f SHA256: 70ae134533610e73144f2218e076d02efe63e9bfdcc0a9398575b8df00603b82 SHA512: 319e90f3be201ae89f2b77c4d611ff4a18d4f99e5fb8b50ab13e6bcc196c0e888cb12c6a3a2dc690b02d0f3f51d6f01527772eaa4092450e4e516ddceb45334a Homepage: https://cran.r-project.org/package=bridgedist Description: CRAN Package 'bridgedist' (An Implementation of the Bridge Distribution with Logit-Link asin Wang and Louis (2003)) An implementation of the bridge distribution with logit-link in R. In Wang and Louis (2003) , such a univariate bridge distribution was derived as the distribution of the random intercept that 'bridged' a marginal logistic regression and a conditional logistic regression. The conditional and marginal regression coefficients are a scalar multiple of each other. Such is not the case if the random intercept distribution was Gaussian. Package: r-cran-bridger2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-shiny, r-cran-shinydashboard, r-cran-plotly, r-cran-bsda, r-cran-outliers Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-bridger2_0.1.0-1.ca2404.1_all.deb Size: 304216 MD5sum: f4395766a7ea4393b70315ae9428c8b4 SHA1: a3c04a84533da0e284592f166206c2a3235aae0a SHA256: 235cb7c4c5a556f72e7bc3128102768f6aa41781e642d13d66c1bd8b87ad429c SHA512: 15a41c67d9771300f62c1373d09093500a75ad97131aba39ed795b88612af6c9414b8a3b25882af00030384edd8bc29c9e3122940904dd8a00e29b2f4f2888c2 Homepage: https://cran.r-project.org/package=bridger2 Description: CRAN Package 'bridger2' (Genome-Wide RNA Degradation Analysis Using BRIC-Seq Data) BRIC-seq is a genome-wide approach for determining RNA stability in mammalian cells. This package provides a series of functions for performing quality check of your BRIC-seq data, calculation of RNA half-life for each transcript and comparison of RNA half-lives between two conditions. Package: r-cran-bridger Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 593 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-patchwork, r-cran-tibble, r-cran-tidyr, r-cran-magrittr, r-cran-ggplot2, r-cran-ggedit, r-cran-glue, r-cran-gridextra, r-cran-kableextra, r-cran-pdftools, r-cran-scales, r-cran-stringr Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-bridger_0.1.0-1.ca2404.1_all.deb Size: 545968 MD5sum: 06d945539d459e943912b2ee2a1d9423 SHA1: defb2c7ac8d4960cd563a2720809966f4a489aee SHA256: 024cfa8dd5d8cdb0a09be2a327bfcc15c7b6a7564ff19f0816f5c514849f1f97 SHA512: 8c16b1a8b05c5fda0ead1650597f80d426fba1ff177e7406d1b6117bc17119795d53cc5c6c69b7863285b984d8be758106c17e6051add5ac35bef5552536c571 Homepage: https://cran.r-project.org/package=bridger Description: CRAN Package 'bridger' (Bridge Hand Generator with Criteria Selector) Produce bridge hands, allowing parameters for hands to offer specific for bidding sequences. Package: r-cran-bridgesampling Architecture: all Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-matrix, r-cran-brobdingnag, r-cran-stringr, r-cran-coda, r-cran-scales Suggests: r-cran-testthat, r-cran-rcpp, r-cran-rcppeigen, r-cran-r2jags, r-cran-rjags, r-cran-runjags, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-bayesfactor, r-cran-rstan, r-cran-rstanarm, r-cran-nimble, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-bridgesampling_1.2-1-1.ca2404.1_all.deb Size: 1176910 MD5sum: 67ef94916c3d9d56436c8e936adf3d25 SHA1: b7ddf7f7ed1691dcb1b8c26494a49cba083cb1e7 SHA256: 785a0ebeae49a14232b45140660aa76967cf8c81b31a5b22c74c985997f0ee7f SHA512: 4aed9765001aea3f358be545f3f84ae14d70386058faa6779d296a11eb067c9a56d52225d3f5aa27ab01f450e0dcad9072e701b1fa1ca6b8f71e853a8ad9c9d5 Homepage: https://cran.r-project.org/package=bridgesampling Description: CRAN Package 'bridgesampling' (Bridge Sampling for Marginal Likelihoods and Bayes Factors) Provides functions for estimating marginal likelihoods, Bayes factors, posterior model probabilities, and normalizing constants in general, via different versions of bridge sampling (Meng & Wong, 1996, ). Gronau, Singmann, & Wagenmakers (2020) . Package: r-cran-bridgr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 945 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-rlang, r-cran-generics, r-cran-tsbox, r-cran-lubridate, r-cran-forecast, r-cran-xts Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bridgr_0.1.2-1.ca2404.1_all.deb Size: 878456 MD5sum: 1e5e4c5686e0f4facc39ee47f394d580 SHA1: b6191d4f4ebffbddc31172710a0f75d59ccb9d46 SHA256: 7f12b61633882a5001bb37b7f30eb29f627a72961f5d443e10808943b161ae1b SHA512: 719026b08be3a92dc1cef6c725feb83fc192e660283bde2f7c853b1ebd3444d541ca5627aacd949d9cf1c2b5f8c4270f145b912a66fde87634dca2ea12231faa Homepage: https://cran.r-project.org/package=bridgr Description: CRAN Package 'bridgr' (Bridging Data Frequencies for Timely Economic Forecasts) Implements bridge models for nowcasting and forecasting macroeconomic variables by linking high-frequency indicator variables (e.g., monthly data) to low-frequency target variables (e.g., quarterly GDP). Simplifies forecasting and aggregating indicator variables to match the target frequency, enabling timely predictions ahead of official data releases. For more on bridge models, see Baffigi, A., Golinelli, R., & Parigi, G. (2004) , Burri (2023) or Schumacher (2016) . Package: r-cran-brightspacer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-config, r-cran-curl, r-cran-dplyr, r-cran-httr2, r-cran-lubridate, r-cran-openssl, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-httptest2, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-testthat, r-cran-withr, r-cran-yaml Filename: pool/dists/noble/main/r-cran-brightspacer_0.1.0-1.ca2404.1_all.deb Size: 288254 MD5sum: 1d48df933e8467de1d26fdef86c11406 SHA1: 3b350a86c7250a5c8bc67c69552bc4b4c69e3c47 SHA256: 715b6b7368c6d3d8f9d4c4ebdfaf7d08b9fee24b36992a6506908ba5c3468ffe SHA512: d32a079d9924e41f1244c5a7d956d86d4cc8845894c42074a606691aad37c8ee79dca15b9bbf4acd0dc39ff0a81a483e85e0a75a31ecf3f596ba91128df0ae26 Homepage: https://cran.r-project.org/package=brightspaceR Description: CRAN Package 'brightspaceR' (Access D2L 'Brightspace' Data Sets via the 'BDS' API) Connect to the D2L 'Brightspace' Data Sets ('BDS') API via 'OAuth2', download all available datasets as tidy data frames with proper types, join them using convenience functions that know the foreign key relationships, and analyse student engagement, performance, and retention with ready-made analytics functions. Package: r-cran-brikmeans Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-cluster, r-cran-depthtools, r-cran-splines2 Filename: pool/dists/noble/main/r-cran-brikmeans_1.0-1.ca2404.1_all.deb Size: 142274 MD5sum: 65f299b1ba9be9f38cf0dfbf632d245e SHA1: 3a79103e489239e39e87bfa008557140f23370f5 SHA256: d133ab3a039df69e70821052aa1f3f3437ee91e1e8f193dda6c75e02efac54e3 SHA512: d169e25864ba74f4bd8fe1d701d1adb50d934eada3bd0c0779a9a1da763df5dbd8c6d57093a4c6fe3c44e552c65076541935d4afdb71bae7db1a2bc7ae443982 Homepage: https://cran.r-project.org/package=briKmeans Description: CRAN Package 'briKmeans' (Package for Brik, Fabrik and Fdebrik Algorithms to InitialiseKmeans) Implementation of the BRIk, FABRIk and FDEBRIk algorithms to initialise k-means. These methods are intended for the clustering of multivariate and functional data, respectively. They make use of the Modified Band Depth and bootstrap to identify appropriate initial seeds for k-means, which are proven to be better options than many techniques in the literature. Torrente and Romo (2021) It makes use of the functions kma and kma.similarity, from the archived package fdakma, by Alice Parodi et al. Package: r-cran-brinda Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-berryfunctions, r-cran-data.table, r-cran-dplyr, r-cran-hmisc, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-brinda_0.1.5-1.ca2404.1_all.deb Size: 52784 MD5sum: 17fe14ce546de2990f7c48d31623b689 SHA1: 4cb8993bcb4407004e7ef70d071cb5c1b65fa612 SHA256: c0507347df16ec3a42064ecf39f6b50f2225abe4e40662cfb006216b57bab8e6 SHA512: 301eef0b1e205d9c32341e85a758125aad3fe22f5ed9a8070a7bd057598a0c4e6425e68c495a6ff496b0c6104a3878fa44cc9a559a94553d55bd1ca8881ac8c1 Homepage: https://cran.r-project.org/package=BRINDA Description: CRAN Package 'BRINDA' (Computation of BRINDA Adjusted Micronutrient Biomarkers forInflammation) Inflammation can affect many micronutrient biomarkers and can thus lead to incorrect diagnosis of individuals and to over- or under-estimate the prevalence of deficiency in a population. Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) is a multi-agency and multi-country partnership designed to improve the interpretation of nutrient biomarkers in settings of inflammation and to generate context-specific estimates of risk factors for anemia (Suchdev (2016) ). In the past few years, BRINDA published a series of papers to provide guidance on how to adjust micronutrient biomarkers, retinol binding protein, serum retinol, serum ferritin by Namaste (2020), soluble transferrin receptor (sTfR), serum zinc, serum and Red Blood Cell (RBC) folate, and serum B-12, using inflammation markers, alpha-1-acid glycoprotein (AGP) and/or C-Reactive Protein (CRP) by Namaste (2020) , Rohner (2017) , McDonald (2020) , and Young (2020) . The BRINDA inflammation adjustment method mainly focuses on Women of Reproductive Age (WRA) and Preschool-age Children (PSC); however, the general principle of the BRINDA method might apply to other population groups. The BRINDA R package is a user-friendly all-in-one R package that uses a series of functions to implement BRINDA adjustment method, as described above. The BRINDA R package will first carry out rigorous checks and provides users guidance to correct data or input errors (if they occur) prior to inflammation adjustments. After no errors are detected, the package implements the BRINDA inflammation adjustment for up to five micronutrient biomarkers, namely retinol-binding-protein, serum retinol, serum ferritin, sTfR, and serum zinc (when appropriate), using inflammation indicators of AGP and/or CRP for various population groups. Of note, adjustment for serum and RBC folate and serum B-12 is not included in the R package, since evidence shows that no adjustment is needed for these micronutrient biomarkers in either WRA or PSC groups (Young (2020) ). Package: r-cran-brinton Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3746 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-rmarkdown, r-cran-glue, r-cran-pander, r-cran-lubridate, r-cran-tibble, r-cran-sm, r-cran-rcolorbrewer, r-cran-forcats, r-cran-ggally, r-cran-patchwork, r-cran-scales Suggests: r-cran-knitr, r-cran-mass, r-cran-hexbin, r-cran-testthat Filename: pool/dists/noble/main/r-cran-brinton_0.2.7-1.ca2404.1_all.deb Size: 2705156 MD5sum: d493b72be6d95caad3b1f053c83117ce SHA1: dd8934cb1c92c40353bb6cbbb704e8f37d9ae6f1 SHA256: 15aea64d3737731351ecbeaecc81ee26c3abf80704342c140a0dc49db0e87438 SHA512: 463308ce425a6838340f28fbf284212f7e27f436fed00fe257826e6dc20d34c96abac176824851f5665db0222295cc85ba65d7747a3c6b3d0ebced8b469c96da Homepage: https://cran.r-project.org/package=brinton Description: CRAN Package 'brinton' (A Graphical EDA Tool) An automated graphical exploratory data analysis (EDA) tool that introduces: a.) wideplot graphics for exploring the structure of a dataset through a grid of variables and graphic types. b.) longplot graphics, which present the entire catalog of available graphics for representing a particular variable using a grid of graphic types and variations on these types. c.) plotup function, which presents a particular graphic for a specific variable of a dataset. The plotup() function also makes it possible to obtain the code used to generate the graphic, meaning that the user can adjust its properties as needed. d.) matrixplot graphics that is a grid of a particular graphic showing bivariate relationships between all pairs of variables of a certain(s) type(s) in a multivariate data set. Package: r-cran-briqr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-httptest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-briqr_0.1.0-1.ca2404.1_all.deb Size: 18016 MD5sum: 2405443d4dda3b040c3c500486df8acb SHA1: 9aa4947343d2def43cf25c364c5aaf4efc52f1dc SHA256: b20539d8bdd3f37cd1248f5bc2870a895d4893fe463f2d9e6751cfcf24471f11 SHA512: 32b6821536d1a6fed5a1cde8a583b12786ee77de52a6a3c2c4cf381b634891b000d59b655c19772539492a2894196ab9597db4b21d473d1befa6ea02b75f9317 Homepage: https://cran.r-project.org/package=briqr Description: CRAN Package 'briqr' (Interface to the 'Briq' API) An interface to the 'Briq' API . 'Briq' is a tool that aims to promote employee engagement by helping employees recognize and reward each other. Employees can praise and thank one another (for achieving a company goal, for example) by giving virtual credits (known as 'briqs' or 'bqs') that can be redeemed for various rewards. The 'Briq' API lets you create, read, update and delete users, user groups, transactions and messages. This package provides functions that simplify getting the users, user groups and transactions of your organization into R. Package: r-cran-brisk Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-hitandrun, r-cran-purrr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-fs, r-cran-testthat, r-cran-tibble, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-brisk_0.1.1-1.ca2404.1_all.deb Size: 208636 MD5sum: 92442e15517abcee24eca78ca59337c9 SHA1: 5df25ceb08b7299425c2c87a5ab839f626eba4e1 SHA256: 0e33a0d53ad468d9801748a7856440c31c5aa52aad956b1b81c9564154741f4b SHA512: 9b5b5b61a9388bf82b2dc762fb0f8c7f16308f8532df69e9352f2b7fa7e028867d37088fa4816d490f1b7499e47ae293d20b5fb5b6e3b7670573e5d0faf13698 Homepage: https://cran.r-project.org/package=brisk Description: CRAN Package 'brisk' (Bayesian Benefit Risk Analysis) Quantitative methods for benefit-risk analysis help to condense complex decisions into a univariate metric describing the overall benefit relative to risk. One approach is to use the multi-criteria decision analysis framework (MCDA), as in Mussen, Salek, and Walker (2007) . Bayesian benefit-risk analysis incorporates uncertainty through posterior distributions which are inputs to the benefit-risk framework. The brisk package provides functions to assist with Bayesian benefit-risk analyses, such as MCDA. Users input posterior samples, utility functions, weights, and the package outputs quantitative benefit-risk scores. The posterior of the benefit-risk scores for each group can be compared. Some plotting capabilities are also included. Package: r-cran-brlrmr Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-brglm, r-cran-mass, r-cran-profilemodel, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-brlrmr_0.1.7-1.ca2404.1_all.deb Size: 68972 MD5sum: 6fb706cad26b03bb7d7e88d8cfd118b1 SHA1: 720cf3d7f69f890432431057f4fe1b1170bdec4b SHA256: c3f50fb6debb11826f13ee7b2f24f682fc011f051dd903a60a40bb5aab51c70e SHA512: 96802d2b828bcc1a00ba56627c553d388e65ac9f78f54c08bcffd08d2383981d381db0cad3a7ac82a1bc86e850d0ce5b60196d0a44fb28cbea886d863b74ac82 Homepage: https://cran.r-project.org/package=brlrmr Description: CRAN Package 'brlrmr' (Bias Reduction with Missing Binary Response) Provides two main functions, il() and fil(). The il() function implements the EM algorithm developed by Ibrahim and Lipsitz (1996) to estimate the parameters of a logistic regression model with the missing response when the missing data mechanism is nonignorable. The fil() function implements the algorithm proposed by Maity et. al. (2017+) to reduce the bias produced by the method of Ibrahim and Lipsitz (1996) . Package: r-cran-brm Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-brm_1.1.1-1.ca2404.1_all.deb Size: 82066 MD5sum: f0fff2a4863487c627e39bffd90e9939 SHA1: f16e9c737be31618c4c1eb2b68b9f80a414be823 SHA256: 9ec9faaedf75d667527007970b3e0a9f932cfb639cf5be8432c8886a65467487 SHA512: 11b7801887b1eec872573aec8dff7e638322ab98fbeaad274540fcab1a5f76f2977581da6c87257965325e7a416bcbb0473b882ae5bb52ecbe1bbd3fd35e76b9 Homepage: https://cran.r-project.org/package=brm Description: CRAN Package 'brm' (Binary Regression Model) Fits novel models for the conditional relative risk, risk difference and odds ratio . Package: r-cran-brms.mmrm Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4890 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brms, r-cran-dplyr, r-cran-ggplot2, r-cran-ggridges, r-cran-mass, r-cran-posterior, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-trialr, r-cran-zoo Suggests: r-cran-bh, r-cran-emmeans, r-cran-fst, r-cran-gt, r-cran-gtsummary, r-cran-knitr, r-cran-markdown, r-cran-mmrm, r-cran-rcpp, r-cran-rcppeigen, r-cran-rcppparallel, r-cran-rmarkdown, r-cran-rstan, r-cran-stanheaders, r-cran-testthat Filename: pool/dists/noble/main/r-cran-brms.mmrm_1.1.1-1.ca2404.1_all.deb Size: 1548994 MD5sum: 6f7cf235d9733385911fda10b86a1d5f SHA1: 63048f5ee4c1305b25ade7e14570c8b44e0e26cd SHA256: d709745138905519f920b237f11dec8b09abbbb9a8a13a6bc3affebe008bb9b1 SHA512: 149973ffaf38647c237f0b0e2e269d0c2bd39be9fdcc0c2ee5941fcde1da30107431fa535384414344674b9b006d7acdfe004ef7b36e40de93372896f73c3c02 Homepage: https://cran.r-project.org/package=brms.mmrm Description: CRAN Package 'brms.mmrm' (Bayesian MMRMs using 'brms') The mixed model for repeated measures (MMRM) is a popular model for longitudinal clinical trial data with continuous endpoints, and 'brms' is a powerful and versatile package for fitting Bayesian regression models. The 'brms.mmrm' R package leverages 'brms' to run MMRMs, and it supports a simplified interfaced to reduce difficulty and align with the best practices of the life sciences. References: Bürkner (2017) , Mallinckrodt (2008) . Package: r-cran-brms Architecture: all Version: 2.23.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8817 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rstan, r-cran-ggplot2, r-cran-loo, r-cran-posterior, r-cran-matrix, r-cran-mgcv, r-cran-rstantools, r-cran-bayesplot, r-cran-bridgesampling, r-cran-glue, r-cran-rlang, r-cran-future, r-cran-future.apply, r-cran-matrixstats, r-cran-nleqslv, r-cran-nlme, r-cran-coda, r-cran-abind, r-cran-backports Suggests: r-cran-testthat, r-cran-emmeans, r-cran-projpred, r-cran-priorsense, r-cran-shinystan, r-cran-splines2, r-cran-rwiener, r-cran-rtdists, r-cran-extradistr, r-cran-processx, r-cran-mice, r-cran-spdep, r-cran-mnormt, r-cran-lme4, r-cran-mcmcglmm, r-cran-ape, r-cran-arm, r-cran-statmod, r-cran-digest, r-cran-diffobj, r-cran-betareg, r-cran-r.rsp, r-cran-gtable, r-cran-shiny, r-cran-knitr, r-cran-rmarkdown, r-cran-ragg, r-cran-colorspace, r-cran-mirai, r-cran-future.mirai Filename: pool/dists/noble/main/r-cran-brms_2.23.0-1.ca2404.1_all.deb Size: 7140264 MD5sum: 2cece98920d8f7126cbd14f22d338626 SHA1: 9e316c62a73d9027cfa6d7f9dbaa87bab855cc9b SHA256: 59a56cbf857201005e5c603606019ef3535b105544bc1287a8d48da0ab16a4a4 SHA512: 5e9ee045a0eb8426e6d13e9b3c3b3b7dd9f280bf22fd3d0fd5d446a504112c7d386ff679a791fca63b90d47f10679b0a55afab4a4e43ae7ac23a266651aff126 Homepage: https://cran.r-project.org/package=brms Description: CRAN Package 'brms' (Bayesian Regression Models using 'Stan') Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) ; Bürkner (2018) ; Bürkner (2021) ; Carpenter et al. (2017) . Package: r-cran-brobdingnag Architecture: all Version: 1.2-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-cubature, r-cran-testthat Filename: pool/dists/noble/main/r-cran-brobdingnag_1.2-9-1.ca2404.1_all.deb Size: 944576 MD5sum: d4ddc8ac79f3fbc2d6e63e28a5157fd6 SHA1: b0b086d6b472d645385791743d3c11b2d2919650 SHA256: 5b0a05ce611f51d867761d1056bb79ef63c631bdc2b762f622b9a7ea02ca0cb9 SHA512: 681c0a440b9ed07bb5cd5747d5ec12a643f1deb065afcab512a9f4757c599387aecd4fa23453dd65a49083c423d60c8095ad6bbe985a05b436b9bb9068a3ccad Homepage: https://cran.r-project.org/package=Brobdingnag Description: CRAN Package 'Brobdingnag' (Very Large Numbers in R) Very large numbers in R. Real numbers are held using their natural logarithms, plus a logical flag indicating sign. Functionality for complex numbers is also provided. The package includes a vignette that gives a step-by-step introduction to using S4 methods. Package: r-cran-brokenadaptiveridge Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cyclops, r-cran-parallellogger, r-cran-bit64 Suggests: r-cran-testthat, r-cran-survival, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-brokenadaptiveridge_1.0.2-1.ca2404.1_all.deb Size: 28124 MD5sum: 68b97cbb4a46630bf8e6884f1feb6af2 SHA1: c6071dca90d3a3b0b0aa1e5c21abd6d7e8824298 SHA256: 3102e5e595b53540f82b0987362851511cda42e646a9809f2472d72a232e9558 SHA512: 4923b7ebd5cf69845f387dffcbfb76cd1d6fb4c5ac0f84a933410ee98ac444e1cf87943adc1532d0ab2dacfaae3a81e1d0f1f13e541cddfad0310fb5758deb05 Homepage: https://cran.r-project.org/package=BrokenAdaptiveRidge Description: CRAN Package 'BrokenAdaptiveRidge' (Broken Adaptive Ridge Regression with Cyclops) Approximates best-subset selection (L0) regression with an iteratively adaptive Ridge (L2) penalty for large-scale models. This package uses Cyclops for an efficient implementation and the iterative method is described in Kawaguchi et al (2020) and Li et al (2021) . Package: r-cran-brokenstick Architecture: all Version: 2.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1535 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-dplyr, r-cran-lme4, r-cran-matrixsampling, r-cran-rlang, r-cran-tidyr Suggests: r-cran-agd, r-cran-bookdown, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-lattice, r-cran-mass, r-cran-matrix, r-cran-mice, r-cran-mvtnorm, r-cran-plyr, r-cran-svglite, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-brokenstick_2.7.0-1.ca2404.1_all.deb Size: 1211832 MD5sum: 6d0ca2f974bb52deab4c520b2039bdbd SHA1: ec1904180d46993da14d870da0f5e6fc30b3dabb SHA256: e75dcac30778e4a58b70ccde226fd0d8a1867b011a0effa4a993ed77a9c90e36 SHA512: c2865aa8783916c0a9d33e5019208eb49747b0b3ac8a980b3dfbcf184965b3383efc1359f737b742dac8a778eb00ff1ab90d3000ea1e04b924553657acd7a900 Homepage: https://cran.r-project.org/package=brokenstick Description: CRAN Package 'brokenstick' (Broken Stick Model for Irregular Longitudinal Data) Data on multiple individuals through time are often sampled at times that differ between persons. Irregular observation times can severely complicate the statistical analysis of the data. The broken stick model approximates each subject’s trajectory by one or more connected line segments. The times at which segments connect (breakpoints) are identical for all subjects and under control of the user. A well-fitting broken stick model effectively transforms individual measurements made at irregular times into regular trajectories with common observation times. Specification of the model requires three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The package contains functions for fitting the broken stick model to data, for predicting curves in new data and for plotting broken stick estimates. The package supports two optimization methods, and includes options to structure the variance-covariance matrix of the random effects. The analyst may use the software to smooth growth curves by a series of connected straight lines, to align irregularly observed curves to a common time grid, to create synthetic curves at a user-specified set of breakpoints, to estimate the time-to-time correlation matrix and to predict future observations. See for additional documentation on background, methodology and applications. Package: r-cran-brolgar Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4979 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fabletools, r-cran-ggplot2, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tsibble, r-cran-vctrs Suggests: r-cran-gapminder, r-cran-gghighlight, r-cran-knitr, r-cran-matrix, r-cran-lme4, r-cran-modelr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tsibbledata, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-brolgar_1.0.2-1.ca2404.1_all.deb Size: 3694038 MD5sum: e9412e88a4eaa56d88f953795d1cbe45 SHA1: 729716699883a2d5cf96b993f7eed7d0ee114db1 SHA256: b0ca640289dad4d1a0ed5f25ce33af8f3873c39a20842ce64e40a4116765af21 SHA512: 077b84815955f6a0b2ce5c1ea3751f5746ef0d638a53ffd061660a4c5eb657854e6235ccecbb7b6437186b19ca5bb6c985ac09447e3c6a3d0db9d9410dbc9579 Homepage: https://cran.r-project.org/package=brolgar Description: CRAN Package 'brolgar' (Browse Over Longitudinal Data Graphically and Analytically in R) Provides a framework of tools to summarise, visualise, and explore longitudinal data. It builds upon the tidy time series data frames used in the 'tsibble' package, and is designed to integrate within the 'tidyverse', and 'tidyverts' (for time series) ecosystems. The methods implemented include calculating features for understanding longitudinal data, including calculating summary statistics such as quantiles, medians, and numeric ranges, sampling individual series, identifying individual series representative of a group, and extending the facet system in 'ggplot2' to facilitate exploration of samples of data. These methods are fully described in the paper "brolgar: An R package to Browse Over Longitudinal Data Graphically and Analytically in R", Nicholas Tierney, Dianne Cook, Tania Prvan (2020) . Package: r-cran-broom.helpers Architecture: all Version: 1.22.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 869 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cards, r-cran-cli, r-cran-dplyr, r-cran-labelled, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-betareg, r-cran-biglm, r-cran-brms, r-cran-broom.mixed, r-cran-cmprsk, r-cran-covr, r-cran-effects, r-cran-emmeans, r-cran-fixest, r-cran-forcats, r-cran-gam, r-cran-gee, r-cran-geepack, r-cran-ggplot2, r-cran-ggeffects, r-cran-ggstats, r-cran-glmmtmb, r-cran-glmtoolbox, r-cran-glue, r-cran-gt, r-cran-gtsummary, r-cran-knitr, r-cran-lavaan, r-cran-lfe, r-cran-lme4, r-cran-logitr, r-cran-marginaleffects, r-cran-margins, r-cran-mass, r-cran-mgcv, r-cran-mice, r-cran-mmrm, r-cran-multgee, r-cran-nnet, r-cran-ordinal, r-cran-parameters, r-cran-parsnip, r-cran-patchwork, r-cran-plm, r-cran-pscl, r-cran-quantreg, r-cran-rmarkdown, r-cran-rstanarm, r-cran-scales, r-cran-spelling, r-cran-survey, r-cran-survival, r-cran-testthat, r-cran-tidycmprsk, r-cran-vgam, r-cran-svyvgam Filename: pool/dists/noble/main/r-cran-broom.helpers_1.22.0-1.ca2404.1_all.deb Size: 560548 MD5sum: 7f128c4193cdbc367949edeef4bdb4e7 SHA1: 30de3d0cc43ea74306e4dd0df213aa926e8493bf SHA256: 0116a55b0f7dabdb80290edfb89ca50c40619edb8ffe8f1bd60bb8eee5afe64e SHA512: 09dbba6e3874dabbdeb16afc0e4bb71ed87dd17f66a5c94218158fcdf781751c8ab0c590deeaca80c0c5aecab1a0ca40164a88ff9a8de28cc6c72ba098f609a0 Homepage: https://cran.r-project.org/package=broom.helpers Description: CRAN Package 'broom.helpers' (Helpers for Model Coefficients Tibbles) Provides suite of functions to work with regression model 'broom::tidy()' tibbles. The suite includes functions to group regression model terms by variable, insert reference and header rows for categorical variables, add variable labels, and more. Package: r-cran-broom.mixed Architecture: all Version: 0.2.9.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5505 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-coda, r-cran-dplyr, r-cran-forcats, r-cran-nlme, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-furrr Suggests: r-cran-brms, r-cran-dotwhisker, r-cran-knitr, r-cran-testthat, r-cran-gamlss, r-cran-gamlss.data, r-cran-ggplot2, r-cran-glmmadaptive, r-cran-glmmtmb, r-cran-lmertest, r-cran-lme4, r-cran-matrix, r-cran-mcmcglmm, r-cran-mediation, r-cran-mgcv, r-cran-mice, r-cran-ordinal, r-cran-pander, r-cran-pbkrtest, r-cran-posterior, r-cran-rstan, r-cran-rstanarm, r-cran-rstantools, r-cran-r2jags, r-cran-tmb, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-broom.mixed_0.2.9.7-1.ca2404.1_all.deb Size: 5321854 MD5sum: d04f18d6ab171c0aba5f3d14119ac806 SHA1: 347e70b7d848c911d96fdb9af68cb7dd25ec2dbb SHA256: 76bd1506668dbef7ce2d8ebd856ad7e0961b6e2bb7b363fbc00b8b3a49e73b1f SHA512: 34938084633bd540337571d0bd5669cc9e651c126f49c53b7e574167d45f7efe13b707bf45f24e346c9f80f8e35b5f31da4ebe9a04f277ef1f2086e998ee8f8d Homepage: https://cran.r-project.org/package=broom.mixed Description: CRAN Package 'broom.mixed' (Tidying Methods for Mixed Models) Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics. Package: r-cran-broom Architecture: all Version: 1.0.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1856 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-backports, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-glue, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-aer, r-cran-auc, r-cran-bbmle, r-cran-betareg, r-cran-biglm, r-cran-bingroup, r-cran-boot, r-cran-btergm, r-cran-car, r-cran-cardata, r-cran-caret, r-cran-cluster, r-cran-cmprsk, r-cran-coda, r-cran-covr, r-cran-drc, r-cran-e1071, r-cran-emmeans, r-cran-epir, r-cran-ergm, r-cran-fixest, r-cran-gam, r-cran-gee, r-cran-geepack, r-cran-ggplot2, r-cran-glmnet, r-cran-glmnetutils, r-cran-gmm, r-cran-hmisc, r-cran-interp, r-cran-irlba, r-cran-joinerml, r-cran-kendall, r-cran-knitr, r-cran-ks, r-cran-lahman, r-cran-lavaan, r-cran-leaps, r-cran-lfe, r-cran-lm.beta, r-cran-lme4, r-cran-lmodel2, r-cran-lmtest, r-cran-lsmeans, r-cran-maps, r-cran-margins, r-cran-mass, r-cran-mclust, r-cran-mediation, r-cran-metafor, r-cran-mfx, r-cran-mgcv, r-cran-mlogit, r-cran-modeldata, r-cran-modeltests, r-cran-muhaz, r-cran-multcomp, r-cran-network, r-cran-nnet, r-cran-ordinal, r-cran-plm, r-cran-polca, r-cran-psych, r-cran-quantreg, r-cran-rmarkdown, r-cran-robust, r-cran-robustbase, r-cran-rsample, r-cran-sandwich, r-cran-spatialreg, r-cran-spdep, r-cran-speedglm, r-cran-spelling, r-cran-survey, r-cran-survival, r-cran-systemfit, r-cran-testthat, r-cran-tseries, r-cran-vars, r-cran-zoo Filename: pool/dists/noble/main/r-cran-broom_1.0.13-1.ca2404.1_all.deb Size: 1536102 MD5sum: 916202e0bef12d28a6acbdaecde4c8c2 SHA1: 617d3779593a3e059d4167faa7b5dc474d093d49 SHA256: ebf7c6fb69624d4d72dfe35d2631de6c4d703c62a1acedeaa891519d51e703c9 SHA512: fc680feaa9c8bfac1f8f9b1bb40c9f9176c9ea847d068053609e1f0b6db9dc09f29438644510b47ade4608dfa39102ec378d569c20e075f86b02729703783bac Homepage: https://cran.r-project.org/package=broom Description: CRAN Package 'broom' (Convert Statistical Objects into Tidy Tibbles) Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures. Package: r-cran-browndog Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl, r-cran-jsonlite, r-cran-httpuv Filename: pool/dists/noble/main/r-cran-browndog_0.2.1-1.ca2404.1_all.deb Size: 30792 MD5sum: 453f35d96da1be96cff8ceaf9ca386b3 SHA1: 2eda8888db9be9863eaaedee54e3df17467ec126 SHA256: 2693e8da3b38b6bf8cf3f28930a9eb0cb07aca9de9c2f22453e29bd959104d36 SHA512: 8d366016245484e2ff9c572c82b6c9af58e9efd7034f6aac660f21c0f6f3f9c0aa6583c170dcaaefb2a20976d6254d15693ce58bd2450547c06fb692b6df0802 Homepage: https://cran.r-project.org/package=BrownDog Description: CRAN Package 'BrownDog' (Brown Dog R Interface) An R interface for the Brown Dog which allows researchers to leverage Brown Dog Services that provides modules to identify the conversion options for a file, to convert file to appropriate format, or to extract data from a file. See for more information. Package: r-cran-brpl Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1154 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-brpl_1.0.2-1.ca2404.1_all.deb Size: 1130080 MD5sum: 7b75a589027122dcb1aa29fe089f5ea6 SHA1: f6736a981f2c3a89890c62d236b4808f6f54ff4c SHA256: dac1622a696fd01963372ed6cedc635af844e9bb3cb376ed6ae67ed23e658207 SHA512: eb147f484a44dfe5e1837e59a3996019c701d92b552ccfed7fd106d83b92fe0e5d15394e21f6c2a55c86c0f5dbbc639a705f58ea75f34b862de38b5458c8c33e Homepage: https://cran.r-project.org/package=BRPL Description: CRAN Package 'BRPL' (Methods for Bivariate Poverty Line Calculations) Provides tools for identifying subgroups within populations based on individual response patterns to specific interventions or treatments. Designed to support researchers and clinicians in exploring heterogeneous treatment effects and developing personalized therapeutic strategies. Offers functionality for analyzing and visualizing the interplay between two variables, thereby enhancing the interpretation of social sustainability metrics. The package focuses on bivariate discriminant analysis and aims to clarify relationships between indicator variables. 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Package: r-cran-bset Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1341 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rstan, r-cran-ggplot2, r-cran-future, r-cran-mvtnorm, r-cran-dplyr, r-cran-surrogaterank, r-cran-rdpack, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-bset_1.0-1.ca2404.1_all.deb Size: 722344 MD5sum: 8a435c0981641bc2ff54f092c0c8a1f8 SHA1: fd39bcee80dd3598fc62ea5f2b2b37fd313fd5d7 SHA256: 7efd3a4acc01d5266d9b735ca09dd458338a8f3390ad23e0c5d9f421c2109c31 SHA512: 17232d4cc8d14476667ed161dbc699620a54417dc830a50eac0897000a9187bfeeb8dfca1ae82361158f518c7ff914a990e03160dd5281ec2302ad02bf4f1524 Homepage: https://cran.r-project.org/package=BSET Description: CRAN Package 'BSET' (A Bayesian Surrogate Evaluation Test) An implementation of the Bayesian Surrogate Evaluation Test (BSET) for assessing the validity of surrogate markers in clinical trials. Provides hypothesis testing tools to evaluate whether a surrogate can reliably estimate the causal effect of a treatment on a primary outcome. Implements the imputation-based Bayesian methodology of Carlotti and Parast (2026) , extending the frequentist rank-based approach of Parast et al. (2024) . Addresses key limitations of the frequentist method, including the lack of causal interpretability and the inability to adjust for covariates in the estimation process. Package: r-cran-bsgof Architecture: all Version: 0.23.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2398 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bsgof_0.23.8-1.ca2404.1_all.deb Size: 2281046 MD5sum: b3389bb9741d4f3828668988899386bd SHA1: bcbeebb1bcf1ab1acd6aa6519751cda871d98e80 SHA256: 7cf8bcf7deac082e741344480c803b99a672b01151a209a0ffc31b5d35901551 SHA512: 024cbb7d39fcf22b10667e8ca221e8dbf793a266a3cc8ba2290d66d30c15e1a5e2199bc02510129cb8d2bffe701876de94230962c4ecafe78615ce61dc70a7f3 Homepage: https://cran.r-project.org/package=bsgof Description: CRAN Package 'bsgof' (Birnbaum-Saunders Goodness-of-Fit Test) Performs goodness of fit test for the Birnbaum-Saunders distribution and provides the maximum likelihood estimate and the method-of-moments estimate. For more details, see Park and Wang (2013) . This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528). 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Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation. Package: r-cran-bshazard Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-epi Filename: pool/dists/noble/main/r-cran-bshazard_1.2-1.ca2404.1_all.deb Size: 51554 MD5sum: 01608a8d77587fdc61cd6698d9261494 SHA1: 49bdcbe685c8116a8ac4d280d2147c6822c9ba32 SHA256: 20aad7cf10965e8d163a253c2009a45a95287fe97a44a2cf87a05b5aa3005296 SHA512: 62e235348c0d7b3a4c04c4be84d77ceec13428c8b26d5e74f3c90a4d711dd05654202d129c098043a32fc6c0676335e0894a2d3ad6b356509fc04628eb26d54e Homepage: https://cran.r-project.org/package=bshazard Description: CRAN Package 'bshazard' (Nonparametric Smoothing of the Hazard Function) The function estimates the hazard function non parametrically from a survival object (possibly adjusted for covariates). The smoothed estimate is based on B-splines from the perspective of generalized linear mixed models. 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Package: r-cran-bsims Architecture: all Version: 0.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2735 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-intrval, r-cran-mefa4, r-cran-mass, r-cran-deldir, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-detect, r-cran-shiny Filename: pool/dists/noble/main/r-cran-bsims_0.3-3-1.ca2404.1_all.deb Size: 1875950 MD5sum: 4c9fc6574933150096d29be292951d33 SHA1: 438cbf74eb7ac39dffb4c88395f91341bacbbb56 SHA256: 2afb4c88c8a4e5e82159a09001fab4d3b4aaaf7e0cc4e2c49b317a40646e6b11 SHA512: 1b077272071ded2bd6b6a81fca41ff5af4227c8fcd4ccad66bdfb707ea7683e98a78b9a35f25d29c3453209112253e48e9bb4ddcb34e9813824d505dcfd48b98 Homepage: https://cran.r-project.org/package=bSims Description: CRAN Package 'bSims' (Agent-Based Bird Point Count Simulator) A highly scientific and utterly addictive bird point count simulator to test statistical assumptions, aid survey design, and have fun while doing it (Solymos 2024 ). The simulations follow time-removal and distance sampling models based on Matsuoka et al. (2012) , Solymos et al. (2013) , and Solymos et al. (2018) , and sound attenuation experiments by Yip et al. (2017) . Package: r-cran-bsitar Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8754 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-rstan, r-cran-loo, r-cran-dplyr, r-cran-rlang, r-cran-rdpack, r-cran-insight, r-cran-data.table, r-cran-collapse, r-cran-marginaleffects, r-cran-sitar, r-cran-magrittr Suggests: r-cran-ggplot2, r-cran-bayesplot, r-cran-posterior, r-cran-testthat, r-cran-ggridges, r-cran-jtools, r-cran-splines2, r-cran-scales, r-cran-kableextra, r-cran-knitr, r-cran-future, r-cran-future.callr, r-cran-future.apply, r-cran-future.mirai, r-cran-mirai, r-cran-doparallel, r-cran-foreach, r-cran-dofuture, r-cran-fastplyr, r-cran-cheapr, r-cran-dtplyr, r-cran-checkmate, r-cran-installr, r-cran-bayestestr, r-cran-r.utils, r-cran-mass, r-cran-matrix, r-cran-tidyr, r-cran-nlme, r-cran-purrr, r-cran-forcats, r-cran-patchwork, r-cran-tibble, r-cran-pracma, r-cran-extradistr, r-cran-bookdown, r-cran-rmarkdown, r-cran-spelling, r-cran-hmisc, r-cran-growthcleanr, r-cran-boot, r-cran-r.rsp, r-cran-abind, r-cran-glue Filename: pool/dists/noble/main/r-cran-bsitar_0.3.3-1.ca2404.1_all.deb Size: 7896060 MD5sum: b07766b45cee8eb1da06e1ff527f7ba9 SHA1: ee4d51b358e8a77cc062828981dab701d5cf804e SHA256: b79e4dcadfde98fd9aeb1dbd1ff5e5c591e6f84640a29f2107254fddc29ca2e1 SHA512: 8c8d7447277e9bf5775367c3b594433a724ce8b5edfd7876f029bf98b5420b8df0ba2920f5a1dec7ba3803a219f8eccf1863182bb192fd56c810c629710e8b06 Homepage: https://cran.r-project.org/package=bsitar Description: CRAN Package 'bsitar' (Bayesian Super Imposition by Translation and Rotation GrowthCurve Analysis) The Super Imposition by Translation and Rotation (SITAR) model is a shape-invariant nonlinear mixed effect model that fits a natural cubic spline mean curve to the growth data and aligns individual-specific growth curves to the underlying mean curve via a set of random effects (see Cole, 2010 for details). 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Since the 'bsitar' package is a front-end to the R package 'brms', it offers excellent support for post-processing of posterior draws via various functions that are directly available from the 'brms' package. In addition, the 'bsitar' package includes various customized functions that allow for the visualization of distance (increase in size with age) and velocity (change in growth rate as a function of age), as well as the estimation of growth spurt parameters such as age at peak growth velocity and peak growth velocity. Package: r-cran-bskyr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 659 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-fs, r-cran-httr2, r-cran-lubridate, r-cran-magick, r-cran-mime, r-cran-opengraph, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-emoji, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vcr, r-cran-withr Filename: pool/dists/noble/main/r-cran-bskyr_0.4.0-1.ca2404.1_all.deb Size: 442354 MD5sum: 5e8fb2dc244c4a95e5667d5ca2c766d1 SHA1: 2b659e5c048fcee4c23c0dabd7b9e3eee8729183 SHA256: 3f8a70197e3a7da4b0c1227e1e3567b4b41ec47f3a2ec3a7ac2d9d55f9d6eb95 SHA512: 437ab63971a16be735201e4ef85e48d1f029db5aeb77238aa144a4c1d734c0a1a22ab9e8422f8803d62e49a0c4daec778951b02725e8e556afeae343513dd402 Homepage: https://cran.r-project.org/package=bskyr Description: CRAN Package 'bskyr' (Interact with 'Bluesky' Social) Collect data from and make posts on 'Bluesky' Social via the Hypertext Transfer Protocol (HTTP) Application Programming Interface (API), as documented at . 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Package: r-cran-bt Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 691 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpart, r-cran-statmod Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bt_0.4-1.ca2404.1_all.deb Size: 571818 MD5sum: 40c1bc49154c79e2e6ac345fadcad8ff SHA1: 083a2639af92d2455e54e335b504f17c0a3993c2 SHA256: d4cca705840683dd7f3584463a71e64d1ca8ecdd47d6d4e0714b2c2b3594a2ae SHA512: cfbe9298473f0949db0bc6235cec8603944ccb5c9f8d8c792dd56a725d51b131862996558f67c15cac0d1cdc4c8c3912fcfaf597908408565ff20d3117ba7446 Homepage: https://cran.r-project.org/package=BT Description: CRAN Package 'BT' ((Adaptive) Boosting Trees Algorithm) Performs (Adaptive) Boosting Trees for Poisson distributed response variables, using log-link function. The code approach is similar to the one used in 'gbm'/'gbm3'. Moreover, each tree in the expansion is built thanks to the 'rpart' package. This package is based on following books and articles Denuit, M., Hainaut, D., Trufin, J. (2019) Denuit, M., Hainaut, D., Trufin, J. (2019) Denuit, M., Hainaut, D., Trufin, J. (2019) Denuit, M., Hainaut, D., Trufin, J. (2022) Denuit, M., Huyghe, J., Trufin, J. (2022) Denuit, M., Trufin, J., Verdebout, T. (2022) . Package: r-cran-btdecaylasso Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-optimx, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-btdecaylasso_0.1.1-1.ca2404.1_all.deb Size: 98078 MD5sum: f88890f3923f50d115663122c3564f2c SHA1: 94e4018c6bcc7028502b69e2c9fe6aa2436e6cf8 SHA256: f5c842027c8c7b8ab9bba908e25183159eee1b4c01b5a77547d1943c2882c519 SHA512: 33f281ab227634787b52ef8206f6932d6a4380caf3c240fd0bf88fbc25fa478923bbaf6f920412d03326fedaf1f23b9f509d1f16aea4b75cd85b6e0a1d4696b3 Homepage: https://cran.r-project.org/package=BTdecayLasso Description: CRAN Package 'BTdecayLasso' (Bradley-Terry Model with Exponential Time Decayed Log-Likelihoodand Adaptive Lasso) We utilize the Bradley-Terry Model to estimate the abilities of teams using paired comparison data. For dynamic approximation of current rankings, we employ the Exponential Decayed Log-likelihood function, and we also apply the Lasso penalty for variance reduction and grouping. The main algorithm applies the Augmented Lagrangian Method described by Masarotto and Varin (2012) . Package: r-cran-btergm Architecture: all Version: 1.11.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 996 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-network, r-cran-sna, r-cran-ergm, r-cran-matrix, r-cran-boot, r-cran-coda, r-cran-rocr, r-cran-igraph, r-cran-statnet.common Suggests: r-cran-fastglm, r-cran-speedglm, r-cran-testthat, r-cran-bergm, r-cran-rsiena, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-btergm_1.11.1-1.ca2404.1_all.deb Size: 873620 MD5sum: 3415827320d1753936d12870e8f161b0 SHA1: 331ab826de577b18801c69677f763fd070ed5567 SHA256: 601d7a5620b3a04900fb7885e55a79bc5432ec85a464bfebb451c7f2f9f746fc SHA512: 277952cd92c453566f8bc23c3fe8afefe76f1150a35da280dd5a8daccd71424f6cd4aa57df163603bf171c12ce82a3398da0a011d11cba596b3d962395283c0b Homepage: https://cran.r-project.org/package=btergm Description: CRAN Package 'btergm' (Temporal Exponential Random Graph Models by BootstrappedPseudolikelihood) Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft . Package: r-cran-btime Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1761 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-runjags, r-cran-vgam, r-cran-matlib Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-btime_1.0.1-1.ca2404.1_all.deb Size: 1326964 MD5sum: 7dbd35d333386044c12fdfad93be45c6 SHA1: 17e051f9c4610b6410127263e1d5354f8e544dac SHA256: 745bdd287734c2ed88733d93d95caaabe95efdbb672d4301577ace628eb68fc4 SHA512: fd5985fc9ed387ed9be4293d32b8fae296cc4c369af114632f5bd0bd99886614f2a1c1a3f502eb3dc852f4bac5924ef09d96c51cb3eb86ee710a8808b20ccd82 Homepage: https://cran.r-project.org/package=BTIME Description: CRAN Package 'BTIME' (Bayesian Hierarchical Models for Single-Cell Protein Data) Bayesian Hierarchical beta-binomial models for modeling cell population to predictors/exposures. This package utilizes 'runjags' to run Gibbs sampling, parallelizing the chains. Options for different covariances/relationship structures between parameters of interest. Package: r-cran-btml Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-randomforest, r-cran-e1071, r-cran-proc Filename: pool/dists/noble/main/r-cran-btml_0.4.0-1.ca2404.1_all.deb Size: 75298 MD5sum: 48e38977addffe66358c357d8dc78fd3 SHA1: 807442d8a1f156923461de310d48cdcb399b95e0 SHA256: 5c1996dbe9ebe3ccc2485d16fb51e60a931bf996496cfa03c5807450955eadbb SHA512: 5e8316195ce54cd6a58fc5a33c72b90baa043d6c3e1c09f4ef0d77b0c707d17246df11d2fda0b7e0e9e8fe09cc403494cddafbe876e8a2c8f72ab6796e077016 Homepage: https://cran.r-project.org/package=btml Description: CRAN Package 'btml' (Bayesian Treed Machine Learning for Personalized Prediction) Generalization of the Bayesian classification and regression tree model that partitions subjects into terminal nodes and tailors predictive model to each terminal node. Package: r-cran-btrm Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-proc, r-cran-arm, r-cran-mass Filename: pool/dists/noble/main/r-cran-btrm_0.2.0-1.ca2404.1_all.deb Size: 63844 MD5sum: 392c5447fd414517200a013ea18716fd SHA1: 8814243b28bba64f98eaa6c1b0d85e1bdb1eb039 SHA256: 0edcbce9cd97f69ae8c122565de4235c069746b9a4ddb66b5f54f95e36d513a0 SHA512: e24d81042c85feebfbe2c0a0c3795f2e61321a451bc7aa3e7a18e7f8be05ac6f73b37cae6b0bbc881b5e58a0ad3b77608e816e87be98b7ab6afc700d5645d297 Homepage: https://cran.r-project.org/package=btrm Description: CRAN Package 'btrm' (Bayesian Treed Regression Model for Personalized Prediction andPrecision Diagnostics) Generalization of the Bayesian classification and regression tree (CART) model that partitions subjects into terminal nodes and tailors regression model to each terminal node. Package: r-cran-btspas Architecture: all Version: 2024.11.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5326 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-actuar, r-cran-coda, r-cran-data.table, r-cran-ggplot2, r-cran-ggforce, r-cran-gridextra, r-cran-plyr, r-cran-reshape2, r-cran-r2jags, r-cran-scales Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-btspas_2024.11.1-1.ca2404.1_all.deb Size: 3399574 MD5sum: dd16f91707d54ced5d3cc2383f9ef0e2 SHA1: 9ec912777234d5d1a02b572faf4957059d2bdac2 SHA256: e6c57ff52f153348a9b76e884a38e345c8bb713b844eb4c437ede8b6be6daa57 SHA512: 4123fd83d2c3d2e0a346ee79f7e48adcabd0397b18d27f44648a44ac69b8ff87758f38fd3e92328957080d4294096a09ef50864e5b016ed007a19dc676a94ba2 Homepage: https://cran.r-project.org/package=BTSPAS Description: CRAN Package 'BTSPAS' (Bayesian Time-Stratified Population Analysis) Provides advanced Bayesian methods to estimate abundance and run-timing from temporally-stratified Petersen mark-recapture experiments. Methods include hierarchical modelling of the capture probabilities and spline smoothing of the daily run size. Theory described in Bonner and Schwarz (2011) . Package: r-cran-bttl Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bttl_1.0.0-1.ca2404.1_all.deb Size: 37754 MD5sum: ef92f7584b15605b84bcfeb56a42e185 SHA1: b5c31367a29ac0e644b312059a486a6df8ccd60b SHA256: 490a24b6c36811ec6e98a3c40b85988cfc1ac65579b46539d708a99138b46378 SHA512: ed44b98be1d22daa81b575532d4d6e4fa1aa2e39ef970f59a5cbb10eeae7e3644d0fefdaf9fe14b74a511db0a7edbf5711f9b7030e384c3f8fe0a0c00592e2ad Homepage: https://cran.r-project.org/package=BTTL Description: CRAN Package 'BTTL' (Bradley-Terry Transfer Learning) Implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2024) , and allows for the statistical modeling of multi-attribute pairwise comparison data. Package: r-cran-btw Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1678 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brio, r-cran-cli, r-cran-clipr, r-cran-dplyr, r-cran-ellmer, r-cran-frontmatter, r-cran-fs, r-cran-jsonlite, r-cran-lifecycle, r-cran-mcptools, r-cran-pkgsearch, r-cran-rlang, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-s7, r-cran-sessioninfo, r-cran-skimr, r-cran-withr, r-cran-xml2 Suggests: r-cran-bslib, r-cran-callr, r-cran-chromote, r-cran-covr, r-cran-dbi, r-cran-devtools, r-cran-diffviewer, r-cran-duckdb, r-cran-evaluate, r-cran-fansi, r-cran-gert, r-cran-gh, r-cran-htmltools, r-cran-pandoc, r-cran-pkgload, r-cran-processx, r-cran-ragg, r-cran-rapp, r-cran-roxygen2, r-cran-shiny, r-cran-shinychat, r-cran-testthat, r-cran-tibble, r-cran-usethis Filename: pool/dists/noble/main/r-cran-btw_1.2.1-1.ca2404.1_all.deb Size: 1389310 MD5sum: 062a68e192186a94cb4c9375355ee371 SHA1: 02efcb8ca4abec49e183718333307bd7e9df343d SHA256: ba0843fe282dec7276d16475788392f2ebb13b4ca391ffe97a3ba047653a33bf SHA512: 9b3bed2200e1eadd28379de226f2d49a2ee0a77ce211d3aaeb6baf42b5567385ac3b555b96b0f32e1ba43daa1613080bdba531de88252a85acde4bdad589fad2 Homepage: https://cran.r-project.org/package=btw Description: CRAN Package 'btw' (A Toolkit for Connecting R and Large Language Models) A complete toolkit for connecting 'R' environments with Large Language Models (LLMs). Provides utilities for describing 'R' objects, package documentation, and workspace state in plain text formats optimized for LLM consumption. Supports multiple workflows: interactive copy-paste to external chat interfaces, programmatic tool registration with 'ellmer' chat clients, batteries-included chat applications via 'shinychat', and exposure to external coding agents through the Model Context Protocol. Project configuration files enable stable, repeatable conversations with project-specific context and preferred LLM settings. Package: r-cran-btwar Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-pracma, r-cran-tseries, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-btwar_1.0.1-1.ca2404.1_all.deb Size: 389224 MD5sum: f8424a5a08d3e2f33dcd9f7bd5dd3bfc SHA1: 262021592436964f4fc0a93e578bea1c7d563cf5 SHA256: 9ec872efa180526a560a628da6576c79db18c5e9c7d4e026e534ddda6bf11f95 SHA512: 218d416f4692d6c009ac6786bdada1400befe23a5a4918a42aa5fae3520ea856c28d911ff708ec8b7a76f43d8db786089f250190c4df12994172ac1653cbbf3b Homepage: https://cran.r-project.org/package=BTWAR Description: CRAN Package 'BTWAR' (Butterworth-Induced Autoregressive Model) Implements the Butterworth-Induced Autoregressive ('BTWAR') model, where autoregressive coefficients are obtained from analog Butterworth filter prototypes mapped into the discrete-time domain using the Matched Z-Transform. The framework establishes a structured connection between frequency-domain filter design and time-domain autoregressive modeling. Model order selection is performed via nested rolling-origin cross-validation. Method described in Bras-Geraldes, Rocha and Martins (2026) . Package: r-cran-btyd Architecture: all Version: 2.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3339 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hypergeo, r-cran-optimx, r-cran-dplyr, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-btyd_2.4.3-1.ca2404.1_all.deb Size: 1092384 MD5sum: 3653540b753810f690df0916ee3f2501 SHA1: 76269448ad44c198374b4f4bf1d3f82c4e2d55dd SHA256: 46b0ec5c91970dcf30bfbdfece2118fc6dff26e5831ba870df8b8b6f2badcf01 SHA512: 2d7e7dab63baa74a3d35b056201d242d2f9cb20bac68e09b91f1a6b1e77ce836a7eeb7d433da8ba4a432402c68a7126f22c3d6faf1e0fd42c33e10daf845e81a Homepage: https://cran.r-project.org/package=BTYD Description: CRAN Package 'BTYD' (Implementing BTYD Models with the Log Sum Exp Patch) Functions for data preparation, parameter estimation, scoring, and plotting for the BG/BB (Fader, Hardie, and Shang 2010 ), BG/NBD (Fader, Hardie, and Lee 2005 ) and Pareto/NBD and Gamma/Gamma (Fader, Hardie, and Lee 2005 ) models. Package: r-cran-bubbleheatmap Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-bubbleheatmap_0.1.1-1.ca2404.1_all.deb Size: 263188 MD5sum: fb47c114ddea27580c5cfb28af79f3c8 SHA1: 78c0bf1e3f3ddd7257bbaa2e8484ae295a1fdd1f SHA256: 0c9bd593d361eaf9297d81d7b5fcc95860714783c8a23539b7f8de0f372d7e97 SHA512: 4769a076c17f5cf7e595683fcebf77378aacb8914e18d3b48c79fd237e15e2dcd99420c09f57a331d1f987317c19f430e08fafee189a792873eb07770d5e0704 Homepage: https://cran.r-project.org/package=bubbleHeatmap Description: CRAN Package 'bubbleHeatmap' (Produces 'bubbleHeatmap' Plots for Visualising Metabolomics Data) Plotting package based on the grid system, combining elements of a bubble plot and heatmap to conveniently display two numerical variables, (represented by color and size) grouped by categorical variables on the x and y axes. This is a useful alternative to a forest plot when the data can be grouped in two dimensions, such as predictors x outcomes. It has particular advantages for visualising the metabolic measures produced by the 'Nightingale Health' metabolomics platform, and templates are included for automatically generating figures from these datasets. Package: r-cran-bubblyr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2852 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-bubblyr_0.1.2-1.ca2404.1_all.deb Size: 2104464 MD5sum: 5f3ab63d15c02ac7ce911d4ca1c730d8 SHA1: 02f6ad594ab1831085d6a8d7d3922213ea9b7100 SHA256: 85cd8c2c51362554bf6ae2529b1fa18ed97882925f4743a69d5d12f5cb159728 SHA512: f5192cd789013f022207a1c0b2d99347a7670822377516f7d2c4db31be41c1b7741d3dc59fe350b84f45244185aeb98e099636defcc859209be46febfe5c6ec5 Homepage: https://cran.r-project.org/package=bubblyr Description: CRAN Package 'bubblyr' (Beautiful Bubbles for 'shiny' and 'rmarkdown' Backgrounds) Creates bubbles within 'shiny' and 'rmarkdown' backgrounds using the 'bubbly-bg' 'JavaScript' library. Package: r-cran-bucky Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich, r-cran-lmtest Suggests: r-cran-mass, r-cran-amelia, r-cran-mice Filename: pool/dists/noble/main/r-cran-bucky_1.0.7-1.ca2404.1_all.deb Size: 77284 MD5sum: 783db7f57caf6f8d5472a22ea4f4ea4d SHA1: 815f7a2b80961b636467cce1be9bda3eed31b69a SHA256: 1936fa8fe607370a357ea17337c90542f5a87ef5a5f4a01df0ab8f1d46faeb23 SHA512: f613d5669adbf2cdc9da7819902b1e7f27372f350ece143f40dd9c75bf40937165be57bba6f85ba8873f159584d7b7c95448373302d4596b6cf1c492d2eecb7f Homepage: https://cran.r-project.org/package=bucky Description: CRAN Package 'bucky' (Bucky's Archive for Data Analysis in the Social Sciences) Provides functions for various statistical techniques commonly used in the social sciences, including functions to compute clustered robust standard errors, combine results across multiply-imputed data sets, and simplify the addition of robust and clustered robust standard errors. Package: r-cran-bucss Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-bucss_1.2.1-1.ca2404.1_all.deb Size: 139972 MD5sum: dcd7eb0e7f193babf334b25593ee132b SHA1: 16d6ec31352ff044ba7d5d87c8615d5203748f0e SHA256: 870a5c0c8fa97492a47cead178441cae755b58d0a23da66704167ddf556de40b SHA512: 728dd8de030f4ccb5f0fa72ec71170b5c502027bb6d7fb731ad3769097d4f37fcc2b992e5c3d3b191c5a298b57570d2a69f3d709b358b8fb6c18abcc278abc13 Homepage: https://cran.r-project.org/package=BUCSS Description: CRAN Package 'BUCSS' (Bias and Uncertainty Corrected Sample Size) Bias- and Uncertainty-Corrected Sample Size. BUCSS implements a method of correcting for publication bias and uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (2017; Psychological Science, 28, 1547-1562). Package: r-cran-budgetivr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-arrangements, r-cran-mass, r-cran-rglpk Filename: pool/dists/noble/main/r-cran-budgetivr_0.1.2-1.ca2404.1_all.deb Size: 116638 MD5sum: ba43daf45f43e52869235c221964a74a SHA1: cfe1ff0aee9cc825597cb89b6a60fe645dacbf67 SHA256: 43f6b94bf01d29f75110b8e4154412683c59feed86086f397b75b60b5ba50391 SHA512: f27de74d110d20c9f16cc322d2cf00722f64f00c45763494c65ad23385500cf144de5df4c7dd8ec37e45514109dd58f9067cb6118c1453735191b483bd2b9497 Homepage: https://cran.r-project.org/package=budgetIVr Description: CRAN Package 'budgetIVr' (Partial Identification of Causal Effects with Mostly InvalidInstruments) A tuneable and interpretable method for relaxing the instrumental variables (IV) assumptions to infer treatment effects in the presence of unobserved confounding. For a treatment-associated covariate to be a valid IV, it must be (a) unconfounded with the outcome and (b) have a causal effect on the outcome that is exclusively mediated by the exposure. There is no general test of the validity of these IV assumptions for any particular pre-treatment covariate. However, if different pre-treatment covariates give differing causal effect estimates when treated as IVs, then we know at least some of the covariates violate these assumptions. 'budgetIVr' exploits this fact by taking as input a minimum budget of pre-treatment covariates assumed to be valid IVs and idenfiying the set of causal effects that are consistent with the user's data and budget assumption. The following generalizations of this principle can be used in this package: (1) a vector of multiple budgets can be assigned alongside corresponding thresholds that model degrees of IV invalidity; (2) budgets and thresholds can be chosen using specialist knowledge or varied in a principled sensitivity analysis; (3) treatment effects can be nonlinear and/or depend on multiple exposures (at a computational cost). The methods in this package require only summary statistics. Confidence sets are constructed under the "no measurement error" (NOME) assumption from the Mendelian randomization literature. For further methodological details, please refer to Penn et al. (2024) . 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The methods in this package are based on those in Stephens (1994) . Bayesian changepoint detection will simply be an option in the function from the package 'bulletxtrctr' which identifies the groove locations. 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The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). 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Package: r-cran-bunching Architecture: all Version: 0.8.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 630 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bb, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-bunching_0.8.6-1.ca2404.1_all.deb Size: 519300 MD5sum: 9949a75f59dab20510afefb4cd30dcda SHA1: 0d88e1de84475ee0bce1e1f21aa1d9a45f64dcaf SHA256: a09876698ab507b295634bd636c82a248af59b6b165dca782b5ad34d8ccb145a SHA512: 1a863585a61a626f94bd2c539557e70b6f2141721e4c79ad511625bec2451f2a1f5bc50cff7540441312493835a426d6a10073200d44c556bd1eea68c1391a96 Homepage: https://cran.r-project.org/package=bunching Description: CRAN Package 'bunching' (Estimate Bunching) Implementation of the bunching estimator for kinks and notches. 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Data previously recorded could change for a number of reasons, such as discovery of an error in model code, a change in methodology or instrument recalibration. Monitoring data sources for these changes is not always possible. Other unnoticed changes could include a jump in time or measurement frequency, due to instrument failure or software updates. Functionality is provided that can be used to check and flag changes to previous data to prevent changes going unnoticed, as well as unexpected jumps in time. 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The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). 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Functions to compute and identify impulse responses, calculate forecasts, forecast error variance decompositions and scenarios are available. Several methods to print, plot and summarise results facilitate analysis. 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Package: r-cran-bvpa Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-bvpa_1.0.0-1.ca2404.1_all.deb Size: 123730 MD5sum: 4859a495ae4f786da53d27a04535609f SHA1: 3e6180a9df10296786f19ff01460dd81240e2372 SHA256: 1b30a0d6462f18de33914ad18db4086493ba60196f8786117b7bfc7f1c584afe SHA512: e414e463dd64589d3debbbbeabd3fbd254d1414214fb073924ac687acdbf85b3c0a6d41099e47d243f92d443805e5b01ce405110765f625ab0185f7c3f24e7aa Homepage: https://cran.r-project.org/package=bvpa Description: CRAN Package 'bvpa' (Bivariate Pareto Distribution) Implements the EM algorithm with one-step Gradient Descent method to estimate the parameters of the Block-Basu bivariate Pareto distribution with location and scale. We also found parametric bootstrap and asymptotic confidence intervals based on the observed Fisher information of scale and shape parameters, and exact confidence intervals for location parameters. Details are in Biplab Paul and Arabin Kumar Dey (2023) "An EM algorithm for absolutely continuous Marshall-Olkin bivariate Pareto distribution with location and scale"; E L Lehmann and George Casella (1998) "Theory of Point Estimation"; Bradley Efron and R J Tibshirani (1994) "An Introduction to the Bootstrap"; A P Dempster, N M Laird and D B Rubin (1977) "Maximum Likelihood from Incomplete Data via the EM Algorithm". 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The package aims to reduce time spent on getting Norwegian city bike data, and lower barriers to start analyzing it. The data is retrieved from Oslo City Bike, Bergen City Bike, and Trondheim City Bike. The data is made available under NLOD 2.0 . 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Package: r-cran-calendrio Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-gggibbous, r-cran-ggimage, r-cran-ggplot2, r-cran-suncalc Filename: pool/dists/noble/main/r-cran-calendrio_0.2.1-1.ca2404.1_all.deb Size: 53454 MD5sum: 86c982b3f8f837f98c3d49b242cf9d93 SHA1: a3ddef9c7aa07bffa9d29b64fb6581d9e2b9a4cc SHA256: 07eb1147156a33b13cb7890655b1de7c5033cb5e1c1dc1090abc5d8d366f155d SHA512: 5d4b72e831d03173099082827e17f7032a59c76e5b4f3c8eefd008d825fab38dd9ff220d98c76286ec32b98ebfd41a14d09eb07320a9326ebeba0087a48b0234 Homepage: https://cran.r-project.org/package=calendRio Description: CRAN Package 'calendRio' ('calendR' Fork with Additional Features (Backwards Compatible)) Fork of 'calendR' R package to generate ready to print calendars with 'ggplot2' (see ) with additional features (backwards compatible). 'calendRio' provides a 'calendR()' function that serves as a drop-in replacement for the upstream version but allows for additional parameters unlocking extra functionality. Package: r-cran-calf Architecture: all Version: 1.0.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-calf_1.0.17-1.ca2404.1_all.deb Size: 113112 MD5sum: 3ac00b465a68a5285a6fa76fb62ab7a3 SHA1: a47159d698f806c75000c23b6b0b3dcd879d8956 SHA256: ea97a3816a8f8dd55d160a40377cbea5d6766e04753a96cf1db35d27e99c9de8 SHA512: c19c7cc83275cc2f4d855818011a107e7df6da5401200d8af6278ff8f34193118ce627e28eb809e512fbf328ceaf5d3041d3c80ee7e336f5fead54aea5542f10 Homepage: https://cran.r-project.org/package=CALF Description: CRAN Package 'CALF' (Coarse Approximation Linear Function) Contains greedy algorithms for coarse approximation linear functions. Package: r-cran-caliberrfimpute Architecture: all Version: 1.0-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-mvtnorm, r-cran-randomforest Suggests: r-cran-missforest, r-cran-rpart, r-cran-survival, r-cran-xtable, r-cran-ranger Filename: pool/dists/noble/main/r-cran-caliberrfimpute_1.0-8-1.ca2404.1_all.deb Size: 510008 MD5sum: 7eacf7ca8b7d77b6a8462bf1cd920952 SHA1: b8dc00a3cbe421b7fde6b4bfb2babc02d6b00cea SHA256: 166706e63215f7958e1247693acf6ac28bfe557d01bdca8b152f82973e4b5a08 SHA512: 6d25df759aa7e96b286dc866df9b6e9f30776a3a641b12033614a7725f919bf055bef991410e04c8eadb63fcc645f9783372d14f9bb705dc2d41148483a123b0 Homepage: https://cran.r-project.org/package=CALIBERrfimpute Description: CRAN Package 'CALIBERrfimpute' (Multiple Imputation Using MICE and Random Forest) Functions to impute using random forest under full conditional specifications (multivariate imputation by chained equations). The methods are described in Shah and others (2014) . Package: r-cran-calibmsm Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2346 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggextra, r-cran-gridextra, r-cran-hmisc, r-cran-mstate, r-cran-rms, r-cran-survival, r-cran-tidyr, r-cran-vgam Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat, r-cran-survminer, r-cran-flexsurv Filename: pool/dists/noble/main/r-cran-calibmsm_1.1.3-1.ca2404.1_all.deb Size: 1941868 MD5sum: 865980b7a22fe59e95f92286c34ba6f7 SHA1: c004d2d2beb831294bfcb260884330fdda48dfa4 SHA256: 88e7ccfad8fb100a7b2d454616fd01bb9430c034224c76890fd67812342ad4e4 SHA512: 1c58ff639439091b8deb18c4f2e4acfbc198e5886e9ce5f322aa1cffe0c83b5e30953ad302cab4b34878fea19c49eca95fc10a6932d4ba78f76c923d9ad14fa7 Homepage: https://cran.r-project.org/package=calibmsm Description: CRAN Package 'calibmsm' (Calibration Plots for the Transition Probabilities fromMultistate Models) Assess the calibration of an existing (i.e. previously developed) multistate model through calibration plots. Calibration is assessed using one of three methods. 1) Calibration methods for binary logistic regression models applied at a fixed time point in conjunction with inverse probability of censoring weights. 2) Calibration methods for multinomial logistic regression models applied at a fixed time point in conjunction with inverse probability of censoring weights. 3) Pseudo-values estimated using the Aalen-Johansen estimator of observed risk. All methods are applied in conjunction with landmarking when required. These calibration plots evaluate the calibration (in a validation cohort of interest) of the transition probabilities estimated from an existing multistate model. While package development has focused on multistate models, calibration plots can be produced for any model which utilises information post baseline to update predictions (e.g. dynamic models); competing risks models; or standard single outcome survival models, where predictions can be made at any landmark time. Please see Pate et al. (2024) and Pate et al. (2024) . Package: r-cran-calibrar Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bb, r-cran-cmaes, r-cran-deoptim, r-cran-dfoptim, r-cran-gensa, r-cran-minqa, r-cran-optimx, r-cran-foreach, r-cran-lbfgsb3c, r-cran-pso, r-cran-rgenoud, r-cran-soma, r-cran-stringr Suggests: r-cran-desolve, r-cran-ibm, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-calibrar_0.9.0-1.ca2404.1_all.deb Size: 561452 MD5sum: b248c65b9010b5a76d5b5d8e5a775f4c SHA1: d104a8f7580149668680f7b4a76eaa376bbb86ac SHA256: b033b7e854e462c1b38162ce40d0d7f4903ee2c4f2e601bdfb770d98846e14a6 SHA512: c0b2f6c8fdb0405ee18a32a10d1cb34a0e79c1bc33b5c098df93c064aefa6ba40c77be8817ee5cb9c71fe793dbd1d326b8484a2786510cd1376c9246357ddbb4 Homepage: https://cran.r-project.org/package=calibrar Description: CRAN Package 'calibrar' (Automated Parameter Estimation for Complex Models) General optimisation and specific tools for the parameter estimation (i.e. calibration) of complex models, including stochastic ones. It implements generic functions that can be used for fitting any type of models, especially those with non-differentiable objective functions, with the same syntax as base::optim. It supports multiple phases estimation (sequential parameter masking), constrained optimization (bounding box restrictions) and automatic parallel computation of numerical gradients. Some common maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs is provided. See for more details. Package: r-cran-calibrate Architecture: all Version: 1.7.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-calibrate_1.7.7-1.ca2404.1_all.deb Size: 350926 MD5sum: 2ee0ac65303abeb652aec98e40f54cb7 SHA1: 1c2f79bb098964c31123c6066b791396c3b9334c SHA256: cae8cb0030e37f3ec265a41c07bcfdfac10f301bb721eb74bef43583242e2cb2 SHA512: ebd9de67c393905b9b3d51c81c6716597cc26a17dd385c519bdf4dbe1b65d96508c63c229e05cb3375c1dddf51cd82d2c691308a7ca3a861e767c7efc97b7912 Homepage: https://cran.r-project.org/package=calibrate Description: CRAN Package 'calibrate' (Calibration of Scatterplot and Biplot Axes) Package for drawing calibrated scales with tick marks on (non-orthogonal) variable vectors in scatterplots and biplots. Also provides some functions for biplot creation and for multivariate analysis such as principal coordinate analysis. Package: r-cran-calibratebinary Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gpfit, r-cran-gelnet, r-cran-kernlab, r-cran-randtoolbox Filename: pool/dists/noble/main/r-cran-calibratebinary_0.1-1.ca2404.1_all.deb Size: 37420 MD5sum: 455970a254e764035b18c9cd42ef0658 SHA1: 50ae2c5bf93b8e37f3c316f87fffd251bdcaa918 SHA256: 3d7bdade67b86983a8f22a78aba6c6bc97c14dc200e8f40ad38e50b7139c6438 SHA512: a2dd1822289099e45d9be32075af17e36910b9b3d7ce9e6b0e28c3593e33ae656afba60cbabeec72acccf2f30de4cb9cc423f0d0338ab5716d1643526b7db75e Homepage: https://cran.r-project.org/package=calibrateBinary Description: CRAN Package 'calibrateBinary' (Calibration for Computer Experiments with Binary Responses) Performs the calibration procedure proposed by Sung et al. (2018+) . This calibration method is particularly useful when the outputs of both computer and physical experiments are binary and the estimation for the calibration parameters is of interest. Package: r-cran-calibratessb Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survey Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-calibratessb_1.4.0-1.ca2404.1_all.deb Size: 190554 MD5sum: 6400d80e8143042e6d226f0ff18ccbfd SHA1: 5c92655cdf22674fe1a45ca4e0b676155cc7f8e6 SHA256: 9540bb88b7a9823e81f1df845d23d9610f670ab190b4295f7e53d2fb9f5cb7ed SHA512: 7da9d5cc1f1cd1b5407a84b01ab696cb6328d9f9be3f8a989ea4dff48865a2517e316ba0ff4ba426a85150bf8b0023766df686fb0b50bc5ae15842b54bd209da Homepage: https://cran.r-project.org/package=CalibrateSSB Description: CRAN Package 'CalibrateSSB' (Weighting and Estimation for Panel Data with Non-Response) Functions to calculate weights, estimates of changes and corresponding variance estimates for panel data with non-response. Partially overlapping samples are handled. Initially, weights are calculated by linear calibration. By default, the 'survey' package is used for this purpose. It is also possible to use 'ReGenesees', which can be installed from . Variances of linear combinations (changes and averages) and ratios are calculated from a covariance matrix based on residuals according to the calibration model. The methodology was presented at the conference, The Use of R in Official Statistics, and is described in Langsrud (2016) . Package: r-cran-calibrationcurves Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rms, r-cran-ggplot2, r-cran-survival, r-cran-hmisc, r-cran-pec, r-cran-riskregression, r-cran-meta, r-cran-metafor, r-cran-zoo, r-cran-lme4, r-cran-mertools, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-rstudioapi, r-cran-mgcv, r-cran-mass, r-cran-matrix, r-cran-testthat Filename: pool/dists/noble/main/r-cran-calibrationcurves_3.1.0-1.ca2404.1_all.deb Size: 2273264 MD5sum: 4a38d2c1845dec3c355b3d2fa0cb44c1 SHA1: d442420ca1da71d904a32205bffb6087a02d6d5c SHA256: d0f1ac85250e1c0502b5619f853fc7afcde7f62fd5a9252a7f6d28951a1978f2 SHA512: 70903776e8351fcbdbeea52a65cce00b647a833ab30a6b776ff6b8a849f5d2d58b78362e1a9e63336e19a52a21fb70d8ae280af0860bdce9c0b8a323073e6e2f Homepage: https://cran.r-project.org/package=CalibrationCurves Description: CRAN Package 'CalibrationCurves' (Calibration Performance) Plots calibration curves and computes statistics for assessing calibration performance. See Lasai et al. (2025) , De Cock Campo (2023) and Van Calster et al. (2016) . Package: r-cran-calibrator Architecture: all Version: 1.2-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 737 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-emulator, r-cran-mvtnorm, r-cran-cubature Filename: pool/dists/noble/main/r-cran-calibrator_1.2-8-1.ca2404.1_all.deb Size: 635876 MD5sum: 76c42b334e5762b70101919e2e5c21de SHA1: 2c0213f76d5611f250ebe91d562dff885aec9aa7 SHA256: 525cd61a72cdb51ee44189fac874a29636d9d858fc3cf61e97b74064cd600661 SHA512: 8d28d08d792c01a9ed39aae00de941396d42e4cfd66c8814c2374478a4a3f6d1b810dc8dd75397ff87ffd6e776d027868f7e4787610a0a224d213911c3dc580a Homepage: https://cran.r-project.org/package=calibrator Description: CRAN Package 'calibrator' (Bayesian Calibration of Complex Computer Codes) Performs Bayesian calibration of computer models as per Kennedy and O'Hagan 2001. The package includes routines to find the hyperparameters and parameters; see the help page for stage1() for a worked example using the toy dataset. A tutorial is provided in the calex.Rnw vignette; and a suite of especially simple one dimensional examples appears in inst/doc/one.dim/. Package: r-cran-calibratr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-proc, r-cran-reshape2, r-cran-foreach, r-cran-fitdistrplus, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-calibratr_0.1.2-1.ca2404.1_all.deb Size: 248080 MD5sum: 71af89373a62db0b1f0f4b8a1651e342 SHA1: 43ca86cd5b86d58662e7fa992979eb5166f37227 SHA256: 1639b6ddbde46618c9078cdf7d31ee4e89c94fd65506d9dbba0aec1ab70d4d01 SHA512: a0f959cabd19cca37d841c453db5e20a0f3a78f53a44ead3f140b5a7f896689f131828f08bd8bfc4ea8b4d9d7f443083a2884f930b17e829236175d75aa8bdbb Homepage: https://cran.r-project.org/package=CalibratR Description: CRAN Package 'CalibratR' (Mapping ML Scores to Calibrated Predictions) Transforms your uncalibrated Machine Learning scores to well-calibrated prediction estimates that can be interpreted as probability estimates. The implemented BBQ (Bayes Binning in Quantiles) model is taken from Naeini (2015, ISBN:0-262-51129-0). Please cite this paper: Schwarz J and Heider D, Bioinformatics 2019, 35(14):2458-2465. Package: r-cran-calidad Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4603 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-purrr, r-cran-survey, r-cran-kableextra, r-cran-stringr, r-cran-haven Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-srvyr Filename: pool/dists/noble/main/r-cran-calidad_0.8.2-1.ca2404.1_all.deb Size: 4537322 MD5sum: 99f43a2b5497f965cd0088bf5c8a7a51 SHA1: 50b530629bd7aed7d427a726f593da5de8c718a5 SHA256: 9dc8c71cfb2a3431ea9b60fb766380c3206ec9f296d78dd8672a452f005f770b SHA512: ebaab3931557b12ac0406d230eb5a8ac452eeebf69a448b0a0044a846a47697e7ad3d726e81e19020a16b8e0dc7aa25220a3861cd52bb3a1eaba4ef27357bc4d Homepage: https://cran.r-project.org/package=calidad Description: CRAN Package 'calidad' (Assesses the Quality of Estimates Made by Complex Sample Designs) Assesses the quality of estimates made by complex sample designs, following the methodology developed by the National Institute of Statistics Chile (Household Survey Standard 2020, ), (Economics Survey Standard 2024, ) and by Economic Commission for Latin America and Caribbean (2020, ), (2024, ). Package: r-cran-callback Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-callback_0.1.3-1.ca2404.1_all.deb Size: 323156 MD5sum: 62dfbd600a35bea4caee6dcded429e44 SHA1: 51fdf9466eaea32a0b7710267ff381545b65b8d7 SHA256: 5fc22ba4643d35ad811d68376074c1609b922f21fe657bd050821db9a438548b SHA512: 2b007246f6b8c868697b044d5483f694b0e03b4865cb10672eff7bc39b91851bdd7232054c67e521c9cc65d677509c74d3a73bd13b0a3dcf1706ada3ac2a3170 Homepage: https://cran.r-project.org/package=callback Description: CRAN Package 'callback' (Computes Statistics from Discrimination Experimental Data) In discrimination experiments candidates are sent on the same test (e.g. job, house rental) and one examines whether they receive the same outcome. 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Package: r-cran-callsync Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-oce, r-cran-seewave, r-cran-signal, r-cran-stringr, r-cran-tuner, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-callsync_0.2.3-1.ca2404.1_all.deb Size: 164966 MD5sum: 396e5e9e6d9c0494a5f1402d242c0d74 SHA1: a8c1ca4c25f3ccbec8570f50bde967e8cbf5f818 SHA256: 12ab5ee41dca0f85203df26ba3be81f5b51c9414e0bbac9d03620054512c1f51 SHA512: 38af0080ca7f28c63a2a17e190f173a2f93b6888ad4d76e62c25900b4563f187d283ccfa937f17a208df29d0e506d3d8ed45c60cea8a37e57977eaa767671795 Homepage: https://cran.r-project.org/package=callsync Description: CRAN Package 'callsync' (Recording Synchronisation, Call Detection and Assignment, AudioAnalysis) Intended to analyse recordings from multiple microphones (e.g., backpack microphones in captive setting). It allows users to align recordings even if there is non-linear drift of several minutes between them. A call detection and assignment pipeline can be used to find vocalisations and assign them to the vocalising individuals (even if the vocalisation is picked up on multiple microphones). The tracing and measurement functions allow for detailed analysis of the vocalisations and filtering of noise. Finally, the package includes a function to run spectrographic cross correlation, which can be used to compare vocalisations. It also includes multiple other functions related to analysis of vocal behaviour. Package: r-cran-calmate Architecture: all Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r.utils, r-cran-aroma.core, r-cran-mass, r-cran-r.methodss3, r-cran-r.oo, r-cran-matrixstats, r-cran-r.filesets Suggests: r-bioc-dnacopy Filename: pool/dists/noble/main/r-cran-calmate_0.13.0-1.ca2404.1_all.deb Size: 205288 MD5sum: 3ceac2de149ab254d7276ddf6bc65319 SHA1: afb8dbd799cd52925e2de6892eaffe6aef59a72f SHA256: c87af026f70df9ff18f16b92ac9acec27f6d3aa4f9a9b9021c60d5554704f0ed SHA512: e65e04d49a368a255f05629a7edd865b898608783e5de1ef4ae5f1f483f75a375884e9e969f8eabc54075e8f104d9a5d6c7faa70b9dd90333b74b598ae880b8e Homepage: https://cran.r-project.org/package=calmate Description: CRAN Package 'calmate' (Improved Allele-Specific Copy Number of SNP Microarrays forDownstream Segmentation) The CalMaTe method calibrates preprocessed allele-specific copy number estimates (ASCNs) from DNA microarrays by controlling for single-nucleotide polymorphism-specific allelic crosstalk. The resulting ASCNs are on average more accurate, which increases the power of segmentation methods for detecting changes between copy number states in tumor studies including copy neutral loss of heterozygosity. CalMaTe applies to any ASCNs regardless of preprocessing method and microarray technology, e.g. Affymetrix and Illumina. Package: r-cran-calmr Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2014 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-future, r-cran-future.apply, r-cran-ga, r-cran-ggnetwork, r-cran-ggplot2, r-cran-lifecycle, r-cran-network, r-cran-patchwork, r-cran-progressr, r-cran-rlang Suggests: r-cran-diagrammer, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-calmr_0.8.1-1.ca2404.1_all.deb Size: 893428 MD5sum: 9a09552b362ecf5eec31a8dc95ff5436 SHA1: 7207631388cc65c80647c99c212401954e5c6377 SHA256: bab3f530b0f3abdb38c387cae8a6bc73a74150d4265d957a78e7dd278a28f2d1 SHA512: e95b0e1f8d0e3aa3d5f6cfa0f38a638320c59359446a886c1a225a6eca062680f01870850369aef914128307ea3ec579c61a728c82b57f6f5d0b17e944d866bb Homepage: https://cran.r-project.org/package=calmr Description: CRAN Package 'calmr' (Canonical Associative Learning Models and their Representations) Implementations of canonical associative learning models, with tools to run experiment simulations, estimate model parameters, and compare model representations. Experiments and results are represented using S4 classes and methods. Package: r-cran-calms Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3619 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-foreign, r-cran-lavaan, r-cran-lsr, r-cran-matchit, r-cran-shiny, r-cran-shinyjs, r-cran-stringr, r-cran-bslib Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rgenoud, r-cran-matching Filename: pool/dists/noble/main/r-cran-calms_1.0-3-1.ca2404.1_all.deb Size: 1754192 MD5sum: 8260fb4f2ccfe22b1675089958e56608 SHA1: ddcbdee3dd318edd2927e8bdca32bac0a993d655 SHA256: 3763be26fa498c2600b830a0f5c7b9dcddc8bbc3681c4a8f2a6e05535747e692 SHA512: 7106364e3d60c7094810bdde2aa5ffc285f887870679fd9fef989e61898949f1eb4a05dffbd902f7d0e70ff3cd31b30e685e9c041ef198bb00548057aa861cb8 Homepage: https://cran.r-project.org/package=calms Description: CRAN Package 'calms' (Comprehensive Analysis of Latent Means) Provides a Shiny application to conduct comprehensive analysis of latent means including the examination of group equivalency, propensity score analysis, measurement invariance analysis, and assessment of latent mean differences of equivalent groups with invariant data. Group equivalency and propensity score analyses are implemented using the 'MatchIt' package [Ho et al. (2011) ], ensuring robust control for covariates. Structural equation modeling and invariance testing rely heavily on the 'lavaan' package [Rosseel (2012) ], providing a flexible and powerful modeling framework. The application also integrates modified functions from Hammack-Brown et al. (2021) to support factor ratio testing and the list-and-delete procedure. Package: r-cran-calpassapi Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-digest, r-cran-jsonlite, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-calpassapi_0.0.3-1.ca2404.1_all.deb Size: 37308 MD5sum: fd5d0d18dae5096b2337c49ffb6a14f5 SHA1: bc1b29781a3c5ef3ae0b79218acaded16616e742 SHA256: 3fe362010175a87aac8ae13b7516b2612db555a322b04b15055ecb34092a59a9 SHA512: 0d0b59f154595eff529478e00376662ef6e9dbfd6d71b8d88aa7c7a6c047d333f18acc3d15a089b39874d9e1230d512b7cf4089b50e9ae2fd36057ad1eb31e07 Homepage: https://cran.r-project.org/package=calpassapi Description: CRAN Package 'calpassapi' (R Interface to Access CalPASS API) Implements methods for querying data from CalPASS using its API. CalPASS Plus. MMAP API V1. . Package: r-cran-camcorder Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1991 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gifski, r-cran-magick, r-cran-rsvg, r-cran-jsonlite, r-cran-rlang, r-cran-svglite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-ragg, r-cran-covr, r-cran-testthat, r-cran-patchwork, r-cran-withr, r-cran-dplyr, r-cran-systemfonts, r-cran-scales, r-cran-extrafont Filename: pool/dists/noble/main/r-cran-camcorder_0.1.0-1.ca2404.1_all.deb Size: 1418976 MD5sum: 06b82c92d613c8adccf7a43aa88191d3 SHA1: 089edfc91851d26dd3a4b76dc0e27c6c6e7a5a95 SHA256: cc6412d92bf631472129bcf23d91e99a032a74c332dcc6a5df42b764ba178443 SHA512: 500e0fb318271aedacfd0f772414aed87cf46649a1439a8683ca7741d6e1afe8c9e007af5a07b97da8ccec7edd00c6227bb21309f05e03d0de8b9329742b1ad6 Homepage: https://cran.r-project.org/package=camcorder Description: CRAN Package 'camcorder' (Record Your Plot History) Record and generate a 'gif' of your 'R' sessions plots. When creating a visualization, there is inevitably iteration and refinement that occurs. Automatically save the plots made to a specified directory, previewing them as they would be saved. Then combine all plots generated into a 'gif' to show the plot refinement over time. Package: r-cran-camea Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-metafor, r-cran-tibble, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-camea_0.1.2-1.ca2404.1_all.deb Size: 88516 MD5sum: 54db1ec0e9f2ce40062a38a86b7dd46b SHA1: ea7a77d8b7c62004d2dc9aa47850979f27b15190 SHA256: 056796b812a9c25d37dda3e76ffb022ce1d89b88199b8bd7f35a305af679ab65 SHA512: 4da4a09cbf376afdafd7c618924c774eda64876468e46e51c84e634667e47c04e2fe2cc35d968c0aa482d1a88dc023737767f7a3fd275259c7dfe4638634f7a2 Homepage: https://cran.r-project.org/package=CaMeA Description: CRAN Package 'CaMeA' (Causal Meta-Analysis for Aggregated Data) A tool for causal meta-analysis. This package implements the aggregation formulas and inference methods proposed in Berenfeld et al. (2025) . Users can input aggregated data across multiple studies and compute causally meaningful aggregated effects of their choice (risk difference, risk ratio, odds ratio, etc) under user-specified population weighting. The built-in function camea() allows to obtain precise variance estimates for these effects and to compare the latter to a classical meta-analysis aggregate, the random effect model, as implemented in the 'metafor' package . Package: r-cran-camelscl Architecture: all Version: 0.1-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-zoo, r-cran-hydrotsm, r-cran-terra Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-camelscl_0.1-10-1.ca2404.1_all.deb Size: 266338 MD5sum: 83d1f3afb237caf068e2b53d49b8b268 SHA1: 009a969230fe738a192d2ac8b8c1fa1b8c1f8648 SHA256: 45097fbac56aad57e5e3b2f3f81f38aebcf575c9d4b2e46dcccb4b1aae6f6e9f SHA512: 9063304745b9a76d684df966e306ad58de0ad78a3ea6b3cd09e06e166e850bdc59ed40c5fd9e281572ea194efabf9c83b93ef764f63b33b8152e6f0bd3cc69da Homepage: https://cran.r-project.org/package=camelsCL Description: CRAN Package 'camelsCL' (Easy Handling of the CAMELS-CL Dataset) Download and handle spatial and temporal data from the CAMELS-CL dataset (Catchment Attributes and Meteorology for Large Sample Studies, Chile) , developed by Alvarez-Garreton et al. (2018) . The package does not generate new data, it only facilitates direct access to the original dataset for hydrological analyses. Package: r-cran-camerondata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1342 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-camerondata_1.0.0-1.ca2404.1_all.deb Size: 1311266 MD5sum: dcdb5f5648ab6d2661a730f2f5e208ae SHA1: 16a5c1895eda95968e71b19b204fc28f7db6dc40 SHA256: 1de9be0d0190e1e96d9f740038ccd3e6068187fdfa076d2596fe1c440baffb22 SHA512: 6eb8f93ff4ee6cc5c20d533260a270f97f17d10cb701445c1ea7d2fd773ff5ccfa97d65d799bcb283a140645458946e920902b774e5a42211f5da29ef78538a2 Homepage: https://cran.r-project.org/package=camerondata Description: CRAN Package 'camerondata' (Datasets from "Microeconometrics: Methods and Applications" byCameron and Trivedi) Quick and easy access to datasets that let you replicate the empirical examples in Cameron and Trivedi (2005) "Microeconometrics: Methods and Applications" (ISBN: 9780521848053).The data are available as soon as you install and load the package (lazy-loading) as data frames. The documentation includes reference to chapter sections and page numbers where the datasets are used. Package: r-cran-camml Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 816 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vam, r-cran-seurat, r-cran-mass, r-cran-matrix, r-cran-biocmanager, r-bioc-org.hs.eg.db, r-bioc-org.dr.eg.db, r-bioc-org.mm.eg.db, r-bioc-annotationdbi, r-cran-seuratobject, r-bioc-edger Suggests: r-cran-sctransform Filename: pool/dists/noble/main/r-cran-camml_1.0.0-1.ca2404.1_all.deb Size: 643810 MD5sum: 4ab940de9431b752c01767420addb7ed SHA1: ed15aa36572a0e376a3393ef26b9b62f0da196c6 SHA256: bad91ab244f851a1a1747becd0b59c740ad1232e8e7892858282995bba7401f5 SHA512: d35f8956641599419f7e2d69303104d7ab8f2a921aa9edf23999a4cbe70c0742eb10e746452955f402aa6b85852e50fa6a0fa9eb8a6a940cdb03e2b225219a7b Homepage: https://cran.r-project.org/package=CAMML Description: CRAN Package 'CAMML' (Cell-Typing using Variance Adjusted Mahalanobis Distances withMulti-Labeling) Creates multi-label cell-types for single-cell RNA-sequencing data based on weighted VAM scoring of cell-type specific gene sets. Schiebout, Frost (2022) . Package: r-cran-campaignmanager Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-campaignmanager_0.1.0-1.ca2404.1_all.deb Size: 22896 MD5sum: 957d53581e3d48ad6ba702178e245fbe SHA1: 370162a7d848d0317fcab3ca0413744f4bf32f8a SHA256: d0d75435725402550aea1a9b1359c488c53807a1e5c3502f33a8f26f10dcb389 SHA512: a4c5883a9f330520a7c8660dadfee0165cb644495d89282b97c2f85e9d01cf2966700866d88a8c19c76d36483883c40888efac427d55e3f8391932a064dc56c1 Homepage: https://cran.r-project.org/package=campaignmanageR Description: CRAN Package 'campaignmanageR' (Connect to Campaign Manager via the 'Windsor.ai' API) Collect marketing data from Campaign Manager using the 'Windsor.ai' API . Package: r-cran-campfin Architecture: all Version: 1.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-fs, r-cran-ggplot2, r-cran-glue, r-cran-httr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-stringdist, r-cran-stringr, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-campfin_1.0.11-1.ca2404.1_all.deb Size: 646670 MD5sum: a084e85594511ec556da9c3fead95b68 SHA1: ee98c28bd5e26670dc7999909280aab005ca6417 SHA256: d912177addcb13e2fefda2280946c09ad6671a8eaaf5dec08503da989d3b83d6 SHA512: 775e4e3a02ebed2e65c95ffaafdd50fbee462b20d1ed3a202a8cf12fc53b297a7b5d7db09355908d5b59b3a224e87f3214b5b02b4eb05402f87697ced74421be Homepage: https://cran.r-project.org/package=campfin Description: CRAN Package 'campfin' (Wrangle Campaign Finance Data) Explore and normalize American campaign finance data. Created by the Investigative Reporting Workshop to facilitate work on The Accountability Project, an effort to collect public data into a central, standard database that is more easily searched: . Package: r-cran-campsis Architecture: all Version: 1.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1482 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-campsismod, r-cran-assertthat, r-cran-digest, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-mass, r-cran-progressr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-bookdown, r-cran-devtools, r-cran-gridextra, r-cran-knitr, r-cran-mrgsolve, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rxode2, r-cran-stringr, r-cran-testthat, r-cran-tictoc, r-cran-vdiffr, r-cran-xfun Filename: pool/dists/noble/main/r-cran-campsis_1.8.2-1.ca2404.1_all.deb Size: 995738 MD5sum: d731ce0871307e27a8868afef1c00742 SHA1: 075d64056e504e20939cb52a6890db165ffc821c SHA256: bd5d263c2a457631df54a5d13c6f0be2dea0b80cb6e4fcba683a1c3c316fe1e7 SHA512: 1c959ccec709636b74e0c49953f8ec023036436bcd7fa0b71c25bce5fa3e3a459aa5895f8b753d9609f25ff39a5cb481345fcfb514d060e3ee3f55a89e9e8d9c Homepage: https://cran.r-project.org/package=campsis Description: CRAN Package 'campsis' (Generic PK/PD Simulation Platform Campsis) A generic, easy-to-use and intuitive pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on the R packages 'rxode2' and 'mrgsolve'. Campsis provides an abstraction layer over the underlying processes of defining a PK/PD model, assembling a custom dataset and running a simulation. The package has a strong dependency on the R package 'campsismod', which allows models to be read from and written to files, including through a JSON-based interface, and to be adapted further on the fly in the R environment. In addition, 'campsis' allows users to assemble datasets in an intuitive manner, including via a JSON-based interface to import Campsis datasets defined using formal JSON schemas distributed with the package. Once the dataset is ready, the package prepares the simulation, calls 'rxode2' or 'mrgsolve' (at the user's choice), and returns the results for the given model, dataset and desired simulation settings. The package itself is licensed under the GPL (>= 3); the JSON schema files shipped in inst/extdata are licensed separately under the Creative Commons Attribution 4.0 International (CC BY 4.0). Package: r-cran-campsismod Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1619 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-ggplot2, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-laplacesdemon, r-cran-magrittr, r-cran-mass, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-devtools, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-xfun Filename: pool/dists/noble/main/r-cran-campsismod_1.3.2-1.ca2404.1_all.deb Size: 1062452 MD5sum: 9cd2e98e15e6b3a551a38faf493e0b42 SHA1: 921110e8fccccdea70b8ff8ef1fbf3ee5150c51a SHA256: 9e9b93de7686382e9f8705fa7be6fdc83f3a60dd2c61f335c06e07a07dbf934b SHA512: 5e532da241807741abd3c931aaabb1df71547199d58c675e79e9f052f47e5aae7344360b06aa965cb2a5058745b15ddc91b5ae205f640d7c8b3b91dc82fc894c Homepage: https://cran.r-project.org/package=campsismod Description: CRAN Package 'campsismod' (Generic Implementation of a PK/PD Model) A generic, easy-to-use and expandable implementation of a pharmacokinetic (PK) / pharmacodynamic (PD) model based on the S4 class system. This package allows the user to read and write pharmacometric models from and to files, including a JSON-based interface to import Campsis models defined using a formal JSON schema distributed with the package. Models can be adapted further on the fly in the R environment using an intuitive API to add, modify or delete equations, ordinary differential equations (ODEs), model parameters or compartment properties (such as infusion duration or rate, bioavailability and initial values). The package also provides export facilities for use with the simulation packages 'rxode2' and 'mrgsolve'. The package itself is licensed under the GPL (>= 3); the JSON schema file shipped in inst/extdata is licensed separately under the Creative Commons Attribution 4.0 International (CC BY 4.0). This package is designed and intended to be used with the package 'campsis', a PK/PD simulation platform built on top of 'rxode2' and 'mrgsolve'. Package: r-cran-camsrad Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-xml2 Suggests: r-cran-ncdf4, r-cran-roxygen2, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-camsrad_0.3.0-1.ca2404.1_all.deb Size: 32894 MD5sum: 1a21c65a73ea546af5fe41552b5714b0 SHA1: d11e7c6b64eecd11b51f06c149741d8c1dc9b478 SHA256: 247483874b2d996aaf21b088007dc47f04ff76beabb1e25ecd9975be0a0bd044 SHA512: 872aadf822b2c0f590db58c411037b23d61d176e002df66fc6f981811819f142dcef6353bda57d3806632ad107a0c8333cf1a59c6aba23c2a316304b0a3208b4 Homepage: https://cran.r-project.org/package=camsRad Description: CRAN Package 'camsRad' (Client for CAMS Radiation Service) Copernicus Atmosphere Monitoring Service (CAMS) radiations service provides time series of global, direct, and diffuse irradiations on horizontal surface, and direct irradiation on normal plane for the actual weather conditions as well as for clear-sky conditions. The geographical coverage is the field-of-view of the Meteosat satellite, roughly speaking Europe, Africa, Atlantic Ocean, Middle East. The time coverage of data is from 2004-02-01 up to 2 days ago. Data are available with a time step ranging from 15 min to 1 month. For license terms and to create an account, please see . Package: r-cran-camtrapdp Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-eml, r-cran-frictionless, r-cran-memoise, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-uuid Suggests: r-cran-jsonlite, r-cran-lubridate, r-cran-testthat, r-cran-tibble, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-camtrapdp_0.5.0-1.ca2404.1_all.deb Size: 200212 MD5sum: eb1ecc2af42b1efffd68ee8401d70677 SHA1: b2f6bda262d2db943ac5dac242c034664e970e6b SHA256: 8747fef08aaccc1e0b1f029749c79f20f706009e5a79787df250899738a7ee06 SHA512: 592f59754235e0d422ef224bc452fc64eb0b191733697ae1b40b65cf0d1dca7cfda0d0c3a8010dcf81108c21b34cb255d3d502728a96abf102da4d93a874a9dc Homepage: https://cran.r-project.org/package=camtrapdp Description: CRAN Package 'camtrapdp' (Read and Manipulate Camera Trap Data Packages) Read and manipulate Camera Trap Data Packages ('Camtrap DP'). 'Camtrap DP' () is a data exchange format for camera trap data. With 'camtrapdp' you can read, filter and transform data (including to Darwin Core) before further analysis in e.g. 'camtraptor' or 'camtrapR'. Package: r-cran-camtrapr Architecture: all Version: 3.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9876 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-generics, r-cran-ggplot2, r-cran-leaflet, r-cran-lubridate, r-cran-secr, r-cran-sf, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboard, r-cran-shinyjs, r-cran-terra Suggests: r-cran-abind, r-cran-bayesplot, r-cran-callr, r-cran-coda, r-cran-corrplot, r-cran-elevatr, r-cran-inext, r-cran-jsonify, r-cran-jsonlite, r-cran-knitr, r-cran-lattice, r-cran-magick, r-cran-mapview, r-cran-mockery, r-cran-nimble, r-cran-nimbleecology, r-cran-overlap, r-cran-patchwork, r-cran-pbapply, r-cran-plotly, r-cran-psych, r-cran-r.rsp, r-cran-raster, r-cran-reshape2, r-cran-ritis, r-cran-rjags, r-cran-rlang, r-cran-rmarkdown, r-cran-rsqlite, r-cran-rstudioapi, r-cran-scales, r-cran-shinywidgets, r-cran-stringr, r-cran-taxize, r-cran-tesseract, r-cran-testthat, r-cran-tibble, r-cran-ubms, r-cran-units, r-cran-unmarked, r-cran-viridislite, r-cran-withr, r-cran-zip Filename: pool/dists/noble/main/r-cran-camtrapr_3.0.4-1.ca2404.1_all.deb Size: 5580242 MD5sum: f8a3dd7eb602f300e410c609ab3dcdf2 SHA1: 0db665cb9a20af2bcc4da1888f54413500001cde SHA256: 9e800fd16151caaaab628e736443e58ee1f5de36ba697c64acd38e53bfe93c4d SHA512: 57c9effe8ebaa1d9e9c375d1e072d5766d7d35560d78c60763f74ecb8bff027f9d350bf42aaca71ed53ca15c78722371deb08eae6ad86cb6d363f37e647e1f06 Homepage: https://cran.r-project.org/package=camtrapR Description: CRAN Package 'camtrapR' (Camera Trap Data Management and Analysis Framework) Management and analysis of camera trap wildlife data through an integrated workflow. Provides functions for image/video organization and metadata extraction, species/individual identification. Creates detection histories for occupancy and spatial capture-recapture analyses, with support for multi-season studies. Includes tools for fitting community occupancy models in JAGS and NIMBLE, and an interactive dashboard for survey data visualization and analysis. Features visualization of species distributions and activity patterns, plus export capabilities for GIS and reports. Emphasizes automation and reproducibility while maintaining flexibility for different study designs. Package: r-cran-canadamaps Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4025 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-rmapshaper, r-cran-dplyr, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-canadamaps_0.1-1.ca2404.1_all.deb Size: 4084738 MD5sum: 7a69621afd72a30f78c96574a83e0c65 SHA1: a25fd30ea0f0f7521230b3057d7775c950ceacc2 SHA256: 30444906388f95d076938d675217787a92ad521e70f39427e6b4feb4c9ba0f37 SHA512: cb07b658fa54e48b0c4fc02db63dcd76b02ee3c387a062d4a4441a73b3d03194105856f20f4ae3ae060ca2da255de7db78921a71d58fa3c2351f5c30b7e07eba Homepage: https://cran.r-project.org/package=canadamaps Description: CRAN Package 'canadamaps' (Maps of the Political and Administrative Divisions of Canada) Terrestrial maps with simplified topologies for Census Divisions, Agricultural Regions, Economic Regions, Federal Electoral Divisions and Provinces. Package: r-cran-canadianmaps Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-sp, r-cran-sf, r-cran-ggrepel, r-cran-rcolorbrewer, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-canadianmaps_2.0.0-1.ca2404.1_all.deb Size: 4182436 MD5sum: 1471ec052026d951af4486d70c80b2a9 SHA1: 539b85ecc23a2c93cfdf2fcda837e62566960414 SHA256: 79f2c316d0fa4284347fd4896e81c25cbe81d823a37a40605cd38e0112bae2ed SHA512: f84fb9295a3f8927212d84a98c407772e8a3698700de559c35653b770f21fe811fcd176df6a1245dde27890a32b0a2558b39f20a15a73f120fb4299abe5d62b4 Homepage: https://cran.r-project.org/package=canadianmaps Description: CRAN Package 'canadianmaps' (Effortlessly Create Stunning Canadian Maps) Simple and seamless access to a variety of 'StatCan' shapefiles for mapping Canadian provinces, regions, forward sortation areas, census divisions, and subdivisions using the popular 'ggplot2' package. Package: r-cran-canaper Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 809 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-assertr, r-cran-assertthat, r-cran-dplyr, r-cran-future.apply, r-cran-phyloregion, r-cran-progressr, r-cran-purrr, r-cran-tibble, r-cran-vegan Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-future, r-cran-tictoc, r-cran-patchwork, r-cran-testthat, r-cran-stringr, r-cran-magrittr, r-cran-covr, r-cran-picante, r-cran-withr, r-cran-fs, r-cran-readr, r-cran-usethis, r-cran-scales, r-cran-colorblindness Filename: pool/dists/noble/main/r-cran-canaper_1.0.1-1.ca2404.1_all.deb Size: 535012 MD5sum: 548e43e5eb8db57ce2a80a4bf72cb904 SHA1: f08838550b0e2142a679a0148439a9704be61c6d SHA256: 72a52bb737711e38c3a00259120d1292fd2617f6b62e6d76d0fd66bc39d615a6 SHA512: 5ce5a8c4f9b17a7842e467f642ccda858c7c04a5643ebac401a2a988582558bb6b03f25ee5b3d30c282390ea17341f95307b494d3c052bf77272f0f50b92ded1 Homepage: https://cran.r-project.org/package=canaper Description: CRAN Package 'canaper' (Categorical Analysis of Neo- And Paleo-Endemism) Provides functions to analyze the spatial distribution of biodiversity, in particular categorical analysis of neo- and paleo-endemism (CANAPE) as described in Mishler et al (2014) . 'canaper' conducts statistical tests to determine the types of endemism that occur in a study area while accounting for the evolutionary relationships of species. Package: r-cran-cancensus Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2071 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-sf, r-cran-geojsonsf, r-cran-rlang, r-cran-readr Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-leaflet, r-cran-mapdeck, r-cran-rmarkdown, r-cran-scales, r-cran-sp, r-cran-tidyr, r-cran-lwgeom, r-cran-xml2, r-cran-testthat, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-cancensus_0.6.0-1.ca2404.1_all.deb Size: 1024288 MD5sum: cf27fb4b5f4aa4880eed3ec2cb56a781 SHA1: d788c105f080a12efeefbb4ac4eb61c86e3a48c4 SHA256: db806936d412cc431e2695c95e2c746c5d1abbc4373a4f5ec918699a6a53b582 SHA512: bb623a0fb262475189d8e4e240b108bc71a350a47195e5401eca210b0e4617e50acc4bfeaed35918e03e56c6a13c5f10994093eea9eca37a5ca06f540564bdcb Homepage: https://cran.r-project.org/package=cancensus Description: CRAN Package 'cancensus' (Access, Retrieve, and Work with Canadian Census Data andGeography) Integrated, convenient, and uniform access to Canadian Census data and geography retrieved using the 'CensusMapper' API. This package produces analysis-ready tidy data frames and spatial data in multiple formats, as well as convenience functions for working with Census variables, variable hierarchies, and region selection. API keys are freely available with free registration at . Census data and boundary geometries are reproduced and distributed on an "as is" basis with the permission of Statistics Canada (Statistics Canada 1996; 2001; 2006; 2011; 2016; 2021). Package: r-cran-cancerevolutionvisualization Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gridextra, r-cran-gtable, r-cran-plyr, r-cran-stringr, r-cran-boutroslab.plotting.general Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-cancerevolutionvisualization_2.0.1-1.ca2404.1_all.deb Size: 270440 MD5sum: d00d987343226168f186f9d4a9d955cf SHA1: 51d7481542747c7e4c286c188969a876cb20f808 SHA256: 6fc4b61cce2ce5818acd3fa1e185921025133bda4244cc21e8f8c8dd1f44d046 SHA512: c8287b66e7778be49a13048a4acf2667179d49831210e58a072c9e11b216c6c61b6a0b843823686d5522ffd0b87261fb865e3e33f7196737ae1e34c0c4d495f4 Homepage: https://cran.r-project.org/package=CancerEvolutionVisualization Description: CRAN Package 'CancerEvolutionVisualization' (Publication Quality Phylogenetic Tree Plots) Generates tree plots with precise branch lengths, gene annotations, and cellular prevalence. The package handles complex tree structures (angles, lengths, etc.) and can be further refined as needed by the user. Package: r-cran-cancergi Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 751 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-systemfit, r-bioc-qvalue, r-cran-survival, r-cran-reshape2, r-cran-igraph Filename: pool/dists/noble/main/r-cran-cancergi_1.0.1-1.ca2404.1_all.deb Size: 729080 MD5sum: c973ce2b94e98c9a00238f58fd0699d8 SHA1: 37127e74603b94c0f2b593eaadce65db3f01deea SHA256: 6f529f3a3e2e2440001dd6018fe2e17532bdfd28dd04378a59e00234998482b0 SHA512: c430d51542aadeb6b14fe5b871078abdfe3fa8d2bbedd3eb41ca9983f64b751faf27072bb69bac9f25e1f252b12f2708344dd0b1000bc25002fcfeb8c791b538 Homepage: https://cran.r-project.org/package=cancerGI Description: CRAN Package 'cancerGI' (Analyses of Cancer Gene Interaction) Functions to perform the following analyses: i) inferring epistasis from RNAi double knockdown data; ii) identifying gene pairs of multiple mutation patterns; iii) assessing association between gene pairs and survival; and iv) calculating the smallworldness of a graph (e.g., a gene interaction network). Data and analyses are described in Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. . Package: r-cran-cancergram Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biogram, r-cran-devtools, r-cran-pbapply, r-cran-ranger, r-cran-shiny, r-cran-stringi, r-cran-dplyr Suggests: r-cran-dt, r-cran-ggplot2, r-cran-pander, r-cran-rmarkdown, r-cran-shinythemes, r-cran-spelling Filename: pool/dists/noble/main/r-cran-cancergram_1.0.0-1.ca2404.1_all.deb Size: 213382 MD5sum: dbe867f1fe5b68b6415c585d585c5bad SHA1: 40b07502f2e4339e2480a958df50fa8a155c5a19 SHA256: 4bbb948ff7dac036146939ed10adc80632f538c9e6e9937c90bd531ae12687dc SHA512: 9a7d4351dadb39855e87c353db49dcc29046992be9714684871447446feceb5d42684bea92d58e4c110333c78ee75bcbf8c36a6c5cd23dfab25ed7c596422142 Homepage: https://cran.r-project.org/package=CancerGram Description: CRAN Package 'CancerGram' (Prediction of Anticancer Peptides) Predicts anticancer peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. The CancerGram model is too large for CRAN and it has to be downloaded separately from the repository: . For more information see: Burdukiewicz et al. (2020) . Package: r-cran-cancerr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cancerr_0.1.1-1.ca2404.1_all.deb Size: 268584 MD5sum: b2767f0d609493f5c6d08fb44e584a52 SHA1: 9cf1b17d8949e94db2211a40b68ff6a95def7733 SHA256: 541f73b803d065d310628e38b0269137f122f0145ef7d8256132cbe73d22b875 SHA512: b83e6f936918ef9a92d25d0cfdb444b1b44a95e85e6cd115f6031233fb80b74ff1320718ccca37bb6bafd5fd2b060f892c057ae27e8404675d3d877a9c409002 Homepage: https://cran.r-project.org/package=cancerR Description: CRAN Package 'cancerR' (Classification of Cancer Using Administrative Data) Classifies the type of cancer using routinely collected data commonly found in cancer registries from pathology reports. The package implements the International Classification of Diseases for Oncology, 3rd Edition site (topography), histology (morphology), and behaviour codes of neoplasms to classify cancer type . Classification in children utilize the International Classification of Childhood Cancer by Steliarova-Foucher et al. (2005) . Adolescent and young adult cancer classification is based on Barr et al. (2020) . Package: r-cran-cancerradarr Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7833 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-epitools, r-cran-gtools, r-cran-magrittr, r-cran-openxlsx, r-cran-plyr, r-cran-purrr, r-cran-rlang, r-cran-rmarkdown, r-cran-stringr, r-cran-tidyr Suggests: r-cran-plotly, r-cran-shiny, r-cran-tidyverse, r-cran-dt, r-cran-testthat, r-cran-knitr, r-cran-quarto Filename: pool/dists/noble/main/r-cran-cancerradarr_3.0.0-1.ca2404.1_all.deb Size: 7170862 MD5sum: d3853927ec1ad447603edb6bdb918a38 SHA1: d68d6e74013cd3510b7a57b87abfe85569db7007 SHA256: 335c3d5d286bf493ce5be2ec6273f52484aaf23d697d193b965c9408291777a6 SHA512: 85348fef81205fec426377527ca736d4e6393101cd71edcd6b294718315590e51b7b658b15a9d15dc9192cbe5ffe7e8fadc24b457c37dae84963a11309e33242 Homepage: https://cran.r-project.org/package=cancerradarr Description: CRAN Package 'cancerradarr' (Cancer RADAR Project Tool) Cancer RADAR is a project which aim is to develop an infrastructure that allows quantifying the risk of cancer by migration background across Europe. This package contains a set of functions cancer registries partners should use to reshape 5 year-age group cancer incidence data into a set of summary statistics (see Boyle & Parkin (1991, ISBN:978-92-832-1195-2)) in lines with Cancer RADAR data protections rules. Package: r-cran-cancerscreening Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-khisr, r-cran-lubridate, r-cran-magrittr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cancerscreening_1.1.1-1.ca2404.1_all.deb Size: 285642 MD5sum: 26879985438a5ced0d89f45d34f46631 SHA1: ba8ab10da664256333af7752003f446ee0f6a3bd SHA256: f52d8915760ce27309ccfea30ebf253d43ff5beb3efc25533f3bdfbefda7fde7 SHA512: b49d3bd94278de4d32e94d8ac568c61c78450e82cf1e864477919e2df350fa429ac771432e2cd2313af1a73974001a152a7b7310d48809f54c4f738349bd0b33 Homepage: https://cran.r-project.org/package=cancerscreening Description: CRAN Package 'cancerscreening' (Streamline Access to Cancer Screening Data) Retrieve cancer screening data for cervical, breast and colorectal cancers from the Kenya Health Information System in a consistent way. Package: r-cran-candisc Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 748 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-heplots, r-cran-car, r-cran-insight, r-cran-dplyr, r-cran-ggplot2, r-cran-mass Suggests: r-cran-rgl, r-cran-cardata, r-cran-corrplot, r-cran-knitr, r-cran-rmarkdown, r-cran-rpart, r-cran-rpart.plot, r-cran-klar, r-cran-lifecycle, r-cran-tidyr, r-cran-testthat, r-cran-rcolorbrewer, r-cran-tinytable Filename: pool/dists/noble/main/r-cran-candisc_1.1.0-1.ca2404.1_all.deb Size: 582668 MD5sum: caf3af88067971ec4c249274f09703f2 SHA1: 31140d7786615508cd5f24c72aaa6c9b3aef21ee SHA256: e206e18f437af29530cac387e366c7eb5d1e6d5234da2ba7d0bcb57529b7851c SHA512: 8c326a789023349665ac56c9dee2547e19b1dc76f7b7a0ba1494da6a38a09ba1174540a44234abe56bd827c4316f537d5c108f3538fa63cfcfe8a93f35ef5d3f Homepage: https://cran.r-project.org/package=candisc Description: CRAN Package 'candisc' (Visualizing Generalized Canonical Discriminant and CanonicalCorrelation Analysis) Functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA' design and is equivalent to canonical correlation analysis between a set of quantitative response variables and a set of dummy variables coded from the factor variable. The 'candisc' package generalizes this to higher-way 'MANOVA' designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are now provided for canonical correlation analysis when all predictors are quantitative. Methods for linear discriminant analysis are now included. Package: r-cran-cane Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-agricolae, r-cran-dplyr, r-cran-emmeans Filename: pool/dists/noble/main/r-cran-cane_0.1.1-1.ca2404.1_all.deb Size: 49740 MD5sum: 9c17a3e965e86b089e001cb9df40ada4 SHA1: 24afc8b89d057534feedf8d532b93d7f3792e76a SHA256: 3d54662c50dbbfd7cb9671d13c5de077bbc8b1c4c75eae7b90f1c2311e37fb63 SHA512: ea22988b4cdfb1a9f0784034398f00a44580f0bd4f412f835022b98fd6ea6329e09a522d332fbe9ebf2d95e815230f45418bf44bea976fc97addac20e9223adb Homepage: https://cran.r-project.org/package=CANE Description: CRAN Package 'CANE' (Comprehensive Groups of Experiments Analysis for NumerousEnvironments) In many cases, experiments must be repeated across multiple seasons or locations to ensure applicability of findings. A single experiment conducted in one location and season may yield limited conclusions, as results can vary under different environmental conditions. In agricultural research, treatment × location and treatment × season interactions play a crucial role. Analyzing a series of experiments across diverse conditions allows for more generalized and reliable recommendations. The 'CANE' package facilitates the pooled analysis of experiments conducted over multiple years, seasons, or locations. It is designed to assess treatment interactions with environmental factors (such as location and season) using various experimental designs. The package supports pooled analysis of variance (ANOVA) for the following designs: (1) 'PooledCRD()': completely randomized design; (2) 'PooledRBD()': randomized block design; (3) 'PooledLSD()': Latin square design; (4) 'PooledSPD()': split plot design; and (5) 'PooledStPD()': strip plot design. Each function provides the following outputs: (i) Individual ANOVA tables based on independent analysis for each location or year; (ii) Testing of homogeneity of error variances among distinct locations using Bartlett’s Chi-Square test; (iii) If Bartlett’s test is significant, 'Aitken’s' transformation, defined as the ratio of the response to the square root of the error mean square, is applied to the response variable; otherwise, the data is used as is; (iv) Combined analysis to obtain a pooled ANOVA table; (v) Multiple comparison tests, including Tukey's honestly significant difference (Tukey's HSD) test, Duncan’s multiple range test (DMRT), and the least significant difference (LSD) test, for treatment comparisons. The statistical theory and steps of analysis of these designs are available in Dean et al. (2017) and Ruíz et al. (2024). By broadening the scope of experimental conclusions, 'CANE' enables researchers to derive robust, widely applicable recommendations. This package is particularly valuable in agricultural research, where accounting for treatment × location and treatment × season interactions is essential for ensuring the validity of findings across multiple settings. Package: r-cran-canek Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-irlba, r-cran-numbers, r-cran-fpc, r-bioc-bluster, r-cran-igraph, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-seurat, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-scater, r-bioc-batchelor, r-bioc-scran, r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-canek_0.2.5-1.ca2404.1_all.deb Size: 3149662 MD5sum: 4e95127660420beb9d6dc6359eb1afbf SHA1: 793b4dd75a5d06fb6909715dd091c12288ee5472 SHA256: 6c89fb100148402e236ad2386c948d46f6824bfe16e81fd4005e1f1d2963da91 SHA512: 06757c896c2ba4c05e102e8cbea2f39d1ea02577e56b96a6e38cc0027ae0109c538b19e552187bd1f6954b81996d1c0f80e83711e5539e85d77fa3d29ada4068 Homepage: https://cran.r-project.org/package=Canek Description: CRAN Package 'Canek' (Batch Correction of Single Cell Transcriptome Data) Non-linear/linear hybrid method for batch-effect correction that uses Mutual Nearest Neighbors (MNNs) to identify similar cells between datasets. Reference: Loza M. et al. (NAR Genomics and Bioinformatics, 2020) . Package: r-cran-canonicalfamilyextra Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-gee, r-cran-geepack, r-cran-superlearner, r-cran-ipred, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-canonicalfamilyextra_1.0.0-1.ca2404.1_all.deb Size: 38098 MD5sum: 71d34f8c7d230f71599a4c680416d352 SHA1: e0451e36a8ca1d00cc95bb1ea603b9afbb95fac5 SHA256: 64708d87c77ebe662dd1b260f441c3bb47fff0a89cb7abb53539d2dc7d62613e SHA512: d1f366e05de13e6e1b0e6b0d5cb4e9d9e4d8b8c3b8a61b7b14f00b38068c013a231b9d41701e4ac221ddbe08759e13597a66ac31a4722ed621bbac1ec64b9978 Homepage: https://cran.r-project.org/package=CanonicalFamilyExtra Description: CRAN Package 'CanonicalFamilyExtra' (Extra Canonical Link Family Objects for Generalized LinearModels) Extra family objects in "weird" scenarios, particularly logistic or log-linear model with unbounded or non-binary/non-integer outcomes. Provides binomial_extra() and poisson_extra() as generalizations of binomial() and poisson(). The use of canonical link with the corresponding working likelihood in glm() ensures convexity, making model fitting reliable and independent of starting value. Robert WM Wedderburn (1974) and Peter McCullagh (1983) justified this method to fit generalized linear (mean) models with quasi-/working likelihood. Package: r-cran-canopy Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1041 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-fields, r-cran-pheatmap, r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-canopy_1.3.0-1.ca2404.1_all.deb Size: 969690 MD5sum: 6953c246c3e38c14c36af89e855d5c22 SHA1: e65b5453570846045db3ce673971c7d8d3b932eb SHA256: fa262f669f64b0a7104b243aabeaeeb496e84ca0b756cf443d0c491bfa4f5b84 SHA512: dcb6d5cae23010cdb29cc403d27308e93f029ce3d41005d32ab1f0f30f39b1459c0de254c6d9ef6901787d1ea6c251f996d9c4ee01702be4087ddf15d9e08f21 Homepage: https://cran.r-project.org/package=Canopy Description: CRAN Package 'Canopy' (Accessing Intra-Tumor Heterogeneity and Tracking Longitudinaland Spatial Clonal Evolutionary History by Next-GenerationSequencing) A statistical framework and computational procedure for identifying the sub-populations within a tumor, determining the mutation profiles of each subpopulation, and inferring the tumor's phylogenetic history. The input are variant allele frequencies (VAFs) of somatic single nucleotide alterations (SNAs) along with allele-specific coverage ratios between the tumor and matched normal sample for somatic copy number alterations (CNAs). These quantities can be directly taken from the output of existing software. Canopy provides a general mathematical framework for pooling data across samples and sites to infer the underlying parameters. For SNAs that fall within CNA regions, Canopy infers their temporal ordering and resolves their phase. When there are multiple evolutionary configurations consistent with the data, Canopy outputs all configurations along with their confidence assessment. Package: r-cran-canprot Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1682 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi, r-cran-multcompview Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-chnosz Filename: pool/dists/noble/main/r-cran-canprot_2.0.0-1.ca2404.1_all.deb Size: 1056880 MD5sum: e972f1b2578becde9b4b6bda8a93fdc7 SHA1: 6d9ca566910debd7e8d163c05f501adeb0b6e4c2 SHA256: f335f38d47c3768a5058835535c24385030c9c2c95a5fb6806f41d0286ba489d SHA512: 5b36d54b7510462e15f77864ce26a49e95593e9936b0d8d9b5b6a155e9533c31d4542a0992c52fbdb2592ca66a53d0a2ba2478b55434eaf4b1c2c034de956c4f Homepage: https://cran.r-project.org/package=canprot Description: CRAN Package 'canprot' (Chemical Analysis of Proteins) Chemical analysis of proteins based on their amino acid compositions. Amino acid compositions can be read from FASTA files and used to calculate chemical metrics including carbon oxidation state and stoichiometric hydration state, as described in Dick et al. (2020) . Other properties that can be calculated include protein length, grand average of hydropathy (GRAVY), isoelectric point (pI), molecular weight (MW), standard molal volume (V0), and metabolic costs (Akashi and Gojobori, 2002 ; Wagner, 2005 ; Zhang et al., 2018 ). A database of amino acid compositions of human proteins derived from UniProt is provided. Package: r-cran-cansim2r Architecture: all Version: 1.14.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-hmisc, r-cran-downloader Filename: pool/dists/noble/main/r-cran-cansim2r_1.14.1-1.ca2404.1_all.deb Size: 27712 MD5sum: 0a8ea9a13e248652726e492bfc81f2d8 SHA1: bf2ab9c7ee4281961fbc1ba5126f6414e508ae45 SHA256: 524710015d958dfd934ccec35b2c135fa778e24268f2c7a1a472209a9270851b SHA512: 4cf834b61e46a5c862c73f987bd79690a1a4681cb8350bb303cbfd2c3cf12ce2cd0e79433964bfe7c87bbffaad959d43560c7d9eef25aae9d43a5907afe980bf Homepage: https://cran.r-project.org/package=CANSIM2R Description: CRAN Package 'CANSIM2R' (Directly Extracts Complete CANSIM Data Tables) Extract CANSIM (Statistics Canada) tables and transform them into readily usable data in panel (wide) format. It can also extract more than one table at a time and produce the resulting merge by time period and geographical region. Package: r-cran-cansim Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 829 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-httr, r-cran-tidyr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-arrow, r-cran-dbi, r-cran-rsqlite, r-cran-dbplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cansim_0.4.4-1.ca2404.1_all.deb Size: 553976 MD5sum: 657e0bd13f6ec96905d96d3b94a21d0a SHA1: 861a766282d6ab74e1f904e7ffbb3b937088cdcf SHA256: 993e83761acc60ec02efc30916c8e67e0b09af272261eb74f91d2599722559c9 SHA512: 195b8ddc991988c804ab9ee7316633781da6165b73dbeb814068c374b917e917631e9bfd84d503a77dde962de0752412ef65825accd4aea06db99ecad0c6a16c Homepage: https://cran.r-project.org/package=cansim Description: CRAN Package 'cansim' (Accessing Statistics Canada Data Table and Vectors) Searches for, accesses, and retrieves Statistics Canada data tables, as well as individual vectors, as tidy data frames. This package enriches the tables with metadata, deals with encoding issues, allows for bilingual English or French language data retrieval, and bundles convenience functions to make it easier to work with retrieved table data. For more efficient data access the package allows for caching data in a local database and database level filtering, data manipulation and summarizing. Package: r-cran-canvasxpress.data Architecture: all Version: 1.34.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4216 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-usethis Filename: pool/dists/noble/main/r-cran-canvasxpress.data_1.34.2-1.ca2404.1_all.deb Size: 4139408 MD5sum: a542c1e52fd341fa48f4bcd59406cd95 SHA1: d2bc25efe811f1e3933c93d82fbedef3e12b5c10 SHA256: 8e7dd09626fc4ee8f1aefb94fa005060009926fc1e6d8cc28910b688d9d99c55 SHA512: a510d27ac927cb873ebf9601204f21ded5e29746b4b734ef38e40fce51b4b9d283868e41df855c4970453e08d5da3ce2bce02f79bd49f2412793b359fc0c5cc6 Homepage: https://cran.r-project.org/package=canvasXpress.data Description: CRAN Package 'canvasXpress.data' (Datasets for the 'canvasXpress' Package) Contains the prepared data that is needed for the 'shiny' application examples in the 'canvasXpress' package. This package also includes datasets used for automated 'testthat' tests. Scotto L, Narayan G, Nandula SV, Arias-Pulido H et al. (2008) . Davis S, Meltzer PS (2007) . Package: r-cran-canvasxpress Architecture: all Version: 1.59.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4491 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools, r-cran-httr, r-cran-jsonlite Suggests: r-cran-shiny, r-cran-canvasxpress.data, r-cran-dplyr, r-cran-dt, r-cran-glue, r-cran-knitr, r-cran-png, r-cran-readr, r-cran-rlang, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-bioc-limma, r-cran-ggplot2, r-cran-survminer, r-cran-s7, r-cran-patchwork, r-cran-ggally, r-cran-ggpubr, r-cran-ggpattern Filename: pool/dists/noble/main/r-cran-canvasxpress_1.59.5-1.ca2404.1_all.deb Size: 1372388 MD5sum: 25ed446716a98383bf0f9ea185ece5e3 SHA1: d58061ef0d73863452a3a7cc22fe985e3655016a SHA256: 87e89815cc6d1bdaf36b583f94d7594461805003d83fc67ae3110e6ef6829caf SHA512: 65db6c16f6ded011ac9e31e3d3bfe60e08610260e4c8e0d28d01278a826a3d44d995411c64be98cd312e1a38d285e40bbff0eb71f0d96886247e63af1b7b81a1 Homepage: https://cran.r-project.org/package=canvasXpress Description: CRAN Package 'canvasXpress' (Visualization Package for CanvasXpress in R) Enables creation of visualizations using the CanvasXpress framework in R. CanvasXpress is a standalone JavaScript library for reproducible research with complete tracking of data and end-user modifications stored in a single PNG image that can be played back. See for more information. Package: r-cran-caop.raa.2024 Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7437 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-tibble, r-cran-dplyr, r-cran-readr, r-cran-stringi, r-cran-glue Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-caop.raa.2024_0.0.5-1.ca2404.1_all.deb Size: 6516498 MD5sum: f7727d4da906bca4ece9bb0bac9117ad SHA1: b10d0afcb20bb660076f38f94de12a79070079df SHA256: f1889cb778e77431531e2cfeaf2d5dec119499406b565281efa6ac8848c9e110 SHA512: 499107435d67c5bf0dddb3523d252ef71955a1afdd4d81d97663434eb25bdd21a8dd33064d9a290652551982337aa2aab99e459193e7539e19103fcb728ad19a Homepage: https://cran.r-project.org/package=CAOP.RAA.2024 Description: CRAN Package 'CAOP.RAA.2024' (Official Administrative Map of the Azores (CAOP 2024)) Provides the official administrative boundaries of the Azores (Região Autónoma dos Açores (RAA)) as defined in the 2024 edition of the Carta Administrativa Oficial de Portugal (CAOP), published by the Direção-Geral do Território (DGT). The package includes convenience functions to import these boundaries as 'sf' objects for spatial analysis in R. Source: . Package: r-cran-cap Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-multigroup Filename: pool/dists/noble/main/r-cran-cap_1.0-1.ca2404.1_all.deb Size: 235656 MD5sum: 9ee66c8242348a8a22b2ba177e4aa603 SHA1: 1e25f4b6c52f84543cc712b1ff9072f2456f3891 SHA256: 0efb08114b63135e99b4048ad4db1c1d412b15c32bee93a62377b57f00503b2d SHA512: f0e679b9c0c4b6c7e79a7b6f66d5ee0ab31c7fac2a6662eec5c893fd28de89e356f0917ce12221daf1d5ac376b90f5a399e44ed4a8949069f7b91df66602da8d Homepage: https://cran.r-project.org/package=cap Description: CRAN Package 'cap' (Covariate Assisted Principal (CAP) Regression for CovarianceMatrix Outcomes) Performs Covariate Assisted Principal (CAP) Regression for covariance matrix outcomes. The method identifies the optimal projection direction which maximizes the log-likelihood function of the log-linear heteroscedastic regression model in the projection space. See Zhao et al. (2018), Covariate Assisted Principal Regression for Covariance Matrix Outcomes, for details. Package: r-cran-cape Architecture: all Version: 3.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2899 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-catools, r-cran-corpcor, r-cran-doparallel, r-cran-evd, r-cran-foreach, r-cran-here, r-cran-igraph, r-cran-matrix, r-cran-pheatmap, r-cran-pracma, r-cran-propagate, r-cran-qtl, r-cran-qtl2, r-cran-qtl2convert, r-cran-r6, r-cran-rcolorbrewer, r-cran-regress, r-cran-shape, r-cran-yaml Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cape_3.1.2-1.ca2404.1_all.deb Size: 1764578 MD5sum: cf93afc6ec4656995d17c03a57608587 SHA1: 939fb8430cd93ac8be9ea3b0b90d35b9a9220755 SHA256: f329d8e651343630f3007bfd8e9661d731c9a94b2a1999019effaa18876c6b82 SHA512: b59425797953856bc597d4234884b4f827f7a1b55d24591173856310acfbdde98e6ec8011c81d63d06d026de155a0ae9cea6a0b02c56e0b1c25602f694c83e01 Homepage: https://cran.r-project.org/package=cape Description: CRAN Package 'cape' (Combined Analysis of Pleiotropy and Epistasis for DiversityOutbred Mice) Combined Analysis of Pleiotropy and Epistasis infers predictive networks between genetic variants and phenotypes. It can be used with standard two-parent populations as well as multi-parent populations, such as the Diversity Outbred (DO) mice, Collaborative Cross (CC) mice, or the multi-parent advanced generation intercross (MAGIC) population of Arabidopsis thaliana. It uses complementary information of pleiotropic gene variants across different phenotypes to resolve models of epistatic interactions between alleles. To do this, cape reparametrizes main effect and interaction coefficients from pairwise variant regressions into directed influence parameters. These parameters describe how alleles influence each other, in terms of suppression and enhancement, as well as how gene variants influence phenotypes. All of the final interactions are reported as directed interactions between pairs of parental alleles. For detailed descriptions of the methods used in this package please see the following references. Carter, G. W., Hays, M., Sherman, A. & Galitski, T. (2012) . Tyler, A. L., Lu, W., Hendrick, J. J., Philip, V. M. & Carter, G. W. (2013) . Package: r-cran-caper Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1023 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-xtable, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-caper_1.0.4-1.ca2404.1_all.deb Size: 876368 MD5sum: f40aa568da243212c01e0ae929adc03c SHA1: e10f634c84f5441ec5f8665c4ad73641c4fe5e33 SHA256: b3b7e05759d3d726b8d7bb735b41dabcf48203118a5576429673bbf4ee4f9163 SHA512: 93f3502eee8fba7327120ef33893272045a2b4f6c0dfa098eaab3a4174c7e2e8a6e9fab0f9bb75b211fc41c9a05c95d5032ec5eb3dff7ba5ea9d2de31d2dd784 Homepage: https://cran.r-project.org/package=caper Description: CRAN Package 'caper' (Comparative Analyses of Phylogenetics and Evolution in R) Functions for performing phylogenetic comparative analyses. 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Users can flexibly choose between two functions based on their specific needs for each procedure: use real patient data from clinical trials to compute allocation probabilities directly, or use built-in simulation functions to generate synthetic patient data. Detailed methodologies and algorithms used in this package are described in the following references: Zhang, L. X., Hu, F., Cheung, S. H., & Chan, W. S. (2007) Zhang, L. X. & Hu, F. (2009) Hu, J., Zhu, H., & Hu, F. (2015) Zhao, W., Ma, W., Wang, F., & Hu, F. (2022) Mukherjee, A., Jana, S., & Coad, S. (2024) . Package: r-cran-carbayesdata Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf Filename: pool/dists/noble/main/r-cran-carbayesdata_3.0-1.ca2404.1_all.deb Size: 355876 MD5sum: 8754ebeeebf6b963347d2f64da42a2f8 SHA1: 9b9a28e4b32cc0988bfbf4be31b2a69acbaa25a5 SHA256: 8d6cfc816e0ac6baa47535ec93bacc45ed19d3137294e5214056d0301b1288c6 SHA512: e20582f6308e92141dbf91a49bb8c5e2f3b782bdbd9c123ac29d0fde25fd39d54a28e18fcf57ad816dfde25143a6b6f02d4df8f13d9bfcf9ab02d8cf05997582 Homepage: https://cran.r-project.org/package=CARBayesdata Description: CRAN Package 'CARBayesdata' (Data Used in the Vignettes Accompanying the CARBayes andCARBayesST Packages) Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. Most of the data relate to the set of 271 Intermediate Zones (IZ) that make up the 2001 definition of the Greater Glasgow and Clyde health board. 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Package: r-cran-carbondata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-readxl Suggests: r-cran-arrow, r-cran-nanoparquet, r-cran-testthat Filename: pool/dists/noble/main/r-cran-carbondata_0.1.0-1.ca2404.1_all.deb Size: 108124 MD5sum: 4afefbc8b23daef07ce8825b5d8ac374 SHA1: efc8c65e3616a6d94d13b9d2a94d212101172d8d SHA256: 0d2a3d641cb65c8e731374e723d7f6f1ed4ed98ef9d5f8bf7871933f258f0ef6 SHA512: f623ae4f24fecbd160edd0d253e2f347c75cce96786b85e718b09e26455822382c39899891a866ac08140b02f950a2fdb603312fac69d30563aaa86fbbd74dbb Homepage: https://cran.r-project.org/package=carbondata Description: CRAN Package 'carbondata' (Access Carbon Market Data from Emissions Trading Systems andVoluntary Registries) Unified access to carbon market data from compliance emissions trading systems ('EU ETS', 'UK ETS', 'RGGI', California Cap-and-Trade) and voluntary carbon markets (Verra, Gold Standard, American Carbon Registry, Climate Action Reserve, via the Berkeley Voluntary Registry Offsets Database and the 'CarbonPlan' 'OffsetsDB' API). Includes cross-market price data from the 'International Carbon Action Partnership' ('ICAP') Allowance Price Explorer , global carbon pricing from the World Bank Carbon Pricing Dashboard , and the historical 'RFF' World Carbon Pricing Database following Dolphin, Pollitt and Newbery (2020) . Data is downloaded from public sources on first use and cached locally. Package: r-cran-carbonpredict Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5049 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-progress, r-cran-lmertest, r-cran-networkd3, r-cran-htmltools, r-cran-htmlwidgets Suggests: r-cran-testthat, r-cran-mockery, r-cran-png Filename: pool/dists/noble/main/r-cran-carbonpredict_2.0.1-1.ca2404.1_all.deb Size: 4777186 MD5sum: 4da92e68d961f1f539984023baaf7ecd SHA1: 68fd33b9c386339914958da4511f4594f260da06 SHA256: d9d1327804e54e24b51ef005bf26ef46c854b8381182f8111723fe34db397a39 SHA512: 7cfc4b4579496c1092d6869b8e58780ff66db8b7f189856f252fb8344cec7557432675bb3786497c3d95db700349be686e3c78673bb1d53467817d10ddc1ffff Homepage: https://cran.r-project.org/package=carbonpredict Description: CRAN Package 'carbonpredict' (Predict Carbon Emissions for UK SMEs) Predict Scope 1, 2 and 3 carbon emissions for UK Small and Medium-sized Enterprises (SMEs), using Standard Industrial Classification (SIC) codes and annual turnover data, as well as Scope 1 carbon emissions for UK farms. The 'carbonpredict' package provides single and batch prediction, plotting, and workflow tools for carbon accounting and reporting. The package utilises pre-trained models, leveraging rich classified transaction data to accurately predict Scope 1, 2 and 3 carbon emissions for UK SMEs as well as identifying emissions hotspots. It also provides Scope 1 carbon emissions predictions for UK farms of types: Cereals ex. rice, Dairy, Mixed farming, Sheep and goats, Cattle & buffaloes, Poultry, Animal production and Support for crop production. The methodology used to produce the estimates in this package is fully detailed in the following peer-reviewed publications: Phillpotts, A., Owen. A., Norman, J., Trendl, A., Gathergood, J., Jobst, Norbert., Leake, D. (2025) "Bridging the SME Reporting Gap: A New Model for Predicting Scope 1 and 2 Emissions" and Wells, J., Trendl, A., Owen, A., Barrett, J., Gridley, J., Jobst, N., Leake, D. (2025) "A Scalable Tool for Farm-Level Carbon Accounting: Evidence from UK Agriculture". Package: r-cran-carbonr Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3574 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-airportr, r-cran-checkmate, r-cran-cowplot, r-cran-dplyr, r-cran-emojifont, r-cran-ggplot2, r-cran-ggpp, r-cran-htmltools, r-cran-lubridate, r-cran-magrittr, r-cran-readxl, r-cran-rlang, r-cran-shiny, r-cran-shinydashboard, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-sp, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-carbonr_0.2.7-1.ca2404.1_all.deb Size: 2556112 MD5sum: 80c321900ee5dfcb37cd07b52529abb3 SHA1: 5eefece211c868fdfcdbe20599eb5e0973c18ae9 SHA256: 2a4a642e4479778365c170d8552366fa4529b9af3441037b40f54d951c1f47ae SHA512: 2aaba1d6fd409bcccdd9e7da90f8ecd266c07b701e4444df2b5da36c3da57796d20ab0ae37f6c6d1457209dfbad0501309b0526bab28cd0f0e679923262f440d Homepage: https://cran.r-project.org/package=carbonr Description: CRAN Package 'carbonr' (Calculate Carbon-Equivalent Emissions) Provides a flexible tool for calculating carbon-equivalent emissions. Mostly using data from the UK Government's Greenhouse Gas Conversion Factors report , it facilitates transparent emissions calculations for various sectors, including travel, accommodation, and clinical activities. The package is designed for easy integration into R workflows, with additional support for 'shiny' applications and community-driven extensions. Package: r-cran-carcass Architecture: all Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-survival, r-cran-expm, r-cran-arm, r-cran-mass Filename: pool/dists/noble/main/r-cran-carcass_1.9-1.ca2404.1_all.deb Size: 107236 MD5sum: 44c65c7976800b305450956d7fe895eb SHA1: b24b6decfc2408514e986d41877e214723497344 SHA256: 8afd662cb830b83d98f9615c9da55ff655abaf84873c07454a315f1c10b2febd SHA512: e8286b54e10234fce60bfdbdc04f4209d3f2cc7f9fb7327586e74cb6e46cc6a5aaf4b9f682b4b41c8c990ee8c4d8222e18ea41ede2baa3c77047b841b3742ee0 Homepage: https://cran.r-project.org/package=carcass Description: CRAN Package 'carcass' (Estimation of the Number of Fatalities from Carcass Searches) The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability. 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This novel card component in 'Bootstrap' provides a flexible and extensible content container with multiple variants and options for building robust 'R' based apps e.g for graph build or machine learning projects. The features rely on a combination of 'JQuery' and 'CSS' styles to improve the card functionality. 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Package: r-cran-cardargus Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2692 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-cli, r-cran-digest, r-cran-gdtools, r-cran-magick, r-cran-rsvg, r-cran-later Suggests: r-cran-base64enc, r-cran-chromote, r-cran-curl, r-cran-gfonts, r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-showtext, r-cran-sysfonts, r-cran-systemfonts, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cardargus_0.2.2-1.ca2404.1_all.deb Size: 887736 MD5sum: 68d8b48fde5978610b724f695b7f9784 SHA1: 713e29ad4a1b5e5c7f1292460e91212e0a86b267 SHA256: 1ffd02dd98dea114fe2cade8195e2b2ea324625f805897c94233b845c3fd21ea SHA512: 58820abe34337686d58f3491dbbda0f5bc32a97318900fa56c5298138e447ab37facf3f4a1ed8bec8741fe7ad2f5718538f516a21862e5c1a44548ab31c107b5 Homepage: https://cran.r-project.org/package=cardargus Description: CRAN Package 'cardargus' (Generate SVG Information Cards with Embedded Fonts and Badges) Create self-contained SVG information cards with embedded 'Google Fonts', shields-style badges, and custom logos. Cards are fully portable SVG files ideal for dashboards, reports, and web applications. Includes functions to export cards to PNG format and display them in 'R Markdown' and 'Quarto' documents. Package: r-cran-cardata Architecture: all Version: 3.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1944 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-car Filename: pool/dists/noble/main/r-cran-cardata_3.0-6-1.ca2404.1_all.deb Size: 1684690 MD5sum: 671c871d2b9119556f6c4cde67f94ce3 SHA1: b57ef2311cfa60f155278c8aad58970ab5558e48 SHA256: 15f42385bda1a57944a593f895e745a9a5e4b50a8b2814b2eb67f2acda918de8 SHA512: 070f8bcd3301f015153ee5eaa88c79a1dec1651f60fa3ef90d0a12455bee053bc08db11ead2222e674e43e9bb85765fac9900bada236ae90dac8a5aa96760483 Homepage: https://cran.r-project.org/package=carData Description: CRAN Package 'carData' (Companion to Applied Regression Data Sets) Datasets to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage (2019). Package: r-cran-cardiacdp Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-purrr, r-cran-rcolorbrewer, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cardiacdp_0.4.2-1.ca2404.1_all.deb Size: 241662 MD5sum: be43025b4ed62f6285492dd256e1ba86 SHA1: bbe163301e023f2adf4d0f6c351ecd802c55254b SHA256: 43bac0d6cd2766fa6036475c1821249b72a9d591e20327cf1d7d3c1d0fcffd62 SHA512: 5b130c39e02d31789b04a58b393175797ab20a51ff0484790ff2acf3bfa91fe8bdd82138e0e96561c3ed56436548a535ab1957e3d1eb84ddde3718fd07763cc6 Homepage: https://cran.r-project.org/package=CardiacDP Description: CRAN Package 'CardiacDP' (Automated Cardiac Data Processing via ACF, GA & Tracking Index) An algorithm developed to efficiently and accurately process complex and variable cardiac data with three key features: 1. employing autocorrelation to identify recurrent heartbeats and use their periods to compute heart rates; 2. incorporating a genetic algorithm framework to minimize data loss due to noise interference and accommodate within-sequence variations; and 3. introducing a tracking index as a moving reference to reduce errors. Lau, Wong, & Gu (2026) . Package: r-cran-cardidates Architecture: all Version: 0.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-pastecs, r-cran-lattice Filename: pool/dists/noble/main/r-cran-cardidates_0.4.9-1.ca2404.1_all.deb Size: 251964 MD5sum: 297a406a9923cc6be148662abc4ff006 SHA1: 8d3a888e9cf026fd490196f1d95ad654e1802c18 SHA256: f8914576bfdb120ab4bf9e668c5e16cbfd9a5e38effbd51cd952a3f2b8f1bdd8 SHA512: 95bb415d587231f8a3b1ad0bf6f93dde994c69512f4f1b1f3f228e0de2944001cb35893eb0a060844e28e7453988ab87f72342253226e972249ae65aa7a10364 Homepage: https://cran.r-project.org/package=cardidates Description: CRAN Package 'cardidates' (Identification of Cardinal Dates in Ecological Time Series) Identification of cardinal dates (begin, time of maximum, end of mass developments) in ecological time series using fitted Weibull functions. 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These data structures are useful for testing, validating, and improving algorithms used in dimensionality reduction, clustering, machine learning, and visualization. Package: r-cran-cardiocurver Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1748 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-signal, r-cran-ggplot2, r-cran-gridextra, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cardiocurver_1.0.0-1.ca2404.1_all.deb Size: 262210 MD5sum: 6df66ebd92e70563b98baca0abcd6aca SHA1: 6bb306cbbbaf1fbcba38b1630e5130f4f4e34659 SHA256: e5ccb706558fc07289c6e355283888eef895709fee6c3cac0fd1fdb8d51e122c SHA512: e35a084145cfd3dfac385b8123e2cadc618db803b12d1a60bee549905e8add5748a8c2938b4507e70d4aff44b4fc7ed4b25d3202f20707462a8e413643bed2d7 Homepage: https://cran.r-project.org/package=CardioCurveR Description: CRAN Package 'CardioCurveR' (Nonlinear Modeling of R-R Interval Dynamics) Automated and robust framework for analyzing R-R interval (RRi) signals using advanced nonlinear modeling and preprocessing techniques. The package implements a dual-logistic model to capture the rapid drop and subsequent recovery of RRi during exercise, as described by Castillo-Aguilar et al. (2025) . In addition, 'CardioCurveR' includes tools for filtering RRi signals using zero-phase Butterworth low-pass filtering and for cleaning ectopic beats via adaptive outlier replacement using local regression and robust statistics. These integrated methods preserve the dynamic features of RRi signals and facilitate accurate cardiovascular monitoring and clinical research. Package: r-cran-cardiodatasets Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cardiodatasets_0.2.0-1.ca2404.1_all.deb Size: 537662 MD5sum: ee466f1dbb62be1d2a01e087006955ce SHA1: b2e66bd9bc342a5504f6b4e6f20f6d2bce9de831 SHA256: c4a6b08faf9e68533a8a722d67d56cb9fe509e7a57c15678645e224c36ccacea SHA512: 41090a4f5e23a6463519c49c8932feedb9151ba298dbe5cec859d30acbe20b18b478700d7a346764d584f32a4ef294ceef6bf929081d527fc32ed2793fd6b412 Homepage: https://cran.r-project.org/package=CardioDataSets Description: CRAN Package 'CardioDataSets' (A Comprehensive Collection of Cardiovascular and Heart DiseaseDatasets) Offers a diverse collection of datasets focused on cardiovascular and heart disease research, including heart failure, myocardial infarction, aortic dissection, transplant outcomes, cardiovascular risk factors, drug efficacy, and mortality trends. Designed for researchers, clinicians, epidemiologists, and data scientists, the package features clinical, epidemiological, and simulated datasets covering a wide range of conditions and treatments such as statins, anticoagulants, and beta blockers. It supports analyses related to disease progression, treatment effects, rehospitalization, and public health outcomes across various cardiovascular patient populations. Package: r-cran-cards Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-cards_0.7.1-1.ca2404.1_all.deb Size: 676138 MD5sum: 454231d84f300860c7a305079beaaf9e SHA1: 429132878aed55ff0531383a09f0a7ccb1fd2070 SHA256: 37d8e4a069f210ac0264b7a5ceba03320cad34030dbaa08cac5978661b1f32ab SHA512: f98264f32d64eb5a107db342ed9056d5a08f4767d00f0bc07e891696b1e7f7f8bb975c518381f50390da190c9a85db4701a3293eaa78576151335dc1e2c2a493 Homepage: https://cran.r-project.org/package=cards Description: CRAN Package 'cards' (Analysis Results Data) Construct CDISC (Clinical Data Interchange Standards Consortium) compliant Analysis Results Data objects. These objects are used and re-used to construct summary tables, visualizations, and written reports. The package also exports utilities for working with these objects and creating new Analysis Results Data objects. Package: r-cran-cardx Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 641 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cards, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-rlang, r-cran-tidyr Suggests: r-cran-aod, r-cran-broom, r-cran-broom.helpers, r-cran-broom.mixed, r-cran-car, r-cran-effectsize, r-cran-emmeans, r-cran-geepack, r-cran-ggsurvfit, r-cran-lme4, r-cran-parameters, r-cran-smd, r-cran-survey, r-cran-survival, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-cardx_0.3.2-1.ca2404.1_all.deb Size: 578966 MD5sum: 5f51ed137eb187cc4acb446416c3c335 SHA1: 7a5c0259f624e8001678e30d7ab5fb0269f723a4 SHA256: 2d81c9de25589786c4c542fcec99aba6765e8c05a645117a81805f572f72d392 SHA512: a4e725e9c810865d816c9e1648d06f110d58750eba35d1780ed5fd4dbd8d3f671f2c6f9df11dcb41facfb35cec6e8d911ef7e5c99ecceae4da7627254c5c9e60 Homepage: https://cran.r-project.org/package=cardx Description: CRAN Package 'cardx' (Extra Analysis Results Data Utilities) Create extra Analysis Results Data (ARD) summary objects. The package supplements the simple ARD functions from the 'cards' package, exporting functions to put statistical results in the ARD format. These objects are used and re-used to construct summary tables, visualizations, and written reports. Package: r-cran-care4cmodel Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-ggplot2, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-care4cmodel_1.0.3-1.ca2404.1_all.deb Size: 248136 MD5sum: b20c9050bc6d464d0c1f46588aff18db SHA1: da30fdcffd8860526b38635d15f941fddde1c12e SHA256: 34cc183c92183a66249500cf6757bfa600cd6df47eed75dcd96967c264bfb86c SHA512: 026220a577e0ef24d30d553124880b7ef39c56f2331646a744d49e006dfb3ee8d51da04fc6d18bd3028f8a3e470cfb9f1cd9f918757f555e6fe643ac63228bc8 Homepage: https://cran.r-project.org/package=care4cmodel Description: CRAN Package 'care4cmodel' (Carbon-Related Assessment of Silvicultural Concepts) A simulation model and accompanying functions that support assessing silvicultural concepts on the forest estate level with a focus on the CO2 uptake by wood growth and CO2 emissions by forest operations. For achieving this, a virtual forest estate area is split into the areas covered by typical phases of the silvicultural concept of interest. Given initial area shares of these phases, the dynamics of these areas is simulated. The typical carbon stocks and flows which are known for all phases are attributed post-hoc to the areas and upscaled to the estate level. CO2 emissions by forest operations are estimated based on the amounts and dimensions of the harvested timber. Probabilities of damage events are taken into account. Package: r-cran-care Architecture: all Version: 1.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corpcor Suggests: r-cran-crossval Filename: pool/dists/noble/main/r-cran-care_1.1.11-1.ca2404.1_all.deb Size: 153940 MD5sum: a7562ab5a153375aa0b3ee592e478765 SHA1: 68a3df2fb80935c4c1b902407cf3ccaded46dc4f SHA256: 95c8352ebd882f52dd663994d3f0b5a9c679dc94a277a9ea8afd4e6480a88054 SHA512: f5985c2f4d8c3a4f359372ec2fb486b0e1cf311a1787a5dc858b2c6f07a2062e721b9563810103a9e11a674e8770bd422a2f767452c31d05aea64b558a7e7db7 Homepage: https://cran.r-project.org/package=care Description: CRAN Package 'care' (High-Dimensional Regression and CAR Score Variable Selection) Implements the regression approach of Zuber and Strimmer (2011) "High-dimensional regression and variable selection using CAR scores" SAGMB 10: 34, . CAR scores measure the correlation between the response and the Mahalanobis-decorrelated predictors. The squared CAR score is a natural measure of variable importance and provides a canonical ordering of variables. This package provides functions for estimating CAR scores, for variable selection using CAR scores, and for estimating corresponding regression coefficients. Both shrinkage as well as empirical estimators are available. Package: r-cran-carecall Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-spelling, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-carecall_0.1.0-1.ca2404.1_all.deb Size: 91934 MD5sum: 38f4d39c18033b6f4c33f74d614aad2c SHA1: 4fe0d0ffde74f966bef3021f3b628f16c6c30d4e SHA256: efa83abe6d1ea6f183ac17dc565f9b946b965187e5f1c884e734114a173c5eca SHA512: 21f533191c04aacc9fcf3f9a13f4acd6770b5fd64884aad1b65d83a198cc5cd62170612a7c75d9af80d4eef6bd149450480db3ff6b3499eddf215ff2ce3df3c8 Homepage: https://cran.r-project.org/package=caRecall Description: CRAN Package 'caRecall' (Government of Canada Vehicle Recalls Database API Wrapper) Provides API access to the Government of Canada Vehicle Recalls Database used by the Defect Investigations and Recalls Division for vehicles, tires, and child car seats. The API wrapper provides access to recall summary information searched using make, model, and year range, as well as detailed recall information searched using recall number. Package: r-cran-caredensity Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixextra, r-cran-data.table, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-caredensity_0.1.0-1.ca2404.1_all.deb Size: 58168 MD5sum: 816e0a1c70003d9705544d3d13a20862 SHA1: 3c49b5aba897395d31253653f3dd8317ae0f50ec SHA256: 88fb71489a34b6b811e693d5bb7fba30a0921377aeb609a4b3e7d175ab4a5727 SHA512: 87d97d2e0426d9fd372ba37252d595cd65aed99d336e71f1fd06747d1a4dc05747337c96af1959efc38955009a176d0aaac275a44a4f34a5b2b4d5567d3eaeda Homepage: https://cran.r-project.org/package=CareDensity Description: CRAN Package 'CareDensity' (Calculate the Care Density or Fragmented Care Density Given aPatient-Sharing Network) Given a patient-sharing network, calculate either the classic care density as proposed by Pollack et al. (2013) or the fragmented care density as proposed by Engels et al. (2024) . By utilizing the 'igraph' and 'data.table' packages, the provided functions scale well for very large graphs. Package: r-cran-careless Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-careless_1.2.2-1.ca2404.1_all.deb Size: 114092 MD5sum: 4a6bae0e992655ce2fbd4d6b629c70d3 SHA1: 613c5807ddca958137b65ce4de1a77e9f3f5657c SHA256: e093c4d95e74dfc9f79ea96faaa44cb3ca0d7c719e4de7f215f60711bb326a9d SHA512: d77fda40a8fc29ccf0f36061dc69c463ffdeb155aa2a4df92c47218f874488c1cb728addb2892971affb3d4b3bc59719d1af10a4cc6a22d9689f8c7bb600a778 Homepage: https://cran.r-project.org/package=careless Description: CRAN Package 'careless' (Procedures for Computing Indices of Careless Responding) When taking online surveys, participants sometimes respond to items without regard to their content. These types of responses, referred to as careless or insufficient effort responding, constitute significant problems for data quality, leading to distortions in data analysis and hypothesis testing, such as spurious correlations. The 'R' package 'careless' provides solutions designed to detect such careless / insufficient effort responses by allowing easy calculation of indices proposed in the literature. It currently supports the calculation of longstring, even-odd consistency, psychometric synonyms/antonyms, Mahalanobis distance, and intra-individual response variability (also termed inter-item standard deviation). For a review of these methods, see Curran (2016) . Package: r-cran-caresid Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ca, r-cran-ggplot2, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-caresid_0.1-1.ca2404.1_all.deb Size: 24254 MD5sum: b0ae7b2ce16ac862b489dc79f58cdc8e SHA1: a63a00a072e02d1ea68e98b044e7faab6c313493 SHA256: a9ca884e5a09e0fe237dbe9c1b4f793abbc170653d6a99452949946081330177 SHA512: e25f2b3be471478cd6c6d2c8848a1ad52db088f086d0e132c425a2d12ba6a0bfc21d52cbd89df186dd32b232f79c241bf9760591e4849d23d07acb318254899d Homepage: https://cran.r-project.org/package=caresid Description: CRAN Package 'caresid' (Correspondence Analysis Plot and Associations Visualisation) Performs a Correspondence Analysis (CA) on a contingency table and creates a scatterplot of the row and column points on the selected dimensions. Optionally, the function can add segments to the plot to visualize significant associations between row and column categories on the basis of positive (unadjusted) standardized residuals larger than a given threshold. Package: r-cran-caretensemble Architecture: all Version: 4.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3486 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-data.table, r-cran-ggplot2, r-cran-lattice, r-cran-patchwork, r-cran-pbapply, r-cran-rlang Suggests: r-cran-mass, r-cran-catools, r-cran-covr, r-cran-earth, r-cran-gbm, r-cran-glmnet, r-cran-klar, r-cran-knitr, r-cran-lintr, r-cran-mgcv, r-cran-mlbench, r-cran-nnet, r-cran-randomforest, r-cran-rmarkdown, r-cran-rhub, r-cran-rpart, r-cran-spelling, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-caretensemble_4.0.1-1.ca2404.1_all.deb Size: 3037966 MD5sum: 0dc7d854b53d8491ba7b524e528559ea SHA1: 143f1b017600abdd342d917b56e4705e7d5c8447 SHA256: a390d71481ce6237a6ea4aa6eb6ff2b6a7c16a6f0786afb3414e6db313e815aa SHA512: 3a9ab7ef362ab2808ab06d9ec417dff4c528de5b93b1ed128790a42c724517d871d3283391221c723403dc634e0aeb7adb0a2b5574d1355fd49c8ec255452c0a Homepage: https://cran.r-project.org/package=caretEnsemble Description: CRAN Package 'caretEnsemble' (Ensembles of Caret Models) Functions for creating ensembles of caret models: caretList() and caretStack(). caretList() is a convenience function for fitting multiple caret::train() models to the same dataset. caretStack() will make linear or non-linear combinations of these models, using a caret::train() model as a meta-model. Package: r-cran-caretforecast Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2240 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-caret, r-cran-magrittr, r-cran-dplyr, r-cran-generics Suggests: r-cran-cubist, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-caretforecast_0.1.3-1.ca2404.1_all.deb Size: 2173740 MD5sum: 4ae3f567b2871e27ad8a608db742eaec SHA1: 3aa89ab642835c98671f3045657e9b1668826846 SHA256: faa1bc7457a9e285518862608aaef45b08f3cb57cce05f7999fad5b325e09981 SHA512: 30489ccdfd9b25c500bd14fcf83f75e23bc562919bcb96fea0d88cdcf4940c6eb3c4e8126bd367bf05f5bb82c1a89b0e24cb1fce297010ed9119984610105cc8 Homepage: https://cran.r-project.org/package=caretForecast Description: CRAN Package 'caretForecast' (Conformal Time Series Forecasting Using State of Art MachineLearning Algorithms) Conformal time series forecasting using the caret infrastructure. It provides access to state-of-the-art machine learning models for forecasting applications. The hyperparameter of each model is selected based on time series cross-validation, and forecasting is done recursively. Package: r-cran-caretsdm Architecture: all Version: 1.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 800 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-blockcv, r-cran-caret, r-cran-checkcli, r-cran-checkmate, r-cran-cli, r-cran-coordinatecleaner, r-cran-data.table, r-cran-dismo, r-cran-dplyr, r-cran-ecdfniche, r-cran-ecospat, r-cran-fs, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggspatial, r-cran-glue, r-cran-gtools, r-cran-httr2, r-cran-lwgeom, r-cran-mapview, r-cran-maxnet, r-cran-parallelly, r-cran-pdp, r-cran-proc, r-cran-progressr, r-cran-purrr, r-cran-raster, r-cran-rgbif, r-cran-rtsne, r-cran-sf, r-cran-stars, r-cran-stringdist, r-cran-stringr, r-cran-terra, r-cran-tidyr, r-cran-usdm Suggests: r-cran-bench, r-cran-biomod2, r-cran-gbm, r-cran-cito, r-cran-covr, r-cran-e1071, r-cran-earth, r-cran-gam, r-cran-here, r-cran-kknn, r-cran-knitr, r-cran-mda, r-cran-naivebayes, r-cran-nnet, r-cran-r.utils, r-cran-randomforest, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-rpart, r-cran-rsnns, r-cran-sdm, r-cran-tibble, r-cran-withr, r-cran-xgboost, r-cran-testthat Filename: pool/dists/noble/main/r-cran-caretsdm_1.8.3-1.ca2404.1_all.deb Size: 741702 MD5sum: 599cfefe5c3e979539f5d3adbf939aa9 SHA1: 6c7995328b5b894e4f02858f1f464a2d7648814b SHA256: 6be183bd56d6de8d0cc5e5eaa8e9a5df3436ac22cd14b26153991ceddb5fc858 SHA512: 7cff5a12133f1d55c2cfffc2db1197b6c7d8f1977e8aa098a8c81bd6707ebf61863613940ce452c440422bf6ac5ca7fc455e151e367275cc46ee52d842789343 Homepage: https://cran.r-project.org/package=caretSDM Description: CRAN Package 'caretSDM' (Build Species Distribution Modeling using 'caret') Use machine learning algorithms and advanced geographic information system tools to build Species Distribution Modeling in a extensible and modern fashion. Package: r-cran-carfima Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-deoptim, r-cran-pracma, r-cran-truncnorm, r-cran-invgamma Filename: pool/dists/noble/main/r-cran-carfima_2.0.2-1.ca2404.1_all.deb Size: 71626 MD5sum: e33cb1f39622ed1f68010a9a6d5fc3dc SHA1: b55abcfa0e12beee71b091657a40a3ef7bfac78b SHA256: c15e1df617d8f307d722fe2ddb50c8f116035a22d294a22a4782909513a37939 SHA512: cc1b33956ef769ae2b3bf39a86ca70014ec617251dac25f8e21998b92e3e13fcc6323416a85ee732103c2b439a88613ac4931bf9ec335aa0db6ac47f952d0880 Homepage: https://cran.r-project.org/package=carfima Description: CRAN Package 'carfima' (Continuous-Time Fractionally Integrated ARMA Process forIrregularly Spaced Long-Memory Time Series Data) We provide a toolbox to fit a continuous-time fractionally integrated ARMA process (CARFIMA) on univariate and irregularly spaced time series data via both frequentist and Bayesian machinery. A general-order CARFIMA(p, H, q) model for p>q is specified in Tsai and Chan (2005) and it involves p+q+2 unknown model parameters, i.e., p AR parameters, q MA parameters, Hurst parameter H, and process uncertainty (standard deviation) sigma. Also, the model can account for heteroscedastic measurement errors, if the information about measurement error standard deviations is known. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces posterior samples of the model parameters via Metropolis-Hastings within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo. These fitting procedures, however, may produce numerical errors if p>2. The toolbox also contains a function to simulate discrete time series data from CARFIMA(p, H, q) process given the model parameters and observation times. Package: r-cran-cargo Architecture: all Version: 0.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-cargo_0.4.9-1.ca2404.1_all.deb Size: 254806 MD5sum: f86a41ba65e4064122715ed6a1f377f3 SHA1: 219d5913487d040fd6c354caf95b91c4a30311a8 SHA256: 9823c1613f8bbf0fc8687073596c7043c4c3ab2bd3aae375c84164df31269b55 SHA512: f7c5ac71b12286cbf1ee2a5ed4212e6fc1af7add83a2850303cbe9c7fa0c91b43e2568c57752810689f5fb492319bc7a0c5f65171e0d3e2ba4e6951e452573ed Homepage: https://cran.r-project.org/package=cargo Description: CRAN Package 'cargo' (Develop R Packages using Rust) A framework is provided to develop R packages using 'Rust' with minimal overhead, and more wrappers are easily added. Help is provided to use 'Cargo' in a manner consistent with CRAN policies. 'Rust' code can also be embedded directly in an R script. The package is not official, affiliated with, nor endorsed by the Rust project. Package: r-cran-caribou Architecture: all Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-caribou_1.1-1-1.ca2404.1_all.deb Size: 55736 MD5sum: 946aa5a4d1651f56a4cc648d2c844c58 SHA1: fdc349d7824db2af4b883c4b6c9cdf145e2d22dd SHA256: 5629617b543f709927b8d62817f4d61152a07db62ab132868d4bdcff6be9b6f4 SHA512: ef10e34e36385727995ec01b74f129afaa283aee234a63d36e8242f605460de43637b4bcd3fe755fea753c1b35d4d8d5e91fc09306281284834be35d645b60ce Homepage: https://cran.r-project.org/package=caribou Description: CRAN Package 'caribou' (Estimation of Caribou Abundance Based on Radio Telemetry Data) Estimation of population size of migratory caribou herds based on large scale aggregations monitored by radio telemetry. It implements the methodology found in the article by Rivest et al. (1998) about caribou abundance estimation. It also includes a function based on the Lincoln-Petersen Index as applied to radio telemetry data by White and Garrott (1990). Package: r-cran-carletonstats Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-patchwork Suggests: r-cran-mass, r-cran-testthat Filename: pool/dists/noble/main/r-cran-carletonstats_2.2-1.ca2404.1_all.deb Size: 173222 MD5sum: 1b05bb859b769c49f95ead509c65db61 SHA1: 2f54a75db8713f60a640d61dbe4810c31ebfda4d SHA256: aaddc842657ff3c4c108e9bb2e79c6df503f9c6845a6e5265c534f050b1654da SHA512: 3ebb5c3c7e4c741205e951d88d3fd482b6e18018b88df61603aa7ef8a45f307bfa035b6c1d455dc0fe251be937fa529b4aeabae74b73e46554fae207b3c5c499 Homepage: https://cran.r-project.org/package=CarletonStats Description: CRAN Package 'CarletonStats' (Functions for Statistics Classes at Carleton College) Includes commands for bootstrapping and permutation tests, a command for created grouped bar plots, and a demo of the quantile-normal plot for data drawn from different distributions. Package: r-cran-carm Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrangements, r-cran-dplyr, r-cran-mass Filename: pool/dists/noble/main/r-cran-carm_2.0.0-1.ca2404.1_all.deb Size: 43524 MD5sum: 4364411d6e824cbc9ca4e5af97f98601 SHA1: 266abf3c9cff29cf6147181e32da29bddb2e3202 SHA256: 6d5c196b147e4f0d3f037125952361e68e5b87e744bbdb552d5455e81c56407e SHA512: 55f896347f82155b26ddee707dfa234a2628b7b94b511f0824bd77d433f6032c7fd07c9a98352bcaf43a091cbd167dbc428d092c5d4fd48a5650f3f32fff650d Homepage: https://cran.r-project.org/package=CARM Description: CRAN Package 'CARM' (Covariate-Adjusted Adaptive Randomization viaMahalanobis-Distance) In randomized controlled trial (RCT), balancing covariate is often one of the most important concern. CARM package provides functions to balance the covariates and generate allocation sequence by covariate-adjusted Adaptive Randomization via Mahalanobis-distance (ARM) for RCT. About what ARM is and how it works please see Y. Qin, Y. Li, W. Ma, H. Yang, and F. Hu (2024). "Adaptive randomization via Mahalanobis distance" Statistica Sinica. . In addition, the package is also suitable for the randomization process of multi-arm trials. For details, please see Yang H, Qin Y, Wang F, et al. (2023). "Balancing covariates in multi-arm trials via adaptive randomization" Computational Statistics & Data Analysis.. Package: r-cran-caroc Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-caroc_0.1.5-1.ca2404.1_all.deb Size: 229672 MD5sum: aa7fd007d36979d86bd067110f90ba6a SHA1: 5f7b0d7f7adb5574fc087bce59040c19d35cd5c2 SHA256: 0e63ece1cf7ce65debcdc00a5f83692fdcc0cddd90f09047c5266ab6658faa71 SHA512: c2e22cd150269aa1fbde0ae67b02d1685fa3aeb3b229b91a883fe76565a141b846f6d48fe877408c9b4059fb08771ba322714155936f82030b92c88b717a15c2 Homepage: https://cran.r-project.org/package=caROC Description: CRAN Package 'caROC' (Continuous Biomarker Evaluation with Adjustment of Covariates) Compute covariate-adjusted specificity at controlled sensitivity level, or covariate-adjusted sensitivity at controlled specificity level, or covariate-adjust receiver operating characteristic curve, or covariate-adjusted thresholds at controlled sensitivity/specificity level. All statistics could also be computed for specific sub-populations given their covariate values. Methods are described in Ziyi Li, Yijian Huang, Datta Patil, Martin G. Sanda (2021+) "Covariate adjustment in continuous biomarker assessment". Package: r-cran-caroline Architecture: all Version: 0.9.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass, r-cran-rsqlite Filename: pool/dists/noble/main/r-cran-caroline_0.9.9-1.ca2404.1_all.deb Size: 202246 MD5sum: 7ef2b448b5315c2591d43e4fe6a6496f SHA1: 5e6535b54195e192c8bc95f48f6cb9d09eb0af0f SHA256: ab3db0a1105d35e2a9a960e71d6459e4b079f393cd775dceafd0f1b401ef80bd SHA512: 354ee4e21c16c56fbfd7457594b7bd2fbe741da560954ce4874fbfdeb1b429904ed5c65b036b765c3ff44fa026b735da5124d1ea8455d8599270c86d8a329c9c Homepage: https://cran.r-project.org/package=caroline Description: CRAN Package 'caroline' (A Collection of Database, Data Structure, Visualization, andUtility Functions for R) The caroline R library contains dozens of functions useful for: database migration (dbWriteTable2), database style joins & aggregation (nerge, groupBy, & bestBy), data structure conversion (nv, tab2df), legend table making (sstable & leghead), automatic legend positioning for scatter and box plots (), plot annotation (labsegs & mvlabs), data visualization (pies, sparge, confound.grid & raPlot), character string manipulation (m & pad), file I/O (write.delim), batch scripting, data exploration, and more. The package's greatest contributions lie in the database style merge, aggregation and interface functions as well as in it's extensive use and propagation of row, column and vector names in most functions. Package: r-cran-carpenter Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-pander, r-cran-tibble, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-carpenter_0.2.3-1.ca2404.1_all.deb Size: 59150 MD5sum: 06b977ee2c0b2efb33d2c1eb0e6123fb SHA1: 211d01df6158c018a88c8e4038e61d94ecbeba66 SHA256: 0bf8e0509d7be073eab7e23f59880a775299c898e78149c6801f09bfd1317cd8 SHA512: 914cc28a2b0742585846093cb021f5537f925ec6f8eb1c65d59adf9b43a95c63686436a57a3b2a2f5f638ab5de1c5440b1134b2b4063dcc36046c3907d63657f Homepage: https://cran.r-project.org/package=carpenter Description: CRAN Package 'carpenter' (Build Common Tables of Summary Statistics for Reports) Mainly used to build tables that are commonly presented for bio-medical/health research, such as basic characteristic tables or descriptive statistics. Package: r-cran-carrier Architecture: all Version: 0.3.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lobstr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-carrier_0.3.0.4-1.ca2404.1_all.deb Size: 26672 MD5sum: d3f8fde5bab5f13bd549ede14969b0a1 SHA1: 4c218039c6e0e6af7939e745d882bf8f700b1a21 SHA256: 1c366f325ae9ae868b8d9e115c1cf5b0a25a2065df0a1749676e90ef930c6a0a SHA512: 611942238c6322ee0ba91a8118f5f24907c7181de3b8aa182b1688d22c0f0daba744abc769aef537e59195d7ef32f5af20a2c851652faafa5e765bce986962e6 Homepage: https://cran.r-project.org/package=carrier Description: CRAN Package 'carrier' (Isolate Functions for Remote Execution) Sending functions to remote processes can be wasteful of resources because they carry their environments with them. With the carrier package, it is easy to create functions that are isolated from their environment. These isolated functions, also called crates, print at the console with their total size and can be easily tested locally before being sent to a remote. 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CARS algorithm iteratively applies the Monte Carlo sub-sampling and exponential variable elimination techniques to identify/select the most informative variables/features subjected to minimal cross-validated RMSE score. The implementation of CARS algorithm is inspired from the work of Li et al. (2009) . This algorithm is widely applied in near-infrared (NIR), mid-infrared (MIR), hyperspectral chemometrics areas, etc. Package: r-cran-cartograflow Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-plotly, r-cran-reshape2, r-cran-rlang, r-cran-sf, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-cartography Filename: pool/dists/noble/main/r-cran-cartograflow_1.0.5-1.ca2404.1_all.deb Size: 372172 MD5sum: ab671b731339c9588e0bbf0fbcb60b47 SHA1: 4ed9b65643c41d4b9990dc65dd26bde179c88dc0 SHA256: b4d70329de76f90e2e623707ad507a05d0a416409e153544b889f84d25c80764 SHA512: 289bf0884c23feca466644d267ab75a11720c7d5d6b620c157e7f527788ed90fe82a152f0c5621a85ed45e8d7cbff64a435d4264f75fd6ab8127b1f009235f29 Homepage: https://cran.r-project.org/package=cartograflow Description: CRAN Package 'cartograflow' (Filtering Matrix for Flow Mapping) Functions to prepare and filter an origin-destination matrix for thematic flow mapping purposes. This comes after Bahoken, Francoise (2016), Mapping flow matrix a contribution, PhD in Geography - Territorial sciences. See Bahoken (2017) . Package: r-cran-cartogram Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-packcircles Filename: pool/dists/noble/main/r-cran-cartogram_0.3.0-1.ca2404.1_all.deb Size: 263228 MD5sum: 8afc09ab0ca5f15abb05cf02a749c4e1 SHA1: 75fca704376840949d6b0e025860e3402cc8f8d7 SHA256: 1ac2d51a3066ab4e2163f257ef0b268b872c7626f619255693bce9b90384d579 SHA512: cc8f49c79c33c4231b0e56d9e5acce78bed43cb3ebdfdb7fdef1b51413bf3af95ded231eee684e7dff6eeea0e5788d96ac867ecfc2e91c13b09b660135fb9e58 Homepage: https://cran.r-project.org/package=cartogram Description: CRAN Package 'cartogram' (Create Cartograms with R) Construct continuous and non-contiguous area cartograms. 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Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) . 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Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) . Package: r-cran-cascadeselect Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12399 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fontawesome, r-cran-htmltools, r-cran-reactr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-cascadeselect_1.1.0-1.ca2404.1_all.deb Size: 830922 MD5sum: 628237dc6f9385755534e1d601f23c9c SHA1: eedd7acb49ff27732e21ee0118ba3fc05bec3e71 SHA256: 51105a13e70c06320d94e2fc0e17e2340559ec87f8880546ee70a22892595ca2 SHA512: 05eae5107d9c5b2c724baab2503fcac1489e1ac5303ec6299c15fb0f79f36968d1e82d446e55d8b66f56a80ec8a4760e2183bc87525e3c47f10109ddcb860f77 Homepage: https://cran.r-project.org/package=cascadeSelect Description: CRAN Package 'cascadeSelect' (A Cascade Select Input for 'Shiny') Provides a cascade select widget for usage in 'Shiny' applications. This is useful for selection of hierarchical choices (e.g. continent, country, city). It is taken from the 'JavaScript' library 'PrimeReact'. 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Change borders, backgrounds, text, margins, layouts, and more. Package: r-cran-cascore Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Suggests: r-cran-testthat, r-cran-igraph Filename: pool/dists/noble/main/r-cran-cascore_0.1.2-1.ca2404.1_all.deb Size: 61004 MD5sum: 22d71c19e378bf9396130c83bcd0b283 SHA1: 666c8427be73ea1923dcbc44972f7a01fb2def0e SHA256: f9a29895011a2b3011b6b79cd075b461406f77d63bf8d2c8ddd8728bc2a0227f SHA512: 2e7ebf4a3fe6d07a3ab17ae4299f0d18dfdf6c688fa31f960355cb50712a42d0ca9012dfbeb69b0535a98d9b772fa3eb69e2c1352af16a9e5672ebab207ee6b4 Homepage: https://cran.r-project.org/package=CASCORE Description: CRAN Package 'CASCORE' (Covariate Assisted Spectral Clustering on Ratios of Eigenvectors) Functions for implementing the novel algorithm CASCORE, which is designed to detect latent community structure in graphs with node covariates. This algorithm can handle models such as the covariate-assisted degree corrected stochastic block model (CADCSBM). CASCORE specifically addresses the disagreement between the community structure inferred from the adjacency information and the community structure inferred from the covariate information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2022) . In addition to CASCORE, this package includes several classical community detection algorithms that are compared to CASCORE in our paper. These algorithms are: Spectral Clustering On Ratios-of Eigenvectors (SCORE), normalized PCA, ordinary PCA, network-based clustering, covariates-based clustering and covariate-assisted spectral clustering (CASC). By providing these additional algorithms, the package enables users to compare their performance with CASCORE in community detection tasks. Package: r-cran-cascsim Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2html, r-cran-fitdistrplus, r-cran-moments, r-cran-copula, r-cran-scatterplot3d Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cascsim_0.4-1.ca2404.1_all.deb Size: 666166 MD5sum: 1b2d6884ea7538937e593674998b6052 SHA1: def9a286a84baa2177f0b7d5ec7027fb2490691c SHA256: 4f9520013011ea0a175059a02a341ff7fd921009c7ae8010ef8e02c7a38c5386 SHA512: f0bb59db87acfeee4dbf37f5988050c74bf398eb3bbd49a3e057d235839b00ae130b4ee2121df7d909ba69ac7319ff938e9c3dc2976e5eb40dbb836041225391 Homepage: https://cran.r-project.org/package=cascsim Description: CRAN Package 'cascsim' (Casualty Actuarial Society Individual Claim Simulator) It is an open source insurance claim simulation engine sponsored by the Casualty Actuarial Society. It generates individual insurance claims including open claims, reopened claims, incurred but not reported claims and future claims. It also includes claim data fitting functions to help set simulation assumptions. It is useful for claim level reserving analysis. Parodi (2013) . 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Our formulation allows for arbitrary functional forms of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival curves for a given covariate profile. This approach accommodates any log-linear hazard function of prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing incidence density via population time plots. Based on the case-base sampling approach of Hanley and Miettinen (2009) , Saarela and Arjas (2015) , and Saarela (2015) . Package: r-cran-casematch Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-casematch_1.1.0-1.ca2404.1_all.deb Size: 48032 MD5sum: 9ddc567f501ea767be201711b2a2cb85 SHA1: 1569347bc85817a89965b1e252f9370257d65640 SHA256: 20432f5f85810ae4d1297fe8d24a17e686ef80e6fa28efcec40266a23070e448 SHA512: 4d09d1cf4f9e3efda93c73350c18474bccf4ddb5187081daa0084d3ffc2360f0b663ee2b77a537c1b8152ade8fb9d351675b8584b59d1c6b17ccc8303543bfad Homepage: https://cran.r-project.org/package=caseMatch Description: CRAN Package 'caseMatch' (Identify Similar Cases for Qualitative Case Studies) Allows users to identify similar cases for qualitative case studies using statistical matching methods. Package: r-cran-cases Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bindata, r-cran-boot, r-cran-copula, r-cran-corrplot, r-cran-dplyr, r-cran-extradistr, r-cran-magrittr, r-cran-matrix, r-cran-multcomp, r-cran-mvtnorm, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-badger, r-cran-glmnet, r-cran-splitstackshape Filename: pool/dists/noble/main/r-cran-cases_0.2.0-1.ca2404.1_all.deb Size: 252492 MD5sum: e3720702c9506623fb27bc0e5a13ac35 SHA1: f54af2a63b01296b58913fcc91172b41ee5befc5 SHA256: 63359a169b1d18ddc0b702f5ef4dd0c3a2b9ec6d3496c814652035816fc2bd57 SHA512: bb6cf7b6617214a5c4a90984edfbec5f0cd83f10bdc215b7329be426b936c68546e079e77ea6f7df99f972fbfe18d237ccad959f8c62cfc623ea542ca68f6966 Homepage: https://cran.r-project.org/package=cases Description: CRAN Package 'cases' (Stratified Evaluation of Subgroup Classification Accuracy) Enables simultaneous statistical inference for the accuracy of multiple classifiers in multiple subgroups (strata). For instance, allows to perform multiple comparisons in diagnostic accuracy studies with co-primary endpoints sensitivity and specificity (Westphal M, Zapf A. Statistical inference for diagnostic test accuracy studies with multiple comparisons. Statistical Methods in Medical Research. 2024;0(0). ). Package: r-cran-casidata Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5132 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-vcdextra, r-cran-car Filename: pool/dists/noble/main/r-cran-casidata_0.2.1-1.ca2404.1_all.deb Size: 4710278 MD5sum: 6de259e2a4c57fce0ee6f4f40321bce4 SHA1: 5974ddadbba8a8ed71f63050d65d6d2875b70e11 SHA256: e5f5f6161efd7317f4b6013e5c4e074f710195b26e70bcd925b57295388ce8f9 SHA512: 9a85dfa05f4c2d8cb0d5f8ce51f08006edb5b7491569137b9c39a812636caa9d373eca87568acef103f525dc526859d8521ca3a41d4c3d48aee43efc7c1e8111 Homepage: https://cran.r-project.org/package=CASIdata Description: CRAN Package 'CASIdata' (Datasets from Computer Age Statistical Inference) Provides the datasets from Efron & Hastie (2016, ISBN: 9781108107952), "Computer Age Statistical Inference: Algorithms, Evidence, and Data Science", in an accessible R format for those who want to use them for study or to try to reproduce analyses from the book. 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The main focus of the package is to enable efficient computation of set retrieval and ranked retrieval metrics across multiple dimensions of a dataset, e.g. document strata or subsets of the label set. The package also provides the possibility of computing bootstrap confidence intervals for all major metrics, with seamless integration of parallel computation and propensity scored variants of standard metrics. Package: r-cran-casino Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-purrr, r-cran-crayon, r-cran-r6, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-beepr Filename: pool/dists/noble/main/r-cran-casino_0.1.0-1.ca2404.1_all.deb Size: 344264 MD5sum: 7463f49fa55d5b00e879b4577ef52953 SHA1: 8e363802e6792059f4aad8c70916b8e2c73a257f SHA256: c405194fd56336c257f82799bdad4197d766919bad7dc0e7cfdedca35c80b527 SHA512: 5e026ea4bf05f83daf656543511e2fccd1f37c265754a2e1a18a49b33631940d7c3a8144a39be95104cfb56bde5a8eb0f76b2bdbf47c496773034dd49764c9c5 Homepage: https://cran.r-project.org/package=casino Description: CRAN Package 'casino' (Play Casino Games) Play casino games in the R console, including poker, blackjack, and a slot machine. Try to build your fortune before you succumb to the gambler's ruin! Package: r-cran-casmi Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-entropyestimation, r-cran-entropy Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-casmi_2.0.0-1.ca2404.1_all.deb Size: 63338 MD5sum: 54507e4980e2260c047a8ce3353639ac SHA1: ee01440b0392885f401268b45e7e50ced1045581 SHA256: 57bc370cc8bd643eaaddfb2704c587374f476398d1122d30fcca4728b8fb70c6 SHA512: ebcb8b3f823633d6d70003cd91c71b60979c304e04b8b453b5eacc085b89be972136dda10512d963ac351e467c59e8ac408b1998dbac181f9fbc7991cb73f607 Homepage: https://cran.r-project.org/package=CASMI Description: CRAN Package 'CASMI' ('CASMI'-Based Functions) Contains Coverage Adjusted Standardized Mutual Information ('CASMI')-based functions. 'CASMI' is a fundamental concept of a series of methods. For more information about 'CASMI' and 'CASMI'-related methods, please refer to the corresponding publications (e.g., a feature selection method, Shi, J., Zhang, J., & Ge, Y. (2019) , and a dataset quality measurement method, Shi, J., Zhang, J., & Ge, Y. (2019) ) or contact the package author for the latest updates. Package: r-cran-cassandra Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bipartite, r-cran-reshape2, r-cran-magrittr, r-cran-vegan, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-boot, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-proc, r-cran-lattice Filename: pool/dists/noble/main/r-cran-cassandra_0.2.0-1.ca2404.1_all.deb Size: 168052 MD5sum: 9f7dc70d483e48ba58e86ef8747e1d36 SHA1: 89fcf50dae5fcedd0c5b7f1f7bbef9093c60ea10 SHA256: f1dc06e5e4f1e3e32a97f219d3c984b0291251a70a866311442b3c41150cf5ea SHA512: 6eddd83fff3ed05cb43719a415159c1243fabb419c43e398c57f0c78f149472351bab8307262bd9cf01038fd21f81d577bf60cf377024481021d6bb0b4061512 Homepage: https://cran.r-project.org/package=cassandRa Description: CRAN Package 'cassandRa' (Finds Missing Links and Metric Confidence Intervals inEcological Bipartite Networks) Provides methods to deal with under sampling in ecological bipartite networks from Terry and Lewis (2020) Ecology Includes tools to fit a variety of statistical network models and sample coverage estimators to highlight most likely missing links. 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Package: r-cran-cassowaryr Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-alphahull, r-cran-splancs, r-cran-interp, r-cran-energy, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-progress, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-mgcv, r-cran-ggally, r-cran-tidyr, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-cassowaryr_2.0.2-1.ca2404.1_all.deb Size: 506930 MD5sum: 31d817f5fc1dd88ad7ad653a0ef62bd4 SHA1: 007d300c0e5c1fbc84abdcd6fb28ae619e5f1675 SHA256: 298222d9989efa44cf7f3182d018c0c26c2e4acef6e2d9a9ac73533e1da971b8 SHA512: 9b912d1b95a1415d1b83ea82897da26f4e67c24919eac347a83aa9fbd84c83e42cb7a22dd739fc17a2c423ec29d6562b52db9215e8b3cb8dde13597d82e0db73 Homepage: https://cran.r-project.org/package=cassowaryr Description: CRAN Package 'cassowaryr' (Compute Scagnostics on Pairs of Numeric Variables in a Data Set) Computes a range of scatterplot diagnostics (scagnostics) on pairs of numerical variables in a data set. A range of scagnostics, including graph and association-based scagnostics described by Leland Wilkinson and Graham Wills (2008) and association-based scagnostics described by Katrin Grimm (2016,ISBN:978-3-8439-3092-5) can be computed. Summary and plotting functions are provided. Package: r-cran-cast Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4988 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-ggplot2, r-cran-fnn, r-cran-plyr, r-cran-zoo, r-cran-data.table, r-cran-sf, r-cran-forcats, r-cran-twosamples, r-cran-terra, r-cran-sp Suggests: r-cran-doparallel, r-cran-lubridate, r-cran-randomforest, r-cran-knitr, r-cran-geodata, r-cran-mapview, r-cran-rmarkdown, r-cran-scales, r-cran-gridextra, r-cran-viridis, r-cran-stars, r-cran-scam, r-cran-rnaturalearth, r-cran-mass, r-cran-rcolorbrewer, r-cran-tmap, r-cran-pcamixdata, r-cran-gower, r-cran-clustmixtype, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cast_1.0.4-1.ca2404.1_all.deb Size: 3760612 MD5sum: 2351ec8679f34eba67dd98b82ad740e1 SHA1: d9e62cce57d3d73ee0a6b6d2305b19d3a12653f8 SHA256: 429ccdf9d3cf1c548fe32e3755fdce6064eb53a49eb9387c4b7d9f45625e1a6e SHA512: 28148656a6504d753788e46f3047dfb20e3d370a6e0dba595f1ea5df20e0b92ef42a7b8e3f034a4cbce9360d5ff56bba6f17bf5bbfd6e5a86ad0037481206cb6 Homepage: https://cran.r-project.org/package=CAST Description: CRAN Package 'CAST' ('caret' Applications for Spatial-Temporal Models) Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018) ; Meyer et al. (2019) ; Meyer and Pebesma (2021) ; Milà et al. (2022) ; Meyer and Pebesma (2022) ; Linnenbrink et al. (2024) ; Schumacher et al. (2025) . The package is described in detail in Meyer et al. (2026) . 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Compares bootstrap samples to a full population using linear regression, employing the R-squared value to represent the proportion of diversity captured. Iteratively increases sample size until a user-defined target R-squared is met. Offers a parallelized R implementation of a previously developed 'python' method. All ploidy levels are supported. For more details, see Sandercock et al. (2024) . Package: r-cran-cat2cat Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2966 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-caret, r-cran-dplyr, r-cran-e1071, r-cran-fixest, r-cran-forcats, r-cran-knitr, r-cran-magrittr, r-cran-randomforest, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-cat2cat_0.6.1-1.ca2404.1_all.deb Size: 2627554 MD5sum: 2274fdbf16a4f6522e5189c3959ad238 SHA1: 74a778ab9fc52e0f5a837a30e7d9173838a6948d SHA256: 23a781224983da3e2fa76d84272f74192db10be30c93c50f4e62d61021a6f457 SHA512: c9f4113ccf79bb1ab5ace804e154772dc806b9baae8fb963dd6e7c28e665e658d2b6845963114f6c6cf362f04be5fa9ad35154b7fdb19b1104896ef4764995b4 Homepage: https://cran.r-project.org/package=cat2cat Description: CRAN Package 'cat2cat' (Handling an Inconsistently Coded Categorical Variable in aLongitudinal Dataset) Unifying an inconsistently coded categorical variable between two different time points in accordance with a mapping table. 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Tools for downloading references and addresses of properties, as well as map images. Package: r-cran-catcont Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-testthat, r-cran-data.table Filename: pool/dists/noble/main/r-cran-catcont_0.5.0-1.ca2404.1_all.deb Size: 28498 MD5sum: 3336af35269379bc72cd1a2bc514ab68 SHA1: b44fe4c7e10b792e11884c4fe1dccf91649533ac SHA256: 4502e0bdc36e58cefb34f214f02211ce24ed33ba55ba60474655ceea99631e24 SHA512: 75793bbabd303c79e9e8e3ac0a2c9e68beaedc9904325000c7f16a0efc91f58d593f123414a1325b7adfa96501a1940a4e5e7fd328258591edfd6fa7473e8cd4 Homepage: https://cran.r-project.org/package=catcont Description: CRAN Package 'catcont' (Test, Identify, Select and Mutate Categorical or ContinuousValues) Methods and utilities for testing, identifying, selecting and mutating objects as categorical or continous types. 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It includes methods for data organization, plotting standard exploratory and analytical plots, predictions, for 100 types of models of increasing complexity, and 72 likelihood models for the data. 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R currently supports a wide variety of tools for the analysis of categorical data. However, many functions are spread across a variety of packages with differing syntax and poor compatibility with each another. prop_test() combines the functions binom.test(), prop.test() and BinomCI() into one output. prop_power() allows for power and sample size calculations for both balanced and unbalanced designs. riskdiff() is used for calculating risk differences and matched_or() is used for calculating matched odds ratios. For further information on methods used that are not documented in other packages see Nathan Mantel and William Haenszel (1959) and Alan Agresti (2002) . Package: r-cran-cati Architecture: all Version: 0.99.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-ade4, r-cran-ape, r-cran-e1071, r-cran-rastervis, r-cran-hypervolume, r-cran-cluster, r-cran-geometry, r-cran-vegan Suggests: r-cran-lattice, r-cran-entropart, r-cran-fbasics, r-cran-picante, r-cran-mice, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cati_0.99.6-1.ca2404.1_all.deb Size: 351382 MD5sum: f50a3637e68c8bc76ebc77b5aac5cc7b SHA1: 3a39e9b9eba4625cf8525b1932d34a8c5cca5c5b SHA256: 6c330c7ff4f18319840355573bd17b1c4890c9b25e83d4ee49b1d81190098d59 SHA512: ef6315c3face79d06f0b5c792492522e3c1e2af1e2f9bf9cf5bdf8ee5d785fec91911042aa13c1bbb4362d5007061e3495537010b1c57cbada581c8d98da6e0b Homepage: https://cran.r-project.org/package=cati Description: CRAN Package 'cati' (Community Assembly by Traits: Individuals and Beyond) Detect and quantify community assembly processes using trait values of individuals or populations, the T-statistics and other metrics, and dedicated null models. Provides tools to analyse 'intraspecific' trait variability and its consequences for community assembly. Implements a framework using individual-level trait data to decompose variance at the population, species, and community levels. Methods are described in 'Taudiere' and 'Violle' (2016) . Package: r-cran-catmap Architecture: all Version: 1.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forestplot, r-cran-metafor Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-catmap_1.6.4-1.ca2404.1_all.deb Size: 57584 MD5sum: 659092c6b1b63d6f4c1c3b1ff36f3e76 SHA1: 36cdb34d235e97c7baefa0e315c8763e2c8038d4 SHA256: 088848225609a025b1f6f408424ab2a7716c5e6395e3e7d463aef92b7f2d79c6 SHA512: f6c77e82c553dd3fa623aa7b5cf0315fa62202e133ee8ae33e01ce6159ca1dd15031df2daadea619cd9a7566dc5dfb7a19c1742dd7ebfa59d2092381a4dc6651 Homepage: https://cran.r-project.org/package=catmap Description: CRAN Package 'catmap' (Case-Control and TDT Meta-Analysis Package) Although many software tools can perform meta-analyses on genetic case-control data, none of these apply to combined case-control and family-based (TDT) studies. This package conducts fixed-effects (with inverse variance weighting) and random-effects [DerSimonian and Laird (1986) ] meta-analyses on combined genetic data. Specifically, this package implements a fixed-effects model [Kazeem and Farrall (2005) ] and a random-effects model [Nicodemus (2008) ] for combined studies. Package: r-cran-catmaply Architecture: all Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 21351 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-testthat, r-cran-viridis, r-cran-lubridate, r-cran-tidyverse, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-catmaply_0.9.5-1.ca2404.1_all.deb Size: 1447854 MD5sum: 49a029bd400b5053f610a88e06148535 SHA1: 00c1b1f76ab7feaf8987205b04f5188d349c0070 SHA256: 4ed79ca78d79cb22cc300f943797d415ea5f855ba61f692fbb42afaec470ed70 SHA512: d56b2f868dfbb1b10b7ae0a5a17f7dc2c2a5d9459e480c55b850364fcfc73dacb30d64ac4f7ba82612330670e0ec6aeb0316fd9538e429cb5adbf797874a3b9c Homepage: https://cran.r-project.org/package=catmaply Description: CRAN Package 'catmaply' (Heatmap for Categorical Data using 'plotly') Methods and plotting functions for displaying categorical data on an interactive heatmap using 'plotly'. Provides functionality for strictly categorical heatmaps, heatmaps illustrating categorized continuous data and annotated heatmaps. Also, there are various options to interact with the x-axis to prevent overlapping axis labels, e.g. via simple sliders or range sliders. Besides the viewer pane, resulting plots can be saved as a standalone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app. Package: r-cran-catool Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-catool_1.0.1-1.ca2404.1_all.deb Size: 82714 MD5sum: 471c6492c190cd162c33c73dcaa1f282 SHA1: 2fc40febf6d2acb21d32d58d2d03b00fe185a036 SHA256: 67350b2de87ce7fcf9d2b8a4be434ce23c8c7ef0344ed1536aad4a94b6a320ac SHA512: 2cd86f7000b99c07382de0dea5bc682812b9c94fb2782f7bb436a7c6da9fdc7928f3f9e94ff19072566f3ed330103b37bc34b9f936986a8f5427679e12ed320b Homepage: https://cran.r-project.org/package=catool Description: CRAN Package 'catool' (Compensation Analysis Tool for Instructor Overload Pay) Calculates equitable overload compensation for college instructors based on institutional policies, enrollment thresholds, and regular teaching load limits. Compensation is awarded only for credit hours that exceed the regular load and meet minimum enrollment criteria. When enrollment is below a specified threshold, pay is prorated accordingly. The package prioritizes compensation from high-enrollment courses, or optionally from low-enrollment courses for fairness, depending on user-defined strategy. Includes tools for flexible policy settings, instructor filtering, and produces clean, audit-ready summary tables suitable for payroll and administrative reporting. Package: r-cran-catpredi Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cpe, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-mgcv, r-cran-rgenoud, r-cran-rms, r-cran-survival Filename: pool/dists/noble/main/r-cran-catpredi_2.0-1.ca2404.1_all.deb Size: 135860 MD5sum: e493845cf42e02226cdd6adaa516812b SHA1: 459bce11786be975787d1bbebaf1a9ade6211402 SHA256: 61947eefb5098dd3efccb26c89026c0d3281b86f630453243a547ed76b76be60 SHA512: b0a9ebb6a6ad67ec795b1cbac8461cafee1819fc4aafd1937ab7ad4b4fb6799a263968c6f9c59c451d7b4b59ef37955c8dbefd936e1eb31d55575852139e9940 Homepage: https://cran.r-project.org/package=CatPredi Description: CRAN Package 'CatPredi' (Optimal Categorisation of Continuous Variables in PredictionModels) Allows the user to categorise a continuous predictor variable in a logistic or a Cox proportional hazards regression setting, by maximising the discriminative ability of the model. I Barrio, I Arostegui, MX Rodriguez-Alvarez, JM Quintana (2015) . I Barrio, MX Rodriguez-Alvarez, L Meira-Machado, C Esteban, I Arostegui (2017) . Package: r-cran-catr Architecture: all Version: 3.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 613 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-catr_3.17-1.ca2404.1_all.deb Size: 552582 MD5sum: 1d40374e5466f2267c4f5bd568250aa8 SHA1: e99e64f1d43dfa7ac7b1ff2874addc8b7067b587 SHA256: 173172e8b45fdc0396d6482d2c8bd9257de1e5bcd40e92f003c31aad862776fd SHA512: bde1532be9317ebbb1f859a49a137b74d5cf2f8a4f3e3dec84264fe3ac40bd8e829d5e08ddd49b6b14d86bf849f7a6e27ac4b7048131736e22e500db9bcb89dc Homepage: https://cran.r-project.org/package=catR Description: CRAN Package 'catR' (Generation of IRT Response Patterns under Computerized AdaptiveTesting) Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Barrada (2017) ). 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The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518). 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Endpoints are co-primary binary endpoints and decisions are made using either Bayesian or frequentist decision rules. Realistic trial trajectories are simulated and the operating characteristics of the designs are calculated. Package: r-cran-catseyes Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-emmeans Filename: pool/dists/noble/main/r-cran-catseyes_0.2.5-1.ca2404.1_all.deb Size: 27624 MD5sum: 403451c0a054100ce1f1b848e0a95121 SHA1: f2533abf77c96a07811dff491e509435cf4842c7 SHA256: 0445564dcdd8e486b0cd951d3e0ffd4a1549fb5cc5991028a3610d17d91b404c SHA512: 70399d05013a4f6525c5d7e6b91973c85baa4ed6c6a7c620c3ca0a3cd8394c23494f9884f35c21458df25dde835a8ecaaf2c615ca5323a86bf0555e19e66c76c Homepage: https://cran.r-project.org/package=catseyes Description: CRAN Package 'catseyes' (Create Catseye Plots Illustrating the Normal Distribution of theMeans) Provides the tools to produce catseye plots, principally by catseyesplot() function which calls R's standard plot() function internally, or alternatively by the catseyes() function to overlay the catseye plot onto an existing R plot window. Catseye plots illustrate the normal distribution of the mean (picture a normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence interval; they are an intuitive way of illustrating and comparing normally distributed estimates, and are arguably a superior alternative to standard confidence intervals, since they show the full distribution rather than fixed quantile bounds. The catseyesplot and catseyes functions require pre-calculated means and standard errors (or standard deviations), provided as numeric vectors; this allows the flexibility of obtaining this information from a variety of sources, such as direct calculation or prediction from a model. Catseye plots, as illustrations of the normal distribution of the means, are described in Cumming (2013 & 2014). Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 27, 7-29. pmid:24220629. Package: r-cran-catt Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-catt_2.0-1.ca2404.1_all.deb Size: 12166 MD5sum: ae141208cd0c4170f6bdee7a9887b55e SHA1: f34bfc15a2f8c1cc373cd35dddfac949d31a13d4 SHA256: c61e58a4ab9b8efe95f6feb5a7e68ab87c48270458f22f9bab3e7de7dcebd40f SHA512: 4b737f47c27799bc8d355f18af1534896df90bab096ce63ed216ce8f2524eb8781dc03fa7f3c71e1dcda76a1890635c8291be97f540fe5d1fc1d89e0fc696842 Homepage: https://cran.r-project.org/package=CATT Description: CRAN Package 'CATT' (The Cochran-Armitage Trend Test) This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default. Package: r-cran-cattexact Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cattexact_0.1.1-1.ca2404.1_all.deb Size: 27154 MD5sum: 8e16de06d27278578770b2874a964b3a SHA1: 971276e45a588f8593cc41b46494634eaee6f1fd SHA256: 6dc92a62024ef9bb8121addff09383b1d8a1ed0fb6c24ac8c5317581bce4bf44 SHA512: 3967e98265f3ae13da397cf0bc004c188c8c4dce57c02686f0b397dd2fc1f45d32fde36be53c4c6b794761018b6baf63f13dd36d4193f40e104a042152351c13 Homepage: https://cran.r-project.org/package=CATTexact Description: CRAN Package 'CATTexact' (Computation of the p-Value for the Exact ConditionalCochran-Armitage Trend Test) Provides functions for computing the one-sided p-values of the Cochran-Armitage trend test statistic for the asymptotic and the exact conditional test. The computation of the p-value for the exact test is performed using an algorithm following an idea by Mehta, et al. (1992) . Package: r-cran-catviz Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-catviz_0.1.1-1.ca2404.1_all.deb Size: 103716 MD5sum: 38424fa6a1b0c29566edfdd1d3ce853b SHA1: b1831376960ab426809cae19e0e7c3079422f313 SHA256: 23daf0bea39b82d8a1ecaec8b332b4caae995e498b5468c82996757879633141 SHA512: 4dd3771190dd9fe0c1095e72b06cbbe87923bc5a32a988e2bc7568b0e292852f0f029c9740248bd8a7b00e6c8643f6f8ee0de461a636225eb865e37659a4b531 Homepage: https://cran.r-project.org/package=catviz Description: CRAN Package 'catviz' (Visualizing Causal Assignment Trees for CSDiD and DR-DDD Designs) Tools for constructing, labeling, and visualizing Causal Assignment Trees (CATs) in settings with staggered adoption. Supports Callaway and Sant'Anna difference-in-differences (CSDiD) and doubly robust difference-in-difference-differences (DR-DDD) designs. The package helps clarify treatment timing, never-treated vs. not-yet-treated composition, and subgroup structure, and produces publication-quality diagrams and summary tables. Current functionality focuses on data-to-node mapping, node counts, cohort-year summaries, and high-quality tree plots suitable for empirical applications prior to estimation. Methods are based on Callaway and Sant'Anna (2021) , Sant'Anna and Zhao (2020) , and Kilanko (2026) . Package: r-cran-cauchycp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-cauchycp_0.1.1-1.ca2404.1_all.deb Size: 19108 MD5sum: ef97f731b5d9648f084c7346ebfaa292 SHA1: 628959e6198033f59d9b2c8a34e611e3f8d38966 SHA256: d7643afa0119b5adcb3f539720afe10a7cf198f60addbf0f05ce6e1bdd6a5fa1 SHA512: 0a726f749eac200fc27e2f2ea23295ab6d6768049bc3af0427af90b90dc39a69ba5462d918cc0b9690d32b7fd857003a37217f910896da39d4e7b7ce9d929c57 Homepage: https://cran.r-project.org/package=CauchyCP Description: CRAN Package 'CauchyCP' (Powerful Test for Survival Data under Non-Proportional Hazards) An omnibus test of change-point Cox regression models to improve the statistical power of detecting signals of non-proportional hazards patterns. The technical details can be found in Hong Zhang, Qing Li, Devan Mehrotra and Judong Shen (2021) . Extensive simulation studies demonstrate that, compared to existing tests under non-proportional hazards, the proposed CauchyCP test 1) controls the type I error better at small alpha levels; 2) increases the power of detecting time-varying effects; and 3) is more computationally efficient. Package: r-cran-cauchypca Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-cauchypca_1.3-1.ca2404.1_all.deb Size: 25042 MD5sum: 5b712f4030eddfcdbef4adae71adf9bf SHA1: 8f1fbfd10ed8c8f1ad373b62802634db8121bc5c SHA256: 0f487448a3227b764dfd6d04227ec6901170d5253536bd2fe00aea0613675735 SHA512: b1051c3d52d46980453cedfb2305c656f14152a215176ebcad658c6d1d52c7198f2a71c0c6347cb7a92408085189c7dd88810ed43868768a97a5724d92f149ca Homepage: https://cran.r-project.org/package=cauchypca Description: CRAN Package 'cauchypca' (Robust Principal Component Analysis Using the CauchyDistribution) A new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2024). "Cauchy robust principal component analysis with applications to high-dimensional data sets". Statistics and Computing, 34: 26. . Package: r-cran-causact Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3209 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-diagrammer, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-cran-rlang, r-cran-purrr, r-cran-tidyr, r-cran-igraph, r-cran-stringr, r-cran-cowplot, r-cran-forcats, r-cran-rstudioapi, r-cran-lifecycle, r-cran-reticulate Suggests: r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-rmarkdown, r-cran-extradistr, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-causact_0.6.0-1.ca2404.1_all.deb Size: 2598390 MD5sum: 1b27a3cdde91b9bf6c028d71eafa77db SHA1: cb3bcb1e63894a0110fa1c0046e32a02133dcad3 SHA256: 3fb579ef612310f2653a5cd67ebc033dc51b2cceb6b42ecff8bea7cb1b7be3e4 SHA512: ec57803d5a46534fda0d66200069fe90e4385b5fe9ffa0d654e0d72864fe9ab1369e514867ab8fde63833f7329145b51516fd9afe92934fcf69b1de31be0684d Homepage: https://cran.r-project.org/package=causact Description: CRAN Package 'causact' (Fast, Easy, and Visual Bayesian Inference) Accelerate Bayesian analytics workflows in 'R' through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on interfacing with the 'numpyro' python package. Package: r-cran-causal.decomp Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-nnet, r-cran-suppdists, r-cran-psweight, r-cran-rlang, r-cran-dyntxregime, r-cran-distr, r-cran-rpart, r-cran-dplyr, r-cran-modelobj, r-cran-magrittr, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-rpart.plot, r-cran-spelling, r-cran-cbps, r-cran-pbmcapply Filename: pool/dists/noble/main/r-cran-causal.decomp_0.2.0-1.ca2404.1_all.deb Size: 272934 MD5sum: e2a1e103cf5380450d1715d10b73c136 SHA1: f9b7932b5501c67f775a4803ca65b63fd9230706 SHA256: f389fcebaf50dab6730cd02ab0cd79dbfe3424b5219bef27e7cac0a64c33a88c SHA512: 232e5f9e953f840875a849d22799b2e6b6dba2e65839ed755c4b00dc5288d080da45df126494ccd4f69b6b058d04ed8b0a3d507891cd1d9f30cd7e0c58a03d88 Homepage: https://cran.r-project.org/package=causal.decomp Description: CRAN Package 'causal.decomp' (Causal Decomposition Analysis) We implement causal decomposition analysis using methods proposed by Park, Lee, and Qin (2022) and Park, Kang, and Lee (2023), which provide researchers with multiple-mediator imputation, single-mediator imputation, and product-of-coefficients regression approaches to estimate the initial disparity, disparity reduction, and disparity remaining (; ). We also implement sensitivity analysis for causal decomposition using R-squared values as sensitivity parameters (Park, Kang, Lee, and Ma, 2023 ). Finally, we include individualized causal decomposition and sensitivity analyses proposed by Park, Kang, and Lee (2025+) . Package: r-cran-causalbatch Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1609 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cdcsis, r-bioc-sva, r-cran-matchit, r-cran-nnet, r-cran-dplyr, r-cran-magrittr, r-bioc-genefilter, r-bioc-biocparallel Suggests: r-cran-tidyr, r-cran-ggpubr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-roxygen2, r-cran-ks, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-causalbatch_1.3.0-1.ca2404.1_all.deb Size: 1093200 MD5sum: 8f67c3c0dcf3c303b051ed48ab056814 SHA1: 7b596e37cc22b47d43e51a3bd14aa735952eb9eb SHA256: 55dfe24f7a87e5185fd5b65369340c863b3abce5148ce6ddcba1768db6a9bfed SHA512: b53e58bdad00753e7e7ab97d436034309d830e83575925700107bf9042ed109fcddefc075c4c8cd83e51a776cc1327d08d23eec4a0de0a7cbcdd0a5b03210ff7 Homepage: https://cran.r-project.org/package=causalBatch Description: CRAN Package 'causalBatch' (Causal Batch Effects) Software which provides numerous functionalities for detecting and removing group-level effects from high-dimensional scientific data which, when combined with additional assumptions, allow for causal conclusions, as-described in our manuscripts Bridgeford et al. (2024) and Bridgeford et al. (2023) . Also provides a number of useful utilities for generating simulations and balancing covariates across multiple groups/batches of data via matching and propensity trimming for more than two groups. 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The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022) . Package: r-cran-causaldata Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Filename: pool/dists/noble/main/r-cran-causaldata_0.1.4-1.ca2404.1_all.deb Size: 3137134 MD5sum: dc2ffc22e711cb6f2fca9177b112c24b SHA1: 67272bd6004370a8687d3ef416d4665694edf4ca SHA256: 69383c3793adaf61683fff36dcb5c05c8f2414d0d49e6d8ba57474361745c3da SHA512: b81fc6c224e042c58d8e3e2530a18c354f9b5fee9856606b4675510f934d9873968ca4f1da04ce4dea4d2fcf3bd3dce66fda5c6d948abfde8e2664a2937957f6 Homepage: https://cran.r-project.org/package=causaldata Description: CRAN Package 'causaldata' (Example Data Sets for Causal Inference Textbooks) Example data sets to run the example problems from causal inference textbooks. Currently, contains data sets for Huntington-Klein, Nick (2021 and 2025) "The Effect" , first and second edition, Cunningham, Scott (2021 and 2025, ISBN-13: 978-0-300-25168-5) "Causal Inference: The Mixtape", and Hernán, Miguel and James Robins (2020) "Causal Inference: What If" . Package: r-cran-causaldef Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1956 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-ggplot2 Suggests: r-cran-survival, r-cran-matchit, r-cran-tmle, r-cran-superlearner, r-cran-grf, r-cran-future.apply, r-cran-glue, r-cran-shiny, r-cran-plumber, r-cran-jsonlite, r-cran-cmprsk, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-causaldef_0.2.0-1.ca2404.1_all.deb Size: 1211792 MD5sum: ce2a3fe189808a98e457f22415fe871b SHA1: 4715fdc08dd82cc2b5ca64c5114d79324321e98e SHA256: 422dfbc51baef55cd16558be29ba1105bd9160e8ca4f3136a8b6c8532cfff0a8 SHA512: f0a08d572b89af4cf3bc0399e843fea49b30e648725f19670b449cedc32da35d8e0e2b125e56de1fd8cda9f0966efd7b1cc282ad2094ea54419928eab825fcd8 Homepage: https://cran.r-project.org/package=causaldef Description: CRAN Package 'causaldef' (Decision-Theoretic Causal Diagnostics via Le Cam Deficiency) Implements Le Cam deficiency theory for causal inference, as described in Akdemir (2026) . Provides theorem-backed bounds together with computable proxy diagnostics for information loss from confounding, selection bias, and distributional shift. Supports continuous, binary, count, survival, and competing risks outcomes. Key features include propensity-score total-variation deficiency proxies, negative control diagnostics, policy regret bounds, and sensitivity analysis via confounding frontiers. Package: r-cran-causaldisco Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-caugi, r-cran-checkmate, r-cran-cli, r-cran-digest, r-cran-dplyr, r-cran-glue, r-cran-gtools, r-cran-lifecycle, r-cran-micd, r-cran-pcalg, r-cran-purrr, r-cran-r6, r-cran-readr, r-cran-rlang, r-cran-s7, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-mockery, r-cran-quarto, r-cran-rhpcblasctl, r-cran-rjava, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr, r-cran-mice Filename: pool/dists/noble/main/r-cran-causaldisco_1.1.0-1.ca2404.1_all.deb Size: 4403884 MD5sum: abb922154af8d7aacb4a2407048b0c6e SHA1: 0d26e65ea57e0046a807dca66dc3407cc2656b47 SHA256: 8d755fc4514a6d81bfa84400f3c672069cc8bb99f87f2b2a48f4578f6bc768b7 SHA512: 7a27ff972675afb91280cc25da8ddaa3358dba0d93f228b24dcb525c874c690e8003a4c2388f7e89f68ba931c90ba6ee523bb419e39795bf33761f2611f8778a Homepage: https://cran.r-project.org/package=causalDisco Description: CRAN Package 'causalDisco' (Tools for Causal Discovery on Observational Data) Tools for causal structure learning from observational data, with emphasis on temporally ordered variables. The package implements the Temporal Peter–Clark (TPC) algorithm (Petersen, Osler & Ekstrøm, 2021; ), the Temporal Greedy Equivalence Search (TGES) algorithm (Larsen, Ekstrøm & Petersen, 2025; ) and Temporal Fast Causal Inference (TFCI). It provides a unified framework for specifying background knowledge, which can be incorporated into the implemented algorithms from the R packages 'bnlearn' (Scutari, 2010; ) and 'pcalg' (Kalish et al., 2012; ), as well as the Java library 'Tetrad' (Scheines et al., 1998; ). The package further includes utilities for visualization, comparison, and evaluation of graph structures, facilitating performance evaluation and methodological studies. Package: r-cran-causaldrf Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 909 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-survey Suggests: r-cran-bayestree, r-cran-dplyr, r-cran-foreign, r-cran-hmisc, r-cran-knitr, r-cran-mass, r-cran-nnet, r-cran-reshape2, r-cran-rmarkdown, r-cran-sas7bdat, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-causaldrf_0.4.2-1.ca2404.1_all.deb Size: 829602 MD5sum: 39fdbb36f5f9da8afd5b8e5b9e783e9e SHA1: 44f7b7b6c1e772c4e2d0123633478cee507d0572 SHA256: e095f6fbef62ec4f2aa0be40b77b1e48235fa790eb5e46c9c7d7a97fa8131361 SHA512: a17ea3533e42299188a1fd12216a7daf18013e5a9eead932f78dfc2003d3f49172f40e475d0891ae42e83a0c9d44c8f93369a95db5d70bad3d167bf82c98cb06 Homepage: https://cran.r-project.org/package=causaldrf Description: CRAN Package 'causaldrf' (Estimating Causal Dose Response Functions) Functions and data to estimate causal dose response functions given continuous, ordinal, or binary treatments. A description of the methods is given in Galagate (2016) . Package: r-cran-causaleffect Architecture: all Version: 1.3.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1043 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Suggests: r-cran-r.rsp, r-cran-xml Filename: pool/dists/noble/main/r-cran-causaleffect_1.3.15-1.ca2404.1_all.deb Size: 872906 MD5sum: 48ba9f71df0e8d298ea26553b615434e SHA1: ac58fcfe8ea8f6930be4db612501067a5288af92 SHA256: d2a23d04d188e00b30dd55bed1279209688169d78d1b68d8c2c3678fd1a53e89 SHA512: 726146cd16accaa3f0be417edeefeb6bfdd22f3090c44b0cf3d2d214c9935cfb804121007e9f01c79ea392060ec57acb46350bc29cb6ad44e14d0e35680c29e5 Homepage: https://cran.r-project.org/package=causaleffect Description: CRAN Package 'causaleffect' (Deriving Expressions of Joint Interventional Distributions andTransport Formulas in Causal Models) Functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) , an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) , and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) . All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) . Package: r-cran-causalgam Architecture: all Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gam Filename: pool/dists/noble/main/r-cran-causalgam_0.1-4-1.ca2404.1_all.deb Size: 50866 MD5sum: e00b66e2258bba2a838afbcb10f5db02 SHA1: 3757a33c2058844ae1aec4f88630b1dcb215deb6 SHA256: 05925f6493a31e05e4cacd2ec97966f360dc239bd3c215387358da34fdd1a9bf SHA512: f679dfd44c9e02b3b144efc8b804d03ebcb64d865eb6616e2a22d257abee49d645dbe44aae87542647598cea375fbbf68f144475c6bb6561557ba877242c8dcb Homepage: https://cran.r-project.org/package=CausalGAM Description: CRAN Package 'CausalGAM' (Estimation of Causal Effects with Generalized Additive Models) Implements various estimators for average treatment effects - an inverse probability weighted (IPW) estimator, an augmented inverse probability weighted (AIPW) estimator, and a standard regression estimator - that make use of generalized additive models for the treatment assignment model and/or outcome model. See: Glynn, Adam N. and Kevin M. Quinn. 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator." Political Analysis. 18: 36-56. Package: r-cran-causalhypergraph Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-stringr, r-cran-useful, r-cran-cna Filename: pool/dists/noble/main/r-cran-causalhypergraph_0.1.0-1.ca2404.1_all.deb Size: 47408 MD5sum: bd7ed96b31ba4296685122f9cbccbff3 SHA1: 74fe9721f70ef6be5fd54ea64dbd80512b4bdd23 SHA256: 3352001a313cbdb318e1045dca07ed40027ef965ffc038e48851ba2489a70f96 SHA512: 90d07474a1daa92c599b5f89b74e3a36527b9ec5de1a35509efaf38c5d7dce8b3db65952cd0df5db18e81c9734735d068a75a56c167e26b68c1e510389169f77 Homepage: https://cran.r-project.org/package=causalHyperGraph Description: CRAN Package 'causalHyperGraph' (Drawing Causal Hypergraphs) Draws causal hypergraph plots from models output by configurational comparative methods such as Coincidence Analysis (CNA) or Qualitative Comparative Analysis (QCA). Package: r-cran-causalimpact Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 947 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bsts, r-cran-assertthat, r-cran-boom, r-cran-ggplot2, r-cran-zoo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-causalimpact_1.4.1-1.ca2404.1_all.deb Size: 424862 MD5sum: b91f8eac6104e34bfa105ce392fa62ed SHA1: 66c8494c13b60d9348e9525e14041001f65c1350 SHA256: aa412ff51286f607e548d05d422fbbfd8722298843c0fe6db48e94ce55779f77 SHA512: f6dae2a927cecf03418745c8a05864a9abf1cb7febfe0aec5392790b80d855d32f2db8c5243152768ce66ba81a98896cf4dde480032cede8e53f93590bc51aee Homepage: https://cran.r-project.org/package=CausalImpact Description: CRAN Package 'CausalImpact' (Inferring Causal Effects using Bayesian Structural Time-SeriesModels) Implements a Bayesian approach to causal impact estimation in time series, as described in Brodersen et al. (2015) . See the package documentation on GitHub to get started. Package: r-cran-causalmbsts Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kfas, r-cran-cholwishart, r-cran-forecast, r-cran-mass, r-cran-matrix, r-cran-mixmatrix Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-causalmbsts_0.1.1-1.ca2404.1_all.deb Size: 144790 MD5sum: 06af06d0f7c218ad55e675386cabce7d SHA1: 4435224934167d6971f763707c79411952a7d7ff SHA256: ea510ae499a9a912cf893487cd64afb632dce411b4c738e991db25148bbf25c8 SHA512: d797c01ba5fb3c843466e9bbd56380f74487cb1abddfd3cefb06abb245d4713fba7ac42c3e237d99a1805d69e1e65a2fc551b1c5ecc82c57fb0de2086abd49c5 Homepage: https://cran.r-project.org/package=CausalMBSTS Description: CRAN Package 'CausalMBSTS' (MBSTS Models for Causal Inference and Forecasting) Infers the causal effect of an intervention on a multivariate response through the use of Multivariate Bayesian Structural Time Series models (MBSTS) as described in Menchetti & Bojinov (2020) . The package also includes functions for model building and forecasting. Package: r-cran-causalmetar Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1186 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-metafor, r-cran-nnet, r-cran-progress, r-cran-superlearner Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-causalmetar_0.1.3-1.ca2404.1_all.deb Size: 1169098 MD5sum: d7ccd88b84188d6fed224be4804fb98c SHA1: 96629a0a0f673daa7c853884076d8df111c76cb6 SHA256: df42cba6e9f07036bd086c04242ce6a1fa371df4433605e80bd55505bd24f993 SHA512: aafdba95cfa786695291bd8249cd0368430396d8699a65d6ca3218baaacd7897d07d82625e7ace9189b86cb9f08845558513f2dccc3979029a26bad5233372d1 Homepage: https://cran.r-project.org/package=CausalMetaR Description: CRAN Package 'CausalMetaR' (Causally Interpretable Meta-Analysis) Provides robust and efficient methods for estimating causal effects in a target population using a multi-source dataset, including those of Dahabreh et al. (2019) , Robertson et al. (2021) , and Wang et al. (2024) . The multi-source data can be a collection of trials, observational studies, or a combination of both, which have the same data structure (outcome, treatment, and covariates). The target population can be based on an internal dataset or an external dataset where only covariate information is available. The causal estimands available are average treatment effects and subgroup treatment effects. See Wang et al. (2025) for a detailed guide on using the package. Package: r-cran-causalmixgpd Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3555 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nimble, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-here, r-cran-cli, r-cran-coda, r-cran-ggmcmc, r-cran-future, r-cran-future.apply, r-cran-crayon, r-cran-dt, r-cran-kableextra, r-cran-plotly, r-cran-matchit, r-cran-codetools Filename: pool/dists/noble/main/r-cran-causalmixgpd_0.8.0-1.ca2404.1_all.deb Size: 3036516 MD5sum: d63f18b5c372657c4cea0a3e14bdab4e SHA1: eed9be69f29ba341ad245ff367945fbbcfb64081 SHA256: 626160b75aea0bb95cae8a15171b42bbfdc5c2a46ab19b14b625b5d11542b954 SHA512: edc3673047102e9f0e1a90c0f060e0d30b42779ca165f9575af0f4d3fb87bdfb020d2fbf8786c88567d9af840517719f7a537d5691a9cb56466a0934028922f1 Homepage: https://cran.r-project.org/package=CausalMixGPD Description: CRAN Package 'CausalMixGPD' (Bayesian Nonparametric Conditional Density Modeling in CausalInference and Clustering with a Heavy-Tail Extension) The presence of a heavy tail is a feature of many scenarios when risk management involves extremely rare events. While parametric distributions may give adequate representation of the mode of data, they are likely to misrepresent heavy tails, and completely nonparametric approaches lack a rigorous mechanism for tail extrapolation; see Pickands (1975) . The package 'CausalMixGPD' implements tools for Bayesian analysis of heavy-tailed outcomes by combining Dirichlet process mixture models for the body of the distribution with optional generalized Pareto tails. The method allows for unconditional and covariate-modulated mixtures, implements MCMC estimation using 'nimble', and extends to mixtures of different arms' outcomes with application to causal inference in the Rubin (1974) framework. Posterior summaries include density functions, quantiles, expected values, survival functions, and causal effects, with an emphasis on tail quantiles and functional measures sensitive to the tail. Package: r-cran-causalmodels Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-causaldata, r-cran-boot, r-cran-multcomp, r-cran-geepack Filename: pool/dists/noble/main/r-cran-causalmodels_0.2.1-1.ca2404.1_all.deb Size: 105170 MD5sum: 104bcb6bc77745ed71dc9b1f3b760d33 SHA1: 96b847b3e1aeb40016ac3d693087b85d779fef3f SHA256: ebd550670fa2ed8f66aa56729ecb9e1d1c6f70e64af68d3be17b7fdcee124282 SHA512: 23f8d67a3fc0fdf9f093e09ee35b5307e6bd2ecbd3e7c050a58345ff4ec210e6a70a5f1a9c853b2dcd3b799477fd98c6b8e12197dfd7ea82ba267a339ec4b5bd Homepage: https://cran.r-project.org/package=CausalModels Description: CRAN Package 'CausalModels' (Causal Inference Modeling for Estimation of Causal Effects) Provides an array of statistical models common in causal inference such as standardization, IP weighting, propensity matching, outcome regression, and doubly-robust estimators. Estimates of the average treatment effects from each model are given with the standard error and a 95% Wald confidence interval (Hernan, Robins (2020) ). Package: r-cran-causalnet Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-scales, r-cran-cowplot Suggests: r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-causalnet_0.1.0-1.ca2404.1_all.deb Size: 66554 MD5sum: 072958851266417d251f0e34e28066db SHA1: aed452a9aad37c0cdcd3e2ebe174d5afbd0f3ddf SHA256: eb530ab2a117b74a001507b40ae83cc3509266513e2847b7efa3804e67049e47 SHA512: 877214f5148b67e2ffd3c946467412cf6dd828e9b961b7a80e6b9a09d6ecd605b3d7490701da10d8ca87c305821c2c9fc55a6e42c273469d55ea95fa66712e68 Homepage: https://cran.r-project.org/package=causalnet Description: CRAN Package 'causalnet' (Directed Causal Network Enumeration and Simulation) Enumerate orientation-consistent directed networks from an undirected or partially directed skeleton, detect feedback loops, summarize topology, and simulate node dynamics via stochastic differential equations. Package: r-cran-causaloptim Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2067 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-shiny, r-cran-rcdd Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-causaloptim_1.0.0-1.ca2404.1_all.deb Size: 847264 MD5sum: c8f343c01131d3e4974c6ac9cb4523da SHA1: b102a1b7b77144c3dff9f4f9d77d3618b19b25f0 SHA256: 50b4dd25d526e06e81503b7e72a5f3e4c25072962f4b8da8c2741ee42341dd7f SHA512: 61415f2f6c97bf0cfc0f58f2fc41b94a2865a7a254e77bbe04c9502f826f5b3ee49ed180979dedbc4023711cd9f068f4a192ba226de38aad60ef191cada18786 Homepage: https://cran.r-project.org/package=causaloptim Description: CRAN Package 'causaloptim' (An Interface to Specify Causal Graphs and Compute Bounds onCausal Effects) When causal quantities are not identifiable from the observed data, it still may be possible to bound these quantities using the observed data. We outline a class of problems for which the derivation of tight bounds is always a linear programming problem and can therefore, at least theoretically, be solved using a symbolic linear optimizer. We extend and generalize the approach of Balke and Pearl (1994) and we provide a user friendly graphical interface for setting up such problems via directed acyclic graphs (DAG), which only allow for problems within this class to be depicted. The user can then define linear constraints to further refine their assumptions to meet their specific problem, and then specify a causal query using a text interface. The program converts this user defined DAG, query, and constraints, and returns tight bounds. The bounds can be converted to R functions to evaluate them for specific datasets, and to latex code for publication. The methods and proofs of tightness and validity of the bounds are described in a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) . Package: r-cran-causalpaf Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-dagitty, r-cran-dplyr, r-cran-forestplot, r-cran-ggdag, r-cran-ggplot2, r-cran-gridextra, r-cran-magrittr, r-cran-mass, r-cran-reshape2, r-cran-rlist Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-causalpaf_1.2.5-1.ca2404.1_all.deb Size: 1149672 MD5sum: 859a649729df9539bf4f8c70b5eaa65c SHA1: cbba5e5bd98e85a20a83d6b9515f2b39dc19c9d9 SHA256: 5deb000b0d468b4641e207d5d1d8b12a0ac1e5c3b72663db75448f9ef0edea00 SHA512: 84468a5f75c3b0f38163b6f1774103015a08383c16e835408ef57e45d0ab9c8c91158f20ab7ca3a5643fce3cb0561bfe28233a43b6fe9875b09573fb1ede0b00 Homepage: https://cran.r-project.org/package=causalPAF Description: CRAN Package 'causalPAF' (Causal Effect for Population Attributable Fractions (PAF)) Calculates population attributable fraction causal effects. The 'causalPAF' package contains a suite of functions for causal analysis calculations of population attributable fractions (PAF) given a causal diagram which apply both: Pathway-specific population attributable fractions (PS-PAFs) O’Connell and Ferguson (2022) and Sequential population attributable fractions Ferguson, O’Connell, and O’Donnell (2020) . Results are presentable in both table and plot format. Package: r-cran-causalplot Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6541 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggforce, r-cran-ggplot2, r-cran-ggtext Suggests: r-cran-shiny, r-cran-shinythemes Filename: pool/dists/noble/main/r-cran-causalplot_0.2.1-1.ca2404.1_all.deb Size: 2415318 MD5sum: 7ac4f8af2dbbb0ec47bb7c3c2b6efa20 SHA1: 93db87034fb602d585efd9ca221c1a743d1b81bc SHA256: 29c7a0e0d3ec0067883a98d8d01eaca69433951d081209a845c60f72c23e59cc SHA512: 9486c6a821f1da3d64f3f46fd52ef406d23ccc5f1bcdf8d02f94d255ea940efbfd62c3d8c9e28d36932841263ff7c8cfa8db27ef81d3a383e9fccf92981c64af Homepage: https://cran.r-project.org/package=causalplot Description: CRAN Package 'causalplot' (Create Publication-Ready Causal Diagrams) Creates publication-ready causal diagrams using 'ggplot2'. Provides simple templates for common causal diagrams (e.g., mediating mechanisms and parallel pathways) with customizable labels, colors, fonts, and export-friendly defaults. Package: r-cran-causalqual Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aer, r-cran-caret, r-cran-cli, r-cran-ggplot2, r-cran-ggsci, r-cran-grf, r-cran-lmtest, r-cran-magrittr, r-cran-ocf, r-cran-rdrobust, r-cran-sandwich, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-causalqual_1.0.0-1.ca2404.1_all.deb Size: 638374 MD5sum: 5bd3c6d1c288f31d31594cc6af914d95 SHA1: fdd3c450e3184879ee38f5c3b812f34c57c83509 SHA256: 3c04ddcfd1e9180686258df574d95d64f63f67ac7af44260172aff40c7330dd4 SHA512: 81c7c5b89be1c593a16fd251b772d107e77e33bb74e5f6e7205600dc967b5a67d55c81c6e9b74769c7586e0a2e4635537b617aeefecb3c8bdbe22f317ea7453f Homepage: https://cran.r-project.org/package=causalQual Description: CRAN Package 'causalQual' (Causal Inference for Qualitative Outcomes) Implements the framework introduced in Di Francesco and Mellace (2025) , shifting the focus to well-defined and interpretable estimands that quantify how treatment affects the probability distribution over outcome categories. It supports selection-on-observables, instrumental variables, regression discontinuity, and difference-in-differences designs. Package: r-cran-causalreg Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-causalreg_0.1.2-1.ca2404.1_all.deb Size: 43920 MD5sum: 1aee12a83cba830b77f8db7b50ee2c8f SHA1: 03afa4dfae37f843dc8532a67bba3381b9bcb3b1 SHA256: bcec7e674cb492df0bbe4703545812dff8b25768d20056922cc2bf8932538a92 SHA512: 81aa8d612f810073d1e143bae334939196a521fd5e3940cb2252137b76f22b21f30ae0e93c09677aaae90727132c77176c838d16714a9591cfd048e7582e6b2c Homepage: https://cran.r-project.org/package=causalreg Description: CRAN Package 'causalreg' (Causal Generalized Linear Models) An implementation of methods for causal discovery in a structural causal model where the conditional distribution of the target node is described by a generalized linear model conditional on its causal parents. Package: r-cran-causalsens Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-causalsens_0.1.3-1.ca2404.1_all.deb Size: 419766 MD5sum: dd73014fe22255245963281c9018022d SHA1: 392ebf30d9f44fddf0b9b04c4a16e55d2c501001 SHA256: 6e1db71680172c7f73092adb2a5f458419f28f39a6a6ba9c4a05ad8a5fb54517 SHA512: cecbb607be3069e6bed5188fcd0f7ea79ccca950ca4e076b29f2046612270cea8557c1dd7f51d3688524b38f4a8623897e3abbf2e49ab906de91ef71a48af4fd Homepage: https://cran.r-project.org/package=causalsens Description: CRAN Package 'causalsens' (Selection Bias Approach to Sensitivity Analysis for CausalEffects) The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects. Package: r-cran-causalspline Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-sandwich, r-cran-boot Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork, r-cran-cobalt, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-causalspline_0.1.0-1.ca2404.1_all.deb Size: 187174 MD5sum: d3d08274fb5b4d91fc935cb64c452f99 SHA1: 48bd7cc1bfd6899c2e4ebabe815e4c9329259358 SHA256: b4f0c1070a51001c142f618a2cf28ebac9498e97b6b7d6279d042d28ca89739e SHA512: 47111e756f98394edd30285f64f3191ceadfad6b396d26574676c0592c64863b5533df5091c45dc58505a601b6a06a9771c61735efa6cf2922c016779c088da5 Homepage: https://cran.r-project.org/package=CausalSpline Description: CRAN Package 'CausalSpline' (Nonlinear Causal Dose-Response Estimation via Splines) Estimates nonlinear causal dose-response functions for continuous treatments using spline-based methods under standard causal assumptions (unconfoundedness / ignorability). Implements three identification strategies: Inverse Probability Weighting (IPW) via the generalised propensity score (GPS), G-computation (outcome regression), and a doubly-robust combination. Natural cubic splines and B-splines are supported for both the exposure-response curve f(T) and the propensity nuisance model. Pointwise confidence bands are obtained via the sandwich estimator or nonparametric bootstrap. Also provides fragility diagnostics including pointwise curvature-based fragility, uncertainty-normalised fragility, and regional integration over user-defined treatment intervals. Builds on the framework of Hirano and Imbens (2004) for continuous treatments and extends it to fully nonparametric spline estimation. Package: r-cran-causalweight Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2625 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ranger, r-cran-mvtnorm, r-cran-np, r-cran-larf, r-cran-hdm, r-cran-superlearner, r-cran-glmnet, r-cran-xgboost, r-cran-e1071, r-cran-fastdummies, r-cran-grf, r-cran-checkmate, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-causalweight_1.1.4-1.ca2404.1_all.deb Size: 2424486 MD5sum: 9dab9c5bffd2030054ab8055b94f9bcb SHA1: 1e0b668f7c1814730e6a00ea5fafe1a297196c1e SHA256: 1801d6a721d6cc37e39547c8d1a837dfa9f51a7bce26dc4a3ae34a9e39e3b15a SHA512: 8f5015268462f695e01da71314ae42b762a7b158e4bffc3ea763ed71dde1c4bb3770523a421228cde463d4d4dcfc0760b171a664b6763d6f9af42b4e21120ac9 Homepage: https://cran.r-project.org/package=causalweight Description: CRAN Package 'causalweight' (Estimation Methods for Causal Inference Based on InverseProbability Weighting and Doubly Robust Estimation) Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects. The models refer to studies of Froelich (2007) , Huber (2012) , Huber (2014) , Huber (2014) , Froelich and Huber (2017) , Hsu, Huber, Lee, and Lettry (2020) , and others. Package: r-cran-causalwins Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-drf, r-cran-factominer, r-cran-grf, r-cran-matchit Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-wins, r-cran-mass Filename: pool/dists/noble/main/r-cran-causalwins_0.1.0-1.ca2404.1_all.deb Size: 212726 MD5sum: c12d01733d8bdab5c30c9f85f079f96d SHA1: 876da75a4950871a54b10551845e9d15e917dbc0 SHA256: c0fcd824baafe70bc7a713ddd23b512da63e52331fc1bddc75d54ee997300141 SHA512: 5dcbe520a06053c501b3aa41d6bc429be6f9819bf30f11f80977629fc48e1408e74e5308b765a5679af9b331a00ba0e89204a92455b9ad97255ec23a9b5e549a Homepage: https://cran.r-project.org/package=causalWins Description: CRAN Package 'causalWins' (Compute the Causal Win Ratio Using Nearest Neighbor Matching) Based on “Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysis” (M. Even and J. Josse, 2025), this package provides implementations of three approaches - nearest neighbor matching, distributional regression forests, and efficient influence functions - to estimate the causal win ratio, win proportion, and net benefit. Package: r-cran-causcor Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-writexls Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-causcor_0.1.3-1.ca2404.1_all.deb Size: 36480 MD5sum: 7737802d9ac7532dee183581f4438eb5 SHA1: af8cb29136bfeb27ba513d8fa638dc68e1f5971b SHA256: b3dae2379fabb900fa92d8dee15562b2fe680d824cf8196bff61c9ce699f21a0 SHA512: dd77a29120e01183983c4ba31e35ac93263a35df2a6d72e381e06291bf39cfe1378b4b01cb139e414daa93d7b89edd9c580a6d534628961eac1d15ed365516ca Homepage: https://cran.r-project.org/package=CausCor Description: CRAN Package 'CausCor' (Calculate Correlations and Estimate Causality) This tool performs pairwise correlation analysis and estimate causality. Particularly, it is useful for detecting the metabolites that would be altered by the gut bacteria. Package: r-cran-causens Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-knitr, r-cran-pkgdown, r-cran-psweight, r-cran-rjags, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-usethis, r-cran-waldo Filename: pool/dists/noble/main/r-cran-causens_0.0.3-1.ca2404.1_all.deb Size: 570020 MD5sum: b71c108e90af4399de35c0cf7fca4e45 SHA1: ce42517b849de4f0740278e00a579ea8d9b601f2 SHA256: 7abfe2fdbd9bf0dfbe46cb8501ef7283e23c58aa0aea8ddc26115b048611a0b6 SHA512: 84db66aab948247cce79a6426f8caa77fbeabfa01543c1ee4a61868a66129fdc42b1b5167175e41b48ce16866db35343420b6e5030e40634a01465bcddfb584d Homepage: https://cran.r-project.org/package=causens Description: CRAN Package 'causens' (Perform Causal Sensitivity Analyses Using Various StatisticalMethods) While data from randomized experiments remain the gold standard for causal inference, estimation of causal estimands from observational data is possible through various confounding adjustment methods. However, the challenge of unmeasured confounding remains a concern in causal inference, where failure to account for unmeasured confounders can lead to biased estimates of causal estimands. Sensitivity analysis within the framework of causal inference can help adjust for possible unmeasured confounding. In `causens`, three main methods are implemented: adjustment via sensitivity functions (Brumback, Hernán, Haneuse, and Robins (2004) and Li, Shen, Wu, and Li (2011) ), Bayesian parametric modelling and Monte Carlo approaches (McCandless, Lawrence C and Gustafson, Paul (2017) ). Package: r-cran-caustests Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-caustests_1.1.1-1.ca2404.1_all.deb Size: 155452 MD5sum: a27bf89a8bcb143b5381a7f181f5b60f SHA1: 4523a52c888b1f713f9a9f6209c3d47e46a84aa2 SHA256: 2e84d6d884d4b9c607f8c5e51301041c847aa628437a5f626da3b92f5e9ae7fb SHA512: 4d26c307d5349bcd72cd0f9e87860ecb54b82ca9c3e1b81dba17503fcc6b7de132d8809b35e3b131d115c3dcb1f83e59d74f8b70bd616659b4f48f9bfecb1ed1 Homepage: https://cran.r-project.org/package=caustests Description: CRAN Package 'caustests' (Multiple Granger Causality Tests for Time Series and Panel Data) Comprehensive suite of Granger causality tests for time series and panel data. For time series: Toda-Yamamoto (1995) , Fourier-based tests with single frequency (Enders and Jones, 2016) and cumulative frequencies (Nazlioglu et al., 2019) , quantile causality tests (Cai et al., 2023) , and Bootstrap Fourier Granger Causality in Quantiles (Cheng et al., 2021) . For panel data: Panel Fourier Toda-Yamamoto (Yilanci and Gorus, 2020) and Panel Quantile Causality tests (Wang and Nguyen, 2022) , as well as Group-Mean and Pooled Fully Modified OLS estimators for panel cointegrating polynomial regressions (Wagner and Reichold, 2023) . All tests include bootstrap inference for robust p-values. Package: r-cran-cavariants Architecture: all Version: 6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggforce, r-cran-ggrepel, r-cran-gridextra, r-cran-ggplot2, r-cran-plotly Filename: pool/dists/noble/main/r-cran-cavariants_6.0-1.ca2404.1_all.deb Size: 159318 MD5sum: 5c56c533300ee684baae582eeb1de4c3 SHA1: 38757c1440b81cb98e9fec9906274bcaf6687081 SHA256: a4e71d88bc4ae4eb0e55fa67818970c664a8f88d25386f0363f2ff0d7d13715a SHA512: ec38f86bf8bae07c96879c583c2f297b3bcc5e2c77e7ed35af69e43063a13aed95bb42c03ee409b29bd27e071f25436d9c89fa8941600b1083136aba9ca9c2fb Homepage: https://cran.r-project.org/package=CAvariants Description: CRAN Package 'CAvariants' (Correspondence Analysis Variants) Provides six variants of two-way correspondence analysis (ca): simple ca, singly ordered ca, doubly ordered ca, non symmetrical ca, singly ordered non symmetrical ca, and doubly ordered non symmetrical ca. Package: r-cran-cayleyr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cayleyr_0.1.0-1.ca2404.1_all.deb Size: 40710 MD5sum: ee701adb65619f5194c32e527d18459c SHA1: 09157e87e59db4256172bd619a8154c54d358e58 SHA256: a097c1dc16ec3c82b9ff38cd274b8e1d241a5ffd7f826071d7950d88c4b13fde SHA512: 36001accfc4e1076501b09509ee8186854d220a95cc9260eddc961be7412dce524ace1d6381b59d85a262dda680e5098f4936e3ea54e8c4a99703693ab7ad728 Homepage: https://cran.r-project.org/package=cayleyR Description: CRAN Package 'cayleyR' (Cayley Graph Analysis for Permutation Puzzles) Implements algorithms for analyzing Cayley graphs of permutation groups, with a focus on the TopSpin puzzle and similar permutation-based combinatorial puzzles. Provides methods for cycle detection, state space exploration, and finding optimal operation sequences in permutation groups generated by shift and reverse operations. Package: r-cran-cbamodel Architecture: all Version: 0.0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-cbamodel_0.0.1.2-1.ca2404.1_all.deb Size: 22704 MD5sum: e674a068d66f77df0a33b29d0880feef SHA1: 70ddd7a512510cfce76a7ba3a6d625ab064c88e0 SHA256: 23518b50494866c888b0e6add8a8d2b41ea100bc65fcecae3b974426be50fd7f SHA512: d84ed4e0eacb613bdf4f74950494b401906f5503cf26c8026c658b37e210714be565db19a7de71d598674f5972233fc87161727852514501c2c1898f93ac3d13 Homepage: https://cran.r-project.org/package=CBAModel Description: CRAN Package 'CBAModel' (Stochastic 3D Structure Model for Binder-Conductive AdditivePhase) Simulation of the stochastic 3D structure model for the nanoporous binder-conductive additive phase in battery cathodes introduced in P. Gräfensteiner, M. Osenberg, A. Hilger, N. Bohn, J. R. Binder, I. Manke, V. Schmidt, M. Neumann (2024) . The model is developed for a binder-conductive additive phase of consisting of carbon black, polyvinylidene difluoride binder and graphite particles. For its stochastic 3D modeling, a three-step procedure based on methods from stochastic geometry is used. First, the graphite particles are described by a Boolean model with ellipsoidal grains. Second, the mixture of carbon black and binder is modeled by an excursion set of a Gaussian random field in the complement of the graphite particles. Third, large pore regions within the mixture of carbon black and binder are described by a Boolean model with spherical grains. Package: r-cran-cbanalysis Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cbanalysis_0.2.0-1.ca2404.1_all.deb Size: 18202 MD5sum: 36e1862f624b3dfb900946eba6fb7b84 SHA1: 6b99cd962483d582ef0e80c06d294fae7f48c20d SHA256: 9f138e5ffda56f2004fb7b5ecb9ecad7c6b5ffdd7d584e582eb435222207b36d SHA512: 12ab899ef81caeab1ef8fa391b93b0ec2d3b303636e393121365795310ec8308636c82216f9d9ec54cb5e5ebaf8cf641c626572f07c3649794f01a07a9b44922 Homepage: https://cran.r-project.org/package=cbanalysis Description: CRAN Package 'cbanalysis' (Coffee Break Descriptive Analysis) A set of functions that helps you to generate descriptive statistics based on the variable types. Package: r-cran-cbass Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cbass_0.1-1.ca2404.1_all.deb Size: 75814 MD5sum: a89b0fa193920c1c3b665cb7d5d09004 SHA1: 5ae2ad923639c5ca48b58b1ad5f889f9bbd0c665 SHA256: b135313bfc3cea05b57137b3bd22a18a810bdf2332c62790a4540b0b3f668800 SHA512: aa99746a0adc23c16f1810a26d9f6691feb4f2458d138ac2d05abdd9cb61873469e77cc14297cbaea44ca1d4729841d5de3b00aee38f641ccdea4c63088f62c8 Homepage: https://cran.r-project.org/package=cbass Description: CRAN Package 'cbass' (Classification -- Bayesian Adaptive Smoothing Splines) Fit multiclass Classification version of Bayesian Adaptive Smoothing Splines (CBASS) to data using reversible jump MCMC. The multiclass classification problem consists of a response variable that takes on unordered categorical values with at least three levels, and a set of inputs for each response variable. The CBASS model consists of a latent multivariate probit formulation, and the means of the latent Gaussian random variables are specified using adaptive regression splines. The MCMC alternates updates of the latent Gaussian variables and the spline parameters. All the spline parameters (variables, signs, knots, number of interactions), including the number of basis functions used to model each latent mean, are inferred. Functions are provided to process inputs, initialize the chain, run the chain, and make predictions. Predictions are made on a probabilistic basis, where, for a given input, the probabilities of each categorical value are produced. See Marrs and Francom (2023) "Multiclass classification using Bayesian multivariate adaptive regression splines" Under review. Package: r-cran-cbassed50 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-drc, r-cran-rlog, r-cran-dplyr, r-cran-ggplot2, r-cran-readxl, r-cran-glue Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cbassed50_0.2.0-1.ca2404.1_all.deb Size: 105214 MD5sum: d3619fcfb9290f26dac374e206572de2 SHA1: 6c4679a32c90691c1d34e586a4109ed65e3aaf0e SHA256: 6377764b4a2ca926b417a9c39617ae761b859e048a7852ecea3d8828ff0fc4bd SHA512: b87d7c91c9422508575447001d7189aa47f243a48a1f2d36529f1e59e7817fd307bcd7c863541f64c46c151e204245fbf8b06d21cefa7823ac869e7693221f0d Homepage: https://cran.r-project.org/package=CBASSED50 Description: CRAN Package 'CBASSED50' (Process CBASS-Derived PAM Data) Tools to process CBASS-derived PAM data efficiently. Minimal requirements are PAM-based photosynthetic efficiency data (or data from any other continuous variable that changes with temperature, e.g. relative bleaching scores) from 4 coral samples (nubbins) subjected to 4 temperature profiles of at least 2 colonies from 1 coral species from 1 site. Please refer to the following CBASS (Coral Bleaching Automated Stress System) papers for in-depth information regarding CBASS acute thermal stress assays, experimental design considerations, and ED5/ED50/ED95 thermal parameters: Nicolas R. Evensen et al. (2023) Christian R. Voolstra et al. (2020) Christian R. Voolstra et al. (2025) . Package: r-cran-cbcgrps Architecture: all Version: 2.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nortest Filename: pool/dists/noble/main/r-cran-cbcgrps_2.8.2-1.ca2404.1_all.deb Size: 78718 MD5sum: 8c89868eeee9592eb61c8479e1bcc731 SHA1: 7a71ff8d94a9311dddd817ace21d3b97c8a24008 SHA256: 4315c293192b88ef39bdf0b87397e3397a7b155ab46b20e795bb272e2a5b8f2f SHA512: cbd9790920bfc4d93c4572b61eacff1c90937c7879c2214f3837aca7d20200301a3de7acc3cc09550cac5404e6069de3f820c93f6d49638acb3453afa7b9ae12 Homepage: https://cran.r-project.org/package=CBCgrps Description: CRAN Package 'CBCgrps' (Compare Baseline Characteristics Between Groups) Compare baseline characteristics between two or more groups. The variables being compared can be factor and numeric variables. The function will automatically judge the type and distribution of the variables, and make statistical description and bivariate analysis. Package: r-cran-cbctools Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastdummies, r-cran-ggplot2, r-cran-idefix, r-cran-logitr, r-cran-randtoolbox, r-cran-rlang Suggests: r-cran-here, r-cran-knitr, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-cbctools_0.7.1-1.ca2404.1_all.deb Size: 1318930 MD5sum: e38d4594bb48720e26b7e98bb4487d3d SHA1: d55d9b1eba55b0bcc9202f7b88e47422d336a2b7 SHA256: 7cebd74bd097b7cab5d357d33d0579411824e08b7a8a610726062630a73dd38f SHA512: 858ff5703f0251846e577a197eec69646d5bcbc4ac8be9a111607aed8bb90648f09f0df00b03cb9b91501fa891ef926d9612082d1aa28c1fb5af05a5960ea5c3 Homepage: https://cran.r-project.org/package=cbcTools Description: CRAN Package 'cbcTools' (Design and Analyze Choice-Based Conjoint Experiments) Design and evaluate choice-based conjoint survey experiments. Generate a variety of survey designs, including random designs, frequency-based designs, and D-optimal designs, as well as "labeled" designs (also known as "alternative-specific designs"), designs with "no choice" options, and designs with dominant alternatives removed. Conveniently inspect and compare designs using a variety of metrics, including design balance, overlap, and D-error, and simulate choice data for a survey design either randomly or according to a utility model defined by user-provided prior parameters. Conduct a power analysis for a given survey design by estimating the same model on different subsets of the data to simulate different sample sizes. Bayesian D-efficient designs using the 'cea' and 'modfed' methods are obtained using the 'idefix' package by Traets et al (2020) . Choice simulation and model estimation in power analyses are handled using the 'logitr' package by Helveston (2023) . 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Users can browse and query genomic data on mutations, copy number alterations and fusions, as well as data on tumor mutational burden ('TMB'), microsatellite instability status ('MSI'), 'FACETS' and select clinical data points (depending on the study). See and Gao et al., (2013) for more information on the cBioPortal web resource. Package: r-cran-cbl Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-foreach, r-cran-glmnet, r-cran-lightgbm Filename: pool/dists/noble/main/r-cran-cbl_0.1.3-1.ca2404.1_all.deb Size: 56752 MD5sum: d9a9a45d9934c17eac93a0dcd408943e SHA1: 1146682563eb894e876c4df9e888ca0929e018bc SHA256: 0eafe6c9f1879206bce0746047dd1393bdcbbf29334c2b22d1b9644eb4b43d3f SHA512: 7d0412b7c9eb870fce36f46d6450db3c2d1fa2f761c83615eb7f7c02edbb20283a66627ea0a91b4a239b53e3e544c814e0bad3e2443460365876d67fa953298d Homepage: https://cran.r-project.org/package=cbl Description: CRAN Package 'cbl' (Causal Discovery under a Confounder Blanket) Methods for learning causal relationships among a set of foreground variables X based on signals from a (potentially much larger) set of background variables Z, which are known non-descendants of X. The confounder blanket learner (CBL) uses sparse regression techniques to simultaneously perform many conditional independence tests, with complementary pairs stability selection to guarantee finite sample error control. CBL is sound and complete with respect to a so-called "lazy oracle", and works with both linear and nonlinear systems. For details, see Watson & Silva (2022) . Package: r-cran-cbpe Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cbpe_0.1.0-1.ca2404.1_all.deb Size: 99798 MD5sum: 180978369f25f6520568753a04d406dc SHA1: a0455a54e2407020b8fd3c75e2df4400001955fb SHA256: d01b61704e5db7238ca6c2549839b96ac3098d4a5a527186af68af2b77d64cfb SHA512: 7288f65214bc0281f2598408e27003d82f89c5d1dfcd3d56e8009edbf6a3ac943d3c206122ba80ed46722d16400996a2ee75633f2b1c7f3bdba4542017ab0588 Homepage: https://cran.r-project.org/package=CBPE Description: CRAN Package 'CBPE' (Correlation-Based Penalized Estimators) Provides correlation-based penalty estimators for both linear and logistic regression models by implementing a new regularization method that incorporates correlation structures within the data. This method encourages a grouping effect where strongly correlated predictors tend to be in or out of the model together. See Tutz and Ulbricht (2009) and Algamal and Lee (2015) . Package: r-cran-cbps Architecture: all Version: 0.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matchit, r-cran-nnet, r-cran-numderiv, r-cran-glmnet Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cbps_0.24-1.ca2404.1_all.deb Size: 360712 MD5sum: 4b4df584bc24dad5170a2c2a83236d44 SHA1: 2c7f077fff59e584ab3d3ba3d0fb7a6231276240 SHA256: 0f55f958be3c6e584509d11ed86aa86d08ceda60443b563b3a18fc68126e8342 SHA512: 2f1c8323655ec8a3ed8f38faa49dba30c95ae0fbaa6ed81450e811389905d966dcb2590b124c2c00bd3c92ddfeaf8fc542a5f6e5d4a76017d081d9390d502475 Homepage: https://cran.r-project.org/package=CBPS Description: CRAN Package 'CBPS' (Covariate Balancing Propensity Score) Implements the covariate balancing propensity score (CBPS) proposed by Imai and Ratkovic (2014) . The propensity score is estimated such that it maximizes the resulting covariate balance as well as the prediction of treatment assignment. The method, therefore, avoids an iteration between model fitting and balance checking. The package also implements optimal CBPS from Fan et al. (in-press) , several extensions of the CBPS beyond the cross-sectional, binary treatment setting. They include the CBPS for longitudinal settings so that it can be used in conjunction with marginal structural models from Imai and Ratkovic (2015) , treatments with three- and four-valued treatment variables, continuous-valued treatments from Fong, Hazlett, and Imai (2018) , propensity score estimation with a large number of covariates from Ning, Peng, and Imai (2020) , and the situation with multiple distinct binary treatments administered simultaneously. In the future it will be extended to other settings including the generalization of experimental and instrumental variable estimates. Package: r-cran-cbrt Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 665 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cbrt_0.2.0-1.ca2404.1_all.deb Size: 556788 MD5sum: ddcdadbd9aff6a34da9c851f0baa5191 SHA1: 699d51ff53b3ca09d910d29fbd407b88741c9753 SHA256: a0f8f678248b435125a7f847bb7c241721efcec3f080c2d85cd35a9f60ca8651 SHA512: 8eb8eedc3a6482c18d84a854de2e6a209748361867de104e2bd19c76e7a6ced50abe964d001eaca85e15ed4ba2ac9ad2a2825a96c54392d9da7012afd0715ef1 Homepage: https://cran.r-project.org/package=CBRT Description: CRAN Package 'CBRT' (CBRT Data on Turkish Economy) The Central Bank of the Republic of Turkey (CBRT) provides one of the most comprehensive time series databases on the Turkish economy. The 'CBRT' package provides functions for accessing the CBRT's electronic data delivery system . It contains the lists of all data categories and data groups for searching the available variables (data series). As of February 17, 2026, there were 47,986 variables in the dataset. The lists of data categories and data groups can be updated by the user at any time. A specific variable, a group of variables, or all variables in a data group can be downloaded at different frequencies using a variety of aggregation methods. Package: r-cran-cbsodatar Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 812 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-whisker, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-shiny, r-cran-testthat, r-cran-sf Filename: pool/dists/noble/main/r-cran-cbsodatar_1.2.1-1.ca2404.1_all.deb Size: 584822 MD5sum: 7c1c03f0a49ebc59cc34299b17586c26 SHA1: 2c5b277e4b60019d6b549ce59efdf7534d9d0c5d SHA256: 07d9508a1fe8c415ec39c9f985b89223cea331ffdb1702471613286c449ffed3 SHA512: eddefc0264d83369d6a3cf5b98ec823eb707adc1f7b0bbe7decc1f1c11b1176786c39829e91694b9eb1b62ca2bf8023ef6e940c68fbb41d0c65a7668616b193e Homepage: https://cran.r-project.org/package=cbsodataR Description: CRAN Package 'cbsodataR' (Statistics Netherlands (CBS) Open Data API Client) The data and meta data from Statistics Netherlands () can be browsed and downloaded. The client uses the open data API of Statistics Netherlands. Package: r-cran-cbsr Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-nlcoptim Filename: pool/dists/noble/main/r-cran-cbsr_1.0.5-1.ca2404.1_all.deb Size: 42498 MD5sum: 0153858184b35cd8efd7b430df612bb8 SHA1: a19504ddecd3abe09ac75c1dfd6d8f4e75c977d3 SHA256: 059aeb8fda72a7d4bc858fa5b2b2378aff4d93b58829175ed4ccd1a9ca8ca575 SHA512: 89fb038710a286218eee6671ce43bf84b3991d1fc3d4bbaa78abcdfbf7a2b140e46d87287443eb93a427089bff55b17ab539b86dbafa79dec4460876c6e19625 Homepage: https://cran.r-project.org/package=CBSr Description: CRAN Package 'CBSr' (Fits Cubic Bezier Spline Functions to Intertemporal and RiskyChoice Data) Uses monotonically constrained Cubic Bezier Splines (CBS) to approximate latent utility functions in intertemporal choice and risky choice data. For more information, see Lee, Glaze, Bradlow, and Kable . Package: r-cran-cbsreps Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-assertthat, r-cran-kfas, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cbsreps_0.1.0-1.ca2404.1_all.deb Size: 151818 MD5sum: 4b30b079e39fa4b420154386d03d37b9 SHA1: 99917a363922329ba8e0a616df61817ff3380b9c SHA256: fe6918315bc21aed9f2acd7de4880aa826094fa7a2cff9425c5244d77f9f820b SHA512: 280112457c9dfa48251c5191a0e1d4a783297741b2e78623dd7659fd078dec8105660ffdf2f19d01bd155b5af6959fde94703f48129f3135bc7177960b44fc88 Homepage: https://cran.r-project.org/package=cbsREPS Description: CRAN Package 'cbsREPS' (Hedonic and Multilateral Index Methods for Real Estate PriceStatistics) Compute price indices using various Hedonic and multilateral methods, including Laspeyres, Paasche, Fisher, and HMTS (Hedonic Multilateral Time series re-estimation with splicing). The central function calculate_price_index() offers a unified interface for running these methods on structured datasets. This package is designed to support index construction workflows for real estate and other domains where quality-adjusted price comparisons over time are essential. The development of this package was funded by Eurostat and Statistics Netherlands (CBS), and carried out by Statistics Netherlands. The HMTS method implemented here is described in Ishaak, Ouwehand and Remøy (2024) . For broader methodological context, see Eurostat (2013, ISBN:978-92-79-25984-5, ). Package: r-cran-cbt Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cbt_1.0-1.ca2404.1_all.deb Size: 23130 MD5sum: 612d883c5403f67ee4adf9c077fd8d43 SHA1: aa1dfc426dc6981c6f1fb7430cb66cee04766815 SHA256: 968021592c6fe686bbf9cfb572e284f359288e9562fe3ad98b872f7250754ae2 SHA512: f30558c6c1f8e072f5c04c41cf5864bfcdaa0c7e752bde678d23c6c566e9de0870d170634de8cb0ae30091d3439d9eee5fa260d77f6308c74cfaa3a7b0f0be30 Homepage: https://cran.r-project.org/package=CBT Description: CRAN Package 'CBT' (Confidence Bound Target Algorithm) The Confidence Bound Target (CBT) algorithm is designed for infinite arms bandit problem. It is shown that CBT algorithm achieves the regret lower bound for general reward distributions. Reference: Hock Peng Chan and Shouri Hu (2018) . 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Fuzz testing helps identify functions lacking sufficient argument validation, and uncovers problematic inputs that, while valid by function signature, may cause issues within the function body. Package: r-cran-cc Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cc_1.0-1.ca2404.1_all.deb Size: 47376 MD5sum: ca461b163351379cbfa8dac1d09fc033 SHA1: 5d33a8cb2d87a42a4923dbd63296e43bd5fc2309 SHA256: 9c9d3eda367a4d9edf0015e8392c02b25c0f1e3b48468108e33302ef12db770b SHA512: ddbc66b77569974445cf86e601cb02cb30aa48cc1694461a072942de6e11e4fb187b1062648ed93e8c80f25cac53b1d55a9d7039c84d3b2e78b575832013c8f6 Homepage: https://cran.r-project.org/package=CC Description: CRAN Package 'CC' (Control Charts) Tools for creating and visualizing statistical process control charts. Control charts are used for monitoring measurement processes, such as those occurring in manufacturing. The objective is to monitor the history of such processes and flag outlying measurements: out-of-control signals. Montgomery, D. (2009, ISBN:978-0-470-16992-6) contains an extensive discussion of the methodology. Package: r-cran-cca Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-fields Filename: pool/dists/noble/main/r-cran-cca_1.2.2-1.ca2404.1_all.deb Size: 61370 MD5sum: eeeb8d5d429d6d7273798b668a40c042 SHA1: 990861ef66ecc07e4b3042e1121c67bff8d45247 SHA256: 70e55412514459df9d188b88789c8d3d1a2098abe42303eaf77722dbfbac82dc SHA512: 1b239e0245d05edcee4aa4fe4909e7f157981e9605d2ee192064738d86e12350f4d578395d1fbc5a94e8be0e8fe82721799df6e47e1cec96dcbe4c4b2f159990 Homepage: https://cran.r-project.org/package=CCA Description: CRAN Package 'CCA' (Canonical Correlation Analysis) Provides a set of functions that extend the 'cancor' function with new numerical and graphical outputs. It also include a regularized extension of the canonical correlation analysis to deal with datasets with more variables than observations. Package: r-cran-ccamlrgis Architecture: all Version: 4.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4403 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-terra, r-cran-magrittr, r-cran-isoband, r-cran-bezier, r-cran-lwgeom Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ccamlrgis_4.3.1-1.ca2404.1_all.deb Size: 4225354 MD5sum: d08aa12d71ab9525868549649b73ff6c SHA1: cfb40a8a9f4b474ae0c79e04a03125d1beb0fd71 SHA256: 3763e1b5d32d0086e56f94092a78eddbe8dd0a0f04c2c57cfb29ab0abfdae37a SHA512: 036c3440eb174b5eb361c3176d6e15e5decc28992de4a9e55a24811ca727ab3fe9135ac32ca6bb128a0c799cef3c504b163978bdc9141da0c2a073a898233bdb Homepage: https://cran.r-project.org/package=CCAMLRGIS Description: CRAN Package 'CCAMLRGIS' (Antarctic Spatial Data Manipulation) Loads and creates spatial data, including layers and tools that are relevant to the activities of the Commission for the Conservation of Antarctic Marine Living Resources. 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Several methods for estimating CCA in high-dimensional settings are implemented. The first set of methods, cca_rrr() (and variants: cca_group_rrr() and cca_graph_rrr()), assumes that one dataset is high-dimensional and the other is low-dimensional, while the second, ecca() (for Efficient CCA) assumes that both datasets are high-dimensional. For both methods, standard l1 regularization as well as group-lasso regularization are available. cca_graph_rrr further supports total variation regularization when there is a known graph structure among the variables of the high-dimensional dataset. In this case, the loadings of the canonical directions of the high-dimensional dataset are assumed to be smooth on the graph. For more details see Donnat and Tuzhilina (2024) and Wu, Tuzhilina and Donnat (2025) . Package: r-cran-cccd Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-proxy, r-cran-deldir, r-cran-fnn Suggests: r-cran-matrix Filename: pool/dists/noble/main/r-cran-cccd_1.6-1.ca2404.1_all.deb Size: 100702 MD5sum: d232398ad168d535cee58bd46ba2d8d5 SHA1: 309cdf2a358234887c9a8794ffb7bc273deab28b SHA256: 3c10a4a4c9bfd745a350473358d5300fd5f4eb889d92c13af8bae392ce9eaf0e SHA512: 5b52bdfaacdb27029e115a46a4806d1ce336e805b3d04e1858e677f5b4d446203db1f263e1f85c11bd7b3be8d252574477e195b78b763b0d2002e2d6ad63de79 Homepage: https://cran.r-project.org/package=cccd Description: CRAN Package 'cccd' (Class Cover Catch Digraphs) Class Cover Catch Digraphs, neighborhood graphs, and relatives. 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Package: r-cran-cccrm Architecture: all Version: 3.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-dplyr, r-cran-deriv, r-cran-tidyselect, r-cran-progressr, r-cran-furrr, r-cran-nlmeu, r-cran-parallelly, r-cran-purrr, r-cran-tidyr, r-cran-lifecycle, r-cran-future, r-cran-mass, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-cccrm_3.0.6-1.ca2404.1_all.deb Size: 176106 MD5sum: 1fc669c32b613abb23a9f22b30f748f2 SHA1: cdcc6dbc34244e064a3162520b3e783fe31fab59 SHA256: a382c1ccde77716670e7fd9bea74e0ba7c547a6e366f94239ee9ec4ef5f167b7 SHA512: 5f775cdf3e702859eebfcd36d8cc7443b61592486f5016c218f5ce3c528fb94e13464bdda679c8b5169f775044c817b52279eade89dfade1ccfc3c96e2bbded9 Homepage: https://cran.r-project.org/package=cccrm Description: CRAN Package 'cccrm' (Concordance Correlation Coefficient for Repeated (andNon-Repeated) Measures) Estimates the Concordance Correlation Coefficient to assess agreement. The scenarios considered are non-repeated measures, non-longitudinal repeated measures (replicates) and longitudinal repeated measures. It also includes the estimation of the one-way intraclass correlation coefficient also known as reliability index. The estimation approaches implemented are variance components and U-statistics approaches. Description of methods can be found in Fleiss (1986) and Carrasco et al. (2013) . Package: r-cran-ccd Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfast Suggests: r-cran-rfast2, r-cran-skellam Filename: pool/dists/noble/main/r-cran-ccd_1.1-1.ca2404.1_all.deb Size: 29828 MD5sum: dd37fad66c02a838e5ba6ebba0568e55 SHA1: 59c5739835c157785d022fc6001c09231f890ba0 SHA256: 177883c05806f50742db7cf7a2ec26b591e65150ae278a428f691594034b77ba SHA512: e80651fd0e1848f6e89a381e5edee54d9e3696f233b6c83dc4a850875d9c15cbd0ce17ff2bd7d4a65c83b931613f194fccf66454a8a074432095ff12a0245111 Homepage: https://cran.r-project.org/package=CCd Description: CRAN Package 'CCd' (The Cauchy-Cacoullos (Discrete Cauchy) Distribution) Maximum likelihood estimation of the Cauchy-Cacoullos (discrete Cauchy) distribution. Probability mass, distribution and quantile function are also included. The reference paper is: Papadatos N. (2022). "The Characteristic Function of the Discrete Cauchy Distribution in Memory of T. Cacoullos". Journal of Statistical Theory Practice, 16(3): 47. . Package: r-cran-ccda Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-ccda_1.1.1-1.ca2404.1_all.deb Size: 29482 MD5sum: 4b12f800ecf4a18745d4a67e4beefd79 SHA1: bc267f6fc0e55aed3496632b9e82a534ab0e36a1 SHA256: fc813981cb104359448ef2fcd4f60632dc8041be92312b948b50e00f0e7bc820 SHA512: d4cb2e4aaf014a2a9d13d93fa6d0fb25610125249ffd2b4fc84c34f7424a50e5fe23a14b91a69c6017b9d206de448317106833ab4cf5157ed5ba6e38b790579b Homepage: https://cran.r-project.org/package=ccda Description: CRAN Package 'ccda' (Combined Cluster and Discriminant Analysis) Implements the combined cluster and discriminant analysis method for finding homogeneous groups of data with known origin as described in Kovacs et. al (2014): Classification into homogeneous groups using combined cluster and discriminant analysis (CCDA). Environmental Modelling & Software. . 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'ccdR' was developed to streamline the process of accessing the information available through the 'CTX' APIs without requiring prior knowledge of how to use APIs. Most data is also available on the CompTox Chemical Dashboard ('CCD') and other resources found at the EPA Computational Toxicology and Exposure Online Resources . Package: r-cran-cchs Architecture: all Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-cchs_0.4.5-1.ca2404.1_all.deb Size: 92238 MD5sum: 85b7958e4d95a7e98c222eae09b38bac SHA1: f954f74744a74ebf34e36a9ca415a648d7fb5c17 SHA256: af4983f5906834f9d16bf0b1cd8f1291022857d6f38973efe58113d0356482fc SHA512: 46dd587a20469ea11baf8dd7560f647f36ba5d393a111ee8a6717254e4d06f8c6df8427ad9f46f00de90961f2b0fe64cf2131e2c151772c2e0b841be07b824cd Homepage: https://cran.r-project.org/package=cchs Description: CRAN Package 'cchs' (Cox Model for Case-Cohort Data with StratifiedSubcohort-Selection) Contains a function, also called 'cchs', that calculates Estimator III of Borgan et al (2000), . This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling. Package: r-cran-cchsflow Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2427 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-haven, r-cran-dplyr, r-cran-sjlabelled, r-cran-stringr, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cchsflow_2.1.0-1.ca2404.1_all.deb Size: 1255214 MD5sum: f8b5fa1b08e5b271bf69dd468f2bf14c SHA1: ce79ec88d66c3b714fc9ab7338bceeecc50e62eb SHA256: 707e3186a7fbe4cf788da6dc49ac07a9d98993753ae0dbb0cba17aa392ba4d82 SHA512: 225d490566f65c7852a5ca0d601258f7451f0abcc8aba2c2ee3c7f56fbe7719bc68abca6f53a9e68199abae4f1ccb380041bf0795a8ef6ab754a04505e247875 Homepage: https://cran.r-project.org/package=cchsflow Description: CRAN Package 'cchsflow' (Transforming and Harmonizing CCHS Variables) Supporting the use of the Canadian Community Health Survey (CCHS) by transforming variables from each cycle into harmonized, consistent versions that span survey cycles (currently, 2001 to 2018). CCHS data used in this library is accessed and adapted in accordance to the Statistics Canada Open Licence Agreement. This package uses rec_with_table(), which was developed from 'sjmisc' rec(). Lüdecke D (2018). "sjmisc: Data and Variable Transformation Functions". Journal of Open Source Software, 3(26), 754. . Package: r-cran-cci Architecture: all Version: 0.3.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1253 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-caret, r-cran-xgboost, r-cran-ranger, r-cran-data.table, r-cran-e1071, r-cran-rlang, r-cran-progress, r-cran-kknn Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cci_0.3.6.1-1.ca2404.1_all.deb Size: 1184902 MD5sum: 7231a7a240b4cdcdf76312f59eb513e1 SHA1: de3791e56ebb9154826663438899ba4d3925703d SHA256: c16543495489f75545f610055a28eddd0fc6bc51f4bce7cc9d25a69ca10a2b14 SHA512: 8742c46a04f74cc28edfce13edf26c2b389054d5096b304083fba3615f9027f59b1e3968b8e1d96ff0a7e992d374ec6e93bc82bce5bca0cc59107841dc22bc77 Homepage: https://cran.r-project.org/package=CCI Description: CRAN Package 'CCI' (Computational Test for Conditional Independence) Tool for performing computational testing for conditional independence between variables in a dataset. 'CCI' implements permutation in combination with Monte Carlo Cross-Validation in generating null distributions and test statistics. For more details see Computational Test for Conditional Independence (2024) . Package: r-cran-ccid Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-idetect, r-cran-hdbinseg, r-cran-genenet, r-cran-gdata Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ccid_1.2.0-1.ca2404.1_all.deb Size: 85014 MD5sum: c5a1339f53c8a6a265164688edd9249b SHA1: 0a47223c55e8a6f852a6d1fba03e737fe7e9d99e SHA256: 06f00c724143a3037ac7c20be440f0a09dc7ef7d6063ad66befd34afec455c24 SHA512: c3e52f7e625afc8a4e0d5852b7194ad747df3c06b3d8ba88502a1f1f9f7853060c1daf3c038464a20a8a62b76e78d8448ecafebaedd705f4e56c8da051f56586 Homepage: https://cran.r-project.org/package=ccid Description: CRAN Package 'ccid' (Cross-Covariance Isolate Detect: a New Change-Point Method forEstimating Dynamic Functional Connectivity) Provides efficient implementation of the Cross-Covariance Isolate Detect (CCID) methodology for the estimation of the number and location of multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. The main routines in the package have been extensively tested on fMRI data. For details on the CCID methodology, please see Anastasiou et al (2022), Cross-covariance isolate detect: A new change-point method for estimating dynamic functional connectivity. Medical Image Analysis, Volume 75. Package: r-cran-cclustr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-e1071, r-cran-fpc, r-cran-viridislite, r-cran-klar, r-cran-clustmixtype, r-cran-proxy, r-cran-mclust Suggests: r-cran-knitr, r-cran-mice, r-cran-mlbench, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cclustr_0.1.1-1.ca2404.1_all.deb Size: 192340 MD5sum: b245065381fd8d66507a4f06f4bfb1ac SHA1: a207dc9d9c485c19d42898afb587ea4aebb71127 SHA256: 4e96edd25249c0f3e5b8929b7fbd775524362042a10f571a58c6c2ac24a59d75 SHA512: d7087ae5047ae99dbbe0a602d1c0df9f3edae0a95daa304412b4ad6d19e0f902786d4c3889b6dc8078df4ba953e2f72ee265977ba237dd1b822cde6820b583f1 Homepage: https://cran.r-project.org/package=cclustr Description: CRAN Package 'cclustr' (Consensus Clustering Methods for Multiple Imputed Data) Provides tools for performing consensus clustering on multiple imputed datasets. The package supports a range of clustering algorithms across imputations, including hierarchical methods (e.g., Ward, single, complete, average) and partition-based approaches such as k-means, k-medoids (PAM), fuzzy clustering, model-based clustering ('mclust'), and methods for mixed or categorical data (k-modes and k-prototypes). A co-assignment matrix is constructed to quantify agreement between partitions, and consensus solutions are derived via hierarchical clustering applied to the resulting dissimilarity matrix. Additional functions are provided for validation and visualization of clustering results, facilitating robust analysis in the presence of missing data. Consensus clustering framework is based on Monti et al. (2003) , rank aggregation methods follow Pihur et al. (2007) , and the PAC (Proportion of Ambiguous Clustering) metric is based on Senbabaoglu et al. (2014) . Package: r-cran-ccm Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ccm_1.2-1.ca2404.1_all.deb Size: 62352 MD5sum: eb799d871dc3647ddc41a7c05c772ac7 SHA1: 7241661c708f5a48f94edde19c3cded9aae03cc6 SHA256: 7e82d294708b6b529d540b4be9492c707f13267bde2f27a214c79c146715468f SHA512: 822d08584f70338eb09758d7981aab064fd24d0d43555b7e8072bad80a5968f71c3e44593764a716edb5817912963fea7d850f79f33084af1c8baf93a8752092 Homepage: https://cran.r-project.org/package=CCM Description: CRAN Package 'CCM' (Correlation Classification Method) Classification method described in Dancik et al (2011) that classifies a sample according to the class with the maximum mean (or any other function of) correlation between the test and training samples with known classes. Package: r-cran-ccmestimator Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ccmestimator_1.0.0-1.ca2404.1_all.deb Size: 102310 MD5sum: f6e1a76b6440f280947b74be53793f54 SHA1: 80d052597f84491e2530fb9cca0d8b7b3cbf880f SHA256: 3142f2b1f92601e4d6efb4ee199be4c356a91640ce1eaf5c5d323f93cb692c2a SHA512: 07fde7c112c6f65bfa95a8a532c6a83a98861460ae5dca71f765192fb2c83a4db42544e0039f6e4ea92a2c8994a3f23a4a53522a982d16cfadd9232627260df7 Homepage: https://cran.r-project.org/package=ccmEstimator Description: CRAN Package 'ccmEstimator' (Comparative Causal Mediation Estimation) Functions to perform comparative causal mediation analysis to compare the mediation effects of different treatments via a common mediator. Results contain the estimates and confidence intervals for the two comparative causal mediation analysis estimands, as well as the ATE and ACME for each treatment. Functions provided in the package will automatically assess the comparative causal mediation analysis scope conditions (i.e. for each comparative causal mediation estimand, a numerator and denominator that are both estimated with the desired statistical significance and of the same sign). Results will be returned for each comparative causal mediation estimand only if scope conditions are met for it. See details in Bansak(2020). Package: r-cran-ccml Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dicer, r-cran-tidyr, r-cran-snftool, r-cran-plyr, r-bioc-consensusclusterplus Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ccml_1.4.0-1.ca2404.1_all.deb Size: 52504 MD5sum: a359eff60563af963af272ce3627b64e SHA1: 7227d2204a2589e19b4c31e76280a0bb2db2e997 SHA256: 4399b04dd9328477b6efd47274c6351488677896bd1270594a9f0e5b0c44d6a8 SHA512: c8e9635fd81713da5a6f0239590bb16fe2e7e92377c03b94c953c6a51a6997032697d326b8bdd5b7a4744e35088df4398ecdc07bc445a7af6da3497be15f2c45 Homepage: https://cran.r-project.org/package=ccml Description: CRAN Package 'ccml' (Consensus Clustering for Different Sample Coverage Data) Consensus clustering, also called meta-clustering or cluster ensembles, has been increasingly used in clinical data. Current consensus clustering methods tend to ensemble a number of different clusters from mathematical replicates with similar sample coverage. As the fact of common variety of sample coverage in the real-world data, a new consensus clustering strategy dealing with such biological replicates is required. This is a two-step consensus clustering package, which is used to input multiple predictive labels with different sample coverage (missing labels). Package: r-cran-ccmm Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-ccmm_1.0-1.ca2404.1_all.deb Size: 94748 MD5sum: 3065e0e672508de4a8f6e9110fa6021f SHA1: c4607af363c26be1d360c2839b74e0bd53d1fbe3 SHA256: d44d18fa15d7f00711717c296cad0b9cb036449dc59882f205dcf134645e112e SHA512: 76fbeda01de0d3e4df5499772e7553759847f986008864f0668deba6ad93b23ddc8258b39df81793c2435e208e1e8cbc56f5ffc2e9e99964487b0f03c49e8383 Homepage: https://cran.r-project.org/package=ccmm Description: CRAN Package 'ccmm' (Compositional Mediation Model) Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional. Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies. (AOAS: In revision). 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Package: r-cran-ccoptimalmatch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ccoptimalmatch_0.1.0-1.ca2404.1_all.deb Size: 233160 MD5sum: 6ee934d15c67a91604ceaae2c76f3905 SHA1: c391725b53f23cf1e104bcd98b1bbc52e92ebd99 SHA256: 18c08835365f622aa11d0ed6fa719f15da96c4cbe35f13122cea963f5e30b324 SHA512: 0865e01d14ac54e1b263698da4540d8fe3a4077b7aebbd162ad638870969e0be6eecb82600ebabd11a7cbcfcf3e5f1cb3fc3879b40905b6fd8a92c0e53c906ce Homepage: https://cran.r-project.org/package=ccoptimalmatch Description: CRAN Package 'ccoptimalmatch' (Implementation of Case-Control Optimal Matching) Cases are matched to controls in an efficient, optimal and computationally flexible way. It uses the idea of sub-sampling in the level of the case, by creating pseudo-observations of controls. The user can select between replacement and without replacement, the number of controls, and several covariates to match upon. See Mamouris (2021) for an overview. 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Including the estimators BT from Bang and Tsiatis (2000) and ZT from Zhao and Tian (2001) . Package: r-cran-ccp Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ccp_1.2-1.ca2404.1_all.deb Size: 45238 MD5sum: 7b093a67e539d17e1f75b845949d6fe3 SHA1: c5b39687dcca0667b403612178e52e0b477ba87d SHA256: 111214c0e96b9f345820300c6bbce44c42bc3d7f8cd9808465916ca8c7baf52b SHA512: 23553d0aeef43f56fca59811d887c94ab43fb99226927a316581fda499f76f6dc72e0f7dd5bd6a3c72cc0eff8d2b5e2a4e467752e51f53fbe5941ce3df321a44 Homepage: https://cran.r-project.org/package=CCP Description: CRAN Package 'CCP' (Significance Tests for Canonical Correlation Analysis (CCA)) Significance tests are provided for canonical correlation analysis, including asymptotic tests and a Monte Carlo method. Package: r-cran-ccpsyc Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-lavaan, r-cran-readr, r-cran-mcmcpack, r-cran-psych, r-cran-ufs, r-cran-xlsx, r-cran-tibble, r-cran-rlang, r-cran-rcppalgos, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ccpsyc_0.2.6-1.ca2404.1_all.deb Size: 117282 MD5sum: f7a8eb01bd4daa8242c7490b9eb72a9f SHA1: c295a715468eaba8b6d85c548c25a3ee5b465e48 SHA256: c2dbe4741bf0d015294ba4ffc8548264b4a6c053dd2ff9082c5dc97917744f08 SHA512: 71dd096807820afd4ae35a0399e66318ebca0a562af7e11b73df7b9552952053b4f13cd0a064d2b6fec5252d705ec2581cd87b2a78209a4dcc0bb4abf1c431ea Homepage: https://cran.r-project.org/package=ccpsyc Description: CRAN Package 'ccpsyc' (Methods for Cross-Cultural Psychology) With the development of new cross-cultural methods this package is intended to combine multiple functions automating and simplifying functions providing a unified analysis approach for commonly employed methods. Package: r-cran-ccremover Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2272 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ccremover_1.0.4-1.ca2404.1_all.deb Size: 2232380 MD5sum: dbb5ed611846a0c8e78fe3fd1402c319 SHA1: 937a820bf8562edd126a30d199fc11b6dba06848 SHA256: 359688cf797ae84f3cccea42843936dd1841b885536ad6b5a1e7540fb0892d40 SHA512: 01b4704ff8a470477822c85e1e104ef0ddd1d4692f84135cd1727d2aefd03fa2bf5677436d1e930a4121c1773e989baf9cdcc25399f1097bb199599ef180d36e Homepage: https://cran.r-project.org/package=ccRemover Description: CRAN Package 'ccRemover' (Removes the Cell-Cycle Effect from Single-Cell RNA-SequencingData) Implements a method for identifying and removing the cell-cycle effect from scRNA-Seq data. The description of the method is in Barron M. and Li J. (2016) . Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. Different from previous methods, ccRemover implements a mechanism that formally tests whether a component is cell-cycle related or not, and thus while it often thoroughly removes the cell-cycle effect, it preserves other features/signals of interest in the data. Package: r-cran-ccsrfind Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ccsrfind_0.1.0-1.ca2404.1_all.deb Size: 684764 MD5sum: 57c2b3b4c1db93e16e993f13fe1d128f SHA1: 4b40d43612046617b005f5f6c5f1039a2f278120 SHA256: c0b9544cfaf29b3b322fc7d1a5ed016ed6077d2935aea2be603e68144b08dc0d SHA512: f42475995b429949759c3ffe1cfdf61d6637fc4df376a9e23749279e44e6b6bc77d02012f45081a1f4f89fd3b595ef06a6a835d02109994c773d42f6614e24cf Homepage: https://cran.r-project.org/package=CCSRfind Description: CRAN Package 'CCSRfind' (Convert ICD-10 Codes to CCSR Codes) Provides a tool for matching ICD-10 codes to corresponding Clinical Classification Software Refined (CCSR) codes. The main function, CCSRfind(), identifies each CCSR code that applies to an individual given their diagnosis codes. It also provides a summary of CCSR codes that are matched to a dataset. The package contains 3 datasets: 'DXCCSR' (mapping of ICD-10 codes to CCSR codes), 'Legend' (conversion of DXCCSR to CCSRfind-usable format for CCSR codes with less than or equal to 1000 ICD-10 diagnosis codes), and 'LegendExtend' (conversion of DXCCSR to CCSRfind-usable format for CCSR codes with more than 1000 ICD-10 dx codes). The disc() function applies grepl() ('base') to multiple columns and is used in CCSRfind(). 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The scheme will be useful in various field like Bioinformatics where the samples are expensive and must be precise in reflecting the population by possessing least sampling variance. 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See the reference section of GitHub README.md , for details of the methods. 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The package includes functions for empirical Q-matrix estimation and validation, such as the Hull method (Nájera, Sorrel, de la Torre, & Abad, 2021, ) and the discrete factor loading method (Wang, Song, & Ding, 2018, ). It also contains dimensionality assessment procedures for CDM, including parallel analysis and automated fit comparison as explored in Nájera, Abad, and Sorrel (2021, ). Other relevant methods and features for CDM applications, such as the restricted DINA model (Nájera et al., 2023; ), the general nonparametric classification method (Chiu et al., 2018; ), and corrected estimation of the classification accuracy via multiple imputation (Kreitchmann et al., 2022; ) are also available. Lastly, the package provides some useful functions for CDM simulation studies, such as random Q-matrix generation and detection of complete/identified Q-matrices. Package: r-cran-cdnbcr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-pracma Suggests: r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cdnbcr_1.1.1-1.ca2404.1_all.deb Size: 140628 MD5sum: 911371aab822ae4c22286363bd37c06a SHA1: 449062e38d18667eed48600e09cbdb9ca4ea3cb4 SHA256: 9b1b1aec3e8548d442a95e20c94e4e7e0b43a14bb246dd1f4f720e7560bf9565 SHA512: e0b77e0e0d83ac808a202e7d057376606c05efb909e3321c9cf4535b17c89d6e0e7ac2d2383e9c0f30a2c69acebe49f4fd5806cb8056c6bd18d890d6d3044a4f Homepage: https://cran.r-project.org/package=cdnbcr Description: CRAN Package 'cdnbcr' (Correlated Destructive Negative Binomial Cure Rate Model) Provides tools for modeling time-to-event data with a cure fraction under correlated destructive negative binomial cure rate models. The models assume multiple latent competing causes with possible dependence and allow for elimination (inactivation) of some initial causes. Estimation is performed via an Expectation-Maximization algorithm, and diagnostic tools based on Cox-Snell residuals are provided. 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Package: r-cran-cdparcoord Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2351 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-plotly, r-cran-freqparcoord, r-cran-partools Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-cdparcoord_1.0.1-1.ca2404.1_all.deb Size: 1749470 MD5sum: 418734600b610dcbaa5edcffa7bb4581 SHA1: ed1b5c96194ff18e92fe4c10484d2e0c1d0ce1d4 SHA256: dfc9179abb2e5a9b23159d2dcc4c0bf39118b04a2e031fc7530e374b8d318f25 SHA512: 852a11a823631a49e0fbbabc54c692e836e08d6c47d0758181806d9bdd2a99eb4ba44b76e4150dcb72bb12e210a6c504a16f81d0b5dfc5bc40bee6ec9bf85177 Homepage: https://cran.r-project.org/package=cdparcoord Description: CRAN Package 'cdparcoord' (Top Frequency-Based Parallel Coordinates) Parallel coordinate plotting with resolutions for large data sets and missing values. Package: r-cran-cdrcr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2063 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-sf, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-purrr, r-cran-rlist, r-cran-rjson, r-cran-tidyr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cdrcr_0.1.1-1.ca2404.1_all.deb Size: 2074402 MD5sum: f9ce4fb81160bc2723d3444e0a275fe1 SHA1: a2feeb14cc5d8b0403c1c9e9830a420df37eb152 SHA256: 4b3c1e2bee23e83e63d0a7dcaf60899544ca3387b3e5ad47b82055980c6d1c28 SHA512: 2765ce45079621d6eb0fb00b64b64b61c5571324c044e403e4031a411682484a7a117990f1e025f7f8ab56daaa25f57dc69b62dfb1ae0630628af2e2734c7c07 Homepage: https://cran.r-project.org/package=cdrcR Description: CRAN Package 'cdrcR' (Load 'CDRC' Data) A wrapper for the 'CDRC' 'API' that returns data frames or 'sf' of 'CDRC' data. The 'API' web reference is:. Package: r-cran-cds Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-limsolve, r-cran-clue, r-cran-colorspace, r-cran-copula Filename: pool/dists/noble/main/r-cran-cds_1.0.4-1.ca2404.1_all.deb Size: 172376 MD5sum: 4caa5a473a980dbaa13605e49c911686 SHA1: 9408b8a2f935f73c3113eb0c99a01f13ff97abcf SHA256: 21dd256af47247f6152cd2ba1c37f455b15588e4bd87244aa0a0971ff9142c41 SHA512: 87b0c270ffd417477efb873042f26b8c205c62cb99f8da7b0e91f26915d75869bc11a3eaf549c75ac2fb8a6bc82fe4f6061a9644450719b8aa73d6b71a16d037 Homepage: https://cran.r-project.org/package=cds Description: CRAN Package 'cds' (Constrained Dual Scaling for Detecting Response Styles) This is an implementation of constrained dual scaling for detecting response styles in categorical data, including utility functions. The procedure involves adding additional columns to the data matrix representing the boundaries between the rating categories. The resulting matrix is then doubled and analyzed by dual scaling. One-dimensional solutions are sought which provide optimal scores for the rating categories. These optimal scores are constrained to follow monotone quadratic splines. Clusters are introduced within which the response styles can vary. The type of response style present in a cluster can be diagnosed from the optimal scores for said cluster, and this can be used to construct an imputed version of the data set which adjusts for response styles. Package: r-cran-cdsampling Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-rglpk Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cdsampling_0.1.6-1.ca2404.1_all.deb Size: 135544 MD5sum: 17edabf63e7b42cb1540d16536dcef01 SHA1: 42850065ebcd12a8149ffa554f73eaf7cf056eab SHA256: 2af8e21ea621bf6b21b4856199fa1390e5b3f487c1da8c6548c9fb6028953407 SHA512: 573528d5b0b3f2ae5fef099ca17b9e9008a541a2b62f7dc0d3e549fb6dcbf284f09420df96c0307ecb252764d903caf4845c2ca38c2e2dd2ce1742c012a6ee79 Homepage: https://cran.r-project.org/package=CDsampling Description: CRAN Package 'CDsampling' (Constrained Sampling in Paid Research Studies) In the context of paid research studies and clinical trials, budget considerations and patient sampling from available populations are subject to inherent constraints. We introduce the 'CDsampling' package, which integrates optimal design theories within the framework of constrained sampling. This package offers the possibility to find both D-optimal approximate and exact allocations for samplings with or without constraints. Additionally, it provides functions to find constrained uniform sampling as a robust sampling strategy with limited model information. Our package offers functions for the computation of the Fisher information matrix under generalized linear models (including regular linear regression model) and multinomial logistic models.To demonstrate the applications, we also provide a simulated dataset and a real dataset embedded in the package. Yifei Huang, Liping Tong, and Jie Yang (2025). Package: r-cran-cdse Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geojsonsf, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-lutz, r-cran-sf, r-cran-terra Suggests: r-cran-maps, r-cran-rsi, r-cran-tibble Filename: pool/dists/noble/main/r-cran-cdse_0.3.2-1.ca2404.1_all.deb Size: 696134 MD5sum: badd89a74ae368c39a13a312f83ad2a5 SHA1: 8a3d893d70bb82c3011045f819f5079a78bd5bc1 SHA256: f34fcc72fa360dc9aa142488ec17314d7a59881a0b406ad2bbfa19d58bc7fd92 SHA512: 4ce65404bdde6bda5fe5f117dc46125372371b0aa4265ec29939c19161c1e2ed02beac70ec384f52b688cc931ce6fcbffc733f1bc3277a105d345537b6ffe6ad Homepage: https://cran.r-project.org/package=CDSE Description: CRAN Package 'CDSE' ('Copernicus Data Space Ecosystem' API Wrapper) Provides interface to the 'Copernicus Data Space Ecosystem' API , mainly for searching the catalog of available data from Copernicus Sentinel missions and obtaining the images for just the area of interest based on selected spectral bands. The package uses the 'Sentinel Hub' REST API interface that provides access to various satellite imagery archives. It allows you to access raw satellite data, rendered images, statistical analysis, and other features. This package is in no way officially related to or endorsed by Copernicus. Package: r-cran-cdsim Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1643 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-trend, r-cran-truncnorm, r-cran-ncdf4, r-cran-lubridate, r-cran-readr, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tidyr, r-cran-vroom, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cdsim_0.1.2-1.ca2404.1_all.deb Size: 1159764 MD5sum: 41035f9db1fc542ffdf799705eb3a595 SHA1: 3ca92416ce1a2e218db3a1ff7fa10c5a8c5ffb87 SHA256: 0a919052c0480f0f1d39fec92d3900dc08b11cb79de0097ee4c4867e910db47e SHA512: fd0fc217f218663276a2160c44e845fd6e05d0db48f44fcb171445fd22aa0cf4bfa049a18e0f83dbe12bab95baaf50a3e9488e8846132b861a1343b7c746fc76 Homepage: https://cran.r-project.org/package=CDSim Description: CRAN Package 'CDSim' (Simulating Climate Data for Research and Modelling) Generate synthetic station-based monthly climate time-series including temperature and rainfall, export to Network Common Data Form (NetCDF), and provide visualization helpers for climate workflows. The approach is inspired by statistical weather generator concepts described in Wilks (1999) and Richardson (1981) . 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Package: r-cran-cdvi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cdvi_0.1.0-1.ca2404.1_all.deb Size: 11880 MD5sum: b7bd4b44a1a4b3374654af0d9469c6af SHA1: bcf1a0b3d525f8cd8941e40b1778c8958d6bbf3f SHA256: acdab130f7c1f283d3ef136d63cdf701d506c0f92988f8705320ed8e13d1f858 SHA512: 452826d12139f0347fb47fbfeda8595ee868021ebb08414f37f48b17af3ba7404b84e4aa12005518c77f25eb1f0f343c045a36bde642315edffaa5d4645be1f1 Homepage: https://cran.r-project.org/package=CDVI Description: CRAN Package 'CDVI' (Cuddy-Della Valle Index for Capturing the Instability in TimeSeries Data) Cuddy-Della valle index gives the degree of instability present in the data by accommodating the effect of a trend. The adjusted R squared value of the best fitted model is chosen. The index is obtained by multiplying the coefficient of variation with square root of one minus the adjusted R-squared value. This package has been developed using concept of Shankar et al. (2022). Package: r-cran-cdvinecopulaconditional Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-combinat, r-cran-vinecopula Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cdvinecopulaconditional_0.1.1-1.ca2404.1_all.deb Size: 134916 MD5sum: 013abba10025bd0fc8cfd0c2df974e92 SHA1: fea532a7fbc01233635c6f70e70dc29e324e06da SHA256: f91958b0c097a950c2ada9831d6d993127df72a84b8713883672c3e71796c39e SHA512: 0aa9c64f792f85ca9164969cff3f325fb7cf8a113f86249544b4a95a87e46ac54ce4b967f0f70131546a61e13a488b291228850d23ebc933a9cb8a9d014ec732 Homepage: https://cran.r-project.org/package=CDVineCopulaConditional Description: CRAN Package 'CDVineCopulaConditional' (Sampling from Conditional C- and D-Vine Copulas) Provides tools for sampling from a conditional copula density decomposed via Pair-Copula Constructions as C- or D- vine. Here, the vines which can be used for such a sampling are those which sample as first the conditioning variables (when following the sampling algorithms shown in Aas et al. (2009) ). The used sampling algorithm is presented and discussed in Bevacqua et al. (2017) , and it is a modified version of that from Aas et al. (2009) . A function is available to select the best vine (based on information criteria) among those which allow for such a conditional sampling. The package includes a function to compare scatterplot matrices and pair-dependencies of two multivariate datasets. Package: r-cran-ceas Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3956 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-lme4, r-cran-readxl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ceas_1.3.0-1.ca2404.1_all.deb Size: 2591928 MD5sum: 8288473b5a3e66ba4e5a283d19cdf1a5 SHA1: e58911ed1070753a1d2023edf3d1f032e8ae5b87 SHA256: 491365d0180b45c4aa12337954ffd50edb95f81735373fef41e402c22406cd58 SHA512: b184088670cb3788cd553d3babf2210eabc63616a05cba27efb9e1ccdfad1eea207682ce8434d56fb1cc7903214b83833b1309ff1c601e49364c0b4bfb614e2f Homepage: https://cran.r-project.org/package=ceas Description: CRAN Package 'ceas' (Cellular Energetics Analysis Software) Measuring cellular energetics is essential to understanding a matrix’s (e.g. cell, tissue or biofluid) metabolic state. The Agilent Seahorse machine is a common method to measure real-time cellular energetics, but existing analysis tools are highly manual or lack functionality. The Cellular Energetics Analysis Software (ceas) R package fills this analytical gap by providing modular and automated Seahorse data analysis and visualization using the methods described by Mookerjee et al. (2017) . Package: r-cran-ceblr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2990 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-readr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ceblr_1.0.0-1.ca2404.1_all.deb Size: 2877742 MD5sum: e48d91ef016ca4b3134fb3463b91cfd9 SHA1: 58d098a6eb068412cb26a2a0af2c3961f959f587 SHA256: f1879af2a1e9d9ec116b90a05747799a55a69d83f5993d40899087abdeb32ac9 SHA512: 19386f5353c384b7a4df16701a1bbede730fa67a2710793ce0d00cfa42ecda6cc6c311d88c626a03a0bf4edea1e28a0cd96e3f3c24cdca45d14c7600196cbe32 Homepage: https://cran.r-project.org/package=ceblR Description: CRAN Package 'ceblR' (Extract Data from the Canadian Elite Basketball League) Gather boxscore and play-by-play data from the Canadian Elite Basketball League (CEBL) to create a repository of basic and advanced statistics for teams and players. Package: r-cran-ceda Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3308 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-limma, r-cran-mixtools, r-cran-ggplot2, r-cran-dplyr, r-cran-ggsci, r-cran-ggridges, r-cran-ggprism Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ceda_1.1.1-1.ca2404.1_all.deb Size: 3259440 MD5sum: d05639d1e766f42511fd4c110427d7a4 SHA1: e5343b7e3dd039dd2385336565bb443ca828b117 SHA256: 7d8bdd073676cba495f730bb9481e2cb7252b5d9ff99de6d09c4cc5d8e0170a9 SHA512: 073f065c4a6e2f18306a45f8e190c54b183b1ce6b0f75cb961f9327403a0ae50e50674ba15a54f5ce28f94584405bb67e7b6d6093111b9380541a1e75a956d28 Homepage: https://cran.r-project.org/package=CEDA Description: CRAN Package 'CEDA' (CRISPR Screen and Gene Expression Differential Analysis) Provides analytical methods for analyzing CRISPR screen data at different levels of gene expression. Multi-component normal mixture models and EM algorithms are used for modeling. Package: r-cran-cedmr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-magrittr, r-cran-mediation, r-cran-rlang, r-cran-rms, r-cran-tidyr Suggests: r-cran-cluster, r-cran-konfound, r-cran-mice, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cedmr_0.1.0-1.ca2404.1_all.deb Size: 111978 MD5sum: 98c79aa3196289ae6e94f1ac31b81445 SHA1: 5f3f9278a71724dc37665f6d2c4b701d57e4c025 SHA256: 635180db94fb200ce55417d96fadb40697bb8b0d5069e16aa433c12dd5db2062 SHA512: 44f9e5e0dfd40a54ece4dc999bc62a92188a70b33f5df344358fc54b3de5a19f76871a4b3e42d6a756e1e8fc70a7493b87773e21e8077176f7b450f6cbd318eb Homepage: https://cran.r-project.org/package=CEDMr Description: CRAN Package 'CEDMr' (Capability-Ecological Developmental Model (CEDM) Analysis) Implements the Capability-Ecological Developmental Model (CEDM) for longitudinal and multilevel data. The package supports estimation and interpretation of models examining how socioeconomic status (SES), health indicators, and contextual factors jointly relate to academic outcomes. Functionality includes: (1) classification of ecological capability regimes (amplifying, neutral, compensatory); (2) estimation of moderated multilevel models with higher-order interaction terms; (3) causal mediation analysis using doubly robust estimation; (4) random-effects within-between (REWB) decomposition; (5) nonlinear moderation using restricted cubic splines; (6) clustering of longitudinal health trajectories; and (7) sensitivity analysis using the impact threshold for a confounding variable (ITCV) and robustness-to-replacement (RIR) measures. The package is designed for use with general longitudinal multilevel datasets. Package: r-cran-ceemdanml Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlibeemd, r-cran-tseries, r-cran-forecast, r-cran-fgarch, r-cran-atsa, r-cran-fints, r-cran-lsts, r-cran-earth, r-cran-caret, r-cran-neuralnet, r-cran-e1071, r-cran-pso Filename: pool/dists/noble/main/r-cran-ceemdanml_0.1.0-1.ca2404.1_all.deb Size: 49542 MD5sum: e3d607c6b188d0134fddf3de1aa4d212 SHA1: 1149fe5589b059a93deb969faab94ebeae76846e SHA256: 5dfdb221ae40e131518cf2557a2418c3412bdbae295433dd2e529a37ebd94926 SHA512: 3ee4d77226053e1ec1489374025e06f69ca20e235fa6d374574612cb82c012cac8aa3c4f993b8d1d46bc81ae2c336adf4b97d3c36bdbbde948512c5d6e74f3c8 Homepage: https://cran.r-project.org/package=CEEMDANML Description: CRAN Package 'CEEMDANML' (CEEMDAN Decomposition Based Hybrid Machine Learning Models) Noise in the time-series data significantly affects the accuracy of the Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression are considered here). Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the time series data into sub-series and help to improve the model performance. The models can achieve higher prediction accuracy than the traditional ML models. Two models have been provided here for time series forecasting. More information may be obtained from Garai and Paul (2023) . Package: r-cran-ceg Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-graph, r-bioc-rgraphviz Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ceg_0.1.0-1.ca2404.1_all.deb Size: 260542 MD5sum: 4ff551cedff0b0dd05fb8c64f0e05131 SHA1: 4e5c45bac9d3260cb719321ad4d9b11784934b86 SHA256: 653dba58e02ef899390ad7174a306824d22c6333371b4b12bb00d9fc711bbfc3 SHA512: b045277b11230c5794f1d42c455874a0efe356409372ec28bd20a9ec88a7c29379d1277e0873b38473551ee8b6d930e3c76fe591cf797001fefa18a128a8bb10 Homepage: https://cran.r-project.org/package=ceg Description: CRAN Package 'ceg' (Chain Event Graph) Create and learn Chain Event Graph (CEG) models using a Bayesian framework. It provides us with a Hierarchical Agglomerative algorithm to search the CEG model space. The package also includes several facilities for visualisations of the objects associated with a CEG. The CEG class can represent a range of relational data types, and supports arbitrary vertex, edge and graph attributes. A Chain Event Graph is a tree-based graphical model that provides a powerful graphical interface through which domain experts can easily translate a process into sequences of observed events using plain language. CEGs have been a useful class of graphical model especially to capture context-specific conditional independences. References: Collazo R, Gorgen C, Smith J. Chain Event Graph. CRC Press, ISBN 9781498729604, 2018 (forthcoming); and Barday LM, Collazo RA, Smith JQ, Thwaites PA, Nicholson AE. The Dynamic Chain Event Graph. Electronic Journal of Statistics, 9 (2) 2130-2169 . Package: r-cran-celestial Architecture: all Version: 1.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rann, r-cran-nistunits, r-cran-pracma Filename: pool/dists/noble/main/r-cran-celestial_1.4.6-1.ca2404.1_all.deb Size: 364362 MD5sum: b1049de58aa3267fd492a5296e5cdfad SHA1: 89cda04989c7fd9a96eed601551c62ce2d9c9c9e SHA256: b56612386f501e61010ae61d610408af1ab70f96fdfe5b8da484d23d1fb37c37 SHA512: b0c914f69736874cf80db3b67b3348e3a9314c91274e0e76f6d15080677abaadbca083ecdbbca061e6eaf33ffa43adcacdacf715e55ee8fff2d205bbd3380685 Homepage: https://cran.r-project.org/package=celestial Description: CRAN Package 'celestial' (Collection of Common Astronomical Conversion Routines andFunctions) Contains a number of common astronomy conversion routines, particularly the HMS and degrees schemes, which can be fiddly to convert between on mass due to the textural nature of the former. It allows users to coordinate match datasets quickly. It also contains functions for various cosmological calculations. Package: r-cran-celldeep Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seurat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-celldeep_1.0.1-1.ca2404.1_all.deb Size: 1490144 MD5sum: 005b1896a10804d706d65b8ffadbd81c SHA1: c6f7a2a31a91aad42da6bbb3bbb8cf905a902642 SHA256: f1e4b49b61bcd9bb81474760082f80c0a8fdee18b000c40eef3b8a524a86c36c SHA512: 05beec28a7e1009570d71db8128a32664f1709dcb6b6679a1c86bfbc8b8979877c94db8d566416da7de5a57cfc2f7b920b6f6bc82c90b5d5635aee3bb7490d31 Homepage: https://cran.r-project.org/package=CellDEEP Description: CRAN Package 'CellDEEP' (Cell DiffErential Expression by Pooling ('CellDEEP')) Pool cells together before running differentially expression (DE) analysis. Tell 'CellDEEP' how many cells you want to pool together (which shall be determined by the overall cell number of data), then run DE analysis. Cheng et al. (2026) . Package: r-cran-cellgeometry Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4296 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circlize, r-bioc-complexheatmap, r-bioc-delayedarray, r-cran-dplyr, r-bioc-ensembldb, r-cran-ggplot2, r-cran-ggrepel, r-cran-gtools, r-cran-matrixstats, r-cran-mcprogress, r-cran-rlang, r-cran-scales Suggests: r-cran-future.apply, r-cran-ggforce, r-cran-ggsci, r-cran-knitr, r-cran-plotly, r-cran-rfast2, r-cran-rmarkdown, r-cran-seriation, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-cellgeometry_0.6.3-1.ca2404.1_all.deb Size: 2187310 MD5sum: 9b271ff7a80bf3377242ce3da262999b SHA1: fa77f3f59f3d0747616d92143c2bcf8f8ff5f9a1 SHA256: 9e270269fef3549380929eebf8de2d3c7d9fd4f34eb4ef676d520391a561250c SHA512: 20167c155f642a4778c0101800865ebcd360beb38662923f6795117eb9591cc284b4f2a23f7fe8755352edeb40f13ed206a2966086366b9f048521c92d2df649 Homepage: https://cran.r-project.org/package=cellGeometry Description: CRAN Package 'cellGeometry' (Geometric Single Cell Deconvolution) Deconvolution of bulk RNA-Sequencing data into proportions of cells based on a reference single-cell RNA-Sequencing dataset using high-dimensional geometric methodology . Package: r-cran-cellkey Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5013 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sdchierarchies, r-cran-rlang, r-cran-digest, r-cran-sdctable, r-cran-ptable, r-cran-cli, r-cran-yaml, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cellkey_1.0.3-1.ca2404.1_all.deb Size: 4968400 MD5sum: db4968badeb3f6399cf8cde418d8291c SHA1: ce1144180a4e8a8a429a11fdde38987efb795e94 SHA256: 2354b4e699de10fb21a9090aa90d38def89307a333a20cdda7a1f928ab76a9c6 SHA512: 66878db1c6c8f32d09804a16625304cc7c192afb351582043b2cd66fe8e5630215e761b5db13071b0ee79343699539dad3dcf48f95ebf668c2813556c5faf58c Homepage: https://cran.r-project.org/package=cellKey Description: CRAN Package 'cellKey' (Consistent Perturbation of Statistical Frequency- And MagnitudeTables) Data from statistical agencies and other institutions often need to be protected before they can be published. This package can be used to perturb statistical tables in a consistent way. The main idea is to add - at the micro data level - a record key for each unit. Based on these keys, for any cell in a statistical table a cell key is computed as a function on the record keys contributing to a specific cell. Values that are added to the cell in order to perturb it are derived from a lookup-table that maps values of cell keys to specific perturbation values. The theoretical basis for the methods implemented can be found in Thompson, Broadfoot and Elazar (2013) which was extended and enhanced by Giessing and Tent (2019) . Package: r-cran-cellorigins Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 773 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-iterpc Filename: pool/dists/noble/main/r-cran-cellorigins_0.1.3-1.ca2404.1_all.deb Size: 346668 MD5sum: 84db4114345178f961ac5dd007c6d008 SHA1: 87f5138264db635265e9792e11ee38d2acace639 SHA256: a0ecf150729a9ca4e1f82bd190ceb0d82392696a57a5021b457251ae2c7319b7 SHA512: 09b720431f180663d95ab8b3b5aa364c1ac9c41f1e1d861266b185325f64ec68a009af1438f92d560fae869f2274a056ba0698c0a767dcdafd21e99507d3661f Homepage: https://cran.r-project.org/package=cellOrigins Description: CRAN Package 'cellOrigins' (Finds RNASeq Source Tissues Using In Situ Hybridisation Data) Finds the most likely originating tissue(s) and developmental stage(s) of tissue-specific RNA sequencing data. The package identifies both pure transcriptomes and mixtures of transcriptomes. The most likely identity is found through comparisons of the sequencing data with high-throughput in situ hybridisation patterns. Typical uses are the identification of cancer cell origins, validation of cell culture strain identities, validation of single-cell transcriptomes, and validation of identity and purity of flow-sorting and dissection sequencing products. Package: r-cran-cellpypes Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1223 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scutils, r-cran-ggplot2, r-cran-matrix, r-cran-rlang, r-cran-viridis, r-cran-cowplot, r-cran-dplyr, r-cran-scales, r-cran-scattermore Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-seurat, r-bioc-deseq2, r-cran-rcppannoy, r-cran-tibble, r-cran-seuratobject Filename: pool/dists/noble/main/r-cran-cellpypes_0.3.0-1.ca2404.1_all.deb Size: 1066838 MD5sum: b604537b29aa3704be896809c72d2b2f SHA1: b130b58f41f398b2b9a6d730488c0bfbf446243d SHA256: 362efb55cc1e58cfe6c8c450199607cef7224291ba75f45445aaaf8c790ee101 SHA512: f9237c164b4bf85d9c50a9c954a28e8463e7d6c94c4f18486bd2862f755f60707579d0c7a0d96f9015dfcb8f5a8e59e65791b1fee316eea46faf688dfe77e885 Homepage: https://cran.r-project.org/package=cellpypes Description: CRAN Package 'cellpypes' (Cell Type Pipes for Single-Cell RNA Sequencing Data) Annotate single-cell RNA sequencing data manually based on marker gene thresholds. Find cell type rules (gene+threshold) through exploration, use the popular piping operator '%>%' to reconstruct complex cell type hierarchies. 'cellpypes' models technical noise to find positive and negative cells for a given expression threshold and returns cell type labels or pseudobulks. Cite this package as Frauhammer (2022) and visit for tutorials and newest features. Package: r-cran-cellranger Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rematch, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cellranger_1.1.0-1.ca2404.1_all.deb Size: 103102 MD5sum: 3ba31e894c8b40552a4f383a3be4b451 SHA1: 4d2797daaa61572b4dda6e01eff286e67544e37d SHA256: c36e80a081ed87be007d76006a1721dba03f00879057a7726940074c02869257 SHA512: aff72b4e99f678a1673266a177deceb0a60976238eea3cffb23ee7212ca4e57751c5150405d607e18207234dd7d8aaf308ade653c764e761b332282a8109fb80 Homepage: https://cran.r-project.org/package=cellranger Description: CRAN Package 'cellranger' (Translate Spreadsheet Cell Ranges to Rows and Columns) Helper functions to work with spreadsheets and the "A1:D10" style of cell range specification. Package: r-cran-celltrackr Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4993 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ellipse, r-cran-pracma Suggests: r-cran-scatterplot3d, r-cran-fractaldim, r-cran-testthat, r-cran-wordspace, r-cran-knitr, r-cran-rmarkdown, r-cran-rspectra, r-cran-uwot, r-cran-dendextend, r-cran-ggplot2, r-cran-ggbeeswarm, r-cran-gridextra, r-cran-mvtnorm, r-cran-jsonlite, r-cran-httr, r-cran-curl Filename: pool/dists/noble/main/r-cran-celltrackr_1.2.2-1.ca2404.1_all.deb Size: 3426762 MD5sum: 62802220e0825461255bfddd0d81ec37 SHA1: 31e632842a4a7a970b70a65c4a4568120e73fa1b SHA256: e6a3b92b2152803f87e9bd2a0d78fea2b56b51a6893db831d5150e65e6f55acc SHA512: 39bb5397c6b6b81f2781912d26707af369c0637c2282a5c65f979cfb1f1efcf88c899d1a60373f5c53d41303d9ea94bcdf2b4577ad58edd59cb65f7475aac9d0 Homepage: https://cran.r-project.org/package=celltrackR Description: CRAN Package 'celltrackR' (Motion Trajectory Analysis) Methods for analyzing (cell) motion in two or three dimensions. Available measures include displacement, confinement ratio, autocorrelation, straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks, steps, or subtracks with varying length. While the methodology has been developed for cell trajectory analysis, it is applicable to anything that moves including animals, people, or vehicles. Some of the methodology implemented in this packages was described by: Beauchemin, Dixit, and Perelson (2007) , Beltman, Maree, and de Boer (2009) , Gneiting and Schlather (2004) , Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) , Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) , Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) , Textor, Sinn, and de Boer (2013) , Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) . Package: r-cran-cellularautomata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gganimate, r-cran-ggplot2, r-cran-patchwork, r-cran-purrr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cellularautomata_0.1.0-1.ca2404.1_all.deb Size: 212882 MD5sum: f17c9911d8ea6d47fa93e73d29dda05c SHA1: 879f40e057e05645f724062db326d9caf60bdf12 SHA256: f2f419c24d55801cd9d70d59c2c3dff4e1e747be7cf8a5e482824b854e72593d SHA512: 7b98c336a53d726e497d8c86b6fab1c2467f74da047626c77b2b207a45d6aa2de5eedeb9ee23203c9adcbcc39692f5dd121f99e7d58d09547b9d343e0b57096d Homepage: https://cran.r-project.org/package=cellularautomata Description: CRAN Package 'cellularautomata' (Cellular Automata) Create cellular automata from 'Wolfram' rules. Allows the creation of 'Wolfram' style plots, as well as of animations. Easy to create multiple plots, for example the output of a rule with different initial states, or the output of many different rules from the same state. The output of a cellular automaton is given as a matrix, making it easy to try to explore the possibility of predicting its time evolution using various statistical tools available in R. Wolfram S. (2002, ISBN:1579550088) "A New Kind of Science". Package: r-cran-cellvolumedist Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm, r-cran-gplots Filename: pool/dists/noble/main/r-cran-cellvolumedist_1.5-1.ca2404.1_all.deb Size: 52852 MD5sum: b455e814ec3cb873b492863b69add655 SHA1: 17c7e79e250f596e5d54efcc839f9fab6d383de5 SHA256: 12f89bbea598a3ed35561ef53af2df1f66d55213ce2e17e049807783467dda0f SHA512: 9361af887d9e0feb0468d8306bfb710c017f905ca1df28ef788ec0fa4fc59757dca7f020b33267e62bf99ecb9a0c89efe5122f0f57897c340b953fe7ef0356e8 Homepage: https://cran.r-project.org/package=cellVolumeDist Description: CRAN Package 'cellVolumeDist' (Functions to Fit Cell Volume Distributions and Thereby EstimateCell Growth Rates and Division Times) Implements a methodology for using cell volume distributions to estimate cell growth rates and division times that is described in the paper, "Cell Volume Distributions Reveal Cell Growth Rates and Division Times", by Michael Halter, John T. Elliott, Joseph B. Hubbard, Alessandro Tona and Anne L. Plant, which appeared in the Journal of Theoretical Biology. In order to reproduce the analysis used to obtain Table 1 in the paper, execute the command "example(fitVolDist)". Package: r-cran-cem Architecture: all Version: 1.1.31-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 757 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-matchit, r-cran-combinat, r-cran-randomforest, r-cran-nlme Suggests: r-cran-amelia Filename: pool/dists/noble/main/r-cran-cem_1.1.31-1.ca2404.1_all.deb Size: 677224 MD5sum: 5a158ced099ff5b42221a96c9b907e14 SHA1: 95dc06a0150e47066eabd4424a61ed5425dc3e70 SHA256: 9290d0aec58839b5e8405cb358d3d96dc87be42d821d4f42204bd1fe46cac44e SHA512: 4e2f0695890373910348024680fc950bd3fdf1839810731585f5b36540cb36dea18dab3961d5084199c3a8c879482894502da105cc3095b35d7212ddb7872eb3 Homepage: https://cran.r-project.org/package=cem Description: CRAN Package 'cem' (Coarsened Exact Matching) Implementation of the Coarsened Exact Matching algorithm discussed along with its properties in Iacus, King, Porro (2011) ; Iacus, King, Porro (2012) and Iacus, King, Porro (2019) . Package: r-cran-cemco Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-nnet, r-cran-rootsolve, r-cran-foreach, r-cran-mass, r-cran-mclust, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-cemco_0.2-1.ca2404.1_all.deb Size: 50974 MD5sum: 7ffb630d0e7ae625c0e767bedd6b16fa SHA1: 630920ba73230895ed39024fce2ab5ec77077576 SHA256: 0a2d080c798ec5dc3d4a49eac13ee2f84253358b086b6d75bf073eee99bc1f92 SHA512: ae305c6f6b1ecbffd7a59687799537761eb8a81433b446e3883962722be32b8a896d1642fe79293bf281689cb8f05eeec978ec8d50e39828dbfe092f951c73ca Homepage: https://cran.r-project.org/package=cemco Description: CRAN Package 'cemco' (Fit 'CemCO' Algorithm) 'CemCO' algorithm, a model-based (Gaussian) clustering algorithm that removes/minimizes the effects of undesirable covariates during the clustering process both in cluster centroids and in cluster covariance structures (Relvas C. & Fujita A., (2020) ). Package: r-cran-cenbar Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-mass, r-cran-mvtnorm, r-cran-glmnet, r-cran-survival, r-cran-cvtools Filename: pool/dists/noble/main/r-cran-cenbar_0.1.1-1.ca2404.1_all.deb Size: 43266 MD5sum: f7265ea7e580c5a745b6bad1de3253f6 SHA1: 5e2e944ff880a9c1e41bbe23e8864f673956ffdc SHA256: c58ba80a44ac3f5c316afea72e0ed5becac8d573ca3202982d21731ee366576c SHA512: c95442847d4187530252d85f20e9fb76678c17783e0db514946fbaa6d5f21a68aaf1946c0748727455ecc419806e153c3343d810af1ad2e83e2512464efa49c7 Homepage: https://cran.r-project.org/package=CenBAR Description: CRAN Package 'CenBAR' (Broken Adaptive Ridge AFT Model with Censored Data) Broken adaptive ridge estimator for censored data is used to select variables and estimate their coefficients in the semi-parametric accelerated failure time model for right-censored survival data. Package: r-cran-cencrne Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2071 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cencrne_1.0.0-1.ca2404.1_all.deb Size: 2038822 MD5sum: c919aa1cedc370cac2f70c9b0c389d21 SHA1: 7ffd2aef9b9d2550da2e952cc70406c36c365506 SHA256: 4ac766c02f25fa6a959c8675c29d51d67ff1d688589a656f12af3b51d0e3a404 SHA512: 0fceaa2e8b65648518c47d1da6bb3633da9dff3b93e34fffee24c5c5e867315b1b1fbfe232dd2ed2577de9870b7acd52b6840d668176df8061a028438a4fd37e Homepage: https://cran.r-project.org/package=cencrne Description: CRAN Package 'cencrne' (Consistent Estimation of the Number of Communities viaRegularized Network Embedding) The network analysis plays an important role in numerous application domains including biomedicine. Estimation of the number of communities is a fundamental and critical issue in network analysis. Most existing studies assume that the number of communities is known a priori, or lack of rigorous theoretical guarantee on the estimation consistency. This method proposes a regularized network embedding model to simultaneously estimate the community structure and the number of communities in a unified formulation. The proposed model equips network embedding with a novel composite regularization term, which pushes the embedding vector towards its center and collapses similar community centers with each other. A rigorous theoretical analysis is conducted, establishing asymptotic consistency in terms of community detection and estimation of the number of communities. Reference: Ren, M., Zhang S. and Wang J. (2022). "Consistent Estimation of the Number of Communities via Regularized Network Embedding". Biometrics, . Package: r-cran-cengam Architecture: all Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-cengam_0.5.4-1.ca2404.1_all.deb Size: 260924 MD5sum: 408ef4712f3238d472529e23c042a06a SHA1: 5e331a8409e9295391636d77daf61b9bbe8fb8fb SHA256: bbf7297e8928ca78bfdd2617dc547e7d26c6f9f0f653d6bb3c26242b3911a95c SHA512: 21232e3399cd32e10495240d0d93a57ad307ea0eb2c5420b72f5f7987c871c888bdecbf83500175f0574f9bb8c275d5960621459ea786ce0ad2c4e1186e7ae46 Homepage: https://cran.r-project.org/package=cenGAM Description: CRAN Package 'cenGAM' (Censored Regression with Smooth Terms) Implementation of Tobit type I and type II families for censored regression using the 'mgcv' package, based on methods detailed in Woods (2016) . Package: r-cran-censable Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-stringr, r-cran-memoise, r-cran-purrr, r-cran-censusapi, r-cran-tinytiger Suggests: r-cran-roxygen2, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-censable_0.0.8-1.ca2404.1_all.deb Size: 445784 MD5sum: fde9b57faebb8d1efcf34dd98d87876f SHA1: c6990265c521d5d7a3664260820f7e3adcad11ae SHA256: c2a3ecf944ab21b361ab1ea3dd9759b75ed7d1adef9baf45860b6cf5d930e495 SHA512: 2e6c38801658280743b47ce6a0de20655c1587f2ac4b9bb5711524906cbc87727d125056d605a392c20455f0907473fc1175e50674723db6178506376d77f2f6 Homepage: https://cran.r-project.org/package=censable Description: CRAN Package 'censable' (Making Census Data More Usable) Creates a common framework for organizing, naming, and gathering population, age, race, and ethnicity data from the Census Bureau. Accesses the API . Provides tools for adding information to existing data to line up with Census data. Package: r-cran-censcov Architecture: all Version: 1.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-censcov_1.0-0-1.ca2404.1_all.deb Size: 49690 MD5sum: 11e1025aeb50af63ef9e6ec8456567f4 SHA1: 58972fbd03d9425ead9c07cc2a1f048b6f38fbfa SHA256: bcf7ce312bde95f2ede2b91a26379a2e0f931f8123f75e99ece8fe6417363ee4 SHA512: f255b755fe17d9ba0ce644cc7a598e733c26ca00e5489ab918286c13ca6a818339369fd33c2ebea973d534a7c02e16817d186508fcf581d2315d5764f4ed90e3 Homepage: https://cran.r-project.org/package=censCov Description: CRAN Package 'censCov' (Linear Regression with a Randomly Censored Covariate) Implementations of threshold regression approaches for linear regression models with a covariate subject to random censoring, including deletion threshold regression and completion threshold regression. Reverse survival regression, which flip the role of response variable and the covariate, is also considered. Package: r-cran-censmfm Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-momtrunc, r-cran-mvtnorm, r-cran-gridextra, r-cran-ggplot2, r-cran-tlrmvnmvt Suggests: r-cran-mixsmsn Filename: pool/dists/noble/main/r-cran-censmfm_3.1-1.ca2404.1_all.deb Size: 153672 MD5sum: 994bc9d4d61e4380a7495a8b55c4e8d4 SHA1: f9fa082d1e464572010b4bd3976ad9b7713cf2fb SHA256: f960f2e7075b94f65c5dd9fafa82d7e84366b1941edc5dc84fe6f7647596a342 SHA512: 04fd573d7603504ec79e9cf5c4ba13594c560781656f362a921dd94312a5b40190b01c9e6bc91e4018591123a52718126066868e4ebcb3c2fd9928922c00e3e7 Homepage: https://cran.r-project.org/package=CensMFM Description: CRAN Package 'CensMFM' (Finite Mixture of Multivariate Censored/Missing Data) It fits finite mixture models for censored or/and missing data using several multivariate distributions. Point estimation and asymptotic inference (via empirical information matrix) are offered as well as censored data generation. Pairwise scatter and contour plots can be generated. Possible multivariate distributions are the well-known normal, Student-t and skew-normal distributions. This package is an complement of Lachos, V. H., Moreno, E. J. L., Chen, K. & Cabral, C. R. B. (2017) for the multivariate skew-normal case. Package: r-cran-censo2017 Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-duckdb, r-cran-httr, r-cran-tibble, r-cran-purrr, r-cran-cli, r-cran-crayon, r-cran-rstudioapi Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-dplyr, r-cran-dbplyr, r-cran-ggplot2, r-cran-chilemapas Filename: pool/dists/noble/main/r-cran-censo2017_0.6.2-1.ca2404.1_all.deb Size: 59860 MD5sum: 751fa72e3c1a685dfcae16d3dceb0ae9 SHA1: fa489b9bda69541ee929d4d4a18de14770bab94f SHA256: 426d98e4d09ab924651680aeb0d1fee5d23b243bf992aee69f94bfd68076395e SHA512: ba26ca3ce2328b22ec941c59b3d34570452abe97a06845ad7ad0ac81f2b064dbec91dc85483451e330d4efefeeba0da55c4455672f61b5b4481726bcbf9467f7 Homepage: https://cran.r-project.org/package=censo2017 Description: CRAN Package 'censo2017' (Base de Datos de Facil Acceso del Censo 2017 de Chile (2017Chilean Census Easy Access Database)) Provee un acceso conveniente a mas de 17 millones de registros de la base de datos del Censo 2017. Los datos fueron importados desde el DVD oficial del INE usando el Convertidor REDATAM creado por Pablo De Grande. Esta paquete esta documentado intencionalmente en castellano asciificado para que funcione sin problema en diferentes plataformas. (Provides convenient access to more than 17 million records from the Chilean Census 2017 database. The datasets were imported from the official DVD provided by the Chilean National Bureau of Statistics by using the REDATAM converter created by Pablo De Grande and in addition it includes the maps accompanying these datasets.) Package: r-cran-censobr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-checkmate, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-duckdb, r-cran-fs, r-cran-glue, r-cran-rlang Suggests: r-cran-covr, r-cran-dbi, r-cran-dbplyr, r-cran-geobr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-kableextra, r-cran-knitr, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-censobr_0.5.0-1.ca2404.1_all.deb Size: 320262 MD5sum: e7c2a46302a33b34d722de12cbfeec3a SHA1: f458ba27777b3a8c6d9217151d59c57603624094 SHA256: 69edc4d7df3a9986a7cb8014da57bf16c9e5996708bf1178d4b63398392267f3 SHA512: 72ea373a2395e9c7afed5c5a970cd3ad4366f443f5c69adacd4ce2fe876d53e6db9b638d1ffea92192650b7b1bbfafb538daf898c55a143b7659196616d3a79a Homepage: https://cran.r-project.org/package=censobr Description: CRAN Package 'censobr' (Download Data from Brazil's Population Census) Easy access to data from Brazil's population censuses. The package provides a simple and efficient way to download and read the data sets and the documentation of all the population censuses taken in and after 1960 in the country. The package is built on top of the 'Arrow' platform , which allows users to work with larger-than-memory census data using 'dplyr' familiar functions. . Package: r-cran-censorcopula Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula Filename: pool/dists/noble/main/r-cran-censorcopula_2.0-1.ca2404.1_all.deb Size: 22738 MD5sum: e1c40f379a544b40a9a3ed22c0923acc SHA1: 859a309370feaddb9658c26b8b08f66d680e8c53 SHA256: dd1d0a30d78515c259719f9d14a1768a9acc1ff7a0a1d05cfd3f853ffb7c5438 SHA512: 67f77c68eca60942338d14736b49f3d0215aee2c09b49ac9a5be5678bb76251d5722b8c82484291ecee8be8a205a128a978d7b37ce0b8fcfb5c6b9384047e4dc Homepage: https://cran.r-project.org/package=censorcopula Description: CRAN Package 'censorcopula' (Estimate Parameter of Bivariate Copula) Implement an interval censor method to break ties when using data with ties to fitting a bivariate copula. Package: r-cran-censored Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-parsnip, r-cran-survival, r-cran-cli, r-cran-dials, r-cran-dplyr, r-cran-generics, r-cran-glue, r-cran-hardhat, r-cran-lifecycle, r-cran-mboost, r-cran-prodlim, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-aorsf, r-cran-coin, r-cran-covr, r-cran-flexsurv, r-cran-glmnet, r-cran-ipred, r-cran-partykit, r-cran-pec, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-censored_0.3.4-1.ca2404.1_all.deb Size: 214170 MD5sum: 1040e4b1981ce7e5243dc354be2ff53b SHA1: d2c9a8cd3cc4135fc6f3c369570b10dbd1be4e2f SHA256: 69b2b537e6bdba540b771e2f625df51c46e33cc25bff0d03c40b785f6bbe7c63 SHA512: 53798d80c9fb99c909fb7fdd1115bc1a5242019fe532004120ed2d521d48f7542087f161c8632ea84c1728078942405ba7730b6a4be03fe42474a798ba6145bc Homepage: https://cran.r-project.org/package=censored Description: CRAN Package 'censored' ('parsnip' Engines for Survival Models) Engines for survival models from the 'parsnip' package. These include parametric models (e.g., Jackson (2016) ), semi-parametric (e.g., Simon et al (2011) ), and tree-based models (e.g., Buehlmann and Hothorn (2007) ). Package: r-cran-censoredaids Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mnormt, r-cran-mvtnorm, r-cran-matrixcalc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-censoredaids_1.0.0-1.ca2404.1_all.deb Size: 434504 MD5sum: cde124355fe634a774a530808fba561a SHA1: 1db56b71e3499d034b2fa7c33b37f58af1eb3e12 SHA256: 5bfe6e2e03d56ce8c0adae650a760b4e8eafc88f2704c91b1a7b654962d972ee SHA512: 0c73b2f965587fbd7632c51959416454259ddbab2dbebf44301910b3a641dfc47c627838e62014cc9956ce4b27592085f556ad493c622205e558c24f59cc440a Homepage: https://cran.r-project.org/package=censoredAIDS Description: CRAN Package 'censoredAIDS' (Estimation of Censored AI/QUAI Demand System via MaximumLikelihood Estimation (MLE)) Tools for estimating censored Almost Ideal (AI) and Quadratic Almost Ideal (QUAI) demand systems using Maximum Likelihood Estimation (MLE). It includes functions for calculating demand share equations and the truncated log-likelihood function for a system of equations, incorporating demographic variables. The package is designed to handle censored data, where some observations may be zero due to non-purchase of certain goods. Package also contains a procedure to approximate demand elasticities numerically and estimate standard errors via Delta Method. It is particularly useful for applied researchers analyzing household consumption data. Package: r-cran-censreg Architecture: all Version: 0.5-38-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maxlik, r-cran-glmmml, r-cran-sandwich, r-cran-misctools, r-cran-plm Suggests: r-cran-aer, r-cran-lmtest Filename: pool/dists/noble/main/r-cran-censreg_0.5-38-1.ca2404.1_all.deb Size: 208480 MD5sum: d89c1561dd6149031c8cd498e6f3aa4a SHA1: 6346d8844590d09e5d575a2718d35e3d31cd8bcc SHA256: 067f739e16e17c04ef6406056f3221d4247731332ffa0f17432ffa6ff1006701 SHA512: b3cb21dd4d785b8a3f4c736824d9be94913759774e4c95f064ae108dcb63d37a0206589eab0538ef399adec4195bb5240a523a82728e0da4999a6a8f56932d3f Homepage: https://cran.r-project.org/package=censReg Description: CRAN Package 'censReg' (Censored Regression (Tobit) Models) Maximum Likelihood estimation of censored regression (Tobit) models with cross-sectional and panel data. Package: r-cran-censregsmsn Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-mnormt, r-cran-cubature, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-censregsmsn_0.0.1-1.ca2404.1_all.deb Size: 124602 MD5sum: 1560fe22f95e221f1a34aaae4926bf4c SHA1: ae6163bab754c9d4fc21f42bf47f0365a1428d09 SHA256: 36bafc7a945c8189daf6726c575f1829fef9cc375b59924950a0dd6e3e6ee393 SHA512: 9a904bcad416b0de9489bdf72e7203c6839e4634a2f98189509bd290994d402e56520930115a6cfa3a81501057eeba7db9405114483db1f9a2ad383e3ec793e0 Homepage: https://cran.r-project.org/package=CensRegSMSN Description: CRAN Package 'CensRegSMSN' (Censored Linear Regression Models under Heavy‑tailedDistributions) Functions for fitting univariate linear regression models under Scale Mixtures of Skew-Normal (SMSN) distributions, considering left, right or interval censoring and missing responses. Estimation is performed via an EM-type algorithm. Includes selection criteria, sample generation and envelope. For details, see Gil, Y.A., Garay, A.M., and Lachos, V.H. (2025) . Package: r-cran-censspatial Architecture: all Version: 3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geor, r-cran-rcpp, r-cran-mvtnorm, r-cran-optimx, r-cran-tmvtnorm, r-cran-msm, r-cran-psych, r-cran-numderiv, r-cran-raster, r-cran-moments, r-cran-lattice, r-cran-tlrmvnmvt Filename: pool/dists/noble/main/r-cran-censspatial_3.6-1.ca2404.1_all.deb Size: 228240 MD5sum: e742bcfb8597819117af22222fcf971a SHA1: 0e2add380545476127c5bf0f26844da2989b1600 SHA256: 5483f3083d6c0e8b7b1100a45faf463a8bb8703b2c2855e02cc0ddffee48d486 SHA512: dead4defddd8434ad7c198300f8dacde785aa6ad3c88ce5392634129ec903bf4ca507b4d02f0b0744da88c2fc8ba2d4943346dec2ab6cd92f754c9f5ee9339ab Homepage: https://cran.r-project.org/package=CensSpatial Description: CRAN Package 'CensSpatial' (Censored Spatial Models) It fits linear regression models for censored spatial data. It provides different estimation methods as the SAEM (Stochastic Approximation of Expectation Maximization) algorithm and seminaive that uses Kriging prediction to estimate the response at censored locations and predict new values at unknown locations. It also offers graphical tools for assessing the fitted model. More details can be found in Ordonez et al. (2018) . Package: r-cran-census2016 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3575 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggrepel, r-cran-magrittr, r-cran-scales, r-cran-testthat, r-cran-png Filename: pool/dists/noble/main/r-cran-census2016_0.2.0-1.ca2404.1_all.deb Size: 3439210 MD5sum: a184cb05fb2830e328db641ca37c8623 SHA1: 5f9f6d8149a5655d138bb3ed8e955514651cf06c SHA256: 4cbe1c797d596c35744031ef122eecb663541a0c82df67c5f619448a0c26f4e9 SHA512: 9b46efb66937125e07e4e222fea8dac7b51b68231e7b82aad85cbddc9ecc08f9dff5a5b7c5db6e6fb8747755f05a8ed5334f7ef3f19c6dd936ffdb54e366a91c Homepage: https://cran.r-project.org/package=Census2016 Description: CRAN Package 'Census2016' (Data from the Australian Census 2016) Contains selected variables from the time series profiles for statistical areas level 2 from the 2006, 2011, and 2016 censuses of population and housing, Australia. Also provides methods for viewing the questions asked for convenience during analysis. Package: r-cran-censusapi Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-censusapi_0.10.0-1.ca2404.1_all.deb Size: 68408 MD5sum: 4ec66a24cbdb63d399c9b69230f0840f SHA1: de561f7ff01d82cd99c0a7f8c4915c8ac59dfdd1 SHA256: 936cce41c5f6e44f7de146622c233151f51fb5cc9137f3823c46b12e4565ccdc SHA512: 83b38b18af7a9dcc10e9ea5419a102bb0e4a84a5dbc825af65a9555e55e29a664d61913670b06672c9207350c6cb40479cedb71c5a596a75c08fb71c0e859774 Homepage: https://cran.r-project.org/package=censusapi Description: CRAN Package 'censusapi' (Retrieve Data from the Census APIs) A wrapper for the U.S. Census Bureau APIs that returns data frames of Census data and metadata. Available datasets include the Decennial Census, American Community Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, Population Estimates and Projections, and more. 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For more information on the CE please visit . For further reading on CE estimate calculations please see the CE Calculation section of the U.S. Bureau of Labor Statistics (BLS) Handbook of Methods at . For further information about CE PUMD please visit . 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Package: r-cran-certara.nlme8 Architecture: all Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1008 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-batchtools, r-cran-reshape, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-certara.nlme8_3.0.2-1.ca2404.1_all.deb Size: 490968 MD5sum: ca9a8b8318e17c246bf02189321cbc6b SHA1: 7f7f189a93eb24de083b8f05f536108cd2c1d012 SHA256: 2900717bc27920dfc9f30aec661fa5de91240c65e9390366e9af8910a5dd9827 SHA512: a44b44a1f5ff502c0e3be0a9218da05a0813f4f2507e3b717e5c3eb780d80c5aa14bac09cce1c84a7ba338babb1c64deecd65d8f8431bf1e72b28ee48aaf30fb Homepage: https://cran.r-project.org/package=Certara.NLME8 Description: CRAN Package 'Certara.NLME8' (Utilities for Certara's Nonlinear Mixed-Effects Modeling Engine) Perform Nonlinear Mixed-Effects (NLME) Modeling using Certara's NLME-Engine. 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Specify engine parameters and select from different run options, including simple estimation, stepwise covariate search, bootstrapping, simulation, visual predictive check, and more. Models are executed using the 'Certara.RsNLME' package. 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The package provides access to the same advanced Maximum Likelihood algorithms used by the NLME-Engine in the Phoenix platform. These tools support a range of analyses, from parametric methods to individual and pooled data, and support integrated use within the Pirana pharmacometric workbench . Execution is supported both locally or on remote machines. 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Suitable for academic awards, professional recognition, and similar uses. Package: r-cran-ces Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-haven, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-ces_1.0.2-1.ca2404.1_all.deb Size: 108228 MD5sum: a81d9c57ddf533a77aa57e24795bbeaf SHA1: 4773c30d75e7fe0ecdc2eb612f34ee7f0a9baeaf SHA256: b57a9bd62935c835a23bac654809e9e82b59b05caeb0a29d90184d2fb59d242f SHA512: 42c8a7aa818514b697298cb4fe90c8e4fa3e1654981c1fef5215a45b2bb302285a2c507af0345087862ff9d19bac5b4b4a30c646e4eb96ce1ea681e078c58fb8 Homepage: https://cran.r-project.org/package=ces Description: CRAN Package 'ces' (Access to Canadian Election Study Data) Provides tools to easily access and analyze Canadian Election Study data. 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Package: r-cran-cfmortality Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cfmortality_0.3.0-1.ca2404.1_all.deb Size: 14100 MD5sum: 0873cfa2add90add9f71749bd601ce02 SHA1: 6356f37ba7a2f4525169a0c3a942414a4fb98332 SHA256: f6e89af027152bbbdd7fbe4bc3f7a0696ec92e13c745d929e809f2a13bfc1fde SHA512: 98ccdba360649c8873c9eee6ede639f614b684732815d6766bee801a65b41b4f8cd5505644f385cd5b2f7b7e60e97c80f47203e40de9d4732508744ada8bfdc6 Homepage: https://cran.r-project.org/package=cfmortality Description: CRAN Package 'cfmortality' (Cystic Fibrosis Survival Prediction Model Based on StanojevicModel) Allows clinicians to predict survival probabilities over the next two years for cystic fibrosis patients, based on the clinical prediction models published in Stanojevic et al. (2019) . 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Package: r-cran-cgam Architecture: all Version: 1.32-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coneproj, r-cran-splines2, r-cran-svdialogs, r-cran-statmod, r-cran-lme4, r-cran-matrix, r-cran-ggplot2, r-cran-dplyr, r-cran-zeallot, r-cran-rlang, r-cran-quadprog, r-cran-mass Suggests: r-cran-roxygen2, r-cran-patchwork, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-cgam_1.32-1.ca2404.1_all.deb Size: 1295724 MD5sum: f9f2772b4287eadba8e1f8c8a4b6e577 SHA1: 501e21f12d79f6671956fb0b34b988a53cb26fd1 SHA256: d8a819f6cbc1de94dd8aa7819ff158cb1ccf3011163e7f2408ac31a09e31be03 SHA512: 36618389f81ee9d381f0362b75ee42d92f2f4044052c32d24ad011f9bf57dd77f3f84fc043c00ff647a32da70ab4cdb94e27a98031e4a3de0133cc9f7ddb90f1 Homepage: https://cran.r-project.org/package=cgam Description: CRAN Package 'cgam' (Constrained Generalized Additive Model) A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Liao X, Meyer MC (2019) for more details. Package: r-cran-cge Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cge_0.3.3-1.ca2404.1_all.deb Size: 289604 MD5sum: 1bf353428711996c7363361a74905838 SHA1: 27302aa0e910b0d8c35ce9346f34760ac71c9cbb SHA256: 22fade92c4be1dac2305caf5766eb77bf597aae10ba1c74f965e89e37579b601 SHA512: 6267996c22927f2c9fd85b0c08ce6e2fb9df3e9e1076a37c1d93b0a0c7797b68a337a20a63963e6cf4dd8480a869aed142d7d30283d74e75bfaf398ff2551f08 Homepage: https://cran.r-project.org/package=CGE Description: CRAN Package 'CGE' (Computing General Equilibrium) Developing general equilibrium models, computing general equilibrium and simulating economic dynamics with structural dynamic models in LI (2019, ISBN: 9787521804225) "General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press". When developing complex general equilibrium models, GE package should be used in addition to this package. Package: r-cran-cgmanalysis Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-parsedate, r-cran-lubridate, r-cran-pracma, r-cran-zoo, r-cran-pastecs, r-cran-readxl, r-cran-readr, r-cran-xml, r-cran-mess, r-cran-rlang Filename: pool/dists/noble/main/r-cran-cgmanalysis_3.2.0-1.ca2404.1_all.deb Size: 207238 MD5sum: 9c5758dd963147f8d89f7ab492e4e9a4 SHA1: 1d7c8e0ffa1742f8a8bc0f943bccd81779051097 SHA256: d7c30ce47e62bd23a6538ee61363e36221de3f3cba54cd6c7735f2ea99006baa SHA512: 986733e9fce543664c332e23ec4d9687a68b9bca77bd97e55513e7e3c84a08d2c9d89412bd73c63e30338a90a2f9a706a4a96dddcf940c4a3c265dca897271f2 Homepage: https://cran.r-project.org/package=cgmanalysis Description: CRAN Package 'cgmanalysis' (Clean and Analyze Continuous Glucose Monitor Data) This code provides several different functions for cleaning and analyzing continuous glucose monitor data. Currently it works with 'Dexcom', 'iPro 2', 'Diasend', 'Libre', or 'Carelink' data. The cleandata() function takes a directory of CGM data files and prepares them for analysis. cgmvariables() iterates through a directory of cleaned CGM data files and produces a single spreadsheet with data for each file in either rows or columns. The column format of this spreadsheet is compatible with REDCap data upload. cgmreport() also iterates through a directory of cleaned data, and produces PDFs of individual and aggregate AGP plots. Please visit to download the new-user guide. Package: r-cran-cgmissingdatar Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-fnn, r-cran-metrics, r-cran-ranger Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cgmissingdatar_0.0.1-1.ca2404.1_all.deb Size: 88438 MD5sum: a45c8edd67656537d5d6b2968a390db3 SHA1: b1649e5581185df27f043944bd2ad5446851846d SHA256: 4dfe71c1de961d6754ebf78d3c145729c9cc795dc351163993d6ac88f6a568d0 SHA512: f913b1c85bc3a96a21874c291ae260d4daf6ca7e100842e6ae0875ff6a5d4fe0c0c06307149f19af4ba596895fcd956d0309f965c3802cfbf735ca050cf14036 Homepage: https://cran.r-project.org/package=CGMissingDataR Description: CRAN Package 'CGMissingDataR' (Missingness Benchmark for Continuous Glucose Monitoring Data) Evaluates predictive performance under feature-level missingness in repeated-measures continuous glucose monitoring-like data. The benchmark injects missing values at user-specified rates, imputes incomplete feature matrices using an iterative chained-equations approach inspired by multivariate imputation by chained equations (MICE; Azur et al. (2011) ), fits Random Forest regression models (Breiman (2001) ) and k-nearest-neighbor regression models (Zhang (2016) ), and reports mean absolute percentage error and R-squared across missingness rates. Package: r-cran-cgmquantify Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyverse, r-cran-ggplot2, r-cran-hms, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cgmquantify_0.1.0-1.ca2404.1_all.deb Size: 64934 MD5sum: 3991c398366ad1dd90f84a91e7731e61 SHA1: d269dba726285f8447553c2f7b91b020f4e8b166 SHA256: 45103d1ded42abdb8225021957c31c4c6eb819615e69d5ceb072f30108156165 SHA512: c86ac31c6919def0d76c8b8390eb6dfcf5f84cdbc3e821a746b011ded218e810f01a54367d821f2aa4194b883cc28441119fd29e7734644bb0517b3cefcbbd37 Homepage: https://cran.r-project.org/package=cgmquantify Description: CRAN Package 'cgmquantify' (Analyzing Glucose and Glucose Variability) Continuous glucose monitoring (CGM) systems provide real-time, dynamic glucose information by tracking interstitial glucose values throughout the day. Glycemic variability, also known as glucose variability, is an established risk factor for hypoglycemia (Kovatchev) and has been shown to be a risk factor in diabetes complications. Over 20 metrics of glycemic variability have been identified. Here, we provide functions to calculate glucose summary metrics, glucose variability metrics (as defined in clinical publications), and visualizations to visualize trends in CGM data. Cho P, Bent B, Wittmann A, et al. (2020) American Diabetes Association (2020) Kovatchev B (2019) Kovdeatchev BP (2017) Tamborlane W V., Beck RW, Bode BW, et al. (2008) Umpierrez GE, P. Kovatchev B (2018) . Package: r-cran-cgnm Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 877 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cgnm_0.9.3-1.ca2404.1_all.deb Size: 583154 MD5sum: 142619eef52c706cf94644ae473b713c SHA1: 12e661eb86435e246f9eb4903e5cde389333cba6 SHA256: de4917150cf13ac48baf6206f3e66412ab978a71694d1fe90825768ac8976204 SHA512: ee59de92461e6d0716c312ca53677a8363b7ca191018a0befefe4763919c450698f6cfcce4738ed22283badbe1d8ef64ffd0028d066ed699f6f368211ac6fc7e Homepage: https://cran.r-project.org/package=CGNM Description: CRAN Package 'CGNM' (Cluster Gauss-Newton Method) Find multiple solutions of a nonlinear least squares problem. Cluster Gauss-Newton method does not assume uniqueness of the solution of the nonlinear least squares problem and compute multiple minimizers. Please cite the following paper when this software is used in your research: Aoki et al. (2020) . Cluster Gauss–Newton method. Optimization and Engineering, 1-31. Please cite the following paper when profile likelihood plot is drawn with this software and used in your research: Aoki and Sugiyama (2024) . Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models. CPT Pharmacometrics Syst Pharmacol.13(1):54-67. GPT based helper bot available at Package: r-cran-cgp Architecture: all Version: 2.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cgp_2.1-1-1.ca2404.1_all.deb Size: 48474 MD5sum: 732728e5332c6cf71a8591cd47abfa53 SHA1: 48e361677a128d8df124aa0313440e3d6f6d17cf SHA256: 64be76341428351aeb955f62ed148dc1c187f722834fcf243f2c039df7a8f3bc SHA512: 5d4c8a5427e88116a33887961d2efd596803f0911e97454c8f5054799ec29beb9fe7b8993f1ab1f9367f6143212f99a855659f6674cf7b4817bd2c17fab8dc72 Homepage: https://cran.r-project.org/package=CGP Description: CRAN Package 'CGP' (Composite Gaussian Process Models) Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) "Composite Gaussian Process Models for Emulating Expensive Functions", Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary. Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP. Package: r-cran-cgpfunctions Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2950 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesfactor, r-cran-desctools, r-cran-dplyr, r-cran-forcats, r-cran-ggmosaic, r-cran-ggplot2, r-cran-ggrepel, r-cran-paletteer, r-cran-partykit, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-sjstats, r-cran-stringr, r-cran-tidyr Suggests: r-cran-bsda, r-cran-ggthemes, r-cran-hrbrthemes, r-cran-janitor, r-cran-knitr, r-cran-lsr, r-cran-magrittr, r-cran-productplots, r-cran-pwr, r-cran-rmarkdown, r-cran-stringi, r-cran-tibble, r-cran-testthat, r-cran-tidyselect Filename: pool/dists/noble/main/r-cran-cgpfunctions_0.6.3-1.ca2404.1_all.deb Size: 1831808 MD5sum: 375277402986c67be28bf85dee24cb23 SHA1: 50346fac99268bde07bfffc2060e20ff582486ac SHA256: 9fde718417c509baa898d484998a01dfffa684a6a75f888fbd2bbacffbd507be SHA512: b7078eea3b22ad8a1b30e619de5c30d1bd1df74a8121b3fc17b890921cbf8dd6ad10c9f2ce817cd40b94a604de8527e25939ac10190f87cadcff385852f2f6c9 Homepage: https://cran.r-project.org/package=CGPfunctions Description: CRAN Package 'CGPfunctions' (Powell Miscellaneous Functions for Teaching and LearningStatistics) Miscellaneous functions useful for teaching statistics as well as actually practicing the art. They typically are not new methods but rather wrappers around either base R or other packages. Package: r-cran-cgr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cgr_0.1.0-1.ca2404.1_all.deb Size: 11320 MD5sum: 4eb354d4633f111c23fb6905e7b1a4fe SHA1: 77d003cd7ecc9aec0b7db3f839d1c1d1028514bb SHA256: 2148b5ad55af5147fc02a7b6d6d21518c04ccdfbd996590c57ee06a79593b908 SHA512: 2eb95e41c0fb1050df79039141cc516f69b0d2dc52ab63d4b4f8c3edcbf1ed342e65053a59723acc306429170c68285cb3d9bfc645315783aeff0ca99200b4dc Homepage: https://cran.r-project.org/package=CGR Description: CRAN Package 'CGR' (Compound Growth Rate for Capturing the Growth Rate Over thePeriod) The compound growth rate indicates the percentage change of a specific variable over a defined period. It is calculated using non-linear models, particularly the exponential model. To estimate the compound growth rates, the growth model is first converted to semilog form and then analyzed using Ordinary Least Squares (OLS) regression. This package has been developed using concept of Shankar et al. (2022). Package: r-cran-cgwtools Architecture: all Version: 4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gmp Filename: pool/dists/noble/main/r-cran-cgwtools_4.1-1.ca2404.1_all.deb Size: 132118 MD5sum: c808e52f08adb93ee27ab1a454068989 SHA1: 6d8e2d76da0e277705c9c7abef14e957f1de64db SHA256: 396d18636623d07d5782d9a14af5d5d36e00934e28a08628aa6376c2f181798d SHA512: f400c42bcdc76cc9381c1e2869f03630097dd01bdd1baceaa964536906c48fa0d67b5a3b94e9fe856714f4bcccb8cfc9e219128eb6d3dfb645dbbcee0f5e6be9 Homepage: https://cran.r-project.org/package=cgwtools Description: CRAN Package 'cgwtools' (Miscellaneous Tools) Functions for performing quick observations or evaluations of data, including a variety of ways to list objects by size, class, etc. The functions 'seqle' and 'reverse.seqle' mimic the base 'rle' but can search for linear sequences. The function 'splatnd' allows the user to generate zero-argument commands without the need for 'makeActiveBinding' . Functions provided to convert from any base to any other base, and to find the n-th greatest max or n-th least min. In addition, functions which mimic Unix shell commands, including 'head', 'tail' ,'pushd' ,and 'popd'. Various other goodies included as well. Package: r-cran-ch Architecture: all Version: 0.1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-clipr, r-cran-ryacas, r-cran-magrittr, r-cran-mass, r-cran-crayon, r-cran-polynom, r-cran-pracma Suggests: r-cran-scales Filename: pool/dists/noble/main/r-cran-ch_0.1.0.2-1.ca2404.1_all.deb Size: 72520 MD5sum: 68a89151c286e1a4a3eb72e39288a818 SHA1: 4e4d0ce2f31a4c0a971edd5527174691fb519f86 SHA256: 79ea0fa56563dcaf074e06d045878ab0ac094345b080802792f804b4f146d949 SHA512: 147026e5154c81ae7d16f365d50bdb025f2060f27487dada4604176dc2ef41452e6e9bba56515f014ca156dea87aa237dbe2928b66b425feadddc075f7e61b1c Homepage: https://cran.r-project.org/package=ch Description: CRAN Package 'ch' (About some Small Functions) The solution to some common problems is proposed, as well as a summary of some small functions. In particular, it provides a useful function for some problems in chemistry. For example, monoa(), monob() and mono() function can be used to calculate The pH of weak acid/base. The ggpng() function can save the PNG format with transparent background. The period_table() function will show the periodic table. Also the show_ruler() function will show the ruler. The show_color() function is funny and easier to show colors. I also provide the symb() function to generate multiple symbols at once. The csv2vcf() function provides an easy method to generate a file. The sym2poly() and sym2coef() function can extract coefficients from polynomials. Package: r-cran-chainbinomial Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-generics Suggests: r-cran-testthat, r-cran-numderiv, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chainbinomial_0.1.5-1.ca2404.1_all.deb Size: 90552 MD5sum: d43ab0eb1b6c293df97b4bb2b34d6326 SHA1: 331d82d350fd0c665027e226e77a4579c740e018 SHA256: 3f865b045efea7dff79635be30ac5267ef5fc8287e1ea4cb51f8e5972ecea717 SHA512: 09c3c84f72bffaf19498063576a9b0aa1bef14b1a79ca5abce3dc5afefb036bbfd659425b408af6bd69a1a366428bc523e04a7386f83930e5733240bc22b358f Homepage: https://cran.r-project.org/package=chainbinomial Description: CRAN Package 'chainbinomial' (Chain Binomial Models for Analysis of Infectious Disease Data) Implements the chain binomial model for analysis of infectious disease data. Contains functions for calculating probabilities of the final size of infectious disease outbreaks using the method from D. Ludwig (1975) and for outbreaks that are not concluded, from Lindstrøm et al. (2024) . The package also contains methods for estimation and regression analysis of secondary attack rates. Package: r-cran-chainladder Architecture: all Version: 0.2.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3512 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-actuar, r-cran-lattice, r-cran-tweedie, r-cran-systemfit, r-cran-statmod, r-cran-cplm, r-cran-ggplot2, r-cran-mass Suggests: r-cran-runit, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chainladder_0.2.21-1.ca2404.1_all.deb Size: 2193122 MD5sum: f801b558face3efdf64b2e6817770ad6 SHA1: 8efda0c2be9b73869c757a45147b411222e9177c SHA256: f4e76ad730e20f967276473260e987f3eef2b818f626a060e741114316224cd6 SHA512: 6e739d47b3badabc61cd131f97478f2ddf9365045924eec535cc0a76238d3db53359c6325a205b8c38ed81e847e97a59fedc58cbcc63f8275bb56de95736a8c2 Homepage: https://cran.r-project.org/package=ChainLadder Description: CRAN Package 'ChainLadder' (Statistical Methods and Models for Claims Reserving in GeneralInsurance) Various statistical methods and models which are typically used for the estimation of outstanding claims reserves in general insurance, including those to estimate the claims development result as required under Solvency II. Package: r-cran-chameleon Architecture: all Version: 0.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clue, r-cran-ggplot2, r-cran-umap Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chameleon_0.2-3-1.ca2404.1_all.deb Size: 155088 MD5sum: 3a87c9c200d89c7e544c7cdb14137c5a SHA1: a74b430e347c46b90b1d5ad4cb69f7665c0b9472 SHA256: 7c3dba7a67141c0d314605206bdfa58a9459a1d23ff74abc92e0665c7befba45 SHA512: f642fc72507aa1d9a3aa2604c187a56fad65a63475d3a36524b1a2251be0b0cfa2502cd29e3a461f189b63daa5eda008fcf88ed6eee02a8a928f8d8ebdad59dc Homepage: https://cran.r-project.org/package=chameleon Description: CRAN Package 'chameleon' (Automatic Colors for Multi-Dimensional Data) Assign distinct colors to arbitrary multi-dimensional data, considering its structure. Package: r-cran-chandwich Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sandwich, r-cran-testthat Filename: pool/dists/noble/main/r-cran-chandwich_1.1.6-1.ca2404.1_all.deb Size: 245200 MD5sum: 7d20b1c03f580dfe0d9a2069ab9e0baf SHA1: 48aa5a244ca807af31cf2460190bb9b8f7ede9be SHA256: bd12467535bbde75f6ea07cfb51edbf48b18d1365b0198c1321077c624dfee0e SHA512: 3845633733757c0e48d11b269c03e9bfed7434dd9f55043ba9145097a1996cadb4c357070376ae68eb4c052c297c0b7f0489e91bdb3720798d685f5f30949204 Homepage: https://cran.r-project.org/package=chandwich Description: CRAN Package 'chandwich' (Chandler-Bate Sandwich Loglikelihood Adjustment) Performs adjustments of a user-supplied independence loglikelihood function using a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) . This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions or for performing inferences that are robust to certain types of model misspecification. Functions for profiling the adjusted loglikelihoods are also provided, as are functions for calculating and plotting confidence intervals, for single model parameters, and confidence regions, for pairs of model parameters. Nested models can be compared using an adjusted likelihood ratio test. Package: r-cran-changepoint.geo Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-changepoint, r-cran-changepoint.np, r-cran-ggplot2, r-cran-rdpack Suggests: r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-changepoint.geo_1.0.3-1.ca2404.1_all.deb Size: 262674 MD5sum: 61ff64503d730404925586047af64c65 SHA1: 937bc83b62a1cd88cee07dbb7861f47421c32d13 SHA256: 94624e2ceee0e0727448f64f0cad31c837f187e5a4d48144e023e89e5a8b817a SHA512: d2a94fb2c2bd09df5ae840a2a149a38318aa36e2da00a420d9126e99860c823b0cb4fdcdb0ff781bfa66db19c2a6f8706b12a561165971d80118cee5a5a74069 Homepage: https://cran.r-project.org/package=changepoint.geo Description: CRAN Package 'changepoint.geo' (Geometrically Inspired Multivariate Changepoint Detection) Implements the high-dimensional changepoint detection method GeomCP and the related mappings used for changepoint detection. These methods view the changepoint problem from a geometrical viewpoint and aim to extract relevant geometrical features in order to detect changepoints. The geomcp() function should be your first point of call. References: Grundy et al. (2020) . Package: r-cran-changepoint.influence Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-data.table, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-changepoint.influence_1.0.2-1.ca2404.1_all.deb Size: 98838 MD5sum: cab63ececc8c71149bdff9bd687786db SHA1: 29595323f23eea90a3b5703f94540869a6859066 SHA256: 51397b62a692413c93d6c09cbb339de79c84c8ff31c7288d52f576a09cacad3c SHA512: 07a3e0041dcd92cef5834bd0900b412573d83e430864daeae88baa4389b9e41c21be0706f1bc61985039a115d73945aebe6a395661294516b7cb228bc75e5ecc Homepage: https://cran.r-project.org/package=changepoint.influence Description: CRAN Package 'changepoint.influence' (Package to Calculate the Influence of the Data on a ChangepointSegmentation) Allows users to input their data, segmentation and function used for the segmentation (and additional arguments) and the package calculates the influence of the data on the changepoint locations, see Wilms et al. (2022) . Currently this can only be used with the changepoint package functions to identify changes, but we plan to extend this. There are options for different types of graphics to assess the influence. Package: r-cran-changepointsvar Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-lars Filename: pool/dists/noble/main/r-cran-changepointsvar_0.1.2-1.ca2404.1_all.deb Size: 44582 MD5sum: 0a602ac6bf0d703b067b5d679a82d06e SHA1: 16c349979c44a06527fc4aabfdc050ac45beb5b0 SHA256: bc594ec433b8636964981bbe394500e438ac6c498b24a8cde071bfdd8a806064 SHA512: ee512faf5d43ba70e8f82717c33fe89fe6c8e98534d173726ceb292185caeff70099d5c68d23bc5877c3f756379e99d10766feb5bd7d4d5221d9dc6cdd08823f Homepage: https://cran.r-project.org/package=changepointsVar Description: CRAN Package 'changepointsVar' (Change-Points Detections for Changes in Variance) Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, . Package: r-cran-changepointtesting Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-changepointtesting_1.2-1.ca2404.1_all.deb Size: 163106 MD5sum: 5c224cda314d5709e73611e8b1360306 SHA1: 4a91dd5e5bb0edfa74c82e424e72e154d696d16c SHA256: c54b55c39a5666c6619d30cb2149803b9c909d2a2ec08ffb207e786222e752ec SHA512: ce306e30a019e358b75345a7d1c190d73f75ac2ad5b801c2664b2eec1c16bd6715f173ea97189fad4b4595ea34561378b197cc58872a832d6e4a746158ebf837 Homepage: https://cran.r-project.org/package=ChangepointTesting Description: CRAN Package 'ChangepointTesting' (Change Point Estimation for Clustered Signals) A multiple testing procedure for clustered alternative hypotheses. It is assumed that the p-values under the null hypotheses follow U(0,1) and that the distributions of p-values from the alternative hypotheses are stochastically smaller than U(0,1). By aggregating information, this method is more sensitive to detecting signals of low magnitude than standard methods. Additionally, sporadic small p-values appearing within a null hypotheses sequence are avoided by averaging on the neighboring p-values. Package: r-cran-changer Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-available, r-cran-devtools, r-cran-git2r Filename: pool/dists/noble/main/r-cran-changer_0.0.5-1.ca2404.1_all.deb Size: 20224 MD5sum: 2efa4132ec04b5a115822632f1e900f0 SHA1: de8744268180f431c9009ffe2d8062e85f91368e SHA256: 93852f772896cda9533a7aedc2e54eee7943bbf0ce8f0f696c9b9d495896afe0 SHA512: fac56be685fb2f2a1271fe30aa557431a7b85fa1449a41d0b4125873bcf4b323686ca015a4240f38d1a1c679bf7c584ed0cee7d874f5a54fb5d39d751e8fd7db Homepage: https://cran.r-project.org/package=changer Description: CRAN Package 'changer' (Change R Package Name) Changing the name of an existing R package is annoying but common task especially in the early stages of package development. This package (mostly) automates this task. Package: r-cran-changeranger Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5022 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-phylobase, r-cran-raster, r-cran-dplyr, r-cran-rangemodelmetadata, r-cran-sf, r-cran-sp Suggests: r-cran-ape, r-cran-tidyverse, r-cran-rmarkdown, r-cran-testthat, r-cran-tinytex, r-cran-knitr, r-cran-picante, r-cran-wallace, r-cran-r.utils, r-cran-dismo Filename: pool/dists/noble/main/r-cran-changeranger_1.1.0-1.ca2404.1_all.deb Size: 1817042 MD5sum: 7e69715ab80e83067331647551acb51d SHA1: b9594badec3b7341b21ec764b851745d617b4a40 SHA256: dcc9c6b9864f4271947f61ab0c2dde7a6630ece60b07abb9dba1926ed77874db SHA512: 3276a50ec8b13f4b89e09eb8134e8feba243ca0e86d40cb8959288f0d29c26e41559692ad894efbadad530377ced567a93fba1343558498d40fd5ecedcfcec54 Homepage: https://cran.r-project.org/package=changeRangeR Description: CRAN Package 'changeRangeR' (Change Metrics for Species Geographic Ranges) Facilitates workflows to reproducibly transform estimates of species’ distributions into metrics relevant for conservation. For example, combining predictions from species distribution models with other maps of environmental data to characterize the proportion of a species’ range that is under protection, calculating metrics used under the International Union for Conservation of Nature (IUCN) Criteria A and B guidelines (Area of Occupancy and Extent of Occurrence), and calculating more general metrics such as taxonomic and phylogenetic diversity, as well as endemism. Also facilitates temporal comparisons among biodiversity metrics to inform efforts towards complementarity and consideration of future scenarios in conservation decisions. 'changeRangeR' also provides tools to determine the effects of modeling decisions through sensitivity tests. Package: r-cran-changes Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nls.multstart, r-cran-ggplot2, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-changes_1.0.1-1.ca2404.1_all.deb Size: 102788 MD5sum: 7071fc3a53dba9bc0cf1830bb25946e8 SHA1: d90e2a72082af6e704224f9651d5edd97ce8354e SHA256: 3d54c9df103a870f1a96810bbc733e876d4240fd16145e3691c6c87a0954de00 SHA512: 842e7181c56d94d77768d772a6777c65cbe08d94dd365bd2c0ffd6501c7f38735815fce4a0ff8c980c16d77eecae54702fb942f67cf6272228de4ec7e58eb5ab Homepage: https://cran.r-project.org/package=changeS Description: CRAN Package 'changeS' (S-Curve Fit for Changepoint Analysis) Estimation of changepoints using an "S-curve" approximation. Formation of confidence intervals for changepoint locations and magnitudes. Both abrupt and gradual changes can be modeled. Package: r-cran-channelattributionapp Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-channelattribution, r-cran-shiny, r-cran-data.table, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-channelattributionapp_1.3-1.ca2404.1_all.deb Size: 127460 MD5sum: 72b7f67785d5715c75f7fb239592c557 SHA1: d10297183eda604446ad9a9e6e90060d17f4d82b SHA256: 0a9782e26317c6d67d7d8d691b8992f88f47c6c98c0ac66566a39746938312db SHA512: c4a6520ec3ba5b07061006ce6fc4b9525d8945a1617f8c9317736f6c6339c4ab6332a61b61818a858e0316d34bc6849aa0b118c774cedf381779cf39c4d77806 Homepage: https://cran.r-project.org/package=ChannelAttributionApp Description: CRAN Package 'ChannelAttributionApp' (Shiny Web Application for the Multichannel Attribution Problem) Shiny Web Application for the Multichannel Attribution Problem. It is a user-friendly graphical interface for package 'ChannelAttribution'. Package: r-cran-chantrics Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 695 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aer, r-cran-chandwich, r-cran-sandwich, r-cran-lmtest, r-cran-rlang, r-cran-progress, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lax, r-cran-covr, r-cran-mass, r-cran-pscl Filename: pool/dists/noble/main/r-cran-chantrics_1.0.0-1.ca2404.1_all.deb Size: 443482 MD5sum: bc030b0c551edc9eb020dcfabdc0803a SHA1: 5eb1b3d99e037b4f8351d4f590acbd2261a5eadb SHA256: ef76b371bac68d1ea1da9a8f5f8fa19e70f6e53c750133ba27733634ce1dec33 SHA512: 066596d9dadb5129bb4cd0e39b92b6906601158d47aa2a9b2c39a3d4cfb4c7c51d7a9d377307e0248625810695176c9f6eca5b42b59063b800e71f89c0a33120 Homepage: https://cran.r-project.org/package=chantrics Description: CRAN Package 'chantrics' (Loglikelihood Adjustments for Econometric Models) Adjusts the loglikelihood of common econometric models for clustered data based on the estimation process suggested in Chandler and Bate (2007) , using the 'chandwich' package , and provides convenience functions for inference on the adjusted models. Package: r-cran-chaosgame Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-colorramps, r-cran-ggplot2, r-cran-gridextra, r-cran-plot3d, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-chaosgame_1.5-1.ca2404.1_all.deb Size: 93604 MD5sum: 69043f4d0b0afa6a0bfb9c6ee050206c SHA1: aa23cbfa7d6044c9b53dca39e2e9f87e2daa2411 SHA256: 00e16b70b3b4e8ce526d76947fb5629c02def242c30ab82a7cd4e8bfcf1ebf8d SHA512: 7cca06fde24e4d7404b3e96a9dc8f9a4a54f324e4cc7155aa8bdf3ddfe39c9999a5cb597f8f027e625cecfd078277743b326c739cb61550dc38f7cc769f8d839 Homepage: https://cran.r-project.org/package=ChaosGame Description: CRAN Package 'ChaosGame' (Chaos Game) The main objective of the package is to enter a word of at least two letters based on which an Iterated Function System with Probabilities is constructed, and a two-dimensional fractal containing the chosen word infinitely often is generated via the Chaos Game. Additionally, the package allows to project the two-dimensional fractal on several three-dimensional surfaces and to transform the fractal into another fractal with uniform marginals. Package: r-cran-chapensk Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bessel Filename: pool/dists/noble/main/r-cran-chapensk_0.4-1.ca2404.1_all.deb Size: 250228 MD5sum: 19743905fcc40fa0c65ef61320261cb4 SHA1: dd52b56769127ce475c2421a89190c1675180b6f SHA256: 094990b97b4ecd080523064e88306d32f5c71fb97d3372b54279c95f435efdbf SHA512: d8a477c6481e31d3a58eaa6ee3036f5fb0ae86f4d20d7f74166d1660680547d77a7e66fc6d79e8d1cdf77aad29254f75bc50ef4d3510ab1ee499f983e2d302f3 Homepage: https://cran.r-project.org/package=chapensk Description: CRAN Package 'chapensk' (Estimation of Gas Properties from the Lennard-Jones Potential) Estimation of gas transport properties (viscosity, diffusion, thermal conductivity) using Chapman-Enskok theory (Chapman and Larmor 1918, ) and of the second virial coefficient (Vargas et al. 2001, ) using the Lennard-Jones (12-6) potential. Up to the third order correction is taken into account for viscosity and thermal conductivity. It is also possible to calculate the binary diffusion coefficients of polar and non-polar gases in non-polar bath gases (Brown et al. 2011, ). 16 collision integrals are calculated with four digit accuracy over the reduced temperature range [0.3, 400] using an interpolation function of Kim and Monroe (2014, ). Package: r-cran-chapgwas Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-plyr Filename: pool/dists/noble/main/r-cran-chapgwas_0.1.3-1.ca2404.1_all.deb Size: 37206 MD5sum: 4913c4cebd930de288df7e7547c64982 SHA1: a7f6cddde2ce5bebc75a887a7bf5901f9abb4325 SHA256: 8200d6fe5d0d1ff2aa37efbaab6751a5382e39d96faef11f34c790c6a607aec1 SHA512: f35851b22fe47897efec194464747c1994113fffcfd1338daefe2541120d3c10f331169103b5536fc9311ab7d9c3989a9418173bfb9bf22059f6d0828afd90db Homepage: https://cran.r-project.org/package=CHAPGWAS Description: CRAN Package 'CHAPGWAS' (CHAP-GWAS: Leveraging Chromosomal Haplotypes to ImproveGenome-Wide Association Studies) CHAP-GWAS (Chromosomal Haplotype-Integrated Genome-Wide Association Study) provides a dynamically adaptive framework for genome-wide association studies (GWAS) that integrates chromosome-scale haplotypes with single nucleotide polymorphism (SNP) analysis. The method identifies and extends haplotype variants based on their phenotypic associations rather than predefined linkage blocks, enabling high-resolution detection of quantitative trait loci (QTL). By leveraging long-range phased haplotype information, CHAP-GWAS improves statistical power and offers a more comprehensive view of the genetic architecture underlying complex traits. Package: r-cran-characterization Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4273 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-andromeda, r-cran-databaseconnector, r-cran-digest, r-cran-featureextraction, r-cran-sqlrender, r-cran-parallellogger, r-cran-resultmodelmanager, r-cran-checkmate, r-cran-dplyr, r-cran-readr, r-cran-rlang Suggests: r-cran-devtools, r-cran-formatr, r-cran-testthat, r-cran-kableextra, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-ohdsishinyappbuilder, r-cran-shiny, r-cran-withr Filename: pool/dists/noble/main/r-cran-characterization_3.0.1-1.ca2404.1_all.deb Size: 3645702 MD5sum: 880ab76225862e6a2d84fe00b09d9af1 SHA1: 4337f46d8fc7a6cf12950c542a61507ecac3af2f SHA256: d872defd7a3424f266ddd0495fe285acdb0725612130930fd97d09854c36dc1b SHA512: 2d6b2c0119bf40e59f5d9ef8ab9839ed9bc01a2462f45af704e310bad44b3542d95fc090df9838dcc411fab0211de6cccf8b93d480885a03ed22542a95030c46 Homepage: https://cran.r-project.org/package=Characterization Description: CRAN Package 'Characterization' (Implement Descriptive Studies Using the Common Data Model) An end-to-end framework that enables users to implement various descriptive studies for a given set of target and outcome cohorts for data mapped to the Observational Medical Outcomes Partnership Common Data Model. Package: r-cran-charanalysis Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2742 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-zoo Suggests: r-cran-ggplot2, r-cran-patchwork, r-cran-ggtext, r-cran-readxl, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-charanalysis_2.0.3-1.ca2404.1_all.deb Size: 573778 MD5sum: ad16e0de3b8b46b3cf517f6a4b32bff5 SHA1: 574f7537341809522b6ebac31e8f33237b38a95e SHA256: 6bd5d9a576f64539a4ed2719a9fbbd8be94125e45097e9999ed3b50cfbd65678 SHA512: 8847f0162471df21b0f7348c850e32c682339a5a07cb321e59fdd7f6f2b9525ce79cc59ce526c5e0a03aa92e545575c73f72d3a3c1b9d781cca38ebdea710ff9 Homepage: https://cran.r-project.org/package=CharAnalysis Description: CRAN Package 'CharAnalysis' (Peak Detection and Fire History from Sediment-Charcoal Records) A program for reconstructing local fire histories from high-resolution, continuously sampled lake-sediment charcoal records. 'CharAnalysis' decomposes a charcoal record into low- and high-frequency components and uses locally defined thresholds to separate fire signal from noise, following the approach of Higuera et al. (2009) , with underlying assumptions and rationale described in Higuera et al. (2010) . The package is designed for macroscopic charcoal records with contiguous sampling fine enough to resolve individual fire events, and is not appropriate for low-resolution or discontinuously sampled records. See the package URL for the User's Guide and application examples. Package: r-cran-charcuterie Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-generics Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vctrs Filename: pool/dists/noble/main/r-cran-charcuterie_0.0.6-1.ca2404.1_all.deb Size: 319052 MD5sum: 30f16fda65e164ec31f208104d4ccb93 SHA1: d04ea380ffb463c42678e62f1a538f529bb94308 SHA256: 82aea0251a0617b5a9eee963ece61442a479820800647427e4a5ad42d483ba37 SHA512: 8ab4829ff5f195777b84b48a26446ce6f06d591260007903bc683c53b40538325effff84c7fb34b4ed183798c1235a569d6e2102513e4c54d8ee765c01607292 Homepage: https://cran.r-project.org/package=charcuterie Description: CRAN Package 'charcuterie' (Handle Strings as Vectors of Characters) Creates a new chars class which looks like a string but is actually a vector of individual characters, making 'strings' iterable. 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Package: r-cran-charisma Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4828 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-plyr, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-tidyr, r-cran-recolorize, r-cran-imager, r-cran-abind, r-cran-jpeg, r-cran-png Suggests: r-cran-pavo, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-charisma_1.0.0-1.ca2404.1_all.deb Size: 4268298 MD5sum: 1e568bdafbb8145b13c61f8d1a358afd SHA1: 5c37fccfce589523d62f7d29fda549b570284c38 SHA256: c0922b2e6f043e34ecba7aa49fd47633205d3a1e701bb4f7266c8d796c9b839c SHA512: 9ad5f18c4249f241a8a5fd44c51566590900b47b92643a2eeb5bf72dc64b97b50fd43bd527fd377ebf085bc4f4be073070a61f720d5d1645717fd8552e888d60 Homepage: https://cran.r-project.org/package=charisma Description: CRAN Package 'charisma' (Reproducible Color Characterization of Digital Images forBiological Studies) Provides a standardized and reproducible framework for characterizing and classifying discrete color classes from digital images of biological organisms. The package automatically determines the presence or absence of 10 human-visible color categories (black, blue, brown, green, grey, orange, purple, red, white, yellow) using a biologically-inspired Color Look-Up Table (CLUT) that partitions HSV color space. Supports both fully automated and semi-automated (interactive) workflows with complete provenance tracking for reproducibility. Pre-processes images using the 'recolorize' package (Weller et al. 2024 ) for spatial-color binning, and integrates with 'pavo' (Maia et al. 2019 ) for color pattern geometry statistics. Designed for high-throughput analysis and seamless integration with downstream evolutionary analyses. Package: r-cran-charlatan Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9389 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-tibble, r-cran-whisker Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ipaddress, r-cran-stringi, r-cran-spelling Filename: pool/dists/noble/main/r-cran-charlatan_0.6.2-1.ca2404.1_all.deb Size: 4143914 MD5sum: 8d3841086e7bcccb949e51d4b15ca939 SHA1: e5be86572198e16883bfdea598a86d769df6b908 SHA256: 41bb1950a38c622d3895de2844ed32686003161b038b04e1a6a451518b7e2f4c SHA512: e85566d66469050c7cb4ec0922e3553c0298c3b34953f4613b5b6db867dffcf1f74901e4d4853c4e6864e7c95b82497db9add639d8e97f4e93322d307ab1b15a Homepage: https://cran.r-project.org/package=charlatan Description: CRAN Package 'charlatan' (Make Fake Data) Make fake data that looks realistic, supporting addresses, person names, dates, times, colors, coordinates, currencies, digital object identifiers ('DOIs'), jobs, phone numbers, 'DNA' sequences, doubles and integers from distributions and within a range. 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Package: r-cran-checkluhn Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-checkluhn_1.1.0-1.ca2404.1_all.deb Size: 17328 MD5sum: 026500f718c44a53a91f3b069f89ad1f SHA1: f0dbf7387d4c5480a231e6729b85de71c10f63c1 SHA256: 965c3e6913c94f02ebda7016da78a060b12c7b8c8d492801de20116e4e8d4b7b SHA512: 52d89dc182d862061667b5ade8df49727e5d3a98229e4adceb11acc7a15e540439854c43860d2e34e0dd25117a562ade711d54c488d1bd9e0d32f201a63ed247 Homepage: https://cran.r-project.org/package=checkLuhn Description: CRAN Package 'checkLuhn' (Checks if a Number is Valid Using the Luhn Algorithm) Confirms if the number is Luhn compliant. Can check if credit card, IMEI number or any other Luhn based number is correct. For more info see: . 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Package: r-cran-cheese Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 448 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cheese_0.1.3-1.ca2404.1_all.deb Size: 158800 MD5sum: 2e80257de5a8d776945f8993f574a64c SHA1: 19b0e3453ac759ebf2778497c87c00ab2b6c4b53 SHA256: 7a43ea71255faddc8bbfb9ae3d416a2b5ef149ae88e40464234447aaac5bc06c SHA512: adc5fd22529fd748099bdb2898ed6dc361e3c0c616fab03ea89ec63e13ee6b4d20686f993e9764adac167164b716949f4dd7943105ec759e609f49bdb2f37d00 Homepage: https://cran.r-project.org/package=cheese Description: CRAN Package 'cheese' (Tools for Working with Data During Statistical Analysis) Contains tools for working with data during statistical analysis, promoting flexible, intuitive, and reproducible workflows. 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Package: r-cran-cheetahr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2643 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-testthat, r-cran-shiny, r-cran-quarto, r-cran-knitr, r-cran-dplyr, r-cran-palmerpenguins Filename: pool/dists/noble/main/r-cran-cheetahr_0.3.0-1.ca2404.1_all.deb Size: 1070880 MD5sum: fc33da3bd1c2a9c2cb1d762cfe2ee739 SHA1: 3491702631aa87b1d755c4ead1c7b615e73a7d7d SHA256: 9326a74776f3fbf5b8825fe5cf3f3c28a92009d0447274d1d250853ac0a27b3d SHA512: 3f410933a3f97317eeb80b48f9ef3cf5abab879a77facd25802c392dc4e02de22bd4b3546fb8cb77612f6b0fb49976a7a5d36efa38fb932bc16121ad46296d2c Homepage: https://cran.r-project.org/package=cheetahR Description: CRAN Package 'cheetahR' (High Performance Tables Using 'Cheetah Grid') An R interface to 'Cheetah Grid', a high-performance JavaScript table widget. 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Package: r-cran-chem.databases Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2461 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-data.table, r-cran-install.load, r-cran-spelling Filename: pool/dists/noble/main/r-cran-chem.databases_1.0.0-1.ca2404.1_all.deb Size: 2483170 MD5sum: 899201b53bf53b7a3c4f7098c8f64e74 SHA1: 04ef1abf4f376f33e65fd416f204f1019377c37a SHA256: 34075ba3c8309471fdda3bf263d95261bb4374207e6d5c4c37bd21dedc7a72ef SHA512: 8d614e19b7116bbefe3a7c78e893ba1f834e6673ec59ee014d3df8fb57fa4bfe53097e036e76dd31b6ab27517d4af6a73a130c8e77258f9fc51cba3c541cd72a Homepage: https://cran.r-project.org/package=chem.databases Description: CRAN Package 'chem.databases' (Collection of 3 Chemical Databases from Public Sources) Contains the Multi-Species Acute Toxicity Database (CAS & SMILES columns only) [United States (US) Department of Health and Human Services (DHHS) National Institutes of Health (NIH) National Cancer Institute (NCI), "Multi-Species Acute Toxicity Database", ] combined with the Toxic Substances Control Act (TSCA) Inventory [United States Environmental Protection Agency (US EPA), "Toxic Substances Control Act (TSCA) Chemical Substance Inventory", ] and the Agency for Toxic Substances and Disease Registry (ATSDR) Database [United States (US) Department of Health and Human Services (DHHS) Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR), "Agency for Toxic Substances and Disease Registry (ATSDR) Database", ] in 2 data sets. One data set has a focus on the latter 2 databases and one data set focuses on the former database. Also contains the collection of chemical data from Wikipedia compiled in the US EPA CompTox Chemicals Dashboard [United States Environmental Protection Agency (US EPA) / Wikimedia Foundation, Inc. "CompTox Chemicals Dashboard v2.2.1", ]. Package: r-cran-chem16s Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4807 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-ggplot2, r-cran-rlang, r-cran-reshape2, r-bioc-phyloseq, r-cran-canprot Suggests: r-cran-tinytest, r-cran-knitr, r-cran-patchwork, r-cran-ggpmisc, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chem16s_1.2.0-1.ca2404.1_all.deb Size: 3456002 MD5sum: 14cdb2cce8196683489d761959ee90ce SHA1: 7bc9ccff4be8126f87aa92537ec652df4874534f SHA256: 267d330e3175a6ab269fb801a902d9dfa3c051f4e06595764ca9088817733783 SHA512: 38b7ad275e874db4f719b766114a3968fcde11cb6c39af448eeca850fa21fa6586e125b1e6269d3279422fe79e34cd23af6f116c9df80c8d2485a71f8c770bc3 Homepage: https://cran.r-project.org/package=chem16S Description: CRAN Package 'chem16S' (Chemical Metrics for Microbial Communities) Combines taxonomic classifications of high-throughput 16S rRNA gene sequences with reference proteomes of archaeal and bacterial taxa to generate amino acid compositions of community reference proteomes. 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Package: r-cran-chemcal Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1079 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass, r-cran-knitr, r-cran-testthat, r-cran-investr, r-cran-covr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chemcal_0.2.3-1.ca2404.1_all.deb Size: 408388 MD5sum: dccbf6cd8289918909d4f28ab55d3fd7 SHA1: 6e35ef1bbf8088882e73f75b1ee408bf7cc61d54 SHA256: 057109df968788ca6bad98dac4985f196871ef2e498660c3217ca8b325fe6fc7 SHA512: c8037793448e84bc44e296adf068fd92be5e125f8fbd77aea83335834cf77a67a5b9cfec4b0e8a93a56d25923c1ae947bc41ca0344b4c4bc9ade1d7d524445ee Homepage: https://cran.r-project.org/package=chemCal Description: CRAN Package 'chemCal' (Calibration Functions for Analytical Chemistry) Simple functions for plotting linear calibration functions and estimating standard errors for measurements according to the Handbook of Chemometrics and Qualimetrics: Part A by Massart et al. (1997) There are also functions estimating the limit of detection (LOD) and limit of quantification (LOQ). The functions work on model objects from - optionally weighted - linear regression (lm) or robust linear regression ('rlm' from the 'MASS' package). 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Package: r-cran-chemist Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-xicor, r-cran-laplacesdemon Filename: pool/dists/noble/main/r-cran-chemist_0.1.5-1.ca2404.1_all.deb Size: 61120 MD5sum: edc7e28a0b02fe1c5be4fca25a3461d0 SHA1: 8638d473b7d62b25c7a64cb40e068cab8c4ff5f9 SHA256: 3a6c11410d60d4f0f61def4986dc91b9768fff8a717713682ca6f3f615162c2b SHA512: d3c6a26c92bde209a7eeac874add4d23bc791d2d39d9dbcab5bba28629581669627f4010cb24e29f9390895233159e58b385681595e7ff6b0387835d0f720d0a Homepage: https://cran.r-project.org/package=CHEMIST Description: CRAN Package 'CHEMIST' (Causal Inference with High-Dimensional Error-Prone Covariatesand Misclassified Treatments) We aim to deal with the average treatment effect (ATE), where the data are subject to high-dimensionality and measurement error. This package primarily contains two functions, which are used to generate artificial data and estimate ATE with high-dimensional and error-prone data accommodated. Package: r-cran-chemmodlab Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-msqc, r-cran-class, r-cran-e1071, r-cran-elasticnet, r-cran-lars, r-cran-mass, r-cran-nnet, r-cran-proc, r-cran-randomforest, r-cran-rpart, r-cran-tree, r-cran-pls, r-cran-caret Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-chemmodlab_2.0.0-1.ca2404.1_all.deb Size: 381906 MD5sum: cfaaaa0f48240c4ba330bef38dc93988 SHA1: 74e2732c6b8065bb17b3e7e1da6a4a672d3483ff SHA256: c06645f5644d9952bc26da3081558a4cef3127d07fa872cd1788048a05ff6a17 SHA512: 60b9ef63c325927c09490928365c52054ac31242eb901758810420290ae13e983a7799b38a6f4e3e655216d6025f82b1188edec1b5ad1095afaa44526a7ed972 Homepage: https://cran.r-project.org/package=chemmodlab Description: CRAN Package 'chemmodlab' (A Cheminformatics Modeling Laboratory for Fitting and AssessingMachine Learning Models) Contains a set of methods for fitting models and methods for validating the resulting models. The statistical methodologies comprise a comprehensive collection of approaches whose validity and utility have been accepted by experts in the Cheminformatics field. As promising new methodologies emerge from the statistical and data-mining communities, they will be incorporated into the laboratory. These methods are aimed at discovering quantitative structure-activity relationships (QSARs). However, the user can directly input their own choices of descriptors and responses, so the capability for comparing models is effectively unlimited. Package: r-cran-chemodiv Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-httr, r-cran-vegan, r-cran-webchem, r-bioc-fmcsr, r-bioc-chemminer, r-cran-hillr, r-cran-ape, r-cran-gunifrac, r-cran-tidygraph, r-cran-igraph, r-cran-ggraph, r-cran-ggplot2, r-cran-gridextra, r-cran-ggdendro, r-cran-tidyr, r-cran-rlang, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-chemodiv_0.3.1-1.ca2404.1_all.deb Size: 579894 MD5sum: bb1464c2b7659e5c63f0c40d86e23ed1 SHA1: 960ee588c318a12e136546f68ece1dfa2ef1b145 SHA256: 1253e049378a26cba9a87e39320b16a67f5f93ffbdb279798d1b7a71e152c5e7 SHA512: 091b5ab8c9474474b15eb9802643d24f503471beaa18db1e55950ef3290e508759328494a894c1b1daf68718e94c33760ec3b38b43711ad13ad4662acd418c6f Homepage: https://cran.r-project.org/package=chemodiv Description: CRAN Package 'chemodiv' (Analysing Chemodiversity of Phytochemical Data) Quantify and visualise various measures of chemical diversity and dissimilarity, for phytochemical compounds and other sets of chemical composition data. Importantly, these measures can incorporate biosynthetic and/or structural properties of the chemical compounds, resulting in a more comprehensive quantification of diversity and dissimilarity. For details, see Petrén, Köllner and Junker (2023) . Package: r-cran-chemometrics Architecture: all Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4346 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpart, r-cran-class, r-cran-e1071, r-cran-mass, r-cran-nnet, r-cran-pcapp, r-cran-robustbase, r-cran-som, r-cran-lars, r-cran-pls, r-cran-mclust Suggests: r-cran-gclus Filename: pool/dists/noble/main/r-cran-chemometrics_1.4.4-1.ca2404.1_all.deb Size: 4086196 MD5sum: 5a1d67378da210417151ded2fb00ef32 SHA1: 491f286c47e6b88e6695a9b8526035fa46f7d4c3 SHA256: 75359978a1fb9e784e5ab1abf100f3a69be214cce71234e2e7d096dc53819114 SHA512: 971cd0f0bf191601dd2246f3df93a98f25af02e5a19280b9e4362b28478dc12b8b4c080f9097fea2f11d69f96b34cb2c98e793aa90a7b141f33a43b0f3972a10 Homepage: https://cran.r-project.org/package=chemometrics Description: CRAN Package 'chemometrics' (Multivariate Statistical Analysis in Chemometrics) R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009). Package: r-cran-chemospec2d Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chemospecutils, r-cran-colorspace, r-cran-readjdx, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-tinytest, r-cran-irlba, r-cran-threeway, r-cran-multiway, r-cran-matrixstats, r-cran-r.utils, r-cran-mlrmbo, r-cran-paramhelpers, r-cran-smoof, r-cran-mlr, r-cran-lhs, r-cran-rcpproll, r-cran-rmarkdown, r-cran-robustbase, r-cran-bookdown, r-cran-cmls Filename: pool/dists/noble/main/r-cran-chemospec2d_0.5.1-1.ca2404.1_all.deb Size: 386674 MD5sum: f09ec14cdacf5485a204918a5b0907f2 SHA1: b42216f5dfeec23b8e85ff058544f71e0b54977f SHA256: 05e56d889f258eb016b3292b31c50d4acf056a73648c8ae0fb2fb0ec9f5714da SHA512: 152b4734f6a8c2a5bd6d4c59642fc39b33c9d5bf69b0a697f778361a54f4f8109070649abb104e7bc72d373b087bc002f205413dc00b16f6b3d8506635896abd Homepage: https://cran.r-project.org/package=ChemoSpec2D Description: CRAN Package 'ChemoSpec2D' (Exploratory Chemometrics for 2D Spectroscopy) A collection of functions for exploratory chemometrics of 2D spectroscopic data sets such as COSY (correlated spectroscopy) and HSQC (heteronuclear single quantum coherence) 2D NMR (nuclear magnetic resonance) spectra. 'ChemoSpec2D' deploys methods aimed primarily at classification of samples and the identification of spectral features which are important in distinguishing samples from each other. Each 2D spectrum (a matrix) is treated as the unit of observation, and thus the physical sample in the spectrometer corresponds to the sample from a statistical perspective. In addition to chemometric tools, a few tools are provided for plotting 2D spectra, but these are not intended to replace the functionality typically available on the spectrometer. 'ChemoSpec2D' takes many of its cues from 'ChemoSpec' and tries to create consistent graphical output and to be very user friendly. Package: r-cran-chemospec Architecture: all Version: 6.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chemospecutils, r-cran-reshape2, r-cran-readjdx, r-cran-patchwork, r-cran-ggplot2, r-cran-plotly, r-cran-lattice, r-cran-magrittr Suggests: r-cran-idpmisc, r-cran-js, r-cran-nbclust, r-cran-clustercrit, r-cran-baseline, r-cran-mclust, r-cran-pls, r-cran-r.utils, r-cran-rcolorbrewer, r-cran-seriation, r-cran-mass, r-cran-pcapp, r-cran-jsonlite, r-cran-signal, r-cran-speaq, r-cran-elasticnet, r-cran-irlba, r-cran-chemometrics, r-cran-amap, r-cran-tinytest, r-cran-roxut Filename: pool/dists/noble/main/r-cran-chemospec_6.3.1-1.ca2404.1_all.deb Size: 3134784 MD5sum: 97883b59b3f32784a71ba4afe79cb6b1 SHA1: c1c0f9c3743790d3665281d833e09e11e16d75b8 SHA256: 95369b2b10f2534eb98f2d2f77fe976a6de42fae46ba46fe0b4d2a319ef4fa31 SHA512: f207b49d29065d1ca084f604e6041a922d45705f9146e8c60db62cd01f01be68decda8c06c82fa9af7b20abf06916e019c45c64d5abafb6c655ac571a3b0c138 Homepage: https://cran.r-project.org/package=ChemoSpec Description: CRAN Package 'ChemoSpec' (Exploratory Chemometrics for Spectroscopy) A collection of functions for top-down exploratory data analysis of spectral data including nuclear magnetic resonance (NMR), infrared (IR), Raman, X-ray fluorescence (XRF) and other similar types of spectroscopy. Includes functions for plotting and inspecting spectra, peak alignment, hierarchical cluster analysis (HCA), principal components analysis (PCA) and model-based clustering. Robust methods appropriate for this type of high-dimensional data are available. ChemoSpec is designed for structured experiments, such as metabolomics investigations, where the samples fall into treatment and control groups. Graphical output is formatted consistently for publication quality plots. ChemoSpec is intended to be very user friendly and to help you get usable results quickly. A vignette covering typical operations is available. 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Chernoff's distribution is defined as the distribution of the maximizer of the two-sided Brownian motion minus quadratic drift. That is, Z = argmax (B(t)-t^2). Package: r-cran-cherry Architecture: all Version: 0.6-15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 688 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bitops, r-cran-lpsolve, r-cran-hommel Suggests: r-cran-mass, r-bioc-multtest Filename: pool/dists/noble/main/r-cran-cherry_0.6-15-1.ca2404.1_all.deb Size: 540098 MD5sum: 5161ce63c91488144a6b1ae0ebe095a9 SHA1: 1ee5d41436043e6dca7e651c7176dd94aedd53ac SHA256: c93a50710a6519580e3e5dba8bad763a3a8a316c37a009ccaeed1917eaf4a788 SHA512: 68d0cc9a14a647a6d10b7ad565f5dc8534e15e86208073ff8c66171ed001118ebef2942f7f5a38f55cec60118fcd3d27096cfa880f1924e16e38e39834161774 Homepage: https://cran.r-project.org/package=cherry Description: CRAN Package 'cherry' (Multiple Testing Methods for Exploratory Research) Provides an alternative approach to multiple testing by calculating a simultaneous upper confidence bounds for the number of true null hypotheses among any subset of the hypotheses of interest, using the methods of Goeman and Solari (2011) . Package: r-cran-cherryblossom Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1378 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cherryblossom_0.1.0-1.ca2404.1_all.deb Size: 1333548 MD5sum: 32a486173da617c1530731e233ae8c96 SHA1: 917b0016d4ed4cec66a84258f75a6fa2a6722770 SHA256: 2ebf27d3f81930b5d7c06634a88ea49ed2e52c6ce3bc8f09b629162eb1b11fec SHA512: e7db5421e2d4abc649c5e2c612508907289a6cc1722ce5e34d7e799578dc3292fad035e4d68f3962f7bc10fc84bf139dc9fa20964ee6f8d4feee3e19cf5a1528 Homepage: https://cran.r-project.org/package=cherryblossom Description: CRAN Package 'cherryblossom' (Cherry Blossom Run Race Results) Race results of the Cherry Blossom Run, which is an annual road race that takes place in Washington, DC. Package: r-cran-chess2plyrs Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-chess2plyrs_0.3.0-1.ca2404.1_all.deb Size: 145134 MD5sum: 68891c77df2edd172d617a13a16d2fda SHA1: 035bee575a5acabdd9bea90cd517fcd59b0d2ebc SHA256: 872860c84ed643117079f064cb71cb93448d48f14677dfeb50f6984104630c8c SHA512: c8973bf59426672862475c4eb9523d5cde2779913ce56ada1130c466843fb13e343b391f7dd42c4ba3caebbf0b31a677b7e66a4c15af49a3af616a7d97e12faf Homepage: https://cran.r-project.org/package=chess2plyrs Description: CRAN Package 'chess2plyrs' (Chess Game Creation and Tools) A chess program which allows the user to create a game, add moves, check for legal moves and game result, plot the board, take back, read and write FEN (Forsyth–Edwards Notation). 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It makes the creation of connectivity matrices easier, i.e. a binary matrix of dimension n x n, where n is the number of nodes (sampling units) indicating the presence (1) or the absence (0) of an edge (link) between pairs of nodes. Different network objects can be produced by 'chessboard': node list, neighbor list, edge list, connectivity matrix. It can also produce objects that will be used later in Moran's Eigenvector Maps (Dray et al. (2006) ) and Asymetric Eigenvector Maps (Blanchet et al. (2008) ), methods available in the package 'adespatial' (Dray et al. (2023) ). This work is part of the FRB-CESAB working group Bridge . Package: r-cran-chessgmoog Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3939 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-chessgmoog_0.1.0-1.ca2404.1_all.deb Size: 3984516 MD5sum: 595c0ff24781b12c6192369c9bc1d096 SHA1: 79d2d3e75ac34aaa37867ebc03f2ccac3b218fbc SHA256: 9f9c2c6ae902547272dd340764013ee49b656b8c29e95451fcabdeef19fce502 SHA512: cf55302a900e1977c113e87a11a6924c3f0e1312a940c0ac04716335a7935938d4bd127e0f8bbfc7360c13ec583090814ba8661c8b4a831bb5fecaae74319a36 Homepage: https://cran.r-project.org/package=ChessGmooG Description: CRAN Package 'ChessGmooG' (FIDE Chess Players Ratings for 2015 and 2020) Datasets of the International Chess Federation's player ratings and country information analysed in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics". Package: r-cran-chest Architecture: all Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-ggplot2, r-cran-survival, r-cran-forestplot, r-cran-mass, r-cran-tibble, r-cran-dplyr Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chest_0.3.7-1.ca2404.1_all.deb Size: 183476 MD5sum: 9c8b91bfe01ab47065b761655b961f73 SHA1: 61f879833d90b2d16dd61567fe97f4cd3d7034c4 SHA256: 5a5ed17cc58d3b58f4d666b54f0470507ea06199f78873787d4d5ec7e522bb69 SHA512: 760f117934ec7b599af2098352ee70365afb6572c8f05c857da9315bf5920009b323b7fdde143c7776eb64bedc6b0c7e9ac7b1e440dc8eee46c82bf5d85397b6 Homepage: https://cran.r-project.org/package=chest Description: CRAN Package 'chest' (Change-in-Estimate Approach to Assess Confounding Effects) Applies the change-in-effect estimate method to assess confounding effects in medical and epidemiological research (Greenland & Pearce (2016) ). It starts with a crude model including only the outcome and exposure variables. At each of the subsequent steps, one variable which creates the largest change among the remaining variables is selected. This process is repeated until all variables have been entered into the model (Wang Z. Stata Journal 2007; 7, Number 2, pp. 183–196). Currently, the 'chest' package has functions for linear regression, logistic regression, negative binomial regression, Cox proportional hazards model and conditional logistic regression. 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Includes functions for data processing, marker position adjustment, volume calculation using convex hulls, and visualization in 2D and 3D. Barber et al. (1996) . TAMIYA Hiroyuki et al. (2021) . 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This package implements a structure to reformat the data with 'dunlin', create reporting tables using 'rtables' and 'tern' with standardized input arguments to enable quick generation of standard outputs. In addition, it also provides comprehensive data checks and script generation functionality. 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Package: r-cran-chi Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-chi_0.1-1.ca2404.1_all.deb Size: 15744 MD5sum: 9717ed2bfd45c51614c2ab8ea2d76267 SHA1: 896972d42e40f17d63dd52a8a60f99e1e7203419 SHA256: f9258fe7b8b80c852050f5050703e31ea7f8421247a9849861890bafad4e1919 SHA512: 31fce7be1fb5686d7d6f61db123e7d76eebd69c82a10f608de0bb25623f3009e697ea9d11a27d226b6bdffd98e4585f189a53dffd71bdfe32f7b52b7e11e6f7e Homepage: https://cran.r-project.org/package=chi Description: CRAN Package 'chi' (The Chi Distribution) Light weight implementation of the standard distribution functions for the chi distribution, wrapping those for the chi-squared distribution in the stats package. 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Converts BAM files into counts of reads linking restriction fragments, and identifies pairs of fragments that interact more than expected by chance. Significant interactions are identified by comparing the observed read count to the expected background rate from a count regression model. Package: r-cran-childdevdata Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1267 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-childdevdata_1.1.0-1.ca2404.1_all.deb Size: 1167516 MD5sum: 94824f0c4a6377291e970d8576ac471a SHA1: 01ad48a3a076182b8993aefe008b128fc8c2fa41 SHA256: 77ddd9f5194d46a21d0775b6ca33924ae66dcd8eb195b64b519a1a1b81987629 SHA512: 7d9dbe9e35427170b50b2d9b2233a61342dba149b64ef10865a03be1a16cc4e8dda9bf53b9ca8f1ccdcea37192fb2ace44d65f9fa700bc710a2ad29e4f46a27d Homepage: https://cran.r-project.org/package=childdevdata Description: CRAN Package 'childdevdata' (Child Development Data) Measuring child development starts by collecting responses to developmental milestones, such as "able to sit" or "says two words". There are many ways to combine such responses into summaries. 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Supports type-level word counts, token-mode searches with simple wildcard patterns and part-of-speech filters, optional stemming, and Zipf-scaled frequencies. Provides normalization per number of tokens or utterances, speaker-role breakdowns, dataset summaries, and export to Excel workbooks for reproducible child language research. The CHILDES database is maintained at . Package: r-cran-childfree Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rio, r-cran-rcurl Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-childfree_0.0.5-1.ca2404.1_all.deb Size: 239734 MD5sum: dcff9e51d128337e82ecbf4a68647229 SHA1: 7fa6e3f28fc29bc0397febde5b98a606e062cc9f SHA256: 6b79f14e2136d9ca3527f95a98f4cab03501673ffb6253c8d50ff97e86afb252 SHA512: 8f326a9d581b5867436c557a986fcb1d3e4c71e0e0262bd88a7fe883a9e6282611ff8e9016b803155f78145b299be29b0ecc4660136aa73d545c87769d17e177 Homepage: https://cran.r-project.org/package=childfree Description: CRAN Package 'childfree' (Access and Harmonize Childfree Demographic Data) Reads demographic data from a variety of public data sources, extracting and harmonizing variables useful for the study of childfree individuals. 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Package: r-cran-chiledataapi Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2600 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-tibble, r-cran-scales Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chiledataapi_0.2.0-1.ca2404.1_all.deb Size: 1397268 MD5sum: 2303301bd2af7150f863a276c678aeeb SHA1: 0e6a1fbb2fc195d9ece8d1f0152fce31b23aacdb SHA256: 916cdd9629f0280b3c29275e4890860f65840251c37ca050030463f49d756099 SHA512: 4f8c0179a68dc2905690d36bb0b30fa66f0b25c542668ee8a11aed53903135bb30885280d83bfd6fd6b82166e031af38b96fd8548155efc62fa576a2fda52b09 Homepage: https://cran.r-project.org/package=ChileDataAPI Description: CRAN Package 'ChileDataAPI' (Access Chilean Data via APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'FINDIC API', 'REST Countries API', 'World Bank API', and 'Nager.Date', retrieving real-time or historical data related to Chile such as financial indicators, holidays, international demographic and geopolitical indicators, and more. Additionally, the package includes curated datasets related to Chile, covering topics such as human rights violations during the Pinochet regime, electoral data, census samples, health surveys, seismic events, territorial codes, and environmental measurements. The package supports research and analysis focused on Chile by integrating open APIs with high-quality datasets from multiple domains. For more information on the APIs, see: 'FINDIC' , 'REST Countries' , 'World Bank API' , and 'Nager.Date' . Package: r-cran-chilemapas Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2835 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-magrittr, r-cran-stringr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-chilemapas_0.4.0-1.ca2404.1_all.deb Size: 2736484 MD5sum: b287141dc6f14b96da013cdad4ebc755 SHA1: 416c3f0e5824b0dc812b92ffa486c452c1d2d173 SHA256: 2ee182f909149637a8b55455fe0850c30fb22f0a882d504cb631aff67a58cba5 SHA512: 3016b358eab3f0dc416e552e5f25f82608d4d52478a17afdae19f90582062309afe08f2ee6f4a910b5d1051118b8d2d924990a44a1f7f4dbac0a62b6d05494c9 Homepage: https://cran.r-project.org/package=chilemapas Description: CRAN Package 'chilemapas' (Mapas de las Divisiones Politicas y Administrativas de Chile(Maps of the Political and Administrative Divisions of Chile)) Mapas terrestres con topologias simplificadas. Estos mapas no tienen precision geodesica, por lo que aplica el DFL-83 de 1979 de la Republica de Chile y se consideran referenciales sin validez legal. No se incluyen los territorios antarticos y bajo ningun evento estos mapas significan que exista una cesion u ocupacion de territorios soberanos en contra del Derecho Internacional por parte de Chile. Esta paquete esta documentado intencionalmente en castellano asciificado para que funcione sin problema en diferentes plataformas. (Terrestrial maps with simplified toplogies. These maps lack geodesic precision, therefore DFL-83 1979 of the Republic of Chile applies and are considered to have no legal validity. Antartic territories are excluded and under no event these maps mean there is a cession or occupation of sovereign territories against International Laws from Chile. This package was intentionally documented in asciified spanish to make it work without problem on different platforms.) Package: r-cran-chillmodels Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-chillr, r-cran-dplyr, r-cran-lubridate, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-chillmodels_1.0.2-1.ca2404.1_all.deb Size: 78674 MD5sum: 43142124bf8d2f09a1adb5e2c074c638 SHA1: 161c2522d8041194fba0078cb9c3c61f29951394 SHA256: ff511d07c1e655f090083d85237f29076faad28612332a8341e7e2c82b7eed49 SHA512: 054b7d01ee4469838c583bd6543d92f22b4fcdbd4485a7feae31bdf0fbae43fed7498352acb4a9422cdf8289d6ee623976cd5f0a62e0130dca1996a921732c40 Homepage: https://cran.r-project.org/package=ChillModels Description: CRAN Package 'ChillModels' (Processing Chill and Heat Models for Temperate Fruit Trees) Calculates the chilling and heat accumulation for studies of the temperate fruit trees. The models in this package are: Utah (Richardson et al., 1974, ISSN:0018-5345), Positive Chill Units - PCU (Linsley-Noaks et al., 1995, ISSN:1017-0316), GDH-A - Growing Degree Hours by Anderson et al.(1986, ISSN:0567-7572), GDH-R - Growing Degree Hours by Richardson et al.(1975, ISSN:0018-5345), North Carolina (Shaltout e Unrath, 1983, ISSN:0003-1062), Landsberg Model (Landsberg, 1974, ISSN:0305-7364), Q10 Model (Bidabe, 1967, ISSN:0031-9368), Jones Model (Jones et al., 2013 ), Low-Chill Model (Gilreath and Buchanan, 1981, ISSN:0003-1062), Model for Cherry "Sweetheart" (Guak and Nielsen, 2013 ), Model for apple "Gala" (Guak and Nielsen, 2013 ), Taiwan Model (Lu et al., 2012 ), Dynamic Model (Fishman et al., 1987, ISSN:0022-5193) adapted from the function Dynamic_Model() of the 'chillR' package (Luedeling, 2018), Unified Model (Chuine et al., 2016 ) and Heat Restriction model. Package: r-cran-chinapis Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3823 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chinapis_0.1.1-1.ca2404.1_all.deb Size: 1993818 MD5sum: e32b099e8e24b03101dfcdaea41c34e3 SHA1: b5a4a7a3f5fc072377cfa49cbb69527bfb58a487 SHA256: 3a727429f2d6be448578e74772494ba5d163f3274666ed73ef2c168062ce0090 SHA512: 2ff792d6a48723c0c50f0b0fdf5520061d2d278e62d54846b5ee5a8ca28a76d167b1d8eddb53f42ecd27d6d44011255f2953df32814853b2bdc82a1290889d93 Homepage: https://cran.r-project.org/package=ChinAPIs Description: CRAN Package 'ChinAPIs' (Access Chinese Data via Public APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'Nager.Date', 'World Bank API', and 'REST Countries API', retrieving real-time or historical data related to China, such as holidays, economic indicators, and international demographic and geopolitical indicators. Additionally, the package includes one of the largest curated collections of open datasets focused on China and Hong Kong, covering topics such as air quality, demographics, input-output tables, epidemiology, political structure, names, and social indicators. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: 'Nager.Date' , 'World Bank API' , and 'REST Countries API' . 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Document term matrix can be generated by only one line of code; detecting encoding, segmenting and removing stop words are done automatically. Some convenient tools are also supplied. Package: r-cran-chinesenames Architecture: all Version: 2025.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brucer, r-cran-data.table Suggests: r-cran-babynames, r-cran-car, r-cran-dplyr, r-cran-glue Filename: pool/dists/noble/main/r-cran-chinesenames_2025.8-1.ca2404.1_all.deb Size: 408432 MD5sum: 6431f5a015e1b0d9df03408686e40c98 SHA1: 1a885239f692bb7b39162943c4525ad200bea5f9 SHA256: 519c3ead73a2bbec9ac3eac52bec26e9d38e01e2f8c9454c4d35aa808f8e1967 SHA512: 790a3b34b4013f47a8791082b04cd7ae82988babd0f3ed9ea68f5a71c860c5c875e433529f678c4f068fb2cd665d675dca961c927d57d03fccd02d78aee09068 Homepage: https://cran.r-project.org/package=ChineseNames Description: CRAN Package 'ChineseNames' (Chinese Name Database 1930-2008) A database of Chinese surnames and given names (1930-2008). This database contains nationwide frequency statistics of 1,806 Chinese surnames and 2,614 Chinese characters used in given names, covering about 1.2 billion Han Chinese population (96.8 percent of the Han Chinese household-registered population born from 1930 to 2008 and still alive in 2008). This package also contains a function for computing multiple indices of Chinese surnames and given names for social science research (e.g., name uniqueness, name gender, name valence, and name warmth/competence). Details are provided at . Package: r-cran-chiopendata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-jsonlite, r-cran-httr, r-cran-janitor, r-cran-rlang Suggests: r-cran-curl, r-cran-knitr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-chiopendata_0.1.0-1.ca2404.1_all.deb Size: 88746 MD5sum: e0fedc0e5f747c8dd66b1837a283adad SHA1: c458adddf147bb2ab8ba4b75155ac818c6a45aeb SHA256: f2631f0793dd579ef924c5df4217737dc9e3f26827679a4b23014342ebf90d31 SHA512: c16465e1a85e2d0539b93208dbf3605c3ce6c5043bed001bd8418ee0f469dfc229183679a1ca886e726d83b465d2273ca897b7329d58a966e01bbce4c7471fb1 Homepage: https://cran.r-project.org/package=chiOpenData Description: CRAN Package 'chiOpenData' (Convenient Access to Chicago Open Data API Endpoints) Provides simple, reproducible access to datasets from the Chicago Open Data portal . Functions return results as tidy tibbles and support optional filtering, sorting, and row limits via the Socrata API. 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Contains functions to normalize and baseline amplification curves, to detect both the start and end of an amplification reaction, several smoothers (e.g., LOWESS, moving average, cubic splines, Savitzky-Golay), a function to detect false positive amplification reactions and a function to determine the amplification efficiency. Quantification point (Cq) methods include the first (FDM) and second approximate derivative maximum (SDM) methods (calculated by a 5-point-stencil) and the cycle threshold method. Data sets of experimental nucleic acid amplification systems ('VideoScan HCU', capillary convective PCR (ccPCR)) and commercial systems are included. Amplification curves were generated by helicase dependent amplification (HDA), ccPCR or PCR. As detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis probes (TaqMan) were used. For more information see: Roediger et al. (2015) . Package: r-cran-chirps Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1920 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-sf, r-cran-terra Suggests: r-cran-climatrends, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-chirps_1.1-1.ca2404.1_all.deb Size: 635314 MD5sum: 10531e0c667d710178ab2eddccb83791 SHA1: e3c9b43bbdaeabae7532b033dee6903a7f9009a4 SHA256: c9bde44c0628c4ec3577673f1af81aea641bc97f0992794e718c24505f4c4d31 SHA512: b9523c31be1e65f9adafc7d15425c5f90e8a6ae903ed0666c780b2f9d2f989cab2a96b6e08cf712c5069ad44de2a63b9358fc9d6c4eb7aa5358b49383c480c79 Homepage: https://cran.r-project.org/package=chirps Description: CRAN Package 'chirps' (API Client for CHIRPS and CHIRTS) API Client for the Climate Hazards Center 'CHIRPS' and 'CHIRTS'. The 'CHIRPS' data is a quasi-global (50°S – 50°N) high-resolution (0.05 arc-degrees) rainfall data set, which incorporates satellite imagery and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. 'CHIRTS' is a quasi-global (60°S – 70°N), high-resolution data set of daily maximum and minimum temperatures. For more details on 'CHIRPS' and 'CHIRTS' data please visit its official home page . 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Package: r-cran-choirbm Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-choirbm_0.0.2-1.ca2404.1_all.deb Size: 227016 MD5sum: 5770ce011b69a376a3f0255b226f47cf SHA1: 8013390379a0b81e21e030eed7756569d1079d28 SHA256: f25c07774bb9339759525d49dca9ae550bc209202fdbe6ca45032b52316c5c6d SHA512: bd8c5b777360274838c7e0ba7423c49ad6f392d33324263a7a92730cd23c0f0a00e0a307ba33e945a8981ebf948613bbb174eca637946a49971ef2c706646896 Homepage: https://cran.r-project.org/package=CHOIRBM Description: CRAN Package 'CHOIRBM' (Plots the CHOIR Body Map) Collection of utility functions for visualizing body map data collected with the Collaborative Health Outcomes Information Registry. 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Package: r-cran-choosegcm Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2320 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-cluster, r-cran-cowplot, r-cran-factoextra, r-cran-ggcorrplot, r-cran-ggplot2, r-cran-httr2, r-cran-reshape2, r-cran-terra, r-cran-usedist Suggests: r-cran-tictoc, r-cran-testthat, r-cran-sf, r-cran-raster, r-cran-rmarkdown, r-cran-stars Filename: pool/dists/noble/main/r-cran-choosegcm_1.3-1.ca2404.1_all.deb Size: 1883262 MD5sum: d46d4f5353cf84c751953524ec0848f5 SHA1: 727f0c958ec7e9632f5b6f9570ffc42b009127ba SHA256: f1bd9ec544da91677cccb1f668b7b1c29f6435df3da3b299c2eb1bac04b94898 SHA512: 0364b885d4df46c22064deedd6a24b0152640c3aca638b8bb7f2a6baaadc6516536266c80c0b509cd1b0567848fb1ed23994284c5bf00b418c165906617d84a7 Homepage: https://cran.r-project.org/package=chooseGCM Description: CRAN Package 'chooseGCM' (Selecting General Circulation Models for Species DistributionModeling) Methods to help selecting General Circulation Models (GCMs) in the context of projecting models to future scenarios. It is provided clusterization algorithms, distance and correlation metrics, as well as a tailor-made algorithm to detect the optimum subset of GCMs that recreate the environment of all GCMs as proposed in Esser et al. (2025) . Package: r-cran-choosepc Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-choosepc_1.0-1.ca2404.1_all.deb Size: 25450 MD5sum: 7131acf29188c12cdcaa535d1b7b3e7f SHA1: 09d56695f0fea5cbd533d6cbeef454f8cfd83b2b SHA256: 16c97fb3e74c4366c7131709795d4b9c5a79614b02a5dd9b1b5eaf08e9bf1fa1 SHA512: 4c8fc55edb04f12dbf9cd2b1a2a67893180651ef2d70bd464617f87f9c27d697eeda78819415d3daceac1984c477773a8aadefcc0282441a0b68717004ff1bee Homepage: https://cran.r-project.org/package=choosepc Description: CRAN Package 'choosepc' (Choose the Number of Principal Components via RecistructionError) One way to choose the number of principal components is via the reconstruction error. This package is designed mainly for this purpose. Graphical representation is also supported, plus some other principal component analysis related functions. References include: Jolliffe I.T. (2002). Principal Component Analysis. and Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press. Package: r-cran-chopin Architecture: all Version: 0.9.9-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5997 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-anticlust, r-cran-cli, r-cran-dplyr, r-cran-exactextractr, r-cran-future, r-cran-future.apply, r-cran-igraph, r-cran-rlang, r-cran-sf, r-cran-sfheaders, r-cran-stars, r-cran-terra, r-cran-mirai, r-cran-collapse Suggests: r-cran-covr, r-cran-devtools, r-cran-targets, r-cran-diagrammer, r-cran-future.mirai, r-cran-knitr, r-cran-lifecycle, r-cran-rmarkdown, r-cran-spatstat.random, r-cran-testthat, r-cran-units, r-cran-withr, r-cran-dggridr, r-cran-h3r Filename: pool/dists/noble/main/r-cran-chopin_0.9.9-5-1.ca2404.1_all.deb Size: 2743382 MD5sum: cca6bc3e33d29ae52d4fb7ca8aabe278 SHA1: e89a25fbcc9980ceaf680a66eadd23de769b4094 SHA256: 87dc3b8e13f2ffcecc94f754d68100e3a34cdcb0974632911dad42ef4a33c86c SHA512: eebbe252205c80a1207d15e85cc9d7b2a44735612dafaf55026f949a3131e90e50d88f287d8720e92169f16f6551992b9e4771760496c7cc50c676a64ee8fbd2 Homepage: https://cran.r-project.org/package=chopin Description: CRAN Package 'chopin' (Spatial Parallel Computing by Hierarchical Data Partitioning) Geospatial data computation is parallelized by grid, hierarchy, or raster files. Based on 'future' (Bengtsson, 2024 ) and 'mirai' (Gao et al., 2025 ) parallel back-ends, 'terra' (Hijmans et al., 2025 ) and 'sf' (Pebesma et al., 2024 ) functions as well as convenience functions in the package can be distributed over multiple threads. The simplest way of parallelizing generic geospatial computation is to start from par_pad_*() functions to par_grid(), par_hierarchy(), or par_multirasters() functions. Virtually any functions accepting classes in 'terra' or 'sf' packages can be used in the three parallelization functions. A common raster-vector overlay operation is provided as a function extract_at(), which uses 'exactextractr' (Baston, 2023 ), with options for kernel weights for summarizing raster values at vector geometries. Other convenience functions for vector-vector operations including simple areal interpolation (summarize_aw()) and summation of exponentially decaying weights (summarize_sedc()) are also provided. Package: r-cran-choplump Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-coin, r-cran-survival Filename: pool/dists/noble/main/r-cran-choplump_1.1.2-1.ca2404.1_all.deb Size: 595798 MD5sum: d3cb24ea7578e252f034ad67cb4ec5d0 SHA1: f190f85c02fa7750debeed0a643332e18cb30315 SHA256: 6e917d0d1db7d998b8986a614822cb98280bfac51ebfd9a7e96bf9a831b734e7 SHA512: 5857b4db5f22599d17b4ef699adc8a73b5fd0f6abe8f01f237fa002c8d2c2f7d8248dd6b5b9660b59f18b08443739a6702cc78b0d1acbf476cb360a5bba584e2 Homepage: https://cran.r-project.org/package=choplump Description: CRAN Package 'choplump' (Permutation Test for Some Positive and Many Zero Responses) Calculates permutation tests that can be powerful for comparing two groups with some positive but many zero responses (see Follmann, Fay, and Proschan ). 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Package: r-cran-chor Architecture: all Version: 0.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-commonsmath Suggests: r-bioc-graph, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-chor_0.0-4-1.ca2404.1_all.deb Size: 395170 MD5sum: dbc5971d4eb2b562250c37868111da91 SHA1: a5662309483b782256746cf6f76d93eb6ea8caa9 SHA256: e50eb1fbc3c7b9bf10c6d1ee98e6db47e498ab2bd02d8e5c03a733e5820adad8 SHA512: f78c571360559ea4109d8d033fa72a0bcb6c9c74c88b9b462dadba4dc1eb625d9a415ee87c0113c6daab28383469dc488db4da57ad41a43fec98aefa385c8776 Homepage: https://cran.r-project.org/package=ChoR Description: CRAN Package 'ChoR' (Chordalysis R Package) Learning the structure of graphical models from datasets with thousands of variables. More information about the research papers detailing the theory behind Chordalysis is available at (KDD 2016, SDM 2015, ICDM 2014, ICDM 2013). 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Package: r-cran-choroplethr Architecture: all Version: 5.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4782 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-stringr, r-cran-ggplot2, r-cran-dplyr, r-cran-r6, r-cran-ggrepel, r-cran-tigris, r-cran-sf, r-cran-tidycensus Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-choroplethr_5.0.1-1.ca2404.1_all.deb Size: 4845922 MD5sum: 56f562efb938058ff3e606c3162b3338 SHA1: af0711bb113942dba4a5f5d7185f5b10a831e5bb SHA256: 388f3fb9ba3b750790ebc9cec12008f23f92ee83de8da86cdf68ac31eee2a4b5 SHA512: 9b7871bc136f0dfad7d619dcb1364cae8e725fdafab7db79cf2fc6998eecb76616658f5f6e2df50e930ebfcab8d5c8179bd67eac2e6ea1233cd42da1f4cf61a4 Homepage: https://cran.r-project.org/package=choroplethr Description: CRAN Package 'choroplethr' (Create Color-Coded Choropleth Maps in R) Easily create color-coded (choropleth) maps in R. No knowledge of cartography or shapefiles needed; go directly from your geographically identified data to a highly customizable map with a single line of code! Supported geographies: U.S. states, counties, census tracts, and zip codes, world countries and sub-country regions (e.g., provinces, prefectures, etc.). 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Package: r-cran-christmas Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-animation Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-christmas_1.4.1-1.ca2404.1_all.deb Size: 178004 MD5sum: 0e1432af25cc76467528461f15ca6ebf SHA1: 285b84598221e1242abe72da9c469a4f704fb4bd SHA256: 4fc48aaa7615eff01c30d49c55da51d71c31422f218c372d847591e23c684751 SHA512: ca9775648af6fd58e09b8191b4aca13153489c8d958bdcc2392ed82d1ff5ad348d65b7998e01c80af7c0a6d03a7b202687a81e8c60c4326222ad896960e04fcf Homepage: https://cran.r-project.org/package=christmas Description: CRAN Package 'christmas' (Generation of Different Animated Christmas Cards) Generation of different Christmas cards, most of them being animated. Most of the cards can be generated in three languages (English, Catalan and Spanish). The collection started in 2009. Package: r-cran-chromconverter Architecture: all Version: 0.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1632 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bitops, r-cran-fs, r-cran-purrr, r-cran-readxl, r-cran-reticulate, r-cran-stringr, r-cran-tidyr, r-cran-rams, r-cran-tibble, r-cran-xml2, r-cran-bit64, r-cran-data.table, r-cran-base64enc, r-cran-jsonlite, r-cran-digest Suggests: r-cran-ncdf4, r-cran-pbapply, r-cran-testthat, r-bioc-mzr Filename: pool/dists/noble/main/r-cran-chromconverter_0.7.5-1.ca2404.1_all.deb Size: 767988 MD5sum: c5130f9af7586c526493622776ab6268 SHA1: 3873cba2a46febd508f7cc63ef45353ee4d21849 SHA256: 70496497cbb09116659e72755c20b1ae5c35e0e9c959aa740bce1c4ea6dbd3a4 SHA512: facd40298b9dba8d7144c5b86cb69383f85be2c96cb950e62eb642384fe322cc7e0bee12c356b11e08c4377aaf263635cb843db00a30f27f23faacef18930c33 Homepage: https://cran.r-project.org/package=chromConverter Description: CRAN Package 'chromConverter' (Chromatographic File Converter) Reads chromatograms from binary formats into R objects. Currently supports conversion of 'Agilent ChemStation', 'Agilent MassHunter', 'Shimadzu LabSolutions', 'ThermoRaw', and 'Varian Workstation' files as well as various text-based formats. In addition to its internal parsers, chromConverter contains bindings to parsers in external libraries, such as 'Aston' , 'Entab' , 'rainbow' , and 'ThermoRawFileParser' . 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It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. Because of the large size of the chromosomes, it is impractical to visualize each element on the same plot. However, the plot provides a magnified view for each of chromosome locus to render additional information and visualization specific for that location. You can map thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the chromosome at a location showing each mapped element in sequential order. The package provide multiple features like visualizing multiple sets, chromosome heat-maps, group annotations, adding hyperlinks, and labelling. The plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R 'Shiny' applications. 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Package: r-cran-chromseq Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-chromseq_0.1.3-1.ca2404.1_all.deb Size: 88514 MD5sum: fe85dc64e6a5912d358b71a4d8fbe58b SHA1: e80e3fa7d79e45a04bb0883a677c733596692881 SHA256: d5c73a32bea6cfd76c5cd287085757fd00b18fa4a7a4ec3937d855e0f5b26df6 SHA512: 3b9fe26221221a0dd3fd98ab99143ddc4798f4cf7b23e3dc6a7b60ad3b82841a543b7f0c758268d33e6bacfc0e7a38e2cb8885134a96ad7931d7150a23ebf4f4 Homepage: https://cran.r-project.org/package=chromseq Description: CRAN Package 'chromseq' (Split Chromosome 'Fasta' File) Chromosome files in the 'Fasta' format usually contain large sequences like human genome. Sometimes users have to split these chromosomes into different files according to their chromosome number. The 'chromseq' can help to handle this. So the selected chromosome sequence can be used for downstream analysis like motif finding. Howard Y. Chang(2019) . 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Package: r-cran-chronometre Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rcppspdlog, r-cran-xptr, r-cran-reticulate, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-chronometre_0.0.2-1.ca2404.1_all.deb Size: 10094 MD5sum: b4ce88984ca3acff1f8e9511523adbef SHA1: 629143bbd04aae4e98813c46c793c8c58b9f58f1 SHA256: e5d839d65cbbf5c0aed392119987cab322c6abedf9b8014cac19532357d9002b SHA512: 96c542d4b2f190d48d9d4a8290d56e5b5d1195ae56003aa6075decd45535abafa05bb7005a1d8a81dff8a6a0c11fd951f3d47e8d26e0ae727450e3e292fa908d Homepage: https://cran.r-project.org/package=chronometre Description: CRAN Package 'chronometre' (A 'chronomètre' is a 'stopwatch') As a 'chronomètre' is a 'stopwatch', this package offers a simple stopwatch, and in particular one that can be shared with Python (using the corresponding package of the same name available via 'PyPi') such that both interpreters operate on the same object instance and shown in the demo file, as well as in the unit tests. Package: r-cran-chronosphere Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 742 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-curl, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-chronosphere_0.6.1-1.ca2404.1_all.deb Size: 593974 MD5sum: 167f7291b65351a73b8dd27b3473aec4 SHA1: fb3eb7530b5c166f0b636efefe9151e2d5a7ab9a SHA256: 3b125e7f50c783f9e8dd903fbeec3d751e9f2f9393cfca9343a119b6d645c919 SHA512: e8ca4f7c0cd39214c14cc2b0084403c7211ddad7d03f84b74fba55adb27b978209ef8c1ec7c095204018c7f4a3efb28d92b7e5bd17b4b8fda288b969b96e47cf Homepage: https://cran.r-project.org/package=chronosphere Description: CRAN Package 'chronosphere' (Evolving Earth System Variables) The implemented functions allow the query, download, and import of remotely-stored and version-controlled data items. 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Package: r-cran-chyper Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-chyper_0.3.1-1.ca2404.1_all.deb Size: 66946 MD5sum: 825eaf2b0fca1e3a04c3106c7a118756 SHA1: cc14daf13a14a807acc74cb7725b2fbf300808b4 SHA256: 6a2b06fb7cab5a04243317d176f7fe0feb8eba6f670721479bdd3c7fac0fd53e SHA512: f11a3b6cadad2a8b4965dc48a0e72fa134ef3aca8e8a07af8910f36caaf41871dae52bda7572d58ec265bfdedc8a7507de45eb1f6270f94b8cee78f1460630d1 Homepage: https://cran.r-project.org/package=chyper Description: CRAN Package 'chyper' (Functions for Conditional Hypergeometric Distributions) An implementation of the probability mass function, cumulative density function, quantile function, random number generator, maximum likelihood estimator, and p-value generator from a conditional hypergeometric distribution: the distribution of how many items are in the overlap of all samples when samples of arbitrary size are each taken without replacement from populations of arbitrary size. Package: r-cran-ci Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-forcats, r-cran-tibble, r-cran-checkmate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ci_0.0.1-1.ca2404.1_all.deb Size: 55882 MD5sum: 7d6c2c5839687cdd93a75a97f3fe6ed3 SHA1: fe0014d3328ca38d78122271af954ffbe7f6e422 SHA256: 24b24a5e7ce114bf939b54cee2a16d12291cec7bf0e8b32c218c8c5098021d8c SHA512: e49a59c373d6b2efaf49873d416d94c1fd621fa8c08432662398542e4b5435ae3910cea3d320cb3e7f8320f802d5d89af8831f375502c3e74e3c994102d010d0 Homepage: https://cran.r-project.org/package=ci Description: CRAN Package 'ci' (Confidence Intervals for Education) An educational package providing intuitive functions for calculating confidence intervals (CI) for various statistical parameters. 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Package: r-cran-cicalc Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cicalc_0.2.1-1.ca2404.1_all.deb Size: 184320 MD5sum: f54c21f978aa85c6dae80150fb1cf098 SHA1: 7e372000c64777973f909540fda6e973c9ae308a SHA256: fe250a43371593b8de9a905c2d0e7fcfb9f394d9938f0d9f4fb9b5f4b4aca053 SHA512: e7289fe0480b7c6a6842bae3412048c62462671d2e535e1a373371223a827a662cb22dd720a53e9c51dd176b1c61f2585ba7a10a00822b7473838efe23f6981c Homepage: https://cran.r-project.org/package=cicalc Description: CRAN Package 'cicalc' (Calculate Confidence Intervals) This calculates a variety of different CIs for proportions and difference of proportions that are commonly used in the pharmaceutical industry including Wald, Wilson, Clopper-Pearson, Agresti-Coull and Jeffreys for proportions. And Miettinen-Nurminen (1985) , Wald, Haldane, and Mee for difference in proportions. 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See Hu Z, Ahmed A, Yau C (2021) for more details. 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Includes optimized 'SQL' search with 'SQLite', fuzzy matching of medical terms ('Jaro-Winkler'), Charlson and Elixhauser comorbidity calculation, 'WHO' 'ICD-11' 'API' integration, and hierarchical code validation. Data from Centro FIC Chile 'DEIS' . Package: r-cran-ciee Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-lavaan, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ciee_0.1.1-1.ca2404.1_all.deb Size: 369708 MD5sum: 5bcbcdb24591ee9e0b29a90c2ff71917 SHA1: 4701cb6a7f3b37ecae4dbd6c7f32b14b4866ef87 SHA256: 06f99ab8e295a2e76f25955ca75588ad0838b3cba25ae0ed7805300a9f2edac2 SHA512: 10c0a7cca31528d0c6d63cceacbf13816657a861d7b995f051c6bc2fb85d5d277962d0e5642de6b1c72aa103ca8ef77cab1531e37b16d94a7c35c9c79d243493 Homepage: https://cran.r-project.org/package=CIEE Description: CRAN Package 'CIEE' (Estimating and Testing Direct Effects in Directed Acyclic Graphsusing Estimating Equations) In many studies across different disciplines, detailed measures of the variables of interest are available. If assumptions can be made regarding the direction of effects between the assessed variables, this has to be considered in the analysis. The functions in this package implement the novel approach CIEE (causal inference using estimating equations; Konigorski et al., 2018, ) for estimating and testing the direct effect of an exposure variable on a primary outcome, while adjusting for indirect effects of the exposure on the primary outcome through a secondary intermediate outcome and potential factors influencing the secondary outcome. The underlying directed acyclic graph (DAG) of this considered model is described in the vignette. CIEE can be applied to studies in many different fields, and it is implemented here for the analysis of a continuous primary outcome and a time-to-event primary outcome subject to censoring. CIEE uses estimating equations to obtain estimates of the direct effect and robust sandwich standard error estimates. Then, a large-sample Wald-type test statistic is computed for testing the absence of the direct effect. Additionally, standard multiple regression, regression of residuals, and the structural equation modeling approach are implemented for comparison. 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In situations where the proportion of diseased subjects does not correspond to the disease prevalence (e.g. case-control studies), this package provides two types of solutions: 1) five methods for estimating confidence intervals for PPV and NPV via ratio of two binomial proportions including Gart & Nam (1988), Walter (1975), MOVER-J (Laud, 2017), Fieller (1954), and Bootstrap (Efron, 1979); 2) three direct methods that compute the confidence intervals including Pepe (2003), Zhou (2007), and Delta. In prospective studies where the proportion of diseased subjects is an unbiased estimate of the disease prevalence, this package provides several methods for calculating the confidence intervals for PPV and NPV including Clopper-Pearson, Wald, Wilson, Agresti-Coull, and Beta. See the Details and References sections in the corresponding functions. 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The input data files are assumed to be in an origin-destination matrix format, with each cell representing a flow count between an origin and a destination area. Columns are expected to represent origins, and rows are expected to represent destinations. The first row and column are assumed to contain labels for each area. See Rodriguez-Vignoli and Rowe (2018) for technical details. Package: r-cran-cimir Architecture: all Version: 0.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-glue, r-cran-stringr, r-cran-dplyr, r-cran-tidyr, r-cran-jsonlite, r-cran-purrr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cimir_0.4-1-1.ca2404.1_all.deb Size: 109710 MD5sum: d813fc0501c382551ecfa723da8ab0ab SHA1: cebd24cb91256e65338d47d81ec0771328f46fd1 SHA256: 6f7f324944495b68436c53ec63a53bb87fa6ac62d7861dc721fe8df190aef3bc SHA512: 70b84efa17c69c5ba165671aa6115f86b95d9ea23caa4800aa95d5fa07cf3d1dbc07c2ecaf8e210d4f53122db97d406e9a5a2eb092d0feefc5937ec4f369fdfb Homepage: https://cran.r-project.org/package=cimir Description: CRAN Package 'cimir' (Interface to the CIMIS Web API) Connect to the California Irrigation Management Information System (CIMIS) Web API. See the CIMIS main page and web API documentation for more information. Package: r-cran-cimple Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-jmbayes2, r-cran-lme4, r-cran-mice, r-cran-nleqslv, r-cran-nlme, r-cran-statmod, r-cran-survival Filename: pool/dists/noble/main/r-cran-cimple_0.1.0-1.ca2404.1_all.deb Size: 243768 MD5sum: 8cfceffe526cdc8cb06ee4e9e945ae17 SHA1: 4908d477e325e6dd59915764ea94980a9e93efc8 SHA256: e3ffcaad7574a817b50e1b888ef39b4191a6c31f2d096879547d866ddc32d53e SHA512: 162074fb686fed22a4abf9a42a734680c31aeec76affc3d9944c2bdd171bb7cb9a76414e75c2c2ce770a097f9c1b6f8757575de84a7c49f16493cac1af55bace Homepage: https://cran.r-project.org/package=CIMPLE Description: CRAN Package 'CIMPLE' (Analysis of Longitudinal Electronic Health Record (EHR) Datawith Possibly Informative Observational Time) Analyzes longitudinal Electronic Health Record (EHR) data with possibly informative observational time. These methods are grouped into two classes depending on the inferential task. One group focuses on estimating the effect of an exposure on a longitudinal biomarker while the other group assesses the impact of a longitudinal biomarker on time-to-diagnosis outcomes. The accompanying paper is Du et al (2024) . Package: r-cran-cimpleg Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4589 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-archive, r-cran-assertthat, r-cran-broom, r-cran-butcher, r-cran-caret, r-cran-data.table, r-cran-dplyr, r-cran-forcats, r-cran-ggextra, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggsci, r-cran-gtools, r-cran-magrittr, r-cran-matrixstats, r-cran-nnls, r-cran-oner, r-cran-parsnip, r-cran-patchwork, r-cran-purrr, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-scales, r-cran-tibble, r-cran-tictoc, r-cran-tidyr, r-cran-tidyselect, r-cran-tsutils, r-cran-tune, r-cran-vroom, r-cran-workflows, r-cran-yardstick Suggests: r-bioc-biobase, r-bioc-biomart, r-cran-c50, r-cran-circlize, r-bioc-epidish, r-cran-furrr, r-cran-future, r-cran-future.apply, r-bioc-geoquery, r-cran-ggbeeswarm, r-cran-ggsignif, r-cran-glmnet, r-cran-knitr, r-bioc-minfi, r-cran-mltools, r-cran-nmf, r-cran-nnet, r-cran-plyr, r-cran-ranger, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rfast, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr, r-bioc-summarizedexperiment, r-cran-testthat, r-cran-withr, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-cimpleg_1.0.1-1.ca2404.1_all.deb Size: 4615874 MD5sum: cd3ef3fbf48cf85b7f8ccecf1ef0eb9e SHA1: 6207329c4ae35068c51429fb9e2fc9aae823b7f4 SHA256: 545a512d1de4de527b64fa46d30cc906243ccd496e72c7bda34d614ee630a44b SHA512: 5bae45e7b8b999ae2cde4b2b3fd5b3dc8150f6cd13a80a8dff25a203323b96ff0d94cb0b688a550199f65664531c99b8bea14ba077021f68a8072981db611a35 Homepage: https://cran.r-project.org/package=CimpleG Description: CRAN Package 'CimpleG' (A Method to Identify Single CpG Sites for Classification andDeconvolution) DNA methylation signatures are usually based on multivariate approaches that require hundreds of sites for predictions. 'CimpleG' is a method for the detection of small CpG methylation signatures used for cell-type classification and deconvolution. 'CimpleG' is time efficient and performs as well as top performing methods for cell-type classification of blood cells and other somatic cells, while basing its prediction on a single DNA methylation site per cell type (but users can also select more sites if they so wish). Users can train cell type classifiers ('CimpleG' based, and others) and directly apply these in a deconvolution of cell mixes context. Altogether, 'CimpleG' provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution. For more details see Maié et al. (2023) . 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For more details, see the paper by Hu et al. . Package: r-cran-cinar Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biocmanager, r-bioc-deseq2, r-cran-dplyr, r-bioc-edger, r-bioc-fgsea, r-bioc-genomicranges, r-cran-ggplot2, r-cran-ggrepel, r-bioc-limma, r-cran-pheatmap, r-bioc-preprocesscore, r-cran-rcolorbrewer, r-bioc-sva, r-cran-writexl Suggests: r-bioc-chipseeker, r-cran-knitr, r-cran-matrix, r-cran-seurat, r-cran-rmarkdown, r-cran-markdown, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene Filename: pool/dists/noble/main/r-cran-cinar_0.2.6-1.ca2404.1_all.deb Size: 3202112 MD5sum: bc660c19a7116a0327daac4f4aa9bfa0 SHA1: e6366d4002c53d9c1bc9e8d9caff560ef7fc2789 SHA256: 49d13869af70ed21b8ef88c51d4cedec2385ba0c39cd7fb1077b31985f1f181d SHA512: 320cf31a3e0622bf691a0ea4a7232dad17873dbfcdca2d0ccfacf2d57f7ece7a57ab4f015905bceea34b889adb29b557bdb359a5967250c4830c054d4c917b6a Homepage: https://cran.r-project.org/package=cinaR Description: CRAN Package 'cinaR' (A Computational Pipeline for Bulk 'ATAC-Seq' Profiles) Differential analyses and Enrichment pipeline for bulk 'ATAC-seq' data analyses. 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"CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis" provides a comprehensive overview of the package functionality Ashtiani et al. (2018) . 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Package: r-cran-cipher Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cipher_1.0.0-1.ca2404.1_all.deb Size: 36770 MD5sum: ef19de7ac2791df866346678cf600507 SHA1: fb308f91645b2c92c33849d175c1822c541b380a SHA256: 007941325df2c08a3a2984f12b973b14372def5cf5d2f2e009b0950e6ce42d6b SHA512: c5257deb391471622b8402af770d319f8d77beb21c292a8342601e8b4c4156a77eb77dca95cbf9662b95eaf369d9c40912cfe4c9264021a9bea8629ded2ac69d Homepage: https://cran.r-project.org/package=cipheR Description: CRAN Package 'cipheR' (Encryption and Decryption with Text Ciphers) Encrypts and decrypts using basic ciphers. None of these should be used in place of real encryption using state of the art tools. The ciphers included use methods described in the ciphers's Wikipedia and cryptography hobby websites. 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Package: r-cran-ciplot Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-multcomp Suggests: r-cran-bsda, r-cran-fmsb Filename: pool/dists/noble/main/r-cran-ciplot_1.0-1.ca2404.1_all.deb Size: 29514 MD5sum: 677e4959554fe5a9ddc9d8a811b924c7 SHA1: a151637724e998b7fd537ed9726fce36baa40457 SHA256: 88844e683ee99dc908b4eb4681f12f4eebe06d1bf3953e38abf27367fe0fb375 SHA512: 7a4e74256c55126fc236dfa28aa6f874ef5a43c94b537b6a016764cd4e638025aad268a887a81d07b68bdf9d9f750469f8f45ec805e1628ac24b949ada712241 Homepage: https://cran.r-project.org/package=CIplot Description: CRAN Package 'CIplot' (Functions to Plot Confidence Interval) Plot confidence interval from the objects of statistical tests such as t.test(), var.test(), cor.test(), prop.test() and fisher.test() ('htest' class), Tukey test [TukeyHSD()], Dunnett test [glht() in 'multcomp' package], logistic regression [glm()], and Tukey or Games-Howell test [posthocTGH() in 'userfriendlyscience' package]. Users are able to set the styles of lines and points. This package contains the function to calculate odds ratios and their confidence intervals from the result of logistic regression. Package: r-cran-cipostselect Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-glmnet, r-cran-magrittr, r-cran-mlbench, r-cran-tictoc Filename: pool/dists/noble/main/r-cran-cipostselect_0.2.2-1.ca2404.1_all.deb Size: 63652 MD5sum: a071ba40b2f7bb4fa5933271c1a33c52 SHA1: 2b932ef480e80991f6917dd9cdb888a8fd75d526 SHA256: 095fd8df787265238f81810314c13c4f07e899b82a6a88b4ce7ff06802140bd7 SHA512: d81c5a21b92ec54db2f0a137f0c2258eb6ee5ea448a6c53f98f649f78a799fd26653db8068981dfdeab0a26427160201534deb4b3d1c6a853f77f5f1384b0718 Homepage: https://cran.r-project.org/package=CIpostSelect Description: CRAN Package 'CIpostSelect' (Confidence Interval Post-Selection of Variable) Calculates confidence intervals after variable selection using repeated data splits. The package offers methods to address the challenges of post-selection inference, ensuring more accurate confidence intervals in models involving variable selection. The two main functions are 'lmps', which records the different models selected across multiple data splits as well as the corresponding coefficient estimates, and 'cips', which takes the lmps object as input to select variables and perform inferences using two types of voting. 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For dev version and change history, see GitHub assaforon/cir. Package: r-cran-circacompare Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-withr Suggests: r-cran-testthat, r-cran-nlme, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-circacompare_0.2.0-1.ca2404.1_all.deb Size: 145668 MD5sum: ba24cdceffa41239610c026aa318cac8 SHA1: 4a9a8714a4acde9c9068cf1fdbe074a691c0921b SHA256: 9d09788da9aa8fce1881431bc5270ef882f088cf8b15e6ae0f3d8b2d5458586b SHA512: ab80a4c10869aafe783c91fe26b3918ebbeb5e75f2d8449a54d10c39c6db33471eed09f7347e69ce1ac903370c7076e967814ad660c3d588cecfc90f3fd96907 Homepage: https://cran.r-project.org/package=circacompare Description: CRAN Package 'circacompare' (Analyses of Circadian Data) Uses non-linear regression to statistically compare two circadian rhythms. 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The algorithm has been validated against event markers using data from the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study, and its methodological details are described in Chen and Sun (2024) . The package provides functions to estimate sleep metrics (e.g., sleep and wake onset times) and circadian rhythm metrics (e.g., mesor, phasor, interdaily stability, intradaily variability), as well as tools for screening actigraphy quality, fitting cosinor models, and performing parametric change point detection. The workflow can also be used to segment long actigraphy sequences into regularized structures for physical activity research. Package: r-cran-circer Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4694 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-rjsonio Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-circer_1.3.3-1.ca2404.1_all.deb Size: 4270552 MD5sum: 78a5d1721f2b3d3004c5730ed14d8a63 SHA1: be8d38133690d15f2653d7c8c8ecb392a7324b0e SHA256: 190ff0cd7455538ec5d3b5ecbbd36cfa0f75af4ebe300de31b27a20d196ffccf SHA512: 905b688973b231f7376566588b379f163e2340161fbf58ba7b052cc6cc69ab2baedfec2ceb682c563d208e1870c101ec7fa5cd854c32b2f757c90828109fe0c4 Homepage: https://cran.r-project.org/package=CirceR Description: CRAN Package 'CirceR' (Construct Cohort Inclusion and Restriction Criteria Expressions) Wraps the 'CIRCE' () 'Java' library allowing cohort definition expressions to be edited and converted to 'Markdown' or 'SQL'. Package: r-cran-circhelp Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1916 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-gamlss, r-cran-mass, r-cran-mathjaxr, r-cran-patchwork Suggests: r-cran-testthat, r-cran-circular, r-cran-knitr, r-cran-rmarkdown, r-cran-bambi, r-cran-mgcv, r-cran-ragg Filename: pool/dists/noble/main/r-cran-circhelp_1.1-1.ca2404.1_all.deb Size: 1458496 MD5sum: b18a55d3b436ba3efe29487202d4a187 SHA1: b6755a9ffb5b57b868768df6c40d32140a2d5886 SHA256: 504fa05df62dbd66965b7903848f4d31ff39571464e0aa4b9d39d49be3b071e5 SHA512: 9ecd4d81ab4e76a798a11139550556131aa456b59133ceda9c02d778424ee1ae723ded0368de682e7e2c6b258b4f95c2e6dfbd5eb90608408a810e6ac2a2a5f3 Homepage: https://cran.r-project.org/package=circhelp Description: CRAN Package 'circhelp' (Circular Analyses Helper Functions) Light-weight functions for computing descriptive statistics in different circular spaces (e.g., 2pi, 180, or 360 degrees), to handle angle-dependent biases, pad circular data, and more. Specifically aimed for psychologists and neuroscientists analyzing circular data. Basic methods are based on Jammalamadaka and SenGupta (2001) , removal of cardinal biases is based on the approach introduced in van Bergen, Ma, Pratte, & Jehee (2015) and Chetverikov and Jehee (2023) . 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Package: r-cran-circlesplot Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3007 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-viridis Filename: pool/dists/noble/main/r-cran-circlesplot_1.1.0-1.ca2404.1_all.deb Size: 1917194 MD5sum: c9887e6a3f7caf73407695f0619244c4 SHA1: b5e6a89beb00d4917c01e2cb579b0ea97918b61c SHA256: e54e0ce470321a8f9eef3d569b064ed717bf3d9e63971b78e35dfec9d6a18be2 SHA512: 291307431581eab2c301e65a5765daa39d27a810058b7313d2c470bfd63c30ac24525e484b83d975c4b860e8d91f9d761c5fb1b722441ff1f756f43ef485178c Homepage: https://cran.r-project.org/package=circlesplot Description: CRAN Package 'circlesplot' (Visualize Proportions with Circles in a Plot) Method for visualizing proportions between objects of different sizes. The proportions are drawn as circles with different diameters, which makes them ideal for visualizing proportions between planets. Package: r-cran-circletyper Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-circletyper_1.0.2-1.ca2404.1_all.deb Size: 21518 MD5sum: dfeba164ad52d506f37ae0bc8353e72d SHA1: 0540e3bdbe91784999ef97de660176013c1edb00 SHA256: 9ab7d9718b4c9ccc80d1867b8ec85db16545a5f5ed48ebf2e3bd59de771b8d33 SHA512: ef630855adcb1b4a9f3552446422c107422d2393d62bd5a46182db98c7281e62350bfb0a98e13e6d828c07a8dc10d73e2f164b10b8fae0e6c587813f14885e63 Homepage: https://cran.r-project.org/package=circletyper Description: CRAN Package 'circletyper' (Curve Text Elements in 'Shiny' Using 'CircleType.js') Enables curving text elements in 'Shiny' apps. Package: r-cran-circlize Architecture: all Version: 0.4.18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3450 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-globaloptions, r-cran-shape, r-cran-colorspace Suggests: r-cran-knitr, r-cran-dendextend, r-bioc-complexheatmap, r-cran-gridbase, r-cran-png, r-cran-markdown, r-cran-bezier, r-cran-covr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-circlize_0.4.18-1.ca2404.1_all.deb Size: 3291964 MD5sum: eb36e62a5b222d17d0b3db62cb5f1d7f SHA1: 2ea68407310d0b8990fe54ba00c8c22dd028bb34 SHA256: 481f03ac04636179311d1dacc1aac352a8dd4537aff70f966602cadb68dff934 SHA512: b51e64fdc1ab5d8d232809bdce044aa533b8aefdbee6d10ae2e12634e60c7c0f4cf88bdc09abb6d23f2534ce36d3befc75fe5747f2a4cdfc3dda830e92e66b00 Homepage: https://cran.r-project.org/package=circlize Description: CRAN Package 'circlize' (Circular Visualization) Circular layout is an efficient way for the visualization of huge amounts of information. Here this package provides an implementation of circular layout generation in R as well as an enhancement of available software. The flexibility of the package is based on the usage of low-level graphics functions such that self-defined high-level graphics can be easily implemented by users for specific purposes. Together with the seamless connection between the powerful computational and visual environment in R, it gives users more convenience and freedom to design figures for better understanding complex patterns behind multiple dimensional data. The package is described in Gu et al. 2014 . Package: r-cran-circlizeplus Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5766 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circlize Suggests: r-cran-png, r-cran-ape, r-cran-dendextend, r-bioc-complexheatmap, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-circlizeplus_0.9.1-1.ca2404.1_all.deb Size: 3858958 MD5sum: ce777d191b0383adb0603bff8cf3d1d4 SHA1: d2b394f755793415de19df7a548cb908c23ae589 SHA256: b394357b099bd9cd7f39998f73f9e926ba416da5dfc00848039930f50cab9490 SHA512: c12257a8b12eb085985845a017fe1fc8d907e645911605f7b0538f402503c736e33b5980625987d17bf92ab8dcfadb3c03627b4ef98988ac8445a96573d36a86 Homepage: https://cran.r-project.org/package=circlizePlus Description: CRAN Package 'circlizePlus' (Using 'ggplot2' Feature to Write Readable R Code for CircularVisualization) A wrapper for 'circlize'. All components are based on classes and objects. Users can use the addition symbol (+) to combine components for a circular visualization with 'ggplot2' style.The package is described in Zhang Z, Cao T, Huang Y and Xia Y (2025) . Package: r-cran-circmle Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circular, r-cran-energy Filename: pool/dists/noble/main/r-cran-circmle_0.3.0-1.ca2404.1_all.deb Size: 126894 MD5sum: 8d11ed48483dff610dcdd5cc354e2b4c SHA1: 004e0be944b293756e8939330622641fe16eff46 SHA256: 61d0ebd41411379b65074bcc9d71ba05678abc0b8c2b95f089102bff65208bc9 SHA512: 44ede1f20f55bebe48cd01b71ecd97f9b898c869a93b5b60cc003edb1497459cbeee4fbac28b912c5fc2ae462ab353e3dfdd610beb40f51608eb1555d436f9d4 Homepage: https://cran.r-project.org/package=CircMLE Description: CRAN Package 'CircMLE' (Maximum Likelihood Analysis of Circular Data) A series of wrapper functions to implement the 10 maximum likelihood models of animal orientation described by Schnute and Groot (1992) . The functions also include the ability to use different optimizer methods and calculate various model selection metrics (i.e., AIC, AICc, BIC). The ability to perform variants of the Hermans-Rasson test and Pycke test is also included as described in Landler et al. (2019) . The latest version also includes a new method to calculate circular-circular and circular-linear distance correlations. Package: r-cran-circnntsr Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-circnntsr_2.3-1.ca2404.1_all.deb Size: 253388 MD5sum: f51096f8695e05f570e86471b9ebae54 SHA1: 02185bbf07a70ffce092a52498d2223001c7840a SHA256: 9ae7ec74950c92e9d79bbd3a04b18e3f5bc514eab6868167fd7ae6a0d32b7f08 SHA512: c46875280d367719d1f4995ca0e6017e4eff005594c2835451436f6502364b3a07ec1cfbd74462a12185efd4ef09fef0847710af6386e9b5a1b544cd5075b597 Homepage: https://cran.r-project.org/package=CircNNTSR Description: CRAN Package 'CircNNTSR' (Statistical Analysis of Circular Data using NonnegativeTrigonometric Sums (NNTS) Models) Includes functions for the analysis of circular data using distributions based on Nonnegative Trigonometric Sums (NNTS). 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The package includes functions for calculation of densities and distributions, for the estimation of parameters, and more. Fernandez-Duran, J.J. and Gregorio-Dominguez, M.M. (2025), ''Multimodal distributions for circular axial data", . Package: r-cran-circnntsrmult Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psychtools, r-cran-circnntsr Filename: pool/dists/noble/main/r-cran-circnntsrmult_0.1.0-1.ca2404.1_all.deb Size: 154722 MD5sum: c0a680642bc96f6e323d8c5222103465 SHA1: baa631ae13515b5797b72a688af1e75ce9d19090 SHA256: 1a537a6c6b25a590e773bc9c91b52428caac9041647701abce8cdd9b9bf08782 SHA512: 01f09e60b5d5da2de045087d16197a3aae4542cc4b39e7d285a2dd42cb688c89dc932d5718a394839c01fb450e244b2aef32a179af4dff1f0f121e6259ab20f1 Homepage: https://cran.r-project.org/package=CircNNTSRmult Description: CRAN Package 'CircNNTSRmult' (Multivariate Circular Data using MNNTS Models) A collection of utilities for the statistical analysis of multivariate circular data using distributions based on Multivariate Nonnegative Trigonometric Sums (MNNTS). 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Fernandez-Duran, J.J. and Gregorio-Dominguez, M.M. (2025), "Multimodal Symmetric Circular Distributions Based on Nonnegative Trigonometric Sums and a Likelihood Ratio Test for Reflective Symmetry", . 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Package: r-cran-circuitscaper Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-juliacall Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-circuitscaper_0.1.0-1.ca2404.1_all.deb Size: 232566 MD5sum: dd1916e67255dd98eb6039a3cdec2cd0 SHA1: 7e6a56d65717186b8e74d42297e18ca6cfe42dd1 SHA256: 887b08d75ca2489c03399427b0f1b479fae363649d954a78115cb66b2d1e9490 SHA512: 42581d40bef0c42e19e167bc3880e03f25008fb4bd49d7058bc94f90087be0186d5aa938ed587a8f69f04cfcfd02487e2d91c47089da6539dbde7d732d287df1 Homepage: https://cran.r-project.org/package=circuitscaper Description: CRAN Package 'circuitscaper' ('Circuitscape' and 'Omniscape' Connectivity Analysis via 'Julia') Provides an R-native interface to the 'Circuitscape.jl' and 'Omniscape.jl' 'Julia' packages for landscape connectivity modeling using circuit theory. Users work entirely in R with familiar objects (SpatRaster, file paths) while 'Julia' handles computation invisibly. Supports all four 'Circuitscape' modes (pairwise, one-to-all, all-to-one, advanced) and 'Omniscape' moving-window analysis. Methods are described in McRae (2006) and Landau et al. (2021) . Package: r-cran-circularboxplots Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circular, r-cran-plotrix, r-cran-rgl, r-cran-rcolorbrewer, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-circularboxplots_0.1.2-1.ca2404.1_all.deb Size: 207130 MD5sum: b761480a325218c67f553dfdc27591b6 SHA1: a82eb03973bab5f5f469c85f71e6598aaba22d48 SHA256: ec44fb32c2cb3913e523bbd1496afc0e4542df8573be2f5bd951551ec609661d SHA512: 7ae4f46856e0f312ac114be5a44cc7239af6fb07081b0474744348ccad510e363152f382af6d09b323b254813827a5c0da2a6d2156a24f1c7b2fc6f932b03c96 Homepage: https://cran.r-project.org/package=CircularBoxplots Description: CRAN Package 'CircularBoxplots' (Grouped Boxplots for Circular Data) Plotting functions to create circular boxplots for grouped data. The primary 2-dimensional version creates concentric circular boxplots for specified groups, scaling the width of each boxplot to adjust for human perception. The 3-dimensional version maps these plots onto a torus which is suitable for periodic circular data such as wind direction over the course of a year. An example dataset of this type is provided for reference. For examples of circular boxplots and additional implementation details, see Berlinski et al. (2026) . Package: r-cran-circularev Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7578 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-mgcv, r-cran-circular, r-cran-npcirc, r-cran-ggplot2 Suggests: r-cran-plotly, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-circularev_0.1.1-1.ca2404.1_all.deb Size: 1294278 MD5sum: 85175677fb1050d161fc9c250e27dcc7 SHA1: e624728bcbcfe06d68f14fdbe456647f5ddd48a4 SHA256: 552c28052792039e5ea89991a3a75db692cd198ccc6e785006ae977864635f44 SHA512: 23eba8f9bda359b85673d699950475622335c0aff718f0625dfa1194e585b68e39bb7bb4ca49d7c4bc3a24b5e3e89dc92a67cc33e633057deb2f663c0078f6ba Homepage: https://cran.r-project.org/package=circularEV Description: CRAN Package 'circularEV' (Extreme Value Analysis for Circular Data) General functions for performing extreme value analysis on a circular domain as part of the statistical methodology in the paper by Konzen, E., Neves, C., and Jonathan, P. (2021). Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. Environmetrics, 32(4), e2667. Package: r-cran-circularkde Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-circularkde_0.1.1-1.ca2404.1_all.deb Size: 82206 MD5sum: 3e02bc1cf023aef7263d7e2c3a1672a4 SHA1: 90d863571fa50bf436883a9ecc53c7e91f3fd032 SHA256: b9c93482e8a815b290eb7b51b0d14cfa29e47bd0bc8d8359c093df555665f7db SHA512: eeb1b68250a63e598ea8586a39a05f92d7599ebfb6e9cfcb6f17aab2b7c3236a07ef4f8aaf11108684b1b00b999356ea6347003e5976feb8749ee9b5ae935033 Homepage: https://cran.r-project.org/package=circularKDE Description: CRAN Package 'circularKDE' (Recent Methods for Kernel Density Estimation of Circular Data) Provides recent kernel density estimation methods for circular data, including adaptive and higher-order techniques. The implementation is based on recent advances in bandwidth selection and circular smoothing. Key methods include adaptive bandwidth selection methods by Zámečník et al. (2024) , complete cross-validation by Hasilová et al. (2024) , Fourier-based plug-in rules by Tenreiro (2022) , and higher-order kernels by Tsuruta & Sagae (2017) . Package: r-cran-cirls Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-osqp, r-cran-coneproj, r-cran-truncatednormal, r-cran-limsolve Suggests: r-cran-dlnm, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cirls_0.4.0-1.ca2404.1_all.deb Size: 124644 MD5sum: 65601a4349859671777658708e1b0b0a SHA1: e0e7df9924cf10443bbe69d73788fde3b34b7b75 SHA256: 9a17e8bd8179ac440e0900aa88ba8a5fc986d48c62065340e377a086d39d8dfe SHA512: be78a871668f0ac8b3fadaa5bbfd64d7c8ff1a519de56fe67c620d4723c63376d5993e5f5be59e662a827df29f66cd990051693b5d3daf04634f71d6d7354c15 Homepage: https://cran.r-project.org/package=cirls Description: CRAN Package 'cirls' (Constrained Iteratively Reweighted Least Squares) Fitting and inference functions for generalized linear models with constrained coefficients. Package: r-cran-cis.dglm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dglm, r-cran-rcolorbrewer, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-cis.dglm_0.1.0-1.ca2404.1_all.deb Size: 59650 MD5sum: 1de1f51504ee059416e6b33d1e924db8 SHA1: d01d9612fe8439396d00439998ad77e6006856d7 SHA256: 5b63a81028b69a961b3a96e5411e4995d862ae3e44674ab1364b41dc5b7006f2 SHA512: befce624747e5ffcd8c6fa04f9fff1c13a86dd2523f99d63a679a14db77608b5a9f19e333d6497a3d613e10162c50ca350bf327b3ac2d3731ffea0c364d41408 Homepage: https://cran.r-project.org/package=CIS.DGLM Description: CRAN Package 'CIS.DGLM' (Covariates, Interaction, and Selection for DGLM) An implementation of double generalized linear model (DGLM) building with variable selection procedures and handling of interaction terms and other complex situations. We also provide a method of handling convergence issues within the dglm() function. The package offers a simulation function for generating simulated data for testing purposes and utilizes the forward stepwise variable selection procedure in model-building. It also provides a new custom bootstrap function for mean and standard deviation estimation and functions for building crossplots and squareplots from a data set. Package: r-cran-cisp Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-gdverse, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-magrittr, r-cran-purrr, r-cran-sdsfun, r-cran-sf, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spedm Filename: pool/dists/noble/main/r-cran-cisp_0.2.0-1.ca2404.1_all.deb Size: 107864 MD5sum: bdf198a3d9d129c012b8efc64377be9c SHA1: 5f8da15204bd95836bbb299ce60a6b2d2c2f767f SHA256: d9188e7b1790bf2af5edbeca0584dfc41aef538dfe9b38084cd866f07e360121 SHA512: e4ffe790920d2be1b05665de08134bc7c98f63de920d5e254157e5cad3504e58769cdf0275eb21d807d6a3772f1a07a4afb25baf799cbe2ff3fa403a8f086ee2 Homepage: https://cran.r-project.org/package=cisp Description: CRAN Package 'cisp' (A Correlation Indicator Based on Spatial Patterns) Utilizes spatial association marginal contributions derived from spatial stratified heterogeneity to capture the degree of correlation between spatial patterns. Package: r-cran-citan Architecture: all Version: 2025.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 970 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-agop, r-cran-rsqlite, r-cran-stringi, r-cran-dbi Filename: pool/dists/noble/main/r-cran-citan_2025.7.1-1.ca2404.1_all.deb Size: 944644 MD5sum: fafed4b0f489f790a78d60199328457a SHA1: 8db3f72a48d76a07a633018602b785ff65f61b44 SHA256: 6d84bbd745714e1ac379f91d888dd990de6fb3d961c710282e6ca88957650f3d SHA512: a1f0d266ecf371db98f49f5ab09dbb936b2e41c1b477455a4a30c81a5a985d1bd13410e7f1712bf071b0dd0ea2f0c7f63d1799fae8c33c254d231450df3fef92 Homepage: https://cran.r-project.org/package=CITAN Description: CRAN Package 'CITAN' (CITation ANalysis Toolpack) Supports quantitative research in scientometrics and bibliometrics. Provides various tools for preprocessing bibliographic data retrieved, e.g., from Elsevier's Scopus, computing bibliometric impact of individuals, or modelling phenomena encountered in the social sciences. This package is deprecated; see 'agop' instead. Package: r-cran-citation Architecture: all Version: 0.12.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desc, r-cran-jsonlite, r-cran-withr, r-cran-yaml Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-citation_0.12.2-1.ca2404.1_all.deb Size: 58650 MD5sum: d66b539e3362a4c7fc9d0f820dbc698f SHA1: 17a29994a44794890fda263a014bc415bca0054c SHA256: b86c289509f122ff84fb2dbed982b4e03ce5ec8008483f7ede3b60e7eed49396 SHA512: 1d764b8dea0b3ced1b7571d9d2a6c1c620ad3b31fb77ebb16fac01e9f953c1352d94a0dfdf6c045663e682da77c6ab6efa5161eb2c241d9d9b2023ab7c39e7b5 Homepage: https://cran.r-project.org/package=citation Description: CRAN Package 'citation' (Software Citation Tools) A collection of functions to extract citation information from 'R' packages and to deal with files in 'citation file format' (), extending the functionality already provided by the citation() function in the 'utils' package. Package: r-cran-citationchaser Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-maditr, r-cran-mess, r-cran-networkd3, r-cran-scales, r-cran-tibble, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-citationchaser_0.0.4-1.ca2404.1_all.deb Size: 242822 MD5sum: bd113d8debfd5c7e45701493263e4d7d SHA1: 5c08a15b5b149735d460c6397ea948332d940b6a SHA256: 485a670bc50c6d3b979f4667efdb23159cd3fde36db19349c86a3570f9212a37 SHA512: ca861d838e7af4a0fe1ce44e2e60807537d609069fb93f45f78369545e0b862c3045d40c3a331a1156da709bd6cea721b273dcc01f44494bccf59e87fc3c4d0b Homepage: https://cran.r-project.org/package=citationchaser Description: CRAN Package 'citationchaser' (Perform Forward and Backwards Chasing in Evidence Syntheses) In searching for research articles, we often want to obtain lists of references from across studies, and also obtain lists of articles that cite a particular study. In systematic reviews, this supplementary search technique is known as 'citation chasing': forward citation chasing looks for all records citing one or more articles of known relevance; backward citation chasing looks for all records referenced in one or more articles. Traditionally, this process would be done manually, and the resulting records would need to be checked one-by-one against included studies in a review to identify potentially relevant records that should be included in a review. This package contains functions to automate this process by making use of the Lens.org API. An input article list can be used to return a list of all referenced records, and/or all citing records in the Lens.org database (consisting of PubMed, PubMed Central, CrossRef, Microsoft Academic Graph and CORE; ). Package: r-cran-citecorp Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-data.table, r-cran-fauxpas, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-vcr, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-citecorp_0.3.0-1.ca2404.1_all.deb Size: 38802 MD5sum: 2f28478caecbb6fcb721a45355431551 SHA1: 97beef69450845407ae13dc35b4b52c05f52f538 SHA256: a26736b7fcdc0281e384f031d1683b223f12ea3c12715ae33575bd5fe345b858 SHA512: bfc657f17b711bfeb8e10275f24ea0a5c4839c68aa4a9ac5ab3372b612a7d6f2c74407ea5da9370a543cf7d59279501489c3cfb93c5db203049e28fa811cc93b Homepage: https://cran.r-project.org/package=citecorp Description: CRAN Package 'citecorp' (Client for the Open Citations Corpus) Client for the Open Citations Corpus (). Includes a set of functions for getting one identifier type from another, as well as getting references and citations for a given identifier. Package: r-cran-citestr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-processx, r-cran-rlang Suggests: r-cran-arrow, r-cran-jsonlite, r-cran-reticulate, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-citestr_0.1.1-1.ca2404.1_all.deb Size: 100956 MD5sum: 4b4a279e7ff26ed9401213015db81b56 SHA1: 3ddc37160e5c17d2838f8ef2a3bc6f2d38df1fe9 SHA256: 09ea26f9a658deff5fa6db26c16b39977fe1206ed29d5c61218d881f2c353835 SHA512: 33e8091176fcb3ab42629fcc46376c990dc9685b71aad320772ec888de38d0b30c8ef095bd75aa404ba61dc6827f51567994b7017fed9536c45d865eae00f613 Homepage: https://cran.r-project.org/package=citestR Description: CRAN Package 'citestR' (Conditional Independence of Missingness Test) Tests whether missingness in explanatory variables is conditionally independent of the outcome, given observed data. Uses multiply-imputed datasets and cross-validated classifiers to produce a test statistic and p-value, with a sensitivity parameter (kappa) for calibrating interpretation. Wraps the 'citest' 'Python' engine via a local 'FastAPI' server over 'HTTP', so no 'reticulate' dependency is needed at runtime. Package: r-cran-cities Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 814 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-plotly, r-cran-tidyr, r-cran-ggthemes Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cities_0.1.3-1.ca2404.1_all.deb Size: 519018 MD5sum: 2d4ac5aa32092049617a746d8211bfc9 SHA1: fc0ca1ed830de970cef116a46cd8b0b2f18f5d35 SHA256: 05b4e6729e41209ceac30b95f5f2e6ae1f0f714e089fcc8aa4fe1b7d35b7c428 SHA512: 9daa8a4a10ec6a80ebae33d963a98aea04fe465fae83a84031b06244ed4523764a81aa3ea63148e5981d1d076126d446dfefdb99bd043cab4bc2c64429905576 Homepage: https://cran.r-project.org/package=cities Description: CRAN Package 'cities' (Clinical Trials with Intercurrent Events Simulator) Simulates clinical trials and summarizes causal effects and treatment policy estimands in the presence of intercurrent events in a transparent and intuitive manner. Package: r-cran-citmic Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1868 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastmatch, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-citmic_0.1.3-1.ca2404.1_all.deb Size: 1852418 MD5sum: 678305cf0aaf1127cdb899d222d62efc SHA1: 5bdcd3a858ef12de222d8f2fe54ba02bed3502b6 SHA256: 07e8c29060070bff4a3e5ce93c50ac1abf24141302cb027a93e26c8e6a6048f1 SHA512: b20e3bb3abc35d50a350d88d6e379c403b932153a04eab3629872d0506c4a6eda9b9ab6d80d738b4d6c15e4fc4e1095a5ba2797e308c02078f8a5d3707f74fd9 Homepage: https://cran.r-project.org/package=CITMIC Description: CRAN Package 'CITMIC' (Estimation of Cell Infiltration Based on Cell Crosstalk) A systematic biology tool was developed to identify cell infiltration via Individualized Cell-Cell interaction network. 'CITMIC' first constructed a weighted cell interaction network through integrating Cell-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific sample, and then, it used a network propagation algorithm on the network to identify cell infiltration for the sample. Ultimately, cell infiltration in the patient dataset was obtained by normalizing the centrality scores of the cells. Package: r-cran-citmre Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-plotly, r-cran-rvest, r-cran-xml2, r-cran-xts Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-citmre_0.1.0-1.ca2404.1_all.deb Size: 241902 MD5sum: 87d6ab63b98c220f201d713be585816e SHA1: 5093f2c3e259666d62eb040b94a78dcb59f5e912 SHA256: 0a436f6059c8d763d148426157e7d56cc14872252d7e60e43c62e854ddb959b8 SHA512: e8e8f89d4bf4ff56ecb0b45c200351043ffa643c43be1a2f008ebfa4f8f21c68a7fa69e58321da1ae63e1cc2acb65e619ffafbe930d01b0f32268689881ea5b5 Homepage: https://cran.r-project.org/package=citmre Description: CRAN Package 'citmre' (Colombian Index Tool Market Rate Exchange) Downloads the Representative Market Rate Exchange (RMRE) from the source. Allows setting the data series in time frequencies, splitting the time series through start and end functions, transforming the data set in log returns or levels, and making a Dynamic graph. Package: r-cran-cito Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3654 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coro, r-cran-checkmate, r-cran-torch, r-cran-gridextra, r-cran-parabar, r-cran-abind, r-cran-progress, r-cran-cli, r-cran-torchvision, r-cran-tibble, r-cran-lme4 Suggests: r-cran-spelling, r-cran-rmarkdown, r-cran-testthat, r-cran-plotly, r-cran-ggraph, r-cran-igraph, r-cran-ggplot2, r-cran-knitr Filename: pool/dists/noble/main/r-cran-cito_1.1-1.ca2404.1_all.deb Size: 2580918 MD5sum: 2e4c91d09c6c351edad2cf783a60c533 SHA1: 68a9265daee341186f423df542bd594dcd36215b SHA256: 94a0a4c647733de374f75247dcad301c16010c066f4cb74b39e33eaca57ca7ad SHA512: d5d0dfaa186e8c37dcd1d13cb746d59dbc53468a3121a524b44165495bc3b3b7910118231745013c70c0667be934aaf4c5afaa394483e129aaa7ce79c68958f3 Homepage: https://cran.r-project.org/package=cito Description: CRAN Package 'cito' (Building and Training Neural Networks) The 'cito' package provides a user-friendly interface for training and interpreting deep neural networks (DNN). 'cito' simplifies the fitting of DNNs by supporting the familiar formula syntax, hyperparameter tuning under cross-validation, and helps to detect and handle convergence problems. DNNs can be trained on CPU, GPU and MacOS GPUs. In addition, 'cito' has many downstream functionalities such as various explainable AI (xAI) metrics (e.g. variable importance, partial dependence plots, accumulated local effect plots, and effect estimates) to interpret trained DNNs. 'cito' optionally provides confidence intervals (and p-values) for all xAI metrics and predictions. At the same time, 'cito' is computationally efficient because it is based on the deep learning framework 'torch'. The 'torch' package is native to R, so no Python installation or other API is required for this package. Package: r-cran-citools Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1331 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arm, r-cran-boot, r-cran-dplyr, r-cran-lme4, r-cran-mass, r-cran-survival Suggests: r-cran-here, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spreda Filename: pool/dists/noble/main/r-cran-citools_0.6.1-1.ca2404.1_all.deb Size: 735938 MD5sum: 6d129606c3c91fdd153294c0463165b7 SHA1: 459f172e6ac13632242323f42d9a8af177614901 SHA256: a6be368162b6f19e6bb0f6ae2e3d4fe2004629648215dc2449b2b06c621deca3 SHA512: 0c6147b879613bd43a14affda6bcd2cf1d680a731f63d66c015c0a0c7b736d2d172782d727c273cd713855bb4599b5cf5a9f305d79ef466c8f5ff51bda5db28b Homepage: https://cran.r-project.org/package=ciTools Description: CRAN Package 'ciTools' (Confidence or Prediction Intervals, Quantiles, and Probabilitiesfor Statistical Models) Functions to append confidence intervals, prediction intervals, and other quantities of interest to data frames. All appended quantities are for the response variable, after conditioning on the model and covariates. This package has a data frame first syntax that allows for easy piping. Currently supported models include (log-) linear, (log-) linear mixed, generalized linear models, generalized linear mixed models, and accelerated failure time models. Package: r-cran-citrus Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ggally, r-cran-clustmixtype, r-cran-treeclust, r-cran-rpart, r-cran-tibble, r-cran-rpart.plot, r-cran-stringr, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-citrus_1.0.2-1.ca2404.1_all.deb Size: 183916 MD5sum: 604e2502a1bbabeec5cade7927f37ed0 SHA1: 4c30db2ff3d8fb83df5b4c698bbca166307cad96 SHA256: 6cb2dd800d7001c861a1bdd5929e7c20001d145ba4e4edc2325bf87a370d7159 SHA512: c85cb07c0a1f437e0bfe1405c32070b20b3b20f2b66cf9700f8d7584f76fecfe2bf64863773d27950186520d2004ebe7f9251b204c15a51f6c7bddb6fbd142d0 Homepage: https://cran.r-project.org/package=citrus Description: CRAN Package 'citrus' (Customer Intelligence Tool for Rapid Understandable Segmentation) A tool to easily run and visualise supervised and unsupervised state of the art customer segmentation. It is built like a pipeline covering the 3 main steps in a segmentation project: pre-processing, modelling, and plotting. Users can either run the pipeline as a whole, or choose to run any one of the three individual steps. It is equipped with a supervised option (tree optimisation) and an unsupervised option (k-clustering) as default models. Package: r-cran-citsr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-nlme, r-cran-clubsandwich, r-cran-ggplot2, r-cran-aiccmodavg Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lmtest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-citsr_0.1.3-1.ca2404.1_all.deb Size: 41642 MD5sum: 307b35ab213add2fcd4bae7e11af1247 SHA1: a8a4c3dee9acab44ca62b0f36651139f5ff67b12 SHA256: e4cc813e402fec249f0cd022e25f90771fa7a593f30ba3ce28983b159c851704 SHA512: a9cb7eb1a095fb84a0cb0e1b4a955890ff7af69f62687a983efd1ae7c83d6079d2c3b2d51ca8d6780bdb4a0033bc71fee5dd8698d363daf2b86d8cdef05f34d8 Homepage: https://cran.r-project.org/package=citsr Description: CRAN Package 'citsr' (Controlled Interrupted Time Series Analysis and Visualization) Implements controlled interrupted time series (CITS) analysis for evaluating interventions in comparative time-series data. The package provides tools for preparing panel time-series datasets, fitting models using generalized least squares (GLS) with optional autoregressive–moving-average (ARMA) error structures, and computing fitted values and robust standard errors using cluster-robust variance estimators (CR2). Visualization functions enable clear presentation of estimated effects and counterfactual trajectories following interventions. Background on methods for causal inference in interrupted time series can be found in Linden and Adams (2011) and Lopez Bernal, Cummins, and Gasparrini (2018) . Package: r-cran-ciu Architecture: all Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-crayon, r-cran-ggplot2, r-cran-ggbeeswarm Suggests: r-cran-mass, r-cran-caret, r-cran-gbm Filename: pool/dists/noble/main/r-cran-ciu_0.8-1.ca2404.1_all.deb Size: 176808 MD5sum: 894e49626863163e4af66140eb8699da SHA1: a1b274365ffa9265e04510bafad7902209ec85e3 SHA256: fba4c29c039d56ba17000a3f8befd3079483f25aadaeae7a60f2eab245477624 SHA512: 7710ce944eea9d85ee309564027c044f11292758547c55f462938f11202ec7b0389612ea6c27489c625c2470b8d2cfa3e62ac52a55d3053494554f3aecaab0a5 Homepage: https://cran.r-project.org/package=ciu Description: CRAN Package 'ciu' (Contextual Importance and Utility) Implementation of the Contextual Importance and Utility (CIU) concepts for Explainable AI (XAI). A description of CIU can be found in e.g. Främling (2020) . Package: r-cran-ciuupi2 Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-functional, r-cran-nloptr, r-cran-pracma, r-cran-precisesums, r-cran-statmod Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ciuupi2_1.0.1-1.ca2404.1_all.deb Size: 97554 MD5sum: 75647a52c8e49971aa1d25ac6cf6ad0c SHA1: 5ee43bd2e8629b2f6fc99632a7e921f7813bf1d2 SHA256: a6f94ec7cd4b69db63531b83993999fc100f6ff31fc73f986e9c044dcf56356a SHA512: c66778437b4ce796764418b37fcf80778b353e802d086e0fc245bf315b1305729927d78df4e2d5145fc251f24f697f039897b55f9ed1e3d2f777114ff562ca11 Homepage: https://cran.r-project.org/package=ciuupi2 Description: CRAN Package 'ciuupi2' (Kabaila and Giri (2009) Confidence Interval) Computes a confidence interval for a specified linear combination of the regression parameters in a linear regression model with iid normal errors with unknown variance when there is uncertain prior information that a distinct specified linear combination of the regression parameters takes a specified number. This confidence interval, found by numerical nonlinear constrained optimization, has the required minimum coverage and utilizes this uncertain prior information through desirable expected length properties. This confidence interval is proposed by Kabaila, P. and Giri, K. (2009) . Package: r-cran-ciuupi Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nloptr, r-cran-statmod, r-cran-functional, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ciuupi_1.2.3-1.ca2404.1_all.deb Size: 133098 MD5sum: d5fd4a0ca743daa94c78d17e1f678c1d SHA1: 928de485ea6482475e1afff7304bdbc5d6584b16 SHA256: d809d10d1e14982edac2e045088dc3429e0eb7dfc925bca5b8b73cbc44c570f9 SHA512: 2e1e1393680b02fcbc535e8a3cfbeafd8d4ecec4852b9a9818b753a20b4a9114412eb77c906fa5bb241b7c5ff187e12ad9d89bdd898115a6f0922f63a96e1ad4 Homepage: https://cran.r-project.org/package=ciuupi Description: CRAN Package 'ciuupi' (Confidence Intervals Utilizing Uncertain Prior Information) Computes a confidence interval for a specified linear combination of the regression parameters in a linear regression model with iid normal errors with known variance when there is uncertain prior information that a distinct specified linear combination of the regression parameters takes a given value. This confidence interval, found by numerical nonlinear constrained optimization, has the required minimum coverage and utilizes this uncertain prior information through desirable expected length properties. This confidence interval has the following three practical applications. Firstly, if the error variance has been accurately estimated from previous data then it may be treated as being effectively known. Secondly, for sufficiently large (dimension of the response vector) minus (dimension of regression parameter vector), greater than or equal to 30 (say), if we replace the assumed known value of the error variance by its usual estimator in the formula for the confidence interval then the resulting interval has, to a very good approximation, the same coverage probability and expected length properties as when the error variance is known. Thirdly, some more complicated models can be approximated by the linear regression model with error variance known when certain unknown parameters are replaced by estimates. This confidence interval is described in Mainzer, R. and Kabaila, P. (2019) , and is a member of the family of confidence intervals proposed by Kabaila, P. and Giri, K. (2009) . Package: r-cran-civ Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aer, r-cran-kcmeans Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-civ_0.1.0-1.ca2404.1_all.deb Size: 40398 MD5sum: cdfe831177f2748fe367af0670df9b4e SHA1: e47dcbc26b38ea2080f27e8355b363795a0c8131 SHA256: b277146f878e47f3f656a39810acc018be5b061c84106210b96baa1f591a6d4e SHA512: c48a68188240f1ad61d55019f13ca53ad91ace93405e87458b5b16594c8b120f993bbb1146c7a35305ad7086cd0f871eebb6deeb93f663450896628cc8917fbc Homepage: https://cran.r-project.org/package=civ Description: CRAN Package 'civ' (Categorical Instrumental Variables) Implementation of the categorical instrumental variable (CIV) estimator proposed by Wiemann (2023) . CIV allows for optimal instrumental variable estimation in settings with relatively few observations per category. To obtain valid inference in these challenging settings, CIV leverages a regularization assumption that implies existence of a latent categorical variable with fixed finite support achieving the same first stage fit as the observed instrument. Package: r-cran-civis Architecture: all Version: 3.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4276 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-httr, r-cran-jsonlite, r-cran-memoise Suggests: r-cran-feather, r-cran-ggplot2, r-cran-knitr, r-cran-lifecycle, r-cran-rmarkdown, r-cran-roxygen2, r-cran-mockery, r-cran-r.utils, r-cran-rstudioapi, r-cran-testthat, r-cran-yaml Filename: pool/dists/noble/main/r-cran-civis_3.1.4-1.ca2404.1_all.deb Size: 3293780 MD5sum: 4083b69af7bfecec9b117ba375e17420 SHA1: 24c9001b23817b442f52afd0d3a46b56bbb18dd7 SHA256: 97eb1923d0228f94260a8e504a26e872b3c0fb1543603cdf52bcb63ff97a463a SHA512: 4090f2cd6bfc4893b8e770dc8db7600d2846b3cec7ecada389795d79e22bb1572f3fd6710b8b6d7765797f3139359af58750dfd3872ffff1051d6f4fbc1e248a Homepage: https://cran.r-project.org/package=civis Description: CRAN Package 'civis' (R Client for the 'Civis Platform API') A convenient interface for making requests directly to the 'Civis Platform API' . Full documentation available 'here' . Package: r-cran-ciw Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-xml, r-cran-data.table Filename: pool/dists/noble/main/r-cran-ciw_0.0.2-1.ca2404.1_all.deb Size: 18696 MD5sum: 37259cde277b70c86ae8c4cda2b7a997 SHA1: 1024fddcb08e7ed6a8c2ebfe6037d8132f26473f SHA256: 968731d5bfa3f3922c2a68b5c30cde8ddff72a55da05255e8a1e213c13026095 SHA512: 36b20e54b89ff6dc300413915ac22d48af84db2080a24bd0d4617325033325492bc480f511021e68766fe0ef4a3ca44e46d3b2835277cce3ebbb47a964ad6695 Homepage: https://cran.r-project.org/package=ciw Description: CRAN Package 'ciw' (Watch the CRAN Incoming Directories) Directory reads and summaries are provided for one or more of the subdirectories of the directory, and a compact summary object is returned. The package name is a contraption of 'CRAN Incoming Watcher'. Package: r-cran-cjamp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 844 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-optimx Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cjamp_0.1.1-1.ca2404.1_all.deb Size: 678956 MD5sum: b58b147cfebded6b1f991f17b58e61ed SHA1: 3c934baa58771c3dbcbf8f05eedc9e88c1fb7262 SHA256: 3feeb2006c63faed7413f9fe68172a5be785918bc6bff728ccd9ac5b02c89dd1 SHA512: c0f6793b581c5dc62f97045feba9c557aaa353221788f50e39e59909c822982d09c3dfcec0b50ef470040291a5f83f6e5e8bc101249f9124adad00aca572cf20 Homepage: https://cran.r-project.org/package=CJAMP Description: CRAN Package 'CJAMP' (Copula-Based Joint Analysis of Multiple Phenotypes) We provide a computationally efficient and robust implementation of the recently proposed C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes) method (Konigorski et al., 2019, submitted). C-JAMP allows estimating and testing the association of one or multiple predictors on multiple outcomes in a joint model, and is implemented here with a focus on large-scale genome-wide association studies with two phenotypes. The use of copula functions allows modeling a wide range of multivariate dependencies between the phenotypes, and previous results are supporting that C-JAMP can increase the power of association studies to identify associated genetic variants in comparison to existing methods (Konigorski, Yilmaz, Pischon, 2016, ; Konigorski, Yilmaz, Bull, 2014, ). In addition to the C-JAMP functions, functions are available to generate genetic and phenotypic data, to compute the minor allele frequency (MAF) of genetic markers, and to estimate the phenotypic variance explained by genetic markers. Package: r-cran-cjar Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 951 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-httr, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-dplyr, r-cran-jsonlite, r-cran-glue, r-cran-jose, r-cran-tibble, r-cran-lubridate, r-cran-progress, r-cran-vctrs, r-cran-stringr, r-cran-rlang, r-cran-memoise, r-cran-openssl, r-cran-httr2 Filename: pool/dists/noble/main/r-cran-cjar_0.2.1-1.ca2404.1_all.deb Size: 840164 MD5sum: f69abd734fd39dc7139bd1545c966c5a SHA1: ce4bd09ddac6871467e023dcb50e8e94be40161c SHA256: de71ffacafdd6b86025ae092a7ebb701b192d368111c091ca7d14dd003dfd280 SHA512: 6895196386d958bb11659031242de05bc6f809a76777ec1821e13b83fc4ce12111c4d57cd7117b56603f8e85b1bc48a1e52ab171632c19cf465398dee79110c6 Homepage: https://cran.r-project.org/package=cjar Description: CRAN Package 'cjar' (R Client for 'Customer Journey Analytics' ('CJA') API) Connect and pull data from the 'CJA' API, which powers 'CJA Workspace' . The package was developed with the analyst in mind and will continue to be developed with the guiding principles of iterative, repeatable, timely analysis. New features are actively being developed and we value your feedback and contribution to the process. Package: r-cran-cjbart Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 828 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bart, r-cran-rlang, r-cran-tidyr, r-cran-ggplot2, r-cran-randomforestsrc, r-cran-rdpack Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cjbart_0.3.2-1.ca2404.1_all.deb Size: 301382 MD5sum: add9918658bb17619cbf40848ad61552 SHA1: e370618986969bf483f5df1878d8d9ee3076694f SHA256: f73a7209c10ddafe03ef316a2cd00d7417cde3d1450b87a7e639a1dd029a7902 SHA512: dddcb2c02d1abd47990f1c2c2b52d7ca19e296cd3775cf45f7b6496bf7098b15c92b274c9a2b7599825ac5d11c1b50e3efd37cc798248ed24030299f098022ff Homepage: https://cran.r-project.org/package=cjbart Description: CRAN Package 'cjbart' (Heterogeneous Effects Analysis of Conjoint Experiments) A tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) . This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) , to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects. Package: r-cran-cjive Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rootsolve, r-cran-ggplot2, r-cran-reshape2, r-cran-fields, r-cran-gplots, r-cran-psych Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cjive_0.1.0-1.ca2404.1_all.deb Size: 108022 MD5sum: 3e541c7c37cc213d694e2664b2fd9af6 SHA1: 4ba6513d778a291beb7d06b7b85c54aeb489fa91 SHA256: 7643f53eb2c740235e1e36b083741d8100627160a7f35f8069fab7f119c171bf SHA512: a04fa56025a09b5cc4b7032b65392ff1205d9c2aca5db39acf5893034913f8b692dbb90157ae1992a9f5e7f5f2a6bfde708de79adea2e188eb26eb90ddc7a0d5 Homepage: https://cran.r-project.org/package=CJIVE Description: CRAN Package 'CJIVE' (Canonical Joint and Individual Variation Explained (CJIVE)) Joint and Individual Variation Explained (JIVE) is a method for decomposing multiple datasets obtained on the same subjects into shared structure, structure unique to each dataset, and noise. The two most common implementations are R.JIVE, an iterative approach, and AJIVE, which uses principal angle analysis. JIVE estimates subspaces but interpreting these subspaces can be challenging with AJIVE or R.JIVE. We expand upon insights into AJIVE as a canonical correlation analysis (CCA) of principal component scores. This reformulation, which we call CJIVE, 1) provides an ordering of joint components by the degree of correlation between corresponding canonical variables; 2) uses a computationally efficient permutation test for the number of joint components, which provides a p-value for each component; and 3) can be used to predict subject scores for out-of-sample observations. Please cite the following article when utilizing this package: Murden, R., Zhang, Z., Guo, Y., & Risk, B. (2022) . Package: r-cran-cjoint Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 635 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sandwich, r-cran-lmtest, r-cran-ggplot2, r-cran-survey, r-cran-matrix, r-cran-dt, r-cran-shiny, r-cran-shinyjs Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cjoint_2.1.3-1.ca2404.1_all.deb Size: 418736 MD5sum: 4d28a17dfa30e76a9b4b599731a85a34 SHA1: 18ffc24e9065e0d37f0d49c00e005663897aa252 SHA256: 35e260afaae86cedd19ff42ec0438aa54f9edd5197fce4db682b6999c512743f SHA512: f502aebd1d64306af955bab8d812e50c4f89a2ad010dcc6b6823553d3e4b83ae5a9e551ffb4d0e0022b98d2470311e2ba388b989061f1490d16e18f5076f1447 Homepage: https://cran.r-project.org/package=cjoint Description: CRAN Package 'cjoint' (AMCE Estimator for Conjoint Experiments) An R implementation of the Average Marginal Component-specific Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto T. (2014) Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments. Political Analysis 22(1):1-30. Package: r-cran-ckanr Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-crul, r-cran-jsonlite, r-cran-dplyr, r-cran-dbplyr, r-cran-magrittr Suggests: r-cran-sf, r-cran-readxl, r-cran-testthat, r-cran-xml2, r-cran-lazyeval Filename: pool/dists/noble/main/r-cran-ckanr_0.7.0-1.ca2404.1_all.deb Size: 435142 MD5sum: da57e425eb1c5fa693ac7209bef8afca SHA1: 0c011da3637f45480f667b0e4b4e42c1761a5661 SHA256: 7152dc282720f931f85cf9ef7bf3e8237083114961589d1ebffaf2f54fb595ad SHA512: 1afeab0b5be2af46448252bdfa23fd8b446e222e8f429b8c0ebb42f064ffd97e81d408c78e799f73baf66eebd787d2ed140763ab0f2f17c2dd4213ad9760b01a Homepage: https://cran.r-project.org/package=ckanr Description: CRAN Package 'ckanr' (Client for the Comprehensive Knowledge Archive Network ('CKAN')API) Client for 'CKAN' API (). 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Package: r-cran-ckat Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform Filename: pool/dists/noble/main/r-cran-ckat_0.1.0-1.ca2404.1_all.deb Size: 35934 MD5sum: 1a09d5b9cffe477b1ac7ba4428df5597 SHA1: 9a660205ed891f28c56e01aeb959923c4cd67080 SHA256: 788f5e2458562528735e78ee14e5d745163661cfbe831399b9d2440fbac423a4 SHA512: 481074cd031dcd546b7f55865661c240cc7555c7f699e162d18dc3673d8022f2223ba6e8b144a4dde0c0d7ed393acf33916820aac02e00d40da725993191145f Homepage: https://cran.r-project.org/package=CKAT Description: CRAN Package 'CKAT' (Composite Kernel Association Test for Pharmacogenetics Studies) Composite Kernel Association Test (CKAT) is a flexible and robust kernel machine based approach to jointly test the genetic main effect and gene-treatment interaction effect for a set of single-nucleotide polymorphisms (SNPs) in pharmacogenetics (PGx) assessments embedded within randomized clinical trials. Package: r-cran-cla Architecture: all Version: 0.96-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1237 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-fgarch, r-cran-frapo, r-cran-matrix, r-cran-sfsmisc Filename: pool/dists/noble/main/r-cran-cla_0.96-3-1.ca2404.1_all.deb Size: 1197210 MD5sum: f3a3d8df584cbab2b7496399cb6e31b6 SHA1: b0618f2bee16207a16fae0e57cd500c7e9dcb32f SHA256: 0eb6e171f17310fa449b36c60f9eb10429f23f7f7e92313e2f57297d0f9a98aa SHA512: d8b597ac11e6a2dbbe4639608b612f687f5fd9d4f724c1c99bf8813f2378220ab58c7aee52acd1363ce6bbf99f63530f40fcda0feaeecfc175c01203e2858251 Homepage: https://cran.r-project.org/package=CLA Description: CRAN Package 'CLA' (Critical Line Algorithm in Pure R) Implements 'Markowitz' Critical Line Algorithm ('CLA') for classical mean-variance portfolio optimization, see Markowitz (1952) . Care has been taken for correctness in light of previous buggy implementations. Package: r-cran-claddis Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2004 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-phytools, r-cran-strap, r-cran-clipr, r-cran-geoscale, r-cran-multicool, r-cran-partitions Suggests: r-cran-rgl, r-cran-testthat Filename: pool/dists/noble/main/r-cran-claddis_0.7.0-1.ca2404.1_all.deb Size: 1378374 MD5sum: ce019f8cc458f4999cbb33fce26eb3a2 SHA1: e8c308159c4a4b373449f97fb37518f560fd633f SHA256: ef839191d4de9d239a34a800410fcdd3e8de456156afcdb5bb98baa6a47f3fbc SHA512: 0373a81378bfa9f0611f78a991d37aca80737154c00b9ba7abc96e3b05fb9e40c360f2827cea51aa588cdc49a9387536c6e6988dee053ff2461131db02924db3 Homepage: https://cran.r-project.org/package=Claddis Description: CRAN Package 'Claddis' (Measuring Morphological Diversity and Evolutionary Tempo) Measures morphological diversity from discrete character data and estimates evolutionary tempo on phylogenetic trees. Imports morphological data from #NEXUS (Maddison et al. (1997) ) format with read_nexus_matrix(), and writes to both #NEXUS and TNT format (Goloboff et al. (2008) ). Main functions are test_rates(), which implements AIC and likelihood ratio tests for discrete character rates introduced across Lloyd et al. (2012) , Brusatte et al. (2014) , Close et al. (2015) , and Lloyd (2016) , and calculate_morphological_distances(), which implements multiple discrete character distance metrics from Gower (1971) , Wills (1998) , Lloyd (2016) , and Hopkins and St John (2018) . This also includes the GED correction from Lehmann et al. (2019) . Multiple functions implement morphospace plots: plot_chronophylomorphospace() implements Sakamoto and Ruta (2012) , plot_morphospace() implements Wills et al. (1994) , plot_changes_on_tree() implements Wang and Lloyd (2016) , and plot_morphospace_stack() implements Foote (1993) . Other functions include safe_taxonomic_reduction(), which implements Wilkinson (1995) , map_dollo_changes() implements the Dollo stochastic character mapping of Tarver et al. (2018) , and estimate_ancestral_states() implements the ancestral state options of Lloyd (2018) . calculate_tree_length() and reconstruct_ancestral_states() implements the generalised algorithms from Swofford and Maddison (1992; no doi). Package: r-cran-claimsproblems Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geometry, r-cran-pracma, r-cran-rgl Filename: pool/dists/noble/main/r-cran-claimsproblems_1.0.0-1.ca2404.1_all.deb Size: 267228 MD5sum: d9db5eb75bfd438d6b43dde6ae811e1f SHA1: 4ed8c3f75e0c9ae7571c7441cca6d0cf89f505d6 SHA256: 9c54ddf8d030c2d2f5bedb0a1735299c37832c771af0ba44b60a789949df2832 SHA512: ed66c46e6ccaee2079e2022c8faffe3954bd97abd5259fb522bb5d44a1cb592f5bd3c8b386d401eae36e93d9863254523075080477106366dd0f2126b0b7743b Homepage: https://cran.r-project.org/package=ClaimsProblems Description: CRAN Package 'ClaimsProblems' (Analysis of Conflicting Claims) The analysis of conflicting claims arises when an amount has to be divided among a set of agents with claims that exceed what is available. A rule is a way of selecting a division among the claimants. This package computes the main rules introduced in the literature from ancient times to the present. The inventory of rules covers the proportional and the adjusted proportional rules, the constrained equal awards and the constrained equal losses rules, the constrained egalitarian, the Piniles’ and the minimal overlap rules, the random arrival and the Talmud rules. Besides, the Dominguez and Thomson and the average-of-awards rules are also included. All of them can be found in the book by W. Thomson (2019), How to divide when there isn't enough. From Aristotle, the Talmud, and Maimonides to the axiomatics of resource allocation', except for the average-of-awards rule, introduced by Mirás Calvo et al. (2022), . In addition, graphical diagrams allow the user to represent, among others, the set of awards, the paths of awards, the schedules of awards of a rule, and some indexes. A good understanding of the similarities and differences between the rules is useful for better decision-making. Therefore, this package could be helpful to students, researchers, and managers alike. For a more detailed explanation of the package, see Mirás Calvo et al. (2023), . Package: r-cran-clam Architecture: all Version: 2.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rintcal, r-cran-rice Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-utf8 Filename: pool/dists/noble/main/r-cran-clam_2.6.3-1.ca2404.1_all.deb Size: 180586 MD5sum: eb680750d54a473b25246c1ca22aa21c SHA1: c3e30beec0435dd063f895e2e5b4a2e667971617 SHA256: 6f1ffc60991d6725e19ca048cb73e0914dae7158429109e46d7385aeb51583c3 SHA512: 6e9a7b906e3e99989de7499ee40535db7812a23dca9884a68b2dd05933fc892711eb1d6070fb64ecaa4e595f9c50c413118353cd1ca244c5eb42e3cbf4903d50 Homepage: https://cran.r-project.org/package=clam Description: CRAN Package 'clam' (Classical Age-Depth Modelling of Cores from Deposits) Performs 'classical' age-depth modelling of dated sediment deposits - prior to applying more sophisticated techniques such as Bayesian age-depth modelling. 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This package provides methods for detecting and visualizing these overlapping regions using partitional clustering techniques based on nearest neighbor distances. Package: r-cran-clarifai Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2600 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clarifai_0.4.2-1.ca2404.1_all.deb Size: 1266860 MD5sum: 43e66191723ae5de58da9485af9c4532 SHA1: 5e61071904d04da419afbde92d74867c38c84165 SHA256: 8beac5cac7d1f2308e4e3e0b33753a82bb4d65d9ed3cf61c74ffc408d4773e1b SHA512: 317a79dfab353ec523360cf73fed1ccca8fe7fcd7c7568cbfca64fa47474e883845e8398f7abe4c5981dcbb3939750e27a95f2839b0a4d18aa72369289ef06da Homepage: https://cran.r-project.org/package=clarifai Description: CRAN Package 'clarifai' (Access to Clarifai API) Get description of images from Clarifai API. 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Currently supported model functions are: lm(), glm() ('binomial', 'poisson', 'negative binomial' links), polr() ('MASS'), clm() ('ordinal'), and multinom() ('nnet'). For more information on the test, see Clarke (2007) . 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Package: r-cran-classifyits Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape2, r-cran-data.table, r-cran-seqinr Suggests: r-cran-formatr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-classifyits_1.0.2-1.ca2404.1_all.deb Size: 198884 MD5sum: da4cb52818f5c74e87b9ae7447593d35 SHA1: 652643a013b672a3992243d4a40421d20dc7e693 SHA256: fe27091d17d21789b286a90710e2f7b5790a11654f7c3bb2b1d6950ba96f0031 SHA512: 5b29368a7aefaa5ee6652e7d506b09de6dcb5a2d29ee63622344dfa1f1ddb9b177d4b25368c36a72802139cf6b53cbd9a88575fc27f3845cc1dc5d4bba7f1ee7 Homepage: https://cran.r-project.org/package=ClassifyITS Description: CRAN Package 'ClassifyITS' (Fungal Assignment Pipeline) Fungi are ubiquitous in Earth's wonderfully diverse ecosystems. The 'ClassifyITS' package aids in the taxonomic classification of environmental internal transcribed spacer (ITS) short-read barcoding data. Unlike previous methods, it employs taxon-specific e-value and percent identity cutoffs at each taxonomic rank from kingdom to species. The package takes a conservative approach and outputs both graphics and user-friendly files to help users manually inspect fungal operational taxonomic units (OTUs) that fail classification at relevant levels (e.g., Phylum). 'ClassifyITS' is based on taxonomic cutoff criteria from "The Global Soil Mycobiome consortium dataset for boosting fungal diversity research" (Fungal Diversity, Tedersoo, 2021, ) and "Best practices in metabarcoding of fungi: From experimental design to results" (Molecular Ecology, Tedersoo, 2022, ). 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The graphical displays include stacked plots, silhouette plots, quasi residual plots, class maps, predictions plots, and predictions correlation plots. Implements the techniques described and illustrated in Raymaekers J., Rousseeuw P.J., Hubert M. (2022). Class maps for visualizing classification results. \emph{Technometrics}, 64(2), 151–165. (open access), Raymaekers J., Rousseeuw P.J.(2022). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. \emph{Journal of Computational and Graphical Statistics}, 31(4), 1332–1343. , and Rousseeuw, P.J. (2026). Explainable Linear and Generalized Linear Models by the Predictions Plot. The American Statistician, 80, 157-163, (open access), and Montalcini, C., Rousseeuw, P.J. (2025). The bixplot: A variation on the boxplot suited for bimodal data, (open access). 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Package: r-cran-cleaner Architecture: all Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-backports, r-cran-crayon, r-cran-knitr, r-cran-pillar, r-cran-rlang, r-cran-vctrs Suggests: r-cran-ggplot2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cleaner_1.5.5-1.ca2404.1_all.deb Size: 158762 MD5sum: 90e7b0100f626b44b3a806788c6e0985 SHA1: fb6b5373de30de1619bc1449e078787b0576c98d SHA256: 79ac09bd8e6f522f6e8df4ebb0fd5f4e68be1f5c952598c94fe885e04900f2c2 SHA512: 22cd8cba36ed6418846f4eb6508dfa0862adfc9fd38727275dd46b265fe488d0428d233bae2709a4b469f788a47daa2c3e19600742a178d7639c905f354f0c42 Homepage: https://cran.r-project.org/package=cleaner Description: CRAN Package 'cleaner' (Fast and Easy Data Cleaning) Data cleaning functions for classes logical, factor, numeric, character, currency and Date to make data cleaning fast and easy. 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Package: r-cran-cleangeo Architecture: all Version: 0.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-sf Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-cleangeo_0.3-1-1.ca2404.1_all.deb Size: 69744 MD5sum: b0fb115a2c3a7671660e41b01ff11b15 SHA1: ff721a2f6e3921a73acc7b42efe34e223b4c6bcd SHA256: 0f1dcccb604b2ca7e6a0733b71636285169707f5e5b450b2f01ca17ebfff1455 SHA512: ffdbd8bab54a80b1734898dd9f16fa3ca5f4f87c4ab239b4132822cd434f202c07d6f819412804bc4b7ee383c631c005f473eacd54fe5e32d4031d8e9c64dc4e Homepage: https://cran.r-project.org/package=cleangeo Description: CRAN Package 'cleangeo' (Cleaning Geometries from Spatial Objects) Provides a set of utility tools to inspect spatial objects, facilitate handling and reporting of topology errors and geometry validity issue with sp objects. 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The package includes non-parametric methods to analyze drug active-ingredient residue (DAR), cleaning agent residue (CAR), and microbial colonies (Mic) for non-Poisson distributions. Additionally, Poisson methods are provided for Mic analysis when Mic data follow a Poisson distribution. Package: r-cran-cleannlp Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4325 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-udpipe, r-cran-reticulate, r-cran-stringi Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-cleannlp_3.1.0-1.ca2404.1_all.deb Size: 3379778 MD5sum: 2fcbf6142ea6f787c2c03217b15c643d SHA1: 4106d4f6e2e893e1f948833ac670c834c4ee05be SHA256: b51aaeaeb2f2d2068130565411336adfe04e3995181202f6a478190b0c41e33e SHA512: 100efe3bb8b76a38e2df80738a0d09d62fd6fe3c6464be0fa9eedd50a8cd56daf30f743b69410e6693691ba08748dfcfd00266bc151f61ffa71d0f195dfaa2e4 Homepage: https://cran.r-project.org/package=cleanNLP Description: CRAN Package 'cleanNLP' (A Tidy Data Model for Natural Language Processing) Provides a set of fast tools for converting a textual corpus into a set of normalized tables. 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Package: r-cran-cleanr Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-fritools, r-cran-pkgload, r-cran-rprojroot Suggests: r-cran-devtools, r-cran-rasciidoc, r-cran-rmarkdown, r-cran-runit, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-cleanr_1.4.0-1.ca2404.1_all.deb Size: 96746 MD5sum: 881fa1f76e76d462c537c72176988860 SHA1: 21249f5d906fd10d0068777f19020185614ec153 SHA256: 07b69ba4bfd43ee1e80be88e4cab4a6f4be762c2e1b4014ef4f5fb39cc37c894 SHA512: 4cf424d78f097ea43957aa9acee2f907c0b7e3a4acbfa1245f4087387d3edfa2df6ded63c35f000397ee262a6ceaaf7389c015c97ed5f3e00ed3a0b84ef174b7 Homepage: https://cran.r-project.org/package=cleanr Description: CRAN Package 'cleanr' (Helps You to Code Cleaner) Check your R code for some of the most common layout flaws. Many tried to teach us how to write code less dreadful, be it implicitly as B. W. Kernighan and D. M. Ritchie (1988) in 'The C Programming Language' did, be it explicitly as R.C. Martin (2008) in 'Clean Code: A Handbook of Agile Software Craftsmanship' did. So we should check our code for files too long or wide, functions with too many lines, too wide lines, too many arguments or too many levels of nesting. Note: This is not a static code analyzer like pylint or the like. Checkout instead. Package: r-cran-cleanrmd Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2655 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-jsonlite, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cleanrmd_0.1.1-1.ca2404.1_all.deb Size: 1746976 MD5sum: 9acf43269ab7001a99210ea861bdf863 SHA1: fa0394e21e215279b5b791cc40b62b4824b3dad8 SHA256: c8c3b3ce28fb71c881614d431a9b938bb604d339f244ee5c91d75eaefdedff22 SHA512: 8ab367fede4b8d1b76b68519dc5b2ec5ba21363b8c5535a75ffb0ba9ae716cb15c6da5bc0a6d99c350773ad549a9071675443b5363a065d18c84eace986b8001 Homepage: https://cran.r-project.org/package=cleanrmd Description: CRAN Package 'cleanrmd' (Clean Class-Less 'R Markdown' HTML Documents) A collection of clean 'R Markdown' HTML document templates using classy-looking classless CSS styles. 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Package: r-cran-clespr Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aer, r-cran-pbivnorm, r-cran-mass, r-cran-magic, r-cran-survival, r-cran-clordr, r-cran-doparallel, r-cran-foreach Filename: pool/dists/noble/main/r-cran-clespr_1.1.2-1.ca2404.1_all.deb Size: 79054 MD5sum: f038678316f5cba33f8d1c934228fe3e SHA1: fc300cf724da697c2c48f63f8b8e79df31961d54 SHA256: db6f289370356d308ac106e4639d4576286760997c21e8bb1483dfdf27059e14 SHA512: 02e301f89b0746a1d0152097c922194cbada37a56ada91e918eab87262bd88718ee5121898d6630d4f06c007c79c689fbad4b393aa730c4e82caf5e6ebddd02f Homepage: https://cran.r-project.org/package=clespr Description: CRAN Package 'clespr' (Composite Likelihood Estimation for Spatial Data) Composite likelihood approach is implemented to estimating statistical models for spatial ordinal and proportional data based on Feng et al. (2014) . 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This package is now superseded by the 'cli' package. Please use 'cli' instead in new projects. Package: r-cran-clic Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-laplacesdemon, r-cran-fbasics Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-clic_0.1-1.ca2404.1_all.deb Size: 41340 MD5sum: 934479c2703f68b0bd6e69df8c6d62d1 SHA1: 334980ed480e3c573e1158d94d18275ea2a12990 SHA256: 46d1f94129287a886d6fc7207014bbba422043a19d2350f589f59b841be3de23 SHA512: 23e8f6229be4e0dd1451fe1bd46fcc64c4876585584e199d22eb9e7ca56b7d1756aef5d24e118d2c570a67161013d98b697234e8c5ae64c0e5b75af7e17fd478 Homepage: https://cran.r-project.org/package=CLIC Description: CRAN Package 'CLIC' (The LIC for Distributed Cosine Regression Analysis) This comprehensive framework for periodic time series modeling is designated as "CLIC" (The LIC for Distributed Cosine Regression Analysis) analysis. 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Package: r-cran-clickableimagemap Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gridextra, r-cran-ggplotify, r-cran-ggplot2, r-cran-gtable Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clickableimagemap_1.0-1.ca2404.1_all.deb Size: 428876 MD5sum: 07d306a5fd69766470e4ba0cd7583c82 SHA1: 40f69ba7cce4de48313fa49d413dba5cc3e0cd6d SHA256: 89da84701e26ad39796954309c26cae470115fc18ab202fcdaa5985547c5a22b SHA512: cf1739f1b4fa44da931c32ac99adef0492cb3fd4ecf0cb8adfe3fa97d7390624be997b0967418ab96038de321ad169c0559b6d751ce534315e29a9d758b5d04f Homepage: https://cran.r-project.org/package=clickableImageMap Description: CRAN Package 'clickableImageMap' (Implement 'tableGrob' Object as a Clickable Image Map) Implement 'tableGrob' object as a clickable image map. 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Package: r-cran-clickhousehttp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dbi, r-cran-httr2, r-cran-jsonlite, r-cran-arrow, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-stringi Filename: pool/dists/noble/main/r-cran-clickhousehttp_1.0.0-1.ca2404.1_all.deb Size: 229198 MD5sum: 8bb35c5163d7ad306480151604bcdd4b SHA1: 8a969e4ec6ef806ac1b96f4cb05381bab0a85f5a SHA256: ec6450e625bc648e28856384f80eec28907426de567f1445acc24998138d0b36 SHA512: e915f718426e15ca318a0daa31440f7243254757b5cb7f4db74acfe367801882893269f17757e3e4018f54f7c43a1070497aed8d5a2fadea49e29549a44a76f9 Homepage: https://cran.r-project.org/package=ClickHouseHTTP Description: CRAN Package 'ClickHouseHTTP' (A Simple HTTP Database Interface to 'ClickHouse') 'ClickHouse' () is an open-source, high performance columnar OLAP (online analytical processing of queries) database management system for real-time analytics using SQL. 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Package: r-cran-cliftlrd Architecture: all Version: 0.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cnltreg, r-cran-liftlrd Suggests: r-cran-fracdiff Filename: pool/dists/noble/main/r-cran-cliftlrd_0.1-2-1.ca2404.1_all.deb Size: 41950 MD5sum: a8f16a8f7557defa51b6e2bfc7138578 SHA1: 5bc2d72afd82957bc311394dbda0edaace5112b7 SHA256: 30c0289e8ebbeaf34cc5493e11061f69053ccfd3dacb74855bb8e052883b1f7f SHA512: 55d7e026444c4ec5fbcc709f9bed0815be91d6a971efed2cc22d83cdbc1c50fa7ed694860336aaf144129d6de2b00771fc501425ce65eb2a5009218faa4e6664 Homepage: https://cran.r-project.org/package=CliftLRD Description: CRAN Package 'CliftLRD' (Complex-Valued Wavelet Lifting Estimators of the Hurst Exponentfor Irregularly Sampled Time Series) Implementation of Hurst exponent estimators based on complex-valued lifting wavelet energy from Knight, M. I and Nunes, M. A. (2018) . 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Package: r-cran-climaemet Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1041 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-httr2, r-cran-jsonlite, r-cran-rappdirs, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-climatol, r-cran-gganimate, r-cran-jpeg, r-cran-knitr, r-cran-lifecycle, r-cran-lubridate, r-cran-mapspain, r-cran-quarto, r-cran-scales, r-cran-sf, r-cran-terra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-climaemet_1.5.1-1.ca2404.1_all.deb Size: 866284 MD5sum: 5b67499c246c753e72a0cd824cdb1107 SHA1: 5afa678d24909aa1e23ce8c45f0d5309775ed514 SHA256: 3084a4015bc50c030cbee0932f1beb239d088a6d53e8811547c40ff590af0ca9 SHA512: ef0485c2a735f825959bac96a979cf0c1177ccd8d2150ff83044b147ecc837a521f451a45be94eea3e9bf5320f93d07dd387e0e6ff00f830352f5e6593fb28a3 Homepage: https://cran.r-project.org/package=climaemet Description: CRAN Package 'climaemet' (Climate AEMET Tools) Tools to download the climatic data of the Spanish Meteorological Agency (AEMET) directly from R using their API and create scientific graphs (climate charts, trend analysis of climate time series, temperature and precipitation anomalies maps, warming stripes graphics, climatograms, etc.). Package: r-cran-climarep Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3780 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-terra, r-cran-sf, r-cran-tidyterra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-climarep_1.0-1.ca2404.1_all.deb Size: 2328756 MD5sum: c0132421c25c69a17cc20ff126984c67 SHA1: 1f7420519eb5fdeddc59b6e1877128a1b93fae5e SHA256: ac3ad3ab00e63df1cd74524322e4daab7bdba09fed23ab81b286cb4a1e3f9e26 SHA512: aae2ea4fdef4f8037bb96350fffd77080c65a59adeb9851452ee5914d476a6a7c437d0054c655a2c6abe6a66d21ad3c950396ced7039b792731d4329294ff0a1 Homepage: https://cran.r-project.org/package=ClimaRep Description: CRAN Package 'ClimaRep' (Estimating Climate Representativeness) Offers tools to estimate the climate representativeness of reference polygons and quantifies its transformation under future climate change scenarios. Approaches described in Mingarro and Lobo (2018) and Mingarro and Lobo (2022) . Package: r-cran-climate Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-archive, r-cran-curl, r-cran-data.table, r-cran-httr, r-cran-stringi, r-cran-xml Suggests: r-cran-dplyr, r-cran-knitr, r-cran-maps, r-cran-testthat, r-cran-tidyr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-climate_1.3.0-1.ca2404.1_all.deb Size: 959440 MD5sum: f677f341b75d6ff950d4b72e3a1fdd76 SHA1: e2cd2ce36fba7891c589b789899681f278032b93 SHA256: a87c14745a37de898cc5ff3287e717d9897bdb7d9e856e450590f3992c739bf5 SHA512: 95a642a67cbf02ebb840c33bba0cc298f0b93040903e1dff55a32e158477e15a6714d97b2b0f2844643d3f2368944faf8e21496f022dace2ef0b3293837956e2 Homepage: https://cran.r-project.org/package=climate Description: CRAN Package 'climate' (Interface to Download Meteorological (and Hydrological) Datasets) Automatize downloading of meteorological and hydrological data from publicly available repositories: OGIMET (), University of Wyoming - atmospheric vertical profiling data (), Polish Institute of Meteorology and Water Management - National Research Institute (), and National Oceanic & Atmospheric Administration (NOAA). This package also allows for searching geographical coordinates for each observation and calculate distances to the nearest stations. Package: r-cran-climatehealth Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-data.table, r-cran-dlnm, r-cran-dplyr, r-cran-epi, r-cran-forcats, r-cran-exactextractr, r-cran-ggplot2, r-cran-ggtext, r-cran-gnm, r-cran-gplots, r-cran-lifecycle, r-cran-lme4, r-cran-lubridate, r-cran-metafor, r-cran-mgcv, r-cran-mixmeta, r-cran-ncdf4, r-cran-patchwork, r-cran-pkgbuild, r-cran-purrr, r-cran-raster, r-cran-rcolorbrewer, r-cran-readr, r-cran-readxl, r-cran-reshape2, r-cran-rlang, r-cran-scales, r-cran-sf, r-cran-spdep, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tseries, r-cran-tsmodel, r-cran-xfun, r-cran-zoo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-dt, r-cran-htmltools, r-cran-mockery, r-cran-mvmeta, r-cran-openxlsx, r-cran-patrick, r-cran-pkgload, r-cran-stringdist, r-cran-terra, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-climatehealth_1.0.1-1.ca2404.1_all.deb Size: 985820 MD5sum: 642a9254c6271e96cb2a356a9a759f1b SHA1: b6a321661d1ef612f4b7e09a70079f05f089b72a SHA256: 908c7f0eba59d9c3f8155297cfa8b47482d64738765310748af8e8bf02bd4b7d SHA512: 8bd666c46dfcf92f160f5c32efa395aa385e7fb209d66d822d5bb02e24273cb97ffa58835825f9a8ef4db91cba1fb837c99902cb4e4f08a9da06fa1b6184d063 Homepage: https://cran.r-project.org/package=climatehealth Description: CRAN Package 'climatehealth' (Statistical Tools for Modelling Climate-Health Impacts) Tools for producing climate-health indicators and supporting official statistics from health and climate data. Implements analytical workflows for temperature-related mortality, wildfire smoke exposure, air pollution, suicides related to extreme heat, malaria, and diarrhoeal disease outcomes, with utilities for descriptive statistics, model validation, attributable fraction and attributable number estimation, relative risk estimation, minimum mortality temperature estimation, and plotting for reporting. These six indicators are endorsed by the United Nations Statistical Commission for inclusion in the Global Set of Environment and Climate Change Statistics. Implemented methods include distributed lag non-linear models (DLNM), quasi-Poisson time-series regression, case-crossover analysis, Bayesian spatio-temporal models using the Integrated Nested Laplace Approximation ('INLA'), and multivariate meta-analysis for sub-national estimates. The package is based on methods developed in the Standards for Official Statistics on Climate-Health Interactions (SOSCHI) project . For methodologies, see Watkins et al. (2025) , Brown et al. (2024) , Pearce et al. (2024) , Byukusenge et al. (2025) , Dzakpa et al. (2025) , and Dzakpa et al. (2025) . Package: r-cran-climatekit Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Suggests: r-cran-testthat, r-cran-terra, r-cran-ncdf4, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-climatekit_0.2.0-1.ca2404.1_all.deb Size: 299346 MD5sum: 77a75ccfa2b1209c1c2c6f457c9a48a3 SHA1: aebce50756bed241e28c3bb3b6242ba777cbf76b SHA256: 3d7ddc0bc49917eb8b870445e3aa36537065959e651c189d4a66e8049a23b5b6 SHA512: 57fcfaf92d00132005b51937c49f601aa97c4fab4e8812db8f102b6355a71b1f6281614cee43546e9dbe84d0a6e6e4869f1ab18208a1949ecd44cc42b9525766 Homepage: https://cran.r-project.org/package=climatekit Description: CRAN Package 'climatekit' (Unified Climate Indices for Temperature, Precipitation, andDrought) Compute the standard suite of climate indices from daily weather observations. Provides the canonical 'ETCCDI' 27 (Expert Team on Climate Change Detection and Indices), the 'ET-SCI' heatwave and cold-wave families plus the Excess Heat Factor of Nairn and Fawcett (2013), and agroclimatic, drought, and human-comfort families. Drought indices ('SPI', 'SPEI') accept a choice of distribution (gamma or Pearson III for SPI; log-logistic or generalised extreme value for SPEI). Reference evapotranspiration is available via Hargreaves and the FAO-56 Penman-Monteith method (Allen et al. 1998). Percentile-based indices support the Zhang (2005) in-base bootstrap. Daily inputs are numeric vectors plus a 'Date' vector; outputs are tidy data frames. Optional gridded support via 'terra' applies any index over a 'SpatRaster' and reads 'netCDF' input. No external API calls; pairs with data packages such as 'readnoaa'. References: Alexander et al. (2006) ; Zhang et al. (2011) ; Zhang et al. (2005) . Package: r-cran-climatestability Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 628 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra Suggests: r-cran-rangebuilder, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-climatestability_0.1.4-1.ca2404.1_all.deb Size: 230030 MD5sum: 984c13f730e69a87c802b56c599a442f SHA1: 3a004dc1c299af079620fd112ad44e50ddc6477d SHA256: 4cb143052b87fb6ff967d7202ba187ee8203b81af9e27586764e6f02deffe538 SHA512: a6a98b72e0b6d460ed7635e547e8ee8f767b4b177de790b8970c0c8a620ffaf402445d504675a49fa61af5b7d1e610bb55b2771d65b8f196456739117990215e Homepage: https://cran.r-project.org/package=climateStability Description: CRAN Package 'climateStability' (Estimating Climate Stability from Climate Model Data) Climate stability measures are not formalized in the literature and tools for generating stability metrics from existing data are nascent. This package provides tools for calculating climate stability from raster data encapsulating climate change as a series of time slices. The methods follow Owens and Guralnick Biodiversity Informatics. Package: r-cran-climatol Architecture: all Version: 4.5-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2726 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-evd, r-cran-fields, r-cran-gstat, r-cran-maps, r-cran-mapdata, r-cran-ncdf4, r-cran-raster, r-cran-readxl, r-cran-rodbc, r-cran-sp Filename: pool/dists/noble/main/r-cran-climatol_4.5-0-1.ca2404.1_all.deb Size: 2364894 MD5sum: 54ea59c083ae1698f4182280afbced04 SHA1: 1a6538a83e57068c493d455f8c57e8de3d3f4069 SHA256: 5d974e3beb3d5e8993aec48989d8380a94e72ec9211fcdda53f10d472c92cb10 SHA512: 84a7805687bd4ee993c8774c5fed44cd60cb1e7c17c090688c1f3488c753e20fa0dc10b4ed31cafd414f8832a90ac48263c648ff10e0e507298b67833a03ec47 Homepage: https://cran.r-project.org/package=climatol Description: CRAN Package 'climatol' (Climate Tools (Series Homogenization and Derived Products)) Functions for the quality control, homogenization and missing data filling of climatological series and to obtain climatological summaries and grids from the results. Also functions to display wind-roses, meteograms, Walter&Lieth diagrams, and more. Package: r-cran-climatrends Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5584 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nasapower Suggests: r-cran-chirps, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-climatrends_1.2-1.ca2404.1_all.deb Size: 3693404 MD5sum: d1941b68cb7bdda924fb8461ca41e510 SHA1: ff02f84c9b0e03c64ea00e7db0bc5890e5826b1a SHA256: a041a0e3fa63694afac84e254358ead61dba655124682fefc4fe959c008bbd3b SHA512: a56b2302a56a51906c8a98af4f682a2d7e745c52f5a88b6b72111df1fa1eca3f5d2ad14e1852c54b04bf2f931874242d632a6d1eb861b9b44b7e3dbd4a23a088 Homepage: https://cran.r-project.org/package=climatrends Description: CRAN Package 'climatrends' (Climate Variability Indices for Ecological Modelling) Supports analysis of trends in climate change, ecological and crop modelling. Package: r-cran-climclass Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geosphere, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-stringr Filename: pool/dists/noble/main/r-cran-climclass_2.1.1-1.ca2404.1_all.deb Size: 2172886 MD5sum: a9af65bbc733d908d1c4a2429be88f8c SHA1: 3e5b005f53a79916f47654921f1d5b23d93f219a SHA256: 559ee5c2c78951f776da7aac8f9371c86334715a965c46b848c2bc1a24ad0eb4 SHA512: 5617ae6489b75b815a8d9b6037908a06747a72f271454dfa3765cae701805d4835ddb8f36be924f497861049f02e08f964931eb6d2812eed6bc94b53ff6dfe37 Homepage: https://cran.r-project.org/package=ClimClass Description: CRAN Package 'ClimClass' (Climate Classification According to Several Indices) Classification of climate according to Koeppen - Geiger, of aridity indices, of continentality indices, of water balance after Thornthwaite, of viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy, Bagnouls-Gaussen. Package: r-cran-climd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49762 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-ncdf4, r-cran-qpdf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-climd_0.1.0-1.ca2404.1_all.deb Size: 2566294 MD5sum: 56976ecaa04a8e58b4900e2a6007c1b5 SHA1: 4f8b819539fe20d4bb3c75275c7fc697f3de2953 SHA256: 0eb8062c021b05418a3598678251c3fcc45a0f25d695251a0526405fd616f4b7 SHA512: 8da9506b098d0758b25721ed403aa63c4cc276038ce76a9580dbd76fca5458404dd3b303b7c7767702b182db79ee18ede36ab51157cb8bbb63dc5b17e84bae22 Homepage: https://cran.r-project.org/package=CLimd Description: CRAN Package 'CLimd' (Generating Rainfall Rasters from IMD NetCDF Data) The developed function is a comprehensive tool for the analysis of India Meteorological Department (IMD) NetCDF rainfall data. Specifically designed to process high-resolution daily gridded rainfall datasets. It provides four key functions to process IMD NetCDF rainfall data and create rasters for various temporal scales, including annual, seasonal, monthly, and weekly rainfall. For method details see, Malik, A. (2019).. It supports different aggregation methods, such as sum, min, max, mean, and standard deviation. These functions are designed for spatio-temporal analysis of rainfall patterns, trend analysis,geostatistical modeling of rainfall variability, identifying rainfall anomalies and extreme events and can be an input for hydrological and agricultural models. Package: r-cran-clime Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve Filename: pool/dists/noble/main/r-cran-clime_0.5.0-1.ca2404.1_all.deb Size: 43566 MD5sum: 1195e0786970c0ac6e69fc56982df9fc SHA1: 15fa0a3bc5347824834aa0cdefd840abbb470c5c SHA256: 1aaebb423af9bf2b3c7611b390e58f47962bb7e32fde2716b5945060fa928988 SHA512: b54f6f9ba3fe75a0a5fcdcaca2ac3756074f0d8927df95f45d53503791fca46e4895404285851dada52c8f6d2846d2471282643fe344db05ff1ef0f81d65b07b Homepage: https://cran.r-project.org/package=clime Description: CRAN Package 'clime' (Constrained L1-Minimization for Inverse (Covariance) MatrixEstimation) A robust constrained L1 minimization method for estimating a large sparse inverse covariance matrix (aka precision matrix), and recovering its support for building graphical models. The computation uses linear programming. The method was published in TT Cai, W Liu, X Luo (2011) . Package: r-cran-climenu Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-keypress Suggests: r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-climenu_0.1.7-1.ca2404.1_all.deb Size: 50470 MD5sum: 252a6e7fc81698f723009de2471d87c1 SHA1: ba5ee2d465ad013c9bc94881279e033dd921a2cb SHA256: 09ccc333855c2be6923a915bee60ab9ccbb01cfe771aef974d711d7c34fdc323 SHA512: 5c6afaac05c39be2e6b184138b2eaa98ab8d1d4a0d60d9ad4a2de9ba9b39ae145b7de90714df0c28c2b4408eeed4fc5fa2543fe97f3c99cba15ede2eb027a5cd Homepage: https://cran.r-project.org/package=climenu Description: CRAN Package 'climenu' (Interactive Command-Line Menus) Provides interactive command-line menu functionality with single and multiple selection menus, keyboard navigation (arrow keys or vi-style j/k), preselection, and graceful fallback for non-interactive environments. Inspired by tools such as 'inquirer.js' , 'pick' , and 'survey' . Designed to be lightweight and easy to integrate into 'R' packages and scripts. Package: r-cran-climenv Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2365 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-climaemet, r-cran-dismo, r-cran-dplyr, r-cran-elevatr, r-cran-exactextractr, r-cran-geodata, r-cran-glue, r-cran-plyr, r-cran-randomforest, r-cran-sf, r-cran-sp, r-cran-ternary, r-cran-terra Suggests: r-cran-covr, r-cran-fs, r-cran-knitr, r-cran-progress, r-cran-raster, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-climenv_1.0.0-1.ca2404.1_all.deb Size: 2039792 MD5sum: 0f49672b31aed12444a74f17b0b6471d SHA1: 461a642fc9e882f3239ee9ae250338e2ce917b9b SHA256: cf86d32fe067e427edec6b7cb55354eaedaa7207ea1e207445244395c43339b9 SHA512: 6c45c8fc1574d85fef137e58484088dfc9893aa9783832d45ef1935bedea9dad7290f02c082b0dcd9f3e63666c0d67b0112ca023091b35bee5a7a9078c15e934 Homepage: https://cran.r-project.org/package=climenv Description: CRAN Package 'climenv' (Download, Extract and Visualise Climate and Elevation Data) Grants access to three widely recognised modelled data sets, namely Global Climate Data (WorldClim 2), Climatologies at high resolution for the earth's land surface areas (CHELSA), and National Aeronautics and Space Administration's (NASA) Shuttle Radar Topography Mission (SRTM). It handles both multi and single geospatial polygon and point data, extracts outputs that can serve as covariates in various ecological studies. Provides two common graphic options – the Walter-Lieth (1960) climate diagram and the Holdridge (1967) life zone classification scheme. Provides one new graphic scheme of our own design which incorporates aspects of both Walter-Leigh and Holdridge. Provides user-friendly access and extraction of globally recognisable data sets to enhance their usability across a broad spectrum of applications. Package: r-cran-climetrics Architecture: all Version: 1.0-15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3829 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rts, r-cran-raster, r-cran-terra, r-cran-zoo, r-cran-yaimpute, r-cran-xts Suggests: r-cran-r.rsp, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-climetrics_1.0-15-1.ca2404.1_all.deb Size: 3170712 MD5sum: 93bdae605a15cdee7d89cea53f5fb489 SHA1: 8572d3fea4d3d94735395a186a21e7bfbe8e5300 SHA256: 2629a3c4a174f47abf37b44f5ece81a706810ccdbc0509a152f7647eb2e05f0f SHA512: 9cd674474d86c9f32146473a067c0d8caf37046631713787a9ab71f25a3cb49dc0945b99d2931036d1e4bc3f235d65812cfe81ead0bdcf7a61cafdf27af227bc Homepage: https://cran.r-project.org/package=climetrics Description: CRAN Package 'climetrics' (Climate Change Metrics) A framework that facilitates spatio-temporal analysis of climate dynamics through exploring and measuring different dimensions of climate change in space and time. Package: r-cran-climextremes Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2193 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-extremes, r-cran-boot Filename: pool/dists/noble/main/r-cran-climextremes_0.3.1-1.ca2404.1_all.deb Size: 735128 MD5sum: 04bbad94ebf2e7de15c6cd40e2931b10 SHA1: ab7ae6dbfb653ed1c9962d2a585a6f0f45aaf474 SHA256: 9f88439b40e74a837ba7de9821943c01179a0393498a6456026d87bdbebe6966 SHA512: 99ecabfe08dfa7bd8a7c36836836f10fe0de222c909667b7d3004866113d1fea23d565210f1b8f0ed9e224bfd7bc6b11fa282e2b00f688bdc23be39f74e1b62c Homepage: https://cran.r-project.org/package=climextRemes Description: CRAN Package 'climextRemes' (Tools for Analyzing Climate Extremes) Functions for fitting GEV and POT (via point process fitting) models for extremes in climate data, providing return values, return probabilities, and return periods for stationary and nonstationary models. Also provides differences in return values and differences in log return probabilities for contrasts of covariate values. Functions for estimating risk ratios for event attribution analyses, including uncertainty. Under the hood, many of the functions use functions from 'extRemes', including for fitting the statistical models. Details are given in Paciorek, Stone, and Wehner (2018) . Package: r-cran-climind Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1696 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spei, r-cran-chron, r-cran-weathermetrics Suggests: r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-climind_0.1-3-1.ca2404.1_all.deb Size: 1634318 MD5sum: 54242f3599ee363c4363971bb10a8c11 SHA1: 06db306312bb9028899bff429267a3628c2c94ce SHA256: 7e223ae367e15227fcf9a454ad445aaad2c69658c97c82670fc52a980d626cf1 SHA512: 8690b86a93038efe5640f1aa3424b6a2361bc409528db45674f7ca50599c2d281654f3a21f21a872cecd638f8dc7301f76d89f1205a56e3d0766cde7be54a2fa Homepage: https://cran.r-project.org/package=ClimInd Description: CRAN Package 'ClimInd' (Climate Indices) Computes 138 standard climate indices at monthly, seasonal and annual resolution. These indices were selected, based on their direct and significant impacts on target sectors, after a thorough review of the literature in the field of extreme weather events and natural hazards. Overall, the selected indices characterize different aspects of the frequency, intensity and duration of extreme events, and are derived from a broad set of climatic variables, including surface air temperature, precipitation, relative humidity, wind speed, cloudiness, solar radiation, and snow cover. The 138 indices have been classified as follow: Temperature based indices (42), Precipitation based indices (22), Bioclimatic indices (21), Wind-based indices (5), Aridity/ continentality indices (10), Snow-based indices (13), Cloud/radiation based indices (6), Drought indices (8), Fire indices (5), Tourism indices (5). Package: r-cran-climmobtools Architecture: all Version: 1.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1241 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-lpsolve, r-cran-matrix, r-cran-rspectra Suggests: r-cran-climatrends, r-cran-gosset, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-plackettluce, r-cran-testthat Filename: pool/dists/noble/main/r-cran-climmobtools_1.8.2-1.ca2404.1_all.deb Size: 636178 MD5sum: 1b99fbe2d1e588e52f0426dc3d5a1fe3 SHA1: 2b0575da500b8300a3b2a4b50d703bd86d0b20b9 SHA256: 429cb01f4e044adcb28ce8ff356d9dbc55b657087c99228a689073dd2d9efae8 SHA512: 27f64bd6042bc1cfe3510e5b988e25299529fd1e6bda70daff17f4d61cc2db994ee3d9492abcf1e3579e82de236c4d643b733f02ff8ac771da9165502f0109d8 Homepage: https://cran.r-project.org/package=ClimMobTools Description: CRAN Package 'ClimMobTools' (API Client for the 'ClimMob' Platform) API client for 'ClimMob', an open source software for decentralized large-N trials with the 'tricot' approach . Developed by van Etten et al. (2019) , it turns the research paradigm on its head; instead of a few researchers designing complicated trials to compare several technologies in search of the best solutions for the target environment, it enables many participants to carry out reasonably simple experiments that taken together can offer even more information. 'ClimMobTools' enables project managers to deep explore and analyse their 'ClimMob' data in R. Package: r-cran-climodr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1438 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-cast, r-cran-corrplot, r-cran-doparallel, r-cran-dplyr, r-cran-lares, r-cran-magrittr, r-cran-rlang, r-cran-stringr, r-cran-terra, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-pls, r-cran-randomforest, r-cran-sp Filename: pool/dists/noble/main/r-cran-climodr_1.0.0-1.ca2404.1_all.deb Size: 1180696 MD5sum: 010d1bb5a03b89bb032866217b29e884 SHA1: 3ecdaba6c8503fefefd1a3055ecf55c4cc2d1d2e SHA256: 10c4fcf528b8674ec586389fa0dad8e015778882abfd24ad8369cca27d24eb18 SHA512: 3d871740815ca227da603c08d1187967c63793dd922220f9ffa9970946ea1a96022d7d1c63664d5d85b2e07a1542caef067e5992cb895f64366123061ccbe4d0 Homepage: https://cran.r-project.org/package=climodr Description: CRAN Package 'climodr' (Climate Modeling with Point Data from Climate Stations) An automated and streamlined workflow for predictive climate mapping using climate station data. Works within an environment the user provides a destined path to - otherwise it's tempdir(). Quick and relatively easy creation of resilient and reproducible climate models, predictions and climate maps, shortening the usually long and complicated work of predictive modelling. For more information, please find the provided URL. Many methods in this package are new, but the main method is based on a workflow from Meyer (2019) and Meyer (2022) , however, it was generalized and adjusted in the context of this package. Package: r-cran-climprojdiags Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1413 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multiapply, r-cran-pcict Suggests: r-cran-knitr, r-cran-testthat, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-climprojdiags_0.3.3-1.ca2404.1_all.deb Size: 1021618 MD5sum: 35303f3623f5e591748c3f57f0461575 SHA1: fca72f5c50da02b52822bc50b69901bc133d4a42 SHA256: f69507304cfa22dc6c969b21b154ae5f2a0d0fc5db67e7a0fae28b698bf114df SHA512: a5cb05cf30aa1d34638efe6858b2f60873e4cbd88f3149686e18e247251c652e4b872d3e310aa886176ebd90d7eafb15582438ad93165794956cd3ae7b1ab485 Homepage: https://cran.r-project.org/package=ClimProjDiags Description: CRAN Package 'ClimProjDiags' (Set of Tools to Compute Various Climate Indices) Set of tools to compute metrics and indices for climate analysis. The package provides functions to compute extreme indices, evaluate the agreement between models and combine theses models into an ensemble. Multi-model time series of climate indices can be computed either after averaging the 2-D fields from different models provided they share a common grid or by combining time series computed on the model native grid. Indices can be assigned weights and/or combined to construct new indices. Package: r-cran-climwin Architecture: all Version: 1.2.33-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1644 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-matrix, r-cran-evd, r-cran-lubridate, r-cran-lme4, r-cran-mumin, r-cran-reshape, r-cran-numderiv, r-cran-rcpproll, r-cran-nlme Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-climwin_1.2.33-1.ca2404.1_all.deb Size: 1207698 MD5sum: 78ea4ae56c79887d069d9b203cae3ad7 SHA1: ab0c04c24382c995ef805326127c924a8189798e SHA256: 4709c20904ae7d7b9fb32cb5aca845f4708a51c7706857b4ed62b911089ba100 SHA512: 0f6ca7a934217fe79b9479a9d2e9beff3b4e7cda37ece40ab86b8874a1a47b88564aae9267e355a6ae0c3eedff7ddbefd912f7808e58511dd005591cc721153f Homepage: https://cran.r-project.org/package=climwin Description: CRAN Package 'climwin' (Climate Window Analysis) Contains functions to detect and visualise periods of climate sensitivity (climate windows) for a given biological response. Please see van de Pol et al. (2016) and Bailey and van de Pol (2016) for details. Package: r-cran-clincompare Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14502 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-haven, r-cran-rlang, r-cran-tidyr Suggests: r-cran-ggplot2, r-cran-openxlsx, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clincompare_1.0.0-1.ca2404.1_all.deb Size: 943690 MD5sum: 804ec41820ebf64d5a96f840d0772ca2 SHA1: 859d7d8039a363ff4538282d6af7abe41531906e SHA256: 3749263654ecbfd7b8112a74849ceb92c5dec3c4e65e95bc503e9a981f4826c4 SHA512: 14f06ca46fd986bd4ede9a9cd79fccea67baf54c71231a9a3df37351f686d0c52f1787664342ef8f9546a3ed64f9b5f4913b58e1e85e5561b10f0e683f418e3d Homepage: https://cran.r-project.org/package=clinCompare Description: CRAN Package 'clinCompare' (Dataset Comparison with 'CDISC' Validation for Clinical TrialData) A general-purpose toolkit for comparing any two data frames with optional 'CDISC' (Clinical Data Interchange Standards Consortium) validation for clinical trial data. Core comparison functions work on arbitrary datasets: variable-level and observation-level comparison, data type checking, metadata attribute analysis (types, labels, lengths, formats), missing value handling, key-based row matching, tolerance-based numeric comparisons, and group-wise comparisons. Optional z-score outlier detection is available when enabled. When working with clinical data, the package additionally validates 'SDTM' (Study Data Tabulation Model) and 'ADaM' (Analysis Data Model) datasets against CDISC standards (SDTM IG 3.3/3.4, ADaM IG 1.1/1.2/1.3), automatically detecting domains and flagging non-conformant variables. Generates unified comparison reports in text or HTML format with interactive dashboards. For CDISC standards, see . Package: r-cran-clindatareview Architecture: all Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5608 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bookdown, r-cran-clinutils, r-cran-crosstalk, r-cran-data.table, r-cran-ggplot2, r-cran-haven, r-cran-htmltools, r-cran-htmlwidgets, r-cran-knitr, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-plotly, r-cran-plyr, r-cran-rmarkdown, r-cran-stringr, r-cran-yaml, r-cran-xml2, r-cran-xfun, r-cran-base64enc Suggests: r-cran-countrycode, r-cran-intextsummarytable, r-cran-patientprofilesvis, r-cran-testthat, r-cran-dt, r-cran-scales Filename: pool/dists/noble/main/r-cran-clindatareview_1.6.2-1.ca2404.1_all.deb Size: 1886992 MD5sum: f5196c07b85653dac691f55b463a69aa SHA1: b9168302bba508c1bb8a6039c99b4ebbceb6a72a SHA256: e10b18ae0a4c46b8d2157559dc2b33e56cd9fdedd4790604dcf35e8e4522020d SHA512: 15c7bfd1b40bdcac495a85f4686905e72352f5b86a4318d38f4d4efe8921d2193de025cee86d4b11473435d8876ca099535d4a0641dcccd3f29bf7078e74d823 Homepage: https://cran.r-project.org/package=clinDataReview Description: CRAN Package 'clinDataReview' (Clinical Data Review Tool) Creation of interactive tables, listings and figures ('TLFs') and associated report for exploratory analysis of data in a clinical trial, e.g. for clinical oversight activities. Interactive figures include sunburst, treemap, scatterplot, line plot and barplot of counts data. Interactive tables include table of summary statistics (as counts of adverse events, enrollment table) and listings. Possibility to compare data (summary table or listing) across two data batches/sets. A clinical data review report is created via study-specific configuration files and template 'R Markdown' reports contained in the package. Package: r-cran-clindr Architecture: all Version: 2.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-shiny, r-cran-foreach, r-cran-dosefinding, r-cran-mvtnorm, r-cran-doparallel, r-cran-ggplot2, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-dplyr, r-cran-glue, r-cran-waiter Filename: pool/dists/noble/main/r-cran-clindr_2.5.2-1.ca2404.1_all.deb Size: 1636670 MD5sum: 5458926d98d3646de3a1e77eaa7f4bca SHA1: 31969008d2c68d6a38ccb3492d18787d1ede97e7 SHA256: 6c53fea618b11dfbadc1ed80863c8dca6008da2ff7e31dfe6f552bac024d2736 SHA512: 257dcb16df596c5411ef3556fa3eafaf024773ba35490fb90435ee022d34c58e42b11bd0ff6c5697e82914aa60e0a222cd67067064aec49c228e08294e23d3be Homepage: https://cran.r-project.org/package=clinDR Description: CRAN Package 'clinDR' (Simulation and Analysis Tools for Clinical Dose ResponseModeling) Bayesian and ML Emax model fitting, graphics and simulation for clinical dose response. The summary data from the dose response meta-analyses in Thomas, Sweeney, and Somayaji (2014) and Thomas and Roy (2016) Wu, Banerjee, Jin, Menon, Martin, and Heatherington(2017) are included in the package. The prior distributions for the Bayesian analyses default to the posterior predictive distributions derived from these references. Package: r-cran-clinicalfair Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-clinicalfair_0.1.0-1.ca2404.1_all.deb Size: 245744 MD5sum: 0d813c97c42eebee8acd83ad1885ad0d SHA1: 360a069e3459ca8a5b235190e236f40539de641a SHA256: a7fd4e1e93a5ffeca1197fc3577aa68bfeb1d980f1fce98e382f2cf2b5dbb2bb SHA512: 89dd19198a881589a2fa82d6db1e2c05c9bd198bc39354e15261eb22cdb46ecdae760c4ae18e91752fb07575e4cc619fd794a3ebd2a6994504b2c2f704d3f22a Homepage: https://cran.r-project.org/package=clinicalfair Description: CRAN Package 'clinicalfair' (Algorithmic Fairness Assessment for Clinical Prediction Models) Post-hoc fairness auditing toolkit for clinical prediction models. Unlike in-processing approaches that modify model training, this package evaluates existing models by computing group-wise fairness metrics (demographic parity, equalized odds, predictive parity, calibration disparity), visualizing disparities across protected attributes, and performing threshold-based mitigation. Supports intersectional analysis across multiple attributes and generates audit reports useful for fairness-oriented auditing in clinical AI settings. Methods described in Obermeyer et al. (2019) and Hardt, Price, and Srebro (2016) . Package: r-cran-clinicalomicsdbr Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr2, r-cran-r6, r-cran-dplyr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-clinicalomicsdbr_1.0.5-1.ca2404.1_all.deb Size: 53210 MD5sum: ea2a6d9d14c1654cd695004c0c0743de SHA1: 94d4965e153b6258396373f80744f9d36ab7e1e9 SHA256: d588a738ed2a5bc2a0a2a729afe6b3af13c83437bf93fa439d5ed000320fcbc5 SHA512: 8e13af7fce08707bfd3f241a6e4e179fcf227fc1408583c93b82b18460b20eef726b7e1a852c037be5b177b8a5b4017a7db580ac06f21b4fb96fb15e17bef7fe Homepage: https://cran.r-project.org/package=clinicalomicsdbR Description: CRAN Package 'clinicalomicsdbR' (Interface with the 'ClinicalOmicsDB' API, Allowing for Easy DataDownloading and Importing) Provides an interface to the 'ClinicalOmicsDB' API, allowing for easy data downloading and importing. 'ClinicalOmicsDB' is a database of clinical and 'omics' data from cancer patients. The database is accessible at . Package: r-cran-clinicalsignificance Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesfactor, r-cran-bayestestr, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-insight, r-cran-lme4, r-cran-purrr, r-cran-rlang, r-cran-snakecase, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-clinicalsignificance_3.0.0-1.ca2404.1_all.deb Size: 737526 MD5sum: 5f11ef577ed182900461a833b112ab40 SHA1: 1f1cc66ddbd4701466d0f45aff20cef350e386dd SHA256: aff9431b4726c6bc11b41282669f4270eea74a519ebdc78cc5a6eb80bfe9bf9b SHA512: a233aad899c1172c43e43fcdc723223e7711fc78f1788274b670a4e829bd93c37e58c205403d31ab631e57690227250a227ac951fcbe41d28a6558e8e7858c3f Homepage: https://cran.r-project.org/package=clinicalsignificance Description: CRAN Package 'clinicalsignificance' (A Toolbox for Clinical Significance Analyses in InterventionStudies) A clinical significance analysis can be used to determine if an intervention has a meaningful or practical effect for patients. You provide a tidy data set plus a few more metrics and this package will take care of it to make your results publication ready. Accompanying package to Claus et al. . Package: r-cran-clinicalutilityrecal Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-caret, r-cran-ggplot2, r-cran-cowplot, r-cran-nloptr Filename: pool/dists/noble/main/r-cran-clinicalutilityrecal_0.1.0-1.ca2404.1_all.deb Size: 105130 MD5sum: 5c05d04badd8afbf3740193c4593b95f SHA1: c81a88e188f5efc233cf81ef6fcd7dc8a68b20c7 SHA256: 559b58eadd43bce6af2e6d92467072d4f71a453a9ca1bfa57748f4c4d105e7a1 SHA512: 914006c194d59a878ab032a91fb65ed161fa66b4fe228b51ecf4d53953adf7e6c803f9d403bbdcf1e78f32f8caff240bf8525acc81397dcca299b3607ae56a24 Homepage: https://cran.r-project.org/package=ClinicalUtilityRecal Description: CRAN Package 'ClinicalUtilityRecal' (Recalibration Methods for Improved Clinical Utility of RiskScores) Recalibrate risk scores (predicting binary outcomes) to improve clinical utility of risk score using weighted logistic or constrained logistic recalibration methods. Additionally, produces plots to assess the potential for recalibration to improve the clinical utility of a risk model. Methods are described in detail in Mishra, A. (2019) "Methods for Risk Markers that Incorporate Clinical Utility" . Package: r-cran-clinify Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1492 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flextable, r-cran-officer, r-cran-magrittr, r-cran-dplyr, r-cran-knitr, r-cran-htmltools, r-cran-tidyselect, r-cran-zoo Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-tplyr, r-cran-rvest, r-cran-withr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-clinify_0.3.0-1.ca2404.1_all.deb Size: 568856 MD5sum: 4ba428b2884b7cda92ba75068b45fb49 SHA1: d9e79613e5f56818eac24dd1996fb14df883282f SHA256: 15c42e3e186e8a7d9454da4eedd31a64fae3b8e5e6a7f57c7990db1d04f4e8b9 SHA512: 7fa52c2e583ef506b0ce9bae00bbb3cf531e8234341a64702a9494cc23b8d7f68fcd38649ae065c48b4b7124dcb09917cd45ead35d2f074fe63e2eff4be6ae0e Homepage: https://cran.r-project.org/package=clinify Description: CRAN Package 'clinify' (Clinical Table Styling Tools and Utilities) The primary motivation of this package is to take the things that are great about the R packages 'flextable' and 'officer' , take the standard and complex pieces of formatting clinical tables for regulatory use, and simplify the tedious pieces. Package: r-cran-clinmon Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1633 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-signal Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clinmon_0.6.0-1.ca2404.1_all.deb Size: 513506 MD5sum: 83b6acb917ba820364fb9ad0414cc2d6 SHA1: 2172a4c343064894bb4163c4a068ad4f7415bad1 SHA256: 9973c5ae4ca08e60622574d022230d826930071df4477b93b5b4ba7c012a9749 SHA512: 3afafb9fd932ff3a502b844acfdbc2b332f8485e7f6dd7984a72293e5e304c67665f85c49e6c246e45f1eadacfcfb3e80077da9001f752cf28f0fa73f5acb60c Homepage: https://cran.r-project.org/package=clinmon Description: CRAN Package 'clinmon' (Hemodynamic Calculations from Clinical Monitoring) Every research team have their own script for calculation of hemodynamic indexes. This package makes it possible to insert a long-format dataframe, and add both periods of interest (trigger-periods), and delete artifacts with deleter-files. Package: r-cran-clinpk Architecture: all Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-clinpk_0.13.0-1.ca2404.1_all.deb Size: 794974 MD5sum: 9c47df6a9060463ee7c20f5bc11e3f9d SHA1: bbfdf9f63ad985d722116ae045024c9221174a41 SHA256: 1a3ed19c4fb5c6fdf1fa0214278fedd2fc27f970246368c9ddc07d60d3ab9802 SHA512: 35483ef1ec983707aff2abb108fdd0c72285b915ecfe397fd127ebc8195a75710edb7aac2155d9b5295ed0fe69a1802a5b06465baa6b3601cd23da557196d19a Homepage: https://cran.r-project.org/package=clinPK Description: CRAN Package 'clinPK' (Clinical Pharmacokinetics Toolkit) Provides equations commonly used in clinical pharmacokinetics and clinical pharmacology, such as equations for dose individualization, compartmental pharmacokinetics, drug exposure, anthropomorphic calculations, clinical chemistry, and conversion of common clinical parameters. Where possible and relevant, it provides multiple published and peer-reviewed equations within the respective R function. Package: r-cran-clinpubr Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1079 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-car, r-cran-desctools, r-cran-dplyr, r-cran-fbasics, r-cran-forestploter, r-cran-ggplot2, r-cran-hmisc, r-cran-rlang, r-cran-rms, r-cran-stringi, r-cran-stringr, r-cran-survival, r-cran-survminer, r-cran-tidyr Suggests: r-cran-caret, r-cran-dcurves, r-cran-dtplyr, r-cran-geepack, r-cran-proc, r-cran-resourceselection, r-cran-rstatix, r-cran-tableone, r-cran-testthat, r-cran-timeroc, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-clinpubr_1.3.0-1.ca2404.1_all.deb Size: 962636 MD5sum: f70b13c01963982636151dd89b29d8c8 SHA1: b01f96288074b32786823d24256898c4ded60775 SHA256: e1cdf2b650bf74c3bbc23c3d45781558c4087625f8cc8f6092a5d8c9bf8a2c6d SHA512: 941dcfd0b971844e80c38d404cdaceab5cc1476a2dbceef223fcf95ffed9dec2803a2aae3e87b92d905fbe3b08286bd8813dd04449a7bb3d598dfa83390e493c Homepage: https://cran.r-project.org/package=clinpubr Description: CRAN Package 'clinpubr' (Clinical Publication) Accelerate the process from clinical data to medical publication, including clinical data cleaning, significant result screening, and the generation of publish-ready tables and figures. Package: r-cran-clinsig Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clinsig_1.2-1.ca2404.1_all.deb Size: 42646 MD5sum: d92b75c39c00ac83aa817dd53072f36c SHA1: 98c5d1291f8ad2826fa8ad5c6c9d6f3cbcb39934 SHA256: 765cdd099ffb6596088ab292196b55d90966963ac6fe9319f1d7fe845fe8f483 SHA512: 730b08a7ba5665bfb6941a0d81fc95baa63f66d7c5265c1f60b5bd655d0c7db7de144437f3ec44f5e6aec405150c95a128457bff95d4001f82e703bc48b3c994 Homepage: https://cran.r-project.org/package=clinsig Description: CRAN Package 'clinsig' (Clinical Significance Functions) Functions for calculating clinical significance. Package: r-cran-clinsigmeasures Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clinsigmeasures_1.2-1.ca2404.1_all.deb Size: 48662 MD5sum: d5ebef79c31490ee34333c14477687e1 SHA1: 709a38a6cb63652796563bc13387c60fb7b99442 SHA256: 3d218ef8828649838150d70c25fe431016b894cd7acae29baac6c2a55cf14c5c SHA512: 09769433194182cbb26a8f1df09df40c8ee0be05e9752b95bba60172345111323340a34c0b362b91912fc9c6516c934936ed634d044ee6d31893276d26373c94 Homepage: https://cran.r-project.org/package=ClinSigMeasures Description: CRAN Package 'ClinSigMeasures' (Clinical Significance Measures) Provides measures of effect sizes for summarized continuous variables as well as diagnostic accuracy statistics for 2x2 table data. Includes functions for Cohen's d, robust effect size, Cohen's q, partial eta-squared, coefficient of variation, odds ratio, likelihood ratios, sensitivity, specificity, positive and negative predictive values, Youden index, number needed to treat, number needed to diagnose, and predictive summary index. Package: r-cran-clinspacy Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-data.table, r-cran-assertthat, r-cran-rappdirs, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clinspacy_1.0.2-1.ca2404.1_all.deb Size: 62942 MD5sum: 53f11f80375e28a06dc9728bb3dfc0c9 SHA1: e29464f04d468ce91289247b93b6132a5f124c0d SHA256: 7eb2c193ae84175c94f010b9e02dc82575d2547fc121128c8b89279da061819f SHA512: e9ba54d91ad53e4e65604dc3f73ef8f47ab53c97bff86397a99250170bfbdeb3e8dc1d11b33de7d775e9febc36dd8a01efa4ec07fd85f732aacff2f6b089bf30 Homepage: https://cran.r-project.org/package=clinspacy Description: CRAN Package 'clinspacy' (Clinical Natural Language Processing using 'spaCy', 'scispaCy',and 'medspaCy') Performs biomedical named entity recognition, Unified Medical Language System (UMLS) concept mapping, and negation detection using the Python 'spaCy', 'scispaCy', and 'medspaCy' packages, and transforms extracted data into a wide format for inclusion in machine learning models. The development of the 'scispaCy' package is described by Neumann (2019) . The 'medspacy' package uses 'ConText', an algorithm for determining the context of clinical statements described by Harkema (2009) . Clinspacy also supports entity embeddings from 'scispaCy' and UMLS 'cui2vec' concept embeddings developed by Beam (2018) . Package: r-cran-clintools Architecture: all Version: 0.9.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1922 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-signal, r-cran-xml2, r-cran-lme4, r-cran-ggplot2, r-cran-proc, r-cran-irr, r-cran-nlme, r-cran-parameters, r-cran-stringi, r-cran-scales, r-cran-dplyr, r-cran-survival, r-cran-pander Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clintools_0.9.10.1-1.ca2404.1_all.deb Size: 791822 MD5sum: 6105abea87e08b0a00b64b705b0232e7 SHA1: 0c67b24cdb444be5eb146ab7527e109ed49ac236 SHA256: ba76118db618741114d7e360c2b0d689f115a9da877ba5ae6547904ef9b8fd83 SHA512: 45f5a0dd9a6fcb78184fdd0a5e8f7e1980ec34a425a495a7342a3c07e455eabc524f5d6fb6e5c7a4a3cf66bd06b9a8bb5347a242e94021cb1541ffb83bba7e8e Homepage: https://cran.r-project.org/package=clintools Description: CRAN Package 'clintools' (Tools for Clinical Research) Every research team have their own script for data management, statistics and most importantly hemodynamic indices. The purpose is to standardize scripts utilized in clinical research. The hemodynamic indices can be used in a long-format dataframe, and add both periods of interest (trigger-periods), and delete artifacts with deleter-files. Transfer function analysis (Claassen et al. (2016) ) and Mx (Czosnyka et al. (1996) ) can be calculated using this package. Package: r-cran-clintrialdata Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4198 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-connector, r-cran-httr, r-cran-jsonlite, r-cran-piggyback Suggests: r-cran-arrow, r-cran-dplyr, r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-clintrialdata_0.1.3-1.ca2404.1_all.deb Size: 2950798 MD5sum: 95414604bdb3d5e6a9127d805e7e218e SHA1: d6ad3b977e8f27186555c44edb64aebe95d79cec SHA256: 969a48ed51b9a828cf17444a86e8496ddcdc9b874828d7dfb5b9ac866ac8e8b2 SHA512: 20aa1a1c18945f9fe4695cb338950b85ad300b179983b983db0abdbb818356696d550a69c979ce8558738613cefa8343789e35999236261cc3891c8c40220066 Homepage: https://cran.r-project.org/package=clinTrialData Description: CRAN Package 'clinTrialData' (Clinical Trial Example Datasets) A collection of clinical trial example datasets from multiple sources including the CDISC Pilot 01 study (CDISC ). All datasets are provided in Parquet format for efficient storage and can be accessed using the 'connector' package. Designed for training, testing, prototyping, and demonstrating clinical data analysis workflows. 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Struggling with complex queries and bulk data extraction? What if you could simplify this process with just a few lines of code? Introducing 'clintrialx' - Fetch clinical trial data from sources like 'ClinicalTrials.gov' and the 'Clinical Trials Transformation Initiative - Access to Aggregate Content of ClinicalTrials.gov' database , supporting pagination and bulk downloads. Also, you can generate HTML reports based on the data obtained from the sources! 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Example datasets in 'SDTM' and 'ADaM' format, containing a subset of patients/domains from the 'CDISC Pilot 01 study' are also available as R datasets to demonstrate the package functionalities. 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(2019) . Package: r-cran-clonetv2 Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3514 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sets, r-cran-ggplot2, r-cran-ggrepel, r-cran-arules, r-cran-dbscan Filename: pool/dists/noble/main/r-cran-clonetv2_2.2.1-1.ca2404.1_all.deb Size: 2649106 MD5sum: b07e2898a1d3703a3f4b24bbf5662337 SHA1: 805e34a29ed5e273011fba14e27ca5684dbbff28 SHA256: f610dec4753b500a83e493bbf0273464483fd947dfb647be0c026c474f2eac34 SHA512: b052e26ada973d4bf1e6f9993fb4c6c094b3819f0a05e1226b2ed786adf6339078efed9737c5ba7f16f854e1c22d159f978b3979aa09d88959835921513610d7 Homepage: https://cran.r-project.org/package=CLONETv2 Description: CRAN Package 'CLONETv2' (Clonality Estimates in Tumor) Analyze data from next-generation sequencing experiments on genomic samples. 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See README on for more details. P Yang et al. (2015) . Package: r-cran-clugenr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4824 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mathjaxr Suggests: r-cran-crul, r-cran-devtools, r-cran-ggplot2, r-cran-knitr, r-cran-lintr, r-cran-patchwork, r-cran-prettydoc, r-cran-rgl, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clugenr_1.0.4-1.ca2404.1_all.deb Size: 2146570 MD5sum: a34aa23e8ff6fa1f39aba41c3324a9d4 SHA1: 3eea285e5ff227b7fb9363e9a6dae48de0840c5a SHA256: 7fd3ddaa9c2f2beb397d4b5b6b87737e6fa7cfe517d089aee1a0088a144a23ff SHA512: d7efb0ec02cb8a9f1f6289da4796afa89d1780c387ab64f510620685f520a58f80fa74bb7885fea022e85c55c00aae90dd1910aab481dcb196bb974d5d2ab596 Homepage: https://cran.r-project.org/package=clugenr Description: CRAN Package 'clugenr' (Multidimensional Cluster Generation Using Support Lines) An implementation of the clugen algorithm for generating multidimensional clusters with arbitrary distributions. Each cluster is supported by a line segment, the position, orientation and length of which guide where the respective points are placed. This package is described in Fachada & de Andrade (2023) . Package: r-cran-clump Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-ggplot2, r-cran-dplyr, r-cran-nbclust, r-cran-amap, r-cran-tableone, r-cran-data.table, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clump_0.8.1-1.ca2404.1_all.deb Size: 96462 MD5sum: 07acffd99145e59df3803d06827ed1c4 SHA1: e0d73120862a54eddca55c9733f08d139678a294 SHA256: 6ce02943f30b4c0a68002aeaf6e416445c3a3395d19ad486bf49c2e8abfd478b SHA512: 27718a64e7c1e0a0d9c2143f8cda935835cf19652aa6a2b1143efae70d510c825a34d5104e77f67a1450e9f1e669cbba2d99b6e4553bcd1781764810add36900 Homepage: https://cran.r-project.org/package=CluMP Description: CRAN Package 'CluMP' (Clustering of Micro Panel Data) Two-step feature-based clustering method designed for micro panel (longitudinal) data with the artificial panel data generator. See Sobisek, Stachova, Fojtik (2018) . Package: r-cran-clusboot Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-fpc, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clusboot_1.2.2-1.ca2404.1_all.deb Size: 52550 MD5sum: 8f0e38e3a780956e80388550486c1eaf SHA1: 6b90faf3bf1902fc28798c879717fdc8f907b625 SHA256: 6908fe8738de66c98c0a85c02adb45a96e2cd6f054d648dfb0ad482777a53e82 SHA512: af4bf5b7d34e0a54c2c6cdbc1efbd798474d32e28785420e5c4608ba45ea72eabe0953bd074b305be4cadfd9ef5efc91f956d7db5b0c4401954bdb12ed180229 Homepage: https://cran.r-project.org/package=ClusBoot Description: CRAN Package 'ClusBoot' (Bootstrap a Clustering Solution to Establish the Stability ofthe Clusters) Providing a cluster allocation for n samples, either with an $n \times p$ data matrix or an $n \times n$ distance matrix, a bootstrap procedure is performed. The proportion of bootstrap replicates where a pair of samples cluster in the same cluster indicates who tightly the samples in a particular cluster clusters together. Package: r-cran-clusevol Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1863 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-cluster, r-cran-fpc, r-cran-viridis, r-cran-clustersim, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-clusevol_1.0.1-1.ca2404.1_all.deb Size: 1864462 MD5sum: 657318a809ae50bd98b27c71d7c507ae SHA1: 33119e9c6a26f3f4ea5710f34f49092941c604b8 SHA256: df70f6e07724cc488b2259502f9bd1617802db2ccc30542cfd050520c6a7b1bb SHA512: c4f78f3b4277ab29c3ef248783042bbb1fc27907e76165fd41960b9721f20c49544bae66c5bf2da7234ee0da9515c9417689b63217eac57d5dc2c04aa5addac1 Homepage: https://cran.r-project.org/package=clusEvol Description: CRAN Package 'clusEvol' (A Procedure for Cluster Evolution Analytics) Cluster Evolution Analytics allows us to use exploratory what if questions in the sense that the present information of an object is plugged-in a dataset in a previous time frame so that we can explore its evolution (and of its neighbors) to the present. See the URL for the papers associated with this package, as for instance, Morales-Oñate and Morales-Oñate (2024) . Package: r-cran-clusscluster Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-venndiagram, r-cran-scales, r-cran-reshape2, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clusscluster_0.1.0-1.ca2404.1_all.deb Size: 210294 MD5sum: 275552be0a86ea61746eea42c4b67daf SHA1: 14a7886d877be7987412179d30569649ab44f4ad SHA256: 5a99594cfd80f7f73877991d8f92cefb07b336701c0c0909d680af1f5d3d02fc SHA512: 63d55ad3fcdf4eb62ba0602d28644a089b9a0ebe501866ec2fd4d4ed4a20b820629048e593ed500ba15d36c1a2cfbf7fc8ab382d1d6be23f8953c4cf7aa10a58 Homepage: https://cran.r-project.org/package=ClussCluster Description: CRAN Package 'ClussCluster' (Simultaneous Detection of Clusters and Cluster-Specific Genes inHigh-Throughput Transcriptome Data) Implements a new method 'ClussCluster' descried in Ge Jiang and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished). Simultaneously perform clustering analysis and signature gene selection on high-dimensional transcriptome data sets. To do so, 'ClussCluster' incorporates a Lasso-type regularization penalty term to the objective function of K- means so that cell-type-specific signature genes can be identified while clustering the cells. Package: r-cran-clust.bin.pair Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-clust.bin.pair_0.1.2-1.ca2404.1_all.deb Size: 35144 MD5sum: 2659c7868c3d87dcf6266cf798f7c856 SHA1: 46da3f04f126be805b0fc484aa7d614a74d76542 SHA256: df75a2ff981975255672b9ccb47e753ca4d9055199d4079c8dc99c74e05e7aef SHA512: 846c1b851277228555b347bb727f0235bb50d88a17fabb802ad7a273f139596985a85adf0c30b4cad831887fd66720c622b58cd0e41e8fd291f83b405c27c2fa Homepage: https://cran.r-project.org/package=clust.bin.pair Description: CRAN Package 'clust.bin.pair' (Statistical Methods for Analyzing Clustered Matched Pair Data) Tests, utilities, and case studies for analyzing significance in clustered binary matched-pair data. The central function clust.bin.pair uses one of several tests to calculate a Chi-square statistic. Implemented are the tests Eliasziw (1991) , Obuchowski (1998) , Durkalski (2003) , and Yang (2010) with McNemar (1947) included for comparison. The utility functions nested.to.contingency and paired.to.contingency convert data between various useful formats. Thyroids and psychiatry are the canonical datasets from Obuchowski and Petryshen (1989) respectively. Package: r-cran-clustblock Architecture: all Version: 5.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-factominer Suggests: r-cran-clustvarlv Filename: pool/dists/noble/main/r-cran-clustblock_5.0.0-1.ca2404.1_all.deb Size: 396312 MD5sum: 236eddcca0286ea553b2de7dbbef07a7 SHA1: d4035a3805516da35ed62ae232b5093bb45b9041 SHA256: 0c98e1811bf3eef5ff2ed289322b9d8cb2dcf5afee95e9f096ae3ed341d3d01c SHA512: e97738faa7844fbf309385228b78ebf5175bb24103cb76a5fc409e4c742eeb3894c3414d53746744d283ff79e1e0291b12794f34c5cfbb83b3546f91f006795c Homepage: https://cran.r-project.org/package=ClustBlock Description: CRAN Package 'ClustBlock' (Clustering of Datasets) Hierarchical and partitioning algorithms to cluster blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. Different thresholds per cluster can be sets. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) , Llobell, Vigneau & Qannari (2019) ) and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) , Llobell, Giacalone, Labenne & Qannari (2019) ) are the core of this package. The CATATIS methods allows to compute some indices and tests to control the quality of CATA data (Llobell, Bonnet & Giacalone (2024) ) . Multivariate analysis and clustering of subjects for quantitative multiblock data, CATA, RATA, Free Sorting and JAR experiments are available. Clustering of observations (products in sensory analysis) in multi-block context (notably with ClusMB strategy) is also included (Llobell & Giacalone (2025) ).Performing clustering based on CATA and liking at the same time is possible thanks to cluscata_liking function (Llobell & Giacalone (2025) ). Package: r-cran-clustcr2 Architecture: all Version: 1.7.3.01-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desctools, r-cran-ggplot2, r-cran-ggseqlogo, r-cran-network, r-cran-plyr, r-cran-rcolorbrewer, r-cran-stringr, r-cran-scales, r-cran-sna, r-cran-vlf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustcr2_1.7.3.01-1.ca2404.1_all.deb Size: 253044 MD5sum: afcbf59f672c70560bc24e5fa37b0ca9 SHA1: 2a984ca794742ea9ad63806f4bb2f9729c4abc2c SHA256: fff9704d575093ca84588b8a73c668efbc22407dd2ca29c7c28c4bd5346a00e0 SHA512: c04b5ea002c1f2976289e22ff350b81a3c6eeb431ba04ed99304692e58154a4b6fb1ec5228cfa789b5847b02d26613579174d5269d0e98aacd6fabb16c5de2a7 Homepage: https://cran.r-project.org/package=ClusTCR2 Description: CRAN Package 'ClusTCR2' (Identifying Similar T Cell Receptor Hyper-Variable Sequenceswith 'ClusTCR2') Enhancing T cell receptor (TCR) sequence analysis, 'ClusTCR2', based on 'ClusTCR' python program, leverages Hamming distance to compare the complement-determining region three (CDR3) sequences for sequence similarity, variable gene (V gene) and length. The second step employs the Markov Cluster Algorithm to identify clusters within an undirected graph, providing a summary of amino acid motifs and matrix for generating network plots. Tailored for single-cell RNA-seq data with integrated TCR-seq information, 'ClusTCR2' is integrated into the Single Cell TCR and Expression Grouped Ontologies (STEGO) R application or 'STEGO.R'. See the two publications for more details. Sebastiaan Valkiers, Max Van Houcke, Kris Laukens, Pieter Meysman (2021) , Kerry A. Mullan, My Ha, Sebastiaan Valkiers, Nicky de Vrij, Benson Ogunjimi, Kris Laukens, Pieter Meysman (2023) . Package: r-cran-clustcurv Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4395 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-ggfortify, r-cran-ggplot2, r-cran-gmedian, r-cran-npregfast, r-cran-rcolorbrewer, r-cran-survival, r-cran-survminer Suggests: r-cran-covr, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustcurv_3.0.1-1.ca2404.1_all.deb Size: 1068770 MD5sum: c207c9cb20bf573bfa1da80eb5991814 SHA1: f8929e553a555e2c3f240105ec0796cf2469b2e2 SHA256: 4466eb81f2f786f2e3d00b3d3742b7c078512fdb8700e41b23bf7f486b3c07cb SHA512: 0bc3afe2d9e206ed48b2141bc2d4d91d0f4330ccc69e74b40767317f846b776d8f82032383de8ac3d9c9cfe9339b9e5a7918ac6e5bdf84aed8ffe549e2b6018d Homepage: https://cran.r-project.org/package=clustcurv Description: CRAN Package 'clustcurv' (Determining Groups in Multiples Curves) A method for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods or other approaches with lower computational cost. The methodology can be applied both in regression and survival framework. Implemented methods are: Grouping multiple survival curves described by Villanueva et al. (2018) . Package: r-cran-clusteff Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qrcm, r-cran-cluster, r-cran-fda, r-cran-ggpubr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-clusteff_0.3.1-1.ca2404.1_all.deb Size: 117300 MD5sum: 1f23648378a07e1de3ba6b3dabdbc146 SHA1: 811d76f24e85bae1d87901d0d1293e735d71f113 SHA256: a470c190a3302924d25e698ba8433c6d94c77c8fc50ec37c543e52bd2e5b6d5b SHA512: a7fae2ebddfb299917699a80908c9db3e7eb4bb7a3034dad04ae6e5982942293d146e6a7801a67d0bebbe520334b5714b602371384e63f7e2f78553408abe1f2 Homepage: https://cran.r-project.org/package=clustEff Description: CRAN Package 'clustEff' (Clusters of Effects Curves in Quantile Regression Models) Clustering method to cluster both effects curves, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2019) . Package: r-cran-cluster.datasets Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cluster.datasets_1.0-1-1.ca2404.1_all.deb Size: 213086 MD5sum: e49afaf548ba6488bd43891b3fb7316a SHA1: a7804bcb688f4e91c02902d54111a25439a6a62c SHA256: 3fd08dec837acefd5eb01a892e8a0f923437ce31890cde903b8cbe7ca153e15b SHA512: fd69eddedf226bc8162856ed9e6896cfe811141fae87351e87dbbcf39e8f795b8a2b326e7456f7233010f672135fc7673601d7176e621fe6423355bfd14c2399 Homepage: https://cran.r-project.org/package=cluster.datasets Description: CRAN Package 'cluster.datasets' (Cluster Analysis Data Sets) A collection of data sets for teaching cluster analysis. Package: r-cran-cluster.obeu Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-cluster, r-cran-clvalid, r-cran-data.tree, r-cran-dendextend, r-cran-jsonlite, r-cran-mclust, r-cran-rcurl, r-cran-reshape, r-cran-reshape2, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cluster.obeu_1.2.3-1.ca2404.1_all.deb Size: 64802 MD5sum: f48a395b0650a49d900db25d437a7a05 SHA1: bb0d10933e296da551a29aaeeb7633753c275f85 SHA256: 567f3c6919527362af1aa4e10b681cdbacdb3baa283c752d89f1698c97a31e3d SHA512: db5e9749efaac7a0a666377a53810735c5d594f122cfa4acc0ac489cd3ac23bce7eb800f65bfb6a03245dda2fc9627a0aa70405b0147c519c09a119f755c1ac3 Homepage: https://cran.r-project.org/package=Cluster.OBeu Description: CRAN Package 'Cluster.OBeu' (Cluster Analysis 'OpenBudgets.eu') Estimate and return the needed parameters for visualisations designed for 'OpenBudgets' data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the 'OpenBudgets' data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to 'JSON' format and use them as input in a different graphical interface. Package: r-cran-clusterability Architecture: all Version: 0.2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-diptest, r-cran-sparsepca, r-cran-elasticnet, r-cran-cluster Suggests: r-cran-testthat, r-cran-plotly, r-cran-bench Filename: pool/dists/noble/main/r-cran-clusterability_0.2.3.0-1.ca2404.1_all.deb Size: 92304 MD5sum: 12c328439e6463502964ea36b6f47837 SHA1: bdf9607a782f7d9411fceab12ed4117dc92330be SHA256: 14f7f08e8175087b9ac1bf3205089fb94a825e6f19befb2ad026e4f1503da62b SHA512: 0035b7ac4766351c0c20e80b1b3c4dac1a071c3d6d34f4fdde675422393b61c5770ee7cdcbb2c7695bbe4e4b44dad1fcb02c15375be860406aabca59d0354088 Homepage: https://cran.r-project.org/package=clusterability Description: CRAN Package 'clusterability' (Performs Tests for Cluster Tendency of a Data Set) Test for cluster tendency (clusterability) of a data set. The methods implemented - reducing the data set to a single dimension using principal component analysis or computing pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test - are described in Adolfsson, Ackerman, and Brownstein (2019) and Laborde et al. (2023) . Such methods can inform whether clustering algorithms are appropriate for a data set. Package: r-cran-clusterbootstrap Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-clusterbootstrap_2.0.0-1.ca2404.1_all.deb Size: 107448 MD5sum: 72947534201253c3b5518620d2e6c527 SHA1: d08902220718b09db2bf1e9806e994f8f2f58d11 SHA256: b5fab9fe311029d33a23e6664a0a8aa7d0ef5320b7eff0ba5c437a462cb5de03 SHA512: 4843c299f18d587ed42f95557a09d2024ba9b069f3cbbda92441a0b07920b7f298483990a0f9b21e5bd2fe65de57367262c6311bc38fff2f64e7edf05b93e885 Homepage: https://cran.r-project.org/package=ClusterBootstrap Description: CRAN Package 'ClusterBootstrap' (Analyze Clustered Data using the Cluster Bootstrap) Provides functionality for the analysis of clustered data using the cluster bootstrap. Package: r-cran-clustercons Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-lattice, r-cran-rcolorbrewer, r-cran-apcluster Suggests: r-cran-latticeextra Filename: pool/dists/noble/main/r-cran-clustercons_1.2-1.ca2404.1_all.deb Size: 268408 MD5sum: 86448b0a6b10e58ce18e4ed8ab5c72bd SHA1: ba49469b7011c18b22a318ed0742ca6424df50b6 SHA256: ae7d421a8e0e3547f4df06a3763f2a9acec9c50889dfc6472943667de693ae62 SHA512: e8934278648cb6b8c9bde7744bf21e411fccae6ea734355297312110ac90952c76369f7aef9fc30e525323e332ad19c2432b3c15eb49a12dd86ed4032229f4a8 Homepage: https://cran.r-project.org/package=clusterCons Description: CRAN Package 'clusterCons' (Consensus Clustering using Multiple Algorithms and Parameters) Functions for calculation of robustness measures for clusters and cluster membership based on generating consensus matrices from bootstrapped clustering experiments in which a random proportion of rows of the data set are used in each individual clustering. This allows the user to prioritise clusters and the members of clusters based on their consistency in this regime. The functions allow the user to select several algorithms to use in the re-sampling scheme and with any of the parameters that the algorithm would normally take. See Simpson, T. I., Armstrong, J. D. & Jarman, A. P. (2010) and Monti, S., Tamayo, P., Mesirov, J. & Golub, T. (2003) . Package: r-cran-clusteredinterference Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-cubature, r-cran-lme4, r-cran-numderiv, r-cran-rootsolve Suggests: r-cran-testthat, r-cran-rprojroot, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-clusteredinterference_1.0.1-1.ca2404.1_all.deb Size: 146440 MD5sum: 9937faac9af45c5eff4e9e2d2f8b008f SHA1: 273111265bc9bbf45fc1efa4b85f9797266e68df SHA256: 0020c6b4a2922ee030e68d8b688e277770e220a6296a3599bf5565bab72fac74 SHA512: 98d1e947e51905eefc8f078a454c9c8983240700249ced956228c6e024d59dc391e98ee47ef9e7e829537b127df02205739d67b2612d104c7d7d45e8c0740da1 Homepage: https://cran.r-project.org/package=clusteredinterference Description: CRAN Package 'clusteredinterference' (Causal Effects from Observational Studies with ClusteredInterference) Estimating causal effects from observational studies assuming clustered (or partial) interference. These inverse probability-weighted estimators target new estimands arising from population-level treatment policies. The estimands and estimators are introduced in Barkley et al. (2017) . Package: r-cran-clusteredmutations Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seriation, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-clusteredmutations_1.0.2-1.ca2404.1_all.deb Size: 954598 MD5sum: 267d6ee44fc8acd4bb955f1e983e7f60 SHA1: 62934e4cf763ab0148e7edc34124d8f071d585d7 SHA256: 9552ed9c3fe7eb42b78280a372f93e538c79cb909c83dabc133e84451855dc81 SHA512: ef7c5ecb724427c8ab826e28e293142f4fd807ea87a53e8d73737d9579591d77622d60cc27ba40cf1f94abd88ab7a3127cd71ec05fe067fb5a99733539004ada Homepage: https://cran.r-project.org/package=ClusteredMutations Description: CRAN Package 'ClusteredMutations' (Location and Visualization of Clustered Somatic Mutations) Identification and visualization of groups of closely spaced mutations in the DNA sequence of cancer genome. The extremely mutated zones are searched in the symmetric dissimilarity matrix using the anti-Robinson matrix properties. Different data sets are obtained to describe and plot the clustered mutations information. Package: r-cran-clustergeneration Architecture: all Version: 1.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-clustergeneration_1.3.8-1.ca2404.1_all.deb Size: 269914 MD5sum: 394d5004f1548cd2f17db70394321eb0 SHA1: 6632ec190aad8072f6214cc94887aa513ac0213d SHA256: 6011996cc20db0405b29502209c0f31533a5aaf410a9af63da93b3436cb0cf6d SHA512: 75135d81df9ab39d9142538a25ddbd909fb8bfd9d00d645d78d8f7ce9a47dcc28d97fc158be0c55c20cee038979709f7903c89dfc11c992dcb5002b6df76710b Homepage: https://cran.r-project.org/package=clusterGeneration Description: CRAN Package 'clusterGeneration' (Random Cluster Generation (with Specified Degree of Separation)) We developed the clusterGeneration package to provide functions for generating random clusters, generating random covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters. The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables. Package: r-cran-clustergvis Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 895 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colorramps, r-cran-dplyr, r-cran-e1071, r-cran-factoextra, r-cran-ggplot2, r-cran-magrittr, r-cran-matrix, r-cran-purrr, r-cran-reshape2, r-cran-scales, r-cran-tibble, r-bioc-singlecellexperiment Suggests: r-bioc-biobase, r-bioc-complexheatmap, r-bioc-clusterprofiler, r-bioc-summarizedexperiment, r-bioc-tcseq, r-cran-circlize, r-cran-igraph, r-cran-knitr, r-bioc-monocle, r-cran-pheatmap, r-cran-rmarkdown, r-cran-seurat, r-cran-wgcna, r-cran-biocmanager Filename: pool/dists/noble/main/r-cran-clustergvis_0.1.4-1.ca2404.1_all.deb Size: 866152 MD5sum: ff1a34582c7d5af3c540e07366aebc5f SHA1: 156b07a2503d760b32db8dc3cd9ca00507d12545 SHA256: 32ad77bd524988a29fc9508a28de6e2dab407dcd7977047bf713899e0811ce18 SHA512: a2822e52654c0946ebeaa9625f08cd607fee94b45f4225cf443f4261e88c277edbc055104e688406465a1d03680d2f0ff179f26ec20677357300c7b1ce61939c Homepage: https://cran.r-project.org/package=ClusterGVis Description: CRAN Package 'ClusterGVis' (One-Step to Cluster and Visualize Gene Expression Data) Streamlining the clustering and visualization of time-series gene expression data from RNA-Seq experiments, this tool supports fuzzy c-means and k-means clustering algorithms. It is compatible with outputs from widely-used packages such as 'Seurat', 'Monocle', and 'WGCNA', enabling seamless downstream visualization and analysis. See Lokesh Kumar and Matthias E Futschik (2007) for more details. Package: r-cran-clusterhap Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clusterhap_0.1-1.ca2404.1_all.deb Size: 46796 MD5sum: 7f97c0a551f6b34246084accfd6bd6d4 SHA1: c1d5e8a104275d0cf9913a983788dfdac95ce95a SHA256: 3c0bcca43ee452e8ad1566605e9adf7b1affaf289235a5e3eac933e0b9a33e26 SHA512: a722c3172a910100a4d10d9d0b3a3044c00ed5c24b460d1d4a150568941a3d8277c4796d0f0a72ac36baeaa1ad61fa8e759e700f8049043a89f6d7f967f2a499 Homepage: https://cran.r-project.org/package=clusterhap Description: CRAN Package 'clusterhap' (Clustering Genotypes in Haplotypes) One haplotype is a combination of SNP (Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci). clusterhap groups together all individuals of a population with the same haplotype. Each group contains individual with the same allele in each SNP, whether or not missing data. Thus, clusterhap groups individuals, that to be imputed, have a non-zero probability of having the same alleles in the entire sequence of SNP's. Moreover, clusterhap calculates such probability from relative frequencies. Package: r-cran-clustering Architecture: all Version: 1.7.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-amap, r-cran-apcluster, r-cran-cluster, r-cran-clusterr, r-cran-data.table, r-cran-dplyr, r-cran-foreach, r-cran-future, r-cran-ggplot2, r-cran-gmp, r-cran-pracma, r-cran-pvclust, r-cran-shiny, r-cran-sqldf, r-cran-xtable, r-cran-toordinal Suggests: r-cran-dt, r-cran-shinyalert, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-tidyverse, r-cran-shinycssloaders Filename: pool/dists/noble/main/r-cran-clustering_1.7.10-1.ca2404.1_all.deb Size: 395286 MD5sum: a5633c969fefe3a785d5e161a3210087 SHA1: acce5850640ac798e16f943625e65abe620b8b45 SHA256: d9d8f821c9768d81719a6e525e2376e3e963776779e39729f43b6d00dca3f6d2 SHA512: 148608a0768039815ba98f56ebabf5d8a280202e840e9921e31e04238155e3cebe80813131f3f69d03b758fb1158190decf91a4e6ae5be701ad8232bcb98b466 Homepage: https://cran.r-project.org/package=Clustering Description: CRAN Package 'Clustering' (Techniques for Evaluating Clustering) The design of this package allows us to run different clustering packages and compare the results between them, to determine which algorithm behaves best from the data provided. See Martos, L.A.P., García-Vico, Á.M., González, P. et al.(2023) "Clustering: an R library to facilitate the analysis and comparison of cluster algorithms.", Martos, L.A.P., García-Vico, Á.M., González, P. et al. "A Multiclustering Evolutionary Hyperrectangle-Based Algorithm" and L.A.P., García-Vico, Á.M., González, P. et al. "An Evolutionary Fuzzy System for Multiclustering in Data Streaming" . Package: r-cran-clustermole Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1399 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-bioc-gseabase, r-bioc-gsva, r-cran-magrittr, r-cran-rlang, r-bioc-singscore, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustermole_1.1.1-1.ca2404.1_all.deb Size: 1380480 MD5sum: fc2d67514e7cf5b7e30215c068cb58eb SHA1: 7d84a868e4b04732d524fae9d58af7bfc7fb7405 SHA256: 90eba7bc030548a46650154204f27ff876cccf27631673dde60428d4a2961f59 SHA512: 3c50e4d8dc03818d5f59f4f731b5f8bfb25155f5e429ff4710907b1ccb49da72536ce0a3068a77efe7e9282f6ed833393fe4510200f5abd1a10de023962db0d9 Homepage: https://cran.r-project.org/package=clustermole Description: CRAN Package 'clustermole' (Unbiased Single-Cell Transcriptomic Data Cell TypeIdentification) Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases. Package: r-cran-clusternomics Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-plyr, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mclust, r-cran-gplots, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-clusternomics_0.1.1-1.ca2404.1_all.deb Size: 308620 MD5sum: b0a680548adb4e7c5efad62ff5f17e42 SHA1: 02cc348d85cfbd2cd900889b794516209ea00bfd SHA256: 69498859a5dc652f357b40502bb093978e425c875e81e585b90dbc000866df9c SHA512: 5b215a87e17b45429ce3469a0f90c0e0e21670f98f6134f5db5826bf24856690bdf103b3daf72f887479de8dad853c21ace960735ed3c77f0b4036197f0bca20 Homepage: https://cran.r-project.org/package=clusternomics Description: CRAN Package 'clusternomics' (Integrative Clustering for Heterogeneous Biomedical Datasets) Integrative context-dependent clustering for heterogeneous biomedical datasets. Identifies local clustering structures in related datasets, and a global clusters that exist across the datasets. Package: r-cran-clusterranktest Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clusterranktest_1.0-1.ca2404.1_all.deb Size: 28898 MD5sum: 53feec732f3f6ede941e53b9823a5aa6 SHA1: 1d9038d9ad8cd17c73a06212386193e750dd191f SHA256: 0688ebb5d72a1d42f6e9c6ffce0a98e8964ad16eceea540472c967d2aa3979d3 SHA512: 71c25ef2c335bda703e5185d5dfdfd707f272b9c64f9f0e06ba436fe810c3857afab3ce1d940ea1c0583bc3bfbe796a9ed9500c568cd8eba52bf012cf5fd8841 Homepage: https://cran.r-project.org/package=ClusterRankTest Description: CRAN Package 'ClusterRankTest' (Rank Tests for Clustered Data) Nonparametric rank based tests (rank-sum tests and signed-rank tests) for clustered data, especially useful for clusters having informative cluster size and intra-cluster group size. Package: r-cran-clusterrepro Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clusterrepro_0.9-1.ca2404.1_all.deb Size: 20194 MD5sum: f244e7c43234956853abb1ea48d26211 SHA1: 20c06f5beef4f8be159351853769e13d8144e4ef SHA256: a2e069d3971beb6e5258e979ba55bcded3c2eb95d9370d300795a0a04e67aa02 SHA512: 5dac6a67894d7ab8818f19d6b7febb2d3ddc98206120f16c72c215d3bfacf2e469e4eb935be9a94485090cf9d0a173b76f1b71930c99085d60ee3cae91317e72 Homepage: https://cran.r-project.org/package=clusterRepro Description: CRAN Package 'clusterRepro' (Reproducibility of Gene Expression Clusters) This is a function for validating microarray clusters via reproducibility, based on the paper referenced below. Package: r-cran-clusterses Architecture: all Version: 2.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aer, r-cran-formula, r-cran-plm, r-cran-sandwich, r-cran-lmtest, r-cran-mlogit, r-cran-dfidx Filename: pool/dists/noble/main/r-cran-clusterses_2.6.5-1.ca2404.1_all.deb Size: 151298 MD5sum: de7e3185e1af9965dbd73429278fed6d SHA1: b2f7b2ccfc4dd783dde1a0213286e875a636040e SHA256: 05c54c5142cbee2cff4429307c45287bb3755ee5cbf7f0ee40752400ed1a9a6b SHA512: 2830de2ef675199a7e802361751b84d6928a2ec562841c57eb01e54de095891de98334d8e476238310ccee4ffe938a4186c8cb901a8122a385fd0db4000125bd Homepage: https://cran.r-project.org/package=clusterSEs Description: CRAN Package 'clusterSEs' (Calculate Cluster-Robust p-Values and Confidence Intervals) Calculate p-values and confidence intervals using cluster-adjusted t-statistics (based on Ibragimov and Muller (2010) , pairs cluster bootstrapped t-statistics, and wild cluster bootstrapped t-statistics (the latter two techniques based on Cameron, Gelbach, and Miller (2008) . Procedures are included for use with GLM, ivreg, plm (pooling or fixed effects), and mlogit models. Package: r-cran-clustertend Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-clustertend_1.7-1.ca2404.1_all.deb Size: 13054 MD5sum: 270cfcb763bbf8b766aaa1d5d376384f SHA1: a77cb3284e8816acf441e9bcf181c9d5829012db SHA256: 67d37ebae88f1307a27666f81a627e5b53b6eadcfd220c4d71d1a5afbccd164f SHA512: ee4cd9c09af62830fb4e559de365f9e57d46aa204720e4bcf7c7b67ef1fc5f684d054898afde74d1de8b38f73d9a5adbf347b0186c2632532774ab44946b4b08 Homepage: https://cran.r-project.org/package=clustertend Description: CRAN Package 'clustertend' (Check the Clustering Tendency) Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins statistic is implemented. See Hopkins and Skellam (1954) . Package: r-cran-clusterv Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 817 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-cluster Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-clusterv_1.1.1-1.ca2404.1_all.deb Size: 752984 MD5sum: e034bc92092fae8fa6ebc9d343fe2332 SHA1: 1a0195e98dada67e26e155ed8c965e1a097cea0b SHA256: a5a0ad812bd1314cd24209cfc44f38c3bae1cd019852344c0a2325f30aced43e SHA512: c78f5f96048c761a5b6634a8bb816d2c2e7354fb5b08f08cf8d83cb6a594dfb2c42c7d2efa2554621bce1f5be5b4b85f92ea9a6349968762826f30fe273f741c Homepage: https://cran.r-project.org/package=clusterv Description: CRAN Package 'clusterv' (Assessment of Cluster Stability by Randomized Maps) The reliability of clusters is estimated using random projections. A set of stability measures is provided to assess the reliability of the clusters discovered by a generic clustering algorithm. The stability measures are taylored to high dimensional data (e.g. DNA microarray data) (Valentini, G (2005), . Package: r-cran-clustervar Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1388 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastdummies, r-cran-mass, r-cran-mvtnorm, r-cran-scales, r-cran-foreach, r-cran-doparallel, r-cran-parabar, r-cran-iterators Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustervar_0.0.8-1.ca2404.1_all.deb Size: 1376400 MD5sum: e345f59238f3c68e24d2841bf73db243 SHA1: 930aaffdc0c97d6779ee4ba0f7a9c725bd7b0de2 SHA256: b699b6dd2ec7ca1e29b60d600c1e6ae41550e4f3897567323b8284b0f1f489a5 SHA512: 9555b4256ff690cb85e5b2becb39d758438359662e5a702b7ddfc2e1989adb4c8598076bf0ca0d42d4cfe25b228033e039c70d8e73926bd41d733e53d0922197 Homepage: https://cran.r-project.org/package=ClusterVAR Description: CRAN Package 'ClusterVAR' (Fitting Latent Class Vector-Autoregressive (VAR) Models) Estimates latent class vector-autoregressive models via EM algorithm on time-series data for model-based clustering and classification. Includes model selection criteria for selecting the number of lags and clusters. Package: r-cran-clusterwebapp Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-shinycssloaders, r-cran-cluster, r-cran-factoextra, r-cran-ggplot2, r-cran-dbscan, r-cran-mclust, r-cran-kernlab, r-cran-rtsne, r-cran-dt, r-cran-dplyr, r-cran-tidyr, r-cran-mlbench, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-clusterwebapp_0.1.3-1.ca2404.1_all.deb Size: 1290720 MD5sum: f81b257c3bf3038b14646ea2340e72e0 SHA1: 26a3586d66b6bcc1ddf151f69eac7320a1d95ae8 SHA256: d9105e2eb926222588815ee2116ca2e2e3bc5fccf251b3f067143de64cb47089 SHA512: 151cc9fcfa4d3b1c15cf029d3937313527b1b8278b07cf10e18d3fc3f857f36ccb22f3b9d1cc03edd46105647fa92143f23635c776f2662b21f4ace65517d6ee Homepage: https://cran.r-project.org/package=clusterWebApp Description: CRAN Package 'clusterWebApp' (Universal Clustering Analysis Platform) An interactive platform for clustering analysis and teaching based on the 'shiny' web application framework. Supports multiple popular clustering algorithms including k-means, hierarchical clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), PAM (Partitioning Around Medoids), GMM (Gaussian Mixture Model), and spectral clustering. Users can upload datasets or use built-in ones, visualize clustering results using dimensionality reduction methods such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE), evaluate clustering quality via silhouette plots, and explore method-specific visualizations and guides. For details on implemented methods, see: Reynolds (2009, ISBN:9781598296975) for GMM; Luxburg (2007) for spectral clustering. Package: r-cran-clustgeo Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1419 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-spdep Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clustgeo_2.1-1.ca2404.1_all.deb Size: 1172344 MD5sum: 70eea58fc4e1f564307e4ebaebf994e2 SHA1: 1bd32296a477acb0f35e9bfb0bf4ef5ef948ffb2 SHA256: 48961b778202f90437797426d1837d5bf50e7fa0c5fe4abc245d462266167770 SHA512: 8b67f0a9bccc5ef4d669ad0b1c69712ec7f02925e9758b3205fea6f5623824231fc4a53ee5a74a89eaaed4e5f54ad77332cfaefd2278629d40928eb62ee560ab Homepage: https://cran.r-project.org/package=ClustGeo Description: CRAN Package 'ClustGeo' (Hierarchical Clustering with Spatial Constraints) Implements a Ward-like hierarchical clustering algorithm including soft spatial/geographical constraints. Package: r-cran-clustimpute Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 768 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clusterr, r-cran-copula, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-ggplot2, r-cran-rlang, r-cran-knitr Suggests: r-cran-ggextra, r-cran-rmarkdown, r-cran-testthat, r-cran-hmisc, r-cran-tictoc, r-cran-spelling, r-cran-corrplot, r-cran-covr Filename: pool/dists/noble/main/r-cran-clustimpute_0.2.4-1.ca2404.1_all.deb Size: 611292 MD5sum: b5f83c502fb645d2cbd37513d4ce5cf0 SHA1: 52135dff73fc6922e07b22d966491977e72ffc14 SHA256: ddb84969655a7a7dc3fc51d101094c11bcde6c686538599918fc8868a9802a26 SHA512: f44cffeb7f40fce762fec4d23e72806828b9c29391569640620508b5420ec30a927367016407eb231efb29ac8f1715bec2b6b8f5b0a51ebed32cf6040bb40d66 Homepage: https://cran.r-project.org/package=ClustImpute Description: CRAN Package 'ClustImpute' (K-Means Clustering with Build-in Missing Data Imputation) This k-means algorithm is able to cluster data with missing values and as a by-product completes the data set. The implementation can deal with missing values in multiple variables and is computationally efficient since it iteratively uses the current cluster assignment to define a plausible distribution for missing value imputation. Weights are used to shrink early random draws for missing values (i.e., draws based on the cluster assignments after few iterations) towards the global mean of each feature. This shrinkage slowly fades out after a fixed number of iterations to reflect the increasing credibility of cluster assignments. See the vignette for details. Package: r-cran-clustlearn Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proxy, r-cran-cli Suggests: r-cran-deldir Filename: pool/dists/noble/main/r-cran-clustlearn_1.0.0-1.ca2404.1_all.deb Size: 130944 MD5sum: fa536bf2cf941b4297279d56287034bd SHA1: d737b4c2e29e71cb864ba3dc0a551d26e8718140 SHA256: 2903d3c3447933ab1ca977207f725d07979986c1aaf473b59b8f5d528ca66cd9 SHA512: 04793c44a0a4ad79592aeb265cdd7d8780b976f7f6fac5e8a1a928eddf374de7d7fc854fbd525da088d4cdeef4deb0ec1346d775dcb0e81ab45b6b6f565ea117 Homepage: https://cran.r-project.org/package=clustlearn Description: CRAN Package 'clustlearn' (Learn Clustering Techniques Through Examples and Code) Clustering methods, which (if asked) can provide step-by-step explanations of the algorithms used, as described in Ezugwu et. al., (2022) ; and datasets to test them on, which highlight the strengths and weaknesses of each technique, as presented in the clustering section of 'scikit-learn' (Pedregosa et al., 2011) . Package: r-cran-clustmc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-procs, r-cran-psych, r-cran-usedist Suggests: r-cran-ggdendro, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustmc_0.1.2-1.ca2404.1_all.deb Size: 108930 MD5sum: a9b38176e3e2c5632b844ccf0129082a SHA1: 4767daf46044cf8b381d9bc4c07b656fa1974354 SHA256: b78f3606d32ef2064f1199adf2be9ad95e1acae62757b06ae9e5fc5cb61fdafd SHA512: e0c0c62614af405690bfb482ac8f85bb3521ccff85f69e2351e4240b34465db6362769ea4916b416832ca305e0242a917d4d550c8f3376b2f46cc73be9080f9a Homepage: https://cran.r-project.org/package=ClustMC Description: CRAN Package 'ClustMC' (Cluster-Based Multiple Comparisons) Multiple comparison techniques are typically applied following an F test from an ANOVA to decide which means are significantly different from one another. As an alternative to traditional methods, cluster analysis can be performed to group the means of different treatments into non-overlapping clusters. Treatments in different groups are considered statistically different. Several approaches have been proposed, with varying clustering methods and cut-off criteria. This package implements cluster-based multiple comparisons tests and also provides a visual representation in the form of a dendrogram. Di Rienzo, J. A., Guzman, A. W., & Casanoves, F. (2002) . Bautista, M. G., Smith, D. W., & Steiner, R. L. (1997) . Package: r-cran-clustmd Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mclust, r-cran-reshape2, r-cran-mass, r-cran-msm, r-cran-mvtnorm, r-cran-truncnorm, r-cran-viridis Filename: pool/dists/noble/main/r-cran-clustmd_1.2.2-1.ca2404.1_all.deb Size: 170336 MD5sum: 9e8031ebf55daf7d8e8b3ee62af3cbf7 SHA1: b35b896a85c29d559cfa80c50ee64950f553a464 SHA256: e8623f96ad778a4a4d637f0dfaf1dc07aaf7a1dabbed7ba86f2360ff633a55a7 SHA512: e47b7dc2efe963ef38004d9072b75058d4ad3035f0f64ef120dbcb5c8fc8e32776c81925c323febe79c456beb93be9f2a7756ff34e1e503578f0de9377460a3a Homepage: https://cran.r-project.org/package=clustMD Description: CRAN Package 'clustMD' (Model Based Clustering for Mixed Data) Model-based clustering of mixed data (i.e. data which consist of continuous, binary, ordinal or nominal variables) using a parsimonious mixture of latent Gaussian variable models. Package: r-cran-clustmixtype Architecture: all Version: 0.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-tibble, r-cran-combinat, r-cran-dplyr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustmixtype_0.4-2-1.ca2404.1_all.deb Size: 222474 MD5sum: ab155d7c11be59959bc0265c1eda7d7c SHA1: d5a386dd06e4b9b5bc56eb254d549e335a5d644b SHA256: 3940c2e0e9b3e6a4a37e567f3f84e723772933e00fadb13fde8d29f30fa3a8d1 SHA512: 2a98bedf19bf16e7ad724692ba46742a3bcb16dc78acbb45fffdb1345375c5d297f1bc7b86d8dad26b88a11c383cc4fc11e67e7a5451aa9dee53098c817a1362 Homepage: https://cran.r-project.org/package=clustMixType Description: CRAN Package 'clustMixType' (k-Prototypes Clustering for Mixed Variable-Type Data) Functions to perform k-prototypes partitioning clustering for mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining and Knowledge Discovery 2, 283-304. Package: r-cran-clustnet Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bidag, r-cran-pcalg, r-bioc-rbgl, r-cran-clue, r-bioc-graph, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggraph, r-cran-ggpubr, r-cran-ggplot2, r-cran-reshape2, r-cran-car, r-cran-ks, r-cran-testthat Filename: pool/dists/noble/main/r-cran-clustnet_1.2.0-1.ca2404.1_all.deb Size: 178544 MD5sum: aa6f288062f4e205849f6c7818f19f1e SHA1: 30d37a953f503b5a3cd770b03293eb257d037d2d SHA256: 2167011a8ebb457128bc8ebd8732f94b6163b22c3b98a3f3fbe1c25bf252dffa SHA512: 1600fdcbaaf6bb4f87af1777ad66dff106c2e163f527024e6a96b6a9e8e3871eec9c415fffa224e4acff59cd35d7840c5a227a2d2541ef7d1c7df192ed705365 Homepage: https://cran.r-project.org/package=clustNet Description: CRAN Package 'clustNet' (Network-Based Clustering) Network-based clustering using a Bayesian network mixture model with optional covariate adjustment. Package: r-cran-clustofvar Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcamixdata Filename: pool/dists/noble/main/r-cran-clustofvar_1.2-1.ca2404.1_all.deb Size: 191664 MD5sum: b6aedb63935f1d34fcf7b5ae91d189db SHA1: d525c922ecec989fb70d531aa2a2af1c213bfbb9 SHA256: 96faf03f4aefda819e779675b7eea0c1743babb08565cb1e39299386ffd9622b SHA512: d6b961d34ea0f9e2621e35a789cba1d07e05232de7fb9fe4458cd30a418c9a437805afd84eb77c0ed381876a830fdc5578f1edb74fe846006c4f39cc7635e2eb Homepage: https://cran.r-project.org/package=ClustOfVar Description: CRAN Package 'ClustOfVar' (Clustering of Variables) Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both. Package: r-cran-clustorus Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2684 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bambi, r-cran-igraph, r-cran-purrr, r-cran-ggplot2, r-cran-rlang, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-clustorus_0.2.2-1.ca2404.1_all.deb Size: 1894336 MD5sum: a3d698f74c285334582f40b68e801c29 SHA1: e8b290ea1576fa01368c6962462b24f4a100799f SHA256: ddcd07038c373614db6308a1d39a2e1f368e772baa4c4ce2d6bb3ace1c67848b SHA512: e8cde62a55510cea7bf8c9c51d61dd0926b975b21b000862feb8f8abc1f69affb10e655edae5382fa19a4af602439373d17a1a8753b70b2481ae32deae973203 Homepage: https://cran.r-project.org/package=ClusTorus Description: CRAN Package 'ClusTorus' (Prediction and Clustering on the Torus by Conformal Prediction) Provides various tools of for clustering multivariate angular data on the torus. The package provides angular adaptations of usual clustering methods such as the k-means clustering, pairwise angular distances, which can be used as an input for distance-based clustering algorithms, and implements clustering based on the conformal prediction framework. Options for the conformal scores include scores based on a kernel density estimate, multivariate von Mises mixtures, and naive k-means clusters. Moreover, the package provides some basic data handling tools for angular data. Package: r-cran-clustra Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3563 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-mgcv, r-cran-mixsim Suggests: r-cran-haven, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clustra_0.2.1-1.ca2404.1_all.deb Size: 2834616 MD5sum: 72ea79aa3cdcae6d3f39d95b8278e2e6 SHA1: b781742d341620bacb9d4fd362d29d4b764da697 SHA256: 6d9e0ca534ccaa1ca1f4a45597c5d209064edbad3ed2db530647acab8f8f6854 SHA512: 3dc520880c3d30d9fd80fb0d674ed2f45438b38fd351a1ebd097c33b70ef04f2693682055ad57e5476cb0c83464a73bef572bca80438b1f0970098a3d5589ea9 Homepage: https://cran.r-project.org/package=clustra Description: CRAN Package 'clustra' (Clustering Longitudinal Trajectories) Clusters longitudinal trajectories over time (can be unequally spaced, unequal length time series and/or partially overlapping series) on a common time axis. Performs k-means clustering on a single continuous variable measured over time, where each mean is defined by a thin plate spline fit to all points in a cluster. Distance is MSE across trajectory points to cluster spline. Provides graphs of derived cluster splines, silhouette plots, and Adjusted Rand Index evaluations of the number of clusters. Scales well to large data with multicore parallelism available to speed computation. Package: r-cran-clustransition Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1044 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-flexclust Filename: pool/dists/noble/main/r-cran-clustransition_1.0-1.ca2404.1_all.deb Size: 976318 MD5sum: d3d2087319d3ab028af5bd5c589d1493 SHA1: d6eb04cab117a5c993dc445f42a3b9930c4324d3 SHA256: bc5b15c4ce95b9fbd0959340a27962024e38dab204c87e9cc21cd833fab91320 SHA512: 595bc1828283039733c61f250078ba57d4deea5d016d361738f121f6694d228bf8e39151eab1f22153bd1b61e4b93ae2f54639674ff9a05a69b8a424f26208ba Homepage: https://cran.r-project.org/package=clusTransition Description: CRAN Package 'clusTransition' (Monitor Changes in Cluster Solutions of Dynamic Datasets) Monitor and trace changes in clustering solutions of accumulating datasets at successive time points. The clusters can adopt External and Internal transition at succeeding time points. The External transitions comprise of Survived, Merged, Split, Disappeared, and newly Emerged candidates. In contrast, Internal transition includes changes in location and cohesion of the survived clusters. The package uses MONIC framework developed by Spiliopoulou, Ntoutsi, Theodoridis, and Schult (2006) . Package: r-cran-clustrd Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rarpack, r-cran-tibble, r-cran-corpcor, r-cran-ggally, r-cran-fpc, r-cran-cluster, r-cran-dplyr, r-cran-plyr, r-cran-ggrepel, r-cran-ca Filename: pool/dists/noble/main/r-cran-clustrd_1.4.0-1.ca2404.1_all.deb Size: 338176 MD5sum: 0c4b76dff4254c2c81b5d5098df4f5f7 SHA1: 3418a88705491a16ae070b4cdc42902aad57963e SHA256: 06e8df2ee0cc4bc0872fd2d09d22f8841f9266bedb56a31ce609a69e335f1137 SHA512: 237821019ab8979b8df692d46ab58e3c05317d677aba9cd910ff6fdda00ccad8bde39f21f146c4e204f4245bd2399d65149e012c21cf6597c56eae8f71f26535 Homepage: https://cran.r-project.org/package=clustrd Description: CRAN Package 'clustrd' (Methods for Joint Dimension Reduction and Clustering) A class of methods that combine dimension reduction and clustering of continuous, categorical or mixed-type data (Markos, Iodice D'Enza and van de Velden 2019; ). For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; ) and reduced K-means (De Soete and Carroll 1994; ); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; ), i-FCB (Iodice D'Enza and Palumbo 2013, ) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; ), which combine multiple correspondence analysis with K-means. For mixed-type data, it provides mixed Reduced K-means and mixed Factorial K-means (van de Velden, Iodice D'Enza and Markos 2019; ), which combine PCA for mixed-type data with K-means. Package: r-cran-clustree Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2462 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggraph, r-cran-checkmate, r-cran-igraph, r-cran-dplyr, r-cran-ggplot2, r-cran-viridis, r-cran-rlang, r-cran-tidygraph, r-cran-ggrepel Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-singlecellexperiment, r-cran-seurat, r-cran-covr, r-bioc-summarizedexperiment, r-cran-pkgdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-clustree_0.5.1-1.ca2404.1_all.deb Size: 1883608 MD5sum: 43fc00cb15a1784b0f8800561344966d SHA1: d78a723d894da0bdbc3406468d8cdd9e073c5302 SHA256: fba8cedfbe589958b0b09707f1730880f7a8fec51061ee858eb5ab2989d96e2c SHA512: 9c2dc1297c2ea4fd8b743c18e0d9ee856a9bab52a164d593d8263d394e4ed2a711d31f26c14db96f73a9389e80fa7676b1b9184e0b0a11573fd1467e32340289 Homepage: https://cran.r-project.org/package=clustree Description: CRAN Package 'clustree' (Visualise Clusterings at Different Resolutions) Deciding what resolution to use can be a difficult question when approaching a clustering analysis. One way to approach this problem is to look at how samples move as the number of clusters increases. This package allows you to produce clustering trees, a visualisation for interrogating clusterings as resolution increases. Package: r-cran-clustringr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-stringi, r-cran-stringr, r-cran-stringdist, r-cran-igraph, r-cran-assertthat, r-cran-forcats, r-cran-rlang, r-cran-tidygraph, r-cran-ggraph, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-clustringr_1.0-1.ca2404.1_all.deb Size: 417814 MD5sum: 7995661034aa75662fdacbed1363c650 SHA1: 8bf4d6da4f251f415ada02cbfcbb7df5bac784e0 SHA256: db448d67a67bb19818ef576e22476bee4b5b75328f6d444a31034900f3cbc871 SHA512: 4bfd168ee06aff8a32b899a369d8e05afdbe745b1044b69a403ad4e0dfd1c5f3e60c010000afb6bf60aa735c43e33879e177eef5dbbbe8dcf744bcf8e15fa2e4 Homepage: https://cran.r-project.org/package=clustringr Description: CRAN Package 'clustringr' (Cluster Strings by Edit-Distance) Returns an edit-distance based clusterization of an input vector of strings. Each cluster will contain a set of strings w/ small mutual edit-distance (e.g., Levenshtein, optimum-sequence-alignment, Damerau-Levenshtein), as computed by stringdist::stringdist(). The set of all mutual edit-distances is then used by graph algorithms (from package 'igraph') to single out subsets of high connectivity. Package: r-cran-clustshiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rmarkdown, r-cran-klar, r-cran-mass, r-cran-dplyr, r-cran-psycho, r-cran-cluster Filename: pool/dists/noble/main/r-cran-clustshiny_0.1.0-1.ca2404.1_all.deb Size: 70932 MD5sum: 1c2f80a4049cdfc4d4f3e94ff1aa3083 SHA1: 3f88fb7154883e9438f1ec795384845219eb4ae3 SHA256: 588d50a0a30026eb90db0927b4e139f4ee9b67dd0c2fe4716ab2406cfd7a7c6c SHA512: c42bbfadaf0f5d566efa05db65f697ba2dd8bb67af7972253e268c4ba1c15d42858530dfe64a6d6f36685dc6884feaf8831ea4fe09d6eab2a3e42284851d3713 Homepage: https://cran.r-project.org/package=CLUSTShiny Description: CRAN Package 'CLUSTShiny' (Interactive Document for Working with Cluster Analysis) An interactive document on the topic of cluster analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at . Package: r-cran-clustvarsel Architecture: all Version: 2.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 724 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust, r-cran-matrix, r-cran-bma, r-cran-foreach, r-cran-iterators Suggests: r-cran-mass, r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-clustvarsel_2.3.5-1.ca2404.1_all.deb Size: 502814 MD5sum: 671d619a2d5a7cbb3a853eff76049c77 SHA1: b52253816b05349c610e13be61d0cab5a39409da SHA256: 7a51342a88555adacb4fa2e4de9d3d349b7b70a0edb236907025477ff38ecbfd SHA512: 09c0ee6e294c8d1c8ca5f79c884f88ef633091a63368e406b830830a0045f0aa597661a8056dc682635bc5c1545cbb3039e39fdef190b7fa02c6060709c6ffa9 Homepage: https://cran.r-project.org/package=clustvarsel Description: CRAN Package 'clustvarsel' (Variable Selection for Gaussian Model-Based Clustering) Variable selection for Gaussian model-based clustering as implemented in the 'mclust' package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting 'mclust' models. By default the algorithm uses a sequential search, but parallelisation is also available. Package: r-cran-clvalid Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-class Suggests: r-bioc-biobase, r-bioc-annotate, r-bioc-go.db, r-bioc-moe430a.db, r-cran-rankaggreg, r-cran-kohonen, r-cran-mclust Filename: pool/dists/noble/main/r-cran-clvalid_0.7-1.ca2404.1_all.deb Size: 606454 MD5sum: c79e58cfa96a20ba5d28577df24fef15 SHA1: 53544a382d112ed8e71e3640fe135029fc0eb47a SHA256: 41f55022c361e2a522214d9ee74c36c6a5f3f1b59726383d414f8282cc40ab5d SHA512: 0bbf51a32c6b3d2db5fe6c8d474b64863b63d83ce27a91cefadd91fd7f05bd8d282824e4a911a7fbadc9604029b10429635f16bab3b1098190bae3174def48d0 Homepage: https://cran.r-project.org/package=clValid Description: CRAN Package 'clValid' (Validation of Clustering Results) Statistical and biological validation of clustering results. This package implements Dunn Index, Silhouette, Connectivity, Stability, BHI and BSI. Further information can be found in Brock, G et al. (2008) . Package: r-cran-cmaes Architecture: all Version: 1.0-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-cmaes_1.0-12-1.ca2404.1_all.deb Size: 43110 MD5sum: fd1d2ea982d98ce2c4abd1d9f6e69668 SHA1: 0fb99c3e0a5c03101bea773dc06fe3c18f908cc6 SHA256: 6e80c6af9b803376d632be9a06c28c7e03e687dc1967e617821f3cbca0ce3f18 SHA512: 2ea3694d6a87f8e2befb7c236ad926332852dd34a123c7f822c54bfd0074251fa098b52e31f3a280c021b42a943f308d988f41e07986910f3747ba60acab9623 Homepage: https://cran.r-project.org/package=cmaes Description: CRAN Package 'cmaes' (Covariance Matrix Adapting Evolutionary Strategy) Single objective optimization using a CMA-ES. Package: r-cran-cmaesr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-paramhelpers, r-cran-bbmisc, r-cran-checkmate, r-cran-smoof, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cmaesr_1.0.3-1.ca2404.1_all.deb Size: 83518 MD5sum: 823fde703fc8a09b4798a4567f036f37 SHA1: 36d299b8542ebabcecfa5cee86e8911ac4915f17 SHA256: 320f0db7b82b05e146a6e109d10f302840399e75a506c617876c25f83df73756 SHA512: 7c3c3dd322b89ee49dbd2bd44180b71c574b48fbe5320613ba666cf081e332739b95b3a1398ff5cbb578a49581a00a1bfa40578315f2ea149b07e7df5311e4b5 Homepage: https://cran.r-project.org/package=cmaesr Description: CRAN Package 'cmaesr' (Covariance Matrix Adaptation Evolution Strategy) Pure R implementation of the Covariance Matrix Adaptation - Evolution Strategy (CMA-ES) with optional restarts (IPOP-CMA-ES). Package: r-cran-cmahalanobis Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-gridextra, r-cran-matrixstats Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cmahalanobis_1.0.0-1.ca2404.1_all.deb Size: 303792 MD5sum: fa0cbb19e95b1f528d505d0278d7debd SHA1: 35dcbb5365672d84340920070f45b9ad7b0e3284 SHA256: 9c9413401c22dbcf9b2201c7b04116d393248ba284b5d9a81887b7fb16596bee SHA512: 11421f67b2f30f50b9d9f4d87691e5c5e35157dd01cd1c6ff4c2486df1ce321100ed3559eb1eb26e6fa0f5d10843cb83b52829e0eb0dbcbbc6eb4a4a28eed120 Homepage: https://cran.r-project.org/package=cmahalanobis Description: CRAN Package 'cmahalanobis' (Calculate Distance Measures for DataFrames) It provides functions that calculate Mahalanobis distance, Euclidean distance, Manhattan distance, Chebyshev distance, Hamming distance, Canberra distance, Minkowski dissimilarity (distance defined for p >= 1), Cosine dissimilarity, Bhattacharyya dissimilarity, Jaccard distance, Hellinger distance, Bray-Curtis dissimilarity, Sorensen-Dice dissimilarity between each pair of species in a list of data frames. These statistics are fundamental in various fields, such as cluster analysis, classification, and other applications of machine learning and data mining, where assessing similarity or dissimilarity between data is crucial. The package is designed to be flexible and easily integrated into data analysis workflows, providing reliable tools for evaluating distances in multidimensional contexts. Package: r-cran-cmanalysis Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-cran-ggplot2, r-cran-factoextra, r-cran-clue, r-cran-igraph, r-cran-stringr, r-cran-pheatmap Filename: pool/dists/noble/main/r-cran-cmanalysis_1.0.3-1.ca2404.1_all.deb Size: 107304 MD5sum: a5d84cf5afc328e61bc9a6f84c566717 SHA1: 3f5210fb72951a4eff883a5062dc40a7137e8126 SHA256: 41487cbc98436e10b1d7a080ae0f1a3bce473cc6cb976fbc650c7e854369699b SHA512: 9426974ce1d163fe2197d61b9f776e19a3a0765716b9d039fe14556e51bfc54ca3d27cfb6397d09b70773f111612785d25c829a21e2531183413afe3462f4149 Homepage: https://cran.r-project.org/package=cmAnalysis Description: CRAN Package 'cmAnalysis' (Process and Visualise Concept Mapping Data) Concept maps are versatile tools used across disciplines to enhance understanding, teaching, brainstorming, and information organization. This package provides functions for processing and visualizing concept mapping data, involving the sequential use of cluster analysis (for sorting participants and statements), multidimensional scaling (for positioning statements in a conceptual space), and visualization techniques, including point cluster maps and dendrograms. The methodology and its validity are discussed in Kampen, J.K., Hageman, J.A., Breuer, M., & Tobi, H. (2025). "The validity of concept mapping: let's call a spade a spade." Qual Quant. . Package: r-cran-cmapviz Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1064 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-ggplot2, r-cran-dplyr, r-cran-scales, r-cran-reshape2, r-cran-stringr Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-cmapviz_0.1.0-1.ca2404.1_all.deb Size: 161940 MD5sum: fe890c673778d65d2df02839539c3202 SHA1: 5e658def24234ab726cce7c700e4371199c6dd0b SHA256: d139d2e2c8a90588d9ffac75f188bdc01c65ff630ded8cd2895e86c81b27f0cb SHA512: 28d3b10c858580a8a6aa70d8c6759093e337c8adeeb01d2db3427a517921bf4124c7d02608d560d22509db1abb8245ee5f79469e332b35cf366851bb9a247304 Homepage: https://cran.r-project.org/package=CMapViz Description: CRAN Package 'CMapViz' (Representation Tool For Output Of Connectivity Map (CMap)Analysis) Automatically displays graphical visualization for exported data table (permutated results) from Connectivity Map (CMap) (2006) . It allows the representation of the statistics (p-value and enrichment) according to each cell lines in the form of a bubble plot. Package: r-cran-cmars Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 514 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-earth, r-cran-rmosek, r-cran-stringr, r-cran-matrix, r-cran-auc, r-cran-ryacas, r-cran-rocr, r-cran-mpv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cmars_0.1.4-1.ca2404.1_all.deb Size: 461486 MD5sum: 8a956cf92378e5de4a2afdc1351ac864 SHA1: 576d98a9d0af6fc687ae1a0460b650f726a60cac SHA256: 391e6e959fa485261be3da8d4b1d00197ef0e03812b5f957b45b0c0af56fd39a SHA512: fd69bc8b8e788af8031486f79a7f29e250ca0887d83f655771da4d55cdca53e1dc1ffceca13230e559b72e53e5e17064509670c47cd7f4b8c33b095dd7a801c6 Homepage: https://cran.r-project.org/package=cmaRs Description: CRAN Package 'cmaRs' (Implementation of the Conic Multivariate Adaptive RegressionSplines in R) An implementation of 'Conic Multivariate Adaptive Regression Splines (CMARS)' in R. See Weber et al. (2011) CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization, . It constructs models by using the terms obtained from the forward step of MARS and then estimates parameters by using 'Tikhonov' regularization and conic quadratic optimization. It is possible to construct models for prediction and binary classification. It provides performance measures for the model developed. The package needs the optimisation software 'MOSEK' to construct the models. Please follow the instructions in 'Rmosek' for the installation. Package: r-cran-cmatching Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matching, r-cran-lmtest, r-cran-multiwayvcov, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-cmatching_2.4-1.ca2404.1_all.deb Size: 86512 MD5sum: 2505292dfbe1c37a780c57f3b6c50a20 SHA1: 11242aa4b5d51fac34a3b10a3d6120bd0c8e73e3 SHA256: 422587af0866d61ebdb8925da9ec19ab1bb4d12d217473b1aa02f20ca6876823 SHA512: 65a0508d046a69cf90840aac08e621c9196d92a2629cca1538dd2836260c3882f26549e98069e0f0df9aeaa79b68fdec9a5a1f01f34a340fa8ca171ff925a565 Homepage: https://cran.r-project.org/package=CMatching Description: CRAN Package 'CMatching' (Matching Algorithms for Causal Inference with Clustered Data) Provides functions to perform matching algorithms for causal inference with clustered data, as described in B. Arpino and M. Cannas (2016) . Pure within-cluster and preferential within-cluster matching are implemented. Both algorithms provide causal estimates with cluster-adjusted estimates of standard errors. Package: r-cran-cmce Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cmce_0.1.0-1.ca2404.1_all.deb Size: 92994 MD5sum: 3063b092b214ca990cb1892c44f3b14c SHA1: 64043ee685aa8889ce219fa35afd50f764a7d4f1 SHA256: f1eb6b2d28466dd1cef5e13d57460f83aff01cd9404e1d29b5aaf09b7177c70a SHA512: 794361546d44da1c36790a331c6902d6e93799e0182f03269e0d25b52bdb3019cb325e01617bfa9110ce21a9837843edbc187984fd03f986e9f53a2ce3605fb6 Homepage: https://cran.r-project.org/package=cmce Description: CRAN Package 'cmce' (Computer Model Calibration for Deterministic and StochasticSimulators) Implements the Bayesian calibration model described in Pratola and Chkrebtii (2018) for stochastic and deterministic simulators. Additive and multiplicative discrepancy models are currently supported. See for more information and examples. 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(2015) . Provides a wide range of pre, inter, and post-processing options when working with cartridge case scan data and their associated comparisons. See the cmcR package website for more details and examples. Package: r-cran-cmdfun Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-purrr, r-cran-r.utils, r-cran-rlang, r-cran-testthat, r-cran-usethis Suggests: r-cran-cli, r-cran-covr, r-cran-knitr, r-cran-processx, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cmdfun_1.0.2-1.ca2404.1_all.deb Size: 87608 MD5sum: 38dab8e50880dc83d70249d41e972b6f SHA1: 7fd1db8f226f82efe55a6a5b72f43e357faebcb2 SHA256: 625dd9c818859be24901d5fc1ccab145a3fa20764dec984c8e94bf3be8d0b644 SHA512: d969d6d6a38753a85e72565207ab255ad05edf965973c3a637152dd38d041863dd2ae5dd86d9e151202ac40294c7ff23005680b10d7e3059324140060e06aacd Homepage: https://cran.r-project.org/package=cmdfun Description: CRAN Package 'cmdfun' (Framework for Building Interfaces to Shell Commands) Writing interfaces to command line software is cumbersome. 'cmdfun' provides a framework for building function calls to seamlessly interface with shell commands by allowing lazy evaluation of command line arguments. 'cmdfun' also provides methods for handling user-specific paths to tool installs or secrets like API keys. Its focus is to equally serve package builders who wish to wrap command line software, and to help analysts stay inside R when they might usually leave to execute non-R software. Package: r-cran-cmfsurrogate Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-cmfsurrogate_1.1-1.ca2404.1_all.deb Size: 50520 MD5sum: b70d9f45b8b95be7b8522321eed7a0e9 SHA1: 0445704b06df86bd1373a404c0d21ca7f61f69c7 SHA256: ffb5a696a6b1dbeeda1719b7daad1af84c08db6a4fcb48a44590a1167454b47d SHA512: 39f671752e10c5041f91dce7ecda3415f020e309b5ead9b5b3bbedf08560e17c0df3223b23f3006d144de9f2ff97d075a6a6966306c0090cd460f08e48c5461c Homepage: https://cran.r-project.org/package=CMFsurrogate Description: CRAN Package 'CMFsurrogate' (Calibrated Model Fusion Approach to Combine Surrogate Markers) Uses a calibrated model fusion approach to optimally combine multiple surrogate markers. Specifically, two initial estimates of optimal composite scores of the markers are obtained; the optimal calibrated combination of the two estimated scores is then constructed which ensures both validity of the final combined score and optimality with respect to the proportion of treatment effect explained (PTE) by the final combined score. The primary function, pte.estimate.multiple(), estimates the PTE of the identified combination of multiple surrogate markers. Details are described in Wang et al (2022) . A tutorial for the package is available at and a Shiny App is available at . Package: r-cran-cmgnd Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppalgos, r-cran-lubridate, r-cran-gnorm, r-cran-purrr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-cmgnd_0.1.1-1.ca2404.1_all.deb Size: 192854 MD5sum: 537515e108d41550a3de6661ddab678c SHA1: df126fceefbefb4c8ae3bf574be753cacddcaf26 SHA256: 49425074ff8b385cb75c26251b75f46500c3d03752780b04e575e9404572bcb7 SHA512: 96ea7d75b73f067275e4bf1c4823b844e3a20b2c79f19997015b18c47e40ce8f81ef2688f3d85788edf345c70ab313f754744765945b115b986f8f20b9cde3f9 Homepage: https://cran.r-project.org/package=cmgnd Description: CRAN Package 'cmgnd' (Constrained Mixture of Generalized Normal Distributions) The 'cmgnd' implements the constrained mixture of generalized normal distributions model, a flexible statistical framework for modelling univariate data exhibiting non-normal features such as skewness, multi-modality, and heavy tails. By imposing constraints on model parameters, the 'cmgnd' reduces estimation complexity while maintaining high descriptive power, offering an efficient solution in the presence of distributional irregularities. For more details see Duttilo and Gattone (2025) and Duttilo et al (2025) . Package: r-cran-cmhc Architecture: all Version: 0.2.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-digest, r-cran-httr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-rlang, r-cran-aws.s3 Suggests: r-cran-knitr, r-cran-scales, r-cran-cancensus, r-cran-ggplot2, r-cran-tidyr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/noble/main/r-cran-cmhc_0.2.10-1.ca2404.1_all.deb Size: 631358 MD5sum: a6c68092cd717a49acded14efd1e34ee SHA1: 8980b29eeec37c1218d0d135145b51feb1aa41c7 SHA256: 114ee2b28361046f3576d9fc28d14e225a866d66bd72ab2ea1d50426591cafae SHA512: 1304827d47042b490c886f96c8f3c6d697ffbef79ac3ab6509b56d80978009ca3485c08edf4271d5fae0f2cf4cc1a1b92ae04b37278cd64cbf0ed68f8aa22b36 Homepage: https://cran.r-project.org/package=cmhc Description: CRAN Package 'cmhc' (Access, Retrieve, and Work with CMHC Data) Wrapper around the Canadian Mortgage and Housing Corporation (CMHC) web interface. It enables programmatic and reproducible access to a wide variety of housing data from CMHC. Package: r-cran-cmhnpa Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-car Filename: pool/dists/noble/main/r-cran-cmhnpa_1.1.1-1.ca2404.1_all.deb Size: 306246 MD5sum: 6810756d7618b47212e0cb8b63ef2afd SHA1: 3de610c9b1225ed2f8e5004d3398d080eaf8f187 SHA256: d2b2edbee6e9cbad4fda573343b6c3eea57b09c7bc5dcafda456707d99eef4b4 SHA512: 895fa6f1d7a8610d172cc6a8e2003d90f8625c9a6e7823ac312274206f3d739b560fa51801d60b12e2f256502c0b23b8b8b6e39e62c6b6e901c6bc73ed9f4b99 Homepage: https://cran.r-project.org/package=CMHNPA Description: CRAN Package 'CMHNPA' (Cochran-Mantel-Haenszel and Nonparametric ANOVA) Cochran-Mantel-Haenszel methods (Cochran (1954) ; Mantel and Haenszel (1959) ; Landis et al. (1978) ) are a suite of tests applicable to categorical data. 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Package: r-cran-cmshiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rmarkdown, r-cran-shinymatrix, r-cran-matrix, r-cran-epitools, r-cran-caret, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-cmshiny_0.1.0-1.ca2404.1_all.deb Size: 70134 MD5sum: 5898053b7381290644b7fc5199fc6640 SHA1: b78cd5feff7dccd232184eed15f6f788ceb01d93 SHA256: 37e0c2aa3d5841aeca947e7e33823e540e87d4c294788f7bc789961f4c796ebe SHA512: 460ce607c30084c14cb1cdba1cdf356852c63d2e71db5a792ed52d53f6558375b29055cef6b416a7a3de23ee0b50d4ce7f1d77d4318b8b1285f86a8a8a62815b Homepage: https://cran.r-project.org/package=CMShiny Description: CRAN Package 'CMShiny' (Interactive Document for Working with Confusion Matrix) An interactive document on the topic of confusion matrix analysis using 'rmarkdown' and 'shiny' packages. 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Package: r-cran-cnmap Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tibble Filename: pool/dists/noble/main/r-cran-cnmap_0.1.2-1.ca2404.1_all.deb Size: 425296 MD5sum: 4e9508fd1b0dd5935d1d58abe8ebc2b5 SHA1: 91bba6d4c1e907deb1335d00a8f1020e9b09211b SHA256: 1af2885edd59ba75efa89ad1848d77d14a3ad9450ef6ededa18ed2c2775aa80f SHA512: 74c853bade3407de5cea479a7581961ce70a0cc24df28e071c9085de51efdbea43b23c04311f73a674cf53903ae176ad0c1a33fb355c42e33eee7eaab6d47841 Homepage: https://cran.r-project.org/package=cnmap Description: CRAN Package 'cnmap' (China Map Data from AutoNavi Map) According to the codes and names of county-level and above administrative divisions released in 2022 by the Ministry of Civil Affairs of the People's Republic of China, the online vector map files were retrieved from the website (available at: ). This study was supported by the National Natural Science Foundation of China (NSFC, Grant No. 42205177). Package: r-cran-cnorm Architecture: all Version: 3.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2060 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-leaps Suggests: r-cran-dt, r-cran-haven, r-cran-foreign, r-cran-knitr, r-cran-markdown, r-cran-readxl, r-cran-rmarkdown, r-cran-shiny, r-cran-shinycssloaders, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cnorm_3.5.4-1.ca2404.1_all.deb Size: 1504336 MD5sum: a087d9cf9aa622eb300b73d368e33594 SHA1: 9560c2a62ba588a5ad26fea1d45f01c5eee57560 SHA256: e95ee4428c6d37a198f923daa4aa3ffd7a4fec14c56189e15ebc3a64fc7e0845 SHA512: b6b206cff4d4c71f7dd25bd36d3fe242d7e02accf759fcbfcc614370e00b0865d9194099c62b2feef811bfb0b74f4d123d9f2e921c8bee56daea1a7a6c6f7ef7 Homepage: https://cran.r-project.org/package=cNORM Description: CRAN Package 'cNORM' (Continuous Norming) A comprehensive toolkit for generating continuous test norms in psychometrics and biometrics, and analyzing model fit. The package offers both distribution-free modeling using Taylor polynomials and parametric modeling using the beta-binomial and the 'Sinh-Arcsinh' distribution. Originally developed for achievement tests, it is applicable to a wide range of mental, physical, or other test scores dependent on continuous or discrete explanatory variables. The package provides several advantages: It minimizes deviations from representativeness in subsamples, interpolates between discrete levels of explanatory variables, and significantly reduces the required sample size compared to conventional norming per age group. cNORM enables graphical and analytical evaluation of model fit, accommodates a wide range of scales including those with negative and descending values, and even supports conventional norming. It generates norm tables including confidence intervals. It also includes methods for addressing representativeness issues through Iterative Proportional Fitting. Based on Lenhard et al. (2016) , Lenhard et al. (2019) , Lenhard and Lenhard (2021) and Gary et al. (2023) . Package: r-cran-cnprep Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4077 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust, r-cran-rlecuyer Filename: pool/dists/noble/main/r-cran-cnprep_2.2-1.ca2404.1_all.deb Size: 4140468 MD5sum: e0031a76d60f5859069a99af3a42c2ae SHA1: 462ed9fb14f77994b8d7f6f738d2712010a4a415 SHA256: 07af7497214df4ba951f19581f760a4b0596554a88ed9a2969794d5a70f6b9ff SHA512: aa7c8946e7e879dc01378ebf2a2abfab0b42d38bd489abe3e8bfe680d9f02296a6455adb47c4718cc13ad7e686bd4e22c48180269caa00dc14ab00f926bd378f Homepage: https://cran.r-project.org/package=CNprep Description: CRAN Package 'CNprep' (Pre-Process DNA Copy Number (CN) Data for Detection of CN Events) DNA copy number data evaluation using both their initial form (copy number as a noisy function of genomic position) and their approximation by a piecewise-constant function (segmentation), for the purpose of identifying genomic regions where the copy number differs from the norm. Package: r-cran-cnps Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-e1071 Filename: pool/dists/noble/main/r-cran-cnps_1.0.0-1.ca2404.1_all.deb Size: 128014 MD5sum: e899be98f20465e721c8b9650ff6bb6b SHA1: 275e4d39b88bf6af0d67e5bbabdfbfaafb4e29d4 SHA256: aae117f6dd756932be9a9f5fed2a77a5b48c8c5ecbbfe1b5f72838e41fc8abee SHA512: 51f7af4dd15fc51dac24177a5dd0207c570d2ab4669e574c6419aa2785e44dff21e03e42148b6c9e1dcab27be5c1962cb5f1096d6e25ca808aaf82f6b5ce3a17 Homepage: https://cran.r-project.org/package=CNPS Description: CRAN Package 'CNPS' (Nonparametric Statistics) We unify various nonparametric hypothesis testing problems in a framework of permutation testing, enabling hypothesis testing on multi-sample, multidimensional data and contingency tables. Most of the functions available in the R environment to implement permutation tests are single functions constructed for specific test problems; to facilitate the use of the package, the package encapsulates similar tests in a categorized manner, greatly improving ease of use. We will all provide functions for self-selected permutation scoring methods and self-selected p-value calculation methods (asymptotic, exact, and sampling). For two-sample tests, we will provide mean tests and estimate drift sizes; we will provide tests on variance; we will provide paired-sample tests; we will provide correlation coefficient tests under three measures. For multi-sample problems, we will provide both ordinary and ordered alternative test problems. For multidimensional data, we will implement multivariate means (including ordered alternatives) and multivariate pairwise tests based on four statistics; the components with significant differences are also calculated. For contingency tables, we will perform permutation chi-square test or ordered alternative. Package: r-cran-cnsigs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 955 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nmf, r-cran-doparallel, r-cran-foreach, r-cran-flexmix, r-cran-limsolve, r-cran-ggplot2, r-cran-snow, r-cran-cowplot, r-cran-pheatmap, r-cran-rcolorbrewer, r-cran-viridislite, r-cran-colorspace Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cnsigs_0.1.0-1.ca2404.1_all.deb Size: 817734 MD5sum: 5dff0b3de3e771b65c7cff438ba202f7 SHA1: 6c95f0927974959680c22fa8281cf1e5983547ae SHA256: 42feb186f64076981de7c4eb6c92f10ce3630de08f163b517dca03c6a96313d4 SHA512: dc8ca22ddbc10909f44781c0867abd2170119ef3c8e8d7ec0a142a94577408bba2b1e2f53a61687aa104687494aaacfad7d851eadc0897687dc471d97438d2a0 Homepage: https://cran.r-project.org/package=CNSigs Description: CRAN Package 'CNSigs' (Analysis of Copy Number Signatures) A workflow to generate and analyze signatures based on copy number data using non-negative matrix factorization (NMF) in an approach similar to that used in mutational signatures. It can be used to extract features from Copy number segment data and use that to find a subset of copy number signatures which can be further used to correlate with other relevant data. For more on 'NMF' see Gaujoux (2013) . 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Creates a "CNV profile curve" to represent an individual’s CNV events across a genomic region so to capture variations in CNV length and dosage. When evaluating association, the CNV profile curve is directly used as a predictor in the regression model, avoiding the need to predefine CNV loci. CNV profile regression estimates CNV effects at each genome position, making the results comparable across different studies. The penalization encourages sparsity in variable selection with a Lasso penalty and encourages effect smoothness between consecutive CNV events with a weighted fusion penalty, where the weight controls the level of smoothing between adjacent CNVs. For more details, see Si (2024) . Package: r-cran-cnvscope Architecture: all Version: 3.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-reshape2, r-cran-magrittr, r-cran-jointseg, r-cran-shiny, r-cran-rcurl, r-cran-foreach, r-bioc-genomicinteractions, r-cran-matrix, r-cran-openimager, r-bioc-biomart, r-cran-matrixstats, r-cran-plyr, r-cran-data.table, r-cran-dplyr, r-cran-doparallel, r-cran-stringr, r-bioc-rtracklayer, r-cran-hmisc Suggests: r-cran-knitr, r-cran-remotes, r-cran-pwr, r-bioc-complexheatmap, r-cran-rmarkdown, r-cran-igraph, r-cran-visnetwork, r-cran-circlize, r-cran-plotly, r-bioc-interactionset, r-bioc-genomicranges, r-bioc-genomicfeatures, r-bioc-iranges, r-cran-rslurm, r-cran-shinythemes, r-cran-shinycssloaders, r-cran-dt, r-cran-logging, r-cran-heatmaply, r-bioc-s4vectors, r-cran-biocmanager, r-cran-shinyjs, r-cran-htmltools, r-cran-htmlwidgets, r-bioc-genomeinfodb, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-tibble, r-cran-smoothie Filename: pool/dists/noble/main/r-cran-cnvscope_3.7.2-1.ca2404.1_all.deb Size: 2896010 MD5sum: 49517755c22b0f7d8b54d638758c7d6f SHA1: ada49e1ae0aae5f8eeb58b30ed53f39d692f61f5 SHA256: 855713b21312d0c4507872f1e295c3e3a285f990fffe4d5ccde34e58d18dd6b0 SHA512: e415b75b184e5734e82124e2ba9d85c1935df320f1171808dc8b771981b17b72e02d0344e099a5a4ac34bc490cacd9d7436d679892ec0405581b74c0ac684ffe Homepage: https://cran.r-project.org/package=CNVScope Description: CRAN Package 'CNVScope' (A Versatile Toolkit for Copy Number Variation Relationship DataAnalysis and Visualization) Provides the ability to create interaction maps, discover CNV map domains (edges), gene annotate interactions, and create interactive visualizations of these CNV interaction maps. Package: r-cran-coalitions Architecture: all Version: 0.6.27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-gtools, r-cran-rvest, r-cran-xml2, r-cran-rlang, r-cran-magrittr, r-cran-lubridate, r-cran-stringr, r-cran-tidyr, r-cran-purrr, r-cran-dplyr, r-cran-ggplot2, r-cran-tibble Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-coalitions_0.6.27-1.ca2404.1_all.deb Size: 178724 MD5sum: ac4016e4080ab0ae245fe726a7d44483 SHA1: 9e7cbd420a900d5382d600c8188604288979d1f2 SHA256: 3a5e85bb00eee5bddd528e2e34c99665e6f1f85e2c046c6cc0574c687c72296e SHA512: 1ea67ded333a51ea628dc40748df24c612a3819fdf3d227a99b783a969267020a437aee71d8c70de6d1baa6d0174406960be5afd876450a1b9ce73adaf5c8551 Homepage: https://cran.r-project.org/package=coalitions Description: CRAN Package 'coalitions' (Bayesian "Now-Cast" Estimation of Event Probabilities inMulti-Party Democracies) An implementation of a Bayesian framework for the opinion poll based estimation of event probabilities in multi-party electoral systems (Bender and Bauer (2018) ). Package: r-cran-coarsedatatools Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack Suggests: r-cran-markdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-coarsedatatools_0.7.2-1.ca2404.1_all.deb Size: 246648 MD5sum: 26bbcbc30aaa2ede38b59d5055d24dff SHA1: 0d6bb1a84ed568fd213550524a186996a8c53021 SHA256: 31ab474594c6448f196acd4275e66e37eb631268e2acfa6b300a1d54af497335 SHA512: bc6e08efce537ef7ddbf04d8e9967ff99c52d13fefd8b09ea4761196f9220d5abea3249ad208195ff223e6059dd354a2a77e0b0c89cb11efe3dffbee6a6ca250 Homepage: https://cran.r-project.org/package=coarseDataTools Description: CRAN Package 'coarseDataTools' (Analysis of Coarsely Observed Data) Functions to analyze coarse data. Specifically, it contains functions to (1) fit parametric accelerated failure time models to interval-censored survival time data, and (2) estimate the case-fatality ratio in scenarios with under-reporting. This package's development was motivated by applications to infectious disease: in particular, problems with estimating the incubation period and the case fatality ratio of a given disease. Sample data files are included in the package. See Reich et al. (2009) , Reich et al. (2012) , and Lessler et al. (2009) . Package: r-cran-coastlinefd Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-tidyr, r-cran-fields, r-cran-readxl, r-cran-writexl, r-cran-ggplot2, r-cran-progress, r-cran-sfheaders Filename: pool/dists/noble/main/r-cran-coastlinefd_1.1.2-1.ca2404.1_all.deb Size: 983104 MD5sum: ab82828d306d2bc5c05853a712f4c369 SHA1: 3c801b00494bf863aa10bbc07df434dee514f6c4 SHA256: f55d8971c1c198d7f139e87e6f35fa212f1e42ecd92acc61b1abcad07598cef7 SHA512: b1849658663069dfce9ccb07b245a76d80f9bd892760f09ebee80cafcf775637699d323df7ad53e213a48a7dd9470fb793ec2e72f5b1c4d73b731ec043c3f000 Homepage: https://cran.r-project.org/package=CoastlineFD Description: CRAN Package 'CoastlineFD' (Calculation of the Fractal Dimension of a Coastline) Calculating the fractal dimension of a coastline using the boxes and dividers methods. Package: r-cran-coat Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit Suggests: r-cran-methcomp Filename: pool/dists/noble/main/r-cran-coat_0.2.2-1.ca2404.1_all.deb Size: 63776 MD5sum: 7836a6dd995ba4bad28ad5660ef515fa SHA1: 09893534cba74d80dac1d4f7b01c170f6fb1e75b SHA256: 5d60c65d323d5a5c148b1058fd40afb5fa31441164fbdadde51f4d6fdffe61fb SHA512: 3958aa31a81e68a8fdb0f1b5afbe1b08cbc348e53fe24f8b006b2f433d3f293944773986fff530eb7c307a4c700f76cae8a8ee06c00bbdb09aa8d9817239f592 Homepage: https://cran.r-project.org/package=coat Description: CRAN Package 'coat' (Conditional Method Agreement Trees (COAT)) Agreement of continuously scaled measurements made by two techniques, devices or methods is usually evaluated by the well-established Bland-Altman analysis or plot. Conditional method agreement trees (COAT), proposed by Karapetyan, Zeileis, Henriksen, and Hapfelmeier (2025) , embed the Bland-Altman analysis in the framework of recursive partitioning to explore heterogeneous method agreement in dependence of covariates. COAT can also be used to perform a Bland-Altman test for differences in method agreement. Package: r-cran-cobalt Architecture: all Version: 4.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3885 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gtable, r-cran-gridextra, r-cran-chk, r-cran-rlang, r-cran-cli Suggests: r-cran-matchit, r-cran-weightit, r-cran-twang, r-cran-twangcontinuous, r-cran-matching, r-cran-optmatch, r-cran-ebal, r-cran-cbps, r-cran-optweight, r-cran-mice, r-cran-matchthem, r-cran-cem, r-cran-sbw, r-cran-gbm, r-cran-brglm2, r-cran-caret, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cobalt_4.6.2-1.ca2404.1_all.deb Size: 2750572 MD5sum: 8b30309ba18bae53c40b83141504a4a2 SHA1: b2d2506591cb76c1cc500a70ab67078db62411cc SHA256: 0a949e26729ae5be8a9b48ba25c84d37c87f316d52792dd35cd74d6451c008ef SHA512: ef0d82e6300dd23a6c7f36835e94025f06ca1fe9aca3d8dda5397d412e0dc90a28a45290dc0f8b5fcd0601e1349f9964f910225f66c88c52e93f2f693e63d704 Homepage: https://cran.r-project.org/package=cobalt Description: CRAN Package 'cobalt' (Covariate Balance Tables and Plots) Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'WeightIt', 'MatchThem', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'cem', 'sbw', and 'designmatch' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with multi-category, continuous, or longitudinal treatments. Package: r-cran-cobenrich Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmvtnorm Filename: pool/dists/noble/main/r-cran-cobenrich_1.0.1-1.ca2404.1_all.deb Size: 27754 MD5sum: cb9b9298c3590738787fc53590a75d89 SHA1: 41e6f02eb3667f3f5e7f73b1cd648b1ccfa07734 SHA256: 41906e485e3e3c6edd65032bd46cb7ce99acd02dd03109c607f6464036e3eff1 SHA512: 8cae78669a004f1e95fec9b5e111de795616f336a68206df4816dd044227041ca1cd1c599946eefb5be23e92298f1f8072aa53253a106bc3b1222aa762f12ac3 Homepage: https://cran.r-project.org/package=cobenrich Description: CRAN Package 'cobenrich' (Using Multiple Continuous Biomarkers for Patient Enrichment inTwo-Stage Clinical Designs) Enrichment strategies play a critical role in modern clinical trial design, especially as precision medicine advances the focus on patient-specific efficacy. Recent developments in enrichment design have introduced biomarker randomness and accounted for the correlation structure between treatment effect and biomarker, resulting in a two-stage threshold enrichment design. We propose novel two-stage enrichment designs capable of handling two or more continuous biomarkers. See Zhang, F. and Gou, J. (2025). Using multiple biomarkers for patient enrichment in two-stage clinical designs. Technical Report. Package: r-cran-cobiclust Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-cluster, r-cran-testthat Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-cobiclust_0.1.2-1.ca2404.1_all.deb Size: 59728 MD5sum: 703758bd1b9cf6d9e814f73049a8e084 SHA1: 7c72d0b66e0228ef75243df4b4d05225a46bae84 SHA256: ef29d5aff84b6637ce6e04e570123a9fc7da53a6ca5c54a02ef37b26e7887315 SHA512: a970b0f611506538c22caff1e664660688722010085c9aeb3f1f0334e29368783df826b933203828bdc5f501905386ee2d97415c32423d216cff7ae9dcd31341 Homepage: https://cran.r-project.org/package=cobiclust Description: CRAN Package 'cobiclust' (Biclustering via Latent Block Model Adapted to OverdispersedCount Data) Implementation of a probabilistic method for biclustering adapted to overdispersed count data. It is a Gamma-Poisson Latent Block Model. 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Package: r-cran-codalomic Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xtable, r-cran-ggplot2, r-cran-reshape2, r-cran-compositions, r-cran-mass, r-cran-broom, r-cran-r2jags, r-cran-ggbiplot, r-cran-zcompositions Suggests: r-cran-testthat, r-cran-gtools, r-cran-knitr Filename: pool/dists/noble/main/r-cran-codalomic_0.1.1-1.ca2404.1_all.deb Size: 949386 MD5sum: dad99b4e25dc9541655fda7a2d6c1aa9 SHA1: 92f55f6fe70b7b68941c9cfb9e4fd957d4ee2c99 SHA256: 55debf5ba08d41051744131f1a6a66c15fdd60e94569bb7b2606de5c7c950b37 SHA512: c3e88c18c3b8f40e4403416b2185e7e457849dd7afc34c03dc838ec22abf46df36c03071554215e56ee9c3a65c23a4e4510ad0d5425810a8b0353e4716995173 Homepage: https://cran.r-project.org/package=CoDaLoMic Description: CRAN Package 'CoDaLoMic' (Compositional Models to Longitudinal Microbiome Data) Implementation of models to analyse compositional microbiome time series taking into account the interaction between groups of bacteria. The models implemented are described in Creus-Martí et al (2018, ISBN:978-84-09-07541-6), Creus-Martí et al (2021) and Creus-Martí et al (2022) . Package: r-cran-codaredistlm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compositions, r-cran-ggplot2, r-cran-broom, r-cran-knitr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-codaredistlm_0.1.0-1.ca2404.1_all.deb Size: 186562 MD5sum: 55e640e2223e2d82ce6d8d2063ed66a1 SHA1: 1d93ebc5a6c92661c45a7e5c9adca917c38faf89 SHA256: 425acafa676e01a19d96e038c5cb596d3a0916cede9d208a6ec4df84f6ddaa73 SHA512: 28e2fde89fdeba402ae27fb379a7f61432592a2887496560f880815d69b4710953d6f8f45396304742aa5f1bb5d6177f89705a34e42a57cdd0442d15d4814d2b Homepage: https://cran.r-project.org/package=codaredistlm Description: CRAN Package 'codaredistlm' (Compositional Data Linear Models with Composition Redistribution) Provided data containing an outcome variable, compositional variables and additional covariates (optional); linearly regress the outcome variable on an isometric log ratio (ilr) transformation of the linearly dependent compositional variables. The package provides predictions (with confidence intervals) in the change (delta) in the outcome/response variable based on the multiple linear regression model and evenly spaced reallocations of the compositional values. The compositional data analysis approach implemented is outlined in Dumuid et al. (2017a) and Dumuid et al. (2017b) . Package: r-cran-codatags Architecture: all Version: 1.43-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-codatags_1.43-1.ca2404.1_all.deb Size: 42058 MD5sum: cdb3bafddc70b8f3ab42201f7dffc920 SHA1: dcec4863262c195496bf3419bae55a9326666b5b SHA256: 9af7a357df9609f577d0bbe24200772376f02e71f055c8be9be57d4690dd929d SHA512: 4ced3cc03380b921970cbe28fb53851e8705b8e87cc316222097fb5944470083bcaa528096f30bd4740d749ddd470c978bac9bea23cf044544d1719c52911d4a Homepage: https://cran.r-project.org/package=CodataGS Description: CRAN Package 'CodataGS' (Genomic Prediction Using SNP Codata) Computes genomic breeding values using external information on the markers. The package fits a linear mixed model with heteroscedastic random effects, where the random effect variance is fitted using a linear predictor and a log link. The method is described in Mouresan, Selle and Ronnegard (2019) . Package: r-cran-code Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-desolve, r-cran-testthat Filename: pool/dists/noble/main/r-cran-code_1.1.1-1.ca2404.1_all.deb Size: 130056 MD5sum: bd283c82df1a9244f4885c970d824b7b SHA1: 6f8e0217c25375cb3202fa0efd05ca16c267321c SHA256: d2b930d9501ea77856af370107e457b536135352e56558e7042360fb1d63a574 SHA512: 59db1a3632944e4b3bba10052bb5a0cf35ed4d15df9c8207c3421cd23b549f04fc0028f60fb6d92a02715b3cb6e030beb65199e08952f803fd209e3f091e9648 Homepage: https://cran.r-project.org/package=cOde Description: CRAN Package 'cOde' (Automated C Code Generation for 'deSolve', 'bvpSolve') Generates all necessary C functions allowing the user to work with the compiled-code interface of ode() and bvptwp(). The implementation supports "forcings" and "events". Also provides functions to symbolically compute Jacobians, sensitivity equations and adjoint sensitivities being the basis for sensitivity analysis. Package: r-cran-codebook Architecture: all Version: 0.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmdpartials, r-cran-forcats, r-cran-vctrs, r-cran-ggplot2, r-cran-stringr, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-jsonlite, r-cran-haven, r-cran-purrr, r-cran-tibble, r-cran-glue, r-cran-likert, r-cran-knitr, r-cran-skimr, r-cran-htmltools, r-cran-labeling, r-cran-labelled, r-cran-future Suggests: r-cran-testthat, r-cran-dt, r-cran-lme4, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shiny, r-cran-miniui, r-cran-roxygen2, r-cran-rio, r-cran-psych Filename: pool/dists/noble/main/r-cran-codebook_0.10.1-1.ca2404.1_all.deb Size: 1330904 MD5sum: ed045291f0d0a19586a1dcd928e7bbae SHA1: 3d25ed3a7ebf37e06871b0c5e79c2b20a51dad8e SHA256: 28996e3b2c682f1f5bf6d2a0272c60cb6676488c035683feaaae7d57446fd3e5 SHA512: 06a24d1d8c2671a07ba25af80705f6f85399d9b00467f671f73ac00eab1d9c215ac64cef783fcc44446bb6699352ad4d6ee70498738dd26f2e109706d9b1206c Homepage: https://cran.r-project.org/package=codebook Description: CRAN Package 'codebook' (Automatic Codebooks from Metadata Encoded in Dataset Attributes) Easily automate the following tasks to describe data frames: Summarise the distributions, and labelled missings of variables graphically and using descriptive statistics. For surveys, compute and summarise reliabilities (internal consistencies, retest, multilevel) for psychological scales. Combine this information with metadata (such as item labels and labelled values) that is derived from R attributes. To do so, the package relies on 'rmarkdown' partials, so you can generate HTML, PDF, and Word documents. Codebooks are also available as tables (CSV, Excel, etc.) and in JSON-LD, so that search engines can find your data and index the metadata. The metadata are also available at your fingertips via RStudio Addins. Package: r-cran-codebookr Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-haven, r-cran-flextable, r-cran-dplyr, r-cran-officer, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-hms, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-codebookr_0.1.9-1.ca2404.1_all.deb Size: 87458 MD5sum: d8bac60ce44894618a10b54898c876fa SHA1: 8e4f693efdef44ff0641a8848771069c6c476f57 SHA256: 6a7e18f1889b723b5ccdffe7c601b59a002c7145941ff35166f155bc133ac1f0 SHA512: 591d0cc8e0553280745829e54996eb9aa7595ef0835427dc8701683e5579c1450a17b4540abdf98b40f92f2b4ea64e757f8796737b122af0bfbdd58c41cb73e4 Homepage: https://cran.r-project.org/package=codebookr Description: CRAN Package 'codebookr' (Create Codebooks from Data Frames) Quickly and easily create codebooks (i.e. data dictionaries) directly from a data frame. Package: r-cran-codebreaker Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-beepr, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-codebreaker_1.0.1-1.ca2404.1_all.deb Size: 79440 MD5sum: 3905dadbbb8bd62c1c458cbd1308fa3a SHA1: 5ebfe8d95bb6a00462a9d7cae1242da89d32463c SHA256: f4e942b4a5424bd09ca4c6e52bcff123722cd6d7a1db8f7f8bc223cfc559f197 SHA512: 9e53216a6c83bec0b077a54489d2d12606858f41d7c7dcd8aa518f2df8223bb7e2080f9e937bda5b0cebf6d5024fe0bf5d08cdc05b29ae38fe69ae24a8bb3dc0 Homepage: https://cran.r-project.org/package=codebreaker Description: CRAN Package 'codebreaker' (Retro Logic Game) Logic game in the style of the early 1980s home computers that can be played in the R console. This game is inspired by Mastermind, a game that became popular in the 1970s. Can you break the code? Package: r-cran-codecollection Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-epi Filename: pool/dists/noble/main/r-cran-codecollection_0.1.3-1.ca2404.1_all.deb Size: 1093848 MD5sum: 5115e80febea7e0abda8ddf52903498a SHA1: a8599a6ea0af0d2aa8026becc138e491f16c5a26 SHA256: 943cc26eb85cd05d0c3c00b5772c681d46a1e315dc14b73fe50075ad448537a2 SHA512: 578293a97f01e3a4dace52405cdfc751bb07e995419d842ae18080417b1a665d779545206dc6b335f15efdf8494dd1a652352a5eeb286bf348f86270c0c485c4 Homepage: https://cran.r-project.org/package=codeCollection Description: CRAN Package 'codeCollection' (Collection of Codes with Labels) Includes several classifications such as International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD10), Anatomical Therapeutic Chemical (ATC) Classification, The International Classification of Diseases for Oncology (ICD-O-3), and International Classification of Primary Care (ICPC). Includes function that adds descriptive label to code value. Depending on classification following languages are available: English, Finnish, Swedish, and Latin. Package: r-cran-codecountr Architecture: all Version: 0.0.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-codecountr_0.0.4.8-1.ca2404.1_all.deb Size: 42902 MD5sum: 458438bce2d837f0f746fd11b88caaf4 SHA1: 44fab9d40c38602869c7a7999fb00865afee7c98 SHA256: f85d7f787b71c29b66859fff9d1ef05959ede082fa88011093d5aeb014aeeb9a SHA512: 5bfddb50aea12e1679091bff4d014b32f4e793ccdcfe5bd43becff09d1beb9a30e7b82b0acc80de1b3a081feb478ebebade5fef3b76e494464e94cfe334ded8e Homepage: https://cran.r-project.org/package=codecountR Description: CRAN Package 'codecountR' (Counting Codes in a Text and Preparing Data for Analysis) Data analysis often requires coding, especially when data are collected through interviews, observations, or questionnaires. As a result, code counting and data preparation are essential steps in the analysis process. Analysts may need to count the codes in a text (Tokenization, counting of pre-established codes, computing the co-occurrence matrix by line) and prepare the data (e.g., min-max normalization, Z-score, robust scaling, Box-Cox transformation, and non-parametric bootstrap). For the Box-Cox transformation (Box & Cox, 1964, ), the optimal Lambda is determined using the log-likelihood method. Non-parametric bootstrap involves randomly sampling data with replacement. Two random number generators are also integrated: a Lehmer congruential generator for uniform distribution and a Box-Muller generator for normal distribution. Package for educational purposes. Package: r-cran-codedepends Architecture: all Version: 0.6.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-codetools, r-bioc-graph, r-cran-xml Suggests: r-bioc-rgraphviz, r-cran-runit, r-cran-knitr, r-cran-highlight, r-cran-rjsonio, r-cran-rcurl, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-codedepends_0.6.7-1.ca2404.1_all.deb Size: 890166 MD5sum: a69104654c532c965ce8e69e7fca557e SHA1: d7b9eac181e0cd80472876bd57fb9e313f0e592b SHA256: 73c6d3acebd7bd4902a25efef7504f58c9ed0f4682472f8d5db84b6204c9d538 SHA512: e9da4f4ea759a7bfdf7bf739c2c1ae491ffb8372fd9479d658363d053ac5b2a04c40bd6d8ccca6c8ec1ff897947c282dd1c2f35765438da5155c1030ba218060 Homepage: https://cran.r-project.org/package=CodeDepends Description: CRAN Package 'CodeDepends' (Analysis of R Code for Reproducible Research and CodeComprehension) Tools for analyzing R expressions or blocks of code and determining the dependencies between them. It focuses on R scripts, but can be used on the bodies of functions. There are many facilities including the ability to summarize or get a high-level view of code, determining dependencies between variables, code improvement suggestions. Package: r-cran-codelist Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-simplermarkdown Filename: pool/dists/noble/main/r-cran-codelist_0.1.0-1.ca2404.1_all.deb Size: 106120 MD5sum: ec83b41fde8a8d595678fc23521af418 SHA1: f0b2d95b519e62df2e7a8e0c34e40885bc3c603c SHA256: 74acb9c79d330d1fef90070a222f4ed3104906c92401f9952613835966a2598f SHA512: 61d891817378fa4880f28b6c8c571169fe0e45af262774079ace80de73717190f347ba6f17ac6aac90b11330074d7a69e8f88193a13b895cda85fdfb8482fb82 Homepage: https://cran.r-project.org/package=codelist Description: CRAN Package 'codelist' (Working with Code Lists) Functions for working with code lists and vectors with codes. These are an alternative for factor that keep track of both the codes and labels. Methods allow for transforming between codes and labels. Also supports hierarchical code lists. Package: r-cran-codelistgenerator Architecture: all Version: 4.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2941 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-dplyr, r-cran-omopgenerics, r-cran-rlang, r-cran-glue, r-cran-stringr, r-cran-stringi, r-cran-tidyr, r-cran-cli, r-cran-purrr, r-cran-clock, r-cran-patientprofiles, r-cran-vctrs, r-cran-jsonlite, r-cran-lifecycle Suggests: r-cran-covr, r-cran-duckdb, r-cran-cdmconnector, r-cran-visomopresults, r-cran-cohortconstructor, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-rpostgres, r-cran-odbc, r-cran-spelling, r-cran-tibble, r-cran-gt, r-cran-flextable, r-cran-omock, r-cran-tictoc Filename: pool/dists/noble/main/r-cran-codelistgenerator_4.0.2-1.ca2404.1_all.deb Size: 1768434 MD5sum: ebb139fcbab924c2e0869e1a1a30df59 SHA1: a11a54be16b82c765c0f990ab1c1a60d105d1666 SHA256: fb2f6e3e66723c96641e49f13c0e6868eb0129ef6fab2ca8272887b129efbe48 SHA512: 019387d379564a918357410142e16ecdad0a511102d9b18f82aae11e98ab06dca129122c2f708bda4f5c3275575911d0be75b5182eae31f37dbfad90175edef8 Homepage: https://cran.r-project.org/package=CodelistGenerator Description: CRAN Package 'CodelistGenerator' (Identify Relevant Clinical Codes and Evaluate Their Use) Generate a candidate code list for the Observational Medical Outcomes Partnership (OMOP) common data model based on string matching. For a given search strategy, a candidate code list will be returned. Package: r-cran-codemeta Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desc, r-cran-jsonlite Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-codemeta_0.1.1-1.ca2404.1_all.deb Size: 164708 MD5sum: 1a14a93f97f2fc6e1aa2fd8b2361a2aa SHA1: 2d7ecf3470f51d4bbc549d3711327d09f281f1cd SHA256: 65bbee09e63733cbc311b1510bdb9371a1ea1d3d5469e6e968ac310892602b74 SHA512: 0c48468169e75c59791537c94849567969dd80f9312951e19821b9435b32619fb98d3f929226f19553129750b4c687039ae14ce955f9aa8a66bf17e404b5b0be Homepage: https://cran.r-project.org/package=codemeta Description: CRAN Package 'codemeta' (A Smaller 'codemetar' Package) The 'Codemeta' Project defines a 'JSON-LD' format for describing software metadata, as detailed at . This package provides core utilities to generate this metadata with a minimum of dependencies. Package: r-cran-codemetar Architecture: all Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1685 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-commonmark, r-cran-crul, r-cran-desc, r-cran-gert, r-cran-gh, r-cran-jsonlite, r-cran-magrittr, r-cran-memoise, r-cran-pingr, r-cran-purrr, r-cran-remotes, r-cran-sessioninfo, r-cran-urltools, r-cran-xml2, r-cran-cli, r-cran-codemeta Suggests: r-cran-withr, r-cran-covr, r-cran-details, r-cran-dplyr, r-cran-jsonld, r-cran-jsonvalidate, r-cran-knitr, r-cran-printr, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-codemetar_0.3.7-1.ca2404.1_all.deb Size: 482546 MD5sum: 7252fa30149dc1ccb41b9005e386cd68 SHA1: 8e76df98d5cf37adbc33fafdf8598e00c00efab4 SHA256: d04f0e80571b5aba28275d4e3cd23c56e62eca3b92d7fa1485ce399914f28b49 SHA512: b0d8777f04ac266eeee3767ca616634119e77367288cf80e422287420f3134881b33a185a7883137f376f97c8668222096685edb8c116c9927e19d3a21f39882 Homepage: https://cran.r-project.org/package=codemetar Description: CRAN Package 'codemetar' (Generate 'CodeMeta' Metadata for R Packages) The 'Codemeta' Project defines a 'JSON-LD' format for describing software metadata, as detailed at . This package provides utilities to generate, parse, and modify 'codemeta.json' files automatically for R packages, as well as tools and examples for working with 'codemeta.json' 'JSON-LD' more generally. Package: r-cran-codename Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tibble Filename: pool/dists/noble/main/r-cran-codename_0.5.0-1.ca2404.1_all.deb Size: 86188 MD5sum: 5835616b683de719878d2a9c6e1dc09b SHA1: f3496d4a0094b86611089c8e107c41d84b75353e SHA256: 963c9121550292d1ae0ca20b1f86e49e6b47029f752f3d4c4b4e4032d8c710ff SHA512: 48860c36455c606cf14168823854ea47f8a5daffd163cd367497db8fceefb8472fe124533877304b9f4fa5ef529625e7da0b923bd583c0e29697014d5b906273 Homepage: https://cran.r-project.org/package=codename Description: CRAN Package 'codename' (Generation of Code Names for Organizations, People, Projects,and Whatever Else) This creates code names that a user can consider for their organizations, their projects, themselves, people in their organizations or projects, or whatever else. The user can also supply a numeric seed (and even a character seed) for maximum reproducibility. Use is simple and the code names produced come in various types too, contingent on what the user may be desiring as a code name or nickname. Package: r-cran-coder Architecture: all Version: 0.13.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 573 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-decoder, r-cran-generics, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-writexl Filename: pool/dists/noble/main/r-cran-coder_0.13.10-1.ca2404.1_all.deb Size: 343648 MD5sum: f15feae940aa8f64f995e3b0195f61eb SHA1: 94b19896a201bac506d1885d6be33808d531d18c SHA256: 58da9a159a231c7ec3a81d004400ef3a04decc39020778d86afb011523744126 SHA512: 40f612b3c604fe7121c9669bd76d561e1e06051702dcd5924964e263e48e7b7cf8751b8b15e56fac5c20a7bc97ef790af81c9b7e5a220546d8d8a8ef65adef1f Homepage: https://cran.r-project.org/package=coder Description: CRAN Package 'coder' (Deterministic Categorization of Items Based on External CodeData) Fast categorization of items based on external code data identified by regular expressions. A typical use case considers patient with medically coded data, such as codes from the International Classification of Diseases ('ICD') or the Anatomic Therapeutic Chemical ('ATC') classification system. Functions of the package relies on a triad of objects: (1) case data with unit id:s and possible dates of interest; (2) external code data for corresponding units in (1) and with optional dates of interest and; (3) a classification scheme ('classcodes' object) with regular expressions to identify and categorize relevant codes from (2). It is easy to introduce new classification schemes ('classcodes' objects) or to use default schemes included in the package. Use cases includes patient categorization based on 'comorbidity indices' such as 'Charlson', 'Elixhauser', 'RxRisk V', or the 'comorbidity-polypharmacy' score (CPS), as well as adverse events after hip and knee replacement surgery. 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The foundation of this package is primarily rooted in the works of Rosenthal, Rosnow, and Rubin (2000, ISBN: 978-0521659802) as well as Sedlmeier and Renkewitz (2018, ISBN: 978-3868943214). 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This package uses Bayesian methods to enforce the chronological ordering of radiocarbon and other dates, for example for trees with multiple radiocarbon dates spaced at exactly known intervals (e.g., 10 annual rings). For methods see Christen 2003 . Another example is sites where the relative chronological position of the dates is taken into account - the ages of dates further down a site must be older than those of dates further up (Buck, Kenworthy, Litton and Smith 1991 ; Nicholls and Jones 2001 ). The paper accompanying this R package is Blaauw et al. 2024 . 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Package: r-cran-cohortcharacteristics Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6324 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-cli, r-cran-stringr, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-snakecase, r-cran-lifecycle, r-cran-purrr, r-cran-clock Suggests: r-cran-cdmconnector, r-cran-codelistgenerator, r-cran-visomopresults, r-cran-cohortconstructor, r-cran-covr, r-cran-dbi, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-drugutilisation, r-cran-dt, r-cran-duckdb, r-cran-flextable, r-cran-ggplot2, r-cran-ggpubr, r-cran-glue, r-cran-gt, r-cran-here, r-cran-hmisc, r-cran-htmltools, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-plotly, r-cran-png, r-cran-reactable, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rsvg, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tictoc, r-cran-withr, r-cran-extrafont Filename: pool/dists/noble/main/r-cran-cohortcharacteristics_1.1.3-1.ca2404.1_all.deb Size: 2681318 MD5sum: da16daa96aff1881bebff5b7c5eceb19 SHA1: cd1f53ef11eebcd4bb745e2f506431c92e7441b4 SHA256: 837063410ea7d9fe5231ac8e856695b0ed7f7ab2b872fba57d6b9bec32029936 SHA512: ae912f22853307eccfc039c8969825288581d29b5a2768c6bb71de6de85b6d57893c90b0d48d2c7e90cba59bdd1bba030bcc3ae8949045a35b3cd7ebe99e4880 Homepage: https://cran.r-project.org/package=CohortCharacteristics Description: CRAN Package 'CohortCharacteristics' (Summarise and Visualise Characteristics of Patients in the OMOPCDM) Summarise and visualise the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Package: r-cran-cohortconstructor Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2018 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-codelistgenerator, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-cdmconnector, r-cran-circer, r-cran-cohortcharacteristics, r-cran-covr, r-cran-dbi, r-cran-diagrammer, r-cran-drugutilisation, r-cran-duckdb, r-cran-ggplot2, r-cran-gt, r-cran-here, r-cran-incidenceprevalence, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-rmarkdown, r-cran-rpostgres, r-cran-scales, r-cran-sqlrender, r-cran-stringr, r-cran-testthat, r-cran-tictoc, r-cran-visomopresults, r-cran-systemfonts Filename: pool/dists/noble/main/r-cran-cohortconstructor_0.6.3-1.ca2404.1_all.deb Size: 1209032 MD5sum: 907c059ed2ad6b9602b6caa961af3deb SHA1: ddc73b57c580b795902478126446956353dbb6ef SHA256: c28ff4de5f8f1ac147072551d333e60f253e7a1dddc664d6edb5d874b420d457 SHA512: e30f5ae3261fbd25b8953762dbafbbb0d021d3e35f8b2e32c3bf6e89d8d1db33d17ca714d4041d35b19452ecf262caacd65f5bfaaecf1a496d6400b7dabbd2bf Homepage: https://cran.r-project.org/package=CohortConstructor Description: CRAN Package 'CohortConstructor' (Build and Manipulate Study Cohorts Using a Common Data Model) Create and manipulate study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model. Package: r-cran-cohortcontrast Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7767 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-cdmconnector, r-cran-cohortconstructor, r-cran-omopgenerics, r-cran-lubridate, r-cran-doparallel, r-cran-foreach, r-cran-data.table, r-cran-cli, r-cran-jsonlite, r-cran-nanoparquet Suggests: r-cran-testthat, r-cran-patientprofiles, r-cran-duckdb, r-cran-dbi, r-cran-rpostgres, r-cran-readr, r-cran-knitr, r-cran-rmarkdown, r-cran-processx, r-cran-bit64, r-cran-reshape2, r-cran-igraph, r-cran-matrix, r-cran-cluster, r-cran-vegan, r-cran-reticulate Filename: pool/dists/noble/main/r-cran-cohortcontrast_1.0.0-1.ca2404.1_all.deb Size: 4307524 MD5sum: 53fcd2e2b0eccddea120b5fa02f625a2 SHA1: 05b99c71169546fe477df1cd49d43b19327cdb6a SHA256: 078604ca36a2b9efa832eb12d58df2221ad98462040884b928442ededdc98a54 SHA512: 3b23a5cb49d369d57c16fdd3878736bb45073ee54c1e9fcb923a3a4e6470dcdf9616bd64eed784e227dbe89905d1521ff2789cd2279f709a4b1d76877e83b770 Homepage: https://cran.r-project.org/package=CohortContrast Description: CRAN Package 'CohortContrast' (Enrichment Analysis of Clinically Relevant Concepts in CommonData Model Cohort Data) Identifies clinically relevant concepts in Observational Medical Outcomes Partnership Common Data Model cohorts using an enrichment-based workflow. Defines target and control cohorts and extracts medical interventions that are over-represented in the target cohort during the observation period. Users can tune filtering and selection thresholds. The workflow includes chi-squared tests for two proportions with Yates continuity correction, logistic tests, and hierarchy and correlation mappings for relevant concepts. The results can be optionally explored using the bundled graphical user interface. For workflow details and examples, see . Package: r-cran-cohortexplorer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 842 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-databaseconnector, r-cran-checkmate, r-cran-dplyr, r-cran-lifecycle, r-cran-parallellogger, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-withr Filename: pool/dists/noble/main/r-cran-cohortexplorer_0.1.0-1.ca2404.1_all.deb Size: 297136 MD5sum: 216f45c9848245370f73f46750d89a7d SHA1: 42a744824978989cea2a9a12c9c32c1822b1c855 SHA256: e5f591976066f6b5ce3d164df9756976a607b16c98266f7fb3aa6278f9e3ed29 SHA512: 466c5b662452b755e92d3151df8ac355bcad37d0976859a8b6eeadcfdcbb3d39d3a5bae8da620873e569866fd3803b2da3adcb91a2e904a9eb49076b421a3f0d Homepage: https://cran.r-project.org/package=CohortExplorer Description: CRAN Package 'CohortExplorer' (Explorer of Profiles of Patients in a Cohort) This software tool is designed to extract data from a randomized subset of individuals within a cohort and make it available for exploration in a shiny application environment. It retrieves date-stamped, event-level records from one or more data sources that represent patient data in the Observational Medical Outcomes Partnership (OMOP) data model format. This tool features a user-friendly interface that enables users to efficiently explore the extracted profiles, thereby facilitating applications, such as reviewing structured profiles. Package: r-cran-cohortgenerator Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2846 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-databaseconnector, r-cran-r6, r-cran-checkmate, r-cran-digest, r-cran-dplyr, r-cran-lubridate, r-cran-parallellogger, r-cran-readr, r-cran-rlang, r-cran-jsonlite, r-cran-resultmodelmanager, r-cran-sqlrender, r-cran-stringi, r-cran-tibble Suggests: r-cran-circer, r-cran-duckdb, r-cran-eunomia, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-zip Filename: pool/dists/noble/main/r-cran-cohortgenerator_1.1.0-1.ca2404.1_all.deb Size: 1411464 MD5sum: 8f0c226de40d65a6d759e6b33e2c8f5d SHA1: d5b8c154e2f92e83b5bcb7b4536f40f6413de75e SHA256: f70b2289d9bad1d064e5e5c6f403df533921593854e19659cff643514d2af5e8 SHA512: d3cac15b292216590818b8748019c541f645d671283037ee1a851ee3dd469900bedc8a57ee39dc4dc9445548d182eb111e6a457263906082e2f0c98bba816b0b Homepage: https://cran.r-project.org/package=CohortGenerator Description: CRAN Package 'CohortGenerator' (Cohort Generation for the OMOP Common Data Model) Generate cohorts and subsets using an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) Database. Cohorts are defined using 'CIRCE' () or SQL compatible with 'SqlRender' (). Package: r-cran-cohortpathways Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-databaseconnector, r-cran-checkmate, r-cran-dplyr, r-cran-lifecycle, r-cran-rlang, r-cran-sqlrender, r-cran-tidyr Suggests: r-cran-remotes, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-cohortpathways_0.0.1-1.ca2404.1_all.deb Size: 32742 MD5sum: 95ac1388fe4a38ebc20527a1b702b990 SHA1: bb773468020af2a371217790db00b2878617dde4 SHA256: c628fa11b8926fa1358504bee5803226363ef55755836031971197e777514e65 SHA512: 2fcf3dda1c85751ba51d5f951b225167c353017d4b1ac61736a0e98ac642b97ea8518a317519f244344c322bbb32293641526689d4869cb9af6a8c6c0279dd60 Homepage: https://cran.r-project.org/package=CohortPathways Description: CRAN Package 'CohortPathways' (Create Pathways from Target to Event Cohorts) Software tool designed to compute the temporal relationship defined as pathways between any two instantiated cohorts. The cohorts are input as Target and event cohorts. Package: r-cran-cohortplat Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1389 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2, r-cran-plotly, r-cran-tidyr, r-cran-doparallel, r-cran-foreach, r-cran-openxlsx, r-cran-forcats, r-cran-epitools, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dt, r-cran-gtools Filename: pool/dists/noble/main/r-cran-cohortplat_1.0.5-1.ca2404.1_all.deb Size: 443586 MD5sum: 564ecc4f7ca378dbdfe1075d7e2d00e9 SHA1: 4be33c4385283bd63abc74b4157cbc2e2a74cf36 SHA256: a69ac0d9024810192ba3e244770090d0e9dd8812646fd6d13abaa804948acb98 SHA512: e782b7bb78704a5b1b3875f5fe160bc13f73014f5faa914dcdd68e4abb1d8c936fd09f114ab5ced91cd05eca6f043d463d574d5bfe3ec3e8c4b9817c8b9c2f50 Homepage: https://cran.r-project.org/package=CohortPlat Description: CRAN Package 'CohortPlat' (Simulation of Cohort Platform Trials for Combination Treatments) A collection of functions dedicated to simulating staggered entry platform trials whereby the treatment under investigation is a combination of two active compounds. In order to obtain approval for this combination therapy, superiority of the combination over the two active compounds and superiority of the two active compounds over placebo need to be demonstrated. A more detailed description of the design can be found in Meyer et al. and a manual in Meyer et al. . Package: r-cran-cohorts Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1214 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-dtplyr, r-cran-magrittr, r-cran-tibble, r-cran-tidyr, r-cran-zoo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cohorts_1.0.1-1.ca2404.1_all.deb Size: 1131582 MD5sum: 703d7479db1e892ce1993ce5121b51f4 SHA1: a4a7df7bb5f2dcfcd497548daa72186d550dda6b SHA256: 776caab5717580388795e7ed4be75862dfd0f7ea06b1720572f6337afc5d5fe8 SHA512: 3de865829fe2b669a03d418a6867be7877c98dc34f1dcf1c26f16facf29a8b23efc5a39ca96381b7fb98354c43702d2f2130f92934fefaf0269e83ff1ed59a87 Homepage: https://cran.r-project.org/package=cohorts Description: CRAN Package 'cohorts' (Cohort Analysis Made Easy) Functions to simplify the process of preparing event and transaction for cohort analysis. Package: r-cran-cohortsurvival Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2997 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-clock, r-cran-dbi, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang, r-cran-survival, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-codelistgenerator, r-cran-roxygen2, r-cran-knitr, r-cran-tictoc, r-cran-rmarkdown, r-cran-ggplot2, r-cran-patchwork, r-cran-cmprsk, r-cran-duckdb, r-cran-gt, r-cran-flextable, r-cran-scales, r-cran-visomopresults, r-cran-extrafont, r-cran-cdmconnector Filename: pool/dists/noble/main/r-cran-cohortsurvival_1.1.1-1.ca2404.1_all.deb Size: 1742904 MD5sum: 1dc39cc8e1b8213f87837d28362057de SHA1: 842dd0dab09a3aa3472a634f9a92c7e33d1e8d6d SHA256: a3484006e85046213d8d01ede9bb384b27f67cb703065ec4c09b6ab5a4174f2b SHA512: 37bb9cec78cf8fbce99f8d7f4753361a68fcbb5865741e20f239d492feb2b14a8277f4a809b0ce7eee5f8fed960805b74349d3dfffabef820f9b21c91dc2e7f9 Homepage: https://cran.r-project.org/package=CohortSurvival Description: CRAN Package 'CohortSurvival' (Estimate Survival from Common Data Model Cohorts) Estimate survival using data mapped to the Observational Medical Outcomes Partnership common data model. Survival can be estimated based on user-defined study cohorts. Package: r-cran-cohortsymmetry Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3582 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cdmconnector, r-cran-dplyr, r-cran-patientprofiles, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-here, r-cran-omopgenerics, r-cran-drugutilisation, r-cran-codelistgenerator Suggests: r-cran-testthat, r-cran-dbi, r-cran-cli, r-cran-odbc, r-cran-rpostgres, r-cran-tidyselect, r-cran-knitr, r-cran-dbplyr, r-cran-omock, r-cran-visomopresults, r-cran-flextable, r-cran-gt, r-cran-ggplot2, r-cran-duckdb, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cohortsymmetry_0.2.4-1.ca2404.1_all.deb Size: 1442104 MD5sum: 220022b08d38f5448cc1a37d455de61a SHA1: 1a99199da0c66f8a388dd402ec6eb65c31e4a825 SHA256: 5777276109cd9008e8307fa03c76cc9fd9cbcbe7a731bdb63c44848ae33ce902 SHA512: be0ee9ae16a13a8b211c040e31923254240f129efec002e52030a33894d08d7341c6bc56faca0aeb0fe59bf9246d27b21dd3e92f85c330a888ea9952dfd01a75 Homepage: https://cran.r-project.org/package=CohortSymmetry Description: CRAN Package 'CohortSymmetry' (Sequence Symmetry Analysis Using the Observational MedicalOutcomes Partnership Common Data Model) Calculating crude sequence ratio, adjusted sequence ratio and confidence intervals using data mapped to the Observational Medical Outcomes Partnership Common Data Model. Package: r-cran-cohorttools Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-epi, r-cran-cmprsk, r-cran-ggplot2, r-cran-survival, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-rsvg, r-cran-mgcv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lattice, r-cran-mstate, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cohorttools_0.1.8-1.ca2404.1_all.deb Size: 60350 MD5sum: d354310fe6ef4b4b6d2bcdd64995ffe5 SHA1: f7e73aa0c112fe2750e149c5ecbb5fd1b63a70ee SHA256: dfc02042830fec0391876423ddf7f322a2cd0e498b15bd2e54fc40b8e2d2af78 SHA512: 3b67882367796c76de7262fd5047313be3df85430c902fca84bf9cac0fc904297d6799dde47f1fe9e172114ca5b1cb8335dfed60144e7da59899316858607b36 Homepage: https://cran.r-project.org/package=cohorttools Description: CRAN Package 'cohorttools' (Cohort Data Analyses) Functions to make lifetables and to calculate hazard function estimate using Poisson regression model with splines. Includes function to draw simple flowchart of cohort study. Function boxesLx() makes boxes of transition rates between states. It utilizes 'Epi' package 'Lexis' data. Package: r-cran-coil Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-aphid, r-cran-seqinr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-coil_1.2.4-1.ca2404.1_all.deb Size: 172918 MD5sum: 3092480f49b56d8912af4e8307bedbd0 SHA1: 58026982b93903237c42bf029e56341d7f5e4552 SHA256: e460fdbea4a00fcbc7724370e3cc2022f4d0862ca1f3de66f84d4b50df39c934 SHA512: 8c6fbb3167a6ba79dd32713ea19aa8716affe857349fa3bebb5f39bca68599adea287fa1e4bf9ab54953a8807231733d7fb33363b5b25a24046516a0adfa0534 Homepage: https://cran.r-project.org/package=coil Description: CRAN Package 'coil' (Contextualization and Evaluation of COI-5P Barcode Data) Designed for the cleaning, contextualization and assessment of cytochrome c oxidase I DNA barcode data (COI-5P, or the five prime portion of COI). It contains functions for placing COI-5P barcode sequences into a common reading frame, translating DNA sequences to amino acids and for assessing the likelihood that a given barcode sequence includes an insertion or deletion error. The error assessment relies on the comparison of input sequences against nucleotide and amino acid profile hidden Markov models (PHMMs) (for details see Durbin et al. 1998, ISBN: 9780521629713) trained on a taxonomically diverse set of reference sequences. The functions are provided as a complete pipeline and are also available individually for efficient and targeted analysis of barcode data. Package: r-cran-coimp Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-cluster, r-cran-nnet, r-cran-gtools, r-cran-locfit Filename: pool/dists/noble/main/r-cran-coimp_2.2-1.ca2404.1_all.deb Size: 229232 MD5sum: ae6b91178245f40017404fc50ba16458 SHA1: d5a0a045511caf8ce10eccac0d46b533f78ebe30 SHA256: 56871b1ce46c8322fd7a27d65e02de3ab97b42429e48d01bcb4ff49b6156d004 SHA512: c01a763591627e8a109b8f4d5c00c61db5d1ea7765c3e811ebb4cb4aeadbd09a75c4e79eec1119aeb3c246c576c34accbb0cf5e8caa059c99816dd51711e7bc5 Homepage: https://cran.r-project.org/package=CoImp Description: CRAN Package 'CoImp' (Parametric and Nonparametric Copula-Based Imputation Methods) Copula-based imputation methods: parametric and nonparametric algorithms for missing multivariate data through conditional copulas. Package: r-cran-coinmarketcapr Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-ggplot2, r-cran-data.table, r-cran-curl, r-cran-cli, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-coinmarketcapr_0.4-1.ca2404.1_all.deb Size: 75906 MD5sum: f3e57783d3c768d0aa9733dd35cb9075 SHA1: dc83f7e274575b00e4c30b7c9a18467b44328992 SHA256: 7eaf6ded72b4cc448a0e7263dd114a52fce407e6c8dbf8aae2b050cf6b56f5cf SHA512: d21d93dc041a4e3f4a10257684809600471469dc3ae354d4884082d8f30646c411368d37530703565c00f343ca07bd4c6cdd7a0fdc391d349c21f04a1cf53d2e Homepage: https://cran.r-project.org/package=coinmarketcapr Description: CRAN Package 'coinmarketcapr' (Get 'Cryptocurrencies' Market Cap Prices from Coin Market Cap) Extract and monitor price and market cap of 'Cryptocurrencies' from 'Coin Market Cap' . 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The package implements seven classical approaches—Wilson, Quesenberry and Hurst, Goodman, Wald (with and without continuity correction), Fitzpatrick and Scott, and Sison and Glaz—along with Bayesian methods based on Dirichlet models. Both equal and unequal Dirichlet priors are supported, providing a broad framework for inference, data analysis, and sensitivity evaluation. 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It is a "development environment" for composite indicators and scoreboards, which includes utilities for construction (indicator selection, denomination, imputation, data treatment, normalisation, weighting and aggregation) and analysis (multivariate analysis, correlation plotting, short cuts for principal component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single hierarchical list called a "coin". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons. It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results. 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It is well known that in a cointegrating regression the ordinary least squares (OLS) estimator of the parameters is super-consistent, i.e. converges at rate equal to the sample size T. When the regressors are endogenous, the limiting distribution of the OLS estimator is contaminated by so-called second order bias terms, see e.g. Phillips and Hansen (1990) . The presence of these bias terms renders inference difficult. Consequently, several modifications to OLS that lead to zero mean Gaussian mixture limiting distributions have been proposed, which in turn make standard asymptotic inference feasible. These methods include the fully modified OLS (FM-OLS) approach of Phillips and Hansen (1990) , the dynamic OLS (D-OLS) approach of Phillips and Loretan (1991) , Saikkonen (1991) and Stock and Watson (1993) and the new estimation approach called integrated modified OLS (IM-OLS) of Vogelsang and Wagner (2014) . 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The package integrates five robust methods into a single function: (1) target encoding of categorical variables based on response values (Micci-Barreca, 2001 (Micci-Barreca, D. 2001 ); (2) automated feature prioritization to preserve key predictors during filtering; (3 and 4) pairwise correlation and VIF filtering across all variable types (numeric–numeric, numeric–categorical, and categorical–categorical); (5) adaptive correlation and VIF thresholds. Together, these methods enable a reliable multicollinearity management in most use cases while maintaining model integrity. The package also supports parallel processing and progress tracking via the packages 'future' and 'progressr', and provides seamless integration with the 'tidymodels' ecosystem through a dedicated recipe step. 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Package: r-cran-colocalization Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-rgl Filename: pool/dists/noble/main/r-cran-colocalization_1.0.2-1.ca2404.1_all.deb Size: 128676 MD5sum: 44ac777cdaec29593cae7ad3c3c251db SHA1: a5884db3c68e35cb10889ba036b313b5b5985404 SHA256: a8561a4f6f0178b9ba33ba9ea64788016d83fdd10c0c40a593d297abce4649fa SHA512: a988cc643aaa3adf424b8216b96ba05bca4cf9e65b641f5c5d49e26f7b8f938ab43518b05ca7eafa8ef036475e3e7ece7ad4b09e244f6506e311bb4e1f4b3f05 Homepage: https://cran.r-project.org/package=colocalization Description: CRAN Package 'colocalization' (Normalized Spatial Intensity Correlation) Calculate the colocalization index, NSInC, in two different ways as described in the paper (Liu et al., 2019. Manuscript submitted for publication.) for multiple-species spatial data which contain the precise locations and membership of each spatial point. The two main functions are nsinc.d() and nsinc.z(). They provide the Pearson’s correlation coefficients of signal proportions in different memberships within a concerned proximity of every signal (or every base signal if single direction colocalization is considered) across all (base) signals using two different ways of normalization. The proximity sizes could be an individual value or a range of values, where the default ranges of values are different for the two functions. Package: r-cran-colocalized Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1809 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-purrr Filename: pool/dists/noble/main/r-cran-colocalized_0.2.0-1.ca2404.1_all.deb Size: 1770544 MD5sum: 6b50670afdc385a5584955e23302334c SHA1: ff400c57dc9591ae5e88095d60e86bd21e50c8d8 SHA256: 59b8da3f1072e38aee8a8de66b0aea2dea718acd57eeffef05a603ac366f2aa8 SHA512: da665e5f7677d1d70b2486440b6affa46c1833339aecf7f88ab78d9fea29dd73de94381da9bd7413e7e0774e0f54ba17bdd3d7945782aeb356bdcb3e0750cd7f Homepage: https://cran.r-project.org/package=colocalized Description: CRAN Package 'colocalized' (Clusters of Colocalized Sequences) Also abbreviates to "CCSeq". Finds clusters of colocalized sequences in .bed annotation files up to a specified cut-off distance. Two sequences are colocalized if they are within the cut-off distance of each other, and clusters are sets of sequences where each sequence is colocalized to at least one other sequence in the cluster. For a set of .bed annotation tables provided in a list along with a cut-off distance, the program will output a file containing the locations of each cluster. Annotated .bed files are from the 'pwmscan' application at . Personal machines might crash or take excessively long depending on the number of annotated sequences in each file and whether chromsearch() or gensearch() is used. Package: r-cran-colocboost Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4491 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfast, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ashr, r-cran-mass, r-cran-susier Filename: pool/dists/noble/main/r-cran-colocboost_1.0.7-1.ca2404.1_all.deb Size: 3217452 MD5sum: 11e5b89eaed8b1226cb25646f51ae0a5 SHA1: 1f72141503d3f930b49b8f86641c80e92a782174 SHA256: fc33e61b5ad43400203504954c7a9876d0d9e95a7b379b6c26d62ba45b662313 SHA512: b26f39f890bed6466818745352d52578ddf044047d7e5c5b30ecc0d47e1124696e1de215ba904e93e8dc17ece72ef5bafd227debee11162b5ada40f1fb2ef68f Homepage: https://cran.r-project.org/package=colocboost Description: CRAN Package 'colocboost' (Multi-Context Colocalization Analysis for QTL and GWAS Studies) A multi-task learning approach to variable selection regression with highly correlated predictors and sparse effects, based on frequentist statistical inference. It provides statistical evidence to identify which subsets of predictors have non-zero effects on which subsets of response variables, motivated and designed for colocalization analysis across genome-wide association studies (GWAS) and quantitative trait loci (QTL) studies. The ColocBoost model is described in Cao et. al. (2025) . Package: r-cran-colocproptest Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-data.table, r-cran-car, r-cran-coloc Suggests: r-cran-knitr, r-cran-plotrix, r-cran-testthat Filename: pool/dists/noble/main/r-cran-colocproptest_0.9.3-1.ca2404.1_all.deb Size: 241082 MD5sum: 8e8a716666613745281e5dd500576e8e SHA1: 33ed14ec0818e161544b1440e661b860c6c76b68 SHA256: 0f9b5894eb3a72040dcc243988074aab0725bb88d7d41ba38fbcabd907f7ef41 SHA512: 66005b7b80ceabcad3d4b6fe316336fc10db75e6fcbc9f8e3864776d8313f4d7beaa65fc3b899d4ce186955534763808ad5b6182ed1092b9ed78e5ba591dc51e Homepage: https://cran.r-project.org/package=colocPropTest Description: CRAN Package 'colocPropTest' (Proportional Testing for Colocalisation Analysis) Colocalisation analysis tests whether two traits share a causal genetic variant in a specified genomic region. Proportional testing for colocalisation has been previously proposed [Wallace (2013) ], but is reimplemented here to overcome barriers to its adoption. Its use is complementary to the fine- mapping based colocalisation method in the 'coloc' package, and may be used in particular to identify false "H3" conclusions in 'coloc'. Package: r-cran-colocr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4542 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-imager, r-cran-magick, r-cran-shiny, r-cran-scales, r-cran-magrittr Suggests: r-cran-testthat, r-cran-shinytest, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-purrr, r-cran-shinybs Filename: pool/dists/noble/main/r-cran-colocr_0.1.1-1.ca2404.1_all.deb Size: 3693316 MD5sum: 5c6b8be17c4919c8a0390c6d035b724f SHA1: 90a3dd5a34b86bfd310a9f78964f024d5fe7da73 SHA256: bef5a856a5abc6f89e2a14adbeb17e48d7f45daae7dc454d4b27cd577e46b052 SHA512: 1161b882fdd65b54874b754fa714e67ad7a73004fc9719bd65aec575e3626b55dcf886e50cea2bfaa69ffc14a4dbef20eec90d1b02dce0d10558042bf3ad1fd6 Homepage: https://cran.r-project.org/package=colocr Description: CRAN Package 'colocr' (Conduct Co-Localization Analysis of Fluorescence MicroscopyImages) Automate the co-localization analysis of fluorescence microscopy images. Selecting regions of interest, extract pixel intensities from the image channels and calculate different co-localization statistics. The methods implemented in this package are based on Dunn et al. (2011) . Package: r-cran-colombiapi Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5240 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-colombiapi_0.3.2-1.ca2404.1_all.deb Size: 4801334 MD5sum: 5e166d416d14cc23d974dfbb8a98bd2a SHA1: e37a91eb92372e81a1078a6cc4f46afed135c00a SHA256: e8a403de305ff06aea28dbe45a06508419a0e286f50ee00025684aab4fad4183 SHA512: 4e6c40f615af4513e9cb3ea98d1f6740089e68945309e3172d1adaf67ec0f65b52f5d320513d0f0b74518eac3b24c4da6d9a8770cf221c7ac636632337a1a564 Homepage: https://cran.r-project.org/package=ColombiAPI Description: CRAN Package 'ColombiAPI' (Access Colombian Data via APIs and Curated Datasets) Provides a comprehensive interface to access diverse public data about Colombia through multiple APIs and curated datasets. The package integrates four different APIs: 'API-Colombia' for Colombian-specific data including geography, culture, tourism, and government information; 'World Bank API' for economic and demographic indicators; 'Nager.Date' for public holidays; and 'REST Countries API' for general country information. The package enables users to explore various aspects of Colombia such as geographic locations, cultural attractions, economic indicators, demographic data, and public holidays. Additionally, 'ColombiAPI' includes curated datasets covering Bogota air stations, business and holiday dates, public schools, Colombian coffee exports, cannabis licenses, Medellin rainfall, malls in Bogota, as well as datasets on indigenous languages, student admissions and school statistics, forest liana mortality, municipal and regional data, connectivity and digital infrastructure, program graduates, vehicle counts, international visitors, and GDP projections. These datasets provide users with a rich and multifaceted view of Colombian social, economic, environmental, and technological information, making 'ColombiAPI' a comprehensive tool for exploring Colombia's diverse data landscape. For more information on the APIs, see: 'API-Colombia' , 'Nager.Date' , 'World Bank API' , and 'REST Countries API' . 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Package: r-cran-colorramps Architecture: all Version: 2.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-colorramps_2.3.4-1.ca2404.1_all.deb Size: 29028 MD5sum: 4665d000cc32d710b5845a1ef3b8c505 SHA1: cfdb9498472748a62919a82d66acc3ce5e05df04 SHA256: 630435897ec6f9010e06996db485a9db3b161070133af3a86d910f839ae448b1 SHA512: 8cb2084180de045fba8f28771b66ee9a7ecd7733f94b4d69aebc374b3b62912796c8500bc877c5d6f1c5d3edb8dcd35fe1c93cecc79ab574ad69dd900e2773d1 Homepage: https://cran.r-project.org/package=colorRamps Description: CRAN Package 'colorRamps' (Builds Color Tables) Builds gradient color maps. 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When many groups are plotted at the same time on multiple axes, for instance stacked bars or scatter plots, effectively ordering colors becomes difficult. This tool iterates through color combinations to find the best solution to maximize visual distinctness of nearby groups, so plots are more friendly toward colorblind users. This is achieved by two distance measurements, distance between groups within the plot, and CIELAB color space distances between colors as described in Carter et al., (2018) . 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You tell 'colors3d' how to map color space onto your data, and it gives you a color for each data point. You can then use these colors to make plots in base 'R', 'ggplot2', or other graphics frameworks. 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This package is deprecated and will someday be removed; for reasons and details please see the README file. Package: r-cran-colorsgen Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace Filename: pool/dists/noble/main/r-cran-colorsgen_1.0.0-1.ca2404.1_all.deb Size: 23008 MD5sum: d038bd5a6e9e562f1ea0adc4925fd4ff SHA1: 1605756ae3350fff972103a4c5700b3318d2cd80 SHA256: 5844b5f4116104ce50dab856012e2e99d874dda9e9c91ff0fd453f31eff6f587 SHA512: 06ccd8fb4987ab342dbdd4a509b8b3297281343fb26202e3faada63a38243fe62641243c315ff2e26f2826784c16be408006ee634bb57e62988c455251b383da Homepage: https://cran.r-project.org/package=colorsGen Description: CRAN Package 'colorsGen' (Generation of Random Colors) Generation of random colors, possibly with a given hue or a given luminosity. This is a port of the JavaScript library 'randomColor' . 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Build complex systems from simpler parts using a spectral product algebra. For light sources, compute CCT, CRI, SSI, and IES TM-30 reports. For object colors, compute optimal colors and Logvinenko coordinates. Work with the standard CIE illuminants and color matching functions, and read spectra from text files, including CGATS files. Estimate a spectrum from its response. A user guide and 9 vignettes are included. 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Popular color palette series have been organized by type and have been scored on several properties such as color-blind-friendliness and fairness (i.e. do colors stand out equally?). Own palettes can also be loaded and analysed. Besides the common palette types (categorical, sequential, and diverging) it also includes cyclic and bivariate color palettes. Furthermore, a color for missing values is assigned to each palette. 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'Colt' styles are defined in themes that can easily be switched, to ensure command line output looks nice on dark as well as light consoles. Package: r-cran-comato Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-lattice, r-cran-gdata, r-cran-xml, r-cran-cluster, r-cran-clustersim Filename: pool/dists/noble/main/r-cran-comato_1.1-1.ca2404.1_all.deb Size: 156674 MD5sum: c0a76b2af5bfed2c15cdf1a5232149ce SHA1: fe894fa7de56f2303c9168c7660fb12d25eb076d SHA256: 31f39a110d32c1fb155637d7cb58f9e21ed0b05d5928a982a68505379fde797f SHA512: 723cb1e3451795045c1bf214ecbd63ecb4901d2f941a03d48d4c8fe1ee278a00b1223be6cb33214fe6acf80f02faf535577bee68c244a5a897c3afe390965abc Homepage: https://cran.r-project.org/package=comato Description: CRAN Package 'comato' (Analysis of Concept Maps and Concept Landscapes) Provides methods for the import/export and automated analysis of concept maps and concept landscapes (sets of concept maps). Package: r-cran-combat.enigma Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-caret, r-cran-matrix, r-cran-nlme Filename: pool/dists/noble/main/r-cran-combat.enigma_1.1.1-1.ca2404.1_all.deb Size: 102604 MD5sum: 60bc9a5a6cc45a62fdb49ebb00a655e6 SHA1: 40df77316fcdf6ee42edd07a4d9b8ae5cd9e76ba SHA256: 31897bad79f0dbc3799bbe973cc18e7aeb9ef356ab01f805a72e69f948f3df43 SHA512: 9997f7a8b589883597cf7694b6fce4e0845cbb2442d9f9885145c1888c721308623b3f496a6ec49c903aa7240e11f89ca9db61099ffda38c38e446d38de86e11 Homepage: https://cran.r-project.org/package=combat.enigma Description: CRAN Package 'combat.enigma' (Fit and Apply ComBat, LMM, or Prescaling Harmonization forENIGMA and Other Multisite MRI Data) Fit and apply ComBat, linear mixed-effects models (LMM), or prescaling to harmonize magnetic resonance imaging (MRI) data from different sites. Briefly, these methods remove differences between sites due to using different scanning devices, and LMM additionally tests linear hypotheses. As detailed in the manual, the original ComBat function was first modified for the harmonization of MRI data (Fortin et al. (2017) ) and then modified again to create separate functions for fitting and applying the harmonization and allow missing values and constant rows for its use within the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium (Radua et al. (2020) ); this package includes the latter version. LMM calls "lme" massively considering specific brain imaging details. Finally, prescaling is a good option for fMRI, where different devices can have varying units of measurement. Package: r-cran-combat Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-corpcor Filename: pool/dists/noble/main/r-cran-combat_0.0.4-1.ca2404.1_all.deb Size: 31228 MD5sum: 7581d8b5157f90648cf309b442edb2fe SHA1: 536982efcb9b7e851a85a4c2862afc6f6d823f2e SHA256: 980444db17b33a856253d386168e9e47211360d36dae62b930c4895040d01101 SHA512: e6fc2384896df76b974eb8e0cba7cb5439f68120301f3555068298309dfdaddcb77e285613c736c224bd255bb1a1d08d53f5ed11395c1f1bddc2cf167ec7739d Homepage: https://cran.r-project.org/package=COMBAT Description: CRAN Package 'COMBAT' (A Combined Association Test for Genes using Summary Statistics) Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. Complementary to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known a priori which test is optimal. Here we proposed COMBined Association Test (COMBAT) to incorporate strengths from multiple existing gene-based tests, including VEGAS, GATES and simpleM. Compared to individual tests, COMBAT shows higher overall performance and robustness across a wide range of genetic models. The algorithm behind this method is described in Wang et al (2017) . 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Features include interactive visualization tools, robust statistical tests, and a range of harmonization techniques. Additionally, 'ComBatFamQC' enables the creation of life-span age trend plots with estimated age-adjusted centiles and facilitates the generation of covariate-corrected residuals for analytical purposes. Methods for harmonization are based on approaches described in Johnson et al., (2007) , Beer et al., (2020) , Pomponio et al., (2020) , and Chen et al., (2021) . Package: r-cran-combcoint Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2760 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-hmisc, r-cran-magrittr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tsdyn, r-cran-urca Suggests: r-cran-mts, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-combcoint_0.2.0-1.ca2404.1_all.deb Size: 2767412 MD5sum: ead4d6a785b07695bc69924bb1e52a99 SHA1: b06432772d440f8aecc9e899dadb2e64b5d6af99 SHA256: 2b5035dcb2c1dc86b8df5edde84b850067d8e5c1be6d2925e0e22dd932998299 SHA512: 9368f68a9c1186ae419fdc5f75bfe4df53c52874e381804790aab1079471898c18fafb63081d001202f5ffebfb655ad2d86096159eb876089da542fe37fc60b1 Homepage: https://cran.r-project.org/package=combcoint Description: CRAN Package 'combcoint' (A Joint Test-Statistic for the Null of Non-Cointegration) Implements a joint cointegration testing approach that combines Engle-Granger, Johansen maximum eigenvalue, Boswijk, and Banerjee tests into a unified test-statistic for the null of non-cointegration. Also see Bayer and Hanck (2013) . 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All methods used can referenced here: Heard & Rubin-Delanchy (2017) . Package: r-cran-combinatorics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-combinatorics_0.1.0-1.ca2404.1_all.deb Size: 13892 MD5sum: 5c99ed50ce3bf8848b453a47bb38b316 SHA1: 33589c3d33d42434ae89c889e7db4e07f1868360 SHA256: 74b4f5d72e0327c0b2d3d5cd6c94a7dc2bbf1b953f95ca2d9ee695a5bb789952 SHA512: 9e9287e448003bb61658d973f7ceed374c4059c0ab4059882d7746ec3a808ada0b894eb47cd6b1b9c80837f0f16cca71f06b49c4ab4d02ea8a64aba0b1b2bdd7 Homepage: https://cran.r-project.org/package=combinatorics Description: CRAN Package 'combinatorics' (Introduction to Some Combinatorial Relations) Determining the value of Stirling numbers of 1st kind and 2nd kind,references: Bóna,Miklós(2017,ISBN 9789813148840). Package: r-cran-combinedevents Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-magrittr, r-cran-rlang, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling, r-cran-covr Filename: pool/dists/noble/main/r-cran-combinedevents_0.1.1-1.ca2404.1_all.deb Size: 89772 MD5sum: 62f48191c8d6d9dcdab69dbfe372bd56 SHA1: 60d2d0a74d57c52a65a9b894bce16fd04df8dd0e SHA256: 745c600cdc438909a46cdc6a4d9404eb9035ba6fc35fc46d81ae75c905e043fd SHA512: 7cd1cee3c06213ec1f1a148ec01cb29a416f2b2083cf640ca05d448174efe170b8d2b6628435b280e1d80b80b1ebb5f12475200f39e6a589ea6fb247a60438c7 Homepage: https://cran.r-project.org/package=combinedevents Description: CRAN Package 'combinedevents' (Calculate Scores and Marks for Track and Field Combined Events) Includes functions to calculate scores and marks for track and field combined events competitions. The functions are based on the scoring tables for combined events set forth by the International Association of Athletics Federation (2001). Package: r-cran-combineportfolio Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-combineportfolio_0.4-1.ca2404.1_all.deb Size: 75702 MD5sum: e98c7a3bf72a06df3ce8fb692f125106 SHA1: 9d458320fe593d45724a5fa61b41bea8b77e8e0b SHA256: 5a24b030be74dc0f105c5c6580006af147eb6e1a079607dce5ded567d36407d1 SHA512: f138c5bdef6a13a09ca62a85de67c6468de078b66b5516e273040e3e72129078426b995200e79d96788e01d92d31ae4aef1e7bd328898b9b9dc78d0617f1c4ad Homepage: https://cran.r-project.org/package=CombinePortfolio Description: CRAN Package 'CombinePortfolio' (Estimation of Optimal Portfolio Weights by Combining SimplePortfolio Strategies) Estimation of optimal portfolio weights as combination of simple portfolio strategies, like the tangency, global minimum variance (GMV) or naive (1/N) portfolio. It is based on a utility maximizing 8-fund rule. Popular special cases like the Kan-Zhou(2007) 2-fund and 3-fund rule or the Tu-Zhou(2011) estimator are nested. Package: r-cran-combins Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-combins_1.2-1.ca2404.1_all.deb Size: 55422 MD5sum: d8a2a6a3ec6fad14aa09cac7aa19e5ec SHA1: 32fa2bfe860c731ad878c66bfade784d88b268c4 SHA256: 2ea995d561adda8a285b35bf224184fe881d803bec23ae06daeb4adb4458efeb SHA512: b1aabc58a094a37b949cafd9a2248de084bd9f71eacbd11627930e4444458caf2c3d1321d517b617ce65e35139675b41a7af55690ff62b3259a18f27822c176a Homepage: https://cran.r-project.org/package=CombinS Description: CRAN Package 'CombinS' (Construction Methods for Series of PBIB Designs via CombinatoryMethod S) Provides constructions of series of partially balanced incomplete block designs (PBIB) based on the combinatory method S, introduced by Rezgui et al. (2014) . This package also offers the associated U-type designs. Version 1.1-1 generalizes the approach to designs with v = wnl treatments. It includes various rectangular and generalized rectangular right angular association schemes with 4, 5, and 7 associated classes. 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Allows to rank and select multi-markers signatures as well as to find the best performing sub-signatures, now also from single-cell RNA-seq datasets. The method used was first published as a Shiny app and described in Mazzara et al. (2017) and further described in Bombaci & Rossi (2019) , and widely expanded as a package as presented in the bioRxiv pre print Ferrari et al. . Package: r-cran-combo Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 769 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-matrix, r-cran-rjags, r-cran-turboem, r-cran-samba Suggests: r-cran-knitr, r-cran-testthat, r-cran-devtools, r-cran-xtable Filename: pool/dists/noble/main/r-cran-combo_1.2.0-1.ca2404.1_all.deb Size: 684104 MD5sum: 3eb1b63970da84f211f09ca2697eba28 SHA1: 9a0827071133d0b062a42aa290dcdd115cae680e SHA256: 259cf9053637b8584fe7b8827325c0b0d887e0720e75384e49a9adcdfbbf0005 SHA512: 1e5bd123bbac62019a24af25e2ec4692c645f80ce7ef9fd434ec7dd1456eb6de557bbeb10636121cd1b42b6adc1a305de5eafe4c9159b6a628322a3238d46173 Homepage: https://cran.r-project.org/package=COMBO Description: CRAN Package 'COMBO' (Correcting Misclassified Binary Outcomes in Association Studies) Use frequentist and Bayesian methods to estimate parameters from a binary outcome misclassification model. These methods correct for the problem of "label switching" by assuming that the sum of outcome sensitivity and specificity is at least 1. A description of the analysis methods is available in Hochstedler and Wells (2023) . 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Reformulates the NP-hard discrete subset selection problem as a continuous optimisation over the hypercube [0,1]^p, solved via a Frank-Wolfe homotopy algorithm with closed-form ridge inner solves. Supports linear (Gaussian), binary logistic, and multinomial regression. For methodological details see Moka, Liquet, Zhu and Muller (2024) and Mathur, Liquet, Muller and Moka (2026) . 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The package implements versions of the generalised covariance measure test (Shah and Peters, 2020, ) and projected covariance measure test (Lundborg et al., 2023, ). The tram-GCM test, for censored responses, is implemented including the Cox model and survival forests (Kook et al., 2024, ). Application examples to variable significance testing and modality selection can be found in Kook and Lundborg (2024, ). 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Australian and Swedish modifications of the Charlson Comorbidity Index are available as well (Sundararajan, 2004 and Ludvigsson, 2021 ), together with different weighting algorithms for both the Charlson and Elixhauser comorbidity scores. 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Package: r-cran-compare Architecture: all Version: 0.2-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-compare_0.2-6-1.ca2404.1_all.deb Size: 504056 MD5sum: a966399c6339160d96caa98735b2f9ca SHA1: 7b07b31f740cf0c64744a22a99f05f2622702a3e SHA256: e4ae13a051bc8e4adc4e9b19c08805a87a5ec12a2f23288e478cbe6ffcf5542c SHA512: 5687521ff0a283ac53b79310b3b4ee027d40646d725c61937da3964780a061034e9114d3f3247f69263393a8a4bffc28bd83127ad936e9d99b50138a052e1064 Homepage: https://cran.r-project.org/package=compare Description: CRAN Package 'compare' (Comparing Objects for Differences) Functions to compare a model object to a comparison object. If the objects are not identical, the functions can be instructed to explore various modifications of the objects (e.g., sorting rows, dropping names) to see if the modified versions are identical. Package: r-cran-comparecausalnetworks Architecture: all Version: 0.2.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-expm, r-cran-data.table Suggests: r-cran-pcalg, r-cran-invariantcausalprediction, r-cran-glmnet, r-cran-backshift, r-cran-kernlab, r-cran-mgcv, r-cran-mboost, r-cran-bnlearn, r-cran-testthat, r-cran-huge, r-cran-flare Filename: pool/dists/noble/main/r-cran-comparecausalnetworks_0.2.6.2-1.ca2404.1_all.deb Size: 234804 MD5sum: b5cfde0b55c4912870bacd4bf193b38b SHA1: d5ac4daf8420a6caba63b31d2539691b711d5258 SHA256: 6b148828c6b84365b244e8e3ccb9db5ea2f47392a211e7e702fc167749adf0c6 SHA512: 453bb5746f7ffc41512b9a8cf20782f8fdfb0ac8946431450ef8a985a52574c0f58347391bc28bb751b3efaad5a7e7458a49c3be1e55a054d5440ee59f688b83 Homepage: https://cran.r-project.org/package=CompareCausalNetworks Description: CRAN Package 'CompareCausalNetworks' (Interface to Diverse Estimation Methods of Causal Networks) Unified interface for the estimation of causal networks, including the methods 'backShift' (from package 'backShift'), 'bivariateANM' (bivariate additive noise model), 'bivariateCAM' (bivariate causal additive model), 'CAM' (causal additive model) (from package 'CAM'; the package is temporarily unavailable on the CRAN repository; formerly available versions can be obtained from the archive), 'hiddenICP' (invariant causal prediction with hidden variables), 'ICP' (invariant causal prediction) (from package 'InvariantCausalPrediction'), 'GES' (greedy equivalence search), 'GIES' (greedy interventional equivalence search), 'LINGAM', 'PC' (PC Algorithm), 'FCI' (fast causal inference), 'RFCI' (really fast causal inference) (all from package 'pcalg') and regression. 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Returns confidence intervals and a p-value for the difference in C-statistics. Useful for evaluating and comparing predictive performance of survival models. Methods implemented for Uno's C are described in Uno et al. (2011) . Package: r-cran-comparedesign Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-ggplot2, r-cran-ggpubr, r-cran-rootsolve, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-comparedesign_2.4.0-1.ca2404.1_all.deb Size: 314588 MD5sum: 53e2a4789b89660745c6b29edbf63459 SHA1: eb4605665a9d96025f643b9abee933bea310bc8d SHA256: afc6dfee0ca3bc8136db925202d951098ea9be88c476c1be71cfb62f7f55dc1f SHA512: cf0c86a42c7cd9d8d431711adce8d3ab4e63cea2d6c928becb1b077b2167487b59657a5f75d1a531764b922705f1d6bf40179a1bd0b667db710b36be33a1da33 Homepage: https://cran.r-project.org/package=CompAREdesign Description: CRAN Package 'CompAREdesign' (Statistical Functions for the Design of Studies with CompositeEndpoints) It has been designed to calculate the required sample size in randomized clinical trials with composite endpoints. It also calculates the expected effect and the probability of observing the composite endpoint, among others. The methodology can be found in Bofill & Gómez (2019) and Gómez & Lagakos (2013) . Package: r-cran-comparedf Architecture: all Version: 2.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-htmltable, r-cran-openxlsx, r-cran-tidyr, r-cran-stringr, r-cran-tibble, r-cran-rlang Suggests: r-cran-testthat, r-cran-futile.logger, r-cran-covr Filename: pool/dists/noble/main/r-cran-comparedf_2.3.5-1.ca2404.1_all.deb Size: 1069798 MD5sum: 472ae7d568c1b40c92852a2be0471c92 SHA1: a98808fa8b03d5f55852659ca500f876ca606e8a SHA256: 8c15a46065b30702648616ac1f2c19ba9ee41cb83be5125a86d7339f70023b24 SHA512: 587990a085828b9c4335d1d934340bad885808af884264e5c8554d78e320721a6a22740d558300deaa2aa7dcc82e4ad05ff231a581c3f503b26876cda9932e21 Homepage: https://cran.r-project.org/package=compareDF Description: CRAN Package 'compareDF' (Do a Git Style Diff of the Rows Between Two Dataframes withSimilar Structure) Compares two dataframes which have the same column structure to show the rows that have changed. Also gives a git style diff format to quickly see what has changed in addition to summary statistics. Package: r-cran-comparegroups Architecture: all Version: 4.10.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4464 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-hardyweinberg, r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra, r-cran-chron, r-cran-writexl, r-cran-flextable, r-cran-officer, r-cran-pmcmrplus, r-cran-rmdformats, r-cran-rstatix Suggests: r-cran-tcltk2, r-cran-shiny, r-cran-shinybs, r-cran-shinyjs, r-cran-shinyjqui, r-cran-shinythemes, r-cran-shinywidgets, r-cran-shinydashboardplus, r-cran-dt, r-cran-readxl, r-cran-haven Filename: pool/dists/noble/main/r-cran-comparegroups_4.10.2-1.ca2404.1_all.deb Size: 3368228 MD5sum: 2b511126bd1fdfd36acf3990dc8cd3e0 SHA1: e1f0412330453626d5117d7bab3a04b922444237 SHA256: 5e11784f95601301e15a70c65265a3c2fc99b8492075e576cddfb510479a6785 SHA512: 797238f01609cfaef9b6ace289ce7028cadfba441aa6c5956bbd6886e6a1f40a3887ecbb56fa1ceba91823875d60524bcdd84126cf6907c08c3876c9b78dc3ad Homepage: https://cran.r-project.org/package=compareGroups Description: CRAN Package 'compareGroups' (Descriptive Analysis by Groups) Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data. Package: r-cran-comparemcmcs Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 906 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nimble, r-cran-r6, r-cran-ggplot2, r-cran-reshape2, r-cran-xtable, r-cran-coda, r-cran-dplyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rjags, r-cran-rstan Filename: pool/dists/noble/main/r-cran-comparemcmcs_0.6.0-1.ca2404.1_all.deb Size: 373888 MD5sum: b887873b0432748cd5dde203fe11f77c SHA1: 6280a0accc7ca051c46daea12b3e3c68026b6f77 SHA256: f17537ec4c5f8b468e6fd2a98ba20409c6b1b17838bddf3bc126cd64f19b94dc SHA512: 754ab2cd9c71c063cb85d863664e55e723bc69c1473cfedf7817fff2016b83b7521a5dad7f3d0b96b44e68c517c964319cabc77e024da8f63dcd5ee00d384504 Homepage: https://cran.r-project.org/package=compareMCMCs Description: CRAN Package 'compareMCMCs' (Compare MCMC Efficiency from 'nimble' and/or Other MCMC Engines) Manages comparison of MCMC performance metrics from multiple MCMC algorithms. These may come from different MCMC configurations using the 'nimble' package or from other packages. Plug-ins for JAGS via 'rjags' and Stan via 'rstan' are provided. It is possible to write plug-ins for other packages. Performance metrics are held in an MCMCresult class along with samples and timing data. It is easy to apply new performance metrics. Reports are generated as html pages with figures comparing sets of runs. It is possible to configure the html pages, including providing new figure components. Package: r-cran-comparemultiplemodels Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ceemdanml Filename: pool/dists/noble/main/r-cran-comparemultiplemodels_0.1.0-1.ca2404.1_all.deb Size: 15340 MD5sum: cab4c2c33eaa4b0925e1402b7356934f SHA1: 8c66bef0886794eada265bc511cb119bb03fd98a SHA256: a70b153c28700ba83f987f3f5a2441b4de3f3bf59cf2bb46e787542fe517ae46 SHA512: a49f2fc556bc3009c9db5cacc8a3cf0834fe1995e6df7c4c7888539994b2808c5eed6a8ef97213b6d2a88719ef37a86c7eaa942ae7adf545a2e487f51086b698 Homepage: https://cran.r-project.org/package=CompareMultipleModels Description: CRAN Package 'CompareMultipleModels' (Finding the Best Model Using Eight Metrics Values) In statistical modeling, multiple models need to be compared based on certain criteria. The method described here uses eight metrics from 'AllMetrics' package. ‘input_df’ is the data frame (at least two columns for comparison) containing metrics values in different rows of a column (which denotes a particular model’s performance). First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as ‘MIN’ and other values are denoted as ‘NA’. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as ‘MAX’ and other values are denoted as ‘NA’. ‘output_df’ contains the similar number of rows (which is 8) and columns (which is number of models to be compared) as of ‘input_df’. Values in ‘output_df’ are corresponding ‘NA’, ‘MIN’ or ‘MAX’. Finally, the column containing minimum number of ‘NA’ values is denoted as the best column. ‘min_NA_col’ gives the name of the best column (model). ‘min_NA_values’ are the corresponding metrics values. ‘BestColumn_metrics’ is the data frame (dimension: 1*8) containing different metrics of the best column (model). ‘best_column_results’ is the final result (a list) containing all of these output elements. In special case, if two columns having equal 'NA', it will be checked among these two column which one is having least 'NA' in first five rows and will be inferred as the best. More details about 'AllMetrics' can be found in Garai (2023) . Package: r-cran-comparer Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gaupro, r-cran-mixopt, r-cran-rmarkdown, r-cran-plyr, r-cran-progress, r-cran-r6 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-ggplot2, r-cran-ggally, r-cran-ggpubr, r-cran-contourfunctions, r-cran-snow, r-cran-tibble, r-cran-lhs, r-cran-dicekriging, r-cran-diceoptim, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-comparer_0.2.4-1.ca2404.1_all.deb Size: 499402 MD5sum: f12437bf4a674b67030b2bfae889fd80 SHA1: a57f9aa50413084a06f3f6af0e29970c1c5b5dbc SHA256: 7e8a48a0f782da2e9e3fa981ee151839c054ce62f0c7bcc6e629c181002334be SHA512: 7ee12bc20f2561692f149d4da0bf0f079790101a0ea4c58586dea8a626ba0bf0e3ac4fd1df8860a69aef0e7ee2f75b1a4b89a1a17ebc785b4d90491a54ed9b8f Homepage: https://cran.r-project.org/package=comparer Description: CRAN Package 'comparer' (Compare Output and Run Time) Quickly run experiments to compare the run time and output of code blocks. The function mbc() can make fast comparisons of code, and will calculate statistics comparing the resulting outputs. It can be used to compare model fits to the same data or see which function runs faster. The R6 class ffexp$new() runs a function using all possible combinations of selected inputs. This is useful for comparing the effect of different parameter values. It can also run in parallel and automatically save intermediate results, which is very useful for long computations. Package: r-cran-comparetests Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-comparetests_1.3-1.ca2404.1_all.deb Size: 38242 MD5sum: 47ca3cd8d0d7d505c1c5a5bf5d606a73 SHA1: 0e917dd156b309873c5fa8674592cb6201a550a9 SHA256: a801677b620884ee31578dc8f93d6d5fdfc1b594a201306a3d5cf5f1374fbeff SHA512: f1b199350ee7d98129f6a0f4a7ba01e598cd2cb6da0eb52c5c13602798a02da5e8e460e15405a44c9773b22eeef5f83f008ea942d1cac9cf33df28d9de2cb3f9 Homepage: https://cran.r-project.org/package=CompareTests Description: CRAN Package 'CompareTests' (Correct for Verification Bias in Diagnostic Accuracy & Agreement) A standard test is observed on all specimens. We treat the second test (or sampled test) as being conducted on only a stratified sample of specimens. Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control. We treat the total sample as stratified two-phase sampling and use inverse probability weighting. We estimate diagnostic accuracy (category-specific classification probabilities; for binary tests reduces to specificity and sensitivity, and also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar's test for binary tests)). See: Katki HA, Li Y, Edelstein DW, Castle PE. Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5) . 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(2020) , fixed-point tests in Klein et al. (2007) , short-term tests, and long-term tests in Logan et al. (2008) . Some commonly used descriptive statistics and plots are also included. 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The functions can be used to generate realistic networks with a wide range of different clustering, density, and average path length. For more information consult research articles by Amiyaal Ilany and Erol Akcay (2016) and Ilany and Erol Akcay (2016) , which have inspired many methods in this package. 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Package: r-cran-complexupset Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4013 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-patchwork, r-cran-scales, r-cran-colorspace Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-tibble, r-cran-ggplot2movies, r-cran-vdiffr, r-cran-jsonlite, r-cran-data.table Filename: pool/dists/noble/main/r-cran-complexupset_1.3.3-1.ca2404.1_all.deb Size: 2665036 MD5sum: 1132848463b93e72f12b0585eebbe7e4 SHA1: 7b1150318f7b53eab9603bb5eeddd4f387f7d25a SHA256: 0528194f3132b4000ffab4a7e9603aa43b9d6e5c45b6b600d2e81a116f07a498 SHA512: 00ac648b226e4590a7c6aa27f82f31af6ebf0edadd703e87b93fbb8c771144a5ce948346206a85ff3d041deab10234fdfd4dc80c823fd23c0cf407ab816f6cc8 Homepage: https://cran.r-project.org/package=ComplexUpset Description: CRAN Package 'ComplexUpset' (Create Complex UpSet Plots Using 'ggplot2' Components) UpSet plots are an improvement over Venn Diagram for set overlap visualizations. Striving to bring the best of the 'UpSetR' and 'ggplot2', this package offers a way to create complex overlap visualisations, using simple and familiar tools, i.e. geoms of 'ggplot2'. For introduction to UpSet concept, see Lex et al. (2014) . Package: r-cran-complmrob Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-robustbase, r-cran-ggplot2, r-cran-boot, r-cran-scales Filename: pool/dists/noble/main/r-cran-complmrob_0.7.1-1.ca2404.1_all.deb Size: 94800 MD5sum: bf4457f4f2069bb5f58c5abcdc778dda SHA1: af870b83950b6963a87688b8e2c18b019d7ca720 SHA256: 6af3c4c1c4ae7510413d67799657a88ff90d6d48f1d3f34f1068a136b8a6c682 SHA512: 730e55da1bc705bda88107de840c0803d26d3971c51a59c6c4d830a41098390de48ec7f7c7b71a13ce149a270402d7802496c81bea9510c665bb179d092b6917 Homepage: https://cran.r-project.org/package=complmrob Description: CRAN Package 'complmrob' (Robust Linear Regression with Compositional Data as Covariates) Robust regression methods for compositional data. The distribution of the estimates can be approximated with various bootstrap methods. These bootstrap methods are available for the compositional as well as for standard robust regression estimates. This allows for direct comparison between them. Package: r-cran-compmix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mvtnorm, r-cran-gglasso, r-cran-higlasso, r-cran-hiernet, r-cran-glmnet, r-cran-superlearner, r-cran-bkmr, r-cran-qgcomp, r-cran-gwqs, r-cran-proc, r-cran-randomforest, r-cran-devtools Filename: pool/dists/noble/main/r-cran-compmix_0.1.0-1.ca2404.1_all.deb Size: 91472 MD5sum: dfb44dd15dd918c88f10e6f2655471e5 SHA1: 5155330c7c8471e9d5ab50272134731bc166d2b7 SHA256: d7e5a4920d7ecd58ca80e7b05c3ef51067de1661f2e3d922b801c358e4f74186 SHA512: 8fbeaf42992c42111231b2edd5f44528b0c0ba0bfbabb5a289f3212fa04ed018be79f4bae450dff81a5790907be8120bab6db63922c9ef95b7704c74a275cf59 Homepage: https://cran.r-project.org/package=CompMix Description: CRAN Package 'CompMix' (A Comprehensive Toolkit for Environmental Mixtures Analysis('CompMix')) Quantitative characterization of the health impacts associated with exposure to chemical mixtures has received considerable attention in current environmental and epidemiological studies. 'CompMix' package allows practitioners to estimate the health impacts from exposure to chemical mixtures data through various statistical approaches, including Lasso, Elastic net, Bayeisan kernel machine regression (BKMR), hierNet, Quantile g-computation, Weighted quantile sum (WQS) and Random forest. Hao W, Cathey A, Aung M, Boss J, Meeker J, Mukherjee B. (2024) "Statistical methods for chemical mixtures: a practitioners guide". . Package: r-cran-compositereliability Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-magrittr, r-cran-plyr, r-cran-psych, r-cran-reshape2, r-cran-tidyr, r-cran-rsolnp Filename: pool/dists/noble/main/r-cran-compositereliability_1.0.3-1.ca2404.1_all.deb Size: 43972 MD5sum: f434ef9ff90989b349b6154fb45bcfe0 SHA1: 1aa792a4152d8dc0967d09db69ad4c9d5d9f587f SHA256: 15c838cf6aef5e6d3da02d8ae1940281e43c2b6cbf5732eceb0b5b84c1fac45b SHA512: 6d3297005f747063c6ada5444f9f969b92b899386b711abd7780c12985dc845bc58aa809939bb7a036cb3f7b546171ceb10f9355aa0bf07200f574f0f45c6759 Homepage: https://cran.r-project.org/package=CompositeReliability Description: CRAN Package 'CompositeReliability' (Determine the Composite Reliability of a Naturalistic,Unbalanced Dataset) The reliability of assessment tools is a crucial aspect of monitoring student performance in various educational settings. It ensures that the assessment outcomes accurately reflect a student's true level of performance. However, when assessments are combined, determining composite reliability can be challenging, especially for naturalistic and unbalanced datasets. This package provides an easy-to-use solution for calculating composite reliability for different assessment types. It allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations. Overall, our approach enhances the reliability of composite assessments, making it suitable for various education contexts. Package: r-cran-compositereliabilityinnesteddesigns Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-magrittr, r-cran-plyr, r-cran-psych, r-cran-reshape2, r-cran-tidyr, r-cran-rsolnp Filename: pool/dists/noble/main/r-cran-compositereliabilityinnesteddesigns_1.0.4-1.ca2404.1_all.deb Size: 44954 MD5sum: 239aa8c0f6e30de678fa1599abe975ba SHA1: 9550229c56e5885969de5b818cf97b16def91f9e SHA256: dba8800105942a04c98a4dd32b4b89aa3d7400c69d2dee317b6a27d93c4ae5ca SHA512: 5fa7fe2c25afb51e54decc826269efe0bd53f39d6587de1a77195447af2914e992bde7c49dc8227eda1f7482f1a87c965b3f815e556dac9291af526ef301d8d6 Homepage: https://cran.r-project.org/package=compositeReliabilityInNestedDesigns Description: CRAN Package 'compositeReliabilityInNestedDesigns' (Optimizing the Composite Reliability in Multivariate NestedDesigns) The reliability of assessment tools is a crucial aspect of monitoring student performance in various educational settings. It ensures that the assessment outcomes accurately reflect a student's true level of performance. However, when assessments are combined, determining composite reliability can be challenging, especially for naturalistic and unbalanced datasets in nested design as is often the case for Workplace-Based Assessments. This package is designed to estimate composite reliability in nested designs using multivariate generalizability theory and enhance the analysis of assessment data. The package allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations, and options to find the set of weights that maximizes the composite reliability or minimizes the standard error of measurement (SEM). Package: r-cran-compositional.mle Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 992 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-algebraic.mle, r-cran-mass, r-cran-numderiv Suggests: r-cran-rmarkdown, r-cran-dplyr, r-cran-knitr, r-cran-ggplot2, r-cran-tibble, r-cran-testthat, r-cran-cli, r-cran-future, r-cran-hypothesize, r-cran-likelihood.model Filename: pool/dists/noble/main/r-cran-compositional.mle_2.0.0-1.ca2404.1_all.deb Size: 553614 MD5sum: 69ec1198f38c821d6281c38466d8ae47 SHA1: 4bc8f140f950505aa6726b82261815a5db20155c SHA256: 41612aec1039c2474747dd4ad61b7b44258010b5538420eb3f78cbbd2b74feb9 SHA512: 75d7c17950bef64c829a8f509500909da7fa25b1e8f35a3489e494b2af1477422c86f3e10980ecaff4177573c6a7627360e5d0755418ed221b9dec634133107d Homepage: https://cran.r-project.org/package=compositional.mle Description: CRAN Package 'compositional.mle' (Compositional Maximum Likelihood Estimation) Provides composable optimization strategies for maximum likelihood estimation (MLE). Solvers are first-class functions that combine via sequential chaining, parallel racing, and random restarts. Implements gradient ascent, Newton-Raphson, quasi-Newton (BFGS), and derivative-free methods with support for constrained optimization and tracing. Returns 'mle' objects compatible with 'algebraic.mle' for downstream analysis. Methods based on Nocedal J, Wright SJ (2006) "Numerical Optimization" . Package: r-cran-compositional Architecture: all Version: 8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bigstatsr, r-cran-cluster, r-cran-emplik, r-cran-glmnet, r-cran-quantreg, r-cran-mass, r-cran-matrix, r-cran-mda, r-cran-minpack.lm, r-cran-mixture, r-cran-nnet, r-cran-osqp, r-cran-quadprog, r-cran-rangen, r-cran-rfast, r-cran-rfast2, r-cran-rgl, r-cran-rnanoflann, r-cran-sn Suggests: r-cran-bigparallelr, r-cran-codalm, r-cran-flexdir Filename: pool/dists/noble/main/r-cran-compositional_8.2-1.ca2404.1_all.deb Size: 1174430 MD5sum: 79f3f899ac0eab296f22b0523c88e175 SHA1: 04bbb2c76d83e5cc1d96cc27df19e6ba64bc5d0b SHA256: 091f54cfcf7f14a3e408386e9b3cfaad836e6ba1993690f5b742d4c90cf61453 SHA512: f19aa8eceb6d7a3bcd2f4ab447163ebf8a599c487b67d7456d4855b0ba481009e67ba3dbbe512028b5c733724919794d6091e5ce27c5ea0dd6c3be965b15dd6a Homepage: https://cran.r-project.org/package=Compositional Description: CRAN Package 'Compositional' (Compositional Data Analysis) Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. We further include functions for percentages (or proportions). The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include: a) Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data--based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. . b) Tsagris M. (2014). "The k--NN algorithm for compositional data: a revised approach with and without zero values present". Journal of Data Science, 12(3): 519--534. . c) Tsagris M. (2015). "A novel, divergence based, regression for compositional data". Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. . d) Tsagris M. (2015). "Regression analysis with compositional data containing zero values". Chilean Journal of Statistics, 6(2): 47--57. . e) Tsagris M., Preston S. and Wood A.T.A. (2016). "Improved supervised classification for compositional data using the alpha-transformation". Journal of Classification, 33(2): 243--261. . f) Tsagris M., Preston S. and Wood A.T.A. (2017). "Nonparametric hypothesis testing for equality of means on the simplex". Journal of Statistical Computation and Simulation, 87(2): 406--422. . g) Tsagris M. and Stewart C. (2018). "A Dirichlet regression model for compositional data with zeros". Lobachevskii Journal of Mathematics, 39(3): 398--412. . h) Alenazi A. (2019). "Regression for compositional data with compositional data as predictor variables with or without zero values". Journal of Data Science, 17(1): 219--238. . i) Tsagris M. and Stewart C. (2020). "A folded model for compositional data analysis". Australian and New Zealand Journal of Statistics, 62(2): 249--277. . j) Alenazi A.A. (2022). "f--divergence regression models for compositional data". Pakistan Journal of Statistics and Operation Research, 18(4): 867--882. . k) Tsagris M. and Stewart C. (2022). "A Review of Flexible Transformations for Modeling Compositional Data". In Advances and Innovations in Statistics and Data Science, pp. 225--234. . l) Alenazi A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. . m) Tsagris M., Alenazi A. and Stewart C. (2023). "Flexible non--parametric regression models for compositional response data with zeros". Statistics and Computing, 33(106). . n) Tsagris. M. (2025). "Constrained least squares simplicial--simplicial regression". Statistics and Computing, 35(27). . o) Sevinc V. and Tsagris. M. (2026). "Energy Based Equality of Distributions Testing for Compositional Data". Communications in Statistics--Simulation and Computation. . p) Tsagris M. and Alzeley O. (2025). "Scalable approximation of the transformation--free linear simplicial--simplicial regression via constrained iterative reweighted least squares". . Package: r-cran-compositionalclust Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compositional, r-cran-doparallel, r-cran-factoextra, r-cran-foreach, r-cran-lowmemtkmeans, r-cran-mixture, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalclust_1.2-1.ca2404.1_all.deb Size: 69066 MD5sum: 4068066cc8356dfd1a68937235644e67 SHA1: 27ce610e8e63cde805b091c52a75321da52553cc SHA256: 540ccc02e1f413a8937fcaf7a06fa59e37e67bb3462c078cfdf62d3a00645f91 SHA512: 2959b48d70affda7bf67e5a3a04cba476eb9d6f6ec387d827a209803e9b677772a2635c8403d84e48b298060e89b298159c7eb0150bf480fa4d2a111e0487a6c Homepage: https://cran.r-project.org/package=CompositionalClust Description: CRAN Package 'CompositionalClust' (Clustering with Compositional Data) Cluster analysis with compositional data using the alpha--transformation. Relevant papers include: Tsagris M. and Kontemeniotis N. (2025), . Tsagris M.T., Preston S. and Wood A.T.A. (2011), . Garcia-Escudero Luis A., Gordaliza Alfonso, Matran Carlos, Mayo-Iscar Agustin. (2008), . Package: r-cran-compositionalhdda Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compositional, r-cran-hdclassif, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalhdda_1.0-1.ca2404.1_all.deb Size: 24268 MD5sum: 8c5265f42c81aae088d746a8f1d0983b SHA1: b0c2570ff4783e59630c0b2f550c54799e7a2ea4 SHA256: 15e57038bf7a7542762e130537b992b4d996df5bd626cb1bf4a9c9da0e266d00 SHA512: cf75cd8b2328bde3fbdf62db0342d7b3b00dd2a736ad59f201e56cfd2a82ffef4ff6b4c0df62f9009ef971268905327ed1d4faa6324cbc1841cae478c450c211 Homepage: https://cran.r-project.org/package=CompositionalHDDA Description: CRAN Package 'CompositionalHDDA' (High Dimensional Discriminant Analysis with Compositional Data) High dimensional discriminant analysis with compositional data is performed. The compositional data are first transformed using the alpha-transformation of Tsagris M., Preston S. and Wood A.T.A. (2011) , and then the High Dimensional Discriminant Analysis (HDDA) algorithm of Bouveyron C. Girard S. and Schmid C. (2007) is applied. Package: r-cran-compositionalml Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boruta, r-cran-compositional, r-cran-doparallel, r-cran-e1071, r-cran-foreach, r-cran-ranger, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalml_1.0-1.ca2404.1_all.deb Size: 68978 MD5sum: 2f800d30775f9b8eef70f8746b1ce15d SHA1: 2db31ea8a46156eef1a02c9911200c0397dafb78 SHA256: e396ade17597d4fb38472f32da0bbac5a024ed013d2797587c8f45b64937d1c8 SHA512: 52637c6ed59d6cd144eab2cbf294d9de2970ee178b4e6d701fb9b63affb0866845b61298427295dde451e82c27bd129cd14f2d609f344b9b88d5feb91e597943 Homepage: https://cran.r-project.org/package=CompositionalML Description: CRAN Package 'CompositionalML' (Machine Learning with Compositional Data) Machine learning algorithms for predictor variables that are compositional data and the response variable is either continuous or categorical. Specifically, the Boruta variable selection algorithm, random forest, support vector machines and projection pursuit regression are included. Relevant papers include: Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. and Alenazi, A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. . Package: r-cran-compositionalnaimp Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compositional, r-cran-rfast, r-cran-rnanoflann Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalnaimp_1.0-1.ca2404.1_all.deb Size: 37596 MD5sum: 137448be24cd7fd851957ebd212acf73 SHA1: 5d99a8a48883e778af83eeffc3b367d70130ac8e SHA256: 9e18723c65e99a88173e7d4248940c4bfca98270e56ea2a480e85860a0bbb1c3 SHA512: 0177222bb7ee4090595dd401bc0d3098dc6192827b4d91a15da3b96cc12f5e240138a9d44001a346288bab990eab6d73572277dcbba0f8fb3d511b4e1996cc86 Homepage: https://cran.r-project.org/package=CompositionalNAimp Description: CRAN Package 'CompositionalNAimp' (Missing Value Imputation with Compositional Data) Functions to perform missing value imputation with compositional data using the Jensen-Shannon divergence based k--NN and a--k--NN algorithms. The functions are based on the following paper: Tsagris M., Alenazi A. and Stewart C. (2026). "A Jensen--Shannon divergence based k--NN algorithm for missing value imputation in compositional data". Journal of Applied Statistics (Accepted for publication). Package: r-cran-compositionalrf Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compositional, r-cran-doparallel, r-cran-foreach, r-cran-multivariaterandomforest, r-cran-rfast Filename: pool/dists/noble/main/r-cran-compositionalrf_1.0-1.ca2404.1_all.deb Size: 23278 MD5sum: 09e3261c659d4167cafc822dfa04bbba SHA1: 9f8db26bb83ccf8e13fedb38d992a1253f470af1 SHA256: e2620a598ce9996f0ee1d709ad32081caa15c84de7de4586036ac1a9c5abbc10 SHA512: 40bd51bfd51866ea3235d4901a265c1201825aad98f0ffb7fcdc9a728bf43eeac10e0a5cc5a0e8ee55a094ec753d55b2203e869c93d903678e17fdd20f94d076 Homepage: https://cran.r-project.org/package=CompositionalRF Description: CRAN Package 'CompositionalRF' (Multivariate Random Forest with Compositional Responses) Non linear regression with compositional responses and Euclidean predictors is performed. The compositional data are first transformed using the additive log-ratio transformation, and then the multivariate random forest of Rahman R., Otridge J. and Pal R. (2017), , is applied. Package: r-cran-compositionalsr Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockcv, r-cran-compositional, r-cran-doparallel, r-cran-foreach, r-cran-gslnls, r-cran-minpack.lm, r-cran-rangen, r-cran-rfast, r-cran-sf, r-cran-spmoran Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-compositionalsr_1.4-1.ca2404.1_all.deb Size: 273036 MD5sum: f5e623e38f0c312f6e630184f17b1292 SHA1: 0ccfa387dbbd01c3eee11e899faf9184bb86d291 SHA256: fcdebd1a8135fdcd3063eea31fbdeec5d11d9479ad57a85b66f7a97c21da88bf SHA512: c53d96e03c2ad0842f62a085dbd1a124fe4a55e110396aedb24631bc8e232eed8d6d1937cb30a08ccde272747614e83ed7ab6d1724ea5ff3a1c5a66cdb82001d Homepage: https://cran.r-project.org/package=CompositionalSR Description: CRAN Package 'CompositionalSR' (Spatial Regression Models with Compositional Data) Spatial and non-spatial regression models with compositional responses (and compositional predictors) using the alpha--transformation. Relevant papers include: Tsagris M. and Pantazis Y. (2026), , Tsagris M. (2015), , Tsagris M.T., Preston S. and Wood A.T.A. (2011), . 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Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) , statistical methods in Emura et al (2012 PLoS ONE) , Emura & Chen (2016 Stat Methods Med Res) , and Emura et al (2019). Algorithms for generating correlated gene expressions are also available. Estimation of survival functions via copula-graphic (CG) estimators is also implemented, which is useful for sensitivity analyses under dependent censoring (Yeh et al 2023 Biomedicines) and factorial survival analyses (Emura et al 2024 Stat Methods Med Res) . Package: r-cran-compoundevents Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-proc, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-compoundevents_1.0-1.ca2404.1_all.deb Size: 67282 MD5sum: 14f856e6cb9a0bdc6eaafe5833fc0829 SHA1: d1c8101bc0e4d92f7dc49b4d1bde8b1b032f7d91 SHA256: 3f9688d04e53c34a19ae78d790ffef58a673f582c1dc9e8a2f03cab9d8c9caed SHA512: f075c281e0cd5e610f23e713a29552bf82550234ebb2329a7dfff48b064d7323c7a539d0688fae0d5c40bc3b069420a7d1cde73d9752a600819569ad7ce559e1 Homepage: https://cran.r-project.org/package=CompoundEvents Description: CRAN Package 'CompoundEvents' (Statistical Modeling of Compound Events) Tools for extracting occurrences, assessing potential driving factors, predicting occurrences, and quantifying impacts of compound events in hydrology and climatology. Please see Hao Zengchao et al. (2022) . 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The detailed description of the methods and the applications of the methods can be found in Bowen Liu, Malwane M.A. Ananda (2023) . Package: r-cran-compr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-compr_1.0-1.ca2404.1_all.deb Size: 252668 MD5sum: 63c2dc6bae049e054914008f3fbeb5b6 SHA1: 8c1e19363ad2a11fa662b49bdee037d16edf8350 SHA256: 666e44e97a3e86cbe7566510671bc5db2de33db53655659733d5927a40a30a90 SHA512: 4f1d9da7fec62226db83556968dfaf9fc4dca89e35c6aeedf088762a1c1aad1a82e0c25fe8e1a4d4ed71521a9639e53f1f5f4250d73e83bc4e313d29427719e7 Homepage: https://cran.r-project.org/package=CompR Description: CRAN Package 'CompR' (Paired Comparison Data Analysis) Different tools for describing and analysing paired comparison data are presented. Main methods are estimation of products scores according Bradley Terry Luce model. 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Package: r-cran-comradesm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4368 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-comradesm_0.1.1-1.ca2404.1_all.deb Size: 4441930 MD5sum: 7f12c68ff6f7d1e0e29a484f5fc44250 SHA1: 6899d03aa41e71e9597e6b7b3cae95f195004d7f SHA256: 2e773728a2650eef7f720f73425b7d41671c58d3877a09c22670f019a5f71730 SHA512: 836dcc6f31d928f0892fae680e74f5585302c19dde9ebf0353217c44000212adc46650c74c70a725dfbe4f5fbb84277257cf8818f53fe648a9c55d41f071173b Homepage: https://cran.r-project.org/package=ComradesM Description: CRAN Package 'ComradesM' (The Comrades Marathon 1921 to 2019) Datasets related to the Comrades Marathon used in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics". The main dataset contains the times of every runner that finished in the time limit for each year the race was run. 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The functions also allow computing the goodness-of-fit measures namely the Akaike-information-criterion (AIC), the Bayesian-information-criterion (BIC), the minimum value of the negative log-likelihood (-2L) function, Anderson-Darling (A) test, Cramer-Von-Mises (W) test, Kolmogorov-Smirnov test, P-value and convergence status. Moreover, some commonly used data sets from the fields of actuarial, reliability, and medical science are also provided. Related works include: a) Tahir, M. H., & Cordeiro, G. M. (2016). Compounding of distributions: a survey and new generalized classes. Journal of Statistical Distributions and Applications, 3, 1-35. . 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For details, see Alcaraz J., Anton-Sanchez L., Monge J.F. (2022) The Concordance Test, an Alternative to Kruskal-Wallis Based on the Kendall-tau Distance: An R Package. The R Journal 14, 26–53 . 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Measures can be calculated in groups or individually. The calculated measure or the resulting vector in table format should help practitioners make more informed decisions. Methods used in this package are from: 1. Chang, E. J., Guerra, S. M., de Souza Penaloza, R. A. & Tabak, B. M. (2005) "Banking concentration: the Brazilian case". 2. Cobham, A. and A. Summer (2013). "Is It All About the Tails? The Palma Measure of Income Inequality". 3. Garcia Alba Idunate, P. (1994). "Un Indice de dominancia para el analisis de la estructura de los mercados". 4. Ginevicius, R. and S. Cirba (2009). "Additive measurement of market concentration" . 5. Herfindahl, O. C. (1950), "Concentration in the steel industry" (PhD thesis). 6. Hirschmann, A. O. (1945), "National power and structure of foreign trade". 7. Melnik, A., O. Shy, and R. Stenbacka (2008), "Assessing market dominance" . 8. Palma, J. G. (2006). "Globalizing Inequality: 'Centrifugal' and 'Centripetal' Forces at Work". 9. Shannon, C. E. 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The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). 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Package: r-cran-conf Architecture: all Version: 1.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3300 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod, r-cran-fitdistrplus, r-cran-pracma, r-cran-rootsolve Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-conf_1.9.3-1.ca2404.1_all.deb Size: 2112400 MD5sum: 4b48b45f5cddaa0b491aa7c80fa59dcb SHA1: 1663237ada95293ff19aea0b71678b10119a445f SHA256: 8856fc1444a7746a0f31f20466a850ee078e175ad8d7e407a85c9cae8c2eccc3 SHA512: 7a7773eb72444ae1269fc5cd3c0c6d8dd64a33aa659bdc3395b53df77544ea0caa691a774a4c8447244d22cf39830fd02c38ba1643bef5c7c23ca6467562b38f Homepage: https://cran.r-project.org/package=conf Description: CRAN Package 'conf' (Visualization and Analysis of Statistical Measures of Confidence) Enables: (1) plotting two-dimensional confidence regions, (2) coverage analysis of confidence region simulations, (3) calculating confidence intervals and the associated actual coverage for binomial proportions, (4) calculating the support values and the probability mass function of the Kaplan-Meier product-limit estimator, and (5) plotting the actual coverage function associated with a confidence interval for the survivor function from a randomly right-censored data set. Each is given in greater detail next. (1) Plots the two-dimensional confidence region for probability distribution parameters (supported distribution suffixes: cauchy, gamma, invgauss, logis, llogis, lnorm, norm, unif, weibull) corresponding to a user-given complete or right-censored dataset and level of significance. The crplot() algorithm plots more points in areas of greater curvature to ensure a smooth appearance throughout the confidence region boundary. An alternative heuristic plots a specified number of points at roughly uniform intervals along its boundary. Both heuristics build upon the radial profile log-likelihood ratio technique for plotting confidence regions given by Jaeger (2016) , and are detailed in a publication by Weld et al. (2019) . (2) Performs confidence region coverage simulations for a random sample drawn from a user- specified parametric population distribution, or for a user-specified dataset and point of interest with coversim(). (3) Calculates confidence interval bounds for a binomial proportion with binomTest(), calculates the actual coverage with binomTestCoverage(), and plots the actual coverage with binomTestCoveragePlot(). Calculates confidence interval bounds for the binomial proportion using an ensemble of constituent confidence intervals with binomTestEnsemble(). Calculates confidence interval bounds for the binomial proportion using a complete enumeration of all possible transitions from one actual coverage acceptance curve to another which minimizes the root mean square error for n <= 15 and follows the transitions for well-known confidence intervals for n > 15 using binomTestMSE(). (4) The km.support() function calculates the support values of the Kaplan-Meier product-limit estimator for a given sample size n using an induction algorithm described in Qin et al. (2023) . The km.outcomes() function generates a matrix containing all possible outcomes (all possible sequences of failure times and right-censoring times) of the value of the Kaplan-Meier product-limit estimator for a particular sample size n. The km.pmf() function generates the probability mass function for the support values of the Kaplan-Meier product-limit estimator for a particular sample size n, probability of observing a failure h at the time of interest expressed as the cumulative probability percentile associated with X = min(T, C), where T is the failure time and C is the censoring time under a random-censoring scheme. The km.surv() function generates multiple probability mass functions of the Kaplan-Meier product-limit estimator for the same arguments as those given for km.pmf(). (5) The km.coverage() function plots the actual coverage function associated with a confidence interval for the survivor function from a randomly right-censored data set for one or more of the following confidence intervals: Greenwood, log-minus-log, Peto, arcsine, and exponential Greenwood. The actual coverage function is plotted for a small number of items on test, stated coverage, failure rate, and censoring rate. The km.coverage() function can print an optional table containing all possible failure/censoring orderings, along with their contribution to the actual coverage function. Package: r-cran-confcons Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mockery, r-cran-vctrs, r-cran-withr, r-cran-rocr, r-cran-covr, r-cran-terra, r-cran-sf, r-cran-blockcv, r-cran-ggplot2, r-cran-ranger, r-cran-ecospat, r-cran-enmeval Filename: pool/dists/noble/main/r-cran-confcons_0.3.2-1.ca2404.1_all.deb Size: 250116 MD5sum: 86d341d3382dcbe82f4324cc6d9f7dd4 SHA1: 83163d3256426a1abbdaf68d9ddb6d04a09e67c3 SHA256: 7f7eba3aabb83e8ef49c066fd98655ceaa51e5b26653c492ea65e1f9a995c1fc SHA512: 6e40607df94068f3cdd809849cf5ee18a88f8e42eab6c22b3b5cafca4bce59c383484e00da4889692da4b57e2e9a8da01241fdece7879ac29a20cbd78d281af0 Homepage: https://cran.r-project.org/package=confcons Description: CRAN Package 'confcons' (Confidence and Consistency of Predictive Distribution Models) Calculate confidence and consistency that measure the goodness-of-fit and transferability of predictive/potential distribution models (including species distribution models) as described by Somodi & Bede-Fazekas et al. (2024) . Package: r-cran-confidence Architecture: all Version: 1.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-markdown, r-cran-plyr, r-cran-xtable, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-confidence_1.1-3-1.ca2404.1_all.deb Size: 281132 MD5sum: fd97e92fb8aaf943bbf103f12336a0c2 SHA1: b50811ce8ad3044ac9f3ced56131b46db20e005f SHA256: a3f664589240fe4ee33814f2800724a3bc558a09cbbb2a9616bf9742587800a2 SHA512: fece17515bcb65311e0b7bf9417df664bcc3d36359918dea55e596298e92e2038f402bb4d71ec23024269c6f0e14173501e4192fa1f13cbfa0f91d7cd49ba0ab Homepage: https://cran.r-project.org/package=confidence Description: CRAN Package 'confidence' (Confidence Estimation of Environmental State Classifications) Functions for estimating and reporting multi-year averages and corresponding confidence intervals and distributions. A potential use case is reporting the chemical and ecological status of surface waters according to the European Water Framework Directive. Package: r-cran-confidencecurves Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-desctools, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-confidencecurves_0.2.0-1.ca2404.1_all.deb Size: 37670 MD5sum: 6293993f881d156fe9f4f8a6c223ff18 SHA1: 1c1867a223a5d40fd3248efc3c30a15766bba3c8 SHA256: 9e95c9082c619ee544276d568a373ffb7a211f97de4b70e9378d937bbf4022ae SHA512: b3f8b8054714ed776120fe5ef6c32974944e4dd4abef023588836b7b8a788d2bae30ac532f5ce4bfe9a1c81e07956f28821aa0f572327121a582cd511e32e12b Homepage: https://cran.r-project.org/package=confidenceCurves Description: CRAN Package 'confidenceCurves' (Frequentist Confidence Analysis for Clinical Trials) Frequentist confidence analysis answers the question: How confident are we in a particular treatment effect? This package calculates the frequentist confidence in a treatment effect of interest given observed data, and returns the family of confidence curves associated with that data. Package: r-cran-confidenceellipse Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cellwise, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-pcapp, r-cran-purrr, r-cran-rgl, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-confidenceellipse_1.1.0-1.ca2404.1_all.deb Size: 2038164 MD5sum: d4415c8ff3ec1e232dd314925414e6b2 SHA1: 07e7ef938c3378711879d20febc239904a7179ee SHA256: 3aa1147b7e908f362de5e45e0e9f1806cc9792ded16dca408b62f7ce61b71bbb SHA512: 3bc2d34583a758bd5f2fd3c052bff2ad753c3202cb1279f2614f6a7fe6400aa37cdc5d8731b4c7e0dfe6a1bdcb22612fbfa9695a07aedbfcb2d1c2540c6e3a0f Homepage: https://cran.r-project.org/package=ConfidenceEllipse Description: CRAN Package 'ConfidenceEllipse' (Computation of 2D and 3D Elliptical Joint Confidence Regions) Computing elliptical joint confidence regions at a specified confidence level. It provides the flexibility to estimate either classical or robust confidence regions, which can be visualized in 2D or 3D plots. The classical approach assumes normality and uses the mean and covariance matrix to define the confidence regions. Alternatively, the robustified version employs estimators like minimum covariance determinant (MCD) and M-estimator, making them less sensitive to outliers and departures from normality. Furthermore, the functions allow users to group the dataset based on categorical variables and estimate separate confidence regions for each group. This capability is particularly useful for exploring potential differences or similarities across subgroups within a dataset. Varmuza and Filzmoser (2009, ISBN:978-1-4200-5947-2). Johnson and Wichern (2007, ISBN:0-13-187715-1). Raymaekers and Rousseeuw (2019) . Package: r-cran-confidencesim Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-confidencecurves, r-cran-genodds, r-cran-rpact Suggests: r-cran-dplyr, r-cran-knitr, r-cran-pbapply, r-cran-plyr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-confidencesim_0.1.0-1.ca2404.1_all.deb Size: 104536 MD5sum: 15b8952227bdb271460eef86717ebab8 SHA1: c0a8e1cd821fd6bf37ca433a7afd2ada0729d82b SHA256: b6470007da82c1b5cb777978b02381f6eea01da60d208e01b7a3aa1c7181ef20 SHA512: c26dbdb268c86f83946437afc72c6759bf9c1ed96a4aad80f8028c24f2e3bc2a22826c8f69a21c664b8bdc2ea0391299687d64660f4f93d7bcef9755c0ddceb9 Homepage: https://cran.r-project.org/package=confidenceSim Description: CRAN Package 'confidenceSim' (Highly Customizable, Parallelized Simulations of FrequentistConfidence Clinical Trials) Simulate one or many frequentist confidence clinical trials based on a specified set of parameters. From a two-arm, single-stage trial to a perpetually run Adaptive Platform Trial, this package offers vast flexibility to customize your trial and observe operational characterisitics over thousands of instances. Package: r-cran-config Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-yaml Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-spelling, r-cran-withr Filename: pool/dists/noble/main/r-cran-config_0.3.2-1.ca2404.1_all.deb Size: 96080 MD5sum: 77938cd86aab5b441a106d5a0fd8eeb8 SHA1: c13601543ff329f19e76fce0134d6a87feefa0a0 SHA256: 3dde366cc8bb40ee5084d993b03be7db98319cc0017c141e3882598d880dd4bc SHA512: ebebc713a76813dae77be81c5cce7ad9577bc53c86057cdb1b2a7c9e90aefc19bd9a019d514602f876408d9c2ae4ddd1c742a5de0d09fb7177078b8beaf73010 Homepage: https://cran.r-project.org/package=config Description: CRAN Package 'config' (Manage Environment Specific Configuration Values) Manage configuration values across multiple environments (e.g. development, test, production). Read values using a function that determines the current environment and returns the appropriate value. Package: r-cran-configparser Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ini, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-configparser_1.0.0-1.ca2404.1_all.deb Size: 56062 MD5sum: f0b786ef415abe14b746ca08a240f946 SHA1: 64712e3da3bb6b38c4e8fdc16b2a6d948959c5ee SHA256: 2fb951f88802ef5735a18f39ed13d560e599776e2fc4098fe0d96c45df7828ed SHA512: f293c7648d2696df206aa53fba85b0b82762286c3b43a9fe45ec98bee3426ca13960d28feee27021a902e4f45469e07737fe86db0a91616c5490a41e5b6eeeb6 Homepage: https://cran.r-project.org/package=ConfigParser Description: CRAN Package 'ConfigParser' (Package to Parse an INI File, Including Variable Interpolation) Enhances the 'ini' package by adding the ability to interpolate variables. The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module) and the keys can be read, where '%(....)s' instances are interpolated by other included options or outside variables. Package: r-cran-configr Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-ini, r-cran-yaml, r-cran-rcpptoml, r-cran-stringr, r-cran-glue Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-configr_0.3.5-1.ca2404.1_all.deb Size: 112548 MD5sum: c70a47cfa62dce17abcf4c26ea6a6f04 SHA1: 4c4aec509a267d8ba73ff744606fda836283f01a SHA256: 15c8ea6b55c293f150d80b7d89912211094f1115da3c99788c3c7c10059e7896 SHA512: dcda982ed76f24e6791e04058a0af0dd7994b605120789fc69c759c9990c8af6158631bcfa10415e3ec0b421e97189b303b895fc3d5cac400897f3a1fd4fd637 Homepage: https://cran.r-project.org/package=configr Description: CRAN Package 'configr' (An Implementation of Parsing and Writing Configuration File(JSON/INI/YAML/TOML)) Implements the JSON, INI, YAML and TOML parser for R setting and writing of configuration file. The functionality of this package is similar to that of package 'config'. Package: r-cran-configular Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-here, r-cran-magrittr, r-cran-stringr Suggests: r-cran-covr, r-cran-docopt, r-cran-git2r, r-cran-lintr, r-cran-precommit, r-cran-roxygen2, r-cran-styler, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-configular_0.1.1-1.ca2404.1_all.deb Size: 36830 MD5sum: 1648c42c8445d339c4f7abb2f73e9c40 SHA1: 3f08bd0ae5ea9bdebd06b4ad3d01a2686db33c1f SHA256: 658506c4d9aefb7ce8f7fe732d1b7cffadfb37360751ac7b2d345821246c57e5 SHA512: b332c663869cb72aa138240a8060843415d29673aa3295ef92514318d58ac2b372a0f92cc6a1181cbda6f960bafeb25dacac5d18b81e0e7177264cfb26c6a5dd Homepage: https://cran.r-project.org/package=configulaR Description: CRAN Package 'configulaR' (Manage Application Settings via '.env' or '.ini' Files) Provides a simple way to manage application settings by loading configuration values from '.env' or '.ini' files. It supports default values, type casting, and environment variable overrides, enabling a clean separation of configuration from code. Ideal for managing credentials, API keys, and deployment-specific settings. Package: r-cran-configural Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-crayon Filename: pool/dists/noble/main/r-cran-configural_0.1.5-1.ca2404.1_all.deb Size: 170360 MD5sum: af07d2a7fb6236f1eeb888b5f3366aef SHA1: d2f47b31bd90a357f80637668819f38ad9c4446e SHA256: 7def786e7ae1a1e940f04b01b462c5f73a8909b2034c5df6f8bd6724538eef9d SHA512: 8a24d1fccc7859bf323a3aa2fd0c90c1d9a90b58c938d4eed88a290fd7b46dd9ee62bdc89574a32adb0c40278156d2cb21fdc9625aeace0714871394d2aaccdb Homepage: https://cran.r-project.org/package=configural Description: CRAN Package 'configural' (Multivariate Profile Analysis) R functions for criterion profile analysis, Davison and Davenport (2002) and meta-analytic criterion profile analysis, Wiernik, Wilmot, Davison, and Ones (2020) . Sensitivity analyses to aid in interpreting criterion profile analysis results are also included. Package: r-cran-confinterpret Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-confinterpret_1.0.0-1.ca2404.1_all.deb Size: 119154 MD5sum: b3d759ef243602c1f39079fe1a7ce097 SHA1: cd1706ffbee2436ceccaa1188c2ad41462603e7e SHA256: 3f214391c6067beab165d3c93282c88684b6e92b45f77bb167736d295803751e SHA512: 8f99a23b257fd1e305daf7c9dbc4dfdeabfbc4a1e93d465524eda2de54a71ca04064a131801833153a646d100fa83f8342bf7d566e308206f9d20a1b809b8942 Homepage: https://cran.r-project.org/package=confinterpret Description: CRAN Package 'confinterpret' (Descriptive Interpretations of Confidence Intervals) Produces descriptive interpretations of confidence intervals. Includes (extensible) support for various test types, specified as sets of interpretations dependent on where the lower and upper confidence limits sit. Provides plotting functions for graphical display of interpretations. Package: r-cran-confintr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-confintr_1.0.2-1.ca2404.1_all.deb Size: 168536 MD5sum: c9c5048d9ec55e4c999b660789a3091b SHA1: 800c02093ef21fb65b6e4221637a6c4976f7ed48 SHA256: 9000c66003213983b2757decba3f438ee4e8d4000db973f0a67b730ac2b83366 SHA512: f94a554f0f1bb86ccb5d669f78ad3a2503434c50c3b6bea3ebf1009114326c6ac05c823146199f9b9f176adf2ba2ff52431503e382e6291fafa3a4fa910a58e9 Homepage: https://cran.r-project.org/package=confintr Description: CRAN Package 'confintr' (Confidence Intervals) Calculates classic and/or bootstrap confidence intervals for many parameters such as the population mean, variance, interquartile range (IQR), median absolute deviation (MAD), skewness, kurtosis, Cramer's V, odds ratio, R-squared, quantiles (incl. median), proportions, different types of correlation measures, difference in means, quantiles and medians. Many of the classic confidence intervals are described in Smithson, M. (2003, ISBN: 978-0761924999). Bootstrap confidence intervals are calculated with the R package 'boot'. Both one- and two-sided intervals are supported. Package: r-cran-confintrob Architecture: all Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-lme4, r-cran-mass, r-cran-mvtnorm, r-cran-tidyr Suggests: r-cran-robustlmm, r-cran-robustvarcomp, r-cran-lmertest, r-cran-testthat, r-cran-xtable, r-cran-ggplot2, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-confintrob_1.1-1-1.ca2404.1_all.deb Size: 66898 MD5sum: 08ac36222ca4c6cff380072214ef3190 SHA1: 726bcefdad2805323eaf4e45d6a700704d596096 SHA256: a07bf20b0bdbb57b550d361610cafcb3ac8ae69b3d59cdf0af10b21d22f046b5 SHA512: 845f4f47a4c948930c5f161af637548a50ea8af10f9ed5a5e67b6ca04416bd97957cf45d8ee1932a8a39c95f821d21b8c61a0d923b17f6c6e664a3b7484f8b2e Homepage: https://cran.r-project.org/package=confintROB Description: CRAN Package 'confintROB' (Confidence Intervals for Robust and Classical Linear Mixed ModelEstimators) The main function calculates confidence intervals (CI) for Mixed Models, utilizing both classical estimators from the lmer() function in the 'lme4' package and robust estimators from the rlmer() function in the 'robustlmm' package, as well as the varComprob() function in the 'robustvarComp' package. Three methods are available: the classical Wald method, the wild bootstrap, and the parametric bootstrap. Bootstrap methods offer flexibility in obtaining lower and upper bounds through percentile or BCa methods. More details are given in Mason, F., Cantoni, E., & Ghisletta, P. (2021) and Mason, F., Cantoni, E., & Ghisletta, P. (2024) . Package: r-cran-confintvariance Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-confintvariance_1.0.2-1.ca2404.1_all.deb Size: 24680 MD5sum: ca2bc5f4a02d900c00eec31f5ba3ced8 SHA1: 35d9e44cac2768c2dd16f7fbafa1594268ce38ca SHA256: d3199795acc43ae5a411ba94aefc67114d9fee4965079bb9b98cb02e502a4e78 SHA512: e6a07a796b08653e82be0ff049d62afdef5c9fd33f1242b2e243075826d99020f2a8033d6e0d9e8512626d19f5be3e8a67b5cfabd283033d4f22a7341b56ca42 Homepage: https://cran.r-project.org/package=ConfIntVariance Description: CRAN Package 'ConfIntVariance' (Confidence Interval for the Univariate Population Variancewithout Normality Assumption) Surrounds the usual sample variance of a univariate numeric sample with a confidence interval for the population variance. This has been done so far only under the assumption that the underlying distribution is normal. Under the hood, this package implements the unique least-variance unbiased estimator of the variance of the sample variance, in a formula that is equivalent to estimating kurtosis and square of the population variance in an unbiased way and combining them according to the classical formula into an estimator of the variance of the sample variance. Both the sample variance and the estimator of its variance are U-statistics. By the theory of U-statistic, the resulting estimator is unique. See Fuchs, Krautenbacher (2016) and the references therein for an overview of unbiased estimation of variances of U-statistics. 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This can make it hard to detect conflicts, particularly when they arise because a package update creates ambiguity that did not previously exist. 'conflicted' takes a different approach, making every conflict an error and forcing you to choose which function to use. Package: r-cran-conflr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-askpass, r-cran-commonmark, r-cran-curl, r-cran-glue, r-cran-httr, r-cran-knitr, r-cran-miniui, r-cran-purrr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-shiny, r-cran-stringi, r-cran-xml2, r-cran-r6, r-cran-rlang Suggests: r-cran-mockery, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-conflr_0.1.1-1.ca2404.1_all.deb Size: 658892 MD5sum: 6722b8f6b0297b15066d6a519aebc869 SHA1: 242e84a947af6e87e54526bae2e0a3e2ca5157cc SHA256: d7f4c354adf16be0baf81151aa5e3388ee6dc761bb656e5480bc0e0378a92e39 SHA512: 22b5eeeb58fdb72b14ef90b6d15bb125b84bf62ba297aa214e00b9e1fae79b335873e30fb074678b09a1cdda059f43e51e4749a8256748c9a084feeb9e91e4eb Homepage: https://cran.r-project.org/package=conflr Description: CRAN Package 'conflr' (Client for 'Confluence' API) Provides utilities for working with various 'Confluence' API , including a functionality to convert an R Markdown document to 'Confluence' format and upload it to 'Confluence' automatically. Package: r-cran-confluxpro Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-tibble, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-rlang, r-cran-furrr, r-cran-progressr, r-cran-ggplot2, r-cran-scales, r-cran-lifecycle Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-diagrammer, r-cran-purrr Filename: pool/dists/noble/main/r-cran-confluxpro_1.3.1-1.ca2404.1_all.deb Size: 1089028 MD5sum: afe945525135eea32d9ebe086cc2c710 SHA1: 6c28438bcafaea24e6f84daf1abd0d932c22882a SHA256: a4093ca371b1ba6d3c2001eb26a8ac1d706addcf2ab09a3c3fa2d4b3863231c8 SHA512: 9b50627ad77eb2ef80d15d4535cff2d6b93f7bff669183e7eb7bb365ccc4f2222a51b67cb5806ded2ab3258df24da098588ffc897d2f540516d925da9ad9303f Homepage: https://cran.r-project.org/package=ConFluxPro Description: CRAN Package 'ConFluxPro' (Soil Gas Analysis and Flux Modeling) Model soil gas fluxes with the Flux-Gradient Method. 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Package: r-cran-confmatrix Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-rdpack, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-confmatrix_0.2.0-1.ca2404.1_all.deb Size: 267022 MD5sum: 59aea27fa0579f3c5bdbdc6ef8208fcb SHA1: d470674cb702665cfff0dfbbd8053191181be2ce SHA256: d897f978db6ab53e054feaea023d97201547425651f806962d62a1bfd41b4c7d SHA512: 2c35de0ea960d02c7a03eca5ef5111696a26dacbe6600facf3eb3faa91603fdce1845edf7b84a32cbba46454bb32ffad892132e6883b973b89f84cfabab2b8d2 Homepage: https://cran.r-project.org/package=ConfMatrix Description: CRAN Package 'ConfMatrix' (Confusion Matrix) Thematic quality indices are provided to facilitate the evaluation and quality control of geospatial data products (e.g. thematic maps, remote sensing classifications, etc.). The indices offered are based on the so-called confusion matrix. This matrix is constructed by comparing the assigned classes or attributes of a set of pairs of positions or objects in the product and the ground truth. In this package it is considered that the classes of the ground truth correspond to the columns and that the classes of the product to be valued correspond to the rows. The package offers two object classes with their methods: 'ConfMatrix' (Confusion matrix) and 'QCCS' (Quality Control Columns Set). The 'ConfMatrix' class of objects offers more than 20 methods based on the confusion matrix. The 'QCCS' class of objects offers a different perspective in which the ground truth is considered to allow the values of the column marginals to be fixed, see Ariza López et al. (2019) and Canran Liu et al. (2007) for more details. The package was created with 'R6'. Package: r-cran-confmeta Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-meta, r-cran-metafor, r-cran-patchwork, r-cran-replicationsuccess, r-cran-scales, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bayesmeta, r-cran-testthat Filename: pool/dists/noble/main/r-cran-confmeta_0.1.0-1.ca2404.1_all.deb Size: 290438 MD5sum: d7bf43b6be2d618811d36be7c113afff SHA1: e792f35489e7bdb5d61811b4fdf8beb6a3cb84bb SHA256: fb1cf8696ac286676a89dce2b10f2acbc3bd2a81dc0879a2b26c30be368f9695 SHA512: 5c8e8f6e9d3040bd3e86f9b2d4e92dbebdbbd0364e98764e74fb8ffb529a84d630e01bf9a950d8f0016c9e40c972b3d544d4b4a31541c89bda5faa0f16ef6926 Homepage: https://cran.r-project.org/package=confMeta Description: CRAN Package 'confMeta' (Confidence Curves and P-Value Functions for Meta-Analysis) Provides tools for the combination of individual study results in meta-analyses using 'p-value' functions. 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Package: r-cran-conforest Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-randomforest, r-cran-rann Filename: pool/dists/noble/main/r-cran-conforest_2.0.1-1.ca2404.1_all.deb Size: 49192 MD5sum: a9905a91e838cafd912b5feb12b884c1 SHA1: cbd2f9cc12ca5cff7404b4239deefcbc80247c6e SHA256: ed985e3bc76571df5eb4cfa2c2f7e67228999dd1c0e6a50ef5fa0b108eeaeb69 SHA512: 46531cd0cc474d4fe0ef9e5f4f536614475879121231f11ea30d338d49bb6f0c558fff14e6a58f9fab24dcca98cbd2b0f8a3d69e915bf9f2f9142343a5365ffa Homepage: https://cran.r-project.org/package=conforest Description: CRAN Package 'conforest' (Conformal Random Forests for Response Surface Emulation) Fits emulators, also known as surrogates or response surfaces, using conformal inference with random forests. The conformal calibration is performed using out-of-bag samples from the forest, eliminating the need for a separate hold-out set. The method is based on Johansson et al. (2014 ). Package: r-cran-conformalbayes Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rstantools, r-cran-loo, r-cran-matrixstats Suggests: r-cran-rstanarm, r-cran-brms, r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-conformalbayes_0.1.4-1.ca2404.1_all.deb Size: 130620 MD5sum: ba85ab25274a1d93a791d74a77a8253c SHA1: 30d80fd784a243800563b83db75844b871aff9ea SHA256: e51bb7c7e25bf39d68437247860bd998d17e57eb6b9bccf58069fe7ce4a2f567 SHA512: df83a9e829ab19b445464a96327e71aa9fa710306c44d45ea48026375c0393f59bfb7fddec73e33a1bea3e6a570e4d45d01933788dad66927024ef6e53cc2a3b Homepage: https://cran.r-project.org/package=conformalbayes Description: CRAN Package 'conformalbayes' (Jackknife(+) Predictive Intervals for Bayesian Models) Provides functions to construct finite-sample calibrated predictive intervals for Bayesian models, following the approach in Barber et al. (2021) . These intervals are calculated efficiently using importance sampling for the leave-one-out residuals. By default, the intervals will also reflect the relative uncertainty in the Bayesian model, using the locally-weighted conformal methods of Lei et al. (2018) . Package: r-cran-conformalclassification Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest, r-cran-foreach, r-cran-doparallel, r-cran-mlbench Filename: pool/dists/noble/main/r-cran-conformalclassification_1.0.0-1.ca2404.1_all.deb Size: 51452 MD5sum: 260207601fcda6d83cae4a3858c9e254 SHA1: e933e389ff93400f8ac5dfc57b7572160c373971 SHA256: 1a38f409bed8a7879cd17019e56a26da061230f553b60b9649380aa8732619c5 SHA512: b9174244c6c39ee7e4c50d1678b0122221a01ed1298851f0f36a09b90c3dae3f0b6e55cb2af80643933f2a3a4419ae9f66aac257150fc7244545383528bae1ae Homepage: https://cran.r-project.org/package=conformalClassification Description: CRAN Package 'conformalClassification' (Transductive and Inductive Conformal Predictions forClassification Problems) Implementation of transductive conformal prediction (see Vovk, 2013, ) and inductive conformal prediction (see Balasubramanian et al., 2014, ISBN:9780124017153) for classification problems. Package: r-cran-conformalforecast Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 891 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-ggdist, r-cran-rlang, r-cran-zoo Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tsibble Filename: pool/dists/noble/main/r-cran-conformalforecast_0.1.1-1.ca2404.1_all.deb Size: 639880 MD5sum: db0d234a5ef59720513fb0efb7d5506c SHA1: 9b6838bbe81e9264fe2aa63f402c5775ec4e10f8 SHA256: 23f3ac530ebd7885323b617a40b74fe04cf238d8e29ebd6b728903065d239627 SHA512: 732d4d85889c3d67f07eae815edeea147bc308b34dd05a2f99f12a2d26801fa2df0482bb18734a92604329386b62b7747149a572077bd983136ce04274d70f08 Homepage: https://cran.r-project.org/package=conformalForecast Description: CRAN Package 'conformalForecast' (Conformal Prediction Methods for Multistep-Ahead Time SeriesForecasting) Methods and tools for performing multistep-ahead time series forecasting using conformal prediction methods including classical conformal prediction, adaptive conformal prediction, conformal PID (Proportional-Integral-Derivative) control, and autocorrelated multistep-ahead conformal prediction. The methods were described by Wang and Hyndman (2024) . Package: r-cran-conformalinference.fd Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-ggnewscale, r-cran-ggpubr, r-cran-scales Suggests: r-cran-roahd, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-conformalinference.fd_1.1.1-1.ca2404.1_all.deb Size: 187710 MD5sum: 19a2da2fd8ca94bb2c2f84a017cd4df0 SHA1: 2da3b1278736daf58ca09e0393cff446693efcd9 SHA256: b41ba041a84030570132033a7a7e010f3cf422e401e53fd0027d97cb2c8baff7 SHA512: 4123b1d4f5687604298a4adbcc2d4e23f31d23263399bd164b9387dec0c76c49b27e6313e34f69db66be4bbbf0a97dd562be6b61e43ce8514a68f67381718252 Homepage: https://cran.r-project.org/package=conformalInference.fd Description: CRAN Package 'conformalInference.fd' (Tools for Conformal Inference for Regression in MultivariateFunctional Setting) It computes full conformal, split conformal and multi split conformal prediction regions when the response has functional nature. Moreover, the package also contain a plot function to visualize the output of the split conformal. To guarantee consistency, the package structure mimics the univariate 'conformalInference' package of professor Ryan Tibshirani. The main references for the code are: Diquigiovanni, Fontana, and Vantini (2021) , Diquigiovanni, Fontana, and Vantini (2021) , Solari, and Djordjilovic (2021) . Package: r-cran-conformalinference.multi Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-glmnet, r-cran-gridextra Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-conformalinference.multi_1.1.2-1.ca2404.1_all.deb Size: 90076 MD5sum: d844f65a8479174a208c15d620230d49 SHA1: dc0b3a82a081c3a7e849a969601a1a259639de7b SHA256: 92796d7a891f73ad945accb38dbb1bb1d342e5161bc8e56abde976367fbfe6fe SHA512: 9f5fa9649d793d8b83a6e6c64aedf50717f55abcc6bab5a36241634d2f0a6e195ee9b4d345cd91e09a698253ef4b4387c2ac892d349c2d64c7e63485a980b4ba Homepage: https://cran.r-project.org/package=conformalInference.multi Description: CRAN Package 'conformalInference.multi' (Conformal Inference Tools for Regression with MultivariateResponse) It computes full conformal, split conformal and multi-split conformal prediction regions when the response variable is multivariate (i.e. dimension is greater than one). Moreover, the package also contains plot functions to visualize the output of the full and split conformal functions. To guarantee consistency, the package structure mimics the univariate package 'conformalInference' by Ryan Tibshirani. See Lei, G’sell, Rinaldo, Tibshirani, & Wasserman (2018) for full and split conformal prediction in regression, and Barber, Candès, Ramdas, & Tibshirani (2023) for extensions beyond exchangeability. Package: r-cran-conformalpvalue Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-conformalpvalue_0.1.0-1.ca2404.1_all.deb Size: 14172 MD5sum: f9f3be8e975a9cf7e040bdb1e2c2ad2e SHA1: 79f65a8f719f621b0e51433fae709bb2f1302da2 SHA256: 30f5fc68cf1ea71a77aee0e9eafedeeae8adbed9807915c64dd3b8c39b15d73a SHA512: b825fa5f51a64cd52737c766ef0fffcacfa36c32e98280b296206eafcf233b5c52030068c6a914ed92823d54baf21b9e0157de635ba36a312a7e61d5d8874639 Homepage: https://cran.r-project.org/package=conformalpvalue Description: CRAN Package 'conformalpvalue' (Computes Conformal p-Values) Computes marginal conformal p-values using conformal prediction in binary classification tasks. Conformal prediction is a framework that augments machine learning algorithms with a measure of uncertainty, in the form of prediction regions that attain a user-specified level of confidence. This package specifically focuses on providing conformal p-values that can be used to assess the confidence of the classification predictions. For more details, see Tyagi and Guo (2023) . Package: r-cran-conformalsmallest Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mvtnorm, r-cran-mass, r-cran-quantregforest Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-repr Filename: pool/dists/noble/main/r-cran-conformalsmallest_1.0-1.ca2404.1_all.deb Size: 840134 MD5sum: 07456a5315c49c1f11229c936430f668 SHA1: e6ecc72a219d0373829cd5427b8e69d7c7ce8c87 SHA256: 5daba521a9c13993bc19cbc979f592bd916b1343cb67d90abf7614754e50133a SHA512: fdcb672a5d73e6dc9e0ac1e12b45fd299dfe23dff6a3101f7b60acf2d800a7cd080363e6c8e9c76ae201e25704d5474c877dd72aaef273fcb854f7e780de46ec Homepage: https://cran.r-project.org/package=ConformalSmallest Description: CRAN Package 'ConformalSmallest' (Efficient Tuning-Free Conformal Prediction) An implementation of efficiency first conformal prediction (EFCP) and validity first conformal prediction (VFCP) that demonstrates both validity (coverage guarantee) and efficiency (width guarantee). To learn how to use it, check the vignettes for a quick tutorial. The package is based on the work by Yang Y., Kuchibhotla A.,(2021) . Package: r-cran-confoundvis Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-confoundvis_0.1.0-1.ca2404.1_all.deb Size: 140820 MD5sum: bb7aad290547191ab08a9d392c6922f1 SHA1: b82aecf71e8a13a35caca73939c045ab7e40f471 SHA256: 7ba8b476cdbd78e7e89103c8d2328cbe3f9bab0aae7fdd4524e53f20c458e1f3 SHA512: 4482a26d6960dc144cb2c4e1ccffe67c6090df2fc4ea8034068603e8b84e3a14969ca32988ed9d45c9d072a86b5cedac09601ac0c1dec6eb31ed56a29fca602a Homepage: https://cran.r-project.org/package=confoundvis Description: CRAN Package 'confoundvis' (Visualization Tools for Sensitivity Analysis of UnmeasuredConfounding) Provides visualization tools for sensitivity analysis to unmeasured confounding in observational studies. Includes contour-based sensitivity plots, robustness curves, and benchmark-oriented graphics that help researchers assess how strong omitted confounding would need to be to attenuate, invalidate, or reverse estimated effects. Supports regression-based sensitivity analysis frameworks, including impact threshold approaches (Frank, 2000, ), partial R-squared methods (Cinelli and Hazlett, 2020, ), and E-value style metrics (VanderWeele and Ding, 2017, ). Emphasizes clear, interpretable, and publication-ready graphical summaries for transparent reporting of causal sensitivity analyses across the social, behavioral, health, and educational sciences. Package: r-cran-confreq Architecture: all Version: 1.6.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gmp, r-cran-vcd Filename: pool/dists/noble/main/r-cran-confreq_1.6.1-3-1.ca2404.1_all.deb Size: 165508 MD5sum: 333eeb3cfb179071e0c12aa94545fd15 SHA1: 8b393f46dd39b9e3661b7bf9d2990e83d19af30f SHA256: a11b21b56d139aaf0b51f6b1bbc4106e33e2aeb39f000263f0736f94713a7a98 SHA512: cd9b6b93efa2f920319f05c2e81ae409bad0a1ef7d719128adb691bd6db8fb6fdfb72463228c90220b5036b249e0b534a463ea220b1cde3a3ebc65d7f35702f3 Homepage: https://cran.r-project.org/package=confreq Description: CRAN Package 'confreq' (Configural Frequencies Analysis Using Log-Linear Modeling) Offers several functions for Configural Frequencies Analysis (CFA), which is a useful statistical tool for the analysis of multiway contingency tables. CFA was introduced by G. A. Lienert as 'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115. Package: r-cran-confsam Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-penalized, r-cran-survival, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-confsam_0.2-1.ca2404.1_all.deb Size: 303162 MD5sum: 78f4ff3e7c33a7f64c1092d15d7c95c4 SHA1: fddc19fccd4b1462416d7dbf9e36c46d43825968 SHA256: 8a66b735d829ec031f9b19dd219b95b351ee5d492ffa9fd3c7623c6db01da0c1 SHA512: add82d5a879d84743765f059c88fb23b370bc713edcf8eae082c6e57b4f5d2f56f4a778632e39568c8983824cff87d64f5505b83cf5fa17214687d5b9ef98dc5 Homepage: https://cran.r-project.org/package=confSAM Description: CRAN Package 'confSAM' (Estimates and Bounds for the False Discovery Proportion, byPermutation) For multiple testing. Computes estimates and confidence bounds for the False Discovery Proportion (FDP), the fraction of false positives among all rejected hypotheses. The methods in the package use permutations of the data. Doing so, they take into account the dependence structure in the data. 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The intended usage is to allow the tool to work with the outputs of machine learning classification models. This tool works with classification problems for binary and multi-classification problems and allows for the record level conversion of the confusion matrix outputs. This is useful, as it allows quick conversion of these objects for storage in database systems and to track ML model performance over time. Traditionally, this approach has been used for highlighting model representation and feature slippage. 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Package: r-cran-conjoint Architecture: all Version: 1.42-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-algdesign, r-cran-fpc, r-cran-broom, r-cran-ggplot2, r-cran-cluster, r-cran-ggfortify Filename: pool/dists/noble/main/r-cran-conjoint_1.42-1.ca2404.1_all.deb Size: 132772 MD5sum: a9c51708d040d1890bd26e94d8a1bdc8 SHA1: e38219882ce6d258ca24e73d13ced0581a05fab9 SHA256: 767fb2a3100123d27ee2965fdd747e10e1aac828346334dbc90140847e960ab6 SHA512: 0bce84e40db27968b24c3fa4537399c4d70fd7d3da5647447266ff4bc4c06600c9d52bc082e20837d9533db56809dead2fd69e5b9addf0427f4695a43acc6cd3 Homepage: https://cran.r-project.org/package=conjoint Description: CRAN Package 'conjoint' (An Implementation of Conjoint Analysis Method) This is a simple R package that allows to measure the stated preferences using traditional conjoint analysis method. Package: r-cran-conjurer Architecture: all Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1504 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-conjurer_1.7.1-1.ca2404.1_all.deb Size: 354918 MD5sum: 1d7c8a00582211fbefa4e30d6f930198 SHA1: 043ae6bf389657df6496217710806fc5c9f49e73 SHA256: eea4ccdb32f299935e01a0f16069fc11979f2bc2cf31c9d5687e74a9779b4f58 SHA512: 63168d8825527fbabb23e4f7871c6f322f4eeb076e0dd6feabcef96ce6b2e64df1a2eb9a89dc615a836306e38ee3f70559ff4c6866e066f1349fc0026d364b74 Homepage: https://cran.r-project.org/package=conjurer Description: CRAN Package 'conjurer' (A Parametric Method for Generating Synthetic Data) Generates synthetic data distributions to enable testing various modelling techniques in ways that real data does not allow. Noise can be added in a controlled manner such that the data seems real. 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The package includes features to explore and visualize the data. To calculate the pairwise concurrent or temporal sequenced relationship between the variables, the package provides seven contingency measures (proportion of agreement, classical & corrected Jaccard, Cohen's kappa, phi correlation coefficient, odds ratio, and log odds ratio), however, others can easily be implemented. The package also includes non-parametric significance tests, that can be applied to test whether the contingency value quantifying the relationship between the variables is significantly higher than chance level. Most importantly this test accounts for auto-dependence and relative frequency.See Bodner et al.(2021) .Finally, a network can be drawn. Variables depicted the nodes of the network, with the node size adapted to the prevalence. The association strength between the variables defines the undirected (concurrent) or directed (temporal sequenced) links between the nodes. 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API documentation varies by 'Posit Connect' installation and version, but the latest documentation is also hosted publicly at . 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It handles the details of communicating with the Connect API correctly, 'OAuth' token caching, and refresh behaviour. Package: r-cran-connected Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-lfe, r-cran-reshape2 Suggests: r-cran-agridat, r-cran-dplyr, r-cran-janitor, r-cran-knitr, r-cran-lme4, r-cran-lucid, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-connected_1.1-1.ca2404.1_all.deb Size: 204728 MD5sum: d72907bdfa44b010492110378d12afc7 SHA1: 04a4b418e0ecfbbd27583022a19be8e2d2b949c1 SHA256: 853224aa1cda37b60f502c963ff7ec6d75e880e2146b00da9115ed11ce917d88 SHA512: 76a92e5ea048ff0b1955cf87c7f7ee9a91658244b74440ea9d61f512d9163a98b2192afada7349d743cf9489b42bc81659b648e05fba4a5db5f76ae989957879 Homepage: https://cran.r-project.org/package=connected Description: CRAN Package 'connected' (Visualize and Improve Connectedness of Factors in Tables) Visualize the connectedness of factors in two-way tables. Perform two-way filtering to improve the degree of connectedness. See Weeks & Williams (1964) . Package: r-cran-connectednessapproach Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3032 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-frequencyconnectedness, r-cran-rmgarch, r-cran-rugarch, r-cran-igraph, r-cran-quantreg, r-cran-mass, r-cran-progress, r-cran-glmnet, r-cran-xts, r-cran-zoo, r-cran-urca, r-cran-moments, r-cran-riskparityportfolio, r-cran-performanceanalytics, r-cran-car, r-cran-l1pack Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-connectednessapproach_1.0.4-1.ca2404.1_all.deb Size: 2977668 MD5sum: 942ec43f7a018d380cde87369521e810 SHA1: cd72dd80de59f6d4502c2152d692a6ac80c7a13b SHA256: 517dd8b8570363a69c85cac5c9a179dbd84ba9d2065b190e4b38c854d7003daf SHA512: 0e2b34f44ee037e74a7e89a738ffc873137a14a759cd782c8e9c69315020c41e0e423d32e6721b0547563ce2abc7664d503bc6dda65ceafb7c2c87555bd7d5ff Homepage: https://cran.r-project.org/package=ConnectednessApproach Description: CRAN Package 'ConnectednessApproach' (Connectedness Approach) The estimation of static and dynamic connectedness measures is created in a modular and user-friendly way. Besides, the time domain connectedness approaches, this package further allows to estimate the frequency connectedness approach, the joint spillover index and the extended joint connectedness approach. In addition, all connectedness frameworks can be based upon orthogonalized and generalized VAR, QVAR, LASSO VAR, Ridge VAR, Elastic Net VAR and TVP-VAR models. Furthermore, the package includes the conditional, decomposed and partial connectedness measures as well as the pairwise connectedness index, influence index and corrected total connectedness index. Finally, a battery of datasets are available allowing to replicate a variety of connectedness papers. Package: r-cran-connection Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-connection_0.1.0-1.ca2404.1_all.deb Size: 17612 MD5sum: c86f61700dcf6e7cd415582f741a5838 SHA1: ab17cb6dd05d40212d1aa5903b5680f606ec41fa SHA256: 04bcb6013b7477496e29a5be3353c4793595d2097c7d2105577b3def3169dbdf SHA512: 7dbc192fd852464a7d1c0208592ed09f2502ef6aea51d2b2082c0638282349fb215a87460d13ace765fbdd986173d452944559872370661a57b0ebf772cdadf1 Homepage: https://cran.r-project.org/package=Connection Description: CRAN Package 'Connection' (Measures of Independence and Connection Without Linear Models) Provides tools to measure connection and independence between variables without relying on linear models. Includes functions to compute Eta squared, Chi-squared, and Cramer V. The main advantage of this package is that it works without requiring parametric assumptions. The methods implemented are based on educational material and statistical decomposition techniques, not directly on previously published software or articles. 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It automates the display of schemata, tables, views, as well as the preview of the table's top 1000 records. Package: r-cran-connectomoda Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-readr Filename: pool/dists/noble/main/r-cran-connectomoda_1.0.0-1.ca2404.1_all.deb Size: 11938 MD5sum: ff9f45f4b0fedfa8279df2442e2540ad SHA1: 333c3e54dfb17af62d2255c6b4feeedfacbfad62 SHA256: 9d050a16b7f25602fddad1f7424df6c78707575228075316c032af9cd840166e SHA512: f8c5e54fcf06e6d3e22a8f2fdddf08390bc501dde1b25478ad804f05cbe0c3d905979d26b25c7b1300be5d402922edc976d8d04420262178aaed771f529d7fc8 Homepage: https://cran.r-project.org/package=connectoModa Description: CRAN Package 'connectoModa' (Download Data from Moda) Connect to WFP's Moda platform to R, download data, and obtain the list of individuals with access to the project along with their access level. 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Package: r-cran-connector Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-checkmate, r-cran-cli, r-cran-dbi, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-haven, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-r6, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-vroom, r-cran-writexl, r-cran-yaml, r-cran-zephyr Suggests: r-cran-dbplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rsqlite, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-usethis, r-cran-whirl, r-cran-withr Filename: pool/dists/noble/main/r-cran-connector_1.0.0-1.ca2404.1_all.deb Size: 442354 MD5sum: 1750a78f1067e24b054412c1362fa537 SHA1: b2245bb2b3bb30f27a880b99e3e5e612a2252181 SHA256: 9817b9de519fbdd884f598183f1c9821d13163a99d6e8b7448de5f48f6b2e87c SHA512: 8a57371c3396a556d04e2cc33e9d1c05d9fb0bad6aa0ec7d2c3566ab34a42b8bc0096168b3a20412085890de25bd21f576101a806e46591c6430214656a7ce1a Homepage: https://cran.r-project.org/package=connector Description: CRAN Package 'connector' (Streamlining Data Access in Clinical Research) Provides a consistent interface for connecting R to various data sources including file systems and databases. Designed for clinical research, 'connector' streamlines access to 'ADAM', 'SDTM' for example. It helps to deal with multiple data formats through a standardized API and centralized configuration. 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The components, Card, Grid, Table, Search, and Filter can be used to produce a showcase page or gallery contained within a static or interactive R Markdown page. Package: r-cran-connmattools Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-igraph, r-cran-mcmc Filename: pool/dists/noble/main/r-cran-connmattools_0.3.5-1.ca2404.1_all.deb Size: 267202 MD5sum: bbb9127b5e7cbbd266adf0d651d97917 SHA1: e94490cc7c0def109ef6ccbd649983009d93e741 SHA256: e314720c639f881e37ff2b64e826d38ba2d92c328b21c4ba3ea34ce20ca7dae1 SHA512: 8b6cdd051174ed5cd6de78c87cf752ace132a500e6da927d60f1cd8888f014fb8de80bdc59150c0e9cdf3a2843029ef4a1543a849b05671ae9b35bfe1603b055 Homepage: https://cran.r-project.org/package=ConnMatTools Description: CRAN Package 'ConnMatTools' (Tools for Working with Connectivity Data) Collects several different methods for analyzing and working with connectivity data in R. Though primarily oriented towards marine larval dispersal, many of the methods are general and useful for terrestrial systems as well. Package: r-cran-conogive Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych, r-cran-mvtnorm, r-cran-checkmate, r-cran-assertthat Suggests: r-cran-testthat, r-cran-psychtools, r-cran-covr Filename: pool/dists/noble/main/r-cran-conogive_1.0.0-1.ca2404.1_all.deb Size: 115884 MD5sum: bb87dc1715b04f8d3c024cddf78ccead SHA1: dadc9c9121d5b6a76d77cf77b732f8ff04f60c6c SHA256: 78683effe8963a68816cd44618b86c73a0aa23b8d8cc847bf0bf73b32e8aa54b SHA512: 6d6958ee6f16b987fe35fa8e56466b761e91de8f19c9c8aec722ef5b82d314193ade188f936e3d3ad78f4938f92ceaee95028455c3f87c6a690dfa435cad150c Homepage: https://cran.r-project.org/package=conogive Description: CRAN Package 'conogive' (Congeneric Normal-Ogive Model) The congeneric normal-ogive model is a popular model for psychometric data (McDonald, R. P. (1997) ). This model estimates the model, calculates theoretical and concrete reliability coefficients, and predicts the latent variable of the model. This is the companion package to Moss (2020) . Package: r-cran-conover.test Architecture: all Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Filename: pool/dists/noble/main/r-cran-conover.test_1.1.7-1.ca2404.1_all.deb Size: 60290 MD5sum: d95fae16766c62bd4ee629745d2107d2 SHA1: ec9fd129b8e2d037a4c6aa2e2118ec78931c10d5 SHA256: 870db03e1770a8972582a64b9123b5133a49bab25617af5549796ae971d5e81b SHA512: 87db43959e048fc60b8646042aefb2a3e5b98beb42c7e273b78d3e48c35e93c46ecafc0f20ce5686fae088ea39dd37872989c137db787ae81c17ed7fc98a9912 Homepage: https://cran.r-project.org/package=conover.test Description: CRAN Package 'conover.test' (Conover-Iman Test of Multiple Comparisons Using Rank Sums) Computes the Conover-Iman test (1979) for 0th-order stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis omnibus test for i0th-order stochastic dominance among k groups (Kruskal and Wallis, 1952). conover.test makes k(k-1)/2 multiple pairwise comparisons based on Conover-Iman t-test-statistic of the rank differences. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Conover-Iman test may be understood as a test for median difference and for mean difference. conover.test accounts for tied ranks. The Conover-Iman test is strictly valid if and only if the corresponding Kruskal-Wallis null hypothesis is rejected. Package: r-cran-conrad Architecture: all Version: 1.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-conrad_1.0.0.1-1.ca2404.1_all.deb Size: 45928 MD5sum: b81315d7d5d000343ad83b2eea1ce6bf SHA1: c1d7754db82189e955452968148411844a78b9d1 SHA256: f8f5ae5693c5cf97e9351316042525f843145a96d1b0001065238d9b199a779f SHA512: 4190f0cae4e543cf0b34a3f7be595d0c3c18ca6442872146b45f2449c4008990365179ec7b4255fcebf9c219cffef11fda99997a8e939d5065b610b2bdc00f0f Homepage: https://cran.r-project.org/package=conrad Description: CRAN Package 'conrad' (Client for the Microsoft's 'Cognitive Services Text to SpeechREST' API) Convert text into synthesized speech and get a list of supported voices for a region. Microsoft's 'Cognitive Services Text to Speech REST' API supports neural text to speech voices, which support specific languages and dialects that are identified by locale. Package: r-cran-conscir Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4798 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-readr, r-cran-readxl, r-cran-stringr, r-cran-rlang, r-cran-shiny, r-cran-ggplot2, r-cran-dplyr, r-cran-lubridate, r-cran-padr, r-cran-openair Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bslib, r-cran-iapws95, r-cran-ternary Filename: pool/dists/noble/main/r-cran-conscir_0.3.0-1.ca2404.1_all.deb Size: 3939506 MD5sum: 8bae2272349455cc4e37a3397244c31c SHA1: afc01a7bf798329fbad325756195fe8ae44cdc40 SHA256: 0025756a1269f0a30456c2d82888059f96e0a57bf5d5472b3a38cfa9e81bc5de SHA512: 580b9758a192a2fee2539abcb0604eb6d5cb28493726077b6a41a7b789c9c726cbb8f8aef87846d1a54e8d2df6f7d637e31a3b6c15bc7591fcea378c5a9793ef Homepage: https://cran.r-project.org/package=ConSciR Description: CRAN Package 'ConSciR' (Tools for Conservation Science) Provides data science tools for conservation science, including methods for environmental data analysis, humidity calculations, sustainability metrics, engineering calculations, and data visualisation. Supports conservators, scientists, and engineers working with cultural heritage preventive conservation data. The package is motivated by the framework outlined in Cosaert and Beltran et al. (2022) "Tools for the Analysis of Collection Environments" . Package: r-cran-consensusclustering Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-igraph, r-cran-cluster, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-consensusclustering_1.5.0-1.ca2404.1_all.deb Size: 121176 MD5sum: 199df335c247ce0e8c446acc36273fef SHA1: 6e43e383543fe41fd2b887e44182181510909bc6 SHA256: 877ddff52b0eb92f1d7017bba1767bd34e05cff4ec176282f510b5539841d01a SHA512: de5237653afa395d7ba7575cdcfb8a113ef0b5514855ebd68f283c24cd6c9f06569ab993f6b73bd6b3baf4b5aa2c5c970574327447c0313cfad62184405f700e Homepage: https://cran.r-project.org/package=ConsensusClustering Description: CRAN Package 'ConsensusClustering' (Consensus Clustering) Clustering, or cluster analysis, is a widely used technique in bioinformatics to identify groups of similar biological data points. Consensus clustering is an extension to clustering algorithms that aims to construct a robust result from those clustering features that are invariant under different sources of variation. For the reference, please cite the following paper: Yousefi, Melograna, et. al., (2023) . Package: r-cran-consensusopls Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6238 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-ggplot2, r-cran-ggrepel, r-cran-plotly, r-cran-psych, r-cran-dt, r-bioc-complexheatmap Filename: pool/dists/noble/main/r-cran-consensusopls_1.1.0-1.ca2404.1_all.deb Size: 3570494 MD5sum: 13daa662fd76a233be56f77b60515e3c SHA1: 1540d899d223e1ca72297a00c901ef8bef484262 SHA256: ff47a71e48574b7ed50a8f5bbe0f5d85791434c016838d7c9deb1e2da84c5242 SHA512: fe18a64ae80dfb4db521438f48894d16224148e7c804cf1295b30c4377501077c8256d17b22318d4e6c50202d5e2f16af141a0f4a0072da4d014b6ecec7e8f5b Homepage: https://cran.r-project.org/package=ConsensusOPLS Description: CRAN Package 'ConsensusOPLS' (Consensus OPLS for Multi-Block Data Fusion) Merging data from multiple sources is a relevant approach for comprehensively evaluating complex systems. However, the inherent problems encountered when analyzing single tables are amplified with the generation of multi-block datasets, and finding the relationships between data layers of increasing complexity constitutes a challenging task. For that purpose, a generic methodology is proposed by combining the strength of established data analysis strategies, i.e. multi-block approaches and the Orthogonal Partial Least Squares (OPLS) framework to provide an efficient tool for the fusion of data obtained from multiple sources. The package enables quick and efficient implementation of the consensus OPLS model for any horizontal multi-block data structures (observation-based matching). Moreover, it offers an interesting range of metrics and graphics to help to determine the optimal number of components and check the validity of the model through permutation tests. Interpretation tools include score and loading plots, Variable Importance in Projection (VIP), functionality predict for SHAP computing, and performance coefficients such as R2, Q2, and DQ2 coefficients. J. Boccard and D.N. Rutledge (2013) . Package: r-cran-conserver Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-ggally, r-cran-ggplot2, r-cran-ggrepel, r-cran-magrittr, r-cran-network, r-cran-rlang, r-cran-scales, r-cran-sna Filename: pool/dists/noble/main/r-cran-conserver_1.0.4-1.ca2404.1_all.deb Size: 175846 MD5sum: c20a407e7eb79110c5e46ec51cfa8712 SHA1: 0a002ede1ef917ab0923db8e5f3272fd8d1802e3 SHA256: 586920258b420431820fc8f4f81a73e4b6ef31dd4c92b2b7d221ff9aa29ae2ec SHA512: 44d6179b98f3efb14614a4703768a50375c91055267e5637974d7e592ee6d7b46dc955bb968d66687d45d5ac9ae92bc94a08c476db1912137417089008f2fc8f Homepage: https://cran.r-project.org/package=conserveR Description: CRAN Package 'conserveR' (Identifying Conservation Prioritization Methods Based on DataAvailability) Helping biologists to choose the most suitable approach to link their research to conservation. After answering few questions on the data available, geographic and taxonomic scope, 'conserveR' ranks existing methods for conservation prioritization and systematic conservation planning by suitability. The methods data base of 'conserveR' contains 133 methods for conservation prioritization based on a systematic review of > 12,000 scientific publications from the fields of spatial conservation prioritization, systematic conservation planning, biogeography and ecology. 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Package: r-cran-consort Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3102 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-diagrammer, r-cran-rmarkdown, r-cran-covr, r-cran-stringi Filename: pool/dists/noble/main/r-cran-consort_1.2.3-1.ca2404.1_all.deb Size: 1080988 MD5sum: 72c8302318c800e862443694150e178a SHA1: 316d524af7de53b45b224f8584cefa17f2858d91 SHA256: 108d6273591614ab6eb126d158ba126d5e7d2b7d9f675ef7a861aaf2e534bbe3 SHA512: 51e07521f8129f972e989f980304927f58ff0ee541edd470be286631716578a00820d3bc6f65aa7ea1222fa9905575b09d0717cc3856c98b0c60e5f388166fb0 Homepage: https://cran.r-project.org/package=consort Description: CRAN Package 'consort' (Create Consort Diagram) To make it easy to create CONSORT diagrams for the transparent reporting of participant allocation in randomized, controlled clinical trials. This is done by creating a standardized disposition data, and using this data as the source for the creation a standard CONSORT diagram. Human effort by supplying text labels on the node can also be achieved. 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Package: r-cran-conspline Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coneproj Filename: pool/dists/noble/main/r-cran-conspline_1.2-1.ca2404.1_all.deb Size: 58008 MD5sum: afc23a0907392a480ecc2b1b70dff529 SHA1: bf123c432bcc26fac91def72ee776ec8a3775ac0 SHA256: dcf730f9064652c52342693caabcbd3903070349796025071f30737e4ce093c8 SHA512: 5c79c4379d240845a7184a44fd5ac638ca783ea1f3782586f0f52be1afc3483cd46330991062a1562925aeb93e5212e26cc086347be131c486080399f919cf13 Homepage: https://cran.r-project.org/package=ConSpline Description: CRAN Package 'ConSpline' (Partial Linear Least-Squares Regression using ConstrainedSplines) Given response y, continuous predictor x, and covariate matrix, the relationship between E(y) and x is estimated with a shape constrained regression spline. Function outputs fits and various types of inference. Package: r-cran-consrank Architecture: all Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-rlist, r-cran-proxy, r-cran-gtools, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-consrank_2.1.5-1.ca2404.1_all.deb Size: 230886 MD5sum: 19f490a0f6e2f4040454b2f419642bb6 SHA1: 61c1fe7aea0d0cd4252d0ec0d5ba6e0fe56d7d47 SHA256: 6623e69a0ef463c6bd08e1e84320336ca6c5f263483f32e4e0adf0ce38a1b916 SHA512: eedfcaa37d0665fba9065c46ba1979cdc4568dde8eb0aa70ef7bf0b80048edeec0649c00f059091bfcc406c17974b926da3255b48118693737fcaa9459768323 Homepage: https://cran.r-project.org/package=ConsRank Description: CRAN Package 'ConsRank' (Compute the Median Ranking(s) According to the Kemeny'sAxiomatic Approach) Compute the median ranking according to the Kemeny's axiomatic approach. Rankings can or cannot contain ties, rankings can be both complete or incomplete. The package contains both branch-and-bound algorithms and heuristic solutions recently proposed. The searching space of the solution can either be restricted to the universe of the permutations or unrestricted to all possible ties. The package also provide some useful utilities for deal with preference rankings, including both element-weight Kemeny distance and correlation coefficient. This release declare as deprecated some functions that are still in the package for compatibility. Next release will not contains these functions. Please type '?ConsRank-deprecated' Essential references: Emond, E.J., and Mason, D.W. (2002) ; D'Ambrosio, A., Amodio, S., and Iorio, C. (2015) ; Amodio, S., D'Ambrosio, A., and Siciliano R. (2016) ; D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017) ; Albano, A., and Plaia, A. (2021) . Package: r-cran-consrankclass Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-consrank, r-cran-janitor, r-cran-pracma, r-cran-rlist, r-cran-proxy, r-cran-smacof, r-cran-gtools Filename: pool/dists/noble/main/r-cran-consrankclass_1.0.2-1.ca2404.1_all.deb Size: 241194 MD5sum: 501418a7733da4cb7dbe41b02d7f5c82 SHA1: 854268c3f5d91fdc84c17169013ab81602e09a38 SHA256: 78493566c0f7d4e03af9148917da1f5c40151427a3fbe2db031baf4eab042872 SHA512: ca0bfaa34a13e72fa29bb0425d9978c5514f5c4bc1c8fa54f67e49fa60646f373b5cdc87c08d67c317f4687a8c0ada169120f632a1c61867fe007eed7a9aa577 Homepage: https://cran.r-project.org/package=ConsRankClass Description: CRAN Package 'ConsRankClass' (Classification and Clustering of Preference Rankings) Tree-based classification and soft-clustering method for preference rankings, with tools for external validation of fuzzy clustering, and Kemeny-equivalent augmented unfolding. It contains the recursive partitioning algorithm for preference rankings, non-parametric tree-based method for a matrix of preference rankings as a response variable. It contains also the distribution-free soft clustering method for preference rankings, namely the K-median cluster component analysis (CCA). The package depends on the 'ConsRank' R package. Options for validate the tree-based method are both test-set procedure and V-fold cross validation. The package contains the routines to compute the adjusted concordance index (a fuzzy version of the adjusted rand index) and the normalized degree of concordance (the corresponding fuzzy version of the rand index). The package also contains routines to perform the Kemeny-equivalent augmented unfolding. The mds endine is the function 'sacofSym' from the package 'smacof'. Essential references: D'Ambrosio, A., Vera, J.F., and Heiser, W.J. (2021) ; D'Ambrosio, A., Amodio, S., Iorio, C., Pandolfo, G., and Siciliano, R. (2021) ; D'Ambrosio, A., and Heiser, W.J. (2019) ; D'Ambrosio, A., and Heiser W.J. (2016) ; Hullermeier, E., Rifqi, M., Henzgen, S., and Senge, R. (2012) ; Marden, J.J. . Package: r-cran-consrq Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-rfast Suggests: r-cran-rfast2, r-cran-cols Filename: pool/dists/noble/main/r-cran-consrq_1.0-1.ca2404.1_all.deb Size: 30764 MD5sum: d1bdcd5f2fc1d15fee2386cc01df031f SHA1: 6cfbdb20c802c47f515a863f12955c712f3eff1f SHA256: 1bc3a54b15569dc7a7d9735e2bf842866107ae5c6b9bfaa0486e94d69dd1244d SHA512: a043eab4097e2d3b88a048ffee4e7bbb66203d78a821bc3b5cfc4c10b9c93eb5779782efc9894e6fb671b4b7fa6c0b1ad71501c90aaff32f581c06d855644d5a Homepage: https://cran.r-project.org/package=consrq Description: CRAN Package 'consrq' (Constrained Quantile Regression) Constrained quantile regression is performed. One constraint is that all beta coefficients (including the constant) cannot be negative, they can be either 0 or strictly positive. Another constraint is that the beta coefficients lie within an interval. References: Koenker R. (2005) Quantile Regression, Cambridge University Press. . 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Optionally, the values with uncertainties and/or units are also provided if the 'errors', 'units' and/or 'quantities' packages are installed. The Committee on Data for Science and Technology (CODATA) is an interdisciplinary committee of the International Council for Science which periodically provides the internationally accepted set of values of the fundamental physical constants. This package contains the "2022 CODATA" version, published on May 2024: Eite Tiesinga, Peter J. Mohr, David B. Newell, and Barry N. Taylor (2024) . Package: r-cran-constellation Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 618 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-devtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-constellation_0.2.0-1.ca2404.1_all.deb Size: 558708 MD5sum: 21cd71fc6d9d4c0e4e4e8168495ba97d SHA1: b24be0ba00c270b634c4214161b42821d5654ebb SHA256: 3d6d48e52a1d6a863590792718d20692535e84543a4aafbd369195f675f4496d SHA512: ba6c9151529636cf3eea0d3c166588328ddf1cf386b22bc6e2e22fb41d6e54969fd4847af7316025c7adcd7de3aadb1b5464eaaef37650a601d609c25bf05d79 Homepage: https://cran.r-project.org/package=constellation Description: CRAN Package 'constellation' (Identify Event Sequences Using Time Series Joins) Examine any number of time series data frames to identify instances in which various criteria are met within specified time frames. In clinical medicine, these types of events are often called "constellations of signs and symptoms", because a single condition depends on a series of events occurring within a certain amount of time of each other. This package was written to work with any number of time series data frames and is optimized for speed to work well with data frames with millions of rows. Package: r-cran-contactdata Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4224 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-countrycode, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-contactdata_1.1.0-1.ca2404.1_all.deb Size: 4135042 MD5sum: 36b4b9cdd54b82ffe4264e3da51eeb09 SHA1: 8588e6a14200834cc060859e86a2765c8cd08801 SHA256: aae00ae320afc6c1fffbad19c9534344cf7a34f4c71cbc36a828f03054e32c22 SHA512: 4199f91c6828d1d8c1c886f3a49e15c3b36768dc97df2df69b2efe227da51902ec48afe6486ebd0ba3e396fc72204678a485ab9e5df54500108fe1cc9a4bae7d Homepage: https://cran.r-project.org/package=contactdata Description: CRAN Package 'contactdata' (Social Contact Matrices for 177 Countries) Data package for the supplementary data in Prem et al. (2017) and Prem et al. . Provides easy access to contact data for 177 countries, for use in epidemiological, demographic or social sciences research. Package: r-cran-contactsurveys Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-oai, r-cran-purrr, r-cran-rlang, r-cran-yesno, r-cran-zen4r Suggests: r-cran-socialmixr, r-cran-knitr, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-testthat, r-cran-withr, r-cran-vcr Filename: pool/dists/noble/main/r-cran-contactsurveys_0.1.0-1.ca2404.1_all.deb Size: 61060 MD5sum: a344b90312411363e47b1ed383103098 SHA1: 11a84677733a88a981b8d0c9f6682410c946b5ac SHA256: e7b1eecb978b34d750e18899546908e728b767d45e9de33d36079bc29dd35f99 SHA512: 17add8641ef8aa09ced00a9cc4a4a409526b41153c4e8ef86893565b747a0311fd50486a7f80a25d3335c3c95040dc619c7106fb44b1277a397b35e065ba88f5 Homepage: https://cran.r-project.org/package=contactsurveys Description: CRAN Package 'contactsurveys' (Download Contact Surveys for Use in Infectious Disease Modelling) Download, cache, and manage social contact survey data from the social contact data community on Zenodo () for use in infectious disease modelling. Provides functions to list available surveys, download survey files with automatic caching, and retrieve citations. Contact survey data describe who contacts whom in a population and are used to parameterise age-structured transmission models, for example via the 'socialmixr' package. The surveys available include those from the POLYMOD study (Mossong et al. (2008) ) and other social contact data shared on Zenodo. Package: r-cran-contagionchannels Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3082 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-waveslim, r-cran-quantreg, r-cran-igraph, r-cran-mass Suggests: r-cran-hdm, r-cran-glmnet, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-patchwork, r-cran-rcolorbrewer, r-cran-viridis, r-cran-scales, r-cran-readxl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-contagionchannels_0.1.3-1.ca2404.1_all.deb Size: 2573144 MD5sum: fb15ec8ca51a32535d68414f235cd03e SHA1: 6540d112df8eee8aa92b055d733955e92c426d28 SHA256: e10c04ec88ea59c32ea88c06d5e32bcf920cfb35de544810d3836082c684c9e4 SHA512: 368e05e157f614b3d7fd38941e866c7b711e7f78c37dd5b006b8395679c84d7aa9583504cd1c2abb0234e9ec2fbf85739d6e88a87b76eba761de6b66ce8eb297 Homepage: https://cran.r-project.org/package=contagionchannels Description: CRAN Package 'contagionchannels' (Two-Stage Detection and Attribution of Cross-Border FinancialContagion Channels) Implementation of a two-stage framework for the joint detection-and-attribution of cross-border financial contagion. Stage one detects directional information flows between equity markets via Wavelet-Quantile Transfer Entropy, combining maximal-overlap discrete wavelet decomposition (Percival and Walden, 2000, ISBN:9780521685085) with the transfer-entropy estimator of Schreiber (2000) and quantile conditioning following Han, Linton, Oka and Whang (2016) . Stage two attributes each significant directional link to one of five mutually exclusive transmission channels (Trade, Financial, Geopolitical, Behavioural, Monetary Policy) through a multi-method structural identification architecture combining instrumental-variables two-stage least squares with channel-specific external instruments (Stock and Watson, 2018) , LASSO-based instrument selection (Belloni, Chernozhukov and Hansen, 2014) , local projections (Jorda, 2005) , heteroskedasticity-based identification (Rigobon, 2003) , and the Cinelli-Hazlett (2020) robustness-value sensitivity bound. Bundled datasets and replication scripts reproduce the headline findings of Bhandari, Parida and Sahu (2026) ; the package is general-purpose and accommodates user-supplied returns and channel proxies. 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The metric and its maximum likelihood estimator (alpha hat) were advanced in Mainali, Slud, et al, 2021 . Four types of confidence intervals and median interval were developed in Mainali and Slud, 2022 . The `finches` dataset is bundled with the package. 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Package: r-cran-coopgame Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1003 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geometry, r-cran-rcdd, r-cran-gtools Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rgl Filename: pool/dists/noble/main/r-cran-coopgame_0.2.2-1.ca2404.1_all.deb Size: 894164 MD5sum: 896d6db9c4b1828a9c326c4b47090ca0 SHA1: 471945fa853bfdc7ec808573bc5ec98d47566b71 SHA256: 2c8a841b43a870c4c85a6823111ce35e9a276f39e9dd836893e04edf0ca0dcbb SHA512: 2d858a0996e412ec1b7e8232d4c69a82e690c92c7cbe49ba9afd9cce6a936c0ef8c0d97e27450f186fd362223ccb79de598dc0425f1aa64046b40f9df8c8aa63 Homepage: https://cran.r-project.org/package=CoopGame Description: CRAN Package 'CoopGame' (Important Concepts of Cooperative Game Theory) The theory of cooperative games with transferable utility offers useful insights into the way parties can share gains from cooperation and secure sustainable agreements, see e.g. one of the books by Chakravarty, Mitra and Sarkar (2015, ISBN:978-1107058798) or by Driessen (1988, ISBN:978-9027727299) for more details. A comprehensive set of tools for cooperative game theory with transferable utility is provided. Users can create special families of cooperative games, like e.g. bankruptcy games, cost sharing games and weighted voting games. There are functions to check various game properties and to compute five different set-valued solution concepts for cooperative games. A large number of point-valued solution concepts is available reflecting the diverse application areas of cooperative game theory. Some of these point-valued solution concepts can be used to analyze weighted voting games and measure the influence of individual voters within a voting body. There are routines for visualizing both set-valued and point-valued solutions in the case of three or four players. Package: r-cran-coordinatecleaner Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2665 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-geosphere, r-cran-ggplot2, r-cran-rgbif, r-cran-rnaturalearth, r-cran-terra, r-cran-tidyselect Suggests: r-cran-countrycode, r-cran-covr, r-cran-knitr, r-cran-magrittr, r-cran-maps, r-cran-rmarkdown, r-cran-rnaturalearthdata, r-cran-sf, r-cran-testthat, r-cran-viridis Filename: pool/dists/noble/main/r-cran-coordinatecleaner_3.0.1-1.ca2404.1_all.deb Size: 2409736 MD5sum: c396ac2888157d62f590cdffa9b6d94c SHA1: d47e1c01e233eeda30d87e134622d692a1de4a90 SHA256: ac0383725a6473ebdd42aaa90b0cff593b15e45451a7e934521a0c126ab0897e SHA512: bbfba2df70895bd7d5372b977a60f6cd59a0f0155b89d72f0c3a672bd52191804b43d95d869e3772a8f00281b9aff3f1d6eed55eed723ed7a150f0966326aae2 Homepage: https://cran.r-project.org/package=CoordinateCleaner Description: CRAN Package 'CoordinateCleaner' (Automated Cleaning of Occurrence Records from BiologicalCollections) Automated flagging of common spatial and temporal errors in biological and paleontological collection data, for the use in conservation, ecology and paleontology. Includes automated tests to easily flag (and exclude) records assigned to country or province centroid, the open ocean, the headquarters of the Global Biodiversity Information Facility, urban areas or the location of biodiversity institutions (museums, zoos, botanical gardens, universities). Furthermore identifies per species outlier coordinates, zero coordinates, identical latitude/longitude and invalid coordinates. Also implements an algorithm to identify data sets with a significant proportion of rounded coordinates. Especially suited for large data sets. The reference for the methodology is: Zizka et al. (2019) . Package: r-cran-coortweet Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3821 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-tidytable, r-cran-rcppsimdjson, r-cran-lubridate, r-cran-igraph, r-cran-stringi Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-coortweet_2.1.2-1.ca2404.1_all.deb Size: 3517524 MD5sum: 2112aaa1a5a7af4b69c0fba84b25049a SHA1: 354fc72602c691ce502bb11717078c3b59b780fd SHA256: be411eb7057114b64bfd62addb33a2beba8cd88f02e7adc3b51e1765dad8e38e SHA512: fb517ade9e07071277a2b43f9452239d0bb1aea97db0f5e9549b42ca99ae0fcf361e3804148d34628c1156812a60899b5d10dc87333c46344155c2731974eb01 Homepage: https://cran.r-project.org/package=CooRTweet Description: CRAN Package 'CooRTweet' (Coordinated Networks Detection on Social Media) Detects a variety of coordinated actions on social media and outputs the network of coordinated users along with related information. Package: r-cran-copbasic Architecture: all Version: 2.2.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2980 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lmomco, r-cran-mvtnorm, r-cran-randtoolbox Suggests: r-cran-copula Filename: pool/dists/noble/main/r-cran-copbasic_2.2.14-1.ca2404.1_all.deb Size: 2667162 MD5sum: 6be0c0b0694e46098285f921b7f70ea3 SHA1: ccfa3d2c5d2d87d721d441021b40b32d00d95b2c SHA256: d8cb258603a1425ac75b32f450b19cc30565690edba12f9ac21530bd40549f9e SHA512: e1033b0563810f7abd29963471ea1aacb7af64cfeb1072f75041fccd1b63a63fcf0000e5139fbd69b401dca5afe34501f6504ecda9ecaebcdb9fa8affc5c6562 Homepage: https://cran.r-project.org/package=copBasic Description: CRAN Package 'copBasic' (General Bivariate Copula Theory and Many Utility Functions) Extensive functions for bivariate copula (bicopula) computations and related operations for bicopula theory. The lower, upper, product, and select other bicopula are implemented along with operations including the diagonal, survival copula, dual of a copula, co-copula, and numerical bicopula density. Level sets, horizontal and vertical sections are supported. Numerical derivatives and inverses of a bicopula are provided through which simulation is implemented. Bicopula composition, convex combination, asymmetry extension, and products also are provided. Support extends to the Kendall Function as well as the Lmoments thereof. Kendall Tau, Spearman Rho and Footrule, Gini Gamma, Blomqvist Beta, Hoeffding Phi, Schweizer- Wolff Sigma, tail dependency, tail order, skewness, and bivariate Lmoments are implemented, and positive/negative quadrant dependency, left (right) increasing (decreasing) are available. Other features include Kullback-Leibler Divergence, Vuong Procedure, spectral measure, and Lcomoments for fit and inference, Lcomoment ratio diagrams, maximum likelihood, and AIC, BIC, and RMSE for goodness-of-fit. Package: r-cran-copcor Architecture: all Version: 2024.7-31-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kyotil Suggests: r-cran-runit, r-cran-r.rsp, r-cran-survival Filename: pool/dists/noble/main/r-cran-copcor_2024.7-31-1.ca2404.1_all.deb Size: 58910 MD5sum: 9bef0eb917137d86215c14904ac67e98 SHA1: 7f6fc13affe75df89e3cb279fe63f5c1190340c6 SHA256: 76c81208b02c7c4dd51399202dea39fc6c90b6ac1487b0134b1521fef8c24d67 SHA512: 014959b42bfc995c7274c5734575f3bcc66f4a000a89a7c8e90f233782a94ea6b8b8079ba1c64b3d14b3f09cf718ae0735e446795cf7920d56369d3f58797359 Homepage: https://cran.r-project.org/package=copcor Description: CRAN Package 'copcor' (Correlates of Protection and Correlates of Risk Functions) Correlates of protection (CoP) and correlates of risk (CoR) study the immune biomarkers associated with an infectious disease outcome, e.g. COVID or HIV-1 infection. This package contains shared functions for analyzing CoP and CoR, including bootstrapping procedures, competing risk estimation, and bootstrapping marginalized risks. Package: r-cran-copcts Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-msm, r-cran-copbasic Filename: pool/dists/noble/main/r-cran-copcts_1.0.0-1.ca2404.1_all.deb Size: 109002 MD5sum: c8d0c395c543410dd4e90a48469691be SHA1: 4c41af7fe09e518e4ea1c536f260768be25f65c8 SHA256: eb0e6240faffc5c2815d12fac558e3987822e3f848c5938f8e025a186c128c03 SHA512: 2668128738c1828638843c1359393392f7890232c5af3b235040a15d4a2377a53da20c3f48fc26ac7bbc37869b3b62580feb7766f1555461b9520030813d4d6b Homepage: https://cran.r-project.org/package=CopCTS Description: CRAN Package 'CopCTS' (Copula-Based Semiparametric Analysis for Time Series Data withDetection Limits) Semiparametric estimation for censored time series with lower detection limit. The latent response is a sequence of stationary process with Markov property of order one. Estimation of copula parameter(COPC) and Conditional quantile estimation are included for five available copula functions. Copula selection methods based on L2 distance from empirical copula function are also included. Package: r-cran-cope Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maps, r-cran-abind, r-cran-fields, r-cran-mass, r-cran-matrix, r-cran-mvtnorm, r-cran-nlme Filename: pool/dists/noble/main/r-cran-cope_0.2.3-1.ca2404.1_all.deb Size: 76062 MD5sum: f6e35f4021dacb81d909baeb4ad2d196 SHA1: 9f797e9c88d127b9bfc8fd72858da596e931c5f9 SHA256: 6c58e893eaf161ebc5d1cbebd614db5a8a8bbdb62e8e2dd4cb29152207442451 SHA512: 84bae1f6aecb97bf09583260b8a791418467132f512d5ad4064b59da4751b0c69980c63f67efc6fe1c0798cd746ef1c3546e33156397fc53e79571da2b463800 Homepage: https://cran.r-project.org/package=cope Description: CRAN Package 'cope' (Coverage Probability Excursion (CoPE) Sets) Provides functions to compute and plot Coverage Probability Excursion (CoPE) sets for real valued functions on a 2-dimensional domain. CoPE sets are obtained from repeated noisy observations of the function on the entire domain. They are designed to bound the excursion set of the target function at a given level from above and below with a predefined probability. The target function can be a parameter in spatially-indexed linear regression. Support by NIH grant R01 CA157528 is gratefully acknowledged. Package: r-cran-copent Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mnormt Filename: pool/dists/noble/main/r-cran-copent_0.5-1.ca2404.1_all.deb Size: 42698 MD5sum: 2d48c8a43c9553fd372d3582a9a7574a SHA1: 7454e55800fbc5791111c0e6a1e039ff63ff814d SHA256: 1cbaa11a0c922d0584d557f0b1f6b32aa84e572ba7512244d05608bb11039213 SHA512: 7178f42ae7b8887595885d58372b6ac49fe035f163ab86294b5adc3b624362a673af2a00d9355dd65659b6efd1282e14c3c0988c57fd6e96c2027c1a7a9cca23 Homepage: https://cran.r-project.org/package=copent Description: CRAN Package 'copent' (Estimating Copula Entropy and Transfer Entropy) The nonparametric methods for estimating copula entropy, transfer entropy, and the statistics for multivariate normality test and two-sample test are implemented. The methods for estimating transfer entropy and the statistics for multivariate normality test and two-sample test are based on the method for estimating copula entropy. The method for change point detection with copula entropy based two-sample test is also implemented. Please refer to Ma and Sun (2011) , Ma (2019) , Ma (2022) , Ma (2023) , and Ma (2024) for more information. Package: r-cran-copernicusclimate Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2784 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-clipr, r-cran-ggplot2, r-cran-knitr, r-cran-refmanager, r-cran-rmarkdown, r-cran-sf, r-cran-stars, r-cran-testthat Filename: pool/dists/noble/main/r-cran-copernicusclimate_0.0.5-1.ca2404.1_all.deb Size: 2011098 MD5sum: a0761d664d06d8a74d3c54637b07c3b8 SHA1: 25db727dd4bbd3e06e6aba493616dc18b85fec6d SHA256: fe2c33adf2a4f513d7b46ab926d7c772b4a60205110d48f9544c3320852dfa4e SHA512: 922f3878335a3861000843c85585741d7b4371c365a3fc476f39e0a2cde58acf19ce973ec3f67f73c271d95c971d6af5bc8a9008ad34e38f64a94fe12e8ac1c8 Homepage: https://cran.r-project.org/package=CopernicusClimate Description: CRAN Package 'CopernicusClimate' (Search Download and Handle Data from Copernicus Climate DataService) Subset and download data from EU Copernicus Climate Data Service: . Import information about the Earth's past, present and future climate from Copernicus into R without the need of external software. Package: r-cran-copernicusdataspace Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2187 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aws.s3, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-memoise, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-clipr, r-cran-jose, r-cran-knitr, r-cran-lwgeom, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-stars, r-cran-testthat Filename: pool/dists/noble/main/r-cran-copernicusdataspace_0.0.1-1.ca2404.1_all.deb Size: 1630464 MD5sum: a40aee990d526c3b9e09f9366e78d7c2 SHA1: f36fb54f82b956c894373cfaf559272621ed2d39 SHA256: 7c7deaea861f3ed84dc5eea6e5819cc605cd105c717c014cd94efdf09e1706b0 SHA512: 457f052ed748bb7fc0ff251cd472a3904cfc88518d6308be279bd2c00f113562ba525c3fe9006b82e5a8a0033cfca3ec9d739910beaf8a42e4863d9167f0dc5d Homepage: https://cran.r-project.org/package=CopernicusDataspace Description: CRAN Package 'CopernicusDataspace' (Search Download and Handle Data from the Copernicus Data SpaceEcosystem) The Copernicus Data Space Ecosystem, is an open ecosystem that provides free instant access to a wide range of data and services from the Copernicus Sentinel missions and more on our planet’s land, oceans and atmosphere. This package provides entry points to several APIs allowing users to access the data directly in R. Package: r-cran-copernicusdem Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7355 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-sf, r-cran-doparallel, r-cran-foreach Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-data.table, r-cran-fitbitviz, r-cran-mapview, r-cran-terra Filename: pool/dists/noble/main/r-cran-copernicusdem_1.0.5-1.ca2404.1_all.deb Size: 2658468 MD5sum: 27a4c7e7cb48745661bd581fe1a0f92e SHA1: 74a791e794991077396f90c876cefb13ea64986e SHA256: f7cdcef242026b5a438fa7822d59b540b4cea1b2bd79c0b661cebfe97611321d SHA512: e617d98ba84ab337db870b7ed10d1e9bb726794c1e9e50319f84da8128f62317901303532b0ce4ce70efa0829c2eb6f1206567f3ee44eb2f1665d3dabe41545f Homepage: https://cran.r-project.org/package=CopernicusDEM Description: CRAN Package 'CopernicusDEM' (Copernicus Digital Elevation Models) Copernicus Digital Elevation Model datasets (DEM) of 90 and 30 meters resolution using the 'awscli' command line tool. The Copernicus (DEM) is included in the Registry of Open Data on 'AWS (Amazon Web Services)' and represents the surface of the Earth including buildings, infrastructure and vegetation. Package: r-cran-copernicusmarine Architecture: all Version: 0.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aws.s3, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-leaflet, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-stars, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-cftime, r-cran-clipr, r-cran-cubelyr, r-cran-curl, r-cran-dt, r-cran-knitr, r-cran-lifecycle, r-cran-ncmeta, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-copernicusmarine_0.4.6-1.ca2404.1_all.deb Size: 701278 MD5sum: c2970cf910d06a69ee8b10f0f78b7b39 SHA1: 93c159aae2f26254b9b3400f87192e1f30391a9d SHA256: bca27ffc4f4b052fcf5ef32afcce875518f0ee74576f583c63dac0349fff791f SHA512: 3b4b40514d217dd665ab0443151a977dc9dc0dd1f5067413bec3542aa875c16a277890b0216fd3c646ec6bb30bedf8bcd4242503ea711c0ea1cc326ad1022f14 Homepage: https://cran.r-project.org/package=CopernicusMarine Description: CRAN Package 'CopernicusMarine' (Search Download and Handle Data from Copernicus Marine ServiceInformation) Subset and download data from EU Copernicus Marine Service Information: . Import data on the oceans physical and biogeochemical state from Copernicus into R without the need of external software. Package: r-cran-copernicusr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-getpass, r-cran-testthat, r-cran-withr, r-cran-terra, r-cran-stars, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-copernicusr_0.1.0-1.ca2404.1_all.deb Size: 72168 MD5sum: e951efd5dcfeab33bf807965656301d4 SHA1: a95871037ef337f567095d339cd04928c277fc88 SHA256: c86d5f8253bd4b01456418e135a6db549c673769c54e008c2a45acbd2953ac3b SHA512: db7e552a6c5537122a2009a418f0a8fc7b28e56d7955c027e77cb6e87a0b7259ad589d483c0f4345693f235436cdbb8da39912b1a62c19897f2c59be5bfee098 Homepage: https://cran.r-project.org/package=copernicusR Description: CRAN Package 'copernicusR' (R Interface to Copernicus Marine Service) Provides an R interface to the Copernicus Marine Service for downloading and accessing marine data. Integrates with the official 'copernicusmarine' Python library through 'reticulate'. Requires Python 3.7+ and a free Copernicus Marine account. See and for more information. Package: r-cran-coppecosenzar Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-coppecosenzar_0.1.3-1.ca2404.1_all.deb Size: 233342 MD5sum: 898aab17a993593ed0c1fcb80da96e56 SHA1: d970aef5f16a4d49417a86d65659414448356abb SHA256: 6cf956759893d5a3a5140dc1fb8f34425e5248c9ae5f97323fbb119cf8593e8c SHA512: 4abe7e2e38fcf807a5f155481b2591a157950612f2c688843aaacf129554c0fdf643fcd8b520c512365a84d9099d06e620d58156cfe76178c1a0c0be774c05a8 Homepage: https://cran.r-project.org/package=coppeCosenzaR Description: CRAN Package 'coppeCosenzaR' (COPPE-Cosenza Fuzzy Hierarchy Model) The program implements the COPPE-Cosenza Fuzzy Hierarchy Model. The model was based on the evaluation of local alternatives, representing regional potentialities, so as to fulfill demands of economic projects. After defining demand profiles in terms of their technological coefficients, the degree of importance of factors is defined so as to represent the productive activity. The method can detect a surplus of supply without the restriction of the distance of classical algebra, defining a hierarchy of location alternatives. In COPPE-Cosenza Model, the distance between factors is measured in terms of the difference between grades of memberships of the same factors belonging to two or more sets under comparison. The required factors are classified under the following linguistic variables: Critical (CR); Conditioning (C); Little Conditioning (LC); and Irrelevant (I). And the alternatives can assume the following linguistic variables: Excellent (Ex), Good (G), Regular (R), Weak (W), Empty (Em), Zero (Z) and Inexistent (In). The model also provides flexibility, allowing different aggregation rules to be performed and defined by the Decision Maker. Such feature is considered in this package, allowing the user to define other aggregation matrices, since it considers the same linguistic variables mentioned. Package: r-cran-cops Architecture: all Version: 1.12-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 914 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cordillera, r-cran-smacofx, r-cran-smacof, r-cran-analogue, r-cran-cmaes, r-cran-crs, r-cran-dfoptim, r-cran-gensa, r-cran-minqa, r-cran-nlcoptim, r-cran-nloptr, r-cran-pso, r-cran-rgenoud, r-cran-rsolnp, r-cran-subplex Suggests: r-cran-r.rsp, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cops_1.12-1-1.ca2404.1_all.deb Size: 583416 MD5sum: 506c7cc5783431ce69040da9e9ad5f8c SHA1: 80460b4093c84fe5c09e4dd2d50a39970ef9d0e5 SHA256: e57b2bf91626f0969d8dfa508ccbfc16256e1106990dc6f31907bf899d4abf3e SHA512: b79188a4951a19f828f55c5f47f3e2fdeb8cc9fe9114c314bc488d0136ee03e2077ef0a859c71b66c6a48f773e21974558665c05b20d0a31d3e32d0faca5087b Homepage: https://cran.r-project.org/package=cops Description: CRAN Package 'cops' (Cluster Optimized Proximity Scaling) Multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration (Rusch, Mair & Hornik, 2021, ). They achieve this by transforming proximities/distances with explicit power functions and penalizing the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, ). There are two variants: One for finding the configuration directly (COPS-C) with given explicit power transformations and implicit ratio, interval and non-metric optimal scaling transformations (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal hyperparameters for the explicit transformations (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different MDS models (most of the functionality in smacofx) in the COPS framework. The package further contains a function for pattern search optimization, the ``Adaptive Luus-Jaakola Algorithm'' (Rusch, Mair & Hornik, 2021,) and a functions to calculate the phi-distances for count data or histograms. Package: r-cran-copsens Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3624 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-pcamethods, r-cran-cvxr, r-cran-mass, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-copsens_0.1.0-1.ca2404.1_all.deb Size: 3616866 MD5sum: efe1a470d4797fd5605f6438a520a41a SHA1: fc7b61dea82c0816cdba1d27fa7aae29ec51bf0f SHA256: a698465ec34c239c6433baa6717aaddecc114a8a9699832ce3936db11b9a3ec1 SHA512: 254b38cdf1607dcb6a4c53835c24ceab16fb91acc240d334eabcb5ca6e907701495dc4a21c9863ff0d3ff5698299d35915337af5e80fa36912285c89d03bd9e0 Homepage: https://cran.r-project.org/package=CopSens Description: CRAN Package 'CopSens' (Copula-Based Sensitivity Analysis for Observational CausalInference) Implements the copula-based sensitivity analysis method, as discussed in Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding , with Gaussian copula adopted in particular. Package: r-cran-copula.markov.survival Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-copula.markov.survival_1.0.0-1.ca2404.1_all.deb Size: 157560 MD5sum: 995498be1e28a068dd63f5c493b3dfaf SHA1: f6a06494e86be2a02a3f221e25b7a254430f2908 SHA256: 1066066d2783e1107c0347bcd4f6933c8a00975bafa55b096dfeb2a02b34f1af SHA512: 32bd49e620f42bad80a0f311c56040fc7c17093baa42c1f8e7eb507a4f9978eda4facc99c59f1a7666222cedbc1085b255a274868e483822a96e5ca0160da757 Homepage: https://cran.r-project.org/package=Copula.Markov.survival Description: CRAN Package 'Copula.Markov.survival' (Copula Markov Model with Dependent Censoring) Perform likelihood estimation and corresponding analysis under the copula-based Markov chain model for serially dependent event times with a dependent terminal event. Available are statistical methods in Huang, Wang and Emura (2020, JJSD accepted). Package: r-cran-copula.markov Architecture: all Version: 2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-copula.markov_2.9-1.ca2404.1_all.deb Size: 239476 MD5sum: a705e0ba1b363af86841b84f9824daea SHA1: 3419920bdc117f341cccdd86ff9800f1e42b1f5a SHA256: a5f9ffe3dbee6c89d83a4fcb3c6ec82dbf7aae2f46ac91c131f8ec3c5e612ba6 SHA512: 073d8e6f2ac2a173315169e768b4331cb5bb20a619436b132db869a1dd52e7cd7adefa0046ee0bd4859fdb804eb5dfa1fd097b18e37b4dcaf1448705c8344390 Homepage: https://cran.r-project.org/package=Copula.Markov Description: CRAN Package 'Copula.Markov' (Copula-Based Estimation and Statistical Process Control forSerially Correlated Time Series) Estimation and statistical process control are performed under copula-based time-series models. Available are statistical methods in Long and Emura (2014 JCSA), Emura et al. (2017 Commun Stat-Simul) , Huang and Emura (2021 Commun Stat-Simul) , Lin et al. (2021 Comm Stat-Simul) , Sun et al. (2020 JSS Series in Statistics), and Huang and Emura (2021, in revision). Package: r-cran-copula.surv Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-copula.surv_3.0-1.ca2404.1_all.deb Size: 178680 MD5sum: 16c8080bc3f3cc60dc5b812bde50b803 SHA1: 3acc4d33343ffb7a4d13972aca28709dcfb1e1c4 SHA256: 41b576eb2a2456feff09164d6caa10e46aa7299ebe4a6c397c19da2e19d923e3 SHA512: 679652e870cbd4087e9a98201b4be39665e011ccf1fd25b6602c2e574f550bd74565558cb4128cb5c690d4ac803c25aaf1c91f44e184e81bd38e860c61769094 Homepage: https://cran.r-project.org/package=Copula.surv Description: CRAN Package 'Copula.surv' (Analysis of Bivariate Survival Data Based on Copulas) Simulating bivariate survival data from copula models. Estimation of the association parameter in copula models. Two different ways to estimate the association parameter in copula models are implemented. A goodness-of-fit test for a given copula model is implemented. See Emura, Lin and Wang (2010) for details. Package: r-cran-copulaboost Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rvinecopulib Filename: pool/dists/noble/main/r-cran-copulaboost_0.1.0-1.ca2404.1_all.deb Size: 85636 MD5sum: d87ac26f49326de8e84fab877c753087 SHA1: 01354280b8fa641a24e16e7f2045dc3d0dc7a699 SHA256: d1450bb0ebf20c80b27dff47b77a4b25501a2aa5a8f9847deeec375b8a90d8f9 SHA512: fd269439ece3aee362c62b748eed79b2858dc47323bd6f6d0ff80841b89c10ff0d0decfd7d1954a795430885ca4f1772a3474858c42b4337f8c95f1db9bf31f6 Homepage: https://cran.r-project.org/package=copulaboost Description: CRAN Package 'copulaboost' (Fitting Additive Copula Regression Models for Binary OutcomeRegression) Additive copula regression for regression problems with binary outcome via gradient boosting [Brant, Hobæk Haff (2022); ]. The fitting process includes a specialised model selection algorithm for each component, where each component is found (by greedy optimisation) among all the D-vines with only Gaussian pair-copulas of a fixed dimension, as specified by the user. When the variables and structure have been selected, the algorithm then re-fits the component where the pair-copula distributions can be different from Gaussian, if specified. Package: r-cran-copulacenr Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-caret, r-cran-copbasic, r-cran-copula, r-cran-corpcor, r-cran-flexsurv, r-cran-foreach, r-cran-icenreg, r-cran-magrittr, r-cran-plotly, r-cran-pracma, r-cran-survival, r-cran-vinecopula Filename: pool/dists/noble/main/r-cran-copulacenr_1.2.4-1.ca2404.1_all.deb Size: 723316 MD5sum: c4b7a56b440e88ddadfb7051ec78e446 SHA1: f744c3fe68deb7638210b67f68b2e3d6b04453ee SHA256: baaac4dfc2ea99c0fd4d86314a4c5138dd8770cba220e5a3676845913fa18550 SHA512: d6847e1b5b35c71b5ab867455ceba4b80338bf89bd9e8c96c6295c586483cdb70473218f1861380ad82c1479ddf7dcfb63b41d8eca2d32ea39b1db25bf252b60 Homepage: https://cran.r-project.org/package=CopulaCenR Description: CRAN Package 'CopulaCenR' (Copula-Based Regression Models for Multivariate Censored Data) Copula-based regression models for multivariate censored data, including bivariate right-censored data, bivariate interval-censored data, and right/interval-censored semi-competing risks data. Currently supports Clayton, Gumbel, Frank, Joe, AMH and Copula2 copula models. For marginal models, it supports parametric (Weibull, Loglogistic, Gompertz) and semiparametric (Cox and transformation) models. Includes methods for convenient prediction and plotting. Also provides a bivariate time-to-event simulation function and an information ratio-based goodness-of-fit test for copula. Method details can be found in Sun et.al (2019) Lifetime Data Analysis, Sun et.al (2021) Biostatistics, Sun et.al (2022) Statistical Methods in Medical Research, Sun et.al (2022) Biometrics, and Sun et al. (2023+) JRSSC. Package: r-cran-copuladata Architecture: all Version: 0.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-copuladata_0.0-2-1.ca2404.1_all.deb Size: 53566 MD5sum: 4be2d13d29c4d1a175c17c649fde0143 SHA1: 050892f773e4a16894cebea19f9173db67c64d16 SHA256: 38a2d2b552f0c903edb6698daf56bf8cf83032da2f06e4a3df4a2228dc1ab4bd SHA512: 95a15276a0b5517edac7cf5fadaf0d58d4d9f84ed47e47e147ab7b5ccd0660eaaf42e4e4a8376671e6f8f79bfa97b3034174256c76ecb91be34e7f07c8b6c379 Homepage: https://cran.r-project.org/package=copulaData Description: CRAN Package 'copulaData' (Data Sets for Copula Modeling) Data sets used for copula modeling in addition to those in the R package 'copula'. These include a random subsample from the US National Education Longitudinal Study (NELS) of 1988 and nursing home data from Wisconsin. Package: r-cran-copulaedas Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-vines, r-cran-mvtnorm, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-copulaedas_1.4.3-1.ca2404.1_all.deb Size: 279796 MD5sum: 10607a83a2acc9efa993154a5e7f122d SHA1: 59e03047e65fc001c318718c9fd3a5c58fba8818 SHA256: 0b3a96ca4dab6896d6bcd0722c872f4f679c7a4025370a15f1fd639c33985eca SHA512: 9bc6fa6347113a7efff60a4b9f14b06848a707fd3326fe93fc27a2e5e28d940a42c2d5f63e0aeacb554ab9e13afe10e630d8236a6d41d656df29c0d0aa65dacd Homepage: https://cran.r-project.org/package=copulaedas Description: CRAN Package 'copulaedas' (Estimation of Distribution Algorithms Based on Copulas) Provides a platform where EDAs (estimation of distribution algorithms) based on copulas can be implemented and studied. The package offers complete implementations of various EDAs based on copulas and vines, a group of well-known optimization problems, and utility functions to study the performance of the algorithms. Newly developed EDAs can be easily integrated into the package by extending an S4 class with generic functions for their main components. Package: r-cran-copulareg Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rvinecopulib Filename: pool/dists/noble/main/r-cran-copulareg_0.1.0-1.ca2404.1_all.deb Size: 40878 MD5sum: f1cbb7409a7226ed535eea15723d575a SHA1: ad758a41b5d0db883c3bb7f5561233a85f8c54f7 SHA256: f918037e3ead60b0e7a0990d746e8c61ccba1360e3e0ee417cf6ecfee72bb7dc SHA512: 58247c9d94fd31309464c7b223ec0c9727674035aa1519b8a5f09cf5b67bd943f1847d43890ebe0b779dda6c44d40f9820f9a7f3afc064edf95d2426b68183a3 Homepage: https://cran.r-project.org/package=copulareg Description: CRAN Package 'copulareg' (Copula Regression) Fits multivariate models in an R-vine pair copula construction framework, in such a way that the conditional copula can be easily evaluated. In addition, the package implements functionality to compute or approximate the conditional expectation via the conditional copula. Package: r-cran-copularemada Architecture: all Version: 1.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1253 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod, r-cran-matlab, r-cran-tensor, r-cran-mc2d Filename: pool/dists/noble/main/r-cran-copularemada_1.7.5-1.ca2404.1_all.deb Size: 568784 MD5sum: 952bbd94e8792b4525132aa9710a78fd SHA1: 6ea824abba6b936e4b17780f04ad137c4759485e SHA256: 866051be83fb76d2581566c47612f73bdb117a748ba0aae863af7745127b223d SHA512: 4d78bcef90f0da69130617c59276bca07d031a85d0c718c5119b940539ae543e87b2f26a7f7dc75cad39341b50d00df99d2d6eaa771186323a9943b0914451fa Homepage: https://cran.r-project.org/package=CopulaREMADA Description: CRAN Package 'CopulaREMADA' (Copula Mixed Models for Multivariate Meta-Analysis of DiagnosticTest Accuracy Studies) The bivariate copula mixed model for meta-analysis of diagnostic test accuracy studies in Nikoloulopoulos (2015) and Nikoloulopoulos (2018) . The vine copula mixed model for meta-analysis of diagnostic test accuracy studies accounting for disease prevalence in Nikoloulopoulos (2017) and also accounting for non-evaluable subjects in Nikoloulopoulos (2020) . The hybrid vine copula mixed model for meta-analysis of diagnostic test accuracy case-control and cohort studies in Nikoloulopoulos (2018) . The D-vine copula mixed model for meta-analysis and comparison of two diagnostic tests in Nikoloulopoulos (2019) . The multinomial quadrivariate D-vine copula mixed model for meta-analysis of diagnostic tests with non-evaluable subjects in Nikoloulopoulos (2020) . The one-factor copula mixed model for joint meta-analysis of multiple diagnostic tests in Nikoloulopoulos (2022) . The multinomial six-variate 1-truncated D-vine copula mixed model for meta-analysis of two diagnostic tests accounting for within and between studies dependence in Nikoloulopoulos (2024) . The 1-truncated D-vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard (Nikoloulopoulos, 2025) . Package: r-cran-copulasfm Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-truncnorm, r-cran-vinecopula, r-cran-mass Filename: pool/dists/noble/main/r-cran-copulasfm_0.2.0-1.ca2404.1_all.deb Size: 41284 MD5sum: 04c4342bd5049842c8a7731a8073e69a SHA1: 59c9f8edd54c1a00a04b2179eff21a2f60f6874a SHA256: 0e7d1a5064bfaa5b564db60168e0a9e4f2fff50d7c16a500776db96a7f876fb0 SHA512: 1b9af9d3f7b18013bed01840ddc7f32100c70fc7430bbc67d28d95eefb8e95b89e5d556e4866f0e66d25da2089f1fe35d2dcdead0951e8a7a85f9d9fb4cb8600 Homepage: https://cran.r-project.org/package=copulaSFM Description: CRAN Package 'copulaSFM' (Copula-Based Stochastic Frontier Models) Provides estimation procedures for copula-based stochastic frontier models for cross-sectional data. The package implements maximum likelihood estimation of stochastic frontier models allowing flexible dependence structures between inefficiency and noise terms through various copula families (e.g., Gaussian and Student-t). It enables estimation of technical efficiency scores, log-likelihood values, and information criteria (AIC and BIC). The implemented framework builds upon stochastic frontier analysis introduced by Aigner, Lovell and Schmidt (1977) and the copula theory described in Joe (2014, ISBN:9781466583221). Empirical applications of copula-based stochastic frontier models can be found in Wiboonpongse et al. (2015) and Maneejuk et al. (2017, ISBN:9783319562176). Package: r-cran-copulasim Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-mvtnorm, r-cran-rlang, r-cran-tibble Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-testthat, r-cran-ball, r-cran-energy Filename: pool/dists/noble/main/r-cran-copulasim_0.0.1-1.ca2404.1_all.deb Size: 70660 MD5sum: a5bc8b9a1bcbae1459582d87b14dcdcf SHA1: 1c5e1baccf9c85d13fdd4807d2d6f4a2c3b1a6f5 SHA256: f9321f1fed9f308d2122d2ac8b39a092c92065d32f6f896a333c037fc0c58490 SHA512: 419e1ee6b43d4d07e04bbeda55ae2e965794b82747333b1b37728cb2a919d3a55b4a8104d8dc688afc6aae2dcd9351a89d2b5e8a2713108f5c27123b922e6148 Homepage: https://cran.r-project.org/package=copulaSim Description: CRAN Package 'copulaSim' (Virtual Patient Simulation by Copula Invariance Property) To optimize clinical trial designs and data analysis methods consistently through trial simulation, we need to simulate multivariate mixed-type virtual patient data independent of designs and analysis methods under evaluation. To make the outcome of optimization more realistic, relevant empirical patient level data should be utilized when it’s available. However, a few problems arise in simulating trials based on small empirical data, where the underlying marginal distributions and their dependence structure cannot be understood or verified thoroughly due to the limited sample size. To resolve this issue, we use the copula invariance property, which can generate the joint distribution without making a strong parametric assumption. The function copula.sim can generate virtual patient data with optional data validation methods that are based on energy distance and ball divergence measurement. The function compare.copula.sim can conduct comparison of marginal mean and covariance of simulated data. To simulate patient-level data from a hypothetical treatment arm that would perform differently from the observed data, the function new.arm.copula.sim can be used to generate new multivariate data with the same dependence structure of the original data but with a shifted mean vector. Package: r-cran-copulasqm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ald, r-cran-vinecopula, r-cran-mass Filename: pool/dists/noble/main/r-cran-copulasqm_0.1.0-1.ca2404.1_all.deb Size: 46758 MD5sum: d03d77b9a73ef1b3f19474ffc41108cc SHA1: 335ffb02efda459f4c72a321d399d3918a37b458 SHA256: fc5ba79fc71f42255a93245cd1a1c0b00814e87543c2bbea3e81d2f81c8003f5 SHA512: 462a7f3c96916f117f5b3a1ba1e5c4a89bc60b3fd7de2e7b6e400e66fac7d4ee3890ee7df537e3d66fbdd0084937f732a5c908cc43cc2020582a73118ae9c79c Homepage: https://cran.r-project.org/package=copulaSQM Description: CRAN Package 'copulaSQM' (Copula Based Stochastic Frontier Quantile Model) Provides estimation procedures for copula-based stochastic frontier quantile models for cross-sectional data. The package implements maximum likelihood estimation of quantile regression models allowing flexible dependence structures between error components through various copula families (e.g., Gaussian and Student-t). It enables estimation of conditional quantile effects, dependence parameters, log-likelihood values, and information criteria (AIC and BIC). The framework combines quantile regression methodology introduced by Koenker and Bassett (1978) with copula theory described in Joe (2014, ISBN:9781466583221). This approach allows modeling heterogeneous effects across quantiles while capturing nonlinear dependence structures between variables. 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Works best when there are only two gene copies and read length >=250 base pairs. High and relatively even coverage are important. 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The data set has a respective gold data set that provides information on which records match based on id. Package: r-cran-corazon Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 788 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-corazon_0.1.0-1.ca2404.1_all.deb Size: 461026 MD5sum: 5132f44dc2cf3d1c8c4c0f9f0134966c SHA1: 943067c5b318146c7a6b8b409fe62da15d891f9c SHA256: efaf0690b4ef1d36dcc1b1bf4babf82e7791391b9df5f2b797449cf3737902e4 SHA512: f6b255ed3d817f2142496fb0bbd54ebae1db40ad4626b6f2958dcfd7b8c2d5e4b77a0923896705e1773f53c72a988d2b6f28996e21a81a4e36a1d521421f901e Homepage: https://cran.r-project.org/package=corazon Description: CRAN Package 'corazon' (Apply 'colorffy' Color Gradients Within 'shiny' Elements) Allows the user to apply nice color gradients to 'shiny' elements. The gradients are extracted from the 'colorffy' website. See . Package: r-cran-corbin Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-corbin_1.0.0-1.ca2404.1_all.deb Size: 41266 MD5sum: 5dca94ab95a1ee42e1cddbd6133bcd11 SHA1: 8521e074231fee3451b609ce1c2a3ac95708397b SHA256: 735077c0d03e78951e71bbdb994a0b45956ec80db57436cae254405982cf4b23 SHA512: db8ef119a57fdd038f2ec660f610fbced5e9171c06891d3942a1d08393fc641d03016249fbe9580f25125ba05270950e3de07de92284bf33a19f680e85a97eae Homepage: https://cran.r-project.org/package=CorBin Description: CRAN Package 'CorBin' (Generate High-Dimensional Binary Data with CorrelationStructures) We design algorithms with linear time complexity with respect to the dimension for three commonly studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend the algorithms to generate binary data of general non-negative correlation matrices with quadratic time complexity. Jiang, W., Song, S., Hou, L. and Zhao, H. "A set of efficient methods to generate high-dimensional binary data with specified correlation structures." The American Statistician. See for a detailed presentation of the method. Package: r-cran-corbouli Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rfast, r-cran-rfast2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-corbouli_0.1.5-1.ca2404.1_all.deb Size: 310826 MD5sum: c6061d4b486a2457ec74a7cc9255f260 SHA1: 4bc88209cad6a627fdcc13ab3c244074309b4fe3 SHA256: 6dab54c15bf8932c7f0b636b184e4f3ded17044ba7bb15fc8e2f66aabd8624ed SHA512: ff52c90d2c2fd9c8e2415a449f861fe2cd75866e2803c54252076bdfd30e58db76c6b3734af4a6c173b4011e9bb0aeee941bb1399f6c0f02170e9835a62c4dbc Homepage: https://cran.r-project.org/package=corbouli Description: CRAN Package 'corbouli' (Corbae-Ouliaris Frequency Domain Filtering) Corbae-Ouliaris frequency domain filtering. According to Corbae and Ouliaris (2006) , this is a solution for extracting cycles from time series, like business cycles etc. when filtering. This method is valid for both stationary and non-stationary time series. Package: r-cran-corclass Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Suggests: r-cran-cairo Filename: pool/dists/noble/main/r-cran-corclass_0.2.1-1.ca2404.1_all.deb Size: 37806 MD5sum: 1354d2f1c15c5468d31d51f6dc04f5bb SHA1: 1da000a131d0f5c51ff7025c8f52f0f14b9f5a86 SHA256: 7801169e42327a02949462af660fdacc16089db759f918bdea5403e458390d7b SHA512: 030264915c6d61801590412cac85eb78d8cf8086a21397751164bcd10945f8d1963b7e60b9dee5395736b9620db80859f1ee0d41ca92b44a1b4b6de07ed02c67 Homepage: https://cran.r-project.org/package=corclass Description: CRAN Package 'corclass' (Correlational Class Analysis) Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules. 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The OPTICS Cordillera measures the amount of 'clusteredness' in a numeric data matrix within a distance-density based framework for a given minimum number of points comprising a cluster, as described in Rusch, Hornik, Mair (2018) . We provide an R native version with methods for printing, summarizing, and plotting the result. Package: r-cran-core Architecture: all Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-core_3.2-1.ca2404.1_all.deb Size: 99872 MD5sum: 85a7f03ab3510bda246ee1246a242aad SHA1: 2885e7fb25be2a3b3e76824473dd029face5ddab SHA256: 80ec77b0c1a261a1d37116f5a0e9e987cf1ae6aaa7a1173aadedff0de5955bdd SHA512: 4aaf481f0e215befb26ab1405b6254cca729b1e6adb75cc7c262fe1d7da45a2b83d9cb888c8a0e781dd9e68dcce3102471e6deb04c0972cf9914c1fb795f5c6c Homepage: https://cran.r-project.org/package=CORE Description: CRAN Package 'CORE' (Cores of Recurrent Events) Given a collection of intervals with integer start and end positions, find recurrently targeted regions and estimate the significance of finding. Randomization is implemented by parallel methods, either using local host machines, or submitting grid engine jobs. 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This package streamlines and accelerates the analysis of CT data generated in the context of environmental science. Included are tools for processing raw DICOM images to characterize sediment composition (sand, peat, etc.). Root analyses are also enabled, including measures of external surface area and volumes for user-defined root size classes. For a detailed description of the application of computed tomography imaging for sediment characterization, see: Davey, E., C. Wigand, R. Johnson, K. Sundberg, J. Morris, and C. Roman. (2011) . 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Ensembl Gene ID row names can be converted to Gene Symbols using, e.g., BioMart. Optionally, data can be clustered and filtered by correlation, tree cutting and/or number of missing values. Genes of interest can be highlighted in the plot and correlation significance be indicated by asterisks encoding corresponding P-Values. Plot dimensions and label measures are adjusted automatically by default. The plot features rely on the heatmap.n2() function in the 'heatmapFlex' package. 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Beaulieu et al (2013) . 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(2020) . It allows for both mean and overdispersion covariates. Package: r-cran-cornerstoner Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3336 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vcd Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cornerstoner_2.0.2-1.ca2404.1_all.deb Size: 2235924 MD5sum: b48263795bb2e0080a408d1877d08266 SHA1: 00bc2fc630e4478c3c6b699bca77c20c5c6d48a8 SHA256: 062797613d0dd521924e2b4da1b041c178d591d81f74b04ff8985fe0faa9bb3b SHA512: d1087af9e3bea4507a92e185740d268713e85b8d604fcbaa4a6a5afebb2c0a5054d533ca4ac07800f0be6eb2e0755659ca9b22ae19ae65a0fe2575124e54c17e Homepage: https://cran.r-project.org/package=CornerstoneR Description: CRAN Package 'CornerstoneR' (Collection of Scripts for Interface Between 'Cornerstone' and'R') Collection of generic 'R' scripts which enable you to use existing 'R' routines in 'Cornerstone'. The desktop application 'Cornerstone' () is a data analysis software provided by 'camLine' that empowers engineering teams to find solutions even faster. The engineers incorporate intensified hands-on statistics into their projects. They benefit from an intuitive and uniquely designed graphical Workmap concept: you design experiments (DoE) and explore data, analyze dependencies, and find answers you can act upon, immediately, interactively, and without any programming. While 'Cornerstone's' interface to the statistical programming language 'R' has been available since version 6.0, the latest interface with 'R' is even much more efficient. 'Cornerstone' release 7.1.1 allows you to integrate user defined 'R' packages directly into the standard 'Cornerstone' GUI. Your engineering team stays in 'Cornerstone's' graphical working environment and can apply 'R' routines, immediately and without the need to deal with programming code. Additionally, your 'R' programming team develops corresponding 'R' packages detached from 'Cornerstone' in their favorite 'R' environment. Learn how to use 'R' packages in 'Cornerstone' 7.1.1 on 'camLineTV' YouTube channel () (available in German). 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Package: r-cran-coroica Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-coroica_1.0.2-1.ca2404.1_all.deb Size: 39374 MD5sum: c1315c64126a5b7b12322a79cdb4f309 SHA1: 8fef2700366f7ffb9e49c0d9b7ff874006497380 SHA256: 8fa88b8bcd6d2e90d78f7836247a84f3299c2672a2568f32b70a6724df76fcab SHA512: 29efb2671c8b5353b0e806d5e054a6b386437b835f02f99b966333db47b54ce68084e007756d90fa62e7b95dd6d027bbda2e9a8a1a9dd80e0bd63ee70c575262 Homepage: https://cran.r-project.org/package=coroICA Description: CRAN Package 'coroICA' (Confounding Robust Independent Component Analysis for Noisy andGrouped Data) Contains an implementation of a confounding robust independent component analysis (ICA) for noisy and grouped data. 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Package: r-cran-corona Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-gganimate, r-cran-ggplot2, r-cran-gridextra, r-cran-qicharts2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-corona_0.3.0-1.ca2404.1_all.deb Size: 509368 MD5sum: fcbe32e2e522e714a6887843a88b8387 SHA1: e269fd9b23feef52f222b019eeaf495c36ce1566 SHA256: 06ccb29a198be5283b40bcf42bd595ea4e27531801bb6e37af012f5f38167703 SHA512: eb5677411c89bac5380a8f531af06be2ad889feba22045b02d3728cacea60e25d07672fa89a1dfca6ce51edea0ec120c5ccd6daefb3e7ba96253a6f045603d22 Homepage: https://cran.r-project.org/package=corona Description: CRAN Package 'corona' (Coronavirus ('Rona') Data Exploration) Manipulate and view coronavirus data and other societally relevant data at a basic level. Package: r-cran-coronanetr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-r.utils, r-cran-readr Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-coronanetr_0.3.0-1.ca2404.1_all.deb Size: 64374 MD5sum: 6725eb32d2372cd7ca7680fa141c7c89 SHA1: 7020dda6e2e4cf2080b4226864f565175d242e92 SHA256: 758338d84df2f64b874d23957f4578edede0da0646489a0cb450e776bc061043 SHA512: 2b671c953ae58261668bfe204ad71c51d9934b73e6fc58c5722d1cb84e4be0a961b125a185415b3a53aca3d438d53bf3958818e21b07bfcec8166710a93f4d38 Homepage: https://cran.r-project.org/package=CoronaNetR Description: CRAN Package 'CoronaNetR' (API Access to 'CoronaNet' Data) Offers access to a database on government responses to the COVID-19 pandemic. To date, the 'CoronaNet' dataset provides the most comprehensive and granular documentation of such government policies in the world, capturing data for 20 broad policy categories alongside many other dimensions, including the initiator, target, and timing of a policy. This package is a programmatic front-end to up-to-date 'CoronaNet' policy records and the 'CoronaNet' policy intensity index scores. For more information, see Cheng et al. (2020) . 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Data source: Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus . Package: r-cran-corpcor Architecture: all Version: 1.6.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-corpcor_1.6.10-1.ca2404.1_all.deb Size: 116064 MD5sum: b57ae27a141519822cf1ac4b228c9476 SHA1: bb0ed85a9e1fe5d89b6c6b7426fa416fad82c6b3 SHA256: 8c6081c1c46c1a455cff4ea6647a5d6545353dca48474df13b0c7644f3223283 SHA512: a030c8caef65cd86b6a338ab93386116744d3fc99b17fa23b10a57b087c66fc8041d3450c3c1b70091fd36046affac991687fa5d3559daeca0429c02e61396ab Homepage: https://cran.r-project.org/package=corpcor Description: CRAN Package 'corpcor' (Efficient Estimation of Covariance and (Partial) Correlation) Implements a James-Stein-type shrinkage estimator for the covariance matrix, with separate shrinkage for variances and correlations. The details of the method are explained in Schafer and Strimmer (2005) and Opgen-Rhein and Strimmer (2007) . The approach is both computationally as well as statistically very efficient, it is applicable to "small n, large p" data, and always returns a positive definite and well-conditioned covariance matrix. In addition to inferring the covariance matrix the package also provides shrinkage estimators for partial correlations and partial variances. The inverse of the covariance and correlation matrix can be efficiently computed, as well as any arbitrary power of the shrinkage correlation matrix. Furthermore, functions are available for fast singular value decomposition, for computing the pseudoinverse, and for checking the rank and positive definiteness of a matrix. 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Package: r-cran-corpora Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3436 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-corpora_0.7-1.ca2404.1_all.deb Size: 3417848 MD5sum: 6cd4d428248def8bdab5d128c4a32f80 SHA1: 4c4e6c37f054d43c11de88247c7f29a6f54dd3ca SHA256: e55a6e007b2a3d89a7c5770b4f578d54515fc0f63a18733c7fc27e31ad0a5ee4 SHA512: cbe779eb81c549d710bfce148718d567ee8924365aa7ba71d0c331240775a7c10e52a156be3e1932bb647979648dec64994e1eefeac7611ce464371aff2522d9 Homepage: https://cran.r-project.org/package=corpora Description: CRAN Package 'corpora' (Statistics and Data Sets for Corpus Frequency Data) Utility functions for the statistical analysis of corpus frequency data. This package is a companion to the open-source course "Statistical Inference: A Gentle Introduction for Computational Linguists and Similar Creatures" ('SIGIL'). 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The package contains 1) a helper function to convert a data frame to a 'corporaexplorerobject' and 2) a 'Shiny' app for fast and flexible exploration of a 'corporaexplorerobject'. The package also includes demo apps with which one can explore Jane Austen's novels and the State of the Union Addresses (data from the 'janeaustenr' and 'sotu' packages respectively). Package: r-cran-corpower Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-osdesign Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-corpower_1.0.4-1.ca2404.1_all.deb Size: 175000 MD5sum: 38eb6bd61c1a9c19def03b444042765f SHA1: fbc4eec33687a45884c4d57e154e5cfe32b02b3d SHA256: 501f39cbccf5bb87a884550404aa9456947cc33fc387e2d7289fdc0e377bb1b7 SHA512: 6f043c0b31ba18eb86e1375a666bcf257ba018f9a4c1300faf23945b7e6313bc5a452168ceb918f77cb1dd335cfb354468ba56205488784a54ed70c6c44e4085 Homepage: https://cran.r-project.org/package=CoRpower Description: CRAN Package 'CoRpower' (Power Calculations for Assessing Correlates of Risk in ClinicalEfficacy Trials) Calculates power for assessment of intermediate biomarker responses as correlates of risk in the active treatment group in clinical efficacy trials, as described in Gilbert, Janes, and Huang, Power/Sample Size Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials (2016, Statistics in Medicine). The methods differ from past approaches by accounting for the level of clinical treatment efficacy overall and in biomarker response subgroups, which enables the correlates of risk results to be interpreted in terms of potential correlates of efficacy/protection. The methods also account for inter-individual variability of the observed biomarker response that is not biologically relevant (e.g., due to technical measurement error of the laboratory assay used to measure the biomarker response), which is important because power to detect a specified correlate of risk effect size is heavily affected by the biomarker's measurement error. The methods can be used for a general binary clinical endpoint model with a univariate dichotomous, trichotomous, or continuous biomarker response measured in active treatment recipients at a fixed timepoint after randomization, with either case-cohort Bernoulli sampling or case-control without-replacement sampling of the biomarker (a baseline biomarker is handled as a trivial special case). In a specified two-group trial design, the computeN() function can initially be used for calculating additional requisite design parameters pertaining to the target population of active treatment recipients observed to be at risk at the biomarker sampling timepoint. Subsequently, the power calculation employs an inverse probability weighted logistic regression model fitted by the tps() function in the 'osDesign' package. Power results as well as the relationship between the correlate of risk effect size and treatment efficacy can be visualized using various plotting functions. To link power calculations for detecting a correlate of risk and a correlate of treatment efficacy, a baseline immunogenicity predictor (BIP) can be simulated according to a specified classification rule (for dichotomous or trichotomous BIPs) or correlation with the biomarker response (for continuous BIPs), then outputted along with biomarker response data under assignment to treatment, and clinical endpoint data for both treatment and placebo groups. 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Noda (1993) ). Additionally there are two plot functions for the resulting correlation matrix: The first one creates colored 2D plots, while the second one generates 3D plots. 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That is, it visualizes its main constituent, namely the distances of the single values to their respective mean. The visualization thereby shows what the etymology of the word correlation contains: In pairwise combination, bringing back (see package Vignette for more details). I hope that the 'correlatio' package may benefit some people in understanding and critically evaluating what Pearson's correlation coefficient summarizes in a single number, i.e., to what degree and why Pearson's correlation coefficient may (or may not) be warranted as a measure of association. 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Uses binary correlation analysis to determine relationship. Default correlation method is the Pearson method. Lian Duan, W Nick Street, Yanchi Liu, Songhua Xu, and Brook Wu (2014) . 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The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) or from Kuk (2004) . The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes. 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References include Mardia K.V., Kent J.T. and Bibby J.M. (1979). "Multivariate Analysis". ISBN: 978-0124712522. London: Academic Press and Owen A. B. (2001). "Empirical likelihood". Chapman and Hall/CRC Press. ISBN: 9781584880714. 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Included panel functions can display points, shading, ellipses, and correlation values with confidence intervals. See Friendly (2002) . 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Package: r-cran-corrmct Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-matrix, r-cran-tibble Filename: pool/dists/noble/main/r-cran-corrmct_0.2.0-1.ca2404.1_all.deb Size: 34410 MD5sum: c29c244b630c13947ca140166bc7c661 SHA1: 8667e2bd295c2fe3376490f3d98ff5557cbe2b9b SHA256: 86f96bb23a4d61a5b21b7b7384e60b7764de95a8d527e596f419b7f3c0d14c81 SHA512: a3b7e7c4f011dc937808920ebc372aa6b3dec6a748ed4a5334034ab7071955375d622a2c94d2164052716bd904864db68d7e9055a19e3465d1e76b8754401829 Homepage: https://cran.r-project.org/package=corrMCT Description: CRAN Package 'corrMCT' (Correlated Weighted Hochberg) Perform additional multiple testing procedure methods to p.adjust(), such as weighted Hochberg (Tamhane, A. C., & Liu, L., 2008) , ICC adjusted Bonferroni method (Shi, Q., Pavey, E. S., & Carter, R. 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For more information about the method, refer to the paper Province MA. (2013) . Package: r-cran-corrmixed Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-psych Filename: pool/dists/noble/main/r-cran-corrmixed_1.1-1.ca2404.1_all.deb Size: 107694 MD5sum: cfbddd371f5b71ffe02196e2f15c38ad SHA1: 7b7d76b8219b1535c38f1fe112c630cb6dce9447 SHA256: 1ebef09326d45c24cd85ca1e64507f5cf66084105f53036f4588597aed2ab252 SHA512: 50de8d20245a5a6082474da4c50f64ad8043666273607c3b07f299e76c31357a4529a0eb4ac619d9240f6ed152fe3199aca2a1e73dcbdd565c66547bff82a08e Homepage: https://cran.r-project.org/package=CorrMixed Description: CRAN Package 'CorrMixed' (Estimate Correlations Between Repeatedly Measured Endpoints(E.g., Reliability) Based on Linear Mixed-Effects Models) In clinical practice and research settings in medicine and the behavioral sciences, it is often of interest to quantify the correlation of a continuous endpoint that was repeatedly measured (e.g., test-retest correlations, ICC, etc.). This package allows for estimating these correlations based on mixed-effects models. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552. Package: r-cran-corrplot Architecture: all Version: 0.95-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5038 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-seriation, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-magrittr, r-cran-prettydoc, r-cran-testthat Filename: pool/dists/noble/main/r-cran-corrplot_0.95-1.ca2404.1_all.deb Size: 3778708 MD5sum: 455bcb64c4574dd83e01e56cdc088e6b SHA1: 09619356543cb761927ae1fa6adf1fda3d3cab87 SHA256: 7de7de0c395fb01c8412851f6bcc2d12a7f148e8febf01df824a6b13bf7c4d75 SHA512: d7f333488379551e053c00020d108809bb6fef6c6ce5051a7b873c2cb2daceb1e8e107d8f07bcfd18425249ffcb7325a1d2c2169105c9a5aceb90fcac7e48882 Homepage: https://cran.r-project.org/package=corrplot Description: CRAN Package 'corrplot' (Visualization of a Correlation Matrix) Provides a visual exploratory tool on correlation matrix that supports automatic variable reordering to help detect hidden patterns among variables. 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It makes it possible to easily perform routine tasks when exploring correlation matrices such as ignoring the diagonal, focusing on the correlations of certain variables against others, or rearranging and visualizing the matrix in terms of the strength of the correlations. 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Package: r-cran-corrtable Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc Suggests: r-cran-waldo, r-cran-withr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-corrtable_0.1.1-1.ca2404.1_all.deb Size: 17372 MD5sum: db13ed871cec5c8898ce3d2dbd2f5ed1 SHA1: ca83f14399f875e7ce38fa6167903de776a64983 SHA256: d9f3286a7a649616ee127b7b6dcada5cbfbcdadf103cd6fb64361cbf23b4eff5 SHA512: 4d93fe64d2a3c2b1dadcfb3f787a6bb0d273a488ec8a02abb1cf41f48e86ceb2ea44a463cde2733158dd0c5fff3ce722208a29dd258dcd71d3cc0e7b21edc947 Homepage: https://cran.r-project.org/package=corrtable Description: CRAN Package 'corrtable' (Creates and Saves Out a Correlation Table with SignificanceLevels Indicated) After using this, a publication-ready correlation table with p-values indicated will be created. The input can be a full data frame; any string and Boolean terms will be dropped as part of functionality. Correlations and p-values are calculated using the 'Hmisc' framework. Output of the correlation_matrix() function is a table of strings; this gets saved out to a '.csv2' with the save_correlation_matrix() function for easy insertion into a paper. For more details about the process, consult . Package: r-cran-corrtoolbox Architecture: all Version: 1.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-binnonnor, r-cran-binordnonnor, r-cran-genord, r-cran-moments, r-cran-mvtnorm, r-cran-psych Filename: pool/dists/noble/main/r-cran-corrtoolbox_1.6.4-1.ca2404.1_all.deb Size: 93686 MD5sum: 982dabcdb724e7bf80763bb0d15110a1 SHA1: 3600148d4413f422753654bb99b54b3e7436aab9 SHA256: 3ded024dd19ad006c430ad0a663a8fd246fb36be81ffb984f56ebf0411f8b5b1 SHA512: cceb430fdfa7d200b3d064dff58629741b8434f16646115de4357a3af4b0dddd9b4b1c2363cb91afdab461c96222212d46928a62694464d09aebbefc71e07d4d Homepage: https://cran.r-project.org/package=CorrToolBox Description: CRAN Package 'CorrToolBox' (Modeling Correlational Magnitude Transformations inDiscretization Contexts) Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts. The details of the method are explained in Demirtas, H. and Vardar-Acar, C. (2017) . Package: r-cran-corrviz Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7337 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-visnetwork, r-cran-plotly, r-cran-dendser, r-cran-gganimate, r-cran-igraph, r-cran-ggraph, r-cran-circlize, r-cran-ggally, r-cran-purrr, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-gifski Filename: pool/dists/noble/main/r-cran-corrviz_0.1.0-1.ca2404.1_all.deb Size: 2582696 MD5sum: 362dbf33e84726c048888791acb9b626 SHA1: 0fafdea29ba85f9856659602dca70d4685f478b9 SHA256: ca3b001bef3305ea4f9e46f475419bb043ee6c3bfdc5eba55ab4f915643db687 SHA512: 15b93c46e0b13226b9f08f599254cab7274bffe94f641b76693e5772e89fffd820d9a1825bb019cb8e5b88910dd8ec62312ebd2ae3de0ed08716362f4215b3bb Homepage: https://cran.r-project.org/package=corrViz Description: CRAN Package 'corrViz' (Visualise Correlations) An investigative tool designed to help users visualize correlations between variables in their datasets. This package aims to provide an easy and effective way to explore and visualize these correlations, making it easier to interpret and communicate results. Package: r-cran-corset Architecture: all Version: 0.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-forecast, r-cran-hts, r-cran-testthat Filename: pool/dists/noble/main/r-cran-corset_0.1-5-1.ca2404.1_all.deb Size: 27844 MD5sum: 06d416b2b083216dbd3caec4b7ef6d40 SHA1: 7fd11d6118d5d206f0a0a2fd4de4c1a64a32c897 SHA256: d2b2b88c5f67fbd95feeabd4961e8472b5e8e25b8c43e4caa87a9814330caf0a SHA512: f4ef02ede67b0f6999be01777fea90c4afbd6cd0f2d317673ecbd417e8908da2c93457722aac699d0b9dae2dd6f53d16157bcea3d981e8cdda0b64f0a5b5b424 Homepage: https://cran.r-project.org/package=corset Description: CRAN Package 'corset' (Arbitrary Bounding of Series and Time Series Objects) Set of methods to constrain numerical series and time series within arbitrary boundaries. Package: r-cran-cortest Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-igraph, r-cran-clustergeneration, r-cran-matrix, r-bioc-biobase, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-cortest_1.0.7-1.ca2404.1_all.deb Size: 132740 MD5sum: 9a83a5ac9d629250402aafd41ed901bf SHA1: ced1536e0d8b894a8dcaebee55e6d39552e887d5 SHA256: 25cf1c3e87a9003e29aed1cf921b687487c6c971c9e966f070ef99b2610e1ad8 SHA512: 49fc705ce06aba7e1f66e30cffaf3ff3cab67a0039a222dce219ccd40504144a0207eafb42f396d306cb274fe325ba24d3ca41240eab1c726912d5eaa593072c Homepage: https://cran.r-project.org/package=corTest Description: CRAN Package 'corTest' (Robust Tests for Equal Correlation) There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. The score statistic U is proportion to difference of two correlations based on different types of correlation in 6 methods. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation. ST5() and ST6() used two different ways to combine ST3() and ST4(). We highly recommend ST5() according to the article titled ''New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs'' published in Scientific Reports. Please see the reference: Yu et al. (2019) . Package: r-cran-corteza Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2213 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-callr, r-cran-codetools, r-cran-curl, r-cran-jsonlite, r-cran-llm.api, r-cran-printify, r-cran-processx, r-cran-saber Suggests: r-cran-fortunes, r-cran-mx.api, r-cran-simplermarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-corteza_0.6.3-1.ca2404.1_all.deb Size: 1995264 MD5sum: 6c24c47bf075bea5d44479a150756a71 SHA1: efa8dc9ff8966a114fddafff8dfe818c6ab3a1e9 SHA256: 2d3dfd8515307a12be42d800dd1c022345f324f76e67e8f61c90435e5295cc47 SHA512: 7c5456a4cdaac90d527b6ae121f38cece5a69ebfdd03e20f33b22441cd33e156b89a6e886198a3bd22c169c6bb1d557a3a43a36066b6a9435952700023852f4f Homepage: https://cran.r-project.org/package=corteza Description: CRAN Package 'corteza' (AI Agent Runtime) An agent runtime that gives Large Language Models (LLMs) from 'Anthropic' , 'OpenAI' , 'Moonshot' , and 'Ollama' direct access to a live R session with managed workspace state. Tools execute as R function calls with provenance tracking, and a deterministic retrieval system keeps relevant objects in context across turns. Three entry points: a shell command-line interface (CLI), a console read-eval-print-loop via chat(), and a Model Context Protocol (MCP) server via serve() for external clients. Package: r-cran-corto Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3987 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gplots, r-cran-knitr, r-cran-rmarkdown, r-cran-pbapply, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-corto_1.2.4-1.ca2404.1_all.deb Size: 3598794 MD5sum: 3d75b3ee1c740994eef8e6586a6458f1 SHA1: bdb924bc460acfb160c91a29610a350b541ebbb4 SHA256: 769b6979a4cd45a73eb01f9b5228496f10139449f672b72e7e720950d9647006 SHA512: ac180ae21e63f4145515485f3e1c7d65930d753aba562aec072582260067ec0d2b037d80694921ebeb1ffaf1306a1c29b619cde4b606c06dc807674664e42163 Homepage: https://cran.r-project.org/package=corto Description: CRAN Package 'corto' (Inference of Gene Regulatory Networks) We present 'corto' (Correlation Tool), a simple package to infer gene regulatory networks and visualize master regulators from gene expression data using DPI (Data Processing Inequality) and bootstrapping to recover edges. An initial step is performed to calculate all significant edges between a list of source nodes (centroids) and target genes. Then all triplets containing two centroids and one target are tested in a DPI step which removes edges. A bootstrapping process then calculates the robustness of the network, eventually re-adding edges previously removed by DPI. The algorithm has been optimized to run outside a computing cluster, using a fast correlation implementation. The package finally provides functions to calculate network enrichment analysis from RNA-Seq and ATAC-Seq signatures as described in the article by Giorgi lab (2020) . Package: r-cran-cortsinescore Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-magrittr, r-cran-dplyr Suggests: r-cran-tibble Filename: pool/dists/noble/main/r-cran-cortsinescore_0.1.0-1.ca2404.1_all.deb Size: 13672 MD5sum: d432d0a6c4cdff872f2db88602bf542b SHA1: facc8235eee475322269ebe0a5d34d92af7b04db SHA256: 6f2478e7f373664c7515390862f6bfe2559daae9a0ea0b0b0c2b0793efcfc06b SHA512: d5445a79f2db08bfc6a4d0cb796ad51a939300309fec2cc25633c34a7924959cd97943d28a09dae6a5bae1797af2b171a14cc7a9f5223396c71c9c8669e7c863 Homepage: https://cran.r-project.org/package=CortSineScore Description: CRAN Package 'CortSineScore' (Compute Cortisol Sine Score (CSS) for Diurnal Cortisol Analysis) Computes a single scalar metric for diurnal cortisol cycle analysis, the Cortisol Sine Score (CSS). The score is calculated as the sum over time points of concentration multiplied by sin(2 * pi * time / 24), giving positive weights to morning time points and negative weights to evening ones. The method is model-free, robust, and suitable for regression, classification, clustering, and biomarker research. Package: r-cran-corx Architecture: all Version: 1.0.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-ggcorrplot, r-cran-glue, r-cran-clipr, r-cran-tidyselect, r-cran-rlang, r-cran-moments, r-cran-ggpubr, r-cran-ggplot2, r-cran-ppcor, r-cran-labelled Suggests: r-cran-covr, r-cran-papaja, r-cran-psych, r-cran-testthat Filename: pool/dists/noble/main/r-cran-corx_1.0.7.3-1.ca2404.1_all.deb Size: 122430 MD5sum: 9f70eaa4b3cd3a4e5efaddfa002dd470 SHA1: 1c0696ead836616c94c04f06a5961e725dc2ea02 SHA256: 783a30d468d4cca81ba2829f8db8f02d42ed63ad9f536c10840888835607f5e6 SHA512: 7589d12e62eb4bb79b1231de1f0f686f5a9b07aa5f92a9d55ac810ac362266c9c5f63bf069dbf28078f5e69446005e98285e682e4bad3c0c4efbe35fc125eaf5 Homepage: https://cran.r-project.org/package=corx Description: CRAN Package 'corx' (Create and Format Correlation Matrices) Create correlation (or partial correlation) matrices. Correlation matrices are formatted with significance stars based on user preferences. Matrices of coefficients, p-values, and number of pairwise observations are returned. Send resultant formatted matrices to the clipboard to be pasted into excel and other programs. A plot method allows users to visualize correlation matrices created with 'corx'. Package: r-cran-cosa Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nloptr, r-cran-msm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cosa_2.1.0-1.ca2404.1_all.deb Size: 375916 MD5sum: 1c2da2075cb45ad6eb766637dbd409ad SHA1: 12587f332959316f4819f5ae89c6904a1340c1f3 SHA256: b6495e5c5de96f93bb589494243a7478cb538ab699d2b15e1365097b9024ccdd SHA512: eccf048e251f2e6f3e533b19c0d93550f71d7a9a428201746433992a409fa80db742f30f9f24a25a9bb8a8576117c29f133347a565d201aeacc8a9d45f4675aa Homepage: https://cran.r-project.org/package=cosa Description: CRAN Package 'cosa' (Bound Constrained Optimal Sample Size Allocation) Implements bound constrained optimal sample size allocation (BCOSSA) framework described in Bulus & Dong (2021) for power analysis of multilevel regression discontinuity designs (MRDDs) and multilevel randomized trials (MRTs) with continuous outcomes. Minimum detectable effect size (MDES) and power computations for MRDDs allow polynomial functional form specification for the score variable (with or without interaction with the treatment indicator). See Bulus (2021) . Package: r-cran-coscorr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-coscorr_1.0.0-1.ca2404.1_all.deb Size: 17218 MD5sum: 0542ea05c2a44ea6c594ed6869dcb8e7 SHA1: 34916c5a2e3de6c12f164075af0505b0ccf01c2c SHA256: 00054f2eb0d93ce3e7d47de0a501d21c74f000dc8b49e145c2da4c6b6b9cc11e SHA512: fd0eb86cda646bd468b8571c8e1136a8b2e1eb49e4e5c55d1ce984cafcfb62f8de74380c796bc4d86346471711c4da9d1e39069b9b360f731b06df3943e9355d Homepage: https://cran.r-project.org/package=cosCorr Description: CRAN Package 'cosCorr' (Cosine-Correlation Coefficient for Vector Variables) Computes the cosine-correlation coefficient for measuring the degree of linear dependence among variables in a multidimensional context. The package implements the generalized cosine-correlation theorem for p-1 variables, providing a quantitative assessment of interrelationships within experimental frameworks. This methodology extends classical correlation measures to higher-dimensional spaces using a dimensional exploration approach based on time scale calculus. Package: r-cran-cosinor2 Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cosinor, r-cran-cowplot, r-cran-scales, r-cran-magrittr, r-cran-stringr, r-cran-purrr, r-cran-ggplot2, r-cran-matrixstats, r-cran-hmisc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cosinor2_0.2.1-1.ca2404.1_all.deb Size: 162050 MD5sum: 4fb9e4e2c4a5089a425934ef982085eb SHA1: d8310cf49f9c5a95e627366aff83eb410d31cab9 SHA256: 3f4cc52f99cd19fc67219d8b71f99d5f6fd72f3f4e8763e37aacc4f68201fcb4 SHA512: 6c6494363111a2ab08b616ec0774277ce428b720e658fa87c2d595cdb99b99a9fbbda496805725e2342d14ecb98c20ccf5980692b982322c08f4d8d7811194d9 Homepage: https://cran.r-project.org/package=cosinor2 Description: CRAN Package 'cosinor2' (Extended Tools for Cosinor Analysis of Rhythms) Statistical procedures for calculating population–mean cosinor, non–stationary cosinor, estimation of best–fitting period, tests of population rhythm differences and more. See Cornélissen, G. (2014). . Package: r-cran-cosinor Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-shiny Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-cosinor_1.2.3-1.ca2404.1_all.deb Size: 96020 MD5sum: d673b652d596240bfd007338346b1a37 SHA1: e7e7e27b8c3004ed900156a77d889dd1935de38b SHA256: edce801187e4fc992cf4f7fdf0f44b887c8d78d3a53d665110f12915b657e002 SHA512: 1cca463f97451f6b8ec27229106899ab78f50a4f5aa357d048d2462a58c8f467c723f92b406fedac9df001885f254b7bde9b8291b7cc7eeac00c69232f09543f Homepage: https://cran.r-project.org/package=cosinor Description: CRAN Package 'cosinor' (Tools for Estimating and Predicting the Cosinor Model) A set of simple functions that transforms longitudinal data to estimate the cosinor linear model as described in Tong (1976). Methods are given to summarize the mean, amplitude and acrophase, to predict the mean annual outcome value, and to test the coefficients. Package: r-cran-cosmic Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 735 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-posterior Suggests: r-cran-arrangements, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-kableextra, r-cran-knitr, r-cran-mass, r-cran-progressr, r-cran-rmarkdown, r-cran-testthat, r-cran-xtable Filename: pool/dists/noble/main/r-cran-cosmic_0.5-1.ca2404.1_all.deb Size: 578436 MD5sum: baf27743b3bb3c64b7a8f5c515879606 SHA1: b0857b1c6071a821e6d6196265e56f225269022e SHA256: bb1c3fa0c82444edad9de04461d452525f6225ee1ca4d9f1fed1aa80cc2b5003 SHA512: 8d997aa3146ad21f9d31d0c915192ed321a657e42dc97c443c6cf1344ff526c265120dbba2ac459ad5818d8af20d6632950f457203b83493a265b444726d9b2f Homepage: https://cran.r-project.org/package=cosmic Description: CRAN Package 'cosmic' (Conditional Ordinal Stereotype Model for Incident-LevelComparison) Implements the Conditional Ordinal Stereotype Model for Incident-Level Comparison (COSMIC), a method for analyzing ordinal outcomes observed across multiple actors within shared events. 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Package: r-cran-cosmicsig Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2959 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-cosmicsig_1.3.1-1.ca2404.1_all.deb Size: 2940006 MD5sum: 3596ceb79bb1cf6d11e9a4e377abf792 SHA1: 11712ea048eb97a5ea620dd0f7a88a43057ba311 SHA256: b519e4daca333f81fb0e7d2fa269bba1c60a0d7b566cb3c4999dd694f57ffb4e SHA512: 36bf9c69e0fdeaf07c3c1c3bf3850c828f8d553c7387da86ce55b77371a16396248279b37942e5319b38d861eb57e5a1e5c821a8fc7c3ed2b5afc4336866bae9 Homepage: https://cran.r-project.org/package=cosmicsig Description: CRAN Package 'cosmicsig' (Mutational Signatures from COSMIC (Catalogue of SomaticMutations in Cancer)) A data package with 2 main package variables: 'signature' and 'etiology'. 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Package: r-cran-cosso Architecture: all Version: 2.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-rglpk, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-cosso_2.1-2-1.ca2404.1_all.deb Size: 188292 MD5sum: d12266deb372349769e0853209c5766d SHA1: d69caa1c6cd5f60b27dd396179e2fba1832b899a SHA256: 87bd83624cf111e103d11d56aa9dbbf9d838b954d60b9f5d0436ef686def5989 SHA512: 4abe7466c947ad9313e4b6e92b6443b6ae519b4c78f9b365144e63e69c87c5aa9ff34123f047160d06e076f5c035e2b17f1b26eeaa3d6856f109a0eea8b3a5a0 Homepage: https://cran.r-project.org/package=cosso Description: CRAN Package 'cosso' (Fit Regularized Nonparametric Regression Models Using COSSOPenalty) The COSSO regularization method automatically estimates and selects important function components by a soft-thresholding penalty in the context of smoothing spline ANOVA models. 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Given two non-stationary series (i.e. locally stationary series) this package can then discover time-varying linear combinations that are second-order stationary. Cardinali, A. and Nason, G.P. (2013) . 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Numbered examples correspond to Feb 2011 preprint . Package: r-cran-count Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-msme, r-cran-sandwich, r-cran-mass Filename: pool/dists/noble/main/r-cran-count_1.3.5-1.ca2404.1_all.deb Size: 381840 MD5sum: 14c6e33886f8059640f6bdb50135c55b SHA1: df169ff67ef5516b7869fc3c7b66e91d13bbc255 SHA256: 60a389db24abc5e7935ad0751673606a0d6a03cb32f6038e96d13e64eb4bcf8a SHA512: 190d2d3a191e5790a24b5d330f9f7959db6f3ea2ed17928273c06ba49bb9a6c186c81a149ee6da5ca934bd96e653ca51f190e8669b50160a9984e0ce2017d131 Homepage: https://cran.r-project.org/package=COUNT Description: CRAN Package 'COUNT' (Functions, Data and Code for Count Data) Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press). 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The functions allow to compute the MLE for the following distributions such as the Bell distribution, the Borel distribution, the Poisson distribution, zero inflated Bell distribution, zero inflated Bell Touchard distribution, zero inflated Poisson distribution, zero one inflated Bell distribution and zero one inflated Poisson distribution. Moreover, the probability mass function (PMF), distribution function (CDF), quantile function (QF) and random numbers generation of the Bell Touchard and zero inflated Bell Touchard distribution are also provided. Package: r-cran-countdown Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-prismatic Suggests: r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-countdown_0.6.0-1.ca2404.1_all.deb Size: 117788 MD5sum: cebaa4c20d3a99df60b824d74602fa63 SHA1: aaac22bcd2b0940523c0974573887a7cf5f586bb SHA256: 096d631b7f5255f35144f9855ba52bc851fd05fe3441b6c7178a47acab0d9e62 SHA512: 30a2a465c5050884a23cdae38b46d2a4e3d73f807912fccf84af3605843009907dada9e3403766fa5b99975b32bca90a75bbefcbd8e3b949eb08574914ec8920 Homepage: https://cran.r-project.org/package=countdown Description: CRAN Package 'countdown' (A Countdown Timer for HTML Presentations, Documents, and WebApps) A simple countdown timer for slides and HTML documents written in 'R Markdown' or 'Quarto'. Integrates fully into 'Shiny' apps. Countdown to something amazing. Package: r-cran-counterfactual Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-survival, r-cran-hmisc, r-cran-foreach, r-cran-dorng, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-counterfactual_1.2-1.ca2404.1_all.deb Size: 465896 MD5sum: 85ed7aef923cd03d17560ea7785ee4eb SHA1: 55894c83fe7b296b3e6b553dc486ed2d8a3008ae SHA256: 53e472743f8a7740cd006f385c1b2a93ebb71a78d51b62c90c1159b9039a7e5d SHA512: b4eb496114a96bc89183ef4ba12b9a4f5b8625cb0132812fea682b591e9729a4b853d2cfdf66d71db15112b9fc0fca66e0f87e3e972c4b34e427439463058b3b Homepage: https://cran.r-project.org/package=Counterfactual Description: CRAN Package 'Counterfactual' (Estimation and Inference Methods for Counterfactual Analysis) Implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions. Package: r-cran-counterfactuals Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-checkmate, r-cran-statmatch, r-cran-iml, r-cran-data.table, r-cran-paradox, r-cran-miesmuschel, r-cran-bbotk Suggests: r-cran-gower, r-cran-randomforest, r-cran-ggally, r-cran-trtf, r-cran-testthat, r-cran-mass, r-cran-r.rsp, r-cran-cowplot, r-cran-covr, r-cran-ggplot2, r-cran-keras, r-cran-rchallenge, r-cran-gamlss.data, r-cran-partykit, r-cran-mlt, r-cran-variables, r-cran-basefun, r-cran-rmarkdown, r-cran-rpart, r-cran-mlr3, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-tidymodels, r-cran-caret, r-cran-mlr Filename: pool/dists/noble/main/r-cran-counterfactuals_1.0.0-1.ca2404.1_all.deb Size: 1099924 MD5sum: 51c0b7fadab7cb7c7e8544a77c46bb64 SHA1: 805aa81627b85f36b2f0a07fa5e61bafad19d731 SHA256: 9fedc5d7443f9f0b3ea42187a06ac9b97e287cb7b923893554637ca2c6e9e0fd SHA512: 9aa231f135472ec65a75943c37d7d03e714991fed6c4c37c70c7fef2e60edc7f526eb7f554376c337ae410d865e599e62861138de34e52f80ffc336ae8189826 Homepage: https://cran.r-project.org/package=counterfactuals Description: CRAN Package 'counterfactuals' (Counterfactual Explanations) Modular and unified R6-based interface for counterfactual explanation methods. The following methods are currently implemented: Burghmans et al. (2022) , Dandl et al. (2020) and Wexler et al. (2019) . Optional extensions allow these methods to be applied to a variety of models and use cases. Once generated, the counterfactuals can be analyzed and visualized by provided functionalities. The package is described in detail in Dandl et al. (2025) . Package: r-cran-counternull Architecture: all Version: 0.2.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-effsize, r-cran-ggplot2, r-cran-randomizr, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-counternull_0.2.12-1.ca2404.1_all.deb Size: 165268 MD5sum: e94729ce84b7e8bc850071c4ba486644 SHA1: 293db63b89757784636a9fa5cba229f4e0786dff SHA256: bd6fbb4ffa9b27f952108f6bc825b434353c0e28ddddc206062039d59faa638a SHA512: d39d46cf0631d10ece2b82a75c076c76b5a552ad449037fdb9ac47c4a9f5538c91d3d2d2011e8dc498664400d0b7393f0c61ed85971ce6254aaa173299c90235 Homepage: https://cran.r-project.org/package=Counternull Description: CRAN Package 'Counternull' (Randomization-Based Inference) Randomization-Based Inference for customized experiments. Computes Fisher-Exact P-Values alongside null randomization distributions. 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This is a statistical data type that only assumes non-negative integer values and is generated by counting. Typically, counting data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. The number of DNA double-strand breaks can be counted in individual cells using various bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. Journal of Laboratory and Precision Medicine. ). The software offers functions for a comprehensive automated evaluation of distribution models of count data. In addition to programmatic interaction, a graphical user interface (web server) is included, which enables fast and interactive data-scientific analyses. The user is supported in selecting the most suitable counting distribution for his own data set. Package: r-cran-countgmifs Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-countgmifs_0.0.2-1.ca2404.1_all.deb Size: 280122 MD5sum: 61ffa94ea57aa786b123054acefa4295 SHA1: 7a703941b1d27090787d85c294ded2ad61274b85 SHA256: b467796bc55f18ecd687906bf0a48877e1e38e8e7482c7ae54b0d8cfa06c8dea SHA512: d7c97b96bdb5edcf2da19e5ae29b06adf78ffb5ce5464bbc6e6954d00c1ab706b51ca4b10ceb14f0059ed5e08582f7a50894e603d44aa39ff31c310cf0a1ae55 Homepage: https://cran.r-project.org/package=countgmifs Description: CRAN Package 'countgmifs' (Discrete Response Regression for High-Dimensional Data) Provides a function for fitting Poisson and negative binomial regression models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method. 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For details, see Adam, T., Langrock, R., and Weiß, C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. . 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See the accompanying preprint manuscript: "Normalizing need not be the norm: count-based math for analyzing single-cell data" Church et al (2022) This tool is specifically designed to analyze count matrices from single cell RNA sequencing assays. The tools implement several count-based approaches for standard steps in single-cell RNA-seq analysis, including scoring genes and cells, comparing cells and clustering, calculating differential gene expression, and several methods for rank reduction. There are many opportunities for further optimization that may prove useful in the analysis of other data. We provide the source code freely available at and encourage users and developers to fork the code for their own purposes. 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Package: r-cran-countseppm Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-expm, r-cran-numderiv, r-cran-lmtest Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-countseppm_3.1-1.ca2404.1_all.deb Size: 413606 MD5sum: d32a7fc20df1fe68c9e392ef562bd434 SHA1: 3bfc66681455d1055e6e272f842a188dd28345b0 SHA256: d829a3ef1ca0e5986a56351ba58dd6c0594994d30cd798d28ea8b8652b783ada SHA512: ca95945af9e75d6c3df8f32c0b92b946d1137cacbad92efd1d8a0568687f4f920971c0e54bf5b4d1cc4db5f53c5972dce9bdd08571c622148df070217078f90d Homepage: https://cran.r-project.org/package=CountsEPPM Description: CRAN Package 'CountsEPPM' (Mean and Variance Modeling of Count Data) Modeling under- and over-dispersed count data using extended Poisson process models as in the article Faddy and Smith (2011) Package: r-cran-counttofpkm Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1988 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-complexheatmap, r-cran-circlize Filename: pool/dists/noble/main/r-cran-counttofpkm_1.0-1.ca2404.1_all.deb Size: 548424 MD5sum: 0e7f3ec8019a5b52d4de978eac86522a SHA1: 655567c23c0b607035df3fa4fa95186d05a95e55 SHA256: e578411f836d47f414a75ee6a63d126b05c57d8d6d2af27740faec5374be7acb SHA512: 49f5a4705490124d670f2bd467919887585d2db72c1370af3cd75d31fbd16b98c3920f52ee0d9caa339e2aae32606604b7ce4de4c129c73c151acd0ef7d488c9 Homepage: https://cran.r-project.org/package=countToFPKM Description: CRAN Package 'countToFPKM' (Convert Counts to Fragments per Kilobase of Transcript perMillion (FPKM)) Implements the algorithm described in Trapnell,C. et al. (2010) . This function takes read counts matrix of RNA-Seq data, feature lengths which can be retrieved using 'biomaRt' package, and the mean fragment lengths which can be calculated using the 'CollectInsertSizeMetrics(Picard)' tool. It then returns a matrix of FPKM normalised data by library size and feature effective length. It also provides the user with a quick and reliable function to generate FPKM heatmap plot of the highly variable features in RNA-Seq dataset. Package: r-cran-counttransformers Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase, r-bioc-limma, r-cran-mass Filename: pool/dists/noble/main/r-cran-counttransformers_0.0.6-1.ca2404.1_all.deb Size: 84158 MD5sum: 06bb96113103bab330346e4ecc479c90 SHA1: 448ab57d0b93818c303eb9d6d588634b4588558a SHA256: 234051d694dc49e67afbdb65e06d16255bd65e9ecb7a57371e4c11e438ebd8ca SHA512: 4a462e2944a155ff22cb2e7ca2c57445287cc0dd2a48bd2436a2824e05c81938a2db3ec72635a83ac2e03e2ba3b60fcbe49e673503b6c65759f08aa03592a042 Homepage: https://cran.r-project.org/package=countTransformers Description: CRAN Package 'countTransformers' (Transform Counts in RNA-Seq Data Analysis) Provide data transformation functions to transform counts in RNA-seq data analysis. Please see the reference: Zhang Z, Yu D, Seo M, Hersh CP, Weiss ST, Qiu W. (2019) . Package: r-cran-countts Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-fastdummies, r-cran-matrixstats, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-countts_0.1.0-1.ca2404.1_all.deb Size: 65980 MD5sum: b442985b183810a04bba6de53fecfb07 SHA1: d50ee748a88f0124a4113bda5d32cf017543c6d8 SHA256: 4f225af48453f9a15ed85ff9362fe001093fdda5717325569771ac41969f2a63 SHA512: acd3223b33df25f9d24e001950764b5d2fd48e72aca74dd16d7fa83720d7521444df9e613f83a610226692841c73b3be7f64adc91262d6aeb4d03dc00efcee36 Homepage: https://cran.r-project.org/package=countts Description: CRAN Package 'countts' (Thomson Sampling for Zero-Inflated Count Outcomes) A specialized tool is designed for assessing contextual bandit algorithms, particularly those aimed at handling overdispersed and zero-inflated count data. It offers a simulated testing environment that includes various models like Poisson, Overdispersed Poisson, Zero-inflated Poisson, and Zero-inflated Overdispersed Poisson. The package is capable of executing five specific algorithms: Linear Thompson sampling with log transformation on the outcome, Thompson sampling Poisson, Thompson sampling Negative Binomial, Thompson sampling Zero-inflated Poisson, and Thompson sampling Zero-inflated Negative Binomial. Additionally, it can generate regret plots to evaluate the performance of contextual bandit algorithms. This package is based on the algorithms by Liu et al. (2023) . Package: r-cran-countyhealthr Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-readr, r-cran-httr, r-cran-stringr, r-cran-jsonlite, r-cran-curl, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-countyhealthr_0.1.5-1.ca2404.1_all.deb Size: 75286 MD5sum: c2f94890dd9ac11b3559d4d14e20fa48 SHA1: bc0b654989a14dd100f2a9042ad8de477f8827bd SHA256: e9b8f9d2a3db20df6fd2905b79254700a7d7cdae80d36ab08ba0fc6efece89a1 SHA512: ef4f013cb5d85dae09bfddf5590f546e6a15b908c4ab351e010a854ae5cf70493f223f9325e7e9e3e2ff59fa829c1b581b0e155512308655ff20e0bbdfdf7cd6 Homepage: https://cran.r-project.org/package=countyhealthR Description: CRAN Package 'countyhealthR' (Programmatic Access to County Health Rankings & Roadmaps Data) Provides a simple interface to pull County Health Rankings & Roadmaps (CHR&R) county-level health data and metadata directly from 'Zenodo' . Users can retrieve data for CHR&R release years 2010 through 2025. CHR&R data support research and decision-making to promote health equity and policies that help all communities thrive. Package: r-cran-coursekata Architecture: all Version: 0.19.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 632 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dslabs, r-cran-ggformula, r-cran-ggplot2, r-cran-glue, r-cran-lifecycle, r-cran-lsr, r-cran-metrics, r-cran-mosaic, r-cran-palmerpenguins, r-cran-purrr, r-cran-remotes, r-cran-rlang, r-cran-supernova, r-cran-vctrs, r-cran-viridislite Suggests: r-cran-fivethirtyeight, r-cran-knitr, r-cran-lubridate, r-cran-mass, r-cran-mockery, r-cran-mockr, r-cran-readr, r-cran-readxl, r-cran-rmarkdown, r-cran-usethis, r-cran-simstudy, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-coursekata_0.19.2-1.ca2404.1_all.deb Size: 463244 MD5sum: ca202f50de877e91bdf6dbab8dd11d90 SHA1: 675602f153f4867272a262a1b031f9beeb209fc2 SHA256: 433f245cafa7f44e5e3c32c5da4baaee96e3f7d749ff24bb10ab08590f67c154 SHA512: 70c3ce8cb567b8efc2bc8c85fc52f8901bd5c0d497d0eefa95ddbfa96b2975fe7cae9fb17589b741b97f193fcae87612ab3464dc3ef329cf838c092fefcb6542 Homepage: https://cran.r-project.org/package=coursekata Description: CRAN Package 'coursekata' (Packages and Functions for 'CourseKata' Courses) Easily install and load all packages and functions used in 'CourseKata' courses. Aid teaching with helper functions and augment generic functions to provide cohesion between the network of packages. Learn more about 'CourseKata' at . Package: r-cran-covadap Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-covadap_1.0.1-1.ca2404.1_all.deb Size: 285600 MD5sum: 0402dff1131aba73e13102a5b938e1ba SHA1: fef7ec4c475df87f95cffdcc01d368437e85f07c SHA256: ff7386a922972f5d8b040445f30f1f3d1627bf13b72e1c08196b1067842e96f9 SHA512: ac347e7a3d0adef8f47e8997156535ce0fa37bcf3f002ed266aca65e408466ca55090813b9a75a8c9eb8b22edaf08390f6eb88e8c31baa606ede2b8d47323f57 Homepage: https://cran.r-project.org/package=covadap Description: CRAN Package 'covadap' (Implement Covariate-Adaptive Randomization) Implementing seven Covariate-Adaptive Randomization to assign patients to two treatments. Three of these procedures can also accommodate quantitative and mixed covariates. Given a set of covariates, the user can generate a single sequence of allocations or replicate the design multiple times by simulating the patients' covariate profiles. At the end, an extensive assessment of the performance of the randomization procedures is provided, calculating several imbalance measures. See Baldi Antognini A, Frieri R, Zagoraiou M and Novelli M (2022) for details. 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Generate datasets with specified correlation structures for continuous variables, adjust mutual information between categorical variables, and manipulate subgroup correlations to intentionally create Simpson's Paradox. Joe (1997) Sklar (1959) . Package: r-cran-covatest Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mathjaxr, r-cran-lubridate, r-cran-v8, r-cran-zoo, r-cran-gstat, r-cran-sp, r-cran-spacetime Suggests: r-cran-sf Filename: pool/dists/noble/main/r-cran-covatest_1.2.5-1.ca2404.1_all.deb Size: 476848 MD5sum: f370fe8ef245ec4aa14e04e3a32f71de SHA1: fafd701d5a3c7ed5a4f4938fb170d3dab2bca036 SHA256: f4f16cc3cc88b31d3c7873fd112033045f7b4c37a261d4018a03bc0f14b80ee7 SHA512: 47cc7171e105fb914d35c00744137ce7f3da3e1227667231bfcd4b7ba3823ddfdbd0d69f2ea263196f44865ded1e6d96e1af1b194ed2baa740509e6f8274edd6 Homepage: https://cran.r-project.org/package=covatest Description: CRAN Package 'covatest' (Tests on Properties of Space-Time Covariance Functions) Tests on properties of space-time covariance functions. Tests on symmetry, separability and for assessing different forms of non-separability are available. Moreover tests on some classes of covariance functions, such that the classes of product-sum models, Gneiting models and integrated product models have been provided. It is the companion R package to the papers of Cappello, C., De Iaco, S., Posa, D., 2018, Testing the type of non-separability and some classes of space-time covariance function models and Cappello, C., De Iaco, S., Posa, D., 2020, covatest: an R package for selecting a class of space-time covariance functions . 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Package: r-cran-covid19.analytics Architecture: all Version: 2.1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5041 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-ape, r-cran-rentrez, r-cran-curl, r-cran-plotly, r-cran-htmlwidgets, r-cran-desolve, r-cran-gplots, r-cran-pheatmap, r-cran-shiny, r-cran-shinydashboard, r-cran-shinycssloaders, r-cran-dt, r-cran-dplyr, r-cran-collapsibletree Suggests: r-cran-knitr, r-cran-devtools, r-cran-roxygen2, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-covid19.analytics_2.1.3.3-1.ca2404.1_all.deb Size: 3292286 MD5sum: f2a54318c45d589a2691b4a3024f7f97 SHA1: 60e7102c500df28d529099325787b0fd2a2cce0a SHA256: 3f2f587c73b69f74b9d6689d4d08e0c322d593b716e154cb1cfe9d1755265aec SHA512: a9c43ba38df7d7eb0706bf8882695cd480b0f80f8e457e645ca1c37784cae3b5b052179629db4f0e3bd115a36a4841337cbf6e6c9ba460719d3f3e9e62355d16 Homepage: https://cran.r-project.org/package=covid19.analytics Description: CRAN Package 'covid19.analytics' (Load and Analyze Live Data from the COVID-19 Pandemic) Load and analyze updated time series worldwide data of reported cases for the Novel Coronavirus Disease (COVID-19) from different sources, including the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) data repository , "Our World in Data" among several others. The datasets reporting the COVID-19 cases are available in two main modalities, as a time series sequences and aggregated data for the last day with greater spatial resolution. Several analysis, visualization and modelling functions are available in the package that will allow the user to compute and visualize total number of cases, total number of changes and growth rate globally or for an specific geographical location, while at the same time generating models using these trends; generate interactive visualizations and generate Susceptible-Infected-Recovered (SIR) model for the disease spread. Package: r-cran-covid19 Architecture: all Version: 3.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r.utils, r-cran-data.table Suggests: r-cran-rsqlite, r-cran-wbstats Filename: pool/dists/noble/main/r-cran-covid19_3.0.3-1.ca2404.1_all.deb Size: 34608 MD5sum: 147418cc0380ccb31ea5040e558ac10b SHA1: bf8ec2e8e530fe46074bfc3e26502862dac97652 SHA256: 232c32f00cf260b806fa162c07e10973d80f7114cdfcc3cbd44a75a52ea1f99b SHA512: 9bb7407d8238fbeb7d3e8862f7b8a726e388f50ab2f9a044fa25337267e52210cce85d7d65d6e24cc80ce77271f037a3a4bc367a3472919f6a2483f3e77b1bc4 Homepage: https://cran.r-project.org/package=COVID19 Description: CRAN Package 'COVID19' (R Interface to COVID-19 Data Hub) Provides a daily summary of COVID-19 cases, deaths, recovered, tests, vaccinations, and hospitalizations for 230+ countries, 760+ regions, and 12000+ administrative divisions of lower level. Includes policy measures, mobility data, and geospatial identifiers. Data source: COVID-19 Data Hub . Package: r-cran-covid19br Architecture: all Version: 1.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3252 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-httr2, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-tidyr Suggests: r-cran-ggrepel, r-cran-kableextra, r-cran-knitr, r-cran-leaflet, r-cran-pracma, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-covid19br_1.0.0.1-1.ca2404.1_all.deb Size: 3273244 MD5sum: 2ff11c38e80ca91130329605c250f9ba SHA1: d6e461d1e761b7fef35815e81bad93094b78da21 SHA256: 60687a81d7f50928c9fdd23a1b334336f8d676bc3f4557cf1a320e3e9704fe41 SHA512: ed4850705d2d76cae5be9e6c1a8dda7044068f29f47ac43f560a4b0af286bd50c3e8d3f6e08222c225129dcdfce0c40700f71b6e1385c05e5b5195bfe99402f5 Homepage: https://cran.r-project.org/package=covid19br Description: CRAN Package 'covid19br' (Brazilian COVID-19 Pandemic Data) Set of functions to import COVID-19 pandemic data into R. The Brazilian COVID-19 data, obtained from the official Brazilian repository at , is available at the country, region, state, and city levels. The package also downloads world-level COVID-19 data from Johns Hopkins University's repository. COVID-19 data is available from the start of follow-up until to May 5, 2023, when the World Health Organization (WHO) declared an end to the Public Health Emergency of International Concern (PHEIC) for COVID-19. Package: r-cran-covid19brazil Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4814 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-dplyr Suggests: r-cran-remotes, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-covid19brazil_0.1.0-1.ca2404.1_all.deb Size: 4872594 MD5sum: 7f643389ae6f8a77cd99fdee7ee3caf9 SHA1: 043229dfd88d18cafc58def287282db416cb4c71 SHA256: 78fb46633e7991d867948bb47143c11826f1120c9b001c49ca457b0e657877ce SHA512: 5a3590a86303aaf994bbb6aeac538838080737498a88e76686fd285e60a1960baa08a1dd7b4ca636bb38072866f0a8caad8a4cc13cd83b795eef9388579412e2 Homepage: https://cran.r-project.org/package=covid19brazil Description: CRAN Package 'covid19brazil' (COVID-19 Dataset for Brazil) Dataset with strategic information about COVID-19 in Brazil. Data for municipalities, states, region and Brazil. Data source: Sistema Unico de Saude - SUS. Package: r-cran-covid19dbcand Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1940 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-data.tree, r-cran-dt, r-cran-networkd3 Filename: pool/dists/noble/main/r-cran-covid19dbcand_0.1.1-1.ca2404.1_all.deb Size: 1497926 MD5sum: b14065e79b789fa6483e1ae0faedbdd4 SHA1: 47abe1d12a4d41a863b40ad41e2e53d1f15a7f4e SHA256: 7d53d7ad187085b96e700c1827412db9fe319df60eb8b3a0d8660e3f0d85148a SHA512: 471d8acd7f4d34048752b9a2fece388040de2b92bf59158aef8121f877aae2ab9d1f8e9d39e6603305b30f37686316db50596aa3b00d43250e4fb90dba786efa Homepage: https://cran.r-project.org/package=covid19dbcand Description: CRAN Package 'covid19dbcand' (Selected 'Drugbank' Drugs for COVID-19 Treatment Related Data inR Format) Provides different datasets parsed from 'Drugbank' database using 'dbparser' package. It is a smaller version from 'dbdataset' package. It contains only information about COVID-19 possible treatment. Package: r-cran-covid19france Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-magrittr, r-cran-readr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-covid19france_0.1.0-1.ca2404.1_all.deb Size: 19174 MD5sum: 09069665b5ff66b9af6524d08962e516 SHA1: 64216469ca913e9b916546e3d80f6bae2c117da0 SHA256: ed1e30fe7cb141ee44325d661e4dabd6b970c5c4774c697f331638360b77e514 SHA512: 469b05f2c078dea1bf4be159ad905900c495658ff1e6b8992b30449586ae6aae7d9c21593664e921162bba7d72b1c1b96b2e0c8fb723f1f511f3af8a6b424d46 Homepage: https://cran.r-project.org/package=covid19france Description: CRAN Package 'covid19france' (Cases of COVID-19 in France) Imports and cleans 'opencovid19-fr' data on COVID-19 in France. Package: r-cran-covid19india Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-epiestim, r-cran-cli, r-cran-gt, r-cran-httr, r-cran-glue, r-cran-janitor, r-cran-scales, r-cran-stringr, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-covid19india_0.1.4-1.ca2404.1_all.deb Size: 84744 MD5sum: 437eb741e64af94d1e1a709829e4ba2c SHA1: 0a938ea0f1e34ba9aa380ce1c532e858c96fd8c3 SHA256: 5459f9b76f94e79ffe74280259bdfcdbf8139f60b1855a39fb24d0d2211df0ad SHA512: 0ba73dbc05880d73c90b4da40f234f68dc80501b6efb5a110566ef9f36976e21449770d25bf27493b5cfe34ba29844d20fe9cbc247b0614c22d4c632e43b1ec4 Homepage: https://cran.r-project.org/package=covid19india Description: CRAN Package 'covid19india' (Pulling Clean Data from Covid19india.org) Pull raw and pre-cleaned versions of national and state-level COVID-19 time-series data from covid19india.org . Easily obtain and merge case count data, testing data, and vaccine data. Also assists in calculating the time-varying effective reproduction number with sensible parameters for COVID-19. Package: r-cran-covid19italy Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools Suggests: r-cran-knitr, r-cran-readr, r-cran-remotes, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-covid19italy_0.3.1-1.ca2404.1_all.deb Size: 2862746 MD5sum: 986ab5f426971b942aedf57c573ff709 SHA1: 507b56e451c4b970936717b5bc8e2f0a22084e93 SHA256: ae474eb1a81338aeff2c4895ca9276a068e193c09f402de91ae1394c8ef2348f SHA512: e12de65649e885c5410a47deca9a75631699c6a0fb4267411040240385b2ff64abfa152ecc66381ecce0cfae8583cd64648061f2743aa75f0ea585dbc08c65bb Homepage: https://cran.r-project.org/package=covid19italy Description: CRAN Package 'covid19italy' (The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Italy Dataset) Provides a daily summary of the Coronavirus (COVID-19) cases in Italy by country, region and province level. Data source: Presidenza del Consiglio dei Ministri - Dipartimento della Protezione Civile . Package: r-cran-covid19sf Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4597 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-mapview, r-cran-plotly, r-cran-sf Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-covid19sf_0.1.2-1.ca2404.1_all.deb Size: 2968382 MD5sum: 358fd9eb67f7e59c45849888b53d1300 SHA1: b29da25bfc2c1e292215083f90da9cd9da379ea4 SHA256: 84ee205584bdd0f73387c24ac30d0a8e90b6dae9bab991ff61c1cea42bf44c3f SHA512: 0b2d705fad2064bc7e499ca0352c2b3b6fe6822925587c033ce12c06b84424daaee77743ab834b978659296b0c93ef6e007bfb8bc6f4032c4c7fc2b9aac4eb2a Homepage: https://cran.r-project.org/package=covid19sf Description: CRAN Package 'covid19sf' (The Covid19 San Francisco Dataset) Provides a verity of summary tables of the Covid19 cases in San Francisco. Data source: San Francisco, Department of Public Health - Population Health Division . Package: r-cran-covid19srilanka Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-covid19srilanka_1.1.0-1.ca2404.1_all.deb Size: 119164 MD5sum: e78b2a2323e30972ea320256285f45e6 SHA1: 53f6f8bdcc67b7958c15de05de9aae0f78d52d2f SHA256: 18893ff6f8ab57dfdead9d1041086ef2a02810446641080bb95e903728107ce7 SHA512: 5feb184a4f926bd84cb293ce66507136a447b326689a4fdc48e55f7d04dc7e82f1556069fde6598345c63c9a01e8ad012f14e16385e3b30a0156138d0968a787 Homepage: https://cran.r-project.org/package=covid19srilanka Description: CRAN Package 'covid19srilanka' (The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data in SriLanka) Provides a daily counts of the Coronavirus (COVID19) cases by districts and country. Data source: Epidemiological Unit, Ministry of Health, Sri Lanka . Package: r-cran-covid19swiss Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1293 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-covid19swiss_0.1.0-1.ca2404.1_all.deb Size: 1218090 MD5sum: b3d15a1d811819c275a17172cd51c7c2 SHA1: 06048a65d755245b4d65bf7f6e79252a7c09ad22 SHA256: 676c89f10ea6566f998c9fd0fa81243acc5e7314b412814854a307610b1aba8a SHA512: b947850e0dd4c4aa24da7d6c31dc5253dac212daaec3719b5749983d02f4bee371e2ee2fa6f531ed33d4ac7a5c4c940e00d379ab1c5d2bc2eaac0613f5e7b6f9 Homepage: https://cran.r-project.org/package=covid19swiss Description: CRAN Package 'covid19swiss' (COVID-19 Cases in Switzerland and Principality of Liechtenstein) Provides a daily summary of the Coronavirus (COVID-19) cases in Switzerland cantons and Principality of Liechtenstein. Data source: Specialist Unit for Open Government Data Canton of Zurich . Package: r-cran-covid19tunisia Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-magrittr, r-cran-readr, r-cran-stringr, r-cran-tidyr, r-cran-knitr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-covid19tunisia_0.1.0-1.ca2404.1_all.deb Size: 56190 MD5sum: 736ec228632a606d8cfde5b7d95db49a SHA1: a193af3b869bcefacf76cb7edf3fd0cc018421fd SHA256: 6d5a2b72de50d3855da54cf0c98f42c920a6ab5b05a252a948854ac5ce40308d SHA512: cb73b5c6918131f1bead8f9e51353fe4d3fc2aba6bdea7b3475fe457198fc6fc0b2da9bf7e1be08103d21c01829de8b42f8efbaf55ec98ab13f9c4699816950f Homepage: https://cran.r-project.org/package=covid19tunisia Description: CRAN Package 'covid19tunisia' (Cases of COVID-19 in Tunisia) Data personally collected about the spread of COVID-19 (SARS-COV-2) in Tunisia . Package: r-cran-covid19us Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-snakecase, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-covid19us_0.1.9-1.ca2404.1_all.deb Size: 83438 MD5sum: 34f3a97eb7be0b55b56344c81cdfce90 SHA1: 8c3790af9244c80970af13a2dabef5c65fcfd234 SHA256: 01f004f41ebd448666ec57e5bf7cb156d24d001e216577f587e13ae43cfa7a3f SHA512: d973bbf5efae422c6011e2863c9ba9f59c3364591ea0fd7c09ed5ec77adad8721ecdfce603d06a8efbec0f60376765d93cd7dfd4884036bfe968f57821cdf487 Homepage: https://cran.r-project.org/package=covid19us Description: CRAN Package 'covid19us' (Cases of COVID-19 in the United States) A wrapper around the 'COVID Tracking Project API' providing data on cases of COVID-19 in the US. Package: r-cran-covidcast Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2723 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-mmwrweek, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-gridextra, r-cran-httptest, r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-covidcast_0.5.3-1.ca2404.1_all.deb Size: 1930792 MD5sum: 9455eb50b905e7c2455e41668ab971c1 SHA1: 41bbb3983731c3626f6813eeb308a52e4bc5d0c5 SHA256: dd63d7846e6adfdc319e68e2b7589482ad6c4329610355dc0d5e360dd44936ca SHA512: c1b93c3fe78340f44089f651c39859c6c65660d9ab9fc4d4bf49b03455082d148fc9e153af96ddb689407fb9c0973f1fb8b5e99fb465225bfacb4df0a2ba86ca Homepage: https://cran.r-project.org/package=covidcast Description: CRAN Package 'covidcast' (Client for Delphi's 'COVIDcast Epidata' API) Tools for Delphi's 'COVIDcast Epidata' API: data access, maps and time series plotting, and basic signal processing. The API includes a collection of numerous indicators relevant to the COVID-19 pandemic in the United States, including official reports, de-identified aggregated medical claims data, large-scale surveys of symptoms and public behavior, and mobility data, typically updated daily and at the county level. All data sources are documented at . Package: r-cran-covidibge Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1088 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-magrittr, r-cran-projmgr, r-cran-rcurl, r-cran-readr, r-cran-readxl, r-cran-survey, r-cran-tibble, r-cran-timedate Suggests: r-cran-convey, r-cran-sipdibge, r-cran-srvyr Filename: pool/dists/noble/main/r-cran-covidibge_0.2.2-1.ca2404.1_all.deb Size: 156162 MD5sum: 78c8521714a9c38b3fdf2545edd535b8 SHA1: 5fca783116e51954c6e791246b08a14436e4030c SHA256: 698a39f286b90da3217df09169faca60da7b073761fff97761b3286c5e669eb5 SHA512: 9cd5b326944c513465f8e8867ff6c5429d3cd5fd40b1a20351cea2787095e3801f3811ac17e168d053d080c7e45e1567d2bf08e36a6f09b0237e6cbe764245b7 Homepage: https://cran.r-project.org/package=COVIDIBGE Description: CRAN Package 'COVIDIBGE' (Downloading, Reading and Analyzing PNAD COVID19 Microdata) Provides tools for downloading, reading and analyzing the COVID19 National Household Sample Survey - PNAD COVID19, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website . Further analysis must be made using package 'survey'. Package: r-cran-covidmutations Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4357 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot, r-cran-seqinr, r-cran-stringr, r-cran-ggpubr, r-cran-dplyr, r-cran-venndiagram Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-covidmutations_0.1.3-1.ca2404.1_all.deb Size: 3124134 MD5sum: 588cfdb67d10ea604d90b6f284613802 SHA1: cab670f00813157a89c8001a91595f7b978b4fda SHA256: a6f2e21f76b8419b3a95bdccaadd825f36b2717e755e27ed38442326c52fa115 SHA512: f2ea30233796bacdde8c15dd8034b363d5dbc6e4c943d46cd207219b1c172497c4aac0d82edfc79d5dfe369b5fbad3c2c10b2fddf83074343fddae92d99b13c2 Homepage: https://cran.r-project.org/package=CovidMutations Description: CRAN Package 'CovidMutations' (Mutation Analysis Toolkit for COVID-19 (Coronavirus Disease2019)) A feasible framework for mutation analysis and reverse transcription polymerase chain reaction (RT-PCR) assay evaluation of COVID-19, including mutation profile visualization, statistics and mutation ratio of each assay. The mutation ratio is conducive to evaluating the coverage of RT-PCR assays in large-sized samples. Mercatelli, D. and Giorgi, F. M. (2020) . Package: r-cran-covidmx Architecture: all Version: 0.7.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1422 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dplyr, r-cran-duckdb, r-cran-fs, r-cran-pins, r-cran-rcurl, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-cowplot, r-cran-crayon, r-cran-dbplyr, r-cran-epiestim, r-cran-ggformula, r-cran-ggplot2, r-cran-ggstream, r-cran-ggtext, r-cran-glue, r-cran-lubridate, r-cran-metbrewer, r-cran-remotes, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-scales, r-cran-sessioninfo, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-covidmx_0.7.7-1.ca2404.1_all.deb Size: 1310152 MD5sum: 7a7f82c06227b43677d6471a989c764a SHA1: bd557bb21670d3136898f9c842128eeb35063a6d SHA256: c2701731101474b747a6da7bcb0b5df8922d945de7f60c42e959386ea1c854ec SHA512: 1325735e26bb77f9451ffdc425c803647558773fb7aa212b9e094bf86d8c8d73a67211e8ca057faf3ceb2d9529f49b3ee925736a81d7acd179d6a4c9e07089bf Homepage: https://cran.r-project.org/package=covidmx Description: CRAN Package 'covidmx' (Descarga y analiza datos de COVID-19 en México) Herramientas para el análisis de datos de COVID-19 en México. Descarga y analiza los datos para COVID-19 de la Direccion General de Epidemiología de México (DGE) , la Red de Infecciones Respiratorias Agudas Graves (Red IRAG) y la Iniciativa Global para compartir todos los datos de influenza (GISAID) . English: Downloads and analyzes data of COVID-19 from the Mexican General Directorate of Epidemiology (DGE), the Network of Severe Acute Respiratory Infections (IRAG network),and the Global Initiative on Sharing All Influenza Data GISAID. Package: r-cran-covidnor Architecture: all Version: 2023.05.18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-glue, r-cran-magrittr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-covidnor_2023.05.18-1.ca2404.1_all.deb Size: 1042198 MD5sum: 035ec1afc63e5eaadc044c56f4712e79 SHA1: 9bb8005edd00d684ff3b7250d934eb6362da4ec4 SHA256: 188a20f4cbbd1c8bfeed3e725446578c1eec653aa639e44935fec37e6bad5720 SHA512: 8bc2fa7b9eba539d518faed619998b92758c6c61d1aa3e04b518151436aa92481c663012afb6fcfffdf3173ec84f86b74094b4ac8da74a88d00ec38f2ef0b1b7 Homepage: https://cran.r-project.org/package=covidnor Description: CRAN Package 'covidnor' (Public COVID-19 Data for Norway) Publicly available COVID-19 data for Norway cleaned and merged into one dataset, including PCR confirmed cases, tests, hospitalisation and vaccination. Package: r-cran-covidprobability Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-covidprobability_0.1.0-1.ca2404.1_all.deb Size: 331228 MD5sum: 4fb29d8dc571904e7b661353e877c5fb SHA1: d6eb9ab62345460e782c727be1e131793c6d8129 SHA256: 4b0c559c84b51591a774c6d527bb34b0786c89fc4007c9606c6d7fd6b03f2604 SHA512: 972009d0557fa603958bcc7fe06ea199bc00cbf5e901b0c4988d163be1510050de0bac284ef06546d8826fc3c497293b156185a9e1738a1949beeaf416b8adf2 Homepage: https://cran.r-project.org/package=covidprobability Description: CRAN Package 'covidprobability' (Estimate the Unit-Wide Probability of COVID-19) We propose a method to estimate the probability of an undetected case of COVID-19 in a defined setting, when a given number of people have been exposed, with a given pretest probability of having COVID-19 as a result of that exposure. Since we are interested in undetected COVID-19, we assume no person has developed symptoms (which would warrant further investigation) and that everyone was tested on a given day, and all tested negative. 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This package is based on model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise. 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A complete descriptions of the implemented tests can be found in the paper Aston, John A. D.; Pigoli, Davide; Tavakoli, Shahin. Tests for separability in nonparametric covariance operators of random surfaces. Ann. Statist. 45 (2017), no. 4, 1431--1461. . 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The visualization of that function looks like a quarter segment of a cowbell giving the package its name. The package has been specifically constructed for the case where minimum and maximum value of the dependent and two independent variables are known a prior, which is usually the case when those values are derived from Likert scales. 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The package was originally written for internal use in the Wilke lab, hence the name (Claus O. Wilke's plot package). It has also been used extensively in the book Fundamentals of Data Visualization. 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Package: r-cran-coxaipw Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-randomforestsrc, r-cran-polspline, r-cran-tidyr, r-cran-ranger, r-cran-pracma, r-cran-gbm Filename: pool/dists/noble/main/r-cran-coxaipw_0.0.3-1.ca2404.1_all.deb Size: 55858 MD5sum: 9ff68c683e6443913457800111f21516 SHA1: 5ca67d8edbe4f74a266229763004f31cf6968a83 SHA256: 2a313ae6c1aff0265df3d9f471c918fe2edf992791604eb8caa470a3dcb69138 SHA512: c4872b16146f56bb2b14b124eecc2e172cb4d5ed8aaf05e12ef3b269d994b8189579c455178973b69fac4d1294b6b0d2c15b0c04f5ec364ec7310b1810362560 Homepage: https://cran.r-project.org/package=CoxAIPW Description: CRAN Package 'CoxAIPW' (Doubly Robust Inference for Cox Marginal Structural Model withInformative Censoring) Doubly robust estimation and inference of log hazard ratio under the Cox marginal structural model with informative censoring. An augmented inverse probability weighted estimator that involves 3 working models, one for conditional failure time T, one for conditional censoring time C and one for propensity score. Both models for T and C can depend on both a binary treatment A and additional baseline covariates Z, while the propensity score model only depends on Z. With the help of cross-fitting techniques, achieves the rate-doubly robust property that allows the use of most machine learning or non-parametric methods for all 3 working models, which are not permitted in classic inverse probability weighting or doubly robust estimators. When the proportional hazard assumption is violated, CoxAIPW estimates a causal estimated that is a weighted average of the time-varying log hazard ratio. Reference: Luo, J. (2023). Statistical Robustness - Distributed Linear Regression, Informative Censoring, Causal Inference, and Non-Proportional Hazards [Unpublished doctoral dissertation]. University of California San Diego.; Luo & Xu (2022) ; Rava (2021) . Package: r-cran-coxbcv Architecture: all Version: 0.0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-survival Filename: pool/dists/noble/main/r-cran-coxbcv_0.0.1.0-1.ca2404.1_all.deb Size: 99690 MD5sum: e28fae0cd1f88a812e671a0c8d86d4bf SHA1: 1c99b983bafc3f4d70bdde8aa9ea213b3995f938 SHA256: 6e1b5900d31b533eb0ce86294b10cbc2a92271bf4332cbe82cc2de1092f27a5d SHA512: e4f791405d87aad56f0b37a9b48f21df5dfe2eb1706ad0b3fe09e8b360b13212200d54eb5a5e1b04cb556725dfbcf3876f58f67a0b3134bf005a3fd5c4ffccbe Homepage: https://cran.r-project.org/package=CoxBcv Description: CRAN Package 'CoxBcv' (Bias-Corrected Sandwich Variance Estimators for Marginal CoxAnalysis of Cluster Randomized Trials) The implementation of bias-corrected sandwich variance estimators for the analysis of cluster randomized trials with time-to-event outcomes using the marginal Cox model, proposed by Wang et al. (under review). 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Package: r-cran-coxicpen Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach Filename: pool/dists/noble/main/r-cran-coxicpen_1.1.0-1.ca2404.1_all.deb Size: 55506 MD5sum: d60483827247c23e2a5f461afb9f7b38 SHA1: 44c41548f93335dbc2d35778de269ed3131c6437 SHA256: 55b11ba571dda820ad1d86600d9f6aaa3991ff1aeea7968ebe033c50fc852628 SHA512: b1d15bf34a9a50dd77959ac1252773770e729e4b5efa18c9b46fbfa01cb150632b10d1d4ac758b55d93fbdce9f474e0a27d810fc1ff4a691ac7bff7a3c8d1bb4 Homepage: https://cran.r-project.org/package=CoxICPen Description: CRAN Package 'CoxICPen' (Variable Selection for Cox's Model with Interval-Censored Data) Perform variable selection for Cox regression model with interval-censored data. Can deal with both low-dimensional and high-dimensional data. Case-cohort design can be incorporated. Two sets of covariates scenario can also be considered. The references are listed in the URL below. Package: r-cran-coxlikelihood Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-coxlikelihood_0.1.0-1.ca2404.1_all.deb Size: 17872 MD5sum: 368991b2f62ed5acd57ef5998a119630 SHA1: 58da430450c876eb1422bf3e81a849ad58c91022 SHA256: ca8d8db54b66d1651f8801f4014b780e260c7a27ba90ce31be8f1f3bb60dcc19 SHA512: 23093c784e52575707f913677e3d347d644f55665cf45609043935e75b96ca084cf20bd8484fecba9776f4811ec1fed38348ff2f9f7ba6b6eb9ec7abd95760e1 Homepage: https://cran.r-project.org/package=CoxLikelihood Description: CRAN Package 'CoxLikelihood' (Robust Likelihood Ratio Test and Confidence Intervals for theCox Model) Calculate the likelihood ratio test p-value and likelihood confidence intervals for misspecified Cox models, as described in Shao and Guo (2025) . Package: r-cran-coxmk Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-survival, r-cran-irlba, r-bioc-gdsfmt, r-cran-bedmatrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-coxmk_0.1.1-1.ca2404.1_all.deb Size: 120268 MD5sum: ab18fd2ff45e4c08020592bb0b7d101e SHA1: 1a8e647c393f89b83ab24ac6047b5419b88b5d21 SHA256: caaf74214cb34d7b48afdad8bf1a7c513258d1ae05a64766576d365a32f009ad SHA512: 5956b8a983e88b55de263b2bc8342c74e463df1c1af79277121c644b37cfc93a113a6c220c2ce78f1502744e60d151f61ec5d5551dd3cdf1bba4eb1b6cc4a67f Homepage: https://cran.r-project.org/package=CoxMK Description: CRAN Package 'CoxMK' (A Model-X Knockoff Method for Genome-Wide Survival AssociationAnalysis) A genome-wide survival framework that integrates sequential conditional independent tuples and saddlepoint approximation method, to provide SNP-level false discovery rate control while improving power, particularly for biobank-scale survival analyses with low event rates. The method is based on model-X knockoffs as described in Barber and Candes (2015) and fast survival analysis methods from Bi et al. (2020) . A shrinkage algorithmic leveraging accelerates multiple knockoffs generation in large genetic cohorts. This CRAN version uses standard Cox regression for association testing. For enhanced performance on very large datasets, users may optionally install the 'SPACox' package from GitHub which provides saddlepoint approximation methods for survival analysis. 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It uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. CPBayes is based on a spike and slab prior. The methodology is available from: A Majumdar, T Haldar, S Bhattacharya, JS Witte (2018) . 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Contains support for hierarchical clustering, k-means, partitioning around medoids, density-based spatial clustering with noise, and manually imposed cluster membership. Mehlhaff (2024) . 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The package contains functions to generate intersection hypotheses according to the closure principle (Bretz, F., Hothorn, T., Westfall, P. (2010) ), an implementation of the computational approach test (Ching-Hui, C., Nabendu, P., Jyh-Jiuan, L. (2010) ) and the combination of both, that is, the closure principle computational approach test. 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The package also contains maximum-likelihood fitting functions for these models. 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Harshman's Parallel Factor Analysis-1 (Parafac) model or Parallel Factor Analysis-2 (Parafac2) model fit to a three-way or four-way data array. See Harshman and Lundy (1994): . Classification using principal component analysis (PCA) fit to a two-way data matrix is also supported. Uses component weights from one mode of a Parafac, Parafac2, or PCA model as features to tune parameters for one or more classification methods via a k-fold cross-validation procedure. Allows for constraints on different tensor modes. Allows for inclusion of additional features alongside features generated by the component model. Supports penalized logistic regression, support vector machine, random forest, feed-forward neural network, regularized discriminant analysis, and gradient boosting machine. Supports binary and multiclass classification. Predicts class labels or class probabilities, and calculates multiple classification performance measures. Implements parallel computing via the 'foreach', 'doParallel', and 'doRNG' packages. Package: r-cran-cpgassoc Architecture: all Version: 2.70-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme Filename: pool/dists/noble/main/r-cran-cpgassoc_2.70-1.ca2404.1_all.deb Size: 1971086 MD5sum: 00da6a8a096bb1f559067397226b25e7 SHA1: 3e5289dd688298d2b423ddd48f362a9cd4d94f39 SHA256: 52f3ade9e460544b388aed5bf1ebf2fc1baa30b2d472a3edeb6ffb0fb6887751 SHA512: b640e286847e435fc5b151d08da9f17a3d2c5d39817e7ed805251824893bbe8bae22ceecd8c3bb44494ac76a148957bf27eb5a4f273966c5ea1e806eaa7d9f27 Homepage: https://cran.r-project.org/package=CpGassoc Description: CRAN Package 'CpGassoc' (Association Between Methylation and a Phenotype of Interest) Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites. Package: r-cran-cpgfilter Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats Filename: pool/dists/noble/main/r-cran-cpgfilter_1.1-1.ca2404.1_all.deb Size: 19166 MD5sum: 3061d4a6a4d0c0918a2fd0a3422149d9 SHA1: f4bc58f768789b5d9298ac78cc66bab02d623b68 SHA256: a874f5fd5fa3710131690b3712fd72ceecb7ce846b556cb93708cc0008d10cfe SHA512: cc9272b496b7ac1cfb71d94b28c3be17a1e28cff1e010ad0fca21a3fa5938b60b53a52d21bded06aa55daf1b0cc7c5c28290d906e415bf11a690a5159b933733 Homepage: https://cran.r-project.org/package=CpGFilter Description: CRAN Package 'CpGFilter' (CpG Filtering Method Based on Intra-Class CorrelationCoefficients) Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design. Package: r-cran-cpgfr Architecture: all Version: 0.0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-osfr, r-cran-lubridate, r-cran-deflatebr, r-cran-curl Filename: pool/dists/noble/main/r-cran-cpgfr_0.0.1.0-1.ca2404.1_all.deb Size: 17696 MD5sum: 6743ef6d480cba244b1b6efb076a77bc SHA1: f28c8005c0b3520dc7c1d8cc57b399077e2a1fda SHA256: 52f26ca0556bd0c019e830fa93517dde9e4362931063c30865e89beb598ea519 SHA512: 79535684ee0d2f4f03b4236658f71eed3d4d12768b79cba60a21aba980af48fd8f8feb71c962206e3bee68171268da7c7932e1c3718e77930df3c45a17979f9e Homepage: https://cran.r-project.org/package=cpgfR Description: CRAN Package 'cpgfR' (Consolidates Information from the Federal Government PaymentCard) Provides access to consolidated information from the Brazilian Federal Government Payment Card. Includes functions to retrieve, clean, and organize data directly from the Transparency Portal and a curated dataset hosted on the Open Science Framework . Useful for public spending analysis, transparency research, and reproducible workflows in auditing or investigative journalism. Package: r-cran-cpi Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-mlr3, r-cran-lgr, r-cran-knockoff Suggests: r-cran-mlr3learners, r-cran-ranger, r-cran-glmnet, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-cpi_0.1.5-1.ca2404.1_all.deb Size: 47392 MD5sum: 8f25c2c00509d00a650ab2d92802c5d8 SHA1: d7320ff5713cc59f880798b0ad2bd97120c2ad32 SHA256: 289491adc6115e7a017b4c5aad54fa27cf8230fb5f21987ad68dcdd3713d2a8f SHA512: b7d154d9de3ce88684b316315b2f9746a862905b2dbe6c16baaf3c2250a3f439ec182d5a348bfad08ea3edd6cc9cbba9c8f23c015d0e38534acb948895195fc1 Homepage: https://cran.r-project.org/package=cpi Description: CRAN Package 'cpi' (Conditional Predictive Impact) A general test for conditional independence in supervised learning algorithms as proposed by Watson & Wright (2021) . Implements a conditional variable importance measure which can be applied to any supervised learning algorithm and loss function. Provides statistical inference procedures without parametric assumptions and applies equally well to continuous and categorical predictors and outcomes. Package: r-cran-cplots Architecture: all Version: 0.5-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circular Filename: pool/dists/noble/main/r-cran-cplots_0.5-0-1.ca2404.1_all.deb Size: 78006 MD5sum: 44502181b4774072bdf7000f3fb2fd4e SHA1: 750ed3f3725225972a9e26a4b1d5bac59f3104a5 SHA256: 12b83d54ed4db35b3d659f5a3a5e420149e88193e8eb6cfeb81f25dc05d4b8e8 SHA512: f42a731106b2ce20bd093ae8dfa3a4063a17cd3fd0d644c9e31c5ddeafb71a3114a1d9dd3e0f294027556a0c422f17922d1047dfe0d277609a1bfa8ce2e504c3 Homepage: https://cran.r-project.org/package=cplots Description: CRAN Package 'cplots' (Plots for Circular Data) Provides functions to produce some circular plots for circular data, in a height- or area-proportional manner. They include bar plots, smooth density plots, stacked dot plots, histograms, multi-class stacked smooth density plots, and multi-class stacked histograms. Package: r-cran-cpmbigdata Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rms, r-cran-hmisc, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-benchmarkme Filename: pool/dists/noble/main/r-cran-cpmbigdata_0.0.2-1.ca2404.1_all.deb Size: 37376 MD5sum: 8245d2cb9406934184687c2197a48a10 SHA1: 67d129f52203622c2f99bf97b895e94de04f3881 SHA256: 1c0797dd09df139391bfdf02aaf6aeb6af33c041a585be68b707a2ba01d491a3 SHA512: a9c82217adeec6bc1b6ea34858c8df59376c9771428863d93afe6663bed9ddb0782bfefe0535c371f9bf1fbfd563f8a96edcf15d6f7ac7ba0ed681071d008a72 Homepage: https://cran.r-project.org/package=cpmBigData Description: CRAN Package 'cpmBigData' (Fitting Semiparametric Cumulative Probability Models for BigData) A big data version for fitting cumulative probability models using the orm() function from the 'rms' package. See Liu et al. (2017) for details. Package: r-cran-cpmcglm Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-plyr, r-cran-abind Filename: pool/dists/noble/main/r-cran-cpmcglm_1.2-1.ca2404.1_all.deb Size: 85152 MD5sum: b3e90dd48ed49a46113c6aa238fdc83a SHA1: 59970a79d18ab1419555a76dc592c8e0534158a0 SHA256: 1d0c4392c990d3d17a74bb15991d2ed6760c7c79f2066dfac779b3c0806db4a6 SHA512: e6f76bf17711eade1413b8e95be453753069c1c38830944c01fe4c34e2d6b76d0adbda0d91739513bf2faa382967c26f449a4d4a26b857878e72058fe2c28c96 Homepage: https://cran.r-project.org/package=CPMCGLM Description: CRAN Package 'CPMCGLM' (Correction of the P-Value after Multiple Coding in GeneralizedLinear Models) We propose to determine the correction of the significance level after multiple coding of an explanatory variable in Generalized Linear Model. The different methods of correction of the p-value are the Single step Bonferroni procedure, and resampling based methods developed by P.H.Westfall in 1993. Resampling methods are based on the permutation and the parametric bootstrap procedure. If some continuous, and dichotomous transformations are performed this package offers an exact correction of the p-value developed by B.Liquet & D.Commenges in 2005. The naive method with no correction is also available. Package: r-cran-cpmerccutoff Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cpmerccutoff_1.0.0-1.ca2404.1_all.deb Size: 80916 MD5sum: 85367c63d8afe6680f8725f7cc724fb9 SHA1: af467c485c086a5c572e5a5f5719d9e405c05714 SHA256: 9bf9f5853ab00b93b82fbf4c217794021691083caa351910f1e255807b6dbc7d SHA512: 17449090ae412f980ed9a0dad91032653fa8fde0dca2dc947a6f1ba729fed6ce96f39ea054ba4f8c077348896e179a13c9e06c58ae87d6c1512e71f20ad2bcb5 Homepage: https://cran.r-project.org/package=CpmERCCutoff Description: CRAN Package 'CpmERCCutoff' (Calculation of Log2 Counts per Million Cutoff from ERCC Controls) Implementation of the empirical method to derive log2 counts per million (CPM) cutoff to filter out lowly expressed genes using ERCC spike-ins as described in Goll and Bosinger et.al (2022). This package utilizes the synthetic mRNA control pairs developed by the External RNA Controls Consortium (ERCC) (ERCC 1 / ERCC 2) that are spiked into sample pairs at known ratios at various absolute abundances. The relationship between the observed and expected fold changes is then used to empirically determine an optimal log2 CPM cutoff for filtering out lowly expressed genes. Package: r-cran-cpmr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-rfast, r-cran-tibble Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-cpmr_0.1.1-1.ca2404.1_all.deb Size: 43018 MD5sum: 8d96680d29feb97459770f0ebeb58c38 SHA1: b4fabdb1a596c0e8ce4f4707a55b10bf930ef83b SHA256: f9029084df319d215ad202870fe09ea751101646b884cab63152f2b28222fc93 SHA512: 265e3809042d8e040e34868deccfdc8493d56d0d99fbf121ac2026e859b7660e353989491ae1534b5ea59b49489c4ba12cc47fb3bcd6072c8341d7ce70ffe1f2 Homepage: https://cran.r-project.org/package=cpmr Description: CRAN Package 'cpmr' (Connectome Predictive Modelling in R) Connectome Predictive Modelling (CPM) (Shen et al. (2017) ) is a method to predict individual differences in behaviour from brain functional connectivity. 'cpmr' provides a simple yet efficient implementation of this method. Package: r-cran-cpncoverageanalysis Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cpncoverageanalysis_1.1.0-1.ca2404.1_all.deb Size: 44566 MD5sum: 0cb168eae5c7392b470a3499b36dfca5 SHA1: a10410c03020f68c41af2db71379cb23707e31fe SHA256: c747286324d6a01e873af511b1cefafc2412b3b4f9c5bc698aaf784c130b9ed7 SHA512: 0f2af158b011ce918cb1d77d49df4456c07699929fae3b357df05ff71fb197adcd73d307bb9f30350116bb96a3c55d4023548182b0138501a09f1b0b5d1914cd Homepage: https://cran.r-project.org/package=CPNCoverageAnalysis Description: CRAN Package 'CPNCoverageAnalysis' (Conceptual Properties Norming Studies as Parameter Estimation) Implementation of conceptual properties norming studies, including estimates of CPNs parameters with their corresponding variances and estimates for the sampling process, and a sampling property function based on a modified empirical distribution from the original data. 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Package: r-cran-cpp4r Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-desc, r-cran-glue, r-cran-withr Suggests: r-cran-mockery, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cpp4r_0.6.0-1.ca2404.1_all.deb Size: 110822 MD5sum: 8bbc4d29e78072c62e9fb81d5beaf707 SHA1: d440ebb22b134cad5eb9e242dd61dd21564f7549 SHA256: b696dc25181ac2f19d40f6e28e2028dc3d1488499ede07592cdea2d844f506e8 SHA512: e39d542d7e25222929a85a5455ceea41437e38e4b839d9869deddf848d6fcc984a924094e4700df4147be6ba3f4b965f4be1a33301b7d01da48368a00d09fecd Homepage: https://cran.r-project.org/package=cpp4r Description: CRAN Package 'cpp4r' (Header-Only 'C++' and 'R' Interface) Provides a header only, 'C++' interface to 'R' with enhancements over 'cpp11'. Enforces copy-on-write semantics consistent with 'R' behavior. 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Package: r-cran-cpp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ineq, r-cran-kappalab, r-cran-mc2d Filename: pool/dists/noble/main/r-cran-cpp_0.1.0-1.ca2404.1_all.deb Size: 141698 MD5sum: a94f718e89fc023d8e8ada825de1baf1 SHA1: f2a73a581d04073909ae746a5bc608411a01dd9f SHA256: 20d6575aa427f3f7c186450cb49ba703220f51b0c0f965ec9465fdbfab15f35e SHA512: 8b8b7c30966a193fb76a101382e9c28cdadaed6cdc7eceafa7517695c9d2f0a639363a58329853479f1166a04419344348c5cf34c0603d946e5e2f9d70f11e80 Homepage: https://cran.r-project.org/package=CPP Description: CRAN Package 'CPP' (Composition of Probabilistic Preferences (CPP)) CPP is a multiple criteria decision method to evaluate alternatives on complex decision making problems, by a probabilistic approach. The CPP was created and expanded by Sant'Anna, Annibal P. (2015) . 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The report appears in the 'RStudio' viewer pane as a formatted 'HTML' file. It is also possible to get this report with a 'shiny' application. 'Cppcheck' can spot many error types and it can also give some recommendations on the code. Package: r-cran-cprobit Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-nortest, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-cprobit_1.0.2-1.ca2404.1_all.deb Size: 85778 MD5sum: 20676367dfcc1359fee6fff2b30b14be SHA1: d9a991fe754f0f7e5388a6638fbfd24bbf257f2f SHA256: 189b6c67ddcf55e52ed2e9d15b3ee536a255fc0cd93a99cde399f75f04cb511a SHA512: a80570bfcec562204e61990cdd848e8061376f43f3249f0af7ac9edefd87e957fbddd7c64dde169358cd46e4f2da7e33bf77ebf55a10bea2fcd8c578b54e5ae7 Homepage: https://cran.r-project.org/package=cprobit Description: CRAN Package 'cprobit' (Conditional Probit Model for Analysing Continuous Outcomes) Implements the three-step workflow for robust analysis of change in two repeated measurements of continuous outcomes, described in Ning et al. (in press), "Robust estimation of the effect of an exposure on the change in a continuous outcome", BMC Medical Research Methodology. Package: r-cran-cprr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cprr_0.2.0-1.ca2404.1_all.deb Size: 22514 MD5sum: 0ec148acf9a63b7cca6de2dd1dab2603 SHA1: f20c3b86e74f750d653f46a01772397b42b20316 SHA256: fd9d10b0cd9ce303296c028b7a69959bbe82af8020e55f2131deba3bd34b52b2 SHA512: c218159d9a5d7d1462ee3b9674d7dbc556947d4abe08400c33545c8ad0b7a42aac80bcd3607e3de2c2696ab7f4b085ebffb079c272e60b59b6dbecc636df54a1 Homepage: https://cran.r-project.org/package=cprr Description: CRAN Package 'cprr' (Functions for Working with Danish CPR Numbers) Calculate date of birth, age, and gender, and generate anonymous sequence numbers from CPR numbers. . Package: r-cran-cpseudomarg Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cpseudomarg_1.0.1-1.ca2404.1_all.deb Size: 47058 MD5sum: 6a3ca35417ad8f018b4a9f8fc5039a81 SHA1: 19a416dba9109576373abfa479eec6250c36abf1 SHA256: 4d850fd1b08028aa91926c9c99265643fcdd13f3d45e969b4858d475e4f3a534 SHA512: 2128d44f79790201a4c750da6a02f9b35033d47c2d603cd9a2bd440fd23ae4d2e591fe4d50503e4add6e86e6926f465110db29932c37dd93e1d1a13b188a44f7 Homepage: https://cran.r-project.org/package=cPseudoMaRg Description: CRAN Package 'cPseudoMaRg' (Constructs a Correlated Pseudo-Marginal Sampler) The primary function makeCPMSampler() generates a sampler function which performs the correlated pseudo-marginal method of Deligiannidis, Doucet and Pitt (2017) . If the 'rho=' argument of makeCPMSampler() is set to 0, then the generated sampler function performs the original pseudo-marginal method of Andrieu and Roberts (2009) . The sampler function is constructed with the user's choice of prior, parameter proposal distribution, and the likelihood approximation scheme. Note that this algorithm is not automatically tuned--each one of these arguments must be carefully chosen. Package: r-cran-cpsr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-tibble Filename: pool/dists/noble/main/r-cran-cpsr_1.0.0-1.ca2404.1_all.deb Size: 23178 MD5sum: 16ba552f6dccfc2f33540a689b2d81b8 SHA1: dbf308f54dc37fd31453a01002e09dd2ba3bef05 SHA256: 840a94124983ef4b1d03d2fe38d5c6a895dc58d82b8bfd5eb726eda07da53f41 SHA512: 3fab59ca8d6a71d6684b6397c65f646546e802073303d3e89668a865ba714b93362d75e95492071e058297f7c25370ad7abb866172dc03406532a2497170d42f Homepage: https://cran.r-project.org/package=cpsR Description: CRAN Package 'cpsR' (Load CPS Microdata into R Using the 'Census Bureau Data' API) Load Current Population Survey (CPS) microdata into R using the 'Census Bureau Data' API (), including basic monthly CPS and CPS ASEC microdata. Package: r-cran-cpsurv Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-muhaz Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cpsurv_1.0.0-1.ca2404.1_all.deb Size: 82472 MD5sum: 13e121c73de69c81f2b5e027786d86d3 SHA1: fa7ba7761e4ba6000846483874a4da442fb98a66 SHA256: 0a99464dbb59315599ebeb8e74dd1695a6682c5e558a41344e6e9e6fbe5fff07 SHA512: 4e8375d599015df991fa1469dbef4a89dbfb6cbafe7b26f7ab1796b689f82e9d40d9697d667fcc1c3dae81950220de235d887c84d5fc9c5419e8e40227843a63 Homepage: https://cran.r-project.org/package=CPsurv Description: CRAN Package 'CPsurv' (Nonparametric Change Point Estimation for Survival Data) Nonparametric change point estimation for survival data based on p-values of exact binomial tests. Package: r-cran-cpsurvsim Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-hmisc, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cpsurvsim_1.2.2-1.ca2404.1_all.deb Size: 53134 MD5sum: 670b05d1301ff98680aeeb20680672ad SHA1: 85bf7a97b1b60779c7af7bc30f79cb78a9911f1d SHA256: a07f6b5a2b9edf095bb936bd41636f41e31fb204f188ffd4adbf7fe07da0e395 SHA512: dc370fb80a9e8f1c8f2fde11d3e154f90a4cb2e0ae0f3a63453419851d62112a405cdd9b222e58ea8e851dc8457071ccb27a318e691bd54640ae3dc1655c551f Homepage: https://cran.r-project.org/package=cpsurvsim Description: CRAN Package 'cpsurvsim' (Simulating Survival Data from Change-Point Hazard Distributions) Simulates time-to-event data with type I right censoring using two methods: the inverse CDF method and our proposed memoryless method. The latter method takes advantage of the memoryless property of survival and simulates a separate distribution between change-points. We include two parametric distributions: exponential and Weibull. Inverse CDF method draws on the work of Rainer Walke (2010), . Package: r-cran-cpsvote Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 595 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-readr, r-cran-dplyr, r-cran-stringr, r-cran-forcats, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survey, r-cran-srvyr, r-cran-here, r-cran-scales, r-cran-ggplot2, r-cran-usmap Filename: pool/dists/noble/main/r-cran-cpsvote_0.1.0-1.ca2404.1_all.deb Size: 406128 MD5sum: 7de7c620a84a0f8f0c55a07dbffd2b4f SHA1: 82207c2b9381126d09a90558f72d1ba32c88da33 SHA256: 353982a64e9888c10884fa3458b82c0322780af73266ceec23eab6e98126cf6b SHA512: 937466a6cfcb86d510ea6c8e50346b998f7fbda4b32b48c2fc9e67cb1721437cd0ff4136a72a9dc818133fee53e2220875537cf37dfbf2bcf01d97f466cad053 Homepage: https://cran.r-project.org/package=cpsvote Description: CRAN Package 'cpsvote' (A Toolbox for Using the CPS’s Voting and Registration Supplement) Provides automated methods for downloading, recoding, and merging selected years of the Current Population Survey's Voting and Registration Supplement, a large N national survey about registration, voting, and non-voting in United States federal elections. Provides documentation for appropriate use of sample weights to generate statistical estimates, drawing from Hur & Achen (2013) and McDonald (2018) . Package: r-cran-cpt Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-nnet, r-cran-randomforest, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-cpt_1.0.2-1.ca2404.1_all.deb Size: 33544 MD5sum: 5479e8e20023fb826f3f77d31db24037 SHA1: 8425444052e2e6483c227ca683e7d040dd96ec6d SHA256: 7faa39ad6a8d2457d70fa539a19439051874e994fdbdb9c0600d4208a851bedb SHA512: 28f0144e1492e02bb923e37070f3a303472bcbb275e94dcc5d90eb8e5ed22781552c9df72014cf429ee9c3c393298d6a60fd6eea79a078718cafce0cbc43ccaf Homepage: https://cran.r-project.org/package=cpt Description: CRAN Package 'cpt' (Classification Permutation Test) Non-parametric test for equality of multivariate distributions. Trains a classifier to classify (multivariate) observations as coming from one of several distributions. If the classifier is able to classify the observations better than would be expected by chance (using permutation inference), then the null hypothesis that the distributions are equal is rejected. Package: r-cran-cptcity Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1308 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cptcity_1.1.1-1.ca2404.1_all.deb Size: 985864 MD5sum: b1291322185fb1973330a8dd8823cb18 SHA1: 99bc33f62b0361d77926a3139540c96f234e35d1 SHA256: 9b526572efd910ce4a026c9d53b485ee38ee7f85548c339cdc42c5d1a5d87901 SHA512: 6dec8c19ad16f6c678ca5eeada4e193128f0998efd5cc40c7f3e0fff3de3c6bb93d0a57152be4e7820b22b69c5b11b4e4f17570cd7967a2c9d39e05b4b69d35f Homepage: https://cran.r-project.org/package=cptcity Description: CRAN Package 'cptcity' ('cpt-city' Colour Gradients) Incorporates colour gradients from the 'cpt-city' web archive available at . Package: r-cran-cquad Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-plm, r-cran-formula Filename: pool/dists/noble/main/r-cran-cquad_2.3-1.ca2404.1_all.deb Size: 198326 MD5sum: 8084bc141cc07f08b18ab0ee1c32d01c SHA1: 9f2775598a07d7b61426206656408b7da0b5c209 SHA256: 1708faa3dd24899d59b6bbfcc1a202098d7d069792effc3a23ff75b2da08ec97 SHA512: ad1dd056378469bc778083d1a1ce18f435c1f413fcc865c0368af12212e99a5622c40a7b0437a3b68ab6ed01c486e66163b174dfadca7086ef5626a2800849d6 Homepage: https://cran.r-project.org/package=cquad Description: CRAN Package 'cquad' (Conditional Maximum Likelihood for Quadratic Exponential Modelsfor Binary Panel Data) Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2018, Econometric Reviews) . The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) . For large time dimensions of the panel, the computation of the proposed models involves a recursive function from Krailo M. D., & Pike M. C. (1984, Journal of the Royal Statistical Society. Series C (Applied Statistics)) and Bartolucci F., Valentini, F. & Pigini C. (2021, Computational Economics . Package: r-cran-cr2 Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-nlme, r-cran-matrix, r-cran-generics, r-cran-magrittr, r-cran-broom, r-cran-dplyr, r-cran-performance, r-cran-tibble Filename: pool/dists/noble/main/r-cran-cr2_0.2.1-1.ca2404.1_all.deb Size: 132244 MD5sum: e1a7105bfea8f28252ae8aa252fa3c3e SHA1: 6ad76e7ce6e039f46f5e2cff602b796a301ef995 SHA256: 803e47e5efe6a71eb7b6ff809e3e25f9e190a46af11e36d6e4992fc8754d1fd5 SHA512: 4b0adb2a9fb7c82e075baf963b94f800457fe98465d5696091717647f6b46275d395bfeef29d8d02382d5135c90de706471b949eec7ad086d0532662e3baf725 Homepage: https://cran.r-project.org/package=CR2 Description: CRAN Package 'CR2' (Compute Cluster Robust Standard Errors with Degrees of FreedomAdjustments) Estimate different types of cluster robust standard errors (CR0, CR1, CR2) with degrees of freedom adjustments. Standard errors are computed based on 'Liang and Zeger' (1986) and Bell and 'McCaffrey' . Functions used in Huang and Li , Huang, 'Wiedermann', and 'Zhang' , and Huang, 'Zhang', and Li (forthcoming: Journal of Research on Educational Effectiveness). Package: r-cran-crabs Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1211 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr, r-cran-desolve, r-cran-dplyr, r-cran-tibble, r-cran-colorspace, r-cran-patchwork, r-cran-latex2exp, r-cran-tidyr, r-cran-pracma, r-cran-ape Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crabs_1.2.0-1.ca2404.1_all.deb Size: 1198704 MD5sum: 6469636f4d2e8dc007109d3e61cc9adf SHA1: 35a44a1d28c3a423d0a66de923897b3c1a4ab530 SHA256: 26da3a0ff12476a38d792adac6ee51d75367d976ce8121680a1d5f601712f7ad SHA512: b833a8e2346a2b5bbc35e8909cd75c407557ee644c2f551ca3c21512a43ba159b45a004e8c2267444d836964b4f7728a6f172d63c36dfc8d0be2c4bb1b7068a1 Homepage: https://cran.r-project.org/package=CRABS Description: CRAN Package 'CRABS' (Congruent Rate Analyses in Birth-Death Scenarios) Features tools for exploring congruent phylogenetic birth-death models. It can construct the pulled speciation- and net-diversification rates from a reference model. Given alternative speciation- or extinction rates, it can construct new models that are congruent with the reference model. Functionality is included to sample new rate functions, and to visualize the distribution of one congruence class. See also Louca & Pennell (2020) . Package: r-cran-cragg Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-expm Suggests: r-cran-testthat, r-cran-knitr, r-cran-mass, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cragg_0.0.1-1.ca2404.1_all.deb Size: 34654 MD5sum: 7412062f6a89b33049e84b3e29f49667 SHA1: 589d1013f6f48ab8b49ded4540c4f9fec8ef8fa3 SHA256: efbb2d57de9aeb954a528b311c99840bc64a420e0448382db228c4dd22c1f651 SHA512: 3aed81dec0ba7bb2e80e98b55610a5deacff729d052dce292c0274f1991aee6a272f8989aa900755436f5f1444b173204715d415b52a7411336b04b4e5955b48 Homepage: https://cran.r-project.org/package=cragg Description: CRAN Package 'cragg' (Tests for Weak Instruments in R) Implements Cragg-Donald (1993) and Stock and Yogo (2005) tests for weak instruments in R. Package: r-cran-cramr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-grf, r-cran-glmnet, r-cran-magrittr, r-cran-doparallel, r-cran-foreach, r-cran-dt, r-cran-data.table, r-cran-keras, r-cran-dplyr, r-cran-purrr, r-cran-r6, r-cran-rjson, r-cran-r.devices, r-cran-itertools, r-cran-iterators Suggests: r-cran-testthat, r-cran-covr, r-cran-kableextra, r-cran-profvis, r-cran-devtools, r-cran-waldo, r-cran-knitr, r-cran-rmarkdown, r-cran-randomforest, r-cran-gbm, r-cran-nnet, r-cran-withr Filename: pool/dists/noble/main/r-cran-cramr_0.1.1-1.ca2404.1_all.deb Size: 3504638 MD5sum: 3de26d594b52b26292c3b5bad258b2fa SHA1: a1331e99f7f22a273c6d9db4d5533322ef565fd3 SHA256: 6b3494fd2a407c96774557c18cc533997129ba953185e54a3005564876e88682 SHA512: 809537b7f965f77f9d304bf048c17e9eec0cd2b7082cd0e2c8690ce00b76a6e48019adf7a05b2a02764bd2dca979bd80b12b3fc5f7042be52b68cd4a8b4e172f Homepage: https://cran.r-project.org/package=cramR Description: CRAN Package 'cramR' (Cram Method for Efficient Simultaneous Learning and Evaluation) Performs the Cram method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning algorithm. In a single pass of batched data, the proposed method repeatedly trains a machine learning algorithm and tests its empirical performance. Because it utilizes the entire sample for both learning and evaluation, cramming is significantly more data-efficient than sample-splitting. Unlike cross-validation, Cram evaluates the final learned model directly, providing sharper inference aligned with real-world deployment. The method naturally applies to both policy learning and contextual bandits, where decisions are based on individual features to maximize outcomes. The package includes cram_policy() for learning and evaluating individualized binary treatment rules, cram_ml() to train and assess the population-level performance of machine learning models, and cram_bandit() for on-policy evaluation of contextual bandit algorithms. For all three functions, the package provides estimates of the average outcome that would result if the model were deployed, along with standard errors and confidence intervals for these estimates. Details of the method are described in Jia, Imai, and Li (2024) and Jia et al. (2025) . Package: r-cran-crane Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtsummary, r-cran-broom, r-cran-broom.helpers, r-cran-cards, r-cran-cardx, r-cran-cli, r-cran-cowplot, r-cran-dplyr, r-cran-flextable, r-cran-ggplot2, r-cran-glue, r-cran-gt, r-cran-labeling, r-cran-lifecycle, r-cran-patchwork, r-cran-rlang, r-cran-survival, r-cran-tidyr Suggests: r-cran-ggtext, r-cran-labelled, r-cran-magick, r-cran-parameters, r-cran-pharmaverseadam, r-cran-testthat, r-cran-webshot2, r-cran-withr Filename: pool/dists/noble/main/r-cran-crane_0.3.1-1.ca2404.1_all.deb Size: 400784 MD5sum: 307764b617902552b0e7edef6bccfa3e SHA1: ec296d0e3fdeab2d72b5bbcc9de0ee0cf5473939 SHA256: 4de339a3f1b1e4150fe4b9499f1b975ed1da9f4fb979eb266db5d5003af8ed36 SHA512: 3040589ed17bb6e95e1a01bdb40842846999135e69ae4fc9f051040fd7956d0610353199109ae6d90f2a2dbac31a4202f26345e136b4d12fa5fbb6e4b2ffa630 Homepage: https://cran.r-project.org/package=crane Description: CRAN Package 'crane' (Supplements the 'gtsummary' Package for Pharmaceutical Reporting) Tables summarizing clinical trial results are often complex and require detailed tailoring prior to submission to a health authority. 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Package: r-cran-cranly Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5444 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-visnetwork, r-cran-colorspace, r-cran-igraph, r-cran-stringr, r-cran-ggplot2, r-cran-countrycode, r-cran-wordcloud, r-cran-tm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cranly_0.6.0-1.ca2404.1_all.deb Size: 1741754 MD5sum: 7d70129a66aa4d447b185aa36439ead7 SHA1: 13a6f19f304ea3bb3fe774cdddec2e4f5bfeda8b SHA256: 68f54fdf75f2ff65427acb39f157edb067e55d49de378262812ef14c451fb4af SHA512: 3c521cc5d78ab508cb1a4964fcc42bc18a8f2c1e484e48abdd5a7b4c1acacfb905eb57291bc9a5a6dbb88331a50108689137ee43e8be781d0e002dd0f167cfb5 Homepage: https://cran.r-project.org/package=cranly Description: CRAN Package 'cranly' (Package Directives and Collaboration Networks in CRAN) Core visualizations and summaries for the CRAN package database. 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Package: r-cran-cransearcher Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-curl, r-cran-shiny, r-cran-shinyjs, r-cran-miniui, r-cran-stringr, r-cran-dt, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-cransearcher_1.0.0-1.ca2404.1_all.deb Size: 1631438 MD5sum: 421baf83bd736ef6102bde9eada0bb30 SHA1: cd5b0b8734ab7e61e2ef11ded53176ba0a39af91 SHA256: 2dca2f3a2cfcf781f78d11532836373024b0c50cbf9e2951c01a0b3cd6f69a0b SHA512: 835a5a31a5928bbb96db0bd820721897c99fd8903010b417bb8b67ac021fb7da97c02ec463d363417826b14d91336e411eed829e2e7f0b28fe0ba5b135d233a3 Homepage: https://cran.r-project.org/package=CRANsearcher Description: CRAN Package 'CRANsearcher' (RStudio Addin for Searching Packages in CRAN Database Based onKeywords) One of the strengths of R is its vast package ecosystem. 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Provides scales for 'ggplot2' for discrete coloring. 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Functions generate bullet and amortising debt schedules, compute credit metrics such as debt service coverage ratios (DSCR), debt yield ratios, and forward loan-to-value ratios (LTV), and expose an explicit property-level operating chain from gross effective income (GEI) to net operating income (NOI) and property before-tax cash flow (PBTCF). The toolkit supports end-to-end scenario execution from a YAML (YAML Ain't Markup Language) configuration file parsed with 'yaml', includes helpers for effective rent, constrained loan underwriting, and simplified SPV-level tax simulations, and ships reproducible vignettes for methodological and applied use cases. 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It relies on a two-stage pseudo-outcome regression, and it is supported by theoretical convergence guarantees. Bargagli-Stoffi, F. J., Cadei, R., Lee, K., & Dominici, F. (2023) Causal rule ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects. arXiv preprint . 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Package: r-cran-creditmodel Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-foreach, r-cran-doparallel, r-cran-glmnet, r-cran-rpart, r-cran-cli, r-cran-xgboost Suggests: r-cran-pdp, r-cran-pmml, r-cran-xml, r-cran-knitr, r-cran-gbm, r-cran-randomforest, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-creditmodel_1.3.1-1.ca2404.1_all.deb Size: 4174690 MD5sum: 039fa6190559f62db25a860303ed2e8b SHA1: c1b0a2ec331bc4247095fdf4496173f69b47c5be SHA256: edf5b1b07d66d33be6d2bf25b9b0b649de458ac633ed23aa1d1bfe0a7a335f21 SHA512: 754f4896b0fba753e7c4622f4110a6216f84dd2ed548d3d5d88dba6ec0a5a4a798a91fe05bdc872d57eaf1049ff9a8a5217f54b3ab4ecf4f1ed7ae77cbf21bbc Homepage: https://cran.r-project.org/package=creditmodel Description: CRAN Package 'creditmodel' (Toolkit for Credit Modeling, Analysis and Visualization) Provides a highly efficient R tool suite for Credit Modeling, Analysis and Visualization.Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyper parameters, data mining and visualization, model evaluation, strategy analysis etc. This package is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster. The references including: 1 Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS; 2 Bezdek, James C.FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences (0098-3004),. Package: r-cran-creditrisk Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-creditrisk_0.1.7-1.ca2404.1_all.deb Size: 83516 MD5sum: 544397ab1f1701c03deeb359ccf8c9f2 SHA1: 4d7cc6bbb427214d7e06e49fbb2c6c0a3259eec2 SHA256: 94c15e4188b23db37a37ecacd0b216cdb67209ada3ad5cfc9cc990785b1cb0ec SHA512: 2b475b2b7ef1a51662360f592961766282e473b43d40db621aa22f91fa2a395033852b903296c041d12a01b777a752dbcd2d0a9cd38dc667722717f8bfcb755a Homepage: https://cran.r-project.org/package=CreditRisk Description: CRAN Package 'CreditRisk' (Evaluation of Credit Risk with Structural and Reduced FormModels) Evaluation of default probability of sovereign and corporate entities based on structural or intensity based models and calibration on market Credit Default Swap quotes. References: Damiano Brigo, Massimo Morini, Andrea Pallavicini (2013) . Print ISBN: 9780470748466, Online ISBN: 9781118818589. © 2013 John Wiley & Sons Ltd. Package: r-cran-creds Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-creds_0.1.0-1.ca2404.1_all.deb Size: 21048 MD5sum: 9936a64957738e0d26ed98b8fa888572 SHA1: 7ca1a6e180d0b6171a0a01c3d8dde792d59c11c0 SHA256: c08ef778f3b2ed160a012224c68c04bb43125af668c34bdaa9eb8ecf1cbbe735 SHA512: f79cfc392553e5f499cfd8def0e189f5328d7f6778e944362eea191dc0b21d76cfd5d85606f144748bf328add06556d1bfd9687ce1a928e90df1e98674055915 Homepage: https://cran.r-project.org/package=CREDS Description: CRAN Package 'CREDS' (Calibrated Ratio Estimator under Double Sampling Design) Population ratio estimator (calibrated) under two-phase random sampling design has gained enormous popularity in the recent time. This package provides functions for estimation population ratio (calibrated) under two phase sampling design, including the approximate variance of the ratio estimator. The improved ratio estimator can be applicable for both the case, when auxiliary data is available at unit level or aggregate level (eg., mean or total) for first phase sampled. Calibration weight of each unit of the second phase sample was calculated. Single and combined inclusion probabilities were also estimated for both phases under two phase random [simple random sampling without replacement (SRSWOR)] sampling. The improved ratio estimator's percentage coefficient of variation was also determined as a measure of accuracy. This package has been developed based on the theoretical development of Islam et al. (2021) and Ozgul (2020) . Package: r-cran-credsubs Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1000 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ff, r-cran-r.rsp, r-cran-shiny Filename: pool/dists/noble/main/r-cran-credsubs_1.1.1-1.ca2404.1_all.deb Size: 876442 MD5sum: eec46a9b7aaf759809103eb9d30c37af SHA1: e9be881d0d492791fd3b8af8b230fcacc378196f SHA256: 7322e7f2afffa33acdebdde21fb647f75b39cacb0f58c14491f50bd26a89cc45 SHA512: 987c8021e2c799701a28cba8a29242a47f13e6f2e18365ab1be6c0fd24b327c3de2fed6d7ce00f62f98e6f6da1db0ddf2559aecaf2dd55069246c71eaafb924d Homepage: https://cran.r-project.org/package=credsubs Description: CRAN Package 'credsubs' (Credible Subsets) Functions for constructing simultaneous credible bands and identifying subsets via the "credible subsets" (also called "credible subgroups") method. Package documentation includes the vignette included in this package, and the paper by Schnell, Fiecas, and Carlin (2020, ). Package: r-cran-creepyalien Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-beepr, r-cran-cli, r-cran-glue Filename: pool/dists/noble/main/r-cran-creepyalien_1.0.0-1.ca2404.1_all.deb Size: 84226 MD5sum: d2dab780cca8c139bb5d2d58f2472ae4 SHA1: 47f22e24fd409ef78c2886dd299f986f820908b0 SHA256: 7c84bdcb17e86cf3cb5dfe34cbae9371e79a098ccf5c0b56a85af49afdbf84fc SHA512: 50b1822ff86f7472d4e034257a8f6a84cf8e43b4cffd853d63d3afab812615e93bc8b39b77feb91bc511bd73d26d4152ca0f39810c3ac9ad7b2e1bfc9239fe38 Homepage: https://cran.r-project.org/package=creepyalien Description: CRAN Package 'creepyalien' (Puzzle Game for the R Console) Puzzle game that can be played in the R console. Help the alien to find the ship. 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Package: r-cran-crew.aws.batch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 601 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-crew, r-cran-paws.common, r-cran-paws.compute, r-cran-paws.management, r-cran-r6, r-cran-rlang, r-cran-tibble Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-crew.aws.batch_0.1.0-1.ca2404.1_all.deb Size: 549650 MD5sum: 81ef1752079774d24116ee7711c52147 SHA1: 503bd35fc2a1d144a83ca08448db6a886586774d SHA256: 1148375b6cc07bf17a19784a76683dfd74d00ff991f1b00f8cce7d8abdbf7d45 SHA512: dd203b4d0746a21cacc3acd41521c2208f075da778af6d45540ba3b6deab05b2be5e00aa85b6518ec4ab5e36a15ec0fe021c785864255b816bcf8b8e6822f53d Homepage: https://cran.r-project.org/package=crew.aws.batch Description: CRAN Package 'crew.aws.batch' (A Crew Launcher Plugin for AWS Batch) In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. 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The 'NNG'-powered 'mirai' R package by Gao (2023) is a sleek and sophisticated scheduler that efficiently processes these intense workloads. The 'crew' package extends 'mirai' with a unifying interface for third-party worker launchers. Inspiration also comes from packages. 'future' by Bengtsson (2021) , 'rrq' by FitzJohn and Ashton (2023) , 'clustermq' by Schubert (2019) ), and 'batchtools' by Lang, Bischel, and Surmann (2017) . 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Package: r-cran-crimedata Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-osfr, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crimedata_0.3.5-1.ca2404.1_all.deb Size: 1083858 MD5sum: cfb12294fe51c38134157a874c516c0e SHA1: 8ba31340891034037a60428c067ee33e50c4560f SHA256: 472f0032efdeaf610a873ac4f38ffa508aefc9a96ca11bb4c5d3c8ae1dcd780b SHA512: 2f85a04d0c0360fb2b2a1e07dc34bbf722937d68543f8dc1e6a6ddbcf3a67cb6d462a3d714b098754ed182bd53bde4489f957dc4624dee3313d897765917a1b6 Homepage: https://cran.r-project.org/package=crimedata Description: CRAN Package 'crimedata' (Access Crime Data from the Open Crime Database) Gives convenient access to publicly available police-recorded open crime data from large cities in the United States that are included in the Crime Open Database . 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CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 . Package: r-cran-crisprdesignr Architecture: all Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2800 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-cran-gbm, r-bioc-genomicranges, r-bioc-biocgenerics, r-bioc-iranges, r-bioc-genomeinfodb, r-bioc-s4vectors, r-bioc-rtracklayer, r-cran-stringr, r-cran-vtreat, r-cran-shiny, r-cran-dt Suggests: r-bioc-bsgenome.scerevisiae.ucsc.saccer3 Filename: pool/dists/noble/main/r-cran-crisprdesignr_1.1.7-1.ca2404.1_all.deb Size: 2516566 MD5sum: e5a68e083497f2befd247ea8261221e3 SHA1: 589d7091d7313ab691e2ae84ebe9774edbb61e3a SHA256: 3520316c8999e241912213488a675157a60cef8a1e88d44b280fd27d2c90a841 SHA512: c998e142670a4e19256d33e7ce921f3439add1ace00d8ef512104ed74b77b04feb59e2080d041a4b410da7799e255da7fe9320fd6b4e375bfa0ea926930c0ed1 Homepage: https://cran.r-project.org/package=crispRdesignR Description: CRAN Package 'crispRdesignR' (Guide Sequence Design for CRISPR/Cas9) Designs guide sequences for CRISPR/Cas9 genome editing and provides information on sequence features pertinent to guide efficiency. Sequence features include annotated off-target predictions in a user-selected genome and a predicted efficiency score based on the model described in Doench et al. (2016) . Users are able to import additional genomes and genome annotation files to use when searching and annotating off-target hits. All guide sequences and off-target data can be generated through the 'R' console with sgRNA_Design() or through 'crispRdesignR's' user interface with crispRdesignRUI(). CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) and the associated protein Cas9 refer to a technique used in genome editing. Package: r-cran-criticality Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 892 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-bnlearn, r-cran-dplyr, r-cran-evd, r-cran-fitdistrplus, r-cran-ggplot2, r-cran-keras, r-cran-magrittr, r-cran-reticulate, r-cran-scales Filename: pool/dists/noble/main/r-cran-criticality_0.9.3-1.ca2404.1_all.deb Size: 386474 MD5sum: 1a4cc686685a9ac3625330419faa5fb4 SHA1: cc9a9d918b661be72b98cceab9dca513da1fa633 SHA256: 324643e8c91ad0394bde9403df07ebd28b7adfc9d117cd96f9f5973108b47988 SHA512: 51ce88a4041451ee9297d77c8ec404cf154f2cd4e01ff0cf3913ec4896c397f642679dfef20ddaa23597c2eb6906fd4b1725a5aea7f0f22429a919a495cc51f8 Homepage: https://cran.r-project.org/package=criticality Description: CRAN Package 'criticality' (Modeling Fissile Material Operations in Nuclear Facilities) A collection of functions for modeling fissile material operations in nuclear facilities, based on Zywiec et al (2021) . Package: r-cran-criticalpath Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-igraph, r-cran-magrittr, r-cran-r6, r-cran-stringr, r-cran-tibble Suggests: r-cran-diagrammer, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-criticalpath_0.2.1-1.ca2404.1_all.deb Size: 283392 MD5sum: ac081e2701f430c8790610f44a04fd39 SHA1: 623b2c10568958bc0fb66b5a775adaa764735546 SHA256: 1201457a90b635f71f067f7a905ec818314c4f3ac2997a428c5ebc138781b3a6 SHA512: 99265b56da33580216162b96c35c0acf7fa2757cff290d09533ae14848b6971370bd3fd05b616a76b9168f317be93e47a1d642f479d0dab42ec8703377886c89 Homepage: https://cran.r-project.org/package=criticalpath Description: CRAN Package 'criticalpath' (An Implementation of the Critical Path Method) An R implementation of the Critical Path Method (CPM). CPM is a method used to estimate the minimum project duration and determine the amount of scheduling flexibility on the logical network paths within the schedule model. The flexibility is in terms of early start, early finish, late start, late finish, total float and free float. Beside, it permits to quantify the complexity of network diagram through the analysis of topological indicators. Finally, it permits to change the activities duration to perform what-if scenario analysis. The package was built based on following references: To make topological sorting and other graph operation, we use Csardi, G. & Nepusz, T. (2005) ; For schedule concept, the reference was Project Management Institute (2017) ; For standards terms, we use Project Management Institute (2017) ; For algorithms on Critical Path Method development, we use Vanhoucke, M. (2013) and Vanhoucke, M. (2014) ; And, finally, for topological definitions, we use Vanhoucke, M. (2009) . Package: r-cran-critpath Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6641 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-diagrammer, r-cran-ggplot2, r-cran-reshape2, r-cran-dplyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-critpath_0.2.3-1.ca2404.1_all.deb Size: 633948 MD5sum: 830d34654eb242f53592a28b06c8fbba SHA1: 6fb94e9fca5ae133b88cd99ce94aa00662ba7380 SHA256: a433198e482b539f6ddd1e59dbc2baaf49bd3296d318cbcc6f4930278f096463 SHA512: 011491475970361d834026f521920e5415bceaade5d0df4dface391d83c5cb712bb8e38c5e7903c35eb043d7bdb9705716d44f01cb42ac1dc4eae7e800d706e0 Homepage: https://cran.r-project.org/package=critpath Description: CRAN Package 'critpath' (Setting the Critical Path in Project Management) Solving the problem of project management using CPM (Critical Path Method), PERT (Program Evaluation and Review Technique) and LESS (Least Cost Estimating and Scheduling) methods. The package sets the critical path, schedule and Gantt chart. In addition, it allows to draw a graph even with marked critical activities. For more information about project management see: Taha H. A. "Operations Research. An Introduction" (2017, ISBN:978-1-292-16554-7), Rama Murthy P. "Operations Research" (2007, ISBN:978-81-224-2944-2), Yuval Cohen & Arik Sadeh (2006) "A New Approach for Constructing and Generating AOA Networks", Journal of Engineering, Computing and Architecture 1. 1-13, Konarzewska I., Jewczak M., Kucharski A. (2020, ISBN:978-83-8220-112-3), Miszczyńska D., Miszczyński M. "Wybrane metody badań operacyjnych" (2000, ISBN:83-907712-0-9). Package: r-cran-crm12comb Architecture: all Version: 0.1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggforce Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crm12comb_0.1.12-1.ca2404.1_all.deb Size: 464398 MD5sum: 3c0599e454b4f07e511047aa9b3422b0 SHA1: e3d196e34f63851c99a4cf1972328e31bc5506a8 SHA256: 1e44c385f98091fa3894843eb9705b60e43511bb0c442774d19838d86941857d SHA512: e1b0a4cac3c4f193c08fdacb67aea90bd0da13d10a9cdd0f9d6cf8edc120878c2f2a9313f02987c3732b4ba5e71e377adfd0a9afb23f49f8b05beb88804322bd Homepage: https://cran.r-project.org/package=crm12Comb Description: CRAN Package 'crm12Comb' (Phase I/II CRM Based Drug Combination Design) Implements the adaptive designs for integrated phase I/II trials of drug combinations via continual reassessment method (CRM) to evaluate toxicity and efficacy simultaneously for each enrolled patient cohort based on Bayesian inference. It supports patients assignment guidance in a single trial using current enrolled data, as well as conducting extensive simulation studies to evaluate operating characteristics before the trial starts. It includes various link functions such as empiric, one-parameter logistic, two-parameter logistic, and hyperbolic tangent, as well as considering multiple prior distributions of the parameters like normal distribution, gamma distribution and exponential distribution to accommodate diverse clinical scenarios. Method using Bayesian framework with empiric link function is described in: Wages and Conaway (2014) . Package: r-cran-crmetrics Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggbeeswarm, r-cran-ggplot2, r-cran-ggpmisc, r-cran-ggpubr, r-cran-ggrepel, r-cran-magrittr, r-cran-matrix, r-cran-r6, r-cran-scales, r-cran-sccore, r-bioc-sparsematrixstats, r-cran-tibble, r-cran-tidyr Suggests: r-cran-conos, r-cran-data.table, r-cran-markdown, r-cran-pagoda2, r-cran-reticulate, r-bioc-rhdf5, r-cran-seurat, r-cran-soupx, r-cran-testthat Filename: pool/dists/noble/main/r-cran-crmetrics_0.3.2-1.ca2404.1_all.deb Size: 428530 MD5sum: e50f7e6d71a97ed7691fa0fc7512e16a SHA1: d77643f8811ae2fddcb110f8c8a000d5a35778dd SHA256: 044fbd54f5ebdc0b8ee6075819283ee8b9c344679753a2ee5799ad3b76ce03ea SHA512: 29c962c87a39318a7bbb52eba2252eed07e13d06454e077ea59ac7dc65c7e73563296e883eb1c4f52d9078144cccdc9bf591f8957e7ce49001adf15cd726de6b Homepage: https://cran.r-project.org/package=CRMetrics Description: CRAN Package 'CRMetrics' (Cell Ranger Output Filtering and Metrics Visualization) Sample and cell filtering as well as visualisation of output metrics from 'Cell Ranger' by Grace X.Y. Zheng et al. (2017) . 'CRMetrics' allows for easy plotting of output metrics across multiple samples as well as comparative plots including statistical assessments of these. 'CRMetrics' allows for easy removal of ambient RNA using 'SoupX' by Matthew D Young and Sam Behjati (2020) or 'CellBender' by Stephen J Fleming et al. (2022) . Furthermore, it is possible to preprocess data using 'Pagoda2' by Nikolas Barkas et al. (2021) or 'Seurat' by Yuhan Hao et al. (2021) followed by embedding of cells using 'Conos' by Nikolas Barkas et al. (2019) . Finally, doublets can be detected using 'scrublet' by Samuel L. Wolock et al. (2019) or 'DoubletDetection' by Gayoso et al. (2020) . In the end, cells are filtered based on user input for use in downstream applications. Package: r-cran-crmn Architecture: all Version: 0.0.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-pcamethods, r-bioc-biobase Filename: pool/dists/noble/main/r-cran-crmn_0.0.21-1.ca2404.1_all.deb Size: 438710 MD5sum: 7cb8aa7043ad9e57e255cc006df38e19 SHA1: df5a9fed08f53e2cd170ad2de9c3a6eb3e938c89 SHA256: 014315597a4863fd101334485a33afa97e55b4942c2c98beb85fe4d9f43faecb SHA512: 4c597c1d79bd5d82fb43e6abc5931b60f4bf443525ec8435d4eb8d6b739f604054cbf556367a2d055a4615148c5e27d41d532391e8e7ce812d488b878dfb7d2f Homepage: https://cran.r-project.org/package=crmn Description: CRAN Package 'crmn' (CCMN and Other Normalization Methods for Metabolomics Data) Implements the Cross-contribution Compensating Multiple standard Normalization (CCMN) method described in Redestig et al. (2009) Analytical Chemistry and other normalization algorithms. Package: r-cran-crmpack Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8145 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-checkmate, r-cran-dplyr, r-cran-futile.logger, r-cran-gensa, r-cran-gridextra, r-cran-kableextra, r-cran-knitr, r-cran-lifecycle, r-cran-magrittr, r-cran-mvtnorm, r-cran-parallelly, r-cran-rdpack, r-cran-rjags, r-cran-rlang, r-cran-survival, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-bookdown, r-cran-broom, r-cran-covr, r-cran-data.tree, r-cran-diagrammer, r-cran-ggmcmc, r-cran-quarto, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-crmpack_2.1.0-1.ca2404.1_all.deb Size: 5126482 MD5sum: f9a04bafaefd97fc4c9dc1ac87178c8d SHA1: 3f5db6af1de142dd8dca2d72f3101bbef1ffde60 SHA256: def3b6a00bdac39df6db3d30ea67684f06c3d2c45fff66ce1060cbbd5f3eaa8b SHA512: 606cbb8c81e15386c786c6714603d56f62c57db8d78cb2ac41696c4c77eea20736fda1d3e0f0ffffc5c6ec8bfcf768bf00895f8ca6c65c47edc813b0b564f5bb Homepage: https://cran.r-project.org/package=crmPack Description: CRAN Package 'crmPack' (Object-Oriented Implementation of Dose Escalation Designs) Implements a wide range of dose escalation designs. The focus is on model-based designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. Bayesian inference is performed via MCMC sampling in JAGS, and it is easy to setup a new design with custom JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanés Bové et al. (2019) . Package: r-cran-crmreg Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-ggplot2, r-cran-gplots, r-cran-pcapp, r-cran-plyr, r-cran-robustbase, r-cran-rrcov Filename: pool/dists/noble/main/r-cran-crmreg_1.0.4-1.ca2404.1_all.deb Size: 92648 MD5sum: d3643f4bb7d6f0e70a4a49367c14fea0 SHA1: 431728f35a374b9608cb9521e32aef294323555e SHA256: 86a5e92550c688b86eeb1a25f8ffd622da3e3f4075003a885201747ba979f4d0 SHA512: 45c68f7dc8df50190e1578e5c25f3a67ce554762c8e2afd788bd013cda9dfc9f7c57aa9cee5bdb856692361d925b86c84d930c82b8e9c3d9698c49ac9d63c324 Homepage: https://cran.r-project.org/package=crmReg Description: CRAN Package 'crmReg' (Cellwise Robust M-Regression and SPADIMO) Method for fitting a cellwise robust linear M-regression model (CRM, Filzmoser et al. (2020) ) that yields both a map of cellwise outliers consistent with the linear model, and a vector of regression coefficients that is robust against vertical outliers and leverage points. As a by-product, the method yields an imputed data set that contains estimates of what the values in cellwise outliers would need to amount to if they had fit the model. The package also provides diagnostic tools for analyzing casewise and cellwise outliers using sparse directions of maximal outlyingness (SPADIMO, Debruyne et al. (2019) ). Package: r-cran-crochet Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crochet_2.3.0-1.ca2404.1_all.deb Size: 54200 MD5sum: 3ba7e2e271e263258a9fba05b19d8e96 SHA1: 482c070a96c8320e091407c3105c8ef589ff320e SHA256: 6be48f8e3bc1aa0f9acea7d8df009559af88ce53ee187ffd74f5e6b61a300657 SHA512: 2f560de27bdd99b0e60bd3a04a2de3dba6a0e3f387a0bf21c27ae65eaa7b33fbd96005f883831f38f4205191633e7234e5eb0a4bccfa872ac62f6d688b9735a9 Homepage: https://cran.r-project.org/package=crochet Description: CRAN Package 'crochet' (Implementation Helper for '[' and '[<-' of Custom Matrix-LikeTypes) Functions to help implement the extraction / subsetting / indexing function '[' and replacement function '[<-' of custom matrix-like types (based on S3, S4, etc.), modeled as closely to the base matrix class as possible (with tests to prove it). Package: r-cran-cromwelldashboard Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-stringr, r-cran-httr, r-cran-dplyr, r-cran-dt Filename: pool/dists/noble/main/r-cran-cromwelldashboard_0.5.1-1.ca2404.1_all.deb Size: 19396 MD5sum: 81f3700f172bf5ba3e0692ed0b966642 SHA1: e7b2ac511a241d04f11f7b3dafdd702abf746bfe SHA256: 0b50e2d4b528b1ac6620f61e8a5f6f7400163fc95ec8b89bcdd1ceee26e0cad7 SHA512: 12016515328e5f6d459050bd1567d2f67b097a8325faaf994d4f90858694952df68941d587cd45fd907e3ecde31b69e538ab77e623b98351409e0e447accfb3a Homepage: https://cran.r-project.org/package=cromwellDashboard Description: CRAN Package 'cromwellDashboard' (A Dashboard to Visualize Scientific Workflows in 'Cromwell') A dashboard supports the usage of 'cromwell'. 'Cromwell' is a scientific workflow engine for command line users. This package utilizes 'cromwell' REST APIs and provides these convenient functions: timing diagrams for running workflows, 'cromwell' engine status, a tabular workflow list. For more information about 'cromwell', visit . Package: r-cran-cronbach Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-rangen, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-cronbach_0.4-1.ca2404.1_all.deb Size: 25598 MD5sum: 634607b712851ba5e416093b349bdf5e SHA1: 24ed4d99c1f6c348a45fc2e8e48574662bfefc95 SHA256: d8dba032bf7ebf3359cb672a3dcc4ed1a1ee43a8b56a751c44f31e1e20471c6b SHA512: c94d03f64a91d21d2967b7d25a0f6ee005bdaa62b9afcd65fbf0e15fa781ca225978a40228baa4b1abe3f06557630308112bcc3dfd052044e3c882f8292c6866 Homepage: https://cran.r-project.org/package=Cronbach Description: CRAN Package 'Cronbach' (Cronbach's Alpha) Cronbach's alpha and various formulas for confidence intervals. The relevant paper is Tsagris M., Frangos C.C. and Frangos C.C. (2013). "Confidence intervals for Cronbach's reliability coefficient". Recent Techniques in Educational Science, 14-16 May, Athens, Greece. Package: r-cran-crone Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1660 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-crone_0.1.1-1.ca2404.1_all.deb Size: 1289380 MD5sum: 84c86105623ffdff7256c26926b05ca8 SHA1: f91cabe2c6da63ec8d29f4ffa6804f1e975d6f95 SHA256: 6ea7168ee6302ce57b1f9a52a62db846c75d706f975784560f085871111ebb7b SHA512: 8cc6d5afad1fe7a4a6fa16fe2b8338bf521da98482603e191f8e834ec3dc6988fbe37b23ff465795a1b31d59bfb5f33c10eb897a27df266749fb313a29490c24 Homepage: https://cran.r-project.org/package=crone Description: CRAN Package 'crone' (Structural Crystallography in 1d) Functions to carry out the most important crystallographic calculations for crystal structures made of 1d Gaussian-shaped atoms, especially useful for methods development. Main reference: E. Smith, G. Evans, J. Foadi (2017) . Package: r-cran-cronologia Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-glue Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-cronologia_0.2.0-1.ca2404.1_all.deb Size: 25554 MD5sum: ded225fa353a557f0962ab0bd8cb6489 SHA1: 8611b40dc05975f76d5a162e0429ce67ab435073 SHA256: 063442f8775e8e3b30fa3569af4a4a28fa1cf7ec346fa8506baa7473e139d75b SHA512: 1dde7b6164648d362797afdc036cea9cf0e9dc274c516e87bde1565d0016a2a0516ff1bcf4f5dcdfb4502f6450eaf64ad6ec6aa88deb184ce087c3f8ac3f67e5 Homepage: https://cran.r-project.org/package=cronologia Description: CRAN Package 'cronologia' (Create an HTML Vertical Timeline from a Data Frame in'rmarkdown' and 'shiny') Creates an HTML vertical timeline from a data frame as an input for 'rmarkdown' documents and 'shiny' applications. Package: r-cran-cronr Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest Suggests: r-cran-miniui, r-cran-shiny, r-cran-shinyfiles, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-cronr_0.6.5-1.ca2404.1_all.deb Size: 79362 MD5sum: 7a334a453ae5b327795b618e4ddefa1d SHA1: 11a4afc37ffde49212cbdff3f029b0e921bc8dd3 SHA256: bd503045eda75acc966413acafe20cfc2d64ff6f550187659803bc6f6be22e89 SHA512: 53e37fc0b68953648ede4ecf4f3fd1dd0a24b837148fd15c0099724c0d79f2b44edefe28e54d7a52ca7e2508b42978086bd28d6642c5684db8d1000b3ee9cf7f Homepage: https://cran.r-project.org/package=cronR Description: CRAN Package 'cronR' (Schedule R Scripts and Processes with the 'cron' Job Scheduler) Create, edit, and remove 'cron' jobs on your unix-alike system. The package provides a set of easy-to-use wrappers to 'crontab'. It also provides an RStudio add-in to easily launch and schedule your scripts. Package: r-cran-crookr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-lidr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-crookr_0.1.0-1.ca2404.1_all.deb Size: 369966 MD5sum: e8937706374b03217cb87b0c7a4a661a SHA1: b5824d3dc288d1b679bca302ad49444139ad6f34 SHA256: 6ca56ca412ffb64c90c9526c363eaabf88fd5d08589eed0a052709974995e876 SHA512: 5c6226123411d4760e11268ec843dcbdbe8eaf0652477f20e862b0356f8e56a570cd6d6442611d974c0221a3f56d6be114e85f4599bc49c03aa2349cc86c54f7 Homepage: https://cran.r-project.org/package=crookR Description: CRAN Package 'crookR' (Synthetic Crook Deformations in Stem Point Clouds) Simulates parameterized single- and double-directional stem deformations in tree point clouds derived from terrestrial or mobile laser scanning, enabling the generation of realistic synthetic datasets for training and validating machine learning models in wood defect detection, quality assessment, and precision forestry. For more details see Pires (2025) . Package: r-cran-crop Architecture: all Version: 0.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-crop_0.0-3-1.ca2404.1_all.deb Size: 14470 MD5sum: 58cbea2464d6160834d5d6b6a9c72545 SHA1: 9e9260dd2ca9e7e8af06982da595dcebd09a72b5 SHA256: 984132b9ec3a483d55a9e2e0cefe712d8b91d885ab00de574079dad802f6a617 SHA512: 590b3786cd5970152a95ee1fea122104534fb3d82344578d82664f42560481e6bb42cc0a72b67038588a02309756058f237e539571cdd251da9a24af74a3744e Homepage: https://cran.r-project.org/package=crop Description: CRAN Package 'crop' (Graphics Cropping Tool) A device closing function which is able to crop graphics (e.g., PDF, PNG files) on Unix-like operating systems with the required underlying command-line tools installed. Package: r-cran-cropbreeding Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-metan, r-cran-rlang Filename: pool/dists/noble/main/r-cran-cropbreeding_0.1.0-1.ca2404.1_all.deb Size: 30136 MD5sum: 38fb84d7094b11a17c135c560d4c21cd SHA1: 763316d0c40fc775670fa3de526b4c043656ece7 SHA256: 35f0965420c1ce0bcd59a0aed9d7018ae311190f8b695c0d1594fa061bb15659 SHA512: 4aa0f9fdb902b78a7544f449a469f86d5ac745937a445bbfa242daa38f2d15c3206d11c71a5f340984ffbbe3130152e22081cdfa8b76ee9345f0d472dd3c53f4 Homepage: https://cran.r-project.org/package=CropBreeding Description: CRAN Package 'CropBreeding' (Stability Analysis in Crop Breeding) Provides tools for crop breeding analysis including Genetic Coefficient of Variation (GCV), Phenotypic Coefficient of Variation (PCV), heritability, genetic advance calculations, stability analysis using the Eberhart-Russell model, two-way ANOVA for genotype-environment interactions, and Additive Main Effects and Multiplicative Interaction (AMMI) analysis. These tools are developed for crop breeding research and stability evaluation under various environmental conditions. The methods are based on established statistical and biometrical principles. Refer to Eberhart and Russell (1966) for stability parameters, Fisher (1935) "The Design of Experiments" , Falconer (1996) "Introduction to Quantitative Genetics" , and Singh and Chaudhary (1985) "Biometrical Methods in Quantitative Genetic Analysis" for foundational methodologies. Package: r-cran-cropcircles Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-magick, r-cran-purrr Filename: pool/dists/noble/main/r-cran-cropcircles_0.2.4-1.ca2404.1_all.deb Size: 62626 MD5sum: 8a05f850cf8d4e8dbbc8532b2e2f411d SHA1: 0cf0997d5adeea536a7bcfc017917cf7af12b9b3 SHA256: bc798c13f856fa89f2682b38f36ab9acd2609d553cf51d68fca0f08ed7f9e706 SHA512: dc9351fa5d277c82a3007ae06dc31513e73b6ba88005992b00029e0b57c23830f338eaa7bbcaac801175aa5ad94570cfaa7e86ae4285211a3a762cad2943beb2 Homepage: https://cran.r-project.org/package=cropcircles Description: CRAN Package 'cropcircles' (Crops an Image to a Circle) Images are cropped to a circle with a transparent background. The function takes a vector of images, either local or from a link, and circle crops the image. Paths to the cropped image are returned for plotting with 'ggplot2'. Also includes cropping to a hexagon, heart, parallelogram, and square. Package: r-cran-cropdatape Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cropdatape_1.0.0-1.ca2404.1_all.deb Size: 192502 MD5sum: 16b7d0e52326b136974aa153f5ffd31e SHA1: 28d1facdbe7f0ecf87c68c25df1d1ee8d8af7c54 SHA256: 880cf42e3b2e6cf7ade678f0eedfb32b63cfdbaf111058f853c38e8a727e9b44 SHA512: a5010b34b869742dbba7b7acf3d41ce6053eb3ee028410c54cc2a4c7eccb373e04e4e3a07dea832e25d75fe97062d04079c3d2496727890d58ac8c46e786824d Homepage: https://cran.r-project.org/package=cropdatape Description: CRAN Package 'cropdatape' (Open Data of Agricultural Production of Crops of Peru) Provides peruvian agricultural production data from the Agriculture Minestry of Peru (MINAGRI). The first version includes 6 crops: rice, quinoa, potato, sweet potato, tomato and wheat; all of them across 24 departments. Initially, in excel files which has been transformed and assembled using tidy data principles, i.e. each variable is in a column, each observation is a row and each value is in a cell. The variables variables are sowing and harvest area per crop, yield, production and price per plot, every one year, from 2004 to 2014. Package: r-cran-cropdemand Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4483 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-terra, r-cran-sf, r-cran-tidyr, r-cran-ncdf4 Filename: pool/dists/noble/main/r-cran-cropdemand_1.0.3-1.ca2404.1_all.deb Size: 3261146 MD5sum: 87e3390e971d24f5833984e75cd0ca03 SHA1: d608772e96f87bc7079afc2d673ff4a65991a363 SHA256: 10e3ed44f387de423674127f530943b1fb3d830dc49ca658122fee8e7a3e05ed SHA512: c763a3243872d7fef326d41039a4c51c83d4cf77ef9b92295ae5cf8ee2aabe0710afde55df3834a93a379423ab8edfa2ad8c5c5c6f6785b44cf0ae48054d22d7 Homepage: https://cran.r-project.org/package=cropDemand Description: CRAN Package 'cropDemand' (Spatial Crop Water Demand for Brazil) Estimation of crop water demand can be processed via this package. As example, the data from 'TerraClimate' dataset () calibrated with automatic weather stations of National Meteorological Institute of Brazil is available in a coarse spatial resolution to do the crop water demand. However, the user have also the option to download the variables directly from 'TerraClimate' repository with the download.terraclimate function and access the original 'TerraClimate' products. If the user believes that is necessary calibrate the variables, there is another function to do it. Lastly, the estimation of the crop water demand present in this package can be run for all the Brazilian territory with 'TerraClimate' dataset. Package: r-cran-cropgrowdays Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-purrrlyr, r-cran-httr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-furrr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cropgrowdays_0.2.2-1.ca2404.1_all.deb Size: 148426 MD5sum: 765be3de34a08f402365182d6da8a2a3 SHA1: a51eca26c23d9e1ee7b4452e57794a65fb00e551 SHA256: 754835baea4a6401aec581d12fd031e4c306a657723fe1d153d08637410eff61 SHA512: 1167876a4513d20756f2964a5c1a777965f64bfd60ced47b1a3b1bb61a6c25f0f123dfc1e91896aa2656dd7452a5080c2466e1e0664de0e29c839dac18a8ec0a Homepage: https://cran.r-project.org/package=cropgrowdays Description: CRAN Package 'cropgrowdays' (Crop Growing Degree Days and Agrometeorological Calculations) Calculate agrometeorological variables for crops including growing degree days (McMaster, GS & Wilhelm, WW (1997) ), cumulative rainfall, number of stress days and cumulative or mean radiation and evaporation. Convert dates to day of year and vice versa. Also, download curated and interpolated Australian weather data from the Queensland Government DES longpaddock website . This data is freely available under the Creative Commons 4.0 licence. Package: r-cran-crops Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sets, r-cran-reshape, r-cran-tidyverse, r-cran-memoise, r-cran-ggplot2, r-cran-magrittr, r-cran-cowplot, r-cran-tibble, r-cran-rdpack Suggests: r-cran-fpop, r-cran-pacman Filename: pool/dists/noble/main/r-cran-crops_1.0.3-1.ca2404.1_all.deb Size: 52320 MD5sum: ce0013363185b3d028428a35fc4eef6e SHA1: 250a57a084b4d5266141265a28e867b6c665dd74 SHA256: f83448bcbda4a2c04e23711e7499bad4beaf231856a7aee6d1922ab07fe022a6 SHA512: 944acfe3f052b0048ddde6206841a3d31cb6345a6bdaf7d4e3e7c1a81bbe22f83be6d9712fd75126da83c638796a876bc121aa9a036b27e8af66a0e4f960cbe3 Homepage: https://cran.r-project.org/package=crops Description: CRAN Package 'crops' (Changepoints for a Range of Penalties (CROPS)) Implements the Changepoints for a Range of Penalties (CROPS) algorithm of Haynes et al. (2017) for finding all of the optimal segmentations for multiple penalty values over a continuous range. Package: r-cran-cropscaper Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-raster, r-cran-sf, r-cran-data.table, r-cran-httr, r-cran-rjsonio Filename: pool/dists/noble/main/r-cran-cropscaper_1.1.5-1.ca2404.1_all.deb Size: 93760 MD5sum: e0e4c2cb641ff47071ae63766eff690f SHA1: aec599d124f2b3d45454783b49b24b5f8fcdd5af SHA256: f5c042f691a32e01cf342f295ce1ad8f43a71223302d0b8f97e652faf9c944da SHA512: e78dcd41e99cd9bbd790b98e38d1d555a593277455d33cd8abd83c64fd5afa8412c7ea42ac5190c67d70bb8e50e1f0901e86d625dd862523a3eb60d7be0a59e0 Homepage: https://cran.r-project.org/package=CropScapeR Description: CRAN Package 'CropScapeR' (Access Cropland Data Layer Data via the 'CropScape' Web Service) Interface to easily access Cropland Data Layer (CDL) data for any area of interest via the 'CropScape' web service. Package: r-cran-cropwaterbalance Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-powersdi, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cropwaterbalance_0.2.0-1.ca2404.1_all.deb Size: 89810 MD5sum: 6b11f1f8c0d25a0ba458294ca362fd82 SHA1: c17574135794b56222b47a4cdf3c6b5e6c411808 SHA256: ad25114e0555e55e55facd93d0f8c2282fe7e2d13699a054579d8df875cd8459 SHA512: f9e98a0a2c3702b7095e73efa505044d72887ebef4abec62e9bb006425a20c646dd3e9fe5058090c39580c1e9f5d2e264ee10ac8632643462c2ec0d99a654063 Homepage: https://cran.r-project.org/package=CropWaterBalance Description: CRAN Package 'CropWaterBalance' (Climate Water Balance for Irrigation Purposes) Calculates daily climate water balance for irrigation purposes and also calculates the reference evapotranspiration (ET) using three methods, Penman and Monteith (Allen et al. 1998, ISBN:92-5-104219-5); Priestley and Taylor (1972) ; or Hargreaves and Samani (1985) . Users may specify a management allowed depletion (MAD), which is used to suggest when to irrigate. The functionality allows for the use of crop and water stress coefficients as well. Package: r-cran-cropzoning Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4414 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-tidyr, r-cran-sf, r-cran-ncdf4, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-cropzoning_1.0.3-1.ca2404.1_all.deb Size: 3204352 MD5sum: 3f28c299f96152ac134b39ce4f02cc4b SHA1: 7e6dacdf777529c1874deda154b92bd07f43bad1 SHA256: d22a86033d5b368eb6a57bbbc3373b8b39b1d17d58f4713016e62610e8aa71e6 SHA512: db5da95622fe34a2ef5f6e454ee635636247b0201c8be9d5bdbecb3a06adf14fbc1012953248f6dc4fec9d3b49b9f153e342fcd2fb7913ad1b96dcbdfb9f2954 Homepage: https://cran.r-project.org/package=cropZoning Description: CRAN Package 'cropZoning' (Climate Crop Zoning Based in Air Temperature for Brazil) Climate crop zoning based in minimum and maximum air temperature. The data used in the package are from 'TerraClimate' dataset (), but, it have been calibrated with automatic weather stations of National Meteorological Institute of Brazil. The climate crop zoning of this package can be run for all the Brazilian territory. Package: r-cran-crosscarry Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gee, r-cran-ggplot2, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-crosscarry_1.2.0-1.ca2404.1_all.deb Size: 63136 MD5sum: a93dbf54d3b4a2d4c674df1ac07543d4 SHA1: aee8e4549631634a1427568a0c37237efda7898a SHA256: deedc0abdf07c1f1d9a7a0fb8bd313f1bf085a10cede52bc9951c8692b8c4137 SHA512: 008a94cb681d22a281f2a0738903599f441752cbfa8feda4d9503c037dba2d61eb63ec069262b97b0d801eedd708a44734fa9d8b65d3601ec5f78622a65d92df Homepage: https://cran.r-project.org/package=CrossCarry Description: CRAN Package 'CrossCarry' (Analysis of Data from a Crossover Design with GEE) Analyze data from a crossover design using generalized estimation equations (GEE), including carryover effects and various correlation structures based on the Kronecker product. It contains functions for semiparametric estimates of carry-over effects in repeated measures and allows estimation of complex carry-over effects. Related work includes: a) Cruz N.A., Melo O.O., Martinez C.A. (2023). "CrossCarry: An R package for the analysis of data from a crossover design with GEE". . b) Cruz N.A., Melo O.O., Martinez C.A. (2023). "A correlation structure for the analysis of Gaussian and non-Gaussian responses in crossover experimental designs with repeated measures". and c) Cruz N.A., Melo O.O., Martinez C.A. (2023). "Semiparametric generalized estimating equations for repeated measurements in cross-over designs". . Package: r-cran-crossclustering Architecture: all Version: 4.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-cluster, r-cran-crayon, r-cran-dplyr, r-cran-flip, r-cran-mclust, r-cran-purrr Suggests: r-cran-covr, r-cran-devtools, r-cran-lintr, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-crossclustering_4.1.3-1.ca2404.1_all.deb Size: 77362 MD5sum: 998e96af07971d2e04ac51df0a0a1613 SHA1: 4bf7df53800cf0ecf02c3f41edec2027cb745b4f SHA256: fe327409399463f86f24eb44edef97013f52f9bf033b8d83ca2730f0508df9d2 SHA512: 58e507616959fac51391ca351b00acc02f4a0abbdaf6077c5431826db82f92991b7a1d91020820bb05db7189e6a691695fe3fae9d966c5edd41c304252bbdc64 Homepage: https://cran.r-project.org/package=CrossClustering Description: CRAN Package 'CrossClustering' (A Partial Clustering Algorithm) Provide the 'CrossClustering' algorithm (Tellaroli et al. (2016) ), which is a partial clustering algorithm that combines the Ward's minimum variance and Complete Linkage algorithms, providing automatic estimation of a suitable number of clusters and identification of outlier elements. 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Package: r-cran-crossexpression Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2903 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfast, r-cran-rann, r-cran-matrix, r-cran-ggplot2, r-cran-dplyr, r-cran-stringr Filename: pool/dists/noble/main/r-cran-crossexpression_1.0.0-1.ca2404.1_all.deb Size: 2932744 MD5sum: 2e0628b8f2bf6fc53a6c338b315a5a22 SHA1: b66a9da1cb9aa818a918428979a85fbf55f4d0d1 SHA256: 472ccb867bf0c280d5e95c7008a6b58f650f8d23ff746a8cb130ca3fbba6eac2 SHA512: 65acd0cf945e77fd41752ce93672d301df1fe3e7ffe2d536540d1e322f25841320ac02181adaffac6fd4e0a2eec573c1201d7d7dcc76a2797931feedfc9105a2 Homepage: https://cran.r-project.org/package=CrossExpression Description: CRAN Package 'CrossExpression' (Cross-Expression Analysis of Spatial Transcriptomics Data) Analyzes spatial transcriptomic data using cells-by-genes and cell location matrices to find gene pairs that coordinate their expression between spatially adjacent cells. It enables quantitative analysis and graphical assessment of these cross-expression patterns. See Sarwar et al. (2025) and for more details. 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Package: r-cran-crossword.r Architecture: all Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-dplyr, r-cran-stringr, r-cran-magrittr, r-cran-jsonlite, r-cran-r6extended Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-crossword.r_0.3.6-1.ca2404.1_all.deb Size: 68028 MD5sum: 63bf7fa2c266c4e5e5baff4b0f032057 SHA1: 21865970957794e0ef4c416de4b0cf24def2763e SHA256: d49dd3903f7aad103e440c68615750362f9a249c674ea7622817af2114a42c72 SHA512: 97781d9058be1db0e25b9ac2750c323df87c4bd05b44b88693bb51661ebee54250cbd4fec24bfa7ee6a64dde5884cf87b7f9d33278f3370c8552d96d1da23160 Homepage: https://cran.r-project.org/package=crossword.r Description: CRAN Package 'crossword.r' (Generating Crosswords from Word Lists) Generate crosswords from a list of words. Package: r-cran-crov Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vgam, r-cran-gtools Filename: pool/dists/noble/main/r-cran-crov_0.3.0-1.ca2404.1_all.deb Size: 142184 MD5sum: 964a7027c97b53b024d6eb0834c615b7 SHA1: 8f427bfe572703f015ad972eb12f13442cc900f2 SHA256: 56b4e23059f617cb09072b1a9f63f7aebd8f112bf9562faa2cb13c43179d84c5 SHA512: e7fab3a4a0fee2edf06884aebcde5793092f12266b90a8d86a8798caabe372daf4d64e95a1c4170b939f862e435a2c7394dd7a78add6e529acc3f27e9b42484f Homepage: https://cran.r-project.org/package=crov Description: CRAN Package 'crov' (Constrained Regression Model for an Ordinal Response and OrdinalPredictors) Fits a constrained regression model for an ordinal response with ordinal predictors and possibly others, Espinosa and Hennig (2019) . The parameter estimates associated with an ordinal predictor are constrained to be monotonic. If a monotonicity direction (isotonic or antitonic) is not specified for an ordinal predictor by the user, then one of the available methods will either establish it or drop the monotonicity assumption. Two monotonicity tests are also available to test the null hypothesis of monotonicity over a set of parameters associated with an ordinal predictor. 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The usual methods 'select', 'filter', 'group_by', 'summarize', and 'collect' are implemented in such a way as to perform as much computation on the server and pull as little data locally as possible. Package: r-cran-crrcbcv Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crrsc, r-cran-abind, r-cran-pracma, r-cran-survival Filename: pool/dists/noble/main/r-cran-crrcbcv_1.0-1.ca2404.1_all.deb Size: 43782 MD5sum: 445c5d044babe54e5b5444382159bfc2 SHA1: b62482bc5a8f48262405f3d92a8843a51c478ee9 SHA256: b0dcb0acf8eab23440a333a18af320ff81af15072497df6ed7b240b5ac011804 SHA512: 14331f1c1f2764e0260059915dd95509953fa93c034377551c52491e58d62d86b567eb0423c6518e5b7905462aabc41c5a61debaf0b24c070a58c42de72c1a2f Homepage: https://cran.r-project.org/package=crrcbcv Description: CRAN Package 'crrcbcv' (Bias-Corrected Variance for Competing Risks Regression withClustered Data) A user friendly function 'crrcbcv' to compute bias-corrected variances for competing risks regression models using proportional subdistribution hazards with small-sample clustered data. Four types of bias correction are included: the MD-type bias correction by Mancl and DeRouen (2001) , the KC-type bias correction by Kauermann and Carroll (2001) , the FG-type bias correction by Fay and Graubard (2001) , and the MBN-type bias correction by Morel, Bokossa, and Neerchal (2003) . Package: r-cran-crrstep Architecture: all Version: 2025.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cmprsk Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crrstep_2025.1.1-1.ca2404.1_all.deb Size: 73736 MD5sum: 77ad7091548c41efdb17e5e3e1779692 SHA1: 0c72f51e67c1d215a16f264cd2cdb69fbec09ebf SHA256: 029157b5b7afd6358ceb74ab90a758fb5e5ccbb1ae2a68a6d340f8f01d4e724c SHA512: 33898a93ea0cf5a86c29f93b31390ca4450048770b645cd8b800c36c3adeb29bc3b4e6188d1115f85d3bc25a9bd4f9855d6cf82dc546cdd2b0171d81dee87a32 Homepage: https://cran.r-project.org/package=crrstep Description: CRAN Package 'crrstep' (Stepwise Covariate Selection for the Fine & Gray Competing RisksRegression Model) Performs forward and backward stepwise regression for the proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events. This version includes improved handling of factors, interactions, and polynomial terms. Package: r-cran-crseeventstudy Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-crseeventstudy_1.2.2-1.ca2404.1_all.deb Size: 74672 MD5sum: 2efb4004ad655dd7c3bfe39939b80761 SHA1: c9cc928b35d90af9bea5eb545446540218f91cf7 SHA256: 7062881f5735e34a28390cb6beefb23eb5b29d17e2dfc9e4ec179d2f5d656b24 SHA512: 51911a18fb63bda4171628cd571daa0d2df6a7a54257abf61e0efa863dc9efa5797985fc2b510bfd9d930639c58ab3f2398f7e600649cd0bde12886c7073c8e4 Homepage: https://cran.r-project.org/package=crseEventStudy Description: CRAN Package 'crseEventStudy' (A Robust and Powerful Test of Abnormal Stock Returns inLong-Horizon Event Studies) Based on Dutta et al. (2018) , this package provides their standardized test for abnormal returns in long-horizon event studies. The methods used improve the major weaknesses of size, power, and robustness of long-run statistical tests described in Kothari/Warner (2007) . Abnormal returns are weighted by their statistical precision (i.e., standard deviation), resulting in abnormal standardized returns. This procedure efficiently captures the heteroskedasticity problem. Clustering techniques following Cameron et al. (2011) are adopted for computing cross-sectional correlation robust standard errors. The statistical tests in this package therefore accounts for potential biases arising from returns' cross-sectional correlation, autocorrelation, and volatility clustering without power loss. Package: r-cran-crsmeta Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-crsmeta_0.3.0-1.ca2404.1_all.deb Size: 28708 MD5sum: 5ec2e24f5e4950dc01b9854a23087366 SHA1: 0119042846546a6105778437567b2659e0c51632 SHA256: dd9bf0b30032d3b60fc8b739d0438157796733577e439bf0af63f6181fd95fb7 SHA512: 705f6c5677829c9badbcc84b65aafb2c11ac70db93d753264f849e58a0c724f5afe6b41505380fb3bf4533c206674d5794c24bc7d3db5e0cf123959537c8cf0e Homepage: https://cran.r-project.org/package=crsmeta Description: CRAN Package 'crsmeta' (Extract Coordinate System Metadata) Obtain coordinate system metadata from various data formats. There are functions to extract a 'CRS' (coordinate reference system, ) in 'EPSG' (European Petroleum Survey Group, ), 'PROJ4' , or 'WKT2' (Well-Known Text 2, ) forms. This is purely for getting simple metadata from in-memory formats, please use other tools for out of memory data sources. Package: r-cran-crsnls Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-crsnls_0.2-1.ca2404.1_all.deb Size: 43286 MD5sum: 93d175af14985c4f60662aa63f12305c SHA1: 67a7c346d9a63d14b28a8371cd9f4eb1dc620740 SHA256: de46bf63d2adf3aa7948fcdf5a11d1621344bc2b3b72d9bc05637d6049467e4c SHA512: ea542441c8174d14d916a2c48db45de66cdbc56a2eaccf57564d06ff9ae4d080388290c2caa44ecfd021ffa6869a48e13e7d05a114012ceae2c70d12cd4d6931 Homepage: https://cran.r-project.org/package=crsnls Description: CRAN Package 'crsnls' (Nonlinear Regression Parameters Estimation by 'CRS4HC' and'CRS4HCe') Functions for nonlinear regression parameters estimation by algorithms based on Controlled Random Search algorithm. Both functions (crs4hc(), crs4hce()) adapt current search strategy by four heuristics competition. In addition, crs4hce() improves adaptability by adaptive stopping condition. Package: r-cran-crso Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crso_0.1.1-1.ca2404.1_all.deb Size: 133620 MD5sum: 7e10df3980b3802aeeb07faa8d97cfac SHA1: d22d2e81169864e5dd6b35c3024884c019c7bd7f SHA256: bfc28c6dbe67f38780b787af63818ec33be068eb95cfec5d885ba252cc2cf40f SHA512: eaac3bf63159da7035250506ebb2ba1b2778533e4d37d3e031b7629054b2ac5a1386117d2ee8318f38c39d9e0db71348ff7295897bdbba8b76a1383cfd5308ec Homepage: https://cran.r-project.org/package=crso Description: CRAN Package 'crso' (Cancer Rule Set Optimization ('crso')) An algorithm for identifying candidate driver combinations in cancer. CRSO is based on a theoretical model of cancer in which a cancer rule is defined to be a collection of two or more events (i.e., alterations) that are minimally sufficient to cause cancer. A cancer rule set is a set of cancer rules that collectively are assumed to account for all of ways to cause cancer in the population. In CRSO every event is designated explicitly as a passenger or driver within each patient. Each event is associated with a patient-specific, event-specific passenger penalty, reflecting how unlikely the event would have happened by chance, i.e., as a passenger. CRSO evaluates each rule set by assigning all samples to a rule in the rule set, or to the null rule, and then calculating the total statistical penalty from all unassigned event. CRSO uses a three phase procedure find the best rule set of fixed size K for a range of Ks. A core rule set is then identified from among the best rule sets of size K as the rule set that best balances rule set size and statistical penalty. Users should consult the 'crso' vignette for an example walk through of a full CRSO run. The full description, of the CRSO algorithm is presented in: Klein MI, Cannataro V, Townsend J, Stern DF and Zhao H. "Identifying combinations of cancer driver in individual patients." BioRxiv 674234 [Preprint]. June 19, 2019. . Please cite this article if you use 'crso'. Package: r-cran-crsra Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2563 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-digest, r-cran-tidytext, r-cran-tibble, r-cran-rcorpora, r-cran-knitr Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-crsra_0.2.3-1.ca2404.1_all.deb Size: 2197602 MD5sum: 0f068d6517f4bbcc188fdf26bb4eea94 SHA1: 4d59cfd75b6a4bd8b9ba48ee4c1db3e34b2fbdb0 SHA256: 9511e4e59c0e55b211150488cf4b7f0889620a34735be1fd2b6a8238fdacadbc SHA512: 039a733c2e8934927bdaf70ef116debbfac9cba840896d99e435194f5119ae605b2c514a2cce9d268339ed7fbb7dfe7343768a2b144f8a2793388c10fa483a18 Homepage: https://cran.r-project.org/package=crsra Description: CRAN Package 'crsra' (Tidying and Analyzing 'Coursera' Research Export Data) Tidies and performs preliminary analysis of 'Coursera' research export data. These export data can be downloaded by anyone who has classes on Coursera and wants to analyze the data. Coursera is one of the leading providers of MOOCs and was launched in January 2012. With over 25 million learners, Coursera is the most popular provider in the world being followed by EdX, the MOOC provider that was a result of a collaboration between Harvard University and MIT, with over 10 million users. Coursera has over 150 university partners from 29 countries and offers a total of 2000+ courses from computer science to philosophy. Besides, Coursera offers 180+ specialization, Coursera's credential system, and four fully online Masters degrees. For more information about Coursera check Coursera's About page on . Package: r-cran-crstools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3916 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-ggplot2, r-cran-terra, r-cran-cli, r-cran-jpeg, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pastclim, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-tidyterra, r-cran-svglite, r-cran-spelling Filename: pool/dists/noble/main/r-cran-crstools_1.0.0-1.ca2404.1_all.deb Size: 2674998 MD5sum: 77acac3b59f3c8bf92c0621682da582d SHA1: 1540436bff1be5a25ecffb54da2c8806efbf5df2 SHA256: 47a4515321c99d171251d609ee574d322d78a4e448260543ee00b5d3fcd91a8b SHA512: 15a9b4aa1c3112c924af6019981d723600fc9220758c1ae559c1cb63713734bec82e74e99db379249a1704650532674d2867dd2c30b5a5d4e695ce171e62c917 Homepage: https://cran.r-project.org/package=crstools Description: CRAN Package 'crstools' (Tools to Work with Projections) Choose the appropriate map projection for a given application, visualise the resulting distortion, and georeference data from unknown projections. The full functionalities of the package are described in Pozzi et al. (2026) (pre-print). 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Package: r-cran-crt2power Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-devtools, r-cran-knitr, r-cran-rootsolve, r-cran-tidyverse, r-cran-tableone, r-cran-foreach, r-cran-mvtnorm, r-cran-tibble, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-crt2power_1.2.2-1.ca2404.1_all.deb Size: 131044 MD5sum: 1ad74fdbcd401ccfa37abac4feaf5b31 SHA1: 7cb3edcb5f1b9321c6f3ff9b3ee4f76675629660 SHA256: 64d64a77f5568654960308cd33dee511f7fd19ad91330c342a9e2f186a6c6b31 SHA512: 2ee014c5f6e468a12940ed7530f7fe6dcb18d1fa431c29a5c596e2d708c41f01b4b6b25cfaae6e298a480c6cd4676f85671ef7aead5aed2a54ac17f6313e7d49 Homepage: https://cran.r-project.org/package=crt2power Description: CRAN Package 'crt2power' (Designing Cluster-Randomized Trials with Two ContinuousCo-Primary Outcomes) Provides methods for powering cluster-randomized trials with two continuous co-primary outcomes using five key design techniques. Includes functions for calculating required sample size and statistical power. For more details on methodology, see Owen et al. (2025) , Yang et al. (2022) , Pocock et al. (1987) , Vickerstaff et al. (2019) , and Li et al. (2020) . 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Package: r-cran-cryptotrackr Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-stringi, r-cran-openssl, r-cran-digest Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cryptotrackr_1.3.3-1.ca2404.1_all.deb Size: 440756 MD5sum: 47342e8a179704732c27e573a88eb78e SHA1: 9d31997d271718517a41eac1c9101405f8ab926d SHA256: f81ab20ac90fb2518038b76e0cdd354cfca3236d764e937523aea938f27185a8 SHA512: 364866d6861805cc4ee9a1428c5df723453e2ddd9ad3eae4ecadf397236eac07394130fb36395d44439f9f530739d380170447507f97243ea2d3c32e7d56e5dd Homepage: https://cran.r-project.org/package=cryptotrackr Description: CRAN Package 'cryptotrackr' (An Interface to Crypto Data Sources) Allows you to connect to data sources across the crypto ecosystem. This data can enable a range of activity such as portfolio tracking, programmatic trading, or industry analysis. The package is described in French (2024) . Package: r-cran-cryptoverse Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1281 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cryptoverse_0.1.0-1.ca2404.1_all.deb Size: 1255012 MD5sum: f29ac678d6a679731bb4aaaa75d747dd SHA1: d1c62d344890e564f15aafca62e37a9c4bb7b95f SHA256: fbd7754ffcef56341f4a3ca1bc1dc6e9334b35bc61292538179538c00c50c25b SHA512: 658f499ec6eb5fe5f6db8b6eb7f51d0fe27e40c889876a01e5e1f96bfde8fb5fe4ce4690acb1dc06ec4f12099c97d8b2c6cbd3d5676b5dbf6fb0968a9bc3816d Homepage: https://cran.r-project.org/package=cryptoverse Description: CRAN Package 'cryptoverse' (Visualization and Analytics for the Cryptoverse) Providing data to quickly visualize and analyze data from several cryptocurrencies. Package: r-cran-cryptowatchr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-cryptowatchr_0.2.0-1.ca2404.1_all.deb Size: 61294 MD5sum: 7bb905b24adbff93a98c0a99c565f38a SHA1: 700bee6f3c3841bdc8cd61c3026abcda6b023267 SHA256: 928ee89c4366716f6f9d85e84c03c2dd1e84cca6805c520335c4e2cd20ad8f78 SHA512: b3ee033fd006882e4507eeb128f7d2e9c323deedb3f3cc48d048916c4645b41cb5373ef922e82701f430fa8f038d3dee51c721c967d8db2c7daa8a3bca1cb4c8 Homepage: https://cran.r-project.org/package=cryptowatchR Description: CRAN Package 'cryptowatchR' (An API Wrapper for 'Cryptowatch') An API wrapper for 'Cryptowatch' to get prices and other information (e.g., volume, trades, order books, bid and ask prices, live quotes, and more) about cryptocurrencies and crypto exchanges. See for a detailed documentation. Package: r-cran-cryptrndtest Architecture: all Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ksamples, r-cran-sfsmisc, r-cran-rmpfr, r-cran-lambertw, r-cran-gmp, r-cran-tseries Suggests: r-cran-r.rsp, r-cran-bbotk Filename: pool/dists/noble/main/r-cran-cryptrndtest_1.2.7-1.ca2404.1_all.deb Size: 341610 MD5sum: 168d35d07ca9c4c5fb6c7d4b9545a6b2 SHA1: 4cd1e0f4c3dee2fe2eff3419188ef67776b13a95 SHA256: e95888a74989a5070e4f9513d6d1e76d56047466c8fdb6d1ce2b39e8b45ebb11 SHA512: 684ce7210163a86e9ec5c17442559d9b9c0869d8a00863b5e881609a21e779de53d012217945afdf9682cace8a4056eaa33ab39697889e3c9e6fa437a67854c8 Homepage: https://cran.r-project.org/package=CryptRndTest Description: CRAN Package 'CryptRndTest' (Statistical Tests for Cryptographic Randomness) Performs cryptographic randomness tests on a sequence of random integers or bits. Included tests are greatest common divisor, birthday spacings, book stack, adaptive chi-square, topological binary, and three random walk tests (Ryabko and Monarev, 2005) . Tests except greatest common divisor and birthday spacings are not covered by standard test suites. In addition to the chi-square goodness-of-fit test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are also generated by some of the cryptographic randomness tests. Package: r-cran-cryst Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-flux, r-cran-pracma Filename: pool/dists/noble/main/r-cran-cryst_0.1.0-1.ca2404.1_all.deb Size: 117314 MD5sum: 45fcc80c341526e4e0b12a65800f139c SHA1: 7a9213928a5ba62f523e829d44b694423ab4200b SHA256: c3ef54b768cbe94c51cfb314d47ae54e7e822ca17c825c5ea21bb4526a86f333 SHA512: 7e329fdb6b2e9b928cd9e6ddb972b583358cde3aa1dd4f1cc6c3dda2c9e766d8c5bf3e6f0eee9e849098f6ba03226ff7378cdf74131105b810d6a4860da85d4d Homepage: https://cran.r-project.org/package=cryst Description: CRAN Package 'cryst' (Calculate the Relative Crystallinity of Starch by XRD and FTIR) Functions to calculate the relative crystallinity of starch by X-ray Diffraction (XRD) and Infrared Spectroscopy (FTIR). Starch is biosynthesized by plants in the form of granules semicrystalline. For XRD, the relative crystallinity is obtained by separating the crystalline peaks from the amorphous scattering region. For FTIR, the relative crystallinity is achieved by setting of a Gaussian holocrystalline-peak in the 800-1300 cm-1 region of FTIR spectrum of starch which is divided into amorphous region and crystalline region. The relative crystallinity of native starch granules varies from 14 of 45 percent. This package was supported by FONDECYT 3150630 and CIPA Conicyt-Regional R08C1002 is gratefully acknowledged. Package: r-cran-crystract Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1222 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-stringr, r-cran-future, r-cran-future.apply, r-cran-geometry Suggests: r-cran-dt, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-crystract_1.0.0-1.ca2404.1_all.deb Size: 855234 MD5sum: 4dd7ab1f4675dc03dff52681092e0c13 SHA1: bf473eee04150a825664a5e3ac1cc8427370565d SHA256: e76deb3e1e6bb369b4b3fe37a18a8ac51dec649bed4015fb86c0f6309683fb62 SHA512: 7bb656b9b76239b959302556aeeafe2817c41552162f93a019e284751ef1bbcef9105bf5072843b50b21b4b97ae9e746141b653ad918815db158c874c4c0fba7 Homepage: https://cran.r-project.org/package=crystract Description: CRAN Package 'crystract' (Crystallographic Information File (CIF) Data Processing Tools) Provides a suite of functions to parse Crystallographic Information Files (.cif), extracting essential data such as chemical formulas, unit cell parameters, atomic coordinates, and symmetry operations. It also includes tools to calculate interatomic distances, identify bonded pairs using various algorithms (minimum_distance, brunner_nn_reciprocal, econ_nn, crystal_nn), determine nearest neighbor counts, and calculate bond angles. The package is designed to facilitate the preparation of crystallographic data for further analysis, including machine learning applications in materials science. Methods are described in: Brunner (1977) ; Hoppe (1979) ; O'Keeffe (1979) ; Shannon (1976) ; Pan et al. (2021) ; Pauling (1960, ISBN:978-0801403330). Package: r-cran-csa Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table, r-cran-scales, r-cran-reshape2, r-cran-moments, r-cran-lmoments, r-cran-foreach, r-cran-ggpubr, r-cran-raster, r-cran-doparallel Suggests: r-cran-testthat, r-cran-colorspace Filename: pool/dists/noble/main/r-cran-csa_0.7.1-1.ca2404.1_all.deb Size: 347504 MD5sum: 5dab87ca2f18aff6d80546c502a6386e SHA1: 0a29a8c915b757d59b491ba62630bedb18f87cdf SHA256: 48a9a598577d6ebc0237de54391b555722be5e10be8876ec0762e7d68f3897ad SHA512: 10c29a5dba07338c1a78f5621dc537fffdab06c6b884fa6969cea41807c7b38b6b1193eff9db8b501d553295fd9e599d7015227fa2d45780b394d38ed51d2f45 Homepage: https://cran.r-project.org/package=csa Description: CRAN Package 'csa' (A Cross-Scale Analysis Tool for Model-Observation Visualizationand Integration) Integration of Earth system data from various sources is a challenging task. 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Package: r-cran-csalert Architecture: all Version: 2024.6.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-magrittr, r-cran-ggplot2, r-cran-glm2, r-cran-cstidy, r-cran-cstime, r-cran-lubridate, r-cran-stringr, r-cran-surveillance Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-glue, r-cran-covidnor, r-cran-csdata, r-cran-csmaps, r-cran-ggrepel, r-cran-plnr Filename: pool/dists/noble/main/r-cran-csalert_2024.6.24-1.ca2404.1_all.deb Size: 184206 MD5sum: c3ef25d0232f090666cf1ec4ebda45a6 SHA1: 07b35b6b3bd99b7c603869c62667c4c55f2c39a5 SHA256: 3c2c5c864971e8773b4d3acf406ee3bb064f41f6a82bc5e090a16fd0d56061e6 SHA512: 0e84bb29fb3389f20f293435908b9bd4c2fd69928ab497bc9949dac472b25c3f5ffec1d1ea2e16b70aa624aa7de3d0239b6e268bf36518034dba8502ddb47407 Homepage: https://cran.r-project.org/package=csalert Description: CRAN Package 'csalert' (Alerts from Public Health Surveillance Data) Helps create alerts and determine trends by using various methods to analyze public health surveillance data. The primary analysis method is based upon a published analytics strategy by Benedetti (2019) . Package: r-cran-csampling Architecture: all Version: 1.2-4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-marg, r-cran-statmod, r-cran-survival Filename: pool/dists/noble/main/r-cran-csampling_1.2-4.1-1.ca2404.1_all.deb Size: 88964 MD5sum: b7165abf8568d9f1a439e04bec2471e7 SHA1: 9c6f6b7805fdd42477b191213ed328c392a87a88 SHA256: 2bb0a3963ccb427563e3f69ef080c31142a60b1b82d895b5512af008ef4b1991 SHA512: d86e31951d7aa200cd07fe9450677de0e41f479f038c47e399111b540176691108d400b145c6b6833ff9b6cad89e5712a7e72611199cffbd230de5f4bfddd8bd Homepage: https://cran.r-project.org/package=csampling Description: CRAN Package 'csampling' (Functions for Conditional Simulation in Regression-Scale Models) Implements Monte Carlo conditional inference for the parameters of a linear nonnormal regression model. Package: r-cran-cschange Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rms, r-cran-survival, r-cran-boot, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-cschange_0.1.7-1.ca2404.1_all.deb Size: 24748 MD5sum: 6dc4b93eba7280f3ab4cb29cab9b5040 SHA1: 7ea98887c1e4e4fb58d2950f6fefcf9c43fb66e1 SHA256: 56ab02ac94f88153861aecd00cf58b060afa405b4d36951d6e52f3c03a761520 SHA512: d310078e4256b262b37ac4dd2828908328a762c45471917bc64d2e715c83073a8ea130b197334c040a66aa201efb7d25b9ea173d12c144ddf2a2f3559f8eab5d Homepage: https://cran.r-project.org/package=CsChange Description: CRAN Package 'CsChange' (Testing for Change in C-Statistic) Calculate the confidence interval and p value for change in C-statistic. The adjusted C-statistic is calculated by using formula as "Somers' Dxy rank correlation"/2+0.5. 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Package: r-cran-csclone Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-mcclust, r-cran-moments, r-bioc-dnacopy Filename: pool/dists/noble/main/r-cran-csclone_1.0-1.ca2404.1_all.deb Size: 55016 MD5sum: 12ac64a0530ddc1a79c6ecf46f279ea2 SHA1: ea8c823c7fc01e9dfca5e2091d713f5870de3585 SHA256: 4565110dcd4e4a0b7a7830f8c85966d92f19716274a98ace2479ccd15121e418 SHA512: f236113ace965e71372a217aa3a942ad28b00a40a8ec6b053daada88e9ad6a80f2ecc7a599e3acbffa639cbc39c00103567b5340d3fa43af2f660782098efcfe Homepage: https://cran.r-project.org/package=CSclone Description: CRAN Package 'CSclone' (Bayesian Nonparametric Modeling in R) Germline and somatic locus data which contain the total read depth and B allele read depth using Bayesian model (Dirichlet Process) to cluster. Meanwhile, the cluster model can deal with the SNVs mutation and the CNAs mutation. Package: r-cran-cscnet Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyverse, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-survival, r-cran-prodlim, r-cran-riskregression, r-cran-glmnet, r-cran-caret, r-cran-recipes, r-cran-parallelly, r-cran-future, r-cran-furrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cscnet_0.1.4-1.ca2404.1_all.deb Size: 118562 MD5sum: 0bd47f02ee94d255508416db872e5520 SHA1: 15963f78a28367c2a786ce69d8f45156dd172d63 SHA256: 39cc137678cfb9a1bf263a4f41049ce0736f75193d6d26ba6de41c3a98898e56 SHA512: ad6d5169a8def1f7e8b9ea83231b3eebde0b2fe5b807215bfd9a6e7b073151db29d152c0a07a22bcf9f52d0d13c5116537a793fe1fc0af6a7e6d32fb16aa788a Homepage: https://cran.r-project.org/package=CSCNet Description: CRAN Package 'CSCNet' (Fitting and Tuning Regularized Cause-Specific Cox Models withElastic-Net Penalty) Flexible tools to fit, tune and obtain absolute risk predictions from regularized cause-specific cox models with elastic-net penalty. Package: r-cran-csdata Architecture: all Version: 2026.3.30-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2202 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat, r-cran-broom, r-cran-pxwebapidata, r-cran-crayon, r-cran-dplyr, r-cran-forcats, r-cran-fs, r-cran-geojsonio, r-cran-ggplot2, r-cran-glue, r-cran-gt, r-cran-knitr, r-cran-lubridate, r-cran-magrittr, r-cran-mapproj, r-cran-ncdf4, r-cran-purrr, r-cran-readxl, r-cran-reshape2, r-cran-rmarkdown, r-cran-rmapshaper, r-cran-rstudioapi, r-cran-stringr, r-cran-sp, r-cran-sf, r-cran-tidyr, r-cran-zoo Filename: pool/dists/noble/main/r-cran-csdata_2026.3.30-1.ca2404.1_all.deb Size: 1839802 MD5sum: bdadf69a0f2527551994d0a03f6f9fae SHA1: b6aed5595e6c1d3bdac221baa648f6026075d74a SHA256: f1dd6c927d66fe0c200c689da136442af5b7e0124ccd684e58a1881c101284d6 SHA512: e7025f713c929dfc3bb38ec0ecee7c7217944eaa2d75466517548947f5d6958c106a68253a4f3a83e7a7ba8b13cc274e6121984e8cd9d84fdb1b555a368d9869 Homepage: https://cran.r-project.org/package=csdata Description: CRAN Package 'csdata' (Structural Data for Norway) Datasets relating to population in municipalities, municipality/county matching, and how different municipalities have merged/redistricted over time from 2006 to 2024. Package: r-cran-csdb Architecture: all Version: 2026.5.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-csutil, r-cran-data.table, r-cran-dbi, r-cran-dplyr, r-cran-fs, r-cran-ggplot2, r-cran-glue, r-cran-odbc, r-cran-r6, r-cran-s7, r-cran-stringr, r-cran-uuid Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-digest, r-cran-crayon Filename: pool/dists/noble/main/r-cran-csdb_2026.5.13-1.ca2404.1_all.deb Size: 379520 MD5sum: b00003246c1ee5ccc0b29271c43f1e11 SHA1: fe2844347ba73122092f8869f66d2945f800e02b SHA256: 78b90625019dc659302ad74a0128656008c45609664d3fb752233eb7f386a4cf SHA512: b95863c6c57d4e2df1bf1d7cc89451b64b99a3462d56439b10db2c0b870f037e1b4db1886dfd3c1ce704aa956163087561a84521646b4c019a4af6b738053b4d Homepage: https://cran.r-project.org/package=csdb Description: CRAN Package 'csdb' (An Abstracted System for Easily Working with Databases withLarge Datasets) Provides object-oriented database management tools for working with large datasets across multiple database systems. Features include robust connection management for 'PostgreSQL' databases, advanced table operations with bulk data loading and upsert functionality, comprehensive data validation through customizable field type and content validators, efficient index management, and cross-database compatibility. Designed for high-performance data operations in surveillance systems and large-scale data processing workflows. Package: r-cran-csdm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rdpack Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-xtsum, r-cran-spelling Filename: pool/dists/noble/main/r-cran-csdm_1.0.1-1.ca2404.1_all.deb Size: 287480 MD5sum: 186eae3f59b318bdeadadc34fed77b94 SHA1: f7bcbb99a2d68c8407a07a35af5b398e5d6e7d9f SHA256: ca69e55691f3a61b52db009fc178505721544bc5b7311479a2e16690fdd9410c SHA512: a176c5fea353b62a1cb45d754c3cfae1ce067436de14db357cc47c4fc72e753d3793858bd7b6d13b2280decd7ae68df7741894461ceb61dd0333e16fb13b2aaf Homepage: https://cran.r-project.org/package=csdm Description: CRAN Package 'csdm' (Cross-Sectional Dependence Models) Provides estimators and utilities for large panel-data models with cross-sectional dependence, including mean group (MG), common correlated effects (CCE) and dynamic CCE (DCCE) estimators, and cross-sectionally augmented ARDL (CS-ARDL) specifications, plus related inference and diagnostics. Package: r-cran-csdownscale Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cstools, r-cran-abind, r-cran-multiapply, r-cran-nnet, r-cran-plyr, r-cran-s2dv, r-cran-climprojdiags, r-cran-proxy, r-cran-easyverification Filename: pool/dists/noble/main/r-cran-csdownscale_0.0.2-1.ca2404.1_all.deb Size: 183680 MD5sum: 2821dcbb9f7bac48ef2aa7557f33a6a0 SHA1: 111ca8fed68d4fe9fd56913b86e6f4c15cba90b3 SHA256: 65351ac9cf4577c1df1ec169dacfd588a4a67a82a83eef1b734d714aedbbb67b SHA512: b115997bf9f9999ab591368e5c1afb9872acd81ec0d76f3dea1007c83ee93bbb3779f2366c9e4ea4230e99125b3c847f3dd42904a3c5362c4ff6a3833990f1cd Homepage: https://cran.r-project.org/package=CSDownscale Description: CRAN Package 'CSDownscale' (Statistical Downscaling of Climate Predictions) Statistical downscaling and bias correction of climate predictions. It includes implementations of commonly used methods such as Analogs, Linear Regression, Logistic Regression, and Bias Correction techniques, as well as interpolation functions for regridding and point-based applications. It facilitates the production of high-resolution and local-scale climate information from coarse-scale predictions, which is essential for impact analyses. The package can be applied in a wide range of sectors and studies, including agriculture, water management, energy, heatwaves, and other climate-sensitive applications. The package was developed within the framework of the European Union Horizon Europe projects Impetus4Change (101081555) and ASPECT (101081460), the Wellcome Trust supported HARMONIZE project (224694/Z/21/Z), and the Spanish national project BOREAS (PID2022-140673OA-I00). Implements the methods described in Duzenli et al. (2024) . Package: r-cran-csem Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2301 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-alabama, r-cran-cli, r-cran-crayon, r-cran-expm, r-cran-future.apply, r-cran-future, r-cran-lavaan, r-cran-magrittr, r-cran-mass, r-cran-matrix, r-cran-matrixcalc, r-cran-matrixstats, r-cran-polycor, r-cran-progressr, r-cran-psych, r-cran-purrr, r-cran-rdpack, r-cran-rlang, r-cran-symmoments, r-cran-truncatednormal, r-cran-lifecycle Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-knitr, r-cran-nnls, r-cran-prettydoc, r-cran-plotly, r-cran-rmarkdown, r-cran-rootsolve, r-cran-listviewer, r-cran-testthat, r-cran-ggplot2, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-csem_0.5.0-1.ca2404.1_all.deb Size: 1733866 MD5sum: f6df11058639fdfd860b2a7086972851 SHA1: eaa9c683d88d2e175bd6b026a554cf3d0eaae4b7 SHA256: 686614bfcdc7c92f04ff99d5c47ad3ac38f094e185c63523b657aee03f7856c1 SHA512: cb89b4eb8cc0dc610361051b7df21e5b50fe8a1d1071f6527d0072ea1683f1101cf85ebf3c4a2c3009800a3189a7956a45ba6bafce8f7db0dd5347c03812ed58 Homepage: https://cran.r-project.org/package=cSEM Description: CRAN Package 'cSEM' (Composite-Based Structural Equation Modeling) Estimate, assess, test, and study linear, nonlinear, hierarchical and multigroup structural equation models using composite-based approaches and procedures, including estimation techniques such as partial least squares path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc), generalized structured component analysis (GSCA), generalized structured component analysis with uniqueness terms (GSCAm), generalized canonical correlation analysis (GCCA), principal component analysis (PCA), factor score regression (FSR) using sum score, regression or bartlett scores (including bias correction using Croon’s approach), as well as several tests and typical postestimation procedures (e.g., verify admissibility of the estimates, assess the model fit, test the model fit etc.). Package: r-cran-cseqpat Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlp, r-cran-tm Filename: pool/dists/noble/main/r-cran-cseqpat_0.1.2-1.ca2404.1_all.deb Size: 16192 MD5sum: a948cf05b4ecd40380626aa3bdf72180 SHA1: 5674f647b1f1d36fd34a33db0568c1cfce6d7ae7 SHA256: 1c779ff331c381ac2c991bc769ea486195f562e3fe221539747f52ae53a28da2 SHA512: 31b7f5d0d1b7f07f2d4e1b0a9f566b800dd64bd43963455fbaf5dfc9626478706c1cea09286c81a2cfe4444a6938f8746abb1e0245dd658e3f96c050e28ca6f2 Homepage: https://cran.r-project.org/package=CSeqpat Description: CRAN Package 'CSeqpat' (Frequent Contiguous Sequential Pattern Mining of Text) Mines contiguous sequential patterns in text. Package: r-cran-csesa Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3069 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-csesa_1.2.0-1.ca2404.1_all.deb Size: 888554 MD5sum: 3bf517810beb3d5ad205993f310f510c SHA1: a94c88b17406c1dcd4a3e18922ba560cecbcddf0 SHA256: 1ee404f87338cb63e4f3d56d22f67d408f4069c860e4d1a7e4ebbd5d9c9d7b3d SHA512: b49571b4aac4128cda9166525b7a0fb05e31051d7c3c43022eaeed41c63f2cb13e6d478a538d971205324ee49ad2d396ab232e0b12b745873eff900f3f961757 Homepage: https://cran.r-project.org/package=CSESA Description: CRAN Package 'CSESA' (CRISPR-Based Salmonella Enterica Serotype Analyzer) Salmonella enterica is a major cause of bacterial food-borne disease worldwide. Serotype identification is the most commonly used typing method to characterize Salmonella isolates. However, experimental serotyping needs great cost on manpower and resources. Recently, we found that the newly incorporated spacer in the clustered regularly interspaced short palindromic repeat (CRISPR) could serve as an effective marker for typing of Salmonella. It was further revealed by Li et. al (2014) that recognized types based on the combination of two newly incorporated spacer in both CRISPR loci showed high accordance with serotypes. Here, we developed an R package 'CSESA' to predict the serotype based on this finding. Considering it’s time saving and of high accuracy, we recommend to predict the serotypes of unknown Salmonella isolates using 'CSESA' before doing the traditional serotyping. Package: r-cran-csgo Architecture: all Version: 0.6.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 617 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fuzzyjoin, r-cran-purrr, r-cran-httr, r-cran-stringr, r-cran-jsonlite, r-cran-magrittr, r-cran-dplyr, r-cran-extrafont, r-cran-ggplot2, r-cran-future, r-cran-furrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-csgo_0.6.7-1.ca2404.1_all.deb Size: 415162 MD5sum: 9735cd8a64fed663c3f2e1652a60449a SHA1: 1b3c4be429c599dce37eabb097c90c4fd46ad657 SHA256: f8f9112051a9af1b1a69edfc78561b56e8703fb7b380377354d01b6866cf042a SHA512: 5301380f222715487341e3777e8b312998421733b28fac67427c23ed5da38e095d3e5587e6cb05d80eeb40a1fbb735993e402fa3582dfcf505d36b9114b339c1 Homepage: https://cran.r-project.org/package=CSGo Description: CRAN Package 'CSGo' (Collecting Counter Strike Global Offensive Data) An implementation of calls designed to collect and organize in an easy way the data from the Steam API specifically for the Counter-Strike Global Offensive Game (CS Go) . Package: r-cran-cshapes Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-rmapshaper, r-cran-sp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cshapes_2.0-1.ca2404.1_all.deb Size: 3698794 MD5sum: 1b79948fcc4db7bc394c09d2c17db1bd SHA1: f68cd41dacfb4babb456314a8c41b63cf578bfca SHA256: 83b383af8e60dcc2c79bf96a338783a03c6746877b17b8cc071b648817942bb2 SHA512: b9678e1eca2b753bb7bc4febc6ffd653ff1ff02001f75eb14535da8a5ad99ef25c94eab0ece01a77e19c88b93fae1f53285d6cff50e4e8e0d4a6fd8c14ae10d1 Homepage: https://cran.r-project.org/package=cshapes Description: CRAN Package 'cshapes' (The CShapes 2.0 Dataset and Utilities) Package for CShapes 2.0, a GIS dataset of country borders (1886-today). Includes functions for data extraction and the computation of distance matrices and -lists. Package: r-cran-cshshydrology Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1924 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-lubridate, r-cran-plotrix, r-cran-timedate, r-cran-stringr, r-cran-ggplot2, r-cran-ggspatial, r-cran-dplyr, r-cran-httr2, r-cran-terra, r-cran-tidyterra, r-cran-tidyhydat, r-cran-whitebox, r-cran-circular, r-cran-mgbt, r-cran-outliers, r-cran-teachingdemos, r-cran-kendall Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-readr Filename: pool/dists/noble/main/r-cran-cshshydrology_1.5.0-1.ca2404.1_all.deb Size: 1236346 MD5sum: c208003ee2009624f3f74cf44027ccae SHA1: 59fb3ee6078dbf0a98f4f66a542d57d8401253c7 SHA256: 4c94ecf9f6746fcd28ea3916b81c634951bb4e3bec3d656a2467ff0806d5706d SHA512: e9db465a8fc6f08301651cd979ac11c39722d2a7bb3d8afafd262a6f8c1c1606b3a17a0ad2e8d6f5a9dcc85570b092f3a604c39c007a5231d90cd3403915d997 Homepage: https://cran.r-project.org/package=CSHShydRology Description: CRAN Package 'CSHShydRology' (Canadian Hydrological Analyses) A collection of user-submitted functions to aid in the analysis of hydrological data, particularly for users in Canada. The functions focus on the use of Canadian data sets, and are suited to Canadian hydrology, such as the important cold region hydrological processes and will work with Canadian hydrological models. The functions are grouped into several themes, currently including Statistical hydrology, Basic data manipulations, Visualization, and Spatial hydrology. Functions developed by the Floodnet project are also included. CSHShydRology has been developed with the assistance of the Canadian Society for Hydrological Sciences (CSHS) which is an affiliated society of the Canadian Water Resources Association (CWRA). As of version 1.2.6, functions now fail gracefully when attempting to download data from a url which is unavailable. Package: r-cran-csindicators Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2916 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multiapply, r-cran-climprojdiags, r-cran-cstools, r-cran-spei, r-cran-lmom, r-cran-lmomco, r-cran-zoo, r-cran-s2dv, r-cran-lubridate, r-cran-geosphere Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-csindicators_1.2.0-1.ca2404.1_all.deb Size: 2185102 MD5sum: 05203b4aea9594813140ec15da554d6d SHA1: b89d376d46895e3561a6f835e00dbf4eef4a9ca0 SHA256: 05dac2c35dc7f76c259dfe779b00c5ac783ca71c5667c3f3805ca7c9a5ee85bc SHA512: a23f85825ee6c200587dfb67757773ff2f82a3991e8d66e336cae7f91cb48161186418f0bb59fd8fc6a91a41911904a5450c6bc9f966d4a9f7556c1d558caf79 Homepage: https://cran.r-project.org/package=CSIndicators Description: CRAN Package 'CSIndicators' (Climate Services' Indicators Based on Sub-Seasonal to DecadalPredictions) Set of generalised tools for the flexible computation of climate related indicators defined by the user. Each method represents a specific mathematical approach which is combined with the possibility to select an arbitrary time period to define the indicator. This enables a wide range of possibilities to tailor the most suitable indicator for each particular climate service application (agriculture, food security, energy, water management, health...). This package is intended for sub-seasonal, seasonal and decadal climate predictions, but its methods are also applicable to other time-scales, provided the dimensional structure of the input is maintained. Additionally, the outputs of the functions in this package are compatible with 'CSTools'. This package is described in Pérez-Zanón et al. (2023) and was developed in the context of the H2020 projects MED-GOLD (776467) and S2S4E (776787) projects, as well as the Horizon Europe project MEDEWSA (101121192) and the national project BOREAS (PID2022-140673OA-I00). See Lledó et al. (2019) and Chou et al., 2023 for details. Package: r-cran-csmaps Architecture: all Version: 2025.8.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5849 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr, r-cran-ggrepel, r-cran-leaflet, r-cran-sf, r-cran-csdata Filename: pool/dists/noble/main/r-cran-csmaps_2025.8.21-1.ca2404.1_all.deb Size: 3845928 MD5sum: fe93a53ed91defc7063790207ee22fb4 SHA1: 0d2b8da46502bb86d414d040660cdd774367db41 SHA256: ede47421ce4a8abb933db903737c95735e510545fefdb6a73af72f39c25326a8 SHA512: b97c3dd8c5958c780820e5b5f6f35f5c9cddef19a692cf7e988e995ae9bf6f85e0ed9bc3b26a4f9b389045ee66856112fc52e20fdf55121d4119c403a510ddae Homepage: https://cran.r-project.org/package=csmaps Description: CRAN Package 'csmaps' (Preformatted Maps of Norway that Don't Need Geolibraries) Provides datasets containing preformatted maps of Norway at the county, municipality, and ward (Oslo only) level for redistricting in 2024, 2020, 2018, and 2017. Multiple layouts are provided (normal, split, and with an insert for Oslo), allowing the user to rapidly create choropleth maps of Norway without any geolibraries. Package: r-cran-csmbuilder Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-desolve, r-cran-rcpp, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-csmbuilder_0.1.0-1.ca2404.1_all.deb Size: 87750 MD5sum: dcc18470c4a0db4fb65d14c9900c7f47 SHA1: 4e43a04b71b06c8a085964ab95f1ecfe9dc9500d SHA256: 9d3654d38c996ed89525e6198a9bc55ca70be5c84e0f34d39a6cf4b01800bc27 SHA512: ed264b9780cfcd34c9e1979de4f95427fdd1f52cf65fccb8d6da073518dcdc2b7a7c7ce10e4d8d7956f4fea11c82c5728c964f3155e201d90f72c3894925d144 Homepage: https://cran.r-project.org/package=csmbuilder Description: CRAN Package 'csmbuilder' (A Collection of Tools for Building Cropping System Models) A collection of tools for designing, implementing, testing, documenting and visualizing dynamic simulation cropping system models. Models are specified as a combination of state variables, parameters, intermediate factors and input data that define a system of ordinary differential equations. Specified models can be used to simulate dynamic processes using numerical integration algorithms. Package: r-cran-csmes Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mco, r-cran-rocr, r-cran-rpart, r-cran-zoo, r-cran-catools, r-cran-data.table Filename: pool/dists/noble/main/r-cran-csmes_1.0.1-1.ca2404.1_all.deb Size: 108728 MD5sum: 0f3b4dcb7d0c1602cdc2403367db7de4 SHA1: c3e00fb998e4812a3e0c39aa129d8a8e7b9ec692 SHA256: b0895459e9b0aa27591ad16b0d38dd5734fb66e5fcb1fd0477a63295dbacecfd SHA512: 3d5f56c81e2242d7b1eae5877f6a836ae1cbd4edde82046c8c131064c761775d0df4524a1b7dcaee56aefcf85fdef5b935146e70cc8716afdace773764a58cf7 Homepage: https://cran.r-project.org/package=CSMES Description: CRAN Package 'CSMES' (Cost-Sensitive Multi-Criteria Ensemble Selection for UncertainCost Conditions) Functions for cost-sensitive multi-criteria ensemble selection (CSMES) (as described in De bock et al. (2020) ) for cost-sensitive learning under unknown cost conditions. Package: r-cran-csmgmm Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-mvtnorm, r-cran-rlang, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-csmgmm_0.4.0-1.ca2404.1_all.deb Size: 114796 MD5sum: f17ba97bc519030d19dac0df3c7432f5 SHA1: 32c3a9c247ea5154c554a063129cc22b0fd06a55 SHA256: 1af87662521fbf4553043e6b50051d9785314aadfaecf8197fe6133389fbc4b7 SHA512: 8ef493959cbbb6a0c5c34fcf19fe8e3f34e4ac9fe64a2a63033cba9b07bd33c25b395813ffd59519feb02a144a75c1339676dc1058dcdb1b78825687bca44333 Homepage: https://cran.r-project.org/package=csmGmm Description: CRAN Package 'csmGmm' (Conditionally Symmetric Multidimensional Gaussian Mixture Model) Implements the conditionally symmetric multidimensional Gaussian mixture model (csmGmm) for large-scale testing of composite null hypotheses in genetic association applications such as mediation analysis, pleiotropy analysis, and replication analysis. In such analyses, we typically have J sets of K test statistics where K is a small number (e.g. 2 or 3) and J is large (e.g. 1 million). For each one of the J sets, we want to know if we can reject all K individual nulls. Please see the vignette for a quickstart guide. The paper describing these methods is "Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies" by Sun R, McCaw Z, & Lin X (Journal of the American Statistical Association 2025, ). Package: r-cran-csmpv Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2853 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-glmnet, r-cran-hmisc, r-cran-rms, r-cran-forestmodel, r-cran-ggplot2, r-cran-ggpubr, r-cran-survminer, r-cran-xgboost, r-cran-scales, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-csmpv_1.0.5-1.ca2404.1_all.deb Size: 1021190 MD5sum: fd8ab98cad930a599135325d07e0e624 SHA1: f8f806f51047eafee98a6d256be23c0af25572bb SHA256: 66fef2d3e6792626ffb710039c97e69389b1e31401527dde85b8b760c9a48ee2 SHA512: 8b9064d0bd42037716b511e48728e8774432c53a9589633fe2138b099ca9ece2f0ff096bfc529bcee9027218496f20a90a268ac41737860fde2150a279c20321 Homepage: https://cran.r-project.org/package=csmpv Description: CRAN Package 'csmpv' (Biomarker Confirmation, Selection, Modelling, Prediction, andValidation) There are diverse purposes such as biomarker confirmation, novel biomarker discovery, constructing predictive models, model-based prediction, and validation. It handles binary, continuous, and time-to-event outcomes at the sample or patient level. - Biomarker confirmation utilizes established functions like glm() from 'stats', coxph() from 'survival', surv_fit(), and ggsurvplot() from 'survminer'. - Biomarker discovery and variable selection are facilitated by three LASSO-related functions LASSO2(), LASSO_plus(), and LASSO2plus(), leveraging the 'glmnet' R package with additional steps. - Eight versatile modeling functions are offered, each designed for predictive models across various outcomes and data types. 1) LASSO2(), LASSO_plus(), LASSO2plus(), and LASSO2_reg() perform variable selection using LASSO methods and construct predictive models based on selected variables. 2) XGBtraining() employs 'XGBoost' for model building and is the only function not involving variable selection. 3) Functions like LASSO2_XGBtraining(), LASSOplus_XGBtraining(), and LASSO2plus_XGBtraining() combine LASSO-related variable selection with 'XGBoost' for model construction. - All models support prediction and validation, requiring a testing dataset comparable to the training dataset. Additionally, the package introduces XGpred() for risk prediction based on survival data, with the XGpred_predict() function available for predicting risk groups in new datasets. The methodology is based on our new algorithms and various references: - Hastie et al. (1992, ISBN 0 534 16765-9), - Therneau et al. (2000, ISBN 0-387-98784-3), - Kassambara et al. (2021) , - Friedman et al. (2010) , - Simon et al. (2011) , - Harrell (2023) , - Harrell (2023) , - Chen and Guestrin (2016) , - Aoki et al. (2023) . 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Package: r-cran-ctablerseh Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survey Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ctablerseh_1.1.2-1.ca2404.1_all.deb Size: 137322 MD5sum: 8f05d44282213bd33b32c74b9fcbfb4e SHA1: c2793ab2397f4e098bbcf8b2fcd3e7a9c2171d8d SHA256: 261767ffc874d9c32dd21f3f96381ecc3ac59783508689873fd032ad41751c2e SHA512: 9628a91fd2069aa685b8cc2338ebd7a7b7a513a25d34c450e4dd13e6ec113e01e8725fca6abfff70209ad378b743de3cad74495795d0f0a4fb13d59543697bac Homepage: https://cran.r-project.org/package=ctablerseh Description: CRAN Package 'ctablerseh' (Processing Survey Data with Confidence Intervals Like 'SPSS'Software) Processes survey data and displays estimation results along with the relative standard error in a table, including the number of samples and also uses a t-distribution approach to compute confidence intervals, similar to 'SPSS' (Statistical Package for the Social Sciences) software. Package: r-cran-ctashiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinymatrix, r-cran-epitools, r-cran-rpivottable Filename: pool/dists/noble/main/r-cran-ctashiny_0.1.0-1.ca2404.1_all.deb Size: 70714 MD5sum: d2e12a5447c1205db9c88105a4892ee2 SHA1: 2f5b6d5144abdc9c056e31297be9bee4beb8d5ec SHA256: dc3197f8707606b475f94424dcd1cbab90fd9ac9f19c3fe3085700c03206493e SHA512: 5cf9489558e79bc00b68ba78959122b03b39a6199ed2a856aab657ce5cc5a71ee98f0f1fe2db32dc5ebc6668fb77166fceb613a839f249ce3ea812ef1448e916 Homepage: https://cran.r-project.org/package=CTAShiny Description: CRAN Package 'CTAShiny' (Interactive Application for Working with Contingency Tables) An interactive application for working with contingency Tables. 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Package: r-cran-ctbi Architecture: all Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ctbi_2.0.5-1.ca2404.1_all.deb Size: 107832 MD5sum: 660773ea540715f15b7ff6d27f6f844e SHA1: 89f25cc1f128c66c06778fe78bb2ee9bc7ed6f55 SHA256: 9545ea456d4c5e078528cea85b241890c0871f5c7c3d2086df648613dd7bcc84 SHA512: 2319b6f3358bebea3d1ef32ec98b90df66abd6edafadd59307872c0d7122cbeb2fde2739bd8f50f7bb68e5ee910652ec31afc1979a712cefcf7c30ae636238fa Homepage: https://cran.r-project.org/package=ctbi Description: CRAN Package 'ctbi' (A Procedure to Clean, Decompose and Aggregate Timeseries) Clean, decompose and aggregate univariate time series following the procedure "Cyclic/trend decomposition using bin interpolation" and the Logbox method for flagging outliers, both detailed in Ritter, F.: Technical note: A procedure to clean, decompose, and aggregate time series, Hydrol. Earth Syst. Sci., 27, 349–361, , 2023. Package: r-cran-ctd Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4331 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-huge, r-cran-ggplot2, r-cran-gplots, r-cran-rcolorbrewer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ctd_1.3-1.ca2404.1_all.deb Size: 4332542 MD5sum: 396018e15b8beb0d154b897ad4f94b89 SHA1: a3a5937b0c2377aca51124408a48331cbef4b96c SHA256: 8291c8d31202237659b0f36d1067b95fa8001ec052c71ea31ae209f06e7bbdb1 SHA512: 0a7cfcd36f6774fa8e5bb1af2d5cf130f29ef5f1a13d8afef99d2210f6fc1d8ad214878497d4258314aedc65eb5cd1da7d4f3007553c4f6529d1116878d1bda7 Homepage: https://cran.r-project.org/package=CTD Description: CRAN Package 'CTD' (A Method for 'Connecting The Dots' in Weighted Graphs) A method for pattern discovery in weighted graphs as outlined in Thistlethwaite et al. (2021) . 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Package: r-cran-cte Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cte_0.1.5-1.ca2404.1_all.deb Size: 281762 MD5sum: 14634d719ce9928423f2bda9d154b5c8 SHA1: 26e73e8fc3b662d648e11f8944beaaf301f4f38b SHA256: 0141708d63b26174c430f8ea9c5ce994def3a203e89a85eb5e0d7be22ad4bf48 SHA512: 6f190104ea16f5a712f16daa2f95ac83f80540e0668d62a8ebacd348f0a0d1b7b84ad18d531d2b817a76f38023f0ed3e7226cad8df99f16d7386efb8f636a16a Homepage: https://cran.r-project.org/package=CTE Description: CRAN Package 'CTE' (Constant Temperature Equivalent) Under natural conditions, nest temperatures fluctuate daily around a mean value, whereas in captivity they are often held constant. The Constant Temperature Equivalent is designed to bridge the gap between the two by calculating a single temperature value for wild nests that corresponds with the amount of development that would occur in an incubator set to the same temperature. The theory and formulas behind this method were developed by Professor Author Georges and are implemented here as a single function. Package: r-cran-ctf Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-iotools Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ctf_0.1.0-1.ca2404.1_all.deb Size: 25474 MD5sum: 1a0696b58607133082de993d7635c709 SHA1: f7e240e100f8b304c58fcf2a5fd35f8af10ca1e3 SHA256: e578cd47649226062316d2cf73c4c6e507ee64247fceb260d8c13198140b6712 SHA512: 38e2c29ed1e0c9262fe557384c423c40b2e6980f8f217007fe0e5dfc40a17ac0c87b1e1faf310dc08af6890eb02ec855295f6f9269ec8f92af251ad84432b132 Homepage: https://cran.r-project.org/package=ctf Description: CRAN Package 'ctf' (Read and Write Column Text Format (CTF)) Column Text Format (CTF) is a new tabular data format designed for simplicity and performance. 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Package: r-cran-ctgdist Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mirt Filename: pool/dists/noble/main/r-cran-ctgdist_0.1.0-1.ca2404.1_all.deb Size: 20506 MD5sum: eadd23f0e6b5adf9d0af18c0eb7360bb SHA1: 816f01a492f1373ef6afaf71cfbc080b61186c1e SHA256: 1358db1422705e65334cfc5dc398a67b48344d232214ab383412fd35b32e7feb SHA512: 042df1b7b511612ffd076657862386dd06bb2bd43973c668d83c95a8ceb8eb0ecdb6e0cef4f8a5c08ef1502c275f93a712388e3c99eeb50ceee708a05ddc65f7 Homepage: https://cran.r-project.org/package=ctgdist Description: CRAN Package 'ctgdist' (Likert Category Distance Calculator) It is assumed that psychological distances between the categories are equal for the measurement instruments consisted of polytomously scored items. According to Muraki, this assumption must be tested. In the examination process of this assumption, the fit indexes are obtained and evaluated. This package provides that this assumption is removed. By with this package, the converted scale values of all items in a measurement instrument can be calculated by estimating a category parameter set for each item. Thus, the calculations can be made without any need to usage of the common category parameter set. Through this package, the psychological distances of the items are scaled. The scaling of a category parameter set for each item cause differentiation of score of the categories will be got from items. Also, the total measurement instrument score of an individual can be calculated according to the scaling of item score categories by with this package.This package provides that the place of individuals related to the structure to be measured with a measurement instrument consisted of polytomously scored items can be reveal more accurately. In this way, it is thought that the results obtained about individuals can be made more sensitive, and the differences between individuals can be revealed more accurately. On the other hand, it can be argued that more accurate evidences can be obtained regarding the psychometric properties of the measurement instruments. Package: r-cran-cthist Architecture: all Version: 2.1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-purrr, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cthist_2.1.12-1.ca2404.1_all.deb Size: 51552 MD5sum: b2278422d07b1d198903586034af2ed1 SHA1: 1c2ee4976ca7731553b25af4e8978d348297036a SHA256: 647b764736d8b6591b50a6ec714aa536e51b928b989ac03a90b74ab593a1acba SHA512: 9a6ed90468805bca746257947d8210bc4aee67ab216308848b2881c3930b94b3428ba342eba49d1368b5a4b971d4a05319c7cebe06098ec46dd3ad6cb6c8afb8 Homepage: https://cran.r-project.org/package=cthist Description: CRAN Package 'cthist' (Clinical Trial Registry History) Retrieves historical versions of clinical trial registry entries from . Package functionality and implementation for v 1.0.0 is documented in Carlisle (2022) . Package: r-cran-cthresher Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-cthresher_1.1.0-1.ca2404.1_all.deb Size: 38830 MD5sum: 2686eae05105a086b072bcb6f24c1291 SHA1: a79e2aa26bf6921a74f4f5b9e415d9052cd9f5de SHA256: e94c20dca1e4e939806f0296f95460ce1af90be21c82e75f858c05c220427c48 SHA512: 0d9477eab4ef7aa33b286e1470f3409efc490aee44fa400c4b1c4ac4f594346bdff6f71c108574d9ff27b83a383bbfaa4968b5e4c370994f7806beaf438e8efb Homepage: https://cran.r-project.org/package=cthreshER Description: CRAN Package 'cthreshER' (Continuous Threshold Expectile Regression) Estimation and inference methods for the continuous threshold expectile regression. It can fit the continuous threshold expectile regression and test the existence of change point, for the paper, "Feipeng Zhang and Qunhua Li (2016). A continuous threshold expectile regression, submitted." Package: r-cran-ctlr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-binom, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-lmtest, r-cran-mass, r-cran-mfp2, r-cran-sandwich Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-ctlr_0.1.0-1.ca2404.1_all.deb Size: 333658 MD5sum: a40a4cf770a5c038ff0adc870b183008 SHA1: 8cf8f12863e789e6741c7ac7e2ba8f3e0609892c SHA256: 1b70353424113c36a34c32864a40e3a28862179b7d6e2ce8008b470d9a39d0a6 SHA512: 3422fa59b83d4131c4598bf2cd004735199bb9bc18ba6c0e7f50c8a89474aec927ded14352d3717f68dded3752929169a35cc3448d60d03afb7e475823974603 Homepage: https://cran.r-project.org/package=ctlr Description: CRAN Package 'ctlr' (Clinical Tolerance Limits for Assessing Agreement) Implements clinical tolerance limits (CTL) methodology for assessing agreement between two measurement methods. Estimates the true latent trait using Best Linear Unbiased Predictors (BLUP), models bias and variance components, and calculates overall and conditional agreement probabilities. Provides visualization tools including tolerance limit plots and conditional probability of agreement plots with confidence bands. This package is based on methods described in Taffé (2016) , Taffé (2019) , and 'Stata' package Taffé (2025) . Package: r-cran-ctm Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jiebar, r-cran-plyr Filename: pool/dists/noble/main/r-cran-ctm_0.2-1.ca2404.1_all.deb Size: 21888 MD5sum: a16efcd4d64cc391efa65489c46bd86b SHA1: 2d7298fcf40654cd7ab2736d4ba7312f59082bb1 SHA256: d97eba5faeeac462b893a014abe99b0122e4d30522f95dbd01d0164728630fe5 SHA512: d066ec131b79a66124d20763cec4ebc9f7c7b85453b535990f214f5d9279025ed47f1707af884de277c9d20c4c4026ab33279a1fab14cd66cacc0e85a7bd1c87 Homepage: https://cran.r-project.org/package=CTM Description: CRAN Package 'CTM' (A Text Mining Toolkit for Chinese Document) The CTM package is designed to solve problems of text mining and is specific for Chinese document. Package: r-cran-ctmcmove Architecture: all Version: 1.2.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-matrix, r-cran-fda, r-cran-gdistance, r-cran-sp Suggests: r-cran-mgcv Filename: pool/dists/noble/main/r-cran-ctmcmove_1.2.10-1.ca2404.1_all.deb Size: 258816 MD5sum: 41339f4c182aadde5b1fd183b28ee58c SHA1: 110f19d5a5be87fa6ff4a3cdccc19b5fa882e5f9 SHA256: e3395b0c8eaf9907e37864e1007280d594fd8e715a5675c8c078290fb29c434d SHA512: 118f5924162751f7f02a82694d0505b3cabd892a1ad2492567800475cf313218d0844eafe681aff36233354f3d46afc42ae423443ad21e37c8d87577e1d2cdeb Homepage: https://cran.r-project.org/package=ctmcmove Description: CRAN Package 'ctmcmove' (Modeling Animal Movement with Continuous-Time Discrete-SpaceMarkov Chains) Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework. As described in Hanks et al. (2015) , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework. Package: r-cran-ctmle Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-superlearner, r-cran-tmle, r-cran-glmnet Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-ctmle_0.1.2-1.ca2404.1_all.deb Size: 166484 MD5sum: 6f1e08cd294157f488d37a5fe1ed426f SHA1: 474274dbe83cb088cf5a67310c85f62cd1bf1fb4 SHA256: 1360442020665cce9f113b9e7862f4fb2626d2058b98e608a1c7b55a86219e5c SHA512: 5628cbefb540c7c0134638455ddefceb92572ed7041467f493000d233cea5d6d100bc8928f3c1f4b9f4ccdaeff66a65d82d3b53f27372f8de23a7c3621f85181 Homepage: https://cran.r-project.org/package=ctmle Description: CRAN Package 'ctmle' (Collaborative Targeted Maximum Likelihood Estimation) Implements the general template for collaborative targeted maximum likelihood estimation. 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The package is described in Calabrese et al (2016) , with models and methods based on those introduced and detailed in Fleming & Calabrese et al (2014) , Fleming et al (2014) , Fleming et al (2015) , Fleming et al (2015) , Fleming et al (2016) , Péron & Fleming et al (2016) , Fleming & Calabrese (2017) , Péron et al (2017) , Fleming et al (2017) , Fleming et al (2018) , Winner & Noonan et al (2018) , Fleming et al (2019) , Noonan & Fleming et al (2019) , Fleming et al (2020) , Noonan et al (2021) , Fleming et al (2022) , Silva et al (2022) , Alston & Fleming et al (2023) . Package: r-cran-ctmva Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-polynom, r-cran-mass, r-cran-mgcv, r-cran-matrix, r-cran-ggplot2, r-cran-rlang, r-cran-vegan, r-cran-viridislite Suggests: r-cran-dplyr, r-cran-wbwdi Filename: pool/dists/noble/main/r-cran-ctmva_1.6.0-1.ca2404.1_all.deb Size: 146176 MD5sum: d998890530d2ca1ff7377e7413edbf70 SHA1: 2c53dea5e8872721d654dbd47a439a224792012e SHA256: d810d7b59b91d27f249b89775af9fcc012862cad77c7251af16e6f84955d9018 SHA512: 497b79538a7a0cc75a68623eec2325f00475e8c14eee8b5d64f987c19f372f84ff85a753339539249159c196ee83d1280b1a38daaabf0f9ad1fe97861872d4f9 Homepage: https://cran.r-project.org/package=ctmva Description: CRAN Package 'ctmva' (Continuous-Time Multivariate Analysis) Implements a basis function or functional data analysis framework for several techniques of multivariate analysis in continuous-time setting. Specifically, we introduced continuous-time analogues of several classical techniques of multivariate analysis, such as principal component analysis, canonical correlation analysis, Fisher linear discriminant analysis, K-means clustering, and so on. Details are in Biplab Paul, Philip T. Reiss, Erjia Cui and Noemi Foa (2025) "Continuous-time multivariate analysis" . Package: r-cran-ctnote Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2560 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi, r-cran-stringr Suggests: r-cran-ape, r-cran-kableextra, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-survival, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-ctnote_0.1.0-1.ca2404.1_all.deb Size: 561022 MD5sum: 4cdee288762a1ccb224de0b16dff08ef SHA1: 08aec1878dacd4f0e706b106a2fad6abff86f89f SHA256: 075cc241692c52b8c2da98bf2c185655138b2ae1f661716469d0dca7a4e032c2 SHA512: 8f5b326c03d787a126dd6e3cac3eb3436e7558926d0cd2f4dc3624ab63949ce478e2f3865f92559e4c2349765af0d5a7bc96c12ce3791fae1ef387bcb8e575e6 Homepage: https://cran.r-project.org/package=CTNote Description: CRAN Package 'CTNote' (CTN Outcomes, Treatments, and Endpoints) The Clinical Trials Network (CTN) of the U.S. National Institute of Drug Abuse sponsored the CTN-0094 research team to harmonize data sets from three nationally-representative clinical trials for opioid use disorder (OUD). The CTN-0094 team herein provides a coded collection of trial outcomes and endpoints used in various OUD clinical trials over the past 50 years. These coded outcome functions are used to contrast and cluster different clinical outcome functions based on daily or weekly patient urine screenings. Note that we abbreviate urine drug screen as "UDS" and urine opioid screen as "UOS". For the example data sets (based on clinical trials data harmonized by the CTN-0094 research team), UDS and UOS are largely interchangeable. Package: r-cran-ctoclient Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-curl, r-cran-httptest2, r-cran-jsonlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ctoclient_0.1.0-1.ca2404.1_all.deb Size: 123612 MD5sum: 2554a1786887384094ccc2b9bfc2a3f3 SHA1: 74b5bd498fd1efa0b63a055ee52228f8b12829e5 SHA256: a237b85c8f82da5bc2758f720fc17a25367fc146d3cbf869fcb21b0086ae42b4 SHA512: 6b5ebe6c6fb00b619551826d7d1b972ca1a870055f092e84e775df6537055bb83923a35b8d46f1b5ba1f1961ad2b6f38a3f3a9776b4715099e23cc2fddd3fd98 Homepage: https://cran.r-project.org/package=ctoclient Description: CRAN Package 'ctoclient' (A Modern and Flexible Data Pipeline for 'SurveyCTO') A modern and flexible R client for the 'SurveyCTO', a mobile and offline data collection platform, providing a modern and consistent interface for programmatic access to server resources. Built on top of the 'httr2' package, it enables secure and efficient data retrieval and returns analysis-ready data through optional tidying. It includes functions to create, upload, and download server datasets, in addition to fetching form data, files, and submission attachments. Robust authentication and request handling make the package suitable for automated survey monitoring and downstream analysis. Package: r-cran-ctost Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2562 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-powertost, r-cran-cli, r-cran-knitr, r-cran-rmarkdown Suggests: r-cran-asciicast Filename: pool/dists/noble/main/r-cran-ctost_1.0.1-1.ca2404.1_all.deb Size: 1699764 MD5sum: 14684f19ad9caed92a57d66167db6880 SHA1: 401e40485fe5c84790b390b6d32a619486b8817a SHA256: 18c6783b9a8b02a71d6499242c427a64a21f9a1950fc982a01dc21b95a2b1d83 SHA512: ea88e4f1062341cd8c7b3036abcbbc48b265c65a7dce253665a053caf2ac3253af21c41a70a12fe2a40455a7bde466e349808b1ff481001c7d2a67f7b1d1771d Homepage: https://cran.r-project.org/package=cTOST Description: CRAN Package 'cTOST' (Finite Sample Correction of the Two One-Sided Tests in theUnivariate Framework) A system containing easy-to-use tools to compute the bioequivalence assessment in the univariate framework using the methods proposed in Boulaguiem et al. (2023) . Package: r-cran-ctpm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-ctmm, r-cran-slouch, r-cran-clusterr Suggests: r-cran-phytools, r-cran-viridis, r-cran-knitr, r-cran-rmarkdown, r-cran-nlme, r-cran-progress Filename: pool/dists/noble/main/r-cran-ctpm_1.0.1-1.ca2404.1_all.deb Size: 268704 MD5sum: f193b2f460c33911ab740e71daf0b511 SHA1: 97fc33244b24031965f5e04aa3770eb26cd63e2b SHA256: a3e7a2eff92e0798f1708271ffae842e5153261bfd1384012b7e483135845313 SHA512: f65519e4507246ab705add44a48403f162e2f9f29d116b201f6e8cb8bd37ea48a1f0f211dbf8b7a14af9ae4303876a6c3404679d5a53993cee1384eedff367ff Homepage: https://cran.r-project.org/package=ctpm Description: CRAN Package 'ctpm' (Continuous-Time Phylogenetic Modeling) Functions for identifying, fitting, and applying continuous-time stochastic models to phylogenetic data. The package is based on methods introduced in Noonan et al. (2021) . Package: r-cran-ctqr Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-pch Suggests: r-cran-car, r-cran-lmtest Filename: pool/dists/noble/main/r-cran-ctqr_2.2-1.ca2404.1_all.deb Size: 99100 MD5sum: 32a8b2fb0745af923a00f20c962ccf99 SHA1: 3bba5aefde7bd098b0eca7494c71985557223c90 SHA256: 47cc53a8ffb77c971d45e4facf3c9ecd53751c09b481640f9673648c0e8e312e SHA512: 7b58c1f244738fc789a32f3b816582d3716c2d141c18f390ccb64c57ffb8551b64834fb1c8cc3898cbcddd6f31fa1ff31e870bdeda06f82e4dfcd559a86eaf6f Homepage: https://cran.r-project.org/package=ctqr Description: CRAN Package 'ctqr' (Censored and Truncated Quantile Regression) Estimation of quantile regression models for survival data. Package: r-cran-ctrdata Architecture: all Version: 1.26.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4848 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-nodbi, r-cran-rvest, r-cran-stringdist, r-cran-tidyr, r-cran-v8, r-cran-httr2, r-cran-stringi, r-cran-lubridate, r-cran-jqr, r-cran-dplyr, r-cran-zip, r-cran-readr, r-cran-rlang, r-cran-htmlwidgets Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-rsqlite, r-cran-mongolite, r-cran-tinytest, r-cran-rpostgres, r-cran-duckdb, r-cran-httr, r-cran-tibble, r-cran-clipr, r-cran-chromote Filename: pool/dists/noble/main/r-cran-ctrdata_1.26.1-1.ca2404.1_all.deb Size: 1828640 MD5sum: b6185578de7bee5748c82661a48645d1 SHA1: d281d77ed4ecbb1fa4f9d10bdacd433e0648d634 SHA256: 20ee175df25baecddc3fb4df7295d475ea68d94d309ed2baf61a99179a157781 SHA512: 2fff5c2b7b640807d9e51bb17d88b6269dc62224d87107e80c729a084092deb166bb611be86708d501340f377017cba57ce02c04f6bfb5b64938f5b4b76e2d34 Homepage: https://cran.r-project.org/package=ctrdata Description: CRAN Package 'ctrdata' (Retrieve and Analyze Clinical Trials Data from Public Registers) A system for querying, retrieving and analyzing protocol- and results-related information on clinical trials from four public registers, the 'European Union Clinical Trials Register' ('EUCTR', ), 'ClinicalTrials.gov' ( and also translating queries the retired classic interface), the 'ISRCTN' () and the 'European Union Clinical Trials Information System' ('CTIS', ). Trial information is downloaded, converted and stored in a database ('PostgreSQL', 'SQLite', 'DuckDB' or 'MongoDB'; via package 'nodbi'). Protocols, statistical analysis plans, informed consent sheets and other documents in registers associated with trials can also be downloaded. Other functions implement trial concepts canonically across registers, identify deduplicated records, easily find and extract variables (fields) of interest even from complex nested data as used by the registers, merge variables and update queries. The package can be used for monitoring, meta- and trend-analysis of the design and conduct as well as of the results of clinical trials across registers. See overview in Herold, R. (2025) . 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Functions provide access a variety of techniques for searching the data using range queries, categorical filtering, and by searching for full-text keywords. Package: r-cran-ctring Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xring, r-cran-functional, r-cran-oro.dicom Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ctring_0.1.0-1.ca2404.1_all.deb Size: 358230 MD5sum: 982ad1962b4f37039347708d6f0aef29 SHA1: e8e5314325dea2263ec6a8680578b78a3f08887e SHA256: 3ca31d0fe8e23db6176ede4616e61e6b65fa4b70a303f92be401198f952c7183 SHA512: 7271722d261f6a17f30f870b296dae6ab9635a18a5db0f16652effde6dedc5c31a49170ed81088ab12c9446a439b1a17381ca92c8832703511c3d184eaf83c4b Homepage: https://cran.r-project.org/package=CTRing Description: CRAN Package 'CTRing' (Density Profiles of Wood from CT Scan Images) Computerized tomography (CT) can be used to assess certain wood properties when wood disks or logs are scanned. Wood density profiles (i.e. variations of wood density from pith to bark) can yield important information used for studies in forest resource assessment, wood quality and dendrochronology studies. The first step consists in transforming grey values from the scan images to density values. The packages then proposes a unique method to automatically locate the pith by combining an adapted Hough Transform method and a one-dimensional edge detector. Tree ring profiles (average ring density, earlywood and latewood density, ring width and percent latewood for each ring) are then obtained. Package: r-cran-ctrlgene Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych Filename: pool/dists/noble/main/r-cran-ctrlgene_1.0.1-1.ca2404.1_all.deb Size: 45782 MD5sum: b790ae84c0a0f46660f45eabdcb2a3eb SHA1: 75dc0e1b16ef4dc1f35fef866adb5dd25c0ee080 SHA256: 7e2caa5199fc1974651f6f738337eef5b88deeb6fce6eb6df246e5f84702587a SHA512: ecb349c3d4e4db54b057f37ead517bc13746b5ae6596dec1e1fc66a6967cbc32cd05e1e1ae0b05959935ce7d878a35ae76d5da196a7f42e2d6f901b814cea883 Homepage: https://cran.r-project.org/package=ctrlGene Description: CRAN Package 'ctrlGene' (Assess the Stability of Candidate Housekeeping Genes) A simple way to assess the stability of candidate housekeeping genes is implemented in this package. Package: r-cran-ctrlvee Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstudioapi, r-cran-httr2, r-cran-xml2, r-cran-purrr, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ctrlvee_0.1.0-1.ca2404.1_all.deb Size: 56874 MD5sum: b2562f9b3678728f24d5ca2888d2626d SHA1: c696ace1b474541ad0cccde569f614629eaed055 SHA256: fdb8c434dc260cb580f0bfa45f0ba9f32c5a64da11097d3638b780c89af6f63b SHA512: 50dfe2d60173b3a3e48f53ea83190c3906d1941857ebaa10047155ea2e52b7a0a1edb04c2aef1aeb53f2aa734e9ee5515ed7c1ae77371732ac40c133fcdd15ed Homepage: https://cran.r-project.org/package=ctrlvee Description: CRAN Package 'ctrlvee' (Extract External 'R' Code and Insert Inline) An 'RStudio' and 'Positron' add-in that prompts the user for a web 'URL', fetches the page content, extracts 'R' code chunks, and inserts those code chunks into the active editor at the current cursor position. Supports extraction of raw 'Markdown' or 'Quarto' source files, 'GitHub' Gist and rendered 'HTML' pages that have markup elements with 'R'-related classes. Package: r-cran-ctsemomx Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openmx, r-cran-data.table, r-cran-expm, r-cran-matrix, r-cran-plyr Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ctsemomx_2.0.0-1.ca2404.1_all.deb Size: 1156532 MD5sum: eda6df7669886673ee12062a87f92810 SHA1: 9b5801e01c076b6237aa4730532df1d9abeda9d4 SHA256: b74ae943c4a04af25f9f1366a3d95c8c2acabaf51f1a8aa327d3745c7edb435c SHA512: 120924d6df4dcbabd5fd9d1bcfd9478be846985d615106bc4dbe292c37b44206f95451db8aac2b318d256a295e0f702d8384e106433ca64529a1107fdcd8571a Homepage: https://cran.r-project.org/package=ctsemOMX Description: CRAN Package 'ctsemOMX' (Continuous Time Structural Equation Modelling - Old'OpenMx'-Based Version) Original 'ctsem' (continuous time structural equation modelling) functionality, based on the 'OpenMx' software, as described in Driver, Oud, Voelkle (2017) , with updated details in vignette. Combines stochastic differential equations representing latent processes with structural equation measurement models. This package is maintained for consistency with the original 'ctsem' paper, but for the much newer and more capable 'ctsem' package, see . Package: r-cran-ctsfeatures Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-astsa, r-cran-latex2exp, r-cran-rdpack, r-cran-bolstad2, r-cran-tsibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ctsfeatures_1.2.2-1.ca2404.1_all.deb Size: 362770 MD5sum: 5c9aafe1be1debb62b487dec58bd5c3c SHA1: d3ce0164449ea931701739a01dfa253e6bf19635 SHA256: ef7bcdfd1a830524e8be7885ed17abc9393d9dcb76aacc4925c8990647f31638 SHA512: ce04fd9bfbba4770e0a665c9c94bedf25f1cbef66cd7e8968b57f54ae6c0115e55b9dec7af1be1761ba64a8cf598352f057e65e5af1ff288978fff3dc96a9b0b Homepage: https://cran.r-project.org/package=ctsfeatures Description: CRAN Package 'ctsfeatures' (Analyzing Categorical Time Series) An implementation of several functions for feature extraction in categorical time series datasets. Specifically, some features related to marginal distributions and serial dependence patterns can be computed. These features can be used to feed clustering and classification algorithms for categorical time series, among others. The package also includes some interesting datasets containing biological sequences. Practitioners from a broad variety of fields could benefit from the general framework provided by 'ctsfeatures'. 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Runtime examples are provided in the package function as well as at . 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Runtime examples are provided in the package function as well as at . Package: r-cran-ctt Architecture: all Version: 2.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ctt_2.3.4-1.ca2404.1_all.deb Size: 83698 MD5sum: af2661abdb07930305be0c9864c0484a SHA1: a1b160f3093912bb9422e5589885c8fd0be447c3 SHA256: f35d1985bfaf9cb4017fc9df8b8c94308bca3f4f4b7263efef8b56ca0cd9927b SHA512: d1ab82ec3c8e3c6d97082afeb09f2506a54a04ff9afd080d77b8f4eb311f03a2e61f37c1a5dc2bf945ab78f9ea1a6475c7f1abd3a4eb47769e411f5cd50f744f Homepage: https://cran.r-project.org/package=CTT Description: CRAN Package 'CTT' (Classical Test Theory Functions) A collection of common test and item analyses from a classical test theory (CTT) framework. Analyses can be applied to both dichotomous and polytomous data. 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These models are based on extensions of the frailty models, capable to accommodating the cure fraction and the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions. Theoretical details regarding the models implemented in the package can be found in Schneider et al. (2022) . 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See Hou, Chambers and Xu (2017) . Package: r-cran-cureplots Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cureplots_1.1.1-1.ca2404.1_all.deb Size: 70390 MD5sum: d4c3a7a8587804a0c55206e672a1a92e SHA1: 13d22020638f47e4c9bb07b139cf161d83ab078d SHA256: faeff0c4c55d53db86f794c97629c57903cb43f84392a815bef56ed42441492e SHA512: 39590b2598a9dc1488bd067615cfc9d33bb36a2cd3735e7069aa8cdf4df66a8e875e2d06d38046932a493a8418380ef7dcec806ca84c5df4fd85868bf16ab2c8 Homepage: https://cran.r-project.org/package=cureplots Description: CRAN Package 'cureplots' (CURE (Cumulative Residual) Plots) Creates 'ggplot2' Cumulative Residual (CURE) plots to check the goodness-of-fit of a count model; or the tables to create a customized version. A dataset of crashes in Washington state is available for illustrative purposes. Package: r-cran-curesurv Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4088 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-survival, r-cran-numderiv, r-cran-randtoolbox, r-cran-bbmle, r-cran-optimx, r-cran-formula, r-cran-deriv, r-cran-statmod Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-xhaz, r-cran-survexp.fr Filename: pool/dists/noble/main/r-cran-curesurv_0.1.2-1.ca2404.1_all.deb Size: 1949484 MD5sum: 1977f4a2df6769af0ce1b8048f0c9858 SHA1: c147c45ca42bc07d64db3b8e4e6ec683a584f076 SHA256: 510d4a46414216e6394da95ca8fe555110c6faaf320f551d897177f6824b2ab1 SHA512: d67712ff52a78838097be3e13f73484c67f7da0900d2edab0dcbb8703413322a7e633144c3001c42631261ec143eaf90f51d1a6324c8620a4b21d16a0d8d14ec Homepage: https://cran.r-project.org/package=curesurv Description: CRAN Package 'curesurv' (Mixture and Non Mixture Parametric Cure Models to Estimate CureIndicators) Fits a variety of cure models using excess hazard modeling methodology such as the mixture model proposed by Phillips et al. (2002) The Weibull distribution is used to represent the survival function of the uncured patients; Fits also non-mixture cure model such as the time-to-null excess hazard model proposed by Boussari et al. (2020) . Package: r-cran-currencyapi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-currencyapi_0.1.0-1.ca2404.1_all.deb Size: 33576 MD5sum: 03f7b940f4957a3b5ee79482bb574a59 SHA1: 28f6cdde62a4906adae50f180c60e74ca77fd811 SHA256: 54362d6ae3acf0102659db3659673ce865c1410f7eb2a16be12c9e59f284011e SHA512: 4d2069743834a58e47b9e3655cfee901cc1a45a7ace0a05a0ee378445c92381866dc2f783697596b1611f055e8fb0d5f9509a1d983c1d15b58f24bd73d7fdb5b Homepage: https://cran.r-project.org/package=currencyapi Description: CRAN Package 'currencyapi' (Client for the 'currencyapi.com' Currency Conversion API) An R client for the 'currencyapi.com' currency conversion API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at . Package: r-cran-currentsurvival Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-cmprsk Filename: pool/dists/noble/main/r-cran-currentsurvival_1.1-1.ca2404.1_all.deb Size: 160268 MD5sum: 8d54bc51b1f0107d5334b96a958afe6c SHA1: 03e2af7d9f81df2eeeeb7acb556fbf8c353dca8b SHA256: db7977afe98d984507d75e9575d2148e0b230e45e914b876322dde6657951c3a SHA512: 30f5f1d761419230944362c9099e5cf2f8ea4d34300199994b95731bc57f8f6de2809c2b82c778f370dcfc2312847d3844f91dd64640b3c151982aa073163816 Homepage: https://cran.r-project.org/package=currentSurvival Description: CRAN Package 'currentSurvival' (Estimation of CCI and CLFS Functions) The currentSurvival package contains functions for the estimation of the current cumulative incidence (CCI) and the current leukaemia-free survival (CLFS). The CCI is the probability that a patient is alive and in any disease remission (e.g. complete cytogenetic remission in chronic myeloid leukaemia) after initiating his or her therapy (e.g. tyrosine kinase therapy for chronic myeloid leukaemia). The CLFS is the probability that a patient is alive and in any disease remission after achieving the first disease remission. Package: r-cran-curricularanalytics Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-igraph, r-cran-visnetwork Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-curricularanalytics_1.0.0-1.ca2404.1_all.deb Size: 273040 MD5sum: 831e5b4afcb4fe17a87917fa45360e07 SHA1: cba2be8c09e4af4ca22a43659aff30bc2063144e SHA256: 88dc7e6ac894210a52abf2a3e20002783ed06b06b3bb40c06a51aa082b91d516 SHA512: 3fb71c1d7cad9a4cc9e2e0490f972ec753dbc884471ce5609a4e68010c364af738d3f0c83e48b84c573c11d742356ce2080a59fc418075cdcc280565b3e808d8 Homepage: https://cran.r-project.org/package=CurricularAnalytics Description: CRAN Package 'CurricularAnalytics' (Exploring and Analyzing Academic Curricula) Provides an implementation of ‘Curricular Analytics’, a framework for analyzing and quantifying the complexity of academic curricula. 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The original tool can be found at . Additional functions to explore curriculum complexity from the literature are also included. 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Package: r-cran-cusumcharter Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-ggplot2, r-cran-data.table Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-cusumcharter_0.1.0-1.ca2404.1_all.deb Size: 230318 MD5sum: f1c8bddede04deea0fc52d4c135ac38b SHA1: d87be4ffc9894d44e1e74389b718010b45049ef9 SHA256: 354401d457fbd0fd487c1911480295471f330ec2ad4a1cec1ddc2d2b371bf7d9 SHA512: 6e443cfe2be14938a4cfa877b1b8decd170fce697402a705eea1bc9ff139be79e54cff35db987f44dab7c46d65d6ea3a0d4ad0e857a5fdf5a266ffa78dfe6cbc Homepage: https://cran.r-project.org/package=cusumcharter Description: CRAN Package 'cusumcharter' (Easier CUSUM Control Charts) Create CUSUM (cumulative sum) statistics from a vector or dataframe. 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Package: r-cran-cutoff Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-set, r-cran-do, r-cran-rocit Filename: pool/dists/noble/main/r-cran-cutoff_1.3-1.ca2404.1_all.deb Size: 55766 MD5sum: 548eb5ad1a21bbed4d79d330a278ba88 SHA1: 288a4b3d0ea4749d33588cf096a2614747489caa SHA256: 801b92221d8d8c3c6d91c511c07782be3d2be01aa19b831c910356bfe3fbc652 SHA512: 94ddbe6da9a5a3889c38b5a984d7f24a8a6d62b5c50710b0504d10fc8bbf37dc3dc6a49dc8d381d74001c7e78c8c4a43dd6a22a47e26b0f9bfed8bf7baa25014 Homepage: https://cran.r-project.org/package=cutoff Description: CRAN Package 'cutoff' (Seek the Significant Cutoff Value) Seek the significant cutoff value for a continuous variable, which will be transformed into a classification, for linear regression, logistic regression, logrank analysis and cox regression. First of all, all combinations will be gotten by combn() function. Then n.per argument, abbreviated of total number percentage, will be used to remove the combination of smaller data group. In logistic, Cox regression and logrank analysis, we will also use p.per argument, patient percentage, to filter the lower proportion of patients in each group. Finally, p value in regression results will be used to get the significant combinations and output relevant parameters. In this package, there is no limit to the number of cutoff points, which can be 1, 2, 3 or more. Still, we provide 2 methods, typical Bonferroni and Duglas G (1994) , to adjust the p value, Missing values will be deleted by na.omit() function before analysis. Package: r-cran-cutools Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cutools_0.1.0-1.ca2404.1_all.deb Size: 28598 MD5sum: d679b3c62db287097a1136134892607f SHA1: 43f93c6741c1f298205e3823018b15975b413c6a SHA256: 1bee0fb16169125169f766866990adf85d2d78b7dc92ccfada5f98cf40c3b4b7 SHA512: 406eec9242716be46aac1b7e593113f0eb2a0f8244b68cca5a13a76277cdb0d9edb2248d0755db279e7506fa4e1d606fe21e52934710aa12c42f9765b138bfc6 Homepage: https://cran.r-project.org/package=CUtools Description: CRAN Package 'CUtools' (Clinical Utility Tools to Analyze a Predictive Model) Package to analyze the clinical utility of a biomarker. It provides the clinical utility curve, clinical utility table, efficacy of a biomarker, clinical efficacy curve and tests to compare efficacy between markers. Package: r-cran-cutpoint Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-plotly, r-cran-rcppalgos, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cutpoint_1.0.0-1.ca2404.1_all.deb Size: 1103246 MD5sum: 415329bd13d3ff6b8989aa3e532c7550 SHA1: fe2a0ef620d18b88384723ae79dc12ab9ba170df SHA256: 694df4a8bff74e69cd817fa7c744c6307dd7a55b651bc56b9552502f23dd7c42 SHA512: 6ac2a7fa17c685d5f00830e883ad640557df63a2d6ae492b753568311d3bb311dbef70233bd4e06977e7e45cc1f0f18fd901c3cf92f8f45cc3aba28a0e7ebf39 Homepage: https://cran.r-project.org/package=cutpoint Description: CRAN Package 'cutpoint' (Estimate Cutpoints of Metric Variables in the Context of CoxRegression) Estimate one or two cutpoints of a metric or ordinal-scaled variable in the multivariable context of survival data or time-to-event data. Visualise the cutpoint estimation process using contour plots, index plots, and spline plots. It is also possible to estimate cutpoints based on the assumption of a U-shaped or inverted U-shaped relationship between the predictor and the hazard ratio. Govindarajulu, U., and Tarpey, T. (2022) . Package: r-cran-cutpointsoehr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-cutpointsoehr_0.1.2-1.ca2404.1_all.deb Size: 25980 MD5sum: eec068f2bc1816cd85b352443d7fbc93 SHA1: aad9fbfa5b2662e4d4348d4c903f837000679f1a SHA256: 03e9ca85b2dc90c8116887dca79c6ed40830b5908f18152d9d279a85977b7fdc SHA512: 586a09e3bb2b4c587a6022fd8c3ff01330dd1e8bbb6adb5c4018d83babb0492f85f8c954912f5aa57811dfe8185366a4235891562ee75a731f6c3e68f1da9c46 Homepage: https://cran.r-project.org/package=CutpointsOEHR Description: CRAN Package 'CutpointsOEHR' (Optimal Equal-HR Method to Find Two Cutpoints for U-ShapedRelationships in Cox Model) Use optimal equal-HR method to determine two optimal cutpoints of a continuous predictor that has a U-shaped relationship with survival outcomes based on Cox regression model. The optimal equal-HR method estimates two optimal cut-points that have approximately the same log hazard value based on Cox regression model and divides individuals into different groups according to their HR values. Package: r-cran-cv Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3899 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-car, r-cran-foreach, r-cran-glmmtmb, r-cran-gtools, r-cran-insight, r-cran-lattice, r-cran-lme4, r-cran-mass, r-cran-nlme Suggests: r-cran-boot, r-cran-cardata, r-cran-dplyr, r-cran-effects, r-cran-islr2, r-cran-knitr, r-cran-latticeextra, r-cran-leaps, r-cran-metrics, r-cran-microbenchmark, r-cran-nnet, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cv_2.0.4-1.ca2404.1_all.deb Size: 2370538 MD5sum: f814196b884bc96f3b83f33240625629 SHA1: 33ed02e5b49e40f142f086feda04bb41708fed44 SHA256: ec8501cfae426d1655be615a419bb3e885d3e86bbe93de24fe91897d44f5a852 SHA512: be2bf21a41b483fb8c2553219c80d4d0befc92365c003880bbb759cbb32ad580d138ef9458795de1deba9d53242e85160257a8ab4a996ded0d17f5c784cd7cc7 Homepage: https://cran.r-project.org/package=cv Description: CRAN Package 'cv' (Cross-Validating Regression Models) Cross-validation methods of regression models that exploit features of various modeling functions to improve speed. Some of the methods implemented in the package are novel, as described in the package vignettes; for general introductions to cross-validation, see, for example, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2021, ISBN 978-1-0716-1417-4, Secs. 5.1, 5.3), "An Introduction to Statistical Learning with Applications in R, Second Edition", and Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009, ISBN 978-0-387-84857-0, Sec. 7.10), "The Elements of Statistical Learning, Second Edition". Package: r-cran-cvap Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-censable, r-cran-cli, r-cran-dplyr, r-cran-fs, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cvap_0.1.6-1.ca2404.1_all.deb Size: 335080 MD5sum: 3e643108de11bcc26bf484c6e0b06132 SHA1: f05edf41560b3d1632123b826cc868d0623f4e87 SHA256: c4195cbd9304bab81373665f791f7af163c529027b1bdd2d7cc6b92205b05d82 SHA512: 02c403df11ceabb2f71051a78be2a83913abd8921cda87be6e7d128364ffd223b3c3f0e00d2ff663975de02492e11adf4791f52192fac9cf6870c1037a6a05f9 Homepage: https://cran.r-project.org/package=cvap Description: CRAN Package 'cvap' (Citizen Voting Age Population) Works with the Citizen Voting Age Population special tabulation from the US Census Bureau . Provides tools to download and process raw data. 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Package: r-cran-cvar Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gbutils, r-cran-rdpack Suggests: r-cran-testthat, r-cran-fgarch, r-cran-performanceanalytics Filename: pool/dists/noble/main/r-cran-cvar_0.6-1.ca2404.1_all.deb Size: 137874 MD5sum: fec8849b7f8dcdfe391e7e1186a93204 SHA1: 8553e131b8c81830a3c1c4ffbae6c17765e21ac8 SHA256: 0e1364e73aa6265f293a67e72301a343cce57f76b1cf297f57080828a276962e SHA512: eaece0c0826cef4a479952d34d89598dcd05ce5b1b603699a007a928107443d2537c9d3b2dadd036475fa74acbf3e3fe47c42adeedaac5b98f1407fdee095937 Homepage: https://cran.r-project.org/package=cvar Description: CRAN Package 'cvar' (Compute Expected Shortfall and Value at Risk for ContinuousDistributions) Compute expected shortfall (ES) and Value at Risk (VaR) from a quantile function, distribution function, random number generator, probability density function, or data. ES is also known as Conditional Value at Risk (CVaR). Virtually any continuous distribution can be specified. The functions are vectorized over the arguments. The computations are done directly from the definitions, see e.g. Acerbi and Tasche (2002) . Some support for GARCH models is provided, as well. Package: r-cran-cvauc Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rocr, r-cran-data.table Filename: pool/dists/noble/main/r-cran-cvauc_1.1.4-1.ca2404.1_all.deb Size: 122878 MD5sum: 07c81b27853e24d857e52d51a6252000 SHA1: 1acd5ca1124cf9d3430742772c0a0f6fb19b2f16 SHA256: f4c0088144afbf2acc09dd4fec4b4ccf15706d70004a12d4d8d92e3f2ace593a SHA512: d6bba90e1454fce40ca57d98cdab5f4bc683460069581973dc2b6729eca4a444d18f4f3a65f3d24be6e333e442946f10b944abb257a99f2bc556000de9135bd3 Homepage: https://cran.r-project.org/package=cvAUC Description: CRAN Package 'cvAUC' (Cross-Validated Area Under the ROC Curve Confidence Intervals) Tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators. The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively. One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods. The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package. Package: r-cran-cvcovest Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-matrix, r-cran-origami, r-cran-coop, r-cran-rdpack, r-cran-rlang, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-assertthat, r-cran-rspectra, r-cran-ggplot2, r-cran-ggpubr, r-cran-rcolorbrewer, r-cran-rmtstat Suggests: r-cran-future, r-cran-future.apply, r-cran-mass, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-cvcovest_1.2.2-1.ca2404.1_all.deb Size: 624518 MD5sum: a9fea706f59aa910f279e14aaa2b4538 SHA1: cd0b7a7aa780bbe7b62316ed63ffaffaff6f5bfe SHA256: 071004bf9a5220c649d6d62443e60a1b7ea3fbc8ab30c9a40e9cdfadbf3c86f6 SHA512: 2a011e925a0bd56d6f0831b6a2e32c95917f3801b7f223760455fd79ab9a8cd4c3ac2f035e58a857fae17d2d2692fc1d9701527d8dce9674705873aabb3bfe02 Homepage: https://cran.r-project.org/package=cvCovEst Description: CRAN Package 'cvCovEst' (Cross-Validated Covariance Matrix Estimation) An efficient cross-validated approach for covariance matrix estimation, particularly useful in high-dimensional settings. This method relies upon the theory of high-dimensional loss-based covariance matrix estimator selection developed by Boileau et al. (2022) to identify the optimal estimator from among a prespecified set of candidates. 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References are 'Panichkitkosolkul' (2013) , 'Altunkaynak' & 'Gamgam' (2018) , 'Albatineh', 'Kibria', Wilcox & 'Zogheib' (2014) . Package: r-cran-cvcrand Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tableone Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cvcrand_0.1.1-1.ca2404.1_all.deb Size: 167124 MD5sum: d0fd9621278737bc439c79c899b3c625 SHA1: b17467e558188be71cdaa9a524552d0377fd4419 SHA256: 45493c21861d34f4613eae890070d6340f075700fb6821678b28328fed9c89fa SHA512: 42f376df5f978be93d8588dd743a99d628b58f8e34b336b76bba28c4c4b6d7c6a91fa814aaaf7302fa77b07d2cdaa6e319be38797d8b39f58a720e3ad576526a Homepage: https://cran.r-project.org/package=cvcrand Description: CRAN Package 'cvcrand' (Efficient Design and Analysis of Cluster Randomized Trials) Constrained randomization by Raab and Butcher (2001) is suitable for cluster randomized trials (CRTs) with a small number of clusters (e.g., 20 or fewer). The procedure of constrained randomization is based on the baseline values of some cluster-level covariates specified. The intervention effect on the individual outcome can then be analyzed through clustered permutation test introduced by Gail, et al. (1996) . Motivated from Li, et al. (2016) , the package performs constrained randomization on the baseline values of cluster-level covariates and clustered permutation test on the individual-level outcomes for cluster randomized trials. Package: r-cran-cvd Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1771 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-cvd_1.0.2-1.ca2404.1_all.deb Size: 1595888 MD5sum: 75b0bb38291622dc3c7b3fbca5eb0b16 SHA1: 1d7342511755a198e171fad766e593a89b973108 SHA256: 7eb60c692b99c169740612d97af27032583c8f5c0971d15234ddbf146f4db3a3 SHA512: 9454e21050c3121d32ab3e83d73f49a634fe14760cd8ebaa71eb8cc995eaf09c95789a2fb8af7c41758e26377b372842ee67408499af0822911fb17b3ebb75dd Homepage: https://cran.r-project.org/package=CVD Description: CRAN Package 'CVD' (Color Vision Deficiencies) Methods for color vision deficiencies (CVD), to help understanding and mitigating issues with CVDs and to generate tests for diagnosis and interpretation. Package: r-cran-cvdprevent Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cachem, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-memoise, r-cran-purrr, r-cran-rappdirs, r-cran-tibble, r-cran-tidyr Suggests: r-cran-forcats, r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-mockery, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat, r-cran-zoo Filename: pool/dists/noble/main/r-cran-cvdprevent_0.2.5-1.ca2404.1_all.deb Size: 456418 MD5sum: f612b446d2db4c6b8e41e0b519946025 SHA1: c4700631c9b5fb0f6bb2adf7676e720490686687 SHA256: adbafcc74b37d70fd670d3846f23ae72c8bc1dfbbc59dfcff85b6a4d7d844246 SHA512: 952ab082e76bad57c517b92ffbc11d414bb51a740d71fab5dccde22394e33b6cf04255fbb745bff4d763f639b3833fdc0242ca4a498c17e27789f76df1392120 Homepage: https://cran.r-project.org/package=cvdprevent Description: CRAN Package 'cvdprevent' (Access and Analyse Data from the 'CVD Prevent' API) Provides an R interface to the 'CVD Prevent' application programming interface (API), allowing users to retrieve and analyse cardiovascular disease prevention data from primary care records across England. The Cardiovascular Disease Prevention Audit (CVDPREVENT) automatically extracts routinely held GP health data to support national reporting and improvement initiatives. See the API documentation for details: . Package: r-cran-cvequality Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-tidyr, r-cran-ggbeeswarm, r-cran-covr Filename: pool/dists/noble/main/r-cran-cvequality_0.2.0-1.ca2404.1_all.deb Size: 71294 MD5sum: e539280f74f62832d97452ffb5a61a36 SHA1: ed2b6519553442f7d3b18170ef202d137e2fa413 SHA256: 7dc89614a320e9886e9beda4ad2c1d98f7b12f55283e0f5c32b1c98335ae333d SHA512: d72fdcd7a90ff3a86a4f1b58323b46475f0378d3034dccb0457b7753f2435eb73b7dde8c54a004010b8bd5ed5796568a1f3b5d1869c463b7cb5e94ffa71b35ed Homepage: https://cran.r-project.org/package=cvequality Description: CRAN Package 'cvequality' (Tests for the Equality of Coefficients of Variation fromMultiple Groups) Contains functions for testing for significant differences between multiple coefficients of variation. Includes Feltz and Miller's (1996) asymptotic test and Krishnamoorthy and Lee's (2014) modified signed-likelihood ratio test. See the vignette for more, including full details of citations. 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This package is a simple wrapper around the popular 'glasso' package and extends and enhances its capabilities. These enhancements include built-in cross validation and visualizations. See Friedman et al. (2008) for details regarding the estimation method. Package: r-cran-cvmaplfam Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-quadprog, r-cran-mgcv, r-cran-mass Filename: pool/dists/noble/main/r-cran-cvmaplfam_0.1.1-1.ca2404.1_all.deb Size: 58274 MD5sum: 896f5d6c714108eef99bbebcbdd0f8b8 SHA1: 253884d2bb3bcfe27744d066dd42fa5c27d93a32 SHA256: 26d6cbf81ace10e72da80e29610bf3ad6d2765a06b43ba0c5875dcccf8e47a59 SHA512: 9b0fc79d8f76d6fb2217472a11f826a4e0b59e27691734a82704eb736ddf8dd1a2f311465401f27f31901f0cf068b50567e8baf9d4b9fa49673c2844cf412f81 Homepage: https://cran.r-project.org/package=cvmaPLFAM Description: CRAN Package 'cvmaPLFAM' (Cross-Validation Model Averaging for Partial Linear FunctionalAdditive Models) Produce an averaging estimate/prediction by combining all candidate models for partial linear functional additive models, using multi-fold cross-validation criterion. More details can be referred to arXiv e-Prints via . Package: r-cran-cvmdisc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cvmdisc_0.1.0-1.ca2404.1_all.deb Size: 51438 MD5sum: 497129ebd0a2c73406ff3614abc5e6b9 SHA1: 27519e4e52eed397fa70df7b190bc8b4ddadbfd6 SHA256: 98edafc2a290d820bc76319228f276e8944e175496ad73cd5148b07e663b344f SHA512: ff048d7b818d0e21846cae283427961884a6aae466da4621506fae79facc085b1140e054324a3f490d48b39e043b6021cca924245d99595099851dba3acad7e1 Homepage: https://cran.r-project.org/package=cvmdisc Description: CRAN Package 'cvmdisc' (Cramer von Mises Tests for Discrete or Grouped Distributions) Implements Cramer-von Mises Statistics for testing fit to (1) fully specified discrete distributions as described in Choulakian, Lockhart and Stephens (1994) (2) discrete distributions with unknown parameters that must be estimated from the sample data, see Spinelli & Stephens (1997) and Lockhart, Spinelli and Stephens (2007) (3) grouped continuous distributions with Unknown Parameters, see Spinelli (2001) . Maximum likelihood estimation (MLE) is used to estimate the parameters. The package computes the Cramer-von Mises Statistics, Anderson-Darling Statistics and the Watson-Stephens Statistics and their p-values. Package: r-cran-cvmgof Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-cvmgof_1.0.3-1.ca2404.1_all.deb Size: 113748 MD5sum: 74752437a44215ec73ee628c2dd3dc35 SHA1: 15fbf2999a2097f01a403ae7ff243332516b211f SHA256: 4ae80ef6e650b8e805c137cc04fde72136cd6b1dd44207303ddab461b2715159 SHA512: 6a4878c99463503971aff7b525430f77dfce7cafdf5844b2c8a41347bab84d5eed918fd2da53d208a503fdfdd284d54d9be1a1e0f1f41cabc35ac2af4803ac4e Homepage: https://cran.r-project.org/package=cvmgof Description: CRAN Package 'cvmgof' (Cramer-von Mises Goodness-of-Fit Tests) It is devoted to Cramer-von Mises goodness-of-fit tests. It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model. Package: r-cran-cvmortalitymult Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stmomo, r-cran-forecast, r-cran-gnm, r-cran-tmap, r-cran-sf Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cvmortalitymult_1.1.1-1.ca2404.1_all.deb Size: 1982824 MD5sum: 90e61006009cef31e4e2ba485b903619 SHA1: b5bc96657804b8210f5ffe75893703e181d0c676 SHA256: 5249f0ebd397ef952bfa8a346f05233e9aee8278ab8829dd6b2d81ca3c819cbc SHA512: 0d2cb3721dce64262e2652c1ef3d16bc56bfff1d391b0d8ab8afb09beb1568a364ecbd775e6c6d5ef5445006dcc02797b44f1bd7a259e2604a3a55e149bcbf9a Homepage: https://cran.r-project.org/package=CvmortalityMult Description: CRAN Package 'CvmortalityMult' (Cross-Validation for Multi-Population Mortality Models) Implementation of cross-validation method for testing the forecasting accuracy of several multi-population mortality models. The family of multi-population includes several multi-population mortality models proposed through the actuarial and demography literature. The package includes functions for fitting and forecast the mortality rates of several populations. Additionally, we include functions for testing the forecasting accuracy of different multi-population models. References, . Atance, D., Debon, A., and Navarro, E. (2020) . Bergmeir, C. & Benitez, J.M. (2012) . Debon, A., Montes, F., & Martinez-Ruiz, F. (2011) . Lee, R.D. & Carter, L.R. (1992) . Russolillo, M., Giordano, G., & Haberman, S. (2011) . Santolino, M. (2023) . Package: r-cran-cvms Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4798 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-groupdata2, r-cran-lifecycle, r-cran-lme4, r-cran-mumin, r-cran-parameters, r-cran-plyr, r-cran-proc, r-cran-purrr, r-cran-rearrr, r-cran-recipes, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-auc, r-cran-covr, r-cran-e1071, r-cran-furrr, r-cran-ggimage, r-cran-ggnewscale, r-cran-knitr, r-cran-merderiv, r-cran-nnet, r-cran-randomforest, r-cran-rmarkdown, r-cran-rsvg, r-cran-testthat, r-cran-xpectr Filename: pool/dists/noble/main/r-cran-cvms_2.0.0-1.ca2404.1_all.deb Size: 3532962 MD5sum: c47178bcef8bf78056b48505caf947b4 SHA1: c241ce35d02513590c05f8b68bad721f50feaa0d SHA256: 017d0d3fefbdddc56279ce7667e96378a376d4be598fbb92eb55866d6d3f72c5 SHA512: 845cb009c0755ce8b6cb467419bc9e70eeca0550d91f5d7cbc63bb2830b6619cad0a64b89b4f56df70fb3508f41dbf3defe43de4f64dc11b244a91b30727b368 Homepage: https://cran.r-project.org/package=cvms Description: CRAN Package 'cvms' (Cross-Validation for Model Selection) Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134). Package: r-cran-cvrisk Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-preventr Suggests: r-cran-testthat, r-cran-covr, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-cvrisk_1.2.1-1.ca2404.1_all.deb Size: 475426 MD5sum: 3fd22790f9a3d432a5e8bd42b5f0ac3d SHA1: a4cb3f975bfe80f6c06ad38ec81b1cc427943b59 SHA256: d31084e65acff1740a66b4d3d0a6759b41625c3ec436bf37e76cfd2cf3dfb741 SHA512: e99ef392fafb09c332505165334721b2cce10a7b8c93c02178172d8111e398e8014eb6cca1bca060c2632ba10a1b00a3426986b3be601c9e6fa3f1ce01205e46 Homepage: https://cran.r-project.org/package=CVrisk Description: CRAN Package 'CVrisk' (Compute Risk Scores for Cardiovascular Diseases) Calculate various cardiovascular disease risk scores from the Framingham Heart Study (FHS), the American College of Cardiology (ACC), and the American Heart Association (AHA) as described in D’agostino, et al (2008) , Goff, et al (2013) , and Mclelland, et al (2015) , and Khan, et al (2024) . 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'cvsem' computes the Kullback Leibler (KL) Divergence between 1) the model implied covariance matrix estimated from the training data and 2) the sample covariance matrix estimated from the test data described in Cudeck, Robert & Browne (1983) . The KL Divergence is computed for each of the specified SEMs allowing for the models to be compared based on their prediction errors. Package: r-cran-cvst Architecture: all Version: 0.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernlab, r-cran-matrix Filename: pool/dists/noble/main/r-cran-cvst_0.2-3-1.ca2404.1_all.deb Size: 86926 MD5sum: 1de0d0ab177dc81f6472309b079afdc9 SHA1: 02caf8852ee936ee3ee6d0565ccda7cd4f62d1cd SHA256: b0515b7ebaf5e1a0734b75f725f9698a31ebfd016ee6719da916b7065cb3bb49 SHA512: ab21f166cce269b67a93582f5ca4d44b7b4cadf839c31e5c236f363ce3d74a3c8f74d429e846a817999c75dc2204e1cd8c6e60142d6fc756619709551a969f4a Homepage: https://cran.r-project.org/package=CVST Description: CRAN Package 'CVST' (Fast Cross-Validation via Sequential Testing) The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. 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The 'CWB' is memory efficient and its design makes running queries fast, see Evert (2011) . The 'cwbtools' package offers pure 'R' tools to create indexed corpus files as well as high-level wrappers for the original 'C' implementation of 'CWB' as exposed by the 'RcppCWB' package (). Additional functionality to add and modify annotations of corpora from within 'R' makes working with 'CWB' indexed corpora much more flexible and convenient. The 'cwbtools' package in combination with the 'R' packages 'RcppCWB' () and 'polmineR' () offers a lightweight infrastructure to support the combination of quantitative and qualitative approaches for working with textual data. Package: r-cran-cwot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatest, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-cwot_0.1.0-1.ca2404.1_all.deb Size: 26652 MD5sum: 2e060f3f9a03e779b19732e90fb91451 SHA1: ce316b19a47229857d15c6006db371321ea9e70a SHA256: 54f28917ba362a1a097d475ecef763f97aafbf7a48297998bdef7c44bbd46172 SHA512: 0295a9331bc4d8b593da5f98f9325d48fa7aaf2210b09ce9325694179d0e3064465f2fe78b11fe31e1335cc0050ada65307ce805bf76d8e8c94e257fcabbe0b5 Homepage: https://cran.r-project.org/package=cwot Description: CRAN Package 'cwot' (Cauchy Weighted Joint Test for Pharmacogenetics Analysis) A flexible and robust joint test of the single nucleotide polymorphism (SNP) main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures, Cauchy weighted joint test (CWOT) and adaptively weighted joint test (AWOT), are proposed to accurately calculate the joint test p-value. The proposed methods are evaluated through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in pharmacogenetics (PGx) association studies. For reference, see Hong Zhang, Devan Mehrotra and Judong Shen (2022) . Package: r-cran-cxr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2061 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mvtnorm, r-cran-optimx Suggests: r-cran-bb, r-cran-deoptimr, r-cran-dfoptim, r-cran-dplyr, r-cran-gensa, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-minqa, r-cran-nloptr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-ucminf Filename: pool/dists/noble/main/r-cran-cxr_1.1.1-1.ca2404.1_all.deb Size: 1323012 MD5sum: eca21de01d69f03575cec5eeef8318e7 SHA1: 893bc1ab672d7a5ba88031b478b95c8ee5550ff1 SHA256: 1b13e233c58addb608737d66674162ab56776f57fdabf156b24dd7317332d34c SHA512: 35d8368a187a51ebe533d428557cd029248fe20e1f004cfe84847ac5d453c3eced3838296f6bd68a6b8be9ca2f11ecfaf3dd28a7d892a5ff6a21c2793e1853c5 Homepage: https://cran.r-project.org/package=cxr Description: CRAN Package 'cxr' (A Toolbox for Modelling Species Coexistence in R) Recent developments in modern coexistence theory have advanced our understanding on how species are able to persist and co-occur with other species at varying abundances. However, applying this mathematical framework to empirical data is still challenging, precluding a larger adoption of the theoretical tools developed by empiricists. This package provides a complete toolbox for modelling interaction effects between species, and calculate fitness and niche differences. The functions are flexible, may accept covariates, and different fitting algorithms can be used. A full description of the underlying methods is available in García-Callejas, D., Godoy, O., and Bartomeus, I. (2020) . Furthermore, the package provides a series of functions to calculate dynamics for stage-structured populations across sites. Package: r-cran-cxxfunplus Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-inline Suggests: r-cran-rcpp Filename: pool/dists/noble/main/r-cran-cxxfunplus_1.0.2-1.ca2404.1_all.deb Size: 151444 MD5sum: 5d015228e2f7011792be5f27fe463bf3 SHA1: b0bcda4cd01969af531919228ea11732745493cd SHA256: bea668fbf93ad09377413a389a4572cd7dcbcc0d1f3a37f641da5ab9a6a80197 SHA512: fac5540650fa9c896df1bc3f828154960c4e6b289e675a345ec7a506cadea801b2175c1795e02b5ee61657c8b6a0394ab5c9d2f5b650b4d3e0d5ad5cce6f4f39 Homepage: https://cran.r-project.org/package=cxxfunplus Description: CRAN Package 'cxxfunplus' (Extend 'cxxfunction' by Saving the Dynamic Shared Objects) Extend 'cxxfunction' by saving the dynamic shared objects for reusing across R sessions. Package: r-cran-cyclestreets Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1547 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-curl, r-cran-dplyr, r-cran-data.table, r-cran-geojsonsf, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-progressr, r-cran-rcppsimdjson, r-cran-readr, r-cran-sf, r-cran-stringr, r-cran-stringi Suggests: r-cran-covr, r-cran-od, r-cran-stplanr Filename: pool/dists/noble/main/r-cran-cyclestreets_1.0.3-1.ca2404.1_all.deb Size: 1482422 MD5sum: 125221ed6db18990ecc430b37e9e4943 SHA1: 6bda1e8b4b353f28418ff1dde4f25b2cb32d7f80 SHA256: 5989af8d4fb1c4dc9648075044e9e38dcaa3b064972b59fdcd27e5fb702077e2 SHA512: a03c780c18149fce361a5ab76b3931f2aa9d106f5230547faaf74f687d201f4b873f07fa0ba88ae01e92a4c0cccff9da39c237d74de869c0aa014b631f62481d Homepage: https://cran.r-project.org/package=cyclestreets Description: CRAN Package 'cyclestreets' (Cycle Routing and Data for Cycling Advocacy) An interface to the cycle routing/data services provided by 'CycleStreets', a not-for-profit social enterprise and advocacy organisation. The application programming interfaces (APIs) provided by 'CycleStreets' are documented at (). The focus of this package is the journey planning API, which aims to emulate the routes taken by a knowledgeable cyclist. An innovative feature of the routing service of its provision of fastest, quietest and balanced profiles. These represent routes taken to minimise time, avoid traffic and compromise between the two, respectively. 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Package: r-cran-cyclocomp Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-crayon, r-cran-desc, r-cran-remotes, r-cran-withr Suggests: r-cran-processx, r-cran-testthat Filename: pool/dists/noble/main/r-cran-cyclocomp_1.1.2-1.ca2404.1_all.deb Size: 33150 MD5sum: ce2f36d01debaffd66115ca59ffd822b SHA1: 94fe77aa1e463412f663e53e70816672b61e3bf7 SHA256: f87a093e9b2704735ffd0bf4ccd6f0a05d5790927a0022ec1cfc33d1c00508c4 SHA512: f8a36e0844c2dd503d252ff4bfc2bd611c56c22adec9c6d9b55b9f3aeb653586fe7b2d541eee0a707a050dd01524d10acc6f280fdfd7d0f5095fb295c349e165 Homepage: https://cran.r-project.org/package=cyclocomp Description: CRAN Package 'cyclocomp' (Cyclomatic Complexity of R Code) Cyclomatic complexity is a software metric (measurement), used to indicate the complexity of a program. 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Package: r-cran-cyclomort Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexsurv, r-cran-lubridate, r-cran-magrittr, r-cran-mvtnorm, r-cran-plyr, r-cran-scales, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-cyclomort_1.0.3-1.ca2404.1_all.deb Size: 247438 MD5sum: 2e54693b691fe71b974cbff603882b36 SHA1: 3649aae9f14049f8d955c7f945c6ab02740d5b31 SHA256: 0aeb58a2178781f27d42fb68e087ad9f6afadaadb0d5921855e9504af076f832 SHA512: 10847aad2a7c780c5b65cf0c8028e32c4222364ea926c772144f0fbeaa5b02edf189dcd091b0a09de9f2023befc691249aa25cc6433545e76853845bd221ac08 Homepage: https://cran.r-project.org/package=cyclomort Description: CRAN Package 'cyclomort' (Survival Modeling with a Periodic Hazard Function) Modeling periodic mortality (or other time-to event) processes from right-censored data. Given observations of a process with a known period (e.g. 365 days, 24 hours), functions determine the number, intensity, timing, and duration of peaks of periods of elevated hazard within a period. The underlying model is a mixed wrapped Cauchy function fitted using maximum likelihoods (details in Gurarie et al. (2020) ). The development of these tools was motivated by the strongly seasonal mortality patterns observed in many wild animal populations. Thus, the respective periods of higher mortality can be identified as "mortality seasons". Package: r-cran-cyclotomic Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-intmap, r-cran-gmp, r-cran-maybe, r-cran-memoise, r-cran-numbers, r-cran-verylargeintegers Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-cyclotomic_1.3.0-1.ca2404.1_all.deb Size: 215842 MD5sum: 3d324ba3bfeae858984e3555fb0e07b4 SHA1: 6550bde47ec0d1c1758bf32afcc5d6aa5ab0baa4 SHA256: 2d152d4a28e8bb65febb6fc130adc610710dbb895768a4a91eeae27c4ca50253 SHA512: d1715494985d03901d42db2c967f8b679b55b1f65789ce44b282c43f781e6c2830ee4b9b2b39fbf8a16c29ee29749e5678c92d4c5fe098acec1d325dba8ea98d Homepage: https://cran.r-project.org/package=cyclotomic Description: CRAN Package 'cyclotomic' (The Field of Cyclotomic Numbers) The cyclotomic numbers are complex numbers that can be thought of as the rational numbers extended with the roots of unity. They are represented exactly, enabling exact computations. They contain the Gaussian rationals (complex numbers with rational real and imaginary parts) as well as the square roots of all rational numbers. They also contain the sine and cosine of all rational multiples of pi. The algorithms implemented in this package are taken from the 'Haskell' package 'cyclotomic', whose algorithms are adapted from code by Martin Schoenert and Thomas Breuer in the 'GAP' project (). Cyclotomic numbers have applications in number theory, algebraic geometry, algebraic number theory, coding theory, and in the theory of graphs and combinatorics. They have connections to the theory of modular functions and modular curves. 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English: It provides a Portuguese translated version of the datasets listed above. 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Time series plots showing aggregated values are automatically created for each data field (column) depending on its contents (e.g. min/max/mean values for numeric data, no. of distinct values for categorical data), as well as overviews for missing values, non-conformant values, and duplicated rows. The resulting reports are shareable and can contribute to forming a transparent record of the entire analysis process. It is designed with Electronic Health Records in mind, but can be used for any type of record-level temporal data (i.e. tabular data where each row represents a single "event", one column contains the "event date", and other columns contain any associated values for the event). 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Package: r-cran-dalex Architecture: all Version: 2.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1375 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ibreakdown, r-cran-ingredients, r-cran-kernelshap Suggests: r-cran-gower, r-cran-ranger, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dalex_2.5.3-1.ca2404.1_all.deb Size: 1081858 MD5sum: 0383537986851e0badfc4c1165cdfb4c SHA1: 0ee2a08436e8dc2f7bc8a9048df0ee57b86fa18e SHA256: 36448efab044d1f784fa556f5a3cd8a77377dab0d98dd7a7a596a33cb1208b21 SHA512: fabe5fe3ac90e8ed170fdadadbb2979a287e29cead6ad617567f11a8182f194fabbf1f21f1185cfd4e44cd1bb707084f392a99963a8ae6925b086cd1fd199aa6 Homepage: https://cran.r-project.org/package=DALEX Description: CRAN Package 'DALEX' (moDel Agnostic Language for Exploration and eXplanation) Any unverified black box model is the path to failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection. DALEX package xrays any model and helps to explore and explain its behaviour. Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance. But such black-box models usually lack direct interpretability. DALEX package contains various methods that help to understand the link between input variables and model output. Implemented methods help to explore the model on the level of a single instance as well as a level of the whole dataset. All model explainers are model agnostic and can be compared across different models. DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration. Find more details in (Biecek 2018) . Package: r-cran-dalextra Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 810 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dalex, r-cran-ggplot2 Suggests: r-cran-auditor, r-cran-gbm, r-cran-ggrepel, r-cran-h2o, r-cran-iml, r-cran-ingredients, r-cran-lime, r-cran-localmodel, r-cran-mlr, r-cran-mlr3, r-cran-ranger, r-cran-recipes, r-cran-reticulate, r-cran-rmarkdown, r-cran-rpart, r-cran-stacks, r-cran-xgboost, r-cran-testthat, r-cran-tidymodels Filename: pool/dists/noble/main/r-cran-dalextra_2.3.1-1.ca2404.1_all.deb Size: 366284 MD5sum: 9939d549eb71cf772f67b9a778e5b63a SHA1: bea4b0207103d0209b395e95333afae927d1bf54 SHA256: 747b68db80fb778f01635692a55138e062641bea17a567bd1e2b57d527d09762 SHA512: 14d3a4419ccf1d0a4609768b377db5e74ff5be4e108acffadbace1e853eb07f038c6d6481c368b4fa9c95a699decce3ca3a09531e638afeb236f967acbc7dc3e Homepage: https://cran.r-project.org/package=DALEXtra Description: CRAN Package 'DALEXtra' (Extension for 'DALEX' Package) Provides wrapper of various machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the interpretable machine learning, there are more and more new ideas for explaining black-box models, that are implemented in 'R'. 'DALEXtra' creates 'DALEX' Biecek (2018) explainer for many type of models including those created using 'python' 'scikit-learn' and 'keras' libraries, and 'java' 'h2o' library. Important part of the package is Champion-Challenger analysis and innovative approach to model performance across subsets of test data presented in Funnel Plot. Package: r-cran-dalsm Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubicbsplines, r-cran-mass, r-cran-plyr Filename: pool/dists/noble/main/r-cran-dalsm_0.9.1-1.ca2404.1_all.deb Size: 368692 MD5sum: fe3347896e9e9cca13f1f3edca03a25e SHA1: a6ad33305cac3d4edd33e16846639079ab661a13 SHA256: 27ce28eb00f8de17e0601c15ad1b51dd260fda227c469f0e3f54141364f026a5 SHA512: 40a2691e32a888a618b9977cbb9fe79ef86cff40efb8ab97fb6e59a74c68b411cb79bcd7bc6729cc0bd51c08a8c59e65e49345fb7e2d3499c46f05711d7167b0 Homepage: https://cran.r-project.org/package=DALSM Description: CRAN Package 'DALSM' (Nonparametric Double Additive Location-Scale Model (DALSM)) Fit of a double additive location-scale model with a nonparametric error distribution from possibly right- or interval censored data. The additive terms in the location and dispersion submodels, as well as the unknown error distribution in the location-scale model, are estimated using Laplace P-splines. For more details, see Lambert (2021) . Package: r-cran-daltoolbox Architecture: all Version: 1.3.747-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fnn, r-cran-caret, r-cran-arules, r-cran-arulessequences, r-cran-class, r-cran-cluster, r-cran-dbscan, r-cran-dplyr, r-cran-e1071, r-cran-ggplot2, r-cran-mclust, r-cran-nnet, r-cran-randomforest, r-cran-reshape, r-cran-tree Suggests: r-cran-adabag, r-cran-glmnet, r-cran-leaps, r-cran-ipred, r-cran-xgboost, r-cran-arulesviz, r-cran-ggally, r-cran-igraph, r-cran-rpart, r-cran-mass Filename: pool/dists/noble/main/r-cran-daltoolbox_1.3.747-1.ca2404.1_all.deb Size: 613860 MD5sum: 9c5ddd9576ec769f357c9bb93e2597ec SHA1: 939511f678c7ea4b50912286ce6a67438073a1bb SHA256: bd5030d2802b9c0e74ec0851f7012407023fca72da80044f0474d987e7dc793f SHA512: de70f8bdbbc59599e6bd7ad503ce647be7aeaf26a4833c4c8c046bf8f81020b0073e25eb39645c8c33134b067a236d1780d1adb80463398f7505ad4b880ff899 Homepage: https://cran.r-project.org/package=daltoolbox Description: CRAN Package 'daltoolbox' (Leveraging Experiment Lines to Data Analytics) The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity in Data Analytics. The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) . Package: r-cran-daltoolboxdp Architecture: all Version: 1.3.747-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tspredit, r-cran-daltoolbox, r-cran-reticulate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-daltoolboxdp_1.3.747-1.ca2404.1_all.deb Size: 277186 MD5sum: 6ca58d3a05b16a95c9f5ea326f1fe3c4 SHA1: 06222cdaaa79b75d8f1fd8cda067e36c556c9339 SHA256: b3691542ca531bb9654f4049501b913ad81f80d524ff77b28060e9834ee8ae03 SHA512: 223bbca8b0ce8f0a76da2cdcf1521fee0a40a48fe0c19fdc3c886391c6b652a45b905391fddf5319fb12c4b22752d427f799f7aaf0b3ce9d183e2fe979147c6c Homepage: https://cran.r-project.org/package=daltoolboxdp Description: CRAN Package 'daltoolboxdp' (Deep Python Extensions for 'daltoolbox') Extends 'daltoolbox' with Python-backed components for deep learning, scikit-learn classification, and time-series forecasting through 'reticulate'. The package provides objects that follow the 'daltoolbox' architecture while delegating model creation, fitting, encoding, and prediction to Python libraries such as 'torch' and 'scikit-learn'. In the package name, 'dp' stands for 'Deep Python'. The overall workflow is inspired by the Experiment Lines approach described in Ogasawara et al. (2009) . Package: r-cran-dam Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 40 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dam_0.0.1-1.ca2404.1_all.deb Size: 9812 MD5sum: 8f8a948767ce966e6a1494b3e26326b6 SHA1: f2c492f21dbc8d58903c5e2e33d423723b7a3422 SHA256: b31011cf45404f035bf5e5acec476d03fc997ae045da19d680ae2fd5898fdf82 SHA512: fc8a6e78b40a64d0f63bc7a0235bacd9260a85620dd7ecf54dde909ce938128a360d3a2c2175b234903e968aec9b44a7f1f69c90c7b06f71522fb366ab781033 Homepage: https://cran.r-project.org/package=dam Description: CRAN Package 'dam' (Data Analysis Metabolomics) A collection of functions which aim to assist common computational workflow for analysis of matabolomic data.. Package: r-cran-damagedetective Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2389 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-matrix, r-cran-patchwork, r-cran-scales, r-cran-rcpphnsw, r-cran-rlang, r-cran-tidyr, r-cran-withr Suggests: r-cran-seurat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-damagedetective_1.0.0-1.ca2404.1_all.deb Size: 864318 MD5sum: 4cbf0716560838eac3b6e2b623ae9401 SHA1: 29b2844688ef31190f5a25ebd80318715acfe00c SHA256: f287236377a16f267fac7c0ac551252cd003f76ec967fffa22d906b6fbeefb79 SHA512: d2f180dfa08ab34d95858c6a28916c725871689e228bd7114d107cab433be0711f3f8105ef3528738bb8efd181f06a9bcacc54b22ee397e51484bd1e2266ab1e Homepage: https://cran.r-project.org/package=DamageDetective Description: CRAN Package 'DamageDetective' (Detecting Damaged Cells in Single-Cell RNA Sequencing Data) Detects and filters damaged cells in single-cell RNA sequencing (scRNA-seq) data using a novel approach inspired by 'DoubletFinder'. 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Package: r-cran-damaoi Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1995 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-fnn, r-cran-magrittr, r-cran-sf, r-cran-units, r-cran-smoothr, r-cran-terra, r-cran-tibble, r-cran-tidyr, r-cran-shiny, r-cran-leaflet, r-cran-shinydashboard Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-damaoi_0.1-1.ca2404.1_all.deb Size: 1753918 MD5sum: 07a70e134adfd300ae65e712388bf019 SHA1: 8ddc52f0e124721dda50a8fb8c5bcfc27c3a9bdd SHA256: 9b9c412e98d77b2562624adbd81b0ba42fbc110354de686b0c378ebacfea96cc SHA512: 245010191127a8f455dd52d5ebd281099d9a46c760c87a8ee70eec1176133be7672c15ef63b31ec4646bf992b3860bcd8b09e5268ae0ae214c4ac9bd674b3a9c Homepage: https://cran.r-project.org/package=damAOI Description: CRAN Package 'damAOI' (Create an 'Area of Interest' Around a Constructed Dam forComparative Impact Evaluations) Define a spatial 'Area of Interest' (AOI) around a constructed dam using hydrology data. Dams have environmental and social impacts, both positive and negative. Current analyses of dams have no consistent way to specify at what spatial extent we should evaluate these impacts. 'damAOI' implements methods to adjust reservoir polygons to match satellite-observed surface water areas, plot upstream and downstream rivers using elevation data and accumulated river flow, and draw buffers clipped by river basins around reservoirs and relevant rivers. This helps to consistently determine the areas which could be impacted by dam construction, facilitating comparative analysis and informed infrastructure investments. Package: r-cran-damiann Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-testthat Filename: pool/dists/noble/main/r-cran-damiann_1.0.0-1.ca2404.1_all.deb Size: 136006 MD5sum: 5eaa185bcac679800a7c4b83f5746ebc SHA1: ebcbb48b5fbff4ea6e0fb9159fe487e9cfd5a194 SHA256: 6db43b6720ccab06653778f6773e51ece865c9a12a1eae775ecf22da63634592 SHA512: a221a921805dfffd749279d64e3e929720d9546a98a7e0caf7b060a40774a68145cf6653c3583d3f636860c6e6efc771c41ed262f871c71e25a4fbe4e4703f17 Homepage: https://cran.r-project.org/package=DamiaNN Description: CRAN Package 'DamiaNN' (Neural Network Numerai) Interactively train neural networks on Numerai, , data. Generate tournament predictions and write them to a CSV. Package: r-cran-damocles Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caper, r-cran-ape, r-cran-desolve, r-cran-matrixstats, r-cran-expm, r-cran-picante, r-cran-matrix, r-cran-ddd, r-cran-hmisc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-damocles_2.3-1.ca2404.1_all.deb Size: 150198 MD5sum: 304f7f337cb8438953c41936f19437b9 SHA1: 790207b79be03b505d2c4d14361e6d9a92094bdc SHA256: 70780a35c78338cf3cd79a8089ffbe93b35ef2b1364442b54e349937e9530a2d SHA512: ef8409ad2d69b05e5968f030ad8b38d11b1c0a4f688e49d8b00d284b100058b278611efd950d2edce0bc826630cb7f0effbe8d475d5c27d3d78756cd6455cc48 Homepage: https://cran.r-project.org/package=DAMOCLES Description: CRAN Package 'DAMOCLES' (Dynamic Assembly Model of Colonization, Local Extinction andSpeciation) Simulates and computes (maximum) likelihood of a dynamical model of community assembly that takes into account phylogenetic history. Package: r-cran-dampack Architecture: all Version: 1.0.2.1000-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ellipse, r-cran-dplyr, r-cran-scales, r-cran-stringr, r-cran-mgcv, r-cran-truncnorm, r-cran-triangle, r-cran-ggrepel, r-cran-tidyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-lintr, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-dampack_1.0.2.1000-1.ca2404.1_all.deb Size: 3423138 MD5sum: d8cc66765fe5d7f40a9d387290f4f6d0 SHA1: d6dc3f341e71f5fbf8d9e8481487eebc5766690c SHA256: 52fc6b67d1a5b72c1e8dab9572d74963972403cd6d04c7f957e9904c68093b7a SHA512: be41b01896fc06d0cc9eac072051a15c2eeac7fa18232a9fd3e8cba5d07a32dc0ff76b75062839713ee0bbbbedeb73827a9f582d1f6b80595978f57f62e8b6ae Homepage: https://cran.r-project.org/package=dampack Description: CRAN Package 'dampack' (Decision-Analytic Modeling Package) A suite of functions for analyzing and visualizing the health economic outputs of mathematical models. This package was developed with funding from the National Institutes of Allergy and Infectious Diseases of the National Institutes of Health under award no. R01AI138783. The content of this package is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The theoretical underpinnings of 'dampack''s functionality are detailed in Hunink et al. (2014) . Package: r-cran-damr Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 942 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-behavr, r-cran-data.table, r-cran-readr Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-ggetho, r-cran-zeitgebr Filename: pool/dists/noble/main/r-cran-damr_0.3.8-1.ca2404.1_all.deb Size: 177956 MD5sum: e42236ff1506f9ec9283cf989bb2795d SHA1: 2813cc7f85336c124e24fabdc7a219e5c5480c99 SHA256: bf92018248e6fdfe29c19533586e197c50f1dc00f28f497c29f20e3c92e50389 SHA512: c165c7f7420d816315825c2da6b25e88628306d33255fe0fa0eea1f3bc7e5d034b936bdc3e56d9e4522faa3d78aaa92e843cfa0b21811446df9c05888144eeb5 Homepage: https://cran.r-project.org/package=damr Description: CRAN Package 'damr' (Interface to Drosophila Activity Monitor System Result Files) Loads behavioural data from the widely used Drosophila Activity Monitor System (DAMS, TriKinetics ) into the rethomics framework. Package: r-cran-dams Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3303 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-fauxpas, r-cran-janitor, r-cran-readxl Suggests: r-cran-ggplot2, r-cran-maps, r-cran-mapproj, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dams_0.3.0-1.ca2404.1_all.deb Size: 3096614 MD5sum: 937214028d62c6f82be47016da533bb7 SHA1: 224a0db39e9c3c201bd96162c0be42cfc7d000b8 SHA256: 927fbd2db39fc54fa46589402d24b04835f6c76087a47769a0380395eb5d12d3 SHA512: e5d2d3cee510cad8fde9efd46893e78bc557ec95059ce94949f9873903405139444e5bf84713449ce67efaa2c6b2d541b24661bb116869acc635340f886373b2 Homepage: https://cran.r-project.org/package=dams Description: CRAN Package 'dams' (Dams in the United States from the National Inventory of Dams(NID)) The single largest source of dams in the United States is the National Inventory of Dams (NID) from the US Army Corps of Engineers. Entire data from the NID cannot be obtained all at once and NID's website limits extraction of more than a couple of thousand records at a time. Moreover, selected data from the NID's user interface cannot not be saved to a file. In order to make the analysis of this data easier, all the data from NID was extracted manually. Subsequently, the raw data was checked for potential errors and cleaned. This package provides sample cleaned data from the NID and provides functionality to access the entire cleaned NID data. 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The statistical testing framework builds on the divide-aggregate composite-null test described by Liu et al. (2022) . The biological application and SNP/gene-based prioritization workflow are motivated by Salamone et al. (under review), "Leveraging trans-gene regulation prioritizes central genes and pathways in asthma". The package provides functions for identifying distal-proximal gene pairs, organizing significant pairs into genomic loci, and visualizing resulting gene networks. Package: r-cran-dani Architecture: all Version: 0.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-epi Filename: pool/dists/noble/main/r-cran-dani_0.1-1-1.ca2404.1_all.deb Size: 38680 MD5sum: e02a4c2d59e9f49b33aeb1189f4ef5b5 SHA1: 41bef1434e63b98432287e9d5296cc9e5b68f55a SHA256: 31a31937590a828ead7989ebd2fdadacad3a34205514c15a3518ef2792b7638d SHA512: 78941a53a78ff0a84f935e21fb6dd16f090aa70921bfecc589213cad43b33597ac964c22059f5449b07e38bc9e27319b8529aa69fa6cdd72799aa5816de1cb77 Homepage: https://cran.r-project.org/package=dani Description: CRAN Package 'dani' (Design and Analysis of Non-Inferiority Trials) Provides tools to help the design and analysis of resilient non-inferiority trials. These include functions for sample size calculations and analyses of trials, with either a risk difference, risk ratio or arc-sine difference margin, and a function to run simulations to design a trial with the methods described in Quartagno et al. (2019) . Package: r-cran-danielbiostatistics10th Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-e1071, r-cran-pracma, r-cran-vcd Suggests: r-cran-bsda, r-cran-car, r-cran-desctools, r-cran-mblm, r-cran-psych, r-cran-reshape2, r-cran-robslopes, r-cran-survival Filename: pool/dists/noble/main/r-cran-danielbiostatistics10th_0.2.6-1.ca2404.1_all.deb Size: 576170 MD5sum: 72d2a07783936542f74a77255453d901 SHA1: f52de4f4cd08940b9c8c03426023c6c0bbb2c0f8 SHA256: c88054991a350458d7400c3e5d5d18542844196f107f0c472d6494d19aec93ba SHA512: 28e4a193405fd221cf4853a92e85bc9a45dccec3f33cd7e0f7e128ac014c9ccee17c2865dbee7c8e906ac1d5876a90b7d6301e3cd7ef551a7be231479223af1c Homepage: https://cran.r-project.org/package=DanielBiostatistics10th Description: CRAN Package 'DanielBiostatistics10th' (Functions for Wayne W. Daniel's Biostatistics, Tenth Edition) Functions to accompany Wayne W. Daniel's Biostatistics: A Foundation for Analysis in the Health Sciences, Tenth Edition. Package: r-cran-danstat Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-readr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-purrr, r-cran-dplyr, r-cran-ggplot2, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-danstat_0.2.0-1.ca2404.1_all.deb Size: 112356 MD5sum: b1ad6b06a6b85f4bfa0e4ffb5949bfbe SHA1: 62a3ba4ff8c24d1738c0b781b3a82f44bca4c588 SHA256: ac61754da5bbeb989722c8426cf8b48a6fc0e0c329fb99e6838d7e00d12ce118 SHA512: 2d6433d9e6f34007d54d27a85f1b1d30967f7dfd5cc698b17bb805044e200d02c0e4d3d76ab879170eba9eab508e38701e06e1bb826db7a49c11521273e29f31 Homepage: https://cran.r-project.org/package=danstat Description: CRAN Package 'danstat' (R Client for the Statistics Denmark Databank API) The purpose of the package is to enable an R function interface into the Statistics Denmark Databank API mainly for research purposes. The Statistics Denmark Databank API has four endpoints, see here for more information and testing the API in their console: . This package mimics the structure of the API and provides four main functions to match the functionality of the API endpoints. Package: r-cran-dapper Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesplot, r-cran-checkmate, r-cran-furrr, r-cran-memoise, r-cran-posterior, r-cran-progressr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dapper_1.1.0-1.ca2404.1_all.deb Size: 83490 MD5sum: c95a5c2972a29db06cfecd973d2b737d SHA1: b037d7cd4d1b21c0dbf1284c7eeceba51a6dc88a SHA256: 5c8ae65a1f1364586df16db6d234603e0e8a5dfabdd07c9f923a93caa7b8d9ad SHA512: ca1b96d72a200f9d562fa4617b45c843a13b5f7716a29dae9996feb6138cbd72d1f1fa1a823c249fc0b96b769e3b2ce2d76c27af4fff0cd489c95e13f8651922 Homepage: https://cran.r-project.org/package=dapper Description: CRAN Package 'dapper' (Data Augmentation for Private Posterior Estimation) A data augmentation based sampler for conducting privacy-aware Bayesian inference. The dapper_sample() function takes an existing sampler as input and automatically constructs a privacy-aware sampler. The process of constructing a sampler is simplified through the specification of four independent modules, allowing for easy comparison between different privacy mechanisms by only swapping out the relevant modules. Probability mass functions for the discrete Gaussian and discrete Laplacian are provided to facilitate analyses dealing with privatized count data. The output of dapper_sample() can be analyzed using many of the same tools from the 'rstan' ecosystem. For methodological details on the sampler see Ju et al. (2022) , and for details on the discrete Gaussian and discrete Laplacian distributions see Canonne et al. (2020) . Package: r-cran-daqapo Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-stringdist, r-cran-stringr, r-cran-tidyr, r-cran-xesreadr, r-cran-rlang, r-cran-bupar, r-cran-readr, r-cran-edear, r-cran-magrittr, r-cran-purrr, r-cran-glue, r-cran-miniui, r-cran-shiny, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-daqapo_0.3.2-1.ca2404.1_all.deb Size: 187468 MD5sum: a4dca3cce60dd57cc7a4ff2508bbce8f SHA1: e83faac617a3486847a47e8fc52f127f891f0d50 SHA256: 282e8da3bda81cbf2c35c3136bce84789528518347df1c5140197eecffa3587c SHA512: 75fcb623396a7c5cdad5365885e984cd26da23a8521d3b9e25410727f51acc1da36405b77fe0a9ccdeefe16334bd3f94ea43ce882d6156021b5ecc011794b6a9 Homepage: https://cran.r-project.org/package=daqapo Description: CRAN Package 'daqapo' (Data Quality Assessment for Process-Oriented Data) Provides a variety of methods to identify data quality issues in process-oriented data, which are useful to verify data quality in a process mining context. Builds on the class for activity logs implemented in the package 'bupaR'. Methods to identify data quality issues either consider each activity log entry independently (e.g. missing values, activity duration outliers,...), or focus on the relation amongst several activity log entries (e.g. batch registrations, violations of the expected activity order,...). Package: r-cran-darand Architecture: all Version: 0.0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-darand_0.0.1.2-1.ca2404.1_all.deb Size: 30438 MD5sum: 0c3d21a89c79498005a3a3be921384fc SHA1: d482395a547d36c0f14e39b1046d5c4bbc42aef0 SHA256: b3b04d6e12192f482ffcb0f01170a977fe5db247066fbfac2fb7151270cf5b5f SHA512: fc7a7b0f514c61ca29cbd5492e39d469f8871a672d77bdbe1a144b1f68fdd72dfb0e32c2646793eb311d33825ed694a28aa017078b609decbd06305a21910c68 Homepage: https://cran.r-project.org/package=DArand Description: CRAN Package 'DArand' (Differential Analysis with Random Reference Genes) Differential Analysis of short RNA transcripts that can be modeled by either Poisson or Negative binomial distribution. The statistical methodology implemented in this package is based on the random selection of references genes (Desaulle et al. (2021) ). Package: r-cran-dark Architecture: all Version: 0.9.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dark_0.9.9-1.ca2404.1_all.deb Size: 399700 MD5sum: a27748b777876a37f0edb736c034d1b6 SHA1: 499315b9064bda95b48cc1f5e4959bccb68527de SHA256: 451f664ef74b1a2a8545e88b303e39d9e25b49e1144beac3ebc944cf30b28a9c SHA512: c0ae0bfe1f7d64f61140c51bc21b8423dbf100b8fb061b81058e0b05a5ebcecf3de77d4ee4bb0fd308af1d5cd7b943f15c7b1ab64c129786f128ad6d6b1a9f31 Homepage: https://cran.r-project.org/package=Dark Description: CRAN Package 'Dark' (The Analysis of Dark Adaptation Data) The recovery of visual sensitivity in a dark environment is known as dark adaptation. In a clinical or research setting the recovery is typically measured after a dazzling flash of light and can be described by the Mahroo, Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take dark adaptation data and use nonlinear regression to find the parameters of the model that 'best' describe the data. They do this by firstly, generating rapid initial objective estimates of data adaptation parameters, then a multi-start algorithm is used to reduce the possibility of a local minimum. There is also a bootstrap method to calculate parameter confidence intervals. The functions rely upon a 'dark' list or object. This object is created as the first step in the workflow and parts of the object are updated as it is processed. Package: r-cran-darkdiv Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-darkdiv_0.3.0-1.ca2404.1_all.deb Size: 35582 MD5sum: e2524447ab759ac11cb914f4260049f3 SHA1: 2a90f9ee2a1bba5f1ac97758ed11f072ae01491f SHA256: fa55f4894767ca3c1482f8c42428af3918f1638f70281e97b6ce625fff7bc8e3 SHA512: 7d0fe2a6713718eecb1453b5df354a59f87b8aa8565ee79fd1988da3a31009f638e710aecfee20a566bac164d087d481ffe1f40134cee16fac9b26ca512c8d3a Homepage: https://cran.r-project.org/package=DarkDiv Description: CRAN Package 'DarkDiv' (Estimating Dark Diversity and Site-Specific Species Pools) Estimation of dark diversity and site-specific species pools using species co-occurrences. It includes implementations of probabilistic dark diversity based on the Hypergeometric distribution, as well as estimations based on the Beals index, which can be transformed to binary predictions using different thresholds, or transformed into a favorability index. All methods include the possibility of using a calibration dataset that is used to estimate the indication matrix between pairs of species, or to estimate dark diversity directly on a single dataset. See De Caceres and Legendre (2008) , Lewis et al. (2016) , Partel et al. (2011) , Real et al. (2017) for further information. Package: r-cran-darksky Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-gridextra, r-cran-gtable, r-cran-ggplot2, r-cran-plyr Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-darksky_1.3.0-1.ca2404.1_all.deb Size: 41356 MD5sum: 760e1e7f8f208fb545f416bad07a621a SHA1: ad6c2faf608b702d05ffd084af054b33c938556d SHA256: 2afeb2b50d7a7149baff1147f515ccf5a135f74323e8dce8cf5e9db97de20074 SHA512: ad4220df7d679a566bae0f9ac1f7abe81274ae70bc74bab17f9879fd762e4c53f7b48a6d71d58a87e8284584818860b7bbb186f731daf1d4a3e64a1b7753d3cd Homepage: https://cran.r-project.org/package=darksky Description: CRAN Package 'darksky' (Tools to Work with the 'Dark Sky' 'API') Provides programmatic access to the 'Dark Sky' 'API' , which provides current or historical global weather conditions. Package: r-cran-dartr.base Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4012 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-dartr.data, r-cran-adegenet, r-cran-ape, r-cran-crayon, r-cran-data.table, r-cran-foreach, r-cran-gridextra, r-cran-patchwork, r-cran-plyr, r-cran-reshape2, r-bioc-snprelate, r-cran-stampp, r-cran-stringr, r-cran-tidyr, r-cran-mass, r-cran-bigstatsr, r-cran-bigsnpr, r-bioc-snpstats, r-cran-gtools Suggests: r-cran-boot, r-cran-devtools, r-cran-directlabels, r-cran-dismo, r-cran-doparallel, r-cran-expm, r-cran-gdistance, r-cran-gganimate, r-cran-ggrepel, r-cran-gtable, r-cran-ggthemes, r-cran-gplots, r-cran-hardyweinberg, r-cran-hierfstat, r-cran-igraph, r-cran-iterpc, r-cran-knitr, r-cran-label.switching, r-cran-lattice, r-cran-leaflet, r-cran-leaflet.minicharts, r-cran-markdown, r-cran-mmod, r-cran-networkd3, r-cran-pegas, r-cran-pheatmap, r-cran-plotly, r-cran-poppr, r-cran-proxy, r-cran-purrr, r-bioc-qvalue, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-rgl, r-cran-rmarkdown, r-cran-rrblup, r-cran-scales, r-cran-seqinr, r-cran-sf, r-cran-shinybs, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-siber, r-cran-stringi, r-cran-tibble, r-cran-vcfr, r-cran-zoo, r-cran-viridis, r-cran-fields, r-cran-testthat, r-cran-ggtern, r-cran-dendextend, r-cran-ggdendro, r-cran-terra, r-cran-r.utils Filename: pool/dists/noble/main/r-cran-dartr.base_1.2.3-1.ca2404.1_all.deb Size: 3745724 MD5sum: 92a3121ddfcfc9d7cae86fd0650852a0 SHA1: d7ecef439b7a8d4a1436dea5b3b5c203ded50622 SHA256: b5bed0fa93da83aabbcf951e6c65ba59571985634eb806ab5f86a67fe9c90903 SHA512: 7213a4eaa0d11dc077139f6cc03233439d4b8612eb5bac7e15292978d23373364a96b00b3c0532adced908d3732d4af219d10a9766fe360566f8338e7d8e3744 Homepage: https://cran.r-project.org/package=dartR.base Description: CRAN Package 'dartR.base' (Analysing 'SNP' and 'Silicodart' Data - Basic Functions) Facilitates the import and analysis of 'SNP' (single nucleotide 'polymorphism') and 'silicodart' (presence/absence) data. The main focus is on data generated by 'DarT' (Diversity Arrays Technology), however, data from other sequencing platforms can be used once 'SNP' or related fragment presence/absence data from any source is imported. Genetic datasets are stored in a derived 'genlight' format (package 'adegenet'), that allows for a very compact storage of data and metadata. Functions are available for importing and exporting of 'SNP' and 'silicodart' data, for reporting on and filtering on various criteria (e.g. 'callrate', 'heterozygosity', 'reproducibility', maximum allele frequency). Additional functions are available for visualization (e.g. Principle Coordinate Analysis) and creating a spatial representation using maps. 'dartR.base' is the 'base' package of the 'dartRverse' suits of packages. To install the other packages, we recommend to install the 'dartRverse' package, that supports the installation of all packages in the 'dartRverse'. If you want to cite 'dartR', you find the information by typing citation('dartR.base') in the console. Package: r-cran-dartr.captive Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1353 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-dartr.sim, r-cran-adegenet, r-cran-crayon, r-cran-ggplot2, r-cran-patchwork, r-cran-stringr, r-cran-data.table, r-cran-gridextra, r-cran-magrittr, r-cran-reshape2, r-cran-tidyr, r-cran-digest Suggests: r-cran-siber, r-cran-gplots, r-cran-fields, r-cran-igraph, r-cran-rrblup, r-cran-scales, r-cran-spelling, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-dartr.captive_1.2.2-1.ca2404.1_all.deb Size: 1255156 MD5sum: 1531453f0203f3a019e837595537edf0 SHA1: 1cd9ff39d70e5708af9eb970727a0be1c3ee84c1 SHA256: 257183c5374787b80830c7782bd044a6ced97e464fe88fb05c07dd1dac774817 SHA512: ef804ff8426e875421980cf16e0f7d4bbd9302b1f5de6b4c73075e7934cfa1d7335b745e283832ca14146c9efcd945205756d3a01dff7133e79c05eceee5c791 Homepage: https://cran.r-project.org/package=dartR.captive Description: CRAN Package 'dartR.captive' (Analysing 'SNP' Data to Support Captive Breeding) Functions are provided that facilitate the analysis of SNP (single nucleotide polymorphism) data to answer questions regarding captive breeding and relatedness between individuals. 'dartR.captive' is part of the 'dartRverse' suit of packages. Gruber et al. (2018) . Mijangos et al. (2022) . Package: r-cran-dartr.data Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5896 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-adegenet Filename: pool/dists/noble/main/r-cran-dartr.data_1.2.2-1.ca2404.1_all.deb Size: 5370084 MD5sum: 39605e524b8a381d5ee06b3965c64b21 SHA1: 732e546bf4dfdcd6e0472a5c5310edd5292849ff SHA256: 7ab202d9943d0f2658372b55f4f4700198e03abb183b949a7b7769a852ed2d7f SHA512: e5b0fdab171d6fad14f4ec3e62a035da419b01e6706d8eb22190ec21e6239bd9a21b9b4a3266d66040cd673b737d7df491f5868ca5f2ef765e856b094f8cef33 Homepage: https://cran.r-project.org/package=dartR.data Description: CRAN Package 'dartR.data' (Auxiliary Data Package for Our Main Package 'dartR') Data package for 'dartR'. Provides data sets to run examples in 'dartR'. This was necessary due to the size limit imposed by 'CRAN'. The data in 'dartR.data' is needed to run the examples provided in the 'dartR' functions. All available data sets are either based on actual data (but reduced in size) and/or simulated data sets to allow the fast execution of examples and demonstration of the functions. Package: r-cran-dartr.popgen Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1226 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-adegenet, r-cran-mass, r-cran-dplyr, r-cran-patchwork, r-cran-crayon, r-cran-ggplot2, r-cran-data.table, r-cran-stringr, r-cran-furrr, r-cran-future, r-cran-ggdendro, r-bioc-lea, r-cran-pillar, r-cran-plyr, r-cran-terra, r-cran-purrr, r-cran-ggpmisc, r-cran-r.utils, r-cran-ape Suggests: r-cran-siber, r-cran-expm, r-cran-fields, r-cran-gplots, r-cran-gridextra, r-cran-igraph, r-cran-iterpc, r-cran-label.switching, r-cran-leaflet, r-cran-proxy, r-bioc-qvalue, r-cran-raster, r-cran-reshape2, r-cran-scales, r-bioc-snpstats, r-cran-tidyr, r-cran-viridis, r-cran-zoo, r-cran-gsubfn, r-cran-sp Filename: pool/dists/noble/main/r-cran-dartr.popgen_1.2.2-1.ca2404.1_all.deb Size: 1199292 MD5sum: 01648900f100f7a35f16fa5be86f7218 SHA1: c5fe99c69ab0a0b737a5139358dc69073e50760e SHA256: 9a623bb9a56def9b1753f4303b8ee7ec940c166d23c896d38b28dc1be24cda86 SHA512: be9dc56fb34d4e70bf7e63e9ac2c7f6249eb89bcac157e0ae64c7f712bd9ecd5bccbe3f597e24b82842ce7ff09d328800f8997170ad0cb139a0769a4ca0c34a1 Homepage: https://cran.r-project.org/package=dartR.popgen Description: CRAN Package 'dartR.popgen' (Analysing 'SNP' and 'Silicodart' Data Generated by Genome-WideRestriction Fragment Analysis) Facilitates the analysis of SNP (single nucleotide polymorphism) and silicodart (presence/absence) data. 'dartR.popgen' provides a suit of functions to analyse such data in a population genetics context. It provides several functions to calculate population genetic metrics and to study population structure. Quite a few functions need additional software to be able to run (gl.run.structure(), gl.blast(), gl.LDNe()). You find detailed description in the help pages how to download and link the packages so the function can run the software. 'dartR.popgen' is part of the the 'dartRverse' suit of packages. Gruber et al. (2018) . Mijangos et al. (2022) . Package: r-cran-dartr.sexlinked Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-adegenet, r-cran-doparallel, r-cran-ggplot2, r-cran-foreach, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-dartr.sexlinked_1.2.2-1.ca2404.1_all.deb Size: 509678 MD5sum: 3c422ecffc504688d48cebb800c96cc7 SHA1: 0d3f242d8d06f64f0a649d3c5df41fa6b105354a SHA256: 2db858ecc4d6a268f60d9594dd9d21c07623b3b44397440d4e94c53d5f7a6bdd SHA512: 5d31028578f2972759f77c06c67da738a36dfc0e49c53ef232930abe7b657eba61a2979e73477343d8f67f4d73e2584c7142f0f87a9b0d589f85b92fae57f0af Homepage: https://cran.r-project.org/package=dartR.sexlinked Description: CRAN Package 'dartR.sexlinked' (Analysing SNP Data to Identify Sex-Linked Markers) Identifies, filters and exports sex linked markers using 'SNP' (single nucleotide polymorphism) data. To install the other packages, we recommend to install the 'dartRverse' package, that supports the installation of all packages in the 'dartRverse'. If you want understand the applied rational to identify sexlinked markers and/or want to cite 'dartR.sexlinked', you find the information by typing citation('dartR.sexlinked') in the console. Package: r-cran-dartr.sim Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1416 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-ggplot2, r-cran-dartr.popgen, r-cran-adegenet, r-cran-shiny, r-cran-fields, r-cran-stringi, r-cran-stringr, r-cran-data.table, r-cran-rcpp, r-cran-shinybs, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-hierfstat, r-cran-reshape2, r-cran-foreach, r-cran-ggrepel, r-cran-dplyr, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-dartr.sim_1.2.2-1.ca2404.1_all.deb Size: 1391104 MD5sum: 0a20d4f53bf6ddd9928463967e686482 SHA1: 3cb718804ec12623c324ce6802eaaef230d42df0 SHA256: 8d52a9d6862836cb337226b2a523b2204c18fecc81de1a164df0ea7160c5574e SHA512: 6529cb01d149e5d1507cf7484afdeace3eea3deb12da39c49d15a14a91cb39919371846b26770032bafe8e086560922f4259733cd1d47149b7ed6c34bcc7b28a Homepage: https://cran.r-project.org/package=dartR.sim Description: CRAN Package 'dartR.sim' (Computer Simulations of 'SNP' Data) Allows to simulate SNP data using genlight objects. For example, it is straight forward to simulate a simple drift scenario with exchange of individuals between two populations or create a new genlight object based on allele frequencies of an existing genlight object. Package: r-cran-dartr.spatial Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dartr.base, r-cran-dartr.data, r-cran-adegenet, r-cran-crayon, r-cran-stampp, r-cran-raster, r-cran-sp, r-cran-tidyr, r-cran-vegan, r-cran-mass, r-cran-ggplot2, r-cran-data.table, r-cran-dismo Suggests: r-cran-mmod, r-cran-gdistance, r-cran-gplots, r-cran-rrblup, r-cran-terra, r-cran-sf, r-cran-popgenreport Filename: pool/dists/noble/main/r-cran-dartr.spatial_1.2.2-1.ca2404.1_all.deb Size: 1314804 MD5sum: 3827f6fc35319cea17a2f46996a52c02 SHA1: e3f3361e4aad6e5c3efe803da2ac2d1db263cf99 SHA256: 9a0ae56e6f3749a1c86268f10e46038ce6f1aeb49aa4888f5a1e8cf44bcef9a4 SHA512: f6f7bdc9227a94c5a81309066d258758a2e42a2fdd53b3daeb0271b11f6dc61d652f1df50009345f47703f0ad6ca68fdfb8329da7dbea9028def3073619dc9aa Homepage: https://cran.r-project.org/package=dartR.spatial Description: CRAN Package 'dartR.spatial' (Applying Landscape Genomic Methods on 'SNP' and 'Silicodart'Data) Provides landscape genomic functions to analyse 'SNP' (single nuclear polymorphism) data, such as least cost path analysis and isolation by distance. Therefore each sample needs to have coordinate data attached (lat/lon) to be able to run most of the functions. 'dartR.spatial' is a package that belongs to the 'dartRverse' suit of packages and depends on 'dartR.base' and 'dartR.data'. Package: r-cran-dartr Architecture: all Version: 2.9.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6054 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-adegenet, r-cran-ggplot2, r-cran-dplyr, r-cran-dartr.data, r-cran-ape, r-cran-crayon, r-cran-data.table, r-cran-fields, r-cran-foreach, r-cran-gridextra, r-cran-mass, r-cran-patchwork, r-cran-plyr, r-cran-popgenreport, r-cran-raster, r-cran-reshape2, r-cran-shiny, r-bioc-snprelate, r-cran-sp, r-cran-stampp, r-cran-stringr, r-cran-tidyr, r-cran-gsubfn, r-cran-purrr Suggests: r-cran-boot, r-cran-devtools, r-cran-directlabels, r-cran-dismo, r-cran-doparallel, r-cran-expm, r-cran-gdistance, r-cran-ggtern, r-cran-gganimate, r-cran-ggrepel, r-cran-gtable, r-cran-ggthemes, r-cran-gplots, r-cran-hardyweinberg, r-cran-hierfstat, r-cran-igraph, r-cran-iterpc, r-cran-knitr, r-cran-label.switching, r-cran-lattice, r-cran-leaflet, r-cran-leaflet.minicharts, r-cran-markdown, r-cran-mmod, r-cran-networkd3, r-cran-pegas, r-cran-pheatmap, r-cran-plotly, r-cran-poppr, r-cran-proxy, r-bioc-qvalue, r-cran-rcolorbrewer, r-cran-rcpp, r-cran-rgl, r-cran-rmarkdown, r-cran-rrblup, r-cran-scales, r-cran-seqinr, r-cran-shinybs, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-siber, r-bioc-snpstats, r-cran-stringi, r-cran-terra, r-cran-tibble, r-cran-vcfr, r-cran-zoo, r-cran-viridis, r-cran-vegan Filename: pool/dists/noble/main/r-cran-dartr_2.9.9.5-1.ca2404.1_all.deb Size: 5259872 MD5sum: 067581a9da5131b80930605c8e0ae01c SHA1: 204820c7168cec7f811b659fddd07c3355fc3e56 SHA256: d8624881972219872010c98f6a6f896bb2dfb019e22fbc6fdc7f4165ea776dd7 SHA512: 4596403d3a239dcff249ff5e7bb990f8745bb3aa3b1c3ea0dc2503715cca648cbd4ea1a569d44cd303aa42bd3bdba706a3dbdfb6d5fff0fef4861a00691b5437 Homepage: https://cran.r-project.org/package=dartR Description: CRAN Package 'dartR' (Importing and Analysing 'SNP' and 'Silicodart' Data Generated byGenome-Wide Restriction Fragment Analysis) Functions are provided that facilitate the import and analysis of 'SNP' (single nucleotide polymorphism) and 'silicodart' (presence/absence) data. The main focus is on data generated by 'DarT' (Diversity Arrays Technology), however, data from other sequencing platforms can be used once 'SNP' or related fragment presence/absence data from any source is imported. Genetic datasets are stored in a derived 'genlight' format (package 'adegenet'), that allows for a very compact storage of data and metadata. Functions are available for importing and exporting of 'SNP' and 'silicodart' data, for reporting on and filtering on various criteria (e.g. 'CallRate', heterozygosity, reproducibility, maximum allele frequency). Additional functions are available for visualization (e.g. Principle Coordinate Analysis) and creating a spatial representation using maps. 'dartR' supports also the analysis of 3rd party software package such as 'newhybrid', 'structure', 'NeEstimator' and 'blast'. Since version 2.0.3 we also implemented simulation functions, that allow to forward simulate 'SNP' dynamics under different population and evolutionary dynamics. Comprehensive tutorials and support can be found at our 'github' repository: github.com/green-striped-gecko/dartR/. If you want to cite 'dartR', you find the information by typing citation('dartR') in the console. Package: r-cran-dartrverse Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1701 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-devtools, r-cran-rcurl, r-cran-httr Suggests: r-cran-dartr.base, r-cran-dartr.data, r-cran-dartr.sim, r-cran-dartr.captive, r-cran-dartr.popgen, r-cran-dartr.spatial Filename: pool/dists/noble/main/r-cran-dartrverse_1.0.6-1.ca2404.1_all.deb Size: 1679910 MD5sum: 1fa326829ab4e7d86671385df3ed970c SHA1: 3d2d0e53c6ea0cf7c43e7ae8922d47f7e3d0b7d0 SHA256: 6267a58a3dee3b78b72feb8a72cfa9ca2c2d5f1294db77313b3e2266ebc8111c SHA512: ff3f8afcc286fe722defd8b5f2bd9dcb619780308f2ebd8f976c4d3b4e7e4c3f6f481d6d3426e6c820f411e4d42b65e5ec092681dfd040c09a45d74cc0a50bdd Homepage: https://cran.r-project.org/package=dartRverse Description: CRAN Package 'dartRverse' (Install and Load the 'dartRverse' Suits of Packages) Provides a single function that supports the installation of all packages belonging to the 'dartRverse'. The 'dartRverse' is a set of packages that work together to analyse SNP (single nuclear polymorphism) data. All packages aim to have a similar 'look and feel' and are based on the same type of data structure ('genlight'), with additional metadata for loci and individuals (samples). For more information visit the 'GitHub' pages . Package: r-cran-dasguptr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2610 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-dasguptr_2.1.0-1.ca2404.1_all.deb Size: 472054 MD5sum: 47d37ae459893ee2d382f4b64de9e465 SHA1: 5007debc7dceecd4cf61ed96e951304c31a5d56a SHA256: afa91c4e15fd71c4f1c446b811baf075dc0cf3fd7c330f2401aed901f211fcaf SHA512: 1594382e48a57887b02a01dc7f0e01f13d582ecf605f18fbc7d0e9bc3e6e1171caf7d2d0f98842b5e7cdf8e9d9891b88b8e6ddaef71021a50efee523205fa44f Homepage: https://cran.r-project.org/package=DasGuptR Description: CRAN Package 'DasGuptR' (Das Gupta Standardisation and Decomposition) Implementation of Das Gupta's standardisation and decomposition of population rates, as set out "Standardization and decomposition of rates: A user’s manual", Das Gupta (1993) . The goal of these methods is to calculate adjusted rates based on compositional 'factors' and quantify the contribution of each factor to the difference in crude rates between populations. The package offers functionality to handle various scenarios for any number of factors and populations, where said factors can be comprised of vectors across sub-populations (including cross-classified population breakdowns), and with the option to specify user-defined rate functions. Package: r-cran-dashboardthemes Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools Suggests: r-cran-testthat, r-cran-lintr, r-cran-knitr, r-cran-rmarkdown, r-cran-glue, r-cran-covr Filename: pool/dists/noble/main/r-cran-dashboardthemes_1.1.6-1.ca2404.1_all.deb Size: 91326 MD5sum: d37c74f8436b3c37f2b83514bf43b144 SHA1: c5f823b7482b44b5fe902af4c3c6a169d2f1bb90 SHA256: 254d8436e3ed1a5e1d8256978e14f53b173cc7e419b5a93b6fdfd55e145c91f0 SHA512: a63a75b5aca7d0278eff59f89621dd1f6e8f2eed727d64eb03e1ad5fc40543cff0d74c251b8366eb05d8d6d948cc344e4a788c7a78dd06ea271d35d0e2e543d9 Homepage: https://cran.r-project.org/package=dashboardthemes Description: CRAN Package 'dashboardthemes' (Customise the Appearance of 'shinydashboard' Applications usingThemes) Allows manual creation of themes and logos to be used in applications created using the 'shinydashboard' package. Removes the need to change the underlying css code by wrapping it into a set of convenient R functions. Package: r-cran-dasst Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2451 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dasst_0.3.4-1.ca2404.1_all.deb Size: 1024626 MD5sum: 3f19f64ed4d8a72d5ab428a35b66feaf SHA1: 9705fd52d426c1bfa36aad9b39fda2c615c3e7e7 SHA256: a6501c2a36c3ffb820487ff1aeb3124f1cbacf7230452743ce9fffe7e2bce239 SHA512: d5fd88021222d70f7272776d957d29e4aad9f74a7334dbdf50d27e285831e016704d77892ebd91b33d6d1fbe9217e5d63d63fe84026baea7262218e80f81c24c Homepage: https://cran.r-project.org/package=Dasst Description: CRAN Package 'Dasst' (Tools for Reading, Processing and Writing 'DSSAT' Files) Provides methods for reading, displaying, processing and writing files originally arranged for the 'DSSAT-CSM' fixed width format. 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Aggregate, cumulate, print, plot, convert to and from data.frame and more. Useful for decision trees, machine learning, finance, conversion from and to JSON, and many other applications. Package: r-cran-data.validator Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1604 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertr, r-cran-shiny.semantic, r-cran-knitr, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-r6, r-cran-rlang, r-cran-rmarkdown, r-cran-htmltools, r-cran-htmlwidgets, r-cran-tibble Suggests: r-cran-covr, r-cran-fixtures, r-cran-fs, r-cran-lintr, r-cran-magrittr, r-cran-rcmdcheck, r-cran-readr, r-cran-shiny, r-cran-spelling, r-cran-targets, r-cran-testthat, r-cran-visnetwork, r-cran-withr Filename: pool/dists/noble/main/r-cran-data.validator_0.2.1-1.ca2404.1_all.deb Size: 624858 MD5sum: 70e461d32d2dea6f6cce5b7918651eda SHA1: 005ebe3dc1bc9948a7b56e2e7b7d684c1f826808 SHA256: ffd6dff6f39fa1abed075476463b69c7369edf3df2bcbb6451c950e23450b15c SHA512: a8269b93f70521c89c337736cb47ecb26acf9c41595ed424efed8487e2c6df802041f19d251eaed1fc20c1ca66de161dd921b68aaa213e0355a200602f3e2fd5 Homepage: https://cran.r-project.org/package=data.validator Description: CRAN Package 'data.validator' (Automatic Data Validation and Reporting) Validate dataset by columns and rows using convenient predicates inspired by 'assertr' package. Generate good looking HTML report or print console output to display in logs of your data processing pipeline. Package: r-cran-data360r Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-reshape2, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-data360r_1.0.9-1.ca2404.1_all.deb Size: 30284 MD5sum: aa8a23ea9be1f212bbc34173f7f519a8 SHA1: c77991c0cafc0170dae70b66d45412622f41e794 SHA256: 9174981166aa630c59738f40c2217be125f33545ba0f9a87554ee7b55bb6d633 SHA512: 6fcb1731fb0f5c527a4f13104b2e65b5390c5d5492fe1646e98f74bce9ac61253d2a8c8a6ceccce75a85f71171a2ba9a0c210b81a042d44a84b8e2f3f54848e3 Homepage: https://cran.r-project.org/package=data360r Description: CRAN Package 'data360r' (Wrapper for 'TCdata360' and 'Govdata360' API) Makes it easy to engage with the Application Program Interface (API) of the 'TCdata360' and 'Govdata360' platforms at and , respectively. These application program interfaces provide access to over 5000 trade, competitiveness, and governance indicator data, metadata, and related information from sources both inside and outside the World Bank Group. Package functions include easier download of data sets, metadata, and related information, as well as searching based on user-inputted query. Package: r-cran-databaseconnector Architecture: all Version: 7.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1435 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava, r-cran-sqlrender, r-cran-stringr, r-cran-readr, r-cran-rlang, r-cran-dbi, r-cran-urltools, r-cran-bit64, r-cran-checkmate, r-cran-digest, r-cran-dbplyr Suggests: r-cran-aws.s3, r-cran-r.utils, r-cran-withr, r-cran-testthat, r-cran-dbitest, r-cran-knitr, r-cran-rmarkdown, r-cran-rsqlite, r-cran-ssh, r-cran-andromeda, r-cran-dplyr, r-cran-rpostgres, r-cran-odbc, r-cran-duckdb, r-cran-bigrquery, r-cran-pool, r-cran-parallellogger, r-cran-azurestor Filename: pool/dists/noble/main/r-cran-databaseconnector_7.1.0-1.ca2404.1_all.deb Size: 1235588 MD5sum: 08380af5bd1667541271c581f99ac964 SHA1: 8008599d33e87c0d235f8a0686941b62380596d4 SHA256: ada536eaee83fbd4db2c006015858e6a86aca452ddc24f57de4814d232555d22 SHA512: 157ad6f320ad2f173027c92c2c5b41227cb56624b39050e337136a867b54663e9e2501f5b3409e98549b0c9256671dde49d51b73d44e34169276ca6e86a5d666 Homepage: https://cran.r-project.org/package=DatabaseConnector Description: CRAN Package 'DatabaseConnector' (Connecting to Various Database Platforms) An R 'DataBase Interface' ('DBI') compatible interface to various database platforms ('PostgreSQL', 'Oracle', 'Microsoft SQL Server', 'Amazon Redshift', 'Microsoft Parallel Database Warehouse', 'IBM Netezza', 'Apache Impala', 'Google BigQuery', 'Snowflake', 'Spark', 'SQLite', and 'InterSystems IRIS'). Also includes support for fetching data as 'Andromeda' objects. Uses either 'Java Database Connectivity' ('JDBC') or other 'DBI' drivers to connect to databases. 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Package: r-cran-datamuseum Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2472 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-rgbif, r-cran-taxize, r-cran-memoise, r-cran-cachem, r-cran-rnaturalearth, r-cran-sf, r-cran-furrr, r-cran-future, r-cran-tibble, r-cran-rlang Suggests: r-cran-r.utils, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-lubridate, r-cran-googlesheets4, r-cran-maps, r-cran-rnaturalearthdata Filename: pool/dists/noble/main/r-cran-datamuseum_0.1.0-1.ca2404.1_all.deb Size: 1807622 MD5sum: 08cdc7fadd559a40449b1b01f8171d9c SHA1: 929e32e4c2628761d15e6ee1927f148c493585fd SHA256: 155c89d5916c016445b7f9c26d3544a162d90b50b1a5793559106bb21b0c858a SHA512: 532aa7ba33689e2d882d168473feec549011f07f753d14a20b0b337e2315f8c9835b9e29874f773f75026878d0475fa410b5772499f5965d645c4eecf8642eb1 Homepage: https://cran.r-project.org/package=datamuseum Description: CRAN Package 'datamuseum' (Spatial and Taxonomic Data Utilities for Specimen Datasets) A management tool for specimen data ranging from public museum collections to private specimen repositories. The main types of data addressed are spatial (coordinates, longitude and latitude) and taxonomic data (ranking and nomenclature validity) with some additional options for user-determined dataset refinement. Combined or individual calls to the online repositories of the Global Biodiversity Information Facility (GBIF) via 'rgbif' and the Integrated Taxonomic Information System (ITIS) via 'taxize' enable built-in taxonomic checks. Package: r-cran-datana Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-hmisc, r-cran-scales Suggests: r-cran-lattice, r-cran-testthat Filename: pool/dists/noble/main/r-cran-datana_1.1.5-1.ca2404.1_all.deb Size: 5236548 MD5sum: a64bd40ee1a8c0d8be114c4a403f97cb SHA1: 9aad991686cc829ca0695e1a257914b680625ec3 SHA256: 299195fcbec51dcbac8eb9a7d0ae4273bcf0dc2fddf502e48a0dbab28ca73c1b SHA512: 3e68013bec4f9d6ff8d84a2618d737ac69f2a950ec8c5d5fa09b83d46238664a05078fe9f94a218c7618c28840bbf4c9966605e006123654f755dc5b94ce39e2 Homepage: https://cran.r-project.org/package=datana Description: CRAN Package 'datana' (Datasets and Functions to Accompany Analisis De Datos Con R) Datasets and functions to accompany the book 'Analisis de datos con el programa estadistico R: una introduccion aplicada' by Salas-Eljatib (2021, ISBN: 9789566086109). The package helps carry out data management, exploratory analyses, and model fitting. Package: r-cran-datanugget Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dosnow, r-cran-foreach, r-cran-rfast Filename: pool/dists/noble/main/r-cran-datanugget_1.4.0-1.ca2404.1_all.deb Size: 81992 MD5sum: 8a419728317c0858700f0371def88d58 SHA1: 7b0195f876a95dd5abc92d2ad0fa972f42562986 SHA256: 8300d19ef97c041ed9aee966e8f1a25d7a6b15c6d6a20bc27cbcb26229a6c751 SHA512: bd52b1e52ab680942775ed0c4134a34845db4366162f860607c9e0f397b2e3881ccc6d47220f7cd2d11189d09bc38240d65acef8f0d8d1f1589a06c8435475db Homepage: https://cran.r-project.org/package=datanugget Description: CRAN Package 'datanugget' (Create, and Refine Data Nuggets) Creating, and refining data nuggets. Data nuggets reduce a large dataset into a small collection of nuggets of data, each containing a center (location), weight (importance), and scale (variability) parameter. Data nugget centers are created by choosing observations in the dataset which are as equally spaced apart as possible. Data nugget weights are created by counting the number observations closest to a given data nugget center. We then say the data nugget 'contains' these observations and the data nugget center is recalculated as the mean of these observations. Data nugget scales are created by calculating the trace of the covariance matrix of the observations contained within a data nugget divided by the dimension of the dataset. Data nuggets are refined by 'splitting' data nuggets which have scales or shapes (defined as the ratio of the two largest eigenvalues of the covariance matrix of the observations contained within the data nugget) Reference paper: [1] Beavers, T. E., Cheng, G., Duan, Y., Cabrera, J., Lubomirski, M., Amaratunga, D., & Teigler, J. E. (2024). Data Nuggets: A Method for Reducing Big Data While Preserving Data Structure. Journal of Computational and Graphical Statistics, 1-21. [2] Cherasia, K. E., Cabrera, J., Fernholz, L. T., & Fernholz, R. (2022). Data Nuggets in Supervised Learning. \emph{In Robust and Multivariate Statistical Methods: Festschrift in Honor of David E. Tyler} (pp. 429-449). Cham: Springer International Publishing. Package: r-cran-dataonderivatives Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr2, r-cran-readr, r-cran-tibble, r-cran-vetr Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dataonderivatives_0.4.0-1.ca2404.1_all.deb Size: 29318 MD5sum: 07238bd094b99b8b09ddd70e6842afcc SHA1: 2e17f2a03a8557aba5e251169e372e0c5b2c9170 SHA256: 96ccb1dc76636d91cd02051738ca7da69e27a522d129e6180545ddc8b43312b6 SHA512: f60cf2ecb55160f9f92bfeeeed2127b52fc35fb6d084cb3aa8d6efa25978a8928d5a83adfab8784583ec836323c5ceb620d3d931c5dd1d2e56221b734a7bd54b Homepage: https://cran.r-project.org/package=dataonderivatives Description: CRAN Package 'dataonderivatives' (Easily Source Publicly Available Data on Derivatives) Post Global Financial Crisis derivatives reforms have lifted the veil off over-the-counter (OTC) derivative markets. Swap Execution Facilities (SEFs) and Swap Data Repositories (SDRs) now publish data on swaps that are traded on or reported to those facilities (respectively). This package provides you the ability to get this data from supported sources. 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Each 'DataONE' repository implements a consistent repository application programming interface. Users call methods in R to access these remote repository functions, such as methods to query the metadata catalog, get access to metadata for particular data packages, and read the data objects from the data repository. Users can also insert and update data objects on repositories that support these methods. Package: r-cran-datapack Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-fs, r-cran-redland, r-cran-xml, r-cran-uuid, r-cran-zip Suggests: r-cran-testthat, r-cran-knitr, r-cran-httr, r-cran-igraph, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-datapack_1.4.2-1.ca2404.1_all.deb Size: 813112 MD5sum: 69d16f33a64ab1b1c0158582c991fb71 SHA1: 920a6533525ee242b8c5f1f7aa9032232e62e3ef SHA256: 4d2b11580329aea568d0c2e1c3dde144c819f46f113de09c1fd8f70e4b76c38e SHA512: 16fa3bc5fdc915d49809e2d8278ac2e719b081e7ca5c4af8c2c07c3ba34fe98cbeec6c93918ee3140f18bbcaa599266dced433ea5974d52eeeee94ed30090606 Homepage: https://cran.r-project.org/package=datapack Description: CRAN Package 'datapack' (A Flexible Container to Transport and Manipulate Data andAssociated Resources) Provides a flexible container to transport and manipulate complex sets of data. These data may consist of multiple data files and associated meta data and ancillary files. Individual data objects have associated system level meta data, and data files are linked together using the OAI-ORE standard resource map which describes the relationships between the files. The OAI- ORE standard is described at . Data packages can be serialized and transported as structured files that have been created following the BagIt specification. The BagIt specification is described at . Package: r-cran-datapackage Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 659 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-yaml, r-cran-jsonlite, r-cran-iso8601 Suggests: r-cran-simplermarkdown, r-cran-data.table, r-cran-codelist, r-cran-laf Filename: pool/dists/noble/main/r-cran-datapackage_0.2.3-1.ca2404.1_all.deb Size: 333296 MD5sum: 9e99c89ab7322366e08eeeb1ff832296 SHA1: 54fae1d0bf696872531bb6833f0a861f3f5e4d35 SHA256: 003937c4f96f7f384cd9464e932f024b190c6569341655a7e0b54fbdf52c2c5a SHA512: 87900f27cb080b2a5507000e114a3dec0f4038ede01342430af0ad0441eeee47d128bfe62cbabe3ecd919d0d59eb9de1d7f7faa009061c2383cca5213de5719e Homepage: https://cran.r-project.org/package=datapackage Description: CRAN Package 'datapackage' (Creating and Reading Data Packages) Open, read data from and modify Data Packages. Data Packages are an open standard for bundling and describing data sets (). When data is read from a Data Package care is taken to convert the data as much a possible to R appropriate data types. The package can be extended with plugins for additional data types. Package: r-cran-datapackager Architecture: all Version: 0.16.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 913 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-desc, r-cran-digest, r-cran-knitr, r-cran-pkgbuild, r-cran-pkgload, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rprojroot, r-cran-usethis, r-cran-yaml Suggests: r-cran-covr, r-cran-data.tree, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-datapackager_0.16.2-1.ca2404.1_all.deb Size: 356336 MD5sum: d03db2077a1e7e29c9a26e08ba2637c5 SHA1: fe9e18857476b76488f8c218c016b66175d28773 SHA256: 3a424f66fdb5d52924511acffd6b2bd4aec970334cfd9df7e4f9c098b54c49f1 SHA512: 94f55f404706aad990dd389a8b9602cdd3a823d83f1d6ef0c696bff9aa8075e5be7496858734d93355bbc5017cab0ef0c0da1806a3f24440e2b9d785bd32d5ff Homepage: https://cran.r-project.org/package=DataPackageR Description: CRAN Package 'DataPackageR' (Construct Reproducible Analytic Data Sets as R Packages) A framework to help construct R data packages in a reproducible manner. Potentially time consuming processing of raw data sets into analysis ready data sets is done in a reproducible manner and decoupled from the usual 'R CMD build' process so that data sets can be processed into R objects in the data package and the data package can then be shared, built, and installed by others without the need to repeat computationally costly data processing. The package maintains data provenance by turning the data processing scripts into package vignettes, as well as enforcing documentation and version checking of included data objects. Data packages can be version controlled on 'GitHub', and used to share data for manuscripts, collaboration and reproducible research. Package: r-cran-datapasta Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 781 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readr, r-cran-clipr, r-cran-rstudioapi Suggests: r-cran-tibble, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-data.table Filename: pool/dists/noble/main/r-cran-datapasta_3.1.0-1.ca2404.1_all.deb Size: 681520 MD5sum: 25556e56fb8aa40cbbc5fc17af3d0e89 SHA1: 0244d270b1dfa671c0485664b7a1a6be5c449e7b SHA256: 743ad795dc8a52a3cf2afd4dcaf4f1ab269d2c5407274632cc18ea179e909971 SHA512: fd4ecf9869705709c795320d969bbe5b828191ec8378ea5f801d44ada8e454ebb8bfb8a5aed91ca3c0091e279c4e921b37d2f66132bb3502cd435845bae34217 Homepage: https://cran.r-project.org/package=datapasta Description: CRAN Package 'datapasta' (R Tools for Data Copy-Pasta) RStudio addins and R functions that make copy-pasting vectors and tables to text painless. 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These data preprocessing methods are developed based on the principles of completeness, accuracy, threshold method, and linear interpolation and through the setting of constraint conditions, time completion & recovery, and fast & efficient calculation and grouping. Key preprocessing steps include deletions of variables and observations, outlier removal, and missing values (NA) interpolation, which are dependent on the incomplete and dispersed degrees of raw data. They clean data more accurately, keep more samples, and add no outliers after interpolation, compared with ordinary methods. Auto-identification of consecutive NA via run-length based grouping is used in observation deletion, outlier removal, and NA interpolation; thus, new outliers are not generated in interpolation. Conditional extremum is proposed to realize point-by-point weighed outlier removal that saves non-outliers from being removed. Plus, time series interpolation with values to refer to within short periods further ensures reliable interpolation. These methods are based on and improved from the reference: Liang, C.-S., Wu, H., Li, H.-Y., Zhang, Q., Li, Z. & He, K.-B. (2020) . Package: r-cran-datapreparation Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1554 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-lubridate, r-cran-stringr, r-cran-matrix, r-cran-progress Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-datapreparation_1.1.2-1.ca2404.1_all.deb Size: 1480282 MD5sum: 746b3c8355fdddb0e7b9f201b25ccead SHA1: 85f72b458f21b0821cbdaddb9bfc6cd43f0bfbc2 SHA256: e31a01f9b0b4d6a242fe097fae3b5360681d48288517f3c3c886ac45d6b05461 SHA512: 03920af27a155f5195cfb161247584c3f408417793dc86766d56cde08e7c085eaa1ee9d5089acb8d23a19217ff75b2b6ab44e4176f5c96efb464f266f9d14b32 Homepage: https://cran.r-project.org/package=dataPreparation Description: CRAN Package 'dataPreparation' (Automated Data Preparation) Do most of the painful data preparation for a data science project with a minimum amount of code; Take advantages of 'data.table' efficiency and use some algorithmic trick in order to perform data preparation in a time and RAM efficient way. Package: r-cran-dataqualitydashboard Architecture: all Version: 2.8.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2808 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-databaseconnector, r-cran-magrittr, r-cran-parallellogger, r-cran-dplyr, r-cran-jsonlite, r-cran-rjava, r-cran-sqlrender, r-cran-plyr, r-cran-stringr, r-cran-rlang, r-cran-tidyselect, r-cran-readr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-shiny, r-cran-ggplot2, r-cran-eunomia, r-cran-r.utils, r-cran-devtools Filename: pool/dists/noble/main/r-cran-dataqualitydashboard_2.8.9-1.ca2404.1_all.deb Size: 977614 MD5sum: cab8b65e1caa6ae450400d26f2904d98 SHA1: 2b2a882926aa13bccd4758d94eed74717b94f049 SHA256: cd2677891358d27ad6ca1221190309b18d37d720f7a49d291ccbf4bae6226b4a SHA512: e6b5fccf14358a36cf3995185712e22799391b20895e6ac363e5df46bc2fcdaf4ed8da37d7cd2a706f70f0ecbf1458bf94ebe90f26667046f6f125f39bb999df Homepage: https://cran.r-project.org/package=DataQualityDashboard Description: CRAN Package 'DataQualityDashboard' (Execute and View Data Quality Checks on OMOP CDM Database) Assesses data quality in Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) databases. Executes data quality checks and provides an R 'shiny' application to view the results. 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The scope of applicable functions rests on the availability of extensive metadata which can be provided in spreadsheet tables. Either standardized (e.g. as 'html5' reports) or individually tailored reports can be generated. For an introduction into the specification of corresponding metadata, please refer to the 'package website' . 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Package: r-cran-datasetsicr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1073 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-datasetsicr_1.0-1.ca2404.1_all.deb Size: 1062010 MD5sum: 3fddd6f8c59a19562db250880ff92e5b SHA1: 5a6039172bbf3c17f04a31c8b88787fe18cb35b8 SHA256: 3cb0f18372dc18a4cc379821ea6129fa1424362406c8b6c682bccd56dff7778f SHA512: ad3becafa87384f7504d8868c6522bdaa5373354cffaafb45846d6f37c5dfea6e9b205ea2994b9b43ff15a0d9de7c99f5f649011540a52103a6657298de9fcc2 Homepage: https://cran.r-project.org/package=datasetsICR Description: CRAN Package 'datasetsICR' (Datasets from the Book "An Introduction to Clustering with R") Companion to the book "An Introduction to Clustering with R" by P. Giordani, M.B. Ferraro and F. Martella (Springer, Singapore, 2020). The datasets are used in some case studies throughout the text. 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It includes time series data, educational metrics, crime records, medical datasets, and oncology research data. Designed to provide researchers, analysts, educators, and data scientists with centralized access to structured and well-documented datasets, this metapackage facilitates reproducible research, data exploration, and teaching applications across a wide range of domains. Included packages: - 'timeSeriesDataSets': Time series data from economics, finance, energy, and healthcare. - 'educationR': Datasets related to education, learning outcomes, and school metrics. - 'crimedatasets': Datasets on global and local crime and criminal behavior. - 'MedDataSets': Datasets related to medicine, public health, treatments, and clinical trials. - 'OncoDataSets': Datasets focused on cancer research, survival, genetics, and biomarkers. 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Package: r-cran-datazoom.social Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-data.table, r-cran-dplyr, r-cran-magrittr, r-cran-pnadcibge, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-datazoom.social_0.1.0-1.ca2404.1_all.deb Size: 62580 MD5sum: 176d333fd817e4f6046156af4eddfa70 SHA1: 50e329163f0e70ac9771d2067a5b5271dd658207 SHA256: 1c0992c6ac782169637ae5ce7f12e7b73d06e4c0f6b3eb33b2090878b8a46d97 SHA512: c2cc57786f9bba7042fb49aecec02a42bf95d7ec1f216a71f3dd6ebd93974b8dbced7e2d78bda9354991949e1d81bd4d3ff1329d16985080610abbcca2f54c62 Homepage: https://cran.r-project.org/package=datazoom.social Description: CRAN Package 'datazoom.social' (Simplify Access to Brazilian Social Data) Provides tools for downloading and processing microdata from the PNAD Contínua (PNADC, Continuous National Household Sample Survey), a rotating panel survey published quarterly by IBGE (Brazilian Institute of Geography and Statistics). Includes panel identification algorithms for linking individuals across survey waves. Package: r-cran-date4ts Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate Suggests: r-cran-testthat, r-cran-renv, r-cran-fuzzr, r-cran-pkgdown, r-cran-devtools, r-cran-usethis, r-cran-covr, r-cran-withr, r-cran-altdoc Filename: pool/dists/noble/main/r-cran-date4ts_0.1.2-1.ca2404.1_all.deb Size: 160608 MD5sum: 42d99c8f6b964c76cd54ac49b3a25ba1 SHA1: 0932a404fb80f79ce1b1c755ddbfd54ef1179089 SHA256: 551b8e4e80409c1a81a586ffd40da4d8018226b1f0dae2b88a7c95ddcc574096 SHA512: 1379210e4d3799644d06878945ea890a5bca25dd827e743842473ad6c0dd2d0d535257cac873e97b4f95836165e4d79e76755596f502802e1df8e755d5b9f2e9 Homepage: https://cran.r-project.org/package=date4ts Description: CRAN Package 'date4ts' (Wrangle and Modify Ts Object with Classic Frequencies and ExactDates) The ts objects in R are managed using a very specific date format (in the form c(2022, 9) for September 2022 or c(2021, 2) for the second quarter of 2021, depending on the frequency, for example). We focus solely on monthly and quarterly series to manage the dates of ts objects. The general idea is to offer a set of functions to manage this date format without it being too restrictive or too imprecise depending on the rounding. This is a compromise between simplicity, precision and use of the basic 'stats' functions for creating and managing time series (ts(), window()). Les objets ts en R sont gérés par un format de date très particulier (sous la forme c(2022, 9) pour septembre 2022 ou c(2021, 2) pour le deuxième trimestre 2021 selon la fréquence par exemple). On se concentre uniquement sur les séries mensuelles et trimestrielles pour gérer les dates des objets ts. Lidée générale est de proposer un ensemble de fonctions pour gérer ce format de date sans que ce soit trop contraignant ou trop imprécis selon les arrondis. Cest un compromis entre simplicité, précision et utilisation des fonctions du package 'stats' de création et de gestion des séries temporelles (ts(), window()). Package: r-cran-dateback Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dateback_1.0.5-1.ca2404.1_all.deb Size: 25846 MD5sum: d137b5f661189d866ffa6d1d0c2d33e2 SHA1: b92b1fab043941c5c1ae1f3162caa8032ba8c2bb SHA256: 8b70c0746798a937231ecbc12cce952df085dcd6d1a1affc12acf36aa945c6a4 SHA512: 9ef93c85e6e0e2d80b6f3fdd3f321d81dd74b1155a4656735b6c7c2b9280d5c84cceaf239fff1bfdd825be02de165fb63186ec6a54015662e5432dfe58c53213 Homepage: https://cran.r-project.org/package=dateback Description: CRAN Package 'dateback' (Collect and Install R Packages on a Specified Date withDependencies) Works as a virtual CRAN snapshot for source packages. It automatically downloads and installs 'tar.gz' files with dependencies, all of which were available on a specific day. Package: r-cran-datelife Architecture: all Version: 0.6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4908 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-abind, r-cran-bold, r-cran-phangorn, r-cran-phytools, r-cran-ips, r-cran-cluster, r-cran-compare, r-cran-geiger, r-cran-stringr, r-cran-rotl, r-cran-paleotree, r-cran-knitcitations, r-cran-phylobase, r-cran-taxize, r-cran-treebase, r-cran-httr, r-cran-plyr, r-cran-phylocomr, r-cran-biocmanager, r-cran-data.table, r-cran-curl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-usethis, r-cran-devtools, r-cran-covr, r-bioc-msa, r-bioc-biostrings Filename: pool/dists/noble/main/r-cran-datelife_0.6.8-1.ca2404.1_all.deb Size: 4282756 MD5sum: 942d2b20719bd21c098fe71eeb3607d5 SHA1: 2ba308de92d3fa0bbad7dd26b0c920a40c45d8fe SHA256: dae16b6ca451f81548f8bb4da82a8c0dae5c4000c1a80e505a3a2b5fcfc61079 SHA512: dc8e710b0ab5046a084d7407e3b3818472142e14e750e2f6c9f1c2d42d6211a110ef9c31909bf469365664d1865b0eaf772d44f1666915c057e9b0687e289435 Homepage: https://cran.r-project.org/package=datelife Description: CRAN Package 'datelife' (Scientific Data on Time of Lineage Divergence for Your Taxa) Methods and workflows to get chronograms (i.e., phylogenetic trees with branch lengths proportional to time), using open, peer-reviewed, state-of-the-art scientific data on time of lineage divergence. This package constitutes the main underlying code of the DateLife web service at . To obtain a single summary chronogram from a group of relevant chronograms, we implement the Super Distance Matrix (SDM) method described in Criscuolo et al. (2006) . To find the grove of chronograms with a sufficiently overlapping set of taxa for summarizing, we implement theorem 1.1. from Ané et al. (2009) . A given phylogenetic tree can be dated using time of lineage divergence data as secondary calibrations (with caution, see Schenk (2016) ). To obtain and apply secondary calibrations, the package implements the congruification method described in Eastman et al. (2013) . Tree dating can be performed with different methods including BLADJ (Webb et al. (2008) ), PATHd8 (Britton et al. (2007) ), mrBayes (Huelsenbeck and Ronquist (2001) ), and treePL (Smith and O'Meara (2012) ). Package: r-cran-daterangepicker Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 992 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-jsonify, r-cran-shiny Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-daterangepicker_0.2.0-1.ca2404.1_all.deb Size: 181144 MD5sum: ab2b04a37794522c9a8646e3a2634080 SHA1: b5f23720aacfd04b6db0657f5f72387cfcd35579 SHA256: dbd07ebfd695ef73db037e70b65a0ca9df85e04fa942b6451e3165686ba3ec0a SHA512: a5771cf2ed9e2cdac186e680bfeb0c8c254d68ceb1e4db97e72cfc10cee68d2b26608c25d40f7180a2b8c0b3c429df53a7c91be900de18ee2fb5867f6758bcd0 Homepage: https://cran.r-project.org/package=daterangepicker Description: CRAN Package 'daterangepicker' (Create a Shiny Date-Range Input) A Shiny Input for date-ranges, which pops up two calendars for selecting dates, times, or predefined ranges like "Last 30 Days". It wraps the JavaScript library 'daterangepicker' which is available at . Package: r-cran-datetime Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-chron Filename: pool/dists/noble/main/r-cran-datetime_0.1.4-1.ca2404.1_all.deb Size: 62568 MD5sum: d2eea8c95e8ea78bb7a6920bf332274f SHA1: 431ed60d4c9e6f07011a69afd31c3cfcb37e8621 SHA256: 65022829afc9e241fd6af9b6fb9d2e239cdef7e6063f14d0dcd453c8e87f5b7d SHA512: 2c50ec192723070cefe89337a6f7874d0c11dd5ab2b8220c647b36fa6d2f87e0ef8f5907ff2a58a4061637c4e4e3eb6729b89068a8b79e6009f44f3a69774046 Homepage: https://cran.r-project.org/package=datetime Description: CRAN Package 'datetime' (Nominal Dates, Times, and Durations) Provides methods for working with nominal dates, times, and durations. Base R has sophisticated facilities for handling time, but these can give unexpected results if, for example, timezone is not handled properly. This package provides a more casual approach to support cases which do not require rigorous treatment. It systematically deconstructs the concepts origin and timezone, and de-emphasizes the display of seconds. It also converts among nominal durations such as seconds, hours, days, and weeks. See '?datetime' and '?duration' for examples. Adapted from 'metrumrg' . 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Package: r-cran-datetimerangepicker Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1367 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-lubridate, r-cran-reactr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-datetimerangepicker_1.1.0-1.ca2404.1_all.deb Size: 163162 MD5sum: f5a015bec52eeebcb8b95b318e6861b2 SHA1: aa74c39c4ca555bc28ebc19ba651c7293b5a340f SHA256: 9b31d2704a129fb7f42cf2c73db2a4691efa15d53934a0702ac54f47c5c1c6d0 SHA512: 312ac300f3a448159777a86be13b6078a05d11fce7ca6bb9d52a01599acfe18b8d81cde1ee546686c79a65e650f44888b5127a407fba24aae21061e27831f515 Homepage: https://cran.r-project.org/package=DateTimeRangePicker Description: CRAN Package 'DateTimeRangePicker' (A Datetime Range Picker Widget for Usage in 'Shiny' Applications) Provides a datetime range picker widget for usage in 'Shiny'. It creates a calendar allowing to select a start date and an end date as well as two fields allowing to select a start time and an end time. 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The package is lightweight (no dependencies, pure R implementations) and relies only on R's standard classes to represent dates and times ('Date' and 'POSIXt'); it aims to provide efficient implementations, through vectorisation and the use of R's native numeric representations of timestamps where possible. Package: r-cran-datetoiso Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-lubridate, r-cran-data.table, r-cran-dplyr, r-cran-purrr, r-cran-glue, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-datetoiso_1.2.1-1.ca2404.1_all.deb Size: 112452 MD5sum: 3f9466c6c2288ba47783c7d3ff055ac0 SHA1: de857ad4b4a9ee93471edf08e2c7bdcbbed9bb55 SHA256: ef25e471e7ac9290a0e60b1c196effe429f32019c5635d2f90a3224d9265a6f4 SHA512: c4cac5ee042e3f272fc62cca21cbc61ef0a1edd8022e26161bacf7042c7ea65b4866ea78a0f114907fc8f3dad3cfd1686b313f3c92449be1ff774b733859fb9c Homepage: https://cran.r-project.org/package=datetoiso Description: CRAN Package 'datetoiso' (Convert and Impute Dates to ISO 8601 Format and Reconcile DataSets) Provides tools for converting and imputing date values to the ISO 8601 standard format and for reconciling differences between two versions of a data set. The package automatically detects date patterns within data frame columns and converts them to consistent ISO-formatted dates, with optional imputation of missing day or month components based on user-defined rules. It also includes functionality to identify inserted, deleted, and updated records, as well as column- and value-level changes, when comparing old and new versions of a data frame. Only one date format may be applied within a single column. Package: r-cran-datoramar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-tibble Filename: pool/dists/noble/main/r-cran-datoramar_0.1.0-1.ca2404.1_all.deb Size: 15366 MD5sum: 236acfea7db9affc96b403dd88c6b6ee SHA1: a205ba1b522d13c406f69bb68e7dec986f3d0902 SHA256: 5c15fda02b2515a1dcd31ff872af19df6b2d0e414dd06d2d6bb0d73cbd33b3d8 SHA512: b5abc2eaf8a99b2afac0cad2e46dc006c62a0fed63856465c5e1471c8722fbad378d4656e33707c1b68bbb22e36423c3cbdd83872f4c2142fb9d87b971b762a2 Homepage: https://cran.r-project.org/package=datoramar Description: CRAN Package 'datoramar' (Interface to the 'Datorama' API) A thin wrapper around the 'Datorama' API. Ideal for analyzing marketing data from . Package: r-cran-datos Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-babynames, r-cran-dplyr, r-cran-forcats, r-cran-fueleconomy, r-cran-gapminder, r-cran-ggplot2, r-cran-lahman, r-cran-nasaweather, r-cran-nycflights13, r-cran-palmerpenguins, r-cran-modeldata, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-yaml Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-datos_0.5.1-1.ca2404.1_all.deb Size: 160430 MD5sum: 590783e6a6dbff98ba7ff4b664ba611a SHA1: e36e848710c9690b4a8953c278932eeb19bf3761 SHA256: 4c31e4a15eea7d73ace397559ca44f01e72583bafa899b778a97ec1766ec9939 SHA512: 9af78742ad7a061033a96074a33edda845c85513843d807eaf50435d06761cdefd1d09edafdaf848cc3a6c58f814d1dad78f9a7d931c87494023e9351fa5ace8 Homepage: https://cran.r-project.org/package=datos Description: CRAN Package 'datos' (Traduce al Español Varios Conjuntos de Datos de Práctica) Provee una versión traducida de los siguientes conjuntos de datos: 'airlines', 'airports', 'AwardsManagers', 'babynames', 'Batting', 'credit_data', 'diamonds', 'faithful', 'fueleconomy', 'Fielding', 'flights', 'gapminder', 'gss_cat', 'iris', 'Managers', 'mpg', 'mtcars', 'atmos', 'palmerpenguins', 'People, 'Pitching', 'planes', 'presidential', 'table1', 'table2', 'table3', 'table4a', 'table4b', 'table5', 'vehicles', 'weather', 'who'. English: It provides a Spanish translated version of the datasets listed above. 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This package provides tools to process and prepare data for visualization and employs the concept of aoristic analysis. Package: r-cran-datr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-datr_0.1.0-1.ca2404.1_all.deb Size: 27010 MD5sum: 3552bb249b9975b4180418619987a306 SHA1: b7d6c7cd066d48156ba2429c190cef8418e12d83 SHA256: 7bcc4d176368720fe69931eca58998b0aaa20c5b0c85e5aef34a8383376fb8a4 SHA512: 592ae0dea8cb87dafe892da46a041d8b78486383eca656415f474c86e1a8a10104ee125ffc80af10ea6c5b0c8ba5dad8599c87e553091bf23250904528e66257 Homepage: https://cran.r-project.org/package=datr Description: CRAN Package 'datr' ('Dat' Protocol Interface) Interface with the 'Dat' p2p network protocol . Clone archives from the network, share your own files, and install packages from the network. 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Package: r-cran-daur Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-daur_1.0-1.ca2404.1_all.deb Size: 33142 MD5sum: e97080acdc12ff101c8cbfe03acdc10b SHA1: b11ec37580913446943ef2afbb5bef41f4bd0030 SHA256: 3a68c3e6a0e43c3aa3771be9af62caeec0168ea15e69df0e092e8719d46e95d2 SHA512: 17134606869af4bf3e805bbf46f069ba82a24ea4c1e82eb892bff7f5b37d16cc9546cb74970f56b90e80bb8e818ab7d686af8ac4563b646e517f8a98d79839db Homepage: https://cran.r-project.org/package=dauR Description: CRAN Package 'dauR' (Datasets for "Sampling and Data Analysis Using R: Theory andPractice") Provides several datasets used throughout the book "Sampling and Data Analysis Using R: Theory and Practice" by Islam (2025, ISBN:978-984-35-8644-5). The datasets support teaching and learning of statistical concepts such as sampling methods, descriptive analysis, estimation and basic data handling. These curated data objects allow instructors, students and researchers to reproduce examples, practice data manipulation and perform hands-on analysis using R. Package: r-cran-davies Architecture: all Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1932 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-davies_1.2-1-1.ca2404.1_all.deb Size: 1935176 MD5sum: c72ae97f8396146a8f23965f79b5223b SHA1: 624a76ca89bfb17c8c424b39ffb617f8753c7c71 SHA256: 08b9d66c115c8e06f8ab401aefbb6c7b71af8834e843b205bbb8cda69aa959c6 SHA512: b9e2d54c17ff33bc76b3efdfce2be27d6e3f2d1d5c1d6119f6facdadd76f4162a558dd5417481af5acbb7fc9e041738863a874cee593cb885477c466e3034e73 Homepage: https://cran.r-project.org/package=Davies Description: CRAN Package 'Davies' (The Davies Quantile Function) Various utilities for the Davies distribution. Package: r-cran-dawar Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-memoise, r-cran-cli, r-cran-httr2, r-cran-sf, r-cran-tidyrss, r-cran-rlang, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat, r-cran-cowplot, r-cran-rmapshaper, r-cran-microbenchmark, r-cran-pkgdown, r-cran-dplyr, r-cran-devtools, r-cran-vcr Filename: pool/dists/noble/main/r-cran-dawar_0.3.2-1.ca2404.1_all.deb Size: 358336 MD5sum: 53ea043f8117297a9163f29250852f87 SHA1: 331c6384edae67e496bb3b507e0469537a180c8f SHA256: a30a5ba0cca666e06295487790e29925d70b4378f8dc0d0b909d4a18e8480559 SHA512: 0a04b345bc74f31fec2ea000327ef0cfce804e25d3a5a48d7401666ab47bcfd13b5138c675b80c1031cbaed2728b0c61a983cfa15ed8d61ba3836a0df0a66a53 Homepage: https://cran.r-project.org/package=dawaR Description: CRAN Package 'dawaR' (An API Wrapper for 'DAWA' - 'The Danish Address Web API') Functions for interacting with all sections of the official 'Danish Address Web API' (also known as 'DAWA') . The development of this package is completely independent from the government agency, Klimadatastyrelsen, who maintains the API. Package: r-cran-daymetr Architecture: all Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 635 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-terra, r-cran-ncdf4, r-cran-httr, r-cran-tidyr, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-knitr, r-cran-markdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-daymetr_1.7.1-1.ca2404.1_all.deb Size: 290952 MD5sum: 279a2c636dd497949acef5ba98f2f25f SHA1: 220d00e491992bd2696b1450e74affd4591dd02d SHA256: a6fd5c37caf66be5a674204386ab1cdfed57efe628949f4aa74186c035cfcd69 SHA512: e86e03c8bef7501a1a1b6bdb96fa38e83965da8eed5cda7932fc3256ad1def70173efe5ba3d7f15ea670e8c7ea46a5be3110ba559ffae0aed99bd53e4e84f2b9 Homepage: https://cran.r-project.org/package=daymetr Description: CRAN Package 'daymetr' (Interface to the 'Daymet' Web Services) Programmatic interface to the 'Daymet' web services (). Allows for easy downloads of 'Daymet' climate data directly to your R workspace or your computer. Routines for both single pixel data downloads and gridded (netCDF) data are provided. Package: r-cran-days2lessons Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-days2lessons_0.1.3-1.ca2404.1_all.deb Size: 45014 MD5sum: b9bf70b529804aef5b52cf41f062b17d SHA1: f41c04d7c81298d250ccea888e64232b60e6aa1a SHA256: 894ffd254104f703df3e70ec6db234bbc90231846eedde0093c6115ef9e1d794 SHA512: 0812699e797ae86833f1d8dacc77dcf67cae253a080804269d75ce1324e96b421b8831868cf514c91f92d6bec6a575daf490181cb3273f6e43f94fd16f15e9af Homepage: https://cran.r-project.org/package=days2lessons Description: CRAN Package 'days2lessons' (Distributes Teachers Lessons On Days in a Balanced Manner) The set of teacher/class lessons is completed with a column that allocates a day to each lesson, so that the distribution of lessons by day, by class, and by teacher is as uniform as possible. . Package: r-cran-daysupply Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lme4, r-cran-rlang, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-daysupply_0.1.0-1.ca2404.1_all.deb Size: 37096 MD5sum: f31c3496d556cfc01596ac1ec6929e14 SHA1: 2c6b200552bcc401cc2b54f99f2e196ecaed01c3 SHA256: 73cdc16447d90af60255f1d4231878a823acf6e57cb2e898bbb88c0400d23a3b SHA512: 319d86dd6d8b53b51dbe99c0a0646b25dc54a321f5482882fa5179f184e365eeb9b78e7491eaa8ebc955f77499041cb492a641169e2ef6f30e3b310c5bd8f21d Homepage: https://cran.r-project.org/package=daySupply Description: CRAN Package 'daySupply' (Calculating Days' Supply and Daily Dose of Prescriptions) Allows clinicians and researchers to compute daily dose (and subsequently days' supply) for prescription refills using the following methods: Fixed window, fixed tablet, defined daily dose (DDD), and Random Effects Warfarin Days' Supply (REWarDS). Daily dose is the computed dose that the patient takes every day. For medications with fixed dosing (e.g. direct oral anticoagulants) this is known and does not need to be estimated. For medications with varying dose such as warfarin, however, the daily dose should be assumed or estimated to allow measurement of drug exposure. Days’ supply is the number of days that patients’ supply of medication will last after each prescription fill. Estimating days’ supply is necessary to calculate drug exposure. The package computes days’ supply and daily dose at both the prescription and patient levels. Results at the prescription level are denoted with “-Rx-” and those at patient level are denoted with “-Pt-”. Package: r-cran-dbacf Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-dbacf_0.2.8-1.ca2404.1_all.deb Size: 50078 MD5sum: 6327b34488b4dcf3c6a53649f548b782 SHA1: 5abbba2161d133b55250108b5ab580c9ccbfb343 SHA256: 52e662788d369eec23627dc8f4fba0d07dc09943bd16fbe53fe4c6088dfa8fa1 SHA512: 4df3a8e9afda462595b11cb02ccd1bf1602f9dda3c19652bee0cacd080d59c15b80f631030b8527f86c6ff8a2b5cefa77608f0db95a62870b3303d18190af789 Homepage: https://cran.r-project.org/package=dbacf Description: CRAN Package 'dbacf' (Autocovariance Estimation via Difference-Based Methods) Provides methods for (auto)covariance/correlation function estimation in change point regression with stationary errors circumventing the pre-estimation of the underlying signal of the observations. Generic, first-order, (m+1)-gapped, difference-based autocovariance function estimator is based on M. Levine and I. Tecuapetla-Gómez (2023) . Bias-reducing, second-order, (m+1)-gapped, difference-based estimator is based on I. Tecuapetla-Gómez and A. Munk (2017) . Robust autocovariance estimator for change point regression with autoregressive errors is based on S. Chakar et al. (2017) . It also includes a general projection-based method for covariance matrix estimation. Package: r-cran-dbcsp Architecture: all Version: 0.0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3800 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-tsdist, r-cran-geigen, r-cran-ggplot2, r-cran-mass, r-cran-matrix, r-cran-paralleldist, r-cran-plyr, r-cran-zoo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dbcsp_0.0.2.2-1.ca2404.1_all.deb Size: 3736354 MD5sum: f9b08dcacef75233919ff56c8fe467fe SHA1: 2080f0a96e7abe36af883e5cf9ddfc4e19e4c420 SHA256: ac834988dbf9d4e0cfc99b2eeae643e06e1a8a7ead5fdf2846f6d7622fcda7ca SHA512: a39d221e8a10aeaa4e1427cf6895a8fc0640cb5e4d8a32adbb2a5590c5307f3a671d0e01a0d4b609a7c5055f429df965d5d5755974f9cd2872bea725a2a4be93 Homepage: https://cran.r-project.org/package=dbcsp Description: CRAN Package 'dbcsp' (Distance-Based Common Spatial Patterns) A way to apply Distance-Based Common Spatial Patterns (DB-CSP) techniques in different fields, both classical Common Spatial Patterns (CSP) as well as DB-CSP. The method is composed of two phases: applying the DB-CSP algorithm and performing a classification. The main idea behind the CSP is to use a linear transform to project data into low-dimensional subspace with a projection matrix, in such a way that each row consists of weights for signals. This transformation maximizes the variance of two-class signal matrices.The dbcsp object is created to compute the projection vectors. For exploratory and descriptive purpose, plot and boxplot functions can be used. Functions train, predict and selectQ are implemented for the classification step. Package: r-cran-dbcvindex Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qpdf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dbcvindex_1.6-1.ca2404.1_all.deb Size: 29548 MD5sum: 223bba3adddebc66cf49892bbb75fe6b SHA1: 9a58ed174a31d4d9aed333fbef4d7b7b87982d92 SHA256: 43496cf5d75fdf82066f8b4257aac227ec38a7b75067183f421a861f5c9ba8a5 SHA512: 09ef51e9d795b2a7f96aab770c94ac62c7f2e1e76702bf8989673392ab2a54adbcdc3fa1d72b8ea893a8282d09c39b0f0ac1d26b4ce2995735c7cbb893f88d21 Homepage: https://cran.r-project.org/package=DBCVindex Description: CRAN Package 'DBCVindex' (Calculates the Density-Based Clustering Validation (DBCV) Index) A metric called 'Density-Based Clustering Validation index' (DBCV) index to evaluate clustering results, following the 'R' implementation by Pablo Andretta Jaskowiak. Original 'DBCV' index article: Moulavi, D., Jaskowiak, P. A., Campello, R. J., Zimek, A., and Sander, J. (April 2014), "Density-based clustering validation", Proceedings of SDM 2014 -- the 2014 SIAM International Conference on Data Mining (pp. 839-847), . A more recent article on the 'DBCV' index: Chicco, D., Sabino, G.; Oneto, L.; Jurman, G. (August 2025), "The DBCV index is more informative than DCSI, CDbw, and VIASCKDE indices for unsupervised clustering internal assessment of concave-shaped and density-based clusters", PeerJ Computer Science 11:e3095 (pp. 1-), . Package: r-cran-dbd Architecture: all Version: 0.0-22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-hmm.discnp, r-cran-mass, r-cran-rmutil, r-cran-spcadjust Filename: pool/dists/noble/main/r-cran-dbd_0.0-22-1.ca2404.1_all.deb Size: 225408 MD5sum: 1e51c3c0ab7c2aaadda02c4f19972f66 SHA1: 9d7e49e4f929f7aaf28efa0001db98e1f3daabd9 SHA256: 34f14c0574896be1132fb4543641131754f60faab00c14d29849493cc0a25bf5 SHA512: 9ac88b54f3e3e6b85d83d2e0bc56afd0e7a2bbe31bfc2cbc30d4089c1b4a1ebf8c2f8de787cb39ac17881bf1f87cc54dd13b48b90118969030091db98e05396f Homepage: https://cran.r-project.org/package=dbd Description: CRAN Package 'dbd' (Discretised Beta Distribution) Tools for working with a new versatile discrete distribution, the db ("discretised Beta") distribution. This package provides density (probability), distribution, inverse distribution (quantile) and random data generation functions for the db family. It provides functions to effect conveniently maximum likelihood estimation of parameters, and a variety of useful plotting functions. It provides goodness of fit tests and functions to calculate the Fisher information, different estimates of the hessian of the log likelihood and Monte Carlo estimation of the covariance matrix of the maximum likelihood parameter estimates. In addition it provides analogous tools for working with the beta-binomial distribution which has been proposed as a competitor to the db distribution. Package: r-cran-dbest Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo Filename: pool/dists/noble/main/r-cran-dbest_1.8-1.ca2404.1_all.deb Size: 87130 MD5sum: 9eb71af98a0a100bfbf73953e32a7a18 SHA1: 3170b9fad4f53836b482f38ade61dfaab2054c09 SHA256: 0ed4f8a81f5ab0aedae16e803f60fabdd756daf97c6bde3ea00bb7fb562fef23 SHA512: 66acf849fe8ad3c0ae513ed262c550ca9e5497668d6043d529781f5a1ca394f63e59f3f5ccbd8d91cfeb53a1c33e75a34d8bebbcf382aa60636a256788448da9 Homepage: https://cran.r-project.org/package=DBEST Description: CRAN Package 'DBEST' (Detecting Breakpoints and Estimating Segments in Trend) A program for analyzing vegetation time series, with two algorithms: 1) change detection algorithm that detects trend changes, determines their type (abrupt or non-abrupt), and estimates their timing, magnitude, number, and direction; 2) generalization algorithm that simplifies the temporal trend into main features. The user can set the number of major breakpoints or magnitude of greatest changes of interest for detection, and can control the generalization process by setting an additional parameter of generalization-percentage. Package: r-cran-dbfit Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfit Filename: pool/dists/noble/main/r-cran-dbfit_2.0-1.ca2404.1_all.deb Size: 94394 MD5sum: d8d23d5b78ffb832fa3f98e8b7334685 SHA1: 97da507550e32848172b194c98f236c2d39b8e89 SHA256: 10d39575145cd67b28ebb09262ebe02d9240e01ce45c56b0730299ceccc20b49 SHA512: 6f8115bdc006b9075d7cf740c36953b54bd7e43811dfa0c4799dea24dab93c03f887df9972a476def3cb5d332aa258e9e1b99961851d7a9682a5fe809e5944d4 Homepage: https://cran.r-project.org/package=DBfit Description: CRAN Package 'DBfit' (A Double Bootstrap Method for Analyzing Linear Models withAutoregressive Errors) Computes the double bootstrap as discussed in McKnight, McKean, and Huitema (2000) . The double bootstrap method provides a better fit for a linear model with autoregressive errors than ARIMA when the sample size is small. Package: r-cran-dbflobr Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-blob, r-cran-chk, r-cran-clisymbols, r-cran-crayon, r-cran-dbi, r-cran-flobr, r-cran-glue, r-cran-rlang, r-cran-rsqlite Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-dbflobr_0.2.2-1.ca2404.1_all.deb Size: 494610 MD5sum: 4605b162c4cda12ea1ebbf65ad33471a SHA1: 28d62dd9a2be267d51702bbe7a3fc0e8eb363f94 SHA256: 12548b770eca685dd776cab2a807395f2000df14ba43767737971cf9a9496384 SHA512: f669cb15e8daf52efc247a821e1b9497874c285753cc93faa7fb1b0ce9ff56dc91f35242da540fc9a2b55ef318e382d33fa259a0eb649f24dca801639b75180f Homepage: https://cran.r-project.org/package=dbflobr Description: CRAN Package 'dbflobr' (Read and Write Files to SQLite Databases) Reads and writes files to SQLite databases as flobs (a flob is a blob that preserves the file extension). 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The DBHC algorithm is an HMM Clustering algorithm that finds a mixture of discrete-output HMMs while using heuristics based on Bayesian Information Criterion (BIC) to search for the optimal number of HMM states and the optimal number of clusters. 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"Time and sample efficient discovery of Markovblankets and direct causal relations". In Proceedings of the ninth ACM SIGKDD international Conference. . Borboudakis G. and Tsamardinos I. (2019). "Forward-backward selection with early dropping". Journal of Machine Learning Research, 20(8): 1--39. . Huo X. and Szekely G.J. (2016). "Fast computing for distance covariance". Technometrics, 58(4): 435--447. . 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Both versions of biased and unbiased estimators of distance covariance and correlation are provided. Test statistics for testing pairwise independence are also implemented. Some data sets are also included. References include: a) Edelmann Dominic, Fokianos Konstantinos and Pitsillou Maria (2019). 'An Updated Literature Review of Distance Correlation and Its Applications to Time Series'. International Statistical Review, 87(2): 237--262. . b) Fokianos Konstantinos and Pitsillou Maria (2018). 'Testing independence for multivariate time series via the auto-distance correlation matrix'. Biometrika, 105(2): 337--352. . c) Fokianos Konstantinos and Pitsillou Maria (2017). 'Consistent testing for pairwise dependence in time series'. Technometrics, 59(2): 262--270. . d) Pitsillou Maria and Fokianos Konstantinos (2016). 'dCovTS: Distance Covariance/Correlation for Time Series'. R Journal, 8(2):324-340. . 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Package: r-cran-dcvar Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-posterior, r-cran-loo, r-cran-ggplot2, r-cran-patchwork, r-cran-bayesplot, r-cran-rlang, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-sn Filename: pool/dists/noble/main/r-cran-dcvar_0.2.0-1.ca2404.1_all.deb Size: 590174 MD5sum: a9da10f662c34ae124544d65595ab24d SHA1: 05601d5b584aec3dba87c8bbb1ce5780369d82d5 SHA256: e19290d1b37db5c26b878ace8857cb59a0cb08edde04eb1d215e817c437a3cd7 SHA512: 4a0270439dc4f7b7f238c5bd36f8336953a3ae422c7da1c7df332f3f9326c939e77d7de3b84e3430fa220be7da1b1c9edb52fc6f5eb6d189fff779ed7ecfa551 Homepage: https://cran.r-project.org/package=dcvar Description: CRAN Package 'dcvar' (Dynamic Copula VAR Models for Time-Varying Dependence) Fits Bayesian copula vector autoregressive models for bivariate time series with dynamic, regime-switching, and constant dependence structures. The package includes simulation, data preparation, estimation with 'Stan' through 'rstan' or 'cmdstanr', posterior summaries, diagnostics, trajectory extraction, fitted and predictive summaries, and approximate leave-one-out cross-validation model comparison for supported fits. For Bayesian computation and model comparison, see Carpenter et al. (2017) and Vehtari, Gelman and Gabry (2017) . Package: r-cran-dda Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dhsic, r-cran-energy, r-cran-foreach Suggests: r-cran-boot, r-cran-doparallel, r-cran-devtools, r-cran-interactions, r-cran-iterators, r-cran-lmtest, r-cran-moments, r-cran-spelling, r-cran-testthat, r-cran-waldo Filename: pool/dists/noble/main/r-cran-dda_0.1.1-1.ca2404.1_all.deb Size: 158942 MD5sum: 4bc79d9a055ada1c76b7968ed00ded56 SHA1: 131976f0212217d9b203167484dff683a9a42a73 SHA256: 1fb9ffa0e7dfd42c7301912a0fbd64a90ec619edb1de530c74d368cc78c673e5 SHA512: 75adf17bb51bb4ab4f469d0672fcf3514d1df3758594c96b002eebacfe7d0c04433f5255ecf273e634255e6b0d688282a6b688d089b456926b851e6664d4a2a5 Homepage: https://cran.r-project.org/package=dda Description: CRAN Package 'dda' (Direction Dependence Analysis) A collection of tests to analyze the causal direction of dependence in linear models (Wiedermann, W., & von Eye, A., 2025, ISBN: 9781009381390). The package includes functions to perform Direction Dependence Analysis for variable distributions, residual distributions, and independence properties of predictors and residuals in competing causal models. In addition, the package contains functions to test the causal direction of dependence in conditional models (i.e., models with interaction terms) For more information see . Package: r-cran-ddecompose Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rifreg, r-cran-formula, r-cran-hmisc, r-cran-pbapply, r-cran-sandwich, r-cran-ranger, r-cran-fastglm Suggests: r-cran-testthat, r-cran-tidyr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-ddecompose_1.0.0-1.ca2404.1_all.deb Size: 1342516 MD5sum: 7bb399fdabfa7ed5375c750decfded7c SHA1: a80f4b1b180799422b8b8af207846c58f4dd0331 SHA256: 28304927393e7d5577676acacb6aeba24ac8b04b732b8f2468bbecb8e46aee7b SHA512: 5e8e4f49bec9b21a9731a59f75d6e981d9ec7b4a5991c2ee88983f04654b57fc64e37e5501e3b95f1da5663740d9925f1f7790a13b8e3659b0fa0e74fd9f52a3 Homepage: https://cran.r-project.org/package=ddecompose Description: CRAN Package 'ddecompose' (Detailed Distributional Decomposition) Implements the Oaxaca-Blinder decomposition method and generalizations of it that decompose differences in distributional statistics beyond the mean. The function ob_decompose() decomposes differences in the mean outcome between two groups into one part explained by different covariates (composition effect) and into another part due to differences in the way covariates are linked to the outcome variable (structure effect). The function further divides the two effects into the contribution of each covariate and allows for weighted doubly robust decompositions. For distributional statistics beyond the mean, the function performs the recentered influence function (RIF) decomposition proposed by Firpo, Fortin, and Lemieux (2018). The function dfl_decompose() divides differences in distributional statistics into an composition effect and a structure effect using inverse probability weighting as introduced by DiNardo, Fortin, and Lemieux (1996). The function also allows to sequentially decompose the composition effect into the contribution of single covariates. References: Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. (2018) . "Decomposing Wage Distributions Using Recentered Influence Function Regressions." Fortin, Nicole M., Thomas Lemieux, and Sergio Firpo. (2011) . "Decomposition Methods in Economics." DiNardo, John, Nicole M. Fortin, and Thomas Lemieux. (1996) . "Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach." Oaxaca, Ronald. (1973) . "Male-Female Wage Differentials in Urban Labor Markets." Blinder, Alan S. (1973) . "Wage Discrimination: Reduced Form and Structural Estimates." Package: r-cran-ddesonn Architecture: all Version: 7.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18953 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-dplyr, r-cran-openxlsx, r-cran-tidyr, r-cran-proc, r-cran-prroc, r-cran-reshape2, r-cran-digest, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-foreach, r-cran-quantmod, r-cran-randomforest, r-cran-reticulate, r-cran-zoo, r-cran-readxl, r-cran-tibble Filename: pool/dists/noble/main/r-cran-ddesonn_7.1.11-1.ca2404.1_all.deb Size: 9713692 MD5sum: 499d5e85fc2336837e38f0856f331136 SHA1: 3a806e4c35baa91c8bd76ddb2d8d1b9c62ceea6e SHA256: 371c0c06bee12c61c48de271f483d89b969f4d7936dd2512e78e67d626c855af SHA512: 233bcfc04091f560ef4ce050565ef1a5727f4333a3a371a9c1376ea6870a00249bcfca8e10142b82188fef177ba9ac81ab135ef688064d90f30cb577cc52c60f Homepage: https://cran.r-project.org/package=DDESONN Description: CRAN Package 'DDESONN' (A Deep Dynamic Experimental Self-Organizing Neural NetworkFramework) Provides a fully native R deep learning framework for constructing, training, evaluating, and inspecting Deep Dynamic Ensemble Self Organizing Neural Networks at research scale. The core engine is an object oriented R6 class-based implementation with explicit control over layer layout, dimensional flow, forward propagation, back propagation, and transparent optimizer state updates. The framework does not rely on external deep learning back ends, enabling direct inspection of model state, reproducible numerical behavior, and fine grained architectural control without requiring compiled dependencies or graphics processing unit specific run times. Users can define dimension agnostic single layer or deep multi-layer networks without hard coded architecture limits, with per layer configuration vectors for activation functions, derivatives, dropout behavior, and initialization strategies automatically aligned to network depth through controlled replication or truncation. Reproducible workflows can be executed through high level helpers for fit, run, and predict across binary classification, multi-class classification, and regression modes. Training pipelines support optional self organization, adaptive learning rate behavior, and structured ensemble orchestration in which candidate models are evaluated under user specified performance metrics and selectively promoted or pruned to refine a primary ensemble, enabling controlled ensemble evolution over successive runs. Ensemble evaluation includes fused prediction strategies in which member outputs may be combined through weighted averaging, arithmetic averaging, or voting mechanisms to generate consolidated metrics for research level comparison and reproducible per-seed assessment. The framework supports multiple optimization approaches, including stochastic gradient descent, adaptive moment estimation, and look ahead methods, alongside configurable regularization controls such as L1, L2, and mixed penalties with separate weight and bias update logic. Evaluation features provide threshold tuning, relevance scoring, receiver operating characteristic and precision recall curve generation, area under curve computation, regression error diagnostics, and report ready metric outputs. The package also includes artifact path management, debug state utilities, structured run level metadata persistence capturing seeds, configuration states, thresholds, metrics, ensemble transitions, fused evaluation artifacts, and model identifiers, as well as reproducible scripts and vignettes documenting end to end experiments. Kingma and Ba (2015) "Adam: A Method for Stochastic Optimization". Hinton et al. (2012) "Neural Networks for Machine Learning (RMSprop lecture notes)". Duchi et al. (2011) "Adaptive Subgradient Methods for Online Learning and Stochastic Optimization". Zeiler (2012) "ADADELTA: An Adaptive Learning Rate Method". Zhang et al. (2019) "Lookahead Optimizer: k steps forward, 1 step back". You et al. (2019) "Large Batch Optimization for Deep Learning: Training BERT in 76 minutes (LAMB)". McMahan et al. (2013) "Ad Click Prediction: a View from the Trenches (FTRL-Proximal)". Klambauer et al. (2017) "Self-Normalizing Neural Networks (SELU)". Maas et al. (2013) "Rectifier Nonlinearities Improve Neural Network Acoustic Models (Leaky ReLU / rectifiers)". Package: r-cran-ddi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-dplyr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-ddi_0.1.0-1.ca2404.1_all.deb Size: 19156 MD5sum: 7f792c63591b07ea7514ff2336d64665 SHA1: 134b1d980f5eac6c5450a140c1f9c069c9187a4a SHA256: cfffb5a6ee16904db5546ff61334d29c28339efb9fde666ecbb6d9bf3727d0ae SHA512: e8fcf19c6b5a25a55d04680324d2134841c6b5af004c098d13fed66cd6fda9f82fc11e8ca2edd6dc56710d8c00e6e1fa8c71eb0aaa2ad41e325167ca972e832c Homepage: https://cran.r-project.org/package=ddi Description: CRAN Package 'ddi' (The Data Defect Index for Samples that May not be IID) Implements Meng's data defect index (ddi), which represents the degree of sample bias relative to an iid sample. The data defect correlation (ddc) represents the correlation between the outcome of interest and the selection into the sample; when the sample selection is independent across the population, the ddc is zero. Details are in Meng (2018) , "Statistical Paradises and Paradoxes in Big Data (I): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election." Survey estimates from the Cooperative Congressional Election Study (CCES) is included to replicate the article's results. Package: r-cran-ddiv Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 678 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-segmented, r-cran-qpdf Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ddiv_0.1.1-1.ca2404.1_all.deb Size: 405234 MD5sum: 9501aad02e0b2aeb5ae182a964224789 SHA1: 5f415643f4b8fb7e6f671b766fbf5ee9700b66e6 SHA256: a20cb9e4caa72fe5dc7ece9a281a9cdbbf07507dddc6de20b20ee0034ab747bb SHA512: 73c2d8e7e90083c40d33e70994b50fd87b0796a45ea9ff3cfaff55bb52608cdfc73706c2f7a3d78154db0cc7fcd657eee97a228b1d36b88046066ba1c7438f8e Homepage: https://cran.r-project.org/package=ddiv Description: CRAN Package 'ddiv' (Data Driven I-v Feature Extraction) The Data Driven I-V Feature Extraction is used to extract Current-Voltage (I-V) features from I-V curves. I-V curves indicate the relationship between current and voltage for a solar cell or Photovoltaic (PV) modules. The I-V features such as maximum power point (Pmp), shunt resistance (Rsh), series resistance (Rs),short circuit current (Isc), open circuit voltage (Voc), fill factor (FF), current at maximum power (Imp) and voltage at maximum power(Vmp) contain important information of the performance for PV modules. The traditional method uses the single diode model to model I-V curves and extract I-V features. This package does not use the diode model, but uses data-driven a method which select different linear parts of the I-V curves to extract I-V features. This method also uses a sampling method to calculate uncertainties when extracting I-V features. Also, because of the partially shaded array, "steps" occurs in I-V curves. The "Segmented Regression" method is used to identify steps in I-V curves. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0007140. Further information can be found in the following paper. [1] Ma, X. et al, 2019. . Package: r-cran-ddiwr Architecture: all Version: 0.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-admisc, r-cran-base64enc, r-cran-declared, r-cran-digest, r-cran-xml2, r-cran-haven, r-cran-readxl, r-cran-writexl Filename: pool/dists/noble/main/r-cran-ddiwr_0.19-1.ca2404.1_all.deb Size: 405746 MD5sum: 569f93c02c3f57db631765c4823a4215 SHA1: d99ee17fe55a4250d83eaa02822173eb4938699d SHA256: 599b2ef0aa427e9b1559c8a61e1d5f9e8c1a5a1f7c89621360ba47827aaae1c5 SHA512: 72e7776958092d32ce98bfecdc8e4689c1b8bc720bb91b7bb8658e32755ea1bf35e70c199bc5ae4055fd45675dd9c8e5fab4c49a958f25d4d783961b04a9186c Homepage: https://cran.r-project.org/package=DDIwR Description: CRAN Package 'DDIwR' (DDI with R) Useful functions for various DDI (Data Documentation Initiative) related inputs and outputs. Converts data files to and from DDI, SPSS, Stata, SAS, R and Excel, including user declared missing values. Package: r-cran-ddl Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-matrix Filename: pool/dists/noble/main/r-cran-ddl_1.0.2-1.ca2404.1_all.deb Size: 34790 MD5sum: d912b59efb0e718b4ff3e66d7835424f SHA1: f8159195bf0d1e8616f6dd183110d45a99250bff SHA256: 1dbbe3a9d11a4189cfc52679d3a9605bd0b4817ef14820b182427f6840d6470e SHA512: ab665d22a346732e0b168db140215364985b36df87f50658e5d8b76dc13585c8a6617b4c9e6087f64a345ac601b8f203c416de16d07f12b4543c685cd1789aad Homepage: https://cran.r-project.org/package=DDL Description: CRAN Package 'DDL' (Doubly Debiased Lasso (DDL)) Statistical inference for the regression coefficients in high-dimensional linear models with hidden confounders. The Doubly Debiased Lasso method was proposed in . Package: r-cran-ddm Architecture: all Version: 1.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ddm_1.0-0-1.ca2404.1_all.deb Size: 182196 MD5sum: f829ff221b943527084960fc67ac5e7e SHA1: 9645208562d026e98084b0bf2382e45f3bd3cb9d SHA256: f65d9be75dc0a896add9eace5a15181db2ff1807d7a96f61f903cbfba666c0e7 SHA512: c5000226dc505b3babf0b79a2ea9fa6086934f2c9f2aed98635483a4e8f8816912eb4d2ba4a6128e8e6d876261875e278ddbe285ac00d2f4f2843628388dd0b1 Homepage: https://cran.r-project.org/package=DDM Description: CRAN Package 'DDM' (Death Registration Coverage Estimation) A set of three two-census methods to the estimate the degree of death registration coverage for a population. Implemented methods include the Generalized Growth Balance method (GGB), the Synthetic Extinct Generation method (SEG), and a hybrid of the two, GGB-SEG. Each method offers automatic estimation, but users may also specify exact parameters or use a graphical interface to guess parameters in the traditional way if desired. 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(2018) . 'ddml' simplifies estimation based on (short-)stacking as discussed in Ahrens et al. (2024) , which leverages multiple base learners to increase robustness to the underlying data generating process. Package: r-cran-ddoutlier Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbscan, r-cran-proxy, r-cran-pracma Filename: pool/dists/noble/main/r-cran-ddoutlier_0.1.0-1.ca2404.1_all.deb Size: 83944 MD5sum: ffb7896f4397d60a2b60ad8292da11c3 SHA1: 735048e53743002720028f7bf8df0402eaa8b6bf SHA256: 30b4790fac75195727f8595bac5e05aeb721e6a6311be6db2b5f16019c8bbaba SHA512: 6374442b2272c998bef06d41fad2f719ef277dd7308c56a77d50e1a2c093f0b0473a118e77a6b32a80f2e907a1226539becfeaa57574209fa7c38b3a03decf51 Homepage: https://cran.r-project.org/package=DDoutlier Description: CRAN Package 'DDoutlier' (Distance & Density-Based Outlier Detection) Outlier detection in multidimensional domains. Implementation of notable distance and density-based outlier algorithms. Allows users to identify local outliers by comparing observations to their nearest neighbors, reverse nearest neighbors, shared neighbors or natural neighbors. For distance-based approaches, see Knorr, M., & Ng, R. T. (1997) , Angiulli, F., & Pizzuti, C. (2002) , Hautamaki, V., & Ismo, K. (2004) and Zhang, K., Hutter, M. & Jin, H. (2009) . For density-based approaches, see Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002) , Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006) , Schubert, E., Zimek, A. & Kriegel, H-P. (2014) , Latecki, L., Lazarevic, A. & Prokrajac, D. (2007) , Papadimitriou, S., Gibbons, P. B., & Faloutsos, C. (2003) , Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) , Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A. (2009) , Zhu, Q., Feng, Ji. & Huang, J. (2016) , Huang, J., Zhu, Q., Yang, L. & Feng, J. (2015) , Tang, B. & Haibo, He. (2017) and Gao, J., Hu, W., Zhang, X. & Wu, Ou. (2011) . Package: r-cran-ddp Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ddp_0.0.3-1.ca2404.1_all.deb Size: 48988 MD5sum: 588507656c990fe97d444eb4722afbda SHA1: 0167b8c7dfdb18175d70b9e948520e317967055e SHA256: 6c2205fca438bf43d25fcf73f3d6b09ab9463bab6936fc7892b411f147236297 SHA512: 6ff5166fdebed9557234bf09e2a788a2c5248045f82a86b95db02024dd8e3c21189f243a2e346bd27a273c453ae653f1b9738d88abfdfe217612969494099de8 Homepage: https://cran.r-project.org/package=ddp Description: CRAN Package 'ddp' (Desirable Dietary Pattern) The desirable Dietary Pattern (DDP)/ PPH score measures the variety of food consumption. The (weighted) score is calculated based on the type of food. This package is intended to calculate the DDP/ PPH score that is faster than traditional method via a manual calculation by BKP (2017) and is simpler than the nutrition survey . The database to create weights and baseline values is the Indonesia national survey in 2017. 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The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package. 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This is the first non-proprietary software for analyzing two-channel ddPCR data. An interactive tool was also created and is available online to facilitate this analysis for anyone who is not comfortable with using R. 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The package aims to simplify the creation of many 'SVG' plot types using a straightforward 'R' API. The package relies on the 'r2d3' 'R' package and the 'D3' 'JavaScript' library. See and respectively. Package: r-cran-ddpm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ddpm_0.1.0-1.ca2404.1_all.deb Size: 202234 MD5sum: 35d14b6a44f614f286a6a001225d8c21 SHA1: 7baae79b9d7003179949ebb2bc503991bd34a2d3 SHA256: 11968555a437a626614637a52e7e5d3286961776df56bce53196ff64a650b598 SHA512: 361ef9054d2081fc753fa29da67dd4d8c7b8f24d9f4611298ceed411eceaaec997339e3a0cf591a18bca4cbb8f3dbe4ecf70807890c7a3d4bd4cd7d629fc4ee8 Homepage: https://cran.r-project.org/package=DDPM Description: CRAN Package 'DDPM' (Data Sets for Discrete Probability Models) A wide collection of univariate discrete data sets from various applied domains related to distribution theory. The functions allow quick, easy, and efficient access to 100 univariate discrete data sets. The data are related to different applied domains, including medical, reliability analysis, engineering, manufacturing, occupational safety, geological sciences, terrorism, psychology, agriculture, environmental sciences, road traffic accidents, demography, actuarial science, law, and justice. The documentation, along with associated references for further details and uses, is presented. Package: r-cran-ddpna Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2450 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggfun, r-cran-ggrepel, r-cran-megena, r-cran-igraph, r-cran-hmisc, r-cran-plyr, r-cran-scales, r-cran-venndiagram Suggests: r-cran-wgcna, r-bioc-biostrings, r-bioc-impute, r-cran-ggfortify Filename: pool/dists/noble/main/r-cran-ddpna_0.4.1-1.ca2404.1_all.deb Size: 2435282 MD5sum: e4c5ab0d51ac67364b0e83a79c45298f SHA1: 13f71416ce98a3262c11264443adde1a64ba2686 SHA256: c63062d8a6d72dc633d4c1624d8854b5e05a34f38ba9a32b662345b6f40b853b SHA512: c05db2e86a322b253a09f03ead6b7a01c2209eb9d962673a6d73bea5ed04438153d11717ad1f849e867e8d3797fa46f50d596d11151f0e0796731fced5415986 Homepage: https://cran.r-project.org/package=DDPNA Description: CRAN Package 'DDPNA' (Disease-Drived Differential Proteins Co-Expression NetworkAnalysis) Functions designed to connect disease-related differential proteins and co-expression network. It provides the basic statics analysis included t test, ANOVA analysis. The network construction is not offered by the package, you can used 'WGCNA' package which you can learn in Peter et al. (2008) . It also provides module analysis included PCA analysis, two enrichment analysis, Planner maximally filtered graph extraction and hub analysis. Package: r-cran-ddpstar Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-moments, r-cran-matrix, r-cran-mass Filename: pool/dists/noble/main/r-cran-ddpstar_1.0-1-1.ca2404.1_all.deb Size: 211684 MD5sum: ac8bb121b1a12800c197bc11805c2c7b SHA1: 471446269d29cdd681f851c4ae107f10c75ce115 SHA256: 0227a4dee6099f871b68c86a74db78ade689fed856bd6cbcb651da51fb196002 SHA512: cc341f42f2042de27aef581f2795607012b62aeced2bc2db3e8dacac9f4298390ec6a30d651cb245bc8a066ba5937409461e451d3fa68a033bf3e9bec18975ff Homepage: https://cran.r-project.org/package=DDPstar Description: CRAN Package 'DDPstar' (Density Regression via Dirichlet Process Mixtures of NormalStructured Additive Regression Models) Implements a flexible, versatile, and computationally tractable model for density regression based on a single-weights dependent Dirichlet process mixture of normal distributions model for univariate continuous responses. The model assumes an additive structure for the mean of each mixture component and the effects of continuous covariates are captured through smooth nonlinear functions. The key components of our modelling approach are penalised B-splines and their bivariate tensor product extension. The proposed method can also easily deal with parametric effects of categorical covariates, linear effects of continuous covariates, interactions between categorical and/or continuous covariates, varying coefficient terms, and random effects. Please see Rodriguez-Alvarez, Inacio et al. (2025) for more details. Package: r-cran-ddst Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-orthopolynom, r-cran-evd Filename: pool/dists/noble/main/r-cran-ddst_1.4-1.ca2404.1_all.deb Size: 170462 MD5sum: b14df99a62fb44d38528e5282909996a SHA1: b06e4a55e7f89ffbd3e678005f72eccd210b9ff5 SHA256: d9d9989a20ae17dee72d96e92622e6a7f5f32b0f18fb445df6a9bb14fbf16cc0 SHA512: c1cae56bec351251990dfeabeba6e20e81e4325834301447418f8aff14c75379104ca3d35d35ad8b21527cb7ded282fbfb96be9b99d3510de69b378469766ec2 Homepage: https://cran.r-project.org/package=ddst Description: CRAN Package 'ddst' (Data Driven Smooth Tests) Smooth testing of goodness of fit. These tests are data driven (alternative hypothesis is dynamically selected based on data). In this package you will find various tests for exponent, Gaussian, Gumbel and uniform distribution. Package: r-cran-ddtlcm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5473 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-data.table, r-cran-extradistr, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggtext, r-bioc-ggtree, r-cran-label.switching, r-cran-matrixstats, r-cran-phylobase, r-cran-polca, r-cran-testthat, r-cran-truncnorm, r-cran-bayeslogit, r-cran-matrix, r-cran-rdpack, r-cran-r.utils Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xfun Filename: pool/dists/noble/main/r-cran-ddtlcm_0.2.1-1.ca2404.1_all.deb Size: 5189278 MD5sum: d12b83b9460bb62990ed2182ad08bc29 SHA1: 957cc8752a1fbbe3549510b23ef3affa65b1349e SHA256: 2b5d00e21d4ba1e2890ffe55316f6f30650c4aaf5e0a3d11851d014cbe0be190 SHA512: 5f6ea1aa3311ccc73cff9765ee7303fa3477b032ed874ab50b242ea2fd1f985de648990fd8e6365b42b331a8f02e4a09336d2ca74f4b9d2a5b4998f450a29b6e Homepage: https://cran.r-project.org/package=ddtlcm Description: CRAN Package 'ddtlcm' (Latent Class Analysis with Dirichlet Diffusion Tree ProcessPrior) Implements a Bayesian algorithm for overcoming weak separation in Bayesian latent class analysis. Reference: Li et al. (2023) . Package: r-cran-deadband Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ttr Filename: pool/dists/noble/main/r-cran-deadband_0.1.0-1.ca2404.1_all.deb Size: 652764 MD5sum: c07084c6817e6bd2e2c12706aaa24c36 SHA1: 349bd9f313eddfcab0808ccc160a005efaf71458 SHA256: 1e9230366ed0f83d2c7e8a0bf1014eebb200ef51021971ae6174cefa91229882 SHA512: e78f8031dd626a2980876ae15d02a1040101e070599168b583ff783dcda69e98b61bae042a5ce2bbbcee3748b9d2b6bc2041b3cc38ecb7e3e3b797b1069c0596 Homepage: https://cran.r-project.org/package=deadband Description: CRAN Package 'deadband' (Statistical Deadband Algorithms Comparison) Statistical deadband algorithms are based on the Send-On-Delta concept as in Miskowicz(2006,). A collection of functions compare effectiveness and fidelity of sampled signals using statistical deadband algorithms. Package: r-cran-dear Architecture: all Version: 1.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 831 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-ggplot2, r-cran-tidyr, r-cran-plotly, r-cran-igraph, r-cran-writexl, r-cran-dplyr, r-cran-gridextra, r-cran-optisolve Filename: pool/dists/noble/main/r-cran-dear_1.5.4-1.ca2404.1_all.deb Size: 788502 MD5sum: 59e1de82062d66984c893908f2df9d91 SHA1: fdf2bc5082b3907d3ccbec27e6ff154ed568fbbe SHA256: 7c2051bd3eff44db8ce5449c3419746c1250ef72225c9a999cff8892bde80ae6 SHA512: 5f5a44809e0d8f8a242e9c6912458adc9932c69948c2c394a53e95b44a686b3f48f8e4d96c31733b6dca83c23de575258184efa6c20a593b66ded175797fbaa4 Homepage: https://cran.r-project.org/package=deaR Description: CRAN Package 'deaR' (Conventional and Fuzzy Data Envelopment Analysis) Set of functions for Data Envelopment Analysis, including classical, fuzzy, cross-efficiency, bootstrapping, and Malmquist models. See: Banker, R.; Charnes, A.; Cooper, W.W. (1984). , Charnes, A.; Cooper, W.W.; Rhodes, E. (1978). and Charnes, A.; Cooper, W.W.; Rhodes, E. (1981). . Package: r-cran-debar Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-aphid, r-cran-seqinr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-debar_0.1.1-1.ca2404.1_all.deb Size: 299678 MD5sum: fa16d9a47215bcac7453c3d55341a2ba SHA1: 2b8b047ca966317c3287153e11971ccb08d48af4 SHA256: 0a779bb3c18c484e2f72b88d4f1e8e7605af30ce033c3a5231eda52e05f786dc SHA512: cf54329e4412a2e24b403389a517b57195470e6b122526a656781c0faf065f4ae8704a8b30b976a9c12bc4253cfc9a2bcde96a8c77482f989bc9d3ce59519def Homepage: https://cran.r-project.org/package=debar Description: CRAN Package 'debar' (A Post-Clustering Denoiser for COI-5P Barcode Data) The 'debar' sequence processing pipeline is designed for denoising high throughput sequencing data for the animal DNA barcode marker cytochrome c oxidase I (COI). 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Package: r-cran-debbi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-randtoolbox, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-debbi_0.1.0-1.ca2404.1_all.deb Size: 282424 MD5sum: 45904d14a6469a8604898630879740f8 SHA1: 57f65508558762924281984a48ff25dcc0f8c099 SHA256: be901ecde2b53b41f691aa16ede84a5520f9fc4b44315b1a37ad17a3fa594704 SHA512: bffc2268cab48a44bde8cd5ab9ba9c8ee41aa3ec65ba88c8ba5283831f34c8433b5bb3c789dfd58526a8d63ce4626ab9102bbf2bb3356d66dd4043c065a900b3 Homepage: https://cran.r-project.org/package=DEBBI Description: CRAN Package 'DEBBI' (Differential Evolution-Based Bayesian Inference) Bayesian inference algorithms based on the population-based "differential evolution" (DE) algorithm. Users can obtain posterior mode (MAP) estimates via DEMAP, posterior samples via DEMCMC, and variational approximations via DEVI. Package: r-cran-debest Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-flexsurv Filename: pool/dists/noble/main/r-cran-debest_0.1.0-1.ca2404.1_all.deb Size: 36084 MD5sum: e8f0d24f48ab012464c5d2604da6008c SHA1: 7b56b54f21949ac14b8ef8f556c2af8a4364b786 SHA256: 076c545962662bc6a20f6888b26ed63dd24122e3a213d9b2f870b545a9e6c921 SHA512: e3288a18fba61f99772f1fe33102e8199c85b8a89b22cf757aac73e112e59ac8307764a9f0c721d7e2a5ec61d2e64a23bb721a1ce81469c96e4a0a0419212ec9 Homepage: https://cran.r-project.org/package=debest Description: CRAN Package 'debest' (Duration Estimation for Biomarker Enrichment Studies and Trials) A general framework using mixture Weibull distributions to accurately predict biomarker-guided trial duration accounting for heterogeneous population. Extensive simulations are performed to evaluate the impact of heterogeneous population and the dynamics of biomarker characteristics and disease on the study duration. Several influential parameters including median survival time, enrollment rate, biomarker prevalence and effect size are identified. Efficiency gains of biomarker-guided trials can be quantitatively compared to the traditional all-comers design. For reference, see Zhang et al. (2024) . 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It also implements calibration using negative control outcomes to enhance robustness. 'debiasedTrialEmulation' facilitates effect estimation for both binary and time-to-event outcomes, supporting risk ratio (RR), odds ratio (OR), and hazard ratio (HR) as effect measures. It integrates statistical modeling and visualization tools to assess covariate balance, equipoise, and bias calibration. Additional methods—including approaches to address immortal time bias, information bias, selection bias, and informative censoring—are under development. Users interested in these extended features are encouraged to contact the package authors. Package: r-cran-debiasinfer Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvxr, r-cran-caret Suggests: r-cran-mass, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-debiasinfer_0.2.1-1.ca2404.1_all.deb Size: 36868 MD5sum: b9b6b75e496727766687317b91588c10 SHA1: 5b0b5f7015f4c206c81425b51757747d04d3b464 SHA256: 54024d6db613ec69961ad6e075e3ab39eba3e2a621b00c2b689ab55ad8c4a20f SHA512: 14846429d3df7d238cf2d0548e327827eb329f0f61c486b739ca9a09177f9cb7631dd2282f77bc8d410b42db43f154d27a89045c953116fe133b355123268802 Homepage: https://cran.r-project.org/package=DebiasInfer Description: CRAN Package 'DebiasInfer' (Efficient Inference on High-Dimensional Linear Model withMissing Outcomes) A statistically and computationally efficient debiasing method for conducting valid inference on the high-dimensional linear regression function with missing outcomes. The reference paper is Zhang, Giessing, and Chen (2023) . Package: r-cran-debinfer Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1444 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-truncdist, r-cran-coda, r-cran-rcolorbrewer, r-cran-mass, r-cran-mvtnorm, r-cran-plyr, r-cran-pbsddesolve Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-r.rsp, r-cran-beanplot Filename: pool/dists/noble/main/r-cran-debinfer_0.4.4-1.ca2404.1_all.deb Size: 1239528 MD5sum: dff757cb4f78da70608ebce265e0d3a5 SHA1: f16e16fcff989deba01322f5093ff20b7355a47b SHA256: 150763974a91fea99f23af8ed29565414126a83cfc2ff0937042ec71c6e50fd5 SHA512: 5062e9ad71baa0d5068f447490fa78b9f50471217912cd86b113f8815b01a45da7b81a0232ea2fe5614613361cb5acb64cbba4560b7e46c3f7b9ed91a8b12e32 Homepage: https://cran.r-project.org/package=deBInfer Description: CRAN Package 'deBInfer' (Bayesian Inference for Differential Equations) A Bayesian framework for parameter inference in differential equations. This approach offers a rigorous methodology for parameter inference as well as modeling the link between unobservable model states and parameters, and observable quantities. Provides templates for the DE model, the observation model and data likelihood, and the model parameters and their prior distributions. A Markov chain Monte Carlo (MCMC) procedure processes these inputs to estimate the posterior distributions of the parameters and any derived quantities, including the model trajectories. Further functionality is provided to facilitate MCMC diagnostics and the visualisation of the posterior distributions of model parameters and trajectories. Package: r-cran-debkeepr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1483 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-vctrs, r-cran-zeallot Suggests: r-cran-covr, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-debkeepr_0.1.1-1.ca2404.1_all.deb Size: 915012 MD5sum: 0bf54f190376511d461acf7cbbde8686 SHA1: ff06029e25c6f90033353767e42e7c50e828abf8 SHA256: 031c4044049a52ed345019de64a7ee2b8dbff6ac02d98ed906265b9182652e39 SHA512: 4d72692be0a812788552ab3f2406fceecfc089b2dff2d4dcb73d3ab1c17289d2620f58949bc2333f45edb9524c5a34d1e3c1463c12d87e9732b9d787c4b0e28b Homepage: https://cran.r-project.org/package=debkeepr Description: CRAN Package 'debkeepr' (Analysis of Non-Decimal Currencies and Double-Entry Bookkeeping) Analysis of historical non-decimal currencies and value systems that use tripartite or tetrapartite systems such as pounds, shillings, and pence. It introduces new vector classes to represent non-decimal currencies, making them compatible with numeric classes, and provides functions to work with these classes in data frames in the context of double-entry bookkeeping. Package: r-cran-deboinr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-ggplot2, r-cran-gridextra, r-cran-pracma, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-deboinr_1.0-1.ca2404.1_all.deb Size: 601250 MD5sum: b86cf36f757319f8d5b1335ab7165ef6 SHA1: 311dd1f40038be1262186d563e9fa39a83037fb4 SHA256: d2ddacc218635703260bdec52c903b0bfe2871ca3d6425a1f52cc187fa7e200a SHA512: 43a197523556b733fab3484c20c973d93497695824440c403965f4f03ccbe0252a3210098209fc0920e183c01245840e6200874fe56f55aa89e48c2ff69bf9e6 Homepage: https://cran.r-project.org/package=DeBoinR Description: CRAN Package 'DeBoinR' (Box-Plots and Outlier Detection for Probability DensityFunctions) Orders a data-set consisting of an ensemble of probability density functions on the same x-grid. Visualizes a box-plot of these functions based on the notion of distance determined by the user. Reports outliers based on the distance chosen and the scaling factor for an interquartile range rule. For further details, see: Alexander C. Murph et al. (2023). "Visualization and Outlier Detection for Probability Density Function Ensembles." . Package: r-cran-debrief Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-profvis, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-debrief_0.1.0-1.ca2404.1_all.deb Size: 306972 MD5sum: 9eaea300cd28b382c07c2b61fb786c16 SHA1: f4a159f69b3f722d743ca9d447ee9ac63fdce399 SHA256: fc2fb6729c6813f1fe6d98f0d0acfbfdd43b346895925f838d80694e6e4b284a SHA512: ef401c3ab0c8276d4aee9ea430b63db48c6f37fc6376501e16ce20754bc880a09fffac80cc0713cd3c5a27dcf7440881e34d1e7fdc7b457a5581127118c057f8 Homepage: https://cran.r-project.org/package=debrief Description: CRAN Package 'debrief' (Text-Based Summaries for 'profvis' Profiling Data) Provides text-based summaries and analysis tools for 'profvis' profiling output. Designed for terminal workflows and artificial intelligence (AI) agent consumption, offering views including hotspot analysis, call trees, source context, caller/callee relationships, and memory allocation breakdowns. Package: r-cran-debtkit Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-debtkit_0.1.2-1.ca2404.1_all.deb Size: 139784 MD5sum: 9c0298ce18bad30a024f6762732ee277 SHA1: edea7a1f0827b58172afdfba9cdc018fe3731bc5 SHA256: 128d9d622e302d1e70366f8427241bfa9de9e90e7f278934bcdcfa5cbbbdc3b8 SHA512: b697cdd2a85ca77d5ab48457e11a513d8c79748ecb62f99294498bf3aaba5e641d3e5a0a3e442a074051c9e6716f12e8d61d6e6ba1fe3c6cb89b04d828dfae5e Homepage: https://cran.r-project.org/package=debtkit Description: CRAN Package 'debtkit' (Debt Sustainability Analysis and Fiscal Risk Assessment) Analyses government debt sustainability using the standard debt dynamics framework from Blanchard (1990) and the IMF Debt Sustainability Analysis methodology (IMF, 2013) and the Sovereign Risk and Debt Sustainability Framework (IMF, 2022). Projects debt-to-GDP paths, decomposes historical debt changes into interest, growth, and primary balance contributions, and estimates fiscal reaction functions following Bohn (1998) . Produces stochastic fan charts via Monte Carlo simulation, standardised stress tests, and IMF- style heat map risk assessments. Computes S1/S2 sustainability gap indicators used by the European Commission. All methods are pure computation with no external dependencies beyond base R; works with fiscal data from any source. 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Package: r-cran-debugr Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rprojroot, r-cran-rstudioapi Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-debugr_0.0.1-1.ca2404.1_all.deb Size: 35096 MD5sum: 93093ecc6af48df0adb38c1fc3e02493 SHA1: cee020ab783aa814640dcc62f2e4e85d1ab313db SHA256: f331e90c1525ce9dae52599802efd8014d7d11c2bfe67978d57895e4678c06cc SHA512: 32e7e37e9c63f60c8f294e4d681507118829b430b24f60a087fefb21a75c7e5ac28ce2dd7c95e9fd99ea8fdc96535c54ea32c19c093dcc2add33c37244b9e5fc Homepage: https://cran.r-project.org/package=debugr Description: CRAN Package 'debugr' (Debug Tool to Watch Objects/Expressions While Running an RScript) Tool to print out the value of R objects/expressions while running an R script. Outputs can be made dependent on user-defined conditions/criteria. 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Package: r-cran-decide Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-decide_1.3-1.ca2404.1_all.deb Size: 65044 MD5sum: 4070202f06715794d099e740a149acb3 SHA1: c639afaf4418481db522de6db5d4dbb688b99070 SHA256: 6c0814f44cb0602e180c4bfc82e698d89a98aa0c3a4b5f2d7fb76fad1c5c0b32 SHA512: a7b47e8571a293213915d61e1a943f49cfbebdada915b7feae0d72218e0ddc27f41166b3834fab6c766d19932786f438b60fdf54dea37663695bd7ef5ca4a3d9 Homepage: https://cran.r-project.org/package=DECIDE Description: CRAN Package 'DECIDE' (DEComposition of Indirect and Direct Effects) Calculates various estimates for measures of educational differentials, the relative importance of primary and secondary effects in the creation of such differentials and compares the estimates obtained from two datasets. Package: r-cran-decision Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-decision_0.1.0-1.ca2404.1_all.deb Size: 13796 MD5sum: e6b0f4b64a0d680731206c67bc4a4ede SHA1: 61ebfbe8ab42ed45cc3004459689b55164878448 SHA256: 57704f6a76520e949e9696f80b0ad53daec7ac24de5dd753f6d98302b68b8029 SHA512: a95fb2c760f7e1985d37ee9554267b7a8c2f36cfcbc3db7d8ecc18b02f60291da9f93a44071bf1917f2721947c77fd239676774a48a4e156b37228c035ccb081 Homepage: https://cran.r-project.org/package=decision Description: CRAN Package 'decision' (Statistical Decision Analysis) Contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making. Package: r-cran-decisiondrift Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-tibble Suggests: r-cran-decisionpaths, r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-decisiondrift_0.1.0-1.ca2404.1_all.deb Size: 193838 MD5sum: 19e24d6bb2ef4ac34cf6d04e842f4d68 SHA1: e792e2ea21e6e96549d6e3d00e573e653f009b9a SHA256: e63de3b795f5d065b03ced8479548ac59c700d7ad53b8b5e4150a1e30d4177b8 SHA512: 5761f24cbf6fae501eaebe52eed97fe6cff580c21aabef394b8292ba8e9b609f2684dc914cf8b73a907295a5124cd33d9bdd3a1063e25b8285cfee4e123fa2a3 Homepage: https://cran.r-project.org/package=DecisionDrift Description: CRAN Package 'DecisionDrift' (Detecting, Decomposing, and Stress-Testing Temporal Change inRepeated Decision Systems) Tools for detecting, decomposing, and stress-testing temporal drift in repeated binary decision systems. Complements the 'decisionpaths' package by shifting focus from path construction to system-level change over time. Implements five core analytic modules: (1) prevalence drift — did the overall decision rate change over time?; (2) transition drift — did the probability of switching or persisting change?; (3) entropy and stability trends — did path complexity evolve?; (4) group-differential drift — did the system drift differently across subgroups?; (5) change-point and regime-shift detection — did the system change abruptly after a policy or model update? Additionally provides a robustness module for testing stability of drift conclusions across analytic choices, and a sensitivity module for probing vulnerability to data problems including missingness, miscoding, and threshold shifts. Defines four original drift indices: the Decision Drift Index (DDI), Transition Drift Index (TDI), Group Differential Drift (GDD), and Cumulative Drift Burden (CDB). Applications include algorithmic audit, AI governance, education, health, and organisational research. Package: r-cran-decisionpaths Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-decisionpaths_0.1.0-1.ca2404.1_all.deb Size: 85114 MD5sum: 187061a315c459ae1f8c8035beeb1f11 SHA1: fd104fcbe269249d15454aa31f17f93214998d89 SHA256: 833335c96ecb4235401826f2942541f31219c0393e2db84fa489dbc75b92c45a SHA512: 61e4a58fe7d92ef6ac8a1408bb59e4f17b4843a5a6e3d6a79557a9c524e50ee8968e524128488239301b4dca16ca809455c17f719c7c27b411bc9431b91cab1d Homepage: https://cran.r-project.org/package=decisionpaths Description: CRAN Package 'decisionpaths' (Construct and Audit Longitudinal Decision Paths) Tools for constructing and auditing longitudinal decision paths from panel data. Implements a decision infrastructure framework for representing institutional AI systems as generators of time-ordered binary decision sequences. Provides functions to build path objects from panel data, summarise per-unit descriptors (dosage, switching rate, onset, duration, longest run), compute the Decision Reliability Index (DRI) following Cronbach (1951) , estimate Shannon decision-path entropy following Shannon (1948) , classify systems by infrastructure type (static, periodic, continuous, human-in-the-loop), and evaluate subgroup disparities in decision exposure and stability. Applications include education, policy, health, and organisational research. 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Decision support is given on two levels: (i) The actual decision level is to choose between two alternatives under probabilistic uncertainty. This package calculates the optimal decision based on maximizing expected welfare. (ii) The meta decision level is to allocate resources to reduce the uncertainty in the underlying decision problem, i.e to increase the current information to improve the actual decision making process. This problem is dealt with using the Value of Information Analysis. The Expected Value of Information for arbitrary prospective estimates can be calculated as well as Individual Expected Value of Perfect Information. The probabilistic calculations are done via Monte Carlo simulations. This Monte Carlo functionality can be used on its own. 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Package: r-cran-declaredesign Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomizr, r-cran-fabricatr, r-cran-estimatr, r-cran-rlang, r-cran-generics Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-aer, r-cran-diffobj, r-cran-dplyr, r-cran-data.table, r-cran-tibble, r-cran-ggplot2, r-cran-future, r-cran-future.apply, r-cran-broom, r-cran-mass, r-cran-matching, r-cran-betareg, r-cran-biglm, r-cran-gam, r-cran-sf, r-cran-reshape2, r-cran-designlibrary, r-cran-coin, r-cran-marginaleffects, r-cran-psych, r-cran-causalqueries, r-cran-rdrobust, r-cran-rdss Filename: pool/dists/noble/main/r-cran-declaredesign_1.1.1-1.ca2404.1_all.deb Size: 331826 MD5sum: edafda4d61dd18076044f5a0b20edec6 SHA1: c6e7ec80eab775aa3948cd8c255cca9dbb685ec5 SHA256: 2a845cbb4c23ced54c1a43eb058e56c2ab37f7f1d8bb36d9c473ff1fae67b26b SHA512: a4f8655fd4a09daf68274e4b6bd0270574bec9b2652767cdacfc02718bb9260c855a10e0035f1a33b8d1b874d73d0ba07392d2f1ce13b79b756ca33ae45f461a Homepage: https://cran.r-project.org/package=DeclareDesign Description: CRAN Package 'DeclareDesign' (Declare and Diagnose Research Designs) Researchers can characterize and learn about the properties of research designs before implementation using `DeclareDesign`. Ex ante declaration and diagnosis of designs can help researchers clarify the strengths and limitations of their designs and to improve their properties, and can help readers evaluate a research strategy prior to implementation and without access to results. It can also make it easier for designs to be shared, replicated, and critiqued. 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Package: r-cran-decoder Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1870 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-dt, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-decoder_1.2.3-1.ca2404.1_all.deb Size: 1633442 MD5sum: e293bdbe95682a2d39e8083298c99a2d SHA1: 40e4397024d229fa1f80929a59b781561484049a SHA256: 03248da31060805fad7167156559872ea5a432eefb8ea547724c35d41a3a3a30 SHA512: 8c874103a2ddbc73713f5300f4996875f1e95adaf19b3718b8c26abd0f4fa74e05dda79b2768eb64ed3ba57c6c26860708e2df4924a83dbc7c40da0d45346b83 Homepage: https://cran.r-project.org/package=decoder Description: CRAN Package 'decoder' (Decode Coded Variables to Plain Text and the Other Way Around) Main function "decode" is used to decode coded key values to plain text. Function "code" can be used to code plain text to code if there is a 1:1 relation between the two. The concept relies on 'keyvalue' objects used for translation. There are several 'keyvalue' objects included in the areas of geographical regional codes, administrative health care unit codes, diagnosis codes and more. It is also easy to extend the use by arbitrary code sets. Package: r-cran-decompdl Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-keras, r-cran-tensorflow, r-cran-reticulate, r-cran-tsutils, r-bioc-biocgenerics, r-cran-magrittr, r-cran-rlibeemd, r-cran-tsdeeplearning, r-cran-vmdecomp Filename: pool/dists/noble/main/r-cran-decompdl_0.1.0-1.ca2404.1_all.deb Size: 84252 MD5sum: 40d6c974f0a5dce16d832a1dbb5c0ef0 SHA1: 273644437214c3bac385dda3954b29442c1cbada SHA256: 35b4296f672fd8242b8aca02fac85c70dd186fe77708ee254169a4acd58a143a SHA512: 3426e8472ffaef6e04eeda6866c7b106b9a7bae70fdea67146519ffcefb946c9000217a8f3fb9ece7f19b6b5e31339dd68cb687f536cc36f97346c2e88b4b67e Homepage: https://cran.r-project.org/package=decompDL Description: CRAN Package 'decompDL' (Decomposition Based Deep Learning Models for Time SeriesForecasting) Hybrid model is the most promising forecasting method by combining decomposition and deep learning techniques to improve the accuracy of time series forecasting. Each decomposition technique decomposes a time series into a set of intrinsic mode functions (IMFs), and the obtained IMFs are modelled and forecasted separately using the deep learning models. Finally, the forecasts of all IMFs are combined to provide an ensemble output for the time series. The prediction ability of the developed models are calculated using international monthly price series of maize in terms of evaluation criteria like root mean squared error, mean absolute percentage error and, mean absolute error. For method details see Choudhary, K. et al. (2023). . Package: r-cran-decompml Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-nnfor, r-cran-rlibeemd, r-cran-vmdecomp Filename: pool/dists/noble/main/r-cran-decompml_0.1.1-1.ca2404.1_all.deb Size: 80656 MD5sum: 5b52fae7e5188124c984ad0ca5e51a3e SHA1: 288405336550ba5fc0fc065badf5b228c636d9bd SHA256: 3cd1a4a6d2832dc97a92e4aafe76230cfd83665b0ac5e4642810bd4ba0156a79 SHA512: e6f97c950ac62aa48f32dd03700dcba1e34c5253dc7f8924571f03c5f8e20e3e492410b0b1ae2047d78173c66b1c117987ec3374171d2351b3ff7ba787e4d73c Homepage: https://cran.r-project.org/package=decompML Description: CRAN Package 'decompML' (Decomposition Based Machine Learning Model) The hybrid model is a highly effective forecasting approach that integrates decomposition techniques with machine learning to enhance time series prediction accuracy. Each decomposition technique breaks down a time series into multiple intrinsic mode functions (IMFs), which are then individually modeled and forecasted using machine learning algorithms. The final forecast is obtained by aggregating the predictions of all IMFs, producing an ensemble output for the time series. The performance of the developed models is evaluated using international monthly maize price data, assessed through metrics such as root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). For method details see Choudhary, K. et al. (2023). . Package: r-cran-decomposedpsf Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psf, r-cran-rlibeemd, r-cran-forecast, r-cran-tseries Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-decomposedpsf_0.2-1.ca2404.1_all.deb Size: 29098 MD5sum: 806a5025ff8d6533c7c92230c852ec67 SHA1: cebd6521510011f42c64da7b5ced49954843d659 SHA256: 27e1c23055e14edbf3f861f0570e90223cdd44d9449e4266c5ea33e1a93001ae SHA512: 72c5bd1635b1e249e504b9fb59a6073fe83eb7c6bd2dd42cce1b8e27f4d1cb9ba4bde171c72e5ede70346aa2262af197c0eb2e29714d82ef80b0d3ef26198715 Homepage: https://cran.r-project.org/package=decomposedPSF Description: CRAN Package 'decomposedPSF' (Time Series Prediction with PSF and Decomposition Methods (EMDand EEMD)) Predict future values with hybrid combinations of Pattern Sequence based Forecasting (PSF), Autoregressive Integrated Moving Average (ARIMA), Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) methods based hybrid methods. Package: r-cran-decomposer Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 933 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-usethis, r-cran-tictoc, r-cran-stratigrapher, r-cran-hexbin, r-cran-colorramps, r-cran-dplyr Suggests: r-cran-emd, r-cran-rssa, r-cran-astrochron Filename: pool/dists/noble/main/r-cran-decomposer_1.0.7-1.ca2404.1_all.deb Size: 821508 MD5sum: 40ce1cedc021c80190e3ce912cf57a77 SHA1: 6d79cf0456e814126db4cf7dc636f444b64261fc SHA256: 70db30cd1418df5b548aa058e6a866f306deb796463b8ab7604fda224df9a20a SHA512: dc2fd9677022e88ec7aad03a162f35c363d532e2e11004efc247f01ec27bb9a96ed4bd7d5338d9150837eb0720eaf5b6276499d58248680b1cb3d6326a972a65 Homepage: https://cran.r-project.org/package=DecomposeR Description: CRAN Package 'DecomposeR' (Empirical Mode Decomposition for Cyclostratigraphy) Tools to apply Ensemble Empirical Mode Decomposition (EEMD) for cyclostratigraphy purposes. Mainly: a new algorithm, extricate, that performs EEMD in seconds, a linear interpolation algorithm using the greatest rational common divisor of depth or time, different algorithms to compute instantaneous amplitude, frequency and ratios of frequencies, and functions to verify and visualise the outputs. The functions were developed during the CRASH project (Checking the Reproducibility of Astrochronology in the Hauterivian). When using for publication please cite Wouters, S., Crucifix, M., Sinnesael, M., Da Silva, A.C., Zeeden, C., Zivanovic, M., Boulvain, F., Devleeschouwer, X., 2022, "A decomposition approach to cyclostratigraphic signal processing". Earth-Science Reviews 225 (103894). . Package: r-cran-decompositionle Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-stringr, r-cran-tidyr, r-cran-purrr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-tibble, r-cran-forcats Filename: pool/dists/noble/main/r-cran-decompositionle_1.0.0-1.ca2404.1_all.deb Size: 41298 MD5sum: f9e5f1d85485de4c969462cd126ddc26 SHA1: 2683d7278bec3e6d22653e2b63cc2676236c5f04 SHA256: b0d9e58d26cb98f2f78ed20d912c93a07dd0e9c8eda4a7dbbb0c568074c5af7a SHA512: 61c327837d8a164b8f617dc23c7d3543c8f21896989ed966eaad0b850f08481a53269887e0990ca258107ada2255fa011fcc1c33543f31b9ccea567ac63c1e5c Homepage: https://cran.r-project.org/package=decompositionLE Description: CRAN Package 'decompositionLE' (Provides Easy Methods to Perform Life Expectancy Decomposition) Provides an easy to use implementation of life expectancy decomposition formulas for age bands, derived from Ponnapalli, K. (2005). A comparison of different methods for decomposition of changes in expectation of life at birth and differentials in life expectancy at birth. Demographic Research, 12, pp.141–172. In addition, there is a decomposition function for disease cause breakdown and a couple helpful plot functions. Package: r-cran-deconstructsigs Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-bioc-bsgenome, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-genomeinfodb Suggests: r-bioc-variantannotation Filename: pool/dists/noble/main/r-cran-deconstructsigs_1.8.0-1.ca2404.1_all.deb Size: 269784 MD5sum: aba9bf3a65e554378ffa304a929d0915 SHA1: a066042a67f6e57215532ff844c454941c08b8c3 SHA256: 03e5177fd417ac8f0eab17e8d3428aaa6e33dfa1a1365f0302bf88aa0d50fbe9 SHA512: a857ee6e09ed549d2ec27a9772f1c91c55180aed47ac9330f6741300979777313e7e65f540d9e35b97f099db4da4cab8cf99d83dcb5c75f6c8ee33ff9ae53e3d Homepage: https://cran.r-project.org/package=deconstructSigs Description: CRAN Package 'deconstructSigs' (Identifies Signatures Present in a Tumor Sample) Takes sample information in the form of the fraction of mutations in each of 96 trinucleotide contexts and identifies the weighted combination of published signatures that, when summed, most closely reconstructs the mutational profile. Package: r-cran-deconvolver Architecture: all Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2890 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-cowplot, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-deconvolver_1.2-1-1.ca2404.1_all.deb Size: 1679726 MD5sum: 7c00ac9223b2fdceb9e5c1855e019065 SHA1: d1ea59a559efb7be7c2c46f737e760c7c57cf2e8 SHA256: 1a36c1700afb1663ad3215496a31836a3a43a8b5b1fd643293df2238308a41ce SHA512: 037f7bcb59686fee50068157d829559d5cdd5b636816a899a6b50f05a3c198983cc49e59a43ab1db365c6b010862a194dd99fdfc7c5f410a04dcaebb3010fbf2 Homepage: https://cran.r-project.org/package=deconvolveR Description: CRAN Package 'deconvolveR' (Empirical Bayes Estimation Strategies) Empirical Bayes methods for learning prior distributions from data. An unknown prior distribution (g) has yielded (unobservable) parameters, each of which produces a data point from a parametric exponential family (f). The goal is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical Bayes methods. Details and examples are in the paper by Narasimhan and Efron (2020, ). Package: r-cran-decorater Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rweka, r-cran-rwekajars, r-cran-rjava Filename: pool/dists/noble/main/r-cran-decorater_0.1.2-1.ca2404.1_all.deb Size: 25222 MD5sum: cb2b4d3e5edb93fbffd890223316da07 SHA1: 203749448e6692005c911ce06e1839966f3a487e SHA256: 409aab89b2a95bc4040c07542c12abdf9cf75ead88b0149f0aacf31c44f60b6e SHA512: 8c09b3ae0578aa6a3d6752aaf82f953009aed62d635aa56c28fed8f41c21dd36b21f0d99cc5270dee3fca53848b20e93a1cdfcf72e06d457cece9175da59802b Homepage: https://cran.r-project.org/package=DecorateR Description: CRAN Package 'DecorateR' (Fit and Deploy DECORATE Trees) DECORATE (Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples) builds an ensemble of J48 trees by recursively adding artificial samples of the training data ("Melville, P., & Mooney, R. J. (2005) "). Package: r-cran-decorators Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-decorators_0.3.0-1.ca2404.1_all.deb Size: 22578 MD5sum: 7142bfcf4122550278b05234ff77aa0e SHA1: f2f23f231203281361aeb4b77a35c0ce529f4015 SHA256: 9fe11b98379288b4d1b0783cdba3ecc00f39f8b8f1f0f643566130bd5025a311 SHA512: 30106cdc846d6f07fac2c6547157afe4ef9533f0e714f36ad03ce0f9fb3b388410a5c0ea94cf0567da6159cfdcfaec67a33c8f2d6509e4dfe5c0a485c907658b Homepage: https://cran.r-project.org/package=decorators Description: CRAN Package 'decorators' (Extend the Behaviour of a Function without Explicitly Modifyingit) A decorator is a function that receives a function, extends its behaviour, and returned the altered function. Any caller that uses the decorated function uses the same interface as it were the original, undecorated function. Decorators serve two primary uses: (1) Enhancing the response of a function as it sends data to a second component; (2) Supporting multiple optional behaviours. An example of the first use is a timer decorator that runs a function, outputs its execution time on the console, and returns the original function's result. An example of the second use is input type validation decorator that during running time tests whether the caller has passed input arguments of a particular class. Decorators can reduce execution time, say by memoization, or reduce bugs by adding defensive programming routines. Package: r-cran-decp Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geigen, r-cran-ggplot2, r-cran-magrittr, r-cran-matrixcalc, r-cran-purrr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-decp_0.1.2-1.ca2404.1_all.deb Size: 56466 MD5sum: e13bb19d4a61fbf592b96a77d3b0b5be SHA1: 1abe216ed877980696265ff3859c2bc01ca4f9f9 SHA256: 5e7d3769d89dfd62d918aecabaebd4df31e45211ee5d55587989a35b18c0c98b SHA512: bbab5f9a07db426728b711704d0f7cf7714c751dfff142c96bc7661e9282bbf1e324ef754b4406e6599363542c97527f12fa61cd30676d8accc06dd9a17e1bbf Homepage: https://cran.r-project.org/package=decp Description: CRAN Package 'decp' (Complete Change Point Analysis) Provides a comprehensive approach for identifying and estimating change points in multivariate time series through various statistical methods. Implements the multiple change point detection methodology from Ryan & Killick (2023) and a novel estimation methodology from Fotopoulos et al. (2023) generalized to fit the detection methodologies. Performs both detection and estimation of change points, providing visualization and summary information of the estimation process for each detected change point. 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This package allows to find connected rows based on data on chosen columns and collapse it into one row. Package: r-cran-deep Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-deep_0.1.0-1.ca2404.1_all.deb Size: 153252 MD5sum: 55c8d1739c6165dc0a5506e1a484d6b7 SHA1: 6408e79cd79680a231912869100cebcdc7272be5 SHA256: 81c22951804a5ed007120895c8399249f907c9612cbcd808d5653954d10e99dc SHA512: 021f2d199d7239a7f93e19647943ffe77180d3335c38f816c0a7390ee28b0c4008360c977866b7af614c3bb943e9110a0c19bf8fcfa4917444ade3eb5f32f4e6 Homepage: https://cran.r-project.org/package=deep Description: CRAN Package 'deep' (A Neural Networks Framework) Explore neural networks in a layer oriented way, the framework is intended to give the user total control of the internals of a net without much effort. Use classes like PerceptronLayer to create a layer of Percetron neurons, and specify how many you want. The package does all the tricky stuff internally leaving you focused in what you want. I wrote this package during a neural networks course to help me with the problem set. 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The main deepdep() function allows to acquire deep dependencies of any package and plot them in an elegant way. It also adds some popularity measures for the packages e.g. in the form of download count through the 'cranlogs' package. Uses the CRAN metadata database and Bioconductor metadata . Other data acquire functions are: get_dependencies(), get_downloads() and get_description(). The deepdep_shiny() function runs shiny application that helps to produce a nice 'deepdep' plot. Package: r-cran-deepdive Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastdummies, r-cran-plyr, r-cran-rpart, r-cran-treeclust, r-cran-data.table, r-cran-stringr Filename: pool/dists/noble/main/r-cran-deepdive_1.0.4-1.ca2404.1_all.deb Size: 86670 MD5sum: e8a4448483ee16294fe0cafe0612401e SHA1: ba86900fc505f82c14916b6585fd017f1f6b7a25 SHA256: e8eebfdb2588e03890772e348a3b816e87f9f24f43384e9e610a4240740a8b74 SHA512: 2cdd8d8388144b3631565ae7b8506d488fbf43fc05005d83cba83275bdde123d1ba014dcab58e1209d9806966ac7e9c65794a73c31d13b66889d96c5263fd6e0 Homepage: https://cran.r-project.org/package=deepdive Description: CRAN Package 'deepdive' (Deep Learning for General Purpose) Aims to provide simple intuitive functions to create quick prototypes of artificial neural network or deep learning models. In addition novel ensemble models like 'deeptree' and 'deepforest' has been included which combines decision trees and neural network. Package: r-cran-deepgmm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-corpcor, r-cran-mclust Filename: pool/dists/noble/main/r-cran-deepgmm_0.2.1-1.ca2404.1_all.deb Size: 86980 MD5sum: 61ee61ae2d9d6ba6df781f68586d3988 SHA1: 2e48bc57f333f68a377a2407d7cdfd01fd1bc956 SHA256: d9f21f09c17395b2267fd4fc509d01347d03423672ec38166fbd2ad90fd7f98f SHA512: 85e2c94a35b5bc323fd23fc1988a0dce239d1e486803140b86f034f05fdbeb053bebcebf358bdecaf3934e7d16d143e658f903461020583dfe2ac4c9c8a41234 Homepage: https://cran.r-project.org/package=deepgmm Description: CRAN Package 'deepgmm' (Deep Gaussian Mixture Models) Deep Gaussian mixture models as proposed by Viroli and McLachlan (2019) provide a generalization of classical Gaussian mixtures to multiple layers. Each layer contains a set of latent variables that follow a mixture of Gaussian distributions. To avoid overparameterized solutions, dimension reduction is applied at each layer by way of factor models. Package: r-cran-deeplearningcausal Architecture: all Version: 0.0.107-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rocr, r-cran-caret, r-cran-neuralnet, r-cran-superlearner, r-cran-ggplot2, r-cran-tidyr, r-cran-magrittr, r-cran-reticulate, r-cran-keras3, r-cran-hmisc Suggests: r-cran-testthat, r-cran-dplyr, r-cran-class, r-cran-xgboost, r-cran-randomforest, r-cran-glmnet, r-cran-ranger, r-cran-gam, r-cran-e1071, r-cran-gbm, r-cran-tensorflow Filename: pool/dists/noble/main/r-cran-deeplearningcausal_0.0.107-1.ca2404.1_all.deb Size: 342618 MD5sum: 50b9f7c9d77202dfb92d897d90980403 SHA1: 26f78ac373b12079586fe742cc260c6d0e3cdbe3 SHA256: 5596769d98a8bf12da926be1b8750bd92ae9c212ec9c30309fcf522f0514d35d SHA512: dfa6eeac4e482bdd333b93b74220fe4f65805537e1ddfb01ead9869f973dba8ea308ccf406f4b03fecc70b0375207a7d87ff8a5ff1c31e920469d38d02dd865f Homepage: https://cran.r-project.org/package=DeepLearningCausal Description: CRAN Package 'DeepLearningCausal' (Causal Inference with Super Learner and Deep Neural Networks) Functions for deep learning estimation of Conditional Average Treatment Effects (CATEs) from meta-learner models and Population Average Treatment Effects on the Treated (PATT) in settings with treatment noncompliance using reticulate, TensorFlow and Keras3. Functions in the package also implements the conformal prediction framework that enables computation and illustration of conformal prediction (CP) intervals for estimated individual treatment effects (ITEs) from meta-learner models. Additional functions in the package permit users to estimate the meta-learner CATEs and the PATT in settings with treatment noncompliance using weighted ensemble learning via the super learner approach and R neural networks. Package: r-cran-deeplr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-utf8, r-cran-httr, r-cran-tibble, r-cran-purrr, r-cran-tokenizers, r-cran-jsonlite, r-cran-readr Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-deeplr_2.1.0-1.ca2404.1_all.deb Size: 165574 MD5sum: 43b812702df999d3451d31fe611c2647 SHA1: e120955732346ce83660459f54602cfc53d64e5c SHA256: 490144d6945076734fc983d70be22298531de776e8b2bab4f1ed4f41eae7e57a SHA512: 852f028f7c0738ea0b1e818c1d769ca4ed0b9abd7085e86c71f77cc8769021b9a4c695abe6e43e759be752174aea57c3524fdcf7acc919ca4620e6eeffb504bf Homepage: https://cran.r-project.org/package=deeplr Description: CRAN Package 'deeplr' (Interface to the 'DeepL' Translation API) A wrapper for the 'DeepL' API , a web service for translating texts between different languages. A DeepL API developer account is required to use the service (see ). Package: r-cran-deepmou Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-skmeans, r-cran-extradistr, r-cran-dplyr, r-cran-rfast, r-cran-entropy, r-cran-ggplot2, r-cran-mass Filename: pool/dists/noble/main/r-cran-deepmou_0.1.1-1.ca2404.1_all.deb Size: 126910 MD5sum: 6209552c09567b594d8f9ce5f1938b3c SHA1: 9f9e1365611493155631c5a665c0c70f03d61442 SHA256: e6c76ea37f53f0a5420ad8efdd6816bdb4c9e3391b0338130505baf1c5f7a744 SHA512: 6b503e51eee96cbab9aaaee2e559895cc773e9fce34944cd263546450cf3edf9d8f32f9b0f1f8e794250bcfee93fad434301ec8297985fb83f77062dc7001f6c Homepage: https://cran.r-project.org/package=deepMOU Description: CRAN Package 'deepMOU' (Clustering of Short Texts by Mixture of Unigrams and Its DeepExtensions) Functions providing an easy and intuitive way for fitting and clusters data using the Mixture of Unigrams models by means the Expectation-Maximization algorithm (Nigam, K. et al. (2000). ), Mixture of Dirichlet-Multinomials estimated by Gradient Descent (Anderlucci, Viroli (2020) ) and Deep Mixture of Multinomials whose estimates are obtained with Gibbs sampling scheme (Viroli, Anderlucci (2020) ). There are also functions for graphical representation of clusters obtained. Package: r-cran-deepnet Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-deepnet_0.2.1-1.ca2404.1_all.deb Size: 60318 MD5sum: 9e1d3563c403b25900dcfa4cd8cdcda1 SHA1: 0971c25a892ae82698e68519662730ced85e9dec SHA256: 0f7d6099e2573eda5052d5e25b9dd79483c18123fb66077271583fc32c70ff25 SHA512: 9893fc087212bb645393553d9df0ef26ec4ed6327d3761f20b515c89ffcf1c39e2e68a89351fbfef85c6eae0fa410e43e6a2a146f85dd692caec08841a31de16 Homepage: https://cran.r-project.org/package=deepnet Description: CRAN Package 'deepnet' (Deep Learning Toolkit in R) Implement some deep learning architectures and neural network algorithms, including BP,RBM,DBN,Deep autoencoder and so on. Package: r-cran-deepnn Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-deepnn_1.2-1.ca2404.1_all.deb Size: 161674 MD5sum: 1ac99dbac6bcd34757af98aa4964f170 SHA1: 54599e4086c0671a2d4be838383df2eac0fcbae9 SHA256: 51d0efb16ce61e263afff1308379c7c70707f0ae8ccbe97b4acb12b576e425e2 SHA512: e0a2e326fae6b24d3e07383c9b030c7de8b5861e3f36fa539740d2a200e7abc828e9c6990f16f66d1cf62db06beb8f9402b23b6724fe730c7cf282a7732f2e2d Homepage: https://cran.r-project.org/package=deepNN Description: CRAN Package 'deepNN' (Deep Learning) Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) Neural Networks YouTube playlist. Michael A. Nielsen Neural Networks and Deep Learning. Package: r-cran-deepredeff Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1611 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-keras, r-cran-magrittr, r-cran-purrr, r-cran-reticulate, r-cran-rlang, r-cran-seqinr, r-cran-tensorflow Suggests: r-cran-covr, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-deepredeff_0.1.1-1.ca2404.1_all.deb Size: 943938 MD5sum: a352a324875a4b33fa22fbaa4a01871e SHA1: b7423ba08f22c664eca30770394b8f3639e86fff SHA256: 690b436d69c5a43618f5ff7b4bcf16fa577f2da0878c2203456a75f9f206d49a SHA512: 93f68830b6b88993b9f711a1e193401b0fe856fdbc01af0b987c672b1a4b5b449d38306786f51e6ca6ad7dc0fb40b92da04b6f16584c0507b77e45fbb42ffaed Homepage: https://cran.r-project.org/package=deepredeff Description: CRAN Package 'deepredeff' (Deep Learning Prediction of Effectors) A tool that contains trained deep learning models for predicting effector proteins. 'deepredeff' has been trained to identify effector proteins using a set of known experimentally validated effectors from either bacteria, fungi, or oomycetes. Documentation is available via several vignettes, and the paper by Kristianingsih and MacLean (2020) . Package: r-cran-deepregression Architecture: all Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensorflow, r-cran-tfprobability, r-cran-keras, r-cran-mgcv, r-cran-dplyr, r-cran-r6, r-cran-reticulate, r-cran-matrix, r-cran-magrittr, r-cran-tfruns, r-cran-coro, r-cran-torchvision, r-cran-luz, r-cran-torch Suggests: r-cran-testthat, r-cran-knitr, r-cran-covr Filename: pool/dists/noble/main/r-cran-deepregression_2.3.2-1.ca2404.1_all.deb Size: 681724 MD5sum: a84db54785d30680830b13495dd78fdd SHA1: 3594c3b6d504f3fc1664d1a6ad7207cb5ea62b0b SHA256: 3ee63091a35f9a2574dbabc8fcd0e5d7735a862c36d8a044a9011da9a8cf095a SHA512: 73bf7721886220335640bac4d6bef61b7b8e6d41f1642b715a17b99b85cde061b57eb67d2e196b468c21b18ee703bfaf01e779b6938a8792e7af6bf828172a31 Homepage: https://cran.r-project.org/package=deepregression Description: CRAN Package 'deepregression' (Fitting Deep Distributional Regression) Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as proposed by Ruegamer et al. (2023) . Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model. Package: r-cran-deeprstudio Architecture: all Version: 0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 929 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-assertthat, r-cran-clipr, r-cran-rstudioapi, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-deeprstudio_0.0.9-1.ca2404.1_all.deb Size: 819052 MD5sum: 6e4cb210ec39bfccae1f2ca9685abf2a SHA1: ff8cf0a09d775991d58c02b5fd63487380161550 SHA256: 023a493059caef098d6a6c5468888d75cd31b332f134c5184396477ddb7b2403 SHA512: a4a3e0dd9cab7f16ef2aeb928b386c5d33a385aa5f5b977870a47ea3bd44b7caad58251f1f318a150f9b2eb2c6476db4aac3583565521580e6bdaefc402fc69b Homepage: https://cran.r-project.org/package=deepRstudio Description: CRAN Package 'deepRstudio' (Seamless Language Translation in 'RStudio' using 'DeepL' API and'Rstudioapi') Enhancing cross-language compatibility within the 'RStudio' environment and supporting seamless language understanding, the 'deepRstudio' package leverages the power of the 'DeepL' API (see ) to enable seamless, fast, accurate, and affordable translation of code comments, documents, and text. This package offers the ability to translate selected text into English (EN), as well as from English into various languages, namely Japanese (JA), Chinese (ZH), Spanish (ES), French (FR), Russian (RU), Portuguese (PT), and Indonesian (ID). With much of the text being written in English, the emphasis is on compatibility from English. It is also designed for developers working on multilingual projects and data analysts collaborating with international teams, simplifying the translation process and making code more accessible and comprehensible to people with diverse language backgrounds. This package uses the 'rstudioapi' package and 'DeepL' API, and is simply implemented, executed from addins or via shortcuts on 'RStudio'. With just a few steps, content can be translated between supported languages, promoting better collaboration and expanding the global reach of work. The functionality of this package works only on 'RStudio' using 'rstudioapi'. Package: r-cran-deepspat Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-matrix, r-cran-reticulate, r-cran-keras, r-cran-tensorflow, r-cran-tfprobability, r-cran-evd, r-cran-spatialextremes, r-cran-fields Filename: pool/dists/noble/main/r-cran-deepspat_0.3.1-1.ca2404.1_all.deb Size: 497240 MD5sum: d3a7ba9b3ea15ba1677ef28cbc8b6e11 SHA1: 3715e15ca7f7a75b10391c83f0a122310235e321 SHA256: 5f4044a04373c65d20bac6c67e4333811a8f4d8d2790b2b3b5d4ab611e773919 SHA512: 77b0a629775efa37786e56b217fb30bb2d753a907853b77343961f5234e206789187ce9aff1a26eb81e800dcb5f8f0295d9c8d0353f8386b9ef4e9bb5f99ac4e Homepage: https://cran.r-project.org/package=deepspat Description: CRAN Package 'deepspat' (Deep Compositional Spatial Models) Deep compositional spatial models are standard spatial covariance models coupled with an injective warping function of the spatial domain. The warping function is constructed through a composition of multiple elemental injective functions in a deep-learning framework. The package implements two cases for the univariate setting; first, when these warping functions are known up to some weights that need to be estimated, and, second, when the weights in each layer are random. In the multivariate setting only the former case is available. Estimation and inference is done using `tensorflow`, which makes use of graphics processing units. For more details see Zammit-Mangion et al. (2022) , Vu et al. (2022) , Vu et al. (2023) , and Shao et al. (2025) . Package: r-cran-deeptime Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4737 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deeptimedata, r-cran-ggplot2, r-cran-ggforce, r-cran-gridextra, r-cran-gtable, r-cran-lattice, r-cran-rlang, r-cran-scales, r-cran-ggfittext, r-cran-curl, r-cran-cli, r-cran-lifecycle, r-cran-grimport2, r-cran-ggh4x Suggests: r-cran-geomtextpath, r-cran-phytools, r-cran-dplyr, r-cran-divdyn, r-cran-gsloid, r-cran-ape, r-cran-palaeoverse, r-cran-paleotree, r-cran-disprity, r-bioc-ggtree, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-ggpattern, r-cran-ggrepel, r-cran-rmacrostrat, r-cran-svglite Filename: pool/dists/noble/main/r-cran-deeptime_2.3.1-1.ca2404.1_all.deb Size: 3701812 MD5sum: ee9dcfb075ad6c72a1388372dff95c00 SHA1: 7f2e3cec21dbff9a481a47009b707ba07b951e69 SHA256: 8b9df554f16f1324be2079edef4a0db4921b6571b34be799606f7a04d398e402 SHA512: 72fa19df80bf142e5f97a3c40f34687932249bed624ca6b723cb131c84a27a007387afe2b5da9462c4248ba1761e19303b35320cf4114104ce498519898e5b74 Homepage: https://cran.r-project.org/package=deeptime Description: CRAN Package 'deeptime' (Plotting Tools for Anyone Working in Deep Time) Extends the functionality of other plotting packages (notably 'ggplot2') to help facilitate the plotting of data over long time intervals, including, but not limited to, geological, evolutionary, and ecological data. The primary goal of 'deeptime' is to enable users to add highly customizable timescales to their visualizations. Other functions are also included to assist with other areas of deep time visualization. Package: r-cran-deeptimedata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4583 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-grimport2, r-cran-rsvg, r-cran-usethis Filename: pool/dists/noble/main/r-cran-deeptimedata_1.0.0-1.ca2404.1_all.deb Size: 4351974 MD5sum: 172f2902dd7201a74453e28c353e89f5 SHA1: 1aa66fa50dabdde419723fe4895584c3ccaa8cb2 SHA256: 1a1cafd55abd91feb47f6eb28016e88fb21c146d1d620c43364b2a704bb1fead SHA512: 059ccd49ad3c844f478a525fc8c4b4ea411c0f79b8505dd2b961641bbadc0e5f5c1c14ebc35bb6859fe7a67cb1d6ad1ece7428f1053d77d8b208fd0f99112243 Homepage: https://cran.r-project.org/package=deeptimedata Description: CRAN Package 'deeptimedata' (Geologic Pattern Data from FGDC Used in 'deeptime') Geologic pattern data from . Access functions are provided in the accompanying package 'deeptime'. Package: r-cran-deet Architecture: all Version: 1.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-activepathways, r-cran-pbapply, r-cran-dplyr, r-cran-ggplot2, r-cran-glmnet, r-cran-ggrepel, r-cran-downloader Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-deet_1.0.12-1.ca2404.1_all.deb Size: 2093962 MD5sum: 2ad94ca5734c37ea390bab6612802351 SHA1: 4b2486ab46d678ae7f3a21c2ec2ce35d0070dc22 SHA256: 47dd6044a5aae346d415a5dc93e17ad03ade4249f43a87f35f8d6d82e618b529 SHA512: a45b0b6b90a576631298db087f82949a4a6874b85fd69925645853f61ebbfb7c2230cba94b9b969dd4c31be5d8864c2325459ec4c12147278700dcdb33b73ebb Homepage: https://cran.r-project.org/package=DEET Description: CRAN Package 'DEET' (Differential Expression Enrichment Tool) Abstract of Manuscript. Differential gene expression analysis using RNA sequencing (RNA-seq) data is a standard approach for making biological discoveries. Ongoing large-scale efforts to process and normalize publicly available gene expression data enable rapid and systematic reanalysis. While several powerful tools systematically process RNA-seq data, enabling their reanalysis, few resources systematically recompute differentially expressed genes (DEGs) generated from individual studies. We developed a robust differential expression analysis pipeline to recompute 3162 human DEG lists from The Cancer Genome Atlas, Genotype-Tissue Expression Consortium, and 142 studies within the Sequence Read Archive. After measuring the accuracy of the recomputed DEG lists, we built the Differential Expression Enrichment Tool (DEET), which enables users to interact with the recomputed DEG lists. DEET, available through CRAN and RShiny, systematically queries which of the recomputed DEG lists share similar genes, pathways, and TF targets to their own gene lists. DEET identifies relevant studies based on shared results with the user’s gene lists, aiding in hypothesis generation and data-driven literature review. Sokolowski, Dustin J., et al. "Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression." Nucleic Acids Research Genomics and Bioinformatics (2023). 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Package: r-cran-degday Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon, r-cran-magrittr, r-cran-zoo Suggests: r-cran-cimir, r-cran-dplyr, r-cran-knitr, r-cran-testthat, r-cran-tidyr, r-cran-units Filename: pool/dists/noble/main/r-cran-degday_0.4.0-1.ca2404.1_all.deb Size: 63698 MD5sum: 22801c268681372e533411dbb3c701a7 SHA1: def77d3bb4123da43f3ccd11304f9766a58f5501 SHA256: b082726a53c3f2888aa48278905671acdb945147e23704bb1bce614fc0e46957 SHA512: 530e9b7bc422df7309c4490138dfd1c0ce1eda03a5a4d66fa6ae605384c6c1ff3a37ec01bbb3331bf6992a181a4eb316ca338146a3b00520c60b7e5325285259 Homepage: https://cran.r-project.org/package=degday Description: CRAN Package 'degday' (Compute Degree Days) Compute degree days from daily min and max temperatures for modeling plant and insect development. 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It supports both univariate and multivariate degradation signals. For multivariate inputs, the signals are merged into a univariate health index prior to modeling. Linear and exponential degradation trajectories are supported (the latter using a log transformation). Remaining Useful Life (RUL) distributions are estimated using Bayesian updating for new units, enabling on-site predictive maintenance. Based on the methodology of Liu and Huang (2016) . Package: r-cran-degre Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-parglm, r-cran-glmmtmb, r-cran-foreach, r-cran-tibble, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggrepel, r-cran-car, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-degre_0.2.0-1.ca2404.1_all.deb Size: 52354 MD5sum: 875c0113c91bba5a8e605039dc3a4b44 SHA1: bea665543672ad2e21f5a7f6ecaf41a8a6888245 SHA256: b40c90899bd9011f24b2b7d8d7fc366aee70762eeb1ad825a8314701f3c95045 SHA512: df31d2ee3ffa66fb1298ec6d791cbbb0ef6a263b6bf561ad4ef54de913e5e3923d4efc7707150f2e017b74b62a8c59e8054fc24ddaf51092cf8970ad3c1f2443 Homepage: https://cran.r-project.org/package=DEGRE Description: CRAN Package 'DEGRE' (Inferring Differentially Expressed Genes using GeneralizedLinear Mixed Models) Genes that are differentially expressed between two or more experimental conditions can be detected in RNA-Seq. A high biological variability may impact the discovery of these genes once it may be divergent between the fixed effects. However, this variability can be covered by the random effects. 'DEGRE' was designed to identify the differentially expressed genes considering fixed and random effects on individuals. These effects are identified earlier in the experimental design matrix. 'DEGRE' has the implementation of preprocessing procedures to clean the near zero gene reads in the count matrix, normalize by 'RLE' published in the 'DESeq2' package, 'Love et al. (2014)' and it fits a regression for each gene using the Generalized Linear Mixed Model with the negative binomial distribution, followed by a Wald test to assess the regression coefficients. Package: r-cran-degreedaycalc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-degreedaycalc_0.1.0-1.ca2404.1_all.deb Size: 25602 MD5sum: a3e81e73810020d3ef13bb7dda1a6ac9 SHA1: 184856beabdccead80411e14e85a84f69ae4497b SHA256: eff8015f0c0e754ab5ea6181a30fee935ba42f69fe8f442603593131fbefd2c3 SHA512: 1437e0f501101d26c41f61075825a643c81ec4a63b2d0180d129fac72eec1a1646c7eecc0c2729192a712f876997370479310e50bdce6b1bdcc69fbd6c39afeb Homepage: https://cran.r-project.org/package=DegreeDayCalc Description: CRAN Package 'DegreeDayCalc' (Degree-Day Phenology Calculator ('shiny' Application)) Provides a 'shiny' application to compute daily and cumulative degree-days from minimum and maximum temperatures using average, single triangle, and single sine methods, with optional upper temperature thresholds. The application maps cumulative thermal accumulation to user-defined developmental stage thresholds and supports exporting tabular and graphical outputs. The degree-day approach follows assumptions described by Higley et al. (1986) . 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The log-density is modelled using a linear combination of penalised B-splines. The multinomial log-likelihood involving the frequencies adds up to a roughness penalty based on the differences in the coefficients of neighbouring B-splines and the log of a root-n approximation of the sampling density of the observed vector of central moments in each class. The so-obtained penalized log-likelihood is maximized using the EM algorithm to get an estimate of the spline parameters and, consequently, of the variable density and related quantities such as quantiles, see Lambert, P. (2021) for details. Package: r-cran-dehogt Architecture: all Version: 0.99.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 784 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/noble/main/r-cran-dehogt_0.99.0-1.ca2404.1_all.deb Size: 240666 MD5sum: 88577a2a2ffa678ac48bae6c0713001d SHA1: 4f8056487a43b24bfb5eb82c96b3463432403ac0 SHA256: 80a0eeecd015c215df93e7bbeb69265ed283124e72c937535433595147814bf0 SHA512: 004915f487d80623b80f4ac73136acacaa548e8851b99efb399cbcc105945f07cb5bc09b421be10be6784ca188c1a645529f6fb11b3590ace1440dfd6577f5ad Homepage: https://cran.r-project.org/package=DEHOGT Description: CRAN Package 'DEHOGT' (Differentially Expressed Heterogeneous Overdispersion Gene Testfor Count Data) Implements a generalized linear model approach for detecting differentially expressed genes across treatment groups in count data. 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Package: r-cran-delaydiscount Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-delaydiscount_0.0.1-1.ca2404.1_all.deb Size: 69606 MD5sum: d7e6022f593ce799de6072d73d0a14a1 SHA1: daed40c9a91e502685d4f0b020203e202fd970a2 SHA256: a9b4ca1a6dbf55ed11d65b3882df838bb07e838809c5fd7a16c73f5bb24b2f76 SHA512: 8fe9b77acfa4eb5843245dbe82bc3a0a19cf110ffee41f7634373938466b1738dd349dfd62fff4cea7c71e93e2e2e4e1a8e2f05a3d07aa46d59cb9016058dacf Homepage: https://cran.r-project.org/package=delaydiscount Description: CRAN Package 'delaydiscount' (Fit Linearized Hyperbolic Model for Delay Discounting Curves) Functions for estimating parameters and hyperparameters of the linearized hyperbolic model, and testing equality of hyperparameters. 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Calculation of DRI, plot of intersubjective correlations (IC), generation of large-language model (LLM) survey data, and permutation tests are supported. Example datasets and a graphical user interface (GUI) are also available to support analysis. For more information, see Niemeyer and Veri (2022) . 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Package: r-cran-delta Architecture: all Version: 0.2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-delta_0.2.0.3-1.ca2404.1_all.deb Size: 87508 MD5sum: 368029a90688b9ab40328e426811527d SHA1: 8e05514b70875be34b87d950bea6895f0e316c5b SHA256: a22be1ac115a1f484c9e3a40e4f2c36a6beef80b1fbbfce54bca77d5bac775f5 SHA512: 381a5683a9076b64a6affe33227fe98e93b54b0db7c0f112274a4caecff54a40ffc5701c730c2bb576a30fab4c2484b4d9e0e50726d76c3f187496d5f4b27027 Homepage: https://cran.r-project.org/package=Delta Description: CRAN Package 'Delta' (Measure of Agreement Between Two Raters) Measure of agreement delta was originally by Martín & Femia (2004) . Since then has been considered as agreement measure for different fields, since their behavior is usually better than the usual kappa index by Cohen (1960) . 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See Shilts et al. (2018) . Package: r-cran-deltaman Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinymatrix, r-cran-xtable, r-cran-shinybs, r-cran-knitr Filename: pool/dists/noble/main/r-cran-deltaman_0.5.0-1.ca2404.1_all.deb Size: 114410 MD5sum: fff61da8cad43418a041428f7d774ae4 SHA1: b575049f329dd7b5e5f1d91824b73a98f38b9555 SHA256: c17e8c4b661fe559da6b5058279fb51c4ebbc27465d1c023a652ca68257b4bf8 SHA512: 80b97c54b1e1454438ec2fd0fa1a049ac89dcc4c6bf3fc8cb1f3cc9d40986102eae6eb10c516c62a004524264301d164c1fac2bf296348a4b79520ba151d602a Homepage: https://cran.r-project.org/package=DeltaMAN Description: CRAN Package 'DeltaMAN' (Delta Measurement of Agreement for Nominal Data) Analysis of agreement for nominal data between two raters using the Delta model. This model is proposed as an alternative to the widespread measure Cohen kappa coefficient, which performs poorly when the marginal distributions are very asymmetric (Martin-Andres and Femia-Marzo (2004), ; Martin-Andres and Femia-Marzo (2008) ). The package also contains a function to perform a massive analysis of multiple raters against a gold standard. A shiny app is also provided to obtain the measures of nominal agreement between two raters. Package: r-cran-deltapif Architecture: all Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 560 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-deriv, r-cran-s7, r-cran-scales Suggests: r-cran-evalue, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-deltapif_0.4.5-1.ca2404.1_all.deb Size: 379790 MD5sum: d7bf2b507eced32d5f3dfaf3bf888085 SHA1: 16614da3e5363f97652e42ccab1b684f9156b4a5 SHA256: 23cd287d2cc96aeccbb2e174f60cf00c33e64691da1a9231d7d99a06c765fd28 SHA512: 9cd974e1af3db5f1a1f79308d5d5d448f4ef5fcda37c5113b1090d41404e85237e6abed7ad5de9e491801e1757d34e38a43a2e0959245288ff38cb56a82b72c4 Homepage: https://cran.r-project.org/package=deltapif Description: CRAN Package 'deltapif' (Estimate Potential Impact and Population Attributable Fractionswith Aggregated Data) Uses the delta-method to estimate the Potential Impact Fraction (PIF) and the Population Attributable Fraction (PAF) from summary data. 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Item purification is supported (Magis and Facon (2014) ). 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Package: r-cran-demor Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-forecast, r-cran-ggplot2, r-cran-magrittr, r-cran-tidyr, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-demor_1.0.10-1.ca2404.1_all.deb Size: 202178 MD5sum: 1848ab48e30523525f21b9d99f672197 SHA1: a9d238f67028c5b64d2d559b43a22d7e10c8400f SHA256: bfb9803ed3fb4d0809d4ac4bd5ca44554ace798da106472ad17f74a9fcdc107d SHA512: 70c2d09f0a0f1c45ce5856fb3f71e9347444a9cff8f8309502e2f214ccc82a47b070842399420a29aa973885062c63999999a91f32a7b837ca48155e28665bf6 Homepage: https://cran.r-project.org/package=demor Description: CRAN Package 'demor' (Methods for Demographic Analysis) Implements life tables, fertility and mortality indicators, decomposition methods, Lee-Carter mortality forecasting, Leslie matrices, and population pyramids for demographic analysis. Methods are described in Preston et al. (2001, ISBN:1557864519) and Ustyuzhanin (2025) . Package: r-cran-demoshiny Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-demoshiny_0.1-1.ca2404.1_all.deb Size: 241742 MD5sum: 6b1e047df1963f8c136936ec2eb97fa8 SHA1: 4e801a2a05fc70a49fe31301a2b4ad9792279e3f SHA256: 7caae4334c6cc35a9dfc6499d10a880a4c7af211e9cefd12a070c2b8cd0ed59b SHA512: 87d2c2c584b8b6b586044570377fbab355be49501497e58b478cd67b5bce47230add1eec9f85ba9c37dcdcc7e088686faab0a6b19cf6d45724af34bdcdb1986c Homepage: https://cran.r-project.org/package=demoShiny Description: CRAN Package 'demoShiny' (Runs a 'Shiny' App as Demo or Lists All Demo 'Shiny' Apps) Mimics the demo functionality for 'Shiny' apps in a package. Apps stored to the package subdirectory inst/shiny can be called by demoShiny(topic). Package: r-cran-demova Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaps Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-demova_1.0-1.ca2404.1_all.deb Size: 57042 MD5sum: c2520040a952958eec4e234eab131279 SHA1: f86f81110e1ac23c54e46fe014b63ff74c78e6b8 SHA256: ec1b167ff4fcc8207100fe5580c9b91f1dc246ecf8621e93d6abbddc4a42ee5b SHA512: 1a4b593695b427390b02f949b05ca798a34d17646b3ca17233c550732fb6350188adaadd06f7ec8b52571e2095e6f6561d6e8ad045b141fc1627141d2d4c7938 Homepage: https://cran.r-project.org/package=DEMOVA Description: CRAN Package 'DEMOVA' (DEvelopment (of Multi-Linear QSPR/QSAR) MOdels VAlidated usingTest Set) Tool for the development of multi-linear QSPR/QSAR models (Quantitative structure-property/activity relationship). Theses models are used in chemistry, biology and pharmacy to find a relationship between the structure of a molecule and its property (such as activity, toxicology but also physical properties). The various functions of this package allows: selection of descriptors based of variances, intercorrelation and user expertise; selection of the best multi-linear regression in terms of correlation and robustness; methods of internal validation (Leave-One-Out, Leave-Many-Out, Y-scrambling) and external using test sets. Package: r-cran-demulticoder Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3894 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-furrr, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-tibble Suggests: r-cran-biocmanager, r-bioc-biostrings, r-bioc-dada2, r-cran-metacoder, r-bioc-shortread, r-bioc-phyloseq, r-cran-rmarkdown, r-cran-rcppparallel, r-cran-testthat Filename: pool/dists/noble/main/r-cran-demulticoder_0.1.2-1.ca2404.1_all.deb Size: 1494526 MD5sum: 477b421eb0849488c0ac232d3f2e7951 SHA1: 4078dc93d35e836a686583f9feccfb8ea2842e24 SHA256: 56a611d1a512f74f4ff2bd47ce4b4d85b1f44817455b55deadeb5f805de51c93 SHA512: e6e27acc08e6cef10297290b8553373ac973d4d97047008e6a69a1540c12f1ccfcc130c19f95e565239c51170b058e03022d476dffb5614f07d5782b89fca1d5 Homepage: https://cran.r-project.org/package=demulticoder Description: CRAN Package 'demulticoder' (Simultaneous Analysis of Multiplexed Metabarcodes) A comprehensive set of wrapper functions for the analysis of multiplex metabarcode data. It includes robust wrappers for 'Cutadapt' and 'DADA2' to trim primers, filter reads, perform amplicon sequence variant (ASV) inference, and assign taxonomy. The package can handle single metabarcode datasets, datasets with two pooled metabarcodes, or multiple datasets simultaneously. The final output is a matrix per metabarcode, containing both ASV abundance data and associated taxonomic assignments. An optional function converts these matrices into 'phyloseq' and 'taxmap' objects. For more information on 'DADA2', including information on how DADA2 infers samples sequences, see Callahan et al. (2016) . For more details on the demulticoder R package see Sudermann et al. (2025) . Package: r-cran-dendextend Architecture: all Version: 1.19.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7001 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-ggplot2, r-cran-viridis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-seriation, r-cran-colorspace, r-cran-ape, r-cran-microbenchmark, r-cran-gplots, r-cran-heatmaply, r-cran-dynamictreecut, r-cran-pvclust, r-cran-corrplot, r-cran-dendser, r-cran-mass, r-cran-cluster, r-cran-fpc, r-cran-circlize, r-cran-covr Filename: pool/dists/noble/main/r-cran-dendextend_1.19.1-1.ca2404.1_all.deb Size: 4813448 MD5sum: 62a21d55feca079cee2edba8c73801f2 SHA1: 457ee4c5aa3472134104a6a408656b9fb559cbc6 SHA256: bf391678dbf0c6ed4b1251d41573b693164ca90094ef5afabe8f239f28f5ba60 SHA512: 10421cc294c208e26347d07702bb48bf4d3f900b70a0c39da5bc97f7ebfa02653bc5c40b42c8ead86b70194c531d4848d566d4cb782773eed207a1cdd8a295e9 Homepage: https://cran.r-project.org/package=dendextend Description: CRAN Package 'dendextend' (Extending 'dendrogram' Functionality in R) Offers a set of functions for extending 'dendrogram' objects in R, letting you visualize and compare trees of 'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters - the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different 'dendrograms' to one another. Package: r-cran-dendroanalyst Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2657 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tidyverse, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-readxl, r-cran-tibble, r-cran-tidyr, r-cran-zoo, r-cran-forecast, r-cran-mgcv, r-cran-minpack.lm, r-cran-pspline, r-cran-moments, r-cran-signal, r-cran-readr, r-cran-boot, r-cran-rlang, r-cran-changepoint, r-cran-waveletcomp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dendroanalyst_0.1.6-1.ca2404.1_all.deb Size: 2320594 MD5sum: f7a601535fc9463dd98feec66a9d1178 SHA1: 806b697fbae9a80d4c87707078a1f7785dc0dc7d SHA256: bace7b8f68ecfdba142e5ffa3add9101b9c2ceec248d41a72b0d9f193a10fdd7 SHA512: 924fd11668f9a73fe7d74aa5775a46611a8648f069f7ad9c3c9a54e1d8ff893e78a453523ef9aab52e780c53afb107bb2bce22c206b031e916bbf315644d00ef Homepage: https://cran.r-project.org/package=dendRoAnalyst Description: CRAN Package 'dendRoAnalyst' (A Tool for Processing and Analyzing Dendrometer Data) There are various functions for managing and cleaning data before the application of different approaches. This includes identifying and erasing sudden jumps in dendrometer data not related to environmental change, identifying the time gaps of recordings, and changing the temporal resolution of data to different frequencies. Furthermore, the package calculates daily statistics of dendrometer data, including the daily amplitude of tree growth. Various approaches can be applied to separate radial growth from daily cyclic shrinkage and expansion due to uptake and loss of stem water. In addition, it identifies periods of consecutive days with user-defined climatic conditions in daily meteorological data, then check what trees are doing during that period. Package: r-cran-dendroextras Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dendroextras_0.2.3-1.ca2404.1_all.deb Size: 32838 MD5sum: 721dbea5f5aade6f94b6daed399c28fb SHA1: a1f72cfa04ab140d01b0078fb0d13f409dc2e1d6 SHA256: e586eaf9fd822278b4b2af8b69d373273353cda37216394fad96eb8e568db319 SHA512: 33c6ae1d5f5c136bcaf816d5f8eafde869c14b19798e7ce00426b2035ec63d0fb725112f93e3b4bc95f244a40735e1a958c168720948c5a640beb0922d9af224 Homepage: https://cran.r-project.org/package=dendroextras Description: CRAN Package 'dendroextras' (Extra Functions to Cut, Label and Colour Dendrogram Clusters) Provides extra functions to manipulate dendrograms that build on the base functions provided by the 'stats' package. The main functionality it is designed to add is the ability to colour all the edges in an object of class 'dendrogram' according to cluster membership i.e. each subtree is coloured, not just the terminal leaves. In addition it provides some utility functions to cut 'dendrogram' and 'hclust' objects and to set/get labels. Package: r-cran-dendrometer Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 933 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-pspline, r-cran-zoo Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-dendrometer_1.1.1-1.ca2404.1_all.deb Size: 569934 MD5sum: 355f8d6460135737c2b9a6bcf76e8cde SHA1: 17be7a08bcc566aebd93a07870bf8d3de07b264e SHA256: 3eebc2145cbfbc44ff65bf53bdd9f8050d96430410f76c7f89dd228572854cd8 SHA512: 7b556529a3f0c3885bf31f931ff3a21ca44858f045bf1314ea3658ca9cd737e3e8f36321f78dea0ae2ccbfcd75007119e2f71c9d947a317fc95bb21a040b28cc Homepage: https://cran.r-project.org/package=dendrometeR Description: CRAN Package 'dendrometeR' (Analyzing Dendrometer Data) Various functions to import, verify, process and plot high-resolution dendrometer data using daily and stem-cycle approaches as described in Deslauriers et al, 2007 . For more details about the package please see: Van der Maaten et al. 2016 . Package: r-cran-dendrometry Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1289 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dendrometry_0.0.4-1.ca2404.1_all.deb Size: 525048 MD5sum: b6d459d37a11fb832f483217e35a94e0 SHA1: 63ff126b37f0079b6090ce254d05b2a1a674c0fa SHA256: 41619daf2fdbe6f2e11b352412e18e1611dd137e61a6194f130f496418411515 SHA512: d6ecd38b663e34df58722e0b0f72a643c4ba35f562fa78e072780bcabeb670d1e09209794089ee90422417491f0b9ffa1d031a61d1847287a60383c75c465ae2 Homepage: https://cran.r-project.org/package=dendrometry Description: CRAN Package 'dendrometry' (Forest Estimations and Dendrometric Computations) Computation of dendrometric and structural parameters from forest inventory data. The objective is to provide a user-friendly R package for researchers, ecologists, foresters, statisticians, loggers and other persons who deal with forest inventory data. The package includes advanced distribution fitting capabilities with multiple estimation methods (Maximum Likelihood, Maximum Product Spacing with ties correction methods following Cheng & Amin (1983), and Method of Moments) for probability distributions commonly used in forestry. Visualization tools with confidence bands using delta method and parametric bootstrap are provided for three-parameter Weibull distribution fitting to diameter data. Useful conversion of angle value from degree to radian, conversion from angle to slope (in percentage) and their reciprocals as well as principal angle determination are also included. Position and dispersion parameters usually found in forest studies are implemented. The package contains Fibonacci series, its extensions and the Golden Number computation. Useful references are Arcadius Y. J. Akossou, Soufianou Arzouma, Eloi Y. Attakpa, Noël H. Fonton and Kouami Kokou (2013) , W. Bonou, R. Glele Kakaï, A.E. Assogbadjo, H.N. Fonton, B. Sinsin (2009) , R. C. H. Cheng and N. A. K. Amin (1983) , and R. C. H. Cheng and M. A. Stephens (1989) . Package: r-cran-dendronetwork Architecture: all Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3425 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplr, r-cran-igraph, r-cran-stringr, r-cran-reshape2, r-bioc-rcy3, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-tidyr, r-cran-foreach, r-cran-lifecycle, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-diagrammer Filename: pool/dists/noble/main/r-cran-dendronetwork_0.5.5-1.ca2404.1_all.deb Size: 1427810 MD5sum: daa87eb9079b4c0e04dd8352877ce93e SHA1: 31811838cd7156cb15e29ffe7fa6b70623a56aa1 SHA256: 3fd79023c5896ffb1f050b33ea70e2916f080d0a1d00041dd60b8eefef39546e SHA512: d89288e4607e50b18c73e7889c9b0d238f58cb6123ec25e12390eed89c97d5e52d9232df8996639007d11a1cda6a15b089d88771b57c5130efbfb9abafe790de Homepage: https://cran.r-project.org/package=dendroNetwork Description: CRAN Package 'dendroNetwork' (Create Networks of Dendrochronological Series using PairwiseSimilarity) Creating dendrochronological networks based on the similarity between tree-ring series or chronologies. The package includes various functions to compare tree-ring curves building upon the 'dplR' package. The networks can be used to visualise and understand the relations between tree-ring curves. These networks are also very useful to estimate the provenance of wood as described in Visser (2021) or wood-use within a structure/context/site as described in Visser and Vorst (2022) . Package: r-cran-dendrosync Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-dendrosync_0.1.5-1.ca2404.1_all.deb Size: 128254 MD5sum: daa54afb28793cae1a72f33cd20cf5e1 SHA1: 82a9868f079378a84219253b83871f9148baa31b SHA256: 5f9456f76cc6111370000cf6fbeb468c76a2ae155dfbc480a1879a2c0f54b591 SHA512: 0f8be921a509fdbcd420f796049b6297ca71f1147bab18756804e07088852d942765612a3683f0432d9a1263143f72874c463377fcd28ecde010ba79d44af7aa Homepage: https://cran.r-project.org/package=DendroSync Description: CRAN Package 'DendroSync' (A Set of Tools for Calculating Spatial Synchrony BetweenTree-Ring Chronologies) Provides functions for the calculation and plotting of synchrony in tree growth from tree-ring width chronologies (TRW index). It combines variance-covariance (VCOV) mixed modelling with functions that quantify the degree to which the TRW chronologies contain a common temporal signal. It also implements temporal trends in spatial synchrony using a moving window. These methods can also be used with other kind of ecological variables that have temporal autocorrelation corrected. Package: r-cran-dendrotools Architecture: all Version: 1.2.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3318 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-brnn, r-cran-reshape2, r-cran-scales, r-cran-oce, r-cran-mlmetrics, r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-plotly, r-cran-randomforest, r-cran-cubist, r-cran-lubridate, r-cran-psych, r-cran-boot, r-cran-viridis, r-cran-dplr Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dendrotools_1.2.16-1.ca2404.1_all.deb Size: 1553380 MD5sum: 4cae0be83b251c86419c44d75a3c4db1 SHA1: c486c9493b2e3d695c678d9d3f47cfad5bc817df SHA256: d8b2e46822daafd0adedc0bf7fa7a3e65cf1de1e3edd4c460c400515ca736e5b SHA512: 63aaa9b65c641acea64ead4409933dd81ae87eda8c91cb0120cc791e1b6b86b1219c00966a6427dd637a53a9e18d11d9a0907f9cb2719b5b70b958bc2f3ee29c Homepage: https://cran.r-project.org/package=dendroTools Description: CRAN Package 'dendroTools' (Linear and Nonlinear Methods for Analyzing Daily and MonthlyDendroclimatological Data) Provides novel dendroclimatological methods, primarily used by the Tree-ring research community. There are four core functions. The first one is daily_response(), which finds the optimal sequence of days that are related to one or more tree-ring proxy records. Similar function is daily_response_seascorr(), which implements partial correlations in the analysis of daily response functions. For the enthusiast of monthly data, there is monthly_response() function. The last core function is compare_methods(), which effectively compares several linear and nonlinear regression algorithms on the task of climate reconstruction. Package: r-cran-dendsort Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1609 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rcolorbrewer, r-cran-gplots, r-cran-seriation, r-cran-gapmap, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dendsort_0.3.4-1.ca2404.1_all.deb Size: 1143812 MD5sum: 6cd92b1243181d2d334ee134f2b3b1d1 SHA1: 90b3c11496ed2fad2278061634887750ed97feb3 SHA256: 4e79ccd2198e44fafb211103cd9bd7b43766c95ebaa3327c3edcb0f88feefd37 SHA512: 4ef26d4fb857cd32c6a1701272f980bf4db9f2bbf6ce0b0e5513658a4b7ec643d7e5e5b0f2376fa4f1d1a2a58410a5a364221bd410e1a110590c63da0385abcd Homepage: https://cran.r-project.org/package=dendsort Description: CRAN Package 'dendsort' (Modular Leaf Ordering Methods for Dendrogram Nodes) An implementation of functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization. This method is described in "dendsort: modular leaf ordering methods for dendrogram representations in R", F1000Research 2014, 3: 177 . Package: r-cran-denguedatahub Architecture: all Version: 4.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2185 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-rvest, r-cran-stringr, r-cran-lifecycle, r-cran-magrittr, r-cran-here, r-cran-purrr, r-cran-xml2, r-cran-tabulapdf, r-cran-rlang Suggests: r-cran-roxygen2, r-cran-tsibble Filename: pool/dists/noble/main/r-cran-denguedatahub_4.1.0-1.ca2404.1_all.deb Size: 1316218 MD5sum: 5c6c4364726c8758678035f5d0d5066d SHA1: 783666b5d14da01980c1bc80790225a6a33afca1 SHA256: 647341cb3c25853c063c5b6e8548f0a0ef97577988f79410ea5e9dc550699d18 SHA512: f74618954b8dcfd295248a7d16ec3db016c37bd6d1670b89968b0af8fe29b69fbaa449aef3078e00b8ec71da6ca72d46e8932d82797442e056f823bb5e37acf6 Homepage: https://cran.r-project.org/package=denguedatahub Description: CRAN Package 'denguedatahub' (A Tidy Format Datasets of Dengue by Country) Provides a weekly, monthly, yearly summary of dengue cases by state/ province/ country. Package: r-cran-denoiseq Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-denoiseq_0.1.1-1.ca2404.1_all.deb Size: 204074 MD5sum: 0e049a1e5a7fac5a2b88d911861f1edb SHA1: 0975f39e5e1e2922c89817a429b808a31487bb9b SHA256: 7fa1ca077f3a721db760660adc32481ddfaa8ea9a189f3b0f2da166f3f893e66 SHA512: 3c17c12b6db8162ba790d638872f1a863d64b80db5cb26580a726677c38bc8d395aedb499ececaa94b81a63d84c9498341a22a6403501630ae7827b10fa1c7dc Homepage: https://cran.r-project.org/package=denoiSeq Description: CRAN Package 'denoiSeq' (Differential Expression Analysis Using a Bottom-Up Model) Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of a bottom-up model for PCR amplification. This model is developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), , and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution. 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Using the functions in the package, that analysis can be repeated using sample life histories, either synthesized from local seroprevalence data using other functions in this package (as in the manuscript) or from some other source. The package provides a vignette which walks through the analysis in the publication, as well as a function to generate a project skeleton for such an analysis. 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Parametric approaches are based on Emura & Konno (2012) and Emura & Pan (2017). A regression approach is based on Emura & Wang (2016). Quasi-independence tests are based on Emura & Wang (2010). Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015). 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This package aims to help users identify novel targets within their data sets based on protein network interactions and publication precedence of target's association with research context based on literature precedence. Methods in this package are described in detail in: Douglas et al. (2025) . Key functionalities of this package also leverage methodologies from previous works, such as: - Szklarczyk et al. (2023) - Winter (2017) . Package: r-cran-descomponer Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-descomponer_1.6-1.ca2404.1_all.deb Size: 287114 MD5sum: edb282f4dff2ca01d1146546a9938041 SHA1: 256edb852bd28e3616b163bc404a9b0ef7bac35e SHA256: ea8b4e1d428cd26962c6c9c4a18845109763f8b35e5e670da27c1d5bac0bf1ac SHA512: fb1ab44ce67db69deaba37faf715896bd97b7ba3334749382043fc7412713f128ed7bb6891e11361fa827dba8844760f951585d579f0a9f65507b8a1a27f928e Homepage: https://cran.r-project.org/package=descomponer Description: CRAN Package 'descomponer' (Seasonal Adjustment by Frequency Analysis) Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain. Package: r-cran-describedata Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-tibble, r-cran-tidyr, r-cran-purrr, r-cran-broom, r-cran-stringr, r-cran-haven, r-cran-ggplot2, r-cran-lmtest, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-describedata_0.1.1-1.ca2404.1_all.deb Size: 69944 MD5sum: 3ad6eea74c3be5352eb35011f7b6adfe SHA1: 41b2632a10fde84af56e3aa679cdf2f88956d8a3 SHA256: 923128dab9eb5ec31a496db64a14a000ec56becd9c876096f69c758c43f378c1 SHA512: bc81f508170395dd5b39ba52a2dce313c9c1439ba629ea6d73f81ef98b26d0cafab7e104e6041068c13f74ae529833e8064105fb2be12aead84ff81adc7d31f4 Homepage: https://cran.r-project.org/package=describedata Description: CRAN Package 'describedata' (Miscellaneous Descriptive Functions) Helper functions for descriptive tasks such as making print-friendly bivariate tables, sample size flow counts, and visualizing sample distributions. 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Package: r-cran-describer Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-testthat, r-cran-lintr Filename: pool/dists/noble/main/r-cran-describer_0.2.0-1.ca2404.1_all.deb Size: 17952 MD5sum: cbf860697e54d14425e0bc4d1cffca5e SHA1: b9be4ad00eef8495a99d9bd929e7094cdcae651e SHA256: d6bbccd964d7c35268b0184f274c83c68f144ea03d57ddc52726aea2983c9da8 SHA512: 9003fc5631c0f675bddb280812e41288e7a9c374e069cb81057a2cc6c9b8e13eff5df0a239588fcbc920477bd327b70962d7498fe9090d7a60cc786d37934ebe Homepage: https://cran.r-project.org/package=describer Description: CRAN Package 'describer' (Describe Data in R Using Common Descriptive Statistics) Allows users to quickly and easily describe data using common descriptive statistics. 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Package: r-cran-descriptiverepresentationcalculator Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-descriptiverepresentationcalculator_1.1.1-1.ca2404.1_all.deb Size: 41774 MD5sum: 1c2f9195d9e3e25218856a82534cad2b SHA1: 623d33733c97ec45802a4619372630d447a76eff SHA256: 11558a80c527d899373585b9dede27390411aa77906a33996a7bfde55270f544 SHA512: 20440bdf939e556d0c2ccdb87c6d79130f938375224262758b440546f60a36c9749b380a2052b8a5a263a562f518f19a104e856297b003be0cc18dee1041d745 Homepage: https://cran.r-project.org/package=DescriptiveRepresentationCalculator Description: CRAN Package 'DescriptiveRepresentationCalculator' (Characterizing Observed and Expected Representation) A system for analyzing descriptive representation, especially for comparing the composition of a political body to the population it represents. 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Calculate descriptive statistical measures in budget data of municipalities across Europe, according to the 'OpenBudgets.eu' data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset. Also, can be used generally to other datasets, to extract visualization parameters, convert them to 'JSON' format and use them as input in a different graphical interface. Package: r-cran-descriptivewh Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-descriptivewh_1.0.3-1.ca2404.1_all.deb Size: 28656 MD5sum: ef5676a338e216c5720f2b61bf7e2644 SHA1: 8a1d6306832c23c3e2808e561990ec82c583bfe3 SHA256: 9e2f66b459faa663bfaf9183cc64b1c6e808a76a53a5e1f09a618f293e440858 SHA512: 979d126264fb2b964f0658d098f66c888ff833f627caaac8703d151c6be720ecf692e7ab2fbac4cc942be6b85d111c470b3325799e0ac6fae92ffbcf535f843e Homepage: https://cran.r-project.org/package=DescriptiveWH Description: CRAN Package 'DescriptiveWH' (Descriptive Statistics) Exploratory analysis of a data base. Using the functions of this package is possible to filter the data set detecting atypical values (outliers) and to perform exploratory analysis through visual inspection or dispersion measures. With this package you can explore the structure of your data using several parameters at the same time joining statistical parameters with different graphics. Finally, this package aid to confirm or reject the hypothesis that your data structure presents a normal distribution. Therefore this package is useful to get a previous insight of your data before to carry out statistical analysis. 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Package: r-cran-descrtab2 Architecture: all Version: 2.1.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1739 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-exact2x2, r-cran-desctools, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-stringr, r-cran-forcats, r-cran-magrittr, r-cran-scales, r-cran-cli, r-cran-kableextra, r-cran-flextable, r-cran-officer, r-cran-knitr, r-cran-rmarkdown, r-cran-haven, r-cran-hmisc Suggests: r-cran-testthat, r-cran-covr, r-cran-tidyverse, r-cran-here, r-cran-shiny, r-cran-exact Filename: pool/dists/noble/main/r-cran-descrtab2_2.1.16-1.ca2404.1_all.deb Size: 505248 MD5sum: 19a044a3691febcb3bd1870cb6224535 SHA1: aa6aa1430c6e3ff0bb14473ec50bd1276672dfb8 SHA256: ea7cf7af15ea9b3f1c0492deac1eadd712e7ddd51b96463bfe748c31e4bbb54f SHA512: f9b53054602944dac62130f279ba0f8f199edc51cd657f2616b870b8c47913bdcef3e9f23c20cadef948d15af2cb4382700c6ee6f2e8ae9e4ab89211601cbb97 Homepage: https://cran.r-project.org/package=DescrTab2 Description: CRAN Package 'DescrTab2' (Publication Quality Descriptive Statistics Tables) Provides functions to create descriptive statistics tables for continuous and categorical variables. By default, summary statistics such as mean, standard deviation, quantiles, minimum and maximum for continuous variables and relative and absolute frequencies for categorical variables are calculated. 'DescrTab2' features a sophisticated algorithm to choose appropriate test statistics for your data and provides p-values. On top of this, confidence intervals for group differences of appropriated summary measures are automatically produces for two-group comparison. Tables generated by 'DescrTab2' can be integrated in a variety of document formats, including .html, .tex and .docx documents. 'DescrTab2' also allows printing tables to console and saving table objects for later use. Package: r-cran-descstat Architecture: all Version: 0.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2237 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-tidyselect, r-cran-forcats, r-cran-cli, r-cran-magrittr Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-descstat_0.1-2-1.ca2404.1_all.deb Size: 708008 MD5sum: f0c8070787f477c3c9c05308702b9b32 SHA1: f4d69e6e68b05822ed29e0910c909f1b7f417451 SHA256: 968aac2314b76d66623833f6381450690d96324b838c75bf432c2402000b8299 SHA512: 8b0e232764331bff81b14b09cdff08b9af5b9924e6c22711475b028d5ac95ebe77f7dbec0872668ea7a00925fc200b3a6c15aefbb6781047822c6b72105d4bc1 Homepage: https://cran.r-project.org/package=descstat Description: CRAN Package 'descstat' (Tools for Descriptive Statistics) A toolbox for descriptive statistics, based on the computation of frequency and contingency tables. Several statistical functions and plot methods are provided to describe univariate or bivariate distributions of factors, integer series and numerical series either provided as individual values or as bins. Package: r-cran-descstatsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-moments Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-descstatsr_0.1.0-1.ca2404.1_all.deb Size: 30378 MD5sum: e8a509d2fe421e7d6c57ef24714ef952 SHA1: c961c309abf28e79151be0329161b0394323eba0 SHA256: ba2f2ef459acb98ebb7756176c0e38a4d5c3b30708c9854197743de119f005ac SHA512: 83531d0f06f69a660ce4e93622b4c03d955caf8b8bd0108ac68dcf8dbff3211ce1e68631d9afdeff7f6089ef4b859c0d82ec2985cb747cb17196f1a7c7c647ac Homepage: https://cran.r-project.org/package=descstatsr Description: CRAN Package 'descstatsr' (Descriptive Univariate Statistics) It generates summary statistics on the input dataset using different descriptive univariate statistical measures on entire data or at a group level. Though there are other packages which does similar job but each of these are deficient in one form or other, in the measures generated, in treating numeric, character and date variables alike, no functionality to view these measures on a group level or the way the output is represented. Given the foremost role of the descriptive statistics in any of the exploratory data analysis or solution development, there is a need for a more constructive, structured and refined version over these packages. This is the idea behind the package and it brings together all the required descriptive measures to give an initial understanding of the data quality, distribution in a faster,easier and elaborative way.The function brings an additional capability to be able to generate these statistical measures on the entire dataset or at a group level. It calculates measures of central tendency (mean, median), distribution (count, proportion), dispersion (min, max, quantile, standard deviation, variance) and shape (skewness, kurtosis). Addition to these measures, it provides information on the data type, count on no. of rows, unique entries and percentage of missing entries. More importantly the measures are generated based on the data types as required by them,rather than applying numerical measures on character and data variables and vice versa. Output as a dataframe object gives a very neat representation, which often is useful when working with a large number of columns. It can easily be exported as csv and analyzed further or presented as a summary report for the data. Package: r-cran-descsuppr Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-plyr, r-cran-descutils, r-cran-tibble, r-cran-dplyr, r-cran-rlang, r-cran-desctools, r-cran-nparcomp, r-cran-rankfd, r-cran-circular, r-cran-glue, r-cran-purrr Suggests: r-cran-roxygen2, r-cran-survival, r-cran-stringr Filename: pool/dists/noble/main/r-cran-descsuppr_1.2-1.ca2404.1_all.deb Size: 169218 MD5sum: 43f7f27ad167dda0acb9c97e63aaea4e SHA1: 876e98a4196b41a352cbce48a7efc8e29f995e57 SHA256: d1484dfafb15489eacf82673a393b369f56e4d1118222dec085dcc8aa70e6ca8 SHA512: f97c6d0136db2a32ff2374dac1300521b5091d8c75c4a972dbdcbdd59e7dba23ba0a073e128b7c5e27897b12e265c05e0231722c13cc73e2e033828dc7343c73 Homepage: https://cran.r-project.org/package=descsuppR Description: CRAN Package 'descsuppR' (Support Functions for (Reproducible) Descriptive Statistics) Contains functions to help with generating tables with descriptive statistics. 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Package: r-cran-designlibrary Architecture: all Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-declaredesign, r-cran-randomizr, r-cran-fabricatr, r-cran-estimatr, r-cran-generics, r-cran-rlang, r-cran-glue Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-designlibrary_0.1.10-1.ca2404.1_all.deb Size: 167030 MD5sum: 0cc7339a535645939d5d86153036cfec SHA1: 3cd5283ae838361bff325d6ec4dcd11cf36cf0f8 SHA256: de9b66232b484e2f917fcfd5bdbc8c65369c6aa25d0b79f2a759f376eb73c3b0 SHA512: 06ab573906c08205c34f0e18d1f3a76240628792ca10b0f417e5068b4de880f06d83c3b0ae8ebb246dca51a46030ef024f7c7c892fd9638f2ad78906502ee41d Homepage: https://cran.r-project.org/package=DesignLibrary Description: CRAN Package 'DesignLibrary' (Library of Research Designs) A simple interface to build designs using the package 'DeclareDesign'. 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Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs. Also, they can be used for the design of randomized experiments, for example, for matching before randomization. By default, 'designmatch' uses the 'highs' optimization solver, but its performance is greatly enhanced by the 'Gurobi' optimization solver and its associated R interface. For their installation, please follow the instructions at and . We have also included directions in the gurobi_installation file in the inst folder. 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Package: r-cran-detpack Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-detpack_1.1.3-1.ca2404.1_all.deb Size: 104776 MD5sum: e7063d45452253cbacfed576c8bca404 SHA1: de81c862652d285958d7594d1c2869845c3e75e2 SHA256: c36d1a55bf081447b82a920e54e406be9d3252f11e0aef5d14f1817f7ed54118 SHA512: e7a58fcc22df55fb4c5f4f249353f65cb7a8612f7d5d8f4e01a593234be87894f84dde249f791fb742ccf60df7e066a0a3d357d213e4530b84d0af4036858790 Homepage: https://cran.r-project.org/package=detpack Description: CRAN Package 'detpack' (Density Estimation and Random Number Generation withDistribution Element Trees) Density estimation for possibly large data sets and conditional/unconditional random number generation or bootstrapping with distribution element trees. The function 'det.construct' translates a dataset into a distribution element tree. To evaluate the probability density based on a previously computed tree at arbitrary query points, the function 'det.query' is available. The functions 'det1' and 'det2' provide density estimation and plotting for one- and two-dimensional datasets. Conditional/unconditional smooth bootstrapping from an available distribution element tree can be performed by 'det.rnd'. For more details on distribution element trees, see: Meyer, D.W. (2016) or Meyer, D.W., Statistics and Computing (2017) and Meyer, D.W. (2017) or Meyer, D.W., Journal of Computational and Graphical Statistics (2018) . Package: r-cran-detrender Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplr, r-cran-tkrplotr Filename: pool/dists/noble/main/r-cran-detrender_1.0.5-1.ca2404.1_all.deb Size: 225462 MD5sum: 2757fc4e70d2c3add2ca990874e6017d SHA1: 10e276986002237b2673f0375125a879c9b12e3c SHA256: 7484cc95841e9d609bc927f9ae9b47ffdd490711f950f667d18da70a8c35c9c6 SHA512: b5312d1cd80a4ab6b0a46f4efeb327bfd2fbecb00b1a24dce522ba9ef1dd3f384de84a51485a90d6c1b178ee0a95be699bf88607152527f449520b61c951e6d5 Homepage: https://cran.r-project.org/package=detrendeR Description: CRAN Package 'detrendeR' (A Graphical User Interface (GUI) to Visualize and AnalyzeDendrochronological Data) A Graphical User Interface (GUI) to import, save, detrend and perform standard tree-ring analyses. The interactive detrending allows the user to check how well the detrending curve fits each time-series and change it when needed. Package: r-cran-dets Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4920 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pheatmap, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-dets_1.0-1.ca2404.1_all.deb Size: 5000520 MD5sum: e1ea78b2cc78a1a6c5d72bd94d126b59 SHA1: a9903ed3b7051fbb3b1fedd0115fc2d27f0fbeed SHA256: 1090313a11bff750d2c6aa64b5de9a9f650d51721e5fbc26f2d5916f1d85e198 SHA512: 181c8a50b389ecad980cc8d3208badfbe95cac980674a2ec9db937ecf2582a5a65c5f013f4c62e886dd42b7dd1f82e1a98ba59fb9784dd81d1783b1505dafe96 Homepage: https://cran.r-project.org/package=deTS Description: CRAN Package 'deTS' (Tissue-Specific Enrichment Analysis) Tissue-specific enrichment analysis to assess lists of candidate genes or RNA-Seq expression profiles. Pei G., Dai Y., Zhao Z. Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission. Package: r-cran-detzrcr Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3174 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-shiny, r-cran-mass, r-cran-dt, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-detzrcr_0.3.3-1.ca2404.1_all.deb Size: 1439398 MD5sum: 7b89595502c0c9ce9d756aeca08637c7 SHA1: cb267e8328e479c034a8ad1259b2d569e2b4ef30 SHA256: e73d30f60c24364fac755d8d1c8e79fbbd993b456010fccb18f2582ab79e96fb SHA512: f67515989dcc0dc9ac748c53a2aa55643ffea60a52a6bc7cbe12fe5ba3625de345cf8cc18fcf8ac6bc7a316e5363018a61b8c1aaae99e6a03ef99b0b1014c341 Homepage: https://cran.r-project.org/package=detzrcr Description: CRAN Package 'detzrcr' (Compare Detrital Zircon Suites) Compare detrital zircon suites by uploading univariate, U-Pb age, or bivariate, U-Pb age and Lu-Hf data, in a 'shiny'-based user-interface. 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Package: r-cran-devore7 Architecture: all Version: 0.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1284 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-lattice Filename: pool/dists/noble/main/r-cran-devore7_0.7.6-1.ca2404.1_all.deb Size: 892528 MD5sum: 34e7a371da2077b4b01bacbd5187f795 SHA1: 5e79c5785b4f7328cceaa8adffd69f052ecd9f39 SHA256: c5db2068e8f145c995be0062a03f90bcad88979d28a98bb8106af249d6ec9695 SHA512: 0057b0db416ccac5b718aea662350dec6aba3ea90c5c7a0d582972e725a26cf051fe1a5aef7f277a0279b5c87778bbe28c467b05a74540abbd34e72878097dfe Homepage: https://cran.r-project.org/package=Devore7 Description: CRAN Package 'Devore7' (Data sets from Devore's "Prob and Stat for Eng (7th ed)") Data sets and sample analyses from Jay L. Devore (2008), "Probability and Statistics for Engineering and the Sciences (7th ed)", Thomson. Package: r-cran-devrate Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1928 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-minpack.lm Filename: pool/dists/noble/main/r-cran-devrate_0.2.6-1.ca2404.1_all.deb Size: 1287782 MD5sum: ad21b352c95fc1c9e776f4c28fd73c70 SHA1: f726a6e4ed6670cb5f7f9cdb7eccee6b94df7ca3 SHA256: 41b2dd46aef257e31932c81f174984764585485a67a98313077c60d127c48530 SHA512: ab04f03749fa14104855fbfe8a3c7338bc1c2f04ab9f7a0d714fa58d52077ac6e0a15ea5419f019bdfebeb513233ead0d77af9e98c6708961fa2d051e2da61d5 Homepage: https://cran.r-project.org/package=devRate Description: CRAN Package 'devRate' (Quantify the Relationship Between Development Rate andTemperature in Ectotherms) A set of functions to quantify the relationship between development rate and temperature and to build phenological models. The package comprises a set of models and estimated parameters borrowed from a literature review in ectotherms. The methods and literature review are described in Rebaudo et al. (2018) , Rebaudo and Rabhi (2018) , and Regnier et al. (2021) . An example can be found in Rebaudo et al. (2017) . Package: r-cran-devtools Architecture: all Version: 2.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-usethis, r-cran-cli, r-cran-desc, r-cran-ellipsis, r-cran-fs, r-cran-lifecycle, r-cran-memoise, r-cran-miniui, r-cran-pak, r-cran-pkgbuild, r-cran-pkgdown, r-cran-pkgload, r-cran-profvis, r-cran-rcmdcheck, r-cran-rlang, r-cran-roxygen2, r-cran-rversions, r-cran-sessioninfo, r-cran-testthat, r-cran-urlchecker, r-cran-withr Suggests: r-cran-biocmanager, r-cran-callr, r-cran-covr, r-cran-curl, r-cran-digest, r-cran-dt, r-cran-foghorn, r-cran-gh, r-cran-httr2, r-cran-knitr, r-cran-lintr, r-cran-quarto, r-cran-remotes, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-spelling, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-devtools_2.5.2-1.ca2404.1_all.deb Size: 463924 MD5sum: ce24d6bfed068e5a58269893d5796277 SHA1: 3abc014aec4aadaf4422c0e366629f9079a54526 SHA256: 1467e3a777c2dff52c40c0f779c1677b3ad0fa9dbc54ba8d25c148ec4d49b1f9 SHA512: 7c007ef9ab84f2b86b589919a8988e4aa3b8a1b99e2cfad49c87f3c0277b4d9e52a096efe07c61c9d833475b5f82625be88a52c1e70e0cb8fff9062337c591d0 Homepage: https://cran.r-project.org/package=devtools Description: CRAN Package 'devtools' (Tools to Make Developing R Packages Easier) Collection of package development tools. Package: r-cran-devtreatrules Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-dyntxregime, r-cran-modelobj Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-devtreatrules_1.1.0-1.ca2404.1_all.deb Size: 397530 MD5sum: 3fe81dfc04416e52f2b58812ecaba89e SHA1: 05b9ffee1ea2f4794583d7279282f3dace45d5e3 SHA256: 62883cc12102ae0a018b222db67875b316adcb11a65f048bc896190d919fcd9b SHA512: 08cc8e65481453a67aeacb1edcf774912af08173f9bfdaa145713e24146031338278119a43db3ae69885c4869ed88c74595d7e0955768cf0fe1fbac9f30299c2 Homepage: https://cran.r-project.org/package=DevTreatRules Description: CRAN Package 'DevTreatRules' (Develop Treatment Rules with Observational Data) Develop and evaluate treatment rules based on: (1) the standard indirect approach of split-regression, which fits regressions separately in both treatment groups and assigns an individual to the treatment option under which predicted outcome is more desirable; (2) the direct approach of outcome-weighted-learning proposed by Yingqi Zhao, Donglin Zeng, A. John Rush, and Michael Kosorok (2012) ; (3) the direct approach, which we refer to as direct-interactions, proposed by Shuai Chen, Lu Tian, Tianxi Cai, and Menggang Yu (2017) . Please see the vignette for a walk-through of how to start with an observational dataset whose design is understood scientifically and end up with a treatment rule that is trustworthy statistically, along with an estimation of rule benefit in an independent sample. Package: r-cran-dexir Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 615 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-stringr Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-ggally, r-cran-fmsb Filename: pool/dists/noble/main/r-cran-dexir_1.0.2-1.ca2404.1_all.deb Size: 556300 MD5sum: 764987be38f00cf8a53b7f349262955f SHA1: 17c61b74bf19c4a5d6c3dc48380f160834f361e5 SHA256: 3a3afc4de20cb77619f2884796a7a5d58296f88c4e425f93b6aba98408afa628 SHA512: 043660c4becb0ef98e0f0d2b82d6f31634a754f97e469bba1c3e727e79c8bcea7085a4d72415e9eff5bd99c674f7abc96b7484aa53727fe4781e557853fd97be Homepage: https://cran.r-project.org/package=DEXiR Description: CRAN Package 'DEXiR' ('DEXi' Library) A software package for using 'DEXi' models. 'DEXi' models are hierarchical qualitative multi-criteria decision models developed according to the method DEX (Decision EXpert, ), using the program 'DEXi' () or 'DEXiWin' (). A typical workflow with 'DEXiR' consists of: (1) reading a '.dxi' file, previously made using the 'DEXi' software (function read_dexi()), (2) making a data frame containing input values of one or more decision alternatives, (3) evaluating those alternatives (function evaluate()), (4) analyzing alternatives (selective_explanation(), plus_minus(), compare_alternatives()), (5) drawing charts. 'DEXiR' is restricted to using models produced externally by the 'DEXi' software and does not provide functionality for creating and/or editing 'DEXi' models directly in 'R'. Package: r-cran-dexisensitivity Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 781 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-algdesign, r-cran-plotrix, r-cran-genalg, r-cran-dplyr, r-cran-ggplot2, r-cran-testthat, r-cran-xml2 Suggests: r-cran-sloop, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-dexisensitivity_1.0.2-1.ca2404.1_all.deb Size: 434640 MD5sum: ccf3bc8841c4aa3220d251a391bbdbf4 SHA1: 88931e3704804a73623af5deb9be0f5f5f22b55c SHA256: e64765c6bc41c3973bb6f752001decee3baeeb661e65baeeaf35cecf382aa0fd SHA512: adb7adb5f7f6f5384f8ef08ed8039c5c9f9600ad309b4408e1b0077a29fc70bc525eeaa910879285a429df27ab50545191380f96ab7953dd97f3e097bec589c8 Homepage: https://cran.r-project.org/package=dexisensitivity Description: CRAN Package 'dexisensitivity' ('DEXi' Decision Tree Analysis and Visualization) Provides a versatile toolkit for analyzing and visualizing 'DEXi' (Decision EXpert for education) decision trees, facilitating multi-criteria decision analysis directly within R. Users can read .dxi files, manipulate decision trees, and evaluate various scenarios. It supports sensitivity analysis through Monte Carlo simulations, one-at-a-time approaches, and variance-based methods, helping to discern the impact of input variations. Additionally, it includes functionalities for generating sampling plans and an array of visualization options for decision trees and analysis results. A distinctive feature is the synoptic table plot, aiding in the efficient comparison of scenarios. Whether for in-depth decision modeling or sensitivity analysis, this package stands as a comprehensive solution. Definition of sensitivity analyses available in Carpani, Bergez and Monod (2012) and detailed description of the package soon available in Alaphilippe et al. (2025) . 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Package: r-cran-df2yaml Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rrapply, r-cran-tibble, r-cran-yaml, r-cran-magrittr, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-prettydoc, r-cran-knitr Filename: pool/dists/noble/main/r-cran-df2yaml_0.3.1-1.ca2404.1_all.deb Size: 186560 MD5sum: a550412bf5b4f97800c2b351f5038382 SHA1: f58dc79dbe288a84361dfd5c8791fc9e2565edf8 SHA256: f374dbc4783dd9b9549696d6cd3e060559932cddc0f39073b7c6b3935cae86ea SHA512: 98b2d9f7b4c6a212d5e8ef3216f6a6474d1d336eb77107d9a2d79bc6bd11e1471e87218ff838e4693e66131f01fcb4a70481b11c29226538806194c59fe3cd54 Homepage: https://cran.r-project.org/package=df2yaml Description: CRAN Package 'df2yaml' (Convert Dataframe to 'YAML') The 'df2yaml' aims to simplify the process of converting 'dataframe' to 'YAML' . 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Package: r-cran-dfa.cancor Architecture: all Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesfactor, r-cran-mass, r-cran-mvoutlier, r-cran-mvn Filename: pool/dists/noble/main/r-cran-dfa.cancor_0.3.9-1.ca2404.1_all.deb Size: 338092 MD5sum: 48fb20b4b413cebc0aeeb38b0ae68229 SHA1: 845f82802726233b76f650cacbe261a24a07f048 SHA256: 97b0f30558335edb1b0f74cad0bb4e07dd6ac4d907694b1bf8eb85239f85c89b SHA512: 022898d65e9471f2b46371224e97222453df05a871857166d2a61c26c7a7b796f44812abbd3f435fe883ce7bae2b8e1f62ba109956aafd767084f518dca357d9 Homepage: https://cran.r-project.org/package=DFA.CANCOR Description: CRAN Package 'DFA.CANCOR' (Linear Discriminant Function and Canonical Correlation Analysis) Produces SPSS- and SAS-like output for linear discriminant function analysis and canonical correlation analysis. 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Package: r-cran-dfrr Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmvtnorm, r-cran-fda, r-cran-mass, r-cran-ggplot2, r-cran-plotly Suggests: r-cran-car Filename: pool/dists/noble/main/r-cran-dfrr_0.1.5-1.ca2404.1_all.deb Size: 152554 MD5sum: 039b19c5da4f2620ee1bdd3a225b4de8 SHA1: 6efcf1434a99d34406c0abe49883e9aa4334f5c7 SHA256: 6f4281b80b54ec59f96ce3793a5fe6f6e0124b6cf8a02a044337dc9456dc00b1 SHA512: ed5414607e39001b7bc1b893624a9312ceb3698922bfc3949ae78515c3fe8f68aaecee8fe72234bb3237694b959a9ab526b69a7200252700456b41c4a73a1a40 Homepage: https://cran.r-project.org/package=dfrr Description: CRAN Package 'dfrr' (Dichotomized Functional Response Regression) Implementing Function-on-Scalar Regression model in which the response function is dichotomized and observed sparsely. 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To learn more about dhlab, visit out site . Package: r-cran-dhs.rates Architecture: all Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape, r-cran-survey, r-cran-haven, r-cran-matrixstats, r-cran-dplyr, r-cran-rlang, r-cran-crayon Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dhs.rates_0.9.2-1.ca2404.1_all.deb Size: 263218 MD5sum: f65caaf67f68ebd0f4d78d6c735293e3 SHA1: 5446189a86713205f3f29d9a9361dc37d82ffe4d SHA256: 8d60bc2b1e7ab134cb7b08dbfdec0e5e5003a54773551eb6cf2f90437ed6b656 SHA512: 1c1a419a2f7529930f06d70ddf4d3dbec34d23a5f891c3f09adb67eb21ae42281f1c6dea9700c9fe7f64f604dfc0fe667506bdd63cd35dd07de1d3bfbbc9ac9b Homepage: https://cran.r-project.org/package=DHS.rates Description: CRAN Package 'DHS.rates' (Calculates Demographic Indicators) Calculates key indicators such as fertility rates (Total Fertility Rate (TFR), General Fertility Rate (GFR), and Age Specific Fertility Rate (ASFR)) using Demographic and Health Survey (DHS) women/individual data, childhood mortality probabilities and rates such as Neonatal Mortality Rate (NNMR), Post-neonatal Mortality Rate (PNNMR), Infant Mortality Rate (IMR), Child Mortality Rate (CMR), and Under-five Mortality Rate (U5MR), and adult mortality indicators such as the Age Specific Mortality Rate (ASMR), Age Adjusted Mortality Rate (AAMR), Age Specific Maternal Mortality Rate (ASMMR), Age Adjusted Maternal Mortality Rate (AAMMR), Age Specific Pregnancy Related Mortality Rate (ASPRMR), Age Adjusted Pregnancy Related Mortality Rate (AAPRMR), Maternal Mortality Ratio (MMR) and Pregnancy Related Mortality Ratio (PRMR). In addition to the indicators, the 'DHS.rates' package estimates sampling errors indicators such as Standard Error (SE), Design Effect (DEFT), Relative Standard Error (RSE) and Confidence Interval (CI). The package is developed according to the DHS methodology of calculating the fertility indicators and the childhood mortality rates outlined in the "Guide to DHS Statistics" (Croft, Trevor N., Aileen M. J. Marshall, Courtney K. Allen, et al. 2018, ) and the DHS methodology of estimating the sampling errors indicators outlined in the "DHS Sampling and Household Listing Manual" (ICF International 2012, ). Package: r-cran-dhsage Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dhsage_0.1.0-1.ca2404.1_all.deb Size: 77424 MD5sum: 3d9dd17fe015b2090cb82f5a00854d64 SHA1: 65657648f69813b126eb252937cf8c6e6cd042af SHA256: c7f9008b42a3fcdfacd3d3379900ed7f299c51b1b7ce973e0c80ef009b2ba13d SHA512: f2ded80e9198b76372edfc577d9b1defb6351da135bc3cadf0c9ee503a29c549adccbaf469dd9913206d16b79788d050bfcae467aaa1cc21ca8af9ee98912de5 Homepage: https://cran.r-project.org/package=dhsage Description: CRAN Package 'dhsage' (Reproductive Age Female Data of Various Demographic HealthSurveys) We provide 70 data sets of females of reproductive age from 19 Asian countries, ranging in age from 15 to 49. The data sets are extracted from demographic and health surveys that were conducted over an extended period of time. Moreover, the functions also provide Whipple’s index as well as age reporting quality such as very rough, rough, approximate, accurate, and highly accurate. Package: r-cran-dhsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-sf, r-cran-spdep, r-cran-viridis, r-cran-nlme, r-cran-mumin, r-cran-tidyr Suggests: r-cran-knitr, r-cran-spdata, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dhsr_0.1.0-1.ca2404.1_all.deb Size: 67862 MD5sum: 9d8018dd9f16dc6c281fa759f31a6fe1 SHA1: 6ca9fbfb017d1f7b7f8dcf55b16e7f86462b6949 SHA256: b2b7a6fd099ef5b211a36ecbfd7d0062cbe6eb33f2b41179c1b8f8e750ef322a SHA512: ad461cda67b23a2381bc085f2e8a838bd33510e23dc364ad288f1fadc184f087d8f58b1300b0f22a43c5f09176695204d80ed2f110313965bfe96d206faf3bdb Homepage: https://cran.r-project.org/package=DHSr Description: CRAN Package 'DHSr' (Create Large Scale Repeated Regression Summary StatisticsDataset and Visualization Seamlessly) Mapping, spatial analysis, and statistical modeling of microdata from sources such as the Demographic and Health Surveys and Integrated Public Use Microdata Series . It can also be extended to other datasets. The package supports spatial correlation index construction and visualization, along with empirical Bayes approximation of regression coefficients in a multistage setup. The main functionality is repeated regression — for example, if we have to run regression for n groups, the group ID should be vertically composed into the variable for the parameter `location_var`. It can perform various kinds of regression, such as Generalized Regression Models, logit, probit, and more. Additionally, it can incorporate interaction effects. The key benefit of the package is its ability to store the regression results performed repeatedly on a dataset by the group ID, along with respective p-values and map those estimates. Package: r-cran-di Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-di_1.1.4-1.ca2404.1_all.deb Size: 283298 MD5sum: c44e97aa492c3426aa1affa31b0cec5a SHA1: 4e623b435564099452f7b850092d62b5ce52739a SHA256: 030f983ec4de8d5bdcf2f2edf57377435d823454d968d7aec6d5c605f99393c7 SHA512: d6a53e50830a7c6ddcff54c6f297f465759b49a3d7134bf2fa1bb06086dfacb41fc7fe6a61b050f42196573c1e15fb434b06d8a575dd30baa716e85717a006e5 Homepage: https://cran.r-project.org/package=di Description: CRAN Package 'di' (Deficit Index (DI)) A set of utilities for calculating the Deficit (frailty) Index (DI) in gerontological studies. The deficit index was first proposed by Arnold Mitnitski and Kenneth Rockwood and represents a proxy measure of aging and also can be served as a sensitive predictor of survival. For more information, see (i)"Accumulation of Deficits as a Proxy Measure of Aging" by Arnold B. Mitnitski et al. (2001), The Scientific World Journal 1, ; (ii) "Frailty, fitness and late-life mortality in relation to chronological and biological age" by Arnold B Mitnitski et al. (2001), BMC Geriatrics2002 2(1), . Package: r-cran-diagcounts Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-diagcounts_0.1.0-1.ca2404.1_all.deb Size: 20432 MD5sum: 7cce342b0b7261983c042880e22d626a SHA1: 3741aa1b18bd0412d687b47f1510368aaf1db4c7 SHA256: 80871473062124642776ada6530deef9e9da77bfeee23eb41463b41c0bf95ace SHA512: 783fb334f37025e205dbc202f9deb155207a4912989e24086f514530880cb87f3c3958113406a8dcc9817ff46ca7837d66ddbba593ccdc1f6173947eae8e2e37 Homepage: https://cran.r-project.org/package=diagcounts Description: CRAN Package 'diagcounts' (Recover Diagnostic Test Accuracy Counts from Reported AccuracyMeasures) Implements a system of linear equations to recover unreported diagnostic test accuracy cell counts from commonly reported measures such as sensitivity, specificity, predictive values, prevalence, and sample size. 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Package: r-cran-diagl1 Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-greekletters, r-cran-conquer, r-cran-lawstat, r-cran-matrixmodels, r-cran-matrix, r-cran-mass, r-cran-cubature, r-cran-doparallel, r-cran-foreach Filename: pool/dists/noble/main/r-cran-diagl1_1.0.1-1.ca2404.1_all.deb Size: 167462 MD5sum: e946be2a42dc3d4e24b2afb37193b844 SHA1: 73829bbafb13cf69304f4c98d707e48873612541 SHA256: 7a5ed47fe61feba4cb3853d7f30d8545e01c976a7877655807a7439b97cb6638 SHA512: 1c1c83971f8fa3fe06fdaecdd2fc2cf67664b89b0985766bb2cb9bbf08ebee6576cab841373bf018bd1f3cca714ea3d825ca9659f71f95ab6ff4c1330093896e Homepage: https://cran.r-project.org/package=diagL1 Description: CRAN Package 'diagL1' (Routines for Fit, Inference and Diagnostics in Linear L1 and LADModels) Diagnostics for linear L1 regression (also known as LAD - Least Absolute Deviations), including: estimation, confidence intervals, tests of hypotheses, measures of leverage, methods of diagnostics for L1 regression, special diagnostics graphs and measures of leverage. The algorithms are based in Dielman (2005) , Elian et al. (2000) and Dodge (1997) . This package builds on the 'quantreg' package, which is a well-established package for tuning quantile regression models. There are also tests to verify if the errors have a Laplace distribution based on the work of Puig and Stephens (2000) . 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These tools can be used to define objects for creating, simulating, or validating values for such parameters. Package: r-cran-dialvalidator Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-stringr Suggests: r-cran-xml2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dialvalidator_0.1.0-1.ca2404.1_all.deb Size: 159308 MD5sum: a3398ed4b3b1e8da59e2633c03485679 SHA1: f0fc5f85cc07fc5a080c49f464502b7e3ad12195 SHA256: 2f14c2cb3ccb33249a58c3150579c20f43a0a2747c2b234690ffa7577a37bc7d SHA512: df5bd1965dbda837a0a4cfe0bee3a09b387eb6d7d91be73c9cd023be1c404c9b73bdfaefffd367ca42aea47a4716a8ff67758d711f9a2374d8a221364baad7c3 Homepage: https://cran.r-project.org/package=dialvalidator Description: CRAN Package 'dialvalidator' (Phone Number Validation Using Google's 'libphonenumber' Metadata) Parses, validates, formats, and classifies phone numbers using Google's 'libphonenumber' metadata. Covers 240+ territories with support for mobile, landline, toll-free, and other number types. Unlike 'dialr', this package requires no Java runtime — metadata is parsed ahead of time from the upstream 'PhoneNumberMetadata.xml' and shipped as a bundled R object. Functions accept character vectors and return simple R types (logical, character, data.frame). Package: r-cran-diaplt Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-diaplt_1.4.0-1.ca2404.1_all.deb Size: 53450 MD5sum: 49cf33b7868930d4ad134f9d3345ff5d SHA1: c818868a761e1d564ac3f353b1f87c747834df8c SHA256: 719c44c77c4597b10614f0608096f2e6e16b49f08bc42c487106216a1e4447d5 SHA512: d9818f3fd92ba851a640e17373f6cb3d72f21c75edade1620bfb6239b9dd81193bd883b113707fddf8c3321d1f89f5895a53007cda39b60fb408f5d78446b8ca Homepage: https://cran.r-project.org/package=diaplt Description: CRAN Package 'diaplt' (Beads Summary Plot of Ranges) Visualize one-factor data frame. Beads plot consists of diamonds of each factor of each data series. A diamond indicates average and range. Look over a data frame with many numeric columns and a factor column. Package: r-cran-diario Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2395 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-tibble, r-cran-httr2, r-cran-keyring Filename: pool/dists/noble/main/r-cran-diario_0.1.1-1.ca2404.1_all.deb Size: 151578 MD5sum: 82108ef324d7da679ef30738289533fb SHA1: ca34d8c455ed487400b68eb8dee3a6368cea404a SHA256: 2e0cac16b22d7be6b8202fe8bd5d3881df5e92a24971b2105eb7e96e7c1ae424 SHA512: 946837208cace718ad96a015d8bfbb31ceee135b49614e0ca64c93dc816fb76a9957c46b0be6e5ceca3328138bd97e6f11e1d8af96a3ecbc12931bc729b57c82 Homepage: https://cran.r-project.org/package=diario Description: CRAN Package 'diario' ('R' Interface to the 'Diariodeobras' Application) Provides a set of functions for securely storing 'API' tokens and interacting with the system. 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Package: r-cran-diathor Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2719 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringdist, r-cran-vegan, r-cran-ggplot2, r-cran-tidyr, r-cran-data.table, r-cran-purrr, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-diathor_0.1.5-1.ca2404.1_all.deb Size: 2635102 MD5sum: 3bdb006dc5e3baa7199a61b943075ffe SHA1: a8a420ef7b6ba90467385d36101729c62726efd6 SHA256: 0c710de58ceee04fed83d58f7b9b3bf6b79b7fa2bafe244882b6c5add7cb8ef1 SHA512: c5b7ce98d205633ff5a21d260dd17f1190238c314061c5bbc8ae3a3225bb8585d849ce8fb109da1acea96808a994abaaaa11f0dd7c27d715f41462e425a191ca Homepage: https://cran.r-project.org/package=diathor Description: CRAN Package 'diathor' (Calculate Ecological Information and Diatom Based Indices) Calculate multiple biotic indices using diatoms from environmental samples. Diatom species are recognized by their species' name using a heuristic search, and their ecological data is retrieved from multiple sources. It includes number/shape of chloroplasts diversity indices, size classes, ecological guilds, and multiple biotic indices. It outputs both a dataframe with all the results and plots of all the obtained data in a defined output folder. - Sample data was taken from Nicolosi Gelis, Cochero & Gómez (2020, ). - The package uses the 'Diat.Barcode' database to calculate morphological and ecological information by Rimet & Couchez (2012, ),and the combined classification of guilds and size classes established by B-Béres et al. (2017, ). - Current diatom-based biotic indices include the DES index by Descy (1979) - EPID index by Dell'Uomo (1996, ISBN: 3950009002) - IDAP index by Prygiel & Coste (1993, ) - ID-CH index by Hürlimann & Niederhauser (2007) - IDP index by Gómez & Licursi (2001, ) - ILM index by Leclercq & Maquet (1987) - IPS index by Coste (1982) - LOBO index by Lobo, Callegaro, & Bender (2002, ISBN:9788585869908) - SLA by Sládeček (1986, ) - TDI index by Kelly, & Whitton (1995, ) - SPEAR(herbicide) index by Wood, Mitrovic, Lim, Warne, Dunlop, & Kefford (2019, ) - PBIDW index by Castro-Roa & Pinilla-Agudelo (2014) - DISP index by Stenger-Kovács et al. (2018, ) - EDI index by Chamorro et al. (2024, ) - DDI index by Álvarez-Blanco et al. (2013, ) - PDISE index by Kahlert et al. (2023, ). 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Package: r-cran-dietcost Architecture: all Version: 1.0.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-rlang, r-cran-dplyr, r-cran-tidyselect, r-cran-xlsx, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-dietcost_1.0.0.0-1.ca2404.1_all.deb Size: 267984 MD5sum: b0ac0e01af2e41c7ac49184689d5bc1c SHA1: b7ae974c473e611a467196f2bddd0e43370e98ee SHA256: 7cf24427ddd45db045a1b8044d6efb80cc4bee56b697fa6e8789be2899aba197 SHA512: 34f3369e661b307490e3edbe50c3f4489ca9b2b7e06a32890b395701ddc3ed6e3023792425a3a8d27675672590e8f2e737d74b4f76114055ad2417e4e4ac950d Homepage: https://cran.r-project.org/package=DIETCOST Description: CRAN Package 'DIETCOST' (Calculate the Cost and Environmental Impact of a Ideal Diet) Easily perform a Monte Carlo simulation to evaluate the cost and carbon, ecological, and water footprints of a set of ideal diets. Pre-processing tools are also available to quickly treat the data, along with basic statistical features to analyze the simulation results — including the ability to establish confidence intervals for selected parameters, such as nutrients and price/emissions. A 'standard version' of the datasets employed is included as well, allowing users easy access to customization. This package brings to R the 'Python' software initially developed by Vandevijvere, Young, Mackay, Swinburn and Gahegan (2018) . Package: r-cran-dietr Architecture: all Version: 1.1.6-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 934 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfishbase Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dietr_1.1.6-1-1.ca2404.1_all.deb Size: 390326 MD5sum: 246c8ee57d7b2448db8c5fcbe2211d9f SHA1: 8aac55d32a6f5dbc39532e485e3652eb962460d5 SHA256: 3eb40d438d86781389f3bfd45425264c5c3b65fefad5c2a50b35c60d35037e69 SHA512: 09f7c69563967efcea3fd0aa354ebc3716d3e083c031b404e2931aa0fc50c78cc2811f41d688b6238837808329e8445bb60ba2bbb39752eac2f67822f59054a2 Homepage: https://cran.r-project.org/package=dietr Description: CRAN Package 'dietr' (Diet Estimated Trophic Levels) Estimates fractional trophic level from quantitative and qualitative diet data and calculates electivity indices in R. Borstein (2020) . Package: r-cran-diezeit Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brew, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-diezeit_0.1-0-1.ca2404.1_all.deb Size: 130688 MD5sum: e1d2feb0b086fc9f09b60245fd169ca5 SHA1: 2c0e2ee5281911a29d8094b633ea10de56abdbae SHA256: 263d7ed0ac10e38973293f1de2e6a74de3aaf0de6b9e8b930e4439432c4a5a68 SHA512: bfb6a5fb69bdcdff0027460d26dfecf705503094ef76d298097e7c6602d9fbff21788d15d547ad11023a0fbdeb39bab65fb4eec6f115232b0221caa2abfafe32 Homepage: https://cran.r-project.org/package=diezeit Description: CRAN Package 'diezeit' (R Interface to the ZEIT ONLINE Content API) A wrapper for the ZEIT ONLINE Content API, available at . 'diezeit' gives access to articles and corresponding metadata from the ZEIT archive and from ZEIT ONLINE. A personal API key is required for usage. Package: r-cran-difboost Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mboost, r-cran-penalized, r-cran-stabs Filename: pool/dists/noble/main/r-cran-difboost_0.4-1.ca2404.1_all.deb Size: 30850 MD5sum: d3903f9c719dbfa0a900924a6eeaaf53 SHA1: d3e44bcd37df4bd46c8c81d6c052dca630bd6dfe SHA256: 5e50c10788b151f2b183643d3f0a9565b958aa339e1c525957c8f838874cbf15 SHA512: 4d4d74fb7d05e6e836c166fbfd26d3c80d867aea15a8d0c60c15011294c1e49d94eb34f2e380bb5e8bc5779a32d1a9b3eee38e89c55c09fbff0daedfd4a625d5 Homepage: https://cran.r-project.org/package=DIFboost Description: CRAN Package 'DIFboost' (Detection of Differential Item Functioning (DIF) in Rasch Modelsby Boosting Techniques) Performs detection of Differential Item Functioning using the method DIFboost as proposed by Schauberger and Tutz (2016) . Package: r-cran-difconet Architecture: all Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gplots, r-cran-stringr, r-cran-data.table, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-difconet_1.0-4-1.ca2404.1_all.deb Size: 83132 MD5sum: 231be56d1943516d8d2399f197716dcf SHA1: 6c7c7ba3c48810f9a9484710486920035208322e SHA256: 7b2283bf16d8028b55f027101c3084a5e936c802e95ee7a1dcb896ef0324fb35 SHA512: 69ab48a4f9baae02f9e9b5656a4375fd5b51e41662db2299fb2555f31cee45039e7abf6ce4d1f85a7d950dc811f7c9b8753246661206ea403bb78b8ba1297ec3 Homepage: https://cran.r-project.org/package=difconet Description: CRAN Package 'difconet' (Differential Coexpressed Networks) Estimation of DIFferential COexpressed NETworks using diverse and user metrics. This package is basically used for three functions related to the estimation of differential coexpression. First, to estimate differential coexpression where the coexpression is estimated, by default, by Spearman correlation. For this, a metric to compare two correlation distributions is needed. The package includes 6 metrics. Some of them needs a threshold. A new metric can also be specified as a user function with specific parameters (see difconet.run). The significance is be estimated by permutations. Second, to generate datasets with controlled differential correlation data. This is done by either adding noise, or adding specific correlation structure. Third, to show the results of differential correlation analyses. Please see for further information. Package: r-cran-diffcor Architecture: all Version: 0.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-diffcor_0.8.4-1.ca2404.1_all.deb Size: 68994 MD5sum: 66572c348198952ef0b2a9b23ec347e5 SHA1: 423079096b0b00ec64cfa7d4e733ea5dd7d9efeb SHA256: 3693808bb8799978dd6760319c50962a8302924b2564ad15a93f11f88667472c SHA512: 9cd074005eddd511f66793bb1b9e0fecd4aef8680e7216d3f7c3a9f447fd503691154e8ad993971135919ce1be92c929d1e9108879392079d878a440a958188e Homepage: https://cran.r-project.org/package=diffcor Description: CRAN Package 'diffcor' (Fisher's z-Tests Concerning Differences Between Correlations) Computations of Fisher's z-tests concerning different kinds of correlation differences. The 'diffpwr' family entails approaches to estimating statistical power via Monte Carlo simulations. Important to note, the Pearson correlation coefficient is sensitive to linear association, but also to a host of statistical issues such as univariate and bivariate outliers, range restrictions, and heteroscedasticity (e.g., Duncan & Layard, 1973 ; Wilcox, 2013 ). Thus, every power analysis requires that specific statistical prerequisites are fulfilled and can be invalid if the prerequisites do not hold. To this end, the 'bootcor' family provides bootstrapping confidence intervals for the incorporated correlation difference tests. Package: r-cran-diffcorr Architecture: all Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 846 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fdrtool, r-cran-igraph, r-bioc-multtest, r-bioc-pcamethods Suggests: r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-diffcorr_0.4.5-1.ca2404.1_all.deb Size: 726728 MD5sum: c41522391396624dd5d2e9bdc35220e2 SHA1: af56fd79a9e57d87fdfc2b145640f931a44bbd3b SHA256: 5d2d92316c4e98818b57bec9cde5efc4a5019838c32d383dbe43c8d97af4cb34 SHA512: 0488441fba7e4480f1aca570569ed4ef84a81dea2755cb19a05c3be4b24f4ba5e30c6d7426c662bdb97dc2051b532413c30b0151095ee0b80b57bf491cb46300 Homepage: https://cran.r-project.org/package=DiffCorr Description: CRAN Package 'DiffCorr' (Analyzing and Visualizing Differential Correlation Networks inBiological Data) A method for identifying pattern changes between 2 experimental conditions in correlation networks (e.g., gene co-expression networks), which builds on a commonly used association measure, such as Pearson's correlation coefficient. This package includes functions to calculate correlation matrices for high-dimensional dataset and to test differential correlation, which means the changes in the correlation relationship among variables (e.g., genes and metabolites) between 2 experimental conditions. Package: r-cran-diffdf Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-assertthat Suggests: r-cran-testthat, r-cran-lubridate, r-cran-knitr, r-cran-rmarkdown, r-cran-purrr, r-cran-dplyr, r-cran-stringi, r-cran-stringr, r-cran-devtools, r-cran-covr, r-cran-bit64, r-cran-withr Filename: pool/dists/noble/main/r-cran-diffdf_1.1.2-1.ca2404.1_all.deb Size: 134924 MD5sum: c59627394cb310d2053e8107ad53fdf4 SHA1: 9ab3fa2b3532aa6f20e43e73af498a992fc2dc8f SHA256: d09a6953c99829f25703e73549a5a907762db2158d0e2f5daf8133d5f369c54d SHA512: a04769ae61862526ea3301b3366ebbc49b2d00afe2158b4cac1b49a2dcc9af63f1d412cd47ca8da306e796bb847f91fdd8dc3beab0f07a431028f67209ee4aa1 Homepage: https://cran.r-project.org/package=diffdf Description: CRAN Package 'diffdf' (Dataframe Difference Tool) Functions for comparing two data.frames against each other. The core functionality is to provide a detailed breakdown of any differences between two data.frames as well as providing utility functions to help narrow down the source of problems and differences. Package: r-cran-diffdfs Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-janitor, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-diffdfs_0.9.0-1.ca2404.1_all.deb Size: 15448 MD5sum: 80f1e80573304c88d8a2c70ed99662af SHA1: 24e5600b579a6eed8b4624aa9b415cba56f8e80a SHA256: dc90d7eaff375021a9203337416eec3804c533025e10f036f7bfc57277af0c9a SHA512: ed24c7010590679abc6a81bb307cd9590e35fa6eacfebc50573153d1a9504da7b6ad7847e5c116eb991e3ec4e89875220296a91db5d3139cedd426b116ec80ad Homepage: https://cran.r-project.org/package=diffdfs Description: CRAN Package 'diffdfs' (Compute the Difference Between Data Frames) Shows you which rows have changed between two data frames with the same column structure. Useful for diffing slowly mutating data. Package: r-cran-diffee Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4074 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-pcapp Filename: pool/dists/noble/main/r-cran-diffee_1.1.0-1.ca2404.1_all.deb Size: 4128276 MD5sum: 8156ad2549c6b4e65cfbb8b0ca253e45 SHA1: c0ce832ef47b86669fd534297ec43007448e294f SHA256: 4101bcf6640e648b45ef95df7351669ec78012f48cb6260b9b4ae0cdcb1730e4 SHA512: 39fdbc77845453990785cc56e63cc2cc7ab334eb7fc816f626c38fcbec4aa899509dfc5fbc3a7a4128c83d2d8988d6306b045da046767148241fcf3bdb8468e7 Homepage: https://cran.r-project.org/package=diffee Description: CRAN Package 'diffee' (Fast and Scalable Learning of Sparse Changes in High-DimensionalGaussian Graphical Model Structure) This is an R implementation of Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure (DIFFEE). The DIFFEE algorithm can be used to fast estimate the differential network between two related datasets. For instance, it can identify differential gene network from datasets of case and control. By performing data-driven network inference from two high-dimensional data sets, this tool can help users effectively translate two aggregated data blocks into knowledge of the changes among entities between two Gaussian Graphical Model. Please run demo(diffeeDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) . Package: r-cran-diffenrich Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1306 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-here, r-cran-rlang, r-cran-stringr, r-cran-reshape2, r-cran-ggnewscale Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-diagram Filename: pool/dists/noble/main/r-cran-diffenrich_0.1.2-1.ca2404.1_all.deb Size: 965276 MD5sum: d72e07e83ddd9427fda30bec1c0c8567 SHA1: 43234f2d6c21103117c86b1a41be14872809770a SHA256: d0999cbc63260f2ea955a2d112b4418031dc205394209478af2508d82daaf542 SHA512: bc25d8f5076141f5a4ee8424ebccccd6b75135886d0389ad3882b2d4d9e7df0925a3f4155e95b49ce0a97ba4be9b8b5b0673cda1a71a8454130a740a87b8016a Homepage: https://cran.r-project.org/package=diffEnrich Description: CRAN Package 'diffEnrich' (Given a List of Gene Symbols, Performs Differential EnrichmentAnalysis) Compare functional enrichment between two experimentally-derived groups of genes or proteins (Peterson, DR., et al.(2018)) . Given a list of gene symbols, 'diffEnrich' will perform differential enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. This package provides a number of functions that are intended to be used in a pipeline. Briefly, the user provides a KEGG formatted species id for either human, mouse or rat, and the package will download and clean species specific ENTREZ gene IDs and map them to their respective KEGG pathways by accessing KEGG's REST API. KEGG's API is used to guarantee the most up-to-date pathway data from KEGG. Next, the user will identify significantly enriched pathways from two gene sets, and finally, the user will identify pathways that are differentially enriched between the two gene sets. In addition to the analysis pipeline, this package also provides a plotting function. Package: r-cran-diffeqr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11068 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-juliacall Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-diffeqr_2.1.0-1.ca2404.1_all.deb Size: 521568 MD5sum: 59b59ed366d2e51a6cbeb9dbe1806c47 SHA1: d88ef6655a2a0cb3d55793e7779d80e8a60d3c5c SHA256: a4a6b8c031ba1233397be619a686c8338b392c0fca7c307808ec15bf18a5276c SHA512: 6e7c4ca49119655921cc8ba3323468c196d424acac200a5cd921c5bafacf9fe41be7dc74c73770d2cd12e3d00e536dde8171bfd424658ad946d28aac9426b4d4 Homepage: https://cran.r-project.org/package=diffeqr Description: CRAN Package 'diffeqr' (Solving Differential Equations (ODEs, SDEs, DDEs, DAEs)) An interface to 'DifferentialEquations.jl' from the R programming language. It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE), delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality, including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods. Supports GPUs, with support for CUDA (NVIDIA), AMD GPUs, Intel oneAPI GPUs, and Apple's Metal (M-series chip GPUs). 'diffeqr' attaches an R interface onto the package, allowing seamless use of this tooling by R users. For more information, see Rackauckas and Nie (2017) . Package: r-cran-differ Architecture: all Version: 0.0-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1196 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-ggplot2, r-cran-tidyr, r-cran-tidyselect, r-cran-rlang, r-cran-raster Filename: pool/dists/noble/main/r-cran-differ_0.0-8-1.ca2404.1_all.deb Size: 390634 MD5sum: e47199710b33c6a8ff072e07a81826b2 SHA1: 975cd43d41c9200a6309cbf422190ee892177055 SHA256: 73a453018a8fef67735df7abbdcf7032063cbbf5e17f8aee0fd225bf8a595311 SHA512: dc2fb4367236a87bf181da13aa3be7b135b4c8698b7736e4a811c19a25d9ec62eaa14c924ec0bd9d4d3f309034e395c14d2e739b0ca4235a9423bd154518ae94 Homepage: https://cran.r-project.org/package=diffeR Description: CRAN Package 'diffeR' (Metrics of Difference for Comparing Pairs of Maps or Pairs ofVariables) Metrics of difference for comparing pairs of variables or pairs of maps representing real or categorical variables at original and multiple resolutions. Package: r-cran-differentes Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boolnet, r-cran-dot, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-differentes_0.3.2-1.ca2404.1_all.deb Size: 42386 MD5sum: 8485e5e29eee3ff4fc300cc82ea8a484 SHA1: 0b7933946a57460df426276370f83e1eeec39926 SHA256: 8bd7481258af6d722ead21355a414387b2fda1c31fd24bbd87f8964c2573eb87 SHA512: 0b6504aef0112a2c5d41aa7f77aa47d2a8fec2583c4bc7472953b5fcd4084fcded25ae0541f3b794db433d5e25e936861ab8eeeb2d3d6377a0881a138ccd032f Homepage: https://cran.r-project.org/package=diffeRenTES Description: CRAN Package 'diffeRenTES' (Computation of TES-Based Cell Differentiation Trees) Computes the ATM (Attractor Transition Matrix) structure and the tree-like structure describing the cell differentiation process (based on the Threshold Ergodic Set concept introduced by Serra and Villani), starting from the Boolean networks with synchronous updating scheme of the 'BoolNet' R package. TESs (Threshold Ergodic Sets) are the mathematical abstractions that represent the different cell types arising during ontogenesis. TESs and the powerful model of biological differentiation based on Boolean networks to which it belongs have been firstly described in "A Dynamical Model of Genetic Networks for Cell Differentiation" Villani M, Barbieri A, Serra R (2011) A Dynamical Model of Genetic Networks for Cell Differentiation. PLOS ONE 6(3): e17703. Package: r-cran-diffmeshgp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-diffmeshgp_0.1.1-1.ca2404.1_all.deb Size: 20874 MD5sum: b790449bb622615ef6eebb31ca7e1007 SHA1: a215f9a5593933b4177fd9272544997f3eb58a59 SHA256: 2527a9d34f0aa165bef82947f493aaeee25a8f17f8e66503978acb39df8e5597 SHA512: 28b6db3d32abdc0d1782cde9e7764af71ef31d547ca252877e586af35284b7bc532799dccfba210ba06eb5bce2d7284d1d1cdd21caacabd638a126218848ae86 Homepage: https://cran.r-project.org/package=diffMeshGP Description: CRAN Package 'diffMeshGP' (Multi-Fidelity Computer Experiments Using the Tuo-Wu-Yu Model) This R function implements the nonstationary Kriging model proposed by Tuo, Wu and Yu (2014) for analyzing multi-fidelity computer outputs. This function computes the maximum likelihood estimates for the model parameters as well as the predictive means and variances of the exact solution. Package: r-cran-diffnet Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-assertthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-diffnet_1.0.2-1.ca2404.1_all.deb Size: 28752 MD5sum: dcdd355180713ea86a7961e9c366f4e4 SHA1: f05fe61ea32d8a88fe8c063024ff6bd10a43a185 SHA256: c396dace717cbf55a5a43b6bcb739d72986374e7c6fe64f74b43209ed7f94029 SHA512: b611f965cfc5dd37e0127c50531e09141d2906f64b6c039179d9cbc7cd4cab7e767b342e66e7c05a80bf48a72ed997e8dfd806bb1478f121dfcd0380f3d28395 Homepage: https://cran.r-project.org/package=DiffNet Description: CRAN Package 'DiffNet' (Identifying Significant Node Scores using Network DiffusionAlgorithm) Designed for network analysis, leveraging the personalized PageRank algorithm to calculate node scores in a given graph. This innovative approach allows users to uncover the importance of nodes based on a customized perspective, making it particularly useful in fields like bioinformatics, social network analysis, and more. 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Example mechanisms include the Laplace mechanism for releasing numeric aggregates, and the exponential mechanism for releasing set elements. A sensitivity sampler (Rubinstein & Alda, 2017) permits sampling target non-private function sensitivity; combined with the generic mechanisms, it permits turn-key privatization of arbitrary programs. 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These models are able to make accurate estimates of the cell composition of bulk RNA-Seq samples from the same context using the advances provided by Deep Learning and the meaningful information provided by scRNA-Seq data. See Torroja and Sanchez-Cabo (2019) for more details. Package: r-cran-digitalpcr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-digitalpcr_1.1.0-1.ca2404.1_all.deb Size: 16764 MD5sum: 44578661697fa330dde1f9787ad25a75 SHA1: 2b163608e1934b31b11421644e406ff5aeead115 SHA256: a2801afe5066057a9e369d8ccbd5bdb7ac34aa76a1dc3ecf653ea618301b7767 SHA512: 71f06af666a53c2e0d4adf6842411210a680169a7e7bcecb3341e6a14e7613d0e0baeb1049743e1bcef8e75c7ec057a7d80f55f15017e411d59b1028e4609a96 Homepage: https://cran.r-project.org/package=digitalPCR Description: CRAN Package 'digitalPCR' (Estimate Copy Number for Digital PCR) The assay sensitivity is the minimum number of copies that the digital PCR assay can detect. Users provide serial dilution results in the format of counts of positive and total reaction wells. The output is the estimated assay sensitivity and the copy number per well in the initial dilute. Package: r-cran-digitize Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readbitmap Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-digitize_0.0.4-1.ca2404.1_all.deb Size: 23838 MD5sum: 5f6ba4725e63645c48ad1479c1a7d538 SHA1: 6a9e393ef01c60fde774061f478e75b75c7e2fb3 SHA256: 7369e68ed328c00171ef6cc9b2b84ff2d3b8e99281077fafdf98138b1c55e6a4 SHA512: 3b172f032ec3359d5cd1019a9b7544887cb9201f2b535d0d9e084d4e86bddd90120227cd103addbfd77d2e18d7e1661055ca5bca6dd899ae6e16150bb462e033 Homepage: https://cran.r-project.org/package=digitize Description: CRAN Package 'digitize' (Use Data from Published Plots in R) Import data from a digital image; it requires user input for calibration and to locate the data points. The end result is similar to 'DataThief' and other other programs that 'digitize' published plots or graphs. Package: r-cran-digittests Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-benford.analysis, r-cran-benfordtests, r-cran-beyondbenford, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-digittests_0.1.2-1.ca2404.1_all.deb Size: 86536 MD5sum: 8fb33715f03da3c54ab20a2606368095 SHA1: 3d70b0f3bac1fff207329028ef67bc35f2fb62e2 SHA256: c654e75d0f299c0f51ce35286fb89beb2aa24d86205594ef2093e7668353ecb9 SHA512: 73e692ece8069641d917a47528db34e8d1a9b10914d324f8a62c450505ad41ac78aa5de0a071af379c4ca3746a5a490b78270e099dfe621befdc4acec6cab7e6 Homepage: https://cran.r-project.org/package=digitTests Description: CRAN Package 'digitTests' (Tests for Detecting Irregular Digit Patterns) Provides statistical tests and support functions for detecting irregular digit patterns in numerical data. The package includes tools for extracting digits at various locations in a number, tests for repeated values, and (Bayesian) tests of digit distributions. Package: r-cran-digss Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-viridis, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-digss_1.0.2-1.ca2404.1_all.deb Size: 144294 MD5sum: ff5b2fa8a639fd4f4d94f0640d11a055 SHA1: 625bc11862d87b383c428e7b13fab05ad549b615 SHA256: 2475ea5c712e392c07899da703382b1393b787c49da487d4c70b298fa186b1a5 SHA512: 18bbfdf84ec165b5c7f5169d31a6692560683fd4275628ec31e93657e278829b5358f65b4f5c9092575c872ca382bb23abbe74a79159a1566b45040811986247 Homepage: https://cran.r-project.org/package=DIGSS Description: CRAN Package 'DIGSS' (Determination of Intervals Using Georeferenced Survey Simulation) Simulation tool to estimate the rate of success that surveys possessing user-specific characteristics have in identifying archaeological sites (or any groups of clouds of objects), given specific parameters of survey area, survey methods, and site properties. The survey approach used is largely based on the work of Kintigh (1988) . Package: r-cran-dilp Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-magrittr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-vegan Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dilp_1.1.0-1.ca2404.1_all.deb Size: 139750 MD5sum: 78d3a8aa7c4e3739b4cb1fab9fcc8f8e SHA1: fdcb960f47bd52f12ec0894a234d51874c00539c SHA256: 0d4c1b6e295697f04fdd9cbc11cd4061993bce13e7a94324e4b2acceb0145f9f SHA512: 8745e2506ea72a4efc09ebda752d3ccc6725f739dc493fc1c09f1bb3b873891b78b390a7fc183986a5ab9511c8adcf33285a9075dba8543b3d3ccee5b973b0af Homepage: https://cran.r-project.org/package=dilp Description: CRAN Package 'dilp' (Reconstruct Paleoclimate and Paleoecology with Leaf Physiognomy) Use leaf physiognomic methods to reconstruct mean annual temperature (MAT), mean annual precipitation (MAP), and leaf dry mass per area (Ma), along with other useful quantitative leaf traits. Methods in this package described in Lowe et al. (in review). Package: r-cran-dimensio Architecture: all Version: 0.14.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arkhe, r-cran-khroma Suggests: r-cran-fontquiver, r-cran-knitr, r-cran-markdown, r-cran-rsvg, r-cran-svglite, r-cran-tinysnapshot, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-dimensio_0.14.1-1.ca2404.1_all.deb Size: 1190886 MD5sum: bc130f5df2b6de35b8ecaad9ead2527b SHA1: e1d16f3f9e2f71e1a24c76c75429d11d3e35ee8d SHA256: 196fdce7e2ecada34f63064eed9c47551f8fbdc324f3fa871fbc6b12b00fe42b SHA512: ea6c5a3ee0a11957a4cdc1318581d8a2b9a713848526193aeaccdbfcfdaf2ede32569175ab3fea9a0e7e664e0a068bd0d92198273117d50aba0d6506cd2975eb Homepage: https://cran.r-project.org/package=dimensio Description: CRAN Package 'dimensio' (Multivariate Data Analysis) Simple Principal Components Analysis (PCA) and (Multiple) Correspondence Analysis (CA) based on the Singular Value Decomposition (SVD). This package provides S4 classes and methods to compute, extract, summarize and visualize results of multivariate data analysis. It also includes methods for partial bootstrap validation described in Greenacre (1984, ISBN: 978-0-12-299050-2) and Lebart et al. (2006, ISBN: 978-2-10-049616-7). Package: r-cran-dimensionsr Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-bibliometrix, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dimensionsr_0.0.3-1.ca2404.1_all.deb Size: 73184 MD5sum: 62188a6fbe0cec47d6de9bc237dae87e SHA1: 47b28949cf583e7abcb8f0ceb255eb4fab4c6fdf SHA256: ca6aaafe914c583c2eebd317cfe36d8629eeaeee3da6034d8c03a2d26d5a9678 SHA512: 5af33e67e496d515f6ba8edae8ebaa263019cf3f23936afefca14b76a9710c033f21a5d6fc6543b73f05f7d9982f2cea75ddcb35db9aec807a46105d75bf291a Homepage: https://cran.r-project.org/package=dimensionsR Description: CRAN Package 'dimensionsR' (Gathering Bibliographic Records from 'Digital ScienceDimensions' Using 'DSL' API) A set of tools to extract bibliographic content from 'Digital Science Dimensions' using 'DSL' API . Package: r-cran-dimodels Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-hnp, r-cran-rootsolve, r-cran-multcomp, r-cran-multcompview Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-dimodels_1.3.3-1.ca2404.1_all.deb Size: 457310 MD5sum: 64e74806bab8e9ee14a767fcc0b7abda SHA1: 8a7a15c6c2937956e290b208b4ac5a85fd33cc58 SHA256: cfee72635967058d61375d549d650ab04d798b59654bd368dcd83f6357f6cfa0 SHA512: 31b8bde0f10197a2f0a0b1943e1d18a580baf6b19743d6ce219a8578d7b1f1f1f21efbdd38581e75647b4b94581fdb59ea996715431a9d74afdd1933b7f54d10 Homepage: https://cran.r-project.org/package=DImodels Description: CRAN Package 'DImodels' (Diversity-Interactions (DI) Models) The 'DImodels' package is suitable for analysing data from biodiversity and ecosystem function studies using the Diversity-Interactions (DI) modelling approach introduced by Kirwan et al. (2009) . Suitable data will contain proportions for each species and a community-level response variable, and may also include additional factors, such as blocks or treatments. The package can perform data manipulation tasks, such as computing pairwise interactions (the DI_data() function), can perform an automated model selection process (the autoDI() function) and has the flexibility to fit a wide range of user-defined DI models (the DI() function). Package: r-cran-dimodelsmulti Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dimodels, r-cran-plyr, r-cran-dplyr, r-cran-nlme, r-cran-reshape2, r-cran-knitr, r-cran-matrix, r-cran-shiny Suggests: r-cran-ggplot2, r-cran-dimodelsvis, r-cran-cli, r-cran-fansi, r-cran-crayon, r-cran-rmarkdown, r-cran-shinydashboard, r-cran-mass, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dimodelsmulti_1.2.1-1.ca2404.1_all.deb Size: 820256 MD5sum: b3b588d31c4f032081d4e9cd008dc16e SHA1: 4ddcf807f49a4c13c86c95b4eeb1dff73630c98d SHA256: 3930f4864d65876cbfcd5133e9f36c4f88ae0fd5a95c9fe5e052334a9f1a31eb SHA512: c5dc86e4ca1b77231e8b2f8e2f73d26b2ce6a3839f2122580ec881bc41a96bef92772f705def17c29fd80cd4639aa8377689063f6747d8160cc93bee0148e909 Homepage: https://cran.r-project.org/package=DImodelsMulti Description: CRAN Package 'DImodelsMulti' (Fit Multivariate Diversity-Interactions Models with RepeatedMeasures) An add-on package to 'DImodels' for the fitting of biodiversity and ecosystem function relationship study data with multiple ecosystem function responses and/or time points. This package uses the multivariate and repeated measures Diversity-Interactions (DI) methods developed by Kirwan et al. (2009) , Finn et al. (2013) , and Dooley et al. (2015) . Package: r-cran-dimodelsvis Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1714 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-colorspace, r-cran-dimodels, r-cran-dplyr, r-cran-forcats, r-cran-ggfortify, r-cran-ggplot2, r-cran-ggtext, r-cran-glue, r-cran-insight, r-cran-metr, r-cran-pieglyph, r-cran-plotwidgets, r-cran-rlang, r-cran-scales, r-cran-tidyr Suggests: r-cran-dimodelsmulti, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-plotly, r-cran-randomforest, r-cran-cowplot, r-cran-nnet, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-dimodelsvis_1.0.4-1.ca2404.1_all.deb Size: 1225774 MD5sum: d868215d1dfc03e5530f46c54afc760e SHA1: 41ec92f00da23502e6f58a38475492e078908066 SHA256: fffe86056c504e2f69118654742a3c5a24c633313348820b7d98bb7950d43809 SHA512: c00a0e858c8ced8f68dc7d7071b76cfb8ca0e34a0f976da41092103c971c5b50870f42d9dd584e6ffba6438d3212a0380a277e5c5a10cb55eef02aaa47aeb98a Homepage: https://cran.r-project.org/package=DImodelsVis Description: CRAN Package 'DImodelsVis' (Visualising and Interpreting Statistical Models Fit toCompositional Data) Statistical models fit to compositional data are often difficult to interpret due to the sum to 1 constraint on data variables. 'DImodelsVis' provides novel visualisations tools to aid with the interpretation of models fit to compositional data. All visualisations in the package are created using the 'ggplot2' plotting framework and can be extended like every other 'ggplot' object. Package: r-cran-dimora Architecture: all Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-minpack.lm, r-cran-numderiv, r-cran-forecast, r-cran-reshape2, r-cran-desolve Filename: pool/dists/noble/main/r-cran-dimora_0.3.6-1.ca2404.1_all.deb Size: 119800 MD5sum: b6b779e5f3f97e65ffbaaf4a662ba075 SHA1: 10dd14aff7937264ff44728bd0172dd92e737d64 SHA256: 1d1f0fb7df3bc430f983c095c82a9fad9a42a221d404aa5acea978c31dfb1bd4 SHA512: 30f2884ee208da9f2f601f23396ec6f28a860e143f8d6b9b6d27ed3daaf0fcbbd42e49d98f04516c315eaa0a7cba532220a6bc5f8129bfdced0bd3b79231d525 Homepage: https://cran.r-project.org/package=DIMORA Description: CRAN Package 'DIMORA' (Diffusion Models R Analysis) The implemented methods are: Standard Bass model, Generalized Bass model (with rectangular shock, exponential shock, and mixed shock. You can choose to add from 1 to 3 shocks), Guseo-Guidolin model and Variable Potential Market model, and UCRCD model. The Bass model consists of a simple differential equation that describes the process of how new products get adopted in a population, the Generalized Bass model is a generalization of the Bass model in which there is a "carrier" function x(t) that allows to change the speed of time sliding. In some real processes the reachable potential of the resource available in a temporal instant may appear to be not constant over time, because of this we use Variable Potential Market model, in which the Guseo-Guidolin has a particular specification for the market function. The UCRCD model (Unbalanced Competition and Regime Change Diachronic) is a diffusion model used to capture the dynamics of the competitive or collaborative transition. Package: r-cran-dinamic.duo Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1389 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biomart, r-cran-dinamic, r-cran-plyr, r-cran-httr Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-dinamic.duo_1.0.4-1.ca2404.1_all.deb Size: 1341882 MD5sum: b16bb451d452d3507e669d0e8633774d SHA1: 56af4ca0eb3622543f139bb31f504ba566a21abb SHA256: a47d4efaeb2db00399d085af2284ae8feec2eb864705fb75d68b940f514bc07d SHA512: 53ff5397ba06704d57028fe1de8d9ba5e5c4e46e39eb00bac223ea0c0b0ce71334212327d73ab97522f9cbc6d1e6833f8b8bbd400d9ca33ccb88acff89177fc0 Homepage: https://cran.r-project.org/package=DiNAMIC.Duo Description: CRAN Package 'DiNAMIC.Duo' (Finding Recurrent DNA Copy Number Alterations and Differences) In tumor tissue, underlying genomic instability can lead to DNA copy number alterations, e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the genome, whereas recurrent alterations are observed in the same genomic region across multiple independent samples, perhaps because they provide a selective growth advantage. Here we use cyclic shift permutations to identify recurrent copy number alterations in a single cohort or recurrent copy number differences in two cohorts based on a common set of genomic markers. Additional functionality is provided to perform downstream analyses, including the creation of summary files and graphics. DiNAMIC.Duo builds upon the original DiNAMIC package of Walter et al. (2011) and leverages the theory developed in Walter et al. (2015) . An article describing DiNAMIC.Duo by Walter et al. (2022) can be found at . Package: r-cran-dinamic Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-dinamic_1.0.1-1.ca2404.1_all.deb Size: 136576 MD5sum: 6a5392c6d58058e0451dcebffc6891ca SHA1: 217339b29938e31f1a46ac9c9fdf561a66cda1a8 SHA256: aa5719fbb6c15aa70fa30797554e82627b38213164b48ea8e030a9a809915758 SHA512: 7411ff1e06c41853ce2f774d44260749aa97e6340d1885e6488ff598fab892d6e2a455c12697410fe9f343bc7e0fb24fa7a0eeed6069594ff5cc07e56fc6cd23 Homepage: https://cran.r-project.org/package=dinamic Description: CRAN Package 'dinamic' (A Method to Analyze Recurrent DNA Copy Number Aberrations inTumors) In tumor tissue, underlying genomic instability can lead to DNA copy number alterations, e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the genome, whereas recurrent alterations are observed in the same genomic region across multiple independent samples, perhaps because they provide a selective growth advantage. This package implements the DiNAMIC procedure for assessing the statistical significance of recurrent DNA copy number aberrations (Bioinformatics (2011) 27(5) 678 - 685). Package: r-cran-dineq Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-dineq_0.1.0-1.ca2404.1_all.deb Size: 216636 MD5sum: c10afbc64cfe9ad1b291a593201c331b SHA1: 225980cdb294719b6276e76bf0db7888fa92e9e9 SHA256: dd063f05ad1affe084298ac05eab7817372e0818522f3a32b55ea5ad26035cd9 SHA512: 708dd8f9d727a14ca2cb4ef87e863f75edf804c64b3e2e693b76ac613b1b54c714dd291b6e674e929bd9a56e3fa6fb73a38c8e186fb00d9a43608d4183071fff Homepage: https://cran.r-project.org/package=dineq Description: CRAN Package 'dineq' (Decomposition of (Income) Inequality) Decomposition of (income) inequality by population sub groups. For a decomposition on a single variable the mean log deviation can be used (see Mookherjee Shorrocks (1982) ). For a decomposition on multiple variables a regression based technique can be used (see Fields (2003) ). Recentered influence function regression for marginal effects of the (income or wealth) distribution (see Firpo et al. (2009) ). Some extensions to inequality functions to handle weights and/or missings. 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Hypothesis testing, discriminant and regression analysis, MLE of distributions and more are included. The standard textbook for such data is the "Directional Statistics" by Mardia, K. V. and Jupp, P. E. (2000). Other references include: a) Paine J.P., Preston S.P., Tsagris M. and Wood A.T.A. (2018). "An elliptically symmetric angular Gaussian distribution". Statistics and Computing 28(3): 689-697. . b) Tsagris M. and Alenazi A. (2019). "Comparison of discriminant analysis methods on the sphere". Communications in Statistics: Case Studies, Data Analysis and Applications 5(4):467--491. . c) Paine J.P., Preston S.P., Tsagris M. and Wood A.T.A. (2020). "Spherical regression models with general covariates and anisotropic errors". Statistics and Computing 30(1): 153--165. . d) Tsagris M. and Alenazi A. (2024). "An investigation of hypothesis testing procedures for circular and spherical mean vectors". Communications in Statistics-Simulation and Computation, 53(3): 1387--1408. . e) Yu Z. and Huang X. (2024). A new parameterization for elliptically symmetric angular Gaussian distributions of arbitrary dimension. Electronic Journal of Statistics, 18(1): 301--334. . f) Tsagris M. and Alzeley O. (2025). "Circular and spherical projected Cauchy distributions: A Novel Framework for Circular and Directional Data Modeling". Australian & New Zealand Journal of Statistics, 67(1): 77--103. . g) Tsagris M., Papastamoulis P. and Kato S. (2025). "Directional data analysis: spherical Cauchy or Poisson kernel-based distribution". Statistics and Computing, 35:51. . h) Alzeley O. and Tsagris (2026). On the generalized circular projected Cauchy distribution. . 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Utilise included pre-built models or specify custom models and allow the 'dirichletprocess' package to handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks for a variety of statistical models including and not limited to: density estimation, clustering and prior distributions in hierarchical models. See Teh, Y. W. (2011) , among many other sources. 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Compare it with the existing EM (Expectation-Maximization)-like methods. Then, distribute and process five methods and compare them, achieving good performance in convergence speed and result quality.The philosophy of the package is described in Guo G. (2022) . Package: r-cran-dirmult Architecture: all Version: 0.1.3-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dirmult_0.1.3-5-1.ca2404.1_all.deb Size: 74698 MD5sum: d9af64e575f11def01e33299d3c93de8 SHA1: dde2cf9eb5f43d64b5beb0e4f96d93d5fc7d71f5 SHA256: 239f7d55f489df5c007beac92d351a2f70f555d2e2ae2318c9125bb1769ac2ce SHA512: cd8f6c9e8a3ce4479fe7ec349dde8689f489bc9d15886ca1799ee450f89f3c5229c8807be50e1001b776171481e0bde56095d74004612f2522cab816d9aff03f Homepage: https://cran.r-project.org/package=dirmult Description: CRAN Package 'dirmult' (Estimation in Dirichlet-Multinomial Distribution) Estimate parameters in Dirichlet-Multinomial and compute log-likelihoods. Package: r-cran-dirttee Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-expectreg, r-cran-formula.tools, r-cran-mass, r-cran-matrix, r-cran-mgcv, r-cran-nloptr, r-cran-provenance, r-cran-rlang, r-cran-survival Filename: pool/dists/noble/main/r-cran-dirttee_1.0.2-1.ca2404.1_all.deb Size: 231284 MD5sum: ecfd91978327ac6156aee270627b45ae SHA1: 729f62adfd2f5fef39f9afb0fe74c3498f52e6b0 SHA256: 916a8a68245a8430fb99b19fc3e630ef1e9fa1707faa0e6d5c84b0fabcab5b0f SHA512: 0833e56a75b2c418b4b37f3b0c62ded588073606e8b425d174bf040bdcf5d2a5bbe10685b1322d311f1c5ef838c67c853dde7e35201599857e73c4f3bc7b04c7 Homepage: https://cran.r-project.org/package=dirttee Description: CRAN Package 'dirttee' (Distributional Regression for Time to Event Data) Semiparametric distributional regression methods (expectile, quantile and mode regression) for time-to-event variables with right-censoring; uses inverse probability of censoring weights or accelerated failure time models with auxiliary likelihoods. 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The aggregated sum of the resulting time series is strictly equal to the low-frequency time series within the benchmarking window. Typically, the low-frequency time series is an annual one, unknown for the last year, and the high frequency one is either quarterly or monthly. See "Methodology of quarterly national accounts", Insee Méthodes N°126, by Insee (2012, ISBN:978-2-11-068613-8, ). Package: r-cran-disaggregatets Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-matrix, r-cran-lars, r-cran-zoo, r-cran-withr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-readxl, r-cran-corrplot, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-disaggregatets_3.0.1-1.ca2404.1_all.deb Size: 149208 MD5sum: 4219843c0f5e046d770c7c8852673d77 SHA1: 5d2ca418d6714dcfa965388ee384959ec33b4968 SHA256: 90c3e74ed679e33482c798241cdc769aa156f12a4d2d9d8f8763118c02d46c38 SHA512: 1eb1f3623511cd38b3ec558381de74b533a8b03487a985eee428e0586549c5cce78ff8a00145a325ce4e81fbc54b18b32e5bd34739d6ec384a7b2706253eca6d Homepage: https://cran.r-project.org/package=DisaggregateTS Description: CRAN Package 'DisaggregateTS' (High-Dimensional Temporal Disaggregation) Provides tools for temporal disaggregation, including: (1) High-dimensional and low-dimensional series generation for simulation studies; (2) A toolkit for temporal disaggregation and benchmarking using low-dimensional indicator series as proposed by Dagum and Cholette (2006, ISBN:978-0-387-35439-2); (3) Novel techniques by Mosley, Gibberd, and Eckley (2022, ) for disaggregating low-frequency series in the presence of high-dimensional indicator matrices. Package: r-cran-disagmethod Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polynom, r-cran-ltsa, r-cran-zoo, r-cran-xts, r-cran-tsbox, r-cran-tswge Filename: pool/dists/noble/main/r-cran-disagmethod_0.1.1-1.ca2404.1_all.deb Size: 62082 MD5sum: 62abb7dd5ca328c61d44bf1367c9bb7b SHA1: 87a2e09da484879269a7604d637bf93cfbbda3f0 SHA256: 2874c7866ab8514e17f4d1dae233994cb3d3e4571fe35de04c6bf407baae7324 SHA512: b1e12d7142a8943408bb1c4d323b5faec668105f1b94f0c5b9e97936016e047a0e0d5f0a97fe64e07a6915c7ed04ce6fab25793c341bac84e13c5e3c3d34901d Homepage: https://cran.r-project.org/package=disagmethod Description: CRAN Package 'disagmethod' (Autoregressive Integrated Moving Average (ARIMA) BasedDisaggregation Methods) We have the code for disaggregation as found in Wei and Stram (1990, ), and Hodgess and Wei (1996, "Temporal Disaggregation of Time Series" in Statistical Science I, Nova Publishing). 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Although the calculation of AUC is standardized, there are differences in AUC based on some assumptions. For example, Myerson et al. (2001) assumed that (with delay discounting data) a researcher would impute an indifference point at zero delay equal to the value of the larger, later outcome. However, this practice is not clearly followed. This imputed zero-delay indifference point plays an important role in log and ordinal versions of AUC. Ordinal and log versions of AUC are described by Borges et al. (2016). The package can calculate all three versions of AUC [and includes a new version: IHS(AUC)], impute indifference points when x = 0, calculate ordinal AUC in the case of Halton sampling of x-values, and account for probability discounting AUC. 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Package: r-cran-discfrail Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-matrix, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-discfrail_0.2-1.ca2404.1_all.deb Size: 392074 MD5sum: 58c9b7959df79a627fbf3e8e7c374e27 SHA1: bb79b58fc854f1fd7f7e05ea63a27235eda0b4f3 SHA256: 5f56456d362b383ab0467c76e2f4a49947aa17e1fdcaf3a7520b0e9d08a6782a SHA512: 9002a9f80c17eed7069a89ad437dd1e9f7e85a61061f0e895ad20a2a69578e9ce62c83a6f5f7e24678d965388686055af4af394e917dded626ad2d4515fa1aff Homepage: https://cran.r-project.org/package=discfrail Description: CRAN Package 'discfrail' (Cox Models for Time-to-Event Data with Nonparametric DiscreteGroup-Specific Frailties) Functions for fitting Cox proportional hazards models for grouped time-to-event data, where the shared group-specific frailties have a discrete nonparametric distribution. The methods proposed in the package is described by Gasperoni, F., Ieva, F., Paganoni, A. M., Jackson, C. H., Sharples, L. (2018) . There are also functions for simulating from these models, with a nonparametric or a parametric baseline hazard function. Package: r-cran-discharge Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmom, r-cran-ggplot2, r-cran-circstats, r-cran-checkmate, r-cran-boot Filename: pool/dists/noble/main/r-cran-discharge_1.0.0-1.ca2404.1_all.deb Size: 276426 MD5sum: ef0be2a4c123f25704c72449ba63c360 SHA1: 6d1418fa944ca1f5bf4ecdf4608f1d94bb3dca3f SHA256: 2aa478fa6538e7ff3801ea3cad1d693e9897028591d924f12a7fa2e8e09d185c SHA512: c67642e4cfe787e43177819e0881b577cca5df8bfac83327804982f68cc8e5e35b46ea3446359c3d23087ca90f1cb72c6bbfa8f5ee682ed15ec4a4ab2ac3d242 Homepage: https://cran.r-project.org/package=discharge Description: CRAN Package 'discharge' (Fourier Analysis of Discharge Data) Computes discrete fast Fourier transform of river discharge data and the derived metrics. The methods are described in J. L. Sabo, D. M. Post (2008) and J. L. Sabo, A. Ruhi, G. W. Holtgrieve, V. Elliott, M. E. Arias, P. B. Ngor, T. A. Räsänsen, S. Nam (2017) . Package: r-cran-disclap Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-disclap_1.5.1-1.ca2404.1_all.deb Size: 23414 MD5sum: 3e63d6bc2c347a8891f202da0abdefb5 SHA1: 1b1d7d2434d115dfd11fd5c1532afc71fdad6f28 SHA256: 5c20cf4a7fe1c196d1136a398ec73b6f49d36c89fe24529fedd21538e620399b SHA512: cb3fe7d7b6f348a476fc24b80417cfd95c2db094250c3608028fa437d4f389d9d75c6e1efae60b0f567c944a09ff379952b6cd2a61ffa8372346c244d311df8a Homepage: https://cran.r-project.org/package=disclap Description: CRAN Package 'disclap' (Discrete Laplace Exponential Family) The discrete Laplace exponential family for use in fitting generalized linear models. Package: r-cran-disclosur Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-pdftools, r-cran-qdap, r-cran-sentimentanalysis, r-cran-stringi, r-cran-stringr, r-cran-syuzhet, r-cran-zoo, r-cran-snowballc, r-cran-tm, r-cran-rlang Filename: pool/dists/noble/main/r-cran-disclosur_0.6.0-1.ca2404.1_all.deb Size: 194214 MD5sum: 3520bb81445ba0805abc5b476b2e9a5f SHA1: d4f5d68f08ae10ff3891524c28c01ac08a834cca SHA256: f4f17f06f214f8de5c614bd30bb6244477fed80a056517b08e1cd1e991a1c053 SHA512: 32b2f2047eb55e2aa5a5e26012f7d96b28860f35f50872e610a9bba13538e90ce6980b3966a76e42a5168c2c8dcf495cdc7adcf89a1b8c59fa4d80a15a0f6492 Homepage: https://cran.r-project.org/package=disclosuR Description: CRAN Package 'disclosuR' (Text Conversion from Nexis Uni PDFs to R Data Frames) Transform 'newswire' and earnings call transcripts as PDF obtained from 'Nexis Uni' to R data frames. Various 'newswires' and 'FairDisclosure' earnings call formats are supported. Further, users can apply several pre-defined dictionaries on the data based on Graffin et al. (2016) and Gamache et al. (2015). Package: r-cran-discnorm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan, r-cran-arules, r-cran-sirt, r-cran-mass, r-cran-pbivnorm, r-cran-cubature, r-cran-copula, r-cran-mnormt, r-cran-gofkernel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-discnorm_0.2.1-1.ca2404.1_all.deb Size: 49436 MD5sum: b91b5428eb6a03a4a5ed29362a2dca16 SHA1: ed0f46233ea17e7abd5488ff574e57eebbb2237f SHA256: 6eb007cd124487b19d78cd177c3eebaf823c93422c25b47fb50139f748c7c1d7 SHA512: e701ae7fc277dbc2924787dded4fa2366751496bb7eac0a841384b820775c50d06315fac6dc0f432b064624df549bb97ca1f438dff22b222ab8f17328d648e62 Homepage: https://cran.r-project.org/package=discnorm Description: CRAN Package 'discnorm' (Test for Discretized Normality in Ordinal Data) Tests whether multivariate ordinal data may stem from discretizing a multivariate normal distribution. The test is described by Foldnes and Grønneberg (2019) . In addition, an adjusted polychoric correlation estimator is provided that takes marginal knowledge into account, as described by Grønneberg and Foldnes (2022) . Package: r-cran-discocvi Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fnn Suggests: r-cran-dbscan, r-cran-testthat Filename: pool/dists/noble/main/r-cran-discocvi_0.1.1-1.ca2404.1_all.deb Size: 105218 MD5sum: c9c5ba4ff4cbe125512fecd1a167aa8a SHA1: 3df34c9d7edc44fbad63a659fcb9a9081f52bcb2 SHA256: 6df1fe622fad318d242327995d56cc12f79ffcc41c709b55a262c9243ba01eb2 SHA512: 9d1078e73a5ae38bdfd24eea92765f9c4019bde2310825e691a44eef479150d7c7c7e53c3eb987ca12575b24c6af26380ed5ecf2b37158e6f60108e75a753ecc Homepage: https://cran.r-project.org/package=discoCVI Description: CRAN Package 'discoCVI' (Implementation of the DISCO Metric for Internal ClusteringEvaluation) Implementation of the DISCO (Density-based Internal Score for Clusterings with nOise) metric, a cluster validity index for evaluating density-based clustering results without ground truth labels. DISCO is the first index to explicitly assess the quality of noise point assignments in addition to cluster quality. It uses density-connectivity distance derived from a minimum spanning tree of the mutual-reachability graph, providing interpretable, bounded scores in [-1, 1]. Higher scores indicate better clustering. Based on Beer et al. (2025) . Package: r-cran-discord Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2418 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-nlsylinks, r-cran-ggpedigree, r-cran-bgmisc, r-cran-dt, r-cran-broom, r-cran-dplyr, r-cran-gridextra, r-cran-ggextra, r-cran-ggplot2, r-cran-janitor, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-scales, r-cran-sessioninfo, r-cran-stargazer, r-cran-snakecase, r-cran-testthat, r-cran-tidyverse, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-discord_1.3-1.ca2404.1_all.deb Size: 1608106 MD5sum: e63a8b03f6c65c7c8c1b9a5214e6af07 SHA1: 83da9e24c60824211fc48400967579b0cb794ba8 SHA256: 662fc45829ff97731577b01d1f5d9046e11de99b70dbbd334c3607ffd58bb316 SHA512: 339498bafbf3bb5079b5a4fcf91916198b62e339b35a19dcfefcba4a684855c53b3f15945c5967331d3b45d001048b62cea0dd94149cc0dff47634c5f0d93eea Homepage: https://cran.r-project.org/package=discord Description: CRAN Package 'discord' (Functions for Discordant Kinship Modeling) Functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Contains data restructuring functions and functions for generating biometrically informed data for kin pairs. See [Garrison and Rodgers, 2016 ], [Sims, Trattner, and Garrison, 2024 ] for empirical examples, and [Garrison and colleagues for theoretical work ]. 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In many settings where it is applicable, researchers want to identify causal effects of policy changes on a treated unit at an aggregate level while having access to data at a finer granularity. This package implements a simple extension of the synthetic controls estimator, developed in Gunsilius (2023) , that takes advantage of this additional structure and provides nonparametric estimates of the heterogeneity within the aggregate unit. The idea is to replicate the quantile function associated with the treated unit by a weighted average of quantile functions of the control units. The package contains tools for aggregating and plotting the resulting distributional estimates, as well as for carrying out inference on them. Package: r-cran-discoursegt Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggally, r-cran-ggplot2, r-cran-ggrepel, r-cran-igraph, r-cran-network Suggests: r-cran-biocmanager, r-cran-formatr, r-cran-ggpubr, r-cran-knitr, r-cran-markdown, r-cran-r.rsp, r-cran-rmarkdown, r-cran-rticles, r-cran-sna, r-cran-testthat Filename: pool/dists/noble/main/r-cran-discoursegt_1.2.0-1.ca2404.1_all.deb Size: 433604 MD5sum: 173609b015c744ce168f6a7460622240 SHA1: 56f58732a6c957da19f6b481886d58e355084ecd SHA256: 8bb3f318ac08be63307d6046e6c7b7c4b5afc87175f5f65fcadc832d2444cb54 SHA512: a0e15e22700aef6ea0428c3ec3d5dc47a623214d38ab0831e5e2a181d096bc585cd397111077150f9485648f1021a1d03530ae9744a9666428956df2c80cf5bd Homepage: https://cran.r-project.org/package=discourseGT Description: CRAN Package 'discourseGT' (Analyze Group Patterns using Graph Theory in EducationalSettings) Analyzes group patterns using discourse analysis data with graph theory mathematics. Takes the order of which individuals talk and converts it to a network edge and weight list. Returns the density, centrality, centralization, and subgroup information for each group. Based on the analytical framework laid out in Chai et al. (2019) . Package: r-cran-discover Architecture: all Version: 3.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-rlang, r-cran-golem, r-cran-shiny, r-cran-config, r-cran-plotly, r-cran-loader, r-cran-cluster, r-cran-ggplot2, r-cran-shinyjs, r-cran-shinyace, r-cran-ggdendro, r-cran-echarts4r, r-cran-htmltools, r-cran-factominer, r-cran-htmlwidgets, r-cran-colourpicker, r-cran-shinydashboard, r-cran-shinycustomloader, r-cran-shinydashboardplus Filename: pool/dists/noble/main/r-cran-discover_3.1.7-1.ca2404.1_all.deb Size: 441756 MD5sum: df599927a2491954d4ebd9937d127ca9 SHA1: d52a626d22948c57c95386d94717a3d28b21830d SHA256: bc7db276baa711ca26a33b2838f79da0e4c91baeb42340122d692e6cd018dba8 SHA512: efcf0bd28a96463d52abf00296689332daf5560c5c79564aa6fffc3fbaf661de4be574cd4c82a42cd562b4dded6d6968ac74dfc160afc6cf4669540e85a23822 Homepage: https://cran.r-project.org/package=discoveR Description: CRAN Package 'discoveR' (Exploratory Data Analysis System) Performs an exploratory data analysis through a 'shiny' interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method. 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The function looks for terms in the title and abstract that also exist in other fields and highlights these as needing attention. suggest_keywords() - this function takes a full text document and produces a list of unigrams, bigrams and trigrams (1-, 2- or 2-word phrases) present in the full text after removing stop words (words with a low utility in natural language processing) that do not occur in the title or abstract that may be suitable candidates for keywords. suggest_title() - this function takes a full text document and produces a list of the most frequently used unigrams, bigrams and trigrams after removing stop words that do not occur in the abstract or keywords that may be suitable candidates for title words. check_title() - this function carries out a number of sub tasks: 1) it compares the length (number of words) of the title with the mean length of titles in major bibliographic databases to assess whether the title is likely to be too short; 2) it assesses the proportion of stop words in the title to highlight titles with low utility in search engines that strip out stop words; 3) it compares the title with a given sample of record titles from an .ris import and calculates a similarity score based on phrase overlap. This highlights the level of uniqueness of the title. This version of the package also contains functions currently in a non-CRAN package called 'litsearchr' . Package: r-cran-discovr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8060 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-learnr, r-cran-ggplot2, r-cran-glue, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-discovr_1.0.0-1.ca2404.1_all.deb Size: 2482774 MD5sum: e50e529702d0e89f3688cedcdbd83ce8 SHA1: 98b9915ca9e2bc44fc6f7ded4866423edfea125b SHA256: 1e1a82a435799dcbfd72ca070f9c673c821d7d7f7233a3d9b15f7a0f2e687f7e SHA512: 7a959a15857d16666e5e8f5584a234e5c9825a06942e5bd15880e6e82ed74b2d4dfd409f7594194c4c9c9323bf07a056ec7bae7b19892d5704aa56c800706dea Homepage: https://cran.r-project.org/package=discovr Description: CRAN Package 'discovr' (Interactive Tutorials and Data for "Discovering Statistics UsingR and RStudio") Interactive 'R' tutorials and datasets for the textbook Field (2026), "Discovering Statistics Using R and RStudio", . Interactive tutorials cover general workflow in 'R' and 'RStudio', summarizing data, visualizing data, fitting models and bias, correlation, the general linear model (GLM), moderation, mediation, missing values, comparing means using the GLM (analysis of variance), comparing adjusted means (analysis of covariance), factorial designs, multilevel models, repeated measures designs, growth models, exploratory factor analysis (EFA), loglinear analysis, and logistic regression. There are no functions, only datasets and interactive tutorials. Package: r-cran-discretedatasets Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 957 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate Filename: pool/dists/noble/main/r-cran-discretedatasets_0.2.0-1.ca2404.1_all.deb Size: 861854 MD5sum: f42b1a7cab55905b998f2f7d4fd5e386 SHA1: 4521faeaef280f9eaa2659d5b2b073eb0ee5c21a SHA256: ee3801fa5a30f87a01d393719bfee257862c0108cd9e5cc75af611fe483d2d0b SHA512: bc3c8f1c6410a285e9fed11476e1e6d04d613e3e735884b9530ea1132d09f650112eebe427e7b981f1db7f8e234f483628b3848e07105b2b0886702ca56334f7 Homepage: https://cran.r-project.org/package=DiscreteDatasets Description: CRAN Package 'DiscreteDatasets' (Example Data Sets for Use with Discrete Statistical Tests) Provides several data sets for use with discrete statistical tests and discrete multiple testing procedures. 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Package: r-cran-discreteqvalue Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-coin, r-cran-exactranktests Filename: pool/dists/noble/main/r-cran-discreteqvalue_1.1-1.ca2404.1_all.deb Size: 36340 MD5sum: 6c83c5bb931f3470689f2ca719f92b63 SHA1: 83f9b69c7d1ee2be3297551a666828dc73e95b8a SHA256: 228580ed1820aa50a12110f7da4ae9ee655cf554ded6faa330d4df94bdd8c416 SHA512: e18f235cbf00f0f51bc707908446a85bf2de2521314fedb2635022194e9239a85ffb190d7d4743debc993ba2490f71a18e4e3766c9a8d628432321707ff53a0a Homepage: https://cran.r-project.org/package=DiscreteQvalue Description: CRAN Package 'DiscreteQvalue' (Improved q-Values for Discrete Uniform and Homogeneous Tests) We consider a multiple testing procedure used in many modern applications which is the q-value method proposed by Storey and Tibshirani (2003), . 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Package: r-cran-discretes Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-ellipsis, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-discretes_0.1.0-1.ca2404.1_all.deb Size: 271404 MD5sum: 76c244d9edfea945cd2780f04dcfec9a SHA1: 199965db60a8113a01670d3a412849dea721bbe5 SHA256: c5dc9717dbac96d0894d6faea99fd55253bcfc5d7e4d1bfbb570fda9266c81f3 SHA512: 29972d3ecbb74cfe6f61db943eb09cb05136997af86c325410a33e34c64e45e8b08d341192f42f636c2bdbeec2f4da58021f0f00003154c974b0b8eb82540672 Homepage: https://cran.r-project.org/package=discretes Description: CRAN Package 'discretes' (Discrete Numeric Series) Provides a framework for representing discrete numeric series (enumerable sets of numbers) that may be finite or infinite. 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Exact and approximate computation methods are provided. For exact p-values, several procedures of determining two-sided p-values are included, which are outlined in more detail in Hirji (2006) . 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Models include flavors of discriminant analysis, such as linear (Fisher (1936) ), regularized (Friedman (1989) ), and flexible (Hastie, Tibshirani, and Buja (1994) ), as well as naive Bayes classifiers (Hand and Yu (2007) ). 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Discursive sophistication captures the complexity of individual attitude expression by quantifying its relative size, range, and constraint. For more information on the measurement approach see: Kraft, Patrick W. 2023. "Women Also Know Stuff: Challenging the Gender Gap in Political Sophistication." American Political Science Review (forthcoming). 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These data are described and made available by Elith et al (2020) to compare species distribution modelling methods. 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This Package includes functions to discretize quantitative variables, calculate conditional probability for each pair of attribute values, distance between every pair of attribute values, significance of attributes, calculate dissimilarity between each pair of objects. 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California Community Colleges Chancellor's Office (2017). Percentage Point Gap Method. . California Community Colleges Chancellor's Office (2014). Guidelines for Measuring Disproportionate Impact in Equity Plans. . 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The package is a dependency for mvp-type packages that use the STL map class: it traps plausible idiom that is ill-defined (implementation-specific) and returns an informative error, rather than returning a possibly incorrect result. To cite the package in publications please use Hankin (2022) . 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Angeles Serrano, Marian Boguna and Alessandro Vespignani in "Extracting the multiscale backbone of complex weighted networks", Proceedings of the National Academy of Sciences 106 (16), 2009. This implementation of the algorithm supports both directed and undirected networks. 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Based on convex hull or integrated covariance Mahalanobis, several indicators are implemented for inter and intra batch dispersion analysis. It is designed to facilitate robust statistical assessment of data variability, supporting applications in exploratory data analysis and quality control, for such datasets as the one found in metabololomics studies. For more details see Salanon (2024) and Salanon (2025) . 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The second is a ridge Mahalanobis distance, which incorporates a ridge correction constant, alpha, to ensure that the covariance matrix is invertible. The third metric is the maximal data piling distance, which computes the orthogonal distance between the affine spaces spanned by each class. These three distances are asymptotically interconnected and are applicable in tasks such as discrimination, clustering, and outlier detection in high-dimensional settings. 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'disttools' can be used to extract the distance between any pair or combination of points encoded by a 'dist' object using only the indices of those points. This is an improvement over existing functionality, which requires either coercing a 'dist' object into a matrix or calculating the one dimensional index corresponding to a pair of observations. Coercion to a matrix is undesirable because doing so doubles the amount of memory required for storage. In contrast, there is no inherent downside to the latter solution. However, in part due to several edge cases, correctly and efficiently implementing such a solution can be challenging. 'disttools' abstracts away these challenges and provides a simple interface to access the data in a 'dist' object using the latter approach. 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Package: r-cran-ditwah Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-ditwah_1.0.1-1.ca2404.1_all.deb Size: 49322 MD5sum: bcdc2bba37fe76e7755845e660e60f40 SHA1: 0c8b85c80259817601448b5e474d23c49e8b0e45 SHA256: 797b0b96be6059970cc7f4e78b8197e75a03777d847c60f33051e1392a720f32 SHA512: 6c1a88ed3949b2037de3ff2bbc8127cc2f9be26c75d2912e1339363fa46ce48d62de0454ca339dcb2d9c28d69c918d7131d75b2130506e2a27c598b461f60e6a Homepage: https://cran.r-project.org/package=Ditwah Description: CRAN Package 'Ditwah' (Ditwah Storm Data and Tools for Storm Monitoring and EarlyWarning November 2025, Sri Lanka) The Ditwah storm began impacting Sri Lanka on 25 November 2025. Ditwah provides a collection of tidy, well-structured datasets to support storm data management, monitoring, and early warning applications in Sri Lanka. The publicly available data were converted to tidy data format for easy analysis. The package processes weather data, flood data and situation report data (families affected, etc.). The package also includes functions for analyzing river level progression and load dashboard visualizations to enhance situational awareness. This is also developed for educational purposes to support learning in data wrangling, visualization, and disaster analytics. Package: r-cran-ditwahlandslide Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-plotly, r-cran-stringr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-ditwahlandslide_1.2.0-1.ca2404.1_all.deb Size: 46520 MD5sum: b833dfaa196dd90edeb2bd1ef0a23af3 SHA1: 3b5b54541ea499515293ac9590f7525167171f06 SHA256: 0ba9a336c94b9894f2a8379ff8a32da9b4268ae422d0e3c66d8647cc587fdeef SHA512: 7ed5975b455ccc2b20a75374bcfe2d9e5a87af0f501be49f6b6914be978c0fdab1155d50626df778670cc318c9f8f5b1a15fa7a815c1cd369fb2a7226d858c97 Homepage: https://cran.r-project.org/package=ditwahLandslide Description: CRAN Package 'ditwahLandslide' (Early Warning Information on Landslides in Sri Lanka During theDitwah Storm) Provides curated early warning data on landslides in Sri Lanka during the Ditwah storm. 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It also provides methods to simulate the effect of bias, random team-data, etc. White paper: 'Philippe J.S. De Brouwer' (2021) . Book (chapter 36): 'Philippe J.S. De Brouwer' (2020, ISBN:978-1-119-63272-6) and 'Philippe J.S. De Brouwer' (2020) . Package: r-cran-dive Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-fme, r-cran-sp Filename: pool/dists/noble/main/r-cran-dive_1.3-1.ca2404.1_all.deb Size: 3226654 MD5sum: 6335140bf3284a2711c2233821b8452d SHA1: 397870cac6261e40f436f56b4f2d22a9ff461400 SHA256: 99b4034d0cae23e1e6f7886dc8aed87807725ffd0268f2ea5d204971c1867bb2 SHA512: c3014b9bf4dd41e8940400d01652aee6dac02426cf61c075f4e594c58b098a3036050de8dd1661df51b182b87b8a1f92b08128364482f41e20b46573072279b5 Homepage: https://cran.r-project.org/package=DivE Description: CRAN Package 'DivE' (Diversity Estimator) Contains functions for the 'DivE' estimator . The 'DivE' estimator is a heuristic approach to estimate the number of classes or the number of species (species richness) in a population. 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These tools work with and complement those of the 'tidyverse' suite, extending the grammar of 'ggplot2' to become a grammar of interactive graphics. The suite provides many visual tools designed for moderately (100s of variables) high dimensional data analysis, through 'zenplots' and novel tools in 'loon', and extends the 'ggplot2' grammar to provide parallel coordinates, Andrews plots, and arbitrary glyphs through 'ggmulti'. The 'diveR' package gathers together and installs all these related packages in a single step. Package: r-cran-diverge Architecture: all Version: 2.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-diverge_2.0.6-1.ca2404.1_all.deb Size: 377776 MD5sum: b755a98ff4de10329e71f2ace89daa5a SHA1: e9625fa8f7317aa9e05f4ecc4ecd98fb741a2843 SHA256: 9edb03a5d07f068b23d023e8c29e74cd9c946020dbefd818aa1917602c5e3ac4 SHA512: bdf71520a7ea5457993facd0a6ffd24f0dc62adf2333def3531ada28f4725f8ee31e7c100e8a97930d63e49f670a35eb1e5f87de64299cc8776a0545f2fbf568 Homepage: https://cran.r-project.org/package=diverge Description: CRAN Package 'diverge' (Evolutionary Trait Divergence Between Sister Species and OtherPaired Lineages) Compares the fit of alternative models of continuous trait differentiation between sister species and other paired lineages. Differences in trait means between two lineages arise as they diverge from a common ancestor, and alternative processes of evolutionary divergence are expected to leave unique signatures in the distribution of trait differentiation in datasets comprised of many lineage pairs. Models include approximations of divergent selection, drift, and stabilizing selection. A variety of model extensions facilitate the testing of process-to-pattern hypotheses. Users supply trait data and divergence times for each lineage pair. The fit of alternative models is compared in a likelihood framework. Package: r-cran-diverse Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proxy, r-cran-reshape2, r-cran-foreign Suggests: r-cran-pheatmap, r-cran-igraph, r-cran-entropart Filename: pool/dists/noble/main/r-cran-diverse_0.1.5-1.ca2404.1_all.deb Size: 76416 MD5sum: b587400ca4c8b8ddd6877431666307b7 SHA1: 47fa24878c46a0eaf62e68e6a40bc4ddfc27a0c8 SHA256: 4ad2dc680a0d9a66e7ee2e9163fc4e7d6fac6239d28ef58a4f1bf39a37684017 SHA512: 6d30c8ad336e0b0e33577fef37ba658bc2df057e063cdf035b4925397068f2beb0e11ffa2e507c014bd90256d8e60b264a73870dd89c453c4b7276ed721402e6 Homepage: https://cran.r-project.org/package=diverse Description: CRAN Package 'diverse' (Diversity Measures for Complex Systems) Computes the most common diversity measures used in social and other sciences, and includes new measures from interdisciplinary research. 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Insights into Hominin Occupation and Carcass Processing at Qesem cave". 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Method documented in dmmOverview.pdf; dmm is an implementation of dispersion mean model described by Searle et al. (1992) "Variance Components", Wiley, NY. Dmm() can do 'MINQUE', 'bias-corrected-ML', and 'REML' variance and covariance component estimates. 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Package: r-cran-dmrnet Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hclust1d, r-cran-glmnet, r-cran-grpreg Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-dmrnet_0.4.1-1.ca2404.1_all.deb Size: 280798 MD5sum: ca9b7dc239faebf63ee755f6e4e070fd SHA1: 96cd5de5f93e27fd224acc51f15c48cbcd3f5590 SHA256: e13a27cd2b7550d5ed033aae9fa1bd2ac134597133db8f8ecd6a314c4f9639f8 SHA512: 3bb20bb03eea2cfc53774a0a8ae35cc5a9f9716c9f63129e99f13e145da611c553cf492cec61eac17ca3b3f47bbe28ce6404d4e803b86c3c91a30bff2174d06f Homepage: https://cran.r-project.org/package=DMRnet Description: CRAN Package 'DMRnet' (Delete or Merge Regressors Algorithms for Linear and LogisticModel Selection and High-Dimensional Data) Model selection algorithms for regression and classification, where the predictors can be continuous or categorical and the number of regressors may exceed the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Szymon Nowakowski, Piotr Pokarowski, Wojciech Rejchel and Agnieszka Sołtys, 2023. Improving Group Lasso for High-Dimensional Categorical Data. In: Computational Science – ICCS 2023. Lecture Notes in Computer Science, vol 14074, p. 455-470. Springer, Cham. . Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka, 2015. Delete or merge regressors for linear model selection. Electronic Journal of Statistics 9(2): 1749-1778. . Piotr Pokarowski and Jan Mielniczuk, 2015. Combined l1 and greedy l0 penalized least squares for linear model selection. Journal of Machine Learning Research 16(29): 961-992. . Piotr Pokarowski, Wojciech Rejchel, Agnieszka Sołtys, Michał Frej and Jan Mielniczuk, 2022. Improving Lasso for model selection and prediction. Scandinavian Journal of Statistics, 49(2): 831–863. . Package: r-cran-dmtest Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1338 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-dmtest_1.0.0-1.ca2404.1_all.deb Size: 1314092 MD5sum: 70c85e6903562a1e8c9bdaf7331552f1 SHA1: 36b0dce53d11769a17c418e27c9d04fb595d7538 SHA256: 3e1bc0903b3766e7be95e2f1b05bf996f97070b00c0d0b6825945c5487b1d4ee SHA512: 2350f8a9343ab8cb6ceacac34218060d97c149c7e6e15dcffce49907a1e2d52215d6fcd43f6e64d30f77b613c31fe41d936d41ac839ca8cc43a959baa377b78c Homepage: https://cran.r-project.org/package=DMtest Description: CRAN Package 'DMtest' (Differential Methylation Tests (DMtest)) Several tests for differential methylation in methylation array data, including one-sided differential mean and variance test. 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It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535. Package: r-cran-dnnsim Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-dnnsim_0.1.1-1.ca2404.1_all.deb Size: 31176 MD5sum: 180da13edf230d77668dd8f9700cbd9e SHA1: 86e20ee8307c672479aae30dcf5a6591cf98b402 SHA256: 864adfaea7197a91557079d42395ebeb1e1eb739351aeea52997276bd5f76f20 SHA512: 4173b5c31b0f005e5b37da2abee207d4fd1b88aa7961aa5d9caac442857a6a4be692cfb5134ad42d0215a8d6fdd49a95049e6f63b022a46a9d9979e2e11df35a Homepage: https://cran.r-project.org/package=DNNSIM Description: CRAN Package 'DNNSIM' (Single-Index Neural Network for Skewed Heavy-Tailed Data) Provides a deep neural network model with a monotonic increasing single index function tailored for periodontal disease studies. 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The models are build on top of exponential random graph models (ERGM) framework. There are functions for simulating or forecasting networks for future time points. Abhirup Mallik & Zack W. Almquist (2019) Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models, Journal of Computational and Graphical Statistics, 28:4, 967-979, . Package: r-cran-do3pca Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdimtools, r-cran-ape, r-cran-phytools, r-cran-matrixcalc, r-cran-mclust, r-cran-nloptr, r-cran-ratematrix Filename: pool/dists/noble/main/r-cran-do3pca_1.0.0-1.ca2404.1_all.deb Size: 54518 MD5sum: 2dcbb503bcc913d26b1187673ca17c06 SHA1: 9464de2f071929d65f91304287030cd95a528dab SHA256: 8998d29c554d1ab4b36b6058dc1b5ca871f5b11afa3d7b2f73980b1d0a4e5096 SHA512: e8a8b9bc174cb18d20dc4c7b7e273d2aac69b336ee67c6448e90c7f421470798810543911f58c158792814c59b5cebe1d14a4022ffa0d77a38bf74af125b8904 Homepage: https://cran.r-project.org/package=do3PCA Description: CRAN Package 'do3PCA' (Probabilistic Phylogenetic Principal Component Analysis) Estimates probabilistic phylogenetic Principal Component Analysis (PCA) and non-phylogenetic probabilistic PCA. 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Package: r-cran-dobin Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 739 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbscan, r-cran-ggplot2, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-outlierso3, r-cran-fnn Filename: pool/dists/noble/main/r-cran-dobin_1.0.4-1.ca2404.1_all.deb Size: 558812 MD5sum: c4a1e977f9cd16719d943b986bdab553 SHA1: 21cdb5e4bb0d28cb22f36bfd354c4ecfd8b835fb SHA256: afb2da43dafd25a365ea5fe8388ccdfd445e75a57b69254346ec2da65f0bfdc8 SHA512: c7e82648631f7d351383183af42e383e1d32dfff25b62ba0db2ead43846924d175d8e58fdb7879549a518fa6887c212a1203bff02c64bd6a00b60ce105c78fd6 Homepage: https://cran.r-project.org/package=dobin Description: CRAN Package 'dobin' (Dimension Reduction for Outlier Detection) A dimension reduction technique for outlier detection. DOBIN: a Distance based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier detection. 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The package implements clustering of barcode lineage trajectories, based on the assumption that similar temporal dynamics indicate comparable relative fitness. It also identifies persistent clonal lineages across time points. Input data can include lineage frequency tables derived from chromosomal barcoding, mutational libraries, or CRISPR/Cas screens. For more details, see Gagné-Leroux et al. (2024) . Package: r-cran-dobson Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dobson_0.4-1.ca2404.1_all.deb Size: 93474 MD5sum: 7814c95930ace2c50a3232d576e1e5c5 SHA1: 827ea96c9839eaeab1f4a4e0359c662b3a0e42aa SHA256: e95b1210e7543d79750800f52cd2e24cab30ca7c6fc290a32733e8e2ad8b3790 SHA512: 2c807da0accdb0a77c3b8de2839f309cc79898622af243cf3071cc0bb9857b5bb00a25d330eba966df93f09e20576bc04c5d507e063d871f9f481de611e23be2 Homepage: https://cran.r-project.org/package=dobson Description: CRAN Package 'dobson' (Data from the GLM Book by Dobson and Barnett) Example datasets from the book "An Introduction to Generalised Linear Models" (Year: 2018, ISBN:9781138741515) by Dobson and Barnett. 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This package is built in support of Yeomans (2021) , which reviews linguistic models of concreteness in several domains. Here, we provide an implementation of the best-performing domain-general model (from Brysbaert et al., (2014) ) as well as two pre-trained models for the feedback and plan-making domains. 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Package: r-cran-doebioresearch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-agricolae Filename: pool/dists/noble/main/r-cran-doebioresearch_0.1.0-1.ca2404.1_all.deb Size: 102756 MD5sum: 329355952bf39761c37e4580326794c8 SHA1: dd0dfa0289abe1e42b14e28dee13171b923a5e3d SHA256: de4827909ca63eab56e2ee59c6dc03f6c7714e3e1d9816dfa0a8511dd4256d88 SHA512: a17c4ee701912f10665678faf895c8bc766655f98335941a448194b626a7721ae985e45f1781ee5dcbf65e0973b4a294b9b374e6d98027e9d489691cef83a287 Homepage: https://cran.r-project.org/package=doebioresearch Description: CRAN Package 'doebioresearch' (Analysis of Design of Experiments for Biological Research) Performs analysis of popular experimental designs used in the field of biological research. The designs covered are completely randomized design, randomized complete block design, factorial completely randomized design, factorial randomized complete block design, split plot design, strip plot design and latin square design. The analysis include analysis of variance, coefficient of determination, normality test of residuals, standard error of mean, standard error of difference and multiple comparison test of means. The package has functions for transformation of data and yield data conversion. Some datasets are also added in order to facilitate examples. Package: r-cran-doem Architecture: all Version: 0.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1349 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-doem_0.0.0.1-1.ca2404.1_all.deb Size: 1342684 MD5sum: 5fddd87b045178b83639e6d93397573f SHA1: 4ca9dbf8cd9e6d0107d30a178c4e1fe54202d5b7 SHA256: c1e9468c87b5605b1636720c7468065bcb4c3f06ba95d562b90c30f01e173d7a SHA512: 09e241f82981601bff7b318409b869cd0ef42d3ca8c0c97960c3ee61232066c09d892b73b287096e446ffad1e9b2f19bd00c8c606c2c891eaf8d9d5def14e38b Homepage: https://cran.r-project.org/package=DOEM Description: CRAN Package 'DOEM' (The Distributed Online Expectation Maximization Algorithms toSolve Parameters of Poisson Mixture Models) The distributed online expectation maximization algorithms are used to solve parameters of Poisson mixture models. The philosophy of the package is described in Guo, G. (2022) . Package: r-cran-doex Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-doex_1.2-1.ca2404.1_all.deb Size: 109200 MD5sum: 4d365195e71865e419c81b4c6e597628 SHA1: 90b50c469ac611caf6c23fc2837d9357f36cbe0d SHA256: 828f5b0e52156586b395ccacefbb83c76932b0c027eb6a09e424f15bf4ed232a SHA512: 0804f6d11d9c6a26084ef3524b25a0485bf6f6ab74b4cc4260ac8ea2ad50510894b59702448fc4d2ad7d370d3d7f28c62dba53c2d051cae475f6b0c5e61a5a9e Homepage: https://cran.r-project.org/package=doex Description: CRAN Package 'doex' (The One-Way Heteroscedastic ANOVA Tests) Contains the heteroscedastic ANOVA tests for normal and two-parameter exponential distributed populations. For normal distributions, Alexander-Govern test by Alexandern and Govern (1994) , Alvandi et al. Generalized F test by Alvandi et al. (2012) , Approximate F test by Asiribo and Gurland (1990) , Box F test by Box (1954) , Brown-Forsythe test by Brown and Forsythe (1974) , B2 test by Ozdemir and Kurt (2006) , Cochran F test by Cochran (1937) , Fiducial Approach test by Li et al. (2011) , Generalized F test by Weerahandi (1995) , Johansen F test by Johansen (1980) , Modified Brown-Forsythe test by Mehrotra (1997) , Modified Welch test by Hartung et al.(2002) , One-Stage test by Chen and Chen (1998) , One-Stage Range test by Chen and Chen (2000) , Parametric Bootstrap test by Krishnamoorhty et al.(2007) , Permutation F test by Berry and Mielke (2002) , Scott-Smith test by Scott and Smith (1971) , Welch test by Welch(1951) , and Welch-Aspin test by Aspin (1948) . These tests are used to test the equality of group means under unequal variance. Also, a modified version of Generalized F-test is improved to test the equality of non-normal group means under unequal variances and a revised version of Generalized F-test is given to test the equality of non-normal group means caused by skewness. Furthermore, it consists some procedures for testing equality of several two-parameter exponentially distributed population means under unequal scale parameters such as generalized p-value, parametric bootstrap and fiducial approach test by Malekzadeh and Jafari (2019) . There is also Hsieh test by Hsieh (1986) for testing equality of location parameters of two-parameter exponentially distributed populations under unequal scale parameters. Package: r-cran-dofuture Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-foreach, r-cran-future, r-cran-future.apply, r-cran-globals, r-cran-iterators Suggests: r-cran-dorng, r-cran-markdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-dofuture_1.2.2-1.ca2404.1_all.deb Size: 175672 MD5sum: e4da5d78550dfdc3794535b752d0909b SHA1: 7f15e4c404bbdcf161e253d5f7c92647c0bfb999 SHA256: 0fa8969b4e2c29453fccf95c847bad62c221b32c5ce2d8bef9a8bb1ced4c4898 SHA512: c7003b132e762827e3112b917a9a0439a0e9814afa8c1d2b7ac319533041cd08d017ac5a16d03f51219d2590e572459a963eadbb246f3fca714d7b3478996d6a Homepage: https://cran.r-project.org/package=doFuture Description: CRAN Package 'doFuture' (Use Foreach to Parallelize via the Future Framework) The 'future' package provides a unifying parallelization framework for R that supports many parallel and distributed backends . 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Package: r-cran-dogesr Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-knitr, r-cran-igraph, r-cran-qpdf, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-devtools, r-cran-tidyr, r-cran-networkd3, r-cran-tibble Filename: pool/dists/noble/main/r-cran-dogesr_0.5.2-1.ca2404.1_all.deb Size: 273428 MD5sum: 024b8e9452f7a009269ded7360accae6 SHA1: e889ee63780c468e419c7b5f2140f9f1643a0fc1 SHA256: 46785f526bb9fbc88e2c280fee54e7d42a1fcf2be39f7ffe57dbb752876f39c2 SHA512: 136b4004b5cc4950b1c4855eb1d6fd9a4b59b1d89d1c783cb0017cab962383e04c3f4b0ed5b59b626709b72fa6cf2cd85acd28e03b32cbcc9278b69362dd6059 Homepage: https://cran.r-project.org/package=dogesr Description: CRAN Package 'dogesr' (Work with the Doges/Dogaresse Dataset) Work with data on Venetian doges and dogaresse and the noble families of the Republic of Venice, and use it for social network analysis, as used in Merelo (2022) . 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Package: r-cran-dola Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3871 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-stringr, r-cran-knitr, r-cran-dplyr, r-cran-reshape2, r-cran-openxlsx, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dola_0.1.0-1.ca2404.1_all.deb Size: 3598014 MD5sum: be15797136e7f0786e622a0f301d118d SHA1: 62abe9903c7ee86c9a9fa6cc8bc6b4f761d9d8bc SHA256: 6b48bb01a22c42cf3fe9376b77507c2653a53ace6a3e0d0be792fcc429f71d81 SHA512: 91280ebc18b8a9ae73324fd16dc3743dcf3eca4af40cd85551855ef6e130b3c59dd035b9fc7c18be26408610923fa58642c2b4dc7e6f8df023b655cab6a4debb Homepage: https://cran.r-project.org/package=DoLa Description: CRAN Package 'DoLa' (Do Currículo Lattes Para o Programa de Pós-Graduação) Managing postgraduate programmes involves extracting information from Lattes CVs. 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Package: r-cran-domc Architecture: all Version: 1.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-iterators Filename: pool/dists/noble/main/r-cran-domc_1.3.8-1.ca2404.1_all.deb Size: 166660 MD5sum: 421d9fba309aed48ce4982b2219cd76b SHA1: 02d72da6f3478002513d29ab3febdee1d0620d6c SHA256: 17540420be10b0c7885f2f07d36e630725940ec19dfc82fd39e9fd13603b3735 SHA512: 1d582e0e5378081bdad9e9a1ddf29aa362fb7810d02e801423766e88e3d58ea5401f2864f23a4790cc7c2fe6b378baed03cf2e917eef18413cdec2bb459d18b1 Homepage: https://cran.r-project.org/package=doMC Description: CRAN Package 'doMC' (Foreach Parallel Adaptor for 'parallel') Provides a parallel backend for the %dopar% function using the multicore functionality of the parallel package. Package: r-cran-domean Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-domean_0.1-1.ca2404.1_all.deb Size: 41648 MD5sum: c595b83cdec51713f7028c3d5a5f0d43 SHA1: 060f2b494a02190b5c8c9cf6252683f8a11d98a5 SHA256: e1550aa1c2d72ed08ab331f91474af759b81ea5bd5e35a467bb44b8e0014c9ab SHA512: 047625e6863c846d7e3ff9c4436603a6f2469ce468a525075a321f39bc1cda93aad3677a81116f4316fc37ca853f36410ecdb07db12777f6ea1e4380b0a15515 Homepage: https://cran.r-project.org/package=Domean Description: CRAN Package 'Domean' (Distributed Online Mean Tests) Distributed Online Mean Tests is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform mean tests in an online, distributed manner, making it highly suitable for large-scale data analysis. 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Package: r-cran-dominanceanalysis Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-lme4, r-cran-boot, r-cran-testthat, r-cran-car, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-pscl, r-cran-dynlm, r-cran-reshape2, r-cran-betareg, r-cran-performance Filename: pool/dists/noble/main/r-cran-dominanceanalysis_2.1.1-1.ca2404.1_all.deb Size: 327342 MD5sum: 26385f74760970c4e50301f154cd17f1 SHA1: ee1e59746a06a4a3a1bb3ba33ad07959a6f446e6 SHA256: 89b3addb7af8d5ac683d50d01a3787d6a15990a64bed855ffafaebad239eb25e SHA512: 09e612b6ef3db027665437f23c5b17409d7c885fcb37fa6da2586e6b639329f240e2107cd014450b80d1fc6d8ecca1b2e22a360ba997309fd5de5bf66bb123df Homepage: https://cran.r-project.org/package=dominanceanalysis Description: CRAN Package 'dominanceanalysis' (Dominance Analysis) Dominance analysis is a method that allows to compare the relative importance of predictors in multiple regression models: ordinary least squares, generalized linear models, hierarchical linear models, beta regression and dynamic linear models. The main principles and methods of dominance analysis are described in Budescu, D. V. (1993) and Azen, R., & Budescu, D. V. (2003) for ordinary least squares regression. Subsequently, the extensions for multivariate regression, logistic regression and hierarchical linear models were described in Azen, R., & Budescu, D. V. (2006) , Azen, R., & Traxel, N. (2009) and Luo, W., & Azen, R. (2013) , respectively. Package: r-cran-domino Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-domino_0.3.1-1.ca2404.1_all.deb Size: 46090 MD5sum: 505c37221c069249dd68c08e684b61c6 SHA1: 34e3141f86702c3f86a8afea062c2abef1782919 SHA256: 479a6f0ae9c5e26ce9aa20dd72dbdb1e4b8d6b9621897356c660cf3cbdeab495 SHA512: 44696e6d6cbfae7c645c4f70532e59b01e762f780a389e19fb049a6ef9cfe90cd94ea73265d8327fc08398081154077c729d8e312fdb010713a4e2536cf9bb8d Homepage: https://cran.r-project.org/package=domino Description: CRAN Package 'domino' (R Console Bindings for the 'Domino Command-Line Client') A wrapper on top of the 'Domino Command-Line Client'. It lets you run 'Domino' commands (e.g., "run", "upload", "download") directly from your R environment. 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Package: r-cran-domir Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1190 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-dominanceanalysis, r-cran-forcats, r-cran-formula, r-cran-ggplot2, r-cran-knitr, r-cran-lme4, r-cran-parameters, r-cran-performance, r-cran-pscl, r-cran-purrr, r-cran-relaimpo, r-cran-rlang, r-cran-rmarkdown, r-cran-stringr, r-cran-systemfit, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-domir_1.3.0-1.ca2404.1_all.deb Size: 1057574 MD5sum: 44e0dfcddc3f2a45d1deb403988924bd SHA1: 47973dcc102be2167e8952b1fe2f958fbbc2b027 SHA256: 29ba075db454d10e27fc5406f0a895bc0ff16962bd3906692ce4583c6edd2e0a SHA512: 7e1c9e31bf1c013538e52dccd218b9d2233ce2da4db055a2a512fc169e3b640f73bdbd0c7cbece39913c812053b03f708cadd1e60aca1b7de8ad9febaee17d59 Homepage: https://cran.r-project.org/package=domir Description: CRAN Package 'domir' (Dominance Analysis Methods) Dominance analysis relative importance methods that are intended for predictive models. 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Leverage layouts to distribute labels effectively. Connect labels to donut segments using pins. Streamline annotation and highlighting. Package: r-cran-doofa Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-combinat Filename: pool/dists/noble/main/r-cran-doofa_1.0-1.ca2404.1_all.deb Size: 41804 MD5sum: dc9339930e93055fbd884fd9ba84e27c SHA1: d279b9b13c8b326d9d42df5ba62ded95b4fd9f12 SHA256: 0f5510bda4d9b48bae20aedbd8141bc84ac9d46c1ad201dee89b49dd676b37ba SHA512: 68ac561740354f17096e394a7efc4c897ad2b99e41b18f660126b3e4ebf4c2ef023eb60cf592253793937983ebbea648dcf06e8d26b5ba5790880cd69b8f7c77 Homepage: https://cran.r-project.org/package=doofa Description: CRAN Package 'doofa' (Designs for Order-of-Addition Experiments) A facility to generate efficient designs for order-of-additions experiments under pair-wise-order model, see Dennis K. J. Lin and Jiayu Peng (2019)."Order-of-addition experiments: A review and some new thoughts". Quality Engineering, 31:1, 49-59, . It also provides a facility to generate component orthogonal arrays under component position model, see Jian-Feng Yang, Fasheng Sun & Hongquan Xu (2020): "A Component Position Model, Analysis and Design for Order-of-Addition Experiments". Technometrics, . 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For our earlier work, we refer to Arntzen et al. (2023) . A paper describing our approach in detail will follow. Package: r-cran-doubt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-unglue Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-doubt_0.1.0-1.ca2404.1_all.deb Size: 55044 MD5sum: b7a871eb56947dd229892fe454407ed1 SHA1: 3ed343c32c468f7fc3b72526220f29589e435a64 SHA256: 78353a18cb6ab99f01533e43502ef797994cd091e0238a16c14316b9723f7d33 SHA512: 6ff828c16b2dca213fb908b48fe113d333d9e13663aa5e383ffa3f852b9bbd016bbdbd29a2cb3e543c0806e788c6b7a459960ef5af3d2ef2173c7b609423a521 Homepage: https://cran.r-project.org/package=doubt Description: CRAN Package 'doubt' (Enable Operators Containing the '?' 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Throughout the two step algorithm of ter Braak et al. (2018) is used. This algorithm combines and extends community- (sample-) and species-level analyses, i.e. the usual community weighted means (CWM)-based regression analysis and the species-level analysis of species-niche centroids (SNC)-based regression analysis. The two steps use canonical correspondence analysis to regress the abundance data on to the traits and (weighted) redundancy analysis to regress the CWM of the orthonormalized traits on to the environmental predictors. The function dc_CA() has an option to divide the abundance data of a site by the site total, giving equal site weights. This division has the advantage that the multivariate analysis corresponds with an unweighted (multi-trait) community-level analysis, instead of being weighted. The first step of the algorithm uses vegan::cca(). The second step uses wrda() but vegan::rda() if the site weights are equal. This version has a predict() function. 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Package: r-cran-dqa Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dqa_0.1.1-1.ca2404.1_all.deb Size: 157542 MD5sum: 9deac530790fefed74536b2fc3363e30 SHA1: 44242a1cfd5a6f1df9ffa341101eb95bc6b2d643 SHA256: a49b9ba1801ae64cf80e314b00e8bc897df633d53b50cfc8950027b5c960865e SHA512: d1b7bf345f643611f6f271344797031ae91f47df35844b3c220498deea9a262fc2260216de0b1695a558cf8a9d9ead431dfdc25d260dbf4baffc961c7cf9dfc2 Homepage: https://cran.r-project.org/package=DQA Description: CRAN Package 'DQA' (Data Quality Assessment Tools) In the context of data quality assessment, this package provides a number of functions for evaluating data quality across various dimensions, including completeness, plausibility, concordance, conformance, currency, timeliness, and correctness. It has been developed based on two well-known frameworks—Michael G. Kahn (2016) and Nicole G. Weiskopf (2017) —for data quality assessment. Using this package, users can evaluate the quality of their datasets, provided that corresponding metadata are available. 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Publication: Mang et al. (2021) . 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Publication: Kapsner et al. (2021) . Package: r-cran-dqcheckr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-readr, r-cran-dbi, r-cran-rsqlite, r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra, r-cran-ggplot2, r-cran-gridextra, r-cran-dplyr, r-cran-tidyr, r-cran-yaml, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dqcheckr_0.1.2-1.ca2404.1_all.deb Size: 168266 MD5sum: 0f69ce5bfbead31fca1041afb3e95dc6 SHA1: a5f0e74341e1d431e82f2df484a29e590d421bfb SHA256: 9dc98b5c1ad84c72800c73a2e30f010864a2b3d6ed106119c68c53e89c203279 SHA512: fd33b1120a459271c49058f4f5c661cd1bc1e667637515556afe84ddc034209c3fa97809ce2ccd1b770268c0c3b7ae6f1fbf2c77e72a38cdd77c91c2979b338c Homepage: https://cran.r-project.org/package=dqcheckr Description: CRAN Package 'dqcheckr' (Automated Data Quality Checks for Recurring Dataset Deliveries) Automates quality verification of recurring external dataset deliveries. For each new file arrival, it runs single-snapshot quality checks, compares the file to the previous delivery, writes a self-contained 'HTML' report, and records summary statistics in a local 'SQLite' database for long-term trend tracking. Supports 'CSV' and fixed-width formats. Custom organisation-specific checks can be supplied as plain R files. Package: r-cran-dqtg.seq Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bb, r-cran-data.table, r-cran-doparallel, r-cran-openxlsx, r-cran-qtl, r-cran-stringr, r-cran-writexl, r-cran-vroom, r-cran-foreach Filename: pool/dists/noble/main/r-cran-dqtg.seq_1.0.2-1.ca2404.1_all.deb Size: 949766 MD5sum: 28bd8fde27443cf7ba92b2ec7c9d718c SHA1: ce530b383d674bc71e19600f78e80a4bcffcf811 SHA256: 210d5f2f8580970726c7e70fe4ec7391e4164ebc08f3f3067ae3be099b47af7f SHA512: 5810fb0c15a0232a40504b76689454cc888180407ed474c7a868df7bb2b89eaa1cc1dde354c6a1888b20660729e1b26dfce4b65f7b258e43ca8d46dfe2499a85 Homepage: https://cran.r-project.org/package=dQTG.seq Description: CRAN Package 'dQTG.seq' (A BSA Software for Detecting All Types of QTLs in BC, DH, RILand F2) The new (dQTG.seq1 and dQTG.seq2) and existing (SmoothLOD, G', deltaSNP and ED) bulked segregant analysis methods are used to identify various types of quantitative trait loci for complex traits via extreme phenotype individuals in bi-parental segregation populations (F2, backcross, doubled haploid and recombinant inbred line). The numbers of marker alleles in extreme low and high pools are used in existing methods to identify trait-related genes, while the numbers of marker alleles and genotypes in extreme low and high pools are used in the new methods to construct a new statistic Gw for identifying trait-related genes. dQTG-seq2 is feasible to identify extremely over-dominant and small-effect genes in F2. Li P, Li G, Zhang YW, Zuo JF, Liu JY, Zhang YM (2022, ). Package: r-cran-dr4pl Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 905 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-tensor, r-cran-rdpack, r-cran-generics, r-cran-rlang, r-cran-glue Suggests: r-cran-drc, r-cran-devtools, r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-dr4pl_2.0.0-1.ca2404.1_all.deb Size: 593006 MD5sum: 7622730678ab1e0531e97be34ff28f97 SHA1: d6be71b2b26141f898487bc0f32557e26da8d7ce SHA256: 495bd12c300fbcaef5121437650db1d3685ce9f0fb01d34104d1df764616435b SHA512: be2753112258cf1fcdcf4393e7b4e7b5a48484c3458d7d10e610695cc24438c82ae3d95c317a1ffcb4814f5fd19164b1d653cd18187695391408b24558547e71 Homepage: https://cran.r-project.org/package=dr4pl Description: CRAN Package 'dr4pl' (Dose Response Data Analysis using the 4 Parameter Logistic (4pl)Model) Models the relationship between dose levels and responses in a pharmacological experiment using the 4 Parameter Logistic model. Traditional packages on dose-response modelling such as 'drc' and 'nplr' often draw errors due to convergence failure especially when data have outliers or non-logistic shapes. This package provides robust estimation methods that are less affected by outliers and other initialization methods that work well for data lacking logistic shapes. We provide the bounds on the parameters of the 4PL model that prevent parameter estimates from diverging or converging to zero and base their justification in a statistical principle. These methods are used as remedies to convergence failure problems. Gadagkar, S. R. and Call, G. B. (2015) Ritz, C. and Baty, F. and Streibig, J. C. and Gerhard, D. (2015) . Package: r-cran-dr Architecture: all Version: 3.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-dr_3.0.11-1.ca2404.1_all.deb Size: 440182 MD5sum: 01b7edd157035cadb56095e88e13110e SHA1: f6abc0060ce971be62047dc7cf5198db964cb3cb SHA256: 4117e49219c766deb3829c8e47dd51851ddac50be0247daca4c7b6c9db0718c5 SHA512: 2bfc1f6af601d14cd7e8a2f6a05442b5bcf132a4e5a5920738b0a01e1a09168d81e825c7af7a1ab60b381652803746bf8deca3c850c4fe1dc7d07db660c72545 Homepage: https://cran.r-project.org/package=dr Description: CRAN Package 'dr' (Methods for Dimension Reduction for Regression) Functions, methods, and datasets for fitting dimension reduction regression, using slicing (methods SAVE and SIR), Principal Hessian Directions (phd, using residuals and the response), and an iterative IRE. Partial methods, that condition on categorical predictors are also available. A variety of tests, and stepwise deletion of predictors, is also included. Also included is code for computing permutation tests of dimension. Adding additional methods of estimating dimension is straightforward. For documentation, see the vignette in the package. With version 3.0.4, the arguments for dr.step have been modified. Package: r-cran-dragonking Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dragonking_0.1.0-1.ca2404.1_all.deb Size: 29764 MD5sum: a4bf1451570fcec9d2804798a945aafd SHA1: 2402a1d7e5c8476f258334e4233087bf24525ed9 SHA256: dc280ccede0176f60c2023d4bb7252cb386d28383c9c3ee660c0cd8435c353bc SHA512: cc665a656b495c0a83316c209880a036dc0a3aa47caf53a244488a5f9645602d422a99d82edb099800b6090c5feeb27709232cacaf9336a3af8a4b01601629c9 Homepage: https://cran.r-project.org/package=dragonking Description: CRAN Package 'dragonking' (Statistical Tools to Identify Dragon Kings) Statistical tests and test statistics to identify events in a dataset that are dragon kings (DKs). The statistical methods in this package were reviewed in Wheatley & Sornette (2015) . Package: r-cran-dragracer Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dragracer_0.1.7-1.ca2404.1_all.deb Size: 232484 MD5sum: d88892974a02dc74b0b96e430dda29a0 SHA1: dc4cb170eba2ed6d6eda4682c5a3f9bc5b977639 SHA256: 667f2291d8762e3d5cad61180178eedaf9bdc33614dcc6eac73eaa5b42f8d76a SHA512: 72e14c6e86d4b2b8803989377f30ea4c4d2e523d3c3fca416078c198571233799832ed8bc4bbb5e9f1acff27398046c2c166af1c3b71ef36e57cc4fec9b84f2e Homepage: https://cran.r-project.org/package=dragracer Description: CRAN Package 'dragracer' (Data Sets for RuPaul's Drag Race) These are data sets for the hit TV show, RuPaul's Drag Race. Data right now include episode-level data, contestant-level data, and episode-contestant-level data. This is a work in progress, and a love letter of a kind to RuPaul's Drag Race and the performers that have appeared on the show. This may not be the most productive use of my time, but I have tenure and what are you going to do about it? I think there is at least some value in this package if it allows the show's fandom to learn more about the R programming language around its contents. 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Package: r-cran-drake Architecture: all Version: 7.13.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2679 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64url, r-cran-digest, r-cran-igraph, r-cran-rlang, r-cran-storr, r-cran-tidyselect, r-cran-txtq, r-cran-vctrs Suggests: r-cran-abind, r-cran-bindr, r-cran-callr, r-cran-cli, r-cran-clustermq, r-cran-crayon, r-cran-curl, r-cran-data.table, r-cran-disk.frame, r-cran-downloader, r-cran-fst, r-cran-future, r-cran-ggplot2, r-cran-ggraph, r-cran-keras, r-cran-knitr, r-cran-lubridate, r-cran-networkd3, r-cran-prettycode, r-cran-progress, r-cran-qs, r-cran-rcpp, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-styler, r-cran-testthat, r-cran-tibble, r-cran-txtplot, r-cran-usethis, r-cran-visnetwork, r-cran-webshot Filename: pool/dists/noble/main/r-cran-drake_7.13.11-1.ca2404.1_all.deb Size: 2274590 MD5sum: d484512c19791aa5f67530904dbdec75 SHA1: 175f4fe41b26cacc500e265f86fa3b7722317fe9 SHA256: ef4a16ed4ea84798097fa02adb1ee98526a56125d5a038a7328a78036de05ac3 SHA512: 42079ab118546d1d4485419cc5e72e24c2ebb0f9cdc9e5cd5a09191c25bc65e78fd627fc5ca424a2e26969a67206d572721c0e66b042f4a62268de3bd26ba649 Homepage: https://cran.r-project.org/package=drake Description: CRAN Package 'drake' (A Pipeline Toolkit for Reproducible Computation at Scale) A general-purpose computational engine for data analysis, drake rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every execution starts from scratch, there is native support for parallel and distributed computing, and completed projects have tangible evidence that they are reproducible. Extensive documentation, from beginner-friendly tutorials to practical examples and more, is available at the reference website and the online manual . Package: r-cran-dramaanalysis Architecture: all Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 799 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-readr, r-cran-data.table, r-cran-httr, r-cran-git2r, r-cran-xml2, r-cran-tokenizers, r-cran-stringr Suggests: r-cran-testthat, r-cran-fmsb, r-cran-knitr, r-cran-magrittr, r-cran-highcharter, r-cran-rmarkdown, r-cran-igraph Filename: pool/dists/noble/main/r-cran-dramaanalysis_3.0.2-1.ca2404.1_all.deb Size: 752774 MD5sum: 51a4f21d6e130b4385da513d6a0f2544 SHA1: 0fecc21343746f2a47c300c8b3e7268407b2533c SHA256: 42ca9efbf05edbb4d0af439d06efa9d6359b8c03beab85bcdd325bb2c84e7afe SHA512: e03e7e03337dcb8a223aaa25c4037d6c94c1b809b7a941e8e159358953c9bbf32567580879e0eaaf1aec2ca020dbb1c4f712058dca07eae9641505fdd6265193 Homepage: https://cran.r-project.org/package=DramaAnalysis Description: CRAN Package 'DramaAnalysis' (Analysis of Dramatic Texts) Analysis of preprocessed dramatic texts, with respect to literary research. The package provides functions to analyze and visualize information about characters, stage directions, the dramatic structure and the text itself. The dramatic texts are expected to be in CSV format, which can be installed from within the package, sample texts are provided. The package and the reasoning behind it are described in Reiter et al. (2017) . Package: r-cran-drape Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-doubleml, r-cran-glmnet, r-cran-hdi, r-cran-knitr, r-cran-matrix, r-cran-mlr3, r-cran-paradox, r-cran-partykit, r-cran-rjson, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-drape_0.0.2-1.ca2404.1_all.deb Size: 112754 MD5sum: d3a10d858591c0da36780aae58f49648 SHA1: 3a241ea452dde567525ced3bbe345f32d910af25 SHA256: 8cc68063427933ed97b8aeab22eeb99eec4b70ed389b813a3e0099d3f7e67788 SHA512: 8bdf4fda319e5e3f1d22a0feb0ef2d7151c25b03181830e0da17cf1a5201708f1a4b84e0d701294e6746419fadd8f387e1bc52699f0016745fddba2d7751630c Homepage: https://cran.r-project.org/package=drape Description: CRAN Package 'drape' (Doubly Robust Average Partial Effects) Doubly robust average partial effect estimation. This implementation contains methods for adding additional smoothness to plug-in regression procedures and for estimating score functions using smoothing splines. Details of the method can be found in Harvey Klyne and Rajen D. Shah (2023) . 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Two primary types of repositories are support: gh-pages at GitHub, as well as local repositories on either the same machine or a local network. Drat is a recursive acronym: Drat R Archive Template. Package: r-cran-draw Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-draw_1.0.0-1.ca2404.1_all.deb Size: 78474 MD5sum: a07b9cd955136d8dd7db051d96c1f18a SHA1: 9019d3e777ac76223fbb3a25252434b42064a08f SHA256: 4cad827ff3ecc8014f27b5f37e8f9539a3349455110f69d71564dd9b2529de62 SHA512: 6a239aa08240714c956a9a2c0523a67169e280dab1de2879a01f35588519ee4a377b9bbb7b73c13871cacc0b701088042ebb9b21642244d7d2f36769583272e0 Homepage: https://cran.r-project.org/package=draw Description: CRAN Package 'draw' (Wrapper Functions for Producing Graphics) A set of user-friendly wrapper functions for creating consistent graphics and diagrams with lines, common shapes, text, and page settings. Compatible with and based on the R 'grid' package. 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Often times, plots, photos are embedded in the web application/file. 'drawer' can take screenshots of these image-like elements, or any part of the HTML document and send to an image editing space called 'canvas' to allow users immediately edit the screenshot(s) within the same document. Users can quickly combine, compare different screenshots, upload their own images and maybe make a scientific figure. Package: r-cran-drawr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rocr Filename: pool/dists/noble/main/r-cran-drawr_1.0.3-1.ca2404.1_all.deb Size: 46236 MD5sum: aed1ea8e7c7958ff7a521d17b3339f95 SHA1: 413bc62490e1c2f9e1ba5922b9a23d95fc3c2db0 SHA256: 57a064a67f45c1305bd92dd2f0cc47b2501ffc44c55430c7b5b18ff08ab818a5 SHA512: 60effa9649c44550f21bf856ed353c8c5247e9317184c64e330fc45fd4a6cd1b3bf55486dd9134e16828d6e0495c884f28e44565d4d0c5d151dc971ef9fd0fd8 Homepage: https://cran.r-project.org/package=DRaWR Description: CRAN Package 'DRaWR' (Discriminative Random Walk with Restart) We present DRaWR, a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types, preserving more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only the relevant properties. We then rerank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. Package: r-cran-drawsample Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lattice, r-cran-tibble, r-cran-psych, r-cran-moments, r-cran-readxl, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-xlsx Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-drawsample_1.0.2-1.ca2404.1_all.deb Size: 249354 MD5sum: eda9fb521c64cc530e93bd0bb0b5151d SHA1: 29afb5c211cb10d30efed974d06aa1c08413427f SHA256: c68703ba05c219a9105cf9b240a96284f5998034132bfc1039208ce1c13056d9 SHA512: 1520a0136ee9d9e1a075c26f5d6f43579d6e2f7ef98b341bdc57c37ea14e9694034e9fef61ebf9d9b9337c4e9ee5230acfc221c093b8ac30906895bd8ea58599 Homepage: https://cran.r-project.org/package=drawsample Description: CRAN Package 'drawsample' (Draw Samples with the Desired Properties from a Data Set) A tool to sample data with the desired properties.Samples can be drawn by purposive sampling with determining distributional conditions, such as deviation from normality (skewness and kurtosis), and sample size in quantitative research studies. For purposive sampling, a researcher has something in mind and participants that fit the purpose of the study are included (Etikan,Musa, & Alkassim, 2015) .Purposive sampling can be useful for answering many research questions (Klar & Leeper, 2019) . Package: r-cran-drayl Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-rconics, r-cran-rmutil, r-cran-cubature Filename: pool/dists/noble/main/r-cran-drayl_1.0-1.ca2404.1_all.deb Size: 41806 MD5sum: b3b7440a4d95e256eca8763e888e57f5 SHA1: a8967b51c45d00c10458aebb989fe4a74758a7cb SHA256: a387027bc4cfffe383d4fecada9ecdfc1dd9d69ecbfeebca92adf621d69a515a SHA512: 8b199809c05867fc386f2ae05e529c8045d8cb159e0a482c0cbe5bffef8fd877c8a9988ab206ca945e981cb20ec4014860f790f0823e5e6e3798c07194cf8a5f Homepage: https://cran.r-project.org/package=DRAYL Description: CRAN Package 'DRAYL' (Computation of Rayleigh Densities of Arbitrary Dimension) We offer an implementation of the series representation put forth in "A series representation for multidimensional Rayleigh distributions" by Wiegand and Nadarajah . Furthermore we have implemented an integration approach proposed by Beaulieu et al. for 3 and 4-dimensional Rayleigh densities (Beaulieu, Zhang, "New simplest exact forms for the 3D and 4D multivariate Rayleigh PDFs with applications to antenna array geometrics", ). Package: r-cran-drbats Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1485 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-ade4, r-cran-coda, r-cran-mass, r-cran-matrix, r-cran-sde Suggests: r-cran-fda, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-drbats_0.1.6-1.ca2404.1_all.deb Size: 862156 MD5sum: 1cc1d6f2a5179af859ab2ddaab20d4fc SHA1: b68fbd89bddd85673da78bbaf49f9986af7ba8de SHA256: 89cc627c90ae83110d308c93772a5ad298d35c895c1f43823f1c5004c763c286 SHA512: 4e54725de2c54646ba025d44e54fe9892cd9c1997aecbad1c3bf4d0ad10dc04ebbfa85ecb081dfda2b65d99177983d17c9ae7039007fd031540726dc9753192f Homepage: https://cran.r-project.org/package=DrBats Description: CRAN Package 'DrBats' (Data Representation: Bayesian Approach That's Sparse) Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016. Package: r-cran-drc Architecture: all Version: 3.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-car, r-cran-gtools, r-cran-multcomp, r-cran-plotrix, r-cran-scales Filename: pool/dists/noble/main/r-cran-drc_3.0-1-1.ca2404.1_all.deb Size: 904064 MD5sum: 0a7a80b0e1b7fcd1f8dd974dcfb1ffd2 SHA1: f57776a516007894f28b12c13a41639eea356b75 SHA256: 2d4ecb91b4c698cfcc984bee17479f164b963ea15377a489cd5bc84c9bc960d0 SHA512: a8b06736079e4b4d0f2bfbc25e8c6a8af55cec2361907c5d63a768b9db691e669160dde1bf79bf1952372705f32e9fee6f2710bca130144111c5ead171244e52 Homepage: https://cran.r-project.org/package=drc Description: CRAN Package 'drc' (Analysis of Dose-Response Curves) Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions. Package: r-cran-drcarlate Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-mass, r-cran-stringr, r-cran-splus2r, r-cran-glmnet, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-drcarlate_1.2.0-1.ca2404.1_all.deb Size: 196458 MD5sum: 9637c8f7c62f34a28e2dae4165d7b20d SHA1: fc415747edb359ef4c4ea5d77d358f8c1adb467f SHA256: 831ab794207e7a99e18474b49e56e4f09e3ad6cc9896892f88e52d4117cd4822 SHA512: 5d5b2125ced2e5b3d7a9343dde51416ae3b93364b10804e7bd85f64959c7b9e8dca9660d581ca6089bd71102733860f72d7cd54c3eed12a199b03727a7792412 Homepage: https://cran.r-project.org/package=drcarlate Description: CRAN Package 'drcarlate' (Improving Estimation Efficiency in CAR with Imperfect Compliance) We provide a list of functions for replicating the results of the Monte Carlo simulations and empirical application of Jiang et al. (2022). In particular, we provide corresponding functions for generating the three types of random data described in this paper, as well as all the estimation strategies. Detailed information about the data generation process and estimation strategy can be found in Jiang et al. (2022) . Package: r-cran-drclass Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-drclass_0.1.0-1.ca2404.1_all.deb Size: 50022 MD5sum: c5550de8545041566154499e2cd828dd SHA1: c607cea31015b487196396b337c7340a0238331b SHA256: 4666081840132acc16ef0ae98c25a18343863bdad64420616dd4b6f79af56fa8 SHA512: 0e4854236bb0004567615e2ca3c279360babaf9229faddd4c61c676ed6f0b2557ae3292f9b733ca656f877b57ba2c5b06c94a4034a8a4539d5da5938dc877638 Homepage: https://cran.r-project.org/package=DRclass Description: CRAN Package 'DRclass' (Consider Ambiguity in Probabilistic Descriptions Using DensityRatio Classes) Consider ambiguity in probabilistic descriptions by replacing a parametric probabilistic description of uncertainty by a non-parametric set of probability distributions in the form of a Density Ratio Class. This is of particular interest in Bayesian inference. The Density Ratio Class is particularly suited for this purpose as it is invariant under Bayesian inference, marginalization, and propagation through a deterministic model. Here, invariant means that the result of the operation applied to a Density Ratio Class is again a Density Ratio Class. In particular the invariance under Bayesian inference thus enables iterative learning within the same framework of Density Ratio Classes. The use of imprecise probabilities in general, and Density Ratio Classes in particular, lead to intervals of characteristics of probability distributions, such as cumulative distribution functions, quantiles, and means. The package is based on a sample of the distribution proportional to the upper bound of the class. Typically this will be a sample from the posterior in Bayesian inference. Based on such a sample, the package provides functions to calculate lower and upper class boundaries and lower and upper bounds of cumulative distribution functions, and quantiles. Rinderknecht, S.L., Albert, C., Borsuk, M.E., Schuwirth, N., Kuensch, H.R. and Reichert, P. (2014) "The effect of ambiguous prior knowledge on Bayesian model parameter inference and prediction." Environmental Modelling & Software. 62, 300-315, 2014. . Sriwastava, A. and Reichert, P. "Robust Bayesian Estimation of Value Function Parameters using Imprecise Priors." Submitted. . Package: r-cran-drcseedgerm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-drc, r-cran-drcte, r-cran-plyr, r-cran-dplyr, r-cran-mvtnorm, r-cran-survival Filename: pool/dists/noble/main/r-cran-drcseedgerm_1.0.1-1.ca2404.1_all.deb Size: 372782 MD5sum: af49ce924044a33851cce9df0cd65300 SHA1: 59c72469bf531aa4bc9fcc4434352a9fcf1a821d SHA256: 647ce84e1f52d86c9103926ccef02e258c749db13657bb345476846b44b2e2e1 SHA512: e6b983ee6bf1bfa4f23b91d9caedc0d4d07cff82c6ba9188c9e622b9cb0ab102d772791ce613423d50442c1b2df083e14b3da0f80d70024e3abaafd2d5983c3d Homepage: https://cran.r-project.org/package=drcSeedGerm Description: CRAN Package 'drcSeedGerm' (Utilities for Data Analyses in Seed Germination/Emergence Assays) Utility functions to be used to analyse datasets obtained from seed germination/emergence assays. Fits several types of seed germination/emergence models, including those reported in Onofri et al. (2018) "Hydrothermal-time-to-event models for seed germination", European Journal of Agronomy, 101, 129-139 . Contains several datasets for practicing. 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Fit non-parametric and parametric time-to-event models. Compare time-to-event curves for different experimental groups. Plots and other displays. It is particularly tailored to the analyses of data from germination and emergence assays. The methods are described in Onofri et al. (2022) "A unified framework for the analysis of germination, emergence, and other time-to-event data in weed science", Weed Science, 70, 259-271 . Package: r-cran-drda Architecture: all Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1196 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-drda_2.0.5-1.ca2404.1_all.deb Size: 1065134 MD5sum: 591b24887c6437fdcde6f9c2d09c0050 SHA1: 3d45b1fbe59daf9b5715b374722afea8246d6522 SHA256: 30731c446b4311f15d6ae51a2b58d22573bbc5de8528fcba0c538579d1b8fa08 SHA512: 870048199ff270e4b960c82b33063797ae20d3d4bf0eacc0243950fbf0fbb6d312398587047c344e1f7bf94d67f0bbd16fae23f9c52329af2f4dd8c9b1f73954 Homepage: https://cran.r-project.org/package=drda Description: CRAN Package 'drda' (Dose-Response Data Analysis) Fit logistic functions to observed dose-response continuous data and evaluate goodness-of-fit measures. See Malyutina A., Tang J., and Pessia A. (2023) . Package: r-cran-drdata Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-shiny Suggests: r-cran-shinydashboard, r-cran-plotly, r-cran-dt, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-readxl, r-cran-caret, r-cran-randomforest, r-cran-rpart, r-cran-rpart.plot, r-cran-e1071, r-cran-class, r-cran-nnet, r-cran-colourpicker, r-cran-glmnet, r-cran-cluster, r-cran-dbscan, r-cran-ggally, r-cran-gbm, r-cran-proc, r-cran-reshape2, r-cran-scales, r-cran-nortest, r-cran-tseries, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-drdata_0.2.0-1.ca2404.1_all.deb Size: 66974 MD5sum: e67b08b2f9d562ed9ac9abee378b352f SHA1: 298fe051c0d6182f1e9f9cb1111547ce7e9dacb3 SHA256: b80f953a9f00955cd9ab481ccc0d45ac35bbc8887b4b6529c143afd8452d5400 SHA512: 1d1914a6a7df7f107fd3b0f8ea23e29100041f969d0bf650d9185078db060344a3b8430f1eb0b7acfa5f115668015bc579ffb9d37c5d647d68cae96311032fd0 Homepage: https://cran.r-project.org/package=DrData Description: CRAN Package 'DrData' (Interactive Statistical Analysis and Machine Learning Platform) A 'Shiny'-based interactive platform for end-to-end data science workflows. Provides modules for data import (CSV, 'Excel', RDS, TXT), data preprocessing (missing value imputation, encoding, scaling, outlier removal), exploratory data analysis with interactive plots and normality tests, supervised learning (regression and classification each with eight algorithms), and unsupervised learning (k-means, hierarchical clustering, density-based spatial clustering of applications with noise). Designed for students and practitioners in data science and artificial intelligence. Package: r-cran-drdimont Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1356 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-dplyr, r-cran-stringr, r-cran-wgcna, r-cran-rfast, r-cran-readr, r-cran-tibble, r-cran-tidyr, r-cran-magrittr, r-cran-rlang, r-cran-reticulate Suggests: r-bioc-go.db, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-drdimont_0.1.6-1.ca2404.1_all.deb Size: 1145568 MD5sum: 12664d5b3698a34c61e67754bb06a208 SHA1: ed32593f3bf5b5031ea5035e49168a8dc337fde8 SHA256: d08e39d8b68de4de89de790323b904424bca0328462ea82f021112f32fd4980c SHA512: 4c7472d050892bf7aa11f151151f61733ac8102fe9e7257f9af233579f671edc3b99fc3b754163e41e2ccf56a0b1e584f0e8262211a9fe3683d3d69066d792dd Homepage: https://cran.r-project.org/package=DrDimont Description: CRAN Package 'DrDimont' (Drug Response Prediction from Differential Multi-Omics Networks) While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. We present a novel network analysis pipeline, DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e., molecular differences that are the source of high differential drug scores can be retrieved. Our proposed pipeline leverages multi-omics data for differential predictions, e.g. on drug response, and includes prior information on interactions. The case study presented in the vignette uses data published by Krug (2020) . The package license applies only to the software and explicitly not to the included data. Package: r-cran-drdrtest Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-superlearner Filename: pool/dists/noble/main/r-cran-drdrtest_0.1-1.ca2404.1_all.deb Size: 53914 MD5sum: 0e0dbef57708c3fbe159153fdf0b1ae6 SHA1: 2a0a60021f0f239684f6638e84a31b2249c404d3 SHA256: 6b46fa994e92270c9d9e48e34a908f701ac20dbf15e2bc4c8ec1e02fa3986ecf SHA512: 53533f0a2889bc58d05e95c2451dd60551431fe6e29b105b7f784fbf787f1a6b843d3f35677cd491b463b6f3e7d3a049b08494048680e4ba12a005017691a7de Homepage: https://cran.r-project.org/package=DRDRtest Description: CRAN Package 'DRDRtest' (A Nonparametric Doubly Robust Test for Continuous TreatmentEffect) Implement the statistical test proposed in Weng et al. (2021) to test whether the average treatment effect curve is constant and whether a discrete covariate is a significant effect modifier. 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Longitudinal dose-response modeling is also supported in a Bayesian model averaging framework as outlined in Payne, Ray, and Thomann (2024) . Functions for plotting and calculating various posterior quantities (e.g. posterior mean, quantiles, probability of minimum efficacious dose, etc.) are also implemented. Copyright Eli Lilly and Company (2019). Package: r-cran-dreamerr Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1768 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-stringmagic Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dreamerr_1.5.0-1.ca2404.1_all.deb Size: 949328 MD5sum: 4788f159240652dbb6a2fbd2e864c92b SHA1: 653adfa88b2fff7a769ed6b8453f8cdcb734efc9 SHA256: 7d6e0f7551f9f4123ce1ea1defa1097c3b1ab2745993944f14086c48e6e2c686 SHA512: b119d7e5bb347c58a16c19e4b0637ed2dc74cfbfdc0e5d9ca9720714967523b262ad4b5c91998bee83f566624087fe542efbf5bcecdbf987c4c8f4a075564977 Homepage: https://cran.r-project.org/package=dreamerr Description: CRAN Package 'dreamerr' (Error Handling Made Easy) Set of tools to facilitate package development and make R a more user-friendly place. Mostly for developers (or anyone who writes/shares functions). 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Package: r-cran-drhotnet Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbsmapping, r-cran-raster, r-cran-sp, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-spatstat, r-cran-spdep Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-drhotnet_2.3-1.ca2404.1_all.deb Size: 396882 MD5sum: 939d7ede3251b7fe1bfdee26d6f56f3a SHA1: d665aa78b6ae66f7c0a2c0e9c54b196c517277de SHA256: 7dcd8dbf9db1073ca9223aece7ca33b62839b0658bfe90d0ffa195218cd5d1d9 SHA512: f4920bd9a170d01c711c5ca2ec5c840e61ac5d2737355f4bb911b23cc101c967a2591d94b3713d28fefa9689d99485f0b3f83746b2d54ab38aea9c177c9e0cd1 Homepage: https://cran.r-project.org/package=DRHotNet Description: CRAN Package 'DRHotNet' (Differential Risk Hotspots in a Linear Network) Performs the identification of differential risk hotspots (Briz-Redon et al. 2019) along a linear network. Given a marked point pattern lying on the linear network, the method implemented uses a network-constrained version of kernel density estimation (McSwiggan et al. 2017) to approximate the probability of occurrence across space for the type of event specified by the user through the marks of the pattern (Kelsall and Diggle 1995) . The goal is to detect microzones of the linear network where the type of event indicated by the user is overrepresented. 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The current version can create folder system for academic project in political science, calculate psychological trait scores, visualize experimental and spatial data, set up color-blind palette, and test for Type I error (false positives) in Qualitative Comparative Analysis (QCA) for crisp-set, multi-value, and fuzzy-set variants. Package: r-cran-drifter Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dalex, r-cran-dplyr, r-cran-tidyr, r-cran-ingredients Suggests: r-cran-testthat, r-cran-ranger Filename: pool/dists/noble/main/r-cran-drifter_0.2.1-1.ca2404.1_all.deb Size: 38934 MD5sum: 2fd5d276bfcde21c2a5f0b7c8d2d42ab SHA1: d101cd3b6a61f4def5e22797f1db85b32d8d2b7b SHA256: 6020325c912fb45ad5679f3bd8f5c4fd5ddf3a43199f1b568c282e441149df83 SHA512: 4f17ea6bfd8406d7d24b2110f1b76b2ff28550935a3f58769519f5ff3918e91a76723d8015a3c143c435fa6733ede80a5c9ef723fd4fef2216554deddb791afe Homepage: https://cran.r-project.org/package=drifter Description: CRAN Package 'drifter' (Concept Drift and Concept Shift Detection for Predictive Models) Concept drift refers to the change in the data distribution or in the relationships between variables over time. 'drifter' calculates distances between variable distributions or variable relations and identifies both types of drift. Key functions are: calculate_covariate_drift() checks distance between corresponding variables in two datasets, calculate_residuals_drift() checks distance between residual distributions for two models, calculate_model_drift() checks distance between partial dependency profiles for two models, check_drift() executes all checks against drift. 'drifter' is a part of the 'DrWhy.AI' universe (Biecek 2018) . Package: r-cran-drillr Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr Filename: pool/dists/noble/main/r-cran-drillr_0.1-1.ca2404.1_all.deb Size: 43248 MD5sum: 1129a817d271675f87bda8b9a0756642 SHA1: 7a8b6b1500144ad8d294cd07ee46f5646cc3cc7c SHA256: 9bbad8da00123cf4e7e7b6ef79b33120a62f810d94bae478ce945046cc21734b SHA512: fdb254a3a755703b933f0405f33053927d83778074f35559d6d3e741db30954cef67824dd6f09d144240e04ff714a41d9f4d92f573486ee74086624df98de46f Homepage: https://cran.r-project.org/package=DrillR Description: CRAN Package 'DrillR' (R Driver for Apache Drill) Provides a R driver for Apache Drill, which could connect to the Apache Drill cluster or drillbit and get result(in data frame) from the SQL query and check the current configuration status. This link contains more information about Apache Drill. Package: r-cran-drimmr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seqinr, r-cran-ggplot2, r-cran-parallelly, r-cran-doparallel, r-cran-foreach, r-cran-dplyr, r-cran-rdpack, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-drimmr_1.0.3-1.ca2404.1_all.deb Size: 229392 MD5sum: ad0348d1cd984dec50862a0998d6980f SHA1: aa0728639c80b740264ff9826de43d7083f5c7ac SHA256: 0fb9614428a8fa2f145f5f690f9e37eff4abe45b9dfdee598eff844415ec32da SHA512: fb3faedab7c80589a4678c3e713d83b53d9161a619e255e48b983b4a1a5281a8273536e2f36c96b24065e7e8490661a4a98bfae403ad3c8cd8464da422b736bb Homepage: https://cran.r-project.org/package=drimmR Description: CRAN Package 'drimmR' (Estimation, Simulation and Reliability of Drifting Markov Models) Performs the drifting Markov models (DMM) which are non-homogeneous Markov models designed for modeling the heterogeneities of sequences in a more flexible way than homogeneous Markov chains or even hidden Markov models. In this context, we developed an R package dedicated to the estimation, simulation and the exact computation of associated reliability of drifting Markov models. The implemented methods are described in Vergne, N. (2008), and Barbu, V.S., Vergne, N. (2019) . Package: r-cran-driver Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5617 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-randomforest, r-bioc-genomicranges, r-bioc-genomeinfodb, r-bioc-genomicfeatures, r-bioc-s4vectors, r-cran-rlang Suggests: r-bioc-org.hs.eg.db, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-driver_0.5.0-1.ca2404.1_all.deb Size: 2883606 MD5sum: 36e2bdddbc14e90b866898c68149438b SHA1: 5c81268c41d582fdd7665fe501a2064baf8ac2b5 SHA256: 40b0e0da53517f96715c93c915eac93e230a125b3518091c65cf1cb144820c46 SHA512: ed668d0bc01af3e5574ac783aa9447e5d23a29bd1d029f867134c428784e469696f05bc076069b6109212822396c517c71f2030f67248c23f3465830cb9976e0 Homepage: https://cran.r-project.org/package=driveR Description: CRAN Package 'driveR' (Prioritizing Cancer Driver Genes Using Genomics Data) Cancer genomes contain large numbers of somatic alterations but few genes drive tumor development. Identifying cancer driver genes is critical for precision oncology. Most of current approaches either identify driver genes based on mutational recurrence or using estimated scores predicting the functional consequences of mutations. 'driveR' is a tool for personalized or batch analysis of genomic data for driver gene prioritization by combining genomic information and prior biological knowledge. As features, 'driveR' uses coding impact metaprediction scores, non-coding impact scores, somatic copy number alteration scores, hotspot gene/double-hit gene condition, 'phenolyzer' gene scores and memberships to cancer-related KEGG pathways. It uses these features to estimate cancer-type-specific probability for each gene of being a cancer driver using the related task of a multi-task learning classification model. The method is described in detail in Ulgen E, Sezerman OU. 2021. driveR: driveR: a novel method for prioritizing cancer driver genes using somatic genomics data. BMC Bioinformatics . 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The package provides: (1) standard MAIC via entropy balancing / exponential tilting; (2) augmented/doubly robust MAIC combining inverse probability weighting with outcome regression; (3) comprehensive covariate balance diagnostics including standardised mean differences, Love plots, and effective sample size; (4) sensitivity analyses including E-values, weight trimming, and variable exclusion analyses; (5) bootstrap confidence intervals; and (6) submission-ready outputs aligned with NICE Decision Support Unit Technical Support Document 18, Cochrane Handbook guidance on indirect comparisons, and ISPOR best practice guidelines. Supports binary (risk difference, risk ratio, odds ratio) and time-to-event (hazard ratio) outcomes. 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'DRomics' is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations) rather than a great number of replicates (no need of replicates). 'DRomics' provides functions 1) to check, normalize and or transform data, 2) to select monotonic or biphasic significantly responding items (e.g. probes, metabolites), 3) to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each selected item, 4) to derive a benchmark dose or concentration and a typology of response from each fitted curve. In the available version data are supposed to be single-channel microarray data in log2, RNAseq data in raw counts, or already pretreated continuous omics data (such as metabolomic data) in log scale. In order to link responses across biological levels based on a common method, 'DRomics' also handles apical data as long as they are continuous and follow a normal distribution for each dose or concentration, with a common standard error. For further details see Delignette-Muller et al (2023) and Larras et al (2018) . 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(2016) and Preussler (2020). Optimal is in the sense of maximal expected utility, where the utility is a function taking into account the expected cost and benefit of the program. It is possible to extend to more complex settings with bias correction (Preussler S et al. (2020) ), multiple phase III trials (Preussler et al. (2019) ), multi-arm trials (Preussler et al. (2019) ), and multiple endpoints (Kieser et al. (2018) ). 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Based on Pye et al (2018) . Package: r-cran-drugsens Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1336 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-knitr, r-cran-roxygen2, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-testthat Filename: pool/dists/noble/main/r-cran-drugsens_0.1.0-1.ca2404.1_all.deb Size: 133158 MD5sum: ab84570556445263ef2e320fec219a93 SHA1: 0da57f00fdeb31acbc2c38b073f18ca554c5c41d SHA256: 6f266af9b1b77f76066aba6dff5e2b63d28670bca9142b5c5280fb832d12fd1b SHA512: f1be4fa9d6769ba5f58e8d3b4e0ccb8eb6b62b79f764367a65df280ba26dc042a1de32acd632b7674d8d456d31bcf93933ce876e58c1fd1e1f54fdeda4317f9b Homepage: https://cran.r-project.org/package=drugsens Description: CRAN Package 'drugsens' (Automated Analysis of 'QuPath' Output Data and MetadataExtraction) A comprehensive toolkit for analyzing microscopy data output from 'QuPath' software. 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Package: r-cran-drugsim2dr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3014 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-pheatmap, r-cran-tidyr, r-cran-reshape2, r-cran-fastmatch Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-drugsim2dr_0.1.2-1.ca2404.1_all.deb Size: 2469258 MD5sum: a4ac5d8cc1200443f62968deb5f28e64 SHA1: e306578bae45c4c4c8009c064808aa41003f5c74 SHA256: 5c1c95bb6de74bad4223a58ddc540acbc87c977cf2ecdfaf3c35cd89606544a7 SHA512: 63521a0cb1782fd9b61b33480cb41b97985bf2fbe8e68fb6cf9f786dc8452a199aaadc1dcbbafe6400b34674ca4ed4b6189d2c2af0bb25a30cdbf1fe85a4bada Homepage: https://cran.r-project.org/package=DrugSim2DR Description: CRAN Package 'DrugSim2DR' (Predict Drug Functional Similarity to Drug Repurposing) A systematic biology tool was developed to repurpose drugs via a drug-drug functional similarity network. 'DrugSim2DR' first predict drug-drug functional similarity in the context of specific disease, and then using the similarity constructed a weighted drug similarity network. Finally, it used a network propagation algorithm on the network to identify drugs with significant target abnormalities as candidate drugs. Package: r-cran-drugutilisation Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3908 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-codelistgenerator, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-bit64, r-cran-cdmconnector, r-cran-cohortconstructor, r-cran-cohortsurvival, r-cran-covr, r-cran-dbi, r-cran-duckdb, r-cran-flextable, r-cran-ggplot2, r-cran-ggtext, r-cran-gt, r-cran-here, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-rmarkdown, r-cran-rpostgres, r-cran-scales, r-cran-testthat, r-cran-tibble, r-cran-visomopresults Filename: pool/dists/noble/main/r-cran-drugutilisation_1.1.0-1.ca2404.1_all.deb Size: 677732 MD5sum: 8d78ad6a6ddfc4091a6393efe0cafc66 SHA1: 16c2958f4e97e819e83f7297bd8d40aaae93e3c5 SHA256: 078438999e353ec20e20f10805843f5269bbb3df4450e698878c3587c5914959 SHA512: a5993984340b40c0a6da3bdc6d7d23982b150ce803ee01cde210aa20cf7be8e764bd03a28660dfb7d25fc44fe88ac140a12854fde1826bad810edbb638c18bef Homepage: https://cran.r-project.org/package=DrugUtilisation Description: CRAN Package 'DrugUtilisation' (Summarise Patient-Level Drug Utilisation in Data Mapped to theOMOP Common Data Model) Summarise patient-level drug utilisation cohorts using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. New users and prevalent users cohorts can be generated and their characteristics, indication and drug use summarised. Package: r-cran-drumr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-audio, r-cran-stringr Filename: pool/dists/noble/main/r-cran-drumr_0.1.0-1.ca2404.1_all.deb Size: 1946640 MD5sum: 5ea320060ee6444ca0f16afb065f6993 SHA1: 0ceee2af61add1c5427b06b9e1a9c074f4d27922 SHA256: 9c45492cbd5f621e15fb20b55b934a9a6458828fecf723733f7920cbd3726ea4 SHA512: 03c3d6dfc84a8f0341af79345680fab927ad46a31dced0559391185da8f80921dbdae496b16c85e3fc99b681c25c118ad83f1aaea73a8c5e273ea84e27d815af Homepage: https://cran.r-project.org/package=drumr Description: CRAN Package 'drumr' (Turn R into a Drum Machine) Includes various functions for playing drum sounds. beat() plays a drum sound from one of the six included drum kits. tempo() sets spacing between calls to beat() in bpm. Together the two functions can be used to create many different drum patterns. Package: r-cran-drviaspcn Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4588 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-gsva, r-bioc-clusterprofiler, r-cran-igraph, r-cran-pheatmap Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-dt Filename: pool/dists/noble/main/r-cran-drviaspcn_0.1.5-1.ca2404.1_all.deb Size: 4506226 MD5sum: 86d9fa71bf576341b38494be60ae4eae SHA1: 087eb06633d7edf9a29e6d8bdb51db25a2ad2e2b SHA256: 8d103dae940f579ca0238348bef87678fba1d8444c805bfa7c75aa0a4c2396f8 SHA512: d71d26147031d7cf68bee2b8327a96bd406dc5cfe8e9a2fbb78145b711ab5174694d107e2a2683e6840d1fd739eb5f7894e90df90e9d673d00ace3adc3fa4c5e Homepage: https://cran.r-project.org/package=DRviaSPCN Description: CRAN Package 'DRviaSPCN' (Drug Repurposing in Cancer via a Subpathway Crosstalk Network) A systematic biology tool was developed to repurpose drugs via a subpathway crosstalk network. The operation modes include 1) calculating centrality scores of SPs in the context of gene expression data to reflect the influence of SP crosstalk, 2) evaluating drug-disease reverse association based on disease- and drug-induced SPs weighted by the SP crosstalk, 3) identifying cancer candidate drugs through perturbation analysis. There are also several functions used to visualize the results. Package: r-cran-ds4psy Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1061 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-unikn Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ds4psy_1.3.0-1.ca2404.1_all.deb Size: 930430 MD5sum: d1bbb3aef94538dbc6e48c87ee1d01ac SHA1: c86c3e66e255e6563e7de93211f84fb51ab65732 SHA256: 680702202ffcfd569f12c995da9820047d4ab70e88119c1541423d5e3cb4d3da SHA512: 02b78edd6c9f5428889979161fe7a9588328abbeb712775acac76959a05503e3af72ad6cac066053c7adfbf5f0385537014e84824aa8e4fc0248f128068988ad Homepage: https://cran.r-project.org/package=ds4psy Description: CRAN Package 'ds4psy' (Data Science for Psychologists) All datasets and functions required for the examples and exercises of the book "Data Science for Psychologists" (by Hansjoerg Neth, Konstanz University, 2026, ), freely available at . The book and corresponding courses introduce principles and methods of data science to students of psychology and other biological or social sciences. The 'ds4psy' package primarily provides datasets, but also functions for data generation and manipulation (e.g., of text and time data) and graphics that are used in the book and its exercises. All functions included in 'ds4psy' are designed to be explicit and instructive, rather than efficient or elegant. Package: r-cran-ds Architecture: all Version: 4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ds_4.0-1.ca2404.1_all.deb Size: 36770 MD5sum: 5571d4e09118fd7dca28d4c0b9dfb386 SHA1: 83f8966cafb5a6e6bc64a31c33ab0c621e148529 SHA256: 2691f026551286aa3fcb476699a7d43086478433d95976f656ab51d3bae47095 SHA512: 0b5c2e8b8fc93e0c0abe0baafa7579aa5121ca73b9138edd9f5a4dd95ae890e27eef829140c27c4ebee6f94f6e979f837dcf73dad39cabd38e479203a4915ddc Homepage: https://cran.r-project.org/package=ds Description: CRAN Package 'ds' (Descriptive Statistics) Performs various analyzes of descriptive statistics, including correlations, graphics and tables. 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Bundesbank Discussion Paper 41/2018. Package: r-cran-dsaide Architecture: all Version: 0.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-adaptivetau, r-cran-desolve, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-lhs, r-cran-nloptr, r-cran-plotly, r-cran-rlang, r-cran-xml Suggests: r-cran-covr, r-cran-devtools, r-cran-emoji, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsaide_0.9.6-1.ca2404.1_all.deb Size: 2637648 MD5sum: a83552657e0455d8405d371b826ab9e5 SHA1: d21bee84856fc996be47837b4e9cd7e64eeb8b21 SHA256: 5d1e5c2e3baa5fb0b64afb73c3d87d6c3228b6c1e294fc1a986fbf506588f451 SHA512: c2f52768562fca336821c54e6a6298c26ec3a073af4ade50eee62b874f5b99e5bec8bc0c3f348892e719f854584d91253cc4c34accaf7e5544d712e3957058a1 Homepage: https://cran.r-project.org/package=DSAIDE Description: CRAN Package 'DSAIDE' (Dynamical Systems Approach to Infectious Disease Epidemiology(Ecology/Evolution)) Exploration of simulation models (apps) of various infectious disease transmission dynamics scenarios. The purpose of the package is to help individuals learn about infectious disease epidemiology (ecology/evolution) from a dynamical systems perspective. All apps include explanations of the underlying models and instructions on what to do with the models. 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The purpose of the package is to help individuals learn about within-host infection and immune response modeling from a dynamical systems perspective. All apps include explanations of the underlying models and instructions on what to do with the models. Package: r-cran-dsam Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-kohonen, r-cran-matrix, r-cran-proc, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-dsam_1.0.2-1.ca2404.1_all.deb Size: 111558 MD5sum: f90d4b3bfe80d5fc54292238eab41815 SHA1: 8081e72f62a3b507a31fddb88e9e11fee0f1d107 SHA256: 48232dc355e517a12e26bd1053ca8e6d02a709c82d4d98c81e00a6a59d9bc077 SHA512: 80a6c0d23421f17ef0df7e44b733adddc056cb5ee0f6102ece3886fdbc53582064f43d9aaa81ca6e1beccd176e4fdb6d43f8213512303fadc763e9c9f6024966 Homepage: https://cran.r-project.org/package=DSAM Description: CRAN Package 'DSAM' (Data Splitting Algorithms for Model Developments) Providing six different algorithms that can be used to split the available data into training, test and validation subsets with similar distribution for hydrological model developments. The dataSplit() function will help you divide the data according to specific requirements, and you can refer to the par.default() function to set the parameters for data splitting. The getAUC() function will help you measure the similarity of distribution features between the data subsets. For more information about the data splitting algorithms, please refer to: Chen et al. (2022) , Zheng et al. (2022) . Package: r-cran-dsample Architecture: all Version: 0.91.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mnormt Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dsample_0.91.3.4-1.ca2404.1_all.deb Size: 51622 MD5sum: fc80e82863e2b1a23bb7eccdde87b0b0 SHA1: 7632a0167d13549e270fc7cdf9aa8895e50a9812 SHA256: 995ca04810da1d332c1569d912349516b0acf657f8bea71ec50798025a11352e SHA512: b5e2f033bf9d2e1ad0b768b02f0462349f86682e59d7a6c7dac7b0a1ba9ada6a151e626e88c41c32dcdfe0bab193e01bb87728ac33bca7a1f09c996415403441 Homepage: https://cran.r-project.org/package=dsample Description: CRAN Package 'dsample' (Discretization-Based Direct Random Sample Generation) Discretization-based random sampling algorithm that is useful for a complex model in high dimension is implemented. The normalizing constant of a target distribution is not needed. Posterior summaries are compared with those by 'OpenBUGS'. The method is described: Wang and Lee (2014) and exercised in Lee (2009) . Package: r-cran-dsb Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4749 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-bioc-limma, r-cran-mclust Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-cowplot, r-cran-spelling Filename: pool/dists/noble/main/r-cran-dsb_2.0.1-1.ca2404.1_all.deb Size: 3781412 MD5sum: 8eeb2b8289cb5c5b1d7befa6b4a7377c SHA1: 8ace5d5e5adfcb7259018cede235bfee4a811172 SHA256: 69468b6f3a9f10522bfff7e444dcb140780759cf59ca565479d465dcbdc65760 SHA512: 298fc8108601fd6ce281370e3213a719ce5a07b4a418f9039e3e2c521ae8b9e3d1a4a201846c0963e4b630897d9aa1d6eeaa0fc617bdf25ff415e5d67d70d86e Homepage: https://cran.r-project.org/package=dsb Description: CRAN Package 'dsb' (Normalize & Denoise Droplet Single Cell Protein Data (CITE-Seq)) This lightweight R package provides a method for normalizing and denoising protein expression data from droplet based single cell experiments. Raw protein Unique Molecular Index (UMI) counts from sequencing DNA-conjugated antibody derived tags (ADT) in droplets (e.g. 'CITE-seq') have substantial measurement noise. Our experiments and computational modeling revealed two major components of this noise: 1) protein-specific noise originating from ambient, unbound antibody encapsulated in droplets that can be accurately inferred via the expected protein counts detected in empty droplets, and 2) droplet/cell-specific noise revealed via the shared variance component associated with isotype antibody controls and background protein counts in each cell. This package normalizes and removes both of these sources of noise from raw protein data derived from methods such as 'CITE-seq', 'REAP-seq', 'ASAP-seq', 'TEA-seq', 'proteogenomic' data from the Mission Bio platform, etc. See the vignette for tutorials on how to integrate dsb with 'Seurat' and 'Bioconductor' and how to use dsb in 'Python'. Please see our paper Mulè M.P., Martins A.J., and Tsang J.S. Nature Communications 2022 for more details on the method. Package: r-cran-dsbase Architecture: all Version: 6.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rann, r-cran-stringr, r-cran-lme4, r-cran-dplyr, r-cran-reshape2, r-cran-polycor, r-cran-gamlss, r-cran-gamlss.dist, r-cran-mice, r-cran-childsds Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsbase_6.3.5-1.ca2404.1_all.deb Size: 1233516 MD5sum: 4a90a2ae6a8082567cc599fdd870ff18 SHA1: 85eb181bacc925eb723c34efa6007bd8bafcfdf7 SHA256: 04f71f418a5506df38fc0efc1ff064c3fbca6c96f0fd8678dde0f2d744e4c695 SHA512: 018f7a7111db68c95bd318f8b640d7379dc948113f13e463281a33da24f75fba6c5357aca0a87eb94a4c9fee47cfa661c4e6cdf8db3b5c0a549cf4779d5a41f1 Homepage: https://cran.r-project.org/package=dsBase Description: CRAN Package 'dsBase' ('DataSHIELD' Server Side Base Functions) Base 'DataSHIELD' functions for the server side. 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Package: r-cran-dsbaseclient Architecture: all Version: 6.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dsi, r-cran-fields, r-cran-metafor, r-cran-meta, r-cran-ggplot2, r-cran-gridextra, r-cran-data.table, r-cran-dplyr Suggests: r-cran-lme4, r-cran-httr, r-cran-spelling, r-cran-tibble, r-cran-testthat, r-cran-e1071, r-cran-desctools, r-cran-dsopal, r-cran-dsmolgenisarmadillo, r-cran-dslite Filename: pool/dists/noble/main/r-cran-dsbaseclient_6.3.5-1.ca2404.1_all.deb Size: 1148184 MD5sum: 5aa168416f3ec13719fdae0dbff30416 SHA1: 514554dd5e3e0ff26582dc9d34417a953c1973f7 SHA256: 22baddd3e661f8b9c9758ac50b567b5910612e4c2d3b333c9a9de9863c359d69 SHA512: 97865a504c68ef66dec26c585df0eb9f11fe1ec6901d800c384abe26c2076996ef3c6a16327c1ac5fdebdfce7a20912195aa6f551a550a87600836861e110646 Homepage: https://cran.r-project.org/package=dsBaseClient Description: CRAN Package 'dsBaseClient' ('DataSHIELD' Client Side Base Functions) Base 'DataSHIELD' functions for the client side. 'DataSHIELD' is a software package which allows you to do non-disclosive federated analysis on sensitive data. 'DataSHIELD' analytic functions have been designed to only share non disclosive summary statistics, with built in automated output checking based on statistical disclosure control. With data sites setting the threshold values for the automated output checks. For more details, see citation('dsBaseClient'). Package: r-cran-dsbayes Architecture: all Version: 2023.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bb Filename: pool/dists/noble/main/r-cran-dsbayes_2023.1.0-1.ca2404.1_all.deb Size: 75200 MD5sum: 59432a8766e575cd2ff7760a90bb90f2 SHA1: 4a40b30c7bac49881d86411db93d6d23ba19808d SHA256: e3d5b5da2188b060bd1e0d4b3f6d91d07879cb7735a2accdfcfd0f3e4fc2eac4 SHA512: 1c09536d53b0b98ea26a5ef09882a827751f9d7392b99063455cbf3e515e406f7aa08deb0bac0dbc379e5bee6dc97c0af921de2d808b3864c8d570f7e0434253 Homepage: https://cran.r-project.org/package=DSBayes Description: CRAN Package 'DSBayes' (Bayesian Subgroup Analysis in Clinical Trials) Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials. For details of the methodology, please refer to D.O. Dixon and R. Simon (1991), Biometrics, 47: 871-881. Package: r-cran-dscoremsm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-timeroc, r-cran-ggplot2, r-cran-survival, r-cran-mstate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dscoremsm_0.1.0-1.ca2404.1_all.deb Size: 269450 MD5sum: 743a98e2e48875674cf833bb2adaa9a2 SHA1: f71b35a250efdc0f4f867dadbbdb408a4a26a1d9 SHA256: a116d7668fb6977347ee420cb48752da564e1903d71f0364aad8e3ba84d4179e SHA512: e41bf621cdda1450a340f43517795c2720a09d338672a3d4056169cf8f4ef32b79e5643f330f094d291747864ee1353d26c71727f05921b80ca6e987050b1653 Homepage: https://cran.r-project.org/package=dscoreMSM Description: CRAN Package 'dscoreMSM' (Survival Proximity Score Matching in Multi-State Survival Model) Implements survival proximity score matching in multi-state survival models. Includes tools for simulating survival data and estimating transition-specific coxph models with frailty terms. The primary methodological work on multistate censored data modeling using propensity score matching has been published by Bhattacharjee et al.(2024) . Package: r-cran-dsfm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 716 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrixcalc, r-cran-sn, r-cran-psych, r-cran-elasticnet, r-cran-sopc Suggests: r-cran-ggplot2, r-cran-cowplot, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsfm_1.0.1-1.ca2404.1_all.deb Size: 691844 MD5sum: b96ffac5bc263b41fae384015454f2c2 SHA1: c96454c056a26ba85657379e200f224cee2cb8f8 SHA256: b8e8bcc3ff252403b5425a40248c9fd46a4cc59fb420dcaf8df843b55b241d33 SHA512: 7a51b7465e5bdedd883f9f170690695aaed73fa5b2cc41b6fa7f229277992397099c383519371f3a8d7c86a885274b20c927e84423df01a5610b176ca618e95d Homepage: https://cran.r-project.org/package=DSFM Description: CRAN Package 'DSFM' (Distributed Skew Factor Model Estimation Methods) Provides a distributed framework for simulating and estimating skew factor models under various skewed and heavy-tailed distributions. The methods support distributed data generation, aggregation of local estimators, and evaluation of estimation performance via mean squared error, relative error, and sparsity measures. The distributed principal component (PC) estimators implemented in the package include 'IPC' (Independent Principal Component),'PPC' (Project Principal Component), 'SPC' (Sparse Principal Component), and other related distributed PC methods. The methodological background follows Guo G. (2023) . Package: r-cran-dsge Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 695 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-coda, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dsge_1.0.0-1.ca2404.1_all.deb Size: 525680 MD5sum: 93499fcfc0213a07f262a91df9f1416d SHA1: 0ba4de88fcc9b5e772816524bcdc3a5424a58028 SHA256: 99d26ff22ba68a556b8936441f0654e7e27da5126e637577af25c03df72730c6 SHA512: e6e44036a93a013491ea513524fccf417ab887553acbd6327cbce295324f46777bbc63ff52b97210613bdf2b93a95691338bf617ce2c678dfda52117e6feba1c Homepage: https://cran.r-project.org/package=dsge Description: CRAN Package 'dsge' (Dynamic Stochastic General Equilibrium Models) Specify, solve, and estimate dynamic stochastic general equilibrium (DSGE) models by maximum likelihood and Bayesian methods. Supports both linear models via an equation-based formula interface and nonlinear models via string-based equations with first-order perturbation (linearization around deterministic steady state). Solution uses the method of undetermined coefficients (Klein, 2000 ). Likelihood evaluated via the Kalman filter. Bayesian estimation uses adaptive Random-Walk Metropolis-Hastings with prior specification. Additional tools include Kalman smoothing, historical shock decomposition, local identification diagnostics, parameter sensitivity analysis, second-order perturbation, occasionally binding constraints, impulse-response functions, forecasting, and robust standard errors. Package: r-cran-dsi Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-progress, r-cran-r6, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsi_1.8.0-1.ca2404.1_all.deb Size: 320020 MD5sum: a79b15146d143b69761d67f708a6a16f SHA1: 41ffe68dc5b2a1d152195c13bc133fb223de39fc SHA256: cbab6751e12bd14270ed352341e398d08930fd235047170999cdd62a70e25c06 SHA512: 0e29105f336a356194e46cb8b3008657435cb89ddf5f944d0bb622f8b4ca9c27e2758d221c7f5e7ba53b4b8fc8f6519bd2887e10ddc98c1425c851c26bed5b9c Homepage: https://cran.r-project.org/package=DSI Description: CRAN Package 'DSI' ('DataSHIELD' Interface) 'DataSHIELD' is an infrastructure and series of R packages that enables the remote and 'non-disclosive' analysis of sensitive research data. This package defines the API that is to be implemented by 'DataSHIELD' compliant data repositories. 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'dsims' repeatedly generates instances of a user defined population within a given survey region. It then generates realisations of a survey design and simulates the detection process. The data are then analysed so that the results can be compared for accuracy and precision across all replications. This process allows users to optimise survey designs for their specific set of survey conditions. The effects of uncertainty in population distribution or parameters can be investigated under a number of simulations so that users can be confident that they have achieved a robust survey design before deploying vessels into the field. The distance sampling designs used in this package from 'dssd' are detailed in Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13: 978-0199225873). General distance sampling methods are detailed in Introduction to Distance Sampling: Estimating Abundance of Biological Populations, Buckland et. al. (2004, ISBN-13: 978-0198509271). Find out more about estimating animal/plant abundance with distance sampling at . 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Dataset containing information about job listings for data science job roles. Package: r-cran-dslabs Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4909 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-dslabs_0.9.1-1.ca2404.1_all.deb Size: 4589956 MD5sum: 6ba00ca381fbef2bc25367d96af12866 SHA1: bfbb8010e9d8fb338e388e17af61ab7dd86cecb1 SHA256: f8cd7ae3f74b5786178b5edf31242534010989ae31c7e8bab63c2e4c0e0e040c SHA512: c4b0f9e11362a159ef87f410f017bed02029797efbac9073a3ad5f0be47339ffed3b7c5f7bb5888f9b9d9bfe338921df7fe3c676a1f0027edf803b5123a834d5 Homepage: https://cran.r-project.org/package=dslabs Description: CRAN Package 'dslabs' (Data Science Labs) Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops. 26 datasets are available for case studies in data visualization, statistical inference, modeling, linear regression, data wrangling and machine learning. 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Detection and remediation of bias in machine learning algorithms. 'Python' interfaces available. 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This 'DataSHIELD Interface' implementation is for analyzing datasets living in the current R session. The purpose of this is primarily for lightweight 'DataSHIELD' analysis package development. Package: r-cran-dsm Architecture: all Version: 2.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-mrds, r-cran-numderiv, r-cran-nlme, r-cran-ggplot2, r-cran-plyr, r-cran-statmod Suggests: r-cran-distance, r-cran-sp, r-cran-tweedie, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsm_2.3.4-1.ca2404.1_all.deb Size: 285422 MD5sum: 6c6007529a763122e22aa9b814c22436 SHA1: 4386bb539d7bd27456c6f74720e71bcc00bb2e48 SHA256: 743be2766428fa9435b4da553ea8569f146f4017fd7e8e6682c1e5169e90e188 SHA512: f035564e5e934bd589e513c6cd739d9ba5f46d6a3cc3c16cdc0d8d3898c3cced59be097a480432fc6459af261f0d38e3af4e79b568f336744c0bf4f7121be059 Homepage: https://cran.r-project.org/package=dsm Description: CRAN Package 'dsm' (Density Surface Modelling of Distance Sampling Data) Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation. Package: r-cran-dsmmr Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-discreteweibull Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dsmmr_1.0.7-1.ca2404.1_all.deb Size: 232770 MD5sum: 2ca76ebbaa9ddbada3ddf44d21201468 SHA1: 55b1080526c576231eac4c45bd9447386db5453d SHA256: d19c828b4c54335cd569ae643b489bd0dfadf90c45201562bf3349572c2cb8a2 SHA512: 505d75c635d9d6d473c0502b1f0bdb85ec68360769927129d10e8e338b3c1b2ac472bd1d0fe0ec71c2e047ff05aa99f875846bdc157bacda88afd908839d5c25 Homepage: https://cran.r-project.org/package=dsmmR Description: CRAN Package 'dsmmR' (Estimation and Simulation of Drifting Semi-Markov Models) Performs parametric and non-parametric estimation and simulation of drifting semi-Markov processes. The definition of parametric and non-parametric model specifications is also possible. Furthermore, three different types of drifting semi-Markov models are considered. These models differ in the number of transition matrices and sojourn time distributions used for the computation of a number of semi-Markov kernels, which in turn characterize the drifting semi-Markov kernel. For the parametric model estimation and specification, several discrete distributions are considered for the sojourn times: Uniform, Poisson, Geometric, Discrete Weibull and Negative Binomial. The non-parametric model specification makes no assumptions about the shape of the sojourn time distributions. Semi-Markov models are described in: Barbu, V.S., Limnios, N. (2008) . Drifting Markov models are described in: Vergne, N. (2008) . Reliability indicators of Drifting Markov models are described in: Barbu, V. S., Vergne, N. (2019) . We acknowledge the DATALAB Project (financed by the European Union with the European Regional Development fund (ERDF) and by the Normandy Region) and the HSMM-INCA Project (financed by the French Agence Nationale de la Recherche (ANR) under grant ANR-21-CE40-0005). 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This package is the 'DataSHIELD' interface implementation to analyze data shared on a 'MOLGENIS Armadillo' server. 'MOLGENIS Armadillo' is a light-weight 'DataSHIELD' server using a file store and an 'RServe' server. 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Inference via bootstrap and recursive sandwich estimation. Estimation and inference for survival outcomes via Dynamic Weighted Survival Modeling (DWSurv). Extension to continuous treatment variables. Wallace et al. (2017) ; Simoneau et al. (2020) . 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Package: r-cran-dtsea Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-bioc-fgsea, r-cran-igraph, r-cran-magrittr, r-cran-tibble, r-cran-tidyr, r-bioc-biocparallel, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-dtsea_0.0.3-1.ca2404.1_all.deb Size: 346080 MD5sum: 606742cc8161b2b1dfd79cb44f152e48 SHA1: c8bbe40c488f267a919fd4f56e6c4bf133339f18 SHA256: 3becfceadb789e6c6fd4ae0a1adc7d1f3f7a8851cc60bc9eb1d7fe7e5a940199 SHA512: 05e27f2364a2d34da5ccffb6f374c005e133e584d271ee50fb6b2e38cbc9aa7e9744ed82147c33508013b1eb3475b72ffb759d812704626a028b5a937ed828c6 Homepage: https://cran.r-project.org/package=DTSEA Description: CRAN Package 'DTSEA' (Drug Target Set Enrichment Analysis) It is a novel tool used to identify the candidate drugs against a particular disease based on the drug target set enrichment analysis. It assumes the most effective drugs are those with a closer affinity in the protein-protein interaction network to the specified disease. (See Gómez-Carballa et al. (2022) and Feng et al. (2022) for disease expression profiles; see Wishart et al. (2018) and Gaulton et al. (2017) for drug target information; see Kanehisa et al. (2021) for the details of KEGG database.) Package: r-cran-dtsg Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1769 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-r6, r-cran-timechange Suggests: r-cran-dygraphs, r-cran-fasttime, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rcppcctz, r-cran-rmarkdown, r-cran-runner, r-cran-tinytest, r-cran-units Filename: pool/dists/noble/main/r-cran-dtsg_2.1.0-1.ca2404.1_all.deb Size: 376294 MD5sum: a96b18a09ccfc263e04a06906337bc36 SHA1: fc9293606121e5c166c32d9895f67897b92d8d4b SHA256: 8cf1b2525f6d9b150dcc8b1ebe8eafe24c4279dc300ceaf405c851d3ac469f42 SHA512: 83ee8a05036a647ab75df9a2a58d71b3d67c6d8994f5f93766c856d10dae2a1f50d838131a0d4f8848c50be7441661dc64c0c8b5dfa056831c3e7e0b7ef95686 Homepage: https://cran.r-project.org/package=DTSg Description: CRAN Package 'DTSg' (A Class for Working with Time Series Data Based on 'data.table'and 'R6' with Largely Optional Reference Semantics) Basic time series functionalities such as listing of missing values, application of arbitrary aggregation as well as rolling (asymmetric) window functions and automatic detection of periodicity. As it is mainly based on 'data.table', it is fast and (in combination with the 'R6' package) offers reference semantics. In addition to its native R6 interface, it provides an S3 interface for those who prefer the latter. Finally yet importantly, its functional approach allows for incorporating functionalities from many other packages. Package: r-cran-dtsr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1316 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvdalab, r-cran-dmwr2, r-cran-cluster, r-cran-mass Filename: pool/dists/noble/main/r-cran-dtsr_0.2.0-1.ca2404.1_all.deb Size: 1310562 MD5sum: de784a42f3fa1bf8ad3001fee0fd02cf SHA1: 74b365a371ceb8556b981fb94391d93f57a60a73 SHA256: 1a2b1a00b3bde8943ff7b08ccc1c37f9adcf79b2a7af65732a2a591fe132b202 SHA512: 270fb0a25cbdc3939d37d3672e884324128eaaf600b8b7071cea6870c8c68df0e1c87e41227761eb94c69bc10999852a32776f7e72afdb403db2bab4f872943c Homepage: https://cran.r-project.org/package=DTSR Description: CRAN Package 'DTSR' (Distributed Trimmed Scores Regression for Handling Missing Data) Provides functions for handling missing data using Distributed Trimmed Scores Regression and other imputation methods. It includes facilities for data imputation, evaluation metrics, and clustering analysis. It is designed to work in distributed computing environments to handle large datasets efficiently. The philosophy of the package is described in Guo G. (2024) . Package: r-cran-dtt Architecture: all Version: 0.1-2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dtt_0.1-2.1-1.ca2404.1_all.deb Size: 21122 MD5sum: 9f30f71ff9b8c040f30577af5286030d SHA1: 243d50a6d82b81d9983746b9ee9d65d78f9d1bce SHA256: 6574fac4482423dd377ed8c639ff0a4acba21a9c289e2cfd71bbc7b23fc0b689 SHA512: 4378d521a917f09563fc8b3c4d27fe03d3b66d51b4aa4d2d0072aca2fd724359ab72be2ba04b8324c72f95ffae81dd2b2ec85706ccae4176addf3cad66ad87b5 Homepage: https://cran.r-project.org/package=dtt Description: CRAN Package 'dtt' (Discrete Trigonometric Transforms) Provides functions for 1D and 2D Discrete Cosine Transform (DCT), Discrete Sine Transform (DST) and Discrete Hartley Transform (DHT). Package: r-cran-dttr2 Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chk, r-cran-hms, r-cran-lifecycle Suggests: r-cran-rlang, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dttr2_0.5.2-1.ca2404.1_all.deb Size: 255934 MD5sum: c6951f3a99dbade51d8c47f073919965 SHA1: 742cd0886f7b7ce3f755c2e19918d95c679750f7 SHA256: ce919e07a33c225b3bc8b289aa70497a7e29ff19d883846ccce50408f1998345 SHA512: 409ed49a7523973c1050b2e946817d3f648c0d444782c40ff57b6048d4a4d4373865e312f23355cf330c5ea3a44106bba3e3beceef5ac1264c29e8db5b0c6e34 Homepage: https://cran.r-project.org/package=dttr2 Description: CRAN Package 'dttr2' (Manipulate Date, POSIXct and hms Vectors) Manipulates date ('Date'), date time ('POSIXct') and time ('hms') vectors. Date/times are considered discrete and are floored whenever encountered. Times are wrapped and time zones are maintained unless explicitly altered by the user. Package: r-cran-dtwbi Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dtw, r-cran-rlist, r-cran-e1071, r-cran-entropy, r-cran-lsa Filename: pool/dists/noble/main/r-cran-dtwbi_1.1-1.ca2404.1_all.deb Size: 76496 MD5sum: 68cbc7ca01a2b3a9675e050b1a6a4316 SHA1: f37f00b542d2e5540ea16710f1ffa83b390c36f0 SHA256: 4baa97008aec0d02d37c455a35a4afb254ace34056d99a3f52bef3b641cac3db SHA512: 7da330aad74c5dc28ef6fea4b930ffe1c97f34c88e7adb678985ce8d1606f16017d9b21bb54a89bfacf4f69c7fd42175cfe3ba793af26d976bd4638f42c07afa Homepage: https://cran.r-project.org/package=DTWBI Description: CRAN Package 'DTWBI' (Imputation of Time Series Based on Dynamic Time Warping) Functions to impute large gaps within time series based on Dynamic Time Warping methods. It contains all required functions to create large missing consecutive values within time series and to fill them, according to the paper Phan et al. (2017), . Performance criteria are added to compare similarity between two signals (query and reference). Package: r-cran-dtwrappers2 Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dtwrappers Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-devtools, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dtwrappers2_0.0.3-1.ca2404.1_all.deb Size: 131104 MD5sum: 31c50e12097c832e6f08324c337cdce6 SHA1: 4f21454a94f4c32ab03208736eb61bc43d4bb1ab SHA256: 5685b39a4c4c6845d6e827a427beb13d8aab7f84d34c014e215a8b1900eed6e5 SHA512: 3191d50f71eee636dfbf9b4db10902c077e4a2d2707e28a7b68618d7b75dd98089e4bccd9cfe95d5e3a8c51cc817d5fc488ebe07dd556b795824d9340084f49f Homepage: https://cran.r-project.org/package=DTwrappers2 Description: CRAN Package 'DTwrappers2' (Extensions of 'DTwrappers') Offers functionality which provides methods for data analyses and cleaning that can be flexibly applied across multiple variables and in groups. These include cleaning accidental text, contingent calculations, counting missing data, and building summarizations of the data. Package: r-cran-dtwrappers Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dtwrappers_0.0.2-1.ca2404.1_all.deb Size: 89146 MD5sum: 318bf9e99cef97008e9ee33406083e46 SHA1: 9615b30a66490bb0f470e43821f0c718562beca2 SHA256: 75338538ec62018e4e68b7de6aa70e1265c6704933a3443eccb2007e1401a5ef SHA512: d559ffbf4c4782b472f2bd6296f9b23cf44219a96152c59ab5e90a2795e918b53926cb8bd3cccb12d5b034870b5b2d5dddc5599619058171a87f0c883641b63f Homepage: https://cran.r-project.org/package=DTwrappers Description: CRAN Package 'DTwrappers' (Simplified Data Analysis with Wrapper Functions for the'Data.Table' Package) Provides functionality for users who are learning R or the techniques of data analysis. Written as a collection of wrapper functions, the 'DTwrapper' package facilitates many core operations of data processing. This is achieved with relatively few requirements about the order of the processing steps or knowledge of specialized syntax. 'DTwrappers' creates coding results along with translations to data.table's code. This enables users to benefit from the speed and efficiency of data.table's calculations. Furthermore, the package also provides the translated code for educational purposes so that users can review working examples of coding syntax and calculations. Package: r-cran-dtwumi Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dtw, r-cran-rlist, r-cran-e1071, r-cran-entropy, r-cran-lsa, r-cran-dtwbi Filename: pool/dists/noble/main/r-cran-dtwumi_1.0-1.ca2404.1_all.deb Size: 72654 MD5sum: 50abcbe4e7b12978780684421ec5d3c0 SHA1: a43165d44b3f9363793c73de9399887bbc8a135e SHA256: 1e77a9012b998bbdc57179a8a23ae17520368e0222bc829871526e1e36082f0d SHA512: 87587e721d9df2758bba52b2d5300de75712ca6d4abdea75df00f625eec5b77e2a4e50c6bf5614ddaef1568868fec5ccf605cba00ec4605ab5491a89f2dc15e8 Homepage: https://cran.r-project.org/package=DTWUMI Description: CRAN Package 'DTWUMI' (Imputation of Multivariate Time Series Based on Dynamic TimeWarping) Functions to impute large gaps within multivariate time series based on Dynamic Time Warping methods. Gaps of size 1 or inferior to a defined threshold are filled using simple average and weighted moving average respectively. Larger gaps are filled using the methodology provided by Phan et al. (2017) : a query is built immediately before/after a gap and a moving window is used to find the most similar sequence to this query using Dynamic Time Warping. To lower the calculation time, similar sequences are pre-selected using global features. Contrary to the univariate method (package 'DTWBI'), these global features are not estimated over the sequence containing the gap(s), but a feature matrix is built to summarize general features of the whole multivariate signal. Once the most similar sequence to the query has been identified, the adjacent sequence to this window is used to fill the gap considered. This function can deal with multiple gaps over all the sequences componing the input multivariate signal. However, for better consistency, large gaps at the same location over all sequences should be avoided. Package: r-cran-dual Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dual_0.0.6-1.ca2404.1_all.deb Size: 242686 MD5sum: cb5bc67ff95ecf9db65237d0312539a0 SHA1: 5156d7c1640b1992f160655ef15dcc879a7a2885 SHA256: b36cfee2f4a7cf9ccbdeed2d0f75ce2015746dfacca8d43572edb4a4ef4388ce SHA512: 054ed9aa7dab73b3f1c10304db893cf72920a63b5f53fa12d73b986ea3b91eace20cde86f4f4502622ecd9540f07a7cdb8a12b88ad96b81792326a75dc1ec78b Homepage: https://cran.r-project.org/package=dual Description: CRAN Package 'dual' (Automatic Differentiation with Dual Numbers) Automatic differentiation is achieved by using dual numbers without providing hand-coded gradient functions. The output value of a mathematical function is returned with the values of its exact first derivative (or gradient). For more details see Baydin, Pearlmutter, Radul, and Siskind (2018) . Package: r-cran-dualscale Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-eba, r-cran-ff, r-cran-ggplot2, r-cran-ggrepel, r-cran-matrix, r-cran-matrixcalc, r-cran-rcolorbrewer, r-cran-glue Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-dualscale_1.0.0-1.ca2404.1_all.deb Size: 137690 MD5sum: 4d6384cb4bafed15305dfdd79ca8d73b SHA1: 8d0d05056ed408fa1e914ddd45d0a43f39f9692f SHA256: c7041a87a51309500600d735c67a743f2535ee9ebd52d0551a89cf578d7536a6 SHA512: 327203e9e6a6d20587a60f585a6b0e37b9032f33ca15eccaefa1a8c34caf856d9001acded79ec357b667594ee9bd7dd873caee0804a137ac3cc8e2193a5edb5a Homepage: https://cran.r-project.org/package=dualScale Description: CRAN Package 'dualScale' (Dual Scaling Analysis of Data) Dual Scaling, developed by Professor Shizuhiko Nishisato (1994, ISBN: 0-9691785-3-6), is a fundamental technique in multivariate analysis used for data scaling and correspondence analysis. Its utility lies in its ability to represent multidimensional data in a lower-dimensional space, making it easier to visualize and understand underlying patterns in complex data. This technique has been implemented to handle various types of data, including Contingency and Frequency data (CF), Multiple-Choice data (MC), Sorting data (SO), Paired-Comparison data (PC), and Rank-Order data (RO), providing users with a powerful tool to explore relationships between variables and observations in various fields, from sociology to ecology, enabling deeper and more efficient analysis of multivariate datasets. Package: r-cran-duawranglr Architecture: all Version: 0.6.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-haven, r-cran-readxl, r-cran-readr, r-cran-digest, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-duawranglr_0.6.7-1.ca2404.1_all.deb Size: 88186 MD5sum: f898dc0c9eb2ec6f9b9b15a329eb7c89 SHA1: a5ef8c0dd150435b221f713480be98f32c74ad9a SHA256: 0ca06f69abb12f641e9e0c6a813d9424ce373b91f49f97abac69282ca4ae6a0a SHA512: e77e219aa35071b749f51874e61abe6ffa24e1e144d676ed70911d808497ba4de705f82c88e72f1b30d2ae8ad6c180f83daef5797d14f9e099c9cbaabd3fa8a7 Homepage: https://cran.r-project.org/package=duawranglr Description: CRAN Package 'duawranglr' (Securely Wrangle Dataset According to Data Usage Agreement) Create shareable data sets from raw data files that contain protected elements. Relying on master crosswalk files that list restricted variables, package functions warn users about possible violations of data usage agreement and prevent writing protected elements. Package: r-cran-dub Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-dub_0.2.0-1.ca2404.1_all.deb Size: 21684 MD5sum: ae08c0af1122dbfe94fbe458073b3211 SHA1: ab7a61b0cdaa8fdf199675788e8af4f76884ef9f SHA256: 881d76cef1f2181bb86bb310dc8a427cb8e0e9579006fba1c5f8c7d3f9b572bc SHA512: 18d4f06c048f2a9d890aa96cf065a4390bbaf2fd16cbfacba4729fdf4c294fb2be1776a6187df06eb4aaca83e8478858cca05ecb6cbecd6e4b798d7ccbc53615 Homepage: https://cran.r-project.org/package=dub Description: CRAN Package 'dub' (Unpacking Assignment for Lists via Pattern Matching) Provides an operator for assigning nested components of a list to names via a concise pattern matching syntax. This is especially convenient for assigning individual names to the multiple values that a function may return in the form of a list, and for extracting deeply nested list components. Package: r-cran-duckdbfs Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 935 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-fs, r-cran-glue Suggests: r-cran-curl, r-cran-sf, r-cran-jsonlite, r-cran-spelling, r-cran-minioclient, r-cran-testthat Filename: pool/dists/noble/main/r-cran-duckdbfs_0.1.2-1.ca2404.1_all.deb Size: 407538 MD5sum: 316119be7ea470aa5b61ea4845628f44 SHA1: 7c9954a08f66cee2c838d2326147a89d3bbbbbca SHA256: cc70be37a235aab17c0be971fc0aee6f6917e12491a7e5a8311cac6118f3f264 SHA512: 51b3ab365f3795458988a102e1be1a0993560ce93d9ac74f28ec3b2f461fa29010729732cb1e2f959aaf4d2a1464661e1b8d68618e3159fb901f526c16df8dcf Homepage: https://cran.r-project.org/package=duckdbfs Description: CRAN Package 'duckdbfs' (High Performance Remote File System, Database and 'Geospatial'Access Using 'duckdb') Provides friendly wrappers for creating 'duckdb'-backed connections to tabular datasets ('csv', parquet, etc) on local or remote file systems. This mimics the behaviour of "open_dataset" in the 'arrow' package, but in addition to 'S3' file system also generalizes to any list of 'http' URLs. Package: r-cran-duckduckr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-duckduckr_1.0.0-1.ca2404.1_all.deb Size: 14892 MD5sum: c5dfc810d0154199086246e6163a11e4 SHA1: f8c7dd6354f26b383453379347483a7efb6e2fa1 SHA256: ce4e620b3036847793dd5120fe9231258823a75fc88c79e04995e65165fe2b6b SHA512: 016fa3022b9669ed15044fbb60d812264f83da990043fe706fd874b6612a11692d90d9d3c09556c4355bd778af7d95db37b5c7230a7df6efbf1e1b4129c975be Homepage: https://cran.r-project.org/package=duckduckr Description: CRAN Package 'duckduckr' (Simple Client for the DuckDuckGo Instant Answer API) Programmatic access to the DuckDuckGo Instant Answer API . Package: r-cran-duckh3 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1726 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-duckspatial, r-cran-glue Suggests: r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-duckh3_0.1.0-1.ca2404.1_all.deb Size: 1727328 MD5sum: bc759ec0d0b727606ce7ebfe196ccee0 SHA1: 3f4c017bbd1bcb82e4ac453d44de72cbcd83f5ce SHA256: 266a38a29b729e448a278de473ea73399e42ee0879aa230b141e96e857e0bbdb SHA512: c84c14bebb534561cdb0b4ea72983e4dbe7b6bba42e552713f9eccd51b416471629359e8983a502bec2b314127f1043ba491718c99fb81af9c938e730c39045f Homepage: https://cran.r-project.org/package=duckh3 Description: CRAN Package 'duckh3' (H3 Extension of 'DuckDB') Fast & memory-efficient functions to analyze and manipulate large data sets. It leverages the fast analytical capabilities of 'DuckDB' and its spatial extension (see ) while maintaining compatibility with R’s spatial data ecosystem to work with spatial vector data. 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Package: r-cran-dwlm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dwlm_0.1.0-1.ca2404.1_all.deb Size: 34134 MD5sum: 3f509b94d675f76bb71f15691ff094f9 SHA1: 0dc09eed84d96d106772fa6e52364b60c3ea22c9 SHA256: 53d4ff81a3741fe8f5d1913f7cea29f6ea95bae82ac119eedbd4f9947d4896c6 SHA512: 1cd626bfa16be4dcc85e01065c40ab67ef7aa050b975dd3f184e76c050eb2e8eb27245e4b3a649ebdca102cc29ee5d2eefe917ee10310fdad2a40aec83fc2f4c Homepage: https://cran.r-project.org/package=dwlm Description: CRAN Package 'dwlm' (Doubly Weighted Linear Model) This linear model solution is useful when both predictor and response have associated uncertainty. The doubly weights linear model solution is invariant on which quantity is used as predictor or response. Based on the results by Reed(1989) and Ripley & Thompson(1987) . Package: r-cran-dwls Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-reshape, r-cran-seurat, r-cran-rocr, r-cran-varhandle, r-cran-dplyr, r-cran-e1071, r-bioc-mast, r-bioc-summarizedexperiment Suggests: r-cran-testthat, r-cran-matrix Filename: pool/dists/noble/main/r-cran-dwls_0.1.0-1.ca2404.1_all.deb Size: 197078 MD5sum: 179fd3f3bb572d603a1ff24b439d4962 SHA1: 1e0ece09fd907515d3044bdea5553c1faf3c05fd SHA256: dfd2fad2ec0becfd3f0aef2442b16c20b8595fe56ee0cad317d763922faf537a SHA512: 714db8125f58842bdd778a969eb4fa9e59b3278cddebf83adc332ee489ca9cf9f9e488f5930980b56f38f63ab3b1fb338a0f0a77667b5ed8a64541b0b32a793a Homepage: https://cran.r-project.org/package=DWLS Description: CRAN Package 'DWLS' (Gene Expression Deconvolution Using Dampened Weighted LeastSquares) The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly,our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations. Dampened weighted least squares ('DWLS') is an estimation method for gene expression deconvolution, in which the cell-type composition of a bulk RNA-seq data set is computationally inferred. This method corrects common biases towards cell types that are characterized by highly expressed genes and/or are highly prevalent, to provide accurate detection across diverse cell types. See: for more information about the development of 'DWLS' and the methods behind our functions. Package: r-cran-dwp Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-gtools, r-cran-invgamma, r-cran-magrittr, r-cran-mass, r-cran-matrixstats, r-cran-mvtnorm, r-cran-plotrix, r-cran-pracma, r-cran-sf, r-cran-statmod, r-cran-vgam Filename: pool/dists/noble/main/r-cran-dwp_1.2-1.ca2404.1_all.deb Size: 420088 MD5sum: d636d5eefba8eb34d8537018ab19bb4f SHA1: 825ff85d2eeb957f131923589c3d308050b6ffcc SHA256: b8bfdc7fcb3773472bcc4f5b96f9bf20df90fb5c7455787f0f43f71d68ea0fbd SHA512: 119a72c9f05ef6a369198924d3ff3bc83f6b1894115bafd38aafc1b53fa52ea58629295f7ee111fed16535d98b6d2aa1513cc07f5faa0d0dbec3d6d60cb60cef Homepage: https://cran.r-project.org/package=dwp Description: CRAN Package 'dwp' (Density-Weighted Proportion) Fit a Poisson regression to carcass distance data and integrate over the searched area at a wind farm to estimate the fraction of carcasses falling in the searched area and format the output for use as the dwp parameter in the 'GenEst' or 'eoa' package for estimating bird and bat mortality, following Dalthorp, et al. 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Provides rich facilities for charting time-series data in R, including highly configurable series- and axis-display and interactive features like zoom/pan and series/point highlighting. Package: r-cran-dykstra Architecture: all Version: 1.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-dykstra_1.0-0-1.ca2404.1_all.deb Size: 22400 MD5sum: bcc4df8352325da5645b22ebea3c82f0 SHA1: c327f4898e51d5f9f3c8f77a3646d3d2a83fec5b SHA256: 01339e0d1f1ffa447f68c0da96c1d246115d64c4dcb1f7b9339e5805fc3826e0 SHA512: 0263767dce54df5df2bed5b11cd6ff843fea571943931a610902191a8708b40e36e1639f6d1da956f3a3d84ae823e635355bdddee5468f6f59ccf7199d10c06a Homepage: https://cran.r-project.org/package=Dykstra Description: CRAN Package 'Dykstra' (Quadratic Programming using Cyclic Projections) Solves quadratic programming problems using Richard L. Dykstra's cyclic projection algorithm. Routine allows for a combination of equality and inequality constraints. See Dykstra (1983) for details. Package: r-cran-dym Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-dym_0.2-1.ca2404.1_all.deb Size: 26362 MD5sum: 8d63d6a7d03d1f876a99209ae9d9eccf SHA1: 13417a1ab222161bf79798f8545ef5d70013b7b6 SHA256: 06b095f13e837189e818033520718102844a7a0689003b2f292ad498d098086d SHA512: f28f36de6a61dfadbef43843830abd6a4c08d64b5b1ec354d2a297448f90431487ddaf4852413f97509adfe74bd2e97ae2fbdbd67e47351414af6c44038b41ca Homepage: https://cran.r-project.org/package=DYM Description: CRAN Package 'DYM' (Did You Mean?) Add a "Did You Mean" feature to the R interactive. 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While there are several excellent logging solutions already in the R ecosystem, I always feel constrained in some way by each of them. Every project is designed differently to solve it's domain specific problem, and ultimately the utility of a logging solution is its ability to adapt to this design. This is the raison d'être for 'dyn.log': to provide a modular design, template mechanics and a configuration-based integration model, so that the logger can integrate deeply into your design, even though it knows nothing about it. 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Package: r-cran-dynafluxr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bspline, r-cran-nlsic, r-cran-optparse, r-cran-qpdf, r-cran-arrapply, r-cran-slam, r-cran-gmresls, r-cran-shiny, r-cran-shinyjs, r-cran-shinyfiles Suggests: r-cran-runit, r-cran-knitr Filename: pool/dists/noble/main/r-cran-dynafluxr_1.0.1-1.ca2404.1_all.deb Size: 266836 MD5sum: 1f6b6b1c71eefd6f00149e7255a23cc7 SHA1: 33647d47844c32125db4313c26b669379d6c09d5 SHA256: a047276b3213ceed5ea0d8cd85779caf798b842fa8bfc8c9ff45794227f70880 SHA512: 4ca43e6291ee43b8a097916adc6148e5b3c6ab8fd3f17f10afa47ad16b5f0e5584d1da4158bf597bc53e18aad50a8fa037006cfed511b67e34ac312919b99d76 Homepage: https://cran.r-project.org/package=dynafluxr Description: CRAN Package 'dynafluxr' (Retrieve Reaction Rate Dynamics from Metabolite ConcentrationTime Courses) Reaction rate dynamics can be retrieved from metabolite concentration time courses. User has to provide corresponding stoichiometric matrix but not a regulation model (Michaelis-Menten or similar). Instead of solving an ordinary differential equation (ODE) system describing the evolution of concentrations, we use B-splines to catch the concentration and rate dynamics then solve a least square problem on their coefficients with non-negativity (and optionally monotonicity) constraints. Constraints can be also set on initial values of concentration. The package 'dynafluxr' can be used as a library but also as an application with command line interface dynafluxr::cli("-h") or graphical user interface dynafluxr::gui(). 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Rationale and model structure are described here: Da Re et al. (2021) and Da Re et al. (2022) . 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Dynamic pricing is also known as life cycle pricing; dynamic uptake is also known as multiple or stacked cohorts, or dynamic disease prevalence. Shafrin (2024) provides an explanation of dynamic value elements, in the context of Generalized Cost Effectiveness Analysis, and Puls (2024) reviews challenges of incorporating such dynamic value elements. This package aims to reduce those challenges. 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These highly flexible functions incorporate spatial and temporal aspects across key SDM stages; including when cleaning and filtering species occurrence data, generating pseudo-absence records, assessing and correcting sampling biases and autocorrelation, extracting explanatory variables and projecting distribution patterns. Throughout, functions utilise Google Earth Engine and Google Drive to minimise the computing power and storage demands associated with species distribution modelling at high spatio-temporal resolution. 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Package: r-cran-earlygating Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-betareg, r-cran-foreach Filename: pool/dists/noble/main/r-cran-earlygating_1.1-1.ca2404.1_all.deb Size: 97692 MD5sum: 8f79f95a2b9bc6f8250c977bbccd6b74 SHA1: 086a2093ec10a9688e2a1f43f9b8b138442363a1 SHA256: 1b55ce3622a7fa96441d2ed0f2e8956b210127dbeeea267673d6b1df6fed4948 SHA512: 337d10377310a984d096208f33f9a5c646bfcd285132b6a09c89bff579d1701ff983c395b536a9ec49633b32f79281044684a53deea6e98dfb5c6113aeda8bb2 Homepage: https://cran.r-project.org/package=earlygating Description: CRAN Package 'earlygating' (Properties of Bayesian Early Gating Designs) Computes the most important properties of four 'Bayesian' early gating designs (two single arm and two randomized controlled designs), such as minimum required number of successes in the experimental group to make a GO decision, operating characteristics and average operating characteristics with respect to the sample size. These might aid in deciding what design to use for the early phase trial. Package: r-cran-earlyr Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-distcrete, r-cran-epiestim, r-cran-epitrix, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-roxygen2, r-cran-incidence, r-cran-knitr, r-cran-rmarkdown, r-cran-projections, r-cran-covr Filename: pool/dists/noble/main/r-cran-earlyr_0.0.5-1.ca2404.1_all.deb Size: 320582 MD5sum: 6c5e2676085964ee8588ede5c25a9c62 SHA1: 17bcf8ba739801accc09fb3882b5380e4bf5ed82 SHA256: 2792efaf8de0b87abd4e4b29f6a7b509af1246cd4eb06909759d52c838531d87 SHA512: e8eb7548133646f880d83b0d8974064b353ba6ad14e1b2613456e82ae4de05a8061afd7442ebc4eed92c4d963356ee343d4c39beb438bc56fea5b64e3321c848 Homepage: https://cran.r-project.org/package=earlyR Description: CRAN Package 'earlyR' (Estimation of Transmissibility in the Early Stages of a DiseaseOutbreak) Implements a simple, likelihood-based estimation of the reproduction number (R0) using a branching process with a Poisson likelihood. This model requires knowledge of the serial interval distribution, and dates of symptom onsets. Infectiousness is determined by weighting R0 by the probability mass function of the serial interval on the corresponding day. It is a simplified version of the model introduced by Cori et al. (2013) . Package: r-cran-earlywarnings Architecture: all Version: 1.1.29-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-moments, r-cran-tgp, r-cran-tseries, r-cran-fields, r-cran-nortest, r-cran-quadprog, r-cran-kendall, r-cran-kernsmooth, r-cran-lmtest, r-cran-som, r-cran-spam, r-cran-knitr Filename: pool/dists/noble/main/r-cran-earlywarnings_1.1.29-1.ca2404.1_all.deb Size: 509872 MD5sum: 807d2baf875661849d085f8e5ad94aca SHA1: 2b3c29924e7ee174a63c4598f24bb37d8ef224c7 SHA256: 0c6dc7e820008751b82ca70a2bc830a1c20dbf6c86d13aa7ea3d2f294d231f36 SHA512: 0546faf8737d0c1ce7664463e04be09cb978082d13d2f9d08fd5cf1b06b2d7dec984d6dd0fdde059a03fa77c16e905c62baaae984da851318498be5234393c3c Homepage: https://cran.r-project.org/package=earlywarnings Description: CRAN Package 'earlywarnings' (Early Warning Signals for Critical Transitions in Time Series) The Early-Warning-Signals Toolbox provides methods for estimating statistical changes in time series that can be used for identifying nearby critical transitions. Package: r-cran-earthdatalogin Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1907 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-openssl, r-cran-purrr, r-cran-base64enc, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-terra, r-cran-rsconnect, r-cran-testthat, r-cran-curl, r-cran-sf, r-cran-fs, r-cran-readr, r-cran-spelling, r-cran-gdalcubes, r-cran-rstac Filename: pool/dists/noble/main/r-cran-earthdatalogin_0.0.3-1.ca2404.1_all.deb Size: 1368080 MD5sum: 64bd4cfbc7ad466f7ff2989a47b240fd SHA1: 04f5cab485f76d829a1ee074d80b374a4ec3a678 SHA256: d421be745a139a9c1755b728d9157ffc101e934f2cfb62f63d7746e4d08858c8 SHA512: 1eb1391e7adfb0912ca261a2cd168d92371e0664223f026d3b48fcca5b8d293f039d3543cec6eac20e34b3e746b6a6d607d9c743da8b69bf9f6125edf6f5cb78 Homepage: https://cran.r-project.org/package=earthdatalogin Description: CRAN Package 'earthdatalogin' (NASA 'EarthData' Access Utilities) Providing easy, portable access to NASA 'EarthData' products through the use of bearer tokens. Much of NASA's public data catalogs hosted and maintained by its 12 Distributed Active Archive Centers ('DAACs') are now made available on the Amazon Web Services 'S3' storage. However, accessing this data through the standard 'S3' API is restricted to only to compute resources running inside 'us-west-2' Data Center in Portland, Oregon, which allows NASA to avoid being charged data egress rates. This package provides public access to the data from any networked device by using the 'EarthData' login application programming interface (API), , providing convenient authentication and access to cloud-hosted NASA 'EarthData' products. This makes access to a wide range of earth observation data from any location straight forward and compatible with R packages that are widely used with cloud native earth observation data (such as 'terra', 'sf', etc.) Package: r-cran-earthtones Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maptiles, r-cran-terra, r-cran-sf Suggests: r-cran-testthat, r-cran-cluster, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-earthtones_0.2.0-1.ca2404.1_all.deb Size: 25386 MD5sum: b10893502dca7bf95b9179e98eea6364 SHA1: 65512012665b60054c31bcff6fbe7bb4163924c7 SHA256: 20564b9f7769bec4ca25d70929e41bcd7bef8ed4cdb65b2f06cfc9239936a8af SHA512: ccc69df02ace100d4d280a9548465fab3c659f43148448cad005e4807d1ad7cac989c728ec176a740c9d79017068acd2b6d3ab782d869fb818536df06f441af6 Homepage: https://cran.r-project.org/package=earthtones Description: CRAN Package 'earthtones' (Derive a Color Palette from a Particular Location on Earth) Downloads a satellite image via ESRI and maptiles (these are originally from a variety of aerial photography sources), translates the image into a perceptually uniform color space, runs one of a few different clustering algorithms on the colors in the image searching for a user-supplied number of colors, and returns the resulting color palette. 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Package: r-cran-easy.glmnet Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-glmnet, r-cran-survival Suggests: r-cran-proc Filename: pool/dists/noble/main/r-cran-easy.glmnet_1.1-1.ca2404.1_all.deb Size: 87146 MD5sum: 218acbc2169145bf44f528d31f288545 SHA1: 7e47fbbf6d779f930f738f00ebbeace04f7c23de SHA256: ac0e90626007434afcf1852156b9a4998330041aee74959d8e8c75515e6d5e02 SHA512: ba271fcdea82a2ead14322102635ae66a9dd8deec8e3bcf8da8fd5886623d749d66e88a307ddea1f2e098a39cc00d1518c69f7926a905cf0b9de15b6fdb5430e Homepage: https://cran.r-project.org/package=easy.glmnet Description: CRAN Package 'easy.glmnet' (Functions to Simplify the Use of 'glmnet' for Machine Learning) Provides several functions to simplify using the 'glmnet' package: converting data frames into matrices ready for 'glmnet'; b) imputing missing variables multiple times; c) fitting and applying prediction models straightforwardly; d) assigning observations to folds in a balanced way; e) cross-validate the models; f) selecting the most representative model across imputations and folds; and g) getting the relevance of the model regressors; as described in several publications: Solanes et al. (2022) , Palau et al. (2023) , Salazar de Pablo et al. (2025) . Package: r-cran-easy.utils Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-fastmatch, r-cran-rlang, r-cran-scales Filename: pool/dists/noble/main/r-cran-easy.utils_0.1.0-1.ca2404.1_all.deb Size: 46634 MD5sum: 62096b277d9ade9756aa301079a44a54 SHA1: 5b95121dfd03983f87a1c5c932edeb87e0f46c23 SHA256: b3b5e9179478f02094a24a234ba43bb87210a9a7bed4f672fee1483347d92fad SHA512: d4915d301ef1bc5b271caac5d5b848cd8dc6dd811dedde323d82c285fc96d63f5afb6c998340704f2729152ae9d4c66eac0077e37c6ae12aa4c0e2d310f27901 Homepage: https://cran.r-project.org/package=easy.utils Description: CRAN Package 'easy.utils' (Frequently Used Functions for Easy R Programming) Some utility functions for validation and data manipulation. These functions can be helpful to reduce internal codes everywhere in package development. 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Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented. Package: r-cran-easyahp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-easyahp_0.1.1-1.ca2404.1_all.deb Size: 23248 MD5sum: 04b8e2e8a769311295037dab1bb3952d SHA1: 3ac50c53271cb3ae22529d4d652b3ab8a505e0fe SHA256: e8c5a7fc074816e2d106a61a16acae88cc172ffeeb9fecf50608da1f7a4f42fa SHA512: c36213661d8767d6e977368c378cc511903aefcee3116bc9c9deab2cbb41e1ca4c82f563c80800cc07b0ac449345f8f59e94539bb3e1847d588dd88c0ec4920b Homepage: https://cran.r-project.org/package=easyAHP Description: CRAN Package 'easyAHP' (Analytic Hierarchy Process (AHP)) Given the scores from decision makers, the analytic hierarchy process can be conducted easily. Package: r-cran-easyalluvial Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2397 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-progress Suggests: r-cran-testthat, r-cran-covr, r-cran-islr, r-cran-nycflights13, r-cran-vdiffr, r-cran-pkgdown, r-cran-mlbench, r-cran-earth, r-cran-workflows, r-cran-future, r-cran-furrr, r-cran-e1071, r-cran-caret, r-cran-parsnip, r-cran-vip, r-cran-rpart, r-cran-glmnet, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-easyalluvial_0.4.0-1.ca2404.1_all.deb Size: 2387076 MD5sum: 7c81980326110fe7a67718188540fcac SHA1: 88debec9d783a8cc99de822f498c362c978f6815 SHA256: 2cc94d8f4735bac8aa846999da3862fe09b3cf605bcdf203d40919fb09f06ebf SHA512: cb99e137b181c0ae297a8189ec35efd9a49f44b2896cfb3df837a057ed78541cfabf10f72634616f06dfd11b858ef53c91ff923fb8ac34d087df8db968c5649e Homepage: https://cran.r-project.org/package=easyalluvial Description: CRAN Package 'easyalluvial' (Generate Alluvial Plots with a Single Line of Code) Alluvial plots are similar to sankey diagrams and visualise categorical data over multiple dimensions as flows. (Rosvall M, Bergstrom CT (2010) Mapping Change in Large Networks. PLoS ONE 5(1): e8694. Their graphical grammar however is a bit more complex then that of a regular x/y plots. The 'ggalluvial' package made a great job of translating that grammar into 'ggplot2' syntax and gives you many options to tweak the appearance of an alluvial plot, however there still remains a multi-layered complexity that makes it difficult to use 'ggalluvial' for explorative data analysis. 'easyalluvial' provides a simple interface to this package that allows you to produce a decent alluvial plot from any dataframe in either long or wide format from a single line of code while also handling continuous data. It is meant to allow a quick visualisation of entire dataframes with a focus on different colouring options that can make alluvial plots a great tool for data exploration. Package: r-cran-easyanova Architecture: all Version: 11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme Filename: pool/dists/noble/main/r-cran-easyanova_11.0-1.ca2404.1_all.deb Size: 480634 MD5sum: 017f8121c12af0921cbebc724b1e4ae7 SHA1: 2c0c48c0736fc100301733f1be307fa8fb7b4f61 SHA256: 4bde4e2d6078731da5d02649f8f46ee3096fce6df86e7868a0acb3a026975f0d SHA512: f2a4b539d79d8d9e4a0c5cc484386f2d3e20388c8bb24b9219535867ae8c87676be175b74782afb9dc08985cec3fad336366b0f0d890a7d94dfd7a635c6639a1 Homepage: https://cran.r-project.org/package=easyanova Description: CRAN Package 'easyanova' (Analysis of Variance and Other Important Complementary Analyses) Perform analysis of variance and other important complementary analyses. The functions are easy to use. Performs analysis in various designs, with balanced and unbalanced data. Package: r-cran-easybgm Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bdgraph, r-cran-bggm, r-cran-bgms, r-cran-dplyr, r-cran-ggplot2, r-cran-hdinterval, r-cran-igraph, r-cran-qgraph, r-cran-coda Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-easybgm_0.4.0-1.ca2404.1_all.deb Size: 226146 MD5sum: be207a673aa06c2a6450d52bb3f80052 SHA1: 20fa4d5448193618f08aeeb01d1fecfb0c82d5d3 SHA256: 4f1550b441d85fac3d67c740fbebe18fa26438e111d7b541d506d6630004f017 SHA512: 7c136bb419af79ba8fae53347ceb2a421dceff3c6fe163882da240500ffd526a881c1b7d411d798e0f5408b4d9e25b6d8f5dffaeab327c1fbb52c0890a263fb5 Homepage: https://cran.r-project.org/package=easybgm Description: CRAN Package 'easybgm' (Extracting and Visualizing Bayesian Graphical Models) Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. The package supports cross-sectional network models fitted using the packages 'BDgraph', 'bgms' and 'BGGM', as well as network comparison tests fitted using the packages 'bgms' and 'BBGM'. The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the posterior density of the parameters. In addition, for 'BDgraph' and 'bgms' it allows to assess the posterior structure space. Furthermore, the package comes with an extensive suite for visualizing results. 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This package can perform descriptive statistics according to different data types. If the data is a continuous variable, the mean and standard deviation or median and quartiles are automatically output; if the data is a categorical variable, the number and percentage are automatically output. In addition, if you enter two variables in this package, the two variables will be described and their relationships will be tested automatically according to their data types. For example, if one of the two input variables is a categorical variable, another variable will be described hierarchically based on the categorical variable and the statistical differences between different groups will be compared using appropriate statistical methods. And for groups of more than two, the post hoc test will be applied. For more information on the methods we used, please see the following references: Libiseller, C. and Grimvall, A. (2002) , Patefield, W. M. (1981) , Hope, A. C. A. (1968) , Mehta, C. R. and Patel, N. R. (1983) , Mehta, C. R. and Patel, N. R. (1986) , Clarkson, D. B., Fan, Y. and Joe, H. (1993) , Cochran, W. G. (1954) , Armitage, P. (1955) , Szabo, A. (2016) , David, F. B. (1972) , Joanes, D. N. and Gill, C. A. (1998) , Dunn, O. J. (1964) , Copenhaver, M. D. and Holland, B. S. (1988) , Chambers, J. M., Freeny, A. and Heiberger, R. M. (1992) , Shaffer, J. P. (1995) , Myles, H. and Douglas, A. W. (1973) , Rahman, M. and Tiwari, R. (2012) , Thode, H. J. (2002) , Jonckheere, A. R. (1954) , Terpstra, T. J. (1952) . 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Compatible model types include lm(), rlm(), glm(), glm.nb(), betareg(), and gam() (from 'mgcv'); nonlinear models via nls(); generalized least squares via gls(); and survival models via coxph() (from 'survival'). Mixed-effects models with random intercepts and/or slopes can be fitted using lmer(), glmer(), glmer.nb(), glmmTMB(), or gam() (from 'mgcv', via smooth terms). Plots are rendered using base R graphics with extensive customization options. Approximate confidence intervals for nls() and betareg() models are computed using the delta method. Robust standard errors for rlm() are computed using the sandwich estimator (Zeileis 2004) . For beta regression using 'betareg', see Cribari-Neto and Zeileis (2010) . For mixed-effects models with 'lme4', see Bates et al. (2015) . For models using 'glmmTMB', see Brooks et al. (2017) . Methods for generalized additive models using 'mgcv' follow Wood (2017) . 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See the package documentation for a more detailed description along with references. 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Package: r-cran-ebase Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2399 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-lubridate, r-cran-r2jags, r-cran-rjags, r-cran-tidyr, r-cran-truncnorm, r-cran-zoo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-ebase_1.1.0-1.ca2404.1_all.deb Size: 825574 MD5sum: ecb0026f087ba60c61aaf094b0fc2407 SHA1: 8d8601b428db43d1c1adbf7bb3ba30dfb651a851 SHA256: 569ce90e0141c2c3ed6121fd994e0735d217bff7d4e4900db60c78c95b9c1b4c SHA512: d86bb98d3b8edc66ffc4f6efc979f7893bcc2b211344882d713f3189e221cb02263fbd51d8ae6e5f84de34fabb9916d69befe818164736792c69880994e0559e Homepage: https://cran.r-project.org/package=EBASE Description: CRAN Package 'EBASE' (Estuarine Bayesian Single-Station Estimation Method forEcosystem Metabolism) Estimate ecosystem metabolism in a Bayesian framework for individual water quality monitoring stations with continuous dissolved oxygen time series. A mass balance equation is used that provides estimates of parameters for gross primary production, respiration, and gas exchange. Methods adapted from Grace et al. (2015) and Wanninkhof (2014) . Details in Beck et al. (2024) . Package: r-cran-ebayesthresh Architecture: all Version: 1.4-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 939 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavethresh Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-ebayesthresh_1.4-12-1.ca2404.1_all.deb Size: 772748 MD5sum: a202870b7dd67cfcae135de633ae1dc7 SHA1: 2d70c418f7fd03e022436536f4765bb18002de8d SHA256: 9e8a1abc5f14bfbe622a9c66a30a2460643659b576f4fa1c92420b7b6cbe85b7 SHA512: 6fc8223e4e4ccfd53ffcfbb6039cc43c371ef2db08b0c00c1011e95254fa52235efbbdc37afa56017f7ee65cf6a06d7751e1bebbfdb8a13f5f46d041fb9069f8 Homepage: https://cran.r-project.org/package=EbayesThresh Description: CRAN Package 'EbayesThresh' (Empirical Bayes Thresholding and Related Methods) Empirical Bayes thresholding using the methods developed by I. M. Johnstone and B. W. Silverman. The basic problem is to estimate a mean vector given a vector of observations of the mean vector plus white noise, taking advantage of possible sparsity in the mean vector. Within a Bayesian formulation, the elements of the mean vector are modelled as having, independently, a distribution that is a mixture of an atom of probability at zero and a suitable heavy-tailed distribution. The mixing parameter can be estimated by a marginal maximum likelihood approach. This leads to an adaptive thresholding approach on the original data. Extensions of the basic method, in particular to wavelet thresholding, are also implemented within the package. Package: r-cran-ebchs Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-fda Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ebchs_0.1.1-1.ca2404.1_all.deb Size: 92420 MD5sum: b2bd58d753e680aae69d4f61fe9e8ab3 SHA1: 92dbae02e66ba2e8f4338f1674dea216c773adc6 SHA256: b0dbe61b8e79772db769d1b29635850dd8b3ae37d82d9f4954a6ee3ed3db70f2 SHA512: 6c35618e1cd28551d34ea3d81372106ef28e4ecff5406f86e98aa7209020fe45d172bbf98d3ee50c773f08fc92888a3a0f3a1904fe102630a220f603fc702346 Homepage: https://cran.r-project.org/package=EBCHS Description: CRAN Package 'EBCHS' (An Empirical Bayes Method for Chi-Squared Data) We provide the main R functions to compute the posterior interval for the noncentrality parameter of the chi-squared distribution. The skewness estimate of the posterior distribution is also available to improve the coverage rate of posterior intervals. Details can be found in Du and Hu (2022) . Package: r-cran-ebci Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat, r-cran-lpsolve, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ebci_1.0.0-1.ca2404.1_all.deb Size: 195540 MD5sum: 3d54e1903af15012c228f96ff64374fa SHA1: 0f16f9c989d43de8867b15c274a75cf996685269 SHA256: 5df92fbb35636054f8d3779a90ce55b7d6a91d63c853b37ff7d444ed02ed5387 SHA512: 709aa59cd720dd0a4c636eb98566103bad8af4d504431d1ce3096ebcb96fb0a6e4df585b924cc5ff1328d857d90ed0d8a49aa8416a110c30b9d13f2754c8745b Homepage: https://cran.r-project.org/package=ebci Description: CRAN Package 'ebci' (Robust Empirical Bayes Confidence Intervals) Computes empirical Bayes confidence estimators and confidence intervals in a normal means model. The intervals are robust in the sense that they achieve correct coverage regardless of the distribution of the means. If the means are treated as fixed, the intervals have an average coverage guarantee. The implementation is based on Armstrong, Kolesár and Plagborg-Møller (2020) . Package: r-cran-ebcobart Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbarts, r-cran-loo, r-cran-posterior, r-cran-univariateml, r-cran-extradistr Filename: pool/dists/noble/main/r-cran-ebcobart_1.1.2-1.ca2404.1_all.deb Size: 252764 MD5sum: 3c80df63d983547a53f2773c5d6ad0ee SHA1: 06f79bd401273e0493b3de1432ea2e663a2ce86e SHA256: f773b5c8fa93b50fab3c8fa70336750c073d186ec72c05b0590adfce84a0a513 SHA512: ab1e42073ccf698dd71c420b5868e30830490eef7a6119946cea0f54da29980044f900aa2e7630dd285a2ac2af9e3f2789f5a6ab34864743a6854384a33791f3 Homepage: https://cran.r-project.org/package=EBcoBART Description: CRAN Package 'EBcoBART' (Co-Data Learning for Bayesian Additive Regression Trees) Estimate prior variable weights for Bayesian Additive Regression Trees (BART). These weights correspond to the probabilities of the variables being selected in the splitting rules of the sum-of-trees. Weights are estimated using empirical Bayes and external information on the explanatory variables (co-data). BART models are fitted using the 'dbarts' 'R' package. See Goedhart and others (2023) for details. Package: r-cran-ebdm Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ebdm_3.0.1-1.ca2404.1_all.deb Size: 57290 MD5sum: 069d61d27decc4d6ada6e1837d3a5210 SHA1: da5d5518cfccd81c2b83937734b5300b439b14a9 SHA256: 474fa603d854d277252628f95a33ac55f8e25cb77fbd2c683efb64a2dde23d47 SHA512: abb880c276d8dea17630e094617f4d6d955a2d9feffd99ea5b949d0c3db90eec6b9959f79795029a2aa2e00a21ab0648cadec3ba13073a02de32eaeb03bb17a4 Homepage: https://cran.r-project.org/package=ebdm Description: CRAN Package 'ebdm' (Estimating Bivariate Dependency from Marginal Data) Provides statistical methods for estimating bivariate dependency (correlation) from marginal summary statistics across multiple studies. The package supports three modules of bivariate joint distribution estimated from marginal summary data: (1) two binary, (2) two continuous, (3) one binary and one continuous These methods enable privacy-preserving joint estimation when individual-level data are unavailable. The approaches are detailed in Shang, Tsao, and Zhang (2025a) and Shang, Tsao, and Zhang (2025b) . Package: r-cran-ebgenotyping Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ebgenotyping_2.0.1-1.ca2404.1_all.deb Size: 31846 MD5sum: 4369eb0ac9685b66218f8ecfa9583fc2 SHA1: 557b57f289413fde987894692778618f3cb28536 SHA256: 00a3e076f3b2f9c1429aee3fc3d06c4223282047d9149bbaee7467cf57ae7d43 SHA512: 3c2df020f60b8df0452dae69427d302b39ee086fb61e43a81db7dad9da4e50eb67f4f8dd3e43d06312ffeee08ec9fb39c36072c52d5c6bd14d918ddb5d6edc79 Homepage: https://cran.r-project.org/package=ebGenotyping Description: CRAN Package 'ebGenotyping' (Genotyping and SNP Detection using Next Generation SequencingData) Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method. Package: r-cran-ebirdst Architecture: all Version: 3.2023.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 891 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-jsonlite, r-cran-magrittr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-scales, r-cran-sf, r-cran-stringr, r-cran-terra, r-cran-viridislite Suggests: r-cran-fields, r-cran-ggplot2, r-cran-knitr, r-cran-lubridate, r-cran-presenceabsence, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-scico, r-cran-testthat, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-ebirdst_3.2023.1-1.ca2404.1_all.deb Size: 443850 MD5sum: 11a9ad8deffbf14d49cea1bce4b9f3d7 SHA1: 99ec5987fde5b4a058ad17d9641fcb05662a6494 SHA256: 59cde7802ed0844ea7bc5ef3cdc885d4f3bd44437fa6d62b634619882fc9f1cb SHA512: c10e00ac8339fa4c9ee2b0a8533af061183c6195606c0da1534407e5fa031aa89eff44ddd7424671b7692d335dce7f9852e3c1e314bdd3365ea1210103d337bf Homepage: https://cran.r-project.org/package=ebirdst Description: CRAN Package 'ebirdst' (Access and Analyze eBird Status and Trends Data Products) Tools for accessing and analyzing eBird Status and Trends Data Products (). eBird () is a global database of bird observations collected by member of the public. eBird Status and Trends uses these data to model global bird distributions, abundances, and population trends at a high spatial and temporal resolution. Package: r-cran-ebm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-ggplot2, r-cran-lattice Suggests: r-cran-htmltools, r-cran-islr2, r-cran-knitr, r-cran-rmarkdown, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-ebm_0.1.0-1.ca2404.1_all.deb Size: 502410 MD5sum: 9fadcec822543345ba0365e4bd0b3937 SHA1: 9a9d1d517818682baf1239cec518ec5f485dddec SHA256: 76ca724a43bb26edea432ad84de7e290aaf8785fe85a9f8196507e221ab90917 SHA512: 5f4d8aaf942d54d9e498b97dc34ede0598440f356cef609b54918a1eaa6ac7d6648243e43ab364f9ed893c32269bdf542d09fa07d20d5037965f95eb064568a3 Homepage: https://cran.r-project.org/package=ebm Description: CRAN Package 'ebm' (Explainable Boosting Machines) An interface to the 'Python' 'InterpretML' framework for fitting explainable boosting machines (EBMs); see Nori et al. (2019) for details. EBMs are a modern type of generalized additive model that use tree-based, cyclic gradient boosting with automatic interaction detection. They are often as accurate as state-of-the-art blackbox models while remaining completely interpretable. Package: r-cran-ebmc Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-rpart, r-cran-c50, r-cran-randomforest, r-cran-proc, r-cran-smotefamily Filename: pool/dists/noble/main/r-cran-ebmc_1.0.1-1.ca2404.1_all.deb Size: 70530 MD5sum: c1c268b1f597bd656f698f9b7e377c1d SHA1: d38808dd0acebdb370e2b6c0f2f1fb0a6c63ac41 SHA256: 52ff53a36c11aa0c46021ffd574a1c1f85437fa4ee858f5ccc55e5ee91eaffdd SHA512: ad1275b01829c39cc129154a48a1c21cd6dfe2eb0205d318a07a094e9593e1c398dee83c6cd5523b0127eedffb34b33609416ca4eb1f05a8ed8c2d269ade543d Homepage: https://cran.r-project.org/package=ebmc Description: CRAN Package 'ebmc' (Ensemble-Based Methods for Class Imbalance Problem) Four ensemble-based methods (SMOTEBoost, RUSBoost, UnderBagging, and SMOTEBagging) for class imbalance problem are implemented for binary classification. Such methods adopt ensemble methods and data re-sampling techniques to improve model performance in presence of class imbalance problem. One special feature offers the possibility to choose multiple supervised learning algorithms to build weak learners within ensemble models. References: Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003) , Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010) , R. Barandela, J. S. Sanchez, R. M. Valdovinos (2003) , Shuo Wang and Xin Yao (2009) , Yoav Freund and Robert E. Schapire (1997) . Package: r-cran-ebnm Architecture: all Version: 1.1-42-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ashr, r-cran-mixsqp, r-cran-truncnorm, r-cran-trust, r-cran-deconvolver, r-cran-magrittr, r-cran-rlang, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-rebayes, r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot, r-cran-mcmc, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-ebnm_1.1-42-1.ca2404.1_all.deb Size: 871034 MD5sum: 4a453ab64bbab3a03008a592413cf334 SHA1: 34c16d4d8bf2576700214b46b306835c0bfd71fe SHA256: 2af3bb2e7730c524e015d3985a02c92dccc06f1988d213e07ad0b50b02e2e4d2 SHA512: fc3bcb2e98a4b657709033bec6a034d8b7eea6a3699f1f796efcb0cae019b28f86b79e9b5aa54dfcf9f8d65bddb177f58e477a664492469b19f72bdbac4f98d6 Homepage: https://cran.r-project.org/package=ebnm Description: CRAN Package 'ebnm' (Solve the Empirical Bayes Normal Means Problem) Provides simple, fast, and stable functions to fit the normal means model using empirical Bayes. For available models and details, see function ebnm(). Our JSS article, Willwerscheid, Carbonetto, and Stephens (2025) , provides a detailed introduction to the package. Package: r-cran-ebprs Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rocr, r-cran-bedmatrix, r-cran-data.table Filename: pool/dists/noble/main/r-cran-ebprs_2.1.0-1.ca2404.1_all.deb Size: 55660 MD5sum: 4ded04b58b0fa49d79209e2c430e5850 SHA1: 628f65cfde3259137adda725b6f46bbb29a795e4 SHA256: 6083b4885f68b2f29c9613608ca30aff336bfc9aa298f88554adb09f53798779 SHA512: 439fabdf7b4fa4db3805f1de44bb60fd487110ff9229e08d51b8afe16e64a774aab9873b75f4f72ae0a37af9abff579d6a9e847db20d753fedeeb3a2254d3635 Homepage: https://cran.r-project.org/package=EBPRS Description: CRAN Package 'EBPRS' (Derive Polygenic Risk Score Based on Emprical Bayes Theory) EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy. No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for each individual in testing data, and evaluate the PRS according to AUC and predictive r2. See Song et al. (2020) for a detailed presentation of the method. Package: r-cran-ebrahim.gof Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-resourceselection, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-ebrahim.gof_1.0.0-1.ca2404.1_all.deb Size: 33034 MD5sum: 16da2193e379bd9e80529102a9bf8e0a SHA1: 8db22df52ff83ae85a81cdc6c6168f1d0d65f414 SHA256: 965ab602caba7d2066dd0e3be8022d9dfba98a8d499eacfe95f70e8d9391413d SHA512: 14d9bf9f5a5191b7a106dfe48c7237511ac082bfeb082fa0ed8c0599ed5da9193e9b6ddc1f1e51190114fc2cbbbe7eda8cb5dc37470913eadfe2079fa7853022 Homepage: https://cran.r-project.org/package=ebrahim.gof Description: CRAN Package 'ebrahim.gof' (Ebrahim-Farrington Goodness-of-Fit Test for Logistic Regression) Implements the Ebrahim-Farrington goodness-of-fit test for logistic regression models, particularly effective for sparse data and binary outcomes. This test provides an improved alternative to the traditional Hosmer-Lemeshow test by using a modified Pearson chi-square statistic with data-dependent grouping. The test is based on Farrington (1996) theoretical framework but simplified for practical implementation with binary data. Includes functions for both the original Farrington test (for grouped data) and the new Ebrahim-Farrington test (for binary data with automatic grouping). For more details see Hosmer (1980) and Farrington (1996) . Package: r-cran-ebrank Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ebrank_1.0.0-1.ca2404.1_all.deb Size: 34464 MD5sum: 16ab253f3554d39b75aec73628cb631b SHA1: 04a7b8a62e1948a98eeee34a79873a6854711bcb SHA256: f266852f6972b02101a7a6093c4799766f275c5968104f5f4d434caf012f34a0 SHA512: 41c7a06e587bfb34eec533f278523679e99797fd840c0f2c3a3f893c866f9de447f8e5ff881b30db175f57bd7110bd44a95511fcc0854d64e64b96aebca628be Homepage: https://cran.r-project.org/package=EBrank Description: CRAN Package 'EBrank' (Empirical Bayes Ranking) Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors. A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given. Package: r-cran-ebreg Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 535 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lars, r-cran-rdpack Suggests: r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-ebreg_0.1.3-1.ca2404.1_all.deb Size: 148510 MD5sum: fd718661d167920c871bbb7fe041058a SHA1: eb3ac06bcf31eb9f6f2c7651d185e3dec8b1bb60 SHA256: 89e83c461a5799a19c4715b1e15363f4740aecfb4dde4df4ccf821fb50cd978f SHA512: 38b7e2a074c10a638627a76cd0b0a0195d91f0e9905271b34c44b01345cef5a344981811c2fc0701fb1d39d216e9076d9f0277ec16a0bd5b1d72635d343a9030 Homepage: https://cran.r-project.org/package=ebreg Description: CRAN Package 'ebreg' (Implementation of the Empirical Bayes Method) Implements a Bayesian-like approach to the high-dimensional sparse linear regression problem based on an empirical or data-dependent prior distribution, which can be used for estimation/inference on the model parameters, variable selection, and prediction of a future response. The method was first presented in Martin, Ryan and Mess, Raymond and Walker, Stephen G (2017) . More details focused on the prediction problem are given in Martin, Ryan and Tang, Yiqi (2019) . Package: r-cran-ebvcube Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3068 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-curl, r-bioc-delayedarray, r-cran-ggplot2, r-bioc-hdf5array, r-cran-httr, r-cran-jsonlite, r-cran-memuse, r-cran-ncdf4, r-cran-ncmeta, r-cran-reshape2, r-bioc-rhdf5, r-cran-stringr, r-cran-terra, r-cran-tidyterra, r-cran-withr Suggests: r-cran-knitr, r-cran-lintr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ebvcube_0.5.2-1.ca2404.1_all.deb Size: 2150458 MD5sum: 1ed464b4b1adf293763bc75f7a4b23d1 SHA1: 1a574292c57ca0abc5a0067b08baba4b29730177 SHA256: 76dae01241623dd404b637df9e5abb888a5bbbf301883b1c76129f635a46636e SHA512: 50af3863c7d8c55f9a1ba036014ea3146395a3a78076e6e551f18302cea2feff86ae49d118f38cd9bdb396c26b6fe37ab7858263f58ad34d2ae19ca6e7806219 Homepage: https://cran.r-project.org/package=ebvcube Description: CRAN Package 'ebvcube' (Working with netCDF for Essential Biodiversity Variables) The concept of Essential Biodiversity Variables (EBV, ) comes with a data structure based on the Network Common Data Form (netCDF). The 'ebvcube' 'R' package provides functionality to easily create, access and visualise this data. The EBV netCDFs can be downloaded from the EBV Data Portal: Christian Langer/ iDiv (2020) . 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'echarty' users build R lists for 'ECharts' API. Lean set of powerful commands. Package: r-cran-echelon Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sf, r-cran-spdata Filename: pool/dists/noble/main/r-cran-echelon_0.2.0-1.ca2404.1_all.deb Size: 121728 MD5sum: a1a379776dfbbf6dea6d9381b5d85ea1 SHA1: dd15501cacd29973caf18e80e43713ec5051b214 SHA256: 7ee98c209ece3ae6863ca8308326b588f5eee3446000be767e50e2a5d7248e24 SHA512: b0f621a299a030a762187758a3c8633ef6b13e5b26aea2389f2045573e91459c697ff82b552cc51125251c76f77345d3f2306add3952cf6b96f7c512addb63a4 Homepage: https://cran.r-project.org/package=echelon Description: CRAN Package 'echelon' (The Echelon Analysis and the Detection of Spatial Clusters usingEchelon Scan Method) Functions for the echelon analysis proposed by Myers et al. 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Traditionally, the LRMC based methods give identical importance to the whole data which results in emphasizing on the commonality of the data and overlooking the subtle but crucial differences. This method aims to overcome the equality assumption problem that exists in the current LRMS based methods. Ensemble correlation-based low-rank matrix completion (ECLRMC) takes consideration of the specific characteristic of each sample and performs LRMC on the set of samples with a strong correlation. It uses an ensemble learning method to improve the imputation performance. Since each sample is analyzed independently this method can be parallelized by distributing imputation across many computation units or GPU platforms. This package provides three different methods (LRMC, CLRMC and ECLRMC) for data imputation. There is also an NRMS function for evaluating the result. Chen, Xiaobo, et al (2017) . 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Allows for easy downloads of weather forecasts and climate reanalysis data in R. Data stores covered include the Climate Data Store (CDS; ), Atmosphere Data Store (ADS; ) and Early Warning Data Store (CEMS; ). Package: r-cran-ecocbo Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpubr, r-cran-rlang, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-parabar, r-cran-parallelly, r-cran-vegan, r-cran-ssp, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ecocbo_1.0.0-1.ca2404.1_all.deb Size: 2047132 MD5sum: de8aa38b91d5bbb6f4229c9898cada15 SHA1: cd192797eac66c5fbe11a9d0e2d266e3f3afcb0e SHA256: f90d648d6f47b2ea3ac6249c305d0fb17662dd486b1783ebfe5eed0be48d9a2a SHA512: 95a51eac671002b77db17020117d736a10ab2e898344babec6806d3f752543f75cdd2dad65e90ffbd7f2aaa97c31f92a371b0b87f6cb5e7e1d7385306021f862 Homepage: https://cran.r-project.org/package=ecocbo Description: CRAN Package 'ecocbo' (Calculating Optimum Sampling Effort in Community Ecology) A system for calculating the optimal sampling effort, based on the ideas of "Ecological cost-benefit optimization" as developed by A. Underwood (1997, ISBN 0 521 55696 1). Data is obtained from simulated ecological communities with prep_data() which formats and arranges the initial data, and then the optimization follows the following procedure of four functions: (1) prep_data() takes the original dataset and creates simulated sets that can be used as a basis for estimating statistical power and type II error. (2) sim_beta() is used to estimate the statistical power for the different sampling efforts specified by the user. (3) sim_cbo() calculates then the optimal sampling effort, based on the statistical power and the sampling costs. Additionally, (4) scompvar() calculates the variation components necessary for (5) Underwood_cbo() to calculate the optimal combination of number of sites and samples depending on either an economic budget or on a desired statistical accuracy. Lastly, (6) plot_power() helps the user visualize the results of sim_beta(). Package: r-cran-ecochange Architecture: all Version: 2.9.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sf, r-cran-rastervis, r-cran-sp, r-cran-ggplot2, r-cran-landscapemetrics, r-cran-tibble, r-cran-httr, r-cran-getpass, r-cran-rlang, r-cran-lattice, r-cran-rasterdt Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-curl, r-cran-xml2, r-cran-rvest, r-cran-viridis Filename: pool/dists/noble/main/r-cran-ecochange_2.9.3.3-1.ca2404.1_all.deb Size: 953882 MD5sum: 57d3b8fbf02b0088a2433c55e92f84a6 SHA1: a70fd9cf031a6c180a942be2d3cefd0220e4ecb2 SHA256: b5d79ec709e8086f42f355466e0b9b865b4f4eea85cec30585a1e9c273fc80ae SHA512: dd4967999196073114475f17576214bd715f7a0521223533931491fb8646ac1d44f9bdb5d786e4094c47c0823fc4f5df41e22d48ffcaddfeaf6c3c7ba8b47c46 Homepage: https://cran.r-project.org/package=ecochange Description: CRAN Package 'ecochange' (Integrating Ecosystem Remote Sensing Products to Derive EBVIndicators) Essential Biodiversity Variables (EBV) are state variables with dimensions on time, space, and biological organization that document biodiversity change. Freely available ecosystem remote sensing products (ERSP) are downloaded and integrated with data for national or regional domains to derive indicators for EBV in the class ecosystem structure (Pereira et al., 2013) , including horizontal ecosystem extents, fragmentation, and information-theory indices. To process ERSP, users must provide a polygon or geographic administrative data map. Downloadable ERSP include Global Surface Water (Peckel et al., 2016) , Forest Change (Hansen et al., 2013) , and Continuous Tree Cover data (Sexton et al., 2013) . Package: r-cran-ecocleanr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geodata, r-cran-geosphere, r-cran-ggplot2, r-cran-mregions2, r-cran-patchwork, r-cran-rlang, r-cran-sdmpredictors, r-cran-sf, r-cran-worrms, r-cran-terra, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rgbif, r-cran-robis, r-cran-ridigbio, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ecocleanr_1.0.3-1.ca2404.1_all.deb Size: 435114 MD5sum: 2660acc5c3825671998d59b896ce94d1 SHA1: 48e69e576e570d8ab2fe50fdadc86481fb50625c SHA256: 954f84e54d7fb74ea779e1979bcce83803a51dfafa9d35c0ec2019992d91420f SHA512: 5ec9892631244a0d87dc0f168e5335502d0c8a55a99fab3396de2e96d0e2f3e0842a14fc9e7d298215aae295b323657ef189177af704ac2c2973f3d8b3655b54 Homepage: https://cran.r-project.org/package=EcoCleanR Description: CRAN Package 'EcoCleanR' (Enhancing Data Quality of Biogeographic Ranges with Applicationfor Marine Invertebrates) Provides step-by-step automation for integrating biodiversity data from multiple online aggregators, merging and cleaning datasets while addressing challenges such as taxonomic inconsistencies, georeferencing issues, and spatial or environmental outliers. Includes functions to extract environmental data and to define the biogeographic ranges in which species are most likely to occur. For methodological details see the associated publication.. 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'ecocomDP' is a flexible data model for harmonizing ecological community surveys, in a research question agnostic format, from source data published across repositories, and with methods that keep the derived data up-to-date as the underlying sources change. Described in O'Brien et al. (2021), . Package: r-cran-ecocopula Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvabund, r-cran-glasso, r-cran-plyr, r-cran-sna, r-cran-mass, r-cran-tweedie, r-cran-igraph, r-cran-betareg, r-cran-doparallel, r-cran-mgcv, r-cran-glm2, r-cran-ordinal, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-ggraph, r-cran-labdsv, r-cran-tidygraph Filename: pool/dists/noble/main/r-cran-ecocopula_1.0.6-1.ca2404.1_all.deb Size: 336630 MD5sum: 0958af32e32557d1ee32e128c76e6fb9 SHA1: a5fe0f3b857249f0707ec5411f86f31f134d298f SHA256: 023fe4895f96240389339c1e2b627ce7464ed2a279e0b8e8deb950f04d2cadf6 SHA512: cd072f7ef6f151cd802bf5adf473b804e03c52d979235519399b0c44ec8deb1cb1ee5310b67d133ac771c9fd2f6615e83141aff449e5c46f752005245488faf5 Homepage: https://cran.r-project.org/package=ecoCopula Description: CRAN Package 'ecoCopula' (Graphical Modelling and Ordination using Copulas) Creates 'graphs' of species associations (interactions) and ordination biplots from co-occurrence data by fitting discrete gaussian copula graphical models. Methods described in Popovic, GC., Hui, FKC., Warton, DI., (2018) . Package: r-cran-ecode Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot, r-cran-rlang, r-cran-stringr Filename: pool/dists/noble/main/r-cran-ecode_0.1.0-1.ca2404.1_all.deb Size: 185790 MD5sum: a52d3f7bc109d4133ba16fbae17a9571 SHA1: c98b350dc6ffc9e6a54b8621670f0bfc5b93cc35 SHA256: 144b2515d726ad04caaa71029b05c53b183655c9bf01c89049d6dd80ede2bdb4 SHA512: 091a224a894888d017d710dbad8c63517763b789b919c1c8a84b27af86dfd28899a77f0d45527ca27775d5150f67c62bfa885f16ffa40fad074e3a35a16d1f15 Homepage: https://cran.r-project.org/package=ecode Description: CRAN Package 'ecode' (Ordinary Differential Equation Systems in Ecology) A framework to simulate ecosystem dynamics through ordinary differential equations (ODEs). 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The package relies on the JAGS software and the 'jagsUI' package to run a Markov chain Monte Carlo approximation of the different variables. Package: r-cran-ecohydmod Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ecohydmod_1.0.0-1.ca2404.1_all.deb Size: 26766 MD5sum: 57fdba4a2d2011e7f2371e1684e736f6 SHA1: 951d014bbfa1a6b43e179f980600c741c2963078 SHA256: dc42917eda25ce51a190a82aa9ffee7f7844cf856e7d4700b1cf3df61b179214 SHA512: 4b9737b365c07b2830dbf47c3c4cf2be44711d9588e25b16cd16fdb5d5f5ce04e4c0141b7cfd8b7acd04462d6aaab60fa0462b18d69c5a2aef29e965f96db2e1 Homepage: https://cran.r-project.org/package=Ecohydmod Description: CRAN Package 'Ecohydmod' (Ecohydrological Modelling) Simulates the soil water balance (soil moisture, evapotranspiration, leakage and runoff), rainfall series by using the marked Poisson process and the vegetation growth through the normalized difference vegetation index (NDVI). Please see Souza et al. (2016) . 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This package provides bug fixes, generalizations and implementations of many state of the art measures. The measures are described in the papers: Lorena et al. (2019) and Lorena et al. (2018) . Package: r-cran-ecolottery Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abc, r-cran-ggplot2 Suggests: r-cran-ape, r-cran-knitr, r-cran-picante, r-cran-rmarkdown, r-cran-testthat, r-cran-vegan Filename: pool/dists/noble/main/r-cran-ecolottery_1.0.2-1.ca2404.1_all.deb Size: 210908 MD5sum: e095801fcd8ff425e4d4bb8f69c6e9d6 SHA1: 47567207ba0a1628eb715d1a758f14c6a9c3e09c SHA256: 1569357e49284fd1ceea6ba4ac397780589685bfdd283fb580ffade19f60c80a SHA512: bc08bb31e7738272d80cee63e603006669a2f0740e28875bf5dfedebe9d95f77d65576ba8fd1279dd6be497ac43029a64bda710564bbda1b7cd5bcfa25e2eceb Homepage: https://cran.r-project.org/package=ecolottery Description: CRAN Package 'ecolottery' (Coalescent-Based Simulation of Ecological Communities) Coalescent-Based Simulation of Ecological Communities as proposed by Munoz et al. (2018) . The package includes a tool for estimating parameters of community assembly by using Approximate Bayesian Computation. Package: r-cran-ecolrxc Architecture: all Version: 0.1.1-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-ecolrxc_0.1.1-10-1.ca2404.1_all.deb Size: 140958 MD5sum: 222733e7db970b6e3416f0701d3be098 SHA1: a914d83536ef620ac593074f7291c1c1bb99f8d4 SHA256: 2f4d28a545eb2c0804d5a223522b2022f1398d1bd8b392366f68bcd01501b58f SHA512: 6db7bc1666ea2dc06bcc72eafef60caeb8ca04141307d23777219cdcbf54890c8f81eab905ed84dad637904e276098168af60e996bd4205d4b943c9d45efc460 Homepage: https://cran.r-project.org/package=ecolRxC Description: CRAN Package 'ecolRxC' (Ecological Inference of RxC Tables by Latent StructureApproaches) Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data building on Thomsen (1987) and Park (2008) approaches. References: Park, W.-H. (2008). ''Ecological Inference and Aggregate Analysis of Election''. PhD Dissertation. University of Michigan. Thomsen, S.R. (1987, ISBN:87-7335-037-2). ''Danish Elections 1920 79: a Logit Approach to Ecological Analysis and Inference''. Politica, Aarhus, Denmark. Package: r-cran-ecoltest Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ecoltest_0.0.1-1.ca2404.1_all.deb Size: 31198 MD5sum: c3ee8925470b1ffe66e9f3da81d42b02 SHA1: bf9b7b5d22df50499dda556e5a5acea5b88459eb SHA256: fd23a2e03756affa4223559787410cdb0b783f96b0b2dba5ee49aa7ea0165ac5 SHA512: de3a8411c1a8487a99b79bae74ba533c7058b69f4c485da6a4f4f0bc6c3dbccb9a28e14ae2b1cc81e1f7864fc3d5395963c8b8e7b5b9c7f5abfbfcacf33006d7 Homepage: https://cran.r-project.org/package=ecolTest Description: CRAN Package 'ecolTest' (Community Ecology Tests) Functions and data sets to perform and demonstrate community ecology statistical tests, including Hutcheson's t-test (Hutcheson (1970) , Zar (2010) ISBN:9780321656865). Package: r-cran-ecometrics Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-forecast, r-cran-ggplot2, r-cran-insight, r-cran-lmtest, r-cran-moments, r-cran-tibble, r-cran-tseries Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-ecometrics_0.1.1-1.ca2404.1_all.deb Size: 107880 MD5sum: a9b1dba0abe995196c0a5383041c69d0 SHA1: f85c867a9fc26671b0d3bd68a1cbd163446fcea8 SHA256: aae305b476c64292c0cb62d68ba736d382599a06241d1565fdf61a9569bee7dc SHA512: 812f50c60f1c7e0985340f4ee2150926f10dd775bbc2a883e0c92efb59c83d3600ffcd612e3c33cfd448d820f505a33a42ac8df6ad7d2bd85634409cbf573cee Homepage: https://cran.r-project.org/package=EcoMetrics Description: CRAN Package 'EcoMetrics' (Econometrics Model Building) An intuitive and user-friendly package designed to aid undergraduate students in understanding and applying econometric methods in their studies, Tailored specifically for Econometrics and Regression Modeling courses, it provides a practical toolkit for modeling and analyzing econometric data with detailed inference capabilities. 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First, model parameters are compiled as a data set and associated metadata for over 300 habitat suitability models developed by the U.S. Fish and Wildlife Service (USFWS 1980, ). Second, functions for conducting habitat suitability analyses both for the models described above as well as generic user-specified model parameterizations. Third, a suite of decision support tools for conducting cost-effectiveness and incremental cost analyses (Robinson et al. 1995, IWR Report 95-R-1, U.S. Army Corps of Engineers). 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Package: r-cran-ecos Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 601 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-xml, r-cran-stringr Filename: pool/dists/noble/main/r-cran-ecos_0.1.7-1.ca2404.1_all.deb Size: 307104 MD5sum: 704c56dc54f6c90d63ea3dd7b5ebedae SHA1: 174eb107b212beaa7f61046bccbd3f905273ef87 SHA256: 2c035e0cb806e457af3f1417004db7f3bfafd02487ef6fa43992dee8811005c5 SHA512: b4eeee1f0c88981f4d3c51d5ea1fa0d822baef22962105de9354f2c2eeec37dc5cc90740964f7afeb9594b97f13c04ded389908fb6d5a2d8e9f95889af677758 Homepage: https://cran.r-project.org/package=ecos Description: CRAN Package 'ecos' (Economic Statistics System of the Bank of Korea) API wrapper to download statistical information from the Economic Statistics System (ECOS) of the Bank of Korea . Package: r-cran-ecosim Architecture: all Version: 1.3-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-stoichcalc Filename: pool/dists/noble/main/r-cran-ecosim_1.3-5-1.ca2404.1_all.deb Size: 117140 MD5sum: 9b3e075b8871187183d01fdb087a47d1 SHA1: d0fb0b686c5cc9e27cfc3f35d92cfc91713fc083 SHA256: 1e2cc532147b20e36d4e1b563c2f572e49549aad03089494a877d428e459f01f SHA512: 0d54b213d80285dde18b7272576f4bfa28c98d268b7187bee6134f2763831ecfa1771b69405ad2a069d2eb7748a784f4403c7d1a4d5244512013facc029a6784 Homepage: https://cran.r-project.org/package=ecosim Description: CRAN Package 'ecosim' (Toolbox for Aquatic Ecosystem Modeling) Classes and methods for implementing aquatic ecosystem models, for running these models, and for visualizing their results. Package: r-cran-ecospace Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fd Suggests: r-cran-vegan, r-cran-knitr, r-cran-rmarkdown, r-cran-data.table Filename: pool/dists/noble/main/r-cran-ecospace_1.4.2-1.ca2404.1_all.deb Size: 212056 MD5sum: 2651df140298c9d526b3402e2af3104d SHA1: ed15eb99c1303b55bcb1129d1287106bfc48e7f9 SHA256: 6633938ce105f108d01cf5d9229c26eae462b24deae90ea16ea92bc9e38ec2ef SHA512: 640b6b8ff5e7e74b314f228ddbc421b1b7ff31d4530b8f9b93534fc934f2517dc07c65af1ed58309d40234fc9068250d428d0c0c4313d2167d199cf8fd6b2c93 Homepage: https://cran.r-project.org/package=ecospace Description: CRAN Package 'ecospace' (Simulating Community Assembly and Ecological DiversificationUsing Ecospace Frameworks) Implements stochastic simulations of community assembly (ecological diversification) using customizable ecospace frameworks (functional trait spaces). Provides a wrapper to calculate common ecological disparity and functional ecology statistical dynamics as a function of species richness. Functions are written so they will work in a parallel-computing environment. 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Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) and Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland. Read Di Cola et al. (2016) for details. Package: r-cran-ecostate Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rtmb, r-cran-tmb, r-cran-mass, r-cran-checkmate, r-cran-ggplot2, r-cran-ggnetwork, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ecostate_0.3.0-1.ca2404.1_all.deb Size: 200144 MD5sum: ed9bc5038d77fc79be7a7955a3e7b050 SHA1: 55b365eae9a56f47b9f37870fc2558c43ea22535 SHA256: 69ba53b9eb86d5b94ec060689812e5b1287b8980ae1d04793fa5b2de7d719bbc SHA512: 1a7afaa677a38b4462cffcb7b4f3801a3c4d5e154ea6660d421f7ad541affc10ee1f3f281302d00a201dda2c0277c4e4578d5f488461bb3b57a0c2769b1ce4d9 Homepage: https://cran.r-project.org/package=ecostate Description: CRAN Package 'ecostate' (State-Space Mass-Balance Model for Marine Ecosystems) Fits a state-space mass-balance model for marine ecosystems, which implements dynamics derived from 'Ecopath with Ecosim' ('EwE') while fitting to time-series of fishery catch, biomass indices, age-composition samples, and weight-at-age data. 'Ecostate' fits biological parameters (e.g., equilibrium mass) and measurement parameters (e.g., catchability coefficients) jointly with residual variation in process errors, and can include Bayesian priors for parameters. Package: r-cran-ecostats Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4734 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvabund, r-cran-ecocopula, r-cran-get, r-cran-mass, r-cran-mgcv, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ade4, r-cran-caper, r-cran-car, r-cran-corrplot, r-cran-daag, r-cran-dharma, r-cran-dplyr, r-cran-gclus, r-cran-ggally, r-cran-ggplot2, r-cran-ggthemes, r-cran-glmnet, r-cran-gllvm, r-cran-glmmtmb, r-cran-gparotation, r-cran-grplasso, r-cran-lattice, r-cran-leaps, r-cran-lme4, r-cran-mcmcglmm, r-cran-multcomp, r-cran-nlme, r-cran-ordinal, r-cran-permute, r-cran-pgirmess, r-cran-phylobase, r-cran-phylosignal, r-cran-psych, r-cran-reshape2, r-cran-smatr, r-cran-testthat, r-cran-vegan, r-cran-vgam, r-cran-covr Filename: pool/dists/noble/main/r-cran-ecostats_1.2.2-1.ca2404.1_all.deb Size: 2644358 MD5sum: 7e7b9a68946478328e7657d70c730efe SHA1: 7bf6070157ba849cd28d7c9d97cd34e96952aa15 SHA256: 5917dd259aae89192b2530ca5907ede0e1b16afcefe6b588953036c32e5680d5 SHA512: fe1c749630391daf6f2ee2b6392b2f4632102c919eb6ac708bfd38961ad432312fe44ace500b9a1f62bdd60a120f04f6c20dbb331cd49e4e7c4eb42dedf176ca Homepage: https://cran.r-project.org/package=ecostats Description: CRAN Package 'ecostats' (Code and Data Accompanying the Eco-Stats Text (Warton 2022)) Functions and data supporting the Eco-Stats text (Warton, 2022, Springer), and solutions to exercises. Functions include tools for using simulation envelopes in diagnostic plots, and a function for diagnostic plots of multivariate linear models. Datasets mentioned in the package are included here (where not available elsewhere) and there is a vignette for each chapter of the text with solutions to exercises. Package: r-cran-ecostatscale Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-desolve Filename: pool/dists/noble/main/r-cran-ecostatscale_1.1-1.ca2404.1_all.deb Size: 49884 MD5sum: de6c8a8bc79d9696bdcdcca725e12878 SHA1: 1732b3de251684037a6e4c6d3d531cff3390e489 SHA256: 2e668fea74ecb2612c59a40bc9bb5905c6646111e3b47ab01d4ef8f84bec3bc2 SHA512: 1ab7d1c1d9301697bf1be427ed88824a315ffd673da01176e055f7e6505b91e9a806e9b18121b658ef7ab50a990048d4d96468028c1a17553d062acc082cb11c Homepage: https://cran.r-project.org/package=ecostatscale Description: CRAN Package 'ecostatscale' (Statistical Scaling Functions for Ecological Systems) Implementation of the scaling functions presented in "General statistical scaling laws for stability in ecological systems" by Clark et al in Ecology Letters . Includes functions for extrapolating variability, resistance, and resilience across spatial and ecological scales, as well as a basic simulation function for producing time series, and a regression routine for generating unbiased parameter estimates. See the main text of the paper for more details. Package: r-cran-ecoteach Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ecoteach_0.1.0-1.ca2404.1_all.deb Size: 184228 MD5sum: d94e174b47054f74cef68e86f23d01c3 SHA1: b845e2baa83ed359e835406769811c49f594c745 SHA256: 42a0bbd2fe7e3ec4778668a47206c9f58380b2646e07bb7314796fc0d1a62029 SHA512: 1bba63411f4554209f34ed639dd478eddf1391bb6d3e7e1a66fb0564134d3bab1d1be384cd7e932ffce8359b099860d8b07c602fd154af49d45c62aa18177abc Homepage: https://cran.r-project.org/package=ecoteach Description: CRAN Package 'ecoteach' (Educational Datasets for Ecology and Agriculture) A collection of curated educational datasets for teaching ecology and agriculture concepts. Includes data on wildlife monitoring, plant treatments, and ecological observations with documentation and examples for educational use. All datasets are derived from published scientific studies and are available under CC0 or compatible licenses. Package: r-cran-ecotolerance Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-raster, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr Suggests: r-cran-terra, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ecotolerance_0.1.0-1.ca2404.1_all.deb Size: 70756 MD5sum: 001813001bd33ee893b3243410d37ead SHA1: 831c0d4d1aabc9823026c8ae5a68c7fc65e458b9 SHA256: 3049764459dcccd088dec39999ba6f1792068fca02803094cc1e22c3d0aa728c SHA512: 580116806826d2245173c2c28e5e050c4c4a3271c004744b920dbc89ee4b29ea527c452cc96de1d881dcdda953f277a55473be8bedbfb0c6479778739dd1a40e Homepage: https://cran.r-project.org/package=ecoTolerance Description: CRAN Package 'ecoTolerance' (Ecological Tolerance Indices) Computes the Road Tolerance Index (RTI) and the Human Footprint Tolerance Index (HFTI) for species occurrence data. It automates data cleaning and integrates spatial data (roads and human footprint) to produce reproducible tolerance metrics for biodiversity and conservation research. The HFTI calculation is based on the global human footprint dataset by Mu et al. (2022) . This package is part of a PhD thesis focused on amphibian ecology in Brazil. Package: r-cran-ecotonefinder Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-colorspace, r-cran-corrplot, r-cran-e1071, r-cran-ggplot2, r-cran-qgraph, r-cran-igraph, r-cran-philentropy, r-cran-plyr, r-cran-purrr, r-cran-reshape, r-cran-rlang, r-cran-rmisc, r-cran-vegan, r-cran-vegclust, r-cran-withr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-ecotonefinder_0.2.3-1.ca2404.1_all.deb Size: 214656 MD5sum: 74a7c9b2834883d4ff1bed4bed6cec87 SHA1: 3fb9925173a215fad3eb8fc9517bb3ce0d6bb373 SHA256: 0c80d4cb3e921bf5be4ef6e117830e4becbfcc22a43bd88abddffe3b2c49758f SHA512: 210578e77575c5b4cb9b7eb231cfda8b01e8a4dc901589cdd6c3eb7162e10cc23ba8a136fba18fc5583bb3cc0737d6c435b56dda8cf576421c0c258d3407e732 Homepage: https://cran.r-project.org/package=EcotoneFinder Description: CRAN Package 'EcotoneFinder' (Characterising and Locating Ecotones and Communities) Analytical methods to locate and characterise ecotones, ecosystems and environmental patchiness along ecological gradients. Methods are implemented for isolated sampling or for space/time series. It includes Detrended Correspondence Analysis (Hill & Gauch (1980) ), fuzzy clustering (De Cáceres et al. (2010) ), biodiversity indices (Jost (2006) ), and network analyses (Epskamp et al. (2012) ) - as well as tools to explore the number of clusters in the data. Functions to produce synthetic ecological datasets are also provided. Package: r-cran-ecotourism Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6080 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-ggbeeswarm, r-cran-sf, r-cran-knitr, r-cran-lubridate, r-cran-quarto, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ecotourism_0.1.0-1.ca2404.1_all.deb Size: 4534954 MD5sum: 695dcfcf53544d5668c440a79fcc34b2 SHA1: 054ccc0ea83a77d96ea6eb1d04b586617d1f2694 SHA256: 2038e1b27072c4098fae45e5382247d23c78b1e230da0db6039d170111a38781 SHA512: 6b32b3a37714072f317d2c26641ffa3b03f994da85d6f2ccea8d040c53d6fb3ed62346e9f0072339b65a8df8fb9d9c4e0643577d20eee161572f5e10d5d376f4 Homepage: https://cran.r-project.org/package=ecotourism Description: CRAN Package 'ecotourism' (Collection of Data on Wildlife Sightings, Tourism Counts, andWeather from Australia) This is a collection of data files for exploring sightings of wild things, relative to weather and tourism patterns in Australia. Package: r-cran-ecotox Architecture: all Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-covr, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-hmisc, r-cran-openxlsx, r-cran-readr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ecotox_1.4.4-1.ca2404.1_all.deb Size: 93494 MD5sum: b4be180c5cf9ee0629388cce05fc380b SHA1: 5d9f1769634a302da459ded5aa0672b1be15da1d SHA256: efe6b5179c54e98e49387818af5ab8b03da85bd52611d014a2df6f1ad380e2d6 SHA512: 530345ee8d0d37bf4c4f72ab033594edcc93edcfd89f167261278f11eead34df222910234c9ee21309fd5291314613f6aab3c81fbcd9f5a71b9c9f7e50293197 Homepage: https://cran.r-project.org/package=ecotox Description: CRAN Package 'ecotox' (Analysis of Ecotoxicology) A simple approach to using a probit or logit analysis to calculate lethal concentration (LC) or time (LT) and the appropriate fiducial confidence limits desired for selected LC or LT for ecotoxicology studies (Finney 1971; Wheeler et al. 2006; Robertson et al. 2007). The simplicity of 'ecotox' comes from the syntax it implies within its functions which are similar to functions like glm() and lm(). In addition to the simplicity of the syntax, a comprehensive data frame is produced which gives the user a predicted LC or LT value for the desired level and a suite of important parameters such as fiducial confidence limits and slope. Finney, D.J. (1971, ISBN: 052108041X); Wheeler, M.W., Park, R.M., and Bailer, A.J. (2006) ; Robertson, J.L., Savin, N.E., Russell, R.M., and Preisler, H.K. (2007, ISBN: 0849323312). Package: r-cran-ecotoxicology Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ecotoxicology_1.0.1-1.ca2404.1_all.deb Size: 213588 MD5sum: 83c325f6aa9af2a58b29073302d408ed SHA1: 687037bb1f51cf559512cd078e3ba4ec8922b84f SHA256: 6018d51481c414491e8ad4b711b9472ebc66366c80130b7ecd9b53e8803d3ec8 SHA512: d080b9b23934eb591885f891fa168cf394c3a43eff5561cb93ecddc4b5633d5cb5edf90d1ebf4ab403ca6fefc58c65144f713fe79cd7533ec833be1de3683a4d Homepage: https://cran.r-project.org/package=ecotoxicology Description: CRAN Package 'ecotoxicology' (Methods for Ecotoxicology) Implementation of the EPA's Ecological Exposure Research Division (EERD) tools (discontinued in 1999) for Probit and Trimmed Spearman-Karber Analysis. Probit and Spearman-Karber methods from Finney's book "Probit analysis a statistical treatment of the sigmoid response curve" with options for most accurate results or identical results to the book. Probit and all the tables from Finney's book (code-generated, not copied) with the generating functions included. Control correction: Abbott, Schneider-Orelli, Henderson-Tilton, Sun-Shepard. 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As the online search interface doesn't come with an API, this package provides the means to easily access and search the database in R. To this end, all raw tables are downloaded from the EPA website and stored in a local SQLite database . 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Package: r-cran-edar Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-flextable, r-cran-ggplot2, r-cran-ggpubr, r-cran-janitor, r-cran-kableextra, r-cran-knitr, r-cran-listr, r-cran-magrittr, r-cran-patchwork, r-cran-rlang, r-cran-rstudioapi, r-cran-scales, r-cran-tidyr, r-cran-xgxr Suggests: r-cran-gt Filename: pool/dists/noble/main/r-cran-edar_0.0.6-1.ca2404.1_all.deb Size: 139016 MD5sum: dbea8baddf947e737e788412b2ef4900 SHA1: a29bc6fc915b4ffb8d6c3bdeae0069f032efc63c SHA256: 4c04623c8e4b4dd0c8c6e966e885c31034077f01d702a54f36f3976545ea3273 SHA512: 99a65ce94027041d8719b750afac748596dd39d1ddcf52d18f4c64f67cce96912a1e770ca2499ecf75998c2afe217fea24be201d010a5169f975aad374fc3371 Homepage: https://cran.r-project.org/package=edar Description: CRAN Package 'edar' (Convenient Functions for Exploratory Data Analysis) A collection of convenient functions to facilitate common tasks in exploratory data analysis. 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Package: r-cran-edbuildmapr Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-spdep, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyselect, r-cran-tmap Filename: pool/dists/noble/main/r-cran-edbuildmapr_0.3.1-1.ca2404.1_all.deb Size: 63944 MD5sum: 397c9ea5b67e779c2c618b21f36cb4af SHA1: 721b9f578ff591b0a88bac0c1e741b6624e5c9e6 SHA256: 89d47b47894df783498ecbfa1c8ae265d71c19bb67d32ca3fc822b0595311680 SHA512: cbce87553c7a41a2d2ec1970f2010d9b8e82753f981341b02db8258eb3b406153314f09ea336e7f547011f12f3be7209a5074389bc9f6201e527e2584af04d4a Homepage: https://cran.r-project.org/package=edbuildmapr Description: CRAN Package 'edbuildmapr' (Download School District Geospatial Data, Perform SpatialAnalysis, and Create Formatted Exportable Maps) Import US Census Bureau, Education Demographic and Geographic Estimates Program, Composite School District Boundaries Files for 2013-2019 with the option to attach the 'EdBuild' master dataset of school district finance, student demographics, and community economic indicators for every school district in the United States. The master dataset is built from the US Census, Annual Survey of School System Finances (F33) and joins data from the National Center for Education Statistics, Common Core of Data; the US Census, Small Area Income and Poverty Estimates; and the US Census, Education Demographic and Geographic Estimates. Additional functions in the package create a dataset of all pairs of school district neighbors as either a dataframe or a shapefile and create formatted maps of selected districts at the state or neighbor level, symbolized by a selected variable in the 'EdBuild' master dataset. For full details about 'EdBuild' data processing please see 'EdBuild' (2020) . Package: r-cran-edcimport Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4464 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-fs, r-cran-glue, r-cran-ggplot2, r-cran-haven, r-cran-lubridate, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-lifecycle Suggests: r-cran-bslib, r-cran-callr, r-cran-crosstable, r-cran-dt, r-cran-gt, r-cran-gtools, r-cran-htmlwidgets, r-cran-janitor, r-cran-knitr, r-cran-openxlsx, r-cran-patchwork, r-cran-plotly, r-cran-quarto, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-testthat, r-cran-shiny, r-cran-usethis, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-edcimport_0.7.0-1.ca2404.1_all.deb Size: 3383526 MD5sum: e7a5cac61d405b6ab455e47517fa2689 SHA1: e33dd92587bf15fb9028babefbf8ba10e4231bf2 SHA256: 56124c79382f8a0debde08b8cd5bd7bc4a3ee77957e98bfadc66f50e82587c09 SHA512: d44d0872ecd2f0ace307f3b529ca355aa84d5242a7e67a29e20647089654e9748d5d0516bd282926ee91c691e37f89cad8ecaecca3b663de8be5c9f6b1735495 Homepage: https://cran.r-project.org/package=EDCimport Description: CRAN Package 'EDCimport' (Import Data from EDC Software) A convenient toolbox to import data exported from Electronic Data Capture (EDC) software 'TrialMaster'. Package: r-cran-edcpr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-edcpr_1.0.1-1.ca2404.1_all.deb Size: 23918 MD5sum: 94756e4c8a478e94fcc4de85703b34d2 SHA1: e74c8c764f0638150c04deceb30ecf5e126a9706 SHA256: 58b056ab084b88a3eca1d792e97df160f931fc58543dcaf1963366a792e78ea3 SHA512: c88858c3ce3874ebdc37b2d0c2819c758b89fc3989a8a3adec930d2c9fac9ef41ebaf7f0165ba21a29dbc1dfc7a38971d075f2ab6d061f204a015bf41bf985b0 Homepage: https://cran.r-project.org/package=edcpR Description: CRAN Package 'edcpR' (Ecological Data Collection and Processing Package) This is the course package for the exercise portion of the "Ecological Data Collection and Processing" course. 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Package: r-cran-edfinr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-rlang, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-stringr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-edfinr_0.1.1-1.ca2404.1_all.deb Size: 177764 MD5sum: a00f264d8d3c8db69a8428fbfaf24807 SHA1: fd2635436c54c0e3c090f4a4dda8492c12546845 SHA256: 9df196e6611117a072de971d54600d2c27067ea9bf4b1ae4985a18a15c7eb730 SHA512: bc0d4993111a15c125fc5a70a245963416b7966dcc9b6e0d9fe189207fa0701d7975f51b691fa029347853d7211b1623924904876446611130ac8108d571eae9 Homepage: https://cran.r-project.org/package=edfinr Description: CRAN Package 'edfinr' (Access Tidy Education Finance Data) Provides easy access to tidy education finance data using Bellwether's methodology to combine NCES F-33 Survey, Census Bureau Small Area Income Poverty Estimates (SAIPE), and community data from the ACS 5-Year Estimates. The package simplifies downloading, caching, and filtering education finance data by year and state, enabling researchers and analysts to explore K-12 education funding patterns, revenue sources, expenditure categories, and demographic factors across U.S. school districts. Package: r-cran-edfreader Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1711 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-edfreader_1.2.1-1.ca2404.1_all.deb Size: 382838 MD5sum: 44c384e0f4223396f0a0cf974fdcb9c8 SHA1: cacdd3f4e0a777e4f6d8291af629adcbd38b901f SHA256: e4b3fbd85b7d1caca1675523fd9d48e72db87baaa7050cb5b2e29184ae81067e SHA512: ff7a9f431d3b13e33adca0f9c625266d2118416db94de1eac8b1dc381583d9a7cda58be4934d0a390bdc074b9a9d5b713041a5d6da609d6deea9c2d19fe88cda Homepage: https://cran.r-project.org/package=edfReader Description: CRAN Package 'edfReader' (Reading EDF(+) and BDF(+) Files) Reads European Data Format files EDF and EDF+, see , BioSemi Data Format files BDF, see , and BDF+ files, see . 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Package: r-cran-edgedata Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-edgedata_0.2.0-1.ca2404.1_all.deb Size: 193082 MD5sum: 79e4b3972f0a87e81776b475be3c1c8d SHA1: 5c595513ec2253cfd43b8eb1a5d70f8694e09f60 SHA256: b4469516e4b5a849aa13974f7c92154df370f4f5b43565e227a15a7b990fd3cc SHA512: 186b3df6140f8c4f9b0e3fa0cd916064cfd4d35f8b46eaf0611be4b11fa4080fe59b0fc002ec20881631e2cbe0ab5e760122054a1398d1c9e590b7b120f22f70 Homepage: https://cran.r-project.org/package=edgedata Description: CRAN Package 'edgedata' (Datasets that Support the EDGE Server DIY Logic) Datasets from most recent CCIIO DIY entry in a tidy format. These support the Centers for Medicare and Medicaid Services' (CMS) risk adjustment Do-It-Yourself (DIY) process, which allows health insurance issuers to calculate member risk profiles under the Health and Human Services-Hierarchical Condition Categories (HHS-HCC) regression model. This regression model is used to calculate risk adjustment transfers. Risk adjustment is a selection mitigation program implemented under the Patient Protection and Affordable Care Act (ACA or Obamacare) in the USA. Under the ACA, health insurance issuers submit claims data to CMS in order for CMS to calculate a risk score under the HHS-HCC regression model. However, CMS does not inform issuers of their average risk score until after the data submission deadline. These data sets can be used by issuers to calculate their average risk score mid-year. More information about risk adjustment and the HHS-HCC model can be found here: . Package: r-cran-edibble Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3742 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-vctrs, r-cran-tibble, r-cran-cli, r-cran-pillar, r-cran-tidyselect, r-cran-nestr, r-cran-algdesign, r-cran-dae, r-cran-r6, r-cran-lifecycle, r-cran-dplyr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-openxlsx2, r-cran-visnetwork, r-cran-blocksdesign, r-cran-knitr, r-cran-scales, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-edibble_1.1.1-1.ca2404.1_all.deb Size: 3104598 MD5sum: d49f5d5d8029ebcb2da3e1aa6d751d61 SHA1: f8a6887cb39195dacaf739ec03d10cf6befc40a2 SHA256: 6e96ef4f371a0021688df5d2e55722e11584efb5e41a29d63f3c2ef12d0dff76 SHA512: c0aed114a331947a65257aea9ee9a3696f59acabd314db4f632ba2ceb98c26f590b7759d2549a2b4da1077cf671af7ecda2d29b7afa18f481d0ebfe000b812a7 Homepage: https://cran.r-project.org/package=edibble Description: CRAN Package 'edibble' (Encapsulating Elements of Experimental Design) A system to facilitate designing comparative (and non-comparative) experiments using the grammar of experimental designs . An experimental design is treated as an intermediate, mutable object that is built progressively by fundamental experimental components like units, treatments, and their relation. The system aids in experimental planning, management and workflow. Package: r-cran-ediblecity Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-magrittr, r-cran-stars, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-ediblecity_0.2.2-1.ca2404.1_all.deb Size: 2190306 MD5sum: cc59463627b9debdb71de8b9c0f46ce6 SHA1: bd93b32ebb395ca06e325dbf7a71a463b2d14358 SHA256: 497e43e0164ecf3c676eae893a0199ee34737392150edfca4ba2bc6ca0956414 SHA512: a1f6ae5b0524b322d89ab8b24f6d7fdf7b9795ec884cfbf6efc3b11765f35160cd55982ce630a682d81ccb1006843901ba407a87d725d60da0d281b940923c29 Homepage: https://cran.r-project.org/package=ediblecity Description: CRAN Package 'ediblecity' (Modeling Urban Agriculture at City Scale) The purpose of this package is to estimate the potential of urban agriculture to contribute to addressing several urban challenges at the city-scale. Within this aim, we selected 8 indicators directly related to one or several urban challenges. Also, a function is provided to compute new scenarios of urban agriculture. Methods are described by Pueyo-Ros, Comas & Corominas (2023) . Package: r-cran-edison Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-edison_1.1.2-1.ca2404.1_all.deb Size: 257826 MD5sum: e7ab5227039d9949d8daad7389b9771b SHA1: 203803128e1a34c7cac5d75d72394f7f9ad5bb7c SHA256: df799a5f485236a524c95b8cefd3f49d991216dfc827b334bc591773d0069dbd SHA512: 6840224ba941a856b14e39392d2a70474da3b138ecbe09096df7eeda31d857b8ac11fce9c29993baafc9fe6d69c08f0bd0de35cb4dca40ac64b60e224ad516e0 Homepage: https://cran.r-project.org/package=EDISON Description: CRAN Package 'EDISON' (Network Reconstruction and Changepoint Detection) Package EDISON (Estimation of Directed Interactions from Sequences Of Non-homogeneous gene expression) runs an MCMC simulation to reconstruct networks from time series data, using a non-homogeneous, time-varying dynamic Bayesian network. Networks segments and changepoints are inferred concurrently, and information sharing priors provide a reduction of the inference uncertainty. Package: r-cran-editbl Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1527 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinyjs, r-cran-dt, r-cran-tibble, r-cran-dplyr, r-cran-rlang, r-cran-uuid, r-cran-fontawesome Suggests: r-cran-testthat, r-cran-dtplyr, r-cran-data.table, r-cran-vctrs, r-cran-rsqlite, r-cran-dbplyr, r-cran-glue, r-cran-dbi, r-cran-bit64, r-cran-knitr, r-cran-dm Filename: pool/dists/noble/main/r-cran-editbl_1.3.0-1.ca2404.1_all.deb Size: 642894 MD5sum: 17c9444eab2e5e1afef7a8cf7fd87720 SHA1: 71978a8a31006b45a70e421ad67bf4908184d619 SHA256: 1ceb25ce35d4ffeed9abc6c0059238cf0298bfbde6100b85d6b3c8bd616fde02 SHA512: 0ac3a23d89aa959a8a6675aeb2a1339ec7688ee411274cd63805583cc2e62fae18f8c315da530f011a9d6df7180fbf0dc2acff58eda5dfbba69f828e3a79e27c Homepage: https://cran.r-project.org/package=editbl Description: CRAN Package 'editbl' ('DT' Extension for CRUD (Create, Read, Update, Delete)Applications in 'shiny') The core of this package is a function eDT() which enhances DT::datatable() such that it can be used to interactively modify data in 'shiny'. By the use of generic 'dplyr' methods it supports many types of data storage, with relational databases ('dbplyr') being the main use case. Package: r-cran-editdata Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2892 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-rstudioapi, r-cran-dt, r-cran-tibble, r-cran-dplyr, r-cran-rio, r-cran-magrittr, r-cran-shinywidgets, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-editdata_0.1.8-1.ca2404.1_all.deb Size: 2112842 MD5sum: c906897090d3f3ae147d7f289adaa8c6 SHA1: 301f47a467100c73f8df5153c244792a3cb72965 SHA256: 8ac379023a22c7fc98f6b1e78977173f7d22d36c11a27946501e73d998ffd027 SHA512: ec8e40809a98434cb3ba4d0ff3426631a1e57ec37436ec816c1a956724a7277dcbd3550eee56f52629d497f3769dd36e934f8827c123756450f895338135a35b Homepage: https://cran.r-project.org/package=editData Description: CRAN Package 'editData' ('RStudio' Addin for Editing a 'data.frame') An 'RStudio' addin for editing a 'data.frame' or a 'tibble'. You can delete, add or update a 'data.frame' without coding. You can get resultant data as a 'data.frame'. In the package, modularized 'shiny' app codes are provided. These modules are intended for reuse across applications. Package: r-cran-editrules Architecture: all Version: 2.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-lpsolveapi Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-editrules_2.9.6-1.ca2404.1_all.deb Size: 519212 MD5sum: 4d494f3599c946fc196b2916d6493d51 SHA1: 79e7b472a593e6d3911ff4eac739ade0c25388c1 SHA256: 6bd179855b177fcd4ee6dd83c825a525f786755e8086a6ff1e1caf64ac43dfc5 SHA512: ac02556d9ca8d63658d8447a47a8f919e32153649ffb68a94ce435ba04d5707f76d8128e77b0df9775d5e47c743efe01f75fcc12c229f6a9f0d3aaaac6e7ca0d Homepage: https://cran.r-project.org/package=editrules Description: CRAN Package 'editrules' (Parsing, Applying, and Manipulating Data Cleaning Rules) Please note: active development has moved to packages 'validate' and 'errorlocate'. Facilitates reading and manipulating (multivariate) data restrictions (edit rules) on numerical and categorical data. Rules can be defined with common R syntax and parsed to an internal (matrix-like format). Rules can be manipulated with variable elimination and value substitution methods, allowing for feasibility checks and more. Data can be tested against the rules and erroneous fields can be found based on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with using the 'igraph' package. Package: r-cran-ediutils Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-httr, r-cran-jsonlite, r-cran-xml2 Suggests: r-cran-knitr, r-cran-readr, r-cran-vcr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ediutils_2.1.0-1.ca2404.1_all.deb Size: 353160 MD5sum: 0512d90238ffb530f581c392537db2b8 SHA1: 8ac1c3dc1f46e30a972bce33fa7d0450282678f8 SHA256: bf4f6ce03300c37c814b475fa019e4ffa02e28be9beb74817dd9339afd065fed SHA512: 6dbe691b8a24622c231b2d8dd1154021b2011f85ea049705a902542cef978a6cb94667fe4805392a4c8c51b5bc2f0ddbe7aca1921290c13f71cf9c1625c71364 Homepage: https://cran.r-project.org/package=EDIutils Description: CRAN Package 'EDIutils' (An API Client for the Environmental Data Initiative Repository) A client for the Environmental Data Initiative repository REST API. The 'EDI' data repository is for publication and reuse of ecological data with emphasis on metadata accuracy and completeness. It is built upon the 'PASTA+' software stack and was developed in collaboration with the US 'LTER' Network . 'EDIutils' includes functions to search and access existing data, evaluate and upload new data, and assist other data management tasks common to repository users. Package: r-cran-edl Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotfunctions, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-edl_1.1-1.ca2404.1_all.deb Size: 398000 MD5sum: c7e9a5110676dff290d1f2072c7a3411 SHA1: 40e658fb1c3bac05390a6c4b1c73d3a35fd47043 SHA256: ea27ea63bf7199f84a2017a6e4ccce3dfd4dcb14dbf5ec159347f0959aad99a2 SHA512: da68b10f4c09afaef7133f08f226ce666426d50a25f4bf75e3cff6d9e9b6a9902843ab4426dd98a182ef5cc60dc38e749ee777cbfada35927951947ea447ab01 Homepage: https://cran.r-project.org/package=edl Description: CRAN Package 'edl' (Toolbox for Error-Driven Learning Simulations with Two-LayerNetworks) Error-driven learning (based on the Widrow & Hoff (1960) learning rule, and essentially the same as Rescorla-Wagner's learning equations (Rescorla & Wagner, 1972, ISBN: 0390718017), which are also at the core of Naive Discrimination Learning, (Baayen et al, 2011, ) can be used to explain bottom-up human learning (Hoppe et al, ), but is also at the core of artificial neural networks applications in the form of the Delta rule. This package provides a set of functions for building small-scale simulations to investigate the dynamics of error-driven learning and it's interaction with the structure of the input. For modeling error-driven learning using the Rescorla-Wagner equations the package 'ndl' (Baayen et al, 2011, ) is available on CRAN at . However, the package currently only allows tracing of a cue-outcome combination, rather than returning the learned networks. To fill this gap, we implemented a new package with a few functions that facilitate inspection of the networks for small error driven learning simulations. Note that our functions are not optimized for training large data sets (no parallel processing), as they are intended for small scale simulations and course examples. (Consider the python implementation 'pyndl' for that purpose.) Package: r-cran-edmdata Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 737 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-edmdata_1.3.0-1.ca2404.1_all.deb Size: 667226 MD5sum: 78fbd5860cb38812db8942f8458b3dba SHA1: 33ed8ad2973849b5ff2f21278ab8e3774b1c1580 SHA256: 01412e02ec02d76909c984407db6d5b41be1a15c7375f8d25cedb7f4287d30d0 SHA512: 46330ede40d12119e662e4d21b6e74f1ebab74b70e5650f052ec195ed2eea65ae6450bd428421a681c604383e44baedabf2526e4d15ee5a3f1828d50872e514f Homepage: https://cran.r-project.org/package=edmdata Description: CRAN Package 'edmdata' (Data Sets for Psychometric Modeling) Collection of data sets from various assessments that can be used to evaluate psychometric models. These data sets have been analyzed in the following papers that introduced new methodology as part of the application section: Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023) , Culpepper, S. A., & Balamuta, J. J. (2021) , Yinghan Chen et al. (2021) , Yinyin Chen et al. (2020) , Culpepper, S. A. (2019a) , Culpepper, S. A. (2019b) , Culpepper, S. A., & Chen, Y. (2019) , Culpepper, S. A., & Balamuta, J. J. (2017) , and Culpepper, S. A. (2015) . Package: r-cran-ednafuns Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-googlesheets4, r-bioc-phyloseq, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-vegan, r-cran-ggplot2, r-cran-vroom, r-bioc-biostrings, r-cran-stringr, r-cran-tidyselect, r-cran-readr Suggests: r-cran-insect, r-cran-testthat, r-cran-tidyverse, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ednafuns_0.1.0-1.ca2404.1_all.deb Size: 140232 MD5sum: 061e6927cc87db94b02e45a8bf8fe906 SHA1: bb20f0449110afffa5b39e1d84d715b833e1a10d SHA256: 07bf66d3cb4bf5be9cbf9badee3031a9b907654b6ab8cfc8478e7d8261ccceac SHA512: 82efdeef9bc3c05531f1aa1b42bd7c1c9627a51dd188c0cd64960dc24c077f3dfe845d6f57e71b123ea317bcc0252e29b6c1b516e8b9168211f17c7d94c4d2eb Homepage: https://cran.r-project.org/package=eDNAfuns Description: CRAN Package 'eDNAfuns' (Working with Metabarcoding Data in a Tidy Format) A series of R functions that come in handy while working with metabarcoding data. The reasoning of doing this is to have the same functions we use all the time stored in a curated, reproducible way. In a way it is all about putting together the grammar of the 'tidyverse' from Wickham et al.(2019) with the functions we have used in community ecology compiled in packages like 'vegan' from Dixon (2003) and 'phyloseq' McMurdie & Holmes (2013) . The package includes functions to read sequences from FAST(A/Q) into a tibble ('fasta_reader' and 'fastq_reader'), to process 'cutadapt' Martin (2011) 'info-file' output. When it comes to sequence counts across samples, the package works with the long format in mind (a three column 'tibble' with Sample, Sequence and counts ), with functions to move from there to the wider format. Package: r-cran-edne.eq Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-edne.eq_1.0-1.ca2404.1_all.deb Size: 36588 MD5sum: b93332d613d5bb5f384d941615a0654a SHA1: 7a972e4690c21a5f7e4cbb793156aa281d5f992f SHA256: b4d6eb002dfd8ff2a2a121d10a7042c7a3c66810bb1d1d97a2f2f33a0dc0a3af SHA512: e280d1da43406779f694afbde6e2a893f34879a9d2bca3d9f297a887459fd80068604ed0e630fa57eeef3557111664bc65705b32eac3ed960069cddcb27e2746 Homepage: https://cran.r-project.org/package=EDNE.EQ Description: CRAN Package 'EDNE.EQ' (Implements the EDNE-Test for Equivalence) Package implements the EDNE-test for equivalence according to Hoffelder et al. (2015) . "EDNE" abbreviates "Euclidean Distance between the Non-standardized Expected values". The EDNE-test for equivalence is a multivariate two-sample equivalence test. Distance measure of the test is the Euclidean distance. The test is an asymptotically valid test for the family of distributions fulfilling the assumptions of the multivariate central limit theorem (see Hoffelder et al.,2015). The function EDNE.EQ() implements the EDNE-test for equivalence according to Hoffelder et al. (2015). The function EDNE.EQ.dissolution.profiles() implements a variant of the EDNE-test for equivalence analyses of dissolution profiles (see Suarez-Sharp et al.,2020 ). EDNE.EQ.dissolution.profiles() checks whether the quadratic mean of the differences of the expected values of both dissolution profile populations is statistically significantly smaller than 10 [\% of label claim]. The current regulatory standard approach for equivalence analyses of dissolution profiles is the similarity factor f2. The statistical hypotheses underlying EDNE.EQ.dissolution.profiles() coincide with the hypotheses for f2 (see Hoffelder et al.,2015, Suarez-Sharp et al., 2020). Package: r-cran-edoif Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-distr, r-cran-igraph, r-cran-ellipsis, r-cran-simpleboot, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-edoif_0.1.4-1.ca2404.1_all.deb Size: 240738 MD5sum: c0e67e908c3bbce986151f020ad2230b SHA1: b8a3206eb4826907e7aa84ed9dcaecc867c710e4 SHA256: 9198035ad00a80ef9ff01ae4505b0d92db411dda6145c12fb4b4c1e3d7dd9c86 SHA512: 16b60b2794a7d22741c77109095e71a5ad6a2eb8e4a607bc7c5a0bf9dabd7f39464cdbe3441931462c96af4a7b9862c9def9e46bd6087775e4be1a07f453b29e Homepage: https://cran.r-project.org/package=EDOIF Description: CRAN Package 'EDOIF' (Empirical Distribution Ordering Inference Framework (EDOIF)) A non-parametric framework based on estimation statistics principle. Its main purpose is to infer orders of empirical distributions from different categories based on a probability of finding a value in one distribution that is greater than an expectation of another distribution. Given a set of ordered-pair of real-category values the framework is capable of 1) inferring orders of domination of categories and representing orders in the form of a graph; 2) estimating magnitude of difference between a pair of categories in forms of mean-difference confidence intervals; and 3) visualizing domination orders and magnitudes of difference of categories. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2020) . Package: r-cran-edotrans Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abcanalysis, r-cran-opgmmassessment Filename: pool/dists/noble/main/r-cran-edotrans_0.2.5-1.ca2404.1_all.deb Size: 71460 MD5sum: eff0cdf7291c95dc8e9a20887626c4dd SHA1: adcbc75b9b1c1decebaef644daaab4eacc67348e SHA256: 15d79af3faf9b5dd534299666e52a47b5b1032fa630497ebf2d961b466171908 SHA512: b9acd20cfc567c8ea0453ce7d7308f996dd56de98180c464649dd7e925df57deb2355f51839a1f49c26d68aecf067555120a61b87df535dcecc5766e8dbc7011 Homepage: https://cran.r-project.org/package=EDOtrans Description: CRAN Package 'EDOtrans' (Euclidean Distance-Optimized Data Transformation) A data transformation method which takes into account the special property of scale non-invariance with a breakpoint at 1 of the Euclidean distance. Package: r-cran-edstan Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-edstan_1.1.0-1.ca2404.1_all.deb Size: 83020 MD5sum: b4f2a14594f3af4722cc3ce404e38b28 SHA1: 7350acfa89690abb51a80174d57e9f39cff1fa1f SHA256: 95c8634b9b3d96991bed86ba19d07f22d47756228677ca1ca5cca236b56a4cf8 SHA512: f09dc6f8b4f11a0741d1ff3b7085d4705d1c40a96d6b87f3de659af798dfc7c7693497d0b1eaf183ae3a93b1dd079838c5b4f857f7d2d6e0bef6da0483f92f62 Homepage: https://cran.r-project.org/package=edstan Description: CRAN Package 'edstan' (Stan Models for Item Response Theory) Streamlines the fitting of common Bayesian item response models using Stan. Package: r-cran-edsurvey Architecture: all Version: 4.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2946 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-lfactors, r-cran-dire, r-cran-data.table, r-cran-formula, r-cran-glm2, r-cran-haven, r-cran-laf, r-cran-lifecycle, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-naepprimer, r-cran-quantreg, r-cran-readxl, r-cran-tibble, r-cran-wcorr, r-cran-naepirtparams, r-cran-wemix, r-cran-xtable, r-cran-xml2 Suggests: r-cran-knitr, r-cran-testthat, r-cran-withr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-edsurvey_4.0.7-1.ca2404.1_all.deb Size: 2397428 MD5sum: 72aa4cca4d0925bdc3c13e934ad4666a SHA1: d17fcda3021a510850d49cbeb0c5af8da5f55de9 SHA256: 27da9bfb689fb6eb507fa7fc07f94138a30915bec1c6ffbcb80c7b087d297713 SHA512: e93942b11f8217264b252950f386e0db2b5447fcc83124e3b0b37e207059f03d92552bc77f44c2caa2ee4c0241b4d1660378ed7ec0480373d0d7c0dc23c7223a Homepage: https://cran.r-project.org/package=EdSurvey Description: CRAN Package 'EdSurvey' (Analysis of NCES Education Survey and Assessment Data) Read in and analyze functions for education survey and assessment data from the National Center for Education Statistics (NCES) , including National Assessment of Educational Progress (NAEP) data and data from the International Assessment Database: Organisation for Economic Co-operation and Development (OECD) , including Programme for International Student Assessment (PISA), Teaching and Learning International Survey (TALIS), Programme for the International Assessment of Adult Competencies (PIAAC), and International Association for the Evaluation of Educational Achievement (IEA) , including Trends in International Mathematics and Science Study (TIMSS), TIMSS Advanced, Progress in International Reading Literacy Study (PIRLS), International Civic and Citizenship Study (ICCS), International Computer and Information Literacy Study (ICILS), and Civic Education Study (CivEd). Package: r-cran-educabr Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6861 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-lifecycle, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-ggplot2, r-cran-jsonlite, r-cran-kableextra, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-systemfonts, r-cran-testthat, r-cran-textshaping, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-educabr_0.9.1-1.ca2404.1_all.deb Size: 5222346 MD5sum: 8467e4fb68445e021d29a7956017fb47 SHA1: 561374b214b86822f0918642be680743238848ee SHA256: 541e3432ca7fc1d2f915c05ab14233117f7692d143380563903667dbf63fdd0f SHA512: c7cac28bc6bda66400585b923551f2d9cf51c6b973a0d1f37b3f1af2545121035b7a3f723b2d8b636eabbf09e09bb5f89128201cdeb33cdde4c92c71b45c56c3 Homepage: https://cran.r-project.org/package=educabR Description: CRAN Package 'educabR' (Download and Process Brazilian Education Data from INEP) Download and process public education data from INEP (Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira). Provides functions to access microdata from the School Census (Censo Escolar), ENEM (Exame Nacional do Ensino Médio), SAEB (Sistema de Avaliação da Educação Básica), Higher Education Census (Censo da Educação Superior), ENADE (Exame Nacional de Desempenho dos Estudantes), ENCCEJA (Exame Nacional para Certificação de Competências de Jovens e Adultos), IDD (Indicador de Diferença entre os Desempenhos Observado e Esperado), CPC (Conceito Preliminar de Curso), IGC (Índice Geral de Cursos), CAPES graduate education data, FUNDEB (Fundo de Manutencao e Desenvolvimento da Educacao Basica), IDEB (Índice de Desenvolvimento da Educação Básica), and other educational datasets. Returns data in tidy format ready for analysis. Data source: INEP Open Data Portal . Package: r-cran-educationdata Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-readr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-educationdata_0.1.5-1.ca2404.1_all.deb Size: 346658 MD5sum: f319826cf5e24639f3af96a2ae7176ec SHA1: 7b0863aeb77eeb2923f7e1e4c414b29bbdda0b57 SHA256: 505976cdf3083442a84ede26a0377e6ab548f8ac0724bece33c2769f25e8aaa7 SHA512: 16d1cc313c3b0a8292cd7436705a19deb9f03bcf95abbf36e09709baad1f7c2eccbc6bf4a9e761fecd3fbbef1abd642e2cb95299209f18da6f2234d94b73f690 Homepage: https://cran.r-project.org/package=educationdata Description: CRAN Package 'educationdata' (Retrieve Records from the Urban Institute's Education DataPortal API) Allows R users to retrieve and parse data from the Urban Institute's Education Data API into a 'data.frame' for analysis. Package: r-cran-educationr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-educationr_0.1.0-1.ca2404.1_all.deb Size: 227148 MD5sum: fa674f4b45c3ff176dbf2a96115db14e SHA1: 5276a304b1229dffb8c40c9cdf2fe1755affd29d SHA256: 5dd938aae9fbd527f978ecbf4d5dae454d3f85e9829e450d3ebea2d522d90be9 SHA512: 979bf242d022a660d28ef1cbf51fae628101acd3da1a0edb6ea9558592efb90c79c3fda6836c338e803a7fcdad10f400a906db4def7821bc3c7dae43b218fa54 Homepage: https://cran.r-project.org/package=educationR Description: CRAN Package 'educationR' (A Comprehensive Collection of Educational Datasets) Provides a comprehensive collection of datasets related to education, covering topics such as student performance, learning methods, test scores, absenteeism, and other educational metrics. This package serves as a resource for educational researchers, data analysts, and statisticians to explore and analyze data in the field of education. Package: r-cran-educineq Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ineq, r-cran-flexsurv Filename: pool/dists/noble/main/r-cran-educineq_0.1.0-1.ca2404.1_all.deb Size: 413658 MD5sum: 2ef3d32ebdfdabed505f670cd803499d SHA1: 0749f2c5fddc774ad9ba9696f2ba950476751c98 SHA256: 28a7568f5887966e8299bdad61d3d730596aac1e80bdf9460958e09aa5e8d97c SHA512: b2cde3f1df95919ad7fcd4e7a1859be93b4569983bbdb5ca3e846945ffa12b7fbc29266b3fd229db7af48d3f82c691efaff71a0a4cc5f90130d261a1b8e652ad Homepage: https://cran.r-project.org/package=educineq Description: CRAN Package 'educineq' (Compute and Decompose Inequality in Education) Easily compute education inequality measures and the distribution of educational attainments for any group of countries, using the data set developed in Jorda, V. and Alonso, JM. (2017) . The package offers the possibility to compute not only the Gini index, but also generalized entropy measures for different values of the sensitivity parameter. In particular, the package includes functions to compute the mean log deviation, which is more sensitive to the bottom part of the distribution; the Theil’s entropy measure, equally sensitive to all parts of the distribution; and finally, the GE measure when the sensitivity parameter is set equal to 2, which gives more weight to differences in higher education. The decomposition of these measures in the components between-country and within-country inequality is also provided. Two graphical tools are also provided, to analyse the evolution of the distribution of educational attainments: The cumulative distribution function and the Lorenz curve. Package: r-cran-edwards97 Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-tibble, r-cran-broom, r-cran-cli, r-cran-withr, r-cran-glue Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-edwards97_0.1.1-1.ca2404.1_all.deb Size: 376944 MD5sum: eb98285665ce8d6bd957c8cf2daff5c2 SHA1: bc8f0e5b9607519bbb07baf45b7ebb95e79616d9 SHA256: eb3e73382cc0a4ff773c99ef1c4f26fdab69536890fb21daafd69a319d76edf9 SHA512: c60920845581f716114c8600834afbb39a76467c084c10daf5d9417c70ac03e65eec3ac7c4c47d56be26d2641d1f9271276b84dfa3779ed6d7ecd0991728b236 Homepage: https://cran.r-project.org/package=edwards97 Description: CRAN Package 'edwards97' (Langmuir Semi-Empirical Coagulation Model) Implements the Edwards (1997) Langmuir-based semi-empirical coagulation model, which predicts the concentration of organic carbon remaining in water after treatment with an Al- or Fe-based coagulant. Data and methods are provided to optimise empirical coefficients. Package: r-cran-ee.data Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7306 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-nnet, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-ee.data_0.1.1-1.ca2404.1_all.deb Size: 7444712 MD5sum: 744e23986be5d711516890e3368aa6a7 SHA1: b5fd771a8e19fa96dffbcb3507606fea3cd022be SHA256: 2d16aacfd9d6c367eee97a646817f54dfb5072c2a6e969f0843d877642e0ecc3 SHA512: be7920fdb1a5c1bdee4fccb3f370c91064e5c61a64a58ccacd0248ac42220bce332919943fb1cc2001377a4ba1db82bdc220b559fb1809b578acbc744277d3bd Homepage: https://cran.r-project.org/package=EE.Data Description: CRAN Package 'EE.Data' (Objects for Predicting Energy Expenditure) This is a data-only package containing model objects that predict human energy expenditure from wearable sensor data. Supported methods include the neural networks of Montoye et al. (2017) and the models of Staudenmayer et al. (2015) , one a linear model and the other a random forest. The package is intended as a spoke for the hub-package 'accelEE', which brings together the above methods and others from packages such as 'Sojourn' and 'TwoRegression.' Package: r-cran-eeaaq Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2460 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-curl, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-gifski, r-cran-gstat, r-cran-htmlwidgets, r-cran-httr, r-cran-leaflet, r-cran-lubridate, r-cran-raster, r-cran-readr, r-cran-sf, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-ggspatial Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rvest, r-cran-readxl, r-cran-digest, r-cran-gh, r-cran-base64enc, r-cran-stringr Filename: pool/dists/noble/main/r-cran-eeaaq_1.0.3-1.ca2404.1_all.deb Size: 1123172 MD5sum: 2943304280fe5fa48fb0ed2438ecf814 SHA1: cb62551cd24329718f99a55b81d04987894d5f2d SHA256: 4a2f6183e9808d89a19f40b3ca26768c0320c3e2235209e9dff5f4cab41dff8e SHA512: 7a34f17fbad2f420885f6254f1904fcd707333ff0e5542f9965ba56facbb35ab4302c75b906a095db5882cdf010b8b3ac47044402eabdf4347830434808a78fb Homepage: https://cran.r-project.org/package=EEAaq Description: CRAN Package 'EEAaq' (Handle Air Quality Data from the European Environment AgencyData Portal) This software downloads and manages air quality data from the European Environmental Agency (EEA) dataflow (). See the web page for details on the EEA's Air Quality Download Service. The package allows dynamically mapping the stations, summarising and time aggregating the measurements and building spatial interpolation maps. See the web page for further information on EEA activities and history. Further details, as well as, an extended vignette of the main functions included in the package, are available at the GitHub web page dedicated to the project. Package: r-cran-eeea Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-eeea_1.0.1-1.ca2404.1_all.deb Size: 94014 MD5sum: ca6173cec6d2274e7ec7419b8f63c494 SHA1: 982f7bd7d8348399742f3830facd24cb8834b3e4 SHA256: 57226842fb7b2e9ff8d9cfca298cd7b0d15b1686463f7f5d3cfa0e49b89e3327 SHA512: bd684db2a2ef1028bc382039996c9b5102429b44ae5e5e25b3f82b9f8ab0d9d03f5226c66d81d47a74259696d21343115de4c2c88405014067cc5e7708f4a627 Homepage: https://cran.r-project.org/package=EEEA Description: CRAN Package 'EEEA' (Explicit Exploration Strategy for Evolutionary Algorithms) Implements an explicit exploration strategy for evolutionary algorithms in order to have a more effective search in solving optimization problems. 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Package: r-cran-eefanalytics Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2jags, r-cran-ggplot2, r-cran-lme4, r-cran-mvtnorm, r-cran-coda, r-cran-mcmcvis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eefanalytics_1.1.5-1.ca2404.1_all.deb Size: 260658 MD5sum: 174334c5d47583b667c1c84d9257d7fe SHA1: 0cac458c808a96b88f4467e332f40373fb6c4f42 SHA256: c93143c115e1e038c083ed34c7b16d6bdc916c698af5315f5577e5e3bf8ae5d3 SHA512: 55a56b90a6ba5b2fb2b68c114f51613fe5ddc378db10ffa66a04ee4ee6808bad5ea5a949020c3b682b52132a635595828f0f64ec8cd3bd406a6134a72b38c964 Homepage: https://cran.r-project.org/package=eefAnalytics Description: CRAN Package 'eefAnalytics' (Robust Analytical Methods for Evaluating EducationalInterventions using Randomised Controlled Trials Designs) Analysing data from evaluations of educational interventions using a randomised controlled trial design. Various analytical tools to perform sensitivity analysis using different methods are supported (e.g. frequentist models with bootstrapping and permutations options, Bayesian models). The included commands can be used for simple randomised trials, cluster randomised trials and multisite trials. The methods can also be used more widely beyond education trials. This package can be used to evaluate other intervention designs using Frequentist and Bayesian multilevel models. Package: r-cran-eegkit Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-eegkitdata, r-cran-bigsplines, r-cran-ica, r-cran-rgl, r-cran-signal Filename: pool/dists/noble/main/r-cran-eegkit_1.0-5-1.ca2404.1_all.deb Size: 560182 MD5sum: 1cdb67d6f3729ba3350980c70035c334 SHA1: d800385c76fcf562417cfc3abd1d3d975f9aac9e SHA256: 819883999f1f0ea92df9f18968c0b9b88717877ec2ee5c01400fbca2ea160458 SHA512: e2893c8213a7928aab2476d3e734402718f84306a42663b829aa05cc7f514ad106540010e5e21b9941b653aaf6ae17dd1241b703710af9d471d00cd12ad45b5f Homepage: https://cran.r-project.org/package=eegkit Description: CRAN Package 'eegkit' (Toolkit for Electroencephalography Data) Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for (i) plotting EEG data, (ii) filtering EEG data, (iii) smoothing EEG data; (iv) frequency domain (Fourier) analysis of EEG data, (v) Independent Component Analysis of EEG data, and (vi) simulating event-related potential EEG data. 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For method details see Jaiswal, R. et al. (2022). . Package: r-cran-eemdsvr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlibeemd, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-eemdsvr_0.1.0-1.ca2404.1_all.deb Size: 24200 MD5sum: 96a01d9312f0664b1e7ab47b2c7d72dd SHA1: f7f0e85980244c908dbfb6bcd8d2071e0622da10 SHA256: 941aff630cccc20e51af85adcd1c10ac4ec02b95129e5ca5d1cdaf11be389940 SHA512: 3c0eb87d9d6f2cff63127f3f0d754cbae621ddb2c9543ea8138732c4edec734be3c3c75d977f866976867022f2221760e9d3960668c2c103422cd4ebcd832cd6 Homepage: https://cran.r-project.org/package=EEMDSVR Description: CRAN Package 'EEMDSVR' (Ensemble Empirical Mode Decomposition and Its Variant BasedSupport Vector Regression Model) Application of Ensemble Empirical Mode Decomposition and its variant based Support Vector regression model for univariate time series forecasting. For method details see Das (2020).. Package: r-cran-eemdtdnn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-rlibeemd Filename: pool/dists/noble/main/r-cran-eemdtdnn_0.1.0-1.ca2404.1_all.deb Size: 29190 MD5sum: c655e123a486112ac88e175cf4862112 SHA1: 8d4d055284351f8fb26eda62f616b803a1779015 SHA256: eaa4aa78fa760d8b215d00fd482676aa64b759cbc86f6cdf200d108bb3015d20 SHA512: 72cacb3c854c0e445cd2fdd4199de9f3fedb26c950e3ca0ba9b726d4deec271c9d459a34848c4ab5a3d20037d5a73f1e369b0659ebdce29eefc3172038c05feb Homepage: https://cran.r-project.org/package=eemdTDNN Description: CRAN Package 'eemdTDNN' (EEMD and Its Variant Based Time Delay Neural Network Model) Forecasting univariate time series with different decomposition based time delay neural network models. For method details see Yu L, Wang S, Lai KK (2008). . Package: r-cran-eeml Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcs, r-cran-weightedensemble, r-cran-topsis Filename: pool/dists/noble/main/r-cran-eeml_0.1.1-1.ca2404.1_all.deb Size: 20778 MD5sum: 1da553d49813010eb3a5e6b1b57000eb SHA1: 275247b14f0c715ee75a3d02ec31cfa7c325d8ed SHA256: 1fd7b13b27904f7dcde5443d57e06579b823ba9c6ad08b6fcf62ad01f098bb01 SHA512: eaf65821fe9d594b642cbdbdd2fe915ba433cbe6c9faedf5f9cb618d2a727f10b34de81fb99d8158419b9bf0591562ac0d59eaf65f786e56cf828a4d5b984873 Homepage: https://cran.r-project.org/package=EEML Description: CRAN Package 'EEML' (Ensemble Explainable Machine Learning Models) We introduced a novel ensemble-based explainable machine learning model using Model Confidence Set (MCS) and two stage Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The model combined the predictive capabilities of different machine-learning models and integrates the interpretability of explainability methods. To develop the proposed algorithm, a two-stage Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) framework was employed. The package has been developed using the algorithm of Paul et al. (2023) and Yeasin and Paul (2024) . 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Different methods are also provided to calculate common metrics such as humification index and fluorescence index. Package: r-cran-eeptools Architecture: all Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1526 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-arm, r-cran-data.table, r-cran-vcd Suggests: r-cran-testthat, r-cran-stringr, r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/noble/main/r-cran-eeptools_1.2.7-1.ca2404.1_all.deb Size: 1444238 MD5sum: 328bf7ca757b0a8223c5fa21b38b9ae3 SHA1: d884e6015cb7d116cee14799cecb9fbd0943922b SHA256: 06960cf2c8fc3a510207c5025419ad921f36711c8582fd9efbbb0974ed490a1e SHA512: 230656dd1fe2d7c4ed9be112ab03d68c47159879d25a63b176fa7d76a34a0cd6572da44f150261ed91b5a4104bffd6852b21fff9630f923bec4b2cd2e10a8c53 Homepage: https://cran.r-project.org/package=eeptools Description: CRAN Package 'eeptools' (Convenience Functions for Education Data) Collection of convenience functions to make working with administrative records easier and more consistent. 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Package: r-cran-eesim Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-viridis Suggests: r-cran-dlnm, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-eesim_0.1.0-1.ca2404.1_all.deb Size: 1543540 MD5sum: 0c517da21a1e125d098d1692f0918a99 SHA1: 6d88daee1b5e8d1eb76d1cb0606bc29817668e10 SHA256: 45af3c23f1b5abfecd2fea2bec6a50a6db6f65a1322a7533fff45840ea70e348 SHA512: 037f07cfe81fcaf2203e79df89214265e9788360839386aa53893d320dd71b988927d36d0587c62597199083b4d0b04bc2c05a788a55fdc6d019e16af65a7a79 Homepage: https://cran.r-project.org/package=eesim Description: CRAN Package 'eesim' (Simulate and Evaluate Time Series for Environmental Epidemiology) Provides functions to create simulated time series of environmental exposures (e.g., temperature, air pollution) and health outcomes for use in power analysis and simulation studies in environmental epidemiology. This package also provides functions to evaluate the results of simulation studies based on these simulated time series. This work was supported by a grant from the National Institute of Environmental Health Sciences (R00ES022631) and a fellowship from the Colorado State University Programs for Research and Scholarly Excellence. Package: r-cran-eespca Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rifle, r-cran-mass, r-cran-pma Filename: pool/dists/noble/main/r-cran-eespca_0.8.0-1.ca2404.1_all.deb Size: 274866 MD5sum: f5ce6ccfb984e84b611538570ec842fc SHA1: 9f9d04a9dc14fca5ecdd1027692a8a37e5eacec6 SHA256: cc742c72184ac348e26d6b5e83923e6e770daeab1211d4a3d57c4ebc99296db6 SHA512: 0ae020d02853c721b97949e1c7bec8d57b53a485594c3708ad9bbd83cc90189770f93c25bce1c7b5f85937b5fe1fb72a94774e8ac83d84ec85b36940754216ab Homepage: https://cran.r-project.org/package=EESPCA Description: CRAN Package 'EESPCA' (Eigenvectors from Eigenvalues Sparse Principal ComponentAnalysis (EESPCA)) Contains logic for computing sparse principal components via the EESPCA method, which is based on an approximation of the eigenvector/eigenvalue identity. Includes logic to support execution of the TPower and rifle sparse PCA methods, as well as logic to estimate the sparsity parameters used by EESPCA, TPower and rifle via cross-validation to minimize the out-of-sample reconstruction error. H. Robert Frost (2021) . Package: r-cran-efa.dimensions Architecture: all Version: 0.1.8.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1035 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-polycor, r-cran-efatools, r-cran-mirt, r-cran-gparotation Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-efa.dimensions_0.1.8.6-1.ca2404.1_all.deb Size: 1016242 MD5sum: 28b5ac44f4443a380c66e72d44ffb048 SHA1: 2b3f040fed42c57e19436d00c271e223444ef404 SHA256: 7815836b1ce6485f6c578d5e0c47a8322a1203ba9b642093ac6c5fe0d16410fc SHA512: 009744fe436e89dfea71c6d82e34f0b07252bd558794a92114b470e9aff5595b8ac86ecf5ee6bccf5b28e45ef5d1eabb0c86f072dd782d1120d84fe1c50b600e Homepage: https://cran.r-project.org/package=EFA.dimensions Description: CRAN Package 'EFA.dimensions' (Exploratory Factor Analysis Functions for AssessingDimensionality) Functions for eleven procedures for determining the number of factors, including functions for parallel analysis and the minimum average partial test. There are also functions for conducting principal components analysis, principal axis factor analysis, maximum likelihood factor analysis, image factor analysis, and extension factor analysis, all of which can take raw data or correlation matrices as input and with options for conducting the analyses using Pearson correlations, Kendall correlations, Spearman correlations, gamma correlations, or polychoric correlations. Varimax rotation, promax rotation, and Procrustes rotations can be performed. Additional functions focus on the factorability of a correlation matrix, the congruences between factors from different datasets, the assessment of local independence, the assessment of factor solution complexity, internal consistency, and for correcting Pearson correlation coefficients for attenuation due to unreliability. Auerswald & Moshagen (2019, ISSN:1939-1463); Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2); Mulaik (2010, ISBN:978-1-4200-9981-2); O'Connor (2000, ); O'Connor (2001, ISSN:0146-6216). 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Package: r-cran-efautilities Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gparotation, r-cran-plyr, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-efautilities_2.1.3-1.ca2404.1_all.deb Size: 324926 MD5sum: 088f98e46f67522911a71e777cc25c33 SHA1: 57bd2ad265b2774c81ca1b4ddfcaf34adb165e58 SHA256: dab06138b080c8997c1a96e0d484431b7ee4ce16f0438c3aa5eacdceb32f305c SHA512: 12cc9e7d0d5a17e745bdc16b1ccb6e47abfa32fa38e5e4d57338bf51dc8c826c0e0e28b04d1d36c7ab731eb0c3b950993e2a9ebeca0cb7972e2f2f3b4555919c Homepage: https://cran.r-project.org/package=EFAutilities Description: CRAN Package 'EFAutilities' (Utility Functions for Exploratory Factor Analysis) A number of utility function for exploratory factor analysis are included in this package. In particular, it computes standard errors for parameter estimates and factor correlations under a variety of conditions. Package: r-cran-efdm Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-efdm_0.2.3-1.ca2404.1_all.deb Size: 265530 MD5sum: 65bf3d538487923802e752cc2fa2c647 SHA1: f97f9f74c27b6b437b1be9b13f29946373fb1cce SHA256: 7f4d882095449ea4c0c8f00521b12790216b8d0d75a00f3888cdea0748c7fb38 SHA512: 5a74ed370d119b912b1d0efe7a6d32d14e7de392269b34012b84ef645792faafaa93548cb2c0e1a3cc299f0fe6fa387b8079118cb51a6b314f5388ee28d67c6d Homepage: https://cran.r-project.org/package=efdm Description: CRAN Package 'efdm' (Simulate Forest Resources with the European Forestry DynamicsModel) An implementation of European Forestry Dynamics Model (EFDM) and an estimation algorithm for the transition probabilities. The EFDM is a large-scale forest model that simulates the development of the forest and estimates volume of wood harvested for any given forested area. This estimate can be broken down by, for example, species, site quality, management regime and ownership category. See Packalen et al. (2015) . Package: r-cran-efdr Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1432 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-matrix, r-cran-foreach, r-cran-doparallel, r-cran-waveslim, r-cran-gstat, r-cran-tidyr, r-cran-dplyr, r-cran-sp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-ggplot2, r-cran-rcurl, r-cran-fields, r-cran-gridextra, r-cran-animation Filename: pool/dists/noble/main/r-cran-efdr_1.3-1.ca2404.1_all.deb Size: 928814 MD5sum: 14c8ca5c589a30a1bd6f5b004c69e931 SHA1: 2b4d58f113ca922c7f007b40185f36785f82becc SHA256: d7de8cb30c7eab975160c47ddaa9f33b543466a5edde3efe0c8ca8593f41ca77 SHA512: 52ce3274831d5e6ff9cab13f123a5961c72ff2620cc0b7780680e35f1c682cd527e72309d6039b102ba6450175c445c4c4d956a12b93a9d98d22c5c6973394b1 Homepage: https://cran.r-project.org/package=EFDR Description: CRAN Package 'EFDR' (Wavelet-Based Enhanced FDR for Detecting Signals from Completeor Incomplete Spatially Aggregated Data) Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies in an image. The image is first transformed into the wavelet domain in order to decorrelate any noise components, following which the coefficients at each resolution are standardised. Statistical tests (in a multiple hypothesis testing setting) are then carried out to find the anomalies. The power of EFDR exceeds that of standard FDR, which would carry out tests on every wavelet coefficient: EFDR choose which wavelets to test based on a criterion described in Shen et al. (2002). The package also provides elementary tools to interpolate spatially irregular data onto a grid of the required size. The work is based on Shen, X., Huang, H.-C., and Cressie, N. 'Nonparametric hypothesis testing for a spatial signal.' Journal of the American Statistical Association 97.460 (2002): 1122-1140. Package: r-cran-eff2 Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcalg, r-bioc-rbgl, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-qgraph Filename: pool/dists/noble/main/r-cran-eff2_1.0.2-1.ca2404.1_all.deb Size: 195348 MD5sum: 07442ef7969eacf8b5eed713ed3aa046 SHA1: 1ee4c3945a7ff5eecf82528628d6d3141ef995f5 SHA256: ee42674d11d0baf305f156887f8f2f0049d7b40266887b60de1d87ff7a127db3 SHA512: 4131aa6d8051c443db4ea43925346461955d0aaf0721cf4631f27c8bdc696a6891be52e304e7dfc20545d815dc1fd1cf76fa6030fcad8b1555eeb4b1209b3e8a Homepage: https://cran.r-project.org/package=eff2 Description: CRAN Package 'eff2' (Efficient Least Squares for Total Causal Effects) Estimate a total causal effect from observational data under linearity and causal sufficiency. The observational data is supposed to be generated from a linear structural equation model (SEM) with independent and additive noise. The underlying causal DAG associated the SEM is required to be known up to a maximally oriented partially directed graph (MPDAG), which is a general class of graphs consisting of both directed and undirected edges, including CPDAGs (i.e., essential graphs) and DAGs. Such graphs are usually obtained with structure learning algorithms with added background knowledge. The program is able to estimate every identified effect, including single and multiple treatment variables. Moreover, the resulting estimate has the minimal asymptotic covariance (and hence shortest confidence intervals) among all estimators that are based on the sample covariance. Package: r-cran-effclust Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fixest Suggests: r-cran-plm, r-cran-data.table Filename: pool/dists/noble/main/r-cran-effclust_0.8.0-1.ca2404.1_all.deb Size: 135238 MD5sum: 03cffb65ce4716f8d97913e4648eb3b9 SHA1: 25d0495a9f29cfa730253e751d5ad6b5710ffec5 SHA256: 73e3fcd1b9bdce61e4607a5784d2686f20d0b5b4873e709e13d601ed68f99d39 SHA512: 5efb7c124d4f139bc40f4cd649bb8663b94fe5344ca6e4d1efcee36ca37c78c5fc2cd20afd7211b770fd9d62af90ba5cba9ace012d94a4e400a8f19f8dca6312 Homepage: https://cran.r-project.org/package=effClust Description: CRAN Package 'effClust' (Calculate Effective Number of Clusters for a Linear Model) Calculates the (approximate) effective number of clusters for a regression model, as described in Carter, Schnepel, and Steigerwald (2017) . The effective number of clusters is a statistic to assess the reliability of asymptotic inference when sampling or treatment assignment is clustered. Methods are implemented for stats::lm(), plm::plm(), and fixest::feols(). There is also a formula method. Package: r-cran-effectcheck Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-stringi, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-xml2, r-cran-rvest, r-cran-glue, r-cran-logger Suggests: r-cran-shiny, r-cran-shinythemes, r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mbess, r-cran-effectsize, r-cran-jsonlite, r-cran-tesseract, r-cran-magick, r-cran-qpdf, r-cran-statcheck Filename: pool/dists/noble/main/r-cran-effectcheck_0.2.3-1.ca2404.1_all.deb Size: 436352 MD5sum: df0cee43fa5b92fe8aad65478a1cd986 SHA1: 3bbdfade731065050818709ab1c7180bafcf7ebd SHA256: 4e97da55a4a8e7c63b6578e371bfb104466783383fde057eb0ffe729d27afd29 SHA512: e25fbbb06a6616ffe2c723d25482221bd6eb91021745cc0a7c11b65ae786c0dbe1993518dd7d5148335688101febc338e3fc97051360b371fbcdf7bee9a242f8 Homepage: https://cran.r-project.org/package=effectcheck Description: CRAN Package 'effectcheck' (Statistical Consistency Checker for Published Research Results) A conservative, assumption-aware statistical consistency checker for published research results. Parses test statistics, effect sizes, and confidence intervals from text, PDF, HTML, and Word documents across multiple citation styles including American Psychological Association (APA), Harvard, Frontiers, PLOS ONE, Scientific Reports, Nature Human Behaviour, PeerJ, eLife, PNAS, and others. Recomputes effect sizes using all plausible variants when design is ambiguous, and validates internal consistency. Supports t-tests, F-tests/ANOVA, correlations, chi-square, z-tests, regression, and nonparametric tests. Provides 'statcheck'-compatible API functions for batch processing of files and directories. Explicitly tracks all assumptions and uncertainty in output. Detects decision errors (significance reversals) similar to 'statcheck'. Note: this package is under active development and results should be independently verified. Use is at the sole responsibility of the user. Contributions and verification reports are welcome. Package: r-cran-effectliter Architecture: all Version: 0.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan, r-cran-shiny, r-cran-dt, r-cran-foreign, r-cran-ggplot2, r-cran-nnet, r-cran-car, r-cran-restriktor, r-cran-ic.infer, r-cran-numderiv Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-effectliter_0.5-1-1.ca2404.1_all.deb Size: 569770 MD5sum: e8fe4f9b9c1e7a975157e76cc6d075d4 SHA1: 3508020c8586c06b41dad6c552ac28543b91b3ca SHA256: 125c3bfa43539ecff8db896a8674ca7e9d05c0764b52b3d26fb4860396cd651b SHA512: 8975366ad2b016b654b78936106d9ab3e08a4add88ec11a61b98b708d15a8cf2853aa696d191fb40d038e73e159050bcf0b44983e9add9f7f1fbd0c2fee966da Homepage: https://cran.r-project.org/package=EffectLiteR Description: CRAN Package 'EffectLiteR' (Average and Conditional Effects) Use structural equation modeling to estimate average and conditional effects of a treatment variable on an outcome variable, taking into account multiple continuous and categorical covariates. Package: r-cran-effectr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seqinr, r-cran-ggplot2, r-cran-shiny, r-cran-reshape2, r-cran-rmarkdown, r-cran-viridis Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-effectr_1.0.2-1.ca2404.1_all.deb Size: 103606 MD5sum: 96d877fa00d995ec8ff6cc245e1766c7 SHA1: 633bcf92814ed348b292e904aa07f6ac13aa1ee7 SHA256: 81ca82f3ed59e5c54d19aa875308dd66bd2efbb45720f98f9cf548b998450cee SHA512: 5ee52697e1f0939ac7795e32bb33861f65b57b71eae394bd8e0db0c78333a9f305fb1610f4ff4980de0724b56b35013bb3cd3cd042bb1671c7ac783e03345129 Homepage: https://cran.r-project.org/package=effectR Description: CRAN Package 'effectR' (Predicts Oomycete Effectors) Predicts cytoplasmic effector proteins using genomic data by searching for motifs of interest using regular expression searches and hidden Markov models (HMM) based in Haas et al. (2009) . 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(2020) . Package: r-cran-effectsizescr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kendall Filename: pool/dists/noble/main/r-cran-effectsizescr_0.1.0-1.ca2404.1_all.deb Size: 20642 MD5sum: 8b29fb4fa1db5bef65136115052850b2 SHA1: 87587533282a5419ff376224a392519ad3e309a3 SHA256: b9b18c559a50e0ccad47b674d20d2ea49124cc1f9f740d8167f12d1a70e60181 SHA512: 6b5dfdd1ef1744725025b81c2e8be6e5dc3791745bbf1177a0b79a3cdd4fb43e2418af340bdfe8f874d91ab5f02ef0ff3efbe858506bad7de0a4223bf0f6708c Homepage: https://cran.r-project.org/package=effectsizescr Description: CRAN Package 'effectsizescr' (Indices for Single-Case Research) Parametric and nonparametric statistics for single-case design. Regarding nonparametric statistics, the index suggested by Parker, Vannest, Davis and Sauber (2011) was included. It combines both nonoverlap and trend to estimate the effect size of a treatment in a single case design. Package: r-cran-effectstars2 Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vgam, r-cran-misctools Suggests: r-cran-diflasso, r-cran-difboost, r-cran-vgamdata Filename: pool/dists/noble/main/r-cran-effectstars2_0.1-3-1.ca2404.1_all.deb Size: 107768 MD5sum: 628f7b1f6137770e881e07ad785258c7 SHA1: 883cc28766e18b81f97d9c898c17d4b52880ce7f SHA256: b84a1281a2d65442686b39ac082a7d2d5eb7e16504f827ba22f996b613aa8d3a SHA512: a744ff05ab0a1d8b35fc15d3ec509acd6d0b113b4512dc53cec02ba10e91690f14a3d63c44912e2acd393aa8f20ed4cf39f767c58640b3afec46cc7e136d38aa Homepage: https://cran.r-project.org/package=EffectStars2 Description: CRAN Package 'EffectStars2' (Effect Stars) Provides functions for the method of effect stars as proposed by Tutz and Schauberger (2013) . Effect stars can be used to visualize estimates of parameters corresponding to different groups, for example in multinomial logit models. Beside the main function 'effectstars' there exist methods for special objects, for example for 'vglm' objects from the 'VGAM' package. Package: r-cran-effectstars Architecture: all Version: 1.9-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vgam Filename: pool/dists/noble/main/r-cran-effectstars_1.9-1-1.ca2404.1_all.deb Size: 261424 MD5sum: 2d28aa5e08218873ba55cc11e3fff500 SHA1: 02cb1eb6a6f2eaa1cf52f3c9b68752b58b055ba7 SHA256: 567a60e163d5d7f2c21c45aee5febfb0478af8ebf2f7dbc873f6432f75601bb5 SHA512: 03bf1b273e9974474e674ad9d3fe1c1deaec2d0440e00f75e9b8876ce38c5cf978752ea6d7547b85f178de630792f16f1b1172ee6685607014af4f95284052d2 Homepage: https://cran.r-project.org/package=EffectStars Description: CRAN Package 'EffectStars' (Visualization of Categorical Response Models) Notice: The package EffectStars2 provides a more up-to-date implementation of effect stars! EffectStars provides functions to visualize regression models with categorical response as proposed by Tutz and Schauberger (2013) . The effects of the variables are plotted with star plots in order to allow for an optical impression of the fitted model. Package: r-cran-effecttreat Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-effecttreat_1.1-1.ca2404.1_all.deb Size: 132912 MD5sum: 6f9d9c335a31275bad4bcd604ac5da58 SHA1: 48b62c0073d2febe1603b23a5f930a95e55686ca SHA256: 124eb5c2c662d52a70024cee74bb99993a047d6a96f5e6cae51fb250f19a2ac3 SHA512: 9fb385973cf3a738e427b10e88fb88c5e112ed3ea05b3582fd78c5370494d9b7465011ff8a48b5b0a39e33126a5a47f6b148a7daedd02a7643c73b99c80c2476 Homepage: https://cran.r-project.org/package=EffectTreat Description: CRAN Package 'EffectTreat' (Prediction of Therapeutic Success) In personalized medicine, one wants to know, for a given patient and his or her outcome for a predictor (pre-treatment variable), how likely it is that a treatment will be more beneficial than an alternative treatment. This package allows for the quantification of the predictive causal association (i.e., the association between the predictor variable and the individual causal effect of the treatment) and related metrics. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552. Package: r-cran-efficientmaxeigenpair Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 448 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-efficientmaxeigenpair_0.1.4-1.ca2404.1_all.deb Size: 381884 MD5sum: 69f336be400c466d529c649ebd3941d2 SHA1: 6fcced54012ff9e2c339f9ee061896c83deacd19 SHA256: 1399c5a5b896bf57f863dd2312ea36ce83d3c5f591bf51176fd10530cab313f1 SHA512: c2c0622a2b124d8ecce4c655f2096c0ee7fdb6326354a683bed0e26a95b497f2853e9dd939134c2be725947982f907c170172fc549f611a7b6c2af28c3ee99c3 Homepage: https://cran.r-project.org/package=EfficientMaxEigenpair Description: CRAN Package 'EfficientMaxEigenpair' (Efficient Initials for Computing the Maximal Eigenpair) An implementation for using efficient initials to compute the maximal eigenpair in R. It provides three algorithms to find the efficient initials under two cases: the tridiagonal matrix case and the general matrix case. Besides, it also provides two algorithms for the next to the maximal eigenpair under these two cases. Package: r-cran-efflog Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-efflog_1.0-1.ca2404.1_all.deb Size: 50226 MD5sum: faed23b5bce7be638d87d043ba3e9902 SHA1: 8755d64b6c5f2031ee45253b33c0a6c2e4086088 SHA256: 9658f6d7b563d869c426480b6b54471339716f4169a7a2d57e5169878107d7f4 SHA512: b77a11d70768ac9c35f4bc79081cc6e511efde37c56de77f0837c510150398c179ef540e4eb1ffc5d005ffc3adf520752d63b269e611e04b76328bf5dc3ac481 Homepage: https://cran.r-project.org/package=efflog Description: CRAN Package 'efflog' (The Causal Effects for a Causal Loglinear Model) Fitting a causal loglinear model and calculating the causal effects for a causal loglinear model with the multiplicative interaction or without the multiplicative interaction, obtaining the natural direct, indirect and the total effect. It calculates also the cell effect, which is a new interaction effect. Package: r-cran-effsize Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-effsize_0.8.1-1.ca2404.1_all.deb Size: 65070 MD5sum: 69703a1868a137b0469e0d112fbe5a3e SHA1: f25fc0e413ec4746ff94776fcedfa1248377f437 SHA256: de74ad12611c1e9a242954f567c30ed918451438f7ec993d84fec02946607838 SHA512: b94e4fbf11cacd4330ad6d211413f204cff2a1046eaccd51dad650107214dbab55e4338f70378ed5c75885f04d2e2856a76c0f60bb4ccf9d9e5fb22df7529c08 Homepage: https://cran.r-project.org/package=effsize Description: CRAN Package 'effsize' (Efficient Effect Size Computation) A collection of functions to compute the standardized effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha-Delaney A). The computation algorithms have been optimized to allow efficient computation even with very large data sets. 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This package implements sparse principal component methods (SPC) and bi-sparse online principal component estimation (SPOC) for parameter estimation. Includes functionality for calculating mean squared error, relative error, and loading matrix sparsity.The philosophy of the package is described in Guo G. (2023) . Package: r-cran-efred Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-efred_0.1.0-1.ca2404.1_all.deb Size: 154684 MD5sum: 7a36fe98ac2bf707672ed1207b7748df SHA1: f3c522606b173a646f32789f436a47ae341df9f5 SHA256: 0ac1a11289480cda2aa4f46760e91871a35be66e65dd920751664593239c82d3 SHA512: 2d0aea04279ac51c94fcfef37a2507529d70a0ca60dffe2c42a388e7ff9e2671f467c04dbf3b3806c671d18935a97c5290b9becf9ae35feed410bf7b6ccb2259 Homepage: https://cran.r-project.org/package=eFRED Description: CRAN Package 'eFRED' (Fetch Data from the Federal Reserve Economic Database) Interact with the FRED API, , to fetch observations across economic series; find information about different economic sources, releases, series, etc.; conduct searches by series name, attributes, or tags; and determine the latest updates. Includes functions for creating panels of related variables with minimal effort and datasets containing data sources, releases, and popular FRED tags. 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Combining these methods in one function (building the cumulative importance values) provides a stable feature selection tool. This selection can also be viewed in a barplot using the barplot_fs() function and proved using the evaluation function efs_eval(). 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Geo-located health outcomes of survey participants may be used to model spatial effects on health in an ego-centred approach. The package contains a range of functions to help explore the spatial structure of the data as well as visualize the fit of exponential models for various metaparameter combinations with respect to the number of lag intervals and maximal distance. Furthermore, the outcome of interest can be adjusted for covariates by fitting a linear regression in a preliminary step before the semi-variogram fitting process. 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This package is an add-on to the EGRET package that performs the WRTDS analysis. The WRTDS modeling method was initially introduced and discussed in Hirsch et al. (2010) , and expanded in Hirsch and De Cicco (2015) . The paper describing the uncertainty and confidence interval calculations is Hirsch et al. (2015) . Package: r-cran-egrni Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1382 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fdrtool, r-cran-gdata, r-cran-mass, r-cran-readr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-egrni_0.1.6-1.ca2404.1_all.deb Size: 770796 MD5sum: bf035d53042d9a8c4834e4b4ab3de41c SHA1: 6d86efe5c1d8f37bf979bd5a429c745d4e7ca88c SHA256: 7001cd43ec207a0b5ace61adb65a821ebe3dc32538270ee1b73c40b0c40d81e1 SHA512: 7a0e0097fa2b4865eece88404fa19b4fbec493236d4fa8dcb5da2ad04eae7c5c65b0a56df1f9f2bb6578d2cbaaa541fbd29ff7d1ce051439f3cb899fafce1bd9 Homepage: https://cran.r-project.org/package=EGRNi Description: CRAN Package 'EGRNi' (Ensemble Gene Regulatory Network Inference) Gene regulatory network constructed using combined score obtained from individual network inference method. The combined score measures the significance of edges in the ensemble network. Fisher's weighted method has been implemented to combine the outcomes of different methods based on the probability values. The combined score follows chi-square distribution with 2n degrees of freedom. . 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As an example, the genetic predisposition for obesity could be manifested through inherited variants that control metabolism through regulation of genes expressed in the brain and/or through the control of fat storage in the adipose tissue by dysregulation of genes expressed in adipose tissue. We present a method eGST (eQTL-based genetic subtyper) that integrates tissue-specific eQTLs with GWAS data for a complex trait to probabilistically assign a tissue of interest to the phenotype of each individual in the study. eGST estimates the posterior probability that an individual's phenotype can be assigned to a tissue based on individual-level genotype data of tissue-specific eQTLs and marginal phenotype data in a genome-wide association study (GWAS) cohort. Under a Bayesian framework of mixture model, eGST employs a maximum a posteriori (MAP) expectation-maximization (EM) algorithm to estimate the tissue-specific posterior probability across individuals. Methodology is available from: A Majumdar, C Giambartolomei, N Cai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019) . Package: r-cran-ehagof Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ehagof_0.1.1-1.ca2404.1_all.deb Size: 67230 MD5sum: 656f7ac6f55b6d8eee8a064b9accafa7 SHA1: a92cf27fcc5d00e88309afbdddacc3eb6cd1ff13 SHA256: 5f0b5b458c75a089440751827c65f6f0f5b44056964bd544cc610745e5ba10cb SHA512: 7796cc455b6395a8641dc210eea316d0d9428e0a38473b4623277419e3665d702ebda3a0de0584db9d130c9dd309389e0accb1bc798f0a345befe58014fbfd9f Homepage: https://cran.r-project.org/package=ehaGoF Description: CRAN Package 'ehaGoF' (Calculates Goodness of Fit Statistics) Calculates 15 different goodness of fit criteria. These are; standard deviation ratio (SDR), coefficient of variation (CV), relative root mean square error (RRMSE), Pearson's correlation coefficients (PC), root mean square error (RMSE), performance index (PI), mean error (ME), global relative approximation error (RAE), mean relative approximation error (MRAE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), coefficient of determination (R-squared), adjusted coefficient of determination (adjusted R-squared), Akaike's information criterion (AIC), corrected Akaike's information criterion (CAIC), Mean Square Error (MSE), Bayesian Information Criterion (BIC) and Normalized Mean Square Error (NMSE). 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(2023) ). Provides functionality for assessing data quality and for improving the reliability and machine interpretability of a dataset. 'eHDPrep' also enables semantic enrichment of a dataset where metavariables are discovered from the relationships between input variables determined from user-provided ontologies. 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Package: r-cran-ei.datasets Architecture: all Version: 0.0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4041 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Filename: pool/dists/noble/main/r-cran-ei.datasets_0.0.1-3-1.ca2404.1_all.deb Size: 4086402 MD5sum: 29d284ce9df3153209b2e48f0a5a8885 SHA1: b009eda6f04ffc4c0bd53ebe43bc4644cd2954aa SHA256: 1fe679234e49ad9cbd844fab008714efbf11cabd055eaa6481f43d73edbad638 SHA512: 2e46833e6e38c0e6bf6e21708dd96cb1c656ca8df90275cf7be717bf98d5f39e0aefb3769afb2cc2715cb0b855e6ec4fa061e45c0bfd1c1b0f96a58dcca906c9 Homepage: https://cran.r-project.org/package=ei.Datasets Description: CRAN Package 'ei.Datasets' (Real Datasets for Assessing Ecological Inference Algorithms) Provides more than 550 data sets of actual election results. Each of the data sets includes aggregate party and candidate outcomes at the voting unit (polling stations) level and two-way cross-tabulated results at the district level. 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Package: r-cran-eicircles Architecture: all Version: 0.0.1-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlcoptim Suggests: r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-eicircles_0.0.1-14-1.ca2404.1_all.deb Size: 256278 MD5sum: 9e29f327f5dab424dd8d7405679e1455 SHA1: 9c979dbf2b852a96d7bbc7bc12ae0da0ed3a4d08 SHA256: d6a7c82a9e7177dbdd95d64b3a3f31be7fb0b1da3a68be3517be439648fa878a SHA512: e63206f9b950b1300453ebd4ecb83f8d77e2b7777865d8f37531a6772e98d932112807f2bcbd619392cf5e9413c1009703a898bf86ac471df68d257faf0e7969 Homepage: https://cran.r-project.org/package=eiCircles Description: CRAN Package 'eiCircles' (Ecological Inference of RxC Tables by Overdispersed-MultinomialModels) Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data using the model described in Forcina et al. (2012), as extension of the model proposed in Brown and Payne (1986). Allows incorporation of covariates. References: Brown, P. and Payne, C. (1986). ''Aggregate data, ecological regression and voting transitions''. Journal of the American Statistical Association, 81, 453–460. . Forcina, A., Gnaldi, M. and Bracalente, B. (2012). ''A revised Brown and Payne model of voting behaviour applied to the 2009 elections in Italy''. Statistical Methods & Applications, 21, 109–119. . Pavia, J.M, and Forcina, A. (2026). ''Simulating electoral behavior''. Modeling Decisions for Artificial Intelligence, MDAI 2025. Lecture Notes in Computer Science, vol 15957, Torra, V., Narukawa, Y., Domingo-Ferrer, J. (eds), Springer, Cham, pp. 54-65. . Acknowledgements: The authors wish to thank Consellería de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIAICO/2023/031) and MICIU/AEI/10.13039/501100011033/FEDER, EU (grant PID2021-128228NB-I00) for supporting this research. Package: r-cran-eider Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 656 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-stringr, r-cran-magrittr, r-cran-jsonlite, r-cran-logger, r-cran-purrr, r-cran-fs, r-cran-tibble, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-eider_1.0.0-1.ca2404.1_all.deb Size: 244540 MD5sum: df7387c74db6002a7943d852f09cdfae SHA1: 04a969178f01ff16dff1f190d96f40bb5a56a9b5 SHA256: c6d03fc1d3a95fd8693d334d7118d14b81762b8da5efb8dfe04cc2bc099c637b SHA512: 890b472396d1e258deba57e30b82006e9bdda2bd18b2ad239c938572cc49ab8c7cd2b8a9424998b9c83070c55023411d4c4fb23bd4bade054b0076950e56da16 Homepage: https://cran.r-project.org/package=eider Description: CRAN Package 'eider' (Declarative Feature Extraction from Tabular Data Records) Extract features from tabular data in a declarative fashion, with a focus on processing medical records. 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These methodologies assess the lack of disaggregated data and provide an approach to obtaining disaggregated territorial-level data. For more details, see the following references: Fernández-Vázquez, E., Díaz-Dapena, A., Rubiera-Morollón, F. et al. (2020) "Spatial Disaggregation of Social Indicators: An Info-Metrics Approach." . Díaz-Dapena, A., Fernández-Vázquez, E., Rubiera-Morollón, F., & Vinuela, A. (2021) "Mapping poverty at the local level in Europe: A consistent spatial disaggregation of the AROPE indicator for France, Spain, Portugal and the United Kingdom." . Package: r-cran-eiexpand Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3750 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-stringr, r-cran-ggplot2, r-cran-ggmap, r-cran-viridis, r-cran-sf, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eiexpand_1.0.5-1.ca2404.1_all.deb Size: 3803898 MD5sum: 37c40a91a6688091421beed3db059ab3 SHA1: 3ddece5d2f0e15d742dd9dd9fb9ab8a02956e3f6 SHA256: 49b1fb86867aca31230ad66998d5bdd6f9bc893914c2cefb94552edb41a315a0 SHA512: fa5ead27b65313c7def6c2b63399bf73eb4124adb5b2295e6bb344ca1d29604a0a06f6ddb1d109438badeacad6c4f474383797bd52db0a0d49f73cd9db3d0079 Homepage: https://cran.r-project.org/package=eiExpand Description: CRAN Package 'eiExpand' (Utilities for Expanding Functionality of 'eiCompare') Augments the 'eiCompare' package's Racially Polarized Voting (RPV) functionality to streamline analyses and visualizations used to support voting rights and redistricting litigation. 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Package: r-cran-eigenmodel Architecture: all Version: 1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-eigenmodel_1.12-1.ca2404.1_all.deb Size: 71980 MD5sum: 4e1752ac371b6be98df41dee45524303 SHA1: a1f4f5964815bd0c7dbc646238cf6fdc3930d2cf SHA256: 2f752987c3f151c847dd399728ee61cd9fbf56bedd54735750eed851f0810cde SHA512: be8077b4e8af9b6997b7d5c3ff35c9e25d2efb6aa4fcb63f59b6d6b8c4b4263048790a663fc84fcdf21307c341f6c89db7042be0ddf2a2a478c78e5dcd48d0ec Homepage: https://cran.r-project.org/package=eigenmodel Description: CRAN Package 'eigenmodel' (Semiparametric Factor and Regression Models for SymmetricRelational Data) Estimation of the parameters in a model for symmetric relational data (e.g., the above-diagonal part of a square matrix), using a model-based eigenvalue decomposition and regression. Missing data is accommodated, and a posterior mean for missing data is calculated under the assumption that the data are missing at random. The marginal distribution of the relational data can be arbitrary, and is fit with an ordered probit specification. See Hoff (2007) . for details on the model. Package: r-cran-eikosograms Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4015 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-eikosograms_1.0.0-1.ca2404.1_all.deb Size: 1055182 MD5sum: aec36b50060960f08b641d832fe5e951 SHA1: 2bcca6cf50674d8b917963e60c2218ab502064d1 SHA256: 9296c2e95642072ad8c864051f4935eb8d09e5d142cf3be33a55658953f77abb SHA512: 76e1eca664c601e78c7ada4d80a67a00dd373092834ab0ad2ca0ed4e963911b1ab17c6d49c8f4a2f5d9a94e516f91e29da1a42e5db07cce2c03e232ba0e5e376 Homepage: https://cran.r-project.org/package=eikosograms Description: CRAN Package 'eikosograms' (Visualizing Probabilities, Frequencies, and ConditionalIndependence for Categorical Variates) An eikosogram (ancient Greek for probability picture) divides the unit square into rectangular regions whose areas, sides, and widths represent various probabilities associated with the values of one or more categorical variates. Rectangle areas are joint probabilities, widths are always marginal (though possibly joint margins, i.e. marginal joint distributions of two or more variates), and heights of rectangles are always conditional probabilities. Eikosograms embed the rules of probability and are useful for introducing elementary probability theory, including axioms, marginal, conditional, and joint probabilities, and their relationships (including Bayes' theorem as a completely trivial consequence). They provide advantages over Venn diagrams for this purpose, particularly in distinguishing probabilistic independence, mutually exclusive events, coincident events, and associations. They also are useful for identifying and understanding conditional independence structure. Eikosograms can be thought of as mosaic plots when only two categorical variates are involved; the layout is quite different when there are more than two variates. Only one categorical variate, designated the "response", presents on the vertical axis and all others, designated the "conditioning" variates, appear on the horizontal. In this way, conditional probability appears only as height and marginal probabilities as widths. The eikosogram is ideal for response models (e.g. logistic models) but equally useful when no variate is distinguished as the response. In such cases, each variate can appear in turn as the response, which is handy for assessing conditional independence in discrete graphical models (i.e. "Bayesian networks" or "BayesNets"). The eikosogram and its value over Venn diagrams in teaching probability is described in W.H. Cherry and R.W. Oldford (2003) , its value in exploring conditional independence structure and relation to graphical and log-linear models is described in R.W. Oldford (2003) , and a number of problems, puzzles, and paradoxes that are easily explained with eikosograms are given in R.W. Oldford (2003) . 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Reshape, rearrange, and combine multidimensional arrays for scientific computing, machine learning, and data analysis. Einops simplifies complex manipulations, making code more maintainable and intuitive. The original implementation is demonstrated in Rogozhnikov (2022) . 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Acknowledgements: The authors wish to thank Generalitat Valenciana, Consellería de Educación, Cultura, Universidades y Empleo (grant CIAICO/2023/031) for supporting this research. 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For more theoretical details on our methods and algorithms see Steyer et al. (2023, ) and Steyer et al. (2023, ). 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Package: r-cran-elcic Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm, r-cran-poisnor, r-cran-bindata, r-cran-geepack, r-cran-wgeesel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-elcic_0.2.1-1.ca2404.1_all.deb Size: 212574 MD5sum: f12fecd24503a516264489a8878afdfd SHA1: e94db45b29992642bd2caf9ee68fb81c949e0098 SHA256: e6a8778fd3aaf745a78cf169b88358f322e38b73d252393219d3e405b1c081ec SHA512: 3407b9d122e570042885ccb968b78ba98c1c7335ce9e844fc88977c0a2da20e85b09813ac3837cb046904d88d79eee9ec63fc874591f1f4591281534b414cd29 Homepage: https://cran.r-project.org/package=ELCIC Description: CRAN Package 'ELCIC' (The Empirical Likelihood-Based Consistent Information Criterion) We developed a consistent and robust information criterion to conduct model selection for semiparametric models. 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Each function allows for implicit and explicit solution of the differential equation using methods like Euler, Backwards implicit, Runge Kutta 4, Crank Nicholson and Backward differentiation formula as well as different number of points for derivative approximation. Several electrochemical processes can be simulated such as: Chronoamperometry, Potential Step, Linear Sweep, Cyclic Voltammetry, Cyclic Voltammetry with electrochemical reaction followed by chemical reaction (EC mechanism) and CV with two following electrochemical reaction (EE mechanism). In update 1.1.0 has been added a general purpose CV function that allow to simulate up to 4 EE mechanism combined with chemical reaction for each species.Update 1.2.0 improved the accuracy of the measurements and allow personalized data resolution for simulation. Bibliography regarding this methods can be found in the following texts. Dieter Britz, Jorg Strutwolf (2016) . Allen J. Bard, Larry R. Faulkner (2000) . 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References: Kedar, O., Harsgor, L. and Sheinerman, R.A. (2016). . Penades, A and Pavia, J.M. (2025) ''The decomposition of seats-to-votes distortion in elections: mean, variance, malapportionment and participation''. Acknowledgements: The authors wish to thank Consellería de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIACO/2023/031) for supporting this research. 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Package: r-cran-electionsbr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-data.table, r-cran-haven, r-cran-readr, r-cran-httr, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-electionsbr_0.5.0-1.ca2404.1_all.deb Size: 158974 MD5sum: 74e66a8a90deedc0e07920006b3b2674 SHA1: 0cdb847d4fb9834319a72b8f75e5875f92e32c28 SHA256: e7c8cc257496495a751a087b9213363c7327aed20ad66b33645348c890ccf8bd SHA512: dff146e3b7d17791c085eb404d1e37bb2b5fef31d6a0db08cd36e1d611ea98fa9a00d092b62b17bf437167d654e36eb98b9a239051cc273f7486caf35d505816 Homepage: https://cran.r-project.org/package=electionsBR Description: CRAN Package 'electionsBR' (R Functions to Download and Clean Brazilian Electoral Data) Offers a set of functions to easily download and clean Brazilian electoral data from the Superior Electoral Court and 'CepespData' websites. Among other features, the package retrieves data on local and federal elections for all positions (city councilor, mayor, state deputy, federal deputy, governor, and president) aggregated by state, city, and electoral zones. Package: r-cran-electivity Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-usethis Filename: pool/dists/noble/main/r-cran-electivity_1.0.2-1.ca2404.1_all.deb Size: 33574 MD5sum: ecf1ef4eb2af7d2450f1b66013d2738a SHA1: 3d0ca2bdc286fa8a4aeb0e36cd3436e39138c790 SHA256: 7cf92ff0dbf3e5ac71b53bb5807a47a0ce8e3728d2029cbabc58bf32b8536791 SHA512: 535cd95695ef506d55f4a3a317ca5aac1c2d7f47f2e7feabfe6886eacfdcde679bedc1a686ff49c68ca299536e38a776fe4280a6aa81071d083b28ad692a0a62 Homepage: https://cran.r-project.org/package=electivity Description: CRAN Package 'electivity' (Algorithms for Electivity Indices) Provides all electivity algorithms (including Vanderploeg and Scavia electivity) that were examined in Lechowicz (1982) , plus the example data that were provided for moth resource utilisation. Package: r-cran-electoral Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ineq, r-cran-tibble Filename: pool/dists/noble/main/r-cran-electoral_0.1.4-1.ca2404.1_all.deb Size: 46228 MD5sum: 2d665d6a79c1e2c8b294867f407b9ce2 SHA1: e3a5f0e57815b5801d5688781a051caa0fe1f3d3 SHA256: 61fafb981e49cea4f8090a61cf0d5a7437b01bda45ae30a47621a7114aadb9d8 SHA512: 38ca14dff30fef699cc54c1cbbeec617dfc722ba9598e48495796585eb0c60ad490de610531aac99701bfc10ce5ba00771fedf3d7846ec38991d95d067f3e441 Homepage: https://cran.r-project.org/package=electoral Description: CRAN Package 'electoral' (Allocating Seats Methods and Party System Scores) Highest averages & largest remainders allocating seats methods and several party system scores. Implemented highest averages allocating seats methods are D'Hondt, Webster, Danish, Imperiali, Hill-Huntington, Dean, Modified Sainte-Lague, equal proportions and Adams. Implemented largest remainders allocating seats methods are Hare, Droop, Hangenbach-Bischoff, Imperial, modified Imperial and quotas & remainders. The main advantage of this package is that ties are always reported and not incorrectly allocated. Party system scores provided are competitiveness, concentration, effective number of parties, party nationalization score, party system nationalization score and volatility. References: Gallagher (1991) . Norris (2004, ISBN:0-521-82977-1). Laakso & Taagepera (1979) . Jones & Mainwaring (2003) . Pedersen (1979) . Golosov (2010) . Golosov (2014) . 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The default thresholds and the treeline definition is based on Paulsen and Körner (2014) , users are free to decide what climate layers they would like to use. Package: r-cran-elexr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-elexr_1.0-1.ca2404.1_all.deb Size: 11922 MD5sum: f51e247a93da735b9382fa2d6a645c98 SHA1: 584d971fdce910c4f080849ce756a27d725021ba SHA256: 17ec6ea91fb473d11c99d878858836ad984ea03f0cc7f080c1f65ed30cc3b39e SHA512: 8ddb25193e52a9c7cc17459eb3faa2ccd6cf1db0dcb60b6176edbdee3bc8a9ebb369a7d6da903b8b43b8d421438b4113a21ae5e14eaca7125854466607c9bcf1 Homepage: https://cran.r-project.org/package=elexr Description: CRAN Package 'elexr' (Load Associated Press Election Results with Elex) Provides R access to election results data. Wraps elex (https://github.com/newsdev/elex/), a Python package and command line tool for fetching and parsing Associated Press election results. 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ELFs describe the relation between aquatic species richness (fish or benthic macroinvertebrates) and stream size characteristics (streamflow or drainage area). Journal publications are available outlining framework methodology (Kleiner et al. (2020) ) and application (Rapp et al. (2020) ). 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It is predicated on the assumption that the error term adheres to a Elliptical distribution. The philosophy of the package is described in Guo G. (2020) . 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Moreover, the batch effect correction/normalization will be carried out, when there are more than one batches of ELISAs. Feng (2018) . 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Drawing on characteristics of classical test theory, Exploratory Likert Scaling (ELiS) supports the user exploring multiple one-dimensional data structures. In common research practice, however, EFA remains the go-to method to uncover the (underlying) structure of a data set. Orthogonal dimensions and the potential of overextraction are often accepted as side effects. As described in Müller-Schneider (2001) ), ELiS confronts these problems. As a result, 'elisr' provides the platform to fully exploit the exploratory potential of the multiple scaling approach itself. Package: r-cran-elist Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-elist_0.2.0-1.ca2404.1_all.deb Size: 105798 MD5sum: 85708111ff4b498577e7996d8506c56d SHA1: 0671240e4c5b01f04ec228ba2c3ee6996c0f5941 SHA256: 415ffb3e6fc03214c2cc89f62da412df68f7d6e3fe9892f8f263eb4a9eea25e1 SHA512: 87967420a108a87bfda36b62bfd371fa0f5cd4358fe724ddb95d83c28257c0227525e223c85cf6d42666d8c318bc2358085eccefbd07133f3651c4ce688c8a34 Homepage: https://cran.r-project.org/package=eList Description: CRAN Package 'eList' (List Comprehension and Tools) Create list comprehensions (and other types of comprehension) similar to those in 'python', 'haskell', and other languages. List comprehension in 'R' converts a regular for() loop into a vectorized lapply() function. 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The training time is very short and the online version allows to update the model using small chunk of the training set at each iteration. The only parameter to tune is the hidden layer size and the learning function. Package: r-cran-elmso Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-elmso_1.0.1-1.ca2404.1_all.deb Size: 27972 MD5sum: 9ab699b9a2b4fbd43fa4bde8a5c56105 SHA1: 8e10203d3f07f8b4049a31ce942aa84c94604378 SHA256: 92e546c875c92d8823c78697eba15c16fce459c47dac9c2755e9d389aea8fced SHA512: fe0fd6c3696dd8b0cf451c0732bb22b23f584f56f50e80477a0cca8d19c67d533a69c09f8b9d7451874a86cace76e61a7b86676edd80b9ada95bee9364b353d2 Homepage: https://cran.r-project.org/package=ELMSO Description: CRAN Package 'ELMSO' (Implementation of the Efficient Large-Scale Online DisplayAdvertising Algorithm) An implementation of the algorithm described in "Efficient Large- Scale Internet Media Selection Optimization for Online Display Advertising" by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below for journal text/citation and for a full-text version of the paper). The algorithm here is designed to allocate budget across a set of online advertising opportunities using a coordinate-descent approach, but it can be used in any resource-allocation problem with a matrix of visitation (in the case of the paper, website page- views) and channels (in the paper, websites). The package contains allocation functions both in the presence of bidding, when allocation is dependent on channel-specific cost curves, and when advertising costs are fixed at each channel. Package: r-cran-elnnpairedcov Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase, r-cran-mass, r-bioc-limma Filename: pool/dists/noble/main/r-cran-elnnpairedcov_0.3.2-1.ca2404.1_all.deb Size: 255520 MD5sum: 1b13cba02c9c96351de4d856effc5c83 SHA1: ed009dbeb53b021a15d33f0e06fff0033cdd06b7 SHA256: 2984578ae6ae06ff7f14cecaded74eadef4418100f5d57ff3a7ce1a81f6f48af SHA512: fb131832b70256fc4a75c88fd2eafbd5e6fc7e505533d7d49d2bfc2632b9d03d705224725e501fdb7ed68c3b877375a33fe3e1c734701dd3ea2bb1d00dfd74e4 Homepage: https://cran.r-project.org/package=eLNNpairedCov Description: CRAN Package 'eLNNpairedCov' (Model-Based Gene Selection for Paired Data) Model-based clustering for paired data based on the regression of a mixture of Bayesian hierarchical models on covariates. Zhang et al. (2023) . Package: r-cran-elooptimized Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-reshape2, r-cran-bammtools, r-cran-magrittr, r-cran-lubridate, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-elooptimized_0.3.2-1.ca2404.1_all.deb Size: 192160 MD5sum: 4b00eee74deb8fd5b9c6f5d4182e7bd4 SHA1: 92959b8c8e0078a287efba83ec3298e096227aca SHA256: d24c7983bab658666323685574ccb90ebd7d4d81e710be9ca33d1d31d8ae4ed6 SHA512: 6207aca1c2d13a737d176ec7f068950e3bb9866015d96ddb36e425bd086f41466aa628437cf132aeafdec3899bd0738153d4f6fc0460f684f6b29a89ed88fa57 Homepage: https://cran.r-project.org/package=EloOptimized Description: CRAN Package 'EloOptimized' (Optimized Elo Rating Method for Obtaining Dominance Ranks) Provides an implementation of the maximum likelihood methods for deriving Elo scores as published in Foerster, Franz et al. (2016) . Package: r-cran-elt Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 771 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-locfit, r-cran-lattice, r-cran-latticeextra, r-cran-xlsx Filename: pool/dists/noble/main/r-cran-elt_1.7-1.ca2404.1_all.deb Size: 662174 MD5sum: 761c859f7d8fa386c995a9c22fb6bad8 SHA1: 53439ac6678bece811c109587267642881cd4bbd SHA256: 331d78f1c606c853e6cc3e0e470fd64ddcc5cd2bff8f6449ac3f8c0621be70be SHA512: 299c41f97c8aaf3f0f1f7d7fe3a6f58556b98e44bae1a696cd3425fe6c4bedd7c5fac207b5bdb8970f6f55d6a0dbac8c2a60c0acc5ddc12e27eda47e15d8e31b Homepage: https://cran.r-project.org/package=ELT Description: CRAN Package 'ELT' (Experience Life Tables) Build experience life tables. Package: r-cran-eltr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-eltr_0.1.0-1.ca2404.1_all.deb Size: 58494 MD5sum: df4ee55ba600628fc9198063d68f09e8 SHA1: 396d6d8cf23e9953c25e365211562fb08fec1e40 SHA256: d9e3cbe53df1f7ed8fb720627dd99d539edc794e52c504f97474f175caa57e65 SHA512: 21282617245b01067753765d18636642938e730d6e5dd104d8e535046e595dbc2ca89e2759b98aa150cbb5bf60b258e680299ef7bc67bb88248a2576eee4eee3 Homepage: https://cran.r-project.org/package=eltr Description: CRAN Package 'eltr' (Utilise Catastrophe Model Event Loss Table Outputs) Provides a tool to run Monte Carlo simulation of catastrophe model event loss tables, using a Poisson frequency and Beta severity distribution. Package: r-cran-em.fuzzy Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fuzzynumbers, r-cran-distrib Filename: pool/dists/noble/main/r-cran-em.fuzzy_1.0-1.ca2404.1_all.deb Size: 67532 MD5sum: 40f352e3eab97acb44fc386dedadc42d SHA1: a21e9d7cd224918c1423352e0355e8e33971a523 SHA256: 10606f91178383421ea60dc2134d6c6bdf63ca9a2ab49438362affc4adf88951 SHA512: d31833bfc23e1900e1ef7d2f8be9e68ec321610521e1d79c9235a50e763b9e4c95c0321aaa6de775e0a8a21659bbc470292abab1df34252ccb7e47a469db7139 Homepage: https://cran.r-project.org/package=EM.Fuzzy Description: CRAN Package 'EM.Fuzzy' (EM Algorithm for Maximum Likelihood Estimation by Non-PreciseInformation) The EM algorithm is a powerful tool for computing maximum likelihood estimates with incomplete data. This package will help to applying EM algorithm based on triangular and trapezoidal fuzzy numbers (as two kinds of incomplete data). A method is proposed for estimating the unknown parameter in a parametric statistical model when the observations are triangular or trapezoidal fuzzy numbers. This method is based on maximizing the observed-data likelihood defined as the conditional probability of the fuzzy data; for more details and formulas see Denoeux (2011) . Package: r-cran-emailjsr Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shiny.i18n, r-cran-httr, r-cran-shinybrowser Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-emailjsr_0.0.2-1.ca2404.1_all.deb Size: 147142 MD5sum: 8f0115a9fca260645593e677a7b6c8ad SHA1: ceba74fa423cd0cecf6a8ee003629ac2144e67b3 SHA256: eeccf4bdae76a408d0ba6880828b94b66668c8a4aed6a4013d709fbdd3527ebc SHA512: 31fff435c50ad75d88a629b4cfd20087d2309599e1917e219271f771048d159af5189a9a4fd32265d52b3fbc4a14214d0b01fa2a322c0d746cb3aca9b106c80a Homepage: https://cran.r-project.org/package=emailjsr Description: CRAN Package 'emailjsr' ('emailjs' Support) Use 'emailjs' API easily in 'R'. This package is not official. . You can send e-mail with 'emailjs' with function, based on 'httr'. You can also make a 'shiny' ui and server function. It can be used for making feedback form, inquiry, and so on. Package: r-cran-emailvalidation Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-emailvalidation_0.1.0-1.ca2404.1_all.deb Size: 20194 MD5sum: 969e233dcb466a5ec3472852d057e268 SHA1: da7d8712f1ffece470d75ec6eaffa344cfa2130c SHA256: 79147d27d35be265f45fd3afcbfc0eff490ada63cdbe2e2457ffbb7fa5f9fe3b SHA512: 2f76b374a87b4c7040e0f2c5de345c68b5b92b58b9499f478416f486df91ae4239f48899b2b989e6b8f392fc078e9aed7dbc256f97758c47a68f5bf2dbbbe511 Homepage: https://cran.r-project.org/package=emailvalidation Description: CRAN Package 'emailvalidation' (Client for the 'emailalvalidation.io' E-Mail Validation API) An R client for the 'emailvalidation.io' e-mail verification API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at . Package: r-cran-emar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-emar_1.0.0-1.ca2404.1_all.deb Size: 19644 MD5sum: 879901d04408812d2d9b9eba07dc89e8 SHA1: 89b2e30ef51c02b8549f60a6e05fa25baee44924 SHA256: 628209bfce8d4a34ae991dcd4b62daea4ed44952f804be16205ee4c35f004dde SHA512: a1edc80e2a812efb65d46f80664997e4f565d63d026740de6f3aaf555313dd94026ce3f930d8f6a6983f272a0bd388a22b98f0f77cd3b5691054ff5bd4b75464 Homepage: https://cran.r-project.org/package=EMAR Description: CRAN Package 'EMAR' (Empirical Model Assessment) A tool that allows users to generate various indices for evaluating statistical models. The fitstat() function computes indices based on the fitting data. The valstat() function computes indices based on the validation data set. Both fitstat() and valstat() will return 16 indices SSR: residual sum of squares, TRE: total relative error, Bias: mean bias, MRB: mean relative bias, MAB: mean absolute bias, MAPE: mean absolute percentage error, MSE: mean squared error, RMSE: root mean square error, Percent.RMSE: percentage root mean squared error, R2: coefficient of determination, R2adj: adjusted coefficient of determination, APC: Amemiya's prediction criterion, logL: Log-likelihood, AIC: Akaike information criterion, AICc: corrected Akaike information criterion, BIC: Bayesian information criterion, HQC: Hannan-Quin information criterion. The lower the better for the SSR, TRE, Bias, MRB, MAB, MAPE, MSE, RMSE, Percent.RMSE, APC, AIC, AICc, BIC and HQC indices. The higher the better for R2 and R2adj indices. Petre Stoica, P., Selén, Y. (2004) \n Zhou et al. (2023) \n Ogana, F.N., Ercanli, I. (2021) \n Musabbikhah et al. (2019) . Package: r-cran-emas Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mediation, r-cran-multilevel, r-cran-ggplot2, r-cran-lavaan Filename: pool/dists/noble/main/r-cran-emas_0.2.4-1.ca2404.1_all.deb Size: 231268 MD5sum: 2168b800e7ebf5c7c4a01df6edff0cb7 SHA1: 8bf856c00226c9d651b25bac1176a83bf253b1a8 SHA256: 435d2cb522a2a7584742f869825797142599e780f142f8e5401864e87dbd6318 SHA512: 23a2ed9ba906ca0d4dde209a087c63f7370e8422408ec02a89ca63f096f00eb68a382eea8209a98adea9d9d8cb1a661ea50991d5b996bc65738bdf4901881e21 Homepage: https://cran.r-project.org/package=EMAS Description: CRAN Package 'EMAS' (Epigenome-Wide Mediation Analysis Study) DNA methylation is essential for human, and environment can change the DNA methylation and affect body status. Epigenome-Wide Mediation Analysis Study (EMAS) can find potential mediator CpG sites between exposure (x) and outcome (y) in epigenome-wide. For more information on the methods we used, please see the following references: Tingley, D. (2014) , Turner, S. D. (2018) , Rosseel, D. (2012) . Package: r-cran-ematools Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-datacombine, r-cran-ggplot2, r-cran-lmertest, r-cran-sjstats, r-cran-anytime, r-cran-plyr Filename: pool/dists/noble/main/r-cran-ematools_0.1.4-1.ca2404.1_all.deb Size: 38008 MD5sum: 4d81ae5d3feb55bb7d96b9cf0daf17f0 SHA1: 2f4ddfb0e46e4cebde42172fe34a14629475a7e8 SHA256: 820cc2eae89840551e3fc682b493bdb257e751932746422512b27a906a304f2a SHA512: cf309c4704e678475a5286758d22b4d83840fcbb251d8a9abd14e4f7bf7bd24a5a000020004990e98cdd070c596051875942ce7be8d26da0b9351e7b207e5bba Homepage: https://cran.r-project.org/package=EMAtools Description: CRAN Package 'EMAtools' (Data Management Tools for Real-Time Monitoring/EcologicalMomentary Assessment Data) Do data management functions common in real-time monitoring (also called: ecological momentary assessment, experience sampling, micro-longitudinal) data, including creating power curves for multilevel data, centering on participant means and merging event-level data into momentary data sets where you need the events to correspond to the nearest data point in the momentary data. This is VERY early release software, and more features will be added over time. Package: r-cran-emayili Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64enc, r-cran-commonmark, r-cran-curl, r-cran-digest, r-cran-dplyr, r-cran-glue, r-cran-htmltools, r-cran-httr, r-cran-logger, r-cran-magrittr, r-cran-mime, r-cran-purrr, r-cran-rmarkdown, r-cran-rvest, r-cran-stringi, r-cran-stringr, r-cran-tidyr, r-cran-urltools, r-cran-xfun, r-cran-xml2 Suggests: r-cran-cld2, r-cran-cld3, r-cran-gpg, r-cran-here, r-cran-jinjar, r-cran-lintr, r-cran-memoise, r-cran-testthat, r-cran-roxygen2, r-cran-showtext, r-cran-microsoft365r Filename: pool/dists/noble/main/r-cran-emayili_0.9.3-1.ca2404.1_all.deb Size: 559344 MD5sum: 6085ab85739823a43187ef8d835705ad SHA1: eaa3e711cf8ccd27655d9a4019202f99170211d9 SHA256: 16359acf7708548726f32d08b643e3e65272dc6eca18f476b011d0532ade566f SHA512: 83f52c6c14519f3284d88a35c98e329270418d29d0867c0fb8a8a017d71d9bd629135445f1c946112ae38ab1dfa2f49618854f44ba3a695c188fbabd18e392c2 Homepage: https://cran.r-project.org/package=emayili Description: CRAN Package 'emayili' (Send Email Messages) A light, simple tool for sending emails with minimal dependencies. Package: r-cran-embalvi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rcolorbrewer Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-embalvi_0.1.0-1.ca2404.1_all.deb Size: 94464 MD5sum: 4d938505e1b61f07aed25a1e0cab08ac SHA1: 60ac54887687fcf23876a2ae93b4f15f4c86e7d2 SHA256: 5454aa68abd8dbdcbf7c0b60eff4e2f0be436f389254a6920817811a0edb80cd SHA512: a9a68841c870da95a9ec9d8c84c33a19203c146296cd89a7f5bb02eeeb4dbc73d947f8ca55d2f8d224e7a24b324aed9d19dd61493c75e2dcc2783c2ce5ef2c18 Homepage: https://cran.r-project.org/package=emBALVI Description: CRAN Package 'emBALVI' (EM Bayesian Adaptive LASSO Variational Inference Based GWAS) Performs Genome-Wide Association Study (GWAS) analysis using Expectation-Maximization Bayesian Adaptive LASSO with Variational Inference (emBALVI). 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Effect encodings using simple generalized linear models or nonlinear models can be used. There are also functions for dimension reduction and other approaches. Package: r-cran-embryogrowth Architecture: all Version: 2025.12.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-optimx, r-cran-numderiv, r-cran-ggplot2, r-cran-helpersmg, r-cran-rdpack Suggests: r-cran-entropy, r-cran-shiny, r-cran-coda, r-cran-polynom, r-cran-car, r-cran-gam, r-cran-pbapply, r-cran-cranlogs, r-cran-mapdata Filename: pool/dists/noble/main/r-cran-embryogrowth_2025.12.22-1.ca2404.1_all.deb Size: 3278198 MD5sum: 7a8b53cd39a5ac2634db7ba173208ce8 SHA1: 6dc90e04c56785cb55805ee9cfd526db8c9b142b SHA256: 10c3794cac2d16ec0c9bedc90661fefbb750caa0863ccd3292c4cf03ad76fc6e SHA512: bde73cf89f82866e21db6e577eb2b190ca8de261e889d4bfb278fe333de6e31306aa46e3e5a5533f1ec71fcafb6b3ed7bb11e85d440fa9ea07eb767c06932b80 Homepage: https://cran.r-project.org/package=embryogrowth Description: CRAN Package 'embryogrowth' (Tools to Analyze the Thermal Reaction Norm of Embryo Growth) Tools to analyze the embryo growth and the sexualisation thermal reaction norms. See for tsd functions; see for thermal reaction norm of embryo growth. 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Package: r-cran-eml Architecture: all Version: 2.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2352 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-digest, r-cran-emld, r-cran-jqr, r-cran-jsonlite, r-cran-uuid, r-cran-rmarkdown, r-cran-dplyr Suggests: r-cran-knitr, r-cran-taxadb, r-cran-tibble, r-cran-testthat, r-cran-covr, r-cran-units, r-cran-htmlwidgets, r-cran-shiny, r-cran-shinyjs, r-cran-spelling Filename: pool/dists/noble/main/r-cran-eml_2.0.7-1.ca2404.1_all.deb Size: 779894 MD5sum: feba488c67f68f6535ee9fb07999b525 SHA1: fadd417cbc75382ab8f5534434172183ba1ed7e4 SHA256: 2f9e7497c59de9bccaa9cf0e723388ff24ded36545081bce56f339b76190c4af SHA512: bd8e5163ac11d91ab8bc381f93690d26167dae57c626f9d71482bf3796d5694831b74362a44023ebd2bd21266c8a4eb4ab599f2ed3d0ab54951994e4d9c8d575 Homepage: https://cran.r-project.org/package=EML Description: CRAN Package 'EML' (Read and Write Ecological Metadata Language Files) Work with Ecological Metadata Language ('EML') files. 'EML' is a widely used metadata standard in the ecological and environmental sciences, described in Jones et al. (2006), . Package: r-cran-emld Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4397 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-jsonlite, r-cran-jsonld, r-cran-yaml Suggests: r-cran-spelling, r-cran-testthat, r-cran-magrittr, r-cran-rmarkdown, r-cran-covr, r-cran-knitr, r-cran-rdflib, r-cran-jqr Filename: pool/dists/noble/main/r-cran-emld_0.5.3-1.ca2404.1_all.deb Size: 329752 MD5sum: 4a0ebdd31d489f99b20df2c0f82b4459 SHA1: b1b23b1f5bae447cd5ab66f066461aa93d8168ed SHA256: eccfdc0c50f09f84db831445e01c0f6021305ca17597052a9db119a75550c4d6 SHA512: 2c026a321d24a9cb50c33b066c1ed89247533971d27a221fa47dd7dfec86ba97d3b083b7cbc2f8974a65b262fc728772130e83aca99917464f72f4a1731b9fd6 Homepage: https://cran.r-project.org/package=emld Description: CRAN Package 'emld' (Ecological Metadata as Linked Data) This is a utility for transforming Ecological Metadata Language ('EML') files into 'JSON-LD' and back into 'EML.' Doing so creates a list-based representation of 'EML' in R, so that 'EML' data can easily be manipulated using standard 'R' tools. This makes this package an effective backend for other 'R'-based tools working with 'EML.' By abstracting away the complexity of 'XML' Schema, developers can build around native 'R' list objects and not have to worry about satisfying many of the additional constraints of set by the schema (such as element ordering, which is handled automatically). Additionally, the 'JSON-LD' representation enables the use of developer-friendly 'JSON' parsing and serialization that may facilitate the use of 'EML' in contexts outside of 'R,' as well as the informatics-friendly serializations such as 'RDF' and 'SPARQL' queries. Package: r-cran-emli Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-emli_0.3.0-1.ca2404.1_all.deb Size: 56394 MD5sum: 54c1c303f90ec107c4533eb83e91d706 SHA1: dd6514c2d22a5f3459f6ff4763fa194125ed6c06 SHA256: 51b514da459ec681bbb26135b496b59ff5f49983ea6c3c2b8ff831285b88b62c SHA512: b97fac63241731ed6871d1fd7c943b5cc949870ed205fad4caf663972b154be8ac0b526937aa69e085cfe6396c10f6eb386e68f8e7e26f6dd40ec1a426b1f15a Homepage: https://cran.r-project.org/package=EMLI Description: CRAN Package 'EMLI' (Computationally Efficient Maximum Likelihood Identification ofLinear Dynamical Systems) Provides implementations of computationally efficient maximum likelihood parameter estimation algorithms for models representing linear dynamical systems. Currently, two such algorithms (one offline and one online) are implemented for the single-output cumulative structural equation model with an additive-noise output measurement equation and assumptions of normality and independence. The corresponding scientific papers are referenced in the descriptions of the functions implementing these algorithms. Package: r-cran-emln Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2226 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-stringr, r-cran-igraph, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-matrix, r-cran-dt, r-cran-hablar, r-cran-rlang Suggests: r-cran-bipartite, r-cran-httpuv, r-cran-testthat Filename: pool/dists/noble/main/r-cran-emln_1.2.0-1.ca2404.1_all.deb Size: 1502098 MD5sum: fb54914c66be9777250c7501adf63c17 SHA1: 13a4dfc4c4b13cd3129d2d765d6ca1249e555231 SHA256: f9cfc2c0c42d74486eab8857d066de556d87fb0abca47cc767088937c864e96e SHA512: cf67eaebd074549df0ca4b3ae38444493d2944495abe6a76ffd1361f04ec18f81d51240e094bd041142ada9df4f059b974add8e7d0f67fd9419770b9d9ff88f1 Homepage: https://cran.r-project.org/package=emln Description: CRAN Package 'emln' (Organize, Handle, and Explore Ecological Multilayer Networks) Data and analysis of ecological multilayer networks, including standardization of data structures and functions to convert between them. Includes an interactive multilayer network visualizer (beta, paper forthcoming), and a collection of 78 empirical ecological multilayer network datasets. This work was supported by research grant ISF (Israel Science Foundation) 1281/20 to Shai Pilosof. Noa Frydman (2023) . Package: r-cran-emmageo Architecture: all Version: 0.9.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gparotation, r-cran-nnls, r-cran-catools, r-cran-shiny Filename: pool/dists/noble/main/r-cran-emmageo_0.9.9-1.ca2404.1_all.deb Size: 593038 MD5sum: fa062438891b7eb0db781cc0a334c52c SHA1: a396e212bc575f82b9590f0706027f01d7a2c555 SHA256: 480e2a784fa19da5f92e63e07b9873aafc50ef025ce1bffc0928f60f93871397 SHA512: 92f0610ddd2fdf6c7f930409aaeff949a400266a9dc0ac9293cffc8777cc6437c91ccfdca98ee235774318c3b61ce6f0fc60c3630a5c7aa255b7380fe67aa0a8 Homepage: https://cran.r-project.org/package=EMMAgeo Description: CRAN Package 'EMMAgeo' (End-Member Modelling of Grain-Size Data) End-member modelling analysis of grain-size data is an approach to unmix a data set's underlying distributions and their contribution to the data set. EMMAgeo provides deterministic and robust protocols for that purpose. Package: r-cran-emmeans Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-estimability, r-cran-mvtnorm, r-cran-numderiv, r-cran-rlang Suggests: r-cran-bayesplot, r-cran-bayestestr, r-cran-biglm, r-cran-brms, r-cran-car, r-cran-coda, r-cran-compositions, r-cran-ggplot2, r-cran-knitr, r-cran-lattice, r-cran-lme4, r-cran-lmertest, r-cran-logspline, r-cran-mass, r-cran-mediation, r-cran-mgcv, r-cran-multcomp, r-cran-multcompview, r-cran-mumin, r-cran-nlme, r-cran-ordinal, r-cran-pbkrtest, r-cran-rmarkdown, r-cran-robmixglm, r-cran-rsm, r-cran-sandwich, r-cran-scales, r-cran-testthat, r-cran-tibble, r-cran-xtable Filename: pool/dists/noble/main/r-cran-emmeans_2.0.3-1.ca2404.1_all.deb Size: 2128408 MD5sum: 5a8fc560f64a034214fcb830e2b212d8 SHA1: f30c3df02b5e00de4f33c1894f15b8529dfdb03a SHA256: 1d4a643bb0a2c4b2cb2c85f35b37d7db7e5aeaeded2f67983f395882b7246a43 SHA512: 018243df294077429be48775e456272b16971ca33bb3734c6a77daa5e73de073991326f099ab3fcd5e9e89fe9a46b2249f024c530b908b5ede66e87c1487c6d1 Homepage: https://cran.r-project.org/package=emmeans Description: CRAN Package 'emmeans' (Estimated Marginal Means, aka Least-Squares Means) Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 . Package: r-cran-emmixssl Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-emmixssl_1.1.1-1.ca2404.1_all.deb Size: 296704 MD5sum: 794f0ab5ce1c9e609b61a333d6875b61 SHA1: 72e657e54dc9cd5a2e2c3f0c235712da6d2143de SHA256: 03edb8add0828e06c948aaa84859fc8e8d30ed1cef96cd86e56da20aabfb99b0 SHA512: b1324a4044e9f63c7bd70b647cacd02c1c1554b6133e5c639c4ba882ef992905ff10c9e86c1df5b3ada2c9e8248c38a6ce7006a125ac07daac2fca35b047d36d Homepage: https://cran.r-project.org/package=EMMIXSSL Description: CRAN Package 'EMMIXSSL' (Semi-Supervised Gaussian Mixture Model with a Missing-DataMechanism) The algorithm of semi-supervised learning based on finite Gaussian mixture models with a missing-data mechanism is designed for a fitting g-class Gaussian mixture model via maximum likelihood (ML). 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Package: r-cran-emmli Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-emmli_0.0.3-1.ca2404.1_all.deb Size: 75694 MD5sum: b30c46a32d3df8633a144cd8a6a64e27 SHA1: f059c93452c6329574579c732f62d6e092322928 SHA256: 5a92e2eaecbf704622ddfeb3c4ce78f1525bc44c5aa6237ad7211f4f853f77bb SHA512: 834cbd1ad0f80890d573c11fb7d7789da5dc3745fce09ad4bd58384333eb2dd774f3548065784ce6172d8dedb85c68869d7de9755de81216a43cd755a1344339 Homepage: https://cran.r-project.org/package=EMMLi Description: CRAN Package 'EMMLi' (A Maximum Likelihood Approach to the Analysis of Modularity) Fit models of modularity to morphological landmarks. Perform model selection on results. Fit models with a single within-module correlation or with separate within-module correlations fitted to each module. Package: r-cran-emmreml Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-emmreml_3.1-1.ca2404.1_all.deb Size: 47286 MD5sum: 8ca7859094c1c53796698529222896d2 SHA1: 1ba4dfc841b0c7a4ec54a5d068307d7ef1b2dcf8 SHA256: f47fa41b8c5b7b669e4dd04c471f8c53ae31fb7dc4dab154367c9ffff4091615 SHA512: 350745f0e7589a652329ae533f4108cc199f9b6a6b139ae2b1b0732216ce92a6b70ea88e82129754e641526c2962e468e51727ed2d249483817a856ed4f39e50 Homepage: https://cran.r-project.org/package=EMMREML Description: CRAN Package 'EMMREML' (Fitting Mixed Models with Known Covariance Structures) The main functions are 'emmreml', and 'emmremlMultiKernel'. 'emmreml' solves a mixed model with known covariance structure using the 'EMMA' algorithm. 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The use of ensemble modeling, which consists of combining predictions from multiple models, has the potential to yields more accurate and robust estimates of lactation patterns than relying solely on single model estimates. The package EMOTIONS fits 47 models for lactation curves and creates ensemble models using model averaging based on Akaike information criterion (AIC), Bayesian information criterion (BIC), root mean square percentage error (RMSPE) and mean squared error (MAE), variance of the predictions, cosine similarity for each model's predictions, and Bayesian Model Average (BMA). The daily production values predicted through the ensemble models can be used to estimate resilience indicators in the package. The package allows the graphical visualization of the model ranks and the predicted lactation curves. Additionally, the packages allows the user to detect milk loss events and estimate residual-based resilience indicators. 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Package: r-cran-emp Architecture: all Version: 2.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rocr Filename: pool/dists/noble/main/r-cran-emp_2.0.6-1.ca2404.1_all.deb Size: 25190 MD5sum: 12b8e65e2b8e6925a1e87d5154dbec0a SHA1: fd0ea6a0077b6ca5ff60de6bb1588840751b786a SHA256: ea3a6bb2b4b2cfe2b1e852f55ac4ffe4c78ee505e4d7cfa9e3dfc9d2a96ae981 SHA512: 27d32f10e9c803c92f91e1921dc4ee083a4890ec817a8d5615c3433a33c9a3557e5b7b2857e9b5662f95a0f7bc743ff16c5f1568b16d9d051b5eef72c568d79d Homepage: https://cran.r-project.org/package=EMP Description: CRAN Package 'EMP' (Expected Maximum Profit Classification Performance Measure) Functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014) , . Package: r-cran-empeaksr Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-empeaksr_0.3.1-1.ca2404.1_all.deb Size: 99598 MD5sum: 471cf2051e790c1281eb7130e5f60d34 SHA1: 587d6286e5bbf8eeef21217e48ac38f9c85649ac SHA256: 1eae171b21a0b27ac49391eb0f1e6b611c4a8ec25ba0a2b7ffc8c922f7776186 SHA512: 8fc44e47d32bc7078a9b3d7417e8d32d4ad649d24f27ce4d6c236ec0e29b1b1832d8fe22e8a98c874d89b2555e2c1b6d7dc3f60becc4a479dfa71afec84eb428 Homepage: https://cran.r-project.org/package=EMpeaksR Description: CRAN Package 'EMpeaksR' (Conducting the Peak Fitting Based on the EM Algorithm) The peak fitting of spectral data is performed by using the frame work of EM algorithm. We adapted the EM algorithm for the peak fitting of spectral data set by considering the weight of the intensity corresponding to the measurement energy steps (Matsumura, T., Nagamura, N., Akaho, S., Nagata, K., & Ando, Y. (2019, 2021 and 2023) , . The package efficiently estimates the parameters of Gaussian mixture model during iterative calculation between E-step and M-step, and the parameters are converged to a local optimal solution. This package can support the investigation of peak shift with two advantages: (1) a large amount of data can be processed at high speed; and (2) stable and automatic calculation can be easily performed. Package: r-cran-emphatic Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 778 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-purrr, r-cran-testthat, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-emphatic_0.1.8-1.ca2404.1_all.deb Size: 170578 MD5sum: 4f2009cf3cf406ffd8a253945f5b09b8 SHA1: 45bd1a8746045c230762db043c865f674f096f7a SHA256: 609c3fffdff5e35ef9c47cbdf8904ca71a1dde8d615ef86d088ebc7552b37bc7 SHA512: 3f956c468e6832313ced74294d58062d18b35501215806f63cc5e447e0f50215def70a33c3b994718b9312e81ab229959c6d917c8d49cb8ccd45569d3d2993d2 Homepage: https://cran.r-project.org/package=emphatic Description: CRAN Package 'emphatic' (Exploratory Analysis of Tabular Data using Colour Highlighting) Tools for exploratory analysis of tabular data using colour highlighting. Highlighting is displayed in any console supporting 'ANSI' colours, and can be converted to 'HTML', 'typst', 'latex' and 'SVG'. 'quarto' and 'rmarkdown' rendering are directly supported. It is also possible to add colour to regular expression matches and highlight differences between two arbitrary R objects. Package: r-cran-empiricaldynamics Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-juliacall, r-cran-cvxr, r-cran-minpack.lm, r-cran-signal, r-cran-lmtest, r-cran-tseries, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-clarabel, r-cran-scs, r-cran-osqp, r-cran-ecosolver, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-empiricaldynamics_0.1.3-1.ca2404.1_all.deb Size: 444058 MD5sum: 7c0406282532c1e0d10334c9101519bc SHA1: 7eea392a43dd44c2c69f8343edfcc18e6b8404dd SHA256: 0a4e7008dd215ca43890f34ff1a594a66050fe88852bfcbbbf0c8422f527d4f0 SHA512: e6c70cdd40c7dbd6bfcbf2181d9ce1579497d4ded54353d44668a9c0d38a20c5f93a9fb8cca78db2b1ee6bd1139e5c9b456f0c6566860efc1cdf4ffdd9266fa6 Homepage: https://cran.r-project.org/package=EmpiricalDynamics Description: CRAN Package 'EmpiricalDynamics' (Empirical Discovery of Differential Equations from Time SeriesData) A comprehensive toolkit for discovering differential and difference equations from empirical time series data using symbolic regression. The package implements a complete workflow from data preprocessing (including Total Variation Regularized differentiation for noisy economic data), visual exploration of dynamical structure, and symbolic equation discovery via genetic algorithms. It leverages a high-performance 'Julia' backend ('SymbolicRegression.jl') to provide industrial-grade robustness, physics-informed constraints, and rigorous out-of-sample validation. Designed for economists, physicists, and researchers studying dynamical systems from observational data. Package: r-cran-emplik2 Architecture: all Version: 1.33-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-emplik2_1.33-1.ca2404.1_all.deb Size: 81644 MD5sum: 3cbfb5bee51c598e74470dedd8445bce SHA1: 726ea2efc7d92ec9e525da7819b4ec5dc884fe3a SHA256: ad0c9d272ccc815d4e3b2eec32b9227d29e895ed0a1700869b5ddcd034f20132 SHA512: 2c9892e2f05f832aff24fda15f2e010ff17d9ea1edb490f7769aa7ee942c77c277a6bd978e45467a79adfc2f994b4f1c8e24ba9705d0666f6a95291e60cb2f5e Homepage: https://cran.r-project.org/package=emplik2 Description: CRAN Package 'emplik2' (Empirical Likelihood Ratio Test for Two-Sample U-Statistics withCensored Data) Calculates the empirical likelihood ratio and p-value for a mean-type hypothesis (or multiple mean-type hypotheses) based on two samples with possible censored data. Package: r-cran-emplikauc Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emplik2, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-emplikauc_0.5-1.ca2404.1_all.deb Size: 447918 MD5sum: a8d2caf2b6e278f4418dffca6cea4b01 SHA1: c1a4f33805df1f890f0458ea98c978975d31cf1e SHA256: e9fd8e6ffb88858fc10a8bd92bc3fa7afdaa1a4fb471c85dd76d332bf492fc1a SHA512: b724a5bb99e192c54f23aa974fd6c45f2c29cbf739f2de7fc44bb01f91a3799d295faa4b64864a895c433b9f60f8f214f065880832c371b4bd3b8bb7301ad4d6 Homepage: https://cran.r-project.org/package=emplikAUC Description: CRAN Package 'emplikAUC' (Empirical Likelihood Ratio Test/Confidence Interval for AUC orpAUC) Test hypotheses and construct confidence intervals for AUC (area under Receiver Operating Characteristic curve) and pAUC (partial area under ROC curve), from the given two samples of test data with disease/healthy subjects. The method used is based on TWO SAMPLE empirical likelihood and PROFILE empirical likelihood, as described in . Package: r-cran-emplikcs Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-monotone Filename: pool/dists/noble/main/r-cran-emplikcs_0.3-1.ca2404.1_all.deb Size: 80420 MD5sum: 7d0303cc5f23e53994c4a51ac1dc466c SHA1: 71d7609b456b3248a7baa0f6244059a9a079f8eb SHA256: 214a56ec2c2ddaeea50009f831c4a94249ba776484aa8926803d77351bb50e1b SHA512: 7e14f9c713b04cea3429297bbaf97a0d8e3bec10597eac12155c72718d5e798838ef25c9241882ed1932b4fbd5fabe434f0753d18ff7199d45d3fcabf561f079 Homepage: https://cran.r-project.org/package=emplikCS Description: CRAN Package 'emplikCS' (Empirical Likelihood with Current Status Data for Mean,Probability, Hazard) Compute the empirical likelihood ratio, -2LogLikRatio (Wilks) statistics, based on current status data for the hypotheses about the parameters of mean or probability or weighted cumulative hazard. Package: r-cran-emreliability Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Filename: pool/dists/noble/main/r-cran-emreliability_1.0.0-1.ca2404.1_all.deb Size: 165144 MD5sum: abdcd541dab54c8ad96b4f1126db8969 SHA1: 954f96c8715f24355ae2dbdb7783a1d5b3be1702 SHA256: d527380d8f4add75abe0546349edc7e3102363ba3d2b72af1c0b945d080e6a11 SHA512: d6f30a59066760a3967a69cdfc715b302088a08f57d48e1af6022774cfffe16bd2a1363cfec136bf0d30294b88534a25d10aefe552941f022b3009efca4bbd7b Homepage: https://cran.r-project.org/package=emreliability Description: CRAN Package 'emreliability' (Test Reliability and CSEM in Educational Measurement) Provides functions for computing test reliability and conditional standard error of measurement (CSEM) based on the methods described in the Reliability in Educational Measurement chapter of the 5th edition of "Educational Measurement" by Lee and Harris (2025, ISBN:9780197654965). Package: r-cran-ems Architecture: all Version: 1.3.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 735 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-survival, r-cran-rms Filename: pool/dists/noble/main/r-cran-ems_1.3.11-1.ca2404.1_all.deb Size: 696208 MD5sum: 05b045b389f24e773579e7a8bec2d3b4 SHA1: 4494c193106377d0e7600404535c2c4add63f263 SHA256: a6f1423c172455eee12b53c651e7e52997928d36f5c26ebdf040e6ec0fcfe201 SHA512: e27836496d5ffe0f6f13f8c61b22862544bf0d11bce7aa3db6bbb44674bdcebe921cd4a687127c6f4a0a736cff54256e92693c8a919ad5f625158281cc0505c7 Homepage: https://cran.r-project.org/package=ems Description: CRAN Package 'ems' (Epimed Solutions Collection for Data Editing, Analysis, andBenchmark of Health Units) Collection of functions related to benchmark with prediction models for data analysis and editing of clinical and epidemiological data. Package: r-cran-emsaov Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Filename: pool/dists/noble/main/r-cran-emsaov_2.3-1.ca2404.1_all.deb Size: 72012 MD5sum: 2c3510601d14ebdb3a7ebffe04b25cdb SHA1: 969d8faaf11d96980a5c3dcfa002379dec27218b SHA256: 0db6b895aea1e618e1ef15a09d957a8cc8d891b5069addec24faa4693b85b7cb SHA512: abb5953a170934ac7a4480401858074f67d2b3faf5b8020ddd9c26214c941fc4dd9ebafdd8a30288095f4b2cb36aaa592190718152046855753a3c50c634135a Homepage: https://cran.r-project.org/package=EMSaov Description: CRAN Package 'EMSaov' (The Analysis of Variance with EMS) Provides the analysis of variance table including the expected mean squares (EMS) for various types of experimental design. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS. Package: r-cran-emsc Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3585 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-emsc_0.9.4-1.ca2404.1_all.deb Size: 1834448 MD5sum: 78f523249058007b0fc34fccc457e0da SHA1: d083d59548e7e8e8e59799f0bc60b1f8beed3fa1 SHA256: fcd6a89a71fcc48c0a89137759c14b0c10add3663ba9e7f4fbe75e27050ac13b SHA512: 96a540d5f92b52de84b507110bd00f4104e3dc493a5784d77353d6022c9df20e39b7f6431ae775b6cccd0c9f1efa3558528c2a587514674b77c3d7e939bf40d5 Homepage: https://cran.r-project.org/package=EMSC Description: CRAN Package 'EMSC' (Extended Multiplicative Signal Correction) Background correction of spectral like data. Handles variations in scaling, polynomial baselines, interferents, constituents and replicate variation. Parameters for corrections are stored for further analysis, and spectra are corrected accordingly. Package: r-cran-emsnm Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-emsnm_1.0-1.ca2404.1_all.deb Size: 66842 MD5sum: 2481253af66280ab1d72640e3b45ecf1 SHA1: 467d148ceab1997f6ed3971f2698ea40ceaef8ec SHA256: c4ba024fc0fda22d3cd6bf1d97e9c5c8d3911cbdbf477c7996c670a47c447d1d SHA512: eead9952f8c263c037adcfc18a63ac68277466443082e49cd6bcdf3fbf68f24d57efccca901e4e73004a6d891456648d571813bf3eb627b93f790d03a5fbad3a Homepage: https://cran.r-project.org/package=EMSNM Description: CRAN Package 'EMSNM' (EM Algorithm for Sigmoid Normal Model) It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure. Package: r-cran-emss Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sampleselection, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-emss_1.1.1-1.ca2404.1_all.deb Size: 79794 MD5sum: 11e651679a829b20ffe837d3d3a0e1f3 SHA1: de96747f3672589bdeb54300713bde86401bb9c9 SHA256: c9c3c8afb2cd5fdcf60667f851fea2936d059b288c4bb3a182542de3bcc37ebb SHA512: 2e42e169d1b92d9fd32193713d66903aa280c9fb63ae3de306fd5dbd1a52a287d72060d3739835cd6e863f5d83c342b00f349c052d7aeb77fcbc45ad937bba8c Homepage: https://cran.r-project.org/package=EMSS Description: CRAN Package 'EMSS' (Some EM-Type Estimation Methods for the Heckman Selection Model) Some EM-type algorithms to estimate parameters for the well-known Heckman selection model are provided in the package. Such algorithms are as follow: ECM(Expectation/Conditional Maximization), ECM(NR)(the Newton-Raphson method is adapted to the ECM) and ECME(Expectation/Conditional Maximization Either). Since the algorithms are based on the EM algorithm, they also have EM’s main advantages, namely, stability and ease of implementation. Further details and explanations of the algorithms can be found in Zhao et al. (2020) . Package: r-cran-emstreer Architecture: all Version: 3.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlpack, r-cran-scatterplot3d, r-cran-ggplot2, r-cran-sf Filename: pool/dists/noble/main/r-cran-emstreer_3.1.3-1.ca2404.1_all.deb Size: 50342 MD5sum: 5b76a237294c184cee5da7067f810ca7 SHA1: 6866e0ab4d71159a22749445971611f9bc41e5bb SHA256: 09b1ff09f74b8f05012fae10fc083ed81207d8304b0c6ad9035a9b34c40a136d SHA512: 8f0aeeeec727670ca1fe22de44c7b2ce0d6780370158a79ccfd0371f32b5c30aa254ceff72f9ceb27dffcc032278512ba43c25a8cc16a4869e4d3b4419b362b3 Homepage: https://cran.r-project.org/package=emstreeR Description: CRAN Package 'emstreeR' (Tools for Fast Computing and Visualizing Euclidean MinimumSpanning Trees) Fast and easy computation of Euclidean Minimum Spanning Trees (EMST) from data, relying on the R API for 'mlpack' - the C++ Machine Learning Library (Curtin et. al., 2013). 'emstreeR' uses the Dual-Tree Boruvka (March, Ram, Gray, 2010, ), which is theoretically and empirically the fastest algorithm for computing an EMST. This package also provides functions and an S3 method for readily visualizing Minimum Spanning Trees (MST) using either the style of the 'base', 'scatterplot3d', or 'ggplot2' libraries; and functions to export the MST output to shapefiles. Package: r-cran-emt Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-emt_1.3.2-1.ca2404.1_all.deb Size: 38628 MD5sum: 2304dc393ab58a207fb5c8e8450dcd56 SHA1: 2534d92896f63ad9c01f050742b6d4bf70472bb8 SHA256: 556e34b6cda42cd6e2a07fa5965b33f24474bb8546daed1492df6b904ca93a02 SHA512: f3ede09a967a15b3e41303199d0c8dd892fc594ef4a9b64a2724794ffae2e3e3a8196992833575ddbe3298ffc14b0a3a75889f41563175cc81a17d97af2bd78f Homepage: https://cran.r-project.org/package=EMT Description: CRAN Package 'EMT' (Exact Multinomial Test: Goodness-of-Fit Test for DiscreteMultivariate Data) Goodness-of-fit tests for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. Monte Carlo methods are provided to make the package capable of solving high-dimensional problems. Package: r-cran-emtscore Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-bioc-aucell, r-bioc-gsva, r-cran-ggpubr, r-bioc-complexheatmap, r-cran-circlize, r-cran-gridextra, r-cran-magrittr, r-cran-gsa, r-cran-nsprcomp, r-cran-stringr, r-cran-foreach, r-cran-doparallel, r-cran-seurat, r-cran-pheatmap, r-cran-paletteer, r-cran-ggthemes, r-cran-curl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-emtscore_0.1.1-1.ca2404.1_all.deb Size: 58122 MD5sum: 5d093f0ff110b78649472b10dcf50aca SHA1: 50f5bcf0092d4f217bee9de8cec33c5900ae25e5 SHA256: 2baa87a80ebff17484a64bf73717bc6771d54526b8b8e35f632d818a666d0aa4 SHA512: 73fea5e244425f8d52ee5db62cbb48a53bdb583408bee91e30621657227f19165795cd3c182d07dacdbdeb9d138407d5574753efb8db8c76147fdb60e843b3c2 Homepage: https://cran.r-project.org/package=EMTscore Description: CRAN Package 'EMTscore' (Calculate 'EMT' Scores Based on 'Omics' Data) Epithelial-Mesenchymal transition ('EMT') is an important form of cellular plasticity that is fully or partially activated in several biological scenarios including development and disease progression. 'EMT' involves altered expression of hundreds of protein-coding and non-protein-coding genes. Recent studies showed the prevalence of partial 'EMT' in multiple processes such as various cancers and organ fibrosis, which necessitates rigorous quantification of the degree of 'EMT'. While traditional gene set scoring methods such as gene set variation analysis have been used to generate 'EMT' scores from omics data, multiple 'EMT' scoring algorithms and 'EMT' gene sets have been used by different groups without standardization. Furthermore, comparisons of 'EMT' scores computed from different methods and/or different EMT gene sets are generally difficult due to both the context dependent nature of 'EMT' and the lack of tools that comprehensively integrate varying components for 'EMT' scoring. To address this problem, we have built a toolbox named 'EMTscore' that enables users to select scoring methods from a list of previously used algorithms and 'EMT' gene sets from a list of gene sets produced from different experiments. We provided several visualization methods for making publication quality plots of 'EMT' scores from 'omics' data. Furthermore, we showed a unique utility of a method based on principal component analysis for scoring divergent 'EMT' processes from a single dataset. Overall, 'EMTscore' provides an integrated solution for assessing the degree and complexity of 'EMT' from 'omics' data, and it paves the way for standardizing the comparison of EMT programs across multiple contexts. Package: r-cran-emulator Architecture: all Version: 1.2-24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-emulator_1.2-24-1.ca2404.1_all.deb Size: 326598 MD5sum: 6eff67986765d165e636bb129185e614 SHA1: 7437908baf4c89d7e914fcefed2538ac8d75c1f9 SHA256: 7b8bf8a0d9bd8d451f6a1d4aa3add643b90136359003d2e6f9d514d2322cfd21 SHA512: 92fb362422bdf5a8b0c398380120f8baadc4af620a34ad0f0aae97934608e6db4625e25f886a02af369f7613905a2215f9a38ba9e378ef85fef36f81bff3ad42 Homepage: https://cran.r-project.org/package=emulator Description: CRAN Package 'emulator' (Bayesian Emulation of Computer Programs) Allows one to estimate the output of a computer program, as a function of the input parameters, without actually running it. The computer program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques that give a PDF of expected program output. This PDF is conditional on a training set of runs, each consisting of a point in parameter space and the model output at that point. The emphasis is on complex codes that take weeks or months to run, and that have a large number of undetermined input parameters; many climate prediction models fall into this class. The emulator essentially determines Bayesian posterior estimates of the PDF of the output of a model, conditioned on results from previous runs and a user-specified prior linear model. The package includes functionality to evaluate quadratic forms efficiently. Package: r-cran-emur Architecture: all Version: 2.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3626 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-stringr, r-cran-uuid, r-cran-base64enc, r-cran-shiny, r-cran-wrassp, r-cran-jsonlite, r-cran-rsqlite, r-cran-dbi, r-cran-httpuv, r-cran-dplyr, r-cran-readr, r-cran-tibble, r-cran-purrr, r-cran-tidyr, r-cran-mime, r-cran-rstudioapi, r-cran-httr, r-cran-v8, r-cran-cli, r-cran-fs, r-cran-tidyselect Suggests: r-cran-mass, r-cran-ggplot2, r-cran-testthat, r-cran-compare, r-cran-knitr, r-cran-rmarkdown, r-cran-matlabr, r-cran-r.matlab Filename: pool/dists/noble/main/r-cran-emur_2.6.0-1.ca2404.1_all.deb Size: 2792844 MD5sum: 88c44c7e9f7d8de61d99754c0f1cce4d SHA1: b1c170af02da420f7f455637ca8bcc32900a3ec8 SHA256: e1e437d1e46dc7e42d495967da057999a384037bc9ec6a9abe0258c832bdf9a7 SHA512: 5e4cb909725fcb7a9fde3cf173c624aa5b5d8d4cbddbaa76844138f85b51747ff1e594c7dc8fb50587a5f7812bb7bb45c0c2f9f4b85a9f05f1723d3677c7432c Homepage: https://cran.r-project.org/package=emuR Description: CRAN Package 'emuR' (Main Package of the EMU Speech Database Management System) Provide the EMU Speech Database Management System (EMU-SDMS) with database management, data extraction, data preparation and data visualization facilities. See for more details. Package: r-cran-enaho Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1741 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-haven, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-enaho_0.2.5-1.ca2404.1_all.deb Size: 238170 MD5sum: 98511c41524790e588a86b3dd65c1b89 SHA1: 8b1097051000e7195be30f67e24296f9fb5638ca SHA256: 52e3177f0d0e213ad06750d5ed7319aa4fae7e69141b78bc67080e0e76fc3e83 SHA512: 079530337022d53af9d0143f556824f51c53fcecdc03f59a261fdd930883aa54ca749756c4bbb5c2effeb25843903a252e59500c90af31b775b03312101b06b8 Homepage: https://cran.r-project.org/package=enaho Description: CRAN Package 'enaho' (Encuesta Nacional de Hogares (Peruvian Home National Survey)) Descarga, lee y analiza bases de la Encuesta Nacional de Hogares (ENAHO) y otras encuestas del Instituto Nacional de Estadística e Informática (INEI) del Perú. (Downloads, reads, and combines data from the Peruvian Home National Survey and other surveys from the National Institute for Statistics (INEI).) Package: r-cran-encdna Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings Filename: pool/dists/noble/main/r-cran-encdna_1.0.2-1.ca2404.1_all.deb Size: 166136 MD5sum: 5ca5255549ee92f97ec62c0578908fa5 SHA1: c320dd327ee95524a8ad8d9302d36e4c2ad699b8 SHA256: 79eab02d11b6b7c155c4649a0b30d3afa59249fae8e5d5e70138b4ed266a4ca9 SHA512: 789da0b31609af40b908318ebcfbe691b2ab078c7b317d3744e7df2c92860f4f250c4a3f08957babd1155d597dd1e5309ec12853eccc56b5943bcbc8d019ae72 Homepage: https://cran.r-project.org/package=EncDNA Description: CRAN Package 'EncDNA' (Encoding of Nucleotide Sequences into Numeric Feature Vectors) We describe fifteen different splice site sequence encoding schemes that have been used in earlier studies for mapping of splice site sequences into numeric feature vectors. These encoding schemes will also be helpful for transforming other nucleotide sequences into numeric forms, provided they are of equal length. These encoding schemes will help the computational biologist working in the field of classification (binary or multiclass) or prediction involving nucleic acid sequences of equal length. Package: r-cran-encode Architecture: all Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-magrittr Filename: pool/dists/noble/main/r-cran-encode_0.3.7-1.ca2404.1_all.deb Size: 47188 MD5sum: 7e32bbd1d9c9b68ee506804fba80da97 SHA1: 55ea040521659a02bc9f34d049d84f99763112e6 SHA256: 14d2e6fd83a0b93e7378967f44be90b924c2d2aec640e2a2e78afded0423f714 SHA512: 9c861b65bf9a402dc443c6f9252f26dee52828ec58a14b3cb495afc3434a689ede08a1fbdd810cc36210b0108bd31e462e331b30117c65f343b7d11b6c5ba7ed Homepage: https://cran.r-project.org/package=encode Description: CRAN Package 'encode' (Represent Ordered Lists and Pairs as Strings) Interconverts between ordered lists and compact string notation. Useful for capturing code lists, and pair-wise codes and decodes, for text storage. Analogous to factor levels and labels. Generics encode() and decode() perform interconversion, while codes() and decodes() extract components of an encoding. The function encoded() checks whether something is interpretable as an encoding. If a vector has an encoded 'guide' attribute, as_factor() uses it to coerce to factor. Package: r-cran-encompasstest Architecture: all Version: 0.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-encompasstest_0.22-1.ca2404.1_all.deb Size: 31392 MD5sum: 5aeed477e21a8ae5df815a1ba8dc5578 SHA1: a3820159a5a0573af9d6b405f8a2c81e9c0fe6b8 SHA256: 9b68155bee9ae4f543ecc7799d8393f11ab634f5e77cb6652446a69b32276180 SHA512: 33bcb35c03778110899d25a26ed77f15172d705d83898794c8f886ffde2e8c8962503c7ceccd21908d016d60ca4fe0371f756211087c0423d7cf3ca5dbd3118d Homepage: https://cran.r-project.org/package=EncompassTest Description: CRAN Package 'EncompassTest' (Direct Multi-Step Forecast Based Comparison of Nested Models viaan Encompassing Test) The encompassing test is developed based on multi-step-ahead predictions of two nested models as in Pitarakis, J. (2023) . The statistics are standardised to a normal distribution, and the null hypothesis is that the larger model contains no additional useful information. P-values will be provided in the output. Package: r-cran-encryptedrmd Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sodium, r-cran-readr, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-encryptedrmd_0.2.1-1.ca2404.1_all.deb Size: 38346 MD5sum: d10bc3599b037b0d0df5b1edd9757b62 SHA1: 321de78cf72758d58aa99738092e087e874ea979 SHA256: 506e9f1163eb0776fa1cd3c2c28ab48beb362bec043583ba400d845e3dc60656 SHA512: 7b3f7213c58cdf7499b829cb75ced95af447e3a77e12627300f4be2ca99a6800aa728819e248580d859d6dde4111bf576791b2f339d204d301af1337788873aa Homepage: https://cran.r-project.org/package=encryptedRmd Description: CRAN Package 'encryptedRmd' (Encrypt Html Reports Using 'Libsodium') Create encrypted html files that are fully self contained and do not require any additional software. Using the package you can encrypt arbitrary html files and also directly create encrypted 'rmarkdown' html reports. Package: r-cran-encryptr Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-openssl, r-cran-purrr, r-cran-readr, r-cran-rlang Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-encryptr_0.1.4-1.ca2404.1_all.deb Size: 93792 MD5sum: 9e598dcf7c50891b145a6a94ce79a0b3 SHA1: 57d4ddb42fbe1f0df0d74ae895c1d30c12e14a57 SHA256: 67fa92a2ed35b582c3a69add83276ec5e90d3ff83a044eb6d3a242dc45aa2559 SHA512: 6c9cf7603f537354ac4a22314e6bc514ff8a5f4bfc5249f1512b5e1b5ce4644008673a5b783381e1a86296b9c58c8ed670f072c2a53e73037383093ab03ff32e Homepage: https://cran.r-project.org/package=encryptr Description: CRAN Package 'encryptr' (Easily Encrypt and Decrypt Data Frame/Tibble Columns or Filesusing RSA Public/Private Keys) It is important to ensure that sensitive data is protected. This straightforward package is aimed at the end-user. Strong RSA encryption using a public/private key pair is used to encrypt data frame or tibble columns. A public key can be shared to allow others to encrypt data to be sent to you. This is particularly aimed a healthcare settings so patient data can be pseudonymised. Package: r-cran-enderecobr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-enderecobr_0.4.1-1.ca2404.1_all.deb Size: 179532 MD5sum: a22158b846564696c686ec4d92e5d4a9 SHA1: b4c24dcecd341df7bfb3de855a3abc622d9905a1 SHA256: 7d2b2f53308f181e1858a8527cc1a960dcb0ee78de648a64ae227eb1ef857d11 SHA512: 08065233954e3e6b55323b2b224fc0eea196451f181d9ee2fb9af7fc0d69b0fded14df419c77f417823ba3d5cee4a5b0fcee332b883ad8a920a6ab846183e8e2 Homepage: https://cran.r-project.org/package=enderecobr Description: CRAN Package 'enderecobr' (Padronizador de Endereços Brasileiros (Brazilian AddressesStandardizer)) Padroniza endereços brasileiros a partir de diferentes critérios. Os métodos de padronização incluem apenas manipulações básicas de strings, não oferecendo suporte a correspondências probabilísticas entre strings. (Standardizes brazilian addresses using different criteria. Standardization methods include only basic string manipulation, not supporting probabilistic matches between strings.) Package: r-cran-endogenous Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-endogenous_1.0-1.ca2404.1_all.deb Size: 56604 MD5sum: 629f116ee0217a8ffa67c3b86e569e7d SHA1: 67183b1c608893abf38458de0469d96638db1289 SHA256: 131eb59d258a444cd7106a83b0f62deb2c2c5c281fde71efbe50e8be40c5abec SHA512: a43846c9cda3c1a04942ce5c0ba970e9b642d726220ed14ce24833a47e9661d53a693b2cc75f3bdd13945156bdbf19997b8fb395217e98d4938e0077c96ca1c1 Homepage: https://cran.r-project.org/package=endogenous Description: CRAN Package 'endogenous' (Classical Simultaneous Equation Models) Likelihood-based approaches to estimate linear regression parameters and treatment effects in the presence of endogeneity. Specifically, this package includes James Heckman's classical simultaneous equation models-the sample selection model for outcome selection bias and hybrid model with structural shift for endogenous treatment. For more information, see the seminal paper of Heckman (1978) in which the details of these models are provided. This package accommodates repeated measures on subjects with a working independence approach. The hybrid model further accommodates treatment effect modification. Package: r-cran-endoswitch Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-maxlik, r-cran-msm Filename: pool/dists/noble/main/r-cran-endoswitch_1.0.0-1.ca2404.1_all.deb Size: 67448 MD5sum: 0f6ebde2db92d31af5f34c47ecbab12a SHA1: 37e98e9a97c70f80434927d43900f04da332609d SHA256: 8c8c7e58e5227a70cc6835a67dffbfbc752f5e295e9899889d178a4deaa7db1d SHA512: fd6191f903a36d2e15b29a2e3db62eee10fb456e6063963dc67076f2d717d1a9d324254236a55831d059ed8954df17bc0d0e05d7f827767ddbd813da49077b8b Homepage: https://cran.r-project.org/package=endoSwitch Description: CRAN Package 'endoSwitch' (Endogenous Switching Regression Models) Maximum likelihood estimation of endogenous switching regression models from Heckman (1979) and estimation of treatment effects. Package: r-cran-endtoend Architecture: all Version: 2.29-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pastecs, r-cran-ggplot2 Suggests: r-cran-hopbyhop, r-cran-opportunistic Filename: pool/dists/noble/main/r-cran-endtoend_2.29-1.ca2404.1_all.deb Size: 37544 MD5sum: 2b79990675814b54590512860554ddf1 SHA1: 93df66b28fe1628900fc4e9082984355c947dcd0 SHA256: d9546e8a59acfc2e987586e9681a0853f08d7b735cbfbacd73d2354328f00c47 SHA512: 0c5558b680879bbacdba9151ebe544f89487d83aaf3637169b7dc1664a86ad6603c881ae50b0018e7318504bc03fb7eafaa42797e90a25d99cc07f75e2de1a20 Homepage: https://cran.r-project.org/package=endtoend Description: CRAN Package 'endtoend' (Transmissions and Receptions in an End to End Network) Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities. Package: r-cran-energygof Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-energy, r-cran-gsl, r-cran-boot, r-cran-fitdistrplus, r-cran-statmod Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-energygof_0.1-1.ca2404.1_all.deb Size: 185414 MD5sum: eb6f8c5e044eb2316ecf8c8c13b0e406 SHA1: a57d40b7884c8bd08fe36741ae1501721f37ddd9 SHA256: d980f5a131605c29e0a79a39c795a23fbccb4c2a3c5518d9cced5a3ca4b13381 SHA512: 9fc56948475a7ce80469f486618e1d6984fb2760a77a2ed73e3b2b5f11b24dfc70f036101857eba5758d22646a7eee43a571a76c8773ef795ec5ae7f58e2b21d Homepage: https://cran.r-project.org/package=energyGOF Description: CRAN Package 'energyGOF' (Goodness-of-Fit Tests for Univariate Data via Energy) Conduct one- and two-sample goodness-of-fit tests for univariate data. In the one-sample case, normal, uniform, exponential, Bernoulli, binomial, geometric, beta, Poisson, lognormal, Laplace, asymmetric Laplace, inverse Gaussian, half-normal, chi-squared, gamma, F, Weibull, Cauchy, and Pareto distributions are supported. egof.test() can also test goodness-of-fit to any distribution with a continuous distribution function. A subset of the available distributions can be tested for the composite goodness-of-fit hypothesis, that is, one can test for distribution fit with unknown parameters. P-values are calculated via parametric bootstrap. Package: r-cran-energyonlinecpm Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-energy, r-cran-mass Filename: pool/dists/noble/main/r-cran-energyonlinecpm_1.0-1.ca2404.1_all.deb Size: 26190 MD5sum: e65be7f3ede03c1b077b096ba21483c5 SHA1: 97fd94b17993577a18c818fb1f7cc0d46f8f30b4 SHA256: 96d2009b662f07a503237a5b781ec18c47dc1ca707238ceb8f5cdf942220ced5 SHA512: 21af002c70f8549359f10c905d179d22b4deeadb76dc5e11de81dab7de74f777ac810ef317bffe7401f8e62108c817b336bbdab4fecae1223d8ff963d2b688a2 Homepage: https://cran.r-project.org/package=EnergyOnlineCPM Description: CRAN Package 'EnergyOnlineCPM' (Distribution Free Multivariate Control Chart Based on EnergyTest) Provides a function for distribution free control chart based on the change point model, for multivariate statistical process control. The main constituent of the chart is the energy test that focuses on the discrepancy between empirical characteristic functions of two random vectors. This new control chart highlights in three aspects. Firstly, it is distribution free, requiring no knowledge of the random processes. Secondly, this control chart can monitor mean and variance simultaneously. Thirdly it is devised for multivariate time series which is more practical in real data application. Fourthly, it is designed for online detection (Phase II), which is central for real time surveillance of stream data. For more information please refer to O. Okhrin and Y.F. Xu (2017) . Package: r-cran-energyr Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-here, r-cran-tidygeocoder, r-cran-usethis, r-cran-testthat Filename: pool/dists/noble/main/r-cran-energyr_0.4-1.ca2404.1_all.deb Size: 241330 MD5sum: 6b77ad656be03801ab5e3eb7ce6f2b6a SHA1: 3c01289fac8f931a9348b4ae15132ca0a8c07d82 SHA256: 3e38cc880257d7e3182f6fd697a19e2544a8b662cd32834a8b95437af0719d90 SHA512: 605f067646bb9bbd4a1cffddbb1e62669f70f1ba240872cf930a87dd4530b9f54b5654fa4e9d0ff7ff893f1d3cec6544efdacc6e69dea7122e35ac69023170a5 Homepage: https://cran.r-project.org/package=energyr Description: CRAN Package 'energyr' (Data Published by the United States Federal Energy RegulatoryCommission) Data published by the United States Federal Energy Regulatory Commission including electric company financial data, natural gas company financial data, hydropower plant data, liquified natural gas plant data, oil company financial data natural gas company financial data, and natural gas storage field data. Package: r-cran-enetlts Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-glmnet, r-cran-reshape, r-cran-cvtools, r-cran-robustbase, r-cran-robusthd Filename: pool/dists/noble/main/r-cran-enetlts_1.1.0-1.ca2404.1_all.deb Size: 241888 MD5sum: 022c5462489a1a696bdac1e86a1172ad SHA1: 23edba429d49d5d76210f734da91e7067405b068 SHA256: 4de65607c5fd8f5715bca435845eb98ee2565d6728d5aea32eb884707fb42c88 SHA512: 8a7c32c282d197ebd989cde7ed8ca7b8d8073074693661fd26ae831580bebb9452bb37d6f99026468b4919c4a37b02db64a918e20d4395c19fcd20425987552d Homepage: https://cran.r-project.org/package=enetLTS Description: CRAN Package 'enetLTS' (Robust and Sparse Methods for High Dimensional Linear and Binaryand Multinomial Regression) Fully robust versions of the elastic net estimator are introduced for linear and binary and multinomial regression, in particular high dimensional data. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied. A reweighting step is added to improve the statistical efficiency of the proposed estimators. Selecting appropriate tuning parameters for elastic net penalties are done via cross-validation. 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This package contains Standard cost engineering and engineering economics methods that are applied to convert between present, future, and annualized costs. Newnan D. (2020) “Engineering Economic Analysis”. Package: r-cran-engression Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch Filename: pool/dists/noble/main/r-cran-engression_0.1.6-1.ca2404.1_all.deb Size: 48894 MD5sum: cf5d8d9db68a628e8d7dd0a4c1aeaef1 SHA1: 0d3392f3e07d55b2232db291de596ed42c86f5a1 SHA256: 901f6c9f5f3d55d64548832dd6faa0b127e1cbee2601c52a56c188661ab4d975 SHA512: e7518a86e6d5eb7cc4f6ee7a7ab550f12d8f7797299e2b0689ce6e60c6d214a9ca8c4ded3dd1aea7a02642f8251e74fa555bf5e8fc166b4f3dbd1709cf453819 Homepage: https://cran.r-project.org/package=engression Description: CRAN Package 'engression' (Engression Modelling) Fits engression models for nonlinear distributional regression. Predictors and targets can be univariate or multivariate. Functionality includes estimation of conditional mean, estimation of conditional quantiles, or sampling from the fitted distribution. Training is done full-batch on CPU (the python version offers GPU-accelerated stochastic gradient descent). Based on "Engression: Extrapolation through the lens of distributional regression" by Xinwei Shen and Nicolai Meinshausen (2024) in JRSSB. Also supports classification (experimental). . 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Package: r-cran-enhancer Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4337 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-crayon Filename: pool/dists/noble/main/r-cran-enhancer_1.1.1-1.ca2404.1_all.deb Size: 4114956 MD5sum: 242a2ffa50728fdc40081dd4e5013b6d SHA1: 813727f9010cc7fb89e8a36e3b4a509ce37a98a7 SHA256: 423bd3e9a99a53abfd45ed94f02ab7f62d058c2ead83e08eb77f59e68d835348 SHA512: ea640738ddc5c509e4e5af0097701f8e4a459151fc33b41d2b24832697cf0c29a2b79cdbd0dc75320e9d2150d53b668697da5466ece7e0931fb83fe11b3a340e Homepage: https://cran.r-project.org/package=enhancer Description: CRAN Package 'enhancer' (Mixed-Effects Models Enhancing Functions) Special functions that enhance other mixed effect model packages by creating overlayed, reduced rank, and reduced model matrices together with multiple data sets to practice the use of these models. For more details see Covarrubias-Pazaran (2016) . Package: r-cran-enmeval Architecture: all Version: 2.0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2676 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-maxnet, r-cran-predicts, r-cran-foreach, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-glmnet, r-cran-rangemodelmetadata, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-rjava, r-cran-spocc, r-cran-rcolorbrewer, r-cran-sf, r-cran-blockcv, r-cran-devtools, r-cran-tibble, r-cran-ecospat, r-cran-geodata, r-cran-usdm Filename: pool/dists/noble/main/r-cran-enmeval_2.0.5.2-1.ca2404.1_all.deb Size: 2492946 MD5sum: a22f17b702c2543c2693d81877e1be5c SHA1: a15d56ff16552c840d6276a9c2e2406d049ed256 SHA256: d92aec279a11e0c8584baaa8102f995ca44abc42d25f5ab0e212d806961784a1 SHA512: 969696248195a4532d6052de9874e16389441e5190b274a280232f0a38a71bab9de4a9af82413a6f9e030efde68d3f11f5c1fc961515b21afcbd6c6b7e303b92 Homepage: https://cran.r-project.org/package=ENMeval Description: CRAN Package 'ENMeval' (Automated Tuning and Evaluations of Ecological Niche Models) Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs cross validation to evaluate models, and returns data tables to aid in selection of optimal model settings that balance goodness-of-fit and model complexity. Also has functions to partition data spatially (or not) for cross validation, to plot multiple visualizations of results, to run null models to estimate significance and effect sizes of performance metrics, and to calculate range overlap between model predictions, among others. The package was originally built for Maxent models (Phillips et al. 2006, Phillips et al. 2017), but the current version allows possible extensions for any modeling algorithm. The extensive vignette, which guides users through most package functionality but unfortunately has a file size too big for CRAN, can be found here on the package's Github Pages website: . Package: r-cran-enmsdmx Architecture: all Version: 1.2.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2169 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aiccmodavg, r-cran-boot, r-cran-cowplot, r-cran-data.table, r-cran-doparallel, r-cran-dt, r-cran-foreach, r-cran-gbm, r-cran-ggplot2, r-cran-ks, r-cran-maxnet, r-cran-mgcv, r-cran-omnibus, r-cran-predicts, r-cran-ranger, r-cran-rjava, r-cran-scales, r-cran-sf, r-cran-shiny, r-cran-sp, r-cran-statisfactory, r-cran-terra Filename: pool/dists/noble/main/r-cran-enmsdmx_1.2.12-1.ca2404.1_all.deb Size: 1990404 MD5sum: 0ca2cc12ad53546288d4d531bfb6b873 SHA1: 14d629936e664cdaff46b0af7abc2c99afdf27e3 SHA256: a08e1288df34227ddaee90b1f336960ecc303f362f1a3cfebb6d29b1e4fdacf5 SHA512: fb43039581ee3c128f46462db51ddd51f4b898a0b3fa34b7015ce1b45785c0b9634f9eb4df9b69f3a13879c032022698d04368700ef53933520b74ef5cafa121 Homepage: https://cran.r-project.org/package=enmSdmX Description: CRAN Package 'enmSdmX' (Species Distribution Modeling and Ecological Niche Modeling) Implements species distribution modeling and ecological niche modeling, including: bias correction, spatial cross-validation, model evaluation, raster interpolation, biotic "velocity" (speed and direction of movement of a "mass" represented by a raster), interpolating across a time series of rasters, and use of spatially imprecise records. The heart of the package is a set of "training" functions which automatically optimize model complexity based number of available occurrences. These algorithms include MaxEnt, MaxNet, boosted regression trees/gradient boosting machines, generalized additive models, generalized linear models, natural splines, and random forests. To enhance interoperability with other modeling packages, no new classes are created. The package works with 'PROJ6' geodetic objects and coordinate reference systems. Package: r-cran-enmtools Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1746 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dismo, r-cran-knitr, r-cran-ggplot2, r-cran-gridextra, r-cran-lhs, r-cran-magrittr, r-cran-enmeval, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-ggpubr, r-cran-forcats, r-cran-terra, r-cran-raster Suggests: r-cran-testthat, r-cran-hypervolume, r-cran-leaflet, r-cran-mgcv, r-cran-ecospat, r-cran-randomforest, r-cran-ranger, r-cran-caret, r-cran-calibratr, r-cran-ape, r-cran-resourceselection, r-cran-reshape2, r-cran-vip, r-cran-pdp, r-cran-fastshap, r-cran-viridis, r-cran-progress, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/noble/main/r-cran-enmtools_1.1.5-1.ca2404.1_all.deb Size: 1660176 MD5sum: c55e74a580c23a0227467d70c69ad8f9 SHA1: a64da6d799b889b249816f4581ffa2e4fbdf3c4d SHA256: 588ebe6bb4cfc6d740352aad6d44de89c4887e678fa656ffe2c62d3244521652 SHA512: 021c2aa5be2fd3f51df67682486f87396d3b20ac0cd2276a285dca60821d2d5529c633776fb0ccdef30d309e9f126c32d29acdc7d662a8462162eeb0614fb909 Homepage: https://cran.r-project.org/package=ENMTools Description: CRAN Package 'ENMTools' (Analysis of Niche Evolution using Niche and Distribution Models) Constructing niche models and analyzing patterns of niche evolution. Acts as an interface for many popular modeling algorithms, and allows users to conduct Monte Carlo tests to address basic questions in evolutionary ecology and biogeography. Warren, D.L., R.E. Glor, and M. Turelli (2008) Glor, R.E., and D.L. Warren (2011) Warren, D.L., R.E. Glor, and M. Turelli (2010) Cardillo, M., and D.L. Warren (2016) D.L. Warren, L.J. Beaumont, R. Dinnage, and J.B. Baumgartner (2019) . Package: r-cran-enpls Architecture: all Version: 6.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2571 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pls, r-cran-spls, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-reshape2, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-enpls_6.1.1-1.ca2404.1_all.deb Size: 1808910 MD5sum: 179fb4175d6ec4800c4a0ffa7325c364 SHA1: 0dc6b2523ad9f8a314eef8cd3cc1262818c15479 SHA256: 68a29d4dd3e4448a18396186956e529db6b5e355cf76379e1d2455abe7319088 SHA512: c60a31626a11f915573c7a9c43058cd215e0866caa8e4441e6eb905a0093326f6abb6e5506b4a29eed01c0e237cafa3af9d79ff194800f10cf9b10c3bf20082d Homepage: https://cran.r-project.org/package=enpls Description: CRAN Package 'enpls' (Ensemble Partial Least Squares Regression) An algorithmic framework for measuring feature importance, outlier detection, model applicability domain evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions. 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This package exhibits high accuracy that it can identify more specific DO terms, which alleviates the over enriched problem. The package includes various statistical models and visualization schemes for discovering the associations between genes and diseases from biological big data. Package: r-cran-enrichintersect Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1387 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-networkd3, r-cran-jsonlite, r-cran-htmlwidgets, r-cran-webshot2 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-enrichintersect_0.7-1.ca2404.1_all.deb Size: 1108194 MD5sum: 5ac8b131d4fd66036f3ab54b94c96035 SHA1: adec5b72a7d2f636db71ccbeec42585f3be0a30f SHA256: 4b3c702d45b11bfd6dbff7db9d6347977e68383176e9d63f739a5620ab9dd236 SHA512: 5452996e7ec7a599eaf96826fa15155cdfdb3f7d8988a38101c114d017dbdc9a1380e401870f992650c2d1357596acf4b73f4330ca8689873a2c639b4d07c0a8 Homepage: https://cran.r-project.org/package=EnrichIntersect Description: CRAN Package 'EnrichIntersect' (Enrichment Analysis and Intersecting Sankey Diagram) A flexible tool for enrichment analysis based on user-defined sets. It allows users to perform over-representation analysis of the custom sets among any specified ranked feature list, hence making enrichment analysis applicable to various types of data from different scientific fields. 'EnrichIntersect' also enables an interactive means to visualize identified associations based on, for example, the mix-lasso model (Zhao et al., 2022 ) or similar methods. Package: r-cran-enrichr Architecture: all Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-curl, r-cran-rjson, r-cran-ggplot2, r-cran-writexls Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-enrichr_3.4-1.ca2404.1_all.deb Size: 256512 MD5sum: 8c743cba34accdbabf3459038eeea106 SHA1: 0c64d88a439d3110248d2191206cc9a5f45e33c8 SHA256: 62f7b6d1874ac1d5e6a73134fbf7905b04ddac14240769ef9c179236270d9808 SHA512: 06d6f6ed12dbf2b229f6817715560993349d34978bce529c098fe61166906c4ff39222019d3d7bd69e4f837dcafe5e52ba758968f0bd3d458dd9c879b8631cb0 Homepage: https://cran.r-project.org/package=enrichR Description: CRAN Package 'enrichR' (Provides an R Interface to 'Enrichr') Provides an R interface to all 'Enrichr' databases. 'Enrichr' is a web-based tool for analysing gene sets and returns any enrichment of common annotated biological features. Quoting from their website 'Enrichment analysis is a computational method for inferring knowledge about an input gene set by comparing it to annotated gene sets representing prior biological knowledge.' See for further details. Package: r-cran-enrichwith Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-whisker, r-cran-suppdists, r-cran-brglm, r-cran-brglm2, r-cran-ggplot2, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-enrichwith_0.5.0-1.ca2404.1_all.deb Size: 274522 MD5sum: 72450b120e74a67f9fb5dd4aa16900d0 SHA1: 2246a2913480f6e8e0cf54d54de225822043df1f SHA256: 65b3d2dd65aeaf7bcba098b0edf287f3f5e0aa8c187e6e4beab9ee441598ffef SHA512: 412aedf6ee14ef3ec6cf22a549b872ba29d19b27f1a5a5b9fb28b84c138650b74250c3d8a76fa26a54543d8a83511974b29c1e6db9ff37eb084743cead95c0f8 Homepage: https://cran.r-project.org/package=enrichwith Description: CRAN Package 'enrichwith' (Methods to Enrich R Objects with Extra Components) Provides the "enrich()" method for augmenting list-like R objects with additional, model-specific components. Methods are currently available for objects of class "family", "link-glm", "lm", "glm", and "betareg". Enriched objects retain their original class and remain compatible with existing methods. For example, enriching a "glm" object produces an "enriched_glm" object that also inherits from "glm". In addition to the standard components, the "enriched_glm" object includes methods for simulation and functions to compute scores, observed and expected information matrices, first-order bias, and other model quantities such as densities, probabilities, and quantiles, which can be evaluated at use-supplied parameter values. The package also provides tools for generating customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects. Package: r-cran-enscat Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2715 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dendextend, r-cran-ggplot2, r-cran-ggdendro, r-cran-seqinr Filename: pool/dists/noble/main/r-cran-enscat_1.1-1.ca2404.1_all.deb Size: 2590854 MD5sum: 9382891647c95a6341d1488f9043996a SHA1: 9e46ee78e6e7ea18f3fcb87d9113e1078ad1390d SHA256: edf29c88b4ed51831e8e847c782e200543e2c9a140fb331fce7788fc9adc9822 SHA512: 67fb02eac18262cef2f02cc1f5fbd30015499d3571248d1b9c96b3eef9b088f6e703e77c3d6df6e0c5e67ee495e5a65fc30be741f5c83fde93f4264bbd9269e6 Homepage: https://cran.r-project.org/package=EnsCat Description: CRAN Package 'EnsCat' (Clustering of Categorical Data) An implementation of the clustering methods of categorical data discussed in Amiri, S., Clarke, B., and Clarke, J. (2015). Clustering categorical data via ensembling dissimilarity matrices. Preprint . Package: r-cran-ensemblebase Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kknn, r-cran-gbm, r-cran-nnet, r-cran-e1071, r-cran-randomforest, r-cran-doparallel, r-cran-foreach, r-cran-glmnet, r-cran-bartmachine Filename: pool/dists/noble/main/r-cran-ensemblebase_1.0.4-1.ca2404.1_all.deb Size: 218776 MD5sum: 72391b318e731e3cf86ed8f71662dbe7 SHA1: ef210c5e99e069069e7ac6692c11a75943073462 SHA256: 83baaa03be0fdd03f593cacfeeece9f642145e88d649ce5ab27771d6e110124c SHA512: b2794130ad14a69dc8ddfa8b2be97359ea9321b0b596223e5e6d2746ae3b089a3dfe289e7f06f6d7dd7df6eed72618516e177d5fe9431bde350dcbeef72d0eae Homepage: https://cran.r-project.org/package=EnsembleBase Description: CRAN Package 'EnsembleBase' (Extensible Package for Parallel, Batch Training of Base Learnersfor Ensemble Modeling) Extensible S4 classes and methods for batch training of regression and classification algorithms such as Random Forest, Gradient Boosting Machine, Neural Network, Support Vector Machines, K-Nearest Neighbors, Penalized Regression (L1/L2), and Bayesian Additive Regression Trees. These algorithms constitute a set of 'base learners', which can subsequently be combined together to form ensemble predictions. This package provides cross-validation wrappers to allow for downstream application of ensemble integration techniques, including best-error selection. All base learner estimation objects are retained, allowing for repeated prediction calls without the need for re-training. For large problems, an option is provided to save estimation objects to disk, along with prediction methods that utilize these objects. This allows users to train and predict with large ensembles of base learners without being constrained by system RAM. 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Package: r-cran-ensemblecv Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ensemblebase Filename: pool/dists/noble/main/r-cran-ensemblecv_0.9-1.ca2404.1_all.deb Size: 61304 MD5sum: 4b0358a0295d5e1deb685c1841c4ca1d SHA1: bb8d3e21a1a4eaccf1a912c030f615419473d6ba SHA256: 35c796eea125469dba3f109e2bcb40f7a6fbac4e3a4551757b36c185934776f1 SHA512: 1ddfb1792198ab5431420bc6aca9a092de05c77f3615663b098a12f6fc1acb87c19513791318a9c8660970de602476eb2b2ecea4ab61c7742cbc06bd6669998c Homepage: https://cran.r-project.org/package=EnsembleCV Description: CRAN Package 'EnsembleCV' (Extensible Package for Cross-Validation-Based Integration ofBase Learners) Extends the base classes and methods of EnsembleBase package for cross-validation-based integration of base learners. 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Package: r-cran-ensemblemos Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ensemblebma, r-cran-chron, r-cran-evd Suggests: r-cran-fields, r-cran-maps Filename: pool/dists/noble/main/r-cran-ensemblemos_0.8.2-1.ca2404.1_all.deb Size: 386732 MD5sum: 67c77ac09cf8e5024f8a90478ae33a58 SHA1: 3acab95011fd6d36b3ab612b2f66089d642008ca SHA256: 37555de8a7f12217672b863e077891e15fc7843f2507aa009ff12a266e853e5f SHA512: b8a636e59babf574fb997e4ac536399238dbe7a92e8900c3d506b62d3d04d8a93332c6366d9b286801e7ce929052da4e1184552e4b96cbb09208d2def7eb66fb Homepage: https://cran.r-project.org/package=ensembleMOS Description: CRAN Package 'ensembleMOS' (Ensemble Model Output Statistics) Ensemble Model Output Statistics to create probabilistic forecasts from ensemble forecasts and weather observations. Package: r-cran-ensemblepcreg Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 517 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ensemblebase Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-ensemblepcreg_1.1.4-1.ca2404.1_all.deb Size: 460202 MD5sum: 522ee84c425d7371753fad53bf601b43 SHA1: 6f19872f5197be9f83c5b9a147b597bc333d105a SHA256: 7f04d9ada37ba508e09cf0853aa95b44826cfc2f4328013a1896d96501da7e02 SHA512: d1e5b5202d6298a4c88e86982402cf57494bdc22d079b3cc3760d7ad40cee042d1b58f0f0d4af7af29ec22b85041ba9c3e69bd15a286ca3f9b2093cf92db34c3 Homepage: https://cran.r-project.org/package=EnsemblePCReg Description: CRAN Package 'EnsemblePCReg' (Extensible Package for Principal-Component-Regression-BasedHeterogeneous Ensemble Meta-Learning) Extends the base classes and methods of 'EnsembleBase' package for Principal-Components-Regression-based (PCR) integration of base learners. 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Package: r-cran-ensemblepenreg Architecture: all Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ensemblebase, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-ensemblepenreg_0.8-1.ca2404.1_all.deb Size: 74432 MD5sum: a7dc752e82624a6c6f41bc556b001d92 SHA1: 9d88d6ea6591887a3494e32d2d63707087c0b308 SHA256: 3ff1cb7fa5881c809235b8e1876a8fd6a657b62755fe0ebff11a42118a3a330c SHA512: 137d5a8c9f8c743cfe04d2ae376b35ea2f7b1256ee93765b4de862bdd7ea077fb5af492518e79c30db37ca38c76965fb6811f2c97d07c4c7f1c7da80eb730ea3 Homepage: https://cran.r-project.org/package=EnsemblePenReg Description: CRAN Package 'EnsemblePenReg' (Extensible Classes and Methods for Penalized-Regression-BasedIntegration of Base Learners) Extending the base classes and methods of EnsembleBase package for Penalized-Regression-based (Ridge and Lasso) integration of base learners. 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Package: r-cran-ensemblepp Architecture: all Version: 1.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ensemblebma, r-cran-crch, r-cran-gamlss, r-cran-ensemblemos, r-cran-specsverification, r-cran-scoringrules, r-cran-glmx, r-cran-ordinal, r-cran-proc, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-ensemblepp_1.0-0-1.ca2404.1_all.deb Size: 252810 MD5sum: 482682799bcf89747b75793f841a414f SHA1: 6a3d976b0fe39824bd9b7b02827a0fa94461c7d6 SHA256: d2925fd4d604c4d78b443db4981b7ba333450dfb3077dd5e31cca6c6bc10daae SHA512: 9321ac1cc393aac86b79684d537acab645c5962bb19ee8d78c975d1c4f5d55abb4581975f0e2bd6c090bbc49aca80e1f4522c18479031855f082186d1d43f87a Homepage: https://cran.r-project.org/package=ensemblepp Description: CRAN Package 'ensemblepp' (Ensemble Postprocessing Data Sets) Data sets for the chapter "Ensemble Postprocessing with R" of the book Stephane Vannitsem, Daniel S. 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And This package also provide write out figures with lots of different formats. Since people may work on the platform without GUI support, the package also provide function to easily write out figures to lots of different type of formats. Now this package provide function to extract colors from all types of figures and pdf files. 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Package: r-cran-epanetreader Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-epanet2toolkit, r-cran-data.table Filename: pool/dists/noble/main/r-cran-epanetreader_1.0.0-1.ca2404.1_all.deb Size: 171678 MD5sum: 72e3341abad9aebb7fcdc16067c3f33d SHA1: 7b440e21d772a0e440d36dfb612cb66427c04662 SHA256: 377f6df4fcf297595a6d235325d9359e5c22183512b8b8cbc7216a8e548b51a4 SHA512: 62f113dfb85cb246434ecebff72c128f5a4e37e56c58e14bbe1c1a55ee9032b2cdd83904d587d264cfb83b9313c8ef91369360fbb483544558d0802a28995635 Homepage: https://cran.r-project.org/package=epanetReader Description: CRAN Package 'epanetReader' (Read Epanet Files into R) Reads water network simulation data in 'Epanet' text-based '.inp' and '.rpt' formats into R. Also reads results from 'Epanet-msx'. Provides basic summary information and plots. The README file has a quick introduction. See for more information on the Epanet software for modeling hydraulic and water quality behavior of water piping systems. 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Package: r-cran-epcr Architecture: all Version: 0.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4771 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-hamlet, r-cran-survival, r-cran-timeroc, r-cran-pracma, r-cran-bolstad2, r-bioc-impute Suggests: r-cran-mass, r-cran-rocr, r-cran-c060, r-cran-matrix, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epcr_0.11.0-1.ca2404.1_all.deb Size: 4697298 MD5sum: a20a3a37374f0b66ed860019040fcb5b SHA1: 600433c51519144d1c4352124a86a3095999e5e5 SHA256: 0f468eef6d4e6fc768d7cb410d677e1c08a2427d71ad9d351ff517d11c1fb32d SHA512: ce1711d9852180d4bed98b215daacca6735d463bef2b3f4abe52e27eab9c1714e91806f6527d8bd4c26772a6f91e89fcd6fa05f3c2bfdfdd5ae918d5a652ce01 Homepage: https://cran.r-project.org/package=ePCR Description: CRAN Package 'ePCR' (Ensemble Penalized Cox Regression for Survival Prediction) The top-performing ensemble-based Penalized Cox Regression (ePCR) framework developed during the DREAM 9.5 mCRPC Prostate Cancer Challenge presented in Guinney J, Wang T, Laajala TD, et al. (2017) is provided here-in, together with the corresponding follow-up work. While initially aimed at modeling the most advanced stage of prostate cancer, metastatic Castration-Resistant Prostate Cancer (mCRPC), the modeling framework has subsequently been extended to cover also the non-metastatic form of advanced prostate cancer (CRPC). Readily fitted ensemble-based model S4-objects are provided, and a simulated example dataset based on a real-life cohort is provided from the Turku University Hospital, to illustrate the use of the package. Functionality of the ePCR methodology relies on constructing ensembles of strata in patient cohorts and averaging over them, with each ensemble member consisting of a highly optimized penalized/regularized Cox regression model. Various cross-validation and other modeling schema are provided for constructing novel model objects. Package: r-cran-epe4md Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4674 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-scales, r-cran-purrr, r-cran-tidyr, r-cran-readxl, r-cran-jrvfinance, r-cran-janitor, r-cran-furrr, r-cran-zoo, r-cran-lubridate, r-cran-tsibble, r-cran-stringr, r-cran-fabletools, r-cran-feasts, r-cran-tibble, r-cran-assertthat, r-cran-forcats, r-cran-future, r-cran-magrittr, r-cran-readr, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-epe4md_0.1.4-1.ca2404.1_all.deb Size: 3866114 MD5sum: 951fc3b7bf72f601cedbbf49ce7962ee SHA1: df7cfe5d2fa292d3e6aba1e08760a5391afb475b SHA256: 62b0d9690c9beca05f18089bd9f509b85f6e735957df803b64d866e147e675d6 SHA512: 3969cc09ab86e82dff23937d4d458728a4f7824eaac1c4b9a2fe915e7bff9f910690271bbe1ea3b6b4e90c71119d7ca6284cb77f89452ea5fd458bc51b04808d Homepage: https://cran.r-project.org/package=epe4md Description: CRAN Package 'epe4md' (EPE's 4MD Model to Forecast the Adoption of DistributedGeneration) EPE's (Empresa de Pesquisa Energética) 4MD (Modelo de Mercado da Micro e Minigeração Distribuída - Micro and Mini Distributed Generation Market Model) model to forecast the adoption of Distributed Generation. Given the user's assumptions, it is possible to estimate how many consumer units will have distributed generation in Brazil over the next 10 years, for example. In addition, it is possible to estimate the installed capacity, the amount of investments that will be made in the country and the monthly energy contribution of this type of generation. . 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The implemented methods are based on INDEC (2016) . As this package works with the argentinian Permanent Household Survey and its main audience is from this country, the documentation was written in Spanish. 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This method provides additional support of exogenous variables and also generates confidence interval. This package provides EWNet model for time series forecasting based on the algorithm by Panja, et al. (2022) and Panja, et al. (2023) . 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These can be used, for example, to analyse the distribution of chain sizes or length of infectious disease outbreaks, as discussed in Farrington et al. (2003) . 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Package: r-cran-epidata Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-rvest, r-cran-xml2, r-cran-tidyr, r-cran-readr, r-cran-stringi, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-epidata_0.4.0-1.ca2404.1_all.deb Size: 506236 MD5sum: 75f1dfbf93f4645559eece619dbbf6cc SHA1: c3e5bde1d3d11d077b70acf352c08464a34fb7ce SHA256: aa8d39ae1649617b26a37eb0debd1e0af77fe09fff53342d0418f819813de20d SHA512: 2346fac7a02f82deb5f2d2187f4a25df1b95f58a34993c78e6c7140b38e04be06bc105fc505b1e2f0f8179b6b9c2c454e15a0cd575af7dc91a689764d796fbfc Homepage: https://cran.r-project.org/package=epidata Description: CRAN Package 'epidata' (Tools to Retrieve Economic Policy Institute Data LibraryExtracts) The Economic Policy Institute () provides researchers, media, and the public with easily accessible, up-to-date, and comprehensive historical data on the American labor force. It is compiled from Economic Policy Institute analysis of government data sources. Use it to research wages, inequality, and other economic indicators over time and among demographic groups. Data is usually updated monthly. Package: r-cran-epidatr Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 638 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cachem, r-cran-checkmate, r-cran-cli, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-mmwrweek, r-cran-openssl, r-cran-purrr, r-cran-rappdirs, r-cran-rlang, r-cran-tibble, r-cran-usethis, r-cran-xml2 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-mapproj, r-cran-maps, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-epidatr_1.2.2-1.ca2404.1_all.deb Size: 415188 MD5sum: 834f6f8a7a0ce11f2be45e3ebbefc1e5 SHA1: 22cb9203ef57ec0e6de7eba0c08ecf8bc52accf1 SHA256: 7c9811bef7b1dd027c05db7f6b3db3fd0619c0b0ad6cb2e93a137c1b21c69a27 SHA512: dc159b532330ecaa87e642403e94a990a940d399194ff0bab0b1297794c5cb25abaf4de028f1edf01e1d76483fa2ef184724ef68f11cd4dd7583105b4ba40e0e Homepage: https://cran.r-project.org/package=epidatr Description: CRAN Package 'epidatr' (Client for Delphi's 'Epidata' API) The Delphi 'Epidata' API provides real-time access to epidemiological surveillance data for influenza, 'COVID-19', and other diseases for the USA at various geographical resolutions, both from official government sources such as the Center for Disease Control (CDC) and Google Trends and private partners such as Facebook and Change 'Healthcare'. It is built and maintained by the Carnegie Mellon University Delphi research group. To cite this API: David C. Farrow, Logan C. Brooks, Aaron 'Rumack', Ryan J. 'Tibshirani', 'Roni' 'Rosenfeld' (2015). Delphi 'Epidata' API. . Package: r-cran-epideaths Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-epideaths_1.1.8-1.ca2404.1_all.deb Size: 45920 MD5sum: be4aa378df7d2ba3591b8f5f97b4621b SHA1: 56b114b79bab38b1d8e58cb5e17fecbe27c1ec3e SHA256: 08aff9bafe163d99469f45988db4bb02fb5fc9efbfcd5e9bdaafe355ae5c4349 SHA512: 4d9cc64a8f73153ca468cc4748190f0ac3506fd549fc0aff00af11adec4ee983cb2461a69ac7a3d47b374ef3a981cbeaa692d2bb02ca5f12c0f3e61384a132a6 Homepage: https://cran.r-project.org/package=epiDeaths Description: CRAN Package 'epiDeaths' (Functions for Calculating Mortality Indicators) Provides functions for calculating mortality indicators. These include geometric interpolation between two periods and projections for future years, as described in the textbook by Laurenti, Mello Jorge, Lebrão and Gotlieb (2005, ISBN:9788512408309), the standardised mortality ratio (Bruce, Pope and Stanistreet, 2018, ISBN:9781118665411), the age-adjusted mortality rate (direct standardisation), years of potential life lost (Gardner and Sanborn, 1990, ; Ma, Ward, Siegel and Jemal, 2015, ), and age-standardised years of potential life lost (Silva Filho et al., 2024 ). Confidence intervals for the standardised mortality ratio are obtained according to Vandenbroucke (1982) and Ulm (1990) . The function also includes a function that produces a graph similar to an age pyramid. Package: r-cran-epidict Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1363 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clipr, r-cran-dplyr, r-cran-readxl, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-dt, r-cran-knitr, r-cran-matchmaker, r-cran-rmarkdown, r-cran-sitrep, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-epidict_0.3.0-1.ca2404.1_all.deb Size: 422690 MD5sum: 2545e61311d8310e56f49c45ad27c350 SHA1: 0062a961260c898898907a91892e26ab68290a6c SHA256: 1a702fcd4a18d9c39b0890322eba04e4ba08065519513cb76501fc8bbedd22bf SHA512: 2896c8ae4eb7337632e1d811fd226108cf0e47ccba087209687ab78a5144c91b9655c924968aa82d40540bdda2cdb3a2a956badf2fc38a620326072c1ba0d9fb Homepage: https://cran.r-project.org/package=epidict Description: CRAN Package 'epidict' (Epidemiology Data Dictionaries and Random Data Generators) The 'R4EPIs' project seeks to provide a set of standardized tools for analysis of outbreak and survey data in humanitarian aid settings. This package currently provides standardized data dictionaries from Medecins Sans Frontieres Operational Centre Amsterdam for outbreak scenarios (Acute Jaundice Syndrome, Cholera, Diphtheria, Measles, Meningitis) and surveys (Retrospective mortality and access to care, Malnutrition, Vaccination coverage and Event Based Surveillance) - as described in the following . In addition, a data generator from these dictionaries is provided. It is also possible to read in any Open Data Kit format data dictionary. Package: r-cran-epidigir Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-sp, r-cran-tm, r-cran-glmnet, r-cran-caret, r-cran-survival Suggests: r-cran-kernlab, r-cran-randomforest, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-usethis, r-cran-testthat Filename: pool/dists/noble/main/r-cran-epidigir_0.1.2-1.ca2404.1_all.deb Size: 83484 MD5sum: 96277ef4d8a3a8e7a13d5a4f2e16cf3d SHA1: 3ac5769f438c002fa1f850a40bf6e2242b73b6a9 SHA256: 8037b0f865cc8d1100d71b7a5ab2992329454aa2f1d7cb5530fb7b09ea7674bf SHA512: f1122ac300ea88eb1597007014b2de122e45331094f9627a1b61b26e98cc7b5f4fe50a0530803ea5f27c6a1d7d3a77b36ae4fdb06bc55c594d1b8720f7ef0e83 Homepage: https://cran.r-project.org/package=EpidigiR Description: CRAN Package 'EpidigiR' (Digital Epidemiological Analysis and Visualization Tools) Integrates methods for epidemiological analysis, modeling, and visualization, including functions for summary statistics, SIR (Susceptible-Infectious-Recovered) modeling, DALY (Disability-Adjusted Life Years) estimation, age standardization, diagnostic test evaluation, NLP (Natural Language Processing) keyword extraction, clinical trial power analysis, survival analysis, SNP (Single Nucleotide Polymorphism) association, and machine learning methods such as logistic regression, k-means clustering, Random Forest, and Support Vector Machine (SVM). Includes datasets for prevalence estimation, SIR modeling, genomic analysis, clinical trials, DALY, diagnostic tests, and survival analysis. Methods are based on Gelman et al. (2013) and Wickham et al. (2019, ISBN:9781492052040>. Package: r-cran-epidisplay Architecture: all Version: 3.7.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 748 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreign, r-cran-survival, r-cran-mass, r-cran-nnet Filename: pool/dists/noble/main/r-cran-epidisplay_3.7.0.0-1.ca2404.1_all.deb Size: 671222 MD5sum: e1ae14ad347875667b793af3ee43d454 SHA1: c03e24fefc329b410735d447176cf0ebed95c97d SHA256: 9e08a524595932f8d529380ee9c351c68ca22ec49df407fe08d80dc3fc9b7876 SHA512: 3c70b819393b6d7043be9b78c258422f84eb58ecc158646935eeb05b147edab49b31c0aa0dcf515facec1d9f66239bff30587fd3e718e3dcaf004f90d886feaf Homepage: https://cran.r-project.org/package=epiDisplay Description: CRAN Package 'epiDisplay' (Epidemiological Data Display Package) Package for data exploration and result presentation. Full 'epicalc' package with data management functions is available at ''. 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Package: r-cran-epidynamics Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-reshape2, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-epidynamics_0.3.1-1.ca2404.1_all.deb Size: 186992 MD5sum: dcbe200dd26f326d902428bd2949cafa SHA1: ee3dc4f1ae7c5fe136a72e31973a6b77572ae6fd SHA256: 91c5382ea2c40a8fc9bd22f8ad68e45b9763ff565212063925d98b909e3affdc SHA512: a244ed7dd01af94c5b00562a42e86c116eda30bbdb4ad118cd0496bc2d7bca108da8dc2ae08e398b3ec6e070afa518c652bd27156670e8e2b6929fddde0d044c Homepage: https://cran.r-project.org/package=EpiDynamics Description: CRAN Package 'EpiDynamics' (Dynamic Models in Epidemiology) Mathematical models of infectious diseases in humans and animals. Both, deterministic and stochastic models can be simulated and plotted. Package: r-cran-epiestim Architecture: all Version: 2.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 947 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coarsedatatools, r-cran-reshape2, r-cran-ggplot2, r-cran-gridextra, r-cran-fitdistrplus, r-cran-coda, r-cran-incidence, r-cran-scales Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epiestim_2.2-5-1.ca2404.1_all.deb Size: 580270 MD5sum: ab9bb6f588c7bb25e5b897071579efbe SHA1: b58a8ba7976bcbed5354e8900fd50091db49628c SHA256: 9babcce4660ea115fd15a2cfe71f4da3bbef628383d58d61bfef730ffe90558c SHA512: 8f617d009ae947ab9252dbed705be00ea6564407481cf3641cf23a14c4426e2a6fe0362a146114c8672f3af65ef960d2d3767cf7558d495388bdf6c99582c613 Homepage: https://cran.r-project.org/package=EpiEstim Description: CRAN Package 'EpiEstim' (Estimate Time Varying Reproduction Numbers from Epidemic Curves) Tools to quantify transmissibility throughout an epidemic from the analysis of time series of incidence as described in Cori et al. (2013) and Wallinga and Teunis (2004) . Package: r-cran-epifitter Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools, r-cran-cowplot, r-cran-desolve, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-minpack.lm, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lemon, r-cran-testthat Filename: pool/dists/noble/main/r-cran-epifitter_1.0.0-1.ca2404.1_all.deb Size: 197222 MD5sum: 47558480f3a66d67e6a19b1c1af65aad SHA1: 92be69c8231315d87d00c143e9d11501d8bb385a SHA256: a498e1baf864d01c1eeea70282fc6d9936b8e581d415e6e4db5f64e50f09fbf2 SHA512: ef2de04ab92f519c1e792ff7234d1d3bb60ba80fe854366e34cc6431913869635641426967e21fb1732830001152dc652754e2a915e075c6d167e32819e42d6f Homepage: https://cran.r-project.org/package=epifitter Description: CRAN Package 'epifitter' (Analysis and Simulation of Plant Disease Progress Curves) Tools for analysis, visualization, and simulation of plant disease progress curves. Includes functions to calculate area-under-the-curve summaries, fit and compare exponential, monomolecular, logistic, and Gompertz models using linear or nonlinear regression, work with single or multiple epidemics, and produce 'ggplot2'-based visualizations. Also includes an experimental powdery mildew dataset for reproducible teaching and research workflows. See Madden, Hughes, and van den Bosch (2007) for background on the epidemiological methods. Package: r-cran-epiflows Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3784 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-epicontacts, r-cran-leaflet, r-cran-ggmap, r-cran-geosphere, r-cran-ggplot2, r-cran-tibble, r-cran-sp, r-cran-htmltools, r-cran-visnetwork Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-outbreaks, r-cran-vdiffr, r-cran-curl, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epiflows_0.2.2-1.ca2404.1_all.deb Size: 1312694 MD5sum: 5bee9705a0a5b0fe73436a182c3ec727 SHA1: 12ca51e492124ed38ac6002fdfba4307092f184d SHA256: 3c3d96bac29be0e20693926ec11155fb2063f7294fe2d83ef3248cf1d12f342d SHA512: 00d9a2fde8cc97488315a988a93fad48ed7d257352deb0e1cfd195dc95065f7da93f1a242073f0d0206775a98d3176150194847d7745a6de0baeb0ee952cebe4 Homepage: https://cran.r-project.org/package=epiflows Description: CRAN Package 'epiflows' (Predicting Disease Spread from Flow Data) Provides functions and classes designed to handle and visualise epidemiological flows between locations. Also contains a statistical method for predicting disease spread from flow data initially described in Dorigatti et al. (2017) . This package is part of the RECON () toolkit for outbreak analysis. Package: r-cran-epiforsk Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cowplot, r-cran-data.table, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-glue, r-cran-grf, r-cran-gridextra, r-cran-hmisc, r-cran-matchit, r-cran-nnet, r-cran-patchwork, r-cran-policytree, r-cran-progressr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-survey, r-cran-survival, r-cran-svyvgam, r-cran-tidyr, r-cran-vgam Suggests: r-cran-cli, r-cran-cvxr, r-cran-furrr, r-cran-future, r-cran-ggsci, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-epiforsk_0.2.2-1.ca2404.1_all.deb Size: 924084 MD5sum: 2720fecf4fd9b170ad86336a92dafed9 SHA1: 3c99d90b4b0595c6f4aaf7f874ec5c8c949367e6 SHA256: a5ae5c2afb0bdceaf41abde4fe77194e67aafb450a74ce1d5fca141cc47614f7 SHA512: 0617dcd396745c1ad1f6572b54fdcbcf96f3649a2f87f9925379ca1eb74f6e97f109e5e4acf455dc2d5e51e0bfe88a32411668d5b580dea041e8d379fdafb903 Homepage: https://cran.r-project.org/package=EpiForsk Description: CRAN Package 'EpiForsk' (Code Sharing at the Department of Epidemiology Research atStatens Serum Institut) This is a collection of assorted functions and examples collected from various projects. Currently we have functionalities for simplifying overlapping time intervals, Charlson comorbidity score constructors for Danish data, getting frequency for multiple variables, getting standardized output from logistic and log-linear regressions, sibling design linear regression functionalities a method for calculating the confidence intervals for functions of parameters from a GLM, Bayes equivalent for hypothesis testing with asymptotic Bayes factor, and several help functions for generalized random forest analysis using 'grf'. Package: r-cran-epigraphdb Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3083 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-tibble, r-cran-httr, r-cran-glue, r-cran-purrr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-devtools, r-cran-usethis, r-cran-pkgdown, r-cran-styler, r-cran-lintr, r-cran-covr, r-cran-igraph, r-cran-gtools, r-cran-stringr, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-epigraphdb_0.2.3-1.ca2404.1_all.deb Size: 1124386 MD5sum: 018a23a427bc9cb77d27c5c56189eaa6 SHA1: 691a53077e8947fe332cedf029bbb01f892d4993 SHA256: 8af7ff2793f50828efc757f1cbd904290a4b08de81f289ca5f93115f4bede5f7 SHA512: 0e942bba8611f743cb87ba05df5256ce0ca051a113bc76a77513901283005085e145ed837a807f7b8fc889741651b8a18d0df7f9c35173c7a7e9ce2dc3b61603 Homepage: https://cran.r-project.org/package=epigraphdb Description: CRAN Package 'epigraphdb' (Interface Package for the 'EpiGraphDB' Platform) The interface package to access data from the 'EpiGraphDB' platform. It provides easy access to the 'EpiGraphDB' platform with functions that query the corresponding REST endpoints on the API and return the response data in the 'tibble' data frame format. Package: r-cran-epikit Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scales, r-cran-dplyr, r-cran-rlang, r-cran-forcats, r-cran-tidyr, r-cran-tibble, r-cran-glue, r-cran-tidyselect, r-cran-ggplot2, r-cran-sf Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epikit_0.2.0-1.ca2404.1_all.deb Size: 177902 MD5sum: 020763146d11d645cfb6547f078608b3 SHA1: d800712577a7d149284dd44a1696026d03358c72 SHA256: 95d624b140c532881eec06eba974d7f51cea694496c17ee4bfc8cf6734cfece9 SHA512: 1eeea0f1693cf1038ca4bcccfbd8d94ab9573773f929a2b933918ebd7b0f06aa25622329c697e2c0ffb167d5c3d8061dd661f14a4d8c3c1fc9d5beb5bcffe984 Homepage: https://cran.r-project.org/package=epikit Description: CRAN Package 'epikit' (Miscellaneous Helper Tools for Epidemiologists) Contains tools for formatting inline code, renaming redundant columns, aggregating age categories, adding survey weights, finding the earliest date of an event, plotting z-curves, generating population counts and formatting proportions with confidence intervals. This is part of the 'R4Epis' project . Package: r-cran-epilogi Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-epilogi_1.2-1.ca2404.1_all.deb Size: 22874 MD5sum: 7a43bf47b8c64b5822e785631c5fbe05 SHA1: f8dd26f5579912097fca126afd53b1ca051cd814 SHA256: 1bb1c23c23ca160aeac0d123281e9787a2e14e0f6f78ce8ea19589863e7ded86 SHA512: 2b435c2826f962d9a79d2bb4f578e78dc0125082be571f79a3a6f20422d60a2beaa86f9c5f577af7b708d31418c662f2a41ea4218332db44183aa69068dfe5a5 Homepage: https://cran.r-project.org/package=epilogi Description: CRAN Package 'epilogi' (The 'epilogi' Variable Selection Algorithm for Continuous Data) The 'epilogi' variable selection algorithm is implemented for the case of continuous response and predictor variables. The relevant paper is: Lakiotaki K., Papadovasilakis Z., Lagani V., Fafalios S., Charonyktakis P., Tsagris M. and Tsamardinos I. (2023). "Automated machine learning for Genome Wide Association Studies". Bioinformatics, 39(9): btad545. . Package: r-cran-epimdr2 Architecture: all Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-desolve, r-cran-plotly, r-cran-polspline, r-cran-ggplot2 Suggests: r-cran-ade4, r-cran-bbmle, r-cran-fields, r-cran-forecast, r-cran-imputets, r-cran-lme4, r-cran-ncf, r-cran-nleqslv, r-cran-nlme, r-cran-nlts, r-cran-plotrix, r-cran-pomp, r-cran-rootsolve, r-cran-rwave, r-cran-rworldmap, r-cran-statnet, r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-epimdr2_1.1-1-1.ca2404.1_all.deb Size: 2367426 MD5sum: 964a6df79ea7a0dbfee62e3374dad8d8 SHA1: b6f5826a25341cd88880766944cddb1f9c5cac33 SHA256: 13a8178211572fb40dabb8eadaab434fd57a59091ac53f703799b6c6af134381 SHA512: f2d3e8b3a59d12f3cf90a227747801ee262a1cd4dd4d42c462cbbebef79002931cd27d1bda1d64bc685b9e8555c70e2214220df898d31933983f62416e46b743 Homepage: https://cran.r-project.org/package=epimdr2 Description: CRAN Package 'epimdr2' (Functions and Data for "Epidemics: Models and Data in R (2ndEdition)") Functions, data sets and shiny apps for "Epidemics: Models and Data in R (2nd edition)" by Ottar N. Bjornstad (2022, ISBN: 978-3-031-12055-8) . The package contains functions to study the Susceptible-Exposed-Infected-Removed SEIR model, spatial and age-structured Susceptible-Infected-Removed SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. Package: r-cran-epimdr Architecture: all Version: 0.6-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2090 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-desolve, r-cran-polspline Suggests: r-cran-ade4, r-cran-bbmle, r-cran-fields, r-cran-forecast, r-cran-imputets, r-cran-lme4, r-cran-ncf, r-cran-nleqslv, r-cran-nlme, r-cran-nlts, r-cran-plotrix, r-cran-pomp, r-cran-rootsolve, r-cran-rwave, r-cran-statnet Filename: pool/dists/noble/main/r-cran-epimdr_0.6-5-1.ca2404.1_all.deb Size: 1257062 MD5sum: ae1c52746fc40c68d61e5f1ae849bad9 SHA1: 3b3997c0c437760d98cdd40dbfa315034efcd1f7 SHA256: 22c5ec8066f3593240fd99a387bac3a7b8409929e6f673b6a3795d384a54462c SHA512: 27acf277e248f8af7f4e7cf8c88cd27be39d1e95e43371859df692694a881fa13313af4ba7597ecfab2dd292f10fcbc9dd89cfda94a1740cd552a8d7f8af240e Homepage: https://cran.r-project.org/package=epimdr Description: CRAN Package 'epimdr' (Functions and Data for "Epidemics: Models and Data in R") Functions, data sets and shiny apps for "Epidemics: Models and Data in R" by Ottar N. Bjornstad (ISBN 978-3-319-97487-3) . The package contains functions to study the S(E)IR model, spatial and age-structured SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the coursera Epidemics Massive Online Open Course . Package: r-cran-epinova Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-scales Suggests: r-cran-deoptim, r-cran-mass, r-cran-numderiv, r-cran-rstan, r-cran-tmb, r-cran-epiestim, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggpubr, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-epinova_0.1.0-1.ca2404.1_all.deb Size: 157946 MD5sum: de57502f8bfb65a0b22f743ab2629ea0 SHA1: 8b64a9710759eb66442f48e21129dcc18926f90b SHA256: 5cefd64d5ad4b7e270bbc0b74a5663db4768868b8ac4b6136952935c962823ea SHA512: d4fa620ceaf41d2259f41b65a7242bb55d81e37f90edb526a93f5a891232eb83fc4c48b9a2ad3977e90b91f67f76c8d0f4b9bbcdea135b9d4a5e965a50524e2d Homepage: https://cran.r-project.org/package=EpiNova Description: CRAN Package 'EpiNova' (Flexible Extended State-Space Epidemiological Models with ModernInference) An extended epidemiological modelling framework that goes beyond the classical SIR (Susceptible-Infectious-Recovered) model. Supports SEIR (Susceptible-Exposed-Infectious-Recovered), SEIRD (Susceptible-Exposed-Infectious-Recovered-Deceased), SVEIRD (Susceptible-Vaccinated-Exposed-Infectious-Recovered-Deceased), and age-stratified compartmental models with flexible intervention functions (spline-based, Gaussian process, or user-defined). Inference is available via maximum likelihood or sequential Monte Carlo (SMC, also known as particle filtering) with no external binary dependencies. Includes a dependency-free real-time effective reproduction number (Rt) estimator, spatial multi-patch models with gravity-model mobility, ensemble forecasting via Bayesian model averaging (BMA), and proper scoring rules including CRPS (Continuous Ranked Probability Score), coverage, and MAE (Mean Absolute Error) for forecast evaluation. Methods follow Anderson and May (1991, ISBN:9780198545996), Doucet, de Freitas, and Gordon (2001) , Cori et al. (2013) , and Gneiting and Raftery (2007) . Package: r-cran-epiomics Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-ggrepel, r-cran-qgcomp, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-epiomics_1.2.0-1.ca2404.1_all.deb Size: 393518 MD5sum: 2754e698be8353a733d7a58b9c75d3c7 SHA1: ff6f75aa9f8aaf844ffaa01c8ea0e915668c5cbd SHA256: 3c6cf12776d29d59af69b3344e1b97feb26994faafeb7b023145dbefc9820d92 SHA512: 3119266d7918d290a9aea4c45c3d7c9616c8f043d0f6447c0290d83b12ea488ec9d486edc8c229effb802897b277ede6b9ba8b78b9c515370d04b8cccd564dbc Homepage: https://cran.r-project.org/package=epiomics Description: CRAN Package 'epiomics' (Analysis of Omics Data in Observational Studies) A collection of fast and flexible functions for analyzing omics data in observational studies. Multiple different approaches for integrating multiple environmental/genetic factors, omics data, and/or phenotype data are implemented. This includes functions for performing omics wide association studies with one or more variables of interest as the exposure or outcome; a function for performing a meet in the middle analysis for linking exposures, omics, and outcomes (as described by Chadeau-Hyam et al., (2010) ); and a function for performing a mixtures analysis across all omics features using quantile-based g-Computation (as described by Keil et al., (2019) ). Package: r-cran-epiparameter Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cachem, r-cran-checkmate, r-cran-cli, r-cran-distcrete, r-cran-distributional, r-cran-epiparameterdb, r-cran-lifecycle, r-cran-pillar, r-cran-rlang Suggests: r-cran-bookdown, r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-epiparameter_0.4.1-1.ca2404.1_all.deb Size: 835396 MD5sum: 3194f6c2fed43fd56e4c482584999b68 SHA1: bb33b413c2488d1a1607567a8f718ad4d15931b2 SHA256: 6923e202936dfbdfd21ff71563ca2226e3da940411cf93a4e577eef547d5cca4 SHA512: 86f3789109417ef5f61d5c6a6c1ea7a7521b8e250ccca0a023a0cdc0575163eb2eddb74ff5e774732af6ccb683c37540585c5b8f816eb72329fb9f7d1fe58d03 Homepage: https://cran.r-project.org/package=epiparameter Description: CRAN Package 'epiparameter' (Classes and Helper Functions for Working with EpidemiologicalParameters) Classes and helper functions for loading, extracting, converting, manipulating, plotting and aggregating epidemiological parameters for infectious diseases. Epidemiological parameters extracted from the literature are loaded from the 'epiparameterDB' R package. Package: r-cran-epiparameterdb Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 854 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dt, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-epiparameterdb_0.1.0-1.ca2404.1_all.deb Size: 185356 MD5sum: ec309ab8dd1cd5296eff465ac659ae01 SHA1: 5d31b0a1e5a45147ff4801630484a4de9859b225 SHA256: 9bf0b6f87e8b0f749348cd023cf4119305b64c1fcc1db6941298dca34a285985 SHA512: 986cb7df783da4f27968d9167ce6c6e8330edb4b492007bff95b21c5f0e8e0b3dbbe211738a72ff58f2a7106a4fc92148b6007d722783dcf9b024bc52d4d3c88 Homepage: https://cran.r-project.org/package=epiparameterDB Description: CRAN Package 'epiparameterDB' (Database of Epidemiological Parameters) A data package containing a database of epidemiological parameters. It stores the data for the 'epiparameter' R package. Epidemiological parameter estimates are extracted from the literature. Package: r-cran-epir Architecture: all Version: 2.0.93-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5469 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-biasedurn, r-cran-pander, r-cran-sf, r-cran-lubridate, r-cran-zoo, r-cran-flextable, r-cran-officer Suggests: r-cran-mass, r-cran-dplyr, r-cran-foreign, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-plyr, r-cran-rcolorbrewer, r-cran-scales, r-cran-sp, r-cran-spdata, r-cran-spatstat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-epir_2.0.93-1.ca2404.1_all.deb Size: 1766914 MD5sum: d662a72a2f25584a2d65d6d3f5c7ee1f SHA1: 1c04a0e7fce193e64ce3de255b1d64a06e3461ea SHA256: 2733f01390f34977e527436b0533f706f2d91f3c073df8902e556bb49e32f42e SHA512: 34e6632715c5fbcbbec6a5cc0c312b8a3147e04b8cf1aefc30a9533f95b5e96ceb0b4264d643d06b09a335ed89dc4e4fa84a630c0ce08485ec5a8eb182370bc5 Homepage: https://cran.r-project.org/package=epiR Description: CRAN Package 'epiR' (Tools for the Analysis of Epidemiological Data) Tools for the analysis of epidemiological and surveillance data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modelling analyses. Package: r-cran-epireport Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-officer, r-cran-flextable, r-cran-zoo, r-cran-png, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epireport_1.0.4-1.ca2404.1_all.deb Size: 2609286 MD5sum: baf11533b502a97c51ef73b942ff2f62 SHA1: 35eb3fa7516ffeaafcb5f318a053b0c0f78ebc98 SHA256: bfd8982ccbddedb00089384bc6ed1f44df9564713dc10776ec3eab08d15e489e SHA512: 4476961033ae5f0452d6f0a09aaf6a8a82944cf1df904f76d1e11c45522b62849010f5682db3e320830ebc56e4315bae7ecc511945657a246936092804cc87e3 Homepage: https://cran.r-project.org/package=EpiReport Description: CRAN Package 'EpiReport' (Epidemiological Report) Drafting an epidemiological report in 'Microsoft Word' format for a given disease, similar to the Annual Epidemiological Reports published by the European Centre for Disease Prevention and Control. Through standalone functions, it is specifically designed to generate each disease specific output presented in these reports and includes: - Table with the distribution of cases by Member State over the last five years; - Seasonality plot with the distribution of cases at the European Union / European Economic Area level, by month, over the past five years; - Trend plot with the trend and number of cases at the European Union / European Economic Area level, by month, over the past five years; - Age and gender bar graph with the distribution of cases at the European Union / European Economic Area level. Two types of datasets can be used: - The default dataset of dengue 2015-2019 data; - Any dataset specified as described in the vignette. Package: r-cran-episcan Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-episcan_0.0.1-1.ca2404.1_all.deb Size: 69392 MD5sum: 8ae1f092c5087b1b79f238393c49e160 SHA1: 72d49d1b7cccc5ccf12b31ef15bc1769cb803cb6 SHA256: 7aafe7f30080eea8f0f9f1bb838498ed31bef25d2f4e3509ac6b6aba8d45015a SHA512: 68c28e9740d1c29364cb889c0da67e63ccc87a0829ff823b56ea079e24f80f5464e3b26c8f8aa8acefa5e85ef60cfbe79f5109b3cec4cd38b06362baed5c51ad Homepage: https://cran.r-project.org/package=episcan Description: CRAN Package 'episcan' (Scan Pairwise Epistasis) Searching genomic interactions with linear/logistic regression in a high-dimensional dataset is a time-consuming task. This package provides some efficient ways to scan epistasis in genome-wide interaction studies (GWIS). Both case-control status (binary outcome) and quantitative phenotype (continuous outcome) are supported (the main references: 1. Kam-Thong, T., D. Czamara, K. Tsuda, K. Borgwardt, C. M. Lewis, A. Erhardt-Lehmann, B. Hemmer, et al. (2011). . 2. Kam-Thong, T., B. Pütz, N. Karbalai, B. Müller-Myhsok, and K. Borgwardt. (2011). .) Package: r-cran-episemble Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3961 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-tidyverse, r-cran-seqinr, r-bioc-biostrings, r-cran-splitstackshape, r-cran-entropy, r-cran-party, r-cran-stringr, r-cran-tibble, r-cran-doparallel, r-cran-e1071, r-cran-caret, r-cran-randomforest, r-cran-gbm, r-cran-foreach, r-cran-ftrcool, r-cran-iterators Filename: pool/dists/noble/main/r-cran-episemble_0.1.1-1.ca2404.1_all.deb Size: 980992 MD5sum: 4927991de3eae484e7199f8cfc7ce923 SHA1: 2e4e5046bf27b9cc2bf4d41d0841eef91fc07389 SHA256: bd3c7edb9e084c2dbe1659da0448bdbc14e67e13b9cf8f5981941a3074e9d229 SHA512: 2f2bb05baf5f8199bfa555952dbaaf75e3f3e5f9cde02474e35a232d62871f208b1447b5637e6375e387a99a377deaff8c3ee79e87c97b9f7e79ef5291acd4f0 Homepage: https://cran.r-project.org/package=EpiSemble Description: CRAN Package 'EpiSemble' (Ensemble Based Machine Learning Approach for PredictingMethylation States) DNA methylation (6mA) is a major epigenetic process by which alteration in gene expression took place without changing the DNA sequence. Predicting these sites in-vitro is laborious, time consuming as well as costly. This 'EpiSemble' package is an in-silico pipeline for predicting DNA sequences containing the 6mA sites. It uses an ensemble-based machine learning approach by combining Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting approach to predict the sequences with 6mA sites in it. This package has been developed by using the concept of Chen et al. (2019) . Package: r-cran-episensr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-actuar, r-cran-boot, r-cran-cli, r-cran-dagitty, r-cran-forcats, r-cran-ggdag, r-cran-magrittr, r-cran-trapezoid, r-cran-triangle, r-cran-truncnorm, r-cran-mass, r-cran-lifecycle Suggests: r-cran-aplore3, r-cran-covr, r-cran-directlabels, r-cran-knitr, r-cran-lattice, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-episensr_2.1.0-1.ca2404.1_all.deb Size: 850710 MD5sum: 57950b29cdfacbd85729177cba34c18a SHA1: cf860014034bb8590d6782b3b3951e88964d5818 SHA256: 81c7c5e29453a8c209dd0ec233138402766d4204d518db34ef2f54c7e01d690a SHA512: 89c373a2fdb7557fb25da1d01191e80b2b280674add72a5407984b4ceba75e286f0129b682003feab7cf3ae22f28cd49a335feb1747bd7157c701d5ebb1ee990 Homepage: https://cran.r-project.org/package=episensr Description: CRAN Package 'episensr' (Basic Sensitivity Analysis of Epidemiological Results) Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Fox M.P., MacLehose R.F., and Lash T.L. "Applying Quantitative Bias Analysis to Epidemiologic Data, second ed.", ('Springer', 2021). Package: r-cran-episignaldetection Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 768 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-isoweek, r-cran-rmarkdown, r-cran-shiny, r-cran-surveillance Suggests: r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-pander Filename: pool/dists/noble/main/r-cran-episignaldetection_0.1.3-1.ca2404.1_all.deb Size: 498748 MD5sum: c7b793c59f1ed605fb2dfb6962a5805a SHA1: c0b08fe5e6383cf2481fc09c8ffb35531e86f71b SHA256: e2678accc2ca437b5f387a7a1d2a15e4058c70e99d5fdae98c110e07b0d75a8a SHA512: d480ce743386fee19d48bc12a930eae7b8d6608cc5fdb3a4690e96ea5a82fc258c29a5fe72afc692a9d7c15280b38bef0c40f2aadadb2b25e7444c7c3b29076e Homepage: https://cran.r-project.org/package=EpiSignalDetection Description: CRAN Package 'EpiSignalDetection' (Signal Detection Analysis) Exploring time series for signal detection. It is specifically designed to detect possible outbreaks using infectious disease surveillance data at the European Union / European Economic Area or country level. Automatic detection tools used are presented in the paper "Monitoring count time series in R: aberration detection in public health surveillance", by Salmon (2016) . The package includes: - Signal Detection tool, an interactive 'shiny' application in which the user can import external data and perform basic signal detection analyses; - An automated report in HTML format, presenting the results of the time series analysis in tables and graphs. This report can also be stratified by population characteristics (see 'Population' variable). This project was funded by the European Centre for Disease Prevention and Control. Package: r-cran-episimr Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-desolve, r-cran-openxlsx, r-cran-dplyr, r-cran-dt, r-cran-shinythemes Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-episimr_1.1-1.ca2404.1_all.deb Size: 24854 MD5sum: 842b405738f201741b8775b0bf8c87ec SHA1: e151df347327d1b4fd7ad0835c2830742f0d6fce SHA256: 57271ca4679e068b56ec6237fb874a8801fe51b3b4e0178919a8409df59ffbb0 SHA512: 70b419f6f48d6e7b428d749b33ac63e85acaab390b6301a8671c0f0c097862f366f0b21a9916f2619e47afc3e7a5e43c949921a3c7ad9cd0be55a2dfd241b785 Homepage: https://cran.r-project.org/package=EpiSimR Description: CRAN Package 'EpiSimR' (A 'Shiny' App to Simulate the Dynamics of Epidemic and EndemicDiseases Spread) The 'EpiSimR' package provides an interactive 'shiny' app based on deterministic compartmental mathematical modeling for simulating and visualizing the dynamics of epidemic and endemic disease spread. It allows users to explore various intervention strategies, including vaccination and isolation, by adjusting key epidemiological parameters. The methodology follows the approach described by Brauer (2008) . Thanks to 'shiny' package. Package: r-cran-episomer Architecture: all Version: 3.0.35-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4555 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-curl, r-cran-dt, r-cran-emayili, r-cran-future, r-cran-httr, r-cran-htmltools, r-cran-jsonlite, r-cran-keyring, r-cran-ggplot2, r-cran-janitor, r-cran-magrittr, r-cran-plotly, r-cran-processx, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-rnaturalearthdata, r-cran-openxlsx, r-cran-shiny, r-cran-stringr, r-cran-tibble, r-cran-xtable, r-cran-httr2, r-cran-lubridate, r-cran-sf, r-cran-cli Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-episomer_3.0.35-1.ca2404.1_all.deb Size: 3213220 MD5sum: 291c5e0b33d35c40b1f733e9eb90a963 SHA1: 6e694746e8002e3bba985631f15183f5d8170467 SHA256: 26931885bbf3a43e38ace563a384b3dbeffe06950f16d62cd9ab4d5a96a06992 SHA512: 72a0d3e009adf47d5fe402f9130ce9f3472ca68a6a68382432b5073c8fa7accc82e90ccb907f50f54f2c59eb1b01db288344251c34a17e8b71495c9ee27115e0 Homepage: https://cran.r-project.org/package=episomer Description: CRAN Package 'episomer' (Early Detection of Public Health Threats from Social Media Data) It allows you to automatically monitor trends of social media messages by time, place and topic aiming at detecting public health threats early through the detection of signals (i.e., an unusual increase in the number of messages per time, topic and location). It was designed to focus on infectious diseases, and it can be extended to all hazards or other fields of study by modifying the topics and keywords. More information on the original package 'epitweetr' is available in the peer-review publication Espinosa et al. (2022) . Package: r-cran-epistandard Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 986 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-tibble Suggests: r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dbi, r-cran-duckdb, r-cran-incidenceprevalence, r-cran-omopgenerics Filename: pool/dists/noble/main/r-cran-epistandard_0.2.0-1.ca2404.1_all.deb Size: 851024 MD5sum: 3bb1cf5973e306620fec0ea8e81ba19e SHA1: d54f7aa40c52f0b374867793be0b0d6e7725dfd6 SHA256: 0f61da66f7c838b9a8694091547664608f50888ea15d66298537f73e8dbaa57a SHA512: dc36bbaf74052c00255f6adf6d5457ab8b03a278f4adc9f4d556ffebcb8e700ca61b7b8992f8db5d7d4ace4b432f319682754b0581b450f0074e1d810e4448f0 Homepage: https://cran.r-project.org/package=EpiStandard Description: CRAN Package 'EpiStandard' (Directly Standardise Rates by Age) Provides functions for age standardisation of epidemiological measures such as incidence and prevalence rates. It allows users to apply standard population structures to observed age-specific estimates in order to obtain comparable summary measures across populations or time periods. Functions support calculation of standardised rates, outcome counts, and corresponding confidence intervals. The tools are designed to facilitate reproducible and transparent adjustment for differences in age distributions in epidemiological and public health research. Package: r-cran-epistats Architecture: all Version: 1.6-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-epir, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epistats_1.6-2-1.ca2404.1_all.deb Size: 401726 MD5sum: decc3085c858a296789677092ea763c7 SHA1: fc5d2041288a7c34c418838b2dfd5ae1ebe0933c SHA256: 7919669b96fb8c08e99b4104420928b0923f6bc6a5f3057aaf65956033db882a SHA512: 2c57cfd7440b3328ce83df9eca73d5b15b64d340638491a06db9bc876be53881131504021e1ddc4a5a410ff48eba932aad4406901dacf4e29ec9ea346e9bd45f Homepage: https://cran.r-project.org/package=EpiStats Description: CRAN Package 'EpiStats' (Tools for Epidemiologists) Provides set of functions aimed at epidemiologists. The package includes commands for measures of association and impact for case control studies and cohort studies. It may be particularly useful for outbreak investigations including univariable analysis and stratified analysis. The functions for cohort studies include the CS(), CSTable() and CSInter() commands. The functions for case control studies include the CC(), CCTable() and CCInter() commands. References - Cornfield, J. 1956. A statistical problem arising from retrospective studies. In Vol. 4 of Proceedings of the Third Berkeley Symposium, ed. J. Neyman, 135-148. Berkeley, CA - University of California Press. Woolf, B. 1955. On estimating the relation between blood group disease. Annals of Human Genetics 19 251-253. Reprinted in Evolution of Epidemiologic Ideas Annotated Readings on Concepts and Methods, ed. S. Greenland, pp. 108-110. Newton Lower Falls, MA Epidemiology Resources. Gilles Desve & Peter Makary, 2007. 'CSTABLE Stata module to calculate summary table for cohort study' Statistical Software Components S456879, Boston College Department of Economics. Gilles Desve & Peter Makary, 2007. 'CCTABLE Stata module to calculate summary table for case-control study' Statistical Software Components S456878, Boston College Department of Economics. Package: r-cran-epistemicgametheory Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-epistemicgametheory_0.1.2-1.ca2404.1_all.deb Size: 33400 MD5sum: b61e32fc2beefaaca8375ed25406dad6 SHA1: f907f4c85f8a2e89a26568cd95bde0e93984f5f2 SHA256: c39aabc3b10b88f8cf6febdf0e672bce20d1869f23b324e06e7e580efdfd30eb SHA512: 7a710d30eba9c513c99c4533b065512062d3654d1b146e1fad2b7001b523f7e7c988d4466648ddbfd07f83f1bbffbc09c28b38e1120e161dff13f9d1afffa6a0 Homepage: https://cran.r-project.org/package=EpistemicGameTheory Description: CRAN Package 'EpistemicGameTheory' (Constructing an Epistemic Model for the Games with Two Players) Constructing an epistemic model such that, for every player i and for every choice c(i) which is optimal, there is one type that expresses common belief in rationality. Package: r-cran-epitab Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kableextra, r-cran-knitr, r-cran-mass, r-cran-survival, r-cran-xml2 Suggests: r-cran-dplyr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epitab_0.2.2-1.ca2404.1_all.deb Size: 154954 MD5sum: 13400d32acc9486d11454ac31ebe2107 SHA1: d876aec27630ab8aec9044b339c1c2bb8aa86bc9 SHA256: 7e5531f6bb65d1d270e3cd79074ba6c862ab1a3d9e78735c3465d5758f3e9290 SHA512: 5855a978a0a9b2232eed28ad0c1d23e386da6f29d01344ee1bdac98e2014723ba506e3dc2c91eb4d568baae4f608db0fa92bb01e29fb4c2569f86e9291449b81 Homepage: https://cran.r-project.org/package=epitab Description: CRAN Package 'epitab' (Flexible Contingency Tables for Epidemiology) Builds contingency tables that cross-tabulate multiple categorical variables and also calculates various summary measures. Export to a variety of formats is supported, including: 'HTML', 'LaTeX', and 'Excel'. Package: r-cran-epitabulate Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-binom, r-cran-dplyr, r-cran-epikit, r-cran-forcats, r-cran-glue, r-cran-gtsummary, r-cran-mass, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-broom.helpers, r-cran-cardx, r-cran-covr, r-cran-matchmaker, r-cran-testthat Filename: pool/dists/noble/main/r-cran-epitabulate_0.1.0-1.ca2404.1_all.deb Size: 81744 MD5sum: 14cf8f83283e6dfc548277f145b10cee SHA1: 0a1eb69459fb221ab5863c32738c08a2d674ceb3 SHA256: 2c078beed8bee7320672ffa62bbd8fcf09dfe223d3776b70f5fe583ddf60d8dc SHA512: 792b4965710288ced3a951d8622da14416e33c39d48cc96dd8e2c2e715d0577d8fb3f598f999045458408199a9ea76fc60bdffb4efbd3f811318904e5a8270eb Homepage: https://cran.r-project.org/package=epitabulate Description: CRAN Package 'epitabulate' (Tables for Epidemiological Analysis) Produces tables for descriptive epidemiological analysis. These tables include attack rates, case fatality ratios, and mortality rates (with appropriate confidence intervals), with additional functionality to calculate Mantel-Haenszel odds, risk, and incidence rate ratios. The methods implemented follow standard epidemiological approaches described in Rothman et al. (2008, ISBN:978-0-19-513554-2). This package is part of the 'R4EPIs' project . Package: r-cran-epitest Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-magrittr, r-cran-mm4lmm, r-cran-purrr, r-cran-stringr Filename: pool/dists/noble/main/r-cran-epitest_1.0.0-1.ca2404.1_all.deb Size: 262604 MD5sum: b6b1805c2c05aa5182e76cc9df51aac4 SHA1: 114df7782fece111910b8b1d93671145a753c8fb SHA256: ad8d8cee53ed5de32540818bd7e2ad111d2354625e6ebdfd335be658476b4fae SHA512: 8a2a544e89183e31e3e6beca987632fa3ce82a4ec00a10146f79c080eb4ed4b15d912c774363bad28b79295bdabca4fc69bb92c9ab277ffb3ea10e6094b0f43f Homepage: https://cran.r-project.org/package=EpiTest Description: CRAN Package 'EpiTest' (Test for Gene x Gene Interactions in Bi-Parental Populations) Provides functions to test for gene x gene interactions in a bi-parental population of inbred lines. The data are fitted with the mixed linear model described in Rio et al. (2022) , that accounts for gene x gene interactions at both the fixed effect and variance levels. The package also provides graphical tools to display the gene x gene interaction trend at the mean level and the variance component analysis. Package: r-cran-epitools Architecture: all Version: 0.5-10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-epitools_0.5-10.1-1.ca2404.1_all.deb Size: 319824 MD5sum: 6667ff41f3551fc33243eaa7d49ffe85 SHA1: b34988329097f9cf0f5628391230f3d8cb4223a0 SHA256: a9473a6b6e4478a40875ae7a8db4786e75c16e6538ba767ab4bc5a5ab8bc95ad SHA512: d2c3191e64e09b291ed2b79faff2254ce34e35fe5f460c0bad4c25a9e1a57fd48594bad37e7ebd4c5c334eb0805901d7528e5e0e73a5c3fe932e87acdfbc8a52 Homepage: https://cran.r-project.org/package=epitools Description: CRAN Package 'epitools' (Epidemiology Tools) Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables. Package: r-cran-epitraxr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1366 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-writexl, r-cran-yaml Suggests: r-cran-dt, r-cran-kableextra, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyjs, r-cran-stringr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-epitraxr_0.5.0-1.ca2404.1_all.deb Size: 260884 MD5sum: 9963168c39c4406b82212fd8b343febe SHA1: 66ad5d6a81805db4739f3fe1116a5bc373dad4cb SHA256: fcf326446f46813ba43bc4f22555545cf12df9bb0c865b382361f6b90e28bc2a SHA512: cd823b400b82202a10977c5189915a6dccf9a847f8a88738bc1bf2e27599a42b8bb8b96dc4890e1cb507dd46f4167f7f9fd571f425c96f9715be3942cdf6f783 Homepage: https://cran.r-project.org/package=epitraxr Description: CRAN Package 'epitraxr' (Manipulate 'EpiTrax' Data and Generate Reports) A fast, flexible tool for generating disease surveillance reports from data exported from 'EpiTrax', a central repository for epidemiological data used by public health officials. 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Package: r-cran-epitrix Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sodium, r-cran-distcrete, r-cran-stringi, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-outbreaks, r-cran-incidence, r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr, r-cran-ggplot2, r-cran-tibble, r-cran-covr Filename: pool/dists/noble/main/r-cran-epitrix_0.4.1-1.ca2404.1_all.deb Size: 155310 MD5sum: a7797b5e00c60acaf0488ad4fc2819b3 SHA1: 0fc73d85c4aebb8afe8b60603315d618fec499a3 SHA256: 7ef66568109c7ed9cbe3b21b8a5098d0650e114bfcb88467be7be0fc7d370fe3 SHA512: e45c396ae5f17c8eabbd1c880deddf0ee47111b30d7010855c3cfdd43ed50f9579301419ba0776f669b27312e38330795a4c0ef32b8e7342ad6928307e45a64d Homepage: https://cran.r-project.org/package=epitrix Description: CRAN Package 'epitrix' (Small Helpers and Tricks for Epidemics Analysis) A collection of small functions useful for epidemics analysis and infectious disease modelling. This includes computation of basic reproduction numbers from growth rates, generation of hashed labels to anonymize data, and fitting discretized Gamma distributions. Package: r-cran-epitweetr Architecture: all Version: 2.2.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4772 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bit64, r-cran-dplyr, r-cran-crul, r-cran-curl, r-cran-dt, r-cran-emayili, r-cran-future, r-cran-httpuv, r-cran-httr, r-cran-htmltools, r-cran-jsonlite, r-cran-keyring, r-cran-knitr, r-cran-lifecycle, r-cran-ggplot2, r-cran-janitor, r-cran-magrittr, r-cran-plotly, r-cran-processx, r-cran-rtweet, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-rnaturalearthdata, r-cran-openxlsx, r-cran-plyr, r-cran-shiny, r-cran-sp, r-cran-stringr, r-cran-tibble, r-cran-tidyverse, r-cran-tidytext, r-cran-xtable, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-epitweetr_2.2.16-1.ca2404.1_all.deb Size: 3404556 MD5sum: d73318b001051e371505548b713ba6f9 SHA1: 90a091ebcaf1fed8ed1130e967200c3b8db001b4 SHA256: 550238322d6cf97cc8f2b4fa2c7ee9410ba8fd4a3b39ed03b7f23cc9261c8268 SHA512: faccb6f4823d71bc80b5f1077b9eb3f5a3f0671647220926f0da320f7a8e19721a4255de4327294972ea08884dfe5c857be39ec1695c0ab8e9fce2d3108bd11d Homepage: https://cran.r-project.org/package=epitweetr Description: CRAN Package 'epitweetr' (Early Detection of Public Health Threats from 'Twitter' Data) It allows you to automatically monitor trends of tweets by time, place and topic aiming at detecting public health threats early through the detection of signals (e.g. an unusual increase in the number of tweets). 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Package: r-cran-epiviz Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 25647 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-classint, r-cran-dplyr, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-plotly, r-cran-sf, r-cran-leaflet, r-cran-htmltools, r-cran-stringr, r-cran-scales, r-cran-tidyr, r-cran-lubridate, r-cran-isoweek, r-cran-forcats, r-cran-slider, r-cran-rlang, r-cran-ellmer, r-cran-jsonlite, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-mockery, r-cran-withr, r-cran-yarrr, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/noble/main/r-cran-epiviz_0.1.2-1.ca2404.1_all.deb Size: 4975036 MD5sum: 9d2507855165a42cde3b4d6df2004fbd SHA1: 28e8dba20250026248c1cae00003e37b01117362 SHA256: 93a1f1d5bcef9b3bf8c6b1114565fafb100f4bef13482de7cffaa6db0b519421 SHA512: 8781136e4eee3af5019ca7bfd774de0547af84687ed1b38897d9832f21d7e2cf9d43177510da88487b60028702632f7f7f4ca893ca775a86f2071cd00b82ff8b Homepage: https://cran.r-project.org/package=epiviz Description: CRAN Package 'epiviz' (Data Visualisation Functions for Epidemiological Data ScienceProducts) Tools for making epidemiological reporting easier with consistent static and dynamic charts and maps. 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Package: r-cran-epiworldrcalibrate Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6485 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-testthat, r-cran-epiworldr Filename: pool/dists/noble/main/r-cran-epiworldrcalibrate_0.1.4-1.ca2404.1_all.deb Size: 5714600 MD5sum: 36f89d5c17d0e7b7a903a6c1e9b92a37 SHA1: 3168dde8d0e60d1cc5b245ce6a3ca444f7e57f6d SHA256: 8e4fb7fea89ba7e866165cf8cfb61ddebec493eb4362710ffb5e9ca855343397 SHA512: 17e582952155b00da6e480249c1952ae1cc81508d43a394a4d3bb01435e4601eb87c96d16d17504890600d3b80f53bde14ca2745ab3f18e720679b9d218f50b9 Homepage: https://cran.r-project.org/package=epiworldRcalibrate Description: CRAN Package 'epiworldRcalibrate' (Fast and Effortless Calibration of Agent-Based Models usingMachine Learning) Provides tools and pre-trained Machine Learning [ML] models for calibration of Agent-Based Models [ABMs] built with the R package 'epiworldR'. Implements methods described in Najafzadehkhoei, Vega Yon, Modenesi, and Meyer (2025) . Users can automatically calibrate ABMs in seconds with pre-trained ML models, effectively focusing on simulation rather than calibration. Bridges a gap by allowing public health practitioners to run their own ABMs without the advanced technical expertise often required by calibration. Package: r-cran-epiworldrshiny Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-ggplot2, r-cran-epiworldr, r-cran-plotly, r-cran-bslib Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-epiworldrshiny_0.2.3-1.ca2404.1_all.deb Size: 283080 MD5sum: 61523194db85f1f080999470f836dd22 SHA1: 6e0050483e64d9f8dc564d7fb841a8b412b0aa4f SHA256: 8e653dac8a7b156e312d46194074c810799d4bcb237a8c2abb2611ace2e523cc SHA512: fb7b72f2db5274553fa0493e0ed5a7fbf9b36941ddab38e327e7d25a50a470e1077beffdb943bcce88f678c6bfb7fcecfde66a30a6fb992b84bab1c79e6ff62a Homepage: https://cran.r-project.org/package=epiworldRShiny Description: CRAN Package 'epiworldRShiny' (A 'shiny' Wrapper of the R Package 'epiworldR') R 'shiny' web apps for epidemiological Agent-Based Models. It provides a user-friendly interface to the Agent-Based Modeling (ABM) R package 'epiworldR' (Meyer et al., 2023) . Some of the main features of the package include the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Recovered (SIR), and Susceptible-Exposed-Infected-Recovered (SEIR) models. 'epiworldRShiny' provides a web-based user interface for running various epidemiological ABMs, simulating interventions, and visualizing results interactively. 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For technical details, see Naghi, Varadi and Zhelonkin (2022), . Package: r-cran-epo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-rlang, r-cran-xts Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-epo_0.1.0-1.ca2404.1_all.deb Size: 17690 MD5sum: 3a8de1fb6bb2dec36ecfd1fe6f2edbbd SHA1: 8ef2b971dd9c3285b9e0d3ebc11e952b63915553 SHA256: a74b4f28c5ce6712366a3b11f5058021afa24259b177f12c0e7815e930212379 SHA512: 2e60dc3ac18ddd762722aa378c365781b46589d3a6c794242eff336e2462285e7d15febaa8a3114171e3e8de585d38020b0eaca6c737671f9ebff3992bc74ae5 Homepage: https://cran.r-project.org/package=epo Description: CRAN Package 'epo' (Enhanced Portfolio Optimization (EPO)) Implements the Enhanced Portfolio Optimization (EPO) method as described in Pedersen, Babu and Levine (2021) . 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Two models are provided, EPoC A where AY + U + R = 0 and EPoC G where Y = GU + E, the matrices R and E are so far treated as noise. For details see the manual page of 'lassoshooting' and the article Rebecka Jörnsten, Tobias Abenius, Teresia Kling, Linnéa Schmidt, Erik Johansson, Torbjörn E M Nordling, Bodil Nordlander, Chris Sander, Peter Gennemark, Keiko Funa, Björn Nilsson, Linda Lindahl, Sven Nelander (2011) . 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Package: r-cran-epos Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 662 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hash, r-cran-ggplot2, r-cran-testthat, r-cran-gridextra, r-cran-topklists, r-cran-stringr, r-cran-xtable, r-cran-mongolite, r-cran-venndiagram, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epos_1.2-1.ca2404.1_all.deb Size: 443888 MD5sum: b04a0e3b78e6b1816183b71db3e02a78 SHA1: e748854d64e287c18b9f0ec1da6d90076de0b97e SHA256: 7564d8492102998c389b25850c0787caa07109550d1f601110994b2864701c05 SHA512: 89eb76c1236ac03191659bdd3d58657dee33fa6bcfecad28858a97c193cac3279efb0226edd46bd41103b31cbdad366b93fdfa46c8f3a818d846eeabd4b6ce0f Homepage: https://cran.r-project.org/package=epos Description: CRAN Package 'epos' (Epilepsy Ontologies' Similarities) Analysis and visualization of similarities between epilepsy ontologies based on text mining results by comparing ranked lists of co-occurring drug terms in the BioASQ corpus. The ranked result lists of neurological drug terms co-occurring with terms from the epilepsy ontologies EpSO, ESSO, EPILONT, EPISEM and FENICS undergo further analysis. The source data to create the ranked lists of drug names is produced using the text mining workflows described in Mueller, Bernd and Hagelstein, Alexandra (2016) , Mueller, Bernd et al. (2017) , Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) , and Mueller, Bernd et al. (2022) . 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String interpolation for 'Shiny' apps or 'R Markdown' and 'knitr'-powered 'Quarto' documents, built on the 'glue' and 'whisker' packages. Package: r-cran-eppofinder Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-dplyr, r-cran-glue, r-cran-purrr, r-cran-checkmate, r-cran-tibble Suggests: r-cran-devtools, r-cran-roxygen2, r-cran-testthat, r-cran-usethis, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-eppofinder_2.0.0-1.ca2404.1_all.deb Size: 345426 MD5sum: 5dd509b80cd4fe452a9f6867f413d3fd SHA1: 038ac085958f2d22a25a22f43d3c42094f100036 SHA256: 70b8ca2a0d56f7579222225a505ce27bae87863928f6ebd589ae6e4ea0285fdb SHA512: aef8cb20dbe11e131f34da1c1722f6d58a329743ebd1e9a02ab967190f049fd0e60488e1c1424fc50e19493ddb2ab2aa5d102f03fd676c2d4e5c6b8bc8aa0799 Homepage: https://cran.r-project.org/package=eppoFindeR Description: CRAN Package 'eppoFindeR' (Interface to the EPPO Database and Public APIs) Provides an interface to the public APIs of the European and Mediterranean Plant Protection Organization (EPPO) database. 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Package: r-cran-epsiwal Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-epsiwal_0.1.0-1.ca2404.1_all.deb Size: 30348 MD5sum: 8b06352ab311167ef160022d90d44c2f SHA1: e5219848c39ea85911e1ef8e9ee59498cfd04eed SHA256: b9e0bae7c6370f4dd8555fdeed6bff5a308ab5b155f61c128020071c41ff6a35 SHA512: ba12717d44b31d806d04c36a453278638bef761b9e97bc385ec85624fdf95a56a70c0d3162123f8fc54ed6977d3438a5b2102bfb89ef155afda22b93ed0bf012 Homepage: https://cran.r-project.org/package=epsiwal Description: CRAN Package 'epsiwal' (Exact Post Selection Inference with Applications to the Lasso) Implements the conditional estimation procedure of Lee, Sun, Sun and Taylor (2016) . This procedure allows hypothesis testing on the mean of a normal random vector subject to linear constraints. Package: r-cran-ept Architecture: all Version: 0.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ept_0.7.6-1.ca2404.1_all.deb Size: 157686 MD5sum: 618bff6e9d20ab6f7ab909b6cae78401 SHA1: e9f690bcf5c5bbf758f37a12616864aae0512702 SHA256: c15fb5f5978ad34d37137bab7b4196381a553f060db90367a420d6f7bde085b3 SHA512: 03af9ed00eae7d6ace2e1e4141757956749d3fb6ee7b3e997e350ab6d640b86df4ad36d1d16d7fbc9176cce465b20c49b4d6c3c41db78c7729300a2db79c14f9 Homepage: https://cran.r-project.org/package=EPT Description: CRAN Package 'EPT' (Ensemble Patch Transform, Visualization and Decomposition) For multiscale analysis, this package carries out ensemble patch transform, its visualization and multiscale decomposition. The detailed procedure is described in Kim et al. (2020), and Oh and Kim (2020). D. Kim, G. Choi, H.-S. Oh, Ensemble patch transformation: a flexible framework for decomposition and filtering of signal, EURASIP Journal on Advances in Signal Processing 30 (2020) 1-27 . H.-S. Oh, D. Kim, Image decomposition by bidimensional ensemble patch transform, Pattern Recognition Letters 135 (2020) 173-179 . Package: r-cran-epts Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-eefanalytics, r-cran-lme4, r-cran-ggplot2, r-cran-ggpubr, r-cran-mvtnorm, r-cran-coda, r-cran-mcmcvis, r-cran-dplyr, r-cran-magrittr, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-epts_1.2.2-1.ca2404.1_all.deb Size: 192294 MD5sum: b431c68a321b0bba0286b8dc79992ad3 SHA1: d74544d0adabb614645eeec742563de1611d4ad1 SHA256: a280452eafee86eac05b9c42f34a9ac08819021bfcf3e0bc77f3604f91ee41a6 SHA512: bd8f38b5352d2c14c5803939ce6f3c50e5ca07598e82e9cf074c25496d422843081e4f75daf48781f37e3cb9147a2f531b69a0ba28a97c238aedd974e01c8fa2 Homepage: https://cran.r-project.org/package=epts Description: CRAN Package 'epts' (Educational Platform Trials Simulator) Simulating multi-arm cluster-randomized, multi-site, and simple randomized trials. Includes functions for conducting multilevel analyses using both Bayesian and Frequentist methods. Supports futility and superiority analyses through Bayesian approaches, along with visualization tools to aid interpretation and presentation of results. 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The 'epubr' package provides functions supporting the reading and parsing of internal e-book content from EPUB files. E-book metadata and text content are parsed separately and joined together in a tidy, nested tibble data frame. E-book formatting is not completely standardized across all literature. It can be challenging to curate parsed e-book content across an arbitrary collection of e-books perfectly and in completely general form, to yield a singular, consistently formatted output. Many EPUB files do not even contain all the same pieces of information in their respective metadata. EPUB file parsing functionality in this package is intended for relatively general application to arbitrary EPUB e-books. However, poorly formatted e-books or e-books with highly uncommon formatting may not work with this package. There may even be cases where an EPUB file has DRM or some other property that makes it impossible to read with 'epubr'. Text is read 'as is' for the most part. The only nominal changes are minor substitutions, for example curly quotes changed to straight quotes. Substantive changes are expected to be performed subsequently by the user as part of their text analysis. Additional text cleaning can be performed at the user's discretion, such as with functions from packages like 'tm' or 'qdap'. Package: r-cran-epwshiftr Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 579 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-eplusr, r-cran-fst, r-cran-future.apply, r-cran-jsonlite, r-cran-pcict, r-cran-progressr, r-cran-psychrolib, r-cran-r6, r-cran-rappdirs, r-cran-rnetcdf, r-cran-units Suggests: r-cran-testthat, r-cran-curl, r-cran-mockery, r-cran-withr, r-cran-pingr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-epwshiftr_0.1.4-1.ca2404.1_all.deb Size: 476722 MD5sum: 52c435be7489bbe7e5215fd637f30cd2 SHA1: 408bbdcb02fba33a452df0b11f3812ab573252e8 SHA256: 26dd20c38002eea18ad70552154041998deecdc5aac50640c42d3de53412454c SHA512: b2f32155f3ff19b6cd0dcc9b97210d09db38097ab44f2181ff7d7297be0d7c39b9e5d0eec9f1f22f0dffc8c57579aa99fe2a7beb6d8ceef7f84a08fea9a171eb Homepage: https://cran.r-project.org/package=epwshiftr Description: CRAN Package 'epwshiftr' (Create Future 'EnergyPlus' Weather Files using 'CMIP6' Data) Query, download climate change projection data from the 'CMIP6' (Coupled Model Intercomparison Project Phase 6) project in the 'ESGF' (Earth System Grid Federation) platform , and create future 'EnergyPlus' Weather ('EPW') files adjusted from climate changes using data from Global Climate Models ('GCM'). Package: r-cran-epxtor Architecture: all Version: 0.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml, r-cran-httr Filename: pool/dists/noble/main/r-cran-epxtor_0.4-1-1.ca2404.1_all.deb Size: 35260 MD5sum: 7138e9b01202cf8d8d8bcca64b800cd8 SHA1: 0d62e136cb54491ca91b67ca1c3aaf79fc95cf67 SHA256: 41463fd46a8094df16b7daebb2486ebde18c9e1fc64ea0c71017bdff6f1e253b SHA512: 9a05928b5b69f2648983724980508e305d09e7f8d6d8e08ee45f9210a78723c7cba6c04308fd7a360ea11ab72a29848d5c3abfddc47cbd0783917817ce167a81 Homepage: https://cran.r-project.org/package=epxToR Description: CRAN Package 'epxToR' (Import 'Epidata' XML Files '.epx') Import data from 'Epidata' XML files '.epx' and convert it to R data structures. Package: r-cran-eq5d Architecture: all Version: 0.16.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3726 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-testthat, r-cran-shiny, r-cran-dt, r-cran-mime, r-cran-readxl, r-cran-ggplot2, r-cran-ggiraph, r-cran-ggiraphextra, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-fsa, r-cran-pmcmrplus, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-eq5d_0.16.3-1.ca2404.1_all.deb Size: 3063648 MD5sum: 95af86fbc7a8a679bcdd4bfefad2c944 SHA1: 5daa334fdfb7fb5f28a823f65a9de07b38e75bf9 SHA256: 0c72405b63fd75db863bcdac3372bec3534176613309e0b49995ae16f525e775 SHA512: 41d71485ef974331c2f879121371af6b602f6be90e587a60f092f33c65d85dfea8d2dfad3c967124d00c43a5bea0ac1eb679760cbb2b61beb16f9d3cd78fa0d9 Homepage: https://cran.r-project.org/package=eq5d Description: CRAN Package 'eq5d' (Methods for Analysing 'EQ-5D' Data and Calculating 'EQ-5D' IndexScores) EQ-5D is a popular health related quality of life instrument used in the clinical and economic evaluation of health care. Developed by the EuroQol group , the instrument consists of two components: health state description and evaluation. For the description component a subject self-rates their health in terms of five dimensions; mobility, self-care, usual activities, pain/discomfort, and anxiety/depression using either a three-level (EQ-5D-3L, ) or a five-level (EQ-5D-5L, ) scale. Frequently the scores on these five dimensions are converted to a single utility index using country specific value sets, which can be used in the clinical and economic evaluation of health care as well as in population health surveys. The eq5d package provides methods to calculate index scores from a subject's dimension scores. 33 TTO and 11 VAS EQ-5D-3L value sets including those for countries in Szende et al (2007) and Szende et al (2014) , 49 EQ-5D-5L EQ-VT value sets, the EQ-5D-5L crosswalk value sets developed by van Hout et al. (2012) , the crosswalk value sets for Bermuda, Jordan and Russia and the van Hout (2021) reverse crosswalk value sets. 13 EQ-5D-Y-3L value sets are also included as are the NICE 'DSU' age-sex based EQ-5D-3L to EQ-5D-5L and EQ-5D-5L to EQ-5D-3L mappings. Methods are also included for the analysis of EQ-5D profiles, including those from the book "Methods for Analyzing and Reporting EQ-5D data" by Devlin et al. (2020) . Additionally a shiny web tool is included to enable the calculation, visualisation and automated statistical analysis of EQ-5D data via a web browser using EQ-5D dimension scores stored in CSV or Excel files. Package: r-cran-eq5dsuite Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2201 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-ggplot2, r-cran-moments, r-cran-rcolorbrewer, r-cran-rlang, r-cran-scales, r-cran-rappdirs Suggests: r-cran-shiny, r-cran-dt, r-cran-bslib, r-cran-readxl, r-cran-spelling, r-cran-testthat, r-cran-withr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-eq5dsuite_2.0.0-1.ca2404.1_all.deb Size: 1643068 MD5sum: 6c16e03122e2a488ed183993d81ac808 SHA1: ed912cdf7a8988ee8f8a8092b25ff88c1513e4e0 SHA256: d0b4410b1ad150fce4c55db00bea465cd36bdf17b6f40c0b6bea7fbfb74c0d8c SHA512: 235f34b0fa03a89682e51a2a06a6da95c0c0c08da7642f7494e29a76da26942b5709238033844f9d23691288161773f71b9f54e3349d2bc84101c313cdbbe86f Homepage: https://cran.r-project.org/package=eq5dsuite Description: CRAN Package 'eq5dsuite' (Handling and Analysing EQ-5d Data) The EQ-5D is a widely-used standarized instrument for measuring Health Related Quality Of Life (HRQOL), developed by the EuroQol group . It assesses five dimensions; mobility, self-care, usual activities, pain/discomfort, and anxiety/depression, using either a three-level (EQ-5D-3L) or five-level (EQ-5D-5L) scale. Scores from these dimensions are commonly converted into a single utility index using country-specific value sets, which are critical in clinical and economic evaluations of healthcare and in population health surveys. The eq5dsuite package enables users to calculate utility index values for the EQ-5D instruments, including crosswalk utilities using the original crosswalk developed by van Hout et al. (2012) (mapping EQ-5D-5L responses to EQ-5D-3L index values), or the recently developed reverse crosswalk by van Hout et al. (2021) (mapping EQ-5D-3L responses to EQ-5D-5L index values). Users are allowed to add and/or remove user-defined value sets. Additionally, the package provides tools to analyze EQ-5D data according to the recommended guidelines outlined in "Methods for Analyzing and Reporting EQ-5D data" by Devlin et al. (2020) . Package: r-cran-eql Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ttutils, r-cran-lattice Filename: pool/dists/noble/main/r-cran-eql_1.0-1-1.ca2404.1_all.deb Size: 112302 MD5sum: 2e9dd58064333096c88bfa2f1dfd9b2f SHA1: 01d76f0bd932d5887c5926082d3f6eebdeeaa4f1 SHA256: 862fa16f61c844f49c1404ce1fdd99dd66df61002d9f59a775c7407dad3a3246 SHA512: a2ea58b431910719ef932306c2098376e52f50a13368a91585ce07d6109628d18684ebb7a10dceb33e5785ef5c3b7198fb122793eb2a47a8c89e8678f7131ebb Homepage: https://cran.r-project.org/package=EQL Description: CRAN Package 'EQL' (Extended-Quasi-Likelihood-Function (EQL)) Computation of the EQL for a given family of variance functions, Saddlepoint-approximations and related auxiliary functions (e.g. Hermite polynomials). Package: r-cran-eqrn Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coro, r-cran-dofuture, r-cran-evd, r-cran-foreach, r-cran-future, r-cran-ismev, r-cran-magrittr, r-cran-torch Filename: pool/dists/noble/main/r-cran-eqrn_0.1.2-1.ca2404.1_all.deb Size: 427370 MD5sum: 98deed6cb6b4d0248ca8b6dc071ce23a SHA1: fce6af49ab855532177fd06a57b0109deedcf693 SHA256: c898ffc1f16b6e6a57859eccd5727a98a860d5332847c6acf3b2afcb9da79cb1 SHA512: d59b406a83cbb0d3f656fae004290f77ba88341a3299e911956b611b605d4982a314832c9b82cb9546b434d7355dfb1ccc91348112235f41cf7132606eecbaad Homepage: https://cran.r-project.org/package=EQRN Description: CRAN Package 'EQRN' (Extreme Quantile Regression Neural Networks for Risk Forecasting) This framework enables forecasting and extrapolating measures of conditional risk (e.g. of extreme or unprecedented events), including quantiles and exceedance probabilities, using extreme value statistics and flexible neural network architectures. It allows for capturing complex multivariate dependencies, including dependencies between observations, such as sequential dependence (time-series). The methodology was introduced in Pasche and Engelke (2024) (also available in preprint: Pasche and Engelke (2022) ). 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For details, see Goeman, Solari, & Stijnen (2010) and Isager & Fitzgerald (2024) . Second, the lddtest() command performs logarithmic density discontinuity equivalence testing for regression discontinuity designs. For reference, see Fitzgerald (2025) . 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Gurusamy,K (2025). 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Gurusamy,K (2025) and Gurusamy,K (2025). Package: r-cran-equalprognosis Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-survival, r-cran-base64enc, r-cran-calibrationcurves, r-cran-mime, r-cran-predtools, r-cran-proc, r-cran-stringr Filename: pool/dists/noble/main/r-cran-equalprognosis_0.1.3-1.ca2404.1_all.deb Size: 257992 MD5sum: aaeae7434809a0ed273dc93e664e5583 SHA1: 1af91c9194679cdff0b2e641dcccb0ebb839f801 SHA256: 85912f654ca1572f94cc9c62d7b2d5a8d2e88c449f4e0bfb093d2bd532ffd5a9 SHA512: c5b0936cb639d0da3889fd5359a52120c67ff2a25bc72bd0071c32d609705ed767ab5a99192fb0d50ce286706e8655212406611529235cd76a7698273727b7bc Homepage: https://cran.r-project.org/package=EQUALPrognosis Description: CRAN Package 'EQUALPrognosis' (Analysing Prognostic Studies) Functions that help with analysis of prognostic study data. This allows users with little experience of developing models to develop models and assess the performance of the prognostic models. This also summarises the information, so the performance of multiple models can be displayed simultaneously. This minor update fixes issues related to memory requirements with large number of simulations and deals with situations when there is overfitting of data. Gurusamy, K (2026). 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'EQUAL-STATS' software is a shiny application with an user-friendly interface to perform complex statistical analysis. Gurusamy,K (2024). 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'EQUAL-STATS' software is a shiny application with an user-friendly interface to perform complex statistical analysis. Gurusamy,K (2024). Package: r-cran-equaltestmi Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-semtools, r-cran-printr Filename: pool/dists/noble/main/r-cran-equaltestmi_0.6.1-1.ca2404.1_all.deb Size: 323016 MD5sum: 6da605c16a4c1a2ff72f46403af9b7b6 SHA1: 964c3566b224d832c33ad2af84d9df2b60e2e946 SHA256: 3072eb048e39306b4c2ebfe44681a1a6f568eda2828b165dbbe38c17e9a5626c SHA512: 84e7fbce95bbcc9d772458a6514471bfd2a35adc17dff998f50e20c66ccb6d1760812853835b39bace2acdc8dac6ac8a1bb60fa716df4e7db79997e51e4df37c Homepage: https://cran.r-project.org/package=equaltestMI Description: CRAN Package 'equaltestMI' (Examine Measurement Invariance via Equivalence Testing andProjection Method) Functions for examining measurement invariance via equivalence testing are included in this package. The traditionally used RMSEA (Root Mean Square Error of Approximation) cutoff values are adjusted based on simulation results. In addition, a projection-based method is implemented to test the equality of latent factor means across groups without assuming the equality of intercepts. For more information, see Yuan, K. H., & Chan, W. (2016) , Deng, L., & Yuan, K. H. (2016) , and Jiang, G., Mai, Y., & Yuan, K. H. (2017) . 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The 'XML' result can then be included in 'HTML', 'Microsoft Word' documents or 'Microsoft PowerPoint' presentations by using a 'Markdown' document or the R package 'officer'. 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Equating types include identity, mean, linear, general linear, equipercentile, circle-arc, and composites of these. Equating methods include synthetic, nominal weights, Tucker, Levine observed score, Levine true score, Braun/Holland, frequency estimation, and chained equating. Plotting and summary methods, and methods for multivariate presmoothing and bootstrap error estimation are also provided. Package: r-cran-equateirt Architecture: all Version: 2.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1942 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod, r-cran-mirt Suggests: r-cran-knitr, r-cran-ltm, r-cran-rmarkdown, r-cran-sna Filename: pool/dists/noble/main/r-cran-equateirt_2.5.2-1.ca2404.1_all.deb Size: 1114284 MD5sum: 8cc2defbd9e131ac752cac1d6d535a09 SHA1: 4d5b75529222ce523c3113c3215d11a7fae0de1a SHA256: fdfb1172e7cb07e6106f5eeea78fae0df1d2e504ecbe4604677e48c72f118d51 SHA512: 8c970360af42dd8649dfc76755874db83b64d99d49e40184027030483d9b617b1b3a2e546c1e698d0e597c1bfb60548214673a3d93ad704206862c6efe9f2a32 Homepage: https://cran.r-project.org/package=equateIRT Description: CRAN Package 'equateIRT' (IRT Equating Methods) Computation of direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items (Battauz (2013) , Battauz (2015) ). Test scoring can be performed by true score equating and observed score equating methods. DIF detection can be performed using a Wald-type test (Battauz (2019) ). The package includes tests to assess the stability of the equating transformations (Battauz(2022) ). 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The primary function of the package, extract_eq(), takes a fitted model object as its input and returns the corresponding 'LaTeX' code for the model. Package: r-cran-equibspd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-equibspd_0.1.0-1.ca2404.1_all.deb Size: 22012 MD5sum: 1a6037c025deabf41c568970d8cfbedb SHA1: 8be0469cae76879e1e2f0a1bdc48bfffb0459605 SHA256: 8951e71da772e3110c7a259c66e530c4f7984d23aa369b0dfad8cae62331dedc SHA512: a52b825d780351921cda60abcaa7cf0aa86e8071b89f36cc6f59a9cf7de47fd720c905758d0b1934300640cc76b4d830c0dbb43bb1d7f674f01a921e441ddcbc Homepage: https://cran.r-project.org/package=equiBSPD Description: CRAN Package 'equiBSPD' (Equivalent Estimation Balanced Split Plot Designs) In agricultural, post-harvest and processing, engineering and industrial experiments factors are often differentiated with ease with which they can change from experimental run to experimental run. This is due to the fact that one or more factors may be expensive or time consuming to change i.e. hard-to-change factors. These factors restrict the use of complete randomization as it may make the experiment expensive and time consuming. Split plot designs can be used for such situations. In general model estimation of split plot designs require the use of generalized least squares (GLS). However for some split-plot designs ordinary least squares (OLS) estimates are equivalent to generalized least squares (GLS) estimates. These types of designs are known in literature as equivalent-estimation split-plot design. For method details see, Macharia, H. and Goos, P.(2010) .Balanced split plot designs are designs which have an equal number of subplots within every whole plot. This package used to construct equivalent estimation balanced split plot designs for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function equivalent_BSPD(). Package: r-cran-equil2 Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-units Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-equil2_1.0.0-1.ca2404.1_all.deb Size: 57010 MD5sum: 70ead535e58564a036fc926812ce0b22 SHA1: 3afeae2ac720bb5b6bba8ef1648c10a163e6ba0f SHA256: fd3e8aa2556b11cf9b7e8d69ac7214d3b19850465bb60020aef79fe0f3279328 SHA512: 3b40a85fbada027c377820b4cf848b2a4151f97fcbd7e147def69a6b7ecf69e7f98541275d1de0492f350d7fa4c9a8872ecc619d68c0d9f6c5a8791c520a61c5 Homepage: https://cran.r-project.org/package=equil2 Description: CRAN Package 'equil2' (Calculate Urinary Saturation with the EQUIL2 Algorithm) Saturation of ionic substances in urine is calculated based on sodium, potassium, calcium, magnesium, ammonia, chloride, phosphate, sulfate, oxalate, citrate, ph, and urate. This program is intended for research use, only. The code within is translated from EQUIL2 Visual Basic code based on Werness, et al (1985) "EQUIL2: a BASIC computer program for the calculation of urinary saturation" to R. The Visual Basic code was kindly provided by Dr. John Lieske of the Mayo Clinic. Package: r-cran-equisurv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-eha Filename: pool/dists/noble/main/r-cran-equisurv_0.1.0-1.ca2404.1_all.deb Size: 54962 MD5sum: e448ece8500b5b57f2e2369202ba70ca SHA1: 104e26144ddc3d41b09c740ce73f62f23bccec7a SHA256: 1b5dd1cc7d52c89873c8d87eb3eb9ee9e1b65432570fe87a6e927cc7ad420f13 SHA512: 4d77f5163db696022c1a61932d9a0d851b61b73632902d020b97f4f78fc36812efc1aaa701b785d25d5be996f9f1bfa0a813c925580591c92c9a76dcc8198ca1 Homepage: https://cran.r-project.org/package=EquiSurv Description: CRAN Package 'EquiSurv' (Modeling, Confidence Intervals and Equivalence of SurvivalCurves) We provide a non-parametric and a parametric approach to investigate the equivalence (or non-inferiority) of two survival curves, obtained from two given datasets. The test is based on the creation of confidence intervals at pre-specified time points. For the non-parametric approach, the curves are given by Kaplan-Meier curves and the variance for calculating the confidence intervals is obtained by Greenwood's formula. The parametric approach is based on estimating the underlying distribution, where the user can choose between a Weibull, Exponential, Gaussian, Logistic, Log-normal or a Log-logistic distribution. Estimates for the variance for calculating the confidence bands are obtained by a (parametric) bootstrap approach. For this bootstrap censoring is assumed to be exponentially distributed and estimates are obtained from the datasets under consideration. All details can be found in K.Moellenhoff and A.Tresch: Survival analysis under non-proportional hazards: investigating non-inferiority or equivalence in time-to-event data . Package: r-cran-equivalencetest Architecture: all Version: 0.0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-polynom, r-cran-rootsolve, r-cran-cubature, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-equivalencetest_0.0.1.1-1.ca2404.1_all.deb Size: 59584 MD5sum: 8e8d4d7a8bdb3ba049d4d15997c9862e SHA1: 0dece82c75c3ac34eae145d143714cd45ab5bfc2 SHA256: 626d83bcdb88162082222d16b4ced4c66fc911546aeb259d4cbb0935fd113d79 SHA512: 5d913feac52c49db4881cb8d1459de159a5c78b1123d410a40111a05162b349ef2826bd519d9103a6c8b90955cae626b546771afe025fdd83af8c9d0515659af Homepage: https://cran.r-project.org/package=equivalenceTest Description: CRAN Package 'equivalenceTest' (Equivalence Test for the Means of Two Normal Distributions) Two methods for performing equivalence test for the means of two (test and reference) normal distributions are implemented. The null hypothesis of the equivalence test is that the absolute difference between the two means are greater than or equal to the equivalence margin and the alternative is that the absolute difference is less than the margin. Given that the margin is often difficult to obtain a priori, it is assumed to be a constant multiple of the standard deviation of the reference distribution. The first method assumes a fixed margin which is a constant multiple of the estimated standard deviation of the reference data and whose variability is ignored. The second method takes into account the margin variability. In addition, some tools to summarize and illustrate the data and test results are included to facilitate the evaluation of the data and interpretation of the results. Package: r-cran-equivnoninf Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biasedurn Filename: pool/dists/noble/main/r-cran-equivnoninf_1.0.2-1.ca2404.1_all.deb Size: 402210 MD5sum: 76feadd6fe96807766a85db32f266522 SHA1: 97d0b478ac23012c8a49f0e45199fdbb6460543f SHA256: 4944df061e8a3b655898e17a1a43a5c15c360dc6b65e4e12c4bce7923db04c15 SHA512: 4424d5883d99078567303cc47ac9cac83cd7268377dc134573230f4ac0827f0561c2e60e805d4983279badb405a91dd8a97f960eb2a7258b4f7b84d6f8fe0454 Homepage: https://cran.r-project.org/package=EQUIVNONINF Description: CRAN Package 'EQUIVNONINF' (Testing for Equivalence and Noninferiority) Making available in R the complete set of programs accompanying S. Wellek's (2010) monograph ''Testing Statistical Hypotheses of Equivalence and Noninferiority. Second Edition'' (Chapman&Hall/CRC). Package: r-cran-equivump Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-equivump_0.1.1-1.ca2404.1_all.deb Size: 23526 MD5sum: 0ae0209cc3246373e3f2d8074bc37a15 SHA1: 8434f83fa23f145775bede79a86b01bc6d7e2f49 SHA256: ff0f01b7052c976bfcd095569f5bf919561e4708732ee8b78c6e56bfeddcd911 SHA512: 987e6151f4c4b505b0e37995510add632ec5d3b559254f35ec70117968f16b9fe4b5ee458f03d16257ee796852e0263ec3466e0a58714177ca407b55ff5a7677 Homepage: https://cran.r-project.org/package=equivUMP Description: CRAN Package 'equivUMP' (Uniformly Most Powerful Invariant Tests of Equivalence) Implementation of uniformly most powerful invariant equivalence tests for one- and two-sample problems (paired and unpaired) as described in Wellek (2010, ISBN:978-1-4398-0818-4). Also one-sided alternatives (non-inferiority and non-superiority tests) are supported. Basically a variant of a t-test with (relaxed) null and alternative hypotheses exchanged. Package: r-cran-er Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3081 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-ggplot2, r-cran-scales, r-cran-gridextra, r-cran-glmnet, r-cran-pls, r-cran-plsvarsel Filename: pool/dists/noble/main/r-cran-er_1.1.2-1.ca2404.1_all.deb Size: 3091792 MD5sum: ff1d096beb7903c40ed3e1335c2fb0d4 SHA1: 73df491caf349a7872de8887a75027fe2c60a59a SHA256: 55972598b83d8b6c7cd497bb8bd85e71f74391257aa0bef30614c56760f02bad SHA512: c69bd99bb5617d64df47d9530cabb29d87e634cc92d3666a627f55027012b5b63b7f6de2126e4f814c3720f172aca325a15270601f9816e46bbc93e2913350f7 Homepage: https://cran.r-project.org/package=ER Description: CRAN Package 'ER' (Effect + Residual Modelling) Multivariate modeling of data after deflation of interfering effects. EF Mosleth et al. (2021) and EF Mosleth et al. 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There are built-in era definitions for many year numbering systems used in contemporary and historic calendars (e.g. Common Era, Islamic 'Hijri' years); year-based time scales used in archaeology, astronomy, geology, and other palaeosciences (e.g. Before Present, SI-prefixed 'annus'); and support for arbitrary user-defined eras. Years can converted from any one era to another using a generalised transformation function. Methods are also provided for robust casting and coercion between years and other numeric types, type-stable arithmetic with years, and pretty-printing in tables. 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See del Castillo, J, Daoudi, J and Lockhart, R (2014) . Package: r-cran-erdbuilder Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2722 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-diagrammer, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gt, r-cran-testthat Filename: pool/dists/noble/main/r-cran-erdbuilder_1.0.0-1.ca2404.1_all.deb Size: 811404 MD5sum: 91d640971aa43335a1acb6b793ac56fd SHA1: 6de7f557f360fcc73ec72c71203c701ded4daaa9 SHA256: fe97c8fdd5e1474a6ab3358396ff84f884af28d41a0d0c8af97f74b549082d80 SHA512: b19ef3ef81f9790c2bbf7c8576f169cd3744911f24578f88f70b1e64f8e9a81355119bb9d7d013fb0ab7b53a1df5c7e76eaa76913d63733e3d6a5cfb27bc463d Homepage: https://cran.r-project.org/package=ERDbuilder Description: CRAN Package 'ERDbuilder' (Entity Relationship Diagrams Builder) Build entity relationship diagrams (ERD) to specify the nature of the relationship between tables in a database. Package: r-cran-erer Architecture: all Version: 4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmtest, r-cran-systemfit, r-cran-tseries, r-cran-urca Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-erer_4.0-1.ca2404.1_all.deb Size: 518042 MD5sum: b584c3381011b48353b64a0f0b2e5dc2 SHA1: 4fa339472839485c8b9cca9d617bb0b3694c6af0 SHA256: b86c71b30c26b5b0e1ac75dda6230cd552b01ff46984faba2a75239e555cd013 SHA512: 1047890348fc4850c7546b18db9a94d43ef4150765fb524a10f32020da814ec6ce7194f2f732b568e9ddea9639082a4d6624a8943f055fdef342bdaf5f1aebbb Homepage: https://cran.r-project.org/package=erer Description: CRAN Package 'erer' (Empirical Research in Economics with R) Several functions, datasets, and sample codes related to empirical research in economics are included. They cover the marginal effects for binary or ordered choice models, static and dynamic Almost Ideal Demand System (AIDS) models, and a typical event analysis in finance. Package: r-cran-erfe Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mvtnorm Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-erfe_0.0.1-1.ca2404.1_all.deb Size: 35366 MD5sum: 2a56d6f005856971fb2ef5b066166a9c SHA1: 72fa0916456649efee9236daa98772e290290550 SHA256: 672e3971124597473ee8c1364847ae0eebf0a24b16b348eafc99c74d887867e9 SHA512: 1e5bc2f89076922c3f529242857cb3fb206ae37e109d43af08ba923738937c4d760312e309db2ba00f348ee1659d5e7cce81e16493b0d0f8604112a91a171488 Homepage: https://cran.r-project.org/package=erfe Description: CRAN Package 'erfe' (Fits Expectile Regression for Panel Fixed Effect Model) Fits the Expectile Regression for Fixed Effect (ERFE) estimator. The ERFE model extends the within-transformation strategy to solve the incidental parameter problem within the expectile regression framework. The ERFE model estimates the regressor effects on the expectiles of the response distribution. The ERFE estimate corresponds to the classical fixed-effect within-estimator when the asymmetric point is 0.5. The paper by Barry, Oualkacha, and Charpentier (2021, ) gives more details about the ERFE model. Package: r-cran-ergmargins Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-ergm, r-cran-matrix, r-cran-btergm, r-cran-dplyr, r-cran-sna, r-cran-network, r-cran-sampling Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-statnet.common, r-cran-rcpp, r-cran-ergm.count Filename: pool/dists/noble/main/r-cran-ergmargins_1.6.1-1.ca2404.1_all.deb Size: 277160 MD5sum: 56fa297c6a48c09e1a7fae981b956028 SHA1: 43b834762a9eccaea9ac317b24a0df5a471d68a5 SHA256: b6aeddded395b0ca415a0876a002bb2e6f2e668c2a04712f03d9b4b49b6ebaaa SHA512: d3deb5f9ae19a28ffa85a7aad73669a388a19b50fae878a6d51a6a2acf1634009a4035784f163b2e2a03facb9dc2301e377d1c9181db6365afca9a705b443c89 Homepage: https://cran.r-project.org/package=ergMargins Description: CRAN Package 'ergMargins' (Process Analysis for Exponential Random Graph Models) Calculates marginal effects and conducts process analysis in exponential family random graph models (ERGM). Includes functions to conduct mediation and moderation analyses and to diagnose multicollinearity. URL: . BugReports: . Duxbury, Scott W (2021) . Long, J. Scott, and Sarah Mustillo (2018) . Mize, Trenton D. (2019) . Karlson, Kristian Bernt, Anders Holm, and Richard Breen (2012) . Duxbury, Scott W (2018) . Duxbury, Scott W, Jenna Wertsching (2023) . Huang, Peng, Carter Butts (2023) . Package: r-cran-ergmharris Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ergmharris_1.0-1.ca2404.1_all.deb Size: 57062 MD5sum: dcccf1541d5c50fbbb26542ca9e52111 SHA1: 320661e3c4fc6573737fa02007427e7d75fe611d SHA256: fa9d3e7d3f005c890cff51e8b753ffb9b1439318c74ac1fe099ebf2ad638906c SHA512: 6a0c8d69a5fb98ca3c0c6da2f22728c09a7787bfc68072dfc0689647870505f290384b41a7843c969e2c8378ce4089d2841f843a79d724d4c13136b45c56d7d2 Homepage: https://cran.r-project.org/package=ergmharris Description: CRAN Package 'ergmharris' (Local Health Department network data set) Data for use with the Sage Introduction to Exponential Random Graph Modeling text by Jenine K. Harris. Network data set consists of 1283 local health departments and the communication links among them along with several attributes. Package: r-cran-erify Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-erify_0.6.0-1.ca2404.1_all.deb Size: 142454 MD5sum: 113e1f2297d0696dcc9009c45b149a48 SHA1: 07cf3af04361bda7ad78224bb02388195c284cee SHA256: 1b4cb2a69b532532d8f28bee18e6bd1236fcb1dfba094e9ec92f932d108fcc0a SHA512: d23bdf405739cd737a2ca1a5dadf3209ecff0689790dde4f619a4760ca4e99be06d5173e7fdae5dce2119a4b009b309cb1c5ff7c0c54521a4bcf9d997d746451 Homepage: https://cran.r-project.org/package=erify Description: CRAN Package 'erify' (Check Arguments and Generate Readable Error Messages) Provides several validator functions for checking if arguments passed by users have valid types, lengths, etc. and for generating informative and well-formatted error messages in a consistent style. 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The Erlang C formula was invented by the Danish Mathematician A.K. Erlang and is used to calculate the number of advisors and the service level. 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Package: r-cran-erp.easy Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2165 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-signal, r-cran-gtools Filename: pool/dists/noble/main/r-cran-erp.easy_1.1.0-1.ca2404.1_all.deb Size: 2176898 MD5sum: d060ca677f7205bd28329816489c67fc SHA1: 144416c585321bf8e97b9de402957f0475292531 SHA256: 672f7ee275071773c4219a0c4748e18bd61415caab4b9292ba7bfea1b4d33e41 SHA512: 45856c05c50d6be0e81abb1f423a289991c254590a362a6bd44542ac8204d928765e78bdc541d9bcdd4a707327d3d03a895051eb6c7440e8b99b8f4bf416daef Homepage: https://cran.r-project.org/package=erp.easy Description: CRAN Package 'erp.easy' (Event-Related Potential (ERP) Data Exploration Made Easy) A set of user-friendly functions to aid in organizing, plotting and analyzing event-related potential (ERP) data. Provides an easy-to-learn method to explore ERP data. Should be useful to those without a background in computer programming, and to those who are new to ERPs (or new to the more advanced ERP software available). Emphasis has been placed on highly automated processes using functions with as few arguments as possible. Expects processed (cleaned) data. 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The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, ). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 ), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, ). 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Package: r-cran-esc Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-esc_0.5.1-1.ca2404.1_all.deb Size: 171782 MD5sum: 9d7fa8da98d05af3666259f1d990564f SHA1: dea33f9a1080d8916962e1b75a8dad9869dd2fad SHA256: 12c970929ee99190b2178b8a34b8870908c2b395b442a77b49f0843b0eea4af7 SHA512: 55562aebd50c660bccdb92a34de16a86d55b40449340f2dbfa0bb7172609d919d26f0ce5cafcf5490b3d69977d031d64f2256339925dd3337a8b8255a74b80f9 Homepage: https://cran.r-project.org/package=esc Description: CRAN Package 'esc' (Effect Size Computation for Meta Analysis) Implementation of the web-based 'Practical Meta-Analysis Effect Size Calculator' from David B. Wilson () in R. Based on the input, the effect size can be returned as standardized mean difference, Cohen's f, Hedges' g, Pearson's r or Fisher's transformation z, odds ratio or log odds, or eta squared effect size. Package: r-cran-escalation Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17068 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-ggplot2, r-cran-gtools, r-cran-dfcrm, r-cran-boin, r-cran-trialr, r-cran-diagrammer, r-cran-rcolorbrewer, r-cran-viridis, r-cran-binom, r-cran-r6, r-cran-mvtnorm, r-cran-iso, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-escalation_0.2.3-1.ca2404.1_all.deb Size: 1706898 MD5sum: 47f8881a2eda0b8d45d0b2d2d6d987ee SHA1: cd10222f5c9dfcbeaf96a75889aa8410f01850d6 SHA256: 6741ab2c8754c4038cc665e0df288dc97fda6c153064deb38917e781792d66ea SHA512: 484f2b7896ac7a71704ca8001bec957a8b5cfa125839dfacabe6fa8a2c95f08a08caed6f1071ebe530de8abdc715230951057f504c25a6a00bb8790e9fe3a2e7 Homepage: https://cran.r-project.org/package=escalation Description: CRAN Package 'escalation' (A Modular Approach to Dose-Finding Clinical Trials) Methods for working with dose-finding clinical trials. We provide implementations of many dose-finding clinical trial designs, including the continual reassessment method (CRM) by O'Quigley et al. (1990) , the toxicity probability interval (TPI) design by Ji et al. (2007) , the modified TPI (mTPI) design by Ji et al. (2010) , the Bayesian optimal interval design (BOIN) by Liu & Yuan (2015) , EffTox by Thall & Cook (2004) ; the design of Wages & Tait (2015) , and the 3+3 described by Korn et al. (1994) . All designs are implemented with a common interface. We also offer optional additional classes to tailor the behaviour of all designs, including avoiding skipping doses, stopping after n patients have been treated at the recommended dose, stopping when a toxicity condition is met, or demanding that n patients are treated before stopping is allowed. By daisy-chaining together these classes using the pipe operator from 'magrittr', it is simple to tailor the behaviour of a dose-finding design so it behaves how the trialist wants. Having provided a flexible interface for specifying designs, we then provide functions to run simulations and calculate dose-paths for future cohorts of patients. Package: r-cran-esci Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3535 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggbeeswarm, r-cran-ggdist, r-cran-ggplot2, r-cran-ggtext, r-cran-glue, r-cran-jmvcore, r-cran-legendry, r-cran-metafor, r-cran-mathjaxr, r-cran-multcomp, r-cran-r6, r-cran-rdpack, r-cran-rlang, r-cran-sadists, r-cran-statpsych, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-esci_1.0.10-1.ca2404.1_all.deb Size: 3042850 MD5sum: dc52a69d40fe55efcc4705670346636a SHA1: f4ffc794f27775e4dd9967fd60cae1bed605a072 SHA256: 2758d0e3508c7ff4771607afb3d765da55a63d886e57e44616fa32376d74b8d5 SHA512: a8fbde4b3bccb41025e2762389c3da22aed4ded5c1316c1b14a297b8ff632074a4136b5161046c72317f15cbb3fa32e1d9bfc835711bf09fb8e079d7a6a49837 Homepage: https://cran.r-project.org/package=esci Description: CRAN Package 'esci' (Estimation Statistics with Confidence Intervals) A collection of functions and 'jamovi' module for the estimation approach to inferential statistics, the approach which emphasizes effect sizes, interval estimates, and meta-analysis. Nearly all functions are based on 'statpsych' and 'metafor'. This package is still under active development, and breaking changes are likely, especially with the plot and hypothesis test functions. Data sets are included for all examples from Cumming & Calin-Jageman (2024) . Package: r-cran-escvtmle Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-superlearner, r-cran-origami, r-cran-dplyr, r-cran-tidyselect, r-cran-mass, r-cran-stringr, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-escvtmle_0.0.2-1.ca2404.1_all.deb Size: 123154 MD5sum: e2238ba94a0bc410bdeecf0df4065e12 SHA1: 2f90f669136dd2ce1b3f09a1c83b280adc019dda SHA256: dd634d683698fb6be97fff5fd1f26e34f13db706ebfb63d0c5d4983958b68658 SHA512: f2d1db6fb13e8955818e299097db4c4c660cf065921264ea6acd140b821c51784f100e32cd112b982ec5d57358657dcba06793779f0b66303bb8430146241a85 Homepage: https://cran.r-project.org/package=EScvtmle Description: CRAN Package 'EScvtmle' (Experiment-Selector CV-TMLE for Integration of Observational andRCT Data) The experiment selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) aims to select the experiment that optimizes the bias-variance tradeoff for estimating a causal average treatment effect (ATE) where different experiments may include a randomized controlled trial (RCT) alone or an RCT combined with real-world data. Using cross-validation, the ES-CVTMLE separates the selection of the optimal experiment from the estimation of the ATE for the chosen experiment. The estimated bias term in the selector is a function of the difference in conditional mean outcome under control for the RCT compared to the combined experiment. In order to help include truly unbiased external data in the analysis, the estimated average treatment effect on a negative control outcome may be added to the bias term in the selector. For more details about this method, please see Dang et al. (2022) . Package: r-cran-esdesign Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-esdesign_1.0.3-1.ca2404.1_all.deb Size: 124434 MD5sum: a808811eb199d7e09eecda66621230f2 SHA1: 0209e028c0a6cfd3dabe491583b6f9a740495830 SHA256: 6b74217031c29a936cc2a34c460911f17c82eb24e8e00df28f51d6d5e224a993 SHA512: 13f6891b829f88883b304f03d35a6a31fc9aa8d904d43c5cf6d4c365af1e3aa862729660fdf3a97c994d487550d94301b1dc566632a5c2cb7698c3d02761bd1f Homepage: https://cran.r-project.org/package=esDesign Description: CRAN Package 'esDesign' (Adaptive Enrichment Designs with Sample Size Re-Estimation) Software of 'esDesign' is developed to implement the adaptive enrichment designs with sample size re-estimation presented in Lin et al. (2021) . In details, three-proposed trial designs are provided, including the AED1-SSR (or ES1-SSR), AED2-SSR (or ES2-SSR) and AED3-SSR (or ES3-SSR). In addition, this package also contains several widely used adaptive designs, such as the Marker Sequential Test (MaST) design proposed Freidlin et al. (2014) , the adaptive enrichment designs without early stopping (AED or ES), the sample size re-estimation procedure (SSR) based on the conditional power proposed by Proschan and Hunsberger (1995), and some useful functions. In details, we can calculate the futility and/or efficacy stopping boundaries, the sample size required, calibrate the value of the threshold of the difference between subgroup-specific test statistics, conduct the simulation studies in AED, SSR, AED1-SSR, AED2-SSR and AED3-SSR. Package: r-cran-esdm Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4630 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-rocr, r-cran-sf, r-cran-shiny, r-cran-units Suggests: r-cran-colorramps, r-cran-colourpicker, r-cran-dichromat, r-cran-dt, r-cran-knitr, r-cran-leafem, r-cran-leaflet, r-cran-maps, r-cran-raster, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-shinybusy, r-cran-shinydashboard, r-cran-shinyjs, r-cran-testthat, r-cran-tmap, r-cran-viridis, r-cran-zip Filename: pool/dists/noble/main/r-cran-esdm_0.4.4-1.ca2404.1_all.deb Size: 3687736 MD5sum: 7998f4c443391c83f3f892b4d7597b10 SHA1: b9fb03c54c55c6b2413dedd5a862de035e063517 SHA256: 752bca6c80961b1c3d33b2987cd14795a47aa27e847351d76965caaa7d7393ec SHA512: cd744147590ab3c1cf1d9330252e384b234945c738ad24f504110f01838c01843c38444312a4273855b058db9b0f3d780829078f843061c1a1dd035a81d414bc Homepage: https://cran.r-project.org/package=eSDM Description: CRAN Package 'eSDM' (Ensemble Tool for Predictions from Species Distribution Models) A tool which allows users to create and evaluate ensembles of species distribution model (SDM) predictions. Functionality is offered through R functions or a GUI (R Shiny app). This tool can assist users in identifying spatial uncertainties and making informed conservation and management decisions. The package is further described in Woodman et al (2019) . Package: r-cran-eselect Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-comparedesign Filename: pool/dists/noble/main/r-cran-eselect_1.1-1.ca2404.1_all.deb Size: 67704 MD5sum: 8011af9e6331257e543da58e333d6d41 SHA1: 7d60db8938d001551a8f82f70ebcaa5e4991d6b7 SHA256: 539d6459bfd81de24bacc2de557baaa796ce4bfa31a785a0b833f6599871db95 SHA512: 94fd251069868503d81c2ec34ef4bbfcb7911661aeb1a62831663d950db366bb94da1f48f6ebcbe8ac7c9f1a8e83bc754b950fc8073f93acc7c331cb680df47b Homepage: https://cran.r-project.org/package=eselect Description: CRAN Package 'eselect' (Adaptive Clinical Trial Designs with Endpoint Selection andSample Size Reassessment) Endpoint selection and sample size reassessment for multiple binary endpoints based on blinded and/or unblinded data. Trial design that allows an adaptive modification of the primary endpoint based on blinded information obtained at an interim analysis. The decision rule chooses the endpoint with the lower estimated required sample size. Additionally, the sample size is reassessed using the estimated event probabilities and correlation between endpoints. The implemented design is proposed in Bofill Roig, M., Gómez Melis, G., Posch, M., and Koenig, F. (2022). . Package: r-cran-esem Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gparotation, r-cran-lavaan, r-cran-magrittr, r-cran-psych, r-cran-tidyr, r-cran-dplyr, r-cran-rlang, r-cran-tibble Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-esem_2.0.0-1.ca2404.1_all.deb Size: 111616 MD5sum: e59e7443468edc7f084008ab6504dfa0 SHA1: c724c81645c57336d40c07770b784c77a33bae68 SHA256: e43596c7735549e3b8af6fc13513f175aadebe6c245464c3b02d64d5b97ac489 SHA512: e866d1123fd068e06ba14d92388612e33374ee28b4b66351557d5d096147ff5dba1e095f13ecacf63b8eb1c990a17b36b9ecaa271b72605fc2cf107935a4a310 Homepage: https://cran.r-project.org/package=esem Description: CRAN Package 'esem' (Exploratory Structural Equation Modeling ESEM) A collection of functions developed to support the tutorial on using Exploratory Structural Equiation Modeling (ESEM) (Asparouhov & Muthén, 2009) ) with Longitudinal Study of Australian Children (LSAC) dataset (Mohal et al., 2023) . The package uses 'tidyverse','psych', 'lavaan','semPlot' and provides additional functions to conduct ESEM. The package provides general functions to complete ESEM, including esem_c(), creation of target matrix (if it is used) make_target(), generation of the Confirmatory Factor Analysis (CFA) model syntax esem_cfa_syntax(). A sample data is provided - the package includes a sample data of the Strengths and Difficulties Questionnaire of the Longitudinal Study of Australian Children (SDQ LSAC) in sdq_lsac(). 'ESEM' package vignette presents the tutorial demonstrating the use of ESEM on SDQ LSAC data. Package: r-cran-esg Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-esg_1.3-1.ca2404.1_all.deb Size: 180236 MD5sum: 8ca88fffde81f8cb498a7c0916010904 SHA1: ee0c174c1f2715371bd4d3518c18f4cc10179ec9 SHA256: b9b293b7dce86c1484517a1c1cd4324ad18c9d3e9d8ae0758d2ce75c3a687cb3 SHA512: 7d6f43c45f21f088491207f435ce9a126106a48bdba6b58c051e87579564fb7a2f538e38f8f48e19940abc0bce4a7358beba90cecfb9afab942bbf75d411d47e Homepage: https://cran.r-project.org/package=ESG Description: CRAN Package 'ESG' (A Package for Asset Projection) Presents a "Scenarios" class containing general parameters, risk parameters and projection results. Risk parameters are gathered together into a ParamsScenarios sub-object. The general process for using this package is to set all needed parameters in a Scenarios object, use the customPathsGeneration method to proceed to the projection, then use xxx_PriceDistribution() methods to get asset prices. Package: r-cran-eshrink Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-eshrink_0.2.0-1.ca2404.1_all.deb Size: 66732 MD5sum: 9df5bb94d89a5f24279af5d4d29e4b43 SHA1: eb0862d43f7f8b7bf1e8db8b9f72a1209dc29a58 SHA256: 7196a3130c5d92f009434971a7ebc5ee4fd101e7859e4bf4a9d39fd55fd98676 SHA512: 542fa43014b8a3e8c2d6034bec71fe19288ba77259e472e2d228ef7f026d7d6b09db0a3a0df2588e122d68f847b9bb88b539e6ec2634a34b31a085049f27803f Homepage: https://cran.r-project.org/package=eshrink Description: CRAN Package 'eshrink' (Shrinkage for Effect Estimation) Computes shrinkage estimators for regression problems. Selects penalty parameter by minimizing bias and variance in the effect estimate, where bias and variance are estimated from the posterior predictive distribution. See Keller and Rice (2017) for more details. Package: r-cran-esir Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3084 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-chron, r-cran-data.table, r-cran-ggplot2, r-cran-gtools, r-cran-scales, r-cran-reshape2, r-cran-rjags Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-esir_0.4.2-1.ca2404.1_all.deb Size: 2800890 MD5sum: a5c5d8a377b3f0ae655e762a76e8184c SHA1: 8607646ff780b63c9de3dba1cd76799f6de8b03a SHA256: 9229e716340bab797cbcc092570b85cdd220be5d29d1f08d16c68dac97e52f8e SHA512: 06b9748ec16e54bc35e052f6fb4181938e7dad5af5bd575f788862a8af84d3103f585b83823d1976e49f0acaeb92c9db4eab3b7760a6ab17bda775b24618f20e Homepage: https://cran.r-project.org/package=eSIR Description: CRAN Package 'eSIR' (Extended State-Space SIR Models) An implementation of extended state-space SIR models developed by Song Lab at UM school of Public Health. There are several functions available by 1) including a time-varying transmission modifier, 2) adding a time-dependent quarantine compartment, 3) adding a time-dependent antibody-immunization compartment. Wang L. (2020) . Package: r-cran-esmisc Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-ggplot2, r-cran-readr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-esmisc_0.0.3-1.ca2404.1_all.deb Size: 416742 MD5sum: 318f0e402bf90babd5417d993e740361 SHA1: 2ee87eb9ba217ead27cd8549e4c6cfe1fdd94297 SHA256: 1773428dd6024c49905d36c317a7f0d72ae72d966e2484220c860a53a842b728 SHA512: 92f1b75a880d7ece1fbfc22eab6cc702f6263b2237fefd9352aeb66ca9fb86ca7d5a0f62e135586de4a73b53a4c28d4051c65fe2d63e3d241192aa445c844c66 Homepage: https://cran.r-project.org/package=esmisc Description: CRAN Package 'esmisc' (Misc Functions of Eduard Szöcs) Misc functions programmed by Eduard Szöcs. Provides read_regnie() to read gridded precipitation data from German Weather Service (DWD, see for more information). Package: r-cran-esmtools Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2460 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64enc, r-cran-dplyr, r-cran-dt, r-cran-fs, r-cran-ggplot2, r-cran-ggpubr, r-cran-htmltools, r-cran-jsonlite, r-cran-kableextra, r-cran-knitr, r-cran-lubridate, r-cran-stringr, r-cran-tidyr Suggests: r-cran-readxl, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-esmtools_1.0.1-1.ca2404.1_all.deb Size: 514874 MD5sum: 60290e7c1977ad6df541a9b32fc130c9 SHA1: 6efa1014cb7e1e75ac1f558a8ae69d6e8f2804ad SHA256: 534486172bce9eddae10b9fc575abccd134c97ad54cca1d658e6a0c10e78d939 SHA512: 624422c1281aefb2a5f296c6bdfc6c1334dd94ac0032aed2154abf9f70596a22def919be97208bce9fd1f018a2d5acd07079613de1785103f9ee0d3539e6765a Homepage: https://cran.r-project.org/package=esmtools Description: CRAN Package 'esmtools' (Preprocessing Experience Sampling Method (ESM) Data) Tailored explicitly for Experience Sampling Method (ESM) data, it contains a suite of functions designed to simplify preprocessing steps and create subsequent reporting. It empowers users with capabilities to extract critical insights during preprocessing, conducts thorough data quality assessments (e.g., design and sampling scheme checks, compliance rate, careless responses), and generates visualizations and concise summary tables tailored specifically for ESM data. Additionally, it streamlines the creation of informative and interactive preprocessing reports, enabling researchers to transparently share their dataset preprocessing methodologies. Finally, it is part of a larger ecosystem which includes a framework and a web gallery (). Package: r-cran-esquisse Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2428 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bslib, r-cran-datamods, r-cran-downlit, r-cran-ggplot2, r-cran-htmltools, r-cran-jsonlite, r-cran-phosphoricons, r-cran-rlang, r-cran-rstudioapi, r-cran-scales, r-cran-shiny, r-cran-shinybusy, r-cran-shinywidgets, r-cran-zip Suggests: r-cran-officer, r-cran-rvg, r-cran-rio, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggthemes, r-cran-hrbrthemes, r-cran-plotly Filename: pool/dists/noble/main/r-cran-esquisse_2.1.0-1.ca2404.1_all.deb Size: 1555678 MD5sum: 00373179f670c136f399d97ff8e4193c SHA1: 6c45ec241b94ced3250088bb4a66990630eb2e85 SHA256: e07653c57a0b4755ff1037240cd5589d69f5a189c32c800585ac0946f410021c SHA512: 6ad8ecfaeb7f14f5e48ab7326d1ad9b3d8aef0ff72d2547b7dde1a3c5a3d602e55081cf140700e2fcb87773e440b5a87b4fb3624ca00ab68a196b54e0d858f09 Homepage: https://cran.r-project.org/package=esquisse Description: CRAN Package 'esquisse' (Explore and Visualize Your Data Interactively) A 'shiny' gadget to create 'ggplot2' figures interactively with drag-and-drop to map your variables to different aesthetics. You can quickly visualize your data accordingly to their type, export in various formats, and retrieve the code to reproduce the plot. Package: r-cran-essentialstools Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ez, r-cran-webpower, r-cran-pwr Filename: pool/dists/noble/main/r-cran-essentialstools_0.1.4-1.ca2404.1_all.deb Size: 22136 MD5sum: 3537d0d658757fe99a7c150e1532102b SHA1: 321aaba4f18b3f1561500a12ee6ae7c9be25346c SHA256: cb0bb7566be2b6307739c0aa8034e36e184019daf3599a5385ad3f2ccceda62d SHA512: db1b3aba504a17b7dc5de259a9de7fbc88d85e1edfec635cb63403c57722cc7cc105176706bafe1bc1f9c7d533abfc8f9d91cafb70e69d0c550a7c00357e1662 Homepage: https://cran.r-project.org/package=essentialstools Description: CRAN Package 'essentialstools' (Datasets and Utilities for Essentials of Statistics for theBehavioral Sciences) Provides instructional datasets and simple wrapper functions for selected analyses used in 'Essentials of Statistics for the Behavioral Sciences' (Gravetter et al., 2026). The package is intended to support textbook examples by distributing data in a form that is easy for students and instructors to access within R. Current functionality includes packaged datasets and convenience wrappers for functions from 'ez', 'pwr', and 'WebPower' for analysis of variance and statistical power calculations. Package: r-cran-essurvey Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-httr, r-cran-haven, r-cran-rvest, r-cran-tibble Suggests: r-cran-foreign, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-essurvey_1.0.8-1.ca2404.1_all.deb Size: 126682 MD5sum: 217bd27883b34f99151c4d21c6822bce SHA1: 2a3cf51465a35bba31907f22160a7e2d6c1fe43f SHA256: c1ae51d02c462d9c67ade9713cd5437d8ed508bb42f51d56ae9a512426957cd3 SHA512: 8d6eeebd383a5b6a47332d610d7392e329c723fa0dd788a649dfe931d1d7d4f46b448ccf4a09b67a528b210f6e8a360adbacb15b9e866d621bca50a33324e8b3 Homepage: https://cran.r-project.org/package=essurvey Description: CRAN Package 'essurvey' (Download Data from the European Social Survey on the Fly) Download data from the European Social Survey directly from their website . There are two families of functions that allow you to download and interactively check all countries and rounds available. Package: r-cran-estadistica Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2039 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-plotly, r-cran-ggplot2, r-cran-rio, r-cran-shiny, r-cran-shinydashboard, r-cran-knitr, r-cran-forecast, r-cran-openxlsx, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-estadistica_1.2.2-1.ca2404.1_all.deb Size: 1243632 MD5sum: f8e7bc2450eaefa856edf596d420bcdc SHA1: 55dd498864c2732f0a54bf8e2b107b7f9a0ef1d1 SHA256: 882926900485d2ccaef83091ecd7e4409e622acb47bb2bb969cfe169976a00fc SHA512: e2c953d4bfe23420d4d1006cc5d8b2ace600009020aa8bab7f8947b3a83408456172a8030e827954acb6eac6a59064ff0da3e117ece2d1ea8f47289279ee70d1 Homepage: https://cran.r-project.org/package=estadistica Description: CRAN Package 'estadistica' (Fundamentos de estadística descriptiva e inferencial) Este paquete pretende apoyar el proceso enseñanza-aprendizaje de estadística descriptiva e inferencial. Las funciones contenidas en el paquete 'estadistica' cubren los conceptos básicos estudiados en un curso introductorio. Muchos conceptos son ilustrados con gráficos dinámicos o web apps para facilitar su comprensión. This package aims to help the teaching-learning process of descriptive and inferential statistics. The functions contained in the package 'estadistica' cover the basic concepts studied in a statistics introductory course. Many concepts are illustrated with dynamic graphs or web apps to make the understanding easier. See: Esteban et al. (2005, ISBN: 9788497323741), Newbold et al.(2019, ISBN:9781292315034 ), Murgui et al. (2002, ISBN:9788484424673) . Package: r-cran-estar Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15401 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-vegan, r-cran-zoo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-matrix, r-cran-hesim, r-cran-cowplot, r-cran-viridis, r-cran-forcats, r-cran-tidyr, r-cran-marss, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-estar_1.0-1-1.ca2404.1_all.deb Size: 1850936 MD5sum: 363606a8ce13f0867b45cc6bd9c22aff SHA1: 95c354bf022761c2da6fc818dfda084e9a31318e SHA256: 28f15b5498c9c03cc1b48c038a886130dda349e0026e4bcae09af7645929e6f2 SHA512: 01cf8d6264b890e2943b1fee0efa0a064283202d2f155052a6adc1cb646181aea63bfc9f5b676b8f77a059241f27082ba835b3195726e572c3c615c3f9a23b6d Homepage: https://cran.r-project.org/package=estar Description: CRAN Package 'estar' (Ecological Stability Metrics) Standardises and facilitates the use of eleven established stability properties that have been used to assess systems’ responses to press or pulse disturbances at different ecological levels (e.g. population, community). There are two sets of functions. The first set corresponds to functions that measure stability at any level of organisation, from individual to community and can be applied to a time series of a system’s state variables (e.g., body mass, population abundance, or species diversity). The properties included in this set are: invariability, resistance, extent and rate of recovery, persistence, and overall ecological vulnerability. The second set of functions can be applied to Jacobian matrices. The functions in this set measure the stability of a community at short and long time scales. In the short term, the community’s response is measured by maximal amplification, reactivity and initial resilience (i.e. initial rate of return to equilibrium). In the long term, stability can be measured as asymptotic resilience and intrinsic stochastic invariability. Figueiredo et al. (2025) . Package: r-cran-estatapi Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-purrr, r-cran-readr, r-cran-dplyr, r-cran-tibble, r-cran-rlang Suggests: r-cran-testthat, r-cran-keyring Filename: pool/dists/noble/main/r-cran-estatapi_0.4.0-1.ca2404.1_all.deb Size: 43246 MD5sum: 246416ea13496f89722d67a2a25f15d9 SHA1: da5d3db3cf5e615d63007f3a06c811bae2c1a413 SHA256: 1a0c062f82dbee179ff0e763b3814b524d49058ba188702296107b93e463f24d SHA512: d51e4943435cb2dc452f3953c091a2dbc59299ab9fb6b0a088c6ed49f283751798c94c178acdde85feba6fbacdebcf137168fab04b7cc165a4ebcdcc3e976c14 Homepage: https://cran.r-project.org/package=estatapi Description: CRAN Package 'estatapi' (R Interface to e-Stat API) Provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government. Package: r-cran-estats Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dcov, r-cran-pdcor, r-cran-rangen, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-estats_1.1-1.ca2404.1_all.deb Size: 49088 MD5sum: a442637cefa4ea8aef2f45a0e7d90171 SHA1: b60900c67f894f0c8a71a1561233160769b20206 SHA256: 5483f987ebefe4147a3282f339b2bda1cd998bfa2cd7946e2d91db606ad539cf SHA512: 094a5d57da1eb4ae3c884a46f19f307ab90cccc868ac1accb89b5d029cd6c0943b2b215990b777fb06fc7da04a80d7a9516c7dccef419dfc35e6ce2c299ae837 Homepage: https://cran.r-project.org/package=estats Description: CRAN Package 'estats' (Fast and Light-Weight Energy Statistics) Fast and memory-less computation of the energy statistics related quantities for vectors and matrices. References include: Szekely G. J. and Rizzo M. L. (2014), . Szekely G. J. and Rizzo M. L. (2023), . Tsagris M. and Papadakis M. (2025). . Package: r-cran-estcrm Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-lattice Filename: pool/dists/noble/main/r-cran-estcrm_1.6-1.ca2404.1_all.deb Size: 111828 MD5sum: eb98ac1e68c706ee4f23ae138605e941 SHA1: d11badb6525ed904cae13472684d7ae6a0b54668 SHA256: c4571d09947498694519dca23698ef5b6722f4b834c68f8c64ece9d4fe07d566 SHA512: beb11976f89dcf4e810e1bac6b2f4d8dc18b24f4612cb90778c3f93601e21810d4df0cf7e44e7aba593603b6479f8704324c01f2f76cd022a0fd8c6bd307b4da Homepage: https://cran.r-project.org/package=EstCRM Description: CRAN Package 'EstCRM' (Calibrating Parameters for the Samejima's Continuous IRT Model) Estimates item and person parameters for the Continuous Response Model (CRM; Samejima, 1973, ), computes item fit residual statistics, draws empirical 3D item category response curves, draws theoretical 3D item category response curves, and generates data under the CRM for simulation studies. Package: r-cran-estempmm Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4026 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-mass, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-estempmm_0.3.2-1.ca2404.1_all.deb Size: 2489294 MD5sum: 6c576f4dda4c7f1484c6b36c52b02b1a SHA1: 2b590c00c683db812a581e9a5463469698d5f811 SHA256: 0f8d542c73f77daf3f181c6a42e353dfaa52ffcd52d9ceb469e3acc80b80fb99 SHA512: 7e7fa7003080d099fb0f17351bbad39dc76e06750ca90df2a78b11fc615260aa730c592fafb29a322a5cf95a6d0c9d766b7f8a0858080d389d6bdc67f5824f38 Homepage: https://cran.r-project.org/package=EstemPMM Description: CRAN Package 'EstemPMM' (Polynomial Maximization Method for Non-Gaussian Regression) Implements the Polynomial Maximization Method ('PMM') for parameter estimation in linear and time series models when error distributions deviate from normality. The 'PMM2' variant achieves lower variance parameter estimates compared to ordinary least squares ('OLS') when errors exhibit significant skewness. The 'PMM3' variant (S=3) targets symmetric platykurtic error distributions, reducing variance when excess kurtosis is negative. Includes automatic method selection ('pmm_dispatch'), linear regression, 'AR'/'MA'/'ARMA'/'ARIMA' models, and bootstrap inference. Methodology described in Zabolotnii, Warsza, and Tkachenko (2018) , Zabolotnii, Tkachenko, and Warsza (2022) , and Zabolotnii, Tkachenko, and Warsza (2023) , and Zabolotnii (2025) . Package: r-cran-ester Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2328 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brms, r-cran-lme4, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-ggplot2, r-cran-rlang, r-cran-foreach, r-cran-doparallel, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ester_0.2.0-1.ca2404.1_all.deb Size: 1107524 MD5sum: c5cbce2808715610a4724f559a75cecd SHA1: c14d5e4fca516540e3914560a37400246293f264 SHA256: 8d960b323327e1d58671c88d4c0f9fd9ecb0a30fde3bcc18ecde2cf5ff96add9 SHA512: 1b4f3ec9d10593af5f0b0655bb98ad304053b2becddecf4afe5dea5e2d14cb5b773f238f77cebb5777bf20bfaf787dcc7707acfdba2bef0fda52edc41329c718 Homepage: https://cran.r-project.org/package=ESTER Description: CRAN Package 'ESTER' (Efficient Sequential Testing with Evidence Ratios) An implementation of sequential testing that uses evidence ratios computed from the weights of a set of models. These weights correspond either to Akaike weights computed from the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC) and following Burnham & Anderson (2004, ) recommendations, or to pseudo-BMA weights computed from the WAIC or the LOO-IC of models fitted with 'brms' and following Yao et al. (2017, ). Package: r-cran-esthtseed Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-esthtseed_0.1.0-1.ca2404.1_all.deb Size: 29524 MD5sum: f0742fe5c161b76317457bb36b75308e SHA1: 570fd7eaf00224b6996a138031f94937bf2a76ab SHA256: e03ec90d4fb32d2f529c0aa37d5c8173a514350bb44775b09597c55c38911c75 SHA512: 5764dd4f97632be24cf0e8e778e31fbc855c43b5011543ea6cf55de04549f1ccf9e6398caec7580639ca14540fa8066c9bdbd5add36ef6bd4be77002d29c801d Homepage: https://cran.r-project.org/package=EstHTSeed Description: CRAN Package 'EstHTSeed' (Hydro Time Analysis of Seed Germination) Dry seed germinates by imbibing water from soil where the physiological process of germination starts after sufficient water has been imbibed by the seed. The germination time of the seed is inversely proportion to the difference between soil water potential and the base seed water potential which is described by hydro time model (Bradford, 2002 ). The parameters of the model like speed of germination, stress tolerance, uniformity of germination are unknown fixed values (Ghosh et al., 2026 ) which are to be estimated using statistical regression model where the validity of the adopted statistical model has been established theoretically. The package will help to estimate the tuning parameter for proportion of viable seeds along with standard error and p- values for inference. Package: r-cran-estimability Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-estimability_1.5.1-1.ca2404.1_all.deb Size: 50362 MD5sum: d3b4aa3a5422df60535c849fc16a0cd8 SHA1: 4ba81ada2e93cc65b55923a852ca7bb33f8d0406 SHA256: 5a38d635f4359a67902df3f4ef830a91074d7d3a13bd92a851095ce795168738 SHA512: bb08b07f239f1299af6941c0b5c8857c33d30db2d493021a07c8f1bf115a29ed532984e3eaca0b1b8fdf8d1a54ecb1e521242da950e37f94cfa2470c02d03a99 Homepage: https://cran.r-project.org/package=estimability Description: CRAN Package 'estimability' (Tools for Assessing Estimability of Linear Predictions) Provides tools for determining estimability of linear functions of regression coefficients, and 'epredict' methods that handle non-estimable cases correctly. Estimability theory is discussed in many linear-models textbooks including Chapter 3 of Monahan, JF (2008), "A Primer on Linear Models", Chapman and Hall (ISBN 978-1-4200-6201-4). Package: r-cran-estimatebreed Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-broom, r-cran-purrr, r-cran-ggrepel, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-nasapower, r-cran-tidyr, r-cran-viridis, r-cran-cowplot, r-cran-sommer, r-cran-lme4, r-cran-minque, r-cran-car, r-cran-lmtest Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-roxygen2, r-cran-dt Filename: pool/dists/noble/main/r-cran-estimatebreed_1.0.2-1.ca2404.1_all.deb Size: 342656 MD5sum: 58440ae184dd31472e0e85846374d94e SHA1: d88fc78205d9f1c29ac4162154760a8449575082 SHA256: cb7976c448b0b09d156cf91a3dc294ec3865679c9e03a7c2b890cf397a9d8201 SHA512: 6b2a20d268e363a34befe7ed3dc0d42559cf69e3bfac7b3faf163b96e5a8bc81ee6ff95cf9c6895269f7de21fbd987b67aa2ea2b03803252159ac21127dfc5d4 Homepage: https://cran.r-project.org/package=EstimateBreed Description: CRAN Package 'EstimateBreed' (Estimation of Environmental Variables and Genetic Parameters) Performs analyzes and estimates of environmental covariates and genetic parameters related to selection strategies and development of superior genotypes. It has two main functionalities, the first being about prediction models of covariates and environmental processes, while the second deals with the estimation of genetic parameters and selection strategies. Designed for researchers and professionals in genetics and environmental sciences, the package combines statistical methods for modeling and data analysis. This includes the plastochron estimate proposed by Porta et al. (2024) , Stress indices for genotype selection referenced by Ghazvini et al. (2024) , the Environmental Stress Index described by Tazzo et al. (2024) , industrial quality indices of wheat genotypes (Szareski et al., 2019), , Ear Indexes estimation (Rigotti et al., 2024), , Selection index for protein and grain yield (de Pelegrin et al., 2017), , Estimation of the ISGR - Genetic Selection Index for Resilience for environmental resilience (Bandeira et al., 2024) , estimation of Leaf Area Index (Meira et al., 2015) , Restriction of control variability (Carvalho et al., 2023) , Risk of Disease Occurrence in Soybeans described by Engers et al. (2024) and estimation of genetic parameters for selection based on balanced experiments (Yadav et al., 2024) . Package: r-cran-estimategroupnetwork Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-qgraph, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-mvtnorm, r-cran-jgl, r-cran-psych Filename: pool/dists/noble/main/r-cran-estimategroupnetwork_0.3.1-1.ca2404.1_all.deb Size: 103262 MD5sum: df7a2c503d82d2c16f7be964689b1f8d SHA1: a712411ef5137ef6489159e1fc82801ebb237768 SHA256: 664f7dd0a799d0536ea6fd7c0ecb3d7b3d44c4ee9a99961b74ecd2093260d836 SHA512: 520a4f440917cfa04b10e0196f9f6fc2c6dc2bb9ce753dee70e47dcb51f25d83ac241ca59318cdfe6fd6fe230e077488427172116f9a53500a2aecf4c4ac291d Homepage: https://cran.r-project.org/package=EstimateGroupNetwork Description: CRAN Package 'EstimateGroupNetwork' (Perform the Joint Graphical Lasso and Selects Tuning Parameters) Can be used to simultaneously estimate networks (Gaussian Graphical Models) in data from different groups or classes via Joint Graphical Lasso. Tuning parameters are selected via information criteria (AIC / BIC / extended BIC) or cross validation. Package: r-cran-estimatew Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixcalc, r-cran-plot.matrix, r-cran-r6 Filename: pool/dists/noble/main/r-cran-estimatew_0.2.0-1.ca2404.1_all.deb Size: 331956 MD5sum: c69fd59e2519195f6746c1c04635ecbf SHA1: f01ac90bd05caf71478192ffd84f7f12ab7ddd78 SHA256: 20a7240db2dce43635bee44f6a13a6233d3972ef38c382bba9753ab4f59af08f SHA512: 61750069e6ed1a3d8bd0f7de2e37163a77248c5925253082954f3b04cf56d92d289f30cdd0cbcfa043b439d77dfa9d02c3fc7b640ada3e3468238815639fa4c2 Homepage: https://cran.r-project.org/package=estimateW Description: CRAN Package 'estimateW' (Estimation of Spatial Weight Matrices) Bayesian estimation of spatial weight matrices in spatial econometric panel models. Allows for estimation of spatial autoregressive (SAR), spatial error (SEM), spatial Durbin (SDM), spatial error Durbin (SDEM) and spatially lagged explanatory variable (SLX) type specifications featuring an unknown spatial weight matrix. Methodological details are given in Krisztin and Piribauer (2022) . Package: r-cran-estimationtools Architecture: all Version: 4.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-deoptim, r-cran-bbmisc, r-cran-ga, r-cran-rdpack, r-cran-numderiv, r-cran-boot, r-cran-autoimage, r-cran-stringr, r-cran-gaussquad, r-cran-car Suggests: r-cran-rmpfr, r-cran-gamlss.dist, r-cran-knitr, r-cran-rmarkdown, r-cran-adequacymodel, r-cran-readr, r-cran-covr, r-cran-testthat, r-cran-vdiffr, r-cran-spelling, r-cran-lifecycle, r-cran-matrixstats, r-cran-lintr, r-cran-v8 Filename: pool/dists/noble/main/r-cran-estimationtools_4.3.1-1.ca2404.1_all.deb Size: 378676 MD5sum: 61096c18b81d6eb5307f06c2d732f8ea SHA1: c1443af365db4cd9daba0fd0813b8c4bbeb39f16 SHA256: 3429ca2f2c24da58273ec605131a3841bfd795ea69ae09584c9184e734119ec1 SHA512: 99294ea81161e8a6f2d133b4c89281f2c02673d644f414ab20dbca24a61460eed1ecc89d3f08f5c986728b8338d00648b8120e09d9caa1398c1bc725914e74ff Homepage: https://cran.r-project.org/package=EstimationTools Description: CRAN Package 'EstimationTools' (Maximum Likelihood Estimation for Probability Functions fromData Sets) Total Time on Test plot and routines for parameter estimation of any lifetime distribution implemented in R via maximum likelihood (ML) given a data set. It is implemented thinking on parametric survival analysis, but it feasible to use in parameter estimation of probability density or mass functions in any field. The main routines 'maxlogL' and 'maxlogLreg' are wrapper functions specifically developed for ML estimation. There are included optimization procedures such as 'nlminb' and 'optim' from base package, and 'DEoptim' Mullen (2011) . Standard errors are estimated with 'numDeriv' Gilbert (2011) or the option 'Hessian = TRUE' of 'optim' function. Package: r-cran-estimators Architecture: all Version: 0.8.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 816 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-extradistr, r-cran-ggh4x, r-cran-ggplot2, r-cran-matrix, r-cran-progress Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-estimators_0.8.5-1.ca2404.1_all.deb Size: 572254 MD5sum: 32255ff155832c3f2b4b0974c5a65ed8 SHA1: 93b02de318c160eac5327d73c8030c3336f69233 SHA256: 651c686a327914db5ac81d30ab96757094053e567a7b1f4d85aa94b8f06e1f1c SHA512: 5c4786b8a03fca3dd1715d29827be0620436098df3eb8edad1ec8047de91e4d77638e2f2c1b22f3e025c66d9ddcd63d1615c4e88eea30ec7793d4c2a2bd487c5 Homepage: https://cran.r-project.org/package=estimators Description: CRAN Package 'estimators' (Parameter Estimation) Implements estimation methods for parameters of common distribution families. The common d, p, q, r function family for each distribution is enriched with the ll, e, and v counterparts, computing the log-likelihood, performing estimation, and calculating the asymptotic variance - covariance matrix, respectively. Parameter estimation is performed analytically whenever possible. Package: r-cran-estimdiagnostics Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-reshape2, r-cran-ggplot2, r-cran-goftest, r-cran-testthat, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-estimdiagnostics_0.0.3-1.ca2404.1_all.deb Size: 99200 MD5sum: 3ff1a3872a1e01e395fd9ad4a0182bd5 SHA1: 84afd85dc0ae66538fce02623cd6d566511e0284 SHA256: b6b2db3324b874e02f0b8081700d179f32d7c22600adafb9244673c9044f7d3d SHA512: 732acdab395ee20041d8cfad66df4fd9c6c38a8498a2812e22863a5f459d099ee65afef6c0eccdc23101ad77d86705bdfabd5fb636df5d46cd441da2d26e39ca Homepage: https://cran.r-project.org/package=EstimDiagnostics Description: CRAN Package 'EstimDiagnostics' (Diagnostic Tools and Unit Tests for Statistical Estimators) Extension of 'testthat' package to make unit tests on empirical distributions of estimators and functions for diagnostics of their finite-sample performance. Package: r-cran-estimraw Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-estimraw_1.0.0-1.ca2404.1_all.deb Size: 21536 MD5sum: 3db1368d4345da1a3c3b9b31329e6174 SHA1: 338cc38dc7d981aa97a8dfd2bb32e0b0b5bf6150 SHA256: cf009df6e7fd37d0400702a18de4c2928718839c92f612033d049a5c1c992854 SHA512: 1969bd167c1580c8ec2bea0819c71857a00491749d654caf068ccd838dc1012d3cde4ef84132cbddd62bf4d4f46b0f2774b7ee48ced633b8387be5c97ee437f9 Homepage: https://cran.r-project.org/package=estimraw Description: CRAN Package 'estimraw' (Estimation of Four-Fold Table Cell Frequencies (Raw Data) fromEffect Size Measures) Estimation of four-fold table cell frequencies (raw data) from risk ratios (relative risks), risk differences and odds ratios. While raw data can be useful for doing meta-analysis, such data is often not provided by primary studies (with summary statistics being solely presented). Therefore, based on summary statistics (namely, risk ratios, risk differences and odds ratios), this package estimates the value of each cell in a 2x2 table according to the equations described in Di Pietrantonj C (2006) . Package: r-cran-estmeansd Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metablue Filename: pool/dists/noble/main/r-cran-estmeansd_1.0.1-1.ca2404.1_all.deb Size: 109560 MD5sum: c69bb458e8968f363da81f91fd4eef7e SHA1: 326a9dc3ca02344a21b6684bc065253a6f9c3a18 SHA256: 2d583f3a040bc7f52b9b45044f0639c44d39e636c77f73110e7846946ee560ba SHA512: a6cf521f26e38e8eb497ed74a246e412362dba552112d8ce09ac3e8ac5ae087629d4022d26ff3bf0f41d4c0c8083e14cd38b6bc4c6585393611fc4a5f7b89e7a Homepage: https://cran.r-project.org/package=estmeansd Description: CRAN Package 'estmeansd' (Estimating the Sample Mean and Standard Deviation from CommonlyReported Quantiles in Meta-Analysis) Implements the methods of McGrath et al. (2020) and Cai et al. (2021) for estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis. These methods can be applied to studies that report the sample median, sample size, and one or both of (i) the sample minimum and maximum values and (ii) the first and third quartiles. The corresponding standard error estimators described by McGrath et al. (2023) are also included. Package: r-cran-estprod Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lazyeval, r-cran-boot, r-cran-minpack.lm, r-cran-formula, r-cran-gmm Filename: pool/dists/noble/main/r-cran-estprod_1.2-1.ca2404.1_all.deb Size: 124198 MD5sum: 8f04afdd09c8b38f3d5faf14b299c108 SHA1: 37039409369b10629211f69a1da94063f7906ef9 SHA256: a70a2cee09912b2eac292ba2920cb05ee91ad29a16df811ca46ac158da6bc511 SHA512: 4a01b717cd1b0c2389bf1c9e48d2d9d9b8f8825dee4778d363bdafb23e152e23c5e451fab7b1d7f22e5d7918bace252bf367a8738406c37631943a93a23e3ab7 Homepage: https://cran.r-project.org/package=estprod Description: CRAN Package 'estprod' (Estimation of Production Functions) Estimation of production functions by the Olley-Pakes, Levinsohn-Petrin and Wooldridge methodologies. The package aims to reproduce the results obtained with the Stata's user written opreg and levpet commands. The first was originally proposed by Olley, G.S. and Pakes, A. (1996) . The second by Levinsohn, J. and Petrin, A. (2003) . And the third by Wooldridge (2009) . Package: r-cran-esvis Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1240 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sfsmisc, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-purrr, r-cran-hmisc, r-cran-tibble Suggests: r-cran-testthat, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-esvis_0.3.1-1.ca2404.1_all.deb Size: 1196328 MD5sum: 291dcd9ae7b63580c6fd4c14bdbb54d7 SHA1: a9121d1619d971b461131ea27be28621b48ae388 SHA256: bd95c536431987f99a2d6df4a46cfb8a7f886a7006bebcdb1516e0b845a396cc SHA512: 9d645be5218bc4b29d5aa3d2dcbaa2ddceea424ae550ff836b2c9e9864584a6dedaab87491f5eaeb47ffae02bcbecd9eaae832b63fd5a1f56295e94eb37d637e Homepage: https://cran.r-project.org/package=esvis Description: CRAN Package 'esvis' (Visualization and Estimation of Effect Sizes) A variety of methods are provided to estimate and visualize distributional differences in terms of effect sizes. Particular emphasis is upon evaluating differences between two or more distributions across the entire scale, rather than at a single point (e.g., differences in means). For example, Probability-Probability (PP) plots display the difference between two or more distributions, matched by their empirical CDFs (see Ho and Reardon, 2012; ), allowing for examinations of where on the scale distributional differences are largest or smallest. The area under the PP curve (AUC) is an effect-size metric, corresponding to the probability that a randomly selected observation from the x-axis distribution will have a higher value than a randomly selected observation from the y-axis distribution. Binned effect size plots are also available, in which the distributions are split into bins (set by the user) and separate effect sizes (Cohen's d) are produced for each bin - again providing a means to evaluate the consistency (or lack thereof) of the difference between two or more distributions at different points on the scale. Evaluation of empirical CDFs is also provided, with built-in arguments for providing annotations to help evaluate distributional differences at specific points (e.g., semi-transparent shading). All function take a consistent argument structure. Calculation of specific effect sizes is also possible. The following effect sizes are estimable: (a) Cohen's d, (b) Hedges' g, (c) percentage above a cut, (d) transformed (normalized) percentage above a cut, (e) area under the PP curve, and (f) the V statistic (see Ho, 2009; ), which essentially transforms the area under the curve to standard deviation units. By default, effect sizes are calculated for all possible pairwise comparisons, but a reference group (distribution) can be specified. Package: r-cran-esviz Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maps, r-cran-climprojdiags, r-cran-sf, r-cran-ggplot2, r-cran-rnaturalearth, r-cran-cowplot, r-cran-s2dv, r-cran-rcolorbrewer, r-cran-cstools, r-cran-easyncdf, r-cran-dplyr, r-cran-plyr, r-cran-data.table, r-cran-reshape2, r-cran-scales, r-cran-foreach, r-cran-rlang, r-cran-kableextra, r-cran-gridextra, r-cran-gtable, r-cran-doparallel, r-cran-mapproj, r-cran-webshot2, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-rnaturalearthdata Filename: pool/dists/noble/main/r-cran-esviz_0.0.2-1.ca2404.1_all.deb Size: 673584 MD5sum: 2d3244ef41f0c0ed285730b34ee4f83a SHA1: 9884174815982c7e31d9459c2218ffd9dfce7070 SHA256: db551b7430b69613eed9ec29543e010ab579df76f8ab6d86764414469d46a69b SHA512: d2ffc277229467b823d916162433c00243afb46a1bdd8b7931ef5d5261ba96c3138dd5b07eb2166caab642f0af38cb25185fc98959982c1436c472244680f9ae Homepage: https://cran.r-project.org/package=esviz Description: CRAN Package 'esviz' (Plotting Functions for Climate Science and Services) A plotting package for climate science and services. Provides a set of functions for visualizing climate data, including maps, time series, scorecards and other diagnostics. Some functions are adapted and extended from the 's2dv' and 'CSTools' packages (Manubens et al. (2018) ; Pérez-Zanón et al. (2022) ), with more consistent and integrated functionalities. Package: r-cran-et.nwfva Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1689 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-et.nwfva_0.2.0-1.ca2404.1_all.deb Size: 1235068 MD5sum: b9280dacfb28bdf074740c50385facd2 SHA1: f5889a97214b740c22fc4974c579cf1f344339c1 SHA256: 1bfe0201b7d5632f5de590a9bb55c7f0e2b097661b12fa3f085637da6d908e52 SHA512: ec1ac038ff06aefe9de7566ea08569fb7546d520fde6fcfb2d2d026532f4e04130e9bd9039a56cb64693a45419f3e8bf7a032cf1fd5da023c598a30702870be5 Homepage: https://cran.r-project.org/package=et.nwfva Description: CRAN Package 'et.nwfva' (Forest Yield Tables for Northwest Germany and their Application) The new yield tables developed by the Northwest German Forest Research Institute (NW-FVA) provide a forest management tool for the five main commercial tree species oak, beech, spruce, Douglas-fir and pine for northwestern Germany. The new method applied for deriving yield tables combines measurements of growth and yield trials with growth simulations using a state-of-the-art single-tree growth simulator. By doing so, the new yield tables reflect the current increment level and the recommended graduated thinning from above is the underlying management concept. The yield tables are provided along with methods for deriving the site index and for interpolating between age and site indices and extrapolating beyond age and site index ranges. The inter-/extrapolations are performed traditionally by the rule of proportion or with a functional approach. Package: r-cran-etable Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-etable_1.3.1-1.ca2404.1_all.deb Size: 136192 MD5sum: d18b6b0307b9b42f7c0720820b199bf5 SHA1: 0807a16735759653f53e6afb2a26f042a7de685d SHA256: 46580442bf5be9ba0cf3bae5890c3ba0698b2155322085bea81f45836c8e50f7 SHA512: 35026fc55e03ab3c0c7734e6a82e65c0fa3f1b265bf9081ed5171a5be3de58b46f3af6ce5d1c68f16c72eac111f659b637f343d9c33c394640ae9e2f2c9f560f Homepage: https://cran.r-project.org/package=etable Description: CRAN Package 'etable' (Easy Table) Creates simple to highly customized tables for a wide selection of descriptive statistics, with or without weighting the data. Package: r-cran-etasbootstrap Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-etas, r-cran-mass, r-cran-spatstat.geom Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-etasbootstrap_0.2.1-1.ca2404.1_all.deb Size: 400702 MD5sum: 303e3b7d87d6c88629cea2d53317fcc8 SHA1: 7696a9464d50d8c9f9ebbc3c42028af4c2db9e98 SHA256: 46478421d6ea3a53aa2864de3ba331f7285db305ccc66c3a2a861a357b77cc2d SHA512: 0ece5423c05276a15a8628d4a564df63bd4e33dfc20da63fcffbb169e59c15ec69b7280bf61cdbedeac835eeae9d614d2970e29c203428f913a1fa41af34a68f Homepage: https://cran.r-project.org/package=ETASbootstrap Description: CRAN Package 'ETASbootstrap' (Bootstrap Confidence Interval Estimation for 'ETAS' ModelParameters) The 2-D spatial and temporal Epidemic Type Aftershock Sequence ('ETAS') Model is widely used to 'decluster' earthquake data catalogs. Usually, the calculation of standard errors of the 'ETAS' model parameter estimates is based on the Hessian matrix derived from the log-likelihood function of the fitted model. However, when an 'ETAS' model is fitted to a local data set over a time period that is limited or short, the standard errors based on the Hessian matrix may be inaccurate. It follows that the asymptotic confidence intervals for parameters may not always be reliable. As an alternative, this package allows for the construction of bootstrap confidence intervals based on empirical quantiles for the parameters of the 2-D spatial and temporal 'ETAS' model. This version improves on Version 0.1.0 of the package by enabling the study space window (renamed 'study region') to be polygonal rather than merely rectangular. A Japan earthquake data catalog is used in a second example to illustrate this new feature. Package: r-cran-etc Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-simcomp, r-cran-multcomp, r-cran-mratios Filename: pool/dists/noble/main/r-cran-etc_1.5-1.ca2404.1_all.deb Size: 55902 MD5sum: 124c0c771bffb128e0cfc537645c8167 SHA1: 9ca9b9ed3b9220683dec2675f963926fa5afaa77 SHA256: 2d59ca4fca0a81910b4668b5adbb9c5f24f6b9757300cd7870703535e877b003 SHA512: d21cae5a00f42d839d0e5ee8f446e44928df43a97d1ad50e9e3b57f26052d81587b34b2585ccad6224c5ea061f1a8886ef731fa55cd74e1a92766b6f8805fa34 Homepage: https://cran.r-project.org/package=ETC Description: CRAN Package 'ETC' (Equivalence to Control) Treatments of a one-way layout, being equivalent to a control, can be selected with this package. Bonferroni adjusted "two one-sided t-tests" (TOST) and related simultaneous confidence intervals are given for both differences or ratios of means of normally distributed data. For the case of equal variances and balanced sample sizes for the treatment groups, the single-step procedure of Bofinger and Bofinger (1995) can be chosen. For non-normal data, the Wilcoxon test is applied. Package: r-cran-etdqualitizer Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-etdqualitizer_1.0.0-1.ca2404.1_all.deb Size: 141636 MD5sum: 287896e3189b2e5bcf92a8e542978e64 SHA1: 2194624ea6e7e5b896d838352e8246e01733ac43 SHA256: 90c4d34fbbb57d952eb0e5c59d2e8bb1e5b19eb9180e784b847528a52a39190e SHA512: 92158ba085e7c8f4de21ad41be45e03778d722e5ffd19919a35907836fbdac63e7f26c75b44a7831143c18e81c6322f4812eca1a5b436a84240f73a2f7c9d35d Homepage: https://cran.r-project.org/package=ETDQualitizer Description: CRAN Package 'ETDQualitizer' (Automated Eye Tracking Data Quality Determination forScreen-Based Eye Trackers) Compute common data quality metrics for accuracy, precision and data loss for screen-based eye trackers. The package supports gaze input in screen pixels or degrees and reports angular measures in degrees where appropriate. If you use this package, please cite Niehorster, D.C., Nyström, M., Hessels, R.S., Benjamins, J.S., Andersson, R., and Hooge, I.T.C. (2026). The fundamentals of eye tracking part 7: Determining data quality. Behavior Research Methods. . Package: r-cran-ethnobotanyr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4851 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circlize, r-cran-cowplot, r-cran-dplyr, r-cran-ggalluvial, r-cran-ggplot2, r-cran-ggridges, r-cran-reshape2, r-cran-magrittr Suggests: r-cran-broom, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-vegan, r-cran-bnlearn, r-cran-causaleffect, r-cran-grain, r-cran-pbapply, r-cran-tidyselect, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-ethnobotanyr_0.2.0-1.ca2404.1_all.deb Size: 2500586 MD5sum: 110b9e8e92f0a0a5dc07ea7e5a03474b SHA1: 52950e8739ed350bf56b3bf554eff1ce92259d39 SHA256: 9e8024ea00ff6033456c623f70e549312c459f24c7e6d0d335d4525eac073d4c SHA512: da3b3132ef7bd8e7fba61bc9ce8db3a1609b9308b8e6e01ea656b88b6a46abc5d408ac9035e41df298ef32b3054bb6bc95b8b74529f4ace94225e9732eeadb3d Homepage: https://cran.r-project.org/package=ethnobotanyR Description: CRAN Package 'ethnobotanyR' (Ethnobotanical Analysis, Decision-Framing, and TEK Modeling) Tools for quantifying Traditional Ecological Knowledge (TEK), modeling TEK in decision frameworks, and designing structured decision-framing exercises in conservation and development contexts. The package implements quantitative ethnobotany indices (Use Value, Relative Frequency of Citation, etc.) but positions them within a larger framework of Bayesian modeling and participatory decision analysis. Includes critical assessment of indices' limitations and case studies of participatory workshops. Package: r-cran-etl Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dbi, r-cran-dbplyr, r-cran-downloader, r-cran-fs, r-cran-janitor, r-cran-lubridate, r-cran-readr, r-cran-rlang, r-cran-rvest, r-cran-tibble, r-cran-usethis, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rsqlite, r-cran-rpostgres, r-cran-rmariadb, r-cran-ggplot2, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-etl_0.4.3-1.ca2404.1_all.deb Size: 127314 MD5sum: e72b0489eed7b60ba6769cd1125b075a SHA1: 24f1c143360cbeafeaa6f7702cf0fd0393d8b31c SHA256: 3b43a0c5846dd955f264c629afb4d975c5aa34184200062e6ede2f69f4b4fa2d SHA512: bacf97e3352bdb178de86ba7818a7a3c7ea1fd6612f842c2bdc538596f47ecf45565f090b4fb56c56e448b00f6135dc14ec44d3d8ac170e6144d014d27b6fc50 Homepage: https://cran.r-project.org/package=etl Description: CRAN Package 'etl' (Extract-Transform-Load Framework for Medium Data) A predictable and pipeable framework for performing ETL (extract-transform-load) operations on publicly-accessible medium-sized data set. This package sets up the method structure and implements generic functions. Packages that depend on this package download specific data sets from the Internet, clean them up, and import them into a local or remote relational database management system. Package: r-cran-etlutils Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ff, r-cran-bit Suggests: r-cran-rsqlite, r-cran-zoo, r-cran-dbi, r-cran-rodbc, r-cran-rjdbc Filename: pool/dists/noble/main/r-cran-etlutils_1.6-1.ca2404.1_all.deb Size: 102310 MD5sum: 9010dc409a315590c32ec79c53842389 SHA1: 1dbe2e3410cc064b8f15baf6f818de885ac4fbbd SHA256: 49f84583c4eddba9304abff61b33283f9fc759f816af1832798101eee2485a99 SHA512: 6ede3a6b4335266354bfef849454485c0ba35aa4f0ecf9275ccbd355b171e890c1e1c6760618eefd7b38b6a77841c7057aa9a9133e96639535cc34fbd9f09072 Homepage: https://cran.r-project.org/package=ETLUtils Description: CRAN Package 'ETLUtils' (Utility Functions to Execute Standard Extract/Transform/LoadOperations (using Package 'ff') on Large Data) Provides functions to facilitate the use of the 'ff' package in interaction with big data in 'SQL' databases (e.g. in 'Oracle', 'MySQL', 'PostgreSQL', 'Hive') by allowing easy importing directly into 'ffdf' objects using 'DBI', 'RODBC' and 'RJDBC'. Also contains some basic utility functions to do fast left outer join merging based on 'match', factorisation of data and a basic function for re-coding vectors. Package: r-cran-etrader Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-httr, r-cran-urltools, r-cran-dplyr, r-cran-rvest, r-cran-purrr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-etrader_0.1.5-1.ca2404.1_all.deb Size: 113612 MD5sum: 89d2bf0dfb41d6efeb4dc0dc6a96a733 SHA1: bddafdcf7d7dabd317e0e2200eb77821c58810e2 SHA256: f4c79ec6844a400c5e92d49de6931cebe5d97cc5da5a76237903f89d7c1bf825 SHA512: 0d13c179c71431db87ecd5423407de22e59f47a26cf2ff491b8ab0f6160becae11880489165ab336da4b10340f4c2ad17d7321a378e59b5ef590a343dec8223d Homepage: https://cran.r-project.org/package=etrader Description: CRAN Package 'etrader' ('ETRADE' API Interface for R) Use R to interface with the 'ETRADE' API . Functions include authentication, trading, quote requests, account information, and option chains. A user will need an ETRADE brokerage account and 'ETRADE' API approval. See README for authentication process and examples. Package: r-cran-etree Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3894 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-braingraph, r-cran-cluster, r-cran-energy, r-cran-fda.usc, r-cran-igraph, r-cran-networkdistance, r-cran-partykit, r-cran-survival, r-cran-tda, r-cran-usedist Suggests: r-cran-knitr, r-cran-mlmetrics, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-etree_0.1.0-1.ca2404.1_all.deb Size: 3120708 MD5sum: aa7d8dfe927756169f6826bafdc80abc SHA1: cd2e45ccd549bd30173099ad872b1518d76db812 SHA256: 90ebc7db28a54fe0c7b98a97195231932f39ec970819bb6a579b5afe0672bddf SHA512: 5e2c4dec1c207f6de827606fbd9e135a85d3fa6f7043ebbde3460f52d714ae9804a0324043cf4c7327f23bd1df216a8cdd464d460b538303d0563de52d1970b3 Homepage: https://cran.r-project.org/package=etree Description: CRAN Package 'etree' (Classification and Regression with Structured and Mixed-TypeData) Implementation of Energy Trees, a statistical model to perform classification and regression with structured and mixed-type data. The model has a similar structure to Conditional Trees, but brings in Energy Statistics to test independence between variables that are possibly structured and of different nature. Currently, the package covers functions and graphs as structured covariates. It builds upon 'partykit' to provide functionalities for fitting, printing, plotting, and predicting with Energy Trees. Energy Trees are described in Giubilei et al. (2022) . Package: r-cran-etrep Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1919 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-shapes, r-cran-morpho, r-cran-matlib, r-cran-rspincalc, r-cran-rotations, r-cran-rvcg, r-cran-fields, r-cran-truncnorm, r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-etrep_1.2.1-1.ca2404.1_all.deb Size: 1902034 MD5sum: 0473b141f855e0cbf26c04b62e1f29fb SHA1: d9dd7334809d85a76b00a1146a5a115553c7def7 SHA256: 0c6a60050a983a16fdbb02f81f54756fb4ad64be8ab62b5665da6e9c016ef8c3 SHA512: 8860b8afd7f857bb481fb18da32f32fdf8ac152775a5e8c5c80d5c4b1c24b3b2f5b228ed321b8fb1d27325b75e8ce60060ea3ef6f4e32c4ae71ecf1df1fbfdde Homepage: https://cran.r-project.org/package=ETRep Description: CRAN Package 'ETRep' (Analysis of Elliptical Tubes Under the Relative CurvatureCondition) Analysis of elliptical tubes with applications in biological modeling. The package is based on the references: Taheri, M., Pizer, S. M., & Schulz, J. (2024) "The Mean Shape under the Relative Curvature Condition." Journal of Computational and Graphical Statistics and arXiv . Mohsen Taheri Shalmani (2024) "Shape Statistics via Skeletal Structures", PhD Thesis, University of Stavanger, Norway . Key features include constructing discrete elliptical tubes, calculating transformations, validating structures under the Relative Curvature Condition (RCC), computing means, and generating simulations. Supports intrinsic and non-intrinsic mean calculations and transformations, size estimation, plotting, and random sample generation based on a reference tube. The intrinsic approach relies on the interior path of the original non-convex space, incorporating the RCC, while the non-intrinsic approach uses a basic robotic arm transformation that disregards the RCC. Package: r-cran-etrm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 920 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-etrm_1.0.1-1.ca2404.1_all.deb Size: 722304 MD5sum: 63ac1cf596144035455133dbc0a57f0d SHA1: 4bcb548692b7806a2a73a67f09a26ba63c815317 SHA256: 1a9f7661a6015836ba0f8c2d47a0adf22e7d6316dc521e586903b7ad09718520 SHA512: ccbe573eff8393580113ffaea9983fcbcf4b1efbe77432426d66bd442c8e4ba591f4c23e58ee967da3f1a318bb980e19ddef81873dce9935c1e0317997ead4ef Homepage: https://cran.r-project.org/package=etrm Description: CRAN Package 'etrm' (Energy Trading and Risk Management) Provides a collection of functions to perform core tasks within Energy Trading and Risk Management (ETRM). Calculation of maximum smoothness forward price curves for electricity and natural gas contracts with flow delivery, as presented in F. E. Benth, S. Koekebakker, and F. Ollmar (2007) and F. E. Benth, J. S. Benth, and S. Koekebakker (2008) . Portfolio insurance trading strategies for price risk management in the forward market, see F. Black (1976) , T. Bjork (2009) , F. Black and R. W. Jones (1987) and H. E. Leland (1980) . Package: r-cran-etrunct Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-etrunct_0.1-1.ca2404.1_all.deb Size: 12146 MD5sum: b8285cc7f6078a691ef8832d280a875e SHA1: 722889d7acd4c4301c1227b16278adba3c9415cf SHA256: 6e06b0c05181b96db124511e4145949f92d3661139a479ad3b89c625b1c6cc51 SHA512: 931c3003deffd82a4848bff6f4cfc5a82ea70e8f6b4b103b759450cf1e06c529341f5ff567527b75b0961373a924a7c964ff9e1daadde84ac8568c35e8b738ac Homepage: https://cran.r-project.org/package=etrunct Description: CRAN Package 'etrunct' (Computes Moments of Univariate Truncated t Distribution) Computes moments of univariate truncated t distribution. There is only one exported function, e_trunct(), which should be seen for details. Package: r-cran-etsi Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hetsurr Filename: pool/dists/noble/main/r-cran-etsi_1.0-1.ca2404.1_all.deb Size: 106698 MD5sum: 0ed82672eb25d6175e161ee7b61f94b7 SHA1: 2300ad6d7084d19bcbfb51f7f00e622bca7c4ff3 SHA256: b875917fde13df90a01c1deb5c55147220517d6be63046c12d979864f30ba876 SHA512: e5b30e674a92f1627ba72cefa41a10697808588b59d2eaf77d8f266d97b74fdb6c979593fe00388653d82b6d366cb3fd0ba1a21b788e85b4b795299ef9033a95 Homepage: https://cran.r-project.org/package=etsi Description: CRAN Package 'etsi' (Efficient Testing Using Surrogate Information) Provides functions for treatment effect estimation, hypothesis testing, and future study design for settings where the surrogate is used in place of the primary outcome for individuals for whom the surrogate is valid, and the primary outcome is purposefully measured in the remaining patients. More details are available in: Knowlton, R., Parast, L. (2024) ``Efficient Testing Using Surrogate Information," Biometrical Journal, 67(6): e70086, . A tutorial for this package can be found at . Package: r-cran-etwfe Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 579 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fixest, r-cran-data.table, r-cran-formula, r-cran-marginaleffects, r-cran-tinyplot Suggests: r-cran-broom, r-cran-did, r-cran-ggplot2, r-cran-knitr, r-cran-modelsummary, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-etwfe_0.6.2-1.ca2404.1_all.deb Size: 211410 MD5sum: 255b87cb1562b56d9a3f08308d3362a4 SHA1: 62560a3e1e6ffc392e82741cac2b9b5d3cf984d0 SHA256: 12c772f4873fa3cdb701ef522b6bf248526597d56280fb1833a2cdf89080a5fb SHA512: 1d0fc72909b02d54dfe032866e07eb92dcdb1a67262597d30bb22a9b29f6930b74ac6255dd727dd9ec14fbf9fd6af5dd765c6ab96ae6f2edbe05d2a56a0afca7 Homepage: https://cran.r-project.org/package=etwfe Description: CRAN Package 'etwfe' (Extended Two-Way Fixed Effects) Convenience functions for implementing extended two-way fixed effect regressions a la Wooldridge (2023, 2025) , . Package: r-cran-euclideansd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Filename: pool/dists/noble/main/r-cran-euclideansd_0.1.0-1.ca2404.1_all.deb Size: 23120 MD5sum: b796c2d07c440cde8a3b6ce570b630f9 SHA1: 9303b8dcabac572db8ee4adc65891c0865bd4490 SHA256: 6ffdec318528e115af6b3cb9b4369974c9fba440bab239ae3373428696dad0a7 SHA512: df37a2a9816b0e0990b3dce7ffe37efb693be4b13a11b10058574eacadfbbbf05478869760339c26acddc66f654d21e766a92c0e9b5097aba3793ad69db08b11 Homepage: https://cran.r-project.org/package=EuclideanSD Description: CRAN Package 'EuclideanSD' (An Euclidean View of Center and Spread) Illustrates the concepts developed in Sarkar and Rashid (2019, ISSN:0025-5742) . This package helps a user guess four things (mean, MD, scaled MSD, and RMSD) before they get the SD. 1) The package displays the Empirical Cumulative Distribution Function (ECDF) of the given data. The user must choose the value of the mean by equating the areas of two colored (blue and green) regions. The package gives feedback to improve the choice until it is correct. Alternatively, the reader may continue with a different guess for the center (not necessarily the mean). 2) The user chooses the values of the Mean Deviation (MD) based on the ECDF of the deviations by equating the areas of two newly colored (blue and green) regions, with feedback from the package until the user guesses correctly. 3) The user chooses the Scaled Mean Squared Deviation (MSD) based on the ECDF of the scaled square deviations by equating the areas of two newly colored (blue and green) regions, with feedback from the package until the user guesses correctly. 4) The user chooses the Root Mean Squared Deviation (RMSD) by ensuring that its intersection with the ECDF of the deviations is at the same height as the intersection between the scaled MSD and the ECDF of the scaled squared deviations. Additionally, the intersection of two blue lines (the green dot) should fall on the vertical line at the maximum deviation. 5) Finally, if the mean is chosen correctly, only then the user can view the population SD (the same as the RMSD) and the sample SD (sqrt(n/(n-1))*RMSD) by clicking the respective buttons. If the mean is chosen incorrectly, the user is asked to correct it. Package: r-cran-eudata Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fs, r-cran-httr2, r-cran-purrr, r-cran-tibble, r-cran-cli, r-cran-rappdirs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-sf, r-cran-glue Filename: pool/dists/noble/main/r-cran-eudata_0.1.3-1.ca2404.1_all.deb Size: 71638 MD5sum: e4eb1c62bfee32dbf5304dd8f3219b5a SHA1: 016cee186720eaedad26f15cd297eb66bce6aef0 SHA256: 4ad5ba13b38c28f8db9c3720440b454470a4cd3c441a240534bfb1e8c609b3e8 SHA512: 95a4966284bed1c64d3009797f0a89f3c01ef7357a5b60a42f6bf63a5858e5004a961536d4c3edbc7eae333fe3b0459e02588d39e80760ad82f7c392af92b8f2 Homepage: https://cran.r-project.org/package=eudata Description: CRAN Package 'eudata' (Access Data from 'GISCO') Access data related to the European union from 'GISCO' , the Geographic Information System of the European Commission, via its rest API at . 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The amount of information required and the format of the results, however, imposes a large extra workload at the end of studies on clinical trial units. In particular, the adverse-event-reporting component requires entering: each unique combination of treatment group and safety event; for every such event above, a further 4 pieces of information (body system, number of occurrences, number of subjects, number exposed) for non-serious events, plus an extra three pieces of data for serious adverse events (numbers of causally related events, deaths, causally related deaths). This package prepares the required statistics needed by EudraCT and formats them into the precise requirements to directly upload an XML file into the web portal, with no further data entry by hand. Package: r-cran-eufmdis.adapt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-magrittr, r-cran-dplyr, r-cran-tibble, r-cran-tidyselect, r-cran-ggplot2, r-cran-shiny, r-cran-shinydashboard, r-cran-shinywidgets, r-cran-htmltools, r-cran-dt Filename: pool/dists/noble/main/r-cran-eufmdis.adapt_0.1.0-1.ca2404.1_all.deb Size: 212494 MD5sum: de2f5e2fac46989b9b3811686d88e2f4 SHA1: d27fbc10adaefb650d365f0c25cd7fa752d9d1ed SHA256: bb6d86d32761d5f77e5e1cdf02abc2c86957208d8e6eea8f152f42fe9464acee SHA512: 4beb601545e342d9a22c010f8ce3a28f696f3075cab8cbe4860544bfa36e1609a0bdf96ea4c7aff512f0a749072db7e9b847162038eb0acc148ccc3390c2308c Homepage: https://cran.r-project.org/package=eufmdis.adapt Description: CRAN Package 'eufmdis.adapt' (Analyse 'EuFMDiS' Output Files via a Shiny App) Analyses 'EuFMDiS' output files in a Shiny App. The distributions of relevant output parameters are described in form of tables (quantiles) and plots. The App is called using eufmdis.adapt::run_adapt(). 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Includes Seasons 2010/2011 until 2019/2020 and a set of interesting covariates. Can be used all purposes. Package: r-cran-eulerian Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-graph Filename: pool/dists/noble/main/r-cran-eulerian_1.0-1.ca2404.1_all.deb Size: 29234 MD5sum: a5d032af8bbb1c9427f32817a02580ce SHA1: 34625a82be3e425c1e6adcbcc13bd38903b18d20 SHA256: 16b1b9077dcdc610fadc580b77afede6240c7dbd1edcb335a1866010eb055cd0 SHA512: 4e4269266f1706a185d84cfe6cb3dbe79fd12b05900fd5bee4955cf6820f6000e5c74550e6f5891041718a959b930440fdfd671cbd15c65e1f633053cd9d1fec Homepage: https://cran.r-project.org/package=eulerian Description: CRAN Package 'eulerian' (eulerian: A package to find eulerian paths from graphs) An eulerian path is a path in a graph which visits every edge exactly once. This package provides methods to handle eulerian paths or cycles. Package: r-cran-eunis.habitats Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2005 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Filename: pool/dists/noble/main/r-cran-eunis.habitats_0.1.0-1.ca2404.1_all.deb Size: 1723608 MD5sum: 44bd9fec698d76d40954d964a220bbee SHA1: 305fa98787752560cb75a2b283214af3c6a3679b SHA256: d5bb8dc357822ec6612aab7d8719539c2c8e7ba8f2c92ee3f15508aa403a30e5 SHA512: 015d50700d81a33dd11ed842c912f2946cd53cc8b31d8a947a16bd086c5d026709e6e1893d7a597fbd46907d4e8f769d7d22e9145d4109bc798deab6481c876b Homepage: https://cran.r-project.org/package=eunis.habitats Description: CRAN Package 'eunis.habitats' (EUNIS Habitat Classification) The EUNIS habitat classification is a comprehensive pan-European system for habitat identification . This is an R data package providing the EUNIS classification system. The classification is hierarchical and covers all types of habitats from natural to artificial, from terrestrial to freshwater and marine. The habitat types are identified by specific codes, names and descriptions and come with schema crosswalks to other habitat typologies. Package: r-cran-eunomia Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readr, r-cran-rlang, r-cran-rsqlite, r-cran-dbi, r-cran-arrow, r-cran-commondatamodel Suggests: r-cran-testthat, r-cran-withr, r-cran-duckdb, r-cran-databaseconnector Filename: pool/dists/noble/main/r-cran-eunomia_2.1.0-1.ca2404.1_all.deb Size: 46468 MD5sum: edaf6005e16b01cdbb8b168ea2030e0c SHA1: f8715479377b0713cc36778d5711c3f774d2dcbc SHA256: d3165889687137ac1b552ae90dfa7b848ee6fa1c5ededc5e771f020732865c33 SHA512: 3172ca48bfa5332b719553480a9e2029b077126e4ab8465362fd0ebc6cb553f24a218e426ff6a5c173c97919ee19c65be22c63563f9045b0b9f0ee5c864a8dc0 Homepage: https://cran.r-project.org/package=Eunomia Description: CRAN Package 'Eunomia' (Standard Dataset Manager for Observational Medical OutcomesPartnership Common Data Model Sample Datasets) Facilitates access to sample datasets from the 'EunomiaDatasets' repository (). Package: r-cran-eurlex Architecture: all Version: 0.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-httr, r-cran-curl, r-cran-rvest, r-cran-pdftools, r-cran-antiword Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidytext Filename: pool/dists/noble/main/r-cran-eurlex_0.4.9-1.ca2404.1_all.deb Size: 349734 MD5sum: 16f10626cde892171a4937388616298f SHA1: c1855d4382da229c97c25c1d6d904bc4dcd707f6 SHA256: 53b60c6f7ef39421efac8185f66620999db503c7b6247ace275c662ad15631a7 SHA512: 17596d615364deed9934516cd3e94a43ae110a3d89ac824b0e3ea7af050bb06956ea8819314ddad7755cca45bf441e4413fc11a3dff9c2b6002b87ff99dbaac5 Homepage: https://cran.r-project.org/package=eurlex Description: CRAN Package 'eurlex' (Retrieve Data on European Union Law) Access to data on European Union laws and court decisions made easy with pre-defined 'SPARQL' queries and 'GET' requests. See Ovadek (2021) . Package: r-cran-eurocordexr Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1768 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-magrittr, r-cran-ncdf4, r-cran-ncdf4.helpers, r-cran-rnetcdf, r-cran-fs, r-cran-pcict, r-cran-lubridate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-eurocordexr_0.2.5-1.ca2404.1_all.deb Size: 846980 MD5sum: 93bd5663415a274ea5b79f8a60d22e92 SHA1: bc131209184e7d32a24f784a08f61cc43153adcf SHA256: a10bd27b30e4e801863b7c4ac5e80ab059172108d86c9f14486b5b3253da8485 SHA512: 2b96e3d76301635e8fa48195c06abbb39ef123b613251e723db5b47bc8b71e1db6ff2cc0b29b8281d8c5cf1503b408fe46d8590cd0ccd1ca6ab0f203e3615a75 Homepage: https://cran.r-project.org/package=eurocordexr Description: CRAN Package 'eurocordexr' (Makes it Easier to Work with Daily 'netCDF' from EURO-CORDEXRCMs) Daily 'netCDF' data from e.g. regional climate models (RCMs) are not trivial to work with. This package, which relies on 'data.table', makes it easier to deal with large data from RCMs, such as from EURO-CORDEX (, ). It has functions to extract single grid cells from rotated pole grids as well as the whole array in long format. Can handle non-standard calendars (360, noleap) and interpolate them to a standard one. Potentially works with many CF-conform 'netCDF' files. Package: r-cran-euroleaguer Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3301 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-euroleaguer_0.2.0-1.ca2404.1_all.deb Size: 3233866 MD5sum: 706cf5149dda8c6c524789d3c238d85e SHA1: 772a7bf7e65c0f0dd93e3f429615e8fa5cda65ba SHA256: 2aea75799ac60fb6ea1094fea06bb29930271be1d600c8ecbbc3ed1f9d310289 SHA512: 6f112a74d6fb048e5903b602d93026af492700187160a56a06ebfbd98bf3a27053ff36f7d7b14134c2d8c164db90c9392ddd82b078cfeeca6fec3e4e3569f256 Homepage: https://cran.r-project.org/package=euroleaguer Description: CRAN Package 'euroleaguer' ('Euroleague basketball API') Unofficial API wrapper for 'Euroleague' and 'Eurocup' basketball API (), it allows to retrieve real-time and historical standard and advanced statistics about competitions, teams, players and games. Package: r-cran-euronext Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-magrittr, r-cran-stringr, r-cran-jsonlite, r-cran-rlang, r-cran-rvest, r-cran-httr2, r-cran-xts, r-cran-dplyr, r-cran-flextable, r-cran-highcharter Suggests: r-cran-lubridate Filename: pool/dists/noble/main/r-cran-euronext_2.0.2-1.ca2404.1_all.deb Size: 260192 MD5sum: 6d1d96e479ef523069895c3e6cd33a2b SHA1: 87b1b591ee0ae82ff8204f3aa5523d8fc4a82a17 SHA256: 3eba60600fe8581899aa2815ed88ff8add470847302e2291a8680f1ad2a13908 SHA512: 44feeac58cee318d968962b7d0de20371f99b1afb636dde43a793beb8fcae9bd89ac245d3c3f16b8846e6cbaaeb4d2f706f944c4e51fb92ae6c69f7b40ecf8df Homepage: https://cran.r-project.org/package=Euronext Description: CRAN Package 'Euronext' (Retrieve Historical Data of Companies Listed on the 'Euronext'Stock Exchange) Provides seamless access to historical data of companies listed on the 'Euronext' Stock Exchange (), enabling users to retrieve real-time information directly within the R environment. With functions tailored for data retrieval and manipulation, users can effortlessly access a wide range of financial data, including stock prices, trading volumes, and more. Leveraging the power of R, this package facilitates efficient analysis and visualization of stock market trends, aiding investors, analysts, and researchers in making informed decisions. By combining ease of use with comprehensive data access, 'Euronext' empowers R users to delve deep into the dynamics of European financial markets, offering valuable insights for various financial applications. Package: r-cran-europeanar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-rdpack Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-europeanar_0.1.0-1.ca2404.1_all.deb Size: 54604 MD5sum: f1d8d5a1b660d251cb9700ce8b6a59ca SHA1: 8a0a618b3488ebb77119eadb0374be624a3557f8 SHA256: 6c1853589097973ba61cf446489b7fc8b4145756d4a5b9daee078be78244f3e8 SHA512: 8338f88b67ef1075c97b88e403a612a29c6705c796f2e2aa712f6f48ebecd0631927d63f6dee5232eec0385e4489900ae353b645766839322f95f3afed264c1a Homepage: https://cran.r-project.org/package=europeanaR Description: CRAN Package 'europeanaR' (Interact with Metadata Records and Media on the EuropeanaRepository) Interact with the Europeana Data Model via a variety of API endpoints that contains digital collections from thousands of institutions around Europe. This translates to millions of Cultural Heritage Objects in the form of image, text, video, sound and 3D, accompanied by rich metadata. The Data Model design principles are based on the core principles and best practices of the Semantic Web and Linked Data efforts to which Europeana contributes (see, e.g., Doerr, Martin, et al. The europeana data model (edm). World Library and Information Congress: 76th IFLA general conference and assembly. Vol. 10. 2010.). The package also provides methods for bulk downloads of specific subsets of items, including both their metadata and their associated media files. Package: r-cran-europepmc Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 787 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-plyr, r-cran-dplyr, r-cran-progress, r-cran-urltools, r-cran-purrr, r-cran-xml2, r-cran-tibble, r-cran-tidyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-europepmc_0.4.3-1.ca2404.1_all.deb Size: 431826 MD5sum: 513ce0649e43d96ee39f808bf8169f7f SHA1: 303d5f825528d639786ebb294956554d6e8992f4 SHA256: adac47c92dc864b809bf90d6eb8c01d401c0143ca14a376fd7d7d0bce74d9246 SHA512: 6685f3fcc4354d86973a6a50ab1417183017fade65d79fd72b6dbf9e4ba0cf7a2f50b1dc2397a5cfcfec06fc2820848e0cb4e32f362474668e9d77e40447c5b3 Homepage: https://cran.r-project.org/package=europepmc Description: CRAN Package 'europepmc' (R Interface to the Europe PubMed Central RESTful Web Service) An R Client for the Europe PubMed Central RESTful Web Service (see for more information). It gives access to both metadata on life science literature and open access full texts. Europe PMC indexes all PubMed content and other literature sources including Agricola, a bibliographic database of citations to the agricultural literature, or Biological Patents. In addition to bibliographic metadata, the client allows users to fetch citations and reference lists. Links between life-science literature and other EBI databases, including ENA, PDB or ChEMBL are also accessible. No registration or API key is required. See the vignettes for usage examples. Package: r-cran-europop Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-readr Filename: pool/dists/noble/main/r-cran-europop_0.3.1-1.ca2404.1_all.deb Size: 26328 MD5sum: 47189e42a4527b230397cbabb556709f SHA1: 9832ecbbccc3821bb02242fcce0e7de800b1e503 SHA256: 84c3e64ede059f3fe6d748fab2fc2f211a307480b8026af57b4f2c41b4670157 SHA512: e28dfab44a5a656e4c3abf5113ed695cc8899c59d9de325864a0e55eb2c60e7a1ce6e7c5a50c3202ab834f72c42c03fc94c7e468d24be3e1e217fa417ddaa87e Homepage: https://cran.r-project.org/package=europop Description: CRAN Package 'europop' (Historical Populations of European Cities, 1500-1800) This dataset contains population estimates of all European cities with at least 10,000 inhabitants during the period 1500-1800. These data are adapted from Jan De Vries, "European Urbanization, 1500-1800" (1984). Package: r-cran-eurosarcbayes Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-vgam, r-cran-data.table, r-cran-plyr, r-cran-clinfun Filename: pool/dists/noble/main/r-cran-eurosarcbayes_1.1-1.ca2404.1_all.deb Size: 357806 MD5sum: 6a37b8ef6304e3dc080f050b321e63c6 SHA1: c2d902b3a525144f6f388ba935641d9b1e9e40a4 SHA256: ad892bb3f70025bf33c2e098fb813e5e59d549b3af2ebfe2309f7c72cd26822e SHA512: 5604f7d4d9110acd256298853cb741957c4bd9fa050c6e93cf21bd975490b9661d138007b6a745a77ca883908de01f51c514a376b6bdec043131426903e10fb5 Homepage: https://cran.r-project.org/package=EurosarcBayes Description: CRAN Package 'EurosarcBayes' (Bayesian Single Arm Sample Size Calculation Software) Bayesian sample size calculation software and examples for EuroSARC clinical trials which utilise Bayesian methodology. These trials rely on binomial based endpoints so the majority of programs found here relate to this sort of endpoint. Developed as part of the EuroSARC FP7 grant. Package: r-cran-eurostat Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-classint, r-cran-countrycode, r-cran-curl, r-cran-digest, r-cran-dplyr, r-cran-httr2, r-cran-isoweek, r-cran-jsonlite, r-cran-lubridate, r-cran-rappdirs, r-cran-readr, r-cran-refmanager, r-cran-regions, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-xml2, r-cran-data.table Suggests: r-cran-giscor, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eurostat_4.0.0-1.ca2404.1_all.deb Size: 567486 MD5sum: 1cf364b4544a990f120ab5fc395b3c67 SHA1: 0d9d3ec7bbaa4d21ed633966bec7f4e8f01003a4 SHA256: 4b5e488bc866443c66c4a4a683eefcb15fdc4f77908f53d742fa6a42c2191caf SHA512: ca50b8cd6326389631fada2c97886549ddfefd80e032325e25162a2e97f2b5e989100699d7027466d8f0f75d87b5a924a3077debe60cc69aeaaed96cc415f683 Homepage: https://cran.r-project.org/package=eurostat Description: CRAN Package 'eurostat' (Tools for Eurostat Open Data) Tools to download data from the Eurostat database together with search and manipulation utilities. Package: r-cran-eva3dm Architecture: all Version: 1.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4795 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-ncdf4 Suggests: r-cran-knitr, r-cran-riem, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eva3dm_1.20-1.ca2404.1_all.deb Size: 3263060 MD5sum: b6c6ae019d9160cad3e2fe641ffb7313 SHA1: e8627bc61a08376b998ae8ab91c097ba380f3e58 SHA256: 93471d8c8fe480e6987a50dbf4bd7554286c0584a31fd187e9373d5486e03cda SHA512: 21578f154664317a4090a8edff74fdc135fcbcb5927c19e64086e34c3a08a555e822b0615eb7e42b85586fa20f742e3aadc52dcbaf255593378079084c844e70 Homepage: https://cran.r-project.org/package=eva3dm Description: CRAN Package 'eva3dm' (Evaluation of 3D Meteorological and Air Quality Models) Provides tools for post-process, evaluate and visualize results from 3d Meteorological and Air Quality models against point observations (i.e. surface stations) and grid (i.e. satellite) observations. Package: r-cran-eva Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2758 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-envstats Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-spatialextremes Filename: pool/dists/noble/main/r-cran-eva_0.2.6-1.ca2404.1_all.deb Size: 2278628 MD5sum: 281130350e2512c28912daf30cd22b3a SHA1: f755802bd5b71d8ffde0db92978582e9420034df SHA256: 112628cd16ac91b722930901a188516bfee82e322665097e25365e19506e775b SHA512: 26b1a84008b0c2309fe55bb7aa7fb7688c5f2f4ab842b015ae85274774687edde20d41e46333f21d461800aa873e9d315b4ad5ccc68a1e0b14f3648dab6eaade Homepage: https://cran.r-project.org/package=eva Description: CRAN Package 'eva' (Extreme Value Analysis with Goodness-of-Fit Testing) Goodness-of-fit tests for selection of r in the r-largest order statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the Generalized Pareto distribution (GPD). Random number generation and density functions for the GEVr distribution. Profile likelihood for return level estimation using the GEVr and Generalized Pareto distributions. P-value adjustments for sequential, multiple testing error control. Non-stationary fitting of GEVr and GPD. Bader, B., Yan, J. & Zhang, X. (2016) . Bader, B., Yan, J. & Zhang, X. (2018) . Package: r-cran-evabic Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-evabic_0.1.4-1.ca2404.1_all.deb Size: 147156 MD5sum: 81eb9d07db0cd7aadccfc1f01b3c3079 SHA1: 71e8fd88ce5aac625a0870221d2959338a4c09eb SHA256: 0e6d4c80e439904146aefeda11bcd4b453d9f44e23d0bc8b01805da751cff875 SHA512: 3689e370f8adc09b2ddc2f7f912e648bd8da452839491bf550757d21d30e3274279b9f74d7d045b50a805bba5f2559a8349748dab74b72c127f7438fe766866f Homepage: https://cran.r-project.org/package=evabic Description: CRAN Package 'evabic' (Evaluation of Binary Classifiers) Evaluates the performance of binary classifiers. Computes confusion measures (TP, TN, FP, FN), derived measures (TPR, FDR, accuracy, F1, DOR, ..), and area under the curve. Outputs are well suited for nested dataframes. Package: r-cran-evacluster Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mlbench, r-cran-emcluster, r-cran-inaparc, r-cran-ppclust, r-cran-fresa.cad, r-cran-mass, r-cran-class, r-cran-nmf, r-cran-cluster, r-cran-rtsne, r-cran-proxy, r-cran-uwot, r-cran-mclust Filename: pool/dists/noble/main/r-cran-evacluster_0.1.0-1.ca2404.1_all.deb Size: 67112 MD5sum: d24bee06fa29e71b641abb95fa355ef6 SHA1: 3950b0dcb653410eaf0b7654093c8ba8255d1717 SHA256: 5be2c12464b6c69a08a608a3a191cd381b453340918626ae978527db271cd5e4 SHA512: a3a95de237eec1385d48387bfbd501a518bd604304d1b4ebde5cb9af0f6a2edffbc1cacc8182d2a216e93b7f39c07032a9314a65dede461d849691c966cda476 Homepage: https://cran.r-project.org/package=Evacluster Description: CRAN Package 'Evacluster' (Evaluation Clustering Methods for Disease Subtypes Diagnosis) Contains a set of clustering methods and evaluation metrics to select the best number of the clusters based on clustering stability. Two references describe the methodology: Fahimeh Nezhadmoghadam, and Jose Tamez-Pena (2021), and Fahimeh Nezhadmoghadam, et al.(2021). Package: r-cran-evalhte Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-cli, r-cran-evalitr, r-cran-ggplot2, r-cran-ggthemes, r-cran-rlang, r-cran-zoo, r-cran-furrr, r-cran-ggdist, r-cran-scales, r-cran-tidyr, r-cran-purrr, r-cran-matrix, r-cran-mass, r-cran-quadprog, r-cran-caret Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-future, r-cran-grf, r-cran-magrittr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-evalhte_0.1.1-1.ca2404.1_all.deb Size: 135436 MD5sum: b55272fd36b6ba8f5cfb362e8a06fcea SHA1: fe3f50f824a969d45dcce3725aca3f4dedbbc1cb SHA256: 32fe68cce74af878c38257f96f7983a91b0c7351356233f8563aa816110d6e30 SHA512: 6dbcbca3a18da76b846455b41a7a7471b05098a2218fb341d5bd5cedde8807a440948e69338c2e0403dc71a4da316b4c2b8c62bc1609cfc990c2b02444168bd9 Homepage: https://cran.r-project.org/package=evalHTE Description: CRAN Package 'evalHTE' (Evaluating Heterogeneous Treatment Effects) Provides various statistical methods for evaluating heterogeneous treatment effects (HTE) in randomized experiments. The package includes tools to estimate uniform confidence bands for estimation of the group average treatment effect sorted by generic machine learning algorithms (GATES). It also provides the tools to identify a subgroup of individuals who are likely to benefit from a treatment the most "exceptional responders" or those who are harmed by it. Detailed reference in Imai and Li (2023) . Package: r-cran-evalitr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1475 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-mass, r-cran-matrix, r-cran-quadprog, r-cran-caret, r-cran-cli, r-cran-e1071, r-cran-forcats, r-cran-gbm, r-cran-ggdist, r-cran-ggplot2, r-cran-ggthemes, r-cran-glmnet, r-cran-grf, r-cran-haven, r-cran-purrr, r-cran-rlang, r-cran-rpart, r-cran-rqpen, r-cran-scales, r-cran-bartcause, r-cran-superlearner Suggests: r-cran-doparallel, r-cran-furrr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bartmachine, r-cran-elasticnet, r-cran-randomforest, r-cran-spelling Filename: pool/dists/noble/main/r-cran-evalitr_1.0.0-1.ca2404.1_all.deb Size: 1111294 MD5sum: 8e677916f31667ec268048955aa0c474 SHA1: 7ce61b1f96c5016a96aaca1471a951cc528b5188 SHA256: 27db17221b83baa439f2b27b534e39ec7ec38b1c07897090648b6280b6bce29c SHA512: 8493cfcf2dfa2c7180eea0f7c504b534bf927b61bb4ee49e09cc8103a6dfe2f507733e376c6e79725771b6a6fab9b2eb401e4006e9d7efa8b7208ccae0347649 Homepage: https://cran.r-project.org/package=evalITR Description: CRAN Package 'evalITR' (Evaluating Individualized Treatment Rules) Provides various statistical methods for evaluating Individualized Treatment Rules under randomized data. The provided metrics include Population Average Value (PAV), Population Average Prescription Effect (PAPE), Area Under Prescription Effect Curve (AUPEC). It also provides the tools to analyze Individualized Treatment Rules under budget constraints. Detailed reference in Imai and Li (2019) . Package: r-cran-evaltest Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-ggplot2, r-cran-ggpubr, r-cran-openxlsx, r-cran-proc, r-cran-readxl, r-cran-shiny, r-cran-shinydashboard, r-cran-binom Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-evaltest_1.0.6-1.ca2404.1_all.deb Size: 214442 MD5sum: 059fc6285cba867906fe0fc986f33c4e SHA1: 27b787d5928f63b843aa954836b649dab85f4224 SHA256: 02b56211390d1cab0db19e34b5dcd27b2ab885705e7416742d959a4179b3d337 SHA512: b31ca6f68e4774211ae768631d42d3f7cd8998f9ee70da82947612fee583273cd078949a301a82107897468597c387761625495e33d250dce7c3027827ea770f Homepage: https://cran.r-project.org/package=EvalTest Description: CRAN Package 'EvalTest' (Tools for Evaluating Diagnostic Test Performance) Evaluates diagnostic test performance using data from laboratory or diagnostic research. 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Package: r-cran-evaluate Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-callr, r-cran-covr, r-cran-ggplot2, r-cran-lattice, r-cran-pkgload, r-cran-ragg, r-cran-rlang, r-cran-knitr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-evaluate_1.0.5-1.ca2404.1_all.deb Size: 102334 MD5sum: 4dfcbb20ac4d36b1dda50d4f3e38bac0 SHA1: 0ad7c041618f7adad5ced939eb5143ee12d259e4 SHA256: 7438124389bb8b761a33b75ce317d5f4d40fc30a8c8e5a69816b0a3a2d4d32f6 SHA512: 57753380195349e09896bcfd1466b3aa907258dc5cdc4fc5d09fb4bb174d4e747fdb49031609261302034aa1cc9c4b3a48ab038321bef281de8fedeaa42c8c5f Homepage: https://cran.r-project.org/package=evaluate Description: CRAN Package 'evaluate' (Parsing and Evaluation Tools that Provide More Details than theDefault) Parsing and evaluation tools that make it easy to recreate the command line behaviour of R. 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(2015) . Package: r-cran-evaluationmeasures Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-evaluationmeasures_1.1.0-1.ca2404.1_all.deb Size: 56876 MD5sum: 90d37974804e2acce7d2587c243ded97 SHA1: e14431a81642d41d4f7016bb94907d08824948c0 SHA256: 3969bd06da73869053c6553bca9a0b3c5fe9232aa2f1b77357b4373ac756f4be SHA512: e767ed83a459c08c859b130b1b89492358271a43ed556150c6a4b6d86555ea4feded7dbfcf6aa95d7e5597d775577d8b8ce7faaec70caf87d8a592252968e416 Homepage: https://cran.r-project.org/package=EvaluationMeasures Description: CRAN Package 'EvaluationMeasures' (Collection of Model Evaluation Measure Functions) Provides Some of the most important evaluation measures for evaluating a model. Just by giving the real and predicted class, measures such as accuracy, sensitivity, specificity, ppv, npv, fmeasure, mcc and ... will be returned. Package: r-cran-evalue Architecture: all Version: 4.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-metafor, r-cran-boot, r-cran-metautility, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-metadat Filename: pool/dists/noble/main/r-cran-evalue_4.1.4-1.ca2404.1_all.deb Size: 828304 MD5sum: 1c32edd1f62c209dfb0caf8f1866db38 SHA1: 4a1a175f108bb50c5bbef071fb21d61ab2fb7c19 SHA256: d80bcf06cdb0dd690ca43def9b8da692a9c87c74a5232d61b3bf17aebff6d1a5 SHA512: 84adece96077f839cbca73274ce82b5a519cbeb7736f79b640815592ee2b5d60e7d3f174b90dae22283093668c1f3f5ff8dddf6d6f8f33b89d71c2cddc956f23 Homepage: https://cran.r-project.org/package=EValue Description: CRAN Package 'EValue' (Sensitivity Analyses for Unmeasured Confounding and Other Biasesin Observational Studies and Meta-Analyses) Conducts sensitivity analyses for unmeasured confounding, selection bias, and measurement error (individually or in combination; VanderWeele & Ding (2017) ; Smith & VanderWeele (2019) ; VanderWeele & Li (2019) ; Smith, Mathur, & VanderWeele (2021) ). 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Provides ~50 functions covering package management, color palette management, data visualization (bar, pie, density, Venn, forest plots), statistical testing (t-test, ANOVA, chi-square, correlation, power analysis), bioinformatics utilities (gene ID conversion, GMT parsing, GEO access), and custom infix operators. Implementation follows tidyverse principles (Wickham et al. (2019) ). Package: r-cran-evapore Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3492 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-lubridate, r-cran-raster, r-cran-twc Suggests: r-cran-rmarkdown, r-cran-ggpubr, r-cran-knitr, r-cran-spelling, r-cran-kableextra, r-cran-tibble, r-cran-testthat Filename: pool/dists/noble/main/r-cran-evapore_1.0.1-1.ca2404.1_all.deb Size: 2450884 MD5sum: 37dfb308fc52eef3121e9ef500115f66 SHA1: b7d35ed4a874cf89ae4cbc3acc944037f8184163 SHA256: 743e618f009ae865eedff89958a5ca1c3a8146583b5cbdbacd9a13bf735eefc0 SHA512: 85ec0941e3ce9d53fcd7931d72763fa65e8f220c2f8b6b3ce75cf50bde9f655cd2fa18421c58b702d26420092e6e0917e65528e07fc8b4fa2515361075d27b28 Homepage: https://cran.r-project.org/package=evapoRe Description: CRAN Package 'evapoRe' (Evapotranspiration R Recipes) An R-based application for exploratory data analysis of global EvapoTranspiration (ET) datasets. 'evapoRe' enables users to download, validate, visualize, and analyze multi-source ET data across various spatio-temporal scales. Also, the package offers calculation methods for estimating potential ET (PET), including temperature-based, combined type, and radiation-based approaches described in : Oudin et al., (2005) . 'evapoRe' supports hydrological modeling, climate studies, agricultural research, and other data-driven fields by facilitating access to ET data and offering powerful analysis capabilities. Users can seamlessly integrate the package into their research applications and explore diverse ET data at different resolutions. Package: r-cran-evapotranspiration Architecture: all Version: 1.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 627 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo Filename: pool/dists/noble/main/r-cran-evapotranspiration_1.16-1.ca2404.1_all.deb Size: 595556 MD5sum: 5fc742b7bd92119020547b2818226536 SHA1: a27780eb5c93a44a0acb2363aaec891dfe85dda5 SHA256: e5440454a3c890673826b821de06b79587c325b544e0a41e4dc2816b725c1682 SHA512: 3b7dcfd703ea79cac409845a8184b5c91711d348bd0df80df090a3712a69f48424370c56a08b87b2552e35e51f434caa5c8392e516a3c5b1b34bcc82cb069ace Homepage: https://cran.r-project.org/package=Evapotranspiration Description: CRAN Package 'Evapotranspiration' (Modelling Actual, Potential and Reference CropEvapotranspiration) Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 21 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations. Package: r-cran-evcgsampler Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3443 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggforce, r-cran-osqp, r-cran-patchwork, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-evcgsampler_1.0.0-1.ca2404.1_all.deb Size: 2274268 MD5sum: a60e9a15694defec2d6f25b24c59ee7d SHA1: 401b34e32c328111da0621614a84ddb1db7401e0 SHA256: 1d0ffa058aa72e9ab4b0f81ac86adca948c1da2dd329ae9acd13428458ac8cbe SHA512: 1b331a6581f60ce00c25584c1e84637a5b97402930f06bb69243acebab3e0e9371b71ef8d41bc37a30e4fa8c9d3cf53f7392028adb8556d9babef56f5f326074 Homepage: https://cran.r-project.org/package=eVCGsampler Description: CRAN Package 'eVCGsampler' (VCG Sampling using Energy-Based Covariate Balancing) Provides a principled framework for sampling Virtual Control Group (VCG) using energy distance-based covariate balancing. The package offers visualization tools to assess covariate balance and includes a permutation test to evaluate the statistical significance of observed deviations. Package: r-cran-evchargcost Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-evchargcost_0.1.0-1.ca2404.1_all.deb Size: 23702 MD5sum: 4409672f8c0a6e0c86ee467ad178fc29 SHA1: cd3a1bce21fd593ec3b9136484f396125e9261b6 SHA256: 4167570f6ec071bc2b449b9946659dc79c85a527b3ee55ceed6bfaa494ad6037 SHA512: 7b62914931cf84926b1661032034794300c9fb5d1a6706785b5f6b76ad01393436d2f4418c6e22f71136e0c582a5489ca259682415b04df2d715bbc8ef6bbdfe Homepage: https://cran.r-project.org/package=EVchargcost Description: CRAN Package 'EVchargcost' (Computes and Plot the Optimal Charging Strategy for ElectricVehicles) The purpose of this library is to compute the optimal charging cost function for a electric vehicle (EV). It is well known that the charging function of a EV is a concave function that can be approximated by a piece-wise linear function, so bigger the state of charge, slower the charging process is. Moreover, the other important function is the one that gives the electricity price. This function is usually step-wise, since depending on the time of the day, the price of the electricity is different. Then, the problem of charging an EV to a certain state of charge is not trivial. This library implements an algorithm to compute the optimal charging cost function, that is, it plots for a given state of charge r (between 0 and 1) the minimum cost we need to pay in order to charge the EV to that state of charge r. The details of the algorithm are described in González-Rodríguez et at (2023) . Package: r-cran-evclass Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-ibelief, r-cran-r.utils Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-nnet Filename: pool/dists/noble/main/r-cran-evclass_2.0.2-1.ca2404.1_all.deb Size: 322530 MD5sum: 54e604a49750aec413e3bac578f029a7 SHA1: 5901eb4c4bf1e02df431061a5c9e2e02b901f7ed SHA256: 6e2f3aecbe963e3f67eeac6c8c9588577b88eed0b31801e5f0ab614020c4d68d SHA512: ccb63caa40cbe9a0ef9c8d4c3fcdcf6b7297a43df719a71a4ba13e70af375cf8645d5ae7cf359ed44cb0b3528d4de95e4f4d72a82e62a63cdf40565739308935 Homepage: https://cran.r-project.org/package=evclass Description: CRAN Package 'evclass' (Evidential Distance-Based Classification) Different evidential classifiers, which provide outputs in the form of Dempster-Shafer mass functions. The methods are: the evidential K-nearest neighbor rule, the evidential neural network, radial basis function neural networks, logistic regression, feed-forward neural networks. Package: r-cran-evclust Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-r.utils, r-cran-limsolve, r-cran-matrix, r-cran-mclust, r-cran-quadprog, r-cran-plyr Suggests: r-cran-kernlab, r-cran-mass Filename: pool/dists/noble/main/r-cran-evclust_2.0.3-1.ca2404.1_all.deb Size: 2958628 MD5sum: 8aba4876a667fc47e8fe1471e50ec2b7 SHA1: c9fd8359aacd29633bea67a316f6832baba9632b SHA256: d48534956eb6c80df546644c527e00dcc5e79fea24b9972e60e4b2a8f17826cb SHA512: ad38b830bd2c4fbb7c9d39e097d0278c0d1c5ca1015bffaf35edcc6f981237ccc94f6c71883564178ca4b437edda1c742058134efd57c733e72968258e614453 Homepage: https://cran.r-project.org/package=evclust Description: CRAN Package 'evclust' (Evidential Clustering) Various clustering algorithms that produce a credal partition, i.e., a set of Dempster-Shafer mass functions representing the membership of objects to clusters. The mass functions quantify the cluster-membership uncertainty of the objects. The algorithms are: Evidential c-Means, Relational Evidential c-Means, Constrained Evidential c-Means, Evidential Clustering, Constrained Evidential Clustering, Evidential K-nearest-neighbor-based Clustering, Bootstrap Model-Based Evidential Clustering, Belief Peak Evidential Clustering, Neural-Network-based Evidential Clustering. Package: r-cran-evcombr Architecture: all Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-evcombr_0.1-4-1.ca2404.1_all.deb Size: 136968 MD5sum: 7468048a2d57b50b3a57d4eff5e24203 SHA1: 2915c7b3d890942dd4debcd05141eee1dcda9eae SHA256: 82701a67781f8f3c961eaae3c874374859d647f30b6f78a58dddc9204871146f SHA512: 0d786360a4b3c420bef0167cabc311dc9f4735e8fce499a63b1f893c9eb1bac795297a70a7156fcd21cd671c000828194b50f0c7a23746e9971fcf8df23d6cdb Homepage: https://cran.r-project.org/package=EvCombR Description: CRAN Package 'EvCombR' (Evidence Combination in R) Combine pieces of evidence in the form of uncertainty representations. Package: r-cran-eve Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-eve_1.0-1.ca2404.1_all.deb Size: 55384 MD5sum: 80e19b37beeb03738095ac906c365fe2 SHA1: dcc43e9aba3a56edfc928f596488d610eb790f31 SHA256: 8bdc1a1f926128371211cb1c9c0f8ac252e3e33a9677f8beff19168669238bc1 SHA512: 604e2da6a1a0b3b139b1b52fc91694ef6026d1f9a93fe012a6cc65044efe0f028579c9fe6b7636aedbe134f1d0e45dcb822b2d543d36268552519c9d03960815 Homepage: https://cran.r-project.org/package=eve Description: CRAN Package 'eve' (The Eigenvalues Entropy as a Classifier Evaluation Measure) The confusion matrix (CM) is used to get a classifier's evaluation measure in order to select a method among many. A stochastic matrix and its transformation are computed from the CM. The eigenvalues of the transformed symmetric matrix are used to get an entropy which appears to be a good evaluation measure. Many other measures, commonly used, are provided for comparison purpose. Package: r-cran-evenbreak Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-combinat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-evenbreak_1.0-1.ca2404.1_all.deb Size: 268518 MD5sum: f791c4e24718d5b5706704db7f4a50e3 SHA1: 6e4ef72aecff26fed2cf3026bfe196d0ce9aa6f9 SHA256: 6be869ba385bf987d7b10ed6959c243c23fefdc2529c4300e4e0798c1dd1fbab SHA512: 62014d6c5a694eec0d10d1e2cf090d00dba9d9c4121b75bfbaa56fa7319c437d17b7f54b19825e370045a3000737bc7c1d8d4fdab2a8ca46dd89545f9643a24f Homepage: https://cran.r-project.org/package=evenBreak Description: CRAN Package 'evenBreak' (A Posteriori Probs of Suits Breaking Evenly Across Four Hands) We quantitatively evaluated the assertion that says if one suit is found to be evenly distributed among the 4 players, the rest of the suits are more likely to be evenly distributed. Our mathematical analyses show that, if one suit is found to be evenly distributed, then a second suit has a slightly elevated probability (ranging between 10% to 15%) of being evenly distributed. If two suits are found to be evenly distributed, then a third suit has a substantially elevated probability (ranging between 30% to 50%) of being evenly distributed.This package refers to methods and authentic data from Ely Culbertson , Gregory Stoll , and details of performing the probability calculations from Jeremy L. Martin , Emile Borel and Andre Cheron (1954) "The Mathematical Theory of Bridge",Antonio Vivaldi and Gianni Barracho (2001, ISBN:0 7134 8663 5) "Probabilities and Alternatives in Bridge", Ken Monzingo (2005) "Hand and Suit Patterns" Ken Monzingo (2005) "Hand and Suit Patterns" . Package: r-cran-eventdatar Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2278 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-eventdatar_0.3.1-1.ca2404.1_all.deb Size: 1381894 MD5sum: 4721746fcfc43d4e0f6d5bf0560e3316 SHA1: 2beaf841e70d62ff804e6f780a53da6d3c1c3b86 SHA256: 2f19b73fa0b4bfb3b501973f0aa4015a9d2d4689965fd9e93710e3c5e83feaf0 SHA512: c500d029155fb528ea9c29668625a954dcf64555c29c5b52d5b04269edc3b5ed605fa141aea3e9e9c976ce2bfad52029502c7f7673a21ddb59538c11b997e05c Homepage: https://cran.r-project.org/package=eventdataR Description: CRAN Package 'eventdataR' (Event Data Repository) Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the 'bupaR' packages. Janssenswillen et al. (2020) . Package: r-cran-eventdetectgui Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2556 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-eventdetectr, r-cran-shinybs, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-xml, r-cran-plotly, r-cran-dt, r-cran-ggplot2 Suggests: r-cran-roxygen2, r-cran-shinytest, r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-eventdetectgui_0.3.0-1.ca2404.1_all.deb Size: 1453022 MD5sum: 5de55889d9f4617c5cae87d7c8631afa SHA1: 75ddf4e43b1ddffc9bdcac09eeb67e362b7a8b63 SHA256: fea8d43613723db89c90641dac461574d269c84ae75484ba102778d256aa0bba SHA512: d2d9bd84a27ac93515a2a755407c40f8328d3be1a1d2664e5be03b1320961670f5e0d0c753a8f344403ee89f0b8263159fc5eaee90f6b8371c603b5de0728957 Homepage: https://cran.r-project.org/package=EventDetectGUI Description: CRAN Package 'EventDetectGUI' (Graphical User Interface for the 'EventDetectR' Package) A graphical user interface for open source event detection. Package: r-cran-eventdetectr Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2062 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-imputets, r-cran-forecast, r-cran-ggplot2, r-cran-gridextra, r-cran-neuralnet Suggests: r-cran-testthat, r-cran-caret, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-eventdetectr_0.3.5-1.ca2404.1_all.deb Size: 1428300 MD5sum: 44e56dd039c845bb2512c8870e661010 SHA1: 75338f0df5995c6a076ea2e2132c4586eb6c394a SHA256: bb7831487a7c8d7d5503e2051f88a724754d5447187d47164e2bf5c2f8d0f973 SHA512: 14078ff5f5496faefe2ee068607bf721846bc86c16779016be8129314ecfd8f134ea3edb3adcf920391a3e049cf26165748c3ef976d888aee25d8bbc3f9cd224 Homepage: https://cran.r-project.org/package=EventDetectR Description: CRAN Package 'EventDetectR' (Event Detection Framework) Detect events in time-series data. Combines multiple well-known R packages like 'forecast' and 'neuralnet' to deliver an easily configurable tool for multivariate event detection. Package: r-cran-eventinterval Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-eventinterval_1.3-1.ca2404.1_all.deb Size: 37468 MD5sum: 1cf276ab1f237fceb5aac5fd8ca481c8 SHA1: 9b99c6e4b10c6dc775a092eae7118e3c181630a2 SHA256: 1f76a24ce415cb8ae650b3acd14ed2d02ea49d7ac3a017e659293f4b2c77a8bb SHA512: a2b01479594d591f8aefac30c823270eb895ff44537c1ad0e2b14501f83a411adbc90737d1709450901b2c8e4d2f0cbabc205d3145414c2ad0735d4659390258 Homepage: https://cran.r-project.org/package=eventInterval Description: CRAN Package 'eventInterval' (Sequential Event Interval Analysis) Functions for analysis of rate changes in sequential events. Package: r-cran-eventpred Architecture: all Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 872 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-magrittr, r-cran-ggplot2, r-cran-plotly, r-cran-survival, r-cran-matrix, r-cran-mvtnorm, r-cran-rstpm2, r-cran-numderiv, r-cran-purrr, r-cran-flexsurv, r-cran-erify, r-cran-shiny, r-cran-rlang, r-cran-lrstat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eventpred_0.2.9-1.ca2404.1_all.deb Size: 647564 MD5sum: 401164aefcac5b88e9e9776e5179b1b2 SHA1: 364552c05fd68cf968de36bc568594c0a73768e6 SHA256: a9ac2134551f3df1166ebb42c6a0dce367c64df18ad349b0dcd4f9ae92f1dd1f SHA512: c9db338c1026f01bfe14e938182c99a384331a578ca33a33f350c96718a037a1028a3fbe3c8caa75464d0c2a2f97ad7143a41c881a8207bb545769c68280832e Homepage: https://cran.r-project.org/package=eventPred Description: CRAN Package 'eventPred' (Event Prediction) Predicts enrollment and events at the design or analysis stage using specified enrollment and time-to-event models through simulations. Package: r-cran-eventpredincure Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-perm, r-cran-msm, r-cran-mlecens, r-cran-kmsurv, r-cran-dplyr, r-cran-rlang, r-cran-plotly, r-cran-survival, r-cran-matrix, r-cran-mvtnorm, r-cran-rstpm2, r-cran-numderiv, r-cran-tmvtnsim, r-cran-erify, r-cran-lubridate, r-cran-flexsurv, r-cran-mass Filename: pool/dists/noble/main/r-cran-eventpredincure_1.0-1.ca2404.1_all.deb Size: 365906 MD5sum: 2703ffbb75075e78440c7b0dab87b274 SHA1: 9cb1e2124d2d9c8a7665f931c42caabc412bc69e SHA256: cacac284b0c589a7c00f86aaf8a32ac35b9ec24d9e808260f99d6ef89ccdbe28 SHA512: c8a12e4b7b741b8212487db5ae42d670d1296bf307c8b04482eca0b67742f64a2b24309977e1348c1149cff5129e0f6b7faf053bf0d6d4bfd2154a0d82a17df0 Homepage: https://cran.r-project.org/package=EventPredInCure Description: CRAN Package 'EventPredInCure' (Event Prediction Including Cured Population) Predicts enrollment and events assumed enrollment and treatment-specific time-to-event models, and calculates test statistics for time-to-event data with cured population based on the simulation.Methods for prediction event in the existence of cured population are as described in : Chen, Tai-Tsang(2016) . Package: r-cran-eventreport Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 952 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-tidyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang, r-cran-scales, r-cran-tidyselect, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-tidyverse, r-cran-tinytable Filename: pool/dists/noble/main/r-cran-eventreport_0.1.2-1.ca2404.1_all.deb Size: 619006 MD5sum: 864d95d9b68b574ca2ecda263d8a40e5 SHA1: 17f72c56e35aee60da8affa55508ca6f10fd8610 SHA256: 9ffa57a310722b7f46adab4c06ff69d82b34c741c45308219fde28c480c4a620 SHA512: 6e754c1a48b1bdf8a7312fff95800f2947cbc91b942670a267ca273df3b6d87d0faa24273d88a09a84f5f4d29732967e5ed38a7556f1438261dee99c7b27028a Homepage: https://cran.r-project.org/package=eventreport Description: CRAN Package 'eventreport' (Diagnose, Visualize, and Aggregate Event Report Level Data) Diagnose, visualize, and aggregate event report level data to the event level. Users provide an event report level dataset, specify their aggregation rules, and the package produces a dataset aggregated at the event level. Also includes the Modes and Agents of Election-Related Violence in Côte d'Ivoire and Kenya (MAVERICK) dataset, an event report level dataset that records all documented instances of electoral violence from the first multiparty election to 2022 in Côte d'Ivoire (1995-2022) and Kenya (1992-2022). For more details see van Baalen and Höglund (2026) . Users of the enclosed MAVERICK dataset should also cite van Baalen and Höglund (2026) . Package: r-cran-eventstream Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4608 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-tensora, r-cran-glmnet, r-cran-dbscan, r-cran-mass, r-cran-changepoint, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-eventstream_0.1.1-1.ca2404.1_all.deb Size: 4596594 MD5sum: 4fc542822d09aef4689f248d1e8d241c SHA1: bdcd30196e0ab53376cb00a195aa89f4ad7e320a SHA256: bcf4a6574c87fceee2dc42778531f704eb243b1b6d4db0250ab4d26784a7e935 SHA512: 0013b1e98595f0670be6817d40edd233128484aa035b3f72a4fc45abacf90a7047dafa5c046d6ef839f36e414ba97463a2f9016870f4085045a702ac1731a2cf Homepage: https://cran.r-project.org/package=eventstream Description: CRAN Package 'eventstream' (Streaming Events and their Early Classification) Implements event extraction and early classification of events in data streams in R. It has the functionality to generate 2-dimensional data streams with events belonging to 2 classes. These events can be extracted and features computed. The event features extracted from incomplete-events can be classified using a partial-observations-classifier (Kandanaarachchi et al. 2018) . Package: r-cran-eventstudyr Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-data.table, r-cran-dplyr, r-cran-estimatr, r-cran-fixest, r-cran-ggplot2, r-cran-mass, r-cran-rlang, r-cran-pracma, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eventstudyr_1.2.0-1.ca2404.1_all.deb Size: 242994 MD5sum: 4975275c522abe0a70d2a2e088bf35ea SHA1: d10a82923a45349f7f9bac8d6cfbc7214ad499bf SHA256: 1974e431d87221e8ba5b054104caa2e939926136f44781be0d91bf1cdbc87427 SHA512: 92c1d82726b4374f0df798d753181836f22298a3bf2769b188a9bf1a61c4affc2b97165c9f19db909e3952c19a663a8f7059e1841dd9fb840998a383662f3189 Homepage: https://cran.r-project.org/package=eventstudyr Description: CRAN Package 'eventstudyr' (Estimation and Visualization of Linear Panel Event Studies) Estimates linear panel event study models. Plots coefficients following the recommendations in Freyaldenhoven et al. (2021) . Includes sup-t bands, testing for key hypotheses, least wiggly path through the Wald region. Allows instrumental variables estimation following Freyaldenhoven et al. (2019) . Package: r-cran-eventtrack Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-muhaz Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rpact, r-cran-fitdistrplus, r-cran-gestate Filename: pool/dists/noble/main/r-cran-eventtrack_1.0.4-1.ca2404.1_all.deb Size: 122856 MD5sum: beca698b2cac00fb72ba2273817c5be2 SHA1: 665be2a744c7ca53c5dd9c725cde43d2ce318615 SHA256: 13b851a4e38f0021b337ffc74e8c55b990dd03832e6d5a7d738c2a4b54370ca6 SHA512: b74aa4766d3890dff8ea002996dfe6a115030744788551fdf774c7c3aafde36e83af55853b26b88ebb42c808a40e1f38f0d174160a35924480fc72f19614b071 Homepage: https://cran.r-project.org/package=eventTrack Description: CRAN Package 'eventTrack' (Event Prediction for Time-to-Event Endpoints) Implements the hybrid framework for event prediction described in Fang & Zheng (2011, ). To estimate the survival function the event prediction is based on, a piecewise exponential hazard function is fit to the time-to-event data to infer the potential change points. Prior to the last identified change point, the survival function is estimated using Kaplan-Meier, and the tail after the change point is fit using piecewise exponential. Package: r-cran-eventwinratios Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-eventwinratios_1.0.0-1.ca2404.1_all.deb Size: 101622 MD5sum: 6f55670e130364a158983521e70a0cb0 SHA1: 3be5db4aee19fd5e2a1fb35fa4d20e6fd3747d18 SHA256: 069763d08cfe52205d8fe975fcf17a302fbfe80ef501a4ab1f71bed012f2bd53 SHA512: 793fc960ba6b5a3ce343fcbe7fdd853a4dd64f563e5698313299b4da65a025e8407456a7dbc877f67e76be3504005d2b29cf563e0b1d885d8113462949f29522 Homepage: https://cran.r-project.org/package=EventWinRatios Description: CRAN Package 'EventWinRatios' (Event-Specific Win Ratios for Terminal and Non-Terminal Events) Provides several confidence interval and testing procedures using event-specific win ratios for semi-competing risks data with non-terminal and terminal events, as developed in Yang et al. (2021). Compared with conventional methods for survival data, these procedures are designed to utilize more data for improved inference procedures with semi-competing risks data. The event-specific win ratios were introduced in Yang and Troendle (2021). In this package, the event-specific win ratios and confidence intervals are obtained for each event type, and several testing procedures are developed for the global null of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazard assumptions, under which the event-specific win ratios converge to the hazard ratios, and a test of equal hazard ratios are provided. For summarizing the treatment effect on all events, confidence intervals for linear combinations of the event-specific win ratios are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al (2021). Also, transformations are used to yield better control of the type one error rates for moderately sized data sets. Package: r-cran-evi Architecture: all Version: 0.2.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-evi_0.2.0-0-1.ca2404.1_all.deb Size: 91112 MD5sum: 341b36f0863b4d7ca514d66c386ccafe SHA1: c6a4009c168d827ff427d046f5bca8b3371ccddf SHA256: 5a0e66ce9eecf19151b71250060740cf5176e81ba62c55dec03a222eb337e937 SHA512: 7459e398c58c00d072b149ec53bc19a099b6e62bb7f435b5f49542f874201c7225968690d8bdecce6deff6efe0a0399feb61d3af2afda0d1fb029e54002c1298 Homepage: https://cran.r-project.org/package=EVI Description: CRAN Package 'EVI' (Epidemic Volatility Index as an Early-Warning Tool) This is an R package implementing the epidemic volatility index (EVI), as discussed by Kostoulas et. al. (2021) and variations by Pateras et. al. (2023). EVI is a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Package: r-cran-evian Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-profilelikelihood, r-cran-sandwich, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-evian_2.1.0-1.ca2404.1_all.deb Size: 120894 MD5sum: 7e909ea1833458917cd91f3e69680642 SHA1: 9316f02394df5a02b2d2062b0e632dcefb3e2197 SHA256: ea3fba41ec448c65b8ea9dfdb6dd76cea363fdf4e4bd9455617ff803a2c1a2d4 SHA512: 0a8db8605011c4fc175b98151104520f106fdf6bd4631aba1cb84c1f4ff8a991dcd4dfb994f67479aa086ce7163dfe0fee84c568e05d23609954fb9456d20056 Homepage: https://cran.r-project.org/package=evian Description: CRAN Package 'evian' (Evidential Analysis of Genetic Association Data) Evidential regression analysis for dichotomous and quantitative outcome data. The following references described the methods in this package: Strug, L. J., Hodge, S. E., Chiang, T., Pal, D. K., Corey, P. N., & Rohde, C. (2010) . Strug, L. J., & Hodge, S. E. (2006) . Royall, R. (1997) . Package: r-cran-evidence Architecture: all Version: 0.8.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-rstanarm, r-cran-loo, r-cran-lattice, r-cran-learnbayes, r-cran-laplacesdemon Filename: pool/dists/noble/main/r-cran-evidence_0.8.10-1.ca2404.1_all.deb Size: 235766 MD5sum: 7a3d13de7d1e3ffed0d73b05c1b13458 SHA1: 821e7d8ba02f3f8bd578f6061c2f19a322cd3edc SHA256: aea76fb4078cc97d4ef820621f72518e3ba646b615728332e3e5d988e2a48245 SHA512: ed14b9759b4cc47aadbcc1f15eba04ef8b4f760a919f33721868b8a9d1abfe75117cd84be3210608234b5158ffb84af07b0fa426b23d8bdcfee7d6c76df8ca74 Homepage: https://cran.r-project.org/package=evidence Description: CRAN Package 'evidence' (Analysis of Scientific Evidence Using Bayesian and LikelihoodMethods) Bayesian (and some likelihoodist) functions as alternatives to hypothesis-testing functions in R base using a user interface patterned after those of R's hypothesis testing functions. See McElreath (2016, ISBN: 978-1-4822-5344-3), Gelman and Hill (2007, ISBN: 0-521-68689-X) (new edition in preparation) and Albert (2009, ISBN: 978-0-387-71384-7) for good introductions to Bayesian analysis and Pawitan (2002, ISBN: 0-19-850765-8) for the Likelihood approach. The functions in the package also make extensive use of graphical displays for data exploration and model comparison. 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When an observational study allows for multiple independent or nearly independent inferences which, if vulnerable, are vulnerable to different biases, we have multiple evidence factors. This package provides methods that respect type I error rate control. Examples are provided of integrated evidence factors analysis in a longitudinal study with continuous outcome and in a case-control study. Karmakar, B., French, B., and Small, D. S. (2019). 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The package reports effect estimates, uncertainty intervals, and likelihood ratios on the log 10 scale derived from a single statistical model. It applies to standard normal mean tests, contingency tables, and regression coefficients, and provides a direct evidential measure while retaining classical error guarantees. For the Evidence Ratio Reporting Standard see Lawless (2026) . 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Rosenbaum (2021) . Package: r-cran-eviewsr Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3773 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-magrittr, r-cran-xts, r-cran-zoo, r-cran-rdpack Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-eviewsr_0.1.7-1.ca2404.1_all.deb Size: 2267038 MD5sum: 18044d1e2e9a7512b5bf07b72b0cb3c1 SHA1: 91bd6b112f893f44394709482ec48d82b66fbad0 SHA256: 940b11d6edabaebe09e545e4af096057f50e3c4bc629f12152a00a1fbd2d8e63 SHA512: 38abdff2a6eb80905aa71931e2dd665a40c5b41557e006f19d9fa52fb3c9fb9da4697256fc21c88b58d71ce77afcd34e5f35672ae4fa4ea02462f595397b64b2 Homepage: https://cran.r-project.org/package=EviewsR Description: CRAN Package 'EviewsR' (A Seamless Integration of 'EViews' and R) It allows running 'EViews' () program from R, R Markdown and Quarto documents. 'EViews' (Econometric Views) is a statistical software for Econometric analysis. This package integrates 'EViews' and R and also serves as an 'EViews' Knit-Engine for 'knitr' package. Write all your 'EViews' commands in R, R Markdown or Quarto documents. For details, please consult our peer-review article Mati S., Civcir I. and Abba S.I (2023) . 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Package: r-cran-evir Architecture: all Version: 1.7-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 514 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-evir_1.7-4-1.ca2404.1_all.deb Size: 464976 MD5sum: 1b3d7d73f002f0fcebd9f05248879807 SHA1: 86a427ead6ef1047cc269ce8b9d5ef2d6b72f565 SHA256: 4946eea0b76667439c0cf0e676b2cd91e1e5d19d1c18aaf3b9694f9bb636d073 SHA512: 607e8c7f0a3b56109190ec76a179a2f14a81a556a447d7b4ce529cf802ccf9f5e84224785088dfb1778d0b4904a4ca97c47d32973505b7f2b9eb98aa71efd130 Homepage: https://cran.r-project.org/package=evir Description: CRAN Package 'evir' (Extreme Values in R) Functions for extreme value theory, which may be divided into the following groups; exploratory data analysis, block maxima, peaks over thresholds (univariate and bivariate), point processes, gev/gpd distributions. Package: r-cran-evmissing Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlssx, r-cran-itp, r-cran-nieve, r-cran-revdbayes, r-cran-rust Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-evmissing_1.0.2-1.ca2404.1_all.deb Size: 335668 MD5sum: 953766993823b7bcc66d46bdcbd6985a SHA1: 6ac36609e59efce301814184da68a3b58a94e6c7 SHA256: d6cfa35a7d08d54a843115ec06d542c61ae286ee2532ff7e8f369c782a0f1def SHA512: c1a683ce129d56c12378fcb87b79c202631af806f6a8f53388326aa3a954170b8cb2b1f076c2b773dd51ca3f9b6d137b20040cb4b7d9c311baff3abd49bae150 Homepage: https://cran.r-project.org/package=evmissing Description: CRAN Package 'evmissing' (Extreme Value Analyses with Missing Data) Performs likelihood-based extreme value inferences with adjustment for the presence of missing values based on Simpson and Northrop (2026) . A Generalised Extreme Value distribution is fitted to block maxima using maximum likelihood estimation, with the location and scale parameters reflecting the numbers of non-missing raw values in each block. A Bayesian version is also provided. For the purposes of comparison, there are options to make no adjustment for missing values or to discard any block maximum for which greater than a percentage of the underlying raw values are missing. Example datasets containing missing values are provided. Package: r-cran-evmix Architecture: all Version: 2.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5042 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-gsl, r-cran-sparsem Filename: pool/dists/noble/main/r-cran-evmix_2.12-1.ca2404.1_all.deb Size: 4890694 MD5sum: cc6c27a48edbc0d031788ea8d83e6079 SHA1: 07380a03d03f09adb76ae8dec978a407697069a5 SHA256: ecf84b1a8981202e4bb1c81e178095467365cadc52619814f68b1b1b1c47281c SHA512: 900fe35eb60cce2e118a039151cfdbff7f4e55676bf4603e3171e93397b66351a0aa5a64bc197602878da7fdb782792f04045a77c2774180aab47695ab71554a Homepage: https://cran.r-project.org/package=evmix Description: CRAN Package 'evmix' (Extreme Value Mixture Modelling, Threshold Estimation andBoundary Corrected Kernel Density Estimation) The usual distribution functions, maximum likelihood inference and model diagnostics for univariate stationary extreme value mixture models are provided. Kernel density estimation including various boundary corrected kernel density estimation methods and a wide choice of kernels, with cross-validation likelihood based bandwidth estimator. Reasonable consistency with the base functions in the 'evd' package is provided, so that users can safely interchange most code. Package: r-cran-evmr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-eva, r-cran-lmomco, r-cran-numderiv, r-cran-rsolnp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-evmr_0.1.0-1.ca2404.1_all.deb Size: 373488 MD5sum: 899d2536633e7dc234f4d0b323cb6fd2 SHA1: 680aa871f537f711305f56c3790bc72e38b0ab99 SHA256: 443414034db8886e1a14cbe58d60da6d0180e6e88682325a3bcd6dd577d145e7 SHA512: c0b2509001e37536e0b6e7632c300e780ed2ed03f20f362ba32d3465b0a9cbe13c6cbcdedca709d9c56b2d8f9578378f12602dc9d8ee17296badb885910bcbd4 Homepage: https://cran.r-project.org/package=evmr Description: CRAN Package 'evmr' (Extreme Value Modeling for r-Largest Order Statistics) Tools for extreme value modeling based on the r-largest order statistics framework. The package provides functions for parameter estimation via maximum likelihood, return level estimation with standard errors, profile likelihood-based confidence intervals, random sample generation, and entropy difference tests for selecting the number of order statistics r. Several r-largest order statistics models are implemented, including the four-parameter kappa (rK4D), generalized logistic (rGLO), generalized Gumbel (rGGD), logistic (rLD), and Gumbel (rGD) distributions. The rK4D methodology is described in Shin et al. (2022) , the rGLO model in Shin and Park (2024) , and the rGGD model in Shin and Park (2025) . The underlying distributions are related to the kappa distribution of Hosking (1994) , the generalized logistic distribution discussed by Ahmad et al. (1988) , and the generalized Gumbel distribution of Jeong et al. (2014) . Penalized likelihood approaches for extreme value estimation follow Martins and Stedinger (2000) and Coles and Dixon (1999) . Selection of r is supported using methods discussed in Bader et al. (2017) . The package is intended for hydrological, climatological, and environmental extreme value analysis. Package: r-cran-evobir Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seqinr, r-cran-ape, r-cran-geiger, r-cran-shiny, r-cran-phytools Filename: pool/dists/noble/main/r-cran-evobir_1.1-1.ca2404.1_all.deb Size: 166552 MD5sum: 4e6b16219619a3d0a0839bafca862991 SHA1: 833dfae18679183a9037da5e5ef542f5bd68446a SHA256: cd1e5cfefcac262e850d94065f926d2bca110247e5ba9aede0422369b791cfcd SHA512: fff3c8616bf2c662685dc401a6bb5e4933142638ae0abf46073f7b2fa71651b54566aed49079a18a6c6d1a819393a04ec86166fadf00de3694ad4adf06f5e8b7 Homepage: https://cran.r-project.org/package=evobiR Description: CRAN Package 'evobiR' (Comparative and Population Genetic Analyses) Comparative analysis of continuous traits influencing discrete states, and utility tools to facilitate comparative analyses. Implementations of ABBA/BABA type statistics to test for introgression in genomic data. 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Functions include sending text, media (image/video/document), audio, stickers, geographic locations, contacts, polls, interactive lists and button messages. Also includes number verification and structured CLI logging for debugging. Package: r-cran-evolutionarygames Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-geometry, r-cran-ggplot2, r-cran-interp, r-cran-mass, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rgl Filename: pool/dists/noble/main/r-cran-evolutionarygames_0.1.2-1.ca2404.1_all.deb Size: 2968228 MD5sum: 0bd43f4b63de1aa2664f9abf1149e6d0 SHA1: 52d4b3d69905638e87770d24d1aa80a8fd398732 SHA256: e264f066fcdb1e38859809099d15f4a666f4adf17d8335f6eff12cf9be4ccae6 SHA512: 3460007a083b9424d2fcbc4923d13e3d6a435633eb3e511a8f8964ab89a3d122fdadf51446c5c511c7da03527f5277090333344b99859ee44d1ca4191701ca2a Homepage: https://cran.r-project.org/package=EvolutionaryGames Description: CRAN Package 'EvolutionaryGames' (Important Concepts of Evolutionary Game Theory) Evolutionary game theory applies game theory to evolving populations in biology, see e.g. one of the books by Weibull (1994, ISBN:978-0262731218) or by Sandholm (2010, ISBN:978-0262195874) for more details. A comprehensive set of tools to illustrate the core concepts of evolutionary game theory, such as evolutionary stability or various evolutionary dynamics, for teaching and academic research is provided. Package: r-cran-evolvability Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-matrix, r-cran-ape, r-cran-lme4 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-evolvability_2.0.1-1.ca2404.1_all.deb Size: 309232 MD5sum: 044bbbc66930e789464752ed87ed2f25 SHA1: a44a11f8d9231a4a64486c91a7417bd254be5fb0 SHA256: c080d8b2cad2448881591140d858784f909e8561c6d385aedf867654d49364fa SHA512: 8beaa373a6efaf2b877f80ad37a3e6d268839d9de904a03fd8090cf89ca5f06be3049d90dd52dcfe7a1ffbcc51a08b4a8f318c25d9ffdb7969b66243f108664f Homepage: https://cran.r-project.org/package=evolvability Description: CRAN Package 'evolvability' (Calculation of Evolvability Parameters) Provides tools for calculating evolvability parameters from estimated G-matrices as defined in Hansen and Houle (2008) and fits phylogenetic comparative models that link the rate of evolution of a trait to the state of another evolving trait (see Hansen et al. 2021 Systematic Biology ). The package was released with Bolstad et al. (2014) , which contains some examples of use. Package: r-cran-evolved Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4582 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-diversitree, r-cran-phytools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lsd, r-cran-paleobiodb, r-cran-bammtools, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-evolved_1.0.0-1.ca2404.1_all.deb Size: 3068204 MD5sum: f79c8337a7353c8b22ffb1a799601ba6 SHA1: 394bc783a98d8b11af8e63c18ef40f097c209bed SHA256: e1b1b622cfa6b6401973c608d7dde7e6498b06420cf51e5a36a3ffd09d501b47 SHA512: 3428ab112649179698e78aff0c3cdb746963c835e590919d8c7ba5a85fdcc9fb6289ceb9cd467ece9f2267c21b3dfb1bc451b28c83b29f08f62932f4e96d10ee Homepage: https://cran.r-project.org/package=evolved Description: CRAN Package 'evolved' (Open Software for Teaching Evolutionary Biology at MultipleScales Through Virtual Inquiries) "Evolutionary Virtual Education" - 'evolved' - provides multiple tools to help educators (especially at the graduate level or in advanced undergraduate level courses) apply inquiry-based learning in general evolution classes. In particular, the tools provided include functions that simulate evolutionary processes (e.g., genetic drift, natural selection within a single locus) or concepts (e.g. Hardy-Weinberg equilibrium, phylogenetic distribution of traits). More than only simulating, the package also provides tools for students to analyze (e.g., measuring, testing, visualizing) datasets with characteristics that are common to many fields related to evolutionary biology. Importantly, the package is heavily oriented towards providing tools for inquiry-based learning - where students follow scientific practices to actively construct knowledge. For additional details, see package's vignettes. Package: r-cran-evomorph Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-geomorph, r-cran-stringr Filename: pool/dists/noble/main/r-cran-evomorph_0.9-1.ca2404.1_all.deb Size: 417574 MD5sum: 3b69e900053162833fffb392bf3ab12f SHA1: 82b2590e82e4dde2257d53286701f4879e5060f4 SHA256: ba724d2568957fc99a2944669168d47e961256dd10f3bc2a48c204649f8e5dea SHA512: 760f5d0de4795c16ac119c183f465395bd3a769cbead77b854d7362044ed9e320e3161d5263a2ec2b98f6b31526e1a8462f488d3e24d0aa396c30d9786cca2b2 Homepage: https://cran.r-project.org/package=Evomorph Description: CRAN Package 'Evomorph' (Evolutionary Morphometric Simulation) Evolutionary process simulation using geometric morphometric data. Manipulation of landmark data files (TPS), shape plotting and distances plotting functions. Package: r-cran-evoper Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rrepast, r-cran-logger, r-cran-boot, r-cran-reshape, r-cran-ggplot2, r-cran-desolve, r-cran-plot3d, r-cran-plyr, r-cran-data.table, r-cran-rnetlogo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-evoper_0.7.0-1.ca2404.1_all.deb Size: 521636 MD5sum: c8d3abdef48f7d556417b6caf3b6a696 SHA1: 40d421b43973aa25a05048cdb76abac03cbc97e4 SHA256: 02520d514a27ec843c2e49e66025b81b8bd3023976744e93672d4412e2557e34 SHA512: 66295163bad8825fffb563c471fd47b807fda4ea152a09bb00893106dcd540a52a1b58d2d6b02a38ed542aa438fffc51b36fb35e37bae53b6bff84f8dd6aefc7 Homepage: https://cran.r-project.org/package=evoper Description: CRAN Package 'evoper' (Evolutionary Parameter Estimation for 'Repast Simphony' Models) The EvoPER, Evolutionary Parameter Estimation for Individual-based Models is an extensible package providing optimization driven parameter estimation methods using metaheuristics and evolutionary computation techniques (Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization for continuous domains, Tabu Search, Evolutionary Strategies, ...) which could be more efficient and require, in some cases, fewer model evaluations than alternatives relying on experimental design. Currently there are built in support for models developed with 'Repast Simphony' Agent-Based framework () and with NetLogo () which are the most used frameworks for Agent-based modeling. Package: r-cran-evophylo Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12483 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-cluster, r-cran-deeptime, r-cran-ggplot2, r-cran-ggrepel, r-bioc-ggtree, r-cran-patchwork, r-bioc-treeio, r-cran-rtsne, r-cran-unglue, r-cran-tidyr, r-cran-tibble, r-cran-phangorn, r-cran-magrittr Suggests: r-cran-devtools, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-evophylo_0.3.5-1.ca2404.1_all.deb Size: 4568456 MD5sum: c6b98e2f4c75f7e63e0fa5ae61d5c678 SHA1: b05783889ad5d2e92b7c3ab0f984b48ec9f82ba4 SHA256: 0ff2874d7f36669f8fe5ef9f3d9bec3bb35a8349a6b21d63ac18084482e85a49 SHA512: 4565692f5c27f4b1818204c57ada28a4b070d5c929e25c9bed840c9f9fd35bca3084eb07c3ecee3184168b66a0ca7ce2e4501364c7b144a660245a17ae8e5d20 Homepage: https://cran.r-project.org/package=EvoPhylo Description: CRAN Package 'EvoPhylo' (Pre- And Postprocessing of Morphological Data from Relaxed ClockBayesian Phylogenetics) Performs automated morphological character partitioning for phylogenetic analyses and analyze macroevolutionary parameter outputs from clock (time-calibrated) Bayesian inference analyses, following concepts introduced by Simões and Pierce (2021) . Package: r-cran-evots Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-paleots, r-cran-mvtnorm, r-cran-plotly, r-cran-pracma, r-cran-mass Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-evots_1.0.3-1.ca2404.1_all.deb Size: 458024 MD5sum: ed3cb716cde516d1f765b3caa70dd6d8 SHA1: 0b65525cb90e13df2ae05feeffaa15e83b74536a SHA256: 73e44ec10c7d18d9e9b793e3de7745fbe564fd83b2b6bae4c6c1c797d76bd30c SHA512: 8e72ef5485e9eff2ef5be5ac283ec756827b6924f1999aea3a26599e2abba33a67c445b1ad9ee1151f0b08a7e58809d4d2ffb3f774b35d581d9e910e54ce5aee Homepage: https://cran.r-project.org/package=evoTS Description: CRAN Package 'evoTS' (Analyses of Evolutionary Time-Series) Facilitates univariate and multivariate analysis of evolutionary sequences of phenotypic change. The package extends the modeling framework available in the 'paleoTS' package. Please see for information about the package and the implemented models. 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Package: r-cran-evsim Architecture: all Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1617 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-jsonlite, r-cran-ggplot2 Suggests: r-cran-carrier, r-cran-mirai, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-evsim_1.7.1-1.ca2404.1_all.deb Size: 1516078 MD5sum: 16d397b849bbd6cb86204d3f83b025f7 SHA1: 65316c82db25a92cd8e237b340c8cf4d379943ef SHA256: a74417488cbb3e76aef2f20597dfa7d08eaa2abec694d5700ed92ec5c47ffbcc SHA512: ebc797beb971564e3fd3739447a976b36e3f2037439103737a5811c5846ffe28b0537cb6ae2504c4de54093eb7a94ec500de52b3eaece25904d2addf24f80372 Homepage: https://cran.r-project.org/package=evsim Description: CRAN Package 'evsim' (Electric Vehicle Charging Sessions Simulation) Simulation of Electric Vehicles charging sessions using Gaussian models, together with time-series power demand calculations. Package: r-cran-evt0 Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-evd Filename: pool/dists/noble/main/r-cran-evt0_1.1.5-1.ca2404.1_all.deb Size: 131556 MD5sum: a71d0583873c60e6c86353c8bb7bf407 SHA1: 89a7def5167ee99b6bc21d55d084ef6cdc6893c8 SHA256: 536e17a096870beaa77878f044775b748cccb5a9c76f1376d8818f0750c231ef SHA512: 3d81b0c058db2fb13444bbe3b72175048c04fc540af61d36247f59c6fe4fd5ddd842c1fbd7dc013f245a76bd85cd71cb582cebaaf9c66ba935e01a0a41edcdb5 Homepage: https://cran.r-project.org/package=evt0 Description: CRAN Package 'evt0' (Mean of Order P, Peaks over Random Threshold Hill and HighQuantile Estimates) The R package proposes extreme value index estimators for heavy tailed models by mean of order p , peaks over random threshold and a bias-reduced estimator . 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The first classifier is based on the generalized Pareto distribution (GPD) and the second classifier is based on the generalized extreme value (GEV) distribution. For details, see Vignotto, E., & Engelke, S. (2018) . 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It calculates the recommended dose for next cohorts and perform simulations to obtain operating characteristics. 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It calculates the next dose as a clinical trial proceeds and performs simulations to obtain operating characteristics. Package: r-cran-ewr Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ewr_1.4-1.ca2404.1_all.deb Size: 40320 MD5sum: 8ee7e2e407e25c3f9cfa46a5216fe6bf SHA1: 03fa35e07af68d6bb04cac275b6846a9edda530a SHA256: c0ad41b20b9f3a3a22249ff6f96b432c7807b40e9aeba1472ff644c3330c949e SHA512: d4d57f06e917516131bfea37c0c8d1915b1087504d0be38f9c87e40ba6ff729e542da6ba212cbfdca3519101cd5082af3d476e286c0ff546e722d75e53d0a173 Homepage: https://cran.r-project.org/package=EwR Description: CRAN Package 'EwR' (Econometrics with R) Function and data sets in the book entitled "R ile Temel Ekonometri", S.Guris, E.C.Akay, B. Guris(2020). The book published in Turkish. It is possible to makes Durbin two stage method for autocorrelation, generalized differencing method for correction autocorrelation, Hausman Test for identification and computes LM, LR and Wald test statistics for redundant variable by using the functions written in this package. Package: r-cran-ews Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-ews_0.2.0-1.ca2404.1_all.deb Size: 122824 MD5sum: 5a3c7c821e587ffeba152cbdb0919d24 SHA1: 4a0baf247e499d8e1d3e40cfb699fafcaa8236cf SHA256: b0cbbcf54350c72eb1bc92ef4974e0d5ac8cd6bdbba93682b64948205e45bddf SHA512: af7522cd179e35b441682ff7f4a66a5a01a6d2652c0621357f238109aa72a5de6d9e09df3dea71598001b76d8f40498ec355ba4fd2c949bbfceba533518c69d5 Homepage: https://cran.r-project.org/package=EWS Description: CRAN Package 'EWS' (Early Warning System) The purpose of Early Warning Systems (EWS) is to detect accurately the occurrence of a crisis, which is represented by a binary variable which takes the value of one when the event occurs, and the value of zero otherwise. EWS are a toolbox for policymakers to prevent or attenuate the impact of economic downturns. Modern EWS are based on the econometric framework of Kauppi and Saikkonen (2008) . Specifically, this framework includes four dichotomous models, relying on a logit approach to model the relationship between yield spreads and future recessions, controlling for recession risk factors. These models can be estimated in a univariate or a balanced panel framework as in Candelon, Dumitrescu and Hurlin (2014) . This package provides both methods for estimating these models and a dataset covering 13 OECD countries over a period of 45 years. In addition, this package also provides methods for the analysis of the propagation mechanisms of an exogenous shock, as well as robust confidence intervals for these response functions using a block-bootstrap method as in Lajaunie (2021). This package constitutes a useful toolbox (data and functions) for scholars as well as policymakers. Package: r-cran-ewsmethods Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3601 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-egg, r-cran-ggplot2, r-cran-gtools, r-cran-forecast, r-cran-foreach, r-cran-infotheo, r-cran-mar, r-cran-moments, r-cran-redm, r-cran-reticulate, r-cran-scales Suggests: r-cran-data.table, r-cran-devtools, r-cran-doparallel, r-cran-knitr, r-cran-fs, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ewsmethods_1.3.3-1.ca2404.1_all.deb Size: 3043156 MD5sum: fc5ec3d9fb76e7dadb7ae2c6bf339d9b SHA1: e5828f418911b4180faf7eb7a7a7041436c4360c SHA256: d78ce5f7c8a2b5ab2afb02175a5646e58ab08e18a84cc97b1ec32caa942358a8 SHA512: 025f5c620712a2831219db61212739242368535e927ce5e274f8d75a460eb7b2e1d244f4558dc635360a4dde6f48eae699c6bbabc9de02e2dc4230d22335c1db Homepage: https://cran.r-project.org/package=EWSmethods Description: CRAN Package 'EWSmethods' (Forecasting Tipping Points at the Community Level) Rolling and expanding window approaches to assessing abundance based early warning signals, non-equilibrium resilience measures, and machine learning. See Dakos et al. (2012) , Deb et al. (2022) , Drake and Griffen (2010) , Ushio et al. (2018) and Weinans et al. (2021) for methodological details. Graphical presentation of the outputs are also provided for clear and publishable figures. Visit the 'EWSmethods' website for more information, and tutorials. Package: r-cran-exact.n Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4750 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr Filename: pool/dists/noble/main/r-cran-exact.n_1.1.1-1.ca2404.1_all.deb Size: 4343938 MD5sum: b05a040ea4472198dce5626e6569466e SHA1: a0094d7475f710a6872055fa9278b7564f89047e SHA256: 7c884619dce68f6e40fb7e288978d6419409a94e931664d661931a1da3b75b80 SHA512: 9b99d451d4f2528c0ba161e4fc72103f9302516a601e081b24858a918767c27af5446fa7ea6c8e6cefca2228f7f75db299fe813801f31a4a61e381b3761fc9ed Homepage: https://cran.r-project.org/package=exact.n Description: CRAN Package 'exact.n' (Exact Samples Sizes and Inference for Clinical Trials withBinary Endpoint) Allows the user to determine minimum sample sizes that achieve target size and power at a specified alternative. For more information, see “Exact samples sizes for clinical trials subject to size and power constraints” by Lloyd, C.J. (2022) Preprint . Package: r-cran-exact2x2 Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1336 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-exactci, r-cran-ssanv Suggests: r-cran-testthat, r-cran-exact, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-exact2x2_1.7.0-1.ca2404.1_all.deb Size: 931330 MD5sum: dc3cad39ad40e68360551f0daef8e0c1 SHA1: 55802b5e8588e2920b758c830b3b88daaf581cb4 SHA256: 1b8ed2a7ee65e5b9f6aa4e9ed9e457aa8bafa7a1b85a156e79ce37de68b2ef1c SHA512: 1306c0065c0fc556cbe471764e1fe0b3cfe9f984db1480038b0d6703e963e824314dcfd26348fbba444de2100f3b3e8e989c36bb96c9498feb77b71a4a4c6d9a Homepage: https://cran.r-project.org/package=exact2x2 Description: CRAN Package 'exact2x2' (Exact Tests and Confidence Intervals for 2x2 Tables) Calculates conditional exact tests (Fisher's exact test, Blaker's exact test, or exact McNemar's test) and unconditional exact tests (including score-based tests on differences in proportions, ratios of proportions, and odds ratios, and Boshcloo's test) with appropriate matching confidence intervals, and provides power and sample size calculations. Gives melded confidence intervals for the binomial case (Fay, et al, 2015, ). Gives boundary-optimized rejection region test (Gabriel, et al, 2018, ), an unconditional exact test for the situation where the controls are all expected to fail. Gives confidence intervals compatible with exact McNemar's or sign tests (Fay and Lumbard, 2021, ). For review of these kinds of exact tests see Fay and Hunsberger (2021, ). Package: r-cran-exact Architecture: all Version: 3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-exact_3.3-1.ca2404.1_all.deb Size: 204234 MD5sum: 2a4ace04740549178b028eae2ae0eeab SHA1: cc90198e1d73c0e2e40e7de6fd8b1d283b80d59f SHA256: 86686192024c251614237e91d9d79a8abc061e936aa48bed985c31c3c2aceec1 SHA512: 9028190f55fec9d4c84cbb127786f340e06e26b2842ee1472f08e5fa76a7b262c72503a967fa55a557ebce7e005a18dc4eedea5046dc31d50779e96611019584 Homepage: https://cran.r-project.org/package=Exact Description: CRAN Package 'Exact' (Unconditional Exact Test) Performs unconditional exact tests and power calculations for 2x2 contingency tables. For comparing two independent proportions, performs Barnard's test (1945) using the original CSM test (Barnard, 1947 ), using Fisher's p-value referred to as Boschloo's test (1970) , or using a Z-statistic (Suissa and Shuster, 1985, ). For comparing two binary proportions, performs unconditional exact test using McNemar's Z-statistic (Berger and Sidik, 2003, ), using McNemar's conditional p-value, using McNemar's Z-statistic with continuity correction, or using CSM test. Calculates confidence intervals for the difference in proportion. This package interacts with pre-computed data available through the ExactData R package, which is available in a 'drat' repository. Install the ExactData R package from GitHub at . The ExactData R package is approximately 85 MB. Package: r-cran-exactamente Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-shiny, r-cran-shinythemes Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-exactamente_0.1.1-1.ca2404.1_all.deb Size: 74792 MD5sum: 978f3560d536e5becc75afa9d7c89bbd SHA1: ec62b5a5cf02b531cabc1b00d36aa3101005c668 SHA256: cdc188c3036a6dd5fb7df24fb1f91bbbb55f6d2565ce973ca068909e76912412 SHA512: 537d0329a344c1d70e280bafbc65ba00eee15ec40201a7fd724f9816bf38de753ce4edde129e2a17b865f68b2516532334c9ec3c5ca562e6eb0aea58f3de701f Homepage: https://cran.r-project.org/package=exactamente Description: CRAN Package 'exactamente' (Explore the Exact Bootstrap Method) Researchers often use the bootstrap to understand a sample drawn from a population with unknown distribution. The exact bootstrap method is a practical tool for exploring the distribution of small sample size data. For a sample of size n, the exact bootstrap method generates the entire space of n to the power of n resamples and calculates all realizations of the selected statistic. The 'exactamente' package includes functions for implementing two bootstrap methods, the exact bootstrap and the regular bootstrap. The exact_bootstrap() function applies the exact bootstrap method following methodologies outlined in Kisielinska (2013) . The regular_bootstrap() function offers a more traditional bootstrap approach, where users can determine the number of resamples. The e_vs_r() function allows users to directly compare results from these bootstrap methods. To augment user experience, 'exactamente' includes the function exactamente_app() which launches an interactive 'shiny' web application. This application facilitates exploration and comparison of the bootstrap methods, providing options for modifying various parameters and visualizing results. 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Package: r-cran-exactcidiff Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-exactcidiff_2.1-1.ca2404.1_all.deb Size: 55354 MD5sum: bb66b1fd4740c6a4b6258ade8d531400 SHA1: b21e069f0a0787e792be90a7df25ff7809e85b1d SHA256: 1b3d22af64230bcf728155a24827e705646b16fa5239332c924dc14b09503ba4 SHA512: 298a4e976b1bb894215ffd637018177c87cc7e0db8be8cedac77bf1a311f98b6eda9c21e9de97807cbfe4b46d721fa56edca8a225c108f475454e6fce87f7e50 Homepage: https://cran.r-project.org/package=ExactCIdiff Description: CRAN Package 'ExactCIdiff' (Inductive Confidence Intervals for the Difference Between TwoProportions) This is a package for exact Confidence Intervals for the difference between two independent or dependent proportions. Package: r-cran-exactcione Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-exactcione_1.0.5-1.ca2404.1_all.deb Size: 100916 MD5sum: 42e11e2726bbfffb38c8515f708ae173 SHA1: c9013f3a142994c390612600379b39892c91e87a SHA256: 341aee8950869be0690f0270d6d8006c89d8c3cecefc3f561d7e9d229b0b358c SHA512: 0cd4073b2381e95d7977b509c8ae65dbf056befd1cccd920df086a5f2cfed6ff39ed217cdc966bc062670a3f1eb4889ab5b54fab2f85bc51112e2436c66ce396 Homepage: https://cran.r-project.org/package=ExactCIone Description: CRAN Package 'ExactCIone' (Admissible Exact Intervals for One-Dimensional DiscreteDistributions) Construct the admissible exact intervals for the binomial proportion, the Poisson mean and the total number of subjects with a certain attribute or the total number of the subjects for the hypergeometric distribution. Both one-sided and two-sided intervals are of interest. This package can be used to calculate the intervals constructed methods developed by Wang (2014) and Wang (2015) . Package: r-cran-exactcox Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biasedurn Filename: pool/dists/noble/main/r-cran-exactcox_0.1.0-1.ca2404.1_all.deb Size: 20882 MD5sum: ee203c8787cf291ac176d27a290e21b3 SHA1: 3bcb902eba67f113af677118dede2ba2d5c9633a SHA256: 5912f4d4cafdda81974a4ca6530cb6348290fb7c2414e97e17c7a8c6695761d1 SHA512: cc1b13bd56bf5884ee5d1b4e50a11e191a4429b3e7f55bb2b632d281dd16c3d0ccc84ba589b87fb9dc2091bf9a4f56a3b802f36ed72a5eb07a3a87a082e7170c Homepage: https://cran.r-project.org/package=ExactCox Description: CRAN Package 'ExactCox' (Exact Test and Exact Confidence Interval for the Cox Model) Performs the exact test on whether there is a difference between two survival curves. Exact confidence interval for the hazard ratio can also be generated for the Cox model. Package: r-cran-exactltre Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixcalc, r-cran-popdemo Filename: pool/dists/noble/main/r-cran-exactltre_0.1.2-1.ca2404.1_all.deb Size: 3257812 MD5sum: efc9d93bb3be85b6a962484a2343c958 SHA1: 00da3399213c45754d29153fc0038ee596d6d825 SHA256: 05f2ed5bb0d8e6220bf5fc7ed77fc150139408202407a112f08f2af22558bf6b SHA512: e5bdf2f5aaa4fdfd0f356b922f2121f490a2db8043e86659fd582c9c51b128fea566dbe835c0da722dca4bb918189ed1257b416cf834579d609ce9aa3911398a Homepage: https://cran.r-project.org/package=exactLTRE Description: CRAN Package 'exactLTRE' (An Exact Method for Life Table Response Experiment (LTRE)Analysis) Life Table Response Experiments (LTREs) are a method of comparative demographic analysis. The purpose is to quantify how the difference or variance in vital rates (stage-specific survival, growth, and fertility) among populations contributes to difference or variance in the population growth rate, "lambda." We provide functions for one-way fixed design and random design LTRE, using either the classical methods that have been in use for several decades, or an fANOVA-based exact method that directly calculates the impact on lambda of changes in matrix elements, for matrix elements and their interactions. The equations and descriptions for the classical methods of LTRE analysis can be found in Caswell (2001, ISBN: 0878930965), and the fANOVA-based exact methods are described in Hernandez et al. (2023) . We also provide some demographic functions, including generation time from Bienvenu and Legendre (2015) . For implementation of exactLTRE where all possible interactions are calculated, we use an operator matrix presented in Poelwijk, Krishna, and Ranganathan (2016) . 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Provides statistical techniques for engineering and processing test data: Classical Test Theory (CTT) with reliability coefficients for continuous ability assessment; Item Response Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank Analysis (LRA) for ordinal clustering with automatic determination of cluster numbers; Biclustering methods including infinite relational models for simultaneous clustering of examinees and items without predefined cluster numbers; and Bayesian Network Models (BNM) for visualizing inter-item dependencies. Features local dependence analysis through LRA and biclustering, parameter estimation, dimensionality assessment, and network structure visualization for educational, psychological, and social science research. 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It is designed for multiple-choice, true-false, and open-ended questions. The toolkit is usable with datasets in 1-0 or other formats. Key analyses include difficulty, discrimination, response-option analysis, and reports. The classical test theory methods used are described in Ebel and Frisbie (1991, ISBN:978-0132892314). 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Sort of like python 'doctests' for R. Package: r-cran-exams.forge.data Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-exams, r-cran-exams.forge Suggests: r-cran-aer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-exams.forge.data_0.1.3-1.ca2404.1_all.deb Size: 435682 MD5sum: 2f9bddcfcc7149af714846dd9c367cec SHA1: 7d00e2f90a0f710da7f4760312fc475669a4401e SHA256: fc5bc546c7fd4b8b11e0fbb53fb98f5389febe197cc3b07ffa9872ef37b20270 SHA512: 3d8a360fd9357fe5d818d2dcc3378865d3364cfb15f63b88f17052d1a18d8a706310b3194cd4b4028bc171a6f89a49eb94726424da7d1f93fe393acb152d1c4e Homepage: https://cran.r-project.org/package=exams.forge.data Description: CRAN Package 'exams.forge.data' (Sample and Precomputed Data for Use with 'exams.forge') Provides a small collection of datasets supporting Pearson correlation and linear regression analysis. 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(2009) . Creating effective student exercises involves challenges such as creating appropriate data sets and ensuring access to intermediate values for accurate explanation of solutions. The functionality includes the generation of univariate and bivariate data including simple time series, functions for theoretical distributions and their approximation, statistical and mathematical calculations for tasks in basic statistics courses as well as general tasks such as string manipulation, LaTeX/HTML formatting and the editing of XML task files for 'Moodle'. Package: r-cran-exams.mylearn Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-exams, r-cran-glue, r-cran-stringr, r-cran-stringi, r-cran-pkgbuild, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-exams.mylearn_1.4-1.ca2404.1_all.deb Size: 42462 MD5sum: dc80b2960ca5fce7afc64bfb7828c95b SHA1: 338480fc0b9ba0789806610ecdfd1fb89ae70e91 SHA256: 17f8bf70b22af5cb9d75f5837f0a0d4886d91fa51ff00e611818a7fcabb2afd9 SHA512: 66b8a79036e333075d25061f7c58a461de66abde35ab9f88db4ea86d7297852d26d825a3b278afcb232356784781672bd84e6c1f8e6ab0e1a415215252bd2edf Homepage: https://cran.r-project.org/package=exams.mylearn Description: CRAN Package 'exams.mylearn' (Question Generation in the 'MyLearn' XML Format) Randomized multiple-select and single-select question generation for the 'MyLearn' teaching and learning platform. 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Package: r-cran-executablepacker Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-automagic, r-cran-cli, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-executablepacker_0.0.2-1.ca2404.1_all.deb Size: 48000 MD5sum: 9be2db45cd4a43d4a1c59aa6f1fdfd71 SHA1: b98e30989ae344ae724f4dc9efc9cd0560142bf7 SHA256: 110b0d01f10b938f9eec5aff48e26940d4c0c747714a6d5feea73dd224391a97 SHA512: edf3c8ca2d38a56d18a0492f567ec78f1ee1fe9fab56703897b55528e9d9fbb27da58fbc16c61121edd6420590646a383db182cdde808d833d5c4b9e6410b94b Homepage: https://cran.r-project.org/package=executablePackeR Description: CRAN Package 'executablePackeR' (Make 'shiny' App to Executable Program) Make your 'shiny' application as executable program. Users do not need to install 'R' and 'shiny' on their system. Package: r-cran-exgaussestim Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-nloptr, r-cran-invgamma, r-cran-dlm, r-cran-fitdistrplus, r-cran-gamlss.dist Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-exgaussestim_0.1.2-1.ca2404.1_all.deb Size: 34270 MD5sum: f8efd68abacdc7b4b0605b4aa99444d8 SHA1: 079365fe106ab2dab18403733e49bff3a06acb3e SHA256: d53d3e1e55548bd4dea5bbe81bafc869c7354e2afded1f683804f28cbf53a382 SHA512: 5588b6b6fbf0f3e896c98799e0e2c88dcc60c77456eb33647b4859dda06d2103b58ad9f44731468f2f10b7328be7a985b109a1a7bfe81898c983f804b90cc63b Homepage: https://cran.r-project.org/package=ExGaussEstim Description: CRAN Package 'ExGaussEstim' (Quantile Maximization Likelihood Estimation and BayesianEx-Gaussian Estimation) Presents two methods to estimate the parameters 'mu', 'sigma', and 'tau' of an ex-Gaussian distribution. Those methods are Quantile Maximization Likelihood Estimation ('QMLE') and Bayesian. The 'QMLE' method allows a choice between three different estimation algorithms for these parameters : 'neldermead' ('NEMD'), 'fminsearch' ('FMIN'), and 'nlminb' ('NLMI'). For more details about the methods you can refer at the following list: Brown, S., & Heathcote, A. (2003) ; McCormack, P. D., & Wright, N. M. (1964) ; Van Zandt, T. (2000) ; El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021) ; Gilks, W. R., Best, N. G., & Tan, K. K. C. (1995) . 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The expansion functions are based on coercing the input to a matrix, treating the columns as features and converting individual columns or combinations into blocks of columns. Currently these include expansion of columns by efficient sparse embedding by vectors of lags, quadratic expansion into squares and unique products, powers by vectors of degree, vectors of orthogonal polynomials functions, and block random affine projection transformations (RAPTs). The transformations are magrittr- and cbind-friendly, and can be used in a building block fashion. For instance, taking the cos() of the output of the RAPT transformation generates a stationary kernel expansion via Bochner's theorem, and this expansion can then be cbind-ed with other features. Additionally, there are utilities for replacing features, removing rows with NAs, creating matrix samples of a given distribution, a simple wrapper for LASSO with CV, a Freeman-Tukey transform, generalizations of the outer function, matrix size-preserving discrete difference by row, plotting, etc. Package: r-cran-expar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast Filename: pool/dists/noble/main/r-cran-expar_0.1.0-1.ca2404.1_all.deb Size: 34622 MD5sum: 818416ca87bfe82d9b5b4eeb4f2474d3 SHA1: fe5bb4f943815a15350ba68b72d2d157702aa46d SHA256: 50055ff3cfca3e808716ae68a7313bcda4ce219eb882d36e85861c7a21b6df07 SHA512: 5d8182129894099440475de042d4475ff9ed2190db51969c82543089be978cee5e3d95fce3527e045c97348ffdc324bc6e484b0e6c824504e4972abf0ef4794a Homepage: https://cran.r-project.org/package=EXPAR Description: CRAN Package 'EXPAR' (Fitting of Exponential Autoregressive (EXPAR) Model) The amplitude-dependent exponential autoregressive (EXPAR) time series model, initially proposed by Haggan and Ozaki (1981) has been implemented in this package. Throughout various studies, the model has been found to adequately capture the cyclical nature of datasets. Parameter estimation of such family of models has been tackled by the approach of minimizing the residual sum of squares (RSS). Model selection among various candidate orders has been implemented using various information criteria, viz., Akaike information criteria (AIC), corrected Akaike information criteria (AICc) and Bayesian information criteria (BIC). An illustration utilizing data of egg price indices has also been provided. 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Parameters of the EXPARMA model can be estimated using this package. The user is provided with the best fitted EXPARMA model for the data set under consideration. Package: r-cran-expbites Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-expbites_0.1.3-1.ca2404.1_all.deb Size: 56802 MD5sum: 494f6c9ad8d11d377f2bf7a4a2653c9f SHA1: 28f7878096436d8a0522d2b58788e66f0bcfe527 SHA256: 6179b49af9d0e29966c97313c3f362c026f2615590ad430d3128e334300e00af SHA512: 71a84a9df7e71885d7a25d7bf9738e8d9e485a874e6934732422bd8e9e9c486828ef07b44c437be4f721af273c8e657f3bd9b878f4ceb5cb3ce08a30c06d2082 Homepage: https://cran.r-project.org/package=ExpBites Description: CRAN Package 'ExpBites' (Analyzing Human Exposure to Mosquito Biting) Tools to analyse human and mosquito behavioral interactions and to compute exposure to mosquito bites estimates. 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It provides a suite of tools to simplify data exploration such as benchmarking, comparing groups, and checking for differences. The outputs translate statistical approaches in applied experience research to human readable output. Package: r-cran-experimentr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-experimentr_0.1.0-1.ca2404.1_all.deb Size: 64492 MD5sum: 81adb7e73721c3c0b0d3d442be37a360 SHA1: 19fcb6e110e395db2ac20ed95c2ea5bd7d9df1ed SHA256: 287e91d6b54233761e0edb6900e67047a7de22379a395a1107d5b69445ae120f SHA512: 0bc64fa4e0972ad66458cb1fd10dee2317f59914b4b60155d1168359f625cfd23fc19765f856b27cdebd7b2b191a097748dbf213e81604b9531bdc0989cd8aeb Homepage: https://cran.r-project.org/package=experimentr Description: CRAN Package 'experimentr' (Datasets Used in Social Science Experiments: A Hands-onIntroduction) Contains all the datasets that were used in Social Science Experiments: A Hands-On Introduction and in its R Companion. Relevant materials can be found at . 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And the package is suitable for RNA-seq and small RNA sequencing data. Besides, two methods of non-additive expression analysis were provided. One is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the gene in hybrid offspring with the expression level in parents. For non-additive expression analysis of RNA-seq data, it is only applicable to hybrid offspring (including two sub-genomes) species for the time being. 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Explanations are generated for individual predictions as well as for models as a whole. Two explanation methods are included, EXPLAIN and IME. The EXPLAIN method is fast but might miss explanations expressed redundantly in the model. The IME method is slower as it samples from all feature subsets. For the EXPLAIN method see Robnik-Sikonja and Kononenko (2008) , and the IME method is described in Strumbelj and Kononenko (2010, JMLR, vol. 11:1-18). All models in package 'CORElearn' are natively supported, for other prediction models a wrapper function is provided and illustrated for models from packages 'randomForest', 'nnet', and 'e1071'. Package: r-cran-explodelayout Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-explodelayout_0.1.3-1.ca2404.1_all.deb Size: 72470 MD5sum: 98e886063416352c3a61d46f50b37349 SHA1: 06d2f4afbc3bafbdb8b527621bfa4117dcb0c2b6 SHA256: 34ea5c20c94ffdc4b69994a2e7fca63a1c9ad977a998f7245ee2122a80bdea03 SHA512: cf585982860be45bd7a553cd39cc56aec4e8f9dc635df4d3ab6f1850b5cc181eb59e22e1202f01ade441abdc2fc3d620a71646b6c57dfaa7633571c653c4b759 Homepage: https://cran.r-project.org/package=ExplodeLayout Description: CRAN Package 'ExplodeLayout' (Calculate Exploded Coordinates Based on Original NodeCoordinates and Node Clustering Membership) Current layout algorithms such as Kamada Kawai do not take into consideration disjoint clusters in a network, often resulting in a high overlap among the clusters, resulting in a visual “hairball” that often is uninterpretable. The ExplodeLayout algorithm takes as input (1) an edge list of a unipartite or bipartite network, (2) node layout coordinates (x, y) generated by a layout algorithm such as Kamada Kawai, (3) node cluster membership generated from a clustering algorithm such as modularity maximization, and (4) a radius to enable the node clusters to be “exploded” to reduce their overlap. The algorithm uses these inputs to generate new layout coordinates of the nodes which “explodes” the clusters apart, such that the edge lengths within the clusters are preserved, while the edge lengths between clusters are recalculated. The modified network layout with nodes and edges are displayed in two dimensions. The user can experiment with different explode radii to generate a layout which has sufficient separation of clusters, while reducing the overall layout size of the network. This package is a basic version of an earlier version called [epl] that searched for an optimal explode radius, and offered multiple ways to separate clusters in a network (Bhavnani et al(2017) ). The example dataset is for a bipartite network, but the algorithm can work also for unipartite networks. Package: r-cran-explor Architecture: all Version: 0.3.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 616 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-highr, r-cran-formatr, r-cran-scatterd3, r-cran-rcolorbrewer Suggests: r-cran-factominer, r-cran-ade4, r-cran-gdatools, r-cran-mass, r-cran-quanteda, r-cran-quanteda.textmodels, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-explor_0.3.10-1.ca2404.1_all.deb Size: 480856 MD5sum: da8f4f53b92191a516f3faf916e684ef SHA1: be3da0faf5b314b111f198c97c5192636049cdb9 SHA256: 79b87c4185fe1417c58561a616a3b7cee040970c501fbbbfdb8987b8e91a2d4c SHA512: 5e0641bfee2b9f96a8cd1da4bd21211a11e8825592a06a86c50cd563e4268e8068b3a1feecdd2853128d38c851f14aafb9497fa737175df4795b8506b5fdde3b Homepage: https://cran.r-project.org/package=explor Description: CRAN Package 'explor' (Interactive Interfaces for Results Exploration) Shiny interfaces and graphical functions for multivariate analysis results exploration. Package: r-cran-exploratory Architecture: all Version: 0.3.31-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dt, r-cran-ggplot2, r-cran-ggridges, r-cran-lemon, r-cran-lm.beta, r-cran-mediation, r-cran-remotes, r-cran-shiny, r-cran-shinydashboard, r-cran-weights Suggests: r-cran-moments Filename: pool/dists/noble/main/r-cran-exploratory_0.3.31-1.ca2404.1_all.deb Size: 196254 MD5sum: b1e922d64b8af38d7ac3ca180034645f SHA1: 0d79233c3c47d6e08339d5f21fa383f9513fc727 SHA256: f62d252e2c76beded21e41503e0d257cd44db09822d11d63cdc93da340fff538 SHA512: 67381c3fbb47a8dd468c71dae08a90935caa206feda2f7872db506e43ba6b20945cf60ad982555e786448dc0c16fd8dcbaaa304eb70ba742db3dd17dfb9e2487 Homepage: https://cran.r-project.org/package=exploratory Description: CRAN Package 'exploratory' (A Tool for Large-Scale Exploratory Analyses) Conduct numerous exploratory analyses in an instant with a point-and-click interface. With one simple command, this tool launches a Shiny App on the local machine. Drag and drop variables in a data set to categorize them as possible independent, dependent, moderating, or mediating variables. Then run dozens (or hundreds) of analyses instantly to uncover any statistically significant relationships among variables. Any relationship thus uncovered should be tested in follow-up studies. This tool is designed only to facilitate exploratory analyses and should NEVER be used for p-hacking. Many of the functions used in this package are previous versions of functions in the R Packages 'kim' and 'ezr'. Selected References: Chang et al. (2021) . Dowle et al. (2021) . Kim (2023) . Kim (2021) . Kim (2020) . Simmons et al. (2011) Tingley et al. (2019) . Wickham et al. (2020) . 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Package: r-cran-explorer Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-explorer_0.1-1.ca2404.1_all.deb Size: 39232 MD5sum: f2c44050107abba699fcf6b632c82b53 SHA1: 224e00580f5a1130df7ee417ab1c9acbcf667fe4 SHA256: 056519a506c5c4ae5eef5308d2e94e14d7445d5dee731a88fa49b4f9233e2ebf SHA512: 0ac200b3ec11e4be4b75fe29d68a1f5f7468b5d97198354b0a08e4ef796ca669cae8cf353f2d6c1e8a31c6a676cccd3c7b9d0b7d9dd35f888e98c1bac7faeedf Homepage: https://cran.r-project.org/package=exploreR Description: CRAN Package 'exploreR' (Tools for Quickly Exploring Data) Simplifies some complicated and labor intensive processes involved in exploring and explaining data. 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Output to 'Microsoft Office' is in editable 'DrawingML' vector format for graphs, and can use corporate template documents for styling. This enables the production of standardized reports and also allows for manual tidy-up of the layout of 'R' graphs in 'Powerpoint' before final publication. Export of graphs is flexible, and functions enable the currently showing R graph or the currently showing 'R' stats object to be exported, but also allow the graphical or tabular output to be passed as objects. The package relies on package 'officer' for export to 'Office' documents,and output files are also fully compatible with 'LibreOffice'. Base 'R', 'ggplot2' and 'lattice' plots are supported, as well as a wide variety of 'R' stats objects, via wrappers to xtable(), broom::tidy() and stargazer(), including aov(), lm(), glm(), lme(), glmnet() and coxph() as well as matrices and data frames and many more... 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Unlike existing algorithms, the CI endpoints are found without the need for a strict prespecified range, can be covariate-dependent, and can be based on weighted samples. This package is motivated by Zeder et al. (2023) and by Pasche et al. (2026) . 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The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms. Package: r-cran-extrpatt Architecture: all Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1793 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-mass Filename: pool/dists/noble/main/r-cran-extrpatt_0.1-4-1.ca2404.1_all.deb Size: 1751708 MD5sum: 6457d328b68baf75ea556fcbda8eb1e9 SHA1: e8a597bb8949b76a682caf5a03655932bd294d1e SHA256: 3309143a7eada438c93e29383ee9054c8a3480c88f08ba031cb30a6d6a1ba718 SHA512: 0410470c695690079b4fb7b80b8dd51808970c37c2767bb07d764fc1378271bffe0ad9c2dfc4200a2ac581a357c6ee9e4d487d3eef000eeeca12d2acd85f5280 Homepage: https://cran.r-project.org/package=ExtrPatt Description: CRAN Package 'ExtrPatt' (Spatial Dependencies and Indices for Extremes) An implementation of 1) the tail pairwise dependence matrix (TPDM) as described in Jiang & Cooley (2020) 2) the extremal pattern index (EPI) as described in Szemkus & Friederichs ('Spatial patterns and indices for heatwave and droughts over Europe using a decomposition of extremal dependency'; submitted to ASCMO 2023). Package: r-cran-exvatools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-openxlsx, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-exvatools_1.0.0-1.ca2404.1_all.deb Size: 419244 MD5sum: be980b455c556814ded67c4fe993464b SHA1: 076b0471c3e83775a03b07d86705510608747056 SHA256: 509b85dabfbd2df8f11b69a20da62985ec5322c8d62ef2538d9d7d085abbd8c8 SHA512: 1fe11eb36a237a32e2ecda2af3dc8691c9245acd3ca8545bf888897928cabef6eaa6b4e9fd68a8408751862d278878967da478c0f5f30c75d811e21d1a55f061 Homepage: https://cran.r-project.org/package=exvatools Description: CRAN Package 'exvatools' (Value Added in Exports and Other Input-Output Table AnalysisTools) Analysis of trade in value added with international input-output tables. Includes commands for easy data extraction, matrix manipulation, decomposition of value added in gross exports and calculation of value added indicators, with full geographical and sector customization. Decomposition methods include Borin and Mancini (2023) , Miroudot and Ye (2021) , Wang et al. (2013) and Koopman et al. (2014) . Package: r-cran-eye Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-english, r-cran-lubridate, r-cran-magrittr, r-cran-pillar, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-eyedata, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eye_1.3.0-1.ca2404.1_all.deb Size: 205178 MD5sum: f884ea5e71eceedc9318201aff4bfce4 SHA1: 643da7a20db2e9782941b83dcbb8e2fd0532dddc SHA256: 939249460b5629ef4685b7dbf05e6f7eaf807cdd03a5d91d6e266e4dccb034e1 SHA512: f36cbfb0f9ae0b961e94d154861ea2e4ae49015f823cfa0a0a1e58911c67f334e067b09f3f5b6081a6bd31999f8b5d9b6186c6867e1701c1ee500e54cf887674 Homepage: https://cran.r-project.org/package=eye Description: CRAN Package 'eye' (Analysis of Eye Data) There is no ophthalmic researcher who has not had headaches from the handling of visual acuity entries. Different notations, untidy entries. This shall now be a matter of the past. Eye makes it as easy as pie to work with VA data - easy cleaning, easy conversion between Snellen, logMAR, ETDRS letters, and qualitative visual acuity shall never pester you again. The eye package automates the pesky task to count number of patients and eyes, and can help to clean data with easy re-coding for right and left eyes. It also contains functions to help reshaping eye side specific variables between wide and long format. Visual acuity conversion is based on Schulze-Bonsel et al. (2006) , Gregori et al. (2010) , Beck et al. (2003) and Bach (2007) . Package: r-cran-eyedata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 943 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-eyedata_0.1.0-1.ca2404.1_all.deb Size: 916288 MD5sum: 3640e797ac4d5abddbe3a7334338e9c1 SHA1: f319cff94e63554eeb66be7457ba13e4362c20e9 SHA256: 3a67b3b2e6ce2fdc7d4d194ca5dbce212042f39acc797ccb2d01dfdc8b0298b7 SHA512: 5b9997ce7b7703944aa5da77eb8ec499c5f3b5e2af60698accefbdf387744b3dc574b2d0f53ac7d93556fc7b07f864adff7feb918bff33abd9d4ea25e2e1517a Homepage: https://cran.r-project.org/package=eyedata Description: CRAN Package 'eyedata' (Open Source Ophthalmic Data Sets Curated for R) Open source data allows for reproducible research and helps advance our knowledge. The purpose of this package is to collate open source ophthalmic data sets curated for direct use. This is real life data of people with intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF), due to age-related macular degeneration or diabetic macular edema. Associated publications of the data sets: Fu et al. (2020) , Moraes et al (2020) , Fasler et al. (2019) , Arpa et al. (2020) , Kern et al. 2020, . Package: r-cran-eyelinker Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1986 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-readr, r-cran-intervals Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-purrr, r-cran-covr Filename: pool/dists/noble/main/r-cran-eyelinker_0.2.2-1.ca2404.1_all.deb Size: 1616036 MD5sum: 2a4ea1172f97ce23f8483c1e79ce3a8b SHA1: 852cc0e9799f33d619d93c90891a585b7429e3d8 SHA256: a8c78085c4420e5ccdf1f5ab9fa294c666958d8cdb3373ba1dc300605a492943 SHA512: 8c8e4e90972a1e8ba05a1c3401d724545a7a58e163f45c16ca7ae193bf665f16fceb8f7badecef26e2ff9e5102764d2d75cf06e0f99fb8cfb20915f0357070b7 Homepage: https://cran.r-project.org/package=eyelinker Description: CRAN Package 'eyelinker' (Import ASC Files from EyeLink Eye Trackers) Imports plain-text ASC data files from EyeLink eye trackers into (relatively) tidy data frames for analysis and visualization. Package: r-cran-eyeread Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-data.table, r-cran-tidyr Suggests: r-cran-testthat, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-eyeread_0.0.4-1.ca2404.1_all.deb Size: 53370 MD5sum: 0b894c1c2c467ebbbc6d6a50d0bfd26d SHA1: b53181d780be4f05254ff74f4cae8057664234c9 SHA256: c5aa10bc1883d29b85f1b85374c8b09c1d25702908e61f3f7fd0f3666ce238ee SHA512: c7f738b710b284cb339a9ebcc890a86078ed832bc198575b581f12a65c1a2bd289775d9d245d2ed8023bd88c5f1694005ee81390d0d06bb81cec836118e18e37 Homepage: https://cran.r-project.org/package=eyeRead Description: CRAN Package 'eyeRead' (Prepare/Analyse Eye Tracking Data for Reading) Functions to prepare and analyse eye tracking data of reading exercises. The functions allow some basic data preparations and code fixations as first and second pass. First passes can be further devided into forward and reading. The package further allows for aggregating fixation times per AOI or per AOI and per type of pass (first forward, first rereading, second). These methods are based on Hyönä, Lorch, and Rinck (2003) and Hyönä, and Lorch (2004) . It is also possible to convert between metric length and visual degrees. Package: r-cran-eyeris Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5790 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-eyelinker, r-cran-dplyr, r-cran-gsignal, r-cran-purrr, r-cran-zoo, r-cran-cli, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-progress, r-cran-data.table, r-cran-withr, r-cran-lifecycle, r-cran-mass, r-cran-viridis, r-cran-fields, r-cran-jsonlite, r-cran-rmarkdown, r-cran-dbi, r-cran-glue, r-cran-base64enc Suggests: r-cran-arrow, r-cran-duckdb, r-cran-knitr, r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-eyeris_3.0.1-1.ca2404.1_all.deb Size: 3497022 MD5sum: d7f302851966b96307143239cda77167 SHA1: 594771fd4f9078b4a4461315dac382880fd021d2 SHA256: 6d326b4d0ad9283e152ea1702d8aafd0da66d167e5916b7859114de447f54630 SHA512: 70e338ce35f3a72c4f4542d5759af7f3064ce04b3fcc0ea0b31f9b73e0a4245190cde91acd1235dc6e33c16b5a54139e49a9b8110e6a5a56c91f8afb94d34351 Homepage: https://cran.r-project.org/package=eyeris Description: CRAN Package 'eyeris' (Flexible, Extensible, & Reproducible Pupillometry Preprocessing) Pupillometry offers a non-invasive window into the mind and has been used extensively as a psychophysiological readout of arousal signals linked with cognitive processes like attention, stress, and emotional states [Clewett et al. (2020) ; Kret & Sjak-Shie (2018) ; Strauch (2024) ]. Yet, despite decades of pupillometry research, many established packages and workflows to date lack design patterns based on Findability, Accessibility, Interoperability, and Reusability (FAIR) principles [see Wilkinson et al. (2016) ]. 'eyeris' provides a modular, performant, and extensible preprocessing framework for pupillometry data with BIDS-like organization and interactive output reports [Esteban et al. (2019) ; Gorgolewski et al. (2016) ]. Development was supported, in part, by the Stanford Wu Tsai Human Performance Alliance, Stanford Ric Weiland Graduate Fellowship, Stanford Center for Mind, Brain, Computation and Technology, NIH National Institute on Aging Grants (R01-AG065255, R01-AG079345), NSF GRFP (DGE-2146755), McKnight Brain Research Foundation Clinical Translational Research Scholarship in Cognitive Aging and Age-Related Memory Loss, American Brain Foundation, and the American Academy of Neurology. Package: r-cran-eyetools Architecture: all Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4050 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggforce, r-cran-ggplot2, r-cran-viridis, r-cran-glue, r-cran-hdf5r, r-cran-lifecycle, r-cran-magick, r-cran-pbapply, r-cran-rlang, r-cran-zoo, r-cran-png Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-eyetools_0.9.2-1.ca2404.1_all.deb Size: 3661868 MD5sum: a05d5b082cc35d022644ccafd43d1daf SHA1: 9e67e51152d26d296de7fe29ed25c96be3298fdb SHA256: 33d9c0adca90523f3aa0fbbf7c35798555db4609b744618fbceb327a311a6230 SHA512: 46b7b84b58d7acd077e816c4644abea9ab0ec92b85cdf4053528f64461193733bd9e7e0fbe2082470cb9d08b0fb6e4a5dbec09cd533ad70d1b689bb71b29f2f4 Homepage: https://cran.r-project.org/package=eyetools Description: CRAN Package 'eyetools' (Analyse Eye Data) Enables the automation of actions across the pipeline, including initial steps of transforming binocular data and gap repair to event-based processing such as fixations, saccades, and entry/duration in Areas of Interest (AOIs). It also offers visualisation of eye movement and AOI entries. These tools take relatively raw (trial, time, x, and y form) data and can be used to return fixations, saccades, and AOI entries and time spent in AOIs. As the tools rely on this basic data format, the functions can work with data from any eye tracking device. Implements fixation and saccade detection using methods proposed by Salvucci and Goldberg (2000) . Package: r-cran-eyetrackingr Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1662 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-broom, r-cran-broom.mixed, r-cran-ggplot2, r-cran-lazyeval, r-cran-rlang, r-cran-zoo, r-cran-tidyr, r-cran-purrr Suggests: r-cran-pbapply, r-cran-knitr, r-cran-lme4, r-cran-glmmtmb, r-cran-mass, r-cran-matrix, r-cran-testthat, r-cran-rmarkdown, r-cran-domc, r-cran-foreach Filename: pool/dists/noble/main/r-cran-eyetrackingr_0.2.2-1.ca2404.1_all.deb Size: 897204 MD5sum: 4d305f63174d202dd8db3dbc51c6c678 SHA1: c522349251e971bce43b339f5ba8d13f359f1a61 SHA256: 9b18eb5c5f5431b4f5f4acd3b019a0f93a93c413d52b690086c5cdbbaf3ead6f SHA512: dc77582da2378082ad03625b253a262c8a2dfef8482369fec31db08a94eebd30f1b4311fc4ad4b2b4b884d6d789efcbedc0b4fd7e286215f56a10eed66a140c9 Homepage: https://cran.r-project.org/package=eyetrackingR Description: CRAN Package 'eyetrackingR' (Eye-Tracking Data Analysis) Addresses tasks along the pipeline from raw data to analysis and visualization for eye-tracking data. Offers several popular types of analyses, including linear and growth curve time analyses, onset-contingent reaction time analyses, as well as several non-parametric bootstrapping approaches. For references to the approach see Mirman, Dixon & Magnuson (2008) , and Barr (2008) . Package: r-cran-eyetrackr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2908 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-eyetrackr_1.0.1-1.ca2404.1_all.deb Size: 2937640 MD5sum: bea50befef246f9951b12831d6fe906a SHA1: f273e924767b016d538aeac4ae4b078460c2e772 SHA256: 7f095a21ee5a5e7e29623c9566c46269e14620ae531922ba554ff0d7d37c4060 SHA512: 6cf35e84566bb9d2883380fc3ffbc3ba6de4d93fc4765cd284e368a60481533f7458dd0e93f5301b5eb70c46b9bb2ba6562589e89d647bf4b9093f89640b3d87 Homepage: https://cran.r-project.org/package=eyeTrackR Description: CRAN Package 'eyeTrackR' (Organising and Analysing Eye-Tracking Data) A set of functions for organising and analysing datasets from experiments run using 'Eyelink' eye-trackers. Organising functions help to clean and prepare eye-tracking datasets for analysis, and mark up key events such as display changes and responses made by participants. Analysing functions help to create means for a wide range of standard measures (such as 'mean fixation durations'), which can then be fed into the appropriate statistical analyses and graphing packages as necessary. Package: r-cran-ez.combat Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ez.combat_1.0.0-1.ca2404.1_all.deb Size: 29004 MD5sum: 7246df4934e6b8d28ff384a17d87828d SHA1: 12cb73362bce149f6cdce61d308d123a1e05ea0a SHA256: 656993ec9b93095c52709540aae527b4c5509862907814df53e0cc9801997ecd SHA512: d042b8b740b0faaf5096f5781d6cf95c419c4906c262a72c9fb94ff333ebb0d7e421b68b54f7ebbc4224eea5df4e1191194bb005cb639fac5d303f3ef3e69794 Homepage: https://cran.r-project.org/package=ez.combat Description: CRAN Package 'ez.combat' (Easy ComBat Harmonization) A dataframe-friendly implementation of ComBat Harmonization which uses an empirical Bayesian framework to remove batch effects. Johnson WE & Li C (2007) "Adjusting batch effects in microarray expression data using empirical Bayes methods." Fortin J-P, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, McInnes M, Phillips ML, Trivedi MH, Weissman MM, & Shinohara RT (2017) "Harmonization of cortical thickness measurements across scanners and sites." Fortin J-P, Parker D, Tun B, Watanabe T, Elliott MA, Ruparel K, Roalf DR, Satterthwaite TD, Gur RC, Gur RE, Schultz RT, Verma R, & Shinohara RT (2017) "Harmonization of multi-site diffusion tensor imaging data." Package: r-cran-ez Architecture: all Version: 4.5-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-ggplot2, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-mgcv, r-cran-plyr, r-cran-reshape2, r-cran-scales, r-cran-stringr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-ez_4.5-0-1.ca2404.1_all.deb Size: 334990 MD5sum: c6c3ebcdf603d3ea33083839b7a95c3c SHA1: 91d35bd2febc9bc89837c84c73e8d59417c9a66e SHA256: 78f1b934892764c954e2ba2d2cb9d7af1f0cd02b77075564dd4370f786d4cccd SHA512: 3fc82c5de446704f9a2be46915811e0616ce4e79cdbca9517331ca57d84cce51653021830417a9666c5c5ead58a0befef17ef9dc7fbe3d5b606414a2fab16c99 Homepage: https://cran.r-project.org/package=ez Description: CRAN Package 'ez' (Easy Analysis and Visualization of Factorial Experiments) Facilitates easy analysis of factorial experiments, including purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss designs, and mixed within-and-between-Ss designs. The functions in this package aim to provide simple, intuitive and consistent specification of data analysis and visualization. Visualization functions also include design visualization for pre-analysis data auditing, and correlation matrix visualization. Finally, this package includes functions for non-parametric analysis, including permutation tests and bootstrap resampling. The bootstrap function obtains predictions either by cell means or by more advanced/powerful mixed effects models, yielding predictions and confidence intervals that may be easily visualized at any level of the experiment's design. Package: r-cran-ezbakr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1440 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-bioc-tximport Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ezbakr_0.1.0-1.ca2404.1_all.deb Size: 867786 MD5sum: 4f7ad5d8d8ccae3997d9c9bfc15267cd SHA1: 77cfaf17aa408b2fce8e1f691605c93078e61b7c SHA256: 079e0416f5fc82b903727bdb423d89ffc4f5e175438dfb6107ab872fa602b85b SHA512: e4daabd3bebb9f3f33fe082b137c0c890b207af5ca3423ff720be36f766d412e1b6637b01bec6a0a2373848eeaf465a4c35f3177c7abda9ac282243412286452 Homepage: https://cran.r-project.org/package=EZbakR Description: CRAN Package 'EZbakR' (Analyze and Integrate Any Type of Nucleotide Recoding RNA-SeqData) A complete rewrite and reimagining of 'bakR' (see 'Vock et al.' (2025) ). Designed to support a wide array of analyses of nucleotide recoding RNA-seq (NR-seq) datasets of any type, including TimeLapse-seq/SLAM-seq/TUC-seq, Start-TimeLapse-seq (STL-seq), TT-TimeLapse-seq (TT-TL-seq), and subcellular NR-seq. 'EZbakR' extends standard NR-seq standard NR-seq mutational modeling to support multi-label analyses (e.g., 4sU and 6sG dual labeling), and implements an improved hierarchical model to better account for transcript-to-transcript variance in metabolic label incorporation. 'EZbakR' also generalized dynamical systems modeling of NR-seq data to support analyses of premature mRNA processing and flow between subcellular compartments. Finally, 'EZbakR' implements flexible and well-powered comparative analyses of all estimated parameters via design matrix-specified generalized linear modeling. Package: r-cran-ezcox Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1064 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forestmodel, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-survival, r-cran-tibble, r-cran-utf8 Suggests: r-cran-covr, r-cran-furrr, r-cran-future, r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ezcox_1.0.4-1.ca2404.1_all.deb Size: 623290 MD5sum: 12071a456bf17142060d3646128eabcb SHA1: 4fa8f71367317c2cfe68df59618ec0006ac43b7d SHA256: 3f004a4586e02c1d0def3af783b84e4c5874c3caa06a158ecf742bc03b9c65b7 SHA512: e40da4f60ae9d2a54dca63b7005a41a5a103f573265df599cfdeb173006b7bcb18ba4b874d2026c0e84f2f16d6e398251b050ecc218af08ae2bbdf0f9bacedac Homepage: https://cran.r-project.org/package=ezcox Description: CRAN Package 'ezcox' (Easily Process a Batch of Cox Models) A tool to operate a batch of univariate or multivariate Cox models and return tidy result. 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Package: r-cran-ezeda Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 834 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-ggally, r-cran-scales, r-cran-magrittr, r-cran-purrr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ezeda_0.1.1-1.ca2404.1_all.deb Size: 542274 MD5sum: b7a510ae224ae6dc112b79d2ec58dae4 SHA1: cd54c47cce81b30eb73967f94f6f46e40fc816e6 SHA256: cbf7099c89407c0e8e4950a0ecb8932c6571f1a82303f22eb701454c47ff8d3c SHA512: 8d2e4e291f496d132737c2692908b7e0e45bfc2ff5728b79d36c85b031ea2cc786a9ace1ffe6c93cc74d297b3430d22a99217d8d0845795875ee39ce5a00ce17 Homepage: https://cran.r-project.org/package=ezEDA Description: CRAN Package 'ezEDA' (Task Oriented Interface for Exploratory Data Analysis) Enables users to create visualizations using functions based on the data analysis task rather than on plotting mechanics. 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Package: r-cran-ezgp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nloptr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ezgp_0.1.0-1.ca2404.1_all.deb Size: 798234 MD5sum: e7c426317e60082bb1cdb513ea761a87 SHA1: d888bbedfc494878b7e6125666fe2b5c615c3837 SHA256: 7910133e34c7ac4ebcbd784b6ae19a55367664a8d29da7c33c7e156bf42684f6 SHA512: b0bff3e3508e78b6663535e41576ca20a5265b8db7dd609a11ce3ecfd844516e561fab438cb39841b3c66326be3aa5255ca714d1b259f1712b15e7fd467002c8 Homepage: https://cran.r-project.org/package=EzGP Description: CRAN Package 'EzGP' (Easy-to-Interpret Gaussian Process Models for ComputerExperiments) Fit model for datasets with easy-to-interpret Gaussian process modeling, predict responses for new inputs. The input variables of the datasets can be quantitative, qualitative/categorical or mixed. 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Package: r-cran-f1pits Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-readr, r-cran-tibble, r-cran-httr, r-cran-jsonlite, r-cran-f1datar Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-f1pits_1.3.1-1.ca2404.1_all.deb Size: 80118 MD5sum: a7476b0e783a0fa5e6ec0dd3917b15a9 SHA1: 065e659a2c62a86dff5eba4646c13fac00083bb9 SHA256: b2428235f62c65b8c115079af1ea373a4e10c286d4bf3223c8af02459d9a912e SHA512: e3c6d31b80111194919f68187aa9f006522ecc518f1f8a312b100bf2a6a51ca1fe3f24ee0d603921541d92d0b811ac4ef9b73e6eb6fe40ccc5e1ff946c1c7a07 Homepage: https://cran.r-project.org/package=f1pits Description: CRAN Package 'f1pits' (F1 Pit Stop Datasets) Formula 1 pit stop data. The package provides information on teams and drivers across seasons (2019 or higher). It also includes a function to visualize pit stop performance. Package: r-cran-faasr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-cli, r-cran-jsonvalidate, r-cran-uuid Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-faasr_2.0.0-1.ca2404.1_all.deb Size: 140014 MD5sum: b70f70ffa0464044f423e649227db108 SHA1: 3a96476899402c34d98dce8c8c704cec09ce68ae SHA256: 255f35fa6d9486e54ef742f3e0666763ad20f788addd506f42091c603eaf9339 SHA512: abd91d119352ed9437c12a3c70b95e6a70cb706aa1b8c0da2f08d8b3ed1bc2a52321a2d5e0981e95e189847165a2bc242022a0ecb3289c352ed8393a78183eb2 Homepage: https://cran.r-project.org/package=FaaSr Description: CRAN Package 'FaaSr' ('FaaSr' Local Test Development Package) Provides a local execution environment for testing and developing the 'FaaSr' workflows without requiring cloud infrastructure. The 'FaaSr' package enables R developers to validate and test workflows locally before deploying to Function-as-a-Service (FaaS) platforms. Key features include: 1) Parsing and validating JSON workflow configurations compliant with the 'FaaSr' schema 2) Simulated S3 storage operations using local file system with local logging 3) Support for conditional branching 4) Support for parallel rank functions execution 5) Workflow cycle detection and validation 6) No cloud credentials or infrastructure required for testing This package is designed for development and testing purposes. For production deployment to cloud FaaS platforms, use the main 'FaaSr' package available at . Package: r-cran-fabci Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-fabci_0.3-1.ca2404.1_all.deb Size: 70698 MD5sum: e4ae773149177476f10e3fa321c6b3ad SHA1: 4c17f262e03f0cf3d899425e63ca9e9dae4a6c3b SHA256: 18a4b41564e1ec4ca9432c22bb914aacd380f80b80b699d8b39b9af29da78dba SHA512: 2a208392fa6a87bdea993c55a4d6605569c07605a94fa17742d02862b2a017884908109db9f07170c32378bf881076b8b25bfe56d968a1926e11577836d38036 Homepage: https://cran.r-project.org/package=fabCI Description: CRAN Package 'fabCI' (FAB Confidence Intervals) Frequentist assisted by Bayes (FAB) confidence interval construction. See 'Adaptive multigroup confidence intervals with constant coverage' by Yu and Hoff and 'Exact adaptive confidence intervals for linear regression coefficients' by Hoff and Yu . Package: r-cran-fabinference Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-fabinference_0.1-1.ca2404.1_all.deb Size: 67818 MD5sum: 1e00c88b3b4bad53c85f2044fee3c323 SHA1: 21cddcbe9c25aa2d904a372447ffb2ffb7933f09 SHA256: afe92310da389a55bc33d35680c9589ab34e79537befa888e5bb77781d29db52 SHA512: e005fc2ff538dda0eb10ba5e34b0627c3c2267a8edf4337bd639999ea24c107a8e37edc5e58d831d4e854684542d50482ed2c9871be4c1c2b5721092823f9a40 Homepage: https://cran.r-project.org/package=FABInference Description: CRAN Package 'FABInference' (FAB p-Values and Confidence Intervals) Frequentist assisted by Bayes (FAB) p-values and confidence interval construction. See Hoff (2019) "Smaller p-values via indirect information", Hoff and Yu (2019) "Exact adaptive confidence intervals for linear regression coefficients", and Yu and Hoff (2018) "Adaptive multigroup confidence intervals with constant coverage". Package: r-cran-fabisearch Architecture: all Version: 0.0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4399 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nmf, r-cran-rgl, r-cran-reshape2, r-cran-foreach, r-cran-doparallel, r-cran-dorng Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fabisearch_0.0.4.5-1.ca2404.1_all.deb Size: 4449890 MD5sum: 09e5b79e298b343dee8c16171c3e5079 SHA1: d1ec8c5f95057cb64a4c456572c4b01fd0f1d92d SHA256: f84261fd43ae30242a6587bec1cd1c5ab8c44cbd484953541e5755d82e2dd04e SHA512: 28932bc656411fb8b19471b9cb25a34804324c28e3c073bb25a561f181cc5afe966dd7d7d959235da1a697283d2b09f6860acdb2e6ff5872d5b2186d158259c9 Homepage: https://cran.r-project.org/package=fabisearch Description: CRAN Package 'fabisearch' (Change Point Detection in High-Dimensional Time Series Networks) Implementation of the Factorized Binary Search (FaBiSearch) methodology for the estimation of the number and the location of multiple change points in the network (or clustering) structure of multivariate high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI) data. FaBiSearch uses non-negative matrix factorization (NMF), an unsupervised dimension reduction technique, and a new binary search algorithm to identify multiple change points. It requires minimal assumptions. Lastly, we provide interactive, 3-dimensional, brain-specific network visualization capability in a flexible, stand-alone function. This function can be conveniently used with any node coordinate atlas, and nodes can be color coded according to community membership, if applicable. The output is an elegantly displayed network laid over a cortical surface, which can be rotated in the 3-dimensional space. The main routines of the package are detect.cps(), for multiple change point detection, est.net(), for estimating a network between stationary multivariate time series, net.3dplot(), for plotting the estimated functional connectivity networks, and opt.rank(), for finding the optimal rank in NMF for a given data set. The functions have been extensively tested on simulated multivariate high-dimensional time series data and fMRI data. For details on the FaBiSearch methodology, please see Ondrus et al. (2021) . For a more detailed explanation and applied examples of the fabisearch package, please see Ondrus and Cribben (2022), preprint. Package: r-cran-fable.ata Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fabletools, r-cran-ataforecasting, r-cran-rlang, r-cran-tibble, r-cran-dplyr, r-cran-tsibble, r-cran-distributional, r-cran-tsbox, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-fable.ata_0.0.6-1.ca2404.1_all.deb Size: 64456 MD5sum: 005ccf67799676b1c6dec3dd0cb89cef SHA1: 26782e5bdffdfcca186c4ad040a999ac8db355f3 SHA256: 493e2c8ff783c7572f087391763be3515fc110d2778e674a1a26116d462ef4da SHA512: 020e899be1ad0d903a517851eb72ff94b921013a2391f7d6ab4efffaa71e3a79074a9451bf1e5870abcbc84121c5dc2242bf693194f5271e74a5de70133a1874 Homepage: https://cran.r-project.org/package=fable.ata Description: CRAN Package 'fable.ata' ('ATAforecasting' Modelling Interface for 'fable' Framework) Allows ATA (Automatic Time series analysis using the Ata method) models from the 'ATAforecasting' package to be used in a tidy workflow with the modeling interface of 'fabletools'. This extends 'ATAforecasting' to provide enhanced model specification and management, performance evaluation methods, and model combination tools. The Ata method (Yapar et al. (2019) ), an alternative to exponential smoothing (described in Yapar (2016) , Yapar et al. (2017) ), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal). Package: r-cran-fable.prophet Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 639 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-fabletools, r-cran-rlang, r-cran-tsibble, r-cran-lubridate, r-cran-prophet, r-cran-dplyr, r-cran-distributional Suggests: r-cran-tsibbledata, r-cran-testthat, r-cran-ggplot2, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fable.prophet_0.1.0-1.ca2404.1_all.deb Size: 510788 MD5sum: 7b0c98aed4e482a3ade461fa3a0605db SHA1: 1d848dc28c600d3eead7e1a6ed6b36df2fffc27f SHA256: f3b9c93be8485b66d99a192e92ee0dc88c57cdf43385979c0c93824ab47ef102 SHA512: 98652f41bdbc0b68acffde0f5dce51ab9e3f9b88e46624a64dfc7a5adfba97d954811be4ae05fbbb31931192ac6063c5bb6f67940b0b79b85042960e99ea89ad Homepage: https://cran.r-project.org/package=fable.prophet Description: CRAN Package 'fable.prophet' (Prophet Modelling Interface for 'fable') Allows prophet models from the 'prophet' package to be used in a tidy workflow with the modelling interface of 'fabletools'. This extends 'prophet' to provide enhanced model specification and management, performance evaluation methods, and model combination tools. Package: r-cran-fablecount Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fabletools, r-cran-tscount, r-cran-glarma, r-cran-fable, r-cran-dplyr, r-cran-tsibble, r-cran-tibble, r-cran-tidyr, r-cran-distributional, r-cran-lubridate, r-cran-stringr, r-cran-tsibbledata Suggests: r-cran-rcpp, r-cran-rlang, r-cran-covr, r-cran-feasts, r-cran-forecast, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fablecount_0.1.0-1.ca2404.1_all.deb Size: 96768 MD5sum: 03e8f10d6531e74d7542234d8d99381e SHA1: 9912e8c59ee64bd90d68b75474b9671f9dffd7e0 SHA256: 867ea1b40d500d54387580669795a220d7714a619c1aebcabb2c4190804cc241 SHA512: 8d5f4bc1024b2a9705c3b40d52b8352954f66de9e45a31caa1eb30182a28c524b483a836fbff638be09f3a59fa45a78429c1d8deda31588593367a4a3e2dd987 Homepage: https://cran.r-project.org/package=fableCount Description: CRAN Package 'fableCount' (INGARCH and GLARMA Models for Count Time Series in FableFramework) Provides a tidy R interface for count time series analysis. It includes implementation of the INGARCH (Integer Generalized Autoregressive Conditional Heteroskedasticity) model from the 'tscount' package and the GLARMA (Generalized Linear Autoregressive Moving Averages) model from the 'glarma' package. Additionally, it offers automated parameter selection algorithms based on the minimization of a penalized likelihood. Package: r-cran-fabletools Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 877 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tsibble, r-cran-tibble, r-cran-ggplot2, r-cran-tidyselect, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-generics, r-cran-r6, r-cran-vctrs, r-cran-distributional, r-cran-progressr, r-cran-lifecycle, r-cran-ggdist, r-cran-scales, r-cran-cli Suggests: r-cran-covr, r-cran-crayon, r-cran-fable, r-cran-future, r-cran-future.apply, r-cran-knitr, r-cran-pillar, r-cran-feasts, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tsibbledata, r-cran-lubridate, r-cran-urca, r-cran-mvtnorm, r-cran-matrix, r-cran-ggtime Filename: pool/dists/noble/main/r-cran-fabletools_0.7.0-1.ca2404.1_all.deb Size: 686702 MD5sum: 4f5b2a49eef962da00e2e3a2d5d8ce17 SHA1: 496865d27026e05f6a458e38d3472343ddea4893 SHA256: e343faff7503f94ca7c264ed0102a7d79fb77399431c8778ad87b1de7cc2789e SHA512: 012dc10a3be5c6a9c594c4199001bd0f1519da1a9f58fd320fcf299a2cd2507ba5a050f7cf58613de5ee912426e1bc361b78c53297637da34e7721551d6ca4aa Homepage: https://cran.r-project.org/package=fabletools Description: CRAN Package 'fabletools' (Core Tools for Packages in the 'fable' Framework) Provides tools, helpers and data structures for developing models and time series functions for 'fable' and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis. Package: r-cran-fabr Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-usethis, r-cran-stringr, r-cran-tidyr, r-cran-purrr, r-cran-janitor, r-cran-fs, r-cran-readr, r-cran-readxl, r-cran-writexl, r-cran-haven, r-cran-lubridate, r-cran-bookdown, r-cran-xfun, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-matrix Filename: pool/dists/noble/main/r-cran-fabr_2.1.1-1.ca2404.1_all.deb Size: 140104 MD5sum: 57841051421afb0577698ae9e597f4d8 SHA1: 36a18103f36c687c8359545e16a50eeca522944f SHA256: a7634eedb6cd015efdd96bff38afcbfa3df0c3c788d4c7015fb1c80d8676ab4e SHA512: 030837eadf782f37427df30e7f06d5aa4f575569fa451b616cff6020acb7969d87e48201eb3dad02a01ef53ed9b070eeb1d811559f52d7fcea03f7baa6b40849 Homepage: https://cran.r-project.org/package=fabR Description: CRAN Package 'fabR' (Wrapper Functions Collection Used in Data Pipelines) The goal of this package is to provide wrapper functions in the data cleaning and cleansing processes. These function helps in messages and interaction with the user, keep track of information in pipelines, help in the wrangling, munging, assessment and visualization of data frame-like material. Package: r-cran-fabricatr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang Suggests: r-cran-testthat, r-cran-data.table, r-cran-mvnfast, r-cran-mass, r-cran-extradistr Filename: pool/dists/noble/main/r-cran-fabricatr_1.0.2-1.ca2404.1_all.deb Size: 146932 MD5sum: cd9c3b79a81c62f71a9041959c9c58d5 SHA1: 79010f37113e4be32738d968d7a06680b3a8449f SHA256: d9845b6a8ebdab058b9d7072bf8270bff10126ee17cdd5b2392b66d7dea22b76 SHA512: c7b646d4c064ef1db93d307d2416c386b72cede6bba5d5b76aea71ae09f7be481c372dd70506b08fbb9086ba049e3dd0375006c9e8c9d15cc3517d05a9d8ad52 Homepage: https://cran.r-project.org/package=fabricatr Description: CRAN Package 'fabricatr' (Imagine Your Data Before You Collect It) Helps you imagine your data before you collect it. Hierarchical data structures and correlated data can be easily simulated, either from random number generators or by resampling from existing data sources. This package is faster with 'data.table' and 'mvnfast' installed. Package: r-cran-fabricerin Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1201 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-glue Filename: pool/dists/noble/main/r-cran-fabricerin_0.1.2-1.ca2404.1_all.deb Size: 888994 MD5sum: 9f653121ad179309db06e5dadb177830 SHA1: cae003998ebbf3336ef303f2c33ca143640037b0 SHA256: 60f338bab4dfbaba5e763aa25eb1b2f538eaff716efa77098e5e29009244683e SHA512: e4cf832d3020deede15c2cf31361dc26ee4eab0302a5694a1dec314e7847a3d0b1901e979b73faeee5a0425ec729c1ff46184788930636c2bda68a293a1d7baf Homepage: https://cran.r-project.org/package=fabricerin Description: CRAN Package 'fabricerin' (Create Easily Canvas in 'shiny' and 'RMarkdown' Documents) Allows the user to implement easily canvas elements within a 'shiny' app or an 'RMarkdown' document. The user can create shapes, images and text elements within the canvas which can also be used as a drawing tool for taking notes. The package relies on the 'fabricjs' 'JavaScript' library. See . Package: r-cran-fabricqueryr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-azureauth, r-cran-httr2, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-stringr, r-cran-jsonlite, r-cran-cli, r-cran-rlang Suggests: r-cran-azurestor, r-cran-dbi, r-cran-odbc, r-cran-readr, r-cran-fs, r-cran-arrow, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fabricqueryr_0.2.1-1.ca2404.1_all.deb Size: 84710 MD5sum: 714f8d4de113a9c4bbd89d8e76ab04e6 SHA1: ee3ca94f820726529b862d5eb037b12790953deb SHA256: 0f14b6ce64c62cd6abc96445845737134936bcf652a58ae4026600b9755ad273 SHA512: 08fdf34efa1176b3e29338bda5696515e8a5bc5192ed112c719654659be90ca0d0699da5c49b17f0d0b79717279b21f28a71744f5d40171fb43e31ea095d2a50 Homepage: https://cran.r-project.org/package=fabricQueryR Description: CRAN Package 'fabricQueryR' (Query Data in 'Microsoft Fabric') Query data hosted in 'Microsoft Fabric'. Provides helpers to open 'DBI' connections to 'SQL' endpoints of 'Lakehouse' and 'Data Warehouse' items; submit 'Data Analysis Expressions' ('DAX') queries to semantic model datasets in 'Microsoft Fabric' and 'Power BI'; read 'Delta Lake' tables stored in 'OneLake' ('Azure Data Lake Storage Gen2'); and execute 'Spark' code via the 'Livy API'. Package: r-cran-face Architecture: all Version: 0.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixcalc, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-face_0.1-8-1.ca2404.1_all.deb Size: 96118 MD5sum: 4a40598a59ae29074f48b08b6f3b3a4f SHA1: c085c918a8c92c6fd9be00e1f50a663229418779 SHA256: 2979f6790995365ee5f69ae4e9703093d227d89a7c1ff47ba62c8df527c9edf1 SHA512: 5785004562b680e4026e6c34dfcd61c9d07185ebdf59ed083097e6cf077b80de0b450eabdaf603b275c16c4003d520ce9d791e72dddd222792f358ead945974e Homepage: https://cran.r-project.org/package=face Description: CRAN Package 'face' (Fast Covariance Estimation for Sparse Functional Data) We implement the Fast Covariance Estimation for Sparse Functional Data paper published in Statistics and Computing . Package: r-cran-facebookadsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-facebookadsr_0.1.0-1.ca2404.1_all.deb Size: 22970 MD5sum: 38ba360f8b12ee52599abce251afbb04 SHA1: 5c70a1776423c4204fc4bea2bd10b1fcac2ab8cd SHA256: d3999ab1eaecc8cad331487d1dc93ce7f6558b89e93f93cfd9cb95a1db4f78ca SHA512: da6383522bef216da099b633198f86c2f03ff9e0644f7944cd6aab5294a63c8e46331a829097426318123ef23470f21481adec17d650e6b69ec56d2dc4d5fd79 Homepage: https://cran.r-project.org/package=facebookadsR Description: CRAN Package 'facebookadsR' (Access to Facebook Ads via the 'Windsor.ai' API) Collect marketing data from facebook Ads using the 'Windsor.ai' API . Use four spaces when indenting paragraphs within the Description. Package: r-cran-facebookleadsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-facebookleadsr_0.1.0-1.ca2404.1_all.deb Size: 23148 MD5sum: b1c6b545b7c8e07e78ef90946d1a1f57 SHA1: 2d16df8af20bd2600a45b52050c9f5e79956da86 SHA256: d24f86af1eb875d7727ce203442b329fb9f007886bdba89093c37db74781315d SHA512: f6209b34bbbee604b226c524cf46a8c6de8392a097db5e49325e0f77f49b810c31f01528035ea9701c7f429f41131226a5a56efba80bb8d1fcc9335d526c66ec Homepage: https://cran.r-project.org/package=facebookleadsR Description: CRAN Package 'facebookleadsR' (Get Facebook Leads Ads Data via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from Facebook Leads Ads using the 'Windsor.ai' API . Package: r-cran-facebookorganicr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-facebookorganicr_0.1.0-1.ca2404.1_all.deb Size: 23212 MD5sum: 55f30c3b1d0d4d76cd43d2e92b5e1169 SHA1: 112315cca44d970ddd03da5632cd08c01ab9109e SHA256: a5e9c9ae0a924c9957a8826867d56a1bf754bed47f369d5669b0cf3a7f6f9bdd SHA512: 029905b23225fe14c4ad041910a388ad6e48bf4e046aa196d561967681b52f7f8b73e45111f05dacc593e1c9d876aac8218e6142140968b586494c2112bca69d Homepage: https://cran.r-project.org/package=facebookorganicR Description: CRAN Package 'facebookorganicR' (Get Data from 'Facebook Organic' via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from 'Facebook Organic' using the 'Windsor.ai' API . Package: r-cran-facerec Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 862 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-knitr, r-cran-stringr, r-cran-snakecase, r-cran-rlang Suggests: r-cran-magick, r-cran-ggplot2, r-cran-purrr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-facerec_0.1.0-1.ca2404.1_all.deb Size: 696616 MD5sum: e6544985b6c57aa433fd004fa24d9316 SHA1: 4c7dea9481cd43f0624f2b8c2a39225c69f0ea98 SHA256: b7c4037da8beaeb582938ea77f873ac2b8994f2a8fd395a2800fc68f4f1f96f7 SHA512: bb28111744c187e2eb00b03ca17fe965d7a735354d23fa0e726a0dce2c88c90a79ca546832adf409c81b4f199a3590a67c8f2f13207647e2ad93cf8aa35d1a0c Homepage: https://cran.r-project.org/package=facerec Description: CRAN Package 'facerec' (An Interface for Face Recognition) Provides an interface to the 'Kairos' Face Recognition API . The API detects faces in images and returns estimates for demographics like gender, ethnicity and age. Package: r-cran-facilityepimath Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-facilityepimath_0.2.1-1.ca2404.1_all.deb Size: 71818 MD5sum: aae96825b5a8b38a8a0b4630217169b5 SHA1: 303ba703175f14c10d04ffcda10507d655a8ecc7 SHA256: a7b1843534efac0443f78ce486afbdaca0f2efc3fa7c7d68afa77a9354c5f64a SHA512: 4496bb5b651104664c10ec75e5749376137ccc9dcacfa88264ea61721b4e7d2e916d5a8da06364487603c2375b2328524a8ced7dee8e5decabf92f88bd025bcd Homepage: https://cran.r-project.org/package=facilityepimath Description: CRAN Package 'facilityepimath' (Analyze Mathematical Models of Healthcare Facility Transmission) Calculate useful quantities for a user-defined differential equation model of infectious disease transmission among individuals in a healthcare facility. Input rates of transition between states of individuals with and without the disease-causing organism, distributions of states at facility admission, relative infectivity of transmissible states, and the facility length of stay distribution. Calculate the model equilibrium and the basic facility reproduction number, as described in Toth et al. (2025) . Package: r-cran-facmodcs Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-xts, r-cran-zoo, r-cran-performanceanalytics, r-cran-lattice, r-cran-sn, r-cran-tseries, r-cran-robustbase, r-cran-robstattm Suggests: r-cran-pcra, r-cran-corrplot, r-cran-lmtest, r-cran-rugarch, r-cran-hh Filename: pool/dists/noble/main/r-cran-facmodcs_1.0-1.ca2404.1_all.deb Size: 427672 MD5sum: e02e4a86e4d071da7d4debe673ef684c SHA1: 5563aae24ef3aa345252a72f27bda11521c12789 SHA256: 3e9d7a4c3c6b0b0fb8571dd97cb5b03ab62ff3ea8b17f2ee8f974b8f60b26722 SHA512: 7aba6a99ccbf86366ec50f19e8f4fe0200e6900a5624a8a070d7e4e26ea4f3ee81dd6b0a13c91b394fec5079acc5b2594cfc3ee29b34867480bfaaf2572be6b7 Homepage: https://cran.r-project.org/package=facmodCS Description: CRAN Package 'facmodCS' (Cross-Section Factor Models) Linear cross-section factor model fitting with least-squares and robust fitting the 'lmrobdetMM()' function from 'RobStatTM'; related volatility, Value at Risk and Expected Shortfall risk and performance attribution (factor-contributed vs idiosyncratic returns); tabular displays of risk and performance reports; factor model Monte Carlo. The package authors would like to thank Chicago Research on Security Prices,LLC for the cross-section of about 300 CRSP stocks data (in the data.table object 'stocksCRSP', and S&P GLOBAL MARKET INTELLIGENCE for contributing 14 factor scores (a.k.a "alpha factors".and "factor exposures") fundamental data on the 300 companies in the data.table object 'factorSPGMI'. The 'stocksCRSP' and 'factorsSPGMI' data are not covered by the GPL-2 license, are not provided as open source of any kind, and they are not to be redistributed in any form. Package: r-cran-facmodts Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-data.table, r-cran-lars, r-cran-lattice, r-cran-leaps, r-cran-performanceanalytics, r-cran-portfolioanalytics, r-cran-r.cache, r-cran-corpcor, r-cran-quadprog, r-cran-robstattm, r-cran-robustbase, r-cran-sandwich, r-cran-sn, r-cran-xts, r-cran-zoo Suggests: r-cran-corrplot, r-cran-hh, r-cran-lmtest, r-cran-r.rsp, r-cran-rugarch, r-cran-strucchange, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-facmodts_1.0-1.ca2404.1_all.deb Size: 250294 MD5sum: 2720fd29b2e16f0ee97fd58f3f3120cf SHA1: e5c09bf200a193e507da8c7241fb93af559bf023 SHA256: 11e27a8ab8362216ff16446e5024676c9290d0031e64bebfb7d2eb25585a02d0 SHA512: 32330c68044855cfd8b828b6513272d4c63c545e8614914e2e5f1a06bca75e6c02aa7a90f33086d35566ae73b9429d5816d3f8df3cfdd6dc6cff98351615060e Homepage: https://cran.r-project.org/package=facmodTS Description: CRAN Package 'facmodTS' (Time Series Factor Models for Asset Returns) Supports teaching methods of estimating and testing time series factor models for use in robust portfolio construction and analysis. Unique in providing not only classical least squares, but also modern robust model fitting methods which are not much influenced by outliers. Includes returns and risk decompositions, with user choice of standard deviation, value-at-risk, and expected shortfall risk measures. "Robust Statistics Theory and Methods (with R)", R. A. Maronna, R. D. Martin, V. J. Yohai, M. Salibian-Barrera (2019) . 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The package is capable of re-assigning instances to clusters (algorithm agnostic), preserves the integrity of the data and does not introduce additional models. 'FACT' is inspired by the principles of model-agnostic interpretation in supervised learning. Therefore, some of the methods presented are based on 'iml', a R Package for Interpretable Machine Learning by Christoph Molnar, Giuseppe Casalicchio, and Bernd Bischl (2018) . Package: r-cran-factchar Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/noble/main/r-cran-factchar_1.0-1.ca2404.1_all.deb Size: 69774 MD5sum: 888d11a279460bc39b42b467c1495195 SHA1: 543a3d6d14d45107a9337f9fcea867d03c74d204 SHA256: 3f6e1d6256fd8ae9cefdec4d771901549c2318416fc88f06a240488076264980 SHA512: b61a1a05595381ca5b6a5f04c01fee8c7fa7f016b3f519ad4f62861dfd1d0442099df8f4c6bb607bfc17c01fd3ad1aaa8669e5c42fd03e050026f2f7e4fc1969 Homepage: https://cran.r-project.org/package=FactChar Description: CRAN Package 'FactChar' (Characterization and Diagnostic Tools for Factorial BlockDesigns) Description: Provides comprehensive tools for analysing and characterizing mixed-level factorial designs arranged in blocks. Includes construction and validation of incidence structures, computation of C-matrices, evaluation of A-, D-, E-, and MV-efficiencies, checking of orthogonal factorial structure (OFS), diagnostics based on Hamming distance, discrepancy measures, B-criterion, Es^2 statistics, J2-distance and J2-efficiency, Phi-p optimality, and symmetry conditions for universal optimality. The methodological framework follows foundational work on factorial and mixed-level design assessment by Xu and Wu (2001) , and Gupta (1983) . These methods assist in selecting, comparing, and studying factorial block designs across a range of experimental situations. 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This package includes tools to check whether a design has orthogonal factorial structure (OFS) with balance or not and is able to find the orthogonality deviation value if not having OFS. This package includes function to evaluate efficiency factor of all factorial effects in two situations, in the first situation if the design is verified with OFS and balance then calculate the efficiencies of all factorial effects using a specific analytical procedure and in the second situation if the design is verified with non-OFS and balance then a new general method has been developed and used to calculate efficiencies under the condition that the design should be proper and equi-replicated, See Gupta, S.C. and Mukerjee, R. (1987): "A Calculus for factorial arrangements". Lecture Notes in Statistics. No. 59, Springer-Verlag, Berlin, New York, . For the easy use of package, 'shiny' app is used for giving inputs and inputs validation. 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It also includes helpers for simplifying clustering analysis workflows and provides 'ggplot2'-based data visualization. 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Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML). Package: r-cran-factoptd Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-partitions Filename: pool/dists/noble/main/r-cran-factoptd_1.0.3-1.ca2404.1_all.deb Size: 20856 MD5sum: 9d791b65b509bb36973b51c078ff8db2 SHA1: f2d6fd57d11265d483f4c7b16eebc0d1541668c4 SHA256: 6b5b9202b536318c2b87bd9fbe32aa8408550187b987b5d96301481471e57c8e SHA512: 8e48a3212570eadafcc6ddda05ee9366e7e9b86b09bcd037a689d4157ff2fdde0730d231042429eddbecadf5397960c43422e696b297335c95c80b589cef749d Homepage: https://cran.r-project.org/package=factoptd Description: CRAN Package 'factoptd' (Factorial Optimal Designs for Two-Colour cDNA MicroarrayExperiments) Computes factorial A-, D- and E-optimal designs for two-colour cDNA microarray experiments. Package: r-cran-factor.switching Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-hdinterval, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-factor.switching_1.4-1.ca2404.1_all.deb Size: 99722 MD5sum: 2eef723bfff02a342ce0d77afe42f08b SHA1: b4a023b4996740992c8c8360b50b7ed75dbb751f SHA256: abc0d22ce1f11e3b2e2be71c5e7031b53b1041b6baae2bc9d61f02fd25b5556a SHA512: d47d57f391771b5d46035105937b7b92b694bbaef7d4bc48304899adc50623acf0dc5e15d4fedd5bfc4c466459b36a7676129d3ffdea93b0a902a56bd94ee8f0 Homepage: https://cran.r-project.org/package=factor.switching Description: CRAN Package 'factor.switching' (Post-Processing MCMC Outputs of Bayesian Factor Analytic Models) A well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup, where Markov chain Monte Carlo (MCMC) methods are used to generate samples from the posterior distribution. The package applies a series of rotation, sign and permutation transformations (Papastamoulis and Ntzoufras (2022) ) into raw MCMC samples of factor loadings, which are provided by the user. The post-processed output is identifiable and can be used for MCMC inference on any parametric function of factor loadings. Comparison of multiple MCMC chains is also possible. 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Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented. Package also supports calculating fairness metrics for regression models. Find more details in (Wiśniewski, Biecek (2021)) . Package: r-cran-fairness Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 716 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-ggplot2, r-cran-proc Suggests: r-cran-devtools, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fairness_1.2.3-1.ca2404.1_all.deb Size: 493042 MD5sum: 8832dd144d99a90b7e7b6ef150a533e1 SHA1: 4bb4ebd2949b4335858c0856c433b37262767131 SHA256: 98521bed9b70c6389438c54676f4c1176144408181cc6aa3945138b004950b07 SHA512: 618408c71075641e079dc954c0ce28dc47ad50e2200645c3fe6d40fc7325a1ab5424ade29fa4527709926e568e47b35830bf62dd9111663dc1587971eaec9008 Homepage: https://cran.r-project.org/package=fairness Description: CRAN Package 'fairness' (Algorithmic Fairness Metrics) Offers calculation, visualization and comparison of algorithmic fairness metrics. Fair machine learning is an emerging topic with the overarching aim to critically assess whether ML algorithms reinforce existing social biases. Unfair algorithms can propagate such biases and produce predictions with a disparate impact on various sensitive groups of individuals (defined by sex, gender, ethnicity, religion, income, socioeconomic status, physical or mental disabilities). Fair algorithms possess the underlying foundation that these groups should be treated similarly or have similar prediction outcomes. The fairness R package offers the calculation and comparisons of commonly and less commonly used fairness metrics in population subgroups. These methods are described by Calders and Verwer (2010) , Chouldechova (2017) , Feldman et al. (2015) , Friedler et al. (2018) and Zafar et al. (2017) . The package also offers convenient visualizations to help understand fairness metrics. Package: r-cran-fairsubset Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fairsubset_1.0-1.ca2404.1_all.deb Size: 30590 MD5sum: 1d83794ef17bded354bd533152a1c851 SHA1: 6af85171e661c6e53a8687fc3fd8480514bb22a7 SHA256: e5b1cc9b1090f4d95755c91cecf62de80088b48223b64339d7422e19f15b3fea SHA512: 9d141f5312504a02e0b799c85090be9caef71a68d94498f7c61cf48ebc53ca3ce7c0e279b15cc75fa42faad44a3680c172872cdfb819de07e0c3f68233eace2d Homepage: https://cran.r-project.org/package=fairsubset Description: CRAN Package 'fairsubset' (Choose Representative Subsets) Allows user to obtain subsets of columns of data or vectors within a list. These subsets will match the original data in terms of average and variation, but have a consistent length of data per column. It is intended for use on automated data generation which may not always output the same N per replicate or sample. 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Each observation is drawn from a multivariate Normal distribution where the mean vector and covariance matrix reflect the desired relationships. Outputs can be used to evaluate the performances of variable selection, graphical modelling, or clustering approaches by comparing the true and estimated structures (B Bodinier et al (2021) ). 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Package: r-cran-fam.recrisk Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fam.recrisk_0.1-1.ca2404.1_all.deb Size: 34442 MD5sum: 64b08aeec1613016211899967cf53b03 SHA1: b4723b4e27ab49f2f36aec9fcddd02a83baa02bb SHA256: 3dd19cad1c3eb56ec0d49a5ac11f30d29b3b7e6867f0e7f08974b816b3634d24 SHA512: 2125cfe5dd5fcb2178db0a2d2ba013cdd9224ee13c7c876134eb717793a45f19efa5dae4731771e5742dab97d076c510033022d6d5ad72d055f7b9a1ecd7843e Homepage: https://cran.r-project.org/package=fam.recrisk Description: CRAN Package 'fam.recrisk' (Familial Recurrence Risk) Given vectors of family sizes and number of affecteds per family, calculates the risk of disease recurrence in an unaffected person, conditional on a family having at least k affected members. 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Appropriate ascertainment correction is utilized to estimate age-dependent penetrance functions either parametrically from the fitted model or nonparametrically from the data. The Expectation and Maximization algorithm can infer missing genotypes and carrier probabilities estimated from family's genotype and phenotype information or from fitted models. Plot functions include pedigrees of simulated families and predicted penetrance curves based on specified parameter values. For more information see Choi, Y.-H., Briollais, L., He, W. and Kopciuk, K. (2021) FamEvent: An R Package for Generating and Modeling Time-to-Event Data in Family Designs, Journal of Statistical Software 97 (7), 1-30. 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This test is general and several special cases are known as other methods: famSKAT, which only focuses on rare variants in family-based data, SKAT, which focuses on rare variants in population-based data (unrelated individuals), and SKAT-RC, which focuses on both rare and common variants in population-based data. When one applies famSKAT-RC and sets the value of phi to 1, famSKAT-RC becomes famSKAT. When one applies famSKAT-RC and set the value of phi to 1 and the kinship matrix to the identity matrix, famSKAT-RC becomes SKAT. When one applies famSKAT-RC and set the kinship matrix (fullkins) to the identity matrix (and phi is not equal to 1), famSKAT-RC becomes SKAT-RC. We also include a small sample synthetic pedigree to demonstrate the method with. For more details see Saad M and Wijsman EM (2014) . 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December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated). 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Package: r-cran-farrell Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-data.table, r-cran-magrittr, r-cran-shinywidgets, r-cran-benchmarking, r-cran-shinycssloaders, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-dplyr, r-cran-dt Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-farrell_0.2.1-1.ca2404.1_all.deb Size: 642542 MD5sum: 2c24e589672c349e7dcd73da42810df8 SHA1: 61b5154796b988247d494123680a61b96a9f39da SHA256: 7a65234f92ad2ed57caffac99bb0cc37caee481499dfffc85dce6683f02493dd SHA512: 94058f835463103dd45adf9e128522d2a25f04617018491a591372dded3823a50ea73959b401b8ebca2682e80947a6bdccabcaabe5f44615f5d725bef74f25a0 Homepage: https://cran.r-project.org/package=farrell Description: CRAN Package 'farrell' (Interactive Interface to Data Envelopment Analysis Modeling) Allows the user to execute interactively radial data envelopment analysis models. The user has the ability to upload a data frame, select the input/output variables, choose the technology assumption to adopt and decide whether to run an input or an output oriented model. When the model is executed a set of results are displayed which include efficiency scores, peers' determination, scale efficiencies' evaluation and slacks' calculation. Fore more information about the theoretical background of the package, please refer to Bogetoft & Otto (2011) . Package: r-cran-fars Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1955 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-magrittr, r-cran-ggplot2, r-cran-plotly, r-cran-sn, r-cran-nloptr, r-cran-ellipse, r-cran-syscselection, r-cran-quantreg, r-cran-tidyr, r-cran-dplyr, r-cran-forcats, r-cran-mass, r-cran-reshape2, r-cran-stringr Suggests: r-cran-r.rsp, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-openxlsx, r-cran-readxl, r-cran-zoo Filename: pool/dists/noble/main/r-cran-fars_0.8.0-1.ca2404.1_all.deb Size: 1768352 MD5sum: d0165eae24c8fe129c1733fbb5e8c75e SHA1: 3f8ab2c8324855bbedcd3a9440645331e98b6da5 SHA256: f5b95a897ae0617da256b44511680fa27a1b56ee2c6d2b51e23b45725d6d3a67 SHA512: a21a771f735fbf846c23422a37a2e8f43f8e903be7b432ea6ada861ab4b9316f71654eb9c473ff66f01fae7b9366189f39123d5982511b5bd4356a746d7fdf6d Homepage: https://cran.r-project.org/package=FARS Description: CRAN Package 'FARS' (Factor-Augmented Regression Scenarios) Provides a comprehensive framework in R for modeling and forecasting economic scenarios based on multi-level dynamic factor model. The package enables users to: (i) extract global and group-specific factors using a flexible multi-level factor structure; (ii) compute asymptotically valid confidence regions for the estimated factors, accounting for uncertainty in the factor loadings; (iii) obtain estimates of the parameters of the factor-augmented quantile regressions together with their standard deviations; (iv) recover full predictive conditional densities from estimated quantiles; (v) obtain risk measures based on extreme quantiles of the conditional densities; (vi) estimate the conditional density and the corresponding extreme quantiles when the factors are stressed. Package: r-cran-fase Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rspectra, r-cran-rtensor, r-cran-splines2 Filename: pool/dists/noble/main/r-cran-fase_1.0.1-1.ca2404.1_all.deb Size: 139270 MD5sum: efbef0094b8e74dc011b83fc9beb6836 SHA1: 4f311e6fb18e442020240c9a5f586656b018ee8c SHA256: f27ccf43a7d5ae72732efa9ff89f00867b219cc78dface3154ab05b83fa2c72c SHA512: fac582389ce4f5cd409b924864835efef28d17947da48942fe219f72163cb7cb847cfa949482b57be99d9ff41ffc7dd0bc0f2cb9f169d88fba2e6b5c2f167fe5 Homepage: https://cran.r-project.org/package=fase Description: CRAN Package 'fase' (Functional Adjacency Spectral Embedding) Latent process embedding for functional network data with the Functional Adjacency Spectral Embedding. Fits smooth latent processes based on cubic spline bases. 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Package: r-cran-faseg Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-faseg_0.1.9-1.ca2404.1_all.deb Size: 41276 MD5sum: a10456cc3888a2f6bc3d3ae5b2870c0e SHA1: 28450a8559d66c049740352f02a2538a100b65cd SHA256: 15304dda05b2fcb3a7ae8dae192f4c8e7817651c41a65d65f937b83e75f5213d SHA512: 0ebdebd33ae825f3fa07c8e042cf9e207af82693f7e0a1c3a71fd7d73b647be4150a5e4c7760ec3b17f354c9e14a118dadabae83fd62b8ca0db5a5623de5d5b2 Homepage: https://cran.r-project.org/package=FASeg Description: CRAN Package 'FASeg' (Joint Segmentation of Correlated Time Series) It contains a function designed to the joint segmentation in the mean of several correlated series. The method is described in the paper X. Collilieux, E. Lebarbier and S. Robin. A factor model approach for the joint segmentation with between-series correlation (2015) . Package: r-cran-fasjem Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Filename: pool/dists/noble/main/r-cran-fasjem_1.1.2-1.ca2404.1_all.deb Size: 359924 MD5sum: 2e67e4f122b5ef5f28a0e7f4e680df98 SHA1: a11f127e4ad6d5b0d384688d5b57a361c75ab167 SHA256: ae73b6d8b7d13f2ce00817157a2593b1a7bcbba0be667b0a81e9efe490558793 SHA512: 74645ba277c4784e40fce9dd8c5623c05d563110f1b17b924d8370723df41d733fbbc5bae8440aab4d27b805e34562dd431bd21b1642e954d17ebfe9000438a7 Homepage: https://cran.r-project.org/package=fasjem Description: CRAN Package 'fasjem' (A Fast and Scalable Joint Estimator for Learning MultipleRelated Sparse Gaussian Graphical Models) This is an R implementation of "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models" (FASJEM). The FASJEM algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogonous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(fasjem) to learn the basic functions provided by this package. For more details, please see . Package: r-cran-fassets Architecture: all Version: 4023.85-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-timedate, r-cran-timeseries, r-cran-fbasics, r-cran-fmultivar, r-cran-robustbase, r-cran-mass, r-cran-sn, r-cran-ecodist, r-cran-mvnormtest, r-cran-energy Suggests: r-cran-mnormt, r-cran-runit Filename: pool/dists/noble/main/r-cran-fassets_4023.85-1.ca2404.1_all.deb Size: 291530 MD5sum: 9cf7ac975c71dbe4dfa69842e6e22ccd SHA1: 61dae5a9d2b60ec702560ee2de0c7f2c55f1c936 SHA256: 08be4e9efb9ec6f9835e03562f95480a805c9492c6e68be2771f56d2d881320e SHA512: ad95fc78d4eff8675a2beeb32856b226584e387452cbf8059cfd57798d660934881cf9f6ea121cbeecf40e9198aa541eba936a7c9f832c4bc7a99b1fd7b431fb Homepage: https://cran.r-project.org/package=fAssets Description: CRAN Package 'fAssets' (Rmetrics - Analysing and Modelling Financial Assets) A collection of functions to manage, to investigate and to analyze data sets of financial assets from different points of view. Package: r-cran-fasster Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fabletools, r-cran-dlm, r-cran-tsibble, r-cran-purrr, r-cran-rlang, r-cran-dplyr, r-cran-distributional, r-cran-vctrs Suggests: r-cran-tsibbledata, r-cran-lubridate, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling, r-cran-covr Filename: pool/dists/noble/main/r-cran-fasster_0.2.0-1.ca2404.1_all.deb Size: 202474 MD5sum: 1b26e870b093aa65d1542f4ae078c972 SHA1: c0c083eb9c62661d5368ee5f302dc0ccf1bdaf52 SHA256: 77ba9ad287f5f4807838f7b40ac2e7b77818f11639bc2b5f1d39156a2b839129 SHA512: 31409b9821a0ba2216002332a1cd9b7ec9ded7f4bdff04f215e8148086baed277ec5256c458922a38554e2e102f1491a5a82fdd8275c7401dd27a4226ffd4adc Homepage: https://cran.r-project.org/package=fasster Description: CRAN Package 'fasster' (Fast Additive Switching of Seasonality, Trend, and ExogenousRegressors) Implementation of the FASSTER (Forecasting with Additive Switching of Seasonality, Trend, and Exogenous Regressors) model for forecasting time series with multiple seasonal patterns. 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Package: r-cran-fasstr Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2969 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-e1071, r-cran-fitdistrplus, r-cran-ggplot2, r-cran-lubridate, r-cran-openxlsx, r-cran-pearsonds, r-cran-plyr, r-cran-purrr, r-cran-rcpproll, r-cran-scales, r-cran-tidyhydat, r-cran-tidyr, r-cran-zyp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fasstr_0.5.3-1.ca2404.1_all.deb Size: 2142798 MD5sum: fd044d80e7b01c2ea0fb5a5946aefd04 SHA1: 5f6a964d4e47de06efb33ad9f02ae30a599538ef SHA256: 46cea62eb8207732b560efcbf85ccc085dc59bc205dca0a93f9e61810bb3086c SHA512: b14568e975e0174e8250f97499bf7f861f2d2bd0cdacb4a4039f3b4038266c05bf4aa0da842ae644bacc0cba356ed18db9b9d1d6e80c71a1e7bdddede059c407 Homepage: https://cran.r-project.org/package=fasstr Description: CRAN Package 'fasstr' (Analyze, Summarize, and Visualize Daily Streamflow Data) The Flow Analysis Summary Statistics Tool for R, 'fasstr', provides various functions to tidy and screen daily stream discharge data, calculate and visualize various summary statistics and metrics, and compute annual trending and volume frequency analyses. It features useful function arguments for filtering of and handling dates, customizing data and metrics, and the ability to pull daily data directly from the Water Survey of Canada hydrometric database (). 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Package: r-cran-fasta Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fasta_0.1.0-1.ca2404.1_all.deb Size: 17518 MD5sum: 6bb21cac98b5b11181caa74b9434d497 SHA1: fe43a7621f044fc97ad33fa1d969e251fd648312 SHA256: 9376abb8d60286eb23a40b046f52d323550b77176c6d6d870204299d0d39e1b6 SHA512: 7017c65f7b12863a61b947f1783e59407fd5ceae98912e93e1211d6e79085d21e02e37749c0c3b266a1a5f6bbbfe44932ea4ac00ae4bb5e81130b040d09559bd Homepage: https://cran.r-project.org/package=fasta Description: CRAN Package 'fasta' (Fast Adaptive Shrinkage/Thresholding Algorithm) A collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in Goldstein, Studer, and Baraniuk (2016) . 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It is based on research in to deep learning best practices undertaken at 'fast.ai', including 'out of the box' support for vision, text, tabular, audio, time series, and collaborative filtering models. Package: r-cran-fastbackward Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastbackward_1.0.1-1.ca2404.1_all.deb Size: 27490 MD5sum: 81fdde4b118328e3f2df7b4275999f9b SHA1: ec4dc3c97683ec35d98bb7252bcf3877948dbade SHA256: caf1b687197d902a73b1194d990fc6c09b1f2b8d3900a9677c5320575067f4d8 SHA512: afdb75bd9a7c1c1aa4cdf1227a730f6106dd010534d275141585b6aa4da9275eaab81a872e4c3df715855c3c28b8bbc27a0414ff70500e6039d326aa9dd41956 Homepage: https://cran.r-project.org/package=fastbackward Description: CRAN Package 'fastbackward' (Fast Backward Elimination Based on Information Criterion) Performs backward elimination with similar syntax to the stepAIC() function from the 'MASS' package. A bounding algorithm is used to avoid fitting unnecessary models, making it much faster. Package: r-cran-fastbioclim Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 799 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-exactextractr, r-cran-future.apply, r-cran-glue, r-cran-progressr, r-cran-purrr, r-cran-qs2, r-cran-rfast, r-cran-sf, r-cran-terra Suggests: r-cran-mockery, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-fastbioclim_0.4.2-1.ca2404.1_all.deb Size: 708008 MD5sum: 9d42f0bf4b987043f665656700b05363 SHA1: ff1bc2c1c2594bf23cb7e7fd8e1889c3897c8caf SHA256: 587c46c21007af3c31224393e47736adbbbe50b77e517e7e71f8fd573b625961 SHA512: 5c407b79a0020c8f4a0652f82a03771cbc6082b18c3da0612e61614bf1aa63f5622a2a39f72af73e0f8986397a665a9449903aa846379dd88ec61aacd219e786 Homepage: https://cran.r-project.org/package=fastbioclim Description: CRAN Package 'fastbioclim' (Scalable and Flexible Derivation of Custom-Time Bioclimatic andEnvironmental Summary Variables) Provides an efficient tool for creating custom-time bioclimatic and derived environmental summary variables from user-supplied raster data for user-defined timeframes. The package overcomes computational bottlenecks by automatically switching between an in-memory framework using the 'terra' package to maximize speed for smaller datasets, and an on-disk tiling framework for rasters that exceed available RAM, leveraging 'exactextractr' and 'Rfast' to process data in chunks. The core functions, derive_bioclim() and derive_statistics(), offer a unified interface with flexibility for custom time periods beyond standard quarters and the use of fixed temporal indices, facilitating the creation of temporally-matched environmental variables for ecological and biogeographical modeling. Visit the package website to find tutorials in English and Spanish. Package: r-cran-fastcub Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-cub Filename: pool/dists/noble/main/r-cran-fastcub_0.0.4-1.ca2404.1_all.deb Size: 271138 MD5sum: ed0a3cb6ae7f44740f7bb43d1cdf60c6 SHA1: c5f28eebd0de9b87fd430fa89605501963db2c4d SHA256: c60421c73676e1f5ee0c49c13b4cdd0ed01559d84d34421148363addf49ffb93 SHA512: f18e3552bc70928b01ca8e30b255d34c7ee51ba949e5b6b90c61ede1f2642f641ae6573f13b6208079283b620f71c7b4aa018017ef167386496d49329c2ec404 Homepage: https://cran.r-project.org/package=FastCUB Description: CRAN Package 'FastCUB' (Fast Estimation of CUB Models via Louis' Identity) For ordinal rating data, consider the accelerated EM algorithm to estimate and test models within the family of CUB models (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions). The procedure is built upon Louis' identity for the observed information matrix. Best-subset variable selection is then implemented since it becomes more feasible from the computational point of view. Package: r-cran-fastdid Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-bmisc, r-cran-collapse, r-cran-dreamerr, r-cran-ggplot2 Suggests: r-cran-did, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-fastdid_1.0.6-1.ca2404.1_all.deb Size: 358894 MD5sum: 8a6f8394e0caf1a246613c6dd3ff3d1c SHA1: 15cfa5ce113b90357b97f2a42cf2b30f5122f6dc SHA256: dc6de581e2a0bfd47fa5ee78e914f4293642eda4e562184d0b8e0178b2326a00 SHA512: 1f1ae4518e9e26c7cbf77270e92a92ffb193d3d48e38bf9da8769b943630c1678e1136d8be559c75c372d76347b3d8e0ac8653816bdcb991789b23c59c116463 Homepage: https://cran.r-project.org/package=fastdid Description: CRAN Package 'fastdid' (Fast Staggered Difference-in-Difference Estimators) A fast and flexible implementation of Callaway and Sant'Anna's (2021) staggered Difference-in-Differences (DiD) estimators, 'fastdid' reduces the computation time from hours to seconds, and incorporates extensions such as time-varying covariates and multiple events. Package: r-cran-fastdummies Architecture: all Version: 1.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-tibble, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-spelling, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-fastdummies_1.7.6-1.ca2404.1_all.deb Size: 42088 MD5sum: 5f94c79c0e95a716eb58b54514bf8164 SHA1: c2b106c3b6d543a1236d3f16f69921133ae4d972 SHA256: 94f0e8217c958f7a23ce6a2467fd1a8f9fcb7d76a24ce36f9809d2ff2831315a SHA512: 8cf464313d323b1bf41106818557d72b8411b1a356b76468996cf8e43aea704e53732b468de1af5c5fb0c45598bc6d0a9f01846b0bd2f3682cf2e1c1f47058b1 Homepage: https://cran.r-project.org/package=fastDummies Description: CRAN Package 'fastDummies' (Fast Creation of Dummy (Binary) Columns and Rows fromCategorical Variables) Creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. Also creates dummy rows from character, factor, and Date columns. This package provides a significant speed increase from creating dummy variables through model.matrix(). Package: r-cran-fasterraster Architecture: all Version: 8.4.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dt, r-cran-omnibus, r-cran-rgrass, r-cran-sf, r-cran-shiny, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fasterraster_8.4.1.2-1.ca2404.1_all.deb Size: 4212326 MD5sum: 5f177996c9df18070a3ef576570d396f SHA1: 3a5591f71f9b9cbdde23eaca2f44d479c8be2cd4 SHA256: 6ec37d5d52222a3af42d16d27cdef6b9cffabcdd9006615ef71cacdfcbc74bc5 SHA512: d3420596d587999377263e42e8938adb8b3726cb4c0b2032c859ac354c1898b5fc900f80e8a79522bcc23504bb6e6dbec5bbea012796fae095e0a43e6585cb4a Homepage: https://cran.r-project.org/package=fasterRaster Description: CRAN Package 'fasterRaster' (Faster Raster and Spatial Vector Processing Using 'GRASS') Processing of large-in-memory/large-on disk rasters and spatial vectors using 'GRASS' . Most functions in the 'terra' package are recreated. Processing of medium-sized and smaller spatial objects will nearly always be faster using 'terra' or 'sf', but for large-in-memory/large-on-disk objects, 'fasterRaster' may be faster. To use most of the functions, you must have the stand-alone version (not the 'OSGeoW4' installer version) of 'GRASS' 8.0 or higher. Package: r-cran-fastfgee Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-gridextra, r-cran-mass, r-cran-matrix, r-cran-mgcv, r-cran-refund, r-cran-rfast, r-cran-supergauss Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sanic, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/noble/main/r-cran-fastfgee_0.1.0-1.ca2404.1_all.deb Size: 304590 MD5sum: 4cf0aa651cd2a49c2a7e2873e86dd4b7 SHA1: 9b83e66787d56456a69be02fc7f449d5bbe08011 SHA256: 24646a185bfa7dccbfb495932fdcb70e8985e5088fcef936f82f0dcdbd388e63 SHA512: d9268d847bf3afecb7619df3964e521670db3f71f0ed4a579a2df22084503337c1b0c40ec11700e07d9af1654dab781733197b60c202955f3c5b403e0b26a39c Homepage: https://cran.r-project.org/package=fastFGEE Description: CRAN Package 'fastFGEE' (Fast Functional Generalized Estimating Equations via a One-StepEstimator) Fits functional generalized estimating equations for longitudinal functional outcomes and covariates using a one-step estimator that is fast even for large cluster sizes or large numbers of clusters. The package supports quasi-likelihoods derived from a range of families, common link functions, and several working correlation structures. Uncertainty quantification is based on sandwich variance estimators and bootstrap procedures that remain valid even when the working correlation is incorrectly specified. The package provides an implementation of the method described in Loewinger et al. (2025) . For irregularly spaced AR(1) precision matrices, the package can optionally use the archived package 'irregulAR1'; if needed, it can be obtained from the CRAN Archive at . Package: r-cran-fastfmm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2356 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-lme4, r-cran-caic4, r-cran-magrittr, r-cran-mgcv, r-cran-mass, r-cran-lsei, r-cran-refund, r-cran-stringr, r-cran-matrix, r-cran-mvtnorm, r-cran-progress, r-cran-ggplot2, r-cran-gridextra, r-cran-rfast, r-cran-lmeresampler Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-fastfmm_1.0.1-1.ca2404.1_all.deb Size: 2235728 MD5sum: 7bb31a42906d48f194e52e4cb0bc4a8b SHA1: 76f387f381a0c95bc08b78b669a4416915315bbb SHA256: 03f604c7f622cab8298fd866ff6960b724da8f1d0ecb3ad819ea81601bab7207 SHA512: d9bd2a8c2d72f5ff6af558242a01a164454e736d4e98558998d2dd41d440d0642295b5325ecba41c6267206f01d9c683659bb9e947f84ab24640292806cb1731 Homepage: https://cran.r-project.org/package=fastFMM Description: CRAN Package 'fastFMM' (Fast Functional Mixed Models using Fast Univariate Inference) Implementation of the fast univariate inference approach (Cui et al. (2022) , Loewinger et al. (2024) , Xin et al. (2025) ) for fitting functional mixed models. User guides and Python package information can be found at . Package: r-cran-fastfocal Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 545 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-withr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-fastfocal_0.1.3-1.ca2404.1_all.deb Size: 305498 MD5sum: 9e5cd91b6888ce49048c703e64d4176b SHA1: a15156e61945980e29e891e6256aee1371ca8635 SHA256: 8dd243f72396a1655a3374cee6937f9ad1647817532ba163fa3ee8b813427893 SHA512: 3c6128af13eef626ce44864086e7d21a6557df3d75e3fd677ac232ec02bb867391e7c38a679b24708e5b0aadd6321e758ee42c06ba7711caed8fbb41a131f20c Homepage: https://cran.r-project.org/package=fastfocal Description: CRAN Package 'fastfocal' (Fast Multiscale Raster Extraction and Moving Window Analysiswith FFT) Provides fast moving-window ("focal") and buffer-based extraction for raster data using the 'terra' package. 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Package: r-cran-fastgraph Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-jmuoutlier Filename: pool/dists/noble/main/r-cran-fastgraph_2.1-1.ca2404.1_all.deb Size: 87162 MD5sum: 31065fb99f4f5793558a43be7fe75562 SHA1: 68d926b22a2b78ba840ee695283164e76a904602 SHA256: bdeff11a5415623221ada90a47b8ab2decd0d1a0cdb539bd8471fb696ebf3c58 SHA512: 66412c8eb8b5812edd7614583e6de6296644282c3fdd85516e9e1ece95560025e177aabb1da6d40f022156d5ce1b749304a26e81a075e10fa73ec2daaf29cc47 Homepage: https://cran.r-project.org/package=fastGraph Description: CRAN Package 'fastGraph' (Fast Drawing and Shading of Graphs of Statistical Distributions) Provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title. Package: r-cran-fastimputation Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 679 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-testthat, r-cran-caret, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-fastimputation_2.2.1-1.ca2404.1_all.deb Size: 623840 MD5sum: f468aef01e9ce775e5f2a3245516df02 SHA1: aaf683460aaa856445d06e99ca814e68e8167459 SHA256: a2501d39349e15353c3ce38e8e7cb05cbe5cfcb2dc1ccd977141cc2dc5e44bd4 SHA512: 67fbba987c032dbeee1a328e23ea06fb8159b9a50c0ab9b8951b305942371a5595a1dca64270823605a1d9b3cb8ca5d401ee9106cc760d1e8cdbca42bba32fca Homepage: https://cran.r-project.org/package=FastImputation Description: CRAN Package 'FastImputation' (Learn from Training Data then Quickly Fill in Missing Data) TrainFastImputation() uses training data to describe a multivariate normal distribution that the data approximates or can be transformed into approximating and stores this information as an object of class 'FastImputationPatterns'. 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Package: r-cran-fastkm Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rarpack Suggests: r-cran-skat, r-cran-survival Filename: pool/dists/noble/main/r-cran-fastkm_1.2-1.ca2404.1_all.deb Size: 88200 MD5sum: 9cadd0f93fbb6aee2da5165f2212defc SHA1: 8cc948709a1a781fce41dbb38b68a05adf51f0dc SHA256: ffba75646bd4b642516640e96a8664aecdb83d42a4ebb4cb3d32ad09ce78659b SHA512: daad7f7fced9e42a86659551db0c3703ea89234ab94d7159f3942d6f92acf407718f6b763c2ad1e47675cac3e35a4a952d9ee768f27c8cf53de86316ab340b18 Homepage: https://cran.r-project.org/package=FastKM Description: CRAN Package 'FastKM' (A Fast Multiple-Kernel Method Based on a Low-Rank Approximation) A computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software 'SKAT' and 'coxKM'. 'coxKM' can be obtained from . Package: r-cran-fastknn Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pdist, r-cran-assertthat Filename: pool/dists/noble/main/r-cran-fastknn_0.0.1-1.ca2404.1_all.deb Size: 19612 MD5sum: f980fa38163b340c497a1456e1e35582 SHA1: e40ceb9584ea05b82b2b62474a4fa6f92c0854b7 SHA256: 91023f309cbf1ef8885ac8898850f4175b44a21c47f931341b385ec7742d358e SHA512: 20192d36c8a6b8fdc86a31618aa8721c333f4997f6ac48395d05b54522fdeb6d06e1ff6c7423e249e1d441495af74b1ea7d3d9e8dc05a3ce075b7e3eb316b963 Homepage: https://cran.r-project.org/package=FastKNN Description: CRAN Package 'FastKNN' (Fast k-Nearest Neighbors) Compute labels for a test set according to the k-Nearest Neighbors classification. This is a fast way to do k-Nearest Neighbors classification because the distance matrix -between the features of the observations- is an input to the function rather than being calculated in the function itself every time. Package: r-cran-fastlaplace Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rspectra, r-cran-bbmle, r-cran-fields Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling, r-cran-mgcv, r-cran-ngspatial, r-cran-mass Filename: pool/dists/noble/main/r-cran-fastlaplace_0.0.2-1.ca2404.1_all.deb Size: 72390 MD5sum: 0c630fd47516b251a9a3a615b096be35 SHA1: 5a188b937586eeb27c843f72f50d16042298ceb1 SHA256: ff9a114d9389c9cec8566cc6638340fbe989c89e6305aaf2a452ea04f20b3f7a SHA512: 0f379673b75cec3d869ea6ccfafca85f8f6a7eb0505d9a2b03ca1d8bb319411c45189b94b3853fe77c832a73322b9434a4cf0d03799771cc3a6dfcce934a9351 Homepage: https://cran.r-project.org/package=fastLaplace Description: CRAN Package 'fastLaplace' (A Fast Laplace Method for Spatial Generalized Linear Mixed Model) Fitting a fast Laplace approximation for Spatial Generalized Linear Mixed Model as described in Park and Lee (2021) . Package: r-cran-fastlogitme Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fastlogitme_0.1.0-1.ca2404.1_all.deb Size: 24966 MD5sum: 0bc498860e9a1bd65a552ecdcf46c727 SHA1: 03f17981a5fea1979051a49393fb26ade6ff2132 SHA256: 2f5b7b67e56f9820b7ef9dc6b576993165b6c65d27f2b8e7d7f279a382a551a0 SHA512: bf61c12ec212472e8f033cca02eb4a4b9a6b3a7fe9f66ffa740779f172b78919fd914554656d4618ddec10198b54e2e01210a599e0006399ad267b2ca5210abf Homepage: https://cran.r-project.org/package=fastlogitME Description: CRAN Package 'fastlogitME' (Basic Marginal Effects for Logit Models) Calculates marginal effects based on logistic model objects such as 'glm' or 'speedglm' at the average (default) or at given values using finite differences. It also returns confidence intervals for said marginal effects and the p-values, which can easily be used as input in stargazer. The function only returns the essentials and is therefore much faster but not as detailed as other functions available to calculate marginal effects. As a result, it is highly suitable for large datasets for which other packages may require too much time or calculating power. Package: r-cran-fastml Architecture: all Version: 0.7.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-recipes, r-cran-dplyr, r-cran-ggplot2, r-cran-reshape2, r-cran-rsample, r-cran-parsnip, r-cran-tune, r-cran-workflows, r-cran-yardstick, r-cran-tibble, r-cran-rlang, r-cran-dials, r-cran-rcolorbrewer, r-cran-dofuture, r-cran-foreach, r-cran-finetune, r-cran-future, r-cran-viridislite, r-cran-magrittr, r-cran-proc, r-cran-janitor, r-cran-stringr, r-cran-broom, r-cran-tidyr, r-cran-purrr, r-cran-survival, r-cran-xgboost Suggests: r-cran-testthat, r-cran-withr, r-cran-baguette, r-cran-c50, r-cran-dalex, r-cran-discrim, r-cran-fairmodels, r-cran-flexsurv, r-cran-ibreakdown, r-cran-iml, r-cran-lime, r-cran-modelstudio, r-cran-pdp, r-cran-plsmod, r-cran-probably, r-cran-ranger, r-cran-rstpm2, r-cran-survrm2, r-cran-aorsf, r-cran-censored, r-cran-crayon, r-cran-kernlab, r-cran-klar, r-cran-kknn, r-cran-keras, r-cran-lightgbm, r-cran-rstanarm, r-cran-patchwork, r-cran-ggally, r-cran-glmnet, r-cran-themis, r-cran-dt, r-cran-upsetr, r-cran-vim, r-cran-dbscan, r-cran-gridextra, r-cran-htmlwidgets, r-cran-kableextra, r-cran-moments, r-cran-naniar, r-cran-plotly, r-cran-scales, r-cran-skimr, r-cran-sparsediscrim, r-cran-knitr, r-cran-rmarkdown, r-cran-pec Filename: pool/dists/noble/main/r-cran-fastml_0.7.8-1.ca2404.1_all.deb Size: 1117808 MD5sum: 8e0b4570771b6331e94a979d888ba5c1 SHA1: b50abd11ece2cab4065ba71465a6ef7617b2308c SHA256: 95bf024e56552fea4755b032fd92958f91ac6a6d612eee7f7c8c48920999f7f6 SHA512: 43bd02762eab49dcceea5ead8aa6c6d55bbc99f848310d3baa7b2ddcbfa2cad7d65b4babd2c03cf5dae62c92df8da13b52b07fabd6aa87042d05412731f85b77 Homepage: https://cran.r-project.org/package=fastml Description: CRAN Package 'fastml' (Guarded Resampling Workflows for Safe and Automated MachineLearning in R) Provides a guarded resampling workflow for training and evaluating machine-learning models. When the guarded resampling path is used, preprocessing and model fitting are re-estimated within each resampling split to reduce leakage risk. Supports multiple resampling schemes, integrates with established engines in the 'tidymodels' ecosystem, and aims to improve evaluation reliability by coordinating preprocessing, fitting, and evaluation within supported workflows. Offers a lightweight AutoML-style workflow by automating model training, resampling, and tuning across multiple algorithms, while keeping evaluation design explicit and user-controlled. Package: r-cran-fastnaivebayes Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1487 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastnaivebayes_2.2.1-1.ca2404.1_all.deb Size: 1214420 MD5sum: 36f113d5e00a6302ccd1e2b6b3214f95 SHA1: 96f81e058ab7e680952d832cd8b09c7391f5515e SHA256: d49510bdfdbc91671011730333ee7960fd87502e82a9068ebada77f59f77b7d4 SHA512: 3991f7106791a8f7e9e622c9f7ad1006fc5b66a4e4d297737cca9db0b09343d0c6058663cd763b19de479b32c7a60ee65e1151c7ba80c6475fd828d0620d1bf5 Homepage: https://cran.r-project.org/package=fastNaiveBayes Description: CRAN Package 'fastNaiveBayes' (Extremely Fast Implementation of a Naive Bayes Classifier) This is an extremely fast implementation of a Naive Bayes classifier. This package is currently the only package that supports a Bernoulli distribution, a Multinomial distribution, and a Gaussian distribution, making it suitable for both binary features, frequency counts, and numerical features. Another feature is the support of a mix of different event models. Only numerical variables are allowed, however, categorical variables can be transformed into dummies and used with the Bernoulli distribution. The implementation is largely based on the paper "A comparison of event models for Naive Bayes anti-spam e-mail filtering" written by K.M. Schneider (2003) . Any issues can be submitted to: . Package: r-cran-fastnet Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-cran-tidygraph Filename: pool/dists/noble/main/r-cran-fastnet_1.0.0-1.ca2404.1_all.deb Size: 180508 MD5sum: 590a957490d44742cf883d5705d30875 SHA1: 5df81b53bc25b5f64b6151324f47c6057ed85a40 SHA256: 5f8b5412ec6356a307e295a47cbafdb4ec2eae3f2c15ff5196d17bc5832ea942 SHA512: b36fbf7793edbfd731e5663be5259b2482dcf62301c4ced79c78c5bde959253944115a164e8f92c91c6ee19dab6f15fa3e568a68fdec93bb9fcb61ae1a9d5ec7 Homepage: https://cran.r-project.org/package=fastnet Description: CRAN Package 'fastnet' (Large-Scale Social Network Analysis) We present an implementation of the algorithms required to simulate large-scale social networks and retrieve their most relevant metrics. Details can be found in the accompanying scientific paper on the Journal of Statistical Software, . Package: r-cran-fastonlinecpt Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-fastonlinecpt_1.0-1.ca2404.1_all.deb Size: 134098 MD5sum: 8a8e24a47736b19bb03b2ce7a3fcd745 SHA1: b10562d09604a9fe17c871b61179536680623480 SHA256: e13ac58c5ee3f3f87697f884211dd04b192fa3bd7ed9b742424d86540bfca523 SHA512: b6e7a6e88e0ec09745350ad5daec020a668a18c37befd7d4d5663b788ef1d992021e287f180a0f2ec8cf54b3b5c07e86c85e585f826c7cd7251b9e8cdd74f881 Homepage: https://cran.r-project.org/package=fastOnlineCpt Description: CRAN Package 'fastOnlineCpt' (Online Multivariate Changepoint Detection) Implementation of a simple algorithm designed for online multivariate changepoint detection of a mean in sparse changepoint settings. The algorithm is based on a modified cusum statistic and guarantees control of the type I error on any false discoveries, while featuring O(1) time and O(1) memory updates per series as well as a proven detection delay. Package: r-cran-fastqcr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1310 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gridextra, r-cran-ggplot2, r-cran-magrittr, r-cran-readr, r-cran-rmarkdown, r-cran-rvest, r-cran-tibble, r-cran-tidyr, r-cran-scales, r-cran-xml2, r-cran-rlang Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-fastqcr_0.1.3-1.ca2404.1_all.deb Size: 1282122 MD5sum: 7f3a19d459e331383053627314299423 SHA1: ea9b575e0f78564453be57e6ca5b416ecd15ba46 SHA256: 4b4feb37e8615e2e9541863da3be2d87cbd1a3326a92138c430e652d77bb7a02 SHA512: a068da012365c9f697fd83f81d3a89ed0eba44eb4b4884fb923938eea98761d82b3cb25f1a04e824a6a90cce6f229358959b427401db149c1f5491ed28a1f08d Homepage: https://cran.r-project.org/package=fastqcr Description: CRAN Package 'fastqcr' (Quality Control of Sequencing Data) 'FASTQC' is the most widely used tool for evaluating the quality of high throughput sequencing data. 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Package: r-cran-fastqrs Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-copula Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sampleselection, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-fastqrs_1.0.0-1.ca2404.1_all.deb Size: 264488 MD5sum: 4f85062ceae69e8abcd5adb27ea0bd16 SHA1: 8021ab409460ed52b2a54853d1b5f6f9a67fdd47 SHA256: 0283d43898fe60265b5d4e1cd18bbb5e68512c934822e57fcc9fc39ee4aeb9aa SHA512: bff65f4e316fc7d90db484b5de94df62b522bc65fd1604e5c12db35abc652f56a519fe5a2a90bffb95c9f5265ff4730a37a521a575a2159afab25cef1bb7a4f3 Homepage: https://cran.r-project.org/package=fastqrs Description: CRAN Package 'fastqrs' (Fast Algorithms for Quantile Regression with Selection) Fast estimation algorithms to implement the Quantile Regression with Selection estimator and the multiplicative Bootstrap for inference. 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Package: r-cran-fastr2 Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2086 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mosaic, r-cran-maxlik, r-cran-numderiv, r-cran-dplyr, r-cran-ggplot2, r-cran-lattice, r-cran-misctools Suggests: r-cran-ggformula, r-cran-mosaiccalc, r-cran-tidyr, r-cran-readr, r-cran-mass, r-cran-faraway, r-cran-hmisc, r-cran-daag, r-cran-multcomp, r-cran-vcd, r-cran-car, r-cran-alr4, r-cran-corrgram, r-cran-bradleyterry2, r-cran-cubature, r-cran-knitr, r-cran-mosaicdata, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fastr2_1.2.5-1.ca2404.1_all.deb Size: 1154056 MD5sum: a7a228c869a0446cb17a99f8ee381b46 SHA1: 4bdc159992b8f715b3c773c90d16c7a0e3ff4161 SHA256: 9aca2b1a1178a9e4fe86c74bf6a4fdcb401d936c3e2d591f8a1719683f00989a SHA512: 04cb8810ba4b5490129c57ba3450e04189028134dacbb69c1cd4f0c5bb310e9fc399533cbf4dd3c7b4c1497d9fdc8a91b8ffc8e738d46b6dd71d4a2e3daef4f7 Homepage: https://cran.r-project.org/package=fastR2 Description: CRAN Package 'fastR2' (Foundations and Applications of Statistics Using R (2nd Edition)) Data sets and utilities to accompany the second edition of "Foundations and Applications of Statistics: an Introduction using R" (R Pruim, published by AMS, 2017), a text covering topics from probability and mathematical statistics at an advanced undergraduate level. R is integrated throughout, and access to all the R code in the book is provided via the snippet() function. Package: r-cran-fastreg Architecture: all Version: 0.8.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2927 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-haven, r-cran-osdc, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-uuid Suggests: r-cran-crew, r-cran-dbplyr, r-cran-devtools, r-cran-duckdb, r-cran-qs2, r-cran-quarto, r-cran-targets, r-cran-testthat, r-cran-tidyselect, r-cran-withr Filename: pool/dists/noble/main/r-cran-fastreg_0.8.17-1.ca2404.1_all.deb Size: 731870 MD5sum: 1fa4982de03cf366b92b5bbcffc91429 SHA1: f35e66354b607168ead5f0f2036e2bc03fc7c5c4 SHA256: 8e4bd7ab8e6452040bfe59a6359342c0dac16cc5a20260e99ba0173fa8937925 SHA512: f8bca2b8b1c8c0a5d193101bc445b8fc856c96a580c2485e72692e637fca6510ecee82a3e764e8c62277d56b1ea516e61400f638e110a056bfca0581e2f376f0 Homepage: https://cran.r-project.org/package=fastreg Description: CRAN Package 'fastreg' (Fast Conversion and Querying of Danish Registers with 'Parquet') Converts large Danish register files ('sas7bdat') into 'Parquet' format with year-based 'Hive' partitioning and chunked reading for larger-than-memory files. 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Package: r-cran-fastrep Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-janitor, r-cran-kableextra, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastrep_0.7-1.ca2404.1_all.deb Size: 118020 MD5sum: d49cb2b0ba6068a7009d0facdb95558f SHA1: c34f23622da9bbfc63a9706d4498e6f4a23c98f8 SHA256: 84ef70b7becb849687359107ebfbb748124f08fe26710bc09a22be285100af4d SHA512: bb0dca5115ccf9f34f807c16998bb2decbb023ed582e12c04ca59fdfcdb62ccc438e0c2de4cb9fea4caed1439528229f0205d6846e1af4a3db959ef9d237f97c Homepage: https://cran.r-project.org/package=fastrep Description: CRAN Package 'fastrep' (Time-Saving Package for Creating Reports) Provides templates for reports in 'rmarkdown' and functions to create tables and summaries of data. Package: r-cran-fastrerandomize Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-assertthat Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fastrerandomize_0.3-1.ca2404.1_all.deb Size: 2084070 MD5sum: fa1fcb8e483a34c6ae08a3efe73ada97 SHA1: 869c329b6895ec84db23e0c5f923c23c32ea8b8f SHA256: bc6eefecdbb472ee64c47937e0514b7879132dce2f8f804e1777e1ed6d50c013 SHA512: 9adc801c455c32fba7f26049f7b74493e461f42ea961c7fd8b4bbede179a589036d21af5bb2e896674f4b12969bcca079df12e185d3ec07cdc9b7c2d61330087 Homepage: https://cran.r-project.org/package=fastrerandomize Description: CRAN Package 'fastrerandomize' (Hardware-Accelerated Rerandomization for Improved Balance) Provides hardware-accelerated tools for performing rerandomization and randomization testing in experimental research. Using a 'JAX' backend, the package enables exact rerandomization inference even for large experiments with hundreds of billions of possible randomizations. Key functionalities include generating pools of acceptable rerandomizations based on covariate balance, conducting exact randomization tests, and performing pre-analysis evaluations to determine optimal rerandomization acceptance thresholds. The package supports various hardware acceleration frameworks including 'CPU', 'CUDA', and 'METAL', making it versatile across accelerated computing environments. This allows researchers to efficiently implement stringent rerandomization designs and conduct valid inference even with large sample sizes. The package is partly based on Jerzak and Goldstein (2023) . Package: r-cran-fastret Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1531 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-cluster, r-cran-data.table, r-cran-dt, r-cran-future, r-cran-ggplot2, r-cran-glmnet, r-cran-htmltools, r-cran-openxlsx, r-cran-promises, r-cran-rcdk, r-cran-rlang, r-cran-shiny, r-cran-shinyhelper, r-cran-shinyjs, r-cran-withr, r-cran-xgboost Suggests: r-cran-callr, r-cran-caret, r-cran-cli, r-cran-devtools, r-cran-knitr, r-cran-languageserver, r-cran-lintr, r-cran-pkgdown, r-cran-pkgbuild, r-cran-pkgload, r-cran-rmarkdown, r-cran-servr, r-cran-tibble, r-cran-testthat, r-cran-toscutil, r-cran-usethis Filename: pool/dists/noble/main/r-cran-fastret_1.3.0-1.ca2404.1_all.deb Size: 1496498 MD5sum: 0781fe5854768cc851ffc968d6691eaa SHA1: 32a7851b46de05f61966e54714c5124030b16e3e SHA256: e838554691265f9cd832ead030f0d8c498cce9bc65f39addd51e25f123fdc82c SHA512: 921123eb53f47757300f3e91aeab2ae85856451859f23b798043235339b0994439ba61ee898a4a3f4eaf607789f3c76e979dc7e42d569b93d39e7f6a003ec644 Homepage: https://cran.r-project.org/package=FastRet Description: CRAN Package 'FastRet' (Retention Time Prediction in Liquid Chromatography) A framework for predicting retention times in liquid chromatography. Users can train custom models for specific chromatography columns, predict retention times using existing models, or adjust existing models to account for altered experimental conditions. The provided functionalities can be accessed either via the R console or via a graphical user interface. Related work: Bonini et al. (2020) . Package: r-cran-fastrg Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-igraph, r-cran-rlang, r-cran-rspectra, r-cran-tibble, r-cran-tidygraph, r-cran-tidyr Suggests: r-cran-covr, r-cran-irlba, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastrg_0.4.0-1.ca2404.1_all.deb Size: 498200 MD5sum: 6df7a4469e8cd9bb50e0bc8bb7c1b4c5 SHA1: 4bc2d9ef91ed377c35e35e530e3b1075922e4584 SHA256: e9d02608eefcb3395bd779a1435aa89fff2542165028ef942e3447d8540d6924 SHA512: 626789d6707380a9ab9bb5dac1290c286b0fb1fd0fc2276064c5feecf40bf8ea3ba40a7e850d73478905d4a6af37d3538088ca52c772370deb7dec8e220d5d61 Homepage: https://cran.r-project.org/package=fastRG Description: CRAN Package 'fastRG' (Sample Generalized Random Dot Product Graphs in Linear Time) Samples generalized random product graphs, a generalization of a broad class of network models. Given matrices X, S, and Y with with non-negative entries, samples a matrix with expectation X S Y^T and independent Poisson or Bernoulli entries using the fastRG algorithm of Rohe et al. (2017) . The algorithm first samples the number of edges and then puts them down one-by-one. As a result it is O(m) where m is the number of edges, a dramatic improvement over element-wise algorithms that which require O(n^2) operations to sample a random graph, where n is the number of nodes. Package: r-cran-fastrhockey Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2094 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-progressr, r-cran-purrr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-rvest, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-crayon, r-cran-curl, r-cran-dbi, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggrepel, r-cran-qs, r-cran-rmarkdown, r-cran-rsqlite, r-cran-stringi, r-cran-testthat, r-cran-usethis, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-fastrhockey_0.4.0-1.ca2404.1_all.deb Size: 2078442 MD5sum: fb33bc717f555760ad4d9b70add65a86 SHA1: 6c98cc035c88220db24086925673d80042372986 SHA256: fe774b475603b08fc76fdc194cb8bdb8e86bfcceead65c6444c54e1a90bdaf23 SHA512: 0eca557849ec4754880e7c8ea4aa4fae35d8b13b9c8bcbb51a63f8f7c770715863bee127a9b599e8fcb35270c3cad250e123b646c6a1f5d0e569ce9a0d4653c0 Homepage: https://cran.r-project.org/package=fastRhockey Description: CRAN Package 'fastRhockey' (Functions to Access Premier Hockey Federation and NationalHockey League Play by Play Data) A utility to scrape and load play-by-play data and statistics from the Premier Hockey Federation (PHF) , formerly known as the National Women's Hockey League (NWHL). Additionally, allows access to the National Hockey League's stats API . Package: r-cran-fastrmodels Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 16525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xgboost Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fastrmodels_2.1.0-1.ca2404.1_all.deb Size: 16886244 MD5sum: b261e52dc84ebdd5d6d0e5a6dd75c332 SHA1: b195b7fe2a7abdbb0dfbf5b778e67db4daa6ef52 SHA256: 56b38ae0cf78efb1bec9171f4a510bf4b2610b3c3c83cc6c599ed4040dbe5b9b SHA512: 9e1b5df8db5faadce0f778f63eea05525f46be739e46d881fe7fcccbab5a1b07d7e0418e57ef4f3a60eda85b667d20d2f80a5d98cee4409dc69c66928da418f7 Homepage: https://cran.r-project.org/package=fastrmodels Description: CRAN Package 'fastrmodels' (Models for the 'nflfastR' Package) A data package that hosts all models for the 'nflfastR' package. Package: r-cran-faststepgraph Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-faststepgraph_0.1.1-1.ca2404.1_all.deb Size: 327360 MD5sum: 4bd9b28a5ed2a50c59937eea39f09aab SHA1: 29e510664903eb51ae0d5c6e5390e41561e170d2 SHA256: 419eecaa5e543df3a4c96aeafaab86874cae0019c7e97942a099e65680b66ca9 SHA512: 35f4e65aebfa5a7e1343ce92275a4455fd86c0a0537d75639980809c8eee5f02fc36f2a15795e97c99853fcc2284fdb0a8c8f001b20b439832d1814afb603762 Homepage: https://cran.r-project.org/package=FastStepGraph Description: CRAN Package 'FastStepGraph' (A Fast Algorithm for Sparse Precision Matrix Estimation) It implements an improved and computationally faster version of the original Stepwise Gaussian Graphical Algorithm for estimating the Omega precision matrix from high-dimensional data. Zamar, R., Ruiz, M., Lafit, G. and Nogales, J. (2021) . Package: r-cran-fastts Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ncvreg, r-cran-rcpproll, r-cran-rlang, r-cran-yardstick Suggests: r-cran-covr, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-fastts_1.0.3-1.ca2404.1_all.deb Size: 395660 MD5sum: 967ad8eba5045ca7c0a52beed7462194 SHA1: a8c2f64cfb609c9d2f57562430d5349cb1db3018 SHA256: 70b028c2afa33fec63b57aa061d07d6f00c310e54560ee9c9dedb8ce7caf0d0b SHA512: ed09cf03dd2fbc1ad4a6398495c437220d98a37ad26599af842bc7d8f5883a789907c862f2ebd19485ed71d77e9f61957566b7785592374b4b75e5f62a30bac4 Homepage: https://cran.r-project.org/package=fastTS Description: CRAN Package 'fastTS' (Fast Time Series Modeling for Seasonal Series with ExogenousVariables) An implementation of sparsity-ranked lasso and related methods for time series data. This methodology is especially useful for large time series with exogenous features and/or complex seasonality. Originally described in Peterson and Cavanaugh (2022) in the context of variable selection with interactions and/or polynomials, ranked sparsity is a philosophy with methods useful for variable selection in the presence of prior informational asymmetry. This situation exists for time series data with complex seasonality, as shown in Peterson and Cavanaugh (2024) , which also describes this package in greater detail. The sparsity-ranked penalization methods for time series implemented in 'fastTS' can fit large/complex/high-frequency time series quickly, even with a high-dimensional exogenous feature set. The method is considerably faster than its competitors, while often producing more accurate predictions. Also included is a long hourly series of arrivals into the University of Iowa Emergency Department with concurrent local temperature. Package: r-cran-fastverse Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-collapse, r-cran-kit, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fastverse_0.3.4-1.ca2404.1_all.deb Size: 107358 MD5sum: e08030e12beddf002b02c2569c910206 SHA1: 714cc94a93a7fee9c9695343c2963b4ad46994d6 SHA256: 6e257be47472609d98a64c9b2b3522e20d2c386c44bf5686da7ee7dca997889e SHA512: dfe7f2cc37410796551a84a5b5f051b6787b40a24723df6f8677b7716d0f6a464a26e84f9b2350b0933fcc3b705647ebf539238755ca96351dd21428817286e5 Homepage: https://cran.r-project.org/package=fastverse Description: CRAN Package 'fastverse' (A Suite of High-Performance Packages for Statistics and DataManipulation) Easy installation, loading and management, of high-performance packages for statistical computing and data manipulation in R. 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Flexible cutoffs are an alternative to fixed cutoffs - rules-of-thumb - regarding an appropriate cutoff for fit indices such as 'CFI' or 'SRMR'. It has been demonstrated that these flexible cutoffs perform better than fixed cutoffs in grey areas where misspecification is not easy to detect. The package provides an alternative to the tool at as it allows to tailor flexible cutoffs to a given dataset and model, which is so far not available in the tool. The package simulates fit indices based on a given dataset and model and then estimates the flexible cutoffs. Some useful functions, e.g., to determine the 'GoF-' or 'BoF-nature' of a fit index, are provided. So far, additional options for a relative use (is a model better than another?) are provided in an exploratory manner. 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Additionally, 26 statistical approaches for the estimation of the number of clusters as well as the mirrored density plot (MD-plot) of clusterability are implemented. The packages is published in Thrun, M.C., Stier Q.: "Fundamental Clustering Algorithms Suite" (2021), SoftwareX, . Moreover, the fundamental clustering problems suite (FCPS) offers a variety of clustering challenges any algorithm should handle when facing real world data, see Thrun, M.C., Ultsch A.: "Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems" (2020), Data in Brief, . Package: r-cran-fcr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-face, r-cran-mgcv, r-cran-fields Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fcr_1.0-1.ca2404.1_all.deb Size: 534160 MD5sum: 1bea0fd05eecd6d809aed4b91b352fd7 SHA1: 7d4658bbc7c76f21891ab2bec58300d74e28b99d SHA256: 6b06638feb3679fb9264db45915c92876cb54c5e2ab518daf7e04da97e56c562 SHA512: e03c38ac34d99269edc5f30e39cb4549b8722d49e163e32aaaf63f5971227b29a2d7bb8ea870b89dcf529a471efd4f39594b0bf5f7cf2a0b2a48669123347211 Homepage: https://cran.r-project.org/package=fcr Description: CRAN Package 'fcr' (Functional Concurrent Regression for Sparse Data) Dynamic prediction in functional concurrent regression with an application to child growth. 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Package: r-cran-fda Architecture: all Version: 6.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4772 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fds, r-cran-desolve Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-lattice Filename: pool/dists/noble/main/r-cran-fda_6.3.0-1.ca2404.1_all.deb Size: 2597562 MD5sum: b248c9f3858731b8458ebeee0aa0d913 SHA1: 5c4050b88b8947186b5ac00af71815cbc294dca9 SHA256: c6ef8611515507aaa7d9bd2954e071e1e7af0f9a180f4f89a57e79696b500994 SHA512: fbc420ac104ca06f2eda19c82f18032a6f71b47b292a5b0043f0ee838d045a6e68e7b6b761a03482529a1f27f25f79ebefad79bea1dfe4e524a4537b36b830fa Homepage: https://cran.r-project.org/package=fda Description: CRAN Package 'fda' (Functional Data Analysis) These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. New York: Springer and in Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book. Matlab versions are available by ftp from . 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The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm (Arnqvist and Sjöstedt de Luna, 2019) . The clustering method is used to analyze annual lake sediment from lake Kassjön (Northern Sweden) which cover more than 6400 years and can be seen as historical records of weather and climate. 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It implements the methods proposed in: Elías, A., Jiménez, R., Paganoni, A. M. and Sangalli, L. M., (2023), "Integrated Depth for Partially Observed Functional Data", Journal of Computational and Graphical Statistics, . Elías, A., Jiménez, R., & Shang, H. L. (2023), "Depth-based reconstruction method for incomplete functional data", Computational Statistics, . Elías, A., Nagy, S. (2024), "Statistical properties of partially observed integrated functional depths", TEST, . 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The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data. 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The core pipeline computes Fraiman-Muniz (FM) functional depth in either its Tukey or Simplicial variant, derives a signed depth by comparing each curve to a reference median curve via the signed distance integral, and feeds the resulting scalar summary into several classifiers: the k-Ranked Nearest Neighbour (k-RNN) rule, a moving-average smoother, a kernel-density Bayes rule, logistic regression on signed depth and distance to the mode, and a generalised additive model (GAM) classifier. Cross-validation routines for tuning the neighbourhood size k and parametric bootstrap confidence intervals are also included. Package: r-cran-fdicdata Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-yaml Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fdicdata_0.1.1-1.ca2404.1_all.deb Size: 47782 MD5sum: f7b95f175318bb1276604afac18564c1 SHA1: c5e2e648427dbbcd2d0932cc2bf53014387a7d3d SHA256: 127df148f09f7d2e4661af8d2e0b5a490eabce21e844543982b854bfc296e37f SHA512: fe903bdac8b673618d48331341f452afc3c2a74a8acbe2be8ef420dcfbd31a77ca1500e4867235f90a2eda6d149d7263b23a5915457fcb16a7d7c1eda43e5441 Homepage: https://cran.r-project.org/package=fdicdata Description: CRAN Package 'fdicdata' (Accessing FDIC Bank Data) Retrieves financial data from Federal Deposit Insurance Corporation (FDIC)-insured institutions and provides access to the FDIC data taxonomy. Package: r-cran-fdid Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-estimatr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-tidyselect, r-cran-rcolorbrewer, r-cran-foreach, r-cran-dofuture, r-cran-future, r-cran-ebal, r-cran-grf, r-cran-car, r-cran-sandwich Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fdid_1.0.2-1.ca2404.1_all.deb Size: 217670 MD5sum: 238759e5ccba9e6ee357f5a2e2d9c710 SHA1: 35c6bda1abd676f06882058ac97dddc496f3cb2c SHA256: eb715d7cf910bffe18f71f21e40b1795c33b9da14c37a44ecb50abc0611ceaa6 SHA512: 8697f808599916a4dd10d2179db5bf0c98fa6161214e2adb83f879fecf1da67a461a919d73ea134cbb89cb43355bba1a0d193361e30dd9ca6c3e519334fca858 Homepage: https://cran.r-project.org/package=fdid Description: CRAN Package 'fdid' (Factorial Difference-in-Differences) Implements the factorial difference-in-differences (FDID) framework for panel data settings where all units are exposed to a universal event but vary in a baseline factor G. 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Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle". Package: r-cran-fdp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-rlang Filename: pool/dists/noble/main/r-cran-fdp_1.0.0-1.ca2404.1_all.deb Size: 285054 MD5sum: 4a00ed90acd527dc6c3290ab54584e14 SHA1: d01dd5feeb1f0b9f25a74b5bef8cf44e73b4d60a SHA256: 968539efcb87cde28a5c96566d2f29bba4861dfc51ba294dc5a27e6ad5920f68 SHA512: 0bc47a3a450517b28912fb96078ae5b38bcd36b1250be310c26a0e4eb150bfb86102210ab5dab5b8a37f878c59437aeada6ceeaabfcd185ac87b0c0abc506db1 Homepage: https://cran.r-project.org/package=fdp Description: CRAN Package 'fdp' (f-Differential Privacy and Gaussian Differential Privacy) Constructs and visualises trade-off functions for f-differential privacy (f-DP) as introduced by Dong et al. (2022) . 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The package contains functions to: - compute 2-Wasserstein distances between Gaussian Processes as in Masarotto, Panaretos & Zemel (2019) ; - compute the Wasserstein barycenter (Frechet mean) as in Masarotto, Panaretos & Zemel (2019) ; - perform analysis of variance testing procedures for functional covariances and tangent space principal component analysis of covariance operators as in Masarotto, Panaretos & Zemel (2022) . - perform a soft-clustering based on the Wasserstein distance where functional data are classified based on their covariance structure as in Masarotto & Masarotto (2023) . 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A score is assigned to each feature based on the tendency of LASSO in including that feature in the models.Finally, the average score and the models are returned as the output. The features with relatively low scores are recommended to be ignored because they can lead to overfitting of the model to the training data. Moreover, for each random subset, the best set of features in terms of global error is returned. They are useful for applying Bolasso, the alternative feature selection method that recommends the intersection of features subsets. 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It includes utilities for extracting structured features from timestamps, IP addresses, and email addresses, enabling enhanced predictive modeling for financial risk assessment. 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Package: r-cran-featurecormatrix Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-featurecormatrix_0.4.0-1.ca2404.1_all.deb Size: 31724 MD5sum: 784f7b001087d5bf8a0d7ec074a464d7 SHA1: f317a28b6b5e59c729388ba00ed29367948abecb SHA256: 9973875da5b10c1d5e92de6433f7e999f1fcfd5195dfe59e6fc64cf8051a34c8 SHA512: 4ad4ea815cb90cad01134633da5c8027d39a03254e823549593ba40229b4120c175b05b3ba0938b55191adc71f4cb7085cedb57934f68a44e3b194491a3bd8e7 Homepage: https://cran.r-project.org/package=featureCorMatrix Description: CRAN Package 'featureCorMatrix' (Measurement Level Independent Feature Correlation Matrix) Uses three different correlation coefficients to calculate measurement-level adequate correlations in a feature matrix: Pearson product-moment correlation coefficient, Intraclass correlation and Cramer's V. 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Features can be constructed using default or custom made feature definitions. Furthermore it's possible to aggregate features and get the summary statistics. Package: r-cran-featurefinder Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpart, r-cran-rpart.plot, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-png Filename: pool/dists/noble/main/r-cran-featurefinder_1.2-1.ca2404.1_all.deb Size: 293720 MD5sum: d78403495eefbdd785f8c6fa9bac9ba3 SHA1: d679b55ee80e9c07a66d7a4bed87b022c15d43a8 SHA256: 06e3b6c3a2de2f38f57c51b7c8848d17e4018c7874fd815b0979a6290777f656 SHA512: ecd3e9f3ea9767d880b71b661e065b54c33f83a955e556188f92568cf5dfddb3c10a29d1ca9e8c36bb3c784a064103901399c71118e6cba3844e03effaed17ca Homepage: https://cran.r-project.org/package=featurefinder Description: CRAN Package 'featurefinder' (Feature Finder) Finds features through a detailed analysis of model residuals using rpart classification and regression trees. Scans the residuals of a model across subsets of the data to identify areas where the model differs from the actual data. Package: r-cran-featureflag Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-withr Filename: pool/dists/noble/main/r-cran-featureflag_0.2.0-1.ca2404.1_all.deb Size: 54010 MD5sum: 1dc960a8d9ecc0007dda524d9f461fdb SHA1: 8f27250896dd0da5d2cef5cb578385d5d45d7350 SHA256: e3b83c2e299bc9ad2c8bdc6a38495f8a1d873732dc87b8724395551063629fd6 SHA512: d45cf5cbefe3c4a3a845c5fc618860be086391f45abb2ea86624efbda75fdcfe7e6871c47539fc2d6283ecda49b28d46a99e10a48fa6f56855c40c24784c345c Homepage: https://cran.r-project.org/package=featureflag Description: CRAN Package 'featureflag' (Turn Features On and Off using Feature Flags) Feature flags allow developers to turn features of their software on and off in form of configuration. This package provides functions for creating feature flags in code. It exposes an interface for defining own feature flags which are enabled based on custom criteria. Package: r-cran-featureimpcluster Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-flexclust, r-cran-clustmixtype, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-attempt, r-cran-clustimpute, r-cran-covr Filename: pool/dists/noble/main/r-cran-featureimpcluster_0.1.5-1.ca2404.1_all.deb Size: 60064 MD5sum: 60ba8838de47aef21c126e44b1958976 SHA1: c2c97136e403e7440f21f4fd1f6be317f4d180f8 SHA256: 5eaeef94d7d120500ddee5239acba5e200e81072fb504600ab6e9138ee1c59fa SHA512: 045914341f8a6365ac20980f883f674d8be611be0570b0395c1652e4303e7983de1fd5f4e07191cd59950536b60d129a1f0c30670b15d792e2691c24f24f5332 Homepage: https://cran.r-project.org/package=FeatureImpCluster Description: CRAN Package 'FeatureImpCluster' (Feature Importance for Partitional Clustering) Implements a novel approach for measuring feature importance in k-means clustering. Importance of a feature is measured by the misclassification rate relative to the baseline cluster assignment due to a random permutation of feature values. An explanation of permutation feature importance in general can be found here: . Package: r-cran-features Architecture: all Version: 2025.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lokern Filename: pool/dists/noble/main/r-cran-features_2025.1-1.ca2404.1_all.deb Size: 26542 MD5sum: 2e2b2e44cb5ab44ebc64191607477400 SHA1: 517e41030268f4bcffd2b0c17cd1b8cc1b691d46 SHA256: 64a4cee1b27cc8780777a4b8f91802df674356a223f629b75d32177c87eaf4e3 SHA512: 3870677e8d65818c8f158c4a1c42904395125901fbebb7531d4c0c8f9488b4a50bb629d012fcb8862eef0f313a68cdd77274247e8314d31e3c1ca3500cb3fb7e Homepage: https://cran.r-project.org/package=features Description: CRAN Package 'features' (Feature Extraction for Discretely-Sampled Functional Data) Discretely-sampled function is first smoothed. Features of the smoothed function are then extracted. Some of the key features include mean value, first and second derivatives, critical points (i.e. local maxima and minima), curvature of cunction at critical points, wiggliness of the function, noise in data, and outliers in data. Package: r-cran-featureterminator Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-caret, r-cran-tibble, r-cran-dplyr, r-cran-lattice, r-cran-e1071, r-cran-randomforest Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-featureterminator_1.0.0-1.ca2404.1_all.deb Size: 126170 MD5sum: 93ca60ff547fd370bbf8eca4a088057f SHA1: 181ddaa4584532fc2de313a9f025b988f442e4c3 SHA256: 2085677dcea2cca126690fbde674572e69c0b990e550df71a4632fe7a75c2395 SHA512: 13a0b1a1e5247dbd3daf87f6f172c507c6df13b54744c4b2fbd7e94aa07260a356a36639e791086b53322b02f0ab4954062d5a0954df9a61740758151af961d0 Homepage: https://cran.r-project.org/package=FeatureTerminatoR Description: CRAN Package 'FeatureTerminatoR' (Feature Selection Engine to Remove Features with MinimalPredictive Power) The aim is to take in data.frame inputs and utilises methods, such as recursive feature engineering, to enable the features to be removed. What this does differently from the other packages, is that it gives you the choice to remove the variables manually, or it automated this process. Feature selection is a concept in machine learning, and statistical pipelines, whereby unimportant, or less predictive variables are eliminated from the analysis, see Boughaci (2018) . Package: r-cran-featurizer Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-featurizer_0.2-1.ca2404.1_all.deb Size: 36154 MD5sum: 9da69560f487ab90f858d506b546e8c2 SHA1: ba3abde1c17166142d30e27eb28fb674d7310c47 SHA256: 41463969d03c45fe53af82872c6ac07a1da62b00b5c29cd61fda87028c81acd6 SHA512: 8cbf49a9027d795e006e6fa97f72d3a0b908bb454f1c084a383aeabac78c6b910b30e34650a449d2bfabc890ba5eac985a8a91e492901e6e4d1ebd492c87972b Homepage: https://cran.r-project.org/package=featurizer Description: CRAN Package 'featurizer' (Some Helper Functions that Help Create Features from Data) A collection of functions that would help one to build features based on external data. Very useful for Data Scientists in data to day work. Many functions create features using parallel computation. Since the nitty gritty of parallel computation is hidden under the hood, the user need not worry about creating clusters and shutting them down. Package: r-cran-fec16 Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2301 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-vroom, r-cran-usethis Suggests: r-cran-testthat, r-cran-scales, r-cran-knitr, r-cran-rmarkdown, r-cran-fs, r-cran-lubridate, r-cran-ggplot2, r-cran-stringr, r-cran-utf8 Filename: pool/dists/noble/main/r-cran-fec16_0.1.6-1.ca2404.1_all.deb Size: 2169540 MD5sum: 6c4978f58c38227b1fb78754fdd7d242 SHA1: 2c3552dbb43b2116567111e8e32c281ff8821384 SHA256: 737312205639df91544c2872dc97ff7ce7c265ab934646d4b7f9f3d8c73bcd14 SHA512: 191ed6c4b726a5694d78f152202599d340d4e61b4fb30785eb6daf75580b975c4fa35930a3675c9dc0b0ee37bb201f18b226427230f4d28dbaa26de7c0fcb4c1 Homepage: https://cran.r-project.org/package=fec16 Description: CRAN Package 'fec16' (Data Package for the 2016 United States Federal Elections) Easily analyze relational data from the United States 2016 federal election cycle as reported by the Federal Election Commission. 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Currently, the package enables extraction from nine datasets: The National Elevation Dataset digital elevation models ( 1 and 1/3 arc-second; USGS); The National Hydrography Dataset (; USGS); The Soil Survey Geographic (SSURGO) database from the National Cooperative Soil Survey (; NCSS), which is led by the Natural Resources Conservation Service (NRCS) under the USDA; the Global Historical Climatology Network (; GHCN), coordinated by National Climatic Data Center at NOAA; the Daymet gridded estimates of daily weather parameters for North America, version 4, available from the Oak Ridge National Laboratory's Distributed Active Archive Center (; DAAC); the International Tree Ring Data Bank; the National Land Cover Database (; NLCD); the Cropland Data Layer from the National Agricultural Statistics Service (; NASS); and the PAD-US dataset of protected area boundaries (; USGS). Package: r-cran-federalregister Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-curl, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-federalregister_0.2.0-1.ca2404.1_all.deb Size: 44844 MD5sum: cc1a57de5198c7d9b4a090dd0e888cb4 SHA1: 930ae22883339063efafe0b8e9a6ee5af9f9d38e SHA256: ca37ebdccae7e12c23bb6e032d951fbff798b09dcf044cf6e6967432c7491d66 SHA512: 163a293ffb56a113c3b4ee470e3d225670213ac5635ea6da0539e08f7aa472017ab3d8af1fd4341ce1b22b5b5ff0ac0759aee1f86648e5b357fe76333d0063e2 Homepage: https://cran.r-project.org/package=federalregister Description: CRAN Package 'federalregister' (Client Package for the U.S. Federal Register API) Access data from the Federal Register API . Package: r-cran-fedirt Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-pracma, r-cran-shiny, r-cran-httr, r-cran-callr, r-cran-dt, r-cran-ggplot2, r-cran-shinyjs Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fedirt_1.1.0-1.ca2404.1_all.deb Size: 147156 MD5sum: c6157ab65f8e7c473877d5fee0e3170b SHA1: 00d032e7fe5781922dd66a84e2bd2073b656a834 SHA256: ebb386754a2f5322a98384af3d262f5195b6f810b97a520d5f39403e4120e9dc SHA512: 380edc9c69f17e90592678725c1c85d25af013d888fa31d193178958af74104407489b747910df3ee91604155158734c81489de3e1bdfac22ed24be54f351a94 Homepage: https://cran.r-project.org/package=FedIRT Description: CRAN Package 'FedIRT' (Federated Item Response Theory Models) Integrate Item Response Theory (IRT) and Federated Learning to estimate traditional IRT models, including the 2-Parameter Logistic (2PL) and the Graded Response Models, with enhanced privacy. It allows for the estimation in a distributed manner without compromising accuracy. A user-friendly 'shiny' application is included. 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Package: r-cran-fedstatapir Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-xml2, r-cran-readsdmx, r-cran-magrittr, r-cran-data.table Filename: pool/dists/noble/main/r-cran-fedstatapir_1.0.3-1.ca2404.1_all.deb Size: 625512 MD5sum: b6b201cb7912d10b81a96bf8e44c6406 SHA1: 06d0fe77e4b47b2f1d810e78748d31fcd06821f0 SHA256: b688d4fbb4d817f408fa510fbefb0fbcc38e34752d145db2de5e9b611439f9ab SHA512: 3ff7b9fe033bd938f86622e0bc0cc0b6fe1532b0abc15d2ed96fc88b88dadf59f671f28b982be4ee7bd5b8257aec64645ea4ca254406e11a16b5e790ef2807fb Homepage: https://cran.r-project.org/package=fedstatAPIr Description: CRAN Package 'fedstatAPIr' (Unofficial API for Fedstat (Rosstat EMISS System) for Automaticand Efficient Data Queries) An API for automatic data queries to the fedstat , using a small set of functions with a common interface. Package: r-cran-fedz1 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fedz1_0.1.0-1.ca2404.1_all.deb Size: 256234 MD5sum: d91efbaac7ef34be41e2a5c8588dd18e SHA1: a2051848cf9b6b5a88fc2e13b6fd24028495595d SHA256: c40870aa78bb0cbaff7cb74342fb3b1566ec6b6fe130224e3ea584ba358ae1c4 SHA512: bad7c4a0ba3b1f9bce0d56935fc5c2deccbd3be278d89c994fcb6c6a71a9a62925658db53c49a45b72ca2eddb972139818aaebab6c5b50da6fd7eba0d7f492b6 Homepage: https://cran.r-project.org/package=fedz1 Description: CRAN Package 'fedz1' (An Easier Access to Financial Accounts of the United States(Z.1)) Flow of funds are financial accounts that are provided by Federal Reserve quarterly. The package contains all datasets , tables and descriptions with functions to understand series and explore them. Package: r-cran-fee Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-oneinfl Filename: pool/dists/noble/main/r-cran-fee_1.0.0-1.ca2404.1_all.deb Size: 52740 MD5sum: 837bc44504403df0e13942744a55b2ca SHA1: 4224a94818a8f7fce7e1c169c907846ddd82fc39 SHA256: 05c4128107b31055e855cfda46500c37bdcdc1c4070d219df62c7bbed62baab7 SHA512: bc4faf576570ebbc0b8031f20a987db83832b8347072e84bd0699ccea7b9c3070254d8b7feee8cb5a0c1345e9c26687889e0db72c446bfc29fb0907ddda43d74 Homepage: https://cran.r-project.org/package=fee Description: CRAN Package 'fee' (Estimate the First-Exposure Effect (FEE) using Count Data Models) Estimates the first-exposure effect (FEE) using a one-inflated positive Poisson model, or a one-inflated zero-truncated negative binomial model. In addition, estimates the marginal FEE, and standard errors for the FEE and marginal FEE. Package: r-cran-feedbackts Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maps, r-cran-mapdata, r-cran-proj4, r-cran-sp, r-cran-gstat, r-cran-automap Filename: pool/dists/noble/main/r-cran-feedbackts_1.5-1.ca2404.1_all.deb Size: 330454 MD5sum: 34475478ee54222a91b1b5de787e0236 SHA1: 97eff3f83c380d7ccdbb3e538a3d5afd3a7a3384 SHA256: aecebb970ec60243f3ad6a79b4cbb681165cd0dad6054948c42569d9ea342011 SHA512: d3985673565259defc9cbf02b7bf3e0869b9d5334a7aa56c1bf1506f55ed4c1742cd4eb38c8ce8eb2721b0b80ea01c87550f7fc8907403f12aa317205aa41921 Homepage: https://cran.r-project.org/package=FeedbackTS Description: CRAN Package 'FeedbackTS' (Analysis of Feedback in Time Series) Analysis of fragmented time directionality to investigate feedback in time series. Tools provided by the package allow the analysis of feedback for a single time series and the analysis of feedback for a set of time series collected across a spatial domain. Package: r-cran-feisr Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-plm, r-cran-rdpack, r-cran-dplyr Suggests: r-cran-texreg, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-feisr_1.3.1-1.ca2404.1_all.deb Size: 288798 MD5sum: 1e5692af14e2b541a7f293d17f7fe2f0 SHA1: 9bd281ad07a8f4bfa361bc1716af1f2a70e6b93b SHA256: 61dc1dd48fe5d78b0bffe805ebf6924b7336ae70ce069705b9da863a6e71d47c SHA512: 75656ddcc8a34de80f7431bd0153d0a6bff397b64a1a1cb10ccaa05bbd8204171c3da30bb146d55cc0e286d94ad322f598af8cff6c65777b652bcdd5f301185d Homepage: https://cran.r-project.org/package=feisr Description: CRAN Package 'feisr' (Estimating Fixed Effects Individual Slope Models) Provides the function feis() to estimate fixed effects individual slope (FEIS) models. The FEIS model constitutes a more general version of the often-used fixed effects (FE) panel model, as implemented in the package 'plm' by Croissant and Millo (2008) . In FEIS models, data are not only person demeaned like in conventional FE models, but detrended by the predicted individual slope of each person or group. Estimation is performed by applying least squares lm() to the transformed data. For more details on FEIS models see Bruederl and Ludwig (2015, ISBN:1446252442); Frees (2001) ; Polachek and Kim (1994) ; Ruettenauer and Ludwig (2020) ; Wooldridge (2010, ISBN:0262294354). To test consistency of conventional FE and random effects estimators against heterogeneous slopes, the package also provides the functions feistest() for an artificial regression test and bsfeistest() for a bootstrapped version of the Hausman test. Package: r-cran-fejiv Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Suggests: r-cran-haven Filename: pool/dists/noble/main/r-cran-fejiv_0.1.1-1.ca2404.1_all.deb Size: 22856 MD5sum: 33762b76e91816849089202f20b6311e SHA1: 4e0a48e44bbee959f96d256bf1f1cf835ab92f48 SHA256: d95297bfbe76162ea69754b4610fe6dfc08a0598a694cbaa80d0dafe9e1c05a1 SHA512: 13a321435127710e5f5c4db87e14f4e469a99d988b94b6472aeef19054cd2a6bd7fa4b6b8706b816fab18fcdb2d7f3fadff753e1d58b4539a3d62c801ea9f25d Homepage: https://cran.r-project.org/package=fejiv Description: CRAN Package 'fejiv' (Fixed Effect Jackknife Instrumental Variables Estimation) Implements the Fixed Effect Jackknife Instrumental Variables ('FEJIV') estimator of Chao, Swanson, and Woutersen (2023) , allowing consistent IV estimation with many (possibly weak) instruments, cluster fixed effects, heteroskedastic errors, and many exogenous covariates. The estimator is recommended by Słoczyński (2024) as an alternative to two-stage least squares when estimating the interacted specification of Angrist and Imbens (1995) . Package: r-cran-felp Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-prettycode, r-cran-callr, r-cran-curl, r-cran-data.table, r-cran-dplyr, r-cran-htmltools, r-cran-magrittr, r-cran-matrixstats, r-cran-memoise, r-cran-miniui, r-cran-reactable, r-cran-rstudioapi, r-cran-rlang, r-cran-shiny, r-cran-stringi Suggests: r-cran-knitr, r-cran-printr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-felp_0.6.0-1.ca2404.1_all.deb Size: 85628 MD5sum: c2d7673843baf14f0b6a1d01ef2588a8 SHA1: 0b77af041b1e0aca9a72852341ad6b0acd94629f SHA256: 75befe9dd24af4b8202d832f237f143a252f372fb993a83f52020e8f90e9f14e SHA512: 50f63bdf6284e1498ac7591ff3ec9337834430f6924de0a0d4060d4f8db0427af9e6020078feb930b6157ab250cf7bd96f6ab2cbf248910fd580a18becc4ad22 Homepage: https://cran.r-project.org/package=felp Description: CRAN Package 'felp' (Functional Help for Functions, Objects, and Packages) Enhance R help system by fuzzy search and preview interface, pseudo-postfix operators, and more. The `?.` pseudo-postfix operator and the `?` prefix operator displays documents and contents (source or structure) of objects simultaneously to help understanding the objects. The `?p` pseudo-postfix operator displays package documents, and is shorter than help(package = foo). Package: r-cran-feltr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-fs, r-cran-geojsonsf, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-httptest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-feltr_0.1.0-1.ca2404.1_all.deb Size: 192080 MD5sum: b810e2b7c69bff5e8465e7705cb8021d SHA1: 1d6752fb04987211af0c6d2190295c8df60111be SHA256: f70642b75e6eb59e26280ceaa81b2f12b162f4b9e9f6b4de8568061e79910725 SHA512: d3c90bda63ffd1d03c1c10d7d786d2c1e4d974c76f3cd47fe9a1082407edc3bcf6fd04d30dfe00953b7068a87692b4f16c31cd2860374e936908ac8b03115727 Homepage: https://cran.r-project.org/package=feltr Description: CRAN Package 'feltr' (Access the Felt API) Upload, download, and edit internet maps with the Felt API (). Allows users to create new maps, edit existing maps, and extract data. Provides tools for working with layers, which represent geographic data, and elements, which are interactive annotations. Spatial data accessed from the API is transformed to work with 'sf'. Package: r-cran-fence Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-lme4, r-cran-ggplot2, r-cran-sae, r-cran-fields, r-cran-snowfall, r-cran-snow Suggests: r-cran-pscl Filename: pool/dists/noble/main/r-cran-fence_1.0-1.ca2404.1_all.deb Size: 152104 MD5sum: 348fd709636fe474875ed40234266f35 SHA1: 04dde99ef4c1e42c6d029c760e47238fba64735a SHA256: e29e61783a04178abc2f804ed4c5e9831ca6a1e8f45370d1a9aa88649b978c90 SHA512: 48f7486137f6c9b4e50a893edeadb397835641cd23619e9f57ba3cf90a8bcd9aff57394166f817a421a5108e9c740679af4c89d349f707851967c8bbda025b14 Homepage: https://cran.r-project.org/package=fence Description: CRAN Package 'fence' (Using Fence Methods for Model Selection) This method is a new class of model selection strategies, for mixed model selection, which includes linear and generalized linear mixed models. The idea involves a procedure to isolate a subgroup of what are known as correct models (of which the optimal model is a member). This is accomplished by constructing a statistical fence, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model is selected from among those within the fence according to a criterion which can be made flexible. References: 1. Jiang J., Rao J.S., Gu Z., Nguyen T. (2008), Fence Methods for Mixed Model Selection. The Annals of Statistics, 36(4): 1669-1692. . 2. Jiang J., Nguyen T., Rao J.S. (2009), A Simplified Adaptive Fence Procedure. Statistics and Probability Letters, 79, 625-629. 3. Jiang J., Nguyen T., Rao J.S. (2010), Fence Method for Nonparametric Small Area Estimation. Survey Methodology, 36(1), 3-11. . 4. Jiming Jiang, Thuan Nguyen and J. Sunil Rao (2011), Invisible fence methods and the identification of differentially expressed gene sets. Statistics and Its Interface, Volume 4, 403-415. . 5. Thuan Nguyen & Jiming Jiang (2012), Restricted fence method for covariate selection in longitudinal data analysis. Biostatistics, 13(2), 303-314. . 6. Thuan Nguyen, Jie Peng, Jiming Jiang (2014), Fence Methods for Backcross Experiments. Statistical Computation and Simulation, 84(3), 644-662. . 7. Jiang, J. (2014), The fence methods, in Advances in Statistics, Hindawi Publishing Corp., Cairo. . 8. Jiming Jiang and Thuan Nguyen (2015), The Fence Methods, World Scientific, Singapore. . Package: r-cran-feprovider Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-poibin Filename: pool/dists/noble/main/r-cran-feprovider_1.1-1.ca2404.1_all.deb Size: 912624 MD5sum: 7212469c00ca87b69f3860688319ec5e SHA1: 56f5d332e5b1e87bcaf49171dc752a2122fd185e SHA256: 7ec8b8b91199c9229ba065060e191e6d974dcd1e92041d5d6dfc6d8d6e8ff290 SHA512: 13e91dcd50f4fb9e9577631805668b719b6f3bfe1205d0f9c99e88fdcf7b448fef336ef55f91e791632c50fafe0fd84df51ae288d8ce09e064158ac603dde984 Homepage: https://cran.r-project.org/package=FEprovideR Description: CRAN Package 'FEprovideR' (Fixed Effects Logistic Model with High-Dimensional Parameters) A structured profile likelihood algorithm for the logistic fixed effects model and an approximate expectation maximization (EM) algorithm for the logistic mixed effects model. Based on He, K., Kalbfleisch, J.D., Li, Y. and Li, Y. (2013) . Package: r-cran-fer Architecture: all Version: 0.94-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-statmod Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fer_0.94-1.ca2404.1_all.deb Size: 61356 MD5sum: 7084f0f9aa99049320ebf604a80162f2 SHA1: 1570dd039781080e028feba8271bfc73a163c75d SHA256: 89a9c127cd5ed3a62beefaf7b682ff07b8e7b03e30cacd40e6ff75ae418b7b70 SHA512: 9aa8a9af665e48d66f655c7d3e489e64084fd463b434628a3ca2630ca4841e28441e5943ca326798d2656ee7c5814303d7bbfce85948dca1b087da79488234af Homepage: https://cran.r-project.org/package=FER Description: CRAN Package 'FER' (Financial Engineering in R) R implementations of standard financial engineering codes; vanilla option pricing models such as Black-Scholes, Bachelier, CEV, and SABR. 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These show paths the optimiser takes in the high-dimensional space in multiple ways: by reducing the dimension using principal component analysis, and also using the tour to show the path on the high-dimensional space. Several botanical colour palettes are included, reflecting the name of the package. A paper describing the methodology can be found at . 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"Modeling fertility in modern populations". Demographic Research, 16(6): 141--194. . Model based averaging is also supported. 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Marine fisheries governance and management practices are very essential to ensure the sustainability of the marine resources. A widely accepted resource management strategy towards this is to derive sustainable fish harvest levels based on the status of marine fish stock. Various fish stock assessment models that describe the biomass dynamics using time series data on fish catch and fishing effort are generally used for this purpose. In the scenario of complex multi-species marine fishery in which different species are caught by a number of fishing gears and each gear harvests a number of species make it difficult to obtain the fishing effort corresponding to each fish species. Since the capacity of the gears varies, the effort made to catch a resource cannot be considered as the sum of efforts expended by different fishing gears. This necessitates standardisation of fishing effort in unit base. 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This package implements the FFA framework proposed by Vidrio- Sahagún et al. (2024) , originally developed in 'MATLAB', now adapted for the 'R' environment. This work was funded by the Flood Hazard Identification and Mapping Program of Environment and Climate Change Canada, as well as the Canada Research Chair (Tier 1) awarded to Dr. Pietroniro. 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Interfaces various web feature services (WFS) and download servers. Allows retrieval, caching and filtering with a wide range of open geodata products, including administrative or non-administrative boundaries, land cover, elevation models, geographic names, and points of interest covering Germany. Can be particularly useful for linking regional statistics to their spatial representations and streamlining workflows that involve spatial data of Germany. 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Rotation testing, described by Langsrud (2005) , is used to compute adjusted single response p-values according to familywise error rates and false discovery rates (FDR). The approach to FDR is described in the appendix of Moen et al. (2005) . Unbalanced designs are handled by Type II sums of squares as argued in Langsrud (2003) . Furthermore, the Type II philosophy is extended to continuous design variables as described in Langsrud et al. (2007) . This means that the method is invariant to scale changes and that common pitfalls are avoided. 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Due to the quantity and complexity of the data, it is useful to calculate the amount by which a subject’s gait deviates from an average normal profile and to represent this deviation as a single number. Such a measure can quantify the overall severity of a condition affecting walking, monitor progress, or evaluate the outcome of an intervention prescribed to improve the gait pattern. This R package provides tools for computing the Functional Gait Deviation Index, a novel index for quantifying gait pathology using multivariate functional principal component analysis. The package supports analysis at the level of both legs combined, individual legs, and individual joints/planes. It includes functions for functional data preprocessing, multivariate functional principal component decomposition, FGDI computation, and visualisation of gait abnormality scores. Further details can be found in Minhas, S. K., Sangeux, M., Polak, J., & Carey, M. (2025). The Functional Gait Deviation Index. Journal of Applied Statistics . 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Package: r-cran-find Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-gtable, r-cran-dplyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-find_0.1.1-1.ca2404.1_all.deb Size: 199588 MD5sum: 19a759f8aaef92740dc3dd505ae35253 SHA1: 999b726a3169d52cb61ea79b1c787ae5089d7bd6 SHA256: 727cdb384e7a2d09d6814377814a75cf27e3d9c78cd9ade801c890c52153ab4e SHA512: ec43b1090906c3dddb944e86c9440bad4f048c7b711956d2e54c112b954e976f946fae2421ee53e027e2d93418f10a85f6a1e3f54be23a1bf0dbafe7426c0e30 Homepage: https://cran.r-project.org/package=FIND Description: CRAN Package 'FIND' (Objective Comparison of Phase I Dose-Finding Designs) Generate decision tables and simulate operating characteristics for phase I dose-finding designs to enable objective comparison across methods. 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Package: r-cran-findgsep Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-ggplot2, r-cran-pracma, r-cran-scales, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-findgsep_1.2.0-1.ca2404.1_all.deb Size: 196146 MD5sum: 54175d33f5e1441299dce046282f4ad4 SHA1: bf1cdf75cfbaec17e368051fbc5c6681cd1643c8 SHA256: f2f71e3fd7b9162b4965422db44527eb8cfc3b461bf07018ac0a63b105e4ff4c SHA512: 8895cef2d0b5b2d97167bcc64ba210cb00c6a692ef162b264e256a0eb673fbe495bb58fc9beb149882ce5b36438b56d17b8b0c871f9d900f13bff585f3fac003 Homepage: https://cran.r-project.org/package=findGSEP Description: CRAN Package 'findGSEP' (Estimate Genome Size of Polyploid Species Using k-MerFrequencies) Provides tools to estimate the genome size of polyploid species using k-mer frequencies. 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Package: r-cran-findinfiles Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon, r-cran-htmlwidgets, r-cran-shiny, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-vctrs Suggests: r-cran-fs, r-cran-shinyace, r-cran-shinyfiles, r-cran-shinyjqui, r-cran-shinyvalidate, r-cran-shinywidgets Filename: pool/dists/noble/main/r-cran-findinfiles_0.5.0-1.ca2404.1_all.deb Size: 66844 MD5sum: 4cb2dc767a31203e1a3e59692a9d340a SHA1: 5aa88ea0e9c41d16fdf86cb65dcccc68784a2024 SHA256: cc74d1ee36b9dcc60950b3fc35e930b872707fa99a4490620ed212dc17db4ba4 SHA512: 1aee85999dd134e0a9e6d1a1341fdfd0056498d4cd123cde3b1202343768fe9ed98981b4f7bdd633433aaf26122e5db425f87f36ca3aed7bd957c456736b6477 Homepage: https://cran.r-project.org/package=findInFiles Description: CRAN Package 'findInFiles' (Find Pattern in Files) Creates a HTML widget which displays the results of searching for a pattern in files in a given folder. 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Package: r-cran-findingit Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-crayon, r-cran-htmlwidgets Suggests: r-cran-shiny, r-cran-r.utils Filename: pool/dists/noble/main/r-cran-findingit_0.1.1-1.ca2404.1_all.deb Size: 54318 MD5sum: 4e3a060d3467fee723e9944705388511 SHA1: 1170ebf9b9c6d6bff2941ee65dbd55c30737f5db SHA256: e952fc422aaa4453e7eadc30055dd0c5d60e44f6a799c4f9257b138d5eea1c2f SHA512: feedc563490bf4eed88a1481a626e57edd6e3bc5d450c7540f53ad1433778dcc6c90f2ff779205855a951f4afe132db0cce13b7051a5cc215d4836e0a61ec010 Homepage: https://cran.r-project.org/package=findInGit Description: CRAN Package 'findInGit' (Find Pattern in Files of All Branches of a 'git' Repository) Creates a HTML widget which displays the results of searching for a pattern in files in a given 'git' repository, including all its branches. 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Package: r-cran-findit Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arm, r-cran-glmnet, r-cran-lars, r-cran-matrix, r-cran-quadprog, r-cran-glinternet, r-cran-igraph, r-cran-sandwich, r-cran-lmtest, r-cran-limsolve Filename: pool/dists/noble/main/r-cran-findit_1.3.0-1.ca2404.1_all.deb Size: 427660 MD5sum: 0f8d614ddd956dd5d8cef4e0f26015b5 SHA1: d58f1582ea484cb205ee0c1a5ca5f0bd4cff9816 SHA256: 562f86f8eae4c62b89a28638bc75cc5f3d9e74d407847210a6e488926b276711 SHA512: d5686c97f52b8c4da261a7f01393f9bc7f4d60e06ccef3c0d921363c9ffea1604160b3da6248c5e9f21441d9ddddda6ef73f426f516172b60fee557d870fbf5d Homepage: https://cran.r-project.org/package=FindIt Description: CRAN Package 'FindIt' (Finding Heterogeneous Treatment Effects) The heterogeneous treatment effect estimation procedure proposed by Imai and Ratkovic (2013). 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Package: r-cran-findn Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-scales Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-findn_0.2.0-1.ca2404.1_all.deb Size: 46386 MD5sum: 187ece906b18a3054558b95a0d8b6b35 SHA1: 41ff7d2fd91ae9ced3c4e3302131ab7775fa549f SHA256: d50c94a476a9dcaba71f4dde3136f82d2c7d250ea8821d9bac3a053c343717f7 SHA512: 010600fa1de84bf9b39560ccc4d6fb097b35e10269bcb475f5171fd3857755adaf0a257721b55bc243e50b5e57fb8e516c8a66786e2e605cdf7695ed03b3fedd Homepage: https://cran.r-project.org/package=findn Description: CRAN Package 'findn' (Simulation Based Sample Size Estimation) Estimates the sample size for a test or a trial based on repeated simulation using a model based approach. Implements a method by Maruo et al. (2018) and an extension. 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Package: r-cran-findr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pdftools, r-cran-stringr Filename: pool/dists/noble/main/r-cran-findr_0.2.1-1.ca2404.1_all.deb Size: 477096 MD5sum: 5591dd173efd0f00830854ded5476ff0 SHA1: defdb75bb771714f8f8b5478ab379e856b81c9f9 SHA256: 553bb9931275446d79c961f9119ec70035fde43fe79d55e3e0b4f2d5074a5d06 SHA512: bac5e857534d2ae85bbebb4f963f100f02122192abd50608bc7fa87caf26ba0036174cf3de4997827b6a38228a0bee6ccd1004d4824fe67e8d81ed0ad9b6b0f0 Homepage: https://cran.r-project.org/package=findR Description: CRAN Package 'findR' (Find Code Snippets, R Scripts, R Markdown, PDF and Text Fileswith Pattern Matching) Scans all directories and subdirectories of a path for code snippets, R scripts, R Markdown, PDF or text files containing a specific pattern. Files found can be copied to a new folder. Package: r-cran-findsvi Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2752 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-stringr, r-cran-tidycensus, r-cran-tidyr, r-cran-tidyselect, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-leaflet, r-cran-htmltools, r-cran-glue, r-cran-sf, r-cran-ggplot2, r-cran-reactable, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-findsvi_0.2.0-1.ca2404.1_all.deb Size: 2217882 MD5sum: 503ef280474035615277c99953f53a66 SHA1: 278b79da061286de10aa62383fe1a4ca3450758a SHA256: 5b09b751904906e7bdcd8eaccf06e45da6314c9334d84bf2d57d9f1580fade84 SHA512: d551ffc5bf7fa7328631039af8204e0ecf30ccfcb36398cf56083e1b3adbff4f836001922e17ae024ba37679c146f7c380b2560f3dfca0b5a455618f56cae942 Homepage: https://cran.r-project.org/package=findSVI Description: CRAN Package 'findSVI' (Calculate Social Vulnerability Index for Communities) Developed by CDC/ATSDR (Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry), Social Vulnerability Index (SVI) serves as a tool to assess the resilience of communities by taking into account socioeconomic and demographic factors. Provided with year(s), region(s) and a geographic level of interest, 'findSVI' retrieves required variables from US census data and calculates SVI for communities in the specified area based on CDC/ATSDR SVI documentation. Reference for the calculation methods: Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B (2011) . Package: r-cran-findviews Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2, r-cran-scales, r-cran-gridextra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-findviews_0.1.3-1.ca2404.1_all.deb Size: 145130 MD5sum: 5ef1a7666fbc4f451bdbeb14e00adcbf SHA1: 115d143e7f60b23f6028ab4198606077727d4ef4 SHA256: cd4856f7332e42acd6b98bd2c7df51404b239e8633260a483b030c364993da05 SHA512: 6fb32117fc5d086cdef947bc2446d739b8af2b140922f2084a41edf19b14d92b505529360dbbc2e804d9c75515697ccd45cb122b4d5329600731c6d415a57257 Homepage: https://cran.r-project.org/package=findviews Description: CRAN Package 'findviews' (A View Generator for Multidimensional Data) A tool to explore wide data sets, by detecting, ranking and plotting groups of statistically dependent columns. Package: r-cran-finepop2 Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-finepop2_0.4-1.ca2404.1_all.deb Size: 117400 MD5sum: 8f18c1027adffafba97732a2473c498c SHA1: 98ec59dbb0e4dd9dac0c0be4f27d1b933cf1fea4 SHA256: f92d86085d2cfd6eee514d8072795ab7e530fdde35dca202d632b33c529bb90a SHA512: 38037acf2123550f07cf63797d395fd4d0d7c0373d4691624d46189d187a2e7abf08196a01b87494bf797d122735330e5576aa998e8570a488676ee0c00d217c Homepage: https://cran.r-project.org/package=FinePop2 Description: CRAN Package 'FinePop2' (Fine-Scale Population Analysis (Rewrite forGene-Trait-Environment Interaction Analysis)) Statistical tool set for population genetics. 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The package provides following functions: 1) empirical Bayes estimator of Fst and other measures of genetic differentiation, 2) regression analysis of environmental effects on genetic differentiation using bootstrap method, 3) interfaces to read and manipulate 'GENEPOP' format data files and allele/haplotype frequency format files. Package: r-cran-finetune Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tune, r-cran-cli, r-cran-dials, r-cran-dplyr, r-cran-ggplot2, r-cran-parsnip, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs, r-cran-workflows Suggests: r-cran-bradleyterry2, r-cran-covr, r-cran-discrim, r-cran-kknn, r-cran-klar, r-cran-lme4, r-cran-modeldata, r-cran-ranger, r-cran-recipes, r-cran-rpart, r-cran-rsample, r-cran-spelling, r-cran-testthat, r-cran-yardstick Filename: pool/dists/noble/main/r-cran-finetune_1.3.0-1.ca2404.1_all.deb Size: 221218 MD5sum: 11324ea5154f204511917e4e0bff37b9 SHA1: ead7453891ed4a904b68e51af618856227a18e3f SHA256: 629ea048bdc09cfd9aa07bb57fdd3eb1b1db876a2b5955433956ad550cf816a2 SHA512: 28fbc3b16d169ab806cac2e34cc49fe935ae6e259e12244cd06baa315aa3251f8b0e01b6cdaaa3c1f559e5a997c4af977ee158cbb9a03b83c546a35e5985e6c7 Homepage: https://cran.r-project.org/package=finetune Description: CRAN Package 'finetune' (Additional Functions for Model Tuning) The ability to tune models is important. 'finetune' enhances the 'tune' package by providing more specialized methods for finding reasonable values of model tuning parameters. Two racing methods described by Kuhn (2014) are included. An iterative search method using generalized simulated annealing (Bohachevsky, Johnson and Stein, 1986) is also included. Package: r-cran-fingraph Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spectralgraphtopology, r-cran-mass, r-cran-progress, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fingraph_0.1.0-1.ca2404.1_all.deb Size: 494198 MD5sum: 8f97e4d41c7df59b3f819cd3b2d6c6cc SHA1: 8f7889e91a366a6140c5562fc271d318b1f27704 SHA256: 744451935e4eaa6d5c7b9aaf974877ea6f86dafba1756f7e05d0b824943a2259 SHA512: e0625a9a7d75ebe3d047e33cad5b98b0ffe0af5b365d7272f41cee17b51f5a61e736dd6974756c946391634baa48f4d4055bddffbb6793251aa3757233896a14 Homepage: https://cran.r-project.org/package=fingraph Description: CRAN Package 'fingraph' (Learning Graphs for Financial Markets) Learning graphs for financial markets with optimization algorithms. This package contains implementations of the algorithms described in the paper: Cardoso JVM, Ying J, and Palomar DP (2021) "Learning graphs in heavy-tailed markets", Advances in Neural Informations Processing Systems (NeurIPS). Package: r-cran-finiteruinprob Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sdprisk, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-finiteruinprob_0.6-1.ca2404.1_all.deb Size: 134430 MD5sum: 8c452cd1dcb66f87cb725131c691ea42 SHA1: 410fd436e72a7e0dc0f36f07f3fc73de7a56bcb3 SHA256: f084320ea7cb96e653ebfc44631de3030f8527c4479c2901dfe4748d23bea74a SHA512: c977b926b4d1040522b15f94e67fc455a451c4729126c91d873e521433fea15818b3dbd9aa30e9805691e7a0b6cc9928481fd42c7ed96d9d5e6fa4369589c9cc Homepage: https://cran.r-project.org/package=finiteruinprob Description: CRAN Package 'finiteruinprob' (Computation of the Probability of Ruin Within a Finite TimeHorizon) In the Cramér–Lundberg risk process perturbed by a Wiener process, this packages provides approximations to the probability of ruin within a finite time horizon. 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Package: r-cran-finlabr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-ggplot2, r-cran-performanceanalytics, r-cran-zoo, r-cran-class, r-cran-quantmod, r-cran-reshape2, r-cran-mclust, r-cran-shiny Suggests: r-cran-bslib, r-cran-ttr, r-cran-dt, r-cran-xts, r-cran-yfr, r-cran-cryptoquotes, r-cran-tidyquant, r-cran-rtsne, r-cran-umap, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-finlabr_1.0.0-1.ca2404.1_all.deb Size: 490918 MD5sum: 96d24b9302cbd330abe9e1bd8ec43af4 SHA1: 75b847b15117166c457ebf9cf5f624f9e5a7c996 SHA256: dd35b417ce1310d12526a09a79592400ec76b3c2439f4a21c0a54bfec26c7015 SHA512: 2a272593b7a7b36a9ada49ab6e895e046053394cf01a352538546e87848fbbdf48bcc277237685a96b07642864470dc187185c1e200cc5688a72db517744d5c4 Homepage: https://cran.r-project.org/package=finlabR Description: CRAN Package 'finlabR' (Portfolio Analytics and Simulation Toolkit) Tools for portfolio construction and risk analytics, including mean-variance optimization, conditional value at risk (expected shortfall) minimization, risk parity, regime clustering, correlation analysis, Monte Carlo simulation, and option pricing. 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Package: r-cran-finnts Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1438 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-modeltime, r-cran-cli, r-cran-cubist, r-cran-dials, r-cran-digest, r-cran-doparallel, r-cran-dplyr, r-cran-earth, r-cran-feasts, r-cran-foreach, r-cran-fs, r-cran-generics, r-cran-glue, r-cran-glmnet, r-cran-gtools, r-cran-hts, r-cran-kernlab, r-cran-lubridate, r-cran-magrittr, r-cran-parsnip, r-cran-plyr, r-cran-purrr, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-rules, r-cran-snakecase, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-timetk, r-cran-tune, r-cran-vroom, r-cran-workflows Suggests: r-cran-arrow, r-cran-azurestor, r-cran-boruta, r-cran-corrr, r-cran-knitr, r-cran-microsoft365r, r-cran-notebookutils, r-cran-qs, r-cran-reactable, r-cran-rmarkdown, r-cran-sparklyr, r-cran-testthat, r-cran-vip Filename: pool/dists/noble/main/r-cran-finnts_0.6.0-1.ca2404.1_all.deb Size: 841330 MD5sum: 608b80567d62f8a7cf246b9fb230dd86 SHA1: 909e95218ceb8401fc8a0250e94ae0c92b8c7576 SHA256: 32e787ee2c87008603c40f8b6447af3e97b65b0d17a7d12b5e932e9bfc610cfb SHA512: 83fd64906bc26a3fb6e659d95aee52981fb454802faecc7eff5c0463e0e36210987ddf955eb5082375981645cd8d60497fbcced1b1129a938c72bcbbbb188eae Homepage: https://cran.r-project.org/package=finnts Description: CRAN Package 'finnts' (Microsoft Finance Time Series Forecasting Framework) Automated time series forecasting developed by Microsoft Finance. 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Package: r-cran-finreportr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-dplyr, r-cran-httr, r-cran-rvest, r-cran-xbrl, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-finreportr_1.0.4-1.ca2404.1_all.deb Size: 49402 MD5sum: 7a27ae31e2318652bcdd6ff9024886c4 SHA1: d6de914328442a6ecb1d66a0e71c3ad4925a1cc5 SHA256: 9dc86e73def3a187ad80df651b9178bdf035802841197021a9176b14d25f34c2 SHA512: 0cbcf182a22dbeceb821a7a0663edf4e1ad7145c6dd0ef17e4d087da65dd10f2c320e3ef568089e2e31cee1f513f86e25079e0da6ddde21697d3d8053e5b845c Homepage: https://cran.r-project.org/package=finreportr Description: CRAN Package 'finreportr' (Financial Data from U.S. Securities and Exchange Commission) Download and display company financial data from the U.S. Securities and Exchange Commission's EDGAR database. 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Package: r-cran-fireexposur Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4849 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geosphere, r-cran-ggplot2, r-cran-ggspatial, r-cran-magrittr, r-cran-maptiles, r-cran-multiscaledtm, r-cran-rlang, r-cran-sf, r-cran-terra, r-cran-tidyr, r-cran-tidyselect, r-cran-tidyterra, r-cran-tmap Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fireexposur_1.2.0-1.ca2404.1_all.deb Size: 3900526 MD5sum: 7ddd7f0ba53270eb0138547e5c40c821 SHA1: 5c8e89dd4528c183f318f5f098c831a2bfcbf6b3 SHA256: 3fb0cdb1b0a52223a3f1d72e918386d53c64a7fafb5856529e4f54fe78229cf3 SHA512: acb94f144d56e1eebf92a7f91e75e5cfe03bf8ad1e7059ae0bbf2efb0901080383bc3317f266bd855fa69dda1665b3f8789ea503c6292bb94c8cafc37cb0d189 Homepage: https://cran.r-project.org/package=fireexposuR Description: CRAN Package 'fireexposuR' (Compute and Visualize Wildfire Exposure) Methods for computing and visualizing wildfire ignition exposure and directional vulnerability that are published in a series of scientific publications are automated by the functions in this package. See Beverly et al. (2010) , Beverly et al. (2021) , and Beverly and Forbes (2023) for background and methodology. Package: r-cran-fireproof Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 546 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-cli, r-cran-curl, r-cran-fiery, r-cran-firesale, r-cran-jose, r-cran-jsonlite, r-cran-r6, r-cran-reqres, r-cran-rlang, r-cran-routr, r-cran-sodium, r-cran-urltools Suggests: r-cran-callr, r-cran-dplyr, r-cran-quarto, r-cran-rmarkdown, r-cran-storr, r-cran-testthat, r-cran-webfakes, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-fireproof_0.1.0-1.ca2404.1_all.deb Size: 463458 MD5sum: 65fc2d7c5963efd21855995ec66c692f SHA1: 060d901c944e3cda87422998c71f5dc5f31d716a SHA256: 156b7c17fab02595e91a77bae5613ee93eb245a39a4d9e89b966f66ceaee2724 SHA512: 1f7952b8496efa37697b25cb42221354f20f57c13bf4a9b356a8a7bdbdb72a56a0642ada6b2425770e36bcf7efdeb91bccea1ee318a937afbf3db3ecc2c403cf Homepage: https://cran.r-project.org/package=fireproof Description: CRAN Package 'fireproof' (Authentication and Authorization for 'fiery' Servers) Provides a plugin for 'fiery' that supports various forms of authorization and authentication schemes. 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Package: r-cran-firesale Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-r6, r-cran-reqres, r-cran-rlang, r-cran-storr Suggests: r-cran-fiery, r-cran-later, r-cran-testthat Filename: pool/dists/noble/main/r-cran-firesale_0.1.1-1.ca2404.1_all.deb Size: 88730 MD5sum: d362af781cef781055cdc6ac49538b12 SHA1: 6ca23b7d8cd9215177edd6d4106147899c2d42fa SHA256: cfb404ac0b2f59a3df1fbf8afc78f7edeed87c0479218d0a933a103eae262226 SHA512: 974888b8d4949f7b27d9c76eb9edbce11e422eb4b6c12e326798df7b83a435539aa8033ec69c33b2b0781f2a901d86c44c056714c0da14b274ef6105c4061629 Homepage: https://cran.r-project.org/package=firesale Description: CRAN Package 'firesale' (Datastore for 'fiery' Web Servers) Provides a persistent datastore for 'fiery' apps. The datastore is build on top of the 'storr' package and can thus be based on a variety of backends. The datastore contains both a global and session-scoped section. Package: r-cran-firestorm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-r6, r-cran-rlang, r-cran-websocket Suggests: r-cran-fiery, r-cran-httpuv, r-cran-later, r-cran-reqres, r-cran-testthat Filename: pool/dists/noble/main/r-cran-firestorm_0.1.0-1.ca2404.1_all.deb Size: 56764 MD5sum: ae4923be913537c6102a2cc1bd1c7d71 SHA1: 2346bf907b4e430cb478569d42c62894a7f35dac SHA256: 9f43dc9302342d144adefb8fee30bbcfd9f98589a2dfce59feb133308e7eff7f SHA512: b369a09b24294072a7f359bf81807e73274c2d984c3378af2bac8b9a5cc85e51fad19cd91c345d67982908995c31895a36d51f438a39e157afe41bc87b4be857 Homepage: https://cran.r-project.org/package=firestorm Description: CRAN Package 'firestorm' (Reverse Proxy and Load Balancing for 'fiery') Provides plugins for setting up 'fiery' apps as a reverse proxy. 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Package: r-cran-firmmatchr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-stringi, r-cran-stringdist, r-cran-zoomerjoin, r-cran-dbi, r-cran-rsqlite, r-cran-cli, r-cran-progressr, r-cran-httr, r-cran-jsonlite, r-cran-glue, r-cran-purrr, r-cran-readr, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-firmmatchr_0.1.3-1.ca2404.1_all.deb Size: 57012 MD5sum: 14868c0c0da6565fad56a1181351b82e SHA1: dd11afe4637e6f4d665899b42a61ba07d7aefaa9 SHA256: 8acdc53373b92b1abf968c607e80da5e42d005b7e52103d01d2e323b1910d4b4 SHA512: d9a5d107296506e8842bc372f0458d38246050ad281b0bdbe17460916ec080f93199ff1522fb6bd0bb0adf1d57321c25b841d5f7b901825253dc82d85c50e6de Homepage: https://cran.r-project.org/package=firmmatchr Description: CRAN Package 'firmmatchr' (Robust Probabilistic Matching for German Company Names) A pipeline for matching messy company name strings against a clean dictionary (e.g., 'Orbis'). Implements a cascading strategy: Exact -> Fuzzy ('zoomerjoin') -> 'FTS5' ('SQLite') -> Rarity Weighted. References: Beniamino Green (2025) ; . Package: r-cran-first Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn, r-cran-twinning Filename: pool/dists/noble/main/r-cran-first_2.1-1.ca2404.1_all.deb Size: 60174 MD5sum: 8629054d9d2a927fa7895848d1112d64 SHA1: cc3a66719ac3c90fc09216f114c3ecf9019edd7e SHA256: 8014191474fca54d434bbe7fb2fb10b26713e842c46563ab08cd1f51090366e2 SHA512: e4b0694aee019ca5384aa166b952a564783d2eca05706dac6deca8825ccc2e41d482e4f8e6ee092285acab5c5cc22a78cf6033f71b2e45aee3970d5462f21822 Homepage: https://cran.r-project.org/package=first Description: CRAN Package 'first' (Factor Importance Ranking and Selection using Total Indices) A model-independent factor importance ranking and selection procedure based on total Sobol' indices. Please see Huang and Joseph (2025) . This research is supported by U.S. National Science Foundation grants DMS-2310637 and DMREF-1921873. Package: r-cran-fiscal Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fiscal_1.0.2-1.ca2404.1_all.deb Size: 140806 MD5sum: c915047a82fb573a305cd898ae0c801f SHA1: ebc846d7d57179c310a09e1a0b6600ef6982307a SHA256: 89c9332f98990dda6358c7a7eb55e1fa73cbb0e7adf36431a8193bb27e2958b7 SHA512: c9d2fcfda869ebbf506c134f7734a235527f3a16cbdc0468721f8a2e71ecdf5196aa929dd8d449de81717d9066b1bd725e3764463d1b36c8bdbd71283568b2f1 Homepage: https://cran.r-project.org/package=Fiscal Description: CRAN Package 'Fiscal' (Income Tax Calculations (UK)) Income tax calculations for England, Northern Ireland and Wales. Estimate annual income tax within the different taxation bands at specified levels of both taxable income and the Personal Allowance, emulating the results obtained at . Calculate the standard Personal Allowance at various levels of taxable income. Estimate the personal allowance required to recoup a specified amount of income tax. Package: r-cran-fish Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda.usc, r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-fish_1.1-1.ca2404.1_all.deb Size: 23406 MD5sum: ac34559b69cdbd68e0b68065a62d5c91 SHA1: f19070e244e8a700da5a0d972d517bfca6ac0d98 SHA256: b304ec678a7d19b3acd39fe679a822b6471b75e117176443fc357356ad6b01ce SHA512: 7ff6e157e92b56ea30ff065bad6e99a8f39f991a3c615f8d4a46add76d2326c222177b47981a800f92adf3ff227ece740e6a9bfa36c724868f5d65bb33a1deef Homepage: https://cran.r-project.org/package=FiSh Description: CRAN Package 'FiSh' (Fisher-Shannon Method) Proposes non-parametric estimates of the Fisher information measure and the Shannon entropy power. More theoretical and implementation details can be found in Guignard et al. . A 'python' version of this work is available on 'github' and 'PyPi' ('FiShPy'). Package: r-cran-fishbc Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-chk, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-fishbc_0.2.1-1.ca2404.1_all.deb Size: 58730 MD5sum: 37e007220e604a299475986ecc0b7505 SHA1: 8715ae08046e8b5360e789b24aca3f3ccd8d6656 SHA256: 58520d47116ee574b7f8446273aa1ce0f0ccb2fc88a628ccc0e07642d7f3dc8d SHA512: f8df1ec66c7ef53ed3fe8687ec93dc1022acec7e0e32faee491ec699b6ee209cbbc3b3c5224d046106ff04994f060c96b2ec2c2a5e56cd7cbd6138fc2cb0bdd9 Homepage: https://cran.r-project.org/package=fishbc Description: CRAN Package 'fishbc' (Fishes of British Columbia) Provides raw and curated data on the codes, classification and conservation status of freshwater fishes in British Columbia. Marine fishes will be added in a future release. Package: r-cran-fishboot Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-ks, r-cran-tropfishr, r-cran-fishmethods Filename: pool/dists/noble/main/r-cran-fishboot_1.0.2-1.ca2404.1_all.deb Size: 218932 MD5sum: f140a053d6faf560f6cfe25088ed1a35 SHA1: 998a9ac77bacbbe290910b43e2ba25007e0f7ba7 SHA256: 1cac079251562942d2fb4c0a5f5fb41c039bee6efcef9719f05f7028da2c3227 SHA512: 3511bd9655c7c63bf3c2e5514417249e2eef1eed56a0ba8ebbdc7e96b35519f6f328c46d321d3c8b27628bd954a1b7883b8da618ae2c533a138d0a166cc6d422 Homepage: https://cran.r-project.org/package=fishboot Description: CRAN Package 'fishboot' (Bootstrap-Based Methods for the Study of Fish Stocks and AquaticPopulations) A suite of bootstrap-based models and tools for analyzing fish stocks and aquatic populations. Designed for ecologists and fisheries scientists, it supports data from length-frequency distributions, tag-and-recapture studies, and hard structure readings (e.g., otoliths). See Schwamborn et al., 2019 for background. The package includes functions for bootstrapped fitting of growth curves and plotting. Package: r-cran-fishdata Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3660 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-magrittr, r-cran-dm, r-cran-ggplot2, r-cran-tidyr, r-cran-diagrammersvg, r-cran-diagrammer Filename: pool/dists/noble/main/r-cran-fishdata_1.0.1-1.ca2404.1_all.deb Size: 1404976 MD5sum: 7369c14bd05d499745bfbe5a869324af SHA1: 537ecda0e2d203489af8194f8346e863dbd3b772 SHA256: f220daecc920766d4f617c36c71d78aa498fb934038b85376fb7ce5cd2b91c1f SHA512: 2fe7aca3d8731ea9d887e3074a3cac51672d353b1dbc100a785733855654ed83fc5ff041db31c5ea92d41895a9234a5c5123fa0aa0945df1941f9411a939784b Homepage: https://cran.r-project.org/package=fishdata Description: CRAN Package 'fishdata' (A Small Collection of Fish Population Datasets) A collection of four datasets based around the population dynamics of migratory fish. Datasets contain both basic size information on a per fish basis, as well as otolith data that contains a per day record of fish growth history. All data in this package was collected by the author, from 2015-2016, in the Wellington region of New Zealand. Package: r-cran-fishdiver Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5874 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-cowplot, r-cran-data.table, r-cran-dplyr, r-cran-factominer, r-cran-geometry, r-cran-ggplot2, r-cran-gridextra, r-cran-lubridate, r-cran-moments, r-cran-patchwork, r-cran-colorspace, r-cran-rgl, r-cran-rfast, r-cran-rlang, r-cran-scales, r-cran-suncalc, r-cran-tidyr, r-cran-waveletcomp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fishdiver_1.1.0-1.ca2404.1_all.deb Size: 5401312 MD5sum: 8697db21b06e79cde8bf189aa3b5dc78 SHA1: 505cd0c485c39f8ddc2c5e6e9ba1f831e873e124 SHA256: 3390a6ed517fad6e4fa06d0bf85390e02951e78b368b35bd59643cfee8bf9969 SHA512: 8b64f1af31f8f58fe528ff29f05ebe3cdd152a24c1e446530ed5f39026162569d18b6ef516519a8ac6aa0ee7c06b063e994b2eb019a5fbf46d8ea0838caea57d Homepage: https://cran.r-project.org/package=FishDiveR Description: CRAN Package 'FishDiveR' (Classify Aquatic Animal Behaviours from Vertical Movement Data) Quantitatively analyse depth time-series data from pop-up satellite archival tags (PSATs) through the application of continuous wavelet transformation (CWT) combined with Principal Component Analysis (PCA), and k-means clustering. Import, crop, and plot depth time-depth records (TDRs). Using CWT to detect important signals within the non-stationary data, we create daily wavelet statistics to summarise vertical movements on different wavelet periods and combine with daily and diel depth statistics. Classify depth time-series with unsupervised k-means clustering into 24-hour periods of vertical movement behaviour with distinct patterns of vertical movement. Plot example days from each behaviour cluster, and plot the TDR coloured by cluster. Based on principals of combining CWT with k-means first developed by Sakamoto (2009) and redeveloped by Beale (2026) . Package: r-cran-fisherem Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-elasticnet, r-cran-ggplot2, r-cran-ellipse, r-cran-plyr Suggests: r-cran-testthat, r-cran-aricode Filename: pool/dists/noble/main/r-cran-fisherem_1.6-1.ca2404.1_all.deb Size: 225572 MD5sum: e2c56c273a46fc8a03532c80a420d511 SHA1: 0bcf2ee248023d6c9ee86173cf90573621bef89f SHA256: 25b8ce7629ad45f77d7eaa8ad216aa16998495a940c65e700beef8daf9833ebe SHA512: 0ab33d2c068ba68fa9a693d16e34350bdd8c1b52dd23f4727f84f7d00a3a2bd0b77ce9c3eba40ca1b02d5ec4a2d67735a11bbc8e867fc81d48ded94a9b894c6f Homepage: https://cran.r-project.org/package=FisherEM Description: CRAN Package 'FisherEM' (The FisherEM Algorithm to Simultaneously Cluster and VisualizeHigh-Dimensional Data) The FisherEM algorithm, proposed by Bouveyron & Brunet (2012) , is an efficient method for the clustering of high-dimensional data. FisherEM models and clusters the data in a discriminative and low-dimensional latent subspace. It also provides a low-dimensional representation of the clustered data. A sparse version of Fisher-EM algorithm is also provided. Package: r-cran-fisheye Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-fisheye_0.2.0-1.ca2404.1_all.deb Size: 16574 MD5sum: 3f7b1987af6e843a01fb0efe3441d529 SHA1: 178eec1c67bbe505b34d616de694d199f2add241 SHA256: ff494236d6dc62b37f48360574c21d8be6a60a736d1fcbc7f88edce189d841c6 SHA512: a6c2db495ecfc35971a0c0704080f91b4ce20cb73175f2ee41d5bf6f983d65c231c0ffdfd424aed7d2cfe0c1f8deb1676e59e5c22b743546626d4bb67515fbc8 Homepage: https://cran.r-project.org/package=fisheye Description: CRAN Package 'fisheye' (Transform Base Maps Using Log-Azimuthal Projection) Base maps are transformed to focus on a specific location using an azimuthal logarithmic distance transformation. Package: r-cran-fishgrowth Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rtmb Suggests: r-cran-areaplot Filename: pool/dists/noble/main/r-cran-fishgrowth_1.0.4-1.ca2404.1_all.deb Size: 226560 MD5sum: 59b7896bac60e0e9b4411025a6229f9e SHA1: fb0a0de73b7d00aa7ef70a18cdc25383571f6c10 SHA256: ae961c8eb8ef49a367bf27be34377006029017cb7af4f7873bc8eeab29fbc67d SHA512: 9756683b77f09262aec15d6510036e1a459b89694f5818b198be8461fc49b711ec89908976337b744febf58ba519c087c67d0bcfea9d13ad21b2e02c3e45798b Homepage: https://cran.r-project.org/package=fishgrowth Description: CRAN Package 'fishgrowth' (Fit Growth Curves to Fish Data) Fit growth models to otoliths and/or tagging data, using the 'RTMB' package and maximum likelihood. The otoliths (or similar measurements of age) provide direct observed coordinates of age and length. The tagging data provide information about the observed length at release and length at recapture at a later time, where the age at release is unknown and estimated as a vector of parameters. The growth models provided by this package can be fitted to otoliths only, tagging data only, or a combination of the two. Growth variability can be modelled as constant or increasing with length. Package: r-cran-fishkirkko2015 Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fishkirkko2015_1.0.0-1.ca2404.1_all.deb Size: 14636 MD5sum: 1bea32e7123325cd8c8e55fd229bdd32 SHA1: ea736dcfb0b4a86a92c733a3cadada0b0266b644 SHA256: 9393209695e6058e6cec2e14d60801a83330efa22c794374e484c329b7a4459b SHA512: dc6027aea448195e58ae9b1a20aab7a12501af4bd44e25680e71dd890d4b1b61b2d54d7711255c5a9050eb34190a6f7cbba4f0a89e687812eb88389889a2086b Homepage: https://cran.r-project.org/package=fishkirkko2015 Description: CRAN Package 'fishkirkko2015' (Dataset of Measurements of Fish Species at Kirkkojarvi Lake,Finland) Dataset of 302 measurements of 11 fish species to accompany the manuscript "Length-weight relationships of six freshwater fish species from lake Kirkkojarvi, Finland". Package: r-cran-fishmechr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1727 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-cli, r-cran-dplyr, r-cran-gsignal, r-cran-pracma, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fishmechr_1.0.3-1.ca2404.1_all.deb Size: 1169038 MD5sum: 4065016fbdd8cd35a5c8a09cc6558c36 SHA1: a5152c0665d7ce20435ecf779b6b7a8704d9d1e0 SHA256: 6621692a46179c47dcf55be912aa49aa854354c2a1eb5f85ecc1a4c5b57c051c SHA512: 604ecc3ee083602848142eacce73346c05903836f5c42a5fbf9911a40aec9ace2e9c92a4039996cf1f36315468e4d75c36caae9f3b6500b00cf4f4cee17a690a Homepage: https://cran.r-project.org/package=fishmechr Description: CRAN Package 'fishmechr' (Computes Kinematic Parameters for Swimming) Processes tracked points on a fish's body and uses them to estimate standard kinematic parameters such as tail beat amplitude and frequency, body wavelength and wavespeed. As part of this, it also estimates the location of the center of mass and the principal swimming axis. The techniques are described in detail in the main vignette and are published in the book chapter Hawkins, O.H., Di Santo, V., Tytell, Eric.D., 2025. "Biomechanics and energetics of swimming", in: Higham, T.E., Lauder, G.V. (Eds.), Integrative Fish Biomechanics, Fish Physiology. Academic Press. . Package: r-cran-fishphylomaker Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 940 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-fishtree, r-cran-geiger, r-cran-knitr, r-cran-phytools, r-cran-progress, r-cran-rfishbase, r-cran-rmarkdown Suggests: r-cran-markdown, r-cran-gh Filename: pool/dists/noble/main/r-cran-fishphylomaker_0.2.0-1.ca2404.1_all.deb Size: 703146 MD5sum: c3f4e8ec6122f88eab68e7c107dc689e SHA1: 5bb17427981d0eeb5aea8e1e5f6a8f6d41765b80 SHA256: 2421e76b7074cefd630e7daba04de4adf160c2462220fe0dd52b9e560f30c00a SHA512: de735aba38d56f1dd65d7f578aa02080509bb50026cbcc52657816cd41ffef0bde7617ca9dcd77e078cd1d7dd52d3cfd4e92459ed703032d68bbef3e856d7014 Homepage: https://cran.r-project.org/package=FishPhyloMaker Description: CRAN Package 'FishPhyloMaker' (Phylogenies for a List of Finned-Ray Fishes) Provides an alternative to facilitate the construction of a phylogeny for fish species from a list of species or a community matrix using as a backbone the phylogenetic tree proposed by Rabosky et al. (2018) . Package: r-cran-fishproxcompanalyzer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fishproxcompanalyzer_0.1.0-1.ca2404.1_all.deb Size: 22044 MD5sum: 2cdd6d3a39fff198fde4df0ba799d067 SHA1: fc31679b02c2565f615e126735571e7d6e520a35 SHA256: 95fe52c692beb0b97a94806bc03f430fdeb78eeede90e2dd5d5e44596abdb017 SHA512: 6fc3a4a69f0e7571f5206fc819dbc4329ca535c2f5267d5c213defa71f523f0e9ec32ece00bcd510eff0b24ae4a38c0409180c11e3039eee12a6466980eb92ff Homepage: https://cran.r-project.org/package=FishProxCompAnalyzer Description: CRAN Package 'FishProxCompAnalyzer' (Proximate Composition Analysis of Fish and Feed Ingredients) The proximate composition analysis is the quantification of main components that constitutes nutritional profile of any food and food products including fish, shellfish, fish feed and their ingredients. Understanding this composition is essential for evaluating their nutritional value and for making informed dietary choices. The primary components typically analyzed include; moisture/ water in foods, crude protein, crude fat/ lipid, total ash, fiber and carbohydrates AOAC(2005,ISBN:0-935584-77-3). In case of fish, shellfish and its products, the proximate composition consists of four primary constituents - water, protein, fat, and ash (mostly minerals). Fish exhibit significant variation in their chemical makeup based on age, sex, environment, and season, both within the same species and between individual fish. There is minimal fluctuation in the content of ash and protein. The lipid concentration varies remarkably and is inversely correlated with the water content. In case of fish, carbohydrates are present in minor quantity so that are quantified by subtracting total of other components from 100 to get percentage of carbohydrates. Package: r-cran-fishresp Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5503 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chron, r-cran-lattice, r-cran-mclust, r-cran-rmr, r-cran-respirometry Filename: pool/dists/noble/main/r-cran-fishresp_1.1.2-1.ca2404.1_all.deb Size: 3770798 MD5sum: 081b89f2e095c315d28ff8957d885d74 SHA1: 4d06d39afe2f5c49b8b9bd08622665348d0d5b03 SHA256: 80336ea3450e08c35d42994f895b9b633dc1fed73dbcaf82206ba40af1f87f4e SHA512: 783503e5d11b7665b0bea6e6f551efd2a9e35127ae82f5be96a7306461a4f97a58493431660ed9061083e39409bdbcfebbf8a39256551ca6a65c208ffeccb82f Homepage: https://cran.r-project.org/package=FishResp Description: CRAN Package 'FishResp' (Analytical Tool for Aquatic Respirometry) Calculates metabolic rate of fish and other aquatic organisms measured using an intermittent-flow respirometry approach. The tool is used to run a set of graphical QC tests of raw respirometry data, correct it for background respiration and chamber effect, filter and extract target values of absolute and mass-specific metabolic rate. Experimental design should include background respiration tests and measuring of one or more metabolic rate traits. The R package is ideally integrated with the pump controller 'PumpResp' and the DO meter 'SensResp' (open-source hardware by FishResp). Raw respirometry data can be also imported from 'AquaResp' (free software), 'AutoResp' ('LoligoSystems'), 'OxyView' ('PreSens'), 'Pyro Oxygen Logger' ('PyroScience') and 'Q-box Aqua' ('QubitSystems'). More information about the R package 'FishResp'is available in the publication by Morozov et al. (2019) . Package: r-cran-fishrman Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 920 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-config, r-cran-countrycode, r-cran-dplyr, r-cran-ggplot2, r-cran-golem, r-cran-httr, r-cran-jsonlite, r-cran-maps, r-cran-sf, r-cran-shiny, r-cran-shinybs, r-cran-shinyjs, r-cran-shinywidgets, r-cran-viridis Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fishrman_1.2.3-1.ca2404.1_all.deb Size: 596712 MD5sum: ce52c4ca00a30d769e0f84bed9a6b26b SHA1: 1685da595df678b2ba0a3fcb5e345ddbcc1859ee SHA256: 8eb4f8dc637357777a67fd24f45a1c934b05aeeef40bfb677215ef0e3508bacb SHA512: 9d105a55bb7d32fc88f5f1787ec265e5881be3499e77fbcb86159cd1bbe204cf62f9380bcbe64d726f76b71373b4fc1432a698eb5b73833fc458a2575ab310c5 Homepage: https://cran.r-project.org/package=fishRman Description: CRAN Package 'fishRman' (The Fisheries Scientist's Toolbox) A bundle of analytics tools for fisheries scientists. A 'shiny' R App is included for a 'no-code' solution for retrieval, analysis, and visualization. Package: r-cran-fishstat Architecture: all Version: 2026.1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4848 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-areaplot Filename: pool/dists/noble/main/r-cran-fishstat_2026.1.0.0-1.ca2404.1_all.deb Size: 4758340 MD5sum: d76056e5aaa1ff4cdf77d83d11f68331 SHA1: bc73a0caed372196b013e10f10b80de4e045ef36 SHA256: 446dd0a24840fa0c40307bc9762b671d990cbfd48157365f65972a13257cb8e5 SHA512: 12b74d0854b08d29c79790aac97690aa4f017a7638ec39baca9413f06cc703667d68446fe3be1bb3e38281d6d72dfaf9642ae777762b914a021f191a4d8738e9 Homepage: https://cran.r-project.org/package=fishstat Description: CRAN Package 'fishstat' (Global Fishery and Aquaculture Statistics) The Food and Agriculture Organization of the United Nations (FAO) FishStat database is the leading source of global fishery and aquaculture statistics and provides unique information for sector analysis and monitoring. This package provides the global production data from all fisheries and aquaculture in R format, ready for analysis. Package: r-cran-fishtree Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 639 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-jsonlite, r-cran-memoise, r-cran-rlang Suggests: r-cran-diversitree, r-cran-geiger, r-cran-hisse, r-cran-knitr, r-cran-markdown, r-cran-phytools, r-cran-picante, r-cran-rfishbase, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fishtree_0.3.4-1.ca2404.1_all.deb Size: 393272 MD5sum: ef37ad9e81b3a9558e249e19d4596e78 SHA1: 907c1f4afa92526e6ef1edd94ff44bf0c5698e25 SHA256: 1eaab3ef22ee5d61e1d1bb3ac98969301723c3c215b7df8607ac75c2150c252d SHA512: f27d4d017dcd74022eb74a932e345a450b4da27b3408e1d2171460252407e269edf959cee3edabf40f83c4969f113e47c1306b45820fefe1eb2810ae4eb7604a Homepage: https://cran.r-project.org/package=fishtree Description: CRAN Package 'fishtree' (Interface to the Fish Tree of Life API) An interface to the Fish Tree of Life API to download taxonomies, phylogenies, fossil calibrations, and diversification rate information for ray-finned fishes. Package: r-cran-fishualize Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1455 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-png, r-cran-downloader, r-cran-httr, r-cran-magrittr, r-cran-stringr, r-cran-dplyr, r-cran-tidyr, r-cran-scales, r-cran-rlang, r-cran-curl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rfishbase, r-cran-rnaturalearth, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-fishualize_0.2.3-1.ca2404.1_all.deb Size: 1020240 MD5sum: 68af71be7161990bbf26479ebe469340 SHA1: 74870779c20783682593970ed7447a18d036ade2 SHA256: 6ac6d952ad94305061b4355eedcd9d3b32f7e58d7d72a55ff8ec8ca0804c3a97 SHA512: f375bc7dd9a562493591d8cebc725d6257cfa7a4c692116d0e7f4390f4437784775b757f202c354c9132dc3abffdc2d64833d7ac104acbd02c36a1f550d50cbf Homepage: https://cran.r-project.org/package=fishualize Description: CRAN Package 'fishualize' (Color Palettes Based on Fish Species) Implementation of color palettes based on fish species. Package: r-cran-fit.models Architecture: all Version: 0.64-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-fit.models_0.64-1.ca2404.1_all.deb Size: 134948 MD5sum: 2b47d0a29f30f530fc7b4a5e920d29ab SHA1: 5479ed4b29c0e5d80d695213abe7d0d4d54560e9 SHA256: 8b5742049d4517c099abcf8af014706773c9544fb62886f1cf9426578f8d31d2 SHA512: 3352f7113996043687f89ca2a45ebb6d54d1701384a3c7baa340adfafada26262b365b24e7b8f6b97e1fefe42ae6d32d33f350fe532fcbc4374b61916d5d8ef2 Homepage: https://cran.r-project.org/package=fit.models Description: CRAN Package 'fit.models' (Compare Fitted Models) The fit.models function and its associated methods (coefficients, print, summary, plot, etc.) were originally provided in the robust package to compare robustly and classically fitted model objects. See chapters 2, 3, and 5 in Insightful (2002) 'Robust Library User's Guide' ). The aim of the fit.models package is to separate this fitted model object comparison functionality from the robust package and to extend it to support fitting methods (e.g., classical, robust, Bayesian, regularized, etc.) more generally. Package: r-cran-fitbitr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 514 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-checkmate, r-cran-devtools, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fitbitr_0.3.0-1.ca2404.1_all.deb Size: 446398 MD5sum: d5b8c5d2c63ce4e6244f9ff2274ba201 SHA1: 395409cfb24a66621ed801082f79842cc8847adc SHA256: c1b8b6c3344c6ec6e18748d93159ca0de2ca6afe8db6c8e67702aa6a989bd3a7 SHA512: 07eca2643eb4b53084831348833be6984e27b4ea189ca956468418173fa3c794d6e232fc2837aa386e6cd24c009d487ca51b39052c9db5d437cf265ad3342105 Homepage: https://cran.r-project.org/package=fitbitr Description: CRAN Package 'fitbitr' (Interface with the 'Fitbit' API) Many 'Fitbit' users, and R-friendly 'Fitbit' users especially, have found themselves curious about their 'Fitbit' data. 'Fitbit' aggregates a large amount of personal data, much of which is interesting for personal research and to satisfy curiosity, and is even potentially useful in medical settings. The goal of 'fitbitr' is to make interfacing with the 'Fitbit' API as streamlined as possible, to make it simple for R users of all backgrounds and comfort levels to analyze their 'Fitbit' data and do whatever they want with it! Currently, 'fitbitr' includes methods for pulling data on activity, sleep, and heart rate, but this list is likely to grow in the future as the package gains more traction and more requests for new methods to be implemented come in. You can find details on the 'Fitbit' API at . Package: r-cran-fitbitscraper Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-stringr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggthemes Filename: pool/dists/noble/main/r-cran-fitbitscraper_0.1.8-1.ca2404.1_all.deb Size: 58820 MD5sum: d4bed0b228ef3802398266d48eb46b34 SHA1: e59da19a500f852697d2b4728a44340893f50bc6 SHA256: ebeaebf40a56c19e11ef5cfef9bbd2614b2668805d5a2f46b32cd990391e8a6d SHA512: 20c3bef9793352d7a14bf0a8a67306001ee72760eadb6a291dc8eea5c865dbfccee2284a87c11b1462b4b6e19cc17e242bd63f7cdf4e31ce97091a0a8233bf5e Homepage: https://cran.r-project.org/package=fitbitScraper Description: CRAN Package 'fitbitScraper' (Scrapes Data from Fitbit) Scrapes data from Fitbit . This does not use the official API, but instead uses the API that the web dashboard uses to generate the graphs displayed on the dashboard after login at . Package: r-cran-fitbitviz Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4979 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-ggplot2, r-cran-lubridate, r-cran-patchwork, r-cran-data.table, r-cran-viridis, r-cran-scales, r-cran-ggthemes, r-cran-paletteer, r-cran-xml, r-cran-hms, r-cran-leaflet, r-cran-sf, r-cran-rstudioapi, r-cran-leafgl, r-cran-raster, r-cran-terra, r-cran-magrittr, r-cran-lifecycle, r-cran-reshape2 Suggests: r-cran-copernicusdem, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dt, r-cran-rgl, r-cran-rayshader, r-cran-magick Filename: pool/dists/noble/main/r-cran-fitbitviz_1.0.8-1.ca2404.1_all.deb Size: 2154904 MD5sum: 6cec7ba8eab64ba2da7958194f9ea7e4 SHA1: a0d687bc755d9b6a5c072b8319bed4124d197d29 SHA256: 9d6ad55e1c7e8a298ed92c14afed1d022f2e5a7c17197cfc15f4ddb6d3069ffc SHA512: 3df0a68a120c4c0e040bbc4aeb1a3d52942715ec27fb0d45f342dbc1668296dc75b1c4b1bf132cc5b15d14031c6694c71e69b909742aec64d21e043c0a45158f Homepage: https://cran.r-project.org/package=fitbitViz Description: CRAN Package 'fitbitViz' ('Fitbit' Visualizations) Visualization of pre-downloaded 'Fitbit' personal health data using 'ggplot2' Visualizations, 'Leaflet' and 3-dimensional 'Rayshader' Maps. The 3-dimensional 'Rayshader' Map requires the installation of the 'CopernicusDEM' R package which includes the 30- and 90-meter elevation data. Package: r-cran-fitconic Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-fitconic_1.2.1-1.ca2404.1_all.deb Size: 92620 MD5sum: 3ed7cd9d34f2a40bd97e9ff1cb9836e3 SHA1: 34fde0bacc5150478434c6ae9b8e57e5c26396bb SHA256: f4987a8fed29fefe1e05516330656588409fc10ab40a1be074d8422c16450b96 SHA512: fe124222af5ea374e8689fd8c397df1c5385eb4522fcaf3c570e8bbf04e71a9b312fb1aaccdfee660c0764c9dc20de283f9c6ddd563f68605a9859f82ca173e9 Homepage: https://cran.r-project.org/package=fitConic Description: CRAN Package 'fitConic' (Fit Data to Any Conic Section) Fit data to an ellipse, hyperbola, or parabola. Bootstrapping is available when needed. The conic curve can be rotated through an arbitrary angle and the fit will still succeed. Helper functions are provided to convert generator coefficients from one style to another, generate test data sets, rotate conic section parameters, and so on. References include Nikolai Chernov (2014) "Fitting ellipses, circles, and lines by least squares" ; A. W. Fitzgibbon, M. Pilu, R. B. Fisher (1999) "Direct Least Squares Fitting of Ellipses" IEEE Trans. PAMI, Vol. 21, pages 476-48; N. Chernov, Q. Huang, and H. Ma (2014) "Fitting quadratic curves to data points", British Journal of Mathematics & Computer Science, 4, 33-60; N. Chernov and H. Ma (2011) "Least squares fitting of quadratic curves and surfaces", Computer Vision, Editor S. R. Yoshida, Nova Science Publishers, pp. 285-302. Package: r-cran-fitdistcp Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4080 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mev, r-cran-extradistr, r-cran-gnorm, r-cran-fdrtool, r-cran-pracma, r-cran-rust, r-cran-actuar, r-cran-fextremes Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fitdistcp_0.2.3-1.ca2404.1_all.deb Size: 3549302 MD5sum: 76c2a58d29eb59d58a3f67ff8d7003b1 SHA1: 8712138ef1485c7b3d22b6eb6a6796d1ca590d19 SHA256: 2abf6ed7c99aaa1e0ae54a06d9e0d18e38cd210c39c979d2bc461cbe8479c696 SHA512: f311ca94d65095395d68960298c3d40b1609dd6fce1ec0337d6bc53d3f1cab5ce48ffcb21c960e4a6aa36e750f3775227fab268ddb88a0b5a423184c45e87dd2 Homepage: https://cran.r-project.org/package=fitdistcp Description: CRAN Package 'fitdistcp' (Distribution Fitting with Calibrating Priors for Commonly UsedDistributions) Generates predictive distributions based on calibrating priors for various commonly used statistical models, including models with predictors. Routines for densities, probabilities, quantiles, random deviates and the parameter posterior are provided. The predictions are generated from the Bayesian prediction integral, with priors chosen to give good reliability (also known as calibration). For homogeneous models, the prior is set to the right Haar prior, giving predictions which are exactly reliable. As a result, in repeated testing, the frequencies of out-of-sample outcomes and the probabilities from the predictions agree. For other models, the prior is chosen to give good reliability. Where possible, the Bayesian prediction integral is solved exactly. Where exact solutions are not possible, the Bayesian prediction integral is solved using the Datta-Mukerjee-Ghosh-Sweeting (DMGS) asymptotic expansion. Optionally, the prediction integral can also be solved using posterior samples generated using Paul Northrop's ratio of uniforms sampling package ('rust'). Results are also generated based on maximum likelihood, for comparison purposes. Various model selection diagnostics and testing routines are included. Based on "Reducing reliability bias in assessments of extreme weather risk using calibrating priors", Jewson, S., Sweeting, T. and Jewson, L. (2024); . Package: r-cran-fitdistrplus Architecture: all Version: 1.2-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4003 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-survival, r-cran-rlang Suggests: r-cran-actuar, r-cran-rgenoud, r-cran-mc2d, r-cran-gamlss.dist, r-cran-knitr, r-cran-ggplot2, r-cran-generalizedhyperbolic, r-cran-rmarkdown, r-cran-hmisc, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-fitdistrplus_1.2-6-1.ca2404.1_all.deb Size: 2866746 MD5sum: c917b08890bb12e5d1bebf678dc69217 SHA1: f38fa24438ecae8657476de11dc64f3c5dcacb5b SHA256: 0a997fd9b1420920df53b501058903d9b0a4617b51ece8ada8f0e35a6aae3776 SHA512: 4bb5ca76504c2091b77f11f8198ea4c5d82bb8204b9b2511337e5cf8cf9f44ce7723e5274a24ff713f0e9caa39c7b07d9e60cf76a9a3d6cd113f95858ab96ca3 Homepage: https://cran.r-project.org/package=fitdistrplus Description: CRAN Package 'fitdistrplus' (Help to Fit of a Parametric Distribution to Non-Censored orCensored Data) Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit estimation (MGE) and maximum spacing estimation (MSE) methods (available only for non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g. Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction to parametric estimation. Package: r-cran-fitdynmix Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-evir, r-cran-mass, r-cran-pracma, r-cran-rdpack, r-cran-ks Filename: pool/dists/noble/main/r-cran-fitdynmix_1.0.2-1.ca2404.1_all.deb Size: 107576 MD5sum: def0d924d44d91138f4d18072fd068d9 SHA1: 43301b8a27dacac19c177b9588ce7658d9b75be3 SHA256: 4c98842527644cb331f2b88a6e173a9869a2cf89048d64999a6175bfc4593c02 SHA512: ae6ba0d88c35d1bccef4bca470f59f320a16bb96316e076ee7130eaa7c8c0f310d2762a4bb2e49028154148c23dcf2ca68cfef4f82f4e8b4756693ce1b29261c Homepage: https://cran.r-project.org/package=FitDynMix Description: CRAN Package 'FitDynMix' (Estimation of Dynamic Mixtures) Estimation of a dynamic lognormal - Generalized Pareto mixture via the Approximate Maximum Likelihood and the Cross-Entropy methods. See Bee, M. (2023) . Package: r-cran-fitheavytail Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1334 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-icsnp, r-cran-mvtnorm, r-cran-ghyp, r-cran-numderiv Suggests: r-cran-ggplot2, r-cran-reshape2, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fitheavytail_0.2.0-1.ca2404.1_all.deb Size: 744190 MD5sum: 7683239aa41e9c3fa108e8ae4cdbf2f6 SHA1: e7e90e4fd96fdad3b11b3585944bebbb0e967828 SHA256: 501df099cdb9ea1716692bfabb5aabe882b82eb11fa81777f3f26cce133eb401 SHA512: 87d0d498c39576b1b59270a99753ba0f423c154c321c8d0a19450d3599e4cc2250dc74b1c6324c703c69e7bf8129d39e19af038bdf8ebcf7ba0e08fe1a8a9284 Homepage: https://cran.r-project.org/package=fitHeavyTail Description: CRAN Package 'fitHeavyTail' (Mean and Covariance Matrix Estimation under Heavy Tails) Robust estimation methods for the mean vector, scatter matrix, and covariance matrix (if it exists) from data (possibly containing NAs) under multivariate heavy-tailed distributions such as angular Gaussian (via Tyler's method), Cauchy, and Student's t distributions. Additionally, a factor model structure can be specified for the covariance matrix. The latest revision also includes the multivariate skewed t distribution. The package is based on the papers: Sun, Babu, and Palomar (2014); Sun, Babu, and Palomar (2015); Liu and Rubin (1995); Zhou, Liu, Kumar, and Palomar (2019); Pascal, Ollila, and Palomar (2021). Package: r-cran-fitlandr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-furrr, r-cran-future.apply, r-cran-ggplot2, r-cran-glue, r-cran-magrittr, r-cran-mass, r-cran-numderiv, r-cran-plotly, r-cran-purrr, r-cran-r.utils, r-cran-rfast, r-cran-rlang, r-cran-rootsolve, r-cran-simlandr, r-cran-sparsevfc, r-cran-tidyr Suggests: r-cran-akima, r-cran-colorramps, r-cran-future, r-cran-knitr Filename: pool/dists/noble/main/r-cran-fitlandr_0.1.1-1.ca2404.1_all.deb Size: 289186 MD5sum: 6587fb7af9f15cfead5ac2694ee859f8 SHA1: dd6937099e37a6145b9d7e9d190c60d14c41b157 SHA256: e00e9184cde97afdfdafba2363d14190ee7272a4d55b3aa96d4746494560f186 SHA512: da2c7a9a7d7decd0eea4026338c29a477c80acf01835faec14a1841fb7a9f9810d4b45222461bedabaa6968f2a9e69f573e80a308640e4d1d0dd2ad9bdbf42b1 Homepage: https://cran.r-project.org/package=fitlandr Description: CRAN Package 'fitlandr' (Fit Vector Fields and Potential Landscapes from IntensiveLongitudinal Data) A toolbox for estimating vector fields from intensive longitudinal data, and construct potential landscapes thereafter. The vector fields can be estimated with two nonparametric methods: the Multivariate Vector Field Kernel Estimator (MVKE) by Bandi & Moloche (2018) and the Sparse Vector Field Consensus (SparseVFC) algorithm by Ma et al. (2013) . The potential landscapes can be constructed with a simulation-based approach with the 'simlandr' package (Cui et al., 2021) , or the Bhattacharya et al. (2011) method for path integration . Package: r-cran-fitmix Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-fitmix_0.1.1-1.ca2404.1_all.deb Size: 43936 MD5sum: dd6983e2463e056942751a3ce53f3e04 SHA1: f3b591b75cbb3dd2454de4a5aecc96dfeb7eb9bb SHA256: d99b8b3d1b208359908f48a2f35ef063d7622d322a519137cde7651b453063f8 SHA512: 1eb73acd154b2e10b79ee594ae5900cffaf13277de728746b5c490de2b4375511314915e0cf090b84ff546e670539619ab1a26e0965c6b33d924411b70da9b90 Homepage: https://cran.r-project.org/package=fitmix Description: CRAN Package 'fitmix' (Finite Mixture Model Fitting of Lifespan Datasets) Fits the lifespan datasets of biological systems such as yeast, fruit flies, and other similar biological units with well-known finite mixture models introduced by Farewell et al. (1982) and Al-Hussaini et al. (2000) . Estimates parameter space fitting of a lifespan dataset with finite mixtures of parametric distributions. Computes the following tasks; 1) Estimates parameter space of the finite mixture model by implementing the expectation maximization (EM) algorithm. 2) Finds a sequence of four goodness-of-fit measures consist of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Kolmogorov-Smirnov (KS), and log-likelihood (log-likelihood) statistics. 3)The initial values is determined by k-means clustering. Package: r-cran-fitnmr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9685 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm, r-cran-abind, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-gslnls Filename: pool/dists/noble/main/r-cran-fitnmr_1.0-1.ca2404.1_all.deb Size: 7029326 MD5sum: a92a2bf7d84b712c421c7a7d6d86e384 SHA1: 660caf5ad43e318e5c3f07cace9808cdcae9e513 SHA256: da3fd6390148831a0c7c6297bdccc8d313d426ece3bf680a2b41c815c7b03b66 SHA512: 9e8ca1b56ffdfa0f1e62ef9663e87c4aa495240f2f3db834cb3465b4d0bbde74bec0c55222538604c8b2a7f442fe45662af882b70ee90207aa83d0904e056d71 Homepage: https://cran.r-project.org/package=fitnmr Description: CRAN Package 'fitnmr' (Multidimensional Nuclear Magnetic Resonance Peak Fitting andAnalysis) Tools for fitting and analyzing 1D-4D nuclear magnetic resonance spectra with analytical models of peak shapes and peak groups. The package reads spectra in 'NMRPipe' format, builds constrained parameter structures for chemical shifts, line widths, scalar couplings, volumes, and phases, and performs nonlinear least-squares optimization for iterative peak discovery or simultaneous fits across multiple spectra. It also provides methods for visualization, preprocessing, and kinetic analysis of 1D time-series data, including automated phase optimization, solvent suppression, time-domain correction for frequency shifts and line broadening, modeling spectra as linear combinations of two component spectra, and exponential rate fitting. Package: r-cran-fitodbod Architecture: all Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmle, r-cran-hypergeo, r-cran-mass, r-cran-mvtnorm, r-cran-rdpack Suggests: r-cran-flextable, r-cran-ggplot2, r-cran-ggthemes, r-cran-gridextra, r-cran-knitr, r-cran-reshape2, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-viridis Filename: pool/dists/noble/main/r-cran-fitodbod_1.5.5-1.ca2404.1_all.deb Size: 729488 MD5sum: e0f0e753e390fb80c3cbde6349f1dc01 SHA1: 680e27f8fb68bdb4f54a29ad5f7a5c2a6eec84a9 SHA256: 0820f33e18f43627f2bc79247d207067d642ce1fc1b4818009f95aaf3c1ccbd5 SHA512: 4c9c66a4400de07300f5639396bcb4985ffb6bd5e289df5e10cecc28c8ca834e12e1367b4258e5a8586f9a036f474d58226bcc770410878ae1f10355f6029d89 Homepage: https://cran.r-project.org/package=fitODBOD Description: CRAN Package 'fitODBOD' (Modeling Over Dispersed Binomial Outcome Data Using BMD and ABD) Contains Probability Mass Functions, Cumulative Mass Functions, Negative Log Likelihood value, parameter estimation and modeling data using Binomial Mixture Distributions (BMD) (Manoj et al (2013) ) and Alternate Binomial Distributions (ABD) (Paul (1985) ), also Journal article to use the package(). Package: r-cran-fitodbodrshiny Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5558 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bslib, r-cran-config, r-cran-flextable, r-cran-ggplot2, r-cran-golem, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyscreenshot Filename: pool/dists/noble/main/r-cran-fitodbodrshiny_1.0.2-1.ca2404.1_all.deb Size: 2805728 MD5sum: 19802b8ccced76dbc3b3184b2155fbe9 SHA1: a42948afbd8df7a754c1d7d83927952e77640082 SHA256: 07217d5b1bca79ed486f0d3b607a32ce533c465599ec27301f7a4294ae1a87b2 SHA512: ecfb565fae4205140c62677d2dbdf9b2348d2e9b8771d5d5d5f28313e68622d305b45eca0a82d86aad0031a4673a3386224e668d09d5849b923529bb15725f7e Homepage: https://cran.r-project.org/package=fitODBODRshiny Description: CRAN Package 'fitODBODRshiny' ('Shiny' Application for R Package 'fitODBOD') For binomial outcome data Alternate Binomial Distributions and Binomial Mixture Distributions are fitted when overdispersion is available. Package: r-cran-fitode Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 883 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmle, r-cran-desolve, r-cran-deriv, r-cran-mass, r-cran-numderiv, r-cran-mvtnorm, r-cran-coda Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fitode_0.1.1-1.ca2404.1_all.deb Size: 690408 MD5sum: f965f036968093e267dced92940e133b SHA1: fb05a584105501331afcc254fc54ae65b5951ac5 SHA256: 8aec3980cc949eaf5a2a7ac19db3d6f6d4d7870ba995b39b6eb070e6c6d802aa SHA512: 4350d73733c77f9804064e7a543d500c9a90c255b263b4e1945eee6b238761f7c6a4f9ecf9b267b1543bb2b3a450117ba9825d3cbf72beea00bd5c20b6fd132f Homepage: https://cran.r-project.org/package=fitode Description: CRAN Package 'fitode' (Tools for Ordinary Differential Equations Model Fitting) Methods and functions for fitting ordinary differential equations (ODE) model in 'R'. Sensitivity equations are used to compute the gradients of ODE trajectories with respect to underlying parameters, which in turn allows for more stable fitting. Other fitting methods, such as MCMC (Markov chain Monte Carlo), are also available. Package: r-cran-fitplc Architecture: all Version: 1.2-3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-car Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fitplc_1.2-3.1-1.ca2404.1_all.deb Size: 89214 MD5sum: e7390741fde847e459eb0e578fd0cd5a SHA1: 63cec9b3225d6168efc6939f515123eae4bb11f0 SHA256: 7822a56e67f487c9c92f52219a7d6f88ad5f17143fd1d817e961e7f878e75385 SHA512: bcd6d7b6085b91630d5e80ff092931414d9fab9b01c0b6d06dc4c2624ccb499f42b054bdebb60722a77e1499183975baafb187ea6edebab0174c2a9c09f9b20e Homepage: https://cran.r-project.org/package=fitplc Description: CRAN Package 'fitplc' (Fit Hydraulic Vulnerability Curves) Fits Weibull or sigmoidal models to percent loss conductivity (plc) curves as a function of plant water potential, computes confidence intervals of parameter estimates and predictions with bootstrap or parametric methods, and provides convenient plotting methods. Package: r-cran-fitplotr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fitplotr_0.1.0-1.ca2404.1_all.deb Size: 57290 MD5sum: 22e72798223378db0c76be788f79b4ed SHA1: 7d92ad6e68fea1fc11166c9f75389d12dda39d92 SHA256: 56cc035dc3e65239b027bce5773443a4d26adf343b5f029cf01186deb23da681 SHA512: 8619c73d72c0e5e64d6c03395562550e7810aadf3e6f5b163b286dfc28ea966dfdc489e61f7f38b0da8fad0f31004ae57ccc0dd36add5d8958b513656d9efafe Homepage: https://cran.r-project.org/package=fitPlotR Description: CRAN Package 'fitPlotR' (Plotting Probability Distributions) Provides functions for plotting probability density functions, distribution functions, survival functions, hazard functions and computing distribution moments. The implementation is inspired by Delignette-Muller and Dutang (2015) . Package: r-cran-fitpoly Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1999 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-devemf, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fitpoly_4.0.0-1.ca2404.1_all.deb Size: 1862464 MD5sum: 520f36adb64edfc7b549f2d35b7ada79 SHA1: a5013713a6a3b79be8ae64c79a0dceeb9d73ebee SHA256: 6324ed81fd54b3414f33ffa76261c67906bbccdf51c9e9f56fe4c6c938d5cb16 SHA512: 0616bb37fc4e34f3cd94a0a0ed2a116471cf58d247023e49278a5fd9ed30a360cce6fd0f28fed31784a71782a22688af065bf305022f1e35d222958f59f4cab8 Homepage: https://cran.r-project.org/package=fitPoly Description: CRAN Package 'fitPoly' (Genotype Calling for Bi-Allelic Marker Assays) Genotyping assays for bi-allelic markers (e.g. SNPs) produce signal intensities for the two alleles. 'fitPoly' assigns genotypes (allele dosages) to a collection of polyploid samples based on these signal intensities. 'fitPoly' replaces the older package 'fitTetra' that was limited (a.o.) to only tetraploid populations whereas 'fitPoly' accepts any ploidy level. Reference: Voorrips RE, Gort G, Vosman B (2011) . New functions added on conversion of data from SNP array software formats, drawing of XY-scatterplots with or without genotype colors, checking against expected F1 segregation patterns, comparing results from two different assays (probes) for the same SNP, recovery from a saveMarkerModels() crash. Package: r-cran-fitps Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 614 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-dplyr, r-cran-hmisc, r-cran-iterators, r-cran-knitr, r-cran-ks, r-cran-pbapply, r-cran-rdpack, r-cran-readxl, r-cran-vgam Suggests: r-cran-sp, r-cran-xtable Filename: pool/dists/noble/main/r-cran-fitps_1.0.1-1.ca2404.1_all.deb Size: 487212 MD5sum: 88e506906843287111c634fbe70a6281 SHA1: 2e1a96bdfabc3e0d8c5761e9ab4551383d5705ba SHA256: a3f0a493afa1ea28c4299d7134a0fd1c6493f1ec3a28b43f153820e5ed4ce098 SHA512: dfabf8f75146a5a912e28c913c85f408f6800f624f3457c08ef0080c755e78f18565a521e5f9f58b65cd79220ea229f1f0a7425f27d3f3f1d02aa48d98208490 Homepage: https://cran.r-project.org/package=fitPS Description: CRAN Package 'fitPS' (Fit Zeta Distributions to Forensic Data) Fits Zeta distributions (discrete power laws) to data that arises from forensic surveys of clothing on the presence of glass and paint in various populations. The general method is described to some extent in Coulson, S.A., Buckleton, J.S., Gummer, A.B., and Triggs, C.M. (2001) , although the implementation differs. Package: r-cran-fitscape Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fitscape_0.1.0-1.ca2404.1_all.deb Size: 25088 MD5sum: 7d64b6cf3f20b2fc32116f29562447e7 SHA1: c8d811346cc2dcee2a83ef13bfe95ea3fe91888f SHA256: b730146a878b3e9319e3d2a2363cb41f5c336a44e75cba30067727d6757c276a SHA512: d806e214cbe14aaa6113e3b8fb2364f82b25d242a361e3298f6db3c9ab100b0d8126210bbbf0452d25f3a962d252e967f56f48fd8dcce6b92ff2fba2a4fa1009 Homepage: https://cran.r-project.org/package=fitscape Description: CRAN Package 'fitscape' (Classes for Fitness Landscapes and Seascapes) Convenient classes to model fitness landscapes and fitness seascapes. A low-level package with which most users will not interact but upon which other packages modeling fitness landscapes and fitness seascapes will depend. 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H. (2001) ), accumulated local effects (Apley D. W. (2016) ), further effects plots, interaction strength, and variable contribution breakdown (Gosiewska and Biecek (2019) ). All tools are implemented to work with case weights and allow for stratified analysis. Furthermore, multiple flashlights can be combined and analyzed together. 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The package's name derives from a play on the fact that lipid scrambling is also sometimes referred to as 'flipping'. The package is originally published as Cotton, R.J., Ploier, B., Goren, M.A., Menon, A.K., and Graumann, J. (2017). "flippant–An R package for the automated analysis of fluorescence-based scramblase assays." BMC Bioinformatics 18, 146. . 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The details of relevant methods are available in Chow et al (1988, ISBN: 007070242X, 9780070702424), and Bobee and Ashkar (1991, ISBN: 0918334683, 9780918334688). 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Package: r-cran-flowermate Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-flowermate_1.1-1.ca2404.1_all.deb Size: 144170 MD5sum: 40875c2b9664a9a6caece055900bd817 SHA1: fdd3825e91e8f03f11e39e7dee6b4af56c1f7f0e SHA256: 55e2bb0b508aa91d37dba4e3be534d47880843b8201ca271f3d72a82414b6be3 SHA512: e9352f97d96024eb81a361ce54f4bffd6c510a2dc8bc0666369a2f0aa3078097d8b50d6c146135aa8056f5e17560c5b369d6a68233328e4ec4f91bd09c759027 Homepage: https://cran.r-project.org/package=FlowerMate Description: CRAN Package 'FlowerMate' (Reciprocity Indices for Style-Polymorphic Plants) Computes unidimensional and multidimensional Reciprocity and Inaccuracy indices. These indices are applicable to common heterostylous populations and to any other type of stylar dimorphic and trimorphic populations, such as in enantiostylous and three-dimensional heterostylous plants. Simón-Porcar, V., A. J. Muñoz-Pajares, J. Arroyo, and S. D. Johnson. (in press) "FlowerMate: multidimensional reciprocity and inaccuracy indices for style-polymorphic plant populations." 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Package: r-cran-flowmapper Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 968 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-forcats, r-cran-scales, r-cran-purrr, r-cran-sfheaders, r-cran-sf, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-flowmapper_0.1.4-1.ca2404.1_all.deb Size: 953146 MD5sum: 4ea79fa54c562ba76711ac3c21df27b9 SHA1: 310878abcba8ef1264f6d4ad808fc376e2d724bc SHA256: dbf1a90b5b23bf5b86494b9ef3c8571e03df12416d75e92fd83bbb204e062050 SHA512: 35f594d17478dd3497f88a65862d6ac1a0ec8f27adfe54c79a5089d8f36c3a10c12069ac2e6594476fe005174a87042b6fd7fa9a250a620fc8fe31f24f39204f Homepage: https://cran.r-project.org/package=flowmapper Description: CRAN Package 'flowmapper' (Draw Flows (Migration, Goods, Money, Information) on 'ggplot2'Plots) Adds flow maps to 'ggplot2' plots. 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Package: r-cran-flowml Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abcanalysis, r-cran-caret, r-cran-data.table, r-cran-dplyr, r-cran-fastshap, r-cran-furrr, r-cran-future, r-cran-magrittr, r-cran-optparse, r-cran-purrr, r-cran-r6, r-cran-readr, r-cran-rjson, r-cran-rlang, r-cran-rsample, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-vip Suggests: r-cran-ada, r-cran-adabag, r-cran-arm, r-cran-bartmachine, r-cran-bst, r-cran-c50, r-cran-catools, r-cran-class, r-cran-cubist, r-cran-e1071, r-cran-earth, r-cran-elasticnet, r-cran-evtree, r-cran-fastica, r-cran-foreach, r-cran-frbs, r-cran-gam, r-cran-gbm, r-cran-ggplot2, r-cran-glmnet, r-cran-h2o, r-cran-hda, r-cran-ipred, r-cran-keras, r-cran-kernlab, r-cran-kknn, r-cran-klar, r-cran-knitr, r-cran-kohonen, r-cran-lars, r-cran-leaps, r-cran-liblinear, r-cran-logicreg, r-cran-mass, r-cran-matrix, r-cran-mboost, r-cran-mda, r-cran-mgcv, r-cran-monomvn, r-cran-neuralnet, r-cran-nnet, r-cran-nnls, r-cran-pamr, r-cran-partdsa, r-cran-party, r-cran-partykit, r-cran-penalized, r-cran-pls, r-cran-plyr, r-cran-proxy, r-cran-quantregforest, r-cran-randomforest, r-cran-ranger, r-cran-rferns, r-cran-rmarkdown, r-cran-rpart, r-cran-rrcov, r-cran-rrcovhd, r-cran-rsnns, r-cran-rweka, r-cran-sda, r-cran-shapviz, r-cran-spls, r-cran-superpc, r-cran-vgam, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-flowml_0.1.3-1.ca2404.1_all.deb Size: 308030 MD5sum: c0b2b3d3324e4d8fb27cc720ae7386d3 SHA1: 40060912c93f0e3b36643df90e5f6bf9f8ceb200 SHA256: 56fceaf9ac19f4c13f6a67f5be9f5c2d6cff8accf99ce5d689bb7dce89bd9a90 SHA512: 7bfbb4981e8101fe483a9916074a9bd34f0fd754eae1a4d8843c26be91fa08d4cde650fc18efe2178e38d0a7762c14517cedfaae62c63e2e3abc04dbfd02aecf Homepage: https://cran.r-project.org/package=flowml Description: CRAN Package 'flowml' (A Backend for a 'nextflow' Pipeline that PerformsMachine-Learning-Based Modeling of Biomedical Data) Provides functionality to perform machine-learning-based modeling in a computation pipeline. Its functions contain the basic steps of machine-learning-based knowledge discovery workflows, including model training and optimization, model evaluation, and model testing. To perform these tasks, the package builds heavily on existing machine-learning packages, such as 'caret' and associated packages. The package can train multiple models, optimize model hyperparameters by performing a grid search or a random search, and evaluates model performance by different metrics. Models can be validated either on a test data set, or in case of a small sample size by k-fold cross validation or repeated bootstrapping. It also allows for 0-Hypotheses generation by performing permutation experiments. Additionally, it offers methods of model interpretation and item categorization to identify the most informative features from a high dimensional data space. The functions of this package can easily be integrated into computation pipelines (e.g. 'nextflow' ) and hereby improve scalability, standardization, and re-producibility in the context of machine-learning. 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This has been built keeping in mind the needs of bioinformatics workflows. However, it is easily extendable to any field where a series of steps (shell commands) are to be executed in a (work)flow. Package: r-cran-flowregenvcost Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo Filename: pool/dists/noble/main/r-cran-flowregenvcost_0.1.1-1.ca2404.1_all.deb Size: 119522 MD5sum: 901ecde29a72335ae2a494ce2d69edd8 SHA1: 5264999de344eadcc9ca59037dcf0f0649138783 SHA256: 7743ae6b3c16b4eaafc69c9b01dc099086de55f73a0de936773f39068057dcde SHA512: 64573c7adcab78a3696e54cb6e849d160ebe752fd965059d56b9eb620e131d38cb1921e0189c27a696327e58479420c15667ce66fc60521eba8338c0569c6497 Homepage: https://cran.r-project.org/package=FlowRegEnvCost Description: CRAN Package 'FlowRegEnvCost' (The Environmental Costs of Flow Regulation) An application to calculate the daily environmental costs of river flow regulation by dams based on García de Jalon et al. 2017 . Package: r-cran-flowscreen Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3265 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zyp, r-cran-changepoint, r-cran-evir Filename: pool/dists/noble/main/r-cran-flowscreen_2.1-1.ca2404.1_all.deb Size: 1624456 MD5sum: 5e78528949d001b99edfb77308db1914 SHA1: 44fde259931e3f8bb080f7eec0c352115ed7112a SHA256: bfa16dcba62d2d743c1ee9806492fb28692ef5d824edd5f5428bf2ddef479869 SHA512: beaa394bbfdf7dc086b5b4dc5d6c6ca0da5f1e45e67ec45926124d2727caf20bcd172beb1952b5866dead7f8e97969e9c003dda7b5ebde47de3b61af6becfc43 Homepage: https://cran.r-project.org/package=FlowScreen Description: CRAN Package 'FlowScreen' (Daily Streamflow Trend and Change Point Screening) Screens daily streamflow time series for temporal trends and change-points. This package has been primarily developed for assessing the quality of daily streamflow time series. 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Package: r-cran-flowtracer Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-comprehenr, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-data.table, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-flowtracer_0.1.1-1.ca2404.1_all.deb Size: 236676 MD5sum: f2d4612ad056b2149516de8be4b1cd22 SHA1: 08ca50af1605951206c6e6f9c33e3538fc858d94 SHA256: ad68df0cd6d9696cb1326a4d03ee80fe521b199df58cfa95f0817b895d9ebc75 SHA512: d44736ad22f2bc61eafae29620302a4a0f54f0679c9b5938cfdaef9c30ae3499fd176b1c52826e1a8c2019cb92a36201dd3fa0d9a8423ca714129a68dd225f07 Homepage: https://cran.r-project.org/package=flowTraceR Description: CRAN Package 'flowTraceR' (Tracing Information Flow for Inter-Software Comparisons in MassSpectrometry-Based Bottom-Up Proteomics) Useful functions to standardize software outputs from ProteomeDiscoverer, Spectronaut, DIA-NN and MaxQuant on precursor, modified peptide and proteingroup level and to trace software differences for identifications such as varying proteingroup denotations for common precursor. Package: r-cran-flps Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1205 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-rcpp, r-cran-mirt, r-cran-mass, r-cran-mvtnorm, r-cran-ggplot2, r-cran-glue Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-lavaan, r-cran-data.table Filename: pool/dists/noble/main/r-cran-flps_1.1.0-1.ca2404.1_all.deb Size: 819396 MD5sum: 848badd4fe8e02cae4bd67bc4fa1c310 SHA1: 5531ccd4092858c260e15c943ed6cc47fbc599db SHA256: f05f5b290f72e4e2817f247fbb5efcb1b9a4654fae5fb40a5977d2844a5c8069 SHA512: 2997a09daa513a03af0f7799174f180e9ee075c8cd3ba7dd74e8c5caa6c2faad7202c6e4f1015c43479a22f49d2eee0242d2d99c27745b742217ba4cf48486d6 Homepage: https://cran.r-project.org/package=flps Description: CRAN Package 'flps' (Fully-Latent Principal Stratification) Simulation and analysis of Fully-Latent Principal Stratification (FLPS) with measurement models. Lee, Adam, Kang, & Whittaker (2023). . This package is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D210036. Package: r-cran-flr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-combinat Filename: pool/dists/noble/main/r-cran-flr_1.0-1.ca2404.1_all.deb Size: 71852 MD5sum: 8faaadac7705d839c5be6fb397c99fde SHA1: 125e3cb059ed8dcd8a09ee99f30fc23fbb800151 SHA256: 1337ee4aee5ecd1c255cd4236cc752955f91bf5738616538a56279e32a8f3684 SHA512: b8deac0973e11a0381b8654d4582337eafc1fed90bc100f1c416bc9a06eed7a6c1bc82c34ff1992bbfc2ca80305effa4289ede7cd67c5f064bad263df1d65fa2 Homepage: https://cran.r-project.org/package=FLR Description: CRAN Package 'FLR' (Fuzzy Logic Rule Classifier) FLR algorithm for classification Package: r-cran-fluidigm Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1860 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-reshape Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-digest Filename: pool/dists/noble/main/r-cran-fluidigm_0.2-1.ca2404.1_all.deb Size: 428044 MD5sum: 84c6f071d757abc5d16842867cadfcc8 SHA1: 2d1bec07782b27b6bbac20fb94fca17bdf59f1de SHA256: 427ee9339c84cd28a29de8ce8833d9df4653404e92d06ddcb80c600d9d60c08d SHA512: 7825087770028aab77607bb2c81d4ea44c0da599894361bf18c6ef7edf0a3426044ce366f496f110f15b40ad3865e9804615f3e01d938a37eb4de5efed5476b4 Homepage: https://cran.r-project.org/package=Fluidigm Description: CRAN Package 'Fluidigm' (Handling Fluidigm Data) Designed to streamline the process of analyzing genotyping data from Fluidigm machines, this package offers a suite of tools for data handling and analysis. It includes functions for converting Fluidigm data to format used by 'PLINK', estimating errors, calculating pairwise similarities, determining pairwise similarity loci, and generating a similarity matrix. Package: r-cran-flumodl Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dlnm, r-cran-mvmeta, r-cran-tsmodel Filename: pool/dists/noble/main/r-cran-flumodl_0.0.3-1.ca2404.1_all.deb Size: 185846 MD5sum: 3792ff5eaf0702326932bc93960cb9a2 SHA1: e82449900d048ea7d0585d66bc0c2036f6b1a0a2 SHA256: 05b8f34ce45c64e81070a2f8e24536337665b315209257f00fef787daa2c3117 SHA512: 246dc1eccd99e0a19369e700c8299843d9809316788490310a95bad712b5c8ba526c7380d47b14eb615a04cca3d15612109632235aeffc4747f90629c0024b4c Homepage: https://cran.r-project.org/package=FluMoDL Description: CRAN Package 'FluMoDL' (Influenza-Attributable Mortality with Distributed-Lag Models) Functions to estimate the mortality attributable to influenza and temperature, using distributed-lag nonlinear models (DLNMs), as first implemented in Lytras et al. (2019) . Full descriptions of underlying DLNM methodology in Gasparrini et al. (DLNMs), (attributable risk from DLNMs) and (multivariate meta-analysis). Package: r-cran-fluorojip Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 676 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scatterplot3d, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fluorojip_0.1.1-1.ca2404.1_all.deb Size: 498378 MD5sum: e2dd043bfa3ea8cff12fb05fcf6b1f86 SHA1: b5669765ff743a0d9446c49affa7ba344b660c1f SHA256: 58a4af0bbdb4c0d31230e5843401c82bbf33ecdd9eb7f17cb6a3104bbfdbcd7b SHA512: 4c3268f2f3cbd2534b8d5df4d47ee352b86da611377b873418c029b479c269f31ddaa172abac6814b864c3925e0ba2a68a8e69c5be7c5d33f22144e898636688 Homepage: https://cran.r-project.org/package=fluorojip Description: CRAN Package 'fluorojip' (Analysis of Chlorophyll a Fluorescence Transient Parameters) Computes chlorophyll a fluorescence transient parameters from fluorescence summary data, including minimum and maximum fluorescence, selected transient steps, and area. Provides standard photosynthetic performance indices and fluxes per reaction center and per cross section. Includes helpers to read exported trace tables in supported 'csv' formats and validation workflows based on bundled example files, as well as visualization tools and an interactive 'shiny' interface. The implemented calculations are based on Strasser et al. (2004) and Stirbet and Govindjee (2011) . Package: r-cran-fluosurv Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1644 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-survival Filename: pool/dists/noble/main/r-cran-fluosurv_1.0.0-1.ca2404.1_all.deb Size: 982952 MD5sum: 74cbff159a6d5d524709afe74ac26c5a SHA1: be84d8b84bcd36fe2f7a3edf1d7a491fbbc3e45f SHA256: 40dcde52af758c7f933b8e9a5d2fa44db7f582e79545fcea5f64de8f71dade88 SHA512: 6126ffa4c8615990f572dda6908dc3287e9f44a6489097b61841877b3474c5939d5d890a0d08d26fb21e93a9f7f0f0a0b419b89fe5fd2d8a67258be4875c96a5 Homepage: https://cran.r-project.org/package=fluoSurv Description: CRAN Package 'fluoSurv' (Estimate Insect Survival from Fluorescence Data) Use spectrophotometry measurements performed on insects as a way to infer pathogens virulence. Insect movements cause fluctuations in fluorescence signal, and functions are provided to estimate when the insect has died as the moment when variance in autofluorescence signal drops to zero. The package provides functions to obtain this estimate together with functions to import spectrophotometry data from a Biotek microplate reader. Details of the method are given in Parthuisot et al. (2018) . Package: r-cran-fluspect Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-fluspect_1.0.0-1.ca2404.1_all.deb Size: 54430 MD5sum: 15d280a3574f823a4fd82cc56074586f SHA1: 293390b2058a900c7422a24d215f440891471010 SHA256: bbb07e91385031dae96db2755a9c2c8cddeb11cecf8987964490fd6aa0f6efe4 SHA512: 5535a473ec071f78aace47ef79dcefb7d3a9edf74c3fbabc0df8343f941304d967a83ebcfe597d2f48768fd743f8cf8e7bda23ad7443e3e352c6dc7b6b690a1f Homepage: https://cran.r-project.org/package=fluspect Description: CRAN Package 'fluspect' (Fluspect-B) A model for leaf fluorescence, reflectance and transmittance spectra. It implements the model introduced by Vilfan et al. (2016) . Fluspect-B calculates the emission of ChlF on both the illuminated and shaded side of the leaf. Other input parameters are chlorophyll and carotenoid concentrations, leaf water, dry matter and senescent material (brown pigments) content, leaf mesophyll structure parameter and ChlF quantum efficiency for the two photosystems, PS-I and PS-II. Package: r-cran-flux Architecture: all Version: 0.3-0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-catools Filename: pool/dists/noble/main/r-cran-flux_0.3-0.1-1.ca2404.1_all.deb Size: 603770 MD5sum: 8190f13c3c481d9850429805cadc9c67 SHA1: eebf2b6ee91635277866ce8528ac518dcb85fa40 SHA256: 4552d67b9b9fc37cd026b3d7c2f77eaf0ab879be57788cafdb2d8992e0feff85 SHA512: 6b6b56724a4df850204bd18791bc83a99008743f3494bcf5d4d34076065c1890efb1b988be0ecdd1b0fbabf5aa6d9603cafc489869726deed82bf15e384c75bb Homepage: https://cran.r-project.org/package=flux Description: CRAN Package 'flux' (Flux Rate Calculation from Dynamic Closed Chamber Measurements) Functions for the calculation of greenhouse gas flux rates from closed chamber concentration measurements. The package follows a modular concept: Fluxes can be calculated in just two simple steps or in several steps if more control in details is wanted. Additionally plot and preparation functions as well as functions for modelling gpp and reco are provided. Package: r-cran-fluxfinder Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-jsonlite, r-cran-lubridate, r-cran-mass Suggests: r-cran-gasfluxes, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-fluxfinder_1.2.2-1.ca2404.1_all.deb Size: 194066 MD5sum: 6e46dfb82b6057f9a37abae15eb83b04 SHA1: 0f652456eca3e940c5e5c82f50af7cebe0ced17c SHA256: 31298e1059bfb60cd53d26722f14ffb517aa7e06982071325eb0ee1c61c4d2df SHA512: 29b2cf8d62ef16954d655802dcda1b3969ed4af37b6ced64414a8b43f9650c8e33d1569c58d0b384fc27861d064d428c3432c5abed05bdf9f08ba42a8b8f4339 Homepage: https://cran.r-project.org/package=fluxfinder Description: CRAN Package 'fluxfinder' (Parsing, Computation, and Diagnostics for Greenhouse GasMeasurements) Parse static-chamber greenhouse gas measurement files generated by a variety of instruments; compute flux rates using multi-observation metadata; and generate diagnostic metrics and plots. Designed to be easy to integrate into reproducible scientific workflows. Package: r-cran-fluxfixer Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gsignal, r-cran-lubridate, r-cran-magrittr, r-cran-ranger, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-xts, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fluxfixer_1.1.0-1.ca2404.1_all.deb Size: 1497290 MD5sum: d6f0823443d627d5599ff61b864c1e70 SHA1: 48fa22c9c5e79153b8651fe3a1399935b32f9f5d SHA256: f072fef78b5dda33c38adb1d7352519ba03c16cab37093c18875489d80bc9bed SHA512: c2c05dcc221080a5932126035c84cdfcb122de16ce70349d674b77054ca09b101591a621375fb13947f5692a7dfed338ac534a3b49868cb868b1a3366f515f96 Homepage: https://cran.r-project.org/package=fluxfixer Description: CRAN Package 'fluxfixer' (Advanced Framework for Sap Flow Data Post-Process) Provides a flexible framework for post-processing thermal dissipation sap flow data using statistical methods and machine learning. This framework includes anomaly correction, outlier removal, gap-filling, trend removal, signal damping correction, and sap flux density calculation. The functions in this package can also apply to other time series with various artifacts. 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It goes from a data frame of gas concentration over time (which can contain several measurements) and a meta data file indicating which measurement was done when, to a data frame of ecosystem gas fluxes including quality diagnostics. Organized with one function per step, maximizing user flexibility and backwards compatibility. Different models to estimate the fluxes from the raw data are available: exponential as described in Zhao et al (2018) , exponential as described in Hutchinson and Mosier (1981) , quadratic, and linear. Other functions include quality assessment, plotting for visual check, calculation of fluxes based on the setup specific parameters (chamber size, plot area, ...), gross primary production and transpiration rate calculation, and light response curves. Package: r-cran-fluxpoint Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blockmatrix, r-cran-corpcor, r-cran-doparallel, r-cran-ggplot2, r-cran-glmnet, r-cran-mass, r-cran-matrix, r-cran-nnls, r-cran-pracma, r-cran-simdesign Filename: pool/dists/noble/main/r-cran-fluxpoint_0.1.2-1.ca2404.1_all.deb Size: 133852 MD5sum: 1b6f6ce6bf7ce56ab2306b517f1799eb SHA1: 8e2fcebf22a4d554faa4e35110ac794f465a0330 SHA256: a136aaa1d2b0f46d05e31003fb3ea916c38a2e20bffd32d5a147c4862611b1ae SHA512: 957ed035ccb0a8da05417ccf66b0797d446d2f5177886bdffc828dac241b4d5682cb5625277c80b3e14ab54b1af0465e55186503c5de00f76f6a04df98387eb6 Homepage: https://cran.r-project.org/package=FluxPoint Description: CRAN Package 'FluxPoint' (Change Point Detection for Non-Stationary and Cross-CorrelatedTime Series) Implements methods for multiple change point detection in multivariate time series with non-stationary dynamics and cross-correlations. The methodology is based on a model in which each component has a fluctuating mean represented by a random walk with occasional abrupt shifts, combined with a stationary vector autoregressive structure to capture temporal and cross-sectional dependence. The framework is broadly applicable to correlated multivariate sequences in which large, sudden shifts occur in all or subsets of components and are the primary targets of interest, whereas small, smooth fluctuations are not. Although random walks are used as a modeling device, they provide a flexible approximation for a wide class of slowly varying or locally smooth dynamics, enabling robust performance beyond the strict random walk setting. Package: r-cran-fluxseparator Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-hmr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-ttr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fluxseparator_1.0.1-1.ca2404.1_all.deb Size: 89528 MD5sum: ea0b96e50ca645e4f90a28fbe40cac5b SHA1: cf1d40dea11190f8e91619a4b96e93971465ddd2 SHA256: 25dd077045bee57849f2d0de970f9ca0d0ccca44fe039fcfca85edaf0984e77f SHA512: 8931bcf6b388bed138510f6d1abfce79150a26bce5d26dc388e787ff1a01a6feb0b44d46ad47c80697683ab201c2d728c2907b6279e26e1afb24e3b8443e00dd Homepage: https://cran.r-project.org/package=FluxSeparator Description: CRAN Package 'FluxSeparator' (Separation of Diffusive and Ebullitive Fluxes) Separates diffusive and ebullitive (bubble) fluxes from continuous concentration measurements using a running variance approach. Ebullitive events are identified when the running variance exceeds a user-set threshold. Diffusive fluxes are calculated via linear regression on the non-ebullitive portion of the data. See Sø et al. (2024) for details. Package: r-cran-fluxtools Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-plotly, r-cran-readr, r-cran-shiny, r-cran-tibble Suggests: r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-shinywidgets, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fluxtools_0.7.1-1.ca2404.1_all.deb Size: 271132 MD5sum: 43fe2b805394d9c03910706934f157da SHA1: d3709b6a0e286a75457eec8c968defd41f12fed9 SHA256: 197d181e7f576a487c70cbe6575bf9ebb57b5002cf32cdc6051de948faf99c36 SHA512: 30f173dad395e32efcc41d3a160152560068e0ef301d89dd774a9606bbd6b442bba4696d64b5b6a18f3c210a816e4e1c889db00c998723b028fd84fecccfa5db Homepage: https://cran.r-project.org/package=fluxtools Description: CRAN Package 'fluxtools' (A 'shiny' App for Reproducible QA/QC of Eddy Covariance Data) An interactive 'shiny'-based tool for exploration and quality assurance and quality control (QA/QC) of eddy covariance flux tower data processing. It generates data-point removal code via user-directed selection from a scatterplot, and can export a cleaned .csv with removed points set to NA plus an R script for reproducibility. Reference: Key (2025) . Package: r-cran-fluxweb Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-fluxweb_2.0.1-1.ca2404.1_all.deb Size: 265472 MD5sum: e75b58a6d44036d904bbe513684c71e1 SHA1: 0af319805132446ad08399a486a6f884c4a5d4be SHA256: d7b6f2d87927f4997fefd318fcb94ee8b35f33d86419240310a5adb6956f8409 SHA512: 10b03e75697d7f21a21d066503a450a27e90b474e3ba9172a58a190e49ee37dc9b9637bfef1902c5e59f7c524516b233af6386a9f191ca0fb7a64db82a13bff2 Homepage: https://cran.r-project.org/package=fluxweb Description: CRAN Package 'fluxweb' (Estimate Energy Fluxes in Food Webs) Compute energy fluxes in trophic networks, from resources to their consumers, and can be applied to systems ranging from simple two-species interactions to highly complex food webs. It implements the approach described in Gauzens et al. (2017) to calculate energy fluxes, which are also used to calculate equilibrium stability. Package: r-cran-fma Architecture: all Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 681 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-fma_2.5-1.ca2404.1_all.deb Size: 476538 MD5sum: 021966d28d099c40867eba1eb7a5ff34 SHA1: 7a5e19a733d9cac3a80793a651523550844d0616 SHA256: d9764f9f125df1cbbaa91fda99b83f273e36b9f2ed6255a88a47dc1d34c9b717 SHA512: cbd827fcf0885b575deea7d7ca99e27c5e68e46c319e48a01562162e7551e251bbddded928c08a498c16ff65855fa32492394b6f4cf2c8b6b133ace35636db55 Homepage: https://cran.r-project.org/package=fma Description: CRAN Package 'fma' (Data Sets from "Forecasting: Methods and Applications" byMakridakis, Wheelwright & Hyndman (1998)) All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998) . Package: r-cran-fmadist Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fitdistrplus, r-cran-actuar, r-cran-envstats, r-cran-extradistr, r-cran-mass, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-fmadist_0.1.2-1.ca2404.1_all.deb Size: 40938 MD5sum: c44cb05a48d48027af09cb32aaa1a4a1 SHA1: 9e606044e680396d8ed3c40b1832f5e86aae836a SHA256: b1ab411e284c83d5d7938f1a7eeb8779bf2baf2b3a1e265e32085c907d15f715 SHA512: ed285a291b5d881f0a575480d06719bfe82b33ee1dab53c538b58bf2df0f473909903832f943657c38d8fdb16e826c9e216114038088948c7977cd8663a52f75 Homepage: https://cran.r-project.org/package=FMAdist Description: CRAN Package 'FMAdist' (Frequentist Model Averaging Distribution) Creation of an input model (fitted distribution) via the frequentist model averaging (FMA) approach and generate random-variates from the distribution specified by "myfit" which is the fitted input model via the FMA approach. See W. X. Jiang and B. L. Nelson (2018), "Better Input Modeling via Model Averaging," Proceedings of the 2018 Winter Simulation Conference, IEEE Press, 1575-1586. Package: r-cran-fmat Architecture: all Version: 2026.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-data.table, r-cran-stringr, r-cran-forcats, r-cran-rvest, r-cran-psych, r-cran-irr, r-cran-glue, r-cran-crayon, r-cran-cli, r-cran-purrr, r-cran-plyr, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-brucer, r-cran-psychwordvec, r-cran-text, r-cran-sweater, r-cran-nlme Filename: pool/dists/noble/main/r-cran-fmat_2026.1-1.ca2404.1_all.deb Size: 116424 MD5sum: a7e1fbfdd653b778bf5e856af377fd9a SHA1: fa3f475915a2a82f178a23ef292acb431381c84f SHA256: 573f24a35636f36e5566072a63b9d48cabf6b012c881e6626dd8859bfddf95ed SHA512: 2f7a14cb8d026a3bcc8cc837140a471ef468ccd95fd08f69c2fc2606a6dc61050c32456b2ae6eb51f3cad76a572273b1e070e390a2718b37676746cc140201c8 Homepage: https://cran.r-project.org/package=FMAT Description: CRAN Package 'FMAT' (The Fill-Mask Association Test) The Fill-Mask Association Test ('FMAT') is an integrative, probability-based social computing method using Masked Language Models to measure conceptual associations (e.g., attitudes, biases, stereotypes, social norms, cultural values) as propositional semantic representations in natural language. 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Package: r-cran-fmbasics Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-fmdates, r-cran-lubridate, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fmbasics_0.3.0-1.ca2404.1_all.deb Size: 510598 MD5sum: 93b7cd23eed53766570d438c4722bde2 SHA1: 60c69d581c1b511eb230bd85bc9df7feb2548933 SHA256: a982d84a52751dde038b060bec70255bbbf83779bb336dff309fb409d1cc9206 SHA512: ffea0ddd29840e38c3709b898d431b45db66f366ff4621a33557228af8add8cd5df21c440249e0674a162816ac41c871a5adfd6a4215be5e5964c0254b4656ec Homepage: https://cran.r-project.org/package=fmbasics Description: CRAN Package 'fmbasics' (Financial Market Building Blocks) Implements basic financial market objects like currencies, currency pairs, interest rates and interest rate indices. 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Package: r-cran-fmcmc Architecture: all Version: 0.5-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1840 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-mass, r-cran-matrix Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-mcmc, r-cran-tinytest, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-fmcmc_0.5-2-1.ca2404.1_all.deb Size: 1353860 MD5sum: 27c0bf5b652d029d29cec21efefbc241 SHA1: 4c103c431e0f8c83730b097e9a2efdc9a18c1c2a SHA256: 83ad41338817c56d680226d873fe48a44c40710d4be7ab0258beeabaf02003c4 SHA512: 08a872f0a7b55ab668720ad03d03372397be0f6e6d2e9db463a361b7544a8dcf4ba9b0a9009bedc96c183f5f1cd6c351bf5b0f8488dc86326d03f171432899a5 Homepage: https://cran.r-project.org/package=fmcmc Description: CRAN Package 'fmcmc' (A friendly MCMC framework) Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing Metropolis-Hastings algorithm in a modular way allowing users to specify automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. 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Package: r-cran-fmeffects Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3329 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-partykit, r-cran-ggparty, r-cran-ggplot2, r-cran-cowplot, r-cran-r6, r-cran-testthat Suggests: r-cran-caret, r-cran-furrr, r-cran-future, r-cran-hexbin, r-cran-knitr, r-cran-mlr3verse, r-cran-parallelly, r-cran-ranger, r-cran-rmarkdown, r-cran-rpart, r-cran-tidymodels Filename: pool/dists/noble/main/r-cran-fmeffects_0.1.4-1.ca2404.1_all.deb Size: 2625534 MD5sum: 0855bac1eee584c943c87d2873c3035e SHA1: c43a9612146ec8d518115fcdaf766636a175413d SHA256: f894b847d6597e356c0d071dd29f585cc2e7fa9fa8573abf147bd4384be10160 SHA512: 1d2e66ce3cd6443727230692de945b28d86e91962f837a26eac72aa8d71cea43fd2b3538c5a8e548f9be656d1f1fae74a5fd4a2a7b05817cbcf0c49b40c91662 Homepage: https://cran.r-project.org/package=fmeffects Description: CRAN Package 'fmeffects' (Model-Agnostic Interpretations with Forward Marginal Effects) Create local, regional, and global explanations for any machine learning model with forward marginal effects. 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Package: r-cran-foodwebr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-crayon, r-cran-codetools, r-cran-diagrammer, r-cran-glue, r-cran-rlang, r-cran-stringr, r-cran-tidygraph Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-foodwebr_1.0.0-1.ca2404.1_all.deb Size: 164260 MD5sum: 64e0166ec61bacf7055998365b40df7c SHA1: 7fc74f160711d117050858ec8ca5fa687cec77b3 SHA256: 648762eb601d630289e9fc2a49067c00cdfa01f252b54f431d3b01fb548ec01e SHA512: e2d1a993a320ce1f49304f131ea22d1d68c616da4aef9cb64e0051efba34c8d0a696e949205119a628fb9d2bdb9b06ebb97831032691829d117479c28bb110de Homepage: https://cran.r-project.org/package=foodwebr Description: CRAN Package 'foodwebr' (Visualise Function Dependencies) Easily create graphs of the inter-relationships between functions in an environment. 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Package: r-cran-footballpenaltiesbl Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-footballpenaltiesbl_1.0.0-1.ca2404.1_all.deb Size: 98462 MD5sum: 6b2c309ccbd2105377c869c41f9f4965 SHA1: 9344e8b7219d45708a1dfa44d403c232455c3048 SHA256: 066706468db00eb6cdfa7578318fccde17c89f3ad863ba46983bda4b2aa3ecfc SHA512: cd3af7539ff140f1ade213cae858667dc7294d348537213212cd487449ec48eaa2bb1ece69f4f836b57d8d7c78c366e2e2cd2d4893a8681f9c6aa35eb5391069 Homepage: https://cran.r-project.org/package=footballpenaltiesBL Description: CRAN Package 'footballpenaltiesBL' (Penalties in the German Men's Football Bundesliga) Basic analysis of all penalties taken in the German men's Bundesliga between the start of its inaugural season and May 2017. The main functions are suitable printing and plotting functions. 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This tools contain functions that facilitate analysis in atmospheric chemistry (especially in ozone pollution). Some functions of time series are also applicable to other fields. For detail please view homepage. Scientific Reference: 1. The Hydroxyl Radical (OH) Reactivity: Roger Atkinson and Janet Arey (2003) . 2. Ozone Formation Potential (OFP): , Zhang et al.(2021) . 3. Aerosol Formation Potential (AFP): Wenjing Wu et al. (2016) . 4. TUV model: . 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This is mainly used to predict the results of the monthly labor market report from the United States Bureau of Labor Statistics for virtually any part of the economy reported by the Bureau of Labor Statistics, but it can be easily modified to work with other types of time series data. For example, the package was used to predict the winning men's and women's time for the 2024 London Marathon. Package: r-cran-forecastlsw Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-locits, r-cran-wavethresh, r-cran-lpacf, r-cran-forecast Filename: pool/dists/noble/main/r-cran-forecastlsw_1.1.1-1.ca2404.1_all.deb Size: 119574 MD5sum: dfd0d52ed11618f5495929b35b1411b1 SHA1: f78e59bda61a58d74a333908fb3eafcfe7554fcd SHA256: ab3b462238b65b0c59e707f7bf01a29e871167a72479dc1696b1eea0fc803c79 SHA512: 36d12e3ca7720c6d063edc461c2b7bf07b1637a43baaff39283984de0ff97174d0c361987c068dab387b6b1a3d9304366fde8ef0ba69492a0add8970f3ee18bf Homepage: https://cran.r-project.org/package=forecastLSW Description: CRAN Package 'forecastLSW' (Forecasting Routines for Locally Stationary Wavelet Processes) Implementation to perform forecasting of locally stationary wavelet processes by examining the local second order structure of the time series. 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'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" . 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Package: r-cran-foreco Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1515 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-osqp, r-cran-cli, r-cran-distributional, r-cran-lifecycle Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-foreco_1.2.1-1.ca2404.1_all.deb Size: 1475488 MD5sum: 495268a693795f10dbaa819bc3903172 SHA1: 0aa842ac350a4508aa149e993b512725d828bcdf SHA256: 0955cffc41d37597c9bd178dfb471431edab018d66fe134a26d04ce02c99fb17 SHA512: 89f2ead7b0dcdd631058b30335185202256176166c70e3ad5c5dfc6873b6ea8abb54463e5222d37273a4e06f8c8044fabce441a8d6fefc76a731427d78caf95f Homepage: https://cran.r-project.org/package=FoReco Description: CRAN Package 'FoReco' (Forecast Reconciliation) Classical (bottom-up and top-down), optimal combination and heuristic point (Di Fonzo and Girolimetto, 2023 ) and probabilistic (Girolimetto et al. 2024 ) forecast reconciliation procedures for linearly constrained time series (e.g., hierarchical or grouped time series) in cross-sectional, temporal, or cross-temporal frameworks. 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Provides multiple Diebold-Mariano test implementations based on fixed-smoothing approaches, including fixed-b methods such as Kiefer and Vogelsang (2005) , and applications to tests for equal predictive accuracy as in Coroneo and Iacone (2020) , alongside conventional large-sample approximations. HAR inference involves nonparametric estimation of the long-run variance, and a key tuning parameter (the truncation parameter) trades off size and power. Lazarus, Lewis, and Stock (2021) theoretically characterize the size-power frontier for the Gaussian multivariate location model. 'ForeComp' computes and visualizes the finite-sample size-power frontier of the Diebold-Mariano test based on fixed-b asymptotics together with the Bartlett kernel. To compute finite-sample size and power, it fits a best approximating ARMA process to the input data and reports how the truncation parameter performs and how robust testing outcomes are to its choice. 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By leveraging information theory metrics, it enables accurate assessment of kinship, particularly when limited genetic evidence is available. With a focus on optimizing statistical power, 'forensIT' empowers investigators to effectively prioritize family members, enhancing the reliability and efficiency of missing person investigations. 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This version of the algorithm relies on random forest algorithm to learn a large set of split points that conserves the relationship between attributes and the target class, and on moment matching optimization to transform this set into a reduced number of cut points matching as well as possible statistical properties of the initial set of split points. For each attribute to be discretized, the set S of its related split points extracted through random forest is mapped to a reduced set C of cut points of size k. This mapping relies on minimizing, for each continuous attribute to be discretized, the distance between the four first moments of S and the four first moments of C subject to some constraints. This non-linear optimization problem is performed using k values ranging from 2 to 'max_splits', and the best solution returned correspond to the value k which optimum solution is the lowest one over the different realizations. ForestDisc is a generalization of RFDisc discretization method initially proposed by Berrado and Runger (2009) , and improved by Berrado et al. in 2012 by adopting the idea of moment matching optimization related by Hoyland and Wallace (2001) . Package: r-cran-forestdynr Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biomass Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-forestdynr_0.0.1-1.ca2404.1_all.deb Size: 47986 MD5sum: 59cbe94d374276769cf6008f6b0cce52 SHA1: 9d03f1440301896f9455557e1494c2e2969499b8 SHA256: d17fbf2e2013ca44c4e74d5a0528e80385beb4c57790fe167290f7d00d5b93df SHA512: 0fa1e625b8d711dc871ffecd2588ad669b9960675574b214c6a5f3a5d996bf44b3e8cf3dc9edbffe751463de3f893ebd3e636186334b9cbfad195db6126daa73 Homepage: https://cran.r-project.org/package=forestdynR Description: CRAN Package 'forestdynR' (Calculate Forest Dynamics) Determines the dynamics of tree species communities (mortality rates, recruitment, loss and gain in basal area, net changes and turnover). Important notes are a) The 'forest_df' argument (data) must contain the columns 'plot' (plot identification), 'spp' (species identification), DBH_1 (Diameter at breast height in first year of measure) and DBH_2 (Diameter at breast height in second year of measure). DBH_1 and DBH_2 must be numeric values; b) example input file in 'data(forest_df_example)'; c) The argument 'inv_time' represents the time between inventories, in years; d) The 'coord' argument must be of the type 'c(longitude, latitude)', with decimal degree values; e) Argument 'add_wd' represents a dataframe with wood density values (g cm-3) format with three columns ('genus', 'species', 'wd'). This argument is set to NULL by default, and if isn't provided, the wood density will be estimated with the getWoodDensity() function from the 'BIOMASS' package. Package: r-cran-forestecology Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1673 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-glue, r-cran-ggplot2, r-cran-stringr, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-ggridges, r-cran-mvnfast, r-cran-sf, r-cran-sfheaders, r-cran-snakecase, r-cran-tibble, r-cran-yardstick, r-cran-forcats Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-patchwork, r-cran-blockcv Filename: pool/dists/noble/main/r-cran-forestecology_0.2.1-1.ca2404.1_all.deb Size: 1345186 MD5sum: a84acbb056a9910cfb1d330edd06d1b1 SHA1: dcef9e3c650119e09cd5fd926a59cc9918198c05 SHA256: 436e529b75d8be36415e1cb36f7d33b376364c32ea5558ae42d29d736476e8b0 SHA512: b1684d4a291c16c8e61d10667e944b36a0cce9adae9b6d910efd6634ab466b21f2c51f26628f561a9508a254795d8be025729405898e16a0c823c5198d2f68ad Homepage: https://cran.r-project.org/package=forestecology Description: CRAN Package 'forestecology' (Fitting and Assessing Neighborhood Models of the Effect ofInterspecific Competition on the Growth of Trees) Code for fitting and assessing models for the growth of trees. In particular for the Bayesian neighborhood competition linear regression model of Allen (2020): methods for model fitting and generating fitted/predicted values, evaluating the effect of competitor species identity using permutation tests, and evaluating model performance using spatial cross-validation. Package: r-cran-forested Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1158 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-forested_0.2.0-1.ca2404.1_all.deb Size: 852992 MD5sum: cef8452adf876b37d6155387e9acca7d SHA1: 57d98bbc62fb4327acdddc17a0bddcb4e14fcab8 SHA256: 039f94554da5ffd8b277e11605c8f8ca4896c724a596600204825a214d4ada89 SHA512: 956fe695d7b57f82b84ccbb30f3b7e37e7e060391551b14501400c06d5f9e47e39251c47bd63226460cf13ccbc829200ff431a60318f8158fe35723c67d85532 Homepage: https://cran.r-project.org/package=forested Description: CRAN Package 'forested' (Forest Attributes in U.S. States) A small subset of plots throughout the U.S. are sampled and assessed "on-the-ground" as forested or non-forested by the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program, but the FIA also has access to remotely sensed data for all land in the country. The 'forested' package contains data frames intended for use in predictive modeling applications where the more easily-accessible remotely sensed data can be used to predict whether a plot is forested or non-forested. Currently, the package provides data for Washington and Georgia. Package: r-cran-forestelementsr Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-magrittr, r-cran-ggplot2, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-rdpack, r-cran-tidyr, r-cran-vctrs, r-cran-stringr, r-cran-tidyselect, r-cran-doby Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-forestelementsr_2.2.0-1.ca2404.1_all.deb Size: 1292900 MD5sum: 1639d7c3d629b0b067e1f6e4418a53b8 SHA1: a6e6699f3b6078830ae2174fe543c5a6486e54bd SHA256: c45106bf46739abfdd3a5e0021d736350a15f5ef92b9f5e6b92d3a33738e199b SHA512: 0960397b99145d0a75ee7f32498e8356e607f87b1aef732d676c046b247d7611269936f3210951939b48716397ed8b3719eac95fecc33cfe2656e4661e829bfe Homepage: https://cran.r-project.org/package=ForestElementsR Description: CRAN Package 'ForestElementsR' (Data Structures and Functions for Working with Forest Data) Provides generic data structures and algorithms for use with forest mensuration data in a consistent framework. The functions and objects included are a collection of broadly applicable tools. More specialized applications should be implemented in separate packages that build on this foundation. Documentation about 'ForestElementsR' is provided by three vignettes included in this package. For an introduction to the field of forest mensuration, refer to the textbooks by Kershaw et al. (2017) , and van Laar and Akca (2007) . Package: r-cran-foresterror Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-purrr Suggests: r-cran-randomforest Filename: pool/dists/noble/main/r-cran-foresterror_1.1.0-1.ca2404.1_all.deb Size: 54532 MD5sum: b1e130daee50b8845db269cb56901853 SHA1: 4727ef8747f007dfb1ef006d087ae632c3a1d347 SHA256: ceea9b60c6d9ce6cea4a319899026ca666bfe6c3c16d92e257eb04af5bf7b13e SHA512: 4f9938aa2bcfa15df00928be68b9477bda1fb167e4bdf34c2667568a99ed72255c3c45c89e618a874e4b7b790fe6f8def0b43d3bf5c3d8fde9065c5d218f2893 Homepage: https://cran.r-project.org/package=forestError Description: CRAN Package 'forestError' (A Unified Framework for Random Forest Prediction ErrorEstimation) Estimates the conditional error distributions of random forest predictions and common parameters of those distributions, including conditional misclassification rates, conditional mean squared prediction errors, conditional biases, and conditional quantiles, by out-of-bag weighting of out-of-bag prediction errors as proposed by Lu and Hardin (2021). This package is compatible with several existing packages that implement random forests in R. Package: r-cran-forestfit Architecture: all Version: 2.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ars, r-cran-pracma Filename: pool/dists/noble/main/r-cran-forestfit_2.4.3-1.ca2404.1_all.deb Size: 453582 MD5sum: 1606719bd1279b393504d378f326c4cd SHA1: 3832ab9df98ad79e40e97b91ebc9612c8211fb11 SHA256: 7c4499de2a35076f8a28cfce804584bd8791320888117e77be5b5585cb6a8482 SHA512: 939bc4f6ffbe5017f9aa8d80f2c84618e9b472618d8ef0dfc47da720d82a824be1030a6eb33751339a486f261cfa030d9a7c1f7eee78ec409a01b2ab8a787c42 Homepage: https://cran.r-project.org/package=ForestFit Description: CRAN Package 'ForestFit' (Statistical Modelling for Plant Size Distributions) Developed for the following tasks. 1 ) Computing the probability density function, cumulative distribution function, random generation, and estimating the parameters of the eleven mixture models. 2 ) Point estimation of the parameters of two - parameter Weibull distribution using twelve methods and three - parameter Weibull distribution using nine methods. 3 ) The Bayesian inference for the three - parameter Weibull distribution. 4 ) Estimating parameters of the three - parameter Birnbaum - Saunders, generalized exponential, and Weibull distributions fitted to grouped data using three methods including approximated maximum likelihood, expectation maximization, and maximum likelihood. 5 ) Estimating the parameters of the gamma, log-normal, and Weibull mixture models fitted to the grouped data through the EM algorithm, 6 ) Estimating parameters of the nonlinear height curve fitted to the height - diameter observation, 7 ) Estimating parameters, computing probability density function, cumulative distribution function, and generating realizations from gamma shape mixture model introduced by Venturini et al. (2008) , 8 ) The Bayesian inference, computing probability density function, cumulative distribution function, and generating realizations from univariate and bivariate Johnson SB distribution, 9 ) Robust multiple linear regression analysis when error term follows skewed t distribution, 10 ) Estimating parameters of a given distribution fitted to grouped data using method of maximum likelihood, and 11 ) Estimating parameters of the Johnson SB distribution through the Bayesian, method of moment, conditional maximum likelihood, and two - percentile method. Package: r-cran-forestgapr Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-powerlaw, r-cran-raster, r-cran-sp, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-vgam, r-cran-viridis Filename: pool/dists/noble/main/r-cran-forestgapr_0.1.7-1.ca2404.1_all.deb Size: 526526 MD5sum: 25ec78c21dc586ac18cb6f10fd091cfa SHA1: 946d4926a9571a41d43839c1e9b247c9645f778c SHA256: 36e025b9cf5db8988c04897224ef32f49e03ec6088889c4288cf48af7e2e2215 SHA512: 1f9aa47d1c555de668c79d2ff4de6f457e3df6e3e174fbb5f1228bbee752413e634df8c0feb725c0eacb079a059eefef481d057b2510af36f6fb24a9c5c8b07b Homepage: https://cran.r-project.org/package=ForestGapR Description: CRAN Package 'ForestGapR' (Tropical Forest Canopy Gaps Analysis) Set of tools for detecting and analyzing Airborne Laser Scanning-derived Tropical Forest Canopy Gaps. Details were published in Silva and others (2019) . Package: r-cran-forestgym Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools Filename: pool/dists/noble/main/r-cran-forestgym_1.0.0-1.ca2404.1_all.deb Size: 44970 MD5sum: 63af5740d10d1aabdf8fceb50cbb5bd0 SHA1: 590d1e7d9b93fa0174563d7040fabf72fec00b1d SHA256: e8babc613d1fa7db3ea5b2fe48e9d7f731d505cb7e9165bd415ab6300283e761 SHA512: 038d8d783096f36a72bd979039ef426960ef28114bce3d7a90e70310afc7321e5f765e55fa21be778c2f30db97197fc02c267c1dabe28dfa9122203c37a7f1e6 Homepage: https://cran.r-project.org/package=forestGYM Description: CRAN Package 'forestGYM' (Forest Growth and Yield Model Based on Clutter Model) The Clutter model is a significant forest growth simulation tool. Grounded on individual trees and comprehensively considering factors such as competition among trees and the impact of environmental elements on growth, it can accurately reflect the growth process of forest stands. It can be applied in areas like forest resource management, harvesting planning, and ecological research. With the help of the Clutter model, people can better understand the dynamic changes of forests and provide a scientific basis for rational forest management and protecting the ecological environment. This R package can effectively realize the construction of forest growth and harvest models based on the Clutter model and achieve optimized forest management.References: Farias A, Soares C, Leite H et al(2021). Guera O, Silva J, Ferreira R, et al(2019). Package: r-cran-foresthes Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-foresthes_2.0.1-1.ca2404.1_all.deb Size: 68724 MD5sum: 684c6cebf016332210d465a4c06b67a2 SHA1: 0cfdbf57f917cc4f06bfdb463d5fdf42be06e641 SHA256: 58705f5a2ca477fa11c5e27c290e4e03803e56a0d36446fde79171ce0953a9c2 SHA512: 71addd706e581d9ecaa876276279f8e3226a7c6d4929be1662f608345aa55b46f0af4613471d1eaa7e9e61847fe61185ed344e152a242b918b0f0666db0c8cb5 Homepage: https://cran.r-project.org/package=forestHES Description: CRAN Package 'forestHES' (Forest Health Evaluation System at the Forest Stand Level) Assessing forest ecosystem health is an effective way for forest resource management.The national forest health evaluation system at the forest stand level using analytic hierarchy process, has a high application value and practical significance. The package can effectively and easily realize the total assessment process, and help foresters to further assess and management forest resources. Package: r-cran-forestinventory Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1400 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-tidyr, r-cran-ggplot2 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-forestinventory_1.0.0-1.ca2404.1_all.deb Size: 1351078 MD5sum: 887ac21ca163536c434fd0ddf5f3e998 SHA1: 80a4895ffeaf056ffaee9e46a6d21ce86a17040d SHA256: e92919dc7f8ebefd8884399c17bd6e6b9df4657a5460318105e5b129b63c9262 SHA512: 5b1a214a2a5650552d2d861ec0242512de0661e69a469907b057844e40ef753835d8c02069a100cf942b5834e212aecd11922e654c5846047f2f5da4256c5165 Homepage: https://cran.r-project.org/package=forestinventory Description: CRAN Package 'forestinventory' (Design-Based Global and Small-Area Estimations for MultiphaseForest Inventories) Extensive global and small-area estimation procedures for multiphase forest inventories under the design-based Monte-Carlo approach are provided. The implementation has been published in the Journal of Statistical Software () and includes estimators for simple and cluster sampling published by Daniel Mandallaz in 2007 (), 2013 (, , , ) and 2016 (). It provides point estimates, their external- and design-based variances and confidence intervals, as well as a set of functions to analyze and visualize the produced estimates. The procedures have also been optimized for the use of remote sensing data as auxiliary information, as demonstrated in 2018 by Hill et al. (). Package: r-cran-forestly Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11475 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brew, r-cran-crosstalk, r-cran-glue, r-cran-htmltools, r-cran-metalite, r-cran-metalite.ae, r-cran-reactable, r-cran-reactr, r-cran-rlang, r-cran-ggplot2, r-cran-uuid Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-r2rtf, r-cran-rmarkdown, r-cran-patchwork, r-cran-testthat Filename: pool/dists/noble/main/r-cran-forestly_0.1.4-1.ca2404.1_all.deb Size: 1346252 MD5sum: df015d09a005ca327344454146a38702 SHA1: f38f37dabc1521d45816614f9f5b6717890c998d SHA256: 04e3117d8d5e44c2513688add629a2415ffacad45944acba861bef6367abc729 SHA512: 2398324da1d5e383340f5874c35983dc59acb6613f2956894fe5155af47849759138d2c4a03eb236ec2fa5f43130ff2ed68d47cb612e82c7167779c69bd8acc7 Homepage: https://cran.r-project.org/package=forestly Description: CRAN Package 'forestly' (Interactive Forest Plot) Interactive forest plot for clinical trial safety analysis using 'metalite', 'reactable', 'plotly', and Analysis Data Model (ADaM) datasets. Includes functionality for adverse event filtering, incidence-based group filtering, hover-over reveals, and search and sort operations. The workflow allows for metadata construction, data preparation, output formatting, and interactive plot generation. Package: r-cran-forestmangr Architecture: all Version: 0.9.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1528 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-tidyr, r-cran-broom, r-cran-purrr, r-cran-plyr, r-cran-tibble, r-cran-systemfit, r-cran-ggpmisc, r-cran-rlang, r-cran-car, r-cran-magrittr, r-cran-minpack.lm, r-cran-fincal, r-cran-scales, r-cran-ggdendro, r-cran-gridextra, r-cran-shiny, r-cran-miniui Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyselect, r-cran-formattable Filename: pool/dists/noble/main/r-cran-forestmangr_0.9.9-1.ca2404.1_all.deb Size: 781448 MD5sum: 76a6bcaa792e3f4160e5b8e0c01fa830 SHA1: 41be319db010f08443de9073ddaa11b8aa38e1cb SHA256: f7e92cbac9c5373bfa78d7fe7832158be780f6d91b98d70bd359b7b74c7b4f8b SHA512: 3c161320cf49cf5b0448c7329e62e9bfd8c171b6b8680e49b78bf9e71dec25bea06f68033ef846022736a85c14a4529a64d52211151957a2d50fca7dd79d6a29 Homepage: https://cran.r-project.org/package=forestmangr Description: CRAN Package 'forestmangr' (Forest Mensuration and Management) Processing forest inventory data with methods such as simple random sampling, stratified random sampling and systematic sampling. There are also functions for yield and growth predictions and model fitting, linear and nonlinear grouped data fitting, and statistical tests. References: Kershaw Jr., Ducey, Beers and Husch (2016). . Package: r-cran-forestmodel Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-broom, r-cran-rlang, r-cran-tibble Suggests: r-cran-survival, r-cran-metafor, r-cran-labelled Filename: pool/dists/noble/main/r-cran-forestmodel_0.6.2-1.ca2404.1_all.deb Size: 116368 MD5sum: 4b52bc424186800bfa0143159bbcaa2c SHA1: 7f18196a50fca72a6a1ff043de475520b88d402d SHA256: 5e2882f8ec23968ffa241e1bfd31f764395ee381990040d9ce70e22d7fa44ee9 SHA512: 8534c50dd2cd58c451cfe6c41c39f82e1135bb8395b3ab630f2ba4384aa7200c5a943fc5d2adbd308c00377973bd976904bee7dd3708a7ba36978ec447ab9ff4 Homepage: https://cran.r-project.org/package=forestmodel Description: CRAN Package 'forestmodel' (Forest Plots from Regression Models) Produces forest plots using 'ggplot2' from models produced by functions such as stats::lm(), stats::glm() and survival::coxph(). Package: r-cran-forestplot Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1693 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-abind Suggests: r-cran-dplyr, r-cran-gmisc, r-cran-greg, r-cran-knitr, r-cran-purrr, r-cran-rlang, r-cran-rmarkdown, r-cran-rmeta, r-cran-rms, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-forestplot_3.2.0-1.ca2404.1_all.deb Size: 1086794 MD5sum: 28a14336e383cb8cf589e76bb1fe4537 SHA1: 2084d0013b25e14b0b501d954f2a48e01d44949e SHA256: 92192b5897f59fad866ed6c67f5f43fd6918bac8918edbda72a5546d806c42fa SHA512: ac146ac77da51543c51fcdbfab5f96b7475d5da72ea3e3d848582812fa1941d0a4aad4f4d1b5d0608a4126bf5e7de051647374d08ee59b559f6c7f6c76cf4120 Homepage: https://cran.r-project.org/package=forestplot Description: CRAN Package 'forestplot' (Advanced Forest Plot Using 'grid' Graphics) Allows the creation of forest plots with advanced features, such as multiple confidence intervals per row, customizable fonts for individual text elements, and flexible confidence interval drawing. It also supports mixing text with mathematical expressions. The package extends the application of forest plots beyond traditional meta-analyses, offering a more general version of the original 'rmeta' package’s forestplot() function. It relies heavily on the 'grid' package for rendering the plots. Package: r-cran-forestploter Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gridextra, r-cran-gtable Suggests: r-cran-gridtext, r-cran-rmarkdown, r-cran-knitr, r-cran-vdiffr, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-forestploter_1.1.4-1.ca2404.1_all.deb Size: 1562948 MD5sum: fb06be2cf8e12449f2016cf7d38163c3 SHA1: e514430e4807c98bac09bff7741ca0490922f7d1 SHA256: daab93cc3f8003213dfb6a6d90d9ae70237b42c7d270ffb6eb15e1912d253a6e SHA512: b8789be6f07c1f950753ac99841842fd4f8532a65bd7dc5d8f3450bbdd33efe9c0043979cc672fdcf2cc1777703f41d045ddb8de8f9ef56c1c3a6b8ba39db0d8 Homepage: https://cran.r-project.org/package=forestploter Description: CRAN Package 'forestploter' (Create a Flexible Forest Plot) Create a forest plot based on the layout of the data. Confidence intervals in multiple columns by groups can be done easily. Editing the plot, inserting/adding text, applying a theme to the plot, and much more. 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By deeply understanding these characteristics of forest populations, scientific basis can be provided for the management, protection and sustainable utilization of forest resources. This R package conducts a systematic analysis of forest population structure and quantitative dynamics through analyzing age structure, compiling life tables, population quantitative dynamic change indices and time series models, in order to provide support for forest population protection and sustainable management. References: Zhang Y, Wang J, Wang X, et al(2024). Yuan G, Guo Q, Xie N, et al(2023). 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References: Atkins et al. 2018 ; Hardiman et al. 2013 ; Parker et al. 2004 . Package: r-cran-forestrk Architecture: all Version: 0.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-ggplot2, r-cran-rapportools, r-cran-partykit, r-cran-pkgkitten, r-cran-knitr, r-cran-mlbench Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-forestrk_0.0-5-1.ca2404.1_all.deb Size: 380252 MD5sum: a503a4fc7f6bd2a8f8aa43c2276edb72 SHA1: 2903baa9608b0771a535bb8fcdb9d76c1eea5892 SHA256: 2a4a73539cdcf54073d2c2d339c557d8a1f323fc28f155d485ff045cae8d12be SHA512: ed7fdde7f37da47f63dae8326a6fe31531afb83974cdbe451918ae22b66f0e26437804ed8e940cce8228686fb2aa0b14b5da1da38f43f6890c9d8d20819925f3 Homepage: https://cran.r-project.org/package=forestRK Description: CRAN Package 'forestRK' (Implements the Forest-R.K. Algorithm for Classification Problems) Provides functions that calculates common types of splitting criteria used in random forests for classification problems, as well as functions that make predictions based on a single tree or a Forest-R.K. model; the package also provides functions to generate importance plot for a Forest-R.K. model, as well as the 2D multidimensional-scaling plot of data points that are colour coded by their predicted class types by the Forest-R.K. model. This package is based on: Bernard, S., Heutte, L., Adam, S., (2008, ISBN:978-3-540-85983-3) "Forest-R.K.: A New Random Forest Induction Method", Fourth International Conference on Intelligent Computing, September 2008, Shanghai, China, pp.430-437. 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The package provides functions to search for 'Fortnite Creative' islands, retrieve detailed metadata about specific islands including titles, descriptions, and tags, and access engagement metrics such as daily active users and play duration. It supports pagination for large result sets and time-series analysis of island performance. The API endpoint is . 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Currently, a single data set of responses to a survey of attendees at useR! 2016 , the R user conference held at Stanford University, Stanford, California, USA, June 27 - June 30 2016. 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Without these types of mutations, the only explanation for observing the four dilocus genotypes (example below) is recombination (Hudson and Kaplan 1985, Genetics 111:147-164). Thus, the presence of all four gametes is also called phylogenetic incompatibility. 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It contains a collection of built-in functions that play the game at various skill levels, for users to test their own functions against. Package: r-cran-fourscores Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fourscores_1.5.1-1.ca2404.1_all.deb Size: 56006 MD5sum: c6a63e58b1c599c89836c8789bf856f9 SHA1: 7ae2c6b49cf26956604c0a35e2dd75fec18c0ea4 SHA256: 8388727cd78847a66ac0afb2b8b352858ccd46317980520d9e2841208503c19b SHA512: 758adff34b2d526501d3e48b3612b837d8b279246675ed6f9646ac8b0367d049f9c50feef66d1836f405fc907a7ffe7beb1131c185abed174a16bff38ea5e4c7 Homepage: https://cran.r-project.org/package=FourScores Description: CRAN Package 'FourScores' (A Game for Human vs. Human or Human vs. AI) A game for two players: Who gets first four in a row (horizontal, vertical or diagonal) wins. 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(2020) . The matrix-variate normal distribution is used as emission distribution. For each hidden state, parsimony is reached via the eigen-decomposition of the covariance matrices of the emission distribution. This produces a family of 98 parsimonious hidden Markov models. 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See the manuscript by Koner and Luo (2023) for details of the PASS formula and computational details. The details of the testing procedure for univariate and multivariate response are presented in Wang (2021) and Koner and Luo (2023) respectively. 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Clustering by merging Gaussian mixture components. Symmetric and asymmetric discriminant projections for visualisation of the separation of groupings. Cluster validation statistics for distance based clustering including corrected Rand index. Standardisation of cluster validation statistics by random clusterings and comparison between many clustering methods and numbers of clusters based on this. Cluster-wise cluster stability assessment. Methods for estimation of the number of clusters: Calinski-Harabasz, Tibshirani and Walther's prediction strength, Fang and Wang's bootstrap stability. Gaussian/multinomial mixture fitting for mixed continuous/categorical variables. Variable-wise statistics for cluster interpretation. DBSCAN clustering. Interface functions for many clustering methods implemented in R, including estimating the number of clusters with kmeans, pam and clara. Modality diagnosis for Gaussian mixtures. For an overview see package?fpc. 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Despite being aware of these problems, people still use numerical methods that fail to account for these and other rounding errors (this pitfall is the first to be highlighted in Circle 1 of Burns (2012) 'The R Inferno' ). This package provides new relational operators useful for performing floating point number comparisons with a set tolerance. 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For information on FQA see Spyreas (2019) . These databases are stored in a sister R package, 'fqadata'. Both packages were developed for the USACE by the U.S. Army Engineer Research and Development Center’s Environmental Laboratory. 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(2015) for more information about floristic quality assessment and the associated database. 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Includes: (1) Fourier Quantile ARDL (FQARDL) - quantile regression with Fourier approximation for analyzing relationships across the conditional distribution; (2) Fourier Nonlinear ARDL (FNARDL) - asymmetric cointegration with partial sum decomposition following Shin, Yu & Greenwood-Nimmo (2014) ; (3) Multi-Threshold NARDL (MTNARDL) - multiple regime asymmetry analysis; (4) Fourier Unit Root Tests - ADF and KPSS tests with Fourier terms following Enders & Lee (2012) and Becker, Enders & Lee (2006) . Features automatic lag and frequency selection, PSS bounds testing following Pesaran, Shin & Smith (2001) , bootstrap cointegration tests, Wald tests for asymmetry, dynamic multiplier computation, and publication-ready visualizations. Ported from Stata/Python by Dr. Merwan Roudane. 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Traditional ARIMA models typically use integer values for differencing, which are suitable for time series with short memory or anti-persistent behaviour. In contrast, the Fractional ARIMA model allows fractional differencing, enabling it to effectively capture long memory characteristics in time series data. The ‘fracARMA’ package is user-friendly and allows users to manually input the fractional differencing parameter, which can be obtained using various estimators such as the GPH estimator, Sperio method, or Wavelet method and many. Additionally, the package enables users to directly feed the time series data, AR order, MA order, fractional differencing parameter, and the proportion of training data as a split ratio, all in a single command. The package is based on the reference from the paper of Irshad and others (2024, ). Package: r-cran-fracdist Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1121 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fracdist_0.1.1-1.ca2404.1_all.deb Size: 1097722 MD5sum: e45f4c68347442e8bf931ea95267200b SHA1: 7ea231ad71863c0a024774a19a55c5c9f43f835d SHA256: d3fc17e5d132e62bd1fcd1f406cf9e9693a08ba007453d1ef644e9e120c99be6 SHA512: 4ea4a7fce5e25c2ef36fdf08db875d6a06f9b638bb4228c8c144637d91c3cbea5650e647a657852f3ea7d702f502731a217164ce0b044e4030d4c1ecd8098e1d Homepage: https://cran.r-project.org/package=fracdist Description: CRAN Package 'fracdist' (Numerical CDFs for Fractional Unit Root and Cointegration Tests) Calculate numerical asymptotic distribution functions of likelihood ratio statistics for fractional unit root tests and tests of cointegration rank. 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Thereby, for three times a simulation with a p-value and once with a q-value is assumed. Hence, these parameters are estimated and displayed. Moreover, functions for simulating random Sierpinski-Carpets with constant and variable probabilities are included. For more details on the method please see Hermann et al. (2015) . Package: r-cran-fractd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 996 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-imager, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fractd_0.1.0-1.ca2404.1_all.deb Size: 204278 MD5sum: 7c5a1134fa068249a41ad26ba69e4304 SHA1: 8be589c08cac08c1e1f6b4e48a7bd6a8844b42c1 SHA256: c8abeeb44c5d7ccfe54e5132ea104e7676bd1272db29dbf47fb2e2ec5e958ac9 SHA512: 4d4e7470a8a4590ce68402835c5f0706a9e711b44a77cfb1ec3acd2a13e4cf5286082cfe5c39bb481dede4a30322ba954ed7543a7379e237e66168bb6b97c095 Homepage: https://cran.r-project.org/package=fractD Description: CRAN Package 'fractD' (Estimation of Fractal Dimension of a Black Area in 2D and 3D(Slices) Images) Estimate the of fractal dimension of a black area in 2D and 3D (slices) images using the box-counting method. See Klinkenberg B. (1994) . Package: r-cran-fraction Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fraction_1.1.1-1.ca2404.1_all.deb Size: 21708 MD5sum: 382fea68572d0e09873592985296fad4 SHA1: a4f5102e3f73e7ac10f41a06cff39545e83e002c SHA256: c99c6bd7ba6a875418dcedfed734e4528ccb6cdb320d2d60899fdc02ca858e69 SHA512: 242ba428d836013407bbe8a5e5140de9f01963df8be5519fab76899e90767ee957c632821e91c6328fca7b83f6eaa94c60d6e6f767a7ec015b2a3a0a447ae175 Homepage: https://cran.r-project.org/package=FRACTION Description: CRAN Package 'FRACTION' (Numeric Number into Fraction) Turn numeric,data.frame,matrix into fraction form. Package: r-cran-fragility Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-meta, r-cran-metafor, r-cran-netmeta, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-fragility_1.6.1-1.ca2404.1_all.deb Size: 401644 MD5sum: 58c36e08485f93dd73867346960e4799 SHA1: f0ef53d74fa6b81a6337425089f9906db08360cf SHA256: 05b3a634be769faa81f9a1fd80d30c531d98b5b986fd4a134a4f1c6557ecd44e SHA512: facd523833989e1c57631fc36b6d7d22e843bfda63145857fc33a7312569dc7aad983710cdd7748a4de8751ef6fd1a09333fedaad01cd3c81652bd9a1ba373e2 Homepage: https://cran.r-project.org/package=fragility Description: CRAN Package 'fragility' (Assessing and Visualizing Fragility of Clinical Results withBinary Outcomes) A collection of user-friendly functions for assessing and visualizing fragility of individual studies (Walsh et al., 2014 ; Lin, 2021 ), conventional pairwise meta-analyses (Atal et al., 2019 ), and network meta-analyses of multiple treatments with binary outcomes (Xing et al., 2020 ). The included functions are designed to: 1) calculate the fragility index (i.e., the minimal event status modifications that can alter the significance or non-significance of the original result) and fragility quotient (i.e., fragility index divided by sample size) at a specific significance level; 2) give the cases of event status modifications for altering the result's significance or non-significance and visualize these cases; 3) visualize the trend of statistical significance as event status is modified; 4) efficiently derive fragility indexes and fragility quotients at multiple significance levels, and visualize the relationship between these fragility measures against the significance levels; and 5) calculate fragility indexes and fragility quotients of multiple datasets (e.g., a collection of clinical trials or meta-analyses) and produce plots of their overall distributions. The outputs from these functions may inform the robustness of clinical results in terms of statistical significance and aid the interpretation of fragility measures. The usage of this package is illustrated in Lin et al. (2023 ) and detailed in Lin and Chu (2022 ). Package: r-cran-fragman Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1810 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fragman_1.0.9-1.ca2404.1_all.deb Size: 1815098 MD5sum: 592efd8d755c8c8e78c7ee4afbdc0db3 SHA1: 3775bd22adebf230009967cd4c0e51a1ca7a0e89 SHA256: bf24f3b871520e73e0d59d8d555b0c9ef0bd6fd7b5fdc92db5dd1e185a0c5064 SHA512: 4d71f5c7dc012e55266210d8ff79dd8b35ed71877da85dc30ba62cbba45507ab900e6625cf66b0e3db0e8d57e4bac940466e1811b260d620ba88aecc7ea5ef4d Homepage: https://cran.r-project.org/package=Fragman Description: CRAN Package 'Fragman' (Fragment Analysis in R) Performs fragment analysis using genetic data coming from capillary electrophoresis machines. These are files with FSA extension which stands for FASTA-type file, and .txt files from Beckman CEQ 8000 system, both contain DNA fragment intensities read by machinery. In addition to visualization, it performs automatic scoring of SSRs (Sample Sequence Repeats; a type of genetic marker very common across the genome) and other type of PCR markers (standing for Polymerase Chain Reaction) in biparental populations such as F1, F2, BC (backcross), and diversity panels (collection of genetic diversity). Package: r-cran-frailtycomprisk Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-dplyr, r-cran-tibble, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-frailtycomprisk_0.1.1-1.ca2404.1_all.deb Size: 91194 MD5sum: 4d0f90778fdd5cc74d739fd7c4100661 SHA1: a99ddcf5277582f23b4a5fa60763370dcec7ac08 SHA256: 7d510355c32f9dd5dba8ca864fe2e8bd38905e42a63bec66a3e3c481f809adab SHA512: e01443030973626065da93eee9ea9105bbe2d5d0a11df597746aff4243c1d0141188458d8e323b442d619c59cff9a540867a01e46a2c37545ced2d15e3a9416d Homepage: https://cran.r-project.org/package=FrailtyCompRisk Description: CRAN Package 'FrailtyCompRisk' (Competing Risks Models for Multi-Center Survival Data withFrailty) Implements methods for analyzing competing risks data in multi-center survival studies using frailty models. The approach relies on a mixed proportional hazards model for the sub-distribution, allowing for cluster-specific random effects. The package provides tools for model estimation with or without frailty using Maximum Likelihood (ML) and Restricted Maximum Likelihood (REML). It supports flexible modeling of between-center heterogeneity and is particularly suited for multi-center clinical trials or registries. Core features include data simulation, likelihood computation, cluster-dependent censoring options, and testing of frailty effects. For methodological details, see Katsahian et al. (2006) . 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See Beyene and Chen (2024) . Package: r-cran-frair Architecture: all Version: 0.5.203-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmle, r-cran-lamw, r-cran-boot, r-cran-rcppparallel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-frair_0.5.203-1.ca2404.1_all.deb Size: 203082 MD5sum: ac58e1d72cfefa13b1bcd60b0ff74aba SHA1: 404bca7ed0fd4571c2834d7f7c72e04654975e06 SHA256: 4683b48ef6e2c138ded103078687f840a68956f7ddb72ece887c9fcc3d5975fc SHA512: 860f9b9fdd17296156e5a6a8d88526e7d336f0654d4a27b621e8e59592383ce29cd423a403a19b3e47cf45559342b07077dc4c5a0f19827c7a8d1d46c897ba5d Homepage: https://cran.r-project.org/package=frair Description: CRAN Package 'frair' (Tools for Functional Response Analysis) Tools to support sensible statistics for functional response analysis. 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Package: r-cran-frapo Architecture: all Version: 0.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cccp, r-cran-rglpk, r-cran-timeseries Filename: pool/dists/noble/main/r-cran-frapo_0.4-2-1.ca2404.1_all.deb Size: 3111892 MD5sum: 4959fe3d67a6d0c917d1f8e4dcf2fc39 SHA1: 440b4626ee89d32a25d18e13ab06f07ae0e32972 SHA256: 30ad0525d040a3dd92009cee3c060af074a7cadfeb264d64b72285b81d0e6b55 SHA512: d5bf6273bbfb7b12b6501bb21368c52b24bc9b6ef65e7a5f88c7a979da938cfd4f5481383662addb6ab81f74275c663b8dac8d941046e3798764b92d9ecc3e5d Homepage: https://cran.r-project.org/package=FRAPO Description: CRAN Package 'FRAPO' (Financial Risk Modelling and Portfolio Optimisation with R) Accompanying package of the book 'Financial Risk Modelling and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package. 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'FRApp' is not limited to the analysis of FRAP experiments only. Any nonlinear mixed-effects models with an asymptotic exponential functional relationship to hierarchical data in various domains can be fitted. The analysis of data available in the package is presented in Di Credico, G., Pelucchi, S., Pauli, F. et al. (2025) . Package: r-cran-frapplot Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-frapplot_0.1.3-1.ca2404.1_all.deb Size: 26636 MD5sum: bde0c38478c8a95d30a160d879b3d2b4 SHA1: b0887f65847cdc9045ee0dea19544cda67d86d42 SHA256: 08c725bc61733c2d9f1f6c0dd5134482632fcc51739d817a13b0d8510a90a5b9 SHA512: 6f8fd4aa035703b05652325fa4306028a91cda2d565d6835b211cf2ecc6a7cd001a6c58412dbf8cb136707b32405c320649190dab4d236dc83339423886fb827 Homepage: https://cran.r-project.org/package=frapplot Description: CRAN Package 'frapplot' (Automatic Data Processing and Visualization for FRAP) Automatically process Fluorescence Recovery After Photobleaching (FRAP) data and generate consistent, publishable figures. Note: this package does not replace 'ImageJ' (or its equivalence) in raw image quantification. Some references about the methods: Sprague, Brian L. (2004) ; Day, Charles A. (2012) . Package: r-cran-frb Architecture: all Version: 2.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rrcov, r-cran-corpcor Suggests: r-cran-robustbase Filename: pool/dists/noble/main/r-cran-frb_2.0-1-1.ca2404.1_all.deb Size: 624036 MD5sum: 59efc3c121f0a996160c699aef782931 SHA1: 53091b3017930f651c2eba7ac70a7af7a3747f0f SHA256: 4184829cd1a293e44b18760a547473a916802b58ba2747523bd5bf21fbb027e8 SHA512: 1dc954d7b8844a697ab014c3c9e7baa506d3cabc340e48bf720f911cad3ace1af7a422a610e1c500cee2fdd85cf53cbec843568a143aa220bbbfb8c23e683d74 Homepage: https://cran.r-project.org/package=FRB Description: CRAN Package 'FRB' (Fast and Robust Bootstrap) Perform robust inference based on applying Fast and Robust Bootstrap on robust estimators (Van Aelst and Willems (2013) ). This method constitutes an alternative to ordinary bootstrap or asymptotic inference. procedures when using robust estimators such as S-, MM- or GS-estimators. The available methods are multivariate regression, principal component analysis and one-sample and two-sample Hotelling tests. It provides both the robust point estimates and uncertainty measures based on the fast and robust bootstrap. 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Moreover, it allows to construct an FRBS model defined by human experts. FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at representing the reasoning of human experts in a set of IF-THEN rules, to handle real-life problems in, e.g., control, prediction and inference, data mining, bioinformatics data processing, and robotics. FRBSs are also known as fuzzy inference systems and fuzzy models. During the modeling of an FRBS, there are two important steps that need to be conducted: structure identification and parameter estimation. Nowadays, there exists a wide variety of algorithms to generate fuzzy IF-THEN rules automatically from numerical data, covering both steps. Approaches that have been used in the past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering methods, genetic algorithms, squares methods, etc. Furthermore, in this version we provide a universal framework named 'frbsPMML', which is adopted from the Predictive Model Markup Language (PMML), for representing FRBS models. PMML is an XML-based language to provide a standard for describing models produced by data mining and machine learning algorithms. Therefore, we are allowed to export and import an FRBS model to/from 'frbsPMML'. Finally, this package aims to implement the most widely used standard procedures, thus offering a standard package for FRBS modeling to the R community. 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Please see the following for details: Raul Cruz-Cano, Mei-Ling Ting Lee, Fast regularized canonical correlation analysis, Computational Statistics & Data Analysis, Volume 70, 2014, Pages 88-100, ISSN 0167-9473 . 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Application to Reverse-Transcriptase Multiplex Ligation-dependent Probe Amplification (RT-MLPA) gene-expression profiling and classification is illustrated in Mareschal, Ruminy et al (2015) . Gene-fusion detection and Sanger sequencing are illustrated in Mareschal, Palau et al (2021) . Examples are provided for genotyping applications as well. Package: r-cran-fsbrain Architecture: all Version: 0.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4843 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reshape, r-cran-freesurferformats, r-cran-pkgfilecache, r-cran-rgl, r-cran-squash, r-cran-fields, r-cran-viridis, r-cran-data.table, r-cran-magick Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-sphereplot, r-cran-misc3d, r-cran-rcolorbrewer, r-cran-rvcg, r-cran-igraph, r-cran-pracma Filename: pool/dists/noble/main/r-cran-fsbrain_0.5.6-1.ca2404.1_all.deb Size: 3861584 MD5sum: 16e8dd981d8fe896c3cbbc547a3f83ab SHA1: b1c87c549a23c0c54743a36b5ccf4eb7f8b8180a SHA256: b6342c7b6e4cc7dabd2065f78685e11e14bc29db2cb77bc3836edb0aed5b645b SHA512: 58ef2bf2f43b2a8275eaf0984adea88985c360336834b8d50b9916ced28ea04051c741a4aee10cb03dc15d8d4325dd33605844f7eded8c8aee9e21004555ba8a Homepage: https://cran.r-project.org/package=fsbrain Description: CRAN Package 'fsbrain' (Managing and Visualizing Brain Surface Data) Provides high-level access to neuroimaging data from standard software packages like 'FreeSurfer' on the level of subjects and groups. Load morphometry data, surfaces and brain parcellations based on atlases. Mask data using labels, load data for specific atlas regions only, and visualize data and statistical results directly in 'R'. 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Package: r-cran-fscaret Architecture: all Version: 0.9.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-gsubfn, r-cran-hmeasure Suggests: r-cran-ada, r-cran-arm, r-cran-boruta, r-cran-bst, r-cran-c50, r-cran-car, r-cran-catools, r-cran-class, r-cran-cubist, r-cran-e1071, r-cran-earth, r-cran-elasticnet, r-cran-ellipse, r-cran-evtree, r-cran-fastica, r-cran-gam, r-cran-gbm, r-cran-glmnet, r-cran-hda, r-cran-hdclassif, r-cran-hmisc, r-cran-ipred, r-cran-kernlab, r-cran-kknn, r-cran-klar, r-cran-kohonen, r-cran-krls, r-cran-lars, r-cran-leaps, r-cran-logicreg, r-cran-mass, r-cran-mboost, r-cran-mda, r-cran-mgcv, r-cran-mlbench, r-cran-neuralnet, r-cran-nnet, r-cran-pamr, r-cran-partdsa, r-cran-party, r-cran-penalized, r-cran-pls, r-cran-proc, r-cran-proxy, r-cran-qrnn, r-cran-quantregforest, r-cran-randomforest, r-cran-rann, r-cran-rferns, r-cran-rocc, r-cran-rpart, r-cran-rrcov, r-cran-rrf, r-cran-rsnns, r-cran-rweka, r-cran-sda, r-cran-sparselda, r-cran-spls, r-cran-stepplr, r-cran-superpc Filename: pool/dists/noble/main/r-cran-fscaret_0.9.4.4-1.ca2404.1_all.deb Size: 281286 MD5sum: afaa72f2827a62af9b0b927c4cfe55bd SHA1: fd1a508128dcf6cb0bb6dc07b538523b3609a440 SHA256: f3802ac975c4feb2548e2d88388c0c9ede7c99cec57f22307bdbdce47c2fcd68 SHA512: d07c616ec77a4e0a76e0eb404e8d816d09833bcdc0708be170f0b22769228fc2d5ab67f90495b2ee3debc42fc71f435a8fe17dce641f4fe21b7222a23c95b121 Homepage: https://cran.r-project.org/package=fscaret Description: CRAN Package 'fscaret' (Automated Feature Selection from 'caret') Automated feature selection using variety of models provided by 'caret' package. This work was funded by Poland-Singapore bilateral cooperation project no 2/3/POL-SIN/2012. Package: r-cran-fsdam Architecture: all Version: 2024.7-30-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kyotil, r-cran-reticulate Suggests: r-cran-r.rsp, r-cran-runit Filename: pool/dists/noble/main/r-cran-fsdam_2024.7-30-1.ca2404.1_all.deb Size: 69278 MD5sum: 80b212d9abbc88c933b55f65e2ff822f SHA1: 0abc56551003d892fa02cd175fa7bd8af99c2b18 SHA256: 93dd1590d12f0d9bcb2a7833b3fdc79017ae4896b40b986da88cad5c1805ea37 SHA512: e0951b1367ee8fd5bbfc2ee91a03a87280d698ef65b61286414523458efc837dec64a73de9c81d694dc357e90d218ceefef1c3c17ca74e71ce583ee6983fe5b1 Homepage: https://cran.r-project.org/package=FSDAM Description: CRAN Package 'FSDAM' (Forward Stepwise Deep Autoencoder-Based Monotone NLDR) FS-DAM performs feature extraction through latent variables identification. Implementation is based on autoencoders with monotonicity and orthogonality constraints. Package: r-cran-fsdar Architecture: all Version: 0.9-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3083 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rjava, r-cran-ggplot2 Suggests: r-cran-robustbase, r-cran-rrcov, r-cran-mass Filename: pool/dists/noble/main/r-cran-fsdar_0.9-1-1.ca2404.1_all.deb Size: 2292344 MD5sum: e6e61f2daf48425ab2cba807a1e3f16c SHA1: 51160455f0109731fc5cf4c09c83e0bf7248ce3f SHA256: b9cabbcc6a1146c0796c27e911638329361f27475727d766bbb4a50dc2f24f23 SHA512: ce2001520afbf8afe79ddb3f1259a135754f1d5baa02ed574bebececed4a33d80eb575f2f39b36ba75dade4418090d7470b78432a4880aa95febb0beb799c1ec Homepage: https://cran.r-project.org/package=fsdaR Description: CRAN Package 'fsdaR' (Robust Data Analysis Through Monitoring and DynamicVisualization) Provides interface to the 'MATLAB' toolbox 'Flexible Statistical Data Analysis (FSDA)' which is comprehensive and computationally efficient software package for robust statistics in regression, multivariate and categorical data analysis. The current R version implements tools for regression: (forward search, S- and MM-estimation, least trimmed squares (LTS) and least median of squares (LMS)), for multivariate analysis (forward search, S- and MM-estimation), for cluster analysis and cluster-wise regression. The distinctive feature of our package is the possibility of monitoring the statistics of interest as a function of breakdown point, efficiency or subset size, depending on the estimator. This is accompanied by a rich set of graphical features, such as dynamic brushing, linking, particularly useful for exploratory data analysis. Package: r-cran-fselector Architecture: all Version: 0.34-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-entropy, r-cran-randomforest, r-cran-rweka Suggests: r-cran-mlbench, r-cran-rpart Filename: pool/dists/noble/main/r-cran-fselector_0.34-1.ca2404.1_all.deb Size: 93996 MD5sum: f30d62678aafd737fc7458249814387f SHA1: e4e59d29db3d9bdcbeddf6ec92cb9d5019eb532a SHA256: d809bfb0a13bc84c8670f73edc8e7bccfff8890c08f2de88c4ed82ebc49fc6aa SHA512: b2cf35b8f4e1fc0baacbff687700a3754bfbb95a38829f68fdb2cc91d672b0d978636f937bff4d77ddd3c60a6b73bc1ca85ce23d6d3ed4c3d88dc37f9d698774 Homepage: https://cran.r-project.org/package=FSelector Description: CRAN Package 'FSelector' (Selecting Attributes) Functions for selecting attributes from a given dataset. Attribute subset selection is the process of identifying and removing as much of the irrelevant and redundant information as possible. Package: r-cran-fsemipar Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1723 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-grpreg, r-cran-dicekriging, r-cran-gtools, r-cran-parallelly, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-gridextra, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-fsemipar_1.1.1-1.ca2404.1_all.deb Size: 1693788 MD5sum: bb4c270324e10bc98d50b1ef87650ea6 SHA1: 857228c9873a4f74fff3ece886373e541498526f SHA256: dd0ec82d2b6a3fe7ac31796a80428bf5bc3b5b1160d18eb72b5ced39249e23ac SHA512: af0f8baffdd0ea058b9a15d2159c26491fa9fc3787e05bb55cca5e75f7275b0c92e67dab68b6914c233c41ea3109d89a137ff3deadeb00f46f16ba2b418d0c9a Homepage: https://cran.r-project.org/package=fsemipar Description: CRAN Package 'fsemipar' (Estimation, Variable Selection and Prediction for FunctionalSemiparametric Models) Routines for the estimation or simultaneous estimation and variable selection in several functional semiparametric models with scalar responses are provided. 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Package: r-cran-fsia Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fsia_1.1.1-1.ca2404.1_all.deb Size: 555654 MD5sum: 0e8a1d2781b3a85069d34f333e00e5bd SHA1: 10d94c96da11efdd87af311ef3de5687e51502d0 SHA256: 785720950c6834c2908c7e90eb1a9c7ef07f6580525f49719d1ae4bd4d3cbbc5 SHA512: a8431f6fd909d0963b35e3563f838a412663b6d92b0b7e11828af02148886000af741d6ab209e790c51000d17f4143dc66a84d4319c3a0219b66b40edb6eb508 Homepage: https://cran.r-project.org/package=fsia Description: CRAN Package 'fsia' (Import and Analysis of OMR Data from FormScanner) Import data of tests and questionnaires from FormScanner. FormScanner is an open source software that converts scanned images to data using optical mark recognition (OMR) and it can be downloaded from . The spreadsheet file created by FormScanner is imported in a convenient format to perform the analyses provided by the package. These analyses include the conversion of multiple responses to binary (correct/incorrect) data, the computation of the number of corrected responses for each subject or item, scoring using weights,the computation and the graphical representation of the frequencies of the responses to each item and the report of the responses of a few subjects. 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Package: r-cran-fsk2r Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2890 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-purrr, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-rlang, r-cran-readxl, r-cran-readtext, r-cran-xml2, r-cran-jsonlite, r-cran-shiny, r-cran-r.utils Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fsk2r_0.2.0-1.ca2404.1_all.deb Size: 1301046 MD5sum: 15d56dfd578eda949daa635563b183c6 SHA1: 8adc0f6359684a01d72541177dcc58f3551cfa5c SHA256: d1e9265838e46267f56bea3ed2aeeba733dd58d6975958dfd9936b6256fa534e SHA512: 9200143b4b9a190adcfa7dee9b2a9035c036fe22c7ab2e8d79a41733967392f7bf76b517a2bc8559e6c3597c59528c9ad1d2d40d188d2f13abd6f0ad0bc55092 Homepage: https://cran.r-project.org/package=FSK2R Description: CRAN Package 'FSK2R' (An Interface Between the 'FSKX' Standard and 'R') Functions for importing, creating, editing and exporting 'FSK' files using the 'R' programming environment. 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The goal is to be able to interface with 'FSL' completely in R, where you pass R objects of class 'nifti', implemented by package 'oro.nifti', and the function executes an 'FSL' command and returns an R object of class 'nifti' if desired. Package: r-cran-fsm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fsm_1.0.0-1.ca2404.1_all.deb Size: 99814 MD5sum: b99e907f5b7d087e190fbd2da761affa SHA1: bae67c63429d6d1eb9e07bbb264aa33eb5c558c3 SHA256: 5220b78185403abf3ceff969a2e82a5f970d2c411a4a191d31623e8f42a12ccd SHA512: 502a5792e66c95b8079a62a56577f25f003b77108f26e78473e0cc9f66288268206225d01ee3b6486da39f6dfeab4f526f0361a5fcab5e09e33665829060d574 Homepage: https://cran.r-project.org/package=FSM Description: CRAN Package 'FSM' (Finite Selection Model) Randomized and balanced allocation of units to treatment groups using the Finite Selection Model (FSM). The FSM was originally proposed and developed at the RAND corporation by Carl Morris to enhance the experimental design for the now famous Health Insurance Experiment. See Morris (1979) for details on the original version of the FSM. Package: r-cran-fsn Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-fsn_0.4-1.ca2404.1_all.deb Size: 41270 MD5sum: eed7374189ff74192f028c5cc496347a SHA1: b95ed6b0468b1d6568b5ed8282fb971e66e9b013 SHA256: 189f3c63a120da9e19d0d2760ae60e8d98fbb1b18f5154692d18bb84339f726c SHA512: 08dea9b9c10fff20bd16cbe5bb61c40e29e871b83e76bd036de0a599cb48960bb786de459338c8607199ec072651832d86a59c3e605ac2d804f9edb089d7cf20 Homepage: https://cran.r-project.org/package=fsn Description: CRAN Package 'fsn' (Rosenthal's Fail Safe Number and Related Functions) Estimation of Rosenthal's fail safe number including confidence intervals. The relevant papers are the following. Konstantinos C. Fragkos, Michail Tsagris and Christos C. Frangos (2014). "Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal's Fail-Safe Number". International Scholarly Research Notices, Volume 2014. . Konstantinos C. Fragkos, Michail Tsagris and Christos C. Frangos (2017). "Exploring the distribution for the estimator of Rosenthal's fail-safe number of unpublished studies in meta-analysis". Communications in Statistics-Theory and Methods, 46(11):5672--5684. . 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Features include spanning header, grouping rows, parsing markdown and so on. Package: r-cran-ftrading Architecture: all Version: 3042.79-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-timedate, r-cran-timeseries, r-cran-fbasics Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-ftrading_3042.79-1.ca2404.1_all.deb Size: 97430 MD5sum: 5054fe691145f12419d0b550cc212501 SHA1: 7c38f227f6d3202d71594cffe4adb9ef33a07d0e SHA256: b936accde6b9e435d2cde364db989b0785cc9f16079d029b9d509c62f7fa1bd9 SHA512: 1a961d758b771ed4094ce339b19cff5f9e8a574ade9ef6beb5c765be91858afba399c5270746880b39a8f88eb86d53a6e1e9681d6140783d7a582502a46ca1be Homepage: https://cran.r-project.org/package=fTrading Description: CRAN Package 'fTrading' (Rmetrics - Trading and Rebalancing Financial Instruments) A collection of functions for trading and rebalancing financial instruments. It implements various technical indicators to analyse time series such as moving averages or stochastic oscillators. Package: r-cran-ftrcool Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2784 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ftrcool_2.0.0-1.ca2404.1_all.deb Size: 1469228 MD5sum: 33746eb4a05cb35ca6d44cea3bcfe9bb SHA1: 16eda249e7569a0ac00442a9c71475422037f72b SHA256: f435270fe1d24b0e70faf0986182dd846594687e8353b918d74a443a7174dc18 SHA512: 89391136ca44f4e26bc58fe3568081cfc5ed0c578eef3ef7a71d3c7a94bba2e2f3098e971c3f0f15d2869e09ac7b803126054571198265fb72af348cdbe956a9 Homepage: https://cran.r-project.org/package=ftrCOOL Description: CRAN Package 'ftrCOOL' (Feature Extraction from Biological Sequences) Extracts features from biological sequences. It contains most features which are presented in related work and also includes features which have never been introduced before. It extracts numerous features from nucleotide and peptide sequences. Each feature converts the input sequences to discrete numbers in order to use them as predictors in machine learning models. There are many features and information which are hidden inside a sequence. Utilizing the package, users can convert biological sequences to discrete models based on chosen properties. References: 'iLearn' 'Z. Chen et al.' (2019) . 'iFeature' 'Z. Chen et al.' (2018) . . 'PseKRAAC' 'Y. Zuo et al.' 'PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition' (2017) . 'iDNA6mA-PseKNC' 'P. Feng et al.' 'iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC' (2019) . 'I. Dubchak et al.' 'Prediction of protein folding class using global description of amino acid sequence' (1995) . 'W. Chen et al.' 'Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome' (2015) . 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This R package 'fuel' implements thirty-nine existing and newly proposed estimators. See Zhang, F., and Gou, J. (2020), A unified framework for estimation in lognormal models, Technical report. 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Functions support creating height-above-ground (HAG) metrics, tiling, and sampling point clouds, generating training datasets, applying trained models to new point clouds, and producing per-point fuel classes such as stems, branches, foliage, and surface fuels. These tools support workflows for forest structure analysis, wildfire behavior modeling, and fuel complexity assessment. Deep learning segmentation relies on the PointNeXt architecture described by Qian et al. (2022) , while ground classification utilizes the Cloth Simulation Filter algorithm by Zhang et al. (2016) . Package: r-cran-fueleconomy Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-fueleconomy_1.0.0-1.ca2404.1_all.deb Size: 241994 MD5sum: 4822e91dc20203f4cbc839eefc474840 SHA1: 7602c679a973b5888fd7e2636124c72f8dc7cb07 SHA256: 44e97db9908d83d234c3a87cd81c38d8bbcb4f4c3f3ec32e6cce444a0381b0b5 SHA512: 052bab68c011701b8134aa718dbf86cef0b2553fd799a48bf629b306e1aa6a0b0cae8f69b55c0b290fd772ec9104230896090789da831168c1357fe7d4564c50 Homepage: https://cran.r-project.org/package=fueleconomy Description: CRAN Package 'fueleconomy' (EPA Fuel Economy Data) Fuel economy data from the EPA, 1985-2015, conveniently packaged for consumption by R users. Package: r-cran-fugue Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fugue_0.1.7-1.ca2404.1_all.deb Size: 69276 MD5sum: 3f30faf0fd1ce4b5b9fed99c327067df SHA1: 054979d6020ceec08b28d8e99e5cfa7366821939 SHA256: bfa1683289401d83a8fd3efe9a4bcb20029961c8f1ce63c58008bc2b22c9b397 SHA512: c9a96fcb629e7f2e95d3334de0980218ee67f4619ea596ac9536b647f52b495860a867d43beecdbe9378c2014795f45571f24d8852e4df3149e2428b776cadea Homepage: https://cran.r-project.org/package=fugue Description: CRAN Package 'fugue' (Sensitivity Analysis Optimized for Matched Sets of Varied Sizes) As in music, a fugue statistic repeats a theme in small variations. Here, the psi-function that defines an m-statistic is slightly altered to maintain the same design sensitivity in matched sets of different sizes. The main functions in the package are sen() and senCI(). For sensitivity analyses for m-statistics, see Rosenbaum (2007) Biometrics 63 456-464 . Package: r-cran-fuj Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fuj_0.2.2-1.ca2404.1_all.deb Size: 172244 MD5sum: 6e59231bbb2e9988be9cfc39153a416e SHA1: c7cd84104a89f9d96b0f26a8252e2c1f179bf56c SHA256: 99747dfde3c7f6ec942baba1643d964f6547d735275837188a2ce5ea23a7edcd SHA512: a4bae6b473a884ecdc81db77ccdbf7cbef18710dcd785539df47ac8574e7ee0ae26640f9140e2480eb6bf3ac24cb223a7c3513b9b26335ac4872337312249f70 Homepage: https://cran.r-project.org/package=fuj Description: CRAN Package 'fuj' (Functions and Utilities for Jordan) Provides core functions and utilities for packages and other code developed by Jordan Mark Barbone. 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Package: r-cran-fullrankmatrix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1788 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-testthat, r-cran-weightit, r-cran-caret, r-cran-plm, r-cran-spelling Filename: pool/dists/noble/main/r-cran-fullrankmatrix_0.1.0-1.ca2404.1_all.deb Size: 1032840 MD5sum: 5abe1630dc5adb457fdf2728e0ebf33b SHA1: 3d4519827365a8e1565442b2170553ade8076e71 SHA256: 552e7d6b26f1c946abd88206de3ac7b7e159e8fe15799330bc60dbc361f444a6 SHA512: 5381fae1eec321fc30d0bd088486a3b5448d7bc7e731f9b25693005462b673d11b37379e1559c4af895a35b74b9dfb651405878599cce687acb3e601a91e31ad Homepage: https://cran.r-project.org/package=fullRankMatrix Description: CRAN Package 'fullRankMatrix' (Generation of Full Rank Design Matrix) Creates a full rank matrix out of a given matrix. The intended use is for one-hot encoded design matrices that should be used in linear models to ensure that significant associations can be correctly interpreted. However, 'fullRankMatrix' can be applied to any matrix to make it full rank. It removes columns with only 0's, merges duplicated columns and discovers linearly dependent columns and replaces them with linearly independent columns that span the space of the original columns. Columns are renamed to reflect those modifications. This results in a full rank matrix that can be used as a design matrix in linear models. The algorithm and some functions are inspired by Kuhn, M. (2008) . Package: r-cran-fullroc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fullroc_0.1.0-1.ca2404.1_all.deb Size: 46878 MD5sum: 2939179247592b9a941354a42f614a25 SHA1: eb68b5b3155584902a23f78d15d87d1b93429ab1 SHA256: 4e459f7662b2a50690bdb7f61ce519d839f02c32318d8c97820872705a1da9bc SHA512: ec23c73d4a6eae19b6a334e75997b9a41ab0f32c2e84e2f6b2469eb78a25f3359c553947f8afa88641394cd78100e926b72b1ad9edd4fc6d402bb6a9bfff35af Homepage: https://cran.r-project.org/package=fullROC Description: CRAN Package 'fullROC' (Plot Full ROC Curves using Eyewitness Lineup Data) Enable researchers to adjust identification rates using the 1/(lineup size) method, generate the full receiver operating characteristic (ROC) curves, and statistically compare the area under the curves (AUC). References: Yueran Yang & Andrew Smith. (2020). "fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves". , Andrew Smith, Yueran Yang, & Gary Wells. (2020). "Distinguishing between investigator discriminability and eyewitness discriminability: A method for creating full receiver operating characteristic curves of lineup identification performance". Perspectives on Psychological Science, 15(3), 589-607. . 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Package: r-cran-funchir Architecture: all Version: 0.3.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-funchir_0.3.0-1-1.ca2404.1_all.deb Size: 32076 MD5sum: 10bc0acd0d76fd6a55d5aaa62d157606 SHA1: 66b690dcd95aeb85f934c8e6427e599ef0474a04 SHA256: 3c803b087b63d80e3ef8f27a80f7a71b5983896ac3075cabef041a2aa50e7813 SHA512: 1ee5fb3950e355802705294e594edb9fd51d3adc5d5d489b2a8ad90555ca9c4e7ee82ab08c47c75791b9b31dec3b820ea572e1698b2b79cacf71be8470e2db3b Homepage: https://cran.r-project.org/package=funchir Description: CRAN Package 'funchir' (Convenience Functions by Michael Chirico) YACFP (Yet Another Convenience Function Package). get_age() is a fast & accurate tool for measuring fractional years between two dates. stale_package_check() tries to identify any library() calls to unused packages. Package: r-cran-funcmap Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvbutils Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-funcmap_1.0.10-1.ca2404.1_all.deb Size: 30430 MD5sum: 6e6fa1ceb7ba6d9f824d53e939b0b13c SHA1: 559ecbb7ceb86f2bb3dc49d80ac103f99d5ffb71 SHA256: 49e2431ee40cbdf6d4f919f15d3623530e1adabac61758a85e3f34832967e379 SHA512: acb464bb330e7d91efbfe021ba89ba89a1c6589aaa013e731192ed7a51f41b022ab446ca3a8dd3d7fc9077481238bac8f8d33c2ab6be1d3c7186b03c2bc1ca69 Homepage: https://cran.r-project.org/package=FuncMap Description: CRAN Package 'FuncMap' (Hive Plots of R Package Function Calls) Analyzes the function calls in an R package and creates a hive plot of the calls, dividing them among functions that only make outgoing calls (sources), functions that have only incoming calls (sinks), and those that have both incoming calls and make outgoing calls (managers). Function calls can be mapped by their absolute numbers, their normalized absolute numbers, or their rank. FuncMap should be useful for comparing packages at a high level for their overall design. Plus, it's just plain fun. The hive plot concept was developed by Martin Krzywinski (www.hiveplot.com) and inspired this package. Note: this package is maintained for historical reasons. HiveR is a full package for creating hive plots. 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In other words, this package will allow users to build deep learning models that have either functional or scalar responses paired with functional and scalar covariates. We implement the theoretical discussion found in Thind, Multani and Cao (2020) through the help of a main fitting and prediction function as well as a number of helper functions to assist with cross-validation, tuning, and the display of estimated functional weights. 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Mills, Alexander F. Wilson, Joan E. Bailey-Wilson, and Momiao Xiong (2013) ). 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Underlying theory for these functions is described in the following publications: Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, . Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights. Psychometrika, 74(4), 589-602, . Waller, N. G. (2016). Fungible Correlation Matrices: A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568. Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF) covariance matrix of standardized regression coefficients: theoretical extensions and finite sample behavior. Psychometrika, 80, 365-378, . Waller, N. G. (2018). Direct Schmid-Leiman transformations and rank-deficient loadings matrices. Psychometrika, 83, 858-870. . 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Package: r-cran-funmediation Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-refund, r-cran-tvem, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-funmediation_1.0.2-1.ca2404.1_all.deb Size: 140728 MD5sum: f88297082631102605b5285cb6d35a6e SHA1: d958f0dacaf239c750e7081a220fc3c22a3abc20 SHA256: b57a7ed5ca41d759202e342294fab5a34cd596693f00b8c56fc029429bf24b86 SHA512: 67187c06a38466b34e67657cd4451105b9de49c8bdac4cdcba4189e46051890bcf2d50e2fd4abc93d9ff06b93a9a85e28b3b45518015cc62ee877eb7628df9e4 Homepage: https://cran.r-project.org/package=funmediation Description: CRAN Package 'funmediation' (Functional Mediation for a Distal Outcome) Fits a functional mediation model with a scalar distal outcome. The method is described in detail by Coffman, Dziak, Litson, Chakraborti, Piper & Li (2021) . The model is similar to that of Lindquist (2012) although allowing a binary outcome as an alternative to a numerical outcome. The current version is a minor bug fix in the vignette. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. Package: r-cran-funmodeling Architecture: all Version: 1.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1359 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-rocr, r-cran-ggplot2, r-cran-gridextra, r-cran-pander, r-cran-reshape2, r-cran-scales, r-cran-dplyr, r-cran-rlang, r-cran-rcolorbrewer, r-cran-moments, r-cran-entropy, r-cran-cli, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-funmodeling_1.9.6-1.ca2404.1_all.deb Size: 1000448 MD5sum: 40fdaa3999049cbadb9f8775867f3f86 SHA1: 1c713de357da6b77bb5bb2c9cdbe7047eecae594 SHA256: 1cea3f2ceee93aaa913a71f3adf01c6d28e1c0d77b06e60b568b5d82809381e8 SHA512: 2d551db3cb2bed10c39a083752df6f6376559012a64559918df38a862f88fb8fbbb9d9f9cbb29f01169b7dc24179b23dca1e693fd6f70dec327664e3467c2a48 Homepage: https://cran.r-project.org/package=funModeling Description: CRAN Package 'funModeling' (Exploratory Data Analysis and Data Preparation Tool-Box) Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book () are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions. 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Package: r-cran-funnelr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-funnelr_0.1.0-1.ca2404.1_all.deb Size: 68142 MD5sum: c7e28e0cea76d7f599af697637eaa01b SHA1: dddc4cce12f1ad98b7227413a3408e760f71691d SHA256: 2291b285c1afc42e24d4b2606858f0b75fe789dfcd89d851d1252601c0b219f0 SHA512: 8a25bf11f599ed6c54a7e64317ffc7ce604f5829f8858628d7070008312ead602627a8c587aa2a0b19a2709ff67e169ca792a0ddbaf43149f3c415fcfd289a3f Homepage: https://cran.r-project.org/package=funnelR Description: CRAN Package 'funnelR' (Funnel Plots for Proportion Data) A set of simplified functions for creating funnel plots for proportion data. This package supports user defined benchmarks, confidence limits and estimation methods (i.e. exact or approximate) based on Spiegelhalter (2005) . Additional routines for returning scored unit level data according to a set of specifications is also implemented for convenience. Specifically, both a categorical and a continuous score variable is returned to the sample data frame, which identifies which observations are deemed extreme or in control. Typically, such variables are useful as stratifications or covariates in further exploratory analyses. Lastly, the plotting routine returns a base funnel plot ('ggplot2'), which can also be tailored. 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Package: r-cran-funprog Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-purrr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-funprog_0.3.0-1.ca2404.1_all.deb Size: 34092 MD5sum: 0fef0fa9dfeaa1057d0cfcb94a96a709 SHA1: cafc6d7e94580d61ba9bb1580ee3c8f7d47e7148 SHA256: 6a6469edce923778b796553803e605f83c83d76f0b475db3584ad403451ab169 SHA512: 8c7b20b29a664a4b0ae6beca3ea21ecf4ebd8affef1e4565714c30209493efd15e0945a4074fee578a891cdccbd636f6ce636c56a23576dc298529b4aa99cf14 Homepage: https://cran.r-project.org/package=funprog Description: CRAN Package 'funprog' (Functional Programming) High-order functions for data manipulation : sort or group data, given one or more auxiliary functions. Functions are inspired by other pure functional programming languages ('Haskell' mainly). 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Package: r-cran-funrar Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 786 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-matrix Suggests: r-cran-ade4, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidytext Filename: pool/dists/noble/main/r-cran-funrar_1.5.0-1.ca2404.1_all.deb Size: 540670 MD5sum: 8b9564d0058eec15acce5097e4d6e543 SHA1: ad6bf9f84de8cc924c46b03da18c101ddf06acff SHA256: 1cc90ae12fc80734d159f35daf17900570208eed3c3cc8ed0f50c8ed47053e38 SHA512: e1daa462b99786c0c7f3feb2202ad5a36f8d22337c960293838a7133a459306604b98468ae67c0b251575e51c039a7bfd4a053bdc9a2625ec743a63c1487fbbc Homepage: https://cran.r-project.org/package=funrar Description: CRAN Package 'funrar' (Functional Rarity Indices Computation) Computes functional rarity indices as proposed by Violle et al. (2017) . Various indices can be computed using both regional and local information. Functional Rarity combines both the functional aspect of rarity as well as the extent aspect of rarity. 'funrar' is presented in Grenié et al. (2017) . Package: r-cran-funreg Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mgcv, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-funreg_1.2.2-1.ca2404.1_all.deb Size: 410886 MD5sum: cb9a105c0a415267e440b1585719f891 SHA1: eb2d67d7d08b472b846f766748e9e6af10a0380e SHA256: 1027e8654528121a4d70f5242fc1239f0996068ff48be8ca3cd003fd1e411b7d SHA512: 2071ba86ab79af4bdb04535689d1d88ff664de9f2a9b57747b3cab89ca56681e3e9841835cfe7edb591825f3046096bdfba18a841473f5731a228fd3de89896a Homepage: https://cran.r-project.org/package=funreg Description: CRAN Package 'funreg' (Functional Regression for Irregularly Timed Data) Performs functional regression, and some related approaches, for intensive longitudinal data (see the book by Walls & Schafer, 2006, Models for Intensive Longitudinal Data, Oxford) when such data is not necessarily observed on an equally spaced grid of times. The approach generally follows the ideas of Goldsmith, Bobb, Crainiceanu, Caffo, and Reich (2011) and the approach taken in their sample code, but with some modifications to make it more feasible to use with long rather than wide, non-rectangular longitudinal datasets with unequal and potentially random measurement times. It also allows easy plotting of the correlation between the smoothed covariate and the outcome as a function of time, which can add additional insights on how to interpret a functional regression. Additionally, it also provides several permutation tests for the significance of the functional predictor. The heuristic interpretation of ``time'' is used to describe the index of the functional predictor, but the same methods can equally be used for another unidimensional continuous index, such as space along a north-south axis. Note that most of the functionality of this package has been superseded by added features after 2016 in the 'pfr' function by Jonathan Gellar, Mathew W. McLean, Jeff Goldsmith, and Fabian Scheipl, in the 'refund' package built by Jeff Goldsmith and co-authors and maintained by Julia Wrobel. The development of the funreg package in 2015 and 2016 was part of a research project supported by Award R03 CA171809-01 from the National Cancer Institute and Award P50 DA010075 from the National Institute on Drug Abuse. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse, the National Cancer Institute, or the National Institutes of Health. Package: r-cran-funspace Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1404 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ade4, r-cran-ape, r-cran-ks, r-cran-mgcv, r-cran-missforest, r-cran-mass, r-cran-paran, r-cran-vegan, r-cran-phytools, r-cran-viridis Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-funspace_0.2.2-1.ca2404.1_all.deb Size: 1354112 MD5sum: 2b5a51364edd367661b8adbc894df33d SHA1: 109225a8661c809d1456c90b61a04737ca099e38 SHA256: 69cc20b7c2b709b1f2b243c83b0eee698c352db1da50e811be1a92c7cbfefedf SHA512: 7ea4471ea7215b431859ae68bab4033d6431ab822d4ab710cc9616d78b3e36e73b50aaf1706b3e965baaa9395eac3e16cb6363892dcd5543b1ec551463d26ab3 Homepage: https://cran.r-project.org/package=funspace Description: CRAN Package 'funspace' (Creating and Representing Functional Trait Spaces) Estimation of functional spaces based on traits of organisms. The package includes functions to impute missing trait values (with or without considering phylogenetic information), and to create, represent and analyse two dimensional functional spaces based on principal components analysis, other ordination methods, or raw traits. It also allows for mapping a third variable onto the functional space. See 'Carmona et al. (2021)' , 'Puglielli et al. (2021)' , 'Carmona et al. (2021)' , 'Carmona et al. (2019)' for more information. 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Other competitive state-of-the-art approaches proposed by Chakraborty and Chaudhuri (2015) , Horvath et al (2013) or Cuevas et al (2004) are also included in the package, as well as procedures to run test result comparisons and power analysis using simulations. 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Package: r-cran-fusedtree Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-splittools, r-cran-survival, r-cran-treeclust, r-cran-partykit Suggests: r-cran-rpart Filename: pool/dists/noble/main/r-cran-fusedtree_1.1.0-1.ca2404.1_all.deb Size: 153436 MD5sum: ff5ec7dbca90585a7419b9626492046c SHA1: 692c7678d9ee2b693a440cad07225e31c9f9c0e4 SHA256: cf2311d2359cfde566acd6921f8dbd969e0cc9a1b3b18ead2c7ac565f1cd55a3 SHA512: 30188da3140f9db420faff5f8f705f2e8ed03cc075e37d0e83f6c6f9a9c0e34754a3523351332a4ce118d9a236723592801e36756f2299cded02fb50b8de32b6 Homepage: https://cran.r-project.org/package=fusedTree Description: CRAN Package 'fusedTree' (Fused Partitioned Regression for Clinical and Omics Data) Fit (generalized) linear regression models in each leaf node of a tree. The tree is constructed using clinical variables only. 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The responses of interest may include a mix of discrete and continuous variables. The responses may share the same set of predictors, however, the models and parameters differ across different platforms. Integrating information from different data sets can enhance the power of model selection. Package is based on Xin Gao, Raymond J. Carroll (2017) . 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Consequently, at the time when the last enrolled participant completes their follow-up, the number of observed endpoints is a random variable. Assuming data collected through an interim timepoint, simulation-based estimation and inferential procedures in the standard right-censored failure time analysis framework are conducted for the distribution of the number of endpoints--in total as well as by treatment arm--at the end of the follow-up period. The future (i.e., yet unobserved) enrollment, endpoint, and dropout times are generated according to mechanisms specified in the simTrial() function in the 'seqDesign' package. A Bayesian model for the endpoint rate, offering the option to specify a robust mixture prior distribution, is used for generating future data (see the vignette for details). Inference can be restricted to participants who received treatment according to the protocol and are observed to be at risk for the endpoint at a specified timepoint. 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These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework . 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This allows you to process futures, as defined by the 'future' package, in parallel out of the box, not only on your local machine or ad-hoc cluster of machines, but also via high-performance compute ('HPC') job schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS', e.g. 'y <- future.apply::future_lapply(files, FUN = process)'. Package: r-cran-future.callr Architecture: all Version: 0.10.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-parallelly, r-cran-callr Suggests: r-cran-globals, r-cran-future.apply, r-cran-listenv, r-cran-markdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-future.callr_0.10.2-1.ca2404.1_all.deb Size: 86238 MD5sum: ea149fea376f4cd4514d0b82aaede0d9 SHA1: cc5e333d28f8d4c5f9e971084980ab72b26b38f3 SHA256: cb75967871a6a13a818e1a20166d20e8e61c39ebebd4ca7d8b48e3850c16c462 SHA512: 1503c2cc97ec764df4810156e6b24bd96c9d49eb27bc4379e3e35e0ca7a45177f127e73a6b544cda6018d8e248e21ba47d8c1e8d5774b8bf693935d296c0c934 Homepage: https://cran.r-project.org/package=future.callr Description: CRAN Package 'future.callr' (A Future API for Parallel Processing using 'callr') Implementation of the Future API on top of the 'callr' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local (Linux, macOS, Windows, ...) machine. Contrary to backends relying on the 'parallel' package (e.g. 'future::multisession') and socket connections, the 'callr' backend provided here can run more than 125 parallel R processes. Package: r-cran-future.mirai Architecture: all Version: 0.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-mirai, r-cran-parallelly Suggests: r-cran-future.tests, r-cran-future.apply, r-cran-listenv Filename: pool/dists/noble/main/r-cran-future.mirai_0.10.1-1.ca2404.1_all.deb Size: 81760 MD5sum: 384ea93ca45d85ba5c7ac559dab8bef1 SHA1: ff346dad7ec7ff7000e8a09235526724369b59db SHA256: 852ad5535b195059721281b1bb9da84fe93af6316c353c60d316762e3becfd8b SHA512: 14e2805ebc2b52735163e98de7b6f875f0cbecf95b2004a51e10a7b77119dfe8dbd8c2e56904fcf96ab6d565fb0ee92f27c6455b6ed527d9532dae4342946dec Homepage: https://cran.r-project.org/package=future.mirai Description: CRAN Package 'future.mirai' (A 'Future' API for Parallel Processing using 'mirai') Implementation of the 'Future' API on top of the 'mirai' package . By using this package, you get to take advantage of the benefits of 'mirai' plus everything else that 'future' and the 'Futureverse' adds on top of it. It allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local machine or across remote machines. Contrary to back-ends relying on the 'parallel' package (e.g. 'multisession') and socket connections, 'mirai_cluster' and 'mirai_multisession', provided here, can run more than 125 parallel R processes. As a reminder, regardless which future backend is used by the user, the code does not have to change, it gives identical results, and behaves exactly the same. Package: r-cran-future.tests Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-future, r-cran-cli, r-cran-crayon, r-cran-prettyunits, r-cran-sessioninfo Suggests: r-cran-commonmark, r-cran-base64enc, r-cran-data.table, r-cran-ff Filename: pool/dists/noble/main/r-cran-future.tests_0.9.0-1.ca2404.1_all.deb Size: 194458 MD5sum: d73cfcbd69be3ccb08b6d44d559e4d42 SHA1: 4c0238351e8d8a5c24cd2f7bbc7ffed031d400aa SHA256: ff987e5fcdcb2af16a8fd6750f5f0f0e8f46af9c81b64a10517a95431eef2389 SHA512: a3ba9b326601ae73dbbc12fdfbb1c2cb7979d37d3096be9be9889f212271ec28b342b92973ef1f1c102d761d20688185dceb96781bc495db7aa08119ef468959 Homepage: https://cran.r-project.org/package=future.tests Description: CRAN Package 'future.tests' (Test Suite for 'Future API' Backends) Backends implementing the 'Future' API , as defined by the 'future' package, should use the tests provided by this package to validate that they meet the minimal requirements of the 'Future' API. The tests can be performed easily from within R or from outside of R from the command line making it straightforward to include them in package tests and in Continuous Integration (CI) pipelines. Package: r-cran-future Architecture: all Version: 1.70.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1661 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-globals, r-cran-listenv, r-cran-parallelly Suggests: r-cran-rhpcblasctl, r-cran-r.rsp, r-cran-markdown Filename: pool/dists/noble/main/r-cran-future_1.70.0-1.ca2404.1_all.deb Size: 926670 MD5sum: 70a73374ef9acadfe957027fd70b6405 SHA1: 32aad025afd048a096fe4eb3f41e132d4d00d489 SHA256: 8136d0c2259b38e627fe6ab7b6f3c96569ad797461acf5937b45ddfe4853d0f5 SHA512: 2a00f62543d168a6a83baad50ce66a315dcbeef0b034d03a2d25c5e743c94eb819d7c7d637c77bbce5d33ff84ba5a48b0b3de0a0f82ec5a9d9f86c1088cb6b09 Homepage: https://cran.r-project.org/package=future Description: CRAN Package 'future' (Unified Parallel and Distributed Processing in R for Everyone) The purpose of this package is to provide a lightweight and unified Future API for sequential and parallel processing of R expression via futures. The simplest way to evaluate an expression in parallel is to use `x %<-% { expression }` with `plan(multisession)`. This package implements sequential, multicore, multisession, and cluster futures. With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines. Extensions to this package implement additional backends for processing futures via compute cluster schedulers, etc. Because of its unified API, there is no need to modify any code in order switch from sequential on the local machine to, say, distributed processing on a remote compute cluster. Another strength of this package is that global variables and functions are automatically identified and exported as needed, making it straightforward to tweak existing code to make use of futures. Package: r-cran-futureverse Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-futurize, r-cran-future.apply, r-cran-furrr, r-cran-dofuture, r-cran-progressr Suggests: r-cran-future.batchtools, r-cran-future.callr, r-cran-future.mirai, r-cran-rhpcblasctl Filename: pool/dists/noble/main/r-cran-futureverse_0.2.0-1.ca2404.1_all.deb Size: 34354 MD5sum: b8e55ba3eb01f514bd48814d69eb2b54 SHA1: b8b8469c322509e1079bb989ede701bc4abda131 SHA256: 8f5e133c31cdbae076c4f664c699bc8ddd1057aa7feb53fbf306ce04d3218400 SHA512: 65d7ba3e6bbd7eb15201c2ebd4d46b910ec56fa20c083285b64ed1059f6870d294dc76e79e42704e989d83d6964ab80a9869c3d79d64e0644906ee36a373409a Homepage: https://cran.r-project.org/package=futureverse Description: CRAN Package 'futureverse' (Install 'Futureverse' in One Go) The 'Futureverse' is a set of packages for parallel and distributed processing with the 'future' package at its core, cf. Bengtsson (2021) . This package is designed to make it easy to install common 'Futureverse' packages in a single step. This package is intended for end-users, interactive use, and R scripts. Packages must not list it as a dependency - instead, explicitly declare each 'Futureverse' package as a dependency as needed. Package: r-cran-futurize Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1613 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future Suggests: r-cran-future.apply, r-cran-foreach, r-cran-dofuture, r-cran-purrr, r-cran-furrr, r-cran-crossmap, r-cran-plyr, r-cran-pbapply, r-bioc-biocparallel, r-cran-boot, r-cran-caret, r-cran-randomforest, r-bioc-deseq2, r-bioc-fgsea, r-bioc-genomicalignments, r-bioc-rsamtools, r-cran-fwb, r-cran-gamlss, r-cran-glmmtmb, r-cran-glmnet, r-bioc-gsva, r-cran-kernelshap, r-cran-lme4, r-cran-metafor, r-cran-mgcv, r-cran-partykit, r-cran-riskregression, r-bioc-scater, r-bioc-scuttle, r-bioc-singlecellexperiment, r-bioc-sva, r-cran-seriation, r-cran-simdesign, r-cran-shapr, r-cran-strucchange, r-cran-tm, r-cran-vegan, r-cran-commonmark, r-cran-base64enc Filename: pool/dists/noble/main/r-cran-futurize_0.3.0-1.ca2404.1_all.deb Size: 425694 MD5sum: cd3f5649e703c22dc4bb6bcfa2179721 SHA1: 67d7bc44fec045a388502b516023f65796783363 SHA256: d6f06f649b55a9b432de0fd4cc0309c080e78b3302e0ef949ce2ca56e832d1f3 SHA512: 7babdfa0aaef60b3db02b60e8d0a987b91f056780e2ab2fb4abdabe48bbca65888ff5ead1ee8ac4851a3d87cc40e6c827ea01c003eb073621980f4488d886093 Homepage: https://cran.r-project.org/package=futurize Description: CRAN Package 'futurize' (Parallelize Common Functions via One Magic Function) The futurize() function transpiles calls to sequential map-reduce functions such as base::lapply(), purrr::map(), 'foreach::foreach() %do% { ... }' into concurrent alternatives, providing you with a simple, straightforward path to scalable parallel computing via the 'future' ecosystem . By combining this function with R's native pipe operator, you have a convenient way for speeding up iterative computations with minimal refactoring, e.g. 'lapply(xs, fcn) |> futurize()', 'purrr::map(xs, fcn) |> futurize()', and 'foreach::foreach(x = xs) %do% { fcn(x) } |> futurize()'. Other map-reduce packages that can be "futurized" are 'BiocParallel', 'plyr', 'crossmap', 'pbapply' packages. There is also support for a growing set of domain-specific packages on CRAN (e.g. 'boot', 'caret', 'fgsea', 'fwb', 'gamlss', 'glmmTMB', 'glmnet', 'kernelshap', 'lme4', 'metafor', 'mgcv', 'partykit', 'riskRegression', 'seriation', 'shapr', 'SimDesign', 'strucchange', 'tm', 'TSP', and 'vegan') and on Bioconductor (e.g. 'DESeq2', 'GenomicAlignments', 'GSVA', 'Rsamtools', 'scater', 'scuttle', 'SingleCellExperiment', and 'sva'). Package: r-cran-fuzzr Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-progress, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fuzzr_0.2.2-1.ca2404.1_all.deb Size: 49894 MD5sum: e842926542ae62fc979c99cbf23369b7 SHA1: 063aebe3c0e6cc48b7458af54f311cb6d44c1bb0 SHA256: 09ba070b4e074f7f7792ef4db5ac209b559f7ba85179977daaddb4361e730580 SHA512: 3d2d6f4ed4adf0fae92e6e1e12c48697ce055ea527f6b8570957b88de2c2bab8be1203e6e04c4703aefcb4eb3c152dc5c5cac7378e8da8010a6ce93e61ea92db Homepage: https://cran.r-project.org/package=fuzzr Description: CRAN Package 'fuzzr' (Fuzz-Test R Functions) Test function arguments with a wide array of inputs, and produce reports summarizing messages, warnings, errors, and returned values. Package: r-cran-fuzzy.p.value Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fuzzynumbers Filename: pool/dists/noble/main/r-cran-fuzzy.p.value_1.1-1.ca2404.1_all.deb Size: 51062 MD5sum: 17ce071c0344c6b6b3cd0c1567fa0242 SHA1: fee890fca3232052d1c437b01fcc14727ef6e757 SHA256: 004a6d48768d794522bc5d2c4b05b6ab229363a7a98bab8961e31123ec1a60e7 SHA512: 242ee0605186ddcc958b7897d1f181036ae22be5e9ac9fa0e815cdcf5d63e0614f8c70c8fca4bae6becd36817ba68fe50c83bfb84ee6bf9e1e7df8d779d78046 Homepage: https://cran.r-project.org/package=Fuzzy.p.value Description: CRAN Package 'Fuzzy.p.value' (Computing Fuzzy p-Value) The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, (2) testing fuzzy hypotheses based on crisp data, and (3) testing fuzzy hypotheses based on fuzzy data. In all cases, the fuzziness of data or/and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package. Package: r-cran-fuzzyahp Architecture: all Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fuzzyahp_0.9.5-1.ca2404.1_all.deb Size: 187176 MD5sum: 9c16e047ba4aef379bfed6fdb3e8d0ca SHA1: f418bb2581b83146aaabf4115548b485cc7fd695 SHA256: 272dd4e488420d6133693a543d97ec8cb712afca7e31d7df9985fdab66d49b98 SHA512: 051622e5a2dfb1137713de8e9f65a38fa6f83f5f9cadab235ef24410bbe438ffea3c5e5d760c8ff581696e0a57336eb7548cd6a14a4cf8b0b2461e6842e57659 Homepage: https://cran.r-project.org/package=FuzzyAHP Description: CRAN Package 'FuzzyAHP' ((Fuzzy) AHP Calculation) Calculation of AHP (Analytic Hierarchy Process - ) with classic and fuzzy weights based on Saaty's pairwise comparison method for determination of weights. Package: r-cran-fuzzyclass Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-catools, r-cran-doparallel, r-cran-dplyr, r-cran-e1071, r-cran-envstats, r-cran-foreach, r-cran-mass, r-cran-mvtnorm, r-cran-purrr, r-cran-rdpack, r-cran-rlang, r-cran-rootsolve, r-cran-tibble, r-cran-tidyr, r-cran-trapezoid Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-maxlik Filename: pool/dists/noble/main/r-cran-fuzzyclass_0.1.7-1.ca2404.1_all.deb Size: 527148 MD5sum: 28868484f739187fedce41de9d6924f9 SHA1: 45ead3dab1b101c1bb171e8de4eb25adec199e7a SHA256: 70cd022710e43869156da1dd31b6e694b158b80a1f940d78f8866627a0aaebd4 SHA512: 34c1181f8001aea45fbb99a59fcfce80cd9cd9586adf82d00f8d3c3459e718fb3e8a19fc438900aebf3f161053947892f77cd13a6ba2c18c1f2764fc77831ce5 Homepage: https://cran.r-project.org/package=FuzzyClass Description: CRAN Package 'FuzzyClass' (Fuzzy and Non-Fuzzy Classifiers) It provides classifiers which can be used for discrete variables and for continuous variables based on the Naive Bayes and Fuzzy Naive Bayes hypothesis. Those methods were developed by researchers belong to the 'Laboratory of Technologies for Virtual Teaching and Statistics (LabTEVE)' and 'Laboratory of Applied Statistics to Image Processing and Geoprocessing (LEAPIG)' at 'Federal University of Paraiba, Brazil'. They considered some statistical distributions and their papers were published in the scientific literature, as for instance, the Gaussian classifier using fuzzy parameters, proposed by 'Moraes, Ferreira and Machado' (2021) . Package: r-cran-fuzzydbscan Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-r6, r-cran-data.table, r-cran-dbscan, r-cran-checkmate Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-factoextra, r-cran-spelling Filename: pool/dists/noble/main/r-cran-fuzzydbscan_0.0.3-1.ca2404.1_all.deb Size: 289148 MD5sum: 37ee635632489b9b1a357d86084a3502 SHA1: 8336ed26b8aff204ec927f4f7d1b20c0afa451f6 SHA256: f548022a24da02a195de3b5609d161b39bd94cdbd0175ffc0796d625fa918079 SHA512: 9df914a736317442c2354533573fca474db7e51472c2f8390ea71af4ce0cda75ed6ccff8404bb4d89cde8c53283951de90c0cfa8245d746732b6f749f5cdacd1 Homepage: https://cran.r-project.org/package=FuzzyDBScan Description: CRAN Package 'FuzzyDBScan' (Run and Predict a Fuzzy DBScan) An interface for training Fuzzy DBScan with both Fuzzy Core and Fuzzy Border. Therefore, the package provides a method to initialize and run the algorithm and a function to predict new data w.t.h. of 'R6'. The package is build upon the paper "Fuzzy Extensions of the DBScan algorithm" from Ienco and Bordogna (2018) . A predict function assigns new data according to the same criteria as the algorithm itself. However, the prediction function freezes the algorithm to preserve the trained cluster structure and treats each new prediction object individually. Package: r-cran-fuzzyforest Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 857 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-mvtnorm Suggests: r-cran-wgcna, r-cran-testthat Filename: pool/dists/noble/main/r-cran-fuzzyforest_1.0.8-1.ca2404.1_all.deb Size: 825876 MD5sum: 05d2d02bb31c55f039b3539cdb0105e0 SHA1: 685417d0492f868d594d342aaa4b3f7d1f142513 SHA256: 50bd433a2f6579d4c75213c72c583681cbbda5d2ea526cdfc4ace5e1a3351e8f SHA512: 2c22e647bf2d06314d364a11cd71e0a721aae1c8deb51458ea3519d96c70d6ef742a5c21efe9510870d5e1dd5fe60e248b4d882048e971f5de80e84eec2a035f Homepage: https://cran.r-project.org/package=fuzzyforest Description: CRAN Package 'fuzzyforest' (Fuzzy Forests) Fuzzy forests, a new algorithm based on random forests, is designed to reduce the bias seen in random forest feature selection caused by the presence of correlated features. Fuzzy forests uses recursive feature elimination random forests to select features from separate blocks of correlated features where the correlation within each block of features is high and the correlation between blocks of features is low. One final random forest is fit using the surviving features. This package fits random forests using the 'randomForest' package and allows for easy use of 'WGCNA' to split features into distinct blocks. See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) for further details. Package: r-cran-fuzzyimputationtest Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fuzzysimres, r-cran-fuzzynumbers, r-cran-missforest, r-cran-miceranger, r-cran-vim, r-cran-fuzzyresampling, r-cran-mice Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fuzzyimputationtest_0.5.2-1.ca2404.1_all.deb Size: 141256 MD5sum: 9a8053231b88b0188a75252f86f7e4be SHA1: 76a9ddb7f41640d63003ef2d2e48cf6925aed550 SHA256: 16a32f1bbb77035e89376aefb378d92ff9b8e61c373ea82ae36870365e5f7aad SHA512: 8d9dc0397adc5c3d98ac24fc1bce77d99dad8294dd627532715a3e7f43ea7ca0851df3018221564b81e89fbe37ce5df0e55f514037b1fc4b09794d40317463ef Homepage: https://cran.r-project.org/package=FuzzyImputationTest Description: CRAN Package 'FuzzyImputationTest' (Imputation Procedures and Quality Tests for Fuzzy Data) Special procedures for the imputation of missing fuzzy numbers are still underdeveloped. The goal of the package is to provide the new d-imputation method (DIMP for short, Romaniuk, M. and Grzegorzewski, P. (2023) "Fuzzy Data Imputation with DIMP and FGAIN" RB/23/2023) and covert some classical ones applied in R packages ('missForest','miceRanger','knn') for use with fuzzy datasets. Additionally, specially tailored benchmarking tests are provided to check and compare these imputation procedures with fuzzy datasets. Package: r-cran-fuzzyjoin Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringdist, r-cran-stringr, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-geosphere, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-ggplot2, r-cran-qdapdictionaries, r-cran-readr, r-cran-rvest, r-cran-rmarkdown, r-cran-maps, r-bioc-iranges, r-cran-covr Filename: pool/dists/noble/main/r-cran-fuzzyjoin_0.1.8-1.ca2404.1_all.deb Size: 129232 MD5sum: cf3c2354dcce220340988a7209088152 SHA1: 03ec551528e8d920585f6a0af5bac49a64e18824 SHA256: 15b65c2ed73476d8d36ccebae58d99a88bc227004612dd78c3e4639a0f101534 SHA512: 6a19b0eb67c1e2cda3bf6e384db716270e09f919d056cf5523cc53cf318e3da7f4741e53c09731d22e9047ed3c6b4e8cd958484310249a8233e779f9c9a7d746 Homepage: https://cran.r-project.org/package=fuzzyjoin Description: CRAN Package 'fuzzyjoin' (Join Tables Together on Inexact Matching) Join tables together based not on whether columns match exactly, but whether they are similar by some comparison. Implementations include string distance and regular expression matching. Package: r-cran-fuzzylink Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rfast, r-cran-reshape2, r-cran-stringdist, r-cran-stringr, r-cran-httr, r-cran-jsonlite, r-cran-httr2, r-cran-ranger, r-cran-ellmer Filename: pool/dists/noble/main/r-cran-fuzzylink_0.4.1-1.ca2404.1_all.deb Size: 63244 MD5sum: c0c90711571c6228d4744d5343080df5 SHA1: 36195bd25b3904147cff4161d653811ea425ad49 SHA256: 4ec1c87cb7ca3aaede3cea9f25c068e002c72a7036d13d051044dfc2e3776ae3 SHA512: 7531d5d8f66df1348317b8f9311408e42f9e1bfabd1d691eedbeb6d0791c583a6b79cdd24619e020806a78baed8a9dfe277767520201fc4f38200fd01c945d1f Homepage: https://cran.r-project.org/package=fuzzylink Description: CRAN Package 'fuzzylink' (Probabilistic Record Linkage Using Pretrained Text Embeddings) Links datasets through fuzzy string matching using pretrained text embeddings. Produces more accurate record linkage when lexical string distance metrics are a poor guide to match quality (e.g., "Patricia" is more lexically similar to "Patrick" than it is to "Trish"). Capable of performing multilingual record linkage. Methods are described in Ornstein (2025) . Package: r-cran-fuzzylp Architecture: all Version: 0.1-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-roi, r-cran-fuzzynumbers, r-cran-roi.plugin.glpk Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-fuzzylp_0.1-7-1.ca2404.1_all.deb Size: 421764 MD5sum: 7b190a6280751ab7118972fb246156e5 SHA1: 5f47da3e19b64e98518ac2156387a6b738cfe3fe SHA256: 770b3795ca49c7759c13ede27835ccac155622f4f7f851ef7db6362beed94e14 SHA512: bed0b19ea9208a2aed05f7594c884f2d42cfcaf3619229380f6288a524937203525a575e094e509643a415c22c899534f30f1650abec3e01c9ec36a72df66663 Homepage: https://cran.r-project.org/package=FuzzyLP Description: CRAN Package 'FuzzyLP' (Fuzzy Linear Programming) Provides methods to solve Fuzzy Linear Programming Problems with fuzzy constraints (following different approaches proposed by Verdegay, Zimmermann, Werners and Tanaka), fuzzy costs, and fuzzy technological matrix. Package: r-cran-fuzzym Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1342 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-fuzzym_0.1.0-1.ca2404.1_all.deb Size: 652468 MD5sum: a10fa1fdb6269d42a6215dff360b30ff SHA1: a0446a3a8a06649bb07d6196f8d50e5e4a7a61f4 SHA256: 3f88fee613b6d0dc38ee3f01f2aecb33f821918e02ead621810d15dfb32359d4 SHA512: ac32ee1f31731b0bcd4b05c2399831039948bbbc7081865d6c72b32fba14e5aaef67fc883924d44b8b85c66ff7ba907364d5f66b2e11c0893329764de94e8086 Homepage: https://cran.r-project.org/package=FuzzyM Description: CRAN Package 'FuzzyM' (Fuzzy Cognitive Maps Operations) Contains functions for operations with fuzzy cognitive maps using t-norm and s-norm operators. T-norms and S-norms are described by Dov M. Gabbay and George Metcalfe (2007) . System indicators are described by Cox, Earl D. (1995) . Executable examples are provided in the "inst/examples" folder. Package: r-cran-fuzzynumbers.ext.2 Architecture: all Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fuzzynumbers Filename: pool/dists/noble/main/r-cran-fuzzynumbers.ext.2_3.2-1.ca2404.1_all.deb Size: 49012 MD5sum: 05794c774325a0ef753ea5c843134d26 SHA1: e5fa74077ac41ab782137fc7e5e098e6e782f32f SHA256: 260d9c6aa02a0b79112411ad0233fe25d7fa58c88f592c7952fa0cf518f74fb3 SHA512: 430fd69153173d57bd2fa3e8ea80e58e561f497aeced0ea8b0660fa6c013200570689a48fb46d5f56e2321fdea8156dc694cdc6c8fb73a29e3f401c700ae8374 Homepage: https://cran.r-project.org/package=FuzzyNumbers.Ext.2 Description: CRAN Package 'FuzzyNumbers.Ext.2' (Apply Two Fuzzy Numbers on a Monotone Function) One can easily draw the membership function of f(x,y) by package 'FuzzyNumbers.Ext.2' in which f(.,.) is supposed monotone and x and y are two fuzzy numbers. This work is possible using function f2apply() which is an extension of function fapply() from Package 'FuzzyNumbers' for two-variable monotone functions. Moreover, this package has the ability of computing the core, support and alpha-cuts of the fuzzy-valued final result. Package: r-cran-fuzzynumbers Architecture: all Version: 0.4-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1045 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-digest Filename: pool/dists/noble/main/r-cran-fuzzynumbers_0.4-7-1.ca2404.1_all.deb Size: 785548 MD5sum: b75bf4c96c0b567d0d379aca4ea18c48 SHA1: 5d9c9d2f0ae5d4415c5cb58230418fb60563d04a SHA256: 30d48254325f9e499c14491c5480e36aa0f18f69b334f7e1d30f922a2b81089b SHA512: 45f9907d7c6050b18ac54b2ec4ceae4274e7844fe1f05b48475803471e4951b7a4e7e9a806ebfa78c524a6f4162cabe575112d48d4ba44eb94eaa6d020e64801 Homepage: https://cran.r-project.org/package=FuzzyNumbers Description: CRAN Package 'FuzzyNumbers' (Tools to Deal with Fuzzy Numbers) S4 classes and methods to deal with fuzzy numbers. They allow for computing any arithmetic operations (e.g., by using the Zadeh extension principle), performing approximation of arbitrary fuzzy numbers by trapezoidal and piecewise linear ones, preparing plots for publications, computing possibility and necessity values for comparisons, etc. Package: r-cran-fuzzypovertyr Architecture: all Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 962 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-sampling, r-cran-ecp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-spelling Filename: pool/dists/noble/main/r-cran-fuzzypovertyr_3.0.2-1.ca2404.1_all.deb Size: 643770 MD5sum: 5e72af75d90b80b29ef54a0dd9afc134 SHA1: 0b30a75690123b43fb054184a8b483b494f31064 SHA256: c8756dfce6d137a517a5606d750d17dbcda4448cc44fae66e5bcb048a819094b SHA512: cad26ea7fb028698d2570ca9b597509e43e85dbb7f27ad8a4e67d9ef13f0e22b161c04441573ca2d20b0b489de94f8711870a2747a60823a9095897994a394dd Homepage: https://cran.r-project.org/package=FuzzyPovertyR Description: CRAN Package 'FuzzyPovertyR' (Estimation of Fuzzy Poverty Measures) Estimates fuzzy measures of poverty and deprivation. It also estimates the sampling variance of these measures using bootstrap or jackknife repeated replications. Package: r-cran-fuzzyq Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster Filename: pool/dists/noble/main/r-cran-fuzzyq_0.1.0-1.ca2404.1_all.deb Size: 58878 MD5sum: 6989bd2b2e1030cb1dfc9fb33f000e8a SHA1: 2a061ed4b8facfa1b305691fcc8cda0b18674b11 SHA256: 8b112fe77b8e07c4008f95293aed18292f4a927a191b6aaa784b8d4683a4b914 SHA512: 3208790260c62ef35906c043c474634a2f384b74736d9f5a3887f332eaf4eddaafc221943477159c50ca516ca1b4fdec7214106405804f8ee69525221a4b1b63 Homepage: https://cran.r-project.org/package=FuzzyQ Description: CRAN Package 'FuzzyQ' (Fuzzy Quantification of Common and Rare Species) Fuzzy clustering of species in an ecological community as common or rare based on their abundance and occupancy. It also includes functions to compute confidence intervals of classification metrics and plot results. See Balbuena et al. (2020, ). Package: r-cran-fuzzyr Architecture: all Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-plyr Filename: pool/dists/noble/main/r-cran-fuzzyr_2.3.2-1.ca2404.1_all.deb Size: 323242 MD5sum: eef839917fbe20245e34e3cfbe5db6fc SHA1: 7923e7ac20c68e7d7e7da11261760910cf587e16 SHA256: e443e1a5f378da9255b5998028f5e4590a35e6670a1dce7b0fe626967d249c24 SHA512: f9ebc9f5902cb98e8fed8ceb9e87ab107ad0a6c1f970063778d1d8ced5abd2254282d291512497b0b350e7bcf8e35e23350fc781ae4acbe2049091eb3c06d039 Homepage: https://cran.r-project.org/package=FuzzyR Description: CRAN Package 'FuzzyR' (Fuzzy Logic Toolkit for R) Design and simulate fuzzy logic systems using Type-1 and Interval Type-2 Fuzzy Logic. This toolkit includes with graphical user interface (GUI) and an adaptive neuro- fuzzy inference system (ANFIS). This toolkit is a continuation from the previous package ('FuzzyToolkitUoN'). Produced by the Intelligent Modelling & Analysis Group (IMA) and Lab for UnCertainty In Data and decision making (LUCID), University of Nottingham. A big thank you to the many people who have contributed to the development/evaluation of the toolbox. Please cite the toolbox and the corresponding paper when using it. More related papers can be found in the NEWS. Package: r-cran-fuzzyreg Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-limsolve, r-cran-quadprog Suggests: r-cran-testthat, r-cran-fuzzynumbers, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-fuzzyreg_0.6.2-1.ca2404.1_all.deb Size: 345168 MD5sum: d16a3eab4f66d139681656953124fb2f SHA1: b2a544f6c90c9ed5cb8b267394d513ac82568be3 SHA256: d8e61a250d2b07881e6604807c74943f83b6873c7b7ce1ae734d2d1a046fbfa1 SHA512: dfd124d45ab0512aa2d06ea52aa164508c9a3c66add09cbb94ab9cd667aeafbd7f636cb62177e2eff15975f5a788c77de0707e53bc429cfa883a83c92c2c69c0 Homepage: https://cran.r-project.org/package=fuzzyreg Description: CRAN Package 'fuzzyreg' (Fuzzy Linear Regression) Estimators for fuzzy linear regression. The functions estimate parameters of fuzzy linear regression models with crisp or fuzzy independent variables (triangular fuzzy numbers are supported). Implements multiple methods for parameter estimation and algebraic operations with triangular fuzzy numbers. Includes functions for summarising, printing and plotting the model fit. Calculates predictions from the model and total error of fit. Individual methods are described in Diamond (1988) , Hung & Yang (2006) , Lee & Tanaka (1999) , Nasrabadi, Nasrabadi & Nasrabady (2005) , Skrabanek, Marek & Pozdilkova (2021) , Tanaka, Hayashi & Watada (1989) , Zeng, Feng & Li (2017) . Package: r-cran-fuzzyresampling Architecture: all Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-fuzzyresampling_0.6.4-1.ca2404.1_all.deb Size: 416372 MD5sum: 57382a933e157bbec91e355432083816 SHA1: 6d480ae605998c54b34f2dbeeabf6ec25f826c80 SHA256: 3eabf59ecd42a62e17477850e43d8b8be4e7f9d112e287334c980e1e98844313 SHA512: f27d1e9f3bdc1accb1522df51c2da34514f0537424972ac873141c866c493696e5b383662d53e6fff368c1898e89681605118e70b7a41556a4707a01f8aba5f4 Homepage: https://cran.r-project.org/package=FuzzyResampling Description: CRAN Package 'FuzzyResampling' (Resampling Methods for Triangular and Trapezoidal Fuzzy Numbers) The classical (i.e. Efron's, see Efron and Tibshirani (1994, ISBN:978-0412042317) "An Introduction to the Bootstrap") bootstrap is widely used for both the real (i.e. "crisp") and fuzzy data. The main aim of the algorithms implemented in this package is to overcome a problem with repetition of a few distinct values and to create fuzzy numbers, which are "similar" (but not the same) to values from the initial sample. To do this, different characteristics of triangular/trapezoidal numbers are kept (like the value, the ambiguity, etc., see Grzegorzewski et al. , Grzegorzewski et al. (2020) , Grzegorzewski et al. (2020) , Grzegorzewski and Romaniuk (2022) , Romaniuk and Hryniewicz (2019) ). Some additional procedures related to these resampling methods are also provided, like calculation of the Bertoluzza et al.'s distance (aka the mid/spread distance, see Bertoluzza et al. (1995) "On a new class of distances between fuzzy numbers") and estimation of the p-value of the one- and two- sample bootstrapped test for the mean (see Lubiano et al. (2016, )). Additionally, there are procedures which randomly generate trapezoidal fuzzy numbers using some well-known statistical distributions. Package: r-cran-fuzzysim Architecture: all Version: 4.54-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 509 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-modeva, r-cran-stringi Suggests: r-cran-aod, r-cran-geodist, r-cran-phylolm, r-cran-raster, r-cran-terra Filename: pool/dists/noble/main/r-cran-fuzzysim_4.54-1.ca2404.1_all.deb Size: 446224 MD5sum: 9ddbfbe6d322ab36705f1f72df11051f SHA1: a9c2e6702cb0b51394b476c750fcf3d0021d51b6 SHA256: 294ad25dd43ad3c1515977af0fd1655004d32b87ef2c9cf9954fe80114b77bd9 SHA512: d9d9d0c93813b55837be7a8b17e6bf83a2d8e41cdf5f3f599fe8e35530893cbe04a50b228aa6a5a06b47e292b3d328281976fcf0a6503adbab6c9c6687057b2b Homepage: https://cran.r-project.org/package=fuzzySim Description: CRAN Package 'fuzzySim' (Fuzzy Similarity in Species Distributions) Functions to compute fuzzy versions of species occurrence patterns based on presence-absence data (including inverse distance interpolation, trend surface analysis, and prevalence-independent favourability obtained from probability of presence), as well as pair-wise fuzzy similarity (based on fuzzy logic versions of commonly used similarity indices) among those occurrence patterns. Includes also functions for model consensus and comparison (overlap and fuzzy similarity, fuzzy loss, fuzzy gain), and for data preparation, such as obtaining unique abbreviations of species names, defining the background region, cleaning and gridding (thinning) point occurrence data onto raster maps, selecting among (pseudo)absences to address survey bias, converting species lists (long format) to presence-absence tables (wide format), transposing part of a data frame, selecting relevant variables for models, assessing the false discovery rate, or analysing and dealing with multicollinearity. Initially described in Barbosa (2015) . Package: r-cran-fuzzyspec Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-thresher, r-cran-fclust, r-cran-mclust, r-cran-mvtnorm, r-cran-np, r-cran-ggplot2, r-cran-viridislite Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-patchwork, r-cran-devtools, r-cran-spelling Filename: pool/dists/noble/main/r-cran-fuzzyspec_1.0.0-1.ca2404.1_all.deb Size: 225994 MD5sum: 3160b28e6b53a367aa740d29c49d8c4a SHA1: 687d326f59e6485e285ef9a91e3f77319aeaeb1e SHA256: b347c6133557204efe98e7114ea51cb37701f21ccf9be2971553053af14760b9 SHA512: 7e0580eea73759d3d33076ce859530d0e9ed89f525eaaebf11272f52c5cd57ea1b412114e5085dade74e41274d6eaa1d7ff0b90d39fae4327def9dc26b816c73 Homepage: https://cran.r-project.org/package=FuzzySpec Description: CRAN Package 'FuzzySpec' (Fuzzy Spectral Clustering with Variable-Weighted AdjacencyMatrices) Implementation of the FVIBES, the Fuzzy Variable-Importance Based Eigenspace Separation algorithm as described in the paper by Ghashti, J.S., Hare, W., and J.R.J. Thompson (2025). Variable-Weighted Adjacency Constructions for Fuzzy Spectral Clustering. Submitted. Package: r-cran-fuzzystatprob Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 758 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multinomialci, r-cran-fuzzynumbers, r-cran-deoptim Suggests: r-cran-markovchain, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-fuzzystatprob_2.0.4-1.ca2404.1_all.deb Size: 709468 MD5sum: 2a6dc47e6013da0e467831015f18a92c SHA1: fb3aae8ed43030fb682ab7a823be15db16a244dc SHA256: 579afcb54ff81b79a1484fca43e74f10c52f9b6b63a1bdac1ac72eff19c7a991 SHA512: eb6f39f7807d222da613e1bb285c0ccece8fc1f0747896c61b72ca2ce617ab5baa379d6adeac0ee88e3a7ad6e4d5d4611e8d98857278abd3b8082f49acde6d7e Homepage: https://cran.r-project.org/package=FuzzyStatProb Description: CRAN Package 'FuzzyStatProb' (Fuzzy Stationary Probabilities from a Sequence of Observationsof an Unknown Markov Chain) An implementation of a method for computing fuzzy numbers representing stationary probabilities of an unknown Markov chain, from which a sequence of observations along time has been obtained. The algorithm is based on the proposal presented by James Buckley in his book on Fuzzy probabilities (Springer, 2005), chapter 6. Package 'FuzzyNumbers' is used to represent the output probabilities. Package: r-cran-fuzzystattra Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-fuzzystattra_1.0-1.ca2404.1_all.deb Size: 165506 MD5sum: 587addcf7ed0df8bdee2eaaa91770382 SHA1: 0bbdac6e23deb9f943497ec1c7b42573c9242721 SHA256: f57c8bb2410b54d44b8493f1617a18232dbc5835a5ad6d8952fdff86dfab2155 SHA512: 2084aa90daa1796e22aae338761f309d083e99169bd5eafc46325a7d82a58ef71a6823124952446e55348aa859dde31f6a7d8fab2e2248aed8be5eaa0b7f87bc Homepage: https://cran.r-project.org/package=FuzzyStatTra Description: CRAN Package 'FuzzyStatTra' (Statistical Methods for Trapezoidal Fuzzy Numbers) The aim of the package is to provide some basic functions for doing statistics with trapezoidal fuzzy numbers. In particular, the package contains several functions for simulating trapezoidal fuzzy numbers, as well as for calculating some central tendency measures (mean and two types of median), some scale measures (variance, ADD, MDD, Sn, Qn, Tn and some M-estimators) and one diversity index and one inequality index. Moreover, functions for calculating the 1-norm distance, the mid/spr distance and the (phi,theta)-wabl/ldev/rdev distance between fuzzy numbers are included, and a function to calculate the value phi-wabl given a sample of trapezoidal fuzzy numbers. Package: r-cran-fuzzystattraeoo Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 434 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6 Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-fuzzystattraeoo_1.0-1.ca2404.1_all.deb Size: 381310 MD5sum: 0b72ca6940f215881fc6a09f60552977 SHA1: a25bd00dc246a13c64717732068d32167d0ea3e0 SHA256: c698a95df6f6e1f5cfbb7c745df9dc7eb30f99ed629ed6870ff16c81570da0c3 SHA512: 0c1659dbde8af46b706f74ff7955ad67d38c9b7f3ba6429334b2a1578bd5326c6250a4945e51527b64f25babca085a6ab82541a673def26c1af4d66ba5931bef Homepage: https://cran.r-project.org/package=FuzzyStatTraEOO Description: CRAN Package 'FuzzyStatTraEOO' (Package 'FuzzyStatTra' in Encapsulated Object OrientedProgramming) The aim of the package is to contain the package 'FuzzyStatTra' in Encapsulated Object Oriented Programming using R6. 'FuzzyStatTra' contains Statistical Methods for Trapezoidal Fuzzy Numbers, whose aim is to provide some basic functions for doing statistical analysis with trapezoidal fuzzy numbers. For more details, you can visit the website of the SMIRE+CoDiRE (Statistical Methods with Imprecise Random Elements and Comparison of Distributions of Random Elements) Research Group (). The most related paper can be found in References. Now, those functions are organized in specific classes and methods. This object-based approach is an important step in making statistical computing more accessible to users. Package: r-cran-fuzzysts Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fuzzynumbers, r-cran-polynom Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fuzzysts_0.4-1.ca2404.1_all.deb Size: 858550 MD5sum: 3db79abdddf2781ae0e38a675ac79405 SHA1: 14050523a2366985886ccf9aa5e0cf10375a0c55 SHA256: 2ce47047e5f5dfb0adb6194c3d24591b4aad3e5b7ea15267d0820cf5e4d82fdc SHA512: c37ea2e7f08d54df10483e8e724f7e8debe77774284bd8fa2a0bf5d9acfbcb91a63dbd3c5c0f335b4c087b7ac92d0662b9bbbc7dfd1ea677c5df19f21715ffc5 Homepage: https://cran.r-project.org/package=FuzzySTs Description: CRAN Package 'FuzzySTs' (Fuzzy Statistical Tools) The main goal of this package is to present various fuzzy statistical tools. It intends to provide an implementation of the theoretical and empirical approaches presented in the book entitled "The signed distance measure in fuzzy statistical analysis. Some theoretical, empirical and programming advances" . For the theoretical approaches, see Berkachy R. and Donze L. (2019) . For the empirical approaches, see Berkachy R. and Donze L. (2016) ). Important (non-exhaustive) implementation highlights of this package are as follows: (1) a numerical procedure to estimate the fuzzy difference and the fuzzy square. (2) two numerical methods of fuzzification. (3) a function performing different possibilities of distances, including the signed distance and the generalized signed distance for instance with all its properties. (4) numerical estimations of fuzzy statistical measures such as the variance, the moment, etc. (5) two methods of estimation of the bootstrap distribution of the likelihood ratio in the fuzzy context. (6) an estimation of a fuzzy confidence interval by the likelihood ratio method. (7) testing fuzzy hypotheses and/or fuzzy data by fuzzy confidence intervals in the Kwakernaak - Kruse and Meyer sense. (8) a general method to estimate the fuzzy p-value with fuzzy hypotheses and/or fuzzy data. (9) a method of estimation of global and individual evaluations of linguistic questionnaires. (10) numerical estimations of multi-ways analysis of variance models in the fuzzy context. The unbalance in the considered designs are also foreseen. Package: r-cran-fuzzywuzzyr Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-r6 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-fuzzywuzzyr_1.0.6-1.ca2404.1_all.deb Size: 191686 MD5sum: 4fbd227175be79e8e2879cae14463dad SHA1: a7c0e5e269f13a54167096c2aa79d96376c04b91 SHA256: 8f46821f5edb55c153c07f6ef21733b003b0da87e050e74ac37cb373557cfae1 SHA512: 06ddafcaeb235d51a8d518d0fd9b112d5c81191ff0f244ea64362deee881219da51202be4c862fe5ce12f960c5e09cebe4158f0cce6fcedc78a151cd5c44f186 Homepage: https://cran.r-project.org/package=fuzzywuzzyR Description: CRAN Package 'fuzzywuzzyR' (Fuzzy String Matching) Fuzzy string matching implementation of the 'fuzzywuzzy' 'python' package. It uses the Levenshtein Distance to calculate the differences between sequences. 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The fractional weighted bootstrap (also known as the Bayesian bootstrap) involves drawing weights randomly that are applied to the data rather than resampling units from the data. See Xu et al. (2020) for details. Package: r-cran-fwdselect Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvtools, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-fwdselect_2.1.1-1.ca2404.1_all.deb Size: 178628 MD5sum: 2653ea3270be434cf2fd8425b5488eaa SHA1: d165859da774dcd074e182d24f92c8555c2e8a11 SHA256: b7d72e91ae9a8ec821538c2f84734769e7cbc4cdc915018d965bc70fd0411c2d SHA512: f4214198ccb1c78018638a3914718a53be06abadeb578242f079a1bb3fbe325ecd7d9bb7982f9f83408f7a3aa7fc9f90c9bc983b141ce5df9493e573d55a2895 Homepage: https://cran.r-project.org/package=FWDselect Description: CRAN Package 'FWDselect' (Selecting Variables in Regression Models) A simple method to select the best model or best subset of variables using different types of data (binary, Gaussian or Poisson) and applying it in different contexts (parametric or non-parametric). Implemented methodology described in: M. Sestelo, N. M. Villanueva, L. Meira-Machado and J. Roca-Pardiñas (2016). FWDselect: an R package for variable selection in regression models. The R Journal, 8 (1), 132-148. . 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However, as conventional phenotyping approaches for measuring fresh biomass is time-consuming, laborious and destructive, image-based phenotyping methods are being widely used now. In the image-based approach, the fresh weight of the above-ground part of the plant depends on the projected area. For determining the projected area, the visual image of the plant is converted into the grayscale image by simply averaging the Red(R), Green (G) and Blue (B) pixel values. Grayscale image is then converted into a binary image using Otsu’s thresholding method Otsu, N. (1979) to separate plant area from the background (image segmentation). The segmentation process was accomplished by selecting the pixels with values over the threshold value belonging to the plant region and other pixels to the background region. The resulting binary image consists of white and black pixels representing the plant and background regions. 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Package: r-cran-g3viz Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-stringr, r-cran-httr2, r-bioc-annotationdbi, r-bioc-org.hs.eg.db, r-cran-htmlwidgets Suggests: r-cran-shiny, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-g3viz_1.2.0-1.ca2404.1_all.deb Size: 837024 MD5sum: 7f605fcc2ce909e2fdfab9c13f49ba93 SHA1: 21d5bd123898a82349e8a353b6b04dbc9455998c SHA256: aededb96a9f61b3fe48ff05ef8600d36b601d602d178e456a46dc1c81610d539 SHA512: 125e099e7499ba3ee6b596d278817401935a2132f6d3f549921773666051d9e6070d87bfac53b693c41f7fa68812c43cb37ba31e479506bdcc9769cb8e60f7cb Homepage: https://cran.r-project.org/package=g3viz Description: CRAN Package 'g3viz' (Interactively Visualize Genetic Mutation Data using aLollipop-Diagram) Interface for 'g3-lollipop' 'JavaScript' library. Visualize genetic mutation data using an interactive lollipop diagram in 'RStudio' or your web browser. Package: r-cran-g6r Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4744 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-igraph, r-cran-roxy.shinylive, r-cran-testthat, r-cran-withr, r-cran-stringr, r-cran-htmltools, r-cran-bslib Filename: pool/dists/noble/main/r-cran-g6r_0.6.0-1.ca2404.1_all.deb Size: 1141390 MD5sum: 8ab95c7da1ec4bf4450ada969954da33 SHA1: 8b6a893011b79234f91020b58d192ce732dbe7c3 SHA256: 80fa181a6a841d788a09fe3fab8e058d576822ebebff6ea56f730ed3b5a7df89 SHA512: e18095f5dbdd22972801ef991078021d15ed5c857647e13a6873ec2c0bbe7d40469a0d1eb6b78384189ed9f4a77735270716c399a7021563b9b12349713e2fb4 Homepage: https://cran.r-project.org/package=g6R Description: CRAN Package 'g6R' (Graph Visualisation Engine Widget for R and 'shiny' Apps) Create stunning network experiences powered by the 'G6' graph visualisation engine 'JavaScript' library . 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Package: r-cran-gaawr2 Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2506 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gap, r-cran-gap.datasets, r-cran-ggplot2, r-cran-survival, r-cran-rdpack Suggests: r-cran-blr, r-cran-bglr, r-bioc-biomart, r-cran-bookdown, r-cran-cairo, r-bioc-ensdb.hsapiens.v75, r-bioc-ensembldb, r-cran-gmmat, r-cran-hardyweinberg, r-cran-haplo.stats, r-cran-httr, r-cran-httpuv, r-cran-jsonlite, r-cran-kableextra, r-cran-knitr, r-cran-mcmcglmm, r-cran-plumber, r-cran-powereqtl, r-cran-r2jags, r-cran-regress, r-bioc-rsamtools, r-cran-snpassoc, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-gaawr2_0.0.7-1.ca2404.1_all.deb Size: 654222 MD5sum: 7578b6490cd6f2199bd73d699a6104c7 SHA1: d1067ec8aa9c269dcb05450f2a49a0c03cddae9a SHA256: 550338573bf8c36c5819e74a86213f139f19318a27687dbe3a2922a51778c953 SHA512: 7f7900513f27f7456fe72803ed2563a0f777be8b0ef59a597590887c82d26f9928908a50afe9c54bee04048ae0ec9251f90e5abe8e86e466a652acffc81d1e76 Homepage: https://cran.r-project.org/package=gaawr2 Description: CRAN Package 'gaawr2' (Genetic Association Analysis) This is a companion to Henry-Stewart talk by Zhao (2026, ), which gathers information, metadata and scripts to showcase modern genetic analysis -- ranging from testing of polymorphic variant(s) for Hardy-Weinberg equilibrium, association with traits using genetic and statistical models, Bayesian implementation, power calculation in study design, and genetic annotation. It also covers R integration with the Linux environment, GitHub, package creation and web applications. The earlier version by Zhao (2009, ) provides a brief introduction to these topics. Package: r-cran-gabb Architecture: all Version: 0.3.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-egg, r-cran-ggforce, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggplotify, r-cran-ggpubr, r-cran-tidyr, r-cran-hotelling, r-cran-pheatmap, r-cran-vegan Suggests: r-cran-factominer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gabb_0.3.10-1.ca2404.1_all.deb Size: 85876 MD5sum: cb2431966e3450e1f66d8727defb09ec SHA1: fc64b8ccae7654e1c37b27eae4cb4b8a05c7011e SHA256: fbe9c35e8e2c1f042b136e5d735f19c851059546229a7db2334600c7b1eea2be SHA512: afb776a19fdb6b56be762eed15744a48eaabee9f33633375ba5b8c338e5e896df34ceaab945d25cc1a163d50023a70c9e11d49762cd542ef2565dd9d9363cb30 Homepage: https://cran.r-project.org/package=GABB Description: CRAN Package 'GABB' (Facilitation of Data Preparation and Plotting Procedures for RDAand PCA Analyses) Help to the occasional R user for synthesis and enhanced graphical visualization of redundancy analysis (RDA) and principal component analysis (PCA) methods and objects. Inputs are : data frame, RDA (package 'vegan') and PCA (package 'FactoMineR') objects. Outputs are : synthesized results of RDA, displayed in console and saved in tables ; displayed and saved objects of PCA graphic visualization of individuals and variables projections with multiple graphic parameters. Package: r-cran-gace Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-forecast Filename: pool/dists/noble/main/r-cran-gace_1.0.0-1.ca2404.1_all.deb Size: 44904 MD5sum: 135cd5eae17c1560581a8b12740d6dce SHA1: d6e8bf328bb5b78d75102e111fa4dafef56a8409 SHA256: 89cc7c10d0f8ed1d22919756cfdfabd3b44559bec22fac69bcfd73a0ba4f4f3f SHA512: a4a2f915284d665a3513b687874710a73c6301abe63db09131e07493539cac858867de4286a532413e1dd18a84c3af257b442ba120576d158c9205f60427ed0a Homepage: https://cran.r-project.org/package=GACE Description: CRAN Package 'GACE' (Generalized Adaptive Capped Estimator for Time SeriesForecasting) Provides deterministic forecasting for weekly, monthly, quarterly, and yearly time series using the Generalized Adaptive Capped Estimator. The method includes preprocessing for missing and extreme values, extraction of multiple growth components (including long-term, short-term, rolling, and drift-based signals), volatility-aware asymmetric capping, optional seasonal adjustment via damped and normalized seasonal factors, and a recursive forecast formulation with moderated growth. The package includes a user-facing forecasting interface and a plotting helper for visualization. Related forecasting background is discussed in Hyndman and Athanasopoulos (2021) and Hyndman and Khandakar (2008) . The method extends classical extrapolative forecasting approaches and is suited for operational and business planning contexts where stability and interpretability are important. Package: r-cran-gacff Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gacff_1.0-1.ca2404.1_all.deb Size: 79158 MD5sum: a58013d8662030c156cdf9c1f4926b15 SHA1: 17bb72fd3284221820b4dea904e86f4d82f62234 SHA256: 523302f46a0ab91665bc199df0cd998da5dc343d6b457eb1b3f4a7b1ed5e07a9 SHA512: 586f15aa078d5c7331947c75260eb0dbaafca7e933aba2d2ca45e2f8408e416010d42b2e62adcdee3403313a30322ba3e667f6510f57d8eb53bae6df50438fa2 Homepage: https://cran.r-project.org/package=GACFF Description: CRAN Package 'GACFF' (Genetic Similarity in User-Based Collaborative Filtering) The genetic algorithm can be used directly to find the similarity of users and more effectively to increase the efficiency of the collaborative filtering method. By identifying the nearest neighbors to the active user, before the genetic algorithm, and by identifying suitable starting points, an effective method for user-based collaborative filtering method has been developed. This package uses an optimization algorithm (continuous genetic algorithm) to directly find the optimal similarities between active users (users for whom current recommendations are made) and others. First, by determining the nearest neighbor and their number, the number of genes in a chromosome is determined. Each gene represents the neighbor's similarity to the active user. By estimating the starting points of the genetic algorithm, it quickly converges to the optimal solutions. The positive point is the independence of the genetic algorithm on the number of data that for big data is an effective help in solving the problem. Package: r-cran-gad Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats Filename: pool/dists/noble/main/r-cran-gad_2.0-1.ca2404.1_all.deb Size: 125864 MD5sum: 2cbfc333d03c708db671defcfb7baa5f SHA1: 432c1496d661269c121a1d5a4f242b3825bec064 SHA256: 380b9ef85b64307fe73a5c3c9fe6bb7f517624b56bd92dc881a5720f54d5abb6 SHA512: 473525265cc1f03acda577f3c3ab5232a9f6e9b31ddb78d643451a4a5e7fcf285471b8bb686cefbb902b9cf92b2c281454319ad5f2aaf6e11461fa397107291d Homepage: https://cran.r-project.org/package=GAD Description: CRAN Package 'GAD' (Analysis of Variance from General Principles) Analysis of complex ANOVA models with any combination of orthogonal/nested and fixed/random factors, as described by Underwood (1997). There are two restrictions: (i) data must be balanced; (ii) fixed nested factors are not allowed. 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Package: r-cran-gadget3 Architecture: all Version: 0.15-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2217 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-rlang, r-cran-tmb Suggests: r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-unittest Filename: pool/dists/noble/main/r-cran-gadget3_0.15-1-1.ca2404.1_all.deb Size: 1294982 MD5sum: 4ee67009cd98acf74fa71465450046ef SHA1: ed9d2263b7154f33cd9fe1405efea568f6082e32 SHA256: 7931cac291136afbc543fb501d4e124b258dccafd28321d10f1401f576cd87fa SHA512: 2b399b007d11e109c046af4b585b267fec366a1862bc15d5f665839bfd31ff7a6952a0c79dbe6a853dc6c3cb30e753376990e883d6780d692288b29e24c24cd2 Homepage: https://cran.r-project.org/package=gadget3 Description: CRAN Package 'gadget3' (Globally-Applicable Area Disaggregated General Ecosystem ToolboxV3) A framework to assist creation of marine ecosystem models, generating either 'R' or 'C++' code which can then be optimised using the 'TMB' package and standard 'R' tools. Principally designed to reproduce gadget2 models in 'TMB', but can be extended beyond gadget2's capabilities. Kasper Kristensen, Anders Nielsen, Casper W. Berg, Hans Skaug, Bradley M. Bell (2016) "TMB: Automatic Differentiation and Laplace Approximation.". Begley, J., & Howell, D. (2004) "An overview of Gadget, the globally applicable area-disaggregated general ecosystem toolbox. ICES.". 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Ding, and D. Cabezon (2019) . Package: r-cran-gains Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1983 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gains_1.2-1.ca2404.1_all.deb Size: 1901032 MD5sum: b33e505d5ff0c96f4d893ecd1bf251f9 SHA1: a0bfe987c74108fafe28ed9b3cc9f785b4eec258 SHA256: ee844fc7d1a0bb1cc7fe5d21928cc4fb93c7f9b34ce010f4f4314c7fdc4a2b0a SHA512: 12f133429f43246b62d9dded46ca4fe96693d2a73cf2cf415bf45ee18f30a519ba3213b44ec71df630ffcd9cbcdfc49aa81ddb25aeab01eb140502345638b58b Homepage: https://cran.r-project.org/package=gains Description: CRAN Package 'gains' (Lift (Gains) Tables and Charts) Constructs gains tables and lift charts for prediction algorithms. Gains tables and lift charts are commonly used in direct marketing applications. The method is described in Drozdenko and Drake (2002), "Optimal Database Marketing", Chapter 11. Package: r-cran-gaipe Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gaipe_1.1-1.ca2404.1_all.deb Size: 38442 MD5sum: 3a3388976763b89467f3dec52b689655 SHA1: 5370512a0e148cab694bc239c74453ad5400c001 SHA256: 8b99998ef29e87c44275b14a41942cec975553bd194c41d46672ff178a441bcf SHA512: f71290c2d6102eb0ab0f661842892f6df2e6928542e565b931c513efbef83c765ad611279144f408637e8d9066a295a821a6b4e8511c4c24aebc1c95efea0557 Homepage: https://cran.r-project.org/package=GAIPE Description: CRAN Package 'GAIPE' (Graphical Extension with Accuracy in Parameter Estimation(GAIPE)) Implements graphical extension with accuracy in parameter estimation (AIPE) on RMSEA for sample size planning in structural equation modeling based on Lin, T.-Z. & Weng, L.-J. (2014) . And, it can also implement AIPE on RMSEA and power analysis on RMSEA. 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Several of these nodes - the "living atlases" () - maintain their own web services using software originally developed by the Atlas of Living Australia ('ALA', ). 'galah' enables the R community to directly access data and resources hosted by 'GBIF' and its partner nodes. Package: r-cran-galahad Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-galahad_2.0.0-1.ca2404.1_all.deb Size: 63834 MD5sum: 6b948875f2ef162180b20f45b2d592bd SHA1: 3fa2aeebff804beb8e9d74b5d58cf9ae5018e756 SHA256: 877a5213f7c754243c381bb2f599fbb548ac5d8c6c860e5574dac260cce77e65 SHA512: 36de401924265e96e87caa8d6cb0d17ff5e9b76a58606343d78ca9d4aa1576080a2f6ea326cff0b2f92b03862c0ef794e939ae82f27ca7db504060b44212cec1 Homepage: https://cran.r-project.org/package=GALAHAD Description: CRAN Package 'GALAHAD' (Geometry-Adaptive Lyapunov-Assured Hybrid Optimizer withSoftplus Reparameterization and Trust-Region Control) Implements the GALAHAD algorithm (Geometry-Adaptive Lyapunov-Assured Hybrid Optimizer), updated in version 2 to replace the hard-clamp positivity constraint of v1 with a numerically smooth softplus reparameterization, add rho-based trust-region adaptation (actual vs. predicted objective reduction), extend convergence detection to include both absolute and relative function-stall criteria, and enrich the per-iteration history with Armijo backtrack counts and trust-region quality ratios. Parameters constrained to be positive (rates, concentrations, scale parameters) are handled in a transformed z-space via the softplus map so that gradients remain well-defined at the constraint boundary. A two-partition API (positive / euclidean) replaces the three-way T/P/E partition of v1; the legacy form is still accepted for backwards compatibility. Designed for biological modeling problems (germination, dose-response, prion RT-QuIC, survival) where rates, concentrations, and unconstrained coefficients coexist. Developed at the Minnesota Center for Prion Research and Outreach (MNPRO), University of Minnesota. Based on Conn et al. (2000) , Barzilai and Borwein (1988) , Xu and An (2024) , Polyak (1969) , Nocedal and Wright (2006, ISBN:978-0-387-30303-1), and Dugas et al. (2009) . Package: r-cran-galaxias Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 990 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corella, r-cran-cli, r-cran-delma, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-usethis, r-cran-withr, r-cran-zip Suggests: r-cran-gt, r-cran-here, r-cran-janitor, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-r.utils, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-galaxias_0.1.2-1.ca2404.1_all.deb Size: 424724 MD5sum: 21d5f6b5a5df77a159f009df4c39b3b8 SHA1: fff6fae0213e4319dc1c98b7727dba7413a02daf SHA256: ffe8d6b596458b07d6f14c1fa6343795b945db2b9be46134cae3aea667cc0af5 SHA512: 8cdcec0be37d3fa792bf14f7019a555a32526de9c3f9a62c04e2ab669d20c1390fd932055f5c14309fb3a9239a2d8cccf3c6ee1a323c7c983709e5bc38336d30 Homepage: https://cran.r-project.org/package=galaxias Description: CRAN Package 'galaxias' (Describe, Package, and Share Biodiversity Data) The Darwin Core data standard is widely used to share biodiversity information, most notably by the Global Biodiversity Information Facility and its partner nodes; but converting data to this standard can be tricky. 'galaxias' is functionally similar to 'devtools', but with a focus on building Darwin Core Archives rather than R packages, enabling data to be shared and re-used with relative ease. For details see Wieczorek and colleagues (2012) . Package: r-cran-galaxyr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-galaxyr_0.1.1-1.ca2404.1_all.deb Size: 273626 MD5sum: eaf997c1746b819cbff3bf989a11565f SHA1: ddabd724d0ad70445cd21b2365dfefe4a8c2c2ce SHA256: 2d55e92afc4863a3a8dfed3b3ae479085f0a06c25b9b12689bb6983741b6128a SHA512: c1037c2f889806c1fcf666f1d58ac7281892668570ae055bcbc6aeaba97b8f3f632e01c189408231e9ccae2447e45ce24bc873aa17bb3f5e16bd44da253e4530 Homepage: https://cran.r-project.org/package=GalaxyR Description: CRAN Package 'GalaxyR' ('Galaxy' API Implementation) On 'Galaxy' platforms like 'Galaxy Europe' , many tools and workflows can run directly on a high-performance computer. 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Package: r-cran-galigor Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-cli, r-cran-crayon, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-rstudioapi, r-cran-tibble, r-cran-gargle, r-cran-ryandexdirect, r-cran-rfacebookstat, r-cran-rvkstat, r-cran-rmytarget, r-cran-rym, r-cran-getproxy, r-cran-rgoogleads, r-cran-rappsflyer Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-galigor_0.2.5-1.ca2404.1_all.deb Size: 73376 MD5sum: 9f33ede59c18f1b1ab295d53285f77b3 SHA1: 1fe64915cd5bfa2313fd49063f7a473c6301d7ff SHA256: f05da5785d3ef4e7633f26e33177771a03ebf94a2bcddf3fc41d265cbbe2248b SHA512: 2fb115f20d48f3dc1c61fac4ab72b37425cf23233ec11ed3505777ba476a5e723b1221faabd506d36451c56950623d88863bfda410ac8387996274340a2f2954 Homepage: https://cran.r-project.org/package=galigor Description: CRAN Package 'galigor' (Collection of Packages for Internet Marketing) Collection of packages for work with API 'Google Ads' , 'Yandex Direct' , 'Yandex Metrica' , 'MyTarget' , 'Vkontakte' , 'Facebook' and 'AppsFlyer' . This packages allows you loading data from ads account and manage your ads materials. Package: r-cran-galisats Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-png Filename: pool/dists/noble/main/r-cran-galisats_2.2.0-1.ca2404.1_all.deb Size: 191730 MD5sum: 18d6482b2eac75472c5c69ac0ac22068 SHA1: 60519280a6782a3e9d2790db2ff004415ae57bb9 SHA256: dadaf7de6b223010c43785ae13ed886ab441fa8b46dbee5f84509239c3500cb7 SHA512: 6a408d5fcd4517727e74e3e1625b8b11ca9cb5fde92771e8df96bcae89b28908e31eea2881f001c1c9a72fb61d5ba76acfb00f6d770693d704f883e962f832a3 Homepage: https://cran.r-project.org/package=galisats Description: CRAN Package 'galisats' (Configuration of Jupiter's Four Largest Satellites) Calculate, plot and animate the configuration of Jupiter's four largest satellites (known as Galilean satellites) for a given date and time (UTC - Coordinated Universal Time). The galsat() function returns numerical values of the satellites’ positions. x – the apparent rectangular coordinate of the satellite with respect to the center of Jupiter’s disk in the equatorial plane in the units of Jupiter’s equatorial radius; X is positive toward the west, y – the apparent rectangular coordinate of the satellite with respect to the center of Jupiter’s disk from the equatorial plane in the units of Jupiter’s equatorial radius; Y is positive toward the north. For more details see Meeus (1988, ISBN 0-943396-22-0) "Astronomical Formulae for Calculators". The galsat_animate() function creates an animation of the Galilean satellites' positions. You provide the starting time, duration, the time step between frames, and the pause between frames. The function delta_t() returns the value of delta-T in units of seconds. Package: r-cran-gallery Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-pracma Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gallery_1.0.0-1.ca2404.1_all.deb Size: 172230 MD5sum: d72a122d41c8e2eb6a4266edc5a381fb SHA1: 3da1e0acceef68e7cb44fdd743128f1240f49539 SHA256: da296612eb836bb5c69b87003a3d6dd6950465c6d476660c0b929359624ac1f9 SHA512: 730efc375880ffe35e9dfd282b4dbc7177f1b1421d3559d38d1b4bff8905e83283410e6edca9bc2de191b6702e3c9b256658da7aebf99ab49fa46f275051a690 Homepage: https://cran.r-project.org/package=gallery Description: CRAN Package 'gallery' (Generate Test Matrices for Numerical Experiments) Generates a variety of structured test matrices commonly used in numerical linear algebra and computational experiments. 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The Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package designed to provide an intuitive and straightforward environment to annotate positional candidate genes and QTLs from high-throughput genetic studies in livestock. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies, etc.), and QTL enrichment in different livestock species including cattle, pigs, sheep, and chicken, among others. Package: r-cran-galts Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-genalg, r-cran-deoptim Filename: pool/dists/noble/main/r-cran-galts_1.3.2-1.ca2404.1_all.deb Size: 26652 MD5sum: ffbdecff88828dc004169b4814993342 SHA1: ea42351259d001945c7738a097f9ae503d9ea00b SHA256: 166ab1beab42bb059344ebd11dab0841d1a20710dc73fc8b4947009ef9a69a22 SHA512: fdcd877ca9fa9a534b3f0b1a6922dfe2748fdcbf0d49e07425848f5d26b3e981d7dca6d70432f6471c690fe3812b870d00a78d3a06283d32aed162c8b53b3bd3 Homepage: https://cran.r-project.org/package=galts Description: CRAN Package 'galts' (Genetic Algorithms and C-Steps Based LTS (Least Trimmed Squares)Estimation) Includes the ga.lts() function that estimates LTS (Least Trimmed Squares) parameters using genetic algorithms and C-steps. ga.lts() constructs a genetic algorithm to form a basic subset and iterates C-steps as defined in Rousseeuw and van-Driessen (2006) to calculate the cost value of the LTS criterion. OLS (Ordinary Least Squares) regression is known to be sensitive to outliers. A single outlying observation can change the values of estimated parameters. LTS is a resistant estimator even the number of outliers is up to half of the data. This package is for estimating the LTS parameters with lower bias and variance in a reasonable time. Version >=1.3 includes the function medmad for fast outlier detection in linear regression. Package: r-cran-galvanizer Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-rlang, r-cran-tidyjson, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-galvanizer_0.5.3-1.ca2404.1_all.deb Size: 230096 MD5sum: a131c1da498131d68fe6782688400203 SHA1: 7ea81e432c4571d731c29a32b83c6ef119f82ad9 SHA256: 5bc162832d5d8ffd076ffc703368b39e04aa4692a499ec577e0b53edd373b219 SHA512: a2ab969685dd87ef70da1245981034053353367f086514cea601e3a7952508965a82b1ff432386271a41cb122195f4d87831b0de91ecceb18be015121d72ca90 Homepage: https://cran.r-project.org/package=galvanizer Description: CRAN Package 'galvanizer' (Interface to Galvanize 'Highbond' Internal Audit Software) An R interface to the Galvanize 'Highbond' API . Package: r-cran-gam.hp Architecture: all Version: 0.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-gam.hp_0.0-5-1.ca2404.1_all.deb Size: 49288 MD5sum: d24f9d366b67f51445fdbfb1f7ba6b96 SHA1: bafbc876faffa9d03066de4df388ff90db702e53 SHA256: 32bb05c8ec08ba986c577838175b59710cd695501892cf22339543d08f133a25 SHA512: 4c31b5919732c9bc5c912148eee28677d91e9d95ea74a31ad07e695f3db35fd963f65d1f5afe27a5169b6a8dceb50bf4db15b9e80be3a2ed181f608cd9cfa5a9 Homepage: https://cran.r-project.org/package=gam.hp Description: CRAN Package 'gam.hp' (Hierarchical Partitioning of Adjusted R2 and Explained Deviancefor Generalized Additive Models) Conducts hierarchical partitioning to calculate individual contributions of each predictor towards adjusted R2 and explained deviance for generalized additive models based on output of 'gam()' and 'bam()' in 'mgcv' package, applying the algorithm in this paper: Lai(2024) . Package: r-cran-gamabiomd Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-traits, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-bioc-biostrings, r-cran-heatmaply, r-cran-reshape2, r-cran-dendextend, r-cran-ape Filename: pool/dists/noble/main/r-cran-gamabiomd_0.2.0-1.ca2404.1_all.deb Size: 87618 MD5sum: 705a0054298a59d477ccb0cfb7323fbd SHA1: 441695e58b952768b5db9f8e8f98c6c31ec8bc92 SHA256: 9ea055eeb60fc83b8fb362a5d656f3b365da0ab08d3256e53041aef4d0f7bb62 SHA512: aab4168845f32ad961812ff1bb05a10830ee2eb23c47bdaba0af31b8622b4fb390335d48c07cd25dd03433a85e19cb22bdb97393adf594b17d894828a780694b Homepage: https://cran.r-project.org/package=GaMaBioMD Description: CRAN Package 'GaMaBioMD' (Diversity Analysis for Sequence Data) The full form is Garai and Mantri Biological Material Diversity. It is an R package designed for the calculation of biological diversity using sequence data. It simplifies the process by requiring only sample IDs and accession numbers. Whether you're analyzing genetic or microbial diversity, It provides efficient tools for diversity analysis. Serially one should go for the functions as presented here expand_accession_ranges(), get_sequence_information(), preprocess_for_alignment(), write_fasta(), SampleID_vs_NumSequences(), data_sampling(), alignment_info(), compute_average_similarity_matrix(), generate_heatmaps(), clustering_average_similarity(), clustering_percent_similarity(), bubble_plot_count(), bubble_plot_percentage(), tree_average_similarity(), tree_percent_similarity(). Till date there are total 15 functions. More details can be found in Faith (1992) . 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Package: r-cran-gasmodel Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1724 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-arrangements, r-cran-copula, r-cran-dplyr, r-cran-ggplot2, r-cran-matrix, r-cran-mvnfast, r-cran-nloptr, r-cran-numderiv, r-cran-pracma, r-cran-tidyr Suggests: r-cran-hms, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gasmodel_0.6.2-1.ca2404.1_all.deb Size: 1237756 MD5sum: 9dadab8fb1bd51241681a2f68fd099a3 SHA1: e8e07536926096694e86c3bfdf707f19137a8c6c SHA256: eb32559bdcf260b0de3094d7ac6fd4c0cde442584a4bba91c8072cb87e7c7133 SHA512: 8e1bd07fb1061de04706cdeb04189cbed95557b4eaf4ca566bd71cd506f4fda0a39757baeabfe19d0aeab774806f946f15623fe3d1a2b64df5d6e49bfe3397c9 Homepage: https://cran.r-project.org/package=gasmodel Description: CRAN Package 'gasmodel' (Generalized Autoregressive Score Models) Estimation, forecasting, and simulation of generalized autoregressive score (GAS) models of Creal, Koopman, and Lucas (2013) and Harvey (2013) . Model specification allows for various data types and distributions, different parametrizations, exogenous variables, joint and separate modeling of exogenous variables and dynamics, higher score and autoregressive orders, custom and unconditional initial values of time-varying parameters, fixed and bounded values of coefficients, and missing values. Model estimation is performed by the maximum likelihood method. Package: r-cran-gatepoints Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gatepoints_0.1.5-1.ca2404.1_all.deb Size: 296790 MD5sum: 030d861397d960418d041712ec947cc7 SHA1: ffe455d45dd3f41a5d82c2e7b2f847ef3d4093ec SHA256: 1632e10290a05a804802f302a17dbfe1efda420fe67dd2d2d71223f8b64e6cec SHA512: 4c6ad26aae14a3d86d4af95d4f916fc427187d0150473e4768218e2fcc73e101b27640decebc8817d77eb0d6142badd8654908666d06957eb64ab994002ea29f Homepage: https://cran.r-project.org/package=gatepoints Description: CRAN Package 'gatepoints' (Easily Gate or Select Points on a Scatter Plot) Allows user to choose/gate a region on the plot and returns points within it. 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For a two-group comparison, the 'gateR' package uses the spatial relative risk function estimated using the 'sparr' package. Details about the 'sparr' package methods can be found in the tutorial: Davies et al. (2018) . Details about kernel density estimation can be found in J. F. Bithell (1990) . More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) . 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Package: r-cran-gauser Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gauser_1.3-1.ca2404.1_all.deb Size: 290764 MD5sum: 22d9088f88c3010fdf711eee10c39f40 SHA1: 8dae083f72ee926af4a06f2dcffc2bdc033d9af2 SHA256: 167c40e82967ba5f9bb6a696a3ca43cff40cbfd4aa0980366cb5a6f14053b14d SHA512: 7ae18f87a687a9d747ce52341d39ee0cd686bb78cf1dceb696dff4b6d6b72d52e7268088ef9768603b7030b0482904e2f77b253224ddd98c2cbbed34fcfadb4f Homepage: https://cran.r-project.org/package=gauseR Description: CRAN Package 'gauseR' (Lotka-Volterra Models for Gause's 'Struggle for Existence') A collection of tools and data for analyzing the Gause microcosm experiments, and for fitting Lotka-Volterra models to time series data. 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The lack of awareness among users of vegetation indices about this non-Gaussian nature could lead to incorrect statistical modeling and interpretation. This package provides tools to accurately handle and analyse such ratios: density function, parameter estimation, simulation. An example on the study of chlorophyll fluorescence can be found in A. El Ghaziri et al. (2023) and another method for parameter estimation is given in Bouhlel et al. (2023) . 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A suggestion is to start by working with functions SuppressSmallCounts() and SuppressDominantCells(). These functions use primary suppression functions for the minimum frequency rule and the dominance rule, respectively. Novel functionality for suppression of disclosive cells is also included. General primary suppression functions can be supplied as input to the general working horse function, GaussSuppressionFromData(). Suppressed frequencies can be replaced by synthetic decimal numbers as described in Langsrud (2019) . 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(2021) presented the approach based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi-trait dissimilarity. This is done using either an analytic or a numerical solution, both available in the function. 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Density, cumulative distribution function, quantiles and moments of the distribution are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulas for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted by the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the visualization and analysis of the results are provided. Variance estimation of the parameters is provided for the method of maximum pseudo-likelihood estimation. A mixture distribution based on the compounding property of the GB2 is presented (denoted as "compound" in the documentation). This mixture distribution is based on the discretization of the distribution of the underlying random scale parameter. The discretization can be left or right tail. Density, cumulative distribution function, moments and quantiles for the mixture distribution are provided. The compound mixture distribution is fitted using the method of maximum pseudo-likelihood estimation. The fit can also incorporate the use of auxiliary information. In this new version of the package, the mixture case is complemented with new functions for variance estimation by linearization and comparative density plots. Package: r-cran-gb2group Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gb2, r-cran-minpack.lm, r-cran-ineq, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-gb2group_0.3.0-1.ca2404.1_all.deb Size: 186584 MD5sum: 182658d58d794e3b5df0ea5d547fb180 SHA1: d0ec5efd84a55c636acb4ad75edbd1fb1f8b81ed SHA256: 3547130b7ad2efeb1a045752c224558fd3d5f29ed42c5b9a52720c48f5fba710 SHA512: dd7cb7d9942daec62a6d37ee756320f41ed7a09aa12196a67f79269b68399bc730331aed80880e15b1bcf588bee748af4f96cf21fa7e172436714782a56d0af7 Homepage: https://cran.r-project.org/package=GB2group Description: CRAN Package 'GB2group' (Estimation of the Generalised Beta Distribution of the SecondKind from Grouped Data) Estimation of the generalized beta distribution of the second kind (GB2) and related models using grouped data in form of income shares. The GB2 family is a general class of distributions that provides an accurate fit to income data. 'GB2group' includes functions to estimate the GB2, the Singh-Maddala, the Dagum, the Beta 2, the Lognormal and the Fisk distributions. 'GB2group' deploys two different econometric strategies to estimate these parametric distributions, the equally weighted minimum distance (EWMD) estimator and the optimally weighted minimum distance (OMD) estimator. Asymptotic standard errors are reported for the OMD estimates. Standard errors of the EWMD estimates are obtained by Monte Carlo simulation. See Jorda et al. (2018) for a detailed description of the estimation procedure. Package: r-cran-gb5mcpred Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4599 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-tidyverse, r-cran-seqinr, r-bioc-biostrings, r-cran-splitstackshape, r-cran-entropy, r-cran-party, r-cran-stringr, r-cran-tibble, r-cran-doparallel, r-cran-e1071, r-cran-caret, r-cran-randomforest, r-cran-gbm, r-cran-foreach, r-cran-ftrcool, r-cran-iterators Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gb5mcpred_0.1.0-1.ca2404.1_all.deb Size: 2523348 MD5sum: fa0cbb6efffeb4af0b633916118cc387 SHA1: 3270826e0b766af2d62442aee6c693a0968099fa SHA256: 57c4b8359c2433d71dba8c17dfa8f928c3139e87d9270462eed08f431420b069 SHA512: 0ccf104f2f3f5b253d157af5b062fbcc2e84931ebd89aa72dfa3d937e98794c0eb27788b6b3518a908eed81365c22f9a110922c6fb38f1c1ecde97ca54013116 Homepage: https://cran.r-project.org/package=GB5mcPred Description: CRAN Package 'GB5mcPred' (Gradient Boosting Algorithm for Predicting Methylation States) DNA methylation of 5-methylcytosine (5mC) is the result of a multi-step, enzyme-dependent process. Predicting these sites in-vitro is laborious, time consuming as well as costly. This ' Gb5mC-Pred ' package is an in-silico pipeline for predicting DNA sequences containing the 5mC sites. It uses a machine learning approach which uses Stochastic Gradient Boosting approach for prediction of the sequences with 5mC sites. This package has been developed by using the concept of Navarez and Roxas (2022) . Package: r-cran-gbass Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-gigrvg, r-cran-bass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lhs, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gbass_2.0.1-1.ca2404.1_all.deb Size: 200872 MD5sum: ca4d2e4e01d8ef017f75a5db4a740020 SHA1: 196567f815d9d55134841244e87cbfb464997503 SHA256: a0a1d903851f3a0cf28611a61c29562134cae69894596ff898533e46a8d76cb0 SHA512: ee0523521b5ee07bd8b6176a90342ee88446dffed3f3273d24e5fd0d649dbff82c888d6b3240c71ba842b346a68ede21acd84f5b152d9bead974c36b624f30e8 Homepage: https://cran.r-project.org/package=GBASS Description: CRAN Package 'GBASS' (Generalized Bayesian Adaptive Smoothing Splines) Bayesian nonlinear regression under a range of likelihood models using generalized Bayesian adaptive smoothing splines. Robust regression with Student's t likelihoods, quantile regression, and related latent-scale models are included as special cases. Package: r-cran-gbfs Architecture: all Version: 1.3.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-stringr, r-cran-jsonlite, r-cran-lubridate, r-cran-httr, r-cran-purrr, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-gbfs_1.3.10-1.ca2404.1_all.deb Size: 71614 MD5sum: adf30bc45461f459af0ed6395713f320 SHA1: 4389c94579cb6e83e8c7b851c16edc3cc55020d2 SHA256: dff3bb2bc0dc97cb663c3c098610250b0c76fb7acb063d0e5e9d2baeaa470efd SHA512: 46027a464f0d43bcb297884ec661e23cead1d18ee32c34b2d741098b3752ed15770d4d774ede59a0e56786f265743182b86c898933a861730bed1d5efd1a231b Homepage: https://cran.r-project.org/package=gbfs Description: CRAN Package 'gbfs' (Interface with Live Bikeshare Data) Supplies a set of functions to interface with bikeshare data following the General Bikeshare Feed Specification, allowing users to query and accumulate tidy datasets for specified cities/bikeshare programs. Package: r-cran-gbifdb Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-duckdbfs Suggests: r-cran-spelling, r-cran-dbplyr, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-minioclient Filename: pool/dists/noble/main/r-cran-gbifdb_1.0.0-1.ca2404.1_all.deb Size: 293898 MD5sum: 4c84780693df9a2ab671badf6bb4d009 SHA1: dfe8da87c7d4e2b9df663e4919b2d1e337c706d3 SHA256: 3eeac72fd7e07d6db1e6708afd788219b5e98ad54dffdad021870834eed96060 SHA512: 608cb83c84f56b97095089e3f6e83e17fd3859642e4cd0365f49e3b530d5492e78ebfa8e32c48f02f4b928c7d952d8430cbd9df87eb6838a3fc1b55dfd75b6c8 Homepage: https://cran.r-project.org/package=gbifdb Description: CRAN Package 'gbifdb' (High Performance Interface to 'GBIF') A high performance interface to the Global Biodiversity Information Facility, 'GBIF'. In contrast to 'rgbif', which can access small subsets of 'GBIF' data through web-based queries to a central server, 'gbifdb' provides enhanced performance for R users performing large-scale analyses on servers and cloud computing providers, providing full support for arbitrary 'SQL' or 'dplyr' operations on the complete 'GBIF' data tables (now over 1 billion records, and over a terabyte in size). 'gbifdb' accesses a copy of the 'GBIF' data in 'parquet' format, which is already readily available in commercial computing clouds such as the Amazon Open Data portal and the Microsoft Planetary Computer, or can be accessed directly without downloading, or downloaded to any server with suitable bandwidth and storage space. The high-performance techniques for local and remote access are described in and respectively. Package: r-cran-gbm.auto Architecture: all Version: 2024.10.01-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3850 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-beepr, r-cran-dismo, r-cran-dplyr, r-cran-gbm, r-cran-ggmap, r-cran-ggplot2, r-cran-ggspatial, r-cran-lifecycle, r-cran-lubridate, r-cran-mapplots, r-cran-metrics, r-cran-readr, r-cran-sf, r-cran-stars, r-cran-starsextra, r-cran-stringi, r-cran-tidyselect, r-cran-viridis Filename: pool/dists/noble/main/r-cran-gbm.auto_2024.10.01-1.ca2404.1_all.deb Size: 3852258 MD5sum: a3e00ac6b4ca3dd9a23a3c2f637b3c78 SHA1: 1859d4593c46df2754269139e87aea4ebef84e87 SHA256: e79aeb976fd567ba0915c33cf1c7bc0a9116b0390c4deb72bc6299249d5c7143 SHA512: 1987e7f5aea9ecffd6711d756bf4eccfbe220259d9b828cdf62615a9203868b96f864a3cef4bc671b3ae98f9d0586e1eff3093a2e49ac11551e13737d08c2808 Homepage: https://cran.r-project.org/package=gbm.auto Description: CRAN Package 'gbm.auto' (Automated Boosted Regression Tree Modelling and Mapping Suite) Automates delta log-normal boosted regression tree abundance prediction. Loops through parameters provided (LR (learning rate), TC (tree complexity), BF (bag fraction)), chooses best, simplifies, & generates line, dot & bar plots, & outputs these & predictions & a report, makes predicted abundance maps, and Unrepresentativeness surfaces. Package core built around 'gbm' (gradient boosting machine) functions in 'dismo' (Hijmans, Phillips, Leathwick & Jane Elith, 2020 & ongoing), itself built around 'gbm' (Greenwell, Boehmke, Cunningham & Metcalfe, 2020 & ongoing, originally by Ridgeway). Indebted to Elith/Leathwick/Hastie 2008 'Working Guide' ; workflow follows Appendix S3. See for published guides and papers using this package. Package: r-cran-gbm2sas Architecture: all Version: 4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gbm Filename: pool/dists/noble/main/r-cran-gbm2sas_4.0-1.ca2404.1_all.deb Size: 22478 MD5sum: 6040881564630182bb20272c35afc1a9 SHA1: f02ae769dc112d37994b54ff913774848fed1a7d SHA256: 98d55bacfcff1f4a180bf5e6749ebefda033dde6eb3809d8fcd5467ee43f1a95 SHA512: e2beae1beda66c4ed56a8effa86a164dea69f281fc3adf5de175018b66d845b48c6271010667f83d59e8c26ddf4459df300ed74d5a35678e9e781399e78fb961 Homepage: https://cran.r-project.org/package=gbm2sas Description: CRAN Package 'gbm2sas' (Convert GBM Object Trees to SAS Code) Writes SAS code to get predicted values from every tree of a gbm.object. Package: r-cran-gbmt Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-gbmt_0.1.4-1.ca2404.1_all.deb Size: 172326 MD5sum: fbaf8df828ac45d166a2e0575585500e SHA1: adfb999ed90f1b611b71ff1f2e0b652002ff54b6 SHA256: d11732a9386662711f20cb29c4fee2c43b983d11ef5003c36589cb92d7fb5bbe SHA512: f679c4269ef4fbad7e20b9303bfc7fac7ef1aa51efb6402ff1f536c4e9bc0effd1d311f8adc1ac6730f2a75c1cd6002c3e18daafe01282497e355a3533eebabc Homepage: https://cran.r-project.org/package=gbmt Description: CRAN Package 'gbmt' (Group-Based Multivariate Trajectory Modeling) Estimation and analysis of group-based multivariate trajectory models (Nagin, 2018 ; Magrini, 2022 ). The package implements an Expectation-Maximization (EM) algorithm allowing unbalanced panel and missing values, and provides several functionalities for prediction and graphical representation. Package: r-cran-gbrd Architecture: all Version: 0.4.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gbrd_0.4.12-1.ca2404.1_all.deb Size: 46758 MD5sum: 0dd2bb1e2838038b0c924453f6af02f6 SHA1: 96a56ddf63864a5c6facad264fee273a0890d3c2 SHA256: 3ba237e03dbc29ce6e6f3785b0c66995e989d1cf6787b8684c44eedcaa1a6b6a SHA512: 76e7cb42c1d88b6621af6f58294df6a239eb57f5c767677ac4668ce558b5e7a8f76e02039179f538af4d70ca6a78db3e099e3543d93054478af2ea2ab85cd214 Homepage: https://cran.r-project.org/package=gbRd Description: CRAN Package 'gbRd' (Utilities for Processing Rd Objects and Files) Provides utilities for processing Rd objects and files. 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Package: r-cran-gbs2ploidy Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1324 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rjags Filename: pool/dists/noble/main/r-cran-gbs2ploidy_1.0-1.ca2404.1_all.deb Size: 1320486 MD5sum: 9f5f5d3be0d9e94817924a9bf150c754 SHA1: 83bf5893730772b98657b19bcb5a8337f9732ec6 SHA256: fde9ee5f18ff590677d582a7a40945215b932ccf1746ece6ea4a807999786426 SHA512: 2e3e1bc495c40055db76d2251f8dc19c2511fa933d2dffcc1bfc91bc097649d5477debf2e7b1c9a24c5f11fcbf132753d5e037eb8719257ed4e79400015fa534 Homepage: https://cran.r-project.org/package=gbs2ploidy Description: CRAN Package 'gbs2ploidy' (Inference of Ploidy from (Genotyping-by-Sequencing) GBS Data) Functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework. Package: r-cran-gbts Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-gbm, r-cran-earth Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gbts_1.2.0-1.ca2404.1_all.deb Size: 115954 MD5sum: a09bd9462cc0b3850740bb37e4b3463f SHA1: c901cb03a2ee1fca6a6fe451902bbf94d235275c SHA256: c435004f8b0d6a91103b62ab375657d16b425cc0dd12ecfa55cb8bbf996d5d58 SHA512: 32e72311f4583a1fadf772d0dd38945c4f001c86632a9697f418fb82560db05692a6b35e3835847f5b7791aa3a1d567e93bb3636e4c5ee9c94467a1301d1c8f6 Homepage: https://cran.r-project.org/package=gbts Description: CRAN Package 'gbts' (Hyperparameter Search for Gradient Boosted Trees) An implementation of hyperparameter optimization for Gradient Boosted Trees on binary classification and regression problems. The current version provides two optimization methods: Bayesian optimization and random search. Instead of giving the single best model, the final output is an ensemble of Gradient Boosted Trees constructed via the method of ensemble selection. Package: r-cran-gbutils Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-testthat, r-cran-classgraph, r-bioc-graph, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-gbutils_0.5.1-1.ca2404.1_all.deb Size: 315668 MD5sum: af1279cf64dcc032d518b5ac996bf97b SHA1: a0a958430d2e57740ac0717758cc7478ed42e0b2 SHA256: 1d3bf06ba894d1bdeac1ee7ab428364b88429a726f8f7f934a7531f57d07f8f5 SHA512: 23cdd4e13ce9dfacf7f1261a362274510d59fe1c50a0d40c96a01f1cb0eab893994e8ffd7b3bf89a32e42f5b0cf874da0ba9b7a4bf89b746461cc6090725e021 Homepage: https://cran.r-project.org/package=gbutils Description: CRAN Package 'gbutils' (Utilities for Simulation, Plots, Quantile Functions andProgramming) Plot density and distribution functions with automatic selection of suitable regions. Numerically invert (compute quantiles) distribution functions. Simulate real and complex numbers from distributions of their magnitude and arguments. Optionally, the magnitudes and/or arguments may be fixed in almost arbitrary ways. Create polynomials from roots given in Cartesian or polar form. Small programming utilities: check if an object is identical to NA, count positional arguments in a call, set intersection of more than two sets, check if an argument is unnamed, compute the graph of S4 classes in packages. Package: r-cran-gcalcium Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-catools, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gcalcium_1.0.0-1.ca2404.1_all.deb Size: 95582 MD5sum: e68140a38a598adfba3f22f11d824623 SHA1: 708e655f715417b99cbaa42c761b91789a326d51 SHA256: 3612a5a5ca87a536f42c0c017db0df7a582e3946c8d4498c716c7ae5e464b74c SHA512: 2a28b8c82a86f2cf5170a73cfda779cab145b03059250d18c36a8835b2b4a203048431d0efb94df8ae28296c297f523753f9a264c8a0bc682b7ba375787a6a49 Homepage: https://cran.r-project.org/package=GCalcium Description: CRAN Package 'GCalcium' (A Data Manipulation and Analysis Package for Calcium IndicatorData) Provides shortcuts in extracting useful data points and summarizing waveform data. It is optimized for speed to work efficiently with large data sets so you can get to the analysis phase more quickly. It also utilizes a user-friendly format for use by both beginners and seasoned R users. Package: r-cran-gcalignr Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2216 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-readr, r-cran-reshape2, r-cran-stringr, r-cran-pbapply, r-cran-tibble Suggests: r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-testthat, r-cran-vegan Filename: pool/dists/noble/main/r-cran-gcalignr_1.0.7-1.ca2404.1_all.deb Size: 1575012 MD5sum: e2cb1e7359c95f7db6fb92673c9c2eef SHA1: 394fff9a6f0976c68e22d012fa8fd53393f90714 SHA256: 7c9d55df9d34dc6b3f09542e9ee016292633a31c8d6255eb41b6f3a9340a8a8a SHA512: 977b7b7c9faa61f3ca60281b4eb3e033e16c96b3f754cf100515d5788d2eab62cb5679ff5ec9d2d529bd1d745b5c9c64bbc3367e2bbe67773014cda541383380 Homepage: https://cran.r-project.org/package=GCalignR Description: CRAN Package 'GCalignR' (Simple Peak Alignment for Gas-Chromatography Data) Aligns peak based on peak retention times and matches homologous peaks across samples. The underlying alignment procedure comprises three sequential steps. (1) Full alignment of samples by linear transformation of retention times to maximise similarity among homologous peaks (2) Partial alignment of peaks within a user-defined retention time window to cluster homologous peaks (3) Merging rows that are likely representing homologous substances (i.e. no sample shows peaks in both rows and the rows have similar retention time means). The algorithm is described in detail in Ottensmann et al., 2018 . Package: r-cran-gcbd Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-dbi, r-cran-rsqlite, r-cran-plyr, r-cran-reshape, r-cran-lattice Filename: pool/dists/noble/main/r-cran-gcbd_0.2.7-1.ca2404.1_all.deb Size: 240830 MD5sum: d941afc0e948892df3c7903386d05746 SHA1: d08e72295b304615f66af24c61e8c9df9d006020 SHA256: de03ac5385d0d76655f38dd922716fd83038872d2ed2bb2d0fd152103bc94689 SHA512: c5e100e205cf4324357ffb6d3071319d444c164c6fdbc25c63e86e0654bc9e253f09e60258b2de166ac8baa70f22e88d015044624b6e329249fed85042bd1d51 Homepage: https://cran.r-project.org/package=gcbd Description: CRAN Package 'gcbd' ('GPU'/CPU Benchmarking in Debian-Based Systems) 'GPU'/CPU Benchmarking on Debian-package based systems This package benchmarks performance of a few standard linear algebra operations (such as a matrix product and QR, SVD and LU decompositions) across a number of different 'BLAS' libraries as well as a 'GPU' implementation. To do so, it takes advantage of the ability to 'plug and play' different 'BLAS' implementations easily on a Debian and/or Ubuntu system. The current version supports - 'Reference BLAS' ('refblas') which are un-accelerated as a baseline - Atlas which are tuned but typically configure single-threaded - Atlas39 which are tuned and configured for multi-threaded mode - 'Goto Blas' which are accelerated and multi-threaded - 'Intel MKL' which is a commercial accelerated and multithreaded version. As for 'GPU' computing, we use the CRAN package - 'gputools' For 'Goto Blas', the 'gotoblas2-helper' script from the ISM in Tokyo can be used. For 'Intel MKL' we use the Revolution R packages from Ubuntu 9.10. 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Carmona R, Gulaya S, Murphy JD, Rose BS, Wu J, Noticewala S,McHale MT, Yashar CM, Vaida F, and Mell LK (2014) . 2. Carmona R, Zakeri K, Green G, Hwang L, Gulaya S, Xu B, Verma R, Williamson CW, Triplett DP, Rose BS, Shen H, Vaida F, Murphy JD, and Mell LK (2016) . 3. Lunn, Mary, and Don McNeil (1995) . 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Package: r-cran-gcuber Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3468 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-readr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-gcuber_0.1.3-1.ca2404.1_all.deb Size: 2579664 MD5sum: 3d4aebabfa3a0a3615efb79c0e87bb59 SHA1: 594e2c4482ab5a0cbf2e9da51f7bd6239ce06da7 SHA256: 4e3753c6ed13f3978f877722a4e3df58cde84b51b2d59285b87f008a237b67c2 SHA512: 0216ca4dc477eee044c06c277a4756e862a725b4304940ae57168c511bc1f76cc2894ed5e718acf6dd1f44f26586628556fcbd0002a725600a7b3a76b91fa695 Homepage: https://cran.r-project.org/package=GCubeR Description: CRAN Package 'GCubeR' (Estimation of Forest Volume, Biomass, and Carbon) Provides tools for estimating forest metrics such as stem volume, biomass, and carbon using regional allometric equations. The package implements widely used models including Dagnelie P., Rondeux J. & Palm R. (2013, ISBN:9782870161258) "Cubage des arbres et des peuplements forestiers - Tables et equations" , Vallet P., Dhote J.-F., Le Moguedec G., Ravart M. & Pignard G. (2006) "Development of total aboveground volume equations for seven important forest tree species in France" , Pauwels D. & Rondeux J. (1999, ISSN:07779992) "Tarifs de cubage pour les petits bois de meleze (Larix sp.) en Ardenne" , Massenet J.-Y. (2006) "Chapitre IV: Estimation du volume" , France Valley (2025) "Bilan Carbone Forestier - Methodologie" . Its modular structure allows transparent integration of bibliographic or user-defined allometric relationships. Package: r-cran-gcxgclab Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3396 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ncdf4, r-cran-dplyr, r-cran-ggplot2, r-cran-ptw, r-cran-nilde, r-cran-zoo, r-cran-nls.multstart, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gcxgclab_1.1.0-1.ca2404.1_all.deb Size: 2535540 MD5sum: e9d7e7361feb84b31921c8be6900ea2c SHA1: 513ced5925a45a3157fa337ec4bce37101db3bad SHA256: 4c7133768568d11d03ef8045b56dea30bc31f690c8225aae9457efde240bc3e9 SHA512: 774b30ab9232afb4cdf646617c0e78892b2c0036f3ca0201b26a56800689ed753a1d31d544c6e965ea3fa196cdd9986184c3e42089a97129896d963df984f6e8 Homepage: https://cran.r-project.org/package=gcxgclab Description: CRAN Package 'gcxgclab' (GCxGC Preprocessing and Analysis) Provides complete detailed preprocessing of two-dimensional gas chromatogram (GCxGC) samples. Baseline correction, smoothing, peak detection, and peak alignment. Also provided are some analysis functions, such as finding extracted ion chromatograms, finding mass spectral data, targeted analysis, and nontargeted analysis with either the 'National Institute of Standards and Technology Mass Spectral Library' or with the mass data. There are also several visualization methods provided for each step of the preprocessing and analysis. Package: r-cran-gd Architecture: all Version: 10.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3835 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bammtools Suggests: r-cran-gdverse, r-cran-knitr, r-cran-rmarkdown, r-cran-spedm Filename: pool/dists/noble/main/r-cran-gd_10.9-1.ca2404.1_all.deb Size: 2711670 MD5sum: 59be64380d3871e774b6ffff4bb3fb0a SHA1: 1bbdf2c29555d1661aa66d109af915aed1426334 SHA256: 71065fcaa8d7d869bf63b0f4542e795b91465e2a0ba9d9d9c444fd1f4470ab4a SHA512: 5f15f91fbf3ce171e30b47300770f4dc4afc2975f566ec360346f66b45610c86a1d8e4cdd4c1da393e15b979e748b3d199e231915c073a9521e2599d6a63fe2b Homepage: https://cran.r-project.org/package=GD Description: CRAN Package 'GD' (Geographical Detectors for Assessing Spatial Factors) Geographical detectors for measuring spatial stratified heterogeneity, as described in Jinfeng Wang (2010) and Jinfeng Wang (2016) . Includes the optimal discretization of continuous data, four primary functions of geographical detectors, comparison of size effects of spatial unit and the visualizations of results. To use the package and to refer the descriptions of the package, methods and case datasets, please cite Yongze Song (2020) . The model has been applied in factor exploration of road performance and multi-scale spatial segmentation for network data, as described in Yongze Song (2018) and Yongze Song (2020) , respectively. 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Package: r-cran-gdelttools Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-dplyr, r-cran-datetimeutils, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gdelttools_1.7-1.ca2404.1_all.deb Size: 51478 MD5sum: 39d6f69d9eee792e037186282b41c90f SHA1: d7a6b041f49423e0bf23484c398b6e7286df5199 SHA256: 30270600f11ac5862b68f6ee2e875ea421b0e8215ae3153598bf183e4791eba7 SHA512: 656d4191cbbd8af9e438886a827744ab8802cfecf05a700f1b12b8162b478733fc24532a198c98e35299db53c71553c9ad8ed886db38cea145e6c0666a51d54b Homepage: https://cran.r-project.org/package=GDELTtools Description: CRAN Package 'GDELTtools' (Download, Slice, and Normalize GDELT V1 Event and Sentiment APIData) The GDELT V1 Event data set is over 41 GB now and growing 250 MB a month. The number of source articles has increased over time and unevenly across countries. 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Package: r-cran-gdi Architecture: all Version: 1.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jpeg, r-cran-png Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gdi_1.10.0-1.ca2404.1_all.deb Size: 197180 MD5sum: 1d7e97295b06e85065b7799d5ef19e62 SHA1: bb5756aba84eec257f6edbc2e8cb622abc31fe19 SHA256: 4e7b396b7325a654c6e2696f6d45c8435b516a415831dee81510e83c659b2308 SHA512: a47daa7fa4f6ea458399df379adeb29e6eec3015d1132a8377deea586fb85f8ef211522727703cbbf7bc8cb6cca6c19f045e529f1693222c9c0d28f9f61f7453 Homepage: https://cran.r-project.org/package=gdi Description: CRAN Package 'gdi' (Volumetric Analysis using Graphic Double Integration) Tools implementing an automated version of the graphic double integration technique (GDI) for volume implementation, and some other related utilities for paleontological image-analysis. 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The epidemic models considered are distance-based model within Susceptible-Infectious-Removed (SIR) compartmental frameworks. Package: r-cran-gdilm.seirs Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm, r-cran-ngspatial Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gdilm.seirs_0.0.6-1.ca2404.1_all.deb Size: 120590 MD5sum: ffc0fccfc1cb4e08710e09ab7c325c2b SHA1: 4ad62f94bce85d5ada1516a357c2b15ac8f7ee59 SHA256: c3372217f47ed783f3fd04c725706dabf3ba88154e6cb19c51ed17ae7d06b450 SHA512: fcbb1be53d1fad5e88bf72794b46967d4994f3d88e679f0685dabb6e5abd6d88fab36c8e0f6f828c736e59785048ab837af80fdada75c353fd456fffa85ca972 Homepage: https://cran.r-project.org/package=GDILM.SEIRS Description: CRAN Package 'GDILM.SEIRS' (Spatial Modeling of Infectious Disease with Reinfection) Geographically Dependent Individual Level Models (GDILMs) within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model infectious disease transmission, incorporating reinfection dynamics. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. It also provides tools for GDILM fitting, parameter estimation, AIC calculation on real pandemic data, and simulation studies customized to user-defined model settings. 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Package: r-cran-gdim Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-dplyr, r-cran-ggplot2, r-cran-irlba, r-cran-progress, r-cran-rlang, r-cran-tibble Suggests: r-cran-epca, r-cran-fastrg, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gdim_0.1.1-1.ca2404.1_all.deb Size: 50738 MD5sum: e1dcb3c885d30eb6476856cf24082c00 SHA1: 4329e7e177d671b0e6df1e02d89344390978b290 SHA256: 4335d4566cd086e44c6809f787c3208bd9e01a42cb6100bbe8aff19386ef4889 SHA512: f41bb028969ff4eede5fac1481ef55e74eea8aeeff16769a30cb3c3d91acfe6df920a69d9e954465be785dc735432efcf4611f6b7c5d255a2c2085df657b7c1a Homepage: https://cran.r-project.org/package=gdim Description: CRAN Package 'gdim' (Estimate Graph Dimension using Cross-Validated Eigenvalues) Cross-validated eigenvalues are estimated by splitting a graph into two parts, the training and the test graph. 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The package implements measures to model dispersal histories first presented by van Etten and Hijmans (2010) . Least-cost distances as well as more complex distances based on (constrained) random walks can be calculated. The distances implemented in the package are used in geographical genetics, accessibility indicators, and may also have applications in other fields of geospatial analysis. 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The package implements the difference method and provides point and interval estimates as well as testing for the natural direct and indirect effects and the mediation proportion. Nevo, Xiao and Spiegelman (2017) . 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The package provides any combination of three modified generalized estimating equations and 11 bias-adjusted covariance estimators. 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The package provides any combination of three GEE methods and 12 covariance estimators. 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All examples from Stefanski & Boos (2002) are published in the corresponding Journal of Statistical Software paper "The Calculus of M-Estimation in R with geex" by Saul & Hudgens (2020) . Also provides an API to compute finite-sample variance corrections. 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The most generalized function for this objective is GE_bias(). However GE_bias() requires specification of many higher order moments of covariates in the model. If users are unsure about how to calculate/estimate these higher order moments, it may be easier to use GE_bias_normal_squaredmis(). This function places many more assumptions on the covariates (most notably that they are all jointly generated from a multivariate normal distribution) and is thus able to automatically calculate many of the higher order moments automatically, necessitating only that the user specify some covariances. There are also functions to solve for the bias through simulation and non-linear equation solvers; these can be used to check your work. Second major functionality is to implement the Bootstrap Inference with Correct Sandwich (BICS) testing procedure, which we have found to provide better finite-sample performance than other inference procedures for testing GxE interaction. More details on these functions are available in Sun, Carroll, Christiani, and Lin (2018) . 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Also includes extracted secondary example datasets for practice and learning purposes, which were obtained from the UNDP Human Development Reports Data Center and the World Bank Gender Data Portal by the author the dataset is available on . References: Miller, Kevin; Vagins, Deborah J. (2021) . Jacques Charmes & Saskia Wieringa (2003) . Gaëlle Ferrant (2010) . 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(2007) for detecting periodically expressed genes from gene expression time series data. 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Platforms available: GPL11532, GPL23126, GPL6244, GPL8300, GPL80, GPL96, GPL570, GPL571, GPL20115, GPL1293, GPL6102, GPL6104, GPL6883, GPL6884, GPL13497, GPL14550, GPL17077, GPL6480. GEO: Gene Expression Omnibus. ID: identifier code. The GEO datasets are downloaded from the URL . More information can be found in the following manuscript: Davide Chicco, "geneExpressionFromGEO: an R package to facilitate data reading from Gene Expression Omnibus (GEO)". Microarray Data Analysis, Methods in Molecular Biology, volume 2401, chapter 12, pages 187-194, Springer Protocols, 2021, . Package: r-cran-genef Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-genef_1.0.1-1.ca2404.1_all.deb Size: 34358 MD5sum: f4f13d3299c8cc6b79fbb065c71c9add SHA1: 167cc7f24b2d1115c23fb7dc9dd2d176d9d8507d SHA256: 07b2190456f3c6988a5e16f399648b73df357f92920dcdb94f011c7e8fe4fcfd SHA512: f1b11ee15a9a80efd2f9c25e290675ee7ec06f47e1735bd7806c2ee7ebb075fa63f4c68717cafd2f2c38fdb5484ed7e7dbd7b4678a6991b09e34f7993f9fe918 Homepage: https://cran.r-project.org/package=GeneF Description: CRAN Package 'GeneF' (Package for Generalized F-Statistics) Implementation of several generalized F-statistics. The current version includes a generalized F-statistic based on the flexible isotonic/monotonic regression or order restricted hypothesis testing. Based on: Y. Lai (2011) . 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Package: r-cran-genehummus Architecture: all Version: 1.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rentrez, r-cran-stringr, r-cran-dplyr, r-cran-httr, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-genehummus_1.0.11-1.ca2404.1_all.deb Size: 89868 MD5sum: 25e36aee80d82ebb4ac014f06e988f5d SHA1: 239bb0e2c0ed54c3d845f473c502025b00db7055 SHA256: e43224829348dab4a36683ff7116f3d9125fea886da1e5277f1dd14eaca8d848 SHA512: fd41f2e3456cd90d0374bb7c23f28de8196d449c889daca5d1671365195ffdd572503dd7e552b459d1c1b2ea929b27c19562326a0dc48a8561533f9c077ffa37 Homepage: https://cran.r-project.org/package=geneHummus Description: CRAN Package 'geneHummus' (A Pipeline to Define Gene Families in Legumes and Beyond) A pipeline with high specificity and sensitivity in extracting proteins from the RefSeq database (National Center for Biotechnology Information). Manual identification of gene families is highly time-consuming and laborious, requiring an iterative process of manual and computational analysis to identify members of a given family. The pipelines implements an automatic approach for the identification of gene families based on the conserved domains that specifically define that family. See Die et al. (2018) for more information and examples. Package: r-cran-genekitr Architecture: all Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2834 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-clusterprofiler, r-cran-dplyr, r-cran-europepmc, r-cran-fst, r-cran-geneset, r-cran-ggplot2, r-cran-ggraph, r-cran-ggvenn, r-cran-igraph, r-cran-magrittr, r-cran-openxlsx, r-cran-stringr, r-cran-stringi, r-cran-tidyr, r-cran-rlang Suggests: r-bioc-annotationdbi, r-cran-cowplot, r-cran-complexupset, r-cran-forcats, r-bioc-fgsea, r-cran-futile.logger, r-cran-ggplotify, r-cran-ggsci, r-cran-ggrepel, r-cran-ggridges, r-cran-ggnewscale, r-cran-goplot, r-bioc-gosemsim, r-cran-labeling, r-cran-pheatmap, r-cran-tm, r-cran-treemap, r-cran-rcolorbrewer, r-cran-rcurl, r-cran-reshape2, r-cran-rio, r-bioc-rrvgo, r-cran-testthat, r-cran-wordcloud, r-cran-knitr, r-cran-rmarkdown, r-cran-xml, r-cran-xml2, r-cran-httr Filename: pool/dists/noble/main/r-cran-genekitr_1.2.8-1.ca2404.1_all.deb Size: 2806112 MD5sum: f2f46fc07b6e537e0cc5d53b92144232 SHA1: cb9598e5d2bac038c5cb96fe290d6890f9810381 SHA256: a0d01d9adb370edd07a59ce74738bf6623cc9d5a289262e94d4b1718a7074a7e SHA512: 84e7a2536b04bf18d2c4d74c3e7b6a40ba43557b41ff209e90175c1625b10cd76d0b7de9b7111dfe9c14e091f1fd948fd5feb059d10a46bfd6f4b43892901ba4 Homepage: https://cran.r-project.org/package=genekitr Description: CRAN Package 'genekitr' (Gene Analysis Toolkit) Provides features for searching, converting, analyzing, plotting, and exporting data effortlessly by inputting feature IDs. Enables easy retrieval of feature information, conversion of ID types, gene enrichment analysis, publication-level figures, group interaction plotting, and result export in one Excel file for seamless sharing and communication. 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Package: r-cran-genenet Architecture: all Version: 1.2.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corpcor, r-cran-longitudinal, r-cran-fdrtool Suggests: r-bioc-graph, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-genenet_1.2.17-1.ca2404.1_all.deb Size: 264982 MD5sum: 8f7f38626e75c00f57dbd62ff38bec99 SHA1: 5fc9272b1b87122e3dc8bc1afa1303aac0768350 SHA256: 98893b48289002975fbc3ccec7659fed415ce863b3f16e9617e37294f83a4888 SHA512: c7ddb5f7c47fda934763512bd74e72e7cf786d819332639fd25fecd6946e1269304e4fc9e7d5d367c464b7a7039f582c5be73a2e0c0c7c0d156c660cc242cc0c Homepage: https://cran.r-project.org/package=GeneNet Description: CRAN Package 'GeneNet' (Modeling and Inferring Gene Networks) Analyzes gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions). 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It supports candidate gene analysis for wheat and rice. Just import your GWAS result as explained in the sample_data file and the function does all the manual search and retrieve candidate genes for you, while exporting the results into ready-to-use output. 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It has been developed by using the concept of "Kern" and "Schrider" (2018). Package: r-cran-generalcorr Architecture: all Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3039 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-np, r-cran-xtable, r-cran-meboot, r-cran-psych, r-cran-lattice Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-generalcorr_1.2.6-1.ca2404.1_all.deb Size: 2641436 MD5sum: 000dfc4d708ef643a12895d3863451dd SHA1: 0334f2e32f3ed097065d206425692d1646b6b2a6 SHA256: 5e194396a42f9c06f12ce4ef0da3eea70e911bb3ea874fb34ffbed20bad69ed4 SHA512: 1a2723d132fdc460bbaf3d9d7c31e4b12119492337e44441b6d806adeea07bc4334f04476fa235513344363b1f145029d48bc77049869df7291a1b4994f895dd Homepage: https://cran.r-project.org/package=generalCorr Description: CRAN Package 'generalCorr' (Generalized Correlations, Causal Paths and Portfolio Selection) Function gmcmtx0() computes a more reliable (general) correlation matrix. Since causal paths from data are important for all sciences, the package provides many sophisticated functions. causeSummBlk() and causeSum2Blk() give easy-to-interpret causal paths. Let Z denote control variables and compare two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1 says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent" than in Y, and the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These inequalities between many absolute values are quantified by four orders of stochastic dominance. Our third criterion Cr3, for the causal path X to Y, requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|. The function parcorVec() reports generalized partials between the first variable and all others. The package provides several R functions including get0outliers() for outlier detection, bigfp() for numerical integration by the trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts, canonRho() for generalized canonical correlations, depMeas() measures nonlinear dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns. Portfolio selection: decileVote(), momentVote(), dif4mtx(), exactSdMtx() can rank several stocks. Functions whose names begin with 'boot' provide bootstrap statistical inference, including a new bootGcRsq() test for "Granger-causality" allowing nonlinear relations. A new tool for evaluation of out-of-sample portfolio performance is outOFsamp(). Panel data implementation is now included. See eight vignettes of the package for theory, examples, and usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}. 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When sample allocation is equal across strata, it is referred to as balanced RSS (BRSS) whereas unequal allocation is called unbalanced RSS (URSS), which is particularly effective for asymmetric or skewed distributions. This package offers practical statistical tools and sampling methods for both BRSS and URSS, emphasizing flexible sampling designs and inference for population means, medians, proportions, and Area Under the Curve (AUC). It incorporates parametric and nonparametric tests, including empirical likelihood ratio (LR) methods. The package provides ranked set sampling methods from a given population, including sampling with imperfect ranking using auxiliary variables. Furthermore, it provides tools for efficient sample allocation in URSS, ensuring greater efficiency than SRS and BRSS. For more details, refer e.g. to Chen et al. (2003) , Ahn et al. (2022) , and Ahn et al. (2024) . 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The Z-score and geometric mean approaches are described in Kim et al. (2018) . 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Package: r-cran-geneset Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rcurl, r-cran-fst, r-cran-stringi, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-geneset_0.2.7-1.ca2404.1_all.deb Size: 365106 MD5sum: 8248275cd36e801b8d00ab82841de15b SHA1: f4c99283a1e4bcb6023cc7e7d81a83af4dafb2a2 SHA256: d0daa79696bba4458a45d0962245904881d7ae4c141d65731f9a382387149df1 SHA512: 5399ac227dd4f144bcc37daf5dc1bbae46be698d8a5ba7e12643ef9569932436189dc5484672d7f2da7d2a5ba92c3916aefeb47d8de2e69db1c88ea7877930eb Homepage: https://cran.r-project.org/package=geneset Description: CRAN Package 'geneset' (Get Gene Sets for Gene Enrichment Analysis) Gene sets are fundamental for gene enrichment analysis. The package 'geneset' enables querying gene sets from public databases including 'GO' (Gene Ontology Consortium. (2004) ), 'KEGG' (Minoru et al. (2000) ), 'WikiPathway' (Marvin et al. (2020) ), 'MsigDb' (Arthur et al. (2015) ), 'Reactome' (David et al. (2011) ), 'MeSH' (Ish et al. (2014) ), 'DisGeNET' (Janet et al. (2017) ), 'Disease Ontology' (Lynn et al. (2011) ), 'Network of Cancer Genes' (Dimitra et al. (2019) ) and 'COVID-19' (Maxim et al. (2020) ). Gene sets are stored in the list object which provides data frame of 'geneset' and 'geneset_name'. The 'geneset' has two columns of term ID and gene ID. The 'geneset_name' has two columns of terms ID and term description. Package: r-cran-geneslope Architecture: all Version: 0.38.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 777 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-slope, r-cran-ggplot2, r-cran-bigmemory Suggests: r-cran-shiny, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geneslope_0.38.3-1.ca2404.1_all.deb Size: 420276 MD5sum: 9fbb61e459d88ba846a0eac481518377 SHA1: e4bc0c2d137033c6c384552906e3edc51315c4e1 SHA256: 7ace869dd47d471a277300e5ad19426768c0be2b87d95875bbdbae6ed981704c SHA512: deabfd824bec89aae298320f768df59c43c9720ed817f10d92603059cb13f07a5f1c8107f28b9ee5dad94fbd2dcc980a7db7ac7926a49ee7668df4dfe827b468 Homepage: https://cran.r-project.org/package=geneSLOPE Description: CRAN Package 'geneSLOPE' (Genome-Wide Association Study with SLOPE) Genome-wide association study (GWAS) performed with SLOPE, short for Sorted L-One Penalized Estimation, a method for estimating the vector of coefficients in linear model. 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Package: r-cran-genesysr Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-dplyr, r-cran-readr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-genesysr_2.2.0-1.ca2404.1_all.deb Size: 106094 MD5sum: 606b588fd84da688bfd3706d35cab303 SHA1: ad0eb033465520159cce22439445d515ed5f963a SHA256: 77ea0215621cbaeace677fc18bdc57f01ba087e9fc4b085cf54079ba9d9ed673 SHA512: 82d8223da78aa3711369e8e5c77b142b4c1799cc97f5982a3891f53b67793ea8f77bc63cdf1c32917914d97a29438a0988f149d78de78292fdb5483ee5e98505 Homepage: https://cran.r-project.org/package=genesysr Description: CRAN Package 'genesysr' (Genesys PGR Client) Access data on plant genetic resources from genebanks around the world published on Genesys (). Your use of data is subject to terms and conditions available at . 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Function include allele frequencies, flagging homo/heterozygotes, flagging carriers of certain alleles, estimating and testing for Hardy-Weinberg disequilibrium, estimating and testing for linkage disequilibrium, ... 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It estimates the effects of each variable while fully adjusting for all other variables that are measured in at least one of the studies. Using algebraic relationships between regression parameters in different dimensions, a set of moment equations is specified for estimating the parameters of a maximal model through information available on sets of parameter estimates from a series of reduced models available from the different studies. The specification of the equations requires a reference dataset to estimate the joint distribution of the covariates. These equations are solved using the generalized method of moments approach, with the optimal weighting of the equations taking into account uncertainty associated with estimates of the parameters of the reduced models. The proposed framework is implemented using iterated reweighted least squares algorithm for fitting generalized linear regression models. For more details about the method, please see pre-print version of the manuscript on generalized meta-analysis by Prosenjit Kundu, Runlong Tang and Nilanjan Chatterjee (2018) .The current version (0.2.0) is updated to address some of the stability issues in the previous version (0.1). 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Implements the methods of Tvedebrink et al (2018) . Package: r-cran-genomic.autocorr Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-magrittr, r-cran-reshape Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-genomic.autocorr_1.0-1-1.ca2404.1_all.deb Size: 24636 MD5sum: 1827e4f5f8dd2464ea38bdf9ca8fb957 SHA1: 0e6034272f57f1cc633f73fd180058fb0c571585 SHA256: cb421adb305de602e3cfa8ce2403deec37a29b01018e898da1f1c9548b32e3fe SHA512: 21e14f3e5a1fa7fd22e0e0ff95fb652d40221717859f7484bd53f088648e0d8188702d7253a2d2c42d74f7e00059b9e77fa4c5b625de75a19a809ec7417ff7ec Homepage: https://cran.r-project.org/package=genomic.autocorr Description: CRAN Package 'genomic.autocorr' (Models Dealing with Spatial Dependency in Genomic Data) Local structure in genomic data often induces dependence between observations taken at different genomic locations. Ignoring this dependence leads to underestimation of the standard error of parameter estimates. This package uses block bootstrapping to estimate asymptotically correct standard errors of parameters from any standard generalised linear model that may be fit by the glm() function. Package: r-cran-genomicper Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-genomicper_1.8-1.ca2404.1_all.deb Size: 170116 MD5sum: 759cb7d4e59e7d36819c57fa33ca21ed SHA1: 6fb60f95c9ade744146f81892142fa5ba8554301 SHA256: 0b1f8bf562630667bee83d140ac921ef435553e964f53ca2683042d3ffbb81ff SHA512: 585193cd65d476fa4f7f085b87fef9d6fbc06b44251632f0a2d89749349257d1828a76f6beb9ee869943c763fe1c4d85ed9915c7bc2d6ee802ba3816af6de528 Homepage: https://cran.r-project.org/package=genomicper Description: CRAN Package 'genomicper' (Circular Genomic Permutation using Genome Wide Associationp-Values) Circular genomic permutation approach uses genome wide association studies (GWAS) results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure. All single nucleotide polymorphisms (SNPs) in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses Fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values and SNPs annotation to genes. Pathways can be obtained from within the package or can be provided by the user. Cabrera et al (2012) . Package: r-cran-genomicsig Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kaos, r-bioc-biostrings, r-cran-entropy, r-cran-seqinr Filename: pool/dists/noble/main/r-cran-genomicsig_0.1.0-1.ca2404.1_all.deb Size: 49836 MD5sum: e52652bbb2f1b7431d9c5d3e5ff6e8bd SHA1: b3b6d4854163dfdb69c7be510a99ee29ce1ed3f6 SHA256: 402a4aa599bab36e3ebeb766533af69cf223adac62eec5154cded599d4c21529 SHA512: 257f8b1ddbf14f63b6d0dcdcf24cfdc4abcd09f1715ac06e1780220b3386f749c4c89e04721970bf4ef27b82d05164c6492fe965f8251b340281af3921668b7d Homepage: https://cran.r-project.org/package=GenomicSig Description: CRAN Package 'GenomicSig' (Computation of Genomic Signatures) Genomic signatures represent unique features within a species' DNA, enabling the differentiation of species and offering broad applications across various fields. This package provides essential tools for calculating these specific signatures, streamlining the process for researchers and offering a comprehensive and time-saving solution for genomic analysis.The amino acid contents are identified based on the work published by Sandberg et al. (2003) and Xiao et al. (2015) . The Average Mutual Information Profiles (AMIP) values are calculated based on the work of Bauer et al. (2008) . The Chaos Game Representation (CGR) plot visualization was done based on the work of Deschavanne et al. (1999) and Jeffrey et al. (1990) . The GC content is calculated based on the work published by Nakabachi et al. (2006) and Barbu et al. (1956) . The Oligonucleotide Frequency Derived Error Gradient (OFDEG) values are computed based on the work published by Saeed et al. (2009) . The Relative Synonymous Codon Usage (RSCU) values are calculated based on the work published by Elek (2018) . 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It includes several functions to calculate commonly used population genomic metrics and a method for reference panel free genotype imputation, which is described in the preprint Gurke & Mayer (2024) . 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Package: r-cran-genoscan Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-skat, r-cran-matrix, r-cran-mass, r-cran-seqminer, r-cran-data.table Filename: pool/dists/noble/main/r-cran-genoscan_0.1-1.ca2404.1_all.deb Size: 108298 MD5sum: e6497918ab562d6c2c4699cb8baf7511 SHA1: cf8185aa8ba0fa642b2b756c1af6c2f744f668cd SHA256: f5342609d28608f2aa31254dc7fda51cd4203057766e8dc17e2b9a8eff09d236 SHA512: 42496e46b3b102d4bef8964fdd8723ba3d055f07fca9bc2539072e1b53da788e456194f435c7d717e53ab91ffc29404b4f2c1a0782f3b5992a1d5027c5e478fd Homepage: https://cran.r-project.org/package=GenoScan Description: CRAN Package 'GenoScan' (A Genome-Wide Scan Statistic Framework for Whole-Genome SequenceData Analysis) Functions for whole-genome sequencing studies, including genome-wide scan, candidate region scan and single window test. Package: r-cran-genotriplo Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shinybs, r-cran-cowplot, r-cran-doparallel, r-cran-dplyr, r-cran-dt, r-cran-foreach, r-cran-ggplot2, r-cran-htmltools, r-cran-processx, r-cran-rlang, r-cran-rmixmod, r-cran-shiny, r-cran-shinythemes, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-genotriplo_1.1.3-1.ca2404.1_all.deb Size: 350516 MD5sum: 149596418eeeb0793f767e9264d0668c SHA1: 990740b68e2afbeaeeac2c1f36c1e5a8b7449766 SHA256: de6b102d1c03f267d52c4a23839ed33c65c2fb0ce8783529555864d6641568f9 SHA512: df875b49fe80c013617657690e4f9e3f52dc9409222f580d526234d9ef6e7fa31fbee874de78612a3622dc69e7f8b8da591fed4a05560aa45d763b070de74360 Homepage: https://cran.r-project.org/package=GenoTriplo Description: CRAN Package 'GenoTriplo' (Genotyping Triploids (or Diploids) from Luminescence Data) Genotyping of triploid individuals from luminescence data (marker probeset A and B). Works also for diploids. Two main functions: Run_Clustering() that regroups individuals with a same genotype based on proximity and Run_Genotyping() that assigns a genotype to each cluster. For Shiny interface use: launch_GenoShiny(). 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Package: r-cran-genpathmox Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-csem, r-cran-diagram, r-cran-matrixcalc Filename: pool/dists/noble/main/r-cran-genpathmox_1.1-1.ca2404.1_all.deb Size: 243984 MD5sum: e82beef0d3697415136c2a49be89f362 SHA1: 64c921a662e7c2b2c716f95158d68eb51d126f84 SHA256: a68dd6445ee90322b001f184584ef07f5ec53b08d00d7a5222396b35429b2123 SHA512: cab3b02522e588a24e1a028a3626b4dec0ab3cdf23a91fa77d93f99cb86171d026e973f3f7be85e56605a4f0530d99ff581a26d6ffc6c5465d715f29db7488e8 Homepage: https://cran.r-project.org/package=genpathmox Description: CRAN Package 'genpathmox' (Pathmox Approach Segmentation Tree Analysis) It provides an interesting solution for handling a high number of segmentation variables in partial least squares structural equation modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez, and Aluja,(2016)) including the F-coefficient test (Lamberti, Sanchez, and Aluja,(2017)) to detect the path coefficients responsible for the identified differences). The package also allows running the hybrid multi-group approach (Lamberti (2021) ). Package: r-cran-genproc Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-progressr Suggests: r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-genproc_0.2.0-1.ca2404.1_all.deb Size: 186254 MD5sum: 45651ad910f9aee6da02c40fdb9e1218 SHA1: 762443b87baa126295930116a89b811d1ed2b0ca SHA256: dd283993f5e2f64806612dc01004ab343cbaf21f180fcf0a4890a5038f99f829 SHA512: 749adadbd31db7c62af08b9fc84dcaf87a778394ea40e25926e60d358f27e1c53d0a85c16dc66b61737471bcc185ee87d7b0201c3e1dfe061ba0e631192918dc Homepage: https://cran.r-project.org/package=genproc Description: CRAN Package 'genproc' (Robust, Logged and Reproducible Iteration at OrganizationalScale) Turns one-off iterative R procedures (such as for loops, lapply() or pmap() from 'purrr') into production-grade workflows by wrapping them with orthogonal, composable execution layers. Two layers are always active: structured logging with real traceback and per-case timing; and reproducibility capture, which records the R version, loaded package versions, execution environment, the exact iteration mask, and a stat-based fingerprint of every input file referenced in the mask (with a diff_inputs() helper to detect silent drift between runs). Parallel execution (built on the 'future' framework, Bengtsson (2021) ), non-blocking background jobs, and opt-in progress reporting (via 'progressr') are implemented as optional, composable layers. Further layers (error replay, content-hash input fingerprinting, content-based case identifiers) are planned and will remain composable with the default layers. Package: r-cran-genpwr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-nleqslv, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-genpwr_1.0.4-1.ca2404.1_all.deb Size: 508058 MD5sum: b37a50d002b00ff645663b6da07075f3 SHA1: 1d0ec8ab5937e8e0aa8cc76fa538a687deaa1edb SHA256: a7656a696febd9f6434ffb41271575165b002c29df1e93eaae42728b1477a6d7 SHA512: 180f025dd82f79670d5e1c970fa2ab99200a236405e29ee26ba068f3dc1aa1c080bfa52eade01870539b6f08737276cea280e74a97582b1dc25fb4af631bf032 Homepage: https://cran.r-project.org/package=genpwr Description: CRAN Package 'genpwr' (Power Calculations Under Genetic Model Misspecification) Power and sample size calculations for genetic association studies allowing for misspecification of the model of genetic susceptibility. "Hum Hered. 2019;84(6):256-271.. Epub 2020 Jul 28." Power and/or sample size can be calculated for logistic (case/control study design) and linear (continuous phenotype) regression models, using additive, dominant, recessive or degree of freedom coding of the genetic covariate while assuming a true dominant, recessive or additive genetic effect. In addition, power and sample size calculations can be performed for gene by environment interactions. These methods are extensions of Gauderman (2002) and Gauderman (2002) and are described in: Moore CM, Jacobson S, Fingerlin TE. Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification. American Society of Human Genetics. October 2018, San Diego. Package: r-cran-genridge Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-rgl, r-cran-colorspace Suggests: r-cran-mass, r-cran-bestglm, r-cran-vcdextra Filename: pool/dists/noble/main/r-cran-genridge_0.8.0-1.ca2404.1_all.deb Size: 334014 MD5sum: 8cc5932ce06153e5a6cf013543d7b33a SHA1: a62ff3b057538725222813f259a3767c219e8526 SHA256: a195dc4ea77086d43acfd88bfe3de322bad2a4b3f8b92b942fa62869a3c97a6c SHA512: 198322688240fd9c92dc36706ecdd4453b5fb7aa123cb0d7a3567471df96a5e77d7ff9040e59ff96beb46bcb478a9f0930e13df09b38a6e0943538a2d0498187 Homepage: https://cran.r-project.org/package=genridge Description: CRAN Package 'genridge' (Generalized Ridge Trace Plots for Ridge Regression) The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors. Package: r-cran-genseir Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-minpack.lm, r-cran-nlsr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-genseir_0.1.1-1.ca2404.1_all.deb Size: 99796 MD5sum: 76f8ba7d683200943f705a0b1b624c01 SHA1: 5ae46a14d7d7465a320ee5f4f89a23413da6abc0 SHA256: 2090eee90c9438c14846348a82ed0ab6c128f3e6bfae0a3173afc2e665078cab SHA512: c8d37ad3c4cdcb01e335582638e491e41f14ed3977678f3d17f056ad5f38e0577ecd175104635b334a4af92e0243f3e2e5a4264cb21a0ff5fa1f035f086b21d4 Homepage: https://cran.r-project.org/package=genSEIR Description: CRAN Package 'genSEIR' (Predict Epidemic Curves with Generalized SEIR Modeling) Performs generalized Susceptible-Exposed-Infected-Recovered (SEIR) modeling to predict epidemic curves. The method is described in Peng et al. (2020) . 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The genset function generates a data set from an initial data set to have the same summary statistics (mean, median, and standard deviation) but opposing regression results. 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The methods are described in Nolan (2016) . Package: r-cran-genstab Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-genstab_1.0.0-1.ca2404.1_all.deb Size: 46716 MD5sum: c7462476abf3bc005adc81f83a160a37 SHA1: e73a2d942219660af7d3d9bf805242b69c1fb008 SHA256: 8e7f13c577ff4e16bf6fac7514243811f76f6bd94377aacc052946e021005ea0 SHA512: 99cba4c25cb53da03795422a8a072cd64f9a93796149c7f982fb834d4598a374ed18f13e4b075f2ac79d2485c56d786ff28af242e929a8f999e6505251e9af92 Homepage: https://cran.r-project.org/package=genstab Description: CRAN Package 'genstab' (Resampling Based Yield Stability Analyses) Several yield stability analyses are mentioned in this package: variation and regression based yield stability analyses. Resampling techniques are integrated with these stability analyses. The function stab.mean() provides the genotypic means and ranks including their corresponding confidence intervals. The function stab.var() provides the genotypic variances over environments including their corresponding confidence intervals. The function stab.fw() is an extended method from the Finlay-Wilkinson method (1963). This method can include several other factors that might impact yield stability. Resampling technique is integrated into this method. A few missing data points or unbalanced data are allowed too. The function stab.fw.check() is an extended method from the Finlay-Wilkinson method (1963). The yield stability is evaluated via common check line(s). Resampling technique is integrated. 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First, a single-color tag sequence is created from an algorithm selected by the user, followed by verification of the combination uniqueness. Three methods to produce color tag sequences are provided. Users can modify the main function core to allow a wide range of applications. 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Package: r-cran-gents Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1801 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gents_0.1.4-1.ca2404.1_all.deb Size: 1468534 MD5sum: 117f28aa35456c0fadeb48cdffe2341f SHA1: c44d21ab21cec5a89756d98a84723aeb80a624ce SHA256: 22bdd05989827f09ba29fcae0913960f31a9bb99b67dc7c757104ee6abda6124 SHA512: 552a43fe508d9532ac9cc2ddfae703ac5f80fa96a64ac8f34aa94b217c4686c3cbf2271ddd54e7214995dbdf6901917e6dc3d49229be80e60792a771eb724ddc Homepage: https://cran.r-project.org/package=genTS Description: CRAN Package 'genTS' (R Shiny App for Creating Simplified Trial Summary (TS) Domain) Make it easy to create simplified trial summary (TS) domain based on FDA FDA guide . 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The calculation is based on the type of endpoints(continuous/binary/time-to-event/ordinal), design (parallel/crossover), hypothesis tests (equality/noninferiority/superiority/equivalence), trial arms noncompliance rates and expected loss of follow-up. Methods are described in: Chow SC, Shao J, Wang H, Lokhnygina Y (2017) , Wittes, J (2002) , Sato, T (2000) , Lachin J M, Foulkes, M A (1986) , Whitehead J(1993) , Julious SA (2023) . Package: r-cran-genular Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-plyr, r-cran-purrr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-genular_1.0.1-1.ca2404.1_all.deb Size: 77694 MD5sum: de804f6bbf8fc734aa5198141c5619a4 SHA1: 07cbd07edb6ebd3fce32452829d3fc29616346d8 SHA256: 7f9174329199bf0f8d8b42e210d509b4dcbe4c6bb0e02fd25337e86f4957d444 SHA512: c1ff5c0473fdf05e5a24e44e6bfef7836b25e7d39687290676001ac9c2274c62ab5c17aa24afffaf7c61095e0fc1610d216f851fd8853b68a03d8205afc1245e Homepage: https://cran.r-project.org/package=genular Description: CRAN Package 'genular' ('Genular' Database API) Provides an interface to the 'Genular' database API (), allowing efficient retrieval and integration of genomic, proteomic, and single-cell data. It supports operations like fetching gene annotations, cell expression profiles, and other information as defined in the 'Genular' database, enabling seamless incorporation of biological data into R workflows. With functions tailored for bioinformatics and machine learning, the package facilitates exploration of cellular heterogeneity, gene-disease relationships, and pathway analysis, streamlining multi-omics data analysis. Package: r-cran-genwin Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pspline Filename: pool/dists/noble/main/r-cran-genwin_1.0-1.ca2404.1_all.deb Size: 983648 MD5sum: 07c088644ff6acf09dd12481cdb914a9 SHA1: 57f8de4cff8819fdca12dc9d0fb0265621e08963 SHA256: 0c70276901553c229d9a2d40082c3bed5a81fb4aabac10b1b2a1ce697112a2f4 SHA512: bd8de6763b0b7832725ef524c1bd599dbb35722ade7953276c3d2f3c13b11755815fb2fff9af5c70787a49fdd2fdf2112daef59521e8d82809361ad0b1a64f5b Homepage: https://cran.r-project.org/package=GenWin Description: CRAN Package 'GenWin' (Spline Based Window Boundaries for Genomic Analyses) Defines window or bin boundaries for the analysis of genomic data. Boundaries are based on the inflection points of a cubic smoothing spline fitted to the raw data. Along with defining boundaries, a technique to evaluate results obtained from unequally-sized windows is provided. Applications are particularly pertinent for, though not limited to, genome scans for selection based on variability between populations (e.g. using Wright's fixations index, Fst, which measures variability in subpopulations relative to the total population). 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Includes historical information from censuses, administrative limits at different levels of aggregation, location of human settlements, among others. Since it is expected that the majority of users will be Spanish-speaking, the documentation of the package prioritizes this language, although an effort is made to also offer annotations in English. 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Package: r-cran-geobounds Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-countrycode, r-cran-dplyr, r-cran-httr2, r-cran-sf Suggests: r-cran-ggplot2, r-cran-jsonlite, r-cran-knitr, r-cran-quarto, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-geobounds_0.1.1-1.ca2404.1_all.deb Size: 399578 MD5sum: 7f00c3cfe866bc697293b43039687d84 SHA1: 4e951cca0514b473292eeaf744dc30f008f906fc SHA256: da5f19e0d75427e37bdd1bd09800ff25de987b8f7448b2a793f920ac4e7e539a SHA512: 005c908edcc7379ec5cc8e844a8b477254ae21742caa0d6d9cb2673343432a200b28d5038612d87dfaf7999b69d94212f95d17ccbb1225a63ced6306ff6622f8 Homepage: https://cran.r-project.org/package=geobounds Description: CRAN Package 'geobounds' (Download Map Data from 'geoBoundaries') Tools to download data from 'geoBoundaries' . Several administration levels available. See Runfola, D. et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLOS ONE 15(4): 1-9. . 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The package offers a wide range of spatial data sets available at various geographic scales and for various years with harmonized attributes, projection and fixed topology. All functions allow for seamless integration sf, DuckDB and Arrow. 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You provide the raw data of locations and timings of treatment and outcome events, specify counterfactual scenarios, and the package estimates causal effects over specified spatial and temporal windows. See Papadogeorgou, et al. (2022) and Mukaigawara, et al. (2024) . 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O pacote é baseado em conjuntos de dados espaciais abertos de endereços brasileiros, utilizando como fonte principal o Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE). O CNEFE é publicado pelo Instituto Brasileiro de Geografia e Estatística (IBGE), órgão oficial de estatísticas e geografia do Brasil. (A simple and efficient method for geolocating data in Brazil. The package is based on open spatial datasets of Brazilian addresses, primarily using the Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE), published by the Instituto Brasileiro de Geografia e Estatística (IBGE), Brazil's official statistics and geography agency.) Package: r-cran-geodadata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3348 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sf Filename: pool/dists/noble/main/r-cran-geodadata_0.1.0-1.ca2404.1_all.deb Size: 3375018 MD5sum: 6c1cff1949fa5409ede92d44d0428122 SHA1: da58e6f66d6dacd5543b91a3a1c8a75c98c99126 SHA256: f58c0e939f45962bd42749055a59c482a071a3e89096664ba3e0aa74948230f7 SHA512: c90a480a7fa6c8590a89710c894ddc4c9daf0aa1be95b77e9828dedca841d56ca4cde36a5aafa94dd1ea34fa7120afaa0bc65c6f48ed14514cb3e309c6ba48c7 Homepage: https://cran.r-project.org/package=geodaData Description: CRAN Package 'geodaData' (Spatial Analysis Datasets for Teaching) Stores small spatial datasets used to teach basic spatial analysis concepts. Datasets are based off of the 'GeoDa' software workbook and data site developed by Luc Anselin and team at the University of Chicago. Datasets are stored as 'sf' objects. Package: r-cran-geodata Architecture: all Version: 0.6-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rappdirs Suggests: r-cran-jsonlite, r-cran-r.utils, r-cran-httr, r-cran-archive Filename: pool/dists/noble/main/r-cran-geodata_0.6-9-1.ca2404.1_all.deb Size: 268422 MD5sum: 8f7484887c180d90fb14231eb74640d7 SHA1: a01f3f6357e1fce3f0f890ac25de2f927ee5b06c SHA256: f947ae5a496c9076e47985dcf6f84982ad1512500d3949dc85fed0202174affb SHA512: 1a12b564a40d8a72500f62698406051e508d6199b5d1e9f0eb11a31b409d2f2d858a868f49bdc37184529f49beef2751d1b03a58cce8bfc5b2972cf0691c0998 Homepage: https://cran.r-project.org/package=geodata Description: CRAN Package 'geodata' (Access Geographic Data) Functions for downloading of geographic data for use in spatial analysis and mapping. 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Package: r-cran-geodeltaaudit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3286 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-janitor, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geodeltaaudit_0.1.0-1.ca2404.1_all.deb Size: 1213198 MD5sum: 351aab2250490033a38261de1091ff4f SHA1: 0166221fc2381c35bd39587d8f5f61c3459e4dcf SHA256: 2247d4eaeb3c25443e96ee9b9e5b5fdba368ed5364e34f27f5155c7aaef49915 SHA512: df79f419ae4b7038900750e9083dcf4cbfa412dfaedb98de63d07cf251e3b46efb47bf8d646dd55b402361795afdf48d86ead6b563d1abae9263df3c27d75f79 Homepage: https://cran.r-project.org/package=geoDeltaAudit Description: CRAN Package 'geoDeltaAudit' (Quantifying Variable Change Induced by Administrative BoundaryTransformations) Tools for auditing how analytic variables change when data are transformed across administrative boundary systems. The package is agnostic to data source, variable type, and administrative geography, and is designed to quantify transformation-induced change without attributing blame to any specific boundary definition or allocation scheme. Package: r-cran-geodetector Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2655 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geodetector_1.0-5-1.ca2404.1_all.deb Size: 1683042 MD5sum: 5036ca24679e4918ba2b4e38781b183c SHA1: 745c745c9dd14aab0cf8ca9698288f05688b6be8 SHA256: d6e6ca0fd3a3fc13f0500e1f9c2b45f4aaacc2ab0806e2dcec6fb85b73763bfd SHA512: 928b5f649640a1111b1c0322e41f990f04977760cf05b88a632805699234b4e6ef38c6004279a9c316b4a4a801c8dce24518cb639940fd4f3402bff87a1ebc4f Homepage: https://cran.r-project.org/package=geodetector Description: CRAN Package 'geodetector' (Stratified Heterogeneity Measure, Dominant Driving ForceDetection, Interaction Relationship Investigation) Spatial stratified heterogeneity (SSH), referring to the within strata are more similar than the between strata, a model with global parameters would be confounded if input data is SSH. Note that the "spatial" here can be either geospatial or the space in mathematical meaning. Geographical detector is a novel tool to investigate SSH: (1) measure and find SSH of a variable Y; (2) test the power of determinant X of a dependent variable Y according to the consistency between their spatial distributions; and (3) investigate the interaction between two explanatory variables X1 and X2 to a dependent variable Y (Wang et al 2014 , Wang, Zhang, and Fu 2016 ). Package: r-cran-geodimension Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3079 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-snakecase, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geodimension_2.0.0-1.ca2404.1_all.deb Size: 3030064 MD5sum: 2b3310e9b6a800c28b560110eb1f6a61 SHA1: 43be015cfa4ee5d6a8a09cff79e7a7007e91c755 SHA256: 56f0c86229455f20663439a909a022eb8cddabc65079433250c6b2f4dccba322 SHA512: e6d72a342fe34cf502cdf788f04f2f66ffc30136dbc75fa5abe564ea2232a146ea7ff9dcbb7945b410c11ce36371c9bf1ec5fec317b2f4491b9302bfd0096442 Homepage: https://cran.r-project.org/package=geodimension Description: CRAN Package 'geodimension' (Definition of Geographic Dimensions) The geographic dimension plays a fundamental role in multidimensional systems. To define a geographic dimension in a star schema, we need a table with attributes corresponding to the levels of the dimension. Additionally, we will also need one or more geographic layers to represent the data using this dimension. The goal of this package is to support the definition of geographic dimensions from layers of geographic information related to each other. It makes it easy to define relationships between layers and obtain the necessary data from them. Package: r-cran-geodl Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 735 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-luz, r-cran-torchvision, r-cran-dplyr, r-cran-terra, r-cran-multiscaledtm, r-cran-psych, r-cran-coro, r-cran-r6, r-cran-readr, r-cran-rlang, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geodl_0.3.1-1.ca2404.1_all.deb Size: 642614 MD5sum: c781c443db6902997deebace78270d23 SHA1: 1b0d3bd257db1f27811698b88bf4adfa5685e33b SHA256: b21322fc4f6cf216dbdf43d7e25ab1e4bc8e017342c23555d4e66c8f1dff8e15 SHA512: dc04220b9669e99d9adc8700961c6de5f3115ffea3d154a4b6e9d4c5058188d8262ca119e1ae708331210f74b903241b2c0ca8c466f1540f0ec453d56ec815ea Homepage: https://cran.r-project.org/package=geodl Description: CRAN Package 'geodl' (Geospatial Semantic Segmentation with Torch and Terra) Provides tools for semantic segmentation of geospatial data using convolutional neural network-based deep learning. Utility functions allow for creating masks, image chips, data frames listing image chips in a directory, and DataSets for use within DataLoaders. Additional functions are provided to serve as checks during the data preparation and training process. A UNet architecture can be defined with 4 blocks in the encoder, a bottleneck block, and 4 blocks in the decoder. The UNet can accept a variable number of input channels, and the user can define the number of feature maps produced in each encoder and decoder block and the bottleneck. Users can also choose to (1) replace all rectified linear unit (ReLU) activation functions with leaky ReLU or swish, (2) implement attention gates along the skip connections, (3) implement squeeze and excitation modules within the encoder blocks, (4) add residual connections within all blocks, (5) replace the bottleneck with a modified atrous spatial pyramid pooling (ASPP) module, and/or (6) implement deep supervision using predictions generated at each stage in the decoder. A unified focal loss framework is implemented after Yeung et al. (2022) . We have also implemented assessment metrics using the 'luz' package including F1-score, recall, and precision. Trained models can be used to predict to spatial data without the need to generate chips from larger spatial extents. Functions are available for performing accuracy assessment. The package relies on 'torch' for implementing deep learning, which does not require the installation of a 'Python' environment. Raster geospatial data are handled with 'terra'. Models can be trained using a Compute Unified Device Architecture (CUDA)-enabled graphics processing unit (GPU); however, multi-GPU training is not supported by 'torch' in 'R'. Package: r-cran-geodrawr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-leaflet, r-cran-shiny, r-cran-shinydashboard Filename: pool/dists/noble/main/r-cran-geodrawr_2.0.0-1.ca2404.1_all.deb Size: 29952 MD5sum: 2fec267952dfe55b1f15e66daa403afa SHA1: cc47f2cbe4b2ae2f2bf5afc254aee5e91e21ba86 SHA256: 9d4319eae11af3140eea01c2b2019fff7be672d682a57176e34c4d30af2102cd SHA512: 8c47e4a37a85245bac5adce1bac63d26750705c37f3c7652b32ac4059db49b5c03a5ba4f922fff127eda58e323c2c0e7754f7d6af74a2ed13bbc11667abc7f5d Homepage: https://cran.r-project.org/package=geodrawr Description: CRAN Package 'geodrawr' (Making Geospatial Objects) Draw geospatial objects by clicks on the map. This packages can help data analyst who want to check their own geospatial hypothesis but has no ready-made geospatial objects. Package: r-cran-geodregr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-zipfr Filename: pool/dists/noble/main/r-cran-geodregr_0.2.0-1.ca2404.1_all.deb Size: 146480 MD5sum: 76efe84f758fe1a980431b3516088d81 SHA1: d9a73d564cb32f7b8aeb59fa52c699e2a97c1cd8 SHA256: 5e4fec2686f568450675dc59afa0f002b8dbfe2f8c9740774f052e8462fe514f SHA512: cc2be8ef3efac678d519eedb20e7b335578bfdd2432268497a13e5f21fc7601be221723205f07f9c553840ee6449fe95e9e2a23bd5b1b969766887a3ebc10d30 Homepage: https://cran.r-project.org/package=GeodRegr Description: CRAN Package 'GeodRegr' (Geodesic Regression) Provides a gradient descent algorithm to find a geodesic relationship between real-valued independent variables and a manifold-valued dependent variable (i.e. geodesic regression). Available manifolds are Euclidean space, the sphere, hyperbolic space, and Kendall's 2-dimensional shape space. Besides the standard least-squares loss, the least absolute deviations, Huber, and Tukey biweight loss functions can also be used to perform robust geodesic regression. Functions to help choose appropriate cutoff parameters to maintain high efficiency for the Huber and Tukey biweight estimators are included, as are functions for generating random tangent vectors from the Riemannian normal distributions on the sphere and hyperbolic space. The n-sphere is a n-dimensional manifold: we represent it as a sphere of radius 1 and center 0 embedded in (n+1)-dimensional space. Using the hyperboloid model of hyperbolic space, n-dimensional hyperbolic space is embedded in (n+1)-dimensional Minkowski space as the upper sheet of a hyperboloid of two sheets. Kendall's 2D shape space with K landmarks is of real dimension 2K-4; preshapes are represented as complex K-vectors with mean 0 and magnitude 1. Details are described in Shin, H.-Y. and Oh, H.-S. (2020) . Also see Fletcher, P. T. (2013) . Package: r-cran-geoelectrics Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3460 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-rgl, r-cran-fields Suggests: r-cran-testthat, r-cran-tkrplot Filename: pool/dists/noble/main/r-cran-geoelectrics_0.2.2-1.ca2404.1_all.deb Size: 899056 MD5sum: fc4d7f638e60d804a9e341ab5b4caad2 SHA1: 35f116fb2db3ea464198a2408ae8dccf3ae177b7 SHA256: 58b7d969732b878268a050a52514b322f2c11822c5fb14ccdd7903be6ed181e4 SHA512: ac7f0b52b7f14063101f9a1474798bd09b9a75b351601dd662767abd04e1adf25e90f17d5195195a41724aa28976e503370496d234b58fd5df4db8b2feb9ed72 Homepage: https://cran.r-project.org/package=geoelectrics Description: CRAN Package 'geoelectrics' (3D-Visualization of Geoelectric Resistivity Measurement Profiles) Visualizes two-dimensional geoelectric resistivity measurement profiles in three dimensions. Package: r-cran-geofacet Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1805 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gtable, r-cran-rnaturalearth, r-cran-sp, r-cran-ggrepel, r-cran-gridextra, r-cran-geogrid, r-cran-rlang, r-cran-httr2 Suggests: r-cran-sf, r-cran-testthat, r-cran-covr, r-cran-lintr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geofacet_0.2.4-1.ca2404.1_all.deb Size: 1196538 MD5sum: f3fe30b472fabf91b97645119ea98d4e SHA1: 3aed4308dda3f82bb44530009ba868edf0ce5b41 SHA256: a07d95b8c76c53a57b99fc03fcbc4985532956d76f7eb58519fd1e6bd436e9c3 SHA512: 7e1ba0266a8eae01b5146e6da005535a99cd8c45e1579c2a8dec1d210706dfb98e8d8233bb2cc4babf648a03dc7b67b38d5601ad5f281ba76250e7eef55666a8 Homepage: https://cran.r-project.org/package=geofacet Description: CRAN Package 'geofacet' ('ggplot2' Faceting Utilities for Geographical Data) Provides geographical faceting functionality for 'ggplot2'. Geographical faceting arranges a sequence of plots of data for different geographical entities into a grid that preserves some of the geographical orientation. Package: r-cran-geofi Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4865 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httpcache, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-xml2, r-cran-yaml Suggests: r-cran-covr, r-cran-ggplot2, r-cran-ggrepel, r-cran-geofacet, r-cran-htmltools, r-cran-httptest, r-cran-janitor, r-cran-knitr, r-cran-leaflet, r-cran-patchwork, r-cran-readr, r-cran-rmarkdown, r-cran-sotkanet, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-geofi_1.2.1-1.ca2404.1_all.deb Size: 3321372 MD5sum: 34284aa0258c428bc097a1c4cb9a6732 SHA1: 3bea164ab023547216b0bc80039900abc50b8d62 SHA256: 1a24a1ab6fcce487e648a654d0aa9860db1939cd6d376f2462a27b9e70f567a8 SHA512: afbd6e164a2f89ab1cd44841795b259deb2d0bb41c2f4e351edc6a933e83fa8c05eee8683b087873d4bc0901a883e7f6048c8eee9a3f630a8bc872c2d6d67205 Homepage: https://cran.r-project.org/package=geofi Description: CRAN Package 'geofi' (Access Finnish Geospatial Data) Designed to simplify geospatial data access from the Statistics Finland Web Feature Service API , the geofi package offers researchers and analysts a set of tools to obtain and harmonize administrative spatial data for a wide range of applications, from urban planning to environmental research. The package contains annually updated time series of municipality key datasets that can be used for data aggregation and language translations. Package: r-cran-geoflow Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-dotenv, r-cran-benchmarkme, r-cran-httr, r-cran-mime, r-cran-jsonlite, r-cran-yaml, r-cran-xml, r-cran-xml2, r-cran-rdflib, r-cran-curl, r-cran-whisker, r-cran-digest, r-cran-dplyr, r-cran-readr, r-cran-arrow, r-cran-zip, r-cran-png, r-cran-uuid, r-cran-sf, r-cran-sfarrow, r-cran-lwgeom, r-cran-smoothr, r-cran-terra, r-cran-geometa, r-cran-geosapi, r-cran-geonapi, r-cran-geonode4r, r-cran-ows4r Suggests: r-cran-testthat, r-cran-readxl, r-cran-gsheet, r-cran-googledrive, r-cran-dbi, r-cran-rapiclient, r-cran-rmariadb, r-cran-rpostgres, r-cran-rpostgresql, r-cran-rsqlite, r-cran-ncdf4, r-cran-thredds, r-cran-eml, r-cran-emld, r-cran-datapack, r-cran-dataone, r-cran-rgbif, r-cran-ocs4r, r-cran-zen4r, r-cran-atom4r, r-cran-d4storagehub4r, r-cran-rmarkdown, r-cran-dataverse, r-cran-blastula, r-cran-waldo Filename: pool/dists/noble/main/r-cran-geoflow_1.2.1-1.ca2404.1_all.deb Size: 1764380 MD5sum: 4c6feb88947b512051b200d75d33bab5 SHA1: 32cc2b7f1a52a603e013a7c5611d7b36493abb75 SHA256: 0032584d098cad33634de76d18bb765eb51471383ec23dcd59bd4cf74cfad52f SHA512: 0f93a2d73b41012d03ea641295bd7a8b3736a691bf1f8db94b9318ec19f32fb5c9cfbb1e6223728b2b2e23ef765531bf3b6868a2e65c45deb2eaacaefaad32c5 Homepage: https://cran.r-project.org/package=geoflow Description: CRAN Package 'geoflow' (Orchestrate Geospatial (Meta)Data Management Workflows andManage FAIR Services) An engine to facilitate the orchestration and execution of metadata-driven data management workflows, in compliance with 'FAIR' (Findable, Accessible, Interoperable and Reusable) data management principles. By means of a pivot metadata model, relying on the 'DublinCore' standard (), a unique source of metadata can be used to operate multiple and inter-connected data management actions. Users can also customise their own workflows by creating specific actions but the library comes with a set of native actions targeting common geographic information and data management, in particular actions oriented to the publication on the web of metadata and data resources to provide standard discovery and access services. At first, default actions of the library were meant to focus on providing turn-key actions for geospatial (meta)data: 1) by creating manage geospatial (meta)data complying with 'ISO/TC211' () and 'OGC' () geographic information standards (eg 19115/19119/19110/19139) and related best practices (eg. 'INSPIRE'); and 2) by facilitating extraction, reading and publishing of standard geospatial (meta)data within widely used software that compound a Spatial Data Infrastructure ('SDI'), including spatial databases (eg. 'PostGIS'), metadata catalogues (eg. 'GeoNetwork', 'CSW' servers), data servers (eg. 'GeoServer'). The library was then extended to actions for other domains: 1) biodiversity (meta)data standard management including handling of 'EML' metadata, and their management with 'DataOne' servers, 2) in situ sensors, remote sensing and model outputs (meta)data standard management by handling part of 'CF' conventions, 'NetCDF' data format and 'OPeNDAP' access protocol, and their management with 'Thredds' servers, 3) generic / domain agnostic (meta)data standard managers ('DublinCore', 'DataCite'), to facilitate the publication of data within (meta)data repositories such as 'Zenodo' () or DataVerse (). The execution of several actions will then allow to cross-reference (meta)data resources in each action performed, offering a way to bind resources between each other (eg. reference 'Zenodo' 'DOI' in 'GeoNetwork'/'GeoServer' metadata, or vice versa reference 'GeoNetwork'/'GeoServer' links in 'Zenodo' or 'EML' metadata). The use of standardized configuration files ('JSON' or 'YAML' formats) allow fully reproducible workflows to facilitate the work of data and information managers. Package: r-cran-geogam Architecture: all Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4750 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mboost, r-cran-mgcv, r-cran-grpreg, r-cran-mass Suggests: r-cran-raster, r-cran-sp Filename: pool/dists/noble/main/r-cran-geogam_0.1-4-1.ca2404.1_all.deb Size: 4815150 MD5sum: d548d00cc4dd8ebec8bc361043dae111 SHA1: 362263e9275834dde8a5fe65fcbdf2a145cffc49 SHA256: 067f5d2c2147bb8e3040231e4dfb0592ecf4bb907a7101286fc88d66f525aa04 SHA512: 637148b2b83ba9e04b786d0ca4e2c5f3928f1d64724a48190d5a86b95f5259dd8f0c5a6da137a86809d2d6d5e70a5a78ce3f35cdf1f3c58ce3396a13fd0cd579 Homepage: https://cran.r-project.org/package=geoGAM Description: CRAN Package 'geoGAM' (Select Sparse Geoadditive Models for Spatial Prediction) A model building procedure to build parsimonious geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study in Berne (Switzerland) is provided. Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A. (2017) . Package: r-cran-geogenr Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6856 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-geomultistar, r-cran-httr, r-cran-readr, r-cran-rolap, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-dbi, r-cran-dbplyr, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-dm, r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-rsqlite, r-cran-snakecase, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geogenr_2.0.1-1.ca2404.1_all.deb Size: 4856148 MD5sum: 0406628f6a25504734054867dc5cf412 SHA1: 7b86ad2132dde7cc8dfae2876addc8d0a5ee11e0 SHA256: 27329b85dd92e2119df1286bbf98a7ca9e07f435ae2b721f5471a9bb213cf924 SHA512: a40d7c936c964cb58de11458bcfb8cfac312c1d8e98a27c339901f217996cc04d0c84a77082a5576c369c6e0de3f35cf90cef11dd13f4b65385818cfc462e2da Homepage: https://cran.r-project.org/package=geogenr Description: CRAN Package 'geogenr' (Generator from American Community Survey Geodatabases) The American Community Survey (ACS) offers geodatabases with geographic information and associated data of interest to researchers in the area. The goal of this package is to generate objects that allow us to access and consult the information available in various formats, such as in 'GeoPackage' format or in multidimensional 'ROLAP' (Relational On-Line Analytical Processing) star format. Package: r-cran-geohabnet Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-geosphere, r-cran-igraph, r-cran-terra, r-cran-yaml, r-cran-stringr, r-cran-memoise, r-cran-viridislite, r-cran-beepr, r-cran-rnaturalearth, r-cran-future.apply, r-cran-future, r-cran-magrittr, r-cran-ggplot2, r-cran-patchwork Suggests: r-cran-devtools, r-cran-knitr, r-cran-lintr, r-cran-mockthat, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geohabnet_2.2-1.ca2404.1_all.deb Size: 797386 MD5sum: 356959094c3279af562c3c2039969550 SHA1: 67481762eaa66593cea12c09c014377409916f42 SHA256: 9b60442bf23750c03e5b4273a109df8075d670254af71b2aeac9aaa1a4f1ae0e SHA512: 0a6a3a15dfb82b322d065993f0328df5001fc8e1b5f4030126f14925a30061e71d3b38d9dafe0ff30912882cb160a3f1cf154791743b81008a60d9436463a391 Homepage: https://cran.r-project.org/package=geohabnet Description: CRAN Package 'geohabnet' (Geographical Risk Analysis Based on Habitat Connectivity) The 'geohabnet' package is designed to perform a geographically or spatially explicit risk analysis of habitat connectivity. Xing et al (2021) proposed the concept of cropland connectivity as a risk factor for plant pathogen or pest invasions. As the functions in 'geohabnet' were initially developed thinking on cropland connectivity, users are recommended to first be familiar with the concept by looking at the Xing et al paper. In a nutshell, a habitat connectivity analysis combines information from maps of host density, estimates the relative likelihood of pathogen movement between habitat locations in the area of interest, and applies network analysis to calculate the connectivity of habitat locations. The functions of 'geohabnet' are built to conduct a habitat connectivity analysis relying on geographic parameters (spatial resolution and spatial extent), dispersal parameters (in two commonly used dispersal kernels: inverse power law and negative exponential models), and network parameters (link weight thresholds and network metrics). The functionality and main extensions provided by the functions in 'geohabnet' to habitat connectivity analysis are a) Capability to easily calculate the connectivity of locations in a landscape using a single function, such as sensitivity_analysis() or msean(). b) As backbone datasets, the 'geohabnet' package supports the use of two publicly available global datasets to calculate cropland density. The backbone datasets in the 'geohabnet' package include crop distribution maps from Monfreda, C., N. Ramankutty, and J. A. Foley (2008) "Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cycles, 22, GB1022" and International Food Policy Research Institute (2019) "Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0, Harvard Dataverse, V4". Users can also provide any other geographic dataset that represents host density. c) Because the 'geohabnet' package allows R users to provide maps of host density (as originally in Xing et al (2021)), host landscape density (representing the geographic distribution of either crops or wild species), or habitat distribution (such as host landscape density adjusted by climate suitability) as inputs, we propose the term habitat connectivity. d) The 'geohabnet' package allows R users to customize parameter values in the habitat connectivity analysis, facilitating context-specific (pathogen- or pest-specific) analyses. e) The 'geohabnet' package allows users to automatically visualize maps of the habitat connectivity of locations resulting from a sensitivity analysis across all customized parameter combinations. The primary functions are msean() and sensitivity analysis(). Most functions in 'geohabnet' provide three main outcomes: i) A map of mean habitat connectivity across parameters selected by the user, ii) a map of variance of habitat connectivity across the selected parameters, and iii) a map of the difference between the ranks of habitat connectivity and habitat density. Each function can be used to generate these maps as 'final' outcomes. Each function can also provide intermediate outcomes, such as the adjacency matrices built to perform the analysis, which can be used in other network analysis. Refer to article at to see examples of each function and how to access each of these outcome types. To change parameter values, the file called 'parameters.yaml' stores the parameters and their values, can be accessed using 'get_parameters()' and set new parameter values with 'set_parameters()'. Users can modify up to ten parameters. Package: r-cran-geoheatmap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4943 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geofacet, r-cran-statebins, r-cran-ggplot2, r-cran-plotly, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-viridislite, r-cran-testthat, r-cran-htmltools Filename: pool/dists/noble/main/r-cran-geoheatmap_0.1.0-1.ca2404.1_all.deb Size: 1807196 MD5sum: b84570933df1ac56519b22e54b77e865 SHA1: e06d2420bda4fb36d7a7426516cadce9a621761a SHA256: dc1fbf1ca1400c38d5c0b7f1b874851a5f0a200820d8e8d9f81b657471f89234 SHA512: fb15cd45c8818d281aa0c511c0f1c8fce93464c70e742a1a8ebe839725cebdde1814a0975b86a4286ec72e0545349894ef3edaa6f01a99a5dfc1e4af22478ac1 Homepage: https://cran.r-project.org/package=geoheatmap Description: CRAN Package 'geoheatmap' (Create Geospatial Cartogram Heatmaps) The functionality provided by this package is an expansion of the code of the 'statebins' package, created by B. Rudis (2022), . It allows for the creation of square choropleths for the entire world, provided an appropriate specified grid is supplied. Package: r-cran-geoidep Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4373 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-sf, r-cran-archive, r-cran-tidyr, r-cran-cli, r-cran-jsonlite, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-leaflet, r-cran-leaflet.extras, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geoidep_0.3.0-1.ca2404.1_all.deb Size: 1492332 MD5sum: edc1da5b86eda169ed142c4e7e031f5e SHA1: af9999954bbb243fe485bafd482941475bd77d3e SHA256: 52809425d72407626c4bbe23b5f4b43870cd718a2a8cdd576df861e29f1b806d SHA512: be1e4d2eaca1a2f15c5615c34cc29a3dc19a9b9e865ce7e6033228fb43bc163af4ec23bf0fbe83c5d69f2f57f9785d3e16be7c30e346536120a4206498c78743 Homepage: https://cran.r-project.org/package=geoidep Description: CRAN Package 'geoidep' (Download Geographic Data on Various Topics Provided and Managedby the Spatial Data Infrastructure of Peru) Provides R users with easy access to official cartographic data from Peru across a range of topics, including society, transport, environment, agriculture, climate, and more. It also includes data from regional government entities and technical-scientific institutions, all managed by Peru's Spatial Data Infrastructure. For more information, please visit: . Package: r-cran-geojson Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-jsonlite, r-cran-protolite, r-cran-jqr, r-cran-magrittr, r-cran-lazyeval Suggests: r-cran-tibble, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-stringi, r-cran-covr Filename: pool/dists/noble/main/r-cran-geojson_0.3.5-1.ca2404.1_all.deb Size: 1029408 MD5sum: 6aefc9877bc8b40fa4e3fbbf9ab30894 SHA1: 6f253fe3999874ef93cf856fb1c2368aaf9633cc SHA256: 4abedbc33eb373367ed146c225cf3e0dd81794ff461e4e939a5d98fa0c146e2d SHA512: 47022da93d8decc7b36e366f5f2a5cdb914a7be1827d9749a3b43afd4ae1288ab4cd2ec084841459b8b66f30de3ab3e1a9d28f62ee5226e8b27418730f854ca6 Homepage: https://cran.r-project.org/package=geojson Description: CRAN Package 'geojson' (Classes for 'GeoJSON') Classes for 'GeoJSON' to make working with 'GeoJSON' easier. Includes S3 classes for 'GeoJSON' classes with brief summary output, and a few methods such as extracting and adding bounding boxes, properties, and coordinate reference systems; working with newline delimited 'GeoJSON'; and serializing to/from 'Geobuf' binary 'GeoJSON' format. Package: r-cran-geojsonio Architecture: all Version: 0.11.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2500 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-geojson, r-cran-geojsonsf, r-cran-jqr, r-cran-jsonlite, r-cran-magrittr, r-cran-readr, r-cran-sf, r-cran-sp, r-cran-v8 Suggests: r-cran-covr, r-cran-dbi, r-cran-gistr, r-cran-leaflet, r-cran-maps, r-cran-rpostgres, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-geojsonio_0.11.3-1.ca2404.1_all.deb Size: 717924 MD5sum: d04da7d46bc1f48ab359636bb01c78f3 SHA1: 7ff8d024346594b5e5a384d9562fcc81e2a2273b SHA256: 3ed4344c347c63ed54d3ae66bcdfdba21a3d18840d284206f247aa5996f042df SHA512: f8110c038bedf819167da0f559c1840e4c129575cb25b98d081c9104ac59e2ca22bb3b2d6e5bd7ff938a09a5d57daeae0dd45e34558fe6777a9db502efdba370 Homepage: https://cran.r-project.org/package=geojsonio Description: CRAN Package 'geojsonio' (Convert Data from and to 'GeoJSON' or 'TopoJSON') Convert data to 'GeoJSON' or 'TopoJSON' from various R classes, including vectors, lists, data frames, shape files, and spatial classes. 'geojsonio' does not aim to replace packages like 'sp', 'rgdal', 'rgeos', but rather aims to be a high level client to simplify conversions of data from and to 'GeoJSON' and 'TopoJSON'. Package: r-cran-geomapdata Architecture: all Version: 2.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1277 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-geomap Filename: pool/dists/noble/main/r-cran-geomapdata_2.0-2-1.ca2404.1_all.deb Size: 1269500 MD5sum: fd7349c503db4ca6534e852c249780d1 SHA1: d63c24c440bdd2f2b7943b85e6a0c42101782970 SHA256: 4604b07376ae7c4fdd4681090b1b8cfae8023f798b8e76c3e5d2f05a23c9192d SHA512: bb9c24151786b020b8c5d58469ff63e27780ce595a3413ede121b5dd24e48961468aeee63b9d1a373533a84b8197b4a67766386d637943b583bfcd3b2fe46aa3 Homepage: https://cran.r-project.org/package=geomapdata Description: CRAN Package 'geomapdata' (Data for Topographic and Geologic Mapping) Data sets included here are for use with package GEOmap. These include world map, USA map, Coso map, Japan Map. Package: r-cran-geomarchetypal Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-geometry, r-cran-archetypal, r-cran-doparallel, r-cran-plot3d, r-cran-distances, r-cran-rlang, r-cran-magrittr, r-cran-dplyr, r-cran-mirai, r-cran-abind, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geomarchetypal_1.0.3-1.ca2404.1_all.deb Size: 572538 MD5sum: ec1b74e98ad90abbc1ca3c00e10fb000 SHA1: 52492dbfd726b1566e5cd43923ac995d7e4117e7 SHA256: 8817b6ed0a1e134705c97cfe082b73059c8576b1927f3c4adb67cb0004f17c96 SHA512: 0991b3fff09904822162d1306859c3cd305cf1b985593d4dff26f8ec13e0275558113f7861030789a49ec59b72e420e6103d4691ebbfaf884d1cb4fbf4d64337 Homepage: https://cran.r-project.org/package=GeomArchetypal Description: CRAN Package 'GeomArchetypal' (Finds the Geometrical Archetypal Analysis of a Data Frame) Performs Geometrical Archetypal Analysis after creating Grid Archetypes which are the Cartesian Product of all minimum, maximum variable values. Since the archetypes are fixed now, we have the ability to compute the convex composition coefficients for all our available data points much faster by using the half part of Principal Convex Hull Archetypal method. Additionally we can decide to keep as archetypes the closer to the Grid Archetypes ones. Finally the number of archetypes is always 2 to the power of the dimension of our data points if we consider them as a vector space. Cutler, A., Breiman, L. (1994) . Morup, M., Hansen, LK. (2012) . Christopoulos, DT. (2024) . Package: r-cran-geomaroc Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geomaroc_0.1.1-1.ca2404.1_all.deb Size: 38422 MD5sum: af26a38bf83ea2f98de6142292744a6f SHA1: bec633bc7001bf4b6644fb7e6bcedc4726d59775 SHA256: 7f323ef494afc81225ef66a3697aad098f3af189e3262b2a90571959f729c09c SHA512: 33d1299e356c9e5a3b7bc27b47bdc2decdb68c80cd37425d6cd1e87cda952c990dea9b86da6cdc5f7d5197925bda5299f336e6a2daa63aa16b2de4703d4664c9 Homepage: https://cran.r-project.org/package=geomaroc Description: CRAN Package 'geomaroc' (Easily Visualize Geographic Data of Morocco) Tools to easily visualize geographic data of Morocco. This package interacts with data available through the 'geomarocdata' package, which is available in a 'drat' repository. The size of the 'geomarocdata' package is approximately 12 MB. Package: r-cran-geomcomb Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-forecastcombinations, r-cran-ggplot2, r-cran-matrix, r-cran-mtsdi, r-cran-psych Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-geomcomb_1.0-1.ca2404.1_all.deb Size: 168946 MD5sum: b031de149a2bcd35b4a9c1dc3e1f19e7 SHA1: 08709543f2827f9e8f2aefc2d276072fe8b8e550 SHA256: 0b1e6414fc1fa6df0a95049d3706ffb2166972f717c3a9703f74a2a1ce66818d SHA512: 9e8c30a7faf86087728674f397b6a846b4dafdb186156574b202528aeb042fc4482e0bb1c518f8194adee83292445b6aa9f4f61219877548bacb180eafd7481b Homepage: https://cran.r-project.org/package=GeomComb Description: CRAN Package 'GeomComb' ((Geometric) Forecast Combination Methods) Provides eigenvector-based (geometric) forecast combination methods; also includes simple approaches (simple average, median, trimmed and winsorized mean, inverse rank method) and regression-based combination. Tools for data pre-processing are available in order to deal with common problems in forecast combination (missingness, collinearity). Package: r-cran-geomerge Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1630 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-spdep, r-cran-raster, r-cran-terra, r-cran-sf, r-cran-geosphere, r-cran-lubridate, r-cran-ggplot2, r-cran-gridextra, r-cran-scales Filename: pool/dists/noble/main/r-cran-geomerge_0.3.4-1.ca2404.1_all.deb Size: 1636072 MD5sum: 8d67c9fff8272eb72860f11129afbf8a SHA1: ed031c38f1ca576ec4421862f591c693362877f6 SHA256: 7e3b3bea09f63252c33adb8b8937b535c4ffe7b0dfe5167b26818d71ccde6257 SHA512: 4bd9f21e2405480164709aeb51b9261b4880e9bce43a9c3880db484fcd70d27e5a6b76541e2eb9ca544ea10de7eea4da418712fb6a2be24fddfdbf3a566e0a1a Homepage: https://cran.r-project.org/package=geomerge Description: CRAN Package 'geomerge' (Geospatial Data Integration) Geospatial data integration framework that merges raster, spatial polygon, and (dynamic) spatial points data into a spatial (panel) data frame at any geographical resolution. Package: r-cran-geometa Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 22074 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-xml, r-cran-httr, r-cran-jsonlite, r-cran-keyring, r-cran-readr, r-cran-crayon Suggests: r-cran-sf, r-cran-ncdf4, r-cran-eml, r-cran-emld, r-cran-units, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-geometa_0.9.3-1.ca2404.1_all.deb Size: 9523884 MD5sum: d33f5bfc815b9f5d1ba7ba4c23c8872f SHA1: 89c74e9434bbcef0b2b1c1c39d99874a0179d844 SHA256: 3af4c04b76917ce253b634aeba24252b0cab58843b00172cfe3add6efb39e907 SHA512: 58b14e9b1d88c48250939a3e3229b9a1899efb9f7632700628810deb1d106fef93e0b6313308abe8497d6fb3086f8a15a9418733befe6a42d5e9653c094aaa33 Homepage: https://cran.r-project.org/package=geometa Description: CRAN Package 'geometa' (Tools for Reading and Writing ISO/OGC Geographic Metadata) Provides facilities to read, write and validate geographic metadata defined with ISO TC211 / OGC ISO geographic information metadata standards, and encoded using the ISO 19139 and ISO 19115-3 (XML) standard technical specifications. This includes ISO 19110 (Feature cataloguing), 19115 (dataset metadata), 19119 (service metadata) and 19136 (GML). Other interoperable schemas from the OGC are progressively supported as well, such as the Sensor Web Enablement (SWE) Common Data Model, the OGC GML Coverage Implementation Schema (GMLCOV), or the OGC GML Referenceable Grid (GMLRGRID). Package: r-cran-geometricmorphometricsmix Architecture: all Version: 0.6.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-mclust Suggests: r-cran-ape, r-cran-morpho, r-cran-geometry, r-cran-nlshrink, r-cran-lmf, r-cran-mass, r-cran-clustergeneration, r-cran-ggplot2, r-cran-rmpfr, r-cran-knitr, r-cran-rmarkdown, r-cran-phytools, r-cran-mvmorph, r-cran-testthat, r-cran-future, r-cran-future.apply Filename: pool/dists/noble/main/r-cran-geometricmorphometricsmix_0.6.1.1-1.ca2404.1_all.deb Size: 273830 MD5sum: 29160e8c0d69944f0eddd189c53f34f1 SHA1: a2466408b063aa81a2730ad4723e6ffd22a5ac43 SHA256: e4a91d7b25707e252722d04e8cfccc0fb9e2c19562dacdcf01625766742ec246 SHA512: 5abc3f8ba05424a2bc733f2f866c2ee8618bee76b85f39474cc029e82b1490b5b293eb777769d881882e89902b884969d614f17d6cac23f8160d1328398200f3 Homepage: https://cran.r-project.org/package=GeometricMorphometricsMix Description: CRAN Package 'GeometricMorphometricsMix' (Heterogeneous Methods for Shape and Other Multidimensional Data) Tools for geometric morphometric analyses and multidimensional data. Implements methods for morphological disparity analysis using bootstrap and rarefaction, as reviewed in Foote (1997) . Includes integration and modularity testing, following Fruciano et al. (2013) , using Escoufier's RV coefficient as test statistic as well as two-block partial least squares - PLS, Rohlf and Corti (2000) . Also includes vector angle comparisons, orthogonal projection for data correction (Burnaby (1966) ; Fruciano (2016) ), and parallel analysis for dimensionality reduction (Buja and Eyuboglu (1992) ). Package: r-cran-geomod Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-caret, r-cran-rastervis, r-cran-randomforest, r-cran-arm, r-cran-kernlab, r-cran-e1071, r-cran-cubist, r-cran-rpart, r-cran-ranger, r-cran-qrnn, r-cran-quantregforest, r-cran-nnet Filename: pool/dists/noble/main/r-cran-geomod_0.1.0-1.ca2404.1_all.deb Size: 112402 MD5sum: dce1ed228c6b7490ed61b11b26d9fea3 SHA1: 2d717fbe1aa7f15e72488a48bd36a3f636385879 SHA256: 64485fa0063741848e07d89ebbc0ec4ea7aa7b4f729bdae4dcfe7dc9cd997fba SHA512: c56f18ac63ee06307915d1d00e8c6c66a43c58010654c7874a74824fd1c879583469a025a6be943b834c65b7bcb5648cf4bfaee7bb17f740660e4d581d7d3c58 Homepage: https://cran.r-project.org/package=geomod Description: CRAN Package 'geomod' (A Computer Program for Geotechnical Investigations) The 'geomod' does spatial prediction of the Geotechnical soil properties. It predicts the spatial distribution of Geotechnical properties of soil e.g. shear strength, permeability, plasticity index, Standard Penetration Test (SPT) counts, etc. The output of the prediction takes the form of a map or a series of maps. It uses the interpolation technique where a single or statistically “best” estimate of spatial occurrence soil property is determined. The interpolation is based on both the sampled data and a variogram model for the spatial correlation of the sampled data. The single estimate is produced by a Kriging technique. Package: r-cran-geomongo Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-r6, r-cran-geojsonr, r-cran-data.table Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-geomongo_1.0.3-1.ca2404.1_all.deb Size: 302366 MD5sum: 5fb197dabf69fd666b41c47915108ca8 SHA1: 78090d6dee76b01d0c9601638c427add245816fe SHA256: 3a52b56023dfe13588d65ea884d8b900ad0d1e6d378b55c397500aa5afed96ac SHA512: a60467a1a4c5533618f24deb5e726ef9419a3d47d167588ca66af79a33e3566e657c3996fc00b48c415b0f22c25b9fac064c8e19e4d6464c466604fdec7f1d21 Homepage: https://cran.r-project.org/package=GeoMongo Description: CRAN Package 'GeoMongo' (Geospatial Queries Using 'PyMongo') Utilizes methods of the 'PyMongo' 'Python' library to initialize, insert and query 'GeoJson' data (see for more information on 'PyMongo'). Furthermore, it allows the user to validate 'GeoJson' objects and to use the console for 'MongoDB' (bulk) commands. The 'reticulate' package provides the 'R' interface to 'Python' modules, classes and functions. Package: r-cran-geomorph Architecture: all Version: 4.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1960 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rrpp, r-cran-matrix, r-cran-jpeg, r-cran-ape, r-cran-ggplot2, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-geomorph_4.1.0-1.ca2404.1_all.deb Size: 1872624 MD5sum: 1fbcc4d898e10a8088b91030073f6403 SHA1: 19507855aadb4993e2569101ba68ca12c54fe166 SHA256: 8312f20c5a68626d7a6f43b348676f2fe01fd6d14c5eb8c900a0b7c9d1df6807 SHA512: 3225c9adcc83d9026d79827b9262e8b75ed49f0a6acfe3a445d0c8c6f52d92c65ff752c7287ecabbf2ac34677aac38d3939cd12bd65c87080dba0585188de95f Homepage: https://cran.r-project.org/package=geomorph Description: CRAN Package 'geomorph' (Geometric Morphometric Analyses of 2D and 3D Landmark Data) Read and manipulate landmark data, generate shape variables via Procrustes analysis for points, curves and surfaces, perform shape analyses, and provide graphical depictions of shapes and patterns of shape variation. 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Package: r-cran-geomultistar Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-purrr, r-cran-rlang, r-cran-rsqlite, r-cran-sf, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geomultistar_1.2.2-1.ca2404.1_all.deb Size: 744232 MD5sum: 6036903269a4f2c48a8437999bf8da98 SHA1: f52c5c0c65ca6223ce889a3190cf7abd9d2afe51 SHA256: 4a4fda77d04b5a120dbe2865ea946b988e977467c03a004fd495a68b84760a71 SHA512: ce69fd4fdae51ca836d703675d2c7441ba5d0107c59bd8e11e1d8098a46d1a699e4c74f3f2275830962feeabc83acf7e1eb4b339ec5fb285c1ccad237d3831a6 Homepage: https://cran.r-project.org/package=geomultistar Description: CRAN Package 'geomultistar' (Multidimensional Queries Enriched with Geographic Data) Multidimensional systems allow complex queries to be carried out in an easy way. 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Package: r-cran-geonames Architecture: all Version: 0.999-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjson Filename: pool/dists/noble/main/r-cran-geonames_0.999-1.ca2404.1_all.deb Size: 65250 MD5sum: b5bb1aba926148ed0bfb0e4fb8928c99 SHA1: 4136c4803555beb97c85268c41ee56f43e13b53d SHA256: 5e0485c638047cf41e14444925d9424837a00e88266eab70adf34a2d0310f501 SHA512: 0101f38421e9a7d722811e7373860e4b7c921d4c96b6820fb8e66bb2b2023e69f274c3516211c1b62faeae790f492330a0c1acffc4333c5ae5de7f47e4e61e58 Homepage: https://cran.r-project.org/package=geonames Description: CRAN Package 'geonames' (Interface to the "Geonames" Spatial Query Web Service) The web service at provides a number of spatial data queries, including administrative area hierarchies, city locations and some country postal code queries. A (free) username is required and rate limits exist. Package: r-cran-geonapi Architecture: all Version: 0.8-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geometa, r-cran-keyring, r-cran-r6, r-cran-openssl, r-cran-httr, r-cran-xml, r-cran-plyr Suggests: r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-geonapi_0.8-1-1.ca2404.1_all.deb Size: 389208 MD5sum: ca616935ac6a2349a71e88c7c6f643fe SHA1: c0d1e7c5f132c51bb112548a46ebc1ada77b7205 SHA256: a04e5db4d92fb391cd91c625e89ccf8d483380339e2b16151eb075e560457257 SHA512: 54d00345ee5960bf6127fa2a7d98dbdeeedc21c97c71f96064d6af0e22ffc29b0a142e080be6993879d3e5ae820b231ce16601d81aac99597986fd38668216c7 Homepage: https://cran.r-project.org/package=geonapi Description: CRAN Package 'geonapi' ('GeoNetwork' API R Interface) Provides an R interface to the 'GeoNetwork' API () allowing to upload and publish metadata in a 'GeoNetwork' web-application and expose it to OGC CSW. Package: r-cran-geonetwork Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geosphere, r-cran-igraph, r-cran-sf Suggests: r-cran-devtools, r-cran-knitr, r-cran-maps, r-cran-mapview, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spdata, r-cran-testthat, r-cran-tmaptools Filename: pool/dists/noble/main/r-cran-geonetwork_0.6.0-1.ca2404.1_all.deb Size: 95838 MD5sum: 7b2a982be2bccc6b3000367e81645c61 SHA1: d2463c2ea9eae327084ffd83c5c46a5d2e96c10e SHA256: 10989d3174456b8cca5e121c80e4c77b3b0ff1a490f03cf6ba2709a713d8c177 SHA512: 507763d63b9bc86a4455063e506a685ed25dc749a75de5f697bb37d75758146d8fc26586dbeb58618ad244ee2ea3a5d2fb9f238aa8d1b8f4ec5f09533a6789bd Homepage: https://cran.r-project.org/package=geonetwork Description: CRAN Package 'geonetwork' (Geographic Networks) Provides classes and methods for handling networks or graphs whose nodes are geographical (i.e. locations in the globe). 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For more information about the 'GeoNode' API, see . Package: r-cran-geonuts Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-eurostat, r-cran-ggplot2, r-cran-giscor, r-cran-units, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geonuts_1.0.1-1.ca2404.1_all.deb Size: 197064 MD5sum: 1d127e168a04c19c8bd343d5c2ec939a SHA1: 340ffa0cb2ec30af4a4b8abef93c3f266cb995b2 SHA256: ce37c91b82c251bdabf34950759d35466a554eb0ff813319b21a56e3d04e9435 SHA512: 83805c35826702498030704c99cd4a3f18cb77985c29db037c868796002c79ca08e78e6464578491a6b7332f1762c9dd2325415786534522cfc0d2e202e590b2 Homepage: https://cran.r-project.org/package=geonuts Description: CRAN Package 'geonuts' (Identification and Visualisation of European NUTS Regions fromGeolocations) Provides functions to identify European NUTS (Nomenclature of Territorial Units for Statistics) regions for geographic coordinates (latitude/longitude) using Eurostat geospatial boundaries. 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Package: r-cran-geoperu Architecture: all Version: 0.0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-data.table, r-cran-httr, r-cran-sf Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-geoperu_0.0.0.2-1.ca2404.1_all.deb Size: 405528 MD5sum: 79f8af11976a66eb8f28cd0e66d45933 SHA1: 178bfce81848b7613fb1424722c2973f95af6798 SHA256: 82f6415e772ab777ea0429f2fb842f3fcd39af5cd04fc76617684f2a88bca877 SHA512: f8f7ca7c9f4921e36b7f87aadb1b7acb7fbece11bfe6c2b56b3dd089aede3cd5e3b935c134e46419a7d54bcce8920f5a18a8333281babc9826d445c118005f0f Homepage: https://cran.r-project.org/package=geoperu Description: CRAN Package 'geoperu' (Download Spatial Datasets of Peru) Provides convenient access to the official spatial datasets of Peru as 'sf' objects in R. This package includes a wide range of geospatial data covering various aspects of Peruvian geography, such as: administrative divisions (Source: INEI ), protected natural areas (Source: GEO ANP - SERNANP ). All datasets are harmonized in terms of attributes, projection, and topology, ensuring consistency and ease of use for spatial analysis and visualization. Package: r-cran-geoprofiler Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-tectonicr, r-cran-terra, r-cran-units Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyterra Filename: pool/dists/noble/main/r-cran-geoprofiler_0.0.3-1.ca2404.1_all.deb Size: 586596 MD5sum: 91db37208cbdc4267eb4ecfa2872c1d7 SHA1: 4d4fc4467a6fc1222d18e3850d210e017e0e741e SHA256: 53561189d3d7dccf59dbd65f9aa264d634c486832a3367e270df5460f8e5c9e8 SHA512: 9ccca2dad6decf5327c854ee80d4285b099afc7d6f58d9e7e086113933deaf4003be44ae667161adddd0a516934c0f145241b9cfa60b9582e0ae93146c1c5440 Homepage: https://cran.r-project.org/package=geoprofiler Description: CRAN Package 'geoprofiler' (Perpendicular Line Transects for Geosciences) Toolset to create perpendicular profile graphs and swath profiles. Method are based on coordinate rotation algorithm by Schaeben et al. (2024) . Package: r-cran-geopsych Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/noble/main/r-cran-geopsych_0.1.0-1.ca2404.1_all.deb Size: 47244 MD5sum: 27837d25574842e8e6945e61e231995f SHA1: 7dcf357c8e1bff4f0359eb12ba27f43f0d0ff321 SHA256: 613b804945103423bf1acb30e951132436b24740074e3fa5741699c1ed3c2cd8 SHA512: d0a5424fe3f2876c64f68683eed1c467eec908bc7c9aa0b2e5276038afbe4e82730a4873cb4d4a6edd05a105abb907a5076cd41fb55d23ebe80b4c3c5b597d07 Homepage: https://cran.r-project.org/package=geopsych Description: CRAN Package 'geopsych' (Methods of Applied Psychology and Psychometrics in GeographicalAnalysis) Integrating applied psychological and psychometric methods into geographical analysis. With the emergence of geo-referenced questionnaires, spatially explicit psychological and psychometric methods can offer a geographically contextualised approach that reflects latent traits and processes at a more local scale, leading to more tailored research and decision-making processes. The implemented methods include Geographically Weighted Cronbach's alpha and its bandwidth selection. See Zhang & Li (2025) . 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Package: r-cran-georefdatar Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-spelling, r-cran-testthat, r-cran-readxl, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-georefdatar_0.6.5-1.ca2404.1_all.deb Size: 230218 MD5sum: dc7ac26c32ffad363ed3da44faa7cd37 SHA1: 43debf182f0ca7527fc0ec9cf83184a1fc07b92a SHA256: 943071558039980de975baeb547a8b9afe8768dc08cad2bbe63411b8f1e0eccb SHA512: 72be862313c4fb918d4fd79e477edd3f443ecf8ac9aa3438c4d1b07f44971a5f60886f80d567eb79375dfdde4be158741d641c4318028277d30aea5180b30523 Homepage: https://cran.r-project.org/package=georefdatar Description: CRAN Package 'georefdatar' (Geosciences Reference Datasets) Reference datasets commonly used in the geosciences. 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Package: r-cran-georob Architecture: all Version: 0.3-23-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-abind, r-cran-constrainedkriging, r-cran-fields, r-cran-lmtest, r-cran-nlme, r-cran-nleqslv, r-cran-quantreg, r-cran-robustbase, r-cran-snowfall Suggests: r-cran-gstat, r-cran-multcomp, r-cran-lattice Filename: pool/dists/noble/main/r-cran-georob_0.3-23-1.ca2404.1_all.deb Size: 2203606 MD5sum: 3a0c397f9dd91022a38676e76132835e SHA1: 69b76902e17d7b98f037843caa20b3667beb393b SHA256: 55759492411d6e8c2d172ad7cb415a0eb56ec6269baa31f195c45774214d393d SHA512: 8a079d849a78e75251d8f1b5a09a287330e2459a9f9fec6fc84bbac3ce25d6b6ce30f8dc0bddf21b59cfbee5b089c7c72fe0b875d18fd87c5f882d1abe74dabe Homepage: https://cran.r-project.org/package=georob Description: CRAN Package 'georob' (Robust Geostatistical Analysis of Spatial Data) Provides functions for efficiently fitting linear models with spatially correlated errors by robust (Kuensch et al. (2011) ) and Gaussian (Harville (1977) ) (Restricted) Maximum Likelihood and for computing robust and customary point and block external-drift Kriging predictions (Cressie (1993) ), along with utility functions for variogram modelling in ad hoc geostatistical analyses, model building, model evaluation by cross-validation, (conditional) simulation of Gaussian processes (Davies and Bryant (2013) ), unbiased back-transformation of Kriging predictions of log-transformed data (Cressie (2006) ). 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This package refers to J.N.K Rao and Isabel Molina (2015, ISBN: 978-1-118-73578-7), Bocci, C., & Petrucci, A. (2016), and Ardiansyah, M., Djuraidah, A., & Kurnia, A. (2018). Package: r-cran-geosapi Architecture: all Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-cli, r-cran-openssl, r-cran-httr, r-cran-xml2, r-cran-magrittr, r-cran-keyring, r-cran-readr Suggests: r-cran-testthat, r-cran-waldo, r-cran-roxygen2, r-cran-shiny, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-geosapi_0.8-1.ca2404.1_all.deb Size: 1926984 MD5sum: 0e6b478b6476c99a4e06a4362c2bb2b3 SHA1: 5daafa2d3a3f0e5748fa565a916e18af290783dd SHA256: 67b4494d2c92b8f1ac566b68cbe559ee795b66d35556840a9867e75035d4502c SHA512: 4dbb0f0770d2ee5e470e9ee8ebea73257a3b7fd4e6e19ca290cc88aabca98c4b6e76feedd67697993a6694fee1643a640414a0f2bb9f9e0e3b1db671cab581b7 Homepage: https://cran.r-project.org/package=geosapi Description: CRAN Package 'geosapi' (GeoServer REST API R Interface) Provides an R interface to the GeoServer REST API, allowing to upload and publish data in a GeoServer web-application and expose data to OGC Web-Services. The package currently supports all CRUD (Create,Read,Update,Delete) operations on GeoServer workspaces, namespaces, datastores (stores of vector data), featuretypes, layers, styles, as well as vector data upload operations. For more information about the GeoServer REST API, see . Package: r-cran-geoscale Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-geoscale_2.0.1-1.ca2404.1_all.deb Size: 95156 MD5sum: 3e3e6b8efcdb862323ef848a3d79a463 SHA1: d705494f9047fec935a37e82a815ddb3def9b117 SHA256: 13a41a4fa2cd899d063c9610bfe5fd6067cc759945a0d3515b4f70708e83cde0 SHA512: 592a0dd9ee681157feb1e727d3446e929e0163200046d1747ec5b63036075659969025ead24f9b9843aadc6cd3a192ee0fc65de9776728b333772e48f50d0d0c Homepage: https://cran.r-project.org/package=geoscale Description: CRAN Package 'geoscale' (Geological Time Scale Plotting) Functionality for adding the geological timescale to bivariate plots. 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Based on a modified version of Welzl's algorithm for smallest circle. Distance calculations are based on the haversine formula. Calculations for distance, midpoint, bearing and more are derived from . Package: r-cran-geosimilarity Architecture: all Version: 3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1632 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2, r-cran-magrittr, r-cran-ggrepel Suggests: r-cran-cowplot, r-cran-viridis, r-cran-car, r-cran-desctools, r-cran-performanceanalytics, r-cran-testthat, r-cran-sdsfun, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-geosimilarity_3.9-1.ca2404.1_all.deb Size: 1471044 MD5sum: 3ceffad0b1153e9d9ebd3b584a076659 SHA1: 37a4c0269f82239e27fe637680920726a843e713 SHA256: 51d9440c184f01489f60b7abd29017d8f602db0348daf336ec02b6b1381dd225 SHA512: aab62278bc89db79c746bd3ae5ca8d15f909a01502e26dcd52ca32e51d42c0f10f42a79c82691138598243c3d101e8d14a78200c3cff06fe14e08ff4e36c61d1 Homepage: https://cran.r-project.org/package=geosimilarity Description: CRAN Package 'geosimilarity' (Geographically Optimal Similarity) Understanding spatial association is essential for spatial statistical inference, including factor exploration and spatial prediction. Geographically optimal similarity (GOS) model is an effective method for spatial prediction, as described in Yongze Song (2022) . GOS was developed based on the geographical similarity principle, as described in Axing Zhu (2018) . GOS has advantages in more accurate spatial prediction using fewer samples and critically reduced prediction uncertainty. Package: r-cran-geospark Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparklyr, r-cran-dplyr, r-cran-dbplyr Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-geospark_0.3.1-1.ca2404.1_all.deb Size: 20322 MD5sum: 6c0e848068bb4e7308611c4c9af69da2 SHA1: 171ce958914f9aae07419012580c3cf578a0be03 SHA256: 9c877e00b5e563c6882ed682ca8f458b51649cb7b28bd4f98058c73b59dc18da SHA512: eacd021d30d918e5252a965cf3873aa3a76f0f02a2e2a8c8270d34bd9cc400849d63155045b8c7be743d3ede0ae16e9a5d4d5436353ff173219ca9bb00ae81bf Homepage: https://cran.r-project.org/package=geospark Description: CRAN Package 'geospark' (Bring Local Sf to Spark) R binds 'GeoSpark' extending 'sparklyr' R package to make distributed 'geocomputing' easier. Sf is a package that provides [simple features] access for R and which is a leading 'geospatial' data processing tool. 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Package: r-cran-geospatialsuite Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2322 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-htmlwidgets, r-cran-leaflet, r-cran-magrittr, r-cran-mice, r-cran-rcolorbrewer, r-cran-rnaturalearth, r-cran-sf, r-cran-stringr, r-cran-terra, r-cran-tigris, r-cran-viridis Suggests: r-cran-knitr, r-cran-pkgnet, r-cran-rmarkdown, r-cran-kableextra, r-cran-nhdplustools, r-cran-zipcoder, r-cran-tidygeocoder, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geospatialsuite_0.2.0-1.ca2404.1_all.deb Size: 1081822 MD5sum: 71aeb5b1f3ff77972b2146c694ac5c55 SHA1: b198171d27750c8f424f37f7fd0763881112ea76 SHA256: 1ec7f5ff407ad8fc115ab846fb63a2221068e60f5274030023d48704f2c8aaeb SHA512: 849ba4301efaea41c1d720f4643627155ec4a01ae7b804d0ee9a8ffd6db497de67176ee57c23496ba7849ad579fd05b24f3cdfb8145201c63259d1a67f4d7711 Homepage: https://cran.r-project.org/package=geospatialsuite Description: CRAN Package 'geospatialsuite' (Comprehensive Geospatiotemporal Analysis and MultimodalIntegration Toolkit) A comprehensive toolkit for geospatiotemporal analysis featuring 60+ vegetation indices, advanced raster visualization, universal spatial mapping, water quality analysis, CDL crop analysis, spatial interpolation, temporal analysis, and terrain analysis. Designed for agricultural research, environmental monitoring, remote sensing applications, and publication-quality mapping with support for any geographic region and robust error handling. Methods include vegetation indices calculations (Rouse et al. 1974), NDVI and enhanced vegetation indices (Huete et al. 1997) , (Akanbi et al. 2024) , spatial interpolation techniques (Cressie 1993, ISBN:9780471002556), water quality indices (McFeeters 1996) , and crop data layer analysis (USDA NASS 2024) . Funding: This material is based upon financial support by the National Science Foundation, EEC Division of Engineering Education and Centers, NSF Engineering Research Center for Advancing Sustainable and Distributed Fertilizer production (CASFER), NSF 20-553 Gen-4 Engineering Research Centers award 2133576. Package: r-cran-geospt Architecture: all Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gstat, r-cran-genalg, r-cran-mass, r-cran-sp, r-cran-minqa, r-cran-fields, r-cran-gsl, r-cran-plyr, r-cran-teachingdemos, r-cran-sgeostat Filename: pool/dists/noble/main/r-cran-geospt_1.0-6-1.ca2404.1_all.deb Size: 540276 MD5sum: 80310c8182a0f7aa059deb7925a803c3 SHA1: 19ad7af0fbeb300142728b3b2a501bdcb0e21c49 SHA256: 92af594e6694a522de70cbc6c218fadf23abac7e22c614fe7cc642d3fb134f7e SHA512: 5a6efb0e403181cfa79398bceee1fb651dd7eda8d1faac109a366345f7a6708037e7a3fd840682e3b2a81d44a5e20dec6f33a8026e138af082f42c1fa7738cca Homepage: https://cran.r-project.org/package=geospt Description: CRAN Package 'geospt' (Geostatistical Analysis and Design of Optimal Spatial SamplingNetworks) Estimation of the variogram through trimmed mean, radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, pocket plot, and design of optimal sampling networks through sequential and simultaneous points methods. 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Package: r-cran-geostats Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 933 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-geostats_1.6-1.ca2404.1_all.deb Size: 900768 MD5sum: 614e9ac40138cc7cd4e4518634af8cb0 SHA1: a6bdb5e0f35bc9ed877f6ed7742d9f7fe6051f90 SHA256: ad0ab66034a5f77ed97a93403e2357f1aa393e058e22fdd201f385eea8de9a88 SHA512: 5f09ac3f7c61c57bfd0c7cba33eb665fb1896e17d52de0c57af421a7c78d64c8e309bdc078b9a86a3f5d1ecaaade56aa8586577e2327ba1644163b874bb53244 Homepage: https://cran.r-project.org/package=geostats Description: CRAN Package 'geostats' (An Introduction to Statistics for Geoscientists) A collection of datasets and simplified functions for an introductory (geo)statistics module at University College London. Provides functionality for compositional, directional and spatial data, including ternary diagrams, Wulff and Schmidt stereonets, and ordinary kriging interpolation. Implements logistic and (additive and centred) logratio transformations. Computes vector averages and concentration parameters for the von-Mises distribution. Includes a collection of natural and synthetic fractals, and a simulator for deterministic chaos using a magnetic pendulum example. The main purpose of these functions is pedagogical. Researchers can find more complete alternatives for these tools in other packages such as 'compositions', 'robCompositions', 'sp', 'gstat' and 'RFOC'. All the functions are written in plain R, with no compiled code and a minimal number of dependencies. Theoretical background and worked examples are available at . Package: r-cran-geotargets Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-targets, r-cran-rlang, r-cran-cli, r-cran-terra, r-cran-withr, r-cran-zip, r-cran-lifecycle, r-cran-gdalraster Suggests: r-cran-crew, r-cran-knitr, r-cran-ncmeta, r-cran-rmarkdown, r-cran-sf, r-cran-stars, r-cran-testthat, r-cran-fs, r-cran-spelling Filename: pool/dists/noble/main/r-cran-geotargets_0.3.1-1.ca2404.1_all.deb Size: 599740 MD5sum: 136b0f55b71cfbc50483aad9ffb8b6ae SHA1: c40295e4b532326a51b7c4cd9368ebfd5f771042 SHA256: f072bf50207890d8bc27c246b5db5581faf3e2fd89975929d092be14c809c1fa SHA512: 11e4c8e4ec3355f2c2533aa720030d4c9d2c4107bd3a38ad3c7b8c618b4f8c14fa7f5b9abc85746e178578d2545c35828e68768d8bf22ec51b76a2f00af50508 Homepage: https://cran.r-project.org/package=geotargets Description: CRAN Package 'geotargets' ('targets' Extensions for Geographic Spatial Formats) Provides extensions for various geographic spatial file formats, such as shape files and rasters. 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Package: r-cran-geotools Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1583 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-geotools_0.1-1.ca2404.1_all.deb Size: 1549088 MD5sum: 4784c27fac4a35cfea361ed5a1a90900 SHA1: 85863330b61aa7b1138acb51e03f872883a3ce72 SHA256: 7ea8da14170416d6ae39ddb92d08bfe7a2ee3bdb51a5d0ab896cb9b60d879f58 SHA512: a81a9b2bdc53f0a58e5c4a89c422e0d5826489ca0967edae0705a952e2ad354d1f2509d633466a82efae546853e2ea6b56f1c312e88c675563e4f57ae40219d5 Homepage: https://cran.r-project.org/package=geotools Description: CRAN Package 'geotools' (Geo tools) Tools Package: r-cran-geotoolsr Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geor, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-geotoolsr_1.2.1-1.ca2404.1_all.deb Size: 75414 MD5sum: 32f38053c597db30ff4b78c0c6d010b0 SHA1: 0e6e66cecb48ac454c989596c351f9d906b7a210 SHA256: af5f38399c82254238f94506ffa0af45d9fd559e3487b1a2a8aba16a0f14fd32 SHA512: eb667c35d6b05e1069b40778b36686a1df7708a683ccd626ca09ceac98e69b94ed75b0a98d8f6e3fdf9158f8836b27c6f676d45c7bf7b6d23fb68c943a6073e8 Homepage: https://cran.r-project.org/package=geotoolsR Description: CRAN Package 'geotoolsR' (Tools to Improve the Use of Geostatistic) The basic idea of this package is provides some tools to help the researcher to work with geostatistics. 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Package: r-cran-geotopbricks Architecture: all Version: 1.5.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3879 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-stringr, r-cran-zoo, r-cran-sf, r-cran-terra Suggests: r-cran-soilwater Filename: pool/dists/noble/main/r-cran-geotopbricks_1.5.9.1-1.ca2404.1_all.deb Size: 3254616 MD5sum: 606b2ddc7da52a6a84d12cbf93bb6e70 SHA1: c3ed47150078bc1d126b4eaed42d1ff2d2c46fee SHA256: f4f284108dd8b5436845cdbe1fc37cb5d699e11766b17336674eb7bbc20eaa19 SHA512: 9cf2c615a333dde9d39cd676e8ad24be20eadca60fb1ba90fdbe2c1d3d3d29d7356f5893b24151a43bcb4793478f297b24e5c357bc97b8635a2b491fb30198d9 Homepage: https://cran.r-project.org/package=geotopbricks Description: CRAN Package 'geotopbricks' (An R Plug-in for the Distributed Hydrological Model GEOtop) It analyzes raster maps and other information as input/output files from the Hydrological Distributed Model GEOtop. 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The package, methods, and case-studies are described in Messier, Reif, and Marvel (2025) and Eccles et al. (2023) . 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The homoscedastic harmonic regression model is based on G. Roerink, M. Menenti and W. Verhoef (2000) . 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Contains methods described by Brunsdon et al., 1996 , Brunsdon et al., 2002 , Harris et al., 2011 , Brunsdon et al., 2007 . Package: r-cran-geozoo Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bitops Suggests: r-cran-tourr, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-geozoo_0.5.1-1.ca2404.1_all.deb Size: 93974 MD5sum: ed853762de652bca4e5abbcec4100fbf SHA1: fef8a97b4328b1184e29d88928759b666ed6780f SHA256: df289cb561832e3d4b7a2e34fd3ff9458c305cf15b024eccda70def6dc718a3c SHA512: 38c0b57c84790c0490a7ee9fc61723e01cd6e853cdb9b641219fa9bd53a5e8bbc9cd94fb72fc97975ab4985da640dada33fc686d8ad34fa29dd2a6de293daf7d Homepage: https://cran.r-project.org/package=geozoo Description: CRAN Package 'geozoo' (Zoo of Geometric Objects) Geometric objects defined in 'geozoo' can be simulated or displayed in the R package 'tourr'. 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Package: r-cran-gephiforr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-gephiforr_0.1.1-1.ca2404.1_all.deb Size: 49050 MD5sum: 8c0e047a07dcbf5d142df976a1482ada SHA1: 3e21ac168b88076b3a187a423e34ecbd13d5aea1 SHA256: bc60b55184ae9d4c4d34e29c099628dfeea1d497bf37ccb8ee7e30c3ae6180ce SHA512: 4cb36f9ca7ca1c0bc1451baf65a61c773632d09a90ea6a3048638c5201bf7c08f0487a6b665fa8b49725efc37a0f397359d7b31f65649c733cb405219db9b611 Homepage: https://cran.r-project.org/package=GephiForR Description: CRAN Package 'GephiForR' ('Gephi' Network Visualization) Implements key features of 'Gephi' for network visualization, including 'ForceAtlas2' (with LinLog mode), network scaling, and network rotations. 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Package: r-cran-geppe Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rangen Suggests: r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-geppe_1.1-1.ca2404.1_all.deb Size: 37708 MD5sum: 0b59eab04acd2c313f76d9103c1711b4 SHA1: cb54e49d8520bafcbe3a8cbac0a5ad21a91c2fef SHA256: 53e8f0ffd3d36ea5e8a11713d80a8ddaf661afc8f880cb37a9fcca391516ff48 SHA512: f99ffa47ef14a4df9f2f6db0584be30accf908e2edf4d362540ebe1b7593831ba792202ca2595d0a44bbb1aef5cd21914ea8fdd3a9866096a98954956db1233a Homepage: https://cran.r-project.org/package=geppe Description: CRAN Package 'geppe' (Generalised Exponential Poisson and Poisson ExponentialDistributions) Maximum likelihood estimation, random values generation, density computation and other functions for the exponential-Poisson generalised exponential-Poisson and Poisson-exponential distributions. References include: Rodrigues G. C., Louzada F. and Ramos P. L. (2018). "Poisson-exponential distribution: different methods of estimation". Journal of Applied Statistics, 45(1): 128--144. . Louzada F., Ramos, P. L. and Ferreira, H. P. (2020). "Exponential-Poisson distribution: estimation and applications to rainfall and aircraft data with zero occurrence". Communications in Statistics--Simulation and Computation, 49(4): 1024--1043. . Barreto-Souza W. and Cribari-Neto F. (2009). "A generalization of the exponential-Poisson distribution". Statistics and Probability Letters, 79(24): 2493--2500. . 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Package: r-cran-gesisdata Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1025 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-foreign, r-cran-magrittr, r-cran-netstat, r-cran-rio, r-cran-rselenium, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gesisdata_0.1.2-1.ca2404.1_all.deb Size: 681556 MD5sum: 3742a7d3a8becb215a3f8c469bc8fd77 SHA1: 1716f04574ccf3e5e09b2d1921809a00ea540414 SHA256: 46392c79d02836502c2e4135a5d4cec0397200aa1519dc79e6a14ec936a2e226 SHA512: 75d60b788c7cc8871fa2569cbecfb7c1ae01bfc7c64cd3e58465e4b576c739a31139ca894d185051beb40130de74e5784f7b2b427adafa61f0566bf2b09c8274 Homepage: https://cran.r-project.org/package=gesisdata Description: CRAN Package 'gesisdata' (Reproducible Data Retrieval from the GESIS Data Archive) Reproducible, programmatic retrieval of datasets from the GESIS Data Archive. The GESIS Data Archive makes available thousands of invaluable datasets, but researchers using these datasets are caught in a bind. The archive's terms and conditions bar dissemination of downloaded datasets to third parties, but to ensure that one's work can be reproduced, assessed, and built upon by others, one must provide access to the raw data one has employed. The 'gesisdata' package cuts this knot by providing registered users with programmatic, reproducible access to GESIS datasets from within 'R'. Package: r-cran-geslar Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 685 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-cli Suggests: r-cran-bslib, r-cran-dt, r-cran-knitr, r-cran-leaflet, r-cran-leafpop, r-cran-lubridate, r-cran-patchwork, r-cran-plotly, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyalert, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-geslar_1.0-1-1.ca2404.1_all.deb Size: 186940 MD5sum: fb14947bce87e816a0afa062b1c31409 SHA1: 50a20952f83250ae5eb449802c6951fccfe7f56a SHA256: c0cf14f67189b060376dc2b1e28f5ed34ad863ba2a78081c0ce60a5c44b764bb SHA512: 762764937ac94dae8503424fb432ee53ba58106ad2566553264a6f24de65f5f480b3da984a97c8155f8d63a9c5a218c94fb4e7e1e732f99922ca7da54fa1ce70 Homepage: https://cran.r-project.org/package=geslaR Description: CRAN Package 'geslaR' (Get and Manipulate the GESLA Dataset) Promote access to the GESLA (Global Extreme Sea Level Analysis) dataset, a higher-frequency sea-level record data from all over the world. It provides functions to download it entirely, or query subsets directly into R, without the need of downloading the full dataset. Also, it provides a built-in web-application, so that users can apply basic filters to select the data of interest, generating informative plots, and showing the selected sites. Package: r-cran-gestalt Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang Suggests: r-cran-magrittr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gestalt_0.2.0-1.ca2404.1_all.deb Size: 195328 MD5sum: 660a425331e1000d7964c0d2a32ac12f SHA1: bec4e1c9aff2c5a430ae10b213156a1c748dc128 SHA256: 6cc5ec9f2b6c02aae0fafa800e88c0cd5653805f3c3fefb62f32cc6b2cfa6e21 SHA512: 897975cb5e86a8d4381b69c141c1971bd92b1dc389d8391b3fc2fb68c6e3e7b0445379b32ce7599dcba75b7d28b27cc05fd099e1e51f50390bd07521d2b733fb Homepage: https://cran.r-project.org/package=gestalt Description: CRAN Package 'gestalt' (Tools for Making and Combining Functions) Provides a suite of function-building tools centered around a (forward) composition operator, %>>>%, which extends the semantics of the 'magrittr' %>% operator and supports 'Tidyverse' quasiquotation. It enables you to construct composite functions that can be inspected and transformed as list-like objects. In conjunction with %>>>%, a compact function constructor, fn(), and a partial-application constructor, partial(), are also provided; both support quasiquotation. Package: r-cran-gestate Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-shiny, r-cran-shinythemes, r-cran-survival Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gestate_1.6.0-1.ca2404.1_all.deb Size: 714100 MD5sum: f0fddccb6b7d428d18286a9046dc831d SHA1: 349105fe3068e1de7514ff1dabafd395ddca82ea SHA256: 1baee110d59ee096e7c8d9d1bbc42afefbe6fb40851b069f61e6430f8fe67a66 SHA512: b37c1474df8dd100c12cff0f47dc7ff25f14233f5d9fc8cb1e6d8e5864a047c3c9a878406f537bf5b8da2715c33c7a8ef439249c62b0e93a0418721a363f0f72 Homepage: https://cran.r-project.org/package=gestate Description: CRAN Package 'gestate' (Generalised Survival Trial Assessment Tool Environment) Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves). Package: r-cran-gesttools Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-datacombine, r-cran-tidyr, r-cran-tibble, r-cran-tidyselect, r-cran-geem, r-cran-rsample, r-cran-nnet, r-cran-magrittr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gesttools_1.3.0-1.ca2404.1_all.deb Size: 95122 MD5sum: dd7b1ae21e7f623e814b7442e449571b SHA1: 475e049f54aab3dc308501ff3516f4f463678d83 SHA256: 568af2e0e4221cb5e5c35cdeb53cf9648f5cd0f947a6c434a064eb352ade16dc SHA512: 3beabfa7f159d48bf3a5a1a5177fc41dfc37cfa39ed136c05a711075ec826177f1b899c9e169ef975a2d575cdb366278814a823a440c93e95a90a933071e0e86 Homepage: https://cran.r-project.org/package=gesttools Description: CRAN Package 'gesttools' (General Purpose G-Estimation for End of Study or Time-VaryingOutcomes) Provides a series of general purpose tools to perform g-estimation using the methods described in Sjolander and Vansteelandt (2016) and Dukes and Vansteelandt . The package allows for g-estimation in a wide variety of circumstances, including an end of study or time-varying outcome, and an exposure that is a binary, continuous, or a categorical variable with three or more categories. The package also supports g-estimation with time-varying causal effects and effect modification by a confounding variable. Package: r-cran-get Architecture: all Version: 1.0-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4415 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-ggplot2, r-cran-gridextra, r-cran-viridislite Suggests: r-cran-crayon, r-cran-geor, r-cran-gstat, r-cran-fda, r-cran-fda.usc, r-cran-locfit, r-cran-mvtnorm, r-cran-patchwork, r-cran-quantreg, r-cran-r.rsp, r-cran-sf, r-cran-sp, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.knet, r-cran-spatstat.linnet, r-cran-spatstat.model, r-cran-spatstat.random, r-cran-testthat Filename: pool/dists/noble/main/r-cran-get_1.0-7-1.ca2404.1_all.deb Size: 4189760 MD5sum: c59d7e4193056aebdcd79292fbc0ec5c SHA1: 4a790022f0dbe79832e209bdd062cb4c9705f145 SHA256: 112df6ce3e189b3c2170a4a8ded3caff6f5d44ebfa3d8b6382bdf6d6eb5b8a02 SHA512: b0b6f1607d248c343396089f4ede3f8c75e4fe505ddf523bac0ada316facd85be4db257898ecf21869b391dea895e4971752345d66f4a6310bb39f9743889161 Homepage: https://cran.r-project.org/package=GET Description: CRAN Package 'GET' (Global Envelopes) Implementation of global envelopes for a set of general d-dimensional vectors T in various applications. A 100(1-alpha)% global envelope is a band bounded by two vectors such that the probability that T falls outside this envelope in any of the d points is equal to alpha. Global means that the probability is controlled simultaneously for all the d elements of the vectors. The global envelopes can be used for graphical Monte Carlo and permutation tests where the test statistic is a multivariate vector or function (e.g. goodness-of-fit testing for point patterns and random sets, functional analysis of variance, functional general linear model, n-sample test of correspondence of distribution functions), for central regions of functional or multivariate data (e.g. outlier detection, functional boxplot) and for global confidence and prediction bands (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See Myllymäki and Mrkvička (2024) , Myllymäki et al. (2017) , Mrkvička and Myllymäki (2023) , Mrkvička et al. (2016) , Mrkvička et al. (2017) , Mrkvička et al. (2020) , Mrkvička et al. (2021) , Myllymäki et al. (2021) , Mrkvička et al. (2022) , Dai et al. (2022) , Dvořák and Mrkvička (2022) , Mrkvička et al. (2023) , and Konstantinou et al. (2024) . Package: r-cran-getable Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr Suggests: r-cran-jsonlite, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-getable_1.0.3-1.ca2404.1_all.deb Size: 24196 MD5sum: 4d5df281181407d3fa5eeaf22def656a SHA1: 3e7788a87478dd22b9eafbc0e64cc85c6463b5e2 SHA256: 463f3e1022a926472347bd051db7701dcdc14acdcdcdf9a83368534cd79f58a1 SHA512: 53eaa8e386ece2bf6fbc9a7d79450b908a017fc9cb1558c6c3c2dbebb6a2fa6e41543f15674a695e1b98333a9ddb925377d216f36062e63abf86115c10a95e53 Homepage: https://cran.r-project.org/package=getable Description: CRAN Package 'getable' (Fetching Tabular Data "Onload" in Compiled R Markdown HTMLDocuments) Dynamically retrieve data from the web to render HTML tables on inspection in R Markdown HTML documents. Package: r-cran-getbcbdata Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-future, r-cran-furrr, r-cran-jsonlite, r-cran-memoise, r-cran-purrr, r-cran-cli, r-cran-parallelly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-getbcbdata_0.9.1-1.ca2404.1_all.deb Size: 34726 MD5sum: e1a91cde271e1f4c410d3b0723ea283b SHA1: 7d28308946a1a9a0541dc6f36e92537bdf9c9daf SHA256: c0725eb682de38b172da9a0e46e6caa47b9ef2ae97d146435765e1701a9172ce SHA512: 151d919772332f6d7a4b5ffeb711239a7a6db6c63cd6a454b82cb4617908eff50c411d20d4e5464b461a60cc96db173c1871650efbc8eb4aa93534c1d2ac9a04 Homepage: https://cran.r-project.org/package=GetBCBData Description: CRAN Package 'GetBCBData' (Imports Datasets from BCB (Central Bank of Brazil) using ItsOfficial API) Downloads and organizes datasets using BCB's API . Offers options for caching with the 'memoise' package and , multicore/multisession with 'furrr' and format of output data (long/wide). Package: r-cran-getcrucldata Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 678 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brio, r-cran-cli, r-cran-data.table, r-cran-fs, r-cran-httr2, r-cran-rlang, r-cran-terra Suggests: r-cran-knitr, r-cran-r.utils, r-cran-rmarkdown, r-cran-roxygen2, r-cran-roxyglobals, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-getcrucldata_2.0.0-1.ca2404.1_all.deb Size: 589352 MD5sum: 4a86e54ed247cbb6bc082c2501ab5185 SHA1: 05e5cb703d4f69254db3cb1ad27b49057c515fac SHA256: 28590f64f46745e80526e0919b705e2fd22e28a91fe983c5905dd2034a8941f4 SHA512: 330e226fdf0f79b562f8a546f08404426557f836c5200f0d125661a0379138de80d82aea42189a54ea626e9a9f821cdd19a23c6fe6f3af2764dd37bdefbbbb8a Homepage: https://cran.r-project.org/package=getCRUCLdata Description: CRAN Package 'getCRUCLdata' ('CRU' 'CL' v. 2.0 Climatology Client) Provides functions that automate downloading and importing University of East Anglia Climate Research Unit ('CRU') 'CL' v. 2.0 climatology data, facilitates the calculation of minimum temperature and maximum temperature and formats the data into a data.table object or a 'terra' 'SpatRaster' object. 'CRU' 'CL' v. 2.0 data are a gridded climatology of 1961-1990 monthly means released in 2002 and cover all land areas (excluding Antarctica) at 10 arc minutes (0.1666667 degree) resolution. For more information see the description of the data provided by the University of East Anglia Climate Research Unit, . Package: r-cran-getdesigns Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-getdesigns_1.2.0-1.ca2404.1_all.deb Size: 21074 MD5sum: 0f6ba2e23623d45872e7395c9bc8817a SHA1: 6cba482ec048615aca8d5915502010fdf8625fea SHA256: 45930098b24d12bc924b44fb39ec7e31c5655eade139b70b214f178c6adb2165 SHA512: 3ee8f2a3e0bb8977eb4bc8fad8395a41e9137c1932153dd7c07f96e92a11be62d9f8d0dc2a4b20c7d561c9f36d642f9dace659e6517af169ca5332fb4cfd534c Homepage: https://cran.r-project.org/package=GETdesigns Description: CRAN Package 'GETdesigns' (Generalized Extended Triangular Designs ('GETdesigns')) Since their introduction by Bose and Nair (1939) , partially balanced incomplete block (PBIB) designs remain an important class of incomplete block designs. The concept of association scheme was used by Bose and Shimamoto (1952) for the classification of these designs. The constraint of resources always motivates the experimenter to advance towards PBIB designs, more specifically to higher associate class PBIB designs from balanced incomplete block designs. It is interesting to note that many times higher associate PBIB designs perform better than their counterpart lower associate PBIB designs for the same set of parameters v, b, r, k and lambda_i (i=1,2...m). This package contains functions named GETD() for generating m-associate (m>=2) class PBIB designs along with parameters (v, b, r, k and lambda_i, i = 1, 2,…,m) based on Generalized Triangular (GT) Association Scheme. It also calculates the Information matrix, Average variance factor and canonical efficiency factor of the generated design. These designs, besides having good efficiency, require smaller number of replications and smallest possible concurrence of treatment pairs. Package: r-cran-getdfpdata2 Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-xml2, r-cran-dplyr, r-cran-readr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-memoise, r-cran-rvest, r-cran-tidyr, r-cran-rcurl, r-cran-shiny, r-cran-writexl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-covr, r-cran-fs Filename: pool/dists/noble/main/r-cran-getdfpdata2_0.6.3-1.ca2404.1_all.deb Size: 52912 MD5sum: 4dc50ebcf62ee30f136b92dc71ff68bd SHA1: 60e828dd17a2b5e042d6df209828c27044335354 SHA256: 0368cdfcf155391e8d7d18ab26f5bdeb8a1798891fc282d3e5b677aa48b6fa05 SHA512: 610de5bde5949c4a4990f7a6f6c82cccb7b2382b65bf3e300dff052d69e4ed639a4638cccd27b58497106b59956a5caf3f1682b914997a52abd7ee3fcf43945f Homepage: https://cran.r-project.org/package=GetDFPData2 Description: CRAN Package 'GetDFPData2' (Reading Annual and Quarterly Financial Reports from B3) Reads annual and quarterly financial reports from companies traded at B3, the Brazilian exchange . 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These are web based interfaces for all financial reports of companies traded at Bovespa. The package is specially designed for large scale data importation, keeping a tabular (long) structure for easier processing. Package: r-cran-getdteval Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 840 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-formulaic, r-cran-microbenchmark Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-testthat, r-cran-covr, r-cran-devtools Filename: pool/dists/noble/main/r-cran-getdteval_0.0.2-1.ca2404.1_all.deb Size: 260438 MD5sum: 3cf3e472930c5d18954b4be7533dd752 SHA1: ad57563290a2fb28c63f005209a3ca30ddffb86b SHA256: 307bed61a837ea35c6aeb417316e9d6dcff265ed066139f46282d65cecdca690 SHA512: b93a847fae9c6b484166cd5f7ba4035d886a0e3331dec6650cadb386d7c25be7618d3cfe99b4cb2075d8b84854fb35d76f6bc38edf944e5ee912421e00894681 Homepage: https://cran.r-project.org/package=getDTeval Description: CRAN Package 'getDTeval' (Translating Coding Statements using get() and eval() forImproved Run-Time Coding Efficiency) The getDTeval() function facilitates the translation of the original coding statement to an optimized form for improved runtime efficiency without compromising on the programmatic coding design. 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All data is downloaded and imported from the ftp site . Package: r-cran-getlattes Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5571 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-dplyr, r-cran-tibble, r-cran-janitor, r-cran-purrr Suggests: r-cran-knitr, r-cran-rlang, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-getlattes_1.0.0-1.ca2404.1_all.deb Size: 4184252 MD5sum: 8c8ff6dc0a1248048d56882425eb6f23 SHA1: 9964e471b9303ec4ef185f22d9196a45787b03d6 SHA256: 95e5fd504a91baed63e45ed2be77b1c88d9199229a76decf30814b9ce6a896f4 SHA512: e67db8fa33e0be03850a3f99d1d567f21d18f77d82a595029f91a520f9389c0f7678fa7b8d2824c2a0c4c7d61c5b58bc9406b8cb48e0391d8595c20d9c278b7e Homepage: https://cran.r-project.org/package=getLattes Description: CRAN Package 'getLattes' (Import and Process Data from the 'Lattes' Curriculum Platform) Tool for import and process data from 'Lattes' curriculum platform (). The Brazilian government keeps an extensive base of curricula for academics from all over the country, with over 5 million registrations. The academic life of the Brazilian researcher, or related to Brazilian universities, is documented in 'Lattes'. Some information that can be obtained: professional formation, research area, publications, academics advisories, projects, etc. 'getLattes' package allows work with 'Lattes' data exported to XML format. 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The M statistic aggregates heterogeneity information across multiple variants to, identify systematic heterogeneity patterns and their direction of effect in meta-analysis. It's primary use is to identify outlier studies, which either show "null" effects or consistently show stronger or weaker genetic effects than average across, the panel of variants examined in a GWAS meta-analysis. In contrast to conventional heterogeneity metrics (Q-statistic, I-squared and tau-squared) which measure random heterogeneity at individual variants, M measures systematic (non-random) heterogeneity across multiple independently associated variants. Systematic heterogeneity can arise in a meta-analysis due to differences in the study characteristics of participating studies. Some of the differences may include: ancestry, allele frequencies, phenotype definition, age-of-disease onset, family-history, gender, linkage disequilibrium and quality control thresholds. 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Package: r-cran-ggalluvial Architecture: all Version: 0.12.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2325 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-lazyeval, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-alluvial, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-babynames, r-cran-sessioninfo, r-cran-ggrepel, r-cran-shiny, r-cran-htmltools, r-cran-sp, r-cran-ggfittext, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggalluvial_0.12.6-1.ca2404.1_all.deb Size: 1538898 MD5sum: 5b03af3ac5a58c64a13c20ebb7a31219 SHA1: 8193811fdf014b2961f6b8e82f344431e9fbab37 SHA256: 34370c8f3f61e103904ab10533af397053594313bdae7e0edd078cc355e6b27b SHA512: 0af0981945693929c01b98a46626e5535eca8fa002ecf92c884540c91592f7827377b1b8db153d9e816ea1527121fd7e36e4993cbf74fe4e5df553a58c08c4e7 Homepage: https://cran.r-project.org/package=ggalluvial Description: CRAN Package 'ggalluvial' (Alluvial Plots in 'ggplot2') Alluvial plots use variable-width ribbons and stacked bar plots to represent multi-dimensional or repeated-measures data with categorical or ordinal variables; see Riehmann, Hanfler, and Froehlich (2005) and Rosvall and Bergstrom (2010) . 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Package: r-cran-ggally Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2083 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-cli, r-cran-dplyr, r-cran-ggstats, r-cran-gtable, r-cran-lifecycle, r-cran-magrittr, r-cran-progress, r-cran-rcolorbrewer, r-cran-rlang, r-cran-s7, r-cran-scales, r-cran-tidyr Suggests: r-cran-airports, r-cran-broom, r-cran-broom.helpers, r-cran-chemometrics, r-cran-crosstalk, r-cran-emmeans, r-cran-geosphere, r-cran-ggforce, r-cran-hmisc, r-cran-igraph, r-cran-intergraph, r-cran-knitr, r-cran-labelled, r-cran-mapproj, r-cran-maps, r-cran-network, r-cran-nnet, r-cran-rmarkdown, r-cran-scagnostics, r-cran-sna, r-cran-spelling, r-cran-survival, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggally_2.4.0-1.ca2404.1_all.deb Size: 1993234 MD5sum: bbe3f3bd110d1fdb3790596f490a7abb SHA1: ab426c882b64cff5f43f693e3aae960d69dce545 SHA256: 2cd0ad231d11c52f43859f2096d97eb9d7ba7633a207e74f67175e55e81f99e6 SHA512: 9ab5f02b1244381fee88b8ad7dc2b8f0d45925ec09be752e3b83398383243490ef21227f0fde6e7f4e49e489211753402803da4621f94dd86680c5774ff1d356 Homepage: https://cran.r-project.org/package=GGally Description: CRAN Package 'GGally' (Extension to 'ggplot2') The R package 'ggplot2' is a plotting system based on the grammar of graphics. 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Package: r-cran-ggchangepoint Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-changepoint.np, r-cran-dplyr, r-cran-ecp, r-cran-ggplot2, r-cran-rdpack, r-cran-tibble Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-gstat Filename: pool/dists/noble/main/r-cran-ggchangepoint_0.1.0-1.ca2404.1_all.deb Size: 183504 MD5sum: 49f41523d1caac3034d62ed3ba81a535 SHA1: 7db1603567c450e2fc827ccdeac3c64c8547b114 SHA256: f497a3f08afb5d7be87a8f04b7579efe5bb64048b85b56975038473897fcd9a3 SHA512: 368b19d17a6950cc909a4359a127a7178c68b2780b3f196e0fcc0afcd5f28a3c876fed6eeb2f8329db922c475ff911e10313eb2a72807211e6193a9b1e218970 Homepage: https://cran.r-project.org/package=ggchangepoint Description: CRAN Package 'ggchangepoint' (Combines Changepoint Analysis with 'ggplot2') R provides fantastic tools for changepoint analysis, but plots generated by the tools do not have the 'ggplot2' style. This tool, however, combines 'changepoint', 'changepoint.np' and 'ecp' together, and uses 'ggplot2' to visualize changepoints. 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Provides a high-level interface to create plots using 'ggplot2'. Package: r-cran-ggchernoff Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-ggchernoff_0.3.0-1.ca2404.1_all.deb Size: 48302 MD5sum: 02437c1309f342cc7f5503f551b21fdf SHA1: ee91733f1726777ca2a84bcf554034fe3f69c653 SHA256: b48b46751e709655c639b9873655ed104181f101afdabfea32087ed404a802a4 SHA512: bce5c5a729f51ff25247e2b79daf2c12f6d74ac0dac31229c4188b7d4a97c80db84f82adb5ca251e693b97dc1f9e6f64680fcaee1d3098f5680425720009ffa1 Homepage: https://cran.r-project.org/package=ggChernoff Description: CRAN Package 'ggChernoff' (Chernoff Faces for 'ggplot2') Provides a Chernoff face geom for 'ggplot2'. Maps multivariate data to human-like faces. Inspired by Chernoff (1973) . Package: r-cran-ggchord Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggnewscale, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-qpdf, r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggchord_0.2.0-1.ca2404.1_all.deb Size: 277522 MD5sum: 7fee3c1927972031012ab30f5342059c SHA1: 007cfd64001f121d3a9b70f26dea1d4d56b64074 SHA256: b563705064571ba53cfe4b0b66366c90d22b6dda65af7fb5b85debed7765809b SHA512: f87f53f577a2176c5abaf70aa86da2e995d656a09ad636039e98dd018af5c7685811c31f4839f1e859f5519c57c8ae046d72a5d579f087190350328ad418878c Homepage: https://cran.r-project.org/package=ggchord Description: CRAN Package 'ggchord' (Multi-Sequence 'BLAST' Alignment Chord Diagram VisualizationTool) A function built on 'ggplot2' that visualizes pairwise 'BLAST' alignment results as chord diagrams, intuitively displaying homologous regions between query and subject sequences. Package: r-cran-ggcleveland Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1182 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rlang, r-cran-magrittr, r-cran-readr, r-cran-egg, r-cran-vctrs, r-cran-lattice, r-cran-tibble, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggcleveland_0.1.0-1.ca2404.1_all.deb Size: 941460 MD5sum: 208820e6949feca425f73c11b8da2ec8 SHA1: dcd5041b2020d20526f70d5beca1bc2063b6a6e2 SHA256: 58470ab7ff28370a67512b8ac706c4b7cde11fe971bbe9c56dbead70a1e32bcf SHA512: 315d523b09aa3db767361c5bfd4c5ab88f0a400ca2494968fce76743d96584780dac14cc30dc9151d7654ef3c8f75b898b79b49f9fe5a2b5d7e836b55ac001c1 Homepage: https://cran.r-project.org/package=ggcleveland Description: CRAN Package 'ggcleveland' (Implementation of Plots from Cleveland's Visualizing Data Book) William S. Cleveland's book 'Visualizing Data' is a classic piece of literature on Exploratory Data Analysis. Although it was written several decades ago, its content is still relevant as it proposes several tools which are useful to discover patterns and relationships among the data under study, and also to assess the goodness of fit o a model. This package provides functions to produce the 'ggplot2' versions of the visualization tools described in this book and is thought to be used in the context of courses on Exploratory Data Analysis. Package: r-cran-ggcompare Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-ggcompare_0.0.6-1.ca2404.1_all.deb Size: 141494 MD5sum: 66fd569384231b22125b82a32533d7cd SHA1: 01041af3a26228743011d8408382d96acb9932f9 SHA256: f45d1baccf3e42f2a99b23d624ce33540c03fa2c7a4c6eba6d00858708bbb2f2 SHA512: f177d2a68b5336d37a52d1cc5fe940eb602cf892bfdb7b7f849ff98e67e05bcf585b4a8778a7f163ce6e5a7eb5e49bef388fc6f65820601c0cf79f95bc259bb2 Homepage: https://cran.r-project.org/package=ggcompare Description: CRAN Package 'ggcompare' (Mean Comparison in 'ggplot2') Add mean comparison annotations to a 'ggplot'. This package provides an easy way to indicate if two or more groups are significantly different in a 'ggplot'. Usually you do not need to specify the test method, you only need to tell stat_compare() whether you want to perform a parametric test or a nonparametric test, and stat_compare() will automatically choose the appropriate test method based on your data. For comparisons between two groups, the p-value is calculated by t-test (parametric) or Wilcoxon rank sum test (nonparametric). For comparisons among more than two groups, the p-value is calculated by One-way ANOVA (parametric) or Kruskal-Wallis test (nonparametric). Package: r-cran-ggcorrheatmap Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 824 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-dplyr, r-cran-dendextend, r-cran-ggnewscale, r-cran-rlang, r-cran-cli Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggcorrheatmap_0.3.0-1.ca2404.1_all.deb Size: 744012 MD5sum: b2f6d83a0b3a2d95bec7b7aebb8926f9 SHA1: d9c1d2d36699b989363ce0e035f8ff3c73bac54b SHA256: 4a175bc76da7e81023a33f9ae2e26f9acd2022facd081ec7a173cfe8823d2511 SHA512: 789ca3658907df33131fa3f559b99ad70d4f4a8d951420717a070fa06304c3b768e296e46b43446f5db58f27dc5485a6bde4b550970ac21caf158fe2f4fb707b Homepage: https://cran.r-project.org/package=ggcorrheatmap Description: CRAN Package 'ggcorrheatmap' (Make Flexible 'ggplot2' Correlation Heatmaps) Create correlation heatmaps with 'ggplot2' and customise them with flexible annotation and clustering. Symmetric heatmaps can use triangular or mixed layouts, removing redundant information or displaying complementary information in the two halves. There is also support for general heatmaps not displaying correlations. Package: r-cran-ggcorrplot Architecture: all Version: 0.1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-spelling, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggcorrplot_0.1.4.1-1.ca2404.1_all.deb Size: 30338 MD5sum: a70c954e86955bf6ca5a08ccfdeece0f SHA1: 95623af90fc7c01a666100c09c13b821678de6d3 SHA256: 6b80bf664774a113f5465762a4cfbaa8077a5eca34e02608a17bfb67894f3314 SHA512: 5a08261badb2109208eadcaa6ce500042355ad442d9620ca811ca97ae07df160fac9d810a3dee40afa668432c910f8511181975077a1ddfc3389faa9f8d2b426 Homepage: https://cran.r-project.org/package=ggcorrplot Description: CRAN Package 'ggcorrplot' (Visualization of a Correlation Matrix using 'ggplot2') The 'ggcorrplot' package can be used to visualize easily a correlation matrix using 'ggplot2'. It provides a solution for reordering the correlation matrix and displays the significance level on the plot. It also includes a function for computing a matrix of correlation p-values. Package: r-cran-ggcorset Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3696 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gghalves Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-viridis, r-cran-metbrewer Filename: pool/dists/noble/main/r-cran-ggcorset_0.5.0-1.ca2404.1_all.deb Size: 2781378 MD5sum: 045b32a5df0416e34c51e03cb8f0d1eb SHA1: 755360e61e6b12e349de9ef04acb40580c2ec546 SHA256: bd8c2e35649add06fcdd3760123ce9510d1565e366ad21c890b8a94002df7d0d SHA512: d437952892f07cbeef02ced132231ed7c47ecf11eb2ea9238b655fd2dc66d15bd020ec012b1af5b075da9f0036c2b7a8df63d1b6286bc889e9cc232b03276da0 Homepage: https://cran.r-project.org/package=ggcorset Description: CRAN Package 'ggcorset' (The Corset Plot) Corset plots are a visualization technique used strictly to visualize repeat measures at 2 time points (such as pre- and post- data). The distribution of measurements are visualized at each time point, whilst the trajectories of individual change are visualized by connecting the pre- and post- values linearly. These lines can be coloured to represent the magnitude of change, or other user-defined value. This method of visualization is ideal for showing the heterogeneity of data, including differences by sub-groups. The package relies on 'ggplot2' allowing for easy integration so that users can customize their visualizations as required. Users can create corset plots using data in either wide or long format using the functions gg_corset() or gg_corset_elongated(), respectively. Package: r-cran-ggdag Architecture: all Version: 0.2.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2566 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dagitty, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggraph, r-cran-ggrepel, r-cran-igraph, r-cran-magrittr, r-cran-pillar, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidygraph Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-ggdag_0.2.13-1.ca2404.1_all.deb Size: 1848042 MD5sum: 1475b4374d970e30987eec3085ceae3d SHA1: b631ab593ea8360229dc673d88ca7333ee0aca95 SHA256: 4881d16262bb327412f13f031f92920e900252476d0273184261f259229377d2 SHA512: 22fe8a2eeccb9bd5ea75843ae7670abdfc7528816d73fa6049870968d99ab6d79294eec24126c23fcbe75fa222685baae38f948cb0626d6a555ef4af581e101f Homepage: https://cran.r-project.org/package=ggdag Description: CRAN Package 'ggdag' (Analyze and Create Elegant Directed Acyclic Graphs) Tidy, analyze, and plot directed acyclic graphs (DAGs). 'ggdag' is built on top of 'dagitty', an R package that uses the 'DAGitty' web tool () for creating and analyzing DAGs. 'ggdag' makes it easy to tidy and plot 'dagitty' objects using 'ggplot2' and 'ggraph', as well as common analytic and graphical functions, such as determining adjustment sets and node relationships. Package: r-cran-ggdark Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1596 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggdark_0.2.1-1.ca2404.1_all.deb Size: 1513256 MD5sum: feb467eab41a466f6e0f1aa46d427d73 SHA1: f25935e2bd62021504d84b26bcde5544c35dbcb4 SHA256: 8ca13fc7793f673da9f7f5645438f647edfa4118c746d82ae0f397641218efb2 SHA512: d1e53bdfa3310200cae0f6d203312f4b944d97da100b2452e95fd0f990b614786357bb1e09b7abfdfb36c466d9af23f66b87935a6711b97b8c638f5449d395b2 Homepage: https://cran.r-project.org/package=ggdark Description: CRAN Package 'ggdark' (Dark Mode for 'ggplot2' Themes) Activate dark mode on your favorite 'ggplot2' theme with dark_mode() or use the dark versions of 'ggplot2' themes, including dark_theme_gray(), dark_theme_minimal(), and others. When a dark theme is applied, all geom color and geom fill defaults are changed to make them visible against a dark background. To restore the defaults to their original values, use invert_geom_defaults(). Package: r-cran-ggdaynight Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggdaynight_0.1.3-1.ca2404.1_all.deb Size: 181838 MD5sum: 7e4fc74fd45547f7c93b9ce58a6f228a SHA1: 4694582808afd67e50e7a67fe7f5ee7eb96b4274 SHA256: 0b58939af0f015113e08592012501efd9d94e1f70ebb4dbbe67cc93ad3589d60 SHA512: 0f4d4f5e9f53ea28712c0153d9001627ec4d2354b1935752a32c01b03c469413d629fe7d8495c511dc8a8d5e3b81d8646d40f52efafefca88c51e82652af6a19 Homepage: https://cran.r-project.org/package=ggdaynight Description: CRAN Package 'ggdaynight' (Add Day/Night Patterns to 'ggplot2' Plots) It provides a custom 'ggplot2' geom to add day/night patterns to plots. It visually distinguishes daytime and nighttime periods. 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Package: r-cran-ggdemetra Architecture: all Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4087 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rjdemetra, r-cran-ggrepel, r-cran-gridextra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggdemetra_0.2.9-1.ca2404.1_all.deb Size: 2426148 MD5sum: 0211538e9fd1b07b08f6c0481ab3362d SHA1: 10d6f7af6f0308114fda11106914871a05bdb173 SHA256: 0a8702e756a1a91e914f258440e76d0f7e366c986350af73f932e23831a0896d SHA512: dc4cb108bd5e8bb16ede9f47adae2e37f1a3d0f542f16db0e4c4cf00801495ee15f718bafc00a548e867b7a8e8dd63a75f7476da9c621eba788516fc54882d99 Homepage: https://cran.r-project.org/package=ggdemetra Description: CRAN Package 'ggdemetra' ('ggplot2' Extension for Seasonal and Trading Day Adjustment with'RJDemetra') Provides 'ggplot2' functions to return the results of seasonal and trading day adjustment made by 'RJDemetra'. 'RJDemetra' is an 'R' interface around 'JDemetra+' (), the seasonal adjustment software officially recommended to the members of the European Statistical System and the European System of Central Banks. Package: r-cran-ggdendro Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-rpart, r-cran-tree, r-cran-testthat, r-cran-knitr, r-cran-cluster, r-cran-scales, r-cran-spelling, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-ggdendro_0.2.0-1.ca2404.1_all.deb Size: 201154 MD5sum: 964eb2c7130f634643bfe63c98bafcd8 SHA1: 0396e1d04b9a62d3e4829f883848315514bed77e SHA256: 7bb60f1fa180899577575854683138da7787b7e37eb76917dcdc8c476bd1de6d SHA512: 1af7ece23d7c9cb0d23cdabd4f10b4ff3607d9e0f340ba91a5234b288304d4e68d273d4609f9cfbebba0b9de1291ebc756891f6ecba37a390950410f600ee358 Homepage: https://cran.r-project.org/package=ggdendro Description: CRAN Package 'ggdendro' (Create Dendrograms and Tree Diagrams Using 'ggplot2') This is a set of tools for dendrograms and tree plots using 'ggplot2'. The 'ggplot2' philosophy is to clearly separate data from the presentation. Unfortunately the plot method for dendrograms plots directly to a plot device without exposing the data. The 'ggdendro' package resolves this by making available functions that extract the dendrogram plot data. The package provides implementations for 'tree', 'rpart', as well as diana and agnes (from 'cluster') diagrams. Package: r-cran-ggdensity Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-isoband, r-cran-vctrs, r-cran-tibble, r-cran-mass, r-cran-scales Suggests: r-cran-vdiffr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggdensity_1.0.1-1.ca2404.1_all.deb Size: 370184 MD5sum: 47c5867c9181d7c9997aa2b44c2659a4 SHA1: a4c89fafb9bb1711eb893e1ee3be8e57e514fb35 SHA256: 0a31546b01ae2d7916801ec26b65b285e2fd12a839a330501d6ba460d9235fbf SHA512: a5a87a6b48950d34e2bcd6e709d99664eb38a3935be0c53a8699096b5edf39ce61a07fa31ea42ad6dda09943ae2d5480b9107fe587277c352b13dc066aa3b7aa Homepage: https://cran.r-project.org/package=ggdensity Description: CRAN Package 'ggdensity' (Interpretable Bivariate Density Visualization with 'ggplot2') The 'ggplot2' package provides simple functions for visualizing contours of 2-d kernel density estimates. 'ggdensity' implements several additional density estimators as well as more interpretable visualizations based on highest density regions instead of the traditional height of the estimated density surface. Package: r-cran-ggdiagram Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9856 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrowheadr, r-cran-bezier, r-cran-cli, r-cran-dplyr, r-cran-farver, r-cran-geomtextpath, r-cran-ggarrow, r-cran-ggforce, r-cran-ggplot2, r-cran-ggtext, r-cran-janitor, r-cran-lavaan, r-cran-magick, r-cran-magrittr, r-cran-pdftools, r-cran-purrr, r-cran-rlang, r-cran-s7, r-cran-scales, r-cran-signs, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tinter, r-cran-tinytex, r-cran-vctrs Suggests: r-cran-knitr, r-cran-marquee, r-cran-quarto, r-cran-rmarkdown, r-cran-simstandard, r-cran-spelling, r-cran-testthat, r-cran-tidyverse, r-cran-viridis Filename: pool/dists/noble/main/r-cran-ggdiagram_0.1.1-1.ca2404.1_all.deb Size: 6886318 MD5sum: 2107ae8b7fc751525fef3e3a73f55cf2 SHA1: ea37623cdf07a5eb94e83a591d6eafe92a96259b SHA256: f02d58da22ece81eaa9aa26f7213980ac25bc2b98bdc7f157605415d820d707e SHA512: 3c3a1a3145cb255d07278936f9d6deab3b441e397d9a2b048d019c15d0541c3d96d73486418a2765121de604588f40fbc4638253c9aa05d7372260455201de9b Homepage: https://cran.r-project.org/package=ggdiagram Description: CRAN Package 'ggdiagram' (Object-Oriented Diagram Plots with 'ggplot2') Creates diagrams with an object-oriented approach. Geometric objects have computed properties with information about themselves (e.g., their area) or about their relationships with other objects (e.g, the distance between their edges). The objects have methods to convert them to geoms that can be plotted in 'ggplot2'. Package: r-cran-ggdibbler Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6450 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-distributional, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-lifecycle, r-cran-scales, r-cran-tidyr, r-cran-tibble, r-cran-cli, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-mgcv, r-cran-fable, r-cran-urca, r-cran-gganimate, r-cran-tidyverse, r-cran-tidygraph, r-cran-ggthemes, r-cran-gifski, r-cran-ggridges, r-cran-quantreg, r-cran-ggdist, r-cran-ggraph, r-cran-feasts, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-ggdibbler_0.6.5-1.ca2404.1_all.deb Size: 5585092 MD5sum: 18ed37405da31fe671e5aaacd60cd524 SHA1: 3b71acf6b4467687a6f1e4beca01aecb9d29911c SHA256: 8029330029414b8f97f7266134ca67914168d27836fa88b0f7b7acd7a5ab55e9 SHA512: d5c3d319050f1bff66a00dfd5019ff1ef8c341a583f6ee4e2c0faa235eab112f52feae0c66e9ed5b1efe735cac42b1bfbfd2f4819d56e662b281098653281753 Homepage: https://cran.r-project.org/package=ggdibbler Description: CRAN Package 'ggdibbler' (Add Uncertainty to Data Visualisations) A 'ggplot2' extension for visualising uncertainty with the goal of signal suppression. Usually, uncertainty visualisation focuses on expressing uncertainty as a distribution or probability, whereas 'ggdibbler' differentiates itself by viewing an uncertainty visualisation as an adjustment to an existing graphic that incorporates the inherent uncertainty in the estimates. You provide the code for an existing plot, but replace any of the variables with a vector of distributions, and it will convert the visualisation into it's signal suppression counterpart. Package: r-cran-ggdiceplot Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-legendry, r-cran-scales, r-cran-tibble Filename: pool/dists/noble/main/r-cran-ggdiceplot_1.2.0-1.ca2404.1_all.deb Size: 78730 MD5sum: 53c9a70b1bfc139b682129c62bd53e6a SHA1: 8bb6591bac12d914c10d94df2c719ec95eddcea3 SHA256: 3b93d8cc2fcdc6293c226b543670675e938f33693e6fe277dbc2574901cff59c SHA512: 8be6553798c2c4cb0e2e70f0f96bc922c6196814a9b85c7d397b78d2cf897b2403719eb7b393987a4a766c359964b3c744d7607264793cdc671f94be247d8f39 Homepage: https://cran.r-project.org/package=ggdiceplot Description: CRAN Package 'ggdiceplot' (DicePlot Visualization for 'ggplot2') Provides 'ggplot2' extensions for creating dice-based visualizations where each dot position represents a specific categorical variable. The package includes 'geom_dice()' for displaying presence/absence of categorical variables using traditional dice patterns. Each dice position (1-6) represents a different category, with dots shown only when that category is present. This allows intuitive visualization of up to 6 categorical variables simultaneously. Package: r-cran-ggdnavis Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1403 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggnewscale, r-cran-ggplot2, r-cran-magick, r-cran-png, r-cran-ragg, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-bslib, r-cran-colourpicker, r-cran-jsonlite, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyjs, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggdnavis_1.0.0-1.ca2404.1_all.deb Size: 752680 MD5sum: 8166335e5dbc2e2f5f073f287491b732 SHA1: 74e7cd1292b6832e5c8c2ab78f6d88d422f25104 SHA256: f3f49ce8dd48b17520b94b1a03516eca0863dcfa1d8b1c317543475407174b98 SHA512: fd6e3c56c5544ca5fd094d2bee962970cf6d3ee43bde2ad5c3d29f6ec2965de844320e955656de3b7f038428e1bca8d8ad86386afcf8c94a9516bd38041d29fc Homepage: https://cran.r-project.org/package=ggDNAvis Description: CRAN Package 'ggDNAvis' ('ggplot2'-Based Tools for Visualising DNA Sequences andModifications) Uses 'ggplot2' to visualise either (a) a single DNA/RNA sequence split across multiple lines, (b) multiple DNA/RNA sequences, each occupying a whole line, or (c) base modifications such as DNA methylation called by modified bases models in Dorado or Guppy. Functions starting with visualise_<>() are the main plotting functions, and functions starting with extract_and_sort_<>() are key helper functions for reading files and reformatting data. Source code is available at , a full non-expert user guide is available at , and an interactive web-app version of the software is available at . Package: r-cran-ggdoe Architecture: all Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 914 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-insight Suggests: r-cran-roxygen2, r-cran-tibble, r-cran-mass, r-cran-viridislite, r-cran-ggrepel, r-cran-patchwork, r-cran-unrepx, r-cran-geomtextpath, r-cran-rsm, r-cran-lhs, r-cran-doe.base Filename: pool/dists/noble/main/r-cran-ggdoe_0.8-1.ca2404.1_all.deb Size: 814634 MD5sum: ab5aad5f07eef8d3e560f25486b0c9fd SHA1: d4a0857c75b0de4970bffebee50299360730a1a3 SHA256: ae0951aa72d31235cde21a1c0f42964b2f895b5f6c4e5d1d56d44531edd7acd4 SHA512: 9892187d818cae9c7fb4f289a8e24ba640826530e83d29322752c666cd1d0e7b4a45949fc9d389b3245d14a172e0751aaf86e1ade895ca595e5713dff884eaf2 Homepage: https://cran.r-project.org/package=ggDoE Description: CRAN Package 'ggDoE' (Modern Graphs for Design of Experiments with 'ggplot2') Generate commonly used plots in the field of design of experiments using 'ggplot2'. 'ggDoE' currently supports the following plots: alias matrix, box cox transformation, boxplots, lambda plot, regression diagnostic plots, half normal plots, main and interaction effect plots for factorial designs, contour plots for response surface methodology, Pareto plot, and two dimensional projections of a latin hypercube design. Package: r-cran-ggdoubleheat Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-ggplot2, r-cran-ggnewscale Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-dplyr, r-cran-scales, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ggdoubleheat_0.1.3-1.ca2404.1_all.deb Size: 206280 MD5sum: 44661e50fbb498b62c098d655d545280 SHA1: 010b6da1647508087e8b505871b2222981675d4a SHA256: 21e1eaa8f09434849a1fbfad2e22c69ea828d29f72002d15fe5b5a6311ea0ca4 SHA512: 3db74101a6b69046652a98d7408951d653ae3042a7d0bf9d3749501fc3d876bcf604f104e4e7a42f66ecd9d15a0d235bf6b04af2c86c1c357d7044793a4f70c4 Homepage: https://cran.r-project.org/package=ggDoubleHeat Description: CRAN Package 'ggDoubleHeat' (A Heatmap-Like Visualization Tool) A data visualization design that provides comparison between two (Double) data sources (usually on a par with each other) on one reformed heatmap, while inheriting 'ggplot2' features. Package: r-cran-gge Architecture: all Version: 1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nipals, r-cran-reshape2 Suggests: r-cran-agridat, r-cran-knitr, r-cran-lattice, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gge_1.10-1.ca2404.1_all.deb Size: 158696 MD5sum: b4876595ba416d729c43555be3383fc6 SHA1: bc520ef578758c0d3a28fb322fa17fc810cf9176 SHA256: 3a73fe4519134fccf9c72cfdef0c174ab9a44c18d35f2f7c5838c06aacbfd7f4 SHA512: dbd16e52fae7a961425d8f034ced86e58366118de38ee944e8dccf2df38c6313493e0858fe52fca05c03acc2a42e9c9472e7bb07c8b17d6238d31fab141ee8a0 Homepage: https://cran.r-project.org/package=gge Description: CRAN Package 'gge' (Genotype Plus Genotype-by-Environment Biplots) Create biplots for GGE (genotype plus genotype-by-environment) and GGB (genotype plus genotype-by-block-of-environments) models. See Laffont et al. (2013) . Package: r-cran-ggeasy Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3996 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-clipr, r-cran-labelled, r-cran-patchwork, r-cran-vdiffr, r-cran-covr Filename: pool/dists/noble/main/r-cran-ggeasy_0.1.6-1.ca2404.1_all.deb Size: 3692150 MD5sum: 80e0d3a65088b7e350104c7d05f64d69 SHA1: 34da689f189ffc924c8712ec149086806e3bc8ee SHA256: ead8a509778d7691feb68623aed1541b1d105e8f63a59fc99334218370423454 SHA512: bd41492c6431ee273fc65dc81e036e927125af672dc9d1c92d6c72fdb8d05a4e2752ac67653ed3189e89656675ae331baab29c16d6c2e44a21457e7008fbe2e1 Homepage: https://cran.r-project.org/package=ggeasy Description: CRAN Package 'ggeasy' (Easy Access to 'ggplot2' Commands) Provides a series of aliases to commonly used but difficult to remember 'ggplot2' sequences. Package: r-cran-ggebiplots Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ggforce, r-cran-scales Filename: pool/dists/noble/main/r-cran-ggebiplots_0.1.3-1.ca2404.1_all.deb Size: 71170 MD5sum: ae9642f6c556aaad84b129097e6cbf41 SHA1: 3596437870b7d2416d8e61680ce7c6db0ad99980 SHA256: 80fac9efdb23cd8fcc51aff9eab18d37cfe6f33c5245a43abd597fd3f049184b SHA512: 612fd8d230855ef46c528650e16019a5553292ffbd09c0db10c466ab60333dfb1aa12c3c4f4abd87bf74e2f7a56bfad9e0d2232d7f68d205d5d43e558db5ceaf Homepage: https://cran.r-project.org/package=GGEBiplots Description: CRAN Package 'GGEBiplots' (GGE Biplots with 'ggplot2') Genotype plus genotype-by-environment (GGE) biplots rendered using 'ggplot2'. Provides a command line interface to all of the functionality contained within the archived package 'GGEBiplotGUI'. 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Includes parallel coordinate plots for exploring large datasets with mostly quantitative features, but also stacked one-dimensional visualisations that more effectively show missingness and complex categorical relationships in smaller datasets. Package: r-cran-ggedit Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 581 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-scales, r-cran-rstudioapi, r-cran-shiny, r-cran-miniui, r-cran-shinybs, r-cran-colourpicker, r-cran-shinyace Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-ggedit_0.4.1-1.ca2404.1_all.deb Size: 515940 MD5sum: 10147b257be17dbdf37eb082ba882a38 SHA1: d8af9f893a5c30ca1a1af9af2a2c4b68a22b324f SHA256: 4a5aea199d0fb021b1e101d6353271be4e29f4d0d25403451cc0dca5cd1f229a SHA512: f20fb0d1acd3e3b16ad44de208d06d7f4e1264538072241d3c54f917f5b1c9129fdf93e6693447f887e14577041dfc390b6f18e78b7c47b0d3a50d84313b81b5 Homepage: https://cran.r-project.org/package=ggedit Description: CRAN Package 'ggedit' (Interactive 'ggplot2' Layer and Theme Aesthetic Editor) Interactively edit 'ggplot2' layer and theme aesthetics definitions. Package: r-cran-ggeffects Architecture: all Version: 2.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1013 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-insight, r-cran-datawizard, r-cran-reformulas Suggests: r-cran-aer, r-cran-afex, r-cran-aod, r-cran-bayestestr, r-cran-betareg, r-cran-brglm, r-cran-brglm2, r-cran-brms, r-cran-broom, r-cran-car, r-cran-cardata, r-cran-clubsandwich, r-cran-dfidx, r-cran-effects, r-cran-effectsize, r-cran-emmeans, r-cran-fixest, r-cran-gam, r-cran-gamlss, r-cran-gamm4, r-cran-gee, r-cran-geepack, r-cran-ggplot2, r-cran-ggrepel, r-cran-glmmadaptive, r-cran-glmmtmb, r-cran-gridextra, r-cran-gt, r-cran-haven, r-cran-htmltools, r-cran-httr2, r-cran-jsonlite, r-cran-knitr, r-cran-lme4, r-cran-logistf, r-cran-logitr, r-cran-marginaleffects, r-cran-modelbased, r-cran-mass, r-cran-matrix, r-cran-mice, r-cran-mcmcglmm, r-cran-mumin, r-cran-mgcv, r-cran-mclogit, r-cran-mlogit, r-cran-nestedlogit, r-cran-nlme, r-cran-nnet, r-cran-ordinal, r-cran-parameters, r-cran-parsnip, r-cran-patchwork, r-cran-pscl, r-cran-plm, r-cran-quantreg, r-cran-rmarkdown, r-cran-rms, r-cran-robustbase, r-cran-rstanarm, r-cran-rstantools, r-cran-sandwich, r-cran-sdmtmb, r-cran-see, r-cran-sjlabelled, r-cran-sjstats, r-cran-speedglm, r-cran-survey, r-cran-survival, r-cran-testthat, r-cran-tibble, r-cran-tinytable, r-cran-vdiffr, r-cran-withr, r-cran-vgam Filename: pool/dists/noble/main/r-cran-ggeffects_2.3.2-1.ca2404.1_all.deb Size: 802836 MD5sum: 59cadd676c95350c55cb9590f22cf14d SHA1: 0c72c626e5d398fcfcb0e10a2d7c6d7cde4c0b06 SHA256: 7ca7a6a81728bc25d46059ccbc7a1cd3e2d8461cc0af0ffa8dc46a46d74c7657 SHA512: 0c9ffbc0aee14efd5e1ebeb173ae519511ec4570d02df1b498a3d8c90eaba98a15b84ac5d568aa443f46a301c487df57feeede5c81ac8fa7083a84504c30d48d Homepage: https://cran.r-project.org/package=ggeffects Description: CRAN Package 'ggeffects' (Create Tidy Data Frames of Marginal Effects for 'ggplot' fromModel Outputs) Compute marginal effects and adjusted predictions from statistical models and returns the result as tidy data frames. These data frames are ready to use with the 'ggplot2'-package. Effects and predictions can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The main functions are ggpredict(), ggemmeans() and ggeffect(). There is a generic plot()-method to plot the results using 'ggplot2'. Package: r-cran-ggenealogy Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1373 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-plyr, r-cran-reshape2, r-cran-plotly Suggests: r-cran-stringr, r-cran-knitr, r-cran-roxygen2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-ggenealogy_1.0.3-1.ca2404.1_all.deb Size: 1261182 MD5sum: 647ee126965e1ab6b27af6b52ce11389 SHA1: 19c8d634335783e7ac5247bb903fa22540925bfd SHA256: a0924fd74e22a716b894ab2f8bcdbfa90e14d19ce381e7bdfcf33a2ab993f79b SHA512: 65731415f7ed1db8cdf2e0c034bb43e410c3c735b18704b3721055b83f97c80a313e61742b2972fb2d5a0bacc0248ac2b770ab030aaef4f59e1851ee97843c34 Homepage: https://cran.r-project.org/package=ggenealogy Description: CRAN Package 'ggenealogy' (Visualization Tools for Genealogical Data) Methods for searching through genealogical data and displaying the results. Plotting algorithms assist with data exploration and publication-quality image generation. Includes interactive genealogy visualization tools. Provides parsing and calculation methods for variables in descendant branches of interest. Uses the Grammar of Graphics. 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Notable among these are the bivariate peelings surveyed by Green (1981, ISBN:978-0-471-28039-2), the bag-and-bolster plots proposed by Rousseeuw &al (1999) , and the minimum spanning trees used by Jolliffe (2002) to represent high-dimensional relationships among data in a low-dimensional plot. Additionally, biplots of singular value--decomposed tabular data, such as from principal components analysis, make use of vectors, calibrated axes, and other representations of variable elements to complement point markers for case elements; see Gabriel (1971) and Gower & Harding (1988) for original proposals. Because they treat the abscissa and ordinate as commensurate or the data elements themselves as point masses or unit vectors, these multivariable tools can be thought of as belonging to geometric data analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux & Rouanet (2005) for foundations. 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Plotting oceanographic spatial data is made as simple as feasible, but also flexible for custom modifications. Data that contain geographic information from anywhere around the globe can be plotted on maps generated by the basemap() or qmap() functions using 'ggplot2' layers separated by the '+' operator. The package uses spatial shape- ('sf') and raster ('stars') files, geospatial packages for R to manipulate, and the 'ggplot2' package to plot these files. The package ships with low-resolution spatial data files and higher resolution files for detailed maps are stored in the 'ggOceanMapsLargeData' repository on GitHub and downloaded automatically when needed. Package: r-cran-ggokabeito Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2783 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-covr, r-cran-ggraph, r-cran-igraph, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggokabeito_0.1.0-1.ca2404.1_all.deb Size: 2302452 MD5sum: b11d4484e3c018d757b14ebc20b15131 SHA1: 437508375571da639d8365bbe04b4cb310416225 SHA256: 434abdde0137b5b093c26987703a64b3bfe8f735950c4570736f0dd3c17056fb SHA512: a7b3b3e3876bf03b843d87ef49d70642aa1a24edff322696c04c1aa036ded22659b26118ee7466101277a5a997d4978e0664221b27cfc1b9a89bb6e9362effaa Homepage: https://cran.r-project.org/package=ggokabeito Description: CRAN Package 'ggokabeito' ('Okabe-Ito' Scales for 'ggplot2' and 'ggraph') Discrete scales for the colorblind-friendly 'Okabe-Ito' palette, including 'color', 'fill', and 'edge_colour'. 'ggokabeito' provides 'ggplot2' and 'ggraph' scales to easily use the 'Okabe-Ito' palette in your data visualizations. Package: r-cran-ggordiplots Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-vegan, r-cran-glue Suggests: r-cran-formatr, r-cran-permute, r-cran-lattice, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggordiplots_0.4.3-1.ca2404.1_all.deb Size: 147464 MD5sum: bd0ae014470e25902c00b52c9f6d0dcc SHA1: e98d95c297259b1b45196a4ee8f72c6152f9e273 SHA256: b5d73fc887331117c24b77d82789fc53d87674ae9b7eba5b24d48a28ed26e474 SHA512: 86b3dd14d4aa90552645cfdacfc85a30970d7c8f5c93c51e0d6a38797c2806b53b771c056af3f57c3c779d56f0ad1fbc460b8e5715f1c7ed60d6d80d7950fafe Homepage: https://cran.r-project.org/package=ggordiplots Description: CRAN Package 'ggordiplots' (Make 'ggplot2' Versions of Vegan's Ordiplots) The 'vegan' package includes several functions for adding features to ordination plots: ordiarrows(), ordiellipse(), ordihull(), ordispider() and ordisurf(). This package adds these same features to ordination plots made with 'ggplot2'. In addition, gg_ordibubble() sizes points relative to the value of an environmental variable. Package: r-cran-ggoutlier Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastknn, r-cran-foreach, r-cran-doparallel, r-cran-scales, r-cran-rcolorbrewer, r-cran-ggforce, r-cran-rlang, r-cran-tidyr, r-cran-rnaturalearth, r-cran-sf, r-cran-ggplot2, r-cran-cowplot Suggests: r-cran-rnaturalearthdata Filename: pool/dists/noble/main/r-cran-ggoutlier_1.0.2-1.ca2404.1_all.deb Size: 481372 MD5sum: 03ecd2ea72d4f61cc8e109efa5cbb78b SHA1: 989c3cbfd68cb6f29a7b17463c17b7acb016aee8 SHA256: 3c1774d0d7ea1584c61493eb5aff33a2c47e94c3cf0f1a1f5c4c5c7bd20cab6e SHA512: 70aa0b1d6c69b7fac099845fe9e8baa15c6612c62e7149537931f8d6a64244c052849011099d7b56372e08d54ed9ff8f2e1397e06ed414d374c07bd64120eecc Homepage: https://cran.r-project.org/package=GGoutlieR Description: CRAN Package 'GGoutlieR' (Identify Individuals with Unusual Geo-Genetic Patterns) Identify and visualize individuals with unusual association patterns of genetics and geography using the approach of Chang and Schmid (2023) . It detects potential outliers that violate the isolation-by-distance assumption using the K-nearest neighbor approach. You can obtain a table of outliers with statistics and visualize unusual geo-genetic patterns on a geographical map. This is useful for landscape genomics studies to discover individuals with unusual geography and genetics associations from a large biological sample. Package: r-cran-ggpackets Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-dplyr, r-cran-backports, r-cran-knitr, r-cran-rmarkdown, r-cran-stringi, r-cran-covr Filename: pool/dists/noble/main/r-cran-ggpackets_0.2.2-1.ca2404.1_all.deb Size: 806354 MD5sum: 5aa3fd3faadbdc8791e70b3f31d2eed7 SHA1: 6b4159f02f1e86b576b77b99e0800023da5d9a8f SHA256: 7f03a97873d22b4ad6253699732d19a9bdbad24090908b4b7776966beb679bcd SHA512: 72cb4b98fa98b86e338caa9a89ab3d7f5dbc3655b4a6b5c1498e5bed12f31212b24f9730d8100f4ab140c0eb2852d02f31515548bea28298a97907f768f6eb05 Homepage: https://cran.r-project.org/package=ggpackets Description: CRAN Package 'ggpackets' (Package Plot Layers for Easier Portability and Modularization) Create groups of 'ggplot2' layers that can be easily migrated from one plot to another, reducing redundant code and improving the ability to format many plots that draw from the same source 'ggpacket' layers. Package: r-cran-ggpage Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidytext Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggpage_0.2.3-1.ca2404.1_all.deb Size: 296568 MD5sum: 4bb8aff1c3c0f57f45f4ed5313302e32 SHA1: 508d389abcc4fcbf55b386a2ff9ef4c296c5a3f2 SHA256: 1a353688aa3cd059eab0138b41f5d24fd633ec232294bb059b43ce930808b193 SHA512: 6f0a7116f51713c4f3896420a8dd570bf51dc9a705a816cc481f105de3ce929b03177e8bf0b9e5242e99a284ddeb0ce383ec66c14e3e88acf700c65bbb4b8f92 Homepage: https://cran.r-project.org/package=ggpage Description: CRAN Package 'ggpage' (Creates Page Layout Visualizations) Facilitates the creation of page layout visualizations in which words are represented as rectangles with sizes relating to the length of the words. Which then is divided in lines and pages for easy overview of up to quite large texts. Package: r-cran-ggparallel Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2999 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-plyr Suggests: r-cran-rcolorbrewer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggparallel_0.4.0-1.ca2404.1_all.deb Size: 2891006 MD5sum: bfc9a7ee8222466ee1453887f63b510b SHA1: 62b89f2ae0319ff4ed8ed36db4121885f20a4df9 SHA256: f260aaf7e05feb5958742be2d1e88ddd17bb0937134dea95a88893efda897c16 SHA512: aa631fc9d016d4d42cd79127c3d470a30b6ebef1d315a89a9309e2f1518bc1bd4f237f2c0a6ea9589dfcdd6c66e94b16378868fab5652ff101d3599516fbbfe5 Homepage: https://cran.r-project.org/package=ggparallel Description: CRAN Package 'ggparallel' (Variations of Parallel Coordinate Plots for Categorical Data) Create hammock plots, parallel sets, and common angle plots with 'ggplot2'. 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'ggparty' provides the necessary tools to create clearly structured and highly customizable visualizations for tree-objects of the class 'party'. 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Package: r-cran-ggpattern Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4177 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-glue, r-cran-gridpattern, r-cran-lifecycle, r-cran-rlang, r-cran-scales, r-cran-vctrs Suggests: r-cran-ambient, r-cran-dplyr, r-cran-gganimate, r-cran-knitr, r-cran-magick, r-cran-mapproj, r-cran-maps, r-cran-png, r-cran-ragg, r-cran-readr, r-cran-rmarkdown, r-cran-sf, r-cran-svglite, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggpattern_1.3.1-1.ca2404.1_all.deb Size: 3147610 MD5sum: a157974d6e7cba31aea8f625e47b0d42 SHA1: 3d43e0142a3d945de78b4964da9dfd691f3bb2cb SHA256: 139bb9e28b05d345efd24b45927774f2dfea993e520cb4ccc623ef72b8b94302 SHA512: a161ebfd6aa37ed6b5ecd716a54e09551b7aa906475b84c647add784368235fab91beabc86fa8b0c51660874ce603e9734e82e264e066c9f7304a93487785f02 Homepage: https://cran.r-project.org/package=ggpattern Description: CRAN Package 'ggpattern' ('ggplot2' Pattern Geoms) Provides 'ggplot2' geoms filled with various patterns. Includes a patterned version of every 'ggplot2' geom that has a region that can be filled with a pattern. Provides a suite of 'ggplot2' aesthetics and scales for controlling pattern appearances. Supports over a dozen builtin patterns (every pattern implemented by 'gridpattern') as well as allowing custom user-defined patterns. Package: r-cran-ggpca Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1072 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-config, r-cran-golem, r-cran-shiny, r-cran-rlang, r-cran-rtsne, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-umap Suggests: r-cran-knitr, r-cran-tibble, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggpca_0.1.3-1.ca2404.1_all.deb Size: 560002 MD5sum: 579b5fafffabc69bbffad53a1b14ba4c SHA1: e113760f1469a4428910bb6fc85384fa5edeea8e SHA256: 24a472bb346f68de888b4f220aa986c6ee3a197a1cea4bb01c059db44747828c SHA512: 621939ab4b90df2c6401667cb8212720bf0a988509db214d7bb5c21e328aeb8dbadc31d7cd258d83c64ec7c1258879458cbcf117cd0cc60cd05d37478af145e6 Homepage: https://cran.r-project.org/package=ggpca Description: CRAN Package 'ggpca' (Publication-Ready PCA, t-SNE, and UMAP Plots) Provides tools for creating publication-ready dimensionality reduction plots, including Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). This package helps visualize high-dimensional data with options for custom labels, density plots, and faceting, using the 'ggplot2' framework Wickham (2016) . Package: r-cran-ggpcp Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2649 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-purrr, r-cran-testthat, r-cran-ggally Filename: pool/dists/noble/main/r-cran-ggpcp_0.2.0-1.ca2404.1_all.deb Size: 2575142 MD5sum: 4085ee62a72af203ea1ec64212d9fda4 SHA1: 585bf9b1b8e3f696452126bd68d320aa2157fa6c SHA256: b7a0e42fd90f8649368d7fae8b7cef6ef7496334eb6ddd5e42a155aba18a2582 SHA512: fda3241ba6b20a294fd9072cb8db55fe46b49145c172f261658d6fa17d339aeb86b086992ac71a1fe8ddabdbcaa4e15d4ec559fe93dc75d84fdbf8abd5bc949e Homepage: https://cran.r-project.org/package=ggpcp Description: CRAN Package 'ggpcp' (Parallel Coordinate Plots in the 'ggplot2' Framework) Modern Parallel Coordinate Plots have been introduced in the 1980s as a way to visualize arbitrarily many numeric variables. 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Package: r-cran-ggpedigree Architecture: all Version: 1.1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3803 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bgmisc, r-cran-ggplot2, r-cran-rlang, r-cran-dplyr, r-cran-stringr, r-cran-plotly, r-cran-scales, r-cran-tidyr Suggests: r-cran-selectr, r-cran-kinship2, r-cran-quadprog, r-cran-ggrepel, r-cran-paletteer, r-cran-mockery, r-cran-patchwork, r-cran-viridis, r-cran-knitr, r-cran-tidyverse, r-cran-purrr, r-cran-data.table, r-cran-discord, r-cran-openmx, r-cran-nlsylinks, r-cran-rmarkdown, r-cran-tibble, r-cran-corrplot, r-cran-showtext, r-cran-sysfonts, r-cran-withr, r-cran-htmlwidgets, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggpedigree_1.1.1.1-1.ca2404.1_all.deb Size: 1091104 MD5sum: dc65a6ef077d4184cc8a323c76f5f918 SHA1: c899200d41f0197f2e160600d34aa82b3975f14a SHA256: eeaf62f551124e71ac3efb7f32e0c8e9785e6ecfedb2f92215afe248bd34db29 SHA512: 304078deaa4b6fb22b185da2d5ad0a3f8e9199a6717b2f8f7c4c95f75db189bddf494eb4af41fed5aa6cdd03de693cbcb2a5f459af5031931f8f6fa85ecba03c Homepage: https://cran.r-project.org/package=ggpedigree Description: CRAN Package 'ggpedigree' (Visualizing Pedigrees with 'ggplot2' and 'plotly') Provides plotting functions for visualizing pedigrees and family trees. The package complements a behavior genetics package 'BGmisc' [Garrison et al. (2024) ] by rendering pedigrees using the 'ggplot2' framework. Features include support for duplicated individuals, complex mating structures, integration with simulated pedigrees, and layout customization. Due to the impending deprecation of kinship2, version 1.0 incorporates the layout helper functions from kinship2. The pedigree alignment algorithms are adapted from 'kinship2' [Sinnwell et al. (2014) ]. We gratefully acknowledge the original authors: Jason Sinnwell, Terry Therneau, Daniel Schaid, and Elizabeth Atkinson for their foundational work. Package: r-cran-ggperiodic Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-sticky, r-cran-tidyselect, r-cran-data.table Suggests: r-cran-covr, r-cran-knitr, r-cran-maps, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggperiodic_1.0.3-1.ca2404.1_all.deb Size: 336486 MD5sum: 7c7499f39933833f77d92b9442313733 SHA1: 3cdc5e8c77079f8ddd607b474ce38823b4ede149 SHA256: ad39c14772d4ee72adc300e0865915729dd31e19318927c8258962f6c35b87e4 SHA512: 9ebb5dca4ddd3d6108fda27317d7e083733650355d652f91cf86c52d500fce53daad133bf4b86b03274dccdb98e86129acb0fd0dcceb969a017a613e38e24945 Homepage: https://cran.r-project.org/package=ggperiodic Description: CRAN Package 'ggperiodic' (Easy Plotting of Periodic Data with 'ggplot2') Implements methods to plot periodic data in any arbitrary range on the fly. Package: r-cran-ggpicrust2 Architecture: all Version: 2.5.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5745 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aplot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggh4x, r-cran-readr, r-cran-tibble, r-cran-tidyr, r-cran-ggprism, r-cran-patchwork, r-cran-ggplotify, r-cran-magrittr, r-cran-progress, r-cran-tidygraph, r-cran-ggraph Suggests: r-bioc-biobase, r-cran-ggdendro, r-bioc-keggrest, r-bioc-complexheatmap, r-bioc-biocgenerics, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-aldex2, r-bioc-deseq2, r-bioc-edger, r-cran-ggally, r-bioc-limma, r-cran-microbiomestat, r-bioc-summarizedexperiment, r-cran-circlize, r-bioc-lefser, r-bioc-maaslin2, r-bioc-metagenomeseq, r-bioc-fgsea, r-bioc-clusterprofiler, r-bioc-enrichplot, r-bioc-dose, r-cran-ggvenndiagram, r-cran-upsetr, r-cran-igraph, r-cran-ggridges, r-cran-ggrepel, r-cran-logging Filename: pool/dists/noble/main/r-cran-ggpicrust2_2.5.16-1.ca2404.1_all.deb Size: 4467648 MD5sum: 77b5ec47595cb734941c2ca387adfdf1 SHA1: e6e638e9a8eac40b617dc3ec56d852a66f8bea5f SHA256: 5c1f556d5d51bb0ea036a846882ecfcbc0d424bd707f905d688b1aa69bf917b7 SHA512: 113af42ee55922baeb8a386b7a4c6aca50d55273fb39f14304a510aa99120b1b54024ca4c2d4b31ffbea1258550ee02ae121895d8b4bf1c183dffa9792e05d91 Homepage: https://cran.r-project.org/package=ggpicrust2 Description: CRAN Package 'ggpicrust2' (Make 'PICRUSt2' Output Analysis and Visualization Easier) Provides a convenient way to analyze and visualize 'PICRUSt2' output with pre-defined plots and functions. Allows for generating statistical plots about microbiome functional predictions and offers customization options. Features a one-click option for creating publication-level plots, saving time and effort in producing professional-grade figures. Streamlines the 'PICRUSt2' analysis and visualization process. For more details, see Yang et al. (2023) . 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Package: r-cran-ggplate Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5160 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-forcats, r-cran-farver Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggplate_0.3.1-1.ca2404.1_all.deb Size: 4730122 MD5sum: 558da701dae1fd990deae71765b3f454 SHA1: 7b422de1609b29170e5aa3596c5311b749342594 SHA256: 7cc04171d8f8e5c16296b78a8c3754455905514d69be06b9f6d29f949539f62a SHA512: e2246600815db8303f1c37e8e7b8cb33680e9b00a809cbe06fb76ef3a27d7d317f434250003a1f3acde28bda4f7e66fae49cd6c376075fdbb899509a5139dc43 Homepage: https://cran.r-project.org/package=ggplate Description: CRAN Package 'ggplate' (Create Layout Plots of Biological Culture Plates and Microplates) Enables users to create simple plots of biological culture plates as well as microplates. 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Package: r-cran-ggplot.multistats Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-scales, r-cran-hexbin, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-ggplot.multistats_1.0.1-1.ca2404.1_all.deb Size: 32986 MD5sum: 095584197a1c3fa4aa36e7cba59f76c2 SHA1: fcd39d3757d741cc35b245ee7e8c12617c5b3386 SHA256: 062594678ed95faaaf30f3a1cac73659b8a9e6ae157529c768844401d34cceaf SHA512: 77afcae9a0b6edb2395bd022aeb125c0aafe9c3b91fe2cf86f68a4b4adcf7092340fe53729ba51d360d6c1811e22476f96bf1324d922f8e07487e52021c5c67b Homepage: https://cran.r-project.org/package=ggplot.multistats Description: CRAN Package 'ggplot.multistats' (Multiple Summary Statistics for Binned Stats/Geometries) Provides the ggplot binning layer stat_summaries_hex(), which functions similar to its singular form, but allows the use of multiple statistics per bin. 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Package: r-cran-ggplot2.utils Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1665 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-checkmate, r-cran-envstats, r-cran-ggpp, r-cran-ggstats, r-cran-survival Suggests: r-cran-dplyr, r-cran-lifecycle, r-cran-testthat, r-cran-tibble, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggplot2.utils_0.3.3-1.ca2404.1_all.deb Size: 1246704 MD5sum: d3b91ef6b6c35e63a2488c8f476b5602 SHA1: 9c3ba44d0163d26f2a1969acf92872bc94c88a0b SHA256: 49196e6df4cd8cca58cfcfa60b0f5284f461287c37dc05a8be5e8350e83bfd42 SHA512: b590a61c19bf50842e3e9a04092df79bf50bbfee66b252bfa139c9ac2aee221929515c83af53e27cbd7ed04a74c23a0415a773af34eb9cc5092ae25452d0e08d Homepage: https://cran.r-project.org/package=ggplot2.utils Description: CRAN Package 'ggplot2.utils' (Selected Utilities Extending 'ggplot2') Selected utilities, in particular 'geoms' and 'stats' functions, extending the 'ggplot2' package. This package imports functions from 'EnvStats' by Millard (2013), 'ggpp' by Aphalo et al. (2023) and 'ggstats' by Larmarange (2023), and then exports them. This package also contains modified code from 'ggquickeda' by Mouksassi et al. (2023) for Kaplan-Meier lines and ticks additions to plots. All functions are tested to make sure that they work reliably. Package: r-cran-ggplot2 Architecture: all Version: 4.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-gtable, r-cran-isoband, r-cran-lifecycle, r-cran-rlang, r-cran-s7, r-cran-scales, r-cran-vctrs, r-cran-withr Suggests: r-cran-broom, r-cran-covr, r-cran-dplyr, r-cran-ggplot2movies, r-cran-hexbin, r-cran-hmisc, r-cran-hms, r-cran-knitr, r-cran-mapproj, r-cran-maps, r-cran-mass, r-cran-mgcv, r-cran-multcomp, r-cran-munsell, r-cran-nlme, r-cran-profvis, r-cran-quantreg, r-cran-quarto, r-cran-ragg, r-cran-rcolorbrewer, r-cran-roxygen2, r-cran-rpart, r-cran-sf, r-cran-svglite, r-cran-testthat, r-cran-tibble, r-cran-vdiffr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-ggplot2_4.0.3-1.ca2404.1_all.deb Size: 7780086 MD5sum: c0da7805177f637c0967e40e3ed6217c SHA1: 98c81c893d6d185b6aeabd349369762bf687fa7f SHA256: 34106918ea5fcf995d7a656b6a1310d711d313124cc47eb5f29cfb8b72bb7688 SHA512: 87c14416f8858de2ae6d2a769dc587142f8f1de2f584d84473c03e9acc68b72a6f8e56748ad2fb2bc5d06753542355043b4fce31f8552d5ab6d91aaef3ca5174 Homepage: https://cran.r-project.org/package=ggplot2 Description: CRAN Package 'ggplot2' (Create Elegant Data Visualisations Using the Grammar of Graphics) A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. Package: r-cran-ggplot2movies Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1280 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ggplot2movies_0.0.1-1.ca2404.1_all.deb Size: 1267886 MD5sum: a7f65a60b247dc86911ffdd1645d5c4e SHA1: 40c61501ebe857382ef67e7477fa7194fe2b4f6e SHA256: bfb1d238a3942f9938b6bcb51abe295c397fe067df76ac654ff0c2225a411a12 SHA512: 690fb1f6a200a7261a46337192680f36cca0a5e9bbcf5c0fa0aff6b4653f6704ecf5e9d163eb2ef5cdecc13e79f346604e9a446fa6f46a0b154b65f3514cc976 Homepage: https://cran.r-project.org/package=ggplot2movies Description: CRAN Package 'ggplot2movies' (Movies Data) A dataset about movies. This was previously contained in ggplot2, but has been moved its own package to reduce the download size of ggplot2. Package: r-cran-ggplotassist Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1418 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-rstudioapi, r-cran-shinywidgets, r-cran-shinyace, r-cran-stringr, r-cran-tidyverse, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-tibble, r-cran-scales, r-cran-ggthemes, r-cran-gcookbook, r-cran-moonbook, r-cran-editdata Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-ggplotassist_0.1.3-1.ca2404.1_all.deb Size: 756340 MD5sum: 89221e5b5c6161c365916abc16a934b8 SHA1: 80485eb1697399b3900a6c0b6820b87b5a57bd4d SHA256: 799ba530a03d6f16289b82eda3e2fb14f852aca35102e032af25f9fcf699cc14 SHA512: abae08e07910972cdc58046c472790679f5e02fa231c5a48447ed0692dbfba1b6a07f4e7a033b541dd8a97be7ee5419fd0e078692a63cdf249e876b729568e94 Homepage: https://cran.r-project.org/package=ggplotAssist Description: CRAN Package 'ggplotAssist' ('RStudio' Addin for Teaching and Learning 'ggplot2') An 'RStudio' addin for teaching and learning making plot using the 'ggplot2' package. You can learn each steps of making plot by clicking your mouse without coding. You can get resultant code for the plot. Package: r-cran-ggplotgui Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotly, r-cran-shiny, r-cran-ggplot2, r-cran-stringr, r-cran-readr, r-cran-haven, r-cran-readxl, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-ggplotgui_1.0.0-1.ca2404.1_all.deb Size: 624780 MD5sum: b355973c36a97a740e3a02b3d3fc9a28 SHA1: 842527306e74b01d0bb889228b6d48f945b4810f SHA256: f37ff092e97db878510f0b9507e3357589baad200ccb3eea0947c793c973fb82 SHA512: 0cbb8c68fa644a2db8a4bb1e2b57dff435d14b9fcf5786d503f1cda1daf09a0d8e4a6ff079361853bb14d116f7b388ea1a95a49963a3cced7fc80728fb0a11a0 Homepage: https://cran.r-project.org/package=ggplotgui Description: CRAN Package 'ggplotgui' (Create Ggplots via a Graphical User Interface) Easily explore data by creating ggplots through a (shiny-)GUI. R-code to recreate graph provided. Package: r-cran-ggplotify Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridgraphics, r-cran-rlang, r-cran-yulab.utils Suggests: r-cran-aplot, r-cran-colorspace, r-cran-cowplot, r-cran-ggimage, r-cran-knitr, r-cran-rmarkdown, r-cran-lattice, r-cran-prettydoc, r-cran-vcd Filename: pool/dists/noble/main/r-cran-ggplotify_0.1.3-1.ca2404.1_all.deb Size: 140018 MD5sum: 8f5b162b578da281f0437036077ee473 SHA1: 547621ca76bf9855748e80e3e481166938ad2b32 SHA256: 3325648e6b475130aea013f9ca9af80e85e44e14af0c867da620798aec16bca6 SHA512: d326fffb470b6c8ac4d9d85415e372a508e038cc211f4f42c5eef14c12debe58d8ef69c8eb42fa232585ec8a8df96bde49a5096e7767f0127ab65dd1e10a997c Homepage: https://cran.r-project.org/package=ggplotify Description: CRAN Package 'ggplotify' (Convert Plot to 'grob' or 'ggplot' Object) Convert plot function call (using expression or formula) to 'grob' or 'ggplot' object that compatible to the 'grid' and 'ggplot2' ecosystem. With this package, we are able to e.g. using 'cowplot' to align plots produced by 'base' graphics, 'ComplexHeatmap', 'eulerr', 'grid', 'lattice', 'magick', 'pheatmap', 'vcd' etc. by converting them to 'ggplot' objects. Package: r-cran-ggplotlyextra Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-rlang Filename: pool/dists/noble/main/r-cran-ggplotlyextra_0.0.1-1.ca2404.1_all.deb Size: 13920 MD5sum: 3904cc4e9aedafeadd2f447fa1059fa9 SHA1: 59df894245f3704026d145175ee71c5094046e42 SHA256: e47ea87811d589af6556ddd6e6fc749e3b2b977d3fbb2b31310fbf18b60273ae SHA512: fcd1f3360c43efa996c856a6a2f19c003ed9d66ae87cc42d74cecad915588e472c358b99929cf5a6978702e696c2612099aa9e79a98b05b08d5403c6f22d2d86 Homepage: https://cran.r-project.org/package=ggplotlyExtra Description: CRAN Package 'ggplotlyExtra' (Extra Convenience Functions for 'Plotly') Convenience functions for smooth conversion from 'ggplot' to 'plotly' where the conversion using ggplotly() usually gives an unexpected labels. The package ease the process of making a 'plotly' figures generated from 'ggplot2' object more aesthetic in terms of labels and customizability. Package: r-cran-ggpmisc Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2460 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggpp, r-cran-ggplot2, r-cran-scales, r-cran-rlang, r-cran-generics, r-cran-polynom, r-cran-confintr, r-cran-splus2r, r-cran-tibble, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-lubridate, r-cran-catools, r-cran-quantreg Suggests: r-cran-broom, r-cran-broom.mixed, r-cran-robustbase, r-cran-mass, r-cran-lmodel2, r-cran-nlme, r-cran-smatr, r-cran-multcomp, r-cran-multcompview, r-cran-mixtools, r-cran-segmented, r-cran-gginnards, r-cran-ggrepel, r-cran-ggtext, r-cran-xdvir, r-cran-marquee, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggpmisc_0.7.0-1.ca2404.1_all.deb Size: 1747526 MD5sum: 22928b2ceac000fa69131d6868db2eb4 SHA1: c6e4ccc5ce5fd11deffd3328fde80e7f87f43c03 SHA256: f4a5512a41555996fd949996f849fd138937186e07b07c70dc8484113904e630 SHA512: 514d34ad25bd272605077f87aee4c8837b0330c68e6f3afc07df9ea1b623a974ddf1887e0146fc50a34183e6d04a2aa254118b5a2d2cd2354bd0b74a5ca3aa60 Homepage: https://cran.r-project.org/package=ggpmisc Description: CRAN Package 'ggpmisc' (Miscellaneous Extensions to 'ggplot2') Extensions to 'ggplot2' respecting the grammar of graphics paradigm. Statistics to locate and tag peaks and valleys and to label plots with the equation of a fitted polynomial model by ordinary least squares, major axis, quantile and robust and resistant regression approaches. Line and model equation for Normal mixture models. Labels for P-value, R^2 or adjusted R^2 or information criteria for fitted models; parametric and non-parametric correlation; ANOVA table or summary table for fitted models as plot insets; annotations for multiple pairwise comparisons with adjusted P-values. Model fit classes for which suitable methods are provided by package 'broom' and 'broom.mixed' are supported as well as user-defined wrappers on model fit functions, allowing model selection and conditional labelling. Scales and stats to build volcano and quadrant plots based on outcomes, fold changes, p-values and false discovery rates. Package: r-cran-ggpmx Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6843 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-yaml, r-cran-r6, r-cran-gtable, r-cran-ggplot2, r-cran-ggforce, r-cran-magrittr, r-cran-stringr, r-cran-assertthat, r-cran-ggally, r-cran-zoo, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-checkmate, r-cran-scales Suggests: r-cran-testthat, r-cran-xtable, r-cran-vdiffr, r-cran-rxode2, r-cran-nlmixr2est, r-cran-nlmixr2data, r-cran-nlme, r-cran-xgxr, r-cran-withr, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-ggpmx_1.3.2-1.ca2404.1_all.deb Size: 3978610 MD5sum: 5bb83616d9908736115882a6a3b7f319 SHA1: 3daa4e56a63ed523699f9fccb80d434bda9568a7 SHA256: 6ca755ec187ff923474186463c1890fa6b75981587c8e810730350fa3094645f SHA512: 3cb8210dbe98f66479118b3da5972d437dc95fdf91e60c6a12ce4385e6c842c7ac42f62f57c50ccc59c5addbe67bfd541087670f0e5a8cc069bb836d2da0ce49 Homepage: https://cran.r-project.org/package=ggPMX Description: CRAN Package 'ggPMX' ('ggplot2' Based Tool to Facilitate Diagnostic Plots for NLMEModels) At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling activities in order to reduce the overall effort required for generating such plots. For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox, called 'ggPMX' to execute them. 'ggPMX' is a toolbox that can generate all diagnostic plots at a quality sufficient for publication and submissions using few lines of code. This package focuses on plots recommended by ISoP . While not required, you can get/install the 'R' 'lixoftConnectors' package in the 'Monolix' installation, as described at the following url . When 'lixoftConnectors' is available, 'R' can use 'Monolix' directly to create the required Chart Data instead of exporting it from the 'Monolix' gui. Package: r-cran-ggpointless Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2275 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-cli, r-cran-farver, r-cran-scales, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-covr, r-cran-testthat, r-cran-ragg, r-cran-vdiffr, r-cran-spelling, r-cran-withr Filename: pool/dists/noble/main/r-cran-ggpointless_0.3.0-1.ca2404.1_all.deb Size: 2123266 MD5sum: 37a1a223ed96903899ad941fa1eec257 SHA1: 557869e74cb45c8363708e4231b403aa4c75038f SHA256: 8f4896c4a64ff170d59c98abdf465c865bb87ec25429cdfb2995755bb61f7fe5 SHA512: e3f5db2c11d2fcfb3f0ffe80f65b9f9c08b84788e8a318c1c55ee43c8c60b8592ca69c88a3b5f4bfd83d85a808a1fb75512f05341551111334f44307135ec5ec Homepage: https://cran.r-project.org/package=ggpointless Description: CRAN Package 'ggpointless' (Extra Geometries and Stats for 'ggplot2') A collection of layers for 'ggplot2'. Provides geoms built on linear and radial gradients from the 'grid' package, giving areas, bars, paths, rectangles, and ridgelines a fading or glowing visual effect. Also includes mathematically driven layers — catenary curves, Chaikin's corner-cutting smoothing (Chaikin, 1974, ), and Fourier-series reconstruction — plus Lexis diagrams, isotype bar charts. Package: r-cran-ggpol Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gtable, r-cran-plyr, r-cran-rlang, r-cran-dplyr, r-cran-tibble, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggpol_0.0.7-1.ca2404.1_all.deb Size: 269022 MD5sum: 5aae833d473645bbb8622572886f7204 SHA1: b7dce7ea93c4807ae8ff4e1d10781e7dc22a99b9 SHA256: 00011c08e338039585dd632b1f500fb407154657cdb9d659a19dde96ac912ad1 SHA512: c660950dbf7ed298930fc2b81c872f596408460437b34162043f56a5b7ef049311f877ba2903d173773e4b275228b148072463e742e798f8133e99bca4772eee Homepage: https://cran.r-project.org/package=ggpol Description: CRAN Package 'ggpol' (Visualizing Social Science Data with 'ggplot2') A 'ggplot2' extension for implementing parliament charts and several other useful visualizations. Package: r-cran-ggpolar Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-ezcox, r-cran-ggnewscale, r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-cli Filename: pool/dists/noble/main/r-cran-ggpolar_0.2.2-1.ca2404.1_all.deb Size: 70650 MD5sum: 1b0fbb4fb5af10d892e737ce4e9f9a80 SHA1: 6e1422111051c2aaa87bb06bf710aab653cba1af SHA256: 24dbf8907de5bac1fdc3316b46d3d840ccd6030dc210c2e251d8141719173577 SHA512: 684f54c4b551150cf512167475915debe7e02c6fa389205e6ac974bf622d214aa65d0ef2543ae723f27f8bf140fa4d2d6b134a384d8075d728b3c0a2473a7f64 Homepage: https://cran.r-project.org/package=ggpolar Description: CRAN Package 'ggpolar' (Dots and Their Connections in Polar Coordinate System) Provides basic graphing functions to fully demonstrate point-to-point connections in a polar coordinate space. Package: r-cran-ggpolypath Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ggpolypath_0.4.0-1.ca2404.1_all.deb Size: 272096 MD5sum: 55b9fee0bd1c0fca9423ad3933de3cf0 SHA1: 6176974ff0c892b9d2011ca208cfe91c3fefea7c SHA256: 305810d722d6f261285614628163ddc5fb57080148f34734ad82eacb2eba47f8 SHA512: 96b36dc8ef2948585252c645288860cfe8080da5d3151b894220a1f074cb2c0d02f960b2a27e8e0581ccac99384be61674383878314ee9b8279be9e9f6213e8f Homepage: https://cran.r-project.org/package=ggpolypath Description: CRAN Package 'ggpolypath' (Polygons with Holes for the Grammar of Graphics) Tools for working with polygons with holes in 'ggplot2', with a new 'geom' for drawing a 'polypath' applying the 'evenodd' or 'winding' rules. Package: r-cran-ggpop Architecture: all Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1702 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-ggimage, r-cran-magick, r-cran-rlang, r-cran-tidyr, r-cran-purrr, r-cran-fontawesome, r-cran-rsvg, r-cran-cli, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot, r-cran-ggforce, r-cran-gganimate, r-cran-ggrepel, r-cran-ggtext, r-cran-scales, r-cran-reactable Filename: pool/dists/noble/main/r-cran-ggpop_1.7.1-1.ca2404.1_all.deb Size: 1097562 MD5sum: 061284acb9219be41f4480a3ea7ae097 SHA1: 62776a5457d8f432063b26aa4899261ca668fbfa SHA256: d443f7b156523fe29806a2a095680fc8c1b0e7961f6f8fe867aee47085c69aa5 SHA512: af921aa5b40f9a865fd0d44016f441ac1feb83be387bdbb3cd9267cd94812889407dfb9203a7b8c6abe7e7f232fda2ece754d287213f5e64fbfec98784043392 Homepage: https://cran.r-project.org/package=ggpop Description: CRAN Package 'ggpop' (Icon-Based Population Charts and Plots for 'ggplot2') Create engaging population and point plots charts in R. 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Package: r-cran-ggpp Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2363 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-vctrs, r-cran-glue, r-cran-gridextra, r-cran-scales, r-cran-tibble, r-cran-dplyr, r-cran-xts, r-cran-zoo, r-cran-mass, r-cran-polynom, r-cran-lubridate, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggrepel, r-cran-gginnards, r-cran-magick, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggpp_0.6.0-1.ca2404.1_all.deb Size: 1696228 MD5sum: 99856f0de2a9cb1b1658d4e44eed4f60 SHA1: 23a704ce8004c9860b72352a967520d5fa0c66ca SHA256: e543fd90bd9006ed25eadb1a2b0ee469272f908f51c3dc8ae843ebea329e078b SHA512: 3cd4531c481ff1c12921f4ffe44bd6e98eebb21bb7d65776ac75d25272a9347cfc17dad927b33fa961db2888c1442ac288417dd63effac64baeb322d22be2a37 Homepage: https://cran.r-project.org/package=ggpp Description: CRAN Package 'ggpp' (Grammar Extensions to 'ggplot2') Extensions to 'ggplot2' respecting the grammar of graphics paradigm. Geometries: geom_table(), geom_plot() and geom_grob() add insets to plots using native data coordinates, while geom_table_npc(), geom_plot_npc() and geom_grob_npc() do the same using "npc" coordinates through new aesthetics "npcx" and "npcy". Statistics: select observations based on 2D density. Positions: radial nudging away from a center point and nudging away from a line or curve; combined stacking and nudging; combined dodging and nudging. Package: r-cran-ggprism Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4076 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-ggplot2, r-cran-glue, r-cran-gtable, r-cran-rlang, r-cran-scales, r-cran-tibble Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggbeeswarm, r-cran-ggnewscale, r-cran-knitr, r-cran-magrittr, r-cran-patchwork, r-cran-rmarkdown, r-cran-rstatix, r-cran-tidyr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-ggprism_1.0.7-1.ca2404.1_all.deb Size: 2736870 MD5sum: fe3c4a41b1627d464eec223b1093cb07 SHA1: 64b60953c09e71b9e0dc200554d830b15ece2971 SHA256: a2da395ee81af13e535975fc98d562df271e160b47b7ad5c7e103ce94de8ecb9 SHA512: 651b5806311174659ead4ef199f1bafed190f3c946fcb6ca5b42e7bf2cc86ad7ed25ba2e2d26df5a3ac51ffce99217318169f5152c1fe2460f4bd11683d4b874 Homepage: https://cran.r-project.org/package=ggprism Description: CRAN Package 'ggprism' (A 'ggplot2' Extension Inspired by 'GraphPad Prism') Provides various themes, palettes, and other functions that are used to customise ggplots to look like they were made in 'GraphPad Prism'. The 'Prism'-look is achieved with theme_prism() and scale_fill|colour_prism(), axes can be changed with custom guides like guide_prism_minor(), and significance indicators added with add_pvalue(). Package: r-cran-ggpubr Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggsci, r-cran-tidyr, r-cran-purrr, r-cran-dplyr, r-cran-cowplot, r-cran-ggsignif, r-cran-scales, r-cran-gridextra, r-cran-glue, r-cran-polynom, r-cran-rlang, r-cran-rstatix, r-cran-tibble, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rcolorbrewer, r-cran-gtable, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggpubr_0.6.3-1.ca2404.1_all.deb Size: 2125720 MD5sum: 743f52c66f7eb3df93ff0517e48b9483 SHA1: 8c976f99269a8b03226366ef394eebac4f99d009 SHA256: 4079d223dc7d75f99ae14a80f5c6cff5bd07717871d2fceef3ae01877e6e2ed8 SHA512: 69420925d087bcfd4e2cb78011a7546fc5c5328629460d045b9f632b8101f06977f2be2a7339813894876816a4d800ce24b8a0beb49f26808e853bf0fbdf2f92 Homepage: https://cran.r-project.org/package=ggpubr Description: CRAN Package 'ggpubr' ('ggplot2' Based Publication Ready Plots) The 'ggplot2' package is excellent and flexible for elegant data visualization in R. However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a 'ggplot', the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills. 'ggpubr' provides some easy-to-use functions for creating and customizing 'ggplot2'- based publication ready plots. Package: r-cran-ggpval Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1466 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggpval_0.2.5-1.ca2404.1_all.deb Size: 1107040 MD5sum: 12607d2ba76001757db6e4fb2773f9b8 SHA1: 20be32fbb99dc09d925e5041c196b71d2f3fef48 SHA256: e163dac08e05a28b8029e1462a83fdbd413b60abd24f0104d1ca89e1920f3a45 SHA512: 01b75481a5a2c7ed00e37b4404d7309557c2f4d424b1f1b5996196752e1e873fcdec3d231de15b9267bfb6cc045fbbc22f8a1d0528e192de6a97274c9a62bc72 Homepage: https://cran.r-project.org/package=ggpval Description: CRAN Package 'ggpval' (Annotate Statistical Tests for 'ggplot2') Automatically performs desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, and adds the resulting p-values to the plot with an annotation bar. Visualizing group differences are frequently performed by boxplots, bar plots, etc. Statistical test results are often needed to be annotated on these plots. This package provides a convenient function that works on 'ggplot2' objects, performs the desired statistical test between groups of interest and annotates the test results on the plot. Package: r-cran-ggqc Architecture: all Version: 0.0.31-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 598 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-testthat, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-plyr Filename: pool/dists/noble/main/r-cran-ggqc_0.0.31-1.ca2404.1_all.deb Size: 442456 MD5sum: 76d2d7d2d4d006e953480574263b8aa1 SHA1: 34350091862859320eaf11695a775d0d40a96167 SHA256: eb5a8941ec469d948b1d4289b94fdb7158fbc2eab059d63198f56745dc3d7567 SHA512: 16366fe7dd52815d5e449fb34c66b60a305b0b805851b397b9733a2e00b84008176246e58f884c72e2f8dfe6ffbc7cb11e64f710f643fbed3a890f9f9d7c554a Homepage: https://cran.r-project.org/package=ggQC Description: CRAN Package 'ggQC' (Quality Control Charts for 'ggplot') Plot single and faceted type quality control charts for 'ggplot'. Package: r-cran-ggqqunif Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scales, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-ggqqunif_0.1.5-1.ca2404.1_all.deb Size: 88850 MD5sum: d11522e051d63b7e41a60b77e977d4a6 SHA1: 5a7582041a8ed967ddd8d1e228dd764350b01f74 SHA256: 2c19e7e052685abd106af8b54507d5431f59eb7eaf4167d6b1ee16d6c14551a7 SHA512: 99c23b7681913ffca01cb01b9db4e36052f0c7e51ec1ccd2449e9b20d3b488cd7d038315e302737e0f093a40e452d464f373fe32935317f11b714dc409c1fa90 Homepage: https://cran.r-project.org/package=ggQQunif Description: CRAN Package 'ggQQunif' (Compare Big Datasets to the Uniform Distribution) A quantile-quantile plot can be used to compare a sample of p-values to the uniform distribution. But when the dataset is big (i.e. > 1e4 p-values), plotting the quantile-quantile plot can be slow. geom_QQ uses all the data to calculate the quantiles, but thins it out in a way that focuses on points near zero before plotting to speed up plotting and decrease file size, when vector graphics are stored. Package: r-cran-ggquickeda Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4853 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-colourpicker, r-cran-dplyr, r-cran-data.table, r-cran-dt, r-cran-formula, r-cran-forcats, r-cran-ggally, r-cran-ggbeeswarm, r-cran-ggh4x, r-cran-ggplot2, r-cran-ggpmisc, r-cran-ggrepel, r-cran-ggridges, r-cran-ggpubr, r-cran-ggstance, r-cran-glue, r-cran-gridextra, r-cran-hmisc, r-cran-markdown, r-cran-patchwork, r-cran-plotly, r-cran-quantreg, r-cran-rlang, r-cran-rms, r-cran-rpostgres, r-cran-scales, r-cran-shiny, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinyjqui, r-cran-stringr, r-cran-survival, r-cran-survminer, r-cran-tibble, r-cran-tidyr, r-cran-table1, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggquickeda_0.3.3-1.ca2404.1_all.deb Size: 2951458 MD5sum: 133d5479a384ae32ddc09fb2c5a85a82 SHA1: 555cd2e44c7ef80d1ba6b90f588d4fc72729875c SHA256: 7ff1fabc7bd115b01b043a61a995f91bdcca2233f786c8790aae1d17bf967dda SHA512: 635e767c0a0a95533e4b85428a8e585437d37e873dc850c2b3614dc5215841b445551b91eccda506275c3fba6899e104f77b69a97394d2391393649a6b402455 Homepage: https://cran.r-project.org/package=ggquickeda Description: CRAN Package 'ggquickeda' (Quickly Explore Your Data Using 'ggplot2' and 'table1' SummaryTables) Quickly and easily perform exploratory data analysis by uploading your data as a 'csv' file. 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We can conclude the following two points: Comparing the prediction results of the model with the real survival situation to see whether the survival rate of the high-risk group is lower than that of the low-level group, and whether the survival time of the high-risk group is shorter than that of the low-risk group. The other is to compare the heat map and scatter plot to see the correlation between the predictors and the outcome. 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Package: r-cran-ggseg3d Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2700 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggseg.formats, r-cran-htmlwidgets, r-cran-knitr, r-cran-lifecycle, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-webshot2 Suggests: r-cran-ggseg.meshes, r-cran-png, r-cran-rayshader, r-cran-rgl, r-cran-rmarkdown, r-cran-shiny, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggseg3d_2.1.1-1.ca2404.1_all.deb Size: 1615120 MD5sum: 2f373aa4c8e49a5503cbdc066bf8303b SHA1: 81b44b5c36b8737ad49eb97002cdbbb5cde19dfa SHA256: e39fb9fd1ebd128ec40951417817d999b1f1657b2779299b6ca119238e58ffa9 SHA512: 81f839cfbbbc7686354ab1021d14636f827f61ae307e7b578555f15c1515e61a4cbfc7eccd21be3636fc8f72ed2df3dd32e3ef05d5ec378c3c4fc64e37bd6d82 Homepage: https://cran.r-project.org/package=ggseg3d Description: CRAN Package 'ggseg3d' (Interactive 3D Brain Atlas Visualization) Plot brain atlases as interactive 3D meshes using 'Three.js' via 'htmlwidgets', or render publication-quality static images through 'rgl' and 'rayshader'. A pipe-friendly API lets you map data onto brain regions, control camera angles, toggle region edges, overlay glass brains, and snapshot or ray-trace the result. Additional atlases are available through the 'ggsegverse' r-universe. Mowinckel & Vidal-Piñeiro (2020) . Package: r-cran-ggseg Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4360 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-ggseg.formats, r-cran-lifecycle, r-cran-sf, r-cran-tidyr Suggests: r-cran-covr, r-cran-devtools, r-cran-here, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggseg_2.1.1-1.ca2404.1_all.deb Size: 3251542 MD5sum: 89b3f835cd30aa5b7219a8ab6134ba98 SHA1: ef0afe320a9fafea51f99d1eab518b3c475fbac0 SHA256: 4b1b6b9c286b4e73f414341771b4cb5021982440932e3d009297282c91e6c8ce SHA512: 2a3cd8a97f48578265b3b3f1115a2f9042eb876caf40e526ba92cb20edcb68ba2b3808212ffda7c973a8a3755779ea7bf57bc7cbe42653bbb100406e690892d1 Homepage: https://cran.r-project.org/package=ggseg Description: CRAN Package 'ggseg' (Plotting Tool for Brain Atlases) Provides a 'ggplot2' geom and position for visualizing brain region data on cortical, subcortical, and white matter tract atlases. Brain atlas geometries are stored as simple features ('sf'), enabling seamless integration with the 'ggplot2' ecosystem including faceting, custom scales, and themes. Mowinckel & Vidal-Piñeiro (2020) . Package: r-cran-ggsegmentedtotalbar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-forcats Suggests: r-cran-testthat, r-cran-devtools, r-cran-remotes, r-cran-roxygen2, r-cran-usethis, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggsegmentedtotalbar_0.1.0-1.ca2404.1_all.deb Size: 97542 MD5sum: 91d8129d23e7c26e909299f633d27321 SHA1: a7376cd661015f25f1d2c98acd9615d86079439d SHA256: 895dab58d1ae62cec141144d56d3adc069d504b86f9cc32327c56bc7c4312cb3 SHA512: 7db514a4f983bae62264a57f6a393240ef17cac883949458e761c61d72a6b1bdb8fffc31f28968c2b5abb528362e8e1c3ed1e6fa7e9fe05287391fbe819d7f9b Homepage: https://cran.r-project.org/package=ggsegmentedtotalbar Description: CRAN Package 'ggsegmentedtotalbar' (Create a Segmented Total Bar Plot with Custom Annotations andLabels) It provides a better alternative for stacked bar plot by creating a segmented total bar plot with custom annotations and labels. 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Package: r-cran-ggsem Architecture: all Version: 0.9.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-blavaan, r-cran-diagrammersvg, r-cran-dplyr, r-cran-ggplot2, r-cran-igraph, r-cran-lavaan, r-cran-network, r-cran-purrr, r-cran-qgraph, r-cran-rcolorbrewer, r-cran-rlang, r-cran-rtsne, r-cran-semplot, r-cran-smplot2, r-cran-stringr, r-cran-tidyr, r-cran-tidysem, r-cran-umap, r-cran-xml2 Suggests: r-cran-colourpicker, r-cran-dt, r-cran-diagrammer, r-cran-memoise, r-cran-shiny, r-cran-shinyjs, r-cran-svglite Filename: pool/dists/noble/main/r-cran-ggsem_0.9.9-1.ca2404.1_all.deb Size: 801262 MD5sum: 56eb2a55464725c4e01476d5e01d6e4a SHA1: 8add53093809156abdcb2d90a2763bc8e8e6f289 SHA256: e9870a462067b174f376e6724d51c258f8e9747a7ab235935297f1bfc9a69ef5 SHA512: c4403df9621007d94f3875e57106b6e2d792df6a0ba9039bc7665c3c4d4e4e1d1cc45c534862f4f951fcc695f298d0a05c10c5d421cb01c7525e608eb83566f4 Homepage: https://cran.r-project.org/package=ggsem Description: CRAN Package 'ggsem' (Interactive Structural Equation Modeling (SEM) and Multi-GroupPath Diagrams) Provides an interactive workflow for visualizing structural equation modeling (SEM), multi-group path diagrams, and network diagrams in R. Users can directly manipulate nodes and edges to create publication-quality figures while maintaining statistical model integrity. Supports integration with 'lavaan', 'OpenMx', 'tidySEM', and 'blavaan' etc. Features include parameter-based aesthetic mapping, generative AI assistance, and complete reproducibility by exporting metadata for script-based workflows. Package: r-cran-ggseqlogo Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggseqlogo_0.2.2-1.ca2404.1_all.deb Size: 738706 MD5sum: 2de76481f3853ab25935fff0d989a0b8 SHA1: 7ae81ebbc658abfe8a4ae047143830f4b56d0a2c SHA256: 3e2810da0c8e54dd89e6a92b8033661fea4de59a23877ea5a22573b2ae7a0eb5 SHA512: 395adfe2a192b6b58b6e9679ff62391cbd8a27f934f389ff21b73ace3ca0ee1d5e6630b7fffaaf4595381d844562587c3dd283927cbcf2236065e05eb975bcd9 Homepage: https://cran.r-project.org/package=ggseqlogo Description: CRAN Package 'ggseqlogo' (A 'ggplot2' Extension for Drawing Publication-Ready SequenceLogos) The extensive range of functions provided by this package makes it possible to draw highly versatile sequence logos. Features include, but not limited to, modifying colour schemes and fonts used to draw the logo, generating multiple logo plots, and aiding the visualisation with annotations. Sequence logos can easily be combined with other plots 'ggplot2' plots. Package: r-cran-ggseqplot Architecture: all Version: 0.8.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1360 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-colorspace, r-cran-dplyr, r-cran-forcats, r-cran-ggh4x, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggtext, r-cran-glue, r-cran-haven, r-cran-patchwork, r-cran-purrr, r-cran-rdpack, r-cran-rlang, r-cran-tidyr, r-cran-traminer Suggests: r-cran-covr, r-cran-ggthemes, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggseqplot_0.8.9-1.ca2404.1_all.deb Size: 1117650 MD5sum: aaefa52939345ed56c39028e85320e45 SHA1: 6a16eab98c7bde89999c091547f8100ecfcdfa19 SHA256: 398df36cf9862d155e3413d2c81ca325c71330ac53d83707dfc9718c8c389f6a SHA512: 93661bde8b493dcf00d3aecceb0a54d3280bfe6e575b2ceb039422e81a87d6c16ff6f9d1456e5c2ae24169182415971ca3d9c119611615c4c3d3c89eb6a68cb6 Homepage: https://cran.r-project.org/package=ggseqplot Description: CRAN Package 'ggseqplot' (Render Sequence Plots using 'ggplot2') A set of wrapper functions that mainly re-produces most of the sequence plots rendered with TraMineR::seqplot(). Whereas 'TraMineR' uses base R to produce the plots this library draws on 'ggplot2'. The plots are produced on the basis of a sequence object defined with TraMineR::seqdef(). The package automates the reshaping and plotting of sequence data. Resulting plots are of class 'ggplot', i.e. components can be added and tweaked using '+' and regular 'ggplot2' functions. Package: r-cran-ggshadow Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 751 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-rlang, r-cran-glue, r-cran-vctrs, r-cran-cli Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ggshadow_0.0.5-1.ca2404.1_all.deb Size: 579690 MD5sum: f865fdad2e760f10387da4f069367af2 SHA1: a880a83ec5aed9d45f43b1fcb3e3a0de7b9c7cc7 SHA256: b6ffd776a97a02671dcb59404eadef6e976a9a1cc6c54a44f05fd69eef621fbe SHA512: a1c1812f84ee2d92dfe7796b26b4d5799295b55908945b9847e8dda2c0d1819f0667f2b65f2a568a7979f49cfda16ef5b57ffea16c6343f2887fb6e707cff871 Homepage: https://cran.r-project.org/package=ggshadow Description: CRAN Package 'ggshadow' (Shadow and Glow Geoms for 'ggplot2') A collection of Geoms for R's 'ggplot2' library. geom_shadowpath(), geom_shadowline(), geom_shadowstep() and geom_shadowpoint() functions draw a shadow below lines to make busy plots more aesthetically pleasing. geom_glowpath(), geom_glowline(), geom_glowstep() and geom_glowpoint() add a neon glow around lines to get a steampunk style. 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Package: r-cran-ggsignif Architecture: all Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1056 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggsignif_0.6.4-1.ca2404.1_all.deb Size: 563624 MD5sum: a661f850b98ddc807670aa8a8d8012f5 SHA1: 641b8b2df3c98548f46d553d3cbea6163d59a5c7 SHA256: 1b1bb4365b98fcdd0432992e3a45d1a9df192fb87087ccc52de3d47ad744671d SHA512: ed676c2e34d3c7ffb52a169dacf93fb16f9069e85b05ede0bad24f03f4fafb3fc14fcdf96b625a1129e1196e09a83594560bc8c326a59a19d893402e2df5e3e7 Homepage: https://cran.r-project.org/package=ggsignif Description: CRAN Package 'ggsignif' (Significance Brackets for 'ggplot2') Enrich your 'ggplots' with group-wise comparisons. This package provides an easy way to indicate if two groups are significantly different. 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Package: r-cran-ggskewboxplots Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-waldo, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggskewboxplots_1.0.0-1.ca2404.1_all.deb Size: 90714 MD5sum: 8bf71cf797b09707839b70c6c15abaee SHA1: 35e945f8d9575122d383396498ffb59e92c76ff1 SHA256: 8fd0ec6b47d03204b741d748483402d168104f75316e41535bb184bde133a908 SHA512: b89e52266b5d189f94e63eebed39658cef2893a9985cea2e3a4108962c26d81b5d73db8adb8727728f812a74a47e59e10d20803feeae39340efb7eaf91fa41a6 Homepage: https://cran.r-project.org/package=ggskewboxplots Description: CRAN Package 'ggskewboxplots' (Skew Boxplot Geoms for 'ggplot2') Provides 'ggplot2' extensions for creating skewed boxplots using several statistical methods (Kimber, 1990 ; Hubert and Vandervieren, 2008 ; Adil et al., 2015 ; Babura et al., 2017 ; Walker et al., 2018 ). 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Package: r-cran-ggsky Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 608 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggsky_0.1.0-1.ca2404.1_all.deb Size: 448004 MD5sum: a8f43c7d95bccfe33207e58177292c82 SHA1: 6e5b6f55f5657374253e8df85f971b11407c9823 SHA256: a984a5ff2990df0a2f701da78380b55843d2cd404bf6f15816dccad7a9fc005f SHA512: db19f50456dcb8c9dacd209f404e08a29dba7a6e50ac315278885584a52393f0cb993d358f15c17f3cc1ad387ef1f6304a46a3f7013b0ee120f9462a2f554449 Homepage: https://cran.r-project.org/package=ggsky Description: CRAN Package 'ggsky' (Galactic and Equatorial Coordinate Implementation for 'ggplot2') Simple tools to draw sky maps in 'ggplot2' using galactic or equatorial coordinates. Includes custom coordinate systems, grid labels, and helpers for sky map breaks. Package: r-cran-ggsmc Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1458 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-poorman, r-cran-ggplot2, r-cran-gganimate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggsmc_0.2.0-1.ca2404.1_all.deb Size: 1124312 MD5sum: 66b089f278c86ec2ed72d78904859781 SHA1: 1c79658e0ad8d8a1e70ef926d41339aafa6bab68 SHA256: 163f88a5eaaaf01fe4f8da8341b97e7ce2d27eae2fd53668eeddfe1378c9007f SHA512: c01c7b9bf7326ab8f7f321956bc1236ea0b649d29b2bafe70a31bbb52aa0cfa9ecd30f117de8863671cf103a8917149cc3121ea6a15ab981350685c0fa373e9f Homepage: https://cran.r-project.org/package=ggsmc Description: CRAN Package 'ggsmc' (Visualising Output from Sequential Monte Carlo andEnsemble-Based Methods) Functions for plotting, and animating, the output of importance samplers, sequential Monte Carlo samplers (SMC) and ensemble-based methods. The package can be used to plot and animate histograms, densities, scatter plots and time series, and to plot the genealogy of an SMC or ensemble-based algorithm. These functions all rely on algorithm output to be supplied in tidy format. A function is provided to transform algorithm output from matrix format (one Monte Carlo point per row) to the tidy format required by the plotting and animating functions. Package: r-cran-ggsoccer Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-ggsoccer_0.2.0-1.ca2404.1_all.deb Size: 269968 MD5sum: 86da990d4cdf5c30d2dbba9ff594312d SHA1: dae6e4fbbf80ff6a5ba0708928f9412db09a56ee SHA256: 88bea4d463a358a7c8dddf49703a5d857677c9abf327abe80993bae731757a94 SHA512: 09bef6b9897e649aa7fc068bfed05fd5e443150910699ddde7f129839b02169905738eb83e9fe202634f3306bb26525a811e3d611fbce8ed9e0a1533daeb2e9a Homepage: https://cran.r-project.org/package=ggsoccer Description: CRAN Package 'ggsoccer' (Plot Soccer Event Data) The 'ggplot2' package provides a powerful set of tools for visualising and investigating data. The 'ggsoccer' package provides a set of functions for elegantly displaying and exploring soccer event data with 'ggplot2'. 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Package: r-cran-ggsolvencyii Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 635 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-tidyr Suggests: r-cran-covr, r-cran-ggmap, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggsolvencyii_0.1.2-1.ca2404.1_all.deb Size: 310348 MD5sum: 45e60b45f65e394e2371a9a07f2c9949 SHA1: 5ca0815afababb725e1a3322c0e40fa117e94843 SHA256: 93643163a38020749f6a679d3c5281d3870a582a23028eb115a45a0ae226da4e SHA512: d91c6cb72dd3d2cfebdfd52953d5f838ecc82e247e045e400319ebb12521dfa8d91525c066bcd1d00560e446da03f2109eb7da26a7b42b3b4b320a5cbe4e0019 Homepage: https://cran.r-project.org/package=ggsolvencyii Description: CRAN Package 'ggsolvencyii' (A 'ggplot2'-Plot of Composition of Solvency II SCR: SF and IM) An implementation of 'ggplot2'-methods to present the composition of Solvency II Solvency Capital Requirement (SCR) as a series of concentric circle-parts. Solvency II (Solvency 2) is European insurance legislation, coming in force by the delegated acts of October 10, 2014. . Additional files, defining the structure of the Standard Formula (SF) method of the SCR-calculation are provided. The structure files can be adopted for localization or for insurance companies who use Internal Models (IM). Options are available for combining smaller components, horizontal and vertical scaling, rotation, and plotting only some circle-parts. With outlines and connectors several SCR-compositions can be compared, for example in ORSA-scenarios (Own Risk and Solvency Assessment). Package: r-cran-ggsom Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-ggplot2, r-cran-kohonen, r-cran-assertthat, r-cran-data.table, r-cran-entropy, r-cran-tibble Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggsom_0.4.0-1.ca2404.1_all.deb Size: 38132 MD5sum: 7f68a876f64184074c52b539032c1e5a SHA1: 5acc8091f59874673158cfb850c73901159ed931 SHA256: d92290c1d0dce7137597222830267856d93f753275ff1416c130ade157a06871 SHA512: 939b73435edc2480146cbe7e3ee50d547f55e46fcbcadaeae4c694bbde4ee41dfe6fe9c3701576271edff395fb9c9173dc00182daed4bb9a1db270bc2f7a24fb Homepage: https://cran.r-project.org/package=ggsom Description: CRAN Package 'ggsom' (New Data Visualisations for SOMs Networks) The aim of this package is to offer more variability of graphics based on the self-organizing maps. 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Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . 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Package: r-cran-ggstats Architecture: all Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1943 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-lifecycle, r-cran-patchwork, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tidyr Suggests: r-cran-betareg, r-cran-broom, r-cran-broom.helpers, r-cran-emmeans, r-cran-glue, r-cran-gtsummary, r-cran-knitr, r-cran-labelled, r-cran-reshape, r-cran-rmarkdown, r-cran-nnet, r-cran-parameters, r-cran-pscl, r-cran-testthat, r-cran-spelling, r-cran-survey, r-cran-survival, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggstats_0.13.0-1.ca2404.1_all.deb Size: 1260880 MD5sum: ba1be4b2a646845debf06d43bcfd4e25 SHA1: a70afddb407b0bae493183e2674bf8ab26ab8c55 SHA256: 4d9ce61f9c43019890ca8ef17390d8ed21bb92fe13565012465b9de92206272b SHA512: ae14eb44dfa18c4bd3b7a55acb8d7b4df2eced2ddbdb74ddc5cb9353a54be18286bc14b674ca59f9bb87e35cddf798c89791ae542380f1d1ab0d84e942a34619 Homepage: https://cran.r-project.org/package=ggstats Description: CRAN Package 'ggstats' (Extension to 'ggplot2' for Plotting Stats) Provides new statistics, new geometries and new positions for 'ggplot2' and a suite of functions to facilitate the creation of statistical plots. Package: r-cran-ggstatsplot Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3815 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-correlation, r-cran-datawizard, r-cran-dplyr, r-cran-forcats, r-cran-ggcorrplot, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggside, r-cran-ggsignif, r-cran-glue, r-cran-insight, r-cran-paletteer, r-cran-parameters, r-cran-patchwork, r-cran-performance, r-cran-purrr, r-cran-rlang, r-cran-statsexpressions, r-cran-tidyr Suggests: r-cran-afex, r-cran-bayesfactor, r-cran-bayestestr, r-cran-gapminder, r-cran-knitr, r-cran-lme4, r-cran-mass, r-cran-metabma, r-cran-metafor, r-cran-metaplus, r-cran-patrick, r-cran-psych, r-cran-rmarkdown, r-cran-rstantools, r-cran-survival, r-cran-testthat, r-cran-tibble, r-cran-vdiffr, r-cran-withr, r-cran-wrs2 Filename: pool/dists/noble/main/r-cran-ggstatsplot_1.0.0-1.ca2404.1_all.deb Size: 3240896 MD5sum: 5e46ca7468d0a0e3e7a795d72d4ff377 SHA1: b8a508283a375aa57d7a5f6c0de731252ef09776 SHA256: 33ab539bc10ae92313649968cf5aa17a1af51eb780b8b3f364935726488fe3a4 SHA512: 9c02bc94ebabcbb1389f3102511f9c2fe05aa4d81aefc1a6102477b41ee730d41f024b98297d0b485ece6c37a74220cbdcff8a35cd613c461b56e9eb05b5426d Homepage: https://cran.r-project.org/package=ggstatsplot Description: CRAN Package 'ggstatsplot' ('ggplot2' Based Plots with Statistical Details) Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) . Package: r-cran-ggstream Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-purrr, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-forcats Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggstream_0.1.0-1.ca2404.1_all.deb Size: 51590 MD5sum: ca9b55b23d1aee963f18d852c96838ee SHA1: 4b1b19a42c4750ef3d8c93fee045f17b8a2a1c5a SHA256: 0431f4362fbc0fb8a9ea914e8b26382503609f398d7cc296fb6cfa4d3a3145d0 SHA512: 96b50dbf9e7984dbe24701793931c1d9c3330291e035b1e2c85cdb79313fdd848f8de2551e402b6357cf1df81a7f22ea6c84fc8b3488091456ff920c58d6c5b8 Homepage: https://cran.r-project.org/package=ggstream Description: CRAN Package 'ggstream' (Create Streamplots in 'ggplot2') Make smoothed stacked area charts in 'ggplot2'. Stream plots are useful to show magnitude trends over time. Package: r-cran-ggstudent Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-scales Filename: pool/dists/noble/main/r-cran-ggstudent_0.1.2-1.ca2404.1_all.deb Size: 54834 MD5sum: 770d2f530361fc46d3240fc584e24042 SHA1: a39799907a956ccdc66d572871a44f19a58f6f57 SHA256: 4561f81b7e8b50ac1989f47b6d2505b31d98e3ce114ae8819750b9dde329e7fc SHA512: 0169e675d7fc86325fd639d6c4ad9d20cac6020dbdd120faf187d91ed706dfda1f6e0ca6005e26f9885fea495afa4b4b9d93be9376bf2d2a9800628d01064ab0 Homepage: https://cran.r-project.org/package=ggstudent Description: CRAN Package 'ggstudent' (Continuous Confidence Interval Plots using t-Distribution) Provides an extension to 'ggplot2' (Wickham, 2016, ) for creating two types of continuous confidence interval plots (Violin CI and Gradient CI plots), typically for the sample mean. These plots contain multiple user-defined confidence areas with varying colours, defined by the underlying t-distribution used to compute standard confidence intervals for the mean of the normal distribution when the variance is unknown. Two types of plots are available, a gradient plot with rectangular areas, and a violin plot where the shape (horizontal width) is defined by the probability density function of the t-distribution. These visualizations are studied in (Helske, Helske, Cooper, Ynnerman, and Besancon, 2021) . Package: r-cran-ggsurveillance Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1364 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-glue, r-cran-isoweek, r-cran-legendry, r-cran-lubridate, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-ggrepel, r-cran-hmisc, r-cran-knitr, r-cran-outbreaks, r-cran-plotly, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ggsurveillance_0.5.2-1.ca2404.1_all.deb Size: 963880 MD5sum: 9ad6561c2b3e865e982bf83bf7ee36a0 SHA1: 511cb6e67f79a51e75a0f77d01649d78c206dff5 SHA256: e3e9931403b1b8c86cc96062c2d957e9457f490d2e466da6532c0e0b70edd5a4 SHA512: 0946d2a24d8d3f27bb044b86e1d3706a8945c0cc8e46e6996b821fd250a29565f3a4b904a10e2e0845aefe36be7cd821b1f667cafaee352ebf98d87cad962704 Homepage: https://cran.r-project.org/package=ggsurveillance Description: CRAN Package 'ggsurveillance' (Tools for Outbreak Investigation/Infectious Disease Surveillance) Create epicurves, epigantt charts, and diverging bar charts using 'ggplot2'. Prepare data for visualisation or other reporting for infectious disease surveillance and outbreak investigation (time series data). Includes tidy functions to solve date based transformations for common reporting tasks, like (A) seasonal date alignment for respiratory disease surveillance, (B) date-based case binning based on specified time intervals like isoweek, epiweek, month and more, (C) automated detection and marking of the new year based on the date/datetime axis of the 'ggplot2', (D) labelling of the last value of a time-series. An introduction on how to use epicurves can be found on the US CDC website (2012, ). Package: r-cran-ggsurvey Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-survey, r-cran-hexbin, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-ggsurvey_1.0.0-1.ca2404.1_all.deb Size: 72974 MD5sum: 5ed132114059e3dff3ff4af81d139f45 SHA1: 8b749c1a707c59365d0b0956be5a78bdd85faf6c SHA256: 7e2e63308e61f926ee54a3b055aa3a3ccfd5fd690a231e67cafa8160cdbf2232 SHA512: f69544563cc7f3e8ebb8a5a19005dfead99c8c2370794fa2f6f6a87836642c7dba6e7233ff0b51d6b9a3af6fe3395116ceda94ffd729d525953846af474bbe43 Homepage: https://cran.r-project.org/package=ggsurvey Description: CRAN Package 'ggsurvey' (Simplifying 'ggplot2' for Survey Data) Functions for survey data including svydesign objects from the 'survey' package that call 'ggplot2' to make bar charts, histograms, boxplots, and hexplots of survey data. Package: r-cran-ggsurvfit Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 699 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-gtable, r-cran-patchwork, r-cran-rlang, r-cran-survival, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tidycmprsk, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-ggsurvfit_1.2.0-1.ca2404.1_all.deb Size: 575666 MD5sum: 54f08b5f4512b7bb4c1744ce4a54178d SHA1: c43eaaf92ca069e2358cf162f519d856d17359d2 SHA256: 9f35095c349ab21c21daa20aef9bb47f9defe76b52519fb86d8b68e64bd13c9c SHA512: 76d5ad754893151e31db96b20cd2feb659a728de3fa4f65f77ad71cf39ce563d172791c5680570102bb7a31ffbcbe35ea50626fcf5a9a2674f3382b2bb34c675 Homepage: https://cran.r-project.org/package=ggsurvfit Description: CRAN Package 'ggsurvfit' (Flexible Time-to-Event Figures) Ease the creation of time-to-event (i.e. survival) endpoint figures. The modular functions create figures ready for publication. Each of the functions that add to or modify the figure are written as proper 'ggplot2' geoms or stat methods, allowing the functions from this package to be combined with any function or customization from 'ggplot2' and other 'ggplot2' extension packages. Package: r-cran-ggswissmaps Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1953 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-dplyr, r-cran-sf, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggswissmaps_0.1.2-1.ca2404.1_all.deb Size: 1666052 MD5sum: f6f95366dfe696ae946df373eeea5ea9 SHA1: 184d6aa9d373b6123e753eea384ee523d8ed1a1f SHA256: 31dae509d00b9a13f333076c1846233510aae1c1155a0a4e904ad3846e533d96 SHA512: 45010de258e4455af904107acc8c02e2c7013e816c56a490f7951f0c44189f8574614ddf70e44e1e20699938a63c1949c78bde58883ed0dd9e3a3e226af9431f Homepage: https://cran.r-project.org/package=ggswissmaps Description: CRAN Package 'ggswissmaps' (Offers Various Swiss Maps as Data Frames and 'ggplot2' Objects) Offers various swiss maps as data frames and 'ggplot2' objects and gives the possibility to add layers of data on the maps. 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Also provides geometries for subgroup bordering and text annotation. Package: r-cran-ggtrendline Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-investr, r-cran-aiccmodavg Filename: pool/dists/noble/main/r-cran-ggtrendline_1.0.3-1.ca2404.1_all.deb Size: 77520 MD5sum: 748ce01ced502593e4d462a82ef13092 SHA1: 34028a6594abe87fbe8b13f56d676b8c6e0f92af SHA256: 1f58a5b716bd55bcad5bcb0075854cb37c7aba8968f62ca652e4af4f3d8ceed8 SHA512: cb4e2b6161b1925b9e560fa2f2453d2c5720d6c2aadc85f379c26289442c08fe7a6f3da26b2912fe18f95c16c263d0ca0591974b15f5979c8bac75f8ddf78fa2 Homepage: https://cran.r-project.org/package=ggtrendline Description: CRAN Package 'ggtrendline' (Add Trendline and Confidence Interval to 'ggplot') Add trendline and confidence interval of linear or nonlinear regression model and show equation to 'ggplot' as simple as possible. For a general overview of the methods used in this package, see Ritz and Streibig (2008) and Greenwell and Schubert Kabban (2014) . Package: r-cran-ggtricks Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2909 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-ggplot2 Suggests: r-cran-covr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggtricks_0.1.0-1.ca2404.1_all.deb Size: 2745498 MD5sum: 21e45d6c6e0cf5c5a8cf6d5f3e814134 SHA1: 98afda865e71162a0bd5d2d4c295b50d9a690379 SHA256: 59baf1cd556221e59de103ba3e07740217755f05283e06bd137ab5012a8becf3 SHA512: 71e71a3e73de9ecfc27cda0d2488e4ddd3a41bad856d6a23c227bb11de942704e95e145ba570514e9ed6d3d37425f48057307f766a0c5bce145e3e442e5fe400 Homepage: https://cran.r-project.org/package=ggtricks Description: CRAN Package 'ggtricks' (Create Sector and Other Charts Easily Using Grammar of Graphics) A collection of several geoms to create graphics, using 'ggplot2' and the Cartesian coordinate system. You use the familiar mapping 'Grammar of Graphics' without the need to do another transformation into polar coordinates. Package: r-cran-ggum Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych, r-cran-abind, r-cran-viridis, r-cran-rdpack, r-cran-xlsx Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggum_0.5-1.ca2404.1_all.deb Size: 238336 MD5sum: be704253d29237369d65bc5744c2f354 SHA1: 2184ae3cefb91bbfc7c81849af125e416a28bf7a SHA256: f85d96ff707403c38995624c8e9ab6dd5fac3df8f34dcd067022f4007d1bcf4d SHA512: 132b8402ae2e967f875708116bf25560f147f393f45f365ef193b63c53382abf40569d631993c43ce6c1b6993690ee89bc043d711e26c5fc2495952de977c79f Homepage: https://cran.r-project.org/package=GGUM Description: CRAN Package 'GGUM' (Generalized Graded Unfolding Model) An implementation of the generalized graded unfolding model (GGUM) in R, see Roberts, Donoghue, and Laughlin (2000) ). It allows to simulate data sets based on the GGUM. It fits the GGUM and the GUM, and it retrieves item and person parameter estimates. Several plotting functions are available (item and test information functions; item and test characteristic curves; item category response curves). Additionally, there are some functions that facilitate the communication between R and 'GGUM2004'. Finally, a model-fit checking utility, MODFIT(), is also available. Package: r-cran-ggupset Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2824 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gtable, r-cran-tibble, r-cran-rlang, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggupset_0.4.1-1.ca2404.1_all.deb Size: 1986346 MD5sum: 6437e4ae079e064e89da0675e1631262 SHA1: 28061aaa3282cf45c434403830f4f7b2f840a828 SHA256: bbe7f598bab7cc9ebb87723ee60d5ac2631cf1f9fb6b6ea1ab4cd26a3ff46c77 SHA512: b57b29870789d926ca9f3664fb78c7900f22bfedde050341642faa115106cea6f4273838ca71fda77c7d8f54f9845214062b7930cdf333403cda3dbbecf2f54a Homepage: https://cran.r-project.org/package=ggupset Description: CRAN Package 'ggupset' (Combination Matrix Axis for 'ggplot2' to Create 'UpSet' Plots) Replace the standard x-axis in 'ggplots' with a combination matrix to visualize complex set overlaps. 'UpSet' has introduced a new way to visualize the overlap of sets as an alternative to Venn diagrams. This package provides a simple way to produce such plots using 'ggplot2'. In addition it can convert any categorical axis into a combination matrix axis. Package: r-cran-ggvariant Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-cli, r-cran-scales Suggests: r-cran-plotly, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggvariant_0.1.0-1.ca2404.1_all.deb Size: 131652 MD5sum: 5907c923e76e96b735f5f517f4027b08 SHA1: ca6f62d142fa822332f938e22af490ec5551a97e SHA256: e986cae860d310e8d37519b7af67460f168920a24a85eba4568e8c8aae16b74d SHA512: 3c5cf3b0f0c70ae047812176b5b217e4c8f1c81491ee0c046b58a5c5dce6f1ca489e6715d059708a3c9f15405579b99219927d6f5e1e3f9ad125a02d7f4f785a Homepage: https://cran.r-project.org/package=ggvariant Description: CRAN Package 'ggvariant' (Tidy, 'ggplot2'-Native Visualization for Genomic Variants) A simple, opinionated toolkit for visualizing genomic variant data using a 'ggplot2'-native grammar. Accepts VCF files or plain data frames and produces publication-ready lollipop plots, consequence summaries, mutational spectrum charts, and cohort-level comparisons with minimal code. Designed for both wet-lab biologists and experienced bioinformaticians. Package: r-cran-ggvegan Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-dplyr, r-cran-tibble, r-cran-vctrs, r-cran-ggplot2, r-cran-tidyr, r-cran-ggrepel, r-cran-generics Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-permute, r-cran-withr Filename: pool/dists/noble/main/r-cran-ggvegan_0.2.1-1.ca2404.1_all.deb Size: 229832 MD5sum: ebf0d1f0dbe5695047d7b4c08a969dd2 SHA1: f518e9a3105f17049ff7e00bc24be561d0155d70 SHA256: 4e77746152fbf17997b3fa98e4925de5cfba250dbf8e9fe3166870d49feadd62 SHA512: dd35f4234a54f86c38a0eae38668dd3e613a0ecced37395acd83ad28bfef041646d28fc219651340df29eb8c5d29745985ac9830e84e35ed7f32a9146b544583 Homepage: https://cran.r-project.org/package=ggvegan Description: CRAN Package 'ggvegan' ('ggplot2' Plots for the 'vegan' Package) Functions to produce 'ggplot2'-based plots of objects produced by functions in the 'vegan' package. Provides 'fortify()', 'autoplot()', and 'tidy()' methods for many of 'vegan''s functions. The aim of 'ggvegan' is to make it easier to work within the 'tidyverse' with 'vegan'. Package: r-cran-ggvenn Architecture: all Version: 0.1.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rlang, r-cran-scales Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggvenn_0.1.19-1.ca2404.1_all.deb Size: 77764 MD5sum: ff416d135a9a42a392607532036d9bbe SHA1: 4edd53f6101ba8c2655940c333cdbcbf02cce7a2 SHA256: b0b33acd7f7a06c5a591c85ee7e5b977d8d511a27034008ce87112176b5ac0fc SHA512: 74629ea30618c8e42c2942024fe586541b3ead3d9a53440d084649b1ae777b236182198cdd0622680a6d84d3faf5489911d1c6ebef49cd8e1e1c7216d8878677 Homepage: https://cran.r-project.org/package=ggvenn Description: CRAN Package 'ggvenn' (Draw Venn Diagram by 'ggplot2') An easy-to-use way to draw pretty Venn diagrams using 'ggplot2'. This package provides functions to create Venn diagrams with customizable colors, labels, and styling options. Package: r-cran-ggvenndiagram Architecture: all Version: 1.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-aplot, r-cran-venn, r-cran-yulab.utils, r-cran-forcats Suggests: r-cran-testthat, r-cran-knitr, r-cran-plotly, r-cran-rcolorbrewer, r-cran-shiny, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ggvenndiagram_1.5.7-1.ca2404.1_all.deb Size: 3832180 MD5sum: 16212976ca0056850ca92a2dcfcb06b3 SHA1: e8d4f66802780f6444a5ae6e5ec743f877850135 SHA256: 1e5e63711f1c55eba640af988e8f8e3f62f15b534b7850412471176f0275980e SHA512: 26cb6d464ad3a174f7a0e84201d6de61def28d01b6bc751a2678387ad1d2d1c7643b7ff0d686cd265355b4383b33638268699e1783023af3676a06401580469e Homepage: https://cran.r-project.org/package=ggVennDiagram Description: CRAN Package 'ggVennDiagram' (A 'ggplot2' Implement of Venn Diagram) Easy-to-use functions to generate 2-7 sets Venn or upset plot in publication quality. 'ggVennDiagram' plot Venn or upset using well-defined geometry dataset and 'ggplot2'. The shapes of 2-4 sets Venn use circles and ellipses, while the shapes of 4-7 sets Venn use irregular polygons (4 has both forms), which are developed and imported from another package 'venn', authored by Adrian Dusa. We provided internal functions to integrate shape data with user provided sets data, and calculated the geometry of every regions/intersections of them, then separately plot Venn in four components, set edges/labels, and region edges/labels. From version 1.0, it is possible to customize these components as you demand in ordinary 'ggplot2' grammar. From version 1.4.4, it supports unlimited number of sets, as it can draw a plain upset plot automatically when number of sets is more than 7. Package: r-cran-ggversa Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-ggversa_0.1.1-1.ca2404.1_all.deb Size: 196260 MD5sum: f8315bddb3304c43c7d3b1be34f4eb7c SHA1: f79be420c8bb3a31f57fbd4b1be80fc7f11a2a0a SHA256: dafa084519219246b505d82726cfa2518341dfad5d55a7718eb9cbe65b5ce51d SHA512: c36f2759294c390b3417620d90b100c5c48f4f3c7c72d9da06f2138b20ff4e1c195046f76ca9ceb93ee027dab47f76648d1af75fc8f0cf5e1c029a93424db07f Homepage: https://cran.r-project.org/package=ggversa Description: CRAN Package 'ggversa' (Conjuntos de Datos para 'Graficas Versatiles con ggplot2') Una coleccion de conjuntos de datos para el libro "Graficas versatiles con ggplot: Analisis visuales de datos", por Raymond L. Tremblay y Julian Hernandez-Serrano. Incluye datos de ecologia, salud publica, educacion, economia y biodiversidad para la ensenanza de visualizacion de datos con 'ggplot2'. Package: r-cran-ggvfields Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1881 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-farver, r-cran-tibble, r-cran-numderiv, r-cran-desolve, r-cran-scales, r-cran-cli Suggests: r-cran-ggdensity, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggvfields_1.0.0-1.ca2404.1_all.deb Size: 1596730 MD5sum: 440c18c4d83a7e5496b2d0dd2c14b3be SHA1: 31754271da813b7b4144924b2844c40fe67d2419 SHA256: d42d84ccb98edb0b1d7e19d526a758872839b48d9f74305a30c21667a144bc42 SHA512: 5e95ff6dacdb9b1677b9333f612183a36bdc21bc8642cc31ceca19597ed464db40c247379046c1f9136561b3138a02b362a90f20c699166fa83d163cf7c65e3e Homepage: https://cran.r-project.org/package=ggvfields Description: CRAN Package 'ggvfields' (Vector Field Visualizations with 'ggplot2') A 'ggplot2' extension for visualizing vector fields in two-dimensional space. Provides flexible tools for creating vector and stream field layers, visualizing gradients and potential fields, and smoothing vector and scalar data to estimate underlying patterns. 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Package: r-cran-ggvolc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggtext, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-ggvolc_0.1.0-1.ca2404.1_all.deb Size: 258978 MD5sum: bede7e7c611cd66b09a29374d473aacd SHA1: c8f76fda98d64bed2972c494567a13fbde33f511 SHA256: 05d6ab8ac83b7d126a9aa9d4bf884cff995e1447e10531d09b501d21102959dd SHA512: 96c0ff6a608b9bb001d99934cb7a9ebc9683b8f9a8b26a7ec2affc016c35d501f0ee4108bed60ad15ffd179f5e3e981880970ef34cc5c96af7aed5df10dec9a0 Homepage: https://cran.r-project.org/package=ggvolc Description: CRAN Package 'ggvolc' (Create Volcano Plots for Differential Gene Expression Data) Provides functionality to create customizable volcano plots for visualizing differential gene expression analysis results. The package offers options to highlight genes of interest, adjust significance thresholds, customize colors, and add informative labels. Designed specifically for RNA-seq data analysis workflows. Package: r-cran-ggvolcano Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 763 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-config, r-cran-ggplot2, r-cran-ggrepel, r-cran-golem, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ggvolcano_0.1.4-1.ca2404.1_all.deb Size: 304508 MD5sum: 1896a423f6475ecb7cdd4bbf6e02fc2f SHA1: 1da0cce8edf070cd238a58ec6c021c31c18a605a SHA256: be1097317ba3f228cf86884159887916a67a8a2569c47a4ffdcea78ae9d45c2f SHA512: d23ffb63f65e24429b32bd0cf3cfb0d49070d5e0fda8a45eb48abce5aaef8c214e80d1b124e47a871484a890cf373cec104182ac4459c695a406c40aeec1ec7b Homepage: https://cran.r-project.org/package=ggvolcano Description: CRAN Package 'ggvolcano' (Publication-Ready Volcano Plots) Provides publication-ready volcano plots for visualizing differential expression results, commonly used in RNA-seq and similar analyses. This tool helps create high-quality visual representations of data using the 'ggplot2' framework Wickham (2016) . Package: r-cran-ggwebgl Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2668 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-htmltools, r-cran-htmlwidgets, r-cran-rlang Suggests: r-cran-chromote, r-cran-knitr, r-cran-magick, r-cran-pkgdown, r-cran-pkgload, r-cran-plotly, r-cran-processx, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ggwebgl_0.4.0-1.ca2404.1_all.deb Size: 2357950 MD5sum: 0e4c82d58657ae0e98e1a292aa1bda1a SHA1: 9ab706622bebe983e007c94d6041aab9f36bfcb2 SHA256: 6dbfe53011b756a51ae8d78269a20cf2bfd11f83875f28b40f0e53361703cb23 SHA512: a014afed3188524aeeebdca29de01d9413889af1913488ebc682312e363551bdccc85d62c41753278fe0aa5550b6fc5da8d60077cd5891f8bd7569ba38c72b7d Homepage: https://cran.r-project.org/package=ggWebGL Description: CRAN Package 'ggWebGL' (Browser-Native 'WebGL' Rendering for R Graphics) Provides browser-native 'WebGL' rendering for R graphics through 'htmlwidgets'. The package supports grammar-style graphics workflows and renderer-ready specifications for dense analytical and scientific scenes, including point, line, trajectory, raster, vector, mesh, and surface layers, shader-driven display modes, timeline controls, structured views, selection metadata, and publication-oriented static export helpers. Rendering stays in the browser, and the core package remains cross-platform without requiring 'CUDA', 'Metal', or 'OpenCL' toolchains. Package: r-cran-ggwidth Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-dplyr, r-cran-forcats, r-cran-patchwork, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-ggwidth_0.1.1-1.ca2404.1_all.deb Size: 188504 MD5sum: 23c0c0937f8da54f991f2d4ea350368a SHA1: 323a22cbe34a0b87a81d64c3d7639ea0be1ce50b SHA256: 31fd2134d659979b5d82681f8027b05ed113e490d6462030776bb14c4b2b596f SHA512: 7d840c2106c422536414c85776dfdeb19564a10c78a56a9cea37e69b02a074305fb64587a725b434540bf3e2c0c86f6aa30906557f4a25142838fc9d9fcd1c2a Homepage: https://cran.r-project.org/package=ggwidth Description: CRAN Package 'ggwidth' (Publication-Quality 'ggplot2' Geom Width) Width helper functions for publication-quality 'ggplot2' visualisation. These functions make it easier to create geoms such as bars that have a consistent width appearance across plots. Package: r-cran-ggx Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sets, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-ggx_0.1.1-1.ca2404.1_all.deb Size: 375550 MD5sum: 755cc08b38466ae71f62994ce1e721d6 SHA1: 874db7e0bae031bfa4694a6bafed748ff967f8a2 SHA256: c085da08e4907982d383d14508f8d923b7dcfbf9b026edebe6c31e1a25005c9c SHA512: 003b84d2dfe7f371c99179401b47f80d5e790ca80416b1147ad2bcf9a53055304a27e8d4159ac60f755e7e4308bacf4c0b9d70c2eb48b0baefaf76e831cf3c05 Homepage: https://cran.r-project.org/package=ggx Description: CRAN Package 'ggx' (A Natural Language Interface to 'ggplot2') The 'ggplot2' package is the state-of-the-art toolbox for creating and formatting graphs. However, it is easy to forget how certain formatting commands are named and sometimes users find themselves asking: How do you rotate the x-axis labels again? Or how do you hide the legend...? This package allows users to issue natural language commands related to theme-related styling of plots (colors, font size and such), which then are translated into valid 'ggplot2' commands. 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Package: r-cran-ghap Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1061 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-pedigreemm, r-cran-sparseinv, r-cran-e1071, r-cran-class, r-cran-data.table, r-cran-stringi Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-ghap_3.0.0-1.ca2404.1_all.deb Size: 1013786 MD5sum: 628d3cf91ab463da9e2808f190e421e8 SHA1: 119835890e34b8b8cfdea91867a81127c20c3703 SHA256: b9df03c5994beefc0846299e964c6e87e2fa329ce7121193c96d64bdf3541a6d SHA512: 47952d98844b821454e1861117248f3757076034e51abec03a13dc765b07fcbb69212bb9b2700cdd4fcb7d815ce8b9e3fb7d6511ea3ad123cfceb7b67a26d702 Homepage: https://cran.r-project.org/package=GHap Description: CRAN Package 'GHap' (Genome-Wide Haplotyping) Haplotype calling from phased marker data. Given user-defined haplotype blocks (HapBlock), the package identifies the different haplotype alleles (HapAllele) present in the data and scores sample haplotype allele genotypes (HapGenotype) based on HapAllele dose (i.e. 0, 1 or 2 copies). The output is not only useful for analyses that can handle multi-allelic markers, but is also conveniently formatted for existing pipelines intended for bi-allelic markers. The package was first described in Bioinformatics by Utsunomiya et al. (2016, ). Since the v2 release, the package provides functions for unsupervised and supervised detection of ancestry tracks. The methods implemented in these functions were described in an article published in Methods in Ecology and Evolution by Utsunomiya et al. (2020, ). The source code for v3 was modified for improved performance and inclusion of new functionality, including analysis of unphased data, runs of homozygosity, sampling methods for virtual gamete mating, mixed model fitting and GWAS. Package: r-cran-ghapps Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gh, r-cran-jose, r-cran-openssl Filename: pool/dists/noble/main/r-cran-ghapps_1.1.1-1.ca2404.1_all.deb Size: 22590 MD5sum: ba42b6847c72a51de922f4f3caa113ac SHA1: a0c729bb340ca832b902c98164322c301557b419 SHA256: 2e3f7dc31326df2cc74827213cf97fe3e4ef452b5440dcdfe8d77e536cdae102 SHA512: a4bcbd04f9617bcc2178e1a94e4d43568a4e2a370e81a53ed3b850a499491dcd5f5522e7ab643e5c4f77135bf406a6d8b1650158eded8a7daa706904e2a8e391 Homepage: https://cran.r-project.org/package=ghapps Description: CRAN Package 'ghapps' (Authenticate as a 'GitHub' App) 'GitHub' apps provide a powerful way to manage fine grained programmatic access to specific 'git' repositories, without having to create dummy users, and which are safer than a personal access token for automated tasks. 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The package implements effective handling and analysis algorithms scaled for genome-wide data and calculates a composite statistic, denoted Ghat, which is used to test for selection on a trait. The package provides a number of simple examples for handling and analysing the genome data and visualising the output and results. Beissinger et al., (2018) . 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Package: r-cran-ghibli Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-prismatic Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-crayon, r-cran-cowplot, r-cran-codemeta Filename: pool/dists/noble/main/r-cran-ghibli_0.3.4-1.ca2404.1_all.deb Size: 762254 MD5sum: 92834c3306f68e36572cbe336aed6f76 SHA1: 1a6b33e108c9a30b8676aace3a752fe46d052369 SHA256: bce8dc73d6988d5107227cf3c5cdf8e0afc520a66560174547d82719793403cd SHA512: 71286ae528d8c5ab14d2ed58afb7b78e17c45beeb6d0c5a71e62b6daf4808fbe1e6af6d43aeede992a2ad47612962b7daac3844f35f3b28ded6096cf213e4a74 Homepage: https://cran.r-project.org/package=ghibli Description: CRAN Package 'ghibli' (Studio Ghibli Colour Palettes) Colour palettes inspired by Studio Ghibli films, ported to R for your enjoyment. 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Package: r-cran-ghrmodel Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6527 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cowplot, r-cran-dlnm, r-cran-dplyr, r-cran-ggplot2, r-cran-ghrexplore, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-sf, r-cran-sn, r-cran-rcolorbrewer, r-cran-colorspace, r-cran-testthat, r-cran-spdep, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ghrmodel_0.1.1-1.ca2404.1_all.deb Size: 5468060 MD5sum: b0148b73c39d6a6f29138aa84de0086d SHA1: 7e19d14c7401b09ced34083e02721c163678c1f3 SHA256: 4c5bfe29c9ccc2877d641188ab8a43a5f05e09e2c87c12dc6fd09e4e83cc2b4c SHA512: 82ca5dae1c6f1e79c7321dd203bfed58bef4e8f8d59f14aec0fefaa3215b8e0e17b3d987d00312f33ee632e5c879881ab2cb3ba1c3ad8395752d86b96e1f0d6e Homepage: https://cran.r-project.org/package=GHRmodel Description: CRAN Package 'GHRmodel' (Bayesian Hierarchical Modelling of Spatio-Temporal Health Data) Supports modeling health outcomes using Bayesian hierarchical spatio-temporal models with complex covariate effects (e.g., linear, non-linear, interactions, distributed lag linear and non-linear models) in the 'INLA' framework. It is designed to help users identify key drivers and predictors of disease risk by enabling streamlined model exploration, comparison, and visualization of complex covariate effects. See an application of the modelling framework in Lowe, Lee, O'Reilly et al. (2021) . Package: r-cran-ghs Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-ghs_0.1-1.ca2404.1_all.deb Size: 1061668 MD5sum: f8beba8d3da3f82c5504b163b6f4abbe SHA1: 08e84ef51642aed1dc97161abc7107d8fe981ff7 SHA256: 8a40fd4db71be442cf7b62dde478dc043eeec57a6b6c058d41a5203c65b00ea8 SHA512: 5b31550f806a4045b3ea0959033d86f3305b02cd590978adcadaf402b9d4e8f7862c3783d25f3a7c53f9a4d540be7121c69eb39ccd2b9c3b9032401ddfa2269a Homepage: https://cran.r-project.org/package=GHS Description: CRAN Package 'GHS' (Graphical Horseshoe MCMC Sampler Using Data Augmented BlockGibbs Sampler) Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) . The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) , and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) . Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) . Package: r-cran-ghypernet Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1620 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbmcapply, r-cran-plyr, r-cran-numbers, r-cran-purrr, r-cran-dplyr, r-cran-rlang, r-cran-reshape2, r-cran-rootsolve, r-cran-texreg Suggests: r-cran-biasedurn, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggraph, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ghypernet_1.1.2-1.ca2404.1_all.deb Size: 897238 MD5sum: 62fc1ec8b65ede1b450d05b2b054db18 SHA1: 3924828b387f840ad4aa369ffa95069a96159467 SHA256: 08839fb54315ce84a44d00a7e184b8eaaf78f1bf7ee0641a5bc3c260b5bda8b9 SHA512: b7aa9eb8ca55cd7395b11d8aef386e9e9acc8f0607b94a1fefdc001fd86e6b7f6c2df4b9435a477907b2366297208771480a25c3dcb06192ffbc6daba453b125 Homepage: https://cran.r-project.org/package=ghypernet Description: CRAN Package 'ghypernet' (Fit and Simulate Generalised Hypergeometric Ensembles of Graphs) Provides functions for model fitting and selection of generalised hypergeometric ensembles of random graphs (gHypEG). To learn how to use it, check the vignettes for a quick tutorial. Please reference its use as Casiraghi, G., Nanumyan, V. (2019) together with those relevant references from the one listed below. The package is based on the research developed at the Chair of Systems Design, ETH Zurich. Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) . Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) . Casiraghi, G., (2017) . Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) . Casiraghi, G. (2019) . Casiraghi, G., Nanumyan, V. (2021) . Casiraghi, G. (2021) . 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Currently it implements Gillespie's exact stochastic simulation algorithm (Direct method) and several approximate methods (Explicit tau-leap, Binomial tau-leap, and Optimized tau-leap). The package also contains a library of template models that can be run as demo models and can easily be customized and extended. Currently the following models are included, 'Decaying-Dimerization' reaction set, linear chain system, logistic growth model, 'Lotka' predator-prey model, Rosenzweig-MacArthur predator-prey model, 'Kermack-McKendrick' SIR model, and a 'metapopulation' SIRS model. Pineda-Krch et al. (2008) . Package: r-cran-gilmour Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 962 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gilmour_0.1.1-1.ca2404.1_all.deb Size: 645908 MD5sum: 1b2c679401925f1439797d308756e30a SHA1: e76d30484eb835d6f5ac51caf42a9c2e10564a92 SHA256: c865703548bdd7e4644ef190ad1f4cdc99301ae78a2f39c53f142f970881729a SHA512: db78154d322b9cb6cc75917d75b33499e00ad461a19b13a98a97a0473575e0b8d78a2e95ed0689fbed2158f728256baca0027cb68bdb3f638166fd9f77b3f8ba Homepage: https://cran.r-project.org/package=gilmour Description: CRAN Package 'gilmour' (The Interpretation of Adjusted Cp Statistic) Several methods may be found for selecting a subset of regressors from a set of k candidate variables in multiple linear regression. One possibility is to evaluate all possible regression models and comparing them using Mallows's Cp statistic (Cp) according to Gilmour original study. Full model is calculated, all possible combinations of regressors are generated, adjusted Cp for each submodel are computed, and the submodel with the minimum adjusted value Cp (ModelMin) is calculated. To identify the final model, the package applies a sequence of hypothesis tests on submodels nested within ModelMin, following the approach outlined in Gilmour's original paper. For more details see the help of the function final_model() and the original study (1996) . Package: r-cran-gim Architecture: all Version: 0.33.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gim_0.33.1-1.ca2404.1_all.deb Size: 372160 MD5sum: 8c113d9db3867b67c954c6698c4e1d73 SHA1: 9d63014c99a929cc92ebeaa554ce5b8c09dcb544 SHA256: 7ad8b796dcec007dfd6c80fe1419bd03a761026bf3d2a561d2f6a8463fcf9c31 SHA512: e3b68795eb7dd9c978657f977b769f04c3564da0e5d85cb3597aa2d03cd08817ec0f81afbe015b18abdfaf65c44cf1f3e311fb188231e5f5f4160c01a8837b3d Homepage: https://cran.r-project.org/package=gim Description: CRAN Package 'gim' (Generalized Integration Model) Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models. 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The functions are primarily related to network analysis on the Norwegian road network. 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The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. For AFT model, it also eliminates the needs to try several different AFT models, owing to the flexible shapes of GLD. The goodness of fit of the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data. References include the following: Su (2015) "Flexible Parametric Quantile Regression Model" , Su (2021) "Flexible parametric accelerated failure time model". Package: r-cran-gldrm Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gldrm_1.6-1.ca2404.1_all.deb Size: 116706 MD5sum: c7082e14aaf5de790b2210c5c72ad0f6 SHA1: 67a03a23e0d8511f5e3a204e8bddd4c9a4d03134 SHA256: a709a401cdd281eec8f838130b1b4b68cfc7ca132df301093df1c3139b7d2bd1 SHA512: cd36bf5e72575c3036c41a4bbd55f75b1c54a79050414143bb9ea249f5228afd4025c0a7b6bae00dd03fd0517be8bcb2f5a5f922a33b3274c84a8dafec85de5f Homepage: https://cran.r-project.org/package=gldrm Description: CRAN Package 'gldrm' (Generalized Linear Density Ratio Models) Fits a generalized linear density ratio model (GLDRM). A GLDRM is a semiparametric generalized linear model. In contrast to a GLM, which assumes a particular exponential family distribution, the GLDRM uses a semiparametric likelihood to estimate the reference distribution. The reference distribution may be any discrete, continuous, or mixed exponential family distribution. The model parameters, which include both the regression coefficients and the cdf of the unspecified reference distribution, are estimated by maximizing a semiparametric likelihood. Regression coefficients are estimated with no loss of efficiency, i.e. the asymptotic variance is the same as if the true exponential family distribution were known. Huang (2014) . Huang and Rathouz (2012) . Rathouz and Gao (2008) . Package: r-cran-gleam Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3803 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gleam_0.8.0-1.ca2404.1_all.deb Size: 2829130 MD5sum: 8bf8c488a4eb66909e554f9125143359 SHA1: 2f8856878d8dd9cd6966f2f3cc6e8322dd8c7ad7 SHA256: 88604d666d14878ebefd57524745850e5139d45d6dc80cbaceedb9140cfdbb74 SHA512: 99f9d93b97e3a441bfc85a79895799851f539e74fec45d0e52bf55c9fe67b6ed03640bf49091952a2b867bd721a64f0ce5f826c994fff99c5986c9d0805f7180 Homepage: https://cran.r-project.org/package=gleam Description: CRAN Package 'gleam' (Global Livestock Environmental Assessment Model (GLEAM-X)) The official implementation of the Global Livestock Environmental Assessment Model (GLEAM) of the Food and Agriculture Organization of the United Nations (FAO) in R. GLEAM-X provides a modular, transparent framework for simulating livestock production systems and quantifying their environmental impacts. Methodological background: MacLeod et al. (2017) "Invited review: A position on the Global Livestock Environmental Assessment Model (GLEAM)" . Further information: . Package: r-cran-glioblastomaehrsdata Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dataexplorer, r-cran-flextable, r-cran-ggplot2, r-cran-rmarkdown, r-cran-summarytools, r-cran-table1, r-cran-tinytex Suggests: r-cran-ragg, r-cran-testthat Filename: pool/dists/noble/main/r-cran-glioblastomaehrsdata_1.1.0-1.ca2404.1_all.deb Size: 78166 MD5sum: b924b884473867d91c42438d3853de9f SHA1: 974909a76e2dbf8ec87d5713347dab801557a592 SHA256: 602d27f8003774104767e40c147c7a273db90feb6712d6d82b8ee4c29582c84e SHA512: 4b86ba3996a8033a72cbaf5bff6b2d9a3880c58b1857f205e30ef5008e1832f1771ee1e523a7fbfc41c0f69b1d05b516f0b2724f0e154c87ba7bf529e72f8fba Homepage: https://cran.r-project.org/package=glioblastomaEHRsData Description: CRAN Package 'glioblastomaEHRsData' (Descriptive Analysis on Three Glioblastoma EHRs Datasets) Provides functions to load and analyze three open Electronic Health Records (EHRs) datasets of patients diagnosed with glioblastoma, previously released under the Creative Common Attribution 4.0 International (CC BY 4.0) license. Users can generate basic descriptive statistics, frequency tables and save descriptive summary tables, as well as create and export univariate or bivariate plots. The package is designed to work with the included datasets and to facilitate quick exploratory data analysis and reporting. More information about these three datasets of EHRs of patients with glioblastoma can be found in this article: Gabriel Cerono, Ombretta Melaiu, and Davide Chicco, 'Clinical feature ranking based on ensemble machine learning reveals top survival factors for glioblastoma multiforme', Journal of Healthcare Informatics Research 8, 1-18 (March 2024). . 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Reference: Bennet A. Zelner (2009) . Package: r-cran-glm2 Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-glm2_1.2.1-1.ca2404.1_all.deb Size: 50046 MD5sum: 7f3ed7dd5a8a2dd800f7c0f4715ce07c SHA1: a6cc40e683c06d372d7a9b7763305e5cfffd60dc SHA256: ffb9e5cfeeacedb8da7f81757745b3c772548a44bf1f79ad387d8d21a68ca64f SHA512: 744b27fb3c8e77b2d4f7525dbfe1417e40a5f2d32659b2fe71799f34d45de86a0eabdf891a4bfa9e829b9b67c8b6c4cd4046c8fd3c3d889e96c9aa784c428272 Homepage: https://cran.r-project.org/package=glm2 Description: CRAN Package 'glm2' (Fitting Generalized Linear Models) Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm. Package: r-cran-glm4 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixmodels Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-glm4_0.1.0-1.ca2404.1_all.deb Size: 255408 MD5sum: 4eedbb5857a304e40adeddd9ed13a3a8 SHA1: 4b0b1761db5ebce2c3999569a4ef7ffad1d65b6c SHA256: 63ead2226ff2821961d520348dba2bfe8e009d943291287e22f8f8c21b475b41 SHA512: bf1b6c4beed747b29ef2cfb54d7f8693e92ba6b55dc494a0357150a821995b9bc17cc1ab270337ce6567c4a86d150e1a41aab95cf17d8bb56e86fce131981b75 Homepage: https://cran.r-project.org/package=glm4 Description: CRAN Package 'glm4' (Fitting Generalized Linear Models Using Sparse Matrices) Fits Generalised Linear Models (GLMs) with sparse and dense 'Matrix' matrices for memory efficiency. Acts as a wrapper for the glm4() function in the 'MatrixModels' package , but adds convenient model methods and functions designed to mimic those associated with the glm() function from the 'stats' package. 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Use branch and bound algorithm so we do not have to fit all models. Package: r-cran-glmc Architecture: all Version: 0.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emplik Filename: pool/dists/noble/main/r-cran-glmc_0.4-1-1.ca2404.1_all.deb Size: 87864 MD5sum: cab9b7d4025bd60a8e6282d135728274 SHA1: b546e68b969be8ed741582d962412f3587c11cf7 SHA256: 3fa88142134d40a8d51a38c4315f5b626f9786a7ddb3d85d1b083c6604c8afdd SHA512: 067793c6d7f24bbadee2baff6da05f558060d8d1464226e8d02fb72c3ad96b24adb237db3e309b32bbbe893fb6c68a6b2877e7cad56ecb7aed6924a40060e5ec Homepage: https://cran.r-project.org/package=glmc Description: CRAN Package 'glmc' (Fitting Generalized Linear Models Subject to Constraints) Fits generalized linear models where the parameters are subject to linear constraints. 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The package returns the estimation of the coefficients in random and fixed part of the mixed models by generalized inference. 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The fitting algorithm is described in more detail in Fokkema, Smits, Zeileis, Hothorn & Kelderman (2018; ). For detecting and modeling subgroups in growth curves with GLMM trees see Fokkema & Zeileis (2024; ). 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The functions implement likelihood-based methods using the Expectation and Maximization (EM) algorithm and optionally apply Firth’s bias correction for improved inference. See Pradhan, Nychka, and Bandyopadhyay (2025) , Maiti and Pradhan (2009) , Maity, Pradhan, and Das (2019) for further methodological details. 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Package: r-cran-glmmadaptive Architecture: all Version: 0.9-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-nlme, r-cran-matrixstats Suggests: r-cran-lattice, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-multcomp, r-cran-emmeans, r-cran-estimability, r-cran-effects, r-cran-dharma, r-cran-optimparallel Filename: pool/dists/noble/main/r-cran-glmmadaptive_0.9-7-1.ca2404.1_all.deb Size: 375424 MD5sum: 41b83e8d49828d663ea2d22733469a1b SHA1: 9308ff9f7aa295bc52e02d928e0d4e48be8029dd SHA256: f02c7473f814064a6a3447b75c0b95c0a581f32aeec5fe294c80d8a323dd1d0a SHA512: b2ee46702a52e5af961f20a9f1a1d672c407d52de9c1dedbe812703b19a62bf0762f1991b2df2f8443106b49f5488fbf02632e5c10aace5e79f7c74599b08fe6 Homepage: https://cran.r-project.org/package=GLMMadaptive Description: CRAN Package 'GLMMadaptive' (Generalized Linear Mixed Models using Adaptive GaussianQuadrature) Fits generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) . Package: r-cran-glmmcosinor Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2012 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-cowplot, r-cran-ggforce, r-cran-ggplot2, r-cran-glmmtmb, r-cran-lme4, r-cran-rlang, r-cran-scales Suggests: r-cran-cosinor, r-cran-covr, r-cran-dharma, r-cran-dplyr, r-cran-dt, r-cran-flextable, r-cran-ftextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-glmmcosinor_0.2.1-1.ca2404.1_all.deb Size: 1151172 MD5sum: 9457bf7546d7baf833e903678d61cbcf SHA1: 072988e2e14085e84754f1dd8b07805f8b0daf87 SHA256: 6502b26ae688e4153dc76fc69ba5504c9701062cc3ca60a55e0b3e78a1c0b7bc SHA512: 4ff99f9238d5e4e2998f510ef278a50509132a22ef36b900470b35e517de10a192513e8cd3533d221d392bf39e8858fa7e28fb53c3b27ef30b3420d1782d1c91 Homepage: https://cran.r-project.org/package=GLMMcosinor Description: CRAN Package 'GLMMcosinor' (Fit a Cosinor Model Using a Generalized Mixed Modeling Framework) Allows users to fit a cosinor model using the 'glmmTMB' framework. This extends on existing cosinor modeling packages, including 'cosinor' and 'circacompare', by including a wide range of available link functions and the capability to fit mixed models. The cosinor model is described by Cornelissen (2014) . Package: r-cran-glmmfel Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-numderiv Suggests: r-cran-testthat, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-nlme, r-cran-mvglmmrank, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-glmmfel_1.0.5-1.ca2404.1_all.deb Size: 131410 MD5sum: c6f2b6660796e56709b09b1a3d1a556f SHA1: 0b1ecbfeccce0e3b4930bae64b63e35aa4e95198 SHA256: 3f61f41499364be362c0918451c23a335d8f62b32581e2db236fa059f304f9f9 SHA512: 06196d8a72f28891d00a45d736726c018da09d13636d08a529adea6b3494c8f692ed144d2c8dc1fd168456201848471cf32962e19e578eb896d99b3a0d6ddd95 Homepage: https://cran.r-project.org/package=glmmFEL Description: CRAN Package 'glmmFEL' (Generalized Linear Mixed Models via Fully Exponential Laplace inEM) Fit generalized linear mixed models (GLMMs) with normal random effects using first-order Laplace, fully exponential Laplace (FEL) with mean-only corrections, and FEL with mean and covariance corrections in the E-step of an expectation-maximization (EM) algorithm. The current development version provides a matrix-based interface (y, X, Z) and supports binary logit and probit, and Poisson log-link models. An EM framework is used to update fixed effects, random effects, and a single variance component tau^2 for G = tau^2 I, with staged approximations (Laplace -> FEL mean-only -> FEL full) for efficiency and stability. A pseudo-likelihood engine glmmFEL_pl() implements the working-response / working-weights linearization approach of Wolfinger and O'Connell (1993) , and is adapted from the implementation used in the 'RealVAMS' package (Broatch, Green, and Karl (2018)) . The FEL implementation follows Karl, Yang, and Lohr (2014) and related work (e.g., Tierney, Kass, and Kadane (1989) ; Rizopoulos, Verbeke, and Lesaffre (2009) ; Steele (1996) ). Package code was drafted with assistance from generative AI tools. Package: r-cran-glmmisrep Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-poisson.glm.mix Filename: pool/dists/noble/main/r-cran-glmmisrep_0.1.1-1.ca2404.1_all.deb Size: 263348 MD5sum: fdc1a27582075eb226b8367d0175a786 SHA1: 703fadf981e31e70d71da9ade63476fd84beccf8 SHA256: 303179bda82a15f5cfe71e19104e99763cde96d2e069f705c10e6318b05f429b SHA512: 458c720009b4940360414397a78bd8bce6fca94307d7d5b40465da61d258f0210bcc18db6ea48432f602fd2f4bee067d4de22d76a16ddf0ea2bfb8d5d9dcdf8d Homepage: https://cran.r-project.org/package=glmMisrep Description: CRAN Package 'glmMisrep' (Generalized Linear Models Adjusting for Misrepresentation) Fit Generalized Linear Models to continuous and count outcomes, as well as estimate the prevalence of misrepresentation of an important binary predictor. Misrepresentation typically arises when there is an incentive for the binary factor to be misclassified in one direction (e.g., in insurance settings where policy holders may purposely deny a risk status in order to lower the insurance premium). This is accomplished by treating a subset of the response variable as resulting from a mixture distribution. Model parameters are estimated via the Expectation Maximization algorithm and standard errors of the estimates are obtained from closed forms of the Observed Fisher Information. For an introduction to the models and the misrepresentation framework, see Xia et. al., (2023) . Package: r-cran-glmmrr Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 773 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-lattice, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-glmmrr_0.6.0-1.ca2404.1_all.deb Size: 633464 MD5sum: b37ff754493523dd8a68996d85a24fda SHA1: 44eb711d7a1136b829cb5c2071021626bad8acd3 SHA256: 7d1b74a3a87e647ec50d18419e14fdfcc70ea29a903e4e95b248bc5c641344d7 SHA512: 82b86d6979aef2948a577ef16f24699f24ff233720acd7505de45b6c291d0ad601020f7a32ae6a1fd7900e8bb78f9c3bc0b2e1ce5a3dd2cc55d314440aa3c212 Homepage: https://cran.r-project.org/package=GLMMRR Description: CRAN Package 'GLMMRR' (Generalized Linear Mixed Model (GLMM) for Binary RandomizedResponse Data) Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data. Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data. RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular. Reference: Fox, J-P, Veen, D. and Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. . Package: r-cran-glmmselect Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-glmmselect_1.2.0-1.ca2404.1_all.deb Size: 109304 MD5sum: 699bbc46389b278091b7f2bd9dacb806 SHA1: 487f35b7c5b53a316cd22aae64d8155879a38093 SHA256: 240a183fcb6d318e6436eb3bed6df6e08523052d71e08e0233f1fb6d792edaf3 SHA512: 21d98fe96fe0637208c45495cf6a97fc6c2a21bce070a6a4c81b295bf223bc66688a16f1d680a05eb29d8c40f4d2d391e1d232569e3c96af7973d761d7305859 Homepage: https://cran.r-project.org/package=GLMMselect Description: CRAN Package 'GLMMselect' (Bayesian Model Selection for Generalized Linear Mixed Models) A Bayesian model selection approach for generalized linear mixed models. Currently, 'GLMMselect' can be used for Poisson GLMM and Bernoulli GLMM. 'GLMMselect' can select fixed effects and random effects simultaneously. Covariance structures for the random effects are a product of a unknown scalar and a known semi-positive definite matrix. 'GLMMselect' can be widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. 'GLMMselect' is based on Xu, Ferreira, Porter, and Franck (202X), Bayesian Model Selection Method for Generalized Linear Mixed Models, Biometrics, under review. Package: r-cran-glmmseq Architecture: all Version: 0.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2973 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-emmeans, r-cran-ggplot2, r-cran-ggpubr, r-cran-glmmtmb, r-cran-mass, r-cran-lme4, r-cran-lmertest, r-cran-kableextra, r-cran-mcprogress, r-cran-plotly, r-bioc-qvalue, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-deseq2, r-bioc-edger Filename: pool/dists/noble/main/r-cran-glmmseq_0.5.7-1.ca2404.1_all.deb Size: 1656780 MD5sum: 0fbdb371190ccae6d87b40bdbf59596e SHA1: 6700639f4e8914e85e351520261d3d0498a9ba1c SHA256: 2c5ed714fbbd60184cf12d1047011f11422c3c16c67fdb16583727dddf746f9a SHA512: e4eff717fc6a4b4ef3c2b39cb5ef0d0f69cbb689ee36851a4be8ce2a94d7e37773f9e7297b97b408af8970d43699879e1432273ed4eda7fac02688797ba747e9 Homepage: https://cran.r-project.org/package=glmmSeq Description: CRAN Package 'glmmSeq' (General Linear Mixed Models for Gene-Level DifferentialExpression) Using mixed effects models to analyse longitudinal gene expression can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, and are less optimal for analysing longitudinal data. This package provides negative binomial and Gaussian mixed effects models to fit gene expression and other biological data across repeated samples. This is particularly useful for investigating changes in RNA-Sequencing gene expression between groups of individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M. J., & Pitzalis, C. (2022) Nature medicine . Package: r-cran-glmnetcr Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-glmnetcr_1.0.7-1.ca2404.1_all.deb Size: 807732 MD5sum: 20d6afd759a485a676817085e2b6cf95 SHA1: 534174f4beb976c7a34f4b558ca45851c2c3c758 SHA256: 7a666bacc131e51d796dd5810cfc02977bb057560da26e98c27406774a4de183 SHA512: f7ad2254e7b07b1b03806cfbee8c48259f860ca5eed4294f4d7d7235cad76f2ab5efbbd755c3252829e8c36f1057ab8c53ca079e3460fcd70e280f822eae0b92 Homepage: https://cran.r-project.org/package=glmnetcr Description: CRAN Package 'glmnetcr' (Fit a Penalized Constrained Continuation Ratio Model forPredicting an Ordinal Response) Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets where the number of covariates exceeds the sample size or when there is collinearity among the covariates. The 'glmnet' fitting algorithm is used to fit the continuation ratio model after data restructuring. Package: r-cran-glmnetr Architecture: all Version: 0.6-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3232 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-survival, r-cran-matrix, r-cran-xgboost, r-cran-smoof, r-cran-mlrmbo, r-cran-paramhelpers, r-cran-randomforestsrc, r-cran-rpart, r-cran-torch, r-cran-aorsf Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-glmnetr_0.6-3-1.ca2404.1_all.deb Size: 2812908 MD5sum: 47ce26537794496d6f0988de828f95f2 SHA1: 5aad063b9d3bcf8eec90ff33971587663cac7385 SHA256: 9c8db3176cae9fb934b7741cc61c5671d9f8da64a6cb348bf2a9a9571ed9a68c SHA512: 1bb51b08b4e19ed628e7f794081c8c2f505d02881cfd2ee6d6ded91263082ee9dd98cfc78b7db44c06768b670266b286480bd140f34a7524185c534c16508e62 Homepage: https://cran.r-project.org/package=glmnetr Description: CRAN Package 'glmnetr' (Nested Cross Validation for the Relaxed Lasso and Other MachineLearning Models) Cross validation informed Relaxed LASSO (or more generally elastic net), gradient boosting machine ('xgboost'), Random Forest ('RandomForestSRC'), Oblique Random Forest ('aorsf'), Artificial Neural Network (ANN), Recursive Partitioning ('RPART') or step wise regression models are fit. Cross validation leave out samples (leading to nested cross validation) or bootstrap out-of-bag samples are used to evaluate and compare performances between these models with results presented in tabular or graphical means. Calibration plots can also be generated, again based upon (outer nested) cross validation or bootstrap leave out (out of bag) samples. Note, at the time of this writing, in order to fit gradient boosting machine models one must install the packages 'DiceKriging' and 'rgenoud' using the install.packages() function. For some datasets, for example when the design matrix is not of full rank, 'glmnet' may have very long run times when fitting the relaxed lasso model, from our experience when fitting Cox models on data with many predictors and many patients, making it difficult to get solutions from either glmnet() or cv.glmnet(). This may be remedied by using the 'path=TRUE' option when calling glmnet() and cv.glmnet(). Within the 'glmnetr' package the approach of path=TRUE is taken by default. other packages doing similar include 'nestedcv' , 'glmnetSE' which may provide different functionality when performing a nested CV. Use of the 'glmnetr' has many similarities to the 'glmnet' package and it could be helpful for the user of 'glmnetr' also become familiar with the 'glmnet' package , with the "An Introduction to 'glmnet'" and "The Relaxed Lasso" being especially useful in this regard. Package: r-cran-glmnetse Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-glmnetse_0.0.1-1.ca2404.1_all.deb Size: 44984 MD5sum: e21181847f6a8a451dfda3f12f37f638 SHA1: a775073d82373677970cd1222b6f8d391f7afb6a SHA256: c44d356e71f4facf5016beec9ddbf9dc77f34caaafd1ce594c741f41347aa6f1 SHA512: d3545985961f1e2b72ff09d69547755c101b618796c86a816b0380a9b1428e76cec7d246c94dbac431fa016b26b4e66187c409629bb37bf96db16f0de33082c8 Homepage: https://cran.r-project.org/package=glmnetSE Description: CRAN Package 'glmnetSE' (Add Nonparametric Bootstrap SE to 'glmnet' for SelectedCoefficients (No Shrinkage)) Builds a LASSO, Ridge, or Elastic Net model with 'glmnet' or 'cv.glmnet' with bootstrap inference statistics (SE, CI, and p-value) for selected coefficients with no shrinkage applied for them. Model performance can be evaluated on test data and an automated alpha selection is implemented for Elastic Net. Parallelized computation is used to speed up the process. The methods are described in Friedman et al. (2010) and Simon et al. (2011) . Package: r-cran-glmnetutils Architecture: all Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-doparallel, r-cran-testthat Filename: pool/dists/noble/main/r-cran-glmnetutils_1.1.9-1.ca2404.1_all.deb Size: 105012 MD5sum: 32ba9a8c973fd37623abc0dacd140bcb SHA1: 8801cde751f12faa3cdfb725313916200809f254 SHA256: 9885b480014c6d68858cc496a64b0c5d18a0b37c64f140669ffe7945fb4d021e SHA512: 130f493f3fd34ca4c67830a2cae6e10c3887d06fb200731c002cf1154f0ed012902a1d05082629dae0cfa7f97c49efa1f35df549682d321c5dc81ad89761eedd Homepage: https://cran.r-project.org/package=glmnetUtils Description: CRAN Package 'glmnetUtils' (Utilities for 'Glmnet') Provides a formula interface for the 'glmnet' package for elasticnet regression, a method for cross-validating the alpha parameter, and other quality-of-life tools. Package: r-cran-glmom Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmomco, r-cran-nleqslv, r-cran-robustbase, r-cran-ismev, r-cran-rsolnp, r-cran-zoo Filename: pool/dists/noble/main/r-cran-glmom_1.3.1-1.ca2404.1_all.deb Size: 363338 MD5sum: dcad24f4d070f3f133128f6f9706dee9 SHA1: 1b518ae339b641be49d4dec34cd9ab87bebd0bd3 SHA256: 001527662fd9b3ca87167c559e062c81776050ce1a4b1ca200055881055af858 SHA512: 0b6bd8a05ae3a6f1468d5048c90c4147c93f1348782ca84a1a53246ff992458d6360fd4b18f6e6b0600a30da4557f00e2d85ad1b8a6fd1b7ccfa98d208559c80 Homepage: https://cran.r-project.org/package=GLmom Description: CRAN Package 'GLmom' (Generalized L-Moments Estimation for Extreme Value Distributions) Provides generalized L-moments estimation methods for the generalized extreme value ('GEV') distribution. Implements both stationary 'GEV' and non-stationary 'GEV11' models where location and scale parameters vary with time. Includes various penalty functions ('Martins'-'Stedinger', Park, Cannon, 'Coles'-Dixon) for shape parameter regularization. Also provides model averaging estimation ('ma.gev') that combines MLE and L-moment methods with multiple weighting schemes for robust high quantile estimation. The 'GLME' methodology is described in Shin et al. (2025a) . The non-stationary L-moment method is based on Shin et al. (2025b) . The model averaging method is described in Shin et al. (2026) . See also 'Hosking' (1990) for L-moments theory and 'Martins' and 'Stedinger' (2000) for penalized likelihood methods. Package: r-cran-glmpack Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-pbrackets, r-cran-nnet, r-cran-effects, r-cran-aer, r-cran-pscl, r-cran-foreign, r-cran-matrix, r-cran-lme4, r-cran-lmtest, r-cran-sandwich, r-cran-censreg, r-cran-plm Filename: pool/dists/noble/main/r-cran-glmpack_0.1.0-1.ca2404.1_all.deb Size: 122248 MD5sum: aa6f9ce587a60efad9c36bba0e67b276 SHA1: 57b81fcda576b790989d11f2b79ed5326d8060e2 SHA256: 3b9b5b0241c833bf52a6e97d69085b8d8756dad7a095cefdae1088fcf71e2ccb SHA512: 2bd3f0a350849fdbb2e20e53c51c2ffeafe4179d97fe9a48546e71661d24500c3ab7bef446973109a5ee8955756f3971c0810cbe0726e42d8aa5526e0d6870b1 Homepage: https://cran.r-project.org/package=GLMpack Description: CRAN Package 'GLMpack' (Data and Code to Accompany Generalized Linear Models, 2ndEdition) Contains all the data and functions used in Generalized Linear Models, 2nd edition, by Jeff Gill and Michelle Torres. Examples to create all models, tables, and plots are included for each data set. Package: r-cran-glmpathcr Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmpath Filename: pool/dists/noble/main/r-cran-glmpathcr_1.0.10-1.ca2404.1_all.deb Size: 808708 MD5sum: 90335ae11d618e55a1a5d63d4e168e11 SHA1: 9bb0eba76280d3626699d50df5ab120534d4f526 SHA256: 4f3c08b793714bad89c3d93d1100ec4e80d111708ee1f57d3ad109562739c6bf SHA512: b9140667e47303b7041983b4ca6b87bccd6ad56e2cc48daf0670d342959751bf47a1bbf523b881762862e7a29f2c1a88533c19f39499b7f20821dccf9b10c0fe Homepage: https://cran.r-project.org/package=glmpathcr Description: CRAN Package 'glmpathcr' (Fit a Penalized Continuation Ratio Model for Predicting anOrdinal Response) Provides a function for fitting a penalized constrained continuation ratio model using the glmpath algorithm and methods for extracting coefficient estimates, predicted class, class probabilities, and plots as described by Archer and Williams (2012) . Package: r-cran-glmpca Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-logisticpca, r-cran-markdown, r-cran-matrix, r-cran-testthat Filename: pool/dists/noble/main/r-cran-glmpca_0.2.0-1.ca2404.1_all.deb Size: 226110 MD5sum: 9e0ed10fc9403ba246103989a5e6d72f SHA1: 30ecaeac96979bd0ddf5be6fecae85cd0c7be6f6 SHA256: 647344c9fa1c5640b59c566c8ed59f7403d0004513e45376938947bb46de04b9 SHA512: a36cd019f3f6af997a1e74500313e873230bab312e6beb7fa0af1ec55490ca5ce8789ee2343de7946a14e1fa77c762befba33a7764914b529e4c0753413903b6 Homepage: https://cran.r-project.org/package=glmpca Description: CRAN Package 'glmpca' (Dimension Reduction of Non-Normally Distributed Data) Implements a generalized version of principal components analysis (GLM-PCA) for dimension reduction of non-normally distributed data such as counts or binary matrices. Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) . Townes FW (2019) . 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The package downloads these search volumes provided by Google Trends and uses them to measure and analyze the distribution of search scores across countries or within countries. The package allows researchers and analysts to use these search scores to investigate global trends based on patterns within these scores. This offers insights such as degree of internationalization of firms and organizations or dissemination of political, social, or technological trends across the globe or within single countries. An outline of the package's methodological foundations and potential applications is available as a working paper: . 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For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2023), . 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Given a variable of interest measured in two groups with scaled survey weights so that their hypothetical populations are of equal size, tlorenz() computes the proportion of members of the group with smaller values (ordered from smallest to largest) needed for their sum to match the sum of the top qth percentile of the group with higher values. rlorenz() shows the fraction of the total value of the group with larger values held by the pth percentile of those in the group with smaller values. Fd() is a survey weighted cumulative distribution function and Eps() is a survey weighted inverse cdf used in rlorenz(). Ramos, Graubard, and Gastwirth (2025) . 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GLOSSA (Global Ocean Species Spatio-temporal Analysis) uses Bayesian Additive Regression Trees (BART; Chipman, George, and McCulloch (2010) ) to model species distributions with intuitive workflows for data upload, processing, model fitting, and result visualization. It supports presence-absence and presence-only data (with pseudo-absence generation), spatial thinning, cross-validation, and scenario-based projections. GLOSSA is designed to facilitate ecological research by providing easy-to-use tools for analyzing and visualizing marine species distributions across different spatial and temporal scales. Optionally, pseudo-absences can be generated within the environmental space using the external package 'flexsdm' (not on CRAN), which can be downloaded from ; this functionality is used conditionally when available and all core features work without it. 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Package: r-cran-glsup Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-glsup_1.0.1-1.ca2404.1_all.deb Size: 23508 MD5sum: 8d17828c3962695fe5420242b656f043 SHA1: 146b6665099db0ee32f444d8d4c38613a122f52b SHA256: 3660a71a1fa069e04152f8dcaffaa15b846c255b478c41e125a9f827c439d05c SHA512: af7fd206366a30fd3ad7cd885460ceae24c6efabdce88649afd763c74b403f9ddf571a52d4de65d6a336bd5e1c976bd6b7e6000652b7062bedd43e0a4bf0484c Homepage: https://cran.r-project.org/package=GLSUP Description: CRAN Package 'GLSUP' (Generalised Linear Step-Up Procedure for Multiple HypothesisTesting) Performs the Generalised Linear Step-up Procedure (GLSUP) with a flexible user-defined sizing function. Functions are also available for creating common sizing functions. Package: r-cran-gluedo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue Filename: pool/dists/noble/main/r-cran-gluedo_0.1.0-1.ca2404.1_all.deb Size: 15232 MD5sum: f86ac2705decd87b638ebeb71a34a3a6 SHA1: aaa6a65cd4d3035a6cee660d1788fc93a289f651 SHA256: 6359411f9a2308bad37b1122493a4c3abf141231a46915ede90fbb555709fe82 SHA512: 253ad82dc81b1cdd896df670992b812fa2b3b6279b7c9d2472ba8fad097819b5d630a60357a552352a0f7e1724a5a16f39acd1e413f0e453483ae1b599e84952 Homepage: https://cran.r-project.org/package=glueDo Description: CRAN Package 'glueDo' (Wrapper Functions for the 'glue' Library) Provides convenient wrapper functions around the 'glue' library for common string interpolation tasks. The package simplifies the process of combining 'glue' string templating with common R functions like message(), warning(), stop(), print(), cat(), and file writing operations. Instead of manually calling glue() and then passing the result to these functions, 'glueDo' provides direct wrapper functions that handle both steps in a single call. This is particularly useful for logging, error handling, and formatted output in R scripts and packages. The main reference for the underlying 'glue' package is Hester and Bryan (2022) . Package: r-cran-gluedown Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-magrittr Suggests: r-cran-covr, r-cran-dplyr, r-cran-httr, r-cran-knitr, r-cran-markdown, r-cran-mockr, r-cran-rmarkdown, r-cran-rvest, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-gluedown_1.0.9-1.ca2404.1_all.deb Size: 274610 MD5sum: 6bd93a6529c75273e74c578adc1ab052 SHA1: 2ba32ee3b299e78203f6eee19e877d8966371717 SHA256: 8440d7f266e9eeca8b6acc8bbf694ffabc2d69af1277404aa71e44741bfacdb8 SHA512: a1a70d8c3227cceca2b9ce4c393a4d4ddd0757f97f12a26ad202d392b6de571bb63960b35c52b7b9bbf42a0f827d28ec9e395f4d53789fe44830f80b885fda7e Homepage: https://cran.r-project.org/package=gluedown Description: CRAN Package 'gluedown' (Wrap Vectors in Markdown Formatting) Ease the transition between R vectors and markdown text. With 'gluedown' and 'rmarkdown', users can create traditional vectors in R, glue those strings together with the markdown syntax, and print those formatted vectors directly to the document. This package primarily uses GitHub Flavored Markdown (GFM), an offshoot of the unambiguous CommonMark specification by John MacFarlane (2019) . Package: r-cran-gluvarpro Architecture: all Version: 7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-pracma, r-cran-scales, r-cran-tidyr, r-cran-zoo Filename: pool/dists/noble/main/r-cran-gluvarpro_7.0-1.ca2404.1_all.deb Size: 305012 MD5sum: 17a8b76414a24b1ec80f47c62d7dd4b3 SHA1: cfdc00e83a43024729773f8400a69d0b0d3e7d9a SHA256: 0dbe3cb222207abb359fa7e9403caa621d4dcb399eeef1bf761a1d73d276bf5d SHA512: 65fcc8365498e79ab49fa9e4530f3141f2413add573bcb2475ca77e8dce8ad4cd906d7c692c58f3758e8aaeb40060102827e48d27413ffd80e7ed37fd6b044e7 Homepage: https://cran.r-project.org/package=gluvarpro Description: CRAN Package 'gluvarpro' (Glucose Variability Measures from Continuous Glucose MonitoringData) Calculate different glucose variability measures, including average measures of glycemia, measures of glycemic variability and measures of glycemic risk, from continuous glucose monitoring data. Boris P. Kovatchev, Erik Otto, Daniel Cox, Linda Gonder-Frederick, and William Clarke (2006) . Jean-Pierre Le Floch, Philippe Escuyer, Eric Baudin, Dominique Baudon, and Leon Perlemuter (1990) . C.M. McDonnell, S.M. Donath, S.I. Vidmar, G.A. Werther, and F.J. Cameron (2005) . Everitt, Brian (1998) . Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) . Dougherty, R. L., Edelman, A. and Hyman, J. M. (1989) . Tukey, J. W. (1977) . F. John Service (2013) . Edmond A. Ryan, Tami Shandro, Kristy Green, Breay W. Paty, Peter A. Senior, David Bigam, A.M. James Shapiro, and Marie-Christine Vantyghem (2004) . F. John Service, George D. Molnar, John W. Rosevear, Eugene Ackerman, Leal C. Gatewood, William F. Taylor (1970) . Sarah E. Siegelaar, Frits Holleman, Joost B. L. Hoekstra, and J. Hans DeVries (2010) . Gabor Marics, Zsofia Lendvai, Csaba Lodi, Levente Koncz, David Zakarias, Gyorgy Schuster, Borbala Mikos, Csaba Hermann, Attila J. Szabo, and Peter Toth-Heyn (2015) . Thomas Danne, Revital Nimri, Tadej Battelino, Richard M. Bergenstal, Kelly L. Close, J. Hans DeVries, SatishGarg, Lutz Heinemann, Irl Hirsch, Stephanie A. Amiel, Roy Beck, Emanuele Bosi, Bruce Buckingham, ClaudioCobelli, Eyal Dassau, Francis J. Doyle, Simon Heller, Roman Hovorka, Weiping Jia, Tim Jones, Olga Kordonouri,Boris Kovatchev, Aaron Kowalski, Lori Laffel, David Maahs, Helen R. Murphy, Kirsten Nørgaard, Christopher G.Parkin, Eric Renard, Banshi Saboo, Mauro Scharf, William V. Tamborlane, Stuart A. Weinzimer, and Moshe Phillip.International consensus on use of continuous glucose monitoring.Diabetes Care, 2017 . Package: r-cran-glvmfit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-glvmfit_0.1.0-1.ca2404.1_all.deb Size: 137026 MD5sum: 97e27a970d9ff601f74dcb1598a924c7 SHA1: 7a9c3a2f3e09ba0fea74634d42413c94abb4a86c SHA256: 7b0bcd554405145583a927ac43f9c494de0ff4ee25430df16155f05e8429e736 SHA512: 0da10369470b5ff4b567d2684783d502dfc869c444480f00eb36eb4f3964c3c38b5c8fae4cb52504ab54ef1c3fa177fa6b124fce2546b721b330cbf64c56dc12 Homepage: https://cran.r-project.org/package=glvmfit Description: CRAN Package 'glvmfit' (Methods to Assess Generalized Latent Variable Model Fit) Provides residual global fit indices for generalized latent variable models. 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For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) . Package: r-cran-gmgm Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 795 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-visnetwork Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gmgm_1.1.3-1.ca2404.1_all.deb Size: 732222 MD5sum: 333067cc3d7c32c3f3109f3d7966beda SHA1: 11b7d4b46b14b94ddcf689e22a0fa67fc1538eea SHA256: 32033333927720c6adc76f53a2515aacf017122f1b7925f65aed40d8deaf6604 SHA512: 384bfc2595188deeedcaeacbc2a6ae55d754b2c5c1afa514bc11caf4671ae22f60b37bd2f0abd2a850f3aa8ed896fd7981db8fac553cda82813d5615ff95de91 Homepage: https://cran.r-project.org/package=gmgm Description: CRAN Package 'gmgm' (Gaussian Mixture Graphical Model Learning and Inference) Gaussian mixture graphical models include Bayesian networks and dynamic Bayesian networks (their temporal extension) whose local probability distributions are described by Gaussian mixture models. 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Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) , Savi Virolainen (2025) , Savi Virolainen (in press) . 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Package: r-cran-goeveg Architecture: all Version: 0.7.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-fields, r-cran-mgcv, r-cran-hmisc, r-cran-data.table Suggests: r-cran-vegdata, r-cran-biodiversityr, r-cran-cluster Filename: pool/dists/noble/main/r-cran-goeveg_0.7.10-1.ca2404.1_all.deb Size: 216638 MD5sum: 20d199543551361a6d5606b6ce818ab3 SHA1: 83659d85f6b3858de9d3d71c5b740839c861ceba SHA256: 31038ad443f945a58f2af19c5a80778252e563580e3895de57b6797e2bd80595 SHA512: 80c86fe99972d56ad78cd4dcb353540128dfc5eaa8e6da0221f0f1adaad337419737530e1bf89e15b6e1c60a3a5723b5e4ab6579e072aa5d6bfa589b6ca05ffb Homepage: https://cran.r-project.org/package=goeveg Description: CRAN Package 'goeveg' (Functions for Community Data and Ordinations) A collection of functions useful in (vegetation) community analyses and ordinations. 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Package: r-cran-gofcat Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon, r-cran-matrix, r-cran-epir, r-cran-reshape, r-cran-stringr, r-cran-vgam Suggests: r-cran-serp, r-cran-dfidx, r-cran-mlogit, r-cran-nnet, r-cran-ordinal, r-cran-mass, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-gofcat_0.1.2-1.ca2404.1_all.deb Size: 160896 MD5sum: 628cd5afb1a407feb675b86526a9b50e SHA1: bb436e12934990f068e7650f895fb8f327d0a28a SHA256: 84c09bad6bd054a64f9a520dd8f837d56386cab197055a1853f1dd25008bddc8 SHA512: 8a43f6e3abb97bb936d27b91f44fd7d5145bbe1aeaf309bd639b55772a4f0bf80ca0df85f1bd2a70e973d960e8c724cc050304d56cfbf138522c3a296ac4883c Homepage: https://cran.r-project.org/package=gofcat Description: CRAN Package 'gofcat' (Goodness-of-Fit Measures for Categorical Response Models) A post-estimation method for categorical response models (CRM). Inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), vglm() and glm() are currently supported. Available tests include the Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal models. The proportional odds, adjacent-category, and constrained continuation-ratio models are particularly supported at ordinal level. Tests for the proportional odds assumptions in ordinal models are also possible with the Brant and the Likelihood-Ratio tests. Moreover, several summary measures of predictive strength (Pseudo R-squared), and some useful error metrics, including, the brier score, misclassification rate and logloss are also available for the binary, multinomial and ordinal models. Ugba, E. R. and Gertheiss, J. (2018) . Package: r-cran-gofcens Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 540 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-actuar, r-cran-fitdistrplus, r-cran-ggplot2, r-cran-gridextra, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rms Filename: pool/dists/noble/main/r-cran-gofcens_1.5-1.ca2404.1_all.deb Size: 447120 MD5sum: 5c6304ec49c16dc05505454f7928f4a8 SHA1: fb66933c30022e870ca1ed85607efe0e2626135b SHA256: f1621902e4594880bb13a18c55e1abd494c6efe768c04970bde5fd5490c6500d SHA512: 9bace38032aa39cbd7f25dcc3d43b865756ce9333f1a16b206820d3a6012ece1b7ca5ffc10446d81b2100cc6eb2e8c2d77853f1cc8b6c108b8a6a147f512bef0 Homepage: https://cran.r-project.org/package=GofCens Description: CRAN Package 'GofCens' (Goodness-of-Fit Methods for Right-Censored Data) Graphical tools and goodness-of-fit tests for right-censored data: 1. Kolmogorov-Smirnov, Cramér-von Mises, and Anderson-Darling tests, which use the empirical distribution function for complete data and are extended for right-censored data. 2. Generalized chi-squared-type test, which is based on the squared differences between observed and expected counts using random cells with right-censored data. 3. A series of graphical tools such as probability or cumulative hazard plots to guide the decision about the most suitable parametric model for the data. These functions share several features as they can handle both complete and right-censored data, and they provide parameter estimates for the distributions under study. Package: r-cran-gofedf Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compquadform, r-cran-mass, r-cran-glm2, r-cran-statmod Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gofedf_1.1.0-1.ca2404.1_all.deb Size: 141634 MD5sum: 9d9321453ab653f46caf0e6c56579137 SHA1: aa2025e6ac168d09704275eb6c05a6e4abb42090 SHA256: 1ce0d80fadb277b07969d315d93e595f683c12ee3ccabe012d2958b0aa359e22 SHA512: 243ab01d003482cbb7d20e5e6d77d035fa499f5c2a81dc50dc68b4ab38b8e1ff760517a379d99abade9f7e37a9527e1598236f3ad30d5d34555efd59085fc9e7 Homepage: https://cran.r-project.org/package=gofedf Description: CRAN Package 'gofedf' (Goodness of Fit Tests Based on Empirical Distribution Functions) Routines that allow the user to run goodness of fit tests based on empirical distribution functions for formal model evaluation in a general likelihood model. In addition, functions are provided to test if a sample follows Normal or Gamma distributions, validate the normality assumptions in a linear model, and examine the appropriateness of a Gamma distribution in generalized linear models with various link functions. Michael Arthur Stephens (1976) . Package: r-cran-gofgamma Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gofgamma_1.0-1.ca2404.1_all.deb Size: 69582 MD5sum: 759940eb9f068973dc439d16f3f05cfc SHA1: ae61e8950f51c622b3b3c03c937a118826dbb3b7 SHA256: 71f269bca659eb9a207ef9af4699245c7daa328d6d460117f537715759e4903b SHA512: 6e3ee511fa621c431eb5ab7ea763a0a3b648823021f90d29fdf289d1f5f482fefa9bdc24ea935b1f0bc1ee644030d2851fc2376a93fbcc314c6d0316c39432f7 Homepage: https://cran.r-project.org/package=gofgamma Description: CRAN Package 'gofgamma' (Goodness-of-Fit Tests for the Gamma Distribution) We implement various classical tests for the composite hypothesis of testing the fit to the family of gamma distributions as the Kolmogorov-Smirnov test, the Cramer-von Mises test, the Anderson Darling test and the Watson test. 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Package: r-cran-gofig Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-rmutil Filename: pool/dists/noble/main/r-cran-gofig_1.0-1.ca2404.1_all.deb Size: 84394 MD5sum: 4a0a9b39e0df3911ef6772643469effd SHA1: 4c8711e431362f32286195a3ca7d52c4c4c653b8 SHA256: 815e6b661911f37ba216931a4596f120c48e597703584bb6a74492639d93f2fe SHA512: 8c977defd8047244f28644abea32bc487d8cc805e8f10d6894b98d56e84acc85cc98ef8d1419045f0c899ec1aa251bec0f3b8dfb1eb65f0ea7d2a02c1ac269a4 Homepage: https://cran.r-project.org/package=gofIG Description: CRAN Package 'gofIG' (Goodness-of-Fit Tests for the Inverse Gaussian Distribution) We implement various tests for the composite hypothesis of testing the fit to the family of inverse Gaussian distributions. Included are methods presented by Allison, J.S., Betsch, S., Ebner, B., and Visagie, I.J.H. 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Different test statistics can be used to determine the goodness-of-fit of the assumed model, see Andrews (1997) , Bierens & Wang (2012) , Dikta & Scheer (2021) and Kremling & Dikta (2024) . As proposed in these papers, the corresponding p-values are approximated using a parametric bootstrap method. 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Package: r-cran-gpk Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gpk_1.0-1.ca2404.1_all.deb Size: 290056 MD5sum: 379b444bf9ca362bc1745e33034d6304 SHA1: c7a2b0d773be0e1c572b3abbb48626a9154fdf4f SHA256: cb577edb0cd3a17ac929049fbaa7509cf4a93fcdc6acfacada0ffabd29ab335a SHA512: 888bfbabbe2833eb0d2aafe9aa2164ce7a9035f129d1c72a4f5bd27ca52c910b8a92284665cb0c2ef6f1c190c9626821d0a09d5fb6495e7e7e3815c9ac94a87b Homepage: https://cran.r-project.org/package=gpk Description: CRAN Package 'gpk' (100 Data Sets for Statistics Education) Collection of datasets as prepared by Profs. A.P. Gore, S.A. Paranjape, and M.B. Kulkarni of Department of Statistics, Poona University, India. With their permission, first letter of their names forms the name of this package, the package has been built by me and made available for the benefit of R users. This collection requires a rich class of models and can be a very useful building block for a beginner. 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Package: r-cran-gplsim Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-minpack.lm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gplsim_1.0.0-1.ca2404.1_all.deb Size: 64992 MD5sum: a77abe58415e694e9d267b7e15db6365 SHA1: a3d4d7f5c01dbf017c3ce6c5b8cfd1d0cff43908 SHA256: 2ffd740def790dff08fad77fc10b6746906f1849bc57bbc57d623aa26fceaa64 SHA512: 30ecfdb727a7d48acf67c123dc3f9116eb622c85349616a69fe0c333674383b1dbaee82750a1dc78a13e687a0d41cf61e492f500129b79aae77df707459af3d8 Homepage: https://cran.r-project.org/package=gplsim Description: CRAN Package 'gplsim' (Spline Estimation for GPLSIM) We provides functions that employ splines to estimate generalized partially linear single index models (GPLSIM), which extend the generalized linear models to include nonlinear effect for some predictors. Please see Y. (2017) at and Y., and R. (2002) at for more details. Package: r-cran-gpltr Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gpltr_1.5-1.ca2404.1_all.deb Size: 522302 MD5sum: 8e268393f86bf4bce969eafe8c6090fb SHA1: de9175ab0406dcb2f8095344def10ad061adb16e SHA256: b3f70cc5ec1b745b58435044f1503111f6453227ed257ef60269c5d105b5b8a6 SHA512: 6a6dd6365411f2954479bd07113e4d0856a0e261d86daf8b70d89d12a707e2e11bfba88ddaad917506720e011f6d488e9c3ca4e9586817276f15888495c06482 Homepage: https://cran.r-project.org/package=GPLTR Description: CRAN Package 'GPLTR' (Generalized Partially Linear Tree-Based Regression Model) Combining a generalized linear model with an additional tree part on the same scale. A four-step procedure is proposed to fit the model and test the joint effect of the selected tree part while adjusting on confounding factors. We also proposed an ensemble procedure based on the bagging to improve prediction accuracy and computed several scores of importance for variable selection. See 'Cyprien Mbogning et al.'(2014) and 'Cyprien Mbogning et al.'(2015) for an overview of all the methods implemented in this package. Package: r-cran-gpmap Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-isotone, r-cran-plyr, r-cran-ggplot2, r-cran-foreach Filename: pool/dists/noble/main/r-cran-gpmap_0.1.3-1.ca2404.1_all.deb Size: 73088 MD5sum: dc36dab2d9fe63439ce0fadc1aa214db SHA1: 58d2d07daacab5dbceaebcce64c9f39970213a03 SHA256: 3d846b4d23324341d6105b0ce8a9818ebc18ca82dc447136fbf725cf3481f140 SHA512: 9250c58b2c59fdc99e2268968c3f29b82a62c99a80938c74fb87fb901c5f7141bc6e440d5d460b453d776b890afa90f4c6eb60258a9af7ed2039c365903b4090 Homepage: https://cran.r-project.org/package=gpmap Description: CRAN Package 'gpmap' (Analysing and Plotting Genotype-Phenotype Maps) Tools for studying genotype-phenotype maps for bi-allelic loci underlying quantitative phenotypes. The 0.1 version is released in connection with the publication of Gjuvsland et al (2013) and implements basic line plots and the monotonicity measures for GP maps presented in the paper. Reference: Gjuvsland AB, Wang Y, Plahte E and Omholt SW (2013) Monotonicity is a key feature of genotype-phenotype maps. Frontier in Genetics 4:216 . Package: r-cran-gpom Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5273 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-rgl, r-cran-float Suggests: r-cran-signal, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gpom_1.4-1.ca2404.1_all.deb Size: 4777822 MD5sum: b928769be3416e9e43b8d26368d69ef8 SHA1: 2ed11638f749612bfc9d1e4516ec528a02c8a739 SHA256: 08fd0e2d495be985e2607b844aab1430ccf80361816ebd765413e0916b184bbc SHA512: b4578bb327912aa2da9bc00ca57de2358783d3ff8b8e8cb0c72f6bdb8ec4433289214301e45b2ec9c2e7f20d7430dd468d4047f4dbe0b70fb8b8b2551cba9935 Homepage: https://cran.r-project.org/package=GPoM Description: CRAN Package 'GPoM' (Generalized Polynomial Modelling) Platform dedicated to the Global Modelling technique. Its aim is to obtain ordinary differential equations of polynomial form directly from time series. It can be applied to single or multiple time series under various conditions of noise, time series lengths, sampling, etc. This platform is developped at the Centre d'Etudes Spatiales de la Biosphere (CESBIO), UMR 5126 UPS/CNRS/CNES/IRD, 18 av. Edouard Belin, 31401 TOULOUSE, FRANCE. The developments were funded by the French program Les Enveloppes Fluides et l'Environnement (LEFE, MANU, projets GloMo, SpatioGloMo and MoMu). The French program Defi InFiNiTi (CNRS) and PNTS are also acknowledged (projects Crops'IChaos and Musc & SlowFast). The method is described in the article : Mangiarotti S. and Huc M. (2019) . Package: r-cran-gpp Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan Filename: pool/dists/noble/main/r-cran-gpp_0.1-1.ca2404.1_all.deb Size: 77530 MD5sum: 91d32df13678cc4f5d02cb85e2ab2c14 SHA1: 2589416bf63920b531ba5717629419ffa30175e2 SHA256: 357fcbf5e515f5252507df36414a61688536f5ebca2264996c5b8cf2f0281d2d SHA512: 641bdcddfcd31344edd1891985ad3f34ef6fe07f10728d51c6aa40c04b16a95314a2c9ab61f649725fb4a5503572958d8c10eb9c49e521f68ba181f7644c3e2a Homepage: https://cran.r-project.org/package=GPP Description: CRAN Package 'GPP' (Gaussian Process Projection) Estimates a counterfactual using Gaussian process projection. It takes a dataframe, creates missingness in the desired outcome variable and estimates counterfactual values based on all information in the dataframe. The package writes Stan code, checks it for convergence and adds artificial noise to prevent overfitting and returns a plot of actual values and estimated counterfactual values using r-base plot. 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GPPM is described in Karch, Brandmaier & Voelkle (2020; ) and Karch (2016; ). Essentially, GPPM is Gaussian process based modeling of longitudinal panel data. 'gppm' also supports regular Gaussian process regression (with a focus on flexible model specification), and multi-task learning. Package: r-cran-gprmortality Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan Filename: pool/dists/noble/main/r-cran-gprmortality_0.1.0-1.ca2404.1_all.deb Size: 493294 MD5sum: 4ec248497685acbabc0b30a39d5cb806 SHA1: c3f078d38ffbc9cd685fa44972cf04bc38c5ef87 SHA256: 8ee13b6de6d537beec381b0991f9c06bee4f1f1a23e101b65bba55881b8b2278 SHA512: 508d0409de1525102e18361eba54d9fac6aca7058ceb2894e58ea17449972db121ebe10488d351baf4d69589f1e4b79702e4727feb9719dcc6ed193607e94e66 Homepage: https://cran.r-project.org/package=GPRMortality Description: CRAN Package 'GPRMortality' (Gaussian Process Regression for Mortality Rates) A Bayesian statistical model for estimating child (under-five age group) and adult (15-60 age group) mortality. The main challenge is how to combine and integrate these different time series and how to produce unified estimates of mortality rates during a specified time span. GPR is a Bayesian statistical model for estimating child and adult mortality rates which its data likelihood is mortality rates from different data sources such as: Death Registration System, Censuses or surveys. There are also various hyper-parameters for completeness of DRS, mean, covariance functions and variances as priors. This function produces estimations and uncertainty (95% or any desirable percentiles) based on sampling and non-sampling errors due to variation in data sources. The GP model utilizes Bayesian inference to update predicted mortality rates as a posterior in Bayes rule by combining data and a prior probability distribution over parameters in mean, covariance function, and the regression model. This package uses Markov Chain Monte Carlo (MCMC) to sample from posterior probability distribution by 'rstan' package in R. Details are given in Wang H, Dwyer-Lindgren L, Lofgren KT, et al. (2012) , Wang H, Liddell CA, Coates MM, et al. (2014) and Mohammadi, Parsaeian, Mehdipour et al. (2017) . 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Functional enrichment analysis, gene identifier conversion and mapping homologous genes across related organisms via the 'g:Profiler' toolkit (). 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Package: r-cran-gpscdf Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nbpmatching, r-cran-nnet, r-cran-mass, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gpscdf_0.1.1-1.ca2404.1_all.deb Size: 26648 MD5sum: b85c6366ec16e6235215506162fde1dd SHA1: e7f6992411da243c8fa41e539b2bc9a79f2e4a9d SHA256: 7d5965ea646fcf48b6a0db1d4427555ac0040ea1a2a0db4628b9bb5543d46ae8 SHA512: 42770c7b8a9f6a6c86f32f9d245c0f86c778f3bdd7069f58b55a2d25bb7ad80ed0d4a5dc97307dcda645e7faa510b3f4ebfcff8acc4411f12abff01538718221 Homepage: https://cran.r-project.org/package=GPSCDF Description: CRAN Package 'GPSCDF' (Generalized Propensity Score Cumulative Distribution Function) Implements the generalized propensity score cumulative distribution function proposed by Greene (2017) . A single scalar balancing score is calculated for any generalized propensity score vector with three or more treatments. This balancing score is used for propensity score matching and stratification in outcome analyses when analyzing either ordinal or multinomial treatments. 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Plots interactive cluster maps and provides a summary dataframe with attributes for each cluster commonly used as covariates in subsequent modeling efforts. Additional functions provide individual keyhole markup language plots for quick assessment, and export of global positioning system exchange format files for navigation purposes. Methods can be found at . Package: r-cran-gptoolsstan Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gptoolsstan_1.0.0-1.ca2404.1_all.deb Size: 36908 MD5sum: 364e57bf747c4435461d003fc22b7d18 SHA1: fdb1708b4dfa80339e7a7677d4e59a425d256772 SHA256: 8f9457c9483305f5de5d08b2aeb6c2a07af65abbd300685773defeac4bfa951a SHA512: ac2d8db947f11fc80d81c94ef19229a7cd589720876bd3d0f2ada4ae3836fc549a02f2c3633be6428a87893f054a3f73f22357a172243aa385bd3b2b9129cf4f Homepage: https://cran.r-project.org/package=gptoolsStan Description: CRAN Package 'gptoolsStan' (Gaussian Processes on Graphs and Lattices in 'Stan') Gaussian processes are flexible distributions to model functional data. Whilst theoretically appealing, they are computationally cumbersome except for small datasets. This package implements two methods for scaling Gaussian process inference in 'Stan'. First, a sparse approximation of the likelihood that is generally applicable and, second, an exact method for regularly spaced data modeled by stationary kernels using fast Fourier methods. Utility functions are provided to compile and fit 'Stan' models using the 'cmdstanr' interface. References: Hoffmann and Onnela (2025) . Package: r-cran-gptr Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-rcurl Filename: pool/dists/noble/main/r-cran-gptr_0.7.0-1.ca2404.1_all.deb Size: 14438 MD5sum: 6cb1d42083eb01b4da5e9d1b3b9b81fa SHA1: 5946fb00d7df7483c5d5fea2bb7526b99ec87d0c SHA256: caca6ecbb5300927b86fe78145f50f2ab4b37e614d39ae30563a1e1b034eb713 SHA512: d251ef8a2011464655bc73ca5558f03f5972079b7229b00c2d06f726b292d6df23842464bcdc7a739c86d103b621c90a5f7abc94bd1b4532fcbbd889cfa9d7cd Homepage: https://cran.r-project.org/package=gptr Description: CRAN Package 'gptr' (A Convenient R Interface with the OpenAI 'ChatGPT' API) A convenient interface with the OpenAI 'ChatGPT' API . 'gptr' allows you to interact with 'ChatGPT', a powerful language model, for various natural language processing tasks. The 'gptr' R package makes talking to 'ChatGPT' in R super easy. It helps researchers and data folks by simplifying the complicated stuff, like asking questions and getting answers. With 'gptr', you can use 'ChatGPT' in R without any hassle, making it simpler for everyone to do cool things with language! Package: r-cran-gptreeo Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-hash, r-cran-dicekriging, r-cran-mlegp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gptreeo_1.0.1-1.ca2404.1_all.deb Size: 475946 MD5sum: c9509c1220314aa0c199361eb7df1af2 SHA1: 83aed0b3332f0117ff2d64e70be736e02e62f653 SHA256: 576943b549bf43bf792078711ce9b230275483fd14be240e6623852c2e46a334 SHA512: 869cecaccd8ca0bf62437bcd73e3395fd774bc604a71fc68032ebd29b01300149e81bb23a015081fdb8c43d097b696b761a0ff2f1e28bea439bc98106a64dce2 Homepage: https://cran.r-project.org/package=GPTreeO Description: CRAN Package 'GPTreeO' (Dividing Local Gaussian Processes for Online Learning Regression) We implement and extend the Dividing Local Gaussian Process algorithm by Lederer et al. (2020) . Its main use case is in online learning where it is used to train a network of local GPs (referred to as tree) by cleverly partitioning the input space. In contrast to a single GP, 'GPTreeO' is able to deal with larger amounts of data. The package includes methods to create the tree and set its parameter, incorporating data points from a data stream as well as making joint predictions based on all relevant local GPs. Package: r-cran-gptstudio Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 889 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-bslib, r-cran-cli, r-cran-colorspace, r-cran-curl, r-cran-fontawesome, r-cran-glue, r-cran-htmltools, r-cran-htmlwidgets, r-cran-httr2, r-cran-ids, r-cran-jsonlite, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-rstudioapi, r-cran-rvest, r-cran-shiny, r-cran-shiny.i18n, r-cran-sseparser, r-cran-stringr, r-cran-waiter, r-cran-yaml Suggests: r-cran-azurermr, r-cran-knitr, r-cran-mockr, r-cran-rmarkdown, r-cran-shinytest2, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-gptstudio_0.4.0-1.ca2404.1_all.deb Size: 633856 MD5sum: 8a199a42c0d58d48b45f83fab7709d21 SHA1: f29c136bc96cf9e3e6f703a55529bc06abcc3276 SHA256: 26738b708c6ec2dbdfb58136e943064c88940e72c90b3fa3cc6f5db201805906 SHA512: 83a002bf20120cde1176b37ca5e696f5465e78aca200cdeeb04b941916695ff87609955fc9ba174b541ce85d594a30b982e0e938165771ee2072223d5c7be176 Homepage: https://cran.r-project.org/package=gptstudio Description: CRAN Package 'gptstudio' (Use Large Language Models Directly in your DevelopmentEnvironment) Large language models are readily accessible via API. 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Unfortunately, very few packages connect R to the GPU, and none of them are transparent enough to run the computations on the GPU without substantial changes to the code. The maintenance of these packages is cumbersome: several of the earlier attempts have been removed from their respective repositories. It would be desirable to have a properly maintained R package that takes advantage of the GPU with minimal changes to the existing code. We have developed the 'GPUmatrix' package (available on CRAN). 'GPUmatrix' mimics the behavior of the Matrix package and extends R to use the GPU for computations. It includes single(FP32) and double(FP64) precision data types, and provides support for sparse matrices. It is easy to learn, and requires very few code changes to perform the operations on the GPU. 'GPUmatrix' relies on either the 'Torch' or 'Tensorflow' R packages to perform the GPU operations. 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Of these non-B DNA structures, the G-quadruplexes are highly stable four-stranded structures that are recognized by distinct subsets of nuclear factors. This package provide functions for predicting intramolecular G quadruplexes. In addition, functions for predicting other intramolecular nonB DNA structures are included. 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For detection probability calculation, we used results from Bhat, U. and Lal, R. (1988) . 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This package implemented a granularity-based dimension-agnostic tool for the identification of spatially variable genes. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), ). 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Package: r-cran-grader Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testthat, r-cran-callr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-grader_2.0.1-1.ca2404.1_all.deb Size: 131210 MD5sum: 6b805aa097f0e7dd35d7d00d749645a0 SHA1: 64b918b3ddf6febf1e973eab9ecac1c6b9a9ba34 SHA256: e7adf2b4458979c6e776701632087dc6f9d94d3f27ab3b514fbfed959a9b2deb SHA512: 36746e9313eb38c1aae7756d8fd2c6652430fa99157a6fa98dc48da98d8c87a3357298cf8d1bd4d72c1aabfa90640a01d2a66cdae91e65c3d39a7f84bfe53ec3 Homepage: https://cran.r-project.org/package=gradeR Description: CRAN Package 'gradeR' (Helps Grade Assignment Submissions in Common R Formats) After being given the location of your students' submissions and a test file, the function runs each file that is an R script, R Markdown file, or Quarto document, and evaluates the results from all the given tests. Results are neatly returned in a data frame that has a row for each student, and a column for each test. Package: r-cran-gradient Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-gradient_1.0.1-1.ca2404.1_all.deb Size: 40736 MD5sum: 1904eaa959ae212c8bc66e0f2120ab1f SHA1: fe7c1fa683a7fab824fdb91509753933ebca248c SHA256: 30e340c61b95d048eb9e30b32646bfd8865fc9295d080a1f0023554fe4509549 SHA512: 62661c9f5a1e7829ca9d005795e0ae8e8fe0621ff7242ba77e6e159f03687e6cbd876e4b923c897fdb1646dd601a2743dcc8bcc0656f2d1b6bc341627b8fa0dc Homepage: https://cran.r-project.org/package=graDiEnt Description: CRAN Package 'graDiEnt' (Stochastic Quasi-Gradient Differential Evolution Optimization) An optim-style implementation of the Stochastic Quasi-Gradient Differential Evolution (SQG-DE) optimization algorithm first published by Sala, Baldanzini, and Pierini (2018; ). This optimization algorithm fuses the robustness of the population-based global optimization algorithm "Differential Evolution" with the efficiency of gradient-based optimization. The derivative-free algorithm uses population members to build stochastic gradient estimates, without any additional objective function evaluations. Sala, Baldanzini, and Pierini argue this algorithm is useful for 'difficult optimization problems under a tight function evaluation budget.' This package can run SQG-DE in parallel and sequentially. Package: r-cran-gradientpickerd3 Architecture: all Version: 0.1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-shiny Filename: pool/dists/noble/main/r-cran-gradientpickerd3_0.1.0.0-1.ca2404.1_all.deb Size: 126198 MD5sum: c8052b7cf5a9b2dbf9c2281d6c64c52c SHA1: 3c92204016f8495f552c3dca7b0e8762cb9d406b SHA256: 6d51e308f1530b9b18025feb1afccc01f7804803745d880db6fec1f37d35adaa SHA512: 368efc2ee2603b7d1ffe368a79c824646389d0287e6eb885aebfda7d3936577928b61c6408468b2fb3639fe38438232c8242a4eac9dd8b348eb329d36de2167f Homepage: https://cran.r-project.org/package=gradientPickerD3 Description: CRAN Package 'gradientPickerD3' (Interactive Color Gradient Picker Using 'htmlwidgets' and theModified JS Script 'jquery-gradient-picker') Widget for an interactive selection and modification of a color gradient. 'gradientPickerD3' allows addition, removement and replacement of color ticks. List of numeric values will automatically translate in their corresponding tick position within the numeric range. App returns a data.frame containing tick values, colors and the positions in percent (0.0 to 1.0) for each color tick in the gradient. The original JS 'jquery-gradient-picker' was implemented by Matt Crinklaw-Vogt (nick: tantaman) . Widget and JS modifications were done by CD. Peikert. Package: r-cran-gradlasso Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-doparallel Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gradlasso_0.1.1-1.ca2404.1_all.deb Size: 138364 MD5sum: f0bf13221061fa6d7b0abcf25725be4c SHA1: cb433a155fa6326802d7cab51e233f60db567045 SHA256: 7ed03b75f62da0f38a66ff49e80f662cb9b8dcf52ca9fe2249083d0a9692f319 SHA512: 4acaacd5991bf41177c161724f2587d2663f8fa766779c2ffad408c71a676729db4c3e39457212b9e95deeec6054d73b1c549fada33b3560f4368834e2ee1cf3 Homepage: https://cran.r-project.org/package=gradLasso Description: CRAN Package 'gradLasso' (Gradient Descent LASSO with Stability Selection and BootstrappedConfidence Intervals) Implements LASSO regression using gradient descent with support for Gaussian, Binomial, Negative Binomial, and Zero-Inflated Negative Binomial (ZINB) families. Features cross-validation for determining lambda, stability selection, and bootstrapping for confidence intervals. Methods described in Tibshirani (1996) and Meinshausen and Buhlmann (2010) . Package: r-cran-grafify Architecture: all Version: 5.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5642 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-car, r-cran-dplyr, r-cran-emmeans, r-cran-hmisc, r-cran-lme4, r-cran-lmertest, r-cran-magrittr, r-cran-mgcv, r-cran-patchwork, r-cran-purrr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-matrix, r-cran-rlang, r-cran-rmarkdown, r-cran-pbkrtest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-grafify_5.1.0-1.ca2404.1_all.deb Size: 4241994 MD5sum: 92d421a75e2edc893ee21ad61b4bba0d SHA1: ed0ca8949856b1af83f67dff41d68c8064ce9639 SHA256: 2b45d8326b03a3c68957380e4829c1b734acc0a0a87aeb5e80bf8a539a877305 SHA512: c0de0aaa5d35435e7c2373a33543f3ba490de61bedfd5fe91d30eeb4042fe6c0511056ced6b2c9bc02f3bc9dd767c9f9db6a6786c743f782ff34ddd4d8cbaa60 Homepage: https://cran.r-project.org/package=grafify Description: CRAN Package 'grafify' (Easy Graphs for Data Visualisation and Linear Models for ANOVA) Easily explore data by plotting graphs with a few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways, for example, by choosing from colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent palettes). Use the simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs, and fit non-linear outcomes as a generalised additive model (gam) using mgcv(). Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans()). Also includes small datasets for practising code and teaching basics before users move on to more complex designs. See vignettes for details on usage . Citation: . Package: r-cran-grafzahl Architecture: all Version: 0.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1513 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-lime, r-cran-quanteda, r-cran-reticulate Suggests: r-cran-knitr, r-cran-quanteda.textmodels, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-grafzahl_0.0.12-1.ca2404.1_all.deb Size: 1131534 MD5sum: 6ac926c04120e74bdbe99f83b2f0ab36 SHA1: dd79f98dbf25def97d73ce69335e7081afca6722 SHA256: 35404b9069cdb39bb8a6589212ad849ac17e56642ffdfffee72fd52e2dc72351 SHA512: 1d5cf3118fff31995b44f8059c087784d3df3f10e6c334226836ca2b124f43ddbd17e2c5b8ef43a3e24be2b2b5067ca264300bfc9f092f7010d7fabbaed52706 Homepage: https://cran.r-project.org/package=grafzahl Description: CRAN Package 'grafzahl' (Supervised Machine Learning for Textual Data Using Transformersand 'Quanteda') Duct tape the 'quanteda' ecosystem (Benoit et al., 2018) to modern Transformer-based text classification models (Wolf et al., 2020) , in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of 'quanteda.textmodels' and provides a function to setup the 'Python' environment to use the pretrained models from 'Hugging Face' . More information: . Package: r-cran-gramevol Architecture: all Version: 2.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rex, r-cran-knitr Filename: pool/dists/noble/main/r-cran-gramevol_2.1-4-1.ca2404.1_all.deb Size: 402224 MD5sum: c77f862cc5346ddd6f6ab073f77fad33 SHA1: c90fc0a218826090aeac578e348648dab72b1177 SHA256: d78180b4cf947ad083e409a80686cb3d895e15d12c3595b228185f1a5db52680 SHA512: 483e1dd2aa1a7f5de4589976053e71cda188b8ac395e654ec7e5777b536374fc87bc3879fd18c9aa0bf778fb66aa13e3961a765050ac8bd28f18fdc7aacfb34a Homepage: https://cran.r-project.org/package=gramEvol Description: CRAN Package 'gramEvol' (Grammatical Evolution for R) A native R implementation of grammatical evolution (GE). GE facilitates the discovery of programs that can achieve a desired goal. This is done by performing an evolutionary optimisation over a population of R expressions generated via a user-defined context-free grammar (CFG) and cost function. Package: r-cran-gramquad Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-gramquad_0.1.1-1.ca2404.1_all.deb Size: 14242 MD5sum: 23441c3692fb4c36206b02af8916ba59 SHA1: 61e998734fd56d5be111bbcdebab97ebddd48a76 SHA256: c5e032fc1dee95dafaa94fd7c16ea060ab6c3f12fd8f981d3063f0b3d2b2b556 SHA512: 0652ac106ec7d3782b0d1bad064a717336cd73b6638885f67f632d9c22cceb5bb1b2cd4bda36dc260d5907f29fb41e0118ea4ee091721473f7b33ff34a47e6ba Homepage: https://cran.r-project.org/package=GramQuad Description: CRAN Package 'GramQuad' (Gram Quadrature) Numerical integration with Gram polynomials (based on [math.NA] 28 Jun 2021, by Irfan Muhammad [School of Computer Science, University of Birmingham, UK]). Package: r-cran-grand Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-grand_0.9.1-1.ca2404.1_all.deb Size: 105426 MD5sum: f72b30088edec9481094642989405f87 SHA1: 1510b99c39eabf37601de237879d2861549380e0 SHA256: ecd29baefe54baef73f2c6de58f4c65dda1aff6d31f0563d5760b377660827ad SHA512: 4d143e53c86cc837735db59300c352e8bb835771bcb2be06e450811f5303c9bc50f54eb06da617192c21e5854a554a135dd712214d0cdbac166bf02a67741c05 Homepage: https://cran.r-project.org/package=grand Description: CRAN Package 'grand' (Guidelines for Reporting About Network Data) Interactively applies the Guidelines for Reporting About Network Data (GRAND) to an 'igraph' object, and generates a uniform narrative or tabular description of the object. Package: r-cran-grandpriv Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-envstats, r-cran-rmutil, r-cran-rspectra, r-cran-diffpriv, r-cran-truncnorm, r-cran-randnet, r-cran-igraph, r-cran-hcd, r-cran-transport Filename: pool/dists/noble/main/r-cran-grandpriv_0.1.3-1.ca2404.1_all.deb Size: 56076 MD5sum: 42ee3f9464eeb0367d12d50e1451415d SHA1: 55a00a160f8081cd76a67bae634853ac130e11e9 SHA256: acaed39eb0885e15ad808738813ec1f6237bcbd60ae0eecbff9320f70847bc47 SHA512: 77704c7cef1e2e5df1c74ff5e8b2b02c3b115ba962e8637fac30e01b96989e504195e300d7ee72af8c0d87beb0e146233f7d5ab7c2fe150510ff832ec71cd5a3 Homepage: https://cran.r-project.org/package=GRANDpriv Description: CRAN Package 'GRANDpriv' (Graph Release with Assured Node Differential Privacy) Implements a novel method for privatizing network data using differential privacy. Provides functions for generating synthetic networks based on LSM (Latent Space Model), applying differential privacy to network latent positions to achieve overall network privatization, and evaluating the utility of privatized networks through various network statistics. The privatize and evaluate functions support both LSM and RDPG (Random Dot Product Graph). For generating RDPG networks, users are encouraged to use the 'randnet' package . For more details, see the "proposed method" section of Liu, Bi, and Li (2025) . Package: r-cran-grangers Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vars, r-cran-tseries Filename: pool/dists/noble/main/r-cran-grangers_0.1.0-1.ca2404.1_all.deb Size: 110252 MD5sum: 4f90a040c658f3646fd90a51df90b5a1 SHA1: b845be0ec540bacafcdfb11732dc6dc8cc4f601b SHA256: 1efb2ea7be4f5885cd7d5f9fb45a2faa1cc385bca8db2934e555d4ae31d51529 SHA512: db87c438d7c7b2e6827de50efba2b9fc59d48ce3a9e06a3e0068616953a741574da35d0b70d2805710266e62c8892c01849cf9a17b1dbe95526ee73d9097d33a Homepage: https://cran.r-project.org/package=grangers Description: CRAN Package 'grangers' (Inference on Granger-Causality in the Frequency Domain) Contains five functions performing the calculation of unconditional and conditional Granger-causality spectra, bootstrap inference on both, and inference on the difference between them via the bootstrap approach of Farne' and Montanari, 2018 . Package: r-cran-grangersearch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vars, r-cran-rlang, r-cran-tibble, r-cran-generics Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-grangersearch_0.1.0-1.ca2404.1_all.deb Size: 112000 MD5sum: fce1de6eb3e3e09ab744d58f51a8026a SHA1: d832aaee98098c5f1629e642968c68f0c5c53f7d SHA256: 18ad0fefef5188e412a4c98576ad664e6458d6082055b3d44841b8a39ad3e6b1 SHA512: af0e3e5e7e3fac1e22de4ca6f987b89a73234ad9c15be417810cb59baecfae66d736922eb3a443eedb78c67e239461eb373a78869e0ccbc554277780d9f2eeff Homepage: https://cran.r-project.org/package=grangersearch Description: CRAN Package 'grangersearch' (Granger Causality Testing for Time Series) Performs Granger causality tests on pairs of time series to determine causal relationships. Uses Vector Autoregressive (VAR) models to test whether one time series helps predict another beyond what the series' own past values provide. Returns structured results including p-values, test statistics, and causality conclusions for both directions. Package: r-cran-granova Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car Suggests: r-cran-mgcv, r-cran-rgl, r-cran-mass, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-granova_2.3-1.ca2404.1_all.deb Size: 83862 MD5sum: 66f461169be84d678212a09af04b7c1a SHA1: e29848cb452c0c5b7878b0e3db50f83990c49ccd SHA256: ea68797950b85052d53f5218a6c69fb49a7887fd7676678b77a5e015f446c2fc SHA512: b85a47acb42b8f37d32b601d76eb452416c1fe4a06d390811f659eafe3432fb521e95eace0a096e12dd4e2c30a547247a0f93c4a831e28e921e701f0e1ad7ed2 Homepage: https://cran.r-project.org/package=granova Description: CRAN Package 'granova' (Graphical Analysis of Variance) This small collection of functions provides what we call elemental graphics for display of analysis of variance results, David C. Hoaglin, Frederick Mosteller and John W. Tukey (1991, ISBN:978-0-471-52735-0), Paul R. Rosenbaum (1989) , Robert M. Pruzek and James E. Helmreich . The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular analysis of variance methods. These functions can be particularly helpful for students and non-statistician analysts. But these methods should be quite generally helpful for work-a-day applications of all kinds, as they can help to identify outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data. Package: r-cran-granovagg Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rcolorbrewer, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-granovagg_1.4.1-1.ca2404.1_all.deb Size: 142566 MD5sum: a914055759e1ef10a06fa19a89a95c40 SHA1: 59d1ab68ae101039b8310c9f69f5cd5d7c977c5b SHA256: 2a1bd817d867a72c66793f2a8fdc11c665e4b129747bef5b720001681a1d132e SHA512: 7e2e04184f60ff2f33d1543b4e68ec8430c7a6fb54d30cdf89bf9088a58e43b0567fe518de07d02f754cf083e4b951a2b4ced879d2d910df1847c815884615dd Homepage: https://cran.r-project.org/package=granovaGG Description: CRAN Package 'granovaGG' (Graphical Analysis of Variance Using ggplot2) Create what we call Elemental Graphics for display of anova results. The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular anova methods. This package represents a modification of the original granova package; the key change is to use 'ggplot2', Hadley Wickham's package based on Grammar of Graphics concepts (due to Wilkinson). The main function is granovagg.1w() (a graphic for one way ANOVA); two other functions (granovagg.ds() and granovagg.contr()) are to construct graphics for dependent sample analyses and contrast-based analyses respectively. (The function granova.2w(), which entails dynamic displays of data, is not currently part of 'granovaGG'.) The 'granovaGG' functions are to display data for any number of groups, regardless of their sizes (however, very large data sets or numbers of groups can be problematic). For granovagg.1w() a specialized approach is used to construct data-based contrast vectors for which anova data are displayed. The result is that the graphics use a straight line to facilitate clear interpretations while being faithful to the standard effect test in anova. The graphic results are complementary to standard summary tables; indeed, numerical summary statistics are provided as side effects of the graphic constructions. granovagg.ds() and granovagg.contr() provide graphic displays and numerical outputs for a dependent sample and contrast-based analyses. The graphics based on these functions can be especially helpful for learning how the respective methods work to answer the basic question(s) that drive the analyses. This means they can be particularly helpful for students and non-statistician analysts. But these methods can be of assistance for work-a-day applications of many kinds, as they can help to identify outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data. In the case of granovagg.1w() and granovagg.ds() several arguments are provided to facilitate flexibility in the construction of graphics that accommodate diverse features of data, according to their corresponding display requirements. See the help files for individual functions. Package: r-cran-grantham Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-grantham_0.1.4-1.ca2404.1_all.deb Size: 227702 MD5sum: 56e29904d9ccd640043ecbde10683a30 SHA1: ca69d7d0df401fc59d5a78e1a5dd3b47cc9ab73f SHA256: 74241428724ec1588dbc9ec132002ac37fe5ba2492eb95945d83f7b059612af0 SHA512: 36a6664f297a23f45013f0d13561c94188c4f5fb1e0821dcba33a9bea8a6f6d23696ff92420a33e239fec7caccddd93a5a8db4221a0393d5077e50ea83d97f07 Homepage: https://cran.r-project.org/package=grantham Description: CRAN Package 'grantham' (Calculate the Grantham Distance) A minimal set of routines to calculate the Grantham distance . The Grantham distance attempts to provide a proxy for the evolutionary distance between two amino acids based on three key chemical properties: composition, polarity and molecular volume. In turn, evolutionary distance is used as a proxy for the impact of missense mutations. The higher the distance, the more deleterious the substitution is expected to be. Package: r-cran-grape Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-grape_0.1.1-1.ca2404.1_all.deb Size: 39420 MD5sum: d6830d8384c73d77da03d2b382aac6f3 SHA1: 53131830952552a756b0a0e2196b4487b8b23859 SHA256: 354781d05def1412ed55a0d76aa0d30cf4dc0401d295474f26ed9e9b6f33cbdc SHA512: 36009e1c3d196c9d3953b897879669f59c336d5df29c2052d32023949b554f8a5c211b686e5a82c4c104ced50b599b86f77dfffa45da6478791165c12fdd0fa3 Homepage: https://cran.r-project.org/package=GRAPE Description: CRAN Package 'GRAPE' (Gene-Ranking Analysis of Pathway Expression) Gene-Ranking Analysis of Pathway Expression (GRAPE) is a tool for summarizing the consensus behavior of biological pathways in the form of a template, and for quantifying the extent to which individual samples deviate from the template. GRAPE templates are based only on the relative rankings of the genes within the pathway and can be used for classification of tissue types or disease subtypes. GRAPE can be used to represent gene-expression samples as vectors of pathway scores, where each pathway score indicates the departure from a given collection of reference samples. The resulting pathway- space representation can be used as the feature set for various applications, including survival analysis and drug-response prediction. Users of GRAPE should use the following citation: Klein MI, Stern DF, and Zhao H. GRAPE: A pathway template method to characterize tissue-specific functionality from gene expression profiles. BMC Bioinformatics, 18:317 (June 2017). Package: r-cran-grapes Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-grapes_1.0.0-1.ca2404.1_all.deb Size: 83500 MD5sum: 826c8551f79fa91a3924fb9be03f3800 SHA1: 96db1426284f8c792931c1d38c00317f0291be21 SHA256: 619be97cbe28ca27f59def9359d572f192c653a1bd418470a06f0d18a89dbfb3 SHA512: c93bb13349162a31cf87c833f2657e9cf1276fcfdff4dacc1bebd134c664ccd706e8050b7319798bf329193e303368a0a2a1e67c53c37ee65bc3f3d68943e85f Homepage: https://cran.r-project.org/package=grapes Description: CRAN Package 'grapes' (Make Binary Operators) Turn arbitrary functions into binary operators. Package: r-cran-grapesagri1 Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinywidgets, r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra, r-cran-magrittr, r-cran-summarytools, r-cran-dplyr, r-cran-pastecs, r-cran-ggpubr, r-cran-hmisc, r-cran-corrplot, r-cran-ggplot2, r-cran-reshape2, r-cran-gridgraphics, r-cran-rcolorbrewer, r-cran-desplot, r-cran-agricolae, r-cran-paireddata, r-cran-gtools, r-cran-rdpack Suggests: r-cran-shinytest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-grapesagri1_1.1.0-1.ca2404.1_all.deb Size: 3504660 MD5sum: d7d82554e6095321e76f2dc486cddddc SHA1: 738457357e702a0592a47d175db3fb3ac51ffffa SHA256: 8dd63505ca5e3e38512e3a394908b1ae5affccb26f0a6f533445fb0e772d7e04 SHA512: 9fe64e245c823056601c02f60da887819c7ee118f1c6c016c40505f6126c84c078f8c153cbe6600011cf3e01f26f95d7b3f2529cffd4e387bc0d41ac8ae00b5b Homepage: https://cran.r-project.org/package=grapesAgri1 Description: CRAN Package 'grapesAgri1' (Collection of Shiny Apps for Agricultural Research Data Analysis) Allows user to have graphical user interface to perform analysis of Agricultural experimental data. On using the functions in this package a Interactive User Interface will pop up. Apps Works by simple upload of files in CSV format. Package: r-cran-graph3d Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-lazyeval Suggests: r-cran-shiny, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-graph3d_0.2.0-1.ca2404.1_all.deb Size: 105008 MD5sum: d40e72150e7a20cb1825dab0c96a7956 SHA1: 0c081ea78e6c200e82cd8bf3469d1b061075c26c SHA256: 0aaab42694825e0a75f46ff0caa57b7c87e23301a0f25a32ab4913a38166a0fb SHA512: 03d2df009a970f8691b172748e8aa4227d6c5fc24aa800e8293e96b03dbc772cf5de007bc2afe31671af99e308500284789efe5a87d932c8aedf10b40ce5fd9e Homepage: https://cran.r-project.org/package=graph3d Description: CRAN Package 'graph3d' (A Wrapper of the JavaScript Library 'vis-graph3d') Create interactive visualization charts to draw data in three dimensional graphs. The graphs can be included in Shiny apps and R markdown documents, or viewed from the R console and 'RStudio' Viewer. Based on the 'vis.js' Graph3d module and the 'htmlwidgets' R package. Package: r-cran-graph4lg Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3346 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-adegenet, r-cran-ggplot2, r-cran-stringr, r-cran-igraph, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-matrix, r-cran-vegan, r-cran-pegas, r-cran-mass, r-cran-tidyr, r-cran-sp, r-cran-sf, r-cran-hierfstat, r-cran-rappdirs, r-cran-gdistance, r-cran-raster, r-cran-foreign, r-cran-ecodist, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-graph4lg_1.8.0-1.ca2404.1_all.deb Size: 2092750 MD5sum: aff19ad13fe9feadf4e6678daa5544d6 SHA1: 31e2489a200e3e65f32b4f8ed63d80bbacccd225 SHA256: 1b72c18d8ad9526391f812ea08402e8e03d9d0fa611bba953aaa945d9bd248df SHA512: 3d05828ecb4f20b3b336bfc613c1762ca58810482204e0e6bf178e9f1ee2967f1dcf777bfd6ba7a1910aab4a0a450817148c7d83b3f56f5b42c309848e546182 Homepage: https://cran.r-project.org/package=graph4lg Description: CRAN Package 'graph4lg' (Build Graphs for Landscape Genetics Analysis) Build graphs for landscape genetics analysis. This set of functions can be used to import and convert spatial and genetic data initially in different formats, import landscape graphs created with 'GRAPHAB' software (Foltete et al., 2012) , make diagnosis plots of isolation by distance relationships in order to choose how to build genetic graphs, create graphs with a large range of pruning methods, weight their links with several genetic distances, plot and analyse graphs, compare them with other graphs. It uses functions from other packages such as 'adegenet' (Jombart, 2008) and 'igraph' (Csardi et Nepusz, 2006) . It also implements methods commonly used in landscape genetics to create graphs, described by Dyer et Nason (2004) and Greenbaum et Fefferman (2017) , and to analyse distance data (van Strien et al., 2015) . Package: r-cran-graphclust Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-blockmodels, r-cran-igraph, r-cran-sclust Filename: pool/dists/noble/main/r-cran-graphclust_1.3-1.ca2404.1_all.deb Size: 116332 MD5sum: b61e27fa15f84471320770fe28f8892c SHA1: 096dd27707c25af4f783d3f7f720b44f6f41e4a2 SHA256: 681b65f219852db264c8fa85eda12fdf3c181a8279256a1224f9d6c6f39546ea SHA512: ee8b83c11e7edd8327b3808f1d4fe7610e40e22c8767fa4924b47859e8fccd1faa0723625dde4ba6fb797b588056e28112002dfcb3b8227d16f883f607e22646 Homepage: https://cran.r-project.org/package=graphclust Description: CRAN Package 'graphclust' (Hierarchical Graph Clustering for a Collection of Networks) Graph clustering using an agglomerative algorithm to maximize the integrated classification likelihood criterion and a mixture of stochastic block models. The method is described in the article "Model-based clustering of multiple networks with a hierarchical algorithm" by T. Rebafka (2022) . Package: r-cran-grapher Architecture: all Version: 1.9-86-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-grapher_1.9-86-5-1.ca2404.1_all.deb Size: 694912 MD5sum: c25540dc8a01d3bddfa2a288df18a3fa SHA1: 09b50f8948fab857ade6afe268a889d67816dac5 SHA256: 63b20d956077a2da70f3f01592275d51d76ffb412a9f098e4e68b804fab72650 SHA512: d97919611b3c0d094314c1bcef43b0f9445075e1812596a0465af2467e36b74596f00daa0f7982c8961e9c5332ad08ecffda2c589385e7e52c5cce44804b9748 Homepage: https://cran.r-project.org/package=GrapheR Description: CRAN Package 'GrapheR' (A Multi-Platform GUI for Drawing Customizable Graphs in R) A multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot. Package: r-cran-grapherator Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1220 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmisc, r-cran-checkmate, r-cran-reshape2, r-cran-vegan, r-cran-ggplot2, r-cran-lhs, r-cran-deldir Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-grapherator_1.0.0-1.ca2404.1_all.deb Size: 756848 MD5sum: 4fcb341b426e9a3063c9e4baeb1fe598 SHA1: 502eb61e018904c234a8993072550dff742d6157 SHA256: 9846d0db7fa8134f414687a696f1ea51a659b3f91f0ef17be9b956de1727a1fb SHA512: e7ff0f647351e4e14bf00848091f852eca3162f4de1da4f5b175bee5a207df9c619900d4110f5abed57c054f97b91567c370eaa2e6bdedb9d550777d5820ab75 Homepage: https://cran.r-project.org/package=grapherator Description: CRAN Package 'grapherator' (A Modular Multi-Step Graph Generator) Set of functions for step-wise generation of (weighted) graphs. Aimed for research in the field of single- and multi-objective combinatorial optimization. Graphs are generated adding nodes, edges and weights. Each step may be repeated multiple times with different predefined and custom generators resulting in high flexibility regarding the graph topology and structure of edge weights. Package: r-cran-graphframes Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparklyr, r-cran-tibble, r-cran-forge Suggests: r-cran-testthat, r-cran-covr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-graphframes_0.1.2-1.ca2404.1_all.deb Size: 70524 MD5sum: c397d357c67457dd336b0f82d706b676 SHA1: 0084b60071e6b31ade5f3efdf531747a432d8a0c SHA256: 8baa03ef736f174f8d522a968b9c85bf90ec497792e297fa3efd4aa6c890fd6c SHA512: b38467570f4501281335034eb5dcc1d8fb53932feb6cc8e246f8869f71199e21c82d4bedf87614265217bda2e7165601d9c00462ce0c6b69a766d4a4ef45fda7 Homepage: https://cran.r-project.org/package=graphframes Description: CRAN Package 'graphframes' (Interface for 'GraphFrames') A 'sparklyr' extension that provides an R interface for 'GraphFrames' . 'GraphFrames' is a package for 'Apache Spark' that provides a DataFrame-based API for working with graphs. Functionality includes motif finding and common graph algorithms, such as PageRank and Breadth-first search. Package: r-cran-graphhopper Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-httr, r-cran-googlepolylines, r-cran-jsonlite, r-cran-tibble, r-cran-dplyr Suggests: r-cran-sf, r-cran-geojsonsf, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-graphhopper_0.1.2-1.ca2404.1_all.deb Size: 326668 MD5sum: 185f7e575d14db3f947a0abd0af166db SHA1: 010081e27820845ee537c331d5bb42019a064e71 SHA256: de0e1ccb37d55f268bf21a36f8ea16c581db5c634e9c3e44baba503035d762c3 SHA512: 3464504b5de10805ef2b3964c449d3ec48da658d78c4b08f419bcc4ba5fac76980ac23e0feb83c8d0eac7ae5de5b6bf841fb94cc6bd5131e2b104a9995501e62 Homepage: https://cran.r-project.org/package=graphhopper Description: CRAN Package 'graphhopper' (An R Interface to the 'GraphHopper' Directions API) Provides a quick and easy access to the 'GraphHopper' Directions API. 'GraphHopper' itself is a routing engine based on 'OpenStreetMap' data. API responses can be converted to simple feature (sf) objects in a convenient way. Package: r-cran-graphicalextremes Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2984 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-mvtnorm, r-cran-rdpack, r-cran-corpcor, r-cran-osqp, r-cran-glmnet, r-cran-glassofast, r-cran-cvxr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-bookdown, r-cran-maps Filename: pool/dists/noble/main/r-cran-graphicalextremes_0.3.5-1.ca2404.1_all.deb Size: 2667576 MD5sum: a96aa94ce9b8a7630465a731f969f9a9 SHA1: df7c9ca66a79ba7d7e9cafb2ade8547217f93152 SHA256: 844c2199130fd6299c3c82c6e1f72245474b30bb222d6b184b33c8b598780d3f SHA512: 1ed15a0891adbd43ce8fa15ec0cd7fb4fbba21af70469d5f7ff2f984ab8a3df8c8428a2803f2aeeeafc68edc32dbdd2b7d3f5a5310fa75d66123ce4f7d6e8f6e Homepage: https://cran.r-project.org/package=graphicalExtremes Description: CRAN Package 'graphicalExtremes' (Statistical Methodology for Graphical Extreme Value Models) Statistical methodology for sparse multivariate extreme value models. Methods are provided for exact simulation and statistical inference for multivariate Pareto distributions on graphical structures as described in the paper 'Graphical Models for Extremes' by Engelke and Hitz (2020) . 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Graphical MCPs include many commonly used procedures as special cases; see Bretz et al. (2011) , Lu (2016) , and Xi et al. (2017) . This package is a low-dependency implementation of graphical MCPs which allow mixed types of tests. It also includes power simulations and visualization of graphical MCPs. Package: r-cran-graphon Architecture: all Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-roptspace, r-cran-rdpack Suggests: r-cran-igraph Filename: pool/dists/noble/main/r-cran-graphon_0.3.6-1.ca2404.1_all.deb Size: 80162 MD5sum: fe7dc02cc15a852853ca4d66e9e674ba SHA1: 3c992d6e5842a64f59f1ee2bd3f1206bf760afb4 SHA256: d2accb4f1d1f5bb9d78640591c297f15f87d18c91ce5886c7d70fe66f12e5758 SHA512: caea05418bda3738f251a08edb776cfac99e5306862f4e547f878f64e30f3d9dbc4cb1bd54688c2a39833f4377c81453dee8b1a55ac31fb7013808e93a68d00b Homepage: https://cran.r-project.org/package=graphon Description: CRAN Package 'graphon' (A Collection of Graphon Estimation Methods) Provides a not-so-comprehensive list of methods for estimating graphon, a symmetric measurable function, from a single or multiple of observed networks. For a detailed introduction on graphon and popular estimation techniques, see the paper by Orbanz, P. and Roy, D.M.(2014) . It also contains several auxiliary functions for generating sample networks using various network models and graphons. Package: r-cran-graphonmix Architecture: all Version: 0.0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 830 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-imager Suggests: r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-graphonmix_0.0.1.0-1.ca2404.1_all.deb Size: 606684 MD5sum: 9b3728895f312a287e1e6d686917799f SHA1: be727e4f888654a7808ad8e3b3060a73e2a058c0 SHA256: dcebfa4d46382e7a86f5b446be0f4a3f874404fc2e39780c4ce980d0a4db733f SHA512: e0d10af2eefc9f279c6609d133237ca695ded3ff4b801481d6598550c8c2f711f3357761fb52bf18c03f46e6606663577bfcdf58e933fd25d7c1622413356ee6 Homepage: https://cran.r-project.org/package=graphonmix Description: CRAN Package 'graphonmix' (Generates Mixture Graphs from Dense and Sparse Graphons) Generates (U,W) mixture graphs where U is a line graph graphon and W is a dense graphon. Graphons are graph limits and graphon U can be written as sequence of positive numbers adding to 1. Graphs are sampled from U and W and joined randomly to obtain the mixture graph. Given a mixture graph, U can be inferred. Kandanaarachchi and Ong (2025) . Package: r-cran-graphpaf Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1497 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-gtools, r-cran-madness, r-cran-mass, r-cran-reshape2, r-cran-survival Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-graphpaf_2.0.1-1.ca2404.1_all.deb Size: 1471362 MD5sum: 004079b8dfbceb9d1abdb2ad076182ed SHA1: 6d8cb8ed1cd79a928643812728b0d98306db790e SHA256: a0d13aeda19c5190696161384878e8fd30561e074a915b3777023c419664ebd6 SHA512: fa2a48d680a3660878bd8e9de00c8234061f33e054a1ee5c250d1d2f9e1b01382ea66218d5da7888a69c27c45701889fde36c806155d5e71a314d39f18497936 Homepage: https://cran.r-project.org/package=graphPAF Description: CRAN Package 'graphPAF' (Estimating and Displaying Population Attributable Fractions) Estimation and display of various types of population attributable fraction and impact fractions. As well as the usual calculations of attributable fractions and impact fractions, functions are provided for attributable fraction nomograms and fan plots, continuous exposures, for pathway specific population attributable fractions, and for joint, average and sequential population attributable fractions. Package: r-cran-graphranktest Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-nbpmatching Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-graphranktest_0.1-1.ca2404.1_all.deb Size: 32872 MD5sum: 3d74151216408174fb61caadb81ac712 SHA1: 9c884a0c77fc3136da8e944614cb1261bf0a62b5 SHA256: 3a00ed2c2d8a5ed331d138a37a9025d548412ea988585d7ae387794e2a7ce09d SHA512: 1d3ec0bee8dec634f739ed860b9e4df48ddd6cfce1352ab3399675eccb8401d260910f2c5180a49e7f144299f2d46491babcadd4fd7e65b9a2074a693989b8aa Homepage: https://cran.r-project.org/package=GraphRankTest Description: CRAN Package 'GraphRankTest' (Rank in Similarity Graph Edge-Count Two-Sample Test (RISE)) Implements the Rank In Similarity Graph Edge-count two-sample test (RISE) for high-dimensional and non-Euclidean data. The method constructs similarity-based graphs, such as k-nearest neighbor graph (k-NNG), k-minimum spanning tree (k-MST), and k-minimum distance non-bipartite pairing (k-MDP), and evaluates rank-based within-sample edge counts with asymptotic and permutation p-values. For methodological details, see Zhou and Chen (2023) . 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Intended to be used for analyzing grouped and right-censored data, which is widely applied in many branches of social sciences. The algorithm implemented is described in Fu et al., (2021) . 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Package: r-cran-greedyexperimentaldesignjars Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 681 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava Filename: pool/dists/noble/main/r-cran-greedyexperimentaldesignjars_1.0-1.ca2404.1_all.deb Size: 597356 MD5sum: 49433c3eb180d5e5897d2b3e4457373f SHA1: 22de5b6785fa3a7577633362f9ed132b4b4b4916 SHA256: 693b681f292dc9eeafaa0f94c4caf74778258ef1fb0511b86b9889a2192105a3 SHA512: 1acd2b8219ecc28bf8af0c03ab060eb4dc8f6199fbc65554b325aa37772514c1024541af13d88e18a9f68a72d1a48982a9f650ee298e1bd3f0efacde0e98c292 Homepage: https://cran.r-project.org/package=GreedyExperimentalDesignJARs Description: CRAN Package 'GreedyExperimentalDesignJARs' (GreedyExperimentalDesign JARs) These are GreedyExperimentalDesign Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without GreedyExperimentalDesign. 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The functions in this package do not work properly on the R console. Characters are used via Unicode and encoded as UTF-8 to ensure that they can be viewed on all operating systems. Other characters related to mathematics are included, such as the infinity symbol. All this accessible from very simple commands. This is a package that can be used for teaching purposes, the statistical notation for hypothesis testing can be written from this package and so it is possible to build a course from the 'swirlify' package. Another utility of this package is to create new summary functions that contain the functional form of the model adjusted with the Greek letters, thus making the transition from statistical theory to practice easier. In addition, it is a natural extension of the 'clisymbols' package. Package: r-cran-greenbook Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-inflater, r-cran-openxlsx, r-cran-officer, r-cran-flextable Filename: pool/dists/noble/main/r-cran-greenbook_0.1.0-1.ca2404.1_all.deb Size: 190312 MD5sum: 8263b8408807238068e4654bf57ae3e7 SHA1: deba994c91c85424b2b9e117253702b1492473fd SHA256: c6547244eaca61562a4c2c35a2364a4dca1935216bfa4506b652f9342293c84c SHA512: 65b1a8e158161be4c2f82a96643054896e39ee1d0ee4274a46e4229a4b0222d02842adf5c9da6b5be6e2f21c44e96cd249e8000baadd11f9fcb0cb0ce53a795c Homepage: https://cran.r-project.org/package=greenbook Description: CRAN Package 'greenbook' (HM Treasury Green Book Cost-Benefit Analysis Primitives) Implements cost-benefit analysis primitives from HM Treasury Green Book guidance (HM Treasury, 2022, 2026): the kinked Social Time Preference Rate ('STPR'), discount factors, net present value ('NPV'), equivalent annual cost, and real-terms rebasing using the GDP deflator. 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Package: r-cran-greenclust Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-greenclust_1.1.1-1.ca2404.1_all.deb Size: 159622 MD5sum: 7c16aa4e30f4522f8898154bd433ef13 SHA1: 350f57a5d07ab54b2115f8ea27a1461f3744c02a SHA256: 4c730bc52f07dd6271c1ce287f2b427166435232316070632dcc3339edfe6d49 SHA512: c354ce0db4fb79369735ece88ee36f2160043df2d1cb900a1dbf1961d7b7410c5b3d29b331decc13b4e90136ef56673e9a4cfebff1ef890d9c5218b5ca092a3b Homepage: https://cran.r-project.org/package=greenclust Description: CRAN Package 'greenclust' (Combine Categories Using Greenacre's Method) Implements a method of iteratively collapsing the rows of a contingency table, two at a time, by selecting the pair of categories whose combination yields a new table with the smallest loss of chi-squared, as described by Greenacre, M.J. 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Package: r-cran-gregry Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gregry_0.1.0-1.ca2404.1_all.deb Size: 45478 MD5sum: 9ae835e5c83a0ad92672bdbf386a1a44 SHA1: fd1bfc003e8eed1cfe50d4fb715a84ffc0aba34a SHA256: 23d010cf71ada102adde840d22a446b785e591a294649d33d3ac7d841b73d836 SHA512: a75bcf859957c9c8ba9a1d5f3214735e2e7150f68d051d0101351cb9711be9db069997cc88c58dd1471f035830af46c05e6c08944373881d735c8a7e72eae126 Homepage: https://cran.r-project.org/package=gregRy Description: CRAN Package 'gregRy' (GREGORY Estimation) Functions which make using the Generalized Regression Estimator(GREG) J.N.K. Rao, Isabel Molina, (2015) and the Generalized Regression Estimator Operating on Resolutions of Y (GREGORY) easier. The functions are designed to work well within a forestry context, and estimate multiple estimation units at once. Compared to other survey estimation packages, this function has greater flexibility when describing the linear model. 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It covers several state-of-the-art univariate and multivariate grey models. A user friendly interface allows users to easily compare the performance of different models for prediction and among others, visualize graphical plots of predicted values within user chosen confidence intervals. Chang, C. (2019) , Li, K., Zhang, T. (2019) , Ou, S. (2012) , Li, S., Zhou, M., Meng, W., Zhou, W. (2019) , Xie, N., Liu, S. (2009) , Shao, Y., Su, H. (2012) , Xie, N., Liu, S., Yang, Y., Yuan, C. (2013) , Li, S., Miao, Y., Li, G., Ikram, M. (2020) , Che, X., Luo, Y., He, Z. (2013) , Zhu, J., Xu, Y., Leng, H., Tang, H., Gong, H., Zhang, Z. (2016) , Luo, Y., Liao, D. (2012) , Bilgil, H. (2020) , Li, D., Chang, C., Chen, W., Chen, C. (2011) , Chen, C. (2008) , Zhou, W., Pei, L. (2020) , Xiao, X., Duan, H. (2020) , Xu, N., Dang, Y. (2015) , Chen, P., Yu, H.(2014) , Zeng, B., Li, S., Meng, W., Zhang, D. (2019) , Liu, L., Wu, L. (2021) , Hu, Y. (2020) , Zhou, P., Ang, B., Poh, K. (2006) , Cheng, M., Li, J., Liu, Y., Liu, B. (2020) , Wang, H., Wang, P., Senel, M., Li, T. (2019) , Ding, S., Li, R. (2020) , Zeng, B., Li, C. (2018) , Xie, N., Liu, S. (2015) , Zeng, X., Yan, S., He, F., Shi, Y. (2019) . 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The difference analysis is based on the 'limma' package, which can cover gene and protein expression profiles (Reference: Matthew E Ritchie , Belinda Phipson , Di Wu , Yifang Hu , Charity W Law , Wei Shi , Gordon K Smyth (2015) ). The GO enrichment analysis is based on the 'clusterProfiler' package and supports three common species: human, mouse, and yeast (Reference: Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He (2012) ). The results of batch difference analysis and enrichment analysis are output in separate folders for easy viewing and further visualization of the results during the process. The results returned a heatmap in R and exported to 3 folders named DEG, go, and merge. 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Baumont "Model Predicting Dynamics of Biomass, Structure and Digestibility of Herbage in Managed Permanent Pastures. 1. Model Description." (2006) ). The implementation in this package contains a few additions to the above cited version of ModVege, such as simulations of management decisions, and influences of snow cover. As such, the model is fit to simulate grass growth in mountainous regions, such as the Swiss Alps. The package also contains routines for calibrating the model and helpful tools for analysing model outputs and performance. 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(2020) Plant Methods, 16. Smoothing of growth trends for individual plants using natural cubic smoothing splines or P-splines is available for removing transient effects and segmented smoothing is available to deal with discontinuities in growth trends. There are graphical tools for assessing the adequacy of trait smoothing, both when using this and other packages, such as those that fit nonlinear growth models. A range of per-unit (plant, pot, plot) growth traits or features can be extracted from the data, including single time points, interval growth rates and other growth statistics, such as maximum growth or days to maximum growth. The package also has tools adapted to inputting data from high-throughput phenotyping facilities, such from a Lemna-Tec Scananalyzer 3D (see for more information). The package 'growthPheno' can also be installed from . 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Group sparse optimization via l_{p,q} regularization. Journal of Machine Learning Research, to appear, 2017". 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Refined secondary boundaries are calculated for a gate-keeping test on a primary and a secondary endpoint in a group sequential design with multiple interim looks. The choices include both the standard boundaries and the boundaries using error spending functions. See Tamhane et al. (2018), "A gatekeeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks", Biometrics, 74(1), 40-48. Package: r-cran-gsse Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-iso, r-cran-zoo Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-gsse_0.1-1.ca2404.1_all.deb Size: 69932 MD5sum: 28ec213610ef8fe46d40a46e0c919650 SHA1: 746ebd4168e065c039ca7fab14b1238607bf1a5f SHA256: 07fe8e871e14f90a198846f0097ef1c1b6d6bcbd8eb062677b75e9c1c78a4f96 SHA512: d9d09efea4a5388d6099fc747b4a56029efea6ef64805d7c55902a0802937005e4951dcbb6d54a240f2ead80c5cd131ac94f73659c1db3be8109ce34f44d368b Homepage: https://cran.r-project.org/package=GSSE Description: CRAN Package 'GSSE' (Genotype-Specific Survival Estimation) We propose a fully efficient sieve maximum likelihood method to estimate genotype-specific distribution of time-to-event outcomes under a nonparametric model. We can handle missing genotypes in pedigrees. We estimate the time-dependent hazard ratio between two genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function. The estimators are calculated via an expectation-maximization algorithm. Package: r-cran-gsstda Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4964 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-survival, r-cran-visnetwork, r-bioc-complexheatmap, r-cran-circlize, r-cran-devtools Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gsstda_1.0.0-1.ca2404.1_all.deb Size: 4053730 MD5sum: f3454c143c33529eaf310461fb56bc37 SHA1: c2ba2897b037b101b543aef6ee5168d9cb612199 SHA256: 16591e7da4ba893af97405759bd115a3c225010f45b8bb491b7dd98d213a464c SHA512: a73ba16b70c67db8f87a9fd1b200aeb6629d532885187e8c58df51ffce27724333e20e4d9571feb8f4a36b0702ebebff81ef8442c74bcee9504a62b6cf2f4057 Homepage: https://cran.r-project.org/package=GSSTDA Description: CRAN Package 'GSSTDA' (Progression Analysis of Disease with Survival using TopologicalData Analysis) Mapper-based survival analysis with transcriptomics data is designed to carry out. Mapper-based survival analysis is a modification of Progression Analysis of Disease (PAD) where survival data is taken into account in the filtering function. More details in: J. Fores-Martos, B. Suay-Garcia, R. Bosch-Romeu, M.C. Sanfeliu-Alonso, A. Falco, J. Climent, "Progression Analysis of Disease with Survival (PAD-S) by SurvMap identifies different prognostic subgroups of breast cancer in a large combined set of transcriptomics and methylation studies" . Package: r-cran-gstar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-xts, r-cran-zoo, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gstar_0.1.0-1.ca2404.1_all.deb Size: 52540 MD5sum: 6dab449d8e20a7fc9857564f0bd74c6c SHA1: ddda1edab902bf8433e9a97ebc99720d523b651d SHA256: 22e3e70699605864479cf5b286e71402c6f158c87d06678b0cd726c520b2323f SHA512: 8e37badfb01f2cec87d9004cce7d33237a66fbe30d5c82066df967b587b0edfa9ce48ddf32817cb361957c97e6f56aeb0213e1a626963f036714bd6717054eb4 Homepage: https://cran.r-project.org/package=gstar Description: CRAN Package 'gstar' (Generalized Space-Time Autoregressive Model) Multivariate time series analysis based on Generalized Space-Time Autoregressive Model by Ruchjana et al.(2012) . Package: r-cran-gstream Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gstream_0.2.0-1.ca2404.1_all.deb Size: 149844 MD5sum: 75e2e20b6a0bd90e746635a8360b71b3 SHA1: ada33d6153c2289b227a60c1bf85f5a7b6cc6d3f SHA256: 50f259a30be5870f01ad11f84bab34bf298375d6577399c5ef3364b693c0a92a SHA512: 26a1ceec2c2fda6f4e18cc150165d7ea437096a7884d0a3a363b69d4db81172fb6ab4990dd7e31e15ffdf3576e3290386957a42b8fdc1ee639364798f3f70766 Homepage: https://cran.r-project.org/package=gStream Description: CRAN Package 'gStream' (Graph-Based Sequential Change-Point Detection for Streaming Data) Uses an approach based on k-nearest neighbor information to sequentially detect change-points. Offers analytic approximations for false discovery control given user-specified average run length. Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available. See references (1) Chen, H. (2019) Sequential change-point detection based on nearest neighbors. The Annals of Statistics, 47(3):1381-1407. (2) Chu, L. and Chen, H. (2018) Sequential change-point detection for high-dimensional and non-Euclidean data . 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Generalização de Mineração de Sequências Restritas no Espaço e no Tempo. In: XXXVI SBBD - Simpósio Brasileiro de Banco de Dados, 2021 ). 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Package: r-cran-gtexr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-curl, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-gtexr_0.2.1-1.ca2404.1_all.deb Size: 415332 MD5sum: 3eda03f42aaaa0dbd2e619751132ce1e SHA1: f25b25f1fa1e7084dbf32cadd70da71e6f5f3f38 SHA256: 10b63b8ead761fd8f32214ab44243b6d902e9442fac46e87f5e83ae2e0ece7d2 SHA512: 1bf1a8448e3dfca42158d2b93607beb5c2c89eba7332a72756cdc9090962adbf388b2fb4c72d8f7c80baf0034911bd01370b0110080c3a2bfb9651e37389fb26 Homepage: https://cran.r-project.org/package=gtexr Description: CRAN Package 'gtexr' (Query the GTEx Portal API) A convenient R interface to the Genotype-Tissue Expression (GTEx) Portal API. The GTEx project is a comprehensive public resource for studying tissue-specific gene expression and regulation in human tissues. Through systematic analysis of RNA sequencing data from 54 non-diseased tissue sites across nearly 1000 individuals, GTEx provides crucial insights into the relationship between genetic variation and gene expression. This data is accessible through the GTEx Portal API enabling programmatic access to human gene expression data. For more information on the API, see . Package: r-cran-gtextras Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4785 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gt, r-cran-commonmark, r-cran-dplyr, r-cran-fontawesome, r-cran-ggplot2, r-cran-glue, r-cran-htmltools, r-cran-paletteer, r-cran-rlang, r-cran-scales, r-cran-knitr, r-cran-cli Suggests: r-cran-base64enc, r-cran-bitops, r-cran-covr, r-cran-fs, r-cran-hms, r-cran-magrittr, r-cran-rvest, r-cran-sass, r-cran-stringr, r-cran-svglite, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-webshot2, r-cran-xml2, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-gtextras_0.6.2-1.ca2404.1_all.deb Size: 3999816 MD5sum: f5c0ee12b3bba4425a9c41047d8fd825 SHA1: a38f3c5af598a512fc472d73f380f74ef47bf2f7 SHA256: 0dcdfaa22880b51ee8d1fa3d430e531dece69e75fcba0f0bdf9de46c881e957c SHA512: 428f173e3ce013a0bb77917030c5b97c4b374547eaafc0c9c917e3e3983114512af41ad96754962041de1e057513505d57aaa19cd522f4bb7b36aa04374f3568 Homepage: https://cran.r-project.org/package=gtExtras Description: CRAN Package 'gtExtras' (Extending 'gt' for Beautiful HTML Tables) Provides additional functions for creating beautiful tables with 'gt'. The functions are generally wrappers around boilerplate or adding opinionated niche capabilities and helpers functions. Package: r-cran-gtexture Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dlookr, r-cran-dplyr, r-cran-fitscape, r-cran-igraph, r-cran-magrittr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gtexture_1.0.1-1.ca2404.1_all.deb Size: 107246 MD5sum: 149144cf466f04e8a4a5948b9e5330e0 SHA1: 7b743be52c7ec0774544c537426a97d7757f77d2 SHA256: 578bf1daf169084d5412c7531396c2a0991a6593e43e71877a8e7ad579341a2d SHA512: 6cd92b545b495c19fea3aa22480c2780acb6b1a817d061bbd6aae0da8ae573c5143b3568b405591daf4f3b0a8a9e9744e4c1ec865e9d2ca2fda1da9be843ac7f Homepage: https://cran.r-project.org/package=gtexture Description: CRAN Package 'gtexture' (Generalized Application of Co-Occurrence Matrices and HaralickTexture) Generalizes application of gray-level co-occurrence matrix (GLCM) metrics to objects outside of images. The current focus is to apply GLCM metrics to the study of biological networks and fitness landscapes that are used in studying evolutionary medicine and biology, particularly the evolution of cancer resistance. The package was developed as part of the author's publication in Physics in Medicine and Biology Barker-Clarke et al. (2023) . A general reference to learn more about mathematical oncology can be found at Rockne et al. (2019) . Package: r-cran-gtfs2emis Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2953 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-furrr, r-cran-future, r-cran-gtfs2gps, r-cran-sf, r-cran-sfheaders, r-cran-terra, r-cran-units, r-cran-parallelly Suggests: r-cran-gtfstools, r-cran-ggplot2, r-cran-knitr, r-cran-lwgeom, r-cran-progressr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gtfs2emis_0.1.2-1.ca2404.1_all.deb Size: 2490160 MD5sum: c5e3ca68ea7a896b2731120dc661d4fa SHA1: 3548c9755e0fe15b66611e84853b0a2bcca5904d SHA256: 0e8ad8095b8c4b33b96c87b25de5528ef3afe5a2bf6ce964d281cbf5c5cbb7f4 SHA512: 5241637a26795313dcc8331f35559a029ab07f5ee334e05112a0ff43aa966cab6a69e28455cab917257575e9e31687ca690f68642c9ad7822183901de1c89856 Homepage: https://cran.r-project.org/package=gtfs2emis Description: CRAN Package 'gtfs2emis' (Estimating Public Transport Emissions from General Transit FeedSpecification (GTFS) Data) A bottom up model to estimate the emission levels of public transport systems based on General Transit Feed Specification (GTFS) data. The package requires two main inputs: i) Public transport data in the GTFS standard format; and ii) Some basic information on fleet characteristics such as fleet age, technology, fuel and Euro stage. As it stands, the package estimates several pollutants at high spatial and temporal resolutions. Pollution levels can be calculated for specific transport routes, trips, time of the day or for the transport system as a whole. The output with emission estimates can be extracted in different formats, supporting analysis on how emission levels vary across space, time and by fleet characteristics. A full description of the methods used in the 'gtfs2emis' model is presented in Vieira, J. P. B.; Pereira, R. H. M.; Andrade, P. R. (2022) . Package: r-cran-gtfsio Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-fs, r-cran-zip, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-gtfsio_1.2.1-1.ca2404.1_all.deb Size: 358674 MD5sum: 82a89245fea8e9f5fd3b572e7168cf3a SHA1: 38d07345195723f50128841d96e39d05b771204c SHA256: be15c085d91f3eaa5cae423b7c241023e72d7552db6913dd891b87165b1b9946 SHA512: 14edde08a05717d11e95d303825d519d5e2eb325780b8acd52e51020a46234d861662797e8c03c9f062690fcc4074dd6d012f962e96ff7829f50d3f26f16549e Homepage: https://cran.r-project.org/package=gtfsio Description: CRAN Package 'gtfsio' (Read and Write General Transit Feed Specification (GTFS) Files) Tools for the development of packages related to General Transit Feed Specification (GTFS) files. Establishes a standard for representing GTFS feeds using R data types. Provides fast and flexible functions to read and write GTFS feeds while sticking to this standard. Defines a basic 'gtfs' class which is meant to be extended by packages that depend on it. And offers utility functions that support checking the structure of GTFS objects. Package: r-cran-gtfswizard Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4352 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-sf, r-cran-tidyr, r-cran-data.table, r-cran-shiny, r-cran-leaflet, r-cran-checkmate, r-cran-dplyr, r-cran-ggplot2, r-cran-gtfsio, r-cran-purrr, r-cran-rlang, r-cran-crayon, r-cran-forcats, r-cran-hrbrthemes, r-cran-stringr, r-cran-tibble, r-cran-plotly, r-cran-leaflet.extras, r-cran-geosphere, r-cran-stplanr, r-cran-glue, r-cran-hms, r-cran-sfnetworks, r-cran-gtfstools, r-cran-tidytransit, r-cran-igraph, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-gtfswizard_1.1.0-1.ca2404.1_all.deb Size: 4398700 MD5sum: 8dea36c1a29b39956088c3d152c1c57a SHA1: c368fe544085489d9dcdef64350f0abfed610fe9 SHA256: 5da94a5aec33676ab57ffe6156db2d67697a7e15e390bcd997f11ec1deedbbe2 SHA512: e185dc5a241678cb191fa86f6ea508308e5e41160f81d5e11039d72f5896a3742b915d86ef1b7505ec29ed06040cc1a7f963250c0d48e46c9984d4c7df9ab191 Homepage: https://cran.r-project.org/package=GTFSwizard Description: CRAN Package 'GTFSwizard' (Exploring and Manipulating 'GTFS' Files) Exploring, analyzing, and manipulating General Transit Feed Specification (GTFS) files, which represent public transportation schedules and geographic data. The package allows users to filter data by routes, trips, stops, and time, generate spatial visualizations, and perform detailed analyses of transit networks, including headway, dwell times, and route frequencies. Designed for transit planners, researchers, and data analysts, 'GTFSwizard' integrates functionalities from popular packages to enable efficient GTFS data manipulation and visualization. Package: r-cran-gtheory Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-gtheory_0.1.2-1.ca2404.1_all.deb Size: 43522 MD5sum: 4e34a3e607dfef94dbafef8607bc6f68 SHA1: 05441be76f1f460fce0480f1d4d1995b0b47f265 SHA256: 0f35dfec26d440c9d05e56828646e3a1c4e5fb6bdd5ac2fb04e43d55256b5a18 SHA512: c1cbaa52fc2b90a5d8f7526ddf36f3a9a99d45f190e1a4a15ab57b3259b14159aaf8cc1d882e97a06458e94bc4978e9d1b956206bd2d70cf7388750cd9ab6b84 Homepage: https://cran.r-project.org/package=gtheory Description: CRAN Package 'gtheory' (Apply Generalizability Theory with R) Estimates variance components, generalizability coefficients, universe scores, and standard errors when observed scores contain variation from one or more measurement facets (e.g., items and raters). Package: r-cran-gtheoryr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gtheoryr_0.1.0-1.ca2404.1_all.deb Size: 28924 MD5sum: b38e196c3f2138917f3e1cf7f1fa4ee6 SHA1: 961acdcd4bd10f17be8bc65791bbc4466de4c45f SHA256: 8291e8ac594191028c136c69c5c237899f52e228548e97e0409a7095bafea495 SHA512: bdb2934d5928d251ef2b34361340ff7b7dd919e672a98c387ab778f81fb880c4a0e5bf9b3c4541db104ade8d8a1594e353fe77ea71d321e05519fc5ebef41500 Homepage: https://cran.r-project.org/package=gtheoryr Description: CRAN Package 'gtheoryr' (Simple Generalizability Theory for Crossed and Nested Designs) Provides a small, beginner-friendly interface for estimating variance components in simple generalizability theory designs. The package currently supports a fully crossed persons-by-items design and a simple items-within-person nested design, along with design-study summaries for relative and absolute decisions. Package: r-cran-gto Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gt, r-cran-magrittr, r-cran-officer, r-cran-rlang, r-cran-xml2 Suggests: r-cran-testthat, r-cran-dplyr, r-cran-tibble, r-cran-covr Filename: pool/dists/noble/main/r-cran-gto_0.1.2-1.ca2404.1_all.deb Size: 283618 MD5sum: 71f9d19926aa002084e22a390a0f7129 SHA1: 4e6f671468cc39833175248678d06e06997742a9 SHA256: 446f25dda596eef7dba7bb8904476d95010b8770d8b0f451e761fedc5a3b2911 SHA512: 32197fc242088a1bb410dbdadc4bae7a64d67294dc56acb9e811498b52a34f19d004df77f92f1e1b73f41f89062f6f9d85b9b49df312bc4cb9c902dd7a04a419 Homepage: https://cran.r-project.org/package=gto Description: CRAN Package 'gto' (Insert 'gt' Tables into Word Documents) Insert tables created by the 'gt' R package into 'Microsoft Word' documents. This gives users the ability to add to their existing word documents the tables made in 'gt' using the familiar 'officer' package and syntax from the 'officeverse'. Package: r-cran-gtranslate Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-rvest Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gtranslate_0.0.1-1.ca2404.1_all.deb Size: 14304 MD5sum: 4fd04e422bd0927aab690e88f1c0f2b0 SHA1: 6700b93a59966a572a0047bafbab7f37b4019cef SHA256: 7cb8b46990467892c68a8031465f7581386697b6017153cd249e0860d84493da SHA512: af3e535f7fc604afabbcb3e299295fbf0b3d66faf1d58afa7f5d48224ab74adbb60fe04fcb4617c87e3b0465449b120e3bddd37194725cf900ce9b49dd2d7c4b Homepage: https://cran.r-project.org/package=gtranslate Description: CRAN Package 'gtranslate' (Translate Between Different Languages) The goal of this package is to translate between different languages without any Google API authentication which is pain and you must pay for the key, This package is free and lightweight. Package: r-cran-gtreg Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1467 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-glue, r-cran-gtsummary, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gtreg_0.4.2-1.ca2404.1_all.deb Size: 1294628 MD5sum: 24dac04e20052c08ca02a142f62e5b7d SHA1: 97a07f27053c4e2390431e80c9d26fdf4f40f33a SHA256: bcd988462047c8e4be8b32e12794a14bf104a5b566b4cdd4ff39c00786cbdb6d SHA512: 02f4fea657e39c35359f5673480752ad09c51ec7e54b4be243b038c748d0429fabdf501516ecb2e8bd63c0765a42a03b5599044a0cb57b1b8abf7c1d8c6ce654 Homepage: https://cran.r-project.org/package=gtreg Description: CRAN Package 'gtreg' (Regulatory Tables for Clinical Research) Creates tables suitable for regulatory agency submission by leveraging the 'gtsummary' package as the back end. 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Designed to be accessible for researchers, particularly those in Low- and Middle-Income Countries (LMIC). Built upon widely accepted statistical methods, including logistic regression (Hosmer et al. 2013, ISBN:9781118548429), log-binomial regression (Spiegelman and Hertzmark 2005 ), Poisson and robust Poisson regression (Zou 2004 ), negative binomial regression (Hilbe 2011, ISBN:9780521179515), and linear regression (Kutner et al. 2005, ISBN:9780071122214). Leverages multiple dependencies to ensure high-quality output and generate reproducible, publication-ready tables in alignment with best practices in epidemiology and applied statistics. Package: r-cran-gtrendshealth Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-httr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-gtrendshealth_1.0.0-1.ca2404.1_all.deb Size: 40722 MD5sum: b133483f2928504d48ca8a2290eb70cc SHA1: 04a2ad395d72c0a5a384cbc12096ebef503c3d24 SHA256: f9a8b19535d4ea24c9752b58e3fc9d12d220166aa6296797ea2b39da4900f140 SHA512: f4231507c0ad415f167b08dd11d4426b6845dec30edecd1006c59f6eeea3f384c961a85ad593df3e0aa38604053eb635b562e14b74889141c74c8b57e4c9ef9f Homepage: https://cran.r-project.org/package=gtrendshealth Description: CRAN Package 'gtrendshealth' (Query the 'Google Trends for Health' API) Connects to the 'Google Trends for Health' API hosted at , allowing projects authorized to use the health research data to query 'Google Trends'. Package: r-cran-gtrendsr Architecture: all Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-jsonlite, r-cran-anytime, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-gtrendsr_1.5.2-1.ca2404.1_all.deb Size: 1032250 MD5sum: ba810ab753eac416a668350419d07795 SHA1: 178053418eaa17272f0ac01294d5746b2410f7f0 SHA256: cf8a1ec54fd571c6669baa39ef2d1d9a7ae3e38b20e4b776250c95aaee3414e4 SHA512: 70543a65d18e05c90b14b5df3bcdf48f1db79ce544f3e240bdacc0339ac380a99b2fe6e76c672ff25b95b2c7c3e3d2008b21587b5e2d5dece6b882e6556dd5f5 Homepage: https://cran.r-project.org/package=gtrendsR Description: CRAN Package 'gtrendsR' (Perform and Display Google Trends Queries) An interface for retrieving and displaying the information returned online by Google Trends is provided. Trends (number of hits) over the time as well as geographic representation of the results can be displayed. 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Given aggregated GWAS dataset(s) and a user-defined significance threshold, the package retrieves significant SNPs from the GWAS Catalog and the Experimental Factor Ontology (EFO), annotates their gene context, and can write a harmonised metadata table in comma-separated values (CSV) format, genomic intervals in the Browser Extensible Data (BED) format, and sequences in the FASTA (text-based sequence) format with user-defined flanking regions for clustered regularly interspaced short palindromic repeats (CRISPR) guide design. For details on the resources and methods see: Buniello et al. (2019) ; Sollis et al. (2023) ; Jinek et al. (2012) ; Malone et al. (2010) ; Experimental Factor Ontology (EFO) . Package: r-cran-gwasbycluster Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase, r-bioc-snpstats, r-cran-rootsolve, r-bioc-limma Filename: pool/dists/noble/main/r-cran-gwasbycluster_0.1.7-1.ca2404.1_all.deb Size: 257878 MD5sum: a4798c950550472339f0725765143e3e SHA1: bbd55c1d568c701156ab5e5bceb2610fe77480cc SHA256: 01a93b9cae9e5f8f292b883486adb8d8004268300bcdf6cea3db4d12964cead6 SHA512: f14ad01a34fe715e60e1953f4fbc86899e9a1b4032a73ff0d1f3e1828f12ce2263302243f12f26f90d06c009fd12f597ca60e129b72b654cbc4fff9cdc18463d Homepage: https://cran.r-project.org/package=GWASbyCluster Description: CRAN Package 'GWASbyCluster' (Identifying Significant SNPs in Genome Wide Association Studies(GWAS) via Clustering) Identifying disease-associated significant SNPs using clustering approach. This package is implementation of method proposed in Xu et al (2019) . Package: r-cran-gwasforest Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-glue Filename: pool/dists/noble/main/r-cran-gwasforest_1.0.0-1.ca2404.1_all.deb Size: 287024 MD5sum: 37576da7c8588cd1248e9d1bb985bc7f SHA1: bdfae0074280d9d59ed8dad40becad4364a04fe1 SHA256: 18165f4f1af83a6d41b0ceb3fef93bb6b8e9b18e94bdde0ab79a6efe86e7511f SHA512: 6d7c9f11c94a94ccb3f33b382718194cf373815e54ee2a2777604d49fb636e1e8b3288e33d250e49ac586c32dca00b42683560acbc4e03343c338f41ba6b4dd7 Homepage: https://cran.r-project.org/package=gwasforest Description: CRAN Package 'gwasforest' (Make Forest Plot with GWAS Data) Extract and reform data from GWAS (genome-wide association study) results, and then make a single integrated forest plot containing multiple windows of which each shows the result of individual SNPs (or other items of interest). Package: r-cran-gwasinspector Architecture: all Version: 1.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ini, r-cran-logger, r-cran-data.table, r-cran-hash, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-rsqlite, r-cran-kableextra, r-cran-r.utils, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-gwasinspector_1.7.4-1.ca2404.1_all.deb Size: 1094940 MD5sum: 50ac6a862f920316bc3172693b35e46c SHA1: 2faad80c4c7f237402c69a550b047290fd9e07dd SHA256: e7ac3236dcdde9688a9e0591a6517e62d59ab504e27bcbe11a9193df9c8c4235 SHA512: 4b04df9fe58b6280e5cfd6d942470cb92834d393c399508e06440e3ff315da72d25200a37c7467317f1789c4494309dc11aa246ff3f2eea60f33f03900131724 Homepage: https://cran.r-project.org/package=GWASinspector Description: CRAN Package 'GWASinspector' (Comprehensive and Easy to Use Quality Control of GWAS Results) When evaluating the results of a genome-wide association study (GWAS), it is important to perform a quality control to ensure that the results are valid, complete, correctly formatted, and, in case of meta-analysis, consistent with other studies that have applied the same analysis. This package was developed to facilitate and streamline this process and provide the user with a comprehensive report. 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This adjustment can occur globally, with the same estimate for the entire study space, or locally, where a beta regression model is fitted for each region, considering only influential locations for that area. Da Silva, A. R. and Lima, A. O. (2017) . 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Package: r-cran-gwrlasso Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qpdf, r-cran-numbers, r-cran-glmnet, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-gwrlasso_0.1.0-1.ca2404.1_all.deb Size: 37932 MD5sum: 57df024c71e0a438765c407d3a58e13a SHA1: 6e397f9516ccf0cf6275a5cedee710db9bf4b720 SHA256: a50e946c217fed36e1a0316547b828a07e90da47bd5daa9ce1baf4baa76a479c SHA512: c6cb365d443400c8e9b411e0c6e90daf19d42a89bd534a0e7356ec815ef8a185aa64858912aa9c576c37361f32e93141abaee7972bdaea9a5f0c9657a165d9d3 Homepage: https://cran.r-project.org/package=GWRLASSO Description: CRAN Package 'GWRLASSO' (A Hybrid Model for Spatial Prediction Through Local Regression) It implements a hybrid spatial model for improved spatial prediction by combining the variable selection capability of LASSO (Least Absolute Shrinkage and Selection Operator) with the Geographically Weighted Regression (GWR) model that captures the spatially varying relationship efficiently. For method details see, Wheeler, D.C.(2009).. The developed hybrid model efficiently selects the relevant variables by using LASSO as the first step; these selected variables are then incorporated into the GWR framework, allowing the estimation of spatially varying regression coefficients at unknown locations and finally predicting the values of the response variable at unknown test locations while taking into account the spatial heterogeneity of the data. Integrating the LASSO and GWR models enhances prediction accuracy by considering spatial heterogeneity and capturing the local relationships between the predictors and the response variable. The developed hybrid spatial model can be useful for spatial modeling, especially in scenarios involving complex spatial patterns and large datasets with multiple predictor variables. 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See Wheeler (2009) and Wheeler (2007) for more details. 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Package: r-cran-gwsignif Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gwsignif_1.2.1-1.ca2404.1_all.deb Size: 22264 MD5sum: 3a4d7160071ab3b2539e53a232e5378b SHA1: a636d78365ec16c9c4679c3561f44a101b0484ae SHA256: 04c9bdde2a661f97ff3f23ff5fb5465b7b5b9d14cd7ff99feb3f0995cafc2a0c SHA512: aa215af76b47261301b61197b014f3f12fb3b7bdc2a84c762184a57bd25fcc4495ae7429a7fcc3338c46920f78137a8cc564e9d9a73c9c238304c9ab61936871 Homepage: https://cran.r-project.org/package=GWsignif Description: CRAN Package 'GWsignif' (Estimating Genome-Wide Significance for Whole Genome SequencingStudies, Either Single SNP Tests or Region-Based Tests) The correlations and linkage disequilibrium between tests can vary as a function of minor allele frequency thresholds used to filter variants, and also varies with different choices of test statistic for region-based tests. Appropriate genome-wide significance thresholds can be estimated empirically through permutation on only a small proportion of the whole genome. Package: r-cran-gwzinbr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp Filename: pool/dists/noble/main/r-cran-gwzinbr_0.1.0-1.ca2404.1_all.deb Size: 157964 MD5sum: 57264cc6cd63f730a2c5c200ed3bf23b SHA1: 9cfd798ae261f60672358a310b40758dec76670a SHA256: 3570933196d730d06f532f0fe89c44d9032b9b91a6423aeb02e771bc6d99d5ab SHA512: 3655c73e49f2f4761e6ac99db187842905ef6b8023a825a4983591073179303be241d1c4ae467eb5aeab160199836f302decb3c333ea0b558ad1e17811ac3256 Homepage: https://cran.r-project.org/package=gwzinbr Description: CRAN Package 'gwzinbr' (Geographically Weighted Zero Inflated Negative BinomialRegression) Fits a geographically weighted regression model using zero inflated probability distributions. Has the zero inflated negative binomial distribution (zinb) as default, but also accepts the zero inflated Poisson (zip), negative binomial (negbin) and Poisson distributions. Can also fit the global versions of each regression model. Da Silva, A. R. & De Sousa, M. D. R. (2023). "Geographically weighted zero-inflated negative binomial regression: A general case for count data", Spatial Statistics . Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). "Geographically weighted regression: a method for exploring spatial nonstationarity", Geographical Analysis, . Yau, K. K. W., Wang, K., & Lee, A. H. (2003). "Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros", Biometrical Journal, . Package: r-cran-gxeprs Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1414 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-gxeprs_1.2-1.ca2404.1_all.deb Size: 991732 MD5sum: a0db5619c9a4c02bee6770a98bd9b7f0 SHA1: 5295435026beecb26cef5630f0a9a1e321df9986 SHA256: 24bbc571bf903cda1386e770d0e3988f66ea959ded035713c08105b8956b5d59 SHA512: 1757dcf81c773446b1a2e6cfb233eb9aa51962de0ad4d3d339f2aee86f99016debae10e90b7ab880364992efd2d37b98ccfc13ec0ee4ca38f4976a92c4b7446e Homepage: https://cran.r-project.org/package=GxEprs Description: CRAN Package 'GxEprs' (Genotype-by-Environment Interaction in Polygenic Score Models) A novel PRS model is introduced to enhance the prediction accuracy by utilising GxE effects. This package performs Genome Wide Association Studies (GWAS) and Genome Wide Environment Interaction Studies (GWEIS) using a discovery dataset. The package has the ability to obtain polygenic risk scores (PRSs) for a target sample. Finally it predicts the risk values of each individual in the target sample. Users have the choice of using existing models (Li et al., 2015) , (Pandis et al., 2013) , (Peyrot et al., 2018) and (Song et al., 2022) , as well as newly proposed models for genomic risk prediction (refer to the URL for more details). Package: r-cran-gxescanr Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lsreg, r-cran-binarydosage Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-gxescanr_3.0.0-1.ca2404.1_all.deb Size: 142682 MD5sum: 548500300c9a0ea7c022322a43642c52 SHA1: c1fc9551c7fd943814d2a9b54d388367022e135f SHA256: 50f2c10b6ac3722daccb7ef10e8d5d21a854e151bec7e936ca9b1c88be5d3462 SHA512: dc6128bb74c5090d707de7870afa4643e2940c7a93816a42ada96ebb62d0d3a27efcb36cbc5db371f407888cd63720b0757e5546e468c3601537cbfcfff3d26b Homepage: https://cran.r-project.org/package=GxEScanR Description: CRAN Package 'GxEScanR' (Run GWAS/GWEIS Scans Using Binary Dosage Files) Tools to run genome-wide association study (GWAS) and genome-wide by environment interaction study (GWEIS) scans using the genetic data stored in a binary dosage file. 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Package: r-cran-haarfisz Architecture: all Version: 4.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavethresh Filename: pool/dists/noble/main/r-cran-haarfisz_4.5.4-1.ca2404.1_all.deb Size: 54478 MD5sum: 1cde8712b152c27009e5482dc9f0d029 SHA1: 3c25a259b0908a1f55b713f1ced0fd5a101e5a15 SHA256: 70ca7248987507c582b47f91ac0948814d48e698ade2213dbe859f6c3957cde2 SHA512: ae7eceb02c115862fd273a7b02275125df1430b187367b993095b1430a913761ceccdfd6a0a182e2abdb1f8a4a7002ec178503fe2563f2c7efb99861ee1dab75 Homepage: https://cran.r-project.org/package=haarfisz Description: CRAN Package 'haarfisz' (Software to Perform Haar Fisz Transforms) A Haar-Fisz algorithm for Poisson intensity estimation. Will denoise Poisson distributed sequences where underlying intensity is not constant. Uses the multiscale variance-stabilization method called the Haar-Fisz transform. Contains functions to carry out the forward and inverse Haar-Fisz transform and denoising on near-Gaussian sequences. Can also carry out cycle-spinning. Main reference: Fryzlewicz, P. and Nason, G.P. (2004) "A Haar-Fisz algorithm for Poisson intensity estimation." Journal of Computational and Graphical Statistics, 13, 621-638. . 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(2008) , the "single sample GSEA" of Barbie et al. (2009) and the "singscore" of Foroutan et al. (2018) . The 'get_sig_info()' function can be used to retrieve information about each signature implemented. Package: r-cran-hadamardr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numbers, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-hadamardr_1.0.0-1.ca2404.1_all.deb Size: 235778 MD5sum: a5ed1126f1c834d38ad24e581d672e59 SHA1: bb991c76796a7c38d743d8ee07a4eb73149d0246 SHA256: eb519c0234db482e20b612e19fe6271685c76b7166c6f6f2333a426f9936f663 SHA512: ebd735a6da102d5dfc287e8d77169b9caf4002e58be07640abcf8160e807a1354462b617c15dc5108492d790ed3fbf8008922826f6da7305f17e68f8397c40ad Homepage: https://cran.r-project.org/package=HadamardR Description: CRAN Package 'HadamardR' (Hadamard Matrix Generation) Generates Hadamard matrices using different construction methods. For those who want to generate Hadamard matrix, a generic function, Hadamard_matrix() is provided. For those who want to generate Hadamard matrix using a particular method, separate functions are available. See Horadam (2007, ISBN:9780691119212) Hadamard Matrices and their applications, Princeton University Press for more information on Hadamard Matrices. 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'HaDeX2' introduces a new standardized and reproducible workflow for the analysis of the HDX-MS data, including uncertainty propagation, data aggregation and visualization on 3D structure. Additionally, it covers data exploration, quality control and generation of publication-quality figures. 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'HaDeX' introduces a new standardized and reproducible workflow for the analysis of the HDX-MS data, including novel uncertainty intervals. Additionally, it covers data exploration, quality control and generation of publication-quality figures. All functionalities are also available in the in-built 'Shiny' app. Package: r-cran-hadibds Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hadibds_1.0.1-1.ca2404.1_all.deb Size: 19036 MD5sum: 8ff2499853e492df01c2982450a8d00c SHA1: 1905f01fcd31d435e9ba5785cae540124fc931c6 SHA256: 012b276655dabd56e37e7bc0ab77df043953da4f8d6ef3e2d8f875f7a01f05b7 SHA512: 42f27c431bb1d4699df75de27b13424dbecb42badc76c86ac2330d109fd2be24368f0251f0bc6856dceecb8fbaaf9cfaabc03a5d551e77864dff2c76b5d76bcf Homepage: https://cran.r-project.org/package=HadIBDs Description: CRAN Package 'HadIBDs' (Incomplete Block Designs using Hadamard Matrix (HadIBDs)) Hadamard matrix based statistical designs are of immense importance as the resultant designs carry various desirable characterizing properties. Constructing Partially Balanced Incomplete Block Designs (PBIBds) using Kronecker product of incidence matrices of Balanced Incomplete Block (BIB) and Partially Balanced Incomplete Block (PBIB) designs is much evident from literature. Here, we have constructed Incomplete Block Designs (IBDs) based on Hadamard matrices and Kronecker product of Hadamard matrices. Package: r-cran-hagis Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1648 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-pander Suggests: r-cran-ape, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-testthat, r-cran-vdiffr, r-cran-vegan Filename: pool/dists/noble/main/r-cran-hagis_4.0.0-1.ca2404.1_all.deb Size: 726438 MD5sum: 3ee8dab679bb34a4c340c470951ffa86 SHA1: 455a2569ab546ddd65a81fb306433603fa75337d SHA256: e10ad8f67e0357135597760a5b187c582706d7a6fb5b1d6589c5e947422b851d SHA512: ef3f933b4b7c60dec86d0524149a8f7f92c0d0266c343eee07c1325f6a39adf76206f1549fe5733f654cfad100b72e47edb85ffebf2051c1abde2206647da9a9 Homepage: https://cran.r-project.org/package=hagis Description: CRAN Package 'hagis' (Analysis of Plant Pathogen Pathotype Complexities, Distributionsand Diversity) Analysis of plant pathogen pathotype survey data. Functions provided calculate distribution of susceptibilities, distribution of complexities with statistics, pathotype frequency distribution, as well as diversity indices for pathotypes. This package is meant to be a direct replacement for Herrmann, Löwer and Schachtel's (1999) Habgood-Gilmour Spreadsheet, 'HaGiS', previously used for pathotype analysis. 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Calculates regression analyses for standard ordinary least squares (OLS or linear) or logistic models. Performs regression models used for causal modeling such as differences-in-differences (DID) and interrupted time series (ITS) models. Provides limited interpretations of model results and a ranking of variable importance in models. Performs propensity score models, top-coding of model outcome variables, and can return new data with the newly formed variables. Conducts Bayesian analysis summaries and graphs, decision curve analysis, and produces some Shewhart control charts. Also performs Cronbach's alpha for various scale items (e.g., survey questions). See Github URL for examples in the README file. For more details on the statistical methods, see Allen & Yen (1979, ISBN:0-8185-0283-5), Angrist & Pischke (2009, ISBN:9780691120355), Cohen (1988, ISBN:0-8058-0283-5), Gebski (2012) , Gelman & Goodrich (2019) , Harrell (2016, ISBN:978-3-319-19424-0), Kline (1999, ISBN:9780415211581), Kruschke (2014, ISBN:9780124058880), Linden (2015) , Merlo (2006) , Muthen & Satorra (1995) , Rabe-Hesketh & Skrondal (2008, ISBN:978-1-59718-040-5), Ryan (2011, ISBN:978-0-470-59074-4), and Vickers & Elkin (2006) . 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Package: r-cran-hansard Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-dplyr, r-cran-tibble, r-cran-lubridate, r-cran-tidyr, r-cran-snakecase Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-hansard_0.8.0-1.ca2404.1_all.deb Size: 380330 MD5sum: 9dd7a8cc51fdba48cf57b5ad8898b4a8 SHA1: 1d5b0d7de299688399e89cc27c066118a4034990 SHA256: 7ace22ebdb4786a416f2d4749b26cf0c1b550bc34adee77e4432bdbdcf2ba5e7 SHA512: f9ded2a29ad73238bcb162fa084194297a972c8698a638ffa9917ad4cd1eecf7b689bfe2304e032510869c10e7626788315e71d3da116b7ae6b33cc3ca62409e Homepage: https://cran.r-project.org/package=hansard Description: CRAN Package 'hansard' (Provides Easy Downloading Capabilities for the UK Parliament API) Provides functions to download data from the APIs. Because of the structure of the API, there is a named function for each type of available data for ease of use, as well as some functions designed to retrieve specific pieces of commonly used data. Functions for each new API will be added as and when they become available. Package: r-cran-hanstat Architecture: all Version: 0.90.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-car, r-cran-crayon, r-cran-ggplot2, r-cran-lmtest, r-cran-olsrr, r-cran-ggpubr, r-cran-devtools Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hanstat_0.90.0-1.ca2404.1_all.deb Size: 31846 MD5sum: 67864522c025332986c10d692ae22874 SHA1: c11bbc59d83f681f09d6664235f69cf56a1b9a5b SHA256: 11562b06e4164edf5bedb5c2191ed5ee9b6a1f7582a7cab8a779718220c109fe SHA512: c9b577da92d10060e5bc816963c68d98b9aea087cdc3a3acf281d3a188194b89314ba1dcc79cafea0049adee16e3762e7b8206db35a033ad208018ee736c42b2 Homepage: https://cran.r-project.org/package=HanStat Description: CRAN Package 'HanStat' (Package for Easy Interpretation of Statistical Methods) A simple and time saving multiple linear regression function (OLS) with interpretation, optional bootstrapping, effect size calculation and all tested requirements. Package: r-cran-hanyupinyin Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1371 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-stringi Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hanyupinyin_0.1.3-1.ca2404.1_all.deb Size: 1130610 MD5sum: 2a49a7d9b1bcbae4ed1f8b54da231035 SHA1: be89503ababcd2caaf1362467e285692ea6d2318 SHA256: 5a4ce006b3ec6b9b6c0d742e43c36c5862d3037358b9acd714f4e13174176f07 SHA512: 151d1f61c76bbd8eba2365587761f7d911fb7df8268c152afa6340c18643255912d642089adb597aad3899fdda7b5f05836d4c40ab9d8433f3d917f9a3018620 Homepage: https://cran.r-project.org/package=hanyupinyin Description: CRAN Package 'hanyupinyin' (Convert Chinese Characters into Hanyu Pinyin) Convert Chinese characters into Hanyu Pinyin (the official romanization system for Standard Chinese) with support for tones, toneless output, initials, URL slugs, and valid R variable names. The package was inspired by the now-orphaned CRAN package 'pinyin' (archived in April 2026 after the maintainer became unreachable). 'hanyupinyin' is a ground-up rewrite using the authoritative Unicode Unihan database, a vectorized engine, and modern R practices. Dictionary data are derived from the Unicode Unihan Database (Unicode Consortium, 2025) . Package: r-cran-hapi Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 919 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmm, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hapi_0.0.3-1.ca2404.1_all.deb Size: 730982 MD5sum: 943cbfb330f72f01e73b79022658e264 SHA1: 5ddd0cf963b91f525f68ef25bd74642e2f7c322b SHA256: d81b3c66fbe9374b5e86cf069ca9762b62cf6c09487260cd48d24ae9e3b643f2 SHA512: b48867ad8accf5e0e27aaa582279e5a249a7dedd05fc0b0a5c724d9b49055e480116eb75a1c5701d5bd164157b160afb3e71c7d79e6b92bcbe28f049954a7207 Homepage: https://cran.r-project.org/package=Hapi Description: CRAN Package 'Hapi' (Inference of Chromosome-Length Haplotypes Using Genomic Data ofSingle Gamete Cells) Inference of chromosome-length haplotypes using a few haploid gametes of an individual. The gamete genotype data may be generated from various platforms including genotyping arrays and sequencing even with low-coverage. Hapi simply takes genotype data of known hetSNPs in single gamete cells as input and report the high-resolution haplotypes as well as confidence of each phased hetSNPs. The package also includes a module allowing downstream analyses and visualization of identified crossovers in the gametes. Package: r-cran-haplo.ccs Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-haplo.stats, r-cran-survival Filename: pool/dists/noble/main/r-cran-haplo.ccs_1.3.3-1.ca2404.1_all.deb Size: 67690 MD5sum: 4a6eb8841dc1066c6748651219677441 SHA1: 203839d8d258c6776def3fc3a5e685fc49a9e745 SHA256: 8487f60a02eb4896470c23c65d3f4467c37f1011ffe68aa141d1f0765e2149aa SHA512: b48efcd6c241533fe0946729cd4cf725eabca5dbf05dabb7e286566f4f16e42ebf08e1bbac650794e7c868ea94c8e695de466d67d61915498bfb08b0516188d9 Homepage: https://cran.r-project.org/package=haplo.ccs Description: CRAN Package 'haplo.ccs' (Estimate Haplotype Relative Risks in Case-Control Data) Haplotype and covariate relative risks in case-control data are estimated by weighted logistic regression. Diplotype probabilities, which are estimated by EM computation with progressive insertion of loci, are utilized as weights. French et al. (2006) . Package: r-cran-haplocatcher Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-caret, r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-lattice, r-cran-randomforest Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-haplocatcher_1.0.4-1.ca2404.1_all.deb Size: 936982 MD5sum: bee5ae832df4a6fc63a72079d952d5f7 SHA1: 6ec9f6139de5ae1cf026b5360642acd0df4ff88e SHA256: dfa3accb76d21d814a70691cd6541979737baf28358bbbfae65d43eb7a0332c3 SHA512: 5c73189fc331ee1f11af1297d50c5936a932adb402abceea141eccd9e335dbc08f99ce10eed367c4832f3755c8ddbd01917d47907f04ea0c388e375c597a2cc3 Homepage: https://cran.r-project.org/package=HaploCatcher Description: CRAN Package 'HaploCatcher' (A Predictive Haplotyping Package) Used for predicting a genotype’s allelic state at a specific locus/QTL/gene. This is accomplished by using both a genotype matrix and a separate file which has categorizations about loci/QTL/genes of interest for the individuals in the genotypic matrix. A training population can be created from a panel of individuals who have been previously screened for specific loci/QTL/genes, and this previous screening could be summarized into a category. Using the categorization of individuals which have been genotyped using a genome wide marker platform, a model can be trained to predict what category (haplotype) an individual belongs in based on their genetic sequence in the region associated with the locus/QTL/gene. These trained models can then be used to predict the haplotype of a locus/QTL/gene for individuals which have been genotyped with a genome wide platform yet not genotyped for the specific locus/QTL/gene. This package is based off work done by Winn et al 2021. For more specific information on this method, refer to . Package: r-cran-haplodiploidequilibrium Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-vcfr, r-cran-data.table, r-cran-matrixstats Filename: pool/dists/noble/main/r-cran-haplodiploidequilibrium_0.1.0-1.ca2404.1_all.deb Size: 73446 MD5sum: af8ac3e1882cf934990497ca2a8f305f SHA1: a43a4f1420025023ade8461819974ed0a85d8c5f SHA256: fe8b2b08f6c769096a12c445df96a4c0ea55bac20dac3f2cbcc23fede0f261de SHA512: c8a54d4f13b1b1a60460051e4dafaba25689c03803ab9ac8cb16adc9e569363441e0592f119f3385a3e688420cc2f94a02041d4db97c6f11b1e44fba6e61d36d Homepage: https://cran.r-project.org/package=HaploDiploidEquilibrium Description: CRAN Package 'HaploDiploidEquilibrium' (Calculate F Statistics Using Mixed Haploid and Diploid OrganismData) Provides functions to estimate population genetics summary statistics from haplo-diploid systems, where one sex is haploid and the other diploid (e.g. Hymenoptera insects). It implements a theoretical model assuming equal sex ratio, random mating, no selection, no mutation, and no gene flow, deriving expected genotype frequencies for both sexes under these equilibrium conditions. The package includes windowed calculations (operating over genomic sliding windows from VCF input) for allele and genotype frequencies, the inbreeding coefficient (Fis), pairwise Fst, Nei's H (gene diversity), Watterson's Theta, and sex-specific reference allele frequencies. Most statistics are agnostic to ploidy, allowing the package to be applied to both strictly haplo-diploid and fully diploid systems. Package: r-cran-haplor Architecture: all Version: 4.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 851 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-xml, r-cran-tibble, r-cran-runit, r-cran-plyr, r-cran-dt, r-cran-rcurl, r-cran-rjsonio Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-haplor_4.0.7-1.ca2404.1_all.deb Size: 302874 MD5sum: 9c34ae236149c61a67ddb8419d8f316b SHA1: 232cfba58bf8e67aa0a8d8a74fbfd5642344dc16 SHA256: 7e930967aacb0f360575d9b4664295b6fcae4a1b5832efa154406d81851c1d3e SHA512: 3efe694685bfe80de13a4f0fb00d326ac777d5194ea2de515fd8af800bd609555f54a312392b8e71310decbf6c1660853da46db9b6dbc91c753c4881fcdc928e Homepage: https://cran.r-project.org/package=haploR Description: CRAN Package 'haploR' (Query 'HaploReg', 'RegulomeDB') A set of utilities for querying 'HaploReg' , 'RegulomeDB' web-based tools. The package connects to 'HaploReg', 'RegulomeDB' searches and downloads results, without opening web pages, directly from R environment. Results are stored in a data frame that can be directly used in various kinds of downstream analyses. Package: r-cran-haplosim Architecture: all Version: 1.8.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-pedigree Filename: pool/dists/noble/main/r-cran-haplosim_1.8.4.2-1.ca2404.1_all.deb Size: 203816 MD5sum: 63588191427c2739d76953571525ea2e SHA1: f777c9c7a318ca7f26a48fa9eaf96e175e7f4bfa SHA256: 35a461eb444998ece7b7a12a1be69366cc2d7cfeb3508f4bb1a50150908a845d SHA512: fc801054900d7b3a5c6d51830e2dae192de0b8f7e62f277076e2770d7ed12c1f1827acb458578058b1886e25a92f65c689945763cca32cdd79f05e1ca133cc13 Homepage: https://cran.r-project.org/package=HaploSim Description: CRAN Package 'HaploSim' (Functions to Simulate Haplotypes) Simulate haplotypes through meioses. Allows specification of population parameters. Package: r-cran-haplotyper Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-haplotyper_0.1-1.ca2404.1_all.deb Size: 46016 MD5sum: fc313fa964c736f14c28847c030e63e0 SHA1: ebba8fd11cb9b4a4973d8d19592defd73a3559f9 SHA256: 9dda2ffeba890691ef1aa9c058a2db6e6197716d5ccdcca6ed9535e697f27e38 SHA512: 72c32fadcdcb2437f232c87b31dde3e0527fe92a6eec656e69560cd507bcb39eb04fad93accf3f3be0b31f76165b0779a017cd5730eb440162872fbd3e68a48e Homepage: https://cran.r-project.org/package=haplotyper Description: CRAN Package 'haplotyper' (Tool for Clustering Genotypes in Haplotypes) Function to identify haplotypes within QTL (Quantitative Trait Loci). One haplotype is a combination of SNP (Single Nucleotide Polymorphisms) within the QTL. This function groups together all individuals of a population with the same haplotype. Each group contains individual with the same allele in each SNP, whether or not missing data. Thus, haplotyper groups individuals, that to be imputed, have a non-zero probability of having the same alleles in the entire sequence of SNP's. Moreover, haplotyper calculates such probability from relative frequencies. Package: r-cran-haplotypes Architecture: all Version: 1.1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 893 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-sna, r-cran-ape, r-cran-phangorn, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-haplotypes_1.1.3.2-1.ca2404.1_all.deb Size: 577806 MD5sum: 4d3e222929130307c754307dd0174795 SHA1: ee5f0d9aba27002052a3c65efcdb6043a91b046d SHA256: 92d7a19644aaa5eccef15a45c0387d22631e4e54c15875744ec43bf035a28b29 SHA512: 6e307e8fdd73789ff14dc11390e46463026f2a343a0390a2117fe1879f5b5737aa32133fe65c84d19a0b5f3335c4a5b7894dd605fde14d659da99dc6b69ebfdf Homepage: https://cran.r-project.org/package=haplotypes Description: CRAN Package 'haplotypes' (Manipulating DNA Sequences and Estimating Unambiguous HaplotypeNetwork with Statistical Parsimony) Provides S4 classes and methods for reading and manipulating aligned DNA sequences, supporting an indel-coding method (only simple indel-coding method is available in the current version), showing base substitutions and indels, calculating absolute pairwise distances between DNA sequences, and collapsing identical DNA sequences into haplotypes or inferring haplotypes using user-provided absolute pairwise character difference matrix. This package also includes S4 classes and methods for estimating genealogical relationships among haplotypes using statistical parsimony and plotting parsimony networks. Package: r-cran-haplovar Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 995 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-magrittr, r-cran-dbscan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-haplovar_0.1.1-1.ca2404.1_all.deb Size: 935448 MD5sum: aac90f9b2d487e5b5fa749f184436323 SHA1: 2e256fc6588ee223900e5923bd4b93284b49c1e6 SHA256: 041cb9a8323733ad35d9ada512acba8732eff12e4ab1024efb881e2b6101e228 SHA512: cce2d14ef1ab2020690483f6db7e6e92ac1d315ef5e96652c3add72eac99294a1524c38791e3a13c90dad4a77f203059d2b1f61ae33fdb8767cee859a8ce2ff8 Homepage: https://cran.r-project.org/package=HaploVar Description: CRAN Package 'HaploVar' (Defining Local Haplotype Variants for Use in Trait Associationand Trait Prediction Analyses) A local haplotyping tool for use in trait association and trait prediction analyses pipelines. 'HaploVar' enables users take single nucleotide polymorphisms (SNPs) (in VCF format) and a linkage disequilibrium (LD) matrix, calculate local haplotypes and format the output to be compatible with a wide range of trait association and trait prediction tools. The local haplotypes are calculated from the LD matrix using a clustering algorithm called density-based spatial clustering of applications with noise ('DBSCAN') (Ester et al., 1996) . Package: r-cran-happign Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1838 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-httr2, r-cran-sf, r-cran-terra, r-cran-xml2 Suggests: r-cran-covr, r-cran-ggplot2, r-cran-httptest2, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tmap Filename: pool/dists/noble/main/r-cran-happign_0.3.8-1.ca2404.1_all.deb Size: 1538392 MD5sum: b49946bbc40ad8bbbb4c3df9b559e575 SHA1: 810fe1814afcda2aa306765ab77ddb16ccefec6b SHA256: b3ad0b1f5cfea3af18aaa82ad1efa16bbb1e1499ce1ae69fb3e26c08782c0dcf SHA512: 34133c796775a9189ab0405005b408db723734b38ee205145371936a4d48b944fcfab5de254c3c202fb54c364a920cffefd0bf54048ad6f3814b2d06b1c9a7bc Homepage: https://cran.r-project.org/package=happign Description: CRAN Package 'happign' (R Interface to 'IGN' Web Services) Automatic open data acquisition from resources of IGN ('Institut National de Information Geographique et forestiere') (). Available datasets include various types of raster and vector data, such as digital elevation models, state borders, spatial databases, cadastral parcels, and more. 'happign' also provide access to API Carto (). Package: r-cran-happytime Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-happytime_0.1.0-1.ca2404.1_all.deb Size: 28476 MD5sum: 0fd3822651cb4626ff0de4aa6b03ee3b SHA1: 16398d6900104b0db20b6fcaa85af5229f509f48 SHA256: 1688c62b77723546bfdde878eb3b3cbac1cf1b48c8647e5f6de9a05cb9ba57b3 SHA512: 765766cffe8a55e58d5a8f9cd701b3e6a9b259b27bda615f733b373db4bab29cc7ffe0fe18bc8f1c3d27e883ad2b586f3ae9c4fcb3fc72c0e8c2a0caa597da30 Homepage: https://cran.r-project.org/package=happytime Description: CRAN Package 'happytime' (Two Games to Relieve the Boredom) There are two interesting games in this package, one is 2048 games(for windows), using up and down to control the direction until there is a 2048 figure. And the other is 'what to eat today',preparing for people who choose difficulties, including most of the delicious Cantonese cuisine. Package: r-cran-harbinger Architecture: all Version: 2.0.757-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 676 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tspredit, r-cran-changepoint, r-cran-daltoolbox, r-cran-forecast, r-cran-ggplot2, r-cran-hht, r-cran-rcpphungarian, r-cran-dplyr, r-cran-dtwclust, r-cran-rugarch, r-cran-patchwork, r-cran-stringr, r-cran-strucchange, r-cran-tsmp, r-cran-wavelets, r-cran-zoo Suggests: r-cran-ocp Filename: pool/dists/noble/main/r-cran-harbinger_2.0.757-1.ca2404.1_all.deb Size: 562298 MD5sum: 870a2dd6fd7358148973f969e88d64c8 SHA1: 366a0372ed4b053543702b7527b86c39519b6071 SHA256: 92569f279315e82feb5301e4b89dd8a22f537a1ea9a54ef810f5795025513271 SHA512: 246d07928f26859970826024bbbaef321b6aac49aef585f26e0c2a0724db5e26687fc021b34cb4cf0cb37e52536fa5d004e1e6227c3c580395585eb9a730730f Homepage: https://cran.r-project.org/package=harbinger Description: CRAN Package 'harbinger' (A Unified Time Series Event Detection Framework) By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the nature of events is often unknown. This work presents Harbinger, a framework for integrating and analyzing event detection methods. Harbinger contains several state-of-the-art methods described in Salles et al. (2020) . Package: r-cran-hardhat Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1258 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-rlang, r-cran-sparsevctrs, r-cran-tibble, r-cran-vctrs Suggests: r-cran-covr, r-cran-crayon, r-cran-devtools, r-cran-knitr, r-cran-matrix, r-cran-modeldata, r-cran-recipes, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-usethis, r-cran-withr Filename: pool/dists/noble/main/r-cran-hardhat_1.4.3-1.ca2404.1_all.deb Size: 854190 MD5sum: c038822cbdff1971ad6af0f424c08618 SHA1: e145b75e9795a71094eeb3698da817f965c898ea SHA256: ec2967e23a31131802f7f2fbd131daadb6dcfc0ce1fb5f9d17b4538c4f5ab789 SHA512: ada9de6acf300c0f7d2b3096e614b2ff8ee8eed49047f42722ecf81799de6a94f031443c541d58d3ea02f5760fa800108d608a7db4b114c9fd8ced31ad0c7738 Homepage: https://cran.r-project.org/package=hardhat Description: CRAN Package 'hardhat' (Construct Modeling Packages) Building modeling packages is hard. A large amount of effort generally goes into providing an implementation for a new method that is efficient, fast, and correct, but often less emphasis is put on the user interface. A good interface requires specialized knowledge about S3 methods and formulas, which the average package developer might not have. The goal of 'hardhat' is to reduce the burden around building new modeling packages by providing functionality for preprocessing, predicting, and validating input. Package: r-cran-harmonicmeanp Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1439 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fmstable Suggests: r-cran-knitr, r-cran-ape, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-harmonicmeanp_3.0.1-1.ca2404.1_all.deb Size: 533302 MD5sum: 84e7d35a9005d4b13795622b31dd1856 SHA1: 2d89135acab4c6b588e00eae1aa21bafc830bb5a SHA256: 123e6795574120c8517d0434f93f32f513a828ecc2cc18b5bcfe45d5d1dd1dd8 SHA512: 3d9cacc5acca7717dbce1dcb64dc0eca19aff03aa00a0ecdd4d6aad49a3b4a735d6eae81e36be3dad53726e4e2d3a7f0b2937036a6f249a3a822b4545527799c Homepage: https://cran.r-project.org/package=harmonicmeanp Description: CRAN Package 'harmonicmeanp' (Harmonic Mean p-Values and Model Averaging by Mean MaximumLikelihood) The harmonic mean p-value (HMP) test combines p-values and corrects for multiple testing while controlling the strong-sense family-wise error rate. It is more powerful than common alternatives including Bonferroni and Simes procedures when combining large proportions of all the p-values, at the cost of slightly lower power when combining small proportions of all the p-values. It is more stringent than controlling the false discovery rate, and possesses theoretical robustness to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is certain to be significant and conversely when the superset is non-significant, the constituent tests are certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem. For detailed examples type vignette("harmonicmeanp") after installation. Version 3.0 addresses errors in versions 1.0 and 2.0 that led function p.hmp to control the familywise error rate only in the weak sense, rather than the strong sense as intended. Package: r-cran-harmonizer Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3326 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-harmonizer_0.3.2-1.ca2404.1_all.deb Size: 1692378 MD5sum: b0b6400eaf573e64ff8a91a7c50ae944 SHA1: 69906f47b1dd8982ca922b47d82adebfdfc99996 SHA256: 17e32b3ca6ba93c51c1b254415123d58823223d742da5022d53829c389b72032 SHA512: 292ce89fd6a83ddcbc52587fdd216b7fc92ba317a5046ebc6a4b6c31b7736badf95071ab1c01d675e09618419c629b3bdd302315a5e2b1db239b74a911705cac Homepage: https://cran.r-project.org/package=harmonizer Description: CRAN Package 'harmonizer' (Harmonizing CN8 and PC8 Product Codes) Several functions are provided to harmonize CN8 (Combined Nomenclature 8 digits) and PC8 (Production Communautaire 8 digits) product codes over time and the classification systems HS6 and BEC. Harmonization of CN8 codes are possible by default from 1995 to 2022 and of PC8 from 2001 to 2021, respectively. Package: r-cran-harmonydata Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-uuid, r-cran-base64enc, r-cran-jsonlite, r-cran-assertthat, r-cran-purrr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-harmonydata_0.3.1-1.ca2404.1_all.deb Size: 35172 MD5sum: 4b82781dba9d43ba3e6d0fe5b54288e8 SHA1: b72dd073a6f19ef085158e80dabcfc0f4c087f0e SHA256: 830553d6dbbad1e0b4f7796f27738f8557a6dcd621bbef262be174dda7acc94f SHA512: 2c140752a953d47c8dc8a80fc53a6d4c61758d14f52fad209928302264d723232365863d07b115835b63e096e9b789148f800b87290edd6062984a2a3052bc06 Homepage: https://cran.r-project.org/package=harmonydata Description: CRAN Package 'harmonydata' (R Library for 'Harmony') 'Harmony' is a tool using AI which allows you to compare items from questionnaires and identify similar content. You can try 'Harmony' at and you can read our blog at or at . Documentation at . Package: r-cran-harplus Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3299 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-openxlsx, r-cran-haven, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-harplus_1.2.0-1.ca2404.1_all.deb Size: 730932 MD5sum: feded0d4c55ea5744caec1a8d689e332 SHA1: d689576f614c8ac3d4937b2b0ad949bd16096728 SHA256: 2b6b4bb5cbf249a9b2c252c62617c0f740214f9c75047d346ab60229d356fc64 SHA512: 8750b46b7c221b8d19fcb650b5c5d5768020a7efd48dc3ecfd6659145992be70d8c3be6c4a9f16ce0e7b99c83db062316dd02fe6704167a4584e9c61871eef61 Homepage: https://cran.r-project.org/package=HARplus Description: CRAN Package 'HARplus' (Enhanced R Package for 'GEMPACK' .har and .sl4 Files) Provides tools for processing and analyzing .har and .sl4 files, making it easier for 'GEMPACK' users and 'GTAP' researchers to handle large economic datasets. It simplifies the management of multiple experiment results, enabling faster and more efficient comparisons without complexity. Users can extract, restructure, and merge data seamlessly, ensuring compatibility across different tools. The processed data can be exported and used in 'R', 'Stata', 'Python', 'Julia', or any software that supports Text, CSV, or 'Excel' formats. Package: r-cran-harr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi Filename: pool/dists/noble/main/r-cran-harr_1.1.0-1.ca2404.1_all.deb Size: 57736 MD5sum: fac6852f3d4c3b25105461cd5b694805 SHA1: 34675514b75a133e95ff30fce902faf68958df70 SHA256: 4c7448076fcd25a2917dfa9126aa4f810211c6ecfa0f83a49073feedb635c7a1 SHA512: 66be9b126f1c2fae5fce91063da3bcba9c0e665357e3b8896ebd90c6c2b8067abe72c7a5185eae4ef6eba23e29e08856833bcfe8793f8b84c4851d81656ad192 Homepage: https://cran.r-project.org/package=HARr Description: CRAN Package 'HARr' ('HAR' ('GEMPACK') File Read/Write Utility) HAR files are generated and consumed by 'GEMPACK' applications. This package reads/writes 'HAR' files (and 'SL4' files) directly using basic R functions. Package: r-cran-harrietr Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 779 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-bioc-ggtree, r-cran-magrittr, r-cran-lazyeval, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-harrietr_0.2.3-1.ca2404.1_all.deb Size: 368686 MD5sum: 3d4d7e4140787080beaff88147de2410 SHA1: 88fc626a0a4926e2f5dba0d1236bf7efafda728b SHA256: 821c266ae952e16c17747d67073404f6d20339df2d3ea2c865b5a4c851b80691 SHA512: 0910d964f8c88061f71eec84375f439b5f9638c2e95c7d6348cc68ce90c998c8e3732fdcfb6686965505176cb62510b3e26572bdfee3effcbc90f86c05311d2c Homepage: https://cran.r-project.org/package=harrietr Description: CRAN Package 'harrietr' (Wrangle Phylogenetic Distance Matrices and Other Utilities) Harriet was Charles Darwin's pet tortoise (possibly). 'harrietr' implements some function to manipulate distance matrices and phylogenetic trees to make it easier to plot with 'ggplot2' and to manipulate using 'tidyverse' tools. Package: r-cran-harrypotter Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 350 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-hexbin, r-cran-testthat Filename: pool/dists/noble/main/r-cran-harrypotter_2.1.1-1.ca2404.1_all.deb Size: 229526 MD5sum: aa470b3f2c7590401fc17042522a5f2c SHA1: b298d7c5f8f6f21f1fe5c5ee1829b05cd6bd3649 SHA256: 33a16c84eba68aac9d4617b0faec55d51032c62d33c9853e7cb3147bcd08e1ba SHA512: 6b581a5b9aa44d3e5c81e35d0c15cf413fab567bbbfc62e8af7888e817b59ebba33049247ea118565ceccaa95a36c0d00c94810ef3d824bc36c95e48ae4f7915 Homepage: https://cran.r-project.org/package=harrypotter Description: CRAN Package 'harrypotter' (Palettes Generated from All "Harry Potter" Movies) Implementation of characteristic palettes inspired in the Wizarding World and the Harry Potter movie franchise. Package: r-cran-harvest.tree Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpart Filename: pool/dists/noble/main/r-cran-harvest.tree_1.1-1.ca2404.1_all.deb Size: 64384 MD5sum: da2e50fac50be2a45a18faa31563c49b SHA1: ee0679847b6f3c2983ebb61082237b8b30c7478e SHA256: c88b37edeca8185ed0b04b8af8bbac5865c79d137734edd382a74bc1a830fe6d SHA512: 37082ffc689cd896672bd42bb1e92e51ccd6526661005202d57349d83b22666ad6d74d8dce7c585e7fd1cc1e3ece4bcafb95f1ba80d4cd72e4e7fc541d80605f Homepage: https://cran.r-project.org/package=Harvest.Tree Description: CRAN Package 'Harvest.Tree' (Harvest the Classification Tree) Aimed at applying the Harvest classification tree algorithm, modified algorithm of classic classification tree.The harvested tree has advantage of deleting redundant rules in trees, leading to a simplify and more efficient tree model.It was firstly used in drug discovery field, but it also performs well in other kinds of data, especially when the region of a class is disconnected. 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Package: r-cran-hash Architecture: all Version: 2.2.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-rbenchmark Filename: pool/dists/noble/main/r-cran-hash_2.2.6.4-1.ca2404.1_all.deb Size: 189150 MD5sum: c9953aaae7e00329fd53c440cd3efb12 SHA1: 9c18734862e46bc4b7645709a4912b76d960b975 SHA256: fc0cf210aba240e3a0ccced5c27e4138cbabfdf4c801166840c2ac2716fa89fe SHA512: 130dc370b9b0d06d765ab2fc46baa74826457ca0920b66c06bcc21ce8e8dda271835e9d79e529829dc10d7e1a90254a647da4005d551bca59ee007daf1bfd8f8 Homepage: https://cran.r-project.org/package=hash Description: CRAN Package 'hash' (Full Featured Implementation of Hash Tables/AssociativeArrays/Dictionaries) Implements a data structure similar to hashes in Perl and dictionaries in Python but with a purposefully R flavor. For objects of appreciable size, access using hashes outperforms native named lists and vectors. Package: r-cran-hashids Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hashids_0.9.0-1.ca2404.1_all.deb Size: 44804 MD5sum: e052d5c985371018dfaa57ee5bb6a4db SHA1: 2f721fc8cc5f705e0ff4494d6ada88b6a07fbc77 SHA256: e58b6012e87170f359b47b8bb5b2d14a05ecb3d63bd1a946be0478ca19ab9f5e SHA512: 008dc8b82a76b877fa5d0136c4173268242554e02ae9d2a9e2c7e5d423cd60a02edeb04dc4e3f50310abd43c2c695debee5ca0f3487fe7276e1d512f3492901c Homepage: https://cran.r-project.org/package=hashids Description: CRAN Package 'hashids' (Generate Short Unique YouTube-Like IDs (Hashes) from Integers) An R port of the hashids library. hashids generates YouTube-like hashes from integers or vector of integers. Hashes generated from integers are relatively short, unique and non-seqential. hashids can be used to generate unique ids for URLs and hide database row numbers from the user. By default hashids will avoid generating common English cursewords by preventing certain letters being next to each other. hashids are not one-way: it is easy to encode an integer to a hashid and decode a hashid back into an integer. Package: r-cran-hassani.sacf Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hassani.sacf_2.0-1.ca2404.1_all.deb Size: 16062 MD5sum: 1eec337d5cfe6f231745c2b3e10f7c83 SHA1: 7c7481dc6d50e5b8caba2f2bd7bbb12d4b0ba124 SHA256: e4ca1785758e70a6a62b1b9960e3b407c4185a923a5b3111c905c5eb95dbd117 SHA512: c448e39edaeea36fac196592525f703d1c92776793b53a63c412e593ec1fdfd75256dfd403bdba7bcc8a6f10cc9bf3544985f9e13392a6119def5b88704f8875 Homepage: https://cran.r-project.org/package=Hassani.SACF Description: CRAN Package 'Hassani.SACF' (Computing Lower Bound of Ljung-Box Test) The Ljung-Box test is one of the most important tests for time series diagnostics and model selection. The Hassani SACF (Sum of the Sample Autocorrelation Function) Theorem , however, indicates that the sum of sample autocorrelation function is always fix for any stationary time series with arbitrary length. This package confirms for sensitivity of the Ljung-Box test to the number of lags involved in the test and therefore it should be used with extra caution. The Hassani SACF Theorem has been described in : Hassani, Yeganegi and M. R. (2019) . Package: r-cran-hassani.silva Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hassani.silva_1.0-1.ca2404.1_all.deb Size: 17154 MD5sum: 352d775f01f9d9d696bdc18e47c3a829 SHA1: c4fa44aff9f0f47b0db8598bbcd70bacee1c1949 SHA256: 70228e9f7cac7caf4f3f246ae94523c138df794063423571aee65847188eca1d SHA512: 98f4edf7571395f80829beab677919faf3f011939c18839d51701362e2daa91708e4e24e8eb71cd83d601a7b7dbd5a2b55b952314a0cb8aa5eccd5b2cf0d5895 Homepage: https://cran.r-project.org/package=Hassani.Silva Description: CRAN Package 'Hassani.Silva' (A Test for Comparing the Predictive Accuracy of Two Sets ofForecasts) A non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS) test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA test is able to serve two distinct purposes. 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Package: r-cran-hassediagram Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-rgraphviz, r-bioc-graph Filename: pool/dists/noble/main/r-cran-hassediagram_0.2.0-1.ca2404.1_all.deb Size: 38734 MD5sum: 6ac0602a2411751851f62edbfb6310ae SHA1: 6c01ce4f221c076455146390da8e2c17dd8d0de7 SHA256: df1619e2ec8f45b564c55c7ddab297acd8d78ebe43a14202d06cbb02458d0fef SHA512: a55818e1cdcbb8e2cbb115b9c1f9f94d16e097adcd4b2eb2eac5480a23feb1c3fc78b1ba1053676aa04d7984bcd57589ef6e59246a8a05e68a75068ae622277d Homepage: https://cran.r-project.org/package=hasseDiagram Description: CRAN Package 'hasseDiagram' (Drawing Hasse Diagram) Drawing Hasse diagram - visualization of transitive reduction of a finite partially ordered set. 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Package: r-cran-hatchr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5454 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggtext, r-cran-lifecycle, r-cran-lubridate, r-cran-rlang, r-cran-tibble Suggests: r-cran-cli, r-cran-ggridges, r-cran-knitr, r-cran-nycflights13, r-cran-patchwork, r-cran-purrr, r-cran-readr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-hatchr_1.0.1-1.ca2404.1_all.deb Size: 4023478 MD5sum: fb47b96cb268d8b35222c65820a8eae1 SHA1: 0cc436c09f771b460bf2c3ee662c9c9d7653ed09 SHA256: 98555bc4fc8a71197a7eb31d723c2223a32fd46bc00d56f90c26a158515fd19f SHA512: 791bc99e458333e913f53f315be48a2ffb081d16bc1fc4caf005592942c68dcdfc1edb88dd6305fb46149144c4ca3ba76ebf0a3f01ffb40c2ac0c18bb7871849 Homepage: https://cran.r-project.org/package=hatchR Description: CRAN Package 'hatchR' (Predict Fish Hatch and Emergence Timing) Predict hatch and emergence timing for a wide range of wild fishes using the effective value framework (Sparks et al., (2019) ). 'hatchR' offers users access to established phenological models and the flexibility to incorporate custom parameterizations using external datasets. Package: r-cran-hatemicoint Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hatemicoint_1.0.1-1.ca2404.1_all.deb Size: 39838 MD5sum: 1bbd142ec3d19cfff48a16b6d3b4b46e SHA1: d92abfe3202fc311966fe18d272072bdc07aa1ae SHA256: ac22fdc01af2e47eea055bcdbc1ed84cb7b69dfafdeb2882f0ac842d02ec3d2d SHA512: aad578f5e74184ecfd3246226fb1dd2078cbe1179731d7299a389ec986a749b56964f74a221e51c441de35ce1ec1258ce44308061c0b90b000ed0f6f8431b512 Homepage: https://cran.r-project.org/package=hatemicoint Description: CRAN Package 'hatemicoint' (Hatemi-J Cointegration Test with Two Unknown Regime Shifts) Implements the Hatemi-J (2008) cointegration test which allows for two unknown structural breaks (regime shifts) in the cointegrating relationship. The test provides three test statistics: ADF* (Augmented Dickey-Fuller), Zt* (Phillips-Perron Z_t), and Za* (Phillips-Perron Z_alpha), along with endogenously determined break dates. Critical values are based on simulations from Hatemi-J (2008) . 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The output is designed to be comparable to results from 'SPSS' (Statistical Package for the Social Sciences) Complex Samples procedures. 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While it is described as a constant difference between the hazard functions given the covariates, we do not assume specific functional forms for the covariates. Rava, D. and Xu, R. (2021) . 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The modeling framework follows the methodological foundations described in area-level models. This package is designed to facilitate a principled Bayesian workflow, enabling users to conduct prior predictive checks, model fitting, posterior predictive checks, model comparison, and sensitivity analysis in a coherent and reproducible manner. It supports flexible model specifications via 'brms' and promotes transparency in model development, aligned with the recommendations of modern Bayesian data analysis practices, implementing methods described in Rao and Molina (2015) . 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Package: r-cran-hcandersenr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4368 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hcandersenr_0.2.0-1.ca2404.1_all.deb Size: 4317824 MD5sum: 2112cbcde7a3db1ff51ebfd499e1a0df SHA1: 288422fee74f860f6bb53a66ee4baebfef28b153 SHA256: 5af2a7cbbd22ff76a8c6ce1267b05c79a7c04301c2298dc8a4c50d9148264bc2 SHA512: 8da6ad280db238755257b221ab5d2926227edbd326bd32d455c3fbc8c2449370dc65a591200e11cb56dfcee4e3dd91e86d2640db083e38780a42ef91916db2f8 Homepage: https://cran.r-project.org/package=hcandersenr Description: CRAN Package 'hcandersenr' (H.C. Andersens Fairy Tales) Texts for H.C. Andersens fairy tales, ready for text analysis. Fairy tales in German, Danish, English, Spanish and French. Package: r-cran-hcci Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hcci_1.2.0-1.ca2404.1_all.deb Size: 50084 MD5sum: 073d8249af487762f792389581a3f8b7 SHA1: 2b9600ce56bbb1da4d92755401c150ee30c12b37 SHA256: 1b96017db0b98ba712effbbb2acbb5401faf84925bfc9fda392329879bafa38c SHA512: 192b379c087a8747e4202a1265d74854ce9bc46fcd67c66b6e0e8ed33601ecc0839964e0242f0bf824c88d953a056cc51f144b89aaf16c86d4b351864e87b4d4 Homepage: https://cran.r-project.org/package=hcci Description: CRAN Package 'hcci' (Interval Estimation of Linear Models with Heteroskedasticity) Calculates the interval estimates for the parameters of linear models with heteroscedastic regression using bootstrap - (Wild Bootstrap) and double bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using the percentile bootstrap and percentile bootstrap double. The package can calculate consistent estimates of the covariance matrix of the parameters of linear regression models with heteroscedasticity of unknown form. The package also provides a function to consistently calculate the covariance matrix of the parameters of linear models with heteroscedasticity of unknown form. The bootstrap methods exported by the package are based on the master's thesis of the first author, available at . The hcci package in previous versions was cited in the book VINOD, Hrishikesh D. Hands-on Intermediate Econometrics Using R: Templates for Learning Quantitative Methods and R Software. 2022, p. 441, ISBN 978-981-125-617-2 (hardcover). The simple bootstrap schemes are based on the works of Cribari-Neto F and Lima M. G. (2009) , while the double bootstrap schemes for the parameters that index the linear models with heteroscedasticity of unknown form are based on the works of Beran (1987) . The use of bootstrap for the calculation of interval estimates in regression models with heteroscedasticity of unknown form from a weighting of the residuals was proposed by Wu (1986) . This bootstrap scheme is known as weighted or wild bootstrap. Package: r-cran-hcd Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-randnet, r-cran-rspectra, r-cran-irlba, r-cran-data.tree, r-cran-data.table, r-cran-stringr, r-cran-dendextend Filename: pool/dists/noble/main/r-cran-hcd_1.0-1.ca2404.1_all.deb Size: 53190 MD5sum: df491a9d154e0247b09d5ee32a78f316 SHA1: 986691627851b5c3e9a646195ba1ee3fc5d2cf54 SHA256: 80ecb0a59d490b8f649b24301098918e140f7da3de55d906408c7694da428d40 SHA512: f3ef283f4d7f4e173a9c0bcbdb1af1435bc11ad5cc4165d648f73f83734d1b3699a1cb35c02af653fa7c5594c8e4e0e65be7990603a0843fe86a065a17df783a Homepage: https://cran.r-project.org/package=HCD Description: CRAN Package 'HCD' (Hierarchical Community Detection by Recursive Partitioning) Hierarchical community detection on networks by a recursive spectral partitioning strategy, which is shown to be effective and efficient in Li, Lei, Bhattacharyya, Sarkar, Bickel, and Levina (2018) . The package also includes a data generating function for a binary tree stochastic block model, a special case of stochastic block model that admits hierarchy between communities. Package: r-cran-hce Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6151 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hce_0.9.3-1.ca2404.1_all.deb Size: 1144180 MD5sum: 466293f6a8225178a46e2131b6a41623 SHA1: 52e93293db7b8512fbc6661e356d5635b6e51ef2 SHA256: 3838f6fb72e6c1decd39f7964f308179e0dfb60cc18d97803c3c0e5471e5d024 SHA512: 63cdfd650bc2055584b0721131901afa4f4ee09be2f800e8f0177d940e88e6a10b0d68b8c306b2ede69d395f05887abc0a04fe463cbe35889c5e10b7711346fc Homepage: https://cran.r-project.org/package=hce Description: CRAN Package 'hce' (Design and Analysis of Hierarchical Composite Endpoints) Simulate and analyze hierarchical composite endpoints. Includes implementation for the kidney hierarchical composite endpoint as defined in Heerspink HL et al (2023) “Development and validation of a new hierarchical composite end point for clinical trials of kidney disease progression” (Journal of the American Society of Nephrology 34 (2): 2025–2038, ). Win odds, also called Wilcoxon-Mann-Whitney or success odds, is the main analysis method. Other win statistics (win probability, win ratio, net benefit) are also implemented in the univariate case, provided there is no censoring. The win probability analysis is based on the Brunner-Munzel test and uses the DeLong-DeLong-Clarke-Pearson variance estimator, as described by Brunner and Konietschke (2025) in “An unbiased rank-based estimator of the Mann–Whitney variance including the case of ties” (Statistical Papers 66 (1): 20, ). Includes implementation of a new Wilson-type, compatible confidence interval for the win odds, as proposed by Schüürhuis, Konietschke, Brunner (2025) in “A new approach to the nonparametric Behrens–Fisher problem with compatible confidence intervals.” (Biometrical Journal 67 (6), ). Stratification and covariate adjustment are performed based on the methodology presented by Koch GG et al. in “Issues for covariance analysis of dichotomous and ordered categorical data from randomized clinical trials and non-parametric strategies for addressing them” (Statistics in Medicine 17 (15-16): 1863–92). For a review, see Gasparyan SB et al (2021) “Adjusted win ratio with stratification: Calculation methods and interpretation” (Statistical Methods in Medical Research 30 (2): 580–611, ). Package: r-cran-hchinamap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5560 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr, r-cran-dplyr, r-cran-shiny, r-cran-colourpicker Filename: pool/dists/noble/main/r-cran-hchinamap_0.1.0-1.ca2404.1_all.deb Size: 577454 MD5sum: a2c105f6b206682c8394bc114c08cbf2 SHA1: 6026073daaaea480705c4d644256588387900d86 SHA256: 7843c50acb51138ee5326ecbb5cfb37f15f1c7b218dad80c10ab65ae3f57cf27 SHA512: d181509e87ee277aaf1e08c6ac01aad0bd3a0002cbf53c83efeb31d733205b31735ee54d46376848d981fe1dc89c4e749a63d74bb646b0de6c77579108e8f34c Homepage: https://cran.r-project.org/package=hchinamap Description: CRAN Package 'hchinamap' (Mapping China and Its Provinces) By binding R functions and the 'Highmaps' chart library, 'hchinamap' package provides a simple way to map China and its provinces. 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Package: r-cran-hclusteasy Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clustersim, r-cran-factoextra, r-cran-readxl Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hclusteasy_0.1.0-1.ca2404.1_all.deb Size: 111944 MD5sum: 5938167c64e80cb94c63c7cefd244cf3 SHA1: ad3a80019544d1ca56045b0313592bab5ecec1e5 SHA256: 402713c6f81dc7691810f576d90e4ef9f795ecbdca9e5a8076ebb6bc4d21feeb SHA512: 6a34bd668114548a5c25fadd09220b00b76cf76fc98e6711abf0c99c9a4c1e125d93a1936f3403f0ce3d8d1c248b27d19e83755a469a26a79b2c6afd0d7f0370 Homepage: https://cran.r-project.org/package=hclusteasy Description: CRAN Package 'hclusteasy' (Determining Hierarchical Clustering Easily) Facilitates hierarchical clustering analysis with functions to read data in 'txt', 'xlsx', and 'xls' formats, apply normalization techniques to the dataset, perform hierarchical clustering and construct scatter plot from principal component analysis to evaluate the groups obtained. Package: r-cran-hclustteach Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hclustteach_0.1.0-1.ca2404.1_all.deb Size: 36628 MD5sum: 0060d308cd447731644323ea69fe2c0c SHA1: b3a21234354aea7b0ee0cd5c1a1ad1ea544dc419 SHA256: 4d0b33c39cbc425b9ab40dcea7d360331453e2e6194e50d5b49dbad5a6dbe160 SHA512: ff03f97ce7a3592cbd3727184175928182c20e2db8798b19e802150e6fe5a11b89420573e8afca96f9da4b8ec3dbe00b5718a59bbc5e4f33aa65ee1fe4d3ad61 Homepage: https://cran.r-project.org/package=hclustTeach Description: CRAN Package 'hclustTeach' (Hierarchical Cluster Analysis (Learning Didactically)) Implements hierarchical clustering methods (single linkage, complete linkage, average linkage, and centroid linkage) with stepwise printing and dendrograms for didactic purposes. Package: r-cran-hcmodelsets Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-ggplot2, r-cran-survival Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-hcmodelsets_1.1.3-1.ca2404.1_all.deb Size: 4158286 MD5sum: bb305dea505e13b0901fe9ede130c19d SHA1: 4ba64da962d90886b8aa4e9a3955431da327c38a SHA256: b4446d1f36c9ac51a9640498a48d7f4eec9fdd9d9c61010a0430d5be00ea05e7 SHA512: c340df97c270f3ab54d49e0170d492ff410af0d78743cf9759d89bed37fa17352da78c9d29ef9094f17af181e17f0b5ec0c1bab184a5554de2bed3b3e7388030 Homepage: https://cran.r-project.org/package=HCmodelSets Description: CRAN Package 'HCmodelSets' (Regression with a Large Number of Potential ExplanatoryVariables) Software for performing the reduction, exploratory and model selection phases of the procedure proposed by Cox, D.R. and Battey, H.S. (2017) for sparse regression when the number of potential explanatory variables far exceeds the sample size. The software supports linear regression, likelihood-based fitting of generalized linear regression models and the proportional hazards model fitted by partial likelihood. Package: r-cran-hcpclust Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-grf, r-cran-quantreg, r-cran-xgboost, r-cran-quantregforest Suggests: r-cran-foreach, r-cran-doparallel, r-cran-dorng, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-fnn, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-hcpclust_0.1.1-1.ca2404.1_all.deb Size: 122342 MD5sum: 84877c033fe290b70ce03e9d0daa3ffa SHA1: 986e85e81e683b556595ea2b14a3486294ae9fc6 SHA256: f112787d7af8ca1bf1a93f500d46f6f9ac2131f62000ddcef33ab99cb786e5ca SHA512: 7b4bfee05ee0ed30e38693b85cfdf801569a5c22fbfff363978524642b8006b3892b36d97e5c390c2c4b8b00ae19f9763da87b1e40b44c1336d8472acdbe3b96 Homepage: https://cran.r-project.org/package=HCPclust Description: CRAN Package 'HCPclust' (Hierarchical Conformal Prediction for Clustered Data withMissing Responses) Implements hierarchical conformal prediction for clustered data with missing responses. The method uses repeated cluster-level splitting and within-cluster subsampling to accommodate dependence, and inverse-probability weighting to correct distribution shift induced by missingness. Conditional densities are estimated by inverting fitted conditional quantiles (linear quantile regression or quantile regression forests), and p-values are aggregated across resampling and splitting steps using the Cauchy combination test. Package: r-cran-hcr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-hcr_0.1.1-1.ca2404.1_all.deb Size: 35764 MD5sum: 0902b9dd2e5988f6e1a13744c0bf5c67 SHA1: 64eaf405f13bdbacfa34e14e0cbbe28d664f2887 SHA256: 2acdcea796e45e9d34ac06706117cd0324938feeece243da71310a21ec955c81 SHA512: bb92d7e919e129d1627719fe3a3c9892d3327c3dec78487c2aed41da0bddab7f9436ed786396c2a68b6e91bbe523065df016032257b302d81ff1165b125b7638 Homepage: https://cran.r-project.org/package=HCR Description: CRAN Package 'HCR' (Causal Discovery from Discrete Data using Hidden CompactRepresentation) This code provides a method to fit the hidden compact representation model as well as to identify the causal direction on discrete data. We implement an effective solution to recover the above hidden compact representation under the likelihood framework. Please see the Causal Discovery from Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) for a description of some of our methods. 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The package supports pre index and post index analysis, patient cohort comparison, and customizable summaries and visualizations for clinical and health economics research. Methods implemented are based on Scott et al. (2022) and Xia et al. (2024) . Package: r-cran-hct Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6487 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hct_0.1.3-1.ca2404.1_all.deb Size: 6563224 MD5sum: fc7acfae505203697dea2654513adda8 SHA1: 1f63d051110bdbaee2ef799f3e2643ab96401c99 SHA256: a0516dc4cf5ad4486e8dcaaf676c52634a0b495c91a847ec8d0085bead63fd2b SHA512: b8d44f844e110495901d3288aa0c69f926df3f2320eb50087d6fd87854d6558ee6e97a4a0ca4fca0df387cb76a13249d6e790cf6e72f221f664502dff881f6f1 Homepage: https://cran.r-project.org/package=HCT Description: CRAN Package 'HCT' (Calculates Significance Criteria and Power for a Single ArmTrial) Given a database of previous treatment/placebo estimates, their standard errors and sample sizes, the program calculates a significance criteria and power estimate that takes into account the among trial variation. Package: r-cran-hctdesign Architecture: all Version: 0.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-diversitree, r-cran-mvtnorm, r-cran-flexsurv, r-cran-survival, r-cran-crayon Filename: pool/dists/noble/main/r-cran-hctdesign_0.7.4-1.ca2404.1_all.deb Size: 126134 MD5sum: 30015f041f04ca1964a488f8764dad4d SHA1: b1c26c01196723bfa92b9214f51d81ad6ccc3a13 SHA256: 002c3295df3336c17247046763545cb442ae8483180d5b9a65ce4007fc142aa9 SHA512: e314b04fd061827e7e2e815611fe3a9d30d45de495909ab7e3e50dd1b2c204152b3ff83f70a6f06d3f1ee38ef03de69949c708a50ddd533826f5378957fb77b2 Homepage: https://cran.r-project.org/package=HCTDesign Description: CRAN Package 'HCTDesign' (Group Sequential Design for Historical Control Trial withSurvival Outcome) It provides functions to design historical controlled trials with survival outcome by group sequential method. The options for interim look boundaries are efficacy only, efficacy & futility or futility only. It also provides the function to monitor the trial for any unplanned look. The package is based on Jianrong Wu, Xiaoping Xiong (2016) and Jianrong Wu, Yimei Li (2020) . Package: r-cran-hctr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-harmonicmeanp, r-cran-mass, r-cran-ncvreg, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-hctr_0.1.1-1.ca2404.1_all.deb Size: 48564 MD5sum: f5406cefec778743d1e585305b19898f SHA1: 1aeb4f3c87282335565713ff54db463983c94716 SHA256: ac95b5651639a6d8b9f9b510f810c88b6315a963d1793f86246af23cd2408c1b SHA512: 4026b696ca55bf7ada37894059ff5435360e043b2aac821e3382d9217d945a69c250e13a8076895dbe525b26a10aa41e0d86ff31634e018758fa6894eb99b32c Homepage: https://cran.r-project.org/package=HCTR Description: CRAN Package 'HCTR' (Higher Criticism Tuned Regression) A novel searching scheme for tuning parameter in high-dimensional penalized regression. We propose a new estimate of the regularization parameter based on an estimated lower bound of the proportion of false null hypotheses (Meinshausen and Rice (2006) ). The bound is estimated by applying the empirical null distribution of the higher criticism statistic, a second-level significance testing, which is constructed by dependent p-values from a multi-split regression and aggregation method (Jeng, Zhang and Tzeng (2019) ). An estimate of tuning parameter in penalized regression is decided corresponding to the lower bound of the proportion of false null hypotheses. Different penalized regression methods are provided in the multi-split algorithm. Package: r-cran-hctrial Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clinfun, r-cran-genbinomapps Filename: pool/dists/noble/main/r-cran-hctrial_0.1.0-1.ca2404.1_all.deb Size: 43182 MD5sum: 8b0a0939358545f0f58a61e5d12d644f SHA1: 43952e05b473632aa37e3ec86fbc441a4ea8152c SHA256: deb55e1bf9a1efdca3cded53b6b67edcdbd16034b558037aa624f6201175833a SHA512: 90c7c2c94ff093dd832c723e3f1df78151b15a39a8e61f8801c0fd897beedf83db7e1f0c20cf35d5f54c097ad648c7e6bcbdc121ef32a93c5a92220e306edc79 Homepage: https://cran.r-project.org/package=hctrial Description: CRAN Package 'hctrial' (Using Historical Controls for Designing Phase II Clinical Trials) Provides functions for designing phase II clinical trials adjusting for the heterogeneity of the population using known subgroups or historical controls. Package: r-cran-hcuptools Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2, r-cran-readr, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-rlang, r-cran-xml2, r-cran-readxl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-data.table, r-cran-pdftools Filename: pool/dists/noble/main/r-cran-hcuptools_1.0.1-1.ca2404.1_all.deb Size: 211962 MD5sum: 8304e27cb47642f78deb3772519c5cb1 SHA1: bf56bf2654d453d302aeae7d44a3f56762733667 SHA256: af008f868c6dd851f938fb927b105cb9beb394a7fe89f246487fa37be39504e5 SHA512: 5914aff246ba5b2fb1f06a9463c2631a0b9363c6dc5fc28d1e39565da17ba322e64474cbb184b263e55e386008371410c7aed7dad9f7e340d1925e4fbc90f29b Homepage: https://cran.r-project.org/package=HCUPtools Description: CRAN Package 'HCUPtools' (Access and Work with HCUP Resources and Datasets) A comprehensive R package for accessing and working with publicly available and free resources from the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP). The package provides streamlined access to HCUP's Clinical Classifications Software Refined (CCSR) mapping files and Summary Trend Tables, enabling researchers and analysts to efficiently map ICD-10-CM diagnosis codes and ICD-10-PCS procedure codes to CCSR categories and access HCUP statistical reports. Key features include: direct download from HCUP website, multiple output formats (long/wide/default), cross-classification support, version management, citation generation, and intelligent caching. The package does not redistribute HCUP data files but facilitates direct download from the official HCUP website, ensuring users always have access to the latest versions and maintain compliance with HCUP data use policies. This package only accesses free public tools and reports; it does NOT access HCUP databases (NIS, KID, SID, NEDS, etc.) that require purchase. For more information, see . 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Package: r-cran-hdanova Architecture: all Version: 0.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1772 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-lme4, r-cran-mass, r-cran-mixlm, r-cran-pls, r-cran-pracma, r-cran-progress, r-cran-rspectra Suggests: r-cran-knitr, r-cran-vegan Filename: pool/dists/noble/main/r-cran-hdanova_0.8.4-1.ca2404.1_all.deb Size: 1121458 MD5sum: b0247623e857ea0880f18825b2bf8891 SHA1: 693d3c292d8994e11cb5842524f06b02d15a8b81 SHA256: d84fe779fd15f6b834312709574a6dc14c6dc7c98ea1c65624ac6b0cc1e651e6 SHA512: 955f49050bcec958b155ad5868f82388b167fe4ef0e4a85223c39a72ba23cb3a1c947404d1d604fe92bb59d7f2de12d73dae436356607274b47da125b35e6d51 Homepage: https://cran.r-project.org/package=HDANOVA Description: CRAN Package 'HDANOVA' (High-Dimensional Analysis of Variance) Functions and datasets to support Smilde, Marini, Westerhuis and Liland (2025, ISBN: 978-1-394-21121-0) "Analysis of Variance for High-Dimensional Data - Applications in Life, Food and Chemical Sciences". This implements and imports a collection of methods for HD-ANOVA data analysis with common interfaces, result- and plotting functions, multiple real data sets and four vignettes covering a range different applications. Package: r-cran-hdar Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2404 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-httr2, r-cran-jsonlite, r-cran-magrittr, r-cran-htmltools, r-cran-stringr, r-cran-scales, r-cran-progress Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hdar_1.0.7-1.ca2404.1_all.deb Size: 1794062 MD5sum: a4c06eb1f26cd16590b9cd4290b2476b SHA1: 238618e2be7101695e2466ca6788da2f153170a6 SHA256: 14f2c4a2f2f2fa2191bdacfc75cdc991d3d4bcbaf648caf96d106ddf75423611 SHA512: 5e55b7db08260dd0ffef42a8b078ad4b5c9b4df899c29d7866eeb4498afec516424eb2068ebbbe75e8f3495f7b8d555746626e1483b9f079e4a34ef72ea8002b Homepage: https://cran.r-project.org/package=hdar Description: CRAN Package 'hdar' ('REST' API Client for Accessing Data on 'WEkEO HDA V2') Provides seamless access to the WEkEO Harmonised Data Access (HDA) API, enabling users to query, download, and process data efficiently from the HDA platform. With 'hdar', researchers and data scientists can integrate the extensive HDA datasets into their R workflows, enhancing their data analysis capabilities. Comprehensive information on the API functionality and usage is available at . Package: r-cran-hdbma Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2jags, r-cran-gplots, r-cran-mass, r-cran-survival, r-cran-lattice Filename: pool/dists/noble/main/r-cran-hdbma_1.0-1.ca2404.1_all.deb Size: 181580 MD5sum: 195501953651cba759e65f75aa7b427d SHA1: 163d18b1f7fc29db51b5839c3e55e72d13fc1a8f SHA256: 7285986e1df3bd25120f47c621e299e719cc0cabd8dba1a3896d50695acebc8c SHA512: b6f53c39a797bb650b8c919b759804fcc22886b0c587c1957c305d294b2c95ff61b3865c961d7df385f617aa50f3e2a24179b67ccf3f621a52a1c53e805e02eb Homepage: https://cran.r-project.org/package=hdbma Description: CRAN Package 'hdbma' (Bayesian Mediation Analysis with High-Dimensional Data) Mediation analysis is used to identify and quantify intermediate effects from factors that intervene the observed relationship between an exposure/predicting variable and an outcome. We use a Bayesian adaptive lasso method to take care of the hierarchical structures and high dimensional exposures or mediators. Package: r-cran-hdbrr Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2917 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-bigstatsr, r-cran-mass Filename: pool/dists/noble/main/r-cran-hdbrr_1.1.4-1.ca2404.1_all.deb Size: 2598886 MD5sum: 2241e7cf3c6e0f78494c57b8d8ad5480 SHA1: a7fd294aaf1abcb0eb5a8b2240b800769d9f76e2 SHA256: 41f7026400387aea0453a3d4565c453533171d509706cdef335b33a56069a5fe SHA512: 6677231e9be888c9d134429fec6052c3e26131893caca1e95eb2ba5f81cfc19a2a4c342fc93fd28eed5173736136433675845dbf62c8b83c51839b61dbe8c77b Homepage: https://cran.r-project.org/package=HDBRR Description: CRAN Package 'HDBRR' (High Dimensional Bayesian Ridge Regression without MCMC) Ridge regression provide biased estimators of the regression parameters with lower variance. The HDBRR ("High Dimensional Bayesian Ridge Regression") function fits Bayesian Ridge regression without MCMC, this one uses the SVD or QR decomposition for the posterior computation. Package: r-cran-hdcate Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-r6, r-cran-hdm, r-cran-locpol, r-cran-caret Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xfun, r-cran-randomforest, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes Filename: pool/dists/noble/main/r-cran-hdcate_0.1.0-1.ca2404.1_all.deb Size: 187406 MD5sum: 7d04b35e4adcb17f7ed51e84ba28d7f7 SHA1: 463542f07a43bc27cd4b4f345085a5cc1367173b SHA256: 45f6cf885a47c9c73062f03ef342764133155ed9bdfa15a338aec7f885713ba2 SHA512: 426f3eacf165c53b03a531b12f06ef59e14af9f65f981f650bafccdae9ba074dfec1b22780a0a192a0232b3a994aa10dd44243718f71bbd002aa1f241fd79629 Homepage: https://cran.r-project.org/package=hdcate Description: CRAN Package 'hdcate' (Estimation of Conditional Average Treatment Effects withHigh-Dimensional Data) A two-step double-robust method to estimate the conditional average treatment effects (CATE) with potentially high-dimensional covariate(s). In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the covariate(s) of interest. The CATE estimator implemented in this package not only allows for high-dimensional data, but also has the “double robustness” property: either the model for the propensity score or the models for the conditional means of the potential outcomes are allowed to be misspecified (but not both). This package is based on the paper by Fan et al., "Estimation of Conditional Average Treatment Effects With High-Dimensional Data" (2022), Journal of Business & Economic Statistics . Package: r-cran-hdci Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-slam, r-cran-foreach, r-cran-iterators, r-cran-doparallel, r-cran-lattice, r-cran-matrix, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-hdci_1.0-2-1.ca2404.1_all.deb Size: 106790 MD5sum: b928ab4922dbfa2f493a45e12cfef39f SHA1: e3a723dca06e71fe3c5d2a438b7e6abd19297ae0 SHA256: b9c4d47a01b750fadd8c4077f48257743eedea685225483e95c03127a7c1caa3 SHA512: 2ffb99d5883676a5ca8886a05fd2d4bcf14c9d69d304535a84bb1323e630dd474389ba45a8bc1fdf5e129f4ae49778850ab184aed27036d3c29318cc9613789a Homepage: https://cran.r-project.org/package=HDCI Description: CRAN Package 'HDCI' (High Dimensional Confidence Interval Based on Lasso andBootstrap) Fits regression models on high dimensional data to estimate coefficients and use bootstrap method to obtain confidence intervals. 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Package: r-cran-hdcuremodels Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2079 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-doparallel, r-cran-flexsurv, r-cran-flexsurvcure, r-cran-foreach, r-cran-ggplot2, r-cran-ggpubr, r-cran-glmnet, r-cran-knockoff, r-cran-mvnfast, r-cran-plyr, r-cran-survival, r-cran-withr Suggests: r-cran-knitr, r-cran-rdsdp, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hdcuremodels_0.0.8-1.ca2404.1_all.deb Size: 1849890 MD5sum: 6f8e031f31d9906eb981576ca76cc90a SHA1: c8e5e0793689d1c2ddb1127d2fc813b8593f29ae SHA256: 33572e90234743f4ad0d18f41685c3ffbb7f491540f4ce366789282fc7bca85c SHA512: 2986f76fb9541c4254ea459b1a43d9b60757ae63aea65070f82edb290db5b9c0fb5e639842854e4c9321bed1b102c242bd03dfaa54edd88d384715154afbb16d Homepage: https://cran.r-project.org/package=hdcuremodels Description: CRAN Package 'hdcuremodels' (High-Dimensional Cure Models) Provides functions for fitting various penalized parametric and semi-parametric mixture cure models with different penalty functions, testing for a significant cure fraction, and testing for sufficient follow-up as described in Fu et al (2022) and Archer et al (2024). 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Package: r-cran-hdf5r.extra Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hdf5r, r-cran-checkmate, r-cran-dplyr, r-cran-easy.utils, r-cran-matrix, r-cran-rlang Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-hdf5r.extra_0.1.0-1.ca2404.1_all.deb Size: 213616 MD5sum: f83900f40055b87acd9d8cd8d6f267c2 SHA1: 6d315c6c713cb41f6cd1c85f36023ec03e44cec6 SHA256: 84c4604334965239064a3584f5cffb2951bfa6a62627eccbfeb8afef99c072fb SHA512: 8b1379994d01f059ef117dd72fe4a91d3fbd66963c84a26da06777aca597e33dff38395415d06876bad083312b728e42a7102d63253c3c186908d1b60a65528c Homepage: https://cran.r-project.org/package=hdf5r.Extra Description: CRAN Package 'hdf5r.Extra' (Extensions for 'HDF5' R Interfaces) Some methods to manipulate 'HDF5' files, extending the 'hdf5r' package. 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Jimenez-Varon, Ying Sun and Han Lin Shang (2024, Journal of Computational and Graphical Statistics). Package: r-cran-hdi Architecture: all Version: 0.1-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2391 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scalreg, r-cran-mass, r-cran-glmnet, r-cran-linprog Suggests: r-cran-matrix Filename: pool/dists/noble/main/r-cran-hdi_0.1-10-1.ca2404.1_all.deb Size: 2408828 MD5sum: 713a1d9cba9d044708b6a5a5b7a42c11 SHA1: bbf787731dec60291ff9543acce6f3d4622d1f2b SHA256: 5286c91820a7187e7f463fc9822e61b9563e9c92a0dcc410485e39e91efdc57a SHA512: fa9f475f6d9eba780a42b8877966ac70b2a45d2464219360fd6881380c11ad0c9caa28fb2e9441ed76466415c9c58e9841b30bbcd29cbb17e300d53ca93113c4 Homepage: https://cran.r-project.org/package=hdi Description: CRAN Package 'hdi' (High-Dimensional Inference) Implementation of multiple approaches to perform inference in high-dimensional models. 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See Waggoner (2023) for more on 'hdImpute', Stekhoven and Bühlmann (2012) for more on 'missForest', and Mayer (2022) for more on 'missRanger'. 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Package: r-cran-hdir Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-npcirc, r-cran-circular, r-cran-rgl, r-cran-directional, r-cran-movmf Suggests: r-cran-ggplot2, r-cran-maps, r-cran-mapproj, r-cran-dirstats Filename: pool/dists/noble/main/r-cran-hdir_1.1.3-1.ca2404.1_all.deb Size: 360874 MD5sum: 05591ad2aef1778e363faea643745f8f SHA1: 67977c2009a788f10cdb118e28ae7f22daa1bafe SHA256: 3222faa3b95649bcc299c688776213b54ae52a92e8e39065903c65197b7fd8cf SHA512: 9056140830b494d2544ca9cf392b2e19621731aac847006a69a031e532e881438a7813157e4ba34a275234c7f512b3ee8ef55a7188c778b8345c712f46862a5e Homepage: https://cran.r-project.org/package=HDiR Description: CRAN Package 'HDiR' (Directional Highest Density Regions) We provide an R tool for computation and nonparametric plug-in estimation of Highest Density Regions (HDRs) and general level sets in the directional setting. Concretely, circular and spherical HDRs can be reconstructed from a data sample following Saavedra-Nieves and Crujeiras (2021) . This library also contains two real datasets in the circular and spherical settings. The first one concerns a problem from animal orientation studies and the second one is related to earthquakes occurrences. Package: r-cran-hdivar Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-abind Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hdivar_1.0.2-1.ca2404.1_all.deb Size: 68526 MD5sum: a33a30e75aec14a36acfb38295f13537 SHA1: 5f5514fa8633cbb56b6341201b58ad7e520b4023 SHA256: 7ea16a0abd5bdf30260ce0c6d998dc58f2cf4f95282a6ec567056e6fcacfb7d2 SHA512: d007964c8156b60d7e55d62b06dc56975d81ceb91c0870cbd7bc28fe68c5b6bb021d0a2f6f4e1cbec08cefcff8aed1a8747e369c488ca249ecd56f2e3f0335c2 Homepage: https://cran.r-project.org/package=hdiVAR Description: CRAN Package 'hdiVAR' (Statistical Inference for Noisy Vector Autoregression) The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2023). "Statistical inference for high-dimensional vector autoregression with measurement error", Statistica Sinica. Package: r-cran-hdm Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1923 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-glmnet, r-cran-ggplot2, r-cran-checkmate, r-cran-formula Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-formatr, r-cran-xtable, r-cran-mvtnorm, r-cran-markdown Filename: pool/dists/noble/main/r-cran-hdm_0.3.2-1.ca2404.1_all.deb Size: 1649800 MD5sum: dfabe9980ef1bb8bf3306697087e1e52 SHA1: 1b786cd333d44e441c7c830237e5af687619e9c8 SHA256: f5b8b56e863d0a566b7f7e4570a35d8c4633ec0dc17d91bfd5fde3ab3d4169e2 SHA512: b2cdf63e785c48704d08ccc823115a27397bd370c1c1d2c0cb37b467297ea1b7c7d4c47ff2a66c54abe7c4564512808c14df9283610f95cfacfd7742b6343841 Homepage: https://cran.r-project.org/package=hdm Description: CRAN Package 'hdm' (High-Dimensional Metrics) Implementation of selected high-dimensional statistical and econometric methods for estimation and inference. Efficient estimators and uniformly valid confidence intervals for various low-dimensional causal/ structural parameters are provided which appear in high-dimensional approximately sparse models. Including functions for fitting heteroscedastic robust Lasso regressions with non-Gaussian errors and for instrumental variable (IV) and treatment effect estimation in a high-dimensional setting. Moreover, the methods enable valid post-selection inference and rely on a theoretically grounded, data-driven choice of the penalty. Chernozhukov, Hansen, Spindler (2016) . Package: r-cran-hdmed Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bama, r-cran-foreach, r-cran-freebird, r-cran-gcdnet, r-cran-genlasso, r-cran-hdi, r-cran-iterators, r-cran-mass, r-cran-mediation, r-cran-ncvreg Filename: pool/dists/noble/main/r-cran-hdmed_1.0.1-1.ca2404.1_all.deb Size: 229790 MD5sum: 5af5235a21bee07d8eb8c53475e3855c SHA1: fee849343d20feda60ff7edb12795c6091e79a7a SHA256: c74b6d9c48fc017088ab34d0b226110fcbc4a4e425a9a20a25475841811a59ae SHA512: 2f3a0459f31d951ee7fc344653a1d8826f782fe28af21e184b62cf5ee22e00eb895d467405588ce23da813b79cfa275393f4f841d058d9b9bcd3351fac80887d Homepage: https://cran.r-project.org/package=hdmed Description: CRAN Package 'hdmed' (Methods for Mediation Analysis with High-Dimensional Mediators) A suite of functions for performing mediation analysis with high-dimensional mediators. In addition to centralizing code from several existing packages for high-dimensional mediation analysis, we provide organized, well-documented functions for a handle of methods which, though programmed their original authors, have not previously been formalized into R packages or been made presentable for public use. The methods we include cover a broad array of approaches and objectives, and are described in detail by both our companion manuscript---"Methods for Mediation Analysis with High-Dimensional DNA Methylation Data: Possible Choices and Comparison"---and the original publications that proposed them. The specific methods offered by our package include the Bayesian sparse linear mixed model (BSLMM) by Song et al. (2019); high-dimensional mediation analysis (HDMA) by Gao et al. (2019); high-dimensional multivariate mediation (HDMM) by Chén et al. (2018); high-dimensional linear mediation analysis (HILMA) by Zhou et al. (2020); high-dimensional mediation analysis (HIMA) by Zhang et al. (2016); latent variable mediation analysis (LVMA) by Derkach et al. (2019); mediation by fixed-effect model (MedFix) by Zhang (2021); pathway LASSO by Zhao & Luo (2022); principal component mediation analysis (PCMA) by Huang & Pan (2016); and sparse principal component mediation analysis (SPCMA) by Zhao et al. (2020). Citations for the corresponding papers can be found in their respective functions. Package: r-cran-hdmfa Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-hdmfa_0.1.1-1.ca2404.1_all.deb Size: 70990 MD5sum: d35330fcd53eb2c3bfe626ec2d85f63d SHA1: f237882f04cdbcb946e0216aa5d06fa1bc4b622a SHA256: f0b772636cac9812e9043d9b30b1e69eaf2afc5bf1697deb385a9ba1b22a2006 SHA512: 3af04a2869dcee51d990b2c786d6a525d0ec4c55a5bef2f710039b9111f65337d5a94d169ee9e456e60b510684f32374e99a80845331652bdcc5ab048f0a4887 Homepage: https://cran.r-project.org/package=HDMFA Description: CRAN Package 'HDMFA' (High-Dimensional Matrix Factor Analysis) High-dimensional matrix factor models have drawn much attention in view of the fact that observations are usually well structured to be an array such as in macroeconomics and finance. In addition, data often exhibit heavy-tails and thus it is also important to develop robust procedures. We aim to address this issue by replacing the least square loss with Huber loss function. We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's Frobenius norm, which leads to a weighted iterative projection approach to compute and learn the parameters and thereby named as Robust-Matrix-Factor-Analysis (RMFA), see the details in He et al. (2023). The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm (IHR), see the details in He et al. (2023) . In this package, we also provide the algorithm for alpha-PCA by Chen & Fan (2021) , the Projected estimation (PE) method by Yu et al. (2022). In addition, the methods for determining the pair of factor numbers are also given. Package: r-cran-hdmt Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1886 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fdrtool, r-bioc-qvalue Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-hdmt_1.0.5-1.ca2404.1_all.deb Size: 1858486 MD5sum: b8946810f5979655552413f0575958ac SHA1: c36c2bb39f67e28ae45fba6aea1209af2e9f56a1 SHA256: 028d5aa7496fd55575161a9f4109754efbb3b5c9c54aa21c64be070da587faa4 SHA512: 3938da95062af5bc4509d78a92cdaa5c3937ac192922bd5e2eb4ac84bee922349a5cc18b1a0e6ece24f1a9a095bb35acf788db95699c13cef1cd96d77a6ca395 Homepage: https://cran.r-project.org/package=HDMT Description: CRAN Package 'HDMT' (A Multiple Testing Procedure for High-Dimensional MediationHypotheses) A multiple-testing procedure for high-dimensional mediation hypotheses. Mediation analysis is of rising interest in epidemiology and clinical trials. Among existing methods for mediation analyses, the popular joint significance (JS) test yields an overly conservative type I error rate and therefore low power. In the R package 'HDMT' we implement a multiple-testing procedure that accurately controls the family-wise error rate (FWER) and the false discovery rate (FDR) when using JS for testing high-dimensional mediation hypotheses. The core of our procedure is based on estimating the proportions of three component null hypotheses and deriving the corresponding mixture distribution of null p-values. Results of the data examples include better-behaved quantile-quantile plots and improved detection of novel mediation relationships on the role of DNA methylation in genetic regulation of gene expression. With increasing interest in mediation by molecular intermediaries such as gene expression, the proposed method addresses an unmet methodological challenge. Methods used in the package refer to James Y. Dai, Janet L. Stanford & Michael LeBlanc (2020) . Package: r-cran-hdmtd Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-igraph Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hdmtd_0.1.4-1.ca2404.1_all.deb Size: 519918 MD5sum: 5abb8527a271c38873133115ffe09c86 SHA1: 9067f8037020ca7df0eaa4d8c631d63529df726e SHA256: 00866db025acedbac97b9356358381f5b5721f6a52d31a493f5c329405d0191d SHA512: 7448c8fd4a2934270a8c334b91ffd5bb3e3968c39b0e675ddce8229bd498647cf2215ad640ac7d6b5f9df5f01d00d9ba10d4b7e2c27bd7775a1d9902987b778d Homepage: https://cran.r-project.org/package=hdMTD Description: CRAN Package 'hdMTD' (Inference for High-Dimensional Mixture Transition DistributionModels) Estimates parameters in Mixture Transition Distribution (MTD) models, a class of high-order Markov chains. The set of relevant pasts (lags) is selected using either the Bayesian Information Criterion or the Forward Stepwise and Cut algorithms. Other model parameters (e.g. transition probabilities and oscillations) can be estimated via maximum likelihood estimation or the Expectation-Maximization algorithm. Additionally, 'hdMTD' includes a perfect sampling algorithm that generates samples of an MTD model from its invariant distribution. For theory, see Ost & Takahashi (2023) . 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Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors. Adjust the shrinkage bias in the predicted PC scores. Dey, R. and Lee, S. (2019) . 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We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's L2 norm, which turns out to do Principal Component Analysis (PCA) on the weighted sample covariance matrix and thereby named as Huber PCA. The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm. In this package we also provide the code for traditional PCA, the Robust Two Step (RTS) method by He et al. (2022) and the Quantile Factor Analysis (QFA) method by Chen et al. (2021) and He et al. (2023). 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Package: r-cran-healthcal Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-healthcal_0.1.1-1.ca2404.1_all.deb Size: 26134 MD5sum: 74b420142ec7c26d76fbf9c15f4c73bc SHA1: b9f51ec8aa0f80326214a9dc56eee6bd595688bd SHA256: db3dc567f262d8f3d0da8671328a8078bc0e709dca8b10ecd8463a3c9cd00c4e SHA512: 21192380c5f2bcf5ab946f1e2ffdb211df45aeeb8a2970d74da129e140f2b8ddfa1f425a609edb82e838cad00eb9badc47a20783d9da0bf240d97d64fcef736d Homepage: https://cran.r-project.org/package=HealthCal Description: CRAN Package 'HealthCal' (Health Calculator) Health Calculator helps to find different parameters like basal metabolic rate, body mass index etc. related to fitness and health of a person. 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Contains functions to implement the semiparametric estimation technique described in Raval, Rosenbaum, and Tenn (2017) "A Semiparametric Discrete Choice Model: An Application to Hospital Mergers" . Package: r-cran-healthdb Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clock, r-cran-data.table, r-cran-dbplyr, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-rsqlite Filename: pool/dists/noble/main/r-cran-healthdb_0.5.0-1.ca2404.1_all.deb Size: 300746 MD5sum: d6ab9a96613c363f376d1e7e4773880a SHA1: 7a7d3d851c3b2b764742cb23af04f6beb35f3951 SHA256: 3573197676c797ea6937e7d4d6fc135c01767c1bd3d5459afae7688778c633f7 SHA512: 1844c2571818bc7c24cf36cdc4da2e87553c2f62ae42cb3a5a2d8a5c7e1fe5883baf8fca76453d6f3ebb05656953d724571b79451badf9c03115bfe4dc449ab4 Homepage: https://cran.r-project.org/package=healthdb Description: CRAN Package 'healthdb' (Working with Healthcare Databases) A system for identifying diseases or events from healthcare databases and preparing data for epidemiological studies. 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Package: r-cran-healthfinance Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate, r-cran-readr, r-cran-scales, r-cran-shiny, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-healthfinance_0.1.0-1.ca2404.1_all.deb Size: 30568 MD5sum: eefcc3954130fa6f1623e064f9e0ac92 SHA1: ad28ebca9abd9409b88465cc8b3fca7b9329ec70 SHA256: 2e7d15fd70e50108af7f9fbf58371807332491114ee9c691cb06b5b2cec2780f SHA512: 6f400f616bb254836ba7d55f02d38720440d7a0dfcc96447604686c567bee809ce65cffc3f1b030b0a43419f1b960b7202a69b220d9f4c9388cf87188e9e2445 Homepage: https://cran.r-project.org/package=healthfinance Description: CRAN Package 'healthfinance' (Financial Projections and Planning for Health Care Practices) Provides a shiny interface for a free, open-source managerial accounting-like system for health care practices. This package allows health care administrators to project revenue with monthly adjustments and procedure-specific boosts up to a 3-year period. Granular data (patient-level) to aggregated data (department- or hospital-level) can all be used as valid inputs provided historical volume and revenue data is available. For more details on managerial accounting techniques, see Brewer et al. (2015, ISBN:9780078025792). Package: r-cran-healthiar Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1912 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-rdpack Suggests: r-cran-sf, r-cran-terra, r-cran-exactextractr, r-cran-testthat, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-healthiar_0.2.4-1.ca2404.1_all.deb Size: 1391790 MD5sum: d0723a94fbe2f78f1b4e3b34a277880d SHA1: c61e4e0d1167a6d7d39c7e65f0628cbb0c21fa47 SHA256: 37ba5068e4a7d43119bd2fa6ae2207ed9aff391825d6883f01dbe706442b9b61 SHA512: d71f146243010bfa3f582af224ea44991cf1b94a337d914f4ba4ce3a19f026d967b0de739441e1a2c015448e268b66b3c73d279651ee43ac4433cb14ab7ef199 Homepage: https://cran.r-project.org/package=healthiar Description: CRAN Package 'healthiar' (Quantifying and Monetizing Health Impacts Attributable toExposure) This R package has been developed with a focus on air pollution and noise but can be applied to other exposures. The initial development has been funded by the European Union project BEST-COST. Disclaimer: It is work in progress and the developers are not liable for any calculation errors or inaccuracies resulting from the use of this package. Selection of relevant references (in chronological order): WHO (2003) , Murray et al. (2003) , Miller & Hurley (2003) , Steenland & Armstrong (2006) , WHO (2011) , GBD 2019 Risk Factors Collaborators (2020) . Package: r-cran-healthmarkers Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1290 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-vctrs, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-pkgload, r-cran-cvrisk, r-cran-di, r-cran-rspiro, r-cran-pooledcohort, r-cran-qrisk3, r-cran-riskscorescvd, r-cran-usethis, r-cran-withr, r-cran-mice, r-cran-missforest Filename: pool/dists/noble/main/r-cran-healthmarkers_0.1.2-1.ca2404.1_all.deb Size: 1182536 MD5sum: 60f302a32368e1721e85b80a08f49dea SHA1: c66cc80391dec88198e4766db9c93c4ffc28ce41 SHA256: 845c50d9416ff7b8c8231daca1ceac92bd6b433c7db2ad97b0994c744242d9d2 SHA512: 6b836bee7b9797640f101848483fe2efeb42538cd4c2123891107194cf84c766ea8e08939db8ddc87c9d35ddfd453e3ac2c1d22b2fb814f2ccee8847b80b254e Homepage: https://cran.r-project.org/package=HealthMarkers Description: CRAN Package 'HealthMarkers' (Toolkit for Clinical, Metabolic, and Cardiovascular BiomarkerCalculations) Computes over 50 specialist health marker functions covering insulin sensitivity and resistance indices (fasting, oral glucose tolerance test, adipose-tissue, tracer, and dual-energy X-ray absorptiometry (DXA)-based), glycaemic and lipid markers, atherogenic and metabolic syndrome scores, liver steatosis and fibrosis scores, and cardiovascular risk algorithms (Framingham Heart Study equations, atherosclerotic cardiovascular disease (ASCVD) Pooled Cohort Equations, the QRISK3 cardiac risk score, and Systematic Coronary Risk Evaluation 2 (SCORE2) including the Older Persons variant (SCORE2-OP)). Also implements renal function (estimated glomerular filtration rate (eGFR), Kidney Failure Risk Equation (KFRE), chronic kidney disease (CKD) staging), pulmonary function (spirometry z-scores, Body-mass index, airflow Obstruction, Dyspnea, and Exercise capacity index (BODE)), inflammatory markers and the inflammatory age clock (iAge), hormonal panels, body composition and anthropometric z-scores, bone turnover markers and fracture risk (Fracture Risk Assessment Tool (FRAX)), frailty and comorbidity indices (Rockwood, Charlson), psychiatric rating scales, and biomarker panels from alternative biofluids (urine, saliva, sweat). Missing value imputation helpers, pre or post computation normalization and a unified all_health_markers() dispatcher that returns all requested marker groups as a single wide tibble are included. Package: r-cran-healthmotionr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15700 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-healthmotionr_0.2.0-1.ca2404.1_all.deb Size: 15599250 MD5sum: ea00a064519c6ca0a149f8d6fb622db1 SHA1: 67798b8e5303cbfbb1039d61e6cba2b87571f557 SHA256: 53dd543ce4b8d3c1abd614590e6e18c2db00e33e2e48109858a0a0d931525d6d SHA512: 1b8d110d2e18dc763003d2c92cc3e7fc1754fa2717309ff7eb5adae482454663c697356fe4e7960f1f606896395ef837f64a456cd9ebb78c3395de515bb6098d Homepage: https://cran.r-project.org/package=healthmotionR Description: CRAN Package 'healthmotionR' (A Comprehensive Collection of Health and Human Motion Datasets) Provides a broad collection of datasets focused on health, biomechanics, and human motion. It includes clinical, physiological, and kinematic information from diverse sources, covering aspects such as surgery outcomes, vital signs, rheumatoid arthritis, osteoarthritis, accelerometry, gait analysis, motion sensing, and biomechanics experiments. Designed for researchers, analysts, and students, the package facilitates exploration and analysis of data related to health monitoring, physical activity, and rehabilitation. Package: r-cran-healthyr.ai Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 809 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-yardstick, r-cran-broom, r-cran-ggrepel, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-forcats, r-cran-recipes, r-cran-purrr, r-cran-h2o, r-cran-dials, r-cran-parsnip, r-cran-tune, r-cran-workflows, r-cran-modeltime Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-healthyr.data, r-cran-scales, r-cran-tidyselect, r-cran-janitor, r-cran-timetk, r-cran-plotly, r-cran-rsample, r-cran-kknn, r-cran-hardhat, r-cran-uwot, r-cran-stringr Filename: pool/dists/noble/main/r-cran-healthyr.ai_0.1.1-1.ca2404.1_all.deb Size: 598062 MD5sum: 6b6ec3ff23c1e4d49d7b328a5766b67b SHA1: 4649b125ba2d16bbd27c1232a42fdad184aefd34 SHA256: eff6c13cf92b0c940d7bfae930a75720f8b809548674907404b1e2385f3b4e2c SHA512: 29fe020a464e5ec314f76ea8c8aac746845afd65af7c61ca44c9aa08bc793817b5e19e23b8ee7c140bf22fff29a5e1e979a3fc771e35ac8984f3ad1a032d8a5a Homepage: https://cran.r-project.org/package=healthyR.ai Description: CRAN Package 'healthyR.ai' (The Machine Learning and AI Modeling Companion to 'healthyR') Hospital machine learning and ai data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include predicting length of stay, and readmits. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything. 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Package: r-cran-healthyr.ts Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-timetk, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2, r-cran-lubridate, r-cran-plotly, r-cran-recipes, r-cran-modeltime, r-cran-cowplot, r-cran-forcats, r-cran-stringi, r-cran-parsnip, r-cran-workflowsets, r-cran-hardhat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-rsample, r-cran-healthyr.ai, r-cran-stringr, r-cran-forecast, r-cran-tidymodels, r-cran-glue, r-cran-xts, r-cran-zoo, r-cran-tsa, r-cran-tune, r-cran-dials, r-cran-workflows, r-cran-tidyselect, r-cran-glmnet, r-cran-earth, r-cran-smooth, r-cran-kernlab Filename: pool/dists/noble/main/r-cran-healthyr.ts_0.3.2-1.ca2404.1_all.deb Size: 2234794 MD5sum: 20d0b7cf500006087c846b605de1375b SHA1: 25162df64ada17605db789713a42baf1d2c073d3 SHA256: 1382e43a0a7c55c15e57f654d3a65d21113f7279fbd2131c83b896bdbef0d571 SHA512: 5c3727936ae4274ca2bec0383f3b3d97104c35092ac6453e6c1ee03242ef572636f538ce09c993cc595cd65044289d999fbbf92c2d3a1ca315e9982f279371ad Homepage: https://cran.r-project.org/package=healthyR.ts Description: CRAN Package 'healthyR.ts' (The Time Series Modeling Companion to 'healthyR') Hospital time series data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative time series hospital data. Some of these include average length of stay, and readmission rates. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything. 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This library provides many useful tools to review common administrative hospital data. Some of these include average length of stay, readmission rates, average net pay amounts by service lines just to name a few. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything. 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This package is designed to make it easy to install and load multiple 'healthyverse' packages in a single step. Package: r-cran-heaping Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 714 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fitdistrplus, r-cran-envstats Suggests: r-cran-vim, r-cran-ranger, r-cran-data.table, r-cran-ggplot2, r-cran-simpop, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-heaping_0.1.0-1.ca2404.1_all.deb Size: 530040 MD5sum: 2bffb362296d149101fbb67f3e67fbaf SHA1: 73ad6786b7fe18e0375a5dd8b0d0eb461f86308b SHA256: b011636ccb2b30157dc1b13a67d6eda42254bc4b400dbdab8a3365c244c058b2 SHA512: 50cac704db198234eeb2d9a5f1c420d558ae3d1384915434f6e50aafb419f993af23a71f0edad830097a97845581048c8f13b225e7a5946ba2433f5568d9042b Homepage: https://cran.r-project.org/package=heaping Description: CRAN Package 'heaping' (Correction of Heaping on Individual Level) Provides methods for correcting heaping (digit preference) in survey data at the individual record level. Age heaping, where respondents disproportionately report ages ending in 0 or 5, is a common phenomenon that can distort demographic analyses. Unlike traditional smoothing methods that only correct aggregated statistics, this package corrects individual values by replacing a calculated proportion of heaped observations with draws from fitted truncated distributions (log-normal, normal, or uniform). Supports 5-year and 10-year heaping patterns, single heap correction, and optional model-based adjustment to preserve covariate relationships. Package: r-cran-heapsofpapers Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2599 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aws.s3, r-cran-curl, r-cran-dplyr, r-cran-fs, r-cran-magrittr, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-heapsofpapers_0.1.0-1.ca2404.1_all.deb Size: 2059638 MD5sum: cc210f1fae84d98e224284eb98aaa523 SHA1: cf4dcb01e7cd5a8242e87e6fb166022093b9788e SHA256: 12d0cdb83cd21060c50618bf38ad4e33d32902f9e793646455c9394a192d20a1 SHA512: ce2446f4de800de075d2634fad256d4d08c3d71b02fd8d587ab4372c43116b7380cb2b254d64f0e893390ba1937a4d7684dc003a4489309dfbf81963118f5ebb Homepage: https://cran.r-project.org/package=heapsofpapers Description: CRAN Package 'heapsofpapers' (Easily Download Heaps of PDF and CSV Files) Makes it easy to download a large number of files such as PDF files and CSV files, while automatically slowing down requests, letting you know where it is up to, and adjusting for files that have already been downloaded. Package: r-cran-heartbeatr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3732 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-av, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-transformr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-heartbeatr_1.0.0-1.ca2404.1_all.deb Size: 2150284 MD5sum: 67aa68c9c6db9ad50771760877310274 SHA1: f128955c1a14b03636b8e493957cf24cdd780008 SHA256: ea1cd82df7e60ad6a920125e4f56ab69eece5c17d5b7d6603e5e8c94344eea4b SHA512: 927687511495fdc0301481d936856f716b9de49366ab7d24129e3afb4dd624eeb4efc0fc0a7d8d5f5fd4c136a15d18490d57427e87680dc676bd9ccc5b57c2a9 Homepage: https://cran.r-project.org/package=heartbeatr Description: CRAN Package 'heartbeatr' (A Workflow to Process Data Collected with PULSE Systems) Given one or multiple paths to files produced by a PULSE multi-channel or a PULSE one-channel system () from a single experiment: [1] check pulse files for inconsistencies and read/merge all data, [2] split across time windows, [3] interpolate and smooth to optimize the dataset, [4] compute the heart rate frequency for each channel/window, and [5] facilitate quality control, summarising and plotting. Heart rate frequency is calculated using the Automatic Multi-scale Peak Detection algorithm proposed by Felix Scholkmann and team. For more details see Scholkmann et al (2012) . Check original code at . ElectricBlue is a non-profit technology transfer startup creating research-oriented solutions for the scientific community (). Package: r-cran-heatex Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-heatex_1.0-1.ca2404.1_all.deb Size: 33060 MD5sum: 70abf8b06e6444274f185653e5849024 SHA1: 085960fe1085914ebc6dfee9f0dacade975edc85 SHA256: 7cfe16add2b8e0d57f7ca0e11479df05d1634644994ecc7b72252df96843a9c0 SHA512: 312666fb2246efabf9607b5e426a020ebcdebb358baf79f2e1d594ac2c29741d5ebd198384015c934f1104ab0662c4c2f6a3c3d5e28c9459473e6673d8a72068 Homepage: https://cran.r-project.org/package=heatex Description: CRAN Package 'heatex' (Heat exchange calculations during physical activity) The heatex package calculates heat storage in the body and the components of heat exchange (conductive, convective, radiative, and evaporative) between the body and the environment during physical activity based on the principles of partitional calorimetry. The program enables heat exchange calculations for a range of environmental conditions when wearing various clothing ensembles. Package: r-cran-heatmap3 Architecture: all Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastcluster Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-heatmap3_1.1.9-1.ca2404.1_all.deb Size: 174344 MD5sum: 1206c60695ac4d33190a895dc82c71f0 SHA1: 9416cc2bbde06e6d6588e5bb003a7a7bedac7ebe SHA256: 4d3a1dcdfad5b5ae74f938519fc30351dd97f3133f2ed5a73f40804ec3260f72 SHA512: aa7b2926aae49a60ac296b65918536dd9ad250416c30832e49d46da0c669fae548304496c4c374a71d09b298b9b120fcb36fb2739f475b56afe12d2aed091cbd Homepage: https://cran.r-project.org/package=heatmap3 Description: CRAN Package 'heatmap3' (An Improved Heatmap Package) An improved heatmap package. Completely compatible with the original R function 'heatmap', and provides more powerful and convenient features. Package: r-cran-heatmapfit Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-heatmapfit_2.0.4-1.ca2404.1_all.deb Size: 39030 MD5sum: 7655d6bc66517b3076cb3875c22a7c81 SHA1: 0923bdbec95c9ba9b62e9276056bad8c1dc350dc SHA256: 31a828fa5f588458dcb37df9723677083b32459972d1c15f18e4df69dbe4f1ce SHA512: 1b62a9ab96153c180d42a1db53ca9e790d26189810785a35c88c301304fa19e489fe4f2a5c665b835f9f7b91c85d3c48c6822956f9aaebd8659d69a9b32a5630 Homepage: https://cran.r-project.org/package=heatmapFit Description: CRAN Package 'heatmapFit' (Fit Statistic for Binary Dependent Variable Models) Generates a fit plot for diagnosing misspecification in models of binary dependent variables, and calculates the related heatmap fit statistic described in Esarey and Pierce (2012) . Package: r-cran-heatmapflex Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1494 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase, r-bioc-heatplus, r-cran-rcolorbrewer Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-heatmapflex_0.1.2-1.ca2404.1_all.deb Size: 1017156 MD5sum: 1000181f7e8f4b4b9c4dd2792802fb77 SHA1: 45c2e0dcd9be8665deebd9a86a10efec4631c44c SHA256: 504ba3b4dfa9343bbc00d7d64246fd9bd0c6abb60ab792132a6448c363d556f6 SHA512: bfd8c890e0bae0d41d53653c69cc84ea89931be2fc1b81eda80c2d68b2b9b4b63840e7960a15066b55bbea4b7bcd2e3481ecbe95bddfbd979fab509c21402037 Homepage: https://cran.r-project.org/package=heatmapFlex Description: CRAN Package 'heatmapFlex' (Tools to Generate Flexible Heatmaps) A set of tools supporting more flexible heatmaps. The graphics is grid-like using the old graphics system. The main function is heatmap.n2(), which is a wrapper around the various functions constructing individual parts of the heatmap, like sidebars, picket plots, legends etc. The function supports zooming and splitting, i.e., having (unlimited) small heatmaps underneath each other in one plot deriving from the same data set, e.g., clustered and ordered by a supervised clustering method. Package: r-cran-heatmaply Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5941 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-viridis, r-cran-ggplot2, r-cran-dendextend, r-cran-magrittr, r-cran-reshape2, r-cran-scales, r-cran-seriation, r-cran-colorspace, r-cran-rcolorbrewer, r-cran-htmlwidgets, r-cran-webshot, r-cran-assertthat, r-cran-egg Suggests: r-cran-knitr, r-cran-covr, r-cran-gplots, r-cran-tidyselect, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-heatmaply_1.6.0-1.ca2404.1_all.deb Size: 1668798 MD5sum: 7a967e46b8a1b92bfd2b9feee968c850 SHA1: 0af0b7c71fb74dd026a8cbae440107e5c1f9c2da SHA256: 3cdbc715e6f3ef82cc16d5873f8e84acc48787fa6a67915193a90ab1a912b852 SHA512: 5eb4e730ea3dc4258b007413499f3c59541ea1029c2793a7aaa37492ec6f2b5a05cf2d91fd0a3ef77e6e3cb7f2faa2bf943ff80dc2f7b79a029227881f249139 Homepage: https://cran.r-project.org/package=heatmaply Description: CRAN Package 'heatmaply' (Interactive Cluster Heat Maps Using 'plotly' and 'ggplot2') Create interactive cluster 'heatmaps' that can be saved as a stand- alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and available in the 'RStudio' viewer pane. Hover the mouse pointer over a cell to show details or drag a rectangle to zoom. A 'heatmap' is a popular graphical method for visualizing high-dimensional data, in which a table of numbers are encoded as a grid of colored cells. The rows and columns of the matrix are ordered to highlight patterns and are often accompanied by 'dendrograms'. 'Heatmaps' are used in many fields for visualizing observations, correlations, missing values patterns, and more. Interactive 'heatmaps' allow the inspection of specific value by hovering the mouse over a cell, as well as zooming into a region of the 'heatmap' by dragging a rectangle around the relevant area. This work is based on the 'ggplot2' and 'plotly.js' engine. It produces similar 'heatmaps' to 'heatmap.2' with the advantage of speed ('plotly.js' is able to handle larger size matrix), the ability to zoom from the 'dendrogram' panes, and the placing of factor variables in the sides of the 'heatmap'. Package: r-cran-heatmapr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-heatmapr_1.1.0-1.ca2404.1_all.deb Size: 615952 MD5sum: 652816b676e30b935008aace6709e5e2 SHA1: acb5731a49c3ebd8e812976c75dbbdb099d87c13 SHA256: f909eb5f72de7d928c7f48291adb837d0274da3353025164a3184009715b8ef2 SHA512: 9b07ca89bd5bc8b553e315f747f0ebda1bbd3f08e47bca03c98372de40afc182a4c693031805a86d24633596de2338ae5420859a98bc98ee1bb010649c9c5ee2 Homepage: https://cran.r-project.org/package=HeatmapR Description: CRAN Package 'HeatmapR' (Create Heatmaps Using Base Graphics) Provides a lightweight framework for creating high quality, complex heatmaps using base graphics. Supports hierarchical clustering with dendrograms, column and row scaling, cluster sub-divisions, customizable cell colours, shapes and sizes, legends, and flexible layouts for arranging multiple heatmaps. Designed to return plot objects that can be easily arranged with other plots without sacrificing resolution. Methods for hierarchical clustering and distance computations are described in Murtagh and Contreras (2012) . Dendrogram visualisation methods are based on the 'ggdendro' package by de Vries and Ripley (2020) . Package: r-cran-heattree Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ape, r-cran-htmlwidgets Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-heattree_0.3.1-1.ca2404.1_all.deb Size: 400282 MD5sum: 3f1a332debcc4df462a578e1a1fe1dd0 SHA1: 356a407d8f56c706618e4208444070a514190a80 SHA256: 3b1d9555324624438faf62a8ee458fa5098848d5784d48908b7da23e554c13b8 SHA512: b60236fc0c3b399b7e4ece21d769a534bc422819863a5503837262d0541414a7ca8c4ebf715d7c1a69eda70327a7d7b25698a6e781ea193b3ab7e56473f20dd3 Homepage: https://cran.r-project.org/package=heattree Description: CRAN Package 'heattree' (A Self-Contained Widget for Interactive Phylogenetic TreeVisualization) Creates self-contained widgets for interactive phylogenetic tree visualization. This package wraps the 'JavaScript' 'heat-tree' package using the 'htmlwidgets' R package. Package: r-cran-heavytails Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-heavytails_0.2.0-1.ca2404.1_all.deb Size: 143154 MD5sum: eee9bc93b9589ab0297e5c676e4aacba SHA1: 46eed53c2cb405b530b4ab459138e59d059a4d21 SHA256: e9eb804dc8c71c572fd0e440673e0345394fa6c260d83fd7c2a2e56036bba845 SHA512: 98bfa77b4e8d5895c210d8a3c4274c6e72f3373b3ecf74670939c63db04b4a37a203aa9c350fdbed498a92bd5ac3ee687e827f55f06977a0c8dce31d3f4f334c Homepage: https://cran.r-project.org/package=heavytails Description: CRAN Package 'heavytails' (Estimators and Algorithms for Heavy-Tailed Distributions) Implements the estimators and algorithms described in Chapters 8 and 9 of the book "The Fundamentals of Heavy Tails: Properties, Emergence, and Estimation" by Nair et al. (2022, ISBN:9781009053730). These include the Hill estimator, Moments estimator, Pickands estimator, Peaks-over-Threshold (POT) method, Power-law fit, and the Double Bootstrap algorithm. 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Package: r-cran-hemispher Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-autothresholdr, r-cran-dplyr, r-cran-jpeg, r-cran-dismo, r-cran-scales, r-cran-sf, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-hemispher_1.1.8-1.ca2404.1_all.deb Size: 4108470 MD5sum: 143193d0051a368904d83d70ea26d9c8 SHA1: 418482b10cc453908a926b0dfcca872d83de017f SHA256: de79dfe84432b221c2530d882f3e20b8efe11fd0115b31f0addeebc5fdd1eadb SHA512: ddc2ac35f7a8ce1e90d3ac25073dfc40fcf5280948b642752df9814cd2faabcbb13f14b65e65d0ca475416aba2e204a9b8eff9b4277682efa7545bcffb2a942f Homepage: https://cran.r-project.org/package=hemispheR Description: CRAN Package 'hemispheR' (Processing Hemispherical Canopy Images) Import and classify canopy fish-eye images, estimate angular gap fraction and derive canopy attributes like leaf area index and openness. Additional information is provided in the study by Chianucci F., Macek M. (2023) . Package: r-cran-henna Architecture: all Version: 0.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abdiv, r-cran-dplyr, r-cran-ggalluvial, r-cran-ggeasy, r-cran-ggforce, r-cran-ggraph, r-cran-ggnewscale, r-cran-ggplot2, r-cran-ggrepel, r-cran-liver, r-cran-paletteer, r-cran-reshape2, r-cran-rlang, r-cran-tidygraph, r-cran-viridis, r-cran-withr Suggests: r-bioc-enhancedvolcano, r-cran-qs2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-henna_0.7.5-1.ca2404.1_all.deb Size: 2372374 MD5sum: 0184809e74b3f3bb508eda06ef127d43 SHA1: 3e5d5847513fe420b1a9571ecc98e0902da40183 SHA256: b6d27b78d50942bc321e3a82baefabeb019ba96fcc2c458524637d14b295a9bf SHA512: 2302539990ddcde596ffd78f1c9462bfee4d4fa11256b3c361f5e3872f62e96a19c8de5e27ce0d46ae0b6cb04a20e9b6b81563ac5cdbadab822d3383cde4213f Homepage: https://cran.r-project.org/package=henna Description: CRAN Package 'henna' (A Versatile Visualization Suite) A visualization suite primarily designed for single-cell RNA-sequencing data analysis applications but well-suited for other purposes as well. It introduces novel plots to represent two-variable and frequency data and optimizes some commonly used plotting options (e.g., correlation, network, density, alluvial and volcano plots) for ease of usage and flexibility. Package: r-cran-heplots Architecture: all Version: 1.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3530 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-car, r-cran-mass, r-cran-magrittr, r-cran-purrr, r-cran-rgl, r-cran-tibble Suggests: r-cran-candisc, r-cran-cardata, r-cran-effects, r-cran-reshape, r-cran-gplots, r-cran-nlme, r-cran-lattice, r-cran-reshape2, r-cran-corrgram, r-cran-animation, r-cran-mvinfluence, r-cran-knitr, r-cran-litedown, r-cran-rmarkdown, r-cran-markdown, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-bookdown, r-cran-patchwork, r-cran-tinytable, r-cran-glue, r-cran-here, r-cran-sleuth2, r-cran-rrcov, r-cran-archdata, r-cran-qqtest, r-cran-vcdextra, r-cran-r.rsp, r-cran-kernsmooth, r-cran-aplpack, r-cran-foreign, r-cran-robustbase, r-cran-effectsize Filename: pool/dists/noble/main/r-cran-heplots_1.8.1-1.ca2404.1_all.deb Size: 2338026 MD5sum: 7a9c14d87675d8629d10ddda060f1d02 SHA1: 157a52cccfad975f8027a78895b328fb459cd844 SHA256: 2316163aa40cee4d41b0ebe2a0b9f02ff7984fbdb2f3b09b6ee653c1f7ea3b45 SHA512: ed7dd42336e1e9717e7dbd3fc7b356df22f68af252f5d2c42395e066b171f0cab958e41d507502b69455c604fc4ab83875b37a66cb9b574cdde0ea3628695574 Homepage: https://cran.r-project.org/package=heplots Description: CRAN Package 'heplots' (Visualizing Hypothesis Tests in Multivariate Linear Models) Provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). It also provides other tools for analysis and graphical display of the models such as robust methods and homogeneity of variance covariance matrices. The related 'candisc' package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables. Package: r-cran-hera Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-evaluate, r-cran-glue, r-cran-irdisplay, r-cran-jsonlite, r-cran-r6, r-cran-repr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-hera_0.1.1-1.ca2404.1_all.deb Size: 164976 MD5sum: 7bba5b850ddbb1a22f33cff8ff6366aa SHA1: 19469e19c6f8a2b5876d06d77152db77000691c9 SHA256: 2bf780c03e57be11230f433b0f7725801dd7e475436218ce4d156a86cf4584b8 SHA512: fe2f7cc07a5164d217cf2cc20bbd076b61342bdeae7848daaf8637381c202044dcf85a2949d4bb51355230df6af9d4893ab5f879bb849a73241a23326eb6267f Homepage: https://cran.r-project.org/package=hera Description: CRAN Package 'hera' (Companion to the 'xeus-r' 'jupyter' Kernel) Set of R functions to be coupled with the 'xeus-r' 'jupyter' kernel in order to drive execution of code in notebook input cells, how R objects are to be displayed in output cells, and handle two way communication with the front end through comms. 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Locations, routes and isolines are returned as 'sf' objects. Package: r-cran-heritability Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1383 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-heritability_1.4-1.ca2404.1_all.deb Size: 1372722 MD5sum: 1c690473cedc46b4716faebf11d52bf9 SHA1: ab8940560557fe7b743b7581d034d4f9bae41a4e SHA256: 39c7a7a576a3a70a81ff614e2108aeedbec4e78864c8b1d724d4028a8635b337 SHA512: 466eb0c62ed9974c3cc20d073e3a1ad1409755cf564216ba1c05dd4cde32f70610e2be62233f68c05994dd28268e0e8fc45315a2677145c2a629df08dc90bd94 Homepage: https://cran.r-project.org/package=heritability Description: CRAN Package 'heritability' (Marker-Based Estimation of Heritability Using Individual Plantor Plot Data) Implements marker-based estimation of heritability when observations on genetically identical replicates are available. These can be either observations on individual plants or plot-level data in a field trial. Heritability can then be estimated using a mixed model for the individual plant or plot data. For comparison, also mixed-model based estimation using genotypic means and estimation of repeatability with ANOVA are implemented. For illustration the package contains several datasets for the model species Arabidopsis thaliana. 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Here we provide functions to calculate various broad-sense heritabilities from 'asreml' and 'lme4' model objects. All methods we have implemented in this package have extensively discussed in the article by Schmidt et al. (2019) . Package: r-cran-heritseq Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2709 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cplm, r-bioc-deseq2, r-cran-lme4, r-cran-pbapply, r-cran-tweedie, r-cran-mass, r-bioc-summarizedexperiment Filename: pool/dists/noble/main/r-cran-heritseq_1.0.2-1.ca2404.1_all.deb Size: 2734116 MD5sum: 8404e676ff9529ed41d3e4b0de5f2b5f SHA1: 55425f0f60f21507bd3270f2529a600359b94a5d SHA256: 6e4951b32d462ff174ee4b0b19f00db28309064a303a7ce6ab534f9c9877099b SHA512: 03544a8c2a6af387956ee89a087aa64903ee2cbf69efd0769b2af05a42a47f0fcc8faf76686fc6077fc2a2fc6d51adb4eed401f40cdf5b9eab64ecc87f480e75 Homepage: https://cran.r-project.org/package=HeritSeq Description: CRAN Package 'HeritSeq' (Heritability of Gene Expression for Next-Generation Sequencing) Statistical framework to analyze heritability of gene expression based on next-generation sequencing data and simulating sequencing reads. Variance partition coefficients (VPC) are computed using linear mixed effects and generalized linear mixed effects models. Compound Poisson and negative binomial models are included. Reference: Rudra, Pratyaydipta, et al. "Model based heritability scores for high-throughput sequencing data." BMC bioinformatics 18.1 (2017): 143. Package: r-cran-hermite Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maxlik Filename: pool/dists/noble/main/r-cran-hermite_1.1.2-1.ca2404.1_all.deb Size: 72958 MD5sum: de5b98ecfe7bc3ce1aef1d4502467ba7 SHA1: 23ee11b2d73b44e2692d5c91c0da556f96fd0119 SHA256: 518295c5787e0ef900ea77fbe3a4122d9f7eb5bb6ddcb9c2e7e20f535ae36a48 SHA512: fadf879d7d3af8067f9c4055819df5300be2d3845131f3a8bab64eea325d49e24a8f678e6f69b65241941425f3e3b62c134d04e405a61e10257084c9b28dfc5b Homepage: https://cran.r-project.org/package=hermite Description: CRAN Package 'hermite' (Generalized Hermite Distribution) Probability functions and other utilities for the generalized Hermite distribution. 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(2012) . A hero is a specific type of sandwich. Dictionary.com (2018) describes a hero as: a large sandwich, usually consisting of a small loaf of bread or long roll cut in half lengthwise and containing a variety of ingredients, as meat, cheese, lettuce, and tomatoes. Also implements the spatio-temporal sandwich smoother of French and Kokoszka (2021) . Package: r-cran-hessrna Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr, r-bioc-deseq2, r-cran-ssizerna, r-cran-rdpack Suggests: r-cran-car, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hessrna_1.0.1-1.ca2404.1_all.deb Size: 38998 MD5sum: 20692d69565bf64b56b853e2ea5e48a4 SHA1: 05b43f90bcc4a1c29e92ad0d6b75b4b6d0278356 SHA256: 89111354c462d8c9f85a949c21444ba108f0a06d9a8e86e0c9358d95ea920576 SHA512: fc61f8c02867d790503e3b37d51a4352196adb123ebc751face3fdc87ad9b2c597ddbf4a9d1400145de4904959eb9e68c80e42517e6e5fc6b62e4ed5080598d1 Homepage: https://cran.r-project.org/package=HEssRNA Description: CRAN Package 'HEssRNA' (Heritability-Based Estimation of Sample Size for RNA-Seq Data) Provides tools for estimating sample sizes primarily based on heritability, while also considering additional parameters such as statistical power and fold change. 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Package: r-cran-hetcorfs Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-polycor, r-cran-dplyr, r-cran-cluster, r-cran-psych Filename: pool/dists/noble/main/r-cran-hetcorfs_1.0.1-1.ca2404.1_all.deb Size: 52688 MD5sum: 17722afc691bd1924b5191080246598f SHA1: 8faa02cb85d03ca1d56fdee02dbe307e6f43a34e SHA256: 9177853ea73af4aad4e4cd184e2c7737816664481123a482c41a8878e07404fc SHA512: bada86d12cbd21a1792eda433e31f04ce3827a474ee67eaab01f8fd1a683e0f822d4d451d1cc306267081055d2a01adbdedba3f402a37f16a285b1695f8aeb38 Homepage: https://cran.r-project.org/package=hetcorFS Description: CRAN Package 'hetcorFS' (Unsupervised Feature Selection using the HeterogeneousCorrelation Matrix) Unsupervised multivariate filter feature selection using the UFS-rHCM or UFS-cHCM algorithms based on the heterogeneous correlation matrix (HCM). The HCM consists of Pearson's correlations between numerical features, polyserial correlations between numerical and ordinal features, and polychoric correlations between ordinal features. Tortora C., Madhvani S., Punzo A. (2025). "Designing unsupervised mixed-type feature selection techniques using the heterogeneous correlation matrix." International Statistical Review . This work was supported by the National Science foundation NSF Grant N 2209974 (Tortora) and by the Italian Ministry of University and Research (MUR) under the PRIN 2022 grant number 2022XRHT8R (CUP: E53D23005950006), as part of ‘The SMILE Project: Statistical Modelling and Inference to Live the Environment’, funded by the European Union – Next Generation EU (Punzo). Package: r-cran-heterfunctionaldata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-heterfunctionaldata_0.1.0-1.ca2404.1_all.deb Size: 63282 MD5sum: 467b4dfe47dd7a1f55ae31e4627f294e SHA1: 4aa3317f42a05636544f7c14e7427e81e11238fe SHA256: 314715c577b93c48c648ccdcfc9613e50f4a8510e9e5c2aa036353a971f11bd0 SHA512: 0436428bac0888c5e8719793bc7b94ada36c87efe0dd50030e65e0a3e1e12067827f26d11a1f23c0bc40743acf57ee31d9c55a6bca83fdbf446b9e485e759a47 Homepage: https://cran.r-project.org/package=HeterFunctionalData Description: CRAN Package 'HeterFunctionalData' (Test of No Main and/or Interaction Effects in Functional Data) Distribution free heteroscedastic tests for functional data. The following tests are included in this package: test of no main treatment or contrast effect and no simple treatment effect given in Wang, Higgins, and Blasi (2010) , no main time effect, and no interaction effect based on original observations given in Wang and Akritas (2010a) and tests based on ranks given in Wang and Akritas (2010b) . Package: r-cran-heterocop Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-igraph, r-cran-matrixcalc, r-cran-foreach, r-cran-stringr, r-cran-dosnow, r-cran-huge Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-heterocop_1.0.1-1.ca2404.1_all.deb Size: 103966 MD5sum: 5ae6f98c8b0129fbf618b1944f872d35 SHA1: ecc956b1b5c9ab5e54ae9343e811a346eeaa5235 SHA256: b744074b63d4cd7525a4d1508eb6a3690a279f80c118795ee841319b9f21e378 SHA512: cea424d628529df614639f63581dc0f91dd885de947aeaf7e20f5a086aad5477974325742769916112185fe0f9c75a868c73ee299d982e5e74d64dff0cc2e925 Homepage: https://cran.r-project.org/package=heterocop Description: CRAN Package 'heterocop' (Semi-Parametric Estimation with Gaussian Copula) A method for estimating the correlation matrix of the Gaussian copula from the observed data. This package also contains a penalized estimation of the corresponding precision matrix, and enables to generate random vectors that are distributed according to a Gaussian copula. Package: r-cran-heteroggm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-mass, r-cran-huge Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-heteroggm_1.0.1-1.ca2404.1_all.deb Size: 311010 MD5sum: 2558458e98759ea70f244f6ff2fc20d8 SHA1: 3df83109afa97f4f009820a398bf798d2d3bc406 SHA256: 547c0c466250f43c76c2232e68e3621c7548e62ef7f7dcb52b46c2a8880e9d35 SHA512: 61873a06956ab86f0c4ac2f7c28a6997b39b9f4d06d2ac2c57e806e0faf1bd64f29eecac3ef79a4e0687744185f443a73ad1b763380c65966e1cdda8b17bd6a6 Homepage: https://cran.r-project.org/package=HeteroGGM Description: CRAN Package 'HeteroGGM' (Gaussian Graphical Model-Based Heterogeneity Analysis) The goal of this package is to user-friendly realizing Gaussian graphical model-based heterogeneity analysis. Recently, several Gaussian graphical model-based heterogeneity analysis techniques have been developed. A common methodological limitation is that the number of subgroups is assumed to be known a priori, which is not realistic. In a very recent study (Ren et al., 2022), a novel approach based on the penalized fusion technique is developed to fully data-dependently determine the number and structure of subgroups in Gaussian graphical model-based heterogeneity analysis. It opens the door for utilizing the Gaussian graphical model technique in more practical settings. Beyond Ren et al. (2022), more estimations and functions are added, so that the package is self-contained and more comprehensive and can provide ``more direct'' insights to practitioners (with the visualization function). Reference: Ren, M., Zhang S., Zhang Q. and Ma S. (2022). Gaussian Graphical Model-based Heterogeneity Analysis via Penalized Fusion. Biometrics, 78 (2), 524-535. Package: r-cran-heterometa Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-mathjaxr Filename: pool/dists/noble/main/r-cran-heterometa_0.5-1.ca2404.1_all.deb Size: 31018 MD5sum: da05e70098da4507a94e62ae3dbd6fae SHA1: b31e45149b9b52b476d97751e3d136a10d446018 SHA256: f5357efe408c5440e5644e5e28fcce98246300d9c48c70194c846cbce603456c SHA512: 43a64ea09038cdffa065694f1b31187858414fec08ba0e0be9e7da57ea0ac31e2971fa39ab48d8aa5bd98b3439b76d50e2c21f93e163bfba7d689e2e9c80c5cb Homepage: https://cran.r-project.org/package=heterometa Description: CRAN Package 'heterometa' (Convert Various Meta-Analysis Heterogeneity Measures) Published meta-analyses routinely present one of the measures of heterogeneity introduced in Higgins and Thompson (2002) . For critiquing articles it is often better to convert to another of those measures. Some conversions are provided here and confidence intervals are also available. Package: r-cran-heteromixgm Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-tmvtnorm, r-cran-glasso, r-cran-bdgraph, r-cran-mass Filename: pool/dists/noble/main/r-cran-heteromixgm_2.0.2-1.ca2404.1_all.deb Size: 110148 MD5sum: 55a4037834f0f6271988298c4d02cc68 SHA1: 3483558c59492ca97e690cd7f240c70962718170 SHA256: 9c86c534a215491576ac46cc7a35436600307f7b362dae73e8b0e7a775e73d73 SHA512: 0b4e5f65da5fd633f0f8b1eb33ce21704b699815bc8dfe074a9ea773115d8025857d4d34d507ce36cef8d0dead9816e0d0060b58ba2e35180a8e271969a0b877 Homepage: https://cran.r-project.org/package=heteromixgm Description: CRAN Package 'heteromixgm' (Copula Graphical Models for Heterogeneous Mixed Data) A multi-core R package that allows for the statistical modeling of multi-group multivariate mixed data using Gaussian graphical models. Combining the Gaussian copula framework with the fused graphical lasso penalty, the 'heteromixgm' package can handle a wide variety of datasets found in various sciences. The package also includes an option to perform model selection using the AIC, BIC and EBIC information criteria, a function that plots partial correlation graphs based on the selected precision matrices, as well as simulate mixed heterogeneous data for exploratory or simulation purposes and one multi-group multivariate mixed agricultural dataset pertaining to maize yields. The package implements the methodological developments found in Hermes et al. (2024) . Package: r-cran-hetools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-polynom Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hetools_1.0.0-1.ca2404.1_all.deb Size: 16662 MD5sum: 8ec49fb0c198e3420927d03203477447 SHA1: e5828365c6ff2561c154fe34eccefe563b398373 SHA256: 4161970fe086db8c0ee1b9c65797951808001123c2a879db84b698572d6ed85e SHA512: d751b82d0e0b7752101a9584e045fd77c3ce4464fcbf8097c4463445174c4f70935f25e37591839b2127ee81db200e5da0b799aaa17cb1fb69c25643b3f162c4 Homepage: https://cran.r-project.org/package=HEtools Description: CRAN Package 'HEtools' (Homomorphic Encryption Polynomials) Homomorphic encryption (Brakerski and Vaikuntanathan (2014) ) using Ring Learning with Errors (Lyubashevsky et al. (2012) ) is a form of Learning with Errors (Regev (2005) ) using polynomial rings over finite fields. Functions to generate the required polynomials (using 'polynom'), with various distributions of coefficients are provided. Additionally, functions to generate and take coefficient modulo are provided. Package: r-cran-hetop Architecture: all Version: 0.2-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2jags Filename: pool/dists/noble/main/r-cran-hetop_0.2-6-1.ca2404.1_all.deb Size: 74518 MD5sum: 7ea261b9da548858ea65b29e3e2fd009 SHA1: 08988cc21f2e3932a1e37d411f15cafce18c3f63 SHA256: 8fef0afc6030a948fb3329f6057432e289e3f522ff87f519ed5ee8b8f792c83c SHA512: ad6745b6da6149732da0118d40360d1d66eab5e045cbc33d302910bdc73ac5285ea7ac87ef26817f352d3adf51d680fcf39ce5acb62e96fd72fe6aa81e6a7885 Homepage: https://cran.r-project.org/package=HETOP Description: CRAN Package 'HETOP' (MLE and Bayesian Estimation of Heteroskedastic Ordered Probit(HETOP) Model) Provides functions for maximum likelihood and Bayesian estimation of the Heteroskedastic Ordered Probit (HETOP) model, using methods described in Lockwood, Castellano and Shear (2018) and Reardon, Shear, Castellano and Ho (2017) . It also provides a general function to compute the triple-goal estimators of Shen and Louis (1998) . 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Connecting post-perturbation cells via cellular trajectories to untreated cells (e.g. by leveraging metabolic labeling information) enables exploitation of intercellular heterogeneity as a combined knock-down and overexpression screen to identify pathway modulators, termed Heterogeneity-seq (see 'Berg et al' ). This package contains functions to generate cellular trajectories based on scSLAM-seq (single-cell, thiol-(SH)-linked alkylation of RNA for metabolic labelling sequencing) time courses, functions to identify pathway modulators and to visualize the results. Package: r-cran-hetsurr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsurrogate Filename: pool/dists/noble/main/r-cran-hetsurr_1.0-1.ca2404.1_all.deb Size: 119316 MD5sum: 258a11029701c74b7333e10a228bfa0b SHA1: 12db5d21c85e5f6672bc0cdceaefef6a5faba14e SHA256: ee17e46c1c4ad58d70c3fe5120feb823b33b33720ce1a9e2b63509e5e012cb98 SHA512: b55e6e123732920500a719eb07640fd167458012612b1f9512a2d132b925023e38dbbfa7b1724bb3d323ed7313318494d17cdc7fc682f2b03f73f5f52ede0ec3 Homepage: https://cran.r-project.org/package=hetsurr Description: CRAN Package 'hetsurr' (Assessing Heterogeneity in the Utility of a Surrogate Marker) Provides a function to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate. The main function can be used for either a continuous or discrete baseline covariate. More details will be available in the future in: Parast, L., Cai, T., Tian L (2021). "Testing for Heterogeneity in the Utility of a Surrogate Marker." Biometrics, In press. 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More details are available in Parast et al (2024) . Package: r-cran-hett Architecture: all Version: 0.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-lattice Filename: pool/dists/noble/main/r-cran-hett_0.3-3-1.ca2404.1_all.deb Size: 66728 MD5sum: 2ed1d9ce2c6870cf35fccc8d26012551 SHA1: 17b7a61e1102fe35f6f68b7f8fd29f626145f0a8 SHA256: 224fd0758a0013c99df248be991adbe8d1f3505965a54a307a4cc983e8808188 SHA512: 0bbda3b3345f23426b60b688e95a85ed323b2543bef6d4567b694fc5c056978bf76dda600d213dc9c1e941282bad37e35ef8cdc06c405c56d896df9e6969a9a3 Homepage: https://cran.r-project.org/package=hett Description: CRAN Package 'hett' (Heteroscedastic t-Regression) Functions for the fitting and summarizing of heteroscedastic t-regression. Package: r-cran-hettest Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hettest_1.0-1.ca2404.1_all.deb Size: 75516 MD5sum: 17b393a0ba0c741d627938d812a6c539 SHA1: 425c92dc5e8b90626d77c3a6c7cac74932f99c80 SHA256: f53ef2cc1185029f8933f2aecdca7167c215bbcfdf651596b20d160b0c77dd9e SHA512: 555d31f02f834e6bdece387c70931058816ecf0faf83f17db894fed61af31972ef3f1607a19f2fe59787a6cfd8c648b43cfbdffed96c519fe867611529e3308c Homepage: https://cran.r-project.org/package=hettest Description: CRAN Package 'hettest' (Testing for a Treatment Effect Using a Heterogeneous SurrogateMarker) Tests for a treatment effect using surrogate marker information accounting for heterogeneity in the utility of the surrogate. Details are described in Parast et al (2022) . 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The output of 'hettreatreg' represents ordinary least squares (OLS) estimates of the effect of a binary treatment as a weighted average of the average treatment effect on the treated (ATT) and the average treatment effect on the untreated (ATU). The program estimates the OLS weights on these parameters, computes the associated model diagnostics, and reports the implicit OLS estimate of the average treatment effect (ATE). See Sloczynski (2019), . Package: r-cran-hettx Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 842 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-quantreg, r-cran-mvtnorm, r-cran-mass, r-cran-foreach, r-cran-doparallel, r-cran-moments, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-purrr Filename: pool/dists/noble/main/r-cran-hettx_1.0.1-1.ca2404.1_all.deb Size: 592558 MD5sum: 20898c93d8d59687b8743104d0607ac4 SHA1: 03d33bfab12c0ca1f7c5826150083054f24a2d93 SHA256: d63c567df454619bd2c0ed53f5c786fcca11953bbdc2a19dc953d614374710d1 SHA512: 02f38914f2fdd01295871380e91472aee0b07ef60c08b41257f54106a0856d7b519ca08572b43503cddb13925dca6e14e16a2d1f0e01bd69f0cbfb91c5da52df Homepage: https://cran.r-project.org/package=hettx Description: CRAN Package 'hettx' (Fisherian and Neymanian Methods for Detecting and MeasuringTreatment Effect Variation) Implements methods developed by Ding, Feller, and Miratrix (2016) , and Ding, Feller, and Miratrix (2018) for testing whether there is unexplained variation in treatment effects across observations, and for characterizing the extent of the explained and unexplained variation in treatment effects. The package includes wrapper functions implementing the proposed methods, as well as helper functions for analyzing and visualizing the results of the test. Package: r-cran-hetu Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-checkmate Suggests: r-cran-cairo, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-covr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-hetu_1.1.0-1.ca2404.1_all.deb Size: 124002 MD5sum: 13a74503c84d6ae501228abd8d3ca699 SHA1: d625cbdf0411419df0d474ca2f54265ebce2db6c SHA256: 6b0e5838a06acf052e1e6f31f50bbdfecd4bb6f66f317ba6f1540dc48acd66b7 SHA512: c2f3a7877de1e3459d4314193b93fdfaaa0de4c884f314845729178b7d719fb350ae987101953c92ac0a66740431380db74823cc2509d1771497c98677fe6548 Homepage: https://cran.r-project.org/package=hetu Description: CRAN Package 'hetu' (Structural Handling of Finnish Personal Identity Codes) Structural handling of Finnish identity codes (natural persons and organizations); extract information, check ID validity and diagnostics. Package: r-cran-heuristica Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 618 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc Suggests: r-cran-devtools, r-cran-ggplot2, r-cran-glmnet, r-cran-knitr, r-cran-plyr, r-cran-reshape, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-heuristica_1.0.3-1.ca2404.1_all.deb Size: 331238 MD5sum: f8c57d4bb28b4dd0ad0b2fa0ab8f934a SHA1: 96c1cbad8cba292bb0e4594320269f16710d3ef4 SHA256: e5a3739d4f24956e63c34be25e886f271fa65341a623877ca447b9ac2aa6430b SHA512: 446edd3a4c6016e30bef887767451d22a4ce613768a2191445c3e26abc3385189565a93ae20945d28b8bf2276670b9ca174577e6a64ea549118c34d82406ee9d Homepage: https://cran.r-project.org/package=heuristica Description: CRAN Package 'heuristica' (Heuristics Including Take the Best and Unit-Weight Linear) Implements various heuristics like Take The Best and unit-weight linear, which do two-alternative choice: which of two objects will have a higher criterion? 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Cluster Regularization via a Hierarchical Feature Regression. Econometrics and Statistics (in press). . 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The small depressions are combined into a single "meta" depression which explicitly models the hysteresis between the storage of water and the connected/contributing areas of the depressions. The largest (greater than 5% of the total depressional area) depression (if it exists) is represented separately to model its gatekeeping, i.e. the blocking of upstream flows until it is filled. The methodolgy is described in detail in Shook and Pomeroy (2025, ). Package: r-cran-hglasso Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glasso, r-cran-mvtnorm, r-cran-igraph, r-cran-fields Filename: pool/dists/noble/main/r-cran-hglasso_1.3-1.ca2404.1_all.deb Size: 103560 MD5sum: d014939d63131dff12b0f0e984e11980 SHA1: f5c7bc4d0eb62ebb3157a4c9c3b6914f0fe30bc5 SHA256: e990532f0fb935c311db8f22817b7fdb0fafe9013a6b78b0b410ffce8b1c1f2b SHA512: 2124686ffd7c5b9d2a4918b8cd2315112913d80b07e577d22e747ff7d6d5a9121e08aa82001380677fcf0d76d86680382f6866f38bf7de4d0fa1d29a557cc698 Homepage: https://cran.r-project.org/package=hglasso Description: CRAN Package 'hglasso' (Learning Graphical Models with Hubs) Implements the hub graphical lasso and hub covariance graph proposal by Tan, KM., London, P., Mohan, K., Lee, S-I., Fazel, M., and Witten, D. (2014). Learning graphical models with hubs. Journal of Machine Learning Research 15(Oct):3297-3331. Package: r-cran-hglm.data Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4963 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-sp Filename: pool/dists/noble/main/r-cran-hglm.data_1.0-2-1.ca2404.1_all.deb Size: 5048870 MD5sum: 1ed5a947d2fbea1976822ccd7e0fb067 SHA1: 1a6b2c8dcf81a137ec5aa8bf540f642536e11757 SHA256: 27d231070a45d2b7b46420820335126a73dd97a7c73268671d8382f5394119eb SHA512: 78151c1fda41a02a53a4b42a5de28bb8c9bb6b1153556480e54d1a9b83b3ab86a1faf23248444664bf90ed1c64fffc05d48f83abc01565f8d26297ea04680991 Homepage: https://cran.r-project.org/package=hglm.data Description: CRAN Package 'hglm.data' (Data for the 'hglm' Package) This data-only package was created for distributing data used in the examples of the 'hglm' package. 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It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model. As statistical models, HGLMs were initially developed by Lee and Nelder (1996) . We provide an implementation (Ronnegard, Alam and Shen 2010) following Lee, Nelder and Pawitan (2006) with algorithms extended for spatial modeling (Alam, Ronnegard and Shen 2015) . 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The traditional Gaussian graphical model and its extensions either have a Gaussian assumption on the data distribution or assume the data are homogeneous. However, there are data with complex distributions violating these two assumptions. For example, the air pollutant concentration records are non-negative and, hence, non-Gaussian. Moreover, due to climate changes, distributions of these concentration records in different months of a year can be far different, which means it is uncertain whether datasets from different months are homogeneous. Methods with a Gaussian or homogeneous assumption may incorrectly model the conditional dependence relationships among variables. Therefore, we propose a heterogeneous graphical model for non-negative data (HGMND) to simultaneously cluster multiple datasets and estimate the conditional dependence matrix of variables from a non-Gaussian and non-negative exponential family in each cluster. 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Depth first traversal(DFS) is a recursive algorithm for searching all the vertices of a graph or tree data structure. Traversal means visiting all the nodes of a graph. Breadth first traversal(BFS) algorithm is used to search a tree or graph data structure for a node that meets a set of criteria. It starts at the tree’s root or graph and searches/visits all nodes at the current depth level before moving on to the nodes at the next depth level. Also, it provides the matrix which is reachable between each node. Implement reference about Baruch Awerbuch (1985) . 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Package development functionalities includes among others tools such as cross-referencing package imports with the description file, analysis of redundant package imports, editing of the description file and the creation of package badges for GitHub. Some of the other functionalities include automatic package installation and loading, plotting points without overlap, creating nice breaks for plots, overview tables and many more handy utility functions. 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Heiberger and Burt Holland. This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print. All chapters have exercises. All functions introduced in the book are in the package. R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package. 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Package: r-cran-hierportfolios Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastcluster, r-cran-cluster Filename: pool/dists/noble/main/r-cran-hierportfolios_1.0.2-1.ca2404.1_all.deb Size: 1098738 MD5sum: 1142f73b310c689ce105c73049e5d25e SHA1: 64f4ca9a15b0da7c96f469f503024f9430c7a65f SHA256: 61b97a59eb88f79cd5e2aae41e23237fa0d4b2f71b26ea283a217cc77c999ec7 SHA512: b8d5372c6a4fe8bc2df8974bed5d11224cda3bf06ab3a804dd486c52dd4c183d2c0214b5e180887b77742f30afb8faa9afba29e7ba1288a167aa7de80b008fe3 Homepage: https://cran.r-project.org/package=HierPortfolios Description: CRAN Package 'HierPortfolios' (Hierarchical Risk Clustering Portfolio Allocation Strategies) Machine learning hierarchical risk clustering portfolio allocation strategies. The implemented methods are: Hierarchical risk parity (De Prado, 2016) . Hierarchical clustering-based asset allocation (Raffinot, 2017) . Hierarchical equal risk contribution portfolio (Raffinot, 2018) . A Constrained Hierarchical Risk Parity Algorithm with Cluster-based Capital Allocation (Pfitzingera and Katzke, 2019) . 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The user has the option to compute the asymptotic, the permutation or the bootstrap based p-value of the test. Some references are: Chen S.X. and Qin Y.L. (2010). , Cai T.T., Liu W., and Xia Y. (2014) and Yu X., Li D., Xue L. and Li, R. (2023) . 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Package: r-cran-hilldiv Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-ggpubr, r-cran-rcolorbrewer, r-cran-data.table, r-cran-ape, r-cran-vegan, r-cran-geiger, r-cran-qgraph, r-cran-fsa Filename: pool/dists/noble/main/r-cran-hilldiv_1.5.1-1.ca2404.1_all.deb Size: 175448 MD5sum: 716f8a6ca14018b3a4f0d2d392ac55d2 SHA1: f6fb1a324f08f3d07dc787a3d59fdf6521abcbe3 SHA256: 81a359d2a15951303cf1bf91f9ab44954eec796e66f9a4a82f9e5b9750781ac9 SHA512: fb03a5543a2e20d573a212b3adb788019f6b656e62da84b6a16ed3df82e0d94c4e1667b481d7d7c246d5286657f3b9416dbf06185261ae91cd34a026e96bb827 Homepage: https://cran.r-project.org/package=hilldiv Description: CRAN Package 'hilldiv' (Integral Analysis of Diversity Based on Hill Numbers) Tools for analysing, comparing, visualising and partitioning diversity based on Hill numbers. 'hilldiv' is an R package that provides a set of functions to assist analysis of diversity for diet reconstruction, microbial community profiling or more general ecosystem characterisation analyses based on Hill numbers, using OTU/ASV tables and associated phylogenetic trees as inputs. The package includes functions for (phylo)diversity measurement, (phylo)diversity profile plotting, (phylo)diversity comparison between samples and groups, (phylo)diversity partitioning and (dis)similarity measurement. All of these grounded in abundance-based and incidence-based Hill numbers. The statistical framework developed around Hill numbers encompasses many of the most broadly employed diversity (e.g. richness, Shannon index, Simpson index), phylogenetic diversity (e.g. Faith's PD, Allen's H, Rao's quadratic entropy) and dissimilarity (e.g. Sorensen index, Unifrac distances) metrics. This enables the most common analyses of diversity to be performed while grounded in a single statistical framework. The methods are described in Jost et al. (2007) , Chao et al. (2010) and Chiu et al. (2014) ; and reviewed in the framework of molecularly characterised biological systems in Alberdi & Gilbert (2019) . 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Methods used in the package refer to Zhang H, Zheng Y, Hou L, Liu L, HIMA: An R Package for High-Dimensional Mediation Analysis. Journal of Data Science. (2025). . 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The implementation is based on the 'STATA' ado h-index and is described in more detail in Bornmann et al. (2019) . Package: r-cran-hindexcalculator Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hindexcalculator_1.0.0-1.ca2404.1_all.deb Size: 336018 MD5sum: bb10bb34f4f63c3de6e7bf19ab45688f SHA1: b2ae906c39310968d151e051e150d20bd13cff6c SHA256: 1aa96f9ca18197a283c7a751ebf2d8783e23f016f5088c2eec504ac227740b2e SHA512: a2168104d5e1cad692411bfbc7531f58c960e26f2e40e516ed3db10162a502bdc88652fcf946edba1bf6b07ad619a5c0b0f3e4194d4fdc255a343efb20110799 Homepage: https://cran.r-project.org/package=hindexcalculator Description: CRAN Package 'hindexcalculator' (H-Index Calculator using Data from a Web of Science (WoS)Citation Report) H(x) is the h-index for the past x years. Here, the h(x) of a scientist/department/etc. can be calculated using the exported excel file from a Web of Science citation report of a search. Also calculated is the year of first publication, total number of publications, and sum of times cited for the specified period. Therefore, for h-10: the date of first publication, total number of publications, and sum of times cited in the past 10 years are calculated. Note: the excel file has to first be saved in a .csv format. 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Package: r-cran-hiphop Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hiphop_0.0.1-1.ca2404.1_all.deb Size: 891536 MD5sum: a3e69403847398f2df2383981fa237a6 SHA1: e94ce64267eb397c9974c942cb039539b43c620f SHA256: aed2bb20de322712646b85df08c01628637cd1f31caef631a5c2545552664c75 SHA512: d37f57893f5ce93d122f4c61dfa8c51d9808f87b8380dcbad789f33b6f88986a88fddb66b525c2da8f82947aaf07ef2a9e7eaaa501aeefc90dbb9c12e58853a3 Homepage: https://cran.r-project.org/package=hiphop Description: CRAN Package 'hiphop' (Parentage Assignment using Bi-Allelic Genetic Markers) Can be used for paternity and maternity assignment and outperforms conventional methods where closely related individuals occur in the pool of possible parents. The method compares the genotypes of offspring with any combination of potentials parents and scores the number of mismatches of these individuals at bi-allelic genetic markers (e.g. Single Nucleotide Polymorphisms). It elaborates on a prior exclusion method based on the Homozygous Opposite Test (HOT; Huisman 2017 ) by introducing the additional exclusion criterion HIPHOP (Homozygous Identical Parents, Heterozygous Offspring are Precluded; Cockburn et al., in revision). Potential parents are excluded if they have more mismatches than can be expected due to genotyping error and mutation, and thereby one can identify the true genetic parents and detect situations where one (or both) of the true parents is not sampled. Package 'hiphop' can deal with (a) the case where there is contextual information about parentage of the mother (i.e. a female has been seen to be involved in reproductive tasks such as nest building), but paternity is unknown (e.g. due to promiscuity), (b) where both parents need to be assigned, because there is no contextual information on which female laid eggs and which male fertilized them (e.g. polygynandrous mating system where multiple females and males deposit young in a common nest, or organisms with external fertilisation that breed in aggregations). For details: Cockburn, A., Penalba, J.V.,Jaccoud, D.,Kilian, A., Brouwer, L., Double, M.C., Margraf, N., Osmond, H.L., van de Pol, M. and Kruuk, L.E.B. (in revision). HIPHOP: improved paternity assignment among close relatives using a simple exclusion method for bi-allelic markers. Molecular Ecology Resources, DOI to be added upon acceptance. 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The goal of the package is to make these available, both for instructional use and for historical research. Some of these present interesting challenges for graphics or analysis in R. 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Related works include: a) Imran, M., Nasir, J. A., & Riaz, S. (2018). Regional pattern of HIV cases in Pakistan. Journal of Postgraduate Medical Institute, 32(1), 9-13. . Package: r-cran-hive Architecture: all Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-xml Filename: pool/dists/noble/main/r-cran-hive_0.2-2-1.ca2404.1_all.deb Size: 94796 MD5sum: a6827615168ed2c9f03cf127d4967a36 SHA1: 0ef543286b102e46257a5b6fe7556ea3feed7c13 SHA256: aa17184987412d6f2eb76b49bf4d74bd972f672a4c504be5bd16048ad1deda90 SHA512: dc36b528da6e4a5cc4690b162ae242e91a28c29307ed116a8cd8f434f9182a2da65704d2678b6d2e07f053e2aa85f70f72f17bc089239b137fb425b43a148d12 Homepage: https://cran.r-project.org/package=hive Description: CRAN Package 'hive' (Hadoop InteractiVE) Hadoop InteractiVE facilitates distributed computing via the MapReduce paradigm through R and Hadoop. 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Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned. The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/). Keywords: networks, food webs, linnet, systems biology, bioinformatics. 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The dashboard is designed to work with a dataset containing specific columns with standardized names. These columns must be present in the input data for the app to function properly: year: Numeric year of the data (e.g. 2010, 2021); sex: Gender classification (e.g. Male, Female); age_group: Age bracket (e.g. 15–24, 25–34); hiv_prevalence: Estimated HIV prevalence percentage; hiv_incidence: Number of new HIV cases per year; aids_deaths: Total AIDS-related deaths; plhiv: Estimated number of people living with HIV; art_coverage: Percentage receiving antiretroviral therapy (ART); testing_coverage: HIV testing services coverage; causes: Description of likely HIV transmission cause (e.g. unprotected sex, drug use). The dataset structure must strictly follow this column naming convention for the dashboard to render correctly. Package: r-cran-hjam Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 769 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpubr, r-cran-dplyr, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hjam_1.0.0-1.ca2404.1_all.deb Size: 714798 MD5sum: a7f0c3366ab2a3f1ad535568d28eb12f SHA1: 1b21a228bb35eeffbcdda0ffe67c2f44ff69bee3 SHA256: 9f467ce7e303e270287399a861f15ef02dbe8ad3d003ae856447cd2aef5eef03 SHA512: 84dfdc6a6b3f53c973cb5a2b4b48d6eaa06eb5a31399920b1ec22809e13b10067ccf92ee5d6dbe5484e6443eebd8ee17f077da8db04c9d7dfb5f1fa37d452a69 Homepage: https://cran.r-project.org/package=hJAM Description: CRAN Package 'hJAM' (Hierarchical Joint Analysis of Marginal Summary Statistics) Provides functions to implement a hierarchical approach which is designed to perform joint analysis of summary statistics using the framework of Mendelian Randomization or transcriptome analysis. Reference: Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). "A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis." . 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All of the data in this package is available in the public domain. 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Functions extract reference data from the Anthony Nolan HLA Informatics Group/ImmunoGeneTics HLA 'GitHub' repository (ANHIG/IMGTHLA) , validate Genotype List (GL) Strings, convert between UNIFORMAT and GL String Code (GLSC) formats, translate HLA alleles and GLSCs across ImmunoPolymorphism Database (IPD) IMGT/HLA Database release versions, identify differences between pairs of alleles at a locus, generate customized, multi-position sequence alignments, trim and convert allele-names across nomenclature epochs, and extend existing data-analysis methods. Package: r-cran-hlidacr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-glue, r-cran-purrr, r-cran-stringr, r-cran-curl, r-cran-usethis, r-cran-urltools Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-vcr Filename: pool/dists/noble/main/r-cran-hlidacr_0.2.0-1.ca2404.1_all.deb Size: 71166 MD5sum: e58a488e9acc2e1e1d098748c05a142c SHA1: 3d32d6898a92f01df8a0521d1444ea7c65953ca5 SHA256: 166fe33d75b4a4b52237a968fb355e0a3d6b88bb3d7a6765ba29ae7b87ac3e82 SHA512: c1b6497ebb78e7bf2ec38a3073f5e66732b1fcd20ba74491abc37a26cce958d7a70e754a87f8ef3f7150fbc6406a09f91e95a3825c2674ee80c1e88adaf3d90c Homepage: https://cran.r-project.org/package=hlidacr Description: CRAN Package 'hlidacr' (Access Data from the 'Hlídač Státu' API) Provides access to datasets published by 'Hlídač státu' , a Czech watchdog, via their API. Package: r-cran-hlmlab Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-scales Suggests: r-cran-shiny, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hlmlab_0.1.0-1.ca2404.1_all.deb Size: 48056 MD5sum: 5d34402f435a530be3b03ead66ee5645 SHA1: e1f0977837b2824413b1f353bf1f60f3fd0e3141 SHA256: 7f9c21cfc423d99f4611608c863ea4bf127d06e673fd4653f89fe4f470207770 SHA512: 3d29e971562b0b6afb34d71a34711ca8de3a3d91c1b3169c1ddfef0c3331211fab87ffe9e7305c2921584df9b379f01c9e0bc1d44531d4c98d2a98ef192822d6 Homepage: https://cran.r-project.org/package=hlmLab Description: CRAN Package 'hlmLab' (Hierarchical Linear Modeling with Visualization andDecomposition) Provides functions for visualization and decomposition in hierarchical linear models (HLM) for applications in education, psychology, and the social sciences. Includes variance decomposition for two-level and three-level data structures following Snijders and Bosker (2012, ISBN:9781849202015), intraclass correlation (ICC) estimation and design effect computation as described in Shrout and Fleiss (1979) , and contextual effect decomposition via the Mundlak (1978) specification distinguishing within- and between-cluster components. Supports visualization of random slopes and cross-level interactions following Hofmann and Gavin (1998) and Hamaker and Muthen (2020) . Multilevel models are estimated using 'lme4' (Bates et al., 2015 ). An optional 'Shiny' application enables interactive exploration of model components and parameter variation. The implementation follows the multilevel modeling framework of Raudenbush and Bryk (2002, ISBN:9780761919049). Package: r-cran-hlrhotrix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggforce, r-cran-patchwork Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hlrhotrix_0.1.0-1.ca2404.1_all.deb Size: 213120 MD5sum: 9b2aa99c126595d68e717dac15348ea1 SHA1: ced079421e26cd301e4edca002dd2e708180d523 SHA256: bb29133dea5024be4b144c8241958334a241138a69312344abc1958c1a4f0618 SHA512: 31e7ad8e87fa50ae1ef7c6457ec97c02f739125d57feb1f8a80c7692ea9795144015c1f1ee0dca89e7182a90c114e14bbd7ff2687e5265c3fb0e5a96063d832d Homepage: https://cran.r-project.org/package=hlrhotrix Description: CRAN Package 'hlrhotrix' (Algebraic Operations and Visualisation for Hl-Rhotrices) Provides constructors for hl-rhotrices of dimension 2, 4 and 6, together with computation of the determinant, adjoint, inverse and eigenvalues under the Robust Multiplication Method (RMM). A 'ggplot2'-based function visualises the rhomboidal layout and the decomposition into principal minors. Package: r-cran-hmc Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-irlba, r-cran-pma, r-cran-mass, r-cran-grpreg Filename: pool/dists/noble/main/r-cran-hmc_1.2-1.ca2404.1_all.deb Size: 120724 MD5sum: 3c7a674f1ee1f1e9d3721cab169d9935 SHA1: c6db81c8d62f306464344fca4d52270f02b4c067 SHA256: dfc004102b317afcb064436278fefd88f0ac3afa64fbc7e9b8fb695f66db9931 SHA512: 2e6c02e20b8526ca4a8e32ba57264b135a84864563fed831ac28a7378039bfc1f1e6e990c12395db10be2a830e575babab00f642f5040c10d52dc5163619a2c9 Homepage: https://cran.r-project.org/package=HMC Description: CRAN Package 'HMC' (High-Dimensional Mean Comparison with Projection andCross-Fitting) Provides interpretable high-dimensional mean comparison methods (HMC). For example, users can apply these methods to assess the difference in gene expression between two treatment groups. It is not a gene-by-gene comparison. Instead, the methods focus on the interplay between features and identify those that are predictive of the group label. The tests are valid frequentist procedures and yield sparse estimates indicating which features contribute to the group differences. Package: r-cran-hmclearn Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1656 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesplot, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-matrix, r-cran-lme4, r-cran-cardata, r-cran-mlbench, r-cran-ggplot2, r-cran-mlmrev, r-cran-testthat, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-hmclearn_0.0.5-1.ca2404.1_all.deb Size: 1068324 MD5sum: 6831ec4ce1698579182b5256a92c540c SHA1: 5b4e8c10ad41a9da7582ac255aaeb32672289f94 SHA256: 5ea900b021f3a70fe96f1f8fb1c01ca1e4739c7864afddd78eb35e3493ddf9e9 SHA512: 76f017a4a5fa82c729ebe0d1c8a35df12a0a2f6aa254d357f23396d8bcd22ab9959efe0cf0351ef691e4bae59e92e9ed015f875a661177a2e54bc8e1e9d33177 Homepage: https://cran.r-project.org/package=hmclearn Description: CRAN Package 'hmclearn' (Fit Statistical Models Using Hamiltonian Monte Carlo) Provide users with a framework to learn the intricacies of the Hamiltonian Monte Carlo algorithm with hands-on experience by tuning and fitting their own models. All of the code is written in R. Theoretical references are listed below:. Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418, Betancourt, Michael (2017) "A Conceptual Introduction to Hamiltonian Monte Carlo" , Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" , Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174. Package: r-cran-hmda Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1227 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-h2o, r-cran-shapley, r-cran-autoensemble, r-cran-h2otools, r-cran-splittools, r-cran-psych, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-hmda_0.3.0-1.ca2404.1_all.deb Size: 1179404 MD5sum: 4b5af4e4cbd216f3e67a83040bf81958 SHA1: 37ab88a8bd1805be78e503cde74233f04734872d SHA256: 90aa8cbd94752bd84ebcc117d3305bfa421172c274c464340b3b43ce94a097df SHA512: 76b906717d4572ade71e3c5eb161fc9c86d00630b910c2e9f508cd2231dae988bc08e368e831c921242ddced582e125087e9c5ff1731fb5e4efa2e86f60ddae7 Homepage: https://cran.r-project.org/package=HMDA Description: CRAN Package 'HMDA' (Holistic Multimodel Domain Analysis for Exploratory MachineLearning) Holistic Multimodel Domain Analysis (HMDA) is a robust and transparent framework designed for exploratory machine learning research, aiming to enhance the process of feature assessment and selection. HMDA addresses key limitations of traditional machine learning methods by evaluating the consistency across multiple high-performing models within a fine-tuned modeling grid, thereby improving the interpretability and reliability of feature importance assessments. Specifically, it computes Weighted Mean SHapley Additive exPlanations (WMSHAP), which aggregate feature contributions from multiple models based on weighted performance metrics. HMDA also provides confidence intervals to demonstrate the stability of these feature importance estimates. This framework is particularly beneficial for analyzing complex, multidimensional datasets common in health research, supporting reliable exploration of mental health outcomes such as suicidal ideation, suicide attempts, and other psychological conditions. Additionally, HMDA includes automated procedures for feature selection based on WMSHAP ratios and performs dimension reduction analyses to identify underlying structures among features. For more details see Haghish (2025) . Package: r-cran-hmdhfdplus Architecture: all Version: 2.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-rvest, r-cran-dplyr, r-cran-janitor, r-cran-lubridate, r-cran-readr, r-cran-stringr, r-cran-tidyr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-rcurl Filename: pool/dists/noble/main/r-cran-hmdhfdplus_2.0.8-1.ca2404.1_all.deb Size: 80120 MD5sum: 528f00234f1f09cd6bddd7fb32b7bd30 SHA1: c474fab4eff2d2ca531eec73e48b9d848539b10b SHA256: 45fe906f4a7d13caff10a803c5bdc8068918239447448b691d255e93d5ac8d0f SHA512: 9ee2ad45e63cd9d9f327da5910fc346684888699ad65c1b5a304ac13b23c7cf99137a1db849e8cc7dd3aab1425be02f4c61a153663a02c787652b2f6d3fa1303 Homepage: https://cran.r-project.org/package=HMDHFDplus Description: CRAN Package 'HMDHFDplus' (Read Human Mortality Database and Human Fertility Database Datafrom the Web) Utilities for reading data from the Human Mortality Database (), Human Fertility Database (), and similar databases from the web or locally into an R session as data.frame objects. These are the two most widely used sources of demographic data to study basic demographic change, trends, and develop new demographic methods. Other supported databases at this time include the Human Fertility Collection (), The Japanese Mortality Database (), and the Canadian Human Mortality Database (). Arguments and data are standardized. Package: r-cran-hmeasure Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass, r-cran-class, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hmeasure_1.0-2-1.ca2404.1_all.deb Size: 379506 MD5sum: e032f4081b62be21700bca26bc640aa3 SHA1: 2134bf266e03b17093e028b79936cad4506c973b SHA256: 7701da9ccae5a48ce2a55878f0107d365e03b127616db7736f98bd8bfadf7918 SHA512: 0741a4385468983939a91af95ae07c5a3e3cffab3f1b4eb95d6b6a79792d79ede320bdf9fe01dbe0b25607969f6fa036be79e060b148b30d72d89644d7a463a6 Homepage: https://cran.r-project.org/package=hmeasure Description: CRAN Package 'hmeasure' (The H-Measure and Other Scalar Classification PerformanceMetrics) Classification performance metrics that are derived from the ROC curve of a classifier. 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Package: r-cran-hmer Architecture: all Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-ggplot2, r-cran-lhs, r-cran-mass, r-cran-r6, r-cran-viridis, r-cran-mvtnorm, r-cran-ggally, r-cran-rlang, r-cran-isoband, r-cran-cluster, r-cran-pdist, r-cran-stringr, r-cran-jsonlite Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-desolve, r-cran-testthat, r-cran-covr, r-cran-progressr Filename: pool/dists/noble/main/r-cran-hmer_1.6.2-1.ca2404.1_all.deb Size: 3190066 MD5sum: 382afebc9bd97e8b6506afdbea969318 SHA1: 90a6bb6307298528b91f052e63c49ad9ee565bb1 SHA256: fa50c1e7938fa656f6fb1e2bdf2c9459a1ede4d5f86d369afadfdf4f327703fb SHA512: 7778cc4866e8890b4f08a6b8236301d23a5a9aa81ddec85230f9cc3f0fa283705b61783076fa71920a784fb3bb057340f2596b6ccdcd6afb35511dd4fddc7da5 Homepage: https://cran.r-project.org/package=hmer Description: CRAN Package 'hmer' (History Matching and Emulation Package) A set of objects and functions for Bayes Linear emulation and history matching. Core functionality includes automated training of emulators to data, diagnostic functions to ensure suitability, and a variety of proposal methods for generating 'waves' of points. For details on the mathematical background, there are many papers available on the topic (see references attached to function help files or the below references); for details of the functions in this package, consult the manual or help files. Iskauskas, A, et al. (2024) . Bower, R.G., Goldstein, M., and Vernon, I. (2010) . Craig, P.S., Goldstein, M., Seheult, A.H., and Smith, J.A. (1997) . Package: r-cran-hmetad Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1169 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brms, r-cran-abind, r-cran-dplyr, r-cran-glue, r-cran-posterior, r-cran-rlang, r-cran-stringr, r-cran-tidybayes, r-cran-tidyr Suggests: r-cran-colorspace, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-hmetad_0.1.2-1.ca2404.1_all.deb Size: 628506 MD5sum: 973d0dd0a11c57794db9bcb9e9592057 SHA1: 55a0cea1caa4bc32428e664db678a710623fba30 SHA256: 1c5ca70fdb9606cf7bddbdcb2a3c5cc8c6bfceaae229cc6ad9639a20f5581145 SHA512: 5e19356177d9e86f933eaa0ea073c46eda96d3186beb3c25d66369e29241d9b1427935f196fa6b693d9cb2e15159e672e1356743f1dc169c119c4c7b56a0b918 Homepage: https://cran.r-project.org/package=hmetad Description: CRAN Package 'hmetad' (Fit the Meta-D' Model of Confidence Ratings Using 'brms') Implementation of Bayesian regressions over the meta-d' model of psychological data from two alternative forced choice tasks with ordinal confidence ratings. For more information, see Maniscalco & Lau (2012) . The package is a front-end to the 'brms' package, which facilitates a wide range of regression designs, as well as tools for efficiently extracting posterior estimates, plotting, and significance testing. Package: r-cran-hmix Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-normalp, r-cran-glogis, r-cran-gld, r-cran-purrr, r-cran-hmm, r-cran-mc2d, r-cran-cubature, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hmix_1.0.3-1.ca2404.1_all.deb Size: 55570 MD5sum: 6a8cf5422ba09ca4b75f534e71381ead SHA1: 2ccff3f0c8276abae2d50070863fe519ca0cd616 SHA256: 2d09c80397c160e8809f10353bef7794f0c0a0dd4231ebc4bf52467cdd5ed928 SHA512: 8d4be8f2a2d160e5a211aba797bb7594a8eb13ff78503619b8c3b3923959869990f317f0f0e89e617e4552ec664f40d279054dc71714b60a1be67b7bc517d0ee Homepage: https://cran.r-project.org/package=hmix Description: CRAN Package 'hmix' (Hidden Markov Model for Predicting Time Sequences with MixtureSampling) An algorithm for time series analysis that leverages hidden Markov models, cluster analysis, and mixture distributions to segment data, detect patterns and predict future sequences. 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The goodness-of-fit test is based on a Cramer-von Mises statistic and uses Rosenblatt's transform and parametric bootstrap to estimate the p-value. The proposed methodologies are described in Nasri, Remillard and Thioub (2020) . 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Inequality constraints on the parameters are allowed and can be tested. Package: r-cran-hmmpa Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hmmpa_1.0.2-1.ca2404.1_all.deb Size: 195194 MD5sum: 05c84704d78a09cbf8881d59756f3f59 SHA1: ed97d864f05f6a634ee2a2916402ad1868b572ab SHA256: 141392b78203183612fe17bf92cbd30675e521c6b0e0a9881950ab7e6efe2154 SHA512: f4c6d226d21c4275e8eec5fb08a5346ac3c1369f0206506dc136f7b9b045143a9e6123df7f9f1c88c080402c0a7c31e27c9c41e414cd3ee7f79c81da0bf960a4 Homepage: https://cran.r-project.org/package=HMMpa Description: CRAN Package 'HMMpa' (Analysing Accelerometer Data Using Hidden Markov Models) Analysing time-series accelerometer data to quantify length and intensity of physical activity using hidden Markov models. It also contains the traditional cut-off point method. Witowski V, Foraita R, Pitsiladis Y, Pigeot I, Wirsik N (2014). . Package: r-cran-hmmr Architecture: all Version: 1.0-0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-depmixs4 Suggests: r-cran-boot Filename: pool/dists/noble/main/r-cran-hmmr_1.0-0.1-1.ca2404.1_all.deb Size: 437948 MD5sum: 3a0e35f33f23cf24e65b9a1ab189e71c SHA1: cc9f046d9cd5f2a0ff03e4d40c322958cbfe2e2b SHA256: 8bccca19aa767c5a42c5e26b1a054274ad488e82e82282398ad1443f0fea64ba SHA512: afffdfd48e33af2f0eb4a17f5c5c832e03a5a881dd97c3e4119d7b25cd364ae31498cad8b8639b5d2eb79b15f506aa0901b04e3f7974ee0470959630daae6508 Homepage: https://cran.r-project.org/package=hmmr Description: CRAN Package 'hmmr' ("Mixture and Hidden Markov Models with R" Datasets and ExampleCode) Datasets and code examples that accompany our book Visser & Speekenbrink (2021), "Mixture and Hidden Markov Models with R", . 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Package: r-cran-hmsr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ga, r-cran-msm, r-cran-uuid Suggests: r-cran-testthat, r-cran-ecr, r-cran-filelock, r-cran-doparallel, r-cran-smoof Filename: pool/dists/noble/main/r-cran-hmsr_1.0.1-1.ca2404.1_all.deb Size: 231564 MD5sum: 17baab7f583c9debedc708878fe82075 SHA1: ba95975f5ba0d7f7de36cbe2b3c6101a8622e389 SHA256: de9ba7367f63d6a757028866006c0a909ebecd44d9356dc57e3da0e78c1303f7 SHA512: 5531aec689eecbb005033b09b6a6b42403ccbf8148fe2a07e79a5ca13c409589590d0038239ccf2c08cff4be141792303770fdc52be46d351186ba0ba74b1b4b Homepage: https://cran.r-project.org/package=hmsr Description: CRAN Package 'hmsr' (Multipopulation Evolutionary Strategy HMS) The HMS (Hierarchic Memetic Strategy) is a composite global optimization strategy consisting of a multi-population evolutionary strategy and some auxiliary methods. 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This is particularly useful for classification in unbalanced dataset, for example, disease severity classification, where under-classification errors (misclassifying patients into less severe categories) are more consequential than other misclassifications. The package implements H-NP umbrella algorithms that controls under-classification errors under user specified control levels with high probability. It supports the creation of H-NP classifiers using scoring functions based on built-in classification methods (including logistic regression, support vector machines, and random forests), as well as user-trained scoring functions. For theoretical details, please refer to Lijia Wang, Y. X. Rachel Wang, Jingyi Jessica Li & Xin Tong (2024) . 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Further, sets of miRNA target genes can be identified by using the targetscan.org API. Package: r-cran-hoardr Architecture: all Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1881 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-rappdirs, r-cran-digest Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hoardr_0.5.5-1.ca2404.1_all.deb Size: 579400 MD5sum: 4f702459d11c564bd751f570343097e1 SHA1: 22d165807af6928b4ff4124835f5353325249a92 SHA256: b5c50cb5193f100e1af22bd27b006909bf29d7740c8681bb85bf1911f36c0f00 SHA512: d2e6e6e9bc5692096fd35ea89db48431e3674da082da0aab659001f8d3ef6df8b77843914099655da97d5b066b34fd27a7bccad0c6f4e72461adcbfb151b4f08 Homepage: https://cran.r-project.org/package=hoardr Description: CRAN Package 'hoardr' (Manage Cached Files) Suite of tools for managing cached files, targeting use in other R packages. Uses 'rappdirs' for cross-platform paths. 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References, Arcagni, A., Grassi, R., Stefani, S., & Torriero, A. (2017) Arcagni, A., Grassi, R., Stefani, S., & Torriero, A. (2021) Arcagni, A., Cerqueti, R., & Grassi, R. (2023) . 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Package: r-cran-hodgestools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rcolorbrewer, r-cran-readr, r-cran-ini, r-cran-qqman, r-cran-data.table, r-cran-recordlinkage Filename: pool/dists/noble/main/r-cran-hodgestools_1.0.0-1.ca2404.1_all.deb Size: 296180 MD5sum: 57efed844002c0a89e1843886784dcc3 SHA1: 65b5651996c4ee8e845e6228efbb265aa700a860 SHA256: d199cdb4f8579f90e0b968b9f6cf0cf1072b2839748d7273302fb40e77252376 SHA512: 8d9079c441ecf6a35ab113f9444933bbfd42d9ebf840437a80b5be4a566fa9888d8f5b84d3d00758b3841b994339985c43e7024f52cd665065a4648b3f686fcf Homepage: https://cran.r-project.org/package=HodgesTools Description: CRAN Package 'HodgesTools' (Common Use Tools for Genomic Analysis) Built by Hodges lab members for current and future Hodges lab members. Other individuals are welcome to use as well. Provides useful functions that the lab uses everyday to analyze various genomic datasets. Critically, only general use functions are provided; functions specific to a given technique are reserved for a separate package. As the lab grows, we expect to continue adding functions to the package to build on previous lab members code. Package: r-cran-hoifcar Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brglm2, r-cran-bb, r-cran-doparallel, r-cran-foreach, r-cran-glmnet, r-cran-mass Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-hoifcar_1.1.1-1.ca2404.1_all.deb Size: 178470 MD5sum: 7437b0b67a62e36ad687e63407825c7c SHA1: 68120eb32206637bfa4562d0461f232c47c678e6 SHA256: 03c5c13409a30c9c9a2d5cf36a39fb2da6f2a3847f92288294b9a73f83ebe5ce SHA512: 66a303dfb29a99e7718d88417f5d88720d566d34aa6bc98413ce1ad5403c43662c53bce51ac12f4b2cd76d0bafd951b4aee9cd79a778fa9a8953f9dc48e55267 Homepage: https://cran.r-project.org/package=HOIFCar Description: CRAN Package 'HOIFCar' (Covariate Adjustment in RCT by Higher-Order Influence Functions) Estimates treatment effects using covariate adjustment methods in Randomized Clinical Trials (RCT) motivated by higher-order influence functions (HOIF). 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Making vocational choices. A theory of vocational personalities and work environments. Lutz, FL: Psychological Assessment. 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After the upload of the omics datasets and a metadata file, single-omics is performed for feature selection and dataset reduction. These datasets are used for pairwise- and multi-omics analyses, where automatic tuning is done to identify correlations between the datasets - the end goal of the recommended 'Holomics' workflow. Methods used in the package were implemented in the package 'mixomics' by Florian Rohart,Benoît Gautier,Amrit Singh,Kim-Anh Lê Cao (2017) and are described there in further detail. 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The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis. 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Package: r-cran-homnormal Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-huxtable Filename: pool/dists/noble/main/r-cran-homnormal_0.1-1.ca2404.1_all.deb Size: 72712 MD5sum: 250c76a3ace8abb4ea7d9ab69716f3ce SHA1: 0edd0aa0bce337ea473a1227581930c5c2bfe2ed SHA256: 3927e39a32f76239adbe990ec732bdc0c29b73a760147c1543f57c7a69f40d2e SHA512: f585bbd05de8061c7b1674cd5649c2ac5044d28b21fa0d78ddcd6d288243181eb62f8f3d1f53021e38521e4d6fee0699db442c231ac6fd754c959b55a95cf582 Homepage: https://cran.r-project.org/package=homnormal Description: CRAN Package 'homnormal' (Tests of Homogeneity of Variances) Most common exact, asymptotic and resample based tests are provided for testing the homogeneity of variances of k normal distributions under normality. These tests are Barlett, Bhandary & Dai, Brown & Forsythe, Chang et al., Gokpinar & Gokpinar, Levene, Liu and Xu, Gokpinar. Also, a data generation function from multiple normal distribution is provided using any multiple normal parameters. Bartlett, M. S. (1937) Bhandary, M., & Dai, H. (2008) Brown, M. B., & Forsythe, A. B. (1974). Chang, C. H., Pal, N., & Lin, J. J. (2017) Gokpinar E. & Gokpinar F. (2017) Liu, X., & Xu, X. (2010) Levene, H. (1960) Gökpınar, E. (2020) . Package: r-cran-homologene Architecture: all Version: 1.4.68.19.3.27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4085 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-r.utils Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-homologene_1.4.68.19.3.27-1.ca2404.1_all.deb Size: 3751574 MD5sum: 870dc7b258e1bacdd1827bb1ce241b85 SHA1: 33b91211405d460b462a118b637c42b806e084b8 SHA256: cc8d6a99253ddb7f5dff95aca9eafdb1cb74b670f7154d917a22047f925569b1 SHA512: 575298a96ab60aeb366f9d1c3148789390ef44eb4df1436ce844d122cf2a06b5713ba2f57e750f2d220217bf2cc23239f12e3f9d419eb7b7e0efe2b33731c99b Homepage: https://cran.r-project.org/package=homologene Description: CRAN Package 'homologene' (Quick Access to Homologene and Gene Annotation Updates) A wrapper for the homologene database by the National Center for Biotechnology Information ('NCBI'). It allows searching for gene homologs across species. Data in this package can be found at . The package also includes an updated version of the homologene database where gene identifiers and symbols are replaced with their latest (at the time of submission) version and functions to fetch latest annotation data to keep updated. 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Package: r-cran-homomorphicencryption Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 482 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-polynom, r-cran-hetools Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-homomorphicencryption_0.9.0-1.ca2404.1_all.deb Size: 69302 MD5sum: ce3831f4f39c5b1ff363b3f3b61f6ba7 SHA1: 0435e0d80b6842dfd6b6142902e76aa5c89e8cec SHA256: d5b15a70af4a695f2d49bd84aba86a89ef4a0bf3c636d769dae62d0a9ee97148 SHA512: 88230b70555eb8671785e716fc3739591e312357b49ca2969f3207f45f385f0faf68a50fb72c56504eacd8fdf795ff1d712f7df7e537c13f7924b5314ea580de Homepage: https://cran.r-project.org/package=HomomorphicEncryption Description: CRAN Package 'HomomorphicEncryption' (BFV, BGV, CKKS Schema for Fully Homomorphic Encryption) Implements the Brakerski-Fan-Vercauteren (BFV, 2012) , Brakerski-Gentry-Vaikuntanathan (BGV, 2014) , and Cheon-Kim-Kim-Song (CKKS, 2016) schema for Fully Homomorphic Encryption. The included vignettes demonstrate the encryption procedures. Package: r-cran-honestdid Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1543 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-foreach, r-cran-matrixstats, r-cran-cvxr, r-cran-ecosolver, r-cran-latex2exp, r-cran-lpsolveapi, r-cran-matrix, r-cran-pracma, r-cran-purrr, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-rglpk, r-cran-mvtnorm, r-cran-truncatednormal Suggests: r-cran-knitr, r-cran-testthat, r-cran-haven, r-cran-lfe, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-honestdid_0.2.8-1.ca2404.1_all.deb Size: 810026 MD5sum: 7f903db31b1a63440c5e415c4743ec35 SHA1: 909cbbb1b3f460a61cf68edc2c993056b0f7969b SHA256: 1212ac2a3a183e31ea58a6bf017e93a9f5b41df6448916a11be3d2398e66e1fa SHA512: 96c9aa9dd85af37607b5f0c6638f5bd31f09744d1d7d37ad5e7f2af594bc16564d8f47afc4e3a2167ed860356fdd527f5c1a67316cedfc5247b97a433b840ddb Homepage: https://cran.r-project.org/package=HonestDiD Description: CRAN Package 'HonestDiD' (Robust Inference in Difference-in-Differences and Event StudyDesigns) Provides functions to conduct robust inference in difference-in-differences and event study designs by implementing the methods developed in Rambachan & Roth (2023) , "A More Credible Approach to Parallel Trends" [Previously titled "An Honest Approach..."]. 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Package: r-cran-hoopr Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2737 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-janitor, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-progressr, r-cran-purrr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-rvest, r-cran-stringr, r-cran-tidyr Suggests: r-cran-crayon, r-cran-curl, r-cran-dbi, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggrepel, r-cran-rmarkdown, r-cran-rsqlite, r-cran-stringi, r-cran-testthat, r-cran-tibble, r-cran-usethis, r-cran-xml2, r-cran-yaml Filename: pool/dists/noble/main/r-cran-hoopr_3.0.0-1.ca2404.1_all.deb Size: 2570742 MD5sum: c03173de3e9711decb5a7dfad2d77f4e SHA1: b4f10f6dee75014c4503cfe3c17a2dd5a1421602 SHA256: 87dd1cd5f88aa699ee45f2c5c1d4ae0cbd7b935312e70b54891573f57ab6d614 SHA512: 127e24f6401151cdc3919da6228ce70834f22a69b8eb4a4a7e39b2244426c8c5847967da6fb6f2b655a94405cd89668f29ae202cf5740daec0c1cf0ee5d74b36 Homepage: https://cran.r-project.org/package=hoopR Description: CRAN Package 'hoopR' (Access Men's Basketball Play by Play Data) A utility to quickly obtain clean and tidy men's basketball play by play data. Provides functions to access live play by play and box score data from ESPN with shot locations when available. It is also a full NBA Stats API wrapper. It is also a scraping and aggregating interface for Ken Pomeroy's men's college basketball statistics website. It provides users with an active subscription the capability to scrape the website tables and analyze the data for themselves. Package: r-cran-hopbyhop Architecture: all Version: 3.41-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pastecs, r-cran-ggplot2 Suggests: r-cran-endtoend, r-cran-opportunistic Filename: pool/dists/noble/main/r-cran-hopbyhop_3.41-1.ca2404.1_all.deb Size: 40850 MD5sum: 17431db1294c9f6275abc870328b6961 SHA1: 12de2a221173e35de187c2258032c33609829d13 SHA256: d4f528d9e79d7cb75dd97e10d5a462581b16997a0b141b86baec181f4eb9b517 SHA512: c944b3fd49f25db0a381c49c4122388606b7372819bb824103dbe4de6a9df68814675ccff165c32c7973916b51c2f22faea327cf5b4fecccba873336df2d5dcc Homepage: https://cran.r-project.org/package=hopbyhop Description: CRAN Package 'hopbyhop' (Transmissions and Receptions in a Hop by Hop Network) Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities. Package: r-cran-hopkins Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-donut, r-cran-pdist, r-cran-rann Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spatstat.data, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hopkins_1.1-1.ca2404.1_all.deb Size: 79680 MD5sum: 05a85716062bb44e5aadc94a719c3498 SHA1: bb52352a7bc90644cb5eb1916bda3057a7c0e56d SHA256: a81e7d691e54af27f426bd738aed1c1853c7358f61075010036fe12eadd5c80e SHA512: 8f46d9aded653e6c8e85f6093533d2723c188c3745504568bb60f7b5857f7747f04ba03814f26d8af46ba45ec0bdcb33473dc9c80561068cb6e6ba68fff82de4 Homepage: https://cran.r-project.org/package=hopkins Description: CRAN Package 'hopkins' (Calculate Hopkins Statistic for Clustering) Calculate Hopkins statistic to assess the clusterability of data. See Wright (2023) . Package: r-cran-horm Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-orthopolynom, r-cran-quantmod, r-cran-rsm Filename: pool/dists/noble/main/r-cran-horm_0.1.4-1.ca2404.1_all.deb Size: 164870 MD5sum: 6092a4264a24f29e2b58dfa6925d5e27 SHA1: ae427a71874c1d5f3f159616284f9c7b76ba7491 SHA256: 644e9f2ad7fddfe27fee6cfc52b6d7e865fe7fb3fb135e7926301d402c4e8a03 SHA512: 6bfba4cf0cbbe03c45cd24d7bd3e6200b25055b19e97e23b148e6a16d731f73d3350793006c558ab24206364e1b090be5f990fa1d4149798961779239560257a Homepage: https://cran.r-project.org/package=HoRM Description: CRAN Package 'HoRM' (Supplemental Functions and Datasets for "Handbook of RegressionMethods") Supplement for the book "Handbook of Regression Methods" by D. S. Young. Some datasets used in the book are included and documented. Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares. Package: r-cran-hornpa Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hornpa_1.1.1-1.ca2404.1_all.deb Size: 15038 MD5sum: d50fb6026e68761e3fdf0272a8b3f768 SHA1: 57315fd07ba73272140b8e67af48cd545e52aa38 SHA256: 60db4e420d5d278125fa7025aef1ddc86050aabaf0f78fa3bc9cf67269d89c41 SHA512: 0e4d07cd012a00b669507e327cdfffbd10d7c054e8b489059f121dc8820fdd6ac4522d10b5cb35d73fe043acc02d8b1f3ea1e7afaa03b9416ec49740227c1445 Homepage: https://cran.r-project.org/package=hornpa Description: CRAN Package 'hornpa' (Horn's (1965) Test to Determine the Number of Components/Factors) A stand-alone function that generates a user specified number of random datasets and computes eigenvalues using the random datasets (i.e., implements Horn's [1965, Psychometrika] parallel analysis ). Users then compare the resulting eigenvalues (the mean or the specified percentile) from the random datasets (i.e., eigenvalues resulting from noise) to the eigenvalues generated with the user's data. Can be used for both principal components analysis (PCA) and common/exploratory factor analysis (EFA). The output table shows how large eigenvalues can be as a result of merely using randomly generated datasets. If the user's own dataset has actual eigenvalues greater than the corresponding eigenvalues, that lends support to retain that factor/component. In other words, if the i(th) eigenvalue from the actual data was larger than the percentile of the (i)th eigenvalue generated using randomly generated data, empirical support is provided to retain that factor/component. Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 32, 179-185. Package: r-cran-horsekicks Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-horsekicks_1.0.2-1.ca2404.1_all.deb Size: 49514 MD5sum: 0db15c66942a7a73e682df789cb24c91 SHA1: 99581cf43fd3ea7c255a907f52c0297299e8577e SHA256: 52d6427081d1f1be02fa270228a35e0cbcdc5de6e92296b5948cec8e82891c78 SHA512: 3198ec037ebda18e34c720f8c19421db3181c2fd97e0f99e7ddb57e8a002405d09a4b5dd041128e834ed92c6f2ebb23904ac29f8bcaa9b068b38fee8dd3846a4 Homepage: https://cran.r-project.org/package=Horsekicks Description: CRAN Package 'Horsekicks' (Provide Extensions to the Prussian Army Death by Horsekick Data) We provide extensions to the classical dataset "Example 4: Death by the kick of a horse in the Prussian Army" first used by Ladislaus von Bortkeiwicz in his treatise on the Poisson distribution "Das Gesetz der kleinen Zahlen", . As well as an extended time series for the horse-kick death data, we also provide, in parallel, deaths by falling from a horse and by drowning. Package: r-cran-horseshoe Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 556 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-hmisc, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-horseshoe_0.2.0-1.ca2404.1_all.deb Size: 355774 MD5sum: 498717ccb7803ba505614923be4c6a41 SHA1: 217ccfb2eaabde6b2fad012fb70707a9a5aeb4d3 SHA256: a90b42ba70e59f3e532f26510ab212d7bbf6beab3d8cc6a5ccdb8f29e001ec4f SHA512: bf5677e7a79259b58a4ac551313b283f44aeb37bdde2227e82d90a77e0e326d1984ed38901100e08d1f32fefd4c9c5afe949bcb8d47b537322f4cb1b02af93bf Homepage: https://cran.r-project.org/package=horseshoe Description: CRAN Package 'horseshoe' (Implementation of the Horseshoe Prior) Contains functions for applying the horseshoe prior to high- dimensional linear regression, yielding the posterior mean and credible intervals, amongst other things. The key parameter tau can be equipped with a prior or estimated via maximum marginal likelihood estimation (MMLE). The main function, horseshoe, is for linear regression. In addition, there are functions specifically for the sparse normal means problem, allowing for faster computation of for example the posterior mean and posterior variance. Finally, there is a function available to perform variable selection, using either a form of thresholding, or credible intervals. Package: r-cran-horseshoenlm Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-msm Suggests: r-cran-boot, r-cran-pgdraw, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-horseshoenlm_0.0.6-1.ca2404.1_all.deb Size: 71440 MD5sum: 5000c535976ace213aa1681f617ca1c2 SHA1: d29725c774b4b1d542f6e8fe0dae6d42be2f69c6 SHA256: f20966254806ee0804152becabb72738fce29b503ba4c6aea594fc66ecc2c227 SHA512: 1457b5f3ac91d31d71437b6c2a1477443f601119ecf069634396a9a71f372557548559f9dd6402b530fe9bf1f88ba81d28f6dbfced84223b0594a3cbeea789fa Homepage: https://cran.r-project.org/package=horseshoenlm Description: CRAN Package 'horseshoenlm' (Nonlinear Regression using Horseshoe Prior) Provides the posterior estimates of the regression coefficients when horseshoe prior is specified. The regression models considered here are logistic model for binary response and log normal accelerated failure time model for right censored survival response. The linear model analysis is also available for completeness. All models provide deviance information criterion and widely applicable information criterion. See Maity et. al. (2019) Maity et. al. (2020). Package: r-cran-horsey Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-tibble, r-cran-dplyr, r-cran-stringr, r-cran-cli, r-cran-rlang Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-horsey_1.0.1-1.ca2404.1_all.deb Size: 42396 MD5sum: 4ba1cb04cebabe879558716592199d46 SHA1: f7f3b34ddabf00553ef9e50ded30508ba7278a58 SHA256: 453082109fd602b9f76ca6ca0313bb9961132124bc1638f2fc506d6fec4562b4 SHA512: 13e5f630f3864a33442ab809c7bd580d46b1f401780caf21f8c091cfaae9c75c3436406c3cdeac822e1780b2b935507c96dc44cd543b7f261e4564636d245e27 Homepage: https://cran.r-project.org/package=horsey Description: CRAN Package 'horsey' (Access to the 'Lichess' API) Package that accesses the 'Lichess' API (). Supports both authenticated and unauthenticated requests. Basic functionality for game and player analysis. Package: r-cran-hosm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maps, r-cran-sf, r-cran-tidyverse, r-cran-units, r-cran-tibble, r-cran-readxl Filename: pool/dists/noble/main/r-cran-hosm_0.1.0-1.ca2404.1_all.deb Size: 23546 MD5sum: 3a0da59645a0003e25e418430f94bccc SHA1: 17f538c771acc74ce0bc22da2b19e1b7cc0f58c7 SHA256: 2901229068de01e6302aabae4ff87d748fbb55f3527699c786654580ff363040 SHA512: 649d48ea08e1b0d0c08ec9414f94a971d71e2171944cfbe0a603a518464f0dc9b8f323b70818375e7ec7f578e7a0cce616337641c2202828686cb25b8a0b363c Homepage: https://cran.r-project.org/package=hosm Description: CRAN Package 'hosm' (High Order Spatial Matrix) Automatically displays the order and spatial weighting matrix of the distance between locations. This concept was derived from the research of Mubarak, Aslanargun, and Siklar (2021) and Mubarak, Aslanargun, and Siklar (2022) . Distance data between locations can be imported from 'Ms. Excel', 'maps' package or created in 'R' programming directly. This package also provides 5 simulations of distances between locations derived from fictitious data, the 'maps' package, and from research by Mubarak, Aslanargun, and Siklar (2022) . 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Methods provided have been used in Donker T, Wallinga J, Grundmann H. (2010) , and Nekkab N, Crépey P, Astagneau P, Opatowski L, Temime L. (2020) . Package: r-cran-hospitals Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-stringr, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hospitals_0.1.0-1.ca2404.1_all.deb Size: 66952 MD5sum: 8e4bdfd492725c795621903477982a8a SHA1: 2b7c3840c4602bc03926bcc16baf6f892a75130f SHA256: 8745575281a4e8f173fab7f72fcc54b12f74d39b2dd6d4fe5530b4f8a95c9f7e SHA512: fc3843cdbb0e5572eede6caffc3cbb695eeebe7d5c1200092057a60fcdc0c10a99d37576944b77ac2a24a518dc4f65dfd9823a8ace19a4b2f5c55848df107d44 Homepage: https://cran.r-project.org/package=hospitals Description: CRAN Package 'hospitals' (Portuguese 'NHS' Hospitals) A data set of the Portuguese 'NHS' hospitals. 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Package: r-cran-hotpatchr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-hotpatchr_0.1.0-1.ca2404.1_all.deb Size: 118598 MD5sum: 821ab3174ea67445219742d72532aeed SHA1: f4bf34f20dab5dba0e26ec638bd993c543db2a76 SHA256: d2ea0f9d8df6a167983def4e9305b662e77b02cc9344265c139766a9e1beb150 SHA512: d40aadffa80c16f5da9ef47e8a44480f1f33497f68f90ed266f3d6497fb8e5ccf87b276d69e81c0e3cf26c763ba754e768a9250f1a9e78ddb6a93cbbed99f13e Homepage: https://cran.r-project.org/package=hotpatchR Description: CRAN Package 'hotpatchR' (Runtime Namespace Patching Utilities for R Packages) Provides utilities for runtime hotpatching of locked R package namespaces. The package enables dynamic injection of function patches into sealed package environments without rebuilding or redeploying the package. This is particularly useful for legacy containerized workflows where package versions are frozen in place. The core functionality includes inject_patch() to inject patches into package namespaces, undo_patch() to restore original functions, apply_hotfix_file() to apply patches from external R scripts, and test_patched_dir() to run test suites against patched packages. The package implements namespace surgery techniques that allow internal callers to automatically see patched functions. Package: r-cran-hotspot Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hotspot_1.0-1.ca2404.1_all.deb Size: 90162 MD5sum: 635aa4887ab3661d91d4dc1331cb8d43 SHA1: e465caede3a9a1a8d7e3affea1b6714d47deabb5 SHA256: 82a698284386ac860902b17f3e562c0dc0280012e7b014e59f67499fc080622c SHA512: 747cf639a4e6e9479959cfc691aa9d02f89ebb2dac28a12f5349a6a8e249b103bea1ad32ce743d4e2c53b31f068ee08de8082fdd51ca79950fa9e43ace96eeb7 Homepage: https://cran.r-project.org/package=hotspot Description: CRAN Package 'hotspot' (Software Hotspot Analysis) Contains data for software hotspot analysis, along with a function performing the analysis itself. 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Package: r-cran-hours2lessons Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-igraph, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hours2lessons_0.1.4-1.ca2404.1_all.deb Size: 60370 MD5sum: e35cd4d67249cf5036f3dec8831905ac SHA1: 5e73a8cbffe5fdba413de00f46630ec652b06c14 SHA256: 76755a210433360acbc62d8719a92834e2ee150d9b71f660b179699da5aa917d SHA512: 09aed756122b6dd892c9e0ae05135dc156ef297cb6e5f2ab187101babb6061b0eff389798fc4ccf1f9acd4c5b13c0a1076837e6cceeb27e5bbcca4b60798efc1 Homepage: https://cran.r-project.org/package=hours2lessons Description: CRAN Package 'hours2lessons' (Alocă Pe Ore Lecțiile Zilei) Lecțiile prof/cls trebuie completate cu un câmp "ora", astfel ca oricare două lecții prof/cls/ora să nu se suprapună într-o aceeași oră. The prof/cls lessons must be completed with a "hour" field ('ora), so that any two prof/cls/ora lessons do not overlap in the same hour. . 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Package: r-cran-hpcwld Architecture: all Version: 0.6-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hpcwld_0.6-5-1.ca2404.1_all.deb Size: 144816 MD5sum: 2d4ddb3124b0c800a8578b904a888a09 SHA1: 2fc538f1326481784505422f6ecf416a2effcf80 SHA256: 177812bf49a19f0c9ad5979d077f2f87b67dbb9e8df442be0658749704134645 SHA512: 87ff3ba3c9497b8f0bb76c0b2e1d7308aad091296c4d709863fcff4d340bebc50490a0595748ce9d6605233d3d4d3f53fb539f48411f601f0ec5ff2818ede881 Homepage: https://cran.r-project.org/package=hpcwld Description: CRAN Package 'hpcwld' (High Performance Cluster Models Based on Kiefer-WolfowitzRecursion) Probabilistic models describing the behavior of workload and queue on a High Performance Cluster and computing GRID under FIFO service discipline basing on modified Kiefer-Wolfowitz recursion. Also sample data for inter-arrival times, service times, number of cores per task and waiting times of HPC of Karelian Research Centre are included, measurements took place from 06/03/2009 to 02/30/2011. Functions provided to import/export workload traces in Standard Workload Format (swf). Stability condition of the model may be verified either exactly, or approximately. Stability analysis: see Rumyantsev and Morozov (2017) , workload recursion: see Rumyantsev (2014) . 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Everitt and Torsten Hothorn, Chapman & Hall/CRC, 2006). The first chapter of the book, which is entitled ''An Introduction to R'', is completely included in this package, for all other chapters, a vignette containing all data analyses is available. Package: r-cran-hscovar Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-foreach, r-cran-rlist, r-cran-pwr Filename: pool/dists/noble/main/r-cran-hscovar_0.4.2-1.ca2404.1_all.deb Size: 309112 MD5sum: 44da7a9dceee4905765dd9e22fce2f68 SHA1: 90bd3f0a94478d0cea061ceb91dbd77404b288bd SHA256: 0c71ca16260bb592a794d0896b48d8dbefcc132ae538d56e5e3b7666028a9a25 SHA512: 18e1f9dea472540d911ae13e36c6798f7bdff257b7a8472acc0fc9f0253f1b9faca797f98b30eab3297ac1d3c3c6c3cd873cd0b96a90e5e2662aa503c4a81c2e Homepage: https://cran.r-project.org/package=hscovar Description: CRAN Package 'hscovar' (Calculation of Covariance Between Markers for Half-Sib Families) The theoretical covariance between pairs of markers is calculated from either paternal haplotypes and maternal linkage disequilibrium (LD) or vise versa. A genetic map is required. Grouping of markers is based on the correlation matrix and a representative marker is suggested for each group. Employing the correlation matrix, optimal sample size can be derived for association studies based on a SNP-BLUP approach. The implementation relies on paternal half-sib families and biallelic markers. If maternal half-sib families are used, the roles of sire/dam are swapped. Multiple families can be considered. Wittenburg, Bonk, Doschoris, Reyer (2020) "Design of Experiments for Fine-Mapping Quantitative Trait Loci in Livestock Populations" . Carlson, Eberle, Rieder, Yi, Kruglyak, Nickerson (2004) "Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium" . 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A general pairwise fusion approach is proposed to deal with the sparsity and homogeneity detection when combining prior convex constraints. We develop an modified alternating direction method of multipliers algorithm (ADMM) to obtain the estimators. Package: r-cran-hse Architecture: all Version: 0.0-28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 34 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hse_0.0-28-1.ca2404.1_all.deb Size: 6930 MD5sum: 533069c1b535d238f5943553fc0db28a SHA1: ef1a1ab580d6cb1e9db7782b6887d8fd4ec5a6ae SHA256: 9be50887f09626a52aa83578ad8015283860f2cb7f757bc8f6d6f8aff00cac17 SHA512: f57195c36a37a2e0c6556654a53777e390290c8af7867ebafa83bc561910afe3ee61bb4f86ddab699b9eb361e7962442fb759d70534cff2e91407ff5dd850c45 Homepage: https://cran.r-project.org/package=hse Description: CRAN Package 'hse' (The hse Distribution) Deprecated. Package: r-cran-hsem Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-numderiv, r-cran-boot, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-hsem_1.0-1.ca2404.1_all.deb Size: 342768 MD5sum: 9a3c601be5719d5c808653b7222f403c SHA1: b0f2b9acb624738d37ab1220a95605e13c13ba7f SHA256: ca40dec012b0d26d11769ceabcc6f5892ab808c14503299e2e6fe0ad7485be39 SHA512: 44b9af00e854374ed5c1493046142a772c82dd088b1ebdc78556209ec71b01c134ebea44f858ec54f69d7ecd9f8f7ae0bc0484c7b607864f068c75afd4c5cd7b Homepage: https://cran.r-project.org/package=hsem Description: CRAN Package 'hsem' (Hierarchical Structural Equation Model) We present this package for fitting structural equation models using the hierarchical likelihood method. This package allows extended structural equation model, including dynamic structural equation model. We illustrate the use of our packages with well-known data sets. Therefore, this package are able to handle two serious problems inadmissible solution and factor indeterminacy . Package: r-cran-hset Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hash Filename: pool/dists/noble/main/r-cran-hset_0.1.1-1.ca2404.1_all.deb Size: 179548 MD5sum: f3f1296be1b82dd534ff640830d0907c SHA1: 98bceb3d363f151a410c52ae48941c29cba99c6a SHA256: 574cba9c00abf07c1317bfa9f37b7c07878fab19bd4558357442efd552832bd1 SHA512: d74dfe1ac6f2106c15cc09676d09105833ff4833c1d240b65a353fe50ab666f5a2ff87d097afa3f12c2ed6db1e095471ac492c9509e6147a2abb01f2278de0e4 Homepage: https://cran.r-project.org/package=hset Description: CRAN Package 'hset' (Sets of Numbers Implemented with Hash Tables) Implementation of S4 class of sets and multisets of numbers. The implementation is based on the hash table from the package 'hash'. Quick operations are allowed when the set is a dynamic object. The implementation is discussed in detail in Ceoldo and Wit (2023) . Package: r-cran-hsetest Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hsetest_0.1.0-1.ca2404.1_all.deb Size: 11338 MD5sum: c8145faa1e354d9ad5138395fdd32cd4 SHA1: 2f38e80eca39aa36623eeb50ca057d889aa42f25 SHA256: 0cb0ac6f95566462b0ca1445c074182cbf5c8a844a7ed48f316e6f1bfcf4f7e0 SHA512: 76ed1bd5ea65ef140ad472fe700a29f3ad6a240bdc9eb0db58092f7fcba87e050bedc56233ff85517bc705fea832511024a2cc2d0480b03c73ab61d6b06c2a08 Homepage: https://cran.r-project.org/package=HSEtest Description: CRAN Package 'HSEtest' (Homogeneity of Stratum Effects Test) To test the homogeneity of stratum effects in stratified paired binary data. Package: r-cran-hspm Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1368 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-sphet, r-cran-spdep, r-cran-matrix Suggests: r-cran-splm Filename: pool/dists/noble/main/r-cran-hspm_1.1-1.ca2404.1_all.deb Size: 1349994 MD5sum: 349512e251d7565ffb1bc316a687b64b SHA1: 71abcdf3b025a97599d01ed028cc31b6f1636237 SHA256: 8bfe20ba6bca8163591951190baac39c3324c7ba94adf20dd034983e4afd480b SHA512: 396dfa037bebfcf24077c171d4e6606274fbb36e3b2890e3d128ed9b1580155c96f20f43f00f8ee3c9e3f3b3a8b1b37afb7539706688d9f9e1576cda5259ede4 Homepage: https://cran.r-project.org/package=hspm Description: CRAN Package 'hspm' (Heterogeneous Spatial Models) Spatial heterogeneity can be specified in various ways. 'hspm' is an ambitious project that aims at implementing various methodologies to control for heterogeneity in spatial models. The current version of 'hspm' deals with spatial and (non-spatial) regimes models. In particular, the package allows to estimate a general spatial regimes model with additional endogenous variables, specified in terms of a spatial lag of the dependent variable, the spatially lagged regressors, and, potentially, a spatially autocorrelated error term. Spatial regime models are estimated by instrumental variables and generalized methods of moments (see Arraiz et al., (2010) , Bivand and Piras, (2015) , Drukker et al., (2013) , Kelejian and Prucha, (2010) ). Package: r-cran-hspor Architecture: all Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corpcor, r-cran-npregfast Filename: pool/dists/noble/main/r-cran-hspor_1.1.9-1.ca2404.1_all.deb Size: 166246 MD5sum: 900523c9de8d287d5efd255d3a637c2d SHA1: 328689b5e57f277451c7216cca0a44a6da1ee5bb SHA256: 7cc1ccc6a1b0c0cda34ef65e4df4838e6a1a4c4974703da6caae33e7714dd8a8 SHA512: f9de87e70cd7a2e3b2a5755fa414b988a12e43763ea81e2fc969b3f28c5a537463c50a88e093b4c6056ad286e4b0524a36ad19b96d2abe6aab8ba3a78b758e93 Homepage: https://cran.r-project.org/package=HSPOR Description: CRAN Package 'HSPOR' (Hidden Smooth Polynomial Regression for Rupture Detection) Several functions that allow by different methods to infer a piecewise polynomial regression model under regularity constraints, namely continuity or differentiability of the link function. The implemented functions are either specific to data with two regimes, or generic for any number of regimes, which can be given by the user or learned by the algorithm. A paper describing all these methods will be submitted soon. The reference will be added to this file as soon as available. Package: r-cran-hstats Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1252 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hstats_1.2.2-1.ca2404.1_all.deb Size: 347898 MD5sum: 4cdf94eb53953a96c55d561cbd6a6941 SHA1: d873a1892219012c27a6ff88fb0d5d30243e996b SHA256: 18cbf8feebf7659ff90f3677800fb9d19679a9debe1d8369d4c86523b9ff3afb SHA512: 8fd96769ce07599941874fbba55d9b77be363c6d2bbb00302e8eeb58c5dcfcf2b22d9a2a9e860c989565e4a1e8f587ae5b977f254ac1400b152f757f38a69686 Homepage: https://cran.r-project.org/package=hstats Description: CRAN Package 'hstats' (Interaction Statistics) Fast, model-agnostic implementation of different H-statistics introduced by Jerome H. Friedman and Bogdan E. Popescu (2008) . These statistics quantify interaction strength per feature, feature pair, and feature triple. 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Package: r-cran-htabim Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-cli, r-cran-checkmate, r-cran-glue, r-cran-purrr, r-cran-tibble, r-cran-scales, r-cran-flextable, r-cran-officer, r-cran-shiny, r-cran-bslib, r-cran-bsicons, r-cran-dt, r-cran-openxlsx, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling, r-cran-covr, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-htabim_0.1.0-1.ca2404.1_all.deb Size: 287592 MD5sum: 80735751e7607642ad09f56240cc7614 SHA1: 4d10ebc78d38867b3fb25b634f496d34aed711cd SHA256: 8cc71aa5d513c8a77cc6252c8098ad68d4e457f980a914b2142f482862497149 SHA512: 60675ef88a08d20dc0f97c5e972d22adfea4dc73b5d33ab50c75bdff6ec630ac38c6f2d9fd768c5415f5cdd914129043a25b3b7e32c62aee75addb82360d519f Homepage: https://cran.r-project.org/package=htaBIM Description: CRAN Package 'htaBIM' (Budget Impact Modelling for Health Technology Assessment) Implements a structured, reproducible framework for budget impact modelling (BIM) in health technology assessment (HTA), following the ISPOR Task Force guidelines (Sullivan et al. (2014) and Mauskopf et al. (2007) ). Provides functions for epidemiology-driven population estimation, market share modelling with flexible uptake dynamics, per-patient cost calculation across multiple cost categories, multi-year budget projections, payer perspective analysis, deterministic sensitivity analysis (DSA), and probabilistic sensitivity analysis (PSA) via Monte Carlo simulation. Produces submission-quality outputs including ISPOR-aligned summary tables, scenario comparison tables, per-patient cost breakdowns, tornado diagrams, PSA histograms, and text and HTML reports compatible with NICE, CADTH, and EU-HTA dossier formats. Ships with an interactive 'shiny' dashboard built on 'bslib' for point-and-click model building and exploration. Package: r-cran-htdv Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan Suggests: r-cran-bayesplot, r-cran-bridgesampling, r-cran-knitr, r-cran-loo, r-cran-posterior, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-htdv_0.2.0-1.ca2404.1_all.deb Size: 245110 MD5sum: 953809403569e3da72df2a24d65978d6 SHA1: 474c8523893e02f64aa77ffa0d01f234994dad49 SHA256: 3ad4e4644a4d7d64555489c25cd8a33d9ef9ace229f1250c3e8cf48a71c07bba SHA512: 7f81863e7433633cd5ba1d283f666824d44dfce05c0795eb7d32920d97ea64a6729afb9dff2249a49485574c89120ce8193fd77f9221d9e95322b33b4dee35be Homepage: https://cran.r-project.org/package=HTDV Description: CRAN Package 'HTDV' (Hypothesis Testing for Dependent Variables with Unbalanced Data) Implements hierarchical Bayesian inference, robust frequentist inference, and distribution-free inference for dependent and unbalanced data under strong-mixing conditions. Supports triangular-array, weighted-sum and mixingale convergence regimes with Whittle and composite likelihoods, heteroskedasticity-and-autocorrelation-consistent variance estimation, block bootstrap with automatic block length, fixed-bandwidth HAR inference, adaptive conformal prediction, Bayesian decision under Region of Practical Equivalence, bridge-sampling Bayes factors, and predictive comparison via the Widely Applicable Information Criterion and leave-future-out cross-validation. Methods follow Andrews (1991) , Kiefer and Vogelsang (2005) , Patton, Politis and White (2009) , Vehtari, Gelman and Gabry (2017) , Kruschke (2018) , and Gibbs and Candes (2021) . Package: r-cran-htestclust Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bootstrap, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-htestclust_0.2.2-1.ca2404.1_all.deb Size: 235328 MD5sum: 729c542b195649d69a48d17949d11bb3 SHA1: 2e03d8917f119a9a7559f1472476d1cd6829ae3f SHA256: 785f076f71b7078c0bdb2020c53b0b82695e8931a7bac7ffa76163fb51461bdc SHA512: 035a42e2fcabe3ce0a5b0b2b0f58d2c91397dbc37c5408b8bc12320f685f93179b82e800c712e3f1f628b3626eb4ea02b34176756744849569e85ba48dee087f Homepage: https://cran.r-project.org/package=htestClust Description: CRAN Package 'htestClust' (Reweighted Marginal Hypothesis Tests for Clustered Data) A collection of reweighted marginal hypothesis tests for clustered data, based on reweighting methods of Williamson, J., Datta, S., and Satten, G. (2003) . The tests in this collection are clustered analogs to well-known hypothesis tests in the classical setting, and are appropriate for data with cluster- and/or group-size informativeness. The syntax and output of functions are modeled after common, recognizable functions native to R. Methods used in the package refer to Gregg, M., Datta, S., and Lorenz, D. (2020) , Nevalainen, J., Oja, H., and Datta, S. (2017) Dutta, S. and Datta, S. (2015) , Lorenz, D., Datta, S., and Harkema, S. (2011) , Datta, S. and Satten, G. (2008) , Datta, S. and Satten, G. (2005) . Package: r-cran-htgm2d Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb, r-cran-gominer, r-cran-htgm, r-cran-gplots, r-cran-jaccard, r-cran-vprint, r-cran-randomgodb, r-cran-hgnchelper Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-htgm2d_1.1.1-1.ca2404.1_all.deb Size: 944006 MD5sum: e967ab66777b4bdfacb36e0ad88bc29e SHA1: dea611f222ce00b709936de46a422d200168cd23 SHA256: 72f974ad3fa5b3306d382c00263bdcdb261f8d9fa4d13f79d7198ae816f77859 SHA512: ab5ce926fdd4597e947ecdef7541e44bfa632fef49af05891ed6bd70064ebc56906ee0f9928c146d52a4b41e1813ec0eaea7d4db434e8ea017151474cee71823 Homepage: https://cran.r-project.org/package=HTGM2D Description: CRAN Package 'HTGM2D' (Two Dimensional High Throughput 'GoMiner') The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) ) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Microarray studies are usually analyzed with BP, whereas proteomics researchers often prefer CC. To capture the benefit of both of those ontologies, I developed a two-dimensional version of 'High-Throughput GoMiner' ('HTGM2D'). I generate a 2D heat map whose axes are any two of BP, MF, or CC, and the value within a picture element of the heat map reflects the Jaccard metric p-value for the number of genes in common for the corresponding pair. Package: r-cran-htgm3d Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1848 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb, r-cran-gominer, r-cran-htgm, r-cran-htgm2d, r-cran-r2html, r-cran-rgl, r-cran-vprint, r-cran-randomgodb, r-cran-stringr, r-cran-salesforcer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-htgm3d_1.0.3-1.ca2404.1_all.deb Size: 1318972 MD5sum: 4f8c24920845fa5ff0cd557553854c19 SHA1: 00a99aaa4b7166fd1d157dacc2c15ce26f0fc9c2 SHA256: 4de99ed8aa6b8eef5a4b6dc908e60e2c920bae0ae2bb25e6d0395208b6bf788b SHA512: 4301d2fe5e0b8f13045490234e60765149606532e8e917a5a8416c7956651ca1ad21f21b249e264061c98d38661e0e32271b1bffcbd4e706c760c855b17e8056 Homepage: https://cran.r-project.org/package=HTGM3D Description: CRAN Package 'HTGM3D' (Three Dimensional High Throughput 'GoMiner') The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) ) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. To capture the benefit of all three ontologies, I developed 'HTGM3D', a three-dimensional version of 'GoMiner'. Package: r-cran-htgm4d Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4560 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb, r-cran-gominer, r-cran-htgm, r-cran-htgm2d, r-cran-gplots, r-cran-vprint, r-cran-randomgodb, r-cran-hgnchelper, r-cran-png, r-cran-magick, r-cran-svglite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-htgm4d_1.0-1.ca2404.1_all.deb Size: 2152202 MD5sum: c2a27e7090ea5857e27e607649dc40fb SHA1: 25bb252010d41eb94d68609363224cb1b877461c SHA256: 9e3aa63ec5f0bd055d7de8950fa7665c8fdbfbdeb56f978766a8837c91b86988 SHA512: 64e3f71fc8c8a79b830909492d9db96364a2c287f778642445d71f014c58fdc8763aac8a42f2de8bb007b915c681cd662d31e99c64e8e950b6cc4ba610abaf61 Homepage: https://cran.r-project.org/package=HTGM4D Description: CRAN Package 'HTGM4D' (Four Dimensional High Throughput 'GoMiner') The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) ) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Microarray studies are usually analyzed with BP, whereas proteomics researchers often prefer CC. To capture the benefit of both of those ontologies, I now present an enhancement of the existing two-dimensional version of 'High-Throughput GoMiner' ('HTGM2D'), which is called 'HTGM4D'. The original 'HTGM2D' is augmented by adding two instances of the original 'GoMiner' genes versus categories heatmaps, aligned with the categories axes of the 'HTGM2D' heatmap. Package: r-cran-htgm Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2290 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minimalistgodb, r-cran-gominer, r-cran-gplots, r-cran-vprint Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-htgm_1.2-1.ca2404.1_all.deb Size: 847250 MD5sum: b2a2164ddc14742fc29fbdb7ff79348a SHA1: 0b50898579a22638b8374e4a5f430c27264a6d64 SHA256: 37b509e0ecf06726438aac8ef778aba93596589ede48bf4d127b65e719d7dbaf SHA512: c279079fcf5f1d72b0602c35b8dfbcf3143875aded021215acf50deb8909a3d9026622d616df787a51773b48221b1e8232c652dfa700405fb01f97f7495f91a8 Homepage: https://cran.r-project.org/package=HTGM Description: CRAN Package 'HTGM' (High Throughput 'GoMiner') Two papers published in the early 2000's (Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) ) and (Zeeberg, B.R., Qin, H., Narashimhan, S., et al. (2005) ) implement 'GoMiner' and 'High Throughput GoMiner' ('HTGM') to map lists of genes to the Gene Ontology (GO) . Until recently, these were hosted on a server at The National Cancer Institute (NCI). In order to continue providing these services to the bio-medical community, I have developed stand-alone versions. The current package 'HTGM' builds upon my recent package 'GoMiner'. The output of 'GoMiner' is a heatmap showing the relationship of a single list of genes and the significant categories into which they map. 'High Throughput GoMiner' ('HTGM') integrates the results of the individual 'GoMiner' analyses. The output of 'HTGM' is a heatmap showing the relationship of the significant categories derived from each gene list. The heatmap has only 2 axes, so the identity of the genes are unfortunately "integrated out of the equation." Because the graphic for the heatmap is implemented in Scalable Vector Graphics (SVG) technology, it is relatively easy to hyperlink each picture element to the relevant list of genes. By clicking on the desired picture element, the user can recover the "lost" genes. Package: r-cran-htm2txt Architecture: all Version: 2.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-htm2txt_2.2.2-1.ca2404.1_all.deb Size: 54850 MD5sum: 7419f1b220a9aa0386167bda3ae18df9 SHA1: 244c03f19e74eefbee6a3df94fd925fb4aac0893 SHA256: e3c19a29400f2fe9e08bebb2cd8dd8aaf05ebb5fcb50a8d5a588384a2212f50b SHA512: a5436193856e6f58aabc08e8f8a2bbba8f993b6fe1fd516968585484bcf319cee5c1dbf8be11e1e08fbaa718cb02e6424a67bf9e3f4b241f1c38aa9f7fbae8cd Homepage: https://cran.r-project.org/package=htm2txt Description: CRAN Package 'htm2txt' (Convert Html into Text) Convert a html document to plain texts by stripping off all html tags. 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Package: r-cran-html2r Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-shiny, r-cran-shinyace, r-cran-shinythemes, r-cran-shinyjqui Filename: pool/dists/noble/main/r-cran-html2r_0.1.0-1.ca2404.1_all.deb Size: 62502 MD5sum: dbeaa0b4c3efcec529d55aaefdf5a374 SHA1: 890530bfe43d1c44a3272ee97554f951de71f4f5 SHA256: a6276d2747e98c64055cb93596b6cc87366a36227d29f4ae0e96e0d0845d6733 SHA512: 1598b262fd3b91da877676ef5e2f9702ae0b74985e250df5db3b4f8583455aa5b0ad589005369aa2af1c3cef04b42a3a8c10a3e17298d5a3db6ed0e6abcd9dd9 Homepage: https://cran.r-project.org/package=html2R Description: CRAN Package 'html2R' (Convert 'HTML' to 'R' with a 'Shiny' App) Provides a 'Shiny' app allowing to convert 'HTML' code to 'R' code (e.g. 'Hello' to 'tags$span("Hello")'), for usage in a 'Shiny' UI. Package: r-cran-html5 Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-html5_1.0.2-1.ca2404.1_all.deb Size: 273738 MD5sum: 40229c1a68bbbd21ce8b59045da1bc95 SHA1: d26bf8798986476d64f99dcf083559a34c8da469 SHA256: 51742c278a5902de80b5ad2bc2552e08b586a2210c6df560b11721974514cc9e SHA512: ed2d50a09836bb6ee43ddd19f164baef3db67717ddc30bfc06910dd9dec009becbdaa8d454d144c6a063b3a5a7d9e981ba3f9fee3e4e390904ccffc9a8c50538 Homepage: https://cran.r-project.org/package=html5 Description: CRAN Package 'html5' (Creates Valid HTML5 Strings) Generates valid HTML tag strings for HTML5 elements documented by Mozilla. Attributes are passed as named lists, with names being the attribute name and values being the attribute value. Attribute values are automatically double-quoted. To declare a DOCTYPE, wrap html() with function doctype(). Mozilla's documentation for HTML5 is available here: . Elements marked as obsolete are not included. 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Tools help with extraction of page titles, links, images, rss feeds, social media handles and page metadata. Package: r-cran-htmlreportr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1900 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mime, r-cran-ggplot2, r-cran-knitr, r-cran-xfun, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-htmlreportr_1.0.0-1.ca2404.1_all.deb Size: 1223286 MD5sum: 93ebfaf5feb074734291cac657c8ef0a SHA1: 34bb6ddcfadc70883d17396360237bbe4a658e00 SHA256: 8cf36c6a62c697ce187657f61da598af4d3d66d4e02d537990a55000b461316a SHA512: d6349d793392036569d9035e48f039898ed1600a8c7511305d55dca96af439bb4af99103608f7b59c82635cdfecfa3bf08e78970a403891e7fb3fec759f2f9f7 Homepage: https://cran.r-project.org/package=htmlreportR Description: CRAN Package 'htmlreportR' ('HTML' Reporting Made Simple(R)) Create compressed, interactive 'HTML' (Hypertext Markup Language) reports with embedded 'Python' code, custom 'JS' ('JavaScript') and 'CSS' (Cascading Style Sheets), and wrappers for 'CanvasXpress' plots, networks and more. Based on , its sister project. 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While allowing advanced layout, the underlying css-structure is simple in order to maximize compatibility with common word processors. The package also contains a few text formatting functions that help outputting text compatible with HTML/LaTeX. 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Package: r-cran-htmlwidgets Architecture: all Version: 1.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2080 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-yaml Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-htmlwidgets_1.6.4-1.ca2404.1_all.deb Size: 416826 MD5sum: 72bfb3ea070b6d882dd438f658206a17 SHA1: 92820af024e344d0379d561b0e18c4301434e2eb SHA256: d6738cf3ed6f96ef01d1b6a81832202623b29eae32d17ad719d8036681453c32 SHA512: 0dd18a6cd0f7cb5d8d1df9cd3ff6ece3ad5c261bf67388f145b5ace35cd1bd86ca60d2282e247ec70cac55f8dfcbb1a38e7f1d7855b6286222d92b8966dae670 Homepage: https://cran.r-project.org/package=htmlwidgets Description: CRAN Package 'htmlwidgets' (HTML Widgets for R) A framework for creating HTML widgets that render in various contexts including the R console, 'R Markdown' documents, and 'Shiny' web applications. Package: r-cran-htmxr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 785 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-plumber2 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-svglite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-htmxr_0.2.0-1.ca2404.1_all.deb Size: 650672 MD5sum: a9adb081d8888adc5430b08a1c381260 SHA1: 1a34cf1f515b1c294f613affe1d08b8b218b7d01 SHA256: 9957446bedfced31511e86dbddb33cc50e165d196f996fcc4d22cc183fd4c7ad SHA512: 2cf8c9bd5803a04983c431ac0c02007c6549663f78a310a71ff5226f88b055ec1b64dd9dee2493dc31141054887c2eca21baf1c87e8576570bc9f6a625388749 Homepage: https://cran.r-project.org/package=htmxr Description: CRAN Package 'htmxr' (Build Modern Web Applications with 'htmx' and 'plumber2') A lightweight framework for building server-driven web applications in 'R'. 'htmxr' combines the simplicity of 'htmx' for partial page updates with the power of 'plumber2' for non-blocking HTTP endpoints. Build interactive dashboards and data applications without writing 'JavaScript', using familiar 'R' patterns inspired by 'Shiny'. For more information on 'htmx', see . Package: r-cran-htrspranalysis Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1617 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-magrittr, r-cran-readxl, r-cran-openxlsx, r-cran-minpack.lm, r-cran-zoo, r-cran-gridextra, r-cran-readr, r-cran-rlang, r-cran-dplyr, r-cran-stringr, r-cran-tidyselect, r-cran-ggplot2, r-cran-purrr, r-cran-forcats, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-htrspranalysis_0.1.3-1.ca2404.1_all.deb Size: 1254858 MD5sum: 87d1dabbfbe314291e276256a2b24d37 SHA1: 1d037bc78f46babf54441b7b5ad824f67667f325 SHA256: 5dc2f5e9608ff68ff6abe30f04890734206699c1446ba81abdf76d406bf7a668 SHA512: 1b723dbf7a5a834368d60ad7923eed7f001d25ccdb59e55cbe56e02e376e3950e09ed548d1ada219d25683a2d5ee767ee6dc56afde3fb9d71526eebb251c5998 Homepage: https://cran.r-project.org/package=htrSPRanalysis Description: CRAN Package 'htrSPRanalysis' (Analysis of Surface Plasmon Resonance Data) Analysis of Surface Plasmon Resonance (SPR) and Biolayer Interferometry data, with automations for high-throughput SPR. This version of the package fits the 1: 1 binding model, with and without bulkshift. It offers optional local or global Rmax fitting. The user must provide a sample sheet and a Carterra output file in Carterra's current format. There is a utility function to convert from Carterra's old output format. The user may run a custom pipeline or use the provided 'Runscript', which will produce a pdf file containing fitted Rmax, ka, kd and standard errors, a plot of the sensorgram and fits, and a plot of residuals. The script will also produce a .csv file with all of the relevant parameters for each spot on the SPR chip. Package: r-cran-htrx Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1291 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastglm, r-cran-caret, r-cran-glmnet, r-cran-tune, r-cran-recipes Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-htrx_1.2.4-1.ca2404.1_all.deb Size: 829332 MD5sum: ce136c51309dec4d286b39c7b0540947 SHA1: 660afb4d4d8a9d3a02adfc1559a4a4909ef4e31e SHA256: 42063be5528f93f5f9ff6c7b5c1cf3ca098cdfe3c8e10e00f65b23dd9a05f50e SHA512: 8875ccf4e525ae93415197a4fc8581ede2df232c68c8c2da0d59e2676ccfc534ca07566b2b0c5f1ca9e73340a2add4d05474ad7991cdb0e46882a1c898a1b18c Homepage: https://cran.r-project.org/package=HTRX Description: CRAN Package 'HTRX' (Haplotype Trend Regression with eXtra Flexibility (HTRX)) Detection of haplotype patterns that include single nucleotide polymorphisms (SNPs) and non-contiguous haplotypes that are associated with a phenotype. Methods for implementing HTRX are described in Yang Y, Lawson DJ (2023) and Barrie W, Yang Y, Irving-Pease E.K, et al (2024) . Package: r-cran-htscluster Architecture: all Version: 2.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-edger, r-cran-plotrix, r-cran-capushe Suggests: r-bioc-htsfilter, r-bioc-biobase Filename: pool/dists/noble/main/r-cran-htscluster_2.0.11-1.ca2404.1_all.deb Size: 511628 MD5sum: d7b201d53d1dbab7973e80c6f788c20b SHA1: bcee626591c38e4448e3e9d538fc04500f78f19f SHA256: de8ea7e9076beb353358f46985d0108e87900979519e6770d827fb5e92871306 SHA512: d8f34de733caa3d447140c833b18fc94e4526a36405d88f7b0129d7622cd7cf1a1c93589d95e1d68603b7aee89bcc7f66e98ddf332c34f663905d2ffbfa92072 Homepage: https://cran.r-project.org/package=HTSCluster Description: CRAN Package 'HTSCluster' (Clustering High-Throughput Transcriptome Sequencing (HTS) Data) A Poisson mixture model is implemented to cluster genes from high- throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is performed using either the EM or CEM algorithm, and the slope heuristics are used for model selection (i.e., to choose the number of clusters). Package: r-cran-htsdegenerater Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparsem Suggests: r-cran-forecast, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-htsdegenerater_0.1.0-1.ca2404.1_all.deb Size: 36450 MD5sum: fc7fe297ab3971920e6953464750930a SHA1: 258a796319843bdef4bfa45eefd9ff08b7e60002 SHA256: 201b5e0aa9975c088446608b1eb58a669656bd83ac4516d9d39fbb798eea7623 SHA512: 13da6a2df218dc56874296da814a2129f80e91ae378bba57bf93580b4749c95f75f3802586a957b37ec04ef830f8967d6eee7a9ff906052de9f1a240265b2693 Homepage: https://cran.r-project.org/package=htsDegenerateR Description: CRAN Package 'htsDegenerateR' (Degenerate Hierarchical Time Series Reconciliation) Takes the MinT implementation of the 'hts' package and adapts it to allow degenerate hierarchical structures. Instead of the "nodes" argument, this function takes an S matrix which is more versatile in the structures it allows. For a demo, see Steinmeister and Pauly (2024). The MinT algorithm is based on Wickramasuriya et al. (2019). Package: r-cran-htseed Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-htseed_0.1.0-1.ca2404.1_all.deb Size: 27716 MD5sum: 1cc6b23fea6b5dde0c53dded35f5efcd SHA1: f1c193306d022d0afb2f6185f89dd3f7a6cb96ef SHA256: 7b48c506d66da23d678f8f32e3975b9d32e6bb3d29ee18f52090a42dff4b6ab4 SHA512: 19a7d375162d4c008a1df55de265304057c0c8356b68a5a2d5f3bea4249a44a38744cf99d628877e350308591ef73f1de680fd860cf134419641656893941a70 Homepage: https://cran.r-project.org/package=HTSeed Description: CRAN Package 'HTSeed' (Fitting of Hydrotime Model for Seed Germination Time Course) The seed germination process starts with water uptake by the seed and ends with the protrusion of radicle and plumule under varying temperatures and soil water potential. Hydrotime is a way to describe the relationship between water potential and seed germination rates at germination percentages. One important quantity before applying hydrotime modeling of germination percentages is to consider the proportion of viable seeds that could germinate under saturated conditions. This package can be used to apply correction factors at various water potentials before estimating parameters like stress tolerance, and uniformity of the hydrotime model. Three different distributions namely, Gaussian, Logistic, and Extreme value distributions have been considered to fit the model to the seed germination time course. Details can be found in Bradford (2002) , and Bradford and Still(2004) . Package: r-cran-htseedglm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-htseedglm_0.1.0-1.ca2404.1_all.deb Size: 19738 MD5sum: b29e0f9fb670839007da94c9e5061c4d SHA1: 4ec60173f2409ce11fd566f7857f86990a2926b4 SHA256: 3f11a930413dae29f0a9a7968ab503fe602d28e76e09c930ac6a98c65efe0dab SHA512: 7b07da895a97e41257e7c947343c8bf5b25bc3db87e2ae294572ce01777a62ca31dbb4984bcc60d945a81c935d1fc55eccc82d14ad2307ca4034cea5b26ea73f Homepage: https://cran.r-project.org/package=HTSeedGLM Description: CRAN Package 'HTSeedGLM' (Hydro Thermal Time Analysis of Seed Germination UsingGeneralised Linear Model) Seed germinates through the physical process of water uptake by dry seed driven by the difference in water potential between the seed and the water. There exists seed-to-seed variability in the base seed water potential. Hence, there is a need for a distribution such that a viable seed with its base seed water potential germinates if and only if the soil water potential is more than the base seed water potential. This package estimates the stress tolerance and uniformity parameters of the seed lot for germination under various temperatures by using the hydro-time model of counts of germinated seeds under various water potentials. The distribution of base seed water potential has been considered to follow Normal, Logistic and Extreme value distribution. The estimated proportion of germinated seeds along with the estimates of stress and uniformity parameters are obtained using a generalised linear model. The significance test of the above parameters for within and between temperatures is also performed in the analysis. Details can be found in Kebreab and Murdoch (1999) and Bradford (2002) . Package: r-cran-htssip Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2046 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-ape, r-cran-magrittr, r-cran-stringr, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-vegan, r-bioc-deseq2, r-bioc-phyloseq, r-cran-coenocliner, r-cran-lazyeval Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-htssip_1.4.1-1.ca2404.1_all.deb Size: 1531592 MD5sum: 05604aad8c9923735c78881e61e66b1e SHA1: 7c751f10bef4a47662bfdc21dca81e1c2e3d04b2 SHA256: 5876ed9e60f8faed928ad20fcc2e234a29dae55f8676a36bab47c1021c317c12 SHA512: 53a5dbbb5de44f07ad4a54e65e4ffa187e3d51f1ba4ef8ca6d6d1fb93ee876367921ace40cb90bc6c1727ff4f958c9f3a4cf8ec43720c12f64e8714031b5f34c Homepage: https://cran.r-project.org/package=HTSSIP Description: CRAN Package 'HTSSIP' (High Throughput Sequencing of Stable Isotope Probing DataAnalysis) Functions for analyzing high throughput sequencing stable isotope probing (HTS-SIP) data. Analyses include high resolution stable isotope probing (HR-SIP), multi-window high resolution stable isotope probing (MW-HR-SIP), and quantitative stable isotope probing (q-SIP). Package: r-cran-httkexamples Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4907 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httk, r-cran-rmarkdown, r-cran-knitr, r-cran-rdpack Suggests: r-cran-dplyr, r-cran-tidyverse, r-cran-xlsx, r-cran-metrics, r-cran-ggplot2, r-cran-ggforce, r-cran-ggpubr, r-cran-ggrepel, r-cran-viridis, r-cran-ggh4x, r-cran-readr, r-cran-tidyr, r-cran-stringr, r-cran-pracma, r-cran-cgwtools, r-cran-openxlsx, r-cran-ggstar, r-cran-latex2exp, r-cran-smatr, r-cran-reshape, r-cran-gdata, r-cran-censreg, r-cran-gmodels, r-cran-gplots, r-cran-scales, r-cran-colorspace, r-cran-gridextra, r-cran-rvcheck Filename: pool/dists/noble/main/r-cran-httkexamples_0.0.1-1.ca2404.1_all.deb Size: 1837112 MD5sum: c9bc42248f1485d1a40f2b0fc6a62f2f SHA1: 7250b143416df2fe15925ef85f6fb76f57bf27e5 SHA256: 3c3d3f52e70e061c840f27ed67985399035b5ba409ffd6bc5461083cab207402 SHA512: 7548e3e8cba071c5633dbfe1fb671fbd71b8080c1e44740ece27491c474a1c2fdb52211bbc056d691990269a0893d3cf304d17cc874238dc86751e8068471a18 Homepage: https://cran.r-project.org/package=httkexamples Description: CRAN Package 'httkexamples' (High-Throughput Toxicokinetics Examples) High throughput toxicokinetics ("HTTK") is the combination of 1) chemical-specific in vitro measurements or in silico predictions and 2) generic mathematical models, to predict absorption, distribution, metabolism, and excretion by the body. HTTK methods have been described by Pearce et al. (2017) () and Breen et al. (2021) (). Here we provide examples (vignettes) applying HTTK to solve various problems in bioinformatics, toxicology, and exposure science. In accordance with Davidson-Fritz et al. (2025) (), whenever a new HTTK model is developed, the code to generate the figures evaluating that model is added as a new vignettte. Package: r-cran-httpcache Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-httr Suggests: r-cran-httptest, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-httpcache_1.2.0-1.ca2404.1_all.deb Size: 62324 MD5sum: d1276882ba7c5df85b62ffff00d7ffb7 SHA1: 5c6735a8db1fc01a5dd64546550d786e9415191b SHA256: e62329eccf10eed7b12a421b4ffe20b16b13992cf75a8a0f35e090c2455fbb84 SHA512: ab288bac6186779abd1704a1e02de6eb90f764b87bfa8216b072d80621ffad776c6c3fee99b8305a117d8c92a1efe38eaf8095be7604dacb887e26e35c321e00 Homepage: https://cran.r-project.org/package=httpcache Description: CRAN Package 'httpcache' (Query Cache for HTTP Clients) In order to improve performance for HTTP API clients, 'httpcache' provides simple tools for caching and invalidating cache. It includes the HTTP verb functions GET, PUT, PATCH, POST, and DELETE, which are drop-in replacements for those in the 'httr' package. These functions are cache-aware and provide default settings for cache invalidation suitable for RESTful APIs; the package also enables custom cache-management strategies. Finally, 'httpcache' includes a basic logging framework to facilitate the measurement of HTTP request time and cache performance. Package: r-cran-httpcode Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-httpcode_0.3.0-1.ca2404.1_all.deb Size: 33856 MD5sum: fae16348bdb989247864d54b3a40c381 SHA1: 7f590c5c7179342a25947fe1c5058cd9dbc66098 SHA256: a22ca4318a9b9718a0493b4d8b0a088071c8ab9397af707c2b90c86c50e43a10 SHA512: 4c3554c85a087e1367fc4b6c372cd9834630ae2e0d1d38f7949d19c857189c4809c0660f33785e9539321eb38645088a3b5c477cc756185891c8c5e5665afd33 Homepage: https://cran.r-project.org/package=httpcode Description: CRAN Package 'httpcode' ('HTTP' Status Code Helper) Find and explain the meaning of 'HTTP' status codes. Functions included for searching for codes by full or partial number, by message, and get appropriate dog and cat images for many status codes. Package: r-cran-httping Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-httpcode, r-cran-lobstr Suggests: r-cran-testthat, r-cran-webfakes Filename: pool/dists/noble/main/r-cran-httping_0.3.0-1.ca2404.1_all.deb Size: 24522 MD5sum: eaa4cd3a916e52170cce23169dfc7d13 SHA1: 8446ec7af18a47c4a33e47a8741f72f4b0ea9a6f SHA256: 1a785a6a1b1e02e418ccdb2054e805326dedc99c8128072c72f45500fef50e0f SHA512: 46d991344fa1146bd7faffa8103effccfe418532473cbfc1b00198d48b82fd9024edb298940bd17590659169e2106bfdaaac86adae3eff67329571d6c335bd0a Homepage: https://cran.r-project.org/package=httping Description: CRAN Package 'httping' ('Ping' 'URLs' to Time 'Requests') A suite of functions to ping 'URLs' and to time 'HTTP' 'requests'. Designed to work with 'httr'. Package: r-cran-httpproblems Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-httpproblems_1.0.1-1.ca2404.1_all.deb Size: 22366 MD5sum: 4a39b00424a29290c9c7848de87023f3 SHA1: b880e4686c6962324ddc72806637a3560b66b85e SHA256: 3c1062c61d7de239a1499c0b62902d632ddb04ee23e362b4e9d338ac6eaa3a5b SHA512: fac84d13535b9fbba3fd029a0f6e327d8850c076b943d822d4df68f47996e7394740232d15151bfdb484605a040973c0bf5dc6cf87fdba028cf7b992683b34ba Homepage: https://cran.r-project.org/package=httpproblems Description: CRAN Package 'httpproblems' (Report Errors in Web Applications with 'Problem Details' (RFC7807)) Tools for emitting the 'Problem Details' structure defined in 'RFC' 7807 for reporting errors from 'HTTP' servers in a standard way. Package: r-cran-httprequest Architecture: all Version: 0.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-httprequest_0.0.11-1.ca2404.1_all.deb Size: 27724 MD5sum: c3d1e93d27469dcf6f8f85a30911cd37 SHA1: a5e4811b7104ef84fb027371cd4b25ae09b23eef SHA256: 9c00a3dd82db0c5dfd6b5574ccaa5300d478bc258210df9852bc096e756804fa SHA512: 7c0d579606c417a33eed721776f8fa61773d4fcb93eab8f5514607c995d2a4fabb69ec05f8051a5e0f7c4a8390bc5906941499e6286f96226febc8c379d49fcb Homepage: https://cran.r-project.org/package=httpRequest Description: CRAN Package 'httpRequest' (Basic HTTP Request) HTTP Request protocols. Implements the GET, POST and multipart POST request. Package: r-cran-httptest2 Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-httr2, r-cran-jsonlite, r-cran-rlang, r-cran-testthat Suggests: r-cran-curl, r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-spelling, r-cran-webfakes, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-httptest2_1.2.2-1.ca2404.1_all.deb Size: 133994 MD5sum: 73b52f993d38686532dbe462a450e59d SHA1: 287fbb5a7b4c32f1a38d53e7a6d0b7abc16ea0ce SHA256: 5b4b9918749f762f1312c4bb80ea078661685720ceb3978a64278fe424fc2c01 SHA512: e2dd71dde9952c5bbe3aa2b96f882043ec540e7d6948622cadcb183e04f9579414e87a1fbc463d927a07bb4b221a7d02a1e6ec56e847499aa311185be48b17f7 Homepage: https://cran.r-project.org/package=httptest2 Description: CRAN Package 'httptest2' (Test Helpers for 'httr2') Testing and documenting code that communicates with remote servers can be painful. This package helps with writing tests for packages that use 'httr2'. It enables testing all of the logic on the R sides of the API without requiring access to the remote service, and it also allows recording real API responses to use as test fixtures. The ability to save responses and load them offline also enables writing vignettes and other dynamic documents that can be distributed without access to a live server. Package: r-cran-httptest Architecture: all Version: 4.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testthat, r-cran-curl, r-cran-digest, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-spelling, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-httptest_4.2.3-1.ca2404.1_all.deb Size: 164974 MD5sum: 7e7b07963852b3d1e5401c6482023da9 SHA1: e3917b46bbdfd828b5c56a3fa78454a6582af2f4 SHA256: a6709b654700811dbd192381a545a184caee142fdf848103649eed4e6f83ac8d SHA512: 7e058eaf6f5b97e49e50bf833c4fd6c2fd57a671b9a3948914c990e9e89efcb9404ba00b834d69cc17df1980b0a32e5c44500588a78ef6ebaa4e1e95a757eed3 Homepage: https://cran.r-project.org/package=httptest Description: CRAN Package 'httptest' (A Test Environment for HTTP Requests) Testing and documenting code that communicates with remote servers can be painful. Dealing with authentication, server state, and other complications can make testing seem too costly to bother with. But it doesn't need to be that hard. This package enables one to test all of the logic on the R sides of the API in your package without requiring access to the remote service. Importantly, it provides three contexts that mock the network connection in different ways, as well as testing functions to assert that HTTP requests were---or were not---made. It also allows one to safely record real API responses to use as test fixtures. The ability to save responses and load them offline also enables one to write vignettes and other dynamic documents that can be distributed without access to a live server. Package: r-cran-httr2 Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 913 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-glue, r-cran-lifecycle, r-cran-magrittr, r-cran-openssl, r-cran-r6, r-cran-rappdirs, r-cran-rlang, r-cran-vctrs, r-cran-withr Suggests: r-cran-askpass, r-cran-bench, r-cran-clipr, r-cran-covr, r-cran-docopt, r-cran-httpuv, r-cran-jose, r-cran-jsonlite, r-cran-knitr, r-cran-later, r-cran-nanonext, r-cran-otel, r-cran-otelsdk, r-cran-paws.common, r-cran-promises, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-webfakes, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-httr2_1.2.2-1.ca2404.1_all.deb Size: 790754 MD5sum: a4be6b16018682b0dd99df3d9a7185e1 SHA1: fa83d9aa1044fa4c850bed8de81baf9994df2b3f SHA256: 6186ad81a35d13ea99b3ac9887869e7401c5c003129e68b0d0d88df2a6ba9ebf SHA512: 7d3818705641849a816eac320d498ec2de2db3c473e5ce13760c891b75e115c56c655b750852a61fc99bb3e352c9a378a8701f2aab6561c577a6ee00e905e65c Homepage: https://cran.r-project.org/package=httr2 Description: CRAN Package 'httr2' (Perform HTTP Requests and Process the Responses) Tools for creating and modifying HTTP requests, then performing them and processing the results. 'httr2' is a modern re-imagining of 'httr' that uses a pipe-based interface and solves more of the problems that API wrapping packages face. Package: r-cran-httr Architecture: all Version: 1.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 580 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-jsonlite, r-cran-mime, r-cran-openssl, r-cran-r6 Suggests: r-cran-covr, r-cran-httpuv, r-cran-jpeg, r-cran-knitr, r-cran-png, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-httr_1.4.8-1.ca2404.1_all.deb Size: 468592 MD5sum: bbbf0b0aa004d6a1cb03de21edde2eb6 SHA1: baa24650637bbeda95fab2afa70878b5d3db7a15 SHA256: 0eb8233af78647c0f351b650e874053e938d99a75afebec9d299627a933f89fb SHA512: 5874fdc43b008b2aa55b331b9b26ef255579f713624b36ac0d1c94a069f63f874efc73d6045665828a74e6287a2b0882ebe8fd052aa0e0c21c6fc66b0799f030 Homepage: https://cran.r-project.org/package=httr Description: CRAN Package 'httr' (Tools for Working with URLs and HTTP) Useful tools for working with HTTP organised by HTTP verbs (GET(), POST(), etc). Configuration functions make it easy to control additional request components (authenticate(), add_headers() and so on). Package: r-cran-hubeau Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2175 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-magrittr, r-cran-purrr, r-cran-tibble, r-cran-urltools Suggests: r-cran-ggplot2, r-cran-hmisc, r-cran-knitr, r-cran-leafpop, r-cran-lubridate, r-cran-mapview, r-cran-rmarkdown, r-cran-sf, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hubeau_0.5.2-1.ca2404.1_all.deb Size: 895704 MD5sum: 19aa8ba210d9dfe7d6a9863e125e2fbc SHA1: e7f5f86514c28c0ddc8a465825d0d439457bbdd7 SHA256: 655d8ba64a6b5165b172b2a842eacff8b75ea4d26f712d416258eadaef17ae6b SHA512: d8243c46d31f55f1b4ad6feec3321c1c6eaa15d0464d9cb8363db47f3625923b1b0066bb093a39819ae1500f9ed2028213d7637c90096693ce958a106945c19a Homepage: https://cran.r-project.org/package=hubeau Description: CRAN Package 'hubeau' (Get Data from the French National Database on Water 'Hub'Eau') Collection of functions to help retrieving data from 'Hub'Eau' the free and public French National APIs on water . 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Package: r-cran-hybriddesign Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-testit, r-cran-resourceselection Filename: pool/dists/noble/main/r-cran-hybriddesign_1.0-1.ca2404.1_all.deb Size: 42790 MD5sum: e2ec368c1ca692aee600369fbec83fc6 SHA1: ec84f160408766e678a6a763ca3b4302004fef96 SHA256: 9fdba9bda3dc95eb187450f41c7c033d0939f6f8b588946f22a15c4be28e2c62 SHA512: d8feb5e728b78399baa71a3cad873fe58187cee93e4c1a2a490a24bd453cc4f43ad5d83abaadbee657f14ed381a099ba444e6126d732f8962fa079b40990b89f Homepage: https://cran.r-project.org/package=HybridDesign Description: CRAN Package 'HybridDesign' (Hybrid Design for Phase I Dose-Finding Studies) The Hybrid design is a combination of model-assisted design (e.g., the modified Toxicity Probability Interval design) with dose-toxicity model-based design for phase I dose-finding studies. 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Package: r-cran-hybridehr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-lubridate, r-cran-magrittr, r-cran-jsonlite, r-cran-openxlsx, r-cran-dbi, r-cran-rsqlite, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hybridehr_0.2.0-1.ca2404.1_all.deb Size: 70852 MD5sum: e63577ed74487f1a3e19d879d0f4cc24 SHA1: 6a227b87403a73234193d3375c961a97b2dc147c SHA256: f1ab6098f3bc0b0c09817f8cd3e53e610ffa85d68d84d8e5191190ae0f7bf974 SHA512: 41d07f3f2b2c95215edb765b98704aef94c8174d186f7f3862bca66c3474f98dfd4d52bbf4b239600414e20c845873b89126e2a6aef7ba894e76603910987c06 Homepage: https://cran.r-project.org/package=hybridEHR Description: CRAN Package 'hybridEHR' (Synthetic Hybrid Electronic Health Record Generation forSARS-Related Research and CT Views) Generates synthetic electronic health record data, including patients, encounters, vitals, laboratory results, medications, procedures, and allergies. The package supports optional SARS-focused and computed tomography (CT) research views and export to CSV, SQLite, and Excel formats for research and development workflows. Package: r-cran-hybridmicrobiomes Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-compositions, r-cran-rlang, r-cran-permanova, r-cran-vegan, r-cran-ks, r-cran-ape, r-cran-geometry, r-cran-stereomorph, r-bioc-phyloseq Filename: pool/dists/noble/main/r-cran-hybridmicrobiomes_0.1.1-1.ca2404.1_all.deb Size: 124536 MD5sum: 87a3fed39376c924e4b3b12820bca9ef SHA1: 5bd4b4c8ff2a541dd26eac7851f1dfa7fce6945d SHA256: fbe81962f61dc759b56bd82cb3d9401b425dd189870dfc81754a64da77b49ed3 SHA512: 5b5dcb42202fe0bc8683cdab382a5768edaa69b9916092ef42cee46454f8e5dabfaf517186f2d1576d45aef8ed4ef4d7d2816c4fa6ff362753f8f8e1e4d59158 Homepage: https://cran.r-project.org/package=HybridMicrobiomes Description: CRAN Package 'HybridMicrobiomes' (Analysis of Host-Associated Microbiomes from Hybrid Organisms) A set of tools to analyze and visualize the relationships between host-associated microbiomes of hybrid organisms and those of their progenitor species. Though not necessary, installing the microViz package is recommended as a check for phyloseq objects. To install microViz from R Universe use the following command: install.packages("microViz", repos = c(davidbarnett = "https://david-barnett.r-universe.dev", getOption("repos"))). To install microViz from GitHub use the following commands: install.packages("devtools") followed by devtools::install_github("david-barnett/microViz"). Package: r-cran-hybridmodels Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-dorng, r-cran-foreach, r-cran-ggplot2, r-cran-gillespiessa, r-cran-reshape2, r-cran-stringr Filename: pool/dists/noble/main/r-cran-hybridmodels_0.3.8-1.ca2404.1_all.deb Size: 139814 MD5sum: 9121a10f2580d5f662007e9f45a0285e SHA1: 7b33386f33197a72d8db8a7b1214e6407512afee SHA256: e799a6714c984ec66bd36b371c05197e6ac268f22df0308bb780273936e5d5e1 SHA512: e6be13da2fe738893b9c83d06d31987d1140fc1a035ea335e52886848d9d4beda68b410a5c8bd1e5e5b1446cb6e21c4e4062815a03e8642881aa988b88bc9586 Homepage: https://cran.r-project.org/package=hybridModels Description: CRAN Package 'hybridModels' (An R Package for the Stochastic Simulation of Disease Spreadingin Dynamic Networks) Simulates stochastic hybrid models for transmission of infectious diseases in dynamic networks. It is a metapopulation model in which each node in the network is a sub-population and disease spreads within nodes and among them, combining two approaches: stochastic simulation algorithm () and individual-based approach, respectively. Equations that models spread within nodes are customizable and there are two link types among nodes: migration and influence (commuting). More information in Fernando S. Marques, Jose H. H. Grisi-Filho, Marcos Amaku et al. (2020) . Package: r-cran-hybridogram Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pheatmap Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hybridogram_0.3.2-1.ca2404.1_all.deb Size: 17296 MD5sum: 519f50a96a195e7541999f647705c6ac SHA1: fc9a072b14ca48989f970cf010501c9d0b23c1d0 SHA256: af8a456bbfc7601ba1d3822e80d09a3649d1ef4c829618793a557884e757ae1a SHA512: 5929df4f991b2565fe5b933641273cc57e37f4edf62a26d26f57c53cb7ca11838bfe4d906b8268dff47430d1ff7006459b30817dbdb588346befa2441d079aaf Homepage: https://cran.r-project.org/package=hybridogram Description: CRAN Package 'hybridogram' (Function that Creates a Heat Map from Hybridization Data) Using hybrid data, this package created a vividly colored hybrid heat map. The input is two files which are auto-selected. The first file has three columns, the first two for pairs of species, with the third column for the hybrid experiment code (an integer). The second file is a list of code and their descriptions in two columns. The output is a figure showing the hybrid heat map with a color legend. Package: r-cran-hybridts Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-nnfor, r-cran-waveletarima, r-cran-metrics Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-hybridts_0.1.0-1.ca2404.1_all.deb Size: 50682 MD5sum: 10827843a302d4e789bf88dabc42e4d3 SHA1: 4a392c712b3b754c699f8729bd333491b2ccafe2 SHA256: 5497224c395f14ccd588e5dd7baa827dfd8c26bcf7adb6777576927f79b1b333 SHA512: 8d6821e0c5ef79a39658da794aa7907f9c0286d6e372bf837877f2eade11f217063f50382af67442bbe590574400d458076e441c2708c060fb60530028246182 Homepage: https://cran.r-project.org/package=hybridts Description: CRAN Package 'hybridts' (Hybrid Time Series Forecasting Using Error Remodeling Approach) Method and tool for generating hybrid time series forecasts using an error remodeling approach. These forecasting approaches utilize a recursive technique for modeling the linearity of the series using a linear method (e.g., ARIMA, Theta, etc.) and then models (forecasts) the residuals of the linear forecaster using non-linear neural networks (e.g., ANN, ARNN, etc.). The hybrid architectures comprise three steps: firstly, the linear patterns of the series are forecasted which are followed by an error re-modeling step, and finally, the forecasts from both the steps are combined to produce the final output. This method additionally provides the confidence intervals as needed. Ten different models can be implemented using this package. This package generates different types of hybrid error correction models for time series forecasting based on the algorithms by Zhang. (2003), Chakraborty et al. (2019), Chakraborty et al. (2020), Bhattacharyya et al. (2021), Chakraborty et al. (2022), and Bhattacharyya et al. (2022) . Package: r-cran-hyd1d Architecture: all Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-httr2, r-cran-curl Suggests: r-cran-dbi, r-cran-rpostgresql, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-devtools, r-cran-pkgdown, r-cran-roxygen2, r-cran-revealjs, r-cran-shiny, r-cran-shiny.i18n, r-cran-shinytime, r-cran-lubridate, r-cran-usethis, r-cran-yaml, r-cran-desc Filename: pool/dists/noble/main/r-cran-hyd1d_0.5.4-1.ca2404.1_all.deb Size: 1778086 MD5sum: db49f06d88cc3c8adfe987a7d3261f68 SHA1: dbd28f30e5e68156cae163bddf84e90ed350b303 SHA256: 82fdda8469280c901ac35b59bb5d3e4052a33802fed295d95e921b42fbfad028 SHA512: d102b323ad16fc2db87e11889478a2b445c2a12d278d476b485da6b665d6e5be3a9a513745eee9298fdd0b58606325f7efd377bae5a59769533d365835351880 Homepage: https://cran.r-project.org/package=hyd1d Description: CRAN Package 'hyd1d' (1d Water Level Interpolation along the Rivers Elbe and Rhine) An S4 class and several functions which utilize internally stored datasets and gauging data enable 1d water level interpolation. The S4 class (WaterLevelDataFrame) structures the computation and visualisation of 1d water level information along the German federal waterways Elbe and Rhine. 'hyd1d' delivers 1d water level data - extracted from the 'FLYS' database - and validated gauging data - extracted from the hydrological database 'WISKI7' - package-internally. For computations near real time gauging data are queried externally from the 'PEGELONLINE REST API' . Package: r-cran-hydflood Architecture: all Version: 0.5.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-terra, r-cran-raster, r-cran-hyd1d, r-cran-rdpack, r-cran-httr2, r-cran-curl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-pkgdown, r-cran-roxygen2, r-cran-testthat, r-cran-plot3d, r-cran-shiny, r-cran-shinyjs, r-cran-shiny.i18n, r-cran-leaflet, r-cran-leaflet.extras, r-cran-leaflet.esri, r-cran-pangaear, r-cran-rgrass, r-cran-stringr Filename: pool/dists/noble/main/r-cran-hydflood_0.5.10-1.ca2404.1_all.deb Size: 1793096 MD5sum: 557c224f209d5a1040f0f87888fb55d5 SHA1: 77bfbfcb9cb8e6d573edeef1604dd67ac6ce7f4c SHA256: 82283cb56d7a54e6327508fbab26dfb4c022d7720ae9c7e4cd875c988aac2ab8 SHA512: 53cead6e84c99086e408a11ee132e2d567087ec176d52cdfef9f8a84f0d8ab5b8c4b131c89d2e8e1cd27515a43299fc059b1ece0ee5eddd0bf45363e417e16cc Homepage: https://cran.r-project.org/package=hydflood Description: CRAN Package 'hydflood' (Flood Extents and Duration along the Rivers Elbe and Rhine) Raster based flood modelling internally using 'hyd1d', an R package to interpolate 1d water level and gauging data. The package computes flood extent and duration through strategies originally developed for 'INFORM', an 'ArcGIS'-based hydro-ecological modelling framework. It does not provide a full, physical hydraulic modelling algorithm, but a simplified, near real time 'GIS' approach for flood extent and duration modelling. Computationally demanding annual flood durations have been computed already and data products were published by Weber (2022) . Package: r-cran-hydra Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-igraph, r-cran-igraphdata, r-cran-matrix, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-hydra_0.1.0-1.ca2404.1_all.deb Size: 49636 MD5sum: 9f8e67dd663b864ffc856012a2cef12c SHA1: 873198e306fee91e56153da2c73eccb52d0ad3f7 SHA256: 6d9f42274b1870cb61e95bf8ecaba98dcb3b89aeddaa81bc362913de2cc71ebe SHA512: 8ab9df8a1128f3cb152694dd1f64c6173950d18896453aa55873f48c06a294dec903b54ac726c8e07fc90b4f8f161b632cf7704281a19df5cd55ef8618ab3c20 Homepage: https://cran.r-project.org/package=hydra Description: CRAN Package 'hydra' (Hyperbolic Embedding) Calculate an optimal embedding of a set of data points into low-dimensional hyperbolic space. This uses the strain-minimizing hyperbolic embedding of Keller-Ressel and Nargang (2019), see . Package: r-cran-hydraulics Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gtools, r-cran-pracma, r-cran-purrr, r-cran-reshape2, r-cran-tibble, r-cran-units Suggests: r-cran-formatdown, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hydraulics_0.7.2-1.ca2404.1_all.deb Size: 823666 MD5sum: 9899c4d60051be7776bdf6a156122a5d SHA1: 71e74277172bf400196fb9d5048beb412599f642 SHA256: a0004e61ebd93f4851f1365cb7f5bb3f0f921f13a7863584bdca57faa0312d9c SHA512: 024f584cfd6d2eb746b4da2ae3c58f61c6aeb8e66ee2e44bcb7452490711deb5bf23b83146b08ed2b2d338870627cc83c398b86cd803dda33cb0e8ef1e0b7c48 Homepage: https://cran.r-project.org/package=hydraulics Description: CRAN Package 'hydraulics' (Basic Pipe and Open Channel Hydraulics) Functions for basic hydraulic calculations related to water flow in circular pipes both flowing full (under pressure), and partially full (gravity flow), and trapezoidal open channels. For pressure flow this includes friction loss calculations by solving the Darcy-Weisbach equation for head loss, flow or diameter, plotting a Moody diagram, matching a pump characteristic curve to a system curve, and solving for flows in a pipe network using the Hardy-Cross method. The Darcy-Weisbach friction factor is calculated using the Colebrook (or Colebrook-White equation), the basis of the Moody diagram, the original citation being Colebrook (1939) . For gravity flow, the Manning equation is used, again solving for missing parameters. The derivation of and solutions using the Darcy-Weisbach equation and the Manning equation are outlined in many fluid mechanics texts such as Finnemore and Maurer (2024, ISBN:978-1-264-78729-6). Some gradually- and rapidly-varied flow functions are included. For the Manning equation solutions, this package uses modifications of original code from the 'iemisc' package by Irucka Embry. Package: r-cran-hydreng Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hydreng_1.0.0-1.ca2404.1_all.deb Size: 216938 MD5sum: fb142bacfdbd9b02a50f9add06cf1320 SHA1: fcbfead7db7865e747a7ec971734cf0ec6831c3e SHA256: d6462ebaa848e8a297be48879bb3c5c14a77b77193c5ce1c127d7f7121c60aa4 SHA512: 3aac74c6f06fac2951129a56563ae35cc421946cfe5158e6c60936faaca337213474fe17d99c05fdedd1263f7a8f07dae5ab404356b7742685915a8b655f9b73 Homepage: https://cran.r-project.org/package=hydReng Description: CRAN Package 'hydReng' (Hydraulic Engineering Tools) The 'hydReng' package provides a set of functions for hydraulic engineering tasks and natural hazard assessments. It includes basic hydraulics (wetted area, wetted perimeter, flow, flow velocity, flow depth, and maximum flow) for open channels with arbitrary geometry under uniform flow conditions. For structures such as circular pipes, weirs, and gates, the package includes calculations for pressure flow, backwater depth, and overflow over a weir crest. Additionally, it provides formulas for calculating bedload transport. The formulas used can be found in standard literature on hydraulics, such as Bollrich (2019, ISBN:978-3-410-29169-5) or Hager (2011, ISBN:978-3-642-77430-0). Package: r-cran-hydrocal Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-hydrocal_1.0.0-1.ca2404.1_all.deb Size: 66672 MD5sum: 3e9a0c4520189c52caa6b21a6f82075a SHA1: b08a5a6230e9010efe485fbe3d657b7412c196c1 SHA256: 25c5f1b5c0d2fcda8ce97e2b5677c3ebd982a8657014beeaad312fa08f545a90 SHA512: 8b3ee379faaa9a76d0700fce8b20e8e4b627dbf7a138c822d8dd5a24b3b84a5e755c262d6e58fe85d848d2143c56234c137835c3606b1d952a7293a6106442b5 Homepage: https://cran.r-project.org/package=HYDROCAL Description: CRAN Package 'HYDROCAL' (Hydraulic Roughness Calculator) Estimates frictional constants for hydraulic analysis of rivers. This HYDRaulic ROughness CALculator (HYDROCAL) was previously developed as a spreadsheet tool and accompanying documentation by McKay and Fischenich (2011, ). 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Package: r-cran-hydrodcindex Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-hydrodcindex_1.0.0-1.ca2404.1_all.deb Size: 143900 MD5sum: 2fc626fa6c721f71a9d601a0972388a3 SHA1: 493a42739a77fb96d5ae1629dfa3872f9e4b870b SHA256: 7d0bf2fc9b6d550a7a0912c398f0335afc7d57381ff916f981472329eb2c1aae SHA512: 58cdb88edd50ab0aacbf76302cc05c1dbcd6d65f597d008049d563153a4a129b416dedc9672557f9afe54f185135a2e6048ae417b577233a8f4ff60de8b0e78a Homepage: https://cran.r-project.org/package=hydroDCindex Description: CRAN Package 'hydroDCindex' (Duration Curve Hydrological Model Indexes) Compute duration curves of daily flow series, both real and modeled, to be compared through indexes of flow duration curves. The package functions include comparative plots and goodness of fit tests. Flow duration curve indexes are based on: Yilmaz et al., (2008) . 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Includes a provider registry, S3 generics 'stations' and 'timeseries', licensing metadata, date-range and 'complete history' modes, rate limiting and retries, optional authentication via environment variables, tidy outputs, UTF-8 to ASCII transliteration, and WGS84 coordinates. Designed for reproducible workflows and straightforward addition of new providers. Background and use cases are described in Farber et al. (2025) and Farber et al. (2023) . Package: r-cran-hydroeval Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-hydroeval_0.1.0-1.ca2404.1_all.deb Size: 164434 MD5sum: 3f8fa4944e28e7f76876be7dd753febe SHA1: ac70d09d14ad7118a2f11259d716b17e3b6eb414 SHA256: 4bc0bf40c6e8beb47a3a5ff7386e0c0ab79b71421e2ab4bdd6bbaf580d10045e SHA512: a6245428c5b8acad5228923ca26c1210f02559d07fe3de2e4ca20f8441eff0f85665aeccb8ee198896700fed255d80b2baed2fc7421b3e835e8965b00d444f62 Homepage: https://cran.r-project.org/package=hydroeval Description: CRAN Package 'hydroeval' (Hydrological Evaluation Metrics and Goodness-of-Fit Summaries) Computes scalar performance metrics and goodness-of-fit summaries for comparing simulated and observed hydrological or regression values. Provides error metrics, percentage bias, Nash-Sutcliffe efficiency, Kling-Gupta efficiency variants, correlation measures, agreement indices, and comparison tables via gof() and gof_compare(). Metric definitions are registry-governed with shared validation, provenance-aware wrappers, and explicit handling of undefined or degenerate cases. Methods include Nash and Sutcliffe (1970) , Gupta et al. (2009) , Kling et al. (2012) , and Willmott et al. (2012) . 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Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed. 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Methods and algorithms implemented are documented in Moore et al. (2019) ), Cormen and Leiserson (2022) and Verdin and Verdin (1999) . 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The models considered are those that are commonly used in soil science. Version 2 of the package has new models for water retention characteristic curves. Package: r-cran-hydromopso Architecture: all Version: 0.1-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 714 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-randtoolbox, r-cran-lhs, r-cran-hydrotsm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-smoof, r-cran-hydrogof, r-cran-airgr, r-cran-tuwmodel Filename: pool/dists/noble/main/r-cran-hydromopso_0.1-14-1.ca2404.1_all.deb Size: 693020 MD5sum: 8649f73450102e204c7296f42ae8b0cd SHA1: d94849015b0fa0c0221a6694a34f7f4633c0bbf8 SHA256: 2af8dd096159ef6a03e3187f46e6dfa69b2f10076522fb39973516f73234a9af SHA512: 564985ce1c03c1e4e261caebf193743785abe3a259e6550541a4436ca9b6fef4bfee91d48f35fa6287f59f72f80022d874110561a964a4db7002e3e08eaf6a85 Homepage: https://cran.r-project.org/package=hydroMOPSO Description: CRAN Package 'hydroMOPSO' (Multi-Objective Optimisation with Focus on Environmental Models) State-of-the-art Multi-Objective Particle Swarm Optimiser (MOPSO), based on the algorithm developed by Lin et al. (2018) with improvements described by Marinao-Rivas & Zambrano-Bigiarini (2020) . This package is inspired by and closely follows the philosophy of the single objective 'hydroPSO' R package ((Zambrano-Bigiarini & Rojas, 2013) ), and can be used for global optimisation of non-smooth and non-linear R functions and R-base models (e.g., 'TUWmodel', 'GR4J', 'GR6J'). However, the main focus of 'hydroMOPSO' is optimising environmental and other real-world models that need to be run from the system console (e.g., 'SWAT+'). 'hydroMOPSO' communicates with the model to be optimised through its input and output files, without requiring modifying its source code. Thanks to its flexible design and the availability of several fine-tuning options, 'hydroMOPSO' can tackle a wide range of multi-objective optimisation problems (e.g., multi-objective functions, multiple model variables, multiple periods). Finally, 'hydroMOPSO' is designed to run on multi-core machines or network clusters, to alleviate the computational burden of complex models with long execution time. Package: r-cran-hydropeak Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-data.table Filename: pool/dists/noble/main/r-cran-hydropeak_0.1.2-1.ca2404.1_all.deb Size: 84706 MD5sum: ff043085aa960451c577e86a93c21151 SHA1: 98477147d8687fadfae83529192f6b229f27fa10 SHA256: 792276d3612ad4975c9a4b606a01267a236bbf5cc9e77b76deb7339eca43b54b SHA512: 5af83c3284f18327592f82cf606aaab99a001b38c336eb34efb5406a2d64c718a18c5cd1b6e8d1d2c6a45d0126ce3e97f73d582dc23b39589139185b43162088 Homepage: https://cran.r-project.org/package=hydropeak Description: CRAN Package 'hydropeak' (Detect and Characterize Sub-Daily Flow Fluctuations) An important environmental impact on running water ecosystems is caused by hydropeaking - the discontinuous release of turbine water because of peaks of energy demand. An event-based algorithm is implemented to detect flow fluctuations referring to increase events (IC) and decrease events (DC). For each event, a set of parameters related to the fluctuation intensity is calculated. The framework is introduced in Greimel et al. (2016) "A method to detect and characterize sub-daily flow fluctuations" and can be used to identify different fluctuation types according to the potential source: e.g., sub-daily flow fluctuations caused by hydropeaking, rainfall, or snow and glacier melt. This is a companion to the package 'hydroroute', which is used to detect and follow hydropower plant-specific hydropeaking waves at the sub-catchment scale and to describe how hydropeaking flow parameters change along the longitudinal flow path as proposed and validated in Greimel et al. (2022). Package: r-cran-hydroportailstats Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-evd, r-cran-mvtnorm, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hydroportailstats_1.1.0-1.ca2404.1_all.deb Size: 206790 MD5sum: de7db876b6e021912b5574f3914027d7 SHA1: b2b9d2e261521cc61a6444af3d2d8f381310cd56 SHA256: 6492bb1f6cf32434e3178c698ce136a20fcae16b4f1c81e931f89fabf5b2699e SHA512: ac50a1369d7b307a3845c3b794ae13432cd4253d383349d8591dda193b4dfce18db261241f304d09b3e17d1c635d26492c3f55b90ae364cbecac91289cec2068 Homepage: https://cran.r-project.org/package=HydroPortailStats Description: CRAN Package 'HydroPortailStats' ('HydroPortail' Statistical Functions) Statistical functions used in the French 'HydroPortail' . This includes functions to estimate distributions, quantile curves and uncertainties, along with various other utilities. Technical details are available (in French) in Renard (2016) . Package: r-cran-hydroroute Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2428 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggpmisc, r-cran-ggplot2, r-cran-gridextra, r-cran-hydropeak, r-cran-lubridate, r-cran-reshape2, r-cran-scales Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-hydroroute_0.1.2-1.ca2404.1_all.deb Size: 819236 MD5sum: 108e529ce5e1bfd3032f2b8b6def3758 SHA1: 706d0e365e03f674830f8d8e7f6aa4a0ae8a6988 SHA256: b6745f8c2142e15ee524860e72c273765d9d79323eefd14b8f3be0792536ea8a SHA512: 4227202e5b311280fb559b425909b068d0d7594612e1958e8c92ea413a0b514845f70412834179bd72587e158102f92a4eeddfcf08517763d9dca956ab20249c Homepage: https://cran.r-project.org/package=hydroroute Description: CRAN Package 'hydroroute' (Trace Longitudinal Hydropeaking Waves) Implements an empirical approach referred to as PeakTrace which uses multiple hydrographs to detect and follow hydropower plant-specific hydropeaking waves at the sub-catchment scale and to describe how hydropeaking flow parameters change along the longitudinal flow path. The method is based on the identification of associated events and uses (linear) regression models to describe translation and retention processes between neighboring hydrographs. Several regression model results are combined to arrive at a power plant-specific model. The approach is proposed and validated in Greimel et al. (2022) . The identification of associated events is based on the event detection implemented in 'hydropeak'. Package: r-cran-hydrostate Architecture: all Version: 0.2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3857 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deoptim, r-cran-sn, r-cran-truncnorm, r-cran-diagram, r-cran-padr, r-cran-zoo, r-cran-checkmate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-hydrostate_0.2.0.0-1.ca2404.1_all.deb Size: 3086714 MD5sum: cb2a325e5e9d6ba08a4aa5e6b36ee2f5 SHA1: dedcb45b48e7a51d7b02d26908fd0bb9f1f07344 SHA256: dbad0e97f5678e5cc7976751a3d9901ea656b8e55fdb359749fae53d06fb33a6 SHA512: 348788c6e9d2cd6bb32604804d5a77f010c8b2a9b12b9dfb8ba0d5d6cbfb6b2ff8ec72bf9eac0d972b7c9c2ba16f1702ccce6442de4a4ccd0715f92016f1c85a Homepage: https://cran.r-project.org/package=hydroState Description: CRAN Package 'hydroState' (Hidden Markov Modelling of Hydrological State Change) Identifies regime changes in streamflow runoff not explained by variations in precipitation. The package builds a flexible set of Hidden Markov Models of annual, seasonal or monthly streamflow runoff with precipitation as a predictor. Suites of models can be built for a single site, ranging from one to three states and each with differing combinations of error models and auto-correlation terms. The most parsimonious model is easily identified by AIC, and useful for understanding catchment drought non-recovery: Peterson TJ, Saft M, Peel MC & John A (2021) . Package: r-cran-hydrostats Architecture: all Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-plyr Filename: pool/dists/noble/main/r-cran-hydrostats_0.2.9-1.ca2404.1_all.deb Size: 211896 MD5sum: 66fbe59716c7c80431c3c763f9d34336 SHA1: 709ee6e831a519039bfa8fe8509403dd8318a928 SHA256: 171fbb527c51f6fedc894c8338abafe5ef1f58dce33744013801cea2070ccb8c SHA512: 07ad837fed3c476797c121906129e3e84d1935d6e3c96abe2e0e63903de2a2d7245eec6de8edf4b7209a2923a0aa9c61d45fe4ee44404955b0a0c2eaacea2155 Homepage: https://cran.r-project.org/package=hydrostats Description: CRAN Package 'hydrostats' (Hydrologic Indices for Daily Time Series Data) Calculates a suite of hydrologic indices for daily time series data that are widely used in hydrology and stream ecology. Package: r-cran-hydrotoolkit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5340 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-lubridate, r-cran-readxl, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-hydrotoolkit_0.1.0-1.ca2404.1_all.deb Size: 1284224 MD5sum: 21f03dc003a890ead9212806019c8329 SHA1: be89e22488756f8b4605f2ff0aa94ea7fa6b4144 SHA256: e37143f9e80b15bd3493c50e945b600f1a0b8fb5d9d927140896fd6d051b7bd9 SHA512: b2e1a470444f982fef7723b3f9b3b3c8aa3fd5925f650175b65c8c35153bcec5a50205c628c92b83fd6e4eb48d10dfbf4473cf69cd57ad9c0390c370633f83c8 Homepage: https://cran.r-project.org/package=hydroToolkit Description: CRAN Package 'hydroToolkit' (Hydrological Tools for Handling Hydro-Meteorological Data fromArgentina and Chile) Read, plot, manipulate and process hydro-meteorological data from Argentina and Chile. Package: r-cran-hydrotsm Architecture: all Version: 0.8-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4278 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-xts, r-cran-e1071, r-cran-lattice, r-cran-classint, r-cran-timechange Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-hydrotsm_0.8-6-1.ca2404.1_all.deb Size: 4078002 MD5sum: eda66c8ebda7fe0f6c7b759ef855343b SHA1: 1edffecae8c85292660cb8693d4b1284e89e1e0f SHA256: 358b7f3471cb0c5451e8a48b60df9f2a112e609dc6cbe90b86f272719d02ea79 SHA512: 66f921a4c8c0c54025d8cb183269e7f4c5726b668279764c9e43f5402168018e130c47c1b08a4b650ffb5428dfd98e7c8b12f1e2ae1772d8b4c23fa5cc71ddf9 Homepage: https://cran.r-project.org/package=hydroTSM Description: CRAN Package 'hydroTSM' (Time Series Management and Analysis for Hydrological Modelling) S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. In particular, this package is highly oriented to hydrological modelling tasks. The focus of this package has been put in providing a collection of tools useful for the daily work of hydrologists (although an effort was made to optimise each function as much as possible, functionality has had priority over speed). Bugs / comments / questions / collaboration of any kind are very welcomed, and in particular, datasets that can be included in this package for academic purposes. Package: r-cran-hyfo Architecture: all Version: 1.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 976 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-zoo, r-cran-sf, r-cran-plyr, r-cran-moments, r-cran-lmom, r-cran-maps, r-cran-sp, r-cran-ncdf4, r-cran-mass, r-cran-data.table Suggests: r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-hyfo_1.4.6-1.ca2404.1_all.deb Size: 675868 MD5sum: 704d004499195f85d091c89faad0e63c SHA1: 3de26d582d7a4dc27c6cdc5c6fc6b6b37814fb40 SHA256: 8a5080a34ee1313528f290d8063895eb84de0d0a34ff8f3d093bf10aa1017c07 SHA512: 4d02700b2d6561415c50db45b482cab7d7049b1adcc33a395be9de886961a9f49af3dca19968f27841574aa744a377b6fa812eb17f345a4e6654258c2b43a158 Homepage: https://cran.r-project.org/package=hyfo Description: CRAN Package 'hyfo' (Hydrology and Climate Forecasting) Focuses on data processing and visualization in hydrology and climate forecasting. 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Package: r-cran-hyper.fit Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magicaxis, r-cran-mass, r-cran-rgl, r-cran-laplacesdemon Filename: pool/dists/noble/main/r-cran-hyper.fit_1.2.2-1.ca2404.1_all.deb Size: 432976 MD5sum: 96bf5e570c28bda2a5b78f2913c56407 SHA1: 929a324eafafc572fb204d110e20e94e36e545fd SHA256: 8b0c572519292f78de35cfa1a6f4f903eb6e5b707092f80c595b8f825a345ded SHA512: 003d0bc72b0bbc9756accad5165e890a9c19c5f7f5b39fc9904cb39c826227a729bcac7c0a6a05b30184d6eccef2933deb9fe5983b83e3f24bf9969c5a9a38b2 Homepage: https://cran.r-project.org/package=hyper.fit Description: CRAN Package 'hyper.fit' (N-Dimensional Hyperplane Fitting with Errors) High level functions for hyperplane fitting (hyper.fit()) and visualising (hyper.plot2d() / hyper.plot3d()). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane. Package: r-cran-hyper.gam Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-mgcv, r-cran-plotly Suggests: r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-hyper.gam_0.3.0-1.ca2404.1_all.deb Size: 21782 MD5sum: 6c6383fe2d8c6b7f3987d3539c54769d SHA1: ab4625cdf133569aa41f26dc404f8490a2b01906 SHA256: 1627c530da74b81f8083b724422a449827498ec4a6195a1e06cb47901d062dfd SHA512: 6f41fb95051a46dc7e9e335ea1a0ecfd01ca650db08096c47c837adc2ae08d904690dbe8378122b049faa09d02d13b71948296455ee4d6dcfbb44294a83f6b47 Homepage: https://cran.r-project.org/package=hyper.gam Description: CRAN Package 'hyper.gam' (Generalized Additive Models with Hyper Column) An interactive HTML widget of the perspective plot for generalized additive models. An alternative solution of the function mgcv::vis.gam(). 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It enables flexible estimations with weight restrictions, non-discretionary variables, and a generalized distance function. Additionally, it allows for the calculation of slacks and super-efficiency scores. The methods are detailed in Öttl et al. (2023), . Furthermore, the package provides a non-linear profitability estimation built upon the DEA framework. Package: r-cran-hyperbolicdist Architecture: all Version: 0.6-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-actuar Filename: pool/dists/noble/main/r-cran-hyperbolicdist_0.6-5-1.ca2404.1_all.deb Size: 365718 MD5sum: 0c84854d7cd359838c99488e1abe0357 SHA1: 1e4498bc88381280a683610edf44b10a97645ba6 SHA256: 8e653625ccd029205ab3172b8e8b2cc4ff6523f99407cf4cd7176dd0cf0708ae SHA512: ca3750904fe34a58ac71ad8365c1700cf134faf795e5ab3d262eda79aff570e408d20031297dc0d382e926e01571f3800804d3c3d119a259df5d69f923eb469a Homepage: https://cran.r-project.org/package=HyperbolicDist Description: CRAN Package 'HyperbolicDist' (The Hyperbolic Distribution) Maintenance has been discontinued for this package. It has been superseded by 'GeneralizedHyperbolic'. 'GeneralizedHyperbolic' includes all the functionality of 'HyperbolicDist' and more and is based on a more rational design. 'HyperbolicDist' provides functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skew-Laplace distribution. Additional functionality is provided for the hyperbolic distribution, including fitting of the hyperbolic to data. Package: r-cran-hypercube Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotly, r-cran-stringr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-hypercube_0.2.1-1.ca2404.1_all.deb Size: 206978 MD5sum: e818132bb1295b0232aaebace6c5fceb SHA1: e295319c8d7b63b007014d658e99b5e86efd367c SHA256: 55c5ba40ba67faa42a78bc9cbe4df0dcb7bc4fd0f09b8605613c1f1e22389a5a SHA512: a03cb2c42c5f2d151a004519812c39a65769b993ec651949ca27b79ba9bc3d5539f0247056b0f9ce038d85bcf1abe2bcf40320a3351ef3a889e3eb407607dbc0 Homepage: https://cran.r-project.org/package=hypercube Description: CRAN Package 'hypercube' (Organizing Data in Hypercubes) Provides functions and methods for organizing data in hypercubes (i.e., a multi-dimensional cube). Cubes are generated from molten data frames. Each cube can be manipulated with five operations: rotation (change.dimensionOrder()), dicing and slicing (add.selection(), remove.selection()), drilling down (add.aggregation()), and rolling up (remove.aggregation()). 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These functions accept as input the DEM of the region of interest (your watershed) and a spatial data frame file specifying delineation of sub-catchments within the watershed. They then generate output in the form of PNG images and HTML files contained in a folder named "HYPSO_OUTPUT" created in the current directory. S. K. Sharma, S. Gajbhiye, et al. (2018) . Omvir Singh, A. Sarangi, and Milap C. Sharma (2006) . James A. Vanderwaal and Herbert Ssegane (2013) . Package: r-cran-hyreg2 Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1287 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexmix, r-cran-bbmle, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hyreg2_1.1.2-1.ca2404.1_all.deb Size: 625258 MD5sum: e9743ed3b01cb0af769d459f250ffaaa SHA1: 9fca1e43616edeb9ee1ed6a38a5f90bedb4f35bf SHA256: 64f55855453560909b7e6a1c60d4f467c1ac9511531afce528e4321e9f10b38d SHA512: a7083f97c775abb3dba9fc82002e33506aa4344520f1d1d71f0c7ebf782079a48c60e3ed7db77e79a39848a47952d040947da2396d88ead4611778ecdd2963bc Homepage: https://cran.r-project.org/package=hyreg2 Description: CRAN Package 'hyreg2' (Estimate Latent Classes on a Mixture of Continuous andDichotomous Data) The hybrid model likelihood as described by Ramos-Goñi et al. (2017) is implemented and and embedded in a latent class framework. 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Package: r-cran-hyrim Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compare, r-cran-polynom, r-cran-grimport2, r-cran-rglpk, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-qpdf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hyrim_2.0.2-1.ca2404.1_all.deb Size: 549824 MD5sum: 0eb46b41dce31b428018ec2d10975bbf SHA1: f1b9bc4058321c143833ef5aee924445f52faac6 SHA256: 9fa676e2bfabb4bd86616c2b4cafae86bedaf53ced32884ccb3eec29218a6218 SHA512: 4870e0186176ee89e9650f6f9ed2a2b3e2bb4641005831b99620bd735898a909b00e9bbbf4b2bb74888f15ac995f3016363ddc1866988d0b01625790693c77ba Homepage: https://cran.r-project.org/package=HyRiM Description: CRAN Package 'HyRiM' (Multicriteria Risk Management using Zero-Sum Games withVector-Valued Payoffs that are Probability Distributions) Construction and analysis of multivalued zero-sum matrix games over the abstract space of probability distributions, which describe the losses in each scenario of defense vs. attack action. The distributions can be compiled directly from expert opinions or other empirical data (insofar available). The package implements the methods put forth in the EU project HyRiM (Hybrid Risk Management for Utility Networks), FP7 EU Project Number 608090. The method has been published in Rass, S., König, S., Schauer, S., 2016. Decisions with Uncertain Consequences-A Total Ordering on Loss-Distributions. PLoS ONE 11, e0168583. , and applied for advanced persistent thread modeling in Rass, S., König, S., Schauer, S., 2017. Defending Against Advanced Persistent Threats Using Game-Theory. PLoS ONE 12, e0168675. . A volume covering the wider range of aspects of risk management, partially based on the theory implemented in the package is the book edited by S. Rass and S. Schauer, 2018. Game Theory for Security and Risk Management: From Theory to Practice. Springer, , ISBN 978-3-319-75267-9. Package: r-cran-hysaint Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-energy, r-cran-pracma, r-cran-selectiveinference, r-cran-variablescreening, r-cran-sis Filename: pool/dists/noble/main/r-cran-hysaint_1.2.1-1.ca2404.1_all.deb Size: 70528 MD5sum: 8688f18cae566610d0d2b3c3245f6ea0 SHA1: b56282e2dead9fa9a2e6f159693ac2ae780f7617 SHA256: b7f775f9f236bdeae018539cae7caf6140f2eb9e528bd6a30b70798d18ba4129 SHA512: cb035917d1448b98dd6cd20d29184075a8aecc3265c4b196fbdfc6752f61082248daa36360d3cca5c95659792df379c5198f81e8c66eee4b7a404c3ec596f089 Homepage: https://cran.r-project.org/package=hySAINT Description: CRAN Package 'hySAINT' (Hybrid Genetic and Simulated Annealing Algorithm for HighDimensional Linear Models with Interaction Effects) We provide a stage-wise selection method using genetic algorithms, designed to efficiently identify main and two-way interactions within high-dimensional linear regression models. Additionally, it implements simulated annealing algorithm during the mutation process. The relevant paper can be found at: Ye, C.,and Yang,Y. (2019) . Package: r-cran-hyspc.testthat Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testthat Filename: pool/dists/noble/main/r-cran-hyspc.testthat_0.2.2-1.ca2404.1_all.deb Size: 17746 MD5sum: a4171a0e41da6dc53b54c8af50279200 SHA1: 243cb9585081a38f8a337e53f7ec9d4ccb1e8470 SHA256: e7465f978101b5d29ad70ef7d913cd8b08c22c3670d6a0ccc800260aa784254d SHA512: 38b34be17762033bdf782b8574bbeda67f518caac1c61c2a651b856e818ee2b8a3942272bd7157387eee3439fce4a3f8a964a2fe328b1a4c0bdbb1663ffd8185 Homepage: https://cran.r-project.org/package=hySpc.testthat Description: CRAN Package 'hySpc.testthat' (Unit Test Add-on for 'testthat') Enhance package 'testthat' by allowing tests to be attached to the function/object they test. 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See Yang, F and A. Parkhurst, "Efficient Estimation of Elliptical Hysteresis with Application to the Characterization of Heat Stress" . 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The families of distributions are Bernoulli, Exponential, Geometric, Inverse Normal, Normal, Gamma, Gumbel, Lognormal, Poisson, and Weibull. This package is an ideal resource to help with the teaching of Statistics. The main references for this package are Casella G. and Berger R. (2003,ISBN:0-534-24312-6 , "Statistical Inference. Second Edition", Duxbury Press) and Hogg, R., McKean, J., and Craig, A. (2019,ISBN:013468699, "Introduction to Mathematical Statistic. Eighth edition", Pearson). 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Package: r-cran-iai Architecture: all Version: 1.10.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-juliacall, r-cran-stringr, r-cran-rlang, r-cran-lifecycle, r-cran-rappdirs, r-cran-ggplot2, r-cran-cowplot, r-cran-rjson Suggests: r-cran-testthat, r-cran-covr, r-cran-xml2, r-cran-withr Filename: pool/dists/noble/main/r-cran-iai_1.10.2-1.ca2404.1_all.deb Size: 520902 MD5sum: 8c02a7b428418afbc394ae529002a8d1 SHA1: 16db81114d92a31f6ec8d4ca9caf595f5312bf67 SHA256: 2b81eba5e498f85b4363fc571bbeaf06d9886b360ff3b6ee057c86ce2b884245 SHA512: 8f35736883a10fbd777af6384f37027bfe0c817c8e69aa8ed94de61722870a51dd221cc2189b54b8d19d88380135012558498569107cb5d0379fa5a6741fc538 Homepage: https://cran.r-project.org/package=iai Description: CRAN Package 'iai' (Interface to 'Interpretable AI' Modules) An interface to the algorithms of 'Interpretable AI' from the R programming language. 'Interpretable AI' provides various modules, including 'Optimal Trees' for classification, regression, prescription and survival analysis, 'Optimal Imputation' for missing data imputation and outlier detection, and 'Optimal Feature Selection' for exact sparse regression. The 'iai' package is an open-source project. The 'Interpretable AI' software modules are proprietary products, but free academic and evaluation licenses are available. Package: r-cran-ialiquor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4063 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ialiquor_0.1.0-1.ca2404.1_all.deb Size: 3900646 MD5sum: 35db960491e387cc9d71215ffa64fcea SHA1: 8cf8d0eba8b21f60581a951beef006e8f795a46d SHA256: 8dc9536b0e523d80d4643ce5abccfc17db73926355d6628515c964cff61c738e SHA512: f16b53d03ab6c9c5988cc3af7c46403839805e6aff93a54fdfb87a397adfb02175008ecc3acf12d06d49df85e056e0a0ed313510f4ef574615e3a66b29dea89f Homepage: https://cran.r-project.org/package=ialiquor Description: CRAN Package 'ialiquor' (Monthly Iowa Liquor Sales Summary) Provides a monthly summary of Iowa liquor (class E) sales from January 2015 to October 2020. See the package website for more information, documentation and examples. Data source: Iowa Data portal . Package: r-cran-ials Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rspectra, r-cran-pracma, r-cran-hdmfa Filename: pool/dists/noble/main/r-cran-ials_0.1.3-1.ca2404.1_all.deb Size: 29834 MD5sum: e609195c12dcff095ebbca584a3d5ccb SHA1: 36cbb32a88f0d24112fafeb6fc5d073cc000e7b4 SHA256: 41d2e0d5eb6d6e7f769cc8151694663e7c2d4a990e2bd9274189ec5571f4e7dc SHA512: 2ba59bb6ab1bcd1cc143b674ad043b07630fad174d7cb7d8c5b7acc74c4edae7cf1e4ebdbec7e29199062aea8fc360e497d5cf16ac81647ebfef32aa4881b736 Homepage: https://cran.r-project.org/package=IALS Description: CRAN Package 'IALS' (Iterative Alternating Least Square Estimation forLarge-Dimensional Matrix Factor Model) The matrix factor model has drawn growing attention for its advantage in achieving two-directional dimension reduction simultaneously for matrix-structured observations. In contrast to the Principal Component Analysis (PCA)-based methods, we propose a simple Iterative Alternating Least Squares (IALS) algorithm for matrix factor model, see the details in He et al. (2023) . Package: r-cran-iarm Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-erm, r-cran-ggplot2, r-cran-gridextra, r-cran-hmisc, r-cran-psychotools, r-cran-vcdextra Filename: pool/dists/noble/main/r-cran-iarm_0.4.3-1.ca2404.1_all.deb Size: 170924 MD5sum: 270c81dbdc0caa6bf6428ab65ffe9b3e SHA1: 1df6e7173a37398949e10f21da2554a8f42244a5 SHA256: b66683e4597a575ca6fac5d41f1d5d72eaad2af91ac9d1426809aad02391608e SHA512: 1c99e2ff55cd15e09cb8e1e3f02420fab663e4b862cb1fa3c519c7f1fd85398675cda26297a5c0032ef0626ef48b2c8a44e2eefa05531d41ff393e7137b660da Homepage: https://cran.r-project.org/package=iarm Description: CRAN Package 'iarm' (Item Analysis in Rasch Models) Tools to assess model fit and identify misfitting items for Rasch models (RM) and partial credit models (PCM). Included are item fit statistics, item characteristic curves, item-restscore association, conditional likelihood ratio tests, assessment of measurement error, estimates of the reliability and test targeting as described in Christensen et al. (Eds.) (2013, ISBN:978-1-84821-222-0). Package: r-cran-iasd Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-iasd_1.1.1-1.ca2404.1_all.deb Size: 33640 MD5sum: 88ce9666e9e11a4870d110bcb39c87f3 SHA1: 1e36ef5575ac34a03f89bf96ff27447a0dca52e2 SHA256: caa7ec7232729db52657cb05897dd9207226282823deb8d1c924b4c2f27547ef SHA512: 113b6ebad5516a96b7ece4f20ed472479ed6bda0133e9ab7f153964665b9841f034d7172d3612505d5b0602bc48e8e38167309e913fe8ffe5d46c2178e934fd8 Homepage: https://cran.r-project.org/package=IASD Description: CRAN Package 'IASD' (Model Selection for Index of Asymmetry Distribution) Calculate AIC's and AICc's of unimodal model (one normal distribution) and bimodal model(a mixture of two normal distributions) which fit the distribution of indices of asymmetry (IAS), and plot their density, to help determine IAS distribution is unimodal or bimodal. Package: r-cran-iat Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lazyeval Filename: pool/dists/noble/main/r-cran-iat_0.3-1.ca2404.1_all.deb Size: 79334 MD5sum: d1566727b034002e3d950de0a055a780 SHA1: ed2fd390241beba6c79050c3743e40c0807c9dac SHA256: 305b1c4cf41eed50e480de4a149650fbc7c0c1fa80adc31ca6234994171c4be4 SHA512: 9898777b2bf2d89029aba6671c297e9b6bc2f262fef70267e55c1a6872689a913ea09c6fac17fcc2eea55e1896fe93aab3b9396e284d702132f8e2e338a6722f Homepage: https://cran.r-project.org/package=IAT Description: CRAN Package 'IAT' (Cleaning and Visualizing Implicit Association Test (IAT) Data) Implements the standard D-Scoring algorithm (Greenwald, Banaji, & Nosek, 2003) for Implicit Association Test (IAT) data and includes plotting capabilities for exploring raw IAT data. Package: r-cran-iatanalytics Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-iatanalytics_0.2.0-1.ca2404.1_all.deb Size: 25122 MD5sum: 8fee1c123bfef681ba989436d23a045e SHA1: 90806551e80b6f15bf3ba0bd43626d896b5f8165 SHA256: 9a2e7f8e2aeff9eff24d1f35a5476de2157404cd149e9574b08bd8ab2cc0860f SHA512: 2ce56aa273a2276e52ba32e2e1dcfe7ee445b50c7dfd21d89af0ba4842875283f2589fa46f25945f371b603d2f5f52ca25f874cbaecdb930d13b337bd2094fd2 Homepage: https://cran.r-project.org/package=IATanalytics Description: CRAN Package 'IATanalytics' (Compute Effect Sizes and Reliability for Implicit AssociationTest (IAT) Data) Quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) . IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) . The output of this function is a data frame that consists of four rows containing the following information: (1) the overall IAT effect size for the participant's dataset, (2) the effect size calculated for odd trials only, (3) the effect size calculated for even trials only, and (4) the proportion of trials with reaction times under 300ms (which is important for exclusion purposes). Items (2) and (3) allow for a measure of the internal consistency of the IAT. Specifically, you can use the subsetted IAT effect sizes for odd and even trials to calculate Cronbach's alpha across participants in the sample. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 220, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds). A sample dataset (titled 'sampledata') is included in this package to practice with. Package: r-cran-iatscore Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-iatscore_0.2.0-1.ca2404.1_all.deb Size: 24682 MD5sum: 111f983b3b240e32c6c0fb89857098f3 SHA1: a530a8a82b249cfd0a43aa5e17e87cb3fc2b3b44 SHA256: b46819a9ed03292324a8b7930f58be5fa8b37b5ca75ca4fb4aa677a7af6517a9 SHA512: bbc5c9756d2ec0d3e36816a9881f62719293714da585094117b5fb73d7c899aa0bb39ab3db2ba999d82281bc31a09d3aad83f934d45bdc9c1f8e1b248b51db30 Homepage: https://cran.r-project.org/package=IATScore Description: CRAN Package 'IATScore' (Scoring Algorithm for the Implicit Association Test (IAT)) This minimalist package is designed to quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) . IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) . Outputted values can be interpreted as effect sizes. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 219, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). The script will tell you how long it took to run the code, the effect size for the participant, and whether that participant should be excluded based on the criteria outlined by Greenwald et al. (2003). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds). Three sample datasets are included in this package (labeled 'IAT', 'TooFastIAT', and 'BriefIAT') to practice with. Package: r-cran-iatscores Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-dplyr, r-cran-reshape2, r-cran-qgraph Suggests: r-cran-nparcomp Filename: pool/dists/noble/main/r-cran-iatscores_0.2.8-1.ca2404.1_all.deb Size: 107036 MD5sum: d02bf4e5eb0e2945e5b321f70b0c4c40 SHA1: 9b9dc6a82108bc3dd7c72debf0c7ae0b25725182 SHA256: 6dfc74857b9b245e25391fcd2a10240598b37ece7f4ea32814e12d1d9fa631cc SHA512: 0386fad3eccf3935ae58418e545c7f292160b1d25bd3c6d5931be6640762fde0d22969c4a477b4e5c28b4df8759b286157898442d4de3a4233de25b6c76ca435 Homepage: https://cran.r-project.org/package=IATscores Description: CRAN Package 'IATscores' (Implicit Association Test Scores Using Robust Statistics) Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003, ) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015, ). Package: r-cran-ib Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 928 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-betareg, r-cran-lme4, r-cran-formula, r-cran-mass, r-cran-matrix, r-cran-rdpack, r-cran-vgam Suggests: r-cran-testthat, r-cran-knitr, r-cran-nlraa, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ib_0.2.1-1.ca2404.1_all.deb Size: 591244 MD5sum: c4b772d3db2a0cb6b1b2542e1dc0c89b SHA1: acbe5fff52956b651ed2d412e38a9f64114b158b SHA256: 2f3546026b77942d87ce30a9aaa20667c71e358735fe3d28c3cefb15735c191b SHA512: 14bab03e633f304cfd43dcd136e948d0c48cc01a5d717606b3545543b87b491ba54e96d64cd075305f58bdfecf88fbd5932765c2eb3a0e0d9dbc8a5fca5b87b9 Homepage: https://cran.r-project.org/package=ib Description: CRAN Package 'ib' (Bias Correction via Iterative Bootstrap) An implementation of the iterative bootstrap procedure of Kuk (1995) to correct the estimation bias of a fitted model object. This procedure has better bias correction properties than the bootstrap bias correction technique. Package: r-cran-ibart Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 679 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bartmachine, r-cran-glmnet, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggpubr Filename: pool/dists/noble/main/r-cran-ibart_1.0.0-1.ca2404.1_all.deb Size: 505604 MD5sum: 71275bd548a2f6c75b0143497234ab39 SHA1: cf78692116378c488a127155bcd79b4a69e456e8 SHA256: a4a877b8d149345c3bd170b34d90ff7de7f84436c590bf6aff1fa6248a478881 SHA512: fd2e137ea45fefd59f36fa6d9d316496a7c104d0fa8b0bd4a2c67c3d181c0a00d97a8888f0ba66c5bc5dcf8bce9d8e6a8d208c88af2223acf173924d023c6465 Homepage: https://cran.r-project.org/package=iBART Description: CRAN Package 'iBART' (Iterative Bayesian Additive Regression Trees DescriptorSelection Method) A statistical method based on Bayesian Additive Regression Trees with Global Standard Error Permutation Test (BART-G.SE) for descriptor selection and symbolic regression. It finds the symbolic formula of the regression function y=f(x) as described in Ye, Senftle, and Li (2023) . 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Package: r-cran-ibb Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-rlang, r-cran-magrittr, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-covr, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-ibb_0.0.2-1.ca2404.1_all.deb Size: 84902 MD5sum: b1f4f87e12ae64db10c719d7bc4ec381 SHA1: 08e0f39babf630287eff5a38ef4e9caf1e22a6c9 SHA256: d910ccead8de99999f59537509b4bc8f382131e25a25f8cb4514c507e6b6d4a0 SHA512: 1f8209434b1f4fae4e588040ce25a70f82a8bdf582626b82c2992488e62666dd8efc93db5ad04af3f2d2e4ab78502373aff95719fda5f6047f4391fa327b4e95 Homepage: https://cran.r-project.org/package=ibb Description: CRAN Package 'ibb' (R Wrapper for Istanbul Municipality Open Data Portal) Call wrappers for Istanbul Metropolitan Municipality's Open Data Portal (Turkish: İstanbul Büyükşehir Belediyesi Açık Veri Portalı) at . Package: r-cran-ibcf.mtme Architecture: all Version: 1.6-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lsa, r-cran-tidyr, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-ibcf.mtme_1.6-0-1.ca2404.1_all.deb Size: 90824 MD5sum: 9c598f91ce4086f10534adfc1671da8a SHA1: 2ff0980f3b69cf0c1192303254c85ec438f581a4 SHA256: c065b9ee597bf60b82a8f154d4606f6f8f0c9ec97c7bc8ae49c23d425819bc3d SHA512: ea620b8c851b5ea43d18579d439c30c9cd32d8e278808037035b2e7571c5f8821fabfefce4893652ff70d78f224da268362f7612c654aca852c60afce231cc46 Homepage: https://cran.r-project.org/package=IBCF.MTME Description: CRAN Package 'IBCF.MTME' (Item Based Collaborative Filtering for Multi-Trait andMulti-Environment Data) Implements the item based collaborative filtering (IBCF) method for continues phenotypes in the context of plant breeding where data are collected for various traits that were studied in various environments proposed by Montesinos-López et al. (2017) . Package: r-cran-ibd Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-car, r-cran-emmeans, r-cran-multcomp Suggests: r-cran-multcompview Filename: pool/dists/noble/main/r-cran-ibd_1.6-1.ca2404.1_all.deb Size: 125050 MD5sum: 1cd4ae30d41e5866caa5e5aa33410aca SHA1: 06d8d06e17c82401db97dbb4dcd095c8c51cff9a SHA256: 3d1c6bab226f4a06a22b8df22622ebdfeea1914caef69f4ab798bba0b76871b1 SHA512: c6e2099be9242ed66a365b4d4eb376d342fee69064bc76b1b6458373139397f1bb60192411ccfb97b777bad4c8a62d88fd49eaddb3ed69324e42691b2b565d85 Homepage: https://cran.r-project.org/package=ibd Description: CRAN Package 'ibd' (Incomplete Block Designs) A collection of several utility functions related to binary incomplete block designs. Contains function to generate A- and D-efficient binary incomplete block designs with given numbers of treatments, number of blocks and block size. Contains function to generate an incomplete block design with specified concurrence matrix. There are functions to generate balanced treatment incomplete block designs and incomplete block designs for test versus control treatments comparisons with specified concurrence matrix. Allows performing analysis of variance of data and computing estimated marginal means of factors from experiments using a connected incomplete block design. Tests of hypothesis of treatment contrasts in incomplete block design set up is supported. Package: r-cran-ibdfindr Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forrel, r-cran-ggplot2, r-cran-ibdsim2, r-cran-pedtools, r-cran-ribd Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ibdfindr_0.3.1-1.ca2404.1_all.deb Size: 353870 MD5sum: 52cb3bb794a487165197de60ed0e3d46 SHA1: c5e7109b744430cd582e2a2a5d3182222b96ecc2 SHA256: c8716ef072b1c9208acd4076fea4f35ff6e3c6655e661ea8b119d821fbe27525 SHA512: 379152e326562b6aa3fcdb43d7049ba348a3b6b569f882f2e6f875d0766be514b95ac90584689b22e0e24403c805240aedfc238e4c28147916c40daa4fa4cb5d Homepage: https://cran.r-project.org/package=ibdfindr Description: CRAN Package 'ibdfindr' (HMM Toolkit for Inferring IBD Segments from SNP Genotypes) Implements continuous-time hidden Markov models (HMMs) to infer identity-by-descent (IBD) segments shared by two individuals from their single-nucleotide polymorphism (SNP) genotypes. Provides posterior probabilities at each marker (forward-backward algorithm), prediction of IBD segments (Viterbi algorithm), and functions for visualising results. Supports both autosomal data and X-chromosomal data. Package: r-cran-ibdinfer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crossdes, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ibdinfer_0.1.0-1.ca2404.1_all.deb Size: 35082 MD5sum: 4b4041711eb3e8e54a537fc36e59e8f8 SHA1: 8fd3bd74ba3e360fea06f4b1d80368dfb90a9c65 SHA256: 838c58cced9ba37ea57852248d8008ac678faf848c2b6b24b77b4a2b31400402 SHA512: 64367402412bf09ba4852cf8e838fb69fea3ae977ad7711181bd52baa6c0e9f61227f60e330c96e8a55e1dcf5262a2e4f90740c03193beb41a1eb8c9750ab8cc Homepage: https://cran.r-project.org/package=IBDInfer Description: CRAN Package 'IBDInfer' (Design-Based Causal Inference Method for Incomplete BlockDesigns) This R package implements methods for estimation and inference under Incomplete Block Designs and Balanced Incomplete Block Designs within a design-based finite-population framework. Based on 'Koo and Pashley' (2026) , it includes block-level estimators and extends to unit-level effects using 'Horvitz-Thompson' and 'Hájek' estimators. The package also provides asymptotic confidence intervals to support valid statistical inference. Package: r-cran-ibelief Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ibelief_1.3.1-1.ca2404.1_all.deb Size: 87272 MD5sum: 4278f49b27a6d3ec15e5e617d17ca161 SHA1: 1dc6e2ed2d3472c898f8180fcb2a942ba8c0a28b SHA256: 5a85528646500511c3bd0552f93e47703db628a855320ad6e8f824c200841235 SHA512: 4f37e7013b9073f3d9ea683b2933797749d0cb279cb6b685120a1b1731b0bc09097185cf8088c2c4e4abefdc36aca5ac4917a0cac9e83d599f578af27a3f6001 Homepage: https://cran.r-project.org/package=ibelief Description: CRAN Package 'ibelief' (Belief Function Implementation) Some basic functions to implement belief functions including: transformation between belief functions using the method introduced by Philippe Smets , evidence combination, evidence discounting, decision-making, and constructing masses. Currently, thirteen combination rules and six decision rules are supported. It can also be used to generate different types of random masses when working on belief combination and conflict management. Package: r-cran-ibfs Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ibfs_1.0.0-1.ca2404.1_all.deb Size: 31902 MD5sum: a418989f864ff0da2af68ed8846ab823 SHA1: 83629a66f1cf0f864903566f4d23d13f2c6d1786 SHA256: 8a84f865b9516c901d54bf2ff14b621e3731bd4ace89b8dbe46c8af68e56ed0f SHA512: 0083d91e99bc791eaf1df1eda06df2841077a35b6b8b790f78633b12e283d234655137c64d0f71f00dadf05c0c6f4a2595e54a96dbb6cd269e85b977b7b90923 Homepage: https://cran.r-project.org/package=IBFS Description: CRAN Package 'IBFS' (Initial Basic Feasible Solution for Transportation Problem) The initial basic feasible solution (IBFS) is a significant step to achieve the minimal total cost (optimal solution) of the transportation problem. However, the existing methods of IBFS do not always provide a good feasible solution which can reduce the number of iterations to find the optimal solution. This initial basic feasible solution can be obtained by using any of the following methods. a) North West Corner Method. b) Least Cost Method. c) Row Minimum Method. d) Column Minimum Method. e) Vogel's Approximation Method. etc. For more technical details about the algorithms please refer below URLs. . . . . Package: r-cran-ibger Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-cli, r-cran-tibble, r-cran-purrr, r-cran-dplyr, r-cran-rlang, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-ggplot2, r-cran-tidyr, r-cran-shiny, r-cran-dt, r-cran-bslib, r-cran-bsicons, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-ibger_0.1.0-1.ca2404.1_all.deb Size: 161150 MD5sum: c5af7b448b9e3926ff79ee8f3d2b3e9c SHA1: fee89456062e481816fd457ee6f8afc67db7d7a0 SHA256: f525ca6f9c7e1444424e11a82026a21501b691f9a234aa0bc27fece5d1105194 SHA512: 5048083e35888684c316ae3a0917956b414390394f212c01fb195cf7de24a849e1754b306c9fca8f2d79d6339a5129e158b0e77fca85faec93b93075daca5607 Homepage: https://cran.r-project.org/package=ibger Description: CRAN Package 'ibger' (Access the 'IBGE' Aggregate Data API from 'R') 'Tidyverse'-friendly interface to the Brazilian Institute of Geography and Statistics ('IBGE') aggregate data 'API' . Query aggregates, variables, localities, periods, and metadata from surveys and censuses conducted by 'IBGE'. Package: r-cran-ibkrcp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ibkrcp_0.1.1-1.ca2404.1_all.deb Size: 52042 MD5sum: 6621e2ceee9880f24b86bdab01f64eb7 SHA1: b2c99d60a13f50fccf3794efb5632026a218c873 SHA256: 71e1559dfb58290bfbf62a87eafbb039d700f692f23c702c468316ffcf3051e6 SHA512: 50537a6a5f6dbcd2cacf9629ba2dbe530605cfef2e241b1385d398b1631506007afc9926fe69af19c2846a4df8d29db2d8a6d5bd86eebdca8d9f8220cf61cef3 Homepage: https://cran.r-project.org/package=ibkrcp Description: CRAN Package 'ibkrcp' (R Client for the Interactive Brokers Client Portal API) Provides a lightweight R interface to the Interactive Brokers (IBKR) Client Portal REST API. Functions cover session management, account and portfolio queries, market data retrieval, and order placement and cancellation. Requires a locally running IBKR Client Portal Gateway. Package: r-cran-iblm Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1785 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-fastdummies, r-cran-ggextra, r-cran-ggplot2, r-cran-purrr, r-cran-scales, r-cran-statmod, r-cran-withr, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-gt, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-iblm_1.0.2-1.ca2404.1_all.deb Size: 1395382 MD5sum: b766735ecefbe2846c87b66195d45b97 SHA1: 8549527b3c9cfed6806bcbae5d96acbe0f7112d5 SHA256: 22ac2fba00b572fc93fc0c72d7a601d3654434ad5eb9884f5cc6c5e5e961b229 SHA512: 23b683f29a99154d38d4c6325e0b53a0dd33804aade432723edade85920f571b83dc810ef04cb124ed014da4438deba5515f735ea3504a566253214d4a12b02b Homepage: https://cran.r-project.org/package=IBLM Description: CRAN Package 'IBLM' (Interpretable Boosted Linear Models) Implements Interpretable Boosted Linear Models (IBLMs). These combine a conventional generalized linear model (GLM) with a machine learning component, such as XGBoost. The package also provides tools within for explaining and analyzing these models. For more details see Gawlowski and Wang (2025) . Package: r-cran-ibmacousticr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-httr, r-cran-xml Filename: pool/dists/noble/main/r-cran-ibmacousticr_0.2.1-1.ca2404.1_all.deb Size: 39244 MD5sum: 0f3a35f58386520afe72f478f8abd9c0 SHA1: a7c402dfe418bc095cc18d18e47f4624b36c94a2 SHA256: 2f72929cbc01cbd1277c68b75ad486519989f1ab2dea4a32b2a5163443a10d21 SHA512: c8f198e361dc23a137d33bce236d8dc6a31ffd473b52b3fc9d0bcf14dacafafde575b6b437ce44b9a1925d45d36c9ba4077721f9688eccbecc2700f8de4060ca Homepage: https://cran.r-project.org/package=ibmAcousticR Description: CRAN Package 'ibmAcousticR' (Connect to Your 'IBM Acoustic' Data) Authentication can be the most difficult part about working with a new API. 'ibmAcousticR' facilitates making a connection to the 'IBM Acoustic' email campaign management API and executing various queries. The 'IBM Acoustic' API documentation is available at . This package is not supported by 'IBM'. Package: r-cran-ibmdbr Architecture: all Version: 1.51.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1423 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rodbc, r-cran-matrix, r-cran-arules, r-cran-mass, r-cran-rpart, r-cran-rpart.plot Suggests: r-cran-ggplot2, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ibmdbr_1.51.0-1.ca2404.1_all.deb Size: 1083202 MD5sum: e324a34cec8b49eb2041529b8b2e1547 SHA1: 803f311263e252e808d609dcaccb33303e9b9c59 SHA256: 2031fdff7ce7e23328832acfcdf81c974fb176572ffafd39fc8460a1a6dfa8ce SHA512: b01563f5534be4d8b42bcdb360f861057d76ff7a32d6f159753bb1f4083ded791bcf9347ae46a0a46fd0903a53b504c2844db07d07977e805209de0014c610cc Homepage: https://cran.r-project.org/package=ibmdbR Description: CRAN Package 'ibmdbR' (IBM in-Database Analytics for R) Functionality required to efficiently use R with IBM(R) Db2(R) Warehouse offerings (formerly IBM dashDB(R)) and IBM Db2 for z/OS(R) in conjunction with IBM Db2 Analytics Accelerator for z/OS. Many basic and complex R operations are pushed down into the database, which removes the main memory boundary of R and allows to make full use of parallel processing in the underlying database. For executing R-functions in a multi-node environment in parallel the idaTApply() function requires the 'SparkR' package (). The optional 'ggplot2' package is needed for the plot.idaLm() function only. Package: r-cran-ibmsunburst Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1244 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-jsonlite Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ibmsunburst_0.1.4-1.ca2404.1_all.deb Size: 328248 MD5sum: 81f521441b168b76b4fd4d8b07646914 SHA1: c0a9968f650eec9a2c547fe85df186fe16001675 SHA256: 475a65cb6e43dcb37c1e9e047dc7c5c814561692c0eef87808be0f4656bd2801 SHA512: 56532849b061ed73a2159164e2c1b441e6fcf9360431017419f91efce77ee4478ade8eb3821f55fd1e22a403c2a5a99cf9ede7a5abe5b9fce9a22edb54968a37 Homepage: https://cran.r-project.org/package=ibmsunburst Description: CRAN Package 'ibmsunburst' (Generate Personality Insights Sunburst Diagrams) Generates Personality Insights sunburst diagrams based on 'IBM Watson' Personality Insights service output. Package: r-cran-ibreakdown Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 945 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-dalex, r-cran-knitr, r-cran-rmarkdown, r-cran-randomforest, r-cran-e1071, r-cran-ranger, r-cran-nnet, r-cran-testthat, r-cran-r2d3, r-cran-jsonlite, r-cran-covr Filename: pool/dists/noble/main/r-cran-ibreakdown_2.1.2-1.ca2404.1_all.deb Size: 440888 MD5sum: aff5c2a97a95d889f7bc723670640c9c SHA1: 5ca0c2759262f17dc9b82ca968cf861f20ea304d SHA256: 8d039057294afcbfe74e763862b94ba6a92ce98b12e81a25f59e9d49c5ea52d2 SHA512: fe969644e6fb566f2c3c5ae6da597407ad859abfd4b4f7e71d4c4592d0fb9c52d3162fb2a2c40e5b659c1361c0fd4fa6fae70bf3f8f8d168fe1aa3a7326fa576 Homepage: https://cran.r-project.org/package=iBreakDown Description: CRAN Package 'iBreakDown' (Model Agnostic Instance Level Variable Attributions) Model agnostic tool for decomposition of predictions from black boxes. Supports additive attributions and attributions with interactions. The Break Down Table shows contributions of every variable to a final prediction. The Break Down Plot presents variable contributions in a concise graphical way. This package works for classification and regression models. It is an extension of the 'breakDown' package (Staniak and Biecek 2018) , with new and faster strategies for orderings. It supports interactions in explanations and has interactive visuals (implemented with 'D3.js' library). The methodology behind is described in the 'iBreakDown' article (Gosiewska and Biecek 2019) This package is a part of the 'DrWhy.AI' universe (Biecek 2018) . Package: r-cran-ibrokers Architecture: all Version: 0.10-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 759 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-ibrokers_0.10-2-1.ca2404.1_all.deb Size: 507334 MD5sum: c8404b101cc443675bc445d633841e47 SHA1: bc6b85fd1176261b4c2991467fd0a1173ae37237 SHA256: 95f67fcb1511ecfcae5920e7517a153b1623a8c42803e6b68566f4a3138c7c4f SHA512: 8b83df7c9ad527c901cce1ff5a9c88d0cf16f8c4c8918e451b8fd76ababc96fe5d7a4c4b25cd9bc01847e005c7b61fd571f618d9bbedf60bd90aa9c1cde43b9d Homepage: https://cran.r-project.org/package=IBrokers Description: CRAN Package 'IBrokers' (R API to Interactive Brokers Trader Workstation) Provides native R access to Interactive Brokers Trader Workstation API. Package: r-cran-ibrtools Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-data.table, r-cran-gtools, r-cran-tidyr, r-cran-fmsb, r-cran-binhf, r-cran-tidyselect Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ibrtools_0.1.3-1.ca2404.1_all.deb Size: 57872 MD5sum: bb8d7139dc62d59c721e7217b71b7f0d SHA1: bc8ddb65aefb9c71934fdb74437ba62f219178a9 SHA256: dea037258bb419ef8997cd3a40a2140e083d20b183af46c8c4932054e9f11b58 SHA512: cfedff62ea14b3bfe1e4b5af5a28898affdacf66f29718d8a20f07eccf89470ab7c1e88a5952daae0c3fd41147c2dcb50cd02d788bb2d6c6a7859c866df4a328 Homepage: https://cran.r-project.org/package=IBRtools Description: CRAN Package 'IBRtools' (Integrating Biomarker-Based Assessments and Radarchart Creation) Several functions to calculate two important indexes (IBR (Integrated Biomarker Response) and IBRv2 (Integrated Biological Response version 2)), it also calculates the standardized values for enzyme activity for each index, and it has a graphing function to perform radarplots that make great data visualization for this type of data. Beliaeff, B., & Burgeot, T. (2002). . Sanchez, W., Burgeot, T., & Porcher, J.-M. (2013).. Devin, S., Burgeot, T., Giambérini, L., Minguez, L., & Pain-Devin, S. (2014). . Minato N. (2022). . Package: r-cran-ic.infer Architecture: all Version: 1.1-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-mvtnorm, r-cran-boot, r-cran-kappalab Suggests: r-cran-relaimpo Filename: pool/dists/noble/main/r-cran-ic.infer_1.1-7-1.ca2404.1_all.deb Size: 331054 MD5sum: a201ce4a401c89752f49e7957aefefeb SHA1: fa7dd27cff8caf91ba757cee73d3b945d0602296 SHA256: 3ec3b4211c3913c2a315636396b4c27969ce9d0521089b1c9059dbb34a46b319 SHA512: 104ac1814f7c1a40536dc35ca50f6b8b3c01d736ce09facc271a107488fef5f4570afb64d3e9d6b9bdfb2c1ef90513696a709b0262eff4165a09d8974b802687 Homepage: https://cran.r-project.org/package=ic.infer Description: CRAN Package 'ic.infer' (Inequality Constrained Inference in Linear Normal Situations) Implements inequality constrained inference. This includes parameter estimation in normal (linear) models under linear equality and inequality constraints, as well as normal likelihood ratio tests involving inequality-constrained hypotheses. For inequality-constrained linear models, averaging over R-squared for different orderings of regressors is also included. Package: r-cran-ic10 Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pamr, r-bioc-impute, r-cran-ic10trainingdata Filename: pool/dists/noble/main/r-cran-ic10_2.0.2-1.ca2404.1_all.deb Size: 78680 MD5sum: 892b56fa45939033ee847ec1ae13c257 SHA1: 0f3728b8440cc11c3c28c541e37e1e8103851109 SHA256: 05135ffdef5457266b555336a1521f88e34e93b1cff7968aa23365b2f590bd90 SHA512: 5b256e487b4b74e04596691198298d5d294ffef7f3716f3fe43a30380177c5fa1a4c3f3042f4434f5cfb476b894fc958560f2b079e09d156f87730c53a2d90b3 Homepage: https://cran.r-project.org/package=iC10 Description: CRAN Package 'iC10' (A Copy Number and Expression-Based Classifier for Breast Tumours) Implementation of the classifier described in the paper Ali HR et al (2014) . It uses copy number and/or expression form breast cancer data, trains a Tibshirani's 'pamr' classifier with the features available and predicts the iC10 group. Package: r-cran-ic10trainingdata Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5765 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ic10trainingdata_2.0.1-1.ca2404.1_all.deb Size: 5856210 MD5sum: 0f19168b0fececd19135def8f648bdbc SHA1: 2fa0972a26e9e8a54a393bfa14cf3f443207529c SHA256: 1b5f444eb339c3a37f31e53b2ea34879970ef180786edc06c47da41458ad80a6 SHA512: 5554821457f64b4fe58899c41eaacc76ceee6245cad57f541978577f29eea401e1d97da9b9e4359854c12f74b81872b34f0ca7b64b95cfd84dbdaa01c44574ed Homepage: https://cran.r-project.org/package=iC10TrainingData Description: CRAN Package 'iC10TrainingData' (Training Datasets for iC10 Package) Training datasets for iC10; which implements the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group. Genomic annotation for the training dataset has been obtained from Mark Dunning's lluminaHumanv3.db package. Package: r-cran-ica Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ica_1.0-3-1.ca2404.1_all.deb Size: 86440 MD5sum: 3b7694b6c1583f7b0a1de8ff61368da6 SHA1: d0c3243e2eb1c50d7ea33a6cfaca36b6f58ad7f4 SHA256: 263bc0c3b3385de29dc01d1cdb30cebece4e101a7c45d321959a71711a9249c4 SHA512: 58d236a4c0ef994f02e29882dc5cfe5d8cf2db08321efbbe3c57b36393b6c28bfac2f9aea809c98b72296a2f15b51939d4e2d72eae77c522c4e931a4f7619147 Homepage: https://cran.r-project.org/package=ica Description: CRAN Package 'ica' (Independent Component Analysis) Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE). Package: r-cran-ical Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 323 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-v8 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-ical_0.1.6-1.ca2404.1_all.deb Size: 68380 MD5sum: 8dbecc2c9f346d688d6fc43ee8bd208f SHA1: 896874cdbe574ed8f13b420fb6c9179b687080f3 SHA256: 07b407d0ef67dbf4fd11e7c6aa72af913652a6796dba230a605900f982203a29 SHA512: bce93b9a2765e1b62a78ff6e83a6aebea4d84e3a36d7e85569c8efb154f2e520e7035998665247a4eb9c38eed756a6cf9d61d495d1bc61b15e16dc9647830d42 Homepage: https://cran.r-project.org/package=ical Description: CRAN Package 'ical' ('iCalendar' Parsing) A simple wrapper around the 'ical.js' library executing 'Javascript' code via 'V8' (the 'Javascript' engine driving the 'Chrome' browser and 'Node.js' and accessible via the 'V8' R package). This package enables users to parse 'iCalendar' files ('.ics', '.ifb', '.iCal', '.iFBf') into lists and 'data.frames' to ultimately do statistics on events, meetings, schedules, birthdays, and the like. Package: r-cran-icamp Architecture: all Version: 1.5.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1352 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan, r-cran-permute, r-cran-ape, r-cran-bigmemory, r-cran-nortest, r-cran-minpack.lm, r-cran-hmisc, r-cran-dirichletreg, r-cran-data.table Filename: pool/dists/noble/main/r-cran-icamp_1.5.12-1.ca2404.1_all.deb Size: 1336892 MD5sum: 6b68a743d4ed63e722265815d5657752 SHA1: b4e299621907fe24c3ddddc65f142281307da584 SHA256: 2a90534b6e65d6958714ea82e6f1c330071023ac5cd43f0e58a7a53a3bd77340 SHA512: 2d53bc9e275be1cf505f52b72fe0ffb80d12c81c792d0d6170f45d8e453d1db53471174d84d4bd7e8e9a87b37713c9b73440acfc81571daaa23af2a44ef1cf5c Homepage: https://cran.r-project.org/package=iCAMP Description: CRAN Package 'iCAMP' (Infer Community Assembly Mechanisms by Phylogenetic-Bin-BasedNull Model Analysis) To implement a general framework to quantitatively infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis, abbreviated as 'iCAMP' (Ning et al 2020) . It can quantitatively assess the relative importance of different community assembly processes, such as selection, dispersal, and drift, for both communities and each phylogenetic group ('bin'). Each bin usually consists of different taxa from a family or an order. The package also provides functions to implement some other published methods, including neutral taxa percentage (Burns et al 2016) based on neutral theory model and quantifying assembly processes based on entire-community null models ('QPEN', Stegen et al 2013) . It also includes some handy functions, particularly for big datasets, such as phylogenetic and taxonomic null model analysis at both community and bin levels, between-taxa niche difference and phylogenetic distance calculation, phylogenetic signal test within phylogenetic groups, midpoint root of big trees, etc. Version 1.3.x mainly improved the function for 'QPEN' and added function 'icamp.cate()' to summarize 'iCAMP' results for different categories of taxa (e.g. core versus rare taxa). Package: r-cran-icams Architecture: all Version: 3.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings, r-bioc-bsgenome, r-cran-data.table, r-cran-dplyr, r-cran-fuzzyjoin, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-iranges, r-cran-lifecycle, r-cran-rcolorbrewer, r-cran-stringi, r-cran-zip Suggests: r-bioc-bsgenome.hsapiens.1000genomes.hs37d5, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-bioc-bsgenome.mmusculus.ucsc.mm10, r-cran-ggplot2, r-cran-reshape2, r-cran-rlang, r-cran-testthat Filename: pool/dists/noble/main/r-cran-icams_3.0.11-1.ca2404.1_all.deb Size: 2787178 MD5sum: d65759d98826c8b6c0c7fde707843ebf SHA1: 438f67f963402b02032da2ca870383d6c264f3c7 SHA256: 549198c7cd1668dcb744e63fafcd533ccc3093b535133444c60cbf3de6a31679 SHA512: 01d8b963e09d4219fcb81a87a812b9924fee7935b0d23eb9ee2a016044011a449de87c77a341d1e6d343f3747f3533956b63eac99c86fd64fbc52bab01bd0337 Homepage: https://cran.r-project.org/package=ICAMS Description: CRAN Package 'ICAMS' (In-Depth Characterization and Analysis of Mutational Signatures('ICAMS')) Analysis and visualization of experimentally elucidated mutational signatures -- the kind of analysis and visualization in Boot et al., "In-depth characterization of the cisplatin mutational signature in human cell lines and in esophageal and liver tumors", Genome Research 2018, and "Characterization of colibactin-associated mutational signature in an Asian oral squamous cell carcinoma and in other mucosal tumor types", Genome Research 2020 . 'ICAMS' stands for In-depth Characterization and Analysis of Mutational Signatures. 'ICAMS' has functions to read in variant call files (VCFs) and to collate the corresponding catalogs of mutational spectra and to analyze and plot catalogs of mutational spectra and signatures. Handles both "counts-based" and "density-based" (i.e. representation as mutations per megabase) mutational spectra or signatures. Package: r-cran-icardafigsr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1007 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-doparallel, r-cran-dplyr, r-cran-ggplot2, r-cran-foreach, r-cran-httr, r-cran-magrittr, r-cran-plotroc, r-cran-plyr, r-cran-raster, r-cran-reshape2, r-cran-sp, r-cran-leaflet Filename: pool/dists/noble/main/r-cran-icardafigsr_1.0.2-1.ca2404.1_all.deb Size: 978120 MD5sum: 111e4e9613036f4177d15a8ce2d17470 SHA1: 44f99028c964591b5b3930d776d16bc3c4041a6d SHA256: 83d8654d7d404937b57935daa2ca8426f6ba50e6535cbcd42b74cfc28032ce76 SHA512: 5b71d304e6d41a712bf7927a0dcea3ce2f7538e4ecfa1f06623cf46de9bc93db0385e1f8b9ab9fc1a7fc164d29e27708e7e11d9de4adb83edafb6a3354c489b5 Homepage: https://cran.r-project.org/package=icardaFIGSr Description: CRAN Package 'icardaFIGSr' (Subsetting using Focused Identification of the GermplasmStrategy (FIGS)) Running Focused Identification of the Germplasm Strategy (FIGS) to make best subsets from Genebank Collection. Package: r-cran-icarh Architecture: all Version: 2.0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-mass, r-cran-ggplot2, r-cran-glue, r-cran-rcurl, r-bioc-kegggraph, r-cran-igraph, r-cran-reshape2, r-cran-mc2d, r-cran-abind, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-icarh_2.0.2.1-1.ca2404.1_all.deb Size: 124810 MD5sum: 2f36e545fef8c3db1c50289c15724914 SHA1: bea4942a2f40dbf7455f4c27bd2de2f19509d8ed SHA256: 4d5374e869bc44c4b75fb5eeaf093ac5274b1005d9b23a7c3a1dbf5f81fe6c72 SHA512: 64638cc9e18b2dd671c29273666c33c269783d4d1fba5aa756ecfce6646926a05ca074b87d17db9095c9dbc977dc1bea4f522b8d716a86c92a1766530f5f253f Homepage: https://cran.r-project.org/package=iCARH Description: CRAN Package 'iCARH' (Integrative Conditional Autoregressive Horseshoe Model) Implements the integrative conditional autoregressive horseshoe model discussed in Jendoubi, T., Ebbels, T.M. Integrative analysis of time course metabolic data and biomarker discovery. BMC Bioinformatics 21, 11 (2020) . The model consists in three levels: Metabolic pathways level modeling interdependencies between variables via a conditional auto-regressive (CAR) component, integrative analysis level to identify potential associations between heterogeneous omic variables via a Horseshoe prior and experimental design level to capture experimental design conditions through a mixed-effects model. The package also provides functions to simulate data from the model, construct pathway matrices, post process and plot model parameters. Package: r-cran-icarus Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3988 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-rglpk, r-cran-slam, r-cran-xtable, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-icarus_0.3.3-1.ca2404.1_all.deb Size: 3981210 MD5sum: ce336f093d3abcba81cca08e09317a31 SHA1: bdb7a2dadfc514f4d3fb5fbd4f3be89917e308af SHA256: 9ea1996f00069c156ad22de375cc220b6c640fd6a6976b1df60240b56720485b SHA512: 729f50d4a5f794544b54a343a4c3073a5839a5c44a87b6b81657d0af1943cb9ea81c75ba63382c236810b4b1d8cb3330046687f87ea93750cc99d4655c5fc141 Homepage: https://cran.r-project.org/package=icarus Description: CRAN Package 'icarus' (Calibrates and Reweights Units in Samples) Provides user-friendly tools for calibration in survey sampling. The package is production-oriented, and its interface is inspired by the famous popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to 'icarus'. In addition to calibration (with linear, raking and logit methods), 'icarus' features functions for calibration on tight bounds and penalized calibration. Package: r-cran-icbiomark Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3896 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-dplyr, r-cran-purrr, r-cran-latex2exp, r-cran-matrixstats, r-cran-ggplot2, r-cran-gglasso, r-cran-prroc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-icbiomark_0.1.4-1.ca2404.1_all.deb Size: 3878626 MD5sum: 9c7c4cbc93bc02505e092e5e64b23964 SHA1: a01cf5cf0ce7f3ece3acef666a05a9162dbdf2d6 SHA256: 910e9566f6274aaee992deeebb72247ffd2ae71a7a080b8849edea9e8489fad7 SHA512: 95769f56a5c88d2c363775a742b1d47e01466b00411dcb4a08b5e92f8c41352a4d4a49b02c99cc8c59f0c1fe9dd045369c9d91dca2755640e11f487fa1802931 Homepage: https://cran.r-project.org/package=ICBioMark Description: CRAN Package 'ICBioMark' (Data-Driven Design of Targeted Gene Panels for EstimatingImmunotherapy Biomarkers) Implementation of the methodology proposed in 'Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) . This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models. Package: r-cran-icc.sample.size Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-icc.sample.size_1.1-1.ca2404.1_all.deb Size: 29608 MD5sum: abfac719df0c844579d3a90b29206440 SHA1: 78869dca0494ad3e4de66ff797b16aad998c2762 SHA256: 947b78e21b130300ffece50bb04ce618177ea4b7306ecd9f05f2d7f2a15589c1 SHA512: 4cfbb32e20c10cf00e869c49bb6c04fc4b9de38be5bc3741a9e66d9b054e073f7dec74f8ab7974a56909908044df515742795b3e7a007cf60e2f09b03a2630ca Homepage: https://cran.r-project.org/package=ICC.Sample.Size Description: CRAN Package 'ICC.Sample.Size' (Calculation of Sample Size and Power for ICC) Provides functions to calculate the requisite sample size for studies where ICC is the primary outcome. Can also be used for calculation of power. In both cases it allows the user to test the impact of changing input variables by calculating the outcome for several different values of input variables. Based off the work of Zou. Zou, G. Y. (2012). Sample size formulas for estimating intraclass correlation coefficients with precision and assurance. Statistics in medicine, 31(29), 3972-3981. Package: r-cran-icc Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-icc_2.4.0-1.ca2404.1_all.deb Size: 47116 MD5sum: bd9ac410bc66b3c5c16b1d213a94b878 SHA1: 1113f8811f4aa0351cb56521e6a390a4223802c9 SHA256: fef44475300269274cc86435e937c4ac776c9ece4885c1ea09c4d1323383b65a SHA512: 478cf020bbc1b57459d01ed7c3e1a552a7765cce9f8a0e11cf64e81f904906bc482cc409f187371c75e974be8f5689cd27dbdb404b9a1f526ed8947547d429f2 Homepage: https://cran.r-project.org/package=ICC Description: CRAN Package 'ICC' (Facilitating Estimation of the Intraclass CorrelationCoefficient) Assist in the estimation of the Intraclass Correlation Coefficient (ICC) from variance components of a one-way analysis of variance and also estimate the number of individuals or groups necessary to obtain an ICC estimate with a desired confidence interval width. Package: r-cran-iccbin Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-lme4 Filename: pool/dists/noble/main/r-cran-iccbin_1.2.0-1.ca2404.1_all.deb Size: 58688 MD5sum: c7d8c290a84c6758f0be9f4ffd1f8b50 SHA1: 6cbcd6f5537701c6f3fe09b185fd0cbc421344b8 SHA256: 40e5ec80a647aba1b95ba4d3eb18234401e7be0c90af14a912d38f4c3b36bb36 SHA512: 6a06f1f192c13ca0cba3997543d7cd00bc654a6c8e773ddbd119d629f5709568f2fa77d0412c3bc3a2ef53ff02b155755b047bd1783daf04ba0b919afd23108a Homepage: https://cran.r-project.org/package=ICCbin Description: CRAN Package 'ICCbin' (Facilitates Clustered Binary Data Generation, and Estimation ofIntracluster Correlation Coefficient (ICC) for Binary Data) Assists in generating binary clustered data, estimates of Intracluster Correlation coefficient (ICC) for binary response in 16 different methods, and 5 different types of confidence intervals. Package: r-cran-icccompare Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-dplyr, r-cran-deriv, r-cran-mass, r-cran-furrr, r-cran-future, r-cran-progressr, r-cran-parallelly, r-cran-bbmle, r-cran-mvtnorm Suggests: r-cran-cccrm Filename: pool/dists/noble/main/r-cran-icccompare_1.1.0-1.ca2404.1_all.deb Size: 75000 MD5sum: 40f611946f6312688341f400730e947c SHA1: 471844523766775ff2cd5769e48deb1a3ae036f1 SHA256: fac4db3a997e3da323cc95dff1e04991f01e2bf076d5f9adb66707269df03ac5 SHA512: 0b6cd1bdc637a0afd0ec7d8047b0521bfa578694d162e9fe363ba35f86c05632bf767c88cabd94519b1ddf2ae67a2e76653521460ece5dcc1c3bdce3b266cbb7 Homepage: https://cran.r-project.org/package=iccCompare Description: CRAN Package 'iccCompare' (Comparison of Dependent Intraclass Correlation Coefficients) Provides methods for testing the equality of dependent intraclass correlation coefficients (ICCs) estimated using linear mixed-effects models. Several of the implemented approaches are based on the work of Donner and Zou (2002) . Package: r-cran-icccounts Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmmtmb, r-cran-ggplot2, r-cran-deriv, r-cran-gridextra, r-cran-vgam, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-icccounts_1.1.4-1.ca2404.1_all.deb Size: 282274 MD5sum: 0d08258d76064937c939cb37a210e2e5 SHA1: 6206332f7da00115ca899d9bddb397e1fc1b70fa SHA256: a582bbc9dc4f52f19c5843872a560d3b7feac6f20f9cf15973aaea4eed92ca85 SHA512: 54015f281a0e2400b8c7b323eb2f540f66b082546e68e8f7948b569f81d96646c5d63893a244373bc4854456471f5bc426f92ece23b06a3da44b6da0876c803b Homepage: https://cran.r-project.org/package=iccCounts Description: CRAN Package 'iccCounts' (Intraclass Correlation Coefficient for Count Data) Estimates the intraclass correlation coefficient (ICC) for count data to assess repeatability (intra-methods concordance) and concordance (between-method concordance). In the concordance setting, the ICC is equivalent to the concordance correlation coefficient estimated by variance components. The ICC is estimated using the estimates from generalized linear mixed models. The within-subjects distributions considered are: Poisson; Negative Binomial with additive and proportional extradispersion; Zero-Inflated Poisson; and Zero-Inflated Negative Binomial with additive and proportional extradispersion. The statistical methodology used to estimate the ICC with count data can be found in Carrasco (2010) . Package: r-cran-iccde Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-iccde_0.3.8-1.ca2404.1_all.deb Size: 22200 MD5sum: 65b0eef7911528a4539aa3b40cb01031 SHA1: 39699320578b4bc633bea16d83cbdf4db28a4400 SHA256: 10085b7f02b4527a463008a4721fe1fef715d3357c32e856a6860ce952b0b0d3 SHA512: 897e9666e3118747b645450972f7018fa80a607a699e7fb9e6f9bbfc62d6b39400c1b7059291600fb772fd1577f3e00e3bc8b2adeb61a3e7d50f139e338a631c Homepage: https://cran.r-project.org/package=iccde Description: CRAN Package 'iccde' (Computation of the Double-Entry Intraclass Correlation) The functions compute the double-entry intraclass correlation, which is an index of profile similarity (Furr, 2010; McCrae, 2008). The double-entry intraclass correlation is a more precise index of the agreement of two empirically observed profiles than the often-used intraclass correlation (McCrae, 2008). Profiles comprising correlations are automatically transformed according to the Fisher z-transformation before the double-entry intraclass correlation is calculated. If the profiles comprise scores such as sum scores from various personality scales, it is recommended to standardize each individual score prior to computation of the double-entry intraclass correlation (McCrae, 2008). See Furr (2010) or McCrae (2008) for details. Package: r-cran-iccforest Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partykit, r-cran-survival, r-cran-icenreg, r-cran-ipred Suggests: r-cran-ltrctrees, r-cran-inum Filename: pool/dists/noble/main/r-cran-iccforest_0.5.1-1.ca2404.1_all.deb Size: 59780 MD5sum: b7a24ab6fbcc0dbf1cefc0d1369e952f SHA1: 390bc58b99d0ae87c278920e0214e3cd8ac9dd99 SHA256: 2df4a50830791850b1f4e5462b794409963a40c427ce14d8e072542a928354fb SHA512: a489d1d14f2df8c35748bb75d504c2a519746763ec18dc334a4ca529168b49b77c1047dead25c57768c19dd65985b75b5a848e4d51c2d7e0a08ba542c634b82d Homepage: https://cran.r-project.org/package=ICcforest Description: CRAN Package 'ICcforest' (An Ensemble Method for Interval-Censored Survival Data) Implements the conditional inference forest approach to modeling interval-censored survival data. It also provides functions to tune the parameters and evaluate the model fit. See Yao et al. (2019) . Package: r-cran-iccmult Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dirmult, r-cran-gtools, r-cran-iccbin, r-cran-lme4 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-iccmult_1.0.1-1.ca2404.1_all.deb Size: 35148 MD5sum: 810c684884647e9fa8a496d7fa34d531 SHA1: ad89c50e21331032eba713ac22043e387de9d670 SHA256: fcb742b5b28af17c1a7ed2dd0fce90267311152c8d6a3288341b416e708946a2 SHA512: 0506aac7257ffc6ef321b80f476d275f0cc1661065af21887b863f2335c057747acafdf5d42dddc0a9b569d09a5841f0d3d68c24f53464bf348e0c3f9f73be91 Homepage: https://cran.r-project.org/package=iccmult Description: CRAN Package 'iccmult' (Intracluster Correlation Coefficient (ICC) in ClusteredCategorical Data) Assists in generating categorical clustered outcome data, estimating the Intracluster Correlation Coefficient (ICC) for nominal or ordinal data with 2+ categories under the resampling and method of moments (MoM) methods, with confidence intervals. Package: r-cran-icctraj Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magic, r-cran-trajectories, r-cran-sp, r-cran-spacetime, r-cran-purrr, r-cran-furrr, r-cran-progressr, r-cran-parallelly, r-cran-future Filename: pool/dists/noble/main/r-cran-icctraj_1.1.0-1.ca2404.1_all.deb Size: 152202 MD5sum: af3981c0e301776293f2e054a0b4e1df SHA1: 4bda2b787c8f9b595bcac29323c06eab30131ea2 SHA256: 51390a3cb78b975b30d5683572cd16e79f60308fa6b810145da12b85e8f49545 SHA512: 72499cc37cfb7caf8848ae2c918f40b6fe9a2db1fb02e7b1970046f6835fdd57b8a3dd6b3a8f2400434dc9484088242c5e73e7061a20c33bd76314470e1694ae Homepage: https://cran.r-project.org/package=iccTraj Description: CRAN Package 'iccTraj' (Estimates the Intraclass Correlation Coefficient for TrajectoryData) Estimates the intraclass correlation coefficient for trajectory data using a matrix of distances between trajectories. The distances implemented are the extended Hausdorff distances (Min et al. 2007) and the discrete Fréchet distance (Magdy et al. 2015) . Package: r-cran-icd.data Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5004 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-icd.data_1.0-1.ca2404.1_all.deb Size: 4408776 MD5sum: 05cd2ee95a3ecc918044ce1d29c80c04 SHA1: 6429458f0af67d6ab47382060da507d35e6dfc40 SHA256: acf25cce52aced2e6628249d37efb65f988389be65b9fdc4823b91d58ff1c0b3 SHA512: b79439c40995c24eef38f912bb6dba03849aa6dc282d4885902652c7fd94dc70afd3ff4f485f6c46101204742729a4cd1b567dd5cfd2315d2d7879c3756f9e3a Homepage: https://cran.r-project.org/package=icd.data Description: CRAN Package 'icd.data' (International Classifcation of Diseases (ICD) Data) Data from the United States Center for Medicare and Medicaid Services (CMS) is included in this package. There are ICD-9 and ICD-10 diagnostic and procedure codes, and lists of the chapter and sub-chapter headings and the ranges of ICD codes they encompass. There are also two sample datasets. These data are used by the 'icd' package for finding comorbidities. Package: r-cran-icd10gm Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1371 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-tidyselect, r-cran-stringi, r-cran-rlang, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rvest Filename: pool/dists/noble/main/r-cran-icd10gm_1.2.5-1.ca2404.1_all.deb Size: 1281842 MD5sum: 47f65f5b9bfb40c4f55f0103d1ae6038 SHA1: 1ad3cd315551842b4835ebf053b60cab10e83e5c SHA256: d72f378bf7d7c81e4591845c6ddc4f8ff28a36aa0f0ded56c6a81e77ad1ea2b3 SHA512: f58b4f405f300b8ed4bdf18f37f89cd9398ae78bb9d4c00999d858d36ef125de6aafbd84fda8e2692a0bd7293273a17ac90dd21bf8e8721b071a1281e060ce2b Homepage: https://cran.r-project.org/package=ICD10gm Description: CRAN Package 'ICD10gm' (Metadata Processing for the German Modification of the ICD-10Coding System) Provides convenient access to the German modification of the International Classification of Diagnoses, 10th revision (ICD-10-GM). It provides functionality to aid in the identification, specification and historisation of ICD-10 codes. Its intended use is the analysis of routinely collected data in the context of epidemiology, medical research and health services research. The underlying metadata are released by the German Institute for Medical Documentation and Information , and are redistributed in accordance with their license. Package: r-cran-icdcomorbid Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-icdcomorbid_1.0.0-1.ca2404.1_all.deb Size: 49112 MD5sum: df99939e57740e1d7d56a77e30562286 SHA1: 99e3ef55f4bb401dafafb8f2227250ee855277e8 SHA256: 73e8c4d98dd53d23c51cae904448f67c32da412a02832a474145b482b68c760b SHA512: 1104d6b409be390ad207c2a7b0a61a664c18ec26bd38811a6719f784f3dec86341996bb6690d017063ea000163c3404a1a736937fd4d7285c56e3b0311611ef1 Homepage: https://cran.r-project.org/package=icdcomorbid Description: CRAN Package 'icdcomorbid' (Mapping ICD Codes to Comorbidity) Provides tools for mapping International Classification of Diseases codes to comorbidity, enabling the identification and analysis of various medical conditions within healthcare data. Package: r-cran-icdglm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-icdglm_1.0.0-1.ca2404.1_all.deb Size: 52028 MD5sum: 7a5c6ccf49915030fde8aa89d9207c61 SHA1: ba9f72974c2b0d09ed195a904569d54d2d87cee4 SHA256: 069635f19448e9b4a3aa957f0554e543c80d8b740f5504e90822cd6ecfa5392e SHA512: 9a632278fde2b6b71162ae7bf1c3d9cc41f39921234eeef6f33433643b118157b13e6fe282622d4c13388e1f0c3e416b7cbd2b0be424da9ab93091e5f97fba96 Homepage: https://cran.r-project.org/package=icdGLM Description: CRAN Package 'icdGLM' (EM by the Method of Weights for Incomplete Categorical Data inGenerlized Linear Models) Provides an estimator for generalized linear models with incomplete data for discrete covariates. The estimation is based on the EM algorithm by the method of weights by Ibrahim (1990) . Package: r-cran-icdpicr2 Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-icdpicr2_2.1.0-1.ca2404.1_all.deb Size: 689220 MD5sum: 644f8941c73e959241cc1a8ab1f2733c SHA1: 7842326155117a020770d0251d2807914c89b65e SHA256: a51b5d3478458e375019438cc3b828f87e1d8288f46c2c22c3c4040fafc8a7cc SHA512: d44844a7aab081eb25028ae66a0d40f45f1b3554a20f879e5d22b85f653b9a5f0742fefe21b9bb13be68e7af3d1c64b9083cf2e748d851f7bfbf0c9096cd5198 Homepage: https://cran.r-project.org/package=icdpicr2 Description: CRAN Package 'icdpicr2' (Categorize Injury Diagnosis Codes) Functions read a dataframe containing one or more International Classification of Diseases Tenth Revision codes per subject. They return original data with injury categorizations and severity scores added. Package: r-cran-icdpicr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1499 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-rcmdcheck Filename: pool/dists/noble/main/r-cran-icdpicr_1.0.1-1.ca2404.1_all.deb Size: 1423102 MD5sum: cdc54806ad71aff94418600a7b975804 SHA1: ceb65f3b30446119074bd7d62aa1911be2a17686 SHA256: 82c7f7c7daa6192bea52c17d36fbe8d722983bf744737e2b718c02bac5df72ac SHA512: 32f2841c0055f6b77e56b6a90332aaae0f030aec717fd577fa7bf2c7887df6b4f3180730be7eea9c09b57dfb25c4aea9860e02fb2d3afe213354737a00298738 Homepage: https://cran.r-project.org/package=icdpicr Description: CRAN Package 'icdpicr' ('ICD' Programs for Injury Categorization in R) Categorization and scoring of injury severity typically involves trained personnel with access to injured persons or their medical records. 'icdpicr' contains a function that provides automated calculation of Abbreviated Injury Scale ('AIS') and Injury Severity Score ('ISS') from International Classification of Diseases ('ICD') codes and may be a useful substitute to manual injury severity scoring. 'ICDPIC' was originally developed in 'Stata', and 'icdpicr' is an open-access update that accepts both 'ICD-9' and 'ICD-10' codes. Package: r-cran-icds Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 681 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-bioc-graphite, r-cran-metap, r-bioc-org.hs.eg.db Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-icds_0.1.3-1.ca2404.1_all.deb Size: 608842 MD5sum: d4cd3b37d13bf3767cab8ac479219eed SHA1: 9c9390ed2c2392daf2daf6781e7175c09961ab82 SHA256: 27834ea4727dd24587e8c78c61c14e8c28efce1975c66a1d6322ca1cc42d77de SHA512: cbe623dc40e083bc75de548af13749cdd9c7e79a4f7b22d19529ad6702dafdd52709850e87434117bfc0f3fbfe7af44db74fa593cf514c854bf08560d21c21be Homepage: https://cran.r-project.org/package=ICDS Description: CRAN Package 'ICDS' (Identification of Cancer Dysfunctional Subpathway with OmicsData) Identify Cancer Dysfunctional Sub-pathway by integrating gene expression, DNA methylation and copy number variation, and pathway topological information. 1)We firstly calculate the gene risk scores by integrating three kinds of data: DNA methylation, copy number variation, and gene expression. 2)Secondly, we perform a greedy search algorithm to identify the key dysfunctional sub-pathways within the pathways for which the discriminative scores were locally maximal. 3)Finally, the permutation test was used to calculate statistical significance level for these key dysfunctional sub-pathways. Package: r-cran-icebox Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sfsmisc Suggests: r-cran-randomforest, r-cran-mass Filename: pool/dists/noble/main/r-cran-icebox_1.1.5-1.ca2404.1_all.deb Size: 168370 MD5sum: 732f9832ade2b88085aa206cc34244ca SHA1: 96739c95b8f24fb5b9df8a0aafc1b98747fbd294 SHA256: 90c8ec9fae655fff9c9a596a38e025a2ed095d09983a2a14ba32fe19599595af SHA512: b43d377930d2f744ebd49025c2439096be3825b66f49717b9d5e8e33e415e6cc04ec6ec31bfb8a220c931ab40ef4a924c9500ddbf002445853252ae5483aae2e Homepage: https://cran.r-project.org/package=ICEbox Description: CRAN Package 'ICEbox' (Individual Conditional Expectation Plot Toolbox) Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist. Package: r-cran-icecdr Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-digest, r-cran-glue, r-cran-httr2, r-cran-rlang Suggests: r-cran-here, r-cran-ncdf4, r-cran-rcdo, r-cran-testthat, r-cran-vcr Filename: pool/dists/noble/main/r-cran-icecdr_1.2.0-1.ca2404.1_all.deb Size: 153716 MD5sum: 61073f0de65d26bdc330de883034a75e SHA1: 867b3f14734185f2473778a0596baf9f71f14c51 SHA256: 87d0af0ffb1a58213cb3f2fa64390726157aa15f885cf13b0b8a57236de82f94 SHA512: df6ae7206f1e269352b5875a615b519ac6be8f2464c5d799c3030d209dcd67aa13611a5dc8dd27e67e4d3340fab86f5b29fe0d886829834f8110c448ff49f713 Homepage: https://cran.r-project.org/package=icecdr Description: CRAN Package 'icecdr' (Download Sea Ice Concentration Data from the NSIDC Climate DataRecord) Programmatic access to NSIDC's sea ice concentration CDR via ERDAPP server and Sea Ice index . Supports caching results and optional fixes for some inconsistencies of the raw files. Package: r-cran-icecream Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-pillar, r-cran-purrr, r-cran-rlang Suggests: r-cran-checkmate, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-icecream_0.2.2-1.ca2404.1_all.deb Size: 42350 MD5sum: 9da88504766a03292a9b62b129409061 SHA1: ec898fd1ed8ea13ee81f7739043ee86cdde337cb SHA256: 50da1d13cc8c2579dd07172362cdb5ab0864331474d10b65074b104573d82550 SHA512: dd12f592f91e32f5c862a8c7ceeb706ed7f8f7a2db49fdc0735bfc0cb76f8c624718f578248e9edca11c52dc9eb75d5dbf155d3bcb199a24c63a86967ff85db0 Homepage: https://cran.r-project.org/package=icecream Description: CRAN Package 'icecream' (Print Debugging Made Sweeter) Provides user-friendly and configurable print debugging via a single function, ic(). Wrap an expression in ic() to print the expression, its value and (where available) its source location. Debugging output can be toggled globally without modifying code. Package: r-cran-iced Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1590 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-knitr, r-cran-lavaan, r-cran-mass, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-iced_0.0.1-1.ca2404.1_all.deb Size: 772334 MD5sum: 4d724fb5b378d82de269455de29e60e1 SHA1: e295568d060e40be97c49351845e1c7a14bdb7e4 SHA256: 2fd3b2b61381f8a5367f9ac0c6c984bdb2f2e5b14ace92883b6de2324b6aaedf SHA512: 327c215a101aa6cba9b2427e54a7dd55d4afd35035213209b2945485b3bf6d8d4206fda1f57a100cd0b26e90dcf22708e201ae1ca168dbbad1449953061afac7 Homepage: https://cran.r-project.org/package=ICED Description: CRAN Package 'ICED' (IntraClass Effect Decomposition) Estimate test-retest reliability for complex sampling strategies and extract variances using IntraClass Effect Decomposition. Developed by Brandmaier et al. (2018) "Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)" Also includes functions to simulate data based on sampling strategy. Unofficial version release name: "Good work squirrels". Package: r-cran-icehmeasures Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-ggplot2, r-cran-survey, r-cran-rlang, r-cran-car Suggests: r-cran-viridislite Filename: pool/dists/noble/main/r-cran-icehmeasures_1.1.0-1.ca2404.1_all.deb Size: 69022 MD5sum: 2fbb268f31c1fd05b786f2718a5944eb SHA1: 992e9670edc9b527548e46e78709630962fa052e SHA256: ac978d9c9fb12a1f8998b2e193c97bfb8cdbffbb292eb167a8d5e5509f3c6029 SHA512: f3ddbfa52c848aa48506ae5c0c5169ee0774435b010af835b481b00406a1b6d5e137a013ed9e12d59210f33a5f8fc931587dc293ed2af66d16fc5c4030f26028 Homepage: https://cran.r-project.org/package=ICEHmeasures Description: CRAN Package 'ICEHmeasures' (The Equiplot Graph and Complex Inequality Measures) Generates the equiplot, an iconic dot-plot graph for visualizing inequalities, as well as three complex inequality measures: the slope index of inequality, the concentration index and the mean absolute difference to the mean. For more details see World Health Organization (2013) . Package: r-cran-iceinfer Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1167 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-iceinfer_1.3-1.ca2404.1_all.deb Size: 1146626 MD5sum: 3990c4419ad0271e515d640f78c6a15e SHA1: 8e1db96beb64b4721dba51178efcb6183123f39c SHA256: b12a78422177e1095481f7455bde10eb71d3684de45345ff9c3d5ff60b906982 SHA512: 6611311e476aa4991268ac681aa8e53f8451e217daf4d412b9af40b5530aa1e283cf956bd58fb1d0059b88864e61283183704844eabdcdabae05eadf92c9fa2a Homepage: https://cran.r-project.org/package=ICEinfer Description: CRAN Package 'ICEinfer' (Incremental Cost-Effectiveness Inference using Two UnbiasedSamples) Given two unbiased samples of patient level data on cost and effectiveness for a pair of treatments, make head-to-head treatment comparisons by (i) generating the bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with specified confidence fraction within [0.50, 0.99] that is equivariant with respect to changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge with economic preferences from an ICE map with specified values of lambda, beta and gamma parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves from an ICE map with specified values of lambda, beta and either gamma or eta parameters. Package: r-cran-icensbkl Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mass, r-bioc-icens, r-cran-teachingdemos, r-cran-mvtnorm, r-cran-smoothsurv, r-cran-gtools Suggests: r-cran-interval, r-cran-logspline, r-cran-mixak, r-cran-bayessurv, r-cran-fhtest, r-cran-icenreg, r-cran-mlecens, r-cran-cubature, r-cran-runjags, r-cran-coda, r-cran-dynsurv Filename: pool/dists/noble/main/r-cran-icensbkl_1.5-1.ca2404.1_all.deb Size: 355238 MD5sum: 18bc11a1ddafc9f8c9497421870e1f82 SHA1: bdef942b159da2831b12c787d0c60d549e710c09 SHA256: 9ea2bd17c4615d0568cb2f855e2d8c3e683960e41399b26ec8a9f82b44add48d SHA512: ba4c04bd707384f95da63addcee53b433ed4e566d4f75809e8ba87b5af22d8add453cfc33fa91dc7edf0207d7cee7983e50601e7b818984a1a6fcba21d65cd52 Homepage: https://cran.r-project.org/package=icensBKL Description: CRAN Package 'icensBKL' (Accompanion to the Book on Interval Censoring by Bogaerts,Komarek, and Lesaffre) Contains datasets and several smaller functions suitable for analysis of interval-censored data. The package complements the book Bogaerts, Komárek and Lesaffre (2017, ISBN: 978-1-4200-7747-6) "Survival Analysis with Interval-Censored Data: A Practical Approach" . Full R code related to the examples presented in the book can be found at . Packages mentioned in the "Suggests" section are used in those examples. Package: r-cran-icertool Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-purrr, r-cran-dt, r-cran-tidyverse, r-cran-readxl, r-cran-ggplot2, r-cran-ggrepel, r-cran-digest, r-cran-shinyhelper Filename: pool/dists/noble/main/r-cran-icertool_0.0.3-1.ca2404.1_all.deb Size: 29964 MD5sum: 075b4e83d36e4fdd1e60407c5364d581 SHA1: b93e79aef05c4972976f4c02fa8699d911e47a22 SHA256: 87083d064d2cfd498104ec8eb4dc18a0f6ee34206a216f9d400cf79a405026eb SHA512: 88398b6184b4c9954365eaa9b6c0c66ff48a0453ea14199bbfd40d33094ef19e1f8077a4cbf714122b1708c0fac38aeb1c0ebafebdb743eaf8ae05738f4674ab Homepage: https://cran.r-project.org/package=icertool Description: CRAN Package 'icertool' (Calculate and Plot ICER) The app will calculate the ICER (incremental cost-effectiveness ratio) Rawlins (2012) from the mean costs and quality-adjusted life years (QALY) Torrance and Feeny (2009) for a set of treatment options, and draw the efficiency frontier in the costs-effectiveness plane. The app automatically identifies and excludes dominated and extended-dominated options from the ICER calculation. Package: r-cran-icesadvice Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-icesadvice_2.1.1-1.ca2404.1_all.deb Size: 61252 MD5sum: 14b9d2d1f918d14a61920166b0051ae7 SHA1: 0a402057cf17869326a7884b1b25a580bf7bd720 SHA256: 71fbbff0379f69ea022e4cc316d821c03195382f5361dce6fcbb2ac98806a7b8 SHA512: 1f81b7653b724360c7d969cf7dafb703c8eb437b9c27b55581050dfee0aef5632d4a3ff384bc64af1ae90e7e38f78e33e4c9daaeaf7fbaa6bddf3ca2ef3a8140 Homepage: https://cran.r-project.org/package=icesAdvice Description: CRAN Package 'icesAdvice' (Functions Related to ICES Advice) A collection of functions that facilitate computational steps related to advice for fisheries management, according to ICES guidelines. These include methods for calculating reference points and model diagnostics. Package: r-cran-icesat2r Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3620 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-sf, r-cran-lwgeom, r-cran-units, r-cran-data.table, r-cran-httr, r-cran-foreach, r-cran-doparallel, r-cran-magrittr, r-cran-leaflet, r-cran-leafgl, r-cran-htmltools, r-cran-htmlwidgets, r-cran-leafsync, r-cran-miniui, r-cran-shiny, r-cran-rnaturalearth, r-cran-lubridate, r-cran-rvest, r-cran-withr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-dt, r-cran-mapview, r-cran-stargazer, r-cran-reshape2, r-cran-plotly, r-cran-geodist, r-cran-copernicusdem, r-cran-terra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-icesat2r_1.0.9-1.ca2404.1_all.deb Size: 3329764 MD5sum: eab21f813b1c4187e2d7258545d259a4 SHA1: 29a63ac348fcee0bc6665c73c1859521bfff7729 SHA256: dddad2e1ee677ddef8d446a5d61708fe4b21532af4f8278ee0225086b571ebae SHA512: bb9ff637cf74f53b07cb49ff05bf0db46d54e8701890d8a9d1abfe3cfaf17f8ad269eab82544953a66bb18aae5c756d2be776d2ecabf316cf6976f47df4f38fa Homepage: https://cran.r-project.org/package=IceSat2R Description: CRAN Package 'IceSat2R' ('ICESat-2' Altimeter Data using R) Programmatic connection to the 'OpenAltimetry' API to download and process 'ATL03' (Global 'Geolocated' Photon Data), 'ATL06' (Land Ice Height), 'ATL07' (Sea Ice Height), 'ATL08' (Land and Vegetation Height), 'ATL10' (Sea Ice 'Freeboard'), 'ATL12' (Ocean Surface Height) and 'ATL13' (Inland Water Surface Height) 'ICESat-2' Altimeter Data. The user has the option to download the data by selecting a bounding box from a 1- or 5-degree grid globally utilizing a shiny application. The 'ICESat-2' mission collects 'altimetry' data of the Earth's surface. The sole instrument on 'ICESat-2' is the Advanced Topographic Laser Altimeter System (ATLAS) instrument that measures ice sheet elevation change and sea ice thickness, while also generating an estimate of global vegetation biomass. 'ICESat-2' continues the important observations of ice-sheet elevation change, sea-ice 'freeboard', and vegetation canopy height begun by 'ICESat' in 2003. Package: r-cran-icesconnect Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-base64enc, r-cran-jsonlite, r-cran-askpass Filename: pool/dists/noble/main/r-cran-icesconnect_1.1.4-1.ca2404.1_all.deb Size: 33538 MD5sum: 3d4af475f6fa7cdc400209ddb3c83dbf SHA1: 3aa5fd34087bd7607f5b1052793610d230cce43b SHA256: 180f5e41284de8f2fd8e09d0ceb88c51e4da06c74d4e991f49123dd1adbc525a SHA512: d4670a010a7fa876405632946d5027fa6690f8e456e5fb742db101d091abf2227d282f4159f9926db752b4bd506b7f11f6026cbbcd8f00befaeca575df298067 Homepage: https://cran.r-project.org/package=icesConnect Description: CRAN Package 'icesConnect' (Provides User Tokens for Access to ICES Web Services) Provides user tokens for ICES web services that require authentication and authorization. Web services covered by this package are ICES VMS database, the ICES DATSU web services, and the ICES SharePoint site . 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ICES is an organization facilitating international collaboration in marine science. 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Package: r-cran-icgor Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-icsurv, r-cran-pracma, r-cran-mass Filename: pool/dists/noble/main/r-cran-icgor_2.0-1.ca2404.1_all.deb Size: 53378 MD5sum: c725971b2ae59ff5871d031fbc895f6f SHA1: fcea9d25cd3c3214e661e88d6d21c7e04e19577b SHA256: afa2454375a987084572e5f767b628e77cc89b605dec24faaa59c74c3aa81df4 SHA512: 5e5c128e596f2204904bd1c0e11c7e55305f3ffb0642aed66fb5bd51fdaa179ad5ce738984631350fc15f2734c72cb632442e9284bef6e35a5182ec1bfb097fe Homepage: https://cran.r-project.org/package=ICGOR Description: CRAN Package 'ICGOR' (Fit Generalized Odds Rate Hazards Model with Interval CensoredData) Generalized Odds Rate Hazards (GORH) model is a flexible model of fitting survival data, including the Proportional Hazards (PH) model and the Proportional Odds (PO) Model as special cases. This package fit the GORH model with interval censored data. Package: r-cran-iclick Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1397 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fportfolio, r-cran-lattice, r-cran-xts, r-cran-boot, r-cran-car, r-cran-coefplot, r-cran-fbasics, r-cran-forecast, r-cran-jfe, r-cran-frapo, r-cran-lmtest, r-cran-lubridate, r-cran-openair, r-cran-paper, r-cran-quantmod, r-cran-rugarch, r-cran-sandwich, r-cran-timedate, r-cran-timeseries, r-cran-zoo Filename: pool/dists/noble/main/r-cran-iclick_1.6.1-1.ca2404.1_all.deb Size: 1384308 MD5sum: b0e4a3b379612ce29bbf36a78e4a9973 SHA1: 69df57f77828c72751b590ec2a18e33f148e4040 SHA256: 238c5e8c1d9c05f36178d482291d6da2e4f4ceb0bc1a148c7783c633910e15a6 SHA512: a8609a13dea81d5fc3187a854ea8c76b63da892ef8032a8ddf196b190613af569b125cb1ebdebb844ebff9247fc9c0699ee5c4775d7e6fb613ae124bcbe94925 Homepage: https://cran.r-project.org/package=iClick Description: CRAN Package 'iClick' (A Button-Based GUI for Financial and Economic Data Analysis) A GUI designed to support the analysis of financial-economic time series data. Package: r-cran-icmm Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1025 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ebayesthresh Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-icmm_1.2-1.ca2404.1_all.deb Size: 1010808 MD5sum: 172c4d3a83110de5b3e1782ee704d133 SHA1: 918f3e7c64f33b0c54209c21463ce6fab75dbcd1 SHA256: 5231adcce7d2c631ad355e3d2102667683e1ba046be349dd0d4f082ec62e6c63 SHA512: 6235dcc259116cefdee77fe096489d21546174c885c0e5bc92ebffc9004997fe6f1878952e48bf4d764a1a68a09fda38e7476a21ebaaf98fe0fea8a91c5868c9 Homepage: https://cran.r-project.org/package=icmm Description: CRAN Package 'icmm' (Empirical Bayes Variable Selection via ICM/M Algorithm) Empirical Bayes variable selection via ICM/M algorithm for normal, binary logistic, and Cox's regression. The basic problem is to fit high-dimensional regression which sparse coefficients. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. More information can be found in the papers listed in the URL below. Package: r-cran-icods Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-icods_1.2-1.ca2404.1_all.deb Size: 232996 MD5sum: 0284bf1af2024a57f0f0ee737e147cac SHA1: c9dec42f5195c893d50bdb12aa1a74e62a73e606 SHA256: 0ba8e375772615ce30e62da1502f62f3f4ecebc4a5d24d1e3773463a661b257f SHA512: fc9d008ad90c56124c14d677719c5e32ba64acf66ae820b4eb53a165ec4bcfee34f064abe041d250955fee65f719b2a163e2f32f8d7982b313bb228d448fa9f4 Homepage: https://cran.r-project.org/package=ICODS Description: CRAN Package 'ICODS' (Data Analysis for ODS and Case-Cohort Designs withInterval-Censoring) Sieve semiparametric likelihood methods for analyzing interval-censored failure time data from an outcome-dependent sampling (ODS) design and from a case-cohort design. Zhou, Q., Cai, J., and Zhou, H. (2018) ; Zhou, Q., Zhou, H., and Cai, J. (2017) . Package: r-cran-icompelm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tsutils, r-cran-ica Suggests: r-cran-forecast Filename: pool/dists/noble/main/r-cran-icompelm_0.1.0-1.ca2404.1_all.deb Size: 41894 MD5sum: 9d351b6d6eaa4e501d876e2b8d81b5a2 SHA1: f34be3fe92c9d95d8e284015a4a06da9a441e3f5 SHA256: 07ffeaf1bfda325d39c3197439909c17926340976fe2c078904096bc67720123 SHA512: a8f624eabf0f5b10cb3cdc3f7f52f9ac2c0c822af64e53ade391542fa158793077fc1a7deab37bf8bfd893407c72168a768bd1887ae496fd947e431ec702ed06 Homepage: https://cran.r-project.org/package=ICompELM Description: CRAN Package 'ICompELM' (Independent Component Analysis Based Extreme Learning Machine) Single Layer Feed-forward Neural networks (SLFNs) have many applications in various fields of statistical modelling, especially for time-series forecasting. However, there are some major disadvantages of training such networks via the widely accepted 'gradient-based backpropagation' algorithm, such as convergence to local minima, dependencies on learning rate and large training time. These concerns were addressed by Huang et al. (2006) , wherein they introduced the Extreme Learning Machine (ELM), an extremely fast learning algorithm for SLFNs which randomly chooses the weights connecting input and hidden nodes and analytically determines the output weights of SLFNs. It shows good generalized performance, but is still subject to a high degree of randomness. To mitigate this issue, this package uses a dimensionality reduction technique given in Hyvarinen (1999) , namely, the Independent Component Analysis (ICA) to determine the input-hidden connections and thus, remove any sort of randomness from the algorithm. This leads to a robust, fast and stable ELM model. Using functions within this package, the proposed model can also be compared with an existing alternative based on the Principal Component Analysis (PCA) algorithm given by Pearson (1901) , i.e., the PCA based ELM model given by Castano et al. (2013) , from which the implemented ICA based algorithm is greatly inspired. 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Package: r-cran-ideafilter Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-ggplot2, r-cran-pillar, r-cran-purrr, r-cran-rcolorbrewer, r-cran-shiny, r-cran-shinytime Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-shinytest, r-cran-shinytest2, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ideafilter_0.2.1-1.ca2404.1_all.deb Size: 206524 MD5sum: dd0020a7d84527e742cf52995904b0b0 SHA1: 8938720e35d839e0feed3b24431d28697cd5a2c9 SHA256: 95fdfc88acca86f24225b3b32bd7ed38f380dca68a7830872d011eb81374b417 SHA512: 59795183308a8a371d91278e0d885a7d68236c2f36c76ebc1dacdc91761d951846d2cd15e721c8418e371379355cefc78ae7f4aa0ced9e9e4654a97f730c3a8b Homepage: https://cran.r-project.org/package=IDEAFilter Description: CRAN Package 'IDEAFilter' (Agnostic, Idiomatic Data Filter Module for Shiny) When added to an existing shiny app, users may subset any developer-chosen R data.frame on the fly. That is, users are empowered to slice & dice data by applying multiple (order specific) filters using the AND (&) operator between each, and getting real-time updates on the number of rows effected/available along the way. Thus, any downstream processes that leverage this data source (like tables, plots, or statistical procedures) will re-render after new filters are applied. The shiny module’s user interface has a 'minimalist' aesthetic so that the focus can be on the data & other visuals. In addition to returning a reactive (filtered) data.frame, 'IDEAFilter' as also returns 'dplyr' filter statements used to actually slice the data. Package: r-cran-idealstan Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1330 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-svdialogs, r-cran-tidyr, r-cran-stringr, r-cran-bayesplot, r-cran-posterior, r-cran-gghighlight, r-cran-ggrepel, r-cran-ggplot2, r-cran-tidybayes, r-cran-lazyeval, r-cran-rlang, r-cran-ggthemes, r-cran-forcats, r-cran-ordbetareg, r-cran-scales, r-cran-shiny, r-cran-tibble Suggests: r-cran-quarto, r-cran-knitr, r-cran-rmarkdown, r-cran-loo, r-cran-lubridate, r-cran-rstan, r-cran-shinystan, r-cran-pscl, r-cran-tinytable, r-cran-testthat Filename: pool/dists/noble/main/r-cran-idealstan_1.0-1.ca2404.1_all.deb Size: 659242 MD5sum: b27ab7ea07db9dbc168687204fde0ce6 SHA1: eb6bbd9ed4e41bc866c59938d39d264718d388b1 SHA256: 3fb94e69cf7405869b1aece767fe8198492504ac83555d27e1bc218159ad673f SHA512: 93b5c53fc19051e00fb55e1dc33beef1841cfeaee10865518cab4ae07a171abb742edaf03363eae08febc2c9420abb11a978bea54efaea44aa9bdeb40a25a4a5 Homepage: https://cran.r-project.org/package=idealstan Description: CRAN Package 'idealstan' (Robust Measurement with 'Stan') Offers item-response theory (IRT) ideal-point measurement modeling for diverse distributions, missing data, and over-time variation. Full and approximate Bayesian sampling with 'Stan' (). Package: r-cran-ideamdb Architecture: all Version: 0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 783 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ideamdb_0.0.9-1.ca2404.1_all.deb Size: 401084 MD5sum: 551d006a818658e6a454995c49b18088 SHA1: 8b113c8ee8221c46f8e752f67215be753b9f5da7 SHA256: 05a797812eb964456ba29cce9425e30555d27ee2cd3859e514ed425ed19ff5a6 SHA512: 58200e5e8ef582ae26c097ba78f7567f9018662b407c9251ce676b327ec8ab8608c6e7432a3a4f0fefaf1fee590a7e082967ef744043e605cecc731bab2e45f0 Homepage: https://cran.r-project.org/package=ideamdb Description: CRAN Package 'ideamdb' (Easy Manipulation of IDEAM's Climatological Data) Time series plain text conversion and data visualization. It allows to transform IDEAM (Instituto de Hidrologia, Meteorologia y Estudios Ambientales) daily series from plain text to CSV files or data frames in R. Additionally, it is possible to obtain exploratory graphs from times series. IDEAM’s data is freely delivered under formal request through the official web page . Package: r-cran-ideanet Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraphdata, r-cran-cliquepercolation, r-cran-cluster, r-cran-colorspace, r-cran-concorr, r-cran-cowplot, r-cran-data.table, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggthemes, r-cran-gridgraphics, r-cran-igraph, r-cran-intergraph, r-cran-jsonlite, r-cran-magrittr, r-cran-matrix, r-cran-moments, r-cran-network, r-cran-readxl, r-cran-reshape2, r-cran-rlang, r-cran-rspectra, r-cran-shiny, r-cran-sna, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-dt, r-cran-devtools, r-cran-egor, r-cran-ergm, r-cran-shinythemes, r-cran-shinywidgets, r-cran-knitr, r-cran-rmarkdown, r-cran-shinycssloaders, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-ideanet_1.1.1-1.ca2404.1_all.deb Size: 2706474 MD5sum: 119b514d1339e36d389e6cf28192456e SHA1: a16d2b6b7980ef3b09d4ee5169a629b51c08c9bd SHA256: 3e1bffbfda5755415153c7538c9380c0e174b4e7ac61d18f3f58f569ec404512 SHA512: 0269db63647602053819b15f8284eb70e421531661f9f44a3214265b3a145cf097a1c764d5674413cee0451a0a2df230a81455dc55c80eb66c39c23d8f6cb778 Homepage: https://cran.r-project.org/package=ideanet Description: CRAN Package 'ideanet' (Integrating Data Exchange and Analysis for Networks ('ideanet')) A suite of convenient tools for social network analysis geared toward students, entry-level users, and non-expert practitioners. ‘ideanet’ features unique functions for the processing and measurement of sociocentric and egocentric network data. These functions automatically generate node- and system-level measures commonly used in the analysis of these types of networks. Outputs from these functions maximize the ability of novice users to employ network measurements in further analyses while making all users less prone to common data analytic errors. Additionally, ‘ideanet’ features an R Shiny graphic user interface that allows novices to explore network data with minimal need for coding. Package: r-cran-ideatools Architecture: all Version: 3.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3337 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite, r-cran-readxl, r-cran-tibble, r-cran-stringi, r-cran-ggimage, r-cran-pdftools, r-cran-ggpubr, r-cran-ggplot2, r-cran-rlang, r-cran-ggtext, r-cran-rmarkdown, r-cran-shiny, r-cran-openxlsx Suggests: r-cran-covr, r-cran-knitr Filename: pool/dists/noble/main/r-cran-ideatools_3.5.2-1.ca2404.1_all.deb Size: 2334864 MD5sum: 2272be480c31cfedbc65f4bfad6b3511 SHA1: 394247ec56d99c82f05596312cfbe2860759ecbd SHA256: 4d0f940a5697de2f41b347c536f30604242ec6ae52af1da412352669925091a9 SHA512: 1812e4e72c7afdd0308e2b48ab4f56dff1bdf2a150a7fe81024dbc6a2ae5502c6f4d899c7290c6795763e8f240a19c0d8169cd61ec73ec1c2b42bfc37eaed9da Homepage: https://cran.r-project.org/package=IDEATools Description: CRAN Package 'IDEATools' (Individual and Group Farm Sustainability Assessments using theIDEA4 Method) Collection of tools to automate the processing of data collected though the IDEA4 method (see Zahm et al. (2018) ). Starting from the original data collecting files this packages provides functions to compute IDEA indicators, draw modern and aesthetic plots, and produce a wide range of reporting materials. Package: r-cran-idendr0 Architecture: all Version: 1.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tkrplot Suggests: r-cran-dendser, r-cran-cluster, r-cran-rcolorbrewer, r-cran-loon, r-cran-mass, r-bioc-flowstats, r-cran-hyperspec Filename: pool/dists/noble/main/r-cran-idendr0_1.5.4-1.ca2404.1_all.deb Size: 1555988 MD5sum: 3e4df104d7e0e104c12a8358d0c68091 SHA1: 44318b705161daab0140aaf72b64e278aa9366be SHA256: 22e6d5b822074455feaef349ff9560f572c35f7cb325b1a9cc700b3e605817c1 SHA512: 835aac1eeb277c78af4e3dfcd8dda9f0c88ba360003794eaea61ced9a40c68ad18d341d06647abb0dabd28f928f16be658912e3a05122e58128b8e87913a345c Homepage: https://cran.r-project.org/package=idendr0 Description: CRAN Package 'idendr0' (Interactive Dendrograms) Interactive dendrogram that enables the user to select and color clusters, to zoom and pan the dendrogram, and to visualize the clustered data not only in a built-in heat map, but also in 'GGobi' interactive plots and user-supplied plots. This is a backport of Qt-based 'idendro' () to base R graphics and Tcl/Tk GUI. Package: r-cran-ider Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-glm2 Filename: pool/dists/noble/main/r-cran-ider_0.1.1-1.ca2404.1_all.deb Size: 933830 MD5sum: 9b8586855e031d3a59d00a7cb269eabe SHA1: 4861ac7f3adb0dff2e2897c4f0f1f8c85fd14e15 SHA256: 21d1614685bc2a9be88328e7417cd42b5d61d4fb34fe5da184724766528b5867 SHA512: 671cae0d78f3c6fcd6948d0d4713d06fe7978231cfa1216bf2b320364fe33052e9a583a93546eb54340f55150438360da41f26bb11856f597779e6fd74cadf45 Homepage: https://cran.r-project.org/package=ider Description: CRAN Package 'ider' (Various Methods for Estimating Intrinsic Dimension) An implementation of various methods for estimating intrinsic dimension of vector-valued dataset or distance matrix. 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Package: r-cran-idetect Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-idetect_0.1.1-1.ca2404.1_all.deb Size: 155878 MD5sum: d8a33e3b7c9bf7252817d2f0f700f1a9 SHA1: a1570a8411761cb5c049ce650ddeed66f5bd7d11 SHA256: 9e5e3a295e6b011113a9570df324dd2f78996b62d648671d5fc54beceff5f17d SHA512: bb08471f5aafc20888c9064f55da901dfcd3d712027dba0605015104d605cd3b147ce74656ff5cd9e1856f8d10d61e4606f667afae050554622732e06cf2c9ae Homepage: https://cran.r-project.org/package=IDetect Description: CRAN Package 'IDetect' (Isolate-Detect Method for Multiple Change-Point Detection) The IDetect provides efficient implementation of the ID methodology for the consistent estimation of the number and location of multiple change-points in one-dimensional data sequences from the `deterministic + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, continuous piecewise-linear signal, continuous piecewise-linear signal with a heavy-tailed noise. Package: r-cran-idf Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-evd, r-cran-ismev, r-cran-rcpproll, r-cran-pbapply, r-cran-fastmatch Filename: pool/dists/noble/main/r-cran-idf_2.1.3-1.ca2404.1_all.deb Size: 284730 MD5sum: 002bacd5837f8a7221c288bd6bf5329d SHA1: 51f49a579828ed1daa0a1882635d6d452ad8ddb7 SHA256: 6ffea39458e9fdf154542d15a85fb34787b71ed66a708b771f1feaf471ad7c2e SHA512: 42037220155548994f700f6aabf5a0469adcc3d977283b766af50e1b162a78d1862bfed8939f576db98820fce57aeae0679c13924ff292ae761be70692b80f96 Homepage: https://cran.r-project.org/package=IDF Description: CRAN Package 'IDF' (Estimation and Plotting of IDF Curves) Intensity-duration-frequency (IDF) curves are a widely used analysis-tool in hydrology to assess extreme values of precipitation [e.g. Mailhot et al., 2007, ]. The package 'IDF' provides functions to estimate IDF parameters for given precipitation time series on the basis of a duration-dependent generalized extreme value distribution [Koutsoyiannis et al., 1998, ]. Package: r-cran-idingo Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-mvtnorm, r-cran-glasso, r-cran-ggmridge, r-cran-visnetwork, r-cran-scales Filename: pool/dists/noble/main/r-cran-idingo_1.0.4-1.ca2404.1_all.deb Size: 305884 MD5sum: 8905b186d091915523f721c0b79820ff SHA1: 9580af8a625a7617a6c2998b151e6e2f4a647443 SHA256: 598c37706848a9c0a4ee3eb3b4d95b1a644afd4ee562c99382150cf89f5aa83f SHA512: d7da9458d629fe54dc3c1b9f35b0718a115907b97a94ee870dc8ca2909e3367634ef166b7aad1959c3f12d2fe9ac180de4e086e123dc36ff467187231872c18a Homepage: https://cran.r-project.org/package=iDINGO Description: CRAN Package 'iDINGO' (Integrative Differential Network Analysis in Genomics) Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The methods are described in Class et. al., (2018) and Ha et. al., (2015) . Package: r-cran-idiogramfish Architecture: all Version: 2.0.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3845 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-crayon, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-scales Suggests: r-cran-rentrez, r-cran-ggplot2, r-cran-ggpubr, r-bioc-ggtree, r-cran-phytools, r-bioc-treeio, r-cran-rmdformats, r-cran-knitr, r-cran-kableextra, r-cran-rvcheck, r-cran-badger, r-cran-rmarkdown, r-cran-rcurl, r-cran-shiny, r-cran-shinydashboard, r-cran-rhandsontable, r-cran-gtools, r-cran-rclipboard, r-cran-clipr, r-cran-shinyjs Filename: pool/dists/noble/main/r-cran-idiogramfish_2.0.13-1.ca2404.1_all.deb Size: 1949076 MD5sum: c832eee143a717410933c92324b95891 SHA1: a05c21cfcf88adb5a1dd6da6565fb0d516bcd883 SHA256: a3f3ed3d0e466cf53226ea54a993239ad73a6fef619d1da8a726d04e8b206c9f SHA512: df6a4266297c96e1f4c15e5b935845b9af27e5e193eaa0cef263d2ffe6aac706f84404b7f9e8b1e6809496c39b69c0750e1e095028e9b036be74ea715804d8a0 Homepage: https://cran.r-project.org/package=idiogramFISH Description: CRAN Package 'idiogramFISH' (Shiny App. Idiograms with Marks and Karyotype Indices) Plot idiograms of karyotypes, plasmids, circular chr. having a set of data.frames for chromosome data and optionally mark data. Two styles of chromosomes can be used: without or with visible chromatids. Supports micrometers, cM and Mb or any unit. Three styles of centromeres are available: triangle, rounded and inProtein; and six styles of marks are available: square (squareLeft), dots, cM (cMLeft), cenStyle, upArrow (downArrow), exProtein (inProtein); its legend (label) can be drawn inline or to the right of karyotypes. Idiograms can also be plotted in concentric circles. It is possible to calculate chromosome indices by Levan et al. (1964) , karyotype indices of Watanabe et al. (1999) and Romero-Zarco (1986) and classify chromosomes by morphology Guerra (1986) and Levan et al. (1964). Package: r-cran-idiolect Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 929 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quanteda, r-cran-caret, r-cran-dplyr, r-cran-fdrtool, r-cran-ggplot2, r-cran-kgrams, r-cran-pbapply, r-cran-proc, r-cran-proxy, r-cran-quanteda.textstats, r-cran-spacyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-readtext, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-idiolect_1.2.0-1.ca2404.1_all.deb Size: 771574 MD5sum: 7dd40031b9cd3e9752bc27ad320347e9 SHA1: 22b0c52104451d56a047a900e19b4134855a5ee3 SHA256: 9f612f3cb75367a579ead8c28b803f449d7bace8a22ca93d345a87e4502ce6c6 SHA512: e919b0630df992c7e98eb48b87fd45952d8cda594cef4249b397e696ad1706000706b0426b3db9918faae80ba56be3726de3607b6f76ec32a7a68d8659d566da Homepage: https://cran.r-project.org/package=idiolect Description: CRAN Package 'idiolect' (Forensic Authorship Analysis) Carry out comparative authorship analysis of disputed and undisputed texts within the Likelihood Ratio Framework for expressing evidence in forensic science. This package contains implementations of well-known algorithms for comparative authorship analysis, such as Smith and Aldridge's (2011) Cosine Delta or Koppel and Winter's (2014) Impostors Method , as well as functions to measure their performance and to calibrate their outputs into Log-Likelihood Ratios. Package: r-cran-idionomics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-forcats, r-cran-forecast, r-cran-ggplot2, r-cran-metafor, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-idionomics_0.1.0-1.ca2404.1_all.deb Size: 182534 MD5sum: ec3ec90f0dcdf0d7191550e025f7b4bf SHA1: cd5cff1293cb5eb4b073f96c2baba49bf68f4020 SHA256: 4680a41dd4373fed2e06c15f8e9f4b8200a172c37e93a359d586b4c005a64b39 SHA512: d9fa6324db021b13db4e59e4a8214531aaf8ec34566376d84beba9df9802d08ae4bd8ce472e03fbb6827c7aa48f40b43b15ba9b08b10d37baed76de99c52466d Homepage: https://cran.r-project.org/package=idionomics Description: CRAN Package 'idionomics' (Conduct Idionomic Analyses for Time Series Modeling) A toolkit for idionomic science, a research philosophy that places the unit of the ensemble (individual/couple/group) at the center of analysis. Rather than assuming a common distribution, a similar enough process for each unit, and fitting a single model to the whole ensemble, idionomic methods model each unit separately, then aggregate upward if sensible. The group-level picture emerges from individual results, not the other way around, while explicitly evaluating whether aggregation is reasonable given the measured level of heterogeneity of effects. The package is built around intensive longitudinal data where each participant contributes a time series. It provides a pipeline from preprocessing through modeling to group-level summaries. Current functions: data quality screening (i_screener()), within-person standardization (pmstandardize()), linear detrending (i_detrender()), per-subject ARIMAX (AutoRegressive Integrated Moving Average with eXogenous inputs) modeling and meta-analysis (iarimax()), individual p-values (i_pval()), Sign Divergence and Equisyncratic Null tests (sden_test()), and directed loop detection (looping_machine()). Methods are described in Hernandez et al. (2024) , Ciarrochi et al. (2024) , and Sahdra et al. (2024) . 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This difficulty arises from different mappings between raw scores and scale scores, complex mathematical relationships, adjustments based on judgmental procedures, and diverse equating functions applied to different assessment forms. An alternative method involves running simulations to explore the effect of incrementing raw scores on mean scale scores. The 'idmact' package provides an implementation of this approach based on the algorithm detailed in Schiel (1998) which was developed to help interpret differences between mean scale scores on the American College Testing (ACT) assessment. The function idmact_subj() within the package offers a framework for running simulations on subject-level scores. In contrast, the idmact_comp() function provides a framework for conducting simulations on composite scores. 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Package: r-cran-idmeasurer Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1186 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-infotheo, r-cran-lme4 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-idmeasurer_1.0.0-1.ca2404.1_all.deb Size: 917806 MD5sum: 37f8a485ead5033d7164732f6f9261c2 SHA1: 83a08c0a1251ce9b2236cc755bc32371bf1c2356 SHA256: 65d4f8c8bd7628d325a6b6222c90f33d1832a0f4f15869ed714e9499431a7f72 SHA512: b21d64cc2750b64e77c9f0deeda8193dc02b6e05e4aa7fc4738160fdf66097f4873fb578329a0b448c19a67cc3efd640e7c137fcb2357d82bd20a06f4e39ebf6 Homepage: https://cran.r-project.org/package=IDmeasurer Description: CRAN Package 'IDmeasurer' (Assessment of Individual Identity in Animal Signals) Provides tools for assessment and quantification of individual identity information in animal signals. This package accompanies a research article by Linhart et al. (2019) : "Measuring individual identity information in animal signals: Overview and performance of available identity metrics". 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And the Morisita estimator is used for the ID estimation, but other tools are included as well. 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'IDMIR' first constructed a weighted miRNA interaction network through integrating miRNA-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific-disease context, and then, it used a network propagation algorithm on the network to identify significantly dysregulated miRNAs. Package: r-cran-idmodelr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1786 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-ggplot2, r-cran-viridis, r-cran-magrittr, r-cran-purrr, r-cran-future, r-cran-furrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-desolve Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-pkgnet, r-cran-dt, r-cran-vdiffr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-idmodelr_0.4.0-1.ca2404.1_all.deb Size: 988632 MD5sum: 19dc5474daf33439529706c141b8525a SHA1: 5488de241d983d4a71c5ad6858ebd5e9c96420b0 SHA256: 2bd24d307cb0bb5c4b7a4c057f22730acfe41bdddb2756ecbd7b9bcb06cf733e SHA512: 23054ad650aa1d59d9deba622f518464f843474e685de33a9d8cc7693ff89f3c4f460952da00061f48b54f1e9c0954a27337d2acc6571f0940b5c80d703ff57d Homepage: https://cran.r-project.org/package=idmodelr Description: CRAN Package 'idmodelr' (Infectious Disease Model Library and Utilities) Explore a range of infectious disease models in a consistent framework. 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For newer modellers, this package reduces the barrier to entry by containing multiple infectious disease models, providing a consistent framework for simulation and visualisation, and signposting towards other, more research focussed, resources. 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To address these issues, a new model has been developed, which converts static data into their 'dynamic' form. 'idopNetwork' is an 'R' interface to this model, it can inferring informative, dynamic, omnidirectional and personalized networks. For more information on functional clustering part, see Kim et al. (2008) , Wang et al. (2011) . For more information on our model, see Chen et al. (2019) , and Cao et al. (2022) . 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Please refer to the COPYRIGHTS file and the text_citation.cff file for the reference copyright information and for the complete citations of the reference sources, respectively. 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Returns tidy tibbles for easy analysis. Trading contracts for difference (CFDs), options and spread bets carries a high risk of losing money. This package is not financial or trading advice. 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C. et al. (2020) . Package: r-cran-igorr Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bitops, r-cran-timechange Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-igorr_0.9.0-1.ca2404.1_all.deb Size: 288048 MD5sum: d99be837544f4281bf5288d2c2436d7b SHA1: d02298b57943dd62698202cb129cc82d05c8e5f0 SHA256: fc1393ac3ad9e5eae091f82dc2e8a52b45b355feedbdcbdef3d76eef74d3ed9f SHA512: c3a64438c8961b1acb898cc19a862dc6bc9856badf96336f28fb177edebf091a6766479af3e20fe2a103b12b55261c589bf9f9e0eeba16b05745c9d8621fec2e Homepage: https://cran.r-project.org/package=IgorR Description: CRAN Package 'IgorR' (Read Binary Files Saved by 'Igor Pro' (Including 'Neuromatic'Data)) Provides function to read data from the 'Igor Pro' data analysis program by 'Wavemetrics'. 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Package: r-cran-igsea Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-igsea_1.2-1.ca2404.1_all.deb Size: 26138 MD5sum: 440062b5ad84c4321b27473a71afadcb SHA1: 1ac4da331ca50e9015267fb5ecf6abe12b43367a SHA256: cdd3ff5195e52734673c7ebcf97748a1667865efc8f88caf42b623d646749163 SHA512: 3588535a8136ca129433e6ed325799186bd7538eb9d9fc6c073b8afeb79f2f6281c25f35b357e9a4664a1fd94ca4dee76ac61ad91c17f2c475a009b657fa549f Homepage: https://cran.r-project.org/package=iGSEA Description: CRAN Package 'iGSEA' (Integrative Gene Set Enrichment Analysis Approaches) To integrate multiple GSEA studies, we propose a hybrid strategy, iGSEA-AT, for choosing random effects (RE) versus fixed effect (FE) models, with an attempt to achieve the potential maximum statistical efficiency as well as stability in performance in various practical situations. 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For reference, see below: 1. Song, J., Carey, M., Zhu, H., Miao, H., Ram´ırez, J. C., & Wu, H. (2018) 2. Wu, S., Wu, H. (2013) 3. Carey, M., Wu, S., Gan, G. & Wu, H. (2016) . 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The method begins with a accept/reject approximate bayes computation (ABC) step applied to a sample of points from the prior distribution of model parameters. Accepted points result in model predictions that are within the initially specified tolerance intervals around the target points. The sample is iteratively updated by drawing additional points from a mixture of multivariate normal distributions, accepting points within tolerance intervals. As the algorithm proceeds, the acceptance intervals are narrowed. The algorithm returns a set of points and sampling weights that account for the adaptive sampling scheme. For more details see Rutter, Ozik, DeYoreo, and Collier (2018) . 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One possible reason for this is that psychologists lack the tools to systematically assess the quality of their theories. Previously a computational model for formal theory evaluation based on the concept of explanatory coherence was developed (Thagard, 1989, ). However, there are possible improvements to this model and it is not available in software that psychologists typically use. Therefore, a new implementation of explanatory coherence based on the Ising model is available in this R-package. 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Package: r-cran-immunaut Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-plyr, r-cran-dplyr, r-cran-caret, r-cran-proc, r-cran-prroc, r-cran-rlang, r-cran-rtsne, r-cran-dbscan, r-cran-fnn, r-cran-igraph, r-cran-fpc, r-cran-mclust, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-r.utils, r-cran-clustersim, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-immunaut_1.0.2-1.ca2404.1_all.deb Size: 179540 MD5sum: 29796b4d25c4b67090bf3f6b98e38aa3 SHA1: 3299e2d15a7c9cc57684c9d35dcbe60c7ac35d45 SHA256: 29fb678426ecbabe30eb4018891ac720a70d687ba4c99976f9a1c7a50ed51385 SHA512: 79d9cfc2f568d6410ff7255d366f5e8cbb557b7368ae6a5e462b3331b642b4632378771299ef4d2b10824d1136f2dd107b51abf4b42cab63f872c0b0f101f2f5 Homepage: https://cran.r-project.org/package=immunaut Description: CRAN Package 'immunaut' (Machine Learning Immunogenicity and Vaccine Response Analysis) Used for analyzing immune responses and predicting vaccine efficacy using machine learning and advanced data processing techniques. 'Immunaut' integrates both unsupervised and supervised learning methods, managing outliers and capturing immune response variability. It performs multiple rounds of predictive model testing to identify robust immunogenicity signatures that can predict vaccine responsiveness. The platform is designed to handle high-dimensional immune data, enabling researchers to uncover immune predictors and refine personalized vaccination strategies across diverse populations. Package: r-cran-immundata Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4662 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-duckplyr, r-cran-checkmate, r-cran-cli, r-cran-dbplyr, r-cran-ggplot2, r-cran-glue, r-cran-jsonlite, r-cran-lifecycle, r-cran-r6, r-cran-readr, r-cran-rlang, r-cran-tibble Suggests: r-cran-duckdb, r-cran-rmarkdown, r-cran-testthat, r-cran-seurat Filename: pool/dists/noble/main/r-cran-immundata_0.0.7-1.ca2404.1_all.deb Size: 3577172 MD5sum: 93a6b236a507f3558e3dc5417ffa458b SHA1: 36fe863490772e25cb6b8ab9d60a4565212a1698 SHA256: acea85a409f0652385b1295c50a4dbf9f173b09e586c0ec44edb8628be6dcaea SHA512: dc16731b2077ed97328c70b3a4f193eb9dd21a8e434f1a00383a09dbe1bfa5955bf77cffa9c5cf24a2fa048236f4e387e91faa386122ade1a9f95461d213b057 Homepage: https://cran.r-project.org/package=immundata Description: CRAN Package 'immundata' (A Unified Data Layer for Large-Scale Single-Cell, Spatial andBulk Immunomics) Provides a unified data layer for single-cell, spatial and bulk T-cell and B-cell immune receptor repertoire data. Think AnnData or SeuratObject, but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or VDJ sequencing data. Package: r-cran-immunesigr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2248 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-immunesigr_0.1.0-1.ca2404.1_all.deb Size: 830364 MD5sum: 62dee3928e13654e1fd353c892c3aaf5 SHA1: 83566c773b5a7fe510a3fa0360c2a768f643fd60 SHA256: 37e16bc795938ce614d74402af2c05a84be4579fa9a41ed6a08e21b701954c25 SHA512: 7673624226e0bc077d1cade5a8329e26c5b5fbc4fb7cd0d5c90bd22dff7a844047f9c9de281a1187211e8dbb8d73b9cd4977730a8d9409813e494204a79c6f08 Homepage: https://cran.r-project.org/package=ImmuneSigR Description: CRAN Package 'ImmuneSigR' (Immune Cell Signature Retrieval and Single-Cell Scoring) Provides a literature-derived database of immune cell markers formatted as Gene Matrix Transposed (GMT) files. Users can search immune cell signatures, retrieve marker lists, export GMT files, create custom marker sets, and score gene-by-cell expression matrices with dependency-free rank-based or mean-expression methods. Cell subpopulations are distinguished by their source PMIDs. For the core curation of the lung cell atlas, see Travaglini et al. (2020) . For the pan-cancer B cell signatures, see Fitzsimons et al. (2024) . Package: r-cran-immunesim Architecture: all Version: 0.8.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4313 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-powerlaw, r-cran-stringdist, r-bioc-biostrings, r-cran-igraph, r-cran-stringr, r-cran-data.table, r-cran-plyr, r-cran-reshape2, r-cran-ggplot2, r-cran-ggthemes, r-cran-rcolorbrewer, r-cran-metrics, r-cran-repmis Filename: pool/dists/noble/main/r-cran-immunesim_0.8.7-1.ca2404.1_all.deb Size: 4382522 MD5sum: 92240b59235e840c65171966823b1edd SHA1: e2c068456e21c2b9c37c0648f7c9c8f22d7f4a5a SHA256: 2975f4b0c0ed8c10d27acd80085f48886d4faae85f395e6ea11d5bf9c889d787 SHA512: e306ec87f896b89c65fa76ce9c131035f40db161c4f7608f9e1cc1e379b6f79ddcca241a59de716a4b769671b8501db73c32496b83210e1e66d383822c20b431 Homepage: https://cran.r-project.org/package=immuneSIM Description: CRAN Package 'immuneSIM' (Tunable Simulation of B- And T-Cell Receptor Repertoires) Simulate full B-cell and T-cell receptor repertoires using an in silico recombination process that includes a wide variety of tunable parameters to introduce noise and biases. Additional post-simulation modification functions allow the user to implant motifs or codon biases as well as remodeling sequence similarity architecture. The output repertoires contain records of all relevant repertoire dimensions and can be analyzed using provided repertoire analysis functions. Preprint is available at bioRxiv (Weber et al., 2019 ). Package: r-cran-immunogenetr Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2851 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-immunogenetr_1.2.0-1.ca2404.1_all.deb Size: 297116 MD5sum: 2d1158c97f7fc42a07275aecc82581f1 SHA1: a082eb314859db8e0746b7b832595d37e5a819be SHA256: c47cbd420700b7a4656e92b6aa162f593a8fef6fcff8265fd19778da24a1ad50 SHA512: 466593b82ec16c0767f2d1ed8c9daa412febe0c2452f0a86e82e549572dddbaa579c4d04db0d7d9dfa2aed3e1513c66645c0d59410b236139df8a5c8e73dd144 Homepage: https://cran.r-project.org/package=immunogenetr Description: CRAN Package 'immunogenetr' (A Comprehensive Toolkit for Clinical HLA Informatics) A comprehensive toolkit for clinical Human Leukocyte Antigen (HLA) informatics, built on 'tidyverse' principles and making use of genotype list string (GL string, Mack et al. (2023) ) for storing and computing HLA genotype data. Specific functionalities include: coercion of HLA data in tabular format to and from GL string; calculation of matching and mismatching in all directions, with multiple output formats; automatic formatting of HLA data for searching within a GL string; truncation of molecular HLA data to a specific number of fields; and reading HLA genotypes in HML files and extracting the GL string. This library is intended for research use. Any application making use of this package in a clinical setting will need to be independently validated according to local regulations. Package: r-cran-imneuron Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlmetrics, r-cran-ggplot2, r-cran-neuralnet Filename: pool/dists/noble/main/r-cran-imneuron_0.1.0-1.ca2404.1_all.deb Size: 22978 MD5sum: 514b6235888017d113675cfdbf59f613 SHA1: 7d817a707c766f290e81b395b7d7c23f5755f231 SHA256: c4d71f068c99ba9fb2429482ff413cbad4ca317d6fa8c56d7d7f0d3f869f3c9c SHA512: da97f02434111e687f351f8c10445b76eed7a2c9894fb6882afce8656f2ee7ff93f7a1ec47671885859249a706a90d131ec2156bcb7c7079fd8f3730d9f1458b Homepage: https://cran.r-project.org/package=Imneuron Description: CRAN Package 'Imneuron' (AI Powered Neural Network Solutions for Regression Tasks) It offers a sophisticated and versatile tool for creating and evaluating artificial intelligence based neural network models tailored for regression analysis on datasets with continuous target variables. 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This package is designed to create and assess Artificial Intelligence based Neural Networks with varying architectures for prediction of volume of forest trees using two input features: height and diameter at breast height, as they are the key factors in predicting volume, therefore development and validation of efficient volume prediction neural network model is necessary. This package has been developed using the algorithm of Tabassum et al. (2022) . Package: r-cran-imola Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4503 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-htmltools, r-cran-magrittr, r-cran-stringi, r-cran-glue, r-cran-yaml Suggests: r-cran-testthat, r-cran-rvest, r-cran-devtools, r-cran-covr, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-imola_0.5.0-1.ca2404.1_all.deb Size: 1874978 MD5sum: a5955716779952815e19684c62d28249 SHA1: 7f6d8a3e3e57ab98896e79e72331bb663d654fe3 SHA256: 6bc02b528843e2c5e6be3bdd8bed23e51d304bbe069d81c5706b3e6531a977e5 SHA512: 5ef4f2ba7dd8f28370d12afc9d7b6acd88aa772c80bf16f2d0abedbea1a1512e1f68f63cc565105fc51f0d0b93540c7045d73f2496a6e42a46de4afe0eb14a49 Homepage: https://cran.r-project.org/package=imola Description: CRAN Package 'imola' (CSS Layouts (Grid and Flexbox) Implementation for R/Shiny) Allows easy creation of CSS layouts (grid and flexbox) directly from R without added CSS. 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Package: r-cran-impact Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-impact_0.1.1-1.ca2404.1_all.deb Size: 15932 MD5sum: d63cf574cfa8dbaf22d9712952ad3854 SHA1: 06ba929976a8335dcc44596de9f07ba9341a7cd9 SHA256: f5fb43bf532638a4aa97c7c1d6e6b997649cca7e494118ea42f801564300acdf SHA512: 400d0d0fad3e1b4aba0918887f64ce00882b4800336e885895e94385ae4850f43f43c3dd810009428da0a77cd642334b6e4040046cbbff83870ded1eb492b528 Homepage: https://cran.r-project.org/package=IMPACT Description: CRAN Package 'IMPACT' (The Impact of Items) Implement a multivariate analysis of the impact of items to identify a bias in the questionnaire validation of Likert-type scale variables. The items requires considering a null value (category doesn't have tendency). Offering frequency, importance and impact of the items. Package: r-cran-impectr Architecture: all Version: 2.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-impectr_2.5.5-1.ca2404.1_all.deb Size: 266074 MD5sum: bba962e8a695ba8d4d4265b47731aed0 SHA1: f167b2bdeb8b52c73066863a1b883fc8112fc877 SHA256: 873ceb7912dc4076808713835a823df907d7172abcdd2d6209a2dc561fb85451 SHA512: 5896b0007d126f1528aebbcfd46b714892f96af895b1e2bfa31162da8be55eac76d0d1227c85cd610589a1fc136ddaed9ac74df4275cecc4c15c5dbf2d4c57fa Homepage: https://cran.r-project.org/package=impectR Description: CRAN Package 'impectR' (Access Data from the 'Impect' API) Pull data from the 'Impect' Customer API . The package can retrieve data such as events or match sums. Package: r-cran-impermanentlosscalc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-impermanentlosscalc_0.1.0-1.ca2404.1_all.deb Size: 25604 MD5sum: 4e8b00fdc8ce4e7f63f70c0c2d91c0f7 SHA1: 9e6444a6aaf116041546048a76c8448ad859fa11 SHA256: 8eeb3a6e359b63b2f4466d1650be8cb2214fd7039a15d771ec47ce1b3bd7504e SHA512: addb1653e32f303c011b0264c2cf5f565812bbc3fb086c9a9cfb523a7cde47e3e67642c91d5cc731645ca628f31a80d6e2066468fd2b773d2e45c45f36224202 Homepage: https://cran.r-project.org/package=impermanentlosscalc Description: CRAN Package 'impermanentlosscalc' (Calculate Impermanent Loss in Automated Market Maker (AMM)Liquidity Pools) Computes the key metrics for assessing the performance of a liquidity provider (LP) position in a weighted multi-asset Automated Market Maker (AMM) pool. Calculates the nominal and percentage impermanent loss (IL) by comparing the portfolio value inside the pool (based on the weighted geometric mean of price ratios) against the value of simply holding the assets outside the pool (based on the weighted arithmetic mean). The primary function, `impermanent_loss()`, incorporates the effect of earned trading fees to provide the LP's net profit and loss relative to a holding strategy, using a methodology derived from Tiruviluamala, N., Port, A., and Lewis, E. (2022) . Package: r-cran-impimp Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-impimp_0.3.1-1.ca2404.1_all.deb Size: 65136 MD5sum: cd2251e9b561959ed2a4c0cf0fa03750 SHA1: 8282ce3f0ed8d792c78cdecbf82cd006790ab953 SHA256: 29252d2651d21b6501097c3c138280a742fe13d80fb0952c3616e45ab9e581b5 SHA512: 543047cd623e07eca18396fd0f8ce6e20af91617dff190972f015adb723956e06205d6cc853ae7ac07c795567a729242536d296bc6fe14da2e5ecc1b7a2306f4 Homepage: https://cran.r-project.org/package=impimp Description: CRAN Package 'impimp' (Imprecise Imputation for Statistical Matching) Imputing blockwise missing data by imprecise imputation, featuring a domain-based, variable-wise, and case-wise strategy. Furthermore, the estimation of lower and upper bounds for unconditional and conditional probabilities based on the obtained imprecise data is implemented. Additionally, two utility functions are supplied: one to check whether variables in a data set contain set-valued observations; and another to merge two already imprecisely imputed data. The method is described in a technical report by Endres, Fink and Augustin (2018, ). 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This pattern is known as "broadcasting" in 'Python' and "implicit expansion" in 'Matlab' and is explained for example in the article "Array programming with NumPy" by C. R. Harris et al. (2020) . 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Functions for preparing the data (both for the IAT and the SC-IAT), plotting the results, and obtaining a table with the scores of implicit measures descriptive statistics are provided. 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Functions and utils needed for illustrating this inferential paradigm are implemented for classroom teaching and further comprehensive research. Two imprecise models are demonstrated using multinomial data and 2x2 contingency table data. The concepts of prior ignorance and imprecision are discussed in lower and upper probabilities. Representation invariance principle, hypothesis testing, decision-making, and further generalization are also illustrated. 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(2016) . Plot, save, or export the calculated probabilities for use in your own research. 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We use a methodological framework which ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. See Mamouris (2023) for an overview. 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(2019) . 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Many imaging studies are designed so that every reader reads every case in all modalities, a fully-crossed study. In this case, the data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects. An MRMC analysis accounts for the variability and correlations from the readers and cases when estimating variances, confidence intervals, and p-values. The functions in this package can treat arbitrary study designs and studies with missing data, not just fully-crossed study designs. An overview of this software, including references presenting details on the methods, can be found here: . 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This package is a comprehensive toolset designed for the fitting and validation of various linear and nonlinear allometric equations (Linear, Log-Linear, Inverse, Quadratic, Cubic, Compound, Power and Exponential) used in the prediction of conifer tree volume. This package is particularly useful for forestry professionals, researchers, and resource managers engaged in assessing and estimating the volume of coniferous trees. This package has been developed using the algorithm of Sharma et al. (2017) . 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Package: r-cran-incidence2 Architecture: all Version: 2.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 870 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-grates, r-cran-data.table, r-cran-pillar, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-tidyselect, r-cran-rlang, r-cran-vctrs, r-cran-ympes Suggests: r-cran-outbreaks, r-cran-ggplot2, r-cran-scales, r-cran-litedown, r-cran-testthat, r-cran-citools, r-cran-withr Filename: pool/dists/noble/main/r-cran-incidence2_2.6.4-1.ca2404.1_all.deb Size: 618608 MD5sum: cc2a4253a3b40877a10c730c6a8b0cdd SHA1: a9212367495e934cb4ee836dcbd4c2bc636fed14 SHA256: eccdf8b64e72d4bb34c4b3c38c9dd0f504ea305f03ac8061ccb0cc7db7ffc2f4 SHA512: bebe851a02637d06ce62b5737d96a3414e0edb73d05e0bed82400821b121f3583e03ec06bc19d89120ebc12d5a3f70432fcc97aae20b021425e7f8d6f25699e8 Homepage: https://cran.r-project.org/package=incidence2 Description: CRAN Package 'incidence2' (Compute, Handle and Plot Incidence of Dated Events) Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence2' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. Package: r-cran-incidence Architecture: all Version: 1.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2361 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-aweek Suggests: r-cran-magrittr, r-cran-outbreaks, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-incidence_1.7.6-1.ca2404.1_all.deb Size: 1385538 MD5sum: cf3f809a3d20b58c52b5824489e863ae SHA1: 55e313bac971a80b8b2e4d703a3e5e45d5af7975 SHA256: 920c22bc341e7c1ecdeb282981f429bdb09fe6ff4c69cf1d0416ffe521504162 SHA512: cc74bbef3432314e7d91206d09915371419ee0ea2630c06e22fb116f3f956e5271e31b29984ef266e611b4bfaa18c4b412139e331ac67a4147ae678ecbc1f3a3 Homepage: https://cran.r-project.org/package=incidence Description: CRAN Package 'incidence' (Compute, Handle, Plot and Model Incidence of Dated Events) Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. In addition, log-linear models can be fitted to 'incidence' objects using 'fit'. This package is part of the RECON () toolkit for outbreak analysis. Package: r-cran-incidenceprevalence Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cdmconnector, r-cran-cli, r-cran-clock, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-magrittr, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-dbplyr, r-cran-rmarkdown, r-cran-rpostgres, r-cran-duckdb, r-cran-dbi, r-cran-odbc, r-cran-here, r-cran-hmisc, r-cran-epitools, r-cran-tictoc, r-cran-testthat, r-cran-spelling, r-cran-gt, r-cran-flextable, r-cran-ggplot2, r-cran-scales, r-cran-visomopresults, r-cran-patchwork, r-cran-binom Filename: pool/dists/noble/main/r-cran-incidenceprevalence_1.2.1-1.ca2404.1_all.deb Size: 804758 MD5sum: df63f24b32d8d327870beef1767a9d0e SHA1: 0368a8f2254be7c6b50cf61194d7ca48c26f6d70 SHA256: 9499a25342b62b1f690ceb1d8299fbdab38f7f5c75f447c9a7d626b2af8b2c4d SHA512: 76ce22515851d47ac750e0f7bae39bbf52b20fcbba6da6f32d60c4698391751988171cdf0e442428ba7399743a61fa071ad9f419e4efaf18ef27b5fd8f70c7f9 Homepage: https://cran.r-project.org/package=IncidencePrevalence Description: CRAN Package 'IncidencePrevalence' (Estimate Incidence and Prevalence using the OMOP Common DataModel) Calculate incidence and prevalence using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Incidence and prevalence can be estimated for the total population in a database or for a stratification cohort. Package: r-cran-incidental Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 647 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-matrixstats, r-cran-numderiv, r-cran-dlnm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-incidental_0.1-1.ca2404.1_all.deb Size: 460618 MD5sum: ea232fa6eb1d8439d66ef15cd4cd7fcb SHA1: 1b880c05ab55e69394f7994905cdf2ade69a3803 SHA256: 126c5929127d23a155eafa22e5eee854f70dfce6546c8d80763136c4588bf6cb SHA512: ba16b7cac1425de5a7af8e3ef6e38c9deca77e2d09ed2bbc03c80adfb91450fa6d59cf311c2cc2899e98c213c2fbfbbb11c108f387907e3db86829229b921101 Homepage: https://cran.r-project.org/package=incidental Description: CRAN Package 'incidental' (Implements Empirical Bayes Incidence Curves) Make empirical Bayes incidence curves from reported case data using a specified delay distribution. Package: r-cran-incidentally Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 927 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-xml2 Suggests: r-cran-backbone, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-incidentally_1.0.4-1.ca2404.1_all.deb Size: 540346 MD5sum: fb83e5a0a25c14bb27a9ec194d37282f SHA1: b41e3505b49c99e4c6baa99931f35786ddd74bc9 SHA256: d52b07062106ea56c1ba5ffa5598248f95e74dce984802921998464dd25064c4 SHA512: b5bca270a4f76115b7542388aef342c305663105c6cf07774f3d8fba61f47c84230492a6624ab818a0a0ec84773f8a50a5d73eb63358b187d377d5625594e98b Homepage: https://cran.r-project.org/package=incidentally Description: CRAN Package 'incidentally' (Generates Incidence Matrices and Bipartite Graphs) Functions to generate incidence matrices and bipartite graphs that have (1) a fixed fill rate, (2) given marginal sums, (3) marginal sums that follow given distributions, or (4) represent bill sponsorships in the US Congress . It can also generate an incidence matrix from an adjacency matrix, or bipartite graph from a unipartite graph, via a social process mirroring team, group, or organization formation , or examine the space of binary matrices with fixed marginals. Package: r-cran-incompair Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-incompair_0.1.0-1.ca2404.1_all.deb Size: 53704 MD5sum: 0055f6e5c844d4ea7c29b94b10867489 SHA1: 0dd08f11aff18dd3d15338a1be5696cfdef6496e SHA256: b88e6aed67c2eb1f999a56e12bef3ca1df9ddf538b6c81e1b444535134efdd14 SHA512: 2de51bf59660adb04c362b2ac15fb3eae537d20852cc71252dac8d3bee014123d0f5050e99500f1b1458365b6c092e443cbeeef71933435ae39134fd008be8f0 Homepage: https://cran.r-project.org/package=IncomPair Description: CRAN Package 'IncomPair' (Comparison of Means for the Incomplete Paired Data) Implements a variety of nonparametric and parametric methods that are commonly used when the data set is a mixture of paired observations and independent samples. The package also calculates and returns values of different tests with their corresponding p-values. Bhoj, D. S. (1991) "Testing equality of means in the presence of correlation and missing data". Dubnicka, S. R., Blair, R. C., and Hettmansperger, T. P. (2002) "Rank-based procedures for mixed paired and two-sample designs". Einsporn, R. L. and Habtzghi, D. (2013) "Combining paired and two-sample data using a permutation test". Ekbohm, G. (1976) "On comparing means in the paired case with incomplete data on both responses". Lin, P. E. and Stivers, L. E. (1974) On difference of means with incomplete data". Maritz, J. S. (1995) "A permutation paired test allowing for missing values". Package: r-cran-incr Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-suncalc, r-cran-lubridate Suggests: r-cran-codetools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-incr_2.1.1-1.ca2404.1_all.deb Size: 180592 MD5sum: d0dca39500f77c8c3c852d31eb851018 SHA1: e3d95b6ed9b90a3eb1a2b66605dc05bf5e276c00 SHA256: 9f53c8ba492c470badf6245758f792a21e522600301b4756aa5903ac8c98125b SHA512: 4fa422634b009c6841ee80f836283077060c6f5198c2432181b671b2619b164dd39027292e83cdeebbcf17e97f7b0f2c6f425a3172a9d1d75cb457ae809ca5f0 Homepage: https://cran.r-project.org/package=incR Description: CRAN Package 'incR' (Analysis of Incubation Data) Suite of functions to study animal incubation. At the core of incR lies an algorithm that allows for the scoring of incubation behaviour. Additionally, several functions extract biologically relevant metrics of incubation such as off-bout number and off-bout duration - for a review of avian incubation studies, see Nests, Eggs, and Incubation: New ideas about avian reproduction (2015) edited by D. Charles Deeming and S. James Reynolds . 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Implements and extends the method of Kassanjee et al. (2012) . Package: r-cran-incvcommunitydetection Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rspectra, r-cran-clusterr, r-cran-irlba, r-cran-cluster, r-cran-rfast, r-cran-data.table, r-cran-imifa Suggests: r-cran-latentnet, r-cran-network, r-cran-rdist, r-cran-igraph, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-incvcommunitydetection_0.1.0-1.ca2404.1_all.deb Size: 288594 MD5sum: 6974fb56a27215393d86c3ed56c2e81d SHA1: aaa3592f088297c0b8475d074ac82e802d277c98 SHA256: bcb957ce52b80bdfd8e94a8b8e648ae14b1f73c48251ac8808e83276178217fe SHA512: f1729367b5bf7444a43928728306c5dc0d645076655c3a85aeb285b33a7e39ce8c68463e014ca5f9778a1585e6f962c38e34ad6aec9689ace7f671284416f1d9 Homepage: https://cran.r-project.org/package=INCVCommunityDetection Description: CRAN Package 'INCVCommunityDetection' (Inductive Node-Splitting Cross-Validation for CommunityDetection) Implements Inductive Node-Splitting Cross-Validation (INCV) for selecting the number of communities in stochastic block models. Provides f-fold and random-split node-level cross-validation, along with competing methods including CROISSANT, Edge Cross-Validation (ECV), and Node Cross-Validation (NCV). Supports both SBM and Degree-Corrected Block Models (DCBM), with multiple loss functions (L2, binomial deviance, AUC). Includes network simulation utilities for SBM, RDPG, and latent space models. Package: r-cran-indago Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1841 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigtabulate, r-cran-bsicons, r-cran-bslib, r-cran-callr, r-cran-checkmate, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-fs, r-cran-ggplot2, r-cran-ggrepel, r-cran-heatmaply, r-cran-hmisc, r-cran-htmltools, r-cran-magrittr, r-cran-matrixstats, r-cran-memuse, r-cran-paletteer, r-cran-pheatmap, r-cran-plotly, r-cran-r.devices, r-cran-readr, r-cran-reshape2, r-cran-rintrojs, r-cran-seqinr, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinywidgets, r-cran-spscomps, r-cran-svglite, r-cran-tibble, r-cran-tidyr, r-cran-upsetjs, r-cran-upsetr Suggests: r-bioc-biocgenerics, r-cran-biocmanager, r-bioc-biostrings, r-bioc-edger, r-cran-harrypotter, r-bioc-htsfilter, r-bioc-limma, r-cran-oompabase, r-cran-palr, r-bioc-rsamtools, r-bioc-rsubread, r-bioc-rtracklayer, r-bioc-s4vectors, r-bioc-shortread, r-cran-testthat, r-bioc-xvector Filename: pool/dists/noble/main/r-cran-indago_1.0.3-1.ca2404.1_all.deb Size: 855562 MD5sum: 0671cff4ee9c1f0ca03d3496f023b369 SHA1: 9860a75961b420ce33a4860f5dd0127bc381d340 SHA256: 3f3fd6088b20b6034b38e1f71221634ec93e7086a7b3750c81d16f786d462ff4 SHA512: 41119c6c3628b6489cd0e7e49342e3eb2318c70f8922ed071d4874b05edcbcd09a6ffa36fe9191953b81a9cbf10c84380b78ef952c9e7a7618282f765eb3a400 Homepage: https://cran.r-project.org/package=inDAGO Description: CRAN Package 'inDAGO' (A GUI for Dual and Bulk RNA-Sequencing Analysis) A 'shiny' app that supports both dual and bulk RNA-seq, with the dual RNA-seq functionality offering the flexibility to perform either a sequential approach (where reads are mapped separately to each genome) or a combined approach (where reads are aligned to a single merged genome). The user-friendly interface automates the analysis process, providing step-by-step guidance, making it easy for users to navigate between different analysis steps, and download intermediate results and publication-ready plots. 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Also functions for market capitalization and volume weighted indexes with fixed number of constituents are available. The main function of the package, indexComp(), provides the derived index, suitable for analysis purposes. The functions indexUpdate(), indexMemberSelection() and indexMembersUpdate() are components of indexComp() and enable one to construct and continuously update an index, e.g. for display on a website. The methodology behind the functions provided gets introduced in Trimborn and Haerdle (2018) . 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Among others, we obtain simple index numbers (in chain or in serie), index numbers for not only a product or weighted index numbers as the Laspeyres index (Laspeyres, 1864), the Paasche index (Paasche, 1874) or the Fisher index (Lapedes, 1978). 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Provides data sets and functions to complete the case studies and contains the book original Rmd files and tutorials. 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The package downloads and processes data from a companion 'GitHub' repository () which contains processed versions of the official INE Atlas data. Functions are provided to fetch data at multiple geographic levels (municipalities, districts, and census tracts), including income indicators, demographic characteristics, and inequality metrics. The data repository is updated every year when new releases are published by INE. 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Computes the Gini coefficient with bootstrap or asymptotic confidence intervals following Davidson (2009) , the extended S-Gini family, Theil T and L indices (generalised entropy family), the Atkinson index, the Kolm absolute inequality index, Palma ratio, Hoover index, percentile ratios, and Lorenz curves. Supports between-within group decomposition following Bourguignon (1979) , income share tabulation, concentration indices for health inequality with Erreygers (2009) correction, Kakwani tax progressivity and Reynolds-Smolensky redistribution indices, Foster-Greer-Thorbecke poverty measures including the Sen index, growth incidence curves following Ravallion and Chen (2003) , and Wolfson polarisation. All functions accept optional survey weights and work with data from any source. 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Package: r-cran-infiltrodiscr Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-testthat, r-cran-tibble, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-infiltrodiscr_0.0.5-1.ca2404.1_all.deb Size: 19762 MD5sum: 58e51d3ecdf536146a73270581b931d7 SHA1: fa0c608260329701d75f266921d4562c1e7abba2 SHA256: 3a1b7947b94a59bad3438f9ead7d87c5d638cac785ecbfb80fb9ee895990b909 SHA512: 1d0573c9c62f42a22d2e19b6e418de3942b97eb0ee32d1d76d7bb1c77c388482dd3effafc856ffc77b8d926dcf86c15b077594823d136dd176838b37a6a7b141 Homepage: https://cran.r-project.org/package=infiltrodiscR Description: CRAN Package 'infiltrodiscR' (Minidisc Infiltrometer Data Management) A set of functions for the modeling of data derived from the Minidisc Infiltrometer device. It calculates cumulative infiltration and square root of time. Also, it calculates the A parameter based on soil physical properties. Package: r-cran-infiniumpurify Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 883 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats Filename: pool/dists/noble/main/r-cran-infiniumpurify_1.3.1-1.ca2404.1_all.deb Size: 872018 MD5sum: 3b4754df67b8488a676286a13d1b228f SHA1: 539a2e3c18734dcc2d40f8e4fcc32c5e5ab5797d SHA256: 46c2d5a0420f620cc126bf275f7cdc4e790e693fcdb45f8179c5d5c4ab64c52b SHA512: 7871cd33a214010fe2ee9519a182599d28bd4c56dfff49ddddf6765753c83e091012d81ea8610b3d4363911f0910fad5008b30490c102c29026e9630de1d38bf Homepage: https://cran.r-project.org/package=InfiniumPurify Description: CRAN Package 'InfiniumPurify' (Estimate and Account for Tumor Purity in Cancer Methylation DataAnalysis) The proportion of cancer cells in solid tumor sample, known as the tumor purity, has adverse impact on a variety of data analyses if not properly accounted for. We develop 'InfiniumPurify', which is a comprehensive R package for estimating and accounting for tumor purity based on DNA methylation Infinium 450k array data. 'InfiniumPurify' provides functionalities for tumor purity estimation. In addition, it can perform differential methylation detection and tumor sample clustering with the consideration of tumor purities. Package: r-cran-infix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-infix_0.1.0-1.ca2404.1_all.deb Size: 26364 MD5sum: b942a8811e7cafa91a82c4c41aef711a SHA1: 8893fc33382c62a443268747285aff364c3cb90f SHA256: 4d78d634ad57a669317e7b90819a725cc53b0fa9d2e4f3ff327bc4f0d62cfea4 SHA512: 0cfe666c447ef49295ad57cdb74053fb011c040190362d99061cd5d4f7fc1b2f447391ae83b7249efd818d0a52322fe8e59b34e777755fce1004aca39ced5ecb Homepage: https://cran.r-project.org/package=infix Description: CRAN Package 'infix' (Basic Infix Binary Operators) Contains a number of infix binary operators that may be useful in day to day practices. Package: r-cran-infixit Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-infixit_0.3.1-1.ca2404.1_all.deb Size: 485198 MD5sum: a2165756cc5e3bba1a939980e3bac12a SHA1: ab41d5d14b63ab11863cceba91e9c656d59cf8f8 SHA256: 39be0dc3116cc5676cf7717c58445c83ee2a4edf545a8f56a7195410f6f98baa SHA512: 378a150a1a699c5a3f1611caf363c32bea09aeaa5add5791926025eb3e58437c8873ea515a1bdd0b4bd97c2a78e46539f0e291bc03b0baeb3bb3ec5a9f6912d1 Homepage: https://cran.r-project.org/package=infixit Description: CRAN Package 'infixit' (Helpful Additional Infix Functions) Infix functions in R are those that comes between its arguments such as %in%, +, and *. These are useful in R programming when manipulating data, performing logical operations, and making new functions. 'infixit' extends the infix functions found in R to simplify frequent tasks, such as finding elements that are NOT in a set, in-line text concatenation, augmented assignment operations, additional logical and control flow operators, and identifying if a number or date lies between two others. Package: r-cran-inflater Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-inflater_0.1.3-1.ca2404.1_all.deb Size: 87406 MD5sum: 0ea1ea7069b3f5f898d7e3eb57b5d003 SHA1: 8208781196e7c237822d606246e59d92df5ccda8 SHA256: 1936320bd43d80f559c9c08c15b2e909838407ff4988ea61d4cea664ee3677f0 SHA512: b699ed7309f9c27944536af89564045f66aaf426b3a6a7c39400dfe4ab18251f7a1411f70a32d3548d05eaaa54c70bc16dd66dd447585044c25c8422fc76fdab Homepage: https://cran.r-project.org/package=inflateR Description: CRAN Package 'inflateR' (Inflation Adjustment for Historical Currency Values) Convert historical monetary values into their present-day equivalents using bundled CPI (Consumer Price Index) and GDP deflator data sourced from the World Bank Development Indicators. 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Package: r-cran-inflation Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seasonal Suggests: r-cran-covr Filename: pool/dists/noble/main/r-cran-inflation_0.1.0-1.ca2404.1_all.deb Size: 268792 MD5sum: 2712649db8940497db54ae4398430d0a SHA1: ee3b783b07aa24f687bc17f411430c93aa678c36 SHA256: 7e7a0bfdbcb7a4f7ad2e34ae50a2ff8f2c9ebaed59f2fed39b39f6748e88dde4 SHA512: 17220d4cea14a534d8ddc8f29cbb9ede24a075beaf22aa8fa5cd07c9f88531342b45aa3e76b47d7b096401a696147050890f31c8dff46cd5f086f7d01886ee2f Homepage: https://cran.r-project.org/package=Inflation Description: CRAN Package 'Inflation' (Core Inflation) Provides access to core inflation functions. Four different core inflation functions are provided. The well known trimmed means, exclusion and double weighing methods, alongside the new Triple Filter method introduced in Ferreira et al. (2016) . Package: r-cran-inflationkit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-inflationkit_0.1.0-1.ca2404.1_all.deb Size: 127128 MD5sum: 967655601af21bd9a278836cd265c8ce SHA1: 640a5ba60f87e1c3d305324a8e8dfe7b8bdb4cdf SHA256: 5a67d7667fb9a95294e13610b26cc00a7369a1332f0d9ab61e2209aeda0690c3 SHA512: 276f133a139939b9c371a7dbd707ff34306318b426d30e2461688c366f45450f2a0c63602d652ce5705308a327a30cf6535b9fe0eebf55710b41faa50f76d743 Homepage: https://cran.r-project.org/package=inflationkit Description: CRAN Package 'inflationkit' (Inflation Decomposition, Core Measures, and Trend Estimation) Tools for analysing inflation dynamics. Computes weighted contributions of price index components, core inflation measures (trimmed mean, weighted median, exclusion-based) following Bryan and Cecchetti (1994) , inflation persistence via sum-of-AR-coefficients, diffusion indices, Phillips curve estimation, breakeven inflation, and trend inflation using the Beveridge-Nelson decomposition and Hodrick-Prescott filter. All functions are pure computation and work with price data from any source. Package: r-cran-inflection Architecture: all Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-inflection_1.3.7-1.ca2404.1_all.deb Size: 279678 MD5sum: a5597f7f290881244adfcb2b9cd8a02a SHA1: 8e0c71b8e3f8671aef9f9d6eeb19688ec8950b6b SHA256: 32c0bffb1c9af508d259040c585c8bc0382d5d4e382575a024ad33e06e1f8d6a SHA512: 45ffbed52487f441e6d35b89d44f56569a73706e39f5a911783e958dbf22f02c0e3398f0201ece68e9b81aa4ab54b655eeda1c36884c296c54854ad727b45d7c Homepage: https://cran.r-project.org/package=inflection Description: CRAN Package 'inflection' (Finds the Inflection Point of a Curve) Implementation of methods Extremum Surface Estimator (ESE) and Extremum Distance Estimator (EDE) to identify the inflection point of a curve . Christopoulos, DT (2014) . Christopoulos, DT (2016) . Christopoulos, DT (2016) . Package: r-cran-inflectssp Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-data.table, r-cran-plotrix, r-cran-tidyr, r-cran-ggplot2, r-cran-xlsx, r-cran-httr, r-cran-jsonlite, r-cran-ggally, r-cran-network, r-cran-rcolorbrewer, r-cran-svglite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-inflectssp_1.6-1.ca2404.1_all.deb Size: 83464 MD5sum: 68d2c19f29c4e7039ca9c7f51fd07cb1 SHA1: 445f6ea52404706789f261988695f09c9e90197e SHA256: 6f9a007304f6bb17dcc4edb017d76861075c8f82eb48249dacceecde74d1ccdf SHA512: 22f7fb71e7c6e7e23b139348813e1d59719708f209a0986a9c1400beaf99ca42b9c42ad670b6bff3f84582cb95fd3dbc6a9f852f864615274aa64ec3649ddd47 Homepage: https://cran.r-project.org/package=InflectSSP Description: CRAN Package 'InflectSSP' (Melt Curve Fitting and Melt Shift Analysis) Analyzes raw abundance data from a cellular thermal shift experiment and calculates melt temperatures and melt shifts for each protein in the experiment. McCracken (2022) . Package: r-cran-inflongitudinal Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-mice Filename: pool/dists/noble/main/r-cran-inflongitudinal_0.1.0-1.ca2404.1_all.deb Size: 812152 MD5sum: d3b6f587bdbab523e13e677b518a08c1 SHA1: 72ee9527cbf6216889e6c876ed3584d5ef54959a SHA256: 5c89c90ac5fb34bc97b7534d1976fe32f75411d3a54db89acc614a588c509c68 SHA512: 3d29d164d4f573e1017e41e83b96519f2a601a825f8f5ff6bff8e0973e0e58058efb4de5bc3224741f626606f8471ed4ac22766e0fcd621893ff431eada57d9a Homepage: https://cran.r-project.org/package=Inflongitudinal Description: CRAN Package 'Inflongitudinal' (Detecting Influential Subjects in Longitudinal Data) Provides methods for detecting influential subjects in longitudinal data, particularly when observations are collected at irregular time points. The package identifies subjects whose response trajectories deviate substantially from population-level patterns, helping to diagnose anomalies and undue influence on model estimates. Package: r-cran-influence.me Architecture: all Version: 0.9-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-lattice Filename: pool/dists/noble/main/r-cran-influence.me_0.9-10-1.ca2404.1_all.deb Size: 84278 MD5sum: 2897802149e25bf20dbdb817f780294b SHA1: 79b8e01b5c29b55a26e00d4ecef690837b21b31a SHA256: 200bcb7004a72ee3a0e75c58193a2223b0b369a162a98e318b26b2776bed8ebd SHA512: 345966fea37e87a7b47138295ac263c7713c31518cb7f99ea8939347860b8255c796f19dea095d13de582df1749a43bcf007b153a44e3ee0d4b72bd201f5fd78 Homepage: https://cran.r-project.org/package=influence.ME Description: CRAN Package 'influence.ME' (Tools for Detecting Influential Data in Mixed Effects Models) Provides a collection of tools for detecting influential cases in generalized mixed effects models. It analyses models that were estimated using 'lme4'. The basic rationale behind identifying influential data is that when single units are omitted from the data, models based on these data should not produce substantially different estimates. To standardize the assessment of how influential a (single group of) observation(s) is, several measures of influence are common practice, such as Cook's Distance. In addition, we provide a measure of percentage change of the fixed point estimates and a simple procedure to detect changing levels of significance. Package: r-cran-influence.sem Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan Filename: pool/dists/noble/main/r-cran-influence.sem_2.4-1.ca2404.1_all.deb Size: 78882 MD5sum: bc299e1a69f317e53b6a3535c7693bfd SHA1: 0d2915ecdb1b3cc226147dddf211c7a9f02200ce SHA256: 34ba091788f0aeb984453feb039617ea2fdea5574b8cd3ed61ac7ffe0dc670d1 SHA512: e759e03d8dbcbd03c40f16e08b32a872b3806ea1cc21d9bb68f8670a57951d65e3e8fc6dfdf6d407facf22fea3cdd08c03548ca938c4795a33a47c3812b2d818 Homepage: https://cran.r-project.org/package=influence.SEM Description: CRAN Package 'influence.SEM' (Case Influence in Structural Equation Models) A set of tools for evaluating several measures of case influence for structural equation models. Package: r-cran-influenceauc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-geigen, r-cran-ggplot2, r-cran-ggrepel, r-cran-rocr Filename: pool/dists/noble/main/r-cran-influenceauc_0.1.2-1.ca2404.1_all.deb Size: 64184 MD5sum: b4e3f321d0b9e4a0f46b51daa46abbe5 SHA1: 1289759a47976d578feb834307fe317aabfa810b SHA256: 5fe9152d5065e3a2afd8b3be8d8acc69292a443178f0ef5361311413d7d800c1 SHA512: 6b736d94f4c86309aef5b1839f3ae3168878aca097bfd23c7232c60729d500a2950e068cddbb575e84aeb1aa433135f425570a762029c4c49eb960d65ca2d317 Homepage: https://cran.r-project.org/package=influenceAUC Description: CRAN Package 'influenceAUC' (Identify Influential Observations in Binary Classification) Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization. Package: r-cran-influenceborrowing Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-krls Filename: pool/dists/noble/main/r-cran-influenceborrowing_0.1.0-1.ca2404.1_all.deb Size: 59310 MD5sum: 34edc6115abf1461719f6ae03b418f4b SHA1: c70677a9ee3cdb35289af4258787e82e2b9981e1 SHA256: b66122513903c68cf9ecbbe11b6fd8f9d8f62f6b447a097d8589bae5316d66f4 SHA512: b0e6de85128b922ae7acb8ae5353940370653c2d3ab409ec18bac919b8c309ab969a04172b6dd9a284b7aff09ce1f40803d21bb2111d1fe926bb8bd805deee6c Homepage: https://cran.r-project.org/package=InfluenceBorrowing Description: CRAN Package 'InfluenceBorrowing' (Adaptive Influence-Based Borrowing for Hybrid Control Trials) Implements the adaptive influence-based borrowing framework proposed by Qinwei Yang, Jingyi Li, Peng Wu, and Shu Yang (2026+) in the paper ``Improving Treatment Effect Estimation in Trials through Adaptive Borrowing of External Controls" for augmenting Randomized Controlled Trials (RCTs) with External Control (EC) data. This package provides a comprehensive workflow to: (1) quantify the comparability of external control samples using influence scores approximated via the influence function of the M-estimator; (2) construct candidate borrowing subsets and select the optimal subset that minimizes the Mean Squared Error (MSE); and (3) calibrate systematic differences in external outcomes using R-learner methods implemented via Ordinary Least Squares or Kernel Ridge Regression. Package: r-cran-influential Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1696 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-janitor, r-cran-ranger, r-cran-foreach, r-cran-doparallel, r-cran-ggplot2, r-cran-rcpp Suggests: r-cran-hmisc, r-cran-mgcv, r-cran-nortest, r-cran-nns, r-cran-biocmanager, r-cran-readr, r-cran-shiny, r-cran-shinythemes, r-cran-shinywidgets, r-cran-shinyjs, r-cran-shinycssloaders, r-cran-colourpicker, r-cran-magrittr, r-cran-dt, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-influential_2.3.0-1.ca2404.1_all.deb Size: 1084954 MD5sum: 8e3f1fbee06319bf142f6578c1a9b04f SHA1: f3a65db5b40387ff745fc7e194abb36151fb4971 SHA256: 40871012d1e17bf678266a2b6f1a440bb7853cf83fd7b5733f432c63bfabcc6f SHA512: 8d9366542b535c36b3b5bc93f83a91d042d7d1ca4c3ef22d0b11ecf814c7bf2f8c9f34f6d7ee6469d2fca055cc1d5655a61f67b7e5c922c529f094119d90ba63 Homepage: https://cran.r-project.org/package=influential Description: CRAN Package 'influential' (Identification and Classification of the Most Influential Nodes) Provides functions for classification and ranking of candidate features, reconstruction of networks from adjacency matrices and data frames, topological analysis, and calculation of centrality measures. The package includes the SIRIR model, which combines leave-one-out cross-validation with the conventional SIR model to rank vertex influence in an unsupervised manner. Additional functions support assessment of dependence and correlation between network centrality measures, as well as estimation of conditional probabilities of deviation from their corresponding means in opposite directions. Package: r-cran-influxdbclient Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-jsonlite, r-cran-bit64, r-cran-nanotime, r-cran-plyr Suggests: r-cran-testthat, r-cran-httptest Filename: pool/dists/noble/main/r-cran-influxdbclient_0.1.2-1.ca2404.1_all.deb Size: 372528 MD5sum: 6423e2dbb6844caf1d3c40d29af6e11e SHA1: 6b6dc8092e4be5a2c435ef2d4091653faa6362f4 SHA256: 24994ba5fe141b72334ba23ee768b88c9b105e5934f9308141703fc291fcb287 SHA512: d17e0fce37c21633f7029d5e347d55eb3e7d06fb28526ce80658e08a965957f46055204c32cf55ad8e55888e3f717f641e9148076faffac85f39f319f1e70ed5 Homepage: https://cran.r-project.org/package=influxdbclient Description: CRAN Package 'influxdbclient' ('InfluxDB' 2.x Client) 'InfluxDB' 2.x time-series database client. Supports both 'InfluxDB' OSS () and Cloud () version. 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Gamma Imputation described in and Risk Score Imputation described in . Package: r-cran-informativesci Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gmcp, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-informativesci_1.0.4-1.ca2404.1_all.deb Size: 109786 MD5sum: 0830a7bff1ac80fce91a4a9defe2ccfa SHA1: aee7a10e591efd6adc8bb9d1f80c394cf3b43ef1 SHA256: d2dc2c3ecaeaabb10c568207a19235f0029e8478dbe0689bf9b4863b52027317 SHA512: acd4a734fc85c4a5e2a9d193e14e0bd7d4d6f945d227773ec7715ffc559c8daaf450c3fb0486bbb1160507f66595a75c8fbc0ba8b735afbccfdf571436e8b154 Homepage: https://cran.r-project.org/package=informativeSCI Description: CRAN Package 'informativeSCI' (Informative Simultaneous Confidence Intervals) Calculation of informative simultaneous confidence intervals for graphical described multiple test procedures and given information weights. Bretz et al. 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Package: r-cran-infoset Architecture: all Version: 4.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1559 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-colorspace, r-cran-dendextend, r-cran-quadprog, r-cran-mixtools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-infoset_4.1.1-1.ca2404.1_all.deb Size: 1479374 MD5sum: 0952e26d494134c0879a23510425cc83 SHA1: bda35a7e1c040f9a0defc84fff7447e2d7bdcd9f SHA256: f6bf46acbd3e345f4f1e4b307d7bfe4fec1a7aea5213a45227a0d6159e375d11 SHA512: f8c399cce7d775b8f63363c2f20b1eefb7f1e321fcbd7a744955a9783bcf88471b2eae5ecad33e344a3bb47e91f17b0580dfec99f763a78c1659ff138dbf98d2 Homepage: https://cran.r-project.org/package=INFOSET Description: CRAN Package 'INFOSET' (Computing a New Informative Distribution Set of Asset Returns) Estimation of the most-left informative set of gross returns (i.e., the informative set). 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This separates the responsibilities of use and construction. Package: r-cran-injuryseverityscore Architecture: all Version: 0.0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-injuryseverityscore_0.0.0.2-1.ca2404.1_all.deb Size: 36034 MD5sum: a9ea765dfcf14c5b7ee7a3e59dc94472 SHA1: fd736d43b9c86ce4d4fbb349f3aa12ee9f5471ab SHA256: 496b58ed3f6c220a4942e084bc78dbb466db7a6e58ec516e9783de8f5e84937f SHA512: 1bde38e9fc95753f815271dbcaa0b0e849919c44e1a128d0cfa872328d45049a568be5fbbfe7f28dd6febf16107333a7c991f42762db8cc3e726edb8ed8ffbcd Homepage: https://cran.r-project.org/package=InjurySeverityScore Description: CRAN Package 'InjurySeverityScore' (Translate ICD-9 into Injury Severity Score) Calculate the injury severity score (ISS) based on the dictionary in 'ICDPIC' from . The original code was written in 'STATA 11'. The original 'STATA' code was written by David Clark, Turner Osler and David Hahn. I implement the same logic for easier access. Ref: David E. Clark & Turner M. Osler & David R. Hahn, 2009. "ICDPIC: Stata module to provide methods for translating International Classification of Diseases (Ninth Revision) diagnosis codes into standard injury categories and/or scores," Statistical Software Components S457028, Boston College Department of Economics, revised 29 Oct 2010. Package: r-cran-injurytools Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1935 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-lubridate, r-cran-metr, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-withr Suggests: r-cran-coxme, r-cran-gridextra, r-cran-kableextra, r-cran-knitr, r-cran-lme4, r-cran-mass, r-cran-pscl, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-spelling, r-cran-survival, r-cran-survminer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-injurytools_2.0.1-1.ca2404.1_all.deb Size: 1257956 MD5sum: 634eb5fe991a93ded94912b9924e4fea SHA1: ec6af571e2774378eb082d7fbeacada4d009fc69 SHA256: e3e997f9e4fd5f4c28bcad8a0d7c536b4da7bfbf8ce990346b7363c7dd46dbdf SHA512: cc923c8dbd7b0c0e65f4dc04717725c2ba13ee6389486ba24d364b7e924d9b4f13af63e32eaa212613a16ad705702cbf186fde06c9336231fa00306cc9aeddc0 Homepage: https://cran.r-project.org/package=injurytools Description: CRAN Package 'injurytools' (A Toolkit for Sports Injury and Illness Data Analysis) Sports Injury Data analysis aims to identify and describe the magnitude of the injury problem, and to gain more insights (e.g. determine potential risk factors) by statistical modelling approaches. The 'injurytools' package provides standardized routines and utilities that simplify such analyses. It offers functions for data preparation, informative visualizations and descriptive and model-based analyses. Package: r-cran-inkar Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2926 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-tibble, r-cran-dplyr, r-cran-rlang, r-cran-cli, r-cran-stringdist, r-cran-tidyr Suggests: r-cran-readxl, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-geodata, r-cran-ggplot2, r-cran-httptest2 Filename: pool/dists/noble/main/r-cran-inkar_0.6.2-1.ca2404.1_all.deb Size: 672874 MD5sum: 034cebf836ceb374093bb546e721125d SHA1: 71f4b136f3656673889fea0ca2beff909bd37645 SHA256: 7025ec067bedd1e1c2a2e8e5d51f47b9ff232b6e39da76c932bef74884485de5 SHA512: 12d2407523a2f7dc9bd224089ef440a9d77dddf36a98ab0ad38ec4281d04724db314610546c82905d772bcf793aeda831820acbe86ef0d0b6e9be59a719fda44 Homepage: https://cran.r-project.org/package=inkaR Description: CRAN Package 'inkaR' (Download and Analyze Spatial Development Data from 'INKAR') A professional R interface to download and analyze spatial development indicators from the 'BBSR' 'INKAR' (Indikatoren und Karten zur Raum- und Stadtentwicklung) database. Features a bilingual interactive wizard, fuzzy search, multi-indicator downloads with automatic tidy merging (long/wide), robust disk caching, and premium 'ggplot2' themes for regional mapping. Package: r-cran-inlabma Architecture: all Version: 0.1-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-matrix, r-cran-spdep Filename: pool/dists/noble/main/r-cran-inlabma_0.1-12-1.ca2404.1_all.deb Size: 85552 MD5sum: faa43a6492efa9b3cc92cfa10c9bc2bf SHA1: d4cd42a49bc3a2ae4cd224b0ba74ddbd17cfbdf5 SHA256: 5ca71a121e0f16e5c45170344a5ef36208c5e2a2cdc6dc5cd87520ddc9bc2384 SHA512: 429d7108430afec3fde9f0ac13fd69ffa3ffd6a744cc3c3103a4b68f93d2293ad938aad994e39de5a5af616e1dd3083b39479bcec21314365cf9249e8f8affac Homepage: https://cran.r-project.org/package=INLABMA Description: CRAN Package 'INLABMA' (Bayesian Model Averaging with INLA) Fit Spatial Econometrics models using Bayesian model averaging on models fitted with INLA. The INLA package can be obtained from . Package: r-cran-inlabru Architecture: all Version: 2.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3771 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmesher, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-matrixmodels, r-cran-matrix, r-cran-plyr, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-withr Suggests: r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-maps, r-cran-mgcv, r-cran-patchwork, r-cran-raster, r-cran-rcolorbrewer, r-cran-rgl, r-cran-rmarkdown, r-cran-scales, r-cran-scoringrules, r-cran-shiny, r-cran-sn, r-cran-sp, r-cran-spatstat.geom, r-cran-spatstat.data, r-cran-sphereplot, r-cran-splancs, r-cran-terra, r-cran-tidyterra, r-cran-testthat, r-cran-tidyr, r-cran-diagrammer Filename: pool/dists/noble/main/r-cran-inlabru_2.13.0-1.ca2404.1_all.deb Size: 3162212 MD5sum: a5dca8bff95c45e57192f30238753873 SHA1: 598b40f8891292fd8956f46f46d88797e9ceca0c SHA256: 4e40231983570a5d99a267d599a7743f82fb86ce5f41add68ba201d2b04e1315 SHA512: 67d8ea58e4f771440d3b3ada20239423d56be44c2bce207852110a4071b08ab92346b22df28c7f6cca90e17b01ecba9931182cbe7e247402aebb06832788b487 Homepage: https://cran.r-project.org/package=inlabru Description: CRAN Package 'inlabru' (Bayesian Latent Gaussian Modelling using INLA and Extensions) Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) . Package: r-cran-inlajoint Architecture: all Version: 25.11.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1078 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-lme4, r-cran-ggplot2, r-cran-matrix, r-cran-nlme, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-survival, r-cran-jmbayes2, r-cran-rstanarm, r-cran-frailtypack, r-cran-smcure, r-cran-fmesher Filename: pool/dists/noble/main/r-cran-inlajoint_25.11.10-1.ca2404.1_all.deb Size: 1023756 MD5sum: 73a149a60da8fd53b9f725927c01199a SHA1: ca8e92e0478f9f4d656c4193eff471fee69618a4 SHA256: 1a9e5b81f8466defa9200cdf9727abfb1e956114f1c8d22e657df93cff6e79e5 SHA512: 0e49339ff16ae12be181c2164ae5e1e4db2cd28a1c69d7a06771bc06c96587d0b0ad78965d5b90db1ad4a9a7f2e01cfc21671c0407d46ce2c4b95aa957e69379 Homepage: https://cran.r-project.org/package=INLAjoint Description: CRAN Package 'INLAjoint' (Multivariate Joint Modeling for Longitudinal and Time-to-EventOutcomes with 'INLA') Estimation of joint models for multivariate longitudinal markers (with various distributions available) and survival outcomes (possibly accounting for competing risks) with Integrated Nested Laplace Approximations (INLA). The flexible and user friendly function joint() facilitates the use of the fast and reliable inference technique implemented in the 'INLA' package for joint modeling. More details are given in the help page of the joint() function (accessible via ?joint in the R console) and the vignette associated to the joint() function (accessible via vignette("INLAjoint") in the R console). Package: r-cran-inlamemi Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1655 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-testthat, r-cran-tibble, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-inlamemi_1.1.0-1.ca2404.1_all.deb Size: 918486 MD5sum: 6ece40ab548ca207f721f029fcaae198 SHA1: 5f8191cfa56e6b268c0f3c331a2be7ac254f7971 SHA256: 84cebe84ec76a50489d24f35b3cd43fb0824db71597b8356bc90827a977f337f SHA512: 594690bcd3eb0c6963421b941a8cc213ef287479ffd54ece159082b6075be3eb043ec95de89831660eec549c76d791fbc4f9212065fef68fe9eff49e41344422 Homepage: https://cran.r-project.org/package=inlamemi Description: CRAN Package 'inlamemi' (Missing Data and Measurement Error Modelling in INLA) Facilitates fitting measurement error and missing data imputation models using integrated nested Laplace approximations, according to the method described in Skarstein, Martino and Muff (2023) . See Skarstein and Muff (2024) for details on using the package. Package: r-cran-inlavaan Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-lavaan Suggests: r-cran-blavaan, r-cran-ggplot2, r-cran-knitr, r-cran-lme4, r-cran-numderiv, r-cran-qrng, r-cran-quarto, r-cran-sn, r-cran-testthat, r-cran-ucminf Filename: pool/dists/noble/main/r-cran-inlavaan_0.2.4-1.ca2404.1_all.deb Size: 2259148 MD5sum: 221cf6faec48fcb11a2ba00b72e9788d SHA1: ed4933417d90197e341094a5406486f4a19b9c2e SHA256: 2498ac930eb491a3119e5d10da59ece8c41a6f3da8ebcc711c789d54f5fac5aa SHA512: 798e5b3c993f66b77328660f5e450e9538f5a8437ba5ffc16c0081798f4880b8354c1b41d0620c05f3c501fd3808b712ebf9c6d739f376643ad27a8ca2d629a7 Homepage: https://cran.r-project.org/package=INLAvaan Description: CRAN Package 'INLAvaan' (Approximate Bayesian Latent Variable Analysis) Implements approximate Bayesian inference for Structural Equation Models (SEM) using a custom adaptation of the Integrated Nested Laplace Approximation (Rue et al., 2009) as described in Jamil and Rue (2026a) . 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Package: r-cran-inlcolor Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1968 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-rlang, r-cran-scales Suggests: r-cran-connectapi, r-cran-covr, r-cran-dichromat, r-cran-httr, r-cran-pkgbuild, r-cran-pkgdown, r-cran-pkgload, r-cran-rcmdcheck, r-cran-renv, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rsconnect, r-cran-tinytest, r-cran-tinytex, r-cran-xtable Filename: pool/dists/noble/main/r-cran-inlcolor_1.0.6-1.ca2404.1_all.deb Size: 328686 MD5sum: 4316942194030c9e3cee47dbe64cf717 SHA1: 4a4ccf9655f1dd20313516376646c777840d10df SHA256: d2bbb04fb310f68458f2d346d077bf24059f5578ddbbef20805d11ed0f9b01fb SHA512: a284547cf771d74ee2d2c4e01a84e009f7832bcd788f7470da71924d356dc538605dbd9851c1c13c2eeee2bb2f9cb1fc0e8884d4676d8467598d6348af5f43fc Homepage: https://cran.r-project.org/package=inlcolor Description: CRAN Package 'inlcolor' (Color Schemes for the USGS Idaho National Laboratory ProjectOffice) A collection of functions for creating color schemes. 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Package: r-cran-inldata Architecture: all Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5626 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-sf, r-cran-stringi, r-cran-terra Suggests: r-cran-archive, r-cran-arrow, r-cran-connectapi, r-cran-covr, r-cran-curl, r-cran-dataretrieval, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-dm, r-cran-fontawesome, r-cran-htmltools, r-cran-htmlwidgets, r-cran-httr, r-cran-inlcolor, r-cran-jsonlite, r-cran-knitr, r-cran-pkgload, r-cran-pkgbuild, r-cran-pkgdown, r-cran-plotrix, r-cran-rappdirs, r-cran-rcmdcheck, r-cran-reactable, r-cran-renv, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rsconnect, r-cran-tinytest, r-cran-v8, r-cran-webmap, r-cran-writexl, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-inldata_1.2.7-1.ca2404.1_all.deb Size: 5625184 MD5sum: d21ef6e87afe1db5c94d8eb354696a02 SHA1: e963cd8357ba2c202e834f1325ad43c8f3038f80 SHA256: 2662f48aa77efc3c7701be2af34209bec00fe8cd2b1562dcce8a29ed15e5c82a SHA512: aca5d7524c16b66478c00210ddd833b11ae203c0f59bc5c47899db70d9e702673e2b0f9899b719199d6ebda226f62463ee543090b66e262bdbef8d7c39447ada Homepage: https://cran.r-project.org/package=inldata Description: CRAN Package 'inldata' (Collection of Datasets for the USGS-INL Monitoring Networks) A collection of analysis-ready datasets for the U.S. Geological Survey - Idaho National Laboratory (USGS-INL) groundwater and surface-water monitoring networks, administered by the USGS-INL Project Office in cooperation with the U.S. Department of Energy. The data collected from wells and surface-water stations at the Idaho National Laboratory and surrounding areas have been used to describe the effects of waste disposal on water contained in the eastern Snake River Plain aquifer, located in the southeastern part of Idaho, and the availability of water for long-term consumptive and industrial use. The package includes long-term monitoring records dating back to measurements from 1922. Geospatial data describing the areas from which samples were collected or observations were made are also included in the package. Bundling this data into a single package significantly reduces the magnitude of data processing for researchers and provides a way to distribute the data along with its documentation in a standard format. Geospatial datasets are made available in a common projection and datum, and geohydrologic data have been structured to facilitate analysis. 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Package: r-cran-inlinedocs Architecture: all Version: 2023.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-future.apply, r-cran-future, r-cran-r.methodss3 Filename: pool/dists/noble/main/r-cran-inlinedocs_2023.9.4-1.ca2404.1_all.deb Size: 240564 MD5sum: 119cc6bb4e0db841edb9c7fcd493e678 SHA1: 7365e25611ef772145f91c236cacf1b2b306477e SHA256: 2f07a3c88e78e124b706850c5f732095d6d43280889ef15c69f5e8afe5caeef0 SHA512: 2f0d77cff025765b09d380981cc897ddb21e56f64f4e41a882c1e9dedf64c9b100a6a0b02394c9dc1f2966ba7908a4fcb2c6e31900d5df23038e33e9d1b63034 Homepage: https://cran.r-project.org/package=inlinedocs Description: CRAN Package 'inlinedocs' (Convert Inline Comments to Documentation) Generates Rd files from R source code with comments. 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Implemented methods are, e.g., 'Connection Weights' described by Olden et al. (2004) , layer-wise relevance propagation ('LRP') described by Bach et al. (2015) , deep learning important features ('DeepLIFT') described by Shrikumar et al. (2017) and gradient-based methods like 'SmoothGrad' described by Smilkov et al. (2017) , 'Gradient x Input' or 'Vanilla Gradient'. Details can be found in the accompanying scientific paper: Koenen & Wright (2024, Journal of Statistical Software, ). 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R is great for installing software. Through the 'installr' package you can automate the updating of R (on Windows, using updateR()) and install new software. Software installation is initiated through a GUI (just run installr()), or through functions such as: install.Rtools(), install.pandoc(), install.git(), and many more. The updateR() command performs the following: finding the latest R version, downloading it, running the installer, deleting the installation file, copy and updating old packages to the new R installation. 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Package: r-cran-insulin.secretion Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-lifecycle, r-cran-npreg, r-cran-rlang Filename: pool/dists/noble/main/r-cran-insulin.secretion_0.0.2-1.ca2404.1_all.deb Size: 47848 MD5sum: b2c57150db949030bd50751a99885101 SHA1: 96e1bdf6d7d4a588882f1fa92f34f9c3f0a07602 SHA256: fa00089f5d01da353cb2ab1042e664be2d9601b03111ade9e1dd49892462fa7d SHA512: b8b2be589e54be85d7937f7ea1a3e1383669b01940a05b8cba67c23bae529f6e5732e603d0af62e8c03b0d6bce744a2c93811ecbc14f99f1c93b369fbd13dffd Homepage: https://cran.r-project.org/package=insulin.secretion Description: CRAN Package 'insulin.secretion' (Insulin Secretion Rate Deconvolution) Calculates insulin secretion rates from C-peptide values based on the methods described in Van Cauter et al. (1992) . 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It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center. 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The proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models. 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First, this solves Fredholm integral equations of the first kind ($f(s) = \int_a^b K(s, y) g(y) dy$) using methods described by Twomey (1963) . Second, this solves Volterra integral equations of the first kind ($f(s) = \int_0^s K(s,y) g(t) dt$) using methods from Betto and Thomas (2021) . Third, this solves Voltera integral equations of the second kind ($g(s) = f(s) + \int_a^s K(s,y) g(y) dy$) using methods from Linz (1969) . Package: r-cran-interactionpower Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 547 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-polynom, r-cran-chngpt, r-cran-rlang, r-cran-tidyr, r-cran-ggbeeswarm, r-cran-matrix Filename: pool/dists/noble/main/r-cran-interactionpower_0.2.4-1.ca2404.1_all.deb Size: 440380 MD5sum: f614c30b7ba19ac0d737381d8981eeb7 SHA1: ee4ae69c51077943be0c3188494bcf46755c40c3 SHA256: 46ef95cca3c84e1fa7419ecf75522ac33bd141ae67e0b2c9878b89270fb47b50 SHA512: 6fc9a3465fce9ea5069aa1161f6399cdb8c1302fda9243b7d9cda3b0e5d9d1d131714fc26080423fe85d1755326b996d77c9e0ead9e0cfeb9e1f8b85fbc8b5c3 Homepage: https://cran.r-project.org/package=InteractionPoweR Description: CRAN Package 'InteractionPoweR' (Power Analyses for Interaction Effects in Cross-SectionalRegressions) Power analysis for regression models which test the interaction of two or three independent variables on a single dependent variable. Includes options for correlated interacting variables and specifying variable reliability. Two-way interactions can include continuous, binary, or ordinal variables. Power analyses can be done either analytically or via simulation. Includes tools for simulating single data sets and visualizing power analysis results. The primary functions are power_interaction_r2() and power_interaction() for two-way interactions, and power_interaction_3way_r2() for three-way interactions. The function run_pos_power_search() provides a stability analysis for two-way interactions. Please cite as: Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR, Olino TM (2023). "Tutorial: Power analyses for interaction effects in cross-sectional regressions." . If you use the stability analyses, please cite: Castillo A, Miller JD, Vize C, Baranger DAA, Lynam DR. "When Do Interaction/Moderation Effects Stabilize in Linear Regression?". 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It also estimates confidence interval for the trio of additive interaction measures using the delta method (see Hosmer and Lemeshow (1992), []), variance recovery method (see Zou (2008), []), or percentile bootstrapping (see Assmann et al. (1996), []). 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Methods used in the package refer to Harrell Jr FE (2015, ISBN:9783319330396); Durrleman S, Simon R. (1989) ; Greenland S. (1995) . 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The package includes calculations of inventory metrics, stock-out calculations and ABC analysis calculations. The package includes revenue management techniques such as Multi-product optimization,logit and polynomial model optimization. The functions are referenced from : 1-Harris, Ford W. (1913). "How many parts to make at once". Factory, The Magazine of Management. 2- Nahmias, S. Production and Operations Analysis. McGraw-Hill International Edition. 3-Silver, E.A., Pyke, D.F., Peterson, R. Inventory Management and Production Planning and Scheduling. 4-Ballou, R.H. Business Logistics Management. 5-MIT Micromasters Program. 6- Columbia University course for supply and demand analysis. 8- Price Elasticity of Demand MATH 104,Mark Mac Lean (with assistance from Patrick Chan) 2011W For further details or correspondence :, . 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The functions can generate independent samples from the closed-form posterior distribution using the inverse stable prior. Inverse stable is a non-conjugate prior for a parameter of an exponential subclass of discrete and continuous data distributions (e.g. Poisson, exponential, inverse gamma, double exponential (Laplace), half-normal/half-Gaussian, etc.). The prior class provides flexibility in capturing a wide array of prior beliefs (right-skewed and left-skewed) as modulated by a parameter that is bounded in (0,1). The generated samples can be used to simulate the prior and posterior predictive distributions. More details can be found in Cahoy and Sedransk (2019) . The package can also be used as a teaching demo for introductory Bayesian courses. 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Includes capabilities to compare multiple series and fit both additive and multiplicative models. Used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Holt (1957) , Winters (1960) , Cleveland, Cleveland, & Terpenning (1990) "STL: A Seasonal-Trend Decomposition Procedure Based on Loess". 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(1951) ); within period analysis via various rankings and coefficients (Sonis and Hewings (2006) , Blair and Miller (2009) , Antras et al (2012) , Hummels, Ishii, and Yi (2001) ); across period analysis with impact analysis (Dietzenbacher, van der Linden, and Steenge (2006) , Sonis, Hewings, and Guo (2006) ); and a variety of table operators. 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The package requires two compulsory user inputs (raw CEPPI BACI trade data, and any acceptable ISO country code) and has 4 optional user inputs (a value chain map, chosen complexity method, number of iterations to be performed, and a trade digit level). Various metrics are calculated, such as Economic- and Product complexity, distance, opportunity gain, and inequality metrics, to facilitate better decision making regarding industrial policy making. 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Package: r-cran-iosmooth Architecture: all Version: 0.94-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-iosmooth_0.94-1.ca2404.1_all.deb Size: 59658 MD5sum: c4b6f501caa97dce88b097877c351ba6 SHA1: 9d0372d3449d15d50c545f1be411c249e799bd4c SHA256: e4f26a99b3aacdada3a4b84e4688829275762d716473ca9edc27334aed729eff SHA512: 5f9b25b26d00ee96d464b347f0a4c616397e3a95b4fe2bd2122332d9632233bbf94a63ba6acb52c3f057ddf74600c63d1a34a8dab7e0283f02ce31f010003605 Homepage: https://cran.r-project.org/package=iosmooth Description: CRAN Package 'iosmooth' (Functions for Smoothing with Infinite Order Flat-Top Kernels) Density, spectral density, and regression estimation using infinite order flat-top kernels. Package: r-cran-iotables Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2521 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-eurostat, r-cran-forcats, r-cran-glue, r-cran-kableextra, r-cran-knitr, r-cran-lubridate, r-cran-magrittr, r-cran-readxl, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-covr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-iotables_0.9.4-1.ca2404.1_all.deb Size: 1861650 MD5sum: b8f05d34468c6c922c7ff3f82f160f90 SHA1: 0cfbe6b07ce278ab24e9a79878c53f0278d89595 SHA256: 94491d6f0213b656018f86a13926b71abc897d0b6cf28b2d6fe334496b720f1f SHA512: b3f80ee55d1af12d00dc4b24af9a8a0a11954b420db49b6d83510e7082ce4659ede5f960a4ce9b9943f9eb28aa53cfffea85dc3b4cf2b4abe453fa5a62116fb0 Homepage: https://cran.r-project.org/package=iotables Description: CRAN Package 'iotables' (Reproducible Input–Output Economics Analysis, Economic andEnvironmental Impact Assessment with Empirical Data) Pre-processing and basic analytical tasks for working with Eurostat's symmetric input–output tables, and basic input–output economics calculations. Part of rOpenGov for open source open government initiatives. Package: r-cran-ip2location.io Architecture: all Version: 0.0.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-mockery Filename: pool/dists/noble/main/r-cran-ip2location.io_0.0.0-2-1.ca2404.1_all.deb Size: 21666 MD5sum: 570d609a3b9c4f6b94580e4e91e3ef58 SHA1: 0648a48b64fb6bc63d9271ce6e48f61bb23fccf4 SHA256: 868502aee7af31cfe29e8521ff78cb3ca4cdb292d31d6d5eea57d8e13d457aca SHA512: d1147bc33329c426e8bdf7fde83028e0237ce52935486d1bd24282d711f715410aea12466755d325ebd3543b58bdcf3edb19d835cbbfba7a2eda412703743d00 Homepage: https://cran.r-project.org/package=ip2location.io Description: CRAN Package 'ip2location.io' (Batch IP Data Retrieval and Storage Using 'IP2Location.io') A system for submitting multiple IP information queries to 'IP2Location.io'’s IP Geolocation API and storing the resulting data in a dataframe. You provide a vector of IP addresses and your 'IP2Location.io' API key. The package returns a dataframe with one row per IP address and a column for each available data field (data fields not included in your API plan will contain NAs). This is the second submission of the package to CRAN. Package: r-cran-ip2location Architecture: all Version: 8.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-jsonlite, r-cran-ggplot2, r-cran-maps, r-cran-scales Filename: pool/dists/noble/main/r-cran-ip2location_8.1.3-1.ca2404.1_all.deb Size: 29262 MD5sum: 438bb84e507ec3bd4ad9e65b4f7c8e4e SHA1: cdb3e7c623d288c09478c06683cd23b043fa6854 SHA256: 5264271e3bd02993a9dc57e802bff47de2a87412a447a47d87c9e64b5bf2d663 SHA512: 4aea9e2ccfe9815b427a55ee9abea1138790f42edd638ea9c3ca2813641157323aadabdad7fe4c4aec202df1419099bf074125ddeb3849da5832d58195ccd7ed Homepage: https://cran.r-project.org/package=ip2location Description: CRAN Package 'ip2location' (Lookup for IP Address Information) Enables the user to find the country, region, district, city, coordinates, zip code, time zone, ISP, domain name, connection type, area code, weather, Mobile Country Code, Mobile Network Code, mobile brand name, elevation, usage type, address type, IAB category and Autonomous system information that any IP address or hostname originates from. Supported IPv4 and IPv6. Please visit to learn more. You may also want to visit for free database download. This package requires 'IP2Location Python' module. At the terminal, please run 'pip install IP2Location' to install the module. Package: r-cran-ip2locationio Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-ip2locationio_1.1.0-1.ca2404.1_all.deb Size: 23534 MD5sum: fa7f3094ea3d253834185e3c613a37aa SHA1: 57a76912b2748d66d6a5664152e2bd18885615e1 SHA256: 34ff118d4d9af85f2ebd587af3681d78a8ffa825df531d5f502bf44c86b03498 SHA512: cef7c5ac5482b6a8027339fb2cdfb8e71f7037f6288ecfd7157f545841149dfad19d86e204aef58b9c69761a224da3a300fd4a6b77826cdc203745c07eaeb380 Homepage: https://cran.r-project.org/package=ip2locationio Description: CRAN Package 'ip2locationio' (Lookup Geolocation and Proxy Information using 'IP2Location.io'API) Query for enriched data such as country, region, city, latitude & longitude, ZIP code, time zone, Autonomous System, Internet Service Provider, domain, net speed, International direct dialing (IDD) code, area code, weather station data, mobile data, elevation, usage type, address type, advertisement category, fraud score, and proxy data with an IP address. You can also query a list of hosted domain names for the IP address too. This package uses the 'IP2Location.io' API to query this data. To get started with a free API key, sign up here . Package: r-cran-ip2proxy Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-jsonlite, r-cran-ggplot2, r-cran-maps, r-cran-scales Filename: pool/dists/noble/main/r-cran-ip2proxy_1.2.0-1.ca2404.1_all.deb Size: 33070 MD5sum: 9a4b312e554cabbde085ba940d1b1dc7 SHA1: 14993bce0211a2ae8540bf20d0e16b47f03699e5 SHA256: 141d51f71436d25ff139ab379da97c2467da6562cac5948eee308bb80bdb7f0b SHA512: 7e912c019d094f05ec8f82e51c63e36791ce4103b0309e92717810e6b9a7605d38c0741997ef768e4a6a06f8e014917bd17bb6dd3b307c888130018acaacdd9e Homepage: https://cran.r-project.org/package=ip2proxy Description: CRAN Package 'ip2proxy' (Lookup for IP Address Proxy Information) Enable user to find the IP addresses which are used as VPN anonymizer, open proxies, web proxies and Tor exits. The package lookup the proxy IP address from IP2Proxy BIN Data file. You may visit for free database download. Package: r-cran-ip2whois Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-ip2whois_1.0.0-1.ca2404.1_all.deb Size: 30802 MD5sum: 40cf88d173781be711eff094c168cb4d SHA1: e6d84d30d238c25b051daa70be57c7e78ddbd89a SHA256: c8bb3c4d376b586db1ed664ab914f65cbf02108a701ef144ce29f8cdbd547602 SHA512: 6098d58087d381920390eca6c27d36fa305c4e4c2d5a6b3f33e8a5fb94052442f5532c156cc1a0053d8add3ba073b5f7375139611c7ba107c6221e8ba5893ce4 Homepage: https://cran.r-project.org/package=ip2whois Description: CRAN Package 'ip2whois' (Lookup 'WHOIS' Information for a Particular Domain) Easily implement the checking of 'WHOIS' information for a particular domain. 'IP2WHOIS' supports the query for 1113 Top-level Domains(TLDs) and 634 Country Code Top-level Domains(ccTLDs). To get started with a free API key, you may sign up at here . Package: r-cran-ipa Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ipa_0.1.0-1.ca2404.1_all.deb Size: 72464 MD5sum: 846fb5582cd601822988e190badc7dec SHA1: ab1131e86831aaac91e624c3bd63384c405ef27e SHA256: 1df4c4517f37add52248449aac9b2748c3a8d52eca0deb2046d1839fd8c4b6ee SHA512: c4feaf6cd096120fb20a21e1e10831804a4ade900f4218a5ba7f0207afeda4e503d121d82532741ab585dd7ba85314f1bc6596b7bea1649da9cc93943b48c896 Homepage: https://cran.r-project.org/package=ipa Description: CRAN Package 'ipa' (Convert Between Phonetic Alphabets) Converts character vectors between phonetic representations. Supports IPA (International Phonetic Alphabet), X-SAMPA (Extended Speech Assessment Methods Phonetic Alphabet), and ARPABET (used by the CMU Pronouncing Dictionary). Package: r-cran-ipadmixture Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-treemap, r-cran-ape Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ipadmixture_0.1.2-1.ca2404.1_all.deb Size: 1008170 MD5sum: ac2aa083d798435d31a50a0c0b9767b3 SHA1: 32b1931acf366a68d381831ee59551dbedfcf735 SHA256: 38b86b70e12d01fcbdc26e8630c9c14353cc42e545a110a6483c903494841d1f SHA512: 0df6f1e08d99fc8fee6fef2410db4d39e4fb3ffd3d518f8298e532e6b77644673cc239d378a9e5b9cdacc4a2a392484819f8977c8d8f0a80507751d6b2c11490 Homepage: https://cran.r-project.org/package=ipADMIXTURE Description: CRAN Package 'ipADMIXTURE' (Iterative Pruning Population Admixture Inference Framework) A data clustering package based on admixture ratios (Q matrix) of population structure. The framework is based on iterative Pruning procedure that performs data clustering by splitting a given population into subclusters until meeting the condition of stopping criteria the same as ipPCA, iNJclust, and IPCAPS frameworks. The package also provides a function to retrieve phylogeny tree that construct a neighbor-joining tree based on a similar matrix between clusters. By given multiple Q matrices with varying a number of ancestors (K), the framework define a similar value between clusters i,j as a minimum number K* that makes majority of members of two clusters are in the different clusters. This K* reflexes a minimum number of ancestors we need to splitting cluster i,j into different clusters if we assign K* clusters based on maximum admixture ratio of individuals. The publication of this package is at Chainarong Amornbunchornvej, Pongsakorn Wangkumhang, and Sissades Tongsima (2020) . Package: r-cran-ipag Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ipag_0.1.0-1.ca2404.1_all.deb Size: 133978 MD5sum: 2fbd6d4508919027a1bfce2183015580 SHA1: 9b35bea882e51f784bad1962aab1d38134e18acd SHA256: 4b22b6c2a57ada64277d1bb2ece094e194f3de5d6f164b01ae0ebec1d93e4563 SHA512: e6918dfda6fa03620452036d9d8392e5b87472a03655d4c7618e1b3b74e6148943ff63ca5684e4de85d70cb2972781e9cda9dadcb0db25a645d79b7492579cc3 Homepage: https://cran.r-project.org/package=IPAG Description: CRAN Package 'IPAG' (Tools for IPAG Courses) Provides a collection of intuitive and user-friendly functions for computing confidence intervals for common statistical tasks, including means, differences in means, proportions, and odds ratios. 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Package: r-cran-ipanema Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-dbi, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-rmysql Filename: pool/dists/noble/main/r-cran-ipanema_1.2.0-1.ca2404.1_all.deb Size: 43874 MD5sum: d490b119b607ee267e08d7e15ac7a386 SHA1: dbe81c2d3a6368ff9b1473ca70f9123a525e1a54 SHA256: 85b7d214981947d1874847f014e8f21efb63dc05df779af38ba0fa3651fe2db0 SHA512: b5e5731468d84de786473db5e36eee7ec66e922c5db258b54c0584c0de8ccb7fc10ec434ca6dfc8a00dd154cd72e1e23fe4943ec788d434f13dc58ef3940ebe1 Homepage: https://cran.r-project.org/package=ipanema Description: CRAN Package 'ipanema' (Read Data from 'LimeSurvey') Read data from 'LimeSurvey' () in a comfortable way. Heavily inspired by 'limer' (), which lacked a few comfort features for me. Package: r-cran-ipbase Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ipbase_0.1.1-1.ca2404.1_all.deb Size: 25958 MD5sum: 2373924bd151355a96482f7acbc1e556 SHA1: 5f0075eecce9a1d76d6c158dd77bbb0ee27ce0c1 SHA256: 6026d0554f1fd162235461daffcb191ed7b5c1fbf58da7b2aeb4a25f1b0f58e6 SHA512: a19f9a616e91bb86ada633e8313e5faee21cbc1d5e6f8053bbbebefea0de1eedda951689b435b7c51be2b59f8932c4a18d5c66953e9effbc57e493ea4d8ffa05 Homepage: https://cran.r-project.org/package=ipbase Description: CRAN Package 'ipbase' (Client for the 'ipbase.com' IP Geolocation API) An R client for the 'ipbase.com' IP Geolocation API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at . Package: r-cran-ipc Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-shiny, r-cran-txtq Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-future, r-cran-promises, r-cran-redux Filename: pool/dists/noble/main/r-cran-ipc_0.1.4-1.ca2404.1_all.deb Size: 352388 MD5sum: be87430487d5b1e09bd8364d1897829c SHA1: 1c921fadb39cf39922a1bbed2393d06a164a7c8a SHA256: 3aca8b2e2216049b6ca660d5f41718b2ecc5d4f054e17305f75a30a3705c60f8 SHA512: 956ec7ab5c34f0c206274e1253961291fb19298e1299d29169b97e64d59ad8cc273d6fdf4cdb315c1d7e2ec8f53b38ea56891baf9dfb65685e566579f28e231e Homepage: https://cran.r-project.org/package=ipc Description: CRAN Package 'ipc' (Tools for Message Passing Between Processes) Provides tools for passing messages between R processes. 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Package: r-cran-ipcaps Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 583 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-expm, r-cran-kris, r-cran-fpc, r-cran-lpcm, r-cran-apcluster, r-cran-rmixmod Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ipcaps_1.1.8-1.ca2404.1_all.deb Size: 472132 MD5sum: cd3fdc83e67c4da5eaec72f391287b64 SHA1: 8092802f9e6377051ba720bad6e93457707d3aa0 SHA256: 89d7d3f0288e8f4343b41e4c6fdef2c09766195e302a3281194c5fa52f4c2396 SHA512: 1ac19761820532a9da3fd75b0f99662569fcb693747e861f18aaab490d982f88c14b427ab78c52c36ee2091f15541dc85a477e1f24c7ee183584a1d9b43c7562 Homepage: https://cran.r-project.org/package=IPCAPS Description: CRAN Package 'IPCAPS' (Iterative Pruning to Capture Population Structure) An unsupervised clustering algorithm based on iterative pruning is for capturing population structure. This version supports ordinal data which can be applied directly to SNP data to identify fine-level population structure and it is built on the iterative pruning Principal Component Analysis ('ipPCA') algorithm as explained in Intarapanich et al. (2009) . The 'IPCAPS' involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and 'Expectation-Maximization' clustering as explained in Lebret et al. (2015) . In each iteration, rough clusters and outliers are also identified using the function rubikclust() from the R package 'KRIS'. Package: r-cran-ipcwk Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-survival Filename: pool/dists/noble/main/r-cran-ipcwk_1.0-1.ca2404.1_all.deb Size: 25184 MD5sum: c13afd93d2d4991eb477988aa67c230a SHA1: caad303e03400de9b015b8f57d8932c775b2c364 SHA256: 98adf613c7d36e596536d3ffa87d43a686527de08505e579be6a2d1ec2f745f2 SHA512: 9733512efbeb3a0010d31f2ef70d27292220e50a4bb6c28e12e543a116d655b08e84b2ac6095653efcdb270e419b19c8fa76d9d23f726f1f2dc4bc5db3c1cca7 Homepage: https://cran.r-project.org/package=IPCWK Description: CRAN Package 'IPCWK' (Kendall's Tau Partial Corr. for Survival Trait and Biomarkers) We propose the inverse probability-of-censoring weighted (IPCW) Kendall's tau to measure the association of the survival trait with biomarkers and Kendall's partial correlation to reflect the relationship of the survival trait with interaction variable conditional on main effects, as described in Wang and Chen (2020) . Package: r-cran-ipcwswitch Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-ipcwswitch_1.0.4-1.ca2404.1_all.deb Size: 86480 MD5sum: 1aef7f11f4df59fb4490fbb823820e2d SHA1: bbe5277ff46b707fdc0d0837b4fabc7e40be3478 SHA256: 14e2a922e0aa4c36324d080ec397af8d4c483d005c16de771a103bccabe614b2 SHA512: a442afdda42bc65fe1f86b4b21b269c983363f0b3d6d4cbd6e245049fb9151c6467302800b2ca2180cd1b83b4247e8dacee9bfafab5be7a59f18a3d86e658e2d Homepage: https://cran.r-project.org/package=ipcwswitch Description: CRAN Package 'ipcwswitch' (Inverse Probability of Censoring Weights to Deal with TreatmentSwitch in Randomized Clinical Trials) Contains functions for formatting clinical trials data and implementing inverse probability of censoring weights to handle treatment switches when estimating causal treatment effect in randomized clinical trials. Package: r-cran-ipd Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3093 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-caret, r-cran-gam, r-cran-ranger, r-cran-mass, r-cran-randomforest, r-cran-tibble Suggests: r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyverse, r-cran-xfun, r-bioc-biocstyle, r-cran-biocmanager Filename: pool/dists/noble/main/r-cran-ipd_0.4.1-1.ca2404.1_all.deb Size: 1580828 MD5sum: a46beed74a7e94e093eb1d987cec285f SHA1: 3e42bfa769f88946afb7f8251c57bad097448b02 SHA256: 45ef5a8c0f6eb530736f3d4564e22ffdcdc4aa8c2e2fe5a2f112b7cb4796ec92 SHA512: ac876d8ae744c86b042768e1bce03cdeb868e2a12fa58701319ee91b1c2cc6e0c219161b491b6ae148b55a08aa68d21ea87b5a73cb23c35fc03c2c619d3d5ae9 Homepage: https://cran.r-project.org/package=ipd Description: CRAN Package 'ipd' (Inference on Predicted Data) Performs valid statistical inference on predicted data (IPD) using recent methods, where for a subset of the data, the outcomes have been predicted by an algorithm. Provides a wrapper function with specified defaults for the type of model and method to be used for estimation and inference. Further provides methods for tidying and summarizing results. Salerno et al., (2025) . Package: r-cran-ipdfilecheck Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-testthat, r-cran-lubridate, r-cran-eeptools, r-cran-hash, r-cran-kableextra, r-cran-gtsummary, r-cran-effsize, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-ipdfilecheck_0.8.1-1.ca2404.1_all.deb Size: 134362 MD5sum: 689a9d77fb4ba2ccfc27aa4aed65ff41 SHA1: eb9809143b0f92301373a87782224e61d02d0fa3 SHA256: c57d2d167bbfc8c9f027e9980b043875bb266c821f3d878ff1d793a7ac83d7a0 SHA512: 5c1456545d6f551253770b9f6a9fb1d9628a0bf2a3e442b093387f84cc0bc94bcdf4a6d53447ef101f419e19e2ab69f055c54c2bfcb9e589832fde0c1a00fb42 Homepage: https://cran.r-project.org/package=IPDFileCheck Description: CRAN Package 'IPDFileCheck' (Basic Functions to Check Readability, Consistency, and Contentof an Individual Participant Data File) Basic checks needed with an individual level participant data from randomised controlled trial. This checks files for existence, read access and individual columns for formats. The checks on format is currently implemented for gender and age formats. Package: r-cran-ipdfromkm Architecture: all Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-survival, r-cran-gridextra, r-cran-readbitmap Filename: pool/dists/noble/main/r-cran-ipdfromkm_0.1.10-1.ca2404.1_all.deb Size: 119808 MD5sum: 2be9e02d72d45a6e1eb654ff459c2342 SHA1: f7534aba1efc3a453e7c06d340700f426e773aea SHA256: b6a6bdec665748c69dd8e4fd28118d0b3e0938323b589e585718cc5698f7d85f SHA512: d053e506aaa9176ccb6b9b9095a8436f72d27083ee50ba6183363e73b9f1ff87e8140f20d44da38eaa1f1b4aafa31f0784e2b0543364400700d2bc1a65fb69a3 Homepage: https://cran.r-project.org/package=IPDfromKM Description: CRAN Package 'IPDfromKM' (Map Digitized Survival Curves Back to Individual Patient Data) An implementation to reconstruct individual patient data from Kaplan-Meier (K-M) survival curves, visualize and assess the accuracy of the reconstruction, then perform secondary analysis on the reconstructed data. We involve a simple function to extract the coordinates form the published K-M curves. The function is developed based on Poisot T. ’s digitize package (2011) . For more complex and tangled together graphs, digitizing software, such as 'DigitizeIt' (for MAC or windows) or 'ScanIt'(for windows) can be used to get the coordinates. Additional information should also be involved to increase the accuracy, like numbers of patients at risk (often reported at 5-10 time points under the x-axis of the K-M graph), total number of patients, and total number of events. The package implements the modified iterative K-M estimation algorithm (modified-iKM) improved upon the approach proposed by Guyot (2012) with some modifications. 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Useful for coastal marine applications where barriers in the landscape preclude interpolation with Euclidean distances. 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Package: r-cran-ipec Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-mass Filename: pool/dists/noble/main/r-cran-ipec_1.1.2-1.ca2404.1_all.deb Size: 219980 MD5sum: 155bda132b55e538c79aabed2d8c19ef SHA1: ac60610eb25e4b6bc4c3bce6eeb03bb35ff1c3dd SHA256: 18d74dd00813e927c6bb3278a34325fb408fdb56559caf8888e8681d109f97b3 SHA512: f0973a8ebe3bbe0867e7e11600bc129e39ecc61ec09b02294ee7f6487e26118ee930b0dd280cfd212c3270ffd121c89d6d83ca917d9996a0b12c5243f4e06875 Homepage: https://cran.r-project.org/package=IPEC Description: CRAN Package 'IPEC' (Root Mean Square Curvature Calculation) Calculates the RMS intrinsic and parameter-effects curvatures of a nonlinear regression model. The curvatures are global measures of assessing whether a model/data set combination is close-to-linear or not. See Bates and Watts (1980) and Ratkowsky and Reddy (2017) for details. 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The package allows easy access to a wide variety of information regarding Post-secondary Institutions, its students, faculty, and their demographics, financial aid, educational and recreational offerings, and completions. This package can be used by students, college counselors, or involved parents interested in pursuing higher education, considering their options, and securing admission into their school of choice. 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Starting data specifications can be found in the vignettes. Final files are saved locally to a location of the user's choice. User-friendly readable files can also be produced for purposes of data review and validation. Package: r-cran-iperform Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-matrixstats Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-iperform_0.0.3-1.ca2404.1_all.deb Size: 243920 MD5sum: 996b796dc44fa7421181b07b4e0aed3c SHA1: 5596892c97110c7ab612701992c2597d3a4f0212 SHA256: ecd949ff5bbbdd1367e7af815e81f5d038a777875f87ec1b418e52130881805e SHA512: 02e0e084b1f0912f18435f05305a5f4612dc16ffd972e00d2928e04483d00a1747f99dbca2877e8a8faafcfc5385324efad20c3674ff660b30c7de481b0c3f99 Homepage: https://cran.r-project.org/package=iperform Description: CRAN Package 'iperform' (Time Series Performance) A tool to calculate the performance of a time series in a specific date or period. It is more intended for data analysis in the fields of finance, banking, telecommunications or operational marketing. Package: r-cran-ipeval Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-prodlim Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ipw, r-cran-riskregression Filename: pool/dists/noble/main/r-cran-ipeval_0.1.0-1.ca2404.1_all.deb Size: 159702 MD5sum: 81f11fcb7c19244b831908da3cb4f8ae SHA1: d370b244f4ff2a4b10ad3a9bb47cd457b907934b SHA256: 07f107ea47be3916903cd4d35be597f51fe334d510d3440b1b8b3315354d9921 SHA512: 8a96cc2777d0100749a9f89a5d1b73ec2e99274e79951d25a2fa9dcab057dd8f8aa651064d5a4770fd0d557051a4cb4e01b48571f7424dde15dfea62860a3e1a Homepage: https://cran.r-project.org/package=ipeval Description: CRAN Package 'ipeval' (Evaluation of Interventional Predictions) Provides methods to evaluate predictive performance of models that estimate risks under hypothetical intervention scenarios (interventional/causal/counterfactual predictions) with observational data subject to treatment-outcome confounding. Inverse probability of treatment weighting (IPTW) is used to construct a pseudopopulation in which all individuals receive a specified intervention, enabling assessment of agreement between predicted risks under the intervention and observed outcomes in the pseudo-population corresponding to that intervention. Package supports binary and time-to-event outcomes under binary interventions made at a single time point. Performance measures supported are AUC (Area Under the receiving operating characteristic Curve), Brier score, observed-expected ratio, and calibration plots. Methods implemented in this package are based on work by Keogh and Van Geloven (2024) . Package: r-cran-ipflasso Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-survival Filename: pool/dists/noble/main/r-cran-ipflasso_1.1-1.ca2404.1_all.deb Size: 55680 MD5sum: be311817209a84d7378c962f7d7136ec SHA1: b3d1f0a0208919913701b4ca69abb711dda961e4 SHA256: cd8e85a03cada43b3b7edf11173542add430b51ac914a6d69b8a9e4c831e4436 SHA512: 3b5aadea73dc03d182a9849accad504147ce171fa83c3e98e7389b4fa3ec59f5b7c7c6b8d6f4232b5fb415b33e9a535431c841a2b87fa88758a36a014c4ea632 Homepage: https://cran.r-project.org/package=ipflasso Description: CRAN Package 'ipflasso' (Integrative Lasso with Penalty Factors) The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen from a set of optional candidates by cross-validation or alternatively generated from the input data. Package: r-cran-ipfr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-tidyr, r-cran-mlr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-ipfr_1.0.2-1.ca2404.1_all.deb Size: 137084 MD5sum: 53c4213da761a4cafb29924b564bc89c SHA1: e9d648821f8a9dc9baab099057bc6fd9a3fc875a SHA256: d22da6646e32c6f4a4fe9e2c9ba9a084710e5bba814f5e999875f03f7dd31fb4 SHA512: 5dce7df098f4d1f24d301a827d49211afde6ca5ea64ea959568142921b5a99214c6b33430b27c4aed75bb77d005c3de53d113f45b7d017856868d7a5b93f1dbb Homepage: https://cran.r-project.org/package=ipfr Description: CRAN Package 'ipfr' (List Balancing for Reweighting and Population Synthesis) Performs iterative proportional updating given a seed table and an arbitrary number of marginal distributions. This is commonly used in population synthesis, survey raking, matrix rebalancing, and other applications. For example, a household survey may be weighted to match the known distribution of households by size from the census. An origin/ destination trip matrix might be balanced to match traffic counts. The approach used by this package is based on a paper from Arizona State University (Ye, Xin, et. al. (2009) ). Some enhancements have been made to their work including primary and secondary target balance/importance, general marginal agreement, and weight restriction. Package: r-cran-ipgeolocation Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2 Filename: pool/dists/noble/main/r-cran-ipgeolocation_0.1.0-1.ca2404.1_all.deb Size: 11930 MD5sum: f73dcfae70017915b677bf2aaa37d587 SHA1: 46d4203cc100cc9216f7146e0f4233d9ddd8024a SHA256: ffc733057d686c26b4ae48fa5147d0d242f195db0cfd2b95777f944aa2924ce0 SHA512: f7d17dcc7bc4ea574ee3e9786d7a67faeaac63dec226baefc64a7a4a115a5d23f73934d401826261abdded932d276b7efd721f448d7183da5bbbd64d4db91ed6 Homepage: https://cran.r-project.org/package=ipgeolocation Description: CRAN Package 'ipgeolocation' (Client for the 'IPGeolocation.io IP Location API') Provides functions to query the 'IPGeolocation.io IP Location API' (). Supports retrieval of IP location, ASN, network, currency, timezone, abuse, and security data. Response filtering is supported using 'fields' and 'excludes' parameters (dot notation supported), and optional objects can be requested via the 'include' parameter. Returns parsed API responses as R objects. 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Typically, application areas consist of transforming raw data into the corresponding advanced fuzzy logic representation and determining the similarity between two objects using advanced fuzzy similarity techniques in various fields of research, such as text classification, pattern recognition, software projects, decision-making, medical diagnosis, and market prediction. Functions are designed to compute the membership, non-membership, hesitant-membership, indeterminacy-membership, and refusal-membership for the input matrices. Furthermore, it also includes a large number of advanced fuzzy logic based similarity measure functions to compute the Intuitionistic fuzzy similarity (IFS), Pythagorean fuzzy similarity (PFS), and Spherical fuzzy similarity (SFS) between two objects or items based on their fuzzy relationships. It also includes working examples for each function with sample data sets. 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The paper of "Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J.(2012) ", proposes estimators of marginal quantiles based on the Inverse Probability Weighting method. 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Package: r-cran-irg Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3215 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rcpproll, r-cran-chk Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-diagrammer, r-cran-ggplot2, r-cran-curl, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-irg_0.1.6-1.ca2404.1_all.deb Size: 849856 MD5sum: 2b9fbffadd69ee917be33a6c624cc2b5 SHA1: cf15929a5bbf4077338f83bf79a5c1dd58742e98 SHA256: a66988acf02ca513b7eb1501c9bf0df3b7cef431227b6cbf1b4d27d5474a06a4 SHA512: 93f7a282eebf397ebae2d7744934947b2a7d10c2ff5451359c3113908e2ba14510ed6552c336c674672197554538b2b7f6f62ec327be2f4f5b6330355cad8013 Homepage: https://cran.r-project.org/package=irg Description: CRAN Package 'irg' (Instantaneous Rate of Green Up) Fits a double logistic function to NDVI time series and calculates instantaneous rate of green (IRG) according to methods described in Bischoff et al. (2012) . 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Among the CAC coefficients covered are Cohen's kappa, Conger's kappa, Fleiss' kappa, Brennan-Prediger coefficient, Gwet's AC1/AC2 coefficients, and Krippendorff's alpha. Multiple sets of weights are proposed for computing weighted analyses. Also included in this package is Bangdiwala's B coefficient. 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Applied to complete (balanced) datasets, these generalizations yield the same results as the common procedures, namely the Intraclass Correlation according to McGraw & Wong (1996) \doi{10.1037/1082-989X.1.1.30} and the Coefficient of Concordance according to Kendall & Babington Smith (1939) \doi{10.1214/aoms/1177732186}. Package: r-cran-irtawsi Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-mirt, r-cran-psych, r-cran-readxl, r-cran-shiny, r-cran-shinywidgets, r-cran-shinycssloaders, r-cran-rmarkdown, r-cran-bs4dash, r-cran-gt, r-cran-diagram, r-cran-writexl, r-cran-mirtcat, r-cran-wrightmap Filename: pool/dists/noble/main/r-cran-irtawsi_0.4.1-1.ca2404.1_all.deb Size: 47310 MD5sum: 5acf64e99be57b10cd93a3350da6610f SHA1: e4002be9f01113510225ef9ee69b6d34e0b67e08 SHA256: 6948b74966c67df46e61a8ce8bc5dc048f19c0a93ef2c2c34bb8a6dc27b382c4 SHA512: 72877d4c6647db30f6ea5fca8b39ea4867b887e2d8f2f469ef68d8190b79f993524753f50b3eca32a921d15c6ed2c9ca61818ecae997664ddaa297aae0520ccc Homepage: https://cran.r-project.org/package=irtawsi Description: CRAN Package 'irtawsi' (Items Response Theory Analysis with Steps and Interpretation) Dichotomous and polytomous data analysis and their scoring using the unidimensional Item Response Theory model (Chalmers (2012) ) with user-friendly graphic User Interface. Suitable for beginners who are learning item response theory. 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The BEMM family includes (1) the BEMM algorithm for 3PL model proposed by Guo & Zheng (2019) ; (2) the BEMM algorithm for 1PLG model and (3) the BEMM algorithm for 1PLAG model proposed by Guo, Wu, Zheng, & Chen (2021) ; (4) the BEMM algorithm for 4PL model proposed by Zheng, Guo, & Kern (2021) ; and (5) their maximum likelihood estimation versions proposed by Zheng, Meng, Guo, & Liu (2018) . Thus, both Bayesian modal estimates and maximum likelihood estimates are available. Package: r-cran-irtdemo Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-fgarch Filename: pool/dists/noble/main/r-cran-irtdemo_0.1.5-1.ca2404.1_all.deb Size: 24260 MD5sum: aac7e843e9a524ab1ea6254d7977a9c3 SHA1: ec0cf450df170eedbea95b613be60b272c54fa0e SHA256: 0e30de568c6239696ab081c50acfb25c8101411e7fcc58fc61d78ad0906f4a28 SHA512: 547527faa93c74c9ba9f44e408eddf67f732df8e2223a9de57d61041a6e81c9828b793a8dfc3dcf8d367aa3f71e5a13a672832be31f5265920f4532ab3d67d8e Homepage: https://cran.r-project.org/package=irtDemo Description: CRAN Package 'irtDemo' (Item Response Theory Demo Collection) Includes a collection of shiny applications to demonstrate or to explore fundamental item response theory (IRT) concepts such as estimation, scoring, and multidimensional IRT models. Package: r-cran-irtest Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 885 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-betafunctions, r-cran-dcurver, r-cran-ggplot2, r-cran-usethis, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-irtest_2.2.0-1.ca2404.1_all.deb Size: 583274 MD5sum: 4c488d86a426d6bdb0a5f31e871f98e4 SHA1: 24921f72064a69e56bd31fae7f09812016b6bc8d SHA256: 559229e4b65ee1724770d411734bbc09b5ef15103086dccd5497a5cc50705c5a SHA512: 049f5300c6f521db0c4fd1c3633eb30099d9a28d5ecc97d41c74ae070156fab50d548ca108938a6f0a057dc704774caa3f9ab94dd68c340bea8914deb617aa30 Homepage: https://cran.r-project.org/package=IRTest Description: CRAN Package 'IRTest' (Parameter Estimation of Item Response Theory with Estimation ofLatent Distribution) Item response theory (IRT) parameter estimation using marginal maximum likelihood and expectation-maximization algorithm (Bock \& Aitkin, 1981 ). Within parameter estimation algorithm, several methods for latent distribution estimation are available. Reflecting some features of the true latent distribution, these latent distribution estimation methods can possibly enhance the estimation accuracy and free the normality assumption on the latent distribution. Package: r-cran-irtgui Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-shiny, r-cran-shinydashboard, r-cran-shinycssloaders, r-cran-readxl, r-cran-mirt, r-cran-psych, r-cran-wrightmap, r-cran-writexl, r-cran-irtoys Filename: pool/dists/noble/main/r-cran-irtgui_0.2-1.ca2404.1_all.deb Size: 53966 MD5sum: f4d1cf44fc2490491ef7c8395301de7e SHA1: dd22e9daf9d90cd211243f22d68e679a6bef2d14 SHA256: bcc1b9c9b0deebbf66cbbe7118368949a3db7917aae603f961208d98c82b3d22 SHA512: ff067b70571d29d7243b4af683c99c2467fa333a6f557e7909c412d5ce9785d92ee2891bb305577a1a364d607d61d63b00461b35f25a76770c505576ab163dfb Homepage: https://cran.r-project.org/package=irtGUI Description: CRAN Package 'irtGUI' (Item Response Theory Analysis with a Graphic User Interface) Performing Item Response Theory analysis such as parameter estimation, ability estimation, data generation, item and model fit analyse, local independence assumption, dimensionality assumption, wright map, characteristic and information curves under various models with a user-friendly Graphic User Interface. Package: r-cran-irtpwr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mirt, r-cran-deriv, r-cran-digest, r-cran-spatstat.random, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-irtpwr_1.0.3-1.ca2404.1_all.deb Size: 173168 MD5sum: 351fd2ec5554614dcdad2e6323ed4832 SHA1: ca21bd7b78fb3a6c39069afe0de33b30cd512898 SHA256: d7858b71a6c1c07cf82774414b1d5d77683a66306b2143f83393f1704555b949 SHA512: 4ca4eb0cd60ed66cc6d9c11e97adcb87fcba64c75010ec9395bda6df0c408d553b8c586268741b3fe2577745b44ba006751b17587c5d91a61ef45a616eb05e54 Homepage: https://cran.r-project.org/package=irtpwr Description: CRAN Package 'irtpwr' (Power Analysis for IRT Models Using the Wald, LR, Score, andGradient Statistics) Implementation of analytical and sampling-based power analyses for the Wald, likelihood ratio (LR), score, and gradient tests. Can be applied to item response theory (IRT) models that are fitted using marginal maximum likelihood estimation. The methods are described in our paper (Zimmer et al. (2022) ). Package: r-cran-irtq Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-rlang, r-cran-reshape2, r-cran-janitor, r-cran-ggplot2, r-cran-gridextra, r-cran-matrix, r-cran-rfast, r-cran-mirt Filename: pool/dists/noble/main/r-cran-irtq_1.0.0-1.ca2404.1_all.deb Size: 3185054 MD5sum: 58ad7cfe214fb396e0c9b2a556f5d031 SHA1: 3f85db717cb5e4b16276f6d956175921b8539fb6 SHA256: 6809880b8e248e8a122c3893623ef00ce03beb8a5338e9aadedb5d66c05335b4 SHA512: 6d56f03a440a7aff24ebf580788394c5589502e913f066a4a53b4348e1922a4e48f5bf5743350c9084a57349691ed91320d1afed89a9be595c0412faa9c098e5 Homepage: https://cran.r-project.org/package=irtQ Description: CRAN Package 'irtQ' (Unidimensional Item Response Theory Modeling) Fit unidimensional item response theory (IRT) models to test data, which includes both dichotomous and polytomous items, calibrate pretest item parameters, estimate examinees' abilities, and examine the IRT model-data fit on item-level in different ways as well as provide useful functions related to IRT analyses such as IRT model-data fit evaluation and differential item functioning analysis. The bring.flexmirt() and write.flexmirt() functions were written by modifying the read.flexmirt() function (Pritikin & Falk (2022) ). The bring.bilog() and bring.parscale() functions were written by modifying the read.bilog() and read.parscale() functions, respectively (Weeks (2010) ). The bisection() function was written by modifying the bisection() function (Howard (2017, ISBN:9780367657918)). The code of the inverse test characteristic curve scoring in the est_score() function was written by modifying the irt.eq.tse() function (González (2014) ). In est_score() function, the code of weighted likelihood estimation method was written by referring to the Pi(), Ji(), and Ii() functions of the catR package (Magis & Barrada (2017) ). Package: r-cran-irtrees Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-diagrammer, r-cran-tidyr Suggests: r-cran-lme4, r-cran-mirt, r-cran-knitr, r-cran-flextable, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-irtrees_1.0.0-1.ca2404.1_all.deb Size: 1096048 MD5sum: fb83ebec5cd8fb6cd741bead51429383 SHA1: f2bd92b67fe4b1e0eb7aa19eb8001ca21cbf1e1b SHA256: aa73e2dc689a78a87c0d4fd70c0e92db10d29e10482b56c44c5a56a12fb220c1 SHA512: 13f40da7e83a56d68b39ed367bb90c117b70acc166b13fd03a91b03efa8d1eb7061e7d4c3dd43e6cdde756801c67538f5676d1b86ae92b4cda9d4a478c2ca16e Homepage: https://cran.r-project.org/package=irtrees Description: CRAN Package 'irtrees' (Estimation of Tree-Based Item Response Models) Helper functions and example data sets to facilitate the estimation of IRTree models from data with different shape and using different software. 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The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) . 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Tools to introduce statistical concepts with a focus on simulation approaches. Functions are verbose, designed to provide ample output for students to understand what each function does. Additionally, most functions are accompanied with plots. The package is designed to be used in an educational setting alongside the ISCAM textbook. 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ISCO-08 is the latest version of the International Standard Classification of Occupations which is used to organise information on labour and jobs. Package: r-cran-iscocrosswalks Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-labourr, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-iscocrosswalks_1.0.0-1.ca2404.1_all.deb Size: 353146 MD5sum: 3616c242d02a17195ffe353b2ce9fd6b SHA1: 4d322e9fc661b4cfebb03b585258b7ac69ecb176 SHA256: d96ad853e961182249f8c29694b9cee78ce961fd4bd6f41b90a60252b9555fb2 SHA512: 70294a31b722a63059c72d3f16e9070f508b42fe832bbcc126155b339ef6576e099b2f4edbd627fd11c78e30520d357b814e067f4cab109912c1486ed40a90bb Homepage: https://cran.r-project.org/package=iscoCrosswalks Description: CRAN Package 'iscoCrosswalks' (Crosswalks Between Classifications of Occupations) Allows the user to perform approximate matching between the occupational classifications using concordances provided by the Institute for Structural Research and Faculty of Economics, University of Warsaw, . The crosswalks offer a complete step-by-step mapping of Standard Occupational Classification (2010) data to the International Standard Classification of Occupations (2008). We propose a mapping method based on the aforementioned research that converts measurements to the smallest possible unit of the target taxonomy, and then performs an aggregation/estimate to the requested degree Occupational Hierarchical level. 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Package: r-cran-isdals Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-vgam Filename: pool/dists/noble/main/r-cran-isdals_3.0.1-1.ca2404.1_all.deb Size: 263386 MD5sum: 70f6acb7dba0b193d5e6183a5c987d25 SHA1: 66a9a44972c782861b51af446c81da4133d4818e SHA256: a139dffc7c8db1109c91ccb62b3c2135be2ede0209a3e4ccfb92dbaa9c0a1b69 SHA512: 40348b3340566f6ebccc6961d9f48220d1833a22550e2212198d8740606e87febc93a4a6bd0bb9bc384b6ed0a7aa05f2c8faa5d3be680ba27e160ca228128413 Homepage: https://cran.r-project.org/package=isdals Description: CRAN Package 'isdals' (Datasets for Introduction to Statistical Data Analysis for theLife Sciences) Provides datasets for the book "Introduction to Statistical Data Analysis for the Life Sciences, Second edition" by Ekstrøm and Sørensen (2014). Package: r-cran-isdparser Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4062 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-data.table, r-cran-lubridate Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-isdparser_0.4.0-1.ca2404.1_all.deb Size: 686368 MD5sum: 42c5931e6088a998f16ff314a0c9d564 SHA1: de93d257ec894bb27ed52f65d1b38ae48354cf8f SHA256: 2dcc722a12a9853d22e6e336b787374f5d65cb92cb47faa49d99af465c0c2cd1 SHA512: ea7513ba373caf0fdcf9717ad53263d1ae97b473455eb74d445aa2726420b1f643d0da0bbceb7072876e8572e862b6ce87c03df136a09d2641616b6872e26976 Homepage: https://cran.r-project.org/package=isdparser Description: CRAN Package 'isdparser' (Parse 'NOAA' Integrated Surface Data Files) Tools for parsing 'NOAA' Integrated Surface Data ('ISD') files, described at . Data includes for example, wind speed and direction, temperature, cloud data, sea level pressure, and more. Includes data from approximately 35,000 stations worldwide, though best coverage is in North America/Europe/Australia. Data is stored as variable length ASCII character strings, with most fields optional. Included are tools for parsing entire files, or individual lines of data. 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Implements methods described in: Dillingham, P.W., Radu, T., Diamond, D., Radu, A. and McGraw, C.M. (2012) , Dillingham, P.W., Alsaedi, B.S.O. and McGraw, C.M. (2017) , Dillingham, P.W., Alsaedi, B.S.O., Radu, A., and McGraw, C.M. (2019) , and Dillingham, P.W., Alsaedi, B.S.O., Granados-Focil, S., Radu, A., and McGraw, C.M. (2020) . Package: r-cran-isfun Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1675 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-irlba Filename: pool/dists/noble/main/r-cran-isfun_1.1.0-1.ca2404.1_all.deb Size: 1674074 MD5sum: 553d31a613a24591df6705ce47c9ba9d SHA1: 235ec18bbea5fc61cd166462fc62850ad99fa248 SHA256: 845057f336479b5caa824dcf3ec569fe9308fb9b02c1110486b352b97e9c742e SHA512: bdb2fa2663ea6bbaa547b68b1c8ac68504832ba02a2c45cf5889587e12966e9ab0c9e29055b8084c84ade227cfd301e063f5e35fc9b99d6ac248018643798534 Homepage: https://cran.r-project.org/package=iSFun Description: CRAN Package 'iSFun' (Integrative Dimension Reduction Analysis for Multi-Source Data) The implement of integrative analysis methods based on a two-part penalization, which realizes dimension reduction analysis and mining the heterogeneity and association of multiple studies with compatible designs. The software package provides the integrative analysis methods including integrative sparse principal component analysis (Fang et al., 2018), integrative sparse partial least squares (Liang et al., 2021) and integrative sparse canonical correlation analysis, as well as corresponding individual analysis and meta-analysis versions. References: (1) Fang, K., Fan, X., Zhang, Q., and Ma, S. (2018). Integrative sparse principal component analysis. Journal of Multivariate Analysis, . (2) Liang, W., Ma, S., Zhang, Q., and Zhu, T. (2021). Integrative sparse partial least squares. Statistics in Medicine, . 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EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data. 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The parameters of the networks can be directly supplied by users or estimated by the 'IsingFit' package by van Borkulo and Epskamp (2016) from empirical data. The Ising model's Boltzmann distribution is preserved for the potential landscape function. The landscape functions can be used for quantifying and visualizing the stability of network states, as well as visualizing the simulation process. Package: r-cran-islandcodes Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-countrycode Suggests: r-cran-dplyr, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-islandcodes_0.1.1-1.ca2404.1_all.deb Size: 51284 MD5sum: 3526c6785bb3aabe78dab0168d92f3a6 SHA1: 9dd0ce7259b2b2535cad4ab9f002a08f02f3f50a SHA256: 8a0d8bcef9462e4392b6c8a7a41478e074f3080b353e5a88d9f733fba68297ab SHA512: 35f0f853847f6f4072f280108e8469139ee58ef1d78f22a0ec6d39682851de97ea939958ff9f326c20648d238226fe3d4a85deb5281c9358fed1fa002a04bf49 Homepage: https://cran.r-project.org/package=islandcodes Description: CRAN Package 'islandcodes' (Reference Data and Helpers for Small Island States andTerritories) A curated reference list of countries and territories with classifications for Small Island Developing States (SIDS), sub-national island jurisdictions (SNIJ), World Bank region and income group, and political association. Sub-sovereign cases such as Aruba, Curacao, Bonaire, Sint Maarten, the French overseas territories, and Aaland Islands are represented with disambiguating codes that standard country-code packages often collapse or omit. Provides predicate helpers and a tidy joiner intended to extend rather than replace 'countrycode'. Source data is maintained at and licensed CC BY 4.0. Package: r-cran-islr2 Architecture: all Version: 1.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4582 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-islr2_1.3-2-1.ca2404.1_all.deb Size: 4128634 MD5sum: 5796cba8d1983ca4f19660a508382d2b SHA1: 6d5cb8502c939e38c91560dd84c428e1e3810f37 SHA256: 85adce033244ad020220d7ae32aed6eaae812f1b623df0e765181e6220f1322d SHA512: fda7bbef80ccca24e30ba6f656234152ecedf5e4efffded23bbbf00f86732c408169ed173846def7f0dbe5d4d0cbef591a4ca7f482c018edb56d050ce57730d1 Homepage: https://cran.r-project.org/package=ISLR2 Description: CRAN Package 'ISLR2' (Introduction to Statistical Learning, Second Edition) We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R, Second Edition'. These include many data-sets that we used in the first edition (some with minor changes), and some new datasets. Package: r-cran-islr Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2936 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-islr_1.4-1.ca2404.1_all.deb Size: 2909740 MD5sum: 4afb26d749487db78487aa1c99ed93b6 SHA1: 0745a1e1a5bd38a86c3228bef221b7ca636f68f1 SHA256: e88f0ef06e07d719e25cee674126f9bbdfb2707dc5e84bd2795284417836e103 SHA512: 5cda138d49c553ee0badc229e79da54026a228a51d6648316661723e73274a6101dc31d17ab5c8ad01b68f7cceac4c072b231ebcad1816bde69cfd04caff5338 Homepage: https://cran.r-project.org/package=ISLR Description: CRAN Package 'ISLR' (Data for an Introduction to Statistical Learning withApplications in R) We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'. Package: r-cran-ism Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xlsx, r-cran-rjava, r-cran-xlsxjars Filename: pool/dists/noble/main/r-cran-ism_0.1.0-1.ca2404.1_all.deb Size: 260380 MD5sum: 421bb9d7d170b9a26cca91929e16e6a9 SHA1: e1a02bbad9e5c09dae2c6d7b4dba26fe59986334 SHA256: 4ebc8bb61933f89e7710e8d1db9db8f1f9b9306e7393a62e5444880ae63c6cd7 SHA512: eef535e261d2a65b78817f400b31bc1e2795366fd5c2e53b369fa94815bf615d1652b22b566ea152acf07737f78e9fc357c527f9e59a328e1894758efc84f051 Homepage: https://cran.r-project.org/package=ISM Description: CRAN Package 'ISM' (Interpretive Structural Modelling (ISM)) The development of ISM was made by Warfield in 1974. ISM is the process of collaborating distinct or related essentials into a simplified and an organized format. Hence, ISM is a methodology that seeks the interrelationships among the various elements considered and endows with a hierarchical and multilevel structure. To run this package user needs to provide a matrix (VAXO) converted into 0's and 1's. Warfield,J.N. (1974) Warfield,J.N. (1974, E-ISSN:2168-2909). Package: r-cran-ismev Architecture: all Version: 1.43-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-ismev_1.43-1.ca2404.1_all.deb Size: 266912 MD5sum: dccef4a87fbe16949153e33ce7d03b0e SHA1: 6e432d696546abc1c726cec83fa41ac08057286d SHA256: 10583b9feb187d9c775ff11cebe7def9f6fc24931819f5e4410e8be44294e4eb SHA512: 60051a15afe342ca4fdeaa6e0d33394dad6a933778d40954d9d751cfb5809fd021fa3838b54e7dafd03f60ba813bd99d71df47666beac21839a39cb54aad8ed1 Homepage: https://cran.r-project.org/package=ismev Description: CRAN Package 'ismev' (An Introduction to Statistical Modeling of Extreme Values) Functions to support the computations carried out in `An Introduction to Statistical Modeling of Extreme Values' by Stuart Coles. The functions may be divided into the following groups; maxima/minima, order statistics, peaks over thresholds and point processes. Package: r-cran-ismtchile Architecture: all Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-ismtchile_2.1.5-1.ca2404.1_all.deb Size: 209788 MD5sum: d74e9b735692038293e686d606bb45cd SHA1: 7d1ea4184338ec059519594941783501892d1a8f SHA256: 90c906b463acdf2a14d9023287855e2c433c610d6b9305197c5e2909b1128076 SHA512: 7735d5de26a76478404ee1ef8b66a4b5a6218ab66dfb418aa87b6e87c5e8804a4b67991b51c5418a3e32713ce6e3959309a3546b48222334564486e7c63d3b57 Homepage: https://cran.r-project.org/package=ismtchile Description: CRAN Package 'ismtchile' (Calculating Socio Material Territorial Index) Paquete creado con el fin de facilitar el cálculo y distribución del índice Socio Material Territorial (ISMT), elaborado por el Observatorio de Ciudades UC. La metodología completa está disponible en "ISMT" () [Observatorio de Ciudades UC (2019)]. || Package created to facilitate the calculation and distribution of the Socio-Material Territorial Index by Observatorio de Ciudades UC. The full methodology is available at "ISMT" () [Observatorio de Ciudades UC (2019)]. Package: r-cran-ismtools Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-igraph, r-cran-visnetwork, r-cran-viridis, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ismtools_0.1.0-1.ca2404.1_all.deb Size: 121766 MD5sum: 0e8b9d6bc669254acc8724c7411cf60c SHA1: ae359b6d9b82afaecbe3c4d930d6d4f3e84e4411 SHA256: 131129997e700fab036ffb7770436e736218166267acb235d974d226e07d045d SHA512: 78d29e8491f2799c7f53c6f404f6ba63a590d22cfddd5bd8877e4d6de58dbe40aed3bb23c8704d3d0dcde2ceaea7850b4d336ffa18fddaa3f34a1bf4c0675795 Homepage: https://cran.r-project.org/package=ISMtools Description: CRAN Package 'ISMtools' (Interpretive Structural Modelling Analysis Tools) A comprehensive toolkit for Interpretive Structural Modelling (ISM) analysis. Provides functions for creating adjacency matrices from various input formats including SSIM (Structural Self-Interaction Matrix), computing reachability matrices using Warshall's algorithm, performing hierarchical level partitioning, MICMAC (Cross-Impact Matrix Multiplication Applied to Classification) analysis, and visualizing ISM structures through both static and interactive diagrams. ISM is a methodology for identifying and summarizing relationships among specific elements which define an issue or problem, as described in Warfield (1974) . Package: r-cran-isni Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-mvtnorm, r-cran-nnet, r-cran-matrixcalc, r-cran-formula, r-cran-lme4 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-isni_1.3-1.ca2404.1_all.deb Size: 201838 MD5sum: ef77f38d73e201d69f005b7927b6ecee SHA1: bee0c28f0aea0301f1c2e209487a336a92b0b98b SHA256: 71b9fa3f4e36866e915e887f74237b69cc01db10d143426f37c3b6b0b200a9a3 SHA512: 014b70706e9272a614786412ed9590e1524908288429c00781b3f5747b81018d0905faf8dad62d4a5e7edf83105ce8f440144c1d08d0b2c7a7dba418b79a7b76 Homepage: https://cran.r-project.org/package=isni Description: CRAN Package 'isni' (Index of Local Sensitivity to Nonignorability) The current version provides functions to compute, print and summarize the Index of Sensitivity to Nonignorability (ISNI) in the generalized linear model for independent data, and in the marginal multivariate Gaussian model and the mixed-effects models for continuous and binary longitudinal/clustered data. It allows for arbitrary patterns of missingness in the regression outcomes caused by dropout and/or intermittent missingness. One can compute the sensitivity index without estimating any nonignorable models or positing specific magnitude of nonignorability. Thus ISNI provides a simple quantitative assessment of how robust the standard estimates assuming missing at random is with respect to the assumption of ignorability. For a tutorial, download at . For more details, see Troxel Ma and Heitjan (2004) and Xie and Heitjan (2004) and Ma Troxel and Heitjan (2005) and Xie (2008) and Xie (2012) and Xie and Qian (2012) . Package: r-cran-iso11784tools Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-stringi, r-cran-dplyr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-iso11784tools_1.2.0-1.ca2404.1_all.deb Size: 62088 MD5sum: 5f49af23b08599a697ab420e503c5ea1 SHA1: 5699aca474b231ad03eeed81525f105e4887bc18 SHA256: af725c576a481a4bfcb1b776f08129b29929e6c32989381c1959b8c2ca9b9a08 SHA512: 77fe7c28cea6f4475e4c032cea536ca280013a4aa439cc655237e2abc936af4b33aa561a271554e3c55e7f7dce93e3f1bc3e599b2cf369dc244865f148a45f39 Homepage: https://cran.r-project.org/package=ISO11784Tools Description: CRAN Package 'ISO11784Tools' (ISO11784 PIT Tag ID Format Converters) Some tools to assist with converting International Organization for Standardization (ISO) standard 11784 (ISO11784) animal ID codes between 4 recognised formats commonly displayed on Passive Integrated Transponder (PIT) tag readers. The most common formats are 15 digit decimal, e.g., 999123456789012, and 13 character hexadecimal 'dot' format, e.g., 3E7.1CBE991A14. These are referred to in this package as isodecimal and isodothex. The other two formats are the raw hexadecimal representation of the ISO11784 binary structure (see ). There are two 'flavours' of this format, a left and a right variation. Which flavour a reader happens to output depends on if the developers decided to reverse the binary number or not before converting to hexadecimal, a decision based on the fact that the PIT tags will transmit their binary code Least Significant Bit (LSB) first, or backwards basically. Package: r-cran-isoboost Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iso, r-cran-isotone, r-cran-rpart Filename: pool/dists/noble/main/r-cran-isoboost_1.0.2-1.ca2404.1_all.deb Size: 102056 MD5sum: e7102402795cdc618453fee0fc902886 SHA1: 290e423b04fd0b11cc335ea3320849ce119cc426 SHA256: 2ecb3619a0df2085510967f8581f945faf28a5a9bd06545d3a4068cce4b253d1 SHA512: 18b0eae300af3d5fd99489b8568d52d571f40fdad92b376204f9f27a3652ac899d04594b2c5af172639fc8646da63bdebd0fb872d53ba5e0181cf21ea5390291 Homepage: https://cran.r-project.org/package=isoboost Description: CRAN Package 'isoboost' (Isotonic Boosting Classification Rules) In classification problems a monotone relation between some predictors and the classes may be assumed. In this package 'isoboost' we propose new boosting algorithms, based on LogitBoost, that incorporate this isotonicity information, yielding more accurate and easily interpretable rules. Package: r-cran-isobxr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-readxl, r-cran-dplyr, r-cran-data.table, r-cran-desolve, r-cran-rlang, r-cran-ggplot2, r-cran-ggrepel, r-cran-qgraph, r-cran-writexl, r-cran-r.utils, r-cran-fs, r-cran-magrittr, r-cran-tidyr, r-cran-gridextra, r-cran-purrr, r-cran-reshape2, r-cran-tictoc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-mass, r-cran-roxygen2, r-cran-tidyselect Filename: pool/dists/noble/main/r-cran-isobxr_2.0.0-1.ca2404.1_all.deb Size: 509702 MD5sum: 61e2ed78727e8517477de6580b97c309 SHA1: 8d6d2b784aed6c8dc78b2f21fda820dc6ed34acc SHA256: 2dfe4c4efac1de9897559f181b7ba1980d5785749748facbe4951f3471b6ece3 SHA512: f484b01978f43fdde1ae700c8483d21daf8d38d954a3e5c34464b88d284c34cb93c9944b4db6981e5f78ae9ca585d4afb2fc11fdc59d3598cc70456d5856f3b5 Homepage: https://cran.r-project.org/package=isobxr Description: CRAN Package 'isobxr' (Stable Isotope Box Modelling in R) A set of functions to run simple and composite box-models to describe the dynamic or static distribution of stable isotopes in open or closed systems. The package also allows the sweeping of many parameters in both static and dynamic conditions. The mathematical models used in this package are derived from Albarede, 1995, Introduction to Geochemical Modelling, Cambridge University Press, Cambridge . Package: r-cran-isocalcr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-isocalcr_0.1.1-1.ca2404.1_all.deb Size: 288334 MD5sum: c1e401f02860c8ead0de0df61854e561 SHA1: 4b63cea3400921a1eb250409fe304651f19146c5 SHA256: 5711f052fe6e5d755c146be27e8f06ecefb4883e80bac40dc0357eaa136c95f2 SHA512: 2c48515d2e044b2deeb86ab310fa9b35cff099b114f70d91ffb62780037dc923dbb1eea56c74a2b9311635d1399807edf4e75a7e15866d37b9ad722ada50bac4 Homepage: https://cran.r-project.org/package=isocalcR Description: CRAN Package 'isocalcR' (Isotope Calculations in R) Perform common calculations based on published stable isotope theory, such as calculating carbon isotope discrimination and intrinsic water use efficiency from wood or leaf carbon isotope composition. See Mathias and Hudiburg (2022) in Global Change Biology . Package: r-cran-isocat Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 739 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-plyr, r-cran-sp, r-cran-magrittr, r-cran-foreach Suggests: r-cran-dendextend, r-cran-doparallel, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-kableextra, r-cran-knitr, r-cran-pvclust, r-cran-rmarkdown, r-cran-rastervis, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-isocat_0.3.0-1.ca2404.1_all.deb Size: 478088 MD5sum: ea2bb45378e70539ab8753f3aa5f0c0b SHA1: 50375f03ffe8230bc4dabda88b2af70f51c57044 SHA256: 797abfe7219b8aa2b2c6bb108502ed663a0a0c82488f8195479964e0db0bd741 SHA512: c408a9f413d2864a98403e81482822000109c2deef3a2e94d789a478ad5745984a61cb322630f8411859a5ca05912e826a00f299be9cb2e72045dd079f897a0f Homepage: https://cran.r-project.org/package=isocat Description: CRAN Package 'isocat' (Isotope Origin Clustering and Assignment Tools) This resource provides tools to create, compare, and post-process spatial isotope assignment models of animal origin. It generates probability-of-origin maps for individuals based on user-provided tissue and environment isotope values (e.g., as generated by IsoMAP, Bowen et al. [2013] ) using the framework established in Bowen et al. (2010) ). The package 'isocat' can then quantitatively compare and cluster these maps to group individuals by similar origin. It also includes techniques for applying four approaches (cumulative sum, odds ratio, quantile only, and quantile simulation) with which users can summarize geographic origins and probable distance traveled by individuals. Campbell et al. [2020] establishes several of the functions included in this package . Package: r-cran-isocheck Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-dplyr, r-cran-plyr Filename: pool/dists/noble/main/r-cran-isocheck_0.1.0-1.ca2404.1_all.deb Size: 103378 MD5sum: 8dccde859e200486d940aad2df98fbc1 SHA1: 196674838cc7f65141a49663991e5d7f5dd3559e SHA256: 85cbf003c9e77c695976b3fee9aeb911f183449ac66b83ecc17c81adf43383ce SHA512: 55dc889d4245d39f10feda4b66869307618e1a750c24d5929789746609564243d0081cbf15f5a829bba192c75018583b6ead9d2c94975fed85df17b049daa71a Homepage: https://cran.r-project.org/package=IsoCheck Description: CRAN Package 'IsoCheck' (Isomorphism Check for Multi-Stage Factorial Designs withRandomization Restrictions) Contains functions to check the isomorphism of multi-stage factorial designs with randomisation restrictions based on balanced spreads and balanced covering stars of PG(n-1,2) as described in Spencer, Ranjan and Mendivil (2019) . Package: r-cran-isocodes Architecture: all Version: 2026.03.28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-isocodes_2026.03.28-1.ca2404.1_all.deb Size: 215796 MD5sum: e211101a8c92801b12d6dea4b19a0962 SHA1: 53950dcd5d62139ddebd47b31eb21b2b7672544a SHA256: 5841236c93b0b7cd2095c3333bd5699db32201f5d9efee77669fc1513f8849cb SHA512: 655b3cca594b59267e073d8c72b7a7e45246345527f82336dc0681bf5a79e6de5568c6a6eb64730ada8d1de8ec9179065e0e432042f643176f5fa2f407bcd0bd Homepage: https://cran.r-project.org/package=ISOcodes Description: CRAN Package 'ISOcodes' (Selected ISO Codes) ISO language, territory, currency, script and character codes. Provides ISO 639 language codes, ISO 3166 territory codes, ISO 4217 currency codes, ISO 15924 script codes, and the ISO 8859 character codes as well as the UN M.49 area codes. Package: r-cran-isocor Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3605 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bslib, r-cran-config, r-cran-dt, r-cran-golem, r-cran-maldiquant, r-cran-markdown, r-cran-plyr, r-cran-shiny, r-cran-shinyalert, r-cran-shinyjs Suggests: r-cran-shinytest2, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-isocor_0.2.8-1.ca2404.1_all.deb Size: 3564984 MD5sum: 07b51d946d0eda7a08a2bd57a7287a86 SHA1: 1cb049a672779680945fb8e721249fb612f079a1 SHA256: 876b96c9f32152159e2221121a14ad241e06eeb26b84ad2b677fc65d3ac4e4a6 SHA512: abc44fb1082984acf69a3c4cbc5590535a6e028cf2d6ca86ceb9ee9d7fe8307ba33730dfd7abf7db9440b31ead2ee1938df9c0caf60990765a3be87aa460251e Homepage: https://cran.r-project.org/package=IsoCor Description: CRAN Package 'IsoCor' (Analyze Isotope Ratios in a 'Shiny'-App) Analyzing Inductively Coupled Plasma - Mass Spectrometry (ICP-MS) measurement data to evaluate isotope ratios (IRs) is a complex process. The 'IsoCor' package facilitates this process and renders it reproducible by providing a function to run a 'Shiny'-App locally in any web browser. In this App the user can upload data files of various formats, select ion traces, apply peak detection and perform calculation of IRs and delta values. Results are provided as figures and tables and can be exported. The App, therefore, facilitates data processing of ICP-MS experiments to quickly obtain optimal processing parameters compared to traditional 'Excel' worksheet based approaches. A more detailed description can be found in the corresponding article . The most recent version of 'IsoCor' can be tested online at . Package: r-cran-isocountry Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-isocountry_0.6.0-1.ca2404.1_all.deb Size: 26168 MD5sum: f93e2b95a55a49b8011a549a7fe28e66 SHA1: a1a23e50aa107491c24eb722b4f449aad7b723b0 SHA256: 8398e50f166d51c20e0cd2562e9779003968a7edd8a0d52f4e94d5befb4f7d26 SHA512: a96eec4a06c6b4db009a1b19e8594555e0b324fcc2a03ab90160614779365206fb734ff10737eb8e31bf9567c74142cdba993499517ee214ed51d429581a132a Homepage: https://cran.r-project.org/package=isocountry Description: CRAN Package 'isocountry' (ISO 3166-1 Country Codes) ISO 3166-1 country codes and ISO 4217 currency codes provided by the International Organization for Standardization. Package: r-cran-isogeochem Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 873 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-shades, r-cran-viridislite, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-spelling, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-isogeochem_1.1.1-1.ca2404.1_all.deb Size: 624606 MD5sum: a1619d66536b77dc9555099b1c8c93cc SHA1: c80aaa33b64db6fd9ef9407ebe2abe0ea6d17b67 SHA256: cd4c854165c129895323899d2b431f918387b30ef5a2fa4da469e2fcfcd03071 SHA512: ef13a8fbac2ffde66318e52f659962088bcd85094da76f2df896d1c6cc7eec5a51251d2ca1c8fc9e6f261c934220d87df316e39251617d9d3bf4c1f28b30f983 Homepage: https://cran.r-project.org/package=isogeochem Description: CRAN Package 'isogeochem' (Tools for Stable Isotope Geochemistry) This toolbox makes working with oxygen, carbon, and clumped isotope data reproducible and straightforward. 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Package: r-cran-isoniche Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-lmodel2 Filename: pool/dists/noble/main/r-cran-isoniche_0.1.1-1.ca2404.1_all.deb Size: 52854 MD5sum: f20f995731ca5dccd362482d9147ee11 SHA1: a13eccdcb1b7a86e4fd56e4f98e084ba84c16939 SHA256: ec94b141fc5ee5dc003865970e1adabc1ddd229c5329665b6fc462d78f5a15c1 SHA512: 4236ba16630cec9dade4fced3547561701af36feae70cd35aa54ce09262dc17f7021ae78fe3c385297e53c5194a00847d22e85d083def1ead3350273a07ddb02 Homepage: https://cran.r-project.org/package=isoniche Description: CRAN Package 'isoniche' (Calculating Density-Independent Niche Breadth Indices fromAbundance Data) Deriving isodar-based niche breadth indices from abundance data of two or more habitats, including several methods based on pairwise isodars, multidimensional isodars, and isodar-adjusted inequality. Package: r-cran-isoorbi Architecture: all Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4485 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-cli, r-cran-glue, r-cran-withr, r-cran-lifecycle, r-cran-tidyr, r-cran-tibble, r-cran-tidyselect, r-cran-dplyr, r-cran-ggplot2, r-cran-scales, r-cran-readr, r-cran-readxl, r-cran-openxlsx, r-cran-purrr, r-cran-prettyunits, r-cran-arrow, r-cran-knitr Suggests: r-cran-devtools, r-cran-fansi, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-forcats Filename: pool/dists/noble/main/r-cran-isoorbi_1.5.2-1.ca2404.1_all.deb Size: 2247236 MD5sum: 66d3908878dfc21a289df5029cb6b3e6 SHA1: 2affe10527434bcd19872fd47d7272755c2cc5b4 SHA256: 51d94eb017b3875f68cb7e61683a4df5fe6f61e145e03257744a8ff94592918a SHA512: 9a4478a81298748abd6073d173012c6f19ee690611ddc569ad2cb06576c9571524f654b75f88e3ad432d477e7e3a0e242f4b2dc645e3d749724ea1bc47e1e72f Homepage: https://cran.r-project.org/package=isoorbi Description: CRAN Package 'isoorbi' (Process Orbitrap Isotopocule Data) Read and process isotopocule data from an Orbitrap Isotope Solutions mass spectrometer. Citation: Kantnerova et al. (Nature Protocols, 2024). Package: r-cran-isopam Architecture: all Version: 3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-cluster, r-cran-fastkmedoids, r-cran-future, r-cran-future.apply, r-cran-ps, r-cran-proxy, r-cran-ggplot2, r-cran-tibble Filename: pool/dists/noble/main/r-cran-isopam_3.6-1.ca2404.1_all.deb Size: 126320 MD5sum: 2ea9ca2d7c8fa38551f2ac88a772ac98 SHA1: dfc0b842acfe95a61bbc7a081a6a0488879a00eb SHA256: 2e6a0554119c2437e8a95dfc969206999bc0ab6a181990228ceea5815f2e799d SHA512: 5e3cb702ecce6c5f5c9900182c5ae43500813e8445558df9e484e43aa87ee00e92064e70bd85a62c7cc084bd04056538e2065e6b9a4c8a579983e0f6de8bb9d7 Homepage: https://cran.r-project.org/package=isopam Description: CRAN Package 'isopam' (Clustering of Sites with Species Data) Clustering algorithm developed for use with plot inventories of species. 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It provides functions to display the data in the ternary space, to add or tune graphical elements and to display statistical summaries. It also includes common ternary diagrams which are useful for the archaeologist (e.g. soil texture charts, ceramic phase diagram). Package: r-cran-isoplotr Architecture: all Version: 6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1557 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-isoplotr_6.8-1.ca2404.1_all.deb Size: 1421624 MD5sum: 1b253b8326fb9d431dfb4931fbf624d0 SHA1: 720fb36fa8c5a709f31c930d5b68501222d6977b SHA256: 45ece3f9bd2017826d475be391b1bdfc13e94f9e13793938d379952972853271 SHA512: e6d232e11836b3d1508dc777d35ec39466ceb3bca1aea46601b9f691f51a565a38459141cac7d9a4e330da57c00fa8da827d72f1d63e69dc40fd127e56ac1cce Homepage: https://cran.r-project.org/package=IsoplotR Description: CRAN Package 'IsoplotR' (Statistical Toolbox for Radiometric Geochronology) Plots U-Pb data on Wetherill and Tera-Wasserburg concordia diagrams. Calculates concordia and discordia ages. Performs linear regression of measurements with correlated errors using 'York', 'Titterington', 'Ludwig' and Omnivariant Generalised Least-Squares ('OGLS') approaches. Generates Kernel Density Estimates (KDEs) and Cumulative Age Distributions (CADs). Produces Multidimensional Scaling (MDS) configurations and Shepard plots of multi-sample detrital datasets using the Kolmogorov-Smirnov distance as a dissimilarity measure. Calculates 40Ar/39Ar ages, isochrons, and age spectra. Computes weighted means accounting for overdispersion. Calculates U-Th-He (single grain and central) ages, logratio plots and ternary diagrams. Processes fission track data using the external detector method and LA-ICP-MS, calculates central ages and plots fission track and other data on radial (a.k.a. 'Galbraith') plots. Constructs total Pb-U, Pb-Pb, Th-Pb, K-Ca, Re-Os, Sm-Nd, Lu-Hf, Rb-Sr and 230Th-U isochrons as well as 230Th-U evolution plots. Package: r-cran-isoplotrgui Architecture: all Version: 6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-isoplotr, r-cran-shinylight Filename: pool/dists/noble/main/r-cran-isoplotrgui_6.8-1.ca2404.1_all.deb Size: 1479204 MD5sum: 6ff932c48762c019ed0a35d6a386f325 SHA1: 891ffddda591d5f9a3db3121724ad80a7839065b SHA256: adcd2559d9f3ab578678cb5218f65f3ccf530cd6ad623cad4c9b53c15405e662 SHA512: 2164114e05aeda3e7b9be3c36803f1666b510e70b092c606c0b3f6840e3984cc59686b3b52dfa421b2b728aa2f1a862ba33423c6652e5fb42d539c912254836f Homepage: https://cran.r-project.org/package=IsoplotRgui Description: CRAN Package 'IsoplotRgui' (Web Interface to 'IsoplotR') Provides a graphical user interface to the 'IsoplotR' package for radiometric geochronology. The GUI runs in an internet browser and can either be used offline, or hosted on a server to provide online access to the 'IsoplotR' toolbox. 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It includes functions for storing, prioritizing (sorting), displaying, adding, deleting, and selecting (filtering) issues based on qualitative and quantitative information. Issues (labels and milestones) are written in lists and categorized into the S3 class to be easily manipulated as datasets in R. 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The 'itol.toolkit' package can support all types of annotation templates. Package: r-cran-itop Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-corpcor Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-nmf, r-cran-pcalg, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-itop_1.0.2-1.ca2404.1_all.deb Size: 91346 MD5sum: 593068ec90462c17e7582d8757a8780f SHA1: 448098b8aa070db3de00d8eac0801b9430df9268 SHA256: d07545682eb079bbf120735387ffb98cf06c1ad9965fd2bcdcd7d55d6a4a8045 SHA512: f9d0abcfddb850bb76b720b8ec993c11b1911376f3776f595c5448d7f12c7404be9f1af354f34087e929ad92b196a64177ab8609c43caed5f40271e25692b4f5 Homepage: https://cran.r-project.org/package=iTOP Description: CRAN Package 'iTOP' (Inferring the Topology of Omics Data) Infers a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples. We based this methodology on the RV coefficient (Robert & Escoufier, 1976, ), a measure of matrix correlation, which we have extended for partial matrix correlations and binary data (Aben et al., 2018, ). Package: r-cran-itos Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rcbalance, r-cran-biasedurn, r-cran-xtable Suggests: r-cran-weightedrank Filename: pool/dists/noble/main/r-cran-itos_1.0.3-1.ca2404.1_all.deb Size: 270496 MD5sum: 3b3eef17a54f331532cad50718827a9c SHA1: 901a299aa18ab1de885aeeaf9f3214cf6160fca3 SHA256: 1a5516fb540f5d0e9d3bc43e424792eaa16267356e7e72c06fbdbb33469cdda6 SHA512: 5a360cb75f48d2cc422cda46a8fe813302804dcdf6bca1119a04ba30782a3a6c9ab6655677e7e7dec4c3ec605c9bce6127ebda365c364133ce73ce125bb41824 Homepage: https://cran.r-project.org/package=iTOS Description: CRAN Package 'iTOS' (Methods and Examples from Introduction to the Theory ofObservational Studies) Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies." Data sets are 'aHDL' from Rosenbaum (2023a) and 'bingeM' from Rosenbaum (2023b) . The function makematch() uses two-criteria matching from Zhang et al. (2023) to create the matched data 'bingeM' from 'binge'. The makematch() function also implements optimal matching (Rosenbaum (1989) ) and matching with fine or near-fine balance (Rosenbaum et al. (2007) and Yang et al (2012) ). The book makes use of two other R packages, 'weightedRank' and 'tightenBlock'. Package: r-cran-itraxr Architecture: all Version: 1.13.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3328 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-compositions, r-cran-readr, r-cran-tiff, r-cran-janitor, r-cran-ggcorrplot, r-cran-rlang, r-cran-tidyr, r-cran-broom, r-cran-tibble, r-cran-stringr, r-cran-munsellinterpol, r-cran-plyr, r-cran-tidyselect Suggests: r-cran-magrittr Filename: pool/dists/noble/main/r-cran-itraxr_1.13.2-1.ca2404.1_all.deb Size: 2381012 MD5sum: 664ff9f9cfbd2610f31df89f3320f75a SHA1: a5678f42faeb095e6b3cea9f4ea1d18417415982 SHA256: 712a505eab3de65d312faf58bb03f541057037c8941cfdf5b7fc6b45277199d7 SHA512: 5e27a8f7759711929f05681e105da10cb4299e817dca550b132f67598e0a40354ef90f641e085271f047698e81b1d3a72498d3717be0086b5162c8702cf4f066 Homepage: https://cran.r-project.org/package=itraxR Description: CRAN Package 'itraxR' (Itrax Data Analysis Tools) Parse, trim, join, visualise and analyse data from Itrax sediment core multi-parameter scanners manufactured by Cox Analytical Systems, Sweden. Functions are provided for parsing XRF-peak area files, line-scan optical images, and radiographic images, alongside accompanying metadata. A variety of data wrangling tasks like trimming, joining and reducing XRF-peak area data are simplified. Multivariate methods are implemented with appropriate data transformation. Package: r-cran-itrimhoch Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-itrimhoch_1.0.0-1.ca2404.1_all.deb Size: 109026 MD5sum: 9282a77f5e0e696d1676bf4ea3e1ee24 SHA1: 68c7606b28564744d5c55e99d4e5b887e181709f SHA256: e52b99d436bfa91571537895899b0bc55970520228314f42dd363d14e9e91b35 SHA512: 582aa92d9d5311ba3a953b78fa022a6267f6c5668072acbe1b824ca792e732550d0c00e5aa53504405a0ed2e8ff804ffa487570ac260caaf0c042946c7da0184 Homepage: https://cran.r-project.org/package=itrimhoch Description: CRAN Package 'itrimhoch' (Improved Trimmed Weighted Hochberg Procedures and Sample SizeOptimization) The improved trimmed weighted Hochberg procedure provides increased statistical power and relaxes the dependence assumptions for familywise error rate control compared to the original weighted Hochberg procedure. This package computes the boundaries required for implementing the proposed methodology and includes sample size optimization methods. See Gou, J., Chang, Y., Li, T., and Zhang, F.(2025). Improved trimmed weighted Hochberg procedures with two endpoints and sample size optimization. Technical Report. Package: r-cran-its.analysis Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-car, r-cran-forecast, r-cran-boot, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-its.analysis_1.6.0-1.ca2404.1_all.deb Size: 37002 MD5sum: 1cab4d5ea5e7902754a32e75b58b0d3c SHA1: 57e50f5a9fd0af3b15e81919cb92e5249eee5699 SHA256: f8af98ca1144e3461039201763f360762d6084e13cb8916f461cc32b062699ef SHA512: 90db57d18c953803ac691e84e1c3b4db5f6dd86baa24afbf2076f015854eeaa5ad448385a05f2cebdd74b46b9e2a92f7914713a584e2e8e1b421ed8cb4a8d3e6 Homepage: https://cran.r-project.org/package=its.analysis Description: CRAN Package 'its.analysis' (Running Interrupted Time Series Analysis) Two functions for running and then post-estimating an Interrupted Time Series Analysis model. This is a solution for running time series analyses on temporally short data. See English (2019) 'The its.analysis R package - Modelling short time series data' for an overview of the method. Package: r-cran-itsadug Architecture: all Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4871 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-plotfunctions Suggests: r-cran-knitr, r-cran-xtable, r-cran-sp, r-cran-data.table Filename: pool/dists/noble/main/r-cran-itsadug_2.5-1.ca2404.1_all.deb Size: 3301338 MD5sum: e4d7463c0199a42af10a06203feb2992 SHA1: fcb0032259ce8678d15109db883c719f966310e4 SHA256: 748c19ced03cfb94c3591650e12f73ec66982dd5c0ed67499f1d14499f98e03b SHA512: d39534d77b15ed35eb27cb815ce6b45a0d6bfada9ec70ee9fcb908d150d386ec8f6d152726b9dac007025750d374eaca7c9fd1d6d75a67dd564a410e592f2fdb Homepage: https://cran.r-project.org/package=itsadug Description: CRAN Package 'itsadug' (Interpreting Time Series and Autocorrelated Data Using GAMMs) GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. 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Package: r-cran-itscalledsoccer Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 588 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httpcache, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-r6, r-cran-tidyr, r-cran-stringi, r-cran-rlang, r-cran-data.table, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-itscalledsoccer_0.3.2-1.ca2404.1_all.deb Size: 552896 MD5sum: 33b8e691c5e2866f55dd21b3839fb73c SHA1: 7aab0c404a57701cb0cedf385738c8eecf517473 SHA256: 04e719eeb5c7035fcf3f7760b1040dcedb4a3f44ded8483dc6e3d2eba59758af SHA512: 3c473c37a4e345317305e3e6cccbea9c51584e4d9018356c2fda4dd2229791639f53378a682e2ab0fe0604fdcab260bdce6ba6f859e548f100be486e95db102a Homepage: https://cran.r-project.org/package=itscalledsoccer Description: CRAN Package 'itscalledsoccer' (American Soccer Analysis API Client) Provides a wrapper around the same API that powers the American Soccer Analysis app. Package: r-cran-itsdm Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-fastshap, r-cran-ggplot2, r-cran-isotree, r-cran-mgcv, r-cran-ncdf4, r-cran-outliertree, r-cran-patchwork, r-cran-raster, r-cran-rlang, r-cran-rocit, r-cran-sf, r-cran-stars, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-itsdm_0.2.2-1.ca2404.1_all.deb Size: 1459210 MD5sum: 39016bb607153703d69ffcc02b550f44 SHA1: 86a8d4b7194b72bbc230560fd76239d9c2f4290a SHA256: de52e9a46cee91d1a767ccab9c00ce74cb9a662cbf5ae9c1c9b1d33963109ce7 SHA512: da171bc11e3dda8cff1ac93cd01e62c0d0a3be625b6abc9f050ea75f806de6e37128e96dca9396a2cd4137d20ae81849a31b89f8f3ba4afcf5cf14fed9f3b692 Homepage: https://cran.r-project.org/package=itsdm Description: CRAN Package 'itsdm' (Isolation Forest-Based Presence-Only Species DistributionModeling) Collection of R functions to do purely presence-only species distribution modeling with isolation forest (iForest) and its variations such as Extended isolation forest and SCiForest. See the details of these methods in references: Liu, F.T., Ting, K.M. and Zhou, Z.H. (2008) , Hariri, S., Kind, M.C. and Brunner, R.J. (2019) , Liu, F.T., Ting, K.M. and Zhou, Z.H. (2010) , Guha, S., Mishra, N., Roy, G. and Schrijvers, O. (2016) , Cortes, D. (2021) . Additionally, Shapley values are used to explain model inputs and outputs. See details in references: Shapley, L.S. (1953) , Lundberg, S.M. and Lee, S.I. (2017) , Molnar, C. (2020) , Štrumbelj, E. and Kononenko, I. (2014) . itsdm also provides functions to diagnose variable response, analyze variable importance, draw spatial dependence of variables and examine variable contribution. As utilities, the package includes a few functions to download bioclimatic variables including 'WorldClim' version 2.0 (see Fick, S.E. and Hijmans, R.J. (2017) ) and 'CMCC-BioClimInd' (see Noce, S., Caporaso, L. and Santini, M. (2020) . Package: r-cran-itsmr Architecture: all Version: 1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-itsmr_1.11-1.ca2404.1_all.deb Size: 171914 MD5sum: 82faaa991014eb689275f0396a5e2a2e SHA1: 6a6d10dac74407a57b89c6c3424201e6bb07c485 SHA256: 64aae8778cc0f771ac51ccdbe61a130879a3e56284b66a5858d0e0676bccfd9e SHA512: 0b9426394cc9f0122d24caf57d1cf6fa52915edc086d05b83a816648d34bbbc8ef7c987b31744c0e84236446f045802986265bffe3b98222c960c653cfadcdf2 Homepage: https://cran.r-project.org/package=itsmr Description: CRAN Package 'itsmr' (Time Series Analysis Using the Innovations Algorithm) Provides functions for modeling and forecasting time series data. Forecasting is based on the innovations algorithm. A description of the innovations algorithm can be found in the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis. Package: r-cran-iucnr Architecture: all Version: 0.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-stringr Suggests: r-cran-rlang, r-cran-testthat Filename: pool/dists/noble/main/r-cran-iucnr_0.0.0.1-1.ca2404.1_all.deb Size: 21636 MD5sum: 84878d2262dcb9e52cbfdba8e6d4d9e8 SHA1: 59757cf31cbb6b138836b389b9c04f6ab6c5fb60 SHA256: 9d92128e10dbffac3c4266902757987b2195ef2bd396a9685dc73fa4c1b0a3d8 SHA512: 5eb8c27f3fed580fd745102635cf7b14c31e6fd5f38d7f2fcf89f1cdf03aebe9ce7e3ad77990c75b4422cc450b4d459ff36b908cc7c0fad48ae43b52a3987b70 Homepage: https://cran.r-project.org/package=iucnr Description: CRAN Package 'iucnr' (IUCN Red List Data) Facilitates access to the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, a comprehensive global inventory of species at risk of extinction. This package streamlines the process of determining conservation status by matching species names with Red List data, providing tools to easily query and retrieve conservation statuses. Designed to support biodiversity research and conservation planning, this package relies on data from the 'iucnrdata' package, available on GitHub . To install the data package, use pak::pak('PaulESantos/iucnrdata'). Package: r-cran-iv.sensemakr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6725 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sensemakr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-iv.sensemakr_0.1.0-1.ca2404.1_all.deb Size: 5224598 MD5sum: fc5a8853a43fa65dbda1b09793d55673 SHA1: f0ec3cc32fa8d0212fc4861124d05fa591bcb408 SHA256: c09dde9575f155e688acb54c06e7ab7d79c181abe2d40ce7fdae8bd728adbdaa SHA512: 7578e569567c7c79c68e5fc74fc51beae6c7511e3cad06c821668476416999da14f25edc5b74d8df305c97e93b0864c0654fe25bb3f91485450e69345c9c9068 Homepage: https://cran.r-project.org/package=iv.sensemakr Description: CRAN Package 'iv.sensemakr' (Sensitivity Analysis Tools for Instrumental Variable Estimates) Implements a suite of sensitivity analysis tools for instrumental variable estimates as described in Cinelli and Hazlett (2025) . 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Package: r-cran-ivcheck Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-testthat, r-cran-fixest, r-cran-ivreg, r-cran-modelsummary, r-cran-broom, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ivcheck_0.1.1-1.ca2404.1_all.deb Size: 253568 MD5sum: 6b8b144d30679452948f17b675840563 SHA1: d92d290bfbb03ae1ad9a3f3fc6601541073f38ce SHA256: c982316f65eee84d165e119066f172de2108cdaa408df247b4b46427f89b0fc0 SHA512: 4b9af502d1b9862a16e23ec26afcec1e89910a7ff38f1ef80dbc859b0fa22a1a85aad062e4acce93a46646146772f78e346d6c99872640dea569d130abaf122b Homepage: https://cran.r-project.org/package=ivcheck Description: CRAN Package 'ivcheck' (Tests for Instrumental Variable Validity) Implements tests for the identifying assumptions of instrumental variable models, the local exclusion restriction and monotonicity conditions required for local average treatment effect identification. Covers Kitagawa (2015) , Mourifie and Wan (2017) , and Frandsen, Lefgren, and Leslie (2023) . Includes a one-shot wrapper that runs all applicable tests on a fitted instrumental variable model. Dispatches on 'fixest' and 'ivreg' model objects. 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Package: r-cran-ivd Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nimble, r-cran-coda, r-cran-ggplot2, r-cran-patchwork, r-cran-future, r-cran-future.apply, r-cran-rstan, r-cran-ggrepel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ivd_1.0.0-1.ca2404.1_all.deb Size: 322200 MD5sum: 3842ddcadbc1bc8690e8fdf8cb5717b8 SHA1: 1e64c0f61454f63c6577d7d71200b8f5dbf50683 SHA256: 3a21b69c5ffabea5686d75f22f5e03728425deb32a00418d5c7ec2a9ca1a71e0 SHA512: 39a03e6fd4e46cc4776d4265e59464cf7ac290d3297ebb9a24e77dfb1b2591abedf420c14ca62ab30c8630d29ae96f7eb1c24bd27986bd2e73268d0d824a1c47 Homepage: https://cran.r-project.org/package=ivd Description: CRAN Package 'ivd' (Individual Variance Detection) Fit mixed-effects location scale models with spike-and-slab priors on the location random effects to identify units with unusual residual variances. The method is described in detail in Carmo, Williams and Rast (2025) . 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This package implements the method described in Marbach and Hangartner (2020) and Hangartner, Marbach, Henckel, Maathuis, Kelz and Keele (2021) . 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Package: r-cran-ivdml Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-ranger, r-cran-xgboost Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ivdml_1.0.1-1.ca2404.1_all.deb Size: 100048 MD5sum: edf72665d817e334d8033254fcdb7f9d SHA1: 9d17864309b9b4d1bfba80e7fdced0c9a7887b4f SHA256: ae5c127d8c026a04c7d89edbdfa05ad3e4f4e8fdfec4a960b9babc625c1619cc SHA512: 5619cacc7cb4b3f4bda2400740f060f510dad1c9fc3e6edd2f35bfe56255dc4e8af147d91af8e89a2330ee558c8c7ce9b0aa6a5d3b511bdf40a1b657ee5b3a26 Homepage: https://cran.r-project.org/package=IVDML Description: CRAN Package 'IVDML' (Double Machine Learning with Instrumental Variables andHeterogeneity) Instrumental variable (IV) estimators for homogeneous and heterogeneous treatment effects with efficient machine learning instruments. The estimators are based on double/debiased machine learning allowing for nonlinear and potentially high-dimensional control variables. Details can be found in Scheidegger, Guo and Bühlmann (2025) "Inference for heterogeneous treatment effects with efficient instruments and machine learning" . 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Furthermore, indicator saturation methods can be used to detect outliers and structural breaks in the sample. Package: r-cran-ivitr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet, r-cran-randomforest, r-cran-dplyr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-ivitr_0.1.0-1.ca2404.1_all.deb Size: 61062 MD5sum: 0bdc21f3ce5b4ac9cb4d8af17b36a396 SHA1: b70ab1ca8dc2e7a8c99b65eafeda5f3710bded44 SHA256: 4d979d2f7719ddf5744f589865a72acc8ff56884ff26a33c338fe452635ce6d4 SHA512: f49d1c950671c212f886294e4eb1230b5ae876bd79730b87a0b1dfc82a78bbd7798b24360abd95e20715327ab6ae7f0b369ca6d504cd00abc5879050ffbced16 Homepage: https://cran.r-project.org/package=ivitr Description: CRAN Package 'ivitr' (Estimate IV-Optimal Individualized Treatment Rules) A method that estimates an IV-optimal individualized treatment rule. An individualized treatment rule is said to be IV-optimal if it minimizes the maximum risk with respect to the putative IV and the set of IV identification assumptions. Please refer to for more details on the methodology and some theory underpinning the method. Function IV-PILE() uses functions in the package 'locClass'. Package 'locClass' can be accessed and installed from the 'R-Forge' repository via the following link: . Alternatively, one can install the package by entering the following in R: 'install.packages("locClass", repos="")'. Package: r-cran-ivmodel Architecture: all Version: 1.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-formula, r-cran-reshape2, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ivmodel_1.9.1-1.ca2404.1_all.deb Size: 417316 MD5sum: 640c60e5ab17eecb6134852bb8924623 SHA1: 69fc140057b43bd4182288abda60ab73e13379d0 SHA256: 344edef3b14655b55d9f67965795b7ee8483166e82e9ef4f086e2cddce76d61e SHA512: 32e5fa4ad1cd1979b130baeb3ed2b0ddeebe9919aea9150427bbba8c0632386dcb0c3ea836ae5af780ada38273b94198d852be81893523f99d73fc3838851807 Homepage: https://cran.r-project.org/package=ivmodel Description: CRAN Package 'ivmodel' (Statistical Inference and Sensitivity Analysis for InstrumentalVariables Model) Carries out instrumental variable estimation of causal effects, including power analysis, sensitivity analysis, and diagnostics. See Kang, Jiang, Zhao, and Small (2020) for details. 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This interpretation can be used to extrapolate from the compliers to estimate treatment effects for other subpopulations. This package provides a flexible set of methods for conducting this extrapolation. It allows for parametric or nonparametric sieve estimation, and allows the user to maintain shape restrictions such as monotonicity. The package operates in the general framework developed by Mogstad, Santos and Torgovitsky (2018) , and accommodates either point identification or partial identification (bounds). In the partially identified case, bounds are computed using either linear programming or quadratically constrained quadratic programming. Support for four solvers is provided. Gurobi and the Gurobi R API can be obtained from . CPLEX can be obtained from . CPLEX R APIs 'Rcplex' and 'cplexAPI' are available from CRAN. MOSEK and the MOSEK R API can be obtained from . The lp_solve library is freely available from , and is included when installing its API 'lpSolveAPI', which is available from CRAN. 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Built with 'ggplot2', the plots are rendered interactive using 'ggiraph', enabling users to hover over points to display detailed information or click to trigger custom actions. 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U., Epskamp, S. (2025) The Invariance Partial Pruning Approach to The Network Comparison in Longitudinal Data. IVPP is a two-step method that first test for global network structural difference with invariance test and then inspect specific edge difference with partial pruning. The package also allows you to compute centrality measures and use radar chart to plot. Analysis of bridge centralities by community pairs is also possible (e.g., the bridge strength from depression to anxiety, and from depression to panic disorder). Package: r-cran-ivreg Architecture: all Version: 0.6-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1367 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-formula, r-cran-lmtest, r-cran-mass Suggests: r-cran-aer, r-cran-effects, r-cran-knitr, r-cran-insight, r-cran-rmarkdown, r-cran-sandwich, r-cran-testthat, r-cran-modelsummary, r-cran-gt, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-ivreg_0.6-7-1.ca2404.1_all.deb Size: 908150 MD5sum: e8fbf4fb92b247343cbe30cbf45cdef1 SHA1: 96ef4d440e7373c41480cbf2610bca988ac85993 SHA256: 5a50e6ada1133b9e25004133d9a467fe61bd08410b195a2a574296f083f30699 SHA512: e379f4187271c50c9629eb15efdc00518efe7d5548d4bab1070301d364ad7a5db982db03e930a479e73b994a48a8f9451dd2874aa0093a52fadfc1487c3e4fd9 Homepage: https://cran.r-project.org/package=ivreg Description: CRAN Package 'ivreg' (Instrumental-Variables Regression by '2SLS', '2SM', or '2SMM',with Diagnostics) Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). The main ivreg() model-fitting function is designed to provide a workflow as similar as possible to standard lm() regression. A wide range of methods is provided for fitted ivreg model objects, including extensive functionality for computing and graphing regression diagnostics in addition to other standard model tools. 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Package: r-cran-ivyplot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr Filename: pool/dists/noble/main/r-cran-ivyplot_0.1.0-1.ca2404.1_all.deb Size: 15622 MD5sum: 336be81e67b828f43973bd4ad449f5a2 SHA1: 6b4523fce926a3c7711b78eb397ab77e892dff1a SHA256: c57d55bf75f40c7d73d5a5e2dd6b2f29a2bc4c4e52e3305eb96cd25e8f658d48 SHA512: 3341f555cf6c7973c34c8bf3432ea152b5f03ce31b8880f595bab528e394d39ee1101ec6f2ed10fcdebe4ace09d115c8ef1c754cd1d26f84082af85386651c1c Homepage: https://cran.r-project.org/package=IVYplot Description: CRAN Package 'IVYplot' (Produces an IVY Plot (Similar to Dot Plot) with/withoutFrequencies) For a single variable, the IVY Plot stacks tied values in the form of leaflets. Five leaflets join to form a leaf. Leaves are stacked vertically. 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Package: r-cran-ixsurface Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ixsurface_0.1.0-1.ca2404.1_all.deb Size: 85554 MD5sum: bb3a46c4329bd5d8f5f63e13a2a69f17 SHA1: c5864166759c274c0e224dd6aaabb9c12bb410da SHA256: 6ae1a89b1827f3d75042c2a296c4bf2fc9ad8766b7c941706cbc1e6a55cefaaa SHA512: 622e93ffacc6962a64532c3143cafde2e29150acb6b6874ac7f90ba16c295f2045e0fca677acddf65dd87c4a0c5400a1454947e4589af96e516b4251f9b0545c Homepage: https://cran.r-project.org/package=ixsurface Description: CRAN Package 'ixsurface' (Interactive 3D Surface Plots for Multi-Factor InteractionVisualization) Visualize interactions between multiple experimental factors using interactive 3D surface plots powered by 'plotly'. Instead of examining combinatorial pairwise interaction plots, map factor combinations to response surfaces and use surface crossings as geometric indicators of interaction effects. Supports continuous, categorical, and mixed factor designs with automatic binning for continuous conditioning variables. 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With the package, user can also find tools to simulate random deviates from zero inflated or hurdle models and obtain maximum likelihood estimate of unknown parameters in these models. Package: r-cran-izmir Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rvest, r-cran-stringr, r-cran-jsonlite, r-cran-readr, r-cran-openxlsx, r-cran-magrittr Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2, r-cran-covr, r-cran-usethis Filename: pool/dists/noble/main/r-cran-izmir_0.1.0-1.ca2404.1_all.deb Size: 18018 MD5sum: b1b172f7408884e98ef16787ec633c0a SHA1: 1637170833943be37e004732afde3465f9b7af65 SHA256: 8abaaa257cb4694168ca29fbc290b2e313dad58f19a4cf64ee793904cac96b0b SHA512: 8e533b6395e28cd6f29f819d920c69e98bcf1b74baeb1af10194babfa1e3523b73e2131bba3891d57bbfc7bba313ee2cf36f70050caffe07a11c27e5b9b4d890 Homepage: https://cran.r-project.org/package=izmir Description: CRAN Package 'izmir' (R Wrapper for Izmir Municipality Open Data Portal) Call the data wrappers for Izmir Metropolitan Municipality's Open Data Portal. This will return all datasets formatted as Excel files (.csv or .xlsx), as well as datasets that require an API key. 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It can be applied whenever it is feasible to conduct many simulations, but works best when the data is approximately Poisson distributed. It was originally designed for demographic inference in evolutionary biology (Naduvilezhath et al., 2011 , Mathew et al., 2013 ). It has optional support for conducting coalescent simulation using the 'coala' package. 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Package: r-cran-jackknifer Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-future.apply Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-jackknifer_2.0.0-1.ca2404.1_all.deb Size: 34526 MD5sum: 5af316afccd4a03eb2f2ff55b88558a8 SHA1: 83a1ac677a7ff9d8377a0ee549a77f2061fadb56 SHA256: 1e6f63ac10e5c8004241564efb1d7e752ffb584346be12522b01579003c0f428 SHA512: 91ff6d18f231a6d9518d0e84bde4886fe20788a19a138cfb8a1a47139c66e456018380107d9e36e0908b40c1e9a65c8b128f54e3ddd47581644aca081aafafd1 Homepage: https://cran.r-project.org/package=jackknifeR Description: CRAN Package 'jackknifeR' (Delete-d Jackknife for Point and Interval Estimation) Implements delete-d jackknife resampling for robust statistical estimation. The package provides both weighted (HC3-adjusted) and unweighted versions of jackknife estimation, with parallel computation support. Suitable for biomedical research and other fields requiring robust variance estimation. 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In this jackstrap method, the package provides two criteria to define outliers: heaviside and k-s test. The technique was developed by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers." . 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The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related unsupervised learning algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq (scRNA-seq). Package: r-cran-jacpop Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-jacpop_0.6-1.ca2404.1_all.deb Size: 14980 MD5sum: b397706e1462ea105c231fc3458b1329 SHA1: 9de814d95ced9bdabc52450346c9fdb4155234f2 SHA256: 2ca2c307b082bc5fd7f18efcf8c13fb9e2c72bc66f845eceab1660092af37dc5 SHA512: 20bbc1240057f82fb6bd82d702106b52c465706701d62d9b2151557e9134bcd37c0da02f8f6a0222a662852b8cf2098b2c251ebb3477fe33c0b1d2776134e693 Homepage: https://cran.r-project.org/package=jacpop Description: CRAN Package 'jacpop' (Jaccard Index for Population Structure Identification) Uses the Jaccard similarity index to account for population structure in sequencing studies. This method was specifically designed to detect population stratification based on rare variants, hence it will be especially useful in rare variant analysis. Package: r-cran-jacquard Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsolnp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-jacquard_1.0.2-1.ca2404.1_all.deb Size: 923682 MD5sum: 28c2e78995c04e782f163db3ed5fb475 SHA1: 71c0cf19f99e074c217c605770d28e5999e9773c SHA256: 9b1843bbb32f4cee98584045209e18657563d593fd6960d728c7d571294d444e SHA512: b0501830f064f5b99d41bcf45467db5c325f4c6bf500e3fea5e9cfbe72ccff0f9f39188eb6a5af111acc91f2475995a3d86c2eb21e3b782bae800b81fd4390cd Homepage: https://cran.r-project.org/package=Jacquard Description: CRAN Package 'Jacquard' (Estimation of Jacquard's Genetic Identity Coefficients) Contains procedures to estimate the nine condensed Jacquard genetic identity coefficients (Jacquard, 1974) by constrained least squares (Graffelman et al., 2024) and by the method of moments (Csuros, 2014) . These procedures require previous estimation of the allele frequencies. Functions are supplied that estimate relationship parameters that derive from the Jacquard coefficients, such as individual inbreeding coefficients and kinship coefficients. Package: r-cran-jadelizardoptions Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-magrittr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-jadelizardoptions_1.0.1-1.ca2404.1_all.deb Size: 24968 MD5sum: e840ac2d80f1ed1338c213731ef957a6 SHA1: 8d8e8eb93683bb5bed283b832f6dafb34028f853 SHA256: 3f329d055752c70b9566288237c3e4244bdc35523fd5ad400f4e264ce05b8edf SHA512: b515ec0380a29ffec1822d36ce6d15c657ac8dd81b71269e0b147b4681baff399edb99bfef5cfd9343be7d8b93381101193368a99415ea6a58fbebfb08909432 Homepage: https://cran.r-project.org/package=jadeLizardOptions Description: CRAN Package 'jadeLizardOptions' (Trading Jade Lizard Option Strategies) Jade Lizard and Reverse Jade Lizard Option Strategies are presented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Russell A. Stultz (“The option strategy desk reference: an essential reference for option traders (First edition.)”, 2019, ISBN: 9781949443912). Package: r-cran-jage Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-data.table, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-jage_0.1.0-1.ca2404.1_all.deb Size: 63970 MD5sum: 5ea4697080c0ebbb24ba4b4b954027ab SHA1: e33163de7638a442547943fa333f14e121e5b664 SHA256: f931fdbcbf3a660eeab3e6bc899778d7a5866f08f6790fa7fe147e3fb1c0a917 SHA512: bfc32ac953f67c59c83c49e86b4cd21d8294c2f21113a0d37b23a3a0479871873c9958152204c39f1f7a3b5a4fc8dbe062edb8ee103460677a34f9d05b4efbbf Homepage: https://cran.r-project.org/package=jage Description: CRAN Package 'jage' (Estimation of Developmental Age) Bayesian methods for estimating developmental age from ordinal dental data. For an explanation of the model used, see Konigsberg (2015) . For details on the conditional correlation correction, see Sgheiza (2022) . Dental scoring is based on Moorrees, Fanning, and Hunt (1963) . Package: r-cran-jaggr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formatr, r-cran-glue Filename: pool/dists/noble/main/r-cran-jaggr_0.1.1-1.ca2404.1_all.deb Size: 71678 MD5sum: 1151a60224daab84f80020b287b15854 SHA1: 3f921d32cac37f01873725cbe1be01956afa1c7f SHA256: 0320ca667dd6204f10161f0fed2028b7a048dfdfad17cd9d8ac57e135295b2a2 SHA512: f3b84baf1f9dc615ff1766ca411136c093bfcf8a827d2aa0d68847218c99952288c4abad7057066a6e8fa83118f68ff475cb2a152e7c7d6e4b74d443b9a9e4cd Homepage: https://cran.r-project.org/package=jaggR Description: CRAN Package 'jaggR' (Supporting Files and Functions for the Book Bayesian Modellingwith 'JAGS') All the data and functions used to produce the book. We do not expect most people to use the package for any other reason than to get simple access to the 'JAGS' model files, the data, and perhaps run some of the simple examples. The authors of the book are David Lucy (now sadly deceased) and James Curran. It is anticipated that a manuscript will be provided to Taylor and Francis around February 2020, with bibliographic details to follow at that point. Until such time, further information can be obtained by emailing James Curran. Package: r-cran-jagshelper Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4010 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jagsui, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jagshelper_0.4.1-1.ca2404.1_all.deb Size: 3866424 MD5sum: 7770bf422038da6409ad8bea63e6fef1 SHA1: 4315f00232cc1f65e1167a1f0efd059a27d31651 SHA256: 68889e06f4f1240d18adc790d39de373007d7ee66e2e7fd2623fd07f2fdb8de8 SHA512: 196d784e08b8d2894d2da71b93e8dc51994c37b69149fa04b9e7d87cdd7e88811bc346455344e4e231a47482e9e63a181fb0dd1e18fdd3eeb82097702becf2d3 Homepage: https://cran.r-project.org/package=jagshelper Description: CRAN Package 'jagshelper' (Extracting and Visualizing Output from 'jagsUI') Tools are provided to streamline Bayesian analyses in 'JAGS' using the 'jagsUI' package. Included are functions for extracting output in simpler format, functions for streamlining assessment of convergence, and functions for producing summary plots of output. Also included is a function that provides a simple template for running 'JAGS' from 'R'. Referenced materials can be found at . Package: r-cran-jagstargets Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-fst, r-cran-posterior, r-cran-purrr, r-cran-qs2, r-cran-r2jags, r-cran-rjags, r-cran-rlang, r-cran-secretbase, r-cran-targets, r-cran-tarchetypes, r-cran-tibble, r-cran-tidyselect, r-cran-withr Suggests: r-cran-dplyr, r-cran-fs, r-cran-knitr, r-cran-qs, r-cran-r.utils, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-jagstargets_1.2.2-1.ca2404.1_all.deb Size: 385616 MD5sum: 1f623e992a530bc37227ba01f280e758 SHA1: 389082e6e88d647d2593eada9a26a1f65109838e SHA256: 3c458fc79119e59bef85ff64870bde1cac2bdc06640cb2de943b7a67ff0b36c6 SHA512: 12d596bf6ead8cb9249291dbae0a60541c4e6b5648480afd5afd80ebdd491cd44357988b8ad562b011f3ce93cd32d4cb9d9d23dd41a4e604d83c28590832b9d6 Homepage: https://cran.r-project.org/package=jagstargets Description: CRAN Package 'jagstargets' (Targets for JAGS Pipelines) Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the 'jagstargets' R package is leverages 'targets' and 'R2jags' to ease this burden. 'jagstargets' makes it super easy to set up scalable JAGS pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than 'targets' alone. For the underlying methodology, please refer to the documentation of 'targets' and 'JAGS' (Plummer 2003) . Package: r-cran-jagstree Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2200 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.tree, r-cran-gtools, r-cran-mcmcplots, r-cran-r2jags, r-cran-tidyverse, r-cran-diagrammer, r-cran-autowmm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jagstree_1.0.1-1.ca2404.1_all.deb Size: 516802 MD5sum: 99499b22ac54ed8f209bd48d1e22838a SHA1: 1b2d29845c6c817f44beb104fb21ce63aa43de9a SHA256: b30850468e97df18dc360e5406505d2ad99911dd52a1ae5829dfa4b0959e1ff2 SHA512: 90119dff091b0fb808af87a7d386acacd859cb57ea09de93980592b67ceff354cdbcfda796297b51a3aaf814a2e2957686b95fcdbb664867ef54ec41ea84b5cc Homepage: https://cran.r-project.org/package=JAGStree Description: CRAN Package 'JAGStree' (Automatically Write 'JAGS' Code for Hierarchical Bayesian Modelson Trees) When relationships between sources of data can be represented by a tree, the generation of appropriate Markov Chain Monte Carlo modeling code to be used with 'JAGS' to run a Bayesian hierarchical model can be automatically generated by this package. Any admissible tree-structured data can be used, under the assumption that node counts are multinomial and branching probabilities are Dirichlet among sibling groups. The methodological basis used to create this package can be found in Flynn (2023) . Package: r-cran-jagsui Architecture: all Version: 1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1706 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-rjags Suggests: r-cran-knitr, r-cran-markdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-jagsui_1.6.3-1.ca2404.1_all.deb Size: 1527108 MD5sum: 0a4e9034cb91c9b683f03958d955bff3 SHA1: 57f7aaf59ab25c6c909fbc1d5713873aa9ecaecd SHA256: 46035358875be5953fee2f49e24158fe3e0bb15940c55d8d0cfb777f3bbca669 SHA512: 99a4ffe5678b9703d2ea03aac71ab9b8330a941257f4ead2aed1c03294658bf8a7bb0fff3e17fee79efafcc9fb380062f9117ed5cd19746fe35222e37cf51df1 Homepage: https://cran.r-project.org/package=jagsUI Description: CRAN Package 'jagsUI' (A Wrapper Around 'rjags' to Streamline 'JAGS' Analyses) A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags'). A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers. Package: r-cran-jalcal Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-jalcal_0.3.0-1.ca2404.1_all.deb Size: 24028 MD5sum: 3fd5d2ef971550ea8748170f1712ea9c SHA1: e77416938cbee04ef8b6438757a639f72dcaa7c9 SHA256: 03847cd809742e25167ca404c8b0eab1ec45bedc732e6871a02055ca9e88a0f0 SHA512: 754b23910e722e256d67b4b4f8586c00cd588427a6b6c3c13b5624a8d2c32c565d0849df80694d5a919ccdbbd14cbf473ee96d15579ec9a0186e58c744f21596 Homepage: https://cran.r-project.org/package=jalcal Description: CRAN Package 'jalcal' (Convert Between Jalaali (Persian or Solar Hijri) and GregorianCalendar Dates) The Jalaali calendar, also known as the Persian or Solar Hijri calendar, is the official calendar of Iran and Afghanistan. It starts on Nowruz, the spring equinox, and follows an astronomical system for determining leap years. Each year consists of 365 or 366 days, divided into 12 months. This package provides functions for converting dates between the Jalaali and Gregorian calendars. The conversion calculations are based on the work of Kazimierz M. Borkowski (1996) (), who used an analytical model of Earth's motion to compute equinoxes from AD 550 to 3800 and determine leap years based on Tehran time. Package: r-cran-jamba Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2570 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace, r-cran-rcolorbrewer, r-cran-kernsmooth, r-cran-withr Suggests: r-cran-crayon, r-cran-farver, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jamba_1.0.4-1.ca2404.1_all.deb Size: 2099730 MD5sum: 41d3e6a028455a578f11535c6e68dcb2 SHA1: 7ba52b3f5162de3c542c21e6a2925bd930776ffc SHA256: c7061333993d03cdf80e06e419e317f4cf01e239d68fa99d09b6ad5bfaca8835 SHA512: a501bb74e843daaeba42e53818fe27904eb7981d1c66c5b7bc6f0a48da4bcb8a3a2a263df04fa7dcbeb7bf018bb4f19c3ae695fa08de298c0fb310e1f6bec2be Homepage: https://cran.r-project.org/package=jamba Description: CRAN Package 'jamba' (Just Analysis Methods Base) Just analysis methods ('jam') base functions focused on bioinformatics. Version- and gene-centric alphanumeric sort, unique name and version assignment, colorized console and 'HTML' output, color ramp and palette manipulation, 'Rmarkdown' cache import, styled 'Excel' worksheet import and export, interpolated raster output from smooth scatter and image plots, list to delimited vector, efficient list tools. Package: r-cran-jamendor Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-jamendor_0.1.1-1.ca2404.1_all.deb Size: 132344 MD5sum: 3575070519413c1166358b96a38010aa SHA1: c2c7938e2084b9f77051b87a7ad3593528f958a9 SHA256: fd529882d5150cf7090ef3751f83cab287afe929dfacc74c47231202dc008461 SHA512: 2f377eedacb9d51f197280788362c933fd449450765cd4c1dd42f9b7f8b00ca7714e760332455866df24b8684cef0deaf238b82d0488c3718a8076646254d49a Homepage: https://cran.r-project.org/package=JamendoR Description: CRAN Package 'JamendoR' (Access to 'Jamendo' API) Provides an interface to 'Jamendo' API . Pull audio, features and other information for a given 'Jamendo' user (including yourself!) or enter an artist's -, album's -, or track's name and retrieve the available information in seconds. Package: r-cran-janeaustenr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1616 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-janeaustenr_1.0.0-1.ca2404.1_all.deb Size: 1620086 MD5sum: 5f5b329a897b8810ca8d99ab5e20937e SHA1: 8b8c3b0011a576193d7f524912cfbc95a5625001 SHA256: 33ff2cafa5780d4edc2636073334378c2eb991bbe014a39c696a72fa81a6e6f5 SHA512: 628eaf1124811098a01fa9e08d357806611d19ffc28e233afd61bbcf1d131c896efffc18186a1e47b960cf090fa681d69f202fdca8298e0ea09b2edf487c3074 Homepage: https://cran.r-project.org/package=janeaustenr Description: CRAN Package 'janeaustenr' (Jane Austen's Complete Novels) Full texts for Jane Austen's 6 completed novels, ready for text analysis. These novels are "Sense and Sensibility", "Pride and Prejudice", "Mansfield Park", "Emma", "Northanger Abbey", and "Persuasion". Package: r-cran-janitor Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-snakecase, r-cran-tidyselect, r-cran-tidyr Suggests: r-cran-dbplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-rsqlite, r-cran-sf, r-cran-testthat, r-cran-tibble, r-cran-tidygraph Filename: pool/dists/noble/main/r-cran-janitor_2.2.1-1.ca2404.1_all.deb Size: 270160 MD5sum: 553405bac3cf292f35b09da205ecaf9b SHA1: 2fac862b548d4bd7139d263600aaf95643ee0caa SHA256: 773815cc4ff1115811bd6ebcb2eca4111ac8b00af400e5f00896309a26dffcc2 SHA512: 99c7ee6e90aea57fa1902cb056bd6009349ad8e3893ee562e3dcb627f009cf9c2ebfc655f5e8b262968d8ce1a634f72a72d7c0a8dc629f9abb0823868e7fe792 Homepage: https://cran.r-project.org/package=janitor Description: CRAN Package 'janitor' (Simple Tools for Examining and Cleaning Dirty Data) The main janitor functions can: perfectly format data.frame column names; provide quick counts of variable combinations (i.e., frequency tables and crosstabs); and explore duplicate records. 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Package: r-cran-janusplot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-ggplot2, r-cran-patchwork, r-cran-cli, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-agridat, r-cran-future, r-cran-future.apply, r-cran-knitr, r-cran-mass, r-cran-palmerpenguins, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-janusplot_0.1.0-1.ca2404.1_all.deb Size: 2449560 MD5sum: 8c256d91d3417ced2d5b978d6bb695a6 SHA1: 81230e5ec4c341cd53d8a33182c67aa4d0a692fe SHA256: a709b3c246bb18452a82ae9fadd2b52246a9ba9d080ff332f59c7565a983f4b1 SHA512: ecff00f272be1128bd2e048304a22792fed7cf8464de5cfbc249f142536e973e8c6e4f89244285ce309ee278ea8dc649a7f2be2806bfc88cf5ea898fc4e2f310 Homepage: https://cran.r-project.org/package=janusplot Description: CRAN Package 'janusplot' (Asymmetric Smoothed-Association Matrices via GAM Fits) Render a pairwise, asymmetric smoothed-association matrix of continuous variables. Each cell shows the fitted spline from an 'mgcv' generalised additive model, with the upper triangle displaying 'gam(x_j ~ s(x_i))' and the lower triangle 'gam(x_i ~ s(x_j))'. Unlike Pearson's correlation matrix, the visualisation is intentionally asymmetric, revealing heteroscedasticity, leverage, and directional non-linearity that a single scalar correlation hides. An asymmetry index and a 24-category shape taxonomy quantify the directional difference and qualitative form of each fitted smooth. Package: r-cran-japanapis Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-japanapis_0.1.1-1.ca2404.1_all.deb Size: 664230 MD5sum: b8ffa72603b727dfb119c18238148a7c SHA1: 2ee7e5dfd460b50899530886cc8b1ff98476bc77 SHA256: d37c754d441503c3d48eda4a1e68fa52532017dfc2a6cbbd67a5bb2801b6f9c0 SHA512: 1e6112329170d863347e8e8918575a452ecc4fcf40ac8f5fcd75dc66d08fd88ff48825492b4fe1920a8954e0d6ff5f78856af50a179fd4bfd691020ceb8e658f Homepage: https://cran.r-project.org/package=JapanAPIs Description: CRAN Package 'JapanAPIs' (Access Japanese Data via Public APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'Nager.Date', 'World Bank API', and 'REST Countries API', retrieving real-time or historical data related to Japan, such as holidays, economic indicators, and international demographic and geopolitical indicators. Additionally, the package includes one of the largest curated collections of open datasets focused on Japan, covering topics such as natural disasters, economic production, vehicle industry, air quality, demographics, and administrative divisions. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: 'Nager.Date' , 'World Bank API' , and 'REST Countries API' . Package: r-cran-japanstat Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr, r-cran-pillar, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-keyring, r-cran-testthat Filename: pool/dists/noble/main/r-cran-japanstat_0.1.0-1.ca2404.1_all.deb Size: 64672 MD5sum: 556964c799177795b53d979225d87fb0 SHA1: c8eeb406bf3e90b5737ea7aff48540d78e48811b SHA256: 40b1a44cff5ef855b7c5f40b295ddd7d727c8ea669dee868701e69cc39767aef SHA512: 62c95ab02d6cdcb05b33e5d922c74ea9b91d290bf61a84a9f4098f0219f97e2681144b2f63ecb2e6ecdf98f564e0147fd08ceb6d87f57b2d3ad7a3a6ab640422 Homepage: https://cran.r-project.org/package=japanstat Description: CRAN Package 'japanstat' (Tools for Easy Use of 'e-Stat' API) Provides tools for using the API of 'e-Stat' (), a portal site for Japanese government statistics. Includes functions for automatic query generation, data collection and formatting. Package: r-cran-jarbes Architecture: all Version: 2.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-r2jags, r-cran-ggplot2, r-cran-ggextra, r-cran-mass, r-cran-gridextra, r-cran-bookdown, r-cran-tidyr, r-cran-kableextra, r-cran-ggally, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-jarbes_2.5.0-1.ca2404.1_all.deb Size: 606258 MD5sum: 0e93df1c4c12120c18834dfe8ceec920 SHA1: f22eae5e1f1940af5bb2bdf0142b8bb17dffdce8 SHA256: 178ece3898a9c0b21088dab6bff5618dcef24cf31ddaa29046d1d6aa2c292825 SHA512: 7b028ce1811726bb874260694349fa6552efacb7c387be5da2db853898550f25a242af84b4a8a9c5bcca525aeed84b7d1b9b12df13d43874255ddff10dc65494 Homepage: https://cran.r-project.org/package=jarbes Description: CRAN Package 'jarbes' (Just a Rather Bayesian Evidence Synthesis) Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS). Package: r-cran-jatsdecoder Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 602 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlp, r-cran-opennlp Filename: pool/dists/noble/main/r-cran-jatsdecoder_1.3.1-1.ca2404.1_all.deb Size: 576114 MD5sum: 0b31ce88f966245a3e5569fc5fbef8ea SHA1: f992d2c24dc2d2787ee64c3b0d9d2b76af40bcc3 SHA256: 122988808927463f5f1ef6028c2e6feb42f69c195a757bf63f00e1de29728098 SHA512: 3c946de45f25257a7eae7068e0db47cd21e2d8fec2773aaae3ecbe9ddf20f6d668567576b3890fd3a4a8f563cbfc2d70717b6806035ea370f841258b8c6df1f3 Homepage: https://cran.r-project.org/package=JATSdecoder Description: CRAN Package 'JATSdecoder' (A Metadata and Text Extraction and Manipulation Tool Set) Provides a function collection to extract metadata, sectioned text and study characteristics from scientific articles in 'NISO-JATS' format. Articles in PDF format can be converted to 'NISO-JATS' with the 'Content ExtRactor and MINEr' ('CERMINE', ). For convenience, two functions bundle the extraction heuristics: JATSdecoder() converts 'NISO-JATS'-tagged XML files to a structured list with elements title, author, journal, history, 'DOI', abstract, sectioned text and reference list. study.character() extracts multiple study characteristics like number of included studies, statistical methods used, alpha error, power, statistical results, correction method for multiple testing, software used. The function get.stats() extracts all statistical results from text and recomputes p-values for many standard test statistics. It performs a consistency check of the reported with the recalculated p-values. An estimation of the involved sample size is performed based on textual reports within the abstract and the reported degrees of freedom within statistical results. In addition, the package contains some useful functions to process text (text2sentences(), text2num(), ngram(), strsplit2(), grep2()). See Böschen, I. (2021) Böschen, I. (2021) , Böschen, I. (2023) , and Böschen, I. (2024) . Package: r-cran-javateak Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-javateak_1.0-1.ca2404.1_all.deb Size: 17646 MD5sum: 6bc14747fc7d2c0d72583110d617a7b4 SHA1: 734ecc1e548b969b518c11cb5b2ad5e720762839 SHA256: 971c8344944ceb65056ea7d4c0167e36328e01dc41c42e0b540f0f70d18dd092 SHA512: 2ccfd5888018193323598c29f9d481ba4052ba90d721908edae42d4bca95cb469e165cc6fa0f36ef4b65b7be38304222c4e02ca5a190fe9c1f595d5f6c3bb2ec Homepage: https://cran.r-project.org/package=javateak Description: CRAN Package 'javateak' (Javanese Teak Above Ground Biomass Estimation) Simplifies the process of estimating above ground biomass components for teak trees using a few basic inputs, based on the equations taken from the journal "Allometric equations for estimating above ground biomass and leaf area of planted teak (Tectona grandis) forests under agroforestry management in East Java, Indonesia" (Purwanto & Shiba, 2006) . This function is most reliable when applied to trees from the same region where the equations were developed, specifically East Java, Indonesia. This function help to estimate the stem diameter at the lowest major living branch (DB) using the stem diameter at breast height with R^2 = 0.969. Estimate the branch dry weight (WB) using the stem diameter at breast height and tree height (R^2 = 0.979). Estimate the stem weight (WS) using the stem diameter at breast height and tree height (R^2 = 0.997. Also estimate the leaf dry weight (WL) using the stem diameter at the lowest major living branch (R^2 = 0.996). Package: r-cran-jaya Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-evaluate, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jaya_1.0.3-1.ca2404.1_all.deb Size: 62894 MD5sum: 34d003b5c5a77fe2a5af1aad669891fc SHA1: 0c79f6c04ead383521ef3a5cc0e0460dc894056f SHA256: dc72a46f8c39a61a8e94f78c63577e0eaea3b2cd1f3f0b1f28575c731e67f155 SHA512: dca390bbf0121cb0889e794e59998439f336ac02629e4e6b4912a1f1a8831091cdc30f33e54d2e8ff146127f85b7ff75fe48551d98aeca9ea53ad33c185e1f66 Homepage: https://cran.r-project.org/package=Jaya Description: CRAN Package 'Jaya' (Gradient-Free Optimization Algorithm for Single andMulti-Objective Problems) An implementation of the Jaya optimization algorithm for both single-objective and multi-objective problems. Jaya is a population-based, gradient-free optimization algorithm capable of solving constrained and unconstrained optimization problems without hyperparameters. This package includes features such as multi-objective Pareto optimization, adaptive population adjustment, and early stopping. For further details, see R.V. Rao (2016) . Package: r-cran-jbrowser Architecture: all Version: 0.10.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-reactr, r-cran-stringr, r-cran-magrittr, r-cran-readr, r-cran-jsonlite, r-cran-httpuv, r-cran-mime, r-cran-cli, r-cran-ids, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-jbrowser_0.10.2-1.ca2404.1_all.deb Size: 1024386 MD5sum: 50ddcfb8daa33909330c99c29b79bd6c SHA1: 0d456d3fc70dc63e02d98800ac5887fe5bb03096 SHA256: 303641604f26f9a9e1380a2dab871daaa62494f5f23d6e40745bc91dbeefc6b2 SHA512: be4f942705c20493d4437cb822b9af0ab240cea82896e688219358b32d77d2b7581b64d3bb821890b0cc12999dc29cc03903c49ba697d7a4cc5eb84e71bee2df Homepage: https://cran.r-project.org/package=JBrowseR Description: CRAN Package 'JBrowseR' (An R Interface to the JBrowse 2 Genome Browser) Provides an R interface to the JBrowse 2 genome browser. 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Package: r-cran-jcalendar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-jcalendar_0.1.0-1.ca2404.1_all.deb Size: 153190 MD5sum: ed3b871f7ebd74094f071efefbeb81e7 SHA1: b122629fda97c2c15032bcb89b725c3240557308 SHA256: 91172825c6062514b67d7c43bddb046ccccd34d522bdcd255e30ecf1b4cb255a SHA512: 34ec0d86c6e5f8f253d44d098668e3c96b74ef8a06ae76632b7dfa231bcb804edd8390b6c9cf4d312bbb77fcf9f98f334681810db07b5a7e98d11dc4bdc70020 Homepage: https://cran.r-project.org/package=jcalendaR Description: CRAN Package 'jcalendaR' (Interconversion Between the Japanese Calendar System and theWestern Calendar) This is a set of simple utility functions to perform mutual conversion between the current Japanese calendar system that Wareki, the old Japanese calendar system that the Kyureki calendar and the Julian and Gregorian calendar. To calculate each calendar method, it converts to the Julian Day Number. 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Reference - Bivariate change point detection - joint detection of changes in expectation and variance, Scandinavian Journal of Statistics, DOI 10.1111/sjos.12547. 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The impact factor is available for those journals only that were included Journal Citation Reports 'JCR'. Package: r-cran-jcvrisk Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-jcvrisk_0.1.3-1.ca2404.1_all.deb Size: 136680 MD5sum: 10cf3011cb8065c89038d8ec5ab6fb28 SHA1: 44638644989adfa3aa1bdf8f2bd5d9efd0116bb1 SHA256: 2133c9287a2d45e76a9fccd6e264a90e27c31ce92f4f2fe3da572ef75acdc7ea SHA512: bee0543de0cb4cb31c127940e7c4619b0595ae2e7176ab1fbebdf71a51cb9575d58502754d2fffc6ab5868a8444a8f8a20575cd6b014634dda735e72d9f22025 Homepage: https://cran.r-project.org/package=Jcvrisk Description: CRAN Package 'Jcvrisk' (Risk Calculator for Cardiovascular Disease in Japan) A calculation tool to obtain the 5-year or 10-year risk of cardiovascular disease from various risk models. 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Package: r-cran-jdmbs Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-png, r-cran-ggplot2 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-jdmbs_1.4-1.ca2404.1_all.deb Size: 143472 MD5sum: e1267ef5f371c7dbd28682ab9ef55ff8 SHA1: 6429edbe492a18f7ac46383f11e5d75d08400c54 SHA256: 9352da44e1cb7930b4b882eeb28cfb1235033892cfe30bf69b41e249b7acc25d SHA512: b76fb5d7b1c7e01461325b87a7c28a5a0773ac0b11a1b884fd2806309c7b90bd065396cb933521b4cef919c12977c3b6c4f524c9984e91f9af4c7bcd0ae5f8fd Homepage: https://cran.r-project.org/package=Jdmbs Description: CRAN Package 'Jdmbs' (Monte Carlo Option Pricing Algorithms for Jump Diffusion Modelswith Correlational Companies) Option is a one of the financial derivatives and its pricing is an important problem in practice. The process of stock prices are represented as Geometric Brownian motion [Black (1973) ] or jump diffusion processes [Kou (2002) ]. In this package, algorithms and visualizations are implemented by Monte Carlo method in order to calculate European option price for three equations by Geometric Brownian motion and jump diffusion processes and furthermore a model that presents jumps among companies affect each other. Package: r-cran-jds.rmd Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bookdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-jds.rmd_0.3.4-1.ca2404.1_all.deb Size: 47396 MD5sum: edb5a32a847dc971864fe04cf13ea55e SHA1: a637a94b323b3f135c8982458a26ed69e4ee523b SHA256: a28f64e0089b9487d2edaa90b5367488bfcb48cde58b6961cb8efdf1416deddd SHA512: 174fadbadece3848efcc769d5502103b9776c50c5e9026cbe2e5678a7dc5704daa59e972f34355d2717078795689240565d820594728117fcdbd4d43450d4779 Homepage: https://cran.r-project.org/package=jds.rmd Description: CRAN Package 'jds.rmd' (R Markdown Templates for Journal of Data Science) Customized R Markdown templates for authoring articles for Journal of Data Science. Package: r-cran-jeek Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-pcapp, r-cran-igraph Filename: pool/dists/noble/main/r-cran-jeek_1.1.1-1.ca2404.1_all.deb Size: 448080 MD5sum: bf892a8d86a7e1dd40b85639f0ae4730 SHA1: 1e4fd3f379aac9fb11a9f02466e6133ea9c8a2c6 SHA256: 983951b65d6cac112fe8d8760104140a468078d7224a52917871e47c7d20016b SHA512: b9e83272446821eb2ee6c097c1e321c858c5079a5c6cadb52e848a157734296cb8d0a925a8f4a2c2deaf74f250ab5e829b4e9aaffff50850317932db44a28d67 Homepage: https://cran.r-project.org/package=jeek Description: CRAN Package 'jeek' (A Fast and Scalable Joint Estimator for Integrating AdditionalKnowledge in Learning Multiple Related Sparse GaussianGraphical Models) Provides a fast and scalable joint estimator for integrating additional knowledge in learning multiple related sparse Gaussian Graphical Models (JEEK). The JEEK algorithm can be used to fast estimate multiple related precision matrices in a large-scale. For instance, it can identify multiple gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogeneous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(jeek) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi "A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models" (ICML 2018) . Package: r-cran-jellyfisher Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1773 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-dplyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jellyfisher_1.1.1-1.ca2404.1_all.deb Size: 352328 MD5sum: f344dcce3a7e15949cb4ae3d95fd2023 SHA1: d8961beedbd0bdbbed5f269a07c6e30929b6ed5e SHA256: c90cf6bf986294e9d7b916d12b44b90884afece67685c7fa31ce906ef74a466f SHA512: d177baf88b699e6a72ac5c1047d970ee5df865fe3b94fe1b12562f916d02ddb514d835415c45a695f06709118b679ccaf43b09b9eaf29a7b0bbb1de97340ce92 Homepage: https://cran.r-project.org/package=jellyfisher Description: CRAN Package 'jellyfisher' (Visualize Spatiotemporal Tumor Evolution with Jellyfish Plots) Generates interactive Jellyfish plots to visualize spatiotemporal tumor evolution by integrating sample and phylogenetic trees into a unified plot. 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Package: r-cran-jetpack Architecture: all Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-renv, r-cran-remotes, r-cran-desc, r-cran-docopt Suggests: r-cran-testthat, r-cran-withr, r-cran-packrat Filename: pool/dists/noble/main/r-cran-jetpack_0.5.5-1.ca2404.1_all.deb Size: 68142 MD5sum: eecb23da1be4ec9b942f2cd6b5a65ff4 SHA1: 5722392db9917b106542d7d02efe996da61bc9f9 SHA256: 4cd919d27ddec756f3fa25eae9b1f98c007aa7e58eac43c251ebda925da49752 SHA512: 6d24ab985a3636bf360a34265755c9a5aa86ce25e9359bf4b7c9d7939f30462fa302fd479ae35d71a00e6ede993ae489e510f2b08f052e931b5536a0e84b7926 Homepage: https://cran.r-project.org/package=jetpack Description: CRAN Package 'jetpack' (A Friendly Package Manager) Manage project dependencies from your DESCRIPTION file. Create a reproducible virtual environment with minimal additional files in your project. 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Package: r-cran-jewel Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-matrixcalc, r-cran-mass, r-cran-smut, r-cran-igraph, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-jewel_2.0.2-1.ca2404.1_all.deb Size: 64032 MD5sum: d8ca761e67cfbf1996934b9b17348228 SHA1: d8ea064812fd934a85ef736920b24266a4a27e97 SHA256: 8f49a373d5583d842774933ce7c563a1e27dcdd29d64b16cdffe8426c43dbc08 SHA512: 9b168a55a1e33dba7e026a136b9520aa2a52c2247fc7fd122eaa8c146c7b5af90eabe5a3a8cc2f8f103c35eb4ac3355f88b95a45dbc19089810b739a72a065a5 Homepage: https://cran.r-project.org/package=jewel Description: CRAN Package 'jewel' (Graphical Models Estimation from Multiple Sources) Estimates networks of conditional dependencies (Gaussian graphical models) from multiple classes of data (similar but not exactly, i.e. measurements on different equipment, in different locations or for various sub-types). 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We solve JGL under two penalty functions: The Fused Graphical Lasso (FGL), which employs a fused penalty to encourage inverse covariance matrices to be similar across classes, and the Group Graphical Lasso (GGL), which encourages similar network structure between classes. FGL is recommended over GGL for most applications. Reference: Danaher P, Wang P, Witten DM. (2013) . 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Package: r-cran-jlctree Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rpart, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-jlctree_0.0.2-1.ca2404.1_all.deb Size: 94582 MD5sum: 099ad263c2d5cb4376a2c41b09de5c29 SHA1: 61a535f9df60f4ab11402ca4a330b3990480bf05 SHA256: 5ab4c3890274dd0405e69ee73d28d1242edf78d0fa690e4250f0c56a5f001ea5 SHA512: c6172c62dd196d60ad7cd111e11847ed6aca01a0f9c73849974b787c54c61164bed95ca2824707a4c900cb4b0a7d490ec085c8ec5ba98f2334c9f7fa651d0628 Homepage: https://cran.r-project.org/package=jlctree Description: CRAN Package 'jlctree' (Joint Latent Class Trees for Joint Modeling of Time-to-Event andLongitudinal Data) Implements the tree-based approach to joint modeling of time-to-event and longitudinal data. 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Package: r-cran-jlme Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-generics, r-cran-juliaconnector, r-cran-juliaformulae, r-cran-mass Suggests: r-cran-broom, r-cran-broom.mixed, r-cran-lme4, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jlme_0.4.1-1.ca2404.1_all.deb Size: 88272 MD5sum: 237d1e88993dcf03e9f018be1e4aba86 SHA1: bc6cabeb844335a8e525c4ce524ec66f66407241 SHA256: e8e7dc54dae613a1f3a703ba36467e3837df8dc247cc4378317279599d35aae6 SHA512: b2fb74e0251b4450b64d1a8c5d97792558224db9dd8fbc339eb27f0c01c5594f0f09a241308eb33cae36e871a1e91968f92a4a1a2027673ab4c3e8f6d2b5babb Homepage: https://cran.r-project.org/package=jlme Description: CRAN Package 'jlme' (Regression Modelling with 'GLM.jl' and 'MixedModels.jl' in'Julia') Bindings to 'Julia' packages 'GLM.jl' and 'MixedModels.jl' , powered by 'JuliaConnectoR'. Fits (generalized) linear (mixed-effects) regression models in 'Julia' using familiar model fitting syntax from R. Offers 'broom'-style data frame summary functionalities for 'Julia' regression models. 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Supports (generalized, mixed-effects) regression models for the calculation of timewise statistics. Provides both a wholesale and a piecemeal interface to the CPA procedure with an emphasis on interpretability and diagnostics. Integrates 'Julia' libraries 'MixedModels.jl' and 'GLM.jl' for performance improvements, with additional functionalities for interfacing with 'Julia' from 'R' powered by the 'JuliaConnectoR' package. Package: r-cran-jm4qtn Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1607 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lsmeans, r-cran-stepreg Filename: pool/dists/noble/main/r-cran-jm4qtn_1.0.0-1.ca2404.1_all.deb Size: 1254420 MD5sum: 872b00b3ddfe89b7bfde180baf5ae45e SHA1: e9617b939d28781fd0b4b5b7b94dbb6384b411c7 SHA256: bbab52839611ee16c4cec2d52d7fb6025da55d482ca9eb0445c062bfe8ec6b9d SHA512: 81f48aa9090b90cd63acfa95dd41888ddc2c460b9f9cd988906b1527d298eb5351698ac96c8b0c8bbbbf08095dc98ab28478e02baebb76df7921bd60e547315f Homepage: https://cran.r-project.org/package=JM4QTN Description: CRAN Package 'JM4QTN' (Joint Mapping for Quantitative Trait Loci) A comprehensive computational framework for joint mapping, developed by Li (2016) , supports quantitative trait locus detection in structured genetic populations. It integrates robust phenotype summarization, computes genotype probabilities, and imputes missing markers for association and linkage mapping. Empirical significance thresholds are estimated via permutation testing coupled with stepwise regression. The framework enables genome-wide scans under both univariate and multivariate trait models, streamlining the discovery of complex genetic architectures. Package: r-cran-jm Architecture: all Version: 1.5-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1061 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-nlme, r-cran-survival Filename: pool/dists/noble/main/r-cran-jm_1.5-2-1.ca2404.1_all.deb Size: 1030274 MD5sum: f4d3495cfba91408da8cab4149d98833 SHA1: 60a97223c0e7cec1cf1b5b6d62de4730a6eb3460 SHA256: 1f40c767f34f0a89d2c35fc3d706b233f699042967971242d48937da31f90278 SHA512: 8cdcdab19d0570973c9be6f8c0825e2ae432b9480e2d90f6221cdebe00858d05a671f6098a1cbaeebdfdba96ef179bb991e3cb9e8e5a0369c4ba38d2d0c32e6c Homepage: https://cran.r-project.org/package=JM Description: CRAN Package 'JM' (Joint Modeling of Longitudinal and Survival Data) Shared parameter models for the joint modeling of longitudinal and time-to-event data. Package: r-cran-jmastats Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-crayon, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-lifecycle, r-cran-lubridate, r-cran-purrr, r-cran-rappdirs, r-cran-readr, r-cran-rlang, r-cran-rvest, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyselect, r-cran-tidyr, r-cran-units, r-cran-xml2 Suggests: r-cran-knitr, r-cran-lwgeom, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jmastats_0.3.0-1.ca2404.1_all.deb Size: 249064 MD5sum: aa49ba5678409266b1d63db6114b09f8 SHA1: 3658e1bc813702d99565d5707cb0b235788af658 SHA256: 299519204cd753cb468f0cbe99e7898aa8a173ba4548bff472f60c331203cfe7 SHA512: 1cd3b2340b0353f526113ad1b1d38d8c4850025776df142a625c05e38b541bfa3a563f15c1f46b675a67fa151a88f04dcef54c90aa46ac4951277a03f58da0c0 Homepage: https://cran.r-project.org/package=jmastats Description: CRAN Package 'jmastats' (Download Weather Data from Japan Meteorological Agency Website) Provides features that allow users to download weather data published by the Japan Meteorological Agency (JMA) website (). 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Package: r-cran-jmbdirect Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jmbayes2, r-cran-joinerml, r-cran-fastjm, r-cran-rstanarm, r-cran-dplyr, r-cran-jmbig Filename: pool/dists/noble/main/r-cran-jmbdirect_0.1.1-1.ca2404.1_all.deb Size: 271378 MD5sum: 513fd673463d91ddf3d2fbe112847088 SHA1: dd3e33e007f81d809e352f1437d9d76507b78593 SHA256: f512214e8d88be916bb08d32222a8cc50cfe64c2c0801d7401b20319356f6cd8 SHA512: 286e9cdc9832d7a59db266fa3322be7499407d8fd85ea899ac19b90c84ee869fcc73b64d9678175075bee3a08dc6fb2f49527fca2f092d3fd4f691539ffb8163 Homepage: https://cran.r-project.org/package=JMbdirect Description: CRAN Package 'JMbdirect' (Joint Model for Longitudinal and Multiple Time to Events Data) Provides model fitting, prediction, and plotting for joint models of longitudinal and multiple time-to-event data, including methods from Rizopoulos (2012) . Useful for handling complex survival and longitudinal data in clinical research. Package: r-cran-jmbig Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jmbayes2, r-cran-joinerml, r-cran-rstanarm, r-cran-fastjm, r-cran-dplyr, r-cran-nlme, r-cran-survival, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-jmbig_0.1.3-1.ca2404.1_all.deb Size: 197744 MD5sum: 7ffa905d19f47588007bedd4b15e9618 SHA1: 98083ec86bcceb5c7a7ec6eb8a12290491ee1f3a SHA256: c0971d16660a0b50af548e9794ae38a182e18725ae9201e73e28ba5690b673ef SHA512: 196d5552f3b2f54e7d878eb56d7617b5a0c0c5f9b27db1ef1b27cadecf8f33a133d1671d6566efee2beaf62e8f7987e36a2d5769aa953ff073a012a5ae73e1a2 Homepage: https://cran.r-project.org/package=jmBIG Description: CRAN Package 'jmBIG' (Joint Longitudinal and Survival Model for Big Data) Provides analysis tools for big data where the sample size is very large. It offers a suite of functions for fitting and predicting joint models, which allow for the simultaneous analysis of longitudinal and time-to-event data. This statistical methodology is particularly useful in medical research where there is often interest in understanding the relationship between a longitudinal biomarker and a clinical outcome, such as survival or disease progression. This can be particularly useful in a clinical setting where it is important to be able to predict how a patient's health status may change over time. Overall, this package provides a comprehensive set of tools for joint modeling of BIG data obtained as survival and longitudinal outcomes with both Bayesian and non-Bayesian approaches. Its versatility and flexibility make it a valuable resource for researchers in many different fields, particularly in the medical and health sciences. Package: r-cran-jmdem Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vgam, r-cran-statmod Filename: pool/dists/noble/main/r-cran-jmdem_1.0.1-1.ca2404.1_all.deb Size: 247490 MD5sum: 55691daa7f208f9990a180636f7174b3 SHA1: 3a3d994b3e0cac383f68076a669d589296e71040 SHA256: 3e2aafee51b68adb704dcc9b198ce113a32eb85c47acbd89dc25f24a64053942 SHA512: 716295e47a7860a755a2e973bfe9c7af64e8c59acc93c4a5c1dfcc325ad03c641c21b3931cbe77848dadbbf2209fc6708ab6d0d2efc8cfde4d3878f4a6afd1b5 Homepage: https://cran.r-project.org/package=jmdem Description: CRAN Package 'jmdem' (Fitting Joint Mean and Dispersion Effects Models) Joint mean and dispersion effects models fit the mean and dispersion parameters of a response variable by two separate linear models, the mean and dispersion submodels, simultaneously. 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Package: r-cran-jmdesign Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-jmdesign_1.6-1.ca2404.1_all.deb Size: 138004 MD5sum: 12ee5a0ba7704e96e3f4726fdf0f2586 SHA1: c26a87ee1d00df306625f9f6ed0b41c2b2e0130d SHA256: f84a46117c423969a138276f1d23a407e253011fb28f7bb2879b7719e7e1fcab SHA512: 5ccba76889d13c5391894fd50bfae58b8af700db57421f0295c0120db9af5b71b408f33935fd31253a0d180680e23d02a15301e6cab29e806141ff3e60d8baf2 Homepage: https://cran.r-project.org/package=JMdesign Description: CRAN Package 'JMdesign' (Joint Modeling of Longitudinal and Survival Data - PowerCalculation) Performs power calculations for joint modeling of longitudinal and survival data with k-th order trajectories when the variance-covariance matrix, Sigma_theta, is unknown. Package: r-cran-jmetrik Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-jmetrik_1.1-1.ca2404.1_all.deb Size: 20824 MD5sum: 006dc6450327faeb86eae4538b3e08d8 SHA1: f9b0b7f6b111f5d94c735b7e3aadf374ca46425b SHA256: 668fb6c3aec360b5dafc913f547773d23973de028731001a1373a33dca1a0f98 SHA512: cf0a8432f1bd2cd37bad3fa0af322df5e35fa2165a78d1e649d03a4e0929c1c6423f580a8b6e698fd4326e2a0fda7c568d9003a1cea59cd8c8e04b1bf41177c6 Homepage: https://cran.r-project.org/package=jmetrik Description: CRAN Package 'jmetrik' (Tools for Interacting with 'jMetrik') The main purpose of this package is to make it easy for userR's to interact with 'jMetrik' an open source application for psychometric analysis. For example it allows useR's to write data frames to file in a format that can be used by 'jMetrik'. 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Package: r-cran-jmisc Architecture: all Version: 0.3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-jmisc_0.3.1.1-1.ca2404.1_all.deb Size: 45122 MD5sum: ffb8d34a4d3ebc3b8a825d3d9ad6f23f SHA1: ad7db80e2713573bed199264d89f3990f8522fc7 SHA256: 49b7008a0a3b96a0e091f021093dfca27c5ee81e7040bbe6d699d9e5cc97ca91 SHA512: 726217871c8bd7477356edb80ff25bdd66502f168d141c7f1ebdc92f82633ec32b1d48384923a2df421aad494ca974c7186c84d2446d1b7d87c6b08b0f9b0f85 Homepage: https://cran.r-project.org/package=Jmisc Description: CRAN Package 'Jmisc' (Julian Miscellaneous Function) Some handy function in R. Package: r-cran-jmsurface Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2575 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-survival, r-cran-mgcv Suggests: r-cran-lme4, r-cran-ggplot2, r-cran-viridis, r-cran-plotly, r-cran-shiny, r-cran-shinydashboard, r-cran-dplyr, r-cran-tidyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jmsurface_0.1.0-1.ca2404.1_all.deb Size: 507058 MD5sum: 0b550940195ec805f8d53aa2927eee72 SHA1: 7714887c57a46ae84b724d4cba373506339a97a5 SHA256: 40f789ca768996d7a852b0b97718643cff88a45493b8135f24c8834c510dbcc5 SHA512: 560321c9b75c46724e6071261231dc2d12f1fdc9492130094e32b4ada570b4581cc030e988d88acb212573062912a32f8a276002a0db76942aa08651be84f5c4 Homepage: https://cran.r-project.org/package=jmSurface Description: CRAN Package 'jmSurface' (Semi-Parametric Association Surfaces for JointLongitudinal-Survival Models) Implements interpretable multi-biomarker fusion in joint longitudinal-survival models via semi-parametric association surfaces. Provides a two-stage estimation framework where Stage 1 fits mixed-effects longitudinal models and extracts Best Linear Unbiased Predictors ('BLUP's), and Stage 2 fits transition-specific penalized Cox models with tensor-product spline surfaces linking latent biomarker summaries to transition hazards. Supports multi-state disease processes with transition-specific surfaces, Restricted Maximum Likelihood ('REML') smoothing parameter selection, effective degrees of freedom ('EDF') diagnostics, dynamic prediction of transition probabilities, and three interpretability visualizations (surface plots, contour heatmaps, marginal effect slices). Methods are described in Bhattacharjee (2025, under review). Package: r-cran-jmuoutlier Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-agricolae, r-cran-coin, r-cran-fastgraph Filename: pool/dists/noble/main/r-cran-jmuoutlier_2.2-1.ca2404.1_all.deb Size: 154388 MD5sum: 81bac3ab32f32194a240aef3d10d30d1 SHA1: d89b1996d853016e7b0a34fcec2b2f7c1d5e25ce SHA256: 73135184873bb279835c1b416795949103136a6d99f1f68fc28942b4cb31e2fa SHA512: 14e9c74249dd1b8acf08fca66259877007bff2a25d4f092c135b427f7bb62b269717aa621424498c5ed82db0632da15378cae01d369b2d1f4299ae7b3ee74b1b Homepage: https://cran.r-project.org/package=jmuOutlier Description: CRAN Package 'jmuOutlier' (Permutation Tests for Nonparametric Statistics) Performs a permutation test on the difference between two location parameters, a permutation correlation test, a permutation F-test, the Siegel-Tukey test, a ratio mean deviance test. Also performs some graphing techniques, such as for confidence intervals, vector addition, and Fourier analysis; and includes functions related to the Laplace (double exponential) and triangular distributions. Performs power calculations for the binomial test. 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Provides data preparation helpers, standard cumulative incidence estimators (restricted mean time gained/lost), and severity weighted extensions that integrate longitudinal ordinal outcomes to summarise treatment benefit. Methods follow Wen, Hu, and Wang (2023) Biometrics 79(3):1635-1645 . 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The joint model can subsequently be used to predict the mean number of events in the presence of competing risks at different time points. Comparisons of the mean number of event functions, e.g. the differences in mean number of events between two exposure groups, are also available. 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The abbreviation table is from 'JabRef'. At the same time, 'Shiny' application is provided to generate 'thebibliography', a reference format that can be directly used for latex paper writing based on 'Rmd' files. 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Package: r-cran-jpmesh Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1709 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaflet, r-cran-memoise, r-cran-miniui, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-shiny, r-cran-tibble, r-cran-units, r-cran-magrittr, r-cran-vctrs Suggests: r-cran-knitr, r-cran-lintr, r-cran-lwgeom, r-cran-testthat, r-cran-rmarkdown, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-jpmesh_2.1.0-1.ca2404.1_all.deb Size: 1151110 MD5sum: a2049ca0aad10e03972cdf065c2a52a6 SHA1: f945ffbec0da7613f445e370c2f4671378a1023e SHA256: 09c6e673ebbe9a3c4b32bbbe254577c909c37d395f36cb8a6fff828212f155bf SHA512: e860d9cc52c245774bf67853d4db81a4f57922777ea335fc0de0e3714042085195ad63528ad7cba4cfa193a45c3eb5c5318e4b7bf0acbcdfb319d060ba387b57 Homepage: https://cran.r-project.org/package=jpmesh Description: CRAN Package 'jpmesh' (Utilities for Japanese Mesh Code) Helpful functions for using mesh code (80km to 100m) data in Japan. 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Package: r-cran-jpsurv Architecture: all Version: 3.0.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-scales Filename: pool/dists/noble/main/r-cran-jpsurv_3.0.20-1.ca2404.1_all.deb Size: 1303938 MD5sum: be1cd4eb1f0757fbfbfa5de34eaa0b23 SHA1: 28944ee388ade4f327e39e14e89bb65e99285772 SHA256: a13229d4ecd7452c6b55e9ed5a8d60c4532b16fc6ee73bea97986c17d2626e53 SHA512: 6705dba518bbc49db6c471afc38ac01565846be3c1d50f7351c98cd4daf1f0a3caf2708ab1aec56c74cd83020115b3ffd897cda54d672f004656868be862c363 Homepage: https://cran.r-project.org/package=JPSurv Description: CRAN Package 'JPSurv' (Joinpoint Model for Relative and Cause-Specific Survival) Contains functions for fitting a joinpoint proportional hazards model to relative survival or cause-specific survival data, including estimates of joinpoint years at which survival trends have changed and trend measures in the hazard and cumulative survival scale. See Yu et al.(2009) . Package: r-cran-jqbr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-jsonlite, r-cran-shiny Suggests: r-cran-bslib, r-cran-packer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-jqbr_1.0.4-1.ca2404.1_all.deb Size: 70060 MD5sum: a5d940f16cbabd5e9e0ee6685b1eed58 SHA1: ed1f95f8760b2ba89bcef1cef95155b6c50443d5 SHA256: bbd5b1fb9aa9071b07532fa4d69fd52ecd7c0cc4251a1b8b4dd3a7937685f53d SHA512: 76ea55b0ef1c05618f432e48dd91f0e45beaace16cd9ced15719e752c9d7e429663e1475c755ad84e65f3191478a49628410749011312d0dd409d9bdb59e8112 Homepage: https://cran.r-project.org/package=jqbr Description: CRAN Package 'jqbr' ('jQuery QueryBuilder' Input for 'Shiny') A highly configurable 'jQuery' plugin offering a simple interface to create complex queries/filters in 'Shiny'. The outputted rules can easily be parsed into a set of 'R' and/or 'SQL' queries and used to filter data. Custom parsing of the rules is also supported. For more information about 'jQuery QueryBuilder' see . Package: r-cran-jql Architecture: all Version: 3.6.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-pdist, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-jql_3.6.9-1.ca2404.1_all.deb Size: 50574 MD5sum: f0b48d09036d176e870d570650b4585b SHA1: f364a441abe7f2de156bd1eeb4b3b83cceb33879 SHA256: 613a2d410c8d737cb9ccc8854cd89edc6c525bb379b9aa8731909ce0bc173f9e SHA512: 0885e7cb3dfa9558a676fc282f6a77131e25c6fa8407caaf5ff8ee296221c55dbb85dbe39cb523e778af7a520e58ad7bfd0e26dcd1d8c0dea5faaf598b9c2ad8 Homepage: https://cran.r-project.org/package=JQL Description: CRAN Package 'JQL' (Jump Q-Learning for Individualized Interval-Valued Dose Rule) We provide tools to estimate the individualized interval-valued dose rule (I2DR) that maximizes the expected beneficial clinical outcome for each individual and returns an optimal interval-valued dose, by using the jump Q-learning (JQL) method. The jump Q-learning method directly models the conditional mean of the response given the dose level and the baseline covariates via jump penalized least squares regression under the framework of Q learning. We develop a searching algorithm by dynamic programming in order to find the optimal I2DR with the time complexity O(n2) and spatial complexity O(n). To alleviate the effects of misspecification of the Q-function, a residual jump Q-learning is further proposed to estimate the optimal I2DR. The outcome of interest includes the best partition of the entire dosage of interest, the regression coefficients of each partition, and the value function under the estimated I2DR as well as the Wald-type confidence interval of value function constructed through the Bootstrap. 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Package: r-cran-jrich Architecture: all Version: 0.60-35-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape Filename: pool/dists/noble/main/r-cran-jrich_0.60-35-1.ca2404.1_all.deb Size: 617324 MD5sum: fb7240006798dc3aaea7579896867e51 SHA1: 02f62b6a48a88b851a2489987f0abb9d028da2e0 SHA256: 2fb84dc9a5299fee45ad7fa6c3663789d350c770384df9656c1b0f77dc4c8b9b SHA512: be4a02627eb4e1e6a4c595d9ca1dd523d5e5ad4d63617608cfe421e7d53dc9f40b452d1243db9c730ce5f3a479202cb98d471db234bd617e6368928394e931a6 Homepage: https://cran.r-project.org/package=jrich Description: CRAN Package 'jrich' (Jack-Knife Support for Evolutionary Distinctiveness Indices Iand W) These functions calculate the taxonomic measures presented in Miranda-Esquivel (2016). The package introduces Jack-knife resampling in evolutionary distinctiveness prioritization analysis, as a way to evaluate the support of the ranking in area prioritization, and the persistence of a given area in a conservation analysis. The algorithm is described in: Miranda-Esquivel, D (2016) . 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A function is used to automatically compare and select models, as well as to present a variety of model-based statistics. Plotting functions are used to present category curves, as well as information, reliability and standard error functions. 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This includes finding the IRR and NPV of regularly spaced cash flows and annuities. Bond pricing and YTM calculations are included. In addition, Black Scholes option pricing and Greeks are also provided. 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Package: r-cran-kaphom Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kaphom_0.3-1.ca2404.1_all.deb Size: 36210 MD5sum: 644d0606b59aab6da3ec362dfc90e21b SHA1: 2d9f04aa123d915c5c567ebf03f2a498ecc7f6d3 SHA256: 03c240908c9b8dbff56e7e8a2a3fa8f56bd8e6b4c303dbb2bd7b8acef4d356e5 SHA512: d503378a676a4dfc5d6994963ca7b8f669700224ac38a0a084e6808e7636fead5a6f4e39e0f3b840f3409eb392f14f9c7918d240945182f663c5f4b8ea27104d Homepage: https://cran.r-project.org/package=kaphom Description: CRAN Package 'kaphom' (Test the Homogeneity of Kappa Statistics) Tests the homogeneity of intraclass kappa statistics obtained from independent studies or a stratified study with binary results. It is desired to compare the kappa statistics obtained in multi-center studies or in a single stratified study to give a common or summary kappa using all available information. If the homogeneity test of these kappa statistics is not rejected, then it is possible to make inferences over a single kappa statistic that summarizes all the studies. Muammer Albayrak, Kemal Turhan, Yasemin Yavuz, Zeliha Aydin Kasap (2019) Jun-mo Nam (2003) Jun-mo Nam (2005) Mousumi Banerjee, Michelle Capozzoli, Laura McSweeney,Debajyoti Sinha (1999) Allan Donner, Michael Eliasziw, Neil Klar (1996) . Package: r-cran-kappagold Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-future.apply, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-dplyr, r-cran-irr, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kappagold_0.4.0-1.ca2404.1_all.deb Size: 85220 MD5sum: 26fdb41ee63cb1bff6aea274d30aeb57 SHA1: 737c6db8c3c1b0788afc685849ba6d2aaf556f88 SHA256: 1f54762fa72cd1b75bd8297c480df10744e555ad9b69f8ca1c7029e444ed4ea3 SHA512: 1f7c4286dfd4fb8b6770c116f4ee2a9673e50784a826a6ab82332ca1672ef35c865a2ad5325448656c5f6ca98bc15ef064d785cac784ec75734a0b9ebfbd8deb Homepage: https://cran.r-project.org/package=kappaGold Description: CRAN Package 'kappaGold' (Agreement of Nominal Scale Raters (with a Gold Standard)) Estimate agreement of a group of raters with a gold standard rating on a nominal scale. 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Package: r-cran-karel Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2652 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-magrittr, r-cran-gganimate, r-cran-gifski Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-karel_0.1.1-1.ca2404.1_all.deb Size: 1420142 MD5sum: 19732305284dba3e65be6df97f0158c7 SHA1: 0b5cbaebf50e1c0278a0c542253832c92dbd62c0 SHA256: e63dee29195530f1219fd34dd07720c550d93d2b5d5b85e6c429fa3a42a4723c SHA512: cbd8d09e63079874137bee2a2cfc6a56f27a414ea07ea5597857a753bff59c2b4fefaba7b2341893bea0f6a8a00c77d29a4e013757767d200798e7a087783ee8 Homepage: https://cran.r-project.org/package=karel Description: CRAN Package 'karel' (Learning programming with Karel the robot) This is the R implementation of Karel the robot, a programming language created by Dr. R. E. Pattis at Stanford University in 1981. Karel is an useful tool to teach introductory concepts about general programming, such as algorithmic decomposition, conditional statements, loops, etc., in an interactive and fun way, by writing programs to make Karel the robot achieve certain tasks in the world she lives in. Originally based on Pascal, Karel was implemented in many languages through these decades, including 'Java', 'C++', 'Ruby' and 'Python'. This is the first package implementing Karel in R. Package: r-cran-karen Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-gaussquad, r-cran-scales, r-cran-mvtnorm, r-cran-tmvtnorm, r-cran-mass, r-cran-igraph, r-cran-xtable, r-cran-stringr, r-cran-abind, r-cran-expm Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-karen_1.0-1.ca2404.1_all.deb Size: 667866 MD5sum: 74068eab4ddc4df410459a1dad2f4bed SHA1: f6fc61e45bf35ceb559035926c965a2929304286 SHA256: 5c6c124e6501a61c35cda07b291de2700ed78bfc5a9775f2d2a2fd6f7c9e1b24 SHA512: 7991d10411ee341a58d51f5f88acca6bdf7d7a6dcdb75bab6103c33ff1d7eae30be458240f16a8cb5d143d52ce0c5a9217a572a22482a011b2527002ed791ee7 Homepage: https://cran.r-project.org/package=Karen Description: CRAN Package 'Karen' (Kalman Reaction Networks) This is a stochastic framework that combines biochemical reaction networks with extended Kalman filter and Rauch-Tung-Striebel smoothing. This framework allows to investigate the dynamics of cell differentiation from high-dimensional clonal tracking data subject to measurement noise, false negative errors, and systematically unobserved cell types. Our tool can provide statistical support to biologists in gene therapy clonal tracking studies for a deeper understanding of clonal reconstitution dynamics. Further details on the methods can be found in L. Del Core et al., (2022) . Package: r-cran-karlen Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2889 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Filename: pool/dists/noble/main/r-cran-karlen_0.0.2-1.ca2404.1_all.deb Size: 2846576 MD5sum: b41255650918e6fec92d77e43dfa66d6 SHA1: 230da7f3829eabba13bd4e80f5a23c973ed962c3 SHA256: e0cd90b5fcbfbc78f358422d93120519308838cb4be9ab821016d88440dff427 SHA512: 5127afe9d28ffae0d058e4a86de74c165738ebea28d2f3d57db3b7e0b6d3d850491a4d1a30023d1dea585a9742f2ad94b959b33699f6675bbcccce4355b1c5f1 Homepage: https://cran.r-project.org/package=karlen Description: CRAN Package 'karlen' (Real-Time PCR Data Sets by Karlen et al. (2007)) Real-time quantitative polymerase chain reaction (qPCR) data sets by Karlen et al. (2007) . Provides one single tabular tidy data set in long format, encompassing 32 dilution series, for seven PCR targets and four biological samples. The targeted amplicons are within the murine genes: Cav1, Ccn2, Eln, Fn1, Rpl27, Hspg2, and Serpine1, respectively. Dilution series: scheme 1 (Cav1, Eln, Hspg2, Serpine1): 1-fold, 10-fold, 50-fold, and 100-fold; scheme 2 (Ccn2, Rpl27, Fn1): 1-fold, 10-fold, 50-fold, 100-fold and 1000-fold. For each concentration there are five replicates, except for the 1000-fold concentration, where only two replicates were performed. Each amplification curve is 40 cycles long. Original raw data file is Additional file 2 from "Statistical significance of quantitative PCR" by Y. Karlen, A. McNair, S. Perseguers, C. Mazza, and N. Mermod (2007) . Package: r-cran-karsts Architecture: all Version: 2.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3505 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circular, r-cran-mvn, r-cran-tcltk2, r-cran-tserieschaos, r-cran-stlplus, r-cran-tseries, r-cran-forecast, r-cran-stinepack, r-cran-missforest, r-cran-nonlineartseries, r-cran-zoo, r-cran-rgl, r-cran-mgcv, r-cran-infotheo, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-karsts_2.4.1-1.ca2404.1_all.deb Size: 3060746 MD5sum: d9c42444942d2f079245d12714b21020 SHA1: 8560a5717efe565c5e251ee31a57c9b97bf83c85 SHA256: b8079480aa83083a48f490a91df4648e2e9e783b2605d42df41de15f2936f483 SHA512: 43ea7fd0ddacdc7ea3315d55e86bea657ea2f57ee3c0fa7ff6170d6dce91cd9cf9c0992ec544b65c37c604bfd7800e01ddb167dea48b327b43a8df305b732cda Homepage: https://cran.r-project.org/package=KarsTS Description: CRAN Package 'KarsTS' (An Interface for Microclimate Time Series Analysis) An R code with a GUI for microclimate time series, with an emphasis on underground environments. 'KarsTS' provides linear and nonlinear methods, including recurrence analysis (Marwan et al. (2007) ) and filling methods (Moffat et al. (2007) ), as well as tools to manipulate easily time series and gap sets. Package: r-cran-karyotapr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1902 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-singlecellexperiment, r-cran-circlize, r-cran-cli, r-bioc-complexheatmap, r-cran-dbscan, r-cran-dplyr, r-cran-fitdistrplus, r-bioc-genomeinfodb, r-bioc-genomicranges, r-cran-ggplot2, r-cran-gtools, r-bioc-iranges, r-cran-magrittr, r-cran-purrr, r-bioc-rhdf5, r-cran-rlang, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-cran-tibble, r-cran-tidyr, r-cran-umap, r-cran-viridislite Suggests: r-bioc-biostrings, r-cran-knitr, r-cran-rmarkdown, r-bioc-rsamtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-karyotapr_1.0.2-1.ca2404.1_all.deb Size: 1431272 MD5sum: da8fabbec8a1a5a00d711a3004dd4240 SHA1: 61016d39b95745c5f393881a1b3593fcaaca5a70 SHA256: 8c8f3a57100336c3962bd3f23c5da1fc4d0b6ee176e08733530a9627ca5daf30 SHA512: f1a29d0f215ec03262942f742d03e468dc7383c87587c26838105e414fdc4a91b629a03744e91876a44c8ccf0ed8178a31b8ffbf89da08d8e3ead77cb3e2f307 Homepage: https://cran.r-project.org/package=karyotapR Description: CRAN Package 'karyotapR' (DNA Copy Number Analysis for Genome-Wide Tapestri Panels) Analysis of DNA copy number in single cells using custom genome-wide targeted DNA sequencing panels for the Mission Bio Tapestri platform. Users can easily parse, manipulate, and visualize datasets produced from the automated 'Tapestri Pipeline', with support for normalization, clustering, and copy number calling. Functions are also available to deconvolute multiplexed samples by genotype and parsing barcoded reads from exogenous lentiviral constructs. Package: r-cran-katex Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-v8 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-katex_1.5.0-1.ca2404.1_all.deb Size: 187990 MD5sum: 12ec475e2da28bf2d288cc0fcf7f85f6 SHA1: 96bb2ce52f718a2061aa5998057e07d00e485fa9 SHA256: f3e6b14a1da6cd9846f59e217cebb4596681019be1c1efdd49fd9d9b71202e3f SHA512: cc3065fe7c3bbae0ba0c738998602ce42935e8b208f8bb83dcc7b97e6efe58a3f8a4c8c4273bdefa2b14273ab82ebcb7e2d868cf061a6934ff5bf66ffb17d541 Homepage: https://cran.r-project.org/package=katex Description: CRAN Package 'katex' (Rendering Math to HTML, 'MathML', or R-Documentation Format) Convert latex math expressions to HTML and 'MathML' for use in markdown documents or package manual pages. The rendering is done in R using the V8 engine (i.e. server-side), which eliminates the need for embedding the 'MathJax' library into your web pages. In addition a 'math-to-rd' wrapper is provided to automatically render beautiful math in R documentation files. Package: r-cran-kayadata Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr, r-cran-forcats, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-scales, r-cran-purrr Suggests: r-cran-broom, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-kayadata_1.4.0-1.ca2404.1_all.deb Size: 700360 MD5sum: d60afe65938ace10155aa97f9dc200b1 SHA1: c5274a56cb84829b33dc9d1e1fbd31191bff472f SHA256: 522dad12f0ffdd0dc73fa63707216a274cac9ae0266eff683d38a8cf6bc841f3 SHA512: d4c5349dfa2d33aee6f3ddbdd84ce1fee1068d61ccc307bf41d3e1aa5a55e6b64d667b7a68f49e42b1497035c9db637ae0e8a576a63909920d9b5a6e8c09d36c Homepage: https://cran.r-project.org/package=kayadata Description: CRAN Package 'kayadata' (Kaya Identity Data for Nations and Regions) Provides data for Kaya identity variables (population, gross domestic product, primary energy consumption, and energy-related CO2 emissions) for the world and for individual nations, and utility functions for looking up data, plotting trends of Kaya variables, and plotting the fuel mix for a given country or region. The Kaya identity (Yoichi Kaya and Keiichi Yokobori, "Environment, Energy, and Economy: Strategies for Sustainability" (United Nations University Press, 1998) and ) expresses a nation's or region's greenhouse gas emissions in terms of its population, per-capita Gross Domestic Product, the energy intensity of its economy, and the carbon-intensity of its energy supply. Package: r-cran-kbmvtskew Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kbmvtskew_1.1.0-1.ca2404.1_all.deb Size: 41200 MD5sum: 9500ca8ee714e2a32db836b6641c3921 SHA1: 0d245a4e69f41d0e0a655afb939d0dbf796aa78b SHA256: 4e024a6086adedcf17de1b984d0f33615f66bb1456ed6dd716ecd4776cfcb3e0 SHA512: 1b962072c03b9d1b0b74b2ef4a9188c5942e8c1e11a0cd69308c574e1cb32f113d26610f580c21e0b01778e7c65d278802ab7a6d13a365542dbcda09c72f6669 Homepage: https://cran.r-project.org/package=KbMvtSkew Description: CRAN Package 'KbMvtSkew' (Khattree-Bahuguna's Univariate and Multivariate Skewness) Computes Khattree-Bahuguna's univariate and multivariate skewness, principal-component-based Khattree-Bahuguna's multivariate skewness. It also provides several measures of univariate or multivariate skewnesses including, Pearson’s coefficient of skewness, Bowley’s univariate skewness and Mardia's multivariate skewness. See Khattree, R. and Bahuguna, M. (2019) . Package: r-cran-kcmeans Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ckmeans.1d.dp, r-cran-mass, r-cran-matrix Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kcmeans_0.1.0-1.ca2404.1_all.deb Size: 26846 MD5sum: a67b8e3aef09a418cddc318af6cea389 SHA1: eb40121598e885e29f1ee0fc685e0ebf2022e37f SHA256: 84a45cfd734559a802a0fd437356134f9b625429aed8f74da35a2eec339fae84 SHA512: 95843d12f4fd56a1890ad01c6599d6eee4c9c4cc0316409734ba6a3d590ca0cbd74be150653bf484c4268d432f73e0300a37ac18de73de1e2475b2d024cb45cb Homepage: https://cran.r-project.org/package=kcmeans Description: CRAN Package 'kcmeans' (Conditional Expectation Function Estimation withK-Conditional-Means) Implementation of the KCMeans regression estimator studied by Wiemann (2023) for expectation function estimation conditional on categorical variables. 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Package: r-cran-kcop Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-dplyr, r-cran-gtools, r-cran-orthopolynom Filename: pool/dists/noble/main/r-cran-kcop_1.0.0-1.ca2404.1_all.deb Size: 47088 MD5sum: d60e3f1b66f386a37d1de4515b235f8d SHA1: 7781dd197607d4e9dee88830bb4086ff067757b2 SHA256: db4d27e8e1f742b7689aa1fb48f82d3d41b8ad4831d99e867dc7ffba8d293b83 SHA512: 58e96ab4ffbecf7ec2f174936021f307484d50be9c1cf1c9aff9ffc5647ad83a8a980a211c1a40ba4c4c77f24dbe2bd76f6dea51b6cd05fd645fb774e708f1ac Homepage: https://cran.r-project.org/package=Kcop Description: CRAN Package 'Kcop' (Smooth Test for Equality of Copulas and Clustering Multivariate) Implements approaches of non-parametric smooth test to compare simultaneously K(K>1) copulas and non-parametric clustering of multivariate populations with arbitrary sizes. See Yves I. Ngounou Bakam and Denys Pommeret (2022) and Yves I. Ngounou Bakam and Denys Pommeret (2022) . Package: r-cran-kcsknnshiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rhandsontable, r-cran-dplyr, r-cran-caret, r-cran-fnn Filename: pool/dists/noble/main/r-cran-kcsknnshiny_0.1.0-1.ca2404.1_all.deb Size: 21912 MD5sum: 508ba020a35632570628b5634d5f0c6f SHA1: 0f0c8eaa090fc02ca63dea6d8cf8ab280d406589 SHA256: 5765778858cae43b190ac89f74bdc55de4932304f53f0fc44c917c0fdf7cf973 SHA512: 7b7d17c197d60be122f0705e67c23fdcd62696b189ff3a090d2757a4ffe7375dacea44a38fc74c131400d6e8d1130c1a66fc0f72e527e7b1b7f71dd7c017bdf9 Homepage: https://cran.r-project.org/package=KCSKNNShiny Description: CRAN Package 'KCSKNNShiny' (K-Nearest Neighbour Classifier) It predicts any attribute (categorical) given a set of input numeric predictor values. Note that only numeric input predictors should be given. The k value can be chosen according to accuracies provided. The attribute to be predicted can be selected from the dropdown provided (select categorical attribute). This is because categorical attributes cannot be given as inputs here. A 'handsontable' is also provided to enter the input predictor values. Package: r-cran-kcsnbshiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rhandsontable, r-cran-dplyr, r-cran-caret, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-kcsnbshiny_0.1.0-1.ca2404.1_all.deb Size: 20886 MD5sum: bf60be4170a798fc42dc9e03286dfbb4 SHA1: 8f6660bfadd86141dc7765dcc72cf4c39b0db57f SHA256: 9f0299273c614f8648f8b0b7d90af9b437a50cfada8ab7290a2bcbc32a417e30 SHA512: fddc651e2e5e4f58dfc08fdb2de67af86902b78efd1e108d4284f7b360041c229ee7f755c016d458d2b5cb7c169d81b0960675d703d53e3ac6861dff573289fa Homepage: https://cran.r-project.org/package=KCSNBShiny Description: CRAN Package 'KCSNBShiny' (Naive Bayes Classifier) Predicts any variable in any categorical dataset for given values of predictor variables. If a dataset contains 4 variables, then any variable can be predicted based on the values of the other three variables given by the user. The user can upload their own datasets and select what variable they want to predict. A 'handsontable' is provided to enter the predictor values and also accuracy of the prediction is also shown. Package: r-cran-kdensity Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-univariateml Suggests: r-cran-extradistr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kdensity_1.2.0-1.ca2404.1_all.deb Size: 317152 MD5sum: 3be595d6eae9e9a447d676565be5753c SHA1: 62e8609876a682d26f004521c684fc705fde1215 SHA256: d7965282cdb75ac7dcab16ae4dc63974e2aeeff4d6f6582006b399c236a6918b SHA512: 2f336424671f82cf381b305dd5fa2f4be4f1fc61abb5026ed8f60cce5627532aff7d4a25c2f29fbf2b4abd8b02759611b59b2572885c5813a63e70db3fd768f0 Homepage: https://cran.r-project.org/package=kdensity Description: CRAN Package 'kdensity' (Kernel Density Estimation with Parametric Starts and AsymmetricKernels) Handles univariate non-parametric density estimation with parametric starts and asymmetric kernels in a simple and flexible way. Kernel density estimation with parametric starts involves fitting a parametric density to the data before making a correction with kernel density estimation, see Hjort & Glad (1995) . Asymmetric kernels make kernel density estimation more efficient on bounded intervals such as (0, 1) and the positive half-line. Supported asymmetric kernels are the gamma kernel of Chen (2000) , the beta kernel of Chen (1999) , and the copula kernel of Jones & Henderson (2007) . User-supplied kernels, parametric starts, and bandwidths are supported. Package: r-cran-kdglm Architecture: all Version: 1.2.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10739 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-extradistr, r-cran-rfast, r-cran-generics, r-cran-rlang, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-plotly, r-cran-tidyverse, r-cran-spdep, r-cran-sf, r-cran-terra, r-cran-geodata Filename: pool/dists/noble/main/r-cran-kdglm_1.2.14-1.ca2404.1_all.deb Size: 2265196 MD5sum: 7a308e77e57676c3202ad4879bac7b21 SHA1: bec0cb2dfdd7fb0c5746631dee0e151bf7b5ca9d SHA256: 6bebd40530fce2f06d5b7930a6e5e2ba3cafd75c25d7db3c7ad08777138f0614 SHA512: 0b521691230037a4b8370aac73a033f6c6ebb4c60cbb47078ea58f85c0dd271f85dcabd94f935c9f4dbb73ba55c9c05cd413d756f4148c633b20c3464895763b Homepage: https://cran.r-project.org/package=kDGLM Description: CRAN Package 'kDGLM' (Bayesian Analysis of Dynamic Generalized Linear Models) Provide routines for filtering and smoothing, forecasting, sampling and Bayesian analysis of Dynamic Generalized Linear Models using the methodology described in Alves et al. (2024) and dos Santos Jr. et al. (2024). Package: r-cran-kdist Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kdist_0.2-1.ca2404.1_all.deb Size: 25934 MD5sum: 7f9b3cf3b75beab8fb4f988a28386bfb SHA1: 87ff29491b5768fb4a2a9aac3de50b9f00a664a7 SHA256: 5720bbbd95bf26ebc8faedb5f15ea13b4f3d8d275da36ae47abbba47df01ffd2 SHA512: 33487e2eb2756059159bc39b4bcb2a39ea791071d57405025510bfa6b2ab139ab683f4219b5860056538d5a5e38f79423ceaf9cde161251363cf75c3ba2a5d4f Homepage: https://cran.r-project.org/package=kdist Description: CRAN Package 'kdist' (K-Distribution and Weibull Paper) Density, distribution function, quantile function and random generation for the K-distribution. A plotting function that plots data on Weibull paper and another function to draw additional lines. See results from package in T Lamont-Smith (2018), submitted J. R. Stat. Soc. Package: r-cran-kdml Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-np, r-cran-mass, r-cran-markdown Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kdml_1.1.1-1.ca2404.1_all.deb Size: 156214 MD5sum: 363cb2139e89e6d3015b4547d595d2ae SHA1: 04d7b0bcbcbc2d94244ecd9306dbdd6d074679ed SHA256: 00b4cd05eed684b3af806958ff4f82576e42cab4a6ed22dfc4094aabc01a11bb SHA512: 0dec8d5f23a41bdf691522449b3a19e2d7e6e900b75a79e32003f35e2b841cc6d591c1ccffa60e66addcc1fb0f01bf43f8b95e7d2d2f23d2bbbafa85bb5b8303 Homepage: https://cran.r-project.org/package=kdml Description: CRAN Package 'kdml' (Kernel Distance Metric Learning for Mixed-Type Data) Distance metrics for mixed-type data consisting of continuous, nominal, and ordinal variables. This methodology uses additive and product kernels to calculate similarity functions and metrics, and selects variables relevant to the underlying distance through bandwidth selection via maximum similarity cross-validation. These methods can be used in any distance-based algorithm, such as distance-based clustering. For further details, we refer the reader to Ghashti and Thompson (2024) for dkps() methodology, and Ghashti (2024) for dkss() methodology. Package: r-cran-kdps Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-progress, r-cran-tibble Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kdps_1.0.0-1.ca2404.1_all.deb Size: 174992 MD5sum: 44d566b988ad84b03e0d1930e024c250 SHA1: 93f5d387e9ae755a93036e70ac1b85ddbd4c5a40 SHA256: 2c39303bb17413e741c62c0d156a9edfee68f4de6216f5b386dd592e5bbad98a SHA512: 74c4e17c41e9b402ad870e4dd639f5baa8cbd36d985e7fc4d7133927170785fdc3ed6be1fa1260247049ee20869cfc072ed2df5ca587a14b6a699a42068d02e8 Homepage: https://cran.r-project.org/package=kdps Description: CRAN Package 'kdps' (Kinship Decouple and Phenotype Selection (KDPS)) A phenotype-aware algorithm for resolving cryptic relatedness in genetic studies. It removes related individuals based on kinship or identity-by-descent (IBD) scores while prioritizing subjects with phenotypes of interest. This approach helps maximize the retention of informative subjects, particularly for rare or valuable traits, and improves statistical power in genetic and epidemiological studies. KDPS supports both categorical and quantitative phenotypes, composite scoring, and customizable pruning strategies using a fuzziness parameter. Benchmark results show improved phenotype retention and high computational efficiency on large-scale datasets like the UK Biobank. Methods used include Manichaikul et al. (2010) for kinship estimation, Purcell et al. (2007) for IBD estimation, and Bycroft et al. (2018) for UK Biobank data reference. 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Package: r-cran-kensyn Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-lme4, r-cran-metafor Filename: pool/dists/noble/main/r-cran-kensyn_0.3-1.ca2404.1_all.deb Size: 71674 MD5sum: ce66d04d32d9c64700bf4f4f8fe3e37f SHA1: 68fb6050448b34fe661c7de2430e6f8b7821b876 SHA256: a64bc3cc4c353db58134a8255bb7f06e0e3aa4aa3832fc888656725ab81391d8 SHA512: 2ec95666ecd39878f34436f278a25f8e28aaa695bf529eafb4670eab9befc3f2002cfaa4cc5fbd5722db80dbf50d1e6647935b09f044c1da9d592dce6ec52cc3 Homepage: https://cran.r-project.org/package=KenSyn Description: CRAN Package 'KenSyn' (Knowledge Synthesis in Agriculture - From Experimental Networkto Meta-Analysis) Demo and dataset accompaying the books : De l'analyse des réseaux expérimentaux à la méta-analyse: Méthodes et applications avec le logiciel R pour les sciences agronomiques et environnementales (Published 2018-06-28, Quae, for french version) by David Makowski, Francois Piraux and Francois Brun - Knowledge Synthesis in Agriculture : from Experimental Network to Meta-Analysis (in preparation for 2018-06, Springer , for English version) by David Makowski, Francois Piraux and Francois Brun A full description of all the material is in both books. ACKNOWLEDGMENTS : The French network "RMT modeling and data analysis for agriculture" () have contributed to the development of this R package. This project and network are lead by ACTA (French Technical Institute for Agriculture) and was funded by a grant from the Ministry of Agriculture and Fishing of France. 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Package: r-cran-kerdiest Architecture: all Version: 1.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-chron, r-cran-date, r-cran-evir Filename: pool/dists/noble/main/r-cran-kerdiest_1.3-1-1.ca2404.1_all.deb Size: 99122 MD5sum: c65a1e6018df5a84b0fd90a5d0d0272c SHA1: 24e7dacc9d9853305d5053e842bfdb6ad5c94234 SHA256: c7e0f72acb7357491069d1a45126752bf76d9864209c60ee4ae8338482e4e338 SHA512: 26b919f7914b36258f21fb5d58b8f6190dcebed2c7142ad781bf5918401ab4eb76e775967d2f2f05cb7fa92be66ca47a46b0b9f25993a5e54c8362b03e166e6a Homepage: https://cran.r-project.org/package=kerdiest Description: CRAN Package 'kerdiest' (Nonparametric Kernel Estimation of Distribution Function) Nonparametric kernel distribution function estimation is performed. Three bandwidth selectors are implemented: the plug-in selectors of Altman and Leger and of Polansky and Baker, and the cross-validation selector of Bowman, Hall and Prvan. The exceedance function, the mean return period and the return level are also computed. For details, see Quintela-del-Río and Estévez-Pérez (2012) . Package: r-cran-kernelfactory Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest, r-cran-auc, r-cran-genalg, r-cran-kernlab Filename: pool/dists/noble/main/r-cran-kernelfactory_0.3.0-1.ca2404.1_all.deb Size: 54854 MD5sum: 31954f8791d6c506f16bad916723fab6 SHA1: d54b2c5780a1b7aca845c5c9434852525c1499a8 SHA256: 1e8223eedbe7d9923bc42dffbcfb6331984bbf49bd1f947ff6e3488918d3ed74 SHA512: 6b49c049874b50c418bac7236106bf4c2f4a39997ce5edabd17c76afd31af5674ac3c7b9da3687f4fef8f0c14ae3e4f15c8ae376bec10c64f29e3e92edccfc08 Homepage: https://cran.r-project.org/package=kernelFactory Description: CRAN Package 'kernelFactory' (Kernel Factory: An Ensemble of Kernel Machines) Binary classification based on an ensemble of kernel machines ("Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913"). Kernel factory is an ensemble method where each base classifier (random forest) is fit on the kernel matrix of a subset of the training data. Package: r-cran-kernelheaping Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-ks, r-cran-sparr, r-cran-sp, r-cran-plyr, r-cran-dplyr, r-cran-fastmatch, r-cran-fitdistrplus, r-cran-gb2, r-cran-magrittr, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-kernelheaping_2.3.0-1.ca2404.1_all.deb Size: 170964 MD5sum: eac4b70f21be5882a076d3f6d37e1f2f SHA1: 7531223d343fed9506c8a73ef21fb16327dd898f SHA256: d32a44e7e44b2775922a69a103aa691e047cbc9f52f3ebdc5e5d9a7a5c5e9e35 SHA512: 4f6c10a27055006d38bbed387a5e34af2a7d7b2ba8f9c76af8ae9f9b965c379fb0d9530eb5e1c1235fdea56ef9343e47272c09a45b199dfee11e0bf667260711 Homepage: https://cran.r-project.org/package=Kernelheaping Description: CRAN Package 'Kernelheaping' (Kernel Density Estimation for Heaped and Rounded Data) In self-reported or anonymised data the user often encounters heaped data, i.e. data which are rounded (to a possibly different degree of coarseness). While this is mostly a minor problem in parametric density estimation the bias can be very large for non-parametric methods such as kernel density estimation. This package implements a partly Bayesian algorithm treating the true unknown values as additional parameters and estimates the rounding parameters to give a corrected kernel density estimate. It supports various standard bandwidth selection methods. Varying rounding probabilities (depending on the true value) and asymmetric rounding is estimable as well: Gross, M. and Rendtel, U. (2016) (). Additionally, bivariate non-parametric density estimation for rounded data, Gross, M. et al. (2016) (), as well as data aggregated on areas is supported. Package: r-cran-kernelphil Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-benchmarkme, r-cran-directlabels, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-hmisc, r-cran-pbapply, r-cran-reshape2, r-cran-rlang, r-cran-terra, r-cran-wordspace Filename: pool/dists/noble/main/r-cran-kernelphil_0.2-1.ca2404.1_all.deb Size: 125114 MD5sum: 5a6eed0fbe32018b3e9e88c1142fbf76 SHA1: ce051c2888f754b6d810f5ca24902cf1f9c1e08e SHA256: ff9ff5eb415580de054998c74ee41417a4a0a037ecdd6518976069adca7b1dc8 SHA512: 342260ae80ca089e9a1cad1a34f83f841d0c2e8da51f42f549f1e7a0c22dd10e60e7ce0c1f5ac699072771679f522235a34054e1943866b09befd227e20aef59 Homepage: https://cran.r-project.org/package=kernelPhil Description: CRAN Package 'kernelPhil' (Kernel Smoothing Tools for Philology and Historical Dialectology) Contains kernel smoothing tools designed for use by historical dialectologists and philologists for exploring spatial and temporal patterns in noisy historical language data, such as that obtained from historical texts. The main way in which these might differ from other implementations of kernel smoothing is that they assume that the function (linguistic variable) being explored has the form of the relative frequency of a series of discrete possibilities (linguistic variants). This package also offers a way of exploring distributions in 2-dimensional space and in time with separate kernels, and tools for identifying appropriate bandwidths for these. Package: r-cran-kernelshap Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dofuture, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kernelshap_0.9.1-1.ca2404.1_all.deb Size: 255298 MD5sum: 01a739a9c27cf118f01e63c1fd3aeb84 SHA1: ec2d6f6d87d2652d003b066fcca325c7b681d4d0 SHA256: 4119ca437eeb223a9ee2af3be2eea08c8389a1d2b952bd2b42c88d608ff56114 SHA512: 7415f82f9e501920ae34d5247f7d1381b4598e41a78a9dddd393f7466d380f3cb7067780008b594979f4d39f8dd6d0718d683e9b79b63eee774e817c0e91f11e Homepage: https://cran.r-project.org/package=kernelshap Description: CRAN Package 'kernelshap' (Kernel SHAP) Efficient implementation of Kernel SHAP (Lundberg and Lee, 2017, ) permutation SHAP, and additive SHAP for model interpretability. For Kernel SHAP and permutation SHAP, if the number of features is too large for exact calculations, the algorithms iterate until the SHAP values are sufficiently precise in terms of their standard errors. The package integrates smoothly with meta-learning packages such as 'tidymodels', 'caret' or 'mlr3'. It supports multi-output models, case weights, and parallel computations. Visualizations can be done using the R package 'shapviz'. Package: r-cran-kernhaz Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-foreach, r-cran-doparallel, r-cran-ga Suggests: r-cran-survival Filename: pool/dists/noble/main/r-cran-kernhaz_0.1.0-1.ca2404.1_all.deb Size: 101636 MD5sum: 45c38c7af5e061a357b623b3ca6b9804 SHA1: 3026d11d864ce8b62e56b811a633b1dca487593f SHA256: 18758b3887c94b0025239189be34835ef408fcdcc4c9ed62466b6bd83e5cf4c2 SHA512: 825019167dc52fa203d6b597bc1abd472ca0e54d1abcde150fceeefade17d930d9bf5ce08693d55a8a955b6f77ce2bc0d95b35f60da2769be7544859fa7eac73 Homepage: https://cran.r-project.org/package=kernhaz Description: CRAN Package 'kernhaz' (Kernel Estimation of Hazard Function in Survival Analysis) Producing kernel estimates of the unconditional and conditional hazard function for right-censored data including methods of bandwidth selection. Package: r-cran-kernopt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kernopt_1.0.0-1.ca2404.1_all.deb Size: 165594 MD5sum: 3987ad3298bdd99bc51c789cddc11585 SHA1: 105c7c13a6a1f5a2a5403da67ed70d1cdb19046f SHA256: 33d4c7f93772fac9e4d5bf901f8adddd39f1b257c72c503033e416fa1fef1185 SHA512: 60be9adba398620379425277c56f62e01b02c599a7bf109de82448c0dcb4a33f6e436f0ad865bc344d4bf88701466ac3db4f40f670c00c88e94c844373c64b97 Homepage: https://cran.r-project.org/package=kernopt Description: CRAN Package 'kernopt' (Estimating Count Data Distributions with Discrete OptimalSymmetric Kernel) Implementation of Discrete Symmetric Optimal Kernel for estimating count data distributions, as described by T. Senga Kiessé and G. Durrieu (2024) .The nonparametric estimator using the discrete symmetric optimal kernel was illustrated on simulated data sets and a real-word data set included in the package, in comparison with two other discrete symmetric kernels. Package: r-cran-kernplus Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circular, r-cran-kernsmooth, r-cran-mixtools Filename: pool/dists/noble/main/r-cran-kernplus_0.1.2-1.ca2404.1_all.deb Size: 67652 MD5sum: 033f51e85c0d30ea7501554a8e7ddd04 SHA1: 95bed8228c3c9833893369ab5ba7ac5943d0dea3 SHA256: 40a425da82e4431ac10a68f04ca3563c733dd9b1da7ab195fb2e3a500c9dd573 SHA512: 7974da27690a3954193f456de8379c7129dc6b6bf5a8b4410f23d10171a4541873fa36706664c39a8e4a0b72e7d8c42f01092a8b2707a9884358acd653688189 Homepage: https://cran.r-project.org/package=kernplus Description: CRAN Package 'kernplus' (A Kernel Regression-Based Multidimensional Wind Turbine PowerCurve) Provides wind energy practitioners with an effective machine learning-based tool that estimates a multivariate power curve and predicts the wind power output for a specific environmental condition. Package: r-cran-kernscr Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-readxl, r-bioc-org.hs.eg.db Filename: pool/dists/noble/main/r-cran-kernscr_1.0.7-1.ca2404.1_all.deb Size: 102002 MD5sum: 73f33562291092d0d31c5ac26a5f45ba SHA1: 786a40a8d584c6702dd7b499d9416302c0c6d578 SHA256: 1a1ac9ac628c947222fedc861fbf4bf9f10fb462007d226462c95feff92f07d8 SHA512: 8b95c2121eb2c368542bff01e06ab965380ccfc547214e89a20e0fbdef9b726fa6968e8d312cb62296ee493727b49df1916218ec30fc832eb713d0667d24e753 Homepage: https://cran.r-project.org/package=kernscr Description: CRAN Package 'kernscr' (Kernel Machine Score Test for Semi-Competing Risks) Kernel Machine Score Test for Pathway Analysis in the Presence of Semi-Competing Risks. Method is detailed in: Neykov, Hejblum & Sinnott (2018) . Package: r-cran-kernstadapt Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1632 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-misc3d, r-cran-sparr, r-cran-spatstat.explore, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.utils, r-cran-spatstat.linnet Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-kernstadapt_0.4.0-1.ca2404.1_all.deb Size: 1570040 MD5sum: dfd52723e181660b47788956793d958b SHA1: 635d4b549ac5b75a556623c3e05e6ba5417a7955 SHA256: dffc872530147b2cac166250a6a6f34843d5ac0a9ee2bfddfdee1514aa0ba4f8 SHA512: 736b6d48859f80540feafe5f446e3bacb9f20bf4191bace3230aedcf8221422eb58a499821a5191d620c2ccc9cf63aa060e1977b16747722be2bcacd6cb5c7e5 Homepage: https://cran.r-project.org/package=kernstadapt Description: CRAN Package 'kernstadapt' (Adaptive Kernel Estimators for Point Process Intensities onLinear Networks) Adaptive estimation of the first-order intensity function of a spatio-temporal point process using kernels and variable bandwidths. The methodology used for estimation is presented in González and Moraga (2022). . Package: r-cran-kerntools Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1823 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-kernlab, r-cran-reshape2, r-cran-stringi Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kerntools_1.2.1-1.ca2404.1_all.deb Size: 1213922 MD5sum: 2fb238ad1329aee7060faa20bae0cdd3 SHA1: cb75b588d24658a598a29e024cf7797beceeed29 SHA256: 5a0ea58e15eaf437861020b21ece24b9c6f5593bec9c7f7d1474271539a7544a SHA512: 37c9ef177c18fb56e1504c50a665fb8baae6913da435c46a90040f2577acd5718e82497685483d5e4ff8db77df31f684b45ebe6d4dabda7f9433aa3b04a7248c Homepage: https://cran.r-project.org/package=kerntools Description: CRAN Package 'kerntools' (Kernel Functions and Tools for Machine Learning Applications) Kernel functions for diverse types of data (including, but not restricted to: nonnegative and real vectors, real matrices, categorical and ordinal variables, sets, strings), plus other utilities like kernel similarity, kernel Principal Components Analysis (PCA) and features' importance for Support Vector Machines (SVMs), which expand other 'R' packages like 'kernlab'. Package: r-cran-kertests Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kertests_0.1.4-1.ca2404.1_all.deb Size: 24768 MD5sum: f7f515adbde6f0c2236b65c2fdc636f8 SHA1: 730ebb0ef06663d64220c2c1c138966f775b26ea SHA256: 7055eb91c4d1789d237d4a954fbd942e955d29242174682aaf09e8f8908dd2e4 SHA512: c222ecc851b004f3a5fec3ec569ba2786dec3c7bb5360904a7402448ce14524cab3c86831fb1ca89bb980630a8c75c56503b7e98c38a843580c25aacab530a76 Homepage: https://cran.r-project.org/package=kerTests Description: CRAN Package 'kerTests' (Generalized Kernel Two-Sample Tests) New kernel-based test and fast tests for testing whether two samples are from the same distribution. They work well particularly for high-dimensional data. Song, H. and Chen, H. (2023) . Package: r-cran-kesernetwork Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 999 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shinybs, r-cran-shiny, r-cran-htmltools, r-cran-config, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-golem, r-cran-plotly, r-cran-reactable, r-cran-rintrojs, r-cran-rlang, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyhelper, r-cran-shinywidgets, r-cran-stringr, r-cran-visnetwork, r-cran-yaml Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-shinytest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kesernetwork_0.1.0-1.ca2404.1_all.deb Size: 699800 MD5sum: 29995069e3a6c9546df51097c40aa89a SHA1: 3f1c581982ab571c91271d31c81f975cabf20511 SHA256: 00a17525d00d595948d8802d1a4b74eb99add161fe26a859ad04f72a3ab55990 SHA512: 95d6072f17a8c915fa5acdb3737b03967f19f427cb66a2480e0ad8595e1e74aa3d614cf9513171cb95c7e791a5e7bbd3a40a2f1416ab413c6247bda94bb960ff Homepage: https://cran.r-project.org/package=kesernetwork Description: CRAN Package 'kesernetwork' (Visualization of the KESER Network) A shiny app to visualize the knowledge networks for the code concepts. Using co-occurrence matrices of EHR codes from Veterans Affairs (VA) and Massachusetts General Brigham (MGB), the knowledge extraction via sparse embedding regression (KESER) algorithm was used to construct knowledge networks for the code concepts. Background and details about the method can be found at Chuan et al. (2021) . Package: r-cran-keyboard Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-iso, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-keyboard_0.1.3-1.ca2404.1_all.deb Size: 269936 MD5sum: 47b916e01a53c2a4f953906680d9e67e SHA1: aafa3cc47af99d78474d6de59bf215d9e51a7b36 SHA256: 3dfccfcbe7ac444a35a63dcc242b8f6d4aef561feb4089d67696a2174a9c4cf4 SHA512: 744af301f17251c05dcd3b7366dca1fcf48a7a7906dd708083e5ae2b0ec9f794f99076efc15541e0f5787c294339c70ba461478b0ab6668fc4b01c5121f957c4 Homepage: https://cran.r-project.org/package=Keyboard Description: CRAN Package 'Keyboard' (Bayesian Designs for Early Phase Clinical Trials) We developed a package 'Keyboard' for designing single-agent, drug-combination, or phase I/II dose-finding clinical trials. The 'Keyboard' designs are novel early phase trial designs that can be implemented simply and transparently, similar to the 3+3 design, but yield excellent performance, comparable to those of more-complicated, model-based designs (Yan F, Mandrekar SJ, Yuan Y (2017) , Li DH, Whitmore JB, Guo W, Ji Y. (2017) , Liu S, Johnson VE (2016) , Zhou Y, Lee JJ, Yuan Y (2019) , Pan H, Lin R, Yuan Y (2020) ). The 'Keyboard' package provides tools for designing, conducting, and analyzing single-agent, drug-combination, and phase I/II dose-finding clinical trials. For more details about how to use this packge, please refer to Li C, Sun H, Cheng C, Tang L, and Pan H. (2022) "A software tool for both the maximum tolerated dose and the optimal biological dose finding trials in early phase designs". Manuscript submitted for publication. Package: r-cran-keyed Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-digest, r-cran-lifecycle, r-cran-pillar, r-cran-rlang, r-cran-tibble, r-cran-vctrs Suggests: r-cran-joinspy, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-uuid Filename: pool/dists/noble/main/r-cran-keyed_0.2.0-1.ca2404.1_all.deb Size: 164624 MD5sum: ae4d49d6813864a093ab79e2e16839f5 SHA1: 1e081758a6ce89061978503d2a2521cc5538de44 SHA256: 031925e2f564a6a9a78457198040769a5ed6566c737b1cece56f7d0b99766315 SHA512: 3c16efb720db9ffe3ecc59b8a12864d33b543f0ddf638d20c4e720d623059183b41479527536bfbfa1311df0b527610fe19c4350728303614e34ca3656f823fc Homepage: https://cran.r-project.org/package=keyed Description: CRAN Package 'keyed' (Explicit Key Assumptions for Flat-File Data) Helps make implicit data assumptions explicit by attaching keys to flat-file data that error when those assumptions are violated. Designed for CSV-first workflows without database infrastructure or version control. Provides key definition, assumption checks, join diagnostics, and automatic drift detection via watched data frames that snapshot before each transformation and report cell-level changes. 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This is done by creating special attribute "keys" which is updated after every change in rows (subsetting, ordering, etc.). This package is designed to work tightly with 'dplyr' package. 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Package: r-cran-keytoenglish Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openssl, r-cran-stringr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-keytoenglish_0.2.1-1.ca2404.1_all.deb Size: 230782 MD5sum: acbe4693cfb4689bbc2487ce3e2a2852 SHA1: f13fd65d5785b9389bffad722ff4f2ad9d5c3beb SHA256: a8df5a85698fcc16f9b620206c953cacb0e09e82e529d48f5dd981b5a8c1a3e1 SHA512: 91cbc154ac0e450a1d10525678dcf102f827700e2fd9ef9b8db9762434a4c325be28cd8fba778c57babd65e10a0c514af38273d43f7a2b0089763c686d62b9b6 Homepage: https://cran.r-project.org/package=keyToEnglish Description: CRAN Package 'keyToEnglish' (Convert Data to Memorable Phrases) Convert keys and other values to memorable phrases. Includes some methods to build lists of words. Package: r-cran-kfa Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3520 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-doparallel, r-cran-flextable, r-cran-foreach, r-cran-gparotation, r-cran-knitr, r-cran-lavaan, r-cran-officer, r-cran-rmarkdown, r-cran-semtools, r-cran-simstandard Suggests: r-cran-semplot Filename: pool/dists/noble/main/r-cran-kfa_0.2.2-1.ca2404.1_all.deb Size: 3379518 MD5sum: e3cbc49055782badacb3950457218729 SHA1: ae84a7c457deaa719c301e1f682b680b5a0dfd5f SHA256: 97e49f576a96585a65b0d403f1355a1fa838c5cb3a1bb852693f8f958a9a3cc4 SHA512: c9ee93d60bcf101dc4c501e705e4b05c30d24fd920c66e1306ef19422c14efe53f5b775c969496b6ce230bd0fd07ec0f3f339b7207bab208232f2267c2d16a79 Homepage: https://cran.r-project.org/package=kfa Description: CRAN Package 'kfa' (K-Fold Cross Validation for Factor Analysis) Provides functions to identify plausible and replicable factor structures for a set of variables via k-fold cross validation. The process combines the exploratory and confirmatory factor analytic approach to scale development (Flora & Flake, 2017) with a cross validation technique that maximizes the available data (Hastie, Tibshirani, & Friedman, 2009) . Also available are functions to determine k by drawing on power analytic techniques for covariance structures (MacCallum, Browne, & Sugawara, 1996) , generate model syntax, and summarize results in a report. Package: r-cran-kfda Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernlab, r-cran-mass Filename: pool/dists/noble/main/r-cran-kfda_1.0.0-1.ca2404.1_all.deb Size: 19976 MD5sum: 128075c6e9e7b11dbf1839a3acba881e SHA1: 5ab2851dbe8af0c55747c3cf40ef8d33c84822ad SHA256: 6df1de54225fd1b7cdd612b8d14a509f8d5f1523b8c2c6dafa18263a70881398 SHA512: 7d19aff6c6c603cb883a639f5af2e46118a160d48016cbb9dd685a466e5cb663d3016b1fa5ea81088d8bc8c3e269510fb61c6d5dafefa13157f36b30468e41d9 Homepage: https://cran.r-project.org/package=kfda Description: CRAN Package 'kfda' (Kernel Fisher Discriminant Analysis) Kernel Fisher Discriminant Analysis (KFDA) is performed using Kernel Principal Component Analysis (KPCA) and Fisher Discriminant Analysis (FDA). There are some similar packages. First, 'lfda' is a package that performs Local Fisher Discriminant Analysis (LFDA) and performs other functions. In particular, 'lfda' seems to be impossible to test because it needs the label information of the data in the function argument. Also, the 'ks' package has a limited dimension, which makes it difficult to analyze properly. This package is a simple and practical package for KFDA based on the paper of Yang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. F. (2004) . Package: r-cran-kfigr Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr Suggests: r-cran-ggplot2, r-cran-markdown Filename: pool/dists/noble/main/r-cran-kfigr_1.2.1-1.ca2404.1_all.deb Size: 44632 MD5sum: 2ca82338429f0852c506ebc090aea5db SHA1: 093177460d45dc2758e9d1c31d01c4e90a09b3f0 SHA256: ee45cb5e7975544916bcbbe7798fc92e2fce9d7f02f107bdf87b5963e46e15d1 SHA512: 1cb94eb83d531be6a314cf45d00b85657dbbef05e16f84c7776753346b26def71fa1b4e37078137ca40760787e136a83c4d7c0867a49efb870e1be5d1511a7ca Homepage: https://cran.r-project.org/package=kfigr Description: CRAN Package 'kfigr' (Integrated Code Chunk Anchoring and Referencing for R MarkdownDocuments) A streamlined cross-referencing system for R Markdown documents generated with 'knitr'. R Markdown is an authoring format for generating dynamic content from R. 'kfigr' provides a hook for anchoring code chunks and a function to cross-reference document elements generated from said chunks, e.g. figures and tables. Package: r-cran-kfino Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2572 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-kfino_1.0.0-1.ca2404.1_all.deb Size: 875654 MD5sum: 39395163fac3ce4672a542b7d026f342 SHA1: 67e94d8d706bd1d44728c13f21ffe106c0790561 SHA256: cbec4ef07efa291df6975b2e44b22f6d925f5554b78c069d62ab9dc473f3ae4e SHA512: 26ba65687f2c2e99bdd8ac71260817f8fd191092b8d2951f3d281fb09fc7b49f4ffe7db3052cc5d430c4d92d00dc2250298ac97e44ca76b586c418738e3a1ba0 Homepage: https://cran.r-project.org/package=kfino Description: CRAN Package 'kfino' (Kalman Filter for Impulse Noised Outliers) A method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. 'kfino' is a robust sequential algorithm allowing to filter data with a large number of outliers. This algorithm is based on simple latent linear Gaussian processes as in the Kalman Filter method and is devoted to detect impulse-noised outliers. These are data points that differ significantly from other observations. 'ML' (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) algorithms were implemented in 'kfino'. The method is described in full details in the following arXiv e-Print: . Package: r-cran-kfpca Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kader, r-cran-pracma, r-cran-fdapace, r-cran-fda Filename: pool/dists/noble/main/r-cran-kfpca_2.0-1.ca2404.1_all.deb Size: 87644 MD5sum: 5801a711c676ba7a6bb894d6c06093f3 SHA1: 7f154a5d3edac2234a339ab53939a7e27918d5c7 SHA256: fec65d9dce4177b83b0fd7dd4ea094e969e526c26232343a67de0c1e69ea48a6 SHA512: 624369ea55c809f8147a7ef022190473b161b7b856a3455f6c3d213b731a82bd953010ccd1c541421e6139569f7d8eb988ea7c018d2e9b44a35e41e64e02ba13 Homepage: https://cran.r-project.org/package=KFPCA Description: CRAN Package 'KFPCA' (Kendall Functional Principal Component Analysis) Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA() and KFPCA_reg(). Moreover, least square estimates of functional principal component scores are also provided. Refer to Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) . Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) . Package: r-cran-kfpls Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda Filename: pool/dists/noble/main/r-cran-kfpls_1.0-1.ca2404.1_all.deb Size: 26778 MD5sum: 7fcf6f59de6667d6d84cdc23ab7655a4 SHA1: d0d3bc5d9dd2387a01c57b9dc2c85a1cc6bf82f4 SHA256: 36a72f773e2649bdfcbf96fdc0343f7f0b164dc8fbc432dc058e1f128ac5fcf0 SHA512: fc75c23fc57ce900c0300c9411286a9251a15c88094076d88accc8f6e6673f95434fde121b9449e190518c50d7dbc6adb0b9cedc109146fb4fb33c2c2f1bd915 Homepage: https://cran.r-project.org/package=KFPLS Description: CRAN Package 'KFPLS' (Kernel Functional Partial Least Squares) Implementation for kernel functional partial least squares (KFPLS) method. KFPLS method is developed for functional nonlinear models, and the method does not require strict constraints for the nonlinear structures. The crucial function of this package is KFPLS(). Package: r-cran-kfre Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-r6 Suggests: r-cran-devtools, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-rprojroot, r-cran-spelling, r-cran-svglite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kfre_0.0.2-1.ca2404.1_all.deb Size: 114136 MD5sum: 1302a0b92d5253cc041ed4b530d4448d SHA1: 39f7018fb3aec3072aa6d00fbba0da934fd9f7ea SHA256: 31bc41e46788b3d7f7581471c0101c2fd0c77859bd35705cb01e4011419fe390 SHA512: 81bba9adfc05b48c7df1c312a86bd69ceeb26c0d47e3d549f05f3b59d26d062b5fcbdff23101bf5b2b127766fd60a3588e3267148866f6c6cbec3bef2df55538 Homepage: https://cran.r-project.org/package=kfre Description: CRAN Package 'kfre' (Kidney Failure Risk Equation (KFRE) Tools) Implements the Kidney Failure Risk Equation (KFRE; Tangri and colleagues (2011) ; Tangri and colleagues (2016) ) to compute 2- and 5-year kidney failure risk using 4-, 6-, and 8-variable models. Includes helpers to append risk columns to data frames, classify chronic kidney disease (CKD) stages and end-stage renal disease (ESRD) outcomes, and evaluate and plot model performance. Package: r-cran-kgc Architecture: all Version: 1.0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3595 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-plyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kgc_1.0.0.2-1.ca2404.1_all.deb Size: 2646720 MD5sum: 7372de1e17a08edfe2609f45100385e6 SHA1: 70a32bca0ff114b9f5e582b10d9ffe5d4297c6e1 SHA256: 608d172e7b0c68b1d4f1449041bcdae9d32897661c1a510b45751ccec48d1d69 SHA512: 5de0e5ecfc4fd4a5302be5da5db8601186a3ed2f13c762500b85690c00b617e90bdf4e7bf781fc8b76288300118137e602e4c28cd6363b4aee226ef63b856c66 Homepage: https://cran.r-project.org/package=kgc Description: CRAN Package 'kgc' (Koeppen-Geiger Climatic Zones) Aids in identifying the Koeppen-Geiger (KG) climatic zone for a given location. The Koeppen-Geiger climate zones were first published in 1884, as a system to classify regions of the earth by their relative heat and humidity through the year, for the benefit of human health, plant and agriculture and other human activity [1]. This climate zone classification system, applicable to all of the earths surface, has continued to be developed by scientists up to the present day. Recently one of use (FZ) has published updated, higher accuracy KG climate zone definitions [2]. In this package we use these updated high-resolution maps as the data source [3]. We provide functions that return the KG climate zone for a given longitude and lattitude, or for a given United States zip code. In addition the CZUncertainty() function will check climate zones nearby to check if the given location is near a climate zone boundary. In addition an interactive shiny app is provided to define the KG climate zone for a given longitude and lattitude, or United States zip code. Digital data, as well as animated maps, showing the shift of the climate zones are provided on the following website . This work was supported by the DOE-EERE SunShot award DE-EE-0007140. [1] W. Koeppen, (2011) . [2] F. Rubel and M. Kottek, (2010) . [3] F. Rubel, K. Brugger, K. Haslinger, and I. Auer, (2016) . Package: r-cran-kgen Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjson, r-cran-reticulate, r-cran-checkmate, r-cran-data.table Suggests: r-cran-future, r-cran-progressr, r-cran-future.apply, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kgen_1.1.1-1.ca2404.1_all.deb Size: 72022 MD5sum: 800ccc2c5251bb862ca2d36ae1869a42 SHA1: e36ae15bfa3b63382ab5f81354310ba1905e2ef1 SHA256: 157c37215aee70a108268a5ecf902c6cf362c3ed0e86a6344290b26ebdd210d9 SHA512: 0f452951ea4e406bbd434816b0e2e279cc9779b2a647d47730f785afd0f1497f1a82a1e056ef4466a1c2df76cc334f84a39ef40eae516283ee0e962a7fde9681 Homepage: https://cran.r-project.org/package=kgen Description: CRAN Package 'kgen' (A Tool for Calculating Stoichiometric Equilibrium Constants (Ks)for Seawater) A unified software package simultaneously implemented in 'Python', 'R', and 'Matlab' providing a uniform and internally-consistent way of calculating stoichiometric equilibrium constants in modern and palaeo seawater as a function of temperature, salinity, pressure and the concentration of magnesium, calcium, sulphate, and fluorine. Package: r-cran-kgode Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-pracma, r-cran-pspline, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-kgode_1.0.5-1.ca2404.1_all.deb Size: 364702 MD5sum: 6e0e6687f503c52a48382b4937667874 SHA1: 4b860c9832df1b8f262fd94f25f9d264508fc642 SHA256: 4558174b542ac170b47ea4ba389cb8e667c626511c81bc6533c92511eb4ceedc SHA512: 7243885844f720391d37d9717f5e0f43ade6a30cbb9cbcf6d4cf0ecfbdfb2d4cf106baf19e5bbeda7885616294246933427aae042b9b8b1aeab0b80b2a00778f Homepage: https://cran.r-project.org/package=KGode Description: CRAN Package 'KGode' (Kernel Based Gradient Matching for Parameter Inference inOrdinary Differential Equations) The kernel ridge regression and the gradient matching algorithm proposed in Niu et al. (2016) and the warping algorithm proposed in Niu et al. (2017) are implemented for parameter inference in differential equations. Four schemes are provided for improving parameter estimation in odes by using the odes regularisation and warping. Package: r-cran-kgp Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tibble Filename: pool/dists/noble/main/r-cran-kgp_1.1.1-1.ca2404.1_all.deb Size: 306422 MD5sum: 1b0a19bcc9e133dbc88f9546ba58c421 SHA1: cdefe3a9b6cc8dd727a86432990d294b969abdaa SHA256: f82e53a802a75b041a6a103ce26a2a1beaf14605e9be0b224dc4ab85b11ef627 SHA512: 20e8e5d5f50b3f95155d7c46e4e5502f2960f23bcfd785301e5ba48edad12fad2eaf5988a2268f4863f1ae1b385b92f2197a666ff847d049081a6a5e132a7b7e Homepage: https://cran.r-project.org/package=kgp Description: CRAN Package 'kgp' (1000 Genomes Project Metadata) Metadata about populations and data about samples from the 1000 Genomes Project, including the 2,504 samples sequenced for the Phase 3 release and the expanded collection of 3,202 samples with 602 additional trios. The data is described in Auton et al. (2015) and Byrska-Bishop et al. (2022) , and raw data is available at . See Turner (2022) for more details. Package: r-cran-kgraph Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1669 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-htmltools, r-cran-igraph, r-cran-magrittr, r-cran-opticskxi, r-cran-plyr, r-cran-proc, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-sgraph, r-cran-shiny Suggests: r-cran-bslib, r-cran-data.table, r-cran-dt, r-cran-knitr, r-cran-nlpembeds, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kgraph_1.2.0-1.ca2404.1_all.deb Size: 1179132 MD5sum: 35cffe3d52861437b978f0eabc75ff09 SHA1: e0923cbdc185cb0c5b6671659e74db5b4b408bb0 SHA256: 6ba89b96358132d26e5ad0fcdae89c801ae3bb906ceeeed6229a0943f976adf5 SHA512: 5ec1891e4a4c2618677dc0e842eef9870ab43e79c67f79127f6be5f01a23ebd70e75284a7c1e70c2a4d13496315cc54d171be0b135afeb36160f21a32cfdba94 Homepage: https://cran.r-project.org/package=kgraph Description: CRAN Package 'kgraph' (Knowledge Graphs Constructions and Visualizations) Knowledge graphs enable to efficiently visualize and gain insights into large-scale data analysis results, as p-values from multiple studies or embedding data matrices. The usual workflow is a user providing a data frame of association studies results and specifying target nodes, e.g. phenotypes, to visualize. The knowledge graph then shows all the features which are significantly associated with the phenotype, with the edges being proportional to the association scores. As the user adds several target nodes and grouping information about the nodes such as biological pathways, the construction of such graphs soon becomes complex. The 'kgraph' package aims to enable users to easily build such knowledge graphs, and provides two main features: first, to enable building a knowledge graph based on a data frame of concepts relationships, be it p-values or cosine similarities; second, to enable determining an appropriate cut-off on cosine similarities from a complete embedding matrix, to enable the building of a knowledge graph directly from an embedding matrix. The 'kgraph' package provides several display, layout and cut-off options, and has already proven useful to researchers to enable them to visualize large sets of p-value associations with various phenotypes, and to quickly be able to visualize embedding results. Two example datasets are provided to demonstrate these behaviors, and several live 'shiny' applications are hosted by the CELEHS laboratory and Parse Health, as the KESER Mental Health application based on Hong C. (2021) . Package: r-cran-kgschart Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-deepnet, r-cran-ggplot2, r-cran-gridextra, r-cran-magrittr, r-cran-matrixstats, r-cran-nnet, r-cran-png, r-cran-shiny, r-cran-stringr Suggests: r-cran-jsonlite, r-cran-shinyjs, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kgschart_1.3.5-1.ca2404.1_all.deb Size: 388484 MD5sum: 91efa33c57ec0efe1da370b3ef7499a9 SHA1: 4e94ac839404b0f9e29b704c835624fd5788f183 SHA256: e310e64d8d7abf8f4a0341c2a652abe2d68401b73d22ad484c703b2246ad8008 SHA512: 309a28e6b330c5ba28ce06b39f24971faee493c1aee3f21ed61749eeb76cbb98dcff3dafe116e07eac2b34c18bcf30e79540e8dd3a0a58e17051d21dbbafe92a Homepage: https://cran.r-project.org/package=kgschart Description: CRAN Package 'kgschart' (KGS Rank Graph Parser) Restore underlining numeric data from rating history graph of KGS (an online platform of the game of go, ). A shiny application is also provided. Package: r-cran-khaos Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lhs, r-cran-testthat Filename: pool/dists/noble/main/r-cran-khaos_2.1.1-1.ca2404.1_all.deb Size: 211772 MD5sum: 634b268a26c157454f54002006d4c13b SHA1: cebb54be482a2c6e1e809ed8493a2b5e77f49118 SHA256: 4ea4f6e768a6b019f18a3cef0a67c686b78d34e4d8c5f32c0d440ad5db2cb76c SHA512: bad93f8d45704d480ac3482b2f15f7805015c518a830c69f1fca01e371cc00d77cb7d5e837e640569734ccebc8a75f02d77f0ab1c761a79151d790dad0396bce Homepage: https://cran.r-project.org/package=khaos Description: CRAN Package 'khaos' (Bayesian Sparse Polynomial Chaos Expansion) Implements Bayesian polynomial chaos expansion methods for surrogate modeling, uncertainty quantification, and sensitivity analysis. Includes sparse regression-based approaches and adaptive Bayesian models based on reversible-jump Markov chain Monte Carlo. Optional screening and basis-enrichment strategies are provided to improve scalability in moderate to high dimensions. Package: r-cran-khisr Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-httr2, r-cran-janitor, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-khisr_1.0.6-1.ca2404.1_all.deb Size: 208976 MD5sum: 55f51240c11b6ac5eef8d4bb94a65272 SHA1: e6673a659398567723d0f386b2f76921818876b8 SHA256: 03f4a239ad1c4b4091c8bb590af96f187f4540f3ba70029b33528c91fa3cefec SHA512: 7d3e6a927d53d7f15619a28a1d15d18813bb2a1dae3e0dfcdbd164b24eb35e370b60f955e378f54af2f34148b606e762e10efd4bcdced03fb0803aeafa8fb7b5 Homepage: https://cran.r-project.org/package=khisr Description: CRAN Package 'khisr' (An R Client to Retrieve Data from DHIS2) Provides a user-friendly interface for interacting with the District Health Information Software 2 (DHIS2) instance. It streamlines data retrieval, empowering researchers, analysts, and healthcare professionals to obtain and utilize data efficiently. Package: r-cran-khq Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-khq_0.2.0-1.ca2404.1_all.deb Size: 95980 MD5sum: 23b04d02bb6cb575a0e756189a795093 SHA1: b3daa439d0ed921628c024815288c38d0da6a781 SHA256: e2ebc517dba21e933afb611ab226b37e494a673fba9f590ef5f548f29b24c51f SHA512: 5af8f184a062c63f5e9aa6cd34558f42690fea832840bf1eda9eeb77c8f0e6c900516a3d8c6cb177557e3decf18d4b8e0112189b792a5f61226dae01b55442bc Homepage: https://cran.r-project.org/package=KHQ Description: CRAN Package 'KHQ' (Methods for Calculating 'KHQ' Scores and 'KHQ5D' Utility IndexScores) The King's Health Questionnaire (KHQ) is a disease-specific, self-administered questionnaire designed specific to assess the impact of Urinary Incontinence (UI) on Quality of Life. The questionnaire was developed by Kelleher and collaborators (1997) . It is a simple, acceptable and reliable measure to use in the clinical setting and a research tool that is useful in evaluating UI treatment outcomes. The KHQ five dimensions (KHQ5D) is a condition-specific preference-based measure developed by Brazier and collaborators (2008) . Although not as popular as the SF6D and EQ-5D , the KHQ5D measures health-related quality of life (HRQoL) specifically for UI, not general conditions like the others two instruments mentioned. The KHQ5D ca be used in the clinical and economic evaluation of health care. The subject self-rates their health in terms of five dimensions: Role Limitation (RL), Physical Limitations (PL), Social Limitations (SL), Emotions (E), and Sleep (S). Frequently the states on these five dimensions are converted to a single utility index using country specific value sets, which can be used in the clinical and economic evaluation of health care as well as in population health surveys. This package provides methods to calculate scores for each dimension of the KHQ; converts KHQ item scores to KHQ5D scores; and also calculates the utility index of the KHQ5D. Package: r-cran-khroma Architecture: all Version: 1.17.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4891 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-fontquiver, r-cran-ggplot2, r-cran-ggraph, r-cran-knitr, r-cran-markdown, r-cran-rsvg, r-cran-scales, r-cran-spacesxyz, r-cran-svglite, r-cran-tinysnapshot, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-khroma_1.17.0-1.ca2404.1_all.deb Size: 3305294 MD5sum: 95664257063afe74c9aa4b1e6b5122b8 SHA1: 13ed487374594e34d8218487f7ecd301bc3692ef SHA256: 6c297ccd2bfe90ccec45fb6fc2873aee37e255359c6dff50d7416203f285aea6 SHA512: 91ce13df12bd6bc2f6e1a2344c9aab9af556cb85c65d1647ad0c2a3020e2d27bc03c41cac1b544eaddc5ba36b98a6cb50a88faf8148edd304c26429dcd651d12 Homepage: https://cran.r-project.org/package=khroma Description: CRAN Package 'khroma' (Colour Schemes for Scientific Data Visualization) Color schemes ready for each type of data (qualitative, diverging or sequential), with colors that are distinct for all people, including color-blind readers. This package provides an implementation of Paul Tol (2018) and Fabio Crameri (2018) color schemes for use with 'graphics' or 'ggplot2'. It provides tools to simulate color-blindness and to test how well the colors of any palette are identifiable. Several scientific thematic schemes (geologic timescale, land cover, FAO soils, etc.) are also implemented. Package: r-cran-kibior Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4983 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-stringr, r-cran-purrr, r-cran-jsonlite, r-cran-rio, r-cran-tibble, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-elastic, r-bioc-biostrings, r-bioc-rsamtools, r-bioc-rtracklayer Suggests: r-cran-ggplot2, r-cran-readr, r-cran-xml2, r-cran-yaml, r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-kibior_0.1.1-1.ca2404.1_all.deb Size: 1761342 MD5sum: 840df921c245cc37c7015c476bd0180c SHA1: c9c29572d443e01201fecdec38f4617f040d1c6c SHA256: e91a48e5ef9f9d245ef78c611b1f8ffe59fcc4c8f366091f33930ab39d5a8f02 SHA512: 66042ce4cbc0c6d16ae7f5d411b309590a42c311643eba64f1e8be7e86223c1943fecd9f3ae17c627c9a6f05885268496aa4a117278fe9c4106766ea8a1f2b42 Homepage: https://cran.r-project.org/package=kibior Description: CRAN Package 'kibior' (A Simple Data Management and Sharing Tool) An interface to store, retrieve, search, join and share datasets, based on Elasticsearch (ES) API. As a decentralized, FAIR and collaborative search engine and database effort, it proposes a simple push/pull/search mechanism only based on ES, a tool which can be deployed on nearly any hardware. It is a high-level R-ES binding to ease data usage using 'elastic' package (S. Chamberlain (2020)) , extends joins from 'dplyr' package (H. Wickham et al. (2020)) and integrates specific biological format importation with Bioconductor packages such as 'rtracklayer' (M. Lawrence and al. (2009) ) , 'Biostrings' (H. Pagès and al. (2020) ) , and 'Rsamtools' (M. Morgan and al. (2020) ) , but also a long list of more common ones with 'rio' (C-h. Chan and al. (2018)) . Package: r-cran-kidney.epi Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1079 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-openxlsx, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kidney.epi_1.4.0-1.ca2404.1_all.deb Size: 540220 MD5sum: 425db16a338b5bbead98036ec0abd16d SHA1: 45e9bcb968cb7ab97607e24217224c7518425881 SHA256: a01c0e1661d17aaaec4e285e5ff30728cf3889040a2a66f917285e34d7b9ed28 SHA512: c46edc68f345f5764a44d5574905536df56a2b323fe6724923dbf11b6ce8c03e16666320788338d43a9f13959c472bb2c37ba10da086f3d9bb27ab18dd201d73 Homepage: https://cran.r-project.org/package=kidney.epi Description: CRAN Package 'kidney.epi' (Kidney-Related Functions for Clinical and EpidemiologicalResearch) Contains kidney care oriented functions. Current version contains functions for calculation of: - Estimated glomerular filtration rate by CKD-EPI (2021 and 2009), MDRD, CKiD, FAS, EKFC, etc. - Kidney Donor Risk Index and Kidney Donor Profile Index for kidney transplant donors. - Citation: Bikbov B. kidney.epi: Kidney-Related Functions for Clinical and Epidemiological Research. Scientific-Tools.Org, . . Package: r-cran-kidsides Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-rsqlite, r-cran-r.utils Suggests: r-cran-ggplot2, r-cran-dbplyr, r-cran-tidyr, r-cran-stringr, r-cran-ggthemes, r-cran-rlang, r-cran-ggrepel, r-cran-scales, r-cran-pacman, r-cran-dplyr, r-cran-rmarkdown, r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-dt, r-cran-gt, r-cran-lobstr, r-cran-prettyunits, r-cran-purrr, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-kidsides_0.5.0-1.ca2404.1_all.deb Size: 24972 MD5sum: a9fc62452af96e5c74411d7f2bdd320c SHA1: 6ab30c632deeb402265af3b5d9d4785fd29e517e SHA256: d9220f1c67bb6c5e3f7b650945f05c60c10552dd5a09242c8819aa2fb18a4b1e SHA512: 38f9674b7412433c383a408c52aa1644c827b1ffb012345cb545b9b55d8c6e97aa926fa9dc6af0502609d2c9b301834d44240e3bbb846207c6a4a71b033fa779 Homepage: https://cran.r-project.org/package=kidsides Description: CRAN Package 'kidsides' (Download, Cache, and Connect to KidSIDES) Caches and then connects to a 'sqlite' database containing half a million pediatric drug safety signals. The database is part of a family of resources catalogued at . The database contains 17 tables where the description table provides a map between the fields the field's details. The database was created by Nicholas Giangreco during his PhD thesis which you can read in Giangreco (2022) . The observations are from the Food and Drug Administration's Adverse Event Reporting System. Generalized additive models estimated drug effects across child development stages for the occurrence of an adverse event when exposed to a drug compared to other drugs. Read more at the methods detailed in Giangreco (2022) . Package: r-cran-kifidi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kifidi_0.1.0-1.ca2404.1_all.deb Size: 22020 MD5sum: 12a9d2ceba346598b793c80795db0f50 SHA1: 086635bd6be617f5f2b66e2ca97e25f49f875ba1 SHA256: 65bd487799ddb7526053df4d1dcbe8ad9d2255f3b77a50d2d3aed7e0ff4efd54 SHA512: e7547efb50624c9e65d39e24b29c87d829f6925b1c43f75985fd05b0dd7211ec7d31897b4b8217dc7484c480fdb96e774ee948ca83d92526e182077f6fc7b2f3 Homepage: https://cran.r-project.org/package=Kifidi Description: CRAN Package 'Kifidi' (Summary Table and Means Plots) Optimized for handling complex datasets in environmental and ecological research, this package offers functionality that is not fully met by general-purpose packages. It provides two key functions, 'summarize_data()', which summarizes datasets, and 'plot_means()', which creates plots with error bars. The 'plot_means()' function incorporates error bars by default, allowing quick visualization of uncertainties, crucial in ecological studies. It also streamlines workflows for grouped datasets (e.g., by species or treatment), making it particularly user-friendly and reducing the complexity and time required for data summarization and visualization. Package: r-cran-kim Architecture: all Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1025 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-remotes Suggests: r-cran-boot, r-cran-ggplot2, r-cran-moments, r-cran-mass, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kim_0.6.4-1.ca2404.1_all.deb Size: 962392 MD5sum: f6fc28a7461373c8bd708a4727981a5a SHA1: 36144e303a21735be135814dd895de6114261862 SHA256: 4684eafae177beb1a73cc92da2fe11b4cf5a35c2073e66f6ec0370cd3387bdfe SHA512: 006dad0c48067184f245fdc8c2673227233d96fc91545347247d3bd2da3fc041cc9c098a88436ef12a219f4bbc96e7a1d121b3b71d9d72051c5d622e8c998883 Homepage: https://cran.r-project.org/package=kim Description: CRAN Package 'kim' (A Toolkit for Behavioral Scientists) A collection of functions for analyzing data typically collected or used by behavioral scientists. Examples of the functions include a function that compares groups in a factorial experimental design, a function that conducts two-way analysis of variance (ANOVA), and a function that cleans a data set generated by Qualtrics surveys. Some of the functions will require installing additional package(s). Such packages and other references are cited within the section describing the relevant functions. Many functions in this package rely heavily on these two popular R packages: Dowle et al. (2021) . Wickham et al. (2021) . Package: r-cran-kimisc Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-memoise, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kimisc_1.0.1-1.ca2404.1_all.deb Size: 119984 MD5sum: 08727a4f096924ad5532919caefbf0b0 SHA1: c3e13ae9dad30895120a80ca6cfdc7d9d9fbd06b SHA256: 5cfd1a8a78ca00881c756d1962702002e5ce429e5ac02e44a1526955578d2714 SHA512: be15aa4e3c9bce4098042f9ae205f20a6cfbe09d6fdeee3717a10425618418d2769a6e528775a4d33a1f08953e3ab1f25ca1e149444136a85d69899bfdf163d8 Homepage: https://cran.r-project.org/package=kimisc Description: CRAN Package 'kimisc' (Kirill's Miscellaneous Functions) A collection of useful functions not found anywhere else, mainly for programming: Pretty intervals, generalized lagged differences, checking containment in an interval, and an alternative interface to assign(). Package: r-cran-kin.cohort Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-kin.cohort_0.7-1.ca2404.1_all.deb Size: 158386 MD5sum: 6121c29f48c56c39aab5b632ab101502 SHA1: 1e96725f7ebbb9c9fbd8523d59b99aa9010c4bdc SHA256: 87ede0bd78c41a42839e15eb3c677a1d8c8eafc3573426b4e6293eeeabe9ce48 SHA512: a5fd23ea9d6b01bbbccc0e1d7e67a5c632e1f18c945736d6d779d02e18a49142774bf5322511f0cc5b2fb5a47d875474db258e6026c9bb4dd988e5ec50ab1e52 Homepage: https://cran.r-project.org/package=kin.cohort Description: CRAN Package 'kin.cohort' (Analysis of Kin-Cohort Studies) Analysis of kin-cohort studies. kin.cohort provides estimates of age-specific cumulative risk of a disease for carriers and noncarriers of a mutation. The cohorts are retrospectively built from relatives of probands for whom the genotype is known. Currently the method of moments and marginal maximum likelihood are implemented. Confidence intervals are calculated from bootstrap samples. Most of the code is a translation from previous 'MATLAB' code by N. Chatterjee. Package: r-cran-kindisperse Architecture: all Version: 0.10.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1736 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-readr, r-cran-shiny, r-cran-shinythemes, r-cran-ggrepel, r-cran-fitdistrplus, r-cran-laplacesdemon, r-cran-here, r-cran-tibble, r-cran-magrittr, r-cran-plotly, r-cran-dplyr, r-cran-rlang, r-cran-tidyselect, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kindisperse_0.10.2-1.ca2404.1_all.deb Size: 870632 MD5sum: 392c4e91580b80c9ad2d679d4a169826 SHA1: 3ad51c6000177242ce5c14e60f5b9f87085d384c SHA256: bdd552621da1f1752cc4a2199c5db381477555d5787704084f4219f740c2b686 SHA512: 073464ca890cd01f7f0ecfdbf25f9f22280320dd102f02ada75c3e0fc7a7ec1c46c2d7c48ff968e9f3334f5b8eddcb59ec39a5edfe4f7d93d2322bdcdd36f2eb Homepage: https://cran.r-project.org/package=kindisperse Description: CRAN Package 'kindisperse' (Simulate and Estimate Close-Kin Dispersal Kernels) Functions for simulating and estimating kinship-related dispersal. Based on the methods described in M. Jasper, T.L. Schmidt., N.W. Ahmad, S.P. Sinkins & A.A. Hoffmann (2019) "A genomic approach to inferring kinship reveals limited intergenerational dispersal in the yellow fever mosquito". Assumes an additive variance model of dispersal in two dimensions, compatible with Wright's neighbourhood area. Simple and composite dispersal simulations are supplied, as well as the functions needed to estimate parent-offspring dispersal for simulated or empirical data, and to undertake sampling design for future field studies of dispersal. For ease of use an integrated Shiny app is also included. Package: r-cran-kindling Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 770 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-torch, r-cran-rlang, r-cran-cli, r-cran-glue, r-cran-vctrs, r-cran-parsnip, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-neuralnettools, r-cran-vip, r-cran-ggplot2, r-cran-tune, r-cran-dials, r-cran-hardhat, r-cran-lifecycle, r-cran-coro Suggests: r-cran-testthat, r-cran-magrittr, r-cran-box, r-cran-recipes, r-cran-workflows, r-cran-rsample, r-cran-yardstick, r-cran-mlbench, r-cran-modeldata, r-cran-knitr, r-cran-rmarkdown, r-cran-dicedesign, r-cran-lhs, r-cran-sfd, r-cran-covr Filename: pool/dists/noble/main/r-cran-kindling_0.3.0-1.ca2404.1_all.deb Size: 577104 MD5sum: 6d827e0cadb6fc3d0ee5417297f4263e SHA1: 8e8a17aa51ef5a2292241deb2020fb3ec8d65880 SHA256: e06b7b588067e4238096b49467e3f2ba7d1e8bd24a2751e67b5e53abf6239cb6 SHA512: 6e5eae34481c4365e921bd20f4330967f2b86c8b4d44b047c43637c2f93d4326f5b40d10d6f9c2cc5e62c1a2ce64081bb264ed1b78092750408b5af9bc598fa8 Homepage: https://cran.r-project.org/package=kindling Description: CRAN Package 'kindling' (Higher-Level Interface of 'torch' Package to Auto-Train NeuralNetworks) Provides a higher-level interface to the 'torch' package for defining, training, and fine-tuning neural networks, including its depth, powered by code generation. This package supports few to several architectures, including feedforward (multi-layer perceptron) and recurrent neural networks (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)), while also reduces boilerplate 'torch' code while enabling seamless integration with 'torch'. The model methods to train neural networks from this package also bridges to titanic ML frameworks in R, namely 'tidymodels' ecosystem, which enables the 'parsnip' model specifications, workflows, recipes, and tuning tools. Package: r-cran-kinematics Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-kinematics_1.0.0-1.ca2404.1_all.deb Size: 672444 MD5sum: 9ab995d9076e30eae8c33422eb656e95 SHA1: 3504984f8bca0f36b9ff674629eeb2d9762f0a5b SHA256: 5874892f9c466316ddd0f0fae019089f854fca4dde3033f2b135db1d21dd4ace SHA512: b32d5878f20a8acd0c53bbebc41c0044209a825a5f3043bf961c62dbe889875ff3b2b64c2e8c7d2e3e4e204b73dae34f9d51367a1b394c982da134d911ad31a3 Homepage: https://cran.r-project.org/package=kinematics Description: CRAN Package 'kinematics' (Studying Sampled Trajectories) Allows analyzing time series representing two-dimensional movements. It accepts a data frame with a time (t), horizontal (x) and vertical (y) coordinate as columns, and returns several dynamical properties such as speed, acceleration or curvature. Package: r-cran-kinesis Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1031 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aion, r-cran-arkhe, r-cran-bslib, r-cran-config, r-cran-dimensio, r-cran-gt, r-cran-isopleuros, r-cran-kairos, r-cran-khroma, r-cran-mirai, r-cran-nexus, r-cran-sass, r-cran-shiny, r-cran-tabula Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-kinesis_0.3.1-1.ca2404.1_all.deb Size: 610184 MD5sum: 30bc34a851c364871ac41347e2891442 SHA1: 4f1052ec921e55243cb66c0f7288e648d3d986dc SHA256: c1f2a112eaa396afbc816953b89df6d936cb5448a11fd8917e74cde4cd64a376 SHA512: 9eaa82a90096440fe80fde1599f1c4c1493310ec7dc674ae010ac1f756b9e7251fb0ab605a777c00965ca050140d0950a3261bda16b7b64bb4f6d608fcfafa51 Homepage: https://cran.r-project.org/package=kinesis Description: CRAN Package 'kinesis' ('shiny' Applications for the 'tesselle' Packages) A collection of 'shiny' applications for the 'tesselle' packages . This package provides applications for archaeological data analysis and visualization. These mainly, but not exclusively, include applications for chronological modelling (e.g. matrix seriation, aoristic analysis) and count data analysis (e.g. diversity measures, compositional data analysis). Package: r-cran-kinformr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kinformr_0.1.2-1.ca2404.1_all.deb Size: 457134 MD5sum: b76585c568a950f42251c1667a75667e SHA1: 7c47e1f7295fcc15f7706b30a4435a83c339773d SHA256: cc63064605da28628713eb684898295266fa2627d3e29d2f17154aaec2e98612 SHA512: b6c952df5344aea9702b68916b8bd0ccb65d21f6b3e92c28d87b22891ccab99fc3992e90a6adfb4ffcdfc8807000fec879dd6e9fd32a18735f9d5f62f6315827 Homepage: https://cran.r-project.org/package=KinformR Description: CRAN Package 'KinformR' (Relationship-Informed Pedigree and Variant Scoring) Comparative evaluation of families and candidate variants in rare-variant association studies. The package can be used for two methodologically overlapping but distinct purposes. First, the prior to any genetic or genomic evaluation, evaluation of relative detection power of pedigrees, can direct recruitment efforts by showing which individuals not yet sampled would be the most meaningful additions to a study. Second, after sequencing and analysis, variants based on association with disease status and familial relationships of individuals, aids in variant prioritization. Methodology is described in Nugent (2025) . Package: r-cran-kingcountyhouses Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 645 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-kingcountyhouses_0.1.0-1.ca2404.1_all.deb Size: 611584 MD5sum: 77a6beae20f702bb331fb5543ffa08a7 SHA1: 5271a98669fc3d50112cefc64eb6900ff7e2b63e SHA256: 0889750f1ac774530c092ace7b9796b10011a6020291461cc17641eb3a1144b7 SHA512: aa4a9c07ff182dc8ff108284743f7f1114d8b44c41b091c044141ed7881f3deab64267c66ec46e230c9e8101840f0e1d7be65ead4a66f06a29df6a4070b33c5c Homepage: https://cran.r-project.org/package=KingCountyHouses Description: CRAN Package 'KingCountyHouses' (Data on House Sales in King County WA) Data on houses in and around Seattle WA are included. Basic characteristics are given along with sale prices. Package: r-cran-kinmixlite Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dnamixtureslite, r-cran-graven, r-cran-statnet.common, r-cran-grbase, r-cran-rsolnp, r-cran-numderiv, r-cran-matrix, r-cran-ribd, r-cran-pedtools Filename: pool/dists/noble/main/r-cran-kinmixlite_2.2.1-1.ca2404.1_all.deb Size: 267672 MD5sum: 086ced445885e32f3df94586197dfcdb SHA1: f600aa38e33178167fde1bdf69a3a9137ca884e4 SHA256: 06916d11ce0502c2f5102bbda7a33e98ba2f2633cd17641b255e21b4ee1be59e SHA512: dcc9a09353907820be69c36976620cd63c64e9749d4c0330f0d63d61a12ce1f67398ff234511f744939ecfb9f82e155e9e709283267dbddedf42bcde4d407973 Homepage: https://cran.r-project.org/package=KinMixLite Description: CRAN Package 'KinMixLite' (Inference About Relationships from DNA Mixtures) Methods for inference about/under complex relationships using peak height data from DNA mixtures: the most basic example would be testing whether a contributor to a mixture is the father of a child of known genotype. This provides most of the functionality of the 'KinMix' package, but with some loss of efficiency and restriction on problem size, as the latter uses 'RHugin' as the Bayes net engine, while this package uses 'gRain'. The package implements the methods introduced in Green, P. J. and Mortera, J. (2017) and Green, P. J. and Mortera, J. (2021) . Package: r-cran-kinship2 Architecture: all Version: 1.9.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 805 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-quadprog, r-cran-knitr Filename: pool/dists/noble/main/r-cran-kinship2_1.9.6.2-1.ca2404.1_all.deb Size: 505204 MD5sum: b0e0d5d696925ba9668f271a76fd6c3b SHA1: 81ecf823cccedcda0e0e7f48c7e7ce5d127f3f16 SHA256: 4b89c412f7b6a6a2afa3ba08ee35941456f3648a95d9b56763bba834130108af SHA512: b1725ac697bea9418118c2696a030663880422fb08cad2e74f2d1a0716f9f9fb48e5d74902a328e34623ade01b7ca321cf9001a0d42814976ce550d4f6af0bcb Homepage: https://cran.r-project.org/package=kinship2 Description: CRAN Package 'kinship2' (Pedigree Functions) Routines to handle family data with a pedigree object. The initial purpose was to create correlation structures that describe family relationships such as kinship and identity-by-descent, which can be used to model family data in mixed effects models, such as in the coxme function. Also includes a tool for pedigree drawing which is focused on producing compact layouts without intervention. Recent additions include utilities to trim the pedigree object with various criteria, and kinship for the X chromosome. Package: r-cran-kinsimu Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 527 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kinsimu_0.1.3-1.ca2404.1_all.deb Size: 419224 MD5sum: cf27cbe6f16edbb767335fe5cf701ac0 SHA1: 74c107a9b218ed92bfaceaf86666795b74058e3c SHA256: d69f163b286274f77e4823d9c81ca4c3a87cf1503682880d7e47f2d6c6c54256 SHA512: fe0a022353263b5d8bea3bc3100529f6aa80da4b745718c5b4f5b1fec6c9ce9a8c00c45065133ed0a4697e2b54f95ec70e995ec8426401bdba845e8b266b1dc6 Homepage: https://cran.r-project.org/package=KINSIMU Description: CRAN Package 'KINSIMU' (Panel Evaluation in Forensic Kinship Analysis) Evaluate specific panels in different aspects: i) Simulation tools related to pedigree researches; ii) calculation for systemic effectiveness indicators, such as probability of exclusion (PE). 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Package: r-cran-kirby21.fmri Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kirby21.base Suggests: r-cran-kirby21.t1 Filename: pool/dists/noble/main/r-cran-kirby21.fmri_1.8.0-1.ca2404.1_all.deb Size: 14042 MD5sum: 4194636d735827c565c256f049ee3d42 SHA1: ba67791a37d5dd00ae8b9a41c918cedd78680ce1 SHA256: 8e19f3bcded07267522ecfda9f42ceec05fae8972b4298594520fd5bb368d882 SHA512: 42f446527e4602bdc39e44b6f5d21099b3b65a21b804c44cc7331a7d7461d325265e4fe0f0770ba9acd5d04c91bb29c98e16f78680d7ea42a047845e292f6bba Homepage: https://cran.r-project.org/package=kirby21.fmri Description: CRAN Package 'kirby21.fmri' (Example Functional Imaging Data from the Multi-Modal MRI'Reproducibility' Resource) Functional magnetic resonance imaging ('fMRI') data from the 'Kirby21' 'reproducibility' study . Package: r-cran-kirby21.t1 Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kirby21.base Filename: pool/dists/noble/main/r-cran-kirby21.t1_1.8.0-1.ca2404.1_all.deb Size: 14922 MD5sum: 13a4bf4d4d7667e5c337ae887af70178 SHA1: 610334b32cf8ce13e22f4834a32b9ff14256c9cd SHA256: ec34dbeeb9573f75f3c15640880650c9101f0f17e97320f05983b142d6650c26 SHA512: dc3271b125c785f77c61b83f0c4f1358891e18cab552ea305cc8318a41c8da2edccdbc33014b90ea8ce68ed0e24403955fcebd0ffc908342fed5428a1cfcc1fe Homepage: https://cran.r-project.org/package=kirby21.t1 Description: CRAN Package 'kirby21.t1' (Example T1 Structural Data from the Multi-Modal MRI'Reproducibility' Resource) Structural T1 magnetic resonance imaging ('MRI') data from the 'Kirby21' 'reproducibility' study . Package: r-cran-kisopenapi Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-httr2, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-kisopenapi_0.0.2-1.ca2404.1_all.deb Size: 75732 MD5sum: 05ca3149056db5e02603c7fb8fb4be6f SHA1: 5c1867a391dd935deef776e7ef2ffcbb703c4bca SHA256: 709d66dbd8600c607039ccaae45479d31fa83d8307781c09a78e1e00323a7671 SHA512: 28f314e54b14b4dedfdd6d87aea86a98bd7ae9a623442ea90bd501d184d0bc2acd5a2a900044fab3c45fc1b0caf8d3fb7fc205f6ae1c7f7b1201b93cbb5de8b8 Homepage: https://cran.r-project.org/package=kisopenapi Description: CRAN Package 'kisopenapi' (Korea Investment & Securities (KIS) Open Trading API) API Wrapper to use Korea Investment & Securities (KIS) trading system that provides various financial services like stock price check, orders and balance check . Package: r-cran-kitagawa Architecture: all Version: 3.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 770 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bessel, r-cran-kelvin, r-cran-psd Suggests: r-cran-dplyr, r-cran-tibble, r-cran-rcolorbrewer, r-cran-signal, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-formatr, r-cran-covr Filename: pool/dists/noble/main/r-cran-kitagawa_3.1.3-1.ca2404.1_all.deb Size: 563974 MD5sum: ad7e52ff825626683fb574cfe6d5f8bc SHA1: 7882db03a6cc57f58a1365a7a0e5aba9eb32f260 SHA256: 31d8f8b0b3d4338f55223ea84e0d0e0e9492c0903337ebb6f3ccb284f3844083 SHA512: 88738f762665edccd4fd901ba6535056d74412c494c24f376ca0d788670bb15f55599fbf170ad734b7461601e90a85afd944e471127fdd51837924cfa63627bb Homepage: https://cran.r-project.org/package=kitagawa Description: CRAN Package 'kitagawa' (Spectral Response of Water Wells to Harmonic Strain and PressureSignals) Provides tools to calculate the theoretical hydrodynamic response of an aquifer undergoing harmonic straining or pressurization, or analyze measured responses. There are two classes of models here, designed for use with confined aquifers: (1) for sealed wells, based on the model of Kitagawa et al (2011, ), and (2) for open wells, based on the models of Cooper et al (1965, ), Hsieh et al (1987, ), Rojstaczer (1988, ), Liu et al (1989, ), and Wang et al (2018, ). Wang's solution is a special exception which allows for leakage out of the aquifer (semi-confined); it is equivalent to Hsieh's model when there is no leakage (the confined case). These models treat strain (or aquifer head) as an input to the physical system, and fluid-pressure (or water height) as the output. The applicable frequency band of these models is characteristic of seismic waves, atmospheric pressure fluctuations, and solid earth tides. Package: r-cran-kitesquare Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2, r-cran-ggh4x, r-cran-scales, r-cran-rlang Suggests: r-cran-knitr, r-cran-quarto Filename: pool/dists/noble/main/r-cran-kitesquare_0.0.2-1.ca2404.1_all.deb Size: 138092 MD5sum: 949de6bc4e8de89542634f9847bf239d SHA1: ec375184262f3c94e4380237c063aa044fae00c4 SHA256: 19833ca8508df4ec2f0fd0d0dee4f4551d26bf7b15b9ac8574767f7bd5dbacd7 SHA512: 542917e55ebed07736af956536fe391d45f2d02f3b8c5b1bd8e92375df27a6a0642fe71fb1ad68f2c5f42acf933bf30c0a44c053eec19f25f0939902b3384d25 Homepage: https://cran.r-project.org/package=kitesquare Description: CRAN Package 'kitesquare' (Visualize Contingency Tables Using Kite-Square Plots) Create a kite-square plot for contingency tables using 'ggplot2', to display their relevant quantities in a single figure (marginal, conditional, expected, observed, chi-squared). The plot resembles a flying kite inside a square if the variables are independent, and deviates from this the more dependence exists. Package: r-cran-kiwisr Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-lubridate, r-cran-dplyr, r-cran-purrr, r-cran-httr2 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kiwisr_0.2.4-1.ca2404.1_all.deb Size: 36472 MD5sum: 73d2bf92a7d4130ce1c8aa5d1367fbe2 SHA1: a414ef3142ac142099226f1e02ccbbcd8d10c622 SHA256: 12983522d6f23d499f03faede2a29e1d5a74d34c041b5275c594de90a30ce804 SHA512: 3e2081b783d7a9616a962231835aa9c8e4e291be1e24fe002d537f50e4b1b2159268ce6a85b177e0472b0807be858dd5d5eb0b2f8831546dc111900d913702ac Homepage: https://cran.r-project.org/package=kiwisR Description: CRAN Package 'kiwisR' (A Wrapper for Querying KISTERS 'WISKI' Databases via the 'KiWIS'API) A wrapper for querying 'WISKI' databases via the 'KiWIS' 'REST' API. 'WISKI' is an 'SQL' relational database used for the collection and storage of water data developed by KISTERS and 'KiWIS' is a 'REST' service that provides access to 'WISKI' databases via HTTP requests (). Contains a list of default databases (called 'hubs') and also allows users to provide their own 'KiWIS' URL. Supports the entire query process- from metadata to specific time series values. All data is returned as tidy tibbles. Package: r-cran-kko Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-grpreg, r-cran-knockoff, r-cran-doparallel, r-cran-foreach, r-cran-extdist Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-kko_1.0.1-1.ca2404.1_all.deb Size: 69924 MD5sum: 2cd556dba1f58770ad0bf9c534037a04 SHA1: 448b0dc489af283c2ca13f249e1ed68d1ee64026 SHA256: f787efa6199581bc131db465104c144348661491655ad5021ff9f1420e61d79c SHA512: 37921060721a6b8444181bfedc2b73a9de87772b1eb8bf2e3cd3ddc704515ff33848dd8beec27cc886e0db0038b328f4e28d2c1bbc9173f13510b8f5884515f5 Homepage: https://cran.r-project.org/package=kko Description: CRAN Package 'kko' (Kernel Knockoffs Selection for Nonparametric Additive Models) A variable selection procedure, dubbed KKO, for nonparametric additive model with finite-sample false discovery rate control guarantee. The method integrates three key components: knockoffs, subsampling for stability, and random feature mapping for nonparametric function approximation. For more information, see the accompanying paper: Dai, X., Lyu, X., & Li, L. (2021). “Kernel Knockoffs Selection for Nonparametric Additive Models”. arXiv preprint . Package: r-cran-klar Architecture: all Version: 1.7-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 619 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-combinat, r-cran-questionr Suggests: r-cran-scatterplot3d, r-cran-som, r-cran-mlbench, r-cran-rpart, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-klar_1.7-4-1.ca2404.1_all.deb Size: 566346 MD5sum: fb7c10da19da46c1add0b794948015c2 SHA1: c68fd2a32e982c2480dd1bc4a6eb6d9c0751d476 SHA256: d7eae6a23961f44f67eaa1314a339b3c4e49b046ffc2999a2d892e4a1d29ffc2 SHA512: 2b157f7d9656479466f7d5bf1a550b9208f48fc8562f6898cb638f68a7e8e005724da38c355c52169a3eff3632809bf53359f4548e61d3022ac5142f1f1795c2 Homepage: https://cran.r-project.org/package=klaR Description: CRAN Package 'klaR' (Classification and Visualization) Miscellaneous functions for classification and visualization, e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, an interface to 'svmlight' and stepclass() wrapper variable selection for supervised classification, partimat() visualization of classification rules and shardsplot() of cluster results as well as kmodes() clustering for categorical data, corclust() variable clustering, variable extraction from different variable clustering models and weight of evidence preprocessing. Package: r-cran-klassr Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tm, r-cran-httr, r-cran-jsonlite, r-cran-igraph Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-kableextra, r-cran-magrittr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-klassr_1.0.5-1.ca2404.1_all.deb Size: 284528 MD5sum: 8c560b002d0b8a3958ae0895a3c4734b SHA1: 6460378105a5bb25218d02b8c0e491dba09fc443 SHA256: 2b2c47e34f72a2cc295b76201bbd6fbafc065013fd4be96cbfe664d2d73e20d9 SHA512: 6554e5aab317a4ceb97ede5838844434b5549645daf0d2035bc2ec5521c2270a29d6799d2205d253bb79fa6740971e2af2f21ec0b3a85f7cab38f524afd88006 Homepage: https://cran.r-project.org/package=klassR Description: CRAN Package 'klassR' (Classifications for Statistics Norway) Functions to search, retrieve, apply and update classification standards and code lists using Statistics Norway's API from the system 'KLASS'. Retrieves classifications by date with options to choose language, hierarchical level and formatting. Package: r-cran-klausur Architecture: all Version: 0.12-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xtable, r-cran-psych Filename: pool/dists/noble/main/r-cran-klausur_0.12-14-1.ca2404.1_all.deb Size: 386186 MD5sum: 6048355a57886d29aba6ed3d67a26506 SHA1: 8608ad74c35079dd16d8379eb8fa170f19373c64 SHA256: 6847c41a34a4aff636eaa033e9f57f038f87c255603734844616fb508b868989 SHA512: 15b635f088ebee9bc2c209fbb92bc860b6f63fb44f95503ae060c190c503d9c06dc1de90e2257060d23c59abc332443e62d5a6706a8a777106590eb977e57820 Homepage: https://cran.r-project.org/package=klausuR Description: CRAN Package 'klausuR' (Multiple Choice Test Evaluation) A set of functions designed to quickly generate results of a multiple choice test. Generates detailed global results, lists for anonymous feedback and personalised result feedback (in LaTeX and/or PDF format), as well as item statistics like Cronbach's alpha or disciminatory power. 'klausuR' also includes a plugin for the R GUI and IDE RKWard, providing graphical dialogs for its basic features. The respective R package 'rkward' cannot be installed directly from a repository, as it is a part of RKWard. To make full use of this feature, please install RKWard from (plugins are detected automatically). Due to some restrictions on CRAN, the full package sources are only available from the project homepage. Package: r-cran-kldest Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rann Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kernsmooth, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kldest_1.0.0-1.ca2404.1_all.deb Size: 110926 MD5sum: 27bc2d58cf2f137e3ad20f0d6a113855 SHA1: 0314f4b23bb677155762a8883c9527bd1e547bb2 SHA256: ab1ee240c4be7dd9c620632d1cd465980cd25d7c6f63b7339de72837c7e656bd SHA512: 5eb50bcd48dd71f958c239d89a636997ad89f18604462309f7940eff2a533bf0c86bb7d8e4b9945a667e6474f11de6a7e235765e2a8680d3f6463c4dbd6e3eef Homepage: https://cran.r-project.org/package=kldest Description: CRAN Package 'kldest' (Sample-Based Estimation of Kullback-Leibler Divergence) Estimation algorithms for Kullback-Leibler divergence between two probability distributions, based on one or two samples, and including uncertainty quantification. Distributions can be uni- or multivariate and continuous, discrete or mixed. Package: r-cran-kldtools Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kldtools_1.2-1.ca2404.1_all.deb Size: 23824 MD5sum: 7bc5fb7ecf260155db4b63daab4a2076 SHA1: 78f4eec98708bd4f10ebe2f4b964ddeb0db63164 SHA256: ff825e7e203fb432d71736b82fc46541cbdcf181977dc6ed57d55e8b25b0e9a2 SHA512: 0309777ca039b3daa869ad3c7426aea501476af3dade29ee78e948085d7fd3f0361a09306eb9ebd908ba3cc19e1423d58a7be0147636348945cb97f58290f611 Homepage: https://cran.r-project.org/package=kldtools Description: CRAN Package 'kldtools' (Kullback-Leibler Divergence and Other Tools to AnalyzeFrequencies) Most importantly, calculates Kullback-Leibler Divergence (KLD), Turing's perspective estimator and their confidence intervals. Package: r-cran-klexdatr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1910 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf Suggests: r-cran-chk, r-cran-covr, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-klexdatr_0.1.2-1.ca2404.1_all.deb Size: 1901130 MD5sum: c4adc5025b6752f0f5d3187c7a6edbea SHA1: f2858147b9660bc3d395a747e1a3d3ac83197574 SHA256: 10de32db25c6f1ba6a617646c03b600f7c50a7e9180bd9b1143dbb6cd04f4974 SHA512: 8a2a8fa6a83b2f755dbf892c25fa6ed3febb3880040e30600698bafcb0e9e77853d14c41be8920d0a44661e1786fb40030186551a084d6f5f6c544436f08302b Homepage: https://cran.r-project.org/package=klexdatr Description: CRAN Package 'klexdatr' (Kootenay Lake Exploitation Study Data) Six relational 'tibbles' from the Kootenay Lake Large Trout Exploitation study. The study which ran from 2008 to 2014 caught, tagged and released large Rainbow Trout and Bull Trout in Kootenay Lake by boat angling. The fish were tagged with internal acoustic tags and/or high reward external tags and subsequently detected by an acoustic receiver array as well as reported by anglers. The data are analysed by Thorley and Andrusak (1994) to estimate the natural and fishing mortality of both species. Package: r-cran-klexp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-klexp_1.0.0-1.ca2404.1_all.deb Size: 22380 MD5sum: a4c10420b0d6a0609911075991a3aaaa SHA1: f59fd8d1f9135d4b76189f0d690afb8360f656b8 SHA256: 95b62653f40b9d18d98defe613416d9924846c3535012786de44aa757601f20e SHA512: 9c38d14b81b5e30a7ff3d7370c19f98f94bcdb4e641407f4388b7eab5f332c1c3e409be734315887b20c4255ad512a35c16da76f19a34927ce9b85bac9e7cb09 Homepage: https://cran.r-project.org/package=KLexp Description: CRAN Package 'KLexp' (Kernel_lasso Expansion) Provides the function to calculate the kernel-lasso expansion, Z-score, and max-min-scale standardization.It can increase the dimension of existed dataset and remove abundant features by lasso. Z Dai, L Jiayi, T Gong, C Wang (2021) . Package: r-cran-klic Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1926 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-cluster, r-cran-coca, r-cran-rcolorbrewer, r-cran-pheatmap Suggests: r-cran-rmosek, r-cran-tikzdevice, r-cran-mclust, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-klic_1.0.4-1.ca2404.1_all.deb Size: 738176 MD5sum: 7991393e086f42263bcdd58fbf3d1bea SHA1: f015a0141bac85ae2a0224314f74c1c3c42af21e SHA256: 76c8ec7a7aafe626188ae52a0c2d28f10f615b4e741b8a6a199b60b2c529127a SHA512: ae3d9e05068028e1ebbdc73fc41038ab341215be99cf448c0ee376d4c40c84fe6d30ecffcfdc6c1e743f9fe35ac5c0d9492ac442a1b37f387a74a766a55e4a60 Homepage: https://cran.r-project.org/package=klic Description: CRAN Package 'klic' (Kernel Learning Integrative Clustering) Kernel Learning Integrative Clustering (KLIC) is an algorithm that allows to combine multiple kernels, each representing a different measure of the similarity between a set of observations. The contribution of each kernel on the final clustering is weighted according to the amount of information carried by it. As well as providing the functions required to perform the kernel-based clustering, this package also allows the user to simply give the data as input: the kernels are then built using consensus clustering. Different strategies to choose the best number of clusters are also available. For further details please see Cabassi and Kirk (2020) . Package: r-cran-klink Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forrel, r-cran-norstr, r-cran-gt, r-cran-openxlsx, r-cran-pedfamilias, r-cran-pedmut, r-cran-pedprobr, r-cran-pedtools, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboard, r-cran-shinyjs, r-cran-verbalisr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-klink_1.2.0-1.ca2404.1_all.deb Size: 706216 MD5sum: 95ccb5286d041a73c17eadb133d44f5b SHA1: 0f220cfcf71af06ac9e27e697fb326c6d9e437dc SHA256: c73d58bcdb4a68e1ae6ad004c2aac4ae6741f44c5dbb399c7ef0dd5858d4224e SHA512: 006bf7a48aeea3347b5c8a90e7d95f9268f6e6e253377cef63cf620fefa2844ab6da8a76d24433efefbe5b74bfa1231aecfc308be12f48a6a435d9567c70ffe1 Homepage: https://cran.r-project.org/package=KLINK Description: CRAN Package 'KLINK' (Kinship Analysis with Linked Markers) A 'shiny' application for forensic kinship testing, based on the 'pedsuite' R packages. 'KLINK' is closely aligned with the (non-R) software 'Familias' and 'FamLink', but offers several unique features, including visualisations and automated report generation. The calculation of likelihood ratios supports pairs of linked markers, and all common mutation models. Package: r-cran-klovan Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1933 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-dplyr, r-cran-fields, r-cran-ggally, r-cran-ggforce, r-cran-ggplot2, r-cran-ggrepel, r-cran-gstat, r-cran-metr, r-cran-pracma, r-cran-sp, r-cran-tibble, r-cran-magrittr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-sf, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-klovan_0.1.0-1.ca2404.1_all.deb Size: 1408852 MD5sum: e5bb153f6aec23bcd8b0bd7646d1c752 SHA1: 978ffec9c15b9330a870ce9c4a03105fb91f5191 SHA256: f0fe8993a1ebf2ad57d63672571f5411a8bb3fecea1f60214b7d2552bb6bd51b SHA512: f03cd5a2cdc4e02a254c9d81c5463b1dbfd40a4fe59013a155025dda51ac0f1f896151bcf226a5cf98da2dc624819d4e206e2f28bdfaaaac39c32d95d07f9e7c Homepage: https://cran.r-project.org/package=klovan Description: CRAN Package 'klovan' (Geostatistics Methods and Klovan Data) A comprehensive set of geostatistical, visual, and analytical methods, in conjunction with the expanded version of the acclaimed J.E. Klovan's mining dataset, are included in 'klovan'. This makes the package an excellent learning resource for Principal Component Analysis (PCA), Factor Analysis (FA), kriging, and other geostatistical techniques. Originally published in the 1976 book 'Geological Factor Analysis', the included mining dataset was assembled by Professor J. E. Klovan of the University of Calgary. Being one of the first applications of FA in the geosciences, this dataset has significant historical importance. As a well-regarded and published dataset, it is an excellent resource for demonstrating the capabilities of PCA, FA, kriging, and other geostatistical techniques in geosciences. For those interested in these methods, the 'klovan' datasets provide a valuable and illustrative resource. Note that some methods require the 'RGeostats' package. Please refer to the README or Additional_repositories for installation instructions. This material is based upon research in the Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3-COE), and supported by the Department of Energy's National Nuclear Security Administration under Award Number DE-NA0004104. Package: r-cran-klsh Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-blink, r-cran-plyr, r-cran-rcpp, r-cran-stringi, r-cran-snowballc Suggests: r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-klsh_0.1.0-1.ca2404.1_all.deb Size: 99902 MD5sum: 513343aa082e150db544c5871ce826a9 SHA1: 90fb032347ee48071b8db978bc99a144e895471f SHA256: 36020b2a934899e658d290b74fe5aaed0b13d7626eca392844faa0aa9888fd2e SHA512: 4545dd28117e7c51638b2a1e980f88c7e2515567e7935e6bf256a1177b93ddbe6d5285bcc6a4f5b349baaf6631f3cd59c64fb198edf55e3a17a195290877daf7 Homepage: https://cran.r-project.org/package=klsh Description: CRAN Package 'klsh' (Blocking for Record Linkage) An implementation of the blocking algorithm KLSH in Steorts, Ventura, Sadinle, Fienberg (2014) , which is a k-means variant of locality sensitive hashing. The method is illustrated with examples and a vignette. Package: r-cran-klustr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 826 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-klustr_0.1.0-1.ca2404.1_all.deb Size: 204226 MD5sum: 3dd9f76e35dff53d4eb15966517d8874 SHA1: e165f22d5fe8abf9924774e4046dcbfbb88a8471 SHA256: 352946b996a6063b255abf3f3942ce7294690a567462d509892713988c1b5bfd SHA512: 0104015c5f38cdf2a79bf8fdf65b5049471cdf64ef4c603390235f23f573653709668a7c0433279334305a8266e52198307894ae0dd78fba66fede12c8226371 Homepage: https://cran.r-project.org/package=klustR Description: CRAN Package 'klustR' (D3 Dynamic Cluster Visualizations) Used to create dynamic, interactive 'D3.js' based parallel coordinates and principal component plots in 'R'. The plots make visualizing k-means or other clusters simple and informative. Package: r-cran-km.ci Architecture: all Version: 0.5-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-km.ci_0.5-6-1.ca2404.1_all.deb Size: 67876 MD5sum: e9ef88a0117c6c835c07fc4ce3c11e41 SHA1: ab632e6a81e1caa7c978b96207e9aff997e87c68 SHA256: 6ca61dc21019bb33ef5742166033f8cb34e4805da76bd1eed42d08391961edff SHA512: f057982f26c53a83fd8c2633cb17c890aa322c9b447e2594b60e98fd3a9a9bdd91b4ca3d0581022873188aa37567056ff616e9fb94e48776844d18bb9b29a8a5 Homepage: https://cran.r-project.org/package=km.ci Description: CRAN Package 'km.ci' (Confidence Intervals for the Kaplan-Meier Estimator) Computes various confidence intervals for the Kaplan-Meier estimator, namely: Peto's CI, Rothman CI, CI's based on Greenwood's variance, Thomas and Grunkemeier CI and the simultaneous confidence bands by Nair and Hall and Wellner. Package: r-cran-kmd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rann, r-cran-proxy, r-cran-mlpack, r-cran-boot, r-cran-igraph Filename: pool/dists/noble/main/r-cran-kmd_0.1.0-1.ca2404.1_all.deb Size: 43486 MD5sum: 18ec07f3065ac40b6e091fa62dd179ba SHA1: 748127fe7d3118528d6301e74dbc4dfd885acda2 SHA256: e8157aa78690d944eddc76599e83341dd653c7291f58fa507caad2c21ebd5da5 SHA512: ae883409579050c7cb82298774f901c39f149201fe99552bd0f8a9ee6cd4a5b795f4ec4a4b350d7c3d8447c15e1fbcbae791e7ddaa80f6e570166e8a858b4e10 Homepage: https://cran.r-project.org/package=KMD Description: CRAN Package 'KMD' (Kernel Measure of Multi-Sample Dissimilarity) Implementations of the kernel measure of multi-sample dissimilarity (KMD) between several samples using K-nearest neighbor graphs and minimum spanning trees. The KMD measures the dissimilarity between multiple samples, based on the observations from them. It converges to the population quantity (depending on the kernel) which is between 0 and 1. A small value indicates the multiple samples are from the same distribution, and a large value indicates the corresponding distributions are different. The population quantity is 0 if and only if all distributions are the same, and 1 if and only if all distributions are mutually singular. The package also implements the tests based on KMD for H0: the M distributions are equal against H1: not all the distributions are equal. Both permutation test and asymptotic test are available. These tests are consistent against all alternatives where at least two samples have different distributions. For more details on KMD and the associated tests, see Huang, Z. and B. Sen (2022) . Package: r-cran-kmeans.knn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-factoextra, r-cran-cluster, r-cran-ggplot2, r-cran-assertthat, r-cran-class, r-cran-caret Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kmeans.knn_0.1.0-1.ca2404.1_all.deb Size: 40428 MD5sum: 7ee4c6ab7a63710640cf13227ffe0706 SHA1: c8b56f9cbccd275c0720c140f277b44c19d68635 SHA256: 6774dab56ef4a9cd617b7c52a1f18e0a2a9d6c5932ca1d734b3aa75ebb9feac0 SHA512: 90e5cc10884a33c0ffa983dcc4c211a467a8bc04cf7b3324bd1e08026002b4ae7edffef6b5d51fe0188c63c77918ac764576018b5ee51c46bdd8db07c7247229 Homepage: https://cran.r-project.org/package=KMEANS.KNN Description: CRAN Package 'KMEANS.KNN' (KMeans and KNN Clustering Package) Implementation of Kmeans clustering algorithm and a supervised KNN (K Nearest Neighbors) learning method. It allows users to perform unsupervised clustering and supervised classification on their datasets. Additional features include data normalization, imputation of missing values, and the choice of distance metric. The package also provides functions to determine the optimal number of clusters for Kmeans and the best k-value for KNN: knn_Function(), find_Knn_best_k(), KMEANS_FUNCTION(), and find_Kmeans_best_k(). Package: r-cran-kmed Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kmed_0.4.2-1.ca2404.1_all.deb Size: 268764 MD5sum: 85a9703b005593a588c9bbe829828f32 SHA1: d8b5706a32a209c60947dd1dc96e8761bd13e116 SHA256: 870786a77f150d870a98cec2bbc4940d8cd2bf52880870c216ec2f9c0051f5cf SHA512: 3060678ee3d25dee1a22da7ae84d521979ad8052ec2d3081fcfc6090f74d5c9ad3cc65e9b4513c0a74e0d9db7314cc162bfe7ec2c280912761cfc1b128528a63 Homepage: https://cran.r-project.org/package=kmed Description: CRAN Package 'kmed' (Distance-Based k-Medoids) Algorithms of distance-based k-medoids clustering: simple and fast k-medoids, ranked k-medoids, and increasing number of clusters in k-medoids. Calculate distances for mixed variable data such as Gower, Podani, Wishart, Huang, Harikumar-PV, and Ahmad-Dey. Cluster validation applies internal and relative criteria. The internal criteria includes silhouette index and shadow values. The relative criterium applies bootstrap procedure producing a heatmap with a flexible reordering matrix algorithm such as complete, ward, or average linkages. The cluster result can be plotted in a marked barplot or pca biplot. Package: r-cran-kmedians Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-genieclust, r-cran-gmedian, r-cran-mvtnorm, r-cran-capushe, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-kmedians_2.2.0-1.ca2404.1_all.deb Size: 61176 MD5sum: 61ea3dc596e2ba87e72798973eecb61e SHA1: c91606c41fafd03ed71ab3cea585c125bb2866bf SHA256: fd8dcdb47c6ab89a82597ef32e5160f7cdc0de5208f8f86fffadda496a123f46 SHA512: 2295e7082769bde0019a48ba76b215d270426462943f488a05a8dce711514967f5744ebcbffb2cdbcdc7b6493a8da38af534e01bc81ac29ca513ddb7ea0da9f0 Homepage: https://cran.r-project.org/package=Kmedians Description: CRAN Package 'Kmedians' (K-Medians) Online, Semi-online, and Offline K-medians algorithms are given. For both methods, the algorithms can be initialized randomly or with the help of a robust hierarchical clustering. The number of clusters can be selected with the help of a penalized criterion. We provide functions to provide robust clustering. Function gen_K() enables to generate a sample of data following a contaminated Gaussian mixture. Functions Kmedians() and Kmeans() consists in a K-median and a K-means algorithms while Kplot() enables to produce graph for both methods. Cardot, H., Cenac, P. and Zitt, P-A. (2013). "Efficient and fast estimation of the geometric median in Hilbert spaces with an averaged stochastic gradient algorithm". Bernoulli, 19, 18-43. . Cardot, H. and Godichon-Baggioni, A. (2017). "Fast Estimation of the Median Covariation Matrix with Application to Online Robust Principal Components Analysis". Test, 26(3), 461-480 . Godichon-Baggioni, A. and Surendran, S. "A penalized criterion for selecting the number of clusters for K-medians" Vardi, Y. and Zhang, C.-H. (2000). "The multivariate L1-median and associated data depth". Proc. Natl. Acad. Sci. USA, 97(4):1423-1426. . Package: r-cran-kmers Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-bioc-biocgenerics, r-bioc-pwalign Suggests: r-cran-rcolorbrewer, r-cran-knitr, r-cran-rmarkdown, r-cran-unittest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kmers_2.1.0-1.ca2404.1_all.deb Size: 62874 MD5sum: af45ea2af42609137a3473255ecc5d92 SHA1: 72133faaa6730a3ab33e46cae98ae3e9f6ca98c8 SHA256: 1e432308393d2aef10534cc2a86b9a3a9549399859270ba50c3b17d8cd459672 SHA512: 628eeec5e81567af2176267916469a0fe5734dda578ba7e572c44b186ca8712118cc4d1f004ed457b54968c2903fd6e0dcc5341860fa2a0b22f3b1076938c73c Homepage: https://cran.r-project.org/package=kmeRs Description: CRAN Package 'kmeRs' (K-Mers Similarity Score Matrix and HeatMap) Similarity Score Matrix and HeatMap for nucleic and amino acid k-mers. Similarity score is evaluated by Point Accepted Mutation (PAM) and BLOcks SUbstitution Matrix (BLOSUM). The 30, 40, 70, 120, 250 and 62, 45, 50, 62, 80, 100 matrix versions are available for PAM and BLOSUM, respectively. Alignment is evaluated by local and global alignment. Package: r-cran-kmi Architecture: all Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mitools, r-cran-survival Filename: pool/dists/noble/main/r-cran-kmi_0.5.5-1.ca2404.1_all.deb Size: 73920 MD5sum: 05f32f88b07dd84a5097bd5300b51826 SHA1: c3a722ad0d674a05a3dfe48c73240390fd0c816d SHA256: f9adf0474a9c40fa4cc6ec4b1c070e8e75fc62459bd90292946af502f97840a3 SHA512: 71492eda505f48f354a0b0811a064206b242695690b538a8b88f10905f72273b4562e88d94e70895fa16e0989414e6fbf88acc28df32d3292564700289b4a997 Homepage: https://cran.r-project.org/package=kmi Description: CRAN Package 'kmi' (Kaplan-Meier Multiple Imputation for the Analysis of CumulativeIncidence Functions in the Competing Risks Setting) Performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions (Allignol and Beyersmann, 2010 ). Package: r-cran-kml3d Architecture: all Version: 2.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clv, r-cran-rgl, r-cran-misc3d, r-cran-longitudinaldata, r-cran-kml Filename: pool/dists/noble/main/r-cran-kml3d_2.5.0-1.ca2404.1_all.deb Size: 297722 MD5sum: ae0778ec93ef51cfbd56dfcee4ad7b40 SHA1: be0f7f11ff06e41259e7534e206d2bd3a10fb3b7 SHA256: 82a8d8756549f984d0479a77a320e808dc6878ea9164f6622dca79411af79fc9 SHA512: 3f77cbc06ecd4952e67fd90000ccfb96e9e61a3f4532e1264f2b2741893a3d14dbdafdd885ac537b7d1167c9f4158121320ef9a2237c8a62da88c9ea754c33f8 Homepage: https://cran.r-project.org/package=kml3d Description: CRAN Package 'kml3d' (K-Means for Joint Longitudinal Data) An implementation of k-means specifically design to cluster joint trajectories (longitudinal data on several variable-trajectories). Like 'kml', it provides facilities to deal with missing value, compute several quality criterion (Calinski and Harabatz, Ray and Turie, Davies and Bouldin, BIC,...) and propose a graphical interface for choosing the 'best' number of clusters. In addition, the 3D graph representing the mean joint-trajectories of each cluster can be exported through LaTeX in a 3D dynamic rotating PDF graph. Package: r-cran-kmltoshape Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-stringr, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kmltoshape_0.1.0-1.ca2404.1_all.deb Size: 24802 MD5sum: 50866cf4def912d343840d9067f28122 SHA1: 9b48b2a9e20a92da238a70494d67bd0f4d016fdb SHA256: e4b07b3cfc19bc7c46ad73a9f68121014b3f21465f632b417a880c910af60934 SHA512: e2ca0359cb22b49c12740631a84ff37c9ee0df08188c9910a968e2873131fd4d2f863e95c7438b85b75731bcd77a91df722f0b47c3add66befc1165652c51c11 Homepage: https://cran.r-project.org/package=KMLtoSHAPE Description: CRAN Package 'KMLtoSHAPE' (Preserving Attribute Values: Converting KML to Shapefile) The developed function is designed to facilitate the seamless conversion of KML (Keyhole Markup Language) files to Shapefiles while preserving attribute values. It provides a straightforward interface for users to effortlessly import KML data, extract relevant attributes, and export them into the widely compatible Shapefile format. The package ensures accurate representation of spatial data while maintaining the integrity of associated attribute information. For details see, Flores, G. (2021). . Whether for spatial analysis, visualization, or data interoperability, it simplifies the conversion process and empowers users to seamlessly work with geospatial datasets. Package: r-cran-kmodr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kmodr_0.2.0-1.ca2404.1_all.deb Size: 24508 MD5sum: c38ea78377b9717446925bf6eb816a90 SHA1: e88eeea7305589c6d18374e47198ec2b84350b57 SHA256: e167aca3f371995da8007d545d1ca3cc43471876748080a71205b24a10df8fe0 SHA512: 551b1f13ef94de1d79ce4a23e0334b7d500912d2edc23c9d485197248c7ef6fb13c88290e676b16a1f9953afdfeb0e0f58c090203b178e030a81edc5b0bc1cc2 Homepage: https://cran.r-project.org/package=kmodR Description: CRAN Package 'kmodR' (K-Means with Simultaneous Outlier Detection) An implementation of the 'k-means--' algorithm proposed by Chawla and Gionis, 2013 in their paper, "k-means-- : A unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13)", and using 'ordering' described by Howe, 2013 in the thesis, Clustering and anomaly detection in tropical cyclones". Useful for creating (potentially) tighter clusters than standard k-means and simultaneously finding outliers inexpensively in multidimensional space. Package: r-cran-kmsurv Architecture: all Version: 0.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kmsurv_0.1-6-1.ca2404.1_all.deb Size: 126500 MD5sum: 61f52aa5453ed96ff69e81a6362fb07e SHA1: 23645ca35c5e0db7633fa92fd84f5ecf398cf451 SHA256: 492e4f274407e52503301b9665b693a666c1ec60cb56a38d6cf2d5fe303d8a39 SHA512: e554cace3b09b4149dd824c2f5da19be7085b205803e3b3b584ef221a81b169cc0d475ddfba0fcaad9a65635453fe3d9eedcdcaf9e870cb9482c28701b475401 Homepage: https://cran.r-project.org/package=KMsurv Description: CRAN Package 'KMsurv' (Datasets from Klein and Moeschberger (1997), Survival Analysis) Datasets and functions for Klein and Moeschberger (1997), "Survival Analysis, Techniques for Censored and Truncated Data", Springer. Package: r-cran-kmunicate Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3626 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-checkmate, r-cran-cowplot, r-cran-ggplot2, r-cran-pammtools, r-cran-tidyr Suggests: r-cran-broom, r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-simsurv, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-kmunicate_0.2.5-1.ca2404.1_all.deb Size: 2101202 MD5sum: d7b523dc46ec0b2192fc050428e336fe SHA1: 4f7268288355b472c871342698049fe241c77a7f SHA256: cf457204bb47afe9e584ccccc63c9249bec8f703c100931d6dea3b2c2e3480ae SHA512: 2618fc231c218f1f0ade8f37a442895a85842e529aa316461e652a703ef64ba31e11390a59cbad5741bd712021dd8b3bbcef545d813cd655ad8377c63fb17e1e Homepage: https://cran.r-project.org/package=KMunicate Description: CRAN Package 'KMunicate' (KMunicate-Style Kaplan–Meier Plots) Produce Kaplan–Meier plots in the style recommended following the KMunicate study by Morris et al. (2019) . The KMunicate style consists of Kaplan-Meier curves with confidence intervals to quantify uncertainty and an extended risk table (per treatment arm) depicting the number of study subjects at risk, events, and censored observations over time. The resulting plots are built using 'ggplot2' and can be further customised to a certain extent, including themes, fonts, and colour scales. Package: r-cran-knapsacksampling Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve Filename: pool/dists/noble/main/r-cran-knapsacksampling_0.1.1-1.ca2404.1_all.deb Size: 27636 MD5sum: 6d167cbecd14462e059655cee7daf7c3 SHA1: 7ec7d8fe7eec8927d470eac2915c55f6a4c149e2 SHA256: fe2417681685cfaa4adbeb8cec09fb2f4336c9eb787319670fc8c5d45e112e92 SHA512: eee8ea3f1a0d10feb25b58f29fd1d51750b8814788fd605929a67ee5ede45be41c9233709f42b5a9877f4803ad838d4f61ef192141015792697c60eb8eaaed39 Homepage: https://cran.r-project.org/package=KnapsackSampling Description: CRAN Package 'KnapsackSampling' (Generate Feasible Samples of a Knapsack Problem) The sampl.mcmc function creates samples of the feasible region of a knapsack problem with both equalities and inequalities constraints. Package: r-cran-kneearrower Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-signal Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kneearrower_1.0.0-1.ca2404.1_all.deb Size: 51210 MD5sum: c5ba9a8ace24239247ab765e721e3455 SHA1: f9251958c08db2add4872064827375c913e9ee19 SHA256: 9eb4167f6cf2960655ac0af7e2e4f2f7011232ac476174928a3c265a09b01400 SHA512: 73959333f6382545701d91166bb7d42535545bfe9ca4d7b4c6805aaf13a95a1dc057937b115f4371071b0fb3117c2aef48de70514f8bae48d46861f928734d1d Homepage: https://cran.r-project.org/package=KneeArrower Description: CRAN Package 'KneeArrower' (Finds Cutoff Points on Knee Curves) Given a set of points around a knee curve, analyzes first and second derivatives to find knee points. Package: r-cran-knfi Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1814 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-readxl, r-cran-stringr, r-cran-vegan, r-cran-tidyr, r-cran-biodiversityr, r-cran-data.table, r-cran-sf, r-cran-plotrix, r-cran-rlang, r-cran-cellranger, r-cran-broom, r-cran-ggplot2, r-cran-sp, r-cran-cowplot, r-cran-ggpubr, r-cran-drat, r-cran-purrr, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-knfi_1.0.2-1.ca2404.1_all.deb Size: 1766654 MD5sum: f98cbe4cb86b13350a0770318b32f6a5 SHA1: 2cc4e6dac43904f23184a31975bfbf26505d43bc SHA256: 7951593380b6983d2db62126467543ca8518f8942f18deabe8dccc171335177f SHA512: cbe4ad1823ecc3bfb7c6b37e4833bc1098d8e438df9c18c4c5b7be30ed2ed1df20d0df9b3e14a2185f315914f859dea039d19023f9ad711ee12fea833d45f337 Homepage: https://cran.r-project.org/package=knfi Description: CRAN Package 'knfi' (Analysis of Korean National Forest Inventory Database) Understanding the current status of forest resources is essential for monitoring changes in forest ecosystems and generating related statistics. In South Korea, the National Forest Inventory (NFI) surveys over 4,500 sample plots nationwide every five years and records 70 items, including forest stand, forest resource, and forest vegetation surveys. Many researchers use NFI as the primary data for research, such as biomass estimation or analyzing the importance value of each species over time and space, depending on the research purpose. However, the large volume of accumulated forest survey data from across the country can make it challenging to manage and utilize such a vast dataset. To address this issue, we developed an R package that efficiently handles large-scale NFI data across time and space. The package offers a comprehensive workflow for NFI data analysis. It starts with data processing, where read_nfi() function reconstructs NFI data according to the researcher's needs while performing basic integrity checks for data quality.Following this, the package provides analytical tools that operate on the verified data. These include functions like summary_nfi() for summary statistics, diversity_nfi() for biodiversity analysis, iv_nfi() for calculating species importance value, and biomass_nfi() and cwd_biomass_nfi() for biomass estimation. Finally, for visualization, the tsvis_nfi() function generates graphs and maps, allowing users to visualize forest ecosystem changes across various spatial and temporal scales. This integrated approach and its specialized functions can enhance the efficiency of processing and analyzing NFI data, providing researchers with insights into forest ecosystems. The NFI Excel files (.xlsx) are not included in the R package and must be downloaded separately. Users can access these NFI Excel files by visiting the Korea Forest Service Forestry Statistics Platform to download the annual NFI Excel files, which are bundled in .zip archives. Please note that this website is only available in Korean, and direct download links can be found in the notes section of the read_nfi() function. 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The package is primarily aimed at authoring in the R 'markdown' format, and can provide outputs for web-based authoring such as linked text for inline citations. Cite using a 'DOI', URL, or 'bibtex' file key. See the package URL for details. 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Package: r-cran-knnwtsim Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-knnwtsim_1.0.0-1.ca2404.1_all.deb Size: 243096 MD5sum: 0993c4932d9d7c32e9a7372d8c9f196b SHA1: a8d0e3ad9e01ff21b3c0f1e3536cbaff53a446d5 SHA256: 58154cea6316ccbec350a768c2d918a4f90c0162653c5f1eab8dc4cd4b57e2f8 SHA512: 946b1aabdcdcd877aef7acedfaf7238c2efb163e0c307e4b36a4206bc36452fff99ffed62fe121d2524bd356166b7cb45c4f2515364aef13be4eb98d466ac515 Homepage: https://cran.r-project.org/package=knnwtsim Description: CRAN Package 'knnwtsim' (K Nearest Neighbor Forecasting with a Tailored Similarity Metric) Functions to implement K Nearest Neighbor forecasting using a weighted similarity metric tailored to the problem of forecasting univariate time series where recent observations, seasonal patterns, and exogenous predictors are all relevant in predicting future observations of the series in question. For more information on the formulation of this similarity metric please see Trupiano (2021) . Package: r-cran-knobi Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-corrplot, r-cran-ggplot2, r-cran-gridextra, r-cran-optimx, r-cran-plot3d, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-icessag Filename: pool/dists/noble/main/r-cran-knobi_1.0.0-1.ca2404.1_all.deb Size: 523590 MD5sum: 5baf3b185dff3c6e6974a703aae9c5c0 SHA1: 3c52c35ad8610205c1194c068cfa8e4af23061e7 SHA256: 2c105aac42921ade58cd726d964ac95d45e4de5418af266ffdc3d23534a5e5cc SHA512: 53fed44b1e6f0e16dccf3a10ee30f39b71c8e64c82db59f1d45a2e31791726c29c3b56a559dc61576b79c6bbb1dd5522e18f7616df856aee0510f6d159b6b649 Homepage: https://cran.r-project.org/package=knobi Description: CRAN Package 'knobi' (Known-Biomass Production Model (KBPM)) Application of a Known Biomass Production Model (KBPM): (1) the fitting of KBPM to each stock; (2) the estimation of the effects of environmental variability; (3) the retrospective analysis to identify regime shifts; (4) the estimation of forecasts. For more details see Schaefer (1954) , Pella and Tomlinson (1969) and MacCall (2002) . Package: r-cran-knockoff Architecture: all Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdsdp, r-cran-matrix, r-cran-corpcor, r-cran-glmnet, r-cran-rspectra, r-cran-gtools Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-lars, r-cran-ranger, r-cran-stabs, r-cran-rptests, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-knockoff_0.3.6-1.ca2404.1_all.deb Size: 177494 MD5sum: 5d4efcc8d1b48c8ee1535bb6f276ff1c SHA1: 799a1633d158715007a2aa5dfb44713d62a6596b SHA256: 4c26b4bb77648d7e593e931b0096164e917c978e52d3f1624dc069c564f2e33a SHA512: 9a936cff9e765f7cf42973583ddab50ebbe6448b041f8889c6c8cc368a1f8f95c6d43a95cf787f591eccc29234fab9e114645bc317451d8c005dcd86f7d9d937 Homepage: https://cran.r-project.org/package=knockoff Description: CRAN Package 'knockoff' (The Knockoff Filter for Controlled Variable Selection) The knockoff filter is a general procedure for controlling the false discovery rate (FDR) when performing variable selection. For more information, see the website below and the accompanying paper: Candes et al., "Panning for gold: model-X knockoffs for high-dimensional controlled variable selection", J. R. Statist. Soc. B (2018) 80, 3, pp. 551-577. Package: r-cran-knockoffhybrid Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-spatest Filename: pool/dists/noble/main/r-cran-knockoffhybrid_1.0.1-1.ca2404.1_all.deb Size: 62866 MD5sum: 813a83d80332027ea2132d5e8059d5af SHA1: 8e1d58e827de3c9f005c5d37df06246ab3ea5c23 SHA256: f808f78f15cfa6542653ec663e5e47c96f9ce33d58c832144d37ee59ba89a865 SHA512: cd50fae53d984bc873ae6d8df5ec2f60b8e38749cefc7439ae6c4de44061415b364ece5c654ce06edfb2eab24f78b26ccce974b290f7b6c7c9ddf021e8aac0c3 Homepage: https://cran.r-project.org/package=KnockoffHybrid Description: CRAN Package 'KnockoffHybrid' (Hybrid Analysis of Population and Trio Data with KnockoffStatistics for FDR Control) Identification of putative causal variants in genome-wide association studies using hybrid analysis of both the trio and population designs. The package implements the method in the paper: Yang, Y., Wang, Q., Wang, C., Buxbaum, J., & Ionita-Laza, I. (2024). KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies. The American Journal of Human Genetics, in press. Package: r-cran-knockoffscreen Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-seqminer, r-cran-bigmemory, r-cran-compquadform, r-cran-data.table, r-cran-spatest, r-cran-irlba Filename: pool/dists/noble/main/r-cran-knockoffscreen_0.3.0-1.ca2404.1_all.deb Size: 84146 MD5sum: 2f5822a817d2346c0ff8bcc97f790596 SHA1: ad6767f181fd0e4e7888b9c8515a268d2d58c300 SHA256: c246fb5f70b0ee8a8795427d0c16cd3d4ccb13a3b8ca6cde4b812f12d0951c46 SHA512: c4c508bd2297bdca1707757128bd44f0fba261d24a3fc1e1e41c5cceed4177b106c96e15da7da4a02da964b4e81524e8567b30fe37ed24875102be0b4188dc41 Homepage: https://cran.r-project.org/package=KnockoffScreen Description: CRAN Package 'KnockoffScreen' (Whole-Genome Sequencing Data Analysis via Knockoff Statistics) Functions for identification of putative causal loci in whole-genome sequencing data. The functions allow genome-wide association scan. It also includes an efficient knockoff generator for genetic data. Package: r-cran-knockofftrio Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-knockofftrio_1.1.0-1.ca2404.1_all.deb Size: 75928 MD5sum: df6666faed6c1d3d215b8f9a5008f7a7 SHA1: ecc146c410d981695c134aa3368cd07cf272cb5e SHA256: 1d47fdc93922901225fbe745d4909cd6752278fd2971e94697114824019b4788 SHA512: 029ed497dbebe40a517cf6fe8ff960132097f4a4f099b88d5c18f50ab919c15193a8d5ac8a0631d6dc8d396d6bae242d13baa5d2653ec940ae2a9e036c695990 Homepage: https://cran.r-project.org/package=KnockoffTrio Description: CRAN Package 'KnockoffTrio' (GWAS with Trio and Duo Data using Knockoff Statistics for FDRControl) Identification of putative causal variants in genome-wide association studies with trio and duo families. The package calculates the W feature statistics from KnockoffTrio and p-values from the family-based association test (FBAT) using trio and/or duo data. Compared to previous versions, a significant improvement has been made in Version 1.1.0 to allow the package to be applied not only to trio families but also to duo families. The package implements the methods in the paper: "Yang, Y., Wang, C., Liu, L., Buxbaum, J., He, Z., & Ionita-Laza, I. (2022). KnockoffTrio: A knockoff framework for the identification of putative causal variants in genome-wide association studies with trio design. The American Journal of Human Genetics, 109(10), 1761-1776." 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Package: r-cran-knowbr Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4366 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fossil, r-cran-mgcv, r-cran-plotrix, r-cran-sp, r-cran-vegan Suggests: r-cran-raster, r-cran-rgbif, r-cran-usdm, r-cran-car, r-cran-idpmisc, r-cran-psych, r-cran-candisc Filename: pool/dists/noble/main/r-cran-knowbr_2.2-1.ca2404.1_all.deb Size: 3585936 MD5sum: 9ec27c3069e8193ba6225248494cf13f SHA1: 68ecc2afd8949fee0431cea9e2c374cf584cb22e SHA256: 40180be5a0ecbb8a204f7c3bc4e6f2d0882faf8a35785e2d9ad63d8054bf9a37 SHA512: d851c877199625d510a98c16e25a684d3904154aa15303d91465b49e2e91b320a66c00e9b6d0e06bb41a542e665e7d52dc76d1051d5cb2733b42796e6c7f232f Homepage: https://cran.r-project.org/package=KnowBR Description: CRAN Package 'KnowBR' (Discriminating Well Surveyed Spatial Units from ExhaustiveBiodiversity Databases) It uses species accumulation curves and diverse estimators to assess, at the same time, the levels of survey coverage in multiple geographic cells of a size defined by the user or polygons. It also enables the geographical depiction of observed species richness, survey effort and completeness values including a background with administrative areas. Package: r-cran-kntnr Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64enc, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-rstudioapi, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kntnr_0.4.4-1.ca2404.1_all.deb Size: 50576 MD5sum: d752bd50e601947685d56a85bd0d661a SHA1: 2d5dd60b14b69cbf53555924add68f94201cfce8 SHA256: 1ad02ae448d97c865bb3006a888d63eac6436260ee04cf7fe59c26c73cd96bcd SHA512: ac3b9a42f4cc15031f86dce19284fd050a58e8a22352127873ff564feb81008c6b76fed789dd369405726074ee78f2990af4da7ce5771c5b88a54faba8669fe0 Homepage: https://cran.r-project.org/package=kntnr Description: CRAN Package 'kntnr' (R Client for 'kintone' API) Retrieve data from 'kintone' () via its API. 'kintone' is an enterprise application platform. Package: r-cran-koboconnectr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr2, r-cran-curl, r-cran-jsonlite, r-cran-mime, r-cran-openssl, r-cran-r6, r-cran-dplyr, r-cran-readxl, r-cran-rlang, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-koboconnectr_2.0.0-1.ca2404.1_all.deb Size: 79672 MD5sum: afaca6f4a34e20b08fec6acf2887e64a SHA1: f263abdc2105e0e1af7babd4375f42a91a4f1288 SHA256: ad2e63736455be3fed42abc008d5dd342330a7b0c82193f80ae51b1d55816556 SHA512: abfbf93d1a39c956e31b01d59c5042c9cb5c7208484f6b12c3ab1e8a2a06d3dbc0d1136f7935c2366a51a5ad6d1710ddbd57baced40225cb184702820cd5335d Homepage: https://cran.r-project.org/package=KoboconnectR Description: CRAN Package 'KoboconnectR' (Download Data from Kobotoolbox to R) Wrapper for 'Kobotoolbox' APIs ver 2 mentioned at , to download data from 'Kobotoolbox' to R. Small and simple package that adds immense convenience for the data professionals using 'Kobotoolbox'. Package: r-cran-kofdata Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-httr, r-cran-xts, r-cran-zoo Suggests: r-cran-tstools, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kofdata_0.2.1-1.ca2404.1_all.deb Size: 48346 MD5sum: 52ec233cf68ac48943d7d8a5b70f515b SHA1: 6f4416a4c8a4e5e87f9a723966b3adef20ea7ab4 SHA256: fea461990d9775e3ac2205d5de79d29cdf705bf7e793c8e8dbf5c567221eee2b SHA512: 6821656d7ff8a35bcd13fe68c7247fb5578f6591ddc3840c11c5123a3782fb30f991357e7bf5e2df665d1c74ab41e422979cfe961b93a33a048bfd7cdac1ed35 Homepage: https://cran.r-project.org/package=kofdata Description: CRAN Package 'kofdata' (Get Data from the 'KOF Datenservice' API) Read Swiss time series data from the 'KOF Data' API, . The API provides macro economic time series data mostly about Switzerland. The package itself is a set of wrappers around the 'KOF Datenservice' API. The 'kofdata' package is able to consume public information as well as data that requires an API token. Package: r-cran-kofm Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensormiss, r-cran-mefm, r-cran-rspectra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kofm_1.1.1-1.ca2404.1_all.deb Size: 53652 MD5sum: b99f75f29fe009e86e5676f2da53866f SHA1: 3619c2537a1cfecff9e8dbb960c0079c8bdcaf60 SHA256: f604689134dffba1dc39944b45355d9ac67dd24fc79c2bb1c6d1d9741d97a68a SHA512: 27ad0f0a1a30f1ed9ec7a0e60eee59b52330dfa7a394d50381fe435d2b5d327292cd9bf63795304e09e06b197bcb79be69cf04ba12b23abe6950d880b12c8584 Homepage: https://cran.r-project.org/package=KOFM Description: CRAN Package 'KOFM' (Test the Kronecker Product Structure in Tensor Factor Models) To test if a tensor time series following a Tucker-decomposition factor model has a Kronecker product structure. Supplementary functions for tensor reshape and its reversal are also included. Package: r-cran-kofnga Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bigmemory Filename: pool/dists/noble/main/r-cran-kofnga_1.3-1.ca2404.1_all.deb Size: 45696 MD5sum: d485d997bd99b76e00966aca4862b4e1 SHA1: c4cd0fbd1bd235e80d0982e8208af71055c91ef2 SHA256: fc1b808c94ef0dc797aed6e18f2731ccc2162b91b52cf65fd9e72fdd1f4cbada SHA512: d6659298f94f264b49dbf4ea815d149e237723aee76f6cddd98d55d34d1836a9f8c2cabc7b63639cdaee61dd2557a3176031ba08beee0814064433b79d9d27bb Homepage: https://cran.r-project.org/package=kofnGA Description: CRAN Package 'kofnGA' (A Genetic Algorithm for Fixed-Size Subset Selection) Provides a function that uses a genetic algorithm to search for a subset of size k from the integers 1:n, such that a user-supplied objective function is minimized at that subset. The selection step is done by tournament selection based on ranks, and elitism may be used to retain a portion of the best solutions from one generation to the next. Population objective function values may optionally be evaluated in parallel. Package: r-cran-kogmwu Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pheatmap Filename: pool/dists/noble/main/r-cran-kogmwu_1.2-1.ca2404.1_all.deb Size: 862068 MD5sum: 4b4aa31d0c1d54318168741fd5e7fa41 SHA1: 7434fce5e9a6421896530142046f68a1dbf57a16 SHA256: 93cdc8474d2476678eec38d6f11ce16cd8c046c37aaf22aa800f800753ddf2c8 SHA512: c40902302ae9a81894ffe8238f7a8e5b6dfe7a776fe988d36041d9485ee34f6217b78b670434c2e5772b92d10a240a70f21c582d32c829afad667278feb21bab Homepage: https://cran.r-project.org/package=KOGMWU Description: CRAN Package 'KOGMWU' (Functional Summary and Meta-Analysis of Gene Expression Data) Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed. Package: r-cran-kokudosuuchi Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kokudosuuchi_1.0.0-1.ca2404.1_all.deb Size: 377584 MD5sum: b0997c3d172547a2ac9e9f9035bfb5c8 SHA1: 307f4ceb128a5d07a1a49e9e1f4dad1c947ec43c SHA256: 10ea27ef4584da0b5c239908eddb8c20fc69165c54cd4a3d4ac1866e37ba1740 SHA512: 9ed26fdab4589f9575c7f58c0b7b09e2f0f448186278e3749c5d10c8b38a47fa22b674b87f70f34eefb8df11545e6cb61f2aefbac7b672b0c5b14814467fcdd3 Homepage: https://cran.r-project.org/package=kokudosuuchi Description: CRAN Package 'kokudosuuchi' (Utilities for 'Kokudo Suuchi') Provides utilities for 'Kokudo Suuchi', the GIS data service of the Japanese government. See for more information. Package: r-cran-kolaide Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-kolaide_0.0.1-1.ca2404.1_all.deb Size: 97328 MD5sum: ef27221106aaf82f8daf85f874df7b88 SHA1: 6bc792f27d834264950e59a0c7f81f5b9bf61acf SHA256: dfb7ecbd40a09ebacb109848d41c8a8340bcf3dc0a9028da461f37e6328772fb SHA512: b433fd097e204b7d6cf53b80dbec5ae689f8f2b100eb67fe761114b090ea85e2d61aa6f0c56e0810e46cc02c06c71eafe3dd8fa998e1782e2dca9b8cc9c1c087 Homepage: https://cran.r-project.org/package=KOLaide Description: CRAN Package 'KOLaide' (Pick and Plot Key Opinion Leaders from a Network GivenConstraints) Assists researchers in choosing Key Opinion Leaders (KOLs) in a network to help disseminate or encourage adoption of an innovation by other network members. Potential KOL teams are evaluated using the ABCDE framework (Neal et al., 2025 ). This framework which considers: (1) the team members' Availability, (2) the Breadth of the team's network coverage, (3) the Cost of recruiting a team of a given size, and (4) the Diversity of the team's members, (5) which are pooled into a single Evaluation score. Package: r-cran-kollar Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 898 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-zoo, r-cran-ggforce, r-cran-tidyr, r-cran-ggpubr, r-cran-jpeg, r-cran-patchwork, r-cran-shiny, r-cran-base64enc, r-cran-magick, r-cran-scales Filename: pool/dists/noble/main/r-cran-kollar_1.1.4-1.ca2404.1_all.deb Size: 840340 MD5sum: 53a05883e3090e6ed21cb482672caa6b SHA1: d405b508e52ffb418fb1b85da99fdd512f0f67ef SHA256: 6dd2905cf0a19a29f1fd519869eb1e43d4e152afba09dc873b12e8eb494257c7 SHA512: a99b90f98a43c7ba0d6eb8e6851aaa910f370a078c13a4391f8afc2ae85a2b8c037e05a6007367ca22f455d1f859af2b644a9d1c9d8c27cd8e5f3003d6811698 Homepage: https://cran.r-project.org/package=kollaR Description: CRAN Package 'kollaR' (Event Classification, Visualization and Analysis of Eye TrackingData) Functions for analysing eye tracking data, including event detection, visualizations and area of interest (AOI) based analyses. The package includes implementations of the IV-T, I-DT, adaptive velocity threshold, and Identification by two means clustering (I2MC) algorithms. See separate documentation for each function. The principles underlying I-VT and I-DT algorithms are described in Salvucci & Goldberg (2000) . Two-means clustering is described in Hessels et al. (2017), . The adaptive velocity threshold algorithm is described in Nyström & Holmqvist (2010),. A documentation of the 'kollaR' can be found in Kleberg et al (2026) . Cite this paper when using 'kollaR' See a demonstration in the URL. Package: r-cran-komaletter Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1517 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmarkdown Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-komaletter_0.5.0-1.ca2404.1_all.deb Size: 1211548 MD5sum: af3fd846d12db56cb8dc6f8e67eb6355 SHA1: 7dc786068cb3036b7cdc63e221c0ecdfd30527fa SHA256: fc19d2fd3228da1f6f927cd6cad53f392de3ccb42075023a4686b9585e18b3fb SHA512: fdf055a1534eecb07c88056fc15208f6aa0256f32de2d827a23cc8107ec0e098b2024e01d7b4616dd70dad1f4fea2ef3af89200397b94a6bab7a4b9d9da0d714 Homepage: https://cran.r-project.org/package=komaletter Description: CRAN Package 'komaletter' (Simply Beautiful PDF Letters from Markdown) Write beautiful yet customizable letters in R Markdown and directly obtain the finished PDF. Smooth generation of PDFs is realized by 'rmarkdown', the 'pandoc-letter' template and the 'KOMA-Script' letter class. 'KOMA-Script' provides enhanced replacements for the standard 'LaTeX' classes with emphasis on typography and versatility. 'KOMA-Script' is particularly useful for international writers as it handles various paper formats well, provides layouts for many common window envelope types (e.g. German, US, French, Japanese) and lets you define your own layouts. The package comes with a default letter layout based on 'DIN 5008B'. Package: r-cran-konfound Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-broom.mixed, r-cran-crayon, r-cran-dplyr, r-cran-ggplot2, r-cran-lavaan, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-lme4, r-cran-tibble, r-cran-ggrepel, r-cran-pbkrtest, r-cran-ppcor Suggests: r-cran-covr, r-cran-devtools, r-cran-forcats, r-cran-knitr, r-cran-rmarkdown, r-cran-mice, r-cran-roxygen2, r-cran-testthat, r-cran-matrix Filename: pool/dists/noble/main/r-cran-konfound_1.0.3-1.ca2404.1_all.deb Size: 292596 MD5sum: a9a89332170fa9b888ffa530404eee30 SHA1: cdc6db0c7582b1ab2990174b4730b812b2738dca SHA256: 3b5e960d488ea0e50ef7eecfafc80739ef9acd70910facd52f115e30db4d6c5a SHA512: 1c83124813b29b2ee97e6dcd0218af7609c6f74e1453ac23ddf06ff77e99cde955a3ef4bef6d3c960c2c884a5dbc3f1b1b38befaa395904765104ea585f42bf3 Homepage: https://cran.r-project.org/package=konfound Description: CRAN Package 'konfound' (Quantify the Robustness of Causal Inferences) Statistical methods that quantify the conditions necessary to alter inferences, also known as sensitivity analysis, are becoming increasingly important to a variety of quantitative sciences. A series of recent works, including Frank (2000) and Frank et al. (2013) extend previous sensitivity analyses by considering the characteristics of omitted variables or unobserved cases that would change an inference if such variables or cases were observed. These analyses generate statements such as "an omitted variable would have to be correlated at xx with the predictor of interest (e.g., the treatment) and outcome to invalidate an inference of a treatment effect". Or "one would have to replace pp percent of the observed data with nor which the treatment had no effect to invalidate the inference". We implement these recent developments of sensitivity analysis and provide modules to calculate these two robustness indices and generate such statements in R. In particular, the functions konfound(), pkonfound() and mkonfound() allow users to calculate the robustness of inferences for a user's own model, a single published study and multiple studies respectively. Package: r-cran-kor.addrlink Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1360 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringdist, r-cran-stringi Filename: pool/dists/noble/main/r-cran-kor.addrlink_1.0.1-1.ca2404.1_all.deb Size: 1340522 MD5sum: 5f265e36dc7a8595296db1abf5933de8 SHA1: d704dc3ce3a9eff82e64d572946775e7415b8430 SHA256: 85ef262794fdf937f580777da9b916c4381cf8b725321f08d7b2f5733a24342e SHA512: 9586843f7100050f81498f93881ee1265c85ee6ce118b8dec330f1bc35ba88bf2594c43b47d867d4e134abe3c52ca0caf8cb4dd7f23f53c133980765740ed453 Homepage: https://cran.r-project.org/package=KOR.addrlink Description: CRAN Package 'KOR.addrlink' (Matching Address Data to Reference Index) Matches a data set with semi-structured address data, e.g., street and house number as a concatenated string, wrongly spelled street names or non-existing house numbers to a reference index. The methods are specifically designed for German municipalities ('KOR'-community) and German address schemes. Package: r-cran-korpus.lang.en Architecture: all Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-korpus, r-cran-sylly.en Filename: pool/dists/noble/main/r-cran-korpus.lang.en_0.1-4-1.ca2404.1_all.deb Size: 21092 MD5sum: 1817a928564f199b518825c6e1d776c4 SHA1: c0c3e534909b73bcb8d2c176b38521de815f3741 SHA256: 8955c7ea4448899e2203bfcfd9dbfacbabcf5f377cb53c48af1080e8958e6bc1 SHA512: 467d8334fbfde38e2be91cf1edf8748c052042e455d9a4c37f6e0866097d3492e06f66799c80cf9ec3a4d420b5e8f236b4775f9e2d56a2af22bf1011b96f8873 Homepage: https://cran.r-project.org/package=koRpus.lang.en Description: CRAN Package 'koRpus.lang.en' (Language Support for 'koRpus' Package: English) Adds support for the English language to the 'koRpus' package. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the package, please consider subscribing to the koRpus-dev mailing list (). Package: r-cran-korpus Architecture: all Version: 0.13-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2090 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sylly, r-cran-data.table, r-cran-matrix Suggests: r-cran-testthat, r-cran-tm, r-cran-snowballc, r-cran-shiny, r-cran-knitr, r-cran-rmarkdown, r-cran-korpus.lang.en Filename: pool/dists/noble/main/r-cran-korpus_0.13-9-1.ca2404.1_all.deb Size: 1278540 MD5sum: add1d159b0b0f3e11ad4db27fbb3b2c5 SHA1: e0f87fcff1e9a28c566676159c76d1b7c7b73af0 SHA256: b38a8f2a17524c12ee0979402de36483bb2a4b1b6dcdb4ad5d4fccd1f5d480e7 SHA512: 4264272d9333c16d80bb1696161738732b456f843b8d0a954b7e4b56bf4e0e2ecc136ae0b3ac4d0e0dadd55f31192ec77611f139d3046afa597bb1ddf89c1ddf Homepage: https://cran.r-project.org/package=koRpus Description: CRAN Package 'koRpus' (Text Analysis with Emphasis on POS Tagging, Readability, andLexical Diversity) A set of tools to analyze texts. Includes, amongst others, functions for automatic language detection, hyphenation, several indices of lexical diversity (e.g., type token ratio, HD-D/vocd-D, MTLD) and readability (e.g., Flesch, SMOG, LIX, Dale-Chall). Basic import functions for language corpora are also provided, to enable frequency analyses (supports Celex and Leipzig Corpora Collection file formats) and measures like tf-idf. Note: For full functionality a local installation of TreeTagger is recommended. It is also recommended to not load this package directly, but by loading one of the available language support packages from the 'l10n' repository . 'koRpus' also includes a plugin for the R GUI and IDE RKWard, providing graphical dialogs for its basic features. The respective R package 'rkward' cannot be installed directly from a repository, as it is a part of RKWard. To make full use of this feature, please install RKWard from (plugins are detected automatically). Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, request features, or discuss the development of the package, please subscribe to the koRpus-dev mailing list (). Package: r-cran-kosel Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-ordinalnet Filename: pool/dists/noble/main/r-cran-kosel_0.0.1-1.ca2404.1_all.deb Size: 36386 MD5sum: 987d643fc4ef7aa1954161a032f14f15 SHA1: 0376c661d4c3adca31e357d0de299abad1466408 SHA256: 65fc166e8846506c0cd99ef1c2d047f6881357a4843c39261bb6baac4900faa3 SHA512: c017396db38aa6fd0e493a1868ac107118cc7fdddf41859fee866a6a952fe61b6d5447a454463256bb79e9026dd5f4b40526f8915d7ed0ddcf4e264f1fb2cbf5 Homepage: https://cran.r-project.org/package=kosel Description: CRAN Package 'kosel' (Variable Selection by Revisited Knockoffs Procedures) Performs variable selection for many types of L1-regularised regressions using the revisited knockoffs procedure. This procedure uses a matrix of knockoffs of the covariates independent from the response variable Y. The idea is to determine if a covariate belongs to the model depending on whether it enters the model before or after its knockoff. The procedure suits for a wide range of regressions with various types of response variables. Regression models available are exported from the R packages 'glmnet' and 'ordinalNet'. Based on the paper linked to via the URL below: Gegout A., Gueudin A., Karmann C. (2019) . Package: r-cran-kosis Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-tibble Filename: pool/dists/noble/main/r-cran-kosis_0.0.1-1.ca2404.1_all.deb Size: 44376 MD5sum: 613d80401e334cfe5742ce916f75b275 SHA1: fee87d3930b99bb991692aa9cddfd17e18c7f45d SHA256: 344886120351a791a37e41865c1f09910ee001b704d8b2429c4009c235660f47 SHA512: bf0ba465e3d176c9c003cd307575481d51d2d5affc0b963924940d9f1e345a64ef7667c6da6493de8106ed05087ccd395b42b0ddbdc0d6aeea6fe12b54be2ce9 Homepage: https://cran.r-project.org/package=kosis Description: CRAN Package 'kosis' (Korean Statistical Information Service (KOSIS)) API wrapper to download statistical information from the Korean Statistical Information Service (KOSIS) . Package: r-cran-kpart Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaps Filename: pool/dists/noble/main/r-cran-kpart_1.2.2-1.ca2404.1_all.deb Size: 16900 MD5sum: a8d3a3b636db6b6a478e60d560c2d8ee SHA1: 51344966f585dde009836f647ed37f58e0f121e4 SHA256: 293abd556d8c53b9469486130ad53cf0547a570b135fab4b2674c9f31fb5048c SHA512: aa3f1bde59b6aa25a951b21b9561cc20a6f1e32dc1b02aae809a9d29c5378d8d04f740b56b4ae27bf7726d0a2ae9435844c6fc3b588e47bdc4a4c89e2366ffde Homepage: https://cran.r-project.org/package=Kpart Description: CRAN Package 'Kpart' (Cubic Spline Fitting with Knot Selection) Cubic spline fitting along with knot selection, includes support for additional variables. Package: r-cran-kpc Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-kernlab, r-cran-rann, r-cran-proxy, r-cran-mlpack Filename: pool/dists/noble/main/r-cran-kpc_0.1.3-1.ca2404.1_all.deb Size: 95050 MD5sum: 1fa0a020d4adaf80fd78c594f13eaf56 SHA1: 49d9c3eb898f35c4947cd1d14c9b2cd9f5bbcac0 SHA256: 39e700cbbe7567454c1d7c28180dd78b9c0b1e9b704b449fc01fea87fdc926f6 SHA512: 262ffd7993506dca9fe31d273967cae6962ddef37130a116850a6dc7ecdc9f1cdbc545fdee65ef01b36280e931b745b818b69a03b935e0b09f633a95a6679b49 Homepage: https://cran.r-project.org/package=KPC Description: CRAN Package 'KPC' (Kernel Partial Correlation Coefficient) Implementations of two empirical versions the kernel partial correlation (KPC) coefficient and the associated variable selection algorithms. KPC is a measure of the strength of conditional association between Y and Z given X, with X, Y, Z being random variables taking values in general topological spaces. As the name suggests, KPC is defined in terms of kernels on reproducing kernel Hilbert spaces (RKHSs). The population KPC is a deterministic number between 0 and 1; it is 0 if and only if Y is conditionally independent of Z given X, and it is 1 if and only if Y is a measurable function of Z and X. One empirical KPC estimator is based on geometric graphs, such as K-nearest neighbor graphs and minimum spanning trees, and is consistent under very weak conditions. The other empirical estimator, defined using conditional mean embeddings (CMEs) as used in the RKHS literature, is also consistent under suitable conditions. Using KPC, a stepwise forward variable selection algorithm KFOCI (using the graph based estimator of KPC) is provided, as well as a similar stepwise forward selection algorithm based on the RKHS based estimator. For more details on KPC, its empirical estimators and its application on variable selection, see Huang, Z., N. Deb, and B. Sen (2022). “Kernel partial correlation coefficient – a measure of conditional dependence” (URL listed below). When X is empty, KPC measures the unconditional dependence between Y and Z, which has been described in Deb, N., P. Ghosal, and B. Sen (2020), “Measuring association on topological spaces using kernels and geometric graphs” , and it is implemented in the functions KMAc() and Klin() in this package. The latter can be computed in near linear time. Package: r-cran-kpcaig Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-kernlab, r-cran-ggplot2, r-cran-progress, r-cran-viridis, r-cran-wallomicsdata Filename: pool/dists/noble/main/r-cran-kpcaig_1.0.1-1.ca2404.1_all.deb Size: 45878 MD5sum: cc17e92468425919e8a7db522486b82e SHA1: 9d5201a046592476b1c48476c30881b3acf33c54 SHA256: d76b1f2abb7a2e4e0628d2cb757848afeb4ff7c5185037b163bf846235e5611b SHA512: a014014019c6768ddeb65344152ca157beaf5afd0cb0b5dfb8fa9155ae243f1ef354ef77f86f0a99120096db8cf58b800fd12ef4c6c09609dd9c8d8467702d80 Homepage: https://cran.r-project.org/package=kpcaIG Description: CRAN Package 'kpcaIG' (Variables Interpretability with Kernel PCA) The kernelized version of principal component analysis (KPCA) has proven to be a valid nonlinear alternative for tackling the nonlinearity of biological sample spaces. However, it poses new challenges in terms of the interpretability of the original variables. 'kpcaIG' aims to provide a tool to select the most relevant variables based on the kernel PCA representation of the data as in Briscik et al. (2023) . It also includes functions for 2D and 3D visualization of the original variables (as arrows) into the kernel principal components axes, highlighting the contribution of the most important ones. Package: r-cran-kpcalg Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcalg, r-cran-energy, r-cran-kernlab, r-cran-mgcv, r-cran-rspectra, r-bioc-graph Suggests: r-bioc-rgraphviz, r-cran-knitr Filename: pool/dists/noble/main/r-cran-kpcalg_1.0.1-1.ca2404.1_all.deb Size: 238824 MD5sum: 9019bb9b035a092ab971b6c99fd1bd37 SHA1: 5929570fc5e03860d22490c462dd00885818a086 SHA256: 9f3ac99fa60a0df7c539f5aee028e49e2051e1c86d9e6f4e7c26886ca3127be0 SHA512: 0402bf40429fa16a3c59c01a1185f0d1657d4a27238f0b4bbca708c555df362abf14841129d349185dc4de4d79c6213ff1664d9d2c71d3c149014caae56e5c2a Homepage: https://cran.r-project.org/package=kpcalg Description: CRAN Package 'kpcalg' (Kernel PC Algorithm for Causal Structure Detection) Kernel PC (kPC) algorithm for causal structure learning and causal inference using graphical models. kPC is a version of PC algorithm that uses kernel based independence criteria in order to be able to deal with non-linear relationships and non-Gaussian noise. Package: r-cran-kpeaks Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kpeaks_1.1.0-1.ca2404.1_all.deb Size: 79408 MD5sum: 8944a0689ce1194952f22cf376dc6606 SHA1: 6968bafe3770f526ff0326cf0a8a45c69536fd14 SHA256: a625e550cce99515521cb3b821f85ab3cb51317d84eceb18478c10176c90a994 SHA512: d26d25610111240a32aaccc377d3100571691d40ccfa9d93c84ef2657bd1151d6debcd0e2a9e774d60d80fd4fb19d810bd305afca9bf1a07f6db3d117268eaf2 Homepage: https://cran.r-project.org/package=kpeaks Description: CRAN Package 'kpeaks' (Determination of K Using Peak Counts of Features for Clustering) The number of clusters (k) is needed to start all the partitioning clustering algorithms. An optimal value of this input argument is widely determined by using some internal validity indices. Since most of the existing internal indices suggest a k value which is computed from the clustering results after several runs of a clustering algorithm they are computationally expensive. On the contrary, the package 'kpeaks' enables to estimate k before running any clustering algorithm. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in a data set. Package: r-cran-kpiwidget Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crosstalk, r-cran-htmlwidgets Suggests: r-cran-dplyr, r-cran-dt, r-cran-flexdashboard, r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kpiwidget_0.1.1-1.ca2404.1_all.deb Size: 130696 MD5sum: de3a3514bf6d29441bf45545465f5f8e SHA1: d10d34fc72e619038597b173b6652c28a4361771 SHA256: 6d18f552cdc7b025605777c9d91553df9eccff798f2bf297fa473c64561e38c4 SHA512: 880bd9a64b67caf008e040c37b06127cca85169b5d16b6d5e737c4c4f25a09e6126e8019bdbf67cafe3a05d784f5e7c69f09f9105c0c7209b773ea96f25b3ea3 Homepage: https://cran.r-project.org/package=kpiwidget Description: CRAN Package 'kpiwidget' (KPI Widgets for Quarto Dashboards with Crosstalk) Provides an easy way to create interactive KPI (key performance indicator) widgets for 'Quarto' dashboards using 'Crosstalk'. The package enables visualization of key metrics in a structured format, supporting interactive filtering and linking with other 'Crosstalk'-enabled components. Designed for use in 'Quarto' Dashboards. Package: r-cran-kpmt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats Filename: pool/dists/noble/main/r-cran-kpmt_0.1.0-1.ca2404.1_all.deb Size: 1184194 MD5sum: d9c0c823f2acd72a927af3b78acc28aa SHA1: 881303652936c05008e1699e575dc37d01c6c0c8 SHA256: b77b6c401542ec8aa66047c3e2dd9356c3e3ce655be96c280bffb2529d894c6f SHA512: a3d8185640cd3f56c49e876c8d84b129caedada921e0694b395b51df1fc11c702d84cc0f9fe168e46d43750217dec7a43e854c4c6e5bc3c5604b3bb70e316b9e Homepage: https://cran.r-project.org/package=kpmt Description: CRAN Package 'kpmt' (Known Population Median Test) Functions that implement the known population median test. Package: r-cran-kpodclustr Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kpodclustr_1.1-1.ca2404.1_all.deb Size: 26412 MD5sum: 38c174a7bcc33e97d9eb5c67a07d8cf6 SHA1: 9c3b60dc4ba6a8b203875ef9546b5ee718351623 SHA256: bf294349ee8f3aa069a2bd2b3c61ea46bd7b08d31e99c3e9d6c59f51fd08bbb5 SHA512: fc5617766de4d2159835fd870de9da04f8b2728e318d4ae01661d99476e1fb46a94e4b5820ae88da5d31b48d5c2a1cbe081f2fe6947cc030fffbdb8558029faf Homepage: https://cran.r-project.org/package=kpodclustr Description: CRAN Package 'kpodclustr' (Method for Clustering Partially Observed Data) Software for k-means clustering of partially observed data from Chi, Chi, and Baraniuk (2016) . Package: r-cran-kqm Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-gtools, r-cran-cluster Filename: pool/dists/noble/main/r-cran-kqm_1.1.1-1.ca2404.1_all.deb Size: 36166 MD5sum: 72b741b5fe502bf39146a112a1ca2bf4 SHA1: a06b9cdd922dbb8965b73308785ce871d9d5520e SHA256: dcd2ca4f4cf2208638523ad7337fb9b5832705e3c2d77bc4e8154094a92c7e70 SHA512: 2fef061d9cd45137dbe4d671e5f2eaaa2abe0f8e6de23098205b93483dad5fd76ea9dd2e47e377a364e15b093868f47538d562e10fb93b132ded6bc577c55d65 Homepage: https://cran.r-project.org/package=KQM Description: CRAN Package 'KQM' (K Quantiles Medoids (KQM) Clustering) K Quantiles Medoids (KQM) clustering applies quantiles to divide data of each dimension into K mean intervals. Combining quantiles of all the dimensions of the data and fully permuting quantiles on each dimension is the strategy to determine a pool of candidate initial cluster centers. To find the best initial cluster centers from the pool of candidate initial cluster centers, two methods based on quantile strategy and PAM strategy respectively are proposed. During a clustering process, medoids of clusters are used to update cluster centers in each iteration. Comparison between KQM and the method of randomly selecting initial cluster centers shows that KQM is almost always getting clustering results with smaller total sum squares of distances. Package: r-cran-krakenr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-dplyr, r-cran-tidyr, r-cran-anytime, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-krakenr_1.0.0-1.ca2404.1_all.deb Size: 66556 MD5sum: 0196d9f84e372268c2b1a9dd319fd657 SHA1: 104ab912c15c22de2fbba7e8fee2c076795b5921 SHA256: f263a157bcc7111cf558bdf73e5a7b86742268814d1ca0d54bacc955cba30671 SHA512: cc5727fc1fba98d54ddd023f8db293b10d5288e5292f13f0cad946e169028416524fa3de400b540bec820a5f1a934f0801746df25c07532859364763c7fb3c66 Homepage: https://cran.r-project.org/package=KrakenR Description: CRAN Package 'KrakenR' (Comprehensive R Interface for Accessing Kraken CryptocurrencyExchange REST API) A comprehensive R interface to access data from the Kraken cryptocurrency exchange REST API . It allows users to retrieve various market data, such as asset information, trading pairs, and price data. The package is designed to facilitate efficient data access for analysis, strategy development, and monitoring of cryptocurrency market trends. Package: r-cran-kraljicmatrix Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kraljicmatrix_0.2.1-1.ca2404.1_all.deb Size: 167332 MD5sum: f5c0ea9d091300eb002f04fe62be1454 SHA1: 5c3f9809dbbe8d3c8fd408db1efb830da79146fd SHA256: b310efe6d43941fa15a13f6220b6318eae90738c3faae820aecd5355b2b0662f SHA512: a3f8178b37958472e4ed00679a26601d61e5185b8450d0564b1db874f683aeaf12c1270276004d57d9e99fe50b7b7b82945961c710710ca0633f42fb8463ba8d Homepage: https://cran.r-project.org/package=KraljicMatrix Description: CRAN Package 'KraljicMatrix' (A Quantified Implementation of the Kraljic Matrix) Implements a quantified approach to the Kraljic Matrix (Kraljic, 1983, ) for strategically analyzing a firm’s purchasing portfolio. It combines multi-objective decision analysis to measure purchasing characteristics and uses this information to place products and services within the Kraljic Matrix. Package: r-cran-kriens Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kriens_0.1-1.ca2404.1_all.deb Size: 22660 MD5sum: 82c79f633f63e0dd2dc0de4245aec3f4 SHA1: 2803c8aa47b481b5e5a95dbc9dcccb6e0689d7da SHA256: 0812e30d26ec84e5f21ea1468b3a767c7f18cd1695ba3a9071eee4edf741eb85 SHA512: b3d7c5165b52a4d1af8bfc415597e4ba75cd986ac3f782178d3885dfd6ada91c67ad59c7df82a956d49f2cb93141faedf7f06163e4dba14ef97476e73a05476c Homepage: https://cran.r-project.org/package=kriens Description: CRAN Package 'kriens' (Continuation Passing Style Development) Provides basic functions for Continuation-Passing Style development. Package: r-cran-kriginv Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dicekriging, r-cran-randtoolbox, r-cran-rgenoud, r-cran-pbivnorm, r-cran-anmc, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kriginv_1.4.2-1.ca2404.1_all.deb Size: 336022 MD5sum: 7e255db0bec22376b171f4a7b8fb084f SHA1: b762b7d84b639f87cc12820a5241d7089921e91a SHA256: 3e6d904dcf9d285cd96ed192ed7c4b8839aff1884d67ccbe1910337bc80d6753 SHA512: 5fc3af76968c8d969ee46f699b3139b0a2b4e042905c5826e5455b882e5f5eced76477910f0d4107a0833d4cf1b24867ecb79b3cd11d4352c3637ca123cd90ba Homepage: https://cran.r-project.org/package=KrigInv Description: CRAN Package 'KrigInv' (Kriging-Based Inversion for Deterministic and Noisy ComputerExperiments) Criteria and algorithms for sequentially estimating level sets of a multivariate numerical function, possibly observed with noise. Package: r-cran-krippendorffsalpha Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-pbapply, r-cran-spam, r-cran-testthat Filename: pool/dists/noble/main/r-cran-krippendorffsalpha_2.0-1.ca2404.1_all.deb Size: 407190 MD5sum: 46ecf5542dee91fb6cee80bde507c6e9 SHA1: d2c21f8b515e5e19bf7e72e71c0c278cb786b167 SHA256: 9e7fd38bb1e13385d659ecd0c7ec0d0cd398fa72cfe207f58d4c7df0f029049f SHA512: 39091d76a1c96c81240c45a8feb487347243f8b3f9824de41c977850c8a1870e5bec46d9aab7772f56dfa3b44d8b2cbd5bd4e1476dec88b2bd14fd9964b1cbb0 Homepage: https://cran.r-project.org/package=krippendorffsalpha Description: CRAN Package 'krippendorffsalpha' (Measuring Agreement Using Krippendorff's Alpha Coefficient) Provides tools for applying Krippendorff's Alpha methodology . Both the customary methodology and Hughes' methodology are supported, the former being preferred for larger datasets, the latter for smaller datasets. The framework supports common and user-defined distance functions, and can accommodate any number of units, any number of coders, and missingness. Interval estimation can be done in parallel for either methodology. Package: r-cran-kris Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rarpack Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kris_1.1.6-1.ca2404.1_all.deb Size: 200002 MD5sum: f6f326c1b1a42542715a207ed0d0e367 SHA1: d17db45fbc3a62793dfd80288aacda8883d48412 SHA256: 4c0b5beabfe846307f21e47a92f67d89c145d8df4f818f955c968df581b24555 SHA512: eefcfe1c2b741b415b1e0ac300aedc98ef5ddb2d2b06c4f5eeb8760db5112cc683eee7d3c7f135223f3bd2dc216b072f95ebdff59cda1bcb33ef6bf4313a20a4 Homepage: https://cran.r-project.org/package=KRIS Description: CRAN Package 'KRIS' (Keen and Reliable Interface Subroutines for BioinformaticAnalysis) Provides useful functions which are needed for bioinformatic analysis such as calculating linear principal components from numeric data and Single-nucleotide polymorphism (SNP) dataset, calculating fixation index (Fst) using Hudson method, creating scatter plots in 3 views, handling with PLINK binary file format, detecting rough structures and outliers using unsupervised clustering, and calculating matrix multiplication in the faster way for big data. Package: r-cran-krls Architecture: all Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice, r-cran-testthat Filename: pool/dists/noble/main/r-cran-krls_1.1-0-1.ca2404.1_all.deb Size: 81014 MD5sum: bebf6bd5f67fe229a1591be9202d2dc3 SHA1: ef762a0ea9a40efc2f90258c4c2f80ae29bf6149 SHA256: 1b488dcf4a3b75175fa96914fd55980af2005580e5f23673125add3f390d26dc SHA512: 6f1efce019c49157ac49efc9d52216c84ffd40532ab0471ba3c4c8f0afc98b179ba7cd037141cd2e3557917e4d1d23e97d3855285bff23727ff285ea36a8ff6f Homepage: https://cran.r-project.org/package=KRLS Description: CRAN Package 'KRLS' (Kernel-Based Regularized Least Squares) Implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y = f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014, ). 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Package: r-cran-kstio Architecture: all Version: 0.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sets, r-cran-kstmatrix, r-cran-openxlsx2, r-cran-readods Filename: pool/dists/noble/main/r-cran-kstio_0.5-1-1.ca2404.1_all.deb Size: 430720 MD5sum: d81f70e7c6e9a8caccf22b0d81edc074 SHA1: 532a8fcdbffa8a36b722d309cd74c3c059a15483 SHA256: 09d1a4a2484d4c2e5fc78cc7965af00451f227a81c5a1b1518e07eaa2eddef9d SHA512: 63270a3e48e3843dece3bfc734407891949f88656be92f202e1222dcea34c5225ced5b1af781e6859d9f39931df9e4c81cd2ed9fe1d0c38fca95b3191d3392b9 Homepage: https://cran.r-project.org/package=kstIO Description: CRAN Package 'kstIO' (Knowledge Space Theory Input/Output) Knowledge space theory by Doignon and Falmagne (1999) is a set- and order-theoretical framework which proposes mathematical formalisms to operationalize knowledge structures in a particular domain. The 'kstIO' package provides basic functionalities to read and write KST data from/to files to be used together with the 'kst', 'kstMatrix', 'pks', or 'DAKS' packages. Package: r-cran-ktensorgraphs Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ktensorgraphs_1.1-1.ca2404.1_all.deb Size: 175890 MD5sum: 48952a1474fee2b9d398efcec55d94f3 SHA1: 78c487d4813f38349e1d2e3d43f518303238197d SHA256: f450318324be19734fff8ef3cacb2c37a47c1143e4672a9edfb367779799fcd2 SHA512: 9479c44c705065d8922e8335526c7e6488b892f048208c0fe303648f4474fdaccb8e51051c9a0814af6d1b53bdfa9d7c95592aaccd7d38858e20c8a6a5d989f3 Homepage: https://cran.r-project.org/package=KTensorGraphs Description: CRAN Package 'KTensorGraphs' (Co-Tucker3 Analysis of Two Sequences of Matrices) Provides a function called COTUCKER3() (Co-Inertia Analysis + Tucker3 method) which performs a Co-Tucker3 analysis of two sequences of matrices, as well as other functions called PCA() (Principal Component Analysis) and BGA() (Between-Groups Analysis), which perform analysis of one matrix, COIA() (Co-Inertia Analysis), which performs analysis of two matrices, PTA() (Partial Triadic Analysis), STATIS(), STATISDUAL() and TUCKER3(), which perform analysis of a sequence of matrices, and BGCOIA() (Between-Groups Co-Inertia Analysis), STATICO() (STATIS method + Co-Inertia Analysis), COSTATIS() (Co-Inertia Analysis + STATIS method), which also perform analysis of two sequences of matrices. Package: r-cran-ktsolve Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb, r-cran-nleqslv, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-ktsolve_1.4-1.ca2404.1_all.deb Size: 21572 MD5sum: 1d1874c705a618178c1887f54025b016 SHA1: 2079624cfcff662136ea08c8793347e6af255bda SHA256: 46ccf3c1f01ec8175191993828752139ebc29cd04943e89bb753333105c725ec SHA512: eb95d7b7241be1ddce6b80464f6bd753b603716eeb0ff46416b3a3bba7cfabc3d44057d7ed1d5fd918525af65a148de334ef5e499d8d2740040c84cc9a475fb9 Homepage: https://cran.r-project.org/package=ktsolve Description: CRAN Package 'ktsolve' (Configurable Function for Solving Families of NonlinearEquations) This is designed for use with an arbitrary set of equations with an arbitrary set of unknowns. The user selects "fixed" values for enough unknowns to leave as many variables as there are equations, which in most cases means the system is properly defined and a unique solution exists. The function, the fixed values and initial values for the remaining unknowns are fed to a nonlinear backsolver. The original version of "TK!Solver" , now a product of Universal Technical Systems () was the inspiration for this function. Package: r-cran-kuenm2 Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4724 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dosnow, r-cran-enmpa, r-cran-foreach, r-cran-fproc, r-cran-glmnet, r-cran-mgcv, r-cran-mop, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-kuenm2_0.1.3-1.ca2404.1_all.deb Size: 3132708 MD5sum: 3e9d407a78c0688025433c07613742e5 SHA1: 14873c503fa896063a3bfd386ea18e600c1ed453 SHA256: 08e45a10f6ea3f425f36656e8991f0c42f5d55894d000c5d5bcb9a4e832da416 SHA512: 6096165b4e40b83100e322f3576ce98ffdfbddef8cb85e7a297c24d49f1a252dff561a9df1f4c559afd4e8acb6485a4a7fd2b008a931a3e6595e388d50550de4 Homepage: https://cran.r-project.org/package=kuenm2 Description: CRAN Package 'kuenm2' (Detailed Development of Ecological Niche Models) A new set of tools to help with the development of detailed ecological niche models using multiple algorithms. Pre-modeling analyses and explorations can be done to prepare data. Model calibration (model selection) can be done by creating and testing models with several parameter combinations. Handy options for producing final models with transfers are included. Other tools to assess extrapolation risks and variability in model transfers are also available. Methodological and theoretical basis for the methods implemented here can be found in: Peterson et al. (2011) , Radosavljevic and Anderson (2014) , Peterson et al. (2018) , Cobos et al. (2019) , Alkishe et al. (2020) , Machado-Stredel et al. (2021) , Arias-Giraldo and Cobos (2024) , Cobos et al. (2024) . Package: r-cran-kuiper.2samp Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-kuiper.2samp_1.0-1.ca2404.1_all.deb Size: 16110 MD5sum: 9b044bb52051ef475a2ce469cdf3f653 SHA1: 35bf2838751d3fa129cecfc69c5fff5d872ad8ca SHA256: 07404a23afa35d8f9d08ac4e4e0cfca614e7c6bcd36d6da2a8cbbe12f21da87f SHA512: 7d3bb19206dad27f04ff67b370afe721a07d9b8cc3bee1c3f8ad898af94bb36a778f1bf2868254c1b5b9dece45b1529d43fadf9ae318c443e76551580d7370f6 Homepage: https://cran.r-project.org/package=kuiper.2samp Description: CRAN Package 'kuiper.2samp' (Two-Sample Kuiper Test) This function performs the two-sample Kuiper test to assess the anomaly of continuous, one-dimensional probability distributions. References used for this method are (1). Kuiper, N. H. (1960). and (2). Paltani, S. (2004). . Package: r-cran-kurt Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-polynom, r-cran-expm Filename: pool/dists/noble/main/r-cran-kurt_1.1-1.ca2404.1_all.deb Size: 55114 MD5sum: e05f16da043858567d964f209b4de119 SHA1: 009c613320a5326d4b6e5e3800a6836d0af5b1de SHA256: aa8ef915e1d684bd9e78467fc52fdae9ba114d929befe07e62dbd4fcfa8e4729 SHA512: b2a586a5226f0b87e12c1fae57153c22a1c831f3ec54f102455e4d0e643f5b72514fe1d11ff37ac0332ae5b4cb1824298ca4ba6c527d98872a78134643c13178 Homepage: https://cran.r-project.org/package=Kurt Description: CRAN Package 'Kurt' (Performs Kurtosis-Based Statistical Analyses) Computes measures of multivariate kurtosis, matrices of fourth-order moments and cumulants, kurtosis-based projection pursuit. Franceschini, C. and Loperfido, N. (2018, ISBN:978-3-319-73905-2). "An Algorithm for Finding Projections with Extreme Kurtosis". Loperfido, N. (2017,ISSN:0024-3795). "A New Kurtosis Matrix, with Statistical Applications". Package: r-cran-kutils Architecture: all Version: 1.73-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreign, r-cran-xtable, r-cran-plyr, r-cran-openxlsx, r-cran-runit Suggests: r-cran-rockchalk Filename: pool/dists/noble/main/r-cran-kutils_1.73-1.ca2404.1_all.deb Size: 727120 MD5sum: ff66f92046cf61f4fb70578a6f4fe019 SHA1: 14336f94ad351d33e7ad270c1691b8bbf5039bec SHA256: 25d0f07bea77bc2983c60c9ec1c1416898c9901c79916be035bc7a3de53f2b15 SHA512: ee8b545f8f1aa58fd176c44dbcffb063ae5d90915f02803733564cdba65ccd66626ee50bbf58b48f27ce046fb4d6dea608d0e1597ed71c2c30645eb521f4ccf0 Homepage: https://cran.r-project.org/package=kutils Description: CRAN Package 'kutils' (Project Management Tools) Tools for data importation, recoding, and inspection. There are functions to create new project folders, R code templates, create uniquely named output directories, and to quickly obtain a visual summary for each variable in a data frame. The main feature here is the systematic implementation of the "variable key" framework for data importation and recoding. We are eager to have community feedback about the variable key and the vignette about it. In version 1.7, the function 'semTable' is removed. It was deprecated since 1.67. That is provided in a separate package, 'semTable'. Package: r-cran-kuzco Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1229 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ellmer, r-cran-gt, r-cran-gtextras, r-cran-imager, r-cran-jsonlite, r-cran-magick, r-cran-ollamar, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-quarto, r-cran-shiny, r-cran-bslib, r-cran-rmarkdown, r-cran-purrr, r-cran-mirai, r-cran-tibble, r-cran-tictoc, r-cran-httr Filename: pool/dists/noble/main/r-cran-kuzco_0.1.0-1.ca2404.1_all.deb Size: 991520 MD5sum: 9e30d2f11940159a4357b6d8777540e9 SHA1: d49338f78149cabaea951a4382b9cc69fb662c9d SHA256: 49fa6a1bd26b21f2e66ec7e3935f21b07c23f22972e345bbbdfe1873f3dd1a70 SHA512: 575278b401e1dd8336b72e217b40582c423711d1291f149797920d59d9eb512343f4d2093090a73b71d86696cfa9826f49f7ee8c25a9d75360e17ad98667569b Homepage: https://cran.r-project.org/package=kuzco Description: CRAN Package 'kuzco' (Computer Vision with Large Language Models) Make computer vision tasks approachable in R by leveraging Large Language Models. Providing fine-tuned prompts, boilerplate functions, and input/output helpers for common computer vision workflows, such as classifying and describing images. Functions are designed to take images as input and return structured data, helping users build practical applications with minimal code. Package: r-cran-kuzur Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2711 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-igraph, r-cran-tibble, r-cran-tidygraph Suggests: r-cran-g6r, r-cran-jsonlite, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-arrow Filename: pool/dists/noble/main/r-cran-kuzur_0.2.3-1.ca2404.1_all.deb Size: 1741182 MD5sum: 173cf433c54acb9b123208263be3c9aa SHA1: d110a84b0f183bf4b8dabfc2124fb619bc292cf8 SHA256: 8256500bda81b9af0e04221d5de54c2e0fe3af3dcbae61edb669371d0afb1822 SHA512: 42b1a7c3a36b1aa7079827b0ee67cec2e60dfd55dee1edc8620126c80824eda4e5635a6befc39cc5ef66635acf430277d4eb5e724ec19c0be0fcea02e210c86f Homepage: https://cran.r-project.org/package=kuzuR Description: CRAN Package 'kuzuR' (Interface to 'kuzu' Graph Database) Provides a high-performance 'R' interface to the 'kuzu' graph database. It uses the 'reticulate' package to wrap the official 'Python' client ('kuzu', 'pandas', and 'networkx'), allowing users to interact with 'kuzu' seamlessly from within 'R'. Key features include managing database connections, executing 'Cypher' queries, and efficiently loading data from 'R' data frames. It also provides seamless integration with the 'R' ecosystem by converting query results directly into popular 'R' data structures, including 'tibble', 'igraph', 'tidygraph', and 'g6R' objects, making 'kuzu's powerful graph computation capabilities readily available for data analysis and visualization workflows in 'R'. The 'kuzu' documentation can be found at . Package: r-cran-kvkapir Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-lifecycle, r-cran-purrr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-kvkapir_0.1.2-1.ca2404.1_all.deb Size: 116040 MD5sum: 014740fe9639721a876d582d023ee44f SHA1: 88ae8db0429343494c89738d95ae82a0862e88e9 SHA256: dd288916f8a24d5b86e00fec816e131b2b55c2388f6c3ab7c328e082bd0d2d05 SHA512: b3eddfdfe9e1fcef29503dfa41c05faaa4409ab8bd687a4b9e6fc2567fc6e568fde718e1a2e0525ffec4d12def8e73d84ee1974cc760e8d5bbe8ab4174e497ea Homepage: https://cran.r-project.org/package=kvkapiR Description: CRAN Package 'kvkapiR' (Interface to the Dutch Chamber of Commerce (KvK) API) Access business registration data from the Dutch Chamber of Commerce (Kamer van Koophandel, KvK) through their official API . Search for companies by name, location, or registration number. Retrieve detailed business profiles, establishment information, and company name histories. Built on 'httr2' for robust API interaction with automatic pagination, error handling, and usage tracking. Package: r-cran-kvr2 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-insight, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-kvr2_0.2.0-1.ca2404.1_all.deb Size: 187156 MD5sum: 0948d244bf94bc794bff67e8612589ec SHA1: 52a87aa51673752a30c80d7aac024975ce4e5a03 SHA256: 1b02f05eabfd1cc2cdcb0775211eb808c4ad62e0b1916899d795b4f1aaf3b9ea SHA512: ac42dae2df36ddd1ec4758065585ed3939e90d1ba1f5155cd023f2453937b3d97a48c9927bb39fbed73f9785ffa88b084a943e853b2a71acde175a1dafec7980 Homepage: https://cran.r-project.org/package=kvr2 Description: CRAN Package 'kvr2' (Calculate and Compare Multiple Definitions of Coefficient ofDetermination) Calculate nine types of coefficients of determination (R-squared) based on the classification by Kvalseth (1985) . This package is designed for educational purposes to demonstrate how R-squared values can fluctuate depending on the choice of formula, particularly in power regression models or linear models without an intercept. By providing a comprehensive list of definitions, it helps users understand the mathematical sensitivity of goodness-of-fit indices. Package: r-cran-kwela Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-kwela_1.0.0-1.ca2404.1_all.deb Size: 139378 MD5sum: 12d5d93dcc7f64192ffcbcaa66ddb733 SHA1: bc57a699de702fbe16cdb7e0686536ffbef6fc69 SHA256: 73e3b64a87124843dee095af8ca9febde7f912f14b2cea9639965008fb0f9570 SHA512: ea8ba58634e93e4e3e134acbb12400cd4b8f1acf5325f0e7b37b6dac1eefc5540b5cce33fda4abef57dcc52e8ccc36493ceae36a138df711ff12275f7f642481 Homepage: https://cran.r-project.org/package=KWELA Description: CRAN Package 'KWELA' (Hierarchical Adaptive 'RT-QuIC' Classification for ComplexMatrices) Extends 'RT-QuIC' (Real-Time Quaking-Induced Conversion) statistical analysis to complex environmental matrices through hierarchical adaptive classification. 'KWELA' is named after a deity of the Fore people of Papua New Guinea, among whom Kuru, a notable human prion disease, was identified. Implements a 6-layer architecture: hard gate biological constraints, per-well adaptive scoring, separation-aware combination, Youden-optimized cutoffs, replicate consensus, and matrix instability detection. Features dual-mode operation (diagnostic/research), auto-profile selection (Standard/Sensitive/Matrix-Robust), RAF integration for artifact detection, matrix-aware baseline correction, and multiple consensus rules. Methods include energy distance (Szekely and Rizzo (2013) ), CRPS (Gneiting and Raftery (2007) ), SSMD (Zhang (2007) ), and Jensen-Shannon divergence (Lin (1991) ). This package implements methodology currently under peer review; please contact the author before publication using this approach. Development followed an iterative human-machine collaboration where all algorithmic design, statistical methodologies, and biological validation logic were conceptualized, tested, and iteratively refined by Richard A. Feiss through repeated cycles of running experimental data, evaluating analytical outputs, and selecting among candidate algorithms and approaches. AI systems ('Anthropic Claude' and 'OpenAI GPT') served as coding assistants and analytical sounding boards under continuous human direction. The selection of statistical methods, evaluation of biological plausibility, and all final methodology decisions were made by the human author. AI systems did not independently originate algorithms, statistical approaches, or scientific methodologies. Package: r-cran-kzs Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3463 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-kzs_1.4.1-1.ca2404.1_all.deb Size: 3491780 MD5sum: c041b89e0305c0388d70669390ac0299 SHA1: abda2bdd24d86e8635e3670ba15475c8901ae949 SHA256: c79b942b2805d51a612055f4067a2b65d68111d0c213de187947aef6ccb86d60 SHA512: b3b0fc472844c5f0e83e1089072f2776914d4133e2d088276e9334f06ac1e91d76cf0dfeebc2342537fa915144f0d041bdcd87d92d675d3dc9aeca7b5af02e3d Homepage: https://cran.r-project.org/package=kzs Description: CRAN Package 'kzs' (Kolmogorov-Zurbenko Spatial Smoothing and Applications) A spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise. Package: r-cran-l0cpt Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-l0cpt_0.2.0-1.ca2404.1_all.deb Size: 60686 MD5sum: eb1a83afc34a1cc501e4f65a14353c31 SHA1: 52a507f725a2f616d6338a2f15b4099b01bf876a SHA256: d1ed4e5938e0a4e1aaa9bb6b1f025e425a8dbe851cfbffbe0c97a0bc4b3aa117 SHA512: 0d5549ef6a31fec8a27150a5f5e166c2bed3db89d91a57ca7c609921989b2b774788ea0fff5b0568f747853446730eb264e97db8613cefdc344e364ae6f97025 Homepage: https://cran.r-project.org/package=L0cpt Description: CRAN Package 'L0cpt' (Change Point Detection with L0 Penalty) Under an L0 penalty framework, a computationally efficient implementation of change point detection is developed. By integrating active set algorithms with warm start initialization, the package achieves linear-time complexity for solving change point detection problems. References: Wen et al. (2020) ; Zhu et al. (2020). Package: r-cran-l0tfinv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1273 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-l0tfinv_0.1.0-1.ca2404.1_all.deb Size: 463126 MD5sum: f0c4b028ffbd5b7a36e45e491e0468d6 SHA1: 2c297c449f2d657182f66c59b7a332436905ce27 SHA256: 8bff3b7429f2424fe38db57f01273fb489c2eaa6e248de4d0a03bd1b4bb31d15 SHA512: cee63cfd1137e5c60558afac813a1828c786deee308a4957ad6d64813bab6a29a1175a0d03ec1c506dd91f9dfa7f0cb092f84dd39fe39e3da197077e4c7230ac Homepage: https://cran.r-project.org/package=L0TFinv Description: CRAN Package 'L0TFinv' (A Splicing Approach to the Inverse Problem of L0 Trend Filtering) Trend filtering is a widely used nonparametric method for knot detection. This package provides an efficient solution for L0 trend filtering, avoiding the traditional methods of using Lagrange duality or Alternating Direction Method of Multipliers algorithms. It employ a splicing approach that minimizes L0-regularized sparse approximation by transforming the L0 trend filtering problem. The package excels in both efficiency and accuracy of trend estimation and changepoint detection in segmented functions. References: Wen et al. (2020) ; Zhu et al. (2020); Wen et al. (2023) . Package: r-cran-l1ball Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vgam Filename: pool/dists/noble/main/r-cran-l1ball_0.1.0-1.ca2404.1_all.deb Size: 33708 MD5sum: 6a43236845397061154bdc75b246ea15 SHA1: 54d2f3aa2396ec15a0decdd9daa56ce702a2c407 SHA256: 596baf4ee7d40b9df1d43ff27015d989bf477b1c5d7ada282dce305e4714f839 SHA512: e855a8e7c5dda2bb63104707c868c9c52f94c47d7c5aab717f6d16e66cdac5aa5ee7c24d3204091fd9f842bbc8cd2534e77e6ca851990a3d71e7f78e2fd9597d Homepage: https://cran.r-project.org/package=l1ball Description: CRAN Package 'l1ball' (L1-Ball Prior for Sparse Regression) Provides function for the l1-ball prior on high-dimensional regression. The main function, l1ball(), yields posterior samples for linear regression, as introduced by Xu and Duan (2020) . 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However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses. We present a LINCS L1000 data peak calling R package l1kdeconv based on a new outlier detection method and an aggregate Gaussian mixture model. Upon the remove of outliers and the borrowing information among similar samples, l1kdeconv shows more stable and better performance than methods commonly used in LINCS L1000 data deconvolution. 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Given a data matrix, find the rotation of the loading matrix with the smallest l1-norm and/or test for the presence of local factors with main function local_factors(). 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Package: r-cran-l2densitygoftest Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fgarch, r-cran-nor1mix, r-cran-boot, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-l2densitygoftest_0.6.0-1.ca2404.1_all.deb Size: 59202 MD5sum: 1ebd8377d013cc7f6890a1ef58cd2dc9 SHA1: 0c840b2e8665b9949078a265dac3b1bf57716130 SHA256: a6668c979c26ef75913802586ec30dc35a7ced40fe28b9e7ea425d663b14ed57 SHA512: d44c2caab7d2c569d2c62bbfcadef243cc2853d37c72cce10f1780e3483c8fb3c3b556d79cf532d6dc97ce6a39c37a52c09bcfc941a6eb57823703db4123ef50 Homepage: https://cran.r-project.org/package=L2DensityGoFtest Description: CRAN Package 'L2DensityGoFtest' (Density Goodness-of-Fit Test) Provides functions for the implementation of a density goodness-of-fit test, based on piecewise approximation of the L2 distance. Package: r-cran-l2e Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 470 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-osqp, r-cran-isotone, r-cran-cobs, r-cran-ncvreg, r-cran-matrix, r-cran-signal, r-cran-robustbase Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-latex2exp Filename: pool/dists/noble/main/r-cran-l2e_2.0-1.ca2404.1_all.deb Size: 403008 MD5sum: 3de30f271237e77b73dda5675385bec5 SHA1: 6432f8700f9f15504c8a7c74a24dacc16d7a16d5 SHA256: 8b21b046e1d18cb2811d8478da18e99ad249720938f45409761d762ec30950d4 SHA512: cb284e7ca930189f0ae36c1b15f93abcde0e3eb951fd9b6f37105ea00a700263c632f3a6d0d0c6f23940d546bb9cf0aa2af9f342dc3d418123d0e26baf7abd2b Homepage: https://cran.r-project.org/package=L2E Description: CRAN Package 'L2E' (Robust Structured Regression via the L2 Criterion) An implementation of a computational framework for performing robust structured regression with the L2 criterion from Chi and Chi (2021+). Improvements using the majorization-minimization (MM) principle from Liu, Chi, and Lange (2022+) added in Version 2.0. Package: r-cran-l2hdchange Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-l2hdchange_1.0-1.ca2404.1_all.deb Size: 387154 MD5sum: 8aa2a24aa67962743a61c790af1be258 SHA1: c169e9e0e9364db114ee87ca4894d097b42ebe24 SHA256: a63ec7e2a47341ff5705809ca00aa66751ea19fbf3c41cf5ecd8203b8d07dad2 SHA512: 491f530338d82a9db92612e1680941b576ffb854ac43d2e84d05d1c70432cb17bebac0cfe099b255c2dd027ba1d1c79f6e626b5d402fe3adfeeb55a659f00075 Homepage: https://cran.r-project.org/package=L2hdchange Description: CRAN Package 'L2hdchange' (L2 Inference for Change Points in High-Dimensional Time Series) Provides a method for detecting multiple change points in high-dimensional time series, targeting dense or spatially clustered signals. See Li et al. (2023) "L2 Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM". arXiv preprint . 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Given the substantial time and expertise required for lab data pre-processing and cleaning and the lack of all-in-one tools tailored for this need, we developed our algorithm 'lab2clean' as an open-source R-package. 'lab2clean' package is set to automate and standardize the intricate process of cleaning clinical laboratory results. With a keen focus on improving the data quality of laboratory result values and units, our goal is to equip researchers with a straightforward, plug-and-play tool, making it smoother for them to unlock the true potential of clinical laboratory data in clinical research and clinical machine learning (ML) model development. Functions to clean & validate result values (Version 1.0) are described in detail in 'Zayed et al. (2024)' . Functions to standardize & harmonize result units (added in Version 2.0) are described in detail in 'Zayed et al. (2025)' . 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This package can be used in order to deal with this problem using various relabelling algorithms. Package: r-cran-labeler Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4887 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmarkdown Suggests: r-cran-blastula, r-cran-keyring, r-cran-testthat Filename: pool/dists/noble/main/r-cran-labeler_0.4.0-1.ca2404.1_all.deb Size: 4490176 MD5sum: 9dc0a63507d0cdcb5a8e25fd6015ed8b SHA1: b6dbdb6015e5c5e770120b0e2829444617e4047d SHA256: da8f5944af60428510e0065d70be3c1f89ff4d5550355e30cf75853b3388d3a8 SHA512: 5d4906300f8bd4de4dad5ebbef7fb7bd07423c35430becab13d78cc789674a916e572c80a201ff62eadba5124e614bfacb0122bcd3aa28755de0d2543a9b192f Homepage: https://cran.r-project.org/package=labeleR Description: CRAN Package 'labeleR' (Automate the Production of Custom Labels, Badges, Certificates,and Other Documents) Create custom labels, badges, certificates and other documents. Automate the production of potentially large numbers of herbarium and collection labels, accreditation badges, attendance and participation certificates, etc, and deliver them automatically. Documents are generated in PDF format, which requires a working installation of 'LaTeX', such as 'TinyTeX'. 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This package provides useful functions to deal with "haven_labelled" and "haven_labelled_spss" classes introduced by 'haven' package. 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Value labels include one-to-one and many-to-one labels for nominal and ordinal variables, as well as numerical range-based value labels for continuous variables. Convert value-labeled variables so each value is replaced by its corresponding value label. Add values-converted-to-labels columns to a value-labeled data frame while preserving parent columns. Filter and subset a value-labeled data frame using labels, while returning results in terms of values. Overlay labels in place of values in common R commands to increase interpretability. Generate tables of value frequencies, with categories expressed as raw values or as labels. Access data frames that show value-to-label mappings for easy reference. 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While variable names are succinct, quick to type, and follow a language's naming conventions, labels may be more illustrative and may use plain text and spaces. R does not provide native support for labels. Some packages, however, have made this feature available. Most notably, the 'Hmisc' package provides labelling methods for a number of different object. Due to design decisions, these methods are not all exported, and so are unavailable for use in package development. The 'labelVector' package supports labels for atomic vectors in a light-weight design that is suitable for use in other packages. Package: r-cran-lablaster Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-ggplot2, r-cran-smooth, r-cran-scales, r-cran-rlang Filename: pool/dists/noble/main/r-cran-lablaster_1.0.1-1.ca2404.1_all.deb Size: 45606 MD5sum: d922c3a4ec2fd8440d684bca4fdd8eea SHA1: 97cf898d35ace97fdde31e6654f53061574d4bc3 SHA256: 95db1f50005a89ef4cf02fe7077edc106f7b003fe48fb1172f82df16d4c84036 SHA512: d78373d8a6510d7f878fcead1271d5fbab8ae3344c93d1811138e20e36a05ca4e8c08b1c8c74058a2c25c6ce9e59780c29d777a99c0ff544054e4e7738695b75 Homepage: https://cran.r-project.org/package=lablaster Description: CRAN Package 'lablaster' (Laser Ablation Blast Through Endpoint Detection) Imports a data frame containing a single time resolved laser ablation mass spectrometry analysis of a foraminifera (or other carbonate shell), then detects when the laser has burnt through the foraminifera test as a function of change in signal over time. Package: r-cran-labnorm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4485 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-glue, r-cran-purrr, r-cran-rappdirs, r-cran-scales, r-cran-tibble, r-cran-withr, r-cran-yesno Suggests: r-cran-covr, r-cran-mockery, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-labnorm_1.0.1-1.ca2404.1_all.deb Size: 3722740 MD5sum: 0e1b3fec833530c47602a8206d66b22a SHA1: 894461351e097b20ea6aef6b2bb75c38f30f19cf SHA256: 164e35ac27cd0db9d0174801b2c22541bd691fabf210e520e744a4f734f6444c SHA512: 97d4b6f2f90762228456f794612b01a6ef2b694d26149e820bf1cb966e8acaac4d97e159db39adf9dc2c5e944b5973008d88a874073dde0963875c9d6cafcf88 Homepage: https://cran.r-project.org/package=labNorm Description: CRAN Package 'labNorm' (Normalize Laboratory Measurements by Age and Sex) Provides functions for normalizing standard laboratory measurements (e.g. hemoglobin, cholesterol levels) according to age and sex, based on the algorithms described in "Personalized lab test models to quantify disease potentials in healthy individuals" (Netta Mendelson Cohen, Omer Schwartzman, Ram Jaschek, Aviezer Lifshitz, Michael Hoichman, Ran Balicer, Liran I. Shlush, Gabi Barbash & Amos Tanay, ). Allows users to easily obtain normalized values for standard lab results, and to visualize their distributions. See more at . Package: r-cran-labourmarketareas Architecture: all Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2098 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-sf, r-cran-data.table, r-cran-spdep, r-cran-tmap Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-labourmarketareas_3.4-1.ca2404.1_all.deb Size: 1207146 MD5sum: 5bc0e6e455d4d0e70b1860051507f9a9 SHA1: 356881f6b2da34cfc0c55abe54d48f10d3c7b2f7 SHA256: e6ea5f827d7aa71831604a68b55b726c74ca3005e766459c245c5f34c1b3cf7d SHA512: 2bf663650a350dcb4aad2b6351b3f2ef4d7e536a274f091b468d59b04fa676507f3d8af95c4e16c6616a773fd29e121d8d5f9790028236d3f0058bd7859a2f67 Homepage: https://cran.r-project.org/package=LabourMarketAreas Description: CRAN Package 'LabourMarketAreas' (Identification, Tuning, Visualisation and Analysis of LabourMarket Areas) Produces Labour Market Areas from commuting flows available at elementary territorial units. It provides tools for automatic tuning based on spatial contiguity. It also allows for statistical analyses and visualisation of the new functional geography. Package: r-cran-labourr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2984 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-cld2, r-cran-magrittr, r-cran-stopwords, r-cran-stringdist Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-labourr_1.0.0-1.ca2404.1_all.deb Size: 2905962 MD5sum: afefaf1c6aaeaa162990017a9f956729 SHA1: df1d4df413506cfe05ca7600e154aeded40db629 SHA256: c446780f32d4d460fe9d37e48adbea955ffda9baa443ea301f0e58293ba57b13 SHA512: 5c34e8c7e1a70dd99c978918c743971bb5ff4e335cc603228972f87c22cbb62481716bf571d59e7c6c5bd1db1a1f921edbb05f7d8c7b3dffeeb30b3ba1ba211c Homepage: https://cran.r-project.org/package=labourR Description: CRAN Package 'labourR' (Classify Multilingual Labour Market Free-Text to StandardizedHierarchical Occupations) Allows the user to map multilingual free-text of occupations to a broad range of standardized classifications. The package facilitates automatic occupation coding (see, e.g., Gweon et al. (2017) and Turrell et al. (2019) ), where the ISCO to ESCO mapping is exploited to extend the occupations hierarchy, Le Vrang et al. (2014) . Document vectorization is performed using the multilingual ESCO corpus. A method based on the nearest neighbor search is used to suggest the closest ISCO occupation. Package: r-cran-labrs Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr Suggests: r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-labrs_0.2.1-1.ca2404.1_all.deb Size: 351740 MD5sum: 00a5fbc5e7d6e995808af4e5c06bb132 SHA1: 73f092d91796317ff400dd99d3481b57117339de SHA256: 23947f42b368edd5cd1a75e35b22248c846c676c17a5e5570b9ddab91413f896 SHA512: 7ca3e5f37ded442eb090c37e69f74a8244d330824f6eeea4eb5ff88a19ea3fed29bad7320b71054408c56123bd1334c44bbc2dd08f118647ff48f0e5437d63df Homepage: https://cran.r-project.org/package=LabRS Description: CRAN Package 'LabRS' (Laboratorio di Ricerca Sociale con R) Libreria di dati, scripts e funzioni che accompagna il libro "Ricerca sociale con R. Concetti e funzioni base per la ricerca sociale". Package: r-cran-labsimplex Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3853 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scatterplot3d, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-frf2 Filename: pool/dists/noble/main/r-cran-labsimplex_0.1.2-1.ca2404.1_all.deb Size: 2827288 MD5sum: e222718ca33581f15ad0ab70d4c73d26 SHA1: 1b132ec7563f8aa85e7af49cb3481e6e2ee3173e SHA256: 264baf5aab092a2a32295238a2b94de6132e850e8b8ea6df2c66eff2153db203 SHA512: f46d5ef0b723841917ac2815e1b8840aa285ea6bf26fe6ffef8ad8ae5a0005ec796f681f5fb597a5c72564be3ea82e1c06e83efd9081ab3c4c40b454365de848 Homepage: https://cran.r-project.org/package=labsimplex Description: CRAN Package 'labsimplex' (Simplex Optimization Algorithms for Laboratory and ManufacturingProcesses) Simplex optimization algorithms as firstly proposed by Spendley et al. (1962) and later modified by Nelder and Mead (1965) for laboratory and manufacturing processes. The package also provides tools for graphical representation of the simplexes and some example response surfaces that are useful in illustrating the optimization process. Package: r-cran-labstatr Architecture: all Version: 1.0.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-labstatr_1.0.13-1.ca2404.1_all.deb Size: 188000 MD5sum: dbda446a793a9cf0e4cdc3628a143430 SHA1: bf240980b9a4c44f5085effe4552a4b26743de2c SHA256: 1aaba509269019b9300a5e9f0d2ea7a3c210099c8967f6578915f7c3153f8b0d SHA512: 89523a48eb6dfbb6c2aadfd9127c1e21cf58ae8e28e18e1ce0d946062451f5018d568895ef3a5ffa138249dac073cb2bdab8e23748cd9fb1eac3a5dd16e6756d Homepage: https://cran.r-project.org/package=labstatR Description: CRAN Package 'labstatR' (Libreria Del Laboratorio Di Statistica Con R) Insieme di funzioni di supporto al volume "Laboratorio di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006. This package contains sets of functions defined in "Laboratorio di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006. Function names and docs are in italian as well. Package: r-cran-labstats Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-labstats_1.0.1-1.ca2404.1_all.deb Size: 83500 MD5sum: 3f4faed79efc68b337955363dcbd3529 SHA1: f00cb8f166d249e26c140b6c74b4f7df5cbba92f SHA256: 338a8372243b8d785a89d8059d993a3829bdffa19b9b44b0cd6318f9b5842d6f SHA512: 71dd741cc0d33e0a09ba2e81e65ff3735b699384346be065574d49e58e15dd82bbd611b1e441341382903f8ac0091ee9317f0ad67fdda8d8dfe621bf006be1bc Homepage: https://cran.r-project.org/package=labstats Description: CRAN Package 'labstats' (Data Sets for the Book "Experimental Design for LaboratoryBiologists") Contains data sets to accompany the book: Lazic SE (2016). "Experimental Design for Laboratory Biologists: Maximising Information and Improving Reproducibility". Cambridge University Press. Package: r-cran-labtnscpss Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3320 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-checkmate, r-cran-data.table, r-cran-stringi, r-cran-ggplot2, r-cran-lubridate, r-cran-purrr Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-devtools Filename: pool/dists/noble/main/r-cran-labtnscpss_1.0-1.ca2404.1_all.deb Size: 2906768 MD5sum: 34f1bf34ca0b02ba14e9b81e275f24fb SHA1: c0f2937078f1a18cf7fd471552d13db6f5323172 SHA256: ca915ba7ee14d25220095b96f750f310ef7b857261a3b1496d20d3e247183096 SHA512: 37691d5c28fbe8a9029ce6910aabcdebf7da87d0a335e3a79d317fbcc3836493a5a383eb25e1144ef3e3e7067f757151c9a5666a0b8b048ca7cba13f69884aab Homepage: https://cran.r-project.org/package=LABTNSCPSS Description: CRAN Package 'LABTNSCPSS' (Calculation of Comorbidity and Frailty Scores) Computes comorbidity indices and combined frailty scores for multiple ICD coding systems, including ICD-10-CA, ICD-10-CM, and ICD-11. The package provides tools to preprocess episode data, map diagnosis codes to chronic categories, propagate conditions across episodes, and generate comorbidity and frailty measures. The methods implemented are original to this package and were developed by the authors for research applications; a manuscript describing the methodology is currently in preparation. Package: r-cran-lacrmr Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-janitor, r-cran-sjmisc, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-httptest, r-cran-mockery Filename: pool/dists/noble/main/r-cran-lacrmr_1.0.5-1.ca2404.1_all.deb Size: 230714 MD5sum: c16885f9a4626e0764732ee0674ebb5e SHA1: 91dce2c23104ab636fd575df0c5e9b19abe5ec0b SHA256: 17349a6ad27717908b00d0dd76ef587837b453e0be812f993d8b1672e768f527 SHA512: 941be77f05284af71bf2da949b9e443f11f3c961d6f4c5f7568b4012f1a866174eebfe8854e0e00058acc197260e5c3dd393545730a628da21c9c6a16143a1b6 Homepage: https://cran.r-project.org/package=lacrmr Description: CRAN Package 'lacrmr' (Connect to the 'Less Annoying CRM' API) Connect to the 'Less Annoying CRM' API with ease to get your crm data in a clean and tidy format. 'Less Annoying CRM' is a simple CRM built for small businesses, more information is available on their website . Package: r-cran-lactater Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-ggtext, r-cran-patchwork, r-cran-lubridate, r-cran-minpack.lm, r-cran-pracma, r-cran-rlang, r-cran-segmented, r-cran-stringr, r-cran-tidyr, r-cran-forcats Suggests: r-cran-bsplus, r-cran-covr, r-cran-datapasta, r-cran-glue, r-cran-knitr, r-cran-miniui, r-cran-rhandsontable, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lactater_0.2.0-1.ca2404.1_all.deb Size: 270896 MD5sum: 00898d3b24ae3fc8731bfde0bd5ab80a SHA1: 4d563175483044924554cec1c4313cca14fc971e SHA256: 5c0a0876562f20b051f98f6c346a82e973081524f2915f9dcef909557b800d9f SHA512: 9ce4596c0222761c07f1bb0ae6a85706893ea22bb701e8a011967e8ccf50a0e2e3610e095dbd2806f0e9ae63061db7eb61c0b825a70d275272b9b4081f78b506 Homepage: https://cran.r-project.org/package=lactater Description: CRAN Package 'lactater' (Tools for Analyzing Lactate Thresholds) Set of tools for analyzing lactate thresholds from a step incremental test to exhaustion. Easily analyze the methods Log-log, Onset of Blood Lactate Accumulation (OBLA), Baseline plus (Bsln+), Dmax, Lactate Turning Point (LTP), and Lactate / Intensity ratio (LTratio) in cycling, running, or swimming. Beaver WL, Wasserman K, Whipp BJ (1985) . Heck H, Mader A, Hess G, Mücke S, Müller R, Hollmann W (1985) . Kindermann W, Simon G, Keul J (1979) . Skinner JS, Mclellan TH (1980) . Berg A, Jakob E, Lehmann M, Dickhuth HH, Huber G, Keul J (1990) PMID 2408033. Zoladz JA, Rademaker AC, Sargeant AJ (1995) . Cheng B, Kuipers H, Snyder A, Keizer H, Jeukendrup A, Hesselink M (1992) . Bishop D, Jenkins DG, Mackinnon LT (1998) . Hughson RL, Weisiger KH, Swanson GD (1987) . Jamnick NA, Botella J, Pyne DB, Bishop DJ (2018) . Hofmann P, Tschakert G (2017) . Hofmann P, Pokan R, von Duvillard SP, Seibert FJ, Zweiker R, Schmid P (1997) . Pokan R, Hofmann P, Von Duvillard SP, et al. (1997) . Dickhuth H-H, Yin L, Niess A, et al. (1999) . Package: r-cran-lactcurvemodels Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-lactcurvemodels_0.1.5-1.ca2404.1_all.deb Size: 137528 MD5sum: f7c9f57a7c76fb2fd0bad4c7a5919170 SHA1: ccf4b1c8d514c98407f92bd8dbc540d3081aaf2a SHA256: cfa7ebaef09248a7f4a742e2c06cf9922014c18adc0c9b15c0ff930161eef4ba SHA512: 3c0c88d5df0afb29555d62b4685a6293e9c77b9e7dd616ebe439f522858a05cd7a3f587448cf7dfa18c122467052679c378bfbb23750997257c299b67147fea9 Homepage: https://cran.r-project.org/package=LactCurveModels Description: CRAN Package 'LactCurveModels' (Lactation Curve Model Fitting for Dairy Animals) Fits up to 20 nonlinear lactation curve models to dairy animal milk yield data. Models fitted include exponential, polynomial, mixed logarithmic, inverse polynomial, and sigmoid families published between 1923 and 2000. Supports batch processing of multiple animals from a single CSV file, with flexible selection of animals and models. Produces per-animal parameter tables, goodness-of-fit metrics including R-squared (R2), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and a serial autocorrelation statistic, 15 diagnostic figures, and combined cross-animal comparison outputs. References: , , , , . Package: r-cran-lactcurves Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-polynom, r-cran-orthopolynom Filename: pool/dists/noble/main/r-cran-lactcurves_1.1.0-1.ca2404.1_all.deb Size: 155982 MD5sum: 0157c1197b4e69d0cdcb6d3fbd9e41b8 SHA1: cd529733ae07c10468e14a07d7134ea412fef292 SHA256: 0f948fda5895a63c8dda6ae825f7c1f3ea7da221018464d4f6fcb88af57fc736 SHA512: a569fddd6666f0b90000d84c951e9c3ab7fc19049bebc1a440911177f3f20d617d904b8c96af2df95a24f4a2fad9f62fea1efcfc00f3e8fa3e7871abcb3843a0 Homepage: https://cran.r-project.org/package=lactcurves Description: CRAN Package 'lactcurves' (Lactation Curve Parameter Estimation) AllCurves() runs multiple lactation curve models and extracts selection criteria for each model. This package summarises the most common lactation curve models from the last century and provides a tool for researchers to quickly decide on which model fits their data best to proceed with their analysis. Start parameters were optimized based on a dataset with 1.7 million Holstein-Friesian cows. If convergence fails, the start parameters need to be manually adjusted. The models included in the package are taken from: (1) Michaelis-Menten: Michaelis, L. and M.L. Menten (1913). (1a) Michaelis-Menten (Rook): Rook, A.J., J. France, and M.S. Dhanoa (1993). (1b) Michaelis-Menten + exponential (Rook): Rook, A.J., J. France, and M.S. Dhanoa (1993). (2) Brody (1923): Brody, S., A.C. Ragsdale, and C.W. Turner (1923). (3) Brody (1924): Brody, S., C.W. Tuner, and A.C. Ragsdale (1924). (4) Schumacher: Schumacher, F.X. (1939) in Thornley, J.H.M. and J. France (2007). (4a) Schumacher (Lopez et al. 2015): Lopez, S. J. France, N.E. Odongo, R.A. McBride, E. Kebreab, O. AlZahal, B.W. McBride, and J. Dijkstra (2015). (5) Parabolic exponential (Adediran): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). (6) Wood: Wood, P.D.P. (1967). (6a) Wood reparameterized (Dhanoa): Dhanoa, M.S. (1981). (6b) Wood non-linear (Cappio-Borlino): Cappio-Borlino, A., G. Pulina, and G. Rossi (1995). (7) Quadratic Polynomial (Dave): Dave, B.K. (1971) in Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). (8) Cobby and Le Du (Vargas): Vargas, B., W.J. Koops, M. Herrero, and J.A.M Van Arendonk (2000). (9) Papajcsik and Bodero 1: Papajcsik, I.A. and J. Bodero (1988). (10) Papajcsik and Bodero 2: Papajcsik, I.A. and J. Bodero (1988). (11) Papajcsik and Bodero 3: Papajcsik, I.A. and J. Bodero (1988). (12) Papajcsik and Bodero 4: Papajcsik, I.A. and J. Bodero (1988). (13) Papajcsik and Bodero 6: Papajcsik, I.A. and J. Bodero (1988). (14) Mixed log model 1 (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). (15) Mixed log model 3 (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). (16) Log-quadratic (Adediran et al. 2012): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). (17) Wilmink: J.B.M. Wilmink (1987). (17a) modified Wilmink (Jakobsen): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). (17b) modified Wilmink (Laurenson & Strucken): Strucken E.M., Brockmann G.A., and Y.C.S.M. Laurenson (2019). (18) Bicompartemental (Ferguson and Boston 1993): Ferguson, J.D., and R. Boston (1993) in Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). (19) Dijkstra: Dijkstra, J., J. France, M.S. Dhanoa, J.A. Maas, M.D. Hanigan, A.J. Rook, and D.E. Beever (1997). (20) Morant and Gnanasakthy (Pollott et al 2000): Pollott, G.E. and E. Gootwine (2000). (21) Morant and Gnanasakthy (Vargas et al 2000): Vargas, B., W.J. Koops, M. Herrero, and J.A.M Van Arendonk (2000). (22) Morant and Gnanasakthy (Adediran et al. 2012): Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). (23) Khandekar (Guo and Swalve): Guo, Z. and H.H. Swalve (1995). (24) Ali and Schaeffer: Ali, T.E. and L.R. Schaeffer (1987). (25) Fractional Polynomial (Elvira et al. 2013): Elvira, L., F. Hernandez, P. Cuesta, S. Cano, J.-V. Gonzalez-Martin, and S. Astiz (2012). (26) Pollott multiplicative (Elvira): Elvira, L., F. Hernandez, P. Cuesta, S. Cano, J.-V. Gonzalez-Martin, and S. Astiz (2012). (27) Pollott modified: Adediran, S.A., D.A. Ratkowsky, D.J. Donaghy, and A.E.O. Malau-Aduli (2012). (28) Monophasic Grossman: Grossman, M. and W.J. Koops (1988). (29) Monophasic Power Transformed (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). (30) Diphasic (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). (31) Diphasic Power Transformed (Grossman 1999): Grossman, M., S.M. Hartz, and W.J. Koops (1999). (32) Legendre Polynomial (3th order): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). (33) Legendre Polynomial (4th order): Jakobsen J.H., P. Madsen, J. Jensen, J. Pedersen, L.G. Christensen, and D.A. Sorensen (2002). (34) Legendre + Wilmink (Lidauer): Lidauer, M. and E.A. Mantysaari (1999). (35) Natural Cubic Spline (3 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999). (36) Natural Cubic Spline (4 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999). (37) Natural Cubic Spline (5 percentiles): White, I.M.S., R. Thompson, and S. Brotherstone (1999) (38) Natural Cubic Spline (defined knots according to Harrell 2001): Jr. Harrell, F.E. (2001). The selection criteria measure the goodness of fit of the model and include: Residual standard error (RSE), R-square (R2), log likelihood, Akaike information criterion (AIC), Akaike information criterion corrected (AICC), Bayesian Information Criterion (BIC), Durbin Watson coefficient (DW). The following model parameters are included: Residual sum of squares (RSS), Residual standard deviation (RSD), F-value (F) based on F-ratio test. Package: r-cran-lacunaritycovariance Architecture: all Version: 1.1-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-rcpproll, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.random Suggests: r-cran-cubature, r-cran-sf, r-cran-terra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lacunaritycovariance_1.1-9-1.ca2404.1_all.deb Size: 440516 MD5sum: 7daaffc44ce30dd6b55aeae317cd745c SHA1: 1ccfebad22b42af57009031871493eb40562c65f SHA256: 1eb938bfef3a7c622c1faee4dde0481bc86befe80a313581c766174c1591eee3 SHA512: 114d3a0c5c4279653af34f951710f2857955bcce71efa7d2d2204c3bf3cb56fa89a1662d45bec8093622bb14b9eb1c056466eea84ae1112a71b74d0f6b4bd12a Homepage: https://cran.r-project.org/package=lacunaritycovariance Description: CRAN Package 'lacunaritycovariance' (Gliding Box Lacunarity and Other Metrics for 2D Random ClosedSets) Functions for estimating the gliding box lacunarity (GBL), covariance, and pair-correlation of a random closed set (RACS) in 2D from a binary coverage map (e.g. presence-absence land cover maps). Contains a number of newly-developed covariance-based estimators of GBL (Hingee et al., 2019) and balanced estimators, proposed by Picka (2000) , for covariance, centred covariance, and pair-correlation. Also contains methods for estimating contagion-like properties of RACS and simulating 2D Boolean models. Binary coverage maps are usually represented as raster images with pixel values of TRUE, FALSE or NA, with NA representing unobserved pixels. A demo for extracting such a binary map from a geospatial data format is provided. Binary maps may also be represented using polygonal sets as the foreground, however for most computations such maps are converted into raster images. The package is based on research conducted during the author's PhD studies. Package: r-cran-lad Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1923 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect Filename: pool/dists/noble/main/r-cran-lad_0.1.0-1.ca2404.1_all.deb Size: 1793926 MD5sum: d0c7fd3fc9698a69d0eff7ec225764c9 SHA1: 97b778df9a749972788c08366b05c1078974e3a2 SHA256: 5dcc62f18e5d8461c90f50d4313cc81b3ae6089fedbb90c40a4b5b6b57f5fbd9 SHA512: 2f5e2395159960f6ca658104f71412582422ec748d6e0be0df821306e585988d2332c1298c48ec47d036bc37466ad5001d422fca8b87c01d15e5a960bcec2127 Homepage: https://cran.r-project.org/package=LAD Description: CRAN Package 'LAD' (Derive Leaf Angle Distribution (LAD) from Measured LeafInclination Angles) Calculate mean statistics and leaf angle distribution type from measured leaf inclination angles. LAD distribution is fitted using a two-parameters (mu, nu) Beta distribution and compared with six theoretical LAD distributions. Additional information is provided in Chianucci and Cesaretti (2022) . Package: r-cran-ladder Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-curl, r-cran-flextable, r-cran-gargle, r-cran-httpuv, r-cran-httr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ladder_0.0.3-1.ca2404.1_all.deb Size: 234900 MD5sum: 1a1081dd2e3970f9d49433270d80d8a9 SHA1: fd79434abf6c3d63a41f1efd3b534cb0599b2fb0 SHA256: 68bb7e5b5b87a16c22cfc8753b9cf94de56a438143cf6a6159ee6155ba2a1f26 SHA512: 7ffc834df53fd400028e1dbc0acec0bbd7e820bd08861e7ac2a477f2874cd2bed0a88500317820a75e79be615dc263f61654fd7965b154027f3a40899723bd37 Homepage: https://cran.r-project.org/package=ladder Description: CRAN Package 'ladder' (Get on to the Slides) Create tables from within R directly on Google Slides presentations. Currently supports matrix, data.frame and 'flextable' objects. Package: r-cran-ladderfuelsr Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1788 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gdata, r-cran-ggplot2, r-cran-magrittr, r-cran-segmented, r-cran-stringr, r-cran-tidyr, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ladderfuelsr_0.0.7-1.ca2404.1_all.deb Size: 720960 MD5sum: f92d21271fdbc17b97ca96df650ef540 SHA1: ca93c503338361b4263b0a4ed5603c6978baa425 SHA256: 6c2e179281e35d26e8896d0c1339bea87978b1ad8837a3b760942e3062edb4b5 SHA512: 974d5f2dae7d499baaac48cfe1951951826bae5cece1692f25f3ed4d584557e2479a501a18769ab4e7d733eb9ec800c281887a8558ec02b2c50df659064291c5 Homepage: https://cran.r-project.org/package=LadderFuelsR Description: CRAN Package 'LadderFuelsR' (Automated Tool for Vertical Fuel Continuity Analysis usingAirborne Laser Scanning Data) Set of tools for analyzing vertical fuel continuity at the tree level using Airborne Laser Scanning data. The workflow consisted of: 1) calculating the vertical height profiles of each segmented tree; 2) identifying gaps and fuel layers; 3) estimating the distance between fuel layers; and 4) retrieving the fuel layers base height and depth. Additionally, other functions recalculate previous metrics after considering distances greater than certain threshold. Moreover, the package calculates: i) the percentage of Leaf Area Density comprised in each fuel layer, ii) remove fuel layers with Leaf Area Density (LAD) percentage less than 10, and iii) recalculate the distances among the reminder ones. On the other hand, it identifies the crown base height (CBH) based on different criteria: the fuel layer with the highest LAD percentage and the fuel layers located at the largest- and at the last-distance. When there is only one fuel layer, it also identifies the CBH performing a segmented linear regression (breaking points) on the cumulative sum of LAD as a function of height. Finally, a collection of plotting functions is developed to represent: i) the initial gaps and fuel layers; ii) the fuels base height, depths and gaps with distances greater than certain threshold and, iii) the CBH based on different criteria. The methods implemented in this package are original and have not been published elsewhere. Package: r-cran-laeken Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3314 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-mass Filename: pool/dists/noble/main/r-cran-laeken_0.5.3-1.ca2404.1_all.deb Size: 3103676 MD5sum: ba5f23af4ab40be9b17177f4a4cc42ee SHA1: e0234422474ebc224d51066491516d211d048149 SHA256: f69a015f102e9425a14a17a69f545d4ed52b459cd80975e3110e0d87655c95c8 SHA512: 3c091f23e8c755c1efbe2cee1a0909ad44b2f97376ca7f93e214a848fd5e07e49a8132a6bc166303e28c43c18cda1db3abe5ca9313df83489b85939f462662cb Homepage: https://cran.r-project.org/package=laeken Description: CRAN Package 'laeken' (Estimation of Indicators on Social Exclusion and Poverty) Estimation of indicators on social exclusion and poverty, as well as Pareto tail modeling for empirical income distributions. Package: r-cran-lagged Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lagged_0.3.2-1.ca2404.1_all.deb Size: 401944 MD5sum: eeeb8da3ed0ae9968446af0dd337ea7b SHA1: b8da9948ff4af6163a537ecca8a4c2facca7d8e7 SHA256: 7bc122999f4978b488e3886f8b754cbc53eccede13df4edcd55eb3ede0fd1998 SHA512: fa98715579f687b6c2465784044354fb91784162555e7c62512ac238587b4080055a8889dae1e33310f373cde08f8eca138c1e1019c5149f73f055125f545f7a Homepage: https://cran.r-project.org/package=lagged Description: CRAN Package 'lagged' (Classes and Methods for Lagged Objects) Provides classes and methods for objects, whose indexing naturally starts from zero. Subsetting, indexing and mathematical operations are defined naturally between lagged objects and lagged and base R objects. Recycling is not used, except for singletons. The single bracket operator doesn't drop dimensions by default. Package: r-cran-lagosne Architecture: all Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1864 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rappdirs, r-cran-purrr, r-cran-magrittr, r-cran-sf, r-cran-curl, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-memoise, r-cran-rlang, r-cran-progress, r-cran-qs2, r-cran-xml2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-r.rsp, r-cran-printr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-lagosne_2.0.5-1.ca2404.1_all.deb Size: 569186 MD5sum: eafe3d0b9077af0b9cf3653762ad869c SHA1: 4f37fe134282e2b0533d224d7cfc5f30d8154a6e SHA256: 5c6b04d8f78ca48aec378e74c93dcc1f0b95aa51cb7495d23c22f23053a5ba52 SHA512: d05831dfe7839ff231cb24d27583fe13ab2629920f2c1b5998023eecebf41db845352d5aed0cb98234f746dad4e0edc045bc8bb3de7e81053215d67915f14a8d Homepage: https://cran.r-project.org/package=LAGOSNE Description: CRAN Package 'LAGOSNE' (Interface to the Lake Multi-Scaled Geospatial and TemporalDatabase) Client for programmatic access to the Lake Multi-scaled Geospatial and Temporal database , with functions for accessing lake water quality and ecological context data for the US. Package: r-cran-lagsarlmtree Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit, r-cran-spatialreg, r-cran-formula Suggests: r-cran-strucchange Filename: pool/dists/noble/main/r-cran-lagsarlmtree_1.0-2-1.ca2404.1_all.deb Size: 8655798 MD5sum: ed0bdbbfd568d644120931ded0ba9e70 SHA1: b18c839661cc3067130b66299df03a5aef48b8d9 SHA256: b894dd0037678173ca04db77f538cddc3db4a9f3b4183bb4dcfd22435be48b64 SHA512: 858ccb2f9697a49f0dda5cfc5800e14f4de983ca285d90d90c62f14ddfaa08846238939e7131ce5fae7899f44f031cf6bfbcc2b75a37f6dba3f037e9b6d9b352 Homepage: https://cran.r-project.org/package=lagsarlmtree Description: CRAN Package 'lagsarlmtree' (Spatial Lag Model Trees) Model-based linear model trees adjusting for spatial correlation using a simultaneous autoregressive spatial lag, Wagner and Zeileis (2019) . Package: r-cran-lagsequential Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lagsequential_0.1.1-1.ca2404.1_all.deb Size: 151988 MD5sum: af05d3ea60008bc77e0336600648a4f7 SHA1: eda02886a5bcce1081a661b23d6c8ecf3e1994ca SHA256: acedcc68779f72f08b32cc5d545f5a62c1d6b3b9560569e00a343a041724687e SHA512: 3973593f7d7431c2a986774aca374d50a7aac8d03a5b3f9fd9f4d68661780cfca149d3273cc37e466b91bb1cd8ede656156dbb897467fbec441b83db270ab3b0 Homepage: https://cran.r-project.org/package=LagSequential Description: CRAN Package 'LagSequential' (Lag-Sequential Categorical Data Analysis) Lag-sequential analysis is a method of assessing of patterns (what tends to follow what?) in sequences of codes. The codes are typically for discrete behaviors or states. The functions in this package read a stream of codes, or a frequency transition matrix, and produce a variety of lag sequential statistics, including transitional frequencies, expected transitional frequencies, transitional probabilities, z values, adjusted residuals, Yule's Q values, likelihood ratio tests of stationarity across time and homogeneity across groups or segments, transformed kappas for unidirectional dependence, bidirectional dependence, parallel and nonparallel dominance, and significance levels based on both parametric and randomization tests. The methods are described in Bakeman & Quera (2011) , O'Connor (1999) , Wampold & Margolin (1982) , and Wampold (1995, ISBN:0-89391-919-5). Package: r-cran-lahman Architecture: all Version: 14.0-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7060 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-lattice, r-cran-ggplot2, r-cran-googlevis, r-cran-data.table, r-cran-vcd, r-cran-reshape2, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-car, r-cran-plyr, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-lahman_14.0-0-1.ca2404.1_all.deb Size: 6852454 MD5sum: 85ded382a04d1f529bc9555bf4bc2e13 SHA1: 218eacecb93311c93e82cfb8b24d9fcb2a91ae7f SHA256: 6c3c9b0b9dd47f7c4ef619b39f8d36182077fcb7899161731ac11bf7b7ccc1c8 SHA512: 066b43b03264fa42b776674069625682ed563ab30663945626db99f85f4878fe1ce407747c5fb2abe06bff25e4b0e89d7bf711b348560d7ae417bb3e3c125211 Homepage: https://cran.r-project.org/package=Lahman Description: CRAN Package 'Lahman' (Sean 'Lahman' Baseball Database) Provides the tables from the 'Sean Lahman Baseball Database' as a set of R data.frames. It uses the data on pitching, hitting and fielding performance and other tables from 1871 through 2024, as recorded in the 2025 version of the database. Documentation examples show how many baseball questions can be investigated. Package: r-cran-lair Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-terra Filename: pool/dists/noble/main/r-cran-lair_0.3.0-1.ca2404.1_all.deb Size: 647890 MD5sum: 0845b5c2474673b8dba5556935252637 SHA1: e74c2a58ee775a089bee49aaba12b6acb9cc79bd SHA256: c8892edc01fe5949a9b8131e7adb09e597f19265ef2b702ce4ae969f69341b66 SHA512: e93bf84bec382b94d865fefb308fcb80e079e98611eb67ee6f158adc5386b12a30448fef1a8d10334bc1a2bdd1b98b89eb1f9bf70fbccdf5865421c76dfe7303 Homepage: https://cran.r-project.org/package=LAIr Description: CRAN Package 'LAIr' (Converting NDVI to LAI of Field, Proximal and Satellite Data) Convert Leaf Area Index (LAI) from the Normalized Difference Vegetation Index (NDVI) using available equations from literature. Detailed description of conversion equations in Bajocco et al. 2022 . Package: r-cran-lakefetch Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-osmdata, r-cran-ggplot2 Suggests: r-cran-nhdplustools, r-cran-jsonlite, r-cran-shiny, r-cran-leaflet, r-cran-base64enc, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lakefetch_0.1.3-1.ca2404.1_all.deb Size: 307206 MD5sum: 92461abb66073e462cfae315534a1858 SHA1: 8b8a522c754ebe44ae6d5a9e4d414202ede0dcab SHA256: c13388e9d27f0c82a3a9e0beb54b915708fcbd44ccaaa242bd689550ce6dccd9 SHA512: c61b63aaad33fed006253949b9cfe1c446e038cf35314605e51351790fe0753bab7d7c0a04c8cdab3d44e99c785bc0726ff8831cc6db520b9eb4c39ee22f82c2 Homepage: https://cran.r-project.org/package=lakefetch Description: CRAN Package 'lakefetch' (Calculate Fetch and Wave Exposure for Lake Sampling Points) Calculates fetch (open water distance) and wave exposure metrics for lake sampling points. Downloads lake boundaries from 'OpenStreetMap', calculates directional fetch using a ray-casting approach, and optionally integrates National Hydrography Dataset ('NHD') data for hydrological context including outlet and inlet locations. Can estimate lake depth from surface area using empirical relationships, and integrate historical weather data for cumulative wave energy calculations. Includes an optional interactive 'shiny' application for visualization. Package: r-cran-lakemorpho Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-sf, r-cran-geosphere, r-cran-cluster Filename: pool/dists/noble/main/r-cran-lakemorpho_1.3.2-1.ca2404.1_all.deb Size: 421790 MD5sum: 035d3fed99168ad781c675183b4824ea SHA1: 17dd1ee11f67fc54f13f1a792e64de5d53dcd20e SHA256: f02f1e7fb79fc5829806e1f5833e2f043a1af8e837bbb8b964ab28153f850452 SHA512: c614205dc6813b63d8ed88ae9c11778eb01bb402c83dfd04a5c3af8c6e14be3a4e63e08d1e7854bb221190d82789b916c041476f206addb0b105008a8c8e6897 Homepage: https://cran.r-project.org/package=lakemorpho Description: CRAN Package 'lakemorpho' (Lake Morphometry Metrics) Lake morphometry metrics are used by limnologists to understand, among other things, the ecological processes in a lake. Traditionally, these metrics are calculated by hand, with planimeters, and increasingly with commercial GIS products. All of these methods work; however, they are either outdated, difficult to reproduce, or require expensive licenses to use. The 'lakemorpho' package provides the tools to calculate a typical suite of these metrics from an input elevation model and lake polygon. The metrics currently supported are: fetch, major axis, minor axis, major/minor axis ratio, maximum length, maximum width, mean width, maximum depth, mean depth, shoreline development, shoreline length, surface area, and volume. Package: r-cran-laketemps Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 960 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-laketemps_0.5.1-1.ca2404.1_all.deb Size: 584926 MD5sum: cd52cfb089729afe4544ddf1ce996b58 SHA1: aea191fb2e0283374f77bebed46adfb4594789aa SHA256: 0ccc984cd60c39bd3399ba292ff45cbb2e87aac5f8bc57bb9d949c4a67321896 SHA512: 575d9d5342e62dc75f7dbbe4a784519abcc5930969bc0c07575c4c5212564907530355947fe425519a8fcb6f5d5fb5b7f4cfacfe406fffaf7db2469daa41189d Homepage: https://cran.r-project.org/package=laketemps Description: CRAN Package 'laketemps' (Lake Temperatures Collected by Situ and Satellite Methods from1985-2009) Lake temperature records, metadata, and climate drivers for 291 global lakes during the time period 1985-2009. Temperature observations were collected using satellite and in situ methods. Climatic drivers and geomorphometric characteristics were also compiled and are included for each lake. Data are part of the associated publication from the Global Lake Temperature Collaboration project (http://www.laketemperature.org). See citation('laketemps') for dataset attribution. Package: r-cran-lakhesis Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readr, r-cran-ca, r-cran-ggplot2, r-cran-rdpack, r-cran-shiny, r-cran-shinydashboard, r-cran-bslib Filename: pool/dists/noble/main/r-cran-lakhesis_0.0.1-1.ca2404.1_all.deb Size: 127288 MD5sum: 18654fc559b3f3c1edf78bbc7573968a SHA1: 56e2d289e4ba0ba98052f9c3b56a911f81686cae SHA256: ccc8547ad0c22e16a3ae790ba63ecc18aeccc7d26ccd36c4a2ad2ddcddec4bdd SHA512: 4ad7c55d596ceaed039861116beb5823c5c51a563be2afe0415695515500c2a56f08776cc9532244abd96330c4b534667d4e5a599a9bb95e8b005608e49e11c8 Homepage: https://cran.r-project.org/package=lakhesis Description: CRAN Package 'lakhesis' (Consensus Seriation for Binary Data) Determining consensus seriations for binary incidence matrices, using a two-step process of Procrustes-fit correspondence analysis for heuristic selection of partial seriations and iterative regression to establish a single consensus. Contains the Lakhesis Calculator, a graphical platform for identifying seriated sequences. Collins-Elliott (2024) . Package: r-cran-lambda.r Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formatr Suggests: r-cran-testit Filename: pool/dists/noble/main/r-cran-lambda.r_1.2.4-1.ca2404.1_all.deb Size: 111356 MD5sum: 830bc7ec8cd97cd630bd203b634b9092 SHA1: db604a185b3e9db237d0a453a7a3160149dc2874 SHA256: 5186e2bc4a338982a27659a671441579e36374797b52dcb0af3d1538f767bf9b SHA512: f6cf66fc07ff1489d3db076f99bfedc4e04c9a7427fbebc1512861cb8074ad4913e353b7843ae7511a185d2669813784462c37fb739a32f1d33a6c4dcfbd88a2 Homepage: https://cran.r-project.org/package=lambda.r Description: CRAN Package 'lambda.r' (Modeling Data with Functional Programming) A language extension to efficiently write functional programs in R. Syntax extensions include multi-part function definitions, pattern matching, guard statements, built-in (optional) type safety. Package: r-cran-lambdats Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-purrr, r-cran-abind, r-cran-ggplot2, r-cran-readr, r-cran-stringr, r-cran-lubridate, r-cran-narray, r-cran-fancova, r-cran-imputets, r-cran-modeest, r-cran-scales, r-cran-tictoc, r-cran-bizdays, r-cran-torch Filename: pool/dists/noble/main/r-cran-lambdats_1.1-1.ca2404.1_all.deb Size: 217390 MD5sum: e865fa590959885a5175305618895659 SHA1: 693b8f02f67c50876cde308534fc3b84f15addd2 SHA256: 3b5cc3a0a40b654ace6957b97ad979f704161098cc95b2e9078b48d8b2b7eae8 SHA512: eaaf07a0eedd1ace5a87fbbd311489e45cc8826ac2642ea13a66e800e0cbf6f7051baba5a48d018afab33d359384b7248bacad82ec508803890346c15db1f436 Homepage: https://cran.r-project.org/package=lambdaTS Description: CRAN Package 'lambdaTS' (Variational Seq2Seq Model with Lambda Transformer for TimeSeries Analysis) Time series analysis based on lambda transformer and variational seq2seq, built on 'Torch'. Package: r-cran-lambdr Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-logger Suggests: r-cran-withr, r-cran-testthat, r-cran-webmockr, r-cran-knitr, r-cran-rmarkdown, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-lambdr_1.2.5-1.ca2404.1_all.deb Size: 320430 MD5sum: 67b2f270fcd77bad2e162cbf4e88cb2d SHA1: d254c5edf7844af6aa37353c222a10ecc0615a0d SHA256: 704dae8303e018d1c4d4114475e384151b0b081c0d67830f9a239d0f9fcd7214 SHA512: 413cbca5bafd5c3557d5ff4bbb977b9d688bebdfa8c241061c6c6cf62c059acfcf73afa958a82af842ce9c8a549a71e24898b85cda87fd6ee093e4767b32a2b5 Homepage: https://cran.r-project.org/package=lambdr Description: CRAN Package 'lambdr' (Create a Runtime for Serving Containerised R Functions on 'AWSLambda') Runtime for serving containers that can execute R code on the 'AWS Lambda' serverless compute service . 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Package: r-cran-laminr Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-lifecycle, r-cran-pkgload, r-cran-purrr, r-cran-r.utils, r-cran-r6, r-cran-reticulate, r-cran-rlang, r-cran-sessioninfo, r-cran-withr Suggests: r-cran-anndata, r-cran-arrow, r-cran-irkernel, r-cran-jsonlite, r-cran-knitr, r-cran-magick, r-cran-readr, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-rsvg, r-cran-testthat, r-cran-yaml Filename: pool/dists/noble/main/r-cran-laminr_1.3.0-1.ca2404.1_all.deb Size: 508580 MD5sum: 65d166079a457898082b79b65ead9070 SHA1: 5cb0a5c29c1bc4599c3ffd79e9b6d1f299605f48 SHA256: e3962e90cbbfdd2fe96571b68cdd695a543deb05d8200b5bfd2751238371ddd1 SHA512: 399248a33ceec0eb4f2bf9e0af2ce22a2e23128b31ee85d2ffc799cd266accdf74f5e48f4027efd8ab7ac1d78fa35ad2783fd3414ce33af42c4f8171931a74a4 Homepage: https://cran.r-project.org/package=laminr Description: CRAN Package 'laminr' (Client for 'LaminDB') Interact with 'LaminDB'. 'LaminDB' is an open-source data framework for biology. This package allows you to query and download data from 'LaminDB' instances. Package: r-cran-lamme Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lamme_0.0.1-1.ca2404.1_all.deb Size: 280732 MD5sum: 6dbf20da55cf48a6666a082b3a1ced59 SHA1: 64b3a5624e163c2d945ef0b0ad96492c00b1e506 SHA256: d45f843d43645978b6aaf8e9fa5f9c203550d95a2492414985a948d0f3b337b7 SHA512: eaccfa120957e504d283f87fabfbf8bec65b7caa1fdb6fc1da42d804da3e58883c9aab86280016ef7caba12db6fae30e90f27c7d7361820ea1c69170747241cc Homepage: https://cran.r-project.org/package=lamme Description: CRAN Package 'lamme' (Log-Analytic Methods for Multiplicative Effects) Log-analytic methods intended for testing multiplicative effects. Package: r-cran-lancor Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrangements, r-cran-boot, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lancor_0.1.3-1.ca2404.1_all.deb Size: 50840 MD5sum: 5afc6f45c2977e544430ee1a74663e9c SHA1: 045565b878e918c2eb02a5d04461bbab8b40b286 SHA256: 927b05c2960fd00e728a76252d091bb3f9963ea6001de8dd96a34bc2adee7db1 SHA512: 05e1f6f7eed287f3e17b3ee2f9fc10b99184299c5fa813acb33740b3fc81e7c57e658a1b14ee27a376d34ac063be06532c072d32a1933a6ced1c27cce9d12e1d Homepage: https://cran.r-project.org/package=lancor Description: CRAN Package 'lancor' (Statistical Inference via Lancaster Correlation) Implementation of the methods described in Holzmann, Klar (2024) . Lancaster correlation is a correlation coefficient which equals the absolute value of the Pearson correlation for the bivariate normal distribution, and is equal to or slightly less than the maximum correlation coefficient for a variety of bivariate distributions. Rank and moment-based estimators and corresponding confidence intervals are implemented, as well as independence tests based on these statistics. Package: r-cran-landcomp Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-landcomp_0.0.5-1.ca2404.1_all.deb Size: 761512 MD5sum: 7cc72bd8fc93e5ca4a0eb2c60eca9c0d SHA1: 363a912d2052d38f8f3f5eb03c86edb11f3aedb4 SHA256: feb2af470b44a44c0e66cead2f7ac7ab6d5c06bf399a6a032fe9849c1b7bca86 SHA512: 8a94fda2c90f47a5d16871cd1cc85ef66c1325202e2111806603b8ac25b50a325b97544bd8b493989f019f641c462b53f244766992480c6b80f7efab439e3998 Homepage: https://cran.r-project.org/package=LandComp Description: CRAN Package 'LandComp' (Analysing Landscape Composition and Structure at Multiple Scales) Changes of landscape diversity and structure can be detected soon if relying on landscape class combinations and analysing patterns at multiple scales. 'LandComp' provides such an opportunity, based on Juhász-Nagy's functions (Juhász-Nagy P, Podani J 1983 ). Functions can handle multilayered data. Requirements of the input: binary data contained by a regular square or hexagonal grid, and the grid should have projected coordinates. Package: r-cran-landest Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-landest_1.2-1.ca2404.1_all.deb Size: 274640 MD5sum: 0cdb9b84a3c7b1455d659290c4b15fb3 SHA1: 2ea692c45952634ca4c7ae4adf9d12403e203eb8 SHA256: 638a55658473c93578042f2a4d314ec12e87912e750d86d7d5a4b174720354be SHA512: a278404a6b71567dd7818c7df5b87edaec933b2e5901f285bdbceae276102dae6d0fca27918afe674ca4b4e0df43caebf4abb7b50afd2b020edbc2e14a30e37c Homepage: https://cran.r-project.org/package=landest Description: CRAN Package 'landest' (Landmark Estimation of Survival and Treatment Effect) Provides functions to estimate survival and a treatment effect using a landmark estimation approach. Package: r-cran-landform Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra Filename: pool/dists/noble/main/r-cran-landform_0.2-1.ca2404.1_all.deb Size: 22320 MD5sum: b0d8967676c84e7b67e6980e695b0bd0 SHA1: 1cb2de8928389a90cc96064cfc1d6e8a5794c078 SHA256: 8ff9e3c442e8e7da36f65e72dc3e3f63f920a3b404543da8881329f09c330fba SHA512: c1f6cee23d9da7508cd4b78f09e92cf00f6f48c311ff8b419cdfa9c10f31f5c5d14e1e5df822068a880d6845f90244178d0128126d605575ee160700ebe0be03 Homepage: https://cran.r-project.org/package=landform Description: CRAN Package 'landform' (Topographic Position Index-Based Landform Classification) Provides a function for classifying a landscape into different categories based on the Topographic Position Index (TPI) and slope. It offers two types of classifications: Slope Position Classification, and Landform Classification. The function internally calculates the TPI for the given landscape and then uses it along with the slope to perform the classification. Optionally, descriptive statistics for every class are calculated and plotted. The classifications are useful for identifying the position of a location on a slope and for identifying broader landform types. Package: r-cran-landmarking Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nlme, r-cran-riskregression, r-cran-dplyr, r-cran-pec, r-cran-prodlim, r-cran-survival, r-cran-mstate, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-jm Filename: pool/dists/noble/main/r-cran-landmarking_1.0.2-1.ca2404.1_all.deb Size: 656348 MD5sum: 8c04a3a1360b678f16c51410ccb1073b SHA1: 8bdbb0c133f0cd89d52f88ec97037e4f2dca2123 SHA256: 761545647bc2e780fd3c69daddcf1b7bb52bb3604320f7613bab1b22e9e2b67f SHA512: 83757bb08190a41404ebc9091c86962f8e4d10e3a945fee72b10fcb6b0695ce9c918edc4d88e5ab7d3da19680a997ecf8d2b74184ac6a42724ed778bc7a362f7 Homepage: https://cran.r-project.org/package=Landmarking Description: CRAN Package 'Landmarking' (Analysis using Landmark Models) The landmark approach allows survival predictions to be updated dynamically as new measurements from an individual are recorded. The idea is to set predefined time points, known as "landmark times", and form a model at each landmark time using only the individuals in the risk set. This package allows the longitudinal data to be modelled either using the last observation carried forward or linear mixed effects modelling. There is also the option to model competing risks, either through cause-specific Cox regression or Fine-Gray regression. To find out more about the methods in this package, please see . Package: r-cran-landmix Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-landmix_1.0-1.ca2404.1_all.deb Size: 68980 MD5sum: 78c608ccd3252f83458bd7c62c7bda29 SHA1: 11a883c37ceae811ad05e895e6a0bf1388047479 SHA256: 3d80ff2b9a4dbc345dd31f5f7f28321876a8992bce8fa4e23cf9e4d32e4b7b59 SHA512: 0f70d3d366d85fcf3c61fa1281f6b886f875e4f310c6695f2f598748728cb0f6a8463f242d639b435f0c5d3bbceb2812c5af73f942888c7e0b180315b3f94ea1 Homepage: https://cran.r-project.org/package=landmix Description: CRAN Package 'landmix' (Landmark Prediction for Mixture Data) Non-parametric prediction of survival outcomes for mixture data that incorporates covariates and a landmark time. Details are described in Garcia (2021) . Package: r-cran-landmulti Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-landpred, r-cran-nmof, r-cran-emdbook, r-cran-snow Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-landmulti_0.5.0-1.ca2404.1_all.deb Size: 181322 MD5sum: b3895b6b836577d97d2eb03cd307f143 SHA1: 313bfb59db782640b57768c80db3c6e147f30929 SHA256: c408f3997615e1b3b2d21cd8182f7764dd78c6a7e1ce4e7845d4a4c8f8f6118b SHA512: e485c9acd67dfbeedffbbde3626880467e012b66d7ee3cc1d3e7534cf2902e6bcd26e359b47ca0394b1a9ef4ae59ed8f9c7bdf2b7497fa451c0363d44a89cec6 Homepage: https://cran.r-project.org/package=landmulti Description: CRAN Package 'landmulti' (Landmark Prediction with Multiple Short-Term Events) Contains functions for a flexible varying-coefficient landmark model by incorporating multiple short-term events into the prediction of long-term survival probability. For more information about landmark prediction please see Li, W., Ning, J., Zhang, J., Li, Z., Savitz, S.I., Tahanan, A., Rahbar.M.H., (2023+). "Enhancing Long-term Survival Prediction with Multiple Short-term Events: Landmarking with A Flexible Varying Coefficient Model". Package: r-cran-landpred Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-quantreg, r-cran-sm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-landpred_2.0-1.ca2404.1_all.deb Size: 193250 MD5sum: 95d8d19faef552d94f53315c8fc68328 SHA1: 6748d8e320b9447660dd4dbcdaac8681e4a3b773 SHA256: b4afee107c3b145b445ef7e99dcf92dc7b405aea28c5e85e4f14b2b5172db52e SHA512: 7118df26bccb58dc61e96dc78a52571ba9205920350659ab46bd9a4e61131fc761614844f49ea581022050f2e151c5bd38fab089778c503ed558d735b90211f3 Homepage: https://cran.r-project.org/package=landpred Description: CRAN Package 'landpred' (Landmark Prediction of a Survival Outcome) Nonparametric methods for landmark prediction of long-term survival outcomes, incorporating covariate and short-term event information. The package supports the construction of flexible varying-coefficient models that use discrete covariates, as well as multiple continuous covariates. The goal is to improve prediction accuracy when censored short-term events are available as predictors, using robust nonparametric procedures that do not require correct model specification and avoid restrictive parametric assumptions found in alternative methods. More information on these methods can be found in Parast et al. 2012 , Parast et al. 2011 , and Parast and Cai 2013 . A tutorial for this package is available here: . 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Package: r-cran-latamverse Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-argentinapi, r-cran-brazildataapi, r-cran-chiledataapi, r-cran-colombiapi, r-cran-peruapis, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-latamverse_0.1.0-1.ca2404.1_all.deb Size: 139086 MD5sum: eccc12e31ef65b60ec52d4d3b7074ec5 SHA1: 458c5ff24ddb65cc30b92005fa07f1169ae9f358 SHA256: 50e08639ec7710402dc56e30d30ef1ef6b6bd727ad1625e5d1e33052116cd775 SHA512: 776a3d788ea9d4f0e3563ff7ac69c70fb7be03c21507554800d5386c6ef6d4c621cab6c791a4d7231647d98283e0399ad72bc2a782267c1fdd571707ecdcd863 Homepage: https://cran.r-project.org/package=Latamverse Description: CRAN Package 'Latamverse' (Latin American Data via 'RESTful' APIs and Curated Datasets) Brings together a comprehensive collection of R packages providing access to API functions and curated datasets from Argentina, Brazil, Chile, Colombia, and Peru. Includes real-time and historical data through public 'RESTful' APIs ('Nager.Date', World Bank API, REST Countries API, and country-specific APIs) and extensive curated collections of open datasets covering economics, demographics, public health, environmental data, political indicators, social metrics, and cultural information. Designed to provide researchers, analysts, educators, and data scientists with centralized access to Latin American data sources, facilitating reproducible research, comparative analysis, and teaching applications focused on these five major Latin American countries. Included packages: - 'ArgentinAPI': API functions and curated datasets for Argentina covering exchange rates, inflation, political figures, national holidays and more. - 'BrazilDataAPI': API functions and curated datasets for Brazil covering postal codes, banks, economic indicators, holidays, company registrations and more. - 'ChileDataAPI': API functions and curated datasets for Chile covering financial indicators ('UF', UTM, Dollar, Euro, Yen, Copper, Bitcoin, 'IPSA' index), holidays and more. - 'ColombiAPI': API functions and curated datasets for Colombia covering geographic locations, cultural attractions, economic indicators, demographic data, national holidays and more. - 'PeruAPIs': API functions and curated datasets for Peru covering economic indicators, demographics, national holidays, administrative divisions, electoral data, biodiversity and more. For more information on the APIs, see: 'Nager.Date' , World Bank API , REST Countries API , 'ArgentinaDatos' API , 'BrasilAPI' , 'FINDIC' , and API-Colombia . Package: r-cran-latbias Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geosphere, r-cran-ggplot2, r-cran-psych, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-sf, r-cran-sp, r-cran-terra, r-cran-tidyr, r-cran-units Suggests: r-cran-elevatr, r-cran-knitr, r-cran-progress, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-testthat Filename: pool/dists/noble/main/r-cran-latbias_1.0.0-1.ca2404.1_all.deb Size: 45622 MD5sum: 4c9739c00497d7255f242133c2b87a82 SHA1: eff821732279ca32038d787516a0a826156138e3 SHA256: e596a91aeca9f13193b1ff5c092ece62248b766d360e7305f0f72b593e71dea8 SHA512: f7e7c27f50d49c91c32ee991a70ef861a09632b1140ae9d404318f670a61159d4649d6d6d29b2fb6cb105b0cd06f33294b6998f50996c1648d50a1b0c3886152 Homepage: https://cran.r-project.org/package=latbias Description: CRAN Package 'latbias' (Calculate the Latitudinal Bias Index) Studies that report shifts in species distributions may be biased by the shape of the study area. The main functionality of this package is to calculate the Latitudinal Bias Index (LBI) for any given shape. The LBI is bounded between +1 (100% probability to exclusively record latitudinal shifts, i.e., range shifts data sampled along a perfectly South-North oriented straight line) and -1 (100% probability to exclusively record longitudinal shifts, i.e., range shifts data sampled along a perfectly East-West oriented straight line). Package: r-cran-latcontrol Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan Filename: pool/dists/noble/main/r-cran-latcontrol_0.1.1-1.ca2404.1_all.deb Size: 24368 MD5sum: d137d7d10d5a502595171100ffbf8f8d SHA1: de222b54ea8b3c10361b4b79ea13bab70a9e7575 SHA256: c94648393714d7120f7aed770266c7582d955d290351fd3251f74d3ebb4649d8 SHA512: 21a71a879e8ce9a4c3c2cf8f340a6f9b91828ec0efb1d926c3a107a7f424177299a1e03f3d2b830bd6ef49d788fbf58b9729d2a3e831e2942751a208b2b4c915 Homepage: https://cran.r-project.org/package=latcontrol Description: CRAN Package 'latcontrol' (Evaluation of the Role of Control Variables in StructuralEquation Models) Various opportunities to evaluate the effects of including one or more control variable(s) in structural equation models onto model-implied variances, covariances, and parameter estimates. The derivation of the methodology employed in this package can be obtained from Blötner (2023) . 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Package: r-cran-latenetwork Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-statip Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-latenetwork_1.0.1-1.ca2404.1_all.deb Size: 93028 MD5sum: e750f4029700e73360985ab56d127d3c SHA1: 24b4ff499c91aa8ecee5f0bb1c092179e2b54f44 SHA256: cd88827960b9fa4c00be9183cc780bb7b069f02b082db7c41ec15c25adf4d417 SHA512: 33127572acf904c36d49a773a0df055ec2d5e111fcde55169d2583bcdc50a32b89e99cd12783d267ea305608d1b9e21a1655f77f598faad6473baa18c4b8800b Homepage: https://cran.r-project.org/package=latenetwork Description: CRAN Package 'latenetwork' (Inference on LATEs under Network Interference of Unknown Form) Estimating causal parameters in the presence of treatment spillover is of great interest in statistics. 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It converts continuous latent variables into ordinal categories to generate Likert scale item responses. Particularly useful for accurately modeling and analyzing survey data that use Likert scales, especially when applying statistical techniques that require metric data. Package: r-cran-latentbma Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-knitr, r-cran-mnormt, r-cran-progress, r-cran-reshape2 Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-latentbma_0.1.3-1.ca2404.1_all.deb Size: 95938 MD5sum: 80acb360f9dbea85a44f2ee7301a0591 SHA1: 90576d214a9caaba61e88bcf83ee33ab3fa63ef6 SHA256: cabe1ca325f31c92d55f8ee6f735bd543f5088a26ee262e3359acf3606e78574 SHA512: 6b41aa63d4ee56a9e8662689eaf944167a314d07f7e468eb0fabee15dda2b879e82bf7827bd7ec2225635daa9dd11cacb330525df12965982cf2ac084c1f808e Homepage: https://cran.r-project.org/package=LatentBMA Description: CRAN Package 'LatentBMA' (Bayesian Model Averaging for Univariate Link Latent GaussianModels) Bayesian model averaging (BMA) algorithms for univariate link latent Gaussian models (ULLGMs). 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Package: r-cran-latentfactor Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmisc, r-cran-car, r-cran-eganet, r-cran-fspe, r-cran-googledrive, r-cran-ineq, r-cran-lavaan, r-cran-matrix, r-cran-mlr, r-cran-mvtnorm, r-cran-psych, r-cran-rstudioapi, r-cran-xgboost Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-latentfactor_0.0.7-1.ca2404.1_all.deb Size: 279660 MD5sum: f2c67cc3562088e49029ba1f2e0a03db SHA1: a3ce9fc12e6a1667bd2e4905197b87a31c5dca0a SHA256: 26667e71adcc9b5cd82862171248f377b0417d1e971ddd070ba603a0a9db1fe9 SHA512: abe6c410c4daa505f5ac53a56b8c4cca2520ecabda6959e30a57f048a7bed4c447e109269b1231c9c746a7119635d86ec156396e7d91add761d38df72ac2a035 Homepage: https://cran.r-project.org/package=latentFactoR Description: CRAN Package 'latentFactoR' (Data Simulation Based on Latent Factors) Generates data based on latent factor models. 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Package: r-cran-latrend Architecture: all Version: 1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-r.utils, r-cran-assertthat, r-cran-foreach, r-cran-data.table, r-cran-magrittr, r-cran-matrixstats, r-cran-rmarkdown, r-cran-rlang Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rcmdcheck, r-cran-pkgdown, r-cran-devtools, r-cran-cluster, r-cran-evaluate, r-cran-lme4, r-cran-reformulas, r-cran-covr, r-cran-lintr, r-cran-tinytex, r-cran-lcmm, r-cran-mixtools, r-cran-flexmix, r-cran-fda, r-cran-funfem, r-cran-gridextra, r-cran-igraph, r-cran-crimcv, r-cran-dtwclust, r-cran-mixak, r-cran-mclust, r-cran-mclustcomp, r-cran-clvalid, r-cran-psych, r-cran-qqplotr, r-cran-doparallel, r-cran-simtool, r-cran-dplyr, r-cran-ggplot2, r-cran-caret, r-cran-tibble, r-cran-clustercrit Filename: pool/dists/noble/main/r-cran-latrend_1.6.3-1.ca2404.1_all.deb Size: 1579364 MD5sum: 436c9876847848c6f81fdb971b1dee88 SHA1: 91d533d44365ffe87d010dc50c5dc2247795be16 SHA256: bc4e89889130b38af160b1407839abcb82953e83984d93f78054ac028e578863 SHA512: 91883af0f863fddb5af6dd089094cee104cfef060d0dc8db0f6c6da1ce4c9f9e07d1204cec570e86f19cc7956b6c2e011044a8c5a9fded9f82a1d6ce85c3763e Homepage: https://cran.r-project.org/package=latrend Description: CRAN Package 'latrend' (A Framework for Clustering Longitudinal Data) A framework for clustering longitudinal datasets in a standardized way. The package provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease. The 'akmedoids' package is available from . Package: r-cran-latte Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-stringr, r-cran-mpoly, r-cran-ggplot2, r-cran-memoise, r-cran-dplyr, r-cran-usethis, r-cran-glue Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-latte_0.2.2-1.ca2404.1_all.deb Size: 316196 MD5sum: 942062b6588b71fbca1ae13d1cd9f536 SHA1: 61eaa59ac776c3f4c0e2d8eea9d8d6a1f5626f63 SHA256: eea10cf7fe5a14724760efe6894affb9de785f848637049d8c2493faa8959e5a SHA512: 526bbfcedf31589070bbdf42f4787db2681a7364e481a16030bce79a9ab54eff0c2443a19001299a90498f303fb594bf4a4121bac4fab80b440e15626db4d179 Homepage: https://cran.r-project.org/package=latte Description: CRAN Package 'latte' (Interface to 'LattE' and '4ti2') Back-end connections to 'LattE' () for counting lattice points and integration inside convex polytopes and '4ti2' () for algebraic, geometric, and combinatorial problems on linear spaces and front-end tools facilitating their use in the 'R' ecosystem. Package: r-cran-latticedensity Architecture: all Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.geom, r-cran-splancs, r-cran-spdep, r-cran-spam, r-cran-sp, r-cran-spatialreg Suggests: r-cran-knitr, r-cran-devtools, r-cran-roxygen2, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-latticedensity_1.2.7-1.ca2404.1_all.deb Size: 572682 MD5sum: 386f646f19ca56b864fb7e01554e5763 SHA1: 0d99e14e2e281c0bee12187c3af036a99addd9a5 SHA256: 7b0bb11160459a5de2749a23d74949329a2b83a254973ab1c5c6cae60f5e1425 SHA512: e67a82ebc2cbca6ccad3040687c0b323888349e2f16f999df62eeefb03b39cc48c2824fc53de09d61ca7668cf5bcedd39cc6abecf000ff46a14d5e9b8b2ba20d Homepage: https://cran.r-project.org/package=latticeDensity Description: CRAN Package 'latticeDensity' (Density Estimation and Nonparametric Regression on IrregularRegions) Functions that compute the lattice-based density and regression estimators for two-dimensional regions with irregular boundaries and holes. The density estimation technique is described in Barry and McIntyre (2011) , while the non-parametric regression technique is described in McIntyre and Barry (2018) . Package: r-cran-latticeextra Architecture: all Version: 0.6-31-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-png, r-cran-jpeg, r-cran-rcolorbrewer, r-cran-interp, r-cran-mass Suggests: r-cran-maps, r-cran-mapproj, r-cran-deldir, r-cran-quantreg, r-cran-zoo, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-latticeextra_0.6-31-1.ca2404.1_all.deb Size: 2177920 MD5sum: b670b6a778082fbc07abd8a9f639609e SHA1: dceb1190f6d0a7c3deeff99877e9b1c5d893ea8d SHA256: cc4f003a5103911d0afdb091937a6c2cb07385ed2fec6a6af858d336cb41c8f0 SHA512: 4fd94e21016754d2a5a1c013cd75e9be372c93c639cecf9abd216a08345cbaef2c63075937d7076eabd8b2b34a9cc177432e44b0c3adb00b8a4f7f0af4ea93f9 Homepage: https://cran.r-project.org/package=latticeExtra Description: CRAN Package 'latticeExtra' (Extra Graphical Utilities Based on Lattice) Building on the infrastructure provided by the lattice package, this package provides several new high-level functions and methods, as well as additional utilities such as panel and axis annotation functions. Package: r-cran-lava Architecture: all Version: 1.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3915 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-future.apply, r-cran-numderiv, r-cran-progressr, r-cran-survival, r-cran-squarem Suggests: r-cran-kernsmooth, r-bioc-rgraphviz, r-cran-data.table, r-cran-ellipse, r-cran-fields, r-cran-future, r-cran-geepack, r-bioc-graph, r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-lavasearch2, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-mets, r-cran-nlme, r-cran-optimx, r-cran-polycor, r-cran-quantreg, r-cran-rgl, r-cran-svglite, r-cran-targeted, r-cran-testthat, r-cran-vdiffr, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-lava_1.9.1-1.ca2404.1_all.deb Size: 2323314 MD5sum: f900b1e5ef5fbf29b1927b17aae4d78a SHA1: c8d14d5d25b9ef94ce2c109a658030f7647c89f1 SHA256: b8149ce8204829c511169edeb0e182aa6d7bf93d5b560b54911dc60aa2e703a0 SHA512: 7c56d3d51bb6723b50cef7d9b239dd53a10fdf788d352a58c1ab19225f19ef516f9660aaaa5501062c91399851a6846fd1a7a7c48d813ac48b48a052fe7ec8d0 Homepage: https://cran.r-project.org/package=lava Description: CRAN Package 'lava' (Latent Variable Models) A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) ). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) ). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models. Package: r-cran-lavaan.mi Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-amelia, r-cran-mass, r-cran-mice, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lavaan.mi_0.1-0-1.ca2404.1_all.deb Size: 531776 MD5sum: ce3e4236e32746c706818ab1c51dcae9 SHA1: 6daa26cee849a454bad455aa4948b4487bf6336d SHA256: b410de7ea8a08568cea1e7f5907ec9c867e93fa6d459e2469e430c340d355274 SHA512: 0ae96175397695d43a68dda0c1703e03617b3894fa7293484d45758af057669f7f2fa2a36eb818176e2760283ea366c94b3829daefe80b35f6cbbd0f8fe215c4 Homepage: https://cran.r-project.org/package=lavaan.mi Description: CRAN Package 'lavaan.mi' (Fit Structural Equation Models to Multiply Imputed Data) The primary purpose of 'lavaan.mi' is to extend the functionality of the R package 'lavaan', which implements structural equation modeling (SEM). When incomplete data have been multiply imputed, the imputed data sets can be analyzed by 'lavaan' using complete-data estimation methods, but results must be pooled across imputations (Rubin, 1987, ). The 'lavaan.mi' package automates the pooling of point and standard-error estimates, as well as a variety of test statistics, using a familiar interface that allows users to fit an SEM to multiple imputations as they would to a single data set using the 'lavaan' package. Package: r-cran-lavaan.printer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lavaan.printer_0.1.0-1.ca2404.1_all.deb Size: 84840 MD5sum: ed19ee16bad6ae219c4db728a1fd947f SHA1: e0d4c07e598b2eb225dc51ebe091940cc7213e00 SHA256: 7a71ccb4db9aec9e43d1e2485bb739c6bf0888b7b6b4f210e68477b846c5dfef SHA512: 0f52ebbe6748f582d7febccf0070945d35df4d34007c6af27b442101a8921db204b1fb42e4543cf5b504971f023ae54c69bfb79a11f497a9b4993acdbc27a7a0 Homepage: https://cran.r-project.org/package=lavaan.printer Description: CRAN Package 'lavaan.printer' (Helper Functions for Printing 'lavaan' Outputs) Helpers for customizing selected outputs from 'lavaan' by Rosseel (2012) and print them. The functions are intended to be used by package developers in their packages and so are not designed to be user-friendly. They are designed to be let developers customize the tables by other functions. Currently the parameter estimates tables of a fitted object are supported. Package: r-cran-lavaan.shiny Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinyace, r-cran-psych, r-cran-lavaan, r-cran-semplot Filename: pool/dists/noble/main/r-cran-lavaan.shiny_1.2-1.ca2404.1_all.deb Size: 49312 MD5sum: 03c28b06743d5043cd966d6187355774 SHA1: be44907f3e21e0490218fb40b79b35232a80b8fe SHA256: 2e97b7ce9c20628eafa46460654734263eb6448e92d88c0e095d6e940c54268e SHA512: 879cc6fa01e9303657bf2098882c179ead869f89fb6965c127757926014a4171905f96064583bab4159dc30736e4bf7b5d08237af128f9ea43792d1892f74bef Homepage: https://cran.r-project.org/package=lavaan.shiny Description: CRAN Package 'lavaan.shiny' (Latent Variable Analysis with Shiny) Interactive shiny application for working with different kinds of latent variable analysis, with the 'lavaan' package. Graphical output for models are provided and different estimators are supported. Package: r-cran-lavaan Architecture: all Version: 0.6-21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4385 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mnormt, r-cran-pbivnorm, r-cran-numderiv, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-lavaan_0.6-21-1.ca2404.1_all.deb Size: 4043500 MD5sum: 093b0db3efa70a77b6339cdfc8716ce9 SHA1: a09b73fa3f95e1f4e3a364e3c40e0dbbfbeed966 SHA256: 64791955fc62d7d0337f14d08198a5d1ef6cec69ee8af89fa2611c4cc73c95d3 SHA512: e4d940d34f1f4d5147fc3b85be0d028a0a62767920c4227a15b8e6a20a6587b3039cb095d3e7cdf53b9a7443da38c2afad16d816507ec7c4577d867d79c5b893 Homepage: https://cran.r-project.org/package=lavaan Description: CRAN Package 'lavaan' (Latent Variable Analysis) Fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models. Package: r-cran-lavaanextra Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1503 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-insight Suggests: r-cran-rempsyc, r-cran-flextable, r-cran-lavaanplot, r-cran-diagrammersvg, r-cran-rsvg, r-cran-png, r-cran-webshot, r-cran-tidysem, r-cran-tmvnsim, r-cran-knitr, r-cran-tibble, r-cran-sjlabelled, r-cran-stringdist, r-cran-psych, r-cran-testthat, r-cran-rmarkdown, r-cran-markdown, r-cran-covr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-lavaanextra_0.2.2-1.ca2404.1_all.deb Size: 1171262 MD5sum: c425125f07d8a5ac7067877c4d7cf017 SHA1: f898605b4dfaf105c580bc0659949ae462463af7 SHA256: 766b6be044e51ff86912037d968a281ab58a2e1b3e7482bdc8bdca9e47c85a25 SHA512: 17bfebbc6a6d521243a75e70f50269ab725ede44298d035b90977ebb06496a6863ff172a100e92f472ccb4dc7650946b9757feab81b30c67a42fff533ee92625 Homepage: https://cran.r-project.org/package=lavaanExtra Description: CRAN Package 'lavaanExtra' (Convenience Functions for Package 'lavaan') Affords an alternative, vector-based syntax to 'lavaan', as well as other convenience functions such as naming paths and defining indirect links automatically, in addition to convenience formatting optimized for a publication and script sharing workflow. Package: r-cran-lavaangui Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2734 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-future, r-cran-haven, r-cran-jsonlite, r-cran-lavaan, r-cran-promises, r-cran-readr, r-cran-readxl, r-cran-shiny, r-cran-colorspace, r-cran-igraph, r-cran-dt, r-cran-plyr, r-cran-digest Filename: pool/dists/noble/main/r-cran-lavaangui_0.4.0-1.ca2404.1_all.deb Size: 925286 MD5sum: 0c3e27bda399ea9d0aed004914550c35 SHA1: 8124154481a04ab3947b4044ace32a71e1d5c441 SHA256: 5fbd9936466aa37918ace14e4d2c951a957ff379278f2d71c129611135f8bc76 SHA512: 83be7c1b9ec2f7370babd015def7eb93dc5aae1eb7898b9b26fa2a6e4fff0f0ddef645ee8eb1a6c7b8da100a1bd53706788ada58322e8d3af6958fd0ce6cf47a Homepage: https://cran.r-project.org/package=lavaangui Description: CRAN Package 'lavaangui' (Graphical User Interface with Integrated 'Diagrammer' for'Lavaan') Provides a graphical user interface with an integrated diagrammer for latent variable models from the 'lavaan' package. It offers two core functions: first, lavaangui() launches a web application that allows users to specify models by drawing path diagrams, fitting them, assessing model fit, and more; second, plot_lavaan() creates interactive path diagrams from models specified in 'lavaan'. After customizing a diagram interactively, export_plot() saves it to a file, enabling reproducible scripts without sacrificing fine-grained control over appearance. Karch (2024) contains a tutorial. Package: r-cran-lavaanplot Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6605 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan, r-cran-diagrammer, r-cran-stringr, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-diagrammersvg, r-cran-rsvg, r-cran-png, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lavaanplot_0.8.1-1.ca2404.1_all.deb Size: 562980 MD5sum: 6c19358279ac0cf0700dbe1c2fbfa153 SHA1: cd2b9b01885d9fb64050822da57825ed70ca347c SHA256: cedf5d9195e71afa774a38db985bbc7b561832e7618b3bea1566655fe2a72826 SHA512: d0f46d35f223fd043d997aa310cbc4107bc572737eb0655339f4de94bd973dffa96267bf5575b7ec85e206b96623b3824e6124449b5a5fa25d1b96cee6e16ad2 Homepage: https://cran.r-project.org/package=lavaanPlot Description: CRAN Package 'lavaanPlot' (Path Diagrams for 'Lavaan' Models via 'DiagrammeR') Plots path diagrams from models in 'lavaan' using the plotting functionality from the 'DiagrammeR' package. 'DiagrammeR' provides nice path diagrams via 'Graphviz', and these functions make it easy to generate these diagrams from a 'lavaan' path model without having to write the DOT language graph specification. Package: r-cran-lavacvxr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavash, r-cran-pracma, r-cran-cvxr Filename: pool/dists/noble/main/r-cran-lavacvxr_1.0.2-1.ca2404.1_all.deb Size: 24918 MD5sum: 99e818e2d24d84e3b4c55df6ede1c1c7 SHA1: 13b4d231b431b80fd5744949c23e54399b7bdf7e SHA256: 71359fcd9a20a5fe305e02dccc1f6bf6426656eb7a2422ce0293919e0264b377 SHA512: 18dbdb529f10cc6b923bd085115d75373512ee076f088b93f727fb877c54a9faf5dc6dc2b58df40d2d207deb5696802a0f554b539d049e84bb4dbc103f25b01e Homepage: https://cran.r-project.org/package=LavaCvxr Description: CRAN Package 'LavaCvxr' (Lava Estimation for the Sum of Sparse and Dense Signals(3Methods)) The lava estimation is used to recover signals that is the sum of a sparse signal and a dense signal. The post-lava method corrects the shrinkage bias of lava. For more information on the lava estimation, see Chernozhukov, Hansen, and Liao (2017) . Package: r-cran-lavash Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-glmnet, r-cran-pracma Filename: pool/dists/noble/main/r-cran-lavash_1.0-1.ca2404.1_all.deb Size: 30520 MD5sum: 1cc5d8db01ead5257c292b9b5b0ed151 SHA1: e5fc2667bd9ac1cccd7e4134df6dcbd3de809b1e SHA256: 9c2d7c3e6cac4b1244df545ed129ca85e110bf395e5c2047a056040026ba5eb4 SHA512: 82421340a2386b34ca052140f3400cf7dce52cfe31736114c943bc549ab5a1a860c05a87f6c88ace63e81c6bac695440cea2d99bdf5318c1b587f4612bdf29f0 Homepage: https://cran.r-project.org/package=Lavash Description: CRAN Package 'Lavash' (Lava Estimation for the Sum of Sparse and Dense Signals) The lava estimation is a new technique to recover signals that is the sum of a sparse and dense signals. The post-lava method corrects the shrinkage bias of lava. For more information on the lava estimation, see Chernozhukov, Hansen, and Liao (2017) . Package: r-cran-lavdiag Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corrplot, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-ggrepel, r-cran-igraph, r-cran-lavaan, r-cran-mgcv, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyselect, r-cran-tidyr, r-cran-vctrs, r-cran-visnetwork, r-cran-withr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lavdiag_0.1.0-1.ca2404.1_all.deb Size: 331824 MD5sum: 76714093e0050237af2142c26ffff835 SHA1: 37c28d66164b12ef60958d7b6b18368c1436eaa2 SHA256: 54b769ff51d02448eb0e94e4304ddc38892c617441b6cb77eb56f1cdde86f739 SHA512: ef169cda3f39c94bb680cea9aa542344cd87ccb36595f78d852f4187339b34d6f5d0d2f6aeb4a2120909089bcf15ef4ea15c14bc41083ae06280cf6efe23036a Homepage: https://cran.r-project.org/package=lavDiag Description: CRAN Package 'lavDiag' (Latent Variable Models Diagnostics) Diagnostics and visualization tools for latent variable models fitted with 'lavaan' (Rosseel, 2012 ). The package provides fast, parallel-safe factor-score prediction (lavPredict_parallel()), data augmentation with model predictions, residuals, delta-method standard errors and confidence intervals (augment()), and model-based latent grids for continuous, ordinal, or mixed indicators (prepare()). It offers item-level empirical versus model curve comparison using generalized additive models for both continuous and ordinal indicators (item_data(), item_plot()) via 'mgcv' (Wood, 2017, ISBN:9781498728331), residual diagnostics including residual correlation tables and plots (resid_cor(), resid_corrplot()) using 'corrplot' (Wei and Simko, 2021 ), and Q–Q checks of residual z-statistics (resid_qq()), optionally with non-overlapping labels from 'ggrepel' (Slowikowski, 2024 ). Heavy computations are parallelized via 'future'/'furrr' (Bengtsson, 2021 ; Vaughan and Dancho, 2018 ). Methods build on established literature and packages listed above. Package: r-cran-lavinteract Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1212 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lavinteract_0.5.1-1.ca2404.1_all.deb Size: 246734 MD5sum: 36873b5ec06a88d1ef4a7fa76265c90e SHA1: f524a9cc010017b49878be23a527696d273ab47c SHA256: 10d049afbbf2a215ef7a6cff4946667be746bed1d2ee7cd48d5e71a5c65620fd SHA512: f502441debfac4e3395909772e5ed1eaa51d2ddce82d9e790b5da8d908ab5374b8646c0ef1e78c2957fd2e99ced07182f2df3f83ef3d50656d09e706583454e4 Homepage: https://cran.r-project.org/package=lavinteract Description: CRAN Package 'lavinteract' (Post-Estimation Utilities for 'lavaan' Fitted Models) Companion toolbox for structural equation models fitted with 'lavaan'. 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Package: r-cran-lbm Architecture: all Version: 0.9.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lbm_0.9.0.2-1.ca2404.1_all.deb Size: 70122 MD5sum: 2b87166770c06e15e2644d6f75c159a5 SHA1: 51ab6e98e4eccc6f9429e254022209a3f769543a SHA256: 4d9c18b22b3e0f38b519da9889c57c748cff2e40f3920ced3aa5700ab12f08b8 SHA512: 68594353afeaa734fe6c0e1378d5ce1a21c60df89c74d1bf90aa480ecdb14b27aa54b002c3376ceb4d1dcb30bb2e940d30c3cc4d946918febc30cbb7165f5e54 Homepage: https://cran.r-project.org/package=lbm Description: CRAN Package 'lbm' (Log Binomial Regression Model in Exact Method) Fit the log binomial regression model (LBM) by Exact method. Limited parameter space of LBM causes trouble to find admissible estimates and fail to converge when MLE is close to or on the boundary of space. 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Package: r-cran-lca Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lca_0.1.1-1.ca2404.1_all.deb Size: 46144 MD5sum: 17a7777ff7f02c6bcaefc6dcf405e9f5 SHA1: 20384302962e272d518281ac6e556c93ea9cdd76 SHA256: ff889f619826af45a138f079014bd63f86d21a344f290577242cdfd527f8e77f SHA512: 2650a625db97242586cbab7907a686eb26d7e99b5728ea9e1e3fea5c246233693204b108ec4b27605310b2fb45f01cf2f0ad0595d3250d7c3f74c3437de7b8b0 Homepage: https://cran.r-project.org/package=LCA Description: CRAN Package 'LCA' (Localised Co-Dependency Analysis) Performs model fitting and significance estimation for Localised Co-Dependency between pairs of features of a numeric dataset. Package: r-cran-lcaextend Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-mvtnorm, r-cran-rms, r-cran-kinship2 Filename: pool/dists/noble/main/r-cran-lcaextend_1.3-1.ca2404.1_all.deb Size: 233824 MD5sum: 2247d7486ccd66b8af9b1a2e0bdfeb63 SHA1: d5567b8030bea5c9af38ae4165680946dd0cf31b SHA256: 491fc06e8d30b97a94e748a7509131aa067f0dd75ab3f020f863c7d123467d4a SHA512: 904d16338ede8c1b017fc48b62698b414b1b8276acbc4328da90fce0ccd51871e293a6972829163b6e9e5fd511aff5ddc8308bf99168c7dd0add1b028516ce70 Homepage: https://cran.r-project.org/package=LCAextend Description: CRAN Package 'LCAextend' (Latent Class Analysis (LCA) with Familial Dependence in ExtendedPedigrees) Latent Class Analysis of phenotypic measurements in pedigrees and model selection based on one of two methods: likelihood-based cross-validation and Bayesian Information Criterion. Computation of individual and triplet child-parents weights in a pedigree is performed using an upward-downward algorithm. The model takes into account the familial dependence defined by the pedigree structure by considering that a class of a child depends on his parents classes via triplet-transition probabilities of the classes. The package handles the case where measurements are available on all subjects and the case where measurements are available only on symptomatic (i.e. affected) subjects. Distributions for discrete (or ordinal) and continuous data are currently implemented. The package can deal with missing data. 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Polynomial mixed-effects models (via 'nlme') describe how concordance, Pearson correlation and accuracy evolve over time. Functions are provided for model fitting, diagnostic plots, extraction of summaries, and non-parametric bootstrap confidence intervals (including parallel computation), following Oliveira et al. (2018) . 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A Bayesian perspective to inference and Markov chain Monte Carlo algorithms to obtain posterior estimates of model parameters. The reference paper is: Beom Seuk Hwang, Zhen Chen, Germaine M.Buck Louis, Paul S. Albert, (2018) "A Bayesian multi-dimensional couple-based latent risk model with an application to infertility". Biometrics, 75, 315-325. . 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This package provides interactive data visualizations for quality control (QC) samples, including total ion current chromatogram (TIC), base peak chromatogram (BPC), mass spectrum, extracted ion chromatogram (XIC), and feature detection results from internal standards or known metabolites. 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Package: r-cran-lcra Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-coda, r-cran-rjags Suggests: r-cran-r2winbugs, r-cran-gtools Filename: pool/dists/noble/main/r-cran-lcra_1.1.5-1.ca2404.1_all.deb Size: 121930 MD5sum: 5451e50906e706d1ee4471c7e69d9cda SHA1: 5404f50b9fba78d3cdf53506e17efde38f06b213 SHA256: 5a622e6d105b4a806f5bb84890eeb5bb337d758fe1ce6ea9fce05dcd78d8ac63 SHA512: c11a45ab932d7ac2eb80daf805aed601f6a022ee104e7d5fe97aa1ea3dbd6ec208d48b69463ba7164c2b071f9a312de88e95de1ae40e8833d6b95c643f94af23 Homepage: https://cran.r-project.org/package=lcra Description: CRAN Package 'lcra' (Bayesian Joint Latent Class and Regression Models) For fitting Bayesian joint latent class and regression models using Gibbs sampling. See the documentation for the model. The technical details of the model implemented here are described in Elliott, Michael R., Zhao, Zhangchen, Mukherjee, Bhramar, Kanaya, Alka, Needham, Belinda L., "Methods to account for uncertainty in latent class assignments when using latent classes as predictors in regression models, with application to acculturation strategy measures" (2020) In press at Epidemiology . 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For details about Latent Change Score Modeling (LCSM) see McArdle (2009) and Grimm, An, McArdle, Zonderman and Resnick (2012) . The package automatically generates 'lavaan' syntax for different model specifications and varying timepoints. The 'lavaan' syntax generated by this package can be returned and further specifications can be added manually. Longitudinal plots as well as simplified path diagrams can be created to visualise data and model specifications. Estimated model parameters and fit statistics can be extracted as data frames. Data for different univariate and bivariate LCSM can be simulated by specifying estimates for model parameters to explore their effects. This package combines the strengths of other R packages like 'lavaan', 'broom', and 'semPlot' by generating 'lavaan' syntax that helps these packages work together. Package: r-cran-lctools Architecture: all Version: 0.2-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-reshape, r-cran-weights, r-cran-pscl, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lctools_0.2-10-1.ca2404.1_all.deb Size: 2144030 MD5sum: d2710e1ce6db9831844bfa35432420ce SHA1: 390695e687a2f133d29ce01bff4107c17aec18b6 SHA256: 7cb25c71e3cdd362e7d63b8edd96336341ae8039e9b1944467952de454a8dfc4 SHA512: fcf2fb55345aa15a44a20e96d96c33b5a701db354930c6e5797e0d7854b27ad1627ff6c484ee0fbb62719f688387339036d8469c70bc6c12c24664c202e2787e Homepage: https://cran.r-project.org/package=lctools Description: CRAN Package 'lctools' (Local Correlation, Spatial Inequalities, Geographically WeightedRegression and Other Tools) Provides researchers and educators with easy-to-learn user friendly tools for calculating key spatial statistics and to apply simple as well as advanced methods of spatial analysis in real data. 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Package: r-cran-ldamatch Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-runit, r-cran-data.table, r-cran-entropy, r-cran-foreach, r-cran-iterators, r-cran-iterpc, r-cran-ksamples, r-cran-car, r-cran-gmp Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat, r-cran-roxygen2, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-ldamatch_1.0.3-1.ca2404.1_all.deb Size: 226980 MD5sum: ef0b13dbb39bba4916d4943883206e43 SHA1: b623918effcca89e805821b85d82c33000cd5cee SHA256: 0fb5d292baaa252bd083e0d18ec345b635aeeb9c92fe129ee39d6e3043159f66 SHA512: 9d831913cb88d4c444bbf2d8b95a270c3176fb7dfa64ad41cd09496f3938f8e6935cb95a8bf87e484a9ec5b47a8b6659c4ff8bff0bf360f72b8788d971673f8e Homepage: https://cran.r-project.org/package=ldamatch Description: CRAN Package 'ldamatch' (Selection of Statistically Similar Research Groups) Select statistically similar research groups by backward selection using various robust algorithms, including a heuristic based on linear discriminant analysis, multiple heuristics based on the test statistic, and parallelized exhaustive search. 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Package: r-cran-ldbod Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rann, r-cran-mnormt Filename: pool/dists/noble/main/r-cran-ldbod_0.1.2-1.ca2404.1_all.deb Size: 44500 MD5sum: 747ef1269f2565c580631c5b4e919f7c SHA1: a404df574461d8448d80e3ee0565982bf53c3a8f SHA256: 3ba190f768d3d1f37a8f96cc44eb21ac7d1731a2bf8d66cce041450659d155b1 SHA512: 14a859a134a3e1e5919f3a84fa47f24e952d5e0f13d7e88cc6cf2b1f59b7c0a8dd69b6ba258f19f458bb10cbff8113041b217542a8b55c45956ba5b29cbe8cb9 Homepage: https://cran.r-project.org/package=ldbod Description: CRAN Package 'ldbod' (Local Density-Based Outlier Detection) Flexible procedures to compute local density-based outlier scores for ranking outliers. Both exact and approximate nearest neighbor search can be implemented, while also accommodating multiple neighborhood sizes and four different local density-based methods. 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Package: r-cran-ldbounds Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-ldbounds_2.0.2-1.ca2404.1_all.deb Size: 228764 MD5sum: 7820ca9c4859acb5decddfe61ba253b3 SHA1: f673cd87bc7ed9e5a6fc3ad7c4ebaa2fcb883b80 SHA256: 6eb55416ee6cfba240da09cadff24ee2ad10f05733060b2f5261f6db6eb9a19a SHA512: abf21c16885fc0278db5832b8639f38f89df8b40afa320541319b8443d48ad5f7d253277bc8c13b0db6d256a732d3cca6310ea3dff45bcf84d9b227d5aba44eb Homepage: https://cran.r-project.org/package=ldbounds Description: CRAN Package 'ldbounds' (Lan-DeMets Method for Group Sequential Boundaries) Computations related to group sequential boundaries. Includes calculation of bounds using the Lan-DeMets alpha spending function approach. Based on FORTRAN program ld98 implemented by Reboussin, et al. (2000) . Package: r-cran-ldcorsv Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ldcorsv_1.3.4-1.ca2404.1_all.deb Size: 72928 MD5sum: 4fa31877a46be75725f6d74ae7359207 SHA1: 96b3da3fcf06aa265abbfa5ceffb7636efbcd140 SHA256: 69a5a3d6729f8f236f3a00d219d04cdcf7d6139d4cd147ac64f19514ccfc7229 SHA512: 117a2cca887951dce5726ddbb31e9987b1b7e9e43836861ae19aa7330d25e3d43d64a3f4a9529f978c62b06f52359af2261e15510ea79ed8d41552d68c52a26d Homepage: https://cran.r-project.org/package=LDcorSV Description: CRAN Package 'LDcorSV' (Linkage Disequilibrium Corrected by the Structure and theRelatedness) Four measures of linkage disequilibrium are provided: the usual r^2 measure, the r^2_S measure (r^2 corrected by the structure sample), the r^2_V (r^2 corrected by the relatedness of genotyped individuals), the r^2_VS measure (r^2 corrected by both the relatedness of genotyped individuals and the structure of the sample). Package: r-cran-ldhmm Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1493 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gnorm, r-cran-optimx, r-cran-xts, r-cran-zoo, r-cran-moments, r-cran-scales, r-cran-ggplot2, r-cran-yaml Suggests: r-cran-knitr, r-cran-testthat, r-cran-depmixs4, r-cran-roxygen2, r-cran-r.rsp, r-cran-shape Filename: pool/dists/noble/main/r-cran-ldhmm_0.6.1-1.ca2404.1_all.deb Size: 1467262 MD5sum: 5afe6ee863b7d438881e8a76d52671eb SHA1: 9c658f22ecd81dc9e18a10794f53062b0b4e92f1 SHA256: 857056a7d54111bfa04cbe5ba66560bfd828153ca31213e433718232bd2ff889 SHA512: 83a1a03ddc0cf6e036ae90c414e939bde28990338a3e0d4612006f72bf3b211b177333eb25241b02e8fd1c3ff7b79a27c77a71bdd70a39cd9a55ae55cfd647e9 Homepage: https://cran.r-project.org/package=ldhmm Description: CRAN Package 'ldhmm' (Hidden Markov Model for Financial Time-Series Based on LambdaDistribution) Hidden Markov Model (HMM) based on symmetric lambda distribution framework is implemented for the study of return time-series in the financial market. Major features in the S&P500 index, such as regime identification, volatility clustering, and anti-correlation between return and volatility, can be extracted from HMM cleanly. Univariate symmetric lambda distribution is essentially a location-scale family of exponential power distribution. Such distribution is suitable for describing highly leptokurtic time series obtained from the financial market. It provides a theoretically solid foundation to explore such data where the normal distribution is not adequate. The HMM implementation follows closely the book: "Hidden Markov Models for Time Series", by Zucchini, MacDonald, Langrock (2016). Package: r-cran-ldlcalc Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2033 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-caret, r-cran-caretensemble, r-cran-lares, r-cran-corrplot, r-cran-rcolorbrewer, r-cran-lattice, r-cran-resample, r-cran-moments, r-cran-ggplot2, r-cran-janitor, r-cran-philentropy Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-cubist, r-cran-earth, r-cran-gbm, r-cran-glmnet, r-cran-gridextra, r-cran-kernlab, r-cran-randomforest, r-cran-tidyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ldlcalc_2.1-1.ca2404.1_all.deb Size: 1810262 MD5sum: 2fa2a74536e8608007d1fa007d528388 SHA1: 39100be6c6d9211f59ef4835932af841c3956bc5 SHA256: 3d20e38879734c922ff9dd8b7b38498872c9bf3d4720e3c8fa349cd82b6bc3e6 SHA512: 2f7ece4a1ffc616663fc31127a5cbd6475659592efcfc41e797d97d194032cae4dfc417539452cc1fe5cc2f292ce1d46ff778c5a034119cf39620ac5afdf0dbc Homepage: https://cran.r-project.org/package=LDLcalc Description: CRAN Package 'LDLcalc' (Calculate and Predict the Low Density Lipoprotein Values) A wide variety of ways to calculate (through equations) or predict (using 9 Machine learning methods as well as a stack algorithm combination of them all) the Low Density Lipoprotein values of patients based on the values of three other metrics, namely Total Cholesterol , Triglycerides and High Density Lipoprotein. 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Package: r-cran-ldlinkr Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-ldlinkr_1.4.0-1.ca2404.1_all.deb Size: 154142 MD5sum: 6f6e2a7fd35d59115553f9bcf5f46141 SHA1: 167cf381595d2b8e150d1e9a68ad1286d10beb16 SHA256: 5edffe63ae94785590b1667f8a8571e020a8955c039822f0376b1a2fd5f160fb SHA512: a1f71c4f57c54bc30ab394f46790847071682c22896dae9b8087ddb480c816d297cb28c9801e491595871b20c645ff962fbe469447784e71261e6a7cb8c31579 Homepage: https://cran.r-project.org/package=LDlinkR Description: CRAN Package 'LDlinkR' (Calculating Linkage Disequilibrium (LD) in Human PopulationGroups of Interest) Provides access to the 'LDlink' API () using the R console. This programmatic access facilitates researchers who are interested in performing batch queries in 1000 Genomes Project (2015) data using 'LDlink'. 'LDlink' is an interactive and powerful suite of web-based tools for querying germline variants in human population groups of interest. For more details, please see Machiela et al. (2015) . Package: r-cran-ldm Architecture: all Version: 6.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 850 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gunifrac, r-cran-vegan, r-cran-permute, r-bioc-biocparallel, r-cran-matrixstats, r-cran-castor, r-cran-phangorn, r-cran-modeest Suggests: r-cran-r.rsp, r-cran-testthat, r-cran-survival Filename: pool/dists/noble/main/r-cran-ldm_6.0.1-1.ca2404.1_all.deb Size: 786974 MD5sum: 871768ff06a4e822fb4993d4221f4e4c SHA1: 9aaad31db0424424e0ca37a9f0a9ffcf071ab5dc SHA256: 366c0af695aa727a141b4f1f19ab9cb1412d08ea4f459f5589ad73df2833aa95 SHA512: c6f295ec8923584958a79f5c55871c983652b41a532021d497dcd5238256eb1e7096a915d503e77a8591f73b979cdea15887e651756da71344745a2888c86d00 Homepage: https://cran.r-project.org/package=LDM Description: CRAN Package 'LDM' (Testing Hypotheses About the Microbiome using the LinearDecomposition Model) A single analysis path that includes distance-based ordination, global tests of any effect of the microbiome, and tests of the effects of individual taxa with false-discovery-rate (FDR) control. It accommodates both continuous and discrete covariates as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based p-values that can control for sample correlations. It can be applied to transformed data, and an omnibus test can combine results from analyses conducted on different transformation scales. It can also be used for testing presence-absence associations based on infinite number of rarefaction replicates, testing mediation effects of the microbiome, analyzing censored time-to-event outcomes, and for compositional analysis by fitting linear models to centered-log-ratio taxa count data. Package: r-cran-ldnn Architecture: all Version: 1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-keras, r-cran-devtools, r-cran-reticulate, r-cran-tensorflow Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ldnn_1.10-1.ca2404.1_all.deb Size: 31010 MD5sum: 97f92b5bbdb2d70803d2c6dd435c08cf SHA1: 9f01151bde4294ed815cdd3712eb7cb930c303e3 SHA256: f9eee996290b7119e43db0ba217e8667637e7a13708a2e0f98f974b970c3092f SHA512: 88168edd79fc8c0624aeb6fbde49681cc4c8018f57742f68bd201dfa1ff9f608afcd8e4f9292ce22b3df571da3a9aeb000eeea73783a11465472c96f1fe7ed73 Homepage: https://cran.r-project.org/package=LDNN Description: CRAN Package 'LDNN' (Longitudinal Data Neural Network) This is a Neural Network regression model implementation using 'Keras', consisting of 10 Long Short-Term Memory layers that are fully connected along with the rest of the inputs. Package: r-cran-ldrtools Architecture: all Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dr Filename: pool/dists/noble/main/r-cran-ldrtools_0.2-2-1.ca2404.1_all.deb Size: 37536 MD5sum: 22d9cdc814700003fd50eb5ab7ced377 SHA1: ad34a2a8ff3375440a4f5e5f34c3815f72c529b8 SHA256: a2395c11fa69a5ecfb2369cb8270d9a49c675c867742b6b0f7f8b6ae3f9b1ab3 SHA512: cf95855335673322b62babc3cada2ea075c75a94e092edc340d556bf5e022f03aad2d51158cdee1eacb156b8e2bd916d53559bd2759ad8afddef953fe4a6d7a0 Homepage: https://cran.r-project.org/package=LDRTools Description: CRAN Package 'LDRTools' (Tools for Linear Dimension Reduction) Linear dimension reduction subspaces can be uniquely defined using orthogonal projection matrices. This package provides tools to compute distances between such subspaces and to compute the average subspace. For details see Liski, E.Nordhausen K., Oja H., Ruiz-Gazen A. (2016) Combining Linear Dimension Reduction Subspaces . Package: r-cran-leabra Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-leabra_0.1.0-1.ca2404.1_all.deb Size: 209524 MD5sum: 1143ba121b9ba6f4d986e0bde022015c SHA1: 9a319c25d48d467b72061013e4e3eebe47c53646 SHA256: 08e4e24e3d789901a917d6a9f0ae357af058afa09b77231c86526333066c600c SHA512: b9c8eb25fe134455137ef612f89f12fbdfd0865e4170049cf9924d384007c660803012a2fe8a61634b323139cc4171f6aaa9785c43ea2b33f62e0c32e7a27d2a Homepage: https://cran.r-project.org/package=leabRa Description: CRAN Package 'leabRa' (The Artificial Neural Networks Algorithm Leabra) The algorithm Leabra (local error driven and associative biologically realistic algorithm) allows for the construction of artificial neural networks that are biologically realistic and balance supervised and unsupervised learning within a single framework. This package is based on the 'MATLAB' version by Sergio Verduzco-Flores, which in turn was based on the description of the algorithm by Randall O'Reilly (1996) . For more general (not 'R' specific) information on the algorithm Leabra see . 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Computes Leadership Trait Analysis scores for seven personality traits -- including need for power, conceptual complexity, and self-confidence -- and classifies leaders into one of eight leadership styles. Also computes Operational Code Analysis scores summarising a leader's beliefs about politics and the use of power. Package: r-cran-leadsense Architecture: all Version: 0.0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 858 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-seewave, r-cran-tidyr, r-cran-reshape2, r-cran-signal Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-leadsense_0.0.2.0-1.ca2404.1_all.deb Size: 826358 MD5sum: 2f4688425c099f7ae1f300a12b91b0ff SHA1: b647a940721903a9c3e73cb661c4b0cff0f50d16 SHA256: a3243d13784507573deabf8181983899d7bc6aab53c668b9c1197204eab23ad5 SHA512: b85a8d502687d0679733cedd225774b7a0228063f21754a883b4abbe5ce7897cf01d84b4342b57753b04e78b6954ff7d399ef2485dfa79f66081a1cb652e061b Homepage: https://cran.r-project.org/package=LeadSense Description: CRAN Package 'LeadSense' (Medtronic Brain Sense Local Field Potencial Analysis) Extracts and creates an analysis pipeline for the JSON data files from Brain Sense sessions using Medtronic's Deep Brain Stimulation surgery electrode implants. Package: r-cran-leaf Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2372 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-reticulate, r-cran-ggplot2, r-cran-dplyr, r-cran-rlang, r-cran-rappdirs, r-cran-rstudioapi Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-leaf_0.1.0-1.ca2404.1_all.deb Size: 726100 MD5sum: 33ef1910b164c02063acab28b3f22d22 SHA1: d275bd3bfdaa4f3779b4cd140997c11030cb81b4 SHA256: 4a77b3ddcba4765202308979215abbf403d07e855f86b173b9e8e6362b948de4 SHA512: c31c6c211191a3564dc2423d24959c60544cb9338c1a90d63430cd22a54acb66d2fa674a468819b81025191cb1190e46290b8f75e63710796873accb312e58bd Homepage: https://cran.r-project.org/package=leaf Description: CRAN Package 'leaf' (Learning Equations for Automated Function Discovery) A unified framework for symbolic regression (SR) and multi-view symbolic regression (MvSR) designed for complex, nonlinear systems, with particular applicability to ecological datasets. The package implements a four-stage workflow: data subset generation, functional form discovery, numerical parameter optimization, and multi-objective evaluation. It provides a high-level formula-style interface that abstracts and extends multiple discovery engines: genetic programming (via PySR), Reinforcement Learning with Monte Carlo Tree Search (via RSRM), and exhaustive generalized linear model search. 'leaf' extends these methods by enabling multi-view discovery, where functional structures are shared across groups while parameters are fitted locally, and by supporting the enforcement of domain-specific constraints, such as sign consistency across groups. The framework automatically handles data normalization, link functions, and back-transformation, ensuring that discovered symbolic equations remain interpretable and valid on the original data scale. Implements methods following ongoing work by the authors (2026, in preparation). Package: r-cran-leafarea Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1273 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-leafarea_0.1.8-1.ca2404.1_all.deb Size: 1241518 MD5sum: 8a2bb99335a31893b8998787d9af60f7 SHA1: b2434a87efe011385e1a686cbc2c9042a33e3118 SHA256: 7f52fd5b431e7bf1eb2b36cc64b8d287a0278923bbc1f11579f5042828a819b3 SHA512: 9ce3979e60b7bbd88244f2631842742ab3e5d048b0fab8de888848b423ef1da0289b96bc14d294d117426a9198b8bb756a7eeef1491535816ff8d9bdce784d85 Homepage: https://cran.r-project.org/package=LeafArea Description: CRAN Package 'LeafArea' (Rapid Digital Image Analysis of Leaf Area) An interface for the image processing program 'ImageJ', which allows a rapid digital image analysis for particle sizes. This package includes function to write an 'ImageJ' macro which is optimized for a leaf area analysis by default. 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Focus is on functionality readily available in Geographic Information Systems such as 'Quantum GIS'. Includes functions to display coordinates of mouse pointer position, query image values via mouse pointer and zoom-to-layer buttons. Additionally, provides a feature type agnostic function to add points, lines, polygons to a map. Package: r-cran-leafgl Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1892 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-leaflet, r-cran-sf, r-cran-yyjsonr Suggests: r-cran-colourvalues, r-cran-sp, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-leafgl_0.2.4-1.ca2404.1_all.deb Size: 1771094 MD5sum: 8c557da408535d8996a4d20eebdcbbe0 SHA1: 35eef6ea61d4ccadb0153eaee689d522f4d15d41 SHA256: 63cbadd7387d037ac364d1f58e124358832585a9b97a08c683a1594a85915590 SHA512: b6c4836ae9dbd1bb503bb3af20d95ee97a108a72001b7a114c7c51c525855a6608add8ec4fd100d10235a5fea80d2689338e6d09ba011adc81fd268a5ee5e1c5 Homepage: https://cran.r-project.org/package=leafgl Description: CRAN Package 'leafgl' (High-Performance 'WebGl' Rendering for Package 'leaflet') Provides bindings to the 'Leaflet.glify' JavaScript library which extends the 'leaflet' JavaScript library to render large data in the browser using 'WebGl'. Package: r-cran-leaflegend Architecture: all Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3485 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-leaflet, r-cran-htmltools, r-cran-base64enc, r-cran-htmlwidgets Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-leaflegend_1.2.8-1.ca2404.1_all.deb Size: 1299084 MD5sum: 0a2964cc46a207f32b7ad8fb3234b6c7 SHA1: 72c557d51f88013917311d855b6198803d595d03 SHA256: 4fd3f6f23f0c5ab6b34bcba31f1e1c0ab318da349d59d531c4a8aae47b9f6eb2 SHA512: ac8228ad14fc444b8eeec2890c890996df04e4ea21347fcf2d518cdb8ee58dbc3473b1d65a0cb098cef06500a6ae51a4cb977817dd2fcfff2ae9a8371ee0a0ee Homepage: https://cran.r-project.org/package=leaflegend Description: CRAN Package 'leaflegend' (Add Custom Legends to 'leaflet' Maps) Provides extensions to the 'leaflet' package to customize legends with images, text styling, orientation, sizing, and symbology and functions to create symbols to plot on maps. Package: r-cran-leaflet.esri Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3715 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaflet, r-cran-leaflet.extras, r-cran-htmltools Suggests: r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-leaflet.esri_1.0.0-1.ca2404.1_all.deb Size: 325926 MD5sum: 13bc30e07a3d58a15431e7e292909a00 SHA1: a379bb5aca496bf83937480b537cf373ed13d5d7 SHA256: 392405dc35348a3366102dc1e0de76980b3dd805513f90663e45a049d7aceb15 SHA512: 70261523335e4eabeccdd4a5524183b3ada3182696f713766dbfebe1402682564bc2ff970fe7dc67f81e17e872deb29d2d19b35766e02494ea8b639e2d2d13e9 Homepage: https://cran.r-project.org/package=leaflet.esri Description: CRAN Package 'leaflet.esri' ('ESRI' Bindings for the 'leaflet' Package) An add-on package to the 'leaflet' package, which provides bindings for 'ESRI' services. This package allows a user to add 'ESRI' provided services such as 'MapService', 'ImageMapService', 'TiledMapService' etc. to a 'leaflet' map. Package: r-cran-leaflet.extras2 Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3799 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaflet, r-cran-htmltools, r-cran-magrittr Suggests: r-cran-jsonlite, r-cran-shiny, r-cran-sf, r-cran-yyjsonr, r-cran-sp, r-cran-testthat, r-cran-fontawesome, r-cran-htmlwidgets, r-cran-xfun, r-cran-covr, r-cran-curl Filename: pool/dists/noble/main/r-cran-leaflet.extras2_1.3.2-1.ca2404.1_all.deb Size: 1187308 MD5sum: dc16eb9acb03ec6fbf7732a107c67867 SHA1: 6360a7a373d61268cdeb7ff63489dcedc40a9e9e SHA256: abdbde786ab0388a21beca34a1c5988defd73fc20ebc730b156a2bbef1fb8796 SHA512: 115bbb8d5e4e3defd8a113a1644e352cab1a85e8b00c70efc36f783219a5109cb0b52156ac36adfac181553ad81b40aff1297e8cb1d076b1e2768549a6269a72 Homepage: https://cran.r-project.org/package=leaflet.extras2 Description: CRAN Package 'leaflet.extras2' (Extra Functionality for 'leaflet' Package) Several 'leaflet' plugins are integrated, which are available as extension to the 'leaflet' package. Package: r-cran-leaflet.extras Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2580 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaflet, r-cran-htmlwidgets, r-cran-htmltools, r-cran-stringr, r-cran-magrittr Suggests: r-cran-jsonlite, r-cran-readr, r-cran-sf, r-cran-xfun, r-cran-testthat Filename: pool/dists/noble/main/r-cran-leaflet.extras_2.0.2-1.ca2404.1_all.deb Size: 1395492 MD5sum: eb53476770c15d589a0cb27ad0c109ec SHA1: 7bc48195c32949d66284b2fd9664220a1884a23a SHA256: a7f6364fdac04af7be5c1685f836a750782a7f8dd756dfb724f9ab36c14e5028 SHA512: 90be810b5236de9af024ef6ab866e1ba0c6df1b51f710168197da5c9e85e2033532d5af6e4c1ab4d6b9b031edcba5811d40d48701814c439e17ea1014f97ef5b Homepage: https://cran.r-project.org/package=leaflet.extras Description: CRAN Package 'leaflet.extras' (Extra Functionality for 'leaflet' Package) The 'leaflet' JavaScript library provides many plugins some of which are available in the core 'leaflet' package, but there are many more. 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Package: r-cran-leaflet.minicharts Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1807 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaflet, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-shiny, r-cran-manipulatewidget, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-leaflet.minicharts_0.6.3-1.ca2404.1_all.deb Size: 529778 MD5sum: cd87f0138a36acfb118b8424cebc02f7 SHA1: 0b73e31e814ead5136182e178b36ffd07daf0c2e SHA256: 1b214afa0c95c89360eee4c9a3173d5f6b2ffe92ec6024ab66d0f1c0bb2843b9 SHA512: e036d995517d0b70ce35eec1d4af6121e328346c4f4c0435f214636d6763b98e5e6cf468074cd4a86f125c5f12391a0544e26b54ee0cac33519aeab2df027583 Homepage: https://cran.r-project.org/package=leaflet.minicharts Description: CRAN Package 'leaflet.minicharts' (Mini Charts for Interactive Maps) Add and modify small charts on an interactive map created with package 'leaflet'. These charts can be used to represent at same time multiple variables on a single map. Package: r-cran-leaflet.providers Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools Suggests: r-cran-jsonlite, r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-leaflet.providers_3.0.0-1.ca2404.1_all.deb Size: 44150 MD5sum: 9fee076603c82bc832b9377666994185 SHA1: dd0708558c6ce267ce89037b86c4d8ead733b160 SHA256: 7f31ef93a08898658c4964a8a1948199348a9aa588bb6b203e87b19041ea7cb7 SHA512: ec3e50af6aaf2dcfc3624e3a3bb879db2dd408fe3564128c74fd6751688f66ac0f2dfe31c217143c979a47d7bf5a5150e61835e4b02ea0556f21bfd63e8446fd Homepage: https://cran.r-project.org/package=leaflet.providers Description: CRAN Package 'leaflet.providers' (Leaflet Providers) Contains third-party map tile provider information from 'Leaflet.js', , to be used with the 'leaflet' R package. Additionally, 'leaflet.providers' enables users to retrieve up-to-date provider information between package updates. Package: r-cran-leaflet Architecture: all Version: 2.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4288 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crosstalk, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jquerylib, r-cran-leaflet.providers, r-cran-magrittr, r-cran-png, r-cran-raster, r-cran-rcolorbrewer, r-cran-rlang, r-cran-scales, r-cran-sf, r-cran-viridislite, r-cran-xfun Suggests: r-cran-knitr, r-cran-maps, r-cran-purrr, r-cran-r6, r-cran-rjsonio, r-cran-rmarkdown, r-cran-s2, r-cran-shiny, r-cran-sp, r-cran-terra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-leaflet_2.2.3-1.ca2404.1_all.deb Size: 2009602 MD5sum: 0d7d4e27c73709df01efb134280639de SHA1: 718c311a51782b7b3368eaa52400e4c82c6033bc SHA256: 7a76f74119d319ded89f03ab4fd22c37cebc118e8316a516be77fe69c1544b99 SHA512: 0cf05ba47a24a0baeeff680e7a5f875a31f950cc6017d8034125ec0669a28f78121622409daa737e27a15d15f1503bb40b24a15c4d1270bfc69b730ff546455b Homepage: https://cran.r-project.org/package=leaflet Description: CRAN Package 'leaflet' (Create Interactive Web Maps with the JavaScript 'Leaflet'Library) Create and customize interactive maps using the 'Leaflet' JavaScript library and the 'htmlwidgets' package. These maps can be used directly from the R console, from 'RStudio', in Shiny applications and R Markdown documents. Package: r-cran-leafpm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-leaflet, r-cran-sf Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-leafpm_0.1.0-1.ca2404.1_all.deb Size: 59302 MD5sum: 078fbe8334431c26891d658a7dc1904d SHA1: b7e838f1913a634a9afeda3e69fffd7a7285640d SHA256: 5b9ef9a52beed6d9e419c74851f2afc07623cba6be8c9981bd30a6b4df8356e6 SHA512: 4aea0eb34380bbabd8db93ec55943c7a1041aa963eb81cdbcb21dfa31a1e713155c3f6214e3584d05485a44167144a2084587139b6a91a37c2f8e5b6ab18f6e4 Homepage: https://cran.r-project.org/package=leafpm Description: CRAN Package 'leafpm' (Leaflet Map Plugin for Drawing and Editing) A collection of tools for interactive manipulation of (spatial) data layers on leaflet web maps. Tools include editing of existing layers, creation of new layers through drawing of shapes (points, lines, polygons), deletion of shapes as well as cutting holes into existing shapes. Provides control over options to e.g. prevent self-intersection of polygons and lines or to enable/disable snapping to align shapes. Package: r-cran-leafpop Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1912 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64enc, r-cran-brew, r-cran-htmltools, r-cran-htmlwidgets, r-cran-sf, r-cran-svglite, r-cran-uuid Suggests: r-cran-lattice, r-cran-leaflet, r-cran-sp Filename: pool/dists/noble/main/r-cran-leafpop_0.1.0-1.ca2404.1_all.deb Size: 1910518 MD5sum: 72c0ec5f41b78a0471301f3351e984b2 SHA1: b0a0c7c6951e42cb8dbfceff40695011579bc937 SHA256: d5ad3b6d7ed85f90abd004ace8e2688d2bc43f6ae188572ceb2bbe950d657cfe SHA512: fbbc907fa6f286b6a435ebb13866f58fe2d93e9dbc8f236cb42d3a9c7af582959d46f75d933bc81383137cb0424c142987e19e956b56af216ea93ed1e8717584 Homepage: https://cran.r-project.org/package=leafpop Description: CRAN Package 'leafpop' (Include Tables, Images and Graphs in Leaflet Pop-Ups) Creates 'HTML' strings to embed tables, images or graphs in pop-ups of interactive maps created with packages like 'leaflet' or 'mapview'. Handles local images located on the file system or via remote URL. Handles graphs created with 'lattice' or 'ggplot2' as well as interactive plots created with 'htmlwidgets'. Package: r-cran-leafr Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lidr, r-cran-sp, r-cran-data.table, r-cran-raster Filename: pool/dists/noble/main/r-cran-leafr_0.3.5-1.ca2404.1_all.deb Size: 447946 MD5sum: 88b91ac03e521ac846b346dad1aad339 SHA1: e225a93dbef1c2b2d622d9cb273f4b80d452c828 SHA256: cb8a83c43bc7ccf41bd5c22998d3974943fcde3bcc02f8fa5464c62fdfd43986 SHA512: 642006bb794c2f09bded62ce5c30bf6cddb34216e49d4867079669e9f92ed1d4911be8025b3335ada1a645da0b61f9d94415b458d93f849938a351e642dc9d73 Homepage: https://cran.r-project.org/package=leafR Description: CRAN Package 'leafR' (Calculates the Leaf Area Index (LAD) and Other Related Functions) A set of functions for analyzing the structure of forests based on the leaf area density (LAD) and leaf area index (LAI) measures calculated from Airborne Laser Scanning (ALS), i.e., scanning lidar (Light Detection and Ranging) data. The methodology is discussed and described in Almeida et al. (2019) and Stark et al. (2012) . Package: r-cran-leafstar Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-leafstar_1.0-1.ca2404.1_all.deb Size: 111878 MD5sum: 51e8e55b66fdb943c646575ceda7463b SHA1: 2a183cae87ad49b58432e44868568cb0a8da0522 SHA256: 496ac537208e288f9da0372b907c6d11d15d63874c658a6ea9316931f63f15c0 SHA512: 0a0494eb641b1338ae2bb67d19c5ed2bd20674552f3d288c3ecfadbc114dc3894462da268ebde95adaf8f6a8983c03769572a79ff0e69af8c9e7e74320fc45b4 Homepage: https://cran.r-project.org/package=leafSTAR Description: CRAN Package 'leafSTAR' (Silhouette to Area Ratio of Tilted Surfaces) Implementation of trigonometric functions to calculate the exposure of flat, tilted surfaces, such as leaves and slopes, to direct solar radiation. It implements the equations in A.G. Escribano-Rocafort, A. Ventre-Lespiaucq, C. Granado-Yela, et al. (2014) in a few user-friendly R functions. All functions handle data obtained with 'Ahmes' 1.0 for Android, as well as more traditional data sources (compass, protractor, inclinometer). The main function (star()) calculates the potential exposure of flat, tilted surfaces to direct solar radiation (silhouette to area ratio, STAR). It is equivalent to the ratio of the leaf projected area to total leaf area, but instead of using area data it uses spatial position angles, such as pitch, roll and course, and information on the geographical coordinates, hour, and date. The package includes additional functions to recalculate STAR with custom settings of location and time, to calculate the tilt angle of a surface, and the minimum angle between two non-orthogonal planes. Package: r-cran-leafsync Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 875 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-leaflet Filename: pool/dists/noble/main/r-cran-leafsync_0.1.0-1.ca2404.1_all.deb Size: 841464 MD5sum: faf9f9182c8f0cc4be7e23fad780c0af SHA1: cfa7aa9112a77a3a34d828be88b04e1b44f1f1f5 SHA256: 078c13d0d154e7da74fb88818411200ac332a42a92c4760e5c7251834032d258 SHA512: 45961ac500e34fe6b0e79bce10914a4bec09e9ca44f575e13fbb22c75574ff0f5cf705afc2a9c74a9fa31d18302d2428d1edb037caf94374948628c29925c706 Homepage: https://cran.r-project.org/package=leafsync Description: CRAN Package 'leafsync' (Small Multiples for Leaflet Web Maps) Create small multiples of several leaflet web maps with (optional) synchronised panning and zooming control. When syncing is enabled all maps respond to mouse actions on one map. 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Having defined independent commands for these two tools makes it easier for the student to understand what the software is performing, and it also helps the student to have a better knowledge on which specific tool they need to use in each situation. Moreover, the hypothesis testing commands provide not only the numeric result on the screen but also a very intuitive graph (which includes the statistic distribution, the observed value of the statistic, the rejection area and the p-value) that is very useful for the student to visualise the process. The regression section includes up to now, a simple linear model, with one single command the student can obtain the numeric summary as well as the corresponding diagram with the adjusted regression model and a legend with basic information (formula of the adjusted model and R-squared). 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We include many of the standard competitor types used in spoken word recognition research, such as functions to find cohorts, neighbors, and rhymes, amongst many others. The package includes documentation for using a variety of lexicon files, including those with form codes made up of multiple letters (i.e., phoneme codes) and also basic orthographies. Importantly, the code makes use of multiple CPU cores and vectorization when possible, making it extremely fast and able to handle large lexicons. Additionally, the package contains documentation for users to easily write new functions, allowing researchers to examine other relationships within a lexicon. Preprint: . Open access: . Citation: Li, Z., Crinnion, A.M. & Magnuson, J.S. (2021). . 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Normalization and missing value imputation are commonly used techniques to address these issues and make the dataset suitable for further downstream analysis. This package provides an optimal combination of normalization and imputation methods for the dataset. The package utilizes three normalization methods and three imputation methods.The statistical evaluation measures named pooled co-efficient of variance, pooled estimate of variance and pooled median absolute deviation are used for selecting the best combination of normalization and imputation method for the given dataset. The user can also visualize the results by using various plots available in this package. The user can also perform the differential expression analysis between two sample groups with the function included in this package. The chosen three normalization methods, three imputation methods and three evaluation measures were chosen for this study based on the research papers published by Välikangas et al. (2016) , Jin et al. (2021) and Srivastava et al. (2023) .This work has published by Sakthivel et al. (2025) . 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Package: r-cran-lidartree Architecture: all Version: 4.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1042 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-sf, r-cran-imager, r-cran-leaps, r-cran-gvlma, r-cran-car, r-cran-reldist, r-cran-lidr Suggests: r-cran-stars Filename: pool/dists/noble/main/r-cran-lidartree_4.0.8-1.ca2404.1_all.deb Size: 1004754 MD5sum: 0c9221855657a5fd62f44f51f8245642 SHA1: 690374c71c97426ea8211797cb11d6b1cf7ddb7f SHA256: 28a29be753cd820e00553a23659279e659106dc9563fcb0cf0a7a1dca0950411 SHA512: 72edf96fa50e93925df0c8dbd1c8dad5eddf1af4571f4287c3bcf51b1aed64a4e74df4b3487352aa6185414942663aa8525bc7de9b591414950c2e1a7f720769 Homepage: https://cran.r-project.org/package=lidaRtRee Description: CRAN Package 'lidaRtRee' (Forest Analysis with Airborne Laser Scanning (LiDAR) Data) Provides functions for forest objects detection, structure metrics computation, model calibration and mapping with airborne laser scanning: co-registration of field plots (Monnet and Mermin (2014) ); tree detection (method 1 in Eysn et al. (2015) ) and segmentation; forest parameters estimation with the area-based approach: model calibration with ground reference, and maps export (Aussenac et al. (2023) ); extraction of both physical (gaps, edges, trees) and statistical features useful for e.g. habitat suitability modeling (Glad et al. (2020) ) and forest maturity mapping (Fuhr et al. (2022) ). Package: r-cran-lievens Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2461 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Filename: pool/dists/noble/main/r-cran-lievens_0.0.1-1.ca2404.1_all.deb Size: 1549688 MD5sum: f12837f8fa9c20236fdaa8fac3252c78 SHA1: 743041f8653b202ecb63727ec0678191ce23a861 SHA256: 2c6cae81f7055752506863897360d0eaddd4e19a2f622b7104b168e071156385 SHA512: be747ca35a92de6c3968641954c5e43df279f0d19aff905cbddf13fbf7f9f1de92d512fa9db4c03e2958043281cbd798c28ab10a27ef67fe7783d4cf5dec4473 Homepage: https://cran.r-project.org/package=lievens Description: CRAN Package 'lievens' (Real-Time PCR Data Sets by Lievens et al. (2012)) Real-time quantitative polymerase chain reaction (qPCR) data sets by Lievens et al. (2012) . Provides one single tabular tidy data set in long format, encompassing three dilution series, targeted against the soybean Lectin endogene. Each dilution series was assayed in one of the following PCR-efficiency-modifying conditions: no PCR inhibition, inhibition by isopropanol and inhibition by tannic acid. The inhibitors were co-diluted along with the dilution series. The co-dilution series consists of a five-point, five-fold serial dilution. For each concentration there are 18 replicates. Each amplification curve is 60 cycles long. Original raw data file is available at the Supplementary Data section at Nucleic Acids Research Online . Package: r-cran-lifecourse Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-traminer Filename: pool/dists/noble/main/r-cran-lifecourse_2.0-1.ca2404.1_all.deb Size: 206624 MD5sum: e5ffd31e8c1e4af3689c6cbaa66e0217 SHA1: 8a42ecf0a4dbdd167ef745647075e9a96952124e SHA256: 92cfd2a9e4cf8bd2bcef2a3d94d0af1dcd0050a1d44e49bdc46ee923e9099936 SHA512: 0f268a431bce44f6bbffa91a64489f7c36ad75b23fbeb60297ce1217634e139ae5fa78b94a8f0fcec5de47ccd9b57813c53cf353586d40cdec09cb517e78fc7c Homepage: https://cran.r-project.org/package=lifecourse Description: CRAN Package 'lifecourse' (Quantification of Lifecourse Fluidity) Provides in built datasets and three functions. These functions are mobility_index, nonStanTest and linkedLives. The mobility_index function facilitates the calculation of lifecourse fluidity, whilst the nonStanTest and the linkedLives functions allow the user to determine the probability that the observed sequence data was due to chance. The linkedLives function acknowledges the fact that some individuals may have identical sequences. The datasets available provide sequence data on marital status(maritalData) and mobility (mydata) for a selected group of individuals from the British Household Panel Study (BHPS). In addition, personal and house ID's for 100 individuals are provided in a third dataset (myHouseID) from the BHPS. Package: r-cran-lifecycle Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang Suggests: r-cran-covr, r-cran-knitr, r-cran-lintr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tidyverse, r-cran-vctrs, r-cran-withr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-lifecycle_1.0.5-1.ca2404.1_all.deb Size: 122228 MD5sum: da3a216ea87451d33c668db58c9205aa SHA1: 1c499f3434b44a2208eb05e01473a306032df79b SHA256: 73c50cf718d108839173ea8e8c8dc305d2d9090c251bdaa4aa71f3c6650b5cea SHA512: 20de4a99fa6b4b701b30ca8d966a324471ab7c8a6139b299e29f9b10292d732068758458d45d6ee9b697ffa957dec2b6a04873618db21b3b0191acde0d6b7306 Homepage: https://cran.r-project.org/package=lifecycle Description: CRAN Package 'lifecycle' (Manage the Life Cycle of your Package Functions) Manage the life cycle of your exported functions with shared conventions, documentation badges, and user-friendly deprecation warnings. Package: r-cran-lifehist Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-optimx, r-cran-bb Filename: pool/dists/noble/main/r-cran-lifehist_1.0-1-1.ca2404.1_all.deb Size: 268658 MD5sum: 95e2c27646578b4f679c7401eec533a4 SHA1: 5e2bd803f07dd759898de31a6282335b6e66a388 SHA256: 6260404c713e9b6a264673236b399282e60944c111f51c1d5cefd6c81080cb11 SHA512: b9dc58e0915ec91ec6f85f5571469b588a4a41ab1f9fa5d51cd338de04e0f675caab548baafd47b565b39b8f5c3773d7be467100bae71002be0ee93b794d2fde Homepage: https://cran.r-project.org/package=LifeHist Description: CRAN Package 'LifeHist' (Life History Models of Individuals) Likelihood-based estimation of individual growth and sexual maturity models for organisms, usually fish and invertebrates. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions. Package: r-cran-lifeinsurancecontracts Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 36 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lifeinsurer Filename: pool/dists/noble/main/r-cran-lifeinsurancecontracts_0.0.6-1.ca2404.1_all.deb Size: 8280 MD5sum: b3e3c8d932a4ab1f59ea783d20768142 SHA1: 1b1df352baa4f849ca89f9655abf84d1e50b5ea8 SHA256: a1fbf5fd255c74ad4edd6d24cdc9305806192269459d0932e9a988376b8581ef SHA512: d6633c7fcfc2a81e4c9ba458de47db953b54e74c90297ad00104848e193e739a622cbae4616f249a57f3cab1aa1b220bf3e63ea05c78c64a0c12809422d39f48 Homepage: https://cran.r-project.org/package=LifeInsuranceContracts Description: CRAN Package 'LifeInsuranceContracts' (Framework for Traditional Life Insurance Contracts) Use of this package is deprecated. It has been renamed to 'LifeInsureR'. Package: r-cran-lifeinsurer Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1539 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-mortalitytables, r-cran-objectproperties, r-cran-lubridate, r-cran-openxlsx, r-cran-dplyr, r-cran-scales, r-cran-abind, r-cran-stringr, r-cran-rlang, r-cran-rmarkdown, r-cran-kableextra, r-cran-pander, r-cran-tidyr Suggests: r-cran-knitr, r-cran-magrittr, r-cran-tibble, r-cran-testthat, r-cran-fs, r-cran-here, r-cran-purrr Filename: pool/dists/noble/main/r-cran-lifeinsurer_1.0.1-1.ca2404.1_all.deb Size: 793120 MD5sum: a84e2fec387ad6ff33f8390cc4337e2b SHA1: 55d1869f654b01df9d0f0417990b7ec51a2c6d29 SHA256: 38ccfd8bb849700f449b3474c66f80e0627a000fd839e1774788e8a37a6fb8ed SHA512: 1076e29243d023dbfdf3a84a02f430ded0d7da3a460a20ed4f5cda43a656d82041efb9d535b2c788da5504f9b4e97467de5a0fc3915191a3443d28243f82c9ea Homepage: https://cran.r-project.org/package=LifeInsureR Description: CRAN Package 'LifeInsureR' (Modelling Traditional Life Insurance Contracts) R6 classes to model traditional life insurance contracts like annuities, whole life insurances or endowments. Such life insurance contracts provide a guaranteed interest and are not directly linked to the performance of a particular investment vehicle, but they typically provide (discretionary) profit participation. This package provides a framework to model such contracts in a very generic (cash-flow-based) way and includes modelling profit participation schemes, dynamic increases or more general contract layers, as well as contract changes (like sum increases or premium waivers). All relevant quantities like premium decomposition, reserves and benefits over the whole contract period are calculated and potentially exported to 'Excel'. Mortality rates are given using the 'MortalityTables' package. Package: r-cran-lifelogr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1214 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-shiny, r-cran-dplyr, r-cran-lubridate, r-cran-modelr, r-cran-stringr, r-cran-tidyr, r-cran-lazyeval, r-cran-r6, r-cran-fitbitscraper, r-cran-tibble, r-cran-plyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lifelogr_0.1.0-1.ca2404.1_all.deb Size: 1006710 MD5sum: 25a089b357b2d6bac1fcf7674c5203cd SHA1: e5f2dea0691e6067471e3fd81c0629cc5c3ec819 SHA256: c0eb87297a8f156cc844d177b20026b2186eb5e62b8aa3a630506f844f6dc78e SHA512: b1b96bc2da9e9f1d3554c2ae021bf360450eecb90164287214a73dbf6107d23f01f7714e479186b305f099b565e4bc8eb3f54ec4a6361c71c2b378ca8ad12fa8 Homepage: https://cran.r-project.org/package=lifelogr Description: CRAN Package 'lifelogr' (Life Logging) Provides a framework for combining self-data (exercise, sleep, etc.) from multiple sources (fitbit, Apple Health), creating visualizations, and experimenting on onself. Package: r-cran-lifemapr Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaflet, r-cran-shiny, r-cran-jsonlite, r-cran-dplyr, r-cran-tidyr, r-cran-leaflet.minicharts, r-cran-htmltools, r-cran-rlang, r-cran-rcurl, r-cran-fastmatch, r-cran-arrow Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-lifemapr_1.1.6-1.ca2404.1_all.deb Size: 2549928 MD5sum: 11f615c6ccefd9f927ed973fcff53008 SHA1: 6502d5947adddd9b920e824bd85bdaa1e3c13434 SHA256: 2437a7772665bf971cadc1311bcb7417525e88dc6bcad2268458c05174b3c46a SHA512: c3cca2314eacb2ba2b665a8ba3527ba99cd24a4a5f0bf2722c2378915fe7198509a78d2b23d11916ca32fb3528741f1fdf20c5beccbbcf1fd3df0c44bac576c4 Homepage: https://cran.r-project.org/package=LifemapR Description: CRAN Package 'LifemapR' (Data Visualisation on 'Lifemap' Tree) Allow to visualise data on the NCBI phylogenetic tree as presented in Lifemap . It takes as input a dataframe with at least a "taxid" column containing NCBI format TaxIds and allows to draw multiple layers with different visualisation tools. Package: r-cran-lifepack Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lifepack_0.0.8-1.ca2404.1_all.deb Size: 25042 MD5sum: a21a1f74ea02fb4c2497db551dddd2bc SHA1: 6b5ab2b0d7f5303747c829eab12c002bcbe0860c SHA256: f75c9e634eefa0eaa63d3d2e7aafc2de68446663eb7ac2a12dbdd22bc655271c SHA512: 358594dca2736ecfeb58f3c08461e6542c86848503de4ae36f01337aa7bf727db53c7fee01c30ca953f978ee6b2efca235c7b1d1187d8a65c1ea74516274ecb0 Homepage: https://cran.r-project.org/package=lifepack Description: CRAN Package 'lifepack' (Insurance Reserve Calculations) Calculates insurance reserves and equivalence premiums using advanced numerical methods, including the Runge-Kutta algorithm and product integrals for transition probabilities. This package is useful for actuarial analyses and life insurance modeling, facilitating accurate financial projections. Package: r-cran-lifer Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-ggplot2, r-cran-jsonlite, r-cran-knitr, r-cran-maptiles, r-cran-readr, r-cran-rmarkdown, r-cran-stringr, r-cran-terra, r-cran-tidyterra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lifer_1.0.3-1.ca2404.1_all.deb Size: 105028 MD5sum: d13d0d8808f7caeb1493a30a2c69b268 SHA1: f25f05da461b714303affb9a5be99d4236b98503 SHA256: a5cffe4e4f849148c91d4068196e68ef8b1c153ef8d3db19e49477740e5ed42e SHA512: 2c190ec127347a9f358b62c4566d9f2c8a64737233fec981e396c0f0af56e20940fd5634f05fe8192246d70593fac27ed3910324189301e670bd16ce0e80b3ad Homepage: https://cran.r-project.org/package=lifeR Description: CRAN Package 'lifeR' (Identify Sites for Your Bird List) A suite of tools to use the 'eBird' database () and APIs to compare users' species lists to recent observations and create a report of the top sites to visit to see new species. Package: r-cran-lifertable Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-lifertable_1.0.1-1.ca2404.1_all.deb Size: 90364 MD5sum: d3b004a2b5390b0f58b8152981a6fdf7 SHA1: 7aa06f76374bfbdf7f0dfb991509e159e17023cd SHA256: db8f6f0e93bfa7fff29d5a12f6c2415e1d49d4e5d99cf85758de6d9b2ab9afe4 SHA512: bd9487ae07888d925b60832ed9c181d23e5577f6cf9da03af55e0bdfb5a49d351b3126aeaff213e0c68180750402c76c36d511af1660839a9b79116786e15f6f Homepage: https://cran.r-project.org/package=Lifertable Description: CRAN Package 'Lifertable' (Life and Fertility Tables Specially for Insects) Life and Fertility Tables are appropriate to study the dynamics of arthropods populations. This package provides utilities for constructing Life Tables and Fertility Tables, related demographic parameters, and some simple graphs of interest. It also offers functions to transform the obtained data into a known format for better manipulation. In addition, two methods for obtaining the confidence interval are included. Package: r-cran-lifetablebuilder Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-dt, r-cran-ggplot2, r-cran-readxl, r-cran-gridextra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lifetablebuilder_0.1.2-1.ca2404.1_all.deb Size: 18514 MD5sum: 1ce834f45da8b4dffde640d75a2d1e41 SHA1: 11b75cb8258bd3032e94c564e26f3b066f0583b4 SHA256: 427ddc7ef7400c712f33ce5175033ca99d128087589f74f51c0110f0219c082a SHA512: 094b3a883d8f57de59cfbd1c9ae8d5310232503944fc320923cd5f4ffa90bc8951d58a52f5936df8485ca5dee5d7796f65b4aaf13c13827a72bf801acbae8654 Homepage: https://cran.r-project.org/package=LifeTableBuilder Description: CRAN Package 'LifeTableBuilder' (Interactive 'shiny' Application for Constructing Life Tables) Provides an interactive 'shiny' application to construct stage-structured life tables from tabular input data. The application includes input validation, demographic calculations, visualization tools, and export of tables and figures to support reproducible workflows in ecological and entomological studies. Methods for life table construction follow classical demographic approaches described in Martinez (2015) . Package: r-cran-lifetablefertility Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Suggests: r-cran-knitr, r-cran-dt, r-cran-ggplot2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lifetablefertility_0.1.0-1.ca2404.1_all.deb Size: 30356 MD5sum: 6a2a00cdd1c525d53904f6e5fe370510 SHA1: ed2d1828f148a540ac6133d679aa02eb80703cb8 SHA256: f6f575d01231f8d018d4bcbb4db7e6df70d3676239d79ced6553915de6b496db SHA512: b7a5826dedb4c369b62b96669487489958ddd56133fe64585bcd7765085ee3f77d60355d48cba4009b4fe8baa77720e5bef10aea167d3b4a159e83b049ed184f Homepage: https://cran.r-project.org/package=LifeTableFertility Description: CRAN Package 'LifeTableFertility' ('shiny' Application for Life Table and Fertility Analysis) Provides a 'shiny' application to construct age-specific life tables and fertility schedules from individual female daily egg records. The application computes age-specific survival and fertility functions and estimates key demographic parameters including the net reproductive rate, mean generation time, intrinsic rate of increase, finite rate of increase and doubling time. Optional confidence intervals can be obtained using percentile bootstrap or delete-1 jackknife resampling at the female level. Methods and definitions follow Stevens (2009) and Rossini et al. (2024) . 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The evidential approach used here is based on the book "Likelihood" by A.W.F. Edwards (1992, ISBN-13 : 978-0801844430), "Statistical Evidence" by R. Royall (1997, ISBN-13 : 978-0412044113), S.N. Goodman & R. Royall (2011) , "Understanding Psychology as a Science" by Z. Dienes (2008, ISBN-13 : 978-0230542310), S. Glover & P. Dixon and others. This package accompanies "Evidence-Based Statistics" by P. Cahusac (2020, ISBN-13 : 978-1119549802) . 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Package: r-cran-lindenmayer Architecture: all Version: 0.1.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr Filename: pool/dists/noble/main/r-cran-lindenmayer_0.1.13-1.ca2404.1_all.deb Size: 28596 MD5sum: af47d4226a8e9a839bce2bd228943cee SHA1: 9a0723dbe7bebdeeac3b333a937f709721124936 SHA256: afd735482c732b82463a1b1f13a7acfd74abd7b7a7e96a86056eb48c2ad572f8 SHA512: 4a26ef322cef35cdc9c252acaba6b307e29766f7aac84ad1db8d6f1c910a4fa148b92822f66e4ba4b8719c38d3db9c7538a54c6741f5a456499ccdeef3f8e419 Homepage: https://cran.r-project.org/package=LindenmayeR Description: CRAN Package 'LindenmayeR' (Functions to Explore L-Systems (Lindenmayer Systems)) L-systems or Lindenmayer systems are parallel rewriting systems which can be used to simulate biological forms and certain kinds of fractals. 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Provides a unified interface for multinomial logistic regression, hierarchical partial-pooling models, and the Piantham approximation for relative reproduction number estimation. Features include rolling-origin backtesting, standardized forecast scoring, lineage collapsing, emergence detection, and sequencing power analysis. Designed for real-time public health surveillance of any variant-resolved pathogen. Methods described in Abousamra, Figgins, and Bedford (2024) . 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Package: r-cran-lineartestr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-sandwich, r-cran-dplyr, r-cran-ggplot2, r-cran-viridis, r-cran-tidyr, r-cran-readr, r-cran-forecast Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lineartestr_1.0.0-1.ca2404.1_all.deb Size: 95610 MD5sum: 5d89e4b7542b16d1ad5ac83ca9a8e39c SHA1: bc97efaad4364c2039018cf9d097168d0e622f4b SHA256: 1e5fa8c496bbb807f80073cd4b38a7d8b3af5916f48b6a8192400cae4a8d46c6 SHA512: 43bd6ec715db28567dc2cc4e16f7de7b31b6c9ad77b1e6c7c93bc700bcb82e1ffaf66c66fb164d394e22e0e294c5c85e0ab79a8f10ec33c559e328ca47e2fc09 Homepage: https://cran.r-project.org/package=lineartestr Description: CRAN Package 'lineartestr' (Linear Specification Testing) Tests whether the linear hypothesis of a model is correct specified using Dominguez-Lobato test. Also Ramsey's RESET (Regression Equation Specification Error Test) test is implemented and Wald tests can be carried out. Although RESET test is widely used to test the linear hypothesis of a model, Dominguez and Lobato (2019) proposed a novel approach that generalizes well known specification tests such as Ramsey's. This test relies on wild-bootstrap; this package implements this approach to be usable with any function that fits linear models and is compatible with the update() function such as 'stats'::lm(), 'lfe'::felm() and 'forecast'::Arima(), for ARMA (autoregressive–moving-average) models. Also the package can handle custom statistics such as Cramer von Mises and Kolmogorov Smirnov, described by the authors, and custom distributions such as Mammen (discrete and continuous) and Rademacher. Manuel A. Dominguez & Ignacio N. Lobato (2019) . Package: r-cran-linelist Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 683 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-callr, r-cran-dplyr, r-cran-knitr, r-cran-outbreaks, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-linelist_2.0.1-1.ca2404.1_all.deb Size: 271552 MD5sum: 73a77b1d7f6cc3535b441426f39b6811 SHA1: 305fdbc14279fb4bd591059d24125efc637fcdb0 SHA256: ea7a2de6a8c033481b45dd8854f4e0191de980af0abb12d190dd4db2d7551443 SHA512: cb223e70d1a960a1ddfdb0bc5b98486521386dad249d859251d4d7322f54bebe5099ed9973517095da2472509d8d8fa855255bb9a401189c4f7a6f16c0ed663b Homepage: https://cran.r-project.org/package=linelist Description: CRAN Package 'linelist' (Tagging and Validating Epidemiological Data) Provides tools to help storing and handling case line list data. 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Package: r-cran-linemap Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1714 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra Suggests: r-cran-tinytest, r-cran-covr Filename: pool/dists/noble/main/r-cran-linemap_0.3.0-1.ca2404.1_all.deb Size: 1654306 MD5sum: b5cd83b0e5a2434c030c290f876768d1 SHA1: d987df9e8d9c425e0800de9a77845438d7a590c0 SHA256: 1e14e10cb982c3f7a19811838b0fb1de187a9438e1fb5f0289f81e72cfcb732f SHA512: aa71a0f78339f31f6156e474969fe071a0af30ece390b7bf53ad45f3e1af7d345e82057b71d4a21f64bcea05858ef2342c51fde4beeb9d1a93ea1e93cbe09567 Homepage: https://cran.r-project.org/package=linemap Description: CRAN Package 'linemap' (Line Maps) Create maps made of lines. The package contains one function: linemap(). linemap() displays a map made of lines using a raster or gridded data. Package: r-cran-lineupjs Architecture: all Version: 4.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-crosstalk, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lintr, r-cran-remotes, r-cran-styler, r-cran-shiny Filename: pool/dists/noble/main/r-cran-lineupjs_4.6.0-1.ca2404.1_all.deb Size: 322788 MD5sum: 96f1baaa7eebaf5fe91bfadb332ac43b SHA1: 3f870f4b99813720729eb1424c75065899b3efce SHA256: cd42912cec54dd196f773f2b2535d7bf3cc7b5c3a965c869f8cc4d6ee063aadc SHA512: f92132c6b04d16c07699a523e1698105904dd4c5e313cca09f4dc23f226f225139868188b7cc5cb723859ae11ee67b24c59e556aedf08035cdaaff7fca900524 Homepage: https://cran.r-project.org/package=lineupjs Description: CRAN Package 'lineupjs' ('HTMLWidget' Wrapper of 'LineUp' for Visual Analysis ofMulti-Attribute Rankings) 'LineUp' is an interactive technique designed to create, visualize and explore rankings of items based on a set of heterogeneous attributes. This is a 'htmlwidget' wrapper around the JavaScript library 'LineUp.js'. It is designed to be used in 'R Shiny' apps and 'R Markddown' files. Due to an outdated 'webkit' version of 'RStudio' it won't work in the integrated viewer. Package: r-cran-linevis Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1818 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crosstalk, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-magrittr, r-cran-rmarkdown, r-cran-shiny Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-linevis_1.0.0-1.ca2404.1_all.deb Size: 365622 MD5sum: 422e854fb84a074368bf0baa39235333 SHA1: 87242412608c9dcb3b3c56161fc8fe96e4c21a3b SHA256: bae189bd2bf7e30f386ba4a5193e83bb694132627b1a3e6d1964fc6bf96fda31 SHA512: 8c9cad182077fef48bffaedf920d5d81c20da81c287a5a4bb890b105ac07a21a45b8a589a9b721330c87a2d19b933557d5072f777cb05e2d5dc0d6786417ec9a Homepage: https://cran.r-project.org/package=linevis Description: CRAN Package 'linevis' (Interactive Time Series Visualizations) Create interactive time series visualizations. 'linevis' includes an extensive API to manipulate time series after creation, and supports getting data out of the visualization. Based on the 'timevis' package and the 'vis.js' Timeline 'JavaScript' library . Package: r-cran-lingglosses Architecture: all Version: 0.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kableextra, r-cran-knitr, r-cran-gt, r-cran-rmarkdown, r-cran-htmltools Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lingglosses_0.0.11-1.ca2404.1_all.deb Size: 63808 MD5sum: 5ce631ed94e09791e32e381d1b03c5f3 SHA1: 15b6ef8d3b78ed17d6496931405e520af05089d4 SHA256: 4d8b9c0a202d930743fb9ae0ff718b38c84479a1648f1db66897525c5e65bc5e SHA512: 838f36a267ca5f298f4c37d78a0e312947edc89fc64f06f3916c94d8c022ea8740179d368b3de123aade3e9a2f864243d7b210f6d21dd2b52088f04c92deb841 Homepage: https://cran.r-project.org/package=lingglosses Description: CRAN Package 'lingglosses' (Interlinear Glossed Linguistic Examples and Abbreviation ListsGeneration) Helps to render interlinear glossed linguistic examples in html 'rmarkdown' documents and then semi-automatically compiles the list of glosses at the end of the document. 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Package: r-cran-lingtypology Architecture: all Version: 1.1.25-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaflet, r-cran-leaflet.minicharts, r-cran-stringdist, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mass, r-cran-sp, r-cran-sf, r-cran-ape Filename: pool/dists/noble/main/r-cran-lingtypology_1.1.25-1.ca2404.1_all.deb Size: 2798574 MD5sum: 94c731e8dda4d0afaba76ad2b4347372 SHA1: 8531738221bdc3548f7c2af3eedd3f435ca5e63b SHA256: 4c310c9e092972eea0eb2bac14a4f15ee648f77e5d159891d3bda3847df9dbce SHA512: 8456480c355cf136c600c603a7fc3e47d3441247ae25aa341f8c76083c5d521bba32a63214b8cbf249ae133558cc21f51da583f475c041a7d68fb47b2b507030 Homepage: https://cran.r-project.org/package=lingtypology Description: CRAN Package 'lingtypology' (Linguistic Typology and Mapping) Provides R with the Glottolog database and some more abilities for purposes of linguistic mapping. The Glottolog database contains the catalogue of languages of the world. This package helps researchers to make a linguistic maps, using philosophy of the Cross-Linguistic Linked Data project , which allows for while at the same time facilitating uniform access to the data across publications. A tutorial for this package is available on GitHub pages and package vignette. Maps created by this package can be used both for the investigation and linguistic teaching. In addition, package provides an ability to download data from typological databases such as WALS, AUTOTYP and some others and to create your own database website. 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Access to Web of Science and Scopus requires personal API keys, while PubMed can be queried without one. The optional deduplication functionality requires the package 'ASySD' available from . Package: r-cran-litriddle Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4910 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-stylo, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-litriddle_1.0.0-1.ca2404.1_all.deb Size: 4796696 MD5sum: a3e08a8cfc43472d51b4f874e5ccad94 SHA1: b6629d19f645284f416b661fba28cb17eb568921 SHA256: e12d321c8e704c7a43437186a48d8cbadd8ce9885819833dee715957901567e8 SHA512: 0d2d9e7bee437cdbe54926ed7b7ea6d5f5847da09fceeb972bef821fffc2bf811279dbbdb8f2b9ae5901d45835ddcb59e29c333e91d91987810282d1bb091b31 Homepage: https://cran.r-project.org/package=litRiddle Description: CRAN Package 'litRiddle' (Dataset and Tools to Research the Riddle of Literary Quality) Dataset and functions to explore quality of literary novels. 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Models span simple empirical to process-motivated forms with differing numbers of free parameters. Provides parameter estimates, uncertainty, and tools for model comparison/selection. Based on Cornwell & Weedon (2013) . Package: r-cran-liureg Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-liureg_1.1.2-1.ca2404.1_all.deb Size: 114312 MD5sum: 086052c4b997c6cce8074f6f79d1b9d7 SHA1: 2ead4db9fc730385ecddaa9b1a605ef1bf386f65 SHA256: 494e3cd7b0ad734cdd8e37770742b3289464f0446a8a32b59aa16273dc244bf6 SHA512: e1c3b4493bc7a26f6a382c8fa130db5133666ebff812d878dafa1410256b80bfd8bd7c728b60104d8fd60514c600da8c7e8c2396d408ce2ac8f4ff5c66bbcb03 Homepage: https://cran.r-project.org/package=liureg Description: CRAN Package 'liureg' (Liu Regression with Liu Biasing Parameters and Statistics) Linear Liu regression coefficient's estimation and testing with different Liu related measures such as MSE, R-squared etc. REFERENCES i. Akdeniz and Kaciranlar (1995) ii. Druilhet and Mom (2008) iii. Imdadullah, Aslam, and Saima (2017) iv. Liu (1993) v. Liu (2001) . Package: r-cran-live Architecture: all Version: 1.5.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlr, r-cran-dplyr, r-cran-breakdown, r-cran-data.table, r-cran-forestmodel, r-cran-shiny, r-cran-mass, r-cran-ggplot2, r-cran-gower, r-cran-e1071 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-glmnet, r-cran-covr, r-cran-dalex, r-cran-rweka, r-cran-mda, r-cran-modeltools Filename: pool/dists/noble/main/r-cran-live_1.5.13-1.ca2404.1_all.deb Size: 200656 MD5sum: ed70b17865f6f99f61e7d895cf76e23b SHA1: 71c6341f4eec4a1a5fc765d2666b9bab0e340565 SHA256: 2c579e814288ac176a12fc8f298d2da6c09404f62746ed619e804077cc5c6bb3 SHA512: 54aba188316d00a27cd5a1b2bd65d39434e5a3e9993ecdbb1db5d0352c7a76fe25e99516a943235e3656b8d6d7812abf13ad035a676f69570ae3e82143aa4812 Homepage: https://cran.r-project.org/package=live Description: CRAN Package 'live' (Local Interpretable (Model-Agnostic) Visual Explanations) Interpretability of complex machine learning models is a growing concern. This package helps to understand key factors that drive the decision made by complicated predictive model (so called black box model). This is achieved through local approximations that are either based on additive regression like model or CART like model that allows for higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) . More details can be found in Staniak, Biecek (2018) . Package: r-cran-liver Architecture: all Version: 1.29-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5891 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-class, r-cran-ggplot2 Suggests: r-cran-proc, r-cran-skimr, r-cran-knitr, r-cran-rmarkdown, r-cran-data.table, r-cran-mltools, r-cran-forcats Filename: pool/dists/noble/main/r-cran-liver_1.29-1.ca2404.1_all.deb Size: 5108064 MD5sum: f148b9d242690a0310d036d149fded54 SHA1: fd2bd4d2d4403759ddad2bda1430eb767e00c436 SHA256: 3f778fc956b37aef0aa0dcb54c13b7d5bdcc6b516b7a9859943be6f561e18359 SHA512: 1bb30e8c795dbfb98c512afa1bd788179d3f2dc03f938a254bfe85c43eb1ee9edfbeddf5444397148451fc5f320bde231b6130f3f445770a785fa927198ca0e5 Homepage: https://cran.r-project.org/package=liver Description: CRAN Package 'liver' (Toolkit and Datasets for Data Science) Provides a collection of helper functions and illustrative datasets to support learning and teaching of data science with R. The package is designed as a companion to the book , making key data science techniques accessible to individuals with minimal coding experience. Functions include tools for data partitioning, performance evaluation, and data transformations (e.g., z-score and min-max scaling). The included datasets are curated to highlight practical applications in data exploration, modeling, and multivariate analysis. An early inspiration for the package came from an ancient Persian idiom about "eating the liver", symbolizing deep and immersive engagement with knowledge. Package: r-cran-ljexm Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fs, r-cran-pdftools, r-cran-rstudioapi, r-cran-webshot Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ljexm_1.0.5-1.ca2404.1_all.deb Size: 24660 MD5sum: dbf1871ffce824626b97f4029ab47072 SHA1: 885ae0a334e2d1c1059b5701207815c618e16d79 SHA256: c968f293b415cf86474215b7ed6dd5f3c502abe7dc2e55e60ce73ed742801bd3 SHA512: c187e7fa0034447d949968aa8424c1cbc5bd84d32ebb295d2f1375b3ec00b5de4be51c656b676473640155072d309cfe79b8b3486d4762f48de3b6a8515b1382 Homepage: https://cran.r-project.org/package=LJexm Description: CRAN Package 'LJexm' (Extract, Convert, and Merge 'pdf' Files from 'zip' Files) Extracts 'zip' files, converts 'Word', 'Excel', and 'html'/'htm' files to 'pdf' format. 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Package: r-cran-ljmp3converter Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fs, r-cran-rstudioapi, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ljmp3converter_1.0.7-1.ca2404.1_all.deb Size: 27648 MD5sum: c37f5c78c3c7dd393ff0fc5cee854f32 SHA1: 8760d1d7b3b23b7a042dd48491db96f321a2fc3e SHA256: 0d76d4de097007996b1e1ca4917adb60eb0bcf07d59fb88b1d79aaae9fa06f88 SHA512: 984150053c92f19e3ae8192158ec88f7757de96fb18452fc2a99bf96d1b0d1e388fda4cd23e60f7224b409d9d837c7f47134dba4810c076573c27dc5785232a5 Homepage: https://cran.r-project.org/package=LJmp3converter Description: CRAN Package 'LJmp3converter' (Convert Video Files to 'mp3' Format, Merge or Trim Audio Filesusing 'FFmpeg') Converts video files to 'mp3', merges multiple audio files and trims audio files using 'FFmpeg', which is dynamically downloaded to avoid bundling any third-party binaries. 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Package: r-cran-lkt Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3390 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparsem, r-cran-matrix, r-cran-data.table, r-cran-glmnet, r-cran-glmnetutils, r-cran-lme4, r-cran-cluster, r-cran-proc, r-cran-crayon, r-cran-hdinterval Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-caret, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-lkt_1.7.0-1.ca2404.1_all.deb Size: 2658240 MD5sum: d911cccd6cfdfabf7adc9eef6314af2e SHA1: 1b4acd59153df2cdadf8b87e8991a134ee5062dc SHA256: 46a13a80e2a5bc27758f65f372fd9c36696ee5dd34f7f23880845a041e555300 SHA512: 9c7466bb6bfc2ac7ede83cb0a4f5f8bd0a909b7808626f1cbb27f98abde1168e18799abca98e8f3884e9b7b2e7119ece67cf12edd26ced3ee20e1bf52078fedc Homepage: https://cran.r-project.org/package=LKT Description: CRAN Package 'LKT' (Logistic Knowledge Tracing) Computes Logistic Knowledge Tracing ('LKT') which is a general method for tracking human learning in an educational software system. Please see Pavlik, Eglington, and Harrel-Williams (2021) . 'LKT' is a method to compute features of student data that are used as predictors of subsequent performance. 'LKT' allows great flexibility in the choice of predictive components and features computed for these predictive components. The system is built on top of 'LiblineaR', which enables extremely fast solutions compared to base glm() in R. Package: r-cran-llama Architecture: all Version: 0.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2251 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlr, r-cran-rjava, r-cran-parallelmap, r-cran-ggplot2, r-cran-checkmate, r-cran-bbmisc, r-cran-plyr, r-cran-data.table Suggests: r-cran-testthat, r-cran-paramhelpers Filename: pool/dists/noble/main/r-cran-llama_0.10.1-1.ca2404.1_all.deb Size: 1951434 MD5sum: d82b9430f39fd4d7bb1187b5c4a8a7b5 SHA1: bcfe20bc76522158b9044b864e7442aeb2cd8885 SHA256: 053fee1fb059028985f1c15d51ddb96d6f83d5075571b2d5c91324d61ace59d3 SHA512: 5dbc353ef3b7605b1d3cae43e9215722567507ac8487d3701ee78e009513ceda339b9df54eb5f845d776552ec9fb20fb4689b58c63ad23a81cbc13ef468f00fb Homepage: https://cran.r-project.org/package=llama Description: CRAN Package 'llama' (Leveraging Learning to Automatically Manage Algorithms) Provides functionality to train and evaluate algorithm selection models for portfolios. Package: r-cran-llbayesireg Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-stanheaders, r-cran-rcpp, r-cran-llogistic, r-cran-rstan, r-cran-mcmcpack, r-cran-mass, r-cran-coda Filename: pool/dists/noble/main/r-cran-llbayesireg_1.0.0-1.ca2404.1_all.deb Size: 49584 MD5sum: 345a7cb03a1dd3a2321ed0cd9407a432 SHA1: 7f3a0df7942d1eeb2c666fd49b7f9b66f4687542 SHA256: 42fb5acd7c2170e6b49305ace61d1ca7fd9969413a641b7518466b6fc426860f SHA512: 66a05a79a118087f9d56be9a6d97645f7120fcf12147c5981bb07a20f6226de4d4a20b56e269f610396806050d5cd0b2b517295acb1e8f9716b30c276a5df69e Homepage: https://cran.r-project.org/package=llbayesireg Description: CRAN Package 'llbayesireg' (The L-Logistic Bayesian Regression) R functions and data sets for the work Paz, R.F., Balakrishnan, N and Bazán, J.L. (2018). L-logistic regression models: Prior sensitivity analysis, robustness to outliers and applications. Brazilian Journal of Probability and Statistics, . Package: r-cran-llic Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vgam, r-cran-dplyr, r-cran-laplacesdemon, r-cran-relliptical, r-cran-ggplot2, r-cran-rlang Filename: pool/dists/noble/main/r-cran-llic_3.0.0-1.ca2404.1_all.deb Size: 19790 MD5sum: 5c8a537a7da950003e51d4dd440838e2 SHA1: 5f68610f652ad204dbe5f78228056280b175ba28 SHA256: 9d69cdf77cd83a8679d6303153c642b9e6bc084a263e12236f5c9842242fb676 SHA512: 97a67b5825d845c7cbe68ea3b5bf650a72ad8b68c6a2d02037df01bb470e7f68af2dd5c2b5d0d9297bc4b7a49beb472824e8e526e5aadb9fe5474bc029d54d0f Homepage: https://cran.r-project.org/package=LLIC Description: CRAN Package 'LLIC' (Likelihood Criterion (LIC) Analysis for Laplace Regression Model) Performs likelihood criterion analysis using the Laplace regression model to determine its optimal subset of variables. The methodology is based on Guo et al. (2023), LIC criterion for optimal subset selection in distributed interval estimation . Package: r-cran-llm.api Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-llm.api_0.1.3-1.ca2404.1_all.deb Size: 141934 MD5sum: b9e61dd108df732dd2b2de96a8c2f06d SHA1: fadd26d326f94c495794f796a9881068302ed9f4 SHA256: 4dd42e9a324bdea2faa14dfc5bd5373d56b2409ab8df8f08954a8bf1976cd56e SHA512: 3f9f1b8cb540164b88041e23b2eea5010be96cf6c76c60e4d30a02f7a1a6421a6a79a0e7418c293ee23eb259d103124946f5c3395454de9416bd9fcd069fc784 Homepage: https://cran.r-project.org/package=llm.api Description: CRAN Package 'llm.api' (Minimal LLM Chat Interface) A minimal-dependency client for Large Language Model chat APIs. Supports 'OpenAI' , 'Anthropic' 'Claude' , 'Moonshot' 'Kimi' , 'Ollama' , and other 'OpenAI'-compatible endpoints. Includes an agent loop with tool use and a 'Model Context Protocol' client . API design is derived from the 'ellmer' package, reimplemented with only base R, 'curl', and 'jsonlite'. Package: r-cran-llm Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partykit, r-cran-stringr, r-cran-rweka, r-cran-survey, r-cran-reghelper, r-cran-scales Suggests: r-cran-mlbench Filename: pool/dists/noble/main/r-cran-llm_1.1.0-1.ca2404.1_all.deb Size: 74392 MD5sum: 3608867fd79b3f6e3558292ef3030758 SHA1: 2e0bea7f601ba2ac7f30c46fbcabb3c8a83b7d6b SHA256: c953b5ea384bc77dcff922fd1c7658e056777f9a7ed88c5a7273e74f6c0ee01c SHA512: af0a1536051e229ce70b2e71b9dfdc16f3a2e07095eb1aaf706ed106c4f73d6272f2c372279d7f72cebc39c64acf43bdc235507e29e17906caaf5fb7d7f3025d Homepage: https://cran.r-project.org/package=LLM Description: CRAN Package 'LLM' (Logit Leaf Model Classifier for Binary Classification) Fits the Logit Leaf Model, makes predictions and visualizes the output. (De Caigny et al., (2018) ). Package: r-cran-llmagentr Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1725 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-dbi, r-cran-rsqlite, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-officer, r-cran-purrr, r-cran-timetk, r-cran-pdftools, r-cran-parsnip, r-cran-recipes, r-cran-workflows, r-cran-rsample, r-cran-modeltime.ensemble, r-cran-modeltime, r-cran-xml2 Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-jsonlite, r-cran-magrittr, r-cran-rlang, r-cran-tidyr, r-cran-ggplot2, r-cran-usethis, r-cran-prophet, r-cran-forcats, r-cran-kernlab, r-cran-xgboost, r-cran-xfun, r-cran-modeltime.resample, r-cran-tidymodels, r-cran-tibble, r-cran-lubridate, r-cran-tesseract, r-cran-rvest, r-cran-fastdummies, r-cran-stringr Filename: pool/dists/noble/main/r-cran-llmagentr_0.3.2-1.ca2404.1_all.deb Size: 1696930 MD5sum: 024232317cbfa1002c70c7750ea57b0d SHA1: ab7c48c122fde44629393c29e4c61a36bc1bece8 SHA256: 66c9c63f557ff80b691e880fdf183b550b80371b318ef27336e9e5932e847128 SHA512: 0b84b3909f919a88d0dc9172542f56bd6a16b8a11695c1b77296c40fff563807fe0bd1c764edf00dc6a414a06ae56785cfcdc7ca10fa8bd2e68ad1de03a98b7e Homepage: https://cran.r-project.org/package=LLMAgentR Description: CRAN Package 'LLMAgentR' (Language Model Agents in R for AI Workflows and Research) Provides modular, graph-based agents powered by large language models (LLMs) for intelligent task execution in R. Supports structured workflows for tasks such as forecasting, data visualization, feature engineering, data wrangling, data cleaning, 'SQL', code generation, weather reporting, and research-driven question answering. Each agent performs iterative reasoning: recommending steps, generating R code, executing, debugging, and explaining results. Includes built-in support for packages such as 'tidymodels', 'modeltime', 'plotly', 'ggplot2', and 'prophet'. Designed for analysts, developers, and teams building intelligent, reproducible AI workflows in R. Compatible with LLM providers such as 'OpenAI', 'Anthropic', 'Groq', and 'Ollama'. Inspired by the Python package 'langagent'. Package: r-cran-llmclean Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-httr2, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-llmclean_0.1.0-1.ca2404.1_all.deb Size: 117102 MD5sum: 7784386cc13e902f383c0ecac63b6d3e SHA1: 7d3b5137614e9a2d2ead904327ac6b64be47c700 SHA256: e873f512bda25c4c543e230eeda2c3973df333a356206266c1ca66397a55fa75 SHA512: 4e8ccbd2a10223378a40a0e419b53a1406c2bc809e35182c3d9a3cb1c23020889f204b165323c4af149bd503d0d72f774c714323163af4c656114937484a95fa Homepage: https://cran.r-project.org/package=llmclean Description: CRAN Package 'llmclean' ('LLM'-Assisted Data Cleaning with Multi-Provider Support) Detects and suggests fixes for semantic inconsistencies in data frames by calling large language models (LLMs) through a unified, provider-agnostic interface. Supported providers include 'OpenAI' ('GPT-4o', 'GPT-4o-mini'), 'Anthropic' ('Claude'), 'Google' ('Gemini'), 'Groq' (free-tier 'LLaMA' and 'Mixtral'), and local 'Ollama' models. The package identifies issues that rule-based tools cannot detect: abbreviation variants, typographic errors, case inconsistencies, and malformed values. Results are returned as tidy data frames with column, row index, detected value, issue type, suggested fix, and confidence score. An offline fallback using statistical and fuzzy-matching methods is provided for use without any API key. Interactive fix application with human review is supported via 'apply_fixes()'. Methods follow de Jonge and van der Loo (2013) and Chaudhuri et al. (2003) . Package: r-cran-llmcoder Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rstudioapi, r-cran-httr2, r-cran-miniui, r-cran-shiny, r-cran-stringi, r-cran-stringr, r-cran-rlang, r-cran-htmltools, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-llmcoder_1.2.0-1.ca2404.1_all.deb Size: 176340 MD5sum: 8e53185c956885e336a39baf4fb414e3 SHA1: d977f69173f3238d14c11f5cd9828db517aa379e SHA256: 62ff0dd651241c8e767e5ac6a9a0494692dc7474f94ac479cc6e6880e8cd4acc SHA512: c2577e7f978bb632adc6595d7b71dbcdadff6643e09b2642ac996fb03b277375b45b0def7f585dd187d5f1c22f48db0ff186e115129831c9ec189b0082d03e0c Homepage: https://cran.r-project.org/package=llmcoder Description: CRAN Package 'llmcoder' (LLM-Powered Code Generation, Error Fixing, and Chat for'RStudio') An 'RStudio' addin that integrates large language model (LLM) assistance directly into the code-editing workflow. Features include: (1) generate R code from inline comments; (2) obtain LLM-assisted fixes for console errors; (3) insert plain-English explanations of selected code blocks; (4) a multi-turn Chat Panel with session-context awareness (loaded packages, global objects, source editor contents, console history). Supports 'OpenAI', 'Anthropic' (Claude), 'DeepSeek', 'Groq', 'Together AI', 'OpenRouter', 'Ollama' (fully local, no API key required), and any 'OpenAI'-compatible custom endpoint (e.g. 'LM Studio', 'vLLM', 'llama.cpp'). Package: r-cran-llmflow Architecture: all Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-glue, r-cran-jsonlite, r-cran-jsonvalidate Suggests: r-cran-ellmer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-llmflow_3.0.2-1.ca2404.1_all.deb Size: 162696 MD5sum: 833e05f953df7ed3b61964914e9d3fea SHA1: c6c27a0dddc23161f26ff2f60fbb5d9653be5ddd SHA256: e2bd02c24ef3b483f9f1e62ea13eff0364ac4c281f3b06b442cfd9622fcc2bb9 SHA512: 6dc6bc6349d334f15b00fa6cf12de1ab5061fbb5b28ff9885c0b4b462c837f43e9210eb1db486c02d939888d72c171271b903a2097687706a5fc6f0583efa675 Homepage: https://cran.r-project.org/package=llmflow Description: CRAN Package 'llmflow' (Reasoning and Acting Workflow for Automated Data Analysis) Provides a framework for integrating Large Language Models (LLMs) with R programming through workflow automation. Built on the ReAct (Reasoning and Acting) architecture, enables bi-directional communication between LLMs and R environments. Features include automated code generation and execution, intelligent error handling with retry mechanisms, persistent session management, structured JSON output validation, and context-aware conversation management. Package: r-cran-llmhelper Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyprompt Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-llmhelper_1.0.0-1.ca2404.1_all.deb Size: 89940 MD5sum: 624e0a3d7e20e223d96900a5c7eed245 SHA1: e5d01004f256d676e70632fe16418c2696af36c3 SHA256: 8c75ce9bdb0aa861ee0a8a20c07de7ddfe8a9aeef0e90ee33b92a5942f27d01a SHA512: 6cd6309826b3c52c4058c74b17975b1eb1adaeff7682ae96867c59cc1e55cb5aca22f0dc6d3e65b14071c5475df284c9e0fcfd4fc1e5ec690e313f0990d71f7e Homepage: https://cran.r-project.org/package=llmhelper Description: CRAN Package 'llmhelper' (Unified Interface for Large Language Model Interactions) Provides a unified interface for interacting with Large Language Models (LLMs) through various providers including OpenAI , Ollama , and other OpenAI-compatible APIs. Features include automatic connection testing, max_tokens limit auto-adjustment, structured JSON responses with schema validation, interactive JSON schema generation, prompt templating, and comprehensive diagnostics. Package: r-cran-llming Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-quanteda, r-cran-stopwords, r-cran-stringi, r-cran-reticulate, r-cran-text, r-cran-dbscan, r-cran-pracma, r-cran-jsonlite, r-cran-matrix, r-cran-text2vec Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-llming_1.2.1-1.ca2404.1_all.deb Size: 72784 MD5sum: e7681add089f1a629777848acf3cc314 SHA1: 27ead69d05404078fe97810345ea3cb0df0cb1b1 SHA256: 116e56f835ee85b3098fb67cb320cb2d4fc3e5ff885fc5a1d092d8b0f49573eb SHA512: ed197189c94277751d2fa50c56110de120849ed932f5f6ff0c1806e6f38d90aa6483e33f0ce294a8f69196044fc228d367d31566c4c014277339238521d2f0fd Homepage: https://cran.r-project.org/package=LLMing Description: CRAN Package 'LLMing' (Large Language Model (LLM) Tools for Psychological Text Analysis) A collection of large language model (LLM) text analysis methods designed with psychological data in mind. Currently, LLMing (aka "lemming") includes a text anomaly detection method based on the angle-based subspace approach described by Zhang, Lin, and Karim (2015) and a text generation method. . 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Package: r-cran-llmtranslate Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Suggests: r-cran-dt, r-cran-readxl, r-cran-writexl, r-cran-openxlsx, r-cran-httr2, r-cran-jsonlite, r-cran-glue, r-cran-later, r-cran-testthat Filename: pool/dists/noble/main/r-cran-llmtranslate_0.3.0-1.ca2404.1_all.deb Size: 54466 MD5sum: d34e8ebf32726cd7c8fd09735a9ca7b2 SHA1: ea2354b764dab9076c92154653fd323b864748a9 SHA256: 2fc71a3e43014a77ec1d1843cc0bd1e65bc154850dc71b874037b3d12a8212d9 SHA512: 3d3aa79164432969b0daf0f4aad35fae4031a5c725262d4f2768bd21dda2133411d7d8a5a096ea2d114c9bca60196abc3b255dfc1b148ad91e32fb4392d4c24d Homepage: https://cran.r-project.org/package=LLMTranslate Description: CRAN Package 'LLMTranslate' ('shiny' App for TRAPD/ISPOR Survey Translation with LLMs) A 'shiny' application to automate forward and back survey translation with optional reconciliation using large language models (LLMs). Supports both item-by-item and batch translation modes for optimal performance and context-aware translations. Handles multi-sheet Excel files and supports OpenAI (GPT), Google Gemini, and Anthropic Claude models. Follows the TRAPD (Translation, Review, Adjudication, Pretesting, Documentation) framework and ISPOR (International Society for Pharmacoeconomics and Outcomes Research) recommendations. See Harkness et al. (2010) and Wild et al. (2005) . Package: r-cran-llogistic Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-llogistic_1.0.3-1.ca2404.1_all.deb Size: 14208 MD5sum: 2c995c18a4cb6ba4e1670a24afdc9c8d SHA1: af01140ce7d512b32601963eb8ddb6eff8dbc712 SHA256: d49a3dd296ba107be0527adbe77e0db2f9d746225183979e7a2f23ed15f20890 SHA512: 3813d77c1107b7772b17a7db10b049e375a1ea07f6626165dbec561eaa362a9a4a892511ada3325c62e6998d0deed271d8d18d9046835806e8d64370dcfbc9be Homepage: https://cran.r-project.org/package=llogistic Description: CRAN Package 'llogistic' (The L-Logistic Distribution) Density, distribution function, quantile function and random generation for the L-Logistic distribution with parameters m and phi. The parameter m is the median of the distribution. 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The package will include (every other update) new functions in order to comprise useful tools in liquid-liquid extraction research. Package: r-cran-lm.beta Architecture: all Version: 1.7-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xtable Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-lm.beta_1.7-3-1.ca2404.1_all.deb Size: 249514 MD5sum: 5bb1f3e8bafbcd15f2b8da142256bf13 SHA1: 14e5d8039d129d0be968385b0f2c2c8e08fbf6e7 SHA256: 20b0c3a3e9d4ec3fdafaa9ed52cbb8d5b2cccbe0ec2930a4457aaef71e585074 SHA512: 2abd8ad7448d21d6f2b2d8c85eb1e2b618c73af9511b3f228d2beaf82336dd3e72b2af2d03aaf4989614c49da31e6c06b5aee312954c91036e04179ec9fb00c7 Homepage: https://cran.r-project.org/package=lm.beta Description: CRAN Package 'lm.beta' (Add Standardized Regression Coefficients to Linear-Model-Objects) Adds standardized regression coefficients to objects created by 'lm'. Also extends the S3 methods 'print', 'summary' and 'coef' with additional boolean argument 'standardized' and provides 'xtable'-support. Package: r-cran-lmboot Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-evd Filename: pool/dists/noble/main/r-cran-lmboot_0.0.1-1.ca2404.1_all.deb Size: 72376 MD5sum: 785c4d56d7f6a9c0fa2c8da4dbb1cde9 SHA1: 869c66d2f2f4364d511956c23bda5942d9574049 SHA256: 625e7b788abdfcd43f921ec2a31c8ebfaf21a2c149128cdbaf2072b8e869d29f SHA512: 01e60695f9b945e3bdc62ccbbac8857b5db68a0bfc8fd84ef89eddd332e26c0168a64a1ba59b84007d3051ba8c178e59f982c191bde9e66b90c06cd61a83cac9 Homepage: https://cran.r-project.org/package=lmboot Description: CRAN Package 'lmboot' (Bootstrap in Linear Models) Various efficient and robust bootstrap methods are implemented for linear models with least squares estimation. Functions within this package allow users to create bootstrap sampling distributions for model parameters, test hypotheses about parameters, and visualize the bootstrap sampling or null distributions. Methods implemented for linear models include the wild bootstrap by Wu (1986) , the residual and paired bootstraps by Efron (1979, ISBN:978-1-4612-4380-9), the delete-1 jackknife by Quenouille (1956) , and the Bayesian bootstrap by Rubin (1981) . Package: r-cran-lmd Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emd, r-cran-ggplot2, r-cran-patchwork, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggformula, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lmd_1.2.1-1.ca2404.1_all.deb Size: 369418 MD5sum: 495a74195dc4a138917cf0c572f36b7e SHA1: 36aee5a9bb079773d0ee5e80c0dadc7b682fe80d SHA256: 24b429fcab8b65b6519861c0b1dd709cca718f7fc33f2eae01e7d5ff32178118 SHA512: 3fe77eb1cbaa795d260be9615cfe3bf9410fab4caea66609f02f956fd90bfeb4d83c53af691cfe8a3dd135c831944a45e602156cc687d9f187a6950db0416556 Homepage: https://cran.r-project.org/package=LMD Description: CRAN Package 'LMD' (A Self-Adaptive Approach for Demodulating Multi-Component Signal) Local Mean Decomposition is an iterative and self-adaptive approach for demodulating, processing, and analyzing multi-component amplitude modulated and frequency modulated signals. This R package is based on the approach suggested by Smith (2005) and the 'Python' library 'PyLMD'. Package: r-cran-lmdiallel Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multcomp, r-cran-plyr, r-cran-sommer, r-cran-enhancer, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-lmdiallel_1.0.2-1.ca2404.1_all.deb Size: 246496 MD5sum: 34ddf94d3b18a7b5ba0490cf57c0a85c SHA1: 05b87fdea68c409ac36d4baa58f411c24fd4efeb SHA256: f3a429162eb59cd931fcb2c77f1c56a20e6ccdebecaad5c999857c6f36eb3741 SHA512: 5c73aec75efe8dd33993a5ea04b25c45b1e30a4c69243a02cbad674ad712b97dc20840f36d34bfab5cfafad729ddbd626c5d2abdd67b46f2f75d2bb9c80f7ef1 Homepage: https://cran.r-project.org/package=lmDiallel Description: CRAN Package 'lmDiallel' (Linear Fixed/Mixed Effects Models for Diallel Crosses) Several service functions to be used to analyse datasets obtained from diallel experiments within the frame of linear models in R, as described in Onofri et al (2020) . Package: r-cran-lmds Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-dynutils, r-cran-irlba, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lmds_0.1.0-1.ca2404.1_all.deb Size: 162004 MD5sum: e6cd3b176690d96b91482efd0eddda6d SHA1: 6d32a1f07ff2314b09376bbbf769c447db953135 SHA256: 60239543142e5705958e3bf477e0697c0d5bd1dabada7aff46fb9712a4fab0e4 SHA512: ce7680b84d48ba415a9a3157f356f37012090927f51424df082602d4847f620739b441d9182eeec2935373a0031897abc1e5e3f85eadc3fc95658bfb53ea27e6 Homepage: https://cran.r-project.org/package=lmds Description: CRAN Package 'lmds' (Landmark Multi-Dimensional Scaling) A fast dimensionality reduction method scaleable to large numbers of samples. Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS, but rather than calculating a complete distance matrix between all pairs of samples, only the distances between a set of landmarks and the samples are calculated. Package: r-cran-lme4breeding Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-crayon, r-cran-enhancer Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-orthopolynom, r-cran-mass, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-lme4breeding_1.1.2-1.ca2404.1_all.deb Size: 365216 MD5sum: 9375ed2f3b8da41537173861f430d155 SHA1: e915325d0f62c4f5f81035ecbcbc0299be7ca19d SHA256: 6c04b0162f90210c9db86136af9fdebe2aaec1b90c365ece5b66558a61c39ed7 SHA512: 092a9991d2fe9ae31393a8b56d15b621741c5fd6c8f0cfa15fd15a2cae2b8a310cfb8dae3245310384e5b3586271f6c8ba1a7e3e40a37e92c8532ad0984ff777 Homepage: https://cran.r-project.org/package=lme4breeding Description: CRAN Package 'lme4breeding' (Breeding-Related Mixed-Effects Models) Fit relationship-based and customized mixed-effects models with complex variance-covariance structures using the 'lme4' machinery. The core computational algorithms are implemented using the 'Eigen' 'C++' library for numerical linear algebra and 'RcppEigen' 'glue'. Package: r-cran-lme4gs Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-matrix Suggests: r-cran-bglr Filename: pool/dists/noble/main/r-cran-lme4gs_0.1-1.ca2404.1_all.deb Size: 392882 MD5sum: 07737d99bf1ec2db97b523948e356c25 SHA1: c145c91438aaa594378bc2de6e62b767a318de1f SHA256: 7178cc17c7ad78e4532936b7ea99dee28f31b80226b26147da764cd40401088e SHA512: 568df2f0f8af9230b2bfb85433da22f4cc1c18338c464ca97aaf9960b1f2068802746e4d7535172f51cad266f10b41cc0eb5eeade20bceb0626bcce8eb00a0b5 Homepage: https://cran.r-project.org/package=lme4GS Description: CRAN Package 'lme4GS' ('lme4' for Genomic Selection) Flexible functions that use 'lme4' as computational engine for fitting models used in Genomic Selection (GS). GS is a technology used for genetic improvement, and it has many advantages over phenotype-based selection. There are several statistical models that adequately approach the statistical challenges in GS, such as in linear mixed models (LMMs). The 'lme4' is the standard package for fitting linear and generalized LMMs in the R-package, but its use for genetic analysis is limited because it does not allow the correlation between individuals or groups of individuals to be defined. The 'lme4GS' package is focused on fitting LMMs with covariance structures defined by the user, bandwidth selection, and genomic prediction. The new package is focused on genomic prediction of the models used in GS and can fit LMMs using different variance-covariance matrices. Several examples of GS models are presented using this package as well as the analysis using real data. For more details see Caamal-Pat et.al. (2021) . Package: r-cran-lmeinfo Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-scdhlm, r-cran-mlmrev, r-cran-cardata, r-cran-lme4, r-cran-matrix, r-cran-merderiv Filename: pool/dists/noble/main/r-cran-lmeinfo_0.3.2-1.ca2404.1_all.deb Size: 137312 MD5sum: 4fe32edd9a36b00ec27140dc52e7e99a SHA1: 4ff2c7281711810edf40a4b98a485fba0cbfe976 SHA256: d42af35741d9d8bc3081c24f6c4824ff4363b963900853b6705e72ec06ca9598 SHA512: 329ac81ff71811747801ab46df74c7193fb2dd09b5b3d46e0fec77f31ce1c6b889095c7c7b2423a96c9e6e91eaad44783f66347935e787110d9df886d755b68a Homepage: https://cran.r-project.org/package=lmeInfo Description: CRAN Package 'lmeInfo' (Information Matrices for 'lmeStruct' and 'glsStruct' Objects) Provides analytic derivatives and information matrices for fitted linear mixed effects (lme) models and generalized least squares (gls) models estimated using lme() (from package 'nlme') and gls() (from package 'nlme'), respectively. The package includes functions for estimating the sampling variance-covariance of variance component parameters using the inverse Fisher information. The variance components include the parameters of the random effects structure (for lme models), the variance structure, and the correlation structure. The expected and average forms of the Fisher information matrix are used in the calculations, and models estimated by full maximum likelihood or restricted maximum likelihood are supported. The package also includes a function for estimating standardized mean difference effect sizes (Pustejovsky, Hedges, and Shadish (2014) ) based on fitted lme or gls models. Package: r-cran-lmerconveniencefunctions Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-lcfdata, r-cran-fields, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-lmerconveniencefunctions_3.0-1.ca2404.1_all.deb Size: 279496 MD5sum: c1bcf1f176854ec1bf6b10b039f40cca SHA1: 431d1e8b79a9822de94a6bbfc712fcd8470e239c SHA256: c151b9c5c66e272cf20036a1fbe91e4823a2aaccfbe2416da85ea6ee6e223d1c SHA512: 3165fd877a60ac5433423c60f4192a13fc917d4d2ce4b19afd092164ea4dbaf02506770555ab3e3363634861887867423e0d55fc737d18231eb6f06d01b01a87 Homepage: https://cran.r-project.org/package=LMERConvenienceFunctions Description: CRAN Package 'LMERConvenienceFunctions' (Model Selection and Post-Hoc Analysis for (G)LMER Models) The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data. Package: r-cran-lmeresampler Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-matrix, r-cran-nlmeu, r-cran-ggplot2, r-cran-ggdist, r-cran-hlmdiag, r-cran-purrr, r-cran-forcats, r-cran-statmod, r-cran-tidyr, r-cran-magrittr, r-cran-tibble Suggests: r-cran-lme4, r-cran-nlme, r-cran-testthat, r-cran-mlmrev, r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel, r-cran-foreach Filename: pool/dists/noble/main/r-cran-lmeresampler_0.2.4-1.ca2404.1_all.deb Size: 393102 MD5sum: 8486a2a37e00ced2dede32299fe9e63c SHA1: f49a225b6dbf249a1ede0e5651ebc877a11bd340 SHA256: 804c3961d118655d48336acbd3a02aa7e6e8e5368588586ad8b3c442265e20ba SHA512: edf2657e029ab74e9b14e2dd63470e121ce4c40dab79afa7ca4ddf327e8b656fb4852b73f032c3f15b635277e4eb4c0c127c3e13b374126e25cfd443d78daf2e Homepage: https://cran.r-project.org/package=lmeresampler Description: CRAN Package 'lmeresampler' (Bootstrap Methods for Nested Linear Mixed-Effects Models) Bootstrap routines for nested linear mixed effects models fit using either 'lme4' or 'nlme'. The provided 'bootstrap()' function implements the parametric, residual, cases, random effect block (REB), and wild bootstrap procedures. An overview of these procedures can be found in Van der Leeden et al. (2008) , Carpenter, Goldstein & Rasbash (2003) , and Chambers & Chandra (2013) . Package: r-cran-lmerperm Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmertest Filename: pool/dists/noble/main/r-cran-lmerperm_0.1.9-1.ca2404.1_all.deb Size: 21736 MD5sum: 7f5f1fcd6bdd6f2290ae2a4bd62a498a SHA1: 321792010581b5ebe70b8a3521ac7ee82136d066 SHA256: 3ba0c8443ee20993895be31b9e7a891ed9f3d7cc81926666827a2f0d58d0843d SHA512: 52263a98faa92ff44cdd525273827dbcb046b6ccc2eba6f681602f06639e3cd6c786ad86b0e2e5a4ccd1741147eb9733bb8ccaf1c27f67749e72ea01812f9c77 Homepage: https://cran.r-project.org/package=lmerPerm Description: CRAN Package 'lmerPerm' (Perform Permutation Test on General Linear and Mixed LinearRegression) We provide a solution for performing permutation tests on linear and mixed linear regression models. It allows users to obtain accurate p-values without making distributional assumptions about the data. By generating a null distribution of the test statistics through repeated permutations of the response variable, permutation tests provide a powerful alternative to traditional parameter tests (Holt et al. (2023) ). In this early version, we focus on the permutation tests over observed t values of beta coefficients, i.e.original t values generated by parameter tests. After generating a null distribution of the test statistic through repeated permutations of the response variable, each observed t values would be compared to the null distribution to generate a p-value. To improve the efficiency,a stop criterion (Anscombe (1953) ) is adopted to force permutation to stop if the estimated standard deviation of the value falls below a fraction of the estimated p-value. By doing so, we avoid the need for massive calculations in exact permutation methods while still generating stable and accurate p-values. Package: r-cran-lmertest Architecture: all Version: 3.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-numderiv, r-cran-mass, r-cran-ggplot2, r-cran-reformulas Suggests: r-cran-pbkrtest Filename: pool/dists/noble/main/r-cran-lmertest_3.2-1-1.ca2404.1_all.deb Size: 513184 MD5sum: 54214ddd34ab3a5d340808e42389c313 SHA1: e8f1828a23a52290ef1d808c919b7cd0aed2b460 SHA256: 43443b8e420e7f0bb148df8fdac204612e9148ef4b56100540aeca7a98eeac7e SHA512: a465726fed823f5ae51eee540f9752c773f0d5a3ea13014417fb8fdfbeb81c30184e89f382f1ffbeb955f0506acd6d1a143c55db5443d65df6e6c0ac4e690b18 Homepage: https://cran.r-project.org/package=lmerTest Description: CRAN Package 'lmerTest' (Tests in Linear Mixed Effects Models) Provides p-values in type I, II or III anova and summary tables for lmer model fits (cf. lme4) via Satterthwaite's degrees of freedom method. A Kenward-Roger method is also available via the pbkrtest package. Model selection methods include step, drop1 and anova-like tables for random effects (ranova). Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed effects are also available. Package: r-cran-lmesplines Architecture: all Version: 1.1.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme Filename: pool/dists/noble/main/r-cran-lmesplines_1.1.20-1.ca2404.1_all.deb Size: 26324 MD5sum: 9047d863ea2f982b3433589299872d6b SHA1: 3ff6f8c98c3695b72a72a450d2aba59286ebdb97 SHA256: d67e5843e12a11a0d9db9acfe7e0ace57f72da3ae0bb10f56c1743c38221acce SHA512: 08d6a5abeb6349a4997665d1656cee27f3dc1e1cd5f7aa9841d2b5608c8b943235d65c570bc79aa153067c9cb3bd6c0534673b12c00f548fb7614d6a4391cca0 Homepage: https://cran.r-project.org/package=lmeSplines Description: CRAN Package 'lmeSplines' (Add Smoothing Spline Modelling Capability to `nlme`) Adds smoothing spline modelling capability to nlme. Fits smoothing spline terms in Gaussian linear and nonlinear mixed-effects models. Package: r-cran-lmf Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lmf_1.2.1-1.ca2404.1_all.deb Size: 190752 MD5sum: 21cba07db8e442507fcdc034f5df1738 SHA1: 53663515c69f44ee9811094a60e7aa074ecf4c60 SHA256: de981266b6e76388d821b5be7c14dcdcf663c15341bf86a342583ef7e39f7243 SHA512: 3a48e6f0729a74c781c8ce5f805ceab5e94db5423bedfcf370938733c506f70313501ea662071b2ef680c39333b8933f9ab7638569a96c3e6b6465538f9a7616 Homepage: https://cran.r-project.org/package=lmf Description: CRAN Package 'lmf' (Functions for Estimation and Inference of Selection inAge-Structured Populations) Provides methods for estimation and statistical inference on directional and fluctuating selection in age-structured populations. Package: r-cran-lmfilter Architecture: all Version: 0.1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-lmfilter_0.1.3.1-1.ca2404.1_all.deb Size: 62958 MD5sum: 2faa30de5697f4730ec2364745a2179e SHA1: df21d2793d7e5a45786a5ce7e9e668718f7b361d SHA256: 4496b84b773ed1e5c375c9e163d85d2946d700a7ebb715be92bacd7f16d4f51d SHA512: 1c61de36e6558f32595e43ae42129113ccb6b688eef19a99d8c7b0672dfb5c7e0817cb308e7d5ce8d79907b3e593f5f7c15b2722075849c83c98d13c51cfc675 Homepage: https://cran.r-project.org/package=LMfilteR Description: CRAN Package 'LMfilteR' (Filter Methods for Parameter Estimation in Linear and Non LinearRegression Models) We present a method based on filtering algorithms to estimate the parameters of linear, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method. Package: r-cran-lmfor Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-spatstat, r-cran-magic, r-cran-mass, r-cran-matrix, r-cran-spatstat.geom Suggests: r-cran-lme4 Filename: pool/dists/noble/main/r-cran-lmfor_1.7-1.ca2404.1_all.deb Size: 635288 MD5sum: 67135a4114a0c731d86023724b3a82c1 SHA1: f80783442848c6d569743a06d1d2001dcc8eb7ec SHA256: 6aae7e1873c09e50a7c5cab41cbc952281549005a33ba331c19cbe92c9f8ef5c SHA512: 0c578bc2c567be257e5cfde59c40ef6046b486cae8622871c312759bef6efdf2edd2792654b02d761c618baadce14f2713fde93eff5dbe13e0573afd0fb04322 Homepage: https://cran.r-project.org/package=lmfor Description: CRAN Package 'lmfor' (Functions for Forest Biometrics) Functions for different purposes related to forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, modelling of diameter distributions and estimation off stand density using ITD. Several empirical datasets are also included. Package: r-cran-lmforc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 755 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lmforc_1.0.0-1.ca2404.1_all.deb Size: 430586 MD5sum: 335b1f4455bfbbfcbd702f6785123d8f SHA1: a928b1c5ac827978aa6551e48a57c80ae2c3b18a SHA256: 0e30dcfa7ed984920d2ea3d4b16fda478c25204ab5fc6ab8e68048cb8613190a SHA512: e6db0153a870d1715b7f93e50f6eeb25ba09c3bbd6027ab8abc62ac501c69815648b73c32d17f221d8901b2d5676586f046d345ec1354f67dc515fe562b4cb7f Homepage: https://cran.r-project.org/package=lmForc Description: CRAN Package 'lmForc' (Linear Model Forecasting) Introduces in-sample, out-of-sample, pseudo out-of-sample, and benchmark model forecast tests and a new class for working with forecast data, Forecast. Package: r-cran-lmhelprs Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-semplot Filename: pool/dists/noble/main/r-cran-lmhelprs_0.4.4-1.ca2404.1_all.deb Size: 489768 MD5sum: 92ce04d360cdfb799c9d01ce7412b945 SHA1: a0faee370883561d6d391368a1b9f5ded6a4b93b SHA256: e7c2279eaf878865e39b126fc94a2c99c745916baef2db0d83165e8b31071f96 SHA512: fcfeef6a96c436ea792f642c4b65128b036dc5cfb6bb5ddab7a87456c11fcea4ec494d85177088a11cceb355e22d43923517eb1fad1a7f8e0c676804c0492a5a Homepage: https://cran.r-project.org/package=lmhelprs Description: CRAN Package 'lmhelprs' (Helper Functions for Linear Model Analysis) A collection of helper functions for multiple regression models fitted by lm(). Most of them are simple functions for simple tasks which can be done with coding, but may not be easy for occasional users of R. Most of the tasks addressed are those sometimes needed when using the 'manymome' package (Cheung and Cheung, 2023, ) and 'stdmod' package (Cheung, Cheung, Lau, Hui, and Vong, 2022, ). However, they can also be used in other scenarios. Package: r-cran-lmls Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-generics Suggests: r-cran-bookdown, r-cran-coda, r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-mgcv, r-cran-mvtnorm, r-cran-numderiv, r-cran-patchwork, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lmls_0.1.1-1.ca2404.1_all.deb Size: 358942 MD5sum: c9f7314e87e32da8ebaa16075e209229 SHA1: 1274bc366444e6a310d4afaab427b5044162d934 SHA256: 9b0278c9f5ee1e651ea56b73ed446e70d25b170640d9a8c90dbc7753dcf87276 SHA512: 05da49516b43f559d0bea4ab3afb152a7fecd22bc3566bd6e33a3bb8b9e2b42edd397e413665fa6063c5fdc2ef418b587f8f2b369f4659f59d5cbbad7406829d Homepage: https://cran.r-project.org/package=lmls Description: CRAN Package 'lmls' (Gaussian Location-Scale Regression) The Gaussian location-scale regression model is a multi-predictor model with explanatory variables for the mean (= location) and the standard deviation (= scale) of a response variable. This package implements maximum likelihood and Markov chain Monte Carlo (MCMC) inference (using algorithms from Girolami and Calderhead (2011) and Nesterov (2009) ), a parametric bootstrap algorithm, and diagnostic plots for the model class. Package: r-cran-lmmot Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maxlik, r-cran-mass Filename: pool/dists/noble/main/r-cran-lmmot_0.1.4-1.ca2404.1_all.deb Size: 43936 MD5sum: 083ef7ea26d201307ae79f3ef2ea3596 SHA1: dc4f59d6b0802bfc1ee0511a3be187fb802ca0d6 SHA256: 9f7ab0a4182760c015530dd9bc526e0f4a02717d1733f10e6e74e4d9122bb2c8 SHA512: 1ac3b1125da6ea28c6ba432c2e425822df1fac63f34a55066e6580d290d44e9904d7bc7194232575bdd2fc6752df8b1bf60a3706f67a6a262017bd14157a42e1 Homepage: https://cran.r-project.org/package=lmmot Description: CRAN Package 'lmmot' (Multiple Ordinal Tobit (MOT) Model) Fit right censored Multiple Ordinal Tobit (MOT) model. Package: r-cran-lmmpar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrixcalc, r-cran-mnormt, r-cran-plyr, r-cran-doparallel, r-cran-bigmemory Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lmmpar_0.1.0-1.ca2404.1_all.deb Size: 16900 MD5sum: 55728787f0c0dfd81c6b64498ae0def0 SHA1: 06da85812c59f65c47cd91eecd91584801ae3f79 SHA256: b10bdbfec4161e96bfa3cc16dc2e5d11727b26b26bd8ef6b215f20ceb7416fdc SHA512: eef4daca1f2e13320510c1bc962df12a7f9f89748473e99a1cfef21b0461d2568d062f42c6ebfb15460d2dfdd439d2d45a9eb7602e548fde4da4e3aefc1c8953 Homepage: https://cran.r-project.org/package=lmmpar Description: CRAN Package 'lmmpar' (Parallel Linear Mixed Model) Embarrassingly Parallel Linear Mixed Model calculations spread across local cores which repeat until convergence. Package: r-cran-lmmstar Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6285 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-lava, r-cran-matrix, r-cran-multcomp, r-cran-nlme, r-cran-numderiv, r-cran-rlang Suggests: r-cran-asht, r-cran-data.table, r-cran-ggh4x, r-cran-ggpubr, r-cran-lattice, r-cran-mvtnorm, r-cran-lme4, r-cran-lmertest, r-cran-mice, r-cran-nlmeu, r-cran-optimx, r-cran-pbapply, r-cran-psych, r-cran-publish, r-cran-qqtest, r-cran-r.rsp, r-cran-reshape2, r-cran-rmcorr, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lmmstar_1.1.0-1.ca2404.1_all.deb Size: 3584106 MD5sum: 108c0fda049dbeaf535b2a71c2937a6c SHA1: 56b82a723d8d8622baa48393a9213b949ef3bd6b SHA256: e194263dd30bf7e213048667d6035b99cfbb22b07ad514b9e27b28650d7fdc3d SHA512: dfc9021d3c234665d26ffbd0b68ef55f67813e3325f9a2322932362a5f12f8ea768a18d11f54c1e5317be879c1679070a2ad5d5ba41528c47210da94cb6b1e0c Homepage: https://cran.r-project.org/package=LMMstar Description: CRAN Package 'LMMstar' (Repeated Measurement Models for Discrete Times) Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers" taught by the section of Biostatistics of the University of Copenhagen. It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...). Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom. Normalized residuals are provided to assess model misspecification. Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients. Predictions can be computed conditional to covariates only or also to outcome values. Package: r-cran-lmodel2 Architecture: all Version: 1.7-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lmodel2_1.7-4-1.ca2404.1_all.deb Size: 343532 MD5sum: 65783a0043c3ecf16ccb0a478396c087 SHA1: 4f2d58c7244c04bb4e07b4355974f122fe70bd68 SHA256: 19ba0e85ede9e11c40f8a36e60d1fd3553b46d568a7e17cc735d63fe991697e7 SHA512: 216e623f155e609b90d82a82a4c7023e972e0fd22c30358d5276c30253f133e5f9141d0968730a44b610f7427e71163999e7f14a2270f14e04b7a0081abdd37f Homepage: https://cran.r-project.org/package=lmodel2 Description: CRAN Package 'lmodel2' (Model II Regression) Computes model II simple linear regression using ordinary least squares (OLS), major axis (MA), standard major axis (SMA), and ranged major axis (RMA). Package: r-cran-lmofit Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2781 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmom, r-cran-pracma, r-cran-ggplot2, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lmofit_0.1.7-1.ca2404.1_all.deb Size: 2465702 MD5sum: 974cc5825c62d01ff68ecd08ff1fd8dc SHA1: a2514a2599ba9005d8cc1b6eb36de4b40972e659 SHA256: 2ea941c14996fee820b4c914e27357ffef31ddc0b462627e04df6362d17f17a2 SHA512: 3fa3378fe85a17be47989367fc6c653b9d784b239f4a0c185ef57e3c495d7444e26376cdf642559e0c7652a070b0e648467d8fb8eb49be438987a733916a3271 Homepage: https://cran.r-project.org/package=LMoFit Description: CRAN Package 'LMoFit' (Advanced L-Moment Fitting of Distributions) A complete framework for frequency analysis is provided by 'LMoFit'. It has functions related to the determination of sample L-moments as in Hosking, J.R.M. (1990) , the fitting of various distributions as in Zaghloul et al. (2020) and Hosking, J.R.M. (2019) , besides plotting and manipulating L-space diagrams as in Papalexiou, S.M. & Koutsoyiannis, D. (2016) for two-shape parametric distributions on the L-moment ratio diagram. Additionally, the quantile, probability density, and cumulative probability functions of various distributions are provided in a user-friendly manner. Package: r-cran-lmomco Architecture: all Version: 2.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3758 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-goftest, r-cran-lmoments, r-cran-mass Suggests: r-cran-copbasic Filename: pool/dists/noble/main/r-cran-lmomco_2.5.5-1.ca2404.1_all.deb Size: 3346976 MD5sum: 69fc52d83346c9d1d3c1ae3eaa7f3427 SHA1: 0eabf5dd0e1ffd653e6087452a2ecd1b90e02bd4 SHA256: 404c00ce219ce6c62bdca30d58fea7b37767bde34cb8fee6788722b01becf131 SHA512: 0491d32f3a880d2fb7c6965dbce7303f89ccb43330f6714fff3450a9e200740ebfa28573c5c4e5a66bbe17fb26c275fcabe4c6f6fe68ea17b55f2b1d562e0678 Homepage: https://cran.r-project.org/package=lmomco Description: CRAN Package 'lmomco' (L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments,and Many Distributions) Extensive functions for Lmoments (LMs) and probability-weighted moments (PWMs), distribution parameter estimation, LMs for distributions, LM ratio diagrams, multivariate Lcomoments, and asymmetric (asy) trimmed LMs (TLMs). Maximum likelihood and maximum product spacings estimation are available. Right-tail and left-tail LM censoring by threshold or indicator variable are available. LMs of residual (resid) and reversed (rev) residual life are implemented along with 13 quantile operators for reliability analyses. Exact analytical bootstrap estimates of order statistics, LMs, and LM var-covars are available. Harri-Coble Tau34-squared Normality Test is available. Distributions with L, TL, and added (+) support for right-tail censoring (RC) encompass: Asy Exponential (Exp) Power [L], Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp Poisson [L], Gen Extreme Value [L], Gen Lambda [L, TL], Gen Logistic [L], Gen Normal [L], Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L], Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile Function [L], Normal [L], 3p log-Normal [L], Pearson Type III [L], Polynomial Density-Quantile 3 and 4 [L], Rayleigh [L], Rev-Gumbel [L+RC], Rice [L], Singh Maddala [L], Slash [TL], 3p Student t [L], Truncated Exponential [L], Wakeby [L], and Weibull [L]. Package: r-cran-lmompi Architecture: all Version: 0.6.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lmom, r-cran-stringr Suggests: r-cran-spei Filename: pool/dists/noble/main/r-cran-lmompi_0.6.7-1.ca2404.1_all.deb Size: 35016 MD5sum: f17678656c46f4ce59ef92cf35e94bf2 SHA1: e0dbf4aca97d42d6f090982dea921e57d7a9be8e SHA256: af3c26cb4a8bb98998492ad68d239e9a7f89836282af673ecf5bbf369b14d599 SHA512: f8e72b8b6f35cd8e36916599dfbab7e3180f57dad8458dd7be097da1cd8dc3095be4672ec4f08c4b3889ff8183d6099eeea628aa561fb68c280a6b7d72e6993e Homepage: https://cran.r-project.org/package=lmomPi Description: CRAN Package 'lmomPi' ((Precipitation) Frequency Analysis and Variability withL-Moments from 'lmom') It is an extension of 'lmom' R package: 'pel...()','cdf...()',qua...()' function families are lumped and called from one function per each family respectively in order to create robust automatic tools to fit data with different probability distributions and then to estimate probability values and return periods. The implemented functions are able to manage time series with constant and/or missing values without stopping the execution with error messages. The package also contains tools to calculate several indices based on variability (e.g. 'SPI' , Standardized Precipitation Index, see and ) for multiple time series or spatially gridded values. Package: r-cran-lmpdata Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-data.table Filename: pool/dists/noble/main/r-cran-lmpdata_0.2.0-1.ca2404.1_all.deb Size: 21722 MD5sum: b1ca7089972c0bb5a6fe790b3904938e SHA1: 25b7ea27cf8ae8f5f01cb82c72c1adee268b9cf2 SHA256: adb0930073970e0d62328b71126bf912b6a6e769eeb6b81d96b40a4212183583 SHA512: 67aa78da478e7af18e2c7943d88adfd5d28662a04033a20663ec5b941dcf98eb5b188849e4f053bb7cbd83dec5530945955d426da5bc620b4e6023e0239ccc46 Homepage: https://cran.r-project.org/package=LMPdata Description: CRAN Package 'LMPdata' (Easy Import of the EU Labour Market Policy Data) European Commission's Labour Market Policy (LMP) database () provides information on labour market interventions, which are government actions to help and support the unemployed and other disadvantaged groups in the transition from unemployment or inactivity to work. 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Cancer informatics, 13, CIN-S14021. Package: r-cran-lmreg Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-lmreg_1.3-1.ca2404.1_all.deb Size: 139032 MD5sum: c699c9084fcfbd39ccc89af0f6ea042d SHA1: 3949b16ca3d15eaaa6dee611213b1fd690f377f4 SHA256: 0d374f2889dce4510fac04d4702689299b6c183b824463b93cff434206da74e9 SHA512: 1ecec6dd6263f7b3f738ed341080e10adf6381c21cb2d6135563d4875f68844bbe12187246307f57c7efaafa7931e11f17d3f1798cb88ad0a5756618646e2477 Homepage: https://cran.r-project.org/package=lmreg Description: CRAN Package 'lmreg' (Data and Functions Used in Linear Models and Regression with R:An Integrated Approach) Data files and a few functions used in the book 'Linear Models and Regression with R: An Integrated Approach' by Debasis Sengupta and Sreenivas Rao Jammalamadaka (2019). 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Package: r-cran-lmviz Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3030 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinyjs, r-cran-lmtest, r-cran-mgcv, r-cran-mass, r-cran-scatterplot3d, r-cran-rgl, r-cran-car Filename: pool/dists/noble/main/r-cran-lmviz_0.2.0-1.ca2404.1_all.deb Size: 2491330 MD5sum: b6297b25a538b386e4de60915bfbb475 SHA1: b6a97877674833421d72498212d4c3832e3a9fc2 SHA256: 1116c7ebc916c9cb32b2e6251e7efffba8206ecabb3bbe679618e7acf32f4edd SHA512: 77ba436d7a7f163a3f7d0506294aaa4d5373eafae9a09970b5b16a37702638f71872252047570e1497a8ae7d360358394f97eabccbb8d3f31a29ed262d592f64 Homepage: https://cran.r-project.org/package=lmviz Description: CRAN Package 'lmviz' (A Package to Visualize Linear Models Features and Play with Them) Contains a suite of shiny applications meant to explore linear model inference feature through simulation and games. 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These diagnostics rely on the analyses in Chattopadhyay and Zubizarreta (2023) where several regression estimators are represented as weighting estimators, in connection to inverse probability weighting. 'lmw' provides tools to diagnose representativeness, balance, extrapolation, and influence for these models, clarifying the target population of inference. Tools are also available to simplify estimating treatment effects for specific target populations of interest. Package: r-cran-lncdiff Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 658 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lncdiff_1.0.0-1.ca2404.1_all.deb Size: 639396 MD5sum: c03ab0addb87ae1e708279fb86ed3fa0 SHA1: 50ebface81800d6cb2a2ff173917d96af09d281d SHA256: 211da4ad93aab710b7b26bc4cf97559b15ecb4eeb9f1068e50eb0de5afbbb4a9 SHA512: 6b490f403024e72cb57050e3e151896c96fa12734e7ec16035d106497bde3d2649a3b9c74bca7b07f591bca6c30752a3f3dbd5e3ad2e998bbcf194aef7efca3f Homepage: https://cran.r-project.org/package=lncDIFF Description: CRAN Package 'lncDIFF' (Long Non-Coding RNA Differential Expression Analysis) We developed an approach to detect differential expression features in long non-coding RNA low counts, using generalized linear model with zero-inflated exponential quasi likelihood ratio test. Methods implemented in this package are described in Li (2019) . Package: r-cran-lncfinder Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2960 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seqinr, r-cran-e1071, r-cran-caret Filename: pool/dists/noble/main/r-cran-lncfinder_1.1.6-1.ca2404.1_all.deb Size: 2991906 MD5sum: 08ca7e96f59f4b1591b2efb7d932510e SHA1: f274c92fe436d8cd4ce8a5c84a72f92b3805425c SHA256: 4f9f29e55e44fee789f7e7032921575f9502e716418d99f753410f9d2308039d SHA512: cbe777ccfd199893f52a995bfb1e85f961991b5b9443cd75c0a89fc86095655f5972238ef213ae0119f9376abf7ca0290356e3ef2b178bfe296cbda855750602 Homepage: https://cran.r-project.org/package=LncFinder Description: CRAN Package 'LncFinder' (LncRNA Identification and Analysis Using Heterologous Features) Long non-coding RNAs identification and analysis. Default models are trained with human, mouse and wheat datasets by employing SVM. 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Package: r-cran-lnirt Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 996 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-coda, r-cran-mcmcse Filename: pool/dists/noble/main/r-cran-lnirt_0.5.1-1.ca2404.1_all.deb Size: 924236 MD5sum: 88295343cbaff71dbd012893a7f6ef43 SHA1: f30e64208d66b5803e480a1b8844784ac12b3fff SHA256: cac1257664f71f2281ff051fca8e6e64b75524d8c5eee3358eb915c4a5601889 SHA512: 47512ed6ae40ccc490784ecd65fac585430eee0f414aa6bb20434f4b581e8c982196b22e6f6c2b7dd615072d5792fbfd151b0de82c63d9e4562db088412faaee Homepage: https://cran.r-project.org/package=LNIRT Description: CRAN Package 'LNIRT' (LogNormal Response Time Item Response Theory Models) Allows the simultaneous analysis of responses and response times in an Item Response Theory (IRT) modelling framework. Supports variable person speed functions (intercept, trend, quadratic), and covariates for item and person (random) parameters. Data missing-by-design can be specified. Parameter estimation is done with a MCMC algorithm. LNIRT replaces the package CIRT, which was written by Rinke Klein Entink. For reference, see the paper by Fox, Klein Entink and Van der Linden (2007), "Modeling of Responses and Response Times with the Package cirt", Journal of Statistical Software, . 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(2022) . Package: r-cran-loa Architecture: all Version: 0.3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1069 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-mass, r-cran-png, r-cran-rgooglemaps, r-cran-openstreetmap, r-cran-sp, r-cran-rcolorbrewer, r-cran-mgcv, r-cran-plyr Suggests: r-cran-sf Filename: pool/dists/noble/main/r-cran-loa_0.3.1.1-1.ca2404.1_all.deb Size: 1021828 MD5sum: c1324b0905a4d79cfa20111b3e172508 SHA1: a7226584386894b665986b7fd354f1bdeff8de6b SHA256: adcb8142f530ef32614a509fca78c1df59596e8c2547657657f7b55789402a94 SHA512: 3b80f9d8f4a97b2d336ea4e66d9f4bc3efd07b2aace8c45c4e192914159a21b9226ef678414af33b1400192c1036d0c65fc9def681247c18483631ec8688254e Homepage: https://cran.r-project.org/package=loa Description: CRAN Package 'loa' (Lattice Options and Add-Ins) Various plots and functions that make use of the lattice/trellis plotting framework. The plots, which include loaPlot(), loaMapPlot() and trianglePlot(), and use panelPal(), a function that extends 'lattice' and 'hexbin' package methods to automate plot subscript and panel-to-panel and panel-to-key synchronization/management. 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It allows renaming, transforming, ordering and removing variables. It includes basic exploratory methods such as the mean, median, mode, normality test, histogram and correlation. 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Package: r-cran-loadshaper Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2354 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-loadshaper_1.1.1-1.ca2404.1_all.deb Size: 1705486 MD5sum: a648705c176ac91353cf97bb026d953e SHA1: 6afee56032aee6a9265134ba42a10904eb0a714d SHA256: 8e5a6bd09b51a4a6f23ac0fef697130dd488671cb6a35ab3d588815acb6893ff SHA512: 04f61116ab73fce44914e4e81cb1b2af519356d3d69c4625b220d7c2d192cb188e9f01c632e7b373689b1d1bfd58360f1e409d9dbb3e225c0934dc17f44643a2 Homepage: https://cran.r-project.org/package=loadshaper Description: CRAN Package 'loadshaper' (Producing Load Shape with Target Peak and Load Factor) Modifying a load shape to match specific peak and load factor is a fundamental component for various power system planning and operation studies. 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It allows for search terms such as keywords, time periods and entity names. It then attempts to clean, or at least flag, filings that could provide incorrect results when seeking to answer the question: How much is being spent on lobbying our Congress and the administration and what issues do they care about? 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Including automated filtering of noise parameters and determination of breakpoints. 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Streamlines the end-to-end workflow from data request to analysis-ready datasets. For advanced high-frequency econometric analysis, see the 'highfrequency' package. Package: r-cran-lobstercatch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lobstercatch_0.1.0-1.ca2404.1_all.deb Size: 64842 MD5sum: 0d69c79f5d12821b8e2e6f1bb32c01da SHA1: cd81dd1ce5f6840d8524eea717ce8d29fca7c9fa SHA256: 1261a9aeb089a82834727e6430c446ad633615d6caa4b83db5e6592ea3f1c774 SHA512: 3eea7708822814fc7fd4610dd80a46d5496086d46fc7001df49dab6f57485c7e084e60e499367e21fdbd51d98a54630125a7ab69b272ac4c3c09548ce700ce86 Homepage: https://cran.r-project.org/package=LobsterCatch Description: CRAN Package 'LobsterCatch' (Models the Capture Processes in American Lobster Trap Fishery) Simulate lobster catch process in a trap fishery. Factors such as lobster density on ocean floor, their movement, trap saturation and bait shrinkage rate can be modeled. Details of the methods for modeling those processes can be found in: Addison and Bell (1997) . Package: r-cran-localfda Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1050 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-localfda_1.0.0-1.ca2404.1_all.deb Size: 1038254 MD5sum: 056ad34391ee1b4f774be64af2020341 SHA1: acb9fe8a21813e47623276b5c7173cc77455e2cb SHA256: 0a9d8f9781236a19caf5acb195b47d02db84333bd641b9515351d702d276114d SHA512: 8041f95d9a81aa4892487bf9182837c131b8a8016bdf7faa48aab907b3ff031825ad205183c434c66806c6153094199a47c5ac089663c001af30447ada8acf9a Homepage: https://cran.r-project.org/package=localFDA Description: CRAN Package 'localFDA' (Localization Processes for Functional Data Analysis) Implementation of a theoretically supported alternative to k-nearest neighbors for functional data to solve problems of estimating unobserved segments of a partially observed functional data sample, functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample. Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria. The package allows the implementation of the methodology and the replication of the results in Elías, A., Jiménez, R. and Yukich, J. (2020) . Package: r-cran-localice Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-checkmate Suggests: r-cran-covr, r-cran-h2o, r-cran-mlbench, r-cran-randomforest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-localice_0.1.1-1.ca2404.1_all.deb Size: 30968 MD5sum: a2447ed7c68a6e3b38017982a664db02 SHA1: 9a24dbf8d9feb0af0f5c58b06af0978bef3b781b SHA256: bfa6b25b2d2ec3b0013596e2e75e42aedcb03faae04c06f7a3f91c647dfb9d2f SHA512: e071a84ecdfcc0aaf38ce2526844830f79c0fa85f32086e915c78bf3af1c2e14ef747f52104ee2e5fe31ddc29646e7f862f40efde06286f9a12964fcac63512b Homepage: https://cran.r-project.org/package=localICE Description: CRAN Package 'localICE' (Local Individual Conditional Expectation) Local Individual Conditional Expectation ('localICE') is a local explanation approach from the field of eXplainable Artificial Intelligence (XAI). localICE is a model-agnostic XAI approach which provides three-dimensional local explanations for particular data instances. The approach is proposed in the master thesis of Martin Walter as an extension to ICE (see Reference). The three dimensions are the two features at the horizontal and vertical axes as well as the target represented by different colors. The approach is applicable for classification and regression problems to explain interactions of two features towards the target. For classification models, the number of classes can be more than two and each class is added as a different color to the plot. The given instance is added to the plot as two dotted lines according to the feature values. The localICE-package can explain features of type factor and numeric of any machine learning model. Automatically supported machine learning packages are 'mlr', 'randomForest', 'caret' or all other with an S3 predict function. For further model types from other libraries, a predict function has to be provided as an argument in order to get access to the model. Reference to the ICE approach: Alex Goldstein, Adam Kapelner, Justin Bleich, Emil Pitkin (2013) . 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Given a treatment selection equation and an outcome equation, the function mte() estimates the MTE via the semiparametric local instrumental variables method or the normal selection model. The function mte_at() evaluates MTE at different values of the latent resistance u with a given X = x, and the function mte_tilde_at() evaluates MTE projected onto the estimated propensity score. The function ace() estimates population-level average causal effects such as ATE, ATT, or the marginal policy relevant treatment effect. 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This package implements an explanation method based on LIME (Local Interpretable Model-agnostic Explanations, see Tulio Ribeiro, Singh, Guestrin (2016) ) in which interpretable inputs are created based on local rather than global behaviour of each original feature. 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(2024) . Package: r-cran-locar Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3580 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seewave, r-cran-tuner, r-cran-matrixstats, r-cran-oce, r-cran-signal, r-cran-synchwave Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-locar_0.2.0-1.ca2404.1_all.deb Size: 2684790 MD5sum: d191bcc4e4d79c6414a2597d239c218e SHA1: d13b33928739ac51114d2c0a359c6ff81560cd24 SHA256: 3225a4d5d39d737cfbb2148a90146e820ad47f63164972059c6a07cea7318f9d SHA512: 978187250200d937979dfbe9c23201e8e52e77d6b90f03ed91a364103c40e063adaf50f3db2dd0a79125ebeb4c385c061186143f4c0019ea32ac38a37c465328 Homepage: https://cran.r-project.org/package=locaR Description: CRAN Package 'locaR' (A Set of Tools for Sound Localization) A set of functions and tools to conduct acoustic source localization, as well as organize and check localization data and results. The localization functions implement the modified steered response power algorithm described by Cobos et al. (2010) . 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Package: r-cran-locationgamer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-locationgamer_0.1.0-1.ca2404.1_all.deb Size: 62712 MD5sum: e6008afe03c88e8770a5d19b278c8278 SHA1: 84683e7d26386f364366172f06b53631485bc488 SHA256: 65f1365492d7954ddc86d88acd4c2155fd21562b03f2046361a3035a55385f28 SHA512: 0a8cbbafad4f50667a3e074ff1fbb690be071c55eb70fbeabae22bd681812e512224e0c98d1adf9beacbae53750aaaf8bf0eefcb1805f27b14904438e5aa0ff3 Homepage: https://cran.r-project.org/package=locationgamer Description: CRAN Package 'locationgamer' (Identification of Location Game Equilibria in Networks) Identification of equilibrium locations in location games (Hotelling (1929) ). In these games, two competing actors place customer-serving units in two locations simultaneously. Customers make the decision to visit the location that is closest to them. The functions in this package include Prim algorithm (Prim (1957) ) to find the minimum spanning tree connecting all network vertices, an implementation of Dijkstra algorithm (Dijkstra (1959) ) to find the shortest distance and path between any two vertices, a self-developed algorithm using elimination of purely dominated strategies to find the equilibrium, and several plotting functions. Package: r-cran-locatt Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-taxize Filename: pool/dists/noble/main/r-cran-locatt_1.2.0-1.ca2404.1_all.deb Size: 281858 MD5sum: de782bdfbb96682814cde69f1aeb47ff SHA1: 0eda33cc6ce2944ee779ec901806cc69f059df68 SHA256: 2edc4eb4c3eb85603fb5977f57cffb61a8d94d602d95ba5afcbdcb4de49ca298 SHA512: 4d82fe5be2483f87407c1e58f7c9343790633c79e23f9eede2af89e17160c1d3314084e1d7475975bc787f2bb39f63ea68cf9683c6d115f556e3d70cf02e8937 Homepage: https://cran.r-project.org/package=LocaTT Description: CRAN Package 'LocaTT' (Geographically-Conscious Taxonomic Assignment for Metabarcoding) A bioinformatics pipeline for performing taxonomic assignment of DNA metabarcoding sequence data while considering geographic location. A detailed tutorial is available at . A manuscript describing these methods is in preparation. 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Package: r-cran-logan Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 952 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pander, r-cran-psych, r-cran-foreign, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-stringr, r-cran-magrittr, r-cran-modules Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-logan_1.0.1-1.ca2404.1_all.deb Size: 758330 MD5sum: bd53ad06445e7b01ad03ca279616006e SHA1: 610d8a44a71bb51e64a5ef6c0b0fd0205ed46f62 SHA256: aa5d55ee838968c93cce2616a307a0d772b974b6d1acc32fb50a8295cfc21cbd SHA512: 76098eb67eca5a30807676eb56dc620667c1a06945ec149ddddc2e6d185ac9944bf840959eb3788c70102922126e25c1a5b7cbef46ed537080b226e16427ddce Homepage: https://cran.r-project.org/package=LOGAN Description: CRAN Package 'LOGAN' (Log File Analysis in International Large-Scale Assessments) Enables users to handle the dataset cleaning for conducting specific analyses with the log files from two international educational assessments: the Programme for International Student Assessment (PISA, ) and the Programme for the International Assessment of Adult Competencies (PIAAC, ). An illustration of the analyses can be found on the LOGAN Shiny app () on your browser. Package: r-cran-logantree Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rocr, r-cran-caret, r-cran-caretensemble, r-cran-dplyr, r-cran-ggplot2, r-cran-rpart.plot, r-cran-tibble, r-cran-gbm Filename: pool/dists/noble/main/r-cran-logantree_0.1.1-1.ca2404.1_all.deb Size: 208360 MD5sum: 02df40ff12771cea08ffe12f374ac2d2 SHA1: 3fa6cc1c8d4b9bfb0ec928b77d96aac715157f42 SHA256: f39d4a22bca756d23480b4b0372dd9f7df6dcd6da6c8e13ac16a2708b0ccc350 SHA512: f15df47647960a0ebd8905c7e3dad36fc0f7705e513feda27012bc3a3adaaa66030ab3ffe1122832c22d3cdec514fe444b8acc93e2c4e6a27a48796d7f7239f8 Homepage: https://cran.r-project.org/package=LOGANTree Description: CRAN Package 'LOGANTree' (Tree-Based Models for the Analysis of Log Files fromComputer-Based Assessments) Enables researchers to model log-file data from computer-based assessments using machine-learning techniques. It allows researchers to generate new knowledge by comparing the performance of three tree-based classification models (i.e., decision trees, random forest, and gradient boosting) to predict student's outcome. It also contains a set of handful functions for the analysis of the features' influence on the modeling. Data from the Climate control item from the 2012 Programme for International Student Assessment (PISA, ) is available for an illustration of the package's capability. He, Q., & von Davier, M. (2015) Boehmke, B., & Greenwell, B. M. (2019) . Package: r-cran-logbin Architecture: all Version: 2.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glm2, r-cran-turboem, r-cran-matrix, r-cran-itertools2, r-cran-iterators Suggests: r-cran-testthat, r-cran-vctrs Filename: pool/dists/noble/main/r-cran-logbin_2.0.6-1.ca2404.1_all.deb Size: 208980 MD5sum: 5d94234ad703e2e29ee4eef74d70664f SHA1: c8911885ffca328909c41c6b6463d8596b298dc8 SHA256: d2d93d21de71e7b7a4d87f579f0974d4ed41c8922bf0ed8fbeda4ce28df7b00b SHA512: 4f83a06d72ae0a57dbaedf0640f9fb754f93520c1fda152f0d6882e7ebd5219a10e2001015d2b74325f7a6f79671d2f4c1a07e0800dd0a51172fc04a03a69a5d Homepage: https://cran.r-project.org/package=logbin Description: CRAN Package 'logbin' (Relative Risk Regression Using the Log-Binomial Model) Methods for fitting log-link GLMs and GAMs to binomial data, including EM-type algorithms with more stable convergence properties than standard methods. Package: r-cran-logcondens Architecture: all Version: 2.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 697 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ks Filename: pool/dists/noble/main/r-cran-logcondens_2.1.9-1.ca2404.1_all.deb Size: 575488 MD5sum: 6c41282de657141dc6912948eb7f255c SHA1: bd03c41e4b0cca508eff370ced144e05ecc58317 SHA256: c831d1730cb69dccec51de41ee43f3cc47bae2128ba9e14507d0d2c7a54e0207 SHA512: b663f9cb2307522dea670eb987a0d1fa39bf9db5748785936d976771edf9b652fe4569ab24d7340e76fd83e4475ec94efbe37a84c4b06cbf61b6e8de28a19612 Homepage: https://cran.r-project.org/package=logcondens Description: CRAN Package 'logcondens' (Estimate a Log-Concave Probability Density from Iid Observations) Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available. Package: r-cran-logcondiscr Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mvtnorm, r-cran-cobs Filename: pool/dists/noble/main/r-cran-logcondiscr_1.0.7-1.ca2404.1_all.deb Size: 87358 MD5sum: 51ac6037800b38890ae38bb42d432a47 SHA1: de609f7ff0639765c98bddc048d1ac56ede20a45 SHA256: ab5bedc9c99451fff1efb6cdfbde95456ef9352dd3d1cc664cba8d7735c45281 SHA512: e77ae2acc72d1028bb100f76dff19e0094349e29c6f1a2381b0a4418674ad9dcac7930a3c2277fb361ad5c04dfe94ba58618a3a9293c3037a11aa8cf4eb0ed2c Homepage: https://cran.r-project.org/package=logcondiscr Description: CRAN Package 'logcondiscr' (Estimate a Log-Concave Probability Mass Function from DiscreteI.i.d. Observations) Given independent and identically distributed observations X(1), ..., X(n), allows to compute the maximum likelihood estimator (MLE) of probability mass function (pmf) under the assumption that it is log-concave, see Weyermann (2007) and Balabdaoui, Jankowski, Rufibach, and Pavlides (2012). The main functions of the package are 'logConDiscrMLE' that allows computation of the log-concave MLE, 'logConDiscrCI' that computes pointwise confidence bands for the MLE, and 'kInflatedLogConDiscr' that computes a mixture of a log-concave PMF and a point mass at k. Package: r-cran-logger Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3464 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-botor, r-cran-cli, r-cran-covr, r-cran-crayon, r-cran-devtools, r-cran-glue, r-cran-jsonlite, r-cran-knitr, r-cran-mirai, r-cran-ntfy, r-cran-pander, r-cran-r.utils, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rpushbullet, r-cran-rsyslog, r-cran-shiny, r-cran-slackr, r-cran-syslognet, r-cran-telegram, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-logger_0.4.2-1.ca2404.1_all.deb Size: 817796 MD5sum: c10c9b0902622d08148e32bb2178a3f7 SHA1: 1b722bf01b4e7b297165af4ccce0ee0b55e47cdc SHA256: cb015eb0762cf6fc86e1dba69c250bc390fed7b936150aa30cec4890b66be18c SHA512: 270aa2f4120bf039afc238b7245531ad65abf06c3b5d3c2af247c1302dc63d16529123b37a63d6b04ca96734a75a3b113e6f8c543ad4411372294a333d78c641 Homepage: https://cran.r-project.org/package=logger Description: CRAN Package 'logger' (A Lightweight, Modern and Flexible Logging Utility) Inspired by the the 'futile.logger' R package and 'logging' Python module, this utility provides a flexible and extensible way of formatting and delivering log messages with low overhead. Package: r-cran-logging Architecture: all Version: 0.10-111-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-crayon Filename: pool/dists/noble/main/r-cran-logging_0.10-111-1.ca2404.1_all.deb Size: 162600 MD5sum: 1370f1ec82a61891053dccb98a60f06b SHA1: ebff70dc516f8bd376bff132bebecf2b49622b3a SHA256: a87ea442edffe78f7cb736a7c7bad6485747e1cdf25ad51a2a86816ebb0f55b0 SHA512: e46f1e53226897cdc344976685028eb720d21fb41ed92aa1bc888127d2e637333781c985bc755e744f2157b825e83fb22bc904e4737554e4dd0a7a85ac509103 Homepage: https://cran.r-project.org/package=logging Description: CRAN Package 'logging' (R Logging Package) Pure R implementation of the ubiquitous log4j package. It offers hierarchic loggers, multiple handlers per logger, level based filtering, space handling in messages and custom formatting. Package: r-cran-loggit Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-loggit_2.1.1-1.ca2404.1_all.deb Size: 59574 MD5sum: baf9b543c0326d144a77a1cc83e0ee79 SHA1: e8bd17fd2df2de4c0ca6913ba71baec5e0fa7b9f SHA256: 48f048c80cc61bdf0d21e981f9b5481fe61637b41c594dd8c020ed2319466ac2 SHA512: 25310613bd051c43526d24d5de8f105f67129a8d2b8a37c2aeb130eb8da6d342417a2263fd97337b85e4f8ab4bda7ff6b67078627ce8d9a30395ec0598700c5b Homepage: https://cran.r-project.org/package=loggit Description: CRAN Package 'loggit' (Modern Logging for the R Ecosystem) An effortless 'ndjson' (newline-delimited 'JSON') logger, with two primary log-writing interfaces. It provides a set of wrappings for base R's message(), warning(), and stop() functions that maintain identical functionality, but also log the handler message to an 'ndjson' log file. 'loggit' also exports its internal 'loggit()' function for powerful and configurable custom logging. No change in existing code is necessary to use this package, and should only require additions to fully leverage the power of the logging system. 'loggit' also provides a log reader for reading an 'ndjson' log file into a data frame, log rotation, and live echo of the 'ndjson' log messages to terminal 'stdout' for log capture by external systems (like containers). 'loggit' is ideal for Shiny apps, data pipelines, modeling work flows, and more. Please see the vignettes for detailed example use cases. Package: r-cran-logib Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-readxl Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-logib_0.2.1-1.ca2404.1_all.deb Size: 102494 MD5sum: 218388fad63d204c31772dfa33e455b3 SHA1: a54b2b3ad15faaf264eea6d25b7801a51423c7ce SHA256: 99c12bf68e9626aa3f6bf2a208c25c75340796e561576fa72ae8bc28502f6a9e SHA512: a713d37f831e11788ce4ca6ede869024c3b1b7f91b2c2ef648bc0e3c31e64debe3c9717ad1d882a500737a08c8ba78f157d69c2fade621e565cb7a47e5b491c1 Homepage: https://cran.r-project.org/package=logib Description: CRAN Package 'logib' (Salary Analysis by the Swiss Federal Office for Gender Equality) Implementation of the Swiss Confederation's standard analysis model for salary analyses in R. The analysis is run at company-level and the model is intended for medium-sized and large companies. It can technically be used with 50 or more employees (apprentices, trainees/interns and expats are not included in the analysis). Employees with at least 100 employees are required by the Gender Equality Act to conduct an equal pay analysis. This package allows users to run the equal salary analysis in R, providing additional transparency with respect to the methodology and simple automation possibilities. Package: r-cran-logibin Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partykit, r-cran-doparallel, r-cran-data.table, r-cran-foreach, r-cran-iterators Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-logibin_0.3-1.ca2404.1_all.deb Size: 76694 MD5sum: 481f36d767202f7f980dc05c1e919b50 SHA1: 9362c0d041ed4c6f5b2025729acef3390e1391d1 SHA256: 60890e351160c1f63f7edc4b950db4c030f17ae6421dd529d8c562b8a83428cd SHA512: 112cb8b6a7c04a660814dea5291cf3ae01f2010fc82471502939165c71a35e7155360fa291471e5e09c17bdb38fed88d474ef3c66821d1053f493bf6a53d1007 Homepage: https://cran.r-project.org/package=logiBin Description: CRAN Package 'logiBin' (Binning Variables to Use in Logistic Regression) Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) . Package: r-cran-logicforest Architecture: all Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-logicreg, r-cran-survival Suggests: r-cran-data.table Filename: pool/dists/noble/main/r-cran-logicforest_2.1.4-1.ca2404.1_all.deb Size: 112700 MD5sum: 3a2a70a0d4be6d06294d713a265befa7 SHA1: a5e8a6528dd05990b36cdcfc2e53531fef30c4d4 SHA256: 12e9f8941fb1cfea9734f4ac45f3f82eeb0df941d22c8df96a3796065fcef60a SHA512: 19f608137be11ce151cf783d1cbda198166ab0cfdf541cbba93f4e84839fe97f0c2d0d0e233fa8817d8f545066074c4cbf8aaf2ca1be4ec7c03e4aa671a87f95 Homepage: https://cran.r-project.org/package=LogicForest Description: CRAN Package 'LogicForest' (Logic Forest) Logic Forest is an ensemble machine learning method that identifies important and interpretable combinations of binary predictors using logic regression trees to model complex relationships with an outcome. Wolf, B.J., Slate, E.H., Hill, E.G. (2010) . Package: r-cran-logihist Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-popbio Filename: pool/dists/noble/main/r-cran-logihist_1.1-1.ca2404.1_all.deb Size: 53392 MD5sum: cceba80dc84992ee7e0a791ab309df5a SHA1: a50779fdbe972de59908155a960172c4f41e3240 SHA256: e05c7af727d5e1421420d7750dd7a7afdfdce9bf2eb3e969c8287c15207efff8 SHA512: 53a3abe8bd8c1f6a648e7f635fba8c8c9ede961e801d9b3c7924a84c16d267d9ff86a80e326e38733e88d7f03afedd02cca5aa5c762c39dd68648201a78d58e4 Homepage: https://cran.r-project.org/package=logihist Description: CRAN Package 'logihist' (Combined Graphs for Logistic Regression) Provides histograms, boxplots and dotplots as alternatives to scatterplots of data when plotting fitted logistic regressions. Package: r-cran-login Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 847 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cookies, r-cran-dbi, r-cran-digest, r-cran-emayili, r-cran-htmltools, r-cran-shiny, r-cran-shinybusy, r-cran-shinyjs, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-login_0.9.3-1.ca2404.1_all.deb Size: 640770 MD5sum: c93658d87528ace9a99f229cfa031bc8 SHA1: 23357bbee8ca8fe800f2ada8bf5b14dfea006882 SHA256: 3685cc2becb3e471b3b6d2d05081924fdfa76735f3c3a6e5bf73ce7f6c4d3284 SHA512: 3e5983b98d7ad7ad4ece65626b8e5e2b43b2d864f61acf7253350060367c37e31fe94f3a8a9a338ff732489537839d528e94ce20a5da0b2701e3ed11f4a8bdb7 Homepage: https://cran.r-project.org/package=login Description: CRAN Package 'login' ('shiny' Login Module) Framework for adding authentication to 'shiny' applications. Provides flexibility as compared to other options for where user credentials are saved, allows users to create their own accounts, and password reset functionality. Bryer (2024) . Package: r-cran-logistic4p Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-logistic4p_1.6-1.ca2404.1_all.deb Size: 86104 MD5sum: 2d98881a51b562cbc129cf38b6eed256 SHA1: aa2031d95b14b664ebf6f6123c2d040069ef0725 SHA256: 451ebc67e74035edec77cde961f53764b491667d82e6f2d79b332c9d89b9c47c SHA512: c5cb9b6401d3ef19370026554545e30eccbe135e815514f5f7deb2c873cd3564fa05390bae0f9dc155550cf12a671f604e4baa4b22b6d086371e438849225131 Homepage: https://cran.r-project.org/package=logistic4p Description: CRAN Package 'logistic4p' (Logistic Regression with Misclassification in DependentVariables) Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables. Package: r-cran-logisticcopula Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brglm2, r-cran-vinecopula, r-cran-rvinecopulib, r-cran-igraph, r-cran-numderiv, r-cran-stringr Filename: pool/dists/noble/main/r-cran-logisticcopula_0.1.0-1.ca2404.1_all.deb Size: 165952 MD5sum: 897811448ea9c8ae734dd33c45609652 SHA1: 8ceaae508cf0d191ed4b2ad91058173fdedc5dec SHA256: 065619f59e88b30d0506383578c59dfb71aba4df94fb791737bc23e11b3b0dd9 SHA512: fd99ba76baf5d5edbfe41f01f3e0cdada19b3cc847b01dbca3bc17f67caa6360a09beb3f312c15cd3463452d7c12d1de6ae774a92afaed321fab3085d021a7bd Homepage: https://cran.r-project.org/package=LogisticCopula Description: CRAN Package 'LogisticCopula' (A Copula Based Extension of Logistic Regression) An implementation of a method of extending a logistic regression model beyond linear effects of the co-variates. The extension in is constructed by first equating the logistic regression model to a naive Bayes model where all the margins are specified to follow natural exponential distributions conditional on Y, that is, a model for Y given X that is specified through the distribution of X given Y, where the columns of X are assumed to be mutually independent conditional on Y. Subsequently, the model is expanded by adding vine - copulas to relax the assumption of mutual independence, where pair-copulas are added in a stage-wise, forward selection manner. Some heuristics are employed during the process of selecting edges, as well as the families of pair-copula models. After each component is added, the parameters are updated by a (smaller) number of gradient steps to maximise the likelihood. When the algorithm has stopped adding edges, based the criterion that a new edge should improve the likelihood more than k times the number new parameters, the parameters are updated with a larger number of gradient steps, or until convergence. Package: r-cran-logisticcurvefitting Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-logisticcurvefitting_0.1.0-1.ca2404.1_all.deb Size: 11014 MD5sum: 45c79c592dc6568a488a16991659938c SHA1: 97f988d07cf2d2d155766d08f3f411185675cb49 SHA256: 62f68ac8803c48cacc29c0e5690f5a5fd632b712037a5f6aba1b88fba0c00ef7 SHA512: bb008127a0265ee71d9520913134d8e9742007d46506696353da18519aad0a0b9cd8326f3fb0b635ab8eba50e01770a7cd887748c29a08810bb612101479ad11 Homepage: https://cran.r-project.org/package=LogisticCurveFitting Description: CRAN Package 'LogisticCurveFitting' (Logistic Curve Fitting by Rhodes Method) A system for fitting Logistic Curve by Rhodes Method. Method for fitting logistic curve by Rhodes Method is described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3). Package: r-cran-logisticensembles Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adabag, r-cran-arm, r-cran-brnn, r-cran-c50, r-cran-car, r-cran-caret, r-cran-corrplot, r-cran-cubist, r-cran-doparallel, r-cran-dplyr, r-cran-e1071, r-cran-gam, r-cran-gbm, r-cran-ggplot2, r-cran-ggplotify, r-cran-glmnet, r-cran-gridextra, r-cran-gt, r-cran-htmltools, r-cran-htmlwidgets, r-cran-ipred, r-cran-klar, r-cran-machineshop, r-cran-magrittr, r-cran-mass, r-cran-mda, r-cran-pls, r-cran-proc, r-cran-purrr, r-cran-randomforest, r-cran-ranger, r-cran-reactable, r-cran-readr, r-cran-rpart, r-cran-scales, r-cran-tidyr, r-cran-tree, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-logisticensembles_1.0.2-1.ca2404.1_all.deb Size: 2297920 MD5sum: 2924d1ca7fe497500b10e762f97b88e1 SHA1: 2defeaf49a933e06c593a780da8c0e503b75ceca SHA256: d51c936385b869664fdebdb158ada2640a55564fb52371d1fada790d4124e7b3 SHA512: c6160a943fd515707af238fbf5ac6031ff986c5ae938a418180532b2a214d3651d3bdc9c223603099e6b6e5593abc8827b25d24294e5fb947963a0e0617d04d1 Homepage: https://cran.r-project.org/package=LogisticEnsembles Description: CRAN Package 'LogisticEnsembles' (Automatically Runs 18 Logistic Models-14 Individual LogisticModels and 4 Ensembles of Models) Automatically returns results from 18 logistic models including 14 individual logistic models and 4 logistic ensembles of models. The package also returns 25 plots, 5 tables, and a summary report. The package automatically builds all 18 models, reports all results, and provides graphics to show how the models performed. This can be used for a wide range of data, such as sports or medical data. The package includes medical data (the Pima Indians data set), and information about the performance of Lebron James. The package can be used to analyze many other examples, such as stock market data. The package automatically returns many values for each model, such as True Positive Rate, True Negative Rate, False Positive Rate, False Negative Rate, Positive Predictive Value, Negative Predictive Value, F1 Score, Area Under the Curve. The package also returns 36 Receiver Operating Characteristic (ROC) curves for each of the 18 models. Package: r-cran-logisticrci Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-logisticrci_1.1-1.ca2404.1_all.deb Size: 42040 MD5sum: 2e4132e97dec2b6fd1b64edaa32e38c9 SHA1: 83f255120a9de7fdb65d7fdc928d4a6921df4554 SHA256: 4c0ce3bcc087cae78dc8790e318cc053f4366c015cfb7ffcf8460aed4c5ed347 SHA512: 703916c0c8dd072449159087942796e7a78c3fa2887a670f27207cea7a4d0a0a2b0578902ecbde41dd1abe702bd1558631dd81cbda938cbebdf48df49f78a491 Homepage: https://cran.r-project.org/package=LogisticRCI Description: CRAN Package 'LogisticRCI' (Linear and Logistic Regression-Based Reliable Change Index) Here we provide an implementation of the linear and logistic regression-based Reliable Change Index (RCI), to be used with lm and binomial glm model objects, respectively, following Moral et al. . The RCI function returns a score assumed to be approximately normally distributed, which is helpful to detect patients that may present cognitive decline. Package: r-cran-logisticrr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-logisticrr_0.3.0-1.ca2404.1_all.deb Size: 102232 MD5sum: 30c6b5eb92232859f3365109cb81db91 SHA1: 6b809457b2f676999711a8f54b9234d67d65fd77 SHA256: bc82a1b84bbd76701129187db59baf7fb91ea2161a37ee7862c2577c6a5d1a8d SHA512: 7dc9b6e7d6002c5f186302f17be287b497b9d8d544e35548b565b59ca50b5696b08c8ace91273f56ec98bd7e376b16e489d39dbdea38cf87da98e103e3e895f9 Homepage: https://cran.r-project.org/package=logisticRR Description: CRAN Package 'logisticRR' (Adjusted Relative Risk from Logistic Regression) Adjusted odds ratio conditional on potential confounders can be directly obtained from logistic regression. However, those adjusted odds ratios have been widely incorrectly interpreted as a relative risk. As relative risk is often of interest in public health, we provide a simple code to return adjusted relative risks from logistic regression model under potential confounders. 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The proposed methods were developed in Escabias et al (2004) and Escabias et al (2005) . Package: r-cran-logitnorm Architecture: all Version: 0.8.39-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-markdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-logitnorm_0.8.39-1.ca2404.1_all.deb Size: 201718 MD5sum: 872a5fffe0f6bb4356e8265b5d2d751b SHA1: e36e17e781176c80060a9c73015d29ac22e3a8e7 SHA256: e98f112324e45e5042db035e5cbc57acb1290da2aa9b600f2c9c882121490566 SHA512: f47aeba7a3b6301b7b5c992c5ca48d29bb4a997a74e2c957ab95b6e3c6b73a041f8d03ea07570d8cdfb1c77b01e2c7da6c25226e305af5315d23a1f87dd66713 Homepage: https://cran.r-project.org/package=logitnorm Description: CRAN Package 'logitnorm' (Functions for the Logitnormal Distribution) Density, distribution, quantile and random generation function for the logitnormal distribution. 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Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a parallelized multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. Mixed logit models can include uncorrelated or correlated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) . More details can be found in Helveston (2023) . 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For complex survey data, models can be fitted from design objects from the 'survey' package. Currently supported models include UNIDIFF (Erikson & Goldthorpe, 1992), a.k.a. log-multiplicative layer effect model (Xie, 1992) , and several association models: Goodman (1979) row-column association models of the RC(M) and RC(M)-L families with one or several dimensions; two skew-symmetric association models proposed by Yamaguchi (1990) and by van der Heijden & Mooijaart (1995) Functions allow computing the intrinsic association coefficient (see Bouchet-Valat (2022) ) and the Altham (1970) index , including via the Bayes shrinkage estimator proposed by Zhou (2015) ; and the RAS/IPF/Deming-Stephan algorithm. 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The log_print() function will print to both the console and the file log, without interfering in other write operations. Package: r-cran-logregequiv Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-logregequiv_0.1.5-1.ca2404.1_all.deb Size: 67148 MD5sum: 83c624e27a212e8c21e41e20bbba537a SHA1: efd9ca324426a5c6fc68ad92c65063bbab101e6f SHA256: 4d25154b133ea16770eeb1b5b5960e227e1da0acbe3a7a3e19c199e891975604 SHA512: 1c72c2bb319b78fcb833159270e7eb8134f370663aa4e7665d2b48c13f119a09ab0b3284d139c166d785dd6bf1d5f2524d588f57b31726bf6464f86610ea955c Homepage: https://cran.r-project.org/package=LogRegEquiv Description: CRAN Package 'LogRegEquiv' (Logistic Regression Equivalence) Tools for assessing equivalence of similar Logistic Regression models. 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Package: r-cran-logrxaddin Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-logrx, r-cran-stringr, r-cran-miniui, r-cran-rstudioapi, r-cran-shiny, r-cran-waiter Filename: pool/dists/noble/main/r-cran-logrxaddin_0.0.1-1.ca2404.1_all.deb Size: 16026 MD5sum: 31389a068357e46d8fadc115a60c8195 SHA1: fb7141e2058ed686bb5e74da2a02cfd31ccf0145 SHA256: 459ea087907a8785864f4b24cdd2838844850d77f4791461ef4849788aaa873c SHA512: cc84feb77df07281b8251c8373a3c94a100203d29adea6d8ab6033b459908fc6e823517130ff79fcbcc9a90155afe6293de60724b82b193d4375e03d3ff3dadd Homepage: https://cran.r-project.org/package=logrxaddin Description: CRAN Package 'logrxaddin' (Addin for the 'logrx' Package) This is an extension package to 'logrx', which is a log creation program focused on Clinical Reporting within the Pharma Industry. This package enables a simple 'shiny-based' Add-in that provides a point and click interface to produce a log for a single program. Package: r-cran-lolliplot Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scales, r-bioc-iranges, r-cran-grimport, r-bioc-genomicranges Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lolliplot_0.2.2-1.ca2404.1_all.deb Size: 105526 MD5sum: 004998cc60806f8b5062c50f55af6f2d SHA1: ddd409469e57eced8a591718274c3f3f7bafb45d SHA256: e67e30104922d30e1d9c900b87b36ba2051ea48740ae2383120be4fcb6038d51 SHA512: 480849cd06384dbfa827aac8c66bd6aa61101be2ecbf6e53fe6ae653837a68bb9bde829b88ca9f3c48fd783246c9dd0cf58304cb90b79dab31725b7715ba8186 Homepage: https://cran.r-project.org/package=lolliplot Description: CRAN Package 'lolliplot' (Plot Variants and Somatic Mutations) Draw lolliplot using GRanges objects. this package was designed only for drawing lolliplot. So, it's faster than 'trackViewer', but un-related functions has been derived. Package: r-cran-lolr Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3756 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-abind, r-cran-mass, r-cran-irlba, r-cran-pls, r-cran-robust, r-cran-robustbase Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-randomforest, r-cran-latex2exp, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-lolr_2.1-1.ca2404.1_all.deb Size: 2661766 MD5sum: 94ead0af6b38e11f601e5ba9e88749eb SHA1: 6dbb98c0935a5739b739ee934031e77849dcc5a6 SHA256: 0b415f4d231b5f4d379c770c87c775077849ccbd18ed453136b36c6bcfd0ebf7 SHA512: 6f9f0995f2d3b09c50d8aebcacdaf3c127a679159145efa39b7711bb0c0c46b2c903ac1f1adfa82919c0338f19b6d32672bb9eeddb6a9b49289ea0a6223d4d28 Homepage: https://cran.r-project.org/package=lolR Description: CRAN Package 'lolR' (Linear Optimal Low-Rank Projection) Supervised learning techniques designed for the situation when the dimensionality exceeds the sample size have a tendency to overfit as the dimensionality of the data increases. To remedy this High dimensionality; low sample size (HDLSS) situation, we attempt to learn a lower-dimensional representation of the data before learning a classifier. That is, we project the data to a situation where the dimensionality is more manageable, and then are able to better apply standard classification or clustering techniques since we will have fewer dimensions to overfit. A number of previous works have focused on how to strategically reduce dimensionality in the unsupervised case, yet in the supervised HDLSS regime, few works have attempted to devise dimensionality reduction techniques that leverage the labels associated with the data. In this package and the associated manuscript Vogelstein et al. (2017) , we provide several methods for feature extraction, some utilizing labels and some not, along with easily extensible utilities to simplify cross-validative efforts to identify the best feature extraction method. Additionally, we include a series of adaptable benchmark simulations to serve as a standard for future investigative efforts into supervised HDLSS. Finally, we produce a comprehensive comparison of the included algorithms across a range of benchmark simulations and real data applications. Package: r-cran-lomb Architecture: all Version: 2.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2652 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-plotly, r-cran-pracma, r-cran-knitr Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-lomb_2.5.0-1.ca2404.1_all.deb Size: 1590968 MD5sum: 5fd4af57373d9a729ba7a0fd6513ae0d SHA1: 0e78af3de303629dc94fcd0094b1381ecd701dd2 SHA256: 0bce5c2bb1ff94d8a328d36cf02ec812c12b0eeecac90139344377ca0817d520 SHA512: 27ff947e4902a5d8985ea0dff217cb4d5acc8ec43b950ddd68e42c7a83582fdc3c5d67120654ee66862eb2eed5fee37406cb498cb43e3e872408dd1d50bc1946 Homepage: https://cran.r-project.org/package=lomb Description: CRAN Package 'lomb' (Lomb-Scargle Periodogram) Computes the Lomb-Scargle Periodogram and actogram for evenly or unevenly sampled time series. Includes a randomization procedure to obtain exact p-values. Partially based on C original by Press et al. (Numerical Recipes) and the Python module Astropy. For more information see Ruf, T. (1999). The Lomb-Scargle periodogram in biological rhythm research: analysis of incomplete and unequally spaced time-series. Biological Rhythm Research, 30(2), 178-201. Package: r-cran-long2lstmarray Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-long2lstmarray_0.2.0-1.ca2404.1_all.deb Size: 54298 MD5sum: b47c5d856f793169839534082acb4bdf SHA1: 70750a1ed50efd32fd63c726d3b68e5e8d8f9db4 SHA256: e65c6f5315cd9ec2199c912f4e12a415fc51633afad441b11b75342dfb4c03b1 SHA512: 58bbefec1cec5e7aca5d5ba647cb6728d70e57ef1ee0d2d0f1ce7e0c637b9de7ea489833ecefea5a35f26313881a606da8d6cea53d0d1bbfb431ba63d3910ace Homepage: https://cran.r-project.org/package=long2lstmarray Description: CRAN Package 'long2lstmarray' (Longitudinal Dataframes into Arrays for Machine LearningTraining) An easy tool to transform 2D longitudinal data into 3D arrays suitable for Long short-term memory neural networks training. The array output can be used by the 'keras' package. Long short-term memory neural networks are described in: Hochreiter, S., & Schmidhuber, J. (1997) . 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Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial. 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'LongDat' is able to take in several data types as input, including count, proportion, binary, ordinal and continuous data. The output table contains p values, effect sizes and 'covariates' of each feature, making the downstream analysis easy. 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The package implements a novel longitudinal attribution method based on a semiparametric additive hazards model with time-dependent covariates, specifically designed to address interval censoring and semi-competing risks via a copula framework. The resulting age-cause-specific contributions to disability prevalence and death probability can be used to quantify and decompose differences in cohort HE between groups. The package supports stepwise replacement decomposition algorithms and is applicable to cohort-based health disparity research across diverse populations. Related methods include Sun et al. (2023) . Package: r-cran-longit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aiccmodavg, r-cran-missforest, r-cran-r2jags, r-cran-rjags Filename: pool/dists/noble/main/r-cran-longit_0.1.0-1.ca2404.1_all.deb Size: 72492 MD5sum: 295e2619be6d0cc606f7de57be4630fc SHA1: 71b85392b09533f5f9672b1617e7a509f369c4a7 SHA256: 5b35fdf4b5a9b8fcc71fbade4020a3237f01bcefc89bd4b096fceb40628fc08c SHA512: 36cc9267fd76f8f2a53f8f355c159b7428905ca94e62feceee299079c2358bfdff1a1a6932441df8e1a8ff793ddd9b123638e04ecdf7960e413f0f1f493b8c86 Homepage: https://cran.r-project.org/package=longit Description: CRAN Package 'longit' (High Dimensional Longitudinal Data Analysis Using MCMC) High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). Currently support mixed effect regression with or without missing observations by considering covariance structures. It provides estimates by missing at random and missing not at random assumptions. In this R package, we present Bayesian approaches that statisticians and clinical researchers can easily use. The functions' methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by Bhattacharjee A (2020) . 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Package: r-cran-longitudinalanal Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-mass, r-cran-dlm Filename: pool/dists/noble/main/r-cran-longitudinalanal_0.2-1.ca2404.1_all.deb Size: 60702 MD5sum: 0f0a130b63f595af6953efa2f0906e67 SHA1: 3e52d8965f880341dc37bc31652994370bf72949 SHA256: 5d8fb5315b2d486b05b52518a69ebd77368dd32a7829b5e925de69e65b329015 SHA512: 3cbf6285e92736e79b951316e4d72e8e940965b800e493299bd593ba3cce362e0fedb9f7fd2f4d9afa43c0e9c8000560a944e4f9cebbfa1c7ae0551c3733f347 Homepage: https://cran.r-project.org/package=longitudinalANAL Description: CRAN Package 'longitudinalANAL' (Longitudinal Data Analysis) Regression analysis of mixed sparse synchronous and asynchronous longitudinal covariates. Please cite the manuscripts corresponding to this package: Sun, Z. et al. (2023) and Liu, C. et al. (2023) . Package: r-cran-longitudinalcascade Architecture: all Version: 0.3.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-survival, r-cran-ggplot2, r-cran-tidyr, r-cran-zoo, r-cran-scales, r-cran-rlang, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-longitudinalcascade_0.3.2.6-1.ca2404.1_all.deb Size: 299560 MD5sum: 9f474d509bc9ea0a5e7b1b2136806d23 SHA1: e0d7180cd0eed9d62235e5a72f2054bb3267c3ed SHA256: 877218ce743ed47e66635653d9201c9faa645c4a333af912302e9aae14bac298 SHA512: 1200da875e859f4b886433bbb697db42b630081f76aec416c33d85012596775a437ee7848ce56fd0b6c4fbeec0c95b53eddbf2e7152bc9525cd3204f94327b15 Homepage: https://cran.r-project.org/package=longitudinalcascade Description: CRAN Package 'longitudinalcascade' (Longitudinal Cascade) Creates a series of sets of graphics and statistics related to the longitudinal cascade, all included in a single object. The longitudinal cascade inputs longitudinal data to identify gaps in the HIV and related cascades by observing differences using time to event and survival methods. The stage definitions are set by the user, with default standard options. Outputs include graphics, datasets, and formal statistical tests. Package: r-cran-longitudinaldata Architecture: all Version: 2.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clv, r-cran-class, r-cran-rgl, r-cran-misc3d Filename: pool/dists/noble/main/r-cran-longitudinaldata_2.4.7-1.ca2404.1_all.deb Size: 536228 MD5sum: b9ea5daeb6391b1c3045a3adbb2b7a2f SHA1: 5593c17bc0197a50e06954f8384911bbf56ad019 SHA256: c0b8b46a17dd6d395fc05e11e7f424fabc9c3533f379d31131f31938d4318309 SHA512: e03dbcff76d03f11028c97a7bb10669d8e6b0c17e36ffa16d64b1f2ffb0d808cdcee287bc6c22205e1d3f08b7956a8ef87452e4c6c7c1318c2f362f3173bc386 Homepage: https://cran.r-project.org/package=longitudinalData Description: CRAN Package 'longitudinalData' (Longitudinal Data) Tools for longitudinal data and joint longitudinal data (used by packages kml and kml3d). Package: r-cran-longiturf Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest, r-cran-rpart, r-cran-mvtnorm, r-cran-latex2exp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-longiturf_0.9-1.ca2404.1_all.deb Size: 155810 MD5sum: 3f063577c47e15b374dd0a9c3b921817 SHA1: e8d806f5844a2286c6cc1e7c5e508cef40d2e498 SHA256: 658ec626e5822db5d0f4d56f54e46cf04130b55309fa1f0312f79006c60ec52e SHA512: e60be32e564eab51f9c2e8d92e859a99ab0dd07f54f2b656fcfb896ac7e9b6102d2dac3f23a785659db634c6e93718a7a053cf2785a415918ac4292e0e62f28e Homepage: https://cran.r-project.org/package=LongituRF Description: CRAN Package 'LongituRF' (Random Forests for Longitudinal Data) Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data. In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020) Random forests for high-dimensional longitudinal data. Package: r-cran-longke Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-bvls, r-cran-fdapace, r-cran-mvtnorm, r-cran-dplyr, r-cran-purrr Filename: pool/dists/noble/main/r-cran-longke_0.1.0-1.ca2404.1_all.deb Size: 56002 MD5sum: 0439562745c3a30f2c346b59c48fc00b SHA1: 50bc222414c4099e2a00cddbd8f6bd95c0b4490d SHA256: 692e34acca516a3a5ecc8f09d25513248985ae4490f5645a09ebae8996f4ad8b SHA512: 6af612335a1e0c2d910cbf56e2e9c5efafb7c941d122bf17f8ba74bb03263b8a3ffa387673f6c7b7ab498a38287e002e0953d7bd5e1db14a56cbc734bfae8cc7 Homepage: https://cran.r-project.org/package=longke Description: CRAN Package 'longke' (Nonparametric Predictive Model for Sparse and IrregularLongitudinal Data) The proposed method aims at predicting the longitudinal mean response trajectory by a kernel-based estimator. The kernel estimator is constructed by imposing weights based on subject-wise similarity on L2 metric space between predictor trajectories as well as time proximity. Users could also perform variable selections to derive functional predictors with predictive significance by the proposed multiplicative model with multivariate Gaussian kernels. Package: r-cran-longmemo Architecture: all Version: 1.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-sfsmisc Filename: pool/dists/noble/main/r-cran-longmemo_1.1-4-1.ca2404.1_all.deb Size: 228436 MD5sum: 02c5f68eca7654c7042efb34ff20a5a5 SHA1: a126ee1f1d74e555066e5f48e7c67ed53496d11e SHA256: 1f1c927e4451f5b7c06ddb80d4cf3faed3ca62a48ca93af980075d477f8625f0 SHA512: 13548f98dcbfe5aca3357192ba5826bc5a81ef62d364fd92fe68161d42a359d21e2f23b6581e6c241e43fccbd82b64dbc5fb66395529e2e4d1bbfda5235f2e5c Homepage: https://cran.r-project.org/package=longmemo Description: CRAN Package 'longmemo' (Statistics for Long-Memory Processes (Book Jan Beran), andRelated Functionality) Datasets and Functionality from 'Jan Beran' (1994). Statistics for Long-Memory Processes; Chapman & Hall. Estimation of Hurst (and more) parameters for fractional Gaussian noise, 'fARIMA' and 'FEXP' models. Package: r-cran-longmixr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3020 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-bioc-consensusclusterplus, r-cran-flexmix, r-cran-statmatch Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-ggalluvial, r-cran-factominer, r-cran-factoextra, r-cran-lme4, r-cran-purrr Filename: pool/dists/noble/main/r-cran-longmixr_1.0.0-1.ca2404.1_all.deb Size: 2150634 MD5sum: f0633080d08904c985fccee818b2fb7b SHA1: 8d47ca7e1cb99633cce9069da9b553ac786c723c SHA256: c2995bcc56bf8b0c438b25f189e0feba52d25764c709ca7faba31740b88f6194 SHA512: c31205794980c2ce3063cc8026fe33bdf5cd2f72284e88ace23bc78f174e560c8ac34aae05c8815e5df5545b6131b53aa126cebc17415bddd98550ffaadc5733 Homepage: https://cran.r-project.org/package=longmixr Description: CRAN Package 'longmixr' (Longitudinal Consensus Clustering with 'flexmix') An adaption of the consensus clustering approach from 'ConsensusClusterPlus' for longitudinal data. The longitudinal data is clustered with flexible mixture models from 'flexmix', while the consensus matrices are hierarchically clustered as in 'ConsensusClusterPlus'. By using the flexibility from 'flexmix' and 'FactoMineR', one can use mixed data types for the clustering. 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(2025) . A tutorial for this package can be found at . Package: r-cran-longurl Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-longurl_0.3.3-1.ca2404.1_all.deb Size: 19336 MD5sum: 57d438865b02e2ae3f86f19dc5919c16 SHA1: 40239ef5732837a74ac35b9f09b2fa300c5df92e SHA256: 20ee2864a0f46ec96f89e24884a2c3bb3660fa4ef6b76235c7e84f3fc1203a2c SHA512: cbcd321132ffd0e6534be9da1f1b0e214062c0c40623deb99128cdbf41981e29869fe2e76dd2370d0cd68c8cd12a0b4abc14bc3cfafe05f85a8cdb37a44d4f71 Homepage: https://cran.r-project.org/package=longurl Description: CRAN Package 'longurl' (Expand Short 'URLs') Tools are provided to expand vectors of short URLs into long 'URLs'. 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Package: r-cran-loo Architecture: all Version: 2.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2611 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-matrixstats, r-cran-posterior Suggests: r-cran-bayesplot, r-cran-brms, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-rstan, r-cran-rstanarm, r-cran-rstantools, r-cran-spdep, r-cran-testthat Filename: pool/dists/noble/main/r-cran-loo_2.9.0-1.ca2404.1_all.deb Size: 1735038 MD5sum: e62d322f3a23b028595d31b03c19587c SHA1: d35b2612e62ab01178842396029b22b1f74fa20f SHA256: 218a875e350fdd3817f762da87f27626daaf02a566b9d9452538bf748982ab4d SHA512: 9f2eebe96552e20f79c4e8fab490f5785d8b176c61093a82fdce822e800975f5003735cd20a709b49d4a026b17ede2a2e73596b63602f7d17eb208c3648be07f Homepage: https://cran.r-project.org/package=loo Description: CRAN Package 'loo' (Efficient Leave-One-Out Cross-Validation and WAIC for BayesianModels) Efficient approximate leave-one-out cross-validation (LOO) for Bayesian models fit using Markov chain Monte Carlo, as described in Vehtari, Gelman, and Gabry (2017) . 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The bandwidth for kernel density estimates is computed using persistent homology, a technique in topological data analysis. Using peak-over-threshold method, a generalized Pareto distribution is fitted to the log of leave-one-out kde values to identify outliers. 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The 'loon.shiny' package can take 'loon' widgets and display a selfsame 'shiny' app. Package: r-cran-loon.tourr Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2992 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-loon, r-cran-tourr, r-cran-mass, r-cran-loon.ggplot, r-cran-tibble Suggests: r-cran-class, r-cran-magrittr, r-cran-tidyverse, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-loon.tourr_0.1.5-1.ca2404.1_all.deb Size: 2144616 MD5sum: 4ecc19ef1d211ff4c643227dc87c1222 SHA1: 058987e4956c0bc3ffc9b2f2d8599e1778ebba77 SHA256: 5f98d2419bb4f0a09540da53469c1cc39cc2ea3ee6773e980bcc555c57fdd8b1 SHA512: f405bb120e4567575273b635875bbe7c73a71455e47450672852577e49a641d950fe8cf339a64ab56d19113b1a5d406c49f8ccc916b139b50061ed300de2e03c Homepage: https://cran.r-project.org/package=loon.tourr Description: CRAN Package 'loon.tourr' (Tour in 'Loon') Implement tour algorithms in interactive graphical system 'loon'. 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Package: r-cran-loopanalyst Architecture: all Version: 1.2-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme Filename: pool/dists/noble/main/r-cran-loopanalyst_1.2-7-1.ca2404.1_all.deb Size: 223550 MD5sum: 00daa005cff02619b18cf6c61d2ce38e SHA1: f9b307a9fec753f106ea7262d67d4df48a9257b4 SHA256: 432769ae7435557193c3be760ea8f5ff3fdf5ff9e87d5c961c6e5414325ff58c SHA512: f15672954e350a5a9b1762ce46ffe2b3b6af000d5050d094f210be77a636bc1b7f68397f0719ae4e5a8ba755899f4d9cf836693ac5633962151e406f88ae2549 Homepage: https://cran.r-project.org/package=LoopAnalyst Description: CRAN Package 'LoopAnalyst' (A Collection of Tools to Conduct Levins' Loop Analysis) Performs Levins' loop analysis of qualitatively-specified complex causal systems. 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References: Pavía and Romero (2024) . Pavía and Romero (2024) . Pavía (2023) . Pavía (2024) . Pavía (2024) . Pavía and Penadés (2024). A bottom-up approach for ecological inference. Romero, Pavía, Martín and Romero (2020) . Acknowledgements: The authors wish to thank Consellería de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grants AICO/2021/257, CIAICO/2023/031) and MICIU/AEI/10.13039/501100011033/FEDER, UE (grant PID2021-128228NB-I00) for supporting this research. 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The primary reference is Mukhopadhyay, S. and Wang, K. (2020, Biometrika); . 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Three main functions are provided in this package: (i) LASER(): it generates specially-designed artificial relevant samples for a given case; (ii) g2l.proc(): computes customized fdr(z|x); and (iii) rEB.proc(): performs empirical Bayes inference based on LASERs. The details can be found in Mukhopadhyay, S., and Wang, K (2021, ). 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Package: r-cran-lpsmooth Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpgraph, r-cran-lpbkg, r-cran-truncnorm, r-cran-nloptr, r-cran-hmisc, r-cran-orthopolynom, r-cran-polynom Filename: pool/dists/noble/main/r-cran-lpsmooth_0.1.3-1.ca2404.1_all.deb Size: 83782 MD5sum: 5a952c1a14544e6b59365fd8752728cf SHA1: d574a7ef3ed6a6af0725a7b5726452cc6c933d6c SHA256: c85879e21a5407e9797c037caf87e67500452b22a9c3464adce8a176106166e1 SHA512: 19abce9c94c9b2402b4dc4b7b63be912545791412178700b51b203c0c78b9cfda3728c6dcffd24b94be133ba8f4f04de667d461c6cbca8f6d146a02686e36143 Homepage: https://cran.r-project.org/package=LPsmooth Description: CRAN Package 'LPsmooth' (LP Smoothed Inference and Graphics) Classical tests of goodness-of-fit aim to validate the conformity of a postulated model to the data under study. In their standard formulation, however, they do not allow exploring how the hypothesized model deviates from the truth nor do they provide any insight into how the rejected model could be improved to better fit the data. To overcome these shortcomings, we establish a comprehensive framework for goodness-of-fit which naturally integrates modeling, estimation, inference and graphics. In this package, the deviance tests and comparison density plots are performed to conduct the LP smoothed inference, where the letter L denotes nonparametric methods based on quantiles and P stands for polynomials. Simulations methods are used to perform variance estimation, inference and post-selection adjustments. Algeri S. and Zhang X. (2020) . Package: r-cran-lqg Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 808 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-lqg_0.1.0-1.ca2404.1_all.deb Size: 792542 MD5sum: e9483057b0d47612400ccbc695e530a9 SHA1: e08177c797a6ce8d8160e173745e4c7fcd8211cd SHA256: 384cead997858bd8bbff4f7736ff340155768463604a83725887be5c4571a999 SHA512: f70bf2126e86ff646d45570ab07217c907d919ebdb251ed7f338f8748716e58a09baea7fe2bb80b3948a7781b519f15d6536c3dd946a5556ee64c2fd4c7ada27 Homepage: https://cran.r-project.org/package=LqG Description: CRAN Package 'LqG' (Robust Group Variable Screening Based on Maximum Lq-LikelihoodEstimation) Produces a group screening procedure that is based on maximum Lq-likelihood estimation, to simultaneously account for the group structure and data contamination in variable screening. The methods are described in Li, Y., Li, R., Qin, Y., Lin, C., & Yang, Y. (2021) Robust Group Variable Screening Based on Maximum Lq-likelihood Estimation. Statistics in Medicine, 40:6818-6834.. 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This family of distribution includes skewed versions of the Normal, Student's t, Laplace, Slash and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses as shown in Galarza et.al.(2020) . It provides estimates and full inference. It also provides envelopes plots for assessing the fit and confidences bands when several quantiles are provided simultaneously. 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Described further in Mantz (2016) . Package: r-cran-lrmest Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-psych Filename: pool/dists/noble/main/r-cran-lrmest_3.0-1.ca2404.1_all.deb Size: 511494 MD5sum: dc27c311dba036e07dddb0c9ab4fa366 SHA1: f3199caeb1a7f297d4f616e3d573645106ec7b90 SHA256: 2ac7ccfc67f6fdb79e94cb4b962efe2ff9a3d03b3a875a577bd11851e4385a00 SHA512: 2be671b8b1403c3cbd852ea9f8af36b0d0e10a0e07269fffb3adb7193b61a7065dac7527ebc68e0fe11130a6de4815575d4ff1ee4f673a4ebe5c1659aeb9887e Homepage: https://cran.r-project.org/package=lrmest Description: CRAN Package 'lrmest' (Different Types of Estimators to Deal with Multicollinearity) When multicollinearity exists among predictor variables of the linear model, least square estimators does not provide a better solution for estimating parameters. 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Some of these estimators are Ordinary Least Square Estimator (OLSE), Ordinary Generalized Ordinary Least Square Estimator (OGOLSE), Ordinary Ridge Regression Estimator (ORRE), Ordinary Generalized Ridge Regression Estimator (OGRRE), Restricted Least Square Estimator (RLSE), Ordinary Generalized Restricted Least Square Estimator (OGRLSE), Ordinary Mixed Regression Estimator (OMRE), Ordinary Generalized Mixed Regression Estimator (OGMRE), Liu Estimator (LE), Ordinary Generalized Liu Estimator (OGLE), Restricted Liu Estimator (RLE), Ordinary Generalized Restricted Liu Estimator (OGRLE), Stochastic Restricted Liu Estimator (SRLE), Ordinary Generalized Stochastic Restricted Liu Estimator (OGSRLE), Type (1),(2),(3) Liu Estimator (Type-1,2,3 LTE), Ordinary Generalized Type (1),(2),(3) Liu Estimator (Type-1,2,3 OGLTE), Type (1),(2),(3) Adjusted Liu Estimator (Type-1,2,3 ALTE), Ordinary Generalized Type (1),(2),(3) Adjusted Liu Estimator (Type-1,2,3 OGALTE), Almost Unbiased Ridge Estimator (AURE), Ordinary Generalized Almost Unbiased Ridge Estimator (OGAURE), Almost Unbiased Liu Estimator (AULE), Ordinary Generalized Almost Unbiased Liu Estimator (OGAULE), Stochastic Restricted Ridge Estimator (SRRE), Ordinary Generalized Stochastic Restricted Ridge Estimator (OGSRRE), Restricted Ridge Regression Estimator (RRRE) and Ordinary Generalized Restricted Ridge Regression Estimator (OGRRRE). To select the best estimator in a practical situation the Mean Square Error (MSE) is used. Using this package scalar MSE value of all the above estimators and Prediction Sum of Square (PRESS) values of some of the estimators can be obtained, and the variation of the MSE and PRESS values for the relevant estimators can be shown graphically. Package: r-cran-lrmf3 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-glue Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-lrmf3_0.1.0-1.ca2404.1_all.deb Size: 193268 MD5sum: c9797af9cfa0de2ab12dc229165d06f5 SHA1: c9048b35b104a4691865046a7e8b035578c3b6df SHA256: 7c5ac46a9e0738040b513f8529a9424b39fd99dd7eff120fb0e73bf47517a298 SHA512: 9f2a6c84a3d575689ca2de5de23a42862b045d7429b77eb8ab134b68f345cb48fd3883380a6d8d03d2f15d135d23d3ea720715e512c1ec7dde62a294cf31d304 Homepage: https://cran.r-project.org/package=LRMF3 Description: CRAN Package 'LRMF3' (Low Rank Matrix Factorization S3 Objects) Provides S3 classes to represent low rank matrix decompositions. 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The package implements two estimators: oss_estimator(), a low-dimensional semi-supervised method, and dantzig_missing(), a high-dimensional approach. The tuning parameter can be selected automatically via cv_dantzig_missing(). See Risebrow and Berrett (2026) . Optional support for the 'gurobi' optimizer via the 'gurobi' R package (available from Gurobi, see ). 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By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome. 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For actually building a vector semantic space, use the package 'lsa' or other specialized software. Downloadable semantic spaces can be found at . 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(Hrsg.). (2016). "Large-Scale Assessment mit R: Methodische Grundlagen der österreichischen Bildungsstandardüberprüfung." Wien: facultas. (ISBN: 978-3-7089-1343-8, ) zur Verfügung. Package: r-cran-lsasim Architecture: all Version: 2.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1075 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-cli, r-cran-polycor Suggests: r-cran-testthat, r-cran-knitr, r-cran-formatr, r-cran-rmarkdown, r-cran-naepirtparams Filename: pool/dists/noble/main/r-cran-lsasim_2.1.6-1.ca2404.1_all.deb Size: 524268 MD5sum: 66076185702f3b8151cb8cab46589fe4 SHA1: e217173e855fe07c77fbb39ca40ba96b27a6458d SHA256: 317385c8d66cc52aa0fd8a925d24117fb919411af90dc5e4282d0dd72d40c7fb SHA512: e7490aada2a5adbfebffdb51dc530796343cf24b74ab3fa4486c319508a2ccc13f733e25c4bfcab015ecd8e3bde988f6bae8789f615477974f02f0c91ce81111 Homepage: https://cran.r-project.org/package=lsasim Description: CRAN Package 'lsasim' (Functions to Facilitate the Simulation of Large Scale AssessmentData) Provides functions to simulate data from large-scale educational assessments, including background questionnaire data and cognitive item responses that adhere to a multiple-matrix sampled design. The theoretical foundation can be found on Matta, T.H., Rutkowski, L., Rutkowski, D. et al. (2018) . Package: r-cran-lsbs Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ks, r-cran-numderiv, r-cran-matrix Filename: pool/dists/noble/main/r-cran-lsbs_0.1-1.ca2404.1_all.deb Size: 31230 MD5sum: 2823dd20dfc59d7200ec2dcb15a4cdbe SHA1: d75a5705ff7f243dc5b3cf3424bc95fc3eb4d1b1 SHA256: 07f5163b2081f78e5b806199f4ddcf714cf7d890429120ff9bf3818047bbcae8 SHA512: a5c6563a671039a397884d197877d243f574ae9f362292a4d77fad73626a27fda72a02759d4acb099efc8fbc1e5d6219db95a7c83c75f6d0c2ce2d1819e8c9a3 Homepage: https://cran.r-project.org/package=lsbs Description: CRAN Package 'lsbs' (Bandwidth Selection for Level Sets and HDR Estimation) Bandwidth selection for kernel density estimators of 2-d level sets and highest density regions. 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Based on the data produced by the simulation model, it performs both linear and state-dependent IRF analysis, providing the tools required by the Counterfactual Monte Carlo (CMC) methodology (Amendola and Pereira (2024) ), including state identification and sensitivity. CMC proposes retrieving the causal effect of shocks by exploiting the opportunity to directly observe the counterfactual in a fully controlled experimental setup. 'LSD' (Laboratory for Simulation Development) is free software available at ). 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Reads object-oriented data produced by 'LSD' simulation models and performs screening and global sensitivity analysis (Sobol decomposition method, Saltelli et al. (2008) ISBN:9780470725177). A Kriging or polynomial meta-model (Kleijnen (2009) ) is estimated using the simulation data to provide the data required by the Sobol decomposition. 'LSD' (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at ). 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It enables the construction of bootstrap and classical confidence intervals for regression coefficients, leveraging intensive simulation studies and real data analysis. Package: r-cran-lsl Architecture: all Version: 0.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-lavaan Filename: pool/dists/noble/main/r-cran-lsl_0.5.6-1.ca2404.1_all.deb Size: 172066 MD5sum: 60e1130997a89cbfb1462915322b71fb SHA1: 7739b476db6128a1a7f9ec5d5558d7b1fa124966 SHA256: 7d9761fcd4efa1439fc649e41e74a083273cad157e879122cac8cf11339ec6c1 SHA512: 622c0f062d6f4b49f76c5ddaaa502ffc554e2e4999e1bff1551b92d01e5a891a947731a6496d5dfe73ecf52c001e5c5dd271c7841b48e1868aaae83ae6b77683 Homepage: https://cran.r-project.org/package=lsl Description: CRAN Package 'lsl' (Latent Structure Learning) Fits structural equation modeling via penalized likelihood. 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Package: r-cran-lsmeans Architecture: all Version: 2.30-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-emmeans Filename: pool/dists/noble/main/r-cran-lsmeans_2.30-2-1.ca2404.1_all.deb Size: 39222 MD5sum: d0d3d97787b567eaaa6fd65a8e1545b6 SHA1: b8751d7646bd5ce4787109b6e303a7bb7d618dbf SHA256: de516d1db01694e20e4b866e93b1f429c0a50a0d18f07e9a56975d3bb221a84d SHA512: 049e787be51de6bf07982c1137b83c977e8f1b7866ff4ec49f17b0c22737a1aa5b0826c86184ef73d1b42064329fbfc6bc12f11cce0b9bbf63f3d6093a8255b0 Homepage: https://cran.r-project.org/package=lsmeans Description: CRAN Package 'lsmeans' (Least-Squares Means) Obtain least-squares means for linear, generalized linear, and mixed models. Compute contrasts or linear functions of least-squares means, and comparisons of slopes. Plots and compact letter displays. Least-squares means were proposed in Harvey, W (1960) "Least-squares analysis of data with unequal subclass numbers", Tech Report ARS-20-8, USDA National Agricultural Library, and discussed further in Searle, Speed, and Milliken (1980) "Population marginal means in the linear model: An alternative to least squares means", The American Statistician 34(4), 216-221 . NOTE: lsmeans now relies primarily on code in the 'emmeans' package. 'lsmeans' will be archived in the near future. 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The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution. 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'LSMRealOptions' provides implementations of the LSM simulation method to value American option products and capital investment projects through real options analysis. 'LSMRealOptions' values capital investment projects with cash flows dependent upon underlying state variables that are stochastically evolving, providing analysis into the timing and critical values at which investment is optimal. 'LSMRealOptions' provides flexibility in the stochastic processes followed by underlying assets, the number of state variables, basis functions and underlying asset characteristics to allow a broad range of assets to be valued through the LSM simulation method. Real options projects are further able to be valued whilst considering construction periods, time-varying initial capital expenditures and path-dependent operational flexibility including the ability to temporarily shutdown or permanently abandon projects after initial investment has occurred. The LSM simulation method was first presented in the prolific work of Longstaff and Schwartz (2001) . Package: r-cran-lsnstat Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-lsnstat_1.0.1-1.ca2404.1_all.deb Size: 24556 MD5sum: 33e43c0f187de9782b5bf41b30d5189e SHA1: 8859976a2bcb0e66a1df12cc4fe23581690939cb SHA256: f35d2ff093fc03d8d59c03d0642bfabcadd88e3acb2511796097f2cfa51a8a12 SHA512: 6548c00987e01a0a28f1c3b55a786a9f6d52c8deb81d812ec54012a416972b785e81a4f7ee02502d21f06713dca666af87baa7ddaa9ead6c5324e83f2f56ed41 Homepage: https://cran.r-project.org/package=lsnstat Description: CRAN Package 'lsnstat' ('La Societe Nouvelle' API Access) Tools facilitating access to the 'macro_data' service of the 'La Societe Nouvelle' API. 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H., Thyholt, K., Næs, T. (2004) "A Comparison of Methods for Analysing Regression Models with Both Spectral and Designed Variables" Journal of Chemometrics, 18(10), 451--464, . 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Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners, Version 0.6. 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Over the past decade, remote sensing has become a key tool for monitoring and predicting environmental variables by using satellite data. This package presents the main applications in remote sensing for land surface monitoring and land cover mapping (soil, vegetation, water...). Tomlinson, C.J., Chapman, L., Thornes, E., Baker, C (2011) . Package: r-cran-lss2 Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg Suggests: r-cran-survival Filename: pool/dists/noble/main/r-cran-lss2_1.1-1.ca2404.1_all.deb Size: 27758 MD5sum: 12eb3e5086c4eaf6035b2f6e8b97b714 SHA1: 9e03f20db1197315a72051604a137bcd0eb3ec5c SHA256: 9d4ae980d6198dfb49ade031a6d01fa9d20b3e9c56792f41ac4921992d856600 SHA512: 963c91add67519d0c3357cb0f3902d409f9b1abd69a8691b4e424b808296e638b61fe0da888e34250c3653e1ea1ca37592aa316ff9f5e12d719a3ccddfa72a5d Homepage: https://cran.r-project.org/package=lss2 Description: CRAN Package 'lss2' (The Accelerated Failure Time Model to Right Censored Data Basedon Least-Squares Principle) Due to lack of proper inference procedure and software, the ordinary linear regression model is seldom used in practice for the analysis of right censored data. This paper presents an S-Plus/R program that implements a recently developed inference procedure (Jin, Lin and Ying, 2006) for the accelerated failure time model based on the least-squares principle. Package: r-cran-lst Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra Filename: pool/dists/noble/main/r-cran-lst_2.0.0-1.ca2404.1_all.deb Size: 48766 MD5sum: b326a34567ba25abf711d0bfe7107671 SHA1: 362ea0a882d77c55480b1f2b2b99aeb7df4c3107 SHA256: 2f9c281ee926154b8906036aae4a257008c103d72db6e74b38a80043d6cb61b5 SHA512: b379d386439e5366c371cc940daf6560a62687b9ba34cd4823c026ae484b67a7bb1f30a027e6bd200290b8f10ce90cc1f5f5fec14319c3316a2edae75ed641be Homepage: https://cran.r-project.org/package=LST Description: CRAN Package 'LST' (Land Surface Temperature Retrieval for Landsat 8) Calculates Land Surface Temperature from Landsat band 10 and 11. Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data. Jimenez-Munoz JC, Cristobal J, Sobrino JA, et al (2009). . Land surface temperature retrieval from LANDSAT TM 5. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004). . Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Srivastava PK, Majumdar TJ, Bhattacharya AK (2009). . Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Valor E (1996). . On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Van de Griend AA, Owe M (1993). . Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Yu X, Guo X, Wu Z (2014). . Calibration and Validation of land surface temperature for Landsat8-TIRS sensor. Land product validation and evolution. Skoković D, Sobrino JA, Jimenez-Munoz JC, Soria G, Julien Y, Mattar C, Cristóbal J. (2014). 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Kaplan (2014) is a textbook for a first or second course in statistics that embraces data wrangling, causal reasoning, modeling, statistical adjustment, and simulation. 'LSTbook' supports the student-centered, tidy, pipeline-oriented computing style featured in the book. 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The input variables of the datasets can be quantitative, qualitative/categorical or mixed. The output variable of the datasets is a scalar (quantitative). The optimization of the likelihood function is done using a successive approximation/relaxation algorithm similar to another GP modeling package "GPM". The modeling method is published in "A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors" by Yichi Zhang, Siyu Tao, Wei Chen, and Daniel W. Apley (2018) . The package is developed in IDEAL of Northwestern University. 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It is particularly useful for researchers studying cognitive aging who wish to work with the most recent release of HRS data. The package provides user-friendly functions for data preprocessing, scoring, and classification allowing users to easily apply the Langa-Weir classification system. For details regarding the; HRS and Langa-Weir classifications . 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Package: r-cran-m2b Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geosphere, r-cran-catools, r-cran-ggplot2, r-cran-randomforest, r-cran-caret Suggests: r-cran-adehabitatlt, r-cran-movehmm, r-cran-knitr, r-cran-diagrammer, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-m2b_1.1.0-1.ca2404.1_all.deb Size: 384046 MD5sum: 96a8b2fda07fa20dc83379cb5155aea8 SHA1: fa1e24e1786912feaeb72e892b9e41d894cb5f2b SHA256: ec2d34c901d32ed67e9bf38462e59b5f949b00821e4485d6ca778c0cecef1a2f SHA512: b42ee2c0fbc75211c57861a56d8c11fe86d3e78983ac67c0c61170a2fc6a236f99e2d62a4d20ff66c161bf963a1c5fbbfab1953540c2a540c68a3d8281b98ee2 Homepage: https://cran.r-project.org/package=m2b Description: CRAN Package 'm2b' (Movement to Behaviour Inference using Random Forest) Prediction of behaviour from movement characteristics using observation and random forest for the analyses of movement data in ecology. From movement information (speed, bearing...) the model predicts the observed behaviour (movement, foraging...) using random forest. The model can then extrapolate behavioural information to movement data without direct observation of behaviours. The specificity of this method relies on the derivation of multiple predictor variables from the movement data over a range of temporal windows. This procedure allows to capture as much information as possible on the changes and variations of movement and ensures the use of the random forest algorithm to its best capacity. The method is very generic, applicable to any set of data providing movement data together with observation of behaviour. Package: r-cran-m2smf Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-m2smf_2.0-1.ca2404.1_all.deb Size: 62284 MD5sum: 172e70a520308904ce100ee20665f12d SHA1: a20b721816a25bdd5fdb09b726dc3b09c81adb60 SHA256: 0c6cc87fc472fe8d9121f65e834865af59effbce4e75e550c611db805178163b SHA512: e412e217345148c2e2b235684d8d280f7d7231f8cf1b9a29a7091341a843946efccc5961c492bf97055dcfa170cb8145d8e2c1a90dfa7020b343ecb4cba50150 Homepage: https://cran.r-project.org/package=M2SMF Description: CRAN Package 'M2SMF' (Multi-Modal Similarity Matrix Factorization for IntegrativeMulti-Omics Data Analysis) A new method to implement clustering from multiple modality data of certain samples, the function M2SMF() jointly factorizes multiple similarity matrices into a shared sub-matrix and several modality private sub-matrices, which is further used for clustering. Along with this method, we also provide function to calculate the similarity matrix and function to evaluate the best cluster number from the original data. 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Along with this method, we also provide function to calculate the similarity matrix and function to evaluate the best cluster number from the original data. Package: r-cran-m3 Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2245 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ncdf4, r-cran-sf, r-cran-maps, r-cran-mapdata Filename: pool/dists/noble/main/r-cran-m3_0.4-1.ca2404.1_all.deb Size: 2023920 MD5sum: ed5cc7b4073703b8f3ba5d85ddfc9aa9 SHA1: 2051a42982f4a809687d11abf10de285bfdf6d95 SHA256: 20ddd2ea0426d945529a4261fa673c6bb7a68518fff2d64a23bb2bd28750a27e SHA512: 22c952e331beecb72b0f662b438fc6a0b1c91d1ff9dc180bd0cfeafe4e7961825dab2bce1bc59314b8f5d5c96734714322628387d077d1b2b261216f7dcb0952 Homepage: https://cran.r-project.org/package=M3 Description: CRAN Package 'M3' (Reading M3 Files) Provides functions to read in and manipulate air quality model output from Models3-formatted files. 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Then the samples are classified by clustering the shared sub-matrix with kmeanspp(), a new version of kmeans() developed here to obtain concordant results. The package also provides the cluster number estimation by rotation cost. Moreover, cluster specific features could be retrieved using hypergeometric tests. Package: r-cran-m5 Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2794 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-stringi, r-cran-lubridate Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-zeallot Filename: pool/dists/noble/main/r-cran-m5_0.1.1-1.ca2404.1_all.deb Size: 1358446 MD5sum: 458c21100d4bf15a4a0b0d1d8bebd138 SHA1: f504ff49afb6b0974a79a2bc2983abe4b17f3ba1 SHA256: 1dfdbe1c4151385acb0a1cdfcf359e3d190fc22a9d7d227cb7fe6c799f408a4a SHA512: 6ab57bf77299a4132138ac0ecfe4768c1c88373ebc7f2a16dc9164fcf5c372ba57d71b8c77ea4f88bfb0cb83e6eb200bafa6fd54daa5c8811749460cf74c306d Homepage: https://cran.r-project.org/package=m5 Description: CRAN Package 'm5' ('M5 Forecasting' Challenges Data) Contains functions, which facilitate downloading, loading and preparing data from 'M5 Forecasting' challenges (by 'University of Nicosia', hosted on 'Kaggle'). The data itself is set of time series of different product sales in 'Walmart'. The package also includes a ready-to-use built-in M5 subset named 'tiny_m5'. For detailed information about the challenges, see: Makridakis, S. & Spiliotis, E. & Assimakopoulos, V. (2020). . Package: r-cran-m61r Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-m61r_0.1.0-1.ca2404.1_all.deb Size: 132242 MD5sum: b111717827fbfc604a8ab2f79b943994 SHA1: ba5b8a76e4221a9c7646d25a547d00994bbaf28c SHA256: d80e5a0eda291ce435de4adf60b63c883ddb0b96e2b083209605a21e58a1a499 SHA512: 7105d4ad14345dd499fde53c6c9c17883ba289351763090bfddd845cba47cb312f418f49036c51c8c0f805587c0bc6a91dc5f9a6b6441fc923d9415fde99a84a Homepage: https://cran.r-project.org/package=m61r Description: CRAN Package 'm61r' (Package About Data Manipulation in Pure Base R) A lightweight, dependency-free data engine for R that provides a grammar for tabular and time-series manipulation. Built entirely on Base R, 'm61r' offers a fluent, chainable API inspired by modern data tools while prioritizing memory efficiency and speed. It includes optimized versions of common data verbs such as filtering, mutation, grouped aggregation, and approximate temporal joins, making it an ideal choice for environments where external dependencies are restricted or where performance in pure R is required. Package: r-cran-maaper Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-genomicranges, r-bioc-genomicalignments, r-bioc-genomicfeatures, r-bioc-genomeinfodb, r-bioc-rsamtools, r-bioc-iranges, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-maaper_1.1.1-1.ca2404.1_all.deb Size: 206812 MD5sum: 67ccafd452a01a5d4aa2798da71485e7 SHA1: 6d4311ae4e9ccec041a9bb65d947775841ad7556 SHA256: d912fdead7815006499872ec544d1d048d46996adac8953e8df86274e470a51d SHA512: a0dbe0bc8941c02d7d1e7ff340ca10efb4510a3238810edde3ea86a0ddacc6df5234a3033073aadf6f71a9ebaca3324a65435c5f3f45711c937f4a6bba7c14dd Homepage: https://cran.r-project.org/package=MAAPER Description: CRAN Package 'MAAPER' (Analysis of Alternative Polyadenylation Using 3' End-LinkedReads) A computational method developed for model-based analysis of alternative polyadenylation (APA) using 3' end-linked reads. It accurately assigns 3' RNA-seq reads to polyA sites through statistical modeling, and generates multiple statistics for APA analysis. Please also see Li WV, Zheng D, Wang R, Tian B (2021) . Package: r-cran-maat Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testdesign, r-cran-readxl, r-cran-mass, r-cran-diagram Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-maat_1.1.1-1.ca2404.1_all.deb Size: 1606400 MD5sum: 3d1147eee925122e5136222adf95a1ea SHA1: 9a0708e54d1c010ea4aec958dd8d09f57db8b5f6 SHA256: b9cc8fbb315bbad6aa76e164030c956a95d0cb3e27b507e134e4101ec2fc8124 SHA512: 09de9813e50565b937332971ee982366d2d051360083d71daa3b9c960a460b810a3f3cd39a0af1f16d66d1e51bd496397ac75d3c20184bd978c5d1780edb9ca9 Homepage: https://cran.r-project.org/package=maat Description: CRAN Package 'maat' (Multiple Administrations Adaptive Testing) Provides an extension of the shadow-test approach to computerized adaptive testing (CAT) implemented in the 'TestDesign' package for the assessment framework involving multiple tests administered periodically throughout the year. This framework is referred to as the Multiple Administrations Adaptive Testing (MAAT) and supports multiple item pools vertically scaled and multiple phases (stages) of CAT within each test. Between phases and tests, transitioning from one item pool (and associated constraints) to another is allowed as deemed necessary to enhance the quality of measurement. Package: r-cran-mabacr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mabacr_0.1.0-1.ca2404.1_all.deb Size: 25438 MD5sum: 58b42d2f3ff44500624247feb31ada94 SHA1: f6a38d15b361859290bea5e3a68d64afe0f9a857 SHA256: aeb2045e896fa19115f2dacaf81b1c7122db911fe545b01ab275f1ac4e6cf633 SHA512: 4163b7c1958486155d07eb6a9b66a03c833d80fca300f6f4e84372c6ef95d72d790e41726d0110f2e54cbe6ef9b14a87b4540206b7d60b1acdb96d48976e888d Homepage: https://cran.r-project.org/package=mabacR Description: CRAN Package 'mabacR' (Assisting Decision Makers) Easy implementation of the MABAC multi-criteria decision method, that was introduced by Pamučar and Ćirović in the work entitled: "The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC)" - - which aimed to choose implements for logistics centers. This package receives data, preferably in a spreadsheet, reads it and applies the mathematical algorithms inherent to the MABAC method to generate a ranking with the optimal solution according to the established criteria, weights and type of criteria. The data will be normalized, weighted by the weights, the border area will be determined, the distances to this border area will be calculated and finally a ranking with the optimal option will be generated. Package: r-cran-macbehaviour Architecture: all Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openxlsx, r-cran-httr, r-cran-dplyr, r-cran-rjson Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-macbehaviour_1.2.8-1.ca2404.1_all.deb Size: 79972 MD5sum: 33d7fdadf2ca75240ee86c06bd58b2aa SHA1: bfe37cef01f646d9b870f2c8b6429d6a8c86065e SHA256: 2fa356b6e472e2fee9ab1b4106fde7098dce1e38ac36242c294cb81a57840dd0 SHA512: 0c85933dca748921e523f80919aaf4ae3673b262533e38ff3be0fb9a0604a3805229cf9e1a17387fd70926157d5d69355c33aa530aedcdb72a247f82e1e80716 Homepage: https://cran.r-project.org/package=MacBehaviour Description: CRAN Package 'MacBehaviour' (Behavioural Studies of Large Language Models) Efficient way to design and conduct psychological experiments for testing the performance of large language models. It simplifies the process of setting up experiments and data collection via language models’ API, facilitating a smooth workflow for researchers in the field of machine behaviour. Package: r-cran-macc Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-nlme, r-cran-optimx, r-cran-mass, r-cran-car Filename: pool/dists/noble/main/r-cran-macc_1.0.1-1.ca2404.1_all.deb Size: 580420 MD5sum: bdb50bc9b51bc204378d8bcf4eb64ff2 SHA1: 42cd8c0887f116a06d9b1cc2bc68f9b3cdfcd087 SHA256: 51d0c6d228cd2c678a5a6537a42dc8408e4108f464cfd69932f8003a0b98e65c SHA512: 36bd520c419e3bf3e92129015446325e504fe7d1fa75089844d2ba243aa16dbdee7a6d09ffd894cf105ae7eaca6cc0507b17761f2e9df425c3ceb6b7c2891542 Homepage: https://cran.r-project.org/package=macc Description: CRAN Package 'macc' (Mediation Analysis of Causality under Confounding) Performs causal mediation analysis under confounding or correlated errors. This package includes a single level mediation model, a two-level mediation model, and a three-level mediation model for data with hierarchical structures. Under the two/three-level mediation model, the correlation parameter is identifiable and is estimated based on a hierarchical-likelihood, a marginal-likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2014), Estimating Mediation Effects under Correlated Errors with an Application to fMRI, for details. Package: r-cran-macer Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rentrez, r-cran-ape, r-cran-httr, r-cran-ggplot2, r-cran-pbapply, r-cran-png Filename: pool/dists/noble/main/r-cran-macer_0.2.1-1.ca2404.1_all.deb Size: 94776 MD5sum: ac3739f53cd37342a2763f83aefb6e3b SHA1: 9590eaabb09348b1c24ac08c3d96981dcbbdb20e SHA256: 2c906f841c552f933130f7d0dab56ca936b732402193f0ae46631a268cbc0991 SHA512: 66348ceabfca7d0da1505c92f9dde8d842583a8ef9f4b5a725cfb3ba5500785a1c542822985df323b2d6eab23fb55346ae2c9144a0f82f06dadd52931dde5c7d Homepage: https://cran.r-project.org/package=MACER Description: CRAN Package 'MACER' (Molecular Acquisition, Cleaning, and Evaluation in R 'MACER') To assist biological researchers in assembling taxonomically and marker focused molecular sequence data sets. 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The Ada and Archibald MacLeish Field Station is a 260-acre patchwork of forest and farmland located in West Whately, MA that provides opportunities for faculty and students to pursue environmental research, outdoor education, and low-impact recreation (see for more information). This package contains weather data over several years, and spatial data on various man-made and natural structures. Package: r-cran-maclogp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bma, r-cran-plot.matrix, r-cran-rlist Filename: pool/dists/noble/main/r-cran-maclogp_0.1.1-1.ca2404.1_all.deb Size: 82268 MD5sum: 8a06cfa8f70aec6206fc8c002bc51518 SHA1: 346d51bf297ddadffb70dc3c45e61fdac4c11d95 SHA256: 44469712aa8cd05c267822393771f00c77a49c355c6ede80c5d25835a7334973 SHA512: 5cf28209808f270bae104aa750b7a126a987baba0e051fe1e2bea823d148419e300376c8af9379df7429346cc632014783c8dfa084f9a6fdbbbec8bf2fc665b3 Homepage: https://cran.r-project.org/package=maclogp Description: CRAN Package 'maclogp' (Measures of Uncertainty for Model Selection) Following the common types of measures of uncertainty for parameter estimation, two measures of uncertainty were proposed for model selection, see Liu, Li and Jiang (2020) . 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Package: r-cran-macro Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-common, r-cran-fmtr, r-cran-crayon Suggests: r-cran-sassy, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-macro_0.1.6-1.ca2404.1_all.deb Size: 660986 MD5sum: 3256fc3a8f1d6670a284f85fa9d08407 SHA1: 5feefa51ea5811db44d0b326550ed5c4004df656 SHA256: c300fb975d9c750948849eaa44d78f3d6bf6fe1d688e211868e1cbcf6532d967 SHA512: 02039c4bec3990ad9c8e893a312ab584e96d9c2f74c8bb728e4a9ee364e1eb0f03f805f24d2382b0d4fb4540c7f0b6b0174696f863e5562da65d0ff12c2f4e4a Homepage: https://cran.r-project.org/package=macro Description: CRAN Package 'macro' (A Macro Language for 'R' Programs) A macro language for 'R' programs, which provides a macro facility similar to 'SAS®'. This package contains basic macro capabilities like defining macro variables, executing conditional logic, and defining macro functions. Package: r-cran-macrobiome Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1272 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-palinsol, r-cran-raster, r-cran-rnaturalearthdata, r-cran-sf, r-cran-strex, r-cran-terra Filename: pool/dists/noble/main/r-cran-macrobiome_0.4.0-1.ca2404.1_all.deb Size: 1261622 MD5sum: 569be7b325153953c7203c4fe57481a9 SHA1: 03fe6a7a33b2c9b27caf49bb4c1c432e38b67e01 SHA256: fae0ff9dc51effa23f7535419e80b63e1b766d09a0afa2b4e4368ec4468a028a SHA512: 1d44dcc0c603822135555a55dd7885614285a0fe88cd66b5415c8e116b7fefb1a189c07d814a4c34c037dc8ab221acb324fd64b6fbb74e4cf5eee77b7150182d Homepage: https://cran.r-project.org/package=macroBiome Description: CRAN Package 'macroBiome' (A Tool for Mapping the Distribution of the Biomes and Bioclimate) Procedures for simulating biomes by equilibrium vegetation models, with a special focus on paleoenvironmental applications. Three widely used equilibrium biome models are currently implemented in the package: the Holdridge Life Zone (HLZ) system (Holdridge 1947, ), the Köppen-Geiger classification (KGC) system (Köppen 1936, ) and the BIOME model (Prentice et al. 1992, ). Three climatic forest-steppe models are also implemented. An approach for estimating monthly time series of relative sunshine duration from temperature and precipitation data (Yin 1999, ) is also adapted, allowing process-based biome models to be combined with high-resolution paleoclimate simulation datasets (e.g., CHELSA-TraCE21k v1.0 dataset: ). Package: r-cran-macrocol Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openxlsx, r-cran-httr, r-cran-lubridate, r-cran-readxl, r-cran-r.utils Filename: pool/dists/noble/main/r-cran-macrocol_0.1.0-1.ca2404.1_all.deb Size: 40268 MD5sum: 4d1e63fcc5ea9191897b0106426a2d9e SHA1: 139f96c2ba1262580cf819d71954109f954fa21a SHA256: 401d203f307fe2695fb7dd94acb4c507895128fcf39101c2c18990b3417fde1f SHA512: 5f2e2134093db4cbd7bae1bd63b7ed419a432b324175b8715b792187e7a501d69c9db4f11dee9c076d0129a024194234450a9607e5ced617e2d821838c1acf04 Homepage: https://cran.r-project.org/package=macrocol Description: CRAN Package 'macrocol' (Colombian Macro-Financial Time Series Generator) This repository aims to contribute to the econometric models' production with Colombian data, by providing a set of web-scrapping functions of some of the main macro-financial indicators. All the sources are public and free, but the advantage of these functions is that they directly download and harmonize the information in R's environment. No need to import or download additional files. You only need an internet connection! Package: r-cran-macrosyntr Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3541 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-tidyr, r-cran-reshape2, r-cran-dplyr, r-cran-stringr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-macrosyntr_0.3.3-1.ca2404.1_all.deb Size: 2351326 MD5sum: a82022bc73f681251fc9b0a0e416f3de SHA1: 15299071ab9a84f049b3a956ef32f42556c433ca SHA256: fc4a6f26436728566f4944e8b872dec4d87cb8c308e213c23e891088ed6414bf SHA512: c9d8f7abc7ee571ab0c63389f4a8e7341591cd81d63327c9c8dc95df28443302a0dae9fc9c6e5e0c6ae20874d2c9642b751200e9c87f5ab97edd916557763efb Homepage: https://cran.r-project.org/package=macrosyntR Description: CRAN Package 'macrosyntR' (Draw Ordered Oxford Grids and Chord Diagrams) Use standard genomics file format (BED) and a table of orthologs to illustrate synteny conservation at the genome-wide scale. Significantly conserved linkage groups are identified as described in Simakov et al. (2020) and displayed on an Oxford Grid (Edwards (1991) ) or a chord diagram as in Simakov et al. (2022) . The package provides a function that uses a network-based greedy algorithm to find communities (Clauset et al. (2004) ) and so automatically order the chromosomes on the plot to improve interpretability. Package: r-cran-macrozoobenthoswatera Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-macrozoobenthoswatera_0.1.0-1.ca2404.1_all.deb Size: 40774 MD5sum: 92ffe002f6acb9d4285041a53a5adea8 SHA1: 0caaedfe779ef510fcce18364bfa895312e2bb8f SHA256: b6e0e325c5acd09c894da0a0a845aa5c1ce9c411c8d8c64c210bf106626cbfcd SHA512: d6856dd08a1c82b57f43ffc1142c8996eec3b942babf6bc6ad5f0d7ab2f94ef25f1743fcefb484d7d071b4187f96e2107c3670a821138458a77888516f28dad9 Homepage: https://cran.r-project.org/package=MacroZooBenthosWaterA Description: CRAN Package 'MacroZooBenthosWaterA' (Fresh Water Quality Analysis Based on Macrozoobenthos) Includes functions for calculating basic indices of macrozoobenthos for water quality and is designed to provide researchers and environmental professionals with a comprehensive tool for evaluating the ecological health of aquatic ecosystems.The package is based on the following references: Paisley, M. F., Trigg, D. J. and Walley, W. J. (2014). Arslan, N., Salur, A., Kalyoncu, H. et al.(2016) . Hilsenhoff W.L. (1987). Hilsenhoff. W.L. (1988) Barbour, M.T., Gerritsen, J., Snyder, B.D., and Stribling, J.B. (1999). Package: r-cran-maczic Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-pscl, r-cran-sandwich, r-cran-survival, r-cran-mediation, r-cran-emplik, r-cran-bb, r-cran-mathjaxr Suggests: r-cran-suppdists Filename: pool/dists/noble/main/r-cran-maczic_1.1.0-1.ca2404.1_all.deb Size: 231538 MD5sum: b484c2a879629552fcb6a502d6c87625 SHA1: 9e3fabee0c84e4b848d2a56224c420de0f13c085 SHA256: 319bc7ff7bf700560f2c8722109813223d327303ecd4a59a469bb591cefcdbca SHA512: db6083cd1cd9f5dfacecb3f1a45be53609416b1c219e0e494b99076feff589ff0f6e910841e0d55612fa8a18abeab5465db47a617441f700b050e3886770dddc Homepage: https://cran.r-project.org/package=maczic Description: CRAN Package 'maczic' (Mediation Analysis for Count and Zero-Inflated Count Data) Performs causal mediation analysis for count and zero-inflated count data without or with a post-treatment confounder; calculates power to detect prespecified causal mediation effects, direct effects, and total effects; performs sensitivity analysis when there is a treatment- induced mediator-outcome confounder as described by Cheng, J., Cheng, N.F., Guo, Z., Gregorich, S., Ismail, A.I., Gansky, S.A. (2018) . Implements Instrumental Variable (IV) method to estimate the controlled (natural) direct and mediation effects, and compute the bootstrap Confidence Intervals as described by Guo, Z., Small, D.S., Gansky, S.A., Cheng, J. (2018) . This software was made possible by Grant R03DE028410 from the National Institute of Dental and Craniofacial Research, a component of the National Institutes of Health. Package: r-cran-mad Architecture: all Version: 0.8-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-metafor, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mad_0.8-3-1.ca2404.1_all.deb Size: 310734 MD5sum: 5845cd6ad175b33ac8ca58a0db366fea SHA1: c6caa8dc40d78426addd19db24b9fb379c865ecf SHA256: 516762124b20fec77f770eb9aedd0d7efaa3663fca23a5202f21367b582dc32f SHA512: 8af28e42586de46eaf44405f01841e10712b4a4ed7ceed91fe136f420fcdffbb51d3a81292891e5ec72ca10142bd1361f077483f18e4fc0bccbf06deb9446a8c Homepage: https://cran.r-project.org/package=MAd Description: CRAN Package 'MAd' (Meta-Analysis with Mean Differences) A collection of functions for conducting a meta-analysis with mean differences data. It uses recommended procedures as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009). 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(2015) ), Maryland Scientific Methods Scale ratings, structured confidence ratings, light-weight difference-in-differences and interrupted-time-series estimators ('Bernal' et al. (2017) ) with cluster-robust standard errors ('Cameron' and 'Miller' (2015) ), pre-treatment balance checks ('Stuart' (2010) ), and cost-effectiveness analysis (cost per outcome, incremental cost-effectiveness ratio, acceptability curves, incremental net benefit, quality-adjusted and disability-adjusted life years). Designed as the evaluation companion to the appraisal package 'greenbook'. Bundled rubric and reference tables carry vintage metadata for reproducibility. 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Propensity Score Matching deals with this issue but current techniques are restricted to binary treatment variables. Moreover, they provide several solutions without providing a comprehensive framework on choosing the best model. The MAGMA R-package addresses these restrictions by offering nearest neighbor matching for two to four groups. It also includes the option to match data of a 2x2 design. In addition, MAGMA includes a framework for evaluating the post-matching balance. The package includes functions for the matching process and matching reporting. We provide a tutorial on MAGMA as vignette. More information on MAGMA can be found in Feuchter, M. D., Urban, J., Scherrer V., Breit, M. L., and Preckel F. (2022) . 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In MAIC, unbiased comparison between outcomes of two trials is facilitated by weighting the subject-level outcomes of one trial with weights derived such that the weighted aggregate measures of the prognostic or effect modifying variables are equal to those of the sample in the comparator trial. The functions and classes included in this package wrap and abstract the process demonstrated in the UK National Institute for Health and Care Excellence Decision Support Unit (NICE DSU)'s example (Phillippo et al, (2016) [see URL]), providing a repeatable and easily specifiable workflow for producing multiple comparison variable sets against a variety of target studies, with preprocessing for a number of aggregate target forms (e.g. mean, median, domain limits). Package: r-cran-maicchecks Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-tidyr, r-cran-ggplot2, r-cran-lpsolve, r-cran-quadprog Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-maicchecks_0.2.0-1.ca2404.1_all.deb Size: 97886 MD5sum: 21827c8a6aaa64a1e0ae9da4a491b0c5 SHA1: 73f2752715dd7dcd29200ffb63cd268054b79951 SHA256: e40911c93105918f983ebd33cc86524b8d94a80db9ae4eb12132ceb4acb75723 SHA512: 3f519890a3f126fe7a6dd43dc45828a9fde6d6d8b83eb8f302e63d198d9616004e6ca53b7d0d7359ad856dc52333010a0da793e4da714fd45ebec293898f2e7f Homepage: https://cran.r-project.org/package=maicChecks Description: CRAN Package 'maicChecks' (Exact Matching and Matching-Adjusted Indirect Comparison (MAIC)) The second version (0.2.0) contains implementation for exact matching which is an alternative to propensity score matching (see Glimm & Yau (2025)). The initial version (0.1.2) contains a collection of easy-to-implement tools for checking whether a MAIC can be conducted, as well as an alternative way of calculating weights (see Glimm & Yau (2021) .) Package: r-cran-maicplus Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1925 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-lubridate, r-cran-matrixstats, r-cran-mass, r-cran-boot, r-cran-stringr, r-cran-lmtest, r-cran-sandwich Suggests: r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-rmarkdown, r-cran-dplyr, r-cran-survminer, r-cran-flexsurv, r-cran-tibble, r-cran-vdiffr, r-cran-checkmate Filename: pool/dists/noble/main/r-cran-maicplus_0.1.2-1.ca2404.1_all.deb Size: 1440940 MD5sum: c1722ddfa52536124b14de548d7d287d SHA1: add662cd16d551efee674a44a937c3287efaa10e SHA256: 8e571d177845c498ceaeb7e95f396d87ad3e2fc16b6d44c7ad199b1c43c9bd1c SHA512: e2fd07155e05acbfcd50c3760ddee54deab68a798a5f4d1852377f16ed8dba4435e406fbabb5f004549759cf3e33cbd2c94255c0edbd59a1342807ab29335644 Homepage: https://cran.r-project.org/package=maicplus Description: CRAN Package 'maicplus' (Matching Adjusted Indirect Comparison) Facilitates performing matching adjusted indirect comparison (MAIC) analysis where the endpoint of interest is either time-to-event (e.g. overall survival) or binary (e.g. objective tumor response). The method is described by Signorovitch et al (2012) . Package: r-cran-maictools Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-boot, r-cran-broom, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-survival, r-cran-survminer, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vim Suggests: r-cran-haven, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-maictools_0.1.1-1.ca2404.1_all.deb Size: 166412 MD5sum: b2c0168779900e830cb39ad6b950e404 SHA1: bb368b7c6149de44ded441d7c2112f6526a46c11 SHA256: f7dc742b1d14384cb919f47df21b369ca289a8cfdbfdfe85a3f171d7a9903a63 SHA512: 5e3750d583061284cfdb80b34a07b457638b918757a5335c0ce9c6b24e1fe45fce4e765f3068d81652d7b3739f58e8b6e5abd31757275c9de81a3fa71274cef1 Homepage: https://cran.r-project.org/package=MAICtools Description: CRAN Package 'MAICtools' (Performing Matched-Adjusted Indirect Comparisons (MAIC)) A generalised workflow for Matching-Adjusted Indirect Comparison (MAIC) analysis, which supports both anchored and non-anchored MAIC methods. In MAIC, unbiased trial outcome comparison is achieved by weighting the subject-level outcomes of the intervention trial so that the weighted aggregate measures of prognostic or effect-modifying variables match those of the comparator trial. Measurements supported include time-to-event (e.g., overall survival) and binary (e.g., objective tumor response). The method is described in Signorovitch et al. (2010) and Signorovitch et al. (2012) . Package: r-cran-maidr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5235 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-curl, r-cran-ggplot2, r-cran-ggplotify, r-cran-gridsvg, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-shiny, r-cran-xml2, r-cran-rlang, r-cran-r6 Suggests: r-cran-testthat, r-cran-lintr, r-cran-styler, r-cran-goodpractice, r-cran-cyclocomp, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-maidr_0.3.0-1.ca2404.1_all.deb Size: 2779596 MD5sum: a403293f2d2fee860b74a8e4c5f673de SHA1: d49ab90cb62df5661e25b58adfa13a92bf6db631 SHA256: 515ef6ba43112fb3cd9cdecc9ac1b7eff92539bc38a3c267a3e25eb529c8193f SHA512: af5fd708cad28c3ba6e1f4529211b85304b49770a3bd6de50b38b79364ed4689bfec3338481ac47dbb86aef76ec1ecae1ddb4fe31b496e1b36b4193a9d9ee4c5 Homepage: https://cran.r-project.org/package=maidr Description: CRAN Package 'maidr' (Multimodal Access and Interactive Data Representation) Provides accessible, interactive visualizations through the 'MAIDR' (Multimodal Access and Interactive Data Representation) system. 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Package: r-cran-mailchimpr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-mailchimpr_0.1.0-1.ca2404.1_all.deb Size: 22944 MD5sum: 68ff4d4e66d4e03e127751f140eb0c8c SHA1: 698f1e8635a1e1bb1c7e3e9f67a12bef7b74e11a SHA256: da05ff77c8e55daa42a7e38d2849ebf5808b8647093f87e96d3faa1a2a342710 SHA512: 82241b590c2040f3a6f97ddde74f3b912642559fea55c87e130d5cf33526cdf31400fedba3b95aed597ce5074b7443c01a577c61f1d319f8b3f360d7ebfbd7dc Homepage: https://cran.r-project.org/package=mailchimpR Description: CRAN Package 'mailchimpR' (Get Mailchimp Data via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from Mailchimp using the 'Windsor.ai' API . 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Package: r-cran-mainexistingdatasets Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-glue, r-cran-golem, r-cran-htmltools, r-cran-htmlwidgets, r-cran-magrittr, r-cran-openxlsx, r-cran-pkgload, r-cran-processx, r-cran-rlang, r-cran-sf, r-cran-shiny, r-cran-spdata, r-cran-tidyr, r-cran-tmap, r-cran-tmaptools Suggests: r-bioc-bioccheck, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mainexistingdatasets_1.0.2-1.ca2404.1_all.deb Size: 52824 MD5sum: 264fc904979ff9c28d3ee2d0008b994a SHA1: 5bf25637e3cc9e52d14c888eddfacc0c3e562254 SHA256: b1e95dd0662c3fd11336ce30bae9424af960bacb423924384c6d9c4bd446860c SHA512: 3b5590669d64688ff851a99ea1943954fbf67b190db1ba89f0797ccaff4955c7e7ff8c6f5f581b35fee75509ee47b7725e6550911f9e49785dccee96640970b5 Homepage: https://cran.r-project.org/package=MainExistingDatasets Description: CRAN Package 'MainExistingDatasets' (Main Existing Human Datasets) Shiny for Open Science to visualize, share, and inventory the main existing human datasets for researchers. Package: r-cran-maive Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clubsandwich Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-varhandle Filename: pool/dists/noble/main/r-cran-maive_0.2.4-1.ca2404.1_all.deb Size: 148144 MD5sum: 9c8b466630794233197da8984a4d4664 SHA1: 037ea6986c8aa9b1fdd11556bd9652154bbc3218 SHA256: 4f733f88eb15364c97079cf6edeeefbebcaf909de6e62b58953fbe5386166db2 SHA512: 712dd274ab79992ae816f3f89f6df33b49fdb8e311bf2f32e381fe75b3bcb0f232e69b20b326c3422e0ef7d5b64b3e120900e6e64a15e20a923dfacfcd35307c Homepage: https://cran.r-project.org/package=MAIVE Description: CRAN Package 'MAIVE' (Meta Analysis Instrumental Variable Estimator) Meta-analysis traditionally assigns more weight to studies with lower standard errors, assuming higher precision. However, in observational research, precision must be estimated and is vulnerable to manipulation, such as p-hacking, to achieve statistical significance. This can lead to spurious precision, invalidating inverse-variance weighting and bias-correction methods like funnel plots. Common methods for addressing publication bias, including selection models, often fail or exacerbate the problem. This package introduces an instrumental variable approach to limit bias caused by spurious precision in meta-analysis. Methods are described in 'Irsova et al.' (2025) . Package: r-cran-majkmeans Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-majkmeans_0.1.0-1.ca2404.1_all.deb Size: 28804 MD5sum: 6ab2eb89aa3560514b71579bdddb7da6 SHA1: b52a3ec574debce0a5c3349c659f4031e6354e76 SHA256: 882b3bcf794866a14786cfcc390f31a946ed976230ded17875d383615a0590df SHA512: d3c6807d5d109d423b9dbfd7ed20467666c817d1f46cd29d943e9d7ce37b9ace955dbf47ed65f442dfdaf47d2c08e69d532e9e221c46f8fcd17f618c5d4da8dd Homepage: https://cran.r-project.org/package=MajKMeans Description: CRAN Package 'MajKMeans' (k-Means Algorithm with a Majorization-Minimization Method) A hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) . The two most important functions in package 'MajKMeans' are cluster_km() and cluster_MajKm(). cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering. 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The two most important functions in package 'MajMinKmeans' are cluster_km() and cluster_MajKm(). Cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering. Another useful function is MajMinOptim(), which helps to find the optimum values of the Majorization-Minimization estimator. 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Package: r-cran-makeit Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-readr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-makeit_1.0.2-1.ca2404.1_all.deb Size: 96642 MD5sum: f8a6921bad223723a0ed81b48c3bdaa1 SHA1: a5cb606b233016bc4575519c485bfec298ccfc59 SHA256: 583da81258137218455957565e4b4152f3f6253ec2ec577be66a5ad51410ca04 SHA512: a0af09f2254856cded651202e5de7ac4e1bea3ab4916cb043d4d4815779a66676d8b739a42d7ed8b2acc604522bf0818a28db7167460c5037ac8eac51342f6f2 Homepage: https://cran.r-project.org/package=makeit Description: CRAN Package 'makeit' (Run R Scripts if Needed) Automation tool to run R scripts if needed, based on last modified time. It comes with no package dependencies, organizational overhead, or structural requirements. In short: run an R script if underlying files have changed, otherwise do nothing. Package: r-cran-makemyprior Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3885 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-shiny, r-cran-shinyjs, r-cran-shinybs, r-cran-visnetwork, r-cran-rlang, r-cran-mass Suggests: r-cran-rstan, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-makemyprior_1.2.2-1.ca2404.1_all.deb Size: 1158960 MD5sum: 3929969fb826b437117afa8580e93213 SHA1: 6ef9d4f18f4e53f67d12fb9950996ff624dde68e SHA256: b21aa45b2201387af8b76e57256f88dca7ff3a382aafd990d4af78f7d01a2b77 SHA512: efb284e859c8d4cd68a6f69c106af05a07d18fd7d97eb56f32a733d5a34b4f26fbb8a3834ccb171cd24f57c80c712526cebf96bd03f108837f41e8034b01efdd Homepage: https://cran.r-project.org/package=makemyprior Description: CRAN Package 'makemyprior' (Intuitive Construction of Joint Priors for Variance Parameters) Tool for easy prior construction and visualization. It helps to formulates joint prior distributions for variance parameters in latent Gaussian models. The resulting prior is robust and can be created in an intuitive way. A graphical user interface (GUI) can be used to choose the joint prior, where the user can click through the model and select priors. An extensive guide is available in the GUI. The package allows for direct inference with the specified model and prior. Using a hierarchical variance decomposition, we formulate a joint variance prior that takes the whole model structure into account. In this way, existing knowledge can intuitively be incorporated at the level it applies to. Alternatively, one can use independent variance priors for each model components in the latent Gaussian model. Details can be found in the accompanying scientific paper: Hem, Fuglstad, Riebler (2024, Journal of Statistical Software, ). 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Package: r-cran-makepipe Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-nomnoml, r-cran-r6, r-cran-roxygen2 Suggests: r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-withr, r-cran-rmarkdown, r-cran-webshot2, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-makepipe_0.2.2-1.ca2404.1_all.deb Size: 361644 MD5sum: 481043a4825f610bd8795c98c05669fc SHA1: 779bf4d8934a02dfa90b2216dce30e361ae289c3 SHA256: 7dc23041fb193dbcbdcf85cba995ba922477284ba9e3a4c4ad70653eee43003a SHA512: 7bb0625ec50b3840e25e53e4ed6f08b1ba479ecf30f548337cbb2a8fa5eca0d7ab3aa1687a540eafd014aa0205a3cf90eaa11603be0b877692a5f2fedfb56ee0 Homepage: https://cran.r-project.org/package=makepipe Description: CRAN Package 'makepipe' (Pipeline Tools Inspired by 'GNU Make') A suite of tools for transforming an existing workflow into a self-documenting pipeline with very minimal upfront costs. Segments of the pipeline are specified in much the same way a 'Make' rule is, by declaring an executable recipe (which might be an R script), along with the corresponding targets and dependencies. When the entire pipeline is run through, only those recipes that need to be executed will be. Meanwhile, execution metadata is captured behind the scenes for later inspection. Package: r-cran-makeproject Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-makeproject_1.0-1.ca2404.1_all.deb Size: 14980 MD5sum: ab4fc1aa3ac4254bd18f27bdc2c8536f SHA1: 90f0f6aa81528e22524ed313ceb948e61138c027 SHA256: 72b32bac09931402bb7e0dcbf14f7d2a2cd36e660e03dad7d12abf6995141e82 SHA512: dd37d3e2c63390fdb316d5cac917daf08dc688c19d837e046975b3f72bd29f5b174acbf8967ca3199f17e56e4e3e8aeb2d8e5490ab093493c3275a22da66edf5 Homepage: https://cran.r-project.org/package=makeProject Description: CRAN Package 'makeProject' (Creates an empty package framework for the LCFD format) This package creates an empty framework of files and directories for the "Load, Clean, Func, Do" structure described by Josh Reich. 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Lets you explore 'make' rules to test the local configuration, or query 'pkg-config' to find compiler flags and libs needed for building packages with external dependencies. Also contains tools to analyze which libraries that a installed R package linked to by inspecting output from 'ldd' in combination with information from your distribution package manager, e.g. 'rpm' or 'dpkg'. 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Package: r-cran-makicoint Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-makicoint_1.0.0-1.ca2404.1_all.deb Size: 94320 MD5sum: 908dc6664b00dac07eb9253fc3755726 SHA1: bd168f480df6329c8ea2f877542a0bf87cc50381 SHA256: 74c331beb1f151a40860b9ae8c34026419a91003ca8d979e4e6744b509b14c0b SHA512: 8ec83e1ccf6ff3ae58f9d93a10c7dc0976bd43e523caaf0e1f98c09d185b39b67c26531db3bf39a8f069b4a87a9585295db6ca854ccdd518fb66877a8007d2e4 Homepage: https://cran.r-project.org/package=makicoint Description: CRAN Package 'makicoint' (Maki Cointegration Test with Structural Breaks) Implements the Maki (2012) cointegration test that allows for an unknown number of structural breaks. The test detects cointegration relationships in the presence of up to five structural breaks in the intercept and/or slope coefficients. Four different model specifications are supported: level shifts, level shifts with trend, regime shifts, and trend with regime shifts. The method is described in Maki (2012) "Tests for cointegration allowing for an unknown number of breaks" . Package: r-cran-makl Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-auc, r-cran-grplasso Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-makl_1.0.1-1.ca2404.1_all.deb Size: 31452 MD5sum: 72754a8a69db97c5d42c5209be680e1c SHA1: e821da5a5d3264325a3b828f87c659b5b491ed64 SHA256: 2be49a6c30ac668975cca79f72e08ce5a47ede633057bbc9fd281818dae52615 SHA512: 37e09aec868c12541d6c952f4b321c571082211aff77799bddb6f87ce0bb0d3a68dc810b4b02b68f8efc5984eba60d4a5485c74502d0728eddff1edd788379fa Homepage: https://cran.r-project.org/package=MAKL Description: CRAN Package 'MAKL' (Multiple Approximate Kernel Learning (MAKL)) R package associated with the Multiple Approximate Kernel Learning (MAKL) algorithm proposed in . The algorithm fits multiple approximate kernel learning (MAKL) models that are fast, scalable and interpretable. Package: r-cran-malani Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-malani_1.0-1.ca2404.1_all.deb Size: 39272 MD5sum: 63d79390709f615c70de37b7aeb21157 SHA1: 2b7602af3ddbe1dc2e847f28a2d992429d65a9c8 SHA256: 8f0875cb34b12e075f039c758882c083eb89a005895c394eed286ce12d74d415 SHA512: f1e1d3fcdcd8bd515cf06759669b8b6932164a30ec495472565294669faa62179fbb4dd04cc3670ca80fda4d827351ca80ea141f1b1e9a716baa79b0cab567b3 Homepage: https://cran.r-project.org/package=malani Description: CRAN Package 'malani' (Machine Learning Assisted Network Inference) Find dark genes. These genes are often disregarded due to no detected mutation or differential expression, but are important in coordinating the functionality in cancer networks. Package: r-cran-malariaatlas Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-xml2, r-cran-gridextra, r-cran-httr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-sf, r-cran-lifecycle, r-cran-terra, r-cran-tidyterra, r-cran-ows4r, r-cran-future.apply, r-cran-lubridate, r-cran-jsonlite, r-cran-stringr, r-cran-ggnewscale Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-palettetown, r-cran-magrittr, r-cran-tibble, r-cran-rdhs Filename: pool/dists/noble/main/r-cran-malariaatlas_1.7.0-1.ca2404.1_all.deb Size: 2111606 MD5sum: 57c0f13b3de6f9b195d145550bd41717 SHA1: bd54c0dbbbc1a40998f6970cca24f080728263e9 SHA256: 84f6508019f0cf246662974d39e5e5e92a139cad91413c3cdf498970029b914f SHA512: 70ff7e99ba49aae59ac0337e1e333a22a0829fd02ba6b7654f5eed533b383f9a48f52c89d3624d029da8f424f2ad43b35269aed33121cffb45714da9430a38c9 Homepage: https://cran.r-project.org/package=malariaAtlas Description: CRAN Package 'malariaAtlas' (An R Interface to Open-Access Malaria Data, Hosted by the'Malaria Atlas Project') A suite of tools to allow you to download all publicly available parasite rate survey points, mosquito occurrence points and raster surfaces from the 'Malaria Atlas Project' servers as well as utility functions for plotting the downloaded data. Package: r-cran-malaytextr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-malaytextr_0.1.3-1.ca2404.1_all.deb Size: 79808 MD5sum: 93a7ff14156681e83c9df1ed448bcf00 SHA1: cc00863cf5d9c59aacc56faad8eebaac62e9d5e4 SHA256: cfb8b4f304e74d41cf6f464a1688a7593024f794e48cd4e2787cd8952f962500 SHA512: 6403050410c6ab4b3d188aef7263249d7a02c4188c0c82a71d51c0353ed756b138547f9ebfa71c1af632bc21d0cb36c0e496730aa50233cea8ad242c30275d71 Homepage: https://cran.r-project.org/package=malaytextr Description: CRAN Package 'malaytextr' (Text Mining for Bahasa Malaysia) It is designed to work with text written in Bahasa Malaysia. We provide functions and data sets that will make working with Bahasa Malaysia text much easier. For word stemming in particular, we will look up the Malay words in a dictionary and then proceed to remove "extra suffix" as explained in Khan, Rehman Ullah, Fitri Suraya Mohamad, Muh Inam UlHaq, Shahren Ahmad Zadi Adruce, Philip Nuli Anding, Sajjad Nawaz Khan, and Abdulrazak Yahya Saleh Al-Hababi (2017) . This package includes a dictionary of Malay words that may be used to perform word stemming, a dataset of Malay stop words, a dataset of sentiment words and a dataset of normalized words. Package: r-cran-maldicellassay Architecture: all Version: 0.4.47-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4939 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-nplr, r-cran-dplyr, r-cran-tidyr, r-cran-forcats, r-cran-scales, r-cran-maldiquant, r-cran-maldiquantforeign, r-cran-tibble, r-cran-svmisc, r-cran-purrr Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-maldicellassay_0.4.47-1.ca2404.1_all.deb Size: 4499200 MD5sum: 27a14de7a75204164b266db98719bcd9 SHA1: a4e705e0f3e04a64495ca76da2b34b0fdfba17ed SHA256: e9c14c102c2bbf99358154f9e228eb992d74ef1323f069e74f67be8cde6eed48 SHA512: acfcbf271789ebdd4df7deb75e086c8dd7072ac9536f30ded2567b0162c46d7041018bfb97209e64e67ee4142bd46efd198520ecec3d2651aae4bed184c86517 Homepage: https://cran.r-project.org/package=MALDIcellassay Description: CRAN Package 'MALDIcellassay' (Automated MALDI Cell Assays Using Dose-Response Curve Fitting) Conduct automated cell-based assays using Matrix-Assisted Laser Desorption/Ionization (MALDI) methods for high-throughput screening of signals responsive to treatments. The package efficiently identifies high variance signals and fits dose-response curves to them. Quality metrics such as Z', V', log2FC, and CRS are provided for evaluating the potential of signals as biomarkers. The methodologies were introduced by Weigt et al. (2018) and refined by Unger et al. (2021) . Package: r-cran-maldipickr Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2821 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-maldiquant, r-cran-readbrukerflexdata, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-coop, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-maldipickr_1.3.1-1.ca2404.1_all.deb Size: 1632950 MD5sum: 7a652da47be19f2a5cdf733db2ac9b4e SHA1: aa1dd0e21aa8700c0397047f7c282f209f387a68 SHA256: 5b18f598fa49bd3bab95e62e3b6a386dd1f2890cdb2d00216322f1cffb435659 SHA512: ae8de5b64c6478627caad90c1e65d4bd2fffcf7c454313660a3d15977269f3bb118ed958850d8ef8103fbb05de94c87a2ac39d5dcc215970f23a970f2d981a7c Homepage: https://cran.r-project.org/package=maldipickr Description: CRAN Package 'maldipickr' (Dereplicate and Cherry-Pick Mass Spectrometry Spectra) Convenient wrapper functions for the analysis of matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) spectra data in order to select only representative spectra (also called cherry-pick). The package covers the preprocessing and dereplication steps (based on Strejcek, Smrhova, Junkova and Uhlik (2018) ) needed to cluster MALDI-TOF spectra before the final cherry-picking step. It enables the easy exclusion of spectra and/or clusters to accommodate complex cherry-picking strategies. Alternatively, cherry-picking using taxonomic identification MALDI-TOF data is made easy with functions to import inconsistently formatted reports. Package: r-cran-maldiquantforeign Architecture: all Version: 0.14.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maldiquant, r-cran-base64enc, r-cran-digest, r-cran-readbrukerflexdata, r-cran-readmzxmldata, r-cran-xml Suggests: r-cran-knitr, r-cran-testthat, r-cran-rnetcdf Filename: pool/dists/noble/main/r-cran-maldiquantforeign_0.14.1-1.ca2404.1_all.deb Size: 375636 MD5sum: 21026c5296fb692523d9d7d8f3225801 SHA1: cd3c3e72b6e4807e5fd228c47b039516fb65f43b SHA256: c5d4eaaeaa33917517f8cb9e1d922bba347f92b455e4c364af11215e097aa19d SHA512: 998709d00e9dc40729b4017ca88220381ba21ae606e3081487c76432ac75cf13bac16c6dc21348146a68fd0a057115d3d18c74f802dd861c7f2edaa191524dfb Homepage: https://cran.r-project.org/package=MALDIquantForeign Description: CRAN Package 'MALDIquantForeign' (Import/Export Routines for 'MALDIquant') Functions for reading (tab, csv, Bruker fid, Ciphergen XML, mzXML, mzML, imzML, Analyze 7.5, CDF, mMass MSD) and writing (tab, csv, mMass MSD, mzML, imzML) different file formats of mass spectrometry data into/from 'MALDIquant' objects. Package: r-cran-maldirppa Architecture: all Version: 1.1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2399 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maldiquant, r-cran-signal, r-cran-robustbase, r-cran-lattice, r-cran-waveslim Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-maldirppa_1.1.0-3-1.ca2404.1_all.deb Size: 1548596 MD5sum: e8875059c59171bf961072d1ea7e962f SHA1: e50c48c95f6c896adf0360462c0873a30bbf8a1d SHA256: 88c91e7050214288c54497506141866318e56e25f08aa81c61cad111d71b4cb4 SHA512: fa96cac57bdbcebc832e0c915bd5474e38e5cebb3085121b89b7dc314c23e645889920372c3c9a9771d4af2dc01470a03c215b899d34dcd7418e80f674e587e8 Homepage: https://cran.r-project.org/package=MALDIrppa Description: CRAN Package 'MALDIrppa' (MALDI Mass Spectrometry Data Robust Pre-Processing and Analysis) Provides methods for quality control and robust pre-processing and analysis of MALDI mass spectrometry data (Palarea-Albaladejo et al. (2018) ). Package: r-cran-mall Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ellmer, r-cran-fs, r-cran-glue, r-cran-jsonlite, r-cran-ollamar, r-cran-rlang Suggests: r-cran-dbplyr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-mall_0.2.0-1.ca2404.1_all.deb Size: 124988 MD5sum: 4e8dbb98a024a5c83350ff382e45310e SHA1: f94c58d23130cd7b3e7fa2a5c22a966af1c81a60 SHA256: 69a9eb455906a2fd68dba83a61b5d6d7fa8f8fb37d9c2004ee9b6c816f39a6df SHA512: 88925620dc68441d45243bf042c864ebbcbb69d9dcc56c4ed159244fcad9b6b2be61692d4beb43af758d539436855c2b46b1a8c0f4c523c6173fab5556ab1e16 Homepage: https://cran.r-project.org/package=mall Description: CRAN Package 'mall' (Run Multiple Large Language Model Predictions Against a Table,or Vectors) Run multiple 'Large Language Model' predictions against a table. 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Package: r-cran-mallet Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4378 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-checkmate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mallet_1.3.0-1.ca2404.1_all.deb Size: 3955758 MD5sum: d689785bee1544a9600b27d441662c32 SHA1: 57c3e0ea181b0c4ae2af71b6330fbcb0be315b92 SHA256: 6263e326000f5d92c91866acdcdfd6658e3aa993ef1f992478205d51c8535d6d SHA512: 4e4cb9a81774b4438cd4ac3a9310025063041283206969afddcb748cb7333436aa9ba5cb68862f62348e5bb075066c317adc1175a96fc31b1601feda03f3ffeb Homepage: https://cran.r-project.org/package=mallet Description: CRAN Package 'mallet' (An R Wrapper for the Java Mallet Topic Modeling Toolkit) An R interface for the Java Machine Learning for Language Toolkit (mallet) to estimate probabilistic topic models, such as Latent Dirichlet Allocation. We can use the R package to read textual data into mallet from R objects, run the Java implementation of mallet directly in R, and extract results as R objects. The Mallet toolkit has many functions, this wrapper focuses on the topic modeling sub-package written by David Mimno. The package uses the rJava package to connect to a JVM. Package: r-cran-malvinas Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-malvinas_0.1.0-1.ca2404.1_all.deb Size: 280252 MD5sum: 84ba9c29729c7b2dd3ae586eb5e63121 SHA1: 65b29221e8a442fe5f00f25206fcc7ee693501a7 SHA256: 2486876c6de1f9e2fc245a71b62f62fdd7ee6908a2d2cc11cb7953380f27a475 SHA512: ecb1a3c6acaa82ff6c1aea4fd1bc7836aa7097883ee69657f88c283b572e1260dc02d36e4be1ef6c133be6771222f7bc7416f292f82e7e790ee620c0dd58b16e Homepage: https://cran.r-project.org/package=malvinas Description: CRAN Package 'malvinas' (Islas Malvinas, Georgias Del Sur y Sándwich Del Sur) Data sets related to the Islas Malvinas /// Sets de datos relacionados a las Islas Malvinas - La Nación Argentina ratifica su legítima e imprescriptible soberanía sobre las islas Malvinas, Georgias del Sur y Sándwich del Sur y los espacios marítimos e insulares correspondientes, por ser parte integrante del territorio nacional. La recuperación de dichos territorios y el ejercicio pleno de la soberanía, respetando el modo de vida de sus habitantes y conforme a los principios del Derecho Internacional, constituyen un objetivo permanente e irrenunciable del pueblo argentino. 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The 2025 update includes comprehensive, detailed species accounts, and distribution information. Package: r-cran-managedcloudprovider Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dockerparallel, r-cran-adagio, r-cran-jsonlite Suggests: r-cran-markdown, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-readr Filename: pool/dists/noble/main/r-cran-managedcloudprovider_1.0.0-1.ca2404.1_all.deb Size: 372838 MD5sum: de7c2481e7cd36f66d791151eafa8eba SHA1: 09f351cb4aae6e18178a6ea35e670d0da31b158d SHA256: b532bbb5a58bad92bca9f58019e503c7f25c8f3624addbadcc1126659f32501f SHA512: e7faee5d52c5bd6d842b76e6ede8874d063b78ceaa207cb9759da9504d345d47b96f890848273ab0aafcd790f38d186944e21d342851bcea4b580121df887555 Homepage: https://cran.r-project.org/package=ManagedCloudProvider Description: CRAN Package 'ManagedCloudProvider' (Providing the Kubernetes-Like Functions for the Non-KubernetesCloud Service) Providing the kubernetes-like class 'ManagedCloudProvider' as a child class of the 'CloudProvider' class in the 'DockerParallel' package. The class is able to manage the cloud instance made by the non-kubernetes cloud service. For creating a provider for the non-kubernetes cloud service, the developer needs to define a reference class inherited from 'ManagedCloudProvider' and define the method for the generics runDockerWorkerContainers(), getDockerWorkerStatus() and killDockerWorkerContainers(). For more information, please see the vignette in this package and . Package: r-cran-managelocalrepo Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-assertthat Filename: pool/dists/noble/main/r-cran-managelocalrepo_0.1.5-1.ca2404.1_all.deb Size: 24714 MD5sum: 336e84452aebedae29ce446a9b75e048 SHA1: 26136dce5560bd1e8afeee53656b0ece9d176123 SHA256: 9abb280ad10e7607ae29d83f438e433349c186f975185264bb4952ee0720e156 SHA512: 7a00e3903e8557a9f5ad787bf5f15b45aa4684c4437add1086d777fea9a65d10d4cf77a5841f4609c341ae049d84f257c756bcdab72ba4984e492086d6675704 Homepage: https://cran.r-project.org/package=managelocalrepo Description: CRAN Package 'managelocalrepo' (Manage a CRAN-Style Local Repository) This will allow easier management of a CRAN-style repository on local networks (i.e. not on CRAN). This might be necessary where hosted packages contain intellectual property owned by a corporation. Package: r-cran-mancie Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 593 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mancie_1.4-1.ca2404.1_all.deb Size: 572502 MD5sum: b2f7e7815298998f74c3c2e5e8893cb6 SHA1: b20e6b0192215728a2b15dcfa09b39a4a77f450e SHA256: 1c4453cc52bd08358fb638e76adf82d13187363e63d433f3b29b4d4645cfb210 SHA512: 2efb0b369f6bc3d23211edaffd4c11c84713aaec83d66229ad9624c0dfb9190b3cbe532ff5804d11de01e7d0aaa4dc2b7f5e0ca3cbb888e66b43534ac169f643 Homepage: https://cran.r-project.org/package=MANCIE Description: CRAN Package 'MANCIE' (Matrix Analysis and Normalization by Concordant InformationEnhancement) High-dimensional data integration is a critical but difficult problem in genomics research because of potential biases from high-throughput experiments. We present MANCIE, a computational method for integrating two genomic data sets with homogenous dimensions from different sources based on a PCA procedure as an approximation to a Bayesian approach. Package: r-cran-mand Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7422 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-msma, r-cran-oro.nifti, r-cran-oro.dicom, r-cran-imager, r-cran-caret Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mand_2.0-1.ca2404.1_all.deb Size: 995768 MD5sum: a2274a9788a740fdbf9234ebe95fe11e SHA1: eea0a8b8f32ec9cee90d80b871558bc7a52474b2 SHA256: cfbb49c0cda3c29ca0d8bd0691d28188556c75b249b8adf8e39c60352e5f5d9d SHA512: 0487884a7d48fb8ffe617866e52fada0c453929adadb2743a8b369ac80e450c1a77d6dc80eb50575264c21c450aa347b69e387794412f94eb8e8847ec326521e Homepage: https://cran.r-project.org/package=mand Description: CRAN Package 'mand' (Multivariate Analysis for Neuroimaging Data) Several functions can be used to analyze neuroimaging data using multivariate methods based on the 'msma' package. The functions used in the book entitled "Multivariate Analysis for Neuroimaging Data" (2021, ISBN-13: 978-0367255329) are contained. Package: r-cran-mandalar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mandalar_0.1.0-1.ca2404.1_all.deb Size: 49648 MD5sum: fb2b300c89b108997358512cd9bdaf1b SHA1: be2739269301231557330cdbe77ddfe39ff59658 SHA256: 0a4b2a0c8ac32a188f166ea41757cc8d67f783c00089730eefa8b319c65aa817 SHA512: 7485d3e2a072bf09b7cda14ffc62dfb237d00f2d61163fc0f6ef9abceeadd506aee0c0c45b5839b7cecd6182ba78ab7a767a2c9200d104be5437dbd7c0d2fb58 Homepage: https://cran.r-project.org/package=MandalaR Description: CRAN Package 'MandalaR' (Building Mandalas from Parametric Equations of Classical Curves) Provides an algorithm for creating mandalas. From the perspective of classic mathematical curves and rigid movements on the plane, the package allows you to select curves and produce mandalas from the curve. The algorithm was developed based on the book by Alcoforado et. al. entitled "Art, Geometry and Mandalas with R" (2022) in press by the USP Open Books Portal. Package: r-cran-manet Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-combinat, r-cran-igraph, r-cran-mclust Filename: pool/dists/noble/main/r-cran-manet_2.0-1.ca2404.1_all.deb Size: 50968 MD5sum: a965859b1cc6544c3e27a63b0d9e7be1 SHA1: 9cc94b90dbc9730f98f0d8a10dc778db0694720b SHA256: 4a475a5fd7cb981d4e1ffe76ad5ccb0356999209321cc93e00b9c149c86e4299 SHA512: 5485b9184a30e192ab878f82a05f7b4e6d534a21f3aed9a72312e94a301bfb7fecf7d3fe0895d79ed30caf9eff0c188fce4b1a3ee3c40d40cbaaedf123232d82 Homepage: https://cran.r-project.org/package=manet Description: CRAN Package 'manet' (Multiple Allocation Model for Actor-Event Networks) Mixture model with overlapping clusters for binary actor-event data. Parameters are estimated in a Bayesian framework. Model and inference are described in Ranciati, Vinciotti, Wit (2017) Modelling actor-event network data via a mixture model under overlapping clusters. Submitted. Package: r-cran-mangrove Architecture: all Version: 1.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-kinship2 Filename: pool/dists/noble/main/r-cran-mangrove_1.21-1.ca2404.1_all.deb Size: 344698 MD5sum: 1befe50a2010507b2589c95adc165895 SHA1: c5e9a48bcce3b13ff4f4bc4d389c19e77c407694 SHA256: 49ddac93399f9d34d064dcf53fa011021f09dcb668054dcf61d8415c1fe1af0f SHA512: ad26b502ab6116b85bcabb675f498049802c0546e54ead5540fc4218cdc117ba90e5765225cf29635c5444b9f3df8a07dee668e9c870e499746381592058da6b Homepage: https://cran.r-project.org/package=Mangrove Description: CRAN Package 'Mangrove' (Risk Prediction on Trees) Methods for performing genetic risk prediction from genotype data. You can use it to perform risk prediction for individuals, or for families with missing data. Package: r-cran-manhattanly Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-plotly, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-manhattanly_0.3.0-1.ca2404.1_all.deb Size: 431526 MD5sum: ccc958746e08d91d23fc0fd469ef50e6 SHA1: c89eabc12fcea2c5780b097b7fd132f41915f555 SHA256: 2ad8c1709c5ae351d7d78b99a2564b5b36d11e8c14e7d1808624b221cc217306 SHA512: 45d50a4c918836938de419f14a822964c35154e6aed503c93093a2a9935c4a01ec0b2afae7ac2743d8f2d8b0c54bb9d5a769ddcafac537afeecf8e1ad7896ac1 Homepage: https://cran.r-project.org/package=manhattanly Description: CRAN Package 'manhattanly' (Interactive Q-Q and Manhattan Plots Using 'plotly.js') Create interactive manhattan, Q-Q and volcano plots that are usable from the R console, in 'Dash' apps, in the 'RStudio' viewer pane, in 'R Markdown' documents, and in 'Shiny' apps. Hover the mouse pointer over a point to show details or drag a rectangle to zoom. A manhattan plot is a popular graphical method for visualizing results from high-dimensional data analysis such as a (epi)genome wide association study (GWAS or EWAS), in which p-values, Z-scores, test statistics are plotted on a scatter plot against their genomic position. Manhattan plots are used for visualizing potential regions of interest in the genome that are associated with a phenotype. Interactive manhattan plots allow the inspection of specific value (e.g. rs number or gene name) by hovering the mouse over a cell, as well as zooming into a region of the genome (e.g. a chromosome) by dragging a rectangle around the relevant area. This work is based on the 'qqman' package and the 'plotly.js' engine. It produces similar manhattan and Q-Q plots as the 'manhattan' and 'qq' functions in the 'qqman' package, with the advantage of including extra annotation information and interactive web-based visualizations directly from R. Once uploaded to a 'plotly' account, 'plotly' graphs (and the data behind them) can be viewed and modified in a web browser. Package: r-cran-manhplot Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3516 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra Suggests: r-cran-r.utils, r-cran-testthat Filename: pool/dists/noble/main/r-cran-manhplot_1.1-1.ca2404.1_all.deb Size: 962846 MD5sum: 9f9d3241e57b0ab2ba017bed944864c3 SHA1: d4dff2839a1c22c1e1c2cf83446abb1f9ca54959 SHA256: 724eb8206c1304c9f513aaae4e27b43d61eafeb75e9c8cbfec46f425a6e1cf99 SHA512: 43a4a54c0977a7b2b01baf1468907beef5d6811511afa74545072bf4c9771b4ace43d1bdece76a795104134828690a08ebf6264913b2aab81686b3611e4a8711 Homepage: https://cran.r-project.org/package=manhplot Description: CRAN Package 'manhplot' (The Manhattan++ Plot) This plot integrates annotation into a manhattan plot. The plot is implemented as a heatmap, which is binned using -log10(p-value) and chromosome position. Annotation currently supported is minor allele frequency and gene function high impact variants. Package: r-cran-manifesto Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-jsonlite, r-cran-pak, r-cran-tomledit Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-manifesto_0.0.2-1.ca2404.1_all.deb Size: 139810 MD5sum: 142c6c933179eb56253f61f96636dbdd SHA1: eaa4557a394a74f29056254a113c65af4f68a35d SHA256: 02f022a3402d66a5787f328f5479a39074600e3a8836cc9d66deed901ed7f8e3 SHA512: 6782e2d62a8c8ffb92b2aa57f5739937d9c93915488a262a766aed79edd91411f2b8dfe5d2cf456c5d2f24a361a6876e444b7ce9d62f26a42a6a64ee5cca7d15 Homepage: https://cran.r-project.org/package=manifesto Description: CRAN Package 'manifesto' (Create Project Manifest Files) Provides 'TOML' representations of packages needed that must be installed to run a project. Includes functions to specify detailed installation functions, validate files, and to use a given file as the requirements for a project. Handles package installations, when necessary, via 'pak'. Package: r-cran-manifestor Architecture: all Version: 1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 812 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nlp, r-cran-tm, r-cran-magrittr, r-cran-httr, r-cran-jsonlite, r-cran-base64enc, r-cran-purrr, r-cran-readr, r-cran-dplyr, r-cran-tidyselect, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-r.rsp, r-cran-haven, r-cran-readxl, r-cran-psych, r-cran-zoo, r-cran-htmlwidgets, r-cran-dt, r-cran-htmltools, r-cran-devtools, r-cran-formatr, r-cran-highr Filename: pool/dists/noble/main/r-cran-manifestor_1.6.3-1.ca2404.1_all.deb Size: 450216 MD5sum: 6e2a93ff4342a833d61daf4aeaafea4f SHA1: 7d59f19b006e724cc2152f90824bbd5d9e890245 SHA256: 375e42bf36c56787cf70cab2499a815fb417f8dfc613122de8d6b898057d744e SHA512: 3843c9a085ed12030459c8c2dbf7b7fa91b4bfdc109dd844e3d9852d8b070f116a48066343883a258429cd1686e8260686804f29eeca5a896945a1364fcf7a4e Homepage: https://cran.r-project.org/package=manifestoR Description: CRAN Package 'manifestoR' (Access and Process Data and Documents of the Manifesto Project) Provides access to coded election programmes from the Manifesto Corpus and to the Manifesto Project's Main Dataset and routines to analyse this data. The Manifesto Project collects and analyses election programmes across time and space to measure the political preferences of parties. The Manifesto Corpus contains the collected and annotated election programmes in the Corpus format of the package 'tm' to enable easy use of text processing and text mining functionality. Specific functions for scaling of coded political texts are included. Package: r-cran-manipulate Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-manipulate_1.0.1-1.ca2404.1_all.deb Size: 45196 MD5sum: 821e7f95e2fa0036dc25228200514192 SHA1: 64077b633c2fe15c1a03c30a9545a4ed9a5ad5b4 SHA256: 926f000c67d6123e286fb3460d8f9be5bd3ad840b1faa4686c9fb19ddb90e33b SHA512: 01f39f8545ac9e3167ddad4c70bf2d6e23162db5e09f142aad064c327f2e5bdbc24232adc00d57dfcb9ea4f3b63f58f1e55fdc3c960fae7c93d003f41acbdaee Homepage: https://cran.r-project.org/package=manipulate Description: CRAN Package 'manipulate' (Interactive Plots for RStudio) Interactive plotting functions for use within RStudio. The manipulate function accepts a plotting expression and a set of controls (e.g. slider, picker, checkbox, or button) which are used to dynamically change values within the expression. When a value is changed using its corresponding control the expression is automatically re-executed and the plot is redrawn. Package: r-cran-manipulatewidget Architecture: all Version: 0.11.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3754 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-htmltools, r-cran-htmlwidgets, r-cran-knitr, r-cran-base64enc, r-cran-codetools, r-cran-webshot, r-cran-shinyjs Suggests: r-cran-dygraphs, r-cran-leaflet, r-cran-plotly, r-cran-xts, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-manipulatewidget_0.11.2-1.ca2404.1_all.deb Size: 2351204 MD5sum: 561fcaf6b4cf7d441d1d78467cc03bde SHA1: dc8cfecde9e374ddb2a94320e6be53c8bf565a24 SHA256: 875e90870804b62b56bdd430ec2f367eae4c215d14cbe7cd254a732cda3e5b0b SHA512: 209aafbe20334e429ca27951f5c408d94074e6f89de892f8c027ccacc7993fac129ccda381f5ebf01185735ea5d25f36ecdd093aa657a43ebbb240c903d82156 Homepage: https://cran.r-project.org/package=manipulateWidget Description: CRAN Package 'manipulateWidget' (Add Even More Interactivity to Interactive Charts) Like package 'manipulate' does for static graphics, this package helps to easily add controls like sliders, pickers, checkboxes, etc. that can be used to modify the input data or the parameters of an interactive chart created with package 'htmlwidgets'. Package: r-cran-mannwhitneycopula Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-tidyverse, r-cran-ggplot2, r-cran-purrr, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-mannwhitneycopula_0.1.1-1.ca2404.1_all.deb Size: 65922 MD5sum: 4d176a9abc95b8e83c06f3517f7eefea SHA1: 0bda9b48f4d1b102dd12a05aa3d042f737a8d967 SHA256: ccdbd6cbd0b766257d27505401f57e9653141485ba6570b196d1783df85385df SHA512: c9a0ec1adfc07bd76ecc5fe159c4e2a1d3ccd29cb534f69f572e5164329bb92025fe0fe9b144dbd3ab3e1e00ce186663faee389a7289c047bb5ff95c64cd2d23 Homepage: https://cran.r-project.org/package=MannWhitneyCopula Description: CRAN Package 'MannWhitneyCopula' (Computing Mann-Whitney Effect Based on Copulas) Computing the Mann-Whitney effect based on copula models. Estimation of the association parameter in survival copula models. A description of the underlying methods is described in Nakazono et al. (2024) and Nakazono et al. (accepted for publication in Statistical Papers). Package: r-cran-manorm2 Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5300 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-locfit, r-cran-scales, r-cran-statmod Suggests: r-cran-gplots, r-cran-desctools, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-manorm2_1.2.2-1.ca2404.1_all.deb Size: 4001014 MD5sum: 6f01c34a955ba3fbb3ca7fe15ef0ee4a SHA1: 8ba447b5a3607847a8ca4bdc9c57c68bdc23a108 SHA256: 5fba6c33b7e467e3ee555da23c3482c8eaaee9d566311940b07939b591f5edc0 SHA512: 3c1ebf88077a6a8f2a3c449fe52bcdb8044bd3ec5074b8b95336bcfd0d7adf760aed52c2502fc34523604c86d95b655c61e2e20bfa8e052ce2fa35e5f8c03879 Homepage: https://cran.r-project.org/package=MAnorm2 Description: CRAN Package 'MAnorm2' (Tools for Normalizing and Comparing ChIP-seq Samples) Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the premier technology for profiling genome-wide localization of chromatin-binding proteins, including transcription factors and histones with various modifications. This package provides a robust method for normalizing ChIP-seq signals across individual samples or groups of samples. It also designs a self-contained system of statistical models for calling differential ChIP-seq signals between two or more biological conditions as well as for calling hypervariable ChIP-seq signals across samples. Refer to Tu et al. (2021) and Chen et al. (2022) for associated statistical details. Package: r-cran-manova.rm Architecture: all Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1091 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-mass, r-cran-matrix, r-cran-magic, r-cran-plotrix, r-cran-ellipse, r-cran-multcomp, r-cran-data.table Suggests: r-cran-nparld, r-cran-knitr, r-cran-rmarkdown, r-cran-hsaur3, r-cran-tidyr, r-cran-ggplot2, r-cran-gfd, r-cran-testthat Filename: pool/dists/noble/main/r-cran-manova.rm_0.5.4-1.ca2404.1_all.deb Size: 524956 MD5sum: 0d8c8b6b940ad5d36b840fa4d6f0b8b0 SHA1: dd2ad59cfaffcedfbf86eeb13af32e40359aa869 SHA256: 2f6b6188c13cb14366cf3928975edb5801f3b47d5d21473262c591a9c17fc2ef SHA512: d74944cca566b3145f66f8a2b04cbd8a5f881c1856579446ec84647099f1a47afb843b50d3f0412bda2c3dc0408a86a633555ce3ca8cc93ff5a526bb63ed19bc Homepage: https://cran.r-project.org/package=MANOVA.RM Description: CRAN Package 'MANOVA.RM' (Resampling-Based Analysis of Multivariate Data and RepeatedMeasures Designs) Implemented are various tests for semi-parametric repeated measures and general MANOVA designs that do neither assume multivariate normality nor covariance homogeneity, i.e., the procedures are applicable for a wide range of general multivariate factorial designs. In addition to asymptotic inference methods, novel bootstrap and permutation approaches are implemented as well. These provide more accurate results in case of small to moderate sample sizes. Furthermore, post-hoc comparisons are provided for the multivariate analyses. Friedrich, S., Konietschke, F. and Pauly, M. (2019) . Package: r-cran-mantaid Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biomart, r-cran-caret, r-cran-keras, r-cran-mlr3tuning, r-cran-mlr3, r-cran-ggplot2, r-cran-data.table, r-cran-magrittr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-ggcorrplot, r-cran-reshape2, r-cran-scutr, r-cran-paradox, r-cran-rcolorbrewer, r-cran-purrr, r-cran-dplyr Suggests: r-cran-mlr3hyperband, r-cran-mlr3learners, r-cran-ranger, r-cran-rpart, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mantaid_1.0.4-1.ca2404.1_all.deb Size: 273424 MD5sum: 6e938ddaac60aac90ba9cb2818b26ee6 SHA1: ecd976e1153ab983f982c579e74012f49c18985c SHA256: 9017c018093e074adf52c9da6204925d0c9bc94bfb36f7d69676bd8ff15e45c3 SHA512: e104d63fc326c7c586adf02f6b6e42aa15e1c74c0a0a68a551b137f24f59743466fc671bf45c75aa0a3bc3ad8af303a714c2982c9bcd405da46108c96090ecaa Homepage: https://cran.r-project.org/package=MantaID Description: CRAN Package 'MantaID' (A Machine-Learning Based Tool to Automate the Identification ofBiological Database IDs) The number of biological databases is growing rapidly, but different databases use different IDs to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed 'MantaID', a data-driven, machine-learning based approach that automates identifying IDs on a large scale. The 'MantaID' model's prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within two minutes. 'MantaID' supports the discovery and exploitation of ID patterns from large quantities of databases. (e.g., up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application, and API were also developed for 'MantaID' to improve applicability. To our knowledge, 'MantaID' is the first tool that enables an automatic, quick, accurate, and comprehensive identification of large quantities of IDs, and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases. Package: r-cran-mantar Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 562 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-mathjaxr, r-cran-matrix, r-cran-glassofast Suggests: r-cran-numderiv, r-cran-mice, r-cran-lavaan, r-cran-qgraph, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mantar_0.2.0-1.ca2404.1_all.deb Size: 367334 MD5sum: 2a920f40e7b5bb068f7ff819c08ec878 SHA1: 6e6640a8ca8e6ce63d73985b1407e179a932ef95 SHA256: 3ca4ac98cfd4121d5ce4ef8d5a2eb858bb888f5f87beb3dc7cd99266b9e0b887 SHA512: 2af5a2c1a480a4a585cfb3518993c18fe88ad1476fd5154aac5f121a09389fb9173d8b07226790c4a2a5d04fe83eaf9a8a143f279da05687cee6d3bc7c99ab94 Homepage: https://cran.r-project.org/package=mantar Description: CRAN Package 'mantar' (Missingness Alleviation for Network Analysis) Provides functionality for estimating cross-sectional network structures representing partial correlations while accounting for missing data. Networks are estimated via neighborhood selection or regularization, with model selection guided by information criteria. Missing data can be handled primarily via multiple imputation or a maximum likelihood-based approach, as demonstrated by Nehler and Schultze (2025a) and Nehler and Schultze (2025b) . Deletion-based approaches are also available but play a secondary role. Package: r-cran-mantis Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1494 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmarkdown, r-cran-knitr, r-cran-reactable, r-cran-dplyr, r-cran-tidyr, r-cran-dygraphs, r-cran-xts, r-cran-ggplot2, r-cran-scales, r-cran-purrr, r-cran-htmltools, r-cran-lubridate Suggests: r-cran-covr, r-cran-testthat, r-cran-codemetar, r-cran-vdiffr, r-cran-shiny, r-cran-diffviewer, r-cran-withr Filename: pool/dists/noble/main/r-cran-mantis_1.0.2-1.ca2404.1_all.deb Size: 1146182 MD5sum: c93655c71241d6773497e646d095581d SHA1: 7d06b73de9888be750b67a1a68e14d2cdde75f33 SHA256: 0d90e4880a5302cb26c8346d07a76db2d8401da45f6bd2229f4daf5c388aa8e8 SHA512: e02e3c11a6c6a7947a9837c92ee26b900852c80f5d9e2d1114d6a70c7a80cf527eb9cb89133cbeefd5f1cdf6460ba6d400064097346f9f602b7f943ff1e1564d Homepage: https://cran.r-project.org/package=mantis Description: CRAN Package 'mantis' (Multiple Time Series Scanner) Generate interactive html reports that enable quick visual review of multiple related time series stored in a data frame. For static datasets, this can help to identify any temporal artefacts that may affect the validity of subsequent analyses. For live data feeds, regularly scheduled reports can help to pro-actively identify data feed problems or unexpected trends that may require action. The reports are self-contained and shareable without a web server. Package: r-cran-manureshed Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1289 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-ggplot2, r-cran-tidyr, r-cran-jsonlite, r-cran-rlang, r-cran-magrittr, r-cran-scales, r-cran-igraph, r-cran-tigris Suggests: r-cran-nhdplustools, r-cran-rcolorbrewer, r-cran-cowplot, r-cran-ggpubr, r-cran-viridis, r-cran-testthat, r-cran-knitr, r-cran-ragg, r-cran-progress, r-cran-rmarkdown, r-cran-shiny, r-cran-shinydashboard, r-cran-leaflet, r-cran-plotly, r-cran-dt, r-cran-exactextractr, r-cran-ggrepel, r-cran-terra Filename: pool/dists/noble/main/r-cran-manureshed_0.1.5-1.ca2404.1_all.deb Size: 615364 MD5sum: 4527a7981a5199e2364169fbcf38fe86 SHA1: 8024a98336e6d9dc6ba2046072590d776bd39928 SHA256: c480148fec78f5681be32c0363343f0f1e62588d4acd5ef13f9f2dd07ca9eec9 SHA512: 724d1951e07c8d4967910b47b6c02fb1795e2d1d5cc29518d85f8069efb1baea061140e3816797790341d74f0c512195773bce2ee43d3dd449da8d27c206957a Homepage: https://cran.r-project.org/package=manureshed Description: CRAN Package 'manureshed' (Spatiotemporal Nutrient Balance Analysis Across Agricultural andMunicipal Systems) A comprehensive framework for analyzing agricultural nutrient balances across multiple spatial scales (county, 'HUC8', 'HUC2') with integration of wastewater treatment plant ('WWTP') effluent loads for both nitrogen and phosphorus. Supports classification of spatial units as nutrient sources, sinks, or balanced areas based on agricultural surplus and deficit calculations. Includes visualization tools, spatial transition probability analysis, and nutrient flow network mapping. Built-in datasets include agricultural nutrient balance data from the Nutrient Use Geographic Information System ('NuGIS'; The Fertilizer Institute and Plant Nutrition Canada, 1987-2016) and U.S. Environmental Protection Agency ('EPA') wastewater discharge data from the 'ECHO' Discharge Monitoring Report ('DMR') Loading Tool (2007-2016) . Data are downloaded on demand from the Open Science Framework ('OSF') repository to minimize package size while maintaining full functionality. The integrated 'manureshed' framework methodology is described in Akanbi et al. (2025) . Designed for nutrient management planning, environmental analysis, and circular economy research at watershed/administrative to national scales. This material is based upon financial support by the National Science Foundation, EEC Division of Engineering Education and Centers, NSF Engineering Research Center for Advancing Sustainable and Distributed Fertilizer Production (CASFER), NSF 20-553 Gen-4 Engineering Research Centers award 2133576. We thank Dr. Robert D. Sabo (U.S. Environmental Protection Agency) for his valuable contributions to the conceptual development and review of this work. We acknowledge Dr. Sheri Spiegal (U.S. Department of Agriculture–Agricultural Research Service) for foundational contributions to the manureshed classification framework (Spiegal et al. 2020) . Package: r-cran-manydata Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-messydates, r-cran-caret, r-cran-dtplyr, r-cran-ggplot2, r-cran-glmnet, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-remotes, r-cran-stringr, r-cran-text2vec, r-cran-tidyr Suggests: r-cran-testthat, r-cran-readr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggvenndiagram, r-cran-rlang Filename: pool/dists/noble/main/r-cran-manydata_1.1.3-1.ca2404.1_all.deb Size: 1619774 MD5sum: 6fcedb311986952069eeb3f46606f58c SHA1: cd22c1a77771add0689d2beacbc53fdc112f0c37 SHA256: db79952b08606bb6b8925cf866a674b41c596b969add5f9e96f645187fa225ce SHA512: 5d4bc5a6cbb8aa709d26689e489116bd7d3f5fc2ac1a605d360fe2d0f458b0bca7fa732dedebd78bcc82b137e7de37488cca543e3c9e371cb7ce3bab9730657a Homepage: https://cran.r-project.org/package=manydata Description: CRAN Package 'manydata' (Many Global Governance Datacubes) This is the core package offering a portal to the many packages universe. It includes functions to help researchers access, work across, and maintain ensembles of datasets on global governance called datacubes. Package: r-cran-manydist Architecture: all Version: 0.4.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-entropy, r-cran-matrix, r-cran-fastdummies, r-cran-data.table, r-cran-philentropy, r-cran-cluster, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-forcats, r-cran-tibble, r-cran-magrittr, r-cran-fpc, r-cran-recipes, r-cran-rsample, r-cran-rfast, r-cran-readr, r-cran-distances Suggests: r-cran-palmerpenguins Filename: pool/dists/noble/main/r-cran-manydist_0.4.9-1.ca2404.1_all.deb Size: 106730 MD5sum: 34da62703106ecb97245e6a2df829390 SHA1: e48d31fe096e911dd17edcef7abaa643dbdf014c SHA256: b8ca62984fa7ef5246338d9ae91878d1e8676273481380a30df522dc352fd4a8 SHA512: 9266f419cd9232b22be0eefedb3c64611e60b743bf2ede2f719b817d55df4683026b4582a89e9f7411a6b67dc6fddb6c84312ba12eb4636137bf7aa987305074 Homepage: https://cran.r-project.org/package=manydist Description: CRAN Package 'manydist' (Unbiased Distances for Mixed-Type Data) A comprehensive framework for calculating unbiased distances in datasets containing mixed-type variables (numerical and categorical). The package implements a general formulation that ensures multivariate additivity and commensurability, meaning that variables contribute equally to the overall distance regardless of their type, scale, or distribution. Supports multiple distance measures including Gower's distance, Euclidean distance, Manhattan distance, and various categorical variable distances such as simple matching, Eskin, occurrence frequency, and association-based distances. Provides tools for variable scaling (standard deviation, range, robust range, and principal component scaling), and handles both independent and association-based category dissimilarities. Implements methods to correct for biases that typically arise from different variable types, distributions, and number of categories. Particularly useful for cluster analysis, data visualization, and other distance-based methods when working with mixed data. Methods based on van de Velden et al. (2024) "Unbiased mixed variables distance". Package: r-cran-manyivsnets Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-igraph, r-cran-ggplot2, r-cran-ggraph, r-cran-aer, r-cran-lmtest, r-cran-sandwich, r-cran-magrittr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-pkgdown, r-cran-knitr, r-cran-rtransferentropy, r-cran-tidyr, r-cran-viridis, r-cran-countrycode, r-cran-spelling Filename: pool/dists/noble/main/r-cran-manyivsnets_0.1.1-1.ca2404.1_all.deb Size: 163344 MD5sum: 3d2342dfe82d367b790062b73a44e22b SHA1: aa50f9316531724718d2847cf5cb41265c8ec243 SHA256: d8c154b849cd0edd522d1c19171b20cf29f5525b993cc16bca6c2ae41b1140c6 SHA512: 1f1f322c57cbcd5aa5a26f05152e965c772d362ccafa736680ff89acae129193b6daacc8e0fe4222bc80095f10be3cc2cf5786e100489e5e90705beb5668b6cb Homepage: https://cran.r-project.org/package=ManyIVsNets Description: CRAN Package 'ManyIVsNets' (Environmental Phillips Curve Analysis with Multiple InstrumentalVariables and Networks) Comprehensive toolkit for Environmental Phillips Curve analysis featuring multidimensional instrumental variable creation, transfer entropy causal discovery, network analysis, and state-of-the-art econometric methods. Implements geographic, technological, migration, geopolitical, financial, and natural risk instruments with robust diagnostics and visualization. Provides 24 different instrumental variable approaches with empirical validation. Methods based on Phillips (1958) , transfer entropy by Schreiber (2000) , and weak instrument tests by Stock and Yogo (2005) . Package: r-cran-manymodelr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3550 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lme4, r-cran-stringr, r-cran-usethis, r-cran-testthat, r-cran-caret, r-cran-metrics Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-manymodelr_0.4.0-1.ca2404.1_all.deb Size: 630050 MD5sum: f870649e058e5b89f1c326673f1e902c SHA1: ba1c1fee46eb26d8555cd0788d6e3331be698388 SHA256: 884814f71a15613561da32f705c13ffce20688b7295eb1d5d278347cb25b9974 SHA512: be8089c0648d3bd9174dc8a62f605a8b68cc24769cb0f0305f242deffeba7066fb8956f91854d0358607106bbaf306169ad9fafc44ed70a1a4c0080fba043532 Homepage: https://cran.r-project.org/package=manymodelr Description: CRAN Package 'manymodelr' (Build and Tune Several Models) Frequently one needs a convenient way to build and tune several models in one go.The goal is to provide a number of machine learning convenience functions. It provides the ability to build, tune and obtain predictions of several models in one function. The models are built using functions from 'caret' with easier to read syntax. Kuhn(2014) . Package: r-cran-manymome.table Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-manymome, r-cran-flextable Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-lavaan, r-cran-officer Filename: pool/dists/noble/main/r-cran-manymome.table_0.4.0-1.ca2404.1_all.deb Size: 70920 MD5sum: c48b7de60cf9a983ff64de8f2ab7b93b SHA1: 727cd6c2dfd5feb25f6199ba8d41682ee1534b3e SHA256: 90b83c1268eb2df22aad24867a0d7c4f2e318c9f60155e49963bd1e88223647a SHA512: 5a44ee3b5fabf80a78a9ea547f93ea088333a9185433d11f7b453a225fe5e6ff441d38a2a951953c55aeadb7e2dfe205e530840763626b2e8475a5d394e674cb Homepage: https://cran.r-project.org/package=manymome.table Description: CRAN Package 'manymome.table' (Publication-Ready Tables for 'manymome' Results) Converts results from the 'manymome' package, presented in Cheung and Cheung (2023) , to publication-ready tables. Package: r-cran-manymome Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3539 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-boot, r-cran-pbapply, r-cran-ggplot2, r-cran-igraph, r-cran-mass, r-cran-lmhelprs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lavaan.mi, r-cran-amelia, r-cran-mice, r-cran-semplot, r-cran-semptools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-manymome_0.3.4-1.ca2404.1_all.deb Size: 2926778 MD5sum: b4c43d98dca24be9ff54b69e6083eb7c SHA1: 062543ebf5794e07e458e254d546db88295a6ae2 SHA256: b79124bb73f5a2a658e38014d33ecddd48031bf4bdb951b900df8f303a1d8497 SHA512: 7ff7f38c2f44b80e14687c657d165e015abf0cefb6229abbd5d34d643598728ad3a4e76eba8dd163e7974aaeb213f1d15539f9cffae5cd7d592a49cc41de69f6 Homepage: https://cran.r-project.org/package=manymome Description: CRAN Package 'manymome' (Mediation, Moderation and Moderated-Mediation After ModelFitting) Computes indirect effects, conditional effects, and conditional indirect effects in a structural equation model or path model after model fitting, with no need to define any user parameters or label any paths in the model syntax, using the approach presented in Cheung and Cheung (2024) . Can also form bootstrap confidence intervals by doing bootstrapping only once and reusing the bootstrap estimates in all subsequent computations. Supports bootstrap confidence intervals for standardized (partially or completely) indirect effects, conditional effects, and conditional indirect effects as described in Cheung (2009) and Cheung, Cheung, Lau, Hui, and Vong (2022) . Model fitting can be done by structural equation modeling using lavaan() or regression using lm(). Package: r-cran-manynet Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2809 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-igraph, r-cran-network, r-cran-pillar, r-cran-tidygraph Suggests: r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-rsiena, r-cran-sna, r-cran-testthat, r-cran-tibble, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-manynet_2.0.1-1.ca2404.1_all.deb Size: 1928220 MD5sum: b8f22efb4519264c435f66737e75ad00 SHA1: b7a2434cedc2111c693bb95fb672f165959d5ec9 SHA256: 7f3d435200088c25929dcb19b209323b55142aa25d8537e181400d937c69ecf6 SHA512: 2bdd9871668c9d64ecb46b9857a78e30ee796291029adf1b54620d97c2f46b563cb4d7d676cd024701003e64d1de9fb3aa92278d84aefca4b1e5e20dcbb33bf2 Homepage: https://cran.r-project.org/package=manynet Description: CRAN Package 'manynet' (Many Ways to Make, Modify, Mark, and Measure Myriad Networks) Many tools for making, modifying, marking, measuring, and motifs and memberships of many different types of networks. All functions operate with matrices, edge lists, and 'igraph', 'network', and 'tidygraph' objects, on directed, multiplex, multimodal, signed, and other networks. The package includes functions for importing and exporting, creating and generating networks, modifying networks and node and tie attributes, and describing networks with sensible defaults. Package: r-cran-manystates Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-manydata, r-cran-knitr, r-cran-purrr, r-cran-stringi Suggests: r-cran-pointblank, r-cran-messydates, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-manystates_1.0.3-1.ca2404.1_all.deb Size: 318640 MD5sum: 8e1e07726de1cfcb6b57cfd4a12874c7 SHA1: 0e6d3004196420a0e75a7e334a3044714934d34a SHA256: 5dd9dfd1a4c8b8129a485653287b65f82c39961b0b59525f66e9dfeaba03cdcc SHA512: 1d6c0db61da968d1b490b922a35e904164f2556b92e4b772bf92cbc040b49dc5ffedd269a2c622af19644ff4d4fa84c8c6ad870de61fdc2f02105dbb1b268f84 Homepage: https://cran.r-project.org/package=manystates Description: CRAN Package 'manystates' (Many Data on State and State-Like Actors in the InternationalSystem) Comprehensively identifying states and state-like actors is difficult. This package provides data on states and state-like entities in the international system across time. The package combines and cross-references several existing datasets consistent with the aims and functions of the manydata package. It also includes functions for identifying state references in text, and for generating fictional state names. Package: r-cran-manytests Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-manytests_1.2-1.ca2404.1_all.deb Size: 24566 MD5sum: db430aa9f44115ef087d081d2a608b27 SHA1: cdc10670f95af0b1b22eea55e04d6542771c6347 SHA256: f4d742bc77bb854bef881b7f095da35af00e30a903d32a61406c30d1a95b241a SHA512: 8134a083fd421e8498271e9a34a1ba687bf394582e2a634a49d9b25a49808d27a880ac8661210755f6eacb1259c0b4db53fce60a4d8b430e1785d64a429c91a1 Homepage: https://cran.r-project.org/package=ManyTests Description: CRAN Package 'ManyTests' (Multiple Testing Procedures of Cox (2011) and Wong and Cox(2007)) Performs the multiple testing procedures of Cox (2011) and Wong and Cox (2007) . Package: r-cran-maoea Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-nsga2r, r-cran-lhs, r-cran-nnet, r-cran-stringr, r-cran-randtoolbox, r-cran-e1071, r-cran-mass, r-cran-gtools, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-maoea_0.6.2-1.ca2404.1_all.deb Size: 192064 MD5sum: 4c6ba639fcb6ce87681da6b8b8888562 SHA1: 5c69313dfcb75e09621dada6f842cbe4d6f6489f SHA256: 6498313eb751f4cbc55572042a2cea0acd5224aa9119b27bb53c223830647706 SHA512: 9477187a58bc6f23dfc68722cf9f6324bb19cd58d4ca54cbc00864b2fcdabd52b8ceb7e99e0adba65ea9a200010aad121fb5fc0a3647409b05bbc9ea9e1fb8e6 Homepage: https://cran.r-project.org/package=MaOEA Description: CRAN Package 'MaOEA' (Many Objective Evolutionary Algorithm) A set of evolutionary algorithms to solve many-objective optimization. Hybridization between the algorithms are also facilitated. Available algorithms are: 'SMS-EMOA' 'NSGA-III' 'MO-CMA-ES' The following many-objective benchmark problems are also provided: 'DTLZ1'-'DTLZ4' from Deb, et al. (2001) and 'WFG4'-'WFG9' from Huband, et al. (2005) . Package: r-cran-map2ncbi Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rentrez Filename: pool/dists/noble/main/r-cran-map2ncbi_1.5-1.ca2404.1_all.deb Size: 83436 MD5sum: 7052795f5a25adacd0bc34aee3641e5d SHA1: 111b9b23b545c69aa9e9816744e26aa512eceec7 SHA256: d04e6b8bde60d51f14cfb8020a5ff4d3d47e344a8fca84ee5ed03415f63cf1aa SHA512: 5d2bc2d8bc70b4d7fdbb25553b83a2ee2fdcb1c26fb9a98cebd741d677908e1d57d2a8e810e85779171fad5502429f1c5e3d2b8edb99e8f81b623a9db3c30d31 Homepage: https://cran.r-project.org/package=Map2NCBI Description: CRAN Package 'Map2NCBI' (Mapping Markers to the Nearest Genomic Feature) Allows the user to generate a list of features (gene, pseudo, RNA, CDS, and/or UTR) directly from NCBI database for any species with a current build available. Option to save downloaded and formatted files is available, and the user can prioritize the feature list based on type and assembly builds present in the current build used. The user can then use the list of features generated or provide a list to map a set of markers (designed for SNP markers with a single base pair position available) to the closest feature based on the map build. This function does require map positions of the markers to be provided and the positions should be based on the build being queried through NCBI. Package: r-cran-map Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-flexmix, r-cran-matrix, r-cran-magrittr Suggests: r-cran-knitr, r-cran-proc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-map_1.0.0-1.ca2404.1_all.deb Size: 95246 MD5sum: 122eb91a5291153b92a5f5a86a64cbc0 SHA1: d778860da9fc84c9c872089f2e277ba61b631995 SHA256: 23e3c408220ed1bb38beeb5aa5f0a1a0efcce37d7d17ac8b095493c6114eb5d9 SHA512: ffacd03998f51e78e2c467eb5d218a3e048cc336dee08ee63709aeb5f09449a94232ac33467f6401ae3810f65c1ff38a03e0f1d9c61b68978b87455abf8b0d38 Homepage: https://cran.r-project.org/package=MAP Description: CRAN Package 'MAP' (Multimodal Automated Phenotyping) Electronic health records (EHR) linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. Towards that end, we developed an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP). Specifically, our proposed method, called MAP (Map Automated Phenotyping algorithm), fits an ensemble of latent mixture models on aggregated ICD and NLP counts along with healthcare utilization. The MAP algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying subjects with phenotype yes/no (See Katherine P. Liao, et al. (2019) .). Package: r-cran-mapa Architecture: all Version: 2.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-rcolorbrewer, r-cran-smooth Filename: pool/dists/noble/main/r-cran-mapa_2.0.7-1.ca2404.1_all.deb Size: 111288 MD5sum: cf64a0a90b959d79506d5729dfca9535 SHA1: 029467dad9c437f0d8cab9dad37008762d42ffca SHA256: 2871477f1bdaf3334af1c941dcd596be2d2f1bc9e58571b7c94215d18f67f69e SHA512: 961b73d936c43e77fbf049527d3c0118181aa7dbd1954ac4837a765f8efa218481b7d985e31b9949130a8b953ef0b763992ae4162734613b5f8e096ad43b1d08 Homepage: https://cran.r-project.org/package=MAPA Description: CRAN Package 'MAPA' (Multiple Aggregation Prediction Algorithm) Functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure. For details see Kourentzes et al. (2014) . 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Package: r-cran-mapbayr Architecture: all Version: 0.10.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 410 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-mrgsolve, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-lubridate, r-cran-minqa, r-cran-scales, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-mapbayr_0.10.2-1.ca2404.1_all.deb Size: 296938 MD5sum: 0b1920d0c32d8ed1f362e4efd4860708 SHA1: 6be7ead174a760dc3794365ed293b21aacd7e39d SHA256: e437a8da328809bd57bc7e3950134397c619f1257bf27abd77aa7ad3041be634 SHA512: 5d860bdf1d6bf03d587bd1762509a68d6abbf44da7abf9a9f7ff6b890cc4035bbbf7a4f9fb789c6cbbf96c8b56632b2438623531b16b70dbb06188a06036c09d Homepage: https://cran.r-project.org/package=mapbayr Description: CRAN Package 'mapbayr' (MAP-Bayesian Estimation of PK Parameters) Performs maximum a posteriori Bayesian estimation of individual pharmacokinetic parameters from a model defined in 'mrgsolve', typically for model-based therapeutic drug monitoring. 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Package: r-cran-mapboxapi Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-sf, r-cran-jsonlite, r-cran-purrr, r-cran-curl, r-cran-dplyr, r-cran-tidyr, r-cran-aws.s3, r-cran-stringi, r-cran-slippymath, r-cran-protolite, r-cran-rlang, r-cran-geojsonsf, r-cran-magick, r-cran-leaflet, r-cran-units, r-cran-raster, r-cran-png, r-cran-jpeg, r-cran-htmltools Suggests: r-cran-ggspatial, r-cran-mapdeck, r-cran-tigris, r-cran-tidycensus, r-cran-tmap, r-cran-mapboxer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mapboxapi_0.6.3-1.ca2404.1_all.deb Size: 264336 MD5sum: 71f5cadac7f54caf52f38848aaf2a2e8 SHA1: 63e5a0654efc9d400e73941ad1f2b71472781d07 SHA256: 9b83997aaf822711e2e6bb7c39bb3a2c5bb52679076c7e2980c2f808519a0ad8 SHA512: 21ba02427b35d23604e5b6065148adba91efec8812ce49e5548be610c6e0f1b315d23f0ae39635b5e687258ae11ce6f559ba89c8624c1bd6147e40a092a67160 Homepage: https://cran.r-project.org/package=mapboxapi Description: CRAN Package 'mapboxapi' (R Interface to 'Mapbox' Web Services) Includes support for 'Mapbox' Navigation APIs, including directions, isochrones, and route optimization; the Search API for forward and reverse geocoding; the Maps API for interacting with 'Mapbox' vector tilesets and visualizing 'Mapbox' maps in R; and 'Mapbox Tiling Service' and 'tippecanoe' for generating map tiles. See for more information about the 'Mapbox' APIs. Package: r-cran-mapboxer Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1463 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-htmlwidgets, r-cran-htmltools, r-cran-yaml, r-cran-purrr, r-cran-geojsonsf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/noble/main/r-cran-mapboxer_0.4.0-1.ca2404.1_all.deb Size: 698830 MD5sum: 6c8439bcfaf763c6da8707136ab2187b SHA1: fc39575a9355c60ae25d0679b8377a853bb63e81 SHA256: 1310b31cd19078349da0bbd7f599245faf8b42040d8fc0870fecef508e3059c2 SHA512: 8d092dc209fb5da179b7b78ae915ad4b20ea614380d8a33c076dd52eddbb5ad1d46dcf30078d950cfd79d4011d42137f276e4f4ff8d5e63b8c32f22e5973d77d Homepage: https://cran.r-project.org/package=mapboxer Description: CRAN Package 'mapboxer' (An R Interface to 'Mapbox GL JS') Makes 'Mapbox GL JS' , an open source JavaScript library that uses WebGL to render interactive maps, available within R via the 'htmlwidgets' package. 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A 'shiny' app allows users to create admixture maps interactively. Jenkins TL (2024) . Package: r-cran-mapper Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastcluster Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mapper_2.4.0-1.ca2404.1_all.deb Size: 266738 MD5sum: 1d0c970f2078a3f94f87c7b4df29c49f SHA1: c3bc3599d48df29fbb4fb2a8b04eb06096154c04 SHA256: a3fd9bfb0dfbf14fb7394a47514fe9bc129d6fe6244cb0a037bf27e88d6d45c0 SHA512: 74e55285b521f673503969a928c0a9b61f9662c942de451276ab09fdafb637432a3e7c86833016236263ad724c0bb8918a0e54114a94718d2f737679d81c6285 Homepage: https://cran.r-project.org/package=mappeR Description: CRAN Package 'mappeR' (Construct Mapper Graphs for Topological and Exploratory DataAnalysis) Topological data analysis (TDA) is a method of data analysis that uses techniques from topology to analyze high-dimensional data. Here we implement Mapper, an algorithm from this area developed by Singh, Mémoli and Carlsson (2007) which generalizes the concept of a Reeb graph . 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Define a filter (lens) function on the data. 2. Perform clustering within each level set. 3. Generate a complex from the clustering results. Package: r-cran-mappestrisk Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geodata, r-cran-ggplot2, r-cran-khroma, r-cran-nls.multstart, r-cran-progress, r-cran-purrr, r-cran-rtpc, r-cran-terra, r-cran-tidyr Suggests: r-cran-covr, r-cran-leaflet, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mappestrisk_0.1.2-1.ca2404.1_all.deb Size: 358392 MD5sum: add5dd23e727514280f19cdb086d63a2 SHA1: d3a3ee968bed9c418495f9444907dfdafb654ba0 SHA256: cca91cd4272af44b96816bf0578dc5d2ff30a324c27da51aadfa60bac5b91177 SHA512: aadf92b68d04a2a57d33ba5e02dc0383ebfbc482910e727b2e68943f710ea0ae33582ff291e2d18c18b4f83662212e40b02ce080a92b1ae0dfc9c8d2121fdd87 Homepage: https://cran.r-project.org/package=mappestRisk Description: CRAN Package 'mappestRisk' (Create Maps Forecasting Risk of Pest Occurrence) There are three different modules: (1) model fitting and selection using a set of the most commonly used equations describing developmental responses to temperature helped by already existing R packages ('rTPC') and nonlinear regression model functions from 'nls.multstart' (Padfield et al. 2021, ), with visualization of model predictions to guide ecological criteria for model selection; (2) calculation of suitability thermal limits, which consist on a temperature interval delimiting the optimal performance zone or suitability; and (3) climatic data extraction and visualization inspired on previous research (Taylor et al. 2019, ), with either exportable rasters, static map images or html, interactive maps. Package: r-cran-mapping Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1296 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tmap, r-cran-cartography, r-cran-ggplot2, r-cran-sf, r-cran-dplyr, r-cran-leaflet, r-cran-tmaptools, r-cran-viridislite, r-cran-httr, r-cran-curl, r-cran-htmltools, r-cran-leafpop, r-cran-leafsync, r-cran-mapview, r-cran-geojsonio, r-cran-jsonlite, r-cran-stringr, r-cran-s2, r-cran-stringi Suggests: r-cran-knitr, r-cran-diagrammer, r-cran-rmarkdown, r-cran-validate, r-cran-unrepx Filename: pool/dists/noble/main/r-cran-mapping_1.4.1-1.ca2404.1_all.deb Size: 1278444 MD5sum: 4d3674436963d7eb887fa6b8d79ca858 SHA1: f95f2913f3e092ae0a1a02672a65573c0ac48e16 SHA256: 97d660bd54a755ffa0b7439cf2e466d639e8c34aff71a70687bc102027203d9a SHA512: 26862d7a6bda812d90f45a83136284c0f47b078b0e984b60c08fb791a4fa8ddbcfa8cb90384e2a684609cf9457943a8655897e18d005e1a37a73d6a0b26d5ae2 Homepage: https://cran.r-project.org/package=mapping Description: CRAN Package 'mapping' (Automatic Download, Linking, Manipulating Coordinates for Maps) Maps are an important tool to visualise variables distribution across different spatial objects. The mapping process requires to link the data with coordinates and then generate the correspondent map. This package provide coordinates, linking and mapping functions for an automatic, flexible and easy approach of external functions. The package provides an easy, flexible and automatic unit. Geographical coordinates are provided in the package and automatically linked with the input data to generate maps with internal provided functions or external functions. Provide an easy, flexible and automatic approach to potentially download updated coordinates, to link statistical units with coordinates and to aggregate variables based on the spatial hierarchy of units. The object returned from the package can be used for thematic maps with the build-in functions provided in mapping or with other packages already available. Package: r-cran-mappingcalc Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-bslib, r-cran-ggplot2, r-cran-readxl, r-cran-haven, r-cran-writexl, r-cran-tibble Suggests: r-cran-dt, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-mappingcalc_2.0.0-1.ca2404.1_all.deb Size: 244718 MD5sum: b4539a75995701fc8dfb8845c9ff94a3 SHA1: 39a18f2171de0b10ed0b153fa67f7835044fc922 SHA256: 3d49da3f67a3f63c81c1fc68becb5c3280ef24fbcdb96502d07fc6b6f5e900d7 SHA512: bb7e42bb8c5e7ccad611f05b9a8bbe13aeab5d1c78dd0d33ce83a3b8db6b35d64dffbb203762ae6e69e5b639c77d98c2345c4ed068833e4a421f2910183671e8 Homepage: https://cran.r-project.org/package=MappingCalc Description: CRAN Package 'MappingCalc' (Mapping Calculator for EQ-5D Utility Scores) Provides a 'shiny' web application to map scores from clinical instruments (PANSS, SQLS, WHODAS 2.0, PHQ-8, EQ-5D-5L) to preference-based EQ-5D-5L health utility values using validated regression-based and beta-mixture mapping algorithms developed from Singapore population studies. Intended for use in health economic evaluations and cost-utility analyses. Methods are based on: Abdin et al. (2019) , Seow et al. (2023) , Abdin et al. (2021) , Abdin et al. (2024) . Package: r-cran-mappings Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mappings_0.2.0-1.ca2404.1_all.deb Size: 41598 MD5sum: d7a6e5ddeba44f52c44f6b2f4628cb3b SHA1: 0ff9b5b6571010c9c03cecac106723d2464bd0c5 SHA256: fb4ba9113769c7939e18df18a6a9cbdb79db376ff39558c67eb83391329110fd SHA512: ccbfe6a4a837a5160f8b9b57a45af88377b75da93e83fa37b2d554f825fcb0e0a0693df87b110cdc8ce8665defc01ea1966617d377abf07e0f2055343981284d Homepage: https://cran.r-project.org/package=mappings Description: CRAN Package 'mappings' (Functions for Transforming Categorical Variables) Easily create functions to map between different sets of values, such as for re-labeling categorical variables. Package: r-cran-mapplots Architecture: all Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-shapefiles Filename: pool/dists/noble/main/r-cran-mapplots_1.5.3-1.ca2404.1_all.deb Size: 354750 MD5sum: 7a0c7c6146da1a479201f16aa131c86c SHA1: fc7d5e8b1eebd0fcc3e6d625127dc9427cfc1e80 SHA256: ebaa111789bf1c0326d30a3c075308801125108c613e5d482c137afc6b515898 SHA512: 5918dad6e8e96a6d87420db2fbb7fec8b4199b004e32be9a1c2ba99206e788fd8bc9e8a11a3431d4424610cb20d56ff2d0b6c9dbe3b9c2a845208fe99ad11bc6 Homepage: https://cran.r-project.org/package=mapplots Description: CRAN Package 'mapplots' (Data Visualisation on Maps) Create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation. 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The package provides helpers for constructing design matrices, generating predictions, and, when the companion runtime helper is available, fitting portable model specifications for cross-language replay. Package: r-cran-marss Architecture: all Version: 3.11.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4950 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-kfas, r-cran-mvtnorm, r-cran-nlme Suggests: r-cran-forecast, r-cran-ggplot2, r-cran-hmisc, r-cran-knitr, r-cran-lme4, r-cran-maps, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-marss_3.11.10-1.ca2404.1_all.deb Size: 3678002 MD5sum: 7e96abbadeab195e31e8f390452fa79d SHA1: dcdfd045ff08729d5bd332175da48be9985bccde SHA256: 3adff7b3927befa111776e94bd2cddd1707e3455f390c3e3d4a804305c488210 SHA512: b924c8ab4ef6b88cf8049ad94bcfae4ab262d846b50376b3d8be847e9caac4bef6235824ac5f7d85a0f7868437431a5d00d64253f00ccb7b18ad0c3e446c91a4 Homepage: https://cran.r-project.org/package=MARSS Description: CRAN Package 'MARSS' (Multivariate Autoregressive State-Space Modeling) The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models, including partially deterministic models. MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB' (using the 'marssTMB' companion package). Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all conditional residual types. See the user guide for examples of dynamic factor analysis, dynamic linear models, outlier and shock detection, and multivariate AR-p models. Online workshops (lectures, eBook, and computer labs) at . Package: r-cran-marssvrhybrid Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-earth Filename: pool/dists/noble/main/r-cran-marssvrhybrid_0.1.0-1.ca2404.1_all.deb Size: 13414 MD5sum: 8ca5fb1ce4b4f29dd1d84f668773a3d1 SHA1: bce7da3a161a3f21dc55275d0ce940685cfde248 SHA256: 08e0e4c2b80883c83ea7d15850b2ee17f07cc9cef64f94b7fdda9ec3e5b7a2d9 SHA512: 451cfa027fa84e4588f57239c63b63b147b4c68978782b648ac2fe2267538f48648d998fa3de732c686407032de6c1762810bd65c2b657723d76c6a3c0aecfd1 Homepage: https://cran.r-project.org/package=MARSSVRhybrid Description: CRAN Package 'MARSSVRhybrid' (MARS SVR Hybrid) Multivariate Adaptive Regression Spline (MARS) based Support Vector Regression (SVR) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits SVR on the extracted important variables. Package: r-cran-marvel Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4531 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-plyr, r-cran-scales Suggests: r-bioc-annotationdbi, r-bioc-biostrings, r-bioc-bsgenome, r-bioc-bsgenome.hsapiens.ncbi.grch38, r-bioc-clusterprofiler, r-cran-factoextra, r-cran-factominer, r-cran-fitdistrplus, r-bioc-genomicranges, r-cran-ggnewscale, r-cran-ggrepel, r-cran-gridextra, r-cran-gtools, r-bioc-iranges, r-cran-kableextra, r-cran-knitr, r-cran-ksamples, r-cran-markdown, r-bioc-mast, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-cran-pheatmap, r-cran-reshape2, r-cran-rmarkdown, r-bioc-s4vectors, r-cran-stringr, r-cran-textclean, r-cran-twosamples, r-bioc-wiggleplotr Filename: pool/dists/noble/main/r-cran-marvel_1.4.0-1.ca2404.1_all.deb Size: 3840752 MD5sum: fb3825df80c3bc3aafa0cd012059e609 SHA1: 8b79041dab322fa02ef817380762e5312985999b SHA256: 842fe17d0048060c661e727b96471467eda92b7006fe260a19fcbeb6528b7197 SHA512: 8368c54c3e8a36b083c74cb2a371be41006ff021b7a7a0574fdaeb234d7befbf5b59f41d4430e887a15ab3926048e6751718c15333273585b14c69ee0c82462e Homepage: https://cran.r-project.org/package=MARVEL Description: CRAN Package 'MARVEL' (Revealing Splicing Dynamics at Single-Cell Resolution) Alternative splicing represents an additional and underappreciated layer of complexity underlying gene expression profiles. 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Package: r-cran-masae Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3073 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-forestinventory, r-cran-josae, r-cran-knitr, r-cran-microbenchmark, r-cran-nlme, r-cran-pkgload, r-cran-rmarkdown, r-cran-rprojroot, r-cran-rsae, r-cran-runit, r-cran-r.rsp, r-cran-sae, r-cran-testthat Filename: pool/dists/noble/main/r-cran-masae_2.0.3-1.ca2404.1_all.deb Size: 1329174 MD5sum: ae26c7f0eb533d72a4cec5f3d9b6e3c1 SHA1: f37a273541c0a5727e0368d148fad220f66ee1ae SHA256: ac84f98d4880a223335eb54134a0de9e953ada8818d6e31c7ab40962721a786d SHA512: 305249c017426fe0594a96b77bf69ffcfec0d4f1d0d5a1241895e3303837a05cf5b1df37d0e7ed4d0bc35069be6e6436895bbcfdc9cd2d5241cc72ea748b5dc6 Homepage: https://cran.r-project.org/package=maSAE Description: CRAN Package 'maSAE' (Mandallaz' Model-Assisted Small Area Estimators) An S4 implementation of the unbiased extension of the model- assisted synthetic-regression estimator proposed by Mandallaz (2013) , Mandallaz et al. 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Package: r-cran-mascarade Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2062 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-lifecycle, r-cran-ggplot2, r-cran-polyclip, r-cran-ggforce, r-cran-vctrs, r-cran-rlang, r-cran-cli, r-cran-systemfonts Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-patchwork, r-cran-ggsci, r-cran-seurat, r-cran-scales, r-cran-seuratobject, r-cran-svglite Filename: pool/dists/noble/main/r-cran-mascarade_0.3.4-1.ca2404.1_all.deb Size: 1523138 MD5sum: 078e4004484937539a08d3148edcbcc6 SHA1: f064c25e0fa94450366287bb597d425a9aa8ad06 SHA256: 6ac401906e7104e341d649d9c77b2942a19e478908582bd8394c59a2925d6f39 SHA512: dc66942637f9f7dc1f9bc702a97b3d5a4cd54e6ad93020236b31a2b94390933174a1304c1abf64b3d0ac4eb2ae300b63953f13ba35409c942828a6446e5a06cc Homepage: https://cran.r-project.org/package=mascarade Description: CRAN Package 'mascarade' (Generating Cluster Masks for Single-Cell Dimensional ReductionPlots) Implements a procedure to automatically generate 2D masks for clusters on dimensional reduction plots from methods like t-SNE (t-distributed stochastic neighbor embedding) or UMAP (uniform manifold approximation and projection), with a focus on single-cell RNA-sequencing data. Package: r-cran-maskedcauses Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2323 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-likelihood.model, r-cran-generics, r-cran-numderiv, r-cran-dist.structure Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-algebraic.mle Filename: pool/dists/noble/main/r-cran-maskedcauses_0.10.0-1.ca2404.1_all.deb Size: 1168322 MD5sum: 53f3af037e80df0252b0704a77d0510d SHA1: 0d3c4de4309d1b2c62cf216a8beb114dde598efc SHA256: 429c9278be11b8b6c26e6a2bd1a1db78678dc39d97094000bdbfc56b13725d8c SHA512: 0376c017a9d6c3d5fb564eefd846667457cbe01045c644cde2b4a160a8c586f532766f500d4d86a018e00b38d348a799e78327e73ee3fa67f50eefc62a3920bd Homepage: https://cran.r-project.org/package=maskedcauses Description: CRAN Package 'maskedcauses' (Likelihood Models for Systems with Masked Component Cause ofFailure) Maximum likelihood estimation for series systems where the component cause of failure is masked. Implements analytical log-likelihood, score, and Hessian functions for exponential, homogeneous Weibull, and heterogeneous Weibull component lifetimes under masked cause conditions (C1, C2, C3). Supports exact, right-censored, left-censored, and interval-censored observations via composable observation functors. Provides random data generation, model fitting, and Fisher information for asymptotic inference. See Lin, Loh, and Bai (1993) and Craiu and Reiser (2006) . Package: r-cran-maskedhaz Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-serieshaz, r-cran-flexhaz, r-cran-algebraic.dist, r-cran-likelihood.model, r-cran-maskedcauses, r-cran-generics, r-cran-numderiv Suggests: r-cran-testthat, r-cran-algebraic.mle, r-cran-hypothesize, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-maskedhaz_0.1.0-1.ca2404.1_all.deb Size: 153504 MD5sum: 8fce434e52f1f46d26d298e4fadbb618 SHA1: 4237e450289dbe161177d7b374597d57e2aeb268 SHA256: 537cb08b0f4cee2011f5a6dbd57cab72587924264edca104f823b8ce4826f38e SHA512: 77b3d3f7d28e595bb8dba3dd220f90aab26288e6af6edd7146584244e332aa28d1f9fd4daf3b92e28fc2e183180bea4f31bfb37198f712f83a5255de53bc1d1d Homepage: https://cran.r-project.org/package=maskedhaz Description: CRAN Package 'maskedhaz' (Masked-Cause Likelihood Models for Series Systems with ArbitraryHazard Components) Likelihood-based inference for series systems with masked component cause of failure, using arbitrary dynamic failure rate component distributions. Computes log-likelihood, score, Hessian, and maximum likelihood estimates for masked data satisfying conditions C1, C2, C3 under general component hazard functions. Implements the 'series_md' protocol defined in the 'maskedcauses' package. Package: r-cran-maskr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-pillar, r-cran-rlang, r-cran-vctrs Suggests: r-cran-dplyr, r-cran-fansi, r-cran-testthat, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-maskr_0.1.0-1.ca2404.1_all.deb Size: 34862 MD5sum: 76afaeab2c9347db968b95cda5c401e3 SHA1: 7db07f800095e6f62cbb9118a9e2bb42cc4cdebd SHA256: 10fb757ada16f0f4f185306a386d512900efe823bf4ee1abe9d35f62a982d6fe SHA512: 6a6f0d47da28d739e473f12527513a1f7e7ed96c739520564f8720ad96b62b311e667bb78b87cd0c368d2929c9c76048f35cda10aa681bad3f4f16a26cebd7e3 Homepage: https://cran.r-project.org/package=maskr Description: CRAN Package 'maskr' (Visual Class for Vectors with Non-Publishing Requirements) Create vectors with sticky flags for elements that should not be displayed. Numeric vectors have basic subset and arithmetic methods implemented. 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These tools can be combined to quantitatively and reproducibly generate a new map or to update an existing map. Methods include expert opinion and data-driven tools to generate thresholds for binary masks. Package: r-cran-masscor Architecture: all Version: 0.0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metrology Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-masscor_0.0.7.1-1.ca2404.1_all.deb Size: 433002 MD5sum: f152b331e41661591314ab3847a1330f SHA1: 3ed5225898f5ab86e027ecc530caa78d3a81e0cd SHA256: 1a88521f4dc9dd5b8a8a137265cca5d1e329331778ae02eab96b26eed6626845 SHA512: 992f14f93df0e250e3ce85e18998dd082d7710aef6a454dcc3f2fc4bd2c45a06d3fdb43a74470819f7e0650eeb4cee83f925108dc019b7a260cd9802bebe2111 Homepage: https://cran.r-project.org/package=masscor Description: CRAN Package 'masscor' (Mass Measurement Corrections) Mass measurement corrections and uncertainties using calibration data, as recommended by EURAMET's guideline No. 18 (2015) ISBN:978-3-942992-40-4 . The package provides classes, functions, and methods for storing information contained in calibration certificates and converting balance readings to both conventional mass and real mass. For the latter, the Magnitude of the Air Buoyancy Correction factor employs models (such as the CIMP-2007 formula revised by Picard, Davis, Gläser, and Fujii (2008) ) to estimate the local air density using measured environmental conditions. Package: r-cran-massextra Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1002 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-demokde Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork, r-cran-visreg, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-massextra_1.2.2-1.ca2404.1_all.deb Size: 690822 MD5sum: dd1ffada24ea124b579b4526a5b2afdd SHA1: 012290534631e7db66c497f64f9c2415b563bbff SHA256: 7bb28647a2cf30d10ffeae01d8bb68f38aca7ae03f281c61068289503c66da6f SHA512: b6a9b039bc8b01345cce5b018706a3107b85b7bc294bfd5822257e4ead4e99e0864e9cdaf4309ccfce88bd6aa06e10aba044605a69779bdd2c403e89acda7552 Homepage: https://cran.r-project.org/package=MASSExtra Description: CRAN Package 'MASSExtra' (Some 'MASS' Enhancements) Some enhancements, extensions and additions to the facilities of the recommended 'MASS' package that are useful mainly for teaching purposes, with more convenient default settings and user interfaces. 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Package: r-cran-massprops Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rolluptree Suggests: r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-massprops_0.3.5-1.ca2404.1_all.deb Size: 297744 MD5sum: f7efe79f5a9ad5ad755e664ebce286fc SHA1: 4e2653f734ea93ff973499655f222de2c4883095 SHA256: 00d7a5a8b7408c31b29db6bce91c788c848dc06190549f62d9f12fa7cbcdcede SHA512: d5b40372b74e65b61ac5baf4dc3f6d7ca3521109322eee71087e0120ecfba0354318590659bcbfa9b0803e1b3c731b7a7f778ad5bfb0339bd161e40a6cb775d3 Homepage: https://cran.r-project.org/package=massProps Description: CRAN Package 'massProps' (Calculate Mass Properties and Uncertainties of Tree Structures) Recursively calculates mass properties (mass, center of mass, moments and products of inertia, and optionally, their uncertainties) for arbitrary decomposition trees. 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Package: r-cran-masswater Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1889 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-flextable, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggspatial, r-cran-httr, r-cran-lubridate, r-cran-maptiles, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-readxl, r-cran-sf, r-cran-tidyr, r-cran-tidyterra, r-cran-tibble, r-cran-units, r-cran-writexl Suggests: r-cran-covr, r-cran-knitr, r-cran-mockery, r-cran-patchwork, r-cran-testthat Filename: pool/dists/noble/main/r-cran-masswater_2.2.1-1.ca2404.1_all.deb Size: 1572014 MD5sum: 87f44d66ca3fd284cbb2d46230d2fece SHA1: 16ab820191d5e44421e99f190472779852aace17 SHA256: d202fda440519c38446262f9009832b596aeedf5b1afefcf437e5f17f55525a5 SHA512: fc863f04ddc37d5b02cfb438eda48d2cf4d8f59d91deee25766c9d67d728dc3cc138d566dec2a42b8252f5cf84fc85d734f704d7e77ae71215f993e591a2b561 Homepage: https://cran.r-project.org/package=MassWateR Description: CRAN Package 'MassWateR' (Quality Control and Analysis of Massachusetts Water Quality Data) Methods for quality control and exploratory analysis of surface water quality data collected in Massachusetts, USA. Functions are developed to facilitate data formatting for the Water Quality Exchange Network and reporting of data quality objectives to state agencies. Quality control methods are from Massachusetts Department of Environmental Protection (2020) . Package: r-cran-mata Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mata_0.7.1-1.ca2404.1_all.deb Size: 20564 MD5sum: 9ace9cc40c8e6775cfa0eb3a8c3775a1 SHA1: 8eaa7fe85fee6943c16c0196327ce9a3d5df37a7 SHA256: 84c53092fe2a9c523d1c2e82b676004939759602701b34068a1f776ecfa45a8d SHA512: 497764c2801cca800a4daa0368e698c7ad938d3957fd90126ec42291ca45be56aacb8962a22f20e424356e012f05b735286f31b05d8d20a3d8c97d2fe1cb06a1 Homepage: https://cran.r-project.org/package=MATA Description: CRAN Package 'MATA' (Model-Averaged Tail Area (MATA) Confidence Interval andDistribution) Calculates Model-Averaged Tail Area Wald (MATA-Wald) confidence intervals, and MATA-Wald confidence densities and distributions, which are constructed using single-model frequentist estimators and model weights. See Turek and Fletcher (2012) and Fletcher et al (2019) for details. Package: r-cran-matahari Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstudioapi, r-cran-tibble, r-cran-readr, r-cran-jsonlite, r-cran-clipr, r-cran-purrr, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-matahari_0.1.3-1.ca2404.1_all.deb Size: 106082 MD5sum: 9097e30851161942bc43d96e6ab66b62 SHA1: bf8c35ab24414014734dbdf9581ef6024dc1dfb3 SHA256: e7a6c9614ceaad345a818fd208ca6ed5b4e076111ac7d657b293b23a111caa42 SHA512: 793d7db8b994a100b4cfae5cf47c5d2cd359625f276f48e2dd95e40bb22c940f3658b9f47c61b86cb9cf6ce896972903d7d4c6e32cf7492cf7a781175af251ca Homepage: https://cran.r-project.org/package=matahari Description: CRAN Package 'matahari' (Spy on Your R Session) Conveniently log everything you type into the R console. Logs are are stored as tidy data frames which can then be analyzed using 'tidyverse' style tools. Package: r-cran-matchedcc Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-binom Suggests: r-cran-testthat, r-cran-readr, r-cran-vctrs, r-cran-stringr, r-cran-purrr, r-cran-knitr, r-cran-rmarkdown, r-cran-rstata Filename: pool/dists/noble/main/r-cran-matchedcc_0.1.1-1.ca2404.1_all.deb Size: 217550 MD5sum: 2a3ac15c87ced3f46522db57f5021d8c SHA1: 84d4f705a25173c35b1a596c5e532028b990fd2d SHA256: 81f427b1c78a73c3cd365680688f2057526f3b5c997964229faf3f5453daca43 SHA512: ea1a90f22db5056ed1297bd7c9389a2d0664bec118f0fed3ac99f3304540fd7d8f7348c85ffd5bc5cb1b73d9b9c084c5a91b5933df5b9c708ae17dae7e125c2b Homepage: https://cran.r-project.org/package=matchedcc Description: CRAN Package 'matchedcc' ('Stata'-Like Matched Case-Control Analysis) Calculate multiple statistics with confidence intervals for matched case-control data including risk difference, risk ratio, relative difference, and the odds ratio. 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Package: r-cran-matchfeat Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clue, r-cran-foreach Filename: pool/dists/noble/main/r-cran-matchfeat_1.0-1.ca2404.1_all.deb Size: 188272 MD5sum: 54cd6068173760e64f7849b02a5aa261 SHA1: 317640ee5862182a218ae8d77dd6b4b28a7379ad SHA256: 43a006d3b01b397570ff756f8edf73ad5e1060abeaa788c9f98cea4c537b67c6 SHA512: 277991deefb80a404a2d2a2003280c50ad36fb225d4dd902b33f007635ad943afba81e86158be848e832ba9ce457c19701f1299a5e1ddd015448e728dc050740 Homepage: https://cran.r-project.org/package=matchFeat Description: CRAN Package 'matchFeat' (One-to-One Feature Matching) Statistical methods to match feature vectors between multiple datasets in a one-to-one fashion. Given a fixed number of classes/distributions, for each unit, exactly one vector of each class is observed without label. 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Package: r-cran-matchgate Architecture: all Version: 0.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-locpol Filename: pool/dists/noble/main/r-cran-matchgate_0.0.10-1.ca2404.1_all.deb Size: 21858 MD5sum: cd4db66f17d24924b37fd450544517ab SHA1: 525f7e0002f3605cf2585b000a5897cf86d63c7c SHA256: e7b4a4d9c4e9aa8af3f3703e0a24dacbcec69af467dde2f7b55d95652c168c6e SHA512: 32da74fab0f7a15647a6fb42c614d9aae41ed5f8f993a68ea6b819f8e53204a9a0b5655536ec77f17968be46fd0637f09fe5711471758ee22e5a01580fdb3560 Homepage: https://cran.r-project.org/package=MatchGATE Description: CRAN Package 'MatchGATE' (Estimate Group Average Treatment Effects with Matching) Two novel matching-based methods for estimating group average treatment effects (GATEs). The match_y1y0() and match_y1y0_bc() functions are used for imputing the potential outcomes based on matching and bias-corrected matching techniques, respectively. 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Package: r-cran-matchingpursuit Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1570 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-edf, r-cran-signal, r-cran-rsqlite, r-cran-desctools, r-cran-imager, r-cran-raster, r-cran-digest Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-latex2exp, r-cran-remotes Filename: pool/dists/noble/main/r-cran-matchingpursuit_1.0.1-1.ca2404.1_all.deb Size: 875188 MD5sum: ee9d4c90a6ce3bc16ac52edb389fbdfc SHA1: d57dc8fbbfae8d2f37ece802c52c9b6350d4c85a SHA256: c13f697b4a69576a2fe00e857a1a53102acea3880ac3653435989f6bed202e36 SHA512: c7b98136212edd442792985aaab485220cbd7a69e2c2f577e31ec6772509af09c22c1185da58cef9da79f359e9af7b2d058db6766d40bf59b8468ac9f6f9159b Homepage: https://cran.r-project.org/package=MatchingPursuit Description: CRAN Package 'MatchingPursuit' (Processing Time Series Data Using the Matching Pursuit Algorithm) Provides tools for analysing and decomposing time series data using the Matching Pursuit (MP) algorithm, a greedy signal decomposition technique that represents complex signals as a linear combination of simpler functions (called atoms) selected from a redundant dictionary. For more details see Mallat and Zhang (1993) , Pati et al. (1993) , Elad (2010) and Różański (2024) . Package: r-cran-matchlinreg Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-matching Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-matchlinreg_0.8.1-1.ca2404.1_all.deb Size: 473740 MD5sum: 6a8f8c1e02ef6ad7f2d496dafe6b0317 SHA1: 044db7b18327907e35a9f18c98608e14bac3484c SHA256: 13ec18d4a93d15a2a693ae66cc900b5ad4ab5469540349fe9ceec0bb600a2d7c SHA512: d66d9491d8a4fe2991b170725891697775d7c05770c871e23a45f7eeb8dcc43eab6c652121fe60a07c6e1fa7b621787784898c2089524966bc4b27d419760655 Homepage: https://cran.r-project.org/package=MatchLinReg Description: CRAN Package 'MatchLinReg' (Combining Matching and Linear Regression for Causal Inference) Core functions as well as diagnostic and calibration tools for combining matching and linear regression for causal inference in observational studies. Package: r-cran-matchmaker Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-forcats, r-cran-cli Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-matchmaker_0.1.1-1.ca2404.1_all.deb Size: 55208 MD5sum: d454a5de1e9df38a9aa522e6a5d9781d SHA1: 605693c0217148245176ad7eac11b624067c5f1e SHA256: a11b9e8eee7ee15ce402aa37217aa81e9eb6c8f3535d6c47b40bc39454b54739 SHA512: 0612fe1f72f8c386cf17dc679a927c5bb11af6739c0c65aa152a4964f87b2eb8f5187803ace62df5661a25f71211657fa242dd431229e8d280f283dad2a2519f Homepage: https://cran.r-project.org/package=matchmaker Description: CRAN Package 'matchmaker' (Flexible Dictionary-Based Cleaning) Provides flexible dictionary-based cleaning that allows users to specify implicit and explicit missing data, regular expressions for both data and columns, and global matches, while respecting ordering of factors. 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Package: r-cran-matchmulti Architecture: all Version: 1.1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 697 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-rcbsubset, r-cran-plyr, r-cran-coin, r-cran-weights, r-cran-mvtnorm, r-cran-mass, r-cran-sandwich, r-cran-magrittr Suggests: r-cran-optmatch, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-matchmulti_1.1.14-1.ca2404.1_all.deb Size: 602656 MD5sum: 30b6da23a4263c9a36e9c19b486d34a7 SHA1: 8f994f5b666a93d73eb2f9805bf27915c14d9b10 SHA256: ffecaa1631b98cff65558d3e708191004e6be2c7e792b557aa54c36ea3263dc9 SHA512: aec2b6f71ae560fdcde86c63b463bf1dd6be116ce7440cdd3ab2eca779bda063264f31052155b183ca3c01a056cf41cbac06a01877d1caf1320dda87c959fd65 Homepage: https://cran.r-project.org/package=matchMulti Description: CRAN Package 'matchMulti' (Optimal Multilevel Matching using a Network Algorithm) Performs multilevel matches for data with cluster- level treatments and individual-level outcomes using a network optimization algorithm. Functions for checking balance at the cluster and individual levels are also provided, as are methods for permutation-inference-based outcome analysis. Details in Pimentel et al. (2018) . The optmatch package, which is useful for running many of the provided functions, may be downloaded from Github at if not available on CRAN. Package: r-cran-matchpointr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chromote, r-cran-cli, r-cran-jsonlite, r-cran-magick, r-cran-purrr, r-cran-rvest, r-cran-stringr, r-cran-tibble, r-cran-xml2 Suggests: r-cran-httr2, r-cran-knitr, r-cran-rmarkdown, r-cran-rsvg, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-matchpointr_0.1.0-1.ca2404.1_all.deb Size: 209276 MD5sum: cd15fbd607222e17578d3d0e9e0e8010 SHA1: 7e9b842aa2f13d7767c6b7b9eb0454fda1e69155 SHA256: 2ce6f647814044f9d03600223bd2a5ae4b6d871df6ba24f341006c66c65bd81d SHA512: 988d58dc606a7c09a5f187cef25e418883cd6ea8ccefdbb94fe904ebce374c49a8db3576b8ce35be3279d0a8a47bfaabdd9d9c90631000a0b3efed3d5b7dd4af Homepage: https://cran.r-project.org/package=matchpointR Description: CRAN Package 'matchpointR' (Tidy Access to Women's Tennis Association (WTA) Data) Scrapes and tidies publicly available data from the Women's Tennis Association website (). Provides helpers to retrieve player biographies, singles and doubles career overviews, match histories, live rankings and aggregate statistics. Dynamic pages are rendered through a headless 'Chrome' session so 'JavaScript'-generated content is fully captured, and all outputs are returned as tidy data frames suitable for downstream analysis or visualisation. Package: r-cran-matchr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang Filename: pool/dists/noble/main/r-cran-matchr_0.1.0-1.ca2404.1_all.deb Size: 110968 MD5sum: 9d5b87e5d2f8193f273f779444243784 SHA1: faeceaf6e9baf1e4881a21cc8d886f2cc5f75752 SHA256: 2c9c5ae275c24d60ae478fdfebce2fe6085c0f55e08d5865121ca058685d2fbf SHA512: 2ceb58f62118173f6ed46dad5716ee184661d300bce6cf50c1d8966c034e6751b4ed75d0101f203bb0295bdcf491541715d60e45ee7bc72ea8e010a04ef998dd Homepage: https://cran.r-project.org/package=matchr Description: CRAN Package 'matchr' (Pattern Matching and Enumerated Types in R) Inspired by pattern matching and enum types in Rust and many functional programming languages, this package offers an updated version of the 'switch' function called 'Match' that accepts atomic values, functions, expressions, and enum variants. Conditions and return expressions are separated by '->' and multiple conditions can be associated with the same return expression using '|'. 'Match' also includes support for 'fallthrough'. The package also replicates the Result and Option enums from Rust. Package: r-cran-matchthem Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 688 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matchit, r-cran-mice, r-cran-rlang, r-cran-survey, r-cran-weightit Suggests: r-cran-amelia, r-cran-cobalt, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-matchthem_1.2.1-1.ca2404.1_all.deb Size: 606910 MD5sum: acc5103d6a4133c05b37de81f9726d1a SHA1: 04173b94ff967d733a9e227cf96daf292ed3bc44 SHA256: 47de8c1086cf83788a5453f05bf21d9171876dd8e4da82a974d5f11b4fb620cf SHA512: f34b7e5a6156ad43f2791ebf409910cc93a9fa9b39367e756e300e2fb1b0753c30fafc4539aa87df7c875b660df4a82bd18290d906b0840615afe610d0c481d3 Homepage: https://cran.r-project.org/package=MatchThem Description: CRAN Package 'MatchThem' (Matching and Weighting Multiply Imputed Datasets) Provides essential tools for the pre-processing techniques of matching and weighting multiply imputed datasets. The package includes functions for matching within and across multiply imputed datasets using various methods, estimating weights for units in the imputed datasets using multiple weighting methods, calculating causal effect estimates in each matched or weighted dataset using parametric or non-parametric statistical models, and pooling the resulting estimates according to Rubin's rules (please see for more details). Package: r-cran-matconv Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-matconv_0.4.2-1.ca2404.1_all.deb Size: 90476 MD5sum: e04c4ced252a81f34671ddbc3daad963 SHA1: cc0ef863c14708d25276bbd7c3ae3e5658262650 SHA256: 875699c0e67b8c2832707947d10a4f3d3dee81bb0e48d80d7f3ddc4a6d4ebdce SHA512: ee9141e6c07c62c9da96ec6fc4fde8c2b983cf06450b0aaa8a4f443e607f09c40e8d764cd823aa040eaf46254d4bf15deec7f26ec549531b905b78fdaaf6dbda Homepage: https://cran.r-project.org/package=matconv Description: CRAN Package 'matconv' (A Code Converter from the Matlab/Octave Language to R) Transferring over a code base from Matlab to R is often a repetitive and inefficient use of time. This package provides a translator for Matlab / Octave code into R code. It does some syntax changes, but most of the heavy lifting is in the function changes since the languages are so similar. Options for different data structures and the functions that can be changed are given. The Matlab code should be mostly in adherence to the standard style guide but some effort has been made to accommodate different number of spaces and other small syntax issues. This will not make the code more R friendly and may not even run afterwards. However, the rudimentary syntax, base function and data structure conversion is done quickly so that the maintainer can focus on changes to the design structure. Package: r-cran-materialmodifier Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2053 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jpeg, r-cran-magrittr, r-cran-png, r-cran-readbitmap, r-cran-stringr, r-cran-downloader, r-cran-imager, r-cran-moments Filename: pool/dists/noble/main/r-cran-materialmodifier_1.2.0-1.ca2404.1_all.deb Size: 2030284 MD5sum: 90a8a6fcd84da1ca471e536ace3e6c10 SHA1: 211c5668adba81dec696498e7767e917f0de58c9 SHA256: 4de7a97a1409dfbe02684e384a4af70502d9e321e4e97c877f4d4f2bdd0f4d7e SHA512: 123148677672835dac052897f06074df842326998b42dc17a91364aa2cb3640ed7e753c582bd8adf42f89db1608eebded1c225b56ec00c5dc764706d0ab6aae5 Homepage: https://cran.r-project.org/package=materialmodifier Description: CRAN Package 'materialmodifier' (Apply Photo Editing Effects) You can apply image processing effects that modifies the perceived material properties of objects in photos, such as gloss, smoothness, and blemishes. This is an implementation of the algorithm proposed by Boyadzhiev et al. (2015) "Band-Sifting Decomposition for Image Based Material Editing". Documentation and practical tips of the package is available at . 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Package: r-cran-matpow Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 878 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bigmemory Filename: pool/dists/noble/main/r-cran-matpow_0.1.2-1.ca2404.1_all.deb Size: 360742 MD5sum: 81f3dcacad24fbf65e4fddec34456734 SHA1: f92f11a63bb7351433e9a9919366c19dbff1d862 SHA256: c1beba6c1cabf9f5a3a534bfb2fd1d9f64a35f06058b4604d11dd4be44dd4e64 SHA512: 20d412084bb6b3e2da0a93a7c350e548618eea4f7bb016c5708d623f45c29774ecd5753c671b4bbe7e8f6a2f63ff9da915daea36a2c9216eaa39538fd00a2af2 Homepage: https://cran.r-project.org/package=matpow Description: CRAN Package 'matpow' (Matrix Powers) A general framework for computing powers of matrices. A key feature is the capability for users to write callback functions, called after each iteration, thus enabling customization for specific applications. Diverse types of matrix classes/matrix multiplication are accommodated. 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Package: r-cran-matrans Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-formatr, r-cran-glmnet, r-cran-mass, r-cran-quadprog Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-matrans_0.2.0-1.ca2404.1_all.deb Size: 155664 MD5sum: 7ff9a62c708e5987ce57f97164e57b02 SHA1: 9a6baa7449f25ee818249c75820299bb2a73b5e8 SHA256: c8bb9b5d4a43b9d537bc8d01b5b97fd3e8c55fa0b5a241ea1f8f72e74107d3ba SHA512: acf28aa811407dd3e6628937882dc5e230d5e24fb83a33c9b04f9fb0eb4ee5bbc9ddce4357f5923c6f50ad8cd6f23430cf9e15c20c138a482907881f81fd0d6a Homepage: https://cran.r-project.org/package=matrans Description: CRAN Package 'matrans' (Model Averaging-Assisted Optimal Transfer Learning) Transfer learning, as a prevailing technique in computer sciences, aims to improve the performance of a target model by leveraging auxiliary information from heterogeneous source data. We provide novel tools for multi-source transfer learning under statistical models based on model averaging strategies, including linear regression models, partially linear models. Unlike existing transfer learning approaches, this method integrates the auxiliary information through data-driven weight assignments to avoid negative transfer. This is the first package for transfer learning based on the optimal model averaging frameworks, providing efficient implementations for practitioners in multi-source data modeling. The details are described in Hu and Zhang (2023) . Package: r-cran-matriks Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-matriks_0.1.5-1.ca2404.1_all.deb Size: 355158 MD5sum: 5e57db38b60319be6b4810c77f4b3c4a SHA1: da78f5d2e2c75064136fb94409a00b66ffa2ab6a SHA256: 6483afae38bacf1d64e7e7ff2d8072afdffaa506365057f80b8c4a40c5bb90de SHA512: 01f3a41cfa72b03556034c9c2c486c28e8ad30e3a7c5974ac8c8037d43b8fc78cb547ca4ddc4f03f67d07adf7a66de6d58d7249c4d5fb14f5012caf972b90623 Homepage: https://cran.r-project.org/package=matRiks Description: CRAN Package 'matRiks' (Generates Raven-Like Matrices According to Rules) Generates Raven like matrices according to different rules and the response list associated to the matrix. The package can generate matrices composed of 4 or 9 cells, along with a response list of 11 elements (the correct response + 10 incorrect responses). The matrices can be generated according to both logical rules (i.e., the relationships between the elements in the matrix are manipulated to create the matrix) and visual-spatial rules (i.e., the visual or spatial characteristics of the elements are manipulated to generate the matrix). The graphical elements of this package are based on the 'DescTools' package. This package has been developed within the PRIN2020 Project (Prot. 20209WKCLL) titled "Computerized, Adaptive and Personalized Assessment of Executive Functions and Fluid Intelligence" and founded by the Italian Ministry of Education and Research. Package: r-cran-matrisk Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-sn, r-cran-dfoptim, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-matrisk_0.1.0-1.ca2404.1_all.deb Size: 37448 MD5sum: 1d393c00a02ac1b5f9b605087e3a2142 SHA1: 9698ddafbc1e27496ab9f31118df6359fb1a26a0 SHA256: 09c4eeb70851e6dab4e032ffc84d3d5a8408d9075df99a50ee6c92568d6ec639 SHA512: 63eb9aad171495a6d7a836ae7bed6a8ff60946063cf6ef4a568b6417d2944028f00f7b2f32dfc29ff672edd8cd78ce3b8dc1693e1a62f93a0c97e8cf050b0a48 Homepage: https://cran.r-project.org/package=matrisk Description: CRAN Package 'matrisk' (Macroeconomic-at-Risk) The Macroeconomics-at-Risk (MaR) approach is based on a two-step semi-parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) to reveal the vulnerability of economic growth to financial conditions, the MaR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers. Package: r-cran-matrixcalc Architecture: all Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-matrixcalc_1.0-6-1.ca2404.1_all.deb Size: 202942 MD5sum: ce1df4cb0fee3008f5bb9e7e5f695674 SHA1: fe102d20f83e000cacfa5d62d698d70a14bd50fc SHA256: deb63f61dd7e47465fdfcc55876b4b69542f6399644ace19ba36a3be2a90402c SHA512: 763a4a96244487fdca8a39fcb4d3d127267da3582b4920db1a2a7533924d9ccc1727f7bbadb8e28527473d67e6bc5574e2ef00275285ad831f85f621d7c2aa8c Homepage: https://cran.r-project.org/package=matrixcalc Description: CRAN Package 'matrixcalc' (Collection of Functions for Matrix Calculations) A collection of functions to support matrix calculations for probability, econometric and numerical analysis. There are additional functions that are comparable to APL functions which are useful for actuarial models such as pension mathematics. This package is used for teaching and research purposes at the Department of Finance and Risk Engineering, New York University, Polytechnic Institute, Brooklyn, NY 11201. Horn, R.A. (1990) Matrix Analysis. ISBN 978-0521386326. Lancaster, P. (1969) Theory of Matrices. ISBN 978-0124355507. Lay, D.C. (1995) Linear Algebra: And Its Applications. ISBN 978-0201845563. Package: r-cran-matrixcut Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-inflection Filename: pool/dists/noble/main/r-cran-matrixcut_0.0.1-1.ca2404.1_all.deb Size: 62510 MD5sum: 6df570102980729aa26d5d807de0c8bd SHA1: b22fa85dbd0a393e398196f465e6688fef294352 SHA256: 3deead7884a2d8d79428949cc0989c4b4cd935b9042a0ff680592d68de3f44d1 SHA512: 07925e5adea7c844738dde0570d1a55d0150b13b92ac4a62243b6c4441345400efacc8fae95b5ddb8a2142aa4675048478d9f599807ea16b6aafe3f2c4d631c8 Homepage: https://cran.r-project.org/package=matrixcut Description: CRAN Package 'matrixcut' (Determines Clustering Threshold Based on Similarity Values) The user must supply a matrix filled with similarity values. The software will search for significant differences between similarity values at different hierarchical levels. The algorithm will return a Loess-smoothed plot of the similarity values along with the inflection point, if there are any. There is the option to search for an inflection point within a specified range. The package also has a function that will return the matrix components at a specified cutoff. References: Mullner. ; Cserhati, Carter. (2020, Journal of Creation 34(3):41-50), . Package: r-cran-matrixeqtl Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-matrixeqtl_2.3-1.ca2404.1_all.deb Size: 329936 MD5sum: 095df5e06084854f115213cdf72da48c SHA1: 9ea3461ce57aef0165877013eaa02c0598090bca SHA256: d4ac93c55dbbfae35771c6f52403edee5dd42d27179addd6d0a0c473dd9c7d7b SHA512: 07c1f2fcfc8b14ce3b02f807c93671855c1eb22eeaa585951c12dd76ac8094942c6c438e49d777693d47a828b6d33c80cf47ce33bcd894989f78bdcf6d6a651e Homepage: https://cran.r-project.org/package=MatrixEQTL Description: CRAN Package 'MatrixEQTL' (Matrix eQTL: Ultra Fast eQTL Analysis via Large MatrixOperations) Matrix eQTL is designed for fast eQTL analysis on large datasets. Matrix eQTL can test for association between genotype and gene expression using linear regression with either additive or ANOVA genotype effects. 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Package: r-cran-mauricer Architecture: all Version: 2.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-beastier Suggests: r-cran-beautier, r-cran-curl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tracerer Filename: pool/dists/noble/main/r-cran-mauricer_2.5.4-1.ca2404.1_all.deb Size: 73786 MD5sum: 322bad420a2d76597c830a68b8f6d554 SHA1: de9daedfc1d3ea8136f69b0dc5404857d9c35b74 SHA256: 907234d3d34bacbdbad14b3150d2ef8bf6d3abb132cb257c7b77d34e03c54caf SHA512: f26d40e47b0f0626b40991c961818892689d4e262b8e749267be1f2c46edcc62deb1fcd2cf268780cf481771d233b8db3df3be08f52e5dd52c3e50071ba1d44a Homepage: https://cran.r-project.org/package=mauricer Description: CRAN Package 'mauricer' (Work with 'BEAST2' Packages) 'BEAST2' () is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAST2' is commonly accompanied by 'BEAUti 2' (), which, among others, allows one to install 'BEAST2' package. This package allows to work with 'BEAST2' packages from 'R'. Package: r-cran-maxaltall Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1381 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-maxaltall_0.1.0-1.ca2404.1_all.deb Size: 141308 MD5sum: 728b9a668c11a1ba1b47bd5e7b941246 SHA1: 46a873b24332f1ae545fda56ce610726bd30de1e SHA256: b02b057e6d7a72ba587966b64598cce5b20f187c2659617dd492e421da6046e2 SHA512: 3001db170485b0b6cca16c1a3d6ceb9734d31ea8b317b6e7b9581560a47487b6b4f60b636127388a9dd11e4cebf464b68130b7542bffee1ad4f1a308827a7129 Homepage: https://cran.r-project.org/package=maxaltall Description: CRAN Package 'maxaltall' ('FASTA' ML and ‘altall’ Sequences from IQ-TREE .state Files) Takes a .state file generated by IQ-TREE as an input and, for each ancestral node present in the file, generates a FASTA-formatted maximum likelihood (ML) sequence as well as an ‘AltAll’ sequence in which uncertain sites, determined by the two parameters thres_1 and thres_2, have the maximum likelihood state swapped with the next most likely state as described in Geeta N. Eick, Jamie T. Bridgham, Douglas P. Anderson, Michael J. Harms, and Joseph W. Thornton (2017), "Robustness of Reconstructed Ancestral Protein Functions to Statistical Uncertainty" . Package: r-cran-maxcombo Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-mstate, r-cran-mcmcpack, r-cran-dplyr, r-cran-mvtnorm, r-cran-survival Filename: pool/dists/noble/main/r-cran-maxcombo_1.0-1.ca2404.1_all.deb Size: 102434 MD5sum: 192e3b7321e10177d7de20354450d42d SHA1: e0a3a6796a01898bc29d14bda1ba3d63fd92402f SHA256: 39e13333bbc3efc235791b9a9834e84346a857da682c28c19596d6a9e82462e0 SHA512: 29559644c98bf2dc4d989b386ef7d13dd12a9652b0005919644263f0f0ebbe3baa4dc203d4a7bc45e1b523a5afa3da740a058b308df4bd311f54b4946b429095 Homepage: https://cran.r-project.org/package=maxcombo Description: CRAN Package 'maxcombo' (The Group Sequential Max-Combo Test for Comparing SurvivalCurves) Functions for comparing survival curves using the max-combo test at a single timepoint or repeatedly at successive respective timepoints while controlling type I error (i.e., the group sequential setting), as published by Prior (2020) . The max-combo test is a generalization of the weighted log-rank test, which itself is a generalization of the log-rank test, which is a commonly used statistical test for comparing survival curves, e.g., during or after a clinical trial as part of an effort to determine if a new drug or therapy is more effective at delaying undesirable outcomes than an established drug or therapy or a placebo. Package: r-cran-maxeff Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-groupedhyperframe, r-cran-doparallel, r-cran-foreach, r-cran-caret, r-cran-rpart, r-cran-spatstat.geom Suggests: r-cran-survival Filename: pool/dists/noble/main/r-cran-maxeff_0.2.3-1.ca2404.1_all.deb Size: 76368 MD5sum: 1e8e71aadc113bb4c59941c418a14ef0 SHA1: 2e1e46cc17a3efa699d2a90f2bec4694229ddb5d SHA256: 5b47e7a585d8555a469e0fc2c98f81de794017383dc1d25b36532c1bbdfd62de SHA512: abb9f47caa2f712086f05b9347ad22820dff2e8864b2b82ef02197999cdbd21467f6bf4ebc41be75ca1e46ac08f83dbc93c6c9d25ee60981e6f1867c3320a59a Homepage: https://cran.r-project.org/package=maxEff Description: CRAN Package 'maxEff' (Additional Predictor with Maximum Effect Size) Methods of selecting one from many numeric predictors for a regression model, to ensure that the additional predictor has the maximum effect size. Package: r-cran-maxent.ot Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2323 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-maxent.ot_1.0.0-1.ca2404.1_all.deb Size: 994062 MD5sum: 29c3892ccdc06c0407cb15b3059441b6 SHA1: fb3269b9fa394547694caeec5006946ee8561923 SHA256: c5c614c45cd542d30b9fcb6ba654ed7933823ec8de8cdc8ada2cc2cbabceb930 SHA512: 96451926897bd4f908fe3e8faa45ef67c23330799ee143f28f7a9d3cf00fa04c75e52bafadd9f4cb78ffd9ac589805846c0be817070ab4cd00f41f7a6110ba46 Homepage: https://cran.r-project.org/package=maxent.ot Description: CRAN Package 'maxent.ot' (Perform Phonological Analyses using Maximum Entropy OptimalityTheory) Fit Maximum Entropy Optimality Theory models to data sets, generate the predictions made by such models for novel data, and compare the fit of different models using a variety of metrics. The package is described in Mayer, C., Tan, A., Zuraw, K. (in press) . Package: r-cran-maxentvariableselection Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1251 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-raster Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-maxentvariableselection_1.0-3-1.ca2404.1_all.deb Size: 409330 MD5sum: 4e7504d4e91326cd3ea4977f8c958600 SHA1: 0ffb464e150beb448402250eb1cf633c888f63af SHA256: 470969cf7a396220a07b53ad496b6c4affdcfacd7872a77d30b57942318e3c12 SHA512: fa54a7da07e8df8226dc0f8cb4f54f37579add27fcbe1c35a9260d12ace12fa529b67e8719adfba693254a8decec33879b50cf3e62b1bc498d54a1dac0d1877b Homepage: https://cran.r-project.org/package=MaxentVariableSelection Description: CRAN Package 'MaxentVariableSelection' (Selecting the Best Set of Relevant Environmental Variables alongwith the Optimal Regularization Multiplier for Maxent NicheModeling) Complex niche models show low performance in identifying the most important range-limiting environmental variables and in transferring habitat suitability to novel environmental conditions (Warren and Seifert, 2011 ; Warren et al., 2014 ). This package helps to identify the most important set of uncorrelated variables and to fine-tune Maxent's regularization multiplier. In combination, this allows to constrain complexity and increase performance of Maxent niche models (assessed by information criteria, such as AICc (Akaike, 1974 ), and by the area under the receiver operating characteristic (AUC) (Fielding and Bell, 1997 ). Users of this package should be familiar with Maxent niche modelling. Package: r-cran-maximin Architecture: all Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plgp Suggests: r-cran-lhs Filename: pool/dists/noble/main/r-cran-maximin_1.0-6-1.ca2404.1_all.deb Size: 46084 MD5sum: 24acfaf9eaf23f9d42e3b1ef651d1446 SHA1: d05722668dd48441cdb55d05d13067dd37e5334d SHA256: 49e5c88fa8917f657343eca45c2750aa489fb1fa671ae9031155fb97a5d85a46 SHA512: 40c44a22529bf916097a7b343d5d6ec5ebc4761791e15bfbda010cd1ef2190942ce58536ceac187f7ef55cba9bd16f5d105602b86760b4b4a708092476a8bfd9 Homepage: https://cran.r-project.org/package=maximin Description: CRAN Package 'maximin' (Space-Filling Design under Maximin Distance) Constructs a space-filling design under the criterion of maximum-minimum distance. Both discrete and continuous searches are provided. 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Package: r-cran-maxlik Architecture: all Version: 1.5-2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1335 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-misctools, r-cran-sandwich, r-cran-generics Suggests: r-cran-mass, r-cran-clue, r-cran-dlm, r-cran-plot3d, r-cran-tibble, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-maxlik_1.5-2.2-1.ca2404.1_all.deb Size: 1103964 MD5sum: c65cac4613af51e40399940ea35729e6 SHA1: 6a266ba9fe0e257188092c7b4621b9c42d2e14b2 SHA256: 8c242a8e4e58a8dd024fe56bf739b2ef1b30bc728fed777e16ca44e7936314d6 SHA512: 556439a3ed9c9b045426f5632b2ab0594ff6f5348a73f6c66f24a9caba9a2bffb8f7f0479ed117ce0d88e05bd4a44fcf1dc1a89d3ed3dc69f3960ba61ee4e0c1 Homepage: https://cran.r-project.org/package=maxLik Description: CRAN Package 'maxLik' (Maximum Likelihood Estimation and Related Tools) Functions for Maximum Likelihood (ML) estimation, non-linear optimization, and related tools. It includes a unified way to call different optimizers, and classes and methods to handle the results from the Maximum Likelihood viewpoint. It also includes a number of convenience tools for testing and developing your own models. 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Package: r-cran-maxnet Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-maxnet_0.1.4-1.ca2404.1_all.deb Size: 66032 MD5sum: af1ac14290bfb2dafe51ab7cc995d4ac SHA1: f4ea6b2796bf2a256946ed8bdbb1a05ab13ae0f6 SHA256: 7ce8f32198394ad3ac2d596a4fc10867ac6ae3852539ff75879e5712c383485a SHA512: 12507d0358685a109d5317957a781dc305133a5d1e4b6e6ecf6922c3b389b78817694c39e57e2003810cf11c39595536003cad37bff04e0f4194519cc2c7b376 Homepage: https://cran.r-project.org/package=maxnet Description: CRAN Package 'maxnet' (Fitting 'Maxent' Species Distribution Models with 'glmnet') Procedures to fit species distributions models from occurrence records and environmental variables, using 'glmnet' for model fitting. 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Brendan Murphy and Adrian E. Raftery (2019, ISBN:9781108644181). Package: r-cran-mbend Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mbend_1.3.1-1.ca2404.1_all.deb Size: 33950 MD5sum: 7ab283c52679b8b1d25894e727040368 SHA1: 7af107297a0ef919136551acbf1c480cd7d8399f SHA256: ec339c7c80959012fb2bdcae6ab2da52ddea227ae15ca5c59455bfec903ae658 SHA512: 1958444dfdd8c3dc49d7c2db78809b927469b107c1cad7d8f381f700282726704a919c1171a11a03126de40f75f65f533750453eda85fa12034c841691b664a4 Homepage: https://cran.r-project.org/package=mbend Description: CRAN Package 'mbend' (Matrix Bending) Bending non-positive-definite (symmetric) matrices to positive-definite, using weighted and unweighted methods. Jorjani, H., et al. (2003) . Schaeffer, L. R. (2014) . Package: r-cran-mbess Architecture: all Version: 4.9.42-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1088 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-lavaan, r-cran-mass, r-cran-mnormt, r-cran-nlme, r-cran-openmx, r-cran-sem, r-cran-semtools Suggests: r-cran-gsl Filename: pool/dists/noble/main/r-cran-mbess_4.9.42-1.ca2404.1_all.deb Size: 1041212 MD5sum: e9b220c2d136294ece0abc86cd536b41 SHA1: 7c0308966a4b91291ab1aa123d4760532c6039fe SHA256: b0b2e1f94b554a68b6418f2a73d0e6f43a66a48a3f5b092bfa3913ac213eacc5 SHA512: e182ab55aa45223d8993a24869067b20187eada57d99cf3f583623a3e178e22c9ea9eefd124bbc6ac11c26f57be4b9178f428d7634a89b4578b39a5a88242ec1 Homepage: https://cran.r-project.org/package=MBESS Description: CRAN Package 'MBESS' (The MBESS R Package) Implements methods that are useful in designing research studies and analyzing data, with particular emphasis on methods that are developed for or used within the behavioral, educational, and social sciences (broadly defined). That being said, many of the methods implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a suite of functions for a variety of related topics, such as effect sizes, confidence intervals for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and minimum-risk point estimation perspectives), mediation analysis, various properties of distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but MBESS became more general and now contains methods applicable and used in a wide variety of fields and is an orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has greatly benefited from others, see for a detailed list of those that have contributed and other details. 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(2013) and adjusted in Robinson et al. (2017) . Designs using MBHdesign can: 1) accommodate, without substantial detrimental effects on spatial balance, legacy sites (Foster et al., 2017 ); 2) be based on points or transects (foster et al. 2020 and produce clustered samples (Foster et al. (in press). Additional information about the package use itself is given in Foster (2021) . Package: r-cran-mbir Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1022 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-effsize, r-cran-psych Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mbir_1.3.5-1.ca2404.1_all.deb Size: 437928 MD5sum: fa4cb50340291a282e297288d85100a5 SHA1: 5e8c4452292edad4331769b85c92d950399731c5 SHA256: f584e533ee77f1d301f93e3454a26324dd3095b06c5dfc12ff68eea4f3f4332e SHA512: 2ce6b8b7d4160b71f166d200a26c0b14f48dfa28f679bc05f6cc8d4e3885a7c9d030fc89fd2c09ed8d04ec9252af47bec1ea76a8197c01f56324b74e6cf4a3d6 Homepage: https://cran.r-project.org/package=mbir Description: CRAN Package 'mbir' (Magnitude-Based Inferences) Allows practitioners and researchers a wholesale approach for deriving magnitude-based inferences from raw data. 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They are very robust (29 or 50 percent breakdown point, respectively), and if no outliers are present, the estimators are very similar to OLS. Package: r-cran-mbmca Architecture: all Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-chippcr Suggests: r-cran-spelling, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-mbmca_1.1-0-1.ca2404.1_all.deb Size: 308698 MD5sum: b3d19f0d15db212ec73667ee5f604fda SHA1: a47ffba93869fb04af9144ad38222a6a769a559b SHA256: 2317cc9b372ab43e28149bc0e3407ce5c678f2a93572f357ecbea3127d6dcf9c SHA512: 3670317fae8ba625de0e7ed63ccb3374f09cb8891434a7b8498da17d0633fc645cc66c8c9fccfbd90afcda2e7cb094f8c1495111384de3c5d5f1bf386d0900ff Homepage: https://cran.r-project.org/package=MBmca Description: CRAN Package 'MBmca' (Nucleic Acid Melting Curve Analysis) Lightweight utilities for nucleic acid melting curve analysis are important in life sciences and diagnostics. 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See Sharif Rahmani E, Lawarde A, Lingasamy P, Moreno SV, Salumets A and Modhukur V (2023), MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches. Front. Genet. 14:1233657. for more details. Package: r-cran-mbmixture Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-mbmixture_0.6-1.ca2404.1_all.deb Size: 45792 MD5sum: 3d5e83e91287e0c75821018b7e7783a2 SHA1: 17a8c490e960a32fd6d8380519887ad4245b9303 SHA256: 706941699e1fe380de1a2f8216cf7c99dbc08cf2fca61dec116f0c303a9bc407 SHA512: f432ea2b5282de91f340d7050108b85f5501e7146b5fa34f86e72dfd7d86cdf6372256cb6d51586048f34a10a487ac486a5d5a40b4dba011fea3e425f0116d7a Homepage: https://cran.r-project.org/package=mbmixture Description: CRAN Package 'mbmixture' (Microbiome Mixture Analysis) Evaluate whether a microbiome sample is a mixture of two samples, by fitting a model for the number of read counts as a function of single nucleotide polymorphism (SNP) allele and the genotypes of two potential source samples. Lobo et al. (2021) . 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(2016) . By modelling dose-response relationships this can connect networks of evidence that might otherwise be disconnected, and can improve precision on treatment estimates. Several common dose-response functions are provided; others may be added by the user. Various characteristics and assumptions can be flexibly added to the models, such as shared class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects models and/or by node-splitting at the treatment level. Package: r-cran-mbnmatime Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3031 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr, r-cran-gridextra, r-cran-dplyr, r-cran-r2jags, r-cran-rjags, r-cran-reshape2, r-cran-magrittr, r-cran-checkmate, r-cran-igraph, r-cran-scales, r-cran-lspline, r-cran-crayon, r-cran-ggplot2, r-cran-ggdist, r-cran-png, r-cran-zoo, r-cran-rdpack Suggests: r-cran-overlapping, r-cran-hmisc, r-cran-rmarkdown, r-cran-testthat, r-cran-rcolorbrewer, r-cran-mcmcplots Filename: pool/dists/noble/main/r-cran-mbnmatime_0.2.6-1.ca2404.1_all.deb Size: 2543418 MD5sum: 896f821686af2a183c59891b0c880d55 SHA1: e989620fae901e0e11cf90f2c415e406ecc6848b SHA256: 2c8ce550da41d5dc21a59a6f55767ea2079756a512d56742e1b6694d37d0d05a SHA512: 44edc0d106ed4435d2a8f6145a9f43a912498411cc1fba911feebac04efb97c2e1710497ceadd3b00b5cd0908d51e837e0985fa53357bcbd05a7d5923ffe2e7d Homepage: https://cran.r-project.org/package=MBNMAtime Description: CRAN Package 'MBNMAtime' (Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models) Fits Bayesian time-course models for model-based network meta-analysis (MBNMA) that allows inclusion of multiple time-points from studies. Repeated measures over time are accounted for within studies by applying different time-course functions, following the method of Pedder et al. (2019) . The method allows synthesis of studies with multiple follow-up measurements that can account for time-course for a single or multiple treatment comparisons. Several general time-course functions are provided; others may be added by the user. Various characteristics can be flexibly added to the models, such as correlation between time points and shared class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects models and/or by node-splitting. Package: r-cran-mbr Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplr, r-cran-mass, r-cran-matrix, r-cran-rfast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mbr_0.0.1-1.ca2404.1_all.deb Size: 84818 MD5sum: 7701438aade7b1e1a517666b1cd61e7f SHA1: aa8240e77aea7f12600d65e0f5162e15a022ea60 SHA256: e8b15d703c7ce67175b5874a7fd535c5a76b2b6544d13881d2579daada78be37 SHA512: 1ed85e01cd0a96537d860e0e2736392490a43fd863b3373c6b7f5912087c6ae31e4f0ee5d24211e692b34eb4947fa6b49b289cb1afbc71c0a8f0665abcc5276e Homepage: https://cran.r-project.org/package=mbr Description: CRAN Package 'mbr' (Mass Balance Reconstruction) Mass-balance-adjusted Regression algorithm for streamflow reconstruction at sub-annual resolution (e.g., seasonal or monthly). 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Usual dimension reduction methods in multivariate regression focus on the reduction of predictors, not responses. The response dimension reduction is theoretically founded in Yoo and Cook (2008) . Later, three model-based response dimension reduction approaches are proposed in Yoo (2016) and Yoo (2019) . The method by Yoo and Cook (2008) is based on non-parametric ordinary least squares, but the model-based approaches are done through maximum likelihood estimation. For two model-based response dimension reduction methods called principal fitted response reduction and unstructured principal fitted response reduction, chi-squared tests are provided for determining the dimension of the response subspace. Package: r-cran-mbreaks Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mbreaks_1.0.1-1.ca2404.1_all.deb Size: 316492 MD5sum: 73da66950fe8ba1847cc82b705c01075 SHA1: acf1e92fe5ee867943fd6627eecacaedc19b789a SHA256: 68e5dbe4049599403040742658a9887d20361a8e9152154a0fcd0fded3539ed2 SHA512: d7d4a6ed2814711a095a3ce64f3dc71657d9e7520aa770c57b72e5757789256073c25fef5209594170389f9c73c235f9d310f1961f39a2e4785ed012ac913750 Homepage: https://cran.r-project.org/package=mbreaks Description: CRAN Package 'mbreaks' (Estimation and Inference for Structural Breaks in LinearRegression Models) Functions provide comprehensive treatments for estimating, inferring, testing and model selecting in linear regression models with structural breaks. The tests, estimation methods, inference and information criteria implemented are discussed in Bai and Perron (1998) "Estimating and Testing Linear Models with Multiple Structural Changes" . Package: r-cran-mbres Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot, r-cran-magrittr, r-cran-tibble, r-cran-dplyr, r-cran-forcats, r-cran-tidyr, r-cran-purrr, r-cran-data.table, r-cran-scales Suggests: r-cran-rprobsup Filename: pool/dists/noble/main/r-cran-mbres_0.1.7-1.ca2404.1_all.deb Size: 114340 MD5sum: 23c63d96d2c168ff4a4d96ef8baaf837 SHA1: 19350068ea80b2259819aaa820317cef596a53f2 SHA256: 1d2ca156d7d972ab1c67ff8de23246211b71c80de6ca6c2e17a6ac633b689a11 SHA512: b1772f78fff568eca767180661e549bfe0fcdd6d3c8899000af9954c00700c973259d5c3fd6f2eac0e0ddd622ef1d94f8549b4afe9092368d521abbc2c59051f Homepage: https://cran.r-project.org/package=mbRes Description: CRAN Package 'mbRes' (Exploration of Multiple Biomarker Responses using Effect Size) Summarize multiple biomarker responses of aquatic organisms to contaminants using Cliff’s delta, as described in Pham & Sokolova (2023) . Package: r-cran-mbsgs Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-mass, r-cran-mgcv, r-cran-mnormt, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-mbsgs_1.2.0-1.ca2404.1_all.deb Size: 98642 MD5sum: 937d96f4737db561ed42fa5fe93deb43 SHA1: 6c434b536844e88ea024ea26ce7508a485bf8fa2 SHA256: 9a56155c6ec2b4b62fff2bdbd1536031e944fe375d4db25b2a21f5476b8853ed SHA512: 7ba379d3b4bda8f761214473627c14c0b596c874e975e4a0afe3219f8eb868e879c865f3228bbc03c801e9a98ae31fe0553c1b5a371712f3ad7b4f3e60d1e4ef Homepage: https://cran.r-project.org/package=MBSGS Description: CRAN Package 'MBSGS' (Multivariate Bayesian Sparse Group Selection with Spike and Slab) An implementation of a Bayesian sparse group model using spike and slab priors in a regression context. It is designed for regression with a multivariate response variable, but also provides an implementation for univariate response. Package: r-cran-mbsp Architecture: all Version: 5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-gigrvg, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-mbsp_5.0-1.ca2404.1_all.deb Size: 36938 MD5sum: 088ae2cf8507c8022929b31ed99eb636 SHA1: 5423c070d7e0458008e997070274e9fc3529bf93 SHA256: 7475570990ffc938c5881c12f531f3b7ac569cc80380a6abfad17bda253e8175 SHA512: 4f69c8cc6bc646214911e69d3237d49826c1c20014edd7e1a2b09dbd7cde064ba10ab1c911b1202866b1b9962caf11ee0aad678f28ae95d42589bd4903d18360 Homepage: https://cran.r-project.org/package=MBSP Description: CRAN Package 'MBSP' (Multivariate Bayesian Model with Shrinkage Priors) Gibbs sampler for fitting multivariate Bayesian linear regression with shrinkage priors (MBSP), using the three parameter beta normal family. The method is described in Bai and Ghosh (2018) . Package: r-cran-mbsts Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kfas, r-cran-matrix, r-cran-pscl, r-cran-mcmcpack, r-cran-mass, r-cran-ggplot2, r-cran-reshape2, r-cran-bbmisc, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mbsts_3.0-1.ca2404.1_all.deb Size: 99918 MD5sum: 42487b1c87838b7b568c9b03665527fb SHA1: d01da4e9c6d3a7fc15f45b68f8ac16778b45f355 SHA256: c546140698551df12895b4a6ee6ff639bf6f8e0a930cfb30ea9ab5a4ec249838 SHA512: 90fa48cd6c33edd1527b4ad26ea14465b2bb37e6ee09e73fccd93f48ce810216ba0b837e414810d5d9c55314d3499109f034a70d46fbf4b2509dddff222c94bd Homepage: https://cran.r-project.org/package=mbsts Description: CRAN Package 'mbsts' (Multivariate Bayesian Structural Time Series) Tools for data analysis with multivariate Bayesian structural time series (MBSTS) models. Specifically, the package provides facilities for implementing general structural time series models, flexibly adding on different time series components (trend, season, cycle, and regression), simulating them, fitting them to multivariate correlated time series data, conducting feature selection on the regression component. Package: r-cran-mbx Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-openxlsx, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rstatix, r-cran-tibble, r-cran-fsa, r-cran-multcompview Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mbx_0.2.0-1.ca2404.1_all.deb Size: 99928 MD5sum: 7aaa495d38779c8c34751b9c314a5299 SHA1: 816ae71dd0d218105e21332140b09129462dc4d9 SHA256: 7b44d18625510a32ec1402d328e7d2fb2976e8c9a6b532846b0bb0fd6cb9582c SHA512: bf2ab4709cdfe78927d170c669041d90be87d2664f2a4c984113760d067679313381fa670c40949112fa58d2b95694165dcdcff7b932e0d68ff663404ac0573a Homepage: https://cran.r-project.org/package=mbX Description: CRAN Package 'mbX' (A Comprehensive Microbiome Data Processing Pipeline) Provides tools for cleaning, processing, and preparing microbiome sequencing data (e.g., 16S rRNA) for downstream analysis. Supports CSV, TXT, and Excel file formats. The main function, ezclean(), automates microbiome data transformation, including format validation, transposition, numeric conversion, and metadata integration. It also handles taxonomic levels efficiently, resolves duplicated taxa entries, and outputs a well-structured, analysis-ready dataset. The companion functions ezstat() run statistical tests and summarize results, while ezviz() produces publication-ready visualizations. Package: r-cran-mc.heterogeneity Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metafor, r-cran-boot.heterogeneity Suggests: r-cran-hsaur3, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mc.heterogeneity_0.1.2-1.ca2404.1_all.deb Size: 57662 MD5sum: 0becf2af8da50fb8ca6c36c44508e279 SHA1: 370315eb94fcb781063745d1b2157448c9130d4b SHA256: 960f799f6bfe5afa68bc614599b1f9f67b84c6c9fa631116133c249480ac2a09 SHA512: 3d0948fa26af7eb011d8ccf9c98d24450488f1c22b316718687dc0d51f64ef682fedc050c432dedcd0615471600dcea89bf24bda0a32deb3330641f01aac8c3e Homepage: https://cran.r-project.org/package=mc.heterogeneity Description: CRAN Package 'mc.heterogeneity' (A Monte Carlo Based Heterogeneity Test for Meta-Analysis) Implements a Monte Carlo Based Heterogeneity Test for standardized mean differences (d), Fisher-transformed Pearson's correlations (r), and natural-logarithm-transformed odds ratio (OR) in Meta-Analysis Studies. Depending on the presence of moderators, this Monte Carlo Based Test can be implemented in the random or mixed-effects model. This package uses rma() function from the R package 'metafor' to obtain parameter estimates and likelihood, so installation of R package 'metafor' is required. This approach refers to the studies of Hedges (1981) , Hedges & Olkin (1985, ISBN:978-0123363800), Silagy, Lancaster, Stead, Mant, & Fowler (2004) , Viechtbauer (2010) , and Zuckerman (1994, ISBN:978-0521432009). Package: r-cran-mc2d Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1826 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-ggplot2, r-cran-ggpubr Suggests: r-cran-fitdistrplus, r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mc2d_0.2.1-1.ca2404.1_all.deb Size: 1410894 MD5sum: cb4d42d8ea9f884e48f1f4d0a7013d9e SHA1: 20a31ca8bdb7855a7a9708d5fdcc8d8aaa5ef9a7 SHA256: d8b419a3b390c2c31f7c79d6aac537e6d8829dc93217f5930703c658f26caab8 SHA512: aa489be0b796845643e903a909d5f66dbd840a935e42ff05d666a4201f07864c7f156ce330b273d6ed1a20d3eb9aaecd9c80136508a4d99f122b81093be1d68a Homepage: https://cran.r-project.org/package=mc2d Description: CRAN Package 'mc2d' (Tools for Two-Dimensional Monte-Carlo Simulations) A complete framework to build and study Two-Dimensional Monte-Carlo simulations, aka Second-Order Monte-Carlo simulations. Also includes various distributions (pert, triangular, Bernoulli, empirical discrete and continuous). Package: r-cran-mcanalysis Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mcanalysis_0.1.0-1.ca2404.1_all.deb Size: 16244 MD5sum: 9da7d461925126c63e833512a98fff2f SHA1: 88a8ea0d1229100935382986676585f5694daf6d SHA256: 04a9692be49b80568f770ada7391447a0fb9d008a35413bc42508f017aa7f1c1 SHA512: b2b5371595e4c962c135c8e3ab9dce83bd619d5d7782805b5f4262ef2540cd98f93b64214c3e70f2a57328b25ceb997ebd959ed4a9a52e12bb642b4f06865021 Homepage: https://cran.r-project.org/package=mcanalysis Description: CRAN Package 'mcanalysis' (Markov Chain Analysis for Structural Behaviour and Stability) Analyses the stability and structural behaviour of export and import patterns across multiple countries using a Markov chain modelling framework. Constructs transition probability matrices to quantify changes in trade shares between successive periods, thereby capturing persistence, structural shifts, and inter-country interdependence in trade performance. By iteratively generating expected trade distributions over time, the approach facilitates assessment of stability, long-run equilibrium tendencies, and comparative dynamics in longitudinal trade data, providing a rigorous tool for empirical analysis of export–import behaviour. Methodological foundations follow standard Markov chain theory as described in Gagniuc (2017) . Package: r-cran-mcauchyd Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-mass, r-cran-lifecycle, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcauchyd_1.3.3-1.ca2404.1_all.deb Size: 60620 MD5sum: 6673b80d9096076530e9e38858dc968e SHA1: d27b535672ed452f7df7b4f90aa1159da17296d6 SHA256: 3108f0250fbade67f215c558398e65619ee65b9bdd6d9cb9c64f8b75f46bb527 SHA512: 3e2db149f11b806c4f914f9e2b99731d4fa00e0f66e3d0ba71d1d5bdfac9a8566c2d5220fb060f63eac12183f1190a084d35985b8fc949f1e7615878d59a0132 Homepage: https://cran.r-project.org/package=mcauchyd Description: CRAN Package 'mcauchyd' (Multivariate Cauchy Distribution; Kullback-Leibler Divergence) Distance between multivariate Cauchy distributions, as presented by N. Bouhlel and D. Rousseau (2022) . Manipulation of multivariate Cauchy distributions. Package: r-cran-mcavariants Architecture: all Version: 2.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-plotly Filename: pool/dists/noble/main/r-cran-mcavariants_2.6.1-1.ca2404.1_all.deb Size: 91540 MD5sum: c716d1d412b3bbd34684a1441c9a38cc SHA1: b2091dda5b770c0d11ce0ece10c96397aa244ef2 SHA256: dada8dae5cebbc2ee2d19094cbe4025932d8c301987519be7ba6d869164851f3 SHA512: d749f1ff341c2918d38b97129387d5a420c9ef7d9f1c6f1f025c249e7704d1c4b81097de33a6a34ff04279d35c31d627ddcef816b244da88d15a41690bb6ef5d Homepage: https://cran.r-project.org/package=MCAvariants Description: CRAN Package 'MCAvariants' (Multiple Correspondence Analysis Variants) Provides two variants of multiple correspondence analysis (ca): multiple ca and ordered multiple ca via orthogonal polynomials of Emerson. Package: r-cran-mcb Architecture: all Version: 0.1.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaps, r-cran-lars, r-cran-mass, r-cran-glmnet, r-cran-ncvreg, r-cran-smoothmest, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-flare, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcb_0.1.15-1.ca2404.1_all.deb Size: 62314 MD5sum: 5788f360bdcbc4e750aec38d4d5a2338 SHA1: 4a01a459ea2cfe333037b870004399c7c7e6c97e SHA256: 3d8326392fdacf55139987ae9a864f6c59bae114f5310e2941889d878105de27 SHA512: ed992d2f54ffb8ded357e94e83d166ad90d6517685d2ba1df5cda4a647dedab3cf50de996199d1ffe5cae7625b9003a173448166251b6af570b4b03eef938f0f Homepage: https://cran.r-project.org/package=mcb Description: CRAN Package 'mcb' (Model Confidence Bounds) When choosing proper variable selection methods, it is important to consider the uncertainty of a certain method. The model confidence bound for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level. A good variable selection method is the one of which the model confidence bound under a certain confidence level has the shortest width. When visualizing the variability of model selection and comparing different model selection procedures, model uncertainty curve is a good graphical tool. A good variable selection method is the one of whose model uncertainty curve will tend to arch towards the upper left corner. This function aims to obtain the model confidence bound and draw the model uncertainty curve of certain single model selection method under a coverage rate equal or little higher than user-given confidential level. About what model confidence bound is and how it work please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403. . Besides, 'flare' is needed only you apply the SQRT or LAD method ('mcb' totally has 8 methods). Although 'flare' has been archived by CRAN, you can still get it in and the latest version is useful for 'mcb'. Package: r-cran-mcbackscattering Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mcbackscattering_0.1.1-1.ca2404.1_all.deb Size: 89118 MD5sum: c3a0f558339d5f8694e0c1949716a7b4 SHA1: 9ee514f003f3f77dcd262dcb5f1f61b06cd17027 SHA256: 9f4b667d3b9c1900ca370db003fcc627bf77fa632af36e283b85cfb0fdb87021 SHA512: 4fea0021e12b824cc31d0efa7b02978dc9ebac1804afdaac8709e6fe736d528a31dd323d37bc4a2d2696cba7bf62b9c738837bc452c072c77bd45153295f4620 Homepage: https://cran.r-project.org/package=MCBackscattering Description: CRAN Package 'MCBackscattering' (Monte Carlo Simulation for Surface Backscattering) Monte Carlo simulation is a stochastic method computing trajectories of photons in media. Surface backscattering is performing calculations in semi-infinite media and summarizing photon flux leaving the surface. This simulation is modeling the optical measurement of diffuse reflectance using an incident light beam. The semi-infinite media is considered to have flat surface. Media, typically biological tissue, is described by four optical parameters: absorption coefficient, scattering coefficient, anisotropy factor, refractive index. The media is assumed to be homogeneous. Computational parameters of the simulation include: number of photons, radius of incident light beam, lowest photon energy threshold, intensity profile (halo) radius, spatial resolution of intensity profile. You can find more information and validation in the Open Access paper. Laszlo Baranyai (2020) . Package: r-cran-mcbette Architecture: all Version: 1.15.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2817 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-babette, r-cran-beautier, r-cran-beastier, r-cran-curl, r-cran-devtools, r-cran-mauricer, r-cran-rmpfr, r-cran-testit, r-cran-txtplot Suggests: r-cran-ape, r-cran-ggplot2, r-cran-hunspell, r-cran-knitr, r-cran-lintr, r-cran-markdown, r-cran-nltt, r-cran-phangorn, r-cran-rappdirs, r-cran-rmarkdown, r-cran-spelling, r-cran-stringr, r-cran-testthat, r-cran-tracerer Filename: pool/dists/noble/main/r-cran-mcbette_1.15.3-1.ca2404.1_all.deb Size: 2036724 MD5sum: e4c67f6eb5976591c06f0f5691a4fc36 SHA1: 90cef0498e5520d251ce6ad25519ec19c6896124 SHA256: 1c5bd6fa0dcd831625d33d2a22b9af7b9cd808dcafbb909b28c859474a2413d2 SHA512: 3fe516dba139b8aede7f917f5cc132528b38af97d4dca4b83777ac16e004234b595e6090d457257f02e0bae3ade655ac2110555985c331ce65d56156a5400a8f Homepage: https://cran.r-project.org/package=mcbette Description: CRAN Package 'mcbette' (Model Comparison Using 'babette') 'BEAST2' () is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'mcbette' allows to do a Bayesian model comparison over some site and clock models, using 'babette' (). Package: r-cran-mcbiopi Architecture: all Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mcbiopi_1.1.7-1.ca2404.1_all.deb Size: 31022 MD5sum: 17d2d7e0253854c2cc8d394c34fb8931 SHA1: e0a4d57f904933414ecbe76ab29da925e2099487 SHA256: a3ba77f4c1835953ed58f9955135204a7acc011303383fe14643205328712540 SHA512: c610783fd93c482a84d7762ee124860d9c67d81b35a84264def455dcaa05bdcae3b1a5f171a92793a82ecec283117c0e924be52bb41297f252b37df5460baf21 Homepage: https://cran.r-project.org/package=mcbiopi Description: CRAN Package 'mcbiopi' (Matrix Computation Based Identification of Prime Implicants) Computes the prime implicants or a minimal disjunctive normal form for a logic expression presented by a truth table or a logic tree. Has been particularly developed for logic expressions resulting from a logic regression analysis, i.e. logic expressions typically consisting of up to 16 literals, where the prime implicants are typically composed of a maximum of 4 or 5 literals. Package: r-cran-mcboost Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-mlr3, r-cran-mlr3misc, r-cran-mlr3pipelines, r-cran-r6, r-cran-rmarkdown, r-cran-rpart, r-cran-glmnet Suggests: r-cran-curl, r-cran-lgr, r-cran-formattable, r-cran-tidyverse, r-cran-practools, r-cran-mlr3learners, r-cran-mlr3oml, r-cran-neuralnet, r-cran-paradox, r-cran-knitr, r-cran-ranger, r-cran-xgboost, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcboost_0.4.4-1.ca2404.1_all.deb Size: 317832 MD5sum: 180c8c5e4221c504716af078dc66526b SHA1: 64591be95f31d42aef94b738df819e011f0beb8d SHA256: 685b37846cb6da85f9fc2b53957caed74fc26084555cdb19dd88f6b3d3541a85 SHA512: 870bf93a85c059c1da346a07b610631e371e4c632b16e5c7a613e6407ffc992f1a22fece1d70b76b2f8eaee3edbfd8c6882624d0224cd2c4c96470b97154ca4b Homepage: https://cran.r-project.org/package=mcboost Description: CRAN Package 'mcboost' (Multi-Calibration Boosting) Implements 'Multi-Calibration Boosting' (2018) and 'Multi-Accuracy Boosting' (2019) for the multi-calibration of a machine learning model's prediction. 'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups. Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are. This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations. Package: r-cran-mcca Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet, r-cran-rpart, r-cran-e1071, r-cran-mass, r-cran-proc, r-cran-caret Suggests: r-cran-rgl Filename: pool/dists/noble/main/r-cran-mcca_0.8.2-1.ca2404.1_all.deb Size: 124406 MD5sum: 7f50b665a06f1613a2de66774fab0020 SHA1: d3d6fe3726acd8515e8e6d716ce67217a053b413 SHA256: eb2d5f1983d7faa1b3cd1ddf11a6cbdabe789758473314722aae96d5bd25d49b SHA512: b8e3f4921b3ddb6caf281690a4f3009ab6439c5e898deaee4c3e26e67a112cb00b5700f9384df69bd78472966b2c8079667fa46af932c792acdd3ab1dbe8ed1e Homepage: https://cran.r-project.org/package=mcca Description: CRAN Package 'mcca' (Multi-Category Classification Accuracy) It contains six common multi-category classification accuracy evaluation measures. All of these measures could be found in Li and Ming (2019) . Specifically, Hypervolume Under Manifold (HUM), described in Li and Fine (2008) . Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in Li, Jiang and Fine (2013) . Polytomous Discrimination Index (PDI), described in Van Calster et al. (2012) . Li et al. (2018) . PDI with variance estimation using Dover et al. (2021) . We described all these above measures and our mcca package in Li, Gao and D'Agostino (2019) . Package: r-cran-mccf1 Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rocr Filename: pool/dists/noble/main/r-cran-mccf1_1.1-1.ca2404.1_all.deb Size: 24194 MD5sum: 933e6a8bfa196090dd96148a08a69112 SHA1: fd54162fb858fc3e82035a8cbb177ac1a6125560 SHA256: 34faac3fe47621efd4779652973763388c7c94fa2df778f1ad732fa81a5be784 SHA512: e9ad5ea8a96312f1252a6f10c7c2379efe4741a0e99af1172e6d8cf81b1f2b099605ddfc4b44435c5ffd5d6a589377f374d13fefe969b8c446581b7165826884 Homepage: https://cran.r-project.org/package=mccf1 Description: CRAN Package 'mccf1' (Creates the MCC-F1 Curve and Calculates the MCC-F1 Metric andthe Best Threshold) The MCC-F1 analysis is a method to evaluate the performance of binary classifications. The MCC-F1 curve is more reliable than the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR)curve under imbalanced ground truth. The MCC-F1 analysis also provides the MCC-F1 metric that integrates classifier performance over varying thresholds, and the best threshold of binary classification. Package: r-cran-mccm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-polycor, r-cran-lavaan, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mccm_0.1.0-1.ca2404.1_all.deb Size: 185168 MD5sum: 54aad285240db3e4e8cb7e0180e54480 SHA1: 68f6259a5b662ace8ed7236a75f3f7c4db834853 SHA256: 4086a5b1eca8e3f07eb780442c58dd631d519116e11d14c502651718599b9e5c SHA512: 64aa354df14a7967ac7f043e69aa59644ad6766e61fabef78d76cf6c64db4841e76b179f3bfe6be7f2b34300fae4a4cb7b3334bee24f78c4212b23ad0a304a10 Homepage: https://cran.r-project.org/package=MCCM Description: CRAN Package 'MCCM' (Mixed Correlation Coefficient Matrix) The IRLS (Iteratively Reweighted Least Squares) and GMM (Generalized Method of Moments) methods are applied to estimate mixed correlation coefficient matrix (Pearson, Polyseries, Polychoric), which can be estimated in pairs or simultaneously. For more information see Peng Zhang and Ben Liu (2024) ; Ben Liu and Peng Zhang (2024) . Package: r-cran-mccmeiv Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-survival, r-cran-mass Filename: pool/dists/noble/main/r-cran-mccmeiv_2.1-1.ca2404.1_all.deb Size: 297032 MD5sum: 6e2e1266a1e6090fc93548be07c68814 SHA1: 0cbd7249f60cd9bc4ca864a2e19d25092cda40a6 SHA256: 33358f9bda3c8e11c79a95884b7191980066ebfd29c040967537a6b39d585791 SHA512: bcf11a0e8ff7f2d01383c434501741c3d6a89bc81c944a34f94209242f6e96f9b3f78f2cc08577cec4786c34d8c37562fcff3c825f699b9c795d1cb3b40629bb Homepage: https://cran.r-project.org/package=mccmeiv Description: CRAN Package 'mccmeiv' (Analysis of Matched Case Control Data with a MismeasuredExposure that is Accompanied by Instrumental Variables) Applying the methodology from Manuel et al. to estimate parameters using a matched case control data with a mismeasured exposure variable that is accompanied by instrumental variables (Submitted). Package: r-cran-mccount Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1675 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cards, r-cran-cli, r-cran-cmprsk, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-boot, r-cran-ggplot2, r-cran-knitr, r-cran-matchit, r-cran-mstate, r-cran-optmatch, r-cran-patchwork, r-cran-rmarkdown, r-cran-spelling, r-cran-survival, r-cran-testthat, r-cran-weightit, r-cran-withr Filename: pool/dists/noble/main/r-cran-mccount_0.1.1-1.ca2404.1_all.deb Size: 1377486 MD5sum: 311cb34c6d0b7378caaf78012e68c15d SHA1: ed13ed44792643e0abca54d989dfdeac1edf4fc1 SHA256: 1dd3b8ee2c3241137a8e7b2e9bb5721fd9f3c117e836a334a01bd56c79b13ab2 SHA512: 24cf8c1832928da4c053a7e954f24ea93d43466e07c81ad498d8e455441f7e18091bb6b28c3c5a2a23449a9822a82ef308e8f624d40af5e0c0e74450a739f17e Homepage: https://cran.r-project.org/package=mccount Description: CRAN Package 'mccount' (Estimate Recurrent Event Burden with Competing Risks) Calculates mean cumulative count (MCC) to estimate the expected cumulative number of recurrent events per person over time in the presence of competing risks and censoring. Implements both the Dong-Yasui equation method and sum of cumulative incidence method described in Dong, et al. (2015) . Supports inverse probability weighting for causal inference as outlined in Gaber, et al. (2023) . Provides S3 methods for printing, summarizing, plotting, and extracting results. Handles grouped analyses and integrates with 'ggplot2' for visualization. Package: r-cran-mccr Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mccr_0.4.4-1.ca2404.1_all.deb Size: 9648 MD5sum: eeeadba5ad5bfb5f820a733cf0c84055 SHA1: 0a57865e0ea8d4ac52221f7bdda0837502697b43 SHA256: 50ae754bc771015a44dffbbd2b6b60e69c5cf6bb16ac7c17a136e92dea28b584 SHA512: 7e67a73f8aac0b68d7dd2a79f9ec2b17295df8bd984fe2d729f9a94632913eb0965424563ee7e22d0f3c2d825878d9bb5422a28f15cadf9fb4d44aa60fb9eca5 Homepage: https://cran.r-project.org/package=mccr Description: CRAN Package 'mccr' (The Matthews Correlation Coefficient) The Matthews correlation coefficient (MCC) score is calculated (Matthews BW (1975) ). Package: r-cran-mcda Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rglpk, r-cran-glpkapi, r-cran-rcolorbrewer, r-cran-combinat, r-cran-triangle, r-cran-plyr, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcda_0.1.0-1.ca2404.1_all.deb Size: 614928 MD5sum: 0df107602c1d1fa7636fde93a6caf44b SHA1: c3766858a7f3b03861974b733471ed468e2bae3d SHA256: 436608697203f61857d5f30c562e966bd842d57ad319ccee1a10c75f1db3cc72 SHA512: 40cf596316a0fcf2c6ed0c72b9adaf0689bdd46abeaf95ac2da1cc42593262c540a7e4477dedf466d1909183bf8256b17b6192a4dafe28d19e8b2db57fb1e896 Homepage: https://cran.r-project.org/package=MCDA Description: CRAN Package 'MCDA' (Support for the Multicriteria Decision Aiding Process) Support for the analyst in a Multicriteria Decision Aiding (MCDA) process with algorithms, preference elicitation and data visualisation functions. Sébastien Bigaret, Richard Hodgett, Patrick Meyer, Tatyana Mironova, Alexandru Olteanu (2017) Supporting the multi-criteria decision aiding process : R and the MCDA package, Euro Journal On Decision Processes, Volume 5, Issue 1 - 4, pages 169 - 194 . Package: r-cran-mcdabench Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4982 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-factoextra, r-cran-ggplot2, r-cran-gplots, r-cran-igraph, r-cran-monochromer, r-cran-networkd3 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mcdabench_1.1.1-1.ca2404.1_all.deb Size: 2141738 MD5sum: ccf631cdfb0b0de11c12f738129df96e SHA1: b834a7bf26e10f9b86b26a9b4deb7f6401ab8e93 SHA256: b6dd975dd0daab8c3fe9a0fd4e48fcc2cbe15e9a7f44c3b12398cdbd37bc00d1 SHA512: d211c1baa43d7337b01ff747fae85df81112d48377931caab3a92f13852a9508d0ad6e12635e830ad09dff95b35a575cedad73f1572905d3477fb9bf9b62e974 Homepage: https://cran.r-project.org/package=mcdabench Description: CRAN Package 'mcdabench' (Benchmarking for Multi-Criteria Decision Analysis) Performs and benchmarks various Multi-Criteria Decision Analysis (MCDA) methods. MCDA is a decision-making framework used to evaluate and rank alternatives based on multiple conflicting criteria using normalization, weighting, and aggregation techniques. The package implements a wide range of MCDA methods including ARAS (Additive Ratio Assessment), AROMAN (Alternative Ranking Order Method Accounting for two-step Normalization), COCOSO (Combined Compromise Solution), CODAS (Combinative Distance-based Assessment), COPRAS (Complex Proportional Assessment), EDAS (Evaluation based on Distance from Average Solution), ELECTRE (Elimination and Choice Expressing Reality) family (I-IV), FUCA (Faire Un Choix Adequat), GRA (Grey Relational Analysis), MABAC (Multi-Attributive Border Approximation Area Comparison), MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis), MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution), MAUT (Multi-Attribute Utility Theory), MAVT (Multi-Attribute Value Theory), MEGAN (Multi-criteria Evaluation with Gradual-weighting and Aggregation of Normalized distance matrices), MOORA (Multi-Objective Optimization on the basis of Ratio Analysis), OCRA (Operational Competitiveness Rating Analysis), ORESTE (Organisation, Rangement Et Synthese De Donnees Relationnelles), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations I-VI), RAM (Root Assessment Method), ROV (Range of Value), SMART (Simple Multi-Attribute Rating Technique), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje), WASPAS (Weighted Aggregated Sum Product Assessment), WPM (Weighted Product Model), and WSM (Weighted Sum Model). The package computes comparative evaluation measures including Spearman rank correlation (Spearman, 1904) , Salabun-Urbaniak's weight similarity index (Salabun and Urbaniak, 2020), Wilcoxon signed-rank test (Wilcoxon, 1945), and permutation- and bootstrap- based entropy difference tests for pairwise method comparisons using Jensen-Shannon divergence (Lin, 1991). It also provides sensitivity and stability analysis of MCDA results. Weight sensitivity analysis is implemented through deterministic and stochastic perturbation of criterion weights, and is also integrated as a built-in step within the MEGAN method framework (Cebeci, 2026). Package: r-cran-mcgf Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-sp Suggests: r-cran-testthat, r-cran-doparallel, r-cran-foreach, r-cran-knitr, r-cran-rmarkdown, r-cran-lubridate, r-cran-dplyr, r-cran-rsolnp Filename: pool/dists/noble/main/r-cran-mcgf_1.1.1-1.ca2404.1_all.deb Size: 902660 MD5sum: a19b1a3b951f8cf474acc72e3ed71f92 SHA1: ac8ecad3829258465aec5f20919e13d8b73bb1bd SHA256: 1940f6a0ab9292886d2bec0d4e9559d7145b930e4dbd5d3bae3094a3e68d8b0f SHA512: 5578e9dad1e9e71870833c2b6268904facb9ed164089849df068f3e3ab204a5b9eaddfa32e94e1285868573f5d6741eeebb0615c9716eb6b95691dd8365b4179 Homepage: https://cran.r-project.org/package=mcgf Description: CRAN Package 'mcgf' (Markov Chain Gaussian Fields Simulation and Parameter Estimation) Simulating and estimating (regime-switching) Markov chain Gaussian fields with covariance functions of the Gneiting class (Gneiting 2002) . It supports parameter estimation by weighted least squares and maximum likelihood methods, and produces Kriging forecasts and intervals for existing and new locations. 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It combines the quantile estimate error-bounding approach of the Raftery and Lewis MCMC run length diagnostic `gibbsit` with the between verses within chain approach of the Gelman and Rubin MCMC convergence diagnostic. Package: r-cran-mchtest Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mchtest_1.0-3-1.ca2404.1_all.deb Size: 390352 MD5sum: f8899877d08387d328d91aefffa9c6a2 SHA1: 4bb28238dc4fca40dd7e777dca12cf15735c3bdd SHA256: 7e3a46d721b9250ef82360fd780731f6ee1985c6c13cfaa03c58a328572b2531 SHA512: 9e87f79e8e5ea9cc717b1710db13633db54f8a6943af3bdc5a571e1c706bd65cedec1f5f63fc6a3f2870d52d9aead3abc7a91a02e7886f7d481f03d284a954c6 Homepage: https://cran.r-project.org/package=MChtest Description: CRAN Package 'MChtest' (Monte Carlo Hypothesis Tests with Sequential Stopping) Performs Monte Carlo hypothesis tests, allowing a couple of different sequential stopping boundaries. For example, a truncated sequential probability ratio test boundary (Fay, Kim and Hachey, 2007 ) and a boundary proposed by Besag and Clifford, 1991 . 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Bisson and Jiwei Zhao (2021) . Package: r-cran-mcl Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-expm Filename: pool/dists/noble/main/r-cran-mcl_1.0-1.ca2404.1_all.deb Size: 19192 MD5sum: a317f3fb422576a78c040da2cb0873d8 SHA1: 4a956dc4bd7931e2ea113e8fc941baeeb21b8df9 SHA256: 5a565dd23182b4aebf1b5c04d43987c18d28b4f532e64102862b02605e4338fd SHA512: 817b398f2e9a302889176ed07ff78b72e93dcb727783060d0c4ef616e4d086b1ac646445ea79c3ac486630fd0ad0d3cca91c505c079999c5417c1d7811918f68 Homepage: https://cran.r-project.org/package=MCL Description: CRAN Package 'MCL' (Markov Cluster Algorithm) Contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached. Package: r-cran-mclink Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-stringr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-mclink_1.1.1-1.ca2404.1_all.deb Size: 260536 MD5sum: 52984f98f5fc904c9a3b966d1d824305 SHA1: 6241407aa5c777f6f4bdd4c5085acb7452f3a866 SHA256: cf1a33696e75ea1212d734741e4c038171a8e79625ffd363c6660af7e0655210 SHA512: 9bb98b6a7c85fda064eb6013b7979d3249ffceb161a761f9739b64ae425bbf928bed134c4e825dd3cb4ec31b5124511160833a80a36e28e68cdbd87c686eb486 Homepage: https://cran.r-project.org/package=mclink Description: CRAN Package 'mclink' (Metabolic Pathway Completeness and Abundance Calculation) Provides tools for analyzing metabolic pathway completeness, abundance, and transcripts using KEGG Orthology (KO) data from (meta)genomic and (meta)transcriptomic studies. Supports both completeness (presence/absence) and abundance-weighted analyses. Includes built-in KEGG reference datasets. For more details see Li et al. (2023) . Package: r-cran-mclogit Architecture: all Version: 0.9.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-memisc, r-cran-nlme Suggests: r-cran-nnet, r-cran-ucminf, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mclogit_0.9.15-1.ca2404.1_all.deb Size: 328636 MD5sum: bc3c19ef464bba8f972df7a4e85825cc SHA1: f067ad01a8c54833f20c0619a6ac0f654b0af29c SHA256: ef221a3bdbf651a6f62f3e0428e2bfab8cc1163ca56157e3ec4e6613cc82832d SHA512: ef6f43c8c477e6c747cc964b8267d2506db5de0de439485580317222c8aec9fad3689b6293205d954691a8916872e1d4b003d9a519ee2d270403aab2ed9d7468 Homepage: https://cran.r-project.org/package=mclogit Description: CRAN Package 'mclogit' (Multinomial Logit Models for Categorical Responses and DiscreteChoices) Provides estimators for multinomial logit models in their conditional logit (for discrete choices) and baseline logit variants (for categorical responses), optionally with overdispersion or random effects. Random effects models are estimated using the PQL technique (based on a Laplace approximation) or the MQL technique (based on a Solomon-Cox approximation). Estimates should be treated with caution if the group sizes are small. Package: r-cran-mcm Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1243 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survey, r-cran-gee, r-cran-dplyr, r-cran-lme4, r-cran-stringr, r-cran-parameters Filename: pool/dists/noble/main/r-cran-mcm_0.1.7-1.ca2404.1_all.deb Size: 1182186 MD5sum: e3c7c4b38cecb82481d31f47594a4832 SHA1: 512c3de9cef625809eaa36cd3bf99d81e2380152 SHA256: 10540f6dea9547c41e29f1e481bd01b61f698afac0f7c83dfa3312b0f005ca7e SHA512: 16fa2ca36a5201b62300384ead975aff7a77e692720a1146132c68bb5fb4f7273a7734779cf14e0b5076e4b9ea3da05c7d8ffc0746bf7ca137618cc29f721a43 Homepage: https://cran.r-project.org/package=MCM Description: CRAN Package 'MCM' (Estimating and Testing Intergenerational Social Mobility Effect) Estimate and test inter-generational social mobility effect on an outcome with cross-sectional or longitudinal data. Package: r-cran-mcmapper Architecture: all Version: 0.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mcmapper_0.0.11-1.ca2404.1_all.deb Size: 62892 MD5sum: 26c251be25a4a1b1d22762aceae71618 SHA1: 163d92427aadf590da1325f933f53e74efa4db6d SHA256: 182a9beaabab6e8a71e29d04e19f876b298831e80d283979007713ae110b976a SHA512: 1482e33f40ffd6ccaad599dec9e3c9117d5014d812263a4c88d5a1cc3c300616f7ca83d7edd5c630cf7882a7f5ca4cc8e6534648a8aa0f2be78c393804af0672 Homepage: https://cran.r-project.org/package=mcmapper Description: CRAN Package 'mcmapper' (Mapping First Moment and C-Statistic to the Parameters ofDistributions for Risk) Provides a series of numerical methods for extracting parameters of distributions for risks based on knowing the expected value and c-statistics (e.g., from a published report on the performance of a risk prediction model). This package implements the methodology described in Sadatsafavi et al (2024) . The core of the package is mcmap(), which takes a pair of (mean, c-statistic) and the distribution type requested. This function provides a generic interface to more customized functions (mcmap_beta(), mcmap_logitnorm(), mcmap_probitnorm()) for specific distributions. Package: r-cran-mcmc.otu Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcglmm, r-cran-ggplot2, r-cran-coda Filename: pool/dists/noble/main/r-cran-mcmc.otu_1.0.10-1.ca2404.1_all.deb Size: 98010 MD5sum: 6ccafe7d31df10cd0a1d432d3be853b6 SHA1: b35a635c7056b04f1610424283e9057901f58de8 SHA256: 81385e03a8b0c6d13fe8a5c9622dda3cb2d04966e595bfdc52e65723eee93607 SHA512: 97fc236955540cc6ff3af1ce7b9cd08c2602dc3fb9d55f953554e5d342d89fa06a31704e0b90b924361ce71c8de868b6e8433fb365e16e960c32d76673e50eef Homepage: https://cran.r-project.org/package=MCMC.OTU Description: CRAN Package 'MCMC.OTU' (Bayesian Analysis of Multivariate Counts Data in DNAMetabarcoding and Ecology) Poisson-lognormal generalized linear mixed model analysis of multivariate counts data using MCMC, aiming to infer the changes in relative proportions of individual variables. The package was originally designed for sequence-based analysis of microbial communities ("metabarcoding", variables = operational taxonomic units, OTUs), but can be used for other types of multivariate counts, such as in ecological applications (variables = species). The results are summarized and plotted using 'ggplot2' functions. Includes functions to remove sample and variable outliers and reformat counts into normalized log-transformed values for correlation and principal component/coordinate analysis. Walkthrough and examples: http://www.bio.utexas.edu/research/matz_lab/matzlab/Methods_files/walkthroughExample_mcmcOTU_R.txt. Package: r-cran-mcmc.qpcr Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcglmm, r-cran-ggplot2, r-cran-coda Filename: pool/dists/noble/main/r-cran-mcmc.qpcr_1.2.4-1.ca2404.1_all.deb Size: 180112 MD5sum: 220e8efcae72525950bdd9e6c6de43c5 SHA1: 68da75eec1665720966f88104ec0f7edc9e10100 SHA256: 973d653d991c1803aa3db51386827a9b1a3b1ff0b35ace105b9d14e6a0482215 SHA512: a95489fa5971b57e0f010c0738be02552d9c0616b5e5dc640d39cc4663e7804b6b396215a134f01ae9b0bfe7000c3e3edebdc4f312d3f0bae16f7753989c851d Homepage: https://cran.r-project.org/package=MCMC.qpcr Description: CRAN Package 'MCMC.qpcr' (Bayesian Analysis of qRT-PCR Data) Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: . Package: r-cran-mcmc4extremes Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-evir Filename: pool/dists/noble/main/r-cran-mcmc4extremes_1.1-1.ca2404.1_all.deb Size: 147444 MD5sum: 68ed938a8f658d652f7bcfd1be3c9540 SHA1: b1a5121c5442ee6eac02ff3fb487ca17b114a1fa SHA256: b81e647daf5e45564d506cf61d919362674a042e0c06217e51419ce6b80b59cc SHA512: adcc3809816be755cd208908c91d432bf17eb48f16ac2e47bb40baf8e4d9a6412334470d35287c2fe5c6171804f3752cdf0b4e7de13fdc3789858b40b18a1e66 Homepage: https://cran.r-project.org/package=MCMC4Extremes Description: CRAN Package 'MCMC4Extremes' (Posterior Distribution of Extreme Value Models in R) Provides some function to perform posterior estimation for some distribution, with emphasis to extreme value distributions. It contains some extreme datasets, and functions that perform the runs of posterior points of the GPD and GEV distribution. The package calculate some important extreme measures like return level for each t periods of time, and some plots as the predictive distribution, and return level plots. Package: r-cran-mcmcderive Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-chk, r-cran-extras, r-cran-mcmcr, r-cran-nlist, r-cran-purrr, r-cran-universals Suggests: r-cran-coda, r-cran-covr, r-cran-doparallel, r-cran-plyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcmcderive_0.1.2-1.ca2404.1_all.deb Size: 32530 MD5sum: f86e43bc969ccf9392ab7515f4ff3d43 SHA1: 26ac30973315d8b4ada042619ad9919ebd3bd46e SHA256: 75ab6740f28e1c7628a4f1e5723933222dd5eb8e20a3ccfb937de4bb4d06c8c4 SHA512: d2821c7fc5d78130f7e61f7d8be83b54d60cb1c132cdba69ea1dabf8e71af063117a217227a89beecbac805fcdb5394786155bb5832013715b43a79b400468d5 Homepage: https://cran.r-project.org/package=mcmcderive Description: CRAN Package 'mcmcderive' (Derive MCMC Parameters) Generates derived parameter(s) from Monte Carlo Markov Chain (MCMC) samples using R code. This allows Bayesian models to be fitted without the inclusion of derived parameters which add unnecessary clutter and slow model fitting. For more information on MCMC samples see Brooks et al. (2011) . Package: r-cran-mcmcensemble Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 976 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future.apply, r-cran-progressr Suggests: r-cran-bayesplot, r-cran-coda, r-cran-mockery, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-mcmcensemble_3.2.0-1.ca2404.1_all.deb Size: 189970 MD5sum: 777e2e3bf4fe864dab0752bbc2b048b9 SHA1: 206637efacd7429eea6d66df47b0ba9418bb0021 SHA256: e86a9a3ecee263f20e71fd15b4a59427cf18c886a64096269504414e2c4459f9 SHA512: d79475af177b5f4d5a5ad306bdfb24c2fd53a531354e7c9433d98060bd86a9a4a30f818fa7c70cf09f4e053eb07d22ff02b7833ff19cab77b1194846d1147657 Homepage: https://cran.r-project.org/package=mcmcensemble Description: CRAN Package 'mcmcensemble' (Ensemble Sampler for Affine-Invariant MCMC) Provides ensemble samplers for affine-invariant Monte Carlo Markov Chain, which allow a faster convergence for badly scaled estimation problems. Two samplers are proposed: the 'differential.evolution' sampler from ter Braak and Vrugt (2008) and the 'stretch' sampler from Goodman and Weare (2010) . Package: r-cran-mcmcoutput Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hdinterval, r-cran-coda, r-cran-mass Filename: pool/dists/noble/main/r-cran-mcmcoutput_0.1.3-1.ca2404.1_all.deb Size: 286544 MD5sum: 0d56bf90bbac7b555cb72185e3ae8fa0 SHA1: 3e3900ad6fb61ff439da9b419168f3c62783726a SHA256: 9461d3738ee9472a412d747f839e2a81087adb98d673ada9825fceaa262da044 SHA512: c972039370d31322629fec19a0df3e148cf00434dc909164baf19aa7848227f7b3535ffe0ad898df8e7bfcf7536d9468adaae29f56117fbe77fae4fbeedd75de Homepage: https://cran.r-project.org/package=mcmcOutput Description: CRAN Package 'mcmcOutput' (Functions to Store, Manipulate and Display Markov Chain MonteCarlo (MCMC) Output) Implements a class ('mcmcOutput') for efficiently storing and handling Markov chain Monte Carlo (MCMC) output, intended as an aid for those writing customized MCMC samplers. A range of constructor methods are provided covering common output formats. Functions are provided to generate summary and diagnostic statistics and to display histograms or density plots of posterior distributions, for the entire output, or subsets of draws, nodes, or parameters. Package: r-cran-mcmcplots Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-sfsmisc, r-cran-colorspace, r-cran-denstrip Filename: pool/dists/noble/main/r-cran-mcmcplots_0.4.3-1.ca2404.1_all.deb Size: 143712 MD5sum: a68226d8eb0f777667403f1114ada543 SHA1: d6689418e79d5e22f99574a2ce2db78921ff0a28 SHA256: ce49f270fb98c99b10f00aaa31c6b0e35b9243afa192d156bdc527a802ab2829 SHA512: b753f8df24b4ddb1f4662d92dce05ed5467c12ec260714e4db3d11276c40efb047c144b8270f4ec8a803ca9892ebc13f614a7b898685fa10f0237cfc85d57ac1 Homepage: https://cran.r-project.org/package=mcmcplots Description: CRAN Package 'mcmcplots' (Create Plots from MCMC Output) Functions for convenient plotting and viewing of MCMC output. 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For more information see Brooks et al. (2011) . Package: r-cran-mcmctreer Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7542 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-sn, r-cran-coda Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mcmctreer_1.1-1.ca2404.1_all.deb Size: 4350516 MD5sum: a8ee0129e8a54afa216b0ab30f85d381 SHA1: bfc75919c2df8fa975eaed37825b2366f9174ef4 SHA256: afa2cc3a51b1ba81379218b0f7f46cbb78230f4c5f91fe7b2dcf997f2ae43335 SHA512: a4fecd62493f5d85348aff617bb6f1bf7d9b2ea2b20efd62f0e323c5a6ad846c8f6d84738b6953c94ceb9fd319e23f7bb4ce1e287d8b501a11d45eef326a0bda Homepage: https://cran.r-project.org/package=MCMCtreeR Description: CRAN Package 'MCMCtreeR' (Prepare MCMCtree Analyses and Plot Bayesian Divergence TimeAnalyses Estimates on Trees) Provides functions to prepare time priors for 'MCMCtree' analyses in the 'PAML' software from Yang (2007) and plot time-scaled phylogenies from any Bayesian divergence time analysis. Most time-calibrated node prior distributions require user-specified parameters. The package provides functions to refine these parameters, so that the resulting prior distributions accurately reflect confidence in known, usually fossil, time information. These functions also enable users to visualise distributions and write 'MCMCtree' ready input files. Additionally, the package supplies flexible functions to visualise age uncertainty on a plotted tree with using node bars, using branch widths proportional to the age uncertainty, or by plotting the full posterior distributions on nodes. Time-scaled phylogenetic plots can be visualised with absolute and geological timescales . All plotting functions are applicable with output from any Bayesian software, not just 'MCMCtree'. Package: r-cran-mcmcvis Architecture: all Version: 0.16.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3597 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-rstan, r-cran-overlapping, r-cran-colorspace Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-posterior Filename: pool/dists/noble/main/r-cran-mcmcvis_0.16.5-1.ca2404.1_all.deb Size: 3122232 MD5sum: 1a431004f36c70f01cabd417808012d7 SHA1: 4b3ab175150fff2304f0d6b911a1d5228c8c097a SHA256: f91c9e8d5e09934afe0c3c8edbc09f0f3c0e345729da9df404bce127d99f4f66 SHA512: 4ac73348f99ebc64ff56819a4cbd95b6b0c5dd464c155b56a70572ec18fb56ba12ad9043c164fb1daedc8f359a57ec25bec1d5eeea3dfb826f597e67475ebf3a Homepage: https://cran.r-project.org/package=MCMCvis Description: CRAN Package 'MCMCvis' (Tools to Visualize, Manipulate, and Summarize MCMC Output) Performs key functions for MCMC analysis using minimal code - visualizes, manipulates, and summarizes MCMC output. Functions support simple and straightforward subsetting of model parameters within the calls, and produce presentable and 'publication-ready' output. MCMC output may be derived from Bayesian model output fit with Stan, NIMBLE, JAGS, and other software. Package: r-cran-mcmiso Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-future Filename: pool/dists/noble/main/r-cran-mcmiso_0.2.0-1.ca2404.1_all.deb Size: 119062 MD5sum: 667e758b8a294adbb9cc805bb5fdded2 SHA1: d1a44f38d07ffa978e6d6f5182d4c92c4007e136 SHA256: 4d6f0a3591e4a0e4e37e38c1922f8235687e547f07f1855a1d9bf6cc9ecdf77b SHA512: f822ec1949156b6d6c39dffe9f559063b8111d38d8489d5ceaef339dea3bc9217f28cd2a4370eb922cd7dd2b39ab723adeb258e2762a8c0ca8d8a721fb016434 Homepage: https://cran.r-project.org/package=McMiso Description: CRAN Package 'McMiso' (Multicore Multivariable Isotonic Regression) Provides functions for isotonic regression and classification when there are multiple independent variables. The functions solve the optimization problem using a projective Bayes approach with recursive sequential update algorithms, and are useful for situations with a relatively large number of covariates. Supports binary outcomes via a Beta-Binomial conjugate model ('miso', 'PBclassifier') and continuous outcomes via a Normal-Inverse-Chi-Squared conjugate model ('misoN'). Parallel computing wrappers ('mcmiso', 'mcPBclassifier', 'mcmisoN') are provided that run the down-up and up-down algorithms simultaneously and return whichever finishes first. The estimation method follows the projective Bayes solution described in Cheung and Diaz (2023) . Package: r-cran-mcmodule Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15269 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mc2d, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-visnetwork, r-cran-igraph, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mcmodule_1.2.0-1.ca2404.1_all.deb Size: 2551688 MD5sum: 86cf20448d07a57de9bfeb9f5bb01bce SHA1: d7402bdde203028acec5b6f2bb150369ba883d31 SHA256: 91d0b3fbe64a710db69221922983fcca3b42bb979acb39217349341fe916b6c9 SHA512: dec9f382bada39167b2711137d6d50c1db42a66d46c9e52783d8bea56473448e275931169dd09129e0e7ef5e86923d5b0692f6bcb493e06143645db93ddccd51 Homepage: https://cran.r-project.org/package=mcmodule Description: CRAN Package 'mcmodule' (Modular Monte Carlo Risk Analysis) Framework for building modular Monte Carlo risk analysis models. It extends the capabilities of 'mc2d' to facilitate working with multiple risk pathways, variates and scenarios. It provides tools to organize risk analysis in independent flexible modules, perform multivariate Monte Carlo node operations, automate the creation of Monte Carlo nodes and visualise risk analysis models. For more details see Ciria (2025) . Package: r-cran-mcmsector Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-haven, r-cran-survey, r-cran-openxlsx, r-cran-magrittr, r-cran-rlang, r-cran-dplyr, r-cran-plyr, r-cran-stringr, r-cran-labelled, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-r2jags, r-cran-rmarkdown, r-cran-testthat, r-cran-mcmsupply Filename: pool/dists/noble/main/r-cran-mcmsector_1.0.2-1.ca2404.1_all.deb Size: 235704 MD5sum: e73022944b19dd971ea6b4cd9defdeec SHA1: 03a32384ade8195f466b3ae0c76af7945c841d7d SHA256: 96bf351c2ff1eaf7caa3fd297d08ce5acf2a72d174feef57e86a4fb403e1f199 SHA512: 958a33a9e332770c03230c6c846978d4d5a4fc47556a47c82a3525214a885511586e437a8716c7f2de1b2bec30646a6fd098f4df46f60b73efa5eb80b5550e6c Homepage: https://cran.r-project.org/package=mcmsector Description: CRAN Package 'mcmsector' (Estimating Subnational Public and Private Contraceptive SupplyShares Over Time) Engaging the private sector in contraceptive method supply is critical for equitable, sustainable, and accessible healthcare systems. This package implements Bayesian hierarchical models to estimate public and private contraceptive supply shares over time at national and subnational levels, using Demographic and Health Survey (DHS) data. Penalized splines are used to track supply shares over time, and spatial correlation structures link national and subnational estimates in data- sparse settings. For more details see Comiskey (2025) . 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Package: r-cran-mcmsupply Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1051 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2jags, r-cran-magrittr, r-cran-foreach, r-cran-tidyverse, r-cran-tidybayes, r-cran-runjags, r-cran-rlang, r-cran-abind, r-cran-dplyr, r-cran-ggplot2, r-cran-plyr, r-cran-readxl, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mcmsupply_1.1.1-1.ca2404.1_all.deb Size: 720124 MD5sum: 73b4b21479a3755a28432bce20ee0acb SHA1: b09df0fdff30be014911d6edc11ded6f2934560b SHA256: ec9a41d0183c3768560b94f21e1393c54bdb393ea9d5fb4f78453620bfb18016 SHA512: 7d9c01120fe2cd917d03de9de2b4b7d82d9491be9f89eb3b63664ab5f16419be6285bfbeef0a94e9ecc239936a10016070636ea129ba6c3f719e2f50bca544f6 Homepage: https://cran.r-project.org/package=mcmsupply Description: CRAN Package 'mcmsupply' (Estimating Public and Private Sector Contraceptive Market SupplyShares) Family Planning programs and initiatives typically use nationally representative surveys to estimate key indicators of a country’s family planning progress. However, in recent years, routinely collected family planning services data (Service Statistics) have been used as a supplementary data source to bridge gaps in the surveys. The use of service statistics comes with the caveat that adjustments need to be made for missing private sector contributions to the contraceptive method supply chain. Evaluating the supply source of modern contraceptives often relies on Demographic Health Surveys (DHS), where many countries do not have recent data beyond 2015/16. Fortunately, in the absence of recent surveys we can rely on statistical model-based estimates and projections to fill the knowledge gap. We present a Bayesian, hierarchical, penalized-spline model with multivariate-normal spline coefficients, to account for across method correlations, to produce country-specific,annual estimates for the proportion of modern contraceptive methods coming from the public and private sectors. This package provides a quick and convenient way for users to access the DHS modern contraceptive supply share data at national and subnational administration levels, estimate, evaluate and plot annual estimates with uncertainty for a sample of low- and middle-income countries. Methods for the estimation of method supply shares at the national level are described in Comiskey, Alkema, Cahill (2022) . 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It contains functions often used in the analysis of education data in the county office including seeing if an item is not in a list, rounding in the manner the general public expects, including logos for districts, switching between district names and their county-district-school codes, accessing the local 'SQL' table and making thematically consistent graphs. 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A method for generation of multi-companion matrices with prespecified spectral properties is provided, as well as some utilities for periodically correlated and multivariate time series models. See Boshnakov (2002) and Boshnakov & Iqelan (2009) . 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The package provides tools to estimate marginalized count regression models for direct inference on the effect of covariates on the marginal mean of the outcome. The methods include the marginalized zero-inflated Poisson (MZIP) model described in Long et al. (2014) and the marginalized zero- and N-inflated binomial (MZNIB) model, which extends marginalized modeling to fractional count outcomes with boundary inflation at zero and the upper limit. 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'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindeløv (submitted) and generalizes the approach described in Carlin, Gelfand, & Smith (1992) and Stephens (1994) . Package: r-cran-mcpan Architecture: all Version: 1.1-22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm, r-cran-multcomp, r-cran-magic, r-cran-mcmcpack, r-cran-plyr Filename: pool/dists/noble/main/r-cran-mcpan_1.1-22-1.ca2404.1_all.deb Size: 366238 MD5sum: abe3658c37301a48516aedcabf9b349e SHA1: 7c06f6e707ac4365f434ace960f59367b075539c SHA256: 370306f55b24291ada2cde94b8955960d44cd573d26060ac35056ddf2cff91fa SHA512: df5b262b6cf01af66fc81008f02c09af8b8ec44878e02b847170b8ef3f163b2d6e7719edcd82dcd178ad393ef55c6b05cc46b714d0c64422c52aae42fa473d33 Homepage: https://cran.r-project.org/package=MCPAN Description: CRAN Package 'MCPAN' (Multiple Comparisons Using Normal Approximation) Multiple contrast tests and simultaneous confidence intervals based on normal approximation. With implementations for binomial proportions in a 2xk setting (risk difference and odds ratio), poly-3-adjusted tumour rates, biodiversity indices (multinomial data) and expected values under lognormal assumption. Approximative power calculation for multiple contrast tests of binomial and Gaussian data. Package: r-cran-mcparalleldo Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r.utils, r-cran-checkmate, r-cran-r6 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-mcparalleldo_1.1.0-1.ca2404.1_all.deb Size: 77450 MD5sum: 79f23acda10dbc9a77177f6abfe342a6 SHA1: 5385f62deb58605a9a46ffe4cc23dd23218db349 SHA256: 072ff7b2ef761b12c08c8f3029333e0d3d636d2856cd4ffa8c763b51ee169222 SHA512: fb6f76f30f1ad0ad7a0590732654010b3e8040f15bef89124eb60becfc35cc81b3da61f51516587dfed7282cc4b6fcf9c0b40d8026c99fdf8b601a72ecc0a091 Homepage: https://cran.r-project.org/package=mcparallelDo Description: CRAN Package 'mcparallelDo' (A Simplified Interface for Running Commands on ParallelProcesses) Provides a function that wraps mcparallel() and mccollect() from 'parallel' with temporary variables and a task handler. Wrapped in this way the results of an mcparallel() call can be returned to the R session when the fork is complete without explicitly issuing a specific mccollect() to retrieve the value. Outside of top-level tasks, multiple mcparallel() jobs can be retrieved with a single call to mcparallelDoCheck(). Package: r-cran-mcpmod Architecture: all Version: 1.0-10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 545 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-lattice Filename: pool/dists/noble/main/r-cran-mcpmod_1.0-10.1-1.ca2404.1_all.deb Size: 509668 MD5sum: dc793f619f9ea6f521022bdac3cc1e6c SHA1: de8ea24eb8eb6b5e4ac1bef5a80f5b4a93341cb4 SHA256: 15a2234ebd6d4a7a3e5032ee4845268a9bb954cd273d7bf5c089d1746e3361ae SHA512: f5c5534499d6b722a72539971a969d34aafb1c92938e8c5c7701352dc5b3b237c19541dff05138961c3423f811ae96fe13416e2954a77a28ad6eb4974907ccae Homepage: https://cran.r-project.org/package=MCPMod Description: CRAN Package 'MCPMod' (Design and Analysis of Dose-Finding Studies) Implements a methodology for the design and analysis of dose-response studies that combines aspects of multiple comparison procedures and modeling approaches (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738-748, ). The package provides tools for the analysis of dose finding trials as well as a variety of tools necessary to plan a trial to be conducted with the MCP-Mod methodology. Please note: The 'MCPMod' package will not be further developed, all future development of the MCP-Mod methodology will be done in the 'DoseFinding' R-package. 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Package: r-cran-mcpmodgeneral Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dosefinding, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-survival Filename: pool/dists/noble/main/r-cran-mcpmodgeneral_0.1-3-1.ca2404.1_all.deb Size: 237616 MD5sum: ed97c4a29101d204c77a266fcc02a56e SHA1: 141f2a1d37d628a0d3fe481c48938b351aeb1cc2 SHA256: b9138094fd1a8bb35a41863c47d7468896bfa4f914c3d5419d4656225995e91d SHA512: 08a50b5468a33f4b0989642430f80e623a7ade0aa8fff2a467520a0194728e1149582ae528f3f5feeb3e7a0bd1c19d78a266075ed75bd88960550e4463547070 Homepage: https://cran.r-project.org/package=MCPModGeneral Description: CRAN Package 'MCPModGeneral' (A Supplement to the 'DoseFinding' Package for the General Case) Analyzes non-normal data via the Multiple Comparison Procedures and Modeling approach (MCP-Mod). Many functions rely on the 'DoseFinding' package. This package makes it so the user does not need to provide or calculate the mu vector and S matrix. Instead, the user typically supplies the data in its raw form, and this package will calculate the needed objects and passes them into the 'DoseFinding' functions. If the user wishes to primarily use the functions provided in the 'DoseFinding' package, a singular function (prepareGen()) will provide mu and S. The package currently handles power analysis and the MCP-Mod procedure for negative binomial, Poisson, and binomial data. The MCP-Mod procedure can also be applied to survival data, but power analysis is not available. Bretz, F., Pinheiro, J. C., and Branson, M. (2005) . Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997) . Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) . 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The package supports content validity analysis, dimensionality assessment, and Classical Test Theory using the 'CTT' package (Willse, 2018) . Item Response Theory (IRT) analyses are conducted via 'mirt' (Chalmers, 2012) . Exploratory Factor Analysis is performed using 'psych' (Revelle, 2025), while Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) are based on the 'lavaan' framework (Rosseel, 2012) . The CFA/SEM module features interactive model specification, automatic model comparison, modification indices, comprehensive fit diagnostics, path diagram visualization, and HTML report generation. The application allows users to upload data, evaluate statistical models, visualize results, and export outputs through an intuitive graphical interface without requiring programming experience. 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Details about the measurement error bias correction methods, see Luan et al. (2023) , Tekwe et al. (2022) , Zhang et al. (2023) , Tekwe et al. (2019) . 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Isobel Claire Gormley and Thomas Brendan Murphy (2010) . 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Package: r-cran-mecor Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-lmertest, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mecor_1.0.0-1.ca2404.1_all.deb Size: 413448 MD5sum: 45e53623acb010403fc3c4c0c20aedb6 SHA1: 0e75fcefbe3dfd20b39c4d7ccfdc58edd51885e7 SHA256: bcd005352050b83ceed712f0436526fdda337af1c9a998bb018e93cc7b7624e3 SHA512: 539db8290a1f9f92b355ca311965fe62ab15b5925817c292291ffd5c6c7546aca0a9efa057f7722c5e5f46f73b7f98b3177c0435396d0b8b966d6198a8e30fee Homepage: https://cran.r-project.org/package=mecor Description: CRAN Package 'mecor' (Measurement Error Correction in Linear Models with a ContinuousOutcome) Covariate measurement error correction is implemented by means of regression calibration by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331), efficient regression calibration by Spiegelman D, Carroll RJ & Kipnis V (2001) and maximum likelihood estimation by Bartlett JW, Stavola DBL & Frost C (2009) . Outcome measurement error correction is implemented by means of the method of moments by Buonaccorsi JP (2010, ISBN:1420066560) and efficient method of moments by Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI & Freedman LS (2014) . Standard error estimation of the corrected estimators is implemented by means of the Delta method by Rosner B, Spiegelman D & Willett WC (1990) and Rosner B, Spiegelman D & Willett WC (1992) , the Fieller method described by Buonaccorsi JP (2010, ISBN:1420066560), and the Bootstrap by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331). 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This package covers topics such as drug effectiveness, vaccine trials, survival rates, infectious disease outbreaks, and medical treatments. The included datasets span various health conditions, including AIDS, cancer, bacterial infections, and COVID-19, along with information on pharmaceuticals and vaccines. These datasets are sourced from the R ecosystem and other R packages, remaining unaltered to ensure data integrity. This package serves as a valuable resource for researchers, analysts, and healthcare professionals interested in conducting medical and public health data analysis in R. 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Additionally, a sample dataset of this kind of data is provided, and some other minor tools useful in epidemiological studies. Package: r-cran-meddra.read Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-meddra.read_0.0.1-1.ca2404.1_all.deb Size: 17174 MD5sum: 224762f5820ba41de772c24fcfec6bf3 SHA1: 0e925ad2efa91ba158a431c9ff94fa2fa39661af SHA256: da848452e95d2923acfcef508c3ea49f6c0b14e033014f8f0da138276b99e7ed SHA512: 4ad9e8fc302d58d0ffeec9717abc3cefc5aea999a07488c03b10f5b4bb071bf60ec3338f85b367f71d004a770298d15cdadc0dec62c5353ed17319c134977250 Homepage: https://cran.r-project.org/package=meddra.read Description: CRAN Package 'meddra.read' (Load and Use 'MedDRA' Data for Clinical Trials) 'MedDRA' data is used for defining adverse events in clinical studies. You can load and merge the data for use in categorizing the adverse events using this package. The package requires the data licensed from 'MedDRA' . 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In addition, for the Bayesian hierarchical model, the package provides functions to assess the sensitivity of results to different specifications of the random effects distributions. 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Here, three functions are given related to three new methods which will generate mating diallel cross designs (Hinkelmann and Kempthorne, 1963) or mating environmental (ME) designs along with design parameters, C matrix, eigenvalues (EVs), degree of fractionations (DF) and canonical efficiency factor (CEF). Another one function is added to check the properties of a given ME diallel cross design. 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Medication entities for the basic medExtractR implementation that can be extracted include drug name, strength, dose amount, dose, frequency, intake time, dose change, and time of last dose. The basic medExtractR is outlined in Weeks, Beck, McNeer, Williams, Bejan, Denny, Choi (2020) . The extended medExtractR_tapering implementation is intended to extract dosing information for more tapering schedules, which are far more complex. The tapering extension allows for the extraction of additional entities including dispense amount, refills, dose schedule, time keyword, transition, and preposition. 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We perform the methods considered in Cheng, Spiegelman, and Li (2021) Estimating the natural indirect effect and the mediation proportion via the product method. 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It numerically and graphically evaluates the sensitivity of causal mediation analysis results to the presence of unmeasured pretreatment confounding. The proposed method has primary advantages over existing methods. First, using an unmeasured pretreatment confounder conditional associations with the treatment, mediator, and outcome as sensitivity parameters, the method enables users to intuitively assess sensitivity in reference to prior knowledge about the strength of a potential unmeasured pretreatment confounder. Second, the method accurately reflects the influence of unmeasured pretreatment confounding on the efficiency of estimation of the causal effects. Third, the method can be implemented in different causal mediation analysis approaches, including regression-based, simulation-based, and propensity score-based methods. It is applicable to both randomized experiments and observational studies. 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Package: r-cran-medlea Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr, r-cran-tm, r-cran-patchwork, r-cran-wordcloud2 Filename: pool/dists/noble/main/r-cran-medlea_1.0.2-1.ca2404.1_all.deb Size: 322616 MD5sum: a29d8af24d29d5f3a62afad584dff2d0 SHA1: e8cd88fe44a244b1862164d063e1f8dd96ce299b SHA256: 56792af61e30b761e7d133befc40ba5ac0edc5b45854d5b2679b6d928cebd35b SHA512: 479db7757c511a80a2099b8f233bbc8b9f6f627eb35593ad7d7fb84e408ebbdee186d766793c8fd31cbf3d54ded9133d62aedbe39906ee8dbf7e6f136b09aa6b Homepage: https://cran.r-project.org/package=MedLEA Description: CRAN Package 'MedLEA' (Morphological and Structural Features of Medicinal Leaves) Contains a dataset of morphological and structural features of 'Medicinal LEAves (MedLEA)'. The features of each species is recorded by manually viewing the medicinal plant repository available at (). You can also download repository of leaf images of 1099 medicinal plants in Sri Lanka. Package: r-cran-medparser Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-medparser_0.2.0-1.ca2404.1_all.deb Size: 19722 MD5sum: e421ef5d5e79e118a11c3b3ac7197ab7 SHA1: df6b77af68573e421ada3e4264612622dbaafaf3 SHA256: c85ff7cf0288e791eff122ab0839709f1af06f187cc33216399f56e72faddc7a SHA512: e9b39a704db0f7e7f7edc13dc2e2eea5aecdc285874d20c425021d43b49e17a9b6d09e41bd5ed262a6d3123de2ef75f116fe83dd226d0d289c8ce03848e99b5e Homepage: https://cran.r-project.org/package=medparser Description: CRAN Package 'medparser' (MedPC Text Parser) Parses information from text files with specific utility aimed at pulling information from Med Associate's (MPC) files. These functions allow for further analysis of MPC files. Package: r-cran-medrxivr Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1501 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-curl, r-cran-jsonlite, r-cran-httr, r-cran-stringr, r-cran-rlang, r-cran-bib2df, r-cran-tibble, r-cran-progress, r-cran-lubridate, r-cran-purrr, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-kableextra, r-cran-spelling Filename: pool/dists/noble/main/r-cran-medrxivr_0.1.4-1.ca2404.1_all.deb Size: 972654 MD5sum: 69e003b79e10ebb2cc28717096a526a3 SHA1: 2e76f9b0d2365aa39f1fda7cc1f848e7e54eb3a8 SHA256: 5652dbcab7d554fc9d84a89185e3c4488c41db5c2e2c2595cfab8e64528fda13 SHA512: 420312b7b48dd7ce7aafd9ad9be5fdeaf2fec642ae264bda482c03b23f97e841a4c26b0d3625ed6d66422e09d0af5c9a432b99de88ef85a78e685fdc1a79d857 Homepage: https://cran.r-project.org/package=medrxivr Description: CRAN Package 'medrxivr' (Access and Search MedRxiv and BioRxiv Preprint Data) An increasingly important source of health-related bibliographic content are preprints - preliminary versions of research articles that have yet to undergo peer review. The two preprint repositories most relevant to health-related sciences are medRxiv and bioRxiv, both of which are operated by the Cold Spring Harbor Laboratory. 'medrxivr' provides programmatic access to the 'Cold Spring Harbour Laboratory (CSHL)' API , allowing users to easily download medRxiv and bioRxiv preprint metadata (e.g. title, abstract, publication date, author list, etc) into R. 'medrxivr' also provides functions to search the downloaded preprint records using regular expressions and Boolean logic, as well as helper functions that allow users to export their search results to a .BIB file for easy import to a reference manager and to download the full-text PDFs of preprints matching their search criteria. Package: r-cran-medscan Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hdmt, r-cran-locfdr, r-cran-qqman, r-bioc-qvalue, r-cran-fdrtool Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-medscan_1.0.2-1.ca2404.1_all.deb Size: 33672 MD5sum: 2316ee3870dd74d4a5d9dfef875ab026 SHA1: 4da348da71ded97696ce6bb8806b0eeb7e9c9bc4 SHA256: 32b642615b9ba173c9682cd4677e48e9a62d0b9a49baf40f83ec99446688ea01 SHA512: bf04859bf65144f36678a360f59c35985c7e88d393d81aca882642e2b4919b9ae6f62364b1cd47189e6e60167c5f9faa3f0f8c1de54492c796032d7bc9fae626 Homepage: https://cran.r-project.org/package=medScan Description: CRAN Package 'medScan' (Large Scale Single Mediator Hypothesis Testing) A collection of methods for large scale single mediator hypothesis testing. The six included methods for testing the mediation effect are Sobel's test, Max P test, joint significance test under the composite null hypothesis, high dimensional mediation testing, divide-aggregate composite null test, and Sobel's test under the composite null hypothesis. Du et al (2023) . Package: r-cran-medseq Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-matrixstats, r-cran-nnet, r-cran-seriation, r-cran-stringdist, r-cran-traminer, r-cran-weightedcluster Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-medseq_1.4.2-1.ca2404.1_all.deb Size: 707270 MD5sum: a07de6eabfb1aa01749449c534fe289b SHA1: b13642456b4743c73d3ca1f46c76cbfda8cf9235 SHA256: a9f39c09d42b614e1676dcf26a898948a504a14a999e07f15cbd9eb566a416c9 SHA512: 3b23ab0fbcb054631cc9dde6fc6377e9e059661abf72ad2f26cfab8d71092115ec0a60aef1d079d3285bdb01ee7f41d9a4baeb4fb5b18d181c3505d93a6ef9a3 Homepage: https://cran.r-project.org/package=MEDseq Description: CRAN Package 'MEDseq' (Mixtures of Exponential-Distance Models with Covariates) Implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2021) . A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics and sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided. Package: r-cran-medxr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 523 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-memoise Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-medxr_0.1.1-1.ca2404.1_all.deb Size: 313180 MD5sum: 2444c90a35af267b1ccd15890b136acb SHA1: bfeecc1ca3a134431b95f11bb9abad29fbd49d42 SHA256: 28215c8c046604b050ac095f670b66544686dfe13d4d4da96f37dfc48910c7c7 SHA512: 48a921fe70d4c645f120c113191c788a9e9fce6ec44fefd42bfa61a0d298c5efc74af438571428c0ec7b8200824390b11a9276ccfb5f96f77998c525454f80de Homepage: https://cran.r-project.org/package=MedxR Description: CRAN Package 'MedxR' (Access Drug Regulatory Data via FDA and Health Canada APIs) Provides functions to access drug regulatory data from public RESTful APIs including the 'FDA Open API' and the 'Health Canada Drug Product Database API', retrieving real-time or historical information on drug approvals, adverse events, recalls, and product details. Additionally, the package includes a curated collection of open datasets focused on drugs, pharmaceuticals, treatments, and clinical studies. These datasets cover diverse topics such as treatment dosages, pharmacological studies, placebo effects, drug reactions, misuses of pain relievers, and vaccine effectiveness. The package supports reproducible research and teaching in pharmacology, medicine, and healthcare by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: 'FDA API' and 'Health Canada API' . Package: r-cran-medzisc Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-betareg, r-cran-glmnet Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-medzisc_0.0.4-1.ca2404.1_all.deb Size: 284078 MD5sum: 3aa6d17ecbe19058c9573b6fecf7c034 SHA1: 8abd3d4e803fcc1e14a18bd87146d2a182b5ebe7 SHA256: 65e4d9f0fd9f2401997e26fa7160277c11b416f81bef2ca211cc3a657eebba2e SHA512: da683c9fcc79af4a2e33ac30da20264b276bd8f3e736b9780088da8aeb2c285c0738519afe99c15e11465508c0bc45a6b6955d7570157e5f06f34ddfeafefb94 Homepage: https://cran.r-project.org/package=MedZIsc Description: CRAN Package 'MedZIsc' (Statistical Framework for Co-Mediators of Zero-InflatedSingle-Cell Data) A causal mediation framework for single-cell data that incorporates two key features ('MedZIsc', pronounced Magics): (1) zero-inflation using beta regression and (2) overdispersed expression counts using negative binomial regression. This approach also includes a screening step based on penalized and marginal models to handle high-dimensionality. Full methodological details are available in our recent preprint by Ahn S and Li Z (2025) . Package: r-cran-meerva Architecture: all Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixcalc Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-meerva_0.2-2-1.ca2404.1_all.deb Size: 329482 MD5sum: 1e0fd36f75187fb338a9ff2b401f041c SHA1: 0056e22a964ea589be28049645866cd41da73dd8 SHA256: b3a5a8009d2e11013de07c3f9137427cc9353d365ed42a8722d5e267ed3f406c SHA512: 78ff435319aa328ed01ae7aad519b7252deb1fea56f500f882c05f7beca46b84865b6c88e5db556e894f16202a42123bc3d94cbecb0572a143850eee92c328ea Homepage: https://cran.r-project.org/package=meerva Description: CRAN Package 'meerva' (Analysis of Data with Measurement Error Using a ValidationSubsample) Sometimes data for analysis are obtained using more convenient or less expensive means yielding "surrogate" variables for what could be obtained more accurately, albeit with less convenience; or less conveniently or at more expense yielding "reference" variables, thought of as being measured without error. Analysis of the surrogate variables measured with error generally yields biased estimates when the objective is to make inference about the reference variables. Often it is thought that ignoring the measurement error in surrogate variables only biases effects toward the null hypothesis, but this need not be the case. Measurement errors may bias parameter estimates either toward or away from the null hypothesis. If one has a data set with surrogate variable data from the full sample, and also reference variable data from a randomly selected subsample, then one can assess the bias introduced by measurement error in parameter estimation, and use this information to derive improved estimates based upon all available data. Formulaically these estimates based upon the reference variables from the validation subsample combined with the surrogate variables from the whole sample can be interpreted as starting with the estimate from reference variables in the validation subsample, and "augmenting" this with additional information from the surrogate variables. This suggests the term "augmented" estimate. The meerva package calculates these augmented estimates in the regression setting when there is a randomly selected subsample with both surrogate and reference variables. Measurement errors may be differential or non-differential, in any or all predictors (simultaneously) as well as outcome. The augmented estimates derive, in part, from the multivariate correlation between regression model parameter estimates from the reference variables and the surrogate variables, both from the validation subset. Because the validation subsample is chosen at random any biases imposed by measurement error, whether non-differential or differential, are reflected in this correlation and these correlations can be used to derive estimates for the reference variables using data from the whole sample. The main functions in the package are meerva.fit which calculates estimates for a dataset, and meerva.sim.block which simulates multiple datasets as described by the user, and analyzes these datasets, storing the regression coefficient estimates for inspection. The augmented estimates, as well as how measurement error may arise in practice, is described in more detail by Kremers WK (2021) and is an extension of the works by Chen Y-H, Chen H. (2000) , Chen Y-H. (2002) , Wang X, Wang Q (2015) and Tong J, Huang J, Chubak J, et al. (2020) . Package: r-cran-meetupapi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-purrr, r-cran-dplyr, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-meetupapi_0.1.0-1.ca2404.1_all.deb Size: 29396 MD5sum: 3b094f44602a0cf8bb54e66eb5ce57cf SHA1: d44076ec6aaad5532bce2e4fc100b66bff48c54a SHA256: cb4adf1eef6e944f9d76c3d18a6af76c425b44e3c26c758457f34c5574040a26 SHA512: 13bc1edbfdfa42956894ea3fc6080d62e968ad11eb82b30d9006c19f331d616f0b839099ec129068d577088c0dc14367367a7af0002dd4a7d9bef5f9404b8a2a Homepage: https://cran.r-project.org/package=meetupapi Description: CRAN Package 'meetupapi' (Access 'Meetup' API) Allows management of 'Meetup' groups via the . Provided are a set of functions that enable fetching information of joined meetups, attendance, and members. This package requires the use of an API key. 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Supports authentication via 'OAuth2' and includes functions for common queries and data manipulation tasks. Package: r-cran-mefa4 Architecture: all Version: 0.3-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-mefa Filename: pool/dists/noble/main/r-cran-mefa4_0.3-12-1.ca2404.1_all.deb Size: 591074 MD5sum: a9c899b15e5419c82f595e34a2ec771f SHA1: 3a084f788dfd1517c67e6880962834a01e0ab9ea SHA256: 5d872070e5bca2602a807ec32c5aeb7d76542a9c796f61673418409b3bba7801 SHA512: 8f80949a20d9d5d103373e06bf41dc4aebc257affe1daa1a9ad996616e4a05f4c96594456ae27e19e1872f59ec122f3b466fca5084a68631caaa1dacd982c640 Homepage: https://cran.r-project.org/package=mefa4 Description: CRAN Package 'mefa4' (Multivariate Data Handling with S4 Classes and Sparse Matrices) An S4 update of the 'mefa' package using sparse matrices for enhanced efficiency. Sparse array-like objects are supported via lists of sparse matrices. Package: r-cran-mefa Architecture: all Version: 3.2-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-vegan, r-cran-mefa4, r-cran-matrix Filename: pool/dists/noble/main/r-cran-mefa_3.2-10-1.ca2404.1_all.deb Size: 251572 MD5sum: 5ad35263e40c77a56f455a2eb51975b9 SHA1: 5d4ce4a4804b393348a8941dcf0aceccfe0eca98 SHA256: 1e74485af0a8e97053e1f5a28fd0daf63f2c2a6ba46b4b6915b924884cccb13a SHA512: f0b8ba5fffb31de4566c2577c642ac51e2c565972b73c7b2186649c95c425683f0f7ee93651f42175a082f19cb4f0c5425a9d9da00d6efc0474ca22ba45ad67c Homepage: https://cran.r-project.org/package=mefa Description: CRAN Package 'mefa' (Multivariate Data Handling in Ecology and Biogeography) A framework package aimed to provide standardized computational environment for specialist work via object classes to represent the data coded by samples, taxa and segments (i.e. subpopulations, repeated measures). It supports easy processing of the data along with cross tabulation and relational data tables for samples and taxa. An object of class `mefa' is a project specific compendium of the data and can be easily used in further analyses. Methods are provided for extraction, aggregation, conversion, plotting, summary and reporting of `mefa' objects. Reports can be generated in plain text or LaTeX format. Vignette contains worked examples. Package: r-cran-mefdind Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rvest, r-cran-stringr Filename: pool/dists/noble/main/r-cran-mefdind_0.1-1.ca2404.1_all.deb Size: 51352 MD5sum: 9842a5af97dd59f77cb74eb16e4c84ee SHA1: b49895f46817bfdd3763d906b3f8e960a0bf59b6 SHA256: c25069b7ae661c6309a155f51740d0bc2804ccdb21ef9a5d8de309b14996700b SHA512: a36c3550afb1c304dbc05a77fbd7cd4956559e2ea8d881e1f73bfb1630c46e305562ef3626f42e99b89f1d2a05a4c4be2524dab441b4f39c0fb0a19ff43e911c Homepage: https://cran.r-project.org/package=mefdind Description: CRAN Package 'mefdind' (Imports Data from MoE Spain) Imports indicator data provided by the Ministry of Education (MoE),Spain. The data is stored at Includes functions for reading, downloading, and selecting data for main series. This package is not sponsored or supported by the MoE Spain. Importa datos con indicadores del Ministerio de Educación y Formación Profesional (MEFD) de Españá. Los datos están en Contiene funciones para leer, descargar, y seleccionar bases de datos de series principales. Este paquete no es patrocinado o respaldado por el MEFD. Package: r-cran-mefm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tensormiss Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mefm_0.1.1-1.ca2404.1_all.deb Size: 54900 MD5sum: 0c0a1b4338b96503feca387971a9e846 SHA1: 4199014e1b02d794f358e79197b34dcdebb62d7f SHA256: efe0e3a12aa86c865aa8ca0b683f1d36efdba10489d290da6e492ab24335a588 SHA512: 141be1ecebd7b95e4a247e387505d78bdd8c4e5d11b4492f4aa753f4b649da9c4517b9ac028201665b4a6649d6dea9b957991af8c191a45b68bbb3097cb59a91 Homepage: https://cran.r-project.org/package=MEFM Description: CRAN Package 'MEFM' (Perform MEFM Estimation on Matrix Time Series) To perform main effect matrix factor model (MEFM) estimation for a given matrix time series as described in Lam and Cen (2024) . Estimation of traditional matrix factor models is also supported. 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For ecological studies, we may need to randomly select many such posterior phylogenies to conduct analyses. This data package serves this purpose by providing a small number (100 or 50) of randomly selected posterior phylogenies (if available) so that we can readily use them for our downstream analyses without repeating the downloading and selecting processes. Package: r-cran-megb Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gbm, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-megb_0.2-1.ca2404.1_all.deb Size: 42998 MD5sum: 4e1e42a82322d4bdfbba462fa6a353d1 SHA1: d61269df14804b0616e4db4272221f99db744843 SHA256: 3ade71a2553b775792471316881b37021f727607371133d002aee93b2461564f SHA512: 625199cfb8a338acbc69bf9dd2c39a77fb64d20f0cc7c1734d82218bf6bb075297d4d25b312f16e656def8173da4332cc1e45af05da4be94243c325c02895640 Homepage: https://cran.r-project.org/package=MEGB Description: CRAN Package 'MEGB' (Gradient Boosting for Longitudinal Data) Gradient boosting is a powerful statistical learning method known for its ability to model complex relationships between predictors and outcomes while performing inherent variable selection. However, traditional gradient boosting methods lack flexibility in handling longitudinal data where within-subject correlations play a critical role. In this package, we propose a novel approach Mixed Effect Gradient Boosting ('MEGB'), designed specifically for high-dimensional longitudinal data. 'MEGB' incorporates a flexible semi-parametric model that embeds random effects within the gradient boosting framework, allowing it to account for within-individual covariance over time. Additionally, the method efficiently handles scenarios where the number of predictors greatly exceeds the number of observations (p>>n) making it particularly suitable for genomics data and other large-scale biomedical studies. 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Package: r-cran-mekko Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mekko_0.1.0-1.ca2404.1_all.deb Size: 85490 MD5sum: cadeab281b8d990fe229ced0373053ba SHA1: c0dbf90510c3537ced613beffa2aac0c381200cd SHA256: 194be33136711ac9a8e106d184bc0e7bcfaf31758e9505c741f231f1ee587838 SHA512: 5afffbc4d157218aa27a7e212179dd292a9bea24ab2dac22723271ee1a8d95fff6a69976916a4a5df8fe848a49ddac86b735128ff8e436011a57abbdafb51c87 Homepage: https://cran.r-project.org/package=mekko Description: CRAN Package 'mekko' (Variable Width Bar Charts: Bar Mekko) Create variable width bar charts i.e. "bar mekko" charts to include important quantitative context. Closely related to mosaic, spine (or spinogram), matrix, submarine, olympic, Mondrian or product plots and tree maps. Package: r-cran-melidosdata Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-hms, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-gt, r-cran-lightlogr, r-cran-testthat, r-cran-vroom Filename: pool/dists/noble/main/r-cran-melidosdata_1.0.6-1.ca2404.1_all.deb Size: 1224288 MD5sum: a0db21b9eda8f1f82791930eb35fc20d SHA1: 2be3c31d0dbdbf2401c35baf9e8c390226b62b87 SHA256: fc49586e4740c6f8fb69bd70c2b06869194076a37a847665d936041c796f7302 SHA512: 4b3f7d2d9961bd1723583de157cfd44fec87ee8bc22c15992362b7e83b1189133808ce3d23de09c26682690ec638503d7b82cf00bacf716dcfd3b50fa3d43360 Homepage: https://cran.r-project.org/package=melidosData Description: CRAN Package 'melidosData' (Load Data from the MeLiDos Field Study) In the MeLiDos field study, personal light exposure data were collected in 9 sites, 7 countries, and 196 participants following the Guidolin et al. (2024) protocol. Data originate from wearable devices collecting personal light exposure at the eye level, chest, and the wrist. Questionnaires were collected via 'REDCap' and contain demographic information as well as chronotype, current conditions, sleep diaries, wear logs, and many more. This package makes loading the data from the respective repositories () into R a breeze. It further contains some quality of life functions for label handling and data from 'REDCap'. Package: r-cran-mem Architecture: all Version: 2.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sm, r-cran-boot, r-cran-rcolorbrewer, r-cran-mclust, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-rcpproll, r-cran-envstats Suggests: r-cran-magick Filename: pool/dists/noble/main/r-cran-mem_2.19-1.ca2404.1_all.deb Size: 419128 MD5sum: 8c601b4eb6db95d51832f1bb86b6e9db SHA1: d18bd4c264eea561925f1d85d3ac8a6a74671f0d SHA256: 95c5a707b1439919303d3ddb47cd0b9dc94701ed385e2a50ca82b4fb0958a0c1 SHA512: 1e91a51f10c96cf8210949a796815fe93d1fd07f11308ac4160134a1b3bf3308de0fb430ccb856f7d7ca94a437130b399fb3510116ef61c73e037d06d3eda005 Homepage: https://cran.r-project.org/package=mem Description: CRAN Package 'mem' (The Moving Epidemic Method) The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) , , allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week. Package: r-cran-memapp Architecture: all Version: 2.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1520 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-shinywidgets, r-cran-shinybs, r-cran-shinyjs, r-cran-rcolorbrewer, r-cran-tidyr, r-cran-dplyr, r-cran-stringr, r-cran-stringi, r-cran-dt, r-cran-formattable, r-cran-ggplot2, r-cran-plotly, r-cran-mem Suggests: r-cran-openxlsx, r-cran-readxl, r-cran-foreign, r-cran-haven, r-cran-readods, r-cran-rodbc, r-cran-magick Filename: pool/dists/noble/main/r-cran-memapp_2.16-1.ca2404.1_all.deb Size: 158358 MD5sum: 7a5c8f2db45206beb47b9425cd0709c3 SHA1: c8adf0b3532faa05f78fd14b6883a75ef0e1767c SHA256: e995c771ae97e9a91a7de2dde07f39ddc751da5ddef0fdf2aa434db446d71d16 SHA512: 8e83fffebb5ec19b88c373b11c84b3dc07f1f89b75d9fa3050c608b3d17be40ddb3b2e121d60dfef1614e9c91ddcaffcdde5a93f8452e267d1b602da45ce4a55 Homepage: https://cran.r-project.org/package=memapp Description: CRAN Package 'memapp' (The Moving Epidemic Method Web Application) The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) , , allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week. 'memapp' is a web application created in the Shiny framework for the 'mem' R package. Package: r-cran-meme Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3724 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridgraphics, r-cran-magick, r-cran-showtext, r-cran-sysfonts Suggests: r-cran-cowplot, r-cran-ggimage, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-shadowtext Filename: pool/dists/noble/main/r-cran-meme_0.2.4-1.ca2404.1_all.deb Size: 2854668 MD5sum: 5260f9108146b2ba00d2e080af44221b SHA1: bcf2fef109185effae64d197fbfbbff79ed5021c SHA256: ce52a3b3b9e4651c4d0bb83fc9ed0dd9f35cc0e008a7697aa9efee159c8ce79f SHA512: 680ceec4d3a3f16a95c4136eb0e5e0b73a1c5852e77fc9b3fd413f9c0c98f54d2a2e1e587083724ac9c3960cdfeedc076be3bc13fa78007d41b3ff7200a0523b Homepage: https://cran.r-project.org/package=meme Description: CRAN Package 'meme' (Create Meme) The word 'Meme' was originated from the book, 'The Selfish Gene', authored by Richard Dawkins (1976). It is a unit of culture that is passed from one generation to another and correlates to the gene, the unit of physical heredity. The internet memes are captioned photos that are intended to be funny, ridiculous. Memes behave like infectious viruses and travel from person to person quickly through social media. The 'meme' package allows users to make custom memes. Package: r-cran-memery Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-showtext, r-cran-sysfonts, r-cran-png, r-cran-jpeg, r-cran-cairo, r-cran-ggplot2, r-cran-cowplot, r-cran-magrittr, r-cran-purrr, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinybs, r-cran-colourpicker Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-magick, r-cran-gifski Filename: pool/dists/noble/main/r-cran-memery_0.6.0-1.ca2404.1_all.deb Size: 112340 MD5sum: 61122679ea09796d34d1990f729002de SHA1: 42c857f20674f206eb38b2e3741f33bf6d1dd8a0 SHA256: c57b6fbc6a735caf4b1f734e10a859ad6c74ae92ce49c398339b38116e33138d SHA512: 7a784e517bc03314c3e03dcf20b664d937ff247cbb335da51c01d9d168a6651bbf7657cefadd642f6f96c5694317ef30996fa953dc20bb62db574813b370dd86 Homepage: https://cran.r-project.org/package=memery Description: CRAN Package 'memery' (Internet Memes for Data Analysts) Generates internet memes that optionally include a superimposed inset plot and other atypical features, combining the visual impact of an attention-grabbing meme with graphic results of data analysis. The package differs from related packages that focus on imitating and reproducing standard memes. Some packages do this by interfacing with online meme generators whereas others achieve this natively. This package takes the latter approach. It does not interface with online meme generators or require any authentication with external websites. It reads images directly from local files or via URL and meme generation is done by the package. While this is similar to the 'meme' package available on CRAN, it differs in that the focus is on allowing for non-standard meme layouts and hybrids of memes mixed with graphs. While this package can be used to make basic memes like an online meme generator would produce, it caters primarily to hybrid graph-meme plots where the meme presentation can be seen as a backdrop highlighting foreground graphs of data analysis results. The package also provides support for an arbitrary number of meme text labels with arbitrary size, position and other attributes rather than restricting to the standard top and/or bottom text placement. This is useful for proper aesthetic interleaving of plots of data between meme image backgrounds and overlain text labels. The package offers a selection of templates for graph placement and appearance with respect to the underlying meme. Graph templates also permit additional template-specific customization. Animated gif support is provided but this is optional and functional only if the 'magick' package is installed. 'magick' is not required unless gif functionality is desired. Package: r-cran-memgene Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-gdistance, r-cran-raster, r-cran-sp, r-cran-vegan Suggests: r-cran-adegenet, r-cran-geosphere, r-cran-knitr Filename: pool/dists/noble/main/r-cran-memgene_1.0.3-1.ca2404.1_all.deb Size: 1392744 MD5sum: 59fa60e102442ba4b407071b8fd3eedf SHA1: 990c6f2ef42b0b491bccee116d9fd9460d64ffe9 SHA256: c659e4e855d183282250dbf9dabffb0f4f245162b749d2c84b88e8ea443cc7bf SHA512: f705a1a0e2d89467024c7186b6a1640708be7d0cb2b31c60294c783dfa6554f2529dc8e86fff07c098fbaf84956423e142b40dee0bc350a968eea9fdde7b109e Homepage: https://cran.r-project.org/package=memgene Description: CRAN Package 'memgene' (Spatial Pattern Detection in Genetic Distance Data Using Moran'sEigenvector Maps) Can detect relatively weak spatial genetic patterns by using Moran's Eigenvector Maps (MEM) to extract only the spatial component of genetic variation. Has applications in landscape genetics where the movement and dispersal of organisms are studied using neutral genetic variation. Package: r-cran-memify Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-memify_0.1.1-1.ca2404.1_all.deb Size: 23944 MD5sum: 36cb417369d8f327b5e63ce5b6c10a51 SHA1: 9b5e8af4707376d645d68e739a6738855c144993 SHA256: bb6e687d381cc2b135ea9fd514f3e69d9f5059c891e3ba139955149cad7b22cf SHA512: f27b85616a9bc3f0368a44938742e37d2eb226f2cbcdd04e4c6433775c90afb766604df4df81dbc55cda5e2058c3efbe6dc1923e34431ae1323343c00c223b66 Homepage: https://cran.r-project.org/package=memify Description: CRAN Package 'memify' (Constructing Functions That Keep State) A simple way to construct and maintain functions that keep state i.e. remember their argument lists. This can be useful when one needs to repeatedly invoke the same function with only a small number of argument changes at each invocation. 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On the one hand the package contains the most popular approaches for testing whether a time series exhibits a break in persistence from I(0) to I(1) or vice versa, such as those of Busetti and Taylor (2004) and Leybourne, Kim, and Taylor (2007). The approach by Martins and Rodrigues (2014), which allows to detect changes from I(d1) to I(d2) with d1 and d2 being non-integers, is included as well. In case the tests reject the null of constant persistence, various breakpoint estimators are available to detect the point of the break as well as the order of integration in the two regimes. On the other hand the package contains the most popular approaches to test for a change-in-mean of a long-memory time series, which were recently reviewed by Wenger, Leschinski, and Sibbertsen (2018). These include memory robust versions of the CUSUM, sup-Wald, and Wilcoxon type tests. The tests either utilize consistent estimates of the long-run variance or a self normalization approach in their test statistics. Betken (2016) Busetti and Taylor (2004) Dehling, Rooch and Taqqu (2012) Harvey, Leybourne and Taylor (2006) Horvath and Kokoszka (1997) Hualde and Iacone (2017) Iacone, Leybourne and Taylor (2014) Leybourne, Kim, Smith, and Newbold (2003) Leybourne and Taylor (2004) Leybourne, Kim, and Taylor (2007): Martins and Rodrigues (2014) Shao (2011) Sibbertsen and Kruse (2009) Wang (2008) Wenger, Leschinski and Sibbertsen (2018) . 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Package: r-cran-memoir Architecture: all Version: 1.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 634 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bookdown, r-cran-distill, r-cran-rmarkdown, r-cran-rmdformats, r-cran-usethis Suggests: r-cran-knitr, r-cran-pkgdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-memoir_1.3-1-1.ca2404.1_all.deb Size: 156096 MD5sum: 7059af6a50afc03e337871d499a4401e SHA1: c2e92032a74ff93051cf9d275626f23b33f3435e SHA256: 166b5cc61c5a5df2ba9b5732199489854a72512ad96c6bcba9c4c5790fdb2ff9 SHA512: 28a71c4fb512cf7189e0af13da4907685d22792af45e7d96f81fd4abc2d43412ad91ef5f6f75933f6dbb0e8ac29c80dc72ed60a30bf9a0de35ce2421b74723bd Homepage: https://cran.r-project.org/package=memoiR Description: CRAN Package 'memoiR' (R Markdown and Bookdown Templates to Publish Documents) Producing high-quality documents suitable for publication directly from R is made possible by the R Markdown ecosystem. 'memoiR' makes it easy. It provides templates to knit memoirs, articles and slideshows with helpers to publish the documents on GitHub Pages and activate continuous integration. 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Hardware Reading and Merging (HRM), which uses order statistics to merge; and MUlti-Correlation HEM (MUCH) which merges using a multivariate normal distribution. The reference paper for HRM is: S. Vilardell, I. Serra, R. Santalla, E. Mezzetti, J. Abella and F. J. Cazorla, "HRM: Merging Hardware Event Monitors for Improved Timing Analysis of Complex MPSoCs," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 11, pp. 3662-3673, Nov. 2020, . For MUCH: S. Vilardell, I. Serra, E. Mezzetti, J. Abella, and F. J. Cazorla. 2021. "MUCH: exploiting pairwise hardware event monitor correlations for improved timing analysis of complex MPSoCs". In Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC '21). Association for Computing Machinery. . This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773). 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Package: r-cran-meta.shrinkage Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-meta.shrinkage_0.1.4-1.ca2404.1_all.deb Size: 27322 MD5sum: ead939da4718e9eca77a1241440a99ee SHA1: 0e214f091bb07a6e0d811c8a8d1d79044453c831 SHA256: 98b047d0127449e6a2e8646fce7e0fd6e7e06a2e7fa09329603ae02e00061b26 SHA512: 1f7bdc7ad9e3eeda67464a96719afa4bdfadf2ee18329e9ab69723745cc0fa40c15553b56d0d67ea3313bfc1f3ed02d44fe5b24fc318b1f93d90f6e226c1ea8c Homepage: https://cran.r-project.org/package=meta.shrinkage Description: CRAN Package 'meta.shrinkage' (Meta-Analyses for Simultaneously Estimating Individual Means) Implement meta-analyses for simultaneously estimating individual means with shrinkage, isotonic regression and pretests. Include our original implementation of the isotonic regression via the pool-adjacent-violators algorithm (PAVA) algorithm. 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The installation of R package INLA is compulsory for successful usage. The INLA package can be obtained from . We recommend the testing version, which can be downloaded by running: install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/testing"), dep=TRUE). 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Package: r-cran-metaanalyser Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-ggvis, r-cran-dt, r-cran-rstudioapi Suggests: r-cran-rmeta Filename: pool/dists/noble/main/r-cran-metaanalyser_0.2.1-1.ca2404.1_all.deb Size: 41490 MD5sum: 5f9aa719be9c7fb704b9d5ab62270f8b SHA1: eb4765845306348afa4ee8cf23c14644a8382234 SHA256: c79836d531fa5527cce1d22110e7da916e5856b141a76a152029565976638169 SHA512: c205aa798735c9a4a7de3b1d38cf83eb64a130f6192e260ecf377b8e8c12e62a070aec0dcb97b44eeec4e51f45785eccd9838baaa28f6ca58e86c96ee5ea8604 Homepage: https://cran.r-project.org/package=MetaAnalyser Description: CRAN Package 'MetaAnalyser' (An Interactive Visualisation of Meta-Analysis as a PhysicalWeighing Machine) An interactive application to visualise meta-analysis data as a physical weighing machine. The interface is based on the Shiny web application framework, though can be run locally and with the user's own data. 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See the packages 'PublicationBias', 'phacking', and 'multibiasmeta'. These package implement methods described in, respectively: Mathur & VanderWeele (2020) ; Mathur (2022) ; Mathur (2022) . 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Therefore, researchers need to transform those information back to the sample mean and standard deviation. This package implemented sample mean estimators by Luo et al. (2016) , sample standard deviation estimators by Wan et al. (2014) , and the best linear unbiased estimators (BLUEs) of location and scale parameters by Yang et al. (2018, submitted) based on sample quantiles derived summaries in a meta-analysis. Package: r-cran-metabodata Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1749 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-fs, r-cran-magrittr, r-cran-piggyback, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-yaml Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-metabodata_0.6.3-1.ca2404.1_all.deb Size: 1681954 MD5sum: 9ef339fdfff97a87048f024dab4a6c07 SHA1: f7ced7ae9b96ed6d23494a842eeaaf3b9dabb7ad SHA256: bda9a9933a009db12b2a349654d228e472b4e175f173511c595448039424f5ef SHA512: 872072e8090beba8d1ba7445d4c0efce412379a64e399077b54fd181cea01951a737675fc6c5568907dcb74fcfad6633062e13bdf4af4f0933bcc36cda4e0c25 Homepage: https://cran.r-project.org/package=metaboData Description: CRAN Package 'metaboData' (Example Metabolomics Data Sets) Data sets from a variety of biological sample matrices, analysed using a number of mass spectrometry based metabolomic analytical techniques. The example data sets are stored remotely using GitHub releases which can be accessed from R using the package. The package also includes the 'abr1' FIE-MS data set from the 'FIEmspro' package . Package: r-cran-metabodecon Architecture: all Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4684 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-mathjaxr, r-cran-readjdx, r-cran-speaq, r-cran-toscutil, r-cran-withr Suggests: r-cran-covr, r-cran-devtools, r-cran-diffobj, r-cran-digest, r-cran-glue, r-bioc-impute, r-cran-knitr, r-cran-lifecycle, r-bioc-massspecwavelet, r-cran-microbenchmark, r-cran-pkgbuild, r-cran-pkgload, r-cran-r.devices, r-cran-rcmdcheck, r-cran-remotes, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis, r-cran-v8, r-cran-vdiffr, r-cran-waldo Filename: pool/dists/noble/main/r-cran-metabodecon_1.6.2-1.ca2404.1_all.deb Size: 3249054 MD5sum: 820fdfab829d8037c61a6dbd7e1e093e SHA1: 0f9966a047d5a15c087d400735a5cf8f1d97b2a4 SHA256: 9a156e0af013cf2d45220ee17c605a3b9fce6f915e5a06a98e3e3bc16ff0742b SHA512: 13b68b5171ffeb661d0593c9eb374d651a9c06f6394ea4fd51dcc349cc78256af8139546f28bfb7d49ffc2ffad031796a0ad65fc47ae7548d78f07d8486e0cb9 Homepage: https://cran.r-project.org/package=metabodecon Description: CRAN Package 'metabodecon' (Deconvolution and Alignment of 1d NMR Spectra) A framework for deconvolution, alignment and postprocessing of 1-dimensional (1d) nuclear magnetic resonance (NMR) spectra, resulting in a data matrix of aligned signal integrals. The deconvolution part uses the algorithm described in Koh et al. (2009) . The alignment part is based on functions from the 'speaq' package, described in Beirnaert et al. (2018) and Vu et al. (2011) . A detailed description and evaluation of an early version of the package, 'MetaboDecon1D v0.2.2', can be found in Haeckl et al. (2021) . Package: r-cran-metabolanalyze Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust, r-cran-mvtnorm, r-cran-ellipse, r-cran-gtools, r-cran-gplots Filename: pool/dists/noble/main/r-cran-metabolanalyze_1.3.1-1.ca2404.1_all.deb Size: 224718 MD5sum: 0890b1c1928e1a270abf175ac1e589ad SHA1: b8d518e64b68614f3c23218565086bb414c6f30b SHA256: 85b8c2abc9210843333f51e0d109da037238b7e04939a874a92c7f7a9b9215db SHA512: e21d20c549fcdac4b865541fde10d6cf6e5b859dc4c5999b3f4e3ad0edbb6a16500f126dee4aafa92e3978729eacc607be7b15eb8a7bed53f23c78d43077b9be Homepage: https://cran.r-project.org/package=MetabolAnalyze Description: CRAN Package 'MetabolAnalyze' (Probabilistic Latent Variable Models for Metabolomic Data) Fits probabilistic principal components analysis, probabilistic principal components and covariates analysis and mixtures of probabilistic principal components models to metabolomic spectral data. Package: r-cran-metabolic Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-magrittr, r-cran-usethis, r-cran-dplyr, r-cran-ggplot2, r-cran-ggfittext, r-cran-cli, r-cran-forcats, r-cran-ggimage, r-cran-patchwork, r-cran-scales, r-cran-stringr, r-cran-tidyr, r-cran-purrr, r-cran-meta, r-cran-glue, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-here, r-cran-rd2roxygen, r-cran-kableextra, r-cran-fansi, r-cran-downloadthis, r-cran-spelling Filename: pool/dists/noble/main/r-cran-metabolic_0.1.2-1.ca2404.1_all.deb Size: 205270 MD5sum: 98bed06e61505f5cc0fa17141830567b SHA1: 968f229b9183b26958a9034263087e117126b1df SHA256: fc8cbd1c09005ca21c2d0da21bd554a62d41e51c74d17c8e20ed2d2072e5c047 SHA512: 5c17af13bd7743c43aab4f982a7a81714f8950aca62d49c18675e57c535675f0f00ab8af67557eb8a762f912d64bad271ea86759230344a70de4fc9172568148 Homepage: https://cran.r-project.org/package=metabolic Description: CRAN Package 'metabolic' (Datasets and Functions for Reproducing Meta-Analyses) Dataset and functions from the meta-analysis published in Medicine & Science in Sports & Exercise. It contains all the data and functions to reproduce the analysis. "Effectiveness of HIIE versus MICT in Improving Cardiometabolic Risk Factors in Health and Disease: A Meta-analysis". Felipe Mattioni Maturana, Peter Martus, Stephan Zipfel, Andreas M Nieß (2020) . Package: r-cran-metabolicsurv Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-superpc, r-cran-glmnet, r-cran-matrixstats, r-cran-survminer, r-cran-survival, r-cran-rms, r-cran-tidyr, r-cran-pls, r-cran-rdpack, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metabolicsurv_1.1.2-1.ca2404.1_all.deb Size: 523646 MD5sum: af730233ef20d7b14b00b3d3afb318d8 SHA1: 7e070fae316c13b5b0a7bb1607c258e1041e305d SHA256: 58bcd301eb073eadf3ffd25aba101588ec680cc0df52e6facf813ba4da4dd7af SHA512: cd410976c275c2c905a41e3d02c0df35abc8c3d73e544a10a1a11dfc8112dd6b9b4b4a03746f8fd6ad111c6e431892b03adbf7f671d275c2d6c457693e5d0a6f Homepage: https://cran.r-project.org/package=MetabolicSurv Description: CRAN Package 'MetabolicSurv' (A Biomarker Validation Approach for Classification andPredicting Survival Using Metabolomics Signature) An approach to identifies metabolic biomarker signature for metabolic data by discovering predictive metabolite for predicting survival and classifying patients into risk groups. Classifiers are constructed as a linear combination of predictive/important metabolites, prognostic factors and treatment effects if necessary. Several methods were implemented to reduce the metabolomics matrix such as the principle component analysis of Wold Svante et al. (1987) , the LASSO method by Robert Tibshirani (1998) , the elastic net approach by Hui Zou and Trevor Hastie (2005) . Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected predictive metabolites and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique. Package: r-cran-metabolicsyndrome Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metabolicsyndrome_0.1.3-1.ca2404.1_all.deb Size: 15068 MD5sum: eb8b277ebe5acf6579f662cfdce1c310 SHA1: f8b9b69aa340eb5f97f0d0b3d366ed3d01b82795 SHA256: 117fafb479b876246682661d0cc8914883b73f440028d9450fd059ab45798246 SHA512: 49d3e2aa22008f65ce2c1b85910c0adf3bc56548871f85e83dfef1cdc2980bf91307ff45c185445d24e28957e785892aa01e78a808e0de4eaefbfb9946478432 Homepage: https://cran.r-project.org/package=MetabolicSyndrome Description: CRAN Package 'MetabolicSyndrome' (Diagnosis of Metabolic Syndrome) The modified Adult Treatment Panel -III guidelines (ATP-III) proposed by American Heart Association (AHA) and National Heart, Lung and Blood Institute (NHLBI) are used widely for the clinical diagnosis of Metabolic Syndrome. The AHA-NHLBI criteria advise using parameters such as waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), triglycerides (TG) and high-density lipoprotein cholesterol (HDLC) for diagnosis of metabolic syndrome. Each parameter has to be interpreted based on the proposed cut-offs, making the diagnosis slightly complex and error-prone. This package is developed by incorporating the modified ATP-III guidelines, and it will aid in the easy and quick diagnosis of metabolic syndrome in busy healthcare settings and also for research purposes. The modified ATP-III-AHA-NHLBI criteria for the diagnosis is described by Grundy et al ., (2005) . Package: r-cran-metabolomicsbasics Architecture: all Version: 1.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-c50, r-cran-caret, r-cran-e1071, r-cran-fs, r-cran-hirestec, r-cran-interpretmsspectrum, r-bioc-pcamethods, r-cran-plyr, r-cran-rpart, r-cran-rlang, r-cran-webchem Suggests: r-bioc-mixomics, r-bioc-ropls, r-bioc-xcms Filename: pool/dists/noble/main/r-cran-metabolomicsbasics_1.4.7-1.ca2404.1_all.deb Size: 321986 MD5sum: 871e6eeb937cf0da4d6098c96da78323 SHA1: 7978d89076737694d0ec7ecaa663bd86713a0b5b SHA256: 3d47093a7a4442e9b72103fd0cac20ddf48b426a7a5b0a8bbb998ee0a8584b05 SHA512: ed166528e049be77569cddb88e4930862cb2cf62fd8cd358dd8de498fab759264de5d3dd5ddf282ec4575fa6ff18d5585d7129f6672848ce3f87d5cbfc1f4182 Homepage: https://cran.r-project.org/package=MetabolomicsBasics Description: CRAN Package 'MetabolomicsBasics' (Basic Functions to Investigate Metabolomics Data Matrices) A set of functions to investigate raw data from (metabol)omics experiments intended to be used on a raw data matrix, i.e. following peak picking and signal deconvolution. Functions can be used to normalize data, detect biomarkers and perform sample classification. A detailed description of best practice usage may be found in the publication . Package: r-cran-metabolssmf Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 973 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-laplacesdemon, r-cran-nmf, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-lsei, r-cran-mclust, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-caroline, r-cran-ggsci, r-cran-biocmanager, r-bioc-biobase Filename: pool/dists/noble/main/r-cran-metabolssmf_0.1.0-1.ca2404.1_all.deb Size: 916750 MD5sum: 4c3c0e2172f807aa663892f1e8423a1f SHA1: f308266f03cece4a9ef04c64caf64da9c80b54cb SHA256: 41bae364d506c4927d8b75f7172a48c0eda20864cd2678decdeb2064cf9c08ae SHA512: fbe754a6fe83da509feb9d1782f46d2cff5dbdba06b9e0864de4e030802fe445adc23105eb51bca171b08be84ae7a38b7e0973c1df6b707f609a0f1bf40c7afd Homepage: https://cran.r-project.org/package=MetabolSSMF Description: CRAN Package 'MetabolSSMF' (Simplex-Structured Matrix Factorisation for MetabolomicsAnalysis) Provides a framework to perform soft clustering using simplex-structured matrix factorisation (SSMF). The package contains a set of functions for determining the optimal number of prototypes, the optimal algorithmic parameters, the estimation confidence intervals and the diversity of clusters. Abdolali, Maryam & Gillis, Nicolas (2020) . Package: r-cran-metabook Architecture: all Version: 0.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-meta, r-cran-netmeta, r-cran-metasens, r-cran-netdose Filename: pool/dists/noble/main/r-cran-metabook_0.2-0-1.ca2404.1_all.deb Size: 177896 MD5sum: 7aa4b344572c13fb0ca40cf8b423876b SHA1: ed0cd58736ca67e91d3b6d9a6441ad275c5e0a07 SHA256: 580ecc057641d023431ddbdea5e53aa27680c285f8c32fecd4dc56e258f97828 SHA512: 39c8942278b53466831de22ef4abdd8c0ba3e16f8b608fbbee505f25178c69c506b6731e1e4f75a895b65af5415c0038f5bbb7573a8bd0030dc6caedfd807abf Homepage: https://cran.r-project.org/package=metabook Description: CRAN Package 'metabook' (Data Sets and Code for "Meta-Analysis with R") Data sets and code supporting the second edition of "Meta-Analysis with R"; first edition: Schwarzer, Carpenter, and Rücker (2015) . Package: r-cran-metaboqc Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr Filename: pool/dists/noble/main/r-cran-metaboqc_1.1-1.ca2404.1_all.deb Size: 53490 MD5sum: ab49ff329f498e1e98e9fbee09073335 SHA1: f91896f282497d524ec43850a8512fda385798b0 SHA256: c8b1c9bb70975f353c2437023aea97ad43e830c2e02ba43ef378935d9db4e4b8 SHA512: 502a0d541e903d83d994d71154edb367a97e9598f6ad46c523b5592b28f50dcb9da49c6a4edc38c236dc0183f705f93f4bfbabcceabbb1a3e5bd7f726706fa7d Homepage: https://cran.r-project.org/package=MetaboQC Description: CRAN Package 'MetaboQC' (Normalize Metabolomic Data using QC Signal) Takes QC signal for each day and normalize metabolomic data that has been acquired in a certain period of time. At least three QC per day are required. Package: r-cran-metabup Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-partitions Filename: pool/dists/noble/main/r-cran-metabup_0.1.3-1.ca2404.1_all.deb Size: 22408 MD5sum: a3d8f4f9661f9318e9ce95a9ec672e3d SHA1: 04f9c1a08e3370a22baf3353a73e1d971cced751 SHA256: b46046b22ac04a5a4094567941fc790089f9a609c892fbf555af12621856bd0a SHA512: 4e6eb9bc56410b25a7c786c35c39319677bce350ca02a9781626615ca7307e7015b8f95711561327e47663d8cfe390a446dd982302cd735ada173b8c825fa209 Homepage: https://cran.r-project.org/package=metabup Description: CRAN Package 'metabup' (Bayesian Meta-Analysis Using Basic Uncertain Pooling) Contains functions that allow Bayesian meta-analysis (1) with binomial data, counts(y) and total counts (n) or, (2) with user-supplied point estimates and associated variances. Case (1) provides an analysis based on the logit transformation of the sample proportion. This methodology is also appropriate for combining data from sample surveys and related sources. The functions can calculate the corresponding similarity matrix. More details can be found in Cahoy and Sedransk (2023), Cahoy and Sedransk (2022) , Evans and Sedransk (2001) , and Malec and Sedransk (1992) . Package: r-cran-metacluster Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-factoextra, r-cran-cluster, r-cran-dbscan, r-cran-dplyr, r-cran-seqinr, r-bioc-biostrings Filename: pool/dists/noble/main/r-cran-metacluster_0.1.1-1.ca2404.1_all.deb Size: 67522 MD5sum: b1bf966293e1be626c536e0e03ff60c7 SHA1: 59355ab9d968a2353c137dc0bbeed4c534a913cc SHA256: 97be8c0b313d53831a42a94cb8bb9a056436085c09deb1ce4a81993b5aee1508 SHA512: aad26dd3978f86ea866fb9d5bbf774f92ec92bfd090f495628d99fe59a9850a0c9569bcc4849392d588c206302848d0b0e08fa03da17038f5f814441c69dcb0b Homepage: https://cran.r-project.org/package=metaCluster Description: CRAN Package 'metaCluster' (Metagenomic Clustering) Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning. Package: r-cran-metacom Architecture: all Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metacom_1.5.3-1.ca2404.1_all.deb Size: 63812 MD5sum: aa36b036d865dc92080aaba01936123a SHA1: cf1306dc90fccca9eba4fcb9e924bc733f3163fe SHA256: 4d20302503279a63a4815efb1c04761c728095ec309b6495299446afdf1ad171 SHA512: 2af8f9caf40449f2ea1053b374f8583001f75fb348fe2eb8893e42299b7b07f7eec11ae3eee8f24eed75204cca1e7de03c7b97d04b99b5e17f9eae49f8170352 Homepage: https://cran.r-project.org/package=metacom Description: CRAN Package 'metacom' (Analysis of the 'Elements of Metacommunity Structure') Functions to analyze coherence, boundary clumping, and turnover following the pattern-based metacommunity analysis of Leibold and Mikkelson 2002 . The package also includes functions to visualize ecological networks, and to calculate modularity as a replacement to boundary clumping. Package: r-cran-metacomp Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-plyr, r-cran-dplyr, r-cran-data.table, r-cran-ggplot2, r-cran-cairo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metacomp_1.1.2-1.ca2404.1_all.deb Size: 256100 MD5sum: ed93bdb4ee7e3ca63bbbb0386ee0780f SHA1: fda16ca566aebeba6f9bfe4cad19c2ad430a341f SHA256: b6d02b57ff6eeefec52fe0f1760cab6e652888c6d6aeac8b3700c7d92a4ff0cd SHA512: 8ff20508d8fcb6f8bf1f6c07d1e9c6e0493581e8299c0a0f2ed7ddbf24bf085ed70f28c736daa97861b58801476142ed14f9a457765f7c4505074014ec2f37c1 Homepage: https://cran.r-project.org/package=MetaComp Description: CRAN Package 'MetaComp' (EDGE Taxonomy Assignments Visualization) Implements routines for metagenome sample taxonomy assignments collection, aggregation, and visualization. Accepts the EDGE-formatted output from GOTTCHA/GOTTCHA2, BWA, Kraken, MetaPhlAn, DIAMOND, and Pangia. Produces SVG and PDF heatmap-like plots comparing taxa abundances across projects. Package: r-cran-metaconfoundr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2386 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-shiny, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-covr, r-cran-knitr, r-cran-metafor, r-cran-patchwork, r-cran-readr, r-cran-rio, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-metaconfoundr_0.1.2-1.ca2404.1_all.deb Size: 1780642 MD5sum: 8e1caa2d55a0b662193a2878942525c3 SHA1: 13ab29c96bf50aad144a13f644023b2fb4a76aa4 SHA256: ce1a64127d3a0a208e31770b2e6d49e19c297315e096a1f24c21b30c8055a96a SHA512: 64732b9707fcca2936ed74efa7cb14046b253b5e8ebde22bae70dea9ede121943ac4dc4e8f82902262bb3de14accbaac01756934e924d0aefdea92b48adb2daf Homepage: https://cran.r-project.org/package=metaconfoundr Description: CRAN Package 'metaconfoundr' (Visualize 'Confounder' Control in Meta-Analyses) Visualize 'confounder' control in meta-analysis. 'metaconfoundr' is an approach to evaluating bias in studies used in meta-analyses based on the causal inference framework. Study groups create a causal diagram displaying their assumptions about the scientific question. From this, they develop a list of important 'confounders'. Then, they evaluate whether studies controlled for these variables well. 'metaconfoundr' is a toolkit to facilitate this process and visualize the results as heat maps, traffic light plots, and more. Package: r-cran-metaconvert Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1227 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-comparedf, r-cran-metafor, r-cran-mvtnorm, r-cran-estimraw, r-cran-rio Suggests: r-cran-testthat, r-cran-metaumbrella, r-cran-toster, r-cran-esc, r-cran-epir, r-cran-compute.es, r-cran-meta, r-cran-effectsize, r-cran-metautility, r-cran-knitr, r-cran-dt, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metaconvert_1.0.3-1.ca2404.1_all.deb Size: 805066 MD5sum: d1427cc7d6607a90e96baa51431eea9b SHA1: 627a335cf71702700a6582f1931114e44bcdca8a SHA256: caa45a7a387548b6dc5945ce6f93d195df622eb0c254f1550a40789a8414289b SHA512: 73043968c2a2ed258d446dcea60adef4b81e3daee2dc4e1817e996477986f1e57c5ec49f94024cc70c8d4a835d181bd262ad6b7ed5688d417f032cf29031cca1 Homepage: https://cran.r-project.org/package=metaConvert Description: CRAN Package 'metaConvert' (An Automatic Suite for Estimation of Various Effect SizeMeasures) Automatically estimate 11 effect size measures from a well-formatted dataset. Various other functions can help, for example, removing dependency between several effect sizes, or identifying differences between two datasets. This package is mainly designed to assist in conducting a systematic review with a meta-analysis but can be useful to any researcher interested in estimating an effect size. Package: r-cran-metacor Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-officer, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metacor_1.2.1-1.ca2404.1_all.deb Size: 76704 MD5sum: bebc53ffa332601bf433cf93e686aaad SHA1: 26f8a0d4bd842c0a30594b7d29da3a4998ec2bf7 SHA256: 567deb8ee8807f202990b7da95ac5624ee3cb6a93b6913e25c5f025f30ee2347 SHA512: ebe3a3d99e0f6f4799eb5039e9758068f23979e0a12d9ce960b17aebab2b41ebbc5a4a1c607828b2f3381854ecaea96638950d367edfdf018242531744eaf941 Homepage: https://cran.r-project.org/package=metacor Description: CRAN Package 'metacor' (Meta-Analytic Effect Size Calculation for Pre-Post Designs withCorrelation Imputation) Tools for the calculation of effect sizes (standardised mean difference) and mean difference in pre-post controlled studies, including robust imputation of missing variances (standard deviation of changes) and correlations (Pearson correlation coefficient). The main function 'metacor_dual()' implements several methods for imputing missing standard deviation of changes or Pearson correlation coefficient, and generates transparent imputation reports. Designed for meta-analyses with incomplete summary statistics. For details on the methods, see Higgins et al. (2023) and Fu et al. (2013). Package: r-cran-metacore Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1970 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-readxl, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-xml2 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metacore_0.3.0-1.ca2404.1_all.deb Size: 1281890 MD5sum: bc2c4c9c1a400e997b0311715eec854a SHA1: a6092b5d26f4004c0e3fada9475c91e8bba391f7 SHA256: 6d7f3a4b3333bd29715c21dfd28e17a28b09a2d3bafcecf5e94c4eac73c5a831 SHA512: b59f6cffb6cd999360a0086a8b82418004aef873165c75e2e001004d3ff916f78b6fb6b69792d2c17eb00906e80ac0745cad97dc6c5b92ff88f2730f4df7ce46 Homepage: https://cran.r-project.org/package=metacore Description: CRAN Package 'metacore' (A Centralized Metadata Object Focus on Clinical Trial DataProgramming Workflows) Create an immutable container holding metadata for the purpose of better enabling programming activities and functionality of other packages within the clinical programming workflow. Package: r-cran-metaculr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-jsonlite, r-cran-progress, r-cran-tidyr, r-cran-verification, r-cran-clipr, r-cran-spatstat.geom, r-cran-ggrepel, r-cran-assertthat, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metaculr_0.4.1-1.ca2404.1_all.deb Size: 437190 MD5sum: 01c19ea65506e368de2b2b64b79d5306 SHA1: 165266ef7f3494397c6e4f1c09e940069d188094 SHA256: 6c27c8e789b50e493ff34fad33a5a6c443b1dd0c55bbfb13911e7563ff67c5c2 SHA512: 3f4400c8b946490f2bd71e7d60ae79180e79f7551ccbaf487ba46d65cd0b6cd6db80ebb06ecde5bc6ae1a751cb8db441ca72d82c4d7460cf616f0c2810bd16c2 Homepage: https://cran.r-project.org/package=MetaculR Description: CRAN Package 'MetaculR' (Analyze Metaculus Predictions and Questions) Login, download, and analyze questions predicted by you and/or the Metaculus community by interacting with the Metaculus API, currently located at . Package: r-cran-metacycle Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1366 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gnm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metacycle_1.2.1-1.ca2404.1_all.deb Size: 1028476 MD5sum: 035dd0ddbdfbc8265f9edca208afc31d SHA1: 65e5273fd7b0391e809385d691bf1f802d21c073 SHA256: adadf7b18dd63bbed4337db8d9a7d982f7c7284c6f82b7062bad1198ab650544 SHA512: f6b430584528a5e9826718789e524e9ec2cf62f586dd7bcac89cafe5ffb27d015a5eea78ff6873ada38b22bc2595a6072a4aeed60fcb2907ba9624babad98b86 Homepage: https://cran.r-project.org/package=MetaCycle Description: CRAN Package 'MetaCycle' (Evaluate Periodicity in Large Scale Data) There are two functions-meta2d and meta3d for detecting rhythmic signals from time-series datasets. For analyzing time-series datasets without individual information, 'meta2d' is suggested, which could incorporates multiple methods from ARSER, JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For analyzing time-series datasets with individual information, 'meta3d' is suggested, which takes use of any one of these three methods to analyze time-series data individual by individual and gives out integrated values based on analysis result of each individual. Package: r-cran-metadat Architecture: all Version: 1.6-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mathjaxr Suggests: r-cran-metafor, r-cran-numderiv, r-cran-biasedurn, r-cran-dfoptim, r-cran-igraph, r-cran-ape, r-cran-testthat, r-cran-digest, r-cran-lme4, r-cran-clubsandwich, r-cran-meta, r-cran-netmeta, r-cran-mvtnorm, r-cran-gridextra, r-cran-rms, r-cran-bayesmeta, r-cran-ellipse, r-cran-metasens, r-cran-mada Filename: pool/dists/noble/main/r-cran-metadat_1.6-0-1.ca2404.1_all.deb Size: 1066034 MD5sum: ca2ae608f7d8fd210d6dd1f9e26f41eb SHA1: 9fee847ebbd06db08449aa6a70b3ecec5c416aa8 SHA256: 4725eaf0b24f2a517a368b90945935ac0869b5daa338be8805048822b98e8a51 SHA512: f17c4e4b1e7de0ab03922a813dc6a8c3f4cb5369960f36c7d69a6072cb418a63f9d33fb0ad5d650178cd4c9a5c3eb002346eef0c6f4c24e6f139d2d05b0262be Homepage: https://cran.r-project.org/package=metadat Description: CRAN Package 'metadat' (Meta-Analysis Datasets) A collection of meta-analysis datasets for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses. Package: r-cran-metadeconfoundr Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1187 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-detectseparation, r-cran-lmtest, r-cran-foreach, r-cran-doparallel, r-cran-logger, r-cran-lme4, r-cran-ggplot2, r-cran-reshape2, r-cran-rlang, r-cran-circlize, r-cran-dplyr, r-cran-ggraph, r-cran-igraph, r-cran-magrittr, r-cran-scales, r-cran-stringr Suggests: r-cran-pander, r-cran-knitr, r-cran-gridextra, r-cran-kableextra, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-metadeconfoundr_1.0.5-1.ca2404.1_all.deb Size: 886054 MD5sum: b29f7ceaf22d71651c90681bb7a05515 SHA1: fdcfb501be54e5f14ba7927b2ea85c398bb9a2dc SHA256: 6610cba3f69502062d5bebae757d4962f923047f4ada6abaddb20d183f8cd97a SHA512: 6115219b77d1ee754340c54b8a65f534812b6405d0802c47a4ea1ca382d3ead626f65e9111044d87b974320844b58817b389733a9314f4593e7b6e55a1aa9422 Homepage: https://cran.r-project.org/package=metadeconfoundR Description: CRAN Package 'metadeconfoundR' (Covariate-Sensitive Analysis of Cross-Sectional High-DimensionalData) Using non-parametric tests, naive associations between omics features and metadata in cross-sectional data-sets are detected. In a second step, confounding effects between metadata associated to the same omics feature are detected and labeled using nested post-hoc model comparison tests, as first described in Forslund, Chakaroun, Zimmermann-Kogadeeva, et al. (2021) . The generated output can be graphically summarized using the built-in plotting function. Package: r-cran-metadigitise Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 697 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magick, r-cran-purrr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metadigitise_1.0.2-1.ca2404.1_all.deb Size: 431822 MD5sum: 239bcd8d888ab1f7f6811b3976c61dab SHA1: d0c13ca84c1c969a0cbbbfe30d55541c803976d3 SHA256: d1e41618e5b271b6702390eae83a2995e0a2600d4e7528a4b19d451482f02cf6 SHA512: 84f8c49f93e06b637f5d02f6c620e1da066c5cdece80c7520b0368df977d00f383ac9e000cde65ffc99c1a4c48f91755bfcf027b1b80eef991177313a475a6cd Homepage: https://cran.r-project.org/package=metaDigitise Description: CRAN Package 'metaDigitise' (Extract and Summarise Data from Published Figures) High-throughput, flexible and reproducible extraction of data from figures in primary research papers. metaDigitise() can extract data and / or automatically calculate summary statistics for users from box plots, bar plots (e.g., mean and errors), scatter plots and histograms. Package: r-cran-metadose Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-metafor, r-cran-rms, r-cran-rlang Filename: pool/dists/noble/main/r-cran-metadose_1.0.1-1.ca2404.1_all.deb Size: 33464 MD5sum: cc7a16d4eaaff9f7ef5d6fffbed809b8 SHA1: 7d662ecf532088cb243f637a71e51a3ae7e72158 SHA256: 088c75dd0b83a2355e425df03ea1339aa7fa917572f228c2d524009ca614e851 SHA512: 319793ea53542bce2378cc60892aae978f95d15e38b5a9a0ce8c8bbcb012ecdfd77308ff3a379db09f163f8cf4e445bb8e815c8d233ee073e0aa6a49963164f3 Homepage: https://cran.r-project.org/package=MetaDose Description: CRAN Package 'MetaDose' (Dose-Response Meta-Regression for Meta-Analysis) Conducting linear and nonlinear dose-response meta-regression using study-level summary data. It supports both continuous and binary outcomes and allows modeling of dose-effect relationships using linear trends or nonlinear restricted cubic splines. The package is designed to facilitate transparent, flexible, and reproducible dose-response meta-analyses, with built-in visualization of fitted dose-response curves. Package: r-cran-metadyn Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openmx, r-cran-matrix, r-cran-fitvarmxid Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-simstatespace, r-cran-mass, r-cran-metasem, r-cran-expm Filename: pool/dists/noble/main/r-cran-metadyn_1.0.1-1.ca2404.1_all.deb Size: 175128 MD5sum: 91480f991d65bb36709b9a8d81abc26e SHA1: 737f11d64589093f9252ccfdf2c91adf7bfd0410 SHA256: a2f0a431f21462f578b72a7f8c93a324df3146901b73c0b97f9fe9ca51450e4c SHA512: c3841b152a860bb8aae6e6afd7199911bf190431f6868a3c404b25e0942b7a8e88195d75269d28f75ee240c5941fe19b7a3598786dadd41d9913ab4df330301a Homepage: https://cran.r-project.org/package=metaDyn Description: CRAN Package 'metaDyn' (Multivariate Meta-Analysis of Dynamic Model Estimates) Fits fixed-, random-, or mixed-effects multivariate meta-analysis models using dynamic model estimates from each individual building on and extending Lee and Gates (2023) . Package: r-cran-metaensembler Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 980 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gridextra, r-cran-caret, r-cran-ggplot2, r-cran-e1071, r-cran-gbm, r-cran-randomforest Suggests: r-cran-knitr, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-metaensembler_0.1.0-1.ca2404.1_all.deb Size: 419086 MD5sum: 6ce5c134650859ecd7cdb2d0b5b151e0 SHA1: 4b680b567e6df9e4da5fd2c202f043a6542e3209 SHA256: 28106b926581f3f9fe13a7c891a50ef3558dedabbc4d3daadc09e36e12fde343 SHA512: 21c571386c535bdc13eecf6536e23bd55e63d4081153237b46eb5166aebf8a3d50f6bce5a8e3044a8f296ef6fea0abba3fb564f6a4a735374e3892b602eb6779 Homepage: https://cran.r-project.org/package=metaEnsembleR Description: CRAN Package 'metaEnsembleR' (Automated Intuitive Package for Meta-Ensemble Learning) Extends the base classes and methods of 'caret' package for integration of base learners. The user can input the number of different base learners, and specify the final learner, along with the train-validation-test data partition split ratio. The predictions on the unseen new data is the resultant of the ensemble meta-learning of the heterogeneous learners aimed to reduce the generalization error in the predictive models. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems. Package: r-cran-metaentropy Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-patchwork, r-cran-beeswarm, r-cran-knitr Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metaentropy_1.3-1.ca2404.1_all.deb Size: 116076 MD5sum: 2d7aa7fd495f219c2ee26947c6db829e SHA1: f12263b039e78601ac3603c01b3f012ca974be89 SHA256: 994c1b512af3060227b47c78f8d9d40049be00779bd87c67458d85ed8f49d020 SHA512: e1a6a9b73e88be3443c1c97af554e18a9c357436e3eb7e9f44d6b8ca92ce189765accfda18b2f5aadca232c2267250cf134c7d1ac601d7d530570b51eb4ccf73 Homepage: https://cran.r-project.org/package=MetaEntropy Description: CRAN Package 'MetaEntropy' (Functional Shannon Entropy for Virome Mutational Analysis) Estimates Shannon entropy, per gene and per genomic position, associated with non-synonymous mutation frequencies in viral populations, such as wastewater samples. The package uses codon translations for functional insights. Each amino acid can be treated as an individual state, resulting in a 20-state entropy computation, or grouped into one of six physicochemical classes, adding further functional context. Provides normalized values (0-1 scale) to facilitate the direct comparison of different genomic positions or total functional entropy across multiple metagenomes. Designed to analyze mutational data using tabular 'Single Nucleotide Variant' (SNV) frequency tables generated by variant callers (e.g., 'iVar' or 'LoFreq'), operating independently of consensus sequence estimation and multiple sequence alignment. Package: r-cran-metafor Architecture: all Version: 5.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5672 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-metadat, r-cran-numderiv, r-cran-nlme, r-cran-mathjaxr, r-cran-pbapply, r-cran-digest Suggests: r-cran-lme4, r-cran-pracma, r-cran-minqa, r-cran-nloptr, r-cran-dfoptim, r-cran-ucminf, r-cran-lbfgsb3c, r-cran-subplex, r-cran-bb, r-cran-rsolnp, r-cran-alabama, r-cran-optimparallel, r-cran-optimx, r-cran-compquadform, r-cran-mvtnorm, r-cran-biasedurn, r-cran-epi, r-cran-survival, r-cran-glmmadaptive, r-cran-glmmtmb, r-cran-car, r-cran-multcomp, r-cran-gsl, r-cran-sp, r-cran-ape, r-cran-boot, r-cran-clubsandwich, r-cran-crayon, r-cran-r.rsp, r-cran-testthat, r-cran-rmarkdown, r-cran-wildmeta, r-cran-emmeans, r-cran-estmeansd, r-cran-metablue, r-cran-rstudioapi, r-cran-glmulti, r-cran-mumin, r-cran-mice, r-cran-amelia, r-cran-calculus Filename: pool/dists/noble/main/r-cran-metafor_5.0-1-1.ca2404.1_all.deb Size: 5258332 MD5sum: 73321a991338088ec2798d2532b512dc SHA1: afdb0c7000b4a5d0d49b9a8186e39e69aac6ab5d SHA256: a163468de5bd9ee389669eebf41c57d02e747550c764744b1b5f0681c25c7869 SHA512: d44124aed3e5b2e4d5c87b92d5b00cff612d42a902c145516b24cbb5fb9a3d030e06d1546a93897b5dc51a7e7dde766e8f8cdd3acf2600f988ec4a3f827619e2 Homepage: https://cran.r-project.org/package=metafor Description: CRAN Package 'metafor' (Meta-Analysis Package for R) A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit equal-, fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) . Package: r-cran-metaforest Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gtable, r-cran-data.table, r-cran-metafor, r-cran-ranger, r-cran-metadat Suggests: r-cran-testthat, r-cran-caret, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-metaforest_0.1.5-1.ca2404.1_all.deb Size: 178816 MD5sum: 3581435c0c3358c3fbfbb354ec4a8d98 SHA1: 48737282278f7002cbf6e4aa5fd1427c11a77945 SHA256: cb40472e2103f5cb8bc2761dc7d1d85d666767f8389efd4d103d453b0943df02 SHA512: 1d1a32e5b4491592d9140e96e2e70dac6c1fa8dc5502cefbf174113850449850e5d1a8cf3f62cbf023cc43c9def7cf4977ef9cf57995b0e776d814a37bf35d3b Homepage: https://cran.r-project.org/package=metaforest Description: CRAN Package 'metaforest' (Exploring Heterogeneity in Meta-Analysis using Random Forests) Conduct random forests-based meta-analysis, obtain partial dependence plots for metaforest and classic meta-analyses, and cross-validate and tune metaforest- and classic meta-analyses in conjunction with the caret package. A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates (Van Lissa, 2020). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size. Package: r-cran-metafrontier Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-numderiv, r-cran-lpsolveapi Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sfar, r-cran-frontier, r-cran-benchmarking, r-cran-plm, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-metafrontier_0.2.2-1.ca2404.1_all.deb Size: 327998 MD5sum: 02232cb69cd0be6f621733015aeffc77 SHA1: d57aa78506f7813e18ae54b8c1cbaddcfb1100f0 SHA256: 35856d4af77cc0770ad1a04cf1d94112a20f3457fc284e90e55530c09abef45d SHA512: 213c4dd8c1a0ba35772da87f4ddd9704e3a4360849a032813c6bcd9357294ad078d7959fe1824c93c1eff7366e22870320d4eb29035240d0b30508c191f22ec5 Homepage: https://cran.r-project.org/package=metafrontier Description: CRAN Package 'metafrontier' (Analysis of Metafrontier Models for Efficiency and Productivity) Implements metafrontier production function models for estimating technical efficiencies and technology gaps for firms operating under different technologies. Supports both stochastic frontier analysis (SFA) and data envelopment analysis (DEA) based metafrontiers. Includes the deterministic metafrontier of Battese, Rao, and O'Donnell (2004) , the stochastic metafrontier of Huang, Huang, and Liu (2014) , and the metafrontier Malmquist productivity index of O'Donnell, Rao, and Battese (2008) . Additional features include panel SFA with time-varying inefficiency, bootstrap confidence intervals for technology gap ratios, latent class metafrontier estimation via the EM algorithm, Murphy-Topel corrected standard errors, and 'ggplot2' visualisation methods. Package: r-cran-metafuse Architecture: all Version: 2.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-mass, r-cran-evd Filename: pool/dists/noble/main/r-cran-metafuse_2.0-1-1.ca2404.1_all.deb Size: 66414 MD5sum: 9d9911c8f93526820a17765fea855b23 SHA1: c64290bef491d1cf9a4feb2f25c8b63a52fa2913 SHA256: f87e31f80decba944c423a535ec67c99df262f752d16557ed693bd5d219079cb SHA512: 7dd2917ad38e48d55591138099df47738ca56e1acc7050a6f602593cce1f4058576c765b0b5562f04f884ca1cb00e56cbdcd47251ddeb95f08069321ede85cfa Homepage: https://cran.r-project.org/package=metafuse Description: CRAN Package 'metafuse' (Fused Lasso Approach in Regression Coefficient Clustering) Fused lasso method to cluster and estimate regression coefficients of the same covariate across different data sets when a large number of independent data sets are combined. Package supports Gaussian, binomial, Poisson and Cox PH models. Package: r-cran-metagam Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 941 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-ggplot2, r-cran-metafor, r-cran-rlang Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metagam_0.4.1-1.ca2404.1_all.deb Size: 594360 MD5sum: d20b535998007d82de236c6586a75a37 SHA1: a243e999c9f31a3ef0da927e459c7c74d0f8e97f SHA256: 52efb3aaaaa69cf45ba12fa7f3a422af4f8dc677974abe3faf1dbb75e8d2b827 SHA512: 21d67d421dc47002213b94b226cc7429b5fb3e2c964c931fd0a59e9633b491df9db94c509dcfe9a2fdfb8547ce49a9c63ad3148e5da763102d0908dfe3c194d4 Homepage: https://cran.r-project.org/package=metagam Description: CRAN Package 'metagam' (Meta-Analysis of Generalized Additive Models) Meta-analysis of generalized additive models and generalized additive mixed models. A typical use case is when data cannot be shared across locations, and an overall meta-analytic fit is sought. 'metagam' provides functionality for removing individual participant data from models computed using the 'mgcv' and 'gamm4' packages such that the model objects can be shared without exposing individual data. Furthermore, methods for meta-analysing these fits are provided. The implemented methods are described in Sorensen et al. (2020), , extending previous works by Schwartz and Zanobetti (2000) and Crippa et al. (2018) . Package: r-cran-metage Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corrplot, r-cran-data.table, r-cran-dplyr, r-cran-emdbook, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggrepel, r-cran-gplots, r-cran-ks, r-cran-purrr, r-cran-qqman, r-cran-rfast, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-viridis Suggests: r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-metage_1.2.2-1.ca2404.1_all.deb Size: 4701060 MD5sum: 3108ef3f0742a3366170750b8452bc2f SHA1: 5c65c6c33691557f883ae9f7af4c5ee4187b89af SHA256: 27668f78d698f464a71ad34f3cbcc07bda9621f38a7bb5b8f7ee9bd9ba26c69e SHA512: 0f445508f92f5b0e4b41e9a9060480f89b9889c3531f344bca67c7407ab64703329f0cf67ce378272b2263ea790452ab5f30061caa066e2502ae2138518ed0bb Homepage: https://cran.r-project.org/package=metaGE Description: CRAN Package 'metaGE' (Meta-Analysis for Detecting Genotype x Environment Associations) Provides functions to perform all steps of genome-wide association meta-analysis for studying Genotype x Environment interactions, from collecting the data to the manhattan plot. The procedure accounts for the potential correlation between studies. In addition to the Fixed and Random models, one can investigate the relationship between QTL effects and some qualitative or quantitative covariate via the test of contrast and the meta-regression, respectively. The methodology is available from: (De Walsche, A., et al. (2025) \doi{10.1371/journal.pgen.1011553}). Package: r-cran-metagear Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1074 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mass, r-cran-metafor, r-cran-stringr Suggests: r-bioc-ebimage, r-cran-ape, r-cran-hexview, r-cran-rcurl, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-metagear_0.7-1.ca2404.1_all.deb Size: 943154 MD5sum: 220e7dc6395a86d8bd3572cdbdb2a483 SHA1: 05e5879d0f187ecdad502802b2f3965473985ef5 SHA256: f61838cede5f2fd37115304a96d4dd90da4ac7ee6e5586a394580713a36534e3 SHA512: 52a9f99615ae46600be4ef9916ea2b4f7bf06ca2b337bd45da4971f769fe887ed993ed8a9ea76acbdb2f3f9a51f7e9ee02b195dd1fdc6eb660d51519394d1323 Homepage: https://cran.r-project.org/package=metagear Description: CRAN Package 'metagear' (Comprehensive Research Synthesis Tools for Systematic Reviewsand Meta-Analysis) Functionalities for facilitating systematic reviews, data extractions, and meta-analyses. It includes a GUI (graphical user interface) to help screen the abstracts and titles of bibliographic data; tools to assign screening effort across multiple collaborators/reviewers and to assess inter- reviewer reliability; tools to help automate the download and retrieval of journal PDF articles from online databases; figure and image extractions from PDFs; web scraping of citations; automated and manual data extraction from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in incomplete or missing study parameters; generation of random effects sizes for Hedges' d, log response ratio, odds ratio, and correlation coefficients for Monte Carlo experiments; covariance equations for modelling dependencies among multiple effect sizes (e.g., effect sizes with a common control); and finally summaries that replicate analyses and outputs from widely used but no longer updated meta-analysis software (i.e., metawin). Funding for this package was supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016) Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for R. Methods in Ecology and Evolution 7, 323-330 . Package: r-cran-metaggr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metaggr_0.3.0-1.ca2404.1_all.deb Size: 331906 MD5sum: f89c916d5c8300e5189fddc061a0e218 SHA1: f93a4a654f1d206a5974c671ce099e29ddccbf27 SHA256: 4d5f08503fcdd6c6622b5189d3abce1c51c118992bfc587bb04105f59cd209a1 SHA512: 1d3da07438ff36af46f5c9e528de351463b308c8971d6b5017a6f930302fd5b63049dd956f446470fef39ad562293f6c38a28fd039477f448685e61f6198c8fb Homepage: https://cran.r-project.org/package=metaggR Description: CRAN Package 'metaggR' (Calculate the Knowledge-Weighted Estimate) According to a phenomenon known as "the wisdom of the crowds," combining point estimates from multiple judges often provides a more accurate aggregate estimate than using a point estimate from a single judge. However, if the judges use shared information in their estimates, the simple average will over-emphasize this common component at the expense of the judges’ private information. Asa Palley & Ville Satopää (2021) "Boosting the Wisdom of Crowds Within a Single Judgment Problem: Selective Averaging Based on Peer Predictions" proposes a procedure for calculating a weighted average of the judges’ individual estimates such that resulting aggregate estimate appropriately combines the judges' collective information within a single estimation problem. The authors use both simulation and data from six experimental studies to illustrate that the weighting procedure outperforms existing averaging-like methods, such as the equally weighted average, trimmed average, and median. This aggregate estimate -- know as "the knowledge-weighted estimate" -- inputs a) judges' estimates of a continuous outcome (E) and b) predictions of others' average estimate of this outcome (P). In this R-package, the function knowledge_weighted_estimate(E,P) implements the knowledge-weighted estimate. Its use is illustrated with a simple stylized example and on real-world experimental data. Package: r-cran-metagroup Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-meta, r-cran-rlang Filename: pool/dists/noble/main/r-cran-metagroup_1.0.2-1.ca2404.1_all.deb Size: 62878 MD5sum: 8b3bd2fd8f7a6a61f9d6cd7a12bf8718 SHA1: b250e49feaacf55d27f0365eb988755c46849685 SHA256: 9d5070ef1bfc64180e85d6a36eba1abf80da91a37760b26d0cbc99213e1b57e4 SHA512: d9ef0a22843bee5392f3f2140d59a243bd591c5e728288619c34eaff6fe349679a709c54078d305810d163c08f1cba2bf58ecce43a8526d0afc7433588b587bc Homepage: https://cran.r-project.org/package=metagroup Description: CRAN Package 'metagroup' (Meaningful Grouping of Studies in Meta-Analysis) Performs meaningful subgrouping in a meta-analysis. This is a two-step process; first, use the iterative grouping functions (e.g., mgbin(), mgcont() ) to partition studies into statistically homogeneous clusters based on their effect size data. Second, use the meaning() function to analyze these new subgroups and understand their composition based on study-level characteristics (e.g., country, setting). This approach helps to uncover hidden structures in meta-analytic data and provide a deeper interpretation of heterogeneity. Package: r-cran-metahelper Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-confintr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metahelper_1.0.0-1.ca2404.1_all.deb Size: 77708 MD5sum: 52dd6c83124d306d0d2d5ba9ef3a3277 SHA1: 700f677ccab8edddde8b711ad36bb6288d1549fc SHA256: a26cbc5a6d3b5f0f38a01fc3018e352e2f040078a3cbdd49cf0f0ad23b6d0045 SHA512: 5615648a6b93feaabd7a1c276914487b52bdf5c2107dc240ed0fe5323fb37549c236b79a1841dbf0d5d63a71263da2e823f033e130bf707387febe16773a545c Homepage: https://cran.r-project.org/package=metaHelper Description: CRAN Package 'metaHelper' (Transforms Statistical Measures Commonly Used for Meta-Analysis) Helps calculate statistical values commonly used in meta-analysis. It provides several methods to compute different forms of standardized mean differences, as well as other values such as standard errors and standard deviations. The methods used in this package are described in the following references: Altman D G, Bland J M. (2011) Borenstein, M., Hedges, L.V., Higgins, J.P.T. and Rothstein, H.R. (2009) Chinn S. (2000) Cochrane Handbook (2011) Cooper, H., Hedges, L. V., & Valentine, J. C. (2009) Cohen, J. (1977) Ellis, P.D. (2009) Goulet-Pelletier, J.-C., & Cousineau, D. (2018) Hedges, L. V. (1981) Hedges L. V., Olkin I. (1985) Murad M H, Wang Z, Zhu Y, Saadi S, Chu H, Lin L et al. (2023) Mayer M (2023) Stackoverflow (2014) Stackoverflow (2018) . Package: r-cran-metaheuristicopt Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-metaheuristicopt_2.0.0-1.ca2404.1_all.deb Size: 242718 MD5sum: 02edf66551f47553d334374d269e4d0d SHA1: a5e6ea3e7ed616a68622ff824e7ea2d37debab09 SHA256: 3f6ef4a279c173d576aaa90aafc2b9f0ae12a510651ff7854e9d652e303176d8 SHA512: 3c837ca7f1712f76e69020b00f52a3fd23295bba88e8160cce87b494e5cef9bee2003a7b5a16dc58af236e93705b3b290c12c92665fc72111f5a2058cce2a001 Homepage: https://cran.r-project.org/package=metaheuristicOpt Description: CRAN Package 'metaheuristicOpt' (Metaheuristic for Optimization) An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 ), grey wolf optimizer (Mirjalili et al., 2014 ), dragonfly algorithm (Mirjalili, 2015 ), firefly algorithm (Yang, 2009 ), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 ), harmony search algorithm (Mahdavi et al., 2007 ), moth flame optimizer (Mirjalili, 2015 , sine cosine algorithm (Mirjalili, 2016 ), whale optimization algorithm (Mirjalili and Lewis, 2016 ), clonal selection algorithm (Castro, 2002 ), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013). Package: r-cran-metahunt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 911 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-quadprog, r-cran-dirichletreg, r-cran-withr Suggests: r-cran-grf, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metahunt_0.1.0-1.ca2404.1_all.deb Size: 512926 MD5sum: ddfee3b5ca6550176abd2eb67abbf935 SHA1: 519f812b41f37ce26e57d16801149e9ec8c07daa SHA256: 18032a75474d9218bb8397a5796cf94adba2abf05ff3ceb0436c5b1f682fe4b9 SHA512: 125a0c514fa963055f45ff2a18fb0d6fd756b2ad6d060e7bb3a52596ee37ee413b30ebd634d52f1c61de50d91d46a66686300d9d065b42e310e6d48d16557bb0 Homepage: https://cran.r-project.org/package=MetaHunt Description: CRAN Package 'MetaHunt' (Privacy-Preserving Meta-Analysis via Low-Rank Basis Hunting) Tools for privacy-preserving meta-analysis of function-valued quantities across heterogeneous studies. Implements the 'MetaHunt' pipeline, including the denoised functional Successive Projection Algorithm (d-fSPA) for basis hunting, constrained weight estimation, Dirichlet regression of weights on study-level covariates, target prediction, and split/cross conformal prediction intervals. Operates on aggregate-level function evaluations, so individual-level data from source studies are not required. Methodology described in Shi, Imai, and Zhang (2026) . Package: r-cran-metainc Architecture: all Version: 0.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-meta, r-cran-ggplot2, r-cran-confintr Suggests: r-cran-metafor Filename: pool/dists/noble/main/r-cran-metainc_0.2-1-1.ca2404.1_all.deb Size: 533146 MD5sum: 12a651c3788ab8fa3ed62ba3ce07c5d4 SHA1: c701106072370343be50c376c7e0758c3e09ac55 SHA256: 0c7644ead633fc3c073b34a23e4b44051a0df1c9bffd1124c19d43f2a817c357 SHA512: e147ff5c8fed6cebed11aeb704c9cf998234c51f87291d8f7ab38e85035f73781f64a1f4862039d73bdbf9d75fdb4c47c3f75157207ac789cfd6ac35cc1e813d Homepage: https://cran.r-project.org/package=metainc Description: CRAN Package 'metainc' (Assessment of Inconsistency in Meta-Analysis using DecisionThresholds) Assessment of inconsistency in meta-analysis by calculating the Decision Inconsistency index (DI) and the Across-Studies Inconsistency (ASI) index. These indices quantify inconsistency taking into account outcome-level decision thresholds. Package: r-cran-metaintegration Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsolnp, r-cran-corpcor, r-cran-mass, r-cran-knitr Filename: pool/dists/noble/main/r-cran-metaintegration_0.1.2-1.ca2404.1_all.deb Size: 79146 MD5sum: 1da746b5603598073e67737baf8cbbeb SHA1: d5c09dd6bf72000f8a740bd378b5a3e40a817fe0 SHA256: 1f37cf71759792258050d5d0d72823cab705713fe5073fff1730306f509a682b SHA512: 8853f7647ac959e3346c1bdd50009c4f8802e860f41944e2a58da22a869eeb00fff2997c7d48398b591f4e7bf5983e805524164c6a82ca6c2cb8270a0aa8180a Homepage: https://cran.r-project.org/package=MetaIntegration Description: CRAN Package 'MetaIntegration' (Ensemble Meta-Prediction Framework) An ensemble meta-prediction framework to integrate multiple regression models into a current study. Gu, T., Taylor, J.M.G. and Mukherjee, B. (2020) . A meta-analysis framework along with two weighted estimators as the ensemble of empirical Bayes estimators, which combines the estimates from the different external models. The proposed framework is flexible and robust in the ways that (i) it is capable of incorporating external models that use a slightly different set of covariates; (ii) it is able to identify the most relevant external information and diminish the influence of information that is less compatible with the internal data; and (iii) it nicely balances the bias-variance trade-off while preserving the most efficiency gain. The proposed estimators are more efficient than the naive analysis of the internal data and other naive combinations of external estimators. 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References: Mestre, F., Canovas, F., Pita, R., Mira, A., Beja, P. (2016) ; Mestre, F., Risk, B., Mira, A., Beja, P., Pita, R. (2017) ; Mestre, F., Pita, R., Mira, A., Beja, P. (2020) . 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The function that estimates the correlation structure is also provided standalone. Another key innovation, the estimation of the correlation parameter from the median product of correlated standard normal variables, is provided, as well as a complete set of functions for their underlying distribution: density, cumulative, quantile, and random deviates. Described in Tu and Ochoa (2025) . 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Package: r-cran-metamedian Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-estmeansd, r-cran-hmisc, r-cran-metablue, r-cran-metafor Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metamedian_1.2.2-1.ca2404.1_all.deb Size: 241416 MD5sum: 7f792afcd39efe6dfc8e486dc2783758 SHA1: 4cacf69b3f32e831c4644abefc83561846a72655 SHA256: 8801c605bd7a12da242b70fae8564fb85ebbe6d3a503abd9e8367abacf4c808d SHA512: 0e74b3b9902c04e214de0ef0323e1c791bb7212e7c4bb98122dbd9d43c350eac3dfc6dfe5b4831fd4c3ccd30b023c8eee50bb1852487c1c7529f97d7656ad6f0 Homepage: https://cran.r-project.org/package=metamedian Description: CRAN Package 'metamedian' (Meta-Analysis of Medians) Implements several methods to meta-analyze studies that report the sample median of the outcome. The methods described by McGrath et al. (2019) , Ozturk and Balakrishnan (2020) , and McGrath et al. (2020a) can be applied to directly meta-analyze the median or difference of medians between groups. Additionally, a number of methods (e.g., McGrath et al. (2020b) , Cai et al. (2021) , and McGrath et al. (2023) ) are implemented to estimate study-specific (difference of) means and their standard errors in order to estimate the pooled (difference of) means. Methods for meta-analyzing median survival times (McGrath et al. (2026) ) are also implemented. See McGrath et al. (2024) for a detailed guide on using the package. Package: r-cran-metamer Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-progress Suggests: r-cran-shiny, r-cran-miniui, r-cran-testthat, r-cran-data.table, r-cran-covr, r-cran-sf Filename: pool/dists/noble/main/r-cran-metamer_0.3.0-1.ca2404.1_all.deb Size: 213216 MD5sum: 52250fb0bade9192531f146becd9d903 SHA1: f58e11d89af41f8c8f98cd4599f6767e7a0d82bf SHA256: 1bd965511119b73ce86fe49b0cd7c84b3ad007586f4f30fe9b1763a76e3e9475 SHA512: 0a779252a161963052cfdd5be55415db6ed18b142b750670d5469fdc630ad182cc5d354c69621b6bf0ff2f2297a356e548b6ef71716b0ebf7add00a52ff82c8a Homepage: https://cran.r-project.org/package=metamer Description: CRAN Package 'metamer' (Create Data with Identical Statistics) Creates data with identical statistics (metamers) using an iterative algorithm proposed by Matejka & Fitzmaurice (2017) . Package: r-cran-metamicrobiomer Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1435 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss, r-cran-meta, r-cran-lme4, r-cran-gdata, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-gridextra, r-cran-lmertest, r-cran-matrixstats, r-cran-zcompositions, r-cran-compositions Suggests: r-cran-testthat, r-cran-rcurl, r-cran-httr, r-cran-repmis, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-gplots, r-cran-magrittr, r-cran-foreign, r-cran-mgcv, r-cran-reshape2, r-cran-caret, r-cran-randomforest, r-cran-tsibble, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-metamicrobiomer_1.2-1.ca2404.1_all.deb Size: 1339618 MD5sum: 3050fe13f794bac2ed5bf4d84875ee7e SHA1: 3bd4313b61857a3e7af8cd971b859d4030532695 SHA256: b23227555cba45d9b6c888a3bd34446c91ceddc8bc71750b3d3507a0c9822aee SHA512: be98bb0a5f802dc954e5395daebb8b68c0a345139aa3ee12e7aa705f71f50086d4f1634590f01579a3667e2ed7c40fde024eb34033107d418b488ed7138412cc Homepage: https://cran.r-project.org/package=metamicrobiomeR Description: CRAN Package 'metamicrobiomeR' (Microbiome Data Analysis & Meta-Analysis with GAMLSS-BEZI &Random Effects) Generalized Additive Model for Location, Scale and Shape (GAMLSS) with zero inflated beta (BEZI) family for analysis of microbiome relative abundance data (with various options for data transformation/normalization to address compositional effects) and random effects meta-analysis models for meta-analysis pooling estimates across microbiome studies are implemented. Random Forest model to predict microbiome age based on relative abundances of shared bacterial genera with the Bangladesh data (Subramanian et al 2014), comparison of multiple diversity indexes using linear/linear mixed effect models and some data display/visualization are also implemented. The reference paper is published by Ho NT, Li F, Wang S, Kuhn L (2019) . Package: r-cran-metamisc Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metafor, r-cran-mvtnorm, r-cran-lme4, r-cran-dplyr, r-cran-plyr, r-cran-proc, r-cran-ggplot2 Suggests: r-cran-coda, r-cran-runjags, r-cran-rjags, r-cran-ggmcmc, r-cran-logistf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metamisc_0.4.0-1.ca2404.1_all.deb Size: 561924 MD5sum: 219767d4660419d1a03f5988cb6e9bb3 SHA1: 72c2d6e971e2b73f5abd3df1d02cecef1882ec71 SHA256: 1b8b6a32ad9f7ec215571ee16e58ad9aec83a21b49a14c053cffc0f159fbcb2c SHA512: 01c41e7bdc42e0a91cba9e1234826594cb501c0f611f6678a3b2ff490e6c4b1783c24a85dbc61cb93acb38d44e60669bc1f69dc596ab44dc7b3c14199f8de6e7 Homepage: https://cran.r-project.org/package=metamisc Description: CRAN Package 'metamisc' (Meta-Analysis of Diagnosis and Prognosis Research Studies) Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) . It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) . Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) . Package: r-cran-metamorphr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1015 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-crayon, r-cran-dplyr, r-cran-ggplot2, r-bioc-impute, r-cran-lifecycle, r-cran-magrittr, r-cran-missforest, r-bioc-pcamethods, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringi, r-cran-tibble, r-cran-tidyr, r-cran-vctrs, r-cran-withr Suggests: r-cran-knitr, r-bioc-qsmooth, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metamorphr_0.3.0-1.ca2404.1_all.deb Size: 659084 MD5sum: ba64a5ff91c3da9f41c7abe1880fa4c8 SHA1: e7c4c69704fe4fdd25495d29a54f1ee4ca37c2bd SHA256: 9151f11d8dc2771aeb3b0a368eed8823776d68be66b3ec109eb3586ef958964f SHA512: 7615fca9914a1c2611a6bd98b2bbc80f3dce70ae28ef2706e2fc315edb339b839c65283208e447aa51dfefbf85154116388b04a7e00db22fcdb5d81d22bab5be Homepage: https://cran.r-project.org/package=metamorphr Description: CRAN Package 'metamorphr' (Tidy and Streamlined Metabolomics Data Workflows) Facilitate tasks typically encountered during metabolomics data analysis including data import, filtering, missing value imputation (Stacklies et al. (2007) , Stekhoven et al. (2012) , Tibshirani et al. (2017) , Troyanskaya et al. (2001) ), normalization (Bolstad et al. (2003) , Dieterle et al. (2006) , Zhao et al. (2020) ) transformation, centering and scaling (Van Den Berg et al. (2006) ) as well as statistical tests and plotting. 'metamorphr' introduces a tidy (Wickham et al. (2019) ) format for metabolomics data and is designed to make it easier to build elaborate analysis workflows and to integrate them with 'tidyverse' packages including 'dplyr' and 'ggplot2'. Package: r-cran-metan Architecture: all Version: 1.19.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3683 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggally, r-cran-ggforce, r-cran-ggplot2, r-cran-ggrepel, r-cran-lme4, r-cran-lmertest, r-cran-magrittr, r-cran-mathjaxr, r-cran-patchwork, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-metan_1.19.0-1.ca2404.1_all.deb Size: 3444484 MD5sum: c199104f020f4dadc40febec038f9eaf SHA1: f6ec8a7ef1646f66041a47e96eb85182897f512d SHA256: 1e8595c15ced47fb73eb0f8cda3be494ecf23b9d29103e989c2b626776042637 SHA512: b786522becfceb37254c6227943cb7b64d116322911c8c6b249fc6f65a70446620a42aaf99b62c3c8900d2f599f590e691536ac3b9488aac7cdaf598792e5214 Homepage: https://cran.r-project.org/package=metan Description: CRAN Package 'metan' (Multi Environment Trials Analysis) Performs stability analysis of multi-environment trial data using parametric and non-parametric methods. Parametric methods includes Additive Main Effects and Multiplicative Interaction (AMMI) analysis by Gauch (2013) , Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003) , geometric adaptability index by Mohammadi & Amri (2008) , joint regression analysis by Eberhart & Russel (1966) , genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method, power law residuals (POLAR) statistics by Doring et al. (2015) , scale-adjusted coefficient of variation by Doring & Reckling (2018) , stability variance by Shukla (1972) , weighted average of absolute scores by Olivoto et al. (2019a) , and multi-trait stability index by Olivoto et al. (2019b) . Non-parametric methods includes superiority index by Lin & Binns (1988) , nonparametric measures of phenotypic stability by Huehn (1990) , TOP third statistic by Fox et al. (1990) . Functions for computing biometrical analysis such as path analysis, canonical correlation, partial correlation, clustering analysis, and tools for inspecting, manipulating, summarizing and plotting typical multi-environment trial data are also provided. Package: r-cran-metanet Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4252 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-dplyr, r-cran-ggplot2, r-cran-ggnewscale, r-cran-ggrepel, r-cran-magrittr, r-cran-reshape2, r-cran-tibble, r-cran-pcutils, r-cran-rlang Suggests: r-cran-pheatmap, r-cran-vegan, r-cran-stringr, r-cran-foreach, r-cran-dosnow, r-cran-snow, r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc, r-cran-hmisc, r-cran-gifski, r-cran-ggraph, r-cran-networkd3, r-cran-ggpmisc, r-bioc-ggtree, r-bioc-treeio, r-cran-circlize, r-cran-jsonify, r-cran-ggpubr, r-cran-corrplot, r-cran-philentropy, r-cran-spatstat.random, r-cran-spatstat.geom, r-cran-sf Filename: pool/dists/noble/main/r-cran-metanet_0.3.1-1.ca2404.1_all.deb Size: 3881916 MD5sum: e381c5ea622e64d4b84f8d239aa3b12c SHA1: a04ea743d5dc0fbdc16cf66eed6e5b8fa3710359 SHA256: 35a54b19387acb20dc2bdabca9454ac0c015d8f6c6a92d64c2aa67b1199d78ee SHA512: b8954d360e9aa02850eb656be64e63c9d793b5b242821399ffc65c53d48f4fd12bef3d4201ec4bb262d77a4ab5697994b1c8f20eb78d9ff9f6e1f2b29219e5a4 Homepage: https://cran.r-project.org/package=MetaNet Description: CRAN Package 'MetaNet' (Network Analysis for Omics Data) Comprehensive network analysis package. Calculate correlation network fastly, accelerate lots of analysis by parallel computing. Support for multi-omics data, search sub-nets fluently. Handle bigger data, more than 10,000 nodes in each omics. Offer various layout method for multi-omics network and some interfaces to other software ('Gephi', 'Cytoscape', 'ggplot2'), easy to visualize. Provide comprehensive topology indexes calculation, including ecological network stability. Package: r-cran-metanetwork Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggally, r-cran-network, r-cran-ggplot2, r-cran-intergraph, r-cran-dplyr, r-cran-igraph, r-cran-matrix, r-cran-visnetwork, r-cran-rcolorbrewer, r-cran-magrittr, r-cran-ggimage, r-cran-rlang, r-cran-sna Suggests: r-cran-spelling, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metanetwork_0.7.0-1.ca2404.1_all.deb Size: 2766482 MD5sum: 42968a1b5ba5c89ad7e2858c07275a30 SHA1: 0d1ee59907c06a0aa721bb05855679952c91eaf4 SHA256: cfdbc20db4eb0acf4453ede22919abbfee4b8c3ed8d602c733180bff4a6be6d6 SHA512: afb186f4d67f5ca65b564592da7e76f43e910ec2ea699ebc79c95a8ce49764455cddd458d10a1e144edd4c383401f55c7d5e186c247402d54c86fb8d35a029a1 Homepage: https://cran.r-project.org/package=metanetwork Description: CRAN Package 'metanetwork' (Handling and Representing Trophic Networks in Space and Time) A toolbox to handle and represent trophic networks in space or time across aggregation levels. This package contains a layout algorithm specifically designed for trophic networks, using dimension reduction on a diffusion graph kernel and trophic levels. Importantly, this package provides a layout method applicable for large trophic networks. Package: r-cran-metanlp Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4804 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-tm, r-cran-textstem, r-cran-lexicon Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-wordcloud, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-metanlp_0.1.4-1.ca2404.1_all.deb Size: 4710986 MD5sum: 04cd5bf50e0b1e6a072d4b7b38cfdf1e SHA1: c619a9d85dc7a1f1f2e0a006796adcb191612e12 SHA256: ed2568fb4c831bf5e046d19d10910bab9602692c6ef49a37f8dad74d02b4c2c4 SHA512: 548e2903c61d6a4d9565ab26aa8b01a25d99ddceffb72bc5887b493e425d9eebc0278211bc156961961d018208c72db4e0bed1e11498afbc6480dbd9aec87256 Homepage: https://cran.r-project.org/package=MetaNLP Description: CRAN Package 'MetaNLP' (Natural Language Processing for Meta Analysis) Given a CSV file with titles and abstracts, the package creates a document-term matrix that is lemmatized and stemmed and can directly be used to train machine learning methods for automatic title-abstract screening in the preparation of a meta analysis. Package: r-cran-metann Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-metann_0.1.0-1.ca2404.1_all.deb Size: 331744 MD5sum: 69206ae78d091586d5be87c119423d97 SHA1: 6878562a0292389e309edb13a334ada4b26a3b38 SHA256: 205cfcc89ee074c367f028787589c09bdfc23c92f22fcb911d7263b3b5e01f1a SHA512: b8204f402abcf868ae53e15e981d525665043c6f8c0c624efcc1b12f05f48529403c1c1caa9f4ca6107cf6f96de7e572b63e9f7f9441c215090e1f0eefa24a11 Homepage: https://cran.r-project.org/package=metANN Description: CRAN Package 'metANN' (Metaheuristic and Gradient-Based Optimization for Neural NetworkTraining and Continuous Problems) Provides tools for general-purpose continuous optimization and feed-forward artificial neural network training using metaheuristic and gradient-based optimization algorithms. The package supports benchmark function optimization, regression, binary classification, and multi-class classification with multilayer perceptrons. The package implements several optimization methods, including particle swarm optimization Kennedy and Eberhart (1995) , differential evolution Storn and Price (1997) , grey wolf optimizer Mirjalili et al. (2014) , secretary bird optimization Fu et al. (2024) , and Adam Kingma and Ba (2015) . Package: r-cran-metansue Architecture: all Version: 2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-metansue_2.6-1.ca2404.1_all.deb Size: 109772 MD5sum: 2b788798271ab81cd7b65cfd4ffe7c43 SHA1: 90a785b4a312c8c752cef7864435008913a088a0 SHA256: 5a9e823a7c5e0f92b4036df2849e3bcce2cae0980b80647bdc9be7d9c9a16f2c SHA512: 7b5782edac05462861d09f4dd1cfe0fe9872a2b29cc94a926bf243ee16958b35d184b468180cf7ecc9f2cc08fe62981a71b4d1b6de0277b35370fb9593281006 Homepage: https://cran.r-project.org/package=metansue Description: CRAN Package 'metansue' (Meta-Analysis of Studies with Non-Statistically SignificantUnreported Effects) Novel method to unbiasedly include studies with Non-statistically Significant Unreported Effects (NSUEs) in a meta-analysis. First, the function calculates the interval where the unreported effects (e.g., t-values) should be according to the threshold of statistical significance used in each study. Afterward, the method uses maximum likelihood techniques to impute the expected effect size of each study with NSUEs, accounting for between-study heterogeneity and potential covariates. Multiple imputations of the NSUEs are then randomly created based on the expected value, variance, and statistical significance bounds. Finally, it conducts a restricted-maximum likelihood random-effects meta-analysis separately for each set of imputations, and it performs estimations from these meta-analyses. Please read the reference in 'metansue' for details of the procedure. Package: r-cran-metap Architecture: all Version: 1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-rdpack, r-cran-tfisher, r-cran-mathjaxr, r-cran-qqconf, r-cran-mutoss Filename: pool/dists/noble/main/r-cran-metap_1.14-1.ca2404.1_all.deb Size: 600614 MD5sum: 1a618c9757488567ff75975c4208485b SHA1: 5cc5bac0150c4b82a8611ed12aba7d9674cf2905 SHA256: 8be458774007dacd87814e32b9ba2d8e887ea6f9845e2d972ae0ac8cc3896cd0 SHA512: 59e185e3b87124f22ce0e4daeb014b7b5255f17f959206b4216c1ba953ebc79477fe446a5da6e579ffcf72b440913f8daa1853ce7687e7dde628f7605526aec5 Homepage: https://cran.r-project.org/package=metap Description: CRAN Package 'metap' (Meta-Analysis of Significance Values) The canonical way to perform meta-analysis involves using effect sizes. When they are not available this package provides a number of methods for meta-analysis of significance values including the methods of Edgington, Fisher, Lancaster, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results; and routines for graphical display. Package: r-cran-metaphonebr Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lifecycle, r-cran-stringi Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metaphonebr_0.0.5-1.ca2404.1_all.deb Size: 40370 MD5sum: 90cb0f56c03a2dfa26908e4b53b370bd SHA1: da658a7db971bf6e6eb11e43f32ae2105dda341b SHA256: 935921e0c4a8a4af360c99d56306729ef562ad45c8136b9f2d08ca706ba39f53 SHA512: 37f763347903e56c0b7958935516234fe7fd3f9c3ec0d8ca1ece41c8adc41544de10c0662236ef532f3859103908f7d298325575b0fa8da09e04de819e76e998 Homepage: https://cran.r-project.org/package=metaphonebr Description: CRAN Package 'metaphonebr' (Custom 'MetaphoneBR' Phonetic Encoding for Brazilian Names) Simplifies Brazilian names phonetically using a custom 'metaphoneBR' algorithm that preserves ending vowels. Useful for name matching processing preserving gender information carried generally by ending vowels in Portuguese. Mation (2025) . Package: r-cran-metaplot Architecture: all Version: 0.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-encode, r-cran-lattice, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-gridextra, r-cran-gtable, r-cran-ggplot2, r-cran-scales Suggests: r-cran-csv, r-cran-nlme Filename: pool/dists/noble/main/r-cran-metaplot_0.8.4-1.ca2404.1_all.deb Size: 367628 MD5sum: 0b8d8068b6c2e5fec252fd4f2bc9d418 SHA1: 01e040f4b83227224efe06fc116ae5d6f62b0e13 SHA256: 7ac8cd853c48e03eb724a360ac7fc9a49adcf3cf5e843545e66a829b737afa80 SHA512: 0ff76a3a8039105bbc21e95acf4a569612d7a6eff402ba4a178e842e1b5eb478e5fde6a3dc6e5fe696035c164e21367c764dd456daeb484275e5ba2231f834f7 Homepage: https://cran.r-project.org/package=metaplot Description: CRAN Package 'metaplot' (Data-Driven Plot Design) Designs plots in terms of core structure. See 'example(metaplot)'. Primary arguments are (unquoted) column names; order and type (numeric or not) dictate the resulting plot. Specify any y variables, x variable, any groups variable, and any conditioning variables to metaplot() to generate density plots, boxplots, mosaic plots, scatterplots, scatterplot matrices, or conditioned plots. Use multiplot() to arrange plots in grids. Wherever present, scalar column attributes 'label' and 'guide' are honored, producing fully annotated plots with minimal effort. Attribute 'guide' is typically units, but may be encoded() to provide interpretations of categorical values (see '?encode'). Utility unpack() transforms scalar column attributes to row values and pack() does the reverse, supporting tool-neutral storage of metadata along with primary data. The package supports customizable aesthetics such as such as reference lines, unity lines, smooths, log transformation, and linear fits. The user may choose between trellis and ggplot output. Compact syntax and integrated metadata promote workflow scalability. Package: r-cran-metaplus Architecture: all Version: 1.0-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmle, r-cran-metafor, r-cran-boot, r-cran-numderiv, r-cran-mass, r-cran-fastghquad, r-cran-lme4, r-cran-rfast Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-metaplus_1.0-8-1.ca2404.1_all.deb Size: 433360 MD5sum: 0c1f435e74e0528778c8f8ecad8c59a1 SHA1: bad6ad40041980b43b5669bd0a79061971794aa7 SHA256: 649d7fc1a0d9ca4678897acf2f3b353f72ce9c0a44a92183170d557851ebc4d9 SHA512: fba7ddcd187b402e2c06cf25269c63af4cce37b4663c588c6d13b9612f4be3a5b30ddeb207145e80762243ed83c453c8000af0ed7d7885085f51191d06ca951f Homepage: https://cran.r-project.org/package=metaplus Description: CRAN Package 'metaplus' (Robust Meta-Analysis and Meta-Regression) Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 or Baker and Jackson, 2008 ) or mixtures of normals (Beath, 2014 ). Package: r-cran-metapost Architecture: all Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gridbezier Suggests: r-cran-grimport Filename: pool/dists/noble/main/r-cran-metapost_1.0-6-1.ca2404.1_all.deb Size: 72464 MD5sum: afea7c75662b0a73e9f51d76a5f23bef SHA1: 7b7bce227cf93b9e6489221585979e66897cf032 SHA256: c05bd11c720d322d69b01cf3709a2ce637b68153d443ba83b88c3e9e8e84f135 SHA512: 1903422a556260cafb89011a4a6c582fb62f3f6127153130c44cd895a36252bf6dd1ce804252db0aae0e3cac0f02c562fec6db7746f7dbbfb12c3197d17416a4 Homepage: https://cran.r-project.org/package=metapost Description: CRAN Package 'metapost' (Interface to 'MetaPost') Provides an interface to 'MetaPost' (Hobby, 1998) . There are functions to generate an R description of a 'MetaPost' curve, functions to generate 'MetaPost' code from an R description, functions to process 'MetaPost' code, and functions to read solved 'MetaPost' paths back into R. Package: r-cran-metapower Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2296 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-tidyr, r-cran-testthat, r-cran-rlang Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metapower_0.2.2-1.ca2404.1_all.deb Size: 1448464 MD5sum: 9dbcee98dbaaced1f612ab1ca4672818 SHA1: c8cfdb7ec9cff552a62010c8494c7465bb824e80 SHA256: f6e02abfe02cdf016bb452fbf6a2baa98698651b4f3ebbe270362995d51ea789 SHA512: e4ca4b32e2c2eeb01d65ff748a7a4ec261d8f6d958b2570c11a10aa45593e7ae3173029ceb65f5d8e20c3d153a4837b3bb24d41f7b4664544d14dd47e41eaeb4 Homepage: https://cran.r-project.org/package=metapower Description: CRAN Package 'metapower' (Power Analysis for Meta-Analysis) A simple and effective tool for computing and visualizing statistical power for meta-analysis, including power analysis of main effects (Jackson & Turner, 2017), test of homogeneity (Pigott, 2012), subgroup analysis, and categorical moderator analysis (Hedges & Pigott, 2004). Package: r-cran-metapro Architecture: all Version: 1.5.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metap Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metapro_1.5.11-1.ca2404.1_all.deb Size: 24662 MD5sum: c547fbfac4bad1f35246396c2b3b7ff7 SHA1: 4d871e7db6cbef94f7bc1088bb7ce201b645fdfb SHA256: 55db49e5a6d67416a5126439ffdaff066b30aaabbf6f3e5a8f69f56e29729f38 SHA512: aa1fb85fc7ecb11481d1086995e747a4f34df2dc9fa56f61b6b478b196fe7171737e250a85079883af932b49684b64a64559c8cc6cdafeb02bef832e11de78d6 Homepage: https://cran.r-project.org/package=metapro Description: CRAN Package 'metapro' (Robust P-Value Combination Methods) The meta-analysis is performed to increase the statistical power by integrating the results from several experiments. The p-values are often combined in meta-analysis when the effect sizes are not available. The 'metapro' R package provides not only traditional methods (Becker BJ (1994, ISBN:0-87154-226-9), Mosteller, F. & Bush, R.R. (1954, ISBN:0201048523) and Lancaster HO (1949, ISSN:00063444)), but also new method named weighted Fisher’s method we developed. While the (weighted) Z-method is suitable for finding features effective in most experiments, (weighted) Fisher’s method is useful for detecting partially associated features. Thus, the users can choose the function based on their purpose. Yoon et al. (2021) "Powerful p-value combination methods to detect incomplete association" . Package: r-cran-metaprotr Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1181 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ade4, r-cran-dendextend, r-cran-dplyr, r-cran-ggforce, r-cran-ggrepel, r-cran-reshape2, r-cran-stringr, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-metaprotr_1.2.2-1.ca2404.1_all.deb Size: 1081948 MD5sum: c00224b786fadfbe1ed7b16dc5f07e27 SHA1: 0696fb11a606febeaec57fa16a21f3308f0a07d0 SHA256: 9c3e093452e95e2764355e66f88c58998d2635df7dce3e75d5c550741fc963fd SHA512: fdcaa73b2e7c71692fc96e379fef1ef890af648a5a8efa8d51adfb7ba577912d4995d5ed2d026c0ba914a0879a1d32542ac6187402e002346a25009e59bfc1f0 Homepage: https://cran.r-project.org/package=metaprotr Description: CRAN Package 'metaprotr' (Metaproteomics Post-Processing Analysis) Set of tools for descriptive analysis of metaproteomics data generated from high-throughput mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, expressed as spectral counts, and to manipulate them in groups of metaproteins. This information can be represented using multiple visualization functions to portray the global metaproteome landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, taxonomic levels and/or functional annotation. The provided tools allow to implement flexible analytical pipelines that can be easily applied to studies interested in metaproteomics analysis. Package: r-cran-metaquant Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gld, r-cran-sld, r-cran-ggplot2, r-cran-plotly, r-cran-magrittr, r-cran-dplyr, r-cran-estmeansd, r-cran-metafor Filename: pool/dists/noble/main/r-cran-metaquant_0.1.3-1.ca2404.1_all.deb Size: 126356 MD5sum: f849c96c96905ba4071ed0dd8f9dac5e SHA1: 0885d0c0dea5d7320aa8925933b3f6f54ae859c9 SHA256: 8c670412a265f0bb762b4b2919c19a653432abffecbefcb4e1a40582a03fa4b9 SHA512: 58ed9247903b10398620a7f8af9ef949ab2572fbee50e2d04879d887ffbade16abe6bbfd1916c85615151c2855c9bac15b3200b954d5f8b601a9e0a0bd7ea633 Homepage: https://cran.r-project.org/package=metaquant Description: CRAN Package 'metaquant' (Meta-Analysis of Quantiles and Functions of Quantiles) Implements a novel density-based approach for estimating unknown parameters, distribution visualisations and meta-analyses of quantiles and ther functions. A detailed vignettes with example datasets and code to prepare data and analyses is available at . The methods are described in the pre-print by De Livera, Prendergast and Kumaranathunga (2024, ). Package: r-cran-metarnaseq Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1359 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-bioc-deseq2, r-cran-venndiagram Filename: pool/dists/noble/main/r-cran-metarnaseq_1.0.8-1.ca2404.1_all.deb Size: 1273334 MD5sum: 0022d89548edf86e963be0a180e3e064 SHA1: 65c92cfff4606cdc80c5b9bf7e8ed63ebc0b59e7 SHA256: 506eedd9a31ee40d118d1fe7d32ef8155840639f40f137e62a9c3a0615f3dcd8 SHA512: 958b048ba178e53c963e3eed887fcd25a75c713ccd2796f30eef8bc0be36b97c0160fe36285bbc0992f116529061b80add06f5c3ca5d4ecbff37cd24b742f600 Homepage: https://cran.r-project.org/package=metaRNASeq Description: CRAN Package 'metaRNASeq' (Meta-Analysis of RNA-Seq Data) Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments. Package: r-cran-metarvm Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2913 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-odin, r-cran-purrr, r-cran-r6, r-cran-tidyr, r-cran-yaml Suggests: r-cran-dde, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metarvm_2.1.0-1.ca2404.1_all.deb Size: 2567532 MD5sum: 88b2f4a98d7e88506930056818a7dab7 SHA1: 0a8781fc7e1cd96c1d3ac47b027e65a02d7ab587 SHA256: cb0eb4371de30d22fa8da53de10d85d56444f7cb1fd784a1fc8a9259f0844c04 SHA512: 7dfe6912296f41c39f98606dfa5bfac2537ab4e711f048c0bd7a876412e9ff1b46bee6f44a99f37f34d811d35b13eb2aa6ebb8fd5bd227957d0c136eef53d652 Homepage: https://cran.r-project.org/package=MetaRVM Description: CRAN Package 'MetaRVM' (Meta-Population Compartmental Model for Respiratory VirusDiseases) Simulates respiratory virus epidemics using meta-population compartmental models following Fadikar et. al. (2025) . 'MetaRVM' implements a stochastic SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) framework with demographic stratification by user provided attributes. It supports complex epidemiological scenarios including asymptomatic and presymptomatic transmission, hospitalization dynamics, vaccination schedules, and time-varying contact patterns via mixing matrices. Package: r-cran-metasdtreg Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ordinal, r-cran-maxlik, r-cran-truncnorm, r-cran-matrix Filename: pool/dists/noble/main/r-cran-metasdtreg_0.2.2-1.ca2404.1_all.deb Size: 108668 MD5sum: 610e8efc551a99d0d7208444efb73bfc SHA1: 4433ce85c8535134cd879500f10559e093cdccbc SHA256: e38dbaeb2d98b2685046854dcbcb020ca00a575b989bfa9775d71c3cf853e5f4 SHA512: baa6ff47ee6e76cd6491e3108077f11df18d856adfa1b96a47fd90f1afb4c6e67ae17fe59c18750a6faab0387329ffd572117675b03bc18b8851e0b5a75003fb Homepage: https://cran.r-project.org/package=metaSDTreg Description: CRAN Package 'metaSDTreg' (Regression Models for Meta Signal Detection Theory) Regression methods for the meta-SDT model. The package implements methods for cognitive experiments of metacognition as described in Kristensen, S. B., Sandberg, K., & Bibby, B. M. (2020). Regression methods for metacognitive sensitivity. Journal of Mathematical Psychology, 94. . Package: r-cran-metasem Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2941 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openmx, r-cran-matrix, r-cran-mass, r-cran-ellipse, r-cran-mvtnorm, r-cran-numderiv, r-cran-lavaan Suggests: r-cran-metafor, r-cran-semplot, r-cran-r.rsp, r-cran-testthat, r-cran-matrixcalc Filename: pool/dists/noble/main/r-cran-metasem_1.5.0-1.ca2404.1_all.deb Size: 2100418 MD5sum: 960a99c6d7d46f762c2bb67e7d95b618 SHA1: 36a6ccd7d081d24b5c42535b33cc1b95a78a22cc SHA256: 8281e7a64df1ad065103a82df141605cb64fb85d44ea0382fb7d4ca73ac6bf6e SHA512: 7bc2116ea14ad2fd8761c763a4a77bb5b152e2e86bcd0c1c595ae14ad8e573d035c9d929105b9fb2fa7afc31a4fac6b5864ebf66e597890d397381904bf16a3c Homepage: https://cran.r-project.org/package=metaSEM Description: CRAN Package 'metaSEM' (Meta-Analysis using Structural Equation Modeling) A collection of functions for conducting meta-analysis using a structural equation modeling (SEM) approach via the 'OpenMx' and 'lavaan' packages. It also implements various procedures to perform meta-analytic structural equation modeling on the correlation and covariance matrices, see Cheung (2015) . Package: r-cran-metasens Architecture: all Version: 1.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-meta Filename: pool/dists/noble/main/r-cran-metasens_1.5-3-1.ca2404.1_all.deb Size: 300558 MD5sum: 12e0156c5f42b9ce952ab81b12dcaae2 SHA1: ee80205a9e7acd726c26087cbb86d024e669bca2 SHA256: 5e595cb3077e7e59fa0acd64dc4707405e237c94f1cab17d82d706ca9a1f6b55 SHA512: 0031158603e43cb461916151862f2a9a4d1305f2edc4d7935aca9ed44c10734bb3ab44e6848049b3c35a35fcb2635010500b4614911155529395300591a9b7e2 Homepage: https://cran.r-project.org/package=metasens Description: CRAN Package 'metasens' (Statistical Methods for Sensitivity Analysis in Meta-Analysis) The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis and to support Schwarzer et al. (2015) , Chapter 5 'Small-Study Effects in Meta-Analysis': - Copas selection model described in Copas & Shi (2001) ; - limit meta-analysis by Rücker et al. (2011) ; - upper bound for outcome reporting bias by Copas & Jackson (2004) ; - imputation methods for missing binary data by Gamble & Hollis (2005) and Higgins et al. (2008) ; - LFK index test and Doi plot by Furuya-Kanamori et al. (2018) . Package: r-cran-metasnf Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5029 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-cluster, r-cran-data.table, r-cran-digest, r-cran-dplyr, r-cran-ggplot2, r-cran-mass, r-cran-mclust, r-cran-progressr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-snftool, r-cran-tibble, r-cran-tidyr Suggests: r-cran-circlize, r-bioc-complexheatmap, r-bioc-interactivecomplexheatmap, r-cran-clv, r-cran-future, r-cran-future.apply, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggalluvial, r-cran-lifecycle, r-cran-dbscan Filename: pool/dists/noble/main/r-cran-metasnf_2.1.2-1.ca2404.1_all.deb Size: 3171442 MD5sum: 43c77c058b03d860eab66550c208eef5 SHA1: c9b31e5ce02e4d10c746187630dfd1af7184715d SHA256: 19b9e8aa77c85f3e6199c17e38fe025b50f9087516e0496a5632b55568158d82 SHA512: 55acb5f7490b5e8d2fa2134c13432a1d348263291eeb9b86b59b953903ce14829befc246df8258840d9d4990077baee5bfa3b91bdf24db601790f79259834823 Homepage: https://cran.r-project.org/package=metasnf Description: CRAN Package 'metasnf' (Meta Clustering with Similarity Network Fusion) Framework to facilitate patient subtyping with similarity network fusion and meta clustering. The similarity network fusion (SNF) algorithm was introduced by Wang et al. (2014) in . SNF is a data integration approach that can transform high-dimensional and diverse data types into a single similarity network suitable for clustering with minimal loss of information from each initial data source. The meta clustering approach was introduced by Caruana et al. (2006) in . Meta clustering involves generating a wide range of cluster solutions by adjusting clustering hyperparameters, then clustering the solutions themselves into a manageable number of qualitatively similar solutions, and finally characterizing representative solutions to find ones that are best for the user's specific context. This package provides a framework to easily transform multi-modal data into a wide range of similarity network fusion-derived cluster solutions as well as to visualize, characterize, and validate those solutions. Core package functionality includes easy customization of distance metrics, clustering algorithms, and SNF hyperparameters to generate diverse clustering solutions; calculation and plotting of associations between features, between patients, and between cluster solutions; and standard cluster validation approaches including resampled measures of cluster stability, standard metrics of cluster quality, and label propagation to evaluate generalizability in unseen data. Associated vignettes guide the user through using the package to identify patient subtypes while adhering to best practices for unsupervised learning. Package: r-cran-metasplines Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-stringr, r-cran-meta, r-cran-optimization Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-splines2 Filename: pool/dists/noble/main/r-cran-metasplines_0.1.1-1.ca2404.1_all.deb Size: 93788 MD5sum: 3e242ca481eeda403ec4d45b3bb82636 SHA1: 5711c506f9c2216c4caea4e9add4d17267930632 SHA256: eca87de61b23827f03a514051d01779ae1319be7385f7c9e4ebec08ad0ca2355 SHA512: 3f98b4f209d157bf5de980dbdf33d048844eeef664cd182ff9da1e5c84552f71415048c4d76bb17f88ad0cadd3d4b6d1268b1ffe4fd36e2984cfa8fd5560a900 Homepage: https://cran.r-project.org/package=metasplines Description: CRAN Package 'metasplines' (Pool Literature-Based and Individual Participant Data BasedSpline Estimates) Pooling estimates reported in meta-analyses (literature-based, LB) and estimates based on individual participant data (IPD) is not straight-forward as the details of the LB nonlinear function estimate are not usually reported. This package pools the nonlinear IPD dose-response estimates based on a natural cubic spline from lm or glm with the pointwise LB estimates and their estimated variances. Details will be presented in Härkänen, Tapanainen, Sares-Jäske, Männistö, Kaartinen and Paalanen (2026) "Novel pooling method for nonlinear cohort analysis and meta-analysis estimates: Predicting health outcomes based on climate-friendly diets" Epidemiology . Package: r-cran-metasubtract Architecture: all Version: 1.60-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-metasubtract_1.60-1.ca2404.1_all.deb Size: 179364 MD5sum: ca0cf604ce0cd34121dc3457416f2106 SHA1: 546bbd8c643b2b01e1f790bd99576a84faf74918 SHA256: bfc66d0a859568e32a683adf1db24b1a81d86aae87a7c7391c7b73f086bb6d34 SHA512: a37136fc43a809dc6e79c48c15da66343d8619ee5378679b52547f8acc3b7f748d1759d95f387af643ffab23497fca0d899db2617bc5f4294b2e1cce3b01532e Homepage: https://cran.r-project.org/package=MetaSubtract Description: CRAN Package 'MetaSubtract' (Subtracting Summary Statistics of One or more Cohorts fromMeta-GWAS Results) If results from a meta-GWAS are used for validation in one of the cohorts that was included in the meta-analysis, this will yield biased (i.e. too optimistic) results. The validation cohort needs to be independent from the meta-Genome-Wide-Association-Study (meta-GWAS) results. 'MetaSubtract' will subtract the results of the respective cohort from the meta-GWAS results analytically without having to redo the meta-GWAS analysis using the leave-one-out methodology. It can handle different meta-analyses methods and takes into account if single or double genomic control correction was applied to the original meta-analysis. It can also handle different meta-analysis methods. It can be used for whole GWAS, but also for a limited set of genetic markers. See for application: Nolte I.M. et al. (2017); . Package: r-cran-metasurvey Architecture: all Version: 0.0.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3708 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-cli, r-cran-glue, r-cran-lifecycle, r-cran-jsonlite, r-cran-r6, r-cran-survey Suggests: r-cran-archive, r-cran-convey, r-cran-httr2, r-cran-haven, r-cran-openxlsx, r-cran-visnetwork, r-cran-roxygen2, r-cran-testthat, r-cran-tibble, r-cran-dplyr, r-cran-knitr, r-cran-foreign, r-cran-rmarkdown, r-cran-rio, r-cran-here, r-cran-gt, r-cran-magrittr, r-cran-shiny, r-cran-bslib, r-cran-bsicons, r-cran-htmltools, r-cran-xml2, r-cran-eph, r-cran-pnadcibge, r-cran-ipumsr Filename: pool/dists/noble/main/r-cran-metasurvey_0.0.21-1.ca2404.1_all.deb Size: 2035798 MD5sum: f407ed71545988a59d19bec7ec4a3f39 SHA1: 6f70e0aeac9895813533cfe80f17b57f6a8437e9 SHA256: 5514b8d703c194a05ffddb040900954156acb51c50c84c2d6bb9918869441859 SHA512: 4320ef1aecff169245f935877c434caa7ee6ac7148dabb227cc5e4c3a7f7c619af45366b950593b775496e64576f773438833d50cd10ed65110e93bb26f795bf Homepage: https://cran.r-project.org/package=metasurvey Description: CRAN Package 'metasurvey' (Reproducible Survey Data Processing with Step Pipelines) Provides a step-based pipeline for reproducible survey data processing, building on the 'survey' package for complex sampling designs. Supports rotating panels with bootstrap replicate weights, and provides a recipe system for sharing and reproducing data transformation workflows across survey editions. Package: r-cran-metasurvival Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metasurvival_0.1.0-1.ca2404.1_all.deb Size: 53608 MD5sum: 5a586ddb47f6a93427fabcba934a4924 SHA1: 8ab1255e0e8bcc5072f767dbfe7f0afc4e35615f SHA256: 6c3e7d89920050dddd902232a18d1227476786d9dd7d3be4bb7a6d12f63d0be6 SHA512: 2a0bffe97b9465cc7ba740da8b94c272af61d5c79774dc0441b6e3396f20dd6104e4144e5a856e46b75e660e1309cea17e46edc8f94ba1a14c54501eb0883931 Homepage: https://cran.r-project.org/package=metaSurvival Description: CRAN Package 'metaSurvival' (Meta-Analysis of a Single Survival Curve) To assess a summary survival curve from survival probabilities and number of at-risk patients collected at various points in time in various studies, and to test the between-strata heterogeneity. 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Package: r-cran-metatest Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-metatest_1.0-5-1.ca2404.1_all.deb Size: 40026 MD5sum: b296113c854bd9198b0bec20ea7a82ae SHA1: 381a1e032ac538cff88acc2ba237932160e46ab8 SHA256: 14da92bd9d62b854d1c18a0ffd0bd79a7efbf3bda4596255e234c60f931263bd SHA512: 6b945ac74b41851a2164f52fba64bae6a376088320ad86f833b2bfc1d7ff843028f72445edf70a89a8bf950f9e42b2868a4a1976834fa70f7c53d2b21be10005 Homepage: https://cran.r-project.org/package=metatest Description: CRAN Package 'metatest' (Fit and Test Metaregression Models) Fits and tests meta regression models and generates a number of useful test statistics: next to t- and z-tests, the likelihood ratio, bartlett corrected likelihood ratio and permutation tests are performed on the model coefficients. Package: r-cran-metathis Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 426 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-knitr, r-cran-magrittr, r-cran-purrr Suggests: r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metathis_1.1.4-1.ca2404.1_all.deb Size: 209064 MD5sum: 6ea21c7bf67665447c47cbb4b797dd17 SHA1: ee56faaf7e7239694cb509295ea50fb0b025f98a SHA256: 73a48d377bb52561b528b35e3efee905912adfd824c100a8c3f1bafc3703f884 SHA512: 63c55e0efa6ff8c35e72e8197e9eb58657fb7fc8116ba1d175f5a75a04b1b41afa88f38e0f41150926c5447b1dedba7fc25c17c27273a8c652fca70d6741cdd7 Homepage: https://cran.r-project.org/package=metathis Description: CRAN Package 'metathis' (HTML Metadata Tags for 'R Markdown' and 'Shiny') Create meta tags for 'R Markdown' HTML documents and 'Shiny' apps for customized social media cards, for accessibility, and quality search engine indexing. 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Package: r-cran-metatools Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-metacore, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-haven, r-cran-pharmaversesdtm, r-cran-safetydata, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metatools_0.3.0-1.ca2404.1_all.deb Size: 325588 MD5sum: 6256786b83ef22ed66018b7dd6e7cf3f SHA1: 2f7420a162392c922c11de8ed2da9c92b81558c1 SHA256: 0014e4f1b8bb4429911db4a015e96374009c9216912cf598ae69f6c16ff11cb2 SHA512: 78a84af532b65d482540e1aa76c75a2756ec04971a8ba78f77d8acbbc8535be2785d7945a9a6920435e2eef3bbbf8a9c9f716e1edabbe64b870dbc67a4bc4dad Homepage: https://cran.r-project.org/package=metatools Description: CRAN Package 'metatools' (Enable the Use of 'metacore' to Help Create and Check Dataset) Uses the metadata information stored in 'metacore' objects to check and build metadata associated columns. Package: r-cran-metaumbrella Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1803 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-meta, r-cran-metaconvert, r-cran-pwr, r-cran-powersurvepi, r-cran-readxl, r-cran-withr, r-cran-writexl, r-cran-xtable Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-spelling, r-cran-dt, r-cran-epir, r-cran-esc, r-cran-metafor Filename: pool/dists/noble/main/r-cran-metaumbrella_1.1.0-1.ca2404.1_all.deb Size: 758212 MD5sum: 891b7cf253d96d2a7f633d84dc56de92 SHA1: e10ea91dafcb220a87a3c6cad89114029d6a79ca SHA256: b0f301c99cfb24eb3c970eab0c6a7574caa1f1190cf1fba345860abd50c0fb3f SHA512: c8be7e2403b67a2ab04b1008831d1fd70052135ffaba6d3bdffbae2ad8622586eac18464961255eb450fb508557bc6246998701e00c298e7bde33715afe4b72e Homepage: https://cran.r-project.org/package=metaumbrella Description: CRAN Package 'metaumbrella' (Umbrella Review Package for R) A comprehensive range of facilities to perform umbrella reviews with stratification of the evidence in R. The package accomplishes this aim by building on three core functions that: (i) automatically perform all required calculations in an umbrella review (including but not limited to meta-analyses), (ii) stratify evidence according to various classification criteria, and (iii) generate a visual representation of the results. Note that if you are not familiar with R, the core features of this package are available from a web browser (). Package: r-cran-metautility Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metafor, r-cran-metadat, r-cran-stringr, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-metautility_2.1.2-1.ca2404.1_all.deb Size: 258564 MD5sum: d2fd9b5c9d062c113385bc6266c71229 SHA1: 58547cf42b08f4e948681b2e6c65f6d9a04ca272 SHA256: e77b2b7bb0d4ad74c386ff1f43fc808c325d3b30a8af2b7d33161bfdefad5378 SHA512: 12688da1d197addba53183e018f7e18e8f2d1ea9ac8da1515bc7d8a5f52f4ef6dd5f8237e9139d68bab13463be66e96308d84e2ec5e1505898b1e04194f0ade7 Homepage: https://cran.r-project.org/package=MetaUtility Description: CRAN Package 'MetaUtility' (Utility Functions for Conducting and Interpreting Meta-Analyses) Contains functions to estimate the proportion of effects stronger than a threshold of scientific importance (function prop_stronger), to nonparametrically characterize the distribution of effects in a meta-analysis (calib_ests, pct_pval), to make effect size conversions (r_to_d, r_to_z, z_to_r, d_to_logRR), to compute and format inference in a meta-analysis (format_CI, format_stat, tau_CI), to scrape results from existing meta-analyses for re-analysis (scrape_meta, parse_CI_string, ci_to_var). Package: r-cran-metavcov Architecture: all Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-mixmeta, r-cran-metasem, r-cran-mvmeta, r-cran-mice Filename: pool/dists/noble/main/r-cran-metavcov_2.1.5-1.ca2404.1_all.deb Size: 204428 MD5sum: cc80c9bee1a80a7315a0c38e04841b72 SHA1: cf4386d0622713ae77535ef2036f86a6f9817ba9 SHA256: 69c98231ca6db1119ec197a64f259ad91b6e847a93f5e00791f0037ad7396312 SHA512: d670e2a6951e8002837a75cf290708243f4aa855db3f9a426d1bdf80d343b8d848616d9dadbe731f5b5760be028ba43f4f2fddda7945fd0c61b4610bcb83c39a Homepage: https://cran.r-project.org/package=metavcov Description: CRAN Package 'metavcov' (Computing Variances and Covariances, Visualization and MissingData Solution for Multivariate Meta-Analysis) Collection of functions to compute within-study covariances for different effect sizes, data visualization, and single and multiple imputations for missing data. 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Package: r-cran-metaviz Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2559 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-nullabor, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-metafor, r-cran-gridextra, r-cran-ggpubr Suggests: r-cran-cairo, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metaviz_0.3.1-1.ca2404.1_all.deb Size: 1662594 MD5sum: ee01aa681bf8e7d388732941c1b4aba0 SHA1: 9f7cadce492d019779d63f8df30282673bf50c4f SHA256: 14ca7005ec0b29748bc0514a304b8409f800d0a6fa7651391c653a509075a3ce SHA512: 8f75b3aec77dfebcf2febe1ba83ef3f7ab7b7af4ed9b344505da150cf322cb386297fa77b66235514fdf1acbc4a12e54eeda346aeef90e86b21d82041dd63f4e Homepage: https://cran.r-project.org/package=metaviz Description: CRAN Package 'metaviz' (Forest Plots, Funnel Plots, and Visual Funnel Plot Inference forMeta-Analysis) A compilation of functions to create visually appealing and information-rich plots of meta-analytic data using 'ggplot2'. 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(2017) ) and model visualization. Package: r-cran-metbrewer Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-metbrewer_0.2.0-1.ca2404.1_all.deb Size: 66182 MD5sum: 746086b64dceb4ec0e848b867e01d133 SHA1: 18c846247aa7774c31abe833608c1227b46b9e21 SHA256: bc2cb61069f52773311f4c2918ad81ea9b6f0f9843114386ff7bce26e76832f5 SHA512: 910d2242e13c4884a5d92e58e726f65343fa89b6dd4045fbd09883323f2dcfec0e0325ea8153253ab53e1aa55afc520e9edc10d94ca6f084356f90d851e254d3 Homepage: https://cran.r-project.org/package=MetBrewer Description: CRAN Package 'MetBrewer' (Color Palettes Inspired by Works at the Metropolitan Museum ofArt) Palettes Inspired by Works at the Metropolitan Museum of Art in New York. Currently contains over 50 color schemes and checks for colorblind-friendliness of palettes. Colorblind accessibility checked using the '{colorblindcheck} package by Jakub Nowosad'. 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It allows the metabolite classification in structurally-related modules and identifies common shared functional groups. The KODAMA algorithm is used to highlight structural similarity between metabolites. See Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. (2017) Bioinformatics , Cacciatore S, Luchinat C, Tenori L. (2014) Proc Natl Acad Sci USA , and Abdel-Shafy EA, Melak T, MacIntyre DA, Zadra G, Zerbini LF, Piazza S, Cacciatore S. (2023) Bioinformatics Advances . Package: r-cran-meteo Architecture: all Version: 2.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3387 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-desctools, r-cran-caret, r-cran-data.table, r-cran-dplyr, r-cran-sp, r-cran-spacetime, r-cran-gstat, r-cran-foreach, r-cran-snowfall, r-cran-doparallel, r-cran-plyr, r-cran-units, r-cran-nabor, r-cran-cast, r-cran-ranger, r-cran-sf, r-cran-sftime, r-cran-raster, r-cran-terra, r-cran-jsonlite Suggests: r-cran-xts Filename: pool/dists/noble/main/r-cran-meteo_2.0-5-1.ca2404.1_all.deb Size: 3364938 MD5sum: 580fbafe86ac3fcabcb28d874b20f368 SHA1: 69ca789dcf8efd5df0efb4321a09bf63c6e760bf SHA256: d1dcdd0064e9186d395b647936338e8cd9881a4803039f9a0ac6eed13979373e SHA512: 9eba04e507878a2d3b909c3457c3d1ba289cd4816a3356468f7afc3158bcda5f3deed58293b0e8ad6e5ce9791d6a6f258c986731ed7854031cd5a6728f871e5b Homepage: https://cran.r-project.org/package=meteo Description: CRAN Package 'meteo' (RFSI & STRK Interpolation for Meteo and Environmental Variables) Random Forest Spatial Interpolation (RFSI, Sekulić et al. (2020) ) and spatio-temporal geostatistical (spatio-temporal regression Kriging (STRK)) interpolation for meteorological (Kilibarda et al. (2014) , Sekulić et al. (2020) ) and other environmental variables. Contains global spatio-temporal models calculated using publicly available data. Package: r-cran-meteoevt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 669 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-ncdf4 Filename: pool/dists/noble/main/r-cran-meteoevt_0.1.0-1.ca2404.1_all.deb Size: 635258 MD5sum: 9d6b0f4e8b81fc5728d03964533b6b98 SHA1: 7573b6ccc078e45b269f45fd1d4026a36b1f143e SHA256: 32b217fd03419f83feee5348e33d1ce99b97252db449fcfd0e38868252fe311a SHA512: 6abc657179ad63706f7096b213b9b0a320581997edcdfe6534edde71ef1abbb4cc838b1ec0683c97d19af688df1bf0ae642a3e443159dfde65b827d5b9bdb24f Homepage: https://cran.r-project.org/package=meteoEVT Description: CRAN Package 'meteoEVT' (Computation and Visualization of Energetic and VorticalAtmospheric Quantities) Energy-Vorticity theory (EVT) is the fundamental theory to describe processes in the atmosphere by combining conserved quantities from hydrodynamics and thermodynamics. The package 'meteoEVT' provides functions to calculate many energetic and vortical quantities, like potential vorticity, Bernoulli function and dynamic state index (DSI) [e.g. Weber and Nevir, 2008, ], for given gridded data, like ERA5 reanalyses. These quantities can be studied directly or can be used for many applications in meteorology, e.g., the objective identification of atmospheric fronts. For this purpose, separate function are provided that allow the detection of fronts based on the thermic front parameter [Hewson, 1998, ], the F diagnostic [Parfitt et al., 2017, ] and the DSI [Mack et al., 2022, ]. Package: r-cran-meteoforecast Architecture: all Version: 0.57-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-zoo, r-cran-ncdf4, r-cran-xml Suggests: r-cran-sf, r-cran-lattice, r-cran-rastervis Filename: pool/dists/noble/main/r-cran-meteoforecast_0.57-1.ca2404.1_all.deb Size: 94850 MD5sum: 740404d75f0763aa24eb29140bb78cc5 SHA1: b28a5a4e19b999ce2fcbccd34fdcf57eb7d62492 SHA256: 684f10d16fefe383b59d65e6ed36baf0718a6bcc2652cacd431569d5bb32ec9c SHA512: 08ab738db08dfc8f96a47015e84fdb9a7609711aa21622f0cc6f18b35cc6c13cfe9ce74daea134446ac8eeb5898b0df6178af1cf6dc3bf18e73c647185039267 Homepage: https://cran.r-project.org/package=meteoForecast Description: CRAN Package 'meteoForecast' (Numerical Weather Predictions) Access to several Numerical Weather Prediction services both in raster format and as a time series for a location. Currently it works with GFS , MeteoGalicia , NAM , and RAP . Package: r-cran-meteospain Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1701 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-glue, r-cran-jsonlite, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tidyr, r-cran-units, r-cran-xml2, r-cran-vctrs, r-cran-cachem, r-cran-cli, r-cran-httr2, r-cran-rvest Suggests: r-cran-ggforce, r-cran-ggplot2, r-cran-keyring, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-meteospain_0.3.1-1.ca2404.1_all.deb Size: 1120114 MD5sum: a6dd02e8b9a7c96957bf94799d558ca6 SHA1: cf9a568cbb9ad89c6ae01093e0daded8661f886e SHA256: 1ff35451548ad707d30f5bf3717d43ed76540b26a6fcedd1b9b8d1f5f843cb7c SHA512: 4138fd6040266badf93e23412c294b47ae1a6810c56719cfbad9f9af85e59821a8d747b5a5cac0968f755a3cb4cf4fe415f0c4660930f6b9bc1c3a57ebbe547f Homepage: https://cran.r-project.org/package=meteospain Description: CRAN Package 'meteospain' (Access to Spanish Meteorological Stations Services) Access to different Spanish meteorological stations data services and APIs (AEMET, SMC, MG, Meteoclimatic...). Package: r-cran-meter Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 947 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nleqslv, r-cran-distr Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-meter_1.2-1.ca2404.1_all.deb Size: 634294 MD5sum: dcc3d0e16352948962f5ac1356d6b1dc SHA1: e80dbb547ccab7f78eb9cd6b1f21a22c9876f258 SHA256: f7103ffa5850f4892c08ed0fea58bf898117227f75cf4a6e2e9bce39a9ef4404 SHA512: ce9a2e5c5ee259904cc402f3c7c502892cf3873a417f87e12da5a5bc1884933f8071ebdec379f7e52e93d71aab84f6a7678853e3f91bc2d373be81d361dae9b1 Homepage: https://cran.r-project.org/package=meteR Description: CRAN Package 'meteR' (Fitting and Plotting Tools for the Maximum Entropy Theory ofEcology (METE)) Fit and plot macroecological patterns predicted by the Maximum Entropy Theory of Ecology (METE). Package: r-cran-metevalue Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2945 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sqldf, r-cran-psych, r-cran-dplyr Suggests: r-cran-rmarkdown, r-cran-prettydoc, r-cran-knitr, r-cran-ggplot2, r-cran-tidyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metevalue_0.2.4-1.ca2404.1_all.deb Size: 2822350 MD5sum: 1908049c9d4f1b19666f4accc55cacc7 SHA1: f7409d1102843335b9a5ff053f08672c6f7bc29f SHA256: a09ac505e6e78da6c983d1b276a0243ebd1d1f08e79b3765ead242e8f5e76383 SHA512: df11eaa91b594d204a9f8834907caa51f7bec0d352053c3b3842b1c34ce21daad89ae04e7e4735f58d19fc630db255c584b92e6f22fae66ed9afaeabc6f2bbc5 Homepage: https://cran.r-project.org/package=metevalue Description: CRAN Package 'metevalue' (E-Value in the Omics Data Association Studies) In the omics data association studies, it is common to conduct the p-value corrections to control the false significance. Beyond the P-value corrections, E-value is recently studied to facilitate multiple testing correction based on V. Vovk and R. Wang (2021) . This package provides E-value calculation for DNA methylation data and RNA-seq data. Currently, five data formats are supported: DNA methylation levels using DMR detection tools (BiSeq, DMRfinder, MethylKit, Metilene and other DNA methylation tools) and RNA-seq data. The relevant references are listed below: Katja Hebestreit and Hans-Ulrich Klein (2022) ; Altuna Akalin et.al (2012) . Package: r-cran-metgen Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chron, r-cran-glmnet, r-cran-mass Filename: pool/dists/noble/main/r-cran-metgen_0.5-1.ca2404.1_all.deb Size: 168200 MD5sum: 7f0925369a96e6c2359a8d85022b0666 SHA1: 9fb47ccecfad5d40de3a1a1c83404ca9a5d91aea SHA256: 8bdd051a2fd4ae67b787ef2089de5bca9358fa608d8ec39796829bc37e39a8c5 SHA512: b3c58c162f2876029277d02d4539647413ecd155c54c228664e820d7f8ca4ffecc37f5822aab33edfd72100532e49a599b1a02ae2dcd3c7a56ec4ccc02b51571 Homepage: https://cran.r-project.org/package=MetGen Description: CRAN Package 'MetGen' (Stochastic Weather Generator) An adaptation of the multi-variable stochastic weather generator proposed in 'Rglimclim' to perform gap-filling and temporal extension at sub-daily resolution. Simulation is performed based on large scale variables and climatic observation data that could be generated from different gauged stations having geographical proximity. SWG relies on reanalyses. Multi-variable dependence is taking into account by using the decomposition of the product rule (in statistics) into conditional probabilities. See . Package: r-cran-methcomp Architecture: all Version: 1.30.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3034 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-nlme Suggests: r-cran-lattice, r-cran-lme4, r-cran-rjags Filename: pool/dists/noble/main/r-cran-methcomp_1.30.2-1.ca2404.1_all.deb Size: 3047996 MD5sum: d546ac3476986ae6a792bc24b4f2284e SHA1: 6fa1569ee071952d2674e1107e5bb6d4accc160b SHA256: 75ade9418661fcab735036efadab18249f2f7dedccdee299baba7f75d2023470 SHA512: 4d83ac2541b674a5d93837040134ecdab8c0e5450469709092a1e25d9228dc87b8ac380e28c12662818e6a85a68c5fb3c691b133ebc293c64c845deee1db1e75 Homepage: https://cran.r-project.org/package=MethComp Description: CRAN Package 'MethComp' (Analysis of Agreement in Method Comparison Studies) Methods (standard and advanced) for analysis of agreement between measurement methods. These cover Bland-Altman plots, Deming regression, Lin's Total deviation index, and difference-on-average regression. See Carstensen B. (2010) "Comparing Clinical Measurement Methods: A Practical Guide (Statistics in Practice)" for more information. Package: r-cran-methcon5 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-methcon5_0.1.0-1.ca2404.1_all.deb Size: 163994 MD5sum: b84a2a00857767b59affffa8a18a3c4c SHA1: c6ad7faf4918a2f377c101c155bb62bec32dfd9d SHA256: b6469d45cee47ae07b28378f77b8da199064977784d41d4203642e404cfbe039 SHA512: 72b2d4ba8c9c5f600f9acb5eec72afd1fffd9d209216d44c56b3ff1be0118ba7f374e1dec01b216a4458cadbec4d620b3227e2c0f9e73d6211f5b05f055f2137 Homepage: https://cran.r-project.org/package=methcon5 Description: CRAN Package 'methcon5' (Identify and Rank CpG DNA Methylation Conservation Along theHuman Genome) Identify and rank CpG DNA methylation conservation along the human genome. Specifically it includes bootstrapping methods to provide ranking which should adjust for the differences in length as without it short regions tend to get higher conservation scores. Package: r-cran-methevolsim Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-ape Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-methevolsim_0.2.1-1.ca2404.1_all.deb Size: 1550178 MD5sum: 5ac28ad6e3f4037a8efde6f8dd22bc21 SHA1: 8628a3686cc4ad2b3ec49fd77509a8a15e264935 SHA256: ed8b97a1f83c2ad4cc87b26fd5cda63513907c3bc88bab28fed972ece309db12 SHA512: 045f380d43a4c014b3dbc83a2fa61edcaf39b0276fb71bd4a382e3d3cd2748206ade51147d00fa8073437eb5642b62ca293186a136505ab411bd3082f41db513 Homepage: https://cran.r-project.org/package=MethEvolSIM Description: CRAN Package 'MethEvolSIM' (Simulate DNA Methylation Dynamics on Different GenomicStructures along Genealogies) DNA methylation is an epigenetic modification involved in genomic stability, gene regulation, development and disease. DNA methylation occurs mainly through the addition of a methyl group to cytosines, for example to cytosines in a CpG dinucleotide context (CpG stands for a cytosine followed by a guanine). Tissue-specific methylation patterns lead to genomic regions with different characteristic methylation levels. E.g. in vertebrates CpG islands (regions with high CpG content) that are associated to promoter regions of expressed genes tend to be unmethylated. 'MethEvolSIM' is a model-based simulation software for the generation and modification of cytosine methylation patterns along a given tree, which can be a genealogy of cells within an organism, a coalescent tree of DNA sequences sampled from a population, or a species tree. The simulations are based on an extension of the model of Grosser & Metzler (2020) and allows for changes of the methylation states at single cytosine positions as well as simultaneous changes of methylation frequencies in genomic structures like CpG islands. Package: r-cran-methodcompare Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 300 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-estimatr, r-cran-lme4, r-cran-matrix, r-cran-mfp, r-cran-rockchalk Filename: pool/dists/noble/main/r-cran-methodcompare_1.1.0-1.ca2404.1_all.deb Size: 267066 MD5sum: cbd8c16997b38c5434bf23d2e691d560 SHA1: 651e05e1fe7b597bbc6cddd4b24d8436c1f24c72 SHA256: 26c2671be46b90aa68a4073fa65cf035a5ff4f94aa971874066287e02107d11b SHA512: e2dd2c3b6df3291e33e162c22db89500f2e466977c66c2d61fdb53cab039656d9e4b9db5c36f6c09db06e0e8b03a940e7e4c88b5ed6b6e572d5116fe150e95ad Homepage: https://cran.r-project.org/package=MethodCompare Description: CRAN Package 'MethodCompare' (Evaluating Bias and Precision in Method Comparison Studies) Evaluate bias and precision in method comparison studies. One provides measurements for each method and it takes care of the estimates. Multiple plots to evaluate bias, precision and compare methods. 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This includes generating experimental designs, uploading and viewing data, and performing various analyses to determine the optimal method. Details of the techniques used in this package are published in Gamble, Granger, & Mannion (2024) . Package: r-cran-meto Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-meto_0.1.1-1.ca2404.1_all.deb Size: 102200 MD5sum: ffa76b9ad0e956c35832fc6e5f33edb4 SHA1: c1099b4eee9bd437b1ef5ed2fd466a72921b666b SHA256: c31aef5f1bbdbf21556dcf07d8361727d4b1dbd398906e96af24d26344cd8105 SHA512: 663697576bca1263ee350e866278546195dad19c8af88318d0559b87d123491a1e0b4e66108823181a7c40abe863062aece1657cebe7914cf3cc8f6e952e7a89 Homepage: https://cran.r-project.org/package=MeTo Description: CRAN Package 'MeTo' (Meteorological Tools) Meteorological Tools following the FAO56 irrigation paper of Allen et al. (1998) [1]. Functions for calculating: reference evapotranspiration (ETref), extraterrestrial radiation (Ra), net radiation (Rn), saturation vapor pressure (satVP), global radiation (Rs), soil heat flux (G), daylight hours, and more. [1] Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9). Package: r-cran-metools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-lubridate, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-metools_1.0.0-1.ca2404.1_all.deb Size: 166240 MD5sum: 25a9e9ce16ab8b9c236a2409c71839d0 SHA1: e6e6cd7329c1e9fc04f105b6efb11ac9aac3f693 SHA256: 0825bcdabf8a83c68e3350f82ab5d6ea28a81acb5c33bbbc6a7ac0cc4df4ff31 SHA512: ee4e5121876bd350dec5dde68a2a01d5f0a256ad1dd1d5574577acf3bf25129b001c8fa1bf0a5e6fe9ee707618d2553ab23d7f3c8737352faa0b6f69025b35a6 Homepage: https://cran.r-project.org/package=metools Description: CRAN Package 'metools' (Macroeconomics Tools) Provides a number of functions to facilitate the handling and production of reports using time series data. The package was developed to be understandable for beginners, so some functions aim to transform processes that would be complex into functions with a few lines. The main advantage of using the 'metools' package is the ease of producing reports and working with time series using a few lines of code, so the code is clean and easy to understand/maintain. Learn more about the 'metools' at . Package: r-cran-metproc Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 661 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gplots, r-cran-fastcluster Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-metproc_1.0.1-1.ca2404.1_all.deb Size: 315130 MD5sum: 9c5563e2e20e8ad95af55466cd3529db SHA1: 6310c767b2a03a14d70b298ff7994554095014f2 SHA256: b85714871782cda8e4a412e3f1d514049ea3e16fa42a6e0ea9939592d5565a04 SHA512: a4d340e4be2dcfd9c62b5cd6a28f1094a201036a7ba2e15bd10d8368ca9ddd9dd2391c84a780dd3716979b0cf9f07be698d750cda3a89d25c82a3563dcd65bad Homepage: https://cran.r-project.org/package=MetProc Description: CRAN Package 'MetProc' (Separate Metabolites into Likely Measurement Artifacts and TrueMetabolites) Split an untargeted metabolomics data set into a set of likely true metabolites and a set of likely measurement artifacts. This process involves comparing missing rates of pooled plasma samples and biological samples. The functions assume a fixed injection order of samples where biological samples are randomized and processed between intermittent pooled plasma samples. By comparing patterns of missing data across injection order, metabolites that appear in blocks and are likely artifacts can be separated from metabolites that seem to have random dispersion of missing data. The two main metrics used are: 1. the number of consecutive blocks of samples with present data and 2. the correlation of missing rates between biological samples and flanking pooled plasma samples. 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Extends 'ggplot2' for better plotting of scalar and vector fields and provides commonly used analysis methods in the atmospheric sciences. Package: r-cran-metrica Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2998 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-dbi, r-cran-rsqlite, r-cran-ggpp, r-cran-minerva, r-cran-energy Suggests: r-cran-purrr, r-cran-knitr, r-cran-rmarkdown, r-cran-apsimx, r-cran-testthat Filename: pool/dists/noble/main/r-cran-metrica_2.1.1-1.ca2404.1_all.deb Size: 2090766 MD5sum: 8e08bdf38f91f0b5ef5e84fc5ee184eb SHA1: 8008b30e5b5e73ae0b7f10bc5b742be4787c686a SHA256: 4d527a6a8b0272fc3fb0b9bf0379c1e69e5a3578e497861c7ad709c997283dcc SHA512: 7fc1938509feb4755f89741be37018ad03f40a9507f6e1a6a2d6e7989411b4a7cc02d8251973039370bcd179f57ae0c3d33f58ca46f8d688f421d7b278933fbb Homepage: https://cran.r-project.org/package=metrica Description: CRAN Package 'metrica' (Prediction Performance Metrics) A compilation of more than 80 functions designed to quantitatively and visually evaluate prediction performance of regression (continuous variables) and classification (categorical variables) of point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes . Package: r-cran-metricminer Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-assertthat, r-cran-openssl, r-cran-gh, r-cran-getpass, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-tidyr, r-cran-googledrive, r-cran-googlesheets4, r-cran-janitor, r-cran-stringr, r-cran-magrittr, r-cran-rvest, r-cran-rprojroot, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-metricminer_1.0.1-1.ca2404.1_all.deb Size: 218760 MD5sum: 86eb79ebc6de7d857531b965184d04b9 SHA1: 7477ef75db2f4c00585cc105ad13daf25fa5f0fc SHA256: 3a70ccac567a16802953574cff4f8eb08446b284cefddda9a5949f3bd98e1cb4 SHA512: 253e3a3a09bd2eaf29992df59b02b2e014c737e0cced96c34b3376af819d429195ee610baa65fa26774d1620ec0731eefb4a2646d21df401ba4d9b3f0a633e31 Homepage: https://cran.r-project.org/package=metricminer Description: CRAN Package 'metricminer' (Mine Metrics from Common Places on the Web) Mine metrics on common places on the web through the power of their APIs (application programming interfaces). 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Package: r-cran-metrics Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-metrics_0.1.4-1.ca2404.1_all.deb Size: 83500 MD5sum: 858155f2d0ec1747c3408f435a186336 SHA1: e229dfc646c8f7d899977afbaad50e3c3adefda6 SHA256: 3af7c6b2f9b5d2407281183b8c85df221a18af2dbd6d58f2f7f9cdb963fef169 SHA512: 5a6c7aed9e6dfcd84932b7c9c7e40f90b2f7a957cb8cab8f1c134f8fe6a006f7e96e1594511a9b0df75bc26d908a00b6d4cce17ad9eda943f0a5d22b5a2e2172 Homepage: https://cran.r-project.org/package=Metrics Description: CRAN Package 'Metrics' (Evaluation Metrics for Machine Learning) An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. 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Chart types include line charts, scatterplots, histograms and rudimentary bar charts. Support for laying out multiple charts into a grid layout is also provided. All charts are interactive and many have an option for line, label and region annotations. 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(1997, ISBN: 978-0412997112). The package also contains a weighted version of generalized R-squared, see e.g. Cohen, J. et al. (2002, ISBN: 978-0805822236). Furthermore, 'dplyr' chains are supported. 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Package: r-cran-metrology Architecture: all Version: 0.9-29-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 883 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-numderiv, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-metrology_0.9-29-2-1.ca2404.1_all.deb Size: 830610 MD5sum: ea147f731b5a05fd55aa0400ab707494 SHA1: 61737629782f8da6c6cc76ab9541aba223ef8ccb SHA256: 0196ff52707d1ebdf50deb105241d2af1d54bed9377488936a506a63660b6cc5 SHA512: b024050e9d7b1b6fbd7fe457e50c0241be1bb0fbbea5c7f847b7b97d4d63f4588917fa690ffec08f4bc9be4ab5bfdd1b2f4c690edb36b10b00d8786a70d0ad78 Homepage: https://cran.r-project.org/package=metRology Description: CRAN Package 'metRology' (Support for Metrological Applications) Provides classes and calculation and plotting functions for metrology applications, including measurement uncertainty estimation and inter-laboratory metrology comparison studies. 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Package: r-cran-metsizer Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-metabolanalyze, r-cran-rfast, r-cran-shiny, r-cran-shinythemes, r-cran-vroom Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-metsizer_2.0.0-1.ca2404.1_all.deb Size: 69648 MD5sum: 3aa7e3a651fc380e061e8a97fb8d167c SHA1: 5f6ffe6cb0e6aa6d911217ac4e13a47230c76610 SHA256: e5ae3d30f94c39044ed5a3f8d4107d03df94c55cf5a782e1a789c07fa558e4e4 SHA512: 08f2b11e52c5c53a5e2525deac9d2a423d372b85562bff2505c6e8cef289a001640745b66ca75d083609c1aa912e76e2e7a2dd54368621666d524ada19d73c6e Homepage: https://cran.r-project.org/package=MetSizeR Description: CRAN Package 'MetSizeR' (A Shiny App for Sample Size Estimation in MetabolomicExperiments) Provides a Shiny application to estimate the sample size required for a metabolomic experiment to achieve a desired statistical power. 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The Meteo France Synop data are made of meteorological data recorded every three hours on 62 French meteorological stations. Package: r-cran-mevr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-envstats, r-cran-foreach, r-cran-doparallel, r-cran-bamlss, r-cran-mgcv Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mevr_1.1.1-1.ca2404.1_all.deb Size: 127516 MD5sum: b957de46a3a6657ab71d867e202d3098 SHA1: 6981946f31b87e05667f2e40d41246feeba8f0b3 SHA256: 80e6f066b3fc560732df088dd35377ef4017ebd926f0c4b006e0704210ea8bb3 SHA512: 78dadcd19e536bbcb8f86484cd056cf7e911a04ac6961c3fc402d347e3a1e3eb279e36c362a1a4e0bf98e9f0ccba78aaa86476c7852cc756c69d11f89f9995ce Homepage: https://cran.r-project.org/package=mevr Description: CRAN Package 'mevr' (Fitting the Metastatistical Extreme Value Distribution MEVD) Extreme value analysis with the metastatistical extreme value distribution MEVD (Marani and Ignaccolo, 2015, ) and some of its variants. In particular, analysis can be performed with the simplified metastatistical extreme value distribution SMEV (Marra et al., 2019, ) and the temporal metastatistical extreme value distribution TMEV (Falkensteiner et al., 2023, ). Parameters can be estimated with probability weighted moments, maximum likelihood and least squares. The data can also be left-censored prior to a fit. Density, distribution function, quantile function and random generation for the MEVD, SMEV and TMEV are included. In addition, functions for the calculation of return levels including confidence intervals are provided. For a description of use cases please see the provided references. Package: r-cran-mexbrewer Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4036 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mexbrewer_0.0.2-1.ca2404.1_all.deb Size: 3979218 MD5sum: b298ea51f83a78e1a03f8ad50ca69cb9 SHA1: 328f857735115456af86342463459c96648d1ff1 SHA256: 8da23e3d08544a1ce582b875c217fbb990e50b43c834e8c2bb36c0f8299bfc17 SHA512: 784511246b98edc8f054b225939c0695a7dff89af5c99d948ea11fd23ffa9e6373fd00f1ccadf1dfb929bf0905e045c284257024778746386a854877ed94d91f Homepage: https://cran.r-project.org/package=MexBrewer Description: CRAN Package 'MexBrewer' (Color Palettes Inspired by Works of Mexican Painters andMuralists) Color palettes inspired by the works of Mexican painters and muralists. The package includes functions that return vectors of colors and also functions to use color and fill scales in 'ggplot2' visualizations. Package: r-cran-mexicodataapi Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 607 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mexicodataapi_0.2.0-1.ca2404.1_all.deb Size: 514980 MD5sum: 44f2ee6614c473f461734fb51edf3ce2 SHA1: 988926e7135f989aa62aa4322a76d50a1e5837f8 SHA256: 104fed79494bc492b77cb6b4d0bfb8c3f9ae7880484e712e23baf924c50152ca SHA512: 00867262e5e445d274f57a96e4e47787aa1e638bdf72bd775531476108b7518448ee86c2b6e9a9a8d19f06235b471be90e78586c16bae5ba21fe74e17862366c Homepage: https://cran.r-project.org/package=MexicoDataAPI Description: CRAN Package 'MexicoDataAPI' (Access Mexican Data via APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'REST Countries API', 'World Bank API', and 'Nager.Date API', covering Mexico's economic indicators, population statistics, literacy rates, international geopolitical information and official public holidays. The package also includes curated datasets related to Mexico such as air quality monitoring stations, pollution zones, income surveys, postal abbreviations, election studies, forest productivity and demographic data by state. It supports research and analysis focused on Mexico by integrating reliable global APIs with structured national datasets drawn from open and academic sources. For more information on the APIs, see: 'REST Countries API' , 'World Bank API' , and 'Nager.Date API' . Package: r-cran-mexicolors Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mexicolors_0.2.0-1.ca2404.1_all.deb Size: 15268 MD5sum: 50aac6c166b562f12595138a9d40b486 SHA1: cac363f181b42f0584b5340892720c4f05d0ed8c SHA256: 3a6753563bfffe785c51a4e1bde47d4151129a7fe6d5f3a6d980a646577863e2 SHA512: 6926b764fff7b98183fbb21aef171880d0abbb28d40c92ed90944b8b6daa8f6823ab206cbaa1b408e363d54ffb346d9402fccb9778fade601ea676dd3e75b727 Homepage: https://cran.r-project.org/package=mexicolors Description: CRAN Package 'mexicolors' (Mexican Politics-Inspired Color Palette Generator) A color palette generator inspired by Mexican politics, with colors ranging from red on the left to gray in the middle and green on the right. Palette options range from only a few colors to several colors, but with discrete and continuous options to offer greatest flexibility to the user. This package allows for a range of applications, from mapping brief discrete scales (e.g., four colors for Morena, PRI, and PAN) to continuous interpolated arrays including dozens of shades graded from red to green. Package: r-cran-mexplorer Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnet, r-bioc-qusage Filename: pool/dists/noble/main/r-cran-mexplorer_1.0.0-1.ca2404.1_all.deb Size: 59428 MD5sum: 2cddda134bd3b078d6fc5b82e81c53eb SHA1: 0a32ee91136c2a01e6758c93b41548899023e678 SHA256: c0c516f4fa3b732dba53685bbdd31cc6319e7bbf6126e335045fd0b789927c90 SHA512: af083cca3f1042bd0aa6475a894cfc9e4dffe3b0fab894200c7329a946a05524de83a97259eaac502e4eb63bc2685cda723a1e43772c1e67fda14d40b7c6d490 Homepage: https://cran.r-project.org/package=mExplorer Description: CRAN Package 'mExplorer' (Identifying Master Gene Regulators from Gene Expression andDNA-Binding Data) The method 'm:Explorer' associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 . Package: r-cran-mf.beta4 Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-devtools, r-cran-ggplot2, r-cran-dplyr, r-cran-ggpubr, r-cran-purrr, r-cran-patchwork, r-cran-tidyr, r-cran-lme4, r-cran-lmertest, r-cran-tidyverse, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mf.beta4_1.1.2-1.ca2404.1_all.deb Size: 1182996 MD5sum: a22f5dfe3e0bcb1ac96fe5c790e65fd1 SHA1: 49853fa79b809e62c8cd31d2c20dff1b9bc6cbc5 SHA256: 360d677e3463932fd8d5c1b35cc81073f31cddcfe238a697abebf4fd316260ce SHA512: e7e5fad5b522c81316a786405a7dee052c1fe6131ed89021ce244fadc0c007a6de43fc07719bbe8ea2a43dd073ef97106f1757d5377e1792c05e9a2067db7ec2 Homepage: https://cran.r-project.org/package=MF.beta4 Description: CRAN Package 'MF.beta4' (Measuring Ecosystem Multi-Functionality and Its Decomposition) Provide simple functions to (i) compute a class of multi-functionality measures for a single ecosystem for given function weights, (ii) decompose gamma multi-functionality for pairs of ecosystems and K ecosystems (K can be greater than 2) into a within-ecosystem component (alpha multi-functionality) and an among-ecosystem component (beta multi-functionality). In each case, the correlation between functions can be corrected for. Based on biodiversity and ecosystem function data, this software also facilitates graphics for assessing biodiversity-ecosystem functioning relationships across scales. 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Package: r-cran-mfag Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mfag_2.3-1.ca2404.1_all.deb Size: 81520 MD5sum: 5d8ef435e5b0654c06dcbebb0f3dbaaf SHA1: b850d99869d470eae370dd7758af948e84328403 SHA256: a34153e87e86e33c129682ec058839e9a54bd6583df72c24ec574b81ae3abc34 SHA512: 202c21d82a0f6f48954c01ae80879821b35f565914c0d227402892e6a5e9030de63880e1579f40db0f78d3c82cc5849c067b4875bb47ec44d25f8a619e1ff26e Homepage: https://cran.r-project.org/package=MFAg Description: CRAN Package 'MFAg' (Multiple Factor Analysis (MFA)) Performs Multiple Factor Analysis method for quantitative, categorical, frequency and mixed data, in addition to generating a lot of graphics, also has other useful functions. Package: r-cran-mfclim Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr Suggests: r-cran-httptest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mfclim_0.1.1-1.ca2404.1_all.deb Size: 42922 MD5sum: 460e197b8a54ff31b303fddc989d8437 SHA1: ef8a2abf87e7c1de51374d04b85446618ae6aab9 SHA256: ec105c032c6850d43f276d6161239ae949910e0e5b4f7fd71c451eb1063ab51d SHA512: cd1bed940623e80fb6c26895cfae5f47d908c59c8b120abb929cb9d0c11c126873836702daba93f21b22d848ecea2b22cb32da236f2a4718c567d1b5e0618b7a Homepage: https://cran.r-project.org/package=mfclim Description: CRAN Package 'mfclim' (Download Archived Meteorological Data from 'Meteo-France') Download meteorological data from the 'Meteo-France' public API 'Données climatologiques' and 'SYNOP' open data archives . 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Related to the idea of specification curve analysis (Simonsohn, Simmons, and Nelson, 2020) . Includes data preprocessing, statistical testing, and visualization of results with confidence intervals. 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The package allows to compute functional diversity indices assessing the distribution of species (and of their dominance) in a given functional space for each assemblage and the overlap between assemblages in a given functional space, see: Chao et al. (2018) , Maire et al. (2015) , Mouillot et al. (2013) , Mouillot et al. (2014) , Ricotta and Szeidl (2009) . Graphical outputs are included. Visit the 'mFD' website for more information, documentation and examples. Package: r-cran-mfdb Architecture: all Version: 7.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3802 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-logging, r-cran-dbi, r-cran-duckdb, r-cran-getpass, r-cran-rlang Suggests: r-cran-dplyr, r-cran-dbplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rsqlite, r-cran-unittest Filename: pool/dists/noble/main/r-cran-mfdb_7.4-0-1.ca2404.1_all.deb Size: 616508 MD5sum: 43e8dd98b3b3b9a816169acf419f31f9 SHA1: 52f4a63e926b4409172cfb3d6b2fac955bf58994 SHA256: 0c68d5eda23a835ab4829522d3ccd7c0cbecd4ce360875f1b73c7ea7cbe313a1 SHA512: 7ea109dfd8210ab45383e55edb4bfda0faac2329276b281ac350216829c3ee37569d9ee63a3e31b668fa455089bc8df52a7b770705cab735c37a11b70a25b72d Homepage: https://cran.r-project.org/package=mfdb Description: CRAN Package 'mfdb' (MareFrame DB Querying Library) Creates and manages a PostgreSQL database suitable for storing fisheries data and aggregating ready for use within a Gadget model. See for more information. Package: r-cran-mfdfa Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numbers Filename: pool/dists/noble/main/r-cran-mfdfa_1.1-1.ca2404.1_all.deb Size: 39804 MD5sum: cec4443c0e44df16a0165ca597f99c58 SHA1: 87abef80fb86ebd36d73cf1f8c02b99ceac6a77f SHA256: dd287866902e5d2c22c63db8b024da646ca9e932b8cd45dfc9a12ec60543f8a9 SHA512: 8d4829352061fe0b5bbb2bd925a89e10e9bdfc7daa44371fbd544f6c3add4036d98deed89e1d6cf30502871bcdbd7f2d30a50181adb54330835616411722c177 Homepage: https://cran.r-project.org/package=MFDFA Description: CRAN Package 'MFDFA' (MultiFractal Detrended Fluctuation Analysis) Contains the MultiFractal Detrended Fluctuation Analysis (MFDFA), MultiFractal Detrended Cross-Correlation Analysis (MFXDFA), and the Multiscale Multifractal Analysis (MMA). The MFDFA() function proposed in this package was used in Laib et al. ( and ). See references for more information. Interested users can find a parallel version of the MFDFA() function on GitHub. Package: r-cran-mfdp Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mfdp_0.2.2-1.ca2404.1_all.deb Size: 29254 MD5sum: 8c72aa5756f2f2d386e8e05fd725f16f SHA1: e5b9d3bc9be72166e0f815e51c1af64ddec54c49 SHA256: 6ff5afcc6005fe6d24f47eb8daece88e0e4f0042486dd81be67ae30296a8001e SHA512: 8fccae5494a38c43e94963f573de70f188a7a2df16bed712b20615004a21465d5625adf21e208be7e5cbf89a70cd37fa7584564b989905dc352ecb29887b121a Homepage: https://cran.r-project.org/package=mFDP Description: CRAN Package 'mFDP' (Control of the Median of the FDP) Methods for controlling the median of the false discovery proportion (mFDP). Depending on the method, simultaneous or non-simultaneous inference is provided. The methods take a vector of p-values or test statistics as input. Package: r-cran-mff Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-randomforest, r-cran-xgboost, r-cran-lightgbm, r-cran-e1071, r-cran-ppclust, r-cran-doparallel, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass Filename: pool/dists/noble/main/r-cran-mff_0.2.0-1.ca2404.1_all.deb Size: 66722 MD5sum: 23780823c03e3c6b57b8394348440120 SHA1: 009d5ca846f6df274b99b580f3bf22bc35e1f763 SHA256: d1c3839db506c5124a72aa0dfd6495c329502bba48ad55e63722df267f5bae7a SHA512: 58eb629acc6614fb73e3b5be5f80f71883b9c0463df02a63051dc39911662e86d04d9167de17a2a1643b9d1d57abab6c476cc496b64efa3f33560d1fba0a5d85 Homepage: https://cran.r-project.org/package=MFF Description: CRAN Package 'MFF' (Meta Fuzzy Functions) Implements Meta Fuzzy Functions (MFFs) for regression Tak and Ucan (2026) by aggregating predictions from multiple base learners using membership weights learned in the prediction space of validation set. The package supports fuzzy and crisp meta-ensemble structures via Fuzzy C-Means (FCM) Tak (2018) , Possibilistic FCM (PFCM) Tak (2021) , Gustafson–Kessel (GK) clustering, and k-means, and provides a workflow to (i) generate validation/test prediction matrices from common regression learners (linear and penalized regression via 'glmnet', random forests, gradient boosting with 'xgboost' and 'lightgbm'), (ii) fit cluster-wise meta fuzzy functions and compute membership-based weights, (iii) tune clustering-related hyperparameters (number of clusters/functions, fuzziness exponent, possibilistic regularization) via grid search on validation loss, and (iv) predict on new/test prediction matrices and evaluate performance using standard regression metrics (MAE, RMSE, MAPE, SMAPE, MSE, MedAE). This enables flexible, interpretable ensemble regression where different base models contribute to different meta components according to learned memberships. Package: r-cran-mfilter Architecture: all Version: 0.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tseries, r-cran-pastecs, r-cran-locfit, r-cran-tserieschaos, r-cran-tsdyn, r-cran-forecast Filename: pool/dists/noble/main/r-cran-mfilter_0.1-5-1.ca2404.1_all.deb Size: 281670 MD5sum: 11a0d4088dcb8c447c4d0e7e7d304a47 SHA1: 9ab385cc7b6e5997d711fd553ae6c729bfec6909 SHA256: 14274a3035a83864c2db85f4bfb8e9a6e29d3d527dcb9a0ecf2390198e6ef7ff SHA512: 418210147b1acc9ceee1d8e4ab635d4e364211562d5281c68da6a9ebe25777f55d8be3785fa21ceb4a85999509c89b12f90a44e2dfdf5423e4c1e235fb5b848d Homepage: https://cran.r-project.org/package=mFilter Description: CRAN Package 'mFilter' (Miscellaneous Time Series Filters) The mFilter package implements several time series filters useful for smoothing and extracting trend and cyclical components of a time series. The routines are commonly used in economics and finance, however they should also be interest to other areas. Currently, Christiano-Fitzgerald, Baxter-King, Hodrick-Prescott, Butterworth, and trigonometric regression filters are included in the package. Package: r-cran-mflica Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 719 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-mflica_0.1.7-1.ca2404.1_all.deb Size: 609310 MD5sum: 8b1797f15b0267ec1b26d0a647e04c92 SHA1: 354cfc59e7105eac68c060e9595577fcecda49ec SHA256: f61cf4661e5f0c7917854b0fc7161a3be7529807b10d126a8c02fe3e8b2be5e3 SHA512: e883457f887f7f92c8fe5930e4b33114e0d1243146a32f58fc290697e825dbdd29de81b9dbb593f7f45c67e35ec4816101b6ef3f357b25ad1c45601deda3c855 Homepage: https://cran.r-project.org/package=mFLICA Description: CRAN Package 'mFLICA' (Leadership-Inference Framework for Multivariate Time Series) A leadership-inference framework for multivariate time series. The framework for multiple-faction-leadership inference from coordinated activities or 'mFLICA' uses a notion of a leader as an individual who initiates collective patterns that everyone in a group follows. Given a set of time series of individual activities, our goal is to identify periods of coordinated activity, find factions of coordination if more than one exist, as well as identify leaders of each faction. For each time step, the framework infers following relations between individual time series, then identifying a leader of each faction whom many individuals follow but it follows no one. A faction is defined as a group of individuals that everyone follows the same leader. 'mFLICA' reports following relations, leaders of factions, and members of each faction for each time step. Please see Chainarong Amornbunchornvej and Tanya Berger-Wolf (2018) for methodology and Chainarong Amornbunchornvej (2021) for software when referring to this package in publications. Package: r-cran-mfo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-tibble, r-cran-minpack.lm, r-cran-openxlsx, r-cran-readxl, r-cran-stringr, r-cran-tidyr Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mfo_0.1.0-1.ca2404.1_all.deb Size: 146724 MD5sum: ba36e97e1000d76ec680e67d48a93586 SHA1: e45c257274ff3adfa09563ca41dd68e4d8877c40 SHA256: 1332ae5d6727409d664cd24583f61544c8659e6e59708f95fde27dd6a92f4249 SHA512: b1f5039d893617f00e6516f74c57460e6808aa2be343a54a462c102a5f211875796e3acbb30fed883c01bbea5c3ac717201f3466eaebd1e2d240190cdf371efd Homepage: https://cran.r-project.org/package=MFO Description: CRAN Package 'MFO' (Maximal Fat Oxidation and Kinetics Calculation) Calculate the maximal fat oxidation, the exercise intensity that elicits the maximal fat oxidation and the SIN model to represent the fat oxidation kinetics. Three variables can be obtained from the SIN model: dilatation, symmetry and translation. Examples of these methods can be found in Montes de Oca et al (2021) and Chenevière et al. (2009) . 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Key references are Royston and Altman (1994) and Royston and Sauerbrei (2008, ISBN:978-0-470-02842-1). In addition, it can model a sigmoid relationship between variable x and an outcome variable y using the approximate cumulative distribution transformation proposed by Royston (2014) . This feature distinguishes it from a standard fractional polynomial function, which lacks the ability to achieve such modeling. Package: r-cran-mfp Architecture: all Version: 1.5.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mfp_1.5.5.1-1.ca2404.1_all.deb Size: 143828 MD5sum: b2a8f084da6cb232d1af1a71e7537e88 SHA1: ea546f9bc7eaedac53341d121dbe5957b1971868 SHA256: 2fdad2eb9a68e7e33574f96ee5ea305aba943e3dca8ca1f03da6e894c286c975 SHA512: 1a29a9d80df16d2594aaa6d9dd9772953f552430cf4068cdc35d154735b499bcab869b61a7e1bdcfceb4435314cc692dd51e5ccd2df5b23d224e3bbc21ea0f71 Homepage: https://cran.r-project.org/package=mfp Description: CRAN Package 'mfp' (Multivariable Fractional Polynomials) Multivariable Fractional Polynomial algorithm for model-building. Fractional polynomials are used to represent curvature in regression models. A key reference is Royston and Altman, 1994. 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References - Michael Messer, Marietta Kirchner, Julia Schiemann, Jochen Roeper, Ralph Neininger and Gaby Schneider (2014), A multiple filter test for the detection of rate changes in renewal processes with varying variance . Stefan Albert, Michael Messer, Julia Schiemann, Jochen Roeper, Gaby Schneider (2017), Multi-scale detection of variance changes in renewal processes in the presence of rate change points . Michael Messer, Kaue M. Costa, Jochen Roeper and Gaby Schneider (2017), Multi-scale detection of rate changes in spike trains with weak dependencies . Michael Messer, Stefan Albert and Gaby Schneider (2018), The multiple filter test for change point detection in time series . Michael Messer, Hendrik Backhaus, Albrecht Stroh and Gaby Schneider (2019+) Peak detection in time series. 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The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'. 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For more information about 'MGDrivE', see our publication: Sánchez et al. (2019) Some of the notable capabilities of 'MGDrivE2' include: incorporation of human populations, epidemiological dynamics, time-varying parameters, and a continuous-time simulation framework with various sampling algorithms for both deterministic and stochastic interpretations. 'MGDrivE2' relies on the genetic inheritance structures provided in package 'MGDrivE', so we suggest installing that package initially. 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Manipulation of multivariate generalised Gaussian distributions (methods presented by Gomez, Gomez-Villegas and Marin (1998) and Pascal, Bombrun, Tourneret and Berthoumieu (2013) ). 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Package: r-cran-mglm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-plyr, r-cran-reshape2, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mglm_0.2.1-1.ca2404.1_all.deb Size: 444570 MD5sum: 9396e1734faf229f1adf8aa97b227a2a SHA1: 28205af604e18595f5db5a619a89796781cc3b96 SHA256: a2a2676267c19876b51a4a14a4fd7505e7a894004a7051321055b11e6191e9f5 SHA512: 0e66ac3a6ffe1dfce87a30238d8d9e536873ced9b65ac65594097e0441a68071e1593251362028461fb845bddc6105c1a473453d2a0ab0165a565a6ac3f85f27 Homepage: https://cran.r-project.org/package=MGLM Description: CRAN Package 'MGLM' (Multivariate Response Generalized Linear Models) Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data. Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported. Package: r-cran-mglmn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvabund, r-cran-snowfall Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mglmn_0.1.0-1.ca2404.1_all.deb Size: 49374 MD5sum: 3d8d3f034be2b8ee6a23630269be27eb SHA1: 6df1dd5f94fea84fa5e42d7b23392ac8d61fbd7e SHA256: e3b0c68417a7175088394269f858fafb43ec54edcf945284ccb12163645bc105 SHA512: 8258c8f5b7fe64d21f843ad15e33083aadad226081a566dc310eb52da9778e5885902edc317f82082f13145815e7d8a22863633955b050886a82622a0d2899bc Homepage: https://cran.r-project.org/package=mglmn Description: CRAN Package 'mglmn' (Model Averaging for Multivariate GLM with Null Models) Tools for univariate and multivariate generalized linear models with model averaging and null model technique. Package: r-cran-mgm Architecture: all Version: 1.2-15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 949 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-stringr, r-cran-hmisc, r-cran-qgraph, r-cran-gtools Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mgm_1.2-15-1.ca2404.1_all.deb Size: 923102 MD5sum: 78dd4a601a39c0117ce1b779005b0dc4 SHA1: 875434aec75352ac5fbf36e649ebc6b405acf835 SHA256: 45dbad6da32fd7bdb7096ee83c1cbe3f0d170f8462cea12bddca065f6cbea2a3 SHA512: 757c24d3fa2964f50984b229fa5b2ceb3086be94f98cc183bca2efb8df73ac605a3ccd3ad74307bb70d36a957c9a0b0ced71486fac88f990be87c91029407f54 Homepage: https://cran.r-project.org/package=mgm Description: CRAN Package 'mgm' (Estimating Time-Varying k-Order Mixed Graphical Models) Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) . Package: r-cran-mgms2 Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4670 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maldiquant, r-cran-maldiquantforeign Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mgms2_1.0.2-1.ca2404.1_all.deb Size: 3487662 MD5sum: 3f572d05d3b6ba8abd0dad9491713e11 SHA1: dbfd0cc8300cd8fa182143e68cb69cb38b345f38 SHA256: e4f1cd3dc547ac4d5f401e6fdf033b596de0bb39d392a2089e667ba9fe4249cc SHA512: 19e1933381884661dd4e50e5cb906ce7bd41a2773558c0d06e0e14ff8fae5728e8cce476cd6d390acd8f720c482f4bbe55cc16769b6388fce9a0de74f1271717 Homepage: https://cran.r-project.org/package=MGMS2 Description: CRAN Package 'MGMS2' ('MGMS2' for Polymicrobial Samples) A glycolipid mass spectrometry technology has the potential to accurately identify individual bacterial species from polymicrobial samples. To develop bacterial identification algorithms (e.g. machine learning) using this glycolipid technology, it is necessary to generate a large number of various in-silico polymicrobial mass spectra that are similar to real mass spectra. 'MGMS2' (Membrane Glycolipid Mass Spectrum Simulator) generates such in-silico mass spectra, considering errors in m/z (mass-to-charge ratio) and variances of intensity values, occasions of missing signature ions, and noise peaks. It estimates summary statistics of monomicrobial mass spectra for each strain or species and simulates polymicrobial glycolipid mass spectra using the summary statistics of monomicrobial mass spectra. References: Ryu, S.Y., Wendt, G.A., Chandler, C.E., Ernst, R.K. and Goodlett, D.R. (2019) "Model-based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids." Gibb, S. and Strimmer, K. (2012) "MALDIquant: a versatile R package for the analysis of mass spectrometry data." Package: r-cran-mgpsdk Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mgpsdk_1.0.0-1.ca2404.1_all.deb Size: 78154 MD5sum: 4048d8500bbcf6680e9ce0fcdf1eeae2 SHA1: 138c048f5cf1b2a087e38074a353c519b91305fd SHA256: 8734659520f4bffe9dbaf74d13892ab91a0971fef677c84d81719fe2d023a875 SHA512: c5c0c840e16407b40240a4351d2a40a2e98f399d828ab32aa65d5450a49519565a4d07bc5c4630b4e29acf59ceb221d84c032824f7ba671aeced62283c40381d Homepage: https://cran.r-project.org/package=MGPSDK Description: CRAN Package 'MGPSDK' (Interact with the Maxar 'MGP' Application Programming Interfaces) Provides an interface to the Maxar Geospatial Platform (MGP) Application Programming Interface. It facilitates imagery searches using the MGP Streaming Application Programming Interface via the Web Feature Service (WFS) method, and supports image downloads through Web Map Service (WMS) and Web Map Tile Service (WMTS) Open Geospatial Consortium (OGC) methods. Additionally, it integrates with the Maxar Geospatial Platform Basemaps Application Programming Interface for accessing Maxar basemaps imagery and seamlines. The package also offers seamless integration with the Maxar Geospatial Platform Discovery Application Programming Interface, allowing users to search, filter, and sort Maxar content, while retrieving detailed metadata in formats like SpatioTemporal Asset Catalog (STAC) and GeoJSON. Package: r-cran-mgpstreamingsdk Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mgpstreamingsdk_0.2.0-1.ca2404.1_all.deb Size: 59490 MD5sum: 7c1c7effe9fb5145c2d1a25d1dce623e SHA1: 9c009f8fb66013b4d438874af32aa463a1f53dc4 SHA256: 5be6a27bc9c6497c7b4faa0e67125b29b1990b27ab44f9b4730e72967910fb20 SHA512: b4f91f4aa552b0b48ba63eaac21fc3da70303a61c3e4ae4fd52e6ac790d959fb95a9d0161c757cf4f2ac7b1ed93a4ac2491a66ff00ed390f5755827b699054df Homepage: https://cran.r-project.org/package=mgpStreamingSDK Description: CRAN Package 'mgpStreamingSDK' (Interact with the Maxar MGP Streaming API) This grants the functionality of the Maxar Geospatial Platform (MGP) Streaming API. It can search for images using the WFS method. It can Download images using WMS WMTS. It can also Download a full resolution image. Package: r-cran-mgsub Architecture: all Version: 1.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mgsub_1.7.3-1.ca2404.1_all.deb Size: 38070 MD5sum: a4c6dc438f2a6733ace78251b29b5c28 SHA1: 6421f9068e90b7c499072667d034d83083cc47b4 SHA256: effe44292bd115d11cab42da4cd8c25fce41d2b13eecd36a1bb37ff4d466118b SHA512: a91463a1749fc68780b100919fd59a05e2dcfab65889c1685a7387a9f6c3bc91ca0081d5a576d1c4a7df8fd8d02301154e364fba5b9b8a4183bfd489f8881dcf Homepage: https://cran.r-project.org/package=mgsub Description: CRAN Package 'mgsub' (Safe, Multiple, Simultaneous String Substitution) Designed to enable simultaneous substitution in strings in a safe fashion. Safe means it does not rely on placeholders (which can cause errors in same length matches). Package: r-cran-mgwnbr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/noble/main/r-cran-mgwnbr_0.3.0-1.ca2404.1_all.deb Size: 110998 MD5sum: 3b958e9fbd64f57715784e73f601c2a1 SHA1: 3bb290ad09de9f561d5aa50297f07eba6dc3581d SHA256: 5180648661d4d61aa2148f33c38309baaaeb7a60e089a8d8b05b88ec975f7666 SHA512: 2d4117e74c18335377d3d7403030f113ba7afb55052d59f0ae142805fc7fe5010b5260a9a9fca5ba199a4b676884e36e49802e59b6409076d2b5a310e67f56db Homepage: https://cran.r-project.org/package=mgwnbr Description: CRAN Package 'mgwnbr' (Multiscale Geographically Weighted Negative Binomial Regression) Fits a geographically weighted regression model with different scales for each covariate. Uses the negative binomial distribution as default, but also accepts the normal, Poisson, or logistic distributions. Can fit the global versions of each regression and also the geographically weighted alternatives with only one scale, since they are all particular cases of the multiscale approach. Hanchen Yu (2024). "Exploring Multiscale Geographically Weighted Negative Binomial Regression", Annals of the American Association of Geographers . Fotheringham AS, Yang W, Kang W (2017). "Multiscale Geographically Weighted Regression (MGWR)", Annals of the American Association of Geographers . Da Silva AR, Rodrigues TCV (2014). "Geographically Weighted Negative Binomial Regression - incorporating overdispersion", Statistics and Computing . Package: r-cran-mgwrhw Architecture: all Version: 1.1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 950 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spgwr, r-cran-sf, r-cran-psych, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mgwrhw_1.1.1.5-1.ca2404.1_all.deb Size: 924984 MD5sum: 883afe3e377a136b5d69a9ac8f15aa26 SHA1: 9c85b54db088490da7db2c7dc2ee02a70e57ce5b SHA256: 097d544252fa9508cefb68567feb390110a827f18f8bef71dc45c3b8b2d885e6 SHA512: 59b7970e1c45e108fbaff13261149bc97b8788ca3486d343507e1a5be2d7c468af60987c03cd076d269333705ce8b92696287e901b00b6eccf22f54a54e8f134 Homepage: https://cran.r-project.org/package=mgwrhw Description: CRAN Package 'mgwrhw' (Displays GWR (Geographically Weighted Regression) and Mixed GWROutput and Map) Display processing results using the GWR (Geographically Weighted Regression) method, display maps, and show the results of the Mixed GWR (Mixed Geographically Weighted Regression) model which automatically selects global variables based on variability between regions. This function refers to Yasin, & Purhadi. (2012). "Mixed Geographically Weighted Regression Model (Case Study the Percentage of Poor Households in Mojokerto 2008)". European Journal of Scientific Research, 188-196. . Package: r-cran-mhcnuggetsr Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rappdirs, r-cran-reticulate, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mhcnuggetsr_1.1-1.ca2404.1_all.deb Size: 125732 MD5sum: 4a85a04ad6839033d1d9e2f1e6bc0a87 SHA1: df5335452e59787fb47abd4a716cc9c36c2a2b3f SHA256: a1b1f5f3a42caa44134bf990ead2f0b79534d245c8e41b0198e31cac8fbc5762 SHA512: 99fd315f37db68ae4b0fcd79b067cbf4dd7e63fdadabd9aa315e9fbb4c8ad43b7ff123279eb95aa151ccd225b294670437882511b40d7480736367a2f215a92c Homepage: https://cran.r-project.org/package=mhcnuggetsr Description: CRAN Package 'mhcnuggetsr' (Call MHCnuggets) MHCnuggets () is a Python tool to predict MHC class I and MHC class II epitopes. This package allows one to call MHCnuggets from R. Package: r-cran-mhctools Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-mhctools_1.6.0-1.ca2404.1_all.deb Size: 256964 MD5sum: 6754a8d8b2a2604984e399f114a3c96e SHA1: 8abf4cf45d436990a628c8175f79900960a6e2ef SHA256: 852c05801e04f85eb71a003fe23ba2153162305ceec9d29286367aace48a9850 SHA512: 63896927532d27d0b4dccbf7cd45b85412f31c0b0f33f2a003d5a4244d83fa93c97c66b33ea71dc07402db627a5f86b76f76f907b985e53f8bfac3b83eee026a Homepage: https://cran.r-project.org/package=MHCtools Description: CRAN Package 'MHCtools' (Analysis of MHC Data in Non-Model Species) Sixteen tools for bioinformatics processing and analysis of major histocompatibility complex (MHC) data. The functions are tailored for amplicon data sets that have been filtered using the dada2 method (for more information on dada2, visit ), but even other types of data sets can be analyzed. The ReplMatch() function matches replicates in data sets in order to evaluate genotyping success. The GetReplTable() and GetReplStats() functions perform such an evaluation. The CreateFas() function creates a fasta file with all the sequences in the data set. The CreateSamplesFas() function creates individual fasta files for each sample in the data set. The DistCalc() function calculates Grantham, Sandberg, or p-distances from pairwise comparisons of all sequences in a data set, and mean distances of all pairwise comparisons within each sample in a data set. The function additionally outputs five tables with physico-chemical z-descriptor values (based on Sandberg et al. 1998) for each amino acid position in all sequences in the data set. These tables may be useful for further downstream analyses, such as estimation of MHC supertypes. The BootKmeans() function is a wrapper for the kmeans() function of the 'stats' package, which allows for bootstrapping. Bootstrapping k-estimates may be desirable in data sets, where e.g. BIC- vs. k-values do not produce clear inflection points ("elbows"). BootKmeans() performs multiple runs of kmeans() and estimates optimal k-values based on a user-defined threshold of BIC reduction. The method is an automated and bootstrapped version of visually inspecting elbow plots of BIC- vs. k-values. The ClusterMatch() function is a tool for evaluating whether different k-means() clustering models identify similar clusters, and summarize bootstrap model stats as means for different estimated values of k. It is designed to take files produced by the BootKmeans() function as input, but other data can be analyzed if the descriptions of the required data formats are observed carefully. The SynDist() function analyses of synonymous variation among aligned protein-coding DNA sequences, that is, nucleotide substitutions that do not translate to changes in the amino acid sequences due to degeneracy of the genetic code. The SynDist() function calculates synonymous nucleotide changes per base and per codon in pairwise sequence comparisons, as well as mean synonymous variation among all pairwise comparisons of the sequences within each sample in a data set. The PapaDiv() function compares parent pairs in the data set and calculate their joint MHC diversity, taking into account sequence variants that occur in both parents. The HpltFind() function infers putative haplotypes from families in the data set. The GetHpltTable() and GetHpltStats() functions evaluate the accuracy of the haplotype inference. The CreateHpltOccTable() function creates a binary (logical) haplotype-sequence occurrence matrix from the output of HpltFind(), for easy overview of which sequences are present in which haplotypes. The HpltMatch() function compares haplotypes to help identify overlapping and potentially identical types. The NestTablesXL() function translates the output from HpltFind() to an Excel workbook, that provides a convenient overview for evaluation and curating of the inferred putative haplotypes. Package: r-cran-mhda Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mhda_2.0-1.ca2404.1_all.deb Size: 50164 MD5sum: 9df6b541223eb4c707576426b2c48bb0 SHA1: 3f69ee8665ded7c959df185d2b6a4368ffe2d0f8 SHA256: 24f11a0042c04e8ba83f2ab8e498cd2ba18788f5c3af0fa8599fc5ea95b8e039 SHA512: d343bce1dcf409403b970090353dc0ccd5f3fd37fb069e3d5ae80f07c38207f8821199fabace70d2a078beb55c71d961e404ef5dfb64144dd7043dd2aa172cc3 Homepage: https://cran.r-project.org/package=MHDA Description: CRAN Package 'MHDA' (Massive Hierarchically Data Analysis) Three main functions about analyzing massive data (missing observations are allowed) considered from multiple layers of categories are demonstrated. Flexible and diverse applications of the function parameters make the data analyses powerful. Package: r-cran-mhg Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mhg_1.1-1.ca2404.1_all.deb Size: 34344 MD5sum: fa270cb59432ab07337b07d8e3f58478 SHA1: 717ee1bbb089badf64657aecf4ae91fe6896c08d SHA256: bbd9ca056c5445d9e8a0b22272c9f9a09ef4f35c08e59e1b46a1cf700379a4eb SHA512: dc72fd666309eb5e76c400e81d09362bd1273e4a22d0c18f1cff1c18b373a08d74b6a6c147fc8de5067a2305af457f14e32411e1c0f762ce43c9c0213445dcbb Homepage: https://cran.r-project.org/package=mHG Description: CRAN Package 'mHG' (Minimum-Hypergeometric Test) Runs a minimum-hypergeometric (mHG) test as described in: Eden, E. (2007). Discovering Motifs in Ranked Lists of DNA Sequences. Haifa. Package: r-cran-mhqol Architecture: all Version: 0.14.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-assertthat, r-cran-tidyr, r-cran-shiny, r-cran-shinyalert, r-cran-dt, r-cran-writexl, r-cran-fmsb Suggests: r-cran-here, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mhqol_0.14.0-1.ca2404.1_all.deb Size: 98948 MD5sum: 911af7a1862572e6a68bce10288b39ca SHA1: d1aae2fb5366b329022cb9b41fc8b933ef40dc5a SHA256: 3688c53ce81d48aff227b9c9284b2aea11cfa243e29b57e2f2730c70d6c73b64 SHA512: a7798129f537445da3358f45210f19fd42771299a9b2159237d00470ba719c9c29006adff6187cb9c5d9775a32cbb850d9f3792d248f467fbdead59568f7566e Homepage: https://cran.r-project.org/package=MHQoL Description: CRAN Package 'MHQoL' (Mental Health Quality of Life Toolkit) Transforms, calculates, and presents results from the Mental Health Quality of Life Questionnaire (MHQoL), a measure of health-related quality of life for individuals with mental health conditions. Provides scoring functions, summary statistics, and visualization tools to facilitate interpretation. For more details see van Krugten et al.(2022) . Package: r-cran-mhtboot Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-mhtboot_1.3.3-1.ca2404.1_all.deb Size: 176016 MD5sum: 9350cba611bf2033c15d8c148063646e SHA1: fd99214dd85f67536368785e3b4c4798843d42f4 SHA256: 5c1b38914b7ad3e32d278997d1a4bdfc7dde274f87c03e995e0cb17ac1923395 SHA512: e153ddaa96cca7aff35c196ea72b915ff3b749dc547e9cf54431230200ed3bae14cd217a999c1216c9d1707cd831d3889285aaba723f7bb644ef2929bc7b9cfd Homepage: https://cran.r-project.org/package=mhtboot Description: CRAN Package 'mhtboot' (Multiple Hypothesis Test Based on Distribution of p Values) A framework for multiple hypothesis testing based on distribution of p values. It is well known that the p values come from different distribution for null and alternatives, in this package we provide functions to detect that change. We provide a method for using the change in distribution of p values as a way to detect the true signals in the data. Package: r-cran-mhtdiscrete Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mhtdiscrete_1.0.1-1.ca2404.1_all.deb Size: 82476 MD5sum: 6bcf4abfc5e1d3b0e7ee05560ef6d55c SHA1: 38fef71ca5c6c7bb8303d1390a754a963fc4c996 SHA256: 60cca9de4effb732b00f35dd67e0cf1fa606913c3eb0262cc2a23c1b8af7f0fc SHA512: b05a25d4998602f1a426969116cb64929ecebb2995bc56b462755c6d562ba6dd2046843cac3390e8a7e566fcb0455539112a9e793cabae506a92170547139bcf Homepage: https://cran.r-project.org/package=MHTdiscrete Description: CRAN Package 'MHTdiscrete' (Multiple Hypotheses Testing for Discrete Data) A comprehensive tool for almost all existing multiple testing methods for discrete data. The package also provides some novel multiple testing procedures controlling FWER/FDR for discrete data. Given discrete p-values and their domains, the [method].p.adjust function returns adjusted p-values, which can be used to compare with the nominal significant level alpha and make decisions. For users' convenience, the functions also provide the output option for printing decision rules. 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We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion. 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Package: r-cran-miamaxent Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-e1071, r-cran-terra, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-ggplot2, r-cran-sf, r-cran-disdat Filename: pool/dists/noble/main/r-cran-miamaxent_1.4.1-1.ca2404.1_all.deb Size: 994124 MD5sum: 0e5891ef05cb2c130e29251e952be617 SHA1: 1c64c86f5a2642d67f53bdd32826a3ca46a1d0d7 SHA256: f76af174e7b912c6b4cd22eac265983d69b0e360bf6088badb5aaeb8f67a7166 SHA512: 3ded91396f987186e1785f25dcbb576aebb5b4ff05652791266340929ec95830a8324504a306ad12554c3480225a76f8e1e1df43368d868cb81752f2eab6a70a Homepage: https://cran.r-project.org/package=MIAmaxent Description: CRAN Package 'MIAmaxent' (A Modular, Integrated Approach to Maximum Entropy DistributionModeling) Tools for training, selecting, and evaluating maximum entropy (and standard logistic regression) distribution models. This package provides tools for user-controlled transformation of explanatory variables, selection of variables by nested model comparison, and flexible model evaluation and projection. It follows principles based on the maximum- likelihood interpretation of maximum entropy modeling, and uses infinitely- weighted logistic regression for model fitting. The package is described in Vollering et al. (2019; ). Package: r-cran-miapack Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-nnet, r-cran-progress Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-miapack_0.1.0-1.ca2404.1_all.deb Size: 115546 MD5sum: 76019bfca1a2cda5a088b1720c658cdd SHA1: 621bd746f7d36f939c1edce2fc702caa0cffb957 SHA256: f8f4f93db53b38790bc2833e9cd770e923a69117d2df90f40604b00ee907a43e SHA512: 9f66c1f59947b13a3962fb2391f62db1bdb3ca0386b799ecb34d3b5d2527585de7f539245f99f654afc15efb59ab2fdb0ee4ae9e1c89039b28320070cab3de09 Homepage: https://cran.r-project.org/package=miapack Description: CRAN Package 'miapack' (Marginalization over Incomplete Auxiliaries) Implements methods to estimate conditional outcome means in settings with missingness-not-at-random and incomplete auxiliary variables. 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Validate the results of an MIC experiment by comparing observed MIC values to a gold standard assay, in line with standards from the International Organization for Standardization (2021) . Package: r-cran-micar Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-micar_1.2.0-1.ca2404.1_all.deb Size: 146278 MD5sum: 2f981f18e52bc13fa4cc4df32c1bc17d SHA1: ad2e88435fa444d3241fa27095cbb06101a3a373 SHA256: e94b06015de5b6df5fffe5103b06b2067e57e815036e62ef448688336857cd04 SHA512: 3e3219c122910d068fa713d38a65d65a60425c968325e97af14f416536df5bf7a1867b55c03de6bd96aca368ff0c0a27c808aaf21dea393ed5e1fd5cb030ab24 Homepage: https://cran.r-project.org/package=micar Description: CRAN Package 'micar' ('Mica' Data Web Portal Client) 'Mica' is a server application used to create data web portals for large-scale epidemiological studies or multiple-study consortia. 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Package: r-cran-micd Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pcalg, r-cran-mice, r-bioc-rbgl, r-cran-rfast Suggests: r-cran-nnet, r-cran-ranger, r-bioc-rgraphviz, r-cran-testthat Filename: pool/dists/noble/main/r-cran-micd_1.1.2-1.ca2404.1_all.deb Size: 161286 MD5sum: 30014964facb9332c6a6f821f8da12c3 SHA1: 6ccadcba335c98149ab9da0aca1de45c9dd5e3a0 SHA256: 3334a5f326eec334b5ab4637c6c1244e9d43589715ba0f052840965ad8383d15 SHA512: 0967d2d4f9e4611bff4e48d314cddcb3742fd7d0690c4c4341b46a04c4d41ff99402e4235c61675177d4e49b161a500ea8e69ee14ccf5205191e93be48ac0800 Homepage: https://cran.r-project.org/package=micd Description: CRAN Package 'micd' (Multiple Imputation in Causal Graph Discovery) Modified functions of the package 'pcalg' and some additional functions to run the PC and the FCI (Fast Causal Inference) algorithm for constraint-based causal discovery in incomplete and multiply imputed datasets. Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020) ; Andrews RM, Bang CW, Didelez V, Witte J, Foraita R (2021) ; Witte J, Foraita R, Didelez V (2022) . 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A large variety of repeated statistical analysis can be performed and finally pooled. Statistical analysis that are available are, among others, Levene's test, Odds and Risk Ratios, One sample proportions, difference between proportions and linear and logistic regression models. Functions can also be used in combination with the Pipe operator. More and more statistical analyses and pooling functions will be added over time. Heymans (2007) . Eekhout (2017) . Wiel (2009) . Marshall (2009) . Sidi (2021) . Lott (2018) . Grund (2021) . 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Package: r-cran-micemd Architecture: all Version: 1.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-matrix, r-cran-mass, r-cran-nlme, r-cran-lme4, r-cran-mvmeta, r-cran-jomo, r-cran-mvtnorm, r-cran-digest, r-cran-abind, r-cran-gjrm, r-cran-mgcv, r-cran-mixmeta, r-cran-pbivnorm Suggests: r-cran-vim, r-cran-ggplot2, r-cran-data.table, r-cran-broom.mixed Filename: pool/dists/noble/main/r-cran-micemd_1.10.1-1.ca2404.1_all.deb Size: 470050 MD5sum: e0c503fd7a4a2a0cd3dcbdf3bf9eb1d2 SHA1: 4a914803566791d426e7d8ed194d8fba5cea4135 SHA256: 886df9fdf36c5123409aa4f206e9b16b5e1df6853b7b4cdc6b26cf3dcd8b4924 SHA512: 665e07d97b2ac09d16b18e2b7531736141e385d841dec8386a8a5c250e2c2ff977828f8f6c0b873d9066409e1bf1d08e26254265a7e72a50f2787f3fa696152e Homepage: https://cran.r-project.org/package=micemd Description: CRAN Package 'micemd' (Multiple Imputation by Chained Equations with Multilevel Data) Addons for the 'mice' package to perform multiple imputation using chained equations with two-level data. Includes imputation methods dedicated to sporadically and systematically missing values. Imputation of continuous, binary or count variables are available. Following the recommendations of Audigier, V. et al (2018) , the choice of the imputation method for each variable can be facilitated by a default choice tuned according to the structure of the incomplete dataset. Allows parallel calculation and overimputation for 'mice'. Package: r-cran-micer Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-micer_0.2.1-1.ca2404.1_all.deb Size: 59860 MD5sum: a8037a81bf2912c9bb899f181ea5e6cb SHA1: d28bcfe18f6e2ee92bf2ee9c666df18bf70d3d35 SHA256: a57e3266f8d46e3d99bb147fc468a28a65d31e20addd6af22282972e38010bca SHA512: 00da1eabf93730009a786aa2277a19896c5ecd266050543e7c17c1b5b936bc59a2b086efa81e744fd19eb558abbde05fcdbbfc8d7e249dcc3bdc901c88c3c64b Homepage: https://cran.r-project.org/package=micer Description: CRAN Package 'micer' (Map Image Classification Efficacy) Map image classification efficacy (MICE) adjusts the accuracy rate relative to a random classification baseline (Shao et al. (2021) and Tang et al. (2024)). Only the proportions from the reference labels are considered, as opposed to the proportions from the reference and predictions, as is the case for the Kappa statistic. This package offers means to calculate MICE and adjusted versions of class-level user's accuracy (i.e., precision) and producer's accuracy (i.e., recall) and F1-scores. Class-level metrics are aggregated using macro-averaging. Functions are also made available to estimate confidence intervals using bootstrapping and statistically compare two classification results. Package: r-cran-miceranger Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3236 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ranger, r-cran-data.table, r-cran-fnn, r-cran-ggplot2, r-cran-crayon, r-cran-corrplot, r-cran-ggpubr, r-cran-desctools, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel, r-cran-testthat Filename: pool/dists/noble/main/r-cran-miceranger_1.5.0-1.ca2404.1_all.deb Size: 969794 MD5sum: a70de06810cc5eb8604c54e29a728c5c SHA1: 583a455ee75408802fc3c035cdce1b940ea4a085 SHA256: 5114ad46e136c7e8ebf0d5c916104df0e32a46e1928fc596b9de52f69458ffff SHA512: ea6b229cbce59ec2913b255409b9c95fa9351db43dbfa1c02138f794ac36bb6b8a7bb3d54f720d691327a37d60841ee4e568188131a8058e3f70b3efc07b56e6 Homepage: https://cran.r-project.org/package=miceRanger Description: CRAN Package 'miceRanger' (Multiple Imputation by Chained Equations with Random Forests) Multiple Imputation has been shown to be a flexible method to impute missing values by Van Buuren (2007) . Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann to impute missing values in datasets. They have the added benefits of returning out of bag error and variable importance estimates, as well as being simple to run in parallel. Package: r-cran-michelrodange Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-michelrodange_1.0.0-1.ca2404.1_all.deb Size: 155516 MD5sum: 9e7b3db9f4d3febdaa10de991f8b1607 SHA1: ff2b79f073a8188f8abee0e9f629172e9ef8b6c6 SHA256: 2b99852501567e2719444d9e310ba3de1924132a3cec8fb321e7d825cf2bea08 SHA512: b3817f49d5b2be234580a273c6def1f182aade54e6072eed7e297cfc0e20ce0514662f9064171f02e534cb0a9322f8cdd03e939281c171450fff0d80ad862e90 Homepage: https://cran.r-project.org/package=michelRodange Description: CRAN Package 'michelRodange' (The Works (in Luxembourguish) of Michel Rodange) Michel Rodange was a Luxembourguish writer and poet who lived in the 19th century. His most notable work is Rodange (1872, ISBN:1166177424), ("Renert oder de Fuuß am Frack an a Ma'nsgrëßt"), but he also wrote many more works, including Rodange, Tockert (1928) ("D'Léierchen - Dem Léiweckerche säi Lidd") and Rodange, Welter (1929) ("Dem Grow Sigfrid seng Goldkuommer"). This package contains three datasets, each made from the plain text versions of his works available on . Package: r-cran-miclust Architecture: all Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doby, r-cran-combinat, r-cran-flexclust, r-cran-irr, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-xtable, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-miclust_1.2.8-1.ca2404.1_all.deb Size: 165496 MD5sum: 09c6ef71d36bff5582d137456fba859b SHA1: d1a23234b03bb3d668eaa895081746574a181cc4 SHA256: f7313ec82c33b74a1685c116ca1bd5d1066c08dfe5a7c13aa4c2625ed56613cb SHA512: 4185e76a3aa52385f28bfd8f6931a2dff4617b8d697a8cb5e8c03d4562a7fe48e5d664e9c2ea6f02842b2f768bd6bebda9ba5b2247f1edb49a83844b3c5fcedc Homepage: https://cran.r-project.org/package=miclust Description: CRAN Package 'miclust' (Multiple Imputation in Cluster Analysis) Implementation of a framework for cluster analysis with selection of the final number of clusters and an optional variable selection procedure. The package is designed to integrate the results of multiple imputed datasets while accounting for the uncertainty that the imputations introduce in the final results. In addition, the package can also be used for a cluster analysis of the complete cases of a single dataset. The package also includes specific methods to summarize and plot the results. The methods are described in Basagana et al. (2013) . Package: r-cran-micompr Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2111 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-biotools, r-cran-mvn, r-cran-testthat, r-cran-knitr, r-cran-roxygen2, r-cran-devtools Filename: pool/dists/noble/main/r-cran-micompr_1.3.0-1.ca2404.1_all.deb Size: 1708750 MD5sum: a3ddd147527bf4285d5d684899ac691e SHA1: 94d5a5c17de351f7aacbaf11ca584dbb83ad3e9f SHA256: d9f32429606a0cf885fb4ce284a26d0b1d90031b8b83eb836c79804a34cb13b9 SHA512: aa62f5ff466b55c91a84a2258d681a8909128bd578829fb23fabf46f424577a1df2b40ad58bc8141906a1d0a05bd54aba53e85b06f57db9b97f6ce168b3dd622 Homepage: https://cran.r-project.org/package=micompr Description: CRAN Package 'micompr' (Multivariate Independent Comparison of Observations) A procedure for comparing multivariate samples associated with different groups. It uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. The procedure is independent of the distributional properties of samples and automatically selects features that best explain their differences, avoiding manual selection of specific points or summary statistics. It is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations. This package is described in Fachada et al. (2016) . Package: r-cran-micoptcm Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-nleqslv, r-cran-survival, r-cran-distr Filename: pool/dists/noble/main/r-cran-micoptcm_1.1-1.ca2404.1_all.deb Size: 75596 MD5sum: 16eafa19e8f1beb48ce27fb68d0152f7 SHA1: 7a4da4148e0ef5950a9c1b3054ffd1365a0a00ea SHA256: 15daf489474613e0b4b8add9a0a3168b5d42b360055fb64df32ceeed04313e6a SHA512: 74da9d1c849b084c3d063dea02037da15ec99741b34f7a7383feae355075979efde1401121e4c0efa28d28f337c7c23cb22b980fb5bf70a0701622fd39df6c71 Homepage: https://cran.r-project.org/package=miCoPTCM Description: CRAN Package 'miCoPTCM' (Promotion Time Cure Model with Mis-Measured Covariates) Fits Semiparametric Promotion Time Cure Models, taking into account (using a corrected score approach or the SIMEX algorithm) or not the measurement error in the covariates, using a backfitting approach to maximize the likelihood. Package: r-cran-microbats Architecture: all Version: 0.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-microbats_0.1-1-1.ca2404.1_all.deb Size: 15738 MD5sum: c7e9abd204e6f916a87f6213ba7290bc SHA1: bb3c75354c5670dd7aa8f9297a9618909d72192c SHA256: e46ea9c3baf761444c9f5a0cee27ce6ffad0a3f7c62305ae900e67941ce236ca SHA512: b6b886c188d7362d5f3f63d3976c50589858e70e95d4a7d4934205968760542f74c1e964017faa4ffe929821b2439dd29278cd3708617ad69405fcba2a581615 Homepage: https://cran.r-project.org/package=microbats Description: CRAN Package 'microbats' (An Implementation of Bat Algorithm in R) A nature-inspired metaheuristic algorithm based on the echolocation behavior of microbats that uses frequency tuning to optimize problems in both continuous and discrete dimensions. This R package makes it easy to implement the standard bat algorithm on any user-supplied function. The algorithm was first developed by Xin-She Yang in 2010 (, ). Package: r-cran-microbial Architecture: all Version: 0.0.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1066 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-plyr, r-cran-magrittr, r-cran-broom, r-bioc-phyloseq, r-cran-vegan, r-cran-rlang, r-cran-ggplot2, r-cran-ggpubr, r-bioc-deseq2, r-bioc-summarizedexperiment, r-bioc-s4vectors, r-cran-rstatix, r-cran-tidyr, r-cran-phangorn, r-cran-randomforest, r-bioc-edger Suggests: r-cran-markdown, r-bioc-dada2, r-cran-rmarkdown, r-cran-knitr, r-bioc-biostrings, r-bioc-decipher, r-cran-mass, r-cran-testthat Filename: pool/dists/noble/main/r-cran-microbial_0.0.22-1.ca2404.1_all.deb Size: 945802 MD5sum: 54138c8a9bf9fdf0acc45f7cfe1f3709 SHA1: b89d222f55f9b31b6000f840d855d1e2457acbf9 SHA256: 16141237c1eab19103de49fefed50fcfa850aa5280898ba8aaf496c0aa6e9ef1 SHA512: e10f50dc7dd23d3d15017ff96cd4fc31b31b11d6ffe08eb82bc7c48449339dc67bf8012f252f2a3e1f114106f36078e17891d914eea8a3af1fdb42cfbdb0d053 Homepage: https://cran.r-project.org/package=microbial Description: CRAN Package 'microbial' (Do 16s Data Analysis and Generate Figures) Provides functions to enhance the available statistical analysis procedures in R by providing simple functions to analysis and visualize the 16S rRNA data.Here we present a tutorial with minimum working examples to demonstrate usage and dependencies. Package: r-cran-microbialgrowth Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlstools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-microbialgrowth_1.0.0-1.ca2404.1_all.deb Size: 774540 MD5sum: 9d3e5981fe6d359418d543b8efd0044c SHA1: b68d37116d4419d2581f4994a9ace433160fb1f4 SHA256: b43a1e7f36925ae61b4a7e671b84379bdaeafcf1fda84d8cd22ea2a213fd0a22 SHA512: 50f874d67d9cc7b90c54dcda0266b0e0d94b57ddd58e02431693c68e8cf75f614c130943a29211896f52176702c6e5649c9b2492159c2b6f72b5b897219b2de1 Homepage: https://cran.r-project.org/package=MicrobialGrowth Description: CRAN Package 'MicrobialGrowth' (Estimates Growth Parameters from Models and Plots the Curve) Fit growth curves to various known microbial growth models automatically to estimate growth parameters. Growth curves can be plotted with their uncertainty band. Growth models are: modified Gompertz model (Zwietering et al. (1990) ), Baranyi model (Baranyi and Roberts (1994) ), Rosso model (Rosso et al. (1993) ) and linear model (Dantigny (2005) ). Package: r-cran-microbiomemqc Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-vegan Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-microbiomemqc_1.0.2-1.ca2404.1_all.deb Size: 21096 MD5sum: a521708365d3076f40060884e040ba88 SHA1: bf5eb999a51cf86dff57d51de7ef082e9defcb56 SHA256: 02466c496eb6ad50e8d03a8018ba2f6092bcfb71eb1955cece05d7745bc95e28 SHA512: 725cafb1831de1c55f48ed0999968e750b7923bd451357e8f42016a30e08e7536509dedd40a2a94d4cfe6cd5f809c68089885992499493ccbb970a09f2815ea9 Homepage: https://cran.r-project.org/package=microbiomeMQC Description: CRAN Package 'microbiomeMQC' (Calculate 4 Key Reporting Measures) Perform calculations for the WHO International Reference Reagents for the microbiome. Using strain, species or genera abundance tables generated through analysis of 16S ribosomal RNA sequencing or shotgun sequencing which included a reference reagent. This package will calculate measures of sensitivity, False positive relative abundance, diversity, and similarity based on mean average abundances with respect to the reference reagent. Package: r-cran-microbiomesurv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 864 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-survival, r-cran-survminer, r-cran-glmnet, r-cran-superpc, r-cran-lmtest, r-cran-gplots, r-cran-tidyr, r-cran-dplyr, r-bioc-microbiome, r-cran-pls Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-microbiomesurv_0.1.0-1.ca2404.1_all.deb Size: 754706 MD5sum: b5608939581f1d0d1a7dc88d1d74265d SHA1: e79c5774189b6f247dd0d8bd5a5f77886868f8ff SHA256: 538f5baa21c23f8f7ec030be8b85556e36fc454314d21764a5f950eb79ac97a0 SHA512: 222caabc7710a5cbce7a3931a6ee38cd82e2760baba38dc3437c272739b0e04ca2c840501670afafcb10ba1424bb0bc518e7c26f83d9c84bb40111922b51649d Homepage: https://cran.r-project.org/package=MicrobiomeSurv Description: CRAN Package 'MicrobiomeSurv' (Biomarker Validation for Microbiome-Based SurvivalClassification and Prediction) An approach to identify microbiome biomarker for time to event data by discovering microbiome for predicting survival and classifying subjects into risk groups. Classifiers are constructed as a linear combination of important microbiome and treatment effects if necessary. Several methods were implemented to estimate the microbiome risk score such as the LASSO method by Robert Tibshirani (1998) , Elastic net approach by Hui Zou and Trevor Hastie (2005) , supervised principle component analysis of Wold Svante et al. (1987) , and supervised partial least squares analysis by Inge S. Helland . Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected microbiome and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique. Package: r-cran-microbtisda Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5839 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-cluster, r-cran-dplyr, r-cran-ggdendro, r-cran-ggplot2, r-cran-ggraph, r-cran-mass, r-cran-mgcv, r-cran-plyr, r-cran-pracma, r-cran-randomforest, r-cran-reshape2, r-cran-scales, r-cran-tibble, r-cran-tidygraph, r-cran-tidyr, r-cran-vegan, r-cran-visnetwork Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-microbtisda_0.1.0-1.ca2404.1_all.deb Size: 5365596 MD5sum: feda050e8c0888e2acfb309e2a4a3c0d SHA1: 545bee35268230aa76b5613def65a8f076aeb924 SHA256: 5f87f4e63c8276759b699240edbb74c835cd6bdf081aca6901c2a258e67a68f9 SHA512: a74080a4d486633785e36632a753002008a7fc6296ad7431c782875167a1b7e1f31041b6633ef33d5ca3598a4c0a36cdc641c9eb2b997f069afe7db8489c4582 Homepage: https://cran.r-project.org/package=MicrobTiSDA Description: CRAN Package 'MicrobTiSDA' (Microbiome Time-Series Data Analysis) Provides tools specifically designed for analyzing longitudinal microbiome data. This tool integrates seven functional modules, providing a systematic framework for microbiome time-series analysis. For more details on inferences involving interspecies interactions see Fisher (2014) . Details on this package are also described in an unpublished manuscript. Package: r-cran-microcontax Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2633 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-microseq Filename: pool/dists/noble/main/r-cran-microcontax_1.2-1.ca2404.1_all.deb Size: 2649814 MD5sum: c9d03160fcb89b0b7204efaca1a4ca03 SHA1: baf4229e1959da3c2ceede38f9568c7d4afaa232 SHA256: 552cc07ac8a333c81debf3e52ee1a072d70be97612bf5faf29a26ce75af8099c SHA512: 5406af99940fd5c0bdf80cc67ada9d7825e94b8636c19585194087d2a54822f12e5cc71ebf93c44ee8369127636ff8ce4adf86f60a41e7dabb3192e834404514 Homepage: https://cran.r-project.org/package=microcontax Description: CRAN Package 'microcontax' (The ConTax Data Package) The consensus taxonomy for prokaryotes is a set of data-sets for best possible taxonomic classification based on 16S rRNA sequence data. Package: r-cran-microcran Architecture: all Version: 0.9.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-plumber, r-cran-assertthat, r-cran-mime, r-cran-xtable Suggests: r-cran-minicran, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-microcran_0.9.0-1-1.ca2404.1_all.deb Size: 73664 MD5sum: 74f295c9b5ec5c435b2795c1d2aa08ea SHA1: 5ce8198177ba236305c887ce409f7f6df0470654 SHA256: 6d03378d54f33393a381a5c8cc56a10c6b81e699ff21ead6541901c8ba635e19 SHA512: 28b09a6a01c00cd339b9d9cfe031df8cf00780058530ded2f2d3f995ac6d01b5561349cbdad89f22fbe4a6c70978ae7a640e5cf96488288f2d6ef218bd3dc22d Homepage: https://cran.r-project.org/package=microCRAN Description: CRAN Package 'microCRAN' (Hosting an Independent CRAN Repository) Stand-alone HTTP capable R-package repository, that fully supports R's install.packages() and available.packages(). It also contains API endpoints for end-users to add/update packages. This package can supplement 'miniCRAN', which has functions for maintaining a local (partial) copy of 'CRAN'. Current version is bare-minimum without any access-control or much security. Package: r-cran-microdatasus Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1453 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-curl, r-cran-data.table, r-cran-dplyr, r-cran-dtplyr, r-cran-foreign, r-cran-lubridate, r-cran-magrittr, r-cran-rcurl, r-cran-read.dbc, r-cran-rlang, r-cran-stringi, r-cran-tibble, r-cran-zip Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-microdatasus_2.3.1-1.ca2404.1_all.deb Size: 1431932 MD5sum: f6dec10f7e4c2bd3f77335c680eab1ab SHA1: d517c91f5d8b85b61a8e9e3d74d9ab1084a6a909 SHA256: 82148cdcecbc75c54a4f44df8a45aa35e2a6f402aecc87fe4d7dc889965857d1 SHA512: 7f7501d14aa352190378b8ea3683aaf495fc1e06bde44227d23a44fb7fe035bcee5a94ad3d6c912bc8a39ce1db0ccda47930c133fd38f32a15006bc851281955 Homepage: https://cran.r-project.org/package=microdatasus Description: CRAN Package 'microdatasus' (Download and Process 'DataSUS' Files) Downloads data files from 'DataSUS' health information systems from and process the data, including labeling categorical variables. Package: r-cran-microdatoses Architecture: all Version: 0.8.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 784 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readr Filename: pool/dists/noble/main/r-cran-microdatoses_0.8.15-1.ca2404.1_all.deb Size: 142742 MD5sum: fd18458b4d5da4d3be4e225e7dfba449 SHA1: 76ff5484b61941f86cfb522b5ccc9cd50856226f SHA256: 179964b355bb2651c4930bd11f367bed32f4b9fa6b15c8028dbbd7736ea1e05c SHA512: e58f6337f1adc484f39430d3c8c6aa0f2a89bd416c879dda995ccbee1eac968c8569fd95d66478db4ddab91026f188d4df34a5761b7fbde0a103abb8d5aafb5c Homepage: https://cran.r-project.org/package=MicroDatosEs Description: CRAN Package 'MicroDatosEs' (Utilities for Official Spanish Microdata) Provides utilities for reading and processing microdata from Spanish official statistics with R. Package: r-cran-microdiluter Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggh4x, r-cran-ggplot2, r-cran-ggthemes, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-rstatix, r-cran-stringr, r-cran-tibble, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-microdiluter_1.0.1-1.ca2404.1_all.deb Size: 194034 MD5sum: 5cc6f8e9294cedc0828159359e1bd8af SHA1: ecbc078a5d54c04e0c1f5224b9143816e1d5adb8 SHA256: 132c00e9e7c841fe347c8003e5abb1992c5d234d2dd3eec741386c54dbd7fda5 SHA512: 1eb56915585cbfe81b7fa25f1b8160cc185ec394c98f980a58fcd962147508ba4ddc267deffaf5fad64725c82176cd1226d9665d0b265ac27f766d40afcdbca4 Homepage: https://cran.r-project.org/package=microdiluteR Description: CRAN Package 'microdiluteR' (Analysis of Broth Microdilution Assays) A framework for analyzing broth microdilution assays in various 96-well plate designs, visualizing results and providing descriptive and (simple) inferential statistics (i.e. summary statistics and sign test). The functions are designed to add metadata to 8 x 12 tables of absorption values, creating a tidy data frame. Users can choose between clean-up procedures via function parameters (which covers most cases) or user prompts (in cases with complex experimental designs). Users can also choose between two validation methods, i.e. exclusion of absorbance values above a certain threshold or manual exclusion of samples. A function for visual inspection of samples with their absorption values over time for certain group combinations helps with the decision. In addition, the package includes functions to subtract the background absorption (usually at time T0) and to calculate the growth performance compared to a baseline. Samples can be visually inspected with their absorption values displayed across time points for specific group combinations. Core functions of this package (i.e. background subtraction, sample validation and statistics) were inspired by the manual calculations that were applied in Tewes and Muller (2020) . Package: r-cran-microeco Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4293 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-ape, r-cran-vegan, r-cran-rlang, r-cran-data.table, r-cran-magrittr, r-cran-dplyr, r-cran-tibble, r-cran-scales, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-igraph, r-cran-lifecycle Suggests: r-cran-gunifrac, r-cran-mass, r-cran-ggpubr, r-cran-randomforest, r-cran-ggdendro, r-cran-ggrepel, r-cran-agricolae, r-cran-gridextra, r-cran-picante, r-cran-pheatmap, r-cran-rgexf, r-cran-mice, r-cran-ggally Filename: pool/dists/noble/main/r-cran-microeco_2.2.0-1.ca2404.1_all.deb Size: 4169596 MD5sum: d905a20e5e009321f984a7b250414470 SHA1: 58ac4e4093881a0a658ff2b640d549be57638c4a SHA256: 44d85830c041e5d9ba979830bab2ca2dbc13a2b0e6b509b96ec0bdf4ae36f354 SHA512: 8b4a7b7d792b70c2304a7b139872e2670914ceb8ad9e376950db6be940becaf627c32e1ca69ea46677037106870ce46fe45bdb77df00d7263df5056aca0b3d5d Homepage: https://cran.r-project.org/package=microeco Description: CRAN Package 'microeco' (Microbial Community Ecology Data Analysis) A series of data analysis approaches for microbiome omics data based on the R6 class. The classes are designed for data preprocessing, taxa abundance plotting, alpha diversity analysis, beta diversity analysis, differential abundance test, null model analysis, network analysis, machine learning, environmental data analysis and functional analysis. Package: r-cran-microhaplot Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1892 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-dplyr, r-cran-ggplot2, r-cran-gtools, r-cran-magrittr, r-cran-scales, r-cran-shiny, r-cran-shinybs, r-cran-tidyr, r-cran-shinywidgets, r-cran-ggiraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-microhaplot_1.0.1-1.ca2404.1_all.deb Size: 1052320 MD5sum: 70cc2cdd68fa9da5b73a1e1ed724f94f SHA1: 51f9cdb668109148021423ef969467274e129dc1 SHA256: 99f04a7b76dd927a24d8aa2726eb1ea450248e24d2a7b85a534f4775d79bda95 SHA512: db95d648cb461f2daa3361a2be38604f9c8537a3fe19997d9fc0d53bc2ab3623fb7944cd095f0c0909d50d08d0aff1dfde31fda766817f09ab014fe47524ba31 Homepage: https://cran.r-project.org/package=microhaplot Description: CRAN Package 'microhaplot' (Microhaplotype Constructor and Visualizer) A downstream bioinformatics tool to construct and assist curation of microhaplotypes from short read sequences. Package: r-cran-microinverterdata Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1465 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-units Suggests: r-cran-httptest2, r-cran-knitr, r-cran-lubridate, r-cran-pins, r-cran-rmarkdown, r-cran-tibble, r-cran-tsibble, r-cran-imputets, r-cran-testthat Filename: pool/dists/noble/main/r-cran-microinverterdata_0.4.0-1.ca2404.1_all.deb Size: 888502 MD5sum: cca8320bbcd7ed50d0dd18c80758f563 SHA1: 2ea52bdc8028d85ea7cc52e85e87781c25e57a80 SHA256: d0612f3e90d437ec9577deef6a55458cc8394fc773c8a868510bd6d8c907b7e7 SHA512: a52b1e1ec88174957892d12774011a6cc7be31d27f6696afe45d54325bea66648fd0bc24392520c689f6a7f12e934a1f6b705a1df92dc706afe334514a62bdf8 Homepage: https://cran.r-project.org/package=microinverterdata Description: CRAN Package 'microinverterdata' (Collect your Microinverter Data) Collect and normalize local microinverter energy and power production data through off-cloud API requests. Currently supports 'APSystems', 'Enphase', and 'Fronius' microinverters. Package: r-cran-micromacromultilevel Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-micromacromultilevel_0.4.0-1.ca2404.1_all.deb Size: 47690 MD5sum: 650fc37cc36b4969117d78455341a772 SHA1: fecb0606029c7cea47bd401655f711cbd5d7c9b9 SHA256: d53c1369213ca1d3321c7a78db74c7f803e1f18cd0dd047f27d298a1104f2356 SHA512: a9eaae87738f562e18915480cac2b1aa684d9ba508ee3fb1e0e0862139c0024b796e554d4c8110b44201cea4023792afd79b2e2f43203f31f8e917807e5aa942 Homepage: https://cran.r-project.org/package=MicroMacroMultilevel Description: CRAN Package 'MicroMacroMultilevel' (Micro-Macro Multilevel Modeling) Most multilevel methodologies can only model macro-micro multilevel situations in an unbiased way, wherein group-level predictors (e.g., city temperature) are used to predict an individual-level outcome variable (e.g., citizen personality). In contrast, this R package enables researchers to model micro-macro situations, wherein individual-level (micro) predictors (and other group-level predictors) are used to predict a group-level (macro) outcome variable in an unbiased way. Package: r-cran-micromap Architecture: all Version: 1.9.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1736 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-sp, r-cran-sf, r-cran-ggplot2 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-micromap_1.9.12-1.ca2404.1_all.deb Size: 1358900 MD5sum: d0b6812193ae076052a58ea2992b2853 SHA1: abb2ea6b68f60501c12d1beb06d9118e6ba78beb SHA256: 67d034d8bb9556b718b7052531176ea9c1ac7242156842a7fe8c269bfab7c589 SHA512: 955a96abd0e86bb59bd4b1de976dadf2bc15617c3171838cc9cb8c5982bf964e53d45412eccf40024a75d8bf21741c51a4a1ca6eaf21da11b4547d3bb2573a64 Homepage: https://cran.r-project.org/package=micromap Description: CRAN Package 'micromap' (Linked Micromap Plots) This group of functions simplifies the creation of linked micromap plots. Please see for additional details. Package: r-cran-micromapst Architecture: all Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3695 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-rcolorbrewer, r-cran-labeling, r-cran-sf, r-cran-spdep, r-cran-rmapshaper, r-cran-readxl, r-cran-writexl Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-micromapst_3.1.1-1.ca2404.1_all.deb Size: 3162076 MD5sum: f1146fe572ac6225d320cc427bcfa0ba SHA1: be11707ae64ca6f0be584f306394b3b950ad1bda SHA256: a01151f368786924673ceaec7b3e7985d938c677ef54371ac92d5847ecd53de4 SHA512: acb3ea6441b2d1a7cbcb63efedf114738b3c021c366617c01f85ebfac4d7b02f22df3ef508e59a086def362091be07142bf2d246933c0d9fd9e45d23d99054db Homepage: https://cran.r-project.org/package=micromapST Description: CRAN Package 'micromapST' (Linked Micromap Plots for U. S. and Other Geographic Areas) Provides the users with the ability to quickly create linked micromap plots for a collection of geographic areas. Linked micromap plots are visualizations of geo-referenced data that link statistical graphics to an organized series of small maps or graphic images. The Help description contains examples of how to use the 'micromapST' function. Contained in this package are border group datasets to support creating linked micromap plots for the 50 U.S. states and District of Columbia (51 areas), the U. S. 20 Seer Registries, the 105 counties in the state of Kansas, the 62 counties of New York, the 24 counties of Maryland, the 29 counties of Utah, the 32 administrative areas in China, the 218 administrative areas in the UK and Ireland (for testing only), the 25 districts in the city of Seoul South Korea, and the 52 counties on the Africa continent. A border group dataset contains the boundaries related to the data level areas, a second layer boundaries, a top or third layer boundary, a parameter list of run options, and a cross indexing table between area names, abbreviations, numeric identification and alias matching strings for the specific geographic area. By specifying a border group, the package create linked micromap plots for any geographic region. The user can create and provide their own border group dataset for any area beyond the areas contained within the package with the 'BuildBorderGroup' function. In April of 2022, it was announced that 'maptools', 'rgdal', and 'rgeos' R packages would be retired in middle to end of 2023 and removed from the CRAN libraries. The 'BuildBorderGroup' function was dependent on these packages. 'micromapST' functions were not impacted by the retired R packages. Upgrading of 'BuildBorderGroup' function was completed and released with version 3.0.0 on August 10, 2023 using the 'sf' R package. References: Carr and Pickle, Chapman and Hall/CRC, Visualizing Data Patterns with Micromaps, CRC Press, 2010. Pickle, Pearson, and Carr (2015), micromapST: Exploring and Communicating Geospatial Patterns in US State Data., Journal of Statistical Software, 63(3), 1-25., . Copyrighted 2013, 2014, 2015, 2016, 2022, 2023, 2024, and 2025 by Carr, Pearson and Pickle. Package: r-cran-micromodal Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools Suggests: r-cran-bslib, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/noble/main/r-cran-micromodal_1.0.0-1.ca2404.1_all.deb Size: 28744 MD5sum: 87ceaf2478bb0ccf4cf7b718a15a2718 SHA1: be56d68d53eb72b896d8b9560ad1b10049fda896 SHA256: 96ca1471cc00be9c8e4e2bc32fdcbd44ccb858c2c638ae4387bc4b37a7b7af7a SHA512: b897807cbbab1a10345190b9df5757fe914af1c7053871bd21c2ad0485d972b90a283bf4dc2970da1af931ec6f3c996eabce9b44f93c5a7e93000cf09c522519 Homepage: https://cran.r-project.org/package=micromodal Description: CRAN Package 'micromodal' (Create Simple and Elegant Modal Dialogs in 'shiny') Enables you to create accessible modal dialogs, with confidence and with minimal configuration. Package: r-cran-microniche Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-microniche_1.0.0-1.ca2404.1_all.deb Size: 81664 MD5sum: aef5beea372253bae81af1132949d0b6 SHA1: 2c99c121f90a1e3571b41a4d6177ae91a5d11566 SHA256: da3921d1348aac8c45808bc47891e60f53d7e4aeaab776dae72a5a68fc354f4d SHA512: 81a6a8391a51911c6582a8f354afb6d2669ac0fc7f2846c97cfdce7e6239d2412997c0f73360b1514d6cca063d01c1963a44696ead3679e8bc70533bfb1e646e Homepage: https://cran.r-project.org/package=MicroNiche Description: CRAN Package 'MicroNiche' (Microbial Niche Measurements) Measures niche breadth and overlap of microbial taxa from large matrices. Niche breadth measurements include Levins' niche breadth (Bn) index, Hurlbert's Bn and Feinsinger's proportional similarity (PS) index. (Feinsinger, P., Spears, E.E., Poole, R.W. (1981) ). Niche overlap measurements include Levin's Overlap (Ludwig, J.A. and Reynolds, J.F. (1988, ISBN:0471832359)) and a Jaccard similarity index of Feinsinger's PS values between taxa pairs, as Proportional Overlap. Package: r-cran-micronutr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-micronutr_0.1.1-1.ca2404.1_all.deb Size: 431464 MD5sum: afbd68121f23b97f405e28c0b86b047d SHA1: bd9f42066f0fa540fd7596f2bf7e40ca7c7c0e33 SHA256: 1e56779ba9103e36a7f048e0f1131ee9396b0d5675216139fe1ef0a9b6b5b8d8 SHA512: 05899ac94c23e690c84a6dfe69707cac44d605d2e1b29bf8ace62cf0513c266ab4a894c076299fa2ded509a8cc0decae071e1f4cee9c79a81525a1f04c81f6b9 Homepage: https://cran.r-project.org/package=micronutr Description: CRAN Package 'micronutr' (Determining Vitamin and Mineral Status of Populations) Vitamin and mineral deficiencies continue to be a significant public health problem. This is particularly critical in developing countries where deficiencies to vitamin A, iron, iodine, and other micronutrients lead to adverse health consequences. Cross-sectional surveys are helpful in answering questions related to the magnitude and distribution of deficiencies of selected vitamins and minerals. This package provides tools for calculating and determining select vitamin and mineral deficiencies based on World Health Organization (WHO) guidelines found at . Package: r-cran-micropan Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1720 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-microseq, r-cran-dplyr, r-cran-stringr, r-cran-igraph, r-cran-tibble, r-cran-rlang Filename: pool/dists/noble/main/r-cran-micropan_2.1-1.ca2404.1_all.deb Size: 1700920 MD5sum: b5277787f97f6ae6f94d2bf2057348e1 SHA1: 53aec2462ca408a8ea22c8db5fc20fe6c21dd24e SHA256: f31b4dd3a3cd8adcc3165b6da1ffea3d1714753fd7e5b02a1830085b56de39f9 SHA512: 1d4e20abff4d8e58fcf6cb1e66480285d7d94926525bfcd08eff94c41c08ed902b809a57d595cf80d0e78b5bd7e0b665337947fa48e44d603985b3a0a19813cf Homepage: https://cran.r-project.org/package=micropan Description: CRAN Package 'micropan' (Microbial Pan-Genome Analysis) A collection of functions for computations and visualizations of microbial pan-genomes. Package: r-cran-microplot Architecture: all Version: 1.0-47-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-hh, r-cran-lattice, r-cran-flextable, r-cran-officer, r-cran-ggplot2, r-cran-htmltools, r-cran-cowplot Suggests: r-cran-reshape2, r-cran-latticeextra, r-cran-xtable, r-cran-markdown, r-cran-rmarkdown, r-cran-knitr, r-cran-htmltable Filename: pool/dists/noble/main/r-cran-microplot_1.0-47-1.ca2404.1_all.deb Size: 381730 MD5sum: fa65d7a0c492004bdf1fe5ca494c416d SHA1: 7386f86d38ba6a8acf9b0f99bec217d2cf92d847 SHA256: 1dd4c9091df8b3eeb97eec6991937aef1d1b777b7ae7de8d94789e5ba6ae84a4 SHA512: 751ac586c03f7f1fa80b47120ae69682fe7aef1d617af6b36b57e7bc5a00361252aa7affefd8e85a5d543e9fbfd8e6188c5b14410eac2e362400b785f5f571ee Homepage: https://cran.r-project.org/package=microplot Description: CRAN Package 'microplot' (Microplots (Sparklines) in 'LaTeX', 'Word', 'HTML', 'Excel') The microplot function writes a set of R graphics files to be used as microplots (sparklines) in tables in either 'LaTeX', 'HTML', 'Word', or 'Excel' files. For 'LaTeX', we provide methods for the Hmisc::latex() generic function to construct 'latex' tabular environments which include the graphs. These can be used directly with the operating system 'pdflatex' or 'latex' command, or by using one of 'Sweave', 'knitr', 'rmarkdown', or 'Emacs org-mode' as an intermediary. For 'MS Word', the msWord() function uses the 'flextable' package to construct 'Word' tables which include the graphs. There are several distinct approaches for constructing HTML files. The simplest is to use the msWord() function with argument filetype="html". Alternatively, use either 'Emacs org-mode' or the htmlTable::htmlTable() function to construct an 'HTML' file containing tables which include the graphs. See the documentation for our as.htmlimg() function. For 'Excel' use on 'Windows', the file examples/irisExcel.xls includes 'VBA' code which brings the individual panels into individual cells in the spreadsheet. Examples in the examples and demo subdirectories are shown with 'lattice' graphics, 'ggplot2' graphics, and 'base' graphics. Examples for 'LaTeX' include 'Sweave' (both 'LaTeX'-style and 'Noweb'-style), 'knitr', 'emacs org-mode', and 'rmarkdown' input files and their 'pdf' output files. Examples for 'HTML' include 'org-mode' and 'Rmd' input files and their webarchive 'HTML' output files. In addition, the as.orgtable() function can display a data.frame in an 'org-mode' document. The examples for 'MS Word' (with either filetype="docx" or filetype="html") work with all operating systems. The package does not require the installation of 'LaTeX' or 'MS Word' to be able to write '.tex' or '.docx' files. Package: r-cran-micropop Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3504 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-visnetwork, r-cran-testthat Suggests: r-cran-rmarkdown, r-cran-r.rsp, r-cran-knitr, r-cran-webshot Filename: pool/dists/noble/main/r-cran-micropop_1.6-1.ca2404.1_all.deb Size: 845000 MD5sum: 937a48676de24ec5b03d7ab022526609 SHA1: 026e5edc203b5b76dfd5b04c5da99f63d581e292 SHA256: 165196ddd0cac72442bc6a6b798364c71bd9be08c59d9c03291813821105e98e SHA512: 67f8a4919febbfa92b1d971726b86b2b00803753b752ce389a44040ce8ff9f1e34f1844b3d8db6e8fa3fee46b2882a8ae7f3afbbf7add83ff482d41fca798ce0 Homepage: https://cran.r-project.org/package=microPop Description: CRAN Package 'microPop' (Process-Based Modelling of Microbial Populations) Modelling interacting microbial populations - example applications include human gut microbiota, rumen microbiota and phytoplankton. Solves a system of ordinary differential equations to simulate microbial growth and resource uptake over time. This version contains network visualisation functions. Package: r-cran-microsec Architecture: all Version: 2.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2621 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-dplyr, r-bioc-biostrings, r-bioc-rsamtools, r-bioc-genomeinfodb, r-bioc-biocgenerics Suggests: r-bioc-bsgenome.hsapiens.ucsc.hg38, r-cran-knitr, r-cran-rmarkdown, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-bsgenome.mmusculus.ucsc.mm10 Filename: pool/dists/noble/main/r-cran-microsec_2.1.6-1.ca2404.1_all.deb Size: 2566234 MD5sum: f65bdfb97768ee416ac6b641b6c030d3 SHA1: 3125eed3d508e9401f5bed6878a8f81ec5863e02 SHA256: a069754c831539e28d27c6e26437a166f0e87b36607462539d6f0b69977cb1a5 SHA512: a3f5f30956be2792d2c2bd832aac7cbda9532d41f331cdc452f6e202ee20ca72070e87612196091804ee2bf494a19730e119a93b2e5aa4ded0c73a9410c13398 Homepage: https://cran.r-project.org/package=MicroSEC Description: CRAN Package 'MicroSEC' (Sequence Error Filter for Formalin-Fixed and Paraffin-EmbeddedSamples) Clinical sequencing of tumor is usually performed on formalin-fixed and paraffin-embedded samples and have many sequencing errors. We found that the majority of these errors are detected in chimeric read caused by single-strand DNA with micro-homology. Our filtering pipeline focuses on the uneven distribution of the artifacts in each read and removes such errors in formalin-fixed and paraffin-embedded samples without over-eliminating the true mutations detected in fresh frozen samples. Package: r-cran-microsoft365r Architecture: all Version: 2.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-azureauth, r-cran-azuregraph, r-cran-curl, r-cran-httr, r-cran-jsonlite, r-cran-r6, r-cran-vctrs, r-cran-mime Suggests: r-cran-openssl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-blastula, r-cran-emayili, r-cran-readr, r-cran-readxl Filename: pool/dists/noble/main/r-cran-microsoft365r_2.4.1-1.ca2404.1_all.deb Size: 775308 MD5sum: 59d55b1f5bdb4b1694e56f2c3cc3c3fe SHA1: c759f3de1de990b9ffafd9d9b9bf9525ce49e7d6 SHA256: 398028c3c1bd75fd6cf482b9c9876f02ed99ade7a12f55a83b5d2fb7e51acda2 SHA512: d8f7625d1d719a901b4b5a2e7261765464cc07fee0daa6936c6c270096e28f1adf4336b5738499e3f7de7775e2f45291cffa88e816cf97a1259c23efa7098f50 Homepage: https://cran.r-project.org/package=Microsoft365R Description: CRAN Package 'Microsoft365R' (Interface to the 'Microsoft 365' Suite of Cloud Services) An interface to the 'Microsoft 365' (formerly known as 'Office 365') suite of cloud services, building on the framework supplied by the 'AzureGraph' package. Enables access from R to data stored in 'Teams', 'SharePoint Online' and 'OneDrive', including the ability to list drive folder contents, upload and download files, send messages, and retrieve data lists. Also provides a full-featured 'Outlook' email client, with the ability to send emails and manage emails and mail folders. Package: r-cran-microsynth Architecture: all Version: 2.0.51-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3128 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survey, r-cran-kernlab, r-cran-lowrankqp, r-cran-pracma Suggests: r-cran-mass, r-cran-xlsx, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-microsynth_2.0.51-1.ca2404.1_all.deb Size: 2133258 MD5sum: 2775516d59c9795c64a397bebcff96ac SHA1: 1cab9bbd30ddd134f0a2b2e2719c4b53f98da3d5 SHA256: f5075dbfdffb010c78f5b02f8065af29526d0871fe8e4c17b1575f4df6df91d5 SHA512: fc60f6091bce5a27a8f287913adcbaf10aabd5d2e75a679492fdbfa6f9678cda34dc65e88a000277817e9bd4f6a78448e66c2584488703bf07bf7cee1429f28c Homepage: https://cran.r-project.org/package=microsynth Description: CRAN Package 'microsynth' (Synthetic Control Methods with Micro- And Meso-Level Data) A generalization of the 'Synth' package that is designed for data at a more granular level (e.g., micro-level). Provides functions to construct weights (including propensity score-type weights) and run analyses for synthetic control methods with micro- and meso-level data; see Robbins, Saunders, and Kilmer (2017) and Robbins and Davenport (2021) . Package: r-cran-micsim Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlecuyer, r-cran-snowfall Suggests: r-cran-glue, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-micsim_3.0.0-1.ca2404.1_all.deb Size: 517788 MD5sum: 418ce39bed96e514f0d2269a1b736b5d SHA1: dfb6a03684a8d4f1e87f039ae50df533b771fce2 SHA256: a4d0201a8fe2703aae28d0770f731d30c604676788881d8b5197aba37eba49fa SHA512: 31f3ed7b7c21d55245bedecb7f614e36488b126e8f8e94f7b5fe02c95059f444bf393dc0704d6c5eeed5adac7bccc42e6284f2830654306ae89e6d7a2f7050f0 Homepage: https://cran.r-project.org/package=MicSim Description: CRAN Package 'MicSim' (Performing Continuous-Time Microsimulation) This toolkit allows performing continuous-time microsimulation for a wide range of life science (demography, social sciences, epidemiology) applications. Individual life-courses are specified by a continuous-time multi-state model as described in Zinn (2014) . Package: r-cran-micss Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-micss_0.3.1-1.ca2404.1_all.deb Size: 167828 MD5sum: 622ae041d629ab1b2b557ca986db6fd2 SHA1: ff25aad2fa8f58d14751fd40275a49aa40c2cf98 SHA256: cdbb014836a91c04e6a0f227cccb971169e1aeda50a41faf2bfae7c75b9a0deb SHA512: bfe026612eea5e2232b02156040a5589fe825016ed0d65e883ae6401fb0b69c9565f5f371a61ed70c28175bf614991f90ca5297b703c1d37e4a25bc2c65ca17f Homepage: https://cran.r-project.org/package=micss Description: CRAN Package 'micss' (Modified Iterative Cumulative Sum of Squares Algorithm) Companion package of Carrion-i-Silvestre & Sansó (2026): "Testing for Constant Unconditional Variance in Heavy-Tailed Time Series". It implements the Modified Iterative Cumulative Sum of Squares Algorithm, which is an extension of the Iterative Cumulative Sum of Squares (ICSS) Algorithm of Inclan and Tiao (1994), and it checks for changes in the unconditional variance of a time series controlling for the tail index of the underlying distribution. The fourth order moment is estimated non-parametrically to avoid the size problems when the innovations are non-Gaussian (see, Sansó et al., 2004). Critical values and p-values are generated using a Generalized Extreme Value distribution approach. References Carrion-i-Silvestre J.J & Sansó A (2026) . Inclan C & Tiao G.C (1994) , Sansó A & Aragó V & Carrion-i-Silvestre J.L (2004) . Package: r-cran-micvar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrixcalc, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-micvar_0.1.0-1.ca2404.1_all.deb Size: 29590 MD5sum: afaf993230a187a25a69592172a163a2 SHA1: 5765f0bb3ca909dbafd4182b465f336d4300e72c SHA256: 22c76475920b7a1a23932a1996e72f292e04d327fa8458a7e824a8ad387ded64 SHA512: 2bcca28c3c4824f7fe186c4c8e90106c4bc6fe908ff7c494f5c8ea3b6ad962f0e705197cf18e96f69fbce4ab406930bb1a425c11d02ebaf5b0fc712a7e69e93c Homepage: https://cran.r-project.org/package=micvar Description: CRAN Package 'micvar' (Order Selection in Vector Autoregression by Mean SquareInformation Criteria) Implements order selection for Vector Autoregressive (VAR) models using the Mean Square Information Criterion (MIC). Unlike standard methods such as AIC and BIC, MIC is likelihood-free. This method consistently estimates VAR order and has robust performance under model misspecification. For more details, see Hellstern and Shojaie (2025) . 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The package includes a set of functions that allows performing full pipeline of analysis including data normalization, summarisation, binary classification, FDR (False Discovery Rate) multiple comparison and the definition of potential biological markers. Package: r-cran-midas2 Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-coda, r-cran-r2jags Filename: pool/dists/noble/main/r-cran-midas2_1.1.0-1.ca2404.1_all.deb Size: 47642 MD5sum: abc4b155b1bc2d970bbc84c8d6d7f6b3 SHA1: 09d65e78fa25ab509e844e57d5a401beea815370 SHA256: f4d9fbb4f0ae3c17c7638aa0290164a98e4b8c66fb131c2b8199b69894c3e6a5 SHA512: f6440e3498f66ce4e6ef1fae6ca7dfc2f624561db5e5602e0d06226ade6eeec0d33f82076e453faa572d7fe0779b04d5ce39ecdddf53386e093ff636d8c6927b Homepage: https://cran.r-project.org/package=midas2 Description: CRAN Package 'midas2' (Bayesian Platform Design with Subgroup EfficacyExploration(MIDAS-2)) The rapid screening of effective and optimal therapies from large numbers of candidate combinations, as well as exploring subgroup efficacy, remains challenging, which necessitates innovative, integrated, and efficient trial designs(Yuan, Y., et al. (2016) ). MIDAS-2 package enables quick and continuous screening of promising combination strategies and exploration of their subgroup effects within a unified platform design framework. We used a regression model to characterize the efficacy pattern in subgroups. Information borrowing was applied through Bayesian hierarchical model to improve trial efficiency considering the limited sample size in subgroups(Cunanan, K. M., et al. (2019) ). MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to accelerate immunotherapy development in an efficient, accurate, and integrated fashion(Wathen, J. K., & Thall, P. F. (2017) ). Package: r-cran-midas Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-midas_1.0.1-1.ca2404.1_all.deb Size: 14498 MD5sum: fc7f676c130abced15ffec125f7c563d SHA1: a869dc829a9dd046e180828e080603a8c9d87e18 SHA256: 68f90e8f65c5e11642d5531f11beca7bc7408c218467c39dc328db3dca699fe0 SHA512: b99402b2597f10a298b0a6c3cfb1baf058ffdd94263f824e1b7a9e019c02854ac67e8c791c0ad96740657cb412d8fb77c1bb0e7d0e1e9683fee6f4fb04846520 Homepage: https://cran.r-project.org/package=midas Description: CRAN Package 'midas' (Turn HTML 'Shiny') Contains functions for converting existing HTML/JavaScript source into equivalent 'shiny' functions. Bootstraps the process of making new 'shiny' functions by allowing us to turn HTML snippets directly into R functions. Package: r-cran-midasim Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-mass, r-cran-pracma, r-cran-scam Suggests: r-cran-vegan, r-bioc-phyloseq, r-cran-rmarkdown, r-cran-knitr, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-midasim_2.0-1.ca2404.1_all.deb Size: 240560 MD5sum: 5d508fee23ff83180809a5a9fc1116ba SHA1: 91e59f39502017ca7653d1d296123212b783e336 SHA256: a73bc3e095090556d5860ce5fc347b380fc8c2e0529311851c469f16dcccd449 SHA512: b4248b89a3d26c11a47ff89c079792954f7461ad3341c7d7ed1facf9ab11596a997814ef4d2fb0a76129449ca157a4afc0627b36f9dc4fb6a0576f8b7079bd8a Homepage: https://cran.r-project.org/package=MIDASim Description: CRAN Package 'MIDASim' (Simulating Realistic Microbiome Data using 'MIDASim') The 'MIDASim' package is a microbiome data simulator for generating realistic microbiome datasets by adapting a user-provided template. It supports the controlled introduction of experimental signals-such as shifts in taxon relative abundances, prevalence, and sample library sizes-to create distinct synthetic populations under diverse simulation scenarios. For more details, see He et al. (2024) . Package: r-cran-midasr Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1316 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich, r-cran-optimx, r-cran-quantreg, r-cran-mass, r-cran-numderiv, r-cran-matrix, r-cran-forecast, r-cran-zoo, r-cran-formula, r-cran-texreg Suggests: r-cran-testthat, r-cran-lubridate, r-cran-xts Filename: pool/dists/noble/main/r-cran-midasr_0.9-1.ca2404.1_all.deb Size: 925000 MD5sum: ca892da76ab063beff53cc944705b397 SHA1: 9d6ab81e58f1332ac9e5d025152f9943c8bff487 SHA256: fc4382a6962e02819a3eb19f54ac2929e5972c4aa65f1c336dbfa43427194c48 SHA512: 95acacf75dd576c74a96c3e3cf908f48e5448a43a7f599bdad293cb9dcd8e49c77ce292f2789d9e0e9afb9ed3e93e28107b1f37139a895cf4dbb4b4f9c66c5bc Homepage: https://cran.r-project.org/package=midasr Description: CRAN Package 'midasr' (Mixed Data Sampling Regression) Methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions. Package: r-cran-midastouch Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mice Filename: pool/dists/noble/main/r-cran-midastouch_1.3-1.ca2404.1_all.deb Size: 22496 MD5sum: 22330c5e61a2c551351e44f7a6fb1c45 SHA1: 04d2c7082a56881c9fc212a587a40ce974b32b6e SHA256: 3efe811d28e01256b3e42b834838d722720922fd2d2ba29f8803123b949344d6 SHA512: a9564741ef6f9b0f7811722b0d17e6cc7512ff6ab85cf628c73873f3497996cfe7538936743aa8ece111bf9a89703fddc3869f1e739d63ddb837fd8c908c8b22 Homepage: https://cran.r-project.org/package=midastouch Description: CRAN Package 'midastouch' (Multiple Imputation by Distance Aided Donor Selection) Contains the function mice.impute.midastouch(). Technically this function is to be run from within the 'mice' package (van Buuren et al. 2011), type ??mice. It substitutes the method 'pmm' within mice by 'midastouch'. The authors have shown that 'midastouch' is superior to default 'pmm'. Many ideas are based on Siddique / Belin 2008's MIDAS. Package: r-cran-midfieldr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1580 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-wrapr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-midfieldr_1.0.3-1.ca2404.1_all.deb Size: 1152394 MD5sum: 614738fb60a3617551756f09b186892c SHA1: 7d50d90a6be3c762b02658d9d2d9670cad54a133 SHA256: f2b2c83e33e7727c4c30f8830559c48ba2a210d5898db5cc583d3c17b998833f SHA512: 2f97843219037addf4dc4794caecb822cb917b1efb83089960caa29e9018e855e5616e40a0f6bcaacead413267409786d2a6af5d76415a7718e893ce7aa2df1e Homepage: https://cran.r-project.org/package=midfieldr Description: CRAN Package 'midfieldr' (Tools and Methods for Working with MIDFIELD Data in 'R') Provides tools and demonstrates methods for working with individual undergraduate student-level records (registrar's data) in 'R'. Tools include filters for program codes, data sufficiency, and timely completion. Methods include gathering blocs of records, computing quantitative metrics such as graduation rate, and creating charts to visualize comparisons. 'midfieldr' interacts with practice data provided in 'midfielddata', an R data package available at . 'midfieldr' also interacts with the full MIDFIELD database for users who have access. As of the transfer of MIDFIELD to the American Society for Engineering Education in 2023, the development, expansion, and study of MIDFIELD has been supported by the National Science Foundation grants 0337629, 0646441, 0729596, 0734062, 0835914, 0935157, 0935058, 0969474, 1025171, 1129383, 1232740, 1329283, 1361058, 1545667, 2142087, 2141903, and 2152441. Package: r-cran-midi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3890 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-plotly, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-withr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-midi_0.1.0-1.ca2404.1_all.deb Size: 3920068 MD5sum: eaae24a0b21fc03c39c23de167e27127 SHA1: 61958ea9cae27a459f682077c442c6c0d18c5053 SHA256: 6a70604bb58bacbbb2b7005ea86d4ca39bff97d9ff9486072718f56230648350 SHA512: 2d5e464729bba6b0f8235f9103e69efe4afccfecaea926121abd5a43f35cce785fb32af37a911927ddeb677ff7fe8993b6e9c6b955cda33d22c4fcee280ce8e8 Homepage: https://cran.r-project.org/package=midi Description: CRAN Package 'midi' (Microstructure Information from Diffusion Imaging) An implementation of a taxonomy of models of restricted diffusion in biological tissues parametrized by the tissue geometry (axis, diameter, density, etc.). This is primarily used in the context of diffusion magnetic resonance (MR) imaging to model the MR signal attenuation in the presence of diffusion gradients. The goal is to provide tools to simulate the MR signal attenuation predicted by these models under different experimental conditions. The package feeds a companion 'shiny' app available at that serves as a graphical interface to the models and tools provided by the package. Models currently available are the ones in Neuman (1974) , Van Gelderen et al. (1994) , Stanisz et al. (1997) , Soderman & Jonsson (1995) and Callaghan (1995) . Package: r-cran-midn Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biasedurn Filename: pool/dists/noble/main/r-cran-midn_1.0-1.ca2404.1_all.deb Size: 36150 MD5sum: 7ff9dc04896efe375008854f70538926 SHA1: 04e243bb048ee87dd4bc620c3939b9be0fc93ca4 SHA256: 4b098ae6bfc47b4df0b7a869ebd25fa6a197f143815ac9e0e19b980a4bc546cc SHA512: 12aaa3f0fe0264fa79a2f6a9bed284faaafcbeaef4e0f0488959b7fed6de2c91b8263ab56009c41ac1b44876ef942ea430c5c50bfaee0c63d4a790710a392547 Homepage: https://cran.r-project.org/package=MIDN Description: CRAN Package 'MIDN' (Nearly Exact Sample Size Calculation for Exact PowerfulNonrandomized Tests for Differences Between BinomialProportions) Implementation of the mid-n algorithms presented in Wellek S (2015) Statistica Neerlandica 69, 358-373 for exact sample size calculation for superiority trials with binary outcome. Package: r-cran-midoc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1081 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arm, r-cran-blorr, r-cran-dagitty, r-cran-glue, r-cran-lifecycle, r-cran-mfp2, r-cran-mice, r-cran-rlang, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-midoc_1.0.0-1.ca2404.1_all.deb Size: 438634 MD5sum: 5e59dae1afee640e9b92ed12bccfd101 SHA1: 273a9facc38b21e2d2c0efc1a7e978b3df5a4e7e SHA256: b638cfeb0df9933c419e03e32ce2caa5672ed3160ed90cbce65c25447a146978 SHA512: eaeee6193d414ea4fb661a0b4e05b6654ea4b39f704ea90a92006b336abad0b531da176399d78c86325d0fbc427eacdd7685fac6220774aa39cd658f5ab52f5d Homepage: https://cran.r-project.org/package=midoc Description: CRAN Package 'midoc' (A Decision-Making System for Multiple Imputation) A guidance system for analysis with missing data. 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Package: r-cran-midr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppeigen, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-khroma, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-scales, r-cran-shapviz, r-cran-testthat, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-midr_0.5.0-1.ca2404.1_all.deb Size: 553062 MD5sum: f1f3f650f58f19918077dd26a3a62b0f SHA1: 388e6aad15aafbb21c1cf8264cea30c1678d939a SHA256: 192631792ae130144e1b3c20c7a5c7c9c660d0f33633976a06c34a7735ab8a3c SHA512: df0a8639d2753a237ff6034fd6d1547a86099cfa874d8e2036be6c090d1244a5d37646897578916b903276de1e545de3c31c31c541e2c45a518bddf4af10ba1e Homepage: https://cran.r-project.org/package=midr Description: CRAN Package 'midr' (Learning from Black-Box Models by Maximum InterpretationDecomposition) The goal of 'midr' is to provide a model-agnostic method for interpreting and explaining black-box predictive models by creating a globally interpretable surrogate model. 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Package: r-cran-midrangemcp Architecture: all Version: 3.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-smr, r-cran-writexl, r-cran-xtable Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-midrangemcp_3.1.3-1.ca2404.1_all.deb Size: 160214 MD5sum: 4e8eeed113339a0d038f6eec39d3ee88 SHA1: a7199cb2a825808c9fcaf9471626c1828a43c514 SHA256: 22664eb388e9965fcf8f7c8d77bbe39be56db7858475af642514ca3a00bdbba0 SHA512: 1c2306e1869451cf9976f0108f4d30bcf56f4228783cb287fba0947f8f670fedae7146343c8fa41fb8cb9cb7ff23740d281d3413d9e6627415e642367fe4da87 Homepage: https://cran.r-project.org/package=midrangeMCP Description: CRAN Package 'midrangeMCP' (Multiple Comparisons Procedures Based on Studentized Midrangeand Range Distributions) Apply tests of multiple comparisons based on studentized 'midrange' and 'range' distributions. 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Package: r-cran-miebl Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-miebl_0.1.0-1.ca2404.1_all.deb Size: 21812 MD5sum: 5a92f5b052efdf4f3cfb3fa7106a6a0d SHA1: 3c9254fb44d35d5592fddaccdf310c8c552ba47a SHA256: 1923efa8a8edd96863cea98b88e1b598b94f0adb8fea5e2f0ba9cf571e03e32f SHA512: 9a545a8ba63d260ced6e7e8e29d06a3b643b0d18e106b92af6346a88f9e7f99c8ca43368a649d739db5a068c0af2b2a61e861c22308370a829a5fc42d3b4d97a Homepage: https://cran.r-project.org/package=miebl Description: CRAN Package 'miebl' (Performance Criteria Modeler for Discrete Trial Training) Provides a tool for computing probabilities and other quantities that are relevant in selecting performance criteria for discrete trial training. 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Package: r-cran-migrate Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-rlang, r-cran-cli, r-cran-glue Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-migrate_0.5.1-1.ca2404.1_all.deb Size: 172874 MD5sum: 112a60965885627f49d329c07aabb314 SHA1: be7e0d49f70803f622b1921c67a4b7c82e347951 SHA256: 49bb8f2f9c35bfde0fbf632e45cae0f1f00e2119cd867edea4f7ff199cd0715d SHA512: 9de47fc0ad37d7a98680b5cabf3a5ab4ee12d00f0d6d0981769a2897be363130d9ef0e03adcb2081ef2666fc184206591a061f18cddd98c13f9d8af10adbf3b0 Homepage: https://cran.r-project.org/package=migrate Description: CRAN Package 'migrate' (Create Credit State Migration (Transition) Matrices) Tools to help convert credit risk data at two timepoints into traditional credit state migration (aka, "transition") matrices. 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Package: r-cran-migrationdetectr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-lifecycle, r-cran-lubridate, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-migrationdetectr_0.1.1-1.ca2404.1_all.deb Size: 81188 MD5sum: 32eb37f99a6662a8c46a2008f4953f98 SHA1: c960d521f59e3ec335ed4c0c84d0f535eeda4f34 SHA256: 1fb88deec2a4f84bd9e20d98b868f406e9503e33cefd7ef532d6ad8934035d71 SHA512: 7b236f7fe3c1b01c13a9e14d6d1d60f779a04cd4dcba0b0ec8378f3c9200705e6fe7a1eaa61ebedcd8fd7a99c1fc367d992c3ef48b22f8b4176134a9b2762fc2 Homepage: https://cran.r-project.org/package=MigrationDetectR Description: CRAN Package 'MigrationDetectR' (Segment-Based Migration Detection Algorithm) Detection of migration events and segments of continuous residence based on irregular time series of location data as published in Chi et al. (2020) . 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Package: r-cran-miipw Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-spatstat, r-cran-mice, r-cran-matrix, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-miipw_0.1.2-1.ca2404.1_all.deb Size: 261226 MD5sum: 6f8a2793cb9f0f4625f3cc23d7151035 SHA1: c13f599421557c7b60739e578af384b1225cd720 SHA256: 35162350c3aece2b60e904bf5d22658ff069690ff933d77e79a6b4a9f2769a60 SHA512: d222a785c036412d4252ad6b7e6cf95489452cff193b31fe8b1d1b632ddb84e94f872441e8e7b0be7bfe50951f6c0a860be976c930cdea695291731f06d70298 Homepage: https://cran.r-project.org/package=MIIPW Description: CRAN Package 'MIIPW' (IPW and Mean Score Methods for Time-Course Missing Data) Contains functions for data analysis of Repeated measurement using GEE. Data may contain missing value in response and covariates. For parameter estimation through Fisher Scoring algorithm, Mean Score and Inverse Probability Weighted method combining with Multiple Imputation are used when there is missing value in covariates/response. Reference for mean score method, inverse probability weighted method is Wang et al(2007). Package: r-cran-miivefa Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-miivsem Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-mnormt, r-cran-lavaan, r-cran-mpsychor Filename: pool/dists/noble/main/r-cran-miivefa_0.1.2-1.ca2404.1_all.deb Size: 57476 MD5sum: 61da33e394be0d1fd7f3ff5df2f7e3ef SHA1: e3b63aaa48b3451cf9dfb229cca31f6035abc47e SHA256: 2356ac0b809b268187db632c0a3bd7c35b2a758df172d49a69f01293b336a3aa SHA512: 2fba9e77fb7c1f0f1acaf7a3c80c86290e0b0fac4f5aebd1cc713a506ef94eb38628461e9a38d5724f02481fe1bd65875054b4da86c7cc8518ac8ee26baff010 Homepage: https://cran.r-project.org/package=MIIVefa Description: CRAN Package 'MIIVefa' (Exploratory Factor Analysis Using Model Implied InstrumentalVariables) Data-driven approach for Exploratory Factor Analysis (EFA) that uses Model Implied Instrumental Variables (MIIVs). The method starts with a one factor model and arrives at a suggested model with enhanced interpretability that allows cross-loadings and correlated errors. Package: r-cran-miivsem Architecture: all Version: 0.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan, r-cran-numderiv, r-cran-matrix, r-cran-car, r-cran-boot Filename: pool/dists/noble/main/r-cran-miivsem_0.5.8-1.ca2404.1_all.deb Size: 279778 MD5sum: 13166d8afbc5eccefaa5b94efd10a2ba SHA1: 368f569fce6c8061ab3b17a15bdd47604ed350e8 SHA256: cee662f21451b3b15e03a2986c2d95221eff1a23787b3ae4c9fa785c28b5a32b SHA512: 9672a2bed8dc74f270ac50a133eef116e2c12a5c911f08789f8a41c3bde79a9223fd15e23268dd5c4138cca7b6069c6b99846feff3c5df68239dc02200894c95 Homepage: https://cran.r-project.org/package=MIIVsem Description: CRAN Package 'MIIVsem' (Model Implied Instrumental Variable (MIIV) Estimation ofStructural Equation Models) Functions for estimating structural equation models using instrumental variables. Package: r-cran-mikropml Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2506 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-e1071, r-cran-glmnet, r-cran-kernlab, r-cran-mlmetrics, r-cran-randomforest, r-cran-rlang, r-cran-rpart, r-bioc-s4vectors, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-tidyselect, r-bioc-treesummarizedexperiment, r-cran-xgboost Suggests: r-cran-assertthat, r-cran-dofuture, r-cran-forcats, r-cran-foreach, r-cran-furrr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-knitr, r-cran-progress, r-cran-progressr, r-cran-purrr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rsample, r-cran-styler, r-cran-testthat, r-cran-tidyr, r-cran-usethis Filename: pool/dists/noble/main/r-cran-mikropml_1.7.0-1.ca2404.1_all.deb Size: 2217904 MD5sum: 33a250141c52a9e292c28b803162e23d SHA1: 8717b4a039afc3959ee6cfa3c3a76447cb481ce8 SHA256: 4f6f9793409b8ad8faed10004c807c043af116cc06b852e255ceda1cb4628c47 SHA512: 5ba040aadc593dea00cb81df07752928c6dd86194783d91adbf97419f563a38ea4b86a09375e193275cd1063cfc2a18f651ccadb0434e73ab5197f17f532a6b4 Homepage: https://cran.r-project.org/package=mikropml Description: CRAN Package 'mikropml' (User-Friendly R Package for Supervised Machine LearningPipelines) An interface to build machine learning models for classification and regression problems. 'mikropml' implements the ML pipeline described by Topçuoğlu et al. (2020) with reasonable default options for data preprocessing, hyperparameter tuning, cross-validation, testing, model evaluation, and interpretation steps. See the website for more information, documentation, and examples. Package: r-cran-milag Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-testthat, r-cran-minpack.lm, r-cran-nlsmicrobio Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-milag_1.0.5-1.ca2404.1_all.deb Size: 445404 MD5sum: 1f68fff38ca9a3f840550f2d65ca49e2 SHA1: 830ac9ab872f3a7f2969c77e4bf5a4f197c5bc66 SHA256: 6bca9e139923098bfd16ba7fb4b90a88690ef80e588a4b2298ebc6ecd3b62e8b SHA512: b4678f724fe6eadc3578c1a7ef1fcedb2ee0c69ae7b05bb163d64178dbb1386636a816e33deeb7678e0758e918b7d0610bf8b75d870ebdd7b26463997c49d590 Homepage: https://cran.r-project.org/package=miLAG Description: CRAN Package 'miLAG' (Calculates Microbial Lag Duration (on the Population Level) fromProvided Growth Curve Data) Microbial growth is often measured by growth curves i.e. a table of population sizes and times of measurements. This package allows to use such growth curve data to determine the duration of "microbial lag phase" i.e. the time needed for microbes to restart divisions. It implements the most commonly used methods to calculate the lag duration, these methods are discussed and described in Opalek et.al. 2022. Citation: Smug, B. J., Opalek, M., Necki, M., & Wloch-Salamon, D. (2024). Microbial lag calculator: A shiny-based application and an R package for calculating the duration of microbial lag phase. Methods in Ecology and Evolution, 15, 301–307 . Package: r-cran-mildsvm Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-e1071, r-cran-kernlab, r-cran-magrittr, r-cran-mvtnorm, r-cran-pillar, r-cran-proc, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-matrix, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mildsvm_0.4.1-1.ca2404.1_all.deb Size: 453216 MD5sum: 7ae901ad60818dab28c6dcc36cb35f6e SHA1: aa747d465659c9b26ac52795079da462cc5cff77 SHA256: 0daf0d4bcd4c0ebb330e1d34219f1cef66680d4f7a40e9a3c705e02782c4165d SHA512: baa65afc31401378431284dab1830632648989df0cf6abe9e997226f585e04943af89a8a2f90482191543f26cd1099c1099daa5467a7545dab1b0884b77e978f Homepage: https://cran.r-project.org/package=mildsvm Description: CRAN Package 'mildsvm' (Multiple-Instance Learning with Support Vector Machines) Weakly supervised (WS), multiple instance (MI) data lives in numerous interesting applications such as drug discovery, object detection, and tumor prediction on whole slide images. The 'mildsvm' package provides an easy way to learn from this data by training Support Vector Machine (SVM)-based classifiers. It also contains helpful functions for building and printing multiple instance data frames. The core methods from 'mildsvm' come from the following references: Kent and Yu (2024) ; Xiao, Liu, and Hao (2018) ; Muandet et al. (2012) ; Chu and Keerthi (2007) ; and Andrews et al. (2003) . Many functions use the 'Gurobi' optimization back-end to improve the optimization problem speed; the 'gurobi' R package and associated software can be downloaded from after obtaining a license. Package: r-cran-milineage Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-data.table, r-cran-geepack Filename: pool/dists/noble/main/r-cran-milineage_2.1-1.ca2404.1_all.deb Size: 278428 MD5sum: 024a5a05bfa79a073c035bf5cdce7720 SHA1: 77d781427e3e366e36c2a7082160940d98bda632 SHA256: 6f2066d13ab459a4513879263bb6e64c1ea7eb93133c03aa3deeadfe8c299f97 SHA512: 6453730bf599204f0d422fe3adcf9402178e7187bffd165cd3a8340a5653dc8d5b409774cd501184d4aa271b8342556eb860e7a027ea60ab1091c13e79f45964 Homepage: https://cran.r-project.org/package=miLineage Description: CRAN Package 'miLineage' (Association Tests for Microbial Lineages on a Taxonomic Tree) A variety of association tests for microbiome data analysis including Quasi-Conditional Association Tests (QCAT) described in Tang Z.-Z. et al.(2017) and Zero-Inflated Generalized Dirichlet Multinomial (ZIGDM) tests described in Tang Z.-Z. & Chen G. (2017, submitted). Package: r-cran-mimdo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mimdo_0.1.0-1.ca2404.1_all.deb Size: 17142 MD5sum: fcc1790bff4f84b3cbb2cd765eb814cb SHA1: 56a08e9eacc0a71c750fd4474313e76defdb0945 SHA256: 2e1462b22504edf3703c8f8790a8c1016d4c21d7516fe07db3c03bd3d062415a SHA512: eecd252ff69b6c12104be7afd92625f3b0420290ecb020171c7cffa86f5cd89b56d13a43678d59bb2d3b71ea03e09686769f5827ed6a3d34b85ff527fa5f0323 Homepage: https://cran.r-project.org/package=mimdo Description: CRAN Package 'mimdo' (Multivariate Imputation by Mahalanobis Distance Optimization) Imputes missing values of an incomplete data matrix by minimizing the Mahalanobis distance of each sample from the overall mean [Labita, GJ.D. and Tubo, B.F. (2024) ]. Package: r-cran-mimer Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4034 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-amr, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-testthat, r-cran-data.table, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-mimer_1.0.6-1.ca2404.1_all.deb Size: 363838 MD5sum: 32a27394882594836184376b9be22398 SHA1: 3678eaa3aec346c51df1f77d992aab5f98f501ac SHA256: 6c77727d4078c8ac735a30f0c3c159aa9b9c42c0ff8f61a50dbea293a45734c0 SHA512: 9023d084fc3e17a2893c45c11973aeef9c6da51be985e1fd48fcb627e34d85dacf9679c4836695218c08bdffd98e55ecfc1af8f6256d4da2d3e6466b75d5ccd8 Homepage: https://cran.r-project.org/package=MIMER Description: CRAN Package 'MIMER' (Data Wrangling for Antimicrobial Resistance Studies) Designed for analyzing the Medical Information Mart for Intensive Care(MIMIC) dataset, a repository of freely accessible electronic health records. MIMER(MIMIC-enabled Research) package, offers a suite of data wrangling functions tailored specifically for preparing the dataset for research purposes, particularly in antimicrobial resistance(AMR) studies. It simplifies complex data manipulation tasks, allowing researchers to focus on their primary inquiries without being bogged down by wrangling complexities. Package: r-cran-mimi Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-softimpute, r-cran-factominer, r-cran-doparallel, r-cran-foreach, r-cran-data.table, r-cran-rarpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mimi_0.2.0-1.ca2404.1_all.deb Size: 342182 MD5sum: 01adbc1fb565b5732d596a9e4824c5b9 SHA1: 10f84700f676edc3a130c30925f77dc3ee65e780 SHA256: 27305091d6ef27a6c5845ac3587c4657e238a0bf4f92485a3511136067bec234 SHA512: d311f14fb1465a4316d5f8e7bf70184a44e0580a3dffb48d23b65e15183d81e24744504a23d6c0ef4c4d04bc2d964f4f8d1ad8fdf7445d05a23b337bab47bd83 Homepage: https://cran.r-project.org/package=mimi Description: CRAN Package 'mimi' (Main Effects and Interactions in Mixed and Incomplete Data) Generalized low-rank models for mixed and incomplete data frames. The main function may be used for dimensionality reduction of imputation of numeric, binary and count data (simultaneously). Main effects such as column means, group effects, or effects of row-column side information (e.g. user/item attributes in recommendation system) may also be modelled in addition to the low-rank model. Geneviève Robin, Olga Klopp, Julie Josse, Éric Moulines, Robert Tibshirani (2018) . Package: r-cran-mimir Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2655 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-dt, r-cran-foreach, r-cran-ggplot2, r-cran-heatmaply, r-cran-matrixstats, r-cran-plotly, r-cran-proc, r-cran-purrr, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyfiles, r-cran-shinydashboard, r-cran-shinyjs, r-cran-shinywidgets, r-cran-survival, r-cran-survminer, r-cran-dplyr, r-cran-fs Suggests: r-cran-testthat, r-cran-ggfortify, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mimir_1.5-1.ca2404.1_all.deb Size: 2414458 MD5sum: c54a8f066c38fa4980e85b5e3a15a07e SHA1: 930f432a12bfc542d12e33575e38874d07a24cfc SHA256: 9059c0cc3b588c4a098b70e80bfe4a0c9832f57ee850a508ecb695b4407f9648 SHA512: 5ebf332659dd6ed152ed1b049bff5c8028677721066e9cedc1d40dc85ede8df9f18020b52a45ffcf4e8e150f349087ef84eec4fb430247892f425f262cc26532 Homepage: https://cran.r-project.org/package=MiMIR Description: CRAN Package 'MiMIR' (Metabolomics-Based Models for Imputing Risk) Provides an intuitive framework for ad-hoc statistical analysis of 1H-NMR metabolomics by Nightingale Health. It allows to easily explore new metabolomics measurements assayed by Nightingale Health, comparing the distributions with a large Consortium (BBMRI-nl); project previously published metabolic scores [, , , , , ]; and calibrate the metabolic surrogate values to a desired dataset. Package: r-cran-mimisbm Architecture: all Version: 0.0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-blockmodels Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mimisbm_0.0.1.3-1.ca2404.1_all.deb Size: 192114 MD5sum: 08772ea2a85d2e8e27e04be8e3e1349a SHA1: 8e523382d393e9832195eae344bde729566bb4c2 SHA256: d90c2088839b7144df7771a26e0ecd57317ad77e115437bdd49f0da814f28459 SHA512: a65c27ffe2e86856a543c593103ed821e45179cd95543a9cc1a9680a5d164ace06e3c650048e5f36709fafa20875af1eda26f401b1dd959a8244f36be689154d Homepage: https://cran.r-project.org/package=mimiSBM Description: CRAN Package 'mimiSBM' (Mixture of Multilayer Integrator Stochastic Block Models) Our approach uses a mixture of multilayer stochastic block models to group co-membership matrices with similar information into components and to partition observations into different clusters. See De Santiago (2023, ISBN: 978-2-87587-088-9). Package: r-cran-mimsunit Architecture: all Version: 0.11.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1064 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-catools, r-cran-tibble, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-plyr, r-cran-readr, r-cran-r.utils, r-cran-stringr, r-cran-xts, r-cran-signal, r-cran-dygraphs, r-cran-shiny, r-cran-rcolorbrewer, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-pkgdown, r-cran-gridextra, r-cran-remotes Filename: pool/dists/noble/main/r-cran-mimsunit_0.11.3-1.ca2404.1_all.deb Size: 438056 MD5sum: 291b5fdc953909c6df2cb1eee4e653cb SHA1: f02f97142211b2bb606641f16e534fa2465e899c SHA256: 9518e00a203d104d465bf007de26328c51676d11c198d9fe53e72250470a7193 SHA512: 8a1893826f6ea9258d78b17c7af965bfa401c45240c9b62a4d1e79cfc93d4936cf83da80d5803ccc926f9edf34559274be47eaee4e51e4bb4fd32b785d9feeeb Homepage: https://cran.r-project.org/package=MIMSunit Description: CRAN Package 'MIMSunit' (Algorithm to Compute Monitor Independent Movement Summary Unit(MIMS-Unit)) The MIMS-unit algorithm is developed to compute Monitor Independent Movement Summary Unit, a measurement to summarize raw accelerometer data while ensuring harmonized results across different devices. It also includes scripts to reproduce results in the related publication (John, D., Tang. Q., Albinali, F. and Intille, S. (2019) ). Package: r-cran-mimsy Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 684 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-lubridate, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-xfun Filename: pool/dists/noble/main/r-cran-mimsy_0.6.5-1.ca2404.1_all.deb Size: 441168 MD5sum: eb17f3e4012542f8f9d059cccfe7fc6a SHA1: a9746add95dc2aba1c70bebd434358dceb1f3ecf SHA256: 59c1cb9b9037a88157e171247fe988479f08aa3ace0150c105b75d690d1473d2 SHA512: cf21917a5e303bfdf8f1141c80f14ec73c429a75de5de0feb7eaa623ff06841f70425b92fe69c9a1787bb71dd790d699dc3424bc65782183320c024fb809f57d Homepage: https://cran.r-project.org/package=mimsy Description: CRAN Package 'mimsy' (Calculate MIMS Dissolved Gas Concentrations Without Getting aHeadache) Calculate dissolved gas concentrations from raw MIMS (Membrane Inlet Mass Spectrometer) signal data. Use mimsy() on a formatted CSV file to return dissolved gas concentrations (mg and microMole) of N2, O2, Ar based on gas solubility at temperature, pressure, and salinity. See references Benson and Krause (1984), Garcia and Gordon (1992), Stull (1947), and Hamme and Emerson (2004) for more information. Easily save the output to a nicely-formatted multi-tab 'Excel' workbook with mimsy.save(). Supports dual-temperature standard calibration for dual-bath MIMS setups. Package: r-cran-min2halfffd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-hrtlfmc, r-cran-shinybusy Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-min2halfffd_0.1.0-1.ca2404.1_all.deb Size: 186456 MD5sum: 1b2f84915834949ab1c9dc22bb213a08 SHA1: abb06021ffefc0baba5b2f2c9c0b7ceea7c80989 SHA256: ce871bd33e560a07dcd64fe5501296d026ae7dd9f32575a9fbedbd4fa102882d SHA512: c1083b5f328ee0950f3cb78844ae0ec39aa4f3d7ce53122c1067b17ac34ee0300343c1bc1895d71789930c478f56d453185115d3a89a576e2a0e670174eb0640 Homepage: https://cran.r-project.org/package=min2HalfFFD Description: CRAN Package 'min2HalfFFD' (Minimally Changed Two-Level Half-Fractional Factorial Designs) In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. This technique can be employed to any half replicate of two level factorial run order where the number of factors are greater than two. For method details see, Bhowmik, A., Varghese, E., Jaggi, S. and Varghese, C. (2017) . This package generates all possible minimally changed two-level half-fractional factorial designs for different experimental setups along with various statistical criteria to measure the performance of these designs through a user-friendly interface. It consist of the function minimal.2halfFFD() which launches the application interface. Package: r-cran-minb Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-pscl Filename: pool/dists/noble/main/r-cran-minb_0.1.0-1.ca2404.1_all.deb Size: 48158 MD5sum: 09869ed15c5e7bfc018e8f57efdcb91b SHA1: 366834a9b4b1557d28f894b93c539a47d367e9ad SHA256: 4b7e5d9ad52420f25db85c06b4afcdb460baf3bc33f0c1d73d43940d1f8ac23a SHA512: 50005f75dcd15fd79a70ad158acce9a8675dc4a152a256138e0431fafa1b7e3181e3ac65e5a3f1ea74111ef9307e36411856c0454aebe766d87aaaca32287036 Homepage: https://cran.r-project.org/package=minb Description: CRAN Package 'minb' (Multiple-Inflated Negative Binomial Model) Count data is prevalent and informative, with widespread application in many fields such as social psychology, personality, and public health. Classical statistical methods for the analysis of count outcomes are commonly variants of the log-linear model, including Poisson regression and Negative Binomial regression. However, a typical problem with count data modeling is inflation, in the sense that the counts are evidently accumulated on some integers. Such an inflation problem could distort the distribution of the observed counts, further bias estimation and increase error, making the classic methods infeasible. Traditional inflated value selection methods based on histogram inspection are easy to neglect true points and computationally expensive in addition. Therefore, we propose a multiple-inflated negative binomial model to handle count data modeling with multiple inflated values, achieving data-driven inflated value selection. The proposed approach provides simultaneous identification of important regression predictors on the target count response as well. More details about the proposed method are described in Li, Y., Wu, M., Wu, M., & Ma, S. (2023) . Package: r-cran-mind Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 776 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-mass, r-cran-matrix, r-cran-dplyr, r-cran-jwileymisc, r-cran-tm Filename: pool/dists/noble/main/r-cran-mind_1.1.0-1.ca2404.1_all.deb Size: 755598 MD5sum: a48b9ca3f7b404e738496926f09babc6 SHA1: b21894adacce6f0dc6124f51dc86a530c31ac8b7 SHA256: 36f678a16fd81ee783dadac0e8a9b2e8ead7ca1d99626c9cb4d6ff5e526449bd SHA512: 6ad91d196452dec3a071656755b8d77c8ae3a476d1e27283b34735e04248008cfec37cf0f6fe4fd3078b6d37c5aa8b3949ad240ac906561ff212940630d2f9e1 Homepage: https://cran.r-project.org/package=mind Description: CRAN Package 'mind' (Multivariate Model Based Inference for Domains) Allows users to produce estimates and MSE for multivariate variables using Linear Mixed Model. The package follows the approach of Datta, Day and Basawa (1999) . Package: r-cran-mindonstats Architecture: all Version: 0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mindonstats_0.11-1.ca2404.1_all.deb Size: 195670 MD5sum: f1ec53c66c7b225eb26bf1489986380f SHA1: b8dc679b94c4bfd8feb6f5aee985fab5330e778b SHA256: f892a596fb43da2f147673bfa9f768bfb3de314f7d69586eeae860924926da7b SHA512: 0f02198470bb691e24f40008ba1bed99c93b8f6dd2391619d7f24a6dd6f05b3c59396ef0eb5e766835251547c589a0977819c148588fec336a42adcfc360d0db Homepage: https://cran.r-project.org/package=MindOnStats Description: CRAN Package 'MindOnStats' (Data sets included in Utts and Heckard's Mind on Statistics) 66 data sets that were imported using read.table() where appropriate but more commonly after converting to a csv file for importing via read.csv(). Package: r-cran-mindr Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3787 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-knitr, r-cran-rmarkdown, r-cran-pdftools, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-mindr_1.4.1-1.ca2404.1_all.deb Size: 888456 MD5sum: 83f3ba015cadf7967fb1974e25feab31 SHA1: 86fb959d36863f869a5dee09bee328c8ccbcd79a SHA256: 577d752946aa6961fe6afc2e6e5ae74598a2938144daabb511a7462bfca81a63 SHA512: ec2df87b0ce3f62f044793555cefe015555869f4a9b6f0a552c53d51a65d0ae56f9e87ac0830cb9d7f374e2c561e92b6e0d3a9fe8b757b080ef40967dce1c7f6 Homepage: https://cran.r-project.org/package=mindr Description: CRAN Package 'mindr' (Generate Mind Maps) Convert Markdown ('.md') or R Markdown ('.Rmd') texts, R scripts, directory structures, and other hierarchical structured documents into mind map widgets or 'Freemind' codes or 'Mermaid' mind map codes, and vice versa. 'Freemind' mind map ('.mm') files can be opened by or imported to common mind map software such as 'Freemind' (). 'Mermaid' mind map codes () can be directly embedded in documents. Package: r-cran-minecitrus Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4573 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-minecitrus_1.0.0-1.ca2404.1_all.deb Size: 4587210 MD5sum: a3985ab6169716840fb112ac383f5e28 SHA1: 981285681d558f331dbeaed3e518e5a084e85286 SHA256: 7bc9be9dcd783eff534d5c73396bb7ee0d301e181132bd59be026e81bb05a795 SHA512: d82136cfc461757841a39552ca41bbc4eb2e7c2fed8021bdfa41a61d05e3eb926397a67680864e157ae1088f901c1f854f7e0845cd012f03ee4f886e540f7ce8 Homepage: https://cran.r-project.org/package=mineCitrus Description: CRAN Package 'mineCitrus' (Extract and Analyze Median Molecule Intensity from 'citrus'Output) Citrus is a computational technique developed for the analysis of high dimensional cytometry data sets. This package extracts, statistically analyzes, and visualizes marker expression from 'citrus' data. This code was used to generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al. “Identification of Enhanced Interferon-Gamma Signaling in Polyarticular Juvenile Idiopathic Arthritis with Mass Cytometry”, JCI-Insight. For more information on Citrus, please see: Bruggner et al. (2014) . To download the 'citrus' package, please see . Package: r-cran-minedfind Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-iso, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-minedfind_0.1.3-1.ca2404.1_all.deb Size: 46396 MD5sum: ccf1e6becd3c74d97c85837fa7f66f5b SHA1: 9f8518dd35219ced79e1f001ff555225c866dae3 SHA256: d0d456371fab1924643d3303fac49806c48b8ed99008e2bdda50ac302e1d09db SHA512: 27f1028c8094ad73e8f2224b3cd2ae4f07d6cfe2fe7e6449890e05e2993c3de8e47c625781b6a604bc1843da3ccf4c1a4f406cdefc7955eccf5582641e3712c6 Homepage: https://cran.r-project.org/package=MinEDfind Description: CRAN Package 'MinEDfind' (A Bayesian Design for Minimum Effective Dosing-Finding Trial) The nonparametric two-stage Bayesian adaptive design is a novel phase II clinical trial design for finding the minimum effective dose (MinED). This design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. It is used to design single-agent trials. Package: r-cran-minesweeper Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-gifski Filename: pool/dists/noble/main/r-cran-minesweeper_1.0.1-1.ca2404.1_all.deb Size: 61040 MD5sum: c7ae56a82beb182008686fbdf148e521 SHA1: 46bbcb7189814929a4d555be8d333abae974244d SHA256: e6cebf3120968f4bd8a25141c52cdc4f9c6d222d738c490aca92410a554bc13b SHA512: 6b24365e04ed95d3e0cd963113bd4f5e705d3da298ae969baf3dda6b39f71f5e4f4f67c8b2ec02cc0a061f8a4dc4a3ba92d0a29b15630ec13d24179114a6ad3d Homepage: https://cran.r-project.org/package=minesweeper Description: CRAN Package 'minesweeper' (Play Minesweeper) Play and record games of minesweeper using a graphics device that supports event handling. Replay recorded games and save GIF animations of them. Based on classic minesweeper as detailed by Crow P. (1997) . Package: r-cran-minesweepr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-complexheatmap, r-bioc-interactivecomplexheatmap, r-cran-dplyr, r-cran-gsignal, r-cran-mgc, r-cran-mmand, r-cran-hms, r-cran-rlang, r-cran-pals Filename: pool/dists/noble/main/r-cran-minesweepr_0.1.1-1.ca2404.1_all.deb Size: 62752 MD5sum: 86dafda5b5dc8dd87071083ff806fa65 SHA1: f2f2e484dba6cc7c3c1508ce5b7e974f1daee848 SHA256: d86d7f5394a70a89b9ab6b364d35bf8ee00565dd5a9522a04e003c58dcb56319 SHA512: aadc7e225501c67d408b66a9c12240d63ffbeda9c759bb5068946e502738c329f12d92caee4557c3d2fbc33de2ee3debc9a656442a8d9ca6830ae78a80e7fdb6 Homepage: https://cran.r-project.org/package=mineSweepR Description: CRAN Package 'mineSweepR' (Mine Sweeper Game) This is the very popular mine sweeper game! The game requires you to find out tiles that contain mines through clues from unmasking neighboring tiles. Each tile that does not contain a mine shows the number of mines in its adjacent tiles. If you unmask all tiles that do not contain mines, you win the game; if you unmask any tile that contains a mine, you lose the game. For further game instructions, please run `help(run_game)` and check details. This game runs in X11-compatible devices with `grDevices::x11()`. Package: r-cran-minfactorial Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fmc Filename: pool/dists/noble/main/r-cran-minfactorial_0.1.0-1.ca2404.1_all.deb Size: 19358 MD5sum: 5aa158d265eb4c235aaa8c3c5143239b SHA1: a08b9fdd1d56e2b1cf9f8d2cd4a5a69ce4d18c94 SHA256: f66dcb043a5dc06c241e551c1a55feec0876deaf37609f0c888a1fb298d91361 SHA512: 1d7676dbd5f459f10d4f9eb213a2b73c8fd35eed949932bf7f1ddb8ec22e8cfd27e1bde34c886154c4d63b9f9efba2d54606d3d1723d0d4295d10786a276b545 Homepage: https://cran.r-project.org/package=minFactorial Description: CRAN Package 'minFactorial' (All Possible Minimally Changed Factorial Run Orders) In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. For method details see, Bhowmik, A.,Varghese, E., Jaggi, S. and Varghese, C. (2017).This package used to construct all possible minimally changed factorial run orders for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function minFactDesign(). Package: r-cran-mini007 Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-diagrammer, r-cran-ellmer, r-cran-glue, r-cran-r6, r-cran-rlang, r-cran-uuid Filename: pool/dists/noble/main/r-cran-mini007_0.3.0-1.ca2404.1_all.deb Size: 531266 MD5sum: f1733216205240da6b675e2dbbedc22c SHA1: 57a1f88194a73bc7310b5921c0baf44ddb918353 SHA256: 7d776ba0e9ec0fd5bc7221a8479edaec4f068c815ed6e5ffe419043a5b7f805d SHA512: 143e2ace9e7c270657f1b60785e7d660fc1bc7305aedfdf7d5184f76af81495edd6881a14d03a9d2fc238bbeaa8203883da75cf529ac1666be98e4c4097a3418 Homepage: https://cran.r-project.org/package=mini007 Description: CRAN Package 'mini007' (Lightweight Framework for Orchestrating Multi-Agent LargeLanguage Models) Provides tools for creating agents with persistent state using R6 classes and the 'ellmer' package . Tracks prompts, messages, and agent metadata for reproducible, multi-turn large language model sessions. Package: r-cran-minicran Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-igraph, r-cran-assertthat Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-withr, r-cran-mockery, r-cran-testthis, r-cran-roxygen2, r-cran-mockr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-minicran_0.3.2-1.ca2404.1_all.deb Size: 412672 MD5sum: 248f6c0d2309ace59bfde4d322f6478b SHA1: 1dc5b39bee2020b3bba1f33688cfe702fc568d3b SHA256: a62a33ef3e4388483bcc8bee8dce1fb0d6fb6dfbfdd530b4e4a0b184103272d1 SHA512: c66926b62807f414f01d538df4523fbbf5bce3990181ae74bba790624e3537e1cb5716cbef6e203c5cfca2d9c12d012372ccd6db997dca95dbeb2ca82839499a Homepage: https://cran.r-project.org/package=miniCRAN Description: CRAN Package 'miniCRAN' (Create a Mini Version of CRAN Containing Only Selected Packages) Makes it possible to create an internally consistent repository consisting of selected packages from CRAN-like repositories. The user specifies a set of desired packages, and 'miniCRAN' recursively reads the dependency tree for these packages, then downloads only this subset. The user can then install packages from this repository directly, rather than from CRAN. This is useful in production settings, e.g. server behind a firewall, or remote locations with slow (or zero) Internet access. Package: r-cran-minidown Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-katex, r-cran-knitr, r-cran-mime, r-cran-rmarkdown, r-cran-sass, r-cran-xfun Suggests: r-cran-callr, r-cran-dplyr, r-cran-purrr, r-cran-shiny, r-cran-testthat, r-cran-tidyr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-minidown_0.4.0-1.ca2404.1_all.deb Size: 85628 MD5sum: 5e5e14d46b1df0392bd675696241670f SHA1: 995eff77da5e9aea88d4dd64862f530773d1e8ab SHA256: 1920ada98b952b01a4c3b7d4648e823e35fe4183e0ccb737fd1cc11f9b95c990 SHA512: bf03816ec68bf8dd6408ee08dc87bf798f8e01fb935e53e581ab35ac2ba37b6334bf42d5cea0d7d74975259242a3ff344056522144958dc69a767253ab09c242 Homepage: https://cran.r-project.org/package=minidown Description: CRAN Package 'minidown' (Create Simple Yet Powerful HTML Documents with Light Weight CSSFrameworks) Create minimal, responsive, and style-agnostic HTML documents with the lightweight CSS frameworks such as 'sakura', 'Water.css', and 'spcss'. Powerful features include table of contents floating as a sidebar, folding codes and results, and more. Package: r-cran-minigui Architecture: all Version: 0.8-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-minigui_0.8-1-1.ca2404.1_all.deb Size: 49812 MD5sum: e90c3cf492473d9fbd05db1126a22b9a SHA1: 34085e374e41e82b2e55f7209245c6c83197e07f SHA256: 69e1daf6510659947185760972e578296af6f9594af16fbc840599596a9b9fe2 SHA512: cef1cce8f9d1e85d2cb73dd56d23f159b6cae10270168013540cf12507b2bb9fab9d16daf15adb27acadbfcfceebf5eef31f856db6310a36469e64225ce08478 Homepage: https://cran.r-project.org/package=miniGUI Description: CRAN Package 'miniGUI' (Tcl/Tk Quick and Simple Function GUI) Quick and simple Tcl/Tk Graphical User Interface to call functions. Also comprises a very simple experimental GUI framework. Package: r-cran-minimalistgodb Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1973 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-minimalistgodb_1.1.0-1.ca2404.1_all.deb Size: 1739786 MD5sum: 8cf7cad1e4c4c9413854128d5d57542e SHA1: 418ef93bd5792a10b2bb01f7b7e2de2d844c21c2 SHA256: 23d6ed9a6a8a6c2e2b1ac7974f50ca55c02c9c4deaf616dffc6f27786d67308d SHA512: bef758b294b45cb09a55c8536ff4addd07dc6bd85431b43857866422a495add837daf72a159b79ff33587dbdd3558115bfe506db8738e68fe47d8984b8daaf5f Homepage: https://cran.r-project.org/package=minimalistGODB Description: CRAN Package 'minimalistGODB' (Build a Minimalist Gene Ontology (GO) Database (GODB)) Normally building a GODB is fairly complicated, involving downloading multiple database files and using these to build e.g. a 'mySQL' database. Accessing this database is also complicated, involving an intimate knowledge of the database in order to construct reliable queries. Here we have a more modest goal, generating GOGOA3, which is a stripped down version of the GODB that was originally restricted to human genes as designated by the HUGO Gene Nomenclature Committee (HGNC) (see ). I have now added about two dozen additional species, namely all species represented on the Gene Ontology download page . This covers most of the model organisms that are commonly used in bio-medical and basic research (assuming that anyone still has a grant to do such research). This can be built in a matter of seconds from 2 easily downloaded files (see and ), and it can be queried by e.g. w<-which(GOGOA3[,"HGNC"] %in% hgncList) where GOGOA3 is a matrix representing the minimalist GODB and hgncList is a list of gene identifiers. This database will be used in my upcoming package 'GoMiner' which is based on my previous publication (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003)). Relevant .RData files are available from GitHub (). 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Package: r-cran-minimap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-minimap_0.1.0-1.ca2404.1_all.deb Size: 49924 MD5sum: 3fa9f81904f74f472a447968003f0a06 SHA1: 2b4c43485d7aeb465143b826f8b0417e306447df SHA256: 00ac145d61bf27e061101cdcc7a1129683ddea68cac6c6240a29432b0e0ad426 SHA512: 3c3f548ca7633287bd2a1fad86030ea28f851f32ff45d30ed59ea1c520b3712b19eebb964bdcb3396549adea157721f30d311099c474f56b40985690136338a9 Homepage: https://cran.r-project.org/package=minimap Description: CRAN Package 'minimap' (Create Tile Grid Maps) Create tile grid maps, which are like choropleth maps except each region is represented with equal visual space. Package: r-cran-minimapr Architecture: all Version: 0.0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-rsamtools, r-cran-pafr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-minimapr_0.0.1.3-1.ca2404.1_all.deb Size: 36132 MD5sum: ae7e83590c00c309fde9a6a5c84a5294 SHA1: 5b18abe0798d70c0cd122be33adcd89bcfa0c3b0 SHA256: 1a1242d32e0e45bf4cb3b1b2223944acb62cf38ba19e4ce82f54af7a9931d73c SHA512: d81119797a6f862278ea9b76fe5c32105dc4fe424904e85f8fe2d744cd212b91d5649049d3197155913170ea06619d8fd3bbbd90e714609c808bd37a11238bcc Homepage: https://cran.r-project.org/package=minimapR Description: CRAN Package 'minimapR' (Wrapper for 'minimap2') Wrapper for 'Minimap2'. 'Minimap2' is a very valuable long read aligner for the Pacbio and Oxford Nanopore Technologies sequencing platforms. 'minimapR' is an R wrapper for 'minimap2' which was developed by Heng Li . *SPECIAL NOTES 1. Examples can only be run from 'GitHub' installation. 2. 'conda' or 'mamba' must be used to install 'minimapR' on your system. 3. For Windows users, 'minimap2' and 'samtools' can be installed via MSYS2, instructions are provided when 'minimap2_installation()' is run. Li, Heng (2018) "Minimap2: pairwise alignment for nucleotide sequences". 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(2007) "Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants." 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The package supports calculation of simple effects, overall effects, and interaction effects for use in factorial meta-analyses. See Gurevitch et al. (2000) , Morris et al. (2007) , Lajeunesse (2011) and Macartney et al. (2022) . 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Streamlines iterative analysis workflows when working with 'ASReml-R' package. Includes utility functions for phenotypic data processing commonly used by animal breeders. 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The package also provides an application of the IPFP to simulate multivariate Bernoulli distributions. 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Package: r-cran-mirai.promises Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nanonext, r-cran-promises Suggests: r-cran-mirai Filename: pool/dists/noble/main/r-cran-mirai.promises_0.5.0-1.ca2404.1_all.deb Size: 19188 MD5sum: e64bdaaedc11eb87dbc3cc2f6173b225 SHA1: cd08baa13d07e3bbc81b77bc71577074b432cf92 SHA256: 2c1a16ad4139f4be62934ac42eb1a07ec55be41b75d7c18f86c876d8d334a196 SHA512: a8e61caa8c29c3b24179d1e2259de60468c383f091ce972701525607466d0cce13f0c92855b6c7a55497792506bbdf99eb8b72a3ba689b70b88eea6ec5bf7cf6 Homepage: https://cran.r-project.org/package=mirai.promises Description: CRAN Package 'mirai.promises' (Make 'Mirai' 'Promises') Allows 'mirai' objects encapsulating asynchronous computations, from the 'mirai' package by Gao (2023) , to be used interchangeably with 'promise' objects from the 'promises' package by Cheng (2021) . This facilitates their use with packages 'plumber' by Schloerke and Allen (2022) and 'shiny' by Cheng, Allaire, Sievert, Schloerke, Xie, Allen, McPherson, Dipert and Borges (2022) . 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Built on 'nanonext' and 'NNG', its non-polling, event-driven architecture scales from a laptop to thousands of processes across high-performance computing clusters and cloud platforms. Features FIFO scheduling with task cancellation and bounded queues, promises for reactive programming, 'OpenTelemetry' distributed tracing, and custom serialization for cross-language data types. Package: r-cran-mirdd Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-amelia, r-cran-rdrobust Filename: pool/dists/noble/main/r-cran-mirdd_0.2.4-1.ca2404.1_all.deb Size: 162046 MD5sum: d2c66ac6ef14551db816f3655a66d608 SHA1: ed4afd7dd77cda9ed668839999959dca382a71d2 SHA256: b6ec99267229fe1a11b857896693fa174aa1187d59f57e33cc016501b18e1537 SHA512: be5b94c9dadce343dcc43c6f6af550c172a3c66cba550db22072a5c5e5977838e65a335cabfb03bfd8036ca3b774d581fa71eb50341c29b1c75906b136a9aa73 Homepage: https://cran.r-project.org/package=MIRDD Description: CRAN Package 'MIRDD' (Diagnostic Tool by Multiple Imputation for RegressionDiscontinuity Designs) Estimates average treatment effects at the cutoff based on sharp regression discontinuity designs (RDD) and multiple imputation regression discontinuity designs (MIRDD). It provides diagnostic tools for RDD by comparing results with those from MIRDD, as proposed in Takahashi (2023) . The package includes datasets from Takahashi (2023) and Takahashi (2026) . Package: r-cran-mirecsurv Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-compoissonreg, r-cran-matrixstats, r-cran-stringi Filename: pool/dists/noble/main/r-cran-mirecsurv_1.0.2-1.ca2404.1_all.deb Size: 61164 MD5sum: 608e491c1ed63325f801845525585be2 SHA1: a353286a821d8ddb2d09fe2a5146d261a93cf8bc SHA256: 63e39272aa620bf55b11ce2b191254bac93d0e69b5549bddada3c9eb33cf4a2d SHA512: 34803a777739cdfac1b04027cb0350a2b512de078c486eb4a63954cb3d8a29a83a6ce2bd4d02574e0ecad79a5a4ba8976edd29091fc5e469a32ee1437383ecce Homepage: https://cran.r-project.org/package=miRecSurv Description: CRAN Package 'miRecSurv' (Left-Censored Recurrent Events Survival Models) Fitting recurrent events survival models for left-censored data with multiple imputation of the number of previous episodes. See Hernández-Herrera G, Moriña D, Navarro A. (2020) . Package: r-cran-miretrieve Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2402 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-openxlsx, r-cran-plotly, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-textclean, r-cran-tidyr, r-cran-tidytext, r-cran-topicmodels, r-cran-wordcloud, r-cran-xml2, r-cran-zoo Suggests: r-cran-kableextra, r-cran-knitr, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-miretrieve_1.3.4-1.ca2404.1_all.deb Size: 2398746 MD5sum: cf99da0cab6bdc82e195294f5bbc3cd0 SHA1: aedd7ce9a998ed508231bd585a3ed91f29a40208 SHA256: 91570d27a5673f803d27e0cf8676ec6d1931e3a73da77b89c922e949eafe0900 SHA512: b0b5926d133be66e9a201dfa35c8932d303c72fb4bf4806bfba3ec593caf118fbc3bf95ffed935c9ab3294fd6e2c7909b1f204682fa0bcea49e907788c720d8c Homepage: https://cran.r-project.org/package=miRetrieve Description: CRAN Package 'miRetrieve' (miRNA Text Mining in Abstracts) Providing tools for microRNA (miRNA) text mining. miRetrieve summarizes miRNA literature by extracting, counting, and analyzing miRNA names, thus aiming at gaining biological insights into a large amount of text within a short period of time. To do so, miRetrieve uses regular expressions to extract miRNAs and tokenization to identify meaningful miRNA associations. In addition, miRetrieve uses the latest miRTarBase version 8.0 (Hsi-Yuan Huang et al. (2020) "miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database" ) to display field-specific miRNA-mRNA interactions. The most important functions are available as a Shiny web application under . Package: r-cran-mirkat Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-compquadform, r-cran-quantreg, r-cran-gunifrac, r-cran-pearsonds, r-cran-lme4, r-cran-matrix, r-cran-permute, r-cran-mixtools, r-cran-survival Suggests: r-cran-knitr, r-cran-vegan, r-cran-rmarkdown, r-cran-magrittr, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-mirkat_1.2.3-1.ca2404.1_all.deb Size: 363822 MD5sum: 60e469b68a5ed61c0de7e693f83591c0 SHA1: 3eb15ed03ff733f9022a30543a5cc2bd7fb579c3 SHA256: e5508950c690207bf89bb1b7006b700bca9da2fc40051db3d47ae6b4d469d5a3 SHA512: 356c4ee3712202813ebe25b147980ef763bdb36bc6e0fd3df3fe108c0e70053cbe1673df4b78084956f174f52a364ec6321f8986b0359bc18cc4ae8335b0ab1d Homepage: https://cran.r-project.org/package=MiRKAT Description: CRAN Package 'MiRKAT' (Microbiome Regression-Based Kernel Association Tests) Test for overall association between microbiome composition data and phenotypes via phylogenetic kernels. The phenotype can be univariate continuous or binary (Zhao et al. (2015) ), survival outcomes (Plantinga et al. (2017) ), multivariate (Zhan et al. (2017) ) and structured phenotypes (Zhan et al. (2017) ). The package can also use robust regression (unpublished work) and integrated quantile regression (Wang et al. (2021) ). In each case, the microbiome community effect is modeled nonparametrically through a kernel function, which can incorporate phylogenetic tree information. Package: r-cran-mirnaqcd Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-proc, r-cran-qpdf Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mirnaqcd_1.1.3-1.ca2404.1_all.deb Size: 594710 MD5sum: 568e490358a256a642c073458d46a42f SHA1: 975e70a49a91f0c321dc97087112e8f00cdbcd7d SHA256: f0e21f324c0bd744cadc002668b3d368a3bf8ba4068bbb202667a781f45ff69b SHA512: 1275239c402931546a332617be3247e3272cb00cff7732940c071c7112fc7d3cae745afb985428f5fe26f63b6d52c1c5e66f420ce0bdb8b0503f4259d7200de6 Homepage: https://cran.r-project.org/package=MiRNAQCD Description: CRAN Package 'MiRNAQCD' (Micro-RNA Quality Control and Diagnosis) A complete and dedicated analytical toolbox for quality control and diagnosis based on subject-related measurements of micro-RNA (miRNA) expressions. The package consists of a set of functions that allow to train, optimize and use a Bayesian classifier that relies on multiplets of measured miRNA expressions. The package also implements the quality control tools required to preprocess input datasets. In addition, the package provides a function to carry out a statistical analysis of miRNA expressions, which can give insights to improve the classifier's performance. The method implemented in the package was first introduced in L. Ricci, V. Del Vescovo, C. Cantaloni, M. Grasso, M. Barbareschi and M. A. Denti, "Statistical analysis of a Bayesian classifier based on the expression of miRNAs", BMC Bioinformatics 16:287, 2015 . The package is thoroughly described in M. Castelluzzo, A. Perinelli, S. Detassis, M. A. Denti and L. Ricci, "MiRNA-QC-and-Diagnosis: An R package for diagnosis based on MiRNA expression", SoftwareX 12:100569, 2020 . Please cite both these works if you use the package for your analysis. DISCLAIMER: The software in this package is for general research purposes only and is thus provided WITHOUT ANY WARRANTY. It is NOT intended to form the basis of clinical decisions. Please refer to the GNU General Public License 3.0 (GPLv3) for further information. Package: r-cran-mirrorselect Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-yulab.utils Filename: pool/dists/noble/main/r-cran-mirrorselect_0.0.3-1.ca2404.1_all.deb Size: 15394 MD5sum: 1fcc84614ef25452c0fc5c3f782aa6cb SHA1: 451a9f63fde06dd3e8ec730e67d09f98bc6b1174 SHA256: ab6bab2edfeea6fdef0d37447a62bc447a451525d79f3f53e9dc5242a07f206e SHA512: 8062936a249f07ffb801e9ee62326d284ce2352f3059a50734d1741b8fd2ea203cd01305f3afcc54ce40ebb5d1e9c892393253b73b4cc424ac222ee10501a65a Homepage: https://cran.r-project.org/package=mirrorselect Description: CRAN Package 'mirrorselect' (Test CRAN/Bioconductor Mirror Speed) Testing CRAN and Bioconductor mirror speed by recording download time of 'src/base/COPYING' (for CRAN) and 'packages/release/bioc/html/ggtree.html' (for Bioconductor). Package: r-cran-mirsea Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2028 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mirsea_1.1.1-1.ca2404.1_all.deb Size: 1351894 MD5sum: 995f05aeb605b9e88672053e97178cdc SHA1: 900f4142bac22abbc67c03bb4a3b0a8825c675d4 SHA256: 22475a80650d91098cbb42739189d3e0adea65f6238cf4c79ee7a5b28297c462 SHA512: f78965f26d184e67fbc71ce0587a9a2a44d9e3ecfeb0ca2e3a30acef654c8c90f123c713ffd27ab3bc32540f8abcc0a5bc73a383cc2f569d391ba0c5c9e4ea95 Homepage: https://cran.r-project.org/package=MiRSEA Description: CRAN Package 'MiRSEA' ('MicroRNA' Set Enrichment Analysis) The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results. 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(2020). A note on exploratory item factor analysis by singular value decomposition. Psychometrika, 1-15, . 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Jiang W., Josse J., Lavielle M., TraumaBase Group (2020) . Package: r-cran-misc3d Architecture: all Version: 0.9-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rgl, r-cran-tkrplot, r-cran-mass, r-cran-maps, r-cran-lattice, r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-misc3d_0.9-2-1.ca2404.1_all.deb Size: 240488 MD5sum: dda92f02cbd55d829ca7dadb71e871fd SHA1: 22e357efc915bcf1e4342c785084d93f93c1db8c SHA256: 994a84822e3ea75b695bb223cab4640b1a1c458a50c2568bd0bcd40d5667a124 SHA512: 72ec7b56eafa506622c2598c3c373f036f91866ab3228c55602b103376f7e564a88ff030c4a64265c29e5253ff063207cab88e2ad651ecbac81d90264b91eeaf Homepage: https://cran.r-project.org/package=misc3d Description: CRAN Package 'misc3d' (Miscellaneous 3D Plots) A collection of miscellaneous 3d plots, including isosurfaces. 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Package: r-cran-miscic Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nnls Filename: pool/dists/noble/main/r-cran-miscic_0.1.0-1.ca2404.1_all.deb Size: 43924 MD5sum: 4813816543c0f60b9b53b8a0f7055d9a SHA1: 310bd409375388891e1a653bb10a953b6d05149b SHA256: 15481f17f2ef33d12e1559ff235ad96cedcae45093f98cd85d05b27bc5be03c6 SHA512: 00e50548f39b8abedc5175ba8f5d7cde7efd6e9f575e83d36af81b1366687b35e084963ad13c8156b0127884192c1c1d1852e6d8f31ce0ef6446f8d554840fd0 Homepage: https://cran.r-project.org/package=miscIC Description: CRAN Package 'miscIC' (Misclassified Interval Censored Time-to-Event Data) Estimation of the survivor function for interval censored time-to-event data subject to misclassification using nonparametric maximum likelihood estimation, implementing the methods of Titman (2017) . 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Package: r-cran-misctools Architecture: all Version: 0.6-30-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest Suggests: r-cran-ecdat Filename: pool/dists/noble/main/r-cran-misctools_0.6-30-1.ca2404.1_all.deb Size: 88744 MD5sum: 46af52a68c04f64b46f17deacf04e5f6 SHA1: f5a897f2909a9f3ad5942f44e7df188acfdb008d SHA256: 1f5f845e8b3b1d32cb8738987632e6d7f09a7940061866692dbbdd11118ecfbe SHA512: 77b7fc0b80a7beeb6f3fc1a33e5fc1c9387e000c3a4948cde1730d8b3cb7a360f049c78dce6d2d51584d8d86cf235eea910bf57bce2dddae92a080f078d4b792 Homepage: https://cran.r-project.org/package=miscTools Description: CRAN Package 'miscTools' (Miscellaneous Tools and Utilities) Miscellaneous small tools and utilities. Many of them facilitate the work with matrices, e.g. inserting rows or columns, creating symmetric matrices, or checking for semidefiniteness. 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Package: r-cran-miselect Architecture: all Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mice, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-miselect_0.9.2-1.ca2404.1_all.deb Size: 145576 MD5sum: 67b42daae84d8f71a7a14242d9f8ce85 SHA1: 122f8b3dc7f40696bc1f4ff02d279c5c7f08944f SHA256: c500e396b54fb77805945f7d3ca6b7d5d8942d1a2dedc971d77dbe69085d913a SHA512: 73969d0d3f3de7aa2efcc5a2f006ce84c7c6d8c2e4d2b9730cd8a91b2ab5bfa94b6df59487ee3c9fb283b6e3d3639a81bf1ce22a4e1e50f0d782da9da6415ff8 Homepage: https://cran.r-project.org/package=miselect Description: CRAN Package 'miselect' (Variable Selection for Multiply Imputed Data) Penalized regression methods, such as lasso and elastic net, are used in many biomedical applications when simultaneous regression coefficient estimation and variable selection is desired. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors, making it difficult to ascertain a final active set without resorting to ad hoc combination rules. 'miselect' presents Stacked Adaptive Elastic Net (saenet) and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes, developed by Du et al (2022) . They, by construction, force selection of the same variables across multiply imputed data. 'miselect' also provides cross validated variants of these methods. 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Package: r-cran-mispr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-mass, r-cran-penalized Filename: pool/dists/noble/main/r-cran-mispr_1.0.0-1.ca2404.1_all.deb Size: 148614 MD5sum: 7bbb6852ae6d4218207f6320778f54b2 SHA1: 65701de9231fe5cd6eabd11563215ab5ecab51ca SHA256: 57ee9b56b63942e8c293d3a54e191ec69da7acef8df511498669584556dc335c SHA512: 618549ccf286e9b4c68716307b73ec836cf0415c4000b64ac9c5fd73bf53e311ce16b89a55f4a6bec49623bf83ad4e570bd15d94a77d35acb80f18ad344c33b3 Homepage: https://cran.r-project.org/package=mispr Description: CRAN Package 'mispr' (Multiple Imputation with Sequential Penalized Regression) Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) . Package: r-cran-misprime Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-mass Filename: pool/dists/noble/main/r-cran-misprime_0.1.0-1.ca2404.1_all.deb Size: 46682 MD5sum: 51333b1a5ae08a8a6d6c8d27a4912046 SHA1: 66f1d6782c31b0a371b80914f7f66bfc2c800d89 SHA256: e36250d2824b2d2c082699f6fef375a6bbadd58904a6026188664b1ce77a8658 SHA512: 71b279211114fde17c28b946cae33189eb755541990aa8c48d67953f98855ea07c00019f1df6eb312340cd6470ecb13adadb1fdd4aa63edb1522ea6c8401d543 Homepage: https://cran.r-project.org/package=misPRIME Description: CRAN Package 'misPRIME' (Partial Replacement Imputation Estimation for Missing Covariates) Partial Replacement Imputation Estimation (PRIME) can overcome problems caused by missing covariates in additive partially linear model. PRIME conducts imputation and regression simultaneously with known and unknown model structure. More details can be referred to Zishu Zhan, Xiangjie Li and Jingxiao Zhang. (2022) . Package: r-cran-misreparma Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mixtools, r-cran-boot, r-cran-tseries Filename: pool/dists/noble/main/r-cran-misreparma_0.0.2-1.ca2404.1_all.deb Size: 34896 MD5sum: 97da07c1a91315bcc6ff5d0becb0eb46 SHA1: c023d3c54035ee7293d5ff5e28c822f24831ebc9 SHA256: a54c7ce7ab5eedf9ba7a0147dc0e6e87f66fb2b0ce4fbf2ef1fa76bbd2fd680e SHA512: 973b5302d746b2b94bc184f43614d43f828003d45d39ac8859e4f0d983e0d638018fe51d4285a90cdda4528bfbaafb50484478a7bf49015c70d13326f9ac6f9c Homepage: https://cran.r-project.org/package=MisRepARMA Description: CRAN Package 'MisRepARMA' (Misreported Time Series Analysis) Provides a simple and trustworthy methodology for the analysis of misreported continuous time series. See Moriña, D, Fernández-Fontelo, A, Cabaña, A, Puig P. (2021) . 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Cronbach's alpha (Cronbach, 1951 ) is one of the most widely used measures of internal consistency in the social, behavioral, and medical sciences (Bland & Altman, 1997 ; Tavakol & Dennick, 2011 ). However, conventional implementations assume complete data, and listwise deletion is often applied when missingness occurs, which can lead to biased or overly optimistic reliability estimates (Enders, 2003 ). This package implements computational strategies including enumeration, Monte Carlo sampling, and optimization algorithms (e.g., Genetic Algorithm, Differential Evolution, Sequential Least Squares Programming) to obtain sharp lower and upper bounds of Cronbach's alpha under arbitrary missing data patterns. The approach is motivated by Manski's partial identification framework and pessimistic bounding ideas from optimization literature. Package: r-cran-misscforest Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partykit Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-misscforest_0.0.8-1.ca2404.1_all.deb Size: 22432 MD5sum: fe502a4d623f4e754397635724897001 SHA1: f20e47d188589457aef87fc6824f58a58f0fa589 SHA256: 813e20c6db8134cc7a0f8d8d9d4e0e3ae5b5f211d26f4aac345bd7cd724d1857 SHA512: 3d63e0bef289f85860690f4eba136244a672d6a8942d35c866dfc833e80033f7ed7cfc942a19e2cbad78bf66652f179a4851cf31961b0a473db759efe8c6fff3 Homepage: https://cran.r-project.org/package=missCforest Description: CRAN Package 'missCforest' (Ensemble Conditional Trees for Missing Data Imputation) Single imputation based on the Ensemble Conditional Trees (i.e. Cforest algorithm Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007) ). Package: r-cran-misscompare Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-amelia, r-cran-data.table, r-cran-dplyr, r-cran-ggdendro, r-cran-ggplot2, r-cran-hmisc, r-cran-ltm, r-cran-magrittr, r-cran-mass, r-cran-matrix, r-cran-mi, r-cran-mice, r-cran-missforest, r-cran-missmda, r-bioc-pcamethods, r-cran-plyr, r-cran-rlang, r-cran-tidyr, r-cran-vim Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-misscompare_1.0.3-1.ca2404.1_all.deb Size: 2728132 MD5sum: 811df6dc0b19c3f94fbcf52c8f088537 SHA1: 9dbc2b66cd197343b35f6b57e662e7b1552c1780 SHA256: 6299dc0c8cc178fb1765bd05e620e0cae3f3c177959a9b0a5bda60ba68012ef9 SHA512: ac30c0251266ac5b82dae65ad62e9c172e3c6796b66496becc86946319d6848af4e1e00916154c135ca0de367b4bb5cbe17594922c2b98932b04d229810b6780 Homepage: https://cran.r-project.org/package=missCompare Description: CRAN Package 'missCompare' (Intuitive Missing Data Imputation Framework) Offers a convenient pipeline to test and compare various missing data imputation algorithms on simulated and real data. These include simpler methods, such as mean and median imputation and random replacement, but also include more sophisticated algorithms already implemented in popular R packages, such as 'mi', described by Su et al. (2011) ; 'mice', described by van Buuren and Groothuis-Oudshoorn (2011) ; 'missForest', described by Stekhoven and Buhlmann (2012) ; 'missMDA', described by Josse and Husson (2016) ; and 'pcaMethods', described by Stacklies et al. (2007) . The central assumption behind 'missCompare' is that structurally different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation algorithms. 'missCompare' takes measurements of your dataset and sets up a sandbox to try a curated list of standard and sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns. 'missCompare' will also impute your real-life dataset for you after the selection of the best performing algorithm in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you assess imputation performance. Package: r-cran-missdiag Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-cobalt Suggests: r-cran-mice, r-cran-sbw, r-cran-ebal Filename: pool/dists/noble/main/r-cran-missdiag_1.0.1-1.ca2404.1_all.deb Size: 145632 MD5sum: 6b40afefeb3e2b04aa9c3d88c4a958ac SHA1: a90749c8db7033c6fe156383e1d3fda8651eaa26 SHA256: 3dd78965d3ba1cfbc4cd497d26eebda7e76d6c089eaece731d5cb69734921a03 SHA512: 2bab8a9880a0b91034eb046cec7a0868b658ce930db1544b622e858713b42d32dcebd63682043c06876ab589a6c430dcdbf592080da43056b909bd844fb48c8c Homepage: https://cran.r-project.org/package=missDiag Description: CRAN Package 'missDiag' (Comparing Observed and Imputed Values under MAR and MCAR) Implements the computation of discrepancy statistics summarizing differences between the density of imputed and observed values and the construction of weights to balance covariates that are part of the missing data mechanism as described in Marbach (2021) . Package: r-cran-missforest Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforest, r-cran-ranger, r-cran-foreach, r-cran-iterators, r-cran-itertools, r-cran-dorng, r-cran-rdpack Suggests: r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-missforest_1.6.1-1.ca2404.1_all.deb Size: 274880 MD5sum: 0b471a927ca6b1907b23d7cba8e3dba6 SHA1: f408d567f94e27c917c7727f63d338ab3686f9ad SHA256: 229bc0297b7f4acf5275f232e484e9f48d1c499ebe733e2c24f0883ee13270fe SHA512: f030a3bf1df5d2195b1486d4d3cc72b632b983e9c6c788e908c41a6be16d9f4603325f7cc4d80da45ae8a32a89d7bfbaa91ed156d445f596b89360749f835388 Homepage: https://cran.r-project.org/package=missForest Description: CRAN Package 'missForest' (Nonparametric Missing Value Imputation using Random Forest) The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest (via 'ranger' or 'randomForest') trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time. Package: r-cran-missforestpredict Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ranger Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-missforestpredict_1.0.1-1.ca2404.1_all.deb Size: 218612 MD5sum: f5e425610bf0454a3863efc13e0d6769 SHA1: 54d830574f020379c12984000863c7effdf91c44 SHA256: 69f1711be5f91191e029f704961edc741e5f20de2142f8984795d713f0537738 SHA512: c9d27bc3ca3bbd51d41d4f8dfe7116dc8b42a16785bc85f09ebd1c6edcfb43e65ecc984756e5351e059ba4a45d987f76d7c33e278bd5687fe7a90cf431ff96ad Homepage: https://cran.r-project.org/package=missForestPredict Description: CRAN Package 'missForestPredict' (Missing Value Imputation using Random Forest for PredictionSettings) Missing data imputation based on the 'missForest' algorithm (Stekhoven, Daniel J (2012) ) with adaptations for prediction settings. The function missForest() is used to impute a (training) dataset with missing values and to learn imputation models that can be later used for imputing new observations. The function missForestPredict() is used to impute one or multiple new observations (test set) using the models learned on the training data. For more details see Albu, E., Gao, S., Wynants, L., & Van Calster, B. (2024). missForestPredict--Missing data imputation for prediction settings . Package: r-cran-missinghandle Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-imputets, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-missinghandle_0.1.1-1.ca2404.1_all.deb Size: 37184 MD5sum: 548dad1c6b4d129218500dfc247aa90c SHA1: ed286f1f7746f8197c2fdeb9e68c1eaa172cd0de SHA256: 057ed766d040aa5b28c3c5ec3160548acce2506943033b456419ee15e1eab5f0 SHA512: b5990b7eb348416614cb0641c8add762b1d42714a97aafa02046cf354274b4c3255262b5fe77adefc6251e3b999a693f3ac8cbe0227f5696142527026359dcd2 Homepage: https://cran.r-project.org/package=MissingHandle Description: CRAN Package 'MissingHandle' (Handles Missing Dates and Data and Converts into Weekly andMonthly from Daily) Many times, you will not find data for all dates. After first January, 2011 you may have next data on 20th January, 2011 and so on. Also available dates may have zero values. Try to gather all such kinds of data in different excel sheets of a single excel file. Every sheet will contain two columns (1st one is dates and second one is the data). After loading all the sheets into different elements of a list, using this you can fill the gaps for all the sheets and mark all the corresponding values as zeros. Here I am talking about daily data. Finally, it will combine all the filled results into one data frame (first column is date and other columns will be corresponding values of your sheets) and give one combined data frame. Number of columns in the data frame will be number of sheets plus one. Then imputation will be done. Daily to monthly and weekly conversion is also possible. More details can be found in Garai and others (2023) . Package: r-cran-missinghe Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggpubr, r-cran-ggmcmc, r-cran-ggthemes, r-cran-bcea, r-cran-ggplot2, r-cran-bayesplot, r-cran-r2jags, r-cran-loo, r-cran-coda, r-cran-mcmcr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-missinghe_1.6.0-1.ca2404.1_all.deb Size: 1495104 MD5sum: 632689b00b93face74a95f7737e153a3 SHA1: c175d26bee3e587325a5c3f09c5e3e9eba7dd651 SHA256: e7585cf8eb6b7cb299fe4efa2d89200d8fd5ff817d2fc0ec0de403c6b87f0ff6 SHA512: c05835ae9ae428270b09b194ba81c48a1761b7e0d9e4230264fd393c021c6d198aa6ae022f9759227fc6bad863f19f407764cdace047a1695c392ffe9e227343 Homepage: https://cran.r-project.org/package=missingHE Description: CRAN Package 'missingHE' (Missing Outcome Data in Health Economic Evaluation) Contains a suite of functions for health economic evaluations with missing outcome data. The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags'). Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models. In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs. The methods implemented are described in Mason (2018) , Molenberghs (2000) and Gabrio (2019) . Package: r-cran-missingplotlsd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-missingplotlsd_0.1.0-1.ca2404.1_all.deb Size: 13128 MD5sum: 68ba37c1ed6763fcb0547e7d01c52f22 SHA1: e7b9d27f206e746f5e38ca7db0e9b118ad2ac6d6 SHA256: b5b3b4fcc207239a49cd85e1866fae11f4c63da7e18cb3696e9eff9578a0d750 SHA512: b6c0a60217e8d46f08cb286b57509a5220534fe935450d3c299223c3b550e30fa99b6e88966b98952c09ca84ef40a23d0775821550b531712143b53b87038777 Homepage: https://cran.r-project.org/package=MissingPlotLSD Description: CRAN Package 'MissingPlotLSD' (Missing Plot in LSD) A system for Analysis of LSD when there is one missing observation. Methods for this process is described in A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3). Package: r-cran-missingplotrbd Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-missingplotrbd_1.1.0-1.ca2404.1_all.deb Size: 11804 MD5sum: 32f415669e10b3ce19bb2db0857b6ec1 SHA1: c47c38d24f96a07f98525ccfc87d421a5d4acf73 SHA256: 90085a8f611cf598573d1252f9f0e65ea7c5078d8af0ab0433c813daacf7f38f SHA512: 6cff7f2738699c23d8fa18d1b942279186411da96117e0f729d70e6f9576dd453dd7aac0d498a2826d26507be70737318ad4ec439f541ffe360e255083148fb6 Homepage: https://cran.r-project.org/package=MissingPlotRBD Description: CRAN Package 'MissingPlotRBD' (Missing Plot in RBD) A system for Analysis of RBD when there is one missing observation. Methods for this process is described in A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3). Package: r-cran-missmda Architecture: all Version: 1.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-factominer, r-cran-ggplot2, r-cran-mice, r-cran-mvtnorm, r-cran-doparallel, r-cran-foreach Suggests: r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-missmda_1.21-1.ca2404.1_all.deb Size: 439454 MD5sum: c8054b266eb374db5c4e078667d5aa33 SHA1: 9e20b0c90a2239aa9f7497d574f6f3772af32003 SHA256: cb82eab01d285c4652cc1048449565af08f873d55baa6cb55b9a252572e12269 SHA512: bdd3c3aac6bddc3f31b913a3864f065a30b37c883d4d92f7fbd45f85f4a9045431804756edcbe123b9c2e1dbe1d2a95c98a9b11208ea9745abe836e4f9e0eccf Homepage: https://cran.r-project.org/package=missMDA Description: CRAN Package 'missMDA' (Handling Missing Values with Multivariate Data Analysis) Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA. Package: r-cran-missmech Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-missmech_1.0.4-1.ca2404.1_all.deb Size: 147970 MD5sum: 2a9dbf72c3064b48136adc5976b56d99 SHA1: bc757815327d8734fe1ea650136b4fc1013faf34 SHA256: 3c82b9dcbee5b2075e9d08787c6c23692919150d9135110cf5ccb0d4ef1326d0 SHA512: 8c6fccf2f8bef2164dd7eccd459a6e45570ad4bae436d3901d3073ac10d5a26d24db80a32a44d7d2938a82be02a80b7eff6709528d8f7ff0bded488a4647b145 Homepage: https://cran.r-project.org/package=MissMech Description: CRAN Package 'MissMech' (Testing Homoscedasticity, Multivariate Normality, and MissingCompletely at Random) To test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR). For detailed description see Jamshidian, M. Jalal, S., and Jansen, C. (2014). "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)", Journal of Statistical Software, 56(6), 1-31. . Package: r-cran-missmethods Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-lpsolve, r-cran-norm, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-missmethods_0.4.0-1.ca2404.1_all.deb Size: 265482 MD5sum: 43af2f3758f5bacf47e2db7f41e2240f SHA1: de5b4873416fbff4a916329f5129e213521514a5 SHA256: eb1038e60cdb857e3c0755ed54ecec5595e667a5ab81b91cb0dfa6a9ffcf301a SHA512: ae5a8e59242eaa437214e22904ac5050645c9982c89c58d06a78fa70dbd2a688e8904a5dfe94e75db3f30a855d7f3771492e97fc635346c2178fba7dc640dc58 Homepage: https://cran.r-project.org/package=missMethods Description: CRAN Package 'missMethods' (Methods for Missing Data) Supply functions for the creation and handling of missing data as well as tools to evaluate missing data methods. Nearly all possibilities of generating missing data discussed by Santos et al. (2019) and some additional are implemented. Functions are supplied to compare parameter estimates and imputed values to true values to evaluate missing data methods. Evaluations of these types are done, for example, by Cetin-Berber et al. (2019) and Kim et al. (2005) . Package: r-cran-misspi Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 669 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lightgbm, r-cran-doparallel, r-cran-dosnow, r-cran-foreach, r-cran-ggplot2, r-cran-glmnet, r-cran-sis, r-cran-plotly Suggests: r-cran-e1071, r-cran-neuralnet Filename: pool/dists/noble/main/r-cran-misspi_0.1.1-1.ca2404.1_all.deb Size: 648372 MD5sum: 14afd81a16ad5a88afe97346e8b5fb6e SHA1: 0cea8789ee24716972e367844ad74d10035b7ae2 SHA256: b1fd4d149547c75a3954a0082c42fff6d5c6e483b0f7d2ce71cd955c96bfa16a SHA512: bbfdccc789a3d35641251bdf388d9130c513c9823445dc8eb419783e247922e13b9b303f5a07dbf7d6e2a699bad602cdd8ffdc188b786e91b2a5b7a92972dbb3 Homepage: https://cran.r-project.org/package=misspi Description: CRAN Package 'misspi' (Missing Value Imputation in Parallel) A framework that boosts the imputation of 'missForest' by Stekhoven, D.J. and Bühlmann, P. (2012) by harnessing parallel processing and through the fast Gradient Boosted Decision Trees (GBDT) implementation 'LightGBM' by Ke, Guolin et al.(2017) . 'misspi' has the following main advantages: 1. Allows embrassingly parallel imputation on large scale data. 2. Accepts a variety of machine learning models as methods with friendly user portal. 3. Supports multiple initializations methods. 4. Supports early stopping that prohibits unnecessary iterations. Package: r-cran-missplot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-missplot_0.1.0-1.ca2404.1_all.deb Size: 17248 MD5sum: 51d95dcdf858fb05cdb83a07516b2c0a SHA1: f568ad0d7fbeb125c2946d23766b4221336fd2be SHA256: fc7c30b62ae89aec9a9b6c13ac45ae082eee405f0edae6f783c5d9e1f37dc32a SHA512: 10983cca760bdfabdadda2749fa2dd7232b14bafce33acbcba7836ef641657f0261a1964f446dcf092499b725a4bee89bdde125d1dc6c5b0eec7d381697ed6ad Homepage: https://cran.r-project.org/package=Missplot Description: CRAN Package 'Missplot' (Missing Plot Technique in Design of Experiment) A system for testing differential effects among treatments in case of Randomised Block Design and Latin Square Design when there is one missing observation. Methods for this process are as described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3). Package: r-cran-misspls Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 564 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mice, r-cran-plsrglm, r-cran-vim Suggests: r-cran-bcv, r-cran-knitr, r-cran-mlbench, r-cran-plsdof, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-misspls_0.2.1-1.ca2404.1_all.deb Size: 495286 MD5sum: cc4cd4faea2b05bd23d66c285d1c9b76 SHA1: 2a42ea195a3cfb4f25f05684677c79e5aca4946e SHA256: 643474f1002d2cc0ff75cee9b3affa1a9d0701e1b927241a38ef9f3ff91d0121 SHA512: 06a159867b58e75b3a4222c7d0923c6f05d50d2fee9758bedd5096a1bebcd9015a2eaca30f599abe618c8484751d38ab8ed246b72dd51cdffe2cc19fb87b48be Homepage: https://cran.r-project.org/package=missPLS Description: CRAN Package 'missPLS' (Methods and Reproducible Workflows for Partial Least Squareswith Missing Data) Methods-first tooling for reproducing and extending the partial least squares regression studies on incomplete data described in Nengsih et al. (2019) . The package provides simulation helpers, missingness generators, imputation wrappers, component-selection utilities, real-data diagnostics, and reproducible study orchestration for Nonlinear Iterative Partial Least Squares (NIPALS)-Partial Least Squares (PLS) workflows. Package: r-cran-missr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-norm, r-cran-tibble, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-missr_1.0.1-1.ca2404.1_all.deb Size: 129656 MD5sum: 356cb6370da48f43240982b8d1e920ad SHA1: bba6138312b603f5a834bea30b49d831913405da SHA256: 8f45c337ed86d71f8f7ee97db3261e9c5554b215b2a88b52fd43c511eaf9f838 SHA512: f3997cf5fce3d2adcd1202a87eccedb51b959ebbb7527d931277726a6ba77ed2b42d1da27921ce8959af57597bc0961eafc1cb398dcc23bf9102c9c684dc46aa Homepage: https://cran.r-project.org/package=missr Description: CRAN Package 'missr' (Classify Missing Data as MCAR, MAR, or MNAR) Classify missing data as missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). This step is required before handling missing data (e.g. mean imputation) so that bias is not introduced. See Little (1988) for the statistical rationale for the methods used. Package: r-cran-missranger Architecture: all Version: 2.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-ranger Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-missranger_2.6.1-1.ca2404.1_all.deb Size: 89798 MD5sum: b705eb7e0cb71ae7e06e15f3879c8dd7 SHA1: c63dfa83230dd93976bc322ff6a7e8d7315128da SHA256: d5a2d698b903fe6dfbfd1595be81e043a5c3b25be02cc128e57388c875ed2588 SHA512: a6256709b7abeb982859cdc7327cfb1d87b64769bf35924120ff1924c2ec934986a1388951d7d0a79a4d2263ebdcf6b74972ebe58884385c844e918c43aa8c9b Homepage: https://cran.r-project.org/package=missRanger Description: CRAN Package 'missRanger' (Fast Imputation of Missing Values) Alternative implementation of the beautiful 'MissForest' algorithm used to impute mixed-type data sets by chaining random forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012) . Under the hood, it uses the lightning fast random forest package 'ranger'. Between the iterative model fitting, we offer the option of using predictive mean matching. This firstly avoids imputation with values not already present in the original data (like a value 0.3334 in 0-1 coded variable). Secondly, predictive mean matching tries to raise the variance in the resulting conditional distributions to a realistic level. This would allow, e.g., to do multiple imputation when repeating the call to missRanger(). Out-of-sample application is supported as well. Package: r-cran-mistr Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1956 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmle Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-pinp Filename: pool/dists/noble/main/r-cran-mistr_0.0.6-1.ca2404.1_all.deb Size: 1655316 MD5sum: 49be1ee97e93acbd24da049a6de696d3 SHA1: fc5c838664670481c9d72f0c36a80905d536b8bc SHA256: d55e5cdfa9524d94b616235080e792ec8a63469359057f7e4e345d365d6a6cc0 SHA512: 1248962a0c78bb2e23f5221a91a39d3ca232e6e543bce2c38b32210ae930fe34eaed054af30b198ffcc59c5f8614e4f050955c3845fc984c7941a88dad3430c4 Homepage: https://cran.r-project.org/package=mistr Description: CRAN Package 'mistr' (Mixture and Composite Distributions) A flexible computational framework for mixture distributions with the focus on the composite models. 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Yanagida') Miscellaneous functions for (1) data handling (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and cluster scores, reading and writing Excel and SPSS files), (2) descriptive statistics (e.g., frequency table, cross tabulation, effect size measures), (3) missing data (e.g., descriptive statistics for missing data, missing data pattern, Little's test of Missing Completely at Random, and auxiliary variable analysis), (4) multilevel data (e.g., multilevel descriptive statistics, within-group and between-group correlation matrix, multilevel confirmatory factor analysis, level-specific fit indices, cross-level measurement equivalence evaluation, multilevel composite reliability, and multilevel R-squared measures), (5) item analysis (e.g., confirmatory factor analysis, coefficient alpha and omega, between-group and longitudinal measurement equivalence evaluation), (6) statistical analysis (e.g., bootstrap confidence intervals, collinearity and residual diagnostics, dominance analysis, between- and within-subject analysis of variance, latent class analysis, t-test, z-test, sample size determination), and (7) functions to interact with 'Blimp' and 'Mplus'. Package: r-cran-misuvi Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-sf, r-cran-tigris Filename: pool/dists/noble/main/r-cran-misuvi_0.1.1-1.ca2404.1_all.deb Size: 381868 MD5sum: 3c7fd1c6593779fabed97fe424cc529d SHA1: 874eca3632144e0ce6e8e12c7a79a11e7d8def11 SHA256: 9cf9abca3cac92305adb5c4dc9faa0922b4f2fdd9b8c21dd7b64ed24523be3e0 SHA512: 353c60b9dbed097a66c14f9db76991c1931033c141aa901bfba2f0b8b54c667c9ddf961efad48fadf00aa5dd86179fba6d8254b2a20a04752dc0f31e71871791 Homepage: https://cran.r-project.org/package=misuvi Description: CRAN Package 'misuvi' (Access the Michigan Substance Use Vulnerability Index (MI-SUVI)) Easily import the MI-SUVI data sets. The user can import data sets with full metrics, percentiles, Z-scores, or rankings. Data is available at both the County and Zip Code Tabulation Area (ZCTA) levels. This package also includes a function to import shape files for easy mapping and a function to access the full technical documentation. All data is sourced from the Michigan Department of Health and Human Services. Package: r-cran-mitey Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6563 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fdrtool, r-cran-ggplot2 Suggests: r-cran-brms, r-cran-broom, r-cran-cowplot, r-cran-dplyr, r-cran-epilps, r-cran-flextable, r-cran-forcats, r-cran-ftextra, r-cran-ggridges, r-cran-gt, r-cran-here, r-cran-isoweek, r-cran-knitr, r-cran-lubridate, r-cran-officer, r-cran-openxlsx, r-cran-outbreaks, r-cran-purrr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tidybayes, r-cran-tidyr, r-cran-viridis, r-cran-zoo Filename: pool/dists/noble/main/r-cran-mitey_0.2.0-1.ca2404.1_all.deb Size: 2842746 MD5sum: 47b016f61947f881889823060fe3ee58 SHA1: d8ea0967a413903cff6fab4de11e757e25dc4c29 SHA256: 1c35cb6f05acfe75bf947bd2f33ced8789585e813f5797e745b6b03d6bc93d5c SHA512: c8977c8ce15434215d26a2c725b579e5513fb28d23e35e2dbcdfa55a3062896aceb7f5097bab6b95203254fa34483faaa2f8ccf7f18d7256434fdee951256845 Homepage: https://cran.r-project.org/package=mitey Description: CRAN Package 'mitey' (Serial Interval and Case Reproduction Number Estimation) Provides methods to estimate serial intervals and time-varying case reproduction numbers from infectious disease outbreak data. Serial intervals measure the time between symptom onset in linked transmission pairs, while case reproduction numbers quantify how many secondary cases each infected individual generates over time. These parameters are essential for understanding transmission dynamics, evaluating control measures, and informing public health responses. The package implements the maximum likelihood framework from Vink et al. (2014) for serial interval estimation and the retrospective method from Wallinga & Lipsitch (2007) for reproduction number estimation. Originally developed for scabies transmission analysis but applicable to other infectious diseases including influenza, COVID-19, and emerging pathogens. Designed for epidemiologists, public health researchers, and infectious disease modelers working with outbreak surveillance data. Package: r-cran-mitml Architecture: all Version: 0.4-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 668 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pan, r-cran-jomo, r-cran-haven Suggests: r-cran-mice, r-cran-miceadds, r-cran-amelia, r-cran-lme4, r-cran-nlme, r-cran-lavaan, r-cran-geepack, r-cran-glmmtmb, r-cran-survival, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mitml_0.4-5-1.ca2404.1_all.deb Size: 516410 MD5sum: 2aafb97038d39e3177687dd1d843bc14 SHA1: a94f9541ea17ef61ffa9fad1bbf2223823e31bdd SHA256: b57972f6d065f79b22715bff8eafe5cefaf0717050e35419a293e0c7accf1902 SHA512: 4d1df97c3ee0a69aaa0e609c4f33faac1e5e6c871e39c2dc0b91d7b37fca5d9c4b037cb70d48cd9db32812781666e859182f0ac72d0fd331ee1bf61b2026b4e3 Homepage: https://cran.r-project.org/package=mitml Description: CRAN Package 'mitml' (Tools for Multiple Imputation in Multilevel Modeling) Provides tools for multiple imputation of missing data in multilevel modeling. Includes a user-friendly interface to the packages 'pan' and 'jomo', and several functions for visualization, data management and the analysis of multiply imputed data sets. 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Package: r-cran-mitools Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi Suggests: r-cran-rodbc, r-cran-foreign Filename: pool/dists/noble/main/r-cran-mitools_2.4-1.ca2404.1_all.deb Size: 266084 MD5sum: 40de1676cd3da90ae2266706de8bae1d SHA1: df996352af8c6d09636505f41351102b62368aa1 SHA256: 3368348157871dde64497ea101082d9608f98a121e3cbbee0b2fe532ad89ce25 SHA512: 28a08773ae41692bc8459f47075e1a66eeed3f98cb8a3c538d601775167016fe74dfe4112d70f2b9f016b0a36336687cc856d8825b8335d4a69d0ada1c516062 Homepage: https://cran.r-project.org/package=mitools Description: CRAN Package 'mitools' (Tools for Multiple Imputation of Missing Data) Tools to perform analyses and combine results from multiple-imputation datasets. Package: r-cran-mitre Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1324 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-plyr, r-cran-dplyr, r-cran-igraph, r-cran-stringr, r-cran-jsonlite, r-cran-rjsonio, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mitre_1.0.0-1.ca2404.1_all.deb Size: 1298736 MD5sum: 98143fb1c7dd98cad835ef7d4240049f SHA1: 6cad892f26f851072ad7f0e4245f24d566ec15e6 SHA256: 148961b9ccfadee389f4fdb63befd44677ac39f10128b259f560aa60921dd1cc SHA512: b75a0c857ce1bb139635e06293f24cd0c0b6bce174bc3f0f3e7a9fc0239332e5addff9657a84b111c26086621e45072dde03a5cf9a4941393297169020f4b834 Homepage: https://cran.r-project.org/package=mitre Description: CRAN Package 'mitre' (Cybersecurity MITRE Standards Data and Digraphs) Extract, transform and load MITRE standards. This package gives you an approach to cybersecurity data sets. All data sets are build on runtime downloading raw data from MITRE public services. MITRE is a government-funded research organization based in Bedford and McLean. Current version includes most used standards as data frames. It also provide a list of nodes and edges with all relationships. Package: r-cran-mittagleffler Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 677 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stabledist Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-animation, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-mittagleffler_0.4.1-1.ca2404.1_all.deb Size: 565706 MD5sum: b88e80eaafe5f918619a07e9ffab7dfb SHA1: 89cb273c0a855318fe2cf0ec5515ba441459727d SHA256: 22211376f38d7123af2a9bb62cf75dfd530a672641c9db08cad38a266f26679b SHA512: f83221e6e37d8ce78dbcb0cd10bf00f79cb369ce665137db4e357bf795f551e64fd95e847fab2c29e8bc4e25b8d21a02fd3abdfaaf3284a6599965c9cff85ab1 Homepage: https://cran.r-project.org/package=MittagLeffleR Description: CRAN Package 'MittagLeffleR' (Mittag-Leffler Family of Distributions) Implements the Mittag-Leffler function, distribution, random variate generation, and estimation. Based on the Laplace-Inversion algorithm by Garrappa, R. (2015) . Package: r-cran-miwqs Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 725 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-condmvnorm, r-cran-ggplot2, r-cran-glm2, r-cran-hmisc, r-cran-invgamma, r-cran-mass, r-cran-matrixnormal, r-cran-mcmcpack, r-cran-mvtnorm, r-cran-purrr, r-cran-rlist, r-cran-rsolnp, r-cran-survival, r-cran-tidyr, r-cran-tmvmixnorm, r-cran-tmvtnorm, r-cran-truncnorm Suggests: r-cran-formatr, r-cran-ggally, r-cran-knitr, r-cran-mice, r-cran-norm, r-cran-pander, r-cran-rmarkdown, r-cran-scales, r-cran-sessioninfo, r-cran-spelling, r-cran-testthat, r-cran-wqs Filename: pool/dists/noble/main/r-cran-miwqs_0.4.4-1.ca2404.1_all.deb Size: 636720 MD5sum: 1970cd132e7b2aec01a7a60c38a179f8 SHA1: d64f31b62988c243b6029a32fe9ecb8eb0e7fef9 SHA256: 672036112b1ce47bf9b0b0b5c6afdafb4fae323ad60acb7fc404d75dd9b7aa2b SHA512: 6fb7902e27f4dd66b2ecc8d52444e1b101f42a32ae76051fb19ae8a3c3e3d2bea00827b1fdbc94ee257d92299b129d74d21192762cf5a89809857407042c9999 Homepage: https://cran.r-project.org/package=miWQS Description: CRAN Package 'miWQS' (Multiple Imputation Using Weighted Quantile Sum Regression) The miWQS package handles the uncertainty due to below the detection limit in a correlated component mixture problem. Researchers want to determine if a set/mixture of continuous and correlated components/chemicals is associated with an outcome and if so, which components are important in that mixture. These components share a common outcome but are interval-censored between zero and low thresholds, or detection limits, that may be different across the components. This package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes (Hargarten & Wheeler (2020) ). The imputation models are: bootstrapping imputation (Lubin et.al (2004) ), univariate Bayesian imputation (Hargarten & Wheeler (2020) ), and multivariate Bayesian regression imputation. Package: r-cran-mixar Architecture: all Version: 0.22.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1539 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb, r-cran-combinat, r-cran-timedate, r-cran-fgarch, r-cran-rdpack, r-cran-gbutils, r-cran-mcmcpack, r-cran-e1071, r-cran-permute, r-cran-mvtnorm Suggests: r-cran-fma, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-mixar_0.22.9-1.ca2404.1_all.deb Size: 1115438 MD5sum: b54ebd506b840682fa847c04f1b3ab7d SHA1: 6d81c5d8ef7c2b10ca88926b333a719406fa44f6 SHA256: d623d9150a7db74638656916b86492200331df817fbb65c6c14f5e06f754d95a SHA512: 369cc14c353ab2f8320f316900e3376b4fe0bc81888857f049c1bad45737e0d51c84d4e274f6fa26cf38b2b8cd586222b8a0620f0ede6c81279eb7333af7a9dc Homepage: https://cran.r-project.org/package=mixAR Description: CRAN Package 'mixAR' (Mixture Autoregressive Models) Model time series using mixture autoregressive (MAR) models. Implemented are frequentist (EM) and Bayesian methods for estimation, prediction and model evaluation. See Wong and Li (2002) , Boshnakov (2009) ), and the extensive references in the documentation. Package: r-cran-mixbox Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gigrvg, r-cran-stabledist Filename: pool/dists/noble/main/r-cran-mixbox_1.2.3-1.ca2404.1_all.deb Size: 230676 MD5sum: d7eb60cc58b44c24010a75e944b4d001 SHA1: 26c05e7380e13f6ac8ebf5258bce3d46ceb2ca9a SHA256: 0fa5dddffc5530bc20aad0c5678b137477cb7ebd6f00a1c22cabfd2035bab3e1 SHA512: afd860f07da30d919ab08407d6bd4fe34a4901597bed55ccfb612a6064720fe8c064f3ced46f872a9d915884d573064a169bbc0457d022f88cb107cc8360773a Homepage: https://cran.r-project.org/package=mixbox Description: CRAN Package 'mixbox' (Observed Fisher Information Matrix for Finite Mixture Model) Developed for the following tasks. 1- simulating realizations from the canonical, restricted, and unrestricted finite mixture models. 2- Monte Carlo approximation for density function of the finite mixture models. 3- Monte Carlo approximation for the observed Fisher information matrix, asymptotic standard error, and the corresponding confidence intervals for parameters of the mixture models sing the method proposed by Basford et al. (1997) . Package: r-cran-mixchar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2391 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-minpack.lm, r-cran-nloptr, r-cran-zoo, r-cran-tmvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-mixchar_0.1.0-1.ca2404.1_all.deb Size: 1407520 MD5sum: d8b4f9fa36b51d79e8867e3399d59d84 SHA1: a85efe925aec75751a6615d04a534a28a0a7c96d SHA256: af3b1a07786fd885b8fde313920f0468104b29752afc0e08fa3a0aa70037b5b8 SHA512: ba4d9e79f98bdbd68e52c4ddc34da2651f4b62907fcfb8c0ad5b023f3a8621da6f52ac359809ce5d94d7a4fe04c1faa3bde97f651d5348566f5b4032647257a5 Homepage: https://cran.r-project.org/package=mixchar Description: CRAN Package 'mixchar' (Mixture Model for the Deconvolution of Thermal Decay Curves) Deconvolution of thermal decay curves allows you to quantify proportions of biomass components in plant litter. Thermal decay curves derived from thermogravimetric analysis (TGA) are imported, modified, and then modelled in a three- or four- part mixture model using the Fraser-Suzuki function. The output is estimates for weights of pseudo-components corresponding to hemicellulose, cellulose, and lignin. For more information see: Müller-Hagedorn, M. and Bockhorn, H. (2007) , Órfão, J. J. M. and Figueiredo, J. L. (2001) , and Yang, H. and Yan, R. and Chen, H. and Zheng, C. and Lee, D. H. and Liang, D. T. (2006) . Package: r-cran-mixcure Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-boot, r-cran-flexsurv, r-cran-survey, r-cran-gam, r-cran-timereg Filename: pool/dists/noble/main/r-cran-mixcure_2.0-1.ca2404.1_all.deb Size: 82706 MD5sum: 6f870ad387d7bc2cf23ee104cfd1d1bb SHA1: 1acefce79cf16377dc831dc323731cdbf5df105b SHA256: 15583155d54c4f7b014e0a365af086840e4d489391b97e585e6f00795effc3bf SHA512: f401601ae6e5defe1becff060c80ec72b67835fc26e735611148133597ddb17652f8dc1e4cc867ab6eb955add6c2eaf3e4fafdd50ec7d96c43145673c0df549b Homepage: https://cran.r-project.org/package=mixcure Description: CRAN Package 'mixcure' (Mixture Cure Models) Implementation of parametric and semiparametric mixture cure models based on existing R packages. See details of the models in Peng and Yu (2020) . Package: r-cran-mixdist Architecture: all Version: 0.5-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mixdist_0.5-5-1.ca2404.1_all.deb Size: 171222 MD5sum: f03f942c51c31fe8c4573795720afe73 SHA1: d92204d8dfd6b912cc5bbcc5bcd07fb5913a315c SHA256: aa155a4634517a15c0589ae54258d89a7cf8c603fe58fda53e3729518cc9fcd2 SHA512: 3bfde9c9e32412d9b4fe6388fb892d050e0789b63dc59e32960234fee739f878d0f3d3f986cfdb17a3eb46a8b6691aba496f2a70b48056a06d470b420a7c2505 Homepage: https://cran.r-project.org/package=mixdist Description: CRAN Package 'mixdist' (Finite Mixture Distribution Models) Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm. Package: r-cran-mixedbiastest Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-plm, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixedbiastest_1.0.2-1.ca2404.1_all.deb Size: 47908 MD5sum: 8f3510588a430314fe1bd75edb360378 SHA1: f9a291c091b68b686f3f40fec0af9bd105d7e58a SHA256: b51770d9e44e0cb7f8269424535186df6d99625d59e84651d304f21b8b768b9c SHA512: 20846470f04c57188b1474036ac4ceff6464325d79a3d44fa0d0a177b5b56a1293d5cf734379783f41542cb32d52bc0075b8063b3c0505b18f884bc8863a8dbd Homepage: https://cran.r-project.org/package=mixedbiastest Description: CRAN Package 'mixedbiastest' (Bias Diagnostic for Linear Mixed Models) Provides a function to perform bias diagnostics on linear mixed models fitted with lmer() from the 'lme4' package. Implements permutation tests for assessing the bias of fixed effects, as described in Karl and Zimmerman (2021) . Karl and Zimmerman (2020) provide R code for implementing the test using 'mvglmmRank' output. Development of this package was assisted by 'GPT o1-preview' for code structure and documentation. Package: r-cran-mixedfact Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixedfact_0.1.1-1.ca2404.1_all.deb Size: 90938 MD5sum: 086037ef1ffe3093237717aaa972e576 SHA1: 174e1144e8cd6dd68e2b9e7eff6969cdfee0267d SHA256: 674fcb568390025647dbff55aefa47861fe2983c169ed066644eb6164ffcc1b2 SHA512: 6793c8e92d6f3818b18129f9ebe9bc0e89f707c89190ad0f63c601c9cbad3778b9dd5c5344f36f88a1735c4bc6ab0290efac6affe1b2ba10fd50c591346f3658 Homepage: https://cran.r-project.org/package=mixedfact Description: CRAN Package 'mixedfact' (Generate and Analyze Mixed-Level Blocked Factorial Designs) Generates blocked designs for mixed-level factorial experiments for a given block size. Internally, it uses finite-field based, collapsed, and heuristic methods to construct block structures that minimize confounding between block effects and factorial effects. The package creates the full treatment combination table, partitions runs into blocks, and computes detailed confounding diagnostics for main effects and two-factor interactions. It also checks orthogonal factorial structure (OFS) and computes efficiencies of factorial effects using the methods of Nair and Rao (1948) . When OFS is not satisfied but the design has equal treatment replications and equal block sizes, a general method based on the C-matrix and custom contrast vectors is used to compute efficiencies. The output includes the generated design, finite-field metadata, confounding summaries, OFS diagnostics, and efficiency results. Package: r-cran-mixediffusion Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-adaptmcmc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixediffusion_1.0.1-1.ca2404.1_all.deb Size: 230846 MD5sum: c1db063cffb3687ffc426c04805b58aa SHA1: efd74772a4e1e463ab1073fe94a00daabf26d555 SHA256: 63eafbd3130b5c379d9c45af3be582fdb9db04ffcf6d3117acc286c848edda7d SHA512: 4dd9937911adad746ec067d3b8bc6ab54c2563171830dddf82f7c20fd6a930939cb64150fe5965a5bca4c4704af78671d925d593ea469cfa785505bb1c3a9adf Homepage: https://cran.r-project.org/package=mixediffusion Description: CRAN Package 'mixediffusion' (Mixed-Effects Diffusion Models with General Drift) Provides tools for likelihood-based inference in one-dimensional stochastic differential equations with mixed effects using expectation–maximization (EM) algorithms. The package supports Wiener and Ornstein–Uhlenbeck diffusion processes with user-specified drift functions, allowing flexible parametric forms including polynomial, exponential, and trigonometric structures. Estimation is performed via Markov chain Monte Carlo EM. Package: r-cran-mixedlevelrsds Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tords, r-cran-frf2, r-cran-mass Filename: pool/dists/noble/main/r-cran-mixedlevelrsds_1.0.0-1.ca2404.1_all.deb Size: 183192 MD5sum: a0a76094e735046c97e2a5be733711f0 SHA1: d67caccfc1e03d71ed3a2bed561ead8a1e30665d SHA256: 98dcec71700fc8c30f252431284cede496c8cba56562a03d05dafd64baf39dce SHA512: 0c7ebf2e451d2fc84e1c9424c3a1c93c6121766353bf3991fddbfdf930fd40820d905a1bb1d5017753831b787768b9ecbb25b27e2c5ca7a3d9ca8cd36b87083a Homepage: https://cran.r-project.org/package=MixedLevelRSDs Description: CRAN Package 'MixedLevelRSDs' (Mixed Level Response Surface Designs) Response Surface Designs (RSDs) involving factors not all at same levels are called Mixed Level RSDs (or Asymmetric RSDs). In many practical situations, RSDs with asymmetric levels will be more suitable as it explores more regions in the design space. (J.S. Mehta and M.N. Das (1968) . "Asymmetric rotatable designs and orthogonal transformations").This package contains function named ATORDs_I() for generating asymmetric third order rotatable designs (ATORDs) based on third order designs given by Das and Narasimham (1962). Function ATORDs_II() generates asymmetric third order rotatable designs developed using t-design of unequal set sizes, which are smaller in size as compared to design generated by function ATORDs_I(). In general, third order rotatable designs can be classified into two classes viz., designs that are suitable for sequential experimentation and designs for non-sequential experimentation. The sequential experimentation approach involves conducting the trials step by step whereas, in the non-sequential experimentation approach, the entire runs are executed in one go (M. N. Das and V. Narasimham (1962) . "Construction of Rotatable Designs through Balanced Incomplete Block Designs"). ATORDs_I() and ATORDs_II() functions generate non-sequential asymmetric third order designs. Function named SeqTORD() generates symmetric sequential third order design in blocks and also gives G-efficiency of the given design. Function named Asymseq() generates asymmetric sequential third order designs in blocks (M. Hemavathi, Eldho Varghese, Shashi Shekhar and Seema Jaggi (2020) . "Sequential asymmetric third order rotatable designs (SATORDs)"). In response surface design, situations may arise in which some of the factors are qualitative in nature (Jyoti Divecha and Bharat Tarapara (2017) . "Small, balanced, efficient, optimal, and near rotatable response surface designs for factorial experiments asymmetrical in some quantitative, qualitative factors"). The Function named QualRSD() generates second order design with qualitative factors along with their D-efficiency and G-efficiency. The function named RotatabilityQ() calculates a measure of rotatability (measure Q, 0 <= Q <= 1) given by Draper and Pukelshiem(1990) for given a design based on a second order model, (Norman R. Draper and Friedrich Pukelsheim(1990) . "Another look at rotatability"). Package: r-cran-mixedlsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-grpreg, r-cran-purrr, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mclust Filename: pool/dists/noble/main/r-cran-mixedlsr_0.1.0-1.ca2404.1_all.deb Size: 190558 MD5sum: a51cc81c29bfa1facdd42fcb413d39b1 SHA1: 057cfa546cb7d2d5d13c57f8ab087d035ac32ebd SHA256: 0a0efb3696644f22fb10c556b035e5d36dea446b7490190b8875102a3fadebfc SHA512: 8716de38f5f0f2bd7a0d844ba33c7de7bd158c9811d003532878fd694b43d6993cc9fe252a23b10d57a13fc138305362cffc932f48e243ec99d058c1bad420f0 Homepage: https://cran.r-project.org/package=mixedLSR Description: CRAN Package 'mixedLSR' (Mixed, Low-Rank, and Sparse Multivariate Regression onHigh-Dimensional Data) Mixed, low-rank, and sparse multivariate regression ('mixedLSR') provides tools for performing mixture regression when the coefficient matrix is low-rank and sparse. 'mixedLSR' allows subgroup identification by alternating optimization with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically performing parameter selection to identify low-rank substructures in the coefficient matrix. Package: r-cran-mixedpoisson Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gaussquad, r-cran-rmpfr, r-cran-mass Filename: pool/dists/noble/main/r-cran-mixedpoisson_2.0-1.ca2404.1_all.deb Size: 54678 MD5sum: 92261c5ded35c911efaf8035a6b9c0f7 SHA1: e174298dc1591bbc84b114a96fd386f6e360a915 SHA256: bc033b1c7b6f174962fcfdafa424088b0e0c543b08a4ecc5e15661f3fdb0b232 SHA512: 4e83b8c55667b3d7ca916effa9158c2aa51ff223940af5f4ac297bd5143b1bad7be421009210d470463fedca0364db28e2d516c2ab9883e5dd22086118768e58 Homepage: https://cran.r-project.org/package=MixedPoisson Description: CRAN Package 'MixedPoisson' (Mixed Poisson Models) The estimation of the parameters in mixed Poisson models. Package: r-cran-mixedpsy Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-beepr, r-cran-boot, r-cran-brglm, r-cran-lme4, r-cran-matrix, r-cran-mnormt, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-tidyselect Filename: pool/dists/noble/main/r-cran-mixedpsy_1.3.0-1.ca2404.1_all.deb Size: 111640 MD5sum: 8e4a494d04b923e51cd306b57afe2916 SHA1: ceb5ac1d3449d04becc5f9933f35b73932bcfb40 SHA256: 1d9e76974de67194b5dca869246b75500a5c821a69ae268e370f5701b111ac22 SHA512: 78c5c4728d9b9ac558db331e2652defa61b0de2c78bbbf98cc68335b91c5b632aec652fffd0fb8452114241a39b75cb0f25070cb4d01be6566fc4be94dfb69a9 Homepage: https://cran.r-project.org/package=MixedPsy Description: CRAN Package 'MixedPsy' (Statistical Tools for the Analysis of Psychophysical Data) Tools for the analysis of psychophysical data in R. This package allows to estimate the Point of Subjective Equivalence (PSE) and the Just Noticeable Difference (JND), either from a psychometric function or from a Generalized Linear Mixed Model (GLMM). Additionally, the package allows plotting the fitted models and the response data, simulating psychometric functions of different shapes, and simulating data sets. For a description of the use of GLMMs applied to psychophysical data, refer to Moscatelli et al. (2012). Package: r-cran-mixedsde Architecture: all Version: 5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 788 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sde, r-cran-moments, r-cran-mass, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-mixedsde_5.0-1.ca2404.1_all.deb Size: 695774 MD5sum: 5b3ad43a7dbc37894854ef76e376ebf4 SHA1: 6e815c4815c11a1b0b4ae46c3f4462c3b8598b0f SHA256: 49d8d7515f27b4c6407332146aea9d0c710a353a88385a700b40edeccb3ff9eb SHA512: 4ec8100dbd21ac7f8a16fd42c722ff825a45f511d6c0201b2f29682d1489bbd3d5eddfe5c4a6ef68b8a9ff543362378b0a8a15227ba373b70ee26e29378e51f5 Homepage: https://cran.r-project.org/package=mixedsde Description: CRAN Package 'mixedsde' (Estimation Methods for Stochastic Differential Mixed EffectsModels) Inference on stochastic differential models Ornstein-Uhlenbeck or Cox-Ingersoll-Ross, with one or two random effects in the drift function. Package: r-cran-mixedts Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-mixedts_1.0.4-1.ca2404.1_all.deb Size: 210054 MD5sum: f11e7067e6f6e4311bd8d7097cf4ae39 SHA1: b4be9dd8ac2644c339d2af5b3c41e67ffde977e2 SHA256: 6a8d282c5ea3267b47c4b01bac1b8734581be9a26b801510708e328b49ea839a SHA512: b60aaf35286eb46e90f9f58e677960fe884fb1cf65d996636ed3b7d4a6294092d56e4c903650bb524a58acc4ec86f1878d8fca4b0c3ab91b124a94fa1e597eaf Homepage: https://cran.r-project.org/package=MixedTS Description: CRAN Package 'MixedTS' (Mixed Tempered Stable Distribution) We provide detailed functions for univariate Mixed Tempered Stable distribution. Package: r-cran-mixfim Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-mvtnorm, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mixfim_1.1-1.ca2404.1_all.deb Size: 65742 MD5sum: 2390bf7c2f354dfb9b0038607f0fea58 SHA1: 5f82e926d8de25cd963d1a8f9c2834782f49d74a SHA256: f7136899719ede25b1968ba718d013f1ec51a5a93fd75139f9eb3fde0e664f67 SHA512: 6b13a7dcbd129b7e03d1a2305a36de9b0943f6094b620a03f36d9af813a5c1a35a87892e54c816469a4d22c51788a8c1c9b7faf07a43bc425919478768020751 Homepage: https://cran.r-project.org/package=MIXFIM Description: CRAN Package 'MIXFIM' (Evaluation of the FIM in NLMEMs using MCMC) Evaluation and optimization of the Fisher Information Matrix in NonLinear Mixed Effect Models using Markov Chains Monte Carlo for continuous and discrete data. Package: r-cran-mixfrac Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixfrac_1.0-1.ca2404.1_all.deb Size: 64692 MD5sum: 5f294b3dc1ec5768725aa26ec11246b8 SHA1: 870df8fce354b5ec978cad036eee369510fd853e SHA256: 0110f8ec0ab065687c920ac874059bfc423ea7cfbaa16b7e94771f474ae07652 SHA512: 5d6d84fbf8103a2fafe67ef1cb7b3b95cca2d12d5dce7a7fe6c4375b35c145f7b6463719bea367cc5e8b341ade489ec8f1ac84ad3e9096335b59ad78e1a76c4c Homepage: https://cran.r-project.org/package=MixFrac Description: CRAN Package 'MixFrac' (Fractional Factorial Designs with Alias and Trend-Free Analysis) Constructs mixed-level and regular fractional factorial designs using coordinate-exchange optimization and automatic generator search. Design quality is evaluated with J2 and balance (H-hat) criteria, alias structures are computed via correlation-based chaining, and deterministic trend-free run orders can be produced following Coster (1993) . Mixed-level design construction follows the NONBPA approach of Pantoja-Pacheco et al. (2021) . Regular fraction identification follows Guo, Simpson and Pignatiello (2007) . Alias structure computation follows Rios-Lira et al.(2021) . Package: r-cran-mixghd Architecture: all Version: 2.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-bessel, r-cran-mvtnorm, r-cran-ghyp, r-cran-numderiv, r-cran-mixture, r-cran-e1071, r-cran-cluster Filename: pool/dists/noble/main/r-cran-mixghd_2.3.7-1.ca2404.1_all.deb Size: 311802 MD5sum: 1c81f50a00fcfb7767cdd12cab1e9a79 SHA1: d11c8d55891848c53ffaa6cba12298388920ceff SHA256: 0dd1f97646780289fc8ea809ced8c93dce2dc872f0cc87733fa7f0e2d723c4d7 SHA512: 31994c33590e3acf9d4af3f1510d28ccba28baeae32f2c9116a899fdd41017ae3f48574c96d02a46b2ca9b933e45397190455cdb2a9801f9987e71d58d8a168f Homepage: https://cran.r-project.org/package=MixGHD Description: CRAN Package 'MixGHD' (Model Based Clustering, Classification and Discriminant AnalysisUsing the Mixture of Generalized Hyperbolic Distributions) Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the generalized hyperbolic distribution. The first model 'MGHD' (Browne and McNicholas (2015) ) is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' (Tortora et al. (2016) ) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD' is the mixture of multiple scaled generalized hyperbolic distributions, the 'cMSGHD' is a 'MSGHD' with convex contour plots and the 'MCGHD', mixture of coalesced generalized hyperbolic distributions is a new more flexible model (Tortora et al. (2019). The paper related to the software can be found at . Package: r-cran-mixhvg Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1446 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-scran, r-cran-seurat, r-cran-matrix, r-bioc-singlecellexperiment, r-bioc-scuttle Filename: pool/dists/noble/main/r-cran-mixhvg_1.0.1-1.ca2404.1_all.deb Size: 1343254 MD5sum: 2d4eacb72b469ee1cd8bdb25eace9562 SHA1: 82f3f24a77f9921c9cf5e09a250ea5e54c19417b SHA256: 02e027c702a1418088603930a30f11a44063f79c753e95ad6ed7271ffca2f186 SHA512: 64b360e0a26371700fd964d13db16f0463953e098a2003f998ee114b0843085a282e2d4cf39f75a33760e830f32cef6c5349076f8f9c6267e8d81d8960ff840e Homepage: https://cran.r-project.org/package=mixhvg Description: CRAN Package 'mixhvg' (Mixture of Multiple Highly Variable Feature Selection Methods) Highly variable gene selection methods, including popular public available methods, and also the mixture of multiple highly variable gene selection methods, . Reference: . Package: r-cran-mixindependr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mixindependr_1.0.0-1.ca2404.1_all.deb Size: 237896 MD5sum: 39d9cc89ca23c7dd48c0e017b9c024d3 SHA1: 31e88db080143e6bce467395a6875d64b89c5dc8 SHA256: 7a8cf2616c6777c2643b3a72773546807531d6f443566f84b18741dd2e3566a4 SHA512: a4c33c257920be805c10a15ac789243456dd37674c45d192d6f85d28edbfc19ae0b60df223866d7cbfb9d8621c4ff2187a6e54c7ccbdde2e9447a5d5fed0dc32 Homepage: https://cran.r-project.org/package=mixIndependR Description: CRAN Package 'mixIndependR' (Genetics and Independence Testing of Mixed Genetic Panels) Developed to deal with multi-locus genotype data, this package is especially designed for those panel which include different type of markers. Basic genetic parameters like allele frequency, genotype frequency, heterozygosity and Hardy-Weinberg test of mixed genetic data can be obtained. In addition, a new test for mutual independence which is compatible for mixed genetic data is developed in this package. Package: r-cran-mixkernel Architecture: all Version: 0.9-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3397 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-mixomics, r-cran-ggplot2, r-cran-reticulate, r-cran-vegan, r-bioc-phyloseq, r-cran-corrplot, r-cran-psych, r-cran-quadprog, r-cran-ldrtools, r-cran-matrix, r-cran-markdown Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-mixkernel_0.9-2-1.ca2404.1_all.deb Size: 2176064 MD5sum: 88de3c49b13fab25323068ad0495332d SHA1: 2658c4a1d1bac6e7503b1b078af3cbf1e659733e SHA256: 06efb58796bb37dd2c434f3fa360790bfe679db933994269114a4b706995c8e2 SHA512: 920038876d0dea000a26cb8054b398d8dba3644d5c0b7d500e40beb0b14d2a8fb020afb59f67b87c0b0cd6e6178cff722cac0c2752facb114e5f49649321dd90 Homepage: https://cran.r-project.org/package=mixKernel Description: CRAN Package 'mixKernel' (Omics Data Integration Using Kernel Methods) Kernel-based methods are powerful methods for integrating heterogeneous types of data. mixKernel aims at providing methods to combine kernel for unsupervised exploratory analysis. Different solutions are provided to compute a meta-kernel, in a consensus way or in a way that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a non linear space and from the multiple source point of view . A method to select (as well as funtions to display) important variables is also provided . Package: r-cran-mixlm Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-pls, r-cran-multcomp, r-cran-pracma, r-cran-leaps Suggests: r-cran-lme4 Filename: pool/dists/noble/main/r-cran-mixlm_1.4.3-1.ca2404.1_all.deb Size: 321796 MD5sum: 5c1ada80138de593dad4f9b40f1d70a0 SHA1: 1b5fdf28b1961ec55a0698f968ff87704808ce39 SHA256: e9611b934a3b16b25220e0279f5dfb68da31370ef38077a09a85d8128e26fc55 SHA512: a392e6132b26035cdfd3560431537a001742d5829f906eabbb93866cfaebd0a6df7828cefca9896ac6ad9f1af80f42fe51099768de52c8111c1a78b8f4d06f4e Homepage: https://cran.r-project.org/package=mixlm Description: CRAN Package 'mixlm' (Mixed Model ANOVA and Statistics for Education) The main functions perform mixed models analysis by least squares or REML by adding the function r() to formulas of lm() and glm(). 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Package: r-cran-mixmashnet Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgm, r-cran-igraph, r-cran-qgraph, r-cran-colorspace, r-cran-future.apply, r-cran-ggplot2, r-cran-eganet, r-cran-networktools, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-patchwork, r-cran-progressr Filename: pool/dists/noble/main/r-cran-mixmashnet_1.0.0-1.ca2404.1_all.deb Size: 3043362 MD5sum: bd17f6292186504378de0a13d6f1994b SHA1: 4df73c0d56295b611da7b4ac1c2f5da114196a98 SHA256: 06e551870dccb5ab14f2c56d2c7abf045916e31b9d6c1aaacad35ab8781ab431 SHA512: 9d429537cbf00bb4d4316c26eb8500c41acc8b149a26058665bf320301a713663f93a8e91e71a40edde415f1238e58f655812c3e0471c10bb7792671cd177028 Homepage: https://cran.r-project.org/package=MixMashNet Description: CRAN Package 'MixMashNet' (Tools for Multilayer and Single Layer Network Modeling) Estimation and bootstrap utilities for single layer and multilayer Mixed Graphical Models, including functions for centrality, bridge metrics, membership stability, and plotting (De Martino et al. (2026) ). Package: r-cran-mixmeta Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-metafor, r-cran-dosresmeta, r-cran-nlme, r-cran-mass, r-cran-dlnm Filename: pool/dists/noble/main/r-cran-mixmeta_1.2.2-1.ca2404.1_all.deb Size: 429510 MD5sum: f9515d204b1b8a2119a1a9e9b5b87c87 SHA1: 1f75ecbf1a0b890dd43d55baae51d81f5f5b5499 SHA256: b6605d4da7f350f7cd14d4143576caa8d14139a8cc1ebc27a9e3f43d03a7d5e3 SHA512: 2d520e0c5129a1ddaf8d0276d3fcc2272cdde1676a246482076996f6bf811958a8aab84c712f744b38602fe2503dc693658f1cd998af9b41d5fb04cf4dcaa153 Homepage: https://cran.r-project.org/package=mixmeta Description: CRAN Package 'mixmeta' (An Extended Mixed-Effects Framework for Meta-Analysis) A collection of functions to perform various meta-analytical models through a unified mixed-effects framework, including standard univariate fixed and random-effects meta-analysis and meta-regression, and non-standard extensions such as multivariate, multilevel, longitudinal, and dose-response models. Package: r-cran-mixoofa Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doofa, r-cran-crossdes, r-cran-mixexp, r-cran-combinat, r-cran-rsolnp Filename: pool/dists/noble/main/r-cran-mixoofa_1.0-1.ca2404.1_all.deb Size: 54524 MD5sum: 341ced65e3eace88f6df356252e2d34e SHA1: 6cab339f123bcf195f7f3448e30ed21d30a7c1ed SHA256: 87b3b856e915d8852e1d002acda5a1a0a7df4a3c169f96625d40321a9eec0cb3 SHA512: 4afc44d3f13d0cbe6dc5b25798333293c064fa2a66403993007cfe021643304ac545779ef91a9dd64ff6475176d2b7e39c4d36e88975ddf671f6977852c8d6a0 Homepage: https://cran.r-project.org/package=mixOofA Description: CRAN Package 'mixOofA' (Design and Analysis of Order-of-Addition Mixture Experiments) A facility to generate various classes of fractional designs for order-of-addition experiments namely fractional order-of-additions orthogonal arrays, see Voelkel, Joseph G. (2019). "The design of order-of-addition experiments." Journal of Quality Technology 51:3, 230-241, . Provides facility to construct component orthogonal arrays, see Jian-Feng Yang, Fasheng Sun and Hongquan Xu (2020). "A Component Position Model, Analysis and Design for Order-of-Addition Experiments." Technometrics, . Supports generation of fractional designs for order-of-addition mixture experiments. Analysis of data from order-of-addition mixture experiments is also supported. Package: r-cran-mixopt Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-splitfngr Suggests: r-cran-contourfunctions, r-cran-gridextra, r-cran-lhs, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixopt_0.1.3-1.ca2404.1_all.deb Size: 96306 MD5sum: 96cde0266826f98929576079fdb79824 SHA1: 3bdda1468dda6d1a6df048a3b3c0bd1a7467431c SHA256: 7fa7c106a836e93953e5015b727674802dfdc6fe0799c971232e0302c6c559a2 SHA512: cb5cfde56c16be66fcaa2a50841d1b291f047eacfa49e2f680d85ca7203e8aec4ad62393a4917fdbd6df53f495be278afb21c7835599f2e570a039c2b9778138 Homepage: https://cran.r-project.org/package=mixopt Description: CRAN Package 'mixopt' (Mixed Variable Optimization) Mixed variable optimization for non-linear functions. Can optimize function whose inputs are a combination of continuous, ordered, and unordered variables. Package: r-cran-mixoptim Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-patchwork, r-cran-desirability, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mixoptim_0.1.2-1.ca2404.1_all.deb Size: 93160 MD5sum: c238b53ee90cb59551def44faa010399 SHA1: d77ce1c4a914bb9e7dbd62b2d1a8c199dd0f94f8 SHA256: 86afd59e88defbea187d4804bdb7eafc4dfa7afcf39cec0fac57410f905c3699 SHA512: 84919298a1ff27e0ce63ff000466d1e080b41df62be8d54b1f49f580f78af059d1517dade15541709b659610797c9f43b465bc512a37d452b535d69a295d91e2 Homepage: https://cran.r-project.org/package=MixOptim Description: CRAN Package 'MixOptim' (Mixture Optimization Algorithm) Simple tools to perform mixture optimization based on the 'desirability' package by Max Kuhn. It also provides a plot routine using 'ggplot2' and 'patchwork'. Package: r-cran-mixphm Architecture: all Version: 0.7-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-lattice Filename: pool/dists/noble/main/r-cran-mixphm_0.7-2-1.ca2404.1_all.deb Size: 92898 MD5sum: 4bc4a70a52406303de9e431ff722b1ac SHA1: f209c1555b8a81a81f58d46ebc92af02f02f1133 SHA256: 45d1289514917e5748c525ffb1af32c4f1a16cfa38d3bb0b864074db9464cc75 SHA512: 06bcc30a4c367e17d7017732026f696c342f4d8abb9579e16ee1f503dd0175202964ce3b69f09ffc18faa92da2fbdaaac11950fb8f98ed2e65ff312ea37071df Homepage: https://cran.r-project.org/package=mixPHM Description: CRAN Package 'mixPHM' (Mixtures of Proportional Hazard Models) Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. 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Package: r-cran-mixpoissonreg Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4828 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbapply, r-cran-formula, r-cran-rfast, r-cran-dplyr, r-cran-gamlss.dist, r-cran-generics, r-cran-ggplot2, r-cran-gridextra, r-cran-lmtest, r-cran-magrittr, r-cran-statmod, r-cran-tibble, r-cran-rlang, r-cran-ggrepel, r-cran-gamlss Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-devtools, r-cran-bbreg, r-cran-testthat, r-cran-covr, r-cran-tidyr, r-cran-ggfortify, r-cran-broom Filename: pool/dists/noble/main/r-cran-mixpoissonreg_1.0.0-1.ca2404.1_all.deb Size: 3028562 MD5sum: d65551cb58fac21542251c0ed384a63d SHA1: 420934bb2d8993230e9c1b8f19f210ade36b7bc1 SHA256: e20b6f2297e8ce81dfb91d52c106503fe52d1290fb1ffdca310ffa1c8d1930d8 SHA512: bcce598b351a3f389aacf3a2bd5e92364a4274c1da0c96300859fd36c9b424c93e669703866f7afa2a2c44aee0eb5ac07333d5f684da8d1ea768700d07fa5398 Homepage: https://cran.r-project.org/package=mixpoissonreg Description: CRAN Package 'mixpoissonreg' (Mixed Poisson Regression for Overdispersed Count Data) Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. The models can have varying precision parameter, where a linear regression structure (through a link function) is assumed to hold on the precision parameter. The Expectation-Maximization algorithm for both these models (Poisson Inverse Gaussian and Negative Binomial) is an important contribution of this package. Another important feature of this package is the set of functions to perform global and local influence analysis. See Barreto-Souza and Simas (2016) for further details. Package: r-cran-mixpower Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-digest, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-mixpower_0.1.0-1.ca2404.1_all.deb Size: 201226 MD5sum: 2fcd2eab4803be64b5213f4f8374d9ea SHA1: ecda748f391c2d801e6d773ee350a93f373b7a20 SHA256: 696e6c78dcfba3be0356b0408e1c31229ffd9ce800d6f8768440480eb5046fa6 SHA512: 17a8e9ec2a8af79b7a49154b0fdcde3c0aeb0630054752e8d5e31f37ca514aeb1c0e30152d3309444006b3090cb914692b765a631926188c9280aa6ad7181e85 Homepage: https://cran.r-project.org/package=mixpower Description: CRAN Package 'mixpower' (Simulation-Based Power Analysis for Mixed-Effects Models) A comprehensive, simulation-based toolkit for power and sample-size analysis for linear and generalized linear mixed-effects models (LMMs and GLMMs). Supports Gaussian, binomial, Poisson, and negative binomial families via 'lme4'; Wald and likelihood-ratio tests; multi-parameter sensitivity grids; power curves and minimum sample-size solvers; parallel evaluation with deterministic seeds; and full reproducibility (manifests, result bundling, and export to CSV/JSON). Delivers thorough diagnostics per run (failure rate, singular-fit rate, effective N) and publication-ready summary tables. References: Bates et al. (2015) "Fitting Linear Mixed-Effects Models Using lme4" ; Green and MacLeod (2016) "SIMR: an R package for power analysis of generalized linear mixed models by simulation" . Package: r-cran-mixraschtools Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixraschtools_1.1.1-1.ca2404.1_all.deb Size: 216236 MD5sum: b38c5995bec3a10972212902d3427042 SHA1: b23508df604ce7c659681e1952c9a621aca7da23 SHA256: fbc4479d54753c17a8f6d1966694f007fdc6314de512b89716831a80c84b6036 SHA512: 0174e8ec4ca5998d35677b10852df36f973f183b5d14dc523f694d106fa03220fca1a0802bc3c228e08b74de15c6a90ada098c5d94915b051a3796898cbd1a4f Homepage: https://cran.r-project.org/package=mixRaschTools Description: CRAN Package 'mixRaschTools' (Plotting and Average Theta Functions for Multiple Class MixedRasch Models) Provides supplemental functions for the 'mixRasch' package (Willse, 2014), including a plotting function to compare item parameters for multiple class models and a function that provides average theta values for each class in a mixture model. Package: r-cran-mixrf Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-randomforest, r-cran-lme4, r-cran-foreach Filename: pool/dists/noble/main/r-cran-mixrf_1.0-1.ca2404.1_all.deb Size: 102068 MD5sum: b017f611309f966aeb952f20dcc0b129 SHA1: 83583fc3794792d16c6f505c446198019dd48c8a SHA256: 4c5d2c3de9df50b6537421f91daef8527c100ec6fc37564c66c14de276578b01 SHA512: 565cca3156e7615a7d181c11fbf372292c7ffdfb35032fc7adfb02d30340521dbdf92ee9811a57d9a5d33255efa3af58baf9e4210bccdc32ecc588e963f10345 Homepage: https://cran.r-project.org/package=MixRF Description: CRAN Package 'MixRF' (A Random-Forest-Based Approach for Imputing Clustered IncompleteData) It offers random-forest-based functions to impute clustered incomplete data. The package is tailored for but not limited to imputing multitissue expression data, in which a gene's expression is measured on the collected tissues of an individual but missing on the uncollected tissues. Package: r-cran-mixsal Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-mixsal_1.0-1.ca2404.1_all.deb Size: 54564 MD5sum: 00617ced87186614ac59056461c80fb3 SHA1: db1a9ab8821d24e66a08662bd93a78554a11f9cf SHA256: 693cfd563f7098b2fc828b7ca8dfdf38484e87b824686a045229703599cef989 SHA512: c556609738a01d34fc939d15481cbc5e425f5f2558abe62a3e4c77c6933c65f7e3ca0fb38b12a520892eac3fce492cc54470694720bbc609d217158687d6a3e0 Homepage: https://cran.r-project.org/package=MixSAL Description: CRAN Package 'MixSAL' (Mixtures of Multivariate Shifted Asymmetric Laplace (SAL)Distributions) The current version of the 'MixSAL' package allows users to generate data from a multivariate SAL distribution or a mixture of multivariate SAL distributions, evaluate the probability density function of a multivariate SAL distribution or a mixture of multivariate SAL distributions, and fit a mixture of multivariate SAL distributions using the Expectation-Maximization (EM) algorithm (see Franczak et. al, 2014, , for details). Package: r-cran-mixsemirob Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gofkernel, r-cran-mass, r-cran-mixtools, r-cran-mvtnorm, r-cran-rlab, r-cran-robustbase, r-cran-ucminf, r-cran-pracma, r-cran-quadprog Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mixsemirob_1.1.1-1.ca2404.1_all.deb Size: 493704 MD5sum: 838220e37777997fd04ca9d0389d3175 SHA1: d155fc88c0b669b3c7004f54f2d36cf8c94be015 SHA256: eca402f15a0d5101cdc676ab4eaebe7a6ae5108b61a1e31476952fd93b6bfe6f SHA512: dab3082c163e267c518f4546557bbe8a1812f6ae5818c27c53171b8cb39a51edaf27bc45481bcc1942c9e02d1806bff2ba498865c4f04d54fd37eba5e53191fa Homepage: https://cran.r-project.org/package=MixSemiRob Description: CRAN Package 'MixSemiRob' (Mixture Models: Parametric, Semiparametric, and Robust) Various functions are provided to estimate parametric mixture models (with Gaussian, t, Laplace, log-concave distributions, etc.) and non-parametric mixture models. The package performs hypothesis tests and addresses label switching issues in mixture models. The package also allows for parameter estimation in mixture of regressions, proportion-varying mixture of regressions, and robust mixture of regressions. Package: r-cran-mixsiar Architecture: all Version: 3.1.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1914 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-r2jags, r-cran-mass, r-cran-rcolorbrewer, r-cran-reshape, r-cran-reshape2, r-cran-lattice, r-cran-mcmcpack, r-cran-ggmcmc, r-cran-coda, r-cran-loo, r-cran-bayesplot, r-cran-splancs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixsiar_3.1.12-1.ca2404.1_all.deb Size: 1359154 MD5sum: 5572ec3936cd02bd7a18d5fa0226dc4d SHA1: dde896cb276be816fe6caefeee61803658f4f146 SHA256: fedef567e17750886558fc6107a80f19a083ba8fe22134ccab861a3e1dc3ec15 SHA512: 4a5156dab18015a68b37fddd7fd5ea65d5179583ec7b53cf12533e9f7c3f36aa5399d38d0c4d6c02bc8ace96d4c5740d7a8e6f0a28b7f2b08136dda0a00ddc15 Homepage: https://cran.r-project.org/package=MixSIAR Description: CRAN Package 'MixSIAR' (Bayesian Mixing Models in R) Creates and runs Bayesian mixing models to analyze biological tracer data (i.e. stable isotopes, fatty acids), which estimate the proportions of source (prey) contributions to a mixture (consumer). 'MixSIAR' is not one model, but a framework that allows a user to create a mixing model based on their data structure and research questions, via options for fixed/ random effects, source data types, priors, and error terms. 'MixSIAR' incorporates several years of advances since 'MixSIR' and 'SIAR'. Package: r-cran-mixsmsn Architecture: all Version: 1.1-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-mixsmsn_1.1-12-1.ca2404.1_all.deb Size: 211272 MD5sum: 15d55987ad51faf8d0022ee61d0517f6 SHA1: 4bebd9c31493e2d99b92073d8561938123261ba9 SHA256: a05c873e6c2a21ebe2311fb7e9cfab79ffc923f722c26069985f036cf91b0296 SHA512: 6e691eb1cbd616be6e81e35c90bdb4bbf4f7230e001b8a25f32e39f176969667c778812cd862487ea5c0f5b06c169d15f29dbb1c641b88f634d6566c7804d96b Homepage: https://cran.r-project.org/package=mixsmsn Description: CRAN Package 'mixsmsn' (Fitting Finite Mixture of Scale Mixture of Skew-NormalDistributions) Functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions, details in Prates, Lachos and Cabral (2013) , Cabral, Lachos and Prates (2012) and Basso, Lachos, Cabral and Ghosh (2010) . Package: r-cran-mixspe Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixspe_0.9.3-1.ca2404.1_all.deb Size: 105092 MD5sum: 3bb9db4501cdedaa8579e27ebda2d29b SHA1: 546441e826657cf309a5ffa3348fcd755398332d SHA256: 5978a82b965b8a32c16b973be09a7c848b4bcdf638e38370f49c5120309517db SHA512: ec37637e92924ced6ba58a12c01098854217504d47814618e7bb50c044d7cf94321872a33ba59a35f7f5b240cca3cc24b5ae1ddea67bc449fcec22b51b6c7077 Homepage: https://cran.r-project.org/package=mixSPE Description: CRAN Package 'mixSPE' (Mixtures of Power Exponential and Skew Power ExponentialDistributions for Use in Model-Based Clustering andClassification) Mixtures of skewed and elliptical distributions are implemented using mixtures of multivariate skew power exponential and power exponential distributions, respectively. A generalized expectation-maximization framework is used for parameter estimation. See citation() for how to cite. Package: r-cran-mixssg Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ars, r-cran-mass, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-mixssg_2.1.1-1.ca2404.1_all.deb Size: 86026 MD5sum: acc378abf8e4f17357c7a4be33fc31bc SHA1: 9caf6f6461fdb3c0cd8891cf20933266ae2b9e7b SHA256: 662e561d6b6734c679ba894d88a945ef2c831b06d8a05ca45745a206836ca1af SHA512: 6a49d80ae511d321c42de6f837473da6f32b640aa061df3d2b1a0dc422c1c4331f9b6929dae29701a79bbb4537bac42dd3767dea9cee45842b396649d2f0ef85 Homepage: https://cran.r-project.org/package=mixSSG Description: CRAN Package 'mixSSG' (Clustering Using Mixtures of Sub Gaussian Stable Distributions) Developed for model-based clustering using the finite mixtures of skewed sub-Gaussian stable distributions developed by Teimouri (2022) and estimating parameters of the symmetric stable distribution within the Bayesian framework. Package: r-cran-mixstable Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 607 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stabledist, r-cran-mixtools, r-cran-nortest, r-cran-openxlsx, r-cran-e1071, r-cran-jsonlite, r-cran-libstable4u, r-cran-mass Suggests: r-cran-ggplot2, r-cran-moments, r-cran-readxl, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mixstable_0.1.0-1.ca2404.1_all.deb Size: 449138 MD5sum: cbea8ace064a041ac350016e642bd403 SHA1: dc724850bd3ca930a44fca8b4137cd8a5d76a100 SHA256: f0ad72069c0a785ccee0af00100ec2cedfd61f39bb138ce700a1cf9f27feafe3 SHA512: d6cb1bc8c51b3bf387793e67191184358f5e21faced1e6f010e9ba1fafd7b3da181f0cef4bbfdb19f59700097158c06a7180182770799e26611f0349dded9c9c Homepage: https://cran.r-project.org/package=MixStable Description: CRAN Package 'MixStable' (Parameter Estimation for Stable Distributions and Their Mixtures) Provides various functions for parameter estimation of one-dimensional stable distributions and their mixtures. It implements a diverse set of estimation methods, including quantile-based approaches, regression methods based on the empirical characteristic function (empirical, kernel, and recursive), and maximum likelihood estimation. For mixture models, it provides stochastic expectation–maximization (SEM) algorithms and Bayesian estimation methods using sampling and importance sampling to overcome the long burn-in period of Markov Chain Monte Carlo (MCMC) strategies. The package also includes tools and statistical tests for analyzing whether a dataset follows a stable distribution. Some of the implemented methods are described in Hajjaji, O., Manou-Abi, S. M., and Slaoui, Y. (2024) . Package: r-cran-mixtox Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm Filename: pool/dists/noble/main/r-cran-mixtox_1.5.0-1.ca2404.1_all.deb Size: 599238 MD5sum: 6bd7572bad6b18fc7ed5b0b44f2632c5 SHA1: 7975a63708ab1fd3d70d5899bc0459fb9ee0a0be SHA256: 546a90e21a19c712ed6d2f527df62d3cd3e1b20dde5d46a217d7ebae1bebc35a SHA512: 37fad38dc696cd5f77381f992e8dfcc9e762eccfc2fecbc036f94e589e3191be79a60c29696c90a9466936c32b5260346128f560574a4e055dc84106d7e4c0d9 Homepage: https://cran.r-project.org/package=mixtox Description: CRAN Package 'mixtox' (Dose Response Curve Fitting and Mixture Toxicity Assessment) Curve Fitting of monotonic(sigmoidal) & non-monotonic(J-shaped) dose-response data. Predicting mixture toxicity based on reference models such as 'concentration addition', 'independent action', and 'generalized concentration addition'. Package: r-cran-mixtree Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-treespace, r-cran-vegan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mixtree_0.0.1-1.ca2404.1_all.deb Size: 128660 MD5sum: 0a6089dcbbc991ba5bb9627c814c4064 SHA1: d1b667844e3648148f93be785e3f7ab18631519a SHA256: 01ca5647948bd67fd7cc910dac08075a63bdad61c6f37be696b848ef58a16f54 SHA512: 23bdf571d7bcf90e29a7e902d0c8a765cb7e961cd12a73fe2a988b1715f19ca0178cd0bb8e5496842eb58975fdad0e496a4baaf23f82c6b637162f1f879b30ce Homepage: https://cran.r-project.org/package=mixtree Description: CRAN Package 'mixtree' (A Statistical Framework for Comparing Sets of Trees) Statistical framework for comparing sets of trees using hypothesis testing methods. Designed for transmission trees, phylogenetic trees, and directed acyclic graphs (DAGs), the package implements chi-squared tests to compare edge frequencies between sets and PERMANOVA to analyse topological dissimilarities with customisable distance metrics, following Anderson (2001) . Package: r-cran-mixtur Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mixtur_1.2.3-1.ca2404.1_all.deb Size: 469996 MD5sum: 695f51bb75a7d0502d703a6fad73420d SHA1: f61fa761a95f774de4380cdde4d3e75b31af9a3e SHA256: f725c9df5f0438e7b2e02fbd96111ccaa3bc11f0c13b8cc3ba1a10ca86212fd2 SHA512: 7d8d00adb7227815c4ec4fa06f1274557a7740088e5c8bf232f43a65e95b2524e6594140219d530bc7108338350a96256b9b418a1dbd1e5d1d0be5da828f35ea Homepage: https://cran.r-project.org/package=mixtur Description: CRAN Package 'mixtur' (Modelling Continuous Report Visual Short-Term Memory Studies) A set of utility functions for analysing and modelling data from continuous report short-term memory experiments using either the 2-component mixture model of Zhang and Luck (2008) or the 3-component mixture model of Bays et al. (2009) . Users are also able to simulate from these models. Package: r-cran-mixturemissing Architecture: all Version: 3.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-mnormt, r-cran-cluster, r-cran-mass, r-cran-numderiv, r-cran-bessel, r-cran-mclust, r-cran-mice Filename: pool/dists/noble/main/r-cran-mixturemissing_3.0.6-1.ca2404.1_all.deb Size: 282116 MD5sum: 71248820c89c33df704f83fa873fcc6c SHA1: f7f47a1ba0f27dfde8ea9a199500027404c7f2ed SHA256: 2bff09fb87ac3d4b2e28898b8b3ef2e9e6974ae544b3f4a06252753a75643de1 SHA512: c95b6e2d44fb41ca79e899374d73f991d452acde0061d006212c3d4bbb5365d56e689cd1c0fdcb489448be7abee1b421510c23856275dfeb43960aafa341305f Homepage: https://cran.r-project.org/package=MixtureMissing Description: CRAN Package 'MixtureMissing' (Robust and Flexible Model-Based Clustering for Data Sets withMissing Values at Random) Implementations of various robust and flexible model-based clustering methods for data sets with missing values at random (Tong and Tortora, 2025, ). Two main models are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, ) and Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, ). Mixtures via some special or limiting cases of the multivariate generalized hyperbolic distribution are also included: Normal-Inverse Gaussian, Symmetric Normal-Inverse Gaussian, Skew-Cauchy, Cauchy, Skew-t, Student's t, Normal, Symmetric Generalized Hyperbolic, Hyperbolic Univariate Marginals, Hyperbolic, and Symmetric Hyperbolic. Funding: This work was partially supported by the National Science foundation NSF Grant NO. 2209974. Package: r-cran-mixtwice Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-alabama, r-cran-ashr, r-cran-fdrtool, r-cran-iso Filename: pool/dists/noble/main/r-cran-mixtwice_2.0-1.ca2404.1_all.deb Size: 2173082 MD5sum: 1045b2f20ddd43aab552e6b011572c3f SHA1: d3733ebbfd57718e461563dfc767efd7208d1529 SHA256: c666c00030618670974629685b8dfe9e0602ab80915d05e6d7e7d4a77f589cd6 SHA512: e697d4b6fe97d8dabd27f81e1fc23e02a6ad46beff30f058e9e4acc6a4993541bb6445ac4c45ef4467cbabdf5fcff9ac1cf65010816abac489d74c9820fcb4c7 Homepage: https://cran.r-project.org/package=MixTwice Description: CRAN Package 'MixTwice' (Large-Scale Hypothesis Testing by Variance Mixing) Implements large-scale hypothesis testing by variance mixing. It takes two statistics per testing unit -- an estimated effect and its associated squared standard error -- and fits a nonparametric, shape-constrained mixture separately on two latent parameters. It reports local false discovery rates (lfdr) and local false sign rates (lfsr). Manuscript describing algorithm of MixTwice: Zheng et al(2021) . Package: r-cran-mixvir Architecture: all Version: 3.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4055 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-glue, r-cran-httr, r-cran-lubridate, r-cran-magrittr, r-cran-plotly, r-cran-readr, r-cran-shiny, r-cran-stringr, r-cran-tidyr, r-cran-vcfr Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-mixvir_3.5.0-1.ca2404.1_all.deb Size: 1784218 MD5sum: 932a97069f5237840447f6c0de567b2e SHA1: 6b59c554873fd042cae4ab3c44ce7a25b35139c1 SHA256: cf182bc36c8e0b0be9e693d7a6152432af38b85bfa5159852d15fde5bdad44b4 SHA512: 8f694f690007492b89ef3da310bf858fafe0b890457a212745a494f5a45ee9941e9b9935b611043a4dbb5f2dc767d0449021bf124e8d43876460f354f546a98a Homepage: https://cran.r-project.org/package=MixviR Description: CRAN Package 'MixviR' (Analysis and Exploration of Mixed Microbial Genomic Samples) Tool for exploring DNA and amino acid variation and inferring the presence of target lineages from microbial high-throughput genomic DNA samples that potentially contain mixtures of variants/lineages. MixviR was originally created to help analyze environmental SARS-CoV-2/Covid-19 samples from environmental sources such as wastewater or dust, but can be applied to any microbial group. Inputs include reference genome information in commonly-used file formats (fasta, bed) and one or more variant call format (VCF) files, which can be generated with programs such as Illumina's DRAGEN, the Genome Analysis Toolkit, or bcftools. See DePristo et al (2011) and Danecek et al (2021) for these tools, respectively. Available outputs include a table of mutations observed in the sample(s), estimates of proportions of target lineages in the sample(s), and an R Shiny dashboard to interactively explore the data. Package: r-cran-mize Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-mize_0.2.5-1.ca2404.1_all.deb Size: 400864 MD5sum: 8ec31cbf353f9bf4ae2718fefe75b2f8 SHA1: d0ec776c533686f0fcb2d379291390ab688c4d48 SHA256: 6417951cc89abc5d93a0d2e00563616f9192e8deeb53e6adfbf613badb7df16c SHA512: dc783f9a16b8b8ab6f5f670e7eb6e6cd03ffa910726eb33c14c499c4eb5ce3fb0d5cb96a21bab6d94f1556efe38b386ce284e60b13bc840a503e024b50df343a Homepage: https://cran.r-project.org/package=mize Description: CRAN Package 'mize' (Unconstrained Numerical Optimization Algorithms) Optimization algorithms implemented in R, including conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited memory BFGS (L-BFGS) methods. Most internal parameters can be set through the call interface. The solvers hold up quite well for higher-dimensional problems. Package: r-cran-mkbo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-tidyselect, r-cran-stringr, r-cran-rlang, r-cran-broom Filename: pool/dists/noble/main/r-cran-mkbo_0.1.0-1.ca2404.1_all.deb Size: 371922 MD5sum: 25643c0bf86354936dec77b0965df0e7 SHA1: 0e68fcbec137102dfa39ee8a4658ecef1198a1be SHA256: 709a635497320aedb32fef4866592aa3abef528f0a6ab43886a55a2cbf7a6bf7 SHA512: 9c2a08d08241f9b11c4990585e09167be207b9955317044033cffdb3e6cb2ef35d35a5b1afb848a76c55e782cf4e619153fb8bc12671f4545efffa4d330863b5 Homepage: https://cran.r-project.org/package=mKBO Description: CRAN Package 'mKBO' (Multi-Group Kitagawa-Blinder-Oaxaca Decomposition) Provides multigroup Kitagawa-Blinder-Oaxaca ('mKBO') decompositions, that allow for more than two groups. Each group is compared to the sample average. For more details see Thaning and Nieuwenhuis (2025) . Package: r-cran-mkclass Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-mkclass_0.5-1.ca2404.1_all.deb Size: 152642 MD5sum: 1dcc9b258c34408bdd142ea07a6bb5af SHA1: 1d50c2b4d3069181dc3282a95fc58c771bec20e3 SHA256: 1ce798677ebc4786dedf4d5c2431c70a2fe7ce411dd26e2de6daa2601495e217 SHA512: 614bd6870c54f30375453967881567ed9d30cb49d32ee2b92b4bcca398106e4ee34883cba4fb0e089992e17b9ce19399feb416a9d52885834846b0309464d8fb Homepage: https://cran.r-project.org/package=MKclass Description: CRAN Package 'MKclass' (Statistical Classification) Performance measures and scores for statistical classification such as accuracy, sensitivity, specificity, recall, similarity coefficients, AUC, GINI index, Brier score and many more. Calculation of optimal cut-offs and decision stumps (Iba and Langley (1991), ) for all implemented performance measures. Hosmer-Lemeshow goodness of fit tests (Lemeshow and Hosmer (1982), ; Hosmer et al (1997), ). Statistical and epidemiological risk measures such as relative risk, odds ratio, number needed to treat (Porta (2014), ). Package: r-cran-mkdescr Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 795 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mkdescr_0.9-1.ca2404.1_all.deb Size: 531310 MD5sum: ba5bb06a403f8957af1f36500877e12d SHA1: a6724eb52fd713157774c035c746b0e0fedc74bc SHA256: 690be8c967404d58da405249594f573c81fb3181743e9bf3dd22d059884137ad SHA512: 9335ab7c16714deb8e55a4d5e7c115aefe4c0efe40c6ccd1b8ffc2d5900d8a4fbebd164f0cfc65c2fde88e44a552d36c368893f9a544f5402c4394b2c4982552 Homepage: https://cran.r-project.org/package=MKdescr Description: CRAN Package 'MKdescr' (Descriptive Statistics) Computation of standardized interquartile range (IQR), Huber-type skipped mean (Hampel (1985), ), robust coefficient of variation (CV) (Arachchige et al. (2019), ), robust signal to noise ratio (SNR), z-score, standardized mean difference (SMD), as well as functions that support graphical visualization such as boxplots based on quartiles (not hinges), negative logarithms and generalized logarithms for 'ggplot2' (Wickham (2016), ISBN:978-3-319-24277-4). Package: r-cran-mkendall Architecture: all Version: 1.5-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mkendall_1.5-4-1.ca2404.1_all.deb Size: 23284 MD5sum: 3c26b2ee7b328daca7bfec312344fe2b SHA1: 3630f754a0bad46a2ad015cc5febaaeb07112d7c SHA256: 8a5697c9db260b9e6277c7318c7f78919a83a20e16c9ba3dc716e46dabb9cdab SHA512: 263e86b7467d4051e370c463e6c207fbf38e21dd2377e7cf8a00691ec326daa79e09d8c29f2c12728abc86064c7ec6a46cc9f39878d61c7c8187daf9706b3aaa Homepage: https://cran.r-project.org/package=MKendall Description: CRAN Package 'MKendall' (Matrix Kendall's Tau and Matrix Elliptical Factor Model) Large-scale matrix-variate data have been widely observed nowadays in various research areas such as finance, signal processing and medical imaging. Modelling matrix-valued data by matrix-elliptical family not only provides a flexible way to handle heavy-tail property and tail dependencies, but also maintains the intrinsic row and column structure of random matrices. We proposed a new tool named matrix Kendall's tau which is efficient for analyzing random elliptical matrices. By applying this new type of Kendell’s tau to the matrix elliptical factor model, we propose a Matrix-type Robust Two-Step (MRTS) method to estimate the loading and factor spaces. See the details in He at al. (2022) . In this package, we provide the algorithms for calculating sample matrix Kendall's tau, the MRTS method and the Matrix Kendall's tau Eigenvalue-Ratio (MKER) method which is used for determining the number of factors. Package: r-cran-mkin Architecture: all Version: 1.2.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3915 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-r6, r-cran-inline, r-cran-numderiv, r-cran-lmtest, r-cran-pkgbuild, r-cran-nlme, r-cran-saemix, r-cran-rlang, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rbenchmark, r-cran-tikzdevice, r-cran-testthat, r-cran-rmarkdown, r-cran-covr, r-cran-vdiffr, r-cran-benchmarkme, r-cran-tibble, r-cran-readxl Filename: pool/dists/noble/main/r-cran-mkin_1.2.10-1.ca2404.1_all.deb Size: 2815812 MD5sum: d5f840d8a289e3cd2b2edbf2e5cc0b0f SHA1: 6b57bfda3c64b6bff8812d8319a54ceff3fad9f9 SHA256: 7874d65574358a3e4d48c8328bac11a6cb1f45681a95de12930b0e5a982e76c8 SHA512: 1b5eee78da34059a7e80d9a2ceba404d57d1e8ece0862de6a3b90c9b69aa6896ba96752c20e78cf02bd1c3cf20a1e139853d54bbc728fcfa109554b8b7953665 Homepage: https://cran.r-project.org/package=mkin Description: CRAN Package 'mkin' (Kinetic Evaluation of Chemical Degradation Data) Calculation routines based on the FOCUS Kinetics Report (2006, 2014). Includes a function for conveniently defining differential equation models, model solution based on eigenvalues if possible or using numerical solvers. If a C compiler (on windows: 'Rtools') is installed, differential equation models are solved using automatically generated C functions. Non-constant errors can be taken into account using variance by variable or two-component error models . Hierarchical degradation models can be fitted using nonlinear mixed-effects model packages as a back end . Please note that no warranty is implied for correctness of results or fitness for a particular purpose. Package: r-cran-mkinfer Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1791 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-mkdescr, r-cran-boot, r-cran-arrangements, r-cran-nlme, r-cran-ggplot2, r-cran-exactranktests, r-cran-miceadds Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-amelia, r-cran-mice, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-mkinfer_1.3-1.ca2404.1_all.deb Size: 1283968 MD5sum: 731ba958f0d55dcf9c59205cd7380f36 SHA1: 00c8a948a51d3c61daf98ad6424edce59be2e7d4 SHA256: efda5158df334aa0ef2d545c91a91d5647956b78399684ed9a39a38045367b65 SHA512: 307be8b3fbadb3c1ac723d63446292eaebe300d9ad86c90e2e4376280686c1a8559419028693f311da286390565fcd448d442fb7d9e3a7ba7e28ce773ce581a6 Homepage: https://cran.r-project.org/package=MKinfer Description: CRAN Package 'MKinfer' (Inferential Statistics) Computation of various confidence intervals (Altman et al. (2000), ISBN:978-0-727-91375-3; Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) including bootstrapped versions (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) as well as Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2), permutation (Janssen (1997), ), bootstrap (Davison and Hinkley (1997), ISBN:978-0-511-80284-3), intersection-union (Sozu et al. (2015), ISBN:978-3-319-22005-5) and multiple imputation (Barnard and Rubin (1999), ) t-test; furthermore, computation of intersection-union z-test as well as multiple imputation Wilcoxon tests. Graphical visualizations: volcano plot, Bland-Altman plots (Bland and Altman (1986), ; Shieh (2018), ), mean difference plot (Boehning et al. (2008), ), plot of test statistic for permutation and bootstrap tests as well as objects of class htest. Package: r-cran-mkle Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mkle_1.0.1-1.ca2404.1_all.deb Size: 31786 MD5sum: 26957b297d77e356c207559348b56818 SHA1: 9c04baf8bfad52125e87b8950b30e971ef31c57b SHA256: 607cbd770e669d0b821c099aac1846e338908f6f4630e1fc8a4a9cc7a2cf11ca SHA512: 132c79dc6dada5d32745e5839b926e82e27a33df9967023200f9471d81e94e5f99da81d76195d1be40aacc703fec5277788c173e44cfdc469679d79cf9d4fbd8 Homepage: https://cran.r-project.org/package=MKLE Description: CRAN Package 'MKLE' (Maximum Kernel Likelihood Estimation) Package for fast computation of the maximum kernel likelihood estimator (mkle). Package: r-cran-mkmeans Architecture: all Version: 3.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-gtools Filename: pool/dists/noble/main/r-cran-mkmeans_3.4.4-1.ca2404.1_all.deb Size: 34032 MD5sum: 937a9c244d001392eadf397db6a80ecb SHA1: 3b3e11ba93f4205257de8d5dd65f765ab0de16ac SHA256: 3d0a6dac690000ff0b1dddfb68e206c8d02e306bcad6c723fbda61f90770a8ba SHA512: 406ec18dd9b8e472d05c49074ca869af156ac170a71e86c3f84c661d2f4b9e247c0b87f43adc14bfdc77f2468c12a96ef6c17f1b96bbfb4be584267524d534ba Homepage: https://cran.r-project.org/package=MKMeans Description: CRAN Package 'MKMeans' (A Modern K-Means (MKMeans) Clustering Algorithm) It's a Modern K-Means clustering algorithm which works for data of any number of dimensions, has no limit with the number of clusters expected, offers both methods with and without initial cluster centers, and can start with any initial cluster centers for the method with initial cluster centers. 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Given a set of items, each with volume and value, it will allocate them to knapsacks of a given size in a way that value of top N knapsacks is as large as possible. Package: r-cran-mkomics Architecture: all Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 700 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-robustbase, r-bioc-limma, r-cran-circlize, r-bioc-complexheatmap Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mkomics_0.8-1.ca2404.1_all.deb Size: 480886 MD5sum: 1fbf090a60e0954dbe5d95b6550b91db SHA1: 5c97fc7f5e6795706cc586918d17c31291ecb7b9 SHA256: e7d98c6820cbb23c3576e2b2600774149caef2b58c34831a3aca8311357d115d SHA512: 7743378bc555b6dc52d8876c5ce11d5596f77cf0c6cdd46d754b9bee35cf84a24ad9277b2e9307918981f0735007f16ed6fd8db82cfd48e2d0635438f2c89591 Homepage: https://cran.r-project.org/package=MKomics Description: CRAN Package 'MKomics' (Omics Data Analysis) Similarity plots based on correlation and median absolute deviation (MAD); adjusting colors for heatmaps; aggregate technical replicates; calculate pairwise fold-changes and log fold-changes; compute one- and two-way ANOVA; simplified interface to package 'limma' (Ritchie et al. 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Package: r-cran-mkpower Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixtests, r-cran-ggplot2, r-cran-mkdescr, r-cran-mkinfer, r-cran-qqplotr, r-cran-coin, r-cran-mvtnorm, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-hexbin Filename: pool/dists/noble/main/r-cran-mkpower_1.1-1.ca2404.1_all.deb Size: 497088 MD5sum: 5349a52683399509acc44d180c503675 SHA1: 1891a8ef747bae51bd4e2d3542c0f7ddea48465d SHA256: fca94b3adec1aa9f6d772667051ec7f72eef7bc61980201eb94f1dce6aab24dd SHA512: 6c582cd9aea88d9b3aa0a9f98e355317b59a1f18531a95ab68078ebed5c3c904f807639b74d0edc8e2df2d490e13823d2e982e5e6ed5415fdb3bdc50fe5f62c1 Homepage: https://cran.r-project.org/package=MKpower Description: CRAN Package 'MKpower' (Power Analysis and Sample Size Calculation) Power analysis and sample size calculation for Welch and Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations of empirical power and type-I-error. Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005), ; Dobbin and Simon (2007), ) as well as for a single proportion (Fleiss et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004), ; Thulin (2014), ), comparing two negative binomial rates (Zhu and Lakkis (2014), ), ANCOVA (Shieh (2020), ), reference ranges (Jennen-Steinmetz and Wellek (2005), ), multiple primary endpoints (Sozu et al. (2015), ISBN:978-3-319-22005-5), and AUC (Hanley and McNeil (1982), ). Package: r-cran-mkssd Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mkssd_1.2-1.ca2404.1_all.deb Size: 22280 MD5sum: 0de925c6ce68b17cc380cc4889728eea SHA1: b0d12e38a9f5a9dcb942812993c7be49131a4a22 SHA256: 058546260e9c7033648a8985e545db96c2ee7a0bc39e14bc48601d97145872ad SHA512: adf22ef539217614c63e12a5510289b6dd3e301ee933de886cbc32cd132dfbcccf7f2600b99ae687febb3fdf68c7f0428035c17a4bb60936daffa696531deaa5 Homepage: https://cran.r-project.org/package=mkssd Description: CRAN Package 'mkssd' (Efficient Multi-Level k-Circulant Supersaturated Designs) Generates efficient balanced non-aliased multi-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has chisquare efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient multi-level k-circulant design through a progress bar. The progress of 100% means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs. Package: r-cran-ml.msbd Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-ml.msbd_1.2.1-1.ca2404.1_all.deb Size: 283204 MD5sum: e035080a7661294233f36b0d7f1c4f0a SHA1: 965c09ff1a3b14a74283c8623b34af8f01808c6d SHA256: db88506c9c52a14d4982aec1e163f1964cfa6bf2904c98ff1f2727b751f4b9df SHA512: 715a9c8fb6319b772710994472b46f552f5f6c9bcc4961be10da1fbf305164c3aefe624a4437e21e9917f18b4a0b1615ef5aa0d409ab996cb99bc8c55d8114e3 Homepage: https://cran.r-project.org/package=ML.MSBD Description: CRAN Package 'ML.MSBD' (Maximum Likelihood Inference on Multi-State Trees) Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani, T. G. Vaughan and T. Stadler (2018) . Package: r-cran-ml2pvae Architecture: all Version: 1.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-keras, r-cran-reticulate, r-cran-tensorflow, r-cran-tfprobability Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-ml2pvae_1.0.0.1-1.ca2404.1_all.deb Size: 269858 MD5sum: d3d20fac732aaa4b2f8319cff300d146 SHA1: 5be6e3a2d2d333124efdca360d8eeeb24b97a27d SHA256: 6efd5e8df2a61dba9f7ace73e04ee2804c8143d471b7a18b11a79ec3b4379526 SHA512: 6b7f384d317a115b16a09bf41e2efddd49dcad4eefcfe0852fe5a74a8d578d3458f662740e033e944e3a7b274e07bc2704faf30ef106217d63fe0f36c9a09199 Homepage: https://cran.r-project.org/package=ML2Pvae Description: CRAN Package 'ML2Pvae' (Variational Autoencoder Models for IRT Parameter Estimation) Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) . This package provides easy-to-use functions which create a variational autoencoder (VAE) to be used for parameter estimation in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter (ML2P) model. To use a neural network as such, nontrivial modifications to the architecture must be made, such as restricting the nonzero weights in the decoder according to some binary matrix Q. The functions in this package allow for straight-forward construction, training, and evaluation so that minimal knowledge of 'tensorflow' or 'keras' is required. Package: r-cran-mlapi Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-matrix Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-mlapi_0.1.1-1.ca2404.1_all.deb Size: 222120 MD5sum: e82d6dba31f4ae54bc94051069f4d23f SHA1: e780ff89ef3e7a0ed8bf86ee1b0605f169e80fbe SHA256: 06b26ca21103625c9e4707ed1a6106607de77a428285d53497b6a9bb4b4cc801 SHA512: 768b8a20e631e4a12a2938b1193b7a175ec7e14427d63b35bf3b419478ec992d965815c48b24fbc41c8fb24893c9202176cd602aebd8e24b8bb1507bc9e1a9a6 Homepage: https://cran.r-project.org/package=mlapi Description: CRAN Package 'mlapi' (Abstract Classes for Building 'scikit-learn' Like API) Provides 'R6' abstract classes for building machine learning models with 'scikit-learn' like API. is a popular module for 'Python' programming language which design became de facto a standard in industry for machine learning tasks. 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Package: r-cran-mlcm Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mlcm_0.4.4-1.ca2404.1_all.deb Size: 114616 MD5sum: cce5579b5941e5796eb60b82a17ac75b SHA1: d672a93eb36e7d3b78018bfdcb47c2c7d8ddbfd0 SHA256: bb424248c66c40a0a07e3cf98954f575e20d4a48279ebc566e5dd4fe294a5ce6 SHA512: c1afcd051741e3cb65edc1d551058b318f87c3ce684ed42202329bd2395dba11d7b305e33d561534cf595398fe9e59c00194d465957f9a294f4d9da44b15e2aa Homepage: https://cran.r-project.org/package=MLCM Description: CRAN Package 'MLCM' (Maximum Likelihood Conjoint Measurement) Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them. This package contains functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact. Reference: Knoblauch & Maloney (2012) "Modeling Psychophysical Data in R". . Package: r-cran-mlcopula Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-igraph, r-cran-kde1d, r-cran-pracma, r-cran-tsp, r-cran-gridcopula Filename: pool/dists/noble/main/r-cran-mlcopula_1.1.0-1.ca2404.1_all.deb Size: 87594 MD5sum: 68ea6357ef00cfed7c041626c68fcc80 SHA1: d0d74b34815cae5c5db97512f1de38d9d6aac294 SHA256: f62d025d2a030700ce3e06f61702ed50d14602d67cef596bbcf0532186a75c59 SHA512: c6f319c982600ea1476f8e93ed081f497ce4be133562dcbe5e0477eb7a1d7e98aa609febd018a29cb0156b224552330ff43011ee26d93918c73e9397a54843fa Homepage: https://cran.r-project.org/package=MLCOPULA Description: CRAN Package 'MLCOPULA' (Classification Models with Copula Functions) Provides several classifiers based on probabilistic models. 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Package: r-cran-mldr.resampling Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-e1071, r-cran-mldr, r-cran-pbapply, r-cran-vecsets Filename: pool/dists/noble/main/r-cran-mldr.resampling_0.2.3-1.ca2404.1_all.deb Size: 134886 MD5sum: cdefdc378f739046595ea857e8fd088c SHA1: c6cd3d7d8881b82114c5b715f0a64902a7eb69ce SHA256: b8c6be3bc06d4ea567fa203de3e9be731bbe4e9e859afba15cff227365bfaefd SHA512: edee627df3d3a8a1b6604a54f204a1b049bd03e7b6dc1a13023f6c5824561a96364b3388b62f8b6123b70323829a79d885d0442ce0c3254b997c0b442652a6a0 Homepage: https://cran.r-project.org/package=mldr.resampling Description: CRAN Package 'mldr.resampling' (Resampling Algorithms for Multi-Label Datasets) Collection of the state of the art multi-label resampling algorithms. The objective of these algorithms is to achieve balance in multi-label datasets. 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Package: r-cran-mlds Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 766 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-mlds_0.5.1-1.ca2404.1_all.deb Size: 549338 MD5sum: 16f548ca4452e8e0cc6110a37f5c91b3 SHA1: 430a992545ac67f02038ee435ac59e9eeee32120 SHA256: f1317bac7ef9ccc172aa158e1385325b4c686fc38dde21d486a8c6d747488001 SHA512: f7c96e79932c3f24608e4ebc40160c366b49aac69cea0b4d4d05febe1cfe349c48a0399eb0ab23352a716cb5d27adf0fb530e534640e90402e60df99f13334da Homepage: https://cran.r-project.org/package=MLDS Description: CRAN Package 'MLDS' (Maximum Likelihood Difference Scaling) Difference scaling is a method for scaling perceived supra-threshold differences. The package contains functions that allow the user to design and run a difference scaling experiment, to fit the resulting data by maximum likelihood and test the internal validity of the estimated scale. Package: r-cran-mle.tools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-fitdistrplus Filename: pool/dists/noble/main/r-cran-mle.tools_1.0.0-1.ca2404.1_all.deb Size: 35600 MD5sum: 03c524d51c4848b2696fd488bb825678 SHA1: 331a6b94a08efb1ea8e9918a2709cc25a2bfb800 SHA256: 9271b386c0d14f7e194a4550eb7de976a4051a95f0721c2ff920a85b0326418a SHA512: 4ac0f1ba767361b9804ecf65f8d2ffb61667f37ba470049f73d692addbeadbedd1d872d65a5a7cdf6ee18f3dde7ee8d6e80ce246b922700af22126f4b52884fa Homepage: https://cran.r-project.org/package=mle.tools Description: CRAN Package 'mle.tools' (Expected/Observed Fisher Information and Bias-Corrected MaximumLikelihood Estimate(s)) Calculates the expected/observed Fisher information and the bias-corrected maximum likelihood estimate(s) via Cox-Snell Methodology. Package: r-cran-mle Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bgfd, r-cran-bivpois, r-cran-ccd, r-cran-compoissonreg, r-cran-compositional, r-cran-directional, r-cran-foldedt, r-cran-geppe, r-cran-gp, r-cran-mn, r-cran-rfast, r-cran-rfast2 Suggests: r-cran-mvcauchy, r-cran-rangen Filename: pool/dists/noble/main/r-cran-mle_1.7-1.ca2404.1_all.deb Size: 184676 MD5sum: 60297dc69a5ca5512da9c828e5c53460 SHA1: 51257f6ed5829093997f4113a734a9c1b9486107 SHA256: db0ef3275108aec5f760ef991b57f63077daa6e92381e0a572d50d52fc9760e1 SHA512: 338db54c45942a255de42a9bd98b671f90a0e5f057a2c9d5c572271ef56d4285bc48a3e1f99740476bcc39a6e5298e5d7c4f45e65afbbacb50c6e3c1cba8ef8a Homepage: https://cran.r-project.org/package=MLE Description: CRAN Package 'MLE' (Maximum Likelihood Estimation of Various Univariate andMultivariate Distributions) Several functions for maximum likelihood estimation of various univariate and multivariate distributions. The list includes more than 100 functions for univariate continuous and discrete distributions, distributions that lie on the real line, the positive line, interval restricted, circular distributions. Further, multivariate continuous and discrete distributions, distributions for compositional and directional data, etc. Some references include Johnson N. L., Kotz S. and Balakrishnan N. (1994). "Continuous Univariate Distributions, Volume 1" , Johnson, Norman L. Kemp, Adrianne W. Kotz, Samuel (2005). "Univariate Discrete Distributions". and Mardia, K. V. and Jupp, P. E. (2000). "Directional Statistics". . Package: r-cran-mlearning Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-class, r-cran-nnet, r-cran-mass, r-cran-e1071, r-cran-randomforest, r-cran-ipred, r-cran-rpart Suggests: r-cran-mlbench, r-cran-rcolorbrewer, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-mlearning_1.2.1-1.ca2404.1_all.deb Size: 231286 MD5sum: 01a19a1770e7794e0fc937cc435bc586 SHA1: af3d4306a227c73afd0a0d7aa7cfa5499bfdac9b SHA256: fdc2653f26612f46a20af3479a2aa2ffa58801f75d59af37cfd1b9238698b31b SHA512: e49a4986a279dbd5c28e7f55e935d4cf93b9a214682cbe26e3ab25e755867921ef31f1ed99c9ba94badc5181280a32478cd80c9416d87d90779c3d498c29fadd Homepage: https://cran.r-project.org/package=mlearning Description: CRAN Package 'mlearning' (Machine Learning Algorithms with Unified Interface and ConfusionMatrices) A unified interface is provided to various machine learning algorithms like linear or quadratic discriminant analysis, k-nearest neighbors, random forest, support vector machine, ... It allows to train, test, and apply cross-validation using similar functions and function arguments with a minimalist and clean, formula-based interface. Missing data are processed the same way as base and stats R functions for all algorithms, both in training and testing. Confusion matrices are also provided with a rich set of metrics calculated and a few specific plots. Package: r-cran-mlece Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nleqslv, r-cran-laplacesdemon, r-cran-sirt, r-cran-ggplot2, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-mlece_2.1.0-1.ca2404.1_all.deb Size: 110760 MD5sum: 52ff03ca0dedd853271284a4e9b451a6 SHA1: f64bfc5d8a624361edda9483a385c4c038a08542 SHA256: 7ebea1a5a5e9c316d919a6f68741774c04dbb0f0f8028ff6944e5e37e7038fc8 SHA512: 1ef0e3d6f737cc6c606f4297f93f31a6250287850526b221f3eaea7650a9ef7e0f624c31e42e0e7e068452296610add3652e56306b961e573c8c415434694a1b Homepage: https://cran.r-project.org/package=MLEce Description: CRAN Package 'MLEce' (Asymptotic Efficient Closed-Form Estimators for MultivariateDistributions) Asymptotic efficient closed-form estimators (MLEces) are provided in this package for three multivariate distributions(gamma, Weibull and Dirichlet) whose maximum likelihood estimators (MLEs) are not in closed forms. Closed-form estimators are strong consistent, and have the similar asymptotic normal distribution like MLEs. But the calculation of MLEces are much faster than the corresponding MLEs. Further details and explanations of MLEces can be found in. Jang, et al. (2023) . Kim, et al. (2023) . Package: r-cran-mlelod Architecture: all Version: 1.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mlelod_1.0.0.1-1.ca2404.1_all.deb Size: 15450 MD5sum: 99fed65a6d227f1b313d9ea03daefd42 SHA1: 3a4ebb7f57bb49f4848d06c8f47a03da47455d2c SHA256: 4cbbab8fa0ed682f0bb17f8c45587c184bb17a0a5d03eab0c63ba69deaecd2c6 SHA512: d86a966e85b39805bcbc0be8a402ef680791b3f69109f6726af0c45ea872abeb5875d85ad54fcec24cbec4a5be925ffd89e2dcf7e1215b04375074743da96cc0 Homepage: https://cran.r-project.org/package=mlelod Description: CRAN Package 'mlelod' (MLE for Normally Distributed Data Censored by Limit of Detection) Values below the limit of detection (LOD) are a problem in several fields of science, and there are numerous approaches for replacing the missing data. We present a new mathematical solution for maximum likelihood estimation that allows us to estimate the true values of the mean and standard deviation for normal distributions and is significantly faster than previous implementations. The article with the details was submitted to JSS and can be currently seen on . Package: r-cran-mlergm Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1307 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ergm, r-cran-network, r-cran-matrix, r-cran-stringr, r-cran-ggally, r-cran-ggplot2, r-cran-cowplot, r-cran-reshape2, r-cran-plyr, r-cran-lpsolve, r-cran-sna, r-cran-statnet.common Suggests: r-cran-rcolorbrewer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mlergm_0.8.1-1.ca2404.1_all.deb Size: 943308 MD5sum: 82cdcd565a6e7c7c9010d96169e36299 SHA1: 20ac29fefc6ba1a5431b3e1d47f6978c72d15a85 SHA256: f4f3239221c69a0027a67fe3af981867a3010f43a2ff00eb67b7b67051616903 SHA512: 4408dac6ee4c94207f90bad1b45de62aed1c7dcba82c1b115c39e35ca6c22f35f57fcde78604b554803689df949b6439d1c357b47fa3a42ef96f7c31d142126a Homepage: https://cran.r-project.org/package=mlergm Description: CRAN Package 'mlergm' (Multilevel Exponential-Family Random Graph Models) Estimates exponential-family random graph models for multilevel network data, assuming the multilevel structure is observed. The scope, at present, covers multilevel models where the set of nodes is nested within known blocks. The estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. MCMLE methods for curved exponential-family random graph models can be found in Hunter and Handcock (JCGS, 2006). The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks. Package: r-cran-mleval Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mleval_0.3-1.ca2404.1_all.deb Size: 409454 MD5sum: 13f1a76af6bdcabc38601152064dd44f SHA1: cfeffeae1495a45bd45d8b312272e5f32fa10872 SHA256: 5d3e88354447401269b72116ec9a704b50d7f703fd09fad4439d72a293664299 SHA512: cbce25ab54cff39f73bef996220534442bec8c56d74e97b8357329b89f1294f600ec95adc792221ecd944313f402cd13da1f72c01dd744fefbb65b62e0b49ac6 Homepage: https://cran.r-project.org/package=MLeval Description: CRAN Package 'MLeval' (Machine Learning Model Evaluation) Straightforward and detailed evaluation of machine learning models. 'MLeval' can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. 'MLeval' accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. 'MLeval' produces a range of evaluation metrics with confidence intervals. Package: r-cran-mlexperiments Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-kdry, r-cran-progress, r-cran-r6, r-cran-splittools Suggests: r-cran-class, r-cran-lintr, r-cran-measures, r-cran-mlbench, r-cran-quarto, r-cran-rbayesianoptimization, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlexperiments_1.0.0-1.ca2404.1_all.deb Size: 591454 MD5sum: 9e6c92c959a3134a5e6aa4d267692cdc SHA1: d0677164fac18fece7363a33b6b2dc3d6a631501 SHA256: 45cc3089062d448e30171673570e23427c6d2126c94e04c203e9722d79077201 SHA512: d0d735dc834c0a14d80da1206ed496db8729423821fb0b58330b09a192179c04136e3b8e327cb6c363923872ecf80ebc21689a6504b8591a589ac886d907699b Homepage: https://cran.r-project.org/package=mlexperiments Description: CRAN Package 'mlexperiments' (Machine Learning Experiments) Provides 'R6' objects to perform parallelized hyperparameter optimization and cross-validation. Hyperparameter optimization can be performed with Bayesian optimization (via 'rBayesianOptimization' ) and grid search. The optimized hyperparameters can be validated using k-fold cross-validation. Alternatively, hyperparameter optimization and validation can be performed with nested cross-validation. While 'mlexperiments' focuses on core wrappers for machine learning experiments, additional learner algorithms can be supplemented by inheriting from the provided learner base class. Package: r-cran-mlf Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mlf_1.2.1-1.ca2404.1_all.deb Size: 47930 MD5sum: 120e17a0b28dd914ca96ae64f4e0e9d2 SHA1: b1f245f37fa6009628b3788dfa0d4e97edd2f2f7 SHA256: ea459d8e257b9af5d7ec5e4cd2bcdc497ee4c79b7f7dc9fc53e3ab93ca5f418c SHA512: 2372d913fa40916900c21e463d2ad84fa6070351666c950490e943a97c734e1db3aead685bb2e1c1d5356823adffbf898570fe751e6c8d17337f157aa4261b7c Homepage: https://cran.r-project.org/package=mlf Description: CRAN Package 'mlf' (Machine Learning Foundations) Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). . Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). . Package: r-cran-mlfdr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nmof Filename: pool/dists/noble/main/r-cran-mlfdr_0.1.0-1.ca2404.1_all.deb Size: 35880 MD5sum: e5b67a5651c3ee8fbdc5cc20ee638ebe SHA1: 0ba600015ff7140ff81b30e39d2a7dcc7c57fa4c SHA256: e77df96124c077d4101862c9187bf9587a8c8e80334b4ed67fe6a015385f887d SHA512: 62adf8279a3c44999be204e36ca45af80ea28756cd524d7a0a83e2344da8f58b91b05878aa3ca5413f262cebb268d7fb9dffc1d56e452fa2027700d935c85667 Homepage: https://cran.r-project.org/package=MLFDR Description: CRAN Package 'MLFDR' (High Dimensional Mediation Analysis using Local False DiscoveryRates) Implements a high dimensional mediation analysis algorithm using Local False Discovery Rates. 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This package offers implementations for several algorithms that extend this to nested structures: 'parent' and 'child' items for both of which constraints can be provided. The fitting algorithms include Iterative Proportional Updating , Hierarchical IPF , Entropy Optimization , and Generalized Raking . Additionally, a number of replication methods is also provided such as 'Truncate, replicate, sample' . Package: r-cran-mlflow Architecture: all Version: 3.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-fs, r-cran-git2r, r-cran-glue, r-cran-httpuv, r-cran-httr, r-cran-ini, r-cran-jsonlite, r-cran-openssl, r-cran-processx, r-cran-purrr, r-cran-rlang, r-cran-swagger, r-cran-tibble, r-cran-withr, r-cran-yaml, r-cran-zeallot Suggests: r-cran-carrier, r-cran-covr, r-cran-h2o, r-cran-keras, r-cran-lintr, r-cran-sparklyr, r-cran-stringi, r-cran-testthat, r-cran-reticulate, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlflow_3.10.1-1.ca2404.1_all.deb Size: 239938 MD5sum: 860524a6af15e7ae2bfb8af52e18bafe SHA1: 15d204e36d6aacdbbeb37a93d246f924d9ec1bc0 SHA256: e0a38ebd1be4f29e618198af305875e52420e5da0853a00f9242c8b6bcf8d0d9 SHA512: 2bb429a8a7e74971a07dc0a38a26a43e825f0bd72b0af18b878cf2c8305d44cef2d58065628ddccf867dfd080ac9ea2e9d42553b3e2848e390bf84860aaadefb Homepage: https://cran.r-project.org/package=mlflow Description: CRAN Package 'mlflow' (Interface to 'MLflow') R interface to 'MLflow', open source platform for the complete machine learning life cycle, see . This package supports installing 'MLflow', tracking experiments, creating and running projects, and saving and serving models. Package: r-cran-mlfs Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2273 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brnn, r-cran-ranger, r-cran-reshape2, r-cran-pscl, r-cran-naivebayes, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect Filename: pool/dists/noble/main/r-cran-mlfs_0.4.3-1.ca2404.1_all.deb Size: 2028674 MD5sum: 76f8684e64ae987e003196d3e8f0733b SHA1: c9f56a30af64b6dca6a6d982b7ed86088a4896a7 SHA256: 4c98e2e54c39832bc68e4dd00fb8ceed855fb1f5bf799eaf34df12cc4ed06465 SHA512: df0494011d07714247453d4fe3db1947085d1052f5dbe0eba7af983e56e7c52f400e2f9376e2200145646dfd271d132b79d63f2b1a5a5c7674624c845d22037a Homepage: https://cran.r-project.org/package=MLFS Description: CRAN Package 'MLFS' (Machine Learning Forest Simulator) Climate-sensitive, single-tree forest simulator based on data-driven machine learning. It simulates the main forest processes— radial growth, height growth, mortality, crown recession, regeneration, and harvesting—so users can assess stand development under climate and management scenarios. The height model is described by Skudnik and Jevšenak (2022) , the basal-area increment model by Jevšenak and Skudnik (2021) , and an overview of the MLFS package, workflow, and applications is provided by Jevšenak, Arnič, Krajnc, and Skudnik (2023), Ecological Informatics . Package: r-cran-mlgdata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mlgdata_0.1.0-1.ca2404.1_all.deb Size: 243038 MD5sum: 493db3d414becdf41d310397b859d9b5 SHA1: f66c04fa91ab971961e3d4ba795b250f45b6a9e9 SHA256: 9db13670855cd5d1aa8ee1cc8fbf13eed83b50dd0f2ee51e507246b31a0f776e SHA512: bbe0a56bf49b675ba3c4fa1df2b936ee1a759cb46155892d8a2a9392ef3175ffce8c043807c5ddf772948adb67680e8c61eaea72e70589c8628326aa8d56fea8 Homepage: https://cran.r-project.org/package=MLGdata Description: CRAN Package 'MLGdata' (Datasets for Use with Salvan, Sartori and Pace (2020)) Contains the datasets for use with the book Salvan, Sartori and Pace (2020, ISBN:978-88-470-4002-1) "Modelli Lineari Generalizzati". Package: r-cran-mlgl Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gglasso, r-cran-mass, r-cran-matrix, r-cran-fastcluster, r-cran-factominer, r-cran-paralleldist Filename: pool/dists/noble/main/r-cran-mlgl_1.0.1-1.ca2404.1_all.deb Size: 193416 MD5sum: 9841abedd8dd30784333fd08fdb4ec83 SHA1: 8a8c8d9858593f8e3b949222d077f616c0ea216d SHA256: b14483c11b04f81b7bcdf52c53d4acbc325a6076ad1f12d5bb7947401b7a6333 SHA512: 4e87d48cc8facc2c3d8787ea8442e324533e9601c8ff37b37a682f4312fbe2bd4d504cc84465840e6453d061ba72f3f8026e99c918577c4400a4007e02881b50 Homepage: https://cran.r-project.org/package=MLGL Description: CRAN Package 'MLGL' (Multi-Layer Group-Lasso) It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2023) ). 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Package: r-cran-mlim Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1078 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-h2o, r-cran-curl, r-cran-mice, r-cran-missranger, r-cran-memuse, r-cran-md.log Filename: pool/dists/noble/main/r-cran-mlim_0.3.0-1.ca2404.1_all.deb Size: 897498 MD5sum: a8de6b1d6961f4da1e58d70767838f47 SHA1: 023dff5d563d97eaae644421361b84fb6b3bfdcb SHA256: 075feb025ef4e18fd80123c62c54ffc9a4fe61279e4d1a3155b610b405cf184b SHA512: 6551f0b3059e82d966d0563c03ee812d4b4530f8a98ae6743fe4f1af0c826332747a905550bde9a936befa22d6802871d7fed7629eec86c5cf92d350c44ae1a4 Homepage: https://cran.r-project.org/package=mlim Description: CRAN Package 'mlim' (Single and Multiple Imputation with Automated Machine Learning) Machine learning algorithms have been used for performing single missing data imputation and most recently, multiple imputations. However, this is the first attempt for using automated machine learning algorithms for performing both single and multiple imputation. Automated machine learning is a procedure for fine-tuning the model automatic, performing a random search for a model that results in less error, without overfitting the data. The main idea is to allow the model to set its own parameters for imputing each variable separately instead of setting fixed predefined parameters to impute all variables of the dataset. Using automated machine learning, the package fine-tunes an Elastic Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting, or Stacked Ensemble machine learning model (from one or a combination of other supported algorithms) for imputing the missing observations. This procedure has been implemented for the first time by this package and is expected to outperform other packages for imputing missing data that do not fine-tune their models. The multiple imputation is implemented via bootstrapping without letting the duplicated observations to harm the cross-validation procedure, which is the way imputed variables are evaluated. Most notably, the package implements automated procedure for handling imputing imbalanced data (class rarity problem), which happens when a factor variable has a level that is far more prevalent than the other(s). This is known to result in biased predictions, hence, biased imputation of missing data. However, the autobalancing procedure ensures that instead of focusing on maximizing accuracy (classification error) in imputing factor variables, a fairer procedure and imputation method is practiced. Package: r-cran-mllmcelltype Architecture: all Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1381 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-r6, r-cran-digest, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-seurat, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mllmcelltype_2.0.5-1.ca2404.1_all.deb Size: 698588 MD5sum: bb858be3f16075648f3aa7ee86afb437 SHA1: 179aab0f3109088fb5777bd0a3a5b3d1f922585b SHA256: 07e47811ce6c13ba2d953757f47d1e150997544c5776da1b6eaeedb85abe366d SHA512: 66af0c9cb8f50725fed30824163a2156f0205662ed273fb28a4bff0fd2e6e5c2e4ab59bdca46b892615277f953533c3e176665bbf4bc346ab45a9c499ce6336d Homepage: https://cran.r-project.org/package=mLLMCelltype Description: CRAN Package 'mLLMCelltype' (Cell Type Annotation Using Large Language Models) Automated cell type annotation for single-cell RNA sequencing data using consensus predictions from multiple large language models. 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The package provides R6-based learners for the following algorithms: 'glmnet' , 'ranger' , 'xgboost' , and 'lightgbm' . These can be used directly with the 'mlexperiments' R package. 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The analysis method is described in Yu and Li (2020), "Third-Variable Effect Analysis with Multilevel Additive Models", PLoS ONE 15(10): e0241072. 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This package aims to provide functions to compute common tasks when estimating multilevel models such as computing the intraclass correlation and design effect, centering variables, estimating the proportion of variance explained at each level, pseudo-R squared, random intercept and slope reliabilities, tests for homogeneity of variance at level-1, and cluster robust and bootstrap standard errors. The tests and statistics reported in the package are from Raudenbush & Bryk (2002, ISBN:9780761919049), Hox et al. (2018, ISBN:9781138121362), and Snijders & Bosker (2012, ISBN:9781849202015). Package: r-cran-mlmi Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-gsl, r-cran-norm, r-cran-cat, r-cran-mix, r-cran-matrix, r-cran-nlme Suggests: r-cran-bootimpute, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlmi_1.1.3-1.ca2404.1_all.deb Size: 103182 MD5sum: 3db2a4025acd5d86461f3e48b6fb039d SHA1: 1513c66c2722948ddaee9053b5c662da40f9b31d SHA256: 66b1293680c693f1c7bc3d45671fefa06878f56d489127d1f5a04a1e73896072 SHA512: abc53136dbc3e080b1542a8dec85bd5890883fd79a9a32b13607370c8fdb35ee3cd233ddaf1cc163e15a6586695223662d652b3355e2ca3ee0935ef957912884 Homepage: https://cran.r-project.org/package=mlmi Description: CRAN Package 'mlmi' (Maximum Likelihood Multiple Imputation) Implements proper and so-called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) . A number of different imputation methods are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using Rubin's rules (for proper imputation), or a modified version for maximum likelihood imputation. For maximum likelihood imputations a likelihood score based approach based on theory by Wang and Robins (1998) is also available. Package: r-cran-mlml2r Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 956 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-bioc-minfi, r-cran-microbenchmark, r-bioc-geoquery, r-cran-knitr, r-cran-rmarkdown, r-bioc-illuminahumanmethylation450kmanifest Filename: pool/dists/noble/main/r-cran-mlml2r_0.3.3-1.ca2404.1_all.deb Size: 610388 MD5sum: ceaf9c12569fe710fb1a6a7e8c78c2b4 SHA1: 161042dbb49b0af06a212a8940f27632409653ad SHA256: 171014bc6dfce7e33db007f16a9c3c4af551bc9b02d77eafe13e11a783c0a328 SHA512: 9d4c2036cf4dfa279cf3a2c8a18b9e1920f15dda3913c767641d9caa047cd4eb6b1d3d7892847ef053929feb7353e612d30f226b329286f25173dc98775ffa36 Homepage: https://cran.r-project.org/package=MLML2R Description: CRAN Package 'MLML2R' (Maximum Likelihood Estimation of DNA Methylation andHydroxymethylation Proportions) Maximum likelihood estimates (MLE) of the proportions of 5-mC and 5-hmC in the DNA using information from BS-conversion, TAB-conversion, and oxBS-conversion methods. One can use information from all three methods or any combination of two of them. Estimates are based on Binomial model by Qu et al. (2013) and Kiihl et al. (2019) . Package: r-cran-mlmm.gwas Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multcompview, r-cran-multcomp, r-cran-coxme, r-cran-sommer, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mlmm.gwas_1.0.6-1.ca2404.1_all.deb Size: 732190 MD5sum: f539a5e8cdce576b4791d6eec322117c SHA1: 708bccde7d968452d8888c7f064867cf7c27985c SHA256: c9bf97ee236ab9c8c05b0a5f250134e6896909c9a6bf596ad98692262a1cef66 SHA512: c331d048cfed3c164166428044fe9e4363c5b152f589ee45139c94efb73b84bef0a8fd4274473ad202df9c145679a9967261d39197fac8a67e2f6254ba9df7dd Homepage: https://cran.r-project.org/package=mlmm.gwas Description: CRAN Package 'mlmm.gwas' (Pipeline for GWAS Using MLMM) Pipeline for Genome-Wide Association Study using Multi-Locus Mixed Model from Segura V, Vilhjálmsson BJ et al. (2012) . The pipeline include detection of associated SNPs with MLMM, model selection by lowest eBIC and p-value threshold, estimation of the effects of the SNPs in the selected model and graphical functions. Package: r-cran-mlmodels Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2534 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-hardhat, r-cran-insight, r-cran-marginaleffects, r-cran-mass, r-cran-matrixcalc, r-cran-maxlik, r-cran-rlang, r-cran-tibble Suggests: r-cran-boot, r-cran-dplyr, r-cran-e1071, r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat, r-cran-wooldridge Filename: pool/dists/noble/main/r-cran-mlmodels_0.1.2-1.ca2404.1_all.deb Size: 1709898 MD5sum: 06f23ec665b379925959e7987e8533d0 SHA1: 437653c7b69fdfb1a30be9e2cb5cb667befbfaea SHA256: edd35a340a91ae03641c03a7ab8892105ef6ec780e069a2d6c7713852d75e6b0 SHA512: 4ba919dd957a80de99c7b4240484606df2dfea4f638d1f9e8540d979ce085a5d359bbb97d91ef0377af59db8bb284db69f3c20531ffbbaad258133ff40340900 Homepage: https://cran.r-project.org/package=mlmodels Description: CRAN Package 'mlmodels' (Maximum Likelihood Models and Tools for Estimation, Prediction,and Testing) Provides a collection of maximum likelihood estimators with a consistent S3 interface. Supported models include Gaussian (linear and log-normal), logit, probit, Poisson, negative binomial (NB1 and NB2), gamma, and beta regression. A distinctive feature is flexible modeling of the scale parameter (variance, dispersion, precision, or shape) alongside the location/mean parameters. The package offers unified predict() methods, multiple variance-covariance estimators (observed information, outer product of gradients, robust/Huber-White, cluster-robust, bootstrap, jackknife), and a full suite of hypothesis tests (Wald, likelihood ratio, information matrix, Vuong, overdispersion, and goodness-of-fit). It is fully compatible with 'marginaleffects' for post-estimation analysis. Methods implemented include Cameron and Trivedi (1990) , for Poisson overdispersion testing, Manjon and Martinez (2014) , for goodness-of-fit testing of count data models, Vuong (1989) , for non-nested likelihood ratio testing, and White (1982) , for information matrix tests. Package: r-cran-mlmoderator Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-dplyr, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-mlmoderator_0.2.1-1.ca2404.1_all.deb Size: 192874 MD5sum: e1bc18f8c838e3516f5f5fb8032ccf69 SHA1: 585e74b95bb7d958b2fc6d4f258fb120effc8423 SHA256: 855cdfd2f003cace08fbc4ac9b10fe89fe58e84b39e65ea196184346e7e08040 SHA512: 59740ed987c6d7aeda47bd174744f46b8085756a05bbe7da0c3bfcf999fc594cf47e852d4b555064bdbaecc5386864cc2eb69c953970c12e2de3b1ece700a157 Homepage: https://cran.r-project.org/package=mlmoderator Description: CRAN Package 'mlmoderator' (Probing, Plotting, and Interpreting Multilevel InteractionEffects) Provides a unified workflow for probing, plotting, and assessing the robustness of cross-level interaction effects in two-level mixed-effects models fitted with 'lme4' (Bates et al., 2015) . Implements simple slopes analysis following Aiken and West (1991, ISBN:9780761907121), Johnson-Neyman intervals following Johnson and Fay (1950) and Bauer and Curran (2005) , and grand- or group-mean centering as described in Enders and Tofighi (2007) . Includes a slope variance decomposition that separates fixed-effect uncertainty from random-slope variance (tau11), a contour surface plot of predicted outcomes over the full predictor-by-moderator space, and robustness diagnostics comprising intraclass correlation coefficient shift analysis and leave-one-cluster-out (LOCO) stability checks. Designed for researchers in education, psychology, biostatistics, epidemiology, organizational science, and other fields where outcomes are clustered within higher-level units. Package: r-cran-mlmoi Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3714 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openxlsx, r-cran-rdpack, r-cran-rmpfr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mlmoi_0.1.2-1.ca2404.1_all.deb Size: 2346262 MD5sum: 3a07b429d3bdb2cadc38b5f5877398d6 SHA1: 8836ab6b25952e155af08d613c083a945ea616d0 SHA256: aa440ba7f6e703d456ef0256538f9f97d3b542199a0333456dd348454c3e34f9 SHA512: 176c6cc6b8e5f9f68dedcc243fc0669dd2bc0b875c67b9afe62e38f3a5feec8b154a408dbfc2dbbfe409ec30c64d4ac5820d7fe519d628f4a8c6f5f8459ea43f Homepage: https://cran.r-project.org/package=MLMOI Description: CRAN Package 'MLMOI' (Estimating Frequencies, Prevalence and Multiplicity of Infection) The implemented methods reach out to scientists that seek to estimate multiplicity of infection (MOI) and lineage (allele) frequencies and prevalences at molecular markers using the maximum-likelihood method described in Schneider (2018) , and Schneider and Escalante (2014) . Users can import data from Excel files in various formats, and perform maximum-likelihood estimation on the imported data by the package's moimle() function. Package: r-cran-mlmorph Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-caret, r-cran-dplyr, r-cran-ggplot2, r-cran-htmltools, r-cran-jsonlite, r-cran-magrittr, r-cran-openxlsx, r-cran-plotly, r-cran-randomforest, r-cran-reactable, r-cran-shiny, r-cran-shinyfiles, r-cran-shinyjs, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlmorph_0.1.1-1.ca2404.1_all.deb Size: 72130 MD5sum: a59fa3abd12d8ba81539c1e7733a3682 SHA1: 17b34d615efcff75925e132dd50f19a3f4418db5 SHA256: 2c57aedfabc98a4f9c0c992f58a91cae5149a88f29f4b8ad8cd06d129cc0e13f SHA512: 61778ba6d56a33a257883245919eca79652a86e2b36821af68392c522c8fb165744a2e8326cd53d2a81923247b0205934b0bacb6152d442a4d29405b9cea911e Homepage: https://cran.r-project.org/package=MLmorph Description: CRAN Package 'MLmorph' (Integrating Morphological Modeling and Machine Learning forDecision Support) Integrating morphological modeling with machine learning to support structured decision-making (e.g., in management and consulting). The package enumerates a morphospace of feasible configurations and uses random forests to estimate class probabilities over that space, bridging deductive model exploration with empirical validation. It includes utilities for factorizing inputs, model training, morphospace construction, and an interactive 'shiny' app for scenario exploration. Package: r-cran-mlmpower Architecture: all Version: 1.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-lme4, r-cran-lmertest, r-cran-vartestnlme Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mlmpower_1.0.11-1.ca2404.1_all.deb Size: 201196 MD5sum: e3604994441c64492ee085c2669b3002 SHA1: 039275ff1fb54991c3589dad251f00357761c2cc SHA256: abe1a55964eb3c25d96091caaf2a58ab57d64d65d80e880572de111783ee16e8 SHA512: a6b4e2c62dbbb469116814a85f3100ea524877b0b0cbad8077061cf61c5106d48c008a495134fccab756fb77ab530e58d6e09aa3c13e6c391f37a6411b58cdde Homepage: https://cran.r-project.org/package=mlmpower Description: CRAN Package 'mlmpower' (Power Analysis and Data Simulation for Multilevel Models) A declarative language for specifying multilevel models, solving for population parameters based on specified variance-explained effect size measures, generating data, and conducting power analyses to determine sample size recommendations. The specification allows for any number of within-cluster effects, between-cluster effects, covariate effects at either level, and random coefficients. Moreover, the models do not assume orthogonal effects, and predictors can correlate at either level and accommodate models with multiple interaction effects. Package: r-cran-mlmrev Architecture: all Version: 1.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2405 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4 Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-mlmrev_1.0-9-1.ca2404.1_all.deb Size: 2080268 MD5sum: 2954841759eeaf35a590f1d08de9c28d SHA1: e8793cd9dd7b95d09f0478d0a9d5bc196df76635 SHA256: 09a866237b9726d46573edf8f5f7ded65939009d6781b8e80ada96787bea79cd SHA512: 01c97e0c15112460ce972d1ed2e945925af0605a64f2284761b81ce2aff4405a361b1852218a83223b46fdc73e13868a03babdb350b058b1951756dbd49b53a4 Homepage: https://cran.r-project.org/package=mlmRev Description: CRAN Package 'mlmRev' (Examples from Multilevel Modelling Software Review) Data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature. Package: r-cran-mlms Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-jsonlite, r-cran-plotrix, r-cran-readxl, r-cran-sf, r-cran-stringi Suggests: r-cran-connectapi, r-cran-covr, r-cran-dm, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-fontawesome, r-cran-htmltools, r-cran-htmlwidgets, r-cran-inldata, r-cran-knitr, r-cran-pkgbuild, r-cran-pkgdown, r-cran-pkgload, r-cran-rcmdcheck, r-cran-reactable, r-cran-renv, r-cran-rmarkdown, r-cran-roxygen2, r-cran-tinytest, r-cran-v8, r-cran-webmap Filename: pool/dists/noble/main/r-cran-mlms_1.0.2-1.ca2404.1_all.deb Size: 595098 MD5sum: f720766d9da46fb3e0927b50e865e27b SHA1: 1c3fef2b9b38e6a6de1b9f19972b62e53fa082d8 SHA256: 370cf46bbf6db64f0eb038b9f56c144400403ee4e09642826971d0da580c3b44 SHA512: eaff58ad6e1554042e234062243ef5e319bdc66efba2eea4211138c70abb44b41b88716a3108d6bbdbff575d47ac5674d1675bf11f7c21ad6273c8a7ec28de8c Homepage: https://cran.r-project.org/package=mlms Description: CRAN Package 'mlms' (Multilevel Monitoring System Data for Wells in the USGS INLAquifer Monitoring Network) Analysis-ready datasets detailing the Multilevel Monitoring System (MLMS) wells within the U.S. Geological Survey's (USGS) aquifer-monitoring network at the Idaho National Laboratory (INL) in Idaho, and the data collected within these wells. Supported by the U.S. Department of Energy (DOE), the USGS collected discrete measurements of hydraulic head at various depths from wells in the eastern Snake River Plain (ESRP) aquifer over several years. These measurements were derived from data on fluid pressure, fluid temperature, and atmospheric pressure. Each well was equipped with an MLMS, which included valved measurement ports, packer bladders, casing segments, and couplers. The MLMS facilitated monitoring at multiple hydraulically isolated depth intervals, reaching significant depths below the land surface. Additionally, groundwater samples were collected from these wells over multiple years and analyzed for various chemical and physical parameters. Package: r-cran-mlmtools Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1021 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-mlmtools_1.0.2-1.ca2404.1_all.deb Size: 933120 MD5sum: e29c1664a36b4826459a5b74af4380da SHA1: 44c01aafc6c6688f31d59771ae654011d2474f5e SHA256: b4043cf5f53d04aea53d2522f95298bb3fef9496e682564ade33126cc3df1e74 SHA512: ff814b3705f05d08d397b07a3822c218459d5f230b19893fa9c897846d3b235145ea759c02410b179f9e1bc9e92f0f60c302bde7ccc63c84816c33c100241fff Homepage: https://cran.r-project.org/package=mlmtools Description: CRAN Package 'mlmtools' (Multi-Level Model Assessment Kit) Multilevel models (mixed effects models) are the statistical tool of choice for analyzing multilevel data (Searle et al, 2009). These models account for the correlated nature of observations within higher level units by adding group-level error terms that augment the singular residual error of a standard OLS regression. Multilevel and mixed effects models often require specialized data pre-processing and further post-estimation derivations and graphics to gain insight into model results. The package presented here, 'mlmtools', is a suite of pre- and post-estimation tools for multilevel models in 'R'. Package implements post-estimation tools designed to work with models estimated using 'lme4''s (Bates et al., 2014) lmer() function, which fits linear mixed effects regression models. Searle, S. R., Casella, G., & McCulloch, C. E. (2009, ISBN:978-0470009598). Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014) . Package: r-cran-mlmts Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2746 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantspec, r-cran-waveslim, r-cran-rfast, r-cran-tsclust, r-cran-forecast, r-cran-tseries, r-cran-tsa, r-cran-tsfeatures, r-cran-tserieschaos, r-cran-freqdom, r-cran-e1071, r-cran-dtw, r-cran-psych, r-cran-complexplus, r-cran-mts, r-cran-matrix, r-cran-ggplot2, r-cran-multiwave, r-cran-mass, r-cran-fda.usc, r-cran-tsdist, r-cran-geigen, r-cran-desctools, r-cran-pracma, r-cran-pspline, r-cran-rdpack, r-cran-clusterr, r-cran-aid, r-cran-caret, r-cran-ranger, r-cran-igraph, r-cran-randomforest Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlmts_1.1.2-1.ca2404.1_all.deb Size: 2680788 MD5sum: 402b84ece7e2010143e114ee312519ca SHA1: fd5b3190dcf5280249c2280bdf8731f357143cd7 SHA256: b6d124c842e1feb0e3cff1793c1ccc1fcc8242e3e619f65f8b2147c701d98e2e SHA512: 716094288c3a84c3506d74797eb5984c71a47400fe280799ad8783c5b0a320f28e3a3ee7cb65bb1b3c00d690ab613af3bb506c02dab28883040149c68dc189a7 Homepage: https://cran.r-project.org/package=mlmts Description: CRAN Package 'mlmts' (Machine Learning Algorithms for Multivariate Time Series) An implementation of several machine learning algorithms for multivariate time series. The package includes functions allowing the execution of clustering, classification or outlier detection methods, among others. It also incorporates a collection of multivariate time series datasets which can be used to analyse the performance of new proposed algorithms. Some of these datasets are stored in GitHub data packages 'ueadata1' to 'ueadata8'. To access these data packages, run 'install.packages(c('ueadata1', 'ueadata2', 'ueadata3', 'ueadata4', 'ueadata5', 'ueadata6', 'ueadata7', 'ueadata8'), repos='')'. The installation takes a couple of minutes but we strongly encourage the users to do it if they want to have available all datasets of mlmts. Practitioners from a broad variety of fields could benefit from the general framework provided by 'mlmts'. Package: r-cran-mlmusingr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1087 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-nlme, r-cran-matrix, r-cran-magrittr, r-cran-broom, r-cran-generics, r-cran-dplyr, r-cran-performance, r-cran-tibble, r-cran-wemix Filename: pool/dists/noble/main/r-cran-mlmusingr_0.4.0-1.ca2404.1_all.deb Size: 1063098 MD5sum: 8f9669e333c4cf1e3ddbf10e8e57712c SHA1: 8f19126476736129d7d1012a46dc4273807f491f SHA256: ba45122e560ca04882c69c61f37ef92e8d50c654c77d6d3e8c76b9cf4b3f60c6 SHA512: c8406a3a817c617bcfe388c59e5e493aa268299809f2b0a7715d3db79b603f9ab2f711fc7e53481521028fc94c242729c4e0d3f6b93be6e2877bf1431882b921 Homepage: https://cran.r-project.org/package=MLMusingR Description: CRAN Package 'MLMusingR' (Practical Multilevel Modeling) Convenience functions and datasets to be used with Practical Multilevel Modeling using R. The package includes functions for calculating group means, group mean centered variables, and displaying some basic missing data information. A function for computing robust standard errors for linear mixed models based on Liang and Zeger (1986) and Bell and 'McCaffrey' (2002) is included as well as a function for checking for level-one homoskedasticity (Raudenbush & Bryk, 2002, ISBN:076191904X). Package: r-cran-mlogit Architecture: all Version: 1.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1432 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dfidx, r-cran-formula, r-cran-zoo, r-cran-lmtest, r-cran-statmod, r-cran-mass, r-cran-rdpack Suggests: r-cran-knitr, r-cran-car, r-cran-nnet, r-cran-lattice, r-cran-aer, r-cran-ggplot2, r-cran-texreg, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mlogit_1.1-3-1.ca2404.1_all.deb Size: 802478 MD5sum: be7b088ba9e5b548db8e9d948c67b29f SHA1: c1c89acb339791035fe7a5b2fa8a1fe537b2e32b SHA256: bed51f1264c7aa5dba95fa05839e6750516558fd5541c59aed46df54ed06c539 SHA512: 786396f6c33fa400b5a2aa4f6b6249057b0bfb7cf4fd46b5e1f209d569fa7325f6cd8ce1117e7f0a8bc3d94f35cd90f5b751af6e9bdaab1b8b6d42b1072c881e Homepage: https://cran.r-project.org/package=mlogit Description: CRAN Package 'mlogit' (Multinomial Logit Models) Maximum likelihood estimation of random utility discrete choice models. The software is described in Croissant (2020) and the underlying methods in Train (2009) . Package: r-cran-mlpreemption Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mlpreemption_1.0.1-1.ca2404.1_all.deb Size: 51100 MD5sum: 2507244c33d4196211e6fcfd4515c460 SHA1: 6f4fae0ce5344ec8cd7ce509bb0fa6581adaf316 SHA256: 9908d2f1d2edb308e1f799320c93994e2c21e043d91c4a53117d1634a297b634 SHA512: 888060eb6e0ce74b752ae87dd817d6c867c23de4ec952c171575acbb2ef488189b6cf814b3f1681331c4df73de480015f73f4b81bf45db98965d59629e3fa72b Homepage: https://cran.r-project.org/package=MLpreemption Description: CRAN Package 'MLpreemption' (Maximum Likelihood Estimation of the Niche Preemption Model) Provides functions for obtaining estimates of the parameter of the niche preemption model (also known as the geometric series), in particular a maximum likelihood estimator (Graffelman, 2021) . The niche preemption model is a widely used model in ecology and biodiversity studies. Package: r-cran-mlpugs Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-progress, r-cran-c50, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-mlpugs_0.2.0-1.ca2404.1_all.deb Size: 69454 MD5sum: 8e2fae15ade31803290eaea1ee184ae2 SHA1: 1de50dcd7f185246f0c2446526d5048511bedac9 SHA256: 6ec7360454c8f64d723959591be3782b82504744536db24b6794eac4a17b60e4 SHA512: 4314d2b60e567e71faf1e6a02832295bb02600592704474af384eadf34461f5102825f214458d7b72641c81a0ffafeecff57497d1fc23da14e5311a614766f85 Homepage: https://cran.r-project.org/package=MLPUGS Description: CRAN Package 'MLPUGS' (Multi-Label Prediction Using Gibbs Sampling (and ClassifierChains)) An implementation of classifier chains (CC's) for multi-label prediction. Users can employ an external package (e.g. 'randomForest', 'C50'), or supply their own. The package can train a single set of CC's or train an ensemble of CC's -- in parallel if running in a multi-core environment. New observations are classified using a Gibbs sampler since each unobserved label is conditioned on the others. The package includes methods for evaluating the predictions for accuracy and aggregating across iterations and models to produce binary or probabilistic classifications. Package: r-cran-mlpwr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1677 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dicekriging, r-cran-digest, r-cran-ggplot2, r-cran-randtoolbox, r-cran-rlist, r-cran-rgenoud Suggests: r-cran-knitr, r-cran-lme4, r-cran-lmertest, r-cran-mirt, r-cran-pwr, r-cran-rmarkdown, r-cran-simr, r-cran-sn, r-cran-tidyr, r-cran-weightsvm Filename: pool/dists/noble/main/r-cran-mlpwr_1.1.1-1.ca2404.1_all.deb Size: 1169470 MD5sum: a95f6c5e117f6ce37aa976935d97a7e5 SHA1: b7a6ca75ded51d6d8104c26c8711dd6bbdfa04ce SHA256: 742a580551e3294b42868513cda9ba37b4bbf44cb5c0572927788c3756fde69e SHA512: 44e151e5a5102e011fc09b189a55e1dabf887da7e2c5d441fdd2c69650fee86ffad45426b1c2aead780f1fe2ef0d2f1a022592346f397157f9a9a52dbad18df6 Homepage: https://cran.r-project.org/package=mlpwr Description: CRAN Package 'mlpwr' (A Power Analysis Toolbox to Find Cost-Efficient Study Designs) We implement a surrogate modeling algorithm to guide simulation-based sample size planning. The method is described in detail in our paper (Zimmer & Debelak (2023) ). It supports multiple study design parameters and optimization with respect to a cost function. It can find optimal designs that correspond to a desired statistical power or that fulfill a cost constraint. We also provide a tutorial paper (Zimmer et al. (2023) ). Package: r-cran-mlquantify Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-randomforest, r-cran-fnn Suggests: r-cran-corelearn Filename: pool/dists/noble/main/r-cran-mlquantify_0.2.0-1.ca2404.1_all.deb Size: 174296 MD5sum: d211e7761ac19178a8be12f6cfe54ec5 SHA1: 0b37caef0fac8b2f60ab0021055b9b2ee619246b SHA256: 8423be18e9115eae8461dc8c6ff5a250c5856870e52886a34508599099eabee8 SHA512: 91bbed4075b766eaf1973329e4addb4e563e40984786f213c69f98efb477a1a5bef2f1894534873967b7532767a1295107b4d6fcf0907e142554d7ecfc1aeb15 Homepage: https://cran.r-project.org/package=mlquantify Description: CRAN Package 'mlquantify' (Algorithms for Class Distribution Estimation) Quantification is a prominent machine learning task that has received an increasing amount of attention in the last years. The objective is to predict the class distribution of a data sample. This package is a collection of machine learning algorithms for class distribution estimation. This package include algorithms from different paradigms of quantification. These methods are described in the paper: A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set size in quantification assessment. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020. . Package: r-cran-mlr3 Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3554 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-evaluate, r-cran-future, r-cran-future.apply, r-cran-lgr, r-cran-mirai, r-cran-mlbench, r-cran-mlr3measures, r-cran-mlr3misc, r-cran-parallelly, r-cran-palmerpenguins, r-cran-paradox, r-cran-uuid Suggests: r-cran-callr, r-cran-codetools, r-cran-future.callr, r-cran-mlr3data, r-cran-progressr, r-cran-remotes, r-cran-rhpcblasctl, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3_1.6.0-1.ca2404.1_all.deb Size: 2719904 MD5sum: c5e5505b2a9ad311b56f678f40babdd1 SHA1: f1d04ee6569c3e99cbf2f4e251afd214e86181b0 SHA256: f19b56668d09815f509a3ebdddf737d376922a08488f5816d5158a51fa0b8623 SHA512: 8f98a2f02f0c234d8b85aaf1ec64e488f9298854fec529abeee8049cad61bca2891ab44de5d94d28bc042ff88f3dc8d7fc9fdd2809facd5a6d3be9f2cdba944b Homepage: https://cran.r-project.org/package=mlr3 Description: CRAN Package 'mlr3' (Machine Learning in R - Next Generation) Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality. Package: r-cran-mlr3batchmark Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-batchtools, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3, r-cran-mlr3misc, r-cran-uuid Suggests: r-cran-renv, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3batchmark_0.2.2-1.ca2404.1_all.deb Size: 38784 MD5sum: 6078d79bb55c585940acf9c05670a694 SHA1: 9b7d8cfa8f3763d900cb455c5af63bf933144b47 SHA256: 880ab1bbce345a0824bb49ce3ec2005750b53b3da7594707e1d2cf582bcb8bb7 SHA512: 1b8213e49933ad7d0037a91b5a583f33ac1dab5c89f593952be8f2a171fb291005da3315bff4d512f7b859507282fe4a7a0c256898f57afd8536db78e3254cc3 Homepage: https://cran.r-project.org/package=mlr3batchmark Description: CRAN Package 'mlr3batchmark' (Batch Experiments for 'mlr3') Extends the 'mlr3' package with a connector to the package 'batchtools'. This allows to run large-scale benchmark experiments on scheduled high-performance computing clusters. Package: r-cran-mlr3benchmark Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-mlr3, r-cran-mlr3learners, r-cran-pmcmrplus, r-cran-rpart, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlr3benchmark_0.1.7-1.ca2404.1_all.deb Size: 156742 MD5sum: b906b9bcf9f7f5269c098187f262a958 SHA1: 0debd2778741ef53713c64ecb1d2320c45f41834 SHA256: 3d77c71d56547dfd57162d74fc96d82b95722f82678d527622ab2bfb14ff2672 SHA512: b71022fa453d7d806f4d693664303fae5434103ff1f26b25060c9920e9e270693c96e1512db7077fc65dece9615f8a90cd5dd937fc5b570c75c600aa753f0702 Homepage: https://cran.r-project.org/package=mlr3benchmark Description: CRAN Package 'mlr3benchmark' (Analysis and Visualisation of Benchmark Experiments) Implements methods for post-hoc analysis and visualisation of benchmark experiments, for 'mlr3' and beyond. Package: r-cran-mlr3cluster Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1524 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-backports, r-cran-checkmate, r-cran-cluster, r-cran-data.table, r-cran-fpc, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-apcluster, r-cran-clue, r-cran-clusterr, r-cran-clustmixtype, r-cran-dbscan, r-cran-e1071, r-cran-kernlab, r-cran-lpcm, r-cran-mclust, r-cran-mirai, r-cran-mlbench, r-cran-protoclust, r-cran-rweka, r-cran-stream, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3cluster_0.3.0-1.ca2404.1_all.deb Size: 1159942 MD5sum: c2b344b03d1c4c05e3d52857004b1035 SHA1: a83f3bd4f34c76a588370332f3e135ed311eb77d SHA256: 080c810b2321e32f0eb6d11528a9d122bee74e16c162bd27f0fcceac52830855 SHA512: 536939afd82176eba6bce88165e765580d70b973b25a002be78395bf4474f390ecb2c81f4b9b30c92643ea9ef208ce88727a723fb30437b86641c69a40e0fa61 Homepage: https://cran.r-project.org/package=mlr3cluster Description: CRAN Package 'mlr3cluster' (Cluster Extension for 'mlr3') Extends the 'mlr3' package with cluster analysis. Package: r-cran-mlr3data Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3244 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mlr3 Filename: pool/dists/noble/main/r-cran-mlr3data_0.9.0-1.ca2404.1_all.deb Size: 3281962 MD5sum: 7e0d45906e8bbab419d70f1d58923212 SHA1: 022d9a96c23ccda60ac21f51346e3954703aa080 SHA256: d1327a2b0f59aa58d6a7b2de0e99f49344c4c3727ed3918bfd4beac83f15f93e SHA512: 24de9971ab09f5922d31995c0761326fa2392f941ba6badcfb4997cefcc0c653a9edbfc9ffedbd75793507c0a6424a9f35adeef9e812a508c1980f7ea622b3af Homepage: https://cran.r-project.org/package=mlr3data Description: CRAN Package 'mlr3data' (Collection of Machine Learning Data Sets for 'mlr3') A small collection of interesting and educational machine learning data sets which are used as examples in the 'mlr3' book (), the use case gallery (), or in other examples. All data sets are properly preprocessed and ready to be analyzed by most machine learning algorithms. Data sets are automatically added to the dictionary of tasks if 'mlr3' is loaded. Package: r-cran-mlr3db Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-future, r-cran-future.apply, r-cran-future.callr, r-cran-hms, r-cran-lgr, r-cran-rsqlite, r-cran-rlang, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-mlr3db_0.7.2-1.ca2404.1_all.deb Size: 580978 MD5sum: 6effb85721a8f441db4d3c6dbbd20331 SHA1: f1a4b4a91a8aa613d0ed1a907623885d17725adb SHA256: ec4c588861d068c61870d480561e12af97b1bcb78a37b4091537740284f951af SHA512: 7aa8e8626d519a729490d8c1d7b2bfd06cfdbc1141a2ccdd44bcf3a23f55608f371143c3ba2eeefc8a3aad512296c712df77a9169a8155bbead106e88a834239 Homepage: https://cran.r-project.org/package=mlr3db Description: CRAN Package 'mlr3db' (Data Base Backend for 'mlr3') Extends the 'mlr3' package with a backend to transparently work with databases such as 'SQLite', 'DuckDB', 'MySQL', 'MariaDB', or 'PostgreSQL'. The package provides three additional backends: 'DataBackendDplyr' relies on the abstraction of package 'dbplyr' to interact with most DBMS. 'DataBackendDuckDB' operates on 'DuckDB' data bases and also on Apache Parquet files. 'DataBackendPolars' operates on 'Polars' data frames. Package: r-cran-mlr3fairness Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1272 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-mlr3, r-cran-mlr3learners, r-cran-mlr3measures, r-cran-mlr3misc, r-cran-mlr3pipelines, r-cran-paradox, r-cran-r6, r-cran-rlang Suggests: r-cran-cccp, r-cran-cvxr, r-cran-pagedown, r-cran-fairml, r-cran-future, r-cran-iml, r-cran-kableextra, r-cran-knitr, r-cran-linprog, r-cran-lgr, r-cran-mlr3viz, r-cran-patchwork, r-cran-ranger, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3fairness_0.4.0-1.ca2404.1_all.deb Size: 919528 MD5sum: 741f34e88bf04826b3289e8230b308c0 SHA1: 9f07b548053a3fb098be4cb66d7272bd8a832faa SHA256: fca7cb9eaf78c584f7311aa24ff404f50c860d62529cbaef91922724fcc725f7 SHA512: e9d51c859524e3eac38468b4f43f7aadee66e0caa250c50e1ea8dae7156f33d8981f2340c4a0e26fb24baabfb35bf108d13e446ac2c88ed67db7e16e991a76aa Homepage: https://cran.r-project.org/package=mlr3fairness Description: CRAN Package 'mlr3fairness' (Fairness Auditing and Debiasing for 'mlr3') Integrates fairness auditing and bias mitigation methods for the 'mlr3' ecosystem. This includes fairness metrics, reporting tools, visualizations and bias mitigation techniques such as "Reweighing" described in 'Kamiran, Calders' (2012) and "Equalized Odds" described in 'Hardt et al.' (2016) . Integration with 'mlr3' allows for auditing of ML models as well as convenient joint tuning of machine learning algorithms and debiasing methods. Package: r-cran-mlr3fda Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2317 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-mlr3pipelines, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-tf Suggests: r-cran-fdboost, r-cran-lme4, r-cran-mboost, r-cran-rpart, r-cran-testthat, r-cran-tsfeatures, r-cran-wavelets, r-cran-withr Filename: pool/dists/noble/main/r-cran-mlr3fda_0.5.0-1.ca2404.1_all.deb Size: 2208108 MD5sum: b5e20e152d4caf4d5ca9391500cae288 SHA1: 7194ece40d30fdfeec6869e8f037b7faa7892298 SHA256: 75af4caa144f11b15508fb3ed6c7e2f8d770da95f32dcb6119818ebfffa2f113 SHA512: f32724ae92cd76ae294458202985b19c5b956ac2e7baec660854cc1a8690733d39dcd73a31b02f59900cb30b099d5e307dfdbed5867800dfa4d05f52fe0474b1 Homepage: https://cran.r-project.org/package=mlr3fda Description: CRAN Package 'mlr3fda' (Extending 'mlr3' to Functional Data Analysis) Extends the 'mlr3' ecosystem to functional analysis by adding support for irregular and regular functional data as defined in the 'tf' package. The package provides 'PipeOps' for preprocessing functional columns and for extracting scalar features, thereby allowing standard machine learning algorithms to be applied afterwards. Available operations include simple functional features such as the mean or maximum, smoothing, interpolation, flattening, and functional 'PCA'. Package: r-cran-mlr3filters Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-boruta, r-cran-care, r-cran-caret, r-cran-carsurv, r-cran-fselectorrcpp, r-cran-knitr, r-cran-lgr, r-cran-mlr3learners, r-cran-mlr3measures, r-cran-mlr3pipelines, r-cran-praznik, r-cran-rpart, r-cran-survival, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-mlr3filters_0.9.1-1.ca2404.1_all.deb Size: 433896 MD5sum: 787ce300392ee215729aff2b70c15a77 SHA1: f740abbc71ff453e686812349ae5b104ffff350e SHA256: b4d0ccf6293b0a9eb09f2454451e3ad3316a604c64493280d304c4187f737e75 SHA512: 14ffe6b3ffdb4dd895f1819264bbe5689cbb935eed723c874a0a16dc29e1d841b9b8d3fa543deb33ccc28b4a7559c84de4efb72882bec6d72ec9115f513ea544 Homepage: https://cran.r-project.org/package=mlr3filters Description: CRAN Package 'mlr3filters' (Filter Based Feature Selection for 'mlr3') Extends 'mlr3' with filter methods for feature selection. Besides standalone filter methods built-in methods of any machine-learning algorithm are supported. Partial scoring of multivariate filter methods is supported. Package: r-cran-mlr3fselect Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1139 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlr3, r-cran-bbotk, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-stabm Suggests: r-cran-e1071, r-cran-fastvoter, r-cran-genalg, r-cran-mirai, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-processx, r-cran-redux, r-cran-rpart, r-cran-rush, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3fselect_1.6.0-1.ca2404.1_all.deb Size: 814874 MD5sum: a59c58ea9beba73507009a61f09cda3a SHA1: 30dc3f314548b0de5113445d360ad65669d591bb SHA256: a8bcbe0c692a8fbc3a89df48b7a0477327302eba33f77d90cc580bc943a09fb5 SHA512: 3b15bf1990a64618be64aa8ed5a01cdf18b14c5c7108a20d990460662f71d9053afce4f28adfe2befffc4c90592687d9a2e6bd326f2f758e10c7381631a744f0 Homepage: https://cran.r-project.org/package=mlr3fselect Description: CRAN Package 'mlr3fselect' (Feature Selection for 'mlr3') Feature selection package of the 'mlr3' ecosystem. It selects the optimal feature set for any 'mlr3' learner. The package works with several optimization algorithms e.g. Random Search, Recursive Feature Elimination, and Genetic Search. Moreover, it can automatically optimize learners and estimate the performance of optimized feature sets with nested resampling. Package: r-cran-mlr3hyperband Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3tuning, r-cran-bbotk, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-uuid Suggests: r-cran-emoa, r-cran-mirai, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-processx, r-cran-redux, r-cran-rpart, r-cran-rush, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlr3hyperband_1.1.0-1.ca2404.1_all.deb Size: 187962 MD5sum: 67b3674bcfaaa57a4685ae06624a078d SHA1: cfea2800e656ee9b782485595c186e4d73e646c9 SHA256: bad68926fed76330cc83c797d5b38ef8395c268fe8c55f60874d5e7b70a28c41 SHA512: ca4413f31927ee9c79e24c8f80df0e100bd7aa8fe529de598640cef7b021bc108bf1e88e8227f622b4b1c1e84197862e1388a5fb470d16ded9eda1acbaa9b4cc Homepage: https://cran.r-project.org/package=mlr3hyperband Description: CRAN Package 'mlr3hyperband' (Hyperband for 'mlr3') Successive Halving (Jamieson and Talwalkar (2016) ) and Hyperband (Li et al. 2018 ) optimization algorithm for the mlr3 ecosystem. The implementation in mlr3hyperband features improved scheduling and parallelizes the evaluation of configurations. The package includes tuners for hyperparameter optimization in mlr3tuning and optimizers for black-box optimization in bbotk. Package: r-cran-mlr3inferr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-future, r-cran-lgr, r-cran-mlr3measures, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-withr Suggests: r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3inferr_0.2.1-1.ca2404.1_all.deb Size: 312718 MD5sum: beaca2ad607160c20b30a215d4dac486 SHA1: 6bbdfcfbff88f9615936e267430ef94a346a8887 SHA256: a72084411edee83df14ed8a86587b6a17b2714b2bd17f67b8f4c29d787a0ce27 SHA512: 2f231f76a4519a5971c55c349e2ead50b56276506121ccbd41e2f77ad7367f00562ede440f345de1f153d24ebae9fdb4f2eeb7ff32d66628f8660bf2d6a440cf Homepage: https://cran.r-project.org/package=mlr3inferr Description: CRAN Package 'mlr3inferr' (Inference on the Generalization Error) Confidence interval and resampling methods for inference on the generalization error. Package: r-cran-mlr3learners Architecture: all Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 857 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-dicekriging, r-cran-e1071, r-cran-glmnet, r-cran-kknn, r-cran-knitr, r-cran-lgr, r-cran-mass, r-cran-nnet, r-cran-pracma, r-cran-ranger, r-cran-rgenoud, r-cran-rmarkdown, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlr3learners_0.12.0-1.ca2404.1_all.deb Size: 600270 MD5sum: 1bbf5a8836267a8be561832bc3bdde59 SHA1: 27313465ce875586a14b33a4a76c5c94966272dd SHA256: bd41c38732d533edf4d50e91579345f7edbd1a2b27ec783e5e36a17056e64b10 SHA512: b61622722e31d8b94bc2f100aa81a775af82362af2b501d567a15d3d6e12eaa0e4175203fc7fd187e01feafa692192e09717c89f6307b2380e8d132181520f85 Homepage: https://cran.r-project.org/package=mlr3learners Description: CRAN Package 'mlr3learners' (Recommended Learners for 'mlr3') Recommended Learners for 'mlr3'. Extends 'mlr3' with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting. Package: r-cran-mlr3measures Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-mlr3misc, r-cran-prroc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3measures_1.3.0-1.ca2404.1_all.deb Size: 340152 MD5sum: 8322f2b102a7c16b71b0ccae00b8d993 SHA1: df986695b8087bb4911ac68919d9a102a1b6b7bf SHA256: 540a097f41fb86431af8e085f8e89973cfaa26fb0cc26af0e58877bfb84cd001 SHA512: bda63d62326e838312e7e5ba0a7c99b13e870b88d832b734bc7e213bf0e85062bbb8baaa953083dc07b1cce746f2cc876839d3b21a508558f20fd8e44523a952 Homepage: https://cran.r-project.org/package=mlr3measures Description: CRAN Package 'mlr3measures' (Performance Measures for 'mlr3') Implements multiple performance measures for supervised learning. Includes over 40 measures for regression and classification. Additionally, meta information about the performance measures can be queried, e.g. what the best and worst possible performances scores are. Package: r-cran-mlr3pipelines Architecture: all Version: 0.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3447 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-digest, r-cran-lgr, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-ggplot2, r-cran-glmnet, r-cran-igraph, r-cran-knitr, r-cran-lme4, r-cran-mlbench, r-cran-bbotk, r-cran-mlr3filters, r-cran-mlr3learners, r-cran-mlr3measures, r-cran-nloptr, r-cran-quanteda, r-cran-rmarkdown, r-cran-rpart, r-cran-stopwords, r-cran-testthat, r-cran-visnetwork, r-cran-bestnormalize, r-cran-fastica, r-cran-kernlab, r-cran-smotefamily, r-cran-evaluate, r-cran-nmf, r-cran-mass, r-cran-gensa, r-cran-vtreat, r-cran-future, r-cran-htmlwidgets, r-cran-ranger, r-cran-themis, r-cran-dimred, r-cran-rspectra, r-cran-rann Filename: pool/dists/noble/main/r-cran-mlr3pipelines_0.11.0-1.ca2404.1_all.deb Size: 2242294 MD5sum: 3700d921a51d021c0a0f82a548096111 SHA1: 7fe4bf1461e2a6fb3e3ebd70ad64d86139d8ae09 SHA256: 3f307f9db6176fa39d8cfb5f784172703ef18929b8b39e273e2e6e3d5700f54e SHA512: df06d7243fb8e75c9068b6d7b848b2dc59ad80d6e9b30b13448b40498f76fae3c03cc3ee0df37f8a789200b194c50fa0e549430b10aae2566b931d1e074adc75 Homepage: https://cran.r-project.org/package=mlr3pipelines Description: CRAN Package 'mlr3pipelines' (Preprocessing Operators and Pipelines for 'mlr3') Dataflow programming toolkit that enriches 'mlr3' with a diverse set of pipelining operators ('PipeOps') that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can therefore be resampled, benchmarked, and tuned. Package: r-cran-mlr3resampling Architecture: all Version: 2026.2.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-r6, r-cran-checkmate, r-cran-paradox, r-cran-mlr3, r-cran-mlr3misc, r-cran-pbdmpi Suggests: r-cran-ggplot2, r-cran-animint2, r-cran-mlr3tuning, r-cran-lgr, r-cran-future, r-cran-future.apply, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-nc, r-cran-rpart, r-cran-directlabels, r-cran-mlr3pipelines, r-cran-glmnet, r-cran-mlr3learners, r-cran-mlr3torch, r-cran-torch Filename: pool/dists/noble/main/r-cran-mlr3resampling_2026.2.24-1.ca2404.1_all.deb Size: 755842 MD5sum: 16f1c94376eaaf8b49bf2da64cbb20e1 SHA1: 096cc877a8d9f61d934d4dab59bdd315ff7f0a31 SHA256: 7e51a2737208cb79c65fcebadf86a163d8444e0a76f0868407289bdb819e496b SHA512: 04e0dbc3d4a6757eddf02f06382103606c387e2ef192c8ba3480c10f34c8af761c957f9d6dc87be217c7a3ac8a9f7cbe2bb1a6ebbaa279226bbeec8b87bbc638 Homepage: https://cran.r-project.org/package=mlr3resampling Description: CRAN Package 'mlr3resampling' (Resampling Algorithms for 'mlr3' Framework) A supervised learning algorithm inputs a train set, and outputs a prediction function, which can be used on a test set. If each data point belongs to a subset (such as geographic region, year, etc), then how do we know if subsets are similar enough so that we can get accurate predictions on one subset, after training on Other subsets? And how do we know if training on All subsets would improve prediction accuracy, relative to training on the Same subset? SOAK, Same/Other/All K-fold cross-validation, can be used to answer these questions, by fixing a test subset, training models on Same/Other/All subsets, and then comparing test error rates (Same versus Other and Same versus All). Also provides code for estimating how many train samples are required to get accurate predictions on a test set. Package: r-cran-mlr3shiny Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlr3, r-cran-mlr3measures, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-mlr3viz, r-cran-metrics, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-shinywidgets, r-cran-shinyalert, r-cran-data.table, r-cran-dt, r-cran-stringr, r-cran-plyr, r-cran-dplyr, r-cran-purrr, r-cran-patchwork, r-cran-ggparty, r-cran-ggally Suggests: r-cran-testthat, r-cran-shinytest, r-cran-devtools, r-cran-ranger, r-cran-e1071, r-cran-xgboost, r-cran-igraph, r-cran-readxl, r-cran-dalex, r-cran-dalextra, r-cran-bslib, r-cran-haven Filename: pool/dists/noble/main/r-cran-mlr3shiny_0.5.0-1.ca2404.1_all.deb Size: 142948 MD5sum: 08531805879556f440b5f447aacf365c SHA1: dd2957aa4bc85d429a00468929c687bcb0925198 SHA256: dfd9dba5ea57191d62a2a51b24e8e87f9c9feb4fd87c56e56cda6fd9392ffe4c SHA512: 27f84e269662079b6cb9c430f66ff15ca5fa8cf6377e14257d13f7ffa9fc72da99e01c4e2db997eecac5331722c2b9957a99c3eccf5862194901d6475d97827f Homepage: https://cran.r-project.org/package=mlr3shiny Description: CRAN Package 'mlr3shiny' (Machine Learning in 'shiny' with 'mlr3') A web-based graphical user interface to provide the basic steps of a machine learning workflow. It uses the functionalities of the 'mlr3' framework. Package: r-cran-mlr3spatial Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-r6, r-cran-sf, r-cran-terra Suggests: r-cran-bench, r-cran-future, r-cran-future.callr, r-cran-knitr, r-cran-mlr3learners, r-cran-paradox, r-cran-ranger, r-cran-raster, r-cran-rmarkdown, r-cran-rpart, r-cran-stars, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3spatial_0.6.1-1.ca2404.1_all.deb Size: 1912896 MD5sum: 5dafdf147207d52845714c5f30cebd7c SHA1: f3980f3f2724c3f804a15a27b614e229b2293123 SHA256: b01e338384b670ac3407941e04db2a995793038972de45358ba4b3daecd62fd5 SHA512: a9bcda83ffacdf6dab9f57d22fd2086201c341702e9e361b291ac308231fbc20691516ea2e18504c368b0b2b19be23796d53c638e567ea272e4ccb5acca76a2b Homepage: https://cran.r-project.org/package=mlr3spatial Description: CRAN Package 'mlr3spatial' (Support for Spatial Objects Within the 'mlr3' Ecosystem) Extends the 'mlr3' ML framework with methods for spatial objects. Data storage and prediction are supported for packages 'terra', 'raster' and 'stars'. Package: r-cran-mlr3spatiotempcv Architecture: all Version: 2.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3438 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-bbotk, r-cran-blockcv, r-cran-caret, r-cran-cast, r-cran-ggsci, r-cran-ggtext, r-cran-here, r-cran-knitr, r-cran-lgr, r-cran-mlr3filters, r-cran-mlr3pipelines, r-cran-mlr3spatial, r-cran-mlr3tuning, r-cran-patchwork, r-cran-plotly, r-cran-rmarkdown, r-cran-rpart, r-cran-sf, r-cran-sperrorest, r-cran-terra, r-cran-testthat, r-cran-twosamples, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-mlr3spatiotempcv_2.3.4-1.ca2404.1_all.deb Size: 2365016 MD5sum: 92e2f1dd114402e59e4f6fbe91abd97a SHA1: 564afc23fecb62259a61a8bda29745db666bfaac SHA256: d2d74d7eea15c5f1274420f6de9e67cd888ea2703307637a84fed9f90c9c5aad SHA512: 37cecd36b37e84a273e891653c1353b391f4cbfa4bb0557ba854323afc0985ab413672df037b7f6e55a40a05585db0b79cb1024224d45e26d7ed7dfae2809e34 Homepage: https://cran.r-project.org/package=mlr3spatiotempcv Description: CRAN Package 'mlr3spatiotempcv' (Spatiotemporal Resampling Methods for 'mlr3') Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at . Package: r-cran-mlr3summary Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-future.apply, r-cran-mlr3, r-cran-mlr3misc Suggests: r-cran-fastshap, r-cran-iml, r-cran-mlr3fairness, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-mlr3tuning, r-cran-future, r-cran-ranger, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3summary_0.1.2-1.ca2404.1_all.deb Size: 92788 MD5sum: 618c7696bb3a23cbd19a42819eb5fe11 SHA1: 108bee985cf3b5bfa150dd6472d637b372d2b069 SHA256: fbda8034a28372da62ae5d6f774c00038e7451638dd9fa65e80e23f74b798654 SHA512: 916b45daa7762f8d985669630400ba2a1f969d805220c94f9b8cfd51f484927128be63d5cb3877523dc371d6edf10278e3a5c1f83025d30f83600602e3450ff9 Homepage: https://cran.r-project.org/package=mlr3summary Description: CRAN Package 'mlr3summary' (Model and Learner Summaries for 'mlr3') Concise and interpretable summaries for machine learning models and learners of the 'mlr3' ecosystem. The package takes inspiration from the summary function for (generalized) linear models but extends it to non-parametric machine learning models, based on generalization performance, model complexity, feature importances and effects, and fairness metrics. Package: r-cran-mlr3superlearner Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlr3learners, r-cran-checkmate, r-cran-lgr, r-cran-mlr3, r-cran-data.table, r-cran-purrr, r-cran-cli, r-cran-glmnet Suggests: r-cran-ranger, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlr3superlearner_0.1.2-1.ca2404.1_all.deb Size: 36464 MD5sum: f2a8417ab62be03c01dd5c9c1fab4d2e SHA1: 64709ba0afc72e6bcf8256cbc15db495f099b6d3 SHA256: 2d0c6863b4be3a5c02738c26cd5bd88d3bd79c5b9b366e1d1e91910821ae3daa SHA512: b77839a10f002bfecea714bc8eb550125448a1c2636effda1940177220a82c600fd105f106c973153fa54ff649cab3599caec58fba116067354085c83dc089f8 Homepage: https://cran.r-project.org/package=mlr3superlearner Description: CRAN Package 'mlr3superlearner' (Super Learner Fitting and Prediction) An implementation of the Super Learner prediction algorithm from van der Laan, Polley, and Hubbard (2007) Installed-Size: 4666 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-mlr3pipelines, r-cran-torch, r-cran-backports, r-cran-cli, r-cran-checkmate, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-withr Suggests: r-cran-callr, r-cran-curl, r-cran-future, r-cran-ggplot2, r-cran-igraph, r-cran-jsonlite, r-cran-knitr, r-cran-mlr3tuning, r-cran-progress, r-cran-rmarkdown, r-cran-rpart, r-cran-viridis, r-cran-visnetwork, r-cran-testthat, r-cran-tibble, r-cran-tfevents, r-cran-torchvision, r-cran-waldo Filename: pool/dists/noble/main/r-cran-mlr3torch_0.3.3-1.ca2404.1_all.deb Size: 2805546 MD5sum: f4749ad261187ecc53596b6863e4b0bb SHA1: 72ce963a836b176263a883568d5e6058ce8a0b9f SHA256: 23c609215a036ec2e7af5a160eb2d3902d2e21f9f49d4827ff8bfe58be019775 SHA512: 37f0d9ffc2ce70d770ae6a180fec8692467e086e8d3676c563bdf14f9835de85e42c6c79d9e5d7257ce7f9a92955f7d8df6510f2ec2df43dec13adea40bd25e4 Homepage: https://cran.r-project.org/package=mlr3torch Description: CRAN Package 'mlr3torch' (Deep Learning with 'mlr3') Deep Learning library that extends the mlr3 framework by building upon the 'torch' package. It allows to conveniently build, train, and evaluate deep learning models without having to worry about low level details. Custom architectures can be created using the graph language defined in 'mlr3pipelines'. Package: r-cran-mlr3tuning Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1323 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-paradox, r-cran-bbotk, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-adagio, r-cran-future, r-cran-gensa, r-cran-irace, r-cran-knitr, r-cran-mirai, r-cran-mlflow, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-nloptr, r-cran-processx, r-cran-redux, r-cran-rush, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlr3tuning_1.6.0-1.ca2404.1_all.deb Size: 909646 MD5sum: 78a756041f049e96597a81c3108649db SHA1: 08f6d06c09d56a0e1863466d30a59868a960d148 SHA256: c99e40844f58cc7c459fea51bd33d867d17ae0f4fb7501cce7363c62aa4d739c SHA512: 5c8d38b9360e38a268a88d4d929629f17ab9ea8df87c11465b862bcb579bc9f1bda946ec375c0c8ec2e8b56ad84f94cd2b9c13cd05763a7a4e983b6628ef0712 Homepage: https://cran.r-project.org/package=mlr3tuning Description: CRAN Package 'mlr3tuning' (Hyperparameter Optimization for 'mlr3') Hyperparameter optimization package of the 'mlr3' ecosystem. It features highly configurable search spaces via the 'paradox' package and finds optimal hyperparameter configurations for any 'mlr3' learner. 'mlr3tuning' works with several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). Moreover, it can automatically optimize learners and estimate the performance of optimized models with nested resampling. Package: r-cran-mlr3tuningspaces Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3tuning, r-cran-cli, r-cran-checkmate, r-cran-data.table, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6 Suggests: r-cran-e1071, r-cran-bbotk, r-cran-glmnet, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-ranger, r-cran-rpart, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlr3tuningspaces_0.6.0-1.ca2404.1_all.deb Size: 303402 MD5sum: 1124c1531e4df9aec1fd5a639c760a8d SHA1: a6c95d5781f973cb26e822fe1e9a6450d05b2136 SHA256: 4ec13e2d855472ec1db17026a53b47094115ce8094dfd749d8458e749eed7721 SHA512: fbe60aebb350c1d34b2ce461012e207735464f3aa5da3611e6eca5576aeee8bf0018150015c1ffed370dd9d89aa706ca10050198049de4b726f553ab16c3e9a4 Homepage: https://cran.r-project.org/package=mlr3tuningspaces Description: CRAN Package 'mlr3tuningspaces' (Search Spaces for 'mlr3') Collection of search spaces for hyperparameter optimization in the 'mlr3' ecosystem. It features ready-to-use search spaces for many popular machine learning algorithms. The search spaces are from scientific articles and work for a wide range of data sets. Package: r-cran-mlr3verse Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlr3, r-cran-bbotk, r-cran-data.table, r-cran-mlr3cluster, r-cran-mlr3data, r-cran-mlr3filters, r-cran-mlr3fselect, r-cran-mlr3hyperband, r-cran-mlr3inferr, r-cran-mlr3learners, r-cran-mlr3mbo, r-cran-mlr3misc, r-cran-mlr3pipelines, r-cran-mlr3tuning, r-cran-mlr3tuningspaces, r-cran-mlr3viz, r-cran-paradox Suggests: r-cran-miesmuschel, r-cran-mlr3batchmark, r-cran-mlr3benchmark, r-cran-mlr3db, r-cran-mlr3fairness, r-cran-mlr3fda, r-cran-mlr3oml, r-cran-mlr3spatial, r-cran-mlr3spatiotempcv, r-cran-mlr3summary, r-cran-mlr3torch, r-cran-rush Filename: pool/dists/noble/main/r-cran-mlr3verse_0.3.1-1.ca2404.1_all.deb Size: 31192 MD5sum: 72f5015d54f0c14d08be10f5f5664f9e SHA1: c0b19a820e80c615bac9aeca039df31540e8d661 SHA256: 3d2a2dd16e5438d86dd6f530ad4a76bd5e485bb6a2dbe5126239e50ca3c4bfcf SHA512: c92512ac010ff10cffd76376d13141bb952976bd5879ad49d2ccd9885aad7f1a0e6d127f2985f6ae315109c826cc32a0c2850a1172ede32e38f1cde341e8162f Homepage: https://cran.r-project.org/package=mlr3verse Description: CRAN Package 'mlr3verse' (Easily Install and Load the 'mlr3' Package Family) The 'mlr3' package family is a set of packages for machine-learning purposes built in a modular fashion. This wrapper package is aimed to simplify the installation and loading of the core 'mlr3' packages. Get more information about the 'mlr3' project at . Package: r-cran-mlr3viz Architecture: all Version: 0.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr3, r-cran-checkmate, r-cran-data.table, r-cran-ggplot2, r-cran-mlr3misc, r-cran-scales, r-cran-viridis Suggests: r-cran-bbotk, r-cran-clue, r-cran-cluster, r-cran-ggally, r-cran-ggdendro, r-cran-ggfortify, r-cran-ggparty, r-cran-glmnet, r-cran-knitr, r-cran-lgr, r-cran-mlr3cluster, r-cran-mlr3filters, r-cran-mlr3fselect, r-cran-mlr3inferr, r-cran-mlr3learners, r-cran-mlr3tuning, r-cran-paradox, r-cran-partykit, r-cran-patchwork, r-cran-precrec, r-cran-ranger, r-cran-rpart, r-cran-testthat, r-cran-vdiffr, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlr3viz_0.11.0-1.ca2404.1_all.deb Size: 346580 MD5sum: 98a774e123b94ddf2e83668ee4cc213c SHA1: c91040121bc93c8008f42c9d9bbf19ebb7dc1d31 SHA256: eb1b549db3588ecbf215c22fac0c8c0000651bdffcc7a997d1aa649c23efb6a6 SHA512: b2aac436309b9b4cf7416b3d58ea5fa91ee3a3d670fe2126f5ea9ad748aab18ec2938b62f519a0317891cedfa35c3f323c42c4d927541129a7f2f13b752efb94 Homepage: https://cran.r-project.org/package=mlr3viz Description: CRAN Package 'mlr3viz' (Visualizations for 'mlr3') Visualization package of the 'mlr3' ecosystem. It features plots for mlr3 objects such as tasks, learners, predictions, benchmark results, tuning instances and filters via the 'autoplot()' generic of 'ggplot2'. The package draws plots with the 'viridis' color palette and applies the minimal theme. Visualizations include barplots, boxplots, histograms, ROC curves, and Precision-Recall curves. Package: r-cran-mlrcpo Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-paramhelpers, r-cran-mlr, r-cran-bbmisc, r-cran-stringi, r-cran-checkmate, r-cran-backports Suggests: r-cran-care, r-cran-party, r-cran-rpart, r-cran-mlbench, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mrmre, r-cran-digest, r-cran-praznik, r-cran-randomforestsrc, r-cran-randomforest, r-cran-ranger, r-cran-rfast, r-cran-fselector, r-cran-fselectorrcpp, r-cran-e1071, r-cran-fnn, r-cran-lintr, r-cran-hmisc, r-cran-fastica, r-cran-rex Filename: pool/dists/noble/main/r-cran-mlrcpo_0.3.8-1.ca2404.1_all.deb Size: 2112376 MD5sum: b94e7e04fb9b37e97d42467690973adf SHA1: c0d62ef7114cacc28761e9e43cbffa1a11777f5e SHA256: af79b58922d92cc8fdb87099876957ba00f814905f00b63c3bedb06ca092b7b8 SHA512: 920cfcda9e6594145c45de34314d49254d6849a3dce9998e8f7ba18a59ca1daed3d91fec7c43b8af1a538d36fb3ba8e44e8ca724d2d4d4fd0185ca839ec9d9dd Homepage: https://cran.r-project.org/package=mlrCPO Description: CRAN Package 'mlrCPO' (Composable Preprocessing Operators and Pipelines for MachineLearning) Toolset that enriches 'mlr' with a diverse set of preprocessing operators. Composable Preprocessing Operators ("CPO"s) are first-class R objects that can be applied to data.frames and 'mlr' "Task"s to modify data, can be attached to 'mlr' "Learner"s to add preprocessing to machine learning algorithms, and can be composed to form preprocessing pipelines. Package: r-cran-mlrintermbo Architecture: all Version: 0.5.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-lhs, r-cran-callr, r-cran-bbotk, r-cran-mlr3tuning Suggests: r-cran-mlr, r-cran-paramhelpers, r-cran-testthat, r-cran-rgenoud, r-cran-dicekriging, r-cran-emoa, r-cran-cmaesr, r-cran-randomforest, r-cran-smoof, r-cran-lgr, r-cran-mlr3, r-cran-mlr3learners, r-cran-mlr3pipelines, r-cran-mlrmbo, r-cran-ranger, r-cran-rpart, r-cran-mco Filename: pool/dists/noble/main/r-cran-mlrintermbo_0.5.1-1-1.ca2404.1_all.deb Size: 155392 MD5sum: 74558bc68c2f45b5c71c67f2c627ae67 SHA1: 3b81da042d7ad76e6f2a5d58cc3916bf7ec4406b SHA256: b8abb11323e4bd78a3696c83f698ce19bba95a1d2cd0fdab76ff6b75eb8aac5c SHA512: 078d54b9e45218ef7e62de11f7978fc866cb9d31817a58f0ad5b3534ed0a522d28736960f8cb7af2fe995246e492f634584893e65db4e26245b895d91095c2b2 Homepage: https://cran.r-project.org/package=mlrintermbo Description: CRAN Package 'mlrintermbo' (Model-Based Optimization for 'mlr3' Through 'mlrMBO') The 'mlrMBO' package can ordinarily not be used for optimization within 'mlr3', because of incompatibilities of their respective class systems. 'mlrintermbo' offers a compatibility interface that provides 'mlrMBO' as an 'mlr3tuning' 'Tuner' object, for tuning of machine learning algorithms within 'mlr3', as well as a 'bbotk' 'Optimizer' object for optimization of general objective functions using the 'bbotk' black box optimization framework. The control parameters of 'mlrMBO' are faithfully reproduced as a 'paradox' 'ParamSet'. Package: r-cran-mlrpro Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-dplyr, r-cran-mass, r-cran-dgof Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlrpro_0.1.3-1.ca2404.1_all.deb Size: 40394 MD5sum: 043c829d4470e5544c03419a6e6cb053 SHA1: 361211ba1df20d90ab0aa828ebc8a016cf78dbfe SHA256: 76ad254efd2d062a65e1b322941a76ecde02592240559245357aa3ed6a90c899 SHA512: cb3f2a329ec4abf470e7f63e90e5738e35c4957f2933654511f9af06981695206419ef7d7e81522e6fac6b4434d9a0c38e2126f5a0a50a02407573824a997302 Homepage: https://cran.r-project.org/package=mlrpro Description: CRAN Package 'mlrpro' (Stepwise Regression with Assumptions Checking) The stepwise regression with assumptions checking and the possible Box-Cox transformation. Package: r-cran-mls3 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-glmnet, r-cran-lightgbm, r-cran-ranger Suggests: r-cran-caret, r-cran-knitr, r-cran-randomforest, r-cran-kernlab Filename: pool/dists/noble/main/r-cran-mls3_0.1.0-1.ca2404.1_all.deb Size: 100808 MD5sum: 67c0888827e85673ebf307c628638f4a SHA1: 2e34193787fded6ee20d6569d1dc8b52b6d08736 SHA256: 8bda90e9bd5ab4c644d578146d44502aa7ac91f2f2ab7557c93972ab1ecb708c SHA512: b658004a9aac7237c1207503ce45e1924d19b1f2fa9b36de4424a982663b2ce9e5de758bb72fda4faa8c3ed2bb1b6a2e0dcee3d5cf941a22f9cb8c4c2bd5e2ea Homepage: https://cran.r-project.org/package=mlS3 Description: CRAN Package 'mlS3' (Unified S3 Interface to Machine Learning Models) Provides a unified and consistent S3 interface for training and predicting with a variety of machine learning models in R. The package wraps popular algorithms (e.g., from 'glmnet', 'lightgbm', 'ranger', 'e1071', and 'caret') under a common workflow based on simple wrap_*() and predict() functions, allowing users to switch between models without changing their code structure. It supports both classification and regression tasks and facilitates rapid experimentation, benchmarking, and comparison of models. By abstracting away package-specific APIs while preserving flexibility in parameter specification, the package streamlines machine learning workflows and promotes reproducibility. Package: r-cran-mlsjunkgen Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlsjunkgen_0.1.2-1.ca2404.1_all.deb Size: 82006 MD5sum: 29e155fdf5cb355e34743620527c1fee SHA1: fbb6480787511d87e8ca576998143b247718bb07 SHA256: 1ccb231d7414142101c93d64252808eb0c3ee967cac8d482aed16b660c7e2320 SHA512: fb8f52eb7f9b318c3a68a01d447725947afa199192d22568a5a60256a9aa9a4d112d4b90265c06a333a19e8581a90e9f57c0b0208413af5eb0ee1268edd54392 Homepage: https://cran.r-project.org/package=mlsjunkgen Description: CRAN Package 'mlsjunkgen' (Use the MLS Junk Generator Algorithm to Generate a Stream ofPseudo-Random Numbers) Generate a stream of pseudo-random numbers generated using the MLS Junk Generator algorithm. Functions exist to generate single pseudo-random numbers as well as a vector, data frame, or matrix of pseudo-random numbers. Package: r-cran-mlsp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gsignal, r-cran-pls, r-cran-glmnet, r-cran-cubist, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-mlsp_0.1.0-1.ca2404.1_all.deb Size: 96062 MD5sum: 80dd2e1c7360305be72b1ad44a5a4b1a SHA1: 067375b4a2ca9b8764f841bc6b4de33f701f4a40 SHA256: 11b13b19b921e5aed54faec812f5448ff59b8cf40eb13c140743e3cdbf57c3f0 SHA512: e12f81436da18340746ce614d775319209db857e0756afa1b807a27e380802fc5436e04fc173da6b3f2e7b54b01fc8ba19423036ee119d105b1930e4066e937c Homepage: https://cran.r-project.org/package=MLSP Description: CRAN Package 'MLSP' (Machine Learning Models for Soil Properties) Creates a spectroscopy guideline with a highly accurate prediction model for soil properties using machine learning or deep learning algorithms such as LASSO, Random Forest, Cubist, etc., and decide which algorithm generates the best model for different soil types. Package: r-cran-mlspatial Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1996 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-readxl, r-cran-dplyr, r-cran-ggplot2, r-cran-randomforest, r-cran-xgboost, r-cran-e1071, r-cran-caret, r-cran-tmap, r-cran-spdep, r-cran-ggpubr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-kernlab, r-cran-writexl, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mlspatial_0.1.1-1.ca2404.1_all.deb Size: 1565784 MD5sum: de20093c97064b1a0dcaf0161ecef737 SHA1: cdbb3d77a3aae5af92a4880e55820cec7d95335e SHA256: 9c902dbdaf7eed45f02fa49f3a545468d9ea16c1a2c3e9d6c4e359a426678d59 SHA512: fe6d290acef7eb5250b102a27bdaa3380a01f751dee56f47ecb885400b61c147e61c7432b48905e93e94fcbf2167c3a72a402c2838a0c32e93f2a0ec189d061c Homepage: https://cran.r-project.org/package=mlspatial Description: CRAN Package 'mlspatial' (Machine Learning and Mapping for Spatial Epidemiology) Provides tools for the integration, visualisation, and modelling of spatial epidemiological data using the method described in Azeez, A., & Noel, C. (2025). 'Predictive Modelling and Spatial Distribution of Pancreatic Cancer in Africa Using Machine Learning-Based Spatial Model' and . It facilitates the analysis of geographic health data by combining modern spatial mapping tools with advanced machine learning (ML) algorithms. 'mlspatial' enables users to import and pre-process shapefile and associated demographic or disease incidence data, generate richly annotated thematic maps, and apply predictive models, including Random Forest, 'XGBoost', and Support Vector Regression, to identify spatial patterns and risk factors. It is suited for spatial epidemiologists, public health researchers, and GIS analysts aiming to uncover hidden geographic patterns in health-related outcomes and inform evidence-based interventions. Package: r-cran-mlstropalr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-stringr, r-cran-opalr, r-cran-fabr, r-cran-madshapr Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-mlstropalr_1.0.3-1.ca2404.1_all.deb Size: 71082 MD5sum: 816e308e56560906ea89f0b4579f408a SHA1: b17635eca085f19bed002c1d6660b9d1b6b64e7e SHA256: a49ca55d6c029e4e379a1c1c1c0abd346d9c668e0f34f888a83130ae4d700c64 SHA512: ad84f43f38797aa27d1d30629b989dee3dc47244d4077970644273d82ccdf678abee3acfda814d664e362ac19d0a09f56aa675897373e6d43aa5fe00c05b70b4 Homepage: https://cran.r-project.org/package=mlstrOpalr Description: CRAN Package 'mlstrOpalr' (Support Compatibility Between 'Maelstrom' R Packages and 'Opal'Environment) Functions to support compatibility between 'Maelstrom' R packages and 'Opal' environment. 'Opal' is the 'OBiBa' core database application for biobanks. It is used to build data repositories that integrates data collected from multiple sources. 'Opal Maelstrom' is a specific implementation of this software. This 'Opal' client is specifically designed to interact with 'Opal Maelstrom' distributions to perform operations on the R server side. The user must have adequate credentials. Please see for complete documentation. 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The package provides R6-based survival learners for the following algorithms: 'glmnet' , 'ranger' , 'xgboost' , and 'rpart' . These can be used directly with the 'mlexperiments' R package. 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Package: r-cran-mltest Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mltest_1.0.3-1.ca2404.1_all.deb Size: 20616 MD5sum: 59b3c4777466fa5ea4ebb345d47d77b6 SHA1: c45597a8ceca9952db2f691c7297ae5a91b54a27 SHA256: ded9ff7065e726f615712b3761a7039774ecdfbe1dc2f96a7a78e417cccc1a60 SHA512: afb6a972b475d98f356863e33a97d88c5f50520206f23be8f8f83f3a8e52f888611b814937f1df92f0bdfc167b01fa2bb007eb925f404addf00ce1e03eb91932 Homepage: https://cran.r-project.org/package=mltest Description: CRAN Package 'mltest' (Classification Evaluation Metrics) A fast, robust and easy-to-use calculation of multi-class classification evaluation metrics based on confusion matrix. Package: r-cran-mltools Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mltools_0.3.5-1.ca2404.1_all.deb Size: 110682 MD5sum: 6218160d877dfc89b8f571346e49e56e SHA1: 7251ef0505c78fb9d918567f859d69dea80b1595 SHA256: 70f63bb475eb510d89b48b417454c22ed62c2a47549a5727fe1e619750dee4cd SHA512: e2375c87e6f479ee331a890d58b54bfc16daaba2cf92ac114b1bcba77dc3a7cb0657b0f8c5dc207e785c52a2c30209f557397aa21f13d03118920b22f9d517fe Homepage: https://cran.r-project.org/package=mltools Description: CRAN Package 'mltools' (Machine Learning Tools) A collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase. Makes heavy use of the 'data.table' package for optimal speed and memory efficiency. 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Package: r-cran-mlvar Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-arm, r-cran-qgraph, r-cran-dplyr, r-cran-clustergeneration, r-cran-mvtnorm, r-cran-corpcor, r-cran-abind, r-cran-mplusautomation, r-cran-graphicalvar, r-cran-rlang Filename: pool/dists/noble/main/r-cran-mlvar_0.6.1-1.ca2404.1_all.deb Size: 310358 MD5sum: 7f156cde43525c76d874d01809c93e98 SHA1: 831ee2b67e30cf9ab8ca75830087e5a34644bbdc SHA256: 48832d490cccebe124cb9a4ede212625fdbe669b2e6af5c871a96fec7c39a5d2 SHA512: 52cd25b374266c75974d1ca2ba24e5041b3b7e76d24a061ce168d6685995d12d078a20aa34f4dc6c91ba5b8770af8f739bb06bc2ba1070247513e3e4e0b0d758 Homepage: https://cran.r-project.org/package=mlVAR Description: CRAN Package 'mlVAR' (Multi-Level Vector Autoregression) Estimates the multi-level vector autoregression model on time-series data. 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Package: r-cran-mlvsbm Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-blockmodels, r-cran-ape, r-cran-magrittr, r-cran-cluster Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggforce, r-cran-spelling, r-cran-cowplot, r-cran-reshape2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-mlvsbm_0.2.4-1.ca2404.1_all.deb Size: 509868 MD5sum: f802a51c618f919f0e0b48655d98f269 SHA1: 87c3f28a9a53b3775f0024998eb7bf7da6c83df9 SHA256: eabff455a487f6ff5b4a6fa2abf33743c4f58a2f7c71d9e6f33fcc4907018e31 SHA512: e01966b2af9bb14bf1fe920624eda7e6ec9ead4bff0ade8328ecfc4cd8eb53cbc6b794dd75c220112b15c384647783464c7a39ea8d9a4ec44bb7319e20d26c70 Homepage: https://cran.r-project.org/package=MLVSBM Description: CRAN Package 'MLVSBM' (A Stochastic Block Model for Multilevel Networks) Simulation, inference and clustering of multilevel networks using a Stochastic Block Model framework as described in Chabert-Liddell, Barbillon, Donnet and Lazega (2021) . 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Package: r-cran-mlwrap Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 872 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-tidyr, r-cran-magrittr, r-cran-dials, r-cran-parsnip, r-cran-recipes, r-cran-rsample, r-cran-tune, r-cran-workflows, r-cran-yardstick, r-cran-glue, r-cran-innsight, r-cran-shapr, r-cran-diagrammer, r-cran-ggbeeswarm, r-cran-ggplot2, r-cran-sensitivity, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-patchwork, r-cran-cli, r-cran-scales Suggests: r-cran-testthat, r-cran-torch, r-cran-brulee, r-cran-ranger, r-cran-kernlab, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-mlwrap_0.4.0-1.ca2404.1_all.deb Size: 791004 MD5sum: afb0d7a37425568cc8ff81133be1798b SHA1: 44fe7761c43dba096f6ca5c5a475f4aa7f854238 SHA256: ed2182cdc5caed48c82f92fd6d07e50445c92546a9e4f690ba1bf525ba1180ab SHA512: dcaeadf124f8444de2e19264d3bdab06e6b26e673647df6230b3c2368f7fcaa88e9ba59f60facedb3a63b935da7a0004fa3c7d7c381b05ea6234d609e2ff0429 Homepage: https://cran.r-project.org/package=MLwrap Description: CRAN Package 'MLwrap' (Machine Learning Modelling for Everyone) A minimal library specifically designed to make the estimation of Machine Learning (ML) techniques as easy and accessible as possible, particularly within the framework of the Knowledge Discovery in Databases (KDD) process in data mining. The package provides essential tools to structure and execute each stage of a predictive or classification modeling workflow, aligning closely with the fundamental steps of the KDD methodology, from data selection and preparation, through model building and tuning, to the interpretation and evaluation of results using Sensitivity Analysis. The 'MLwrap' workflow is organized into four core steps; preprocessing(), build_model(), fine_tuning(), and sensitivity_analysis(). It also includes global and pairwise interaction analysis based on Friedman’s H-statistic to support a more detailed interpretation of complex feature relationships.These steps correspond, respectively, to data preparation and transformation, model construction, hyperparameter optimization, and sensitivity analysis. The user can access comprehensive model evaluation results including fit assessment metrics, plots, predictions, and performance diagnostics for ML models implemented through 'Neural Networks', 'Random Forest', 'XGBoost' (Extreme Gradient Boosting), and 'Support Vector Machines' (SVM) algorithms. By streamlining these phases, 'MLwrap' aims to simplify the implementation of ML techniques, allowing analysts and data scientists to focus on extracting actionable insights and meaningful patterns from large datasets, in line with the objectives of the KDD process. Package: r-cran-mlxr Architecture: all Version: 4.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 638 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-xml, r-cran-rcpp, r-cran-reshape2, r-cran-gridextra, r-cran-shiny Filename: pool/dists/noble/main/r-cran-mlxr_4.2.0-1.ca2404.1_all.deb Size: 614948 MD5sum: 4d34d9375b6b69673a1697c777bb8d9e SHA1: 337ff7f6357cae5bc24f9098c251951024fd77ad SHA256: 3d2d8b56f63ea511a5c0f9f139381775ef0c31023ee60cafb6bea4424d54367f SHA512: c36fb9ca5cbdba0942e0b6a3d92f5081061fe123eea1f09065872a5c99cc43c155c0adeab680281c849f11faa50da6edb2e2a17ebd6126298b419c86f194820c Homepage: https://cran.r-project.org/package=mlxR Description: CRAN Package 'mlxR' (Simulation of Longitudinal Data) Simulation and visualization of complex models for longitudinal data. The models are encoded using the model coding language 'Mlxtran' and automatically converted into C++ codes. That allows one to implement very easily complex ODE-based models and complex statistical models, including mixed effects models, for continuous, count, categorical, and time-to-event data. Package: r-cran-mm2sdata Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4822 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-mm2sdata_1.0.3-1.ca2404.1_all.deb Size: 4894808 MD5sum: 9fb40daea6c734952907d1f037ea865e SHA1: 82fab3eb0395b5bea8fa4326bb399b0f8e73048b SHA256: 0e3f1cb522e0a9a3d41aa4b492587b2b4a252f22483c079dadcb1371638dce1f SHA512: 4319e85e67df79050f26d89c513126ba878d3205ac86ad034542606ff2022d57281eb9585d08071c14f9d53516c66da922ef48f72e72b7103872a460d19cbce0 Homepage: https://cran.r-project.org/package=MM2Sdata Description: CRAN Package 'MM2Sdata' (Gene Expression Datasets for the 'MM2S' Package) Gene Expression datasets for the 'MM2S' package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) . 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Package: r-cran-mma Architecture: all Version: 10.8-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gbm, r-cran-survival, r-cran-car, r-cran-gplots, r-cran-lattice Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mma_10.8-1-1.ca2404.1_all.deb Size: 929142 MD5sum: d8965d0e21f3b707fa4b220a40050061 SHA1: 28ac441df6dea430ae7e562d1c81a68957983880 SHA256: ce06a18b523f4b5645a0112ba46e900dc990a88603e7d898d6a6bcfb93f8f6eb SHA512: 5cf45e80d445e4bfe1492b489d1353f29392a8729da00845da506b8f17e3d86bacb5fd8645f0024a3c42cab595c63e38fdf9cfde1135920011e6a5be46931c1f Homepage: https://cran.r-project.org/package=mma Description: CRAN Package 'mma' (Multiple Mediation Analysis) Used for general multiple mediation analysis. The analysis method is described in Yu and Li (2022) (ISBN: 9780367365479) "Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS", published by Chapman and Hall/CRC; and Yu et al.(2017) "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23. 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Package: r-cran-mmac Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 816 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mosaic, r-cran-mosaiccalc, r-cran-plotly Filename: pool/dists/noble/main/r-cran-mmac_1.0-1.ca2404.1_all.deb Size: 760462 MD5sum: 3a0eeadf695ef5598e8805cb9aa37073 SHA1: 7a917d0470c71b2cdb6957919ad9163f00903306 SHA256: ab06479b21d0187abf53b6269f5ea6e2c9b2402344c1fd13cf4711ffaf8caa3d SHA512: 4d6e3da0d71b7ae656aa733aa5b70f85c144c5b387b53de8d2a40c20b37aab98a4599b0eb5b87c116cec1b822aaef0e7b21ae891ea3542d856ae131add0f5b4a Homepage: https://cran.r-project.org/package=MMAC Description: CRAN Package 'MMAC' (Data for Mathematical Modeling and Applied Calculus) Contains the data sets for the first and second editions of the textbook "Mathematical Modeling and Applied Calculus" by Joel Kilty and Alex M. McAllister. The first edition of the book was published by Oxford University Press in 2018 with ISBN-13: 978-019882472. The second edition is expected to be published in January 2027. Package: r-cran-mmad Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmad_2.0.1-1.ca2404.1_all.deb Size: 41088 MD5sum: eeeaaf102aa7094a8f0f22b5826a29c4 SHA1: 1df41ec3ec7b2f855c84a4b798c48715163b0d9b SHA256: d1952409238a4bda00a97c92aa793df69a3a6054b1dee427c2bfe5006146fa9f SHA512: 62c6fcc239187b0317be3f44e60645f468eca0866160f2ca30d1de6cfa6b1684eda1eae39c2d2dc266c78ea6b2891e1580aa593ca2584ca70f912b67247fbe9c Homepage: https://cran.r-project.org/package=MMAD Description: CRAN Package 'MMAD' (An R Package of Minorization-Maximization Algorithm via theAssembly--Decomposition Technology) The minorization-maximization (MM) algorithm is a powerful tool for maximizing nonconcave target function. However, for most existing MM algorithms, the surrogate function in the minorization step is constructed in a case-specific manner and requires manual programming. To address this limitation, we develop the R package MMAD, which systematically integrates the assembly--decomposition technology in the MM framework. This new package provides a comprehensive computational toolkit for one-stop inference of complex target functions, including function construction, evaluation, minorization and optimization via MM algorithm. By representing the target function through a hierarchical composition of assembly functions, we design a hierarchical algorithmic structure that supports both bottom-up operations (construction, evaluation) and top-down operation (minorization). Package: r-cran-mmaqshiny Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15863 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-cairo, r-cran-xts, r-cran-lubridate, r-cran-zoo, r-cran-catools, r-cran-ggplot2, r-cran-data.table, r-cran-dt, r-cran-dplyr, r-cran-leaflet, r-cran-stringr, r-cran-shiny, r-cran-xml, r-cran-shinyjs, r-cran-plotly Suggests: r-cran-testthat, r-cran-devtools, r-cran-usethis, r-cran-shinytest Filename: pool/dists/noble/main/r-cran-mmaqshiny_1.0.0-1.ca2404.1_all.deb Size: 3268424 MD5sum: 9a0300718156d7eb3f894907a04812c2 SHA1: dcdeddcd2fb7d2690d92ed53c62a456e5feea671 SHA256: 3474647763d044f4f0296731276174a3a9b8f748a44cf6663678fa4b0c3b0a79 SHA512: 485b6f85b8446caece4749361a7cac32811416451b39993c95452b98f471d284594c95bf94c3e5f60916996ca8305f94ee2c0a1a6c255df3095329b17a4e19db Homepage: https://cran.r-project.org/package=mmaqshiny Description: CRAN Package 'mmaqshiny' (Explore Air-Quality Mobile-Monitoring Data) Mobile-monitoring or "sensors on a mobile platform", is an increasingly popular approach to measure high-resolution pollution data at the street level. Coupled with location data, spatial visualisation of air-quality parameters helps detect localized areas of high air-pollution, also called hotspots. In this approach, portable sensors are mounted on a vehicle and driven on predetermined routes to collect high frequency data (1 Hz). 'mmaqshiny' is for analysing, visualising and spatial mapping of high-resolution air-quality data collected by specific devices installed on a moving platform. 1 Hz data of PM2.5 (mass concentrations of particulate matter with size less than 2.5 microns), Black carbon mass concentrations (BC), ultra-fine particle number concentrations, carbon dioxide along with GPS coordinates and relative humidity (RH) data collected by popular portable instruments (TSI DustTrak-8530, Aethlabs microAeth-AE51, TSI CPC3007, LICOR Li-830, Garmin GPSMAP 64s, Omega USB RH probe respectively). It incorporates device specific cleaning and correction algorithms. RH correction is applied to DustTrak PM2.5 following the Chakrabarti et al., (2004) . Provision is given to add linear regression coefficients for correcting the PM2.5 data (if required). BC data will be cleaned for the vibration generated noise, by adopting the statistical procedure as explained in Apte et al., (2011) , followed by a loading correction as suggested by Ban-Weiss et al., (2009) . For the number concentration data, provision is given for dilution correction factor (if a diluter is used with CPC3007; default value is 1). The package joins the raw, cleaned and corrected data from the above said instruments and outputs as a downloadable csv file. Package: r-cran-mmarch.ac Architecture: all Version: 3.3.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1854 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-refund, r-cran-denseflmm, r-cran-dplyr, r-cran-xlsx, r-cran-survival, r-cran-tidyr, r-cran-zoo, r-cran-ineq, r-cran-cosinor, r-cran-cosinor2, r-cran-abind, r-cran-accelerometry, r-cran-actcr, r-cran-actfrag, r-cran-minpack.lm, r-cran-kableextra, r-cran-ggir Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mmarch.ac_3.3.4.0-1.ca2404.1_all.deb Size: 1085448 MD5sum: b1557dca285828285187dc0097d36259 SHA1: 6fbe350ad08176e657322839b56caacdbcc51d92 SHA256: 13ea9af8f6181fe22a71d973f96dd52ac102905c707af2214e2cf9184ec5c7f6 SHA512: 2c0f040c7d9ad510119e0c8f84f71cbc0d470d1712fe2510898937b266a240bbd0027bf0c80321450f5050c8f007427d4a268df0fbb95bccdaf653368721c36a Homepage: https://cran.r-project.org/package=mMARCH.AC Description: CRAN Package 'mMARCH.AC' (Processing of Accelerometry Data with 'GGIR' in mMARCH) Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of studies of clinical and community samples that employ common clinical, biological, and digital mobile measures across involved studies. One of the main scientific goals of mMARCH sites is developing a better understanding of the inter-relationships between accelerometry-measured physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. Currently, there is no consensus on a standard procedure for a data processing pipeline of raw accelerometry data, and few open-source tools to facilitate their development. The R package 'GGIR' is the most prominent open-source software package that offers great functionality and tremendous user flexibility to process raw accelerometry data. However, even with 'GGIR', processing done in a harmonized and reproducible fashion requires a non-trivial amount of expertise combined with a careful implementation. In addition, novel accelerometry-derived features of PA/SL/CR capturing multiscale, time-series, functional, distributional and other complimentary aspects of accelerometry data being constantly proposed and become available via non-GGIR R implementations. To address these issues, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data, extracting features available through 'GGIR' as well as through non-GGIR R packages, implementing several data and feature quality checks, merging all features of PA/SL/CR together, and performing multiple analyses including Joint Individual Variation Explained (JIVE), an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR. In detail, the pipeline generates all necessary R/Rmd/shell files for data processing after running 'GGIR' for accelerometer data. In module 1, all csv files in the 'GGIR' output directory were read, transformed and then merged. In module 2, the 'GGIR' output files were checked and summarized in one excel sheet. In module 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In module 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data. Reference: Guo W, Leroux A, Shou S, Cui L, Kang S, Strippoli MP, Preisig M, Zipunnikov V, Merikangas K (2022) Processing of accelerometry data with GGIR in Motor Activity Research Consortium for Health (mMARCH) Journal for the Measurement of Physical Behaviour, 6(1): 37-44. Package: r-cran-mmb Architecture: all Version: 0.13.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-foreach, r-cran-doparallel Suggests: r-cran-devtools, r-cran-testthat, r-cran-covr, r-cran-e1071, r-cran-caret, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggpubr, r-cran-cowplot, r-cran-philentropy, r-cran-rtsne Filename: pool/dists/noble/main/r-cran-mmb_0.13.3-1.ca2404.1_all.deb Size: 196458 MD5sum: dea24100a4a8b807149c61e30d179558 SHA1: a12b9f9e01d0159e573a60c92ce7a471c7e6b349 SHA256: 5748aaec91c54c6e9cbc6aae0df9672b1d28f0151591a6fbf685e1705d833cf0 SHA512: e58704552182e04b62eb34b53d3e81840ac3cb61b3d3ac501b89caba39a48a620cd6cb799ec87ce621c3f5f5fbdb3114d2d7093475a32c9ffc1bcdecd94bf8e2 Homepage: https://cran.r-project.org/package=mmb Description: CRAN Package 'mmb' (Arbitrary Dependency Mixed Multivariate Bayesian Models) Supports Bayesian models with full and partial (hence arbitrary) dependencies between random variables. Discrete and continuous variables are supported, and conditional joint probabilities and probability densities are estimated using Kernel Density Estimation (KDE). The full general form, which implements an extension to Bayes' theorem, as well as the simple form, which is just a Bayesian network, both support regression through segmentation and KDE and estimation of probability or relative likelihood of discrete or continuous target random variables. This package also provides true statistical distance measures based on Bayesian models. Furthermore, these measures can be facilitated on neighborhood searches, and to estimate the similarity and distance between data points. Related work is by Bayes (1763) and by Scutari (2010) . Package: r-cran-mmbcv Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmbcv_0.3.0-1.ca2404.1_all.deb Size: 104134 MD5sum: d47e302172aa6c2be8d6ade6346aeeba SHA1: e28b131e391cde24706c2f521475a2324bc4cf88 SHA256: a6fda4c9a71b3014dabbf9c3f4f67f560a1eaa5a52e094aa887b3eab85cd763a SHA512: b4b5d299f82af1b519cb971d7cac425f223e782d5c10ce39592097077709d23b33b220aa26d2dfe36b346df9a764e39a8d53e534dde43ab6072bd0bc574693a8 Homepage: https://cran.r-project.org/package=mmbcv Description: CRAN Package 'mmbcv' (Multistate Model Bias-Corrected Robust Variance) Computes robust and bias-corrected sandwich variance estimators for multi-state Cox models with clustered time-to-event data. The methodology extends the marginal Cox model bias-correction framework of Wang et al. (2023) to the multi-state setting. Package: r-cran-mmc Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mass Filename: pool/dists/noble/main/r-cran-mmc_0.0.3-1.ca2404.1_all.deb Size: 30374 MD5sum: a8fe33b36860f4e15b22ca4b4724a52c SHA1: e831f69d08f2921a3757cab41488c935b99f85de SHA256: 4989f205ebec2a6e30ffa8ed834743a4092aab33a3c75169f94ebb705b88afe7 SHA512: aa36fe7bfeb1381514f5b3eef883e051969ab0a46409918f66b8f9c27ea9219cf7cfad92abb3c3825b713d2c30a1bd5f7da7a9741849fb5c9ac56e29f3ad6f93 Homepage: https://cran.r-project.org/package=mmc Description: CRAN Package 'mmc' (Multivariate Measurement Error Correction) Provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates. Package: r-cran-mmcards Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmcards_0.1.1-1.ca2404.1_all.deb Size: 36398 MD5sum: 48d4455c81daeb22e838f23c5f442850 SHA1: 4f66b132db5fab54eb28af59d415e32d94466d3f SHA256: 7c4c209e229e2ecf6c1d287524fd4536357714b685062f97533007f6eb162201 SHA512: 5d3ee2a88dc3b47631ca896f15597b4a524909f4353de939add4235a1abd4c46060e68df68d4858b34fa1adc7705d7596d87fdda4a262ff7820568e4481b5fd9 Homepage: https://cran.r-project.org/package=mmcards Description: CRAN Package 'mmcards' (Playing Cards Utility Functions) Early insights in probability theory were largely influenced by questions about gambling and games of chance, as noted by Blitzstein and Hwang (2019, ISBN:978-1138369917). In modern times, playing cards continue to serve as an effective teaching tool for probability, statistics, and even 'R' programming, as demonstrated by Grolemund (2014, ISBN:978-1449359010). The 'mmcards' package offers a collection of utility functions designed to aid in the creation, manipulation, and utilization of playing card decks in multiple formats. These include a standard 52-card deck, as well as alternative decks such as decks defined by custom anonymous functions and custom interleaved decks. Optimized for the development of educational 'shiny' applications, the package is particularly useful for teaching statistics and probability through card-based games. Functions include shuffle_deck(), which creates either a shuffled standard deck or a shuffled custom alternative deck; deal_card(), which takes a deck and returns a list object containing both the dealt card and the updated deck; and i_deck(), which adds image paths to card objects, further enriching the package's utility in the development of interactive 'shiny' application card games. Package: r-cran-mmcmcbayes Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6042 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack Suggests: r-cran-ksamples, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mmcmcbayes_0.2.0-1.ca2404.1_all.deb Size: 6150850 MD5sum: b3223d524ada30dd487815e3dfea268d SHA1: c7ad3dd919fced224510151ce9b085552b3940bb SHA256: 4673a6b25b593f6e59911f8fb7a13e8ddc95a3437ea2e1f8c4394cf0f1ca570e SHA512: 74888ba3c73fa66bcd94e53a3d924aedd54563e1ad7a937c604f36aa610c92088d3738f6f87ffa21e39c27a3e7c78210af8ebdc69aa5bb85c594440880e51bfb Homepage: https://cran.r-project.org/package=mmcmcBayes Description: CRAN Package 'mmcmcBayes' (Multistage MCMC Method for Detecting DMRs) Implements differential methylation region (DMR) detection using a multistage Markov chain Monte Carlo (MCMC) algorithm based on the alpha-skew generalized normal (ASGN) distribution. Version 0.2.0 removes the Anderson-Darling test stage, improves computational efficiency of the core ASGN and multistage MCMC routines, and adds convenience functions for summarizing and visualizing detected DMRs. The methodology is based on Yang (2025) . Package: r-cran-mmcsd Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-purrr, r-cran-rlist, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-simstudy, r-cran-kableextra, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-mmcsd_1.0.0-1.ca2404.1_all.deb Size: 231506 MD5sum: 8d706c4a831577c03a970e50fca013f5 SHA1: 0275c4c02a7c6ae2683ca5ad1bf950803092671b SHA256: 62419fb72e2de810017f633a19df9f9c415cb4487350b7cfc6dd70a787f213b1 SHA512: c9052e583a7582fcf707fce10d11301c194547811502d3a2b3ecc181f5f46e23d0f25682cf7090302932fcb041808cee4aaa07c8ff44522cb871c2d6cc0e2966 Homepage: https://cran.r-project.org/package=Mmcsd Description: CRAN Package 'Mmcsd' (Modeling Complex Longitudinal Data in a Quick and Easy Way) Matching longitudinal methodology models with complex sampling design. It fits fixed and random effects models and covariance structured models so far. It also provides tools to perform statistical tests considering these specifications as described in : Pacheco, P. H. (2021). "Modeling complex longitudinal data in R: development of a statistical package." . Package: r-cran-mmd Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-plyr, r-cran-bigmemory Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mmd_1.0.0-1.ca2404.1_all.deb Size: 65178 MD5sum: 17d9415b85c05731608145e663ad37da SHA1: 980c33a6b625b64c4b8cc47549a6126680f5fe00 SHA256: 57fd4ea7beaf25e403aebfb4fceec49474d623c7e4177c6f3bfddfe9902867a5 SHA512: e9307eecd45ddc89542488731dd03721839873571bcfa3ec476a7d7717cc619239efb8d64d69a4d187cedcf19691dfc7ca8e6bb2520182d7d40983e530dbfafe Homepage: https://cran.r-project.org/package=MMD Description: CRAN Package 'MMD' (Minimal Multilocus Distance (MMD) for Source Attribution andLoci Selection) The aim of the package is two-fold: (i) To implement the MMD method for attribution of individuals to sources using the Hamming distance between multilocus genotypes. (ii) To select informative genetic markers based on information theory concepts (entropy, mutual information and redundancy). The package implements the functions introduced by Perez-Reche, F. J., Rotariu, O., Lopes, B. S., Forbes, K. J. and Strachan, N. J. C. Mining whole genome sequence data to efficiently attribute individuals to source populations. Scientific Reports 10, 12124 (2020) . See more details and examples in the README file. 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Please see the reference: Li X, Fu Y, Wang X, DeMeo DL, Tantisira K, Weiss ST, Qiu W. (2018) . 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Package: r-cran-mmeln Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mmeln_1.5-1.ca2404.1_all.deb Size: 128304 MD5sum: 4abdcec6428ad65e892021e210209a74 SHA1: 8540ccaea3f5a86efb42e98980e52aaface30710 SHA256: 44c8f548f158680e559b63214458da2f40c16bbd3259ecbe323f0629f0cf1169 SHA512: 5600344b26c4556205c230ce027e91dda60a6e99ed43986876a9fa098788a77c938c33377cfe0a7f03f93e107f5f711647c0d869e3e4c9b9b3034b67374f58fa Homepage: https://cran.r-project.org/package=mmeln Description: CRAN Package 'mmeln' (Estimation of Multinormal Mixture Distribution) Fit multivariate mixture of normal distribution using covariance structure. Package: r-cran-mmem Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-jointdiag, r-cran-lme4, r-cran-matrixcalc, r-cran-psych, r-cran-stringr Filename: pool/dists/noble/main/r-cran-mmem_0.1.1-1.ca2404.1_all.deb Size: 48674 MD5sum: 43dd87c1ccefb40e5fe3fc9497c6ad1d SHA1: f3cd9b36a240a1b689e89e8b4f07573e1570efc3 SHA256: 03f21baa8b8c23e0921be8ff43f7713eeabb478fa8c4ca343b0e282a79cd23ae SHA512: a12d73f1e241abeb5e24a2a2206356b7ebb579e8a970824862b9ead3ee9b92820ea73fc9242890c13a0420bd055c9ecd70de60ef80cb505deeff9408c5d2a95d Homepage: https://cran.r-project.org/package=MMeM Description: CRAN Package 'MMeM' (Multivariate Mixed Effects Model) Analyzing data under multivariate mixed effects model using multivariate REML and multivariate Henderson3 methods. See Meyer (1985) and Wesolowska Janczarek (1984) . Package: r-cran-mmequiv Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-httr2, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-dplyr, r-cran-httptest2, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-mmequiv_1.0.0-1.ca2404.1_all.deb Size: 164780 MD5sum: 285be41552deebcdd4f257b6ddde7ecf SHA1: 28104e7b12426c39b3d43ebdef362438342fa95e SHA256: 3dfd23b5f4fe765b7db4c2fdaf324aeed540618617fcc6b416ec020208b9ac03 SHA512: 02af711d96602ec4f1ad90e538bde988f8cd1f49b5c8140a7494184a99f25581682550772aeea9c10b570667a28c51f02dfa1e98b5e423969dd7ded2f8f0a7bd Homepage: https://cran.r-project.org/package=mmequiv Description: CRAN Package 'mmequiv' (Calculate Standardized Morphine Milligram Equivalent Doses) Calculate morphine milligram equivalents (MME) for opioid dose comparison using standardized methods. Can directly call the 'NIH HEAL MME Online Calculator' API or replicate API calculations on the user's local machine from the comfort of 'R'. Creation of the 'NIH HEAL MME Online Calculator' and the MME calculations implemented in this package are described in Adams MCB, Sward KA, Perkins ML, Hurley RW (2025) . Package: r-cran-mmibain Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2068 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bain, r-cran-broom, r-cran-car, r-cran-dt, r-cran-e1071, r-cran-ggplot2, r-cran-igraph, r-cran-lavaan, r-cran-mmcards, r-cran-psych, r-cran-shiny, r-cran-shinythemes Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmibain_0.2.0-1.ca2404.1_all.deb Size: 1837894 MD5sum: 716cfd91601a82818990209768e297e7 SHA1: 78ff0d39efd9f85d2896fe11b5eaf47ee37c5d02 SHA256: 09a988cdb898a5a43aa4062ced15467d31f3b3a0503e9bc82dd6a5f00e42e6c5 SHA512: a43b96903442d29afd0d6093356acfe4341660b3e044a3bb3d2d384fd8787f8244a13a6f81037e0905e7d61f22e30f2c92624058bcc228b7b111e08c3501a596 Homepage: https://cran.r-project.org/package=mmibain Description: CRAN Package 'mmibain' (Bayesian Informative Hypotheses Evaluation Web Applications) Researchers often have expectations about the relations between means of different groups or standardized regression coefficients; using informative hypothesis testing to incorporate these expectations into the analysis through order constraints increases statistical power Vanbrabant and Rosseel (2020) . Another valuable tool, the Bayes factor, can evaluate evidence for multiple hypotheses without concerns about multiple testing, and can be used in Bayesian updating Hoijtink, Mulder, van Lissa & Gu (2019) . The 'bain' R package enables informative hypothesis testing using the Bayes factor. The 'mmibain' package provides 'shiny' web applications based on 'bain'. The RepliCrisis() function launches a 'shiny' card game to simulate the evaluation of replication studies while the mmibain() function launches a 'shiny' application to fit Bayesian informative hypotheses evaluation models from 'bain'. Package: r-cran-mmicats Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-broom.mixed, r-cran-clusterses, r-cran-dt, r-cran-lmertest, r-cran-mass, r-cran-mmcards, r-cran-pool, r-cran-robust, r-cran-robustbase, r-cran-rpostgres, r-cran-shiny, r-cran-shinythemes Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmicats_0.2.0-1.ca2404.1_all.deb Size: 1875232 MD5sum: 3eead84c481301019cf90e1a3bccf217 SHA1: 2a483043833d08dc1b7b2483da3bf080e5c08410 SHA256: c62f52c5be92864bd4ec4fa7bf772aec7aba7160cde53c2f90e73a963f46d273 SHA512: ffca9c80c353aefb56a1c29887064c1e34083d6f55a3edfd8f5c3d748b6170c3128d0a0e993e53eac5cd2b70eb893fde1633d23bbc2ac11bb473bb0775870d5e Homepage: https://cran.r-project.org/package=mmiCATs Description: CRAN Package 'mmiCATs' (Cluster Adjusted t Statistic Applications) Simulation results detailed in Esarey and Menger (2019) demonstrate that cluster adjusted t statistics (CATs) are an effective method for correcting standard errors in scenarios with a small number of clusters. The 'mmiCATs' package offers a suite of tools for working with CATs. The mmiCATs() function initiates a 'shiny' web application, facilitating the analysis of data utilizing CATs, as implemented in the cluster.im.glm() function from the 'clusterSEs' package. Additionally, the pwr_func_lmer() function is designed to simplify the process of conducting simulations to compare mixed effects models with CATs models. For educational purposes, the CloseCATs() function launches a 'shiny' application card game, aimed at enhancing users' understanding of the conditions under which CATs should be preferred over random intercept models. Package: r-cran-mminp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3666 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-omicspls Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-prettydoc, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mminp_0.1.0-1.ca2404.1_all.deb Size: 3630364 MD5sum: ea6f2ad6c2be85f412233c3ba018f2a6 SHA1: 4a05ef43b200fb929d3f56c94941a2bdf0f788af SHA256: 111110cad7fac32c36d10d3d472a894a526ebce3f85ff9dcc556206bcfbc8c41 SHA512: b0bd3474f92cfa84b1578883a167f2946a1276f1877d615930efd3fdd3bce343fd215464126600e2429286cfc3cc57dbc0b16dba475750fb5a3cb5e9d22360c6 Homepage: https://cran.r-project.org/package=MMINP Description: CRAN Package 'MMINP' (Microbe-Metabolite Interactions-Based Metabolic ProfilesPredictor) Implements a computational framework to predict microbial community-based metabolic profiles with 'O2PLS' model. It provides procedures of model training and prediction. Paired microbiome and metabolome data are needed for modeling, and the trained model can be applied to predict metabolites of analogous environments using new microbial feature abundances. Package: r-cran-mmints Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-pool, r-cran-rpostgres, r-cran-shiny, r-cran-shinyauthr, r-cran-sodium Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmints_0.2.0-1.ca2404.1_all.deb Size: 54738 MD5sum: bef0f0ba10000f5e96ff63d0e4722134 SHA1: cee6b689bf99ffd108469f635cc2ed9c798cff35 SHA256: 2727a2c391a93825ca38636fea97213b0dcf484f04d78031d142fa9d2c1ccf47 SHA512: c2433953fa15ea2b63d94093ebd9ff13be72ada6e2b0d0481e55cec93096e17adc111a0ba24fc183c7d17d23df6699cf10e09ea0097e7477ef422c195cc6c232 Homepage: https://cran.r-project.org/package=mmints Description: CRAN Package 'mmints' (Workflows for Building Web Applications) Sharing statistical methods or simulation frameworks through 'shiny' applications often requires workflows for handling data. To help save and display simulation results, the postgresUI() and postgresServer() functions in 'mmints' help with persistent data storage using a 'PostgreSQL' database. The 'mmints' package also offers data upload functionality through the csvUploadUI() and csvUploadServer() functions which allow users to upload data, view variables and their types, and edit variable types before fitting statistical models within the 'shiny' application. These tools aim to enhance efficiency and user interaction in 'shiny' based statistical and simulation applications. Package: r-cran-mmirestriktor Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2092 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-mass, r-cran-mmcards, r-cran-pool, r-cran-restriktor, r-cran-rpostgres, r-cran-shiny, r-cran-shinythemes Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmirestriktor_0.3.1-1.ca2404.1_all.deb Size: 1859798 MD5sum: 5e881912809315cda287f45d7c0c2927 SHA1: b3507e5b8e14eb0fb9e9258e7818c7cdc9dd1996 SHA256: 41ecf47547a60a5ab0b0a9c32a3d6561201687bf8815e116cd66ceea17b47e98 SHA512: 853633c0e688b8b35f3d58052de318e306fd9a110937a155aba79ae81647945493c765484a2653fdf1b7b8a31628f98a816428546e84165962f4dcdaa7a815cc Homepage: https://cran.r-project.org/package=mmirestriktor Description: CRAN Package 'mmirestriktor' (Informative Hypothesis Testing Web Applications) Offering enhanced statistical power compared to traditional hypothesis testing methods, informative hypothesis testing allows researchers to explicitly model their expectations regarding the relationships among parameters. An important software tool for this framework is 'restriktor'. The 'mmirestriktor' package provides 'shiny' web applications to implement some of the basic functionality of 'restriktor'. The mmirestriktor() function launches a 'shiny' application for fitting and analyzing models with constraints. The FbarCards() function launches a card game application which can help build intuition about informative hypothesis testing. The iht_interpreter() helps interpret informative hypothesis testing results based on guidelines in Vanbrabant and Rosseel (2020) . Package: r-cran-mmlr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-mmlr_0.2.0-1.ca2404.1_all.deb Size: 70282 MD5sum: 3fe2b7dd2e7df1289276b3ea92eb4593 SHA1: a54df6932ddd13f38de5e5f6dfeae33ec6bf3b29 SHA256: 59c32dd8b81ce7be0de9d679caf3c56a87838059c61565c6df75c0500bf90207 SHA512: 11f885646d1f95e48e44c67b916ea23e78405e4c9e2d04b3f0afa696870609f0215277c6e07f3f1a5bccd955cd482db3f826dc478e78ab8d76d2764979fba12a Homepage: https://cran.r-project.org/package=MMLR Description: CRAN Package 'MMLR' (Fitting Markov-Modulated Linear Regression Models) A set of tools for fitting Markov-modulated linear regression, where responses Y(t) are time-additive, and model operates in the external environment, which is described as a continuous time Markov chain with finite state space. Model is proposed by Alexander Andronov (2012) and algorithm of parameters estimation is based on eigenvalues and eigenvectors decomposition. Markov-switching regression models have the same idea of varying the regression parameters randomly in accordance with external environment. The difference is that for Markov-modulated linear regression model the external environment is described as a continuous-time homogeneous irreducible Markov chain with known parameters while switching models consider Markov chain as unobserved and estimation procedure involves estimation of transition matrix. These models have significant differences in terms of the analytical approach. Also, package provides a set of data simulation tools for Markov-modulated linear regression (for academical/research purposes). Research project No. 1.1.1.2/VIAA/1/16/075. Package: r-cran-mmmgee Architecture: all Version: 1.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mvtnorm Suggests: r-cran-geepack, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmmgee_1.20-1.ca2404.1_all.deb Size: 137254 MD5sum: 08bf6d86227431cc351ad75becf53fb0 SHA1: 2cb4291f96a9daf2570aadc847bdfa30051a3527 SHA256: befb9eb0936e9b68ac2c9a8b53f7a172cd599f6df03b1208a09d25c123e11dd1 SHA512: add8427045c80637dfd73b89c09103fff98a211365cb271407f27975f99a69ebbbdff12311db2925f756c672b7a5edbda94896dffe70d3196283083885dba92a Homepage: https://cran.r-project.org/package=mmmgee Description: CRAN Package 'mmmgee' (Simultaneous Inference for Multiple Linear Contrasts in GEEModels) Provides global hypothesis tests, multiple testing procedures and simultaneous confidence intervals for multiple linear contrasts of regression coefficients in a single generalized estimating equation (GEE) model or across multiple GEE models. GEE models are fit by a modified version of the 'geeM' package. Package: r-cran-mmoc Architecture: all Version: 0.1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4028 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spectrum, r-cran-igraph, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-snftool, r-cran-plotly Filename: pool/dists/noble/main/r-cran-mmoc_0.1.1.0-1.ca2404.1_all.deb Size: 1102396 MD5sum: d54e8714926ea8d94b05e9a210ac030f SHA1: af7355cbd698c266188d37a0fa0b6cb8d28b5ec3 SHA256: 4308970ce315eff3724b51b060b0c87363eed7839e0c8150eed889b2d6b742c3 SHA512: 53c0ce0eb4e27089ce9e23015ba3313209d269eedb4a77a3604e49aadab5c9026f256eca9d2d680e8d7af0c845a7982f612e91da04789476f9b4b1bb13bfab91 Homepage: https://cran.r-project.org/package=MMOC Description: CRAN Package 'MMOC' (Multi-Omic Spectral Clustering using the Flag Manifold) Multi-omic (or any multi-view) spectral clustering methods often assume the same number of clusters across all datasets. We supply methods for multi-omic spectral clustering when the number of distinct clusters differs among the omics profiles (views). Package: r-cran-mmod Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-adegenet, r-cran-pegas Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mmod_1.3.3-1.ca2404.1_all.deb Size: 290092 MD5sum: 3f5a113c0cceb28598b54d298ca644bc SHA1: 8579245bda2873b0428807f06954c473164979de SHA256: 1c55029abd6d06769735da6e5fa6d714c8890b2ad4a74182dfe2ad11b3462bcc SHA512: 237622f92535334d27e822d9b99c18310e60640bc044754b45d90cebd55ddfd85145364ab5e952f8a1586fcc8b9b8c02624ba2bbfc2260b11a80937a6733f744 Homepage: https://cran.r-project.org/package=mmod Description: CRAN Package 'mmod' (Modern Measures of Population Differentiation) Provides functions for measuring population divergence from genotypic data. Package: r-cran-mmodely Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 759 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caper, r-cran-caroline, r-cran-ape Filename: pool/dists/noble/main/r-cran-mmodely_0.2.5-1.ca2404.1_all.deb Size: 492362 MD5sum: f8cd6b269656d94dd35530dae15b3e95 SHA1: d8778269ce13d9b029b2826727e14246ea47cb3a SHA256: 3b262ce1d2a38eebe0a1e5d3f7eb7e129218de336b63d3855ac78e31cfa77b17 SHA512: 5ebe09c0677058d247d711f3690bc2ca06b6e8511e3aa02103b34a2988f5346ba202c128e375fda4d5586a53c8deac5ce5f5a5e2ee538a51dbe84f79cf132264 Homepage: https://cran.r-project.org/package=mmodely Description: CRAN Package 'mmodely' (Modeling Multivariate Origins Determinants - EvolutionaryLineages in Ecology) Perform multivariate modeling of evolved traits, with special attention to understanding the interplay of the multi-factorial determinants of their origins in complex ecological settings (Stephens, 2007 ). This software primarily concentrates on phylogenetic regression analysis, enabling implementation of tree transformation averaging and visualization functionality. Functions additionally support information theoretic approaches (Grueber, 2011 ; Garamszegi, 2011 ) such as model averaging and selection of phylogenetic models. Accessory functions are also implemented for coef standardization (Cade 2015), selection uncertainty, and variable importance (Burnham & Anderson 2000). There are other numerous functions for visualizing confounded variables, plotting phylogenetic trees, as well as reporting and exporting modeling results. Lastly, as challenges to ecology are inherently multifarious, and therefore often multi-dataset, this package features several functions to support the identification, interpolation, merging, and updating of missing data and outdated nomenclature. Package: r-cran-mmpa Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mmpa_1.2.0-1.ca2404.1_all.deb Size: 70046 MD5sum: 261fb50687a5a1de4d99be45ceb23d00 SHA1: 01fa64959047148177333bafb900aab1de5f59a1 SHA256: 4a031b1dd831dec99fe0b2c39ae3b28eaa1d8ec9a4600806eaaef8876110dbb0 SHA512: 1c27ae3dd5719a373702e9c7c8e267ed1c8590292735b37958177aecaf6da3a2dce8a02582104833c71c5f2e249cc433c596c90702339a6317e07c45ad1848d6 Homepage: https://cran.r-project.org/package=mMPA Description: CRAN Package 'mMPA' (Implementation of Marker-Assisted Mini-Pooling with Algorithm) To determine the number of quantitative assays needed for a sample of data using pooled testing methods, which include mini-pooling (MP), MP with algorithm (MPA), and marker-assisted MPA (mMPA). To estimate the number of assays needed, the package also provides a tool to conduct Monte Carlo (MC) to simulate different orders in which the sample would be collected to form pools. Using MC avoids the dependence of the estimated number of assays on any specific ordering of the samples to form pools. 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It combines two different methods to generate non-normal data, one with user-specified multivariate skewness and kurtosis (more details can be found in the paper: Qu, Liu, & Zhang, 2019 ), and the other with the given marginal skewness and kurtosis. The latter one is the widely-used Vale and Maurelli's method. It also contains a function to calculate univariate and multivariate (Mardia's Test) skew and kurtosis. 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Huixuan, Gao (2005, ISBN:9787301078587), "Applied Multivariate Statistical Analysis". Package: r-cran-mnpplasmonr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mnpplasmonr_0.1.0-1.ca2404.1_all.deb Size: 14096 MD5sum: 3cc38ce96b702c2f6e8df7263458ddf5 SHA1: 623912125ce863d9f62ce6883026891937e5b79e SHA256: a5ec44bb331df57798838e2632b3920da1f8a8de7dde9a4ec758f694913d80bf SHA512: f11c4e16f0657f2e87ce2ad5f3030c0c55e37e8d0582e9b92ad58c1b6d88be9da69f3b5cc4b8af0f314fa8574ba992f4da8517cceea5bdc98a05290a9c8a1ec8 Homepage: https://cran.r-project.org/package=mnpPlasmonR Description: CRAN Package 'mnpPlasmonR' (Optical Response of Metallic Nanoparticles (Drude + Rayleigh)) Computes dielectric response and optical cross-sections of metallic nanoparticles using Drude dielectric model and Rayleigh approximation. Package: r-cran-mnreadr Architecture: all Version: 2.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-nlme, r-cran-tibble Filename: pool/dists/noble/main/r-cran-mnreadr_2.1.7-1.ca2404.1_all.deb Size: 140524 MD5sum: 12e53392b51b8a7aa76154a7126c9c7f SHA1: 7efcee7fe34fa663c730ffde66c06bf376f03251 SHA256: 5842bf993d27c19d3dce3ec84e5945655d2c5d51eb4dd9b99a9033e7bd154999 SHA512: ace77f75d15e232bf8c8710f2450ccb4ac9f3aae942121c45c69b29b6026717ad671890c56c9dc871c8d6bcf8465dc8d391cb53f6852fa4f3665e33b4b4fe844 Homepage: https://cran.r-project.org/package=mnreadR Description: CRAN Package 'mnreadR' (MNREAD Parameters Estimation and Curve Plotting) Allows to analyze the reading data obtained with the MNREAD Acuity Chart, a continuous-text reading acuity chart for normal and low vision. Provides the necessary functions to plot the MNREAD curve and estimate automatically the four MNREAD parameters: Maximum Reading Speed, Critical Print Size, Reading Acuity and Reading Accessibility Index. Parameters can be estimated either with the standard method or with a nonlinear mixed-effects (NLME) modeling. See Calabrese et al. 2018 for more details . 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In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis. 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We implement new and time honoured L^2-type tests of multivariate normality, such as the Baringhaus-Henze-Epps-Pulley (BHEP) test, the Henze-Zirkler test, the test of Henze-Jiménes-Gamero, the test of Henze-Jiménes-Gamero-Meintanis, the test of Henze-Visage, the Dörr-Ebner-Henze test based on harmonic oscillator and the Dörr-Ebner-Henze test based on a double estimation in a PDE. Secondly, we include the measures of multivariate skewness and kurtosis by Mardia, Koziol, Malkovich and Afifi and Móri, Rohatgi and Székely, as well as the associated tests. Thirdly, we include the tests of multivariate normality by Cox and Small, the 'energy' test of Székely and Rizzo, the tests based on spherical harmonics by Manzotti and Quiroz and the test of Pudelko. All the functions and tests need the data to be a n x d matrix where n is the samplesize (number of rows) and d is the dimension (number of columns). 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The framework allows you to list available modules and select a module of interest using a basic e-mail interface. After selecting a specific module, you can either run it as is or provide input via the e-mail interface. After parsing your request, R will send the results back to your mobile device. 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Associated publication: Pook et al. (2020) . 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Package: r-cran-moc.gapbk Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nsga2r, r-cran-foreach, r-cran-doparallel Suggests: r-cran-amap, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-moc.gapbk_0.2.1-1.ca2404.1_all.deb Size: 73328 MD5sum: 4891edb3c93c903530c65914b5b1342a SHA1: d345198a2fbfeada2a1e67d326b6fe502b20d55c SHA256: cfbca2efcf6cbf80359f6aa386b32cc615384067b45e6b4b4c1f52e16c9c6e06 SHA512: 78dc2d56a7afe0e8ae4cdf4bb5737b8077faf7b9e7799bd4bfe9409ed2d7a76186d4d441cd8a3537ee9c9eddb01d2426a018d47890455c0d0386678c980588d5 Homepage: https://cran.r-project.org/package=moc.gapbk Description: CRAN Package 'moc.gapbk' (Multi-Objective Clustering Algorithm Guided by a-PrioriBiological Knowledge) Implements the Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge ('MOC-GaPBK') proposed by Parraga-Alava and others (2018) . 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Package: r-cran-mocca Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cclust, r-cran-clue, r-cran-cluster, r-cran-class Filename: pool/dists/noble/main/r-cran-mocca_1.4-1.ca2404.1_all.deb Size: 49422 MD5sum: 334aa1fb3579ad85a20ad06745a949a7 SHA1: eaee3d8d087b814e2a6b90742fbbc9c990eab354 SHA256: a3e97219ba51fa529d9d2c52cfa0d37ebd74be4fe3b2c077ccb20fd8fe92eb96 SHA512: 1a1803c8e65d45d6e61358a525e1db495768140708e2a69a118e0adbc26e96eb5d1c4f63f4f1e7df02fdab340cf01f860f0d7f85791215a60076f75c9617e85d Homepage: https://cran.r-project.org/package=MOCCA Description: CRAN Package 'MOCCA' (Multi-Objective Optimization for Collecting Cluster Alternatives) Provides methods to analyze cluster alternatives based on multi-objective optimization of cluster validation indices. For details see Kraus et al. (2011) . 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These novel modules remove unwanted technical variation, identify open chromatin, robustly models repeated measures in single cell data, implement advanced statistical frameworks to model zero-inflation for differential and co-accessibility analyses, and integrate with existing databases and modules for downstream analyses to reveal biological insights. MOCHA provides a statistical foundation for complex downstream analysis to help advance the potential of single cell ATAC-seq for applied studies. Methods for zero-inflated statistics are as described in: Ghazanfar, S., Lin, Y., Su, X. et al. (2020) . Pimentel, Ronald Silva, "Kendall's Tau and Spearman's Rho for Zero-Inflated Data" (2009) . Package: r-cran-mockery Architecture: all Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-testthat Suggests: r-cran-knitr, r-cran-r6, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mockery_0.4.5-1.ca2404.1_all.deb Size: 41604 MD5sum: 83683121009f40b5ebc88de27fdedccf SHA1: 689f5fa906aab9725c6067d97964e9cbbda69b4e SHA256: bb7310d2027addf19cf194ce709ac60e15bfc2c4563a3803d6048d77f3fec8e7 SHA512: 1282735d8c497d6297646593af2ce6c1f6edeee60d36823ed469f127c90d6efca2c5e750fb1d4788d06373ffe7824b23ca40a89330fdd75da0f423a1b558d715 Homepage: https://cran.r-project.org/package=mockery Description: CRAN Package 'mockery' (Mocking Library for R) The two main functionalities of this package are creating mock objects (functions) and selectively intercepting calls to a given function that originate in some other function. It can be used with any testing framework available for R. 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Package: r-cran-mockr Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-withr Suggests: r-cran-covr, r-cran-fs, r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-mockr_0.2.2-1.ca2404.1_all.deb Size: 42736 MD5sum: f89ca12dacfc5c7aad525a8fa083fc99 SHA1: 17d24adf0829629bee58dec5f8be4351f610612b SHA256: 1d6e0181d1878742a7fd005292bddc589a5069fd98584c4e85a508593488f987 SHA512: ca7dbd21cd05a5203f966c677ec332e4a7c5337973e0cf73479feb02917aca878826265b2d0ba59aa7f1d6d951d91c737e9f20917807d16438d25f5ea48f223d Homepage: https://cran.r-project.org/package=mockr Description: CRAN Package 'mockr' (Mocking in R) Provides a means to mock a package function, i.e., temporarily substitute it for testing. Designed as a drop-in replacement for the now deprecated 'testthat::with_mock()' and 'testthat::local_mock()'. Package: r-cran-mockthat Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang Suggests: r-cran-testthat, r-cran-pkgload, r-cran-curl, r-cran-jsonlite, r-cran-withr Filename: pool/dists/noble/main/r-cran-mockthat_0.2.8-1.ca2404.1_all.deb Size: 36890 MD5sum: 97a9a698bee22090b4cfd1da5d76c9cf SHA1: a59d66d639814f11a4885f90f15e393387e6ee70 SHA256: b2edf35caf574bee23ee0668f291ca3af0734cf48661eec01df0446a0f6c12cb SHA512: 427dd9885f584bbaa26e0ffb2fbb8b815126193d0f9c394e59ba104c4cdd50d4e26b8c3067aa7d516248d5ed589dc2fa34fd6e949cd060e0ceac12c63a5b8ed2 Homepage: https://cran.r-project.org/package=mockthat Description: CRAN Package 'mockthat' (Function Mocking for Unit Testing) With the deprecation of mocking capabilities shipped with 'testthat' as of 'edition 3' it is left to third-party packages to replace this functionality, which in some test-scenarios is essential in order to run unit tests in limited environments (such as no Internet connection). Mocking in this setting means temporarily substituting a function with a stub that acts in some sense like the original function (for example by serving a HTTP response that has been cached as a file). The only exported function 'with_mock()' is modeled after the eponymous 'testthat' function with the intention of providing a drop-in replacement. Package: r-cran-mod09nrt Architecture: all Version: 0.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mod09nrt_0.14-1.ca2404.1_all.deb Size: 33912 MD5sum: 1cd4e8b85d73c4138f71e7fca60f41d5 SHA1: 7eaaae180b6d7955b20bfd4d3e2d3edf1dd32bc0 SHA256: dcc8243a4cec3e6633005f069919340f368268798d8c09ee977394fe5cf2eb17 SHA512: 82ae09f86752de104fe79e7509aa73d2c1aa825d90a4f6a9822b1a131c90cebff160edaf812b6332ca7773febe65b83543bc186f9dd4e75dd57538475c025f2f Homepage: https://cran.r-project.org/package=mod09nrt Description: CRAN Package 'mod09nrt' (Extraction of Bands from MODIS Surface Reflectance Product MOD09NRT) Package for processing downloaded MODIS Surface reflectance Product HDF files. Specifically, MOD09 surface reflectance product files, and the associated MOD03 geolocation files (for MODIS-TERRA). The package will be most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath products; , and adds the directory with the MRTSwath executable to the default R PATH by editing ~/.Rprofile. Package: r-cran-mod2rm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-mod2rm_0.2.1-1.ca2404.1_all.deb Size: 51462 MD5sum: 31f6ed7612aa02830911568c7de9501f SHA1: 3bc07a1881a188c4ecbbb507b9e558aadd3331ca SHA256: 8e24914fddc85f036d47dee7909283a0492f7e311291d79cad2b1e673b3e2234 SHA512: 20de0dc3ce50ece2cd4dd43d6bd33023388571869198295f568a4358df29160dbdc5be7c4c6bcb01dfb60041514b0ed0444e1b791a762fa1eeabdb827e605b7d Homepage: https://cran.r-project.org/package=mod2rm Description: CRAN Package 'mod2rm' (Moderation Analysis for Two-Instance Repeated Measures Designs) Multiple moderation analysis for two-instance repeated measures designs, with up to three simultaneous moderators (dichotomous and/or continuous) with additive or multiplicative relationship. Includes analyses of simple slopes and conditional effects at (automatically determined or manually set) values of the moderator(s), as well as an implementation of the Johnson-Neyman procedure for determining regions of significance in single moderator models. Based on Montoya, A. K. (2018) "Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models" . Package: r-cran-mod Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-mod_0.1.3-1.ca2404.1_all.deb Size: 49280 MD5sum: ab125fb7b114063923cf8b85c1d47664 SHA1: d5fc5360e7e31280e7fd7daf3b2454cc8705f09a SHA256: 8020f6f187263843d94491253bc6727bc3f376a11012e42a0123280ac30db0e2 SHA512: 9520d10f4d819f035b770506528bc5e0d5e866d39f4a24474733870aa87775f7fa11269a4532a7f3e5437e653f0fd4e03a6efcfb26b817d14989c1fce64a03cb Homepage: https://cran.r-project.org/package=mod Description: CRAN Package 'mod' (Lightweight and Self-Contained Modules for Code Organization) Creates modules inline or from a file. Modules can contain any R object and be nested. Each module have their own scope and package "search path" that does not interfere with one another or the user's working environment. Package: r-cran-modacdc Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ccp, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-genieclust, r-cran-genio, r-cran-ggplot2, r-cran-partition, r-cran-tibble, r-cran-tidyr Suggests: r-cran-cca Filename: pool/dists/noble/main/r-cran-modacdc_2.0.1-1.ca2404.1_all.deb Size: 105662 MD5sum: e9e5b87f36da3e3152342593a3c651f4 SHA1: a183199012a1696415fd58d79b1af452376e3895 SHA256: 0446388123550c62cc5d02c522b6ecb4156c8503a6a4f223ac4f45c84c1738e7 SHA512: 0137b243c3e062b57cd676caaf4f510a889f1e8050d8104989a439328e567f7222674698d0f843f81581a9ea15374b23f230430397da78bb2e762cbe97eb262a Homepage: https://cran.r-project.org/package=modACDC Description: CRAN Package 'modACDC' (Association of Covariance for Detecting DifferentialCo-Expression) A series of functions to implement association of covariance for detecting differential co-expression (ACDC), a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types. Users can use the default method which identifies modules by Partition or may supply their own modules. Also included are functions to choose an information loss criterion (ILC) for Partition using OmicS-data-based Complex trait Analysis (OSCA) and Genome-wide Complex trait Analysis (GCTA). The manuscript describing these methods is as follows: Queen K, Nguyen MN, Gilliland F, Chun S, Raby BA, Millstein J. "ACDC: a general approach for detecting phenotype or exposure associated co-expression" (2023) . Package: r-cran-modalcens Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-modalcens_0.1.0-1.ca2404.1_all.deb Size: 36534 MD5sum: 5aa04336ae813c787f9a57e44a5c1cde SHA1: 04b94005dba64c7d89b46b0141aaf393fcc9d2b4 SHA256: d99811f79accd3dff018b9d3cc42cb7639f7c7e2851d3bb488aa5c96d6fba4dd SHA512: 490e08981f62da0a7f92ec92515034e4f5d1d8e023e753b43a551c83e4737fecd59781cf575a01783ff3ee93b69f7e6b05a554dad7b7bdf2af48e230db7d6f3c Homepage: https://cran.r-project.org/package=ModalCens Description: CRAN Package 'ModalCens' (Parametric Modal Regression with Right Censoring) Implements parametric modal regression for continuous positive distributions of the exponential family under right censoring. Provides functions to link the conditional mode to a linear predictor using reparameterizations for Gamma, Beta, Weibull, and Inverse Gaussian families. Includes maximum likelihood estimation via numerical optimization, asymptotic inference based on the observed Fisher information matrix, and model diagnostics using randomized quantile residuals. Package: r-cran-modalclust Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-zoo, r-cran-class Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-modalclust_0.7-1.ca2404.1_all.deb Size: 412222 MD5sum: 04eabd007abb40919e103a3b76e96c8f SHA1: eae4670f485fc9fc42e6af26d160d1f3f2da1e88 SHA256: c4e25f00f579660fa73a83c3b06724ceb94befad15c4d26b5bf40ae2fa726cb2 SHA512: 6e478d0a5020dde323f194606939f53670285a953b4535ef9e76053852aa44369b6738ea44e13526d5ad48e054844bb8a39be6efb51e10a49fd250700faf9907 Homepage: https://cran.r-project.org/package=Modalclust Description: CRAN Package 'Modalclust' (Hierarchical Modal Clustering) Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors. These model-based non-parametric clustering techniques can extract clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default. Package: r-cran-modalforecast Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1177 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-forecast, r-cran-ggplot2, r-cran-gridextra, r-cran-scales Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-modalforecast_0.1.0-1.ca2404.1_all.deb Size: 894986 MD5sum: 79a8670ddd7f8d12104ce49a8ffbc18f SHA1: 613e35009098395b32db7ec3b42b7c0c47046c5c SHA256: 25722980bf213e65dd170ccb9de418e77701d6aa213b7fdbcdeac1dbeeebe355 SHA512: 9b95980b99d087da8173e68da564c946ca8d19cd37b078b52b8f8887944de8f35344236aca754c60aeb17097715c7f8c77711b700226be2ca7326f8110e4c9db Homepage: https://cran.r-project.org/package=ModalForecast Description: CRAN Package 'ModalForecast' (Parametric Modal ARIMA Models using the SKD Family) Implements parametric modal Autoregressive Integrated Moving Average (ARIMA) models utilizing the Skewed Distribution (SKD) family. Current distributions supported are the Skew-Normal, Skewed Student-t, and Skewed Laplace. The conditional mode is parameterized and optimized via maximum likelihood using analytical gradients. Includes comprehensive residual diagnostics, robustness options (heavy tails, asymmetry), robust parametric bootstrap prediction intervals, and classical asymptotic inference via the Fisher Information matrix. Methods are described in Galarza, C.E., Lachos, V.H., Cabral, C.R.B., & Castro, L.M. (2017) . Package: r-cran-modeest Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fbasics, r-cran-stable, r-cran-stabledist, r-cran-statip Suggests: r-cran-evd, r-cran-knitr, r-cran-mvtnorm, r-cran-testthat, r-cran-vgam Filename: pool/dists/noble/main/r-cran-modeest_2.4.0-1.ca2404.1_all.deb Size: 146522 MD5sum: 59eb60f53df4a7948a1912ee6e2dca3e SHA1: 1041f1122014f20343e23e51618b44fcf00836ba SHA256: 79b5f91fd51400dc1240b74c9ebea7e4c637ad0f99354a35db139d4dc904322f SHA512: 0552bfd6bb28d0436f47b18c2c2724c0285de35c209779310170b512248229bebdd8431a2bfd21b79c7e503a170baec3aa9a45705df62fabd4cfd05ebd0100ae Homepage: https://cran.r-project.org/package=modeest Description: CRAN Package 'modeest' (Mode Estimation) Provides estimators of the mode of univariate data or univariate distributions. Package: r-cran-modehunt Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-modehunt_1.0.8-1.ca2404.1_all.deb Size: 106924 MD5sum: f1af9fc5ef7bbba3d525bf405cf1f13a SHA1: 718bec48e5a636d890198e04927b79a163107d97 SHA256: 60fcb025ba24e2266c3fc3ae1bf7b48eb7a4b34861f097004e144c56dea32afa SHA512: 73d4b71c25feb24abd75bdbfc0e7460736b26a23ad0036bc8328c70fea8b77f1ed8ab2dff5556885c84fd14159bda45377be7fb9c4494421be34cde152e3e62f Homepage: https://cran.r-project.org/package=modehunt Description: CRAN Package 'modehunt' (Multiscale Analysis for Density Functions) Given independent and identically distributed observations X(1), ..., X(n) from a density f, provides five methods to perform a multiscale analysis about f as well as the necessary critical values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive correction term Gamma in either method offers another two approaches which are more powerful on small scales and less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically equivalent to the first and was introduced in Rufibach and Walther (2010). Package: r-cran-model4you Architecture: all Version: 0.9-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit, r-cran-sandwich, r-cran-ggplot2, r-cran-formula, r-cran-gridextra, r-cran-survival, r-cran-rlang Suggests: r-cran-mvtnorm, r-cran-th.data, r-cran-psychotools, r-cran-strucchange, r-cran-plyr, r-cran-knitr, r-cran-ggbeeswarm, r-cran-mass Filename: pool/dists/noble/main/r-cran-model4you_0.9-9-1.ca2404.1_all.deb Size: 152102 MD5sum: f5ef82dc868259e3dd760d837f1b6785 SHA1: 28c449d54eacfcf56879f80c92388c5087ccc7d1 SHA256: 072240da48813c50708c9bf866b5090615bfe775d7badfb7e843edc575918b3a SHA512: cc4764791ea84facdabb4b3f62895e9332ac71790f7d14ea042a11fa8bba76d4b3b147a80af7d704931af002feb1ff6d0313730898d7599b99355e8d6dfd38a1 Homepage: https://cran.r-project.org/package=model4you Description: CRAN Package 'model4you' (Stratified and Personalised Models Based on Model-Based Treesand Forests) Model-based trees for subgroup analyses in clinical trials and model-based forests for the estimation and prediction of personalised treatment effects (personalised models). Currently partitioning of linear models, lm(), generalised linear models, glm(), and Weibull models, survreg(), is supported. Advanced plotting functionality is supported for the trees and a test for parameter heterogeneity is provided for the personalised models. For details on model-based trees for subgroup analyses see Seibold, Zeileis and Hothorn (2016) ; for details on model-based forests for estimation of individual treatment effects see Seibold, Zeileis and Hothorn (2017) . Package: r-cran-modelbased Architecture: all Version: 0.15.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1084 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayestestr, r-cran-datawizard, r-cran-insight, r-cran-parameters Suggests: r-cran-afex, r-cran-bh, r-cran-betareg, r-cran-boot, r-cran-bootes, r-cran-brglm2, r-cran-brms, r-cran-broom, r-cran-car, r-cran-cardata, r-cran-coda, r-cran-collapse, r-cran-correlation, r-cran-coxme, r-cran-curl, r-cran-discovr, r-cran-easystats, r-cran-effectsize, r-cran-emmeans, r-cran-formula, r-cran-gamm4, r-cran-gganimate, r-cran-ggplot2, r-cran-glmmtmb, r-cran-httr2, r-cran-knitr, r-cran-lme4, r-cran-lmertest, r-cran-logspline, r-cran-mass, r-cran-matchit, r-cran-matrix, r-cran-marginaleffects, r-cran-mice, r-cran-mgcv, r-cran-mvtnorm, r-cran-nanoparquet, r-cran-nestedlogit, r-cran-nnet, r-cran-ordinal, r-cran-palmerpenguins, r-cran-performance, r-cran-patchwork, r-cran-pbkrtest, r-cran-poorman, r-cran-pscl, r-cran-rcppeigen, r-cran-rdatasets, r-cran-report, r-cran-rmarkdown, r-cran-rstanarm, r-cran-rtdists, r-cran-rwiener, r-cran-sandwich, r-cran-scales, r-cran-see, r-cran-survival, r-cran-testthat, r-cran-tinyplot, r-cran-tinytable, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-modelbased_0.15.0-1.ca2404.1_all.deb Size: 867508 MD5sum: c895897833df2bfcfd1340133a965a4f SHA1: 2139f9f3388109d9455bb69c877773c2a6dd02bf SHA256: 0555129cd2be89bbd10bbfffb11bddd851a47630e051db35e90bcaae4716f964 SHA512: 96afa05001c1ead6c95302dc17fee18cfe032502cd8aa1014633ac99bdd14c370499c94e8eead0ff5709221b36e90a248f682b7311f7216b6c4ad5bcee6f3eda Homepage: https://cran.r-project.org/package=modelbased Description: CRAN Package 'modelbased' (Estimation of Model-Based Predictions, Contrasts and Means) Implements a general interface for model-based estimations for a wide variety of models, used in the computation of marginal means, contrast analysis and predictions. For a list of supported models, see 'insight::supported_models()'. Package: r-cran-modelbpp Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1017 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-pbapply, r-cran-igraph, r-cran-manymome Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-modelbpp_0.3.0-1.ca2404.1_all.deb Size: 802552 MD5sum: 6b2c7442006fe2ba27937d660077d1ab SHA1: 4484871d638c53f564cd83261bb319f5595e0396 SHA256: 50ff384b75895c8657b3f95665adfc2da0d55449791cc7845ebc9acad6604efd SHA512: efdcacecdd5d60f5d6e2f4d666cbc65ff1e87c426297fb4210afd1992fde2e2acde43eb6fdc0fb50061b344e3dffc7802f011bc742799e5203464799fd069fb9 Homepage: https://cran.r-project.org/package=modelbpp Description: CRAN Package 'modelbpp' (Model BIC Posterior Probability) Fits the neighboring models of a fitted structural equation model and assesses the model uncertainty of the fitted model based on BIC posterior probabilities (BPP), using the method presented in Wu, Cheung, and Leung (2020) . See Pesigan, Cheung, Wu, Chang, and Leung (2026) for an introduction to the package. Package: r-cran-modelc Architecture: all Version: 1.0.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-modelc_1.0.0.0-1.ca2404.1_all.deb Size: 39634 MD5sum: 3af6af0945688993b32f3a437f562427 SHA1: 6e4a79dc3d4460b0e626c395719f4658222f7052 SHA256: dc9f6552fb887a8896ee466127d3cf1837b438fb47ebbea607c98020ee8f9b2a SHA512: 86a502f8cb0f767ecaf1d9150a86e063be9e0d4a52bb1ca13ea1817f97b91fe1991821ba0805ac28093e7ecb0287a8be7e81e6f4cafa2dc23980a5810dc742d1 Homepage: https://cran.r-project.org/package=modelc Description: CRAN Package 'modelc' (A Linear Model to 'SQL' Compiler) This is a cross-platform linear model to 'SQL' compiler. It generates 'SQL' from linear and generalized linear models. Its interface consists of a single function, modelc(), which takes the output of lm() or glm() functions (or any object which has the same signature) and outputs a 'SQL' character vector representing the predictions on the scale of the response variable as described in Dunn & Smith (2018) and originating in Nelder & Wedderburn (1972) . The resultant 'SQL' can be included in a 'SELECT' statement and returns output similar to that of the glm.predict() or lm.predict() predictions, assuming numeric types are represented in the database using sufficient precision. Currently log and identity link functions are supported. 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This package uses 'DALEX' explainers to describe global model behavior. We can see how well models behave (tabs: Model Performance, Auditor), how much each variable contributes to predictions (tabs: Variable Response) and which variables are the most important for a given model (tabs: Variable Importance). We can also compare Concept Drift for pairs of models (tabs: Drifter). Additionally, data available on the website can be easily recreated in current R session. Work on this package was financially supported by the NCN Opus grant 2017/27/B/ST6/01307 at Warsaw University of Technology, Faculty of Mathematics and Information Science. 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Package: r-cran-modelscompete4 Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-boot, r-cran-ggplot2, r-cran-nonnest2, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-modelscompete4_0.2.6-1.ca2404.1_all.deb Size: 91464 MD5sum: 430af4f48d1122c4effa2a44b4b12d84 SHA1: 4efbb59dace42e7b5b20b71090488e23d9b5ad9f SHA256: 1ee769f92fda8e63ee99243d53b47fad09f82acf5bcf709b0353373aff63b868 SHA512: 25caced6441a251733ddc575cd4e073bb04544d6b5f150e8a1de8f246d929358e146a9b8f5d45415f6a56188b93c271aa272795dea2b3f7067b2303400db94f3 Homepage: https://cran.r-project.org/package=modelscompete4 Description: CRAN Package 'modelscompete4' (Compare Nested and Non-Nested Structural Equation Models) A comprehensive package for comparing multiple Structural Equation Models (SEM). Supports both nested and non-nested model comparisons, chi-square difference tests, and extraction of multiple fit indices including AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), CFI (Comparative Fit Index), TLI (Tucker-Lewis Index), RMSEA (Root Mean Square Error of Approximation), and SRMR (Standardized Root Mean Square Residual). Built on top of the 'lavaan' package for seamless SEM model comparison workflows. The Vuong test (Vuong, 1989) for non-nested models is used as the statistical test. Package: r-cran-modelsse Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-delaporte Filename: pool/dists/noble/main/r-cran-modelsse_0.1-3-1.ca2404.1_all.deb Size: 133008 MD5sum: 20f5d355df4a53947e7df3083f59068f SHA1: e8702c2b3bb5eed56df8f9893d95c1d32642b218 SHA256: 9d6a43c80674b7f74da7abf5475f4ea534388f2653c185f26fee738d263ca843 SHA512: 6773bcce254ae20e8e253fd7f77ea158e2044ecdd928138d9eac0c3e6e799f19cdf7a1c68a238263ff679ee38dc25b74ae2a107ad8fe7f2465bf2b6463eb202c Homepage: https://cran.r-project.org/package=modelSSE Description: CRAN Package 'modelSSE' (Modelling Infectious Disease Superspreading from Contact TracingData) Comprehensive analytical tools are provided to characterize infectious disease superspreading from contact tracing surveillance data. The underlying theoretical frameworks of this toolkit include branching process with transmission heterogeneity (Lloyd-Smith et al. (2005) ), case cluster size distribution (Nishiura et al. (2012) , Blumberg et al. (2014) , and Kucharski and Althaus (2015) ), and decomposition of reproduction number (Zhao et al. (2022) ). Package: r-cran-modelstudio Architecture: all Version: 3.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dalex, r-cran-ingredients, r-cran-ibreakdown, r-cran-r2d3, r-cran-jsonlite, r-cran-progress, r-cran-digest Suggests: r-cran-parallelmap, r-cran-ranger, r-cran-xgboost, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-modelstudio_3.1.2-1.ca2404.1_all.deb Size: 333526 MD5sum: 5c24d2563191c4fc69a04ec01600fa98 SHA1: 6f4700f55efb993270901ce64f6f1e615d57deb2 SHA256: 06935a3399f59874dcae758df681cc8e450efa73097122bbafba78ed637f4410 SHA512: f16b03f404e37d83e09d4afc8bf32cd1a1c409d232aa7c0d7b747c5e12b81e6c15bd9ae8593822ed897b8a23a8cee3483d7f7d3302f7b878a7fd42c32ae91c48 Homepage: https://cran.r-project.org/package=modelStudio Description: CRAN Package 'modelStudio' (Interactive Studio for Explanatory Model Analysis) Automate the explanatory analysis of machine learning predictive models. Generate advanced interactive model explanations in the form of a serverless HTML site with only one line of code. This tool is model-agnostic, therefore compatible with most of the black-box predictive models and frameworks. The main function computes various (instance and model-level) explanations and produces a customisable dashboard, which consists of multiple panels for plots with their short descriptions. It is possible to easily save the dashboard and share it with others. modelStudio facilitates the process of Interactive Explanatory Model Analysis introduced in Baniecki et al. (2023) . Package: r-cran-modelsummary Architecture: all Version: 2.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3298 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-generics, r-cran-glue, r-cran-insight, r-cran-parameters, r-cran-performance, r-cran-tables, r-cran-tinytable Suggests: r-cran-aer, r-cran-altdoc, r-cran-amelia, r-cran-betareg, r-cran-bookdown, r-cran-brms, r-cran-broom, r-cran-broom.mixed, r-cran-car, r-cran-clubsandwich, r-cran-correlation, r-cran-covr, r-cran-did, r-cran-digest, r-cran-dt, r-cran-estimatr, r-cran-fixest, r-cran-flextable, r-cran-future, r-cran-future.apply, r-cran-gamlss, r-cran-ggdist, r-cran-ggplot2, r-cran-glmmtmb, r-cran-gh, r-cran-gt, r-cran-gtextras, r-cran-haven, r-cran-huxtable, r-cran-labelled, r-cran-irdisplay, r-cran-ivreg, r-cran-kableextra, r-cran-knitr, r-cran-lavaan, r-cran-lfe, r-cran-lme4, r-cran-lmtest, r-cran-magick, r-cran-magrittr, r-cran-marginaleffects, r-cran-mass, r-cran-mgcv, r-cran-mice, r-cran-nlme, r-cran-nnet, r-cran-officer, r-cran-openxlsx, r-cran-pandoc, r-cran-pscl, r-cran-psych, r-cran-randomizr, r-cran-rdatasets, r-cran-remotes, r-cran-rmarkdown, r-cran-rstanarm, r-cran-rsvg, r-cran-sandwich, r-cran-spelling, r-cran-survey, r-cran-survival, r-cran-tibble, r-cran-tictoc, r-cran-tidyselect, r-cran-tidyverse, r-cran-tinysnapshot, r-cran-tinytest, r-cran-tinytex, r-cran-webshot2, r-cran-wesanderson Filename: pool/dists/noble/main/r-cran-modelsummary_2.6.0-1.ca2404.1_all.deb Size: 3207782 MD5sum: 70c5068be5bc5c0b6ccabd4b10948e5c SHA1: 0495412855cc23a4f0091f68a64ce43372efdf6e SHA256: b8742257bde099f26e3cf3800246574a85b560a0f64d7091b1353bed406ae655 SHA512: f58f9dc0b66e1ba58af2770b089a948c6a141209d63123593559a17bc9e1ddceab9d2dc9bf18d07c2e41ed082a0a08d361c5e9f77b03500ab07e72af88717ae0 Homepage: https://cran.r-project.org/package=modelsummary Description: CRAN Package 'modelsummary' (Summary Tables and Plots for Statistical Models and Data:Beautiful, Customizable, and Publication-Ready) Create beautiful and customizable tables to summarize several statistical models side-by-side. Draw coefficient plots, multi-level cross-tabs, dataset summaries, balance tables (a.k.a. "Table 1s"), and correlation matrices. This package supports dozens of statistical models, and it can produce tables in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel, RTF, JPG, or PNG. Tables can easily be embedded in 'Rmarkdown' or 'knitr' dynamic documents. Details can be found in Arel-Bundock (2022) . Package: r-cran-modeltests Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-purrr, r-cran-testthat, r-cran-tibble Suggests: r-cran-covr Filename: pool/dists/noble/main/r-cran-modeltests_0.1.8-1.ca2404.1_all.deb Size: 72622 MD5sum: e1af153f33ecbd3ad8f8353e547fe77b SHA1: b533424d39689eee57b7af449484ffd86c164a3f SHA256: 73370002e3e7084cdf95f6dc5cdb914a25bb350a7dd1468fa759d5dcb2955ef8 SHA512: 29b9c70b30dd994a5135e0f7f4b8e6ddb64716420dd7e48e2468106ca08a81b8cb490282fc24d47bc3a16eef4f359fbf33d04af078ee934df27c1d5e4183fe1f Homepage: https://cran.r-project.org/package=modeltests Description: CRAN Package 'modeltests' (Testing Infrastructure for Broom Model Generics) Provides a number of testthat tests that can be used to verify that tidy(), glance() and augment() methods meet consistent specifications. This allows methods for the same generic to be spread across multiple packages, since all of those packages can make the same guarantees to users about returned objects. Package: r-cran-modeltime.ensemble Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2045 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-modeltime, r-cran-modeltime.resample, r-cran-tune, r-cran-rsample, r-cran-yardstick, r-cran-workflows, r-cran-recipes, r-cran-timetk, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-stringr, r-cran-rlang, r-cran-cli, r-cran-generics, r-cran-magrittr, r-cran-tictoc, r-cran-doparallel, r-cran-foreach, r-cran-glmnet Suggests: r-cran-gt, r-cran-dials, r-cran-earth, r-cran-testthat, r-cran-tidymodels, r-cran-xgboost, r-cran-lubridate, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-modeltime.ensemble_1.1.0-1.ca2404.1_all.deb Size: 1458234 MD5sum: 48f9e8bef0bcfea5f8c94ad84ced4f8c SHA1: 2ad12897e0223b543710a9a02b87f86cab1ecfcf SHA256: e00889f5beeab1c789b4f53888adbf535d5e87ccdf998703e3c18f7a0a6afe1a SHA512: fa0deb4a8bccb40eb37c3c9a88f503dbea3c650965f635730601c95592afdefb1aabc6c1e8f69149ca4c19aa526b460c6ea922f5637511120f7ec64afa517e3f Homepage: https://cran.r-project.org/package=modeltime.ensemble Description: CRAN Package 'modeltime.ensemble' (Ensemble Algorithms for Time Series Forecasting with Modeltime) A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability. Package: r-cran-modeltime.resample Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-modeltime, r-cran-tune, r-cran-rsample, r-cran-workflows, r-cran-recipes, r-cran-yardstick, r-cran-timetk, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-stringr, r-cran-ggplot2, r-cran-plotly, r-cran-cli, r-cran-magrittr, r-cran-rlang, r-cran-progressr, r-cran-tictoc, r-cran-hardhat, r-cran-withr Suggests: r-cran-testthat, r-cran-tidymodels, r-cran-tidyquant, r-cran-glmnet, r-cran-lubridate, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-modeltime.resample_0.3.0-1.ca2404.1_all.deb Size: 1802304 MD5sum: 6875d355552bec4f8fe6b4a935318ccc SHA1: 51827ff00e234aa35600b33c5b8574059aeda2c8 SHA256: 4f7f0ccad22ba9da9510a895ca901d953eab0847befdd6b6842a05236afd5ecf SHA512: 18ff1ef9121d8a73be8a04b7cdabba2434d50dc340d07b1f1ef47ae4cc55936729c2bb5802b60a92b127b122e4408f01c0373cde07b0e7811dd5bd1a41f59f7d Homepage: https://cran.r-project.org/package=modeltime.resample Description: CRAN Package 'modeltime.resample' (Resampling Tools for Time Series Forecasting) A 'modeltime' extension that implements forecast resampling tools that assess time-based model performance and stability for a single time series, panel data, and cross-sectional time series analysis. Package: r-cran-modeltime Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stanheaders, r-cran-timetk, r-cran-parsnip, r-cran-dials, r-cran-yardstick, r-cran-workflows, r-cran-hardhat, r-cran-rlang, r-cran-glue, r-cran-plotly, r-cran-reactable, r-cran-gt, r-cran-ggplot2, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-forcats, r-cran-scales, r-cran-janitor, r-cran-parallelly, r-cran-doparallel, r-cran-foreach, r-cran-magrittr, r-cran-forecast, r-cran-xgboost, r-cran-prophet, r-cran-cli, r-cran-tidymodels Suggests: r-cran-rstan, r-cran-slider, r-cran-sparklyr, r-cran-dorng, r-cran-workflowsets, r-cran-recipes, r-cran-rsample, r-cran-tune, r-cran-lubridate, r-cran-testthat, r-cran-kernlab, r-cran-glmnet, r-cran-thief, r-cran-smooth, r-cran-greybox, r-cran-earth, r-cran-randomforest, r-cran-trelliscopejs, r-cran-knitr, r-cran-rmarkdown, r-cran-webshot, r-cran-qpdf, r-cran-tsrepr, r-cran-future, r-cran-dofuture Filename: pool/dists/noble/main/r-cran-modeltime_1.3.5-1.ca2404.1_all.deb Size: 3032070 MD5sum: f2d80560403c9786e8b25266ec4f4031 SHA1: 09e8cfecdb34a2c15468bad39f0c2c372c06a4e1 SHA256: e86eb59d62b328f50ac040335b22518210bf422c6253be66f4d9426dbf66d018 SHA512: feed9f3373f4fc853527699dfb7bd977e4730486b0e22afcf137952d6320b2c93427cbc60a89bc3b382cfc78450789af29714de0df8eb9d21d177a12d95d0b0c Homepage: https://cran.r-project.org/package=modeltime Description: CRAN Package 'modeltime' (The Tidymodels Extension for Time Series Modeling) The time series forecasting framework for use with the 'tidymodels' ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" (). Refer to "Prophet: forecasting at scale" (.). Package: r-cran-modeltools Architecture: all Version: 0.2-24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-modeltools_0.2-24-1.ca2404.1_all.deb Size: 219236 MD5sum: a66596a6e3d8ff43b2425c525daf6f21 SHA1: 25689a04224a9f36235712fbb1dd1935628f5c8f SHA256: a960229c7286ac0746a6cc0fd0e7ad4688371db20d449d21a415ab435fe34bfd SHA512: 00c2a4755447d1b51c70e6e4c71d27a79a7489c749ec49ddc17f46506fe998a637984d645bd390a9981220bf218d713886128cdc49b3624dec1325015a43006c Homepage: https://cran.r-project.org/package=modeltools Description: CRAN Package 'modeltools' (Tools and Classes for Statistical Models) A collection of tools to deal with statistical models. The functionality is experimental and the user interface is likely to change in the future. The documentation is rather terse, but packages `coin' and `party' have some working examples. However, if you find the implemented ideas interesting we would be very interested in a discussion of this proposal. Contributions are more than welcome! Package: r-cran-modeltuning Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1789 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future.apply, r-cran-progressr, r-cran-r6, r-cran-rlang Suggests: r-cran-matrix, r-cran-e1071, r-cran-future, r-cran-parallelly, r-cran-paws, r-cran-rsample, r-cran-rpart, r-cran-yardstick, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-modeltuning_0.1.3-1.ca2404.1_all.deb Size: 1629814 MD5sum: 0afbc7dcb9c1eb9c8c74613dd62eeb01 SHA1: 1a170f600f7981ed9cbf6de539dae8df1cfbb97b SHA256: a813e0b12295ef174dd33d9248cd4e947dd2232729b0958b1189986186fbfce6 SHA512: a8a5d43e71dd95db6eafbf4d1250401f373a50101f35a680bb026128faf8e302fe332261c6f4c3dbaff7fdc7ad03cfd847af24f8f6d3dfa8ae35cddc82eac3bc Homepage: https://cran.r-project.org/package=modeltuning Description: CRAN Package 'modeltuning' (Model Selection and Tuning Utilities) Provides a lightweight framework for model selection and hyperparameter tuning in R. The package offers intuitive tools for grid search, cross-validation, and combined grid search with cross-validation that work seamlessly with virtually any modeling package. Designed for flexibility and ease of use, it standardizes tuning workflows while remaining fully compatible with a wide range of model interfaces and estimation functions. Package: r-cran-modelwordcloud Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-modelwordcloud_0.1-1.ca2404.1_all.deb Size: 19326 MD5sum: 4316649b205ffa5652c19438082dc231 SHA1: 04a5c1e6891e7e9171c556a28f1ec058c53a4d44 SHA256: 050e85238e1a942393420b5daa83de8129503d87e51b72bbbf130e6a3193e568 SHA512: 02ceecb57bd6aeeabc8496fa1176fc46b1b846ef39120f47014cc0422f101010f7bfef9aa049b810b15608821be6262fe563022ead21d5d47bddcf5a1382c0c4 Homepage: https://cran.r-project.org/package=modelwordcloud Description: CRAN Package 'modelwordcloud' (Model Word Clouds) Makes a word cloud of text, sized by the frequency of the word, and colored either by user-specified colors or colored by the strength of the coefficient of that text derived from a regression model. Package: r-cran-moder Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-moder_0.2.1-1.ca2404.1_all.deb Size: 77064 MD5sum: bcb1434764ff43208ac25fdc13ee1cf4 SHA1: 1848b3ac5be63232fa2a482689fd64633cb9683e SHA256: 43f6319221041468c61c7c5907f35f80fe4f8c18c7d7d62803872c9a17242d21 SHA512: 9680b942fc863857710d1b3ff5cd15d4b0fa9016cf8c78228cc07069b94d24b161d13ef540469055163cc54c3734f33b6a070fe324ac58744eb127fdf5880127 Homepage: https://cran.r-project.org/package=moder Description: CRAN Package 'moder' (Mode Estimation) Determines single or multiple modes (most frequent values). Checks if missing values make this impossible, and returns 'NA' in this case. Dependency-free source code. See Franzese and Iuliano (2019) . Package: r-cran-moderate.mediation Architecture: all Version: 0.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-cowplot, r-cran-distr, r-cran-ggplot2, r-cran-reshape2, r-cran-dosnow, r-cran-scales, r-cran-foreach, r-cran-earth Filename: pool/dists/noble/main/r-cran-moderate.mediation_0.0.12-1.ca2404.1_all.deb Size: 246248 MD5sum: cd35a8697100adf16096af229efb9a18 SHA1: 651a091098a86cf2666417af4cbc30675ba57585 SHA256: 520b2a6a34327c37df4ffc88756e951efaeb921dcc6d66e8b72ef514c9600982 SHA512: e8877a10dafd7f0fd0e1f66bad84641ab8d33afdb34571ef7918baf2876c3b3a79c9f986f1947f1c4dd522f3d3be4ddf3f88eb190a955040bc7568a6a58c9959 Homepage: https://cran.r-project.org/package=moderate.mediation Description: CRAN Package 'moderate.mediation' (Causal Moderated Mediation Analysis) Causal moderated mediation analysis using the methods proposed by Qin and Wang (2023) . Causal moderated mediation analysis is crucial for investigating how, for whom, and where a treatment is effective by assessing the heterogeneity of mediation mechanism across individuals and contexts. This package enables researchers to estimate and test the conditional and moderated mediation effects, assess their sensitivity to unmeasured pre-treatment confounding, and visualize the results. The package is built based on the quasi-Bayesian Monte Carlo method, because it has relatively better performance at small sample sizes, and its running speed is the fastest. The package is applicable to a treatment of any scale, a binary or continuous mediator, a binary or continuous outcome, and one or more moderators of any scale. 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In the location case, one can thus obtain halfspace depth contours in two to six dimensions. Hallin, M., Paindaveine, D. and Šiman, M. (2010) Multivariate quantiles and multiple-output regression quantiles: from L1 optimization to halfspace depth. Annals of Statistics 38, 635-669 For more references about the method, see Help pages. 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Bertrand, E. Claeys and M. Maumy-Bertrand (2019, ISBN:9782100793525, Dunod, Paris). The first chapter of the book is dedicated to an introduction to the R statistical software. The second chapter deals with correlation analysis: Pearson, Spearman and Kendall simple, multiple and partial correlation coefficients. New wrapper functions for permutation tests or bootstrap of matrices of correlation are provided with the package. The third chapter is dedicated to data exploration with factorial analyses (PCA, CA, MCA, MDA) and clustering. The fourth chapter is dedicated to regression analysis: fitting and model diagnostics are detailed. The exercises focus on covariance analysis, logistic regression, Poisson regression, two-way analysis of variance for fixed or random factors. Various example datasets are shipped with the package: for instance on pokemon, world of warcraft, house tasks or food nutrition analyses. 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The focus is on the emergence of argument-marking systems (Dowty (1991) , Van Valin 1999, Dryer 2002, Lestrade 2015a), i.e. noun marking (Aristar (1997) , Lestrade (2010) ), person indexing (Ariel 1999, Dahl (2000) , Bhat 2004), and word order (Dryer 2013), but extensions are foreseen. Agents start out with a protolanguage (a language without grammar; Bickerton (1981) , Jackendoff 2002, Arbib (2015) ) and interact through language games (Steels 1997). Over time, grammatical constructions emerge that may or may not become obligatory (for which the tolerance principle is assumed; Yang 2016). Throughout the simulation, uniformitarianism of principles is assumed (Hopper (1987) , Givon (1995) , Croft (2000), Saffran (2001) , Heine & Kuteva 2007), in which maximal psychological validity is aimed at (Grice (1975) , Levelt 1989, Gaerdenfors 2000) and language representation is usage based (Tomasello 2003, Bybee 2010). 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Package: r-cran-monitos Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-shiny, r-cran-shinydashboard Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-monitos_0.1.6-1.ca2404.1_all.deb Size: 166124 MD5sum: e4e9821818f8551bce7141ead3114f6b SHA1: ea6d33489b30b12a7291657522a7b7236cb7db18 SHA256: 21bb65766bd03e5f94d13d400395d12fae7e798aa4ebd3758e8db6d557473276 SHA512: 058afdd1c754fbf184b92c139b45c6ffada9bf0b9aa70ed9a44337619a4c79a15c95d0eeeef705e7ce76475d8783fd445d2865572ec1dc74f03fe0b15db51a6d Homepage: https://cran.r-project.org/package=monitOS Description: CRAN Package 'monitOS' (Monitoring Overall Survival in Pivotal Trials in IndolentCancers) These guidelines are meant to provide a pragmatic, yet rigorous, help to drug developers and decision makers, since they are shaped by three fundamental ingredients: the clinically determined margin of detriment on OS that is unacceptably high (delta null); the benefit on OS that is plausible given the mechanism of action of the novel intervention (delta alt); and the quantity of information (i.e. survival events) it is feasible to accrue given the clinical and drug development setting. The proposed guidelines facilitate transparent discussions between stakeholders focusing on the risks of erroneous decisions and what might be an acceptable trade-off between power and the false positive error rate. Package: r-cran-monmlp Architecture: all Version: 1.1.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-optimx Filename: pool/dists/noble/main/r-cran-monmlp_1.1.5-1-1.ca2404.1_all.deb Size: 58956 MD5sum: 07da4b253d713a658e4172cf966f410a SHA1: 011d0a2ff8ad3e88e35c203f099432b72fb72ba0 SHA256: 9c807bf35a9e4ff6bee118ad02b7a9e061c7db27432cab4392b6c4447f1b4782 SHA512: 40feab18c0c5c9d2d3ff97834e3f23a7d5e3cd079195b96d9565e95b086161231d0d1a6a1cbc659b8675cb562a221c93dd094f562880333ff8124b8a5ed13631 Homepage: https://cran.r-project.org/package=monmlp Description: CRAN Package 'monmlp' (Multi-Layer Perceptron Neural Network with Optional MonotonicityConstraints) Train and make predictions from a multi-layer perceptron neural network with optional partial monotonicity constraints. Package: r-cran-monobin Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-monobin_0.2.4-1.ca2404.1_all.deb Size: 122394 MD5sum: 8badf26df6dd2ecce17b3ff2909f6f57 SHA1: 5723d60dd0ec88fe418d35990395fe195da78e15 SHA256: 020b3dc98462121d1286fa666e3dbe2215c211358492507fdae61c2bf9fbfa38 SHA512: 4ada1741a688291aa1fb45093103d5ef3e885718acabaca288e67e70e68b26c6c28a45756c7fa4ab9ee51ab05a3fef8f3dc26d2aae2e3c56b060ef0c2471d963 Homepage: https://cran.r-project.org/package=monobin Description: CRAN Package 'monobin' (Monotonic Binning for Credit Rating Models) Performs monotonic binning of numeric risk factor in credit rating models (PD, LGD, EAD) development. All functions handle both binary and continuous target variable. Functions that use isotonic regression in the first stage of binning process have an additional feature for correction of minimum percentage of observations and minimum target rate per bin. Additionally, monotonic trend can be identified based on raw data or, if known in advance, forced by functions' argument. Missing values and other possible special values are treated separately from so-called complete cases. Package: r-cran-monobinshiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-monobin, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-monobinshiny_0.1.0-1.ca2404.1_all.deb Size: 113686 MD5sum: f29e1581290a3cf84573b39e86b1bd3e SHA1: 6abdfdecea6f28c2ec2dab5d37b75b1e68f1cf97 SHA256: 3a74c880bc7002e945d8f53cb6d7228e8cfce30a6ba020ee9b82607f07d3ba4d SHA512: adca802055a30c6e740cb5e93fb60a71536d2e0ac5966188b95c3a8a183c11ed8a2ab55ef7cc71ab2bdec81014bca56e439b8537320d6e5bb1b5e57f2daac679 Homepage: https://cran.r-project.org/package=monobinShiny Description: CRAN Package 'monobinShiny' (Shiny User Interface for 'monobin' Package) This is an add-on package to the 'monobin' package that simplifies its use. It provides shiny-based user interface (UI) that is especially handy for less experienced 'R' users as well as for those who intend to perform quick scanning of numeric risk factors when building credit rating models. The additional functions implemented in 'monobinShiny' that do no exist in 'monobin' package are: descriptive statistics, special case and outliers imputation. The function descriptive statistics is exported and can be used in 'R' sessions independently from the user interface, while special case and outlier imputation functions are written to be used with shiny UI. Package: r-cran-monochromer Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-monochromer_0.2.0-1.ca2404.1_all.deb Size: 81516 MD5sum: dda8c29160aeae9f685b17fc4c7a6fbd SHA1: 06011236daa699210724c2be82d92904f0dc4b41 SHA256: 5254cfb1ae4b2dcd14e29bfb2d082eafaf24fab769f50cebe7885dd46357ec0b SHA512: 92cf64b6f2f72997d1347aa545c732620f8b94aa9619c353479afd4d8935694011b051327659dbde636becc2b2bbbe92a35c94310b23dcd750a399dac05757b7 Homepage: https://cran.r-project.org/package=monochromeR Description: CRAN Package 'monochromeR' (Easily Create, View and Use Monochrome Colour Palettes) Generate a monochrome palette from a starting colour for a specified number of colours. The package can also be used to display colour palettes in the plot window, with or without hex codes and colour labels. Package: r-cran-monoclust Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-permute, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-mice, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-monoclust_1.2.1-1.ca2404.1_all.deb Size: 241364 MD5sum: 3be6002facb67cce8eb4b975072c9f1b SHA1: ef1b93b1eb8c57584ee3750fb525a73e27bd61ac SHA256: 4f3d8778eecccad86a98a3d9ce36efa19baf75bbe03533583dece61a7ffcc588 SHA512: cd0babc2b7c29749e1b7f5713ca850aac42113bd2c4b78b22940c153a03ccb51e5eb9f0a0d705935ec8727117e19efa825c6c6e90b9dc3d070302b24607e468b Homepage: https://cran.r-project.org/package=monoClust Description: CRAN Package 'monoClust' (Perform Monothetic Clustering with Extensions to Circular Data) Implementation of the Monothetic Clustering algorithm (Chavent, 1998 ) on continuous data sets. A lot of extensions are included in the package, including applying Monothetic clustering on data sets with circular variables, visualizations with the results, and permutation and cross-validation based tests to support the decision on the number of clusters. Package: r-cran-monographar Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 999 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circular, r-cran-png, r-cran-raster, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-rpart, r-cran-sf, r-cran-shiny, r-cran-shinydashboard, r-cran-shinythemes, r-cran-shinywidgets, r-cran-sp, r-cran-terra Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-monographar_1.3.1-1.ca2404.1_all.deb Size: 680044 MD5sum: 8a119e60525adade03d2c86051e06732 SHA1: 3c9f1ee00028653fdb710720bd57778214a45915 SHA256: 03b34151359985535132cb262a5b62d3e284a16be1092d12efa9e9289509ee7f SHA512: 735c7650f4660163673aebaf74409dff8c328e8ac959544a0b9b3c879d7719edf6eabea9365fac47ebe8d19941c170146557759e9071597762c9fda60641fd25 Homepage: https://cran.r-project.org/package=monographaR Description: CRAN Package 'monographaR' (Taxonomic Monographs Tools) Contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, identification keys (dichotomous and interactive), and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology histograms and distributional and diversity maps are also available. Package: r-cran-monoinc Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compare, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-sitar Filename: pool/dists/noble/main/r-cran-monoinc_1.1-1.ca2404.1_all.deb Size: 171034 MD5sum: 61e998f1974596566ed5d4272d6ce6a2 SHA1: 87e6b7a6712c9c8a19f141eb6a86ee3cbba0d6b3 SHA256: 6b166a9b4813bf6f37fbe3268ec6497f9556c97b0fcfd3df61132197e4263a2f SHA512: 79b6a5919fe14de689e5fe7f093942acdb4cfeeda1b37573a33a4b609569233f65b49766f4f3d87530c43f98f2094b992a9bd6ba8d32bde34ac03e339014eb19 Homepage: https://cran.r-project.org/package=MonoInc Description: CRAN Package 'MonoInc' (Monotonic Increasing) Various imputation methods are utilized in this package, where one can flag and impute non-monotonic data that is outside of a prespecified range. Package: r-cran-monophy Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-phytools, r-cran-phangorn, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-testthat, r-cran-paleotree, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-monophy_1.3.2-1.ca2404.1_all.deb Size: 326082 MD5sum: 9165984c02f1a2544e97d59863a1f16f SHA1: f5aecf9ccff8165c5e79d6ba84f5553e038c218a SHA256: 574b2af03032e88f365708ed1526066c0530194d06e9ecc0490d276e39d65cbc SHA512: 5b5f5b6a8cf7144255059a1763e69a6087551526869059f892c221a8e7d72cd22eadef7fadcbf40fda59bba70ce8758b6463dd82e7eb47eaa1eb1815bb10fde9 Homepage: https://cran.r-project.org/package=MonoPhy Description: CRAN Package 'MonoPhy' (Explore Monophyly of Taxonomic Groups in a Phylogeny) Requires rooted phylogeny as input and creates a table of genera, their monophyly-status, which taxa cause problems in monophyly etc. Different information can be extracted from the output and a plot function allows visualization of the results in a number of ways. "MonoPhy: a simple R package to find and visualize monophyly issues." Schwery, O. & O'Meara, B.C. (2016) . Package: r-cran-monotonehazardratio Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fdrtool, r-cran-kernsmooth, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-monotonehazardratio_0.2.0-1.ca2404.1_all.deb Size: 36364 MD5sum: 8e02d17e8353df3a7d1283c19b65a4c9 SHA1: e03f242915c187b2c6255cfc293d79cfa24ba29f SHA256: dfb2686ade2503339b11b37721a54a5165506216ee2a833027d2082113942c82 SHA512: 0f7be77f0db7561661c4e735433b65d61e4f54c527876975aa56bd51bd79ab2fab1610760d85c0a082b979e8d45345980572dc27ca1965a4c90b75e6abc48305 Homepage: https://cran.r-project.org/package=MonotoneHazardRatio Description: CRAN Package 'MonotoneHazardRatio' (Nonparametric Estimation and Inference of a Monotone HazardRatio Function) Nonparametric estimation and inference of a non-decreasing monotone hazard ratio from a right censored survival dataset. The estimator is based on a generalized Grenander typed estimator, and the inference procedure relies on direct plugin estimation of a first order derivative. More details please refer to the paper "Nonparametric inference under a monotone hazard ratio order" by Y. Wu and T. Westling (2023) . Package: r-cran-monotonicity Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmtest, r-cran-mass, r-cran-sandwich Suggests: r-cran-testthat, r-cran-xts Filename: pool/dists/noble/main/r-cran-monotonicity_1.3.1-1.ca2404.1_all.deb Size: 106514 MD5sum: f4ed722e268504563700533e05390fda SHA1: 98771422f1b7c09bc6edf0b9ba04dc73a0acf50a SHA256: e0c462a24248962136645bd5ab4e7b6fa8c68573619679e4432accfe44dbe3e5 SHA512: aa9024ed9538294cb74d49690bd82f5f2ecc5a31ba205428e43d3e9c5072dc698ac43776b52821c3dd8fc67dfeac6a7f7b47a8b3b888214671b2fd207430e074 Homepage: https://cran.r-project.org/package=monotonicity Description: CRAN Package 'monotonicity' (Test for Monotonicity in Expected Asset Returns, Sorted byPortfolios) Test for monotonicity in financial variables sorted by portfolios. It is conventional practice in empirical research to form portfolios of assets ranked by a certain sort variable. A t-test is then used to consider the mean return spread between the portfolios with the highest and lowest values of the sort variable. Yet comparing only the average returns on the top and bottom portfolios does not provide a sufficient way to test for a monotonic relation between expected returns and the sort variable. This package provides nonparametric tests for the full set of monotonic patterns by Patton, A. and Timmermann, A. (2010) and compares the proposed results with extant alternatives such as t-tests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations. Package: r-cran-monte.carlo.se Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-monte.carlo.se_0.1.1-1.ca2404.1_all.deb Size: 173322 MD5sum: e06b6e04855e88327d4f2e88e49b11aa SHA1: e7330e0059f7bb34fc15e59edfcaf8406d2d9a84 SHA256: 70263a046e47066607eb713bb3e9a93ae3e786f773ded8007f3072b47cf6e6f2 SHA512: 099a006a20cab1211dfb0197e8c0d0d5bc71373d40c54a6d300e1a20eddb33f58a9f867d5dfa4bb3605936d9942395058c08730be74ec024b94d876be13a2c52 Homepage: https://cran.r-project.org/package=Monte.Carlo.se Description: CRAN Package 'Monte.Carlo.se' (Monte Carlo Standard Errors) Computes Monte Carlo standard errors for summaries of Monte Carlo output. Summaries and their standard errors are based on columns of Monte Carlo simulation output. Dennis D. Boos and Jason A. Osborne (2015) . Package: r-cran-montecarlo Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-snow, r-cran-abind, r-cran-codetools, r-cran-rlecuyer, r-cran-snowfall, r-cran-reshape Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-montecarlo_1.0.6-1.ca2404.1_all.deb Size: 98122 MD5sum: 4cd5f7dfdd0b832bdad91579235a49fb SHA1: b45e6cfc4bfecbacc63732596709473c6f6e20e5 SHA256: a26d343f776ea1d8e03e2eeedc5c9324dc0da6ac071c1e3d46cc4b9f9115da30 SHA512: 1d619d7efc580bb1cf58c8bc2545e4dcfa655c09590a4753d15e3497a3cd3514cd55891bdeacb67315d12b6ddd31f6b0c226aca7c589d7af2c6a5f219ed9b817 Homepage: https://cran.r-project.org/package=MonteCarlo Description: CRAN Package 'MonteCarlo' (Automatic Parallelized Monte Carlo Simulations) Simplifies Monte Carlo simulation studies by automatically setting up loops to run over parameter grids and parallelising the Monte Carlo repetitions. It also generates LaTeX tables. Package: r-cran-montecarlosem Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-lavaan Filename: pool/dists/noble/main/r-cran-montecarlosem_2.0.0-1.ca2404.1_all.deb Size: 72186 MD5sum: cd5050573d4eac9f35b1bdbfa54c4c13 SHA1: 815d89b1767dc2a4dbe7f51c2ca61da6c20e6d81 SHA256: 7b6d75f24d0b1d5a5320c7b69850e111417bf992b5523bb4d0d78a252bfe1178 SHA512: 6aa6a5c8a1bc370ef77cb2d53b3ca72e091315fab3a82fab948ae6d27bdeb8121a958741e3d3d0ceac53f00486c49ac7419eca02de39e7b2d4bda2cc909cfc5f Homepage: https://cran.r-project.org/package=MonteCarloSEM Description: CRAN Package 'MonteCarloSEM' (Monte Carlo Simulation for Structural Equation Modeling) Provides tools to conduct Monte Carlo simulations under different conditions (e.g., varying sample size, data normality) for structural equation models (SEMs). Data can be simulated based on user-defined factor loadings and correlations, with optional non-normality added via Fleishman's power method (1978) . Once generated, models can be estimated using 'lavaan'. This package facilitates testing model performance across multiple simulation scenarios. When data generation is completed (or when generated data sets are given) model tests can also be run. Please cite as "Orçan, F. (2021). MonteCarloSEM An R Package to Simulate Data for SEM. International Journal of Assessment Tools in Education, 8 (3), 704-713." 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This makes it easy to quickly create a quiz that can be randomly replicated with new datasets, questions, and options for answers. 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'Moodle' is an open source Learning Management System (LMS) developed by MoodleHQ. For more information about Moodle, visit . Package: r-cran-moonbook Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1824 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nortest, r-cran-survival, r-cran-sjmisc, r-cran-stringr, r-cran-magrittr, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr, r-cran-ggplot2, r-cran-sjlabelled, r-cran-ztable, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-moonbook_0.3.1-1.ca2404.1_all.deb Size: 1246618 MD5sum: 57c722313f1fec680bfa2759bc0c207a SHA1: a2965369a731a3586c8e4bc43df6133178815f56 SHA256: a22062c112bc73e0834ab0656ff0e7e99eb5877d97524ee61f2c1bcd310448a5 SHA512: 5561321983616669ab49e0e1e7a32d15ace5fffcc2a7b0c854a32fd86f35ccc14da65aef25433f0143405a83e1b1007ccd0f5735325c71bf12db9dc5c8047b47 Homepage: https://cran.r-project.org/package=moonBook Description: CRAN Package 'moonBook' (Functions and Datasets for the Book by Keon-Woong Moon) Several analysis-related functions for the book entitled "R statistics and graph for medical articles" (written in Korean), version 1, by Keon-Woong Moon with Korean demographic data with several plot functions. Package: r-cran-moonboot Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-moonboot_2.0.1-1.ca2404.1_all.deb Size: 64774 MD5sum: 2f0cd147a53b7df60ec451345a95aa90 SHA1: ad71f2ca9ffdccfcb46630c2deb0ba654ffced99 SHA256: 3f13db47f56dadc205f43ea86ab658f12c1f3979a329a0773fe349eaa939ef6e SHA512: 3858e96c71469a8d114ad22c92340a68c545d9527d12019cfe282a385c3937b7cf47c5225444348b89fa1cd9ceb4dadb5f19e46c77ac017d9b0727178a2baa16 Homepage: https://cran.r-project.org/package=moonboot Description: CRAN Package 'moonboot' (m-Out-of-n Bootstrap Functions) Functions and examples based on the m-out-of-n bootstrap suggested by Politis, D.N. and Romano, J.P. (1994) . Additionally there are functions to estimate the scaling factor tau and the subsampling size m. For a detailed description and a full list of references, see Dalitz, C. and Lögler, F. (2025) . Package: r-cran-moonlit Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4263 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-suncalc, r-cran-lubridate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-moonlit_0.1.1-1.ca2404.1_all.deb Size: 914358 MD5sum: cfa62ad6ea75d4351f937bce074c2226 SHA1: 099399684b607916c798cf38a8ba46e4f13d86d9 SHA256: 4b0f4e9f2b11958cf2be35fbff9b97ae1093e74daae474cbf593a33f673fc6f4 SHA512: ed29afa9dc5178a13083f9c9d857a667d831d4a90a21e873875206ec3d01b090b26595afd699af855697ade9dea53f3754996089ac133fd8e46763b85c36ce88 Homepage: https://cran.r-project.org/package=moonlit Description: CRAN Package 'moonlit' (Predicting Moonlight Intensity for a Given Time and Location) Tools for predicting moonlight intensity on the ground based on the position of the moon, atmospheric conditions, and other factors. Provides functions to calculate moonlight intensity and related statistics for ecological and behavioral research, offering more accurate estimates than simple moon phase calculations. The underlying model is described in Smielak (2023) . Package: r-cran-mooplot Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-collapse, r-cran-matrixstats, r-cran-moocore Suggests: r-cran-extrafont, r-cran-viridislite, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-mooplot_0.1.1-1.ca2404.1_all.deb Size: 165778 MD5sum: 7ddccb1bd69d671194e498f8be5c68b6 SHA1: 55867264d9cae74935b285acfedd3d1d78018ebc SHA256: 1e20bc9c3ecc7a0977bb542b006aa89a9b82a6c89e713f5f224e4be3f2d0b325 SHA512: c2c4f78298a9da3677d081901348b88b3170554e8eecaace2626721c6239a4f5da4ca7bc22f69fd0e5b2a1d354155b0e46dff1cd3e96869d2a46648389c82670 Homepage: https://cran.r-project.org/package=mooplot Description: CRAN Package 'mooplot' (Graphical Visualizations for Multi-Objective Optimization) Visualization of multi-dimensional data arising in multi-objective optimization, including plots of the empirical attainment function (EAF), M. López-Ibáñez, L. Paquete, and T. Stützle (2010) , and symmetric Vorob'ev expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) , among others. Package: r-cran-moose Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-moose_0.0.1-1.ca2404.1_all.deb Size: 14938 MD5sum: de1e111557735f8985e9e308382db15a SHA1: b4ed145f7b66c4a373bc0a2fde72e45fd15bb52d SHA256: 4b6832a1b8f740f11e6995491f51aeb4feea16c61eefc146da4fe6fd722bf1d4 SHA512: 7e88c1dc3b41c5a4ffb6bf600e76ebdd4e64f39c6f219b04c562ffa3edca3660478c577bc3146a2bd78096b7e549f3896a96c81c04d1fa06a41564e7ce6908cf Homepage: https://cran.r-project.org/package=moose Description: CRAN Package 'moose' (Mean Squared Out-of-Sample Error Projection) Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) . It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper. Package: r-cran-mopac Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1921 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-lubridate, r-cran-tibble, r-cran-readr, r-cran-hms Filename: pool/dists/noble/main/r-cran-mopac_0.1.0-1.ca2404.1_all.deb Size: 1284376 MD5sum: 5f725a768b26b31fee0c8dbdce7b931b SHA1: c6fe69676b9683cc1d6726961e63b2bcb7ec3eac SHA256: 4f9b92acaa0cb41a0c6c658ab980685e5ca569ba2b69af88403605fea5a67967 SHA512: 78c1a7a14ba865c8b84a43faaba687506d0288bf3ce9e5a86a5c70b45b13a77168489f5c72541e306e0b9ec157f185016f08acb9e864a9d2607defcff6237e28 Homepage: https://cran.r-project.org/package=mopac Description: CRAN Package 'mopac' (Collection of Datasets Pertaining to Loop 1 "Mopac") Provides real & simulated datasets containing time-series traffic observations and additional information pertaining to Loop 1 "Mopac" located in Austin, Texas. Package: r-cran-moqa Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych, r-cran-gplots, r-cran-readr Filename: pool/dists/noble/main/r-cran-moqa_2.0.0-1.ca2404.1_all.deb Size: 83774 MD5sum: 9e315a14a130e4f606bb7aa0fd1e326a SHA1: 0ff45e96dd1046367e8b9805048ac1002325a681 SHA256: d3175c17f4844e0563333ac4aa65eb31a8d8ef141e36f4347120ca5453f39d66 SHA512: c91eac911079ed90c8d001c9bb9bc56a2b8ad4759afb669b9cb859ca9e438c68a698a9a8ad770fe50d69ea01798f955f461ea36d9656947447d5186b88b735f4 Homepage: https://cran.r-project.org/package=MOQA Description: CRAN Package 'MOQA' (Basic Quality Data Assurance for Epidemiological Research) With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The 'MOQA' package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See for more information. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) . . 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Also modules and a shiny app for conditional inference trees. 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Package: r-cran-morestopwords Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4561 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-cld2 Filename: pool/dists/noble/main/r-cran-morestopwords_0.2.0-1.ca2404.1_all.deb Size: 729656 MD5sum: 451ad3a8e8e74c63fbca987a3ba492e5 SHA1: 10b50831e6c37d073110fe00903f076281c7bb76 SHA256: 411e713ed5256387895c40ca9814fef3eec4e1b1614b30bcc97ef1ca3efb63e1 SHA512: 291c664c08c06dede3f7c4e0ec2d9261ce58a35094da160cb052aa585e0d8083bd8430256ed3d3481ddae9fd08217e8e89b82f08e4085fd2d2b3edd125bb8867 Homepage: https://cran.r-project.org/package=morestopwords Description: CRAN Package 'morestopwords' (All Stop Words in One Place) A standalone package combining several stop-word lists for 65 languages with a median of 329 stop words for language and over 1,000 entries for English, Breton, Latin, Slovenian, and Ancient Greek! The user automatically gets access to all the unique stop words contained in: the 'StopwordISO' repository; the 'Natural Language Toolkit' for 'python'; the 'Snowball' stop-word list; the R package 'quanteda'; the 'marimo' repository; the 'Perseus' project; and A. Berra's list of stop words for Ancient Greek and Latin. Package: r-cran-morph Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-reshape2, r-cran-igraph, r-cran-stringr Filename: pool/dists/noble/main/r-cran-morph_1.1.0-1.ca2404.1_all.deb Size: 77170 MD5sum: a5ee30fa4119a05caa25734afc9baf9f SHA1: 4d4dd3661ae90daaede0bdd4ac24ad18e88f656b SHA256: 884bb5c63c49aae4e7600b2c9cf8c43c0b693ccc3831018ff36a4f47bc887626 SHA512: e5f940e09fd1e6f09bc9840f25d36b530e11b2a34faf36f415da2778166b8d655a96bd949263e9804bad9c5fca96320f8b8b7b06daa726f9c3fc4ae07e54bb37 Homepage: https://cran.r-project.org/package=morph Description: CRAN Package 'morph' (3D Segmentation of Voxels into Morphologic Classes) Automatically segments a 3D array of voxels into mutually exclusive morphological elements. This package extends existing work for segmenting 2D binary raster data. A paper documenting this approach has been accepted for publication in the journal Landscape Ecology. Detailed references will be updated here once those are known. 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Calculate two and three-dimensional morphometric maps, cross-sectional geometric parameters, and semilandmarks on the periosteal and endosteal contours of each cross section. 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We provide tools to extend geometric morphometric principles to the study of non-physical structures, hormone profiles, as outlined in Ehrlich et al (2021) . Easily transform daily measures into multivariate landmark-based data. Includes custom functions to apply multivariate methods for data exploration as well as hypothesis testing. Also includes 'shiny' web app to streamline data exploration. Developed to study menstrual cycle hormones but functions have been generalized and should be applicable to any biomarker over any time period. 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After reducing data dimensionality with the built-in function for data ordination, regions are fitted as segmented linear regressions along the serial structure. Every region boundary position and increasing number of regions are iteratively fitted and the best model (number of regions and boundary positions) is selected with an information criterion. This package expands on the previous 'regions' package (Jones et al., Science 2018) with improved computation and more fitting and plotting options. 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Statistical and graphical tools provide a comprehensive framework for checking and manipulating input data, statistical analyses, and visualization of results. Several methods are provided for the analysis of raw data, to make the dataset ready for downstream analyses. Integrated statistical methods include hierarchical classification, principal component analysis, principal coordinates analysis, non-metric multidimensional scaling, and multiple discriminant analyses: canonical, stepwise, and classificatory (linear, quadratic, and the non-parametric k nearest neighbours). The philosophy of the package is described in Šlenker et al. 2022. 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The world's second oldest ongoing agricultural experiment, the Morrow Plots measure the impact of crop rotation and fertility treatments on corn yields. The data includes planting information and annual yield measures for corn grown continuously and in rotation with other crops, in treated and untreated soil. 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'binary' (e.g., survival, mobility), 'count' (e.g., reproduction) and 'continuous' (e.g., growth as length, weight). Estimation procedures can be used without a deep knowledge of their underlying probabilistic model or inference methods. Rather, they were designed to behave as well as possible without requiring a user to provide values for some obscure parameters. That said, models can also be used as a first step to tailor new models for more specific situations. Package: r-cran-mort Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2058 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mort_0.0.1-1.ca2404.1_all.deb Size: 1702126 MD5sum: 8aaefac4de3828c335c11b6e12f2031a SHA1: b92f9a97774fa1183a77f090938ee35eb9845559 SHA256: 541fbb2329b4044a7399c8d781c4c6c3703d6596b6a4bb61a021743f434d0adf SHA512: eeac649481440e4b58f59076a91f8837aaf11737a01ca46086cc90a90b08a9a8744bea39158edc6d4b5d94bd888c9f055a7c5eac55700f28d6ff102355b03356 Homepage: https://cran.r-project.org/package=mort Description: CRAN Package 'mort' (Identifying Potential Mortalities and Expelled Tags in AquaticAcoustic Telemetry Arrays) A toolkit for identifying potential mortalities and expelled tags in aquatic acoustic telemetry arrays. 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It takes demographic data in different formats and displays the result in a standard life table as well as plots the relevant indices (percentage of deaths, survivorship, probability of death, life expectancy, percentage of population). It also checks for possible biases in the age structure and applies corrections to life tables. 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These associations can be used to improve forecasts. Here we have implemented a method for forecasting female life expectancy based on analysis of the gap between female life expectancy in a country compared with the record level of female life expectancy in the world. Second, to forecast male life expectancy, the gap between male life expectancy and female life expectancy in a country is analysed. We named this method the Double-Gap model. For a detailed description of the method see Pascariu et al. (2018). . Package: r-cran-mortalitylaws Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcurl, r-cran-pbapply, r-cran-tidyr, r-cran-rvest, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mortalitylaws_2.2.0-1.ca2404.1_all.deb Size: 560870 MD5sum: 7606dc5547202899c6f7e1e81374d55d SHA1: 6373a2939c1b924975d085d32d4de9100d0ca638 SHA256: 07153df97d3e3cfe0ad61dc0f27e39ee9fd07b0e567bd8b8553cf7b4f51f7caa SHA512: 567dd3d7f5270e616fca224bb8c36f3a4eb50a8ea23f47c4c44858b6a1db9291617f373df6d6912a335fbd0b924578fe8f0d4080aaa731a1db13d71bfa566058 Homepage: https://cran.r-project.org/package=MortalityLaws Description: CRAN Package 'MortalityLaws' (Parametric Mortality Models, Life Tables and HMD) Fit the most popular human mortality 'laws', and construct full and abridge life tables given various input indices. A mortality law is a parametric function that describes the dying-out process of individuals in a population during a significant portion of their life spans. For a comprehensive review of the most important mortality laws see Tabeau (2001) . Practical functions for downloading data from various human mortality databases are provided as well. Package: r-cran-mortalitytables Architecture: all Version: 2.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2725 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-pracma Suggests: r-cran-knitr, r-cran-tidyverse, r-cran-reshape2, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mortalitytables_2.0.5-1.ca2404.1_all.deb Size: 1256364 MD5sum: da616f05862cf31c8a17990aadf0c00a SHA1: b87311faee6bf7a9a7c246681a931fb17aeab5fa SHA256: 5aa434bd5603dd38a70f4cdacbe68794ee93b06ea42d8e771d903c37430d239d SHA512: 11333fa7e73f218c5dac17498169ff2d5b9e1344606cf78fc9b3b954d2254333082576a362e761b2b63a8f19f4f34526f547893075f4d80de14a76e087cbd2ae Homepage: https://cran.r-project.org/package=MortalityTables Description: CRAN Package 'MortalityTables' (A Framework for Various Types of Mortality / Life Tables) Classes to implement, analyze and plot cohort life tables for actuarial calculations. Birth-year dependent cohort mortality tables using a yearly trend to extrapolate from a base year are implemented, as well as period life table, cohort life tables using an age shift, and merged life tables. Additionally, several data sets from various countries are included to provide widely-used tables out of the box. Package: r-cran-mortar Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-gert, r-cran-glue, r-cran-purrr, r-cran-r.utils, r-cran-rlang, r-cran-usethis Suggests: r-cran-knitr, r-cran-renv, r-cran-rmarkdown, r-cran-stringr, r-cran-tarchetypes, r-cran-targets, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-mortar_0.4.0-1.ca2404.1_all.deb Size: 66924 MD5sum: 01a9732e74cf91d1d38094b281e68d66 SHA1: eee3830a242555b3b3d0d72f111ccd0229244e6c SHA256: 8bc26b6aa8448f2e79be394eb86bd09319b8d9d5099d719fea673744b775ebe2 SHA512: fae16d2fb073b30a1d0d9d318d314e31d901ea84e8205df861339bff43dc980df8d1a8eebbf0bfc185da1fb5b89127c4f203799a9f7c5cc27f0f3e3ac6e2061b Homepage: https://cran.r-project.org/package=mortar Description: CRAN Package 'mortar' (Standardize Data Science Workflows) Helper functions to standardizes common workflows in the USGS Data Science Community of Practice to produce more robust, reproducible pipelines. It contains helper functions to standardize (1) the organization of project repositories and (2) the creation ofpipelines from the 'targets' R Package (Landau et al. (2026) ), using the DS CoP best practices. We draw upon community developed best practices as well as certain USGS-specific requirements. See Shrycock et al. (2023) for examples of these USGS requirements. Package: r-cran-mortsoa Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-mortsoa_0.1.0-1.ca2404.1_all.deb Size: 87744 MD5sum: 5a7a84ae152433ba8a7f739c2f300133 SHA1: 13f8eb497445b1344abaa54eb8cc142bd3d7e0c8 SHA256: 065032fd8be31fffb227c09a78b3e6e5288f35dedc22d7fc3967818a57b28e00 SHA512: 800ccf88f721c94a3829be4faa71aabda6def0f8a443f865323d654d050bd199fe7754de01d2f389343f5d39b22668d70d41aff68b6b1ffa635dfe11134e628d Homepage: https://cran.r-project.org/package=mortSOA Description: CRAN Package 'mortSOA' (Obtain Data from the Society of Actuaries 'Mortality and OtherRate Tables' Site) The Society of Actuaries (SOA) provides an extensive online database called 'Mortality and Other Rate Tables' ('MORT') at . This database contains mortality, lapse, and valuation tables that cover a variety of product types and nations. Users of the database can download any tables in 'Excel', 'CSV', or 'XML' formats. This package provides convenience functions that read 'XML' formats from the database and return R objects. Package: r-cran-mos Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hypergeo2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-moments Filename: pool/dists/noble/main/r-cran-mos_0.1.3-1.ca2404.1_all.deb Size: 124910 MD5sum: 76af1a8b12531972722840262fd447a3 SHA1: 6a7d89aece578138d5482cfc3fb34b3dfd722aef SHA256: b3ae9917c709872128513609bb5a5354c53aaddee7686cf7adc97d8a9efc0ec0 SHA512: 80ebe3164d92587391025e2f8cc656c62296a104ba62bc8bf62ed0930afb4583027adc2c1c739de83ca3f9e3ac8a2faeb88109e34a9fa6b295875fb46a194b4f Homepage: https://cran.r-project.org/package=mos Description: CRAN Package 'mos' (Simulation and Moment Computation for Order Statistics) Provides a comprehensive set of tools for working with order statistics, including functions for simulating order statistics, censored samples (Type I and Type II), and record values from various continuous distributions. Additionally, it offers functions to compute moments (mean, variance, skewness, kurtosis) of order statistics for several continuous distributions. These tools assist researchers and statisticians in understanding and analyzing the properties of order statistics and related data. The methods and algorithms implemented in this package are based on several published works, including Ahsanullah et al (2013, ISBN:9789491216831), Arnold and Balakrishnan (2012, ISBN:1461236444), Harter and Balakrishnan (1996, ISBN:9780849394522), Balakrishnan and Sandhu (1995) , Genç (2012) , Makouei et al (2021) and Nagaraja (2013) . Package: r-cran-mosaic Architecture: all Version: 1.10.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3874 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-lattice, r-cran-ggformula, r-cran-mosaicdata, r-cran-matrix, r-cran-mosaiccore, r-cran-ggplot2, r-cran-rlang, r-cran-purrr, r-cran-mass, r-cran-tidyr Suggests: r-cran-ggstance, r-cran-ggridges, r-cran-vdiffr, r-cran-lubridate, r-cran-magrittr, r-cran-nhanes, r-cran-rcurl, r-cran-sp, r-cran-vcd, r-cran-testthat, r-cran-knitr, r-cran-mapproj, r-cran-rgl, r-cran-rmarkdown, r-cran-covr, r-cran-formatr, r-cran-palmerpenguins, r-cran-ggrepel, r-cran-readr, r-cran-ggdendro, r-cran-gridextra, r-cran-latticeextra, r-cran-glue, r-cran-broom, r-cran-leaflet Filename: pool/dists/noble/main/r-cran-mosaic_1.10.2-1.ca2404.1_all.deb Size: 3073936 MD5sum: 7ee38d88c167ccd6790ee8173f1d841e SHA1: 50cdbc6bc61ebc9db33d78dd605bf39a9b107cd6 SHA256: 129f558ce4b67192377e412fb43c59696fa6b0fd1b1caf58a33a3a942006327b SHA512: fd2866ea3057f133d2fef31c3095795ec38ab7cb2332baea0283978d5277b5f7c150b630ca9dd25b770c7e775652887c7c138b998a48c138643d08af428f0e06 Homepage: https://cran.r-project.org/package=mosaic Description: CRAN Package 'mosaic' (Project MOSAIC Statistics and Mathematics Teaching Utilities) Data sets and utilities from Project MOSAIC () used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all. Package: r-cran-mosaiccalc Architecture: all Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3643 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-calculus, r-cran-deriv, r-cran-dplyr, r-cran-ggformula, r-cran-ggplot2, r-cran-glue, r-cran-mass, r-cran-matrix, r-cran-metr, r-cran-mosaiccore, r-cran-mosaic, r-cran-orthopolynom, r-cran-rlang, r-cran-ryacas, r-cran-sp, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-palmerpenguins, r-cran-rmarkdown, r-cran-cubature, r-cran-plotly, r-cran-mosaicdata Filename: pool/dists/noble/main/r-cran-mosaiccalc_0.6.4-1.ca2404.1_all.deb Size: 2738286 MD5sum: 4385c5d772899a15f58297e3033b1d70 SHA1: 34fd79d7b9cbd013b2390af1ed5221dc7c8a9bbe SHA256: d76c13f3e1140d9a54131be5990aa0edec544fd88693112fffe351d8411cc85c SHA512: 8af53ea626293a689b36c26ab220b49fa3db81304ee59f45ccfb52d1ee81cee30675ca91329cd043a98447becdadf4224cf79f7ffbc4d0b6919e13ce60ede841 Homepage: https://cran.r-project.org/package=mosaicCalc Description: CRAN Package 'mosaicCalc' (R-Language Based Calculus Operations for Teaching) Software to support the introductory *MOSAIC Calculus* textbook ), one of many data- and modeling-oriented educational resources developed by Project MOSAIC (). Provides symbolic and numerical differentiation and integration, as well as support for applied linear algebra (for data science), and differential equations/dynamics. Includes grammar-of-graphics-based functions for drawing vector fields, trajectories, etc. The software is suitable for general use, but intended mainly for teaching calculus. Package: r-cran-mosaiccore Architecture: all Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-mass Suggests: r-cran-mosaicdata, r-cran-mosaic, r-cran-ggformula, r-cran-nhanes, r-cran-testthat, r-cran-mosaiccalc Filename: pool/dists/noble/main/r-cran-mosaiccore_0.9.5-1.ca2404.1_all.deb Size: 195740 MD5sum: 94eea23a55eded7aa4ec344c7928a3cd SHA1: e873b1c77ca2023666580380f556a430afcabf36 SHA256: db0742608e3a0161661cb372d967341dc3ba41f7648faa299d49741ef085929e SHA512: c8d1cbd68303f256d7af55ced136921e6a2e005cc69809034aa203d59b7b2a5564ed6d40950837c1a485dd3e519272e90b8eb1a380b7c5e856a504748a665ce0 Homepage: https://cran.r-project.org/package=mosaicCore Description: CRAN Package 'mosaicCore' (Common Utilities for Other MOSAIC-Family Packages) Common utilities used in other MOSAIC-family packages are collected here. Package: r-cran-mosaicdata Architecture: all Version: 0.20.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1699 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-lattice, r-cran-mosaic, r-cran-reshape2, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-ggformula Filename: pool/dists/noble/main/r-cran-mosaicdata_0.20.4-1.ca2404.1_all.deb Size: 1599156 MD5sum: da73772bc47bff14c5f6588c1c69d318 SHA1: 027e5681512778fbc812ca462f244f2baa7139ed SHA256: 83611f690cac9eebd525f2bfa69e1f2e5325682423c09e90cb3e05d942b91abf SHA512: 535fe5a2c22f2a10042fb82f93be9f00c769d3d33b6a1354d904909ed0234fc6e3375fb4855bfffc68bb998fc4d66dc62abcbc37a7bf77b1a3877eb8a6978bb5 Homepage: https://cran.r-project.org/package=mosaicData Description: CRAN Package 'mosaicData' (Project MOSAIC Data Sets) Data sets from Project MOSAIC () used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all. Package: r-cran-mosaicmodel Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 993 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mosaiccore, r-cran-dplyr, r-cran-caret, r-cran-ggplot2, r-cran-ggformula, r-cran-lazyeval, r-cran-knitr, r-cran-mass, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyverse Suggests: r-cran-mosaic, r-cran-mosaicdata, r-cran-randomforest, r-cran-rpart Filename: pool/dists/noble/main/r-cran-mosaicmodel_0.3.0-1.ca2404.1_all.deb Size: 785444 MD5sum: c969fd92d29968d0cee3d0189869b9e7 SHA1: 023dc1e879d6ab5b54643ce8607236beef2be64c SHA256: 52b7e718dadca1f3dd2e49645861051fe9fae0dc3b753c5c0e70604896bc1099 SHA512: e2039216674034374fda536f183648b37744df04b4acb05a03b3612ab6cf57646b00700834cb606b26433633a4a6c96093af9881cfb05507cb1677c380bee652 Homepage: https://cran.r-project.org/package=mosaicModel Description: CRAN Package 'mosaicModel' (An Interface to Statistical Modeling Independent of ModelArchitecture) Provides functions for evaluating, displaying, and interpreting statistical models. The goal is to abstract the operations on models from the particular architecture of the model. For instance, calculating effect sizes rather than looking at coefficients. The package includes interfaces to both regression and classification architectures, including lm(), glm(), rlm() in 'MASS', random forests and recursive partitioning, k-nearest neighbors, linear and quadratic discriminant analysis, and models produced by the 'caret' package's train(). It's straightforward to add in other other model architectures. Package: r-cran-mosalloc Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ecosolver, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mosalloc_1.2.5-1.ca2404.1_all.deb Size: 177702 MD5sum: 2b7379c92c8bd52b96f16e7b42a4ddc9 SHA1: 19a09a65f11493c92e99982ccef2e41af679dca4 SHA256: fb59d09dee5633bdc665a97593ca76f14f68d2b38531bd3429c4ac42b36fb5b2 SHA512: 28490cd833a2f8b7c078a4d4e3156ef648fdddb171947dd6a848c9ee22d80dd75e9106a42e7257ff33772a42a22de8cb4a0f0fdcf0545f7de2008161aaab0545 Homepage: https://cran.r-project.org/package=MOSAlloc Description: CRAN Package 'MOSAlloc' (Constraint Multiobjective Sample Allocation) Provides a framework for multipurpose optimal resource allocation in survey sampling, extending the classical optimal allocation principles introduced by Tschuprow (1923) and Neyman (1934) to multidomain and multivariate allocation problems. The primary method mosalloc() allows for the consideration of precision and cost constraints at the subpopulation level while minimizing either a vector of sampling errors or survey costs across a broad range of optimal sample allocation problems. The approach supports both single- and multistage designs. For single-stage stratified random sampling, the mosallocSTRS() function offers a user- friendly interface. Sensitivity analysis is supported through the problem's dual variables, which are naturally obtained via the internal use of the Embedded Conic Solver from the 'ECOSolveR' package. See Willems (2025, ) for a detailed description of the theory behind 'MOSAlloc'. Package: r-cran-mosclust Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-clusterv Filename: pool/dists/noble/main/r-cran-mosclust_1.0.2-1.ca2404.1_all.deb Size: 366252 MD5sum: e0f75a083948dae4b89ee75ba5649482 SHA1: 9023aee856aedd7d7b101bd8570d60d9af36da97 SHA256: b96db1aabaf4f716ef2993679abf99431f6468fc27b80ad8cd7122a4cbafbb89 SHA512: c58ddbf0a29ad2fee37268b421706f390f950bef917ba9f913994c2a393ca02618e86c39144905b7426af0ea35a7cb82d69a2b76276065034d07040158b4f341 Homepage: https://cran.r-project.org/package=mosclust Description: CRAN Package 'mosclust' (Model Order Selection for Clustering) Stability based methods for model order selection in clustering problems (Valentini, G (2007), ). Using multiple perturbations of the data the stability of clustering solutions is assessed. Different perturbations may be used: resampling techniques, random projections and noise injection. Stability measures for the estimate of clustering solutions and statistical tests to assess their significance are provided. Package: r-cran-mosemiind Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-pracma, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mosemiind_0.1.2-1.ca2404.1_all.deb Size: 16472 MD5sum: f9ccfb1a150ba0d83aeb966e0e23c680 SHA1: 10f6dcfc5649ead6e4b2af9036315937258bc1fa SHA256: 38f43d5e52bcb692466bdd03ca42106ad5ce5e6995c7af645ceeaf1c264a76ef SHA512: 25995c2cbccf1cd4d0bd0e3acebc38c2deeed72bd6c81f643f9f57a3c8f3203c84ba66b71ee0d02b0bc91e85c3a694b57b9d01c335f9ef7494fd5a8f7717b6b1 Homepage: https://cran.r-project.org/package=MOsemiind Description: CRAN Package 'MOsemiind' (Marshall-Olkin Shock Models with Semi-Independent Time) Provides tools for analyzing Marshall-Olkin shock models semi-independent time. It includes interactive 'shiny' applications for exploring copula-based dependence structures, along with functions for modeling and visualization. The methods are based on Mijanovic and Popovic (2024, submitted) "An R package for Marshall-Olkin shock models with semi-independent times." Package: r-cran-mosqcontrol Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-assertthat, r-cran-pracma, r-cran-nlcoptim, r-cran-nloptr, r-cran-sfsmisc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mosqcontrol_0.1.0-1.ca2404.1_all.deb Size: 317114 MD5sum: 47e4875ca0fe5da116176881f8abbe06 SHA1: 99b4d1e0d360b7b864b70b2d4e2447c9589abe8a SHA256: 29db892b265035ab9a76c457d1e2c6bf462b5a580f2c6db24746d6dee72f8b69 SHA512: 86d8c18a43cda92d20c71817b7ebe85ef796d2be4cc2903a41d29ef421352d1e38e955f80bbd302b3ae3f45eab8f33ae6099683e99bc089708061f337ee76ef0 Homepage: https://cran.r-project.org/package=mosqcontrol Description: CRAN Package 'mosqcontrol' (Mosquito Control Resource Optimization) This project aims to make an accessible model for mosquito control resource optimization. The model uses data provided by users to estimate the mosquito populations in the sampling area for the sampling time period, and the optimal time to apply a treatment or multiple treatments. Package: r-cran-moss Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-dbscan, r-cran-rtsne Suggests: r-bioc-annotate, r-cran-bigparallelr, r-cran-bigstatsr, r-cran-future.apply, r-cran-scatterpie, r-cran-clvalid, r-bioc-complexheatmap, r-cran-fpc, r-cran-ggplot2, r-cran-ggpmisc, r-cran-ggthemes, r-cran-gridextra, r-cran-irlba, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat, r-cran-viridis, r-cran-spelling, r-cran-venndiagram Filename: pool/dists/noble/main/r-cran-moss_0.2.2-1.ca2404.1_all.deb Size: 3344364 MD5sum: afb4c6410f73d1842e252a20efda2958 SHA1: 091adf5a76db7b8d7bae20db7d7618d215d6327d SHA256: 77fe22b3953d80df03541a05506a052da2a7d72734f637b1ee86afb519a6854e SHA512: d86233cb050fac0e003bc7465a52e1bdaca0482e676280e347c9f5978dc39f358af3dff74a169718478464739f11ea40ba5f1440fb4e5e60b3a64a1306fadf40 Homepage: https://cran.r-project.org/package=MOSS Description: CRAN Package 'MOSS' (Multi-Omic Integration via Sparse Singular Value Decomposition) High dimensionality, noise and heterogeneity among samples and features challenge the omic integration task. Here we present an omic integration method based on sparse singular value decomposition (SVD) to deal with these limitations, by: a. obtaining the main axes of variation of the combined omics, b. imposing sparsity constraints at both subjects (rows) and features (columns) levels using Elastic Net type of shrinkage, and c. allowing both linear and non-linear projections (via t-Stochastic Neighbor Embedding) of the omic data to detect clusters in very convoluted data (Gonzalez-Reymundez et. al, 2022) . Package: r-cran-most Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-most_0.1.2-1.ca2404.1_all.deb Size: 60806 MD5sum: e24ae28ae939268e90cad8a1f159e3b0 SHA1: d8fc20c697c307320e393edb2cc8c8ed35579940 SHA256: 7dc49fbbabc4b629af572d1dc9dd6059df57a5c29148d7d7a634788264c85a39 SHA512: 6cbc0b8567d8d287c7bfeb4e08ea59bb2a4bf7875a08c8e7a0539a01d4da2a6fe406c448a2ec4b44e1fbc0158d487d14d497c8c53ea15602a35bde3f00247f77 Homepage: https://cran.r-project.org/package=MOST Description: CRAN Package 'MOST' (Multiphase Optimization Strategy) Provides functions similar to the 'SAS' macros previously provided to accompany Collins, Dziak, and Li (2009) and Dziak, Nahum-Shani, and Collins (2012) , papers which outline practical benefits and challenges of factorial and fractional factorial experiments for scientists interested in developing biological and/or behavioral interventions, especially in the context of the multiphase optimization strategy (see Collins, Kugler & Gwadz 2016) . The package currently contains three functions. First, RelativeCosts1() draws a graph of the relative cost of complete and reduced factorial designs versus other alternatives. Second, RandomAssignmentGenerator() returns a dataframe which contains a list of random numbers that can be used to conveniently assign participants to conditions in an experiment with many conditions. Third, FactorialPowerPlan() estimates the power, detectable effect size, or required sample size of a factorial or fractional factorial experiment, for main effects or interactions, given several possible choices of effect size metric, and allowing pretests and clustering. Package: r-cran-mote Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-car, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mote_1.2.2-1.ca2404.1_all.deb Size: 325978 MD5sum: e5b89cefa32b97e5d09afff94eedb830 SHA1: 6c833551d4a51076d2c111efc7bec9e5f7d00d51 SHA256: 8f7536064727ee65b0037d28c9a79496c86769bf91d11ac18490f0d107a337c4 SHA512: d59074ff19d364b87a8d4819839a88f4e3f0e28dd9354d3e4ce5468a0c5678caf67fb87208b2b77d86b7e31cf60ab5c50d2d6eb9f4074b5c3a11dcc139206c3c Homepage: https://cran.r-project.org/package=MOTE Description: CRAN Package 'MOTE' (Effect Size and Confidence Interval Calculator) Measure of the Effect ('MOTE') is an effect size calculator, including a wide variety of effect sizes in the mean differences family (all versions of d) and the variance overlap family (eta, omega, epsilon, r). 'MOTE' provides non-central confidence intervals for each effect size, relevant test statistics, and output for reporting in APA Style (American Psychological Association, 2010, ) with 'LaTeX'. In research, an over-reliance on p-values may conceal the fact that a study is under-powered (Halsey, Curran-Everett, Vowler, & Drummond, 2015 ). A test may be statistically significant, yet practically inconsequential (Fritz, Scherndl, & Kühberger, 2012 ). Although the American Psychological Association has long advocated for the inclusion of effect sizes (Wilkinson & American Psychological Association Task Force on Statistical Inference, 1999 ), the vast majority of peer-reviewed, published academic studies stop short of reporting effect sizes and confidence intervals (Cumming, 2013, ). 'MOTE' simplifies the use and interpretation of effect sizes and confidence intervals. Package: r-cran-motherduck Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2345 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-assertthat, r-cran-cli, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-glue, r-cran-purrr, r-cran-stringr, r-cran-httr2, r-cran-rlang, r-cran-janitor, r-cran-tibble Suggests: r-cran-testthat, r-cran-quarto, r-cran-contoso, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-motherduck_0.2.1-1.ca2404.1_all.deb Size: 2321288 MD5sum: 8e1ae7c414a16b537264b9142776d3b1 SHA1: 6a6e741932982e6571499ecc90b33ec26fa6f7f3 SHA256: 3b8685944b8778382271f4575369c39cc9f088b50451866959aa00cc0add33a8 SHA512: 887228ea092909debeecc04b2ae38cc27c64f9e7d127d0922286c408686373f410f208b225ef146ecec1ccb3ec12c9b390e64251f93eae601edad68ec40e66fa Homepage: https://cran.r-project.org/package=motherduck Description: CRAN Package 'motherduck' (Utilities for Managing a 'Motherduck' Database) Provides helper functions, metadata utilities, and workflows for administering and managing databases on the 'Motherduck' cloud platform. Some features require a 'Motherduck' account (). Package: r-cran-motifcluster Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-rspectra Suggests: r-cran-covr, r-cran-knitr, r-cran-mclust, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-motifcluster_0.2.3-1.ca2404.1_all.deb Size: 450472 MD5sum: c66deca240b2a310013539f8fed16739 SHA1: 6e1f7b20c705b08751e0f9edda08145eb82cd94b SHA256: 2f20f21eed93d6001c5b2113befd5c820f6994c41372b123e7fd58ceb1fd8d11 SHA512: 9217c6812c6769e83c167154ba2481ef1243f84dd767b4d30c1c466af66e844a6a2c951501537ce4b2b4a08338edb627bcb32985d30c1a56d10128332c1f42e4 Homepage: https://cran.r-project.org/package=motifcluster Description: CRAN Package 'motifcluster' (Motif-Based Spectral Clustering of Weighted Directed Networks) Tools for spectral clustering of weighted directed networks using motif adjacency matrices. Methods perform well on large and sparse networks, and random sampling methods for generating weighted directed networks are also provided. Based on methodology detailed in Underwood, Elliott and Cucuringu (2020) . Package: r-cran-motifr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-intergraph, r-cran-network, r-cran-purrr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-reticulate, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-tidygraph Suggests: r-cran-ergm, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-motifr_1.0.0-1.ca2404.1_all.deb Size: 610402 MD5sum: 02e12be390e23c9a98be73e616d0fa1e SHA1: 6259b981ba4b30de51c7f0d991ab2d08b7e11e42 SHA256: c70641ce53906407399ed942415ac7daa4067359de627ae1c8a08991ea47b8c1 SHA512: a0bc36827491d89ccbb0c301b89435b9aef8562c34c9edbe244f2b1bb02adce4d371240449bdb88c8e3dd9e80df1f4d0252031b3a0e029ea0a56aef69a22e41b Homepage: https://cran.r-project.org/package=motifr Description: CRAN Package 'motifr' (Motif Analysis in Multi-Level Networks) Tools for motif analysis in multi-level networks. Multi-level networks combine multiple networks in one, e.g. social-ecological networks. Motifs are small configurations of nodes and edges (subgraphs) occurring in networks. 'motifr' can visualize multi-level networks, count multi-level network motifs and compare motif occurrences to baseline models. It also identifies contributions of existing or potential edges to motifs to find critical or missing edges. The package is in many parts an R wrapper for the excellent 'SESMotifAnalyser' 'Python' package written by Tim Seppelt. Package: r-cran-motorneuron Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2736 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dygraphs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-motorneuron_1.0.0-1.ca2404.1_all.deb Size: 229496 MD5sum: 7085370bdf46497bcc97bba82ee4a984 SHA1: e670e169535a075729c6329085748d5202e701a8 SHA256: 60a6773068ef6c3994ea0459c8eb823a277156d18bc82718a37a811af7eea44a SHA512: cb62162a677ae2927a6786a9bae8fd4c7f34be2c99aec182000695d1b620d1222891a05ab1265ffc5755635efdaa18bd27ec76645a7f204cf09154478fd1cbf8 Homepage: https://cran.r-project.org/package=motoRneuron Description: CRAN Package 'motoRneuron' (Analyzing Paired Neuron Discharge Times for Time-DomainSynchronization) The temporal relationship between motor neurons can offer explanations for neural strategies. We combined functions to reduce neuron action potential discharge data and analyze it for short-term, time-domain synchronization. Even more so, motoRneuron combines most available methods for the determining cross correlation histogram peaks and most available indices for calculating synchronization into simple functions. See Nordstrom, Fuglevand, and Enoka (1992) for a more thorough introduction. Package: r-cran-moult Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 768 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-moult_2.3.1-1.ca2404.1_all.deb Size: 679818 MD5sum: 6dc1305fb263e1507cceca58752770c8 SHA1: 5d7559702f70ec94dce3f37e38b4c8f5b1138d2d SHA256: ae68ec56539b9a75db83c0ce150dcdd098db021c4281c32734a98e343be50a65 SHA512: c51f2e55e44a1c666df6163dfc2243a71555c0718d4b698ff8c56281d25d1270d9969e94340a2b8cc7474ea62f5bcb2bdbd840f593b26ba577acb10f7a648553 Homepage: https://cran.r-project.org/package=moult Description: CRAN Package 'moult' (Models for Analysing Moult in Birds) Functions to estimate start and duration of moult from moult data, based on models developed in Underhill and Zucchini (1988, 1990). Package: r-cran-mountainplot Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Suggests: r-cran-knitr, r-cran-latticeextra, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mountainplot_1.4-1.ca2404.1_all.deb Size: 110762 MD5sum: fec6d62c4c4477e15c37b42b383f22f9 SHA1: 534a277d3c3e9294f3207dbdf8df7a86dd055dcd SHA256: d7b093fe46a4296901ad8c758c4283dd029221111de46b70ebeb0f123a8c11b3 SHA512: f8c5afe108a66c8f7befd8714b433ecebbdc9869954bfa3a3668b31662213902a072c9699c0752e5be78ff55870b860114d18e8ee9367bbb15f4810e7a7b5602 Homepage: https://cran.r-project.org/package=mountainplot Description: CRAN Package 'mountainplot' (Mountain Plots, Folded Empirical Cumulative Distribution Plots) Lattice functions for drawing folded empirical cumulative distribution plots, or mountain plots. A mountain plot is similar to an empirical CDF plot, except that the curve increases from 0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at the right side. See Monti (1995) . Package: r-cran-mousetrajectory Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lifecycle, r-cran-signal Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-mousetrajectory_0.2.1-1.ca2404.1_all.deb Size: 88664 MD5sum: 3d7a4b2a04f6f10e69c3d857470b5c24 SHA1: ddd3b74999a1be945d4190521bc87b6236f4afea SHA256: b4f9dc0ab7646382a567592eceab440f8b2acca428e5281f221a8fc83867d93f SHA512: 73c61d7bf16965cec04bf80de8dd73fa524d5dc3d2f61fc4a2df628fb4d21f5317c1986f3b0caddb1281f8364fd756f4af97317e42e752a12e58b2b009566bbb Homepage: https://cran.r-project.org/package=mousetRajectory Description: CRAN Package 'mousetRajectory' (Mouse Trajectory Analyses for Behavioural Scientists) Helping psychologists and other behavioural scientists to analyze mouse movement (and other 2-D trajectory) data. Bundles together several functions that compute spatial measures (e.g., maximum absolute deviation, area under the curve, sample entropy) or provide a shorthand for procedures that are frequently used (e.g., time normalization, linear interpolation, extracting initiation and movement times). For more information on these dependent measures, see Wirth et al. (2020) . Package: r-cran-move2 Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4435 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-sf, r-cran-rlang, r-cran-units, r-cran-tidyselect, r-cran-dplyr, r-cran-tibble, r-cran-vroom, r-cran-cli, r-cran-vctrs, r-cran-bit64 Suggests: r-cran-knitr, r-cran-askpass, r-cran-digest, r-cran-keyring, r-cran-xml2, r-cran-curl, r-cran-magrittr, r-cran-purrr, r-cran-ggplot2, r-cran-testthat, r-cran-rmarkdown, r-cran-lwgeom, r-cran-s2, r-cran-move, r-cran-raster, r-cran-withr, r-cran-lubridate, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-circular, r-cran-tidyr, r-cran-gganimate, r-cran-prettymapr, r-cran-gifski, r-cran-ggspatial Filename: pool/dists/noble/main/r-cran-move2_0.5.0-1.ca2404.1_all.deb Size: 3284672 MD5sum: 5731ddad2440b28061237458ade38169 SHA1: de35cef9f0104a5692af4b83408817d713e3ad0c SHA256: e1cfba0eed75888277869a0f12c3923c976c91a2aa3e3f16d74a99aef5618ab6 SHA512: 4fb7f750572091e654cce1b1f56184bdd52c46e3072c3b9cf1b76ea3205b0ef3f38cb2bf77516e96a698b42872dbe1ad855d7c76ebf7cf713600830f4d015685 Homepage: https://cran.r-project.org/package=move2 Description: CRAN Package 'move2' (Processing and Analysing Animal Trajectories) Tools to handle, manipulate and explore trajectory data, with an emphasis on data from tracked animals. The package is designed to support large studies with several million location records and keep track of units where possible. Data import directly from 'movebank' and files is facilitated. Package: r-cran-movecost Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 980 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chron, r-cran-gdistance, r-cran-elevatr, r-cran-matrix, r-cran-raster, r-cran-sf, r-cran-sp, r-cran-terra Filename: pool/dists/noble/main/r-cran-movecost_2.2-1.ca2404.1_all.deb Size: 962656 MD5sum: cc236969ef18022601e74794a12dec74 SHA1: 6412213222663377b7170af2021cf35222a18fdb SHA256: 152049eb29f2a283bf3f0164a0135e55a5f775da4c5bb3f599444fc6fb87bf8c SHA512: 3e78f8346c712dbf63655bc760de01ba2d0cefce60fccc2916b5348e071c1919d8576782c393c1d38afef2055766732c672afe774b752033e38e9cb3be4951cf Homepage: https://cran.r-project.org/package=movecost Description: CRAN Package 'movecost' (Calculation of Slope-Dependant Accumulated Cost Surface,Least-Cost Paths, Least-Cost Corridors, Least-Cost NetworksRelated to Human Movement Across the Landscape) Provides the facility to calculate non-isotropic accumulated cost surface, least-cost paths, least-cost corridors, least-cost networks using a number of human-movement-related cost functions that can be selected by the user. It just requires a Digital Terrain Model, a start location and (optionally) destination locations. See Alberti (2019) . Package: r-cran-movedesign Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4036 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayestestr, r-cran-bsplus, r-cran-combinat, r-cran-config, r-cran-crayon, r-cran-ctmm, r-cran-data.table, r-cran-dplyr, r-cran-fontawesome, r-cran-gdtools, r-cran-ggiraph, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggtext, r-cran-golem, r-cran-gsl, r-cran-lubridate, r-cran-parsedate, r-cran-quarto, r-cran-reactable, r-cran-rintrojs, r-cran-rlang, r-cran-scales, r-cran-shiny, r-cran-shinyalert, r-cran-shinybusy, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyfeedback, r-cran-shinyjs, r-cran-shinywidgets, r-cran-stringr, r-cran-terra, r-cran-tidyr, r-cran-viridis Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shinytest2 Filename: pool/dists/noble/main/r-cran-movedesign_0.3.2-1.ca2404.1_all.deb Size: 3431148 MD5sum: 7d321f4e2f72a58b1dd25641352869be SHA1: dd6699bd87f1335585d29e6d0561025ede4c7bdf SHA256: 6974bc1e2ae2d02821b8b99ac7ed3ad22bf42cfb09e7e5eebfc21f7e180454e2 SHA512: 1a6bed4c674c7e379bddc3a0d1ce082582e3ac18b30f5ed79ea84c6fffdb870fc319fa4745cbed09126e353f05aaed38a4e75491cf699a40fd892023789d06c2 Homepage: https://cran.r-project.org/package=movedesign Description: CRAN Package 'movedesign' (Study Design Toolbox for Movement Ecology Studies) Toolbox and 'shiny' application to help researchers design movement ecology studies, focusing on two key objectives: estimating home range areas, and estimating fine-scale movement behavior, specifically speed and distance traveled. It provides interactive simulations and methodological guidance to support study planning and decision-making. The application is described in Silva et al. (2023) . Package: r-cran-moveez Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2823 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-biplotez, r-cran-gganimate, r-cran-ggplot2, r-cran-gpabin Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-tibble, r-cran-scales, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-moveez_1.2.0-1.ca2404.1_all.deb Size: 1991512 MD5sum: 8ce5402f16a1afe6ca08a8e5ae75e6b8 SHA1: 243da12d2b8c103316a175822aee7cbcd5c6ae20 SHA256: 9b1aed28bb5a575794e142276a06b171953e88637e88c62906c4f5ad213d09d6 SHA512: 76047f35871ed746d114c682d0aefa017ebf11bc235c74a8f5eae4d26963d3d773f5aec52931eb5ec067daaabd6681435e5256464b7651414b75a095f46c9c79 Homepage: https://cran.r-project.org/package=moveEZ Description: CRAN Package 'moveEZ' (Animated Biplots) Create animated biplots that enables dynamic visualisation of temporal or sequential changes in multivariate data by animating a single biplot across the levels of a time variable. It builds on objects from the 'biplotEZ' package, Lubbe S, le Roux N, Nienkemper-Swanepoel J, Ganey R, Buys R, Adams Z, Manefeldt P (2024) , allowing users to create animated biplots that reveal how both samples and variables evolve over time. Package: r-cran-movegroup Architecture: all Version: 2024.03.05-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2760 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-beepr, r-cran-dplyr, r-cran-ggmap, r-cran-knitr, r-cran-lubridate, r-cran-magick, r-cran-move, r-cran-purrr, r-cran-raster, r-cran-rlang, r-cran-sf, r-cran-sp, r-cran-stars, r-cran-starsextra, r-cran-stringr, r-cran-terra, r-cran-tidyr, r-cran-tidyselect, r-cran-viridis Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-movegroup_2024.03.05-1.ca2404.1_all.deb Size: 2108546 MD5sum: 2297a327359c431a4bec82bd11a8568f SHA1: 877f041bb3cc65da086a15f9ecbb2c1fba166e48 SHA256: 38fbb030642c71806c28c3cb0e49778297fd322a990ec1741665dbd24861f1c5 SHA512: 00f87f8f6c720961f7324444ecfc8c31b0dbbe1b839e90a34b1f3ae579c94d755df664733e437fb289058b8fcb792359c4dc906c89a20d614bd9e14d0d2d28a6 Homepage: https://cran.r-project.org/package=movegroup Description: CRAN Package 'movegroup' (Visualizing and Quantifying Space Use Data for Groups of Animals) Offers an easy and automated way to scale up individual-level space use analysis to that of groups. Contains a function from the 'move' package to calculate a dynamic Brownian bridge movement model from movement data for individual animals, as well as functions to visualize and quantify space use for individuals aggregated in groups. Originally written with passive acoustic telemetry in mind, this package also provides functionality to account for unbalanced acoustic receiver array designs, and satellite tag data. Package: r-cran-movementsync Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-circular, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-hms, r-cran-igraph, r-cran-lmtest, r-cran-osfr, r-cran-rlang, r-cran-scales, r-cran-signal, r-cran-tidyr, r-cran-waveletcomp, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-movementsync_0.1.5-1.ca2404.1_all.deb Size: 1961272 MD5sum: 62254cac70e783010fc3acf7ad6f4825 SHA1: fc57c6f5b060719c9a233c6d77d36b36c0871bfd SHA256: 39a9fd84565510a82d91035f8889cb011cd1f1caeee1f8f91eeebd753a680439 SHA512: 32beaa17d69b9434850e690dda121d859a2335a81fff182ffa1db0bb3c5971c8708ad174996dc203a6575b9e64feb22f15ae20d0c5c709e5459246caf83be172 Homepage: https://cran.r-project.org/package=movementsync Description: CRAN Package 'movementsync' (Analysis and Visualisation of Musical Audio and Video MovementSynchrony Data) Analysis and visualisation of synchrony, interaction, and joint movements from audio and video movement data of a group of music performers. The demo is data described in Clayton, Leante, and Tarsitani (2021) , while example analyses can be found in Clayton, Jakubowski, and Eerola (2019) . Additionally, wavelet analysis techniques have been applied to examine movement-related musical interactions, as shown in Eerola et al. (2018) . Package: r-cran-movieroc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rms, r-cran-animation, r-cran-intrval, r-cran-gtools, r-cran-e1071, r-cran-robustbase, r-cran-rsolnp, r-cran-ks, r-cran-zoo Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-movieroc_0.1.2-1.ca2404.1_all.deb Size: 2068714 MD5sum: 34cae615f6e08bf81aab9eb4f087b5bf SHA1: b69c1dc866100a0613ae57b86dc4f69fc7d0cc83 SHA256: 25dc73a1cef51c5a7af739b6f1025a43b205f08f2c22dcb15df8099c7e05428b SHA512: 0d3d3a00f57afcf4974b35400e8464a5ca9aadb6fd34b5e15ff794be69555c301d4d47c1acc621d3cadb5bf6e9b306e2638124f079adf7d3de5c9c82aae835e3 Homepage: https://cran.r-project.org/package=movieROC Description: CRAN Package 'movieROC' (Visualizing the Decision Rules Underlying Binary Classification) Visualization of decision rules for binary classification and Receiver Operating Characteristic (ROC) curve estimation under different generalizations proposed in the literature: - making the classification subsets flexible to cover those scenarios where both extremes of the marker are associated with a higher risk of being positive, considering two thresholds (gROC() function); - transforming the marker by a proper function trying to improve the classification performance (hROC() function); - when dealing with multivariate markers, considering a proper transformation to univariate space trying to maximize the resulting AUC of the TPR for each FPR (multiROC() function). The classification regions behind each point of the ROC curve are displayed in both static graphics (plot_buildROC(), plot_regions() or plot_funregions() function) or videos (movieROC() function). Package: r-cran-mpactr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-cli, r-cran-ggplot2, r-cran-r6, r-cran-readr, r-cran-treemapify, r-cran-viridis Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mpactr_0.1.0-1.ca2404.1_all.deb Size: 535764 MD5sum: 31d43e2e15bc694542cab8b6da01519a SHA1: 692acd66e5224a8556bba2690d0286ad579a5154 SHA256: 293a7b545421e380d1779608ad7b2a10c5ee67dfb6418cbd4abf5388b56737d0 SHA512: 84fffc8f9630a683555aa96d15269cefd4862bd648dc74759c4abd79220386f2b7e73590253cc27dd240fba02590c75c1c619f91e3c9f682d7acfddcac458e2a Homepage: https://cran.r-project.org/package=mpactr Description: CRAN Package 'mpactr' (Correction of Preprocessed MS Data) An 'R' implementation of the 'python' program Metabolomics Peak Analysis Computational Tool ('MPACT') (Robert M. Samples, Sara P. Puckett, and Marcy J. Balunas (2023) ). Filters in the package serve to address common errors in tandem mass spectrometry preprocessing, including: (1) isotopic patterns that are incorrectly split during preprocessing, (2) features present in solvent blanks due to carryover between samples, (3) features whose abundance is greater than user-defined abundance threshold in a specific group of samples, for example media blanks, (4) ions that are inconsistent between technical replicates, and (5) in-source fragment ions created during ionization before fragmentation in the tandem mass spectrometry workflow. Package: r-cran-mpae Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-rcmdrmisc Suggests: r-cran-car, r-cran-gbm, r-cran-leaps, r-cran-lmtest, r-cran-glmnet, r-cran-mgcv, r-cran-np, r-cran-neuralnettools, r-cran-pdp, r-cran-vivid, r-cran-plot3d, r-cran-appliedpredictivemodeling, r-cran-islr Filename: pool/dists/noble/main/r-cran-mpae_0.1.2-1.ca2404.1_all.deb Size: 123154 MD5sum: c8e17acfe99283ad938a045d771c5df5 SHA1: 553c0cf6d9b8ae75eb93ec64ce0a13d16dd63956 SHA256: f7afcd1472d1d2086ea4d3a10c605fa5cb4f4e11333510ae3f028deb251006db SHA512: 9c4955f3b6e173dca887e3998f1d58fe2ac6a3c9149b224dd961ac6fe93acab594b51b8825b1a1651caae84390dfacb01d770389c588a571c24f891c5e2df8cb Homepage: https://cran.r-project.org/package=mpae Description: CRAN Package 'mpae' (Metodos Predictivos de Aprendizaje Estadistico (StatisticalLearning Predictive Methods)) Functions and datasets used in the book: Fernandez-Casal, R., Costa, J. and Oviedo-de la Fuente, M. (2024) "Metodos predictivos de aprendizaje estadistico" . Package: r-cran-mpathr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 955 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-readr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mpathr_1.0.4-1.ca2404.1_all.deb Size: 301192 MD5sum: 4b1f5072f9cfebad6b065e574ea8159d SHA1: d439b6d94e824d1d728ba670f672c367024e2770 SHA256: 7f007df68c06b6f9c74ffccbf941fd23d1d0d2f28b883b847f6928cb2bfcda37 SHA512: 4a062f461588aafbbc293dc11ab1fa6fc62966ca6e90753b98349b7bdb8604f476f305e6216f69902f37ba6fe6fad73b354bf73e0fe649ac881f3b29487cc50f Homepage: https://cran.r-project.org/package=mpathr Description: CRAN Package 'mpathr' (Easily Handling Data from the ‘m-Path’ Platform) Provides tools for importing and cleaning Experience Sampling Method (ESM) data collected via the 'm-Path' platform. The goal is to provide with a few utility functions to be able to read and perform some common operations in ESM data collected through the 'm-Path' platform (). Functions include raw data handling, format standardization, and basic data checks, as well as to calculate the response rate in data from ESM studies. Package: r-cran-mpathsenser Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4882 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-furrr, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-rsqlite, r-cran-tibble, r-cran-tidyr Suggests: r-cran-cli, r-cran-curl, r-cran-ggplot2, r-cran-httr, r-cran-kableextra, r-cran-knitr, r-cran-lintr, r-cran-progressr, r-cran-rmarkdown, r-cran-rvest, r-cran-sodium, r-cran-spelling, r-cran-testthat, r-cran-vroom Filename: pool/dists/noble/main/r-cran-mpathsenser_1.2.4-1.ca2404.1_all.deb Size: 3979730 MD5sum: 630c7ed77a438ebd40660f56923442e5 SHA1: fa2e3f0345d402725b3876d11c29c3f8add42b2d SHA256: 885c1c6886c0f7adda27d4d90a633eb225b83843cda6eb0815b7229238e072c2 SHA512: 2f9e3d71c70616268688c7557fed55d50f48198dde8e85ee1278aeb41b1e20c44a2da2a1cbbc93a15efb854d2fa40f3fc1cf380054f4f6bc0216d403f241fd86 Homepage: https://cran.r-project.org/package=mpathsenser Description: CRAN Package 'mpathsenser' (Process and Analyse Data from m-Path Sense) Overcomes one of the major challenges in mobile (passive) sensing, namely being able to pre-process the raw data that comes from a mobile sensing app, specifically 'm-Path Sense' . The main task of 'mpathsenser' is therefore to read 'm-Path Sense' JSON files into a database and provide several convenience functions to aid in data processing. Package: r-cran-mpci Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mpci_1.0.7-1.ca2404.1_all.deb Size: 44188 MD5sum: d2b328fc5f151e10624d4340e31d3a3e SHA1: 9790cab158d9a34093bbc56a79908847d688b2be SHA256: e742a64e3cd6b56f053dd95ae5c9ed7c017ed167ac7f26de2b1f0a811014cd9e SHA512: d9bc1ccedd893d621e483a0ba970242b1d2f306716a8b82d42eb5ff81d7e0f8509ec232189dcbfae6762f177521154a7a282aab3d803eaecc3a603322289c00c Homepage: https://cran.r-project.org/package=MPCI Description: CRAN Package 'MPCI' (Multivariate Process Capability Indices (MPCI)) It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included. Package: r-cran-mpdir Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 601 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-mpdir_0.3-1.ca2404.1_all.deb Size: 392876 MD5sum: 3c636ead53ab537764af6dc69f035208 SHA1: d116995740859d047c9253c926756b090cb02514 SHA256: cfc348f6fb02ea927883dadc3c96f620cbc6ee69d0c591d98f2a06d33f60e8b9 SHA512: f4959488bc8a318bc5dabc575b81a20b08dc4df3a1e9c27691137d9d2e246157bb458b7668452ae98d2197b1d8d7623cd705fcc2d8eec6b5c285a34117e287b8 Homepage: https://cran.r-project.org/package=MPDiR Description: CRAN Package 'MPDiR' (Data Sets and Scripts for Modeling Psychophysical Data in R) Data sets and scripts for Modeling Psychophysical Data in R (Springer). Package: r-cran-mpge Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mpge_1.0.1-1.ca2404.1_all.deb Size: 78476 MD5sum: 9de90b63468e0046bc019d4669d2132c SHA1: a3d38a5a2cc371f93f86af5686d2642d3469b27c SHA256: 9f83d49f36bf6be09007fcbc06da4495f1886f4f7b5a9303fd58d55a3a2547d0 SHA512: 29783704980aeaf121d825f254742403b35bffd4b82eb07866b8e5eda68dbc6bf97cdd9726a6c93f611ad9e158ad7a2dce7c6a554b868f78ce99275b5239c09a Homepage: https://cran.r-project.org/package=MPGE Description: CRAN Package 'MPGE' (A Two-Step Approach to Testing Overall Effect ofGene-Environment Interaction for Multiple Phenotypes) Interaction between a genetic variant (e.g., a single nucleotide polymorphism) and an environmental variable (e.g., physical activity) can have a shared effect on multiple phenotypes (e.g., blood lipids). We implement a two-step method to test for an overall interaction effect on multiple phenotypes. In first step, the method tests for an overall marginal genetic association between the genetic variant and the multivariate phenotype. The genetic variants which show an evidence of marginal overall genetic effect in the first step are prioritized while testing for an overall gene-environment interaction effect in the second step. Methodology is available from: A Majumdar, KS Burch, T Haldar, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) . Package: r-cran-mpi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-doparallel, r-cran-foreach, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-mpi_0.1.0-1.ca2404.1_all.deb Size: 37802 MD5sum: 04d3ad15f95c8f2e2924738c21acc77d SHA1: c05911422ce5f389105d95ae4690f4c03b3e6eb3 SHA256: 117caaf732c4b3f7e9ef3ca711b3f948345f7d38bcae2e249987186581246139 SHA512: c47a9bad771315e4185366db05d0cb0995da5f77df7b70d9a6310d011c069cd43667cd8482e62df0929b9bba40c21de4b89fe7c36df3a157342faef35033fc05 Homepage: https://cran.r-project.org/package=MPI Description: CRAN Package 'MPI' (Computation of Multidimensional Poverty Index (MPI)) Computing package for Multidimensional Poverty Index (MPI) using Alkire-Foster method. Given N individuals, each person has D indicators of deprivation, the package compute MPI value to represent the degree of poverty in a population. The inputs are 1) an N by D matrix, which has the element (i,j) represents whether an individual i is deprived in an indicator j (1 is deprived and 0 is not deprived), and 2) the deprivation threshold. The main output is the MPI value, which has the range between zero and one. MPI value is approaching one if almost all people are deprived in all indicators, and it is approaching zero if almost no people are deprived in any indicator. Please see Alkire S., Chatterjee, M., Conconi, A., Seth, S. and Ana Vaz (2014) for The Alkire-Foster methodology. Package: r-cran-mpindex Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-jsonlite, r-cran-purrr, r-cran-tibble, r-cran-openxlsx Suggests: r-cran-rlang, r-cran-testthat, r-cran-lifecycle, r-cran-knitr, r-cran-rmarkdown, r-cran-gt Filename: pool/dists/noble/main/r-cran-mpindex_0.2.1-1.ca2404.1_all.deb Size: 134076 MD5sum: d8a57147a2b5dd4ed1bac67ac6cbb2a2 SHA1: 9d72f5269f860b915c6bbad8791a4c2cdc3a6aa3 SHA256: ef49fac2c60b7b25f94852978f3d88cc5cc3a7f9b19c2742a77c820569876081 SHA512: fabf2587e09972e3574ff38bc1bfc32968aab148ea1c662f9463b2fbb341258bdcc9c10c4f66e02bbf7e4b32e8c1877a17df791a7677dfec3c0b1aee264b46c8 Homepage: https://cran.r-project.org/package=mpindex Description: CRAN Package 'mpindex' (Multidimensional Poverty Index (MPI)) A set of easy-to-use functions for computing the Multidimensional Poverty Index (MPI). Package: r-cran-mpitbr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1648 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-survey Suggests: r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-stringi, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-mpitbr_1.0.1-1.ca2404.1_all.deb Size: 1454034 MD5sum: 4ea589402d394839e0defb72f4c903dc SHA1: a21af8d1a6ac533979a9ddd40a6659a284e2f05d SHA256: 83a1201df5df0428edfa1209ac293406a5637522f1e5a607abedfa11f6f3b736 SHA512: 17bfeb0f52acb889db94c383070d38a32aa7ba7a1626985540b2a189d306dc4b54cc5a1bceb1871ad7f84bdb52e9480069f3555e9c52cf6aefc13ac9d40fefb6 Homepage: https://cran.r-project.org/package=mpitbR Description: CRAN Package 'mpitbR' (Calculate Alkire-Foster Multidimensional Poverty Measures) Estimate Multidimensional Poverty Indices disaggregated by population subgroups based on the Alkire and Foster method (2011) . This includes the calculation of standard errors and confidence intervals. Other partial indices such as incidence, intensity and indicator-specific measures as well as intertemporal changes analysis can also be estimated. The standard errors and confidence intervals are calculated considering the complex survey design. Package: r-cran-mplot Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaps, r-cran-foreach, r-cran-bestglm, r-cran-doparallel, r-cran-dorng, r-cran-plyr, r-cran-shinydashboard, r-cran-shiny, r-cran-glmnet, r-cran-googlevis, r-cran-ggplot2, r-cran-reshape2, r-cran-scales, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-mvoutlier, r-cran-glmulti, r-cran-rmarkdown, r-cran-dt, r-cran-mass Filename: pool/dists/noble/main/r-cran-mplot_1.0.6-1.ca2404.1_all.deb Size: 204376 MD5sum: 1c6db321c3520e5d27bbbeb26dd77d9c SHA1: 3f6b20dbefb962affc459d43576213ba24fc533c SHA256: 6c952e4771fd6170b7b1bd92b83073cde360d491a54956198b8708ce64179288 SHA512: fadcb9fbd65731d698fcbf080c09dfdddc43dc93e6cff3f013ff91e6bbc4f3d84ab63fd68b89b5a0401f5b1fed4fe19799669d368692d2a5f041d608d3affc10 Homepage: https://cran.r-project.org/package=mplot Description: CRAN Package 'mplot' (Graphical Model Stability and Variable Selection Procedures) Model stability and variable inclusion plots [Mueller and Welsh (2010, ); Murray, Heritier and Mueller (2013, )] as well as the adaptive fence [Jiang et al. (2008, ); Jiang et al. (2009, )] for linear and generalised linear models. Package: r-cran-mplusautomation Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3386 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-plyr, r-cran-gsubfn, r-cran-coda, r-cran-xtable, r-cran-lattice, r-cran-texreg, r-cran-pander, r-cran-digest, r-cran-ggplot2, r-cran-data.table, r-cran-fastdummies, r-cran-checkmate Suggests: r-bioc-rhdf5, r-cran-relimp, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mplusautomation_1.2-1.ca2404.1_all.deb Size: 2500112 MD5sum: 7b5d1aaf3aaa1640c1397099472b4a81 SHA1: a933202726c5f378aa5c203a7ab513851a50f6c7 SHA256: 4575c50f03b81bb9b132b98eba4f2f3e814ff80f9d3d3f77fe1dac5360f755f4 SHA512: 7b05717981c71e3c7a517825956674851bf4593da019214ef4cc97c2e99e42ae756d80f96bc542b3d7dca70f9991353cb97eac4f3b737778f43d1d9028a23951 Homepage: https://cran.r-project.org/package=MplusAutomation Description: CRAN Package 'MplusAutomation' (An R Package for Facilitating Large-Scale Latent VariableAnalyses in Mplus) Leverages the R language to automate latent variable model estimation and interpretation using 'Mplus', a powerful latent variable modeling program developed by Muthen and Muthen (). Specifically, this package provides routines for creating related groups of models, running batches of models, and extracting and tabulating model parameters and fit statistics. Package: r-cran-mpluslgm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mplusautomation, r-cran-magrittr, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-stringr, r-cran-purrr, r-cran-ggplot2, r-cran-glue Filename: pool/dists/noble/main/r-cran-mpluslgm_1.0.0-1.ca2404.1_all.deb Size: 92090 MD5sum: 3e1558007ea884b8293d3cf81c0d52fd SHA1: 840de7b96cd7106ceb1102e30b2a2cc3e0d94f9c SHA256: ac137dd004c3dc31f43a7a9f048623f3c221651419d4a6b60efb6ece7d146471 SHA512: 550b2285beec8e4f22b92afdbf75c76451edaf01175816e20d42b7d399ee6fae0e6470a800c974f7a19b2e7fff535d0198b4149366d37ad766072037aae5474b Homepage: https://cran.r-project.org/package=MplusLGM Description: CRAN Package 'MplusLGM' (Automate Latent Growth Mixture Modelling in 'Mplus') Provide a suite of functions for conducting and automating Latent Growth Modeling (LGM) in 'Mplus', including Growth Curve Model (GCM), Growth-Based Trajectory Model (GBTM) and Latent Class Growth Analysis (LCGA). The package builds upon the capabilities of the 'MplusAutomation' package (Hallquist & Wiley, 2018) to streamline large-scale latent variable analyses. “MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus.” Structural Equation Modeling, 25(4), 621–638. The workflow implemented in this package follows the recommendations outlined in Van Der Nest et al. (2020). “An Overview of Mixture Modeling for Latent Evolutions in Longitudinal Data: Modeling Approaches, Fit Statistics, and Software.” Advances in Life Course Research, 43, Article 100323. . Package: r-cran-mplusparallel.automation Architecture: all Version: 0.0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mplusautomation, r-cran-dplyr, r-cran-furrr, r-cran-future Suggests: r-cran-knitr, r-cran-mvtnorm, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mplusparallel.automation_0.0.1.1-1.ca2404.1_all.deb Size: 39182 MD5sum: f4310e179e24f827fc25df2412c866ee SHA1: af1c59b051570ef5e7e18bc463a9c4f40f3f328b SHA256: ec6a175495d52b9ad5a628df5b19e4b5c9ae9559e5a7fdae7a4c2eeb30bba358 SHA512: e65024741d48033c569d3ae9a15754d308e58cedb394d2bb9925d38f94f22713b2611fc6244c1cb3c95b1fa491fc3674153f47a9ffd863c81d6317adc8714183 Homepage: https://cran.r-project.org/package=mplusParallel.automation Description: CRAN Package 'mplusParallel.automation' (Parallel Processing Automation for 'Mplus') Offers automation tools to parallelize 'Mplus' operations when using 'R' for data generation. 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Package: r-cran-mpmaggregate Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-expm Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-rcompadre, r-cran-kableextra, r-cran-rphylopic, r-cran-collidr, r-cran-png Filename: pool/dists/noble/main/r-cran-mpmaggregate_0.2.5-1.ca2404.1_all.deb Size: 430948 MD5sum: 072d56c618e8a98dc2dd050242d33c44 SHA1: ae33fc612a2c2a8783d4727b053caf8c48ee30a6 SHA256: 2c81e0f0cbe53734ac6f182abea9caf48d8bf90eb3ea406a6a2dfb2efc67627d SHA512: 462efe2450be73c2f9c051e4bb57f80a8f47f9569eca5133e4fd42da7a4e4abc00985569f3a2611113af53ac48ba73ca5bd96c9cf5a1c2f1f7dc26a278ccc3d2 Homepage: https://cran.r-project.org/package=mpmaggregate Description: CRAN Package 'mpmaggregate' (Aggregate Matrix Population Models) Aggregates matrix population models (MPMs) in both the lambda (stable growth rate) and R0 (net reproductive rate) frameworks, including standard and elasticity-consistent aggregators. Standard aggregation in the lambda framework maintains consistent lambda and stable stage distribution, while standard aggregation in the R0 framework maintains consistent R0 and cohort stable stage distribution. Elasticity-consistent aggregators maintain these same consistencies with respect to the chosen framework and additionally preserve consistent reproductive values in the lambda framework and cohort reproductive values in the R0 framework. Aggregation can take the form of general-to-general MPM (mpm_aggregate) or Leslie-to-Leslie MPM (leslie_aggregate). 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Package: r-cran-mpower Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1545 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-boot, r-cran-dplyr, r-cran-dosnow, r-cran-foreach, r-cran-ggplot2, r-cran-mass, r-cran-magrittr, r-cran-purrr, r-cran-snow, r-cran-sbgcop, r-cran-rlang, r-cran-reshape2, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-bma, r-cran-bkmr, r-cran-bws, r-cran-infinitefactor, r-cran-knitr, r-cran-nhanes, r-cran-qgcomp, r-cran-rmarkdown, r-cran-rstan, r-cran-testthat, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-mpower_0.1.0-1.ca2404.1_all.deb Size: 1484566 MD5sum: 73d5b9d885d4f6264864eab19b1446e6 SHA1: c2cf1bde3505d3adae92f475b3a8467e6c81c0e2 SHA256: bdb30529887aa35ed382c625dde920080ae6abfbccbfaa764a6b1870df1eda96 SHA512: 7cef32f6e8fc238f2668addb301b99c133d5f8d382eeffcfff42606dbd9f089aff1c26c2050398bb38524f0c184aecb81aa3c5fbd2784be4910086f3f08d631c Homepage: https://cran.r-project.org/package=mpower Description: CRAN Package 'mpower' (Power Analysis via Monte Carlo Simulation for Correlated Data) A flexible framework for power analysis using Monte Carlo simulation for settings in which considerations of the correlations between predictors are important. 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The population must be composed of crosses between a set of at least three parents (e.g. factorial design, 'diallel', or nested association mapping). The functions cover data processing, QTL detection, and results visualization. The implemented methodology is described in Garin, Wimmer, Mezmouk, Malosetti and van Eeuwijk (2017) , in Garin, Malosetti and van Eeuwijk (2020) , and in Garin, Diallo, Tekete, Thera, ..., and Rami (2024) . 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Version 0.1.0 includes thirteen series covering the United States, United Kingdom, and Australia: for the US, the policy news shock of Nakamura and Steinsson (2018) , the orthogonalised surprise of Bauer and Swanson (2023) , the target and path factors of the Swanson (2021) extension of Gurkaynak, Sack, and Swanson (2005), the pure monetary policy and central bank information shocks of Jarocinski and Karadi (2020) , the informationally-robust shock of Miranda-Agrippino and Ricco (2021) , and the shadow federal funds rate of Wu and Xia (2016) ; for the UK, the UK Monetary Policy Event-Study Database of Braun, Miranda-Agrippino, and Saha (2025) , the high-frequency surprise of Cesa-Bianchi, Thwaites, and Vicondoa (2020) , and the narrative shock of Cloyne and Hurtgen (2016) ; for Australia, the three-component RBA surprise of Hambur and Haque (2023) and the credit-spread-augmented RBA narrative shock of Beckers (2020). Helpers support date alignment, frequency conversion, and shock cumulation. All data is bundled; no runtime network access is required. 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The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016, ) offers a principled alternative in which results for all possible combinations of reasonable modeling choices are reported. MPTmultiverse performs a multiverse analysis for multinomial processing tree (MPT, Riefer & Batchelder, 1988, ) models combining maximum-likelihood/frequentist and Bayesian estimation approaches with different levels of pooling (i.e., data aggregation) as described in Singmann et al. (2024, ). For the frequentist approaches, no pooling (with and without parametric or nonparametric bootstrap) and complete pooling are implemented using MPTinR . For the Bayesian approaches, no pooling, complete pooling, and three different variants of partial pooling are implemented using TreeBUGS . The main function is fit_mpt() which performs the multiverse analysis in one call. 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Multiple comparisons and simultaneous confidence interval estimations can be performed for ratios of treatment means in the normal one-way layout with homogeneous and heterogeneous treatment variances, according to Dilba et al. (2007) and Hasler and Hothorn (2008) . Confidence interval estimations for ratios of linear combinations of linear model parameters like in (multiple) slope ratio and parallel line assays can be carried out. Moreover, it is possible to calculate the sample sizes required in comparisons with a control based on relative margins. For the simple two-sample problem, functions for a t-test for ratio-formatted hypotheses and the corresponding confidence interval are provided assuming homogeneous or heterogeneous group variances. 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The namesake function mrbin() converts 1D or 2D Nuclear Magnetic Resonance data into a matrix of values suitable for further data analysis and performs basic processing steps in a reproducible way. Negative values, a common issue in such data, can be replaced by positive values (). All used parameters are stored in a readable text file and can be restored from that file to enable exact reproduction of the data at a later time. The function fia() ranks features according to their impact on classifier models, especially artificial neural network models. 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Includes implementation of Oguamalam et al. (2023) . Package: r-cran-mrcv Architecture: all Version: 0.4-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tables Suggests: r-cran-geepack Filename: pool/dists/noble/main/r-cran-mrcv_0.4-0-1.ca2404.1_all.deb Size: 429870 MD5sum: 667ccc567c036c0c0a6fff5829edceff SHA1: 202fe623f63fefa00ac28eb3de80530c2041bba6 SHA256: 1dc3c2fc8485da866579387992b0f400fcc058cacdad6f69949cb6c8341ab650 SHA512: a7c2a9619b4e6fb5e2e7d3fdb8eb16d7a50b9bf0038d673d88f40246cfb198c89f4116134def91b2432b3c93f73d503d48fe0cb136732ca26a8f425f48951189 Homepage: https://cran.r-project.org/package=MRCV Description: CRAN Package 'MRCV' (Methods for Analyzing Multiple Response Categorical Variables(MRCVs)) Provides functions for analyzing the association between one single response categorical variable (SRCV) and one multiple response categorical variable (MRCV), or between two or three MRCVs. A modified Pearson chi-square statistic can be used to test for marginal independence for the one or two MRCV case, or a more general loglinear modeling approach can be used to examine various other structures of association for the two or three MRCV case. Bootstrap- and asymptotic-based standardized residuals and model-predicted odds ratios are available, in addition to other descriptive information. Statisical methods implemented are described in Bilder et al. (2000) , Bilder and Loughin (2004) , Bilder and Loughin (2007) , and Koziol and Bilder (2014) . Package: r-cran-mrds Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optimx, r-cran-mgcv, r-cran-numderiv, r-cran-nloptr, r-cran-rsolnp, r-cran-rdpack Suggests: r-cran-distance, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-mrds_3.0.1-1.ca2404.1_all.deb Size: 989806 MD5sum: 8b0888a41d3dbfe6197ce8ac312f8042 SHA1: 4c3e98178cf98035a1cabe2f24ef5d3fc8c7b76d SHA256: 9341fe63f00a6c0fb93b790ea87fb891fdb7fe689a269ba9c8bbf0389cd2811e SHA512: 463c0e93d0bae2d14fc4a0048f95c1438dfd27e037dbf3827ffa66a0057cdd91c28b8ec7b98ca4592b3352120d96645073de742a8fdfa14c6f808e6081a1eecf Homepage: https://cran.r-project.org/package=mrds Description: CRAN Package 'mrds' (Mark-Recapture Distance Sampling) Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator. Package: r-cran-mreg Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mreg_1.2.1-1.ca2404.1_all.deb Size: 61512 MD5sum: e0ef1602516f635219e5718ca01137f2 SHA1: 65048e92c868cbe48a55035b4ab616b70e103193 SHA256: d1a399f439b04e36d2e4c6ed2f157ac435a0130d4048e12f62743cb8cc709b8b SHA512: 026609ea0f92dd88c8f568249584f95a94542a7342523618f88837b95da63c6cf9c2bc45a073637455927473e47530f05ad2893dc7c82bcfbaeb8e018db19c04 Homepage: https://cran.r-project.org/package=mreg Description: CRAN Package 'mreg' (Fits Regression Models When the Outcome is Partially Missing) Implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics. 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Package: r-cran-mrf Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deoptim, r-cran-forecast, r-cran-monmlp, r-cran-nnfor, r-cran-wavelets Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mrf_0.1.9-1.ca2404.1_all.deb Size: 166374 MD5sum: 667c63112e3ccc1f3abf173402be792a SHA1: 338d8d57b2c4f9414dedd8aa1d66d905fc0fcce1 SHA256: 4c77f9e674d54b50a77f9051014f118191fa8283c96ca94eda077bfd03e5cb6e SHA512: 3a4dc97ff6b0d9693bbadd62e67b311c3db6f450d7ce00057c3c12587f5979a6afe239fe635b3f4e2b3dcf153c0030eaf793bb46e322d4b44a909dc6e8ee0297 Homepage: https://cran.r-project.org/package=mrf Description: CRAN Package 'mrf' (Multiresolution Forecasting) Forecasting of univariate time series using feature extraction with variable prediction methods is provided. Feature extraction is done with a redundant Haar wavelet transform with filter h = (0.5, 0.5). The advantage of the approach compared to typical Fourier based methods is an dynamic adaptation to varying seasonalities. Currently implemented prediction methods based on the selected wavelets levels and scales are a regression and a multi-layer perceptron. Forecasts can be computed for horizon 1 or higher. Model selection is performed with an evolutionary optimization. Selection criteria are currently the AIC criterion, the Mean Absolute Error or the Mean Root Error. The data is split into three parts for model selection: Training, test, and evaluation dataset. The training data is for computing the weights of a parameter set. The test data is for choosing the best parameter set. The evaluation data is for assessing the forecast performance of the best parameter set on new data unknown to the model. This work is published in Stier, Q.; Gehlert, T.; Thrun, M.C. Multiresolution Forecasting for Industrial Applications. Processes 2021, 9, 1697. . Package: r-cran-mrfa Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-glmnet, r-cran-grplasso, r-cran-plyr, r-cran-randtoolbox, r-cran-foreach Filename: pool/dists/noble/main/r-cran-mrfa_0.6-1.ca2404.1_all.deb Size: 130628 MD5sum: ee397e4831076f073462a71bc6baad76 SHA1: f33d18c1ccee08985cdc99f4b4958a3c93ec64fb SHA256: d1cb654686d540ac07986ea90779021b697b2624123911c7c9e2e86f23eb0a43 SHA512: c1074d1ebef4fd8e345850543b3ea03b8976624ea60a1e72db2378d024f1495fd27c6ddfcf1a44212280dda60896d3566d35bba72ccb7237d11de654b58af087 Homepage: https://cran.r-project.org/package=MRFA Description: CRAN Package 'MRFA' (Fitting and Predicting Large-Scale Nonlinear Regression Problemsusing Multi-Resolution Functional ANOVA (MRFA) Approach) Performs the MRFA approach proposed by Sung et al. (2020) to fit and predict nonlinear regression problems, particularly for large-scale and high-dimensional problems. The application includes deterministic or stochastic computer experiments, spatial datasets, and so on. 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Models can incorporate additional covariates, allowing users to estimate how interactions between nodes in the graph are predicted to change across covariate gradients. The general methods implemented in this package are described in Clark et al. (2018) . 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Calculate the likelihood of a MRHawkes process with given hazard rate functions and offspring density function for an (increasing) sequence of event times. Calculate the Rosenblatt residuals of the event times. Predict future event times based on observed event times up to a given time. For details see Stindl and Chen (2018) . Package: r-cran-mri Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mri_1.0.1-1.ca2404.1_all.deb Size: 25260 MD5sum: c9974ee021a8ad11ef934e7ce5cfeab5 SHA1: 94bcb557a8d615cf6cc6fb8ad64880cffd7ca69a SHA256: 7a70efdc46692fa4af0e5d2f5b98c80997b6eb11fde6d171cd9ee48bfa6491cb SHA512: 4359a0682ff110ffb387acda7ca274ca5a15f33bf8f78434328d742815a6a432df4ed7a7013737f03fa9d9e01157c1ad6dafa6df1459f9d785db39d6d2695934 Homepage: https://cran.r-project.org/package=mri Description: CRAN Package 'mri' (Modified Rand and Wallace Indices) It provides functions to compute the values of different modifications of the Rand and Wallace indices. The indices are used to measure the stability or similarity of two partitions obtained on two different sets of units with a non-empty intercept. Splitting and merging of clusters can (depends on the selected index) have a different effect on the value of the indices. The indices are proposed in Cugmas and Ferligoj (2018) . Package: r-cran-mriml Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1712 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-ggplot2, r-cran-patchwork, r-cran-purrr, r-cran-recipes, r-cran-rsample, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-tune, r-cran-workflows, r-cran-yardstick, r-cran-flashlight, r-cran-future.apply, r-cran-metricsweighted, r-cran-finetune, r-cran-hstats Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ape, r-cran-vegan, r-cran-hardhat, r-cran-ggrepel, r-cran-themis, r-cran-mrfcov, r-cran-lme4, r-cran-randomforest, r-cran-ggnetwork, r-cran-igraph, r-cran-tidymodels, r-cran-tidyverse, r-cran-parsnip, r-cran-gridextra, r-cran-future, r-cran-generics, r-cran-missforest, r-cran-kernelshap, r-cran-shapviz Filename: pool/dists/noble/main/r-cran-mriml_2.2.0-1.ca2404.1_all.deb Size: 1329952 MD5sum: 63a8b859289b1289e3e5766b93e20d6d SHA1: fe75c3c1c6025f94bc4919f32c4ce2e00dc71d90 SHA256: c0ef0a79384d9737605f99158e7de5ae9a2875c654ea3d7906ddb327b53b86ad SHA512: b15afc852adeffd3f6eac2394eb0b9305432db66ee5855d2891b0ef0d0700f1f91b3eddcf66a28322113608514dc80ef264487256dca66213324c60cbec32eb2 Homepage: https://cran.r-project.org/package=mrIML Description: CRAN Package 'mrIML' (Multi-Response (Multivariate) Interpretable Machine Learning) Builds and interprets multi-response machine learning models using 'tidymodels' syntax. Users can supply a tidy model, and 'mrIML' automates the process of fitting multiple response models to multivariate data and applying interpretable machine learning techniques across them. For more details see Fountain-Jones (2021) and Fountain-Jones et al. (2024) . Package: r-cran-mrmcaov Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 569 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mvtnorm, r-cran-progress, r-cran-tibble, r-cran-trust Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mrmcaov_0.3.1-1.ca2404.1_all.deb Size: 371236 MD5sum: 78f76a7a563f67318bc40594bba7469f SHA1: 66907a96d5244ec0e8e528850178ea4fa8338fd9 SHA256: b81536a1a4c63a1185e73e4f4f86aaa471e00969c54089b2bdb7db08161bc503 SHA512: a2b83cbb590f4f7829f2400b8bbc25c9a17a42bcbd2a20084fbc65765d341ef70e78dec2f6a75d827e13bfb899cf5237e11f96c3f496a47c28e46ce4a6ecb13b Homepage: https://cran.r-project.org/package=MRMCaov Description: CRAN Package 'MRMCaov' (Multi-Reader Multi-Case Analysis of Variance) Estimation and comparison of the performances of diagnostic tests in multi-reader multi-case studies where true case statuses (or ground truths) are known and one or more readers provide test ratings for multiple cases. Reader performance metrics are provided for area under and expected utility of ROC curves, likelihood ratio of positive or negative tests, and sensitivity and specificity. ROC curves can be estimated empirically or with binormal or binormal likelihood-ratio models. Statistical comparisons of diagnostic tests are based on the ANOVA model of Obuchowski-Rockette and the unified framework of Hillis (2005) . The ANOVA can be conducted with data from a full factorial, nested, or partially paired study design; with random or fixed readers or cases; and covariances estimated with the DeLong method, jackknifing, or an unbiased method. Smith and Hillis (2020) . Package: r-cran-mrmcbinary Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools, r-cran-survival Filename: pool/dists/noble/main/r-cran-mrmcbinary_1.0.6-1.ca2404.1_all.deb Size: 107482 MD5sum: 2e24661fe5fff8e35bb860dd5fd3d257 SHA1: ad81446e9bd5759fe8dcdd31a8a8393bc0661b2f SHA256: 54dcd6e0bdc90face6efd1be4d984eaec16af3eab03a5aae182b92ed5911a921 SHA512: 183f3d39f092523b84bfb24215d5ec1704482fc7378efb12e1a57c146c3b281a52c462a5a22c34a4bfdc2da7200d304e395f2d01de0991c6a6cb15791da5864d Homepage: https://cran.r-project.org/package=MRMCbinary Description: CRAN Package 'MRMCbinary' (Multi-Reader Multi-Case Analysis of Binary Diagnostic Tests) Implements methods for comparing sensitivities and specificities in balanced (or fully crossed) multi-reader multi-case (MRMC) studies with binary diagnostic test results. It implements conditional logistic regression and provides score tests equivalent to Cochran's Q test (which corresponds to McNemar's test when comparing two modalities only). The methodology is based on Lee et al. (2026) . Package: r-cran-mrmcsamplesize Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fpow Filename: pool/dists/noble/main/r-cran-mrmcsamplesize_1.0.0-1.ca2404.1_all.deb Size: 32908 MD5sum: 45573a9acdba86de5e4aa3e801d22ad4 SHA1: 34dcf26876e39d099da98fd4245c1ead917bba2f SHA256: 564a8f5a841afd19d4ec6846f0ee355f1ad0569233eff442379f7423c42a5fb7 SHA512: a633122110a9f3bc0251cb25b9402d51e73357536d5c36118ff6949b7e8f2703d7f17cdaebda59d9202fa49fe417caf2f5d4c129e46f837f2c4bb3baba6d6a7b Homepage: https://cran.r-project.org/package=MRMCsamplesize Description: CRAN Package 'MRMCsamplesize' (Sample Size Estimations for Planning Multi-Reader Multi-Case(MRMC) Studies Without Pilot Data) Sample size estimations for MRMC studies based on the Obuchowski-Rockette (OR) methodology is implemented. The function can calculate sample sizes where the endpoint of interest in the study is either ROC AUC (Area-Under-the-Receiver-Operating-Characteristics-Curve) or sensitivity. The package can also return sample sizes for studies expected to have clustering effect (e.g.- multiple pulmonary nodules per patient). All calculations assume that the study design is fully crossed (paired-reader, paired-case) where each reader reads/interprets each case and that there are two interventions/imaging-modalities/techniques in the study. In addition to MRMC, it can also be used to estimate sample sizes for standalone studies where sensitivity or AUC are the primary endpoints. The methods implemented are based on the methods described in Zhou et.al. (2011) and Obuchowski (2000) . Package: r-cran-mrmediation Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-mass Filename: pool/dists/noble/main/r-cran-mrmediation_1.0.1-1.ca2404.1_all.deb Size: 63446 MD5sum: 1a267be73b1d946a6d537cbcdd671b4e SHA1: ed76072d7213b9cbbf6419ea3ba7f65004e52557 SHA256: 706fbbbdfa7966070fe9262218ad20c786564c08ecba3d1e5d0a20ca2e465079 SHA512: 32f65a86c131f095d276aafa93adf40a49b79adf33f989fec8d6fe832db9b4fc19e87d538dc8923021f2c12e4c30f2de3b5289d2b0f5cbae0c7fb192bea037af Homepage: https://cran.r-project.org/package=MRmediation Description: CRAN Package 'MRmediation' (A Causal Mediation Method with Methylated Region (MR) as theMediator) A causal mediation approach under the counterfactual framework to test the significance of total, direct and indirect effects. In this approach, a group of methylated sites from a predefined region are utilized as the mediator, and the functional transformation is used to reduce the possible high dimension in the region-based methylated sites and account for their location information. 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Package: r-cran-mrpc Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1994 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-compositions, r-cran-dynamictreecut, r-cran-ggally, r-cran-fastcluster, r-cran-gtools, r-bioc-graph, r-cran-hmisc, r-cran-mice, r-cran-network, r-cran-pcalg, r-cran-psych, r-bioc-rgraphviz, r-cran-wgcna, r-cran-plyr Filename: pool/dists/noble/main/r-cran-mrpc_3.2.0-1.ca2404.1_all.deb Size: 1866066 MD5sum: ee3f6a58fe48d151d2235a8a705299b0 SHA1: f8d79dfbc6f9b7551105dd4188ef6ab201d131a5 SHA256: 721b3e269c9be74b290d56f6a3d7f8407b40ac3890400219eb6baa5cd237008d SHA512: 371aa101b515e846943a5e5f95916b2b04f2bf71088b00ce2e26899e05a5720d9aa298418debd9bfda20b999ee16975dd4c2bd9ef33b41b4cba8acae7672c37f Homepage: https://cran.r-project.org/package=MRPC Description: CRAN Package 'MRPC' (PC Algorithm with the Principle of Mendelian Randomization) A PC Algorithm with the Principle of Mendelian Randomization. This package implements the MRPC (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also contains functions to simulate data under a certain topology, to visualize a graph in different ways, and to compare graphs and quantify the differences. See Badsha and Fu (2019) , Badsha, Martin and Fu (2021) , Kvamme and Badsha, et al. (2025) . Package: r-cran-mrpostman Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1862 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-r6, r-cran-stringr, r-cran-stringi, r-cran-magrittr, r-cran-assertthat, r-cran-base64enc, r-cran-rvest, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mrpostman_1.1.4-1.ca2404.1_all.deb Size: 1292932 MD5sum: 2fef712c7a2c6efcc3ced666aab2d47f SHA1: 941d31e74190d1b6d76843d601dfee453ac3508e SHA256: 5dd68059a938538488691835ea75e103cd6e992d52091fdfd5fc42e9f7c100d6 SHA512: 7bb928d9869c78bc27409a50dcf542e79f28d8f8312c2b80d237c588e5157bf08f1178555d0097fb6a9bd8721e944d5210b345cd71587a1bd164c28178a252e4 Homepage: https://cran.r-project.org/package=mRpostman Description: CRAN Package 'mRpostman' (An IMAP Client for R) An easy-to-use IMAP client that provides tools for message searching, selective fetching of message attributes, mailbox management, attachment extraction, and several other IMAP features, paving the way for e-mail data analysis in R. Package: r-cran-mrpstrata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1371 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-progress, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mrpstrata_0.1.0-1.ca2404.1_all.deb Size: 755990 MD5sum: 5d45554c04f5436d3d8e650255782552 SHA1: e1c44b4080daeacbba9573c6a1fc0d0d78267762 SHA256: 230cd5bf48d2534dcdc00d7d77205da6468ceb9cfa420d5ed7fd5c2f710d45ab SHA512: 0466a798d90491329233157310051cc925b6fbf806bbb2eae8d810cc7b08280f8013fd0e1e5cb98e76b3123dd0e65aead3c6208cd1d103c6171eac781d324d27 Homepage: https://cran.r-project.org/package=mrPStrata Description: CRAN Package 'mrPStrata' (Multiply Robust Estimation in Causal Survival Analysis withTreatment Noncompliance) Provides multiply robust estimators of principal survival causal effects among always-takers, compliers, and never-takers in studies with treatment noncompliance. The methods are based on Cheng et al. (2026) . Package: r-cran-mrqol Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mrqol_1.0.1-1.ca2404.1_all.deb Size: 35488 MD5sum: 5ee08a50595dc22477f058786aacca4c SHA1: 520695cf1310591efd163c31b97ca1441a2a7e8d SHA256: 06d3cdd0ea65d911167d57ac6ddfc8ad87e28e1f01052306667140e5c41ef3a9 SHA512: 599634c73c6aa1ecd1d91e7cbc8c2ada200893ef6ac5735a556f5326e6416741be4b0b63e2d25cae21869870e9ca01effb73c4333ebda12c6faaf4cd40b17daf Homepage: https://cran.r-project.org/package=MRQoL Description: CRAN Package 'MRQoL' (Minimal Clinically Important Difference and Response ShiftEffect for Health-Related Quality of Life) To calculate the Minimal Clinically Important Difference by applying the Anchor-based method and the Response shift effect by applying the Then-Test method. Package: r-cran-mrreg Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-mrreg_0.1.6-1.ca2404.1_all.deb Size: 117194 MD5sum: d47e30ca0e0ea237110daf5de6a0d452 SHA1: d9bdfbe0afba51d5488067db6782a4ca99dd9fd6 SHA256: 5c26bd09b8b6e91d3127e56e9817db1076130804c371ce09d649ac302e74c096 SHA512: 3fcff56c89bd22525e623f8bf2449837ba827f9e28764f154474535965bb3a18abeb196470f08587fa02588774098261d89da53a413676ae1ba599ae22b9c3a5 Homepage: https://cran.r-project.org/package=MRReg Description: CRAN Package 'MRReg' (MDL Multiresolution Linear Regression Framework) We provide the framework to analyze multiresolution partitions (e.g. country, provinces, subdistrict) where each individual data point belongs to only one partition in each layer (e.g. i belongs to subdistrict A, province P, and country Q). We assume that a partition in a higher layer subsumes lower-layer partitions (e.g. a nation is at the 1st layer subsumes all provinces at the 2nd layer). Given N individuals that have a pair of real values (x,y) that generated from independent variable X and dependent variable Y. Each individual i belongs to one partition per layer. Our goal is to find which partitions at which highest level that all individuals in the these partitions share the same linear model Y=f(X) where f is a linear function. The framework deploys the Minimum Description Length principle (MDL) to infer solutions. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2021) . Package: r-cran-mrregression Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mrregression_1.0.0-1.ca2404.1_all.deb Size: 243832 MD5sum: cef7b615d812b66066311cd568367e02 SHA1: a5ab7589b29de90e6767f1c710e6b1f35cdc74fc SHA256: ef69c700439dc1e9737f029390cda822e8179e4b4c4d0191ae453f43f1a514c5 SHA512: 6629010e66c611f8493866fb6869501bfa8b5e9e6d070e9f53d63b41271109c9ddceaa2c102de9daa6f4239b25a430d2abf08acba792e6317088515f74b5a5f2 Homepage: https://cran.r-project.org/package=mrregression Description: CRAN Package 'mrregression' (Regression Analysis for Very Large Data Sets via Merge andReduce) Frequentist and Bayesian linear regression for large data sets. Useful when the data does not fit into memory (for both frequentist and Bayesian regression), to make running time manageable (mainly for Bayesian regression), and to reduce the total running time because of reduced or less severe memory-spillover into the virtual memory. This is an implementation of Merge & Reduce for linear regression as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler, C. (2020). 'Streaming statistical models via Merge & Reduce'. International Journal of Data Science and Analytics, 1-17, . Package: r-cran-mrstdcrt Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geepack, r-cran-lme4, r-cran-nlme, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mrstdcrt_0.1.1-1.ca2404.1_all.deb Size: 183890 MD5sum: a22fd93287e78b71b60c365dd8cdf44d SHA1: ca37c4204e296216143dc591ec4aa84193e7787c SHA256: cda2b29a2731fa15e484e301306eb55ece32e1dedf89c61035aafbc527b53795 SHA512: 28311e2a7440032a6d0cb0b8d86edc97a6b55955c2d8830a2052333713d23903518ea9a8a204dc7f92b460132f3979f799584b5162696b29e2a23407e2c7eda9 Homepage: https://cran.r-project.org/package=MRStdCRT Description: CRAN Package 'MRStdCRT' (Model-Robust Standardization in Cluster-Randomized Trials) Implements model-robust standardization for cluster-randomized trials (CRTs). Provides functions that standardize user-specified regression models to estimate marginal treatment effects. The targets include the cluster-average and individual-average treatment effects, with utilities for variance estimation and example simulation datasets. Methods are described in Li, Tong, Fang, Cheng, Kahan, and Wang (2025) . Package: r-cran-mrstdlcrt Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 853 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reformulas, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-tidyselect, r-cran-gee, r-cran-lme4, r-cran-ggplot2, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mrstdlcrt_0.1.1-1.ca2404.1_all.deb Size: 667374 MD5sum: 3d868159ae77da791d3f81d61d26c71e SHA1: ce475deb0c9924ee2fed9fade7b5be1205fcbd3f SHA256: e03f0341abe29febe565a69254af55f8814cdfb15dbc46b1b4019923b043fc4c SHA512: 133d8f3cd4ba24b4b6c56d6fe25adc706a3a6c8dcbc1c5674158e5791bef251c8c3ff383ffffeb51c938af3bd41394206f8cf8f3be95e338b74951a392be59d7 Homepage: https://cran.r-project.org/package=MRStdLCRT Description: CRAN Package 'MRStdLCRT' (Model-Robust Standardization for Longitudinal Cluster-RandomizedTrials) Provides estimation and leave-one-cluster-out jackknife standard errors for four longitudinal cluster-randomized trial estimands: horizontal individual average treatment effect (h-iATE), horizontal cluster average treatment effect (h-cATE), vertical individual average treatment effect (v-iATE), and vertical cluster-period average treatment effect (v-cATE), using unadjusted and augmented (model-robust standardization) estimators. The working model may be fit using linear mixed models for continuous outcomes or generalized estimating equations and generalized linear mixed models for binary outcomes. Period inclusion for aggregation is determined automatically: only periods with both treated and control clusters are included in the construction of the marginal means and treatment effect contrasts. See Fang et al. (2025) . 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Generates plots similar to those used previously in Alexandrov et al. (2020) and Rozen et al. (2026). Package: r-cran-msigseg Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1988 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-ggpubr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-msigseg_0.2.0-1.ca2404.1_all.deb Size: 1821686 MD5sum: d84f5967d54dfcb9540db1532c03ca30 SHA1: c795673dd69c6e9a0eca76a8e1df11aeef686e59 SHA256: 8d75e4fae12a8ffdcc20b11537d9c9348644d79e2d5175441aa4eeefa320846c SHA512: 85e7219feba57defd5198bbff96a88f374072a12759fa3da1de23dc455ea452498ee8820706203d6d2b1d606a55edf0a6c71a73e6e793f73f7d6bb491c97c128 Homepage: https://cran.r-project.org/package=MSigSeg Description: CRAN Package 'MSigSeg' (Multiple SIGnal SEGmentation) Traditional methods typically detect breakpoints from individual signals, which means that when applied separately to multiple signals, the breakpoints are not aligned. However, this package implements a common breakpoint detection approach for multiple piecewise constant signals, resulting in increased detection sensitivity and specificity. By employing various techniques, optimal performance is ensured, and computation is accelerated. We hope that this package will be beneficial for researchers in signal processing, bioinformatics, economy, and other related fields. The segmentation(), lambda_estimator() functions are the main functions of this package. Package: r-cran-msigtools Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clue, r-cran-philentropy, r-cran-quadprog, r-cran-sets Suggests: r-cran-cosmicsig, r-cran-icams, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-msigtools_1.0.7-1.ca2404.1_all.deb Size: 68204 MD5sum: ae3fa9725d2c82ca1e725ea62332025a SHA1: 8f5d6ce5fa70afe6da2d8d6a315e59efb470d504 SHA256: 6269129da6aadc59cb4d25b28cf8f9dfcc3c8a084f8d8d07e97bc11c50b2cd5a SHA512: e2eb40e4f940f5fa5e8028296bf65be7b1043b46cbfb2d3c1c1435d3d0030bc54ce8ee144d24b8aac672a2fca87721f178c8b61ea4b3eb0440157a3bfd12fb88 Homepage: https://cran.r-project.org/package=mSigTools Description: CRAN Package 'mSigTools' (Mutational Signature Analysis Tools) Utility functions for mutational signature analysis as described in Alexandrov, L. B. (2020) . This package provides two groups of functions. One is for dealing with mutational signature "exposures" (i.e. the counts of mutations in a sample that are due to each mutational signature). The other group of functions is for matching or comparing sets of mutational signatures. 'mSigTools' stands for mutational Signature analysis Tools. 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This approach includes both spoke and matrix models for interpreting AP-MS data in a network context. The "spoke" model considers only bait-prey interactions, whereas the "matrix" model assumes that each of the identified proteins (baits and prey) in a given AP-MS experiment interacts with each of the others. The spoke model has a high false-negative rate, whereas the matrix model has a high false-positive rate. Although, both statistical models have merits, a combination of both models has shown to increase the performance of machine learning classifiers in terms of their capabilities in discrimination between true and false positive interactions. 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Get access to tumor main types, identifiers and utility routines to map across to other tumor classification systems. Package: r-cran-msltrend Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-forecast, r-cran-plyr, r-cran-rssa, r-cran-tseries, r-cran-zoo Filename: pool/dists/noble/main/r-cran-msltrend_1.0-1.ca2404.1_all.deb Size: 136538 MD5sum: 4fad216a98f977a090f11c72a3d41202 SHA1: 28200c9557fb8dc0e3418ac23ac6baf8a9f4951c SHA256: e31dc55c5861ea905ffab6dad8012aea810bf41cc60d14df1d1025886c5d2c1c SHA512: 9da110e379a4604343c5c0302221304c5eca91e21dbb28a6759c91d9b4bee5875f411569e90aed05179dc18f5797ff456e5668c4596a552ba5a77774963592b3 Homepage: https://cran.r-project.org/package=msltrend Description: CRAN Package 'msltrend' (Improved Techniques to Estimate Trend, Velocity and Accelerationfrom Sea Level Records) Analysis of annual average ocean water level time series from long (minimum length 80 years) individual records, providing improved estimates of trend (mean sea level) and associated real-time velocities and accelerations. 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Developed by the Mine Sustainability Modeling Group (MSMG) at Missouri University of Science and Technology under NSF (National Science Foundation) funding (Award No. 2219086). See Pizzol (2022) for the original 'Python' implementation that inspired this tool. 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For more details, see McCurdy (2025), "Introduction to Data Science with R" . Package: r-cran-msmwra Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-msmwra_1.5-1.ca2404.1_all.deb Size: 30086 MD5sum: 0f811673f058e86900cc8bffffd0cd11 SHA1: cdc902b004a1c5413f7d911f2c601dd13470f42d SHA256: 20b8bc5cfeea33d9e5eb73eeb8757a60b86d1dc0d43711aeaac7e5aa6e329067 SHA512: 3615ddf2292ad98faf300df06fa2891c8cb36b68abd2a769b75b263941d991279f096967117a91bc364953223f4907ed266366ff6e289f5b071d14314e5c4411 Homepage: https://cran.r-project.org/package=MSMwRA Description: CRAN Package 'MSMwRA' (Multivariate Statistical Methods with R Applications) Data sets in the book entitled "Multivariate Statistical Methods with R Applications", H.Bulut (2018). The book was published in Turkish and the original name of this book will be "R Uygulamalari ile Cok Degiskenli Istatistiksel Yontemler". 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Package: r-cran-msprt Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nleqslv, r-cran-ggplot2, r-cran-ggpubr, r-cran-foreach, r-cran-iterators, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-msprt_3.0-1.ca2404.1_all.deb Size: 517304 MD5sum: 4196ae588dc035dd75c9ddf4b1c95dc9 SHA1: 0322925ddde240b24a2bedbb237fa59ba0aae7fd SHA256: af87c1caebf290f351961baef0b8ee8e8aff2072ced49b521c609bb11d543f8b SHA512: bb49be2d3dd14d617276c0ddcd40eee0c04d9d03ddf9f25eb2c93e90cd5f42a45b868e04a5cb8fdda1f802725ba8bedebd92e227506bcc07aaee69fc06be4b57 Homepage: https://cran.r-project.org/package=MSPRT Description: CRAN Package 'MSPRT' (A Modified Sequential Probability Ratio Test (MSPRT)) Given the maximum available sample size (N) for an experiment, and the target levels of Type I and II error probabilities, this package designs a modified SPRT (MSPRT). For any designed MSPRT the package can also obtain its operating characteristics and implement the test for a given sequentially observed data. The MSPRT is defined in a manner very similar to Wald's initial proposal. The proposed test has shown evidence of reducing the average sample size required to perform statistical hypothesis tests at specified levels of significance and power. Currently, the package implements one-sample proportion tests, one and two-sample z tests, and one and two-sample t tests. A brief user guidance for this package is provided below. One can also refer to the supplemental information for the same. Package: r-cran-msqc Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl Filename: pool/dists/noble/main/r-cran-msqc_1.1.0-1.ca2404.1_all.deb Size: 132734 MD5sum: 04a793e2e411cc427dbe75d4adbcc84e SHA1: 4c51a82606cc9571b8ea8c690db9818c3fb138e3 SHA256: f6e3141e9df4fb6d901bf1003d4fcd6952c1ff84aa510ad7c3b0914783dd573e SHA512: b9341e1b4a74e44e400ae8c201b60dbcda060ca0ddfc3942cdd46bf4af55bce0d3b2b3f9253d388c58c33824161dcf108e49eb799220c1cc54d891948312d0bc Homepage: https://cran.r-project.org/package=MSQC Description: CRAN Package 'MSQC' (Multivariate Statistical Quality Control) This is a toolkit for multivariate process monitoring. It computes several multivariate control charts e.g. Hotelling, Chi-squared, MEWMA, MCUSUM and Generalized Variance. Ten didactic datasets are included. 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Offers filtering of decision variables through item–module eligibility and the application of explicit bounds to simplify the MILP model and accelerate the optimization process. Supports bottom up, top down, and hybrid assembly strategies; enemy-item and enemy-stimulus exclusions; stimulus all in/all out or partial selection; anchor item/stimulus specification; and item exposure control. Accommodates both single-objective and multi-objective optimization ('weighted sum', 'maximin', 'capped maximin', 'minimax', and 'goal programming'). Enables simultaneous assembly of multiple panels with item and stimulus content balancing and exposure control. Provides analytical evaluation of assembled MST performance within seconds. Includes tools for diagnosing infeasible optimization models by systematically identifying sources of infeasibility and reformulating models with slack variables to restore feasibility.Methods implemented in this package build on established work in optimal test assembly (van der Linden, 2005 ), item-set constrained test assembly (van der Linden, 2000 ), hybrid assembly (Xiong, 2018 ), recursion-based analytic methods (Lim et al., 2021 ), and classification evaluation (Rudner, 2000 ; Rudner, 2005 ). 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Package: r-cran-mtlgmm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-caret, r-cran-mclust Filename: pool/dists/noble/main/r-cran-mtlgmm_0.1.0-1.ca2404.1_all.deb Size: 111444 MD5sum: e0a99affcb543a2ae2c2137e7c3219be SHA1: 66d1a4e7bb47294e783f591e618c4463d95af1d5 SHA256: fd60222d42977ab71c23b4c83c7135778ee25206591da10f4da0d52934e4e5ae SHA512: 4a288c9808a3fe65df34faab52dc5d4660a00bb1831f8518c9e3b58cf875f07732460ff8f90bab3bc96499df7f7792503f014bbb8f011df05d0034b7c1f302b4 Homepage: https://cran.r-project.org/package=mtlgmm Description: CRAN Package 'mtlgmm' (Unsupervised Multi-Task and Transfer Learning on GaussianMixture Models) Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-task GMM learning procedure based on the Expectation-Maximization (EM) algorithm that not only can effectively utilize unknown similarity between related tasks but is also robust against a fraction of outlier tasks from arbitrary sources. The proposed procedure is shown to achieve minimax optimal rate of convergence for both parameter estimation error and the excess mis-clustering error, in a wide range of regimes. Moreover, we generalize our approach to tackle the problem of transfer learning for GMMs, where similar theoretical results are derived. Finally, we demonstrate the effectiveness of our methods through simulations and a real data analysis. To the best of our knowledge, this is the first work studying multi-task and transfer learning on GMMs with theoretical guarantees. This package implements the algorithms proposed in Tian, Y., Weng, H., & Feng, Y. (2022) . Package: r-cran-mtps Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-rpart, r-cran-mass, r-cran-e1071, r-cran-class Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-mtps_1.0.2-1.ca2404.1_all.deb Size: 249426 MD5sum: 4a427e6e8c7e57170317585865f0e9f1 SHA1: a36d3aea90763ace2b551c523d340ef9ab2356e4 SHA256: aba8f4c0b45d24bfaf0ecf07edd28cac9198aca68eeed360f2e426d275a57e79 SHA512: 94cc2037a112fca24bda588be09436e46d17673bfdcfd396f640d22c47c2154a1dcd31cff74aa20088273c6351678d2639d4f5a70aaa3fbd0489850adf27d099 Homepage: https://cran.r-project.org/package=MTPS Description: CRAN Package 'MTPS' (Multi-Task Prediction using Stacking Algorithms) Simultaneous multiple outcomes prediction based on revised stacking algorithms, which enables the integration of information from predictions of individual models. An implementation of methodologies proposed in our paper: Li Xing, Mary L Lesperance, Xuekui Zhang. (2019) Bioinformatics, "Simultaneous prediction of multiple outcomes using revised stacking algorithms" . Package: r-cran-mtrank Architecture: all Version: 0.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-meta, r-cran-netmeta, r-cran-plackettluce, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-mtrank_0.2-0-1.ca2404.1_all.deb Size: 199200 MD5sum: 572ef7179093dce086896439513411e6 SHA1: 1eeabc37fd7c656a7431ca433cee23ce860fc8ec SHA256: 16c909aabc756f68b9a94fbcb272144d8709bcd0835f3182f885f7710bfe670a SHA512: 18553a48c775cb00510ee0fe82c2d7700fc22495a4a7bd85a034fcb81c0b7c353b5918b49379c245344e39363cc89253136e19120be29cdb56d06b41f9bedfe3 Homepage: https://cran.r-project.org/package=mtrank Description: CRAN Package 'mtrank' (Ranking using Probabilistic Models and Treatment Choice Criteria) Estimation of treatment hierarchies in network meta-analysis using a novel frequentist approach based on treatment choice criteria (TCC) and probabilistic ranking models, as described by Evrenoglou et al. (2024) . The TCC are defined using a rule based on the smallest worthwhile difference (SWD). Using the defined TCC, the NMA estimates (i.e., treatment effects and standard errors) are first transformed into treatment preferences, indicating either a treatment preference (e.g., treatment A > treatment B) or a tie (treatment A = treatment B). These treatment preferences are then synthesized using a probabilistic ranking model, which estimates the latent ability parameter of each treatment and produces the final treatment hierarchy. This parameter represents each treatments ability to outperform all the other competing treatments in the network. Here the terms ability to outperform indicates the propensity of each treatment to yield clinically important and beneficial effects when compared to all the other treatments in the network. Consequently, larger ability estimates indicate higher positions in the ranking list. Package: r-cran-mtreering Architecture: all Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2957 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-png, r-cran-jpeg, r-cran-tiff, r-cran-bmp, r-cran-magick, r-cran-imager, r-cran-dplr, r-cran-spatstat.geom, r-cran-measuring, r-cran-shiny, r-cran-dplyr, r-cran-shinydashboard, r-cran-shinywidgets Suggests: r-cran-testthat, r-cran-knitr, r-cran-shinytest, r-cran-mockery, r-cran-spelling Filename: pool/dists/noble/main/r-cran-mtreering_1.4.5-1.ca2404.1_all.deb Size: 2887226 MD5sum: b6e86949fa574680e9308ecff4e3993e SHA1: 1a62a193682e0d8a4fb06e78a0073eb5d0a076ea SHA256: 4d56d801162b4d1d6296ea05e50af886fefe60775706c909f018ee869b6cb2b4 SHA512: 1771b1785b9b15d008ae97efe6ec529984d4ca4b783013bf18158aaa587fd3b38dc536a80c61f6a4e597099b69cc1982de70dd709eaa083208bb14764dac9b46 Homepage: https://cran.r-project.org/package=MtreeRing Description: CRAN Package 'MtreeRing' (A Shiny Application for Automatic Measurements of Tree-RingWidths on Digital Images) Use morphological image processing and edge detection algorithms to automatically measure tree ring widths on digital images. 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Temporal patterns can be modeled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalized additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region. Package: r-cran-mtsta Architecture: all Version: 0.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 839 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-janitor, r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-mtsta_0.0.0.1-1.ca2404.1_all.deb Size: 784712 MD5sum: efa2e96908d63fec81e5de8a76a657c0 SHA1: 09242e3fdca61a52d2df54367c2eaed402e8b395 SHA256: 329e62f720319a5dfd7b4c204d443d42cee45e5417117e216a283f7598cde777 SHA512: 3a6045a2a22464f6a12e888b647a131c8fd9d330ed31e04e296def9b7588a7025cfebd9880a8360c1312458d12f4c3777e2262d668f11ec57fadedf6363f70e3 Homepage: https://cran.r-project.org/package=mtsta Description: CRAN Package 'mtsta' (Accessing the Red List of Montane Tree Species of the TropicalAndes) Access the 'Red List of Montane Tree Species of the Tropical Andes' Tejedor Garavito et al.(2014, ISBN:978-1-905164-60-8). This package allows users to search for globally threatened tree species within the andean montane forests, including cloud forests and seasonal (wet) forests above 1500 m a.s.l. 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One is a family of Mahalanobis-Taguchi (MT) methods (in the broad sense) for diagnosis (see Woodall, W. H., Koudelik, R., Tsui, K. L., Kim, S. B., Stoumbos, Z. G., and Carvounis, C. P. (2003) ) and the other is a family of Taguchi (T) methods for forecasting (see Kawada, H., and Nagata, Y. (2015) ). The MT package contains three basic methods for the family of MT methods and one basic method for the family of T methods. The MT method (in the narrow sense), the Mahalanobis-Taguchi Adjoint (MTA) methods, and the Recognition-Taguchi (RT) method are for the MT method and the two-sided Taguchi (T1) method is for the family of T methods. In addition, the Ta and Tb methods, which are the improved versions of the T1 method, are included. 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Package: r-cran-muckrock Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2466 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-muckrock_0.1.0-1.ca2404.1_all.deb Size: 2470850 MD5sum: c16d7195d7f1ef1e16359d5abe6c1092 SHA1: 8a9ee2d40929b13b9e2bb7f58b98d7bd7663b269 SHA256: f996c332ad7246b917dbac8cbc65fa61aa11a0a296ef3a1a290b7306cc3f6c24 SHA512: 196207a4b3f5c69aabb6eb94bcdc36de072ea78953ee38eccedc32a6b4f9351edf84dfa5bc4003c3785da8c4861c1980997380d6067d3dd3c6ca507ec0ff3ffb Homepage: https://cran.r-project.org/package=muckrock Description: CRAN Package 'muckrock' (Data on Freedom of Information Act Requests) A data package containing public domain information on requests made by the 'MuckRock' (https://www.muckrock.com/) project under the United States Freedom of Information Act. Package: r-cran-mudfold Architecture: all Version: 1.1.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-glmnet, r-cran-mgcv, r-cran-ggplot2, r-cran-zoo, r-cran-reshape2, r-cran-broom, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-mudfold_1.1.21-1.ca2404.1_all.deb Size: 396258 MD5sum: d3982efce233749141e2eed64900b898 SHA1: ba6e248aef21d285f399b08763ed6af2df935d6f SHA256: 2bd1f8162c85740fe9f003559cbdad4f1fb4bb2f1da6f2ea82f82deaca2aaa13 SHA512: 98bff761c4c07da592b242fdf541daa04696efb160dfb09a5dc4fa9c22d483ecc21e4533d7dcbf6016d77b52023a00d0901116c6efba524cedb04f87f6664765 Homepage: https://cran.r-project.org/package=mudfold Description: CRAN Package 'mudfold' (Multiple UniDimensional unFOLDing) Nonparametric unfolding item response theory (IRT) model for dichotomous data (see W.H. Van Schuur (1984). Structure in Political Beliefs: A New Model for Stochastic Unfolding with Application to European Party Activists, and W.J.Post (1992). Nonparametric Unfolding Models: A Latent Structure Approach). The package implements MUDFOLD (Multiple UniDimensional unFOLDing), an iterative item selection algorithm that constructs unfolding scales from dichotomous preferential-choice data without explicitly assuming a parametric form of the item response functions. Scale diagnostics from Post(1992) and estimates for the person locations proposed by Johnson(2006) and Van Schuur(1984) are also available. This model can be seen as the unfolding variant of Mokken(1971) scaling method. Package: r-cran-muerelativerisk Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-muerelativerisk_0.1.1-1.ca2404.1_all.deb Size: 22532 MD5sum: bf014eefe881370d3f0793c5644544cb SHA1: c472414ac428f0aa514ab3cd902ba23d008c84b9 SHA256: 8720849521ed6709ccbe644a03cb209ffa873fe074bd5b868e90aa8bbed2f058 SHA512: f3ee55fa7690460d873f984cafb74de3b66d21e8b744f9b8aeba6363f7ffcfd703a0454cb6959aa888f26e1c016a92fca6563bd2055cce93e3bb3c9f9d5a19a2 Homepage: https://cran.r-project.org/package=mueRelativeRisk Description: CRAN Package 'mueRelativeRisk' (Relative Risk Based on the Ratio of Median Unbiased Estimates) Implements an estimator for relative risk based on the median unbiased estimator. The relative risk estimator is well defined and performs satisfactorily for a wide range of data configurations. The details of the method are available in Carter et al (2010) . Package: r-cran-mugs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 784 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-fastdummies, r-cran-dosnow, r-cran-dplyr, r-cran-grplasso, r-cran-foreach, r-cran-glmnet, r-cran-grpreg, r-cran-inline, r-cran-mvtnorm, r-cran-proc, r-cran-rcpparmadillo, r-cran-rsvd Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mugs_0.1.0-1.ca2404.1_all.deb Size: 542612 MD5sum: c79e97e85551cad6922391ac339ee4bb SHA1: b0436d81444580e2ed5bffa016c8b12ac6cde93a SHA256: aeabf5c88eb9fe302ed72a47c4cd3a7416f9f7bdd898a5e3e031c31d766c9212 SHA512: a4cd7dcb754bbfdfa389a8161302171b9d70442a56a27e3d582dee4b25d91b906751ecea804b5777e24ee859412b9e8d7d25d8f95c1a0be770eb577436bc8010 Homepage: https://cran.r-project.org/package=MUGS Description: CRAN Package 'MUGS' (Multisource Graph Synthesis with EHR Data) We develop Multi-source Graph Synthesis (MUGS), an algorithm designed to create embeddings for pediatric Electronic Health Record (EHR) codes by leveraging graphical information from three distinct sources: (1) pediatric EHR data, (2) EHR data from the general patient population, and (3) existing hierarchical medical ontology knowledge shared across different patient populations. See Li et al. (2024) for details. 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All components 'Material UI' from the company 'MUI' are available and all inputs have usage examples in R. Package: r-cran-muir Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-diagrammer, r-cran-dplyr, r-cran-stringr Suggests: r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-muir_0.1.0-1.ca2404.1_all.deb Size: 43848 MD5sum: 3a8ccae80726d774c15b7af3b29e3e89 SHA1: 8133d5e8c3e2cbe774f9161d96b9383bde107f38 SHA256: 5e9cb18ac1e048a7f3ddbbf8092ccb48334a2789237c8115c138659b3389694a SHA512: 712591de3fbabef8bfaa0ba3189851566b790588580d78d2846763a527f0b64b16f73ebe9697ae9d6421df4d361bc35693abd51c7f2fe75c13c695095113aff6 Homepage: https://cran.r-project.org/package=muir Description: CRAN Package 'muir' (Exploring Data with Tree Data Structures) A simple tool allowing users to easily and dynamically explore or document a data set using a tree structure. 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Muller plots are plots which combine information about succession of different OTUs (genotypes, phenotypes, species, ...) and information about dynamics of their abundances (populations or frequencies) over time. They are powerful and fascinating tools to visualize evolutionary dynamics. They may be employed also in study of diversity and its dynamics, i.e. how diversity emerges and how changes over time. They are called Muller plots in honor of Hermann Joseph Muller which used them to explain his idea of Muller's ratchet (Muller, 1932, American Naturalist). A big difference between Muller plots and normal box plots of abundances is that a Muller plot depicts not only the relative abundances but also succession of OTUs based on their genealogy/phylogeny/parental relation. In a Muller plot, horizontal axis is time/generations and vertical axis represents relative abundances of OTUs at the corresponding times/generations. 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The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple testing procedures. 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It consists of four sections. The first section uses a dynamic scheme to indicate which possible alternatives to follow depending on the fulfillment of the assumptions of the model. It also presents an analysis on the fulfillment of the assumptions of linearity, homoscedasticity, normality, and independence in the residuals of the model, as well as dynamic statistical graphs on the residuals of the model. The second section presents an analysis with a non-parametric approach of Kruskal Wallis. After Kruskal Wallis, a Post-Hoc analysis of multiple comparisons on the medians of the treatments is carried out. The third section presents a classical parametric ANOVA. Following classical ANOVA, a post-hoc analysis of multiple comparisons on the medians of the treatments, factor levels by Dunn's test, and statistical graphs for the treatments and factor levels are shown. Additionally, a post-hoc analysis of multiple comparisons on the means of the treatments is done. 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Package: r-cran-multicca Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fda, r-cran-geigen, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-multicca_0.1.0-1.ca2404.1_all.deb Size: 80326 MD5sum: 82237ce64790bd2ec1879b2400f029b3 SHA1: d3217b9567429711994a1933f9529828656677e8 SHA256: 814b8f69ac5175b9765e4a065113a46a670e76c35d054fef14bdaffa1494e870 SHA512: 5aee294db5804352bf68f9b6b71dbed8135be2bada0bfeac5ab42ecc58bc6fdd582a9e3c85d56129474f9f1063b4f9e6bb800f66e3a132757292a1a241d441d4 Homepage: https://cran.r-project.org/package=multiCCA Description: CRAN Package 'multiCCA' (Multiple Canonical Correlation Analysis (Kernel and Functional)) Implements methods for multiple canonical correlation analysis (CCA) for more than two data blocks, with a focus on multivariate repeated measures and functional data. The package provides two approaches: (i) multiple kernel CCA, which embeds each data block into a reproducing kernel Hilbert space to capture nonlinear dependencies, and (ii) multiple functional CCA, which represents repeated measurements as smooth functions and performs analysis in a Hilbert space framework. Both approaches are formulated via covariance operators and solved as generalized eigenvalue problems with regularization to ensure numerical stability. The methods allow estimation of canonical variables, generalized canonical correlations, and low-dimensional representations for exploratory analysis and visualization of dependence structures across multiple feature sets. The implementation follows the framework developed in Górecki, Krzyśko, Gnettner and Kokoszka (2025) . Package: r-cran-multichull Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-plotly, r-cran-shiny, r-cran-shinythemes, r-cran-dt Filename: pool/dists/noble/main/r-cran-multichull_3.0.1-1.ca2404.1_all.deb Size: 76610 MD5sum: 5fff34fb79e08c57dffaa9f27385dd9f SHA1: 50f046ab8dddaeb9d124e8f9c90b628b67178fd7 SHA256: 1351e92f9a175ef78f11a9e0b6291c63b5532fdf5816074a8c88afa32a67d7d0 SHA512: 950198e02ee317a3e3046e5bed802d9bc5ee61df10cf4f953ee00698b190385b201eadc153844af034995cfcaa3bca0e216fd55b82c0cd00f6d8e934020fecec Homepage: https://cran.r-project.org/package=multichull Description: CRAN Package 'multichull' (A Generic Convex-Hull-Based Model Selection Method) Given a set of models for which a measure of model (mis)fit and model complexity is provided, CHull(), developed by Ceulemans and Kiers (2006) , determines the models that are located on the boundary of the convex hull and selects an optimal model by means of the scree test values. Package: r-cran-multiclasspairs Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2709 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ranger, r-cran-boruta, r-cran-dunn.test, r-cran-caret, r-cran-e1071, r-cran-rdist Suggests: r-cran-biocmanager, r-bioc-biobase, r-bioc-switchbox, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-leukemiaseset, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-multiclasspairs_0.4.3-1.ca2404.1_all.deb Size: 1287986 MD5sum: 92f7392e961a2e231ebf67dd8cc4fbc9 SHA1: a88edfa19bae0f1c20d4652ee12b282269169a31 SHA256: afd89ba6ca22fc6e8cfdc28346e56e930686693ba50b3cc13831a19aeb307d24 SHA512: 06abc621ceb3da34fdafb79dd45654af6989492a8fc5688876cfe6620016d5c3bae124acf5013dec745620d8f32d9c86b729e5c839371720a8d91667d2db5c4e Homepage: https://cran.r-project.org/package=multiclassPairs Description: CRAN Package 'multiclassPairs' (Build MultiClass Pair-Based Classifiers using TSPs or RF) A toolbox to train a single sample classifier that uses in-sample feature relationships. The relationships are represented as feature1 < feature2 (e.g. gene1 < gene2). We provide two options to go with. First is based on 'switchBox' package which uses Top-score pairs algorithm. Second is a novel implementation based on random forest algorithm. For simple problems we recommend to use one-vs-rest using TSP option due to its simplicity and for being easy to interpret. For complex problems RF performs better. Both lines filter the features first then combine the filtered features to make the list of all the possible rules (i.e. rule1: feature1 < feature2, rule2: feature1 < feature3, etc...). Then the list of rules will be filtered and the most important and informative rules will be kept. The informative rules will be assembled in an one-vs-rest model or in an RF model. We provide a detailed description with each function in this package to explain the filtration and training methodology in each line. Reference: Marzouka & Eriksson (2021) . Package: r-cran-multiclassroc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-proc Filename: pool/dists/noble/main/r-cran-multiclassroc_0.1.0-1.ca2404.1_all.deb Size: 15686 MD5sum: dffb0c45711f46c344e30d811e6320b0 SHA1: 0f928556777acf2a1b5004c062b8f037e7e7ea82 SHA256: 7e83aa6cf84808d3c81c180aac9df7fef52151bf94286e34a565562977aacb15 SHA512: e17d5cf7b2833a5c65d87b602eb64ba9895df132e2b9135079d0963205b42bdcdb3b59f1af384545fbb226e7ca739a9ec33505b562cc798791cbeeab9992378a Homepage: https://cran.r-project.org/package=MultiClassROC Description: CRAN Package 'MultiClassROC' (ROC Curves for Multi-Class Analysis) Function multiroc() can be used for computing and visualizing Receiver Operating Characteristics (ROC) and Area Under the Curve (AUC) for multi-class classification problems. It supports both One-vs-One approach by M.Bishop, C. (2006, ISBN:978-0-387-31073-2) and One-vs-All approach by Murphy P., K. (2012, ISBN:9780262018029). Package: r-cran-multicmp Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-multicmp_1.1-1.ca2404.1_all.deb Size: 33314 MD5sum: b4525ab1fd3888857a916cb20c6d83c9 SHA1: ab3c864e5c1912caf0aa9ac096378c3a50dc53ac SHA256: 776f018c0b9c48e6f335c6552b4b8c1d9bb331f0b7dba01b73f3ede3f2c4ab23 SHA512: e50fbc9c47ac9502519392fc758d1b8222279a1e31675e82c3a0ae6f92afd933d961104c3e0005c103cd32a09e27e34dcb16b20da11efa78e5078610d6bc0326 Homepage: https://cran.r-project.org/package=multicmp Description: CRAN Package 'multicmp' (Flexible Modeling of Multivariate Count Data via theMultivariate Conway-Maxwell-Poisson Distribution) A toolkit containing statistical analysis models motivated by multivariate forms of the Conway-Maxwell-Poisson (COM-Poisson) distribution for flexible modeling of multivariate count data, especially in the presence of data dispersion. Currently the package only supports bivariate data, via the bivariate COM-Poisson distribution described in Sellers et al. (2016) . Future development will extend the package to higher-dimensional data. Package: r-cran-multicoll Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-multicoll_2.0-1.ca2404.1_all.deb Size: 75632 MD5sum: f744cba96d4133dcf6852928bd74465c SHA1: b41a0e99b336a1753df5dc68638a92463159cb28 SHA256: 9fccb524945855bbc27b5e0719f4f6b95b5d9fbca068012a8d9ca6caab7633bd SHA512: ab9bf35753335dfba511a45707418043eb396e868af33a35cabcb42549cc2efe49fb2685b107677a52def23fbbe28bd63bfca9b203521019ce01c06234c337e7 Homepage: https://cran.r-project.org/package=multiColl Description: CRAN Package 'multiColl' (Collinearity Detection in a Multiple Linear Regression Model) The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed. Package: r-cran-multid Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glmnet, r-cran-proc, r-cran-lavaan, r-cran-emmeans, r-cran-lme4, r-cran-quantreg, r-cran-lmertest, r-cran-ggpubr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-overlapping, r-cran-rio Filename: pool/dists/noble/main/r-cran-multid_1.0.2-1.ca2404.1_all.deb Size: 227056 MD5sum: 01646396405940fde05cdb59b4afa04b SHA1: 4858b8d568c2bb5c2d2bfeee93f5e67723659e90 SHA256: 50cc98d58ed728918683030d6c632c434339e2895f11cfb263e500266b412394 SHA512: 461b73edeb7ed20d552cf31d2b23b7890be597182228689cf6c1c616de234f1331d9424fdeb20a62a90ad6f0be1e1ec822532c05a3e73d1b53ec4048c24b304f Homepage: https://cran.r-project.org/package=multid Description: CRAN Package 'multid' (Multivariate Difference Between Two Groups) Estimation of multivariate differences between two groups (e.g., multivariate sex differences) with regularized regression methods and predictive approach. See Ilmarinen et al. (2023) . Deconstructing difference score correlations (e.g., gender-equality paradox), see Ilmarinen & Lönnqvist (2024) . Includes also tools that help in understanding difference score reliability, conditional intra-class correlations, tail-dependency, and heterogeneity of variance estimates. Package development was supported by the Academy of Finland research grant 338891. Package: r-cran-multideggs Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3015 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-knitr, r-cran-mass, r-cran-magrittr, r-cran-pbapply, r-cran-pbmcapply, r-cran-rmarkdown, r-cran-sfsmisc, r-cran-shiny, r-cran-shinydashboard, r-cran-visnetwork Suggests: r-cran-kernlab, r-cran-nestedcv, r-cran-pls, r-bioc-qvalue, r-cran-randomforest, r-cran-ranger, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multideggs_1.2.1-1.ca2404.1_all.deb Size: 1183918 MD5sum: 7a5ebc77d4079ba14e24fcb3c05dee0b SHA1: e74077d76102001d4f2ca2277cb103fd098ea61c SHA256: b2009fca64db7c33633a24f4951919badd95a7a462e391f66d77683394bc04eb SHA512: c39d1c3dcee44fe18ddbe55d16f443c279ede5000277beab7f96346a5074a1741d5fbe4490d8eb57bbb134c0a9f52eb9993fac6a11dcaaada585b02f401febbd Homepage: https://cran.r-project.org/package=multiDEGGs Description: CRAN Package 'multiDEGGs' (Multi-Omic Differentially Expressed Gene-Gene Pairs) Performs multi-omic differential network analysis by revealing differential interactions between molecular entities (genes, proteins, transcription factors, or other biomolecules) across the omic datasets provided. For each omic dataset, a differential network is constructed where links represent statistically significant differential interactions between entities. These networks are then integrated into a comprehensive visualization using distinct colors to distinguish interactions from different omic layers. This unified display allows interactive exploration of cross-omic patterns, such as differential interactions present at both transcript and protein levels. For each link, users can access differential statistical significance metrics (p values or adjusted p values, calculated via robust or traditional linear regression with interaction term) and differential regression plots. The methods implemented in this package are described in Sciacca et al. (2023) . Package: r-cran-multideploy Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gh, r-cran-base64enc, r-cran-cli Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multideploy_0.1.0-1.ca2404.1_all.deb Size: 73296 MD5sum: 48a7ecab82ad4999bcf42c022569fe9b SHA1: d3f65691ae505357d5b6ffd85ac9e82a00a60d75 SHA256: e966ed9ca08b964d42f1e0ca12cc075f0503b664bccf709f6b0b569952425679 SHA512: c3f83fd13ad9e7ed62ae444682d5e3adbfc4e34649ee1796753644aef03e06c0bd8def1d253b84d0a2898bcfc877806340387fb559e63f1d441e1c2079796efb Homepage: https://cran.r-project.org/package=multideploy Description: CRAN Package 'multideploy' (Deploy File Changes Across Multiple 'GitHub' Repositories) Deploy file changes across multiple 'GitHub' repositories using the 'GitHub' 'Web API' . Allows synchronizing common files, Continuous Integration ('CI') workflows, or configurations across many repositories with a single command. Package: r-cran-multidimbio Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lme4, r-bioc-pcamethods, r-cran-misc3d, r-cran-mass, r-cran-rcolorbrewer, r-cran-gridgraphics Filename: pool/dists/noble/main/r-cran-multidimbio_1.2.5-1.ca2404.1_all.deb Size: 143212 MD5sum: dc50c0a0eb5bc8b94f394df01a46b25a SHA1: e2c49bb3f841add641fbb460536c1bdd22217d48 SHA256: c1937db2497fb35514a7f2a554be7bd8ff73547e71992e7ee889d6c63b4b33fc SHA512: 450019d6ff0eef33dccac9ca91ccc0a653d13af6d4bcb489d3497efac70b8a1ad2b1cc2d5a80fe3f482ca6a5501b6aefa0692af3078e234f24853721ef04b5a6 Homepage: https://cran.r-project.org/package=multiDimBio Description: CRAN Package 'multiDimBio' (Multivariate Analysis and Visualization for Biological Data) Code to support a systems biology research program from inception through publication. The methods focus on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations. The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques. As a result, we encourage suggested additions to the package, both methodological and graphical. 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The method is described in Cheng and Demirtas (2026) . 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The algorithm finds the optimal Pareto front and, if requested, selects a possible symmetrical design on it. The symmetrical design is selected based on two techniques: minimum distance with the Utopia point or the 'TOPSIS' approach. 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Package: r-cran-multifamm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2881 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-fundata, r-cran-mfpca, r-cran-mgcv, r-cran-sparseflmm, r-cran-zoo Filename: pool/dists/noble/main/r-cran-multifamm_0.1.1-1.ca2404.1_all.deb Size: 2789934 MD5sum: b5675605bbaf61f4ffae8cb30f01fd5c SHA1: 24055a68bca99691e27715abf27a44df8d7273be SHA256: 6c881975241e7e77bd156f60d4e179317a189dca11f00d74d54a4a1263121900 SHA512: 8becbfe294a7153cadf01c5de8841a145f5c27435d9eb9fd21bfa39db3f4fa4fb3cc9569ad6f341d2a8aa5840a0d77bbf7711d8bea06d128f95bcc05bc6f7dcb Homepage: https://cran.r-project.org/package=multifamm Description: CRAN Package 'multifamm' (Multivariate Functional Additive Mixed Models) An implementation for multivariate functional additive mixed models (multiFAMM), see Volkmann et al. (2021, ). It builds on developed methods for univariate sparse functional regression models and multivariate functional principal component analysis. This package contains the function to run a multiFAMM and some convenience functions useful when working with large models. An additional package on GitHub contains more convenience functions to reproduce the analyses of the corresponding paper (). 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These procedures enable us to evaluate the overall hypothesis regarding equality, as well as specific hypotheses defined by contrasts. In particular, we can perform post hoc tests to examine particular comparisons of interest. Different experimental designs are supported, e.g., one-way and multi-way analysis of variance for functional data. 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Specifically, given the appropriate data, the functions are able to perform t-tests, analyses of variance, and mixed models for the provided data and return summary statistics and plots. The function is also able to return for all those tests p-values, confidence intervals, and Bayes factors. The methods are described in Lonsdorf, Gerlicher, Klingelhofer-Jens, & Krypotos (2022) . Since November 2025, this package contains code from the 'ez' R package (Copyright (c) 2016-11-01, Michael A. Lawrence ), originally distributed under the 'GPL' (equal and above 2) license. 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Package: r-cran-multigroup.vaccine Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1560 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-shiny, r-cran-bslib, r-cran-htmltools, r-cran-socialmixr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multigroup.vaccine_0.1.1-1.ca2404.1_all.deb Size: 458750 MD5sum: e8844dcef1513978ae48733f2b218c89 SHA1: aa09647b5007efdfec83ef67395172495711833f SHA256: eea4b9287154f5b7271ca4ab7a5805066d5a6c5d2c2e5f36fc6c979f5420c36b SHA512: 2ab3415b916731c900e6b6196d4eb6284154871bb0b0ff943ec7cc1567a1c4b77d734add0d389dbe33212dbc19f2d2f238108fbb484c90f300e6e05f99f150ea Homepage: https://cran.r-project.org/package=multigroup.vaccine Description: CRAN Package 'multigroup.vaccine' (Analyze Outbreak Models of Multi-Group Populations withVaccination) Model infectious disease dynamics in populations with multiple subgroups having different vaccination rates, transmission characteristics, and contact patterns. Calculate final and intermediate outbreak sizes, form age-structured contact models with automatic fetching of U.S. census data, and explore vaccination scenarios with an interactive 'shiny' dashboard for a model with two subgroups, as described in Nguyen et al. (2024) and Duong et al. (2026) . 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The first technique called multigroup PCA (mgPCA) this multivariate exploration approach that has the idea of considering the structure of groups and / or different types of variables. On the other hand, the second multivariate technique called Multigroup Dimensionality Reduction (MDR) it is another multivariate exploration method that is based on projections. In addition, a method called Single Dimension Exploration (SDE) was incorporated for to analyze the exploration of the data. It could help us in a better way to observe the behavior of the multigroup data with certain variables of interest. 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The inflation of type-1 error rate comes from two sources (S1) repeated testing individual hypothesis and (S2) simultaneous testing multiple hypotheses. The 'MultiGroupSequential' package is intended to help researchers to tackle this challenge. The procedures provided include the sequential procedures described in Luo and Quan (2023) and the graphical procedure proposed by Maurer and Bretz (2013) . Luo and Quan (2013) describes three procedures, and the functions to implement these procedures are (1) seqgspgx() implements a sequential graphical procedure based on the group-sequential p-values; (2) seqgsphh() implements a sequential Hochberg/Hommel procedure based on the group-sequential p-values; and (3) seqqvalhh() implements a sequential Hochberg/Hommel procedure based on the q-values. In addition, seqmbgx() implements the sequential graphical procedure described in Maurer and Bretz (2013). 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Package: r-cran-multikink Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-quantreg, r-cran-gam, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multikink_0.2.0-1.ca2404.1_all.deb Size: 83574 MD5sum: 54aad6d5d3738fc6f6db7b2d27059602 SHA1: 693b79ec373c2224b484412f051d8a6deb7cd916 SHA256: b5efadbcc40b29955745598928eefb37546ce165dc13aa0c55d5eb6761f1cd2c SHA512: fddcc593a09c05f8dc47078813f21512dd2d6b80634a4dfe9905f2d62c302ae951ff070dbbaad44ef634276df6443ac767469129cd67d38bc10d96b6930c1c6a Homepage: https://cran.r-project.org/package=MultiKink Description: CRAN Package 'MultiKink' (Estimation and Inference for Multi-Kink Quantile Regression) Estimation and inference for multiple kink quantile regression for longitudinal data and the i.i.d data. A bootstrap restarting iterative segmented quantile algorithm is proposed to estimate the multiple kink quantile regression model conditional on a given number of change points. The number of kinks is also allowed to be unknown. In such case, the backward elimination algorithm and the bootstrap restarting iterative segmented quantile algorithm are combined to select the number of change points based on a quantile BIC. For longitudinal data, we also develop the GEE estimator to incorporate the within-subject correlations. A score-type based test statistic is also developed for testing the existence of kink effect. The package is based on the paper, ``Wei Zhong, Chuang Wan and Wenyang Zhang (2022). Estimation and inference for multikink quantile regression, JBES'' and ``Chuang Wan, Wei Zhong, Wenyang Zhang and Changliang Zou (2022). Multi-kink quantile regression for longitudinal data with application to progesterone data analysis, Biometrics". Package: r-cran-multilandr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4954 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-ggally, r-cran-ggplot2, r-cran-tidyterra, r-cran-gridextra, r-cran-landscapemetrics, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multilandr_1.0.0-1.ca2404.1_all.deb Size: 4646402 MD5sum: b0835e57e4ce7fd44c43b5d12103a375 SHA1: 2ce0f2425bda66844331b5c7391658848f933b8f SHA256: d254e2a2a539e1d4858fa77795335047d0e749fc3690ef46efc13232123b32fa SHA512: 58edcaec1bc3d6461606e0093afd392a1d3f713de0148d105fd9c558ba2c585fc5bbfc9769edc27150bc9ab2d48174e4ffe1a46a7acb699fa84ef4a6e67e26e9 Homepage: https://cran.r-project.org/package=multilandr Description: CRAN Package 'multilandr' (Landscape Analysis at Multiple Spatial Scales) Provides a tidy workflow for landscape-scale analysis. 'multilandr' offers tools to generate landscapes at multiple spatial scales and compute landscape metrics, primarily using the 'landscapemetrics' package. It also features utility functions for plotting and analyzing multi-scale landscapes, exploring correlations between metrics, filtering landscapes based on specific conditions, generating landscape gradients for a given metric, and preparing datasets for further statistical analysis. Documentation about 'multilandr' is provided in an introductory vignette included in this package and in the paper by Huais (2024) ; see citation("multilandr") for details. 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Package: r-cran-multilevel Architecture: all Version: 2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-mass Filename: pool/dists/noble/main/r-cran-multilevel_2.8-1.ca2404.1_all.deb Size: 498564 MD5sum: c0baa32e94b508f4543f29aa9f1cd9f9 SHA1: 8e510761334289deed3a70e7e82c52f6261ec36b SHA256: 1b5314bfc39dd5f9b2162669c60d65ee299f41b235a01303dab3f4f22f608fc9 SHA512: a334da5e6ad59049d8867536ee21391edda20432cbcf3e798d4bfeccc8529c068645bf9b40d777de62ab18773f3c4473c0f7459cb2616faf49017b2db6552498 Homepage: https://cran.r-project.org/package=multilevel Description: CRAN Package 'multilevel' (Multilevel Functions) Tools used by organizational researchers for the analysis of multilevel data. Includes four broad sets of tools. First, functions for estimating within-group agreement and reliability indices. Second, functions for manipulating multilevel and longitudinal (panel) data. Third, simulations for estimating power and generating multilevel data. Fourth, miscellaneous functions for estimating reliability and performing simple calculations and data transformations. Package: r-cran-multilevelcoda Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-compositions, r-cran-brms, r-cran-extraoperators, r-cran-ggplot2, r-cran-foreach, r-cran-future, r-cran-dofuture, r-cran-abind, r-cran-shiny, r-cran-shinystan, r-cran-loo, r-cran-bayesplot, r-cran-emmeans, r-cran-plotly, r-cran-htmltools, r-cran-bslib, r-cran-dt, r-cran-fs Suggests: r-cran-testthat, r-cran-covr, r-cran-withr, r-cran-knitr, r-cran-rmarkdown, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-multilevelcoda_1.3.3-1.ca2404.1_all.deb Size: 2793160 MD5sum: d21bd77ba2f419c13a6ab50f73b133ba SHA1: 66674700d81da56003ab049e96e324ab452feb8f SHA256: d073f5e1787abe64164438eb91d89aa9dcf685aea59166d4b766849e7c2a585e SHA512: 5f6417bf45f08cc780a56ef50fdd91c6b833533e1c542464423bb9b109988b3cc51f1793eb290a0ed53cb9f9598d89e6eb57257a5b1b9b7623b33c901272b4ba Homepage: https://cran.r-project.org/package=multilevelcoda Description: CRAN Package 'multilevelcoda' (Estimate Bayesian Multilevel Models for Compositional Data) Implement Bayesian multilevel modelling for compositional data. 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Package: r-cran-multilevelmediation Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-furrr, r-cran-future, r-cran-matrixcalc, r-cran-mcmcpack, r-cran-nlme, r-cran-parallelly, r-cran-tidyr, r-cran-brms, r-cran-posterior, r-cran-glmmtmb Suggests: r-cran-testthat, r-cran-boot, r-cran-bh Filename: pool/dists/noble/main/r-cran-multilevelmediation_0.4.1-1.ca2404.1_all.deb Size: 158700 MD5sum: bd64e995be36754ff051d023cf544d6f SHA1: 3cd9725793275bdc44d82c883f73f94ad545350c SHA256: 030252a1a6deb6936c118af8b904b8a127eb254330d05e907f1488cf9f360182 SHA512: d4d0b4bafa174a1e8d45598fbcb14e74cefd44f4fee5b44ae4a306383f692c98688bc7a65c7f58d5328be9a154db4c5ada143eb82020661960cbe0f18b5b452c Homepage: https://cran.r-project.org/package=multilevelmediation Description: CRAN Package 'multilevelmediation' (Utility Functions for Multilevel Mediation Analysis) The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models for multilevel mediation, the option of a moderating variable for either the a, b, or both paths, and covariates. Currently the 1-1-1 model is supported and several options of random effects; the initial code for bootstrapping was evaluated in simulations by Falk, Vogel, Hammami, and Miočević (2024) . Support for Bayesian estimation using 'brms' comprises ongoing work. Currently only continuous mediators and outcomes are supported. Factors for any predictors must be numerically represented. Package: r-cran-multilevelmod Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-parsnip, r-cran-dplyr, r-cran-lme4, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-withr Suggests: r-cran-covr, r-cran-gee, r-cran-ggplot2, r-cran-knitr, r-cran-nlme, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidymodels Filename: pool/dists/noble/main/r-cran-multilevelmod_1.0.0-1.ca2404.1_all.deb Size: 198068 MD5sum: 9004961584a585a11ca89efa412b084a SHA1: 8a81ff844006656cccf25b8fed9ff349d90025fd SHA256: ad0ffd56ec39ca67e67b97635f09290adc18935a5dab58a15c55020e2a73d6fc SHA512: 757d6fd814f598b444d29ca797b39a4f5a74b3d343d57332931903c30f4a08357b9d2e00396b2cbe91338ee72de4e5ae0988becef13c63a2c71838c8432a3526 Homepage: https://cran.r-project.org/package=multilevelmod Description: CRAN Package 'multilevelmod' (Model Wrappers for Multi-Level Models) Bindings for hierarchical regression models for use with the 'parsnip' package. Models include longitudinal generalized linear models (Liang and Zeger, 1986) , and mixed-effect models (Pinheiro and Bates) . Package: r-cran-multileveloptimalbayes Architecture: all Version: 0.0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multileveloptimalbayes_0.0.4.0-1.ca2404.1_all.deb Size: 126312 MD5sum: 289dfb9ddae4e83d9f3a8c6cd182feff SHA1: 8f70f0f56b73a2cc47ccbc1c12dbcfdfddbf0bb4 SHA256: f24e36acdad3a02f73be639c498e637fa3036f03f412ff3d7ef7f5e117249768 SHA512: 86eccae8f80f87903559881374aec9ab6c69933e9c6f353625985cef2388603cb9e43e633ee56dbcb461a3a9b8d891e75d115358f84f4f0cfcf0fd089245b3b8 Homepage: https://cran.r-project.org/package=MultiLevelOptimalBayes Description: CRAN Package 'MultiLevelOptimalBayes' (Regularized Bayesian Estimator for Two-Level Latent VariableModels) Implements a regularized Bayesian estimator that optimizes the estimation of between-group coefficients for multilevel latent variable models by minimizing mean squared error (MSE) and balancing variance and bias. The package provides more reliable estimates in scenarios with limited data, offering a robust solution for accurate parameter estimation in two-level latent variable models. It is designed for researchers in psychology, education, and related fields who face challenges in estimating between-group effects under small sample sizes and low intraclass correlation coefficients. The package includes comprehensive S3 methods for result objects: print(), summary(), coef(), se(), vcov(), confint(), as.data.frame(), dim(), length(), names(), and update() for enhanced usability and integration with standard R workflows. Dashuk et al. (2025a) derived the optimal regularized Bayesian estimator; Dashuk et al. (2025b) extended it to the multivariate case; and Luedtke et al. (2008) formalized the two-level latent variable framework. Package: r-cran-multilevelpsa Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3245 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-xtable, r-cran-mass, r-cran-party, r-cran-plyr, r-cran-psagraphics, r-cran-psych, r-cran-reshape Suggests: r-cran-knitr, r-cran-matchit, r-cran-mice, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multilevelpsa_1.3.1-1.ca2404.1_all.deb Size: 2948048 MD5sum: 21458c08318871c5b7525fa1f4bf3e99 SHA1: ecc1ba21e97bc73317613a649aaefad909f676b0 SHA256: b712aaf503cb7dd8c96b0f893b87c3c6c5eb764613bd5c2b766d41cc346a71cb SHA512: 3576de4cc697ef34102a3a59d05c2a4f73f7495ff39f93b20f87edb4af886a1bb9296229edaa698012b8dcb28ca4ea363657776d5601e6da17fc9be5877f6381 Homepage: https://cran.r-project.org/package=multilevelPSA Description: CRAN Package 'multilevelPSA' (Multilevel Propensity Score Analysis) Conducts and visualizes propensity score analysis for multilevel, or clustered data. Bryer & Pruzek (2011) . 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Includes marginal and conditional 'R2' estimates for linear mixed effects models based on Johnson (2014) . Package: r-cran-multilinguer Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sys, r-cran-rappdirs, r-cran-usethis, r-cran-askpass Filename: pool/dists/noble/main/r-cran-multilinguer_0.2.4-1.ca2404.1_all.deb Size: 618594 MD5sum: f46a5fb36e90d1b51b50c47ffc638002 SHA1: 1917fd3a769e5428bbd72a1c1ea7a092c1242ec9 SHA256: c3a98d1896bfb3e5406d3f9ea605c1c2a2d619952cccba35dabb957df2a06f2f SHA512: 3b8601b4eddde4762cbaaefda7d51d9f24c20f1112f503477d070d483d37283e704ce175fdd7403b38ccc37f6d53cec00a27d87871a107b226bb986ae6d7d2a1 Homepage: https://cran.r-project.org/package=multilinguer Description: CRAN Package 'multilinguer' (Gentle Language Installer for R User) Provides install functions of other languages such as 'java', 'python'. Package: r-cran-multimarker Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-truncnorm, r-cran-ordinalnet Filename: pool/dists/noble/main/r-cran-multimarker_1.0.1-1.ca2404.1_all.deb Size: 74118 MD5sum: 9fc57c1f0ab0f0ce2e4e2048b0dd1fd9 SHA1: e19cdae546d2dd030165485b47296d8c492bbf64 SHA256: cd803274b14fee2b17444b4f9e54d5cba0ff507b4a3bab46ac48f3d13bff8d30 SHA512: 2291bf9916b203d271e3e5ce0acca92a016701667a76a191181b97f93a1de97b332f76201b3b2c497df4cc67e639991d5254357f39c6b20888e908ae8d00a81b Homepage: https://cran.r-project.org/package=multiMarker Description: CRAN Package 'multiMarker' (Latent Variable Model to Infer Food Intake from MultipleBiomarkers) A latent variable model based on factor analytic and mixture of experts models, designed to infer food intake from multiple biomarkers data. 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Package: r-cran-multimedia Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1643 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brms, r-cran-glmnetutils, r-cran-ranger, r-cran-tidyselect, r-cran-mass, r-bioc-s4vectors, r-bioc-summarizedexperiment, r-cran-cli, r-cran-dplyr, r-cran-fansi, r-cran-formula.tools, r-cran-ggplot2, r-cran-glue, r-cran-minilnm, r-cran-patchwork, r-bioc-phyloseq, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-tidygraph, r-cran-tidyr Suggests: r-cran-compositions, r-cran-ggdist, r-cran-ggraph, r-cran-ggrepel, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-vroom Filename: pool/dists/noble/main/r-cran-multimedia_0.2.0-1.ca2404.1_all.deb Size: 1202130 MD5sum: 0640c1f0329da659a7e413e3c180410e SHA1: d851a28a7cf33e9cb7e9f636cd7c4849fdc34532 SHA256: 8a3a7f93afd570248a411b6ac4f17b4dc744660f914257b1df6feb0a182c176e SHA512: b68e6c10baae24498bad7f4ad5327186a44e217d744e52b3772cfe3df451855f0aacb32b35fdca0aaca2cdde0fdcf8a5c9b0900e8739596377bd63acc210c868 Homepage: https://cran.r-project.org/package=multimedia Description: CRAN Package 'multimedia' (Multimodal Mediation Analysis) Multimodal mediation analysis is an emerging problem in microbiome data analysis. Multimedia make advanced mediation analysis techniques easy to use, ensuring that all statistical components are transparent and adaptable to specific problem contexts. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis. More details are available in Jiang et al. (2024) "multimedia: Multimodal Mediation Analysis of Microbiome Data" . Package: r-cran-multimediate Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rmutil, r-cran-mass, r-cran-mvtnorm, r-cran-timereg Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multimediate_0.1.6-1.ca2404.1_all.deb Size: 331614 MD5sum: cd5b268a7d98fcd8e5f67d27223c9792 SHA1: 4dedf6705e60d56132561457ff0210cb1d759903 SHA256: 6390650c9e58b757048fb0bd0f1fad07c02c00ce0ad092dfe59652c7bf00ed23 SHA512: a3620529b732180eaadf6c97e3af2bda56f9b07e6588c91eb16bc060e10290e1f4193616c3f24338c5113bfbf14f0086dbb6376d95c9ff42e94b2327c48b75c0 Homepage: https://cran.r-project.org/package=multimediate Description: CRAN Package 'multimediate' (Causal Mediation Analysis in Presence of Multiple MediatorsUncausally Related) Estimates key quantities in causal mediation analysis - including average causal mediation effects (indirect effects), average direct effects, total effects, and proportions mediated - in the presence of multiple uncausally related mediators. Methods are described by Jerolon et al., (2021) and extended to accommodate survival outcomes as described by Domingo-Relloso et al., (2024) . Package: r-cran-multimix Architecture: all Version: 1.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-multimix_1.0-10-1.ca2404.1_all.deb Size: 139102 MD5sum: b95bff433dd966491d87082e301e90e6 SHA1: 0fd0bf71963645061c3d608f3d1b4768db0e215b SHA256: 4165adf02d325ed3e9cf6302b7e53a5dc82de4f2ec6289ce399f7d2ba293ed0b SHA512: 4126af006c9e5b470e9b97482e990bf95d9e14c1e7fff0add646c38c42b331b3d896d9f868dd035280542d37fa83b1e773cc5a2fc584f7498fcd3c3feb8f3e77 Homepage: https://cran.r-project.org/package=multimix Description: CRAN Package 'multimix' (Fit Mixture Models Using the Expectation Maximisation (EM)Algorithm) A set of functions which use the Expectation Maximisation (EM) algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, 39(1), 1--22) to take a finite mixture model approach to clustering. The package is designed to cluster multivariate data that have categorical and continuous variables and that possibly contain missing values. The method is described in Hunt, L. and Jorgensen, M. (1999) Australian & New Zealand Journal of Statistics 41(2), 153--171 and Hunt, L. and Jorgensen, M. (2003) Mixture model clustering for mixed data with missing information, Computational Statistics & Data Analysis, 41(3-4), 429--440. Package: r-cran-multimodtest Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1881 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyverse, r-cran-mass, r-cran-sis, r-cran-glmnet, r-cran-ncvreg, r-cran-mbess, r-cran-survival, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-multimodtest_1.0-1.ca2404.1_all.deb Size: 1876446 MD5sum: 847879aaf4b9c6f134bbe16f281c853f SHA1: 5825297e7fefd0223598ce5d3d9fec3d2b8711cd SHA256: 54154a572c50d485786684bc66926b8e6fdce5302638c21809fc56a70ed32aa4 SHA512: a3ed12644c44dc681b668f10cf881221d9494c36cc38fbf5e8a1aa9d792fb5f60184dcfc090af37148df8a2628ee5f961eda8390c50d3eb63fd773b5f8c7a490 Homepage: https://cran.r-project.org/package=multiModTest Description: CRAN Package 'multiModTest' (Information Assessment for Individual Modalities in MultimodalRegression Models) Provides methods for quantifying the information gain contributed by individual modalities in multimodal regression models. Information gain is measured using Expected Relative Entropy (ERE) or pseudo-R² metrics, with corresponding p-values and confidence intervals. Currently supports linear and logistic regression models with plans for extension to additional Generalized Linear Models and Cox proportional hazard model. Package: r-cran-multimolang Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrow Suggests: r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-multimolang_0.1.1-1.ca2404.1_all.deb Size: 214480 MD5sum: d983fbb36775d6c96755f3e7e19c4818 SHA1: 8d3389fd4e0e023e3319e6e9f9308ce9df034e05 SHA256: 34375218a065dd0cbf09f1f591628387e8519a19e5647cd08227b854433e0021 SHA512: 33c301f1a2a921064a931e748f2ec7a3db52fd5012e2f4e029b55f70981e489056043e3364ca20dd20b1ae1959da68a6f8120e794a0955429974f1e19d1e8fbb Homepage: https://cran.r-project.org/package=multimolang Description: CRAN Package 'multimolang' ('multimolang': Multimodal Language Analysis) Process 'OpenPose' human body keypoints for computer vision, including data structuring and user-defined linear transformations for standardization. It optionally, includes metadata extraction from filenames in the UCLA 'NewsScape' archive. Package: r-cran-multimorbidity Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-magrittr, r-cran-stringr, r-cran-rlang, r-cran-tidyselect, r-cran-tidyr, r-cran-sqldf Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-multimorbidity_0.5.1-1.ca2404.1_all.deb Size: 288550 MD5sum: bd4414ccd6532f15d8cdb25f2583bded SHA1: f1015cb400df2e6d5673c8dabcd86e980b3ef7d8 SHA256: ac3e0ba44a47ed9d3b84ef0aec31020aa124c971069bba7bd6e4eb89fa9a2ba3 SHA512: e580353a7a4b57f430f40ff3393a336bb90009a5b72498f9aa5c1c3ed43822ba2f0c9197ffaf9b6ca9b39badb21a0b1d8d6f6f26d22ca38ee7254bf770c21d0d Homepage: https://cran.r-project.org/package=multimorbidity Description: CRAN Package 'multimorbidity' (Harmonizing Various Comorbidity, Multimorbidity, and FrailtyMeasures) Identifying comorbidities, frailty, and multimorbidity in claims and administrative data is often a duplicative process. The functions contained in this package are meant to first prepare the data to a format acceptable by all other packages, then provide a uniform and simple approach to generate comorbidity and multimorbidity metrics based on these claims data. The package is ever evolving to include new metrics, and is always looking for new measures to include. The citations used in this package include the following publications: Anne Elixhauser, Claudia Steiner, D. Robert Harris, Rosanna M. Coffey (1998) , Brian J Moore, Susan White, Raynard Washington, et al. (2017) , Mary E. Charlson, Peter Pompei, Kathy L. Ales, C. Ronald MacKenzie (1987) , Richard A. Deyo, Daniel C. Cherkin, Marcia A. Ciol (1992) , Hude Quan, Vijaya Sundararajan, Patricia Halfon, et al. (2005) , Dae Hyun Kim, Sebastian Schneeweiss, Robert J Glynn, et al. (2018) , Melissa Y Wei, David Ratz, Kenneth J Mukamal (2020) , Kathryn Nicholson, Amanda L. Terry, Martin Fortin, et al. (2015) , Martin Fortin, José Almirall, and Kathryn Nicholson (2017). Package: r-cran-multiness Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-multiness_1.0.2-1.ca2404.1_all.deb Size: 373354 MD5sum: 0c0aee2b03d1d63bf13396c41700417c SHA1: f131f38850e35b756625b68d2db365be81deb521 SHA256: 0ce622e401db8cf079fecd794aa710266a367c0d693cbd4b81ea0a4412c27705 SHA512: 30adcc0d5ad8bd777079c1ecb07a33f91db51e4c2af77b6362a56a64bf184ea6f5bde2c64c14d084be9b51666a9a2faa359d369bcb17363f2ee347264f27bcdf Homepage: https://cran.r-project.org/package=multiness Description: CRAN Package 'multiness' (MULTIplex NEtworks with Shared Structure) Model fitting and simulation for Gaussian and logistic inner product MultiNeSS models for multiplex networks. The package implements a convex fitting algorithm with fully adaptive parameter tuning, including options for edge cross-validation. For more details see MacDonald et al. (2020). Package: r-cran-multinmix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nimble, r-cran-clustergeneration, r-cran-mvtnorm, r-cran-extradistr, r-cran-rstan, r-cran-abind, r-cran-coda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multinmix_0.1.0-1.ca2404.1_all.deb Size: 177966 MD5sum: db056bd71676632dbc8cb41493f86f07 SHA1: 5b2f74fda959abb2cebe02c0417ece9ab9f29488 SHA256: 1341d1c39b5bd144c9a20fef80878619a68f51b6c280f5450c7de7b0358d8d8e SHA512: 3db151766f2cd03368b100df7de9cd37be773cc380a92174cdc6c2601715694b43b4035c1a07c45f7272ddad7149bcb10b856533b8ad251a842f1c2d4186e2b1 Homepage: https://cran.r-project.org/package=MultiNMix Description: CRAN Package 'MultiNMix' (Multi-Species N-Mixture (MNM) Models with 'nimble') Simulating data and fitting multi-species N-mixture models using 'nimble'. Includes features for handling zero-inflation and temporal correlation, Bayesian inference, model diagnostics, parameter estimation, and predictive checks. Designed for ecological studies with zero-altered or time-series data. Mimnagh, N., Parnell, A., Prado, E., & Moral, R. A. (2022) . Royle, J. A. (2004) . Package: r-cran-multinomialci Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-multinomialci_1.2-1.ca2404.1_all.deb Size: 20336 MD5sum: 1ce411723fc8180b5640c6c46c9c4268 SHA1: 10f0b7306aff560a788e9ad978aa558ba063582e SHA256: b467f0d72a4cd6d48f896a44f50504c877cce68af74e8990866bfc09915f3263 SHA512: 77314efae3e22c89c1bfcec5a8b2af34612406523e8ef65bec3374434e9ffa4fefcb026c69ac9d5236e803dab09bca33f0c42e210af50facf25108faca3432d3 Homepage: https://cran.r-project.org/package=MultinomialCI Description: CRAN Package 'MultinomialCI' (Simultaneous Confidence Intervals for Multinomial ProportionsAccording to the Method by Sison and Glaz) An implementation of a method for building simultaneous confidence intervals for the probabilities of a multinomial distribution given a set of observations, proposed by Sison and Glaz in their paper: Sison, C.P and J. Glaz. Simultaneous confidence intervals and sample size determination for multinomial proportions. Journal of the American Statistical Association, 90:366-369 (1995). The method is an R translation of the SAS code implemented by May and Johnson in their paper: May, W.L. and W.D. Johnson. Constructing two-sided simultaneous confidence intervals for multinomial proportions for small counts in a large number of cells. Journal of Statistical Software 5(6) (2000). Paper and code available at . Package: r-cran-multinttestfunc Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-statmod, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multinttestfunc_0.3.0-1.ca2404.1_all.deb Size: 144344 MD5sum: ab896ee15f5421c44a202ef49ca5899a SHA1: 291ec37673813e8da9107e16e930232e07916672 SHA256: e1e1919a4f69265b80cf603cadbc99bd60dfbe7f62b6040595df48ceb7099052 SHA512: 906294c9927f137a07eeae351f6b4a42bf6e2746da0375e78cf465b7545e8f9f5ffe0ecf884899fba593ecf6b6d940f4a604e97bb89c59cdf6879d640dfad0d9 Homepage: https://cran.r-project.org/package=multIntTestFunc Description: CRAN Package 'multIntTestFunc' (Provides Test Functions for Multivariate Integration) Provides implementations of functions that can be used to test multivariate integration routines. The package covers six different integration domains (unit hypercube, unit ball, unit sphere, standard simplex, non-negative real numbers and R^n). For each domain several functions with different properties (smooth, non-differentiable, ...) are available. The functions are available in all dimensions n >= 1. For each function the exact value of the integral is known and implemented to allow testing the accuracy of multivariate integration routines. Details on the available test functions can be found at on the development website. Package: r-cran-multiobjectivemdp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-linprog, r-cran-lintools, r-cran-nsga2r, r-cran-pracma, r-cran-prodlim Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multiobjectivemdp_1.0.0-1.ca2404.1_all.deb Size: 222390 MD5sum: f115b83db5e91e2b92a29ce0e6ffa7d1 SHA1: a5b5711cf7038824760a26ddec923300b077c9b0 SHA256: 952547d8bd3041541e7bb39a5f0688d5a49e8fb827284780f16bea1dbcf909d7 SHA512: e3c0165b527ca48442743dfefc514d03080c19e407732688a5bed199083283acc0853efc32bdf7f895baa76a6af6a1d9b2049a29c60a3b3d84dfa90027c316a7 Homepage: https://cran.r-project.org/package=multiobjectiveMDP Description: CRAN Package 'multiobjectiveMDP' (Solution Methods for Multi-Objective Markov Decision Processes) Compendium of the most representative algorithms in print---vector-valued dynamic programming, linear programming, policy iteration, the weighting factor approach---for solving multi-objective Markov decision processes, with or without reward discount, over a finite or infinite horizon. Mifrani, A. (2024) ; Mifrani, A. & Noll, D. ; Wakuta, K. (1995) . 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(2025) ). Supports the definition and solution of conservation problems across nested H3 resolutions with resolution-specific features, costs, and management attributes, including cross-scale connectivity penalties derived from parent-child relationships. Also includes utilities to evaluate solutions using multiscale-aware diagnostics and to post-process optimization outputs into alternative area-targeted conservation scenarios. Package: r-cran-multiselect Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-multiselect_0.1.0-1.ca2404.1_all.deb Size: 25796 MD5sum: 9ddcc2fe34d3f853ad8ac619610b0c31 SHA1: 17d051533fd65590a81f54e5083d5b4a9fe4f6a1 SHA256: 397f1fb8dfd87fd7b84702f7e823df0bd2afeb7bf2cf8181ad3807f5b1fd7b24 SHA512: 7a7a78190146d50516b18da9f754075175c561fca9760384de8a5aaa52d9aa524a8f04be220c4e4923e50b498dee4363c25374f5ce012d382310a6504eeff6d1 Homepage: https://cran.r-project.org/package=multiselect Description: CRAN Package 'multiselect' (Selecting Combinations of Predictors by Leveraging Multiple AUCsfor an Ordered Multilevel Outcome) Uses multiple AUCs to select a combination of predictors when the outcome has multiple (ordered) levels and the focus is discriminating one particular level from the others. This method is most naturally applied to settings where the outcome has three levels. (Meisner, A, Parikh, CR, and Kerr, KF (2017) .) Package: r-cran-multisensi Architecture: all Version: 2.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sensitivity, r-cran-knitr Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-multisensi_2.1-1-1.ca2404.1_all.deb Size: 1038132 MD5sum: e392f1b6d14aeee71008ab4434dccaae SHA1: f78ab71f3996586210c99dc3b53eb1fcee723d5f SHA256: 0719ad0bf72fe017e315784dc2155caf11e91dd97ce0fd1c2839f24e704446ee SHA512: 8869c8914b372a3fdae1f152650246286bf5a77b507af19569a15fe3fd3ca1855fd14cbe999dfe8ea52c6b92f09d3df1059285496814d6b438223472d5dc771f Homepage: https://cran.r-project.org/package=multisensi Description: CRAN Package 'multisensi' (Multivariate Sensitivity Analysis) Functions to perform sensitivity analysis on a model with multivariate output. Package: r-cran-multisite.accuracy Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coxme, r-cran-lme4, r-cran-lmertest, r-cran-logistf, r-cran-metafor, r-cran-proc, r-cran-survival Filename: pool/dists/noble/main/r-cran-multisite.accuracy_1.3-1.ca2404.1_all.deb Size: 38366 MD5sum: 253f0f6fa418fc0b9f8295654ed35c64 SHA1: 2b9d53239c5821e3c8ccbb121b37f45da3ee3ac0 SHA256: 903d9ce6c6e2545797ea298e3f99d3b946628a49ac200139d87e8b6e50e69ddd SHA512: 21d15696f35c703d3bbfc7570ac5282e5e76aa25c36d28605ce6482c72212fc291ab41ea7a3879cc9f150c1feba637c0209353193136c16ce47467e4b100bfeb Homepage: https://cran.r-project.org/package=multisite.accuracy Description: CRAN Package 'multisite.accuracy' (Estimation of Accuracy in Multisite Machine-Learning Models) The effects of the site may severely bias the accuracy of a multisite machine-learning model, even if the analysts removed them when fitting the model in the 'training set' and applying the model in the 'test set' (Solanes et al., Neuroimage 2023, 265:119800). This simple R package estimates the accuracy of a multisite machine-learning model unbiasedly, as described in (Solanes et al., Psychiatry Research: Neuroimaging 2021, 314:111313). It currently supports the estimation of sensitivity, specificity, balanced accuracy (for binary or multinomial variables), the area under the curve, correlation, mean squarer error, and hazard ratio for binomial, multinomial, gaussian, and survival (time-to-event) outcomes. Package: r-cran-multisitemediation Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-statmod, r-cran-psych, r-cran-mass, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-multisitemediation_0.0.4-1.ca2404.1_all.deb Size: 255452 MD5sum: 6217722f573963bfb6b5afa7e8297f9b SHA1: 8b67b1e6e4e1004f4e066e41507d4c876220061f SHA256: 437c60c8fd1a6a0d8763a6fa215b9a3c556db3240a77caa8cf2de2f4ab609b15 SHA512: 3d5e03501942421596aacf5f508269a31a283cb72f39598854d4d93b980e0f0980d9dd4038424e31f12fda1a7b8af552c7015d325e42ad7e895c07d4d299f4c9 Homepage: https://cran.r-project.org/package=MultisiteMediation Description: CRAN Package 'MultisiteMediation' (Causal Mediation Analysis in Multisite Trials) Multisite causal mediation analysis using the methods proposed by Qin and Hong (2017) , Qin, Hong, Deutsch, and Bein (2019) , and Qin, Deutsch, and Hong (2021) . It enables causal mediation analysis in multisite trials, in which individuals are assigned to a treatment or a control group at each site. It allows for estimation and hypothesis testing for not only the population average but also the between-site variance of direct and indirect effects transmitted through one single mediator or two concurrent (conditionally independent) mediators. This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. This package also provides a function that can further incorporate a sample weight and a nonresponse weight for multisite causal mediation analysis in the presence of complex sample and survey designs and non-random nonresponse, to enhance both the internal validity and external validity. The package also provides a weighting-based balance checking function for assessing the remaining overt bias. Package: r-cran-multiskew Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maxskew Filename: pool/dists/noble/main/r-cran-multiskew_1.1.1-1.ca2404.1_all.deb Size: 44192 MD5sum: 73d5dddfcb4a194b1a080e69070c43a9 SHA1: 7d67622cb26296ea2ad6080f2dcf1768816f0770 SHA256: cb36a560831a89c2067445c40daceca492378319eaaa9814c58ac865234d35c2 SHA512: 74dfab559cf2031c0c692c411a60880c19e2c452adbe3748140bda10fe4e658f8b1801ed49ca7507599f2eecec06e6a33677b867c9a05591c596381e8eb53f10 Homepage: https://cran.r-project.org/package=MultiSkew Description: CRAN Package 'MultiSkew' (Measures, Tests and Removes Multivariate Skewness) Computes the third multivariate cumulant of either the raw, centered or standardized data. Computes the main measures of multivariate skewness, together with their bootstrap distributions. Finally, computes the least skewed linear projections of the data. 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Package: r-cran-multispline Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-mgcv, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-lmertest, r-cran-knitr, r-cran-rmarkdown, r-cran-reformulas, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-multispline_0.2.0-1.ca2404.1_all.deb Size: 148780 MD5sum: 0185ea3eb99ced34e0af8e5bd0dfeac5 SHA1: 23e04539821a11e5854de6334a21960342bf1ff4 SHA256: 715f65326d47dc2af95728353222a50f10798cc08e01d0d7ab7ee806411fdb66 SHA512: 818b161be1767119ce65666d18c2fe1e2177928363fb2bbae605f28e208732687c1d048a7034babab407dbceec9b50cd1ac6db2726648c4778ae0ad3c531b016 Homepage: https://cran.r-project.org/package=MultiSpline Description: CRAN Package 'MultiSpline' (Spline-Based Nonlinear Modeling for Multilevel and LongitudinalData) Provides a unified framework for fitting, predicting, and interpreting nonlinear relationships in single-level, multilevel, and longitudinal regression models. 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Package: r-cran-multistatm Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrangements, r-cran-matrix, r-cran-mass, r-cran-mvtnorm Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-multistatm_2.0.0-1.ca2404.1_all.deb Size: 264620 MD5sum: 201b3aa89fe84c3236a06049f0a98143 SHA1: db7978ecb74f7b83b4be1e29503f1200cebda0e8 SHA256: 49d2f54dff39ef259ac7b8c4494a2839ade02de5c369cdbf62688d24c0069e0e SHA512: c2d00dc0c45e95409bb210590f377a8875059a4f68b7a586d45346070568a7380cb2d44bbbbdcd5347a6fa445653be961a48bd6dbce77b0acbfba6a13c1227f4 Homepage: https://cran.r-project.org/package=MultiStatM Description: CRAN Package 'MultiStatM' (Multivariate Statistical Methods) Algorithms to build set partitions and commutator matrices and their use in the construction of multivariate d-Hermite polynomials; estimation and derivation of theoretical vector moments and vector cumulants of multivariate distributions; conversion formulae for multivariate moments and cumulants. Applications to estimation and derivation of multivariate measures of skewness and kurtosis; estimation and derivation of asymptotic covariances for d-variate Hermite polynomials, multivariate moments and cumulants and measures of skewness and kurtosis. The formulae implemented are discussed in Terdik (2021, ISBN:9783030813925), "Multivariate Statistical Methods". 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Provides functions for data analysis, visualization, and network metrics calculation. Methods are based on He et al. (2026) . 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The package is primary written for the course Multivariate analysis and for the course Computer intensive methods at the masters program of Applied Statistics at University of Ljubljana. 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The package estimates dissimilarity measures, builds dendrograms, obtains MANOVA, principal components, canonical variables, etc. (Pacote com metodologias de analise multivariada para avaliação de experimentos. O pacote estima medidas de dissimilaridade, construi de dendogramas, obtem a MANOVA, componentes principais, variaveis canonicas, etc.) Package: r-cran-multivariatetrendanalysis Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-resample, r-cran-vgam, r-cran-zoo Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-multivariatetrendanalysis_0.1.3-1.ca2404.1_all.deb Size: 61892 MD5sum: 30e45f6225ac3abd4e576a31f24da3c1 SHA1: 31d743ff3cc7ee3777f128cd1d8ddb35a71d5f01 SHA256: 9c950861ece07db40c88e83db57eb13e84d2b8dfb3594e9d7846fd82a60a40da SHA512: 86c1cd673c494466ffdb126d44e43209c962ce7cf2a8e990ffa1a1352e18c261e652b783d951edd0acfc885f63d3d960162a5538f361a2f5a74ae95ed90f8ea2 Homepage: https://cran.r-project.org/package=MultivariateTrendAnalysis Description: CRAN Package 'MultivariateTrendAnalysis' (Univariate and Multivariate Trend Testing) With foundations on the work by Goutali and Chebana (2024) , this package contains various univariate and multivariate trend tests. The main functions regard the Multivariate Dependence Trend and Multivariate Overall Trend tests as proposed by Goutali and Chebana (2024), as well as a plotting function that proves useful as a summary and complement of the tests. Although many packages and methods carry univariate tests, the Mann-Kendall and Spearman's rho test implementations are included in the package with an adapted version to hydrological formulation (e.g. as in Rao and Hamed 1998 or Chebana 2022 ). For better understanding of the example use of the functions, three datasets are included. These are synthetic data and shouldn't be used beyond that purpose. Package: r-cran-multivarious Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1883 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-chk, r-cran-glmnet, r-cran-corpcor, r-cran-matrix, r-cran-rsvd, r-cran-svd, r-cran-pls, r-cran-irlba, r-cran-rspectra, r-cran-proxy, r-cran-matrixstats, r-cran-ggplot2, r-cran-ggrepel, r-cran-future.apply, r-cran-tibble, r-cran-dplyr, r-cran-crayon, r-cran-mass, r-cran-cli, r-cran-withr, r-cran-assertthat, r-cran-future, r-cran-geigen, r-cran-primme, r-cran-gparotation, r-cran-lifecycle Suggests: r-cran-covr, r-cran-randomforest, r-cran-testthat, r-cran-magrittr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-multivarious_0.3.1-1.ca2404.1_all.deb Size: 1089638 MD5sum: bf37859d7648f78ee4de7ffc186398e4 SHA1: 9bde245e012f328adf84ef116b488e05f4016292 SHA256: 876e819a088f3cd03cd450e039debc20e47c2060c0f3ff8ec554b6789c7ad49f SHA512: 9d9a219b4c9b2f6c472e9cb757e2199c7d0e539581764116c1ed0716b792e58929aec66fb9fdc50dfa61c70f0a2443eb9422832d08d14c0b990c4d0b23124fe1 Homepage: https://cran.r-project.org/package=multivarious Description: CRAN Package 'multivarious' (Extensible Data Structures for Multivariate Analysis) Provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks. Package: r-cran-multivarmi Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-binordnonnor, r-cran-corrtoolbox, r-cran-corpcor, r-cran-matrix, r-cran-moments, r-cran-norm, r-cran-poisnonnor Suggests: r-cran-poisbinordnonnor Filename: pool/dists/noble/main/r-cran-multivarmi_1.0-1.ca2404.1_all.deb Size: 60836 MD5sum: 76645588b741ada00a4aed73ee45d6f3 SHA1: 56af351f524b399e2146a593c595192a3c5d3a57 SHA256: aa1a559c4a7ce98e321b8e41814eba3ac1562d33ee69062b26bc678426c8b608 SHA512: 09cfdf1f6e87dbf81419172fbf75d9f40abda6613f954927c80e6780c45ecb5d6c0e7f47c9e1d090cddecb422d0cbe6affab4ded51203490372d6916de4c234b Homepage: https://cran.r-project.org/package=MultiVarMI Description: CRAN Package 'MultiVarMI' (Multiple Imputation for Multivariate Data) Fully parametric Bayesian multiple imputation framework for massive multivariate data of different variable types as seen in Demirtas, H. 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The method is described in the paper Perrot-Dockès et al. (2017) . Package: r-cran-multivator Architecture: all Version: 1.1-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3648 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-emulator, r-cran-mvtnorm, r-cran-mathjaxr Suggests: r-cran-abind Filename: pool/dists/noble/main/r-cran-multivator_1.1-11-1.ca2404.1_all.deb Size: 3456180 MD5sum: 35ec9a6c179f2119ac0f52ca01dca633 SHA1: 72e4f29bd0f856a4118302f8d028cbf41f439976 SHA256: 231e3264e9f0deb913eb94126c855d91d0626de5b1374014baa1bd762336a2a3 SHA512: 5628db6f9c7e4bb06623a9edfed5dc48ea90da0133f3b3387c5d0a63f0f58597fb52e42cb518b489a6e896096c8ac472a25196883d0e6a7b91cc117199e03e95 Homepage: https://cran.r-project.org/package=multivator Description: CRAN Package 'multivator' (A Multivariate Emulator) A multivariate generalization of the emulator package. Package: r-cran-multiverse Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4345 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-r6, r-cran-tidyr, r-cran-tibble, r-cran-magrittr, r-cran-tidyselect, r-cran-formatr, r-cran-collections, r-cran-evaluate, r-cran-rstudioapi, r-cran-berryfunctions, r-cran-furrr, r-cran-styler, r-cran-distributional, r-cran-jsonlite, r-cran-readr Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-highr, r-cran-rmarkdown, r-cran-covr, r-cran-broom, r-cran-boot, r-cran-gganimate, r-cran-gifski, r-cran-forcats, r-cran-stringr, r-cran-cowplot, r-cran-tidybayes, r-cran-png, r-cran-stringi, r-cran-modelr, r-cran-future Filename: pool/dists/noble/main/r-cran-multiverse_0.6.2-1.ca2404.1_all.deb Size: 2613564 MD5sum: ff0c48b0f18b0a26c7708a230df51087 SHA1: 966acc5d51048451934af15eb6f2dc90ff5fe58b SHA256: fe4bc4b0c2a72be1bc1b4eb5276b2bbdae4b3f1ac24d29ef461b4c79163128e1 SHA512: 811f9c42b03f9716b1ad47648438b752fee895bd2c9e5ab840b8267fd537eb87b3aa549192639132724b42738dc6e0f95ae6108baf555d33da250cd5543d184b Homepage: https://cran.r-project.org/package=multiverse Description: CRAN Package 'multiverse' (Create 'multiverse analysis' in R) Implement 'multiverse' style analyses (Steegen S., Tuerlinckx F, Gelman A., Vanpaemal, W., 2016) to show the robustness of statistical inference. 'Multiverse analysis' is a philosophy of statistical reporting where paper authors report the outcomes of many different statistical analyses in order to show how fragile or robust their findings are. The 'multiverse' package (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021) allows users to concisely and flexibly implement 'multiverse-style' analysis, which involve declaring alternate ways of performing an analysis step, in R and R Notebooks. Package: r-cran-multiwave Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-signal Filename: pool/dists/noble/main/r-cran-multiwave_2.0-1.ca2404.1_all.deb Size: 993428 MD5sum: 4444641fc7eb0033c01baba816eb6eb4 SHA1: a828a6e2a354228fab1c26d557585cfa94e82496 SHA256: 88f3883a1bb835c8ace5993d8e53bc3fae6054bedf79d0aad123f55c7e9772b4 SHA512: 133bf6c33fb65929fdec8daaf74e32075d109fe9e4d3e1a738add852540b0d0d4aef2655efc774831115fb9ec530c06de09e4d1f4d03775a099070716de0168a Homepage: https://cran.r-project.org/package=multiwave Description: CRAN Package 'multiwave' (Estimation of Multivariate Long-Memory Models Parameters) Computation of an estimation of the long-memory parameters and the long-run covariance matrix using a multivariate model (Lobato (1999) ; Shimotsu (2007) ). Two semi-parametric methods are implemented: a Fourier based approach (Shimotsu (2007) ) and a wavelet based approach (Achard and Gannaz (2016) ; Achard and Gannaz (2024) ). Real and complex wavelets are implemented. 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Fit models include N-way Canonical Polyadic Decomposition, Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis. Package: r-cran-multiwayregression Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-multiwayregression_1.2-1.ca2404.1_all.deb Size: 311548 MD5sum: ad8519daf49dace706d3146dbf473481 SHA1: f02fe60b3f2be6a9298a1e42702bc2ba0f85a9ab SHA256: c95aa6c66cb081c5d5f708589d924c5341337208646cea3f1c5876ce292217f6 SHA512: cefb72050f368c32dad066d247f69bda8719e8bdc02236bc0cd76f3f087b10e7edcc502a490bd298212eabd0782df0f7fee59f1892fc224fcef6cc943a9888cd Homepage: https://cran.r-project.org/package=MultiwayRegression Description: CRAN Package 'MultiwayRegression' (Perform Tensor-on-Tensor Regression) Functions to predict one multi-way array (i.e., a tensor) from another multi-way array, using a low-rank CANDECOMP/PARAFAC (CP) factorization and a ridge (L_2) penalty [Lock, EF (2018) ]. Also includes functions to sample from the Bayesian posterior of a tensor-on-tensor model. Package: r-cran-multiwayvcov Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich, r-cran-boot Suggests: r-cran-lmtest Filename: pool/dists/noble/main/r-cran-multiwayvcov_1.2.3-1.ca2404.1_all.deb Size: 120322 MD5sum: 52f5babaa8503c1694c835be6fc7914c SHA1: 2019d6d7833b794ab71213273fddb6523ffd8f39 SHA256: d7bc4c639a005e04d9803434dd2fa1075d60868694b95ae77c8ab1d0a8059f50 SHA512: 836fb75744c63e2590f99874b624a8c4e12c544a624090a1561aa02038d0158f7d58484746bcbc2207e76310b53591d36b066a8be5ad3101241658f39a941413 Homepage: https://cran.r-project.org/package=multiwayvcov Description: CRAN Package 'multiwayvcov' (Multi-Way Standard Error Clustering) Exports two functions implementing multi-way clustering using the method suggested by Cameron, Gelbach, & Miller (2011) and cluster (or block) bootstrapping for estimating variance-covariance matrices. Normal one and two-way clustering matches the results of other common statistical packages. Missing values are handled transparently and rudimentary parallelization support is provided. Package: r-cran-multpois Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-dfidx, r-cran-dplyr, r-cran-lme4, r-cran-plyr Suggests: r-cran-emmeans, r-cran-knitr, r-cran-lmertest, r-cran-nnet, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-multpois_0.3.3-1.ca2404.1_all.deb Size: 124852 MD5sum: a367b0b3f831b697c73c2068bbb21b04 SHA1: 772627b0ad5188bd0e8941268cbfde1bb30ba608 SHA256: 36c32285003254d9e0f9ad08244f53cf320674dcf5ec7648897ee6bb1058c265 SHA512: 795905e0b2a1da6470b9662fc9ff43977210a4bbce23dc5a3cac33f3b26bcb36ec3f6e048d14396c17b28f8fd3dab93e58c7adce3ecbc436105e9d53bbe15f3c Homepage: https://cran.r-project.org/package=multpois Description: CRAN Package 'multpois' (Analyze Nominal Response Data with the Multinomial-Poisson Trick) Dichotomous responses having two categories can be analyzed with stats::glm() or lme4::glmer() using the family=binomial option. Unfortunately, polytomous responses with three or more unordered categories cannot be analyzed similarly because there is no analogous family=multinomial option. For between-subjects data, nnet::multinom() can address this need, but it cannot handle random factors and therefore cannot handle repeated measures. To address this gap, we transform nominal response data into counts for each categorical alternative. These counts are then analyzed using (mixed) Poisson regression as per Baker (1994) . Omnibus analyses of variance can be run along with post hoc pairwise comparisons. For users wishing to analyze nominal responses from surveys or experiments, the functions in this package essentially act as though stats::glm() or lme4::glmer() provide a family=multinomial option. Package: r-cran-multregcmp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-mvnfast, r-cran-progress, r-cran-bayesplot, r-cran-ggplot2, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-multregcmp_0.1.0-1.ca2404.1_all.deb Size: 117662 MD5sum: 6a6aebc7a4a3c7d39f6dc8ff0ba30143 SHA1: 8cee199ff561de326911187da5fd79cc0d8da5eb SHA256: a8de0c0fae5ed40f6cece49e70db10c4078acfca57f1fdd7eef423ad5f534ee5 SHA512: 6fbad89ce792d269127186ceb37e840a86ef392a396fcbb579ebc95c93cc84fc4e0b6fa82c44f892696edfd99c955bcb102bfd07643dd4001a84a701d094ee82 Homepage: https://cran.r-project.org/package=MultRegCMP Description: CRAN Package 'MultRegCMP' (Bayesian Multivariate Conway-Maxwell-Poisson Regression Modelfor Correlated Count Data) Fits a Bayesian Regression Model for multivariate count data. This model assumes that the data is distributed according to the Conway-Maxwell-Poisson distribution, and for each response variable it is associate different covariates. This model allows to account for correlations between the counts by using latent effects based on the Chib and Winkelmann (2001) proposal. Package: r-cran-multvardiv Architecture: all Version: 1.0.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-mass, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-multvardiv_1.0.15-1.ca2404.1_all.deb Size: 152794 MD5sum: 61e13bc168fda514150b93e947789957 SHA1: 40e78a6a512039c0b145b50750a06068e03281b0 SHA256: 93ef2a3aed96b773d34b576351274155e3d3ef7e5b546d096ac053c71768418a SHA512: 05ecb88008bed68bb356f2d31b2cf25759d4351f49ea0a6f2cac4dbd440a4f4b19ff2b2c93db5a6b3ea3515b2425fe09a4622a26d04739de0b3ca67b2e8fa96b Homepage: https://cran.r-project.org/package=multvardiv Description: CRAN Package 'multvardiv' (Multivariate Generalized Gaussian Distribution, Multivariate tDistribution, Multivariate Cauchy Distribution, StatisticalDivergence) Multivariate generalized Gaussian distribution, Multivariate Cauchy distribution, Multivariate t distribution. Distance between two distributions (see N. Bouhlel and A. Dziri (2019): , N. Bouhlel and D. Rousseau (2022): , N. Bouhlel and D. Rousseau (2023): ). Manipulation of these multivariate probability distributions. This package replaces 'mggd', 'mcauchyd' and 'mstudentd'. Package: r-cran-multxpert Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-multxpert_0.1.1-1.ca2404.1_all.deb Size: 81946 MD5sum: baf752541789beb9157bddfe5ab0c73b SHA1: 984060e788195491d01ccc5dbf55b59b081eb418 SHA256: 31a1491fd3f1b8a7358c408cfdc482c434eb6a1ee1acad3584d1cb5de7826ee0 SHA512: 2bdff0a697769d36221f935d363b2941fe9fe8ef4c9ea5b24769a209a46a184f63fe8badeae02e457eae069a82d399090edca72d78a74620dd46e6f0549d94ea Homepage: https://cran.r-project.org/package=multxpert Description: CRAN Package 'multxpert' (Common Multiple Testing Procedures and Gatekeeping Procedures) Implementation of commonly used p-value-based and parametric multiple testing procedures (computation of adjusted p-values and simultaneous confidence intervals) and parallel gatekeeping procedures based on the methodology presented in the book "Multiple Testing Problems in Pharmaceutical Statistics" (edited by Alex Dmitrienko, Ajit C. Tamhane and Frank Bretz) published by Chapman and Hall/CRC Press 2009. Package: r-cran-mulvariaterandomforestvarimp Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multivariaterandomforest, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mulvariaterandomforestvarimp_0.0.2-1.ca2404.1_all.deb Size: 30986 MD5sum: c7f964798473e93ac47a417b7c4053a8 SHA1: cb162c976d5417fc8174b503d8c2c251c908619f SHA256: 02a24ccc8e909bc128e3f4639ca415e4084bf4924ef32dfe04dd6959c2b4d064 SHA512: 8a86a16b15f5022ac26b290f9971b153843a9ffd57ae7d6e351638e6c2bdb49c05471b76d946aea07e5e724789a03c2990f338c00f400c23c33b5f18ae179523 Homepage: https://cran.r-project.org/package=MulvariateRandomForestVarImp Description: CRAN Package 'MulvariateRandomForestVarImp' (Variable Importance Measures for Multivariate Random Forests) Calculates two sets of post-hoc variable importance measures for multivariate random forests. 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Reference: Chauvel, N., Grall, J., Thiébaut, E., Houbin, C., Pezy, J.P. (in press). "A general-purpose Multivariate Marine Recovery Index for quantifying the influence of human activities on benthic habitat ecological status". Ecological Indicators. 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Package: r-cran-mus Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-desctools, r-cran-pander Filename: pool/dists/noble/main/r-cran-mus_0.1.6-1.ca2404.1_all.deb Size: 129824 MD5sum: ef2e6fcc9827744a3f5fe3329b9c5a2d SHA1: 7e6a794733e9e6c8a1f3ae4821e4eee1cc24c06d SHA256: 5253b1b18d404ffbed3b148bc0c5b48cc77fd7c211fc089d50fa5e3314733b29 SHA512: 49d95f8730eb45adeaffcfd8b09a4042a5f977fca29f4a17667fe6aef8388c63e2b811503b36e3ac3d54d6632cdc5b43e22ddf527464f98f000c0271dffd1720 Homepage: https://cran.r-project.org/package=MUS Description: CRAN Package 'MUS' (Monetary Unit Sampling and Estimation Methods, Widely Used inAuditing) Sampling and evaluation methods to apply Monetary Unit Sampling (or in older literature Dollar Unit Sampling) during an audit of financial statements. Package: r-cran-musclesynergies Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1629 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-ggplot2, r-cran-gridextra, r-cran-plyr, r-cran-proxy, r-cran-reshape2, r-cran-signal, r-cran-umap Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-musclesynergies_1.2.5-1.ca2404.1_all.deb Size: 1456846 MD5sum: d16e8e7db11b99029f244d7ff9a47298 SHA1: fbe2a9fc8546314aa3076d269f70dd97ad592a36 SHA256: 9f564634558d85a613be5bb51376ffce3adaccff0ccb1888dc443c336555a248 SHA512: 837eb02bf614377ef50b7cc6d15652f8724b46ef8554737dcc253cd9f2ced5ec3771e41bd889072e2548aff9fe77519c53d3f12d854ec0acd55f9279059a7a7d Homepage: https://cran.r-project.org/package=musclesyneRgies Description: CRAN Package 'musclesyneRgies' (Extract Muscle Synergies from Electromyography) Provides a framework to factorise electromyography (EMG) data. Tools are provided for raw data pre-processing, non negative matrix factorisation, classification of factorised data and plotting of obtained outcomes. In particular, reading from ASCII files is supported, along with wide-used filtering approaches to process EMG data. All steps include one or more sensible defaults that aim at simplifying the workflow. Yet, all functions are largely tunable at need. Example data sets are included. Package: r-cran-music Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-audio, r-cran-crayon Filename: pool/dists/noble/main/r-cran-music_0.1.2-1.ca2404.1_all.deb Size: 71172 MD5sum: 058cc68b07441a0aa612e21e5217da86 SHA1: 4197711c36780fbede2334dbf5bec54a5cd830b3 SHA256: 05b7c163e3ef530c9ad8880908e576839e271e8d5a37459741ca2cdbe51f329a SHA512: 3ece6827dadbb25cf426307f9b7c496d3ea68e96b06b8a4009c7508ce09d7d0a8856d9cdad4021756fd7afcf7e7ac928071d9ae7a10385ce38db5d0a44f407a4 Homepage: https://cran.r-project.org/package=music Description: CRAN Package 'music' (Learn and Experiment with Music Theory) An aid for learning and using music theory. You can build chords, scales, and chord progressions using 12-note equal temperament tuning (12-ET) or user-defined tuning. Includes functions to visualize notes on a piano using ASCII plots in the console and to plot waveforms using base graphics. It allows simple playback of notes and chords using the 'audio' package. Package: r-cran-musicmct Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-musicmct_0.4.0-1.ca2404.1_all.deb Size: 2268270 MD5sum: 02f9574ae56fc8706107960e0dd4d856 SHA1: 74070cf24a5a1a54e7006b023ed97cf0bda402f5 SHA256: 521a7edd00b0993e388a67d2b4c2bb76538005833cacba5f2c5ac800362ca92d SHA512: 4b2346b542a808ebdc5fb98e238feb318164044967c5cdaa910bf8feca9f1261de59db5001a3d0e255a5d09d7718337dc63e46ab0bdcd24f03bea037195d773a Homepage: https://cran.r-project.org/package=musicMCT Description: CRAN Package 'musicMCT' (Analyze the Structure of Musical Scales) Analysis of musical scales (& modes, grooves, etc.) in the vein of Sherrill 2025 . The initials MCT in the package title refer to the article's title: "Modal Color Theory." Offers support for conventional musical pitch class set theory as developed by Forte (1973, ISBN: 9780300016109) and David Lewin (1987, ISBN: 9780300034936), as well as for the continuous geometries of Callender, Quinn, & Tymoczko (2008) . Identifies structural properties of scales and calculates derived values (sign vector, color number, brightness ratio, etc.). Creates plots such as "brightness graphs" which visualize these properties. Package: r-cran-musicnmr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2518 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seewave Filename: pool/dists/noble/main/r-cran-musicnmr_1.0-1.ca2404.1_all.deb Size: 2535570 MD5sum: dbaa1b863e32903131dbd97a93ce7eb0 SHA1: 6da91b2b08f9fce2d06dbae192304a964aaafaaa SHA256: 831adfc355004b1f3d7355477df29a1cd00c5a44f1616cd3afd05952cd43f333 SHA512: d2741e37e46d9f8ddc5a83e9845aba4c4fa5d1f4a56d06c29295667878cbbbb030d4ed0227aacf415007fd239abe07783d2ba1316d30ae1bc82f95ed8e653d28 Homepage: https://cran.r-project.org/package=musicNMR Description: CRAN Package 'musicNMR' (Conversion of Nuclear Magnetic Resonance Spectra in Audio Files) A collection of functions for converting and visualization the free induction decay of mono dimensional nuclear magnetic resonance (NMR) spectra into an audio file. It facilitates the conversion of Bruker datasets in files WAV. The sound of NMR signals could provide an alternative to the current representation of the individual metabolic fingerprint and supply equally significant information. The package includes also NMR spectra of the urine samples provided by four healthy donors. Based on Cacciatore S, Saccenti E, Piccioli M. Hypothesis: the sound of the individual metabolic phenotype? Acoustic detection of NMR experiments. OMICS. 2015;19(3):147-56. . Package: r-cran-musicxml Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 950 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-gganimate, r-cran-av Filename: pool/dists/noble/main/r-cran-musicxml_1.0.1-1.ca2404.1_all.deb Size: 429032 MD5sum: ff73510a9b5d195d45a5b9dc3187537c SHA1: 17563e49124cddef27c17716cff6ce5fb07e3be6 SHA256: 9d69cf2cd848ce7d75fdfe170617ab97cbb47d71d0e0ecf281e6002c7944e135 SHA512: d4bee0fb49c34c5ceca034c582260502efa91444013015e2177b4cc5223a8f6081bc8e80abea42d69732c981e094fb70314f27bb2f8f02293410db948d9fbf68 Homepage: https://cran.r-project.org/package=musicXML Description: CRAN Package 'musicXML' (Data Sonification using 'musicXML') A set of tools to facilitate data sonification and handle the 'musicXML' format . Several classes are defined for basic musical objects such as note pitch, note duration, note, measure and score. Moreover, sonification utilities functions are provided, e.g. to map data into musical attributes such as pitch, loudness or duration. A typical sonification workflow hence looks like: get data; map them to musical attributes; create and write the 'musicXML' score, which can then be further processed using specialized music software (e.g. 'MuseScore', 'GuitarPro', etc.). Examples can be found in the blog , the presentation by Renard and Le Bescond (2022, ) or the poster by Renard et al. (2023, ). Package: r-cran-mutationtypes Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertions, r-cran-cli, r-cran-data.table Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mutationtypes_0.0.1-1.ca2404.1_all.deb Size: 74378 MD5sum: d4a3936447b560234df2c9af3e291dd4 SHA1: 96422ec48338a2fa93eccc2f69a00ef6f47fe202 SHA256: a39c47e94d2bbf631ba99e2ac0f15adc0bdbb4639e4a99994985ced387a8ee85 SHA512: 25ffcf7533b28b728b8b381e205fcc0ce0b017b2ec202ad9692a330bf59e3bbb4bd024bf6f1ed1ade82266300b4f7d188313ab621c2cf712f3f243db4c3b4ec2 Homepage: https://cran.r-project.org/package=mutationtypes Description: CRAN Package 'mutationtypes' (Validate and Convert Mutational Impacts Using Standard GenomicDictionaries) Check concordance of a vector of mutation impacts with standard dictionaries such as Sequence Ontology (SO) , Mutation Annotation Format (MAF) or Prediction and Annotation of Variant Effects (PAVE) . 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The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users). Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining. Package: r-cran-mvdfa Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-longmemo, r-cran-pbapply, r-cran-desolve, r-cran-robper, r-cran-mvtnorm, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mvdfa_0.0.4-1.ca2404.1_all.deb Size: 170162 MD5sum: ab65bc4d913cc5190de53d1c5422a1da SHA1: dce09d60b6e9ea547725b34b974d164b013cd239 SHA256: eda91aa8d48b4a7c08407e297d434600d131fb229b3a0427390feb57198aa85d SHA512: 670a426ba3e8578101ad9cd492fb04aae28f2a61cb3d37bb8ec1e4799498353356950566c0f870bbd016f8b247e56ee12279a30a391ba8762385ec89dad79483 Homepage: https://cran.r-project.org/package=mvDFA Description: CRAN Package 'mvDFA' (Multivariate Detrended Fluctuation Analysis) This R package provides an implementation of multivariate extensions of a well-known fractal analysis technique, Detrended Fluctuations Analysis (DFA; Peng et al., 1995), for multivariate time series: multivariate DFA (mvDFA). Several coefficients are implemented that take into account the correlation structure of the multivariate time series to varying degrees. These coefficients may be used to analyze long memory and changes in the dynamic structure that would by univariate DFA. Therefore, this R package aims to extend and complement the original univariate DFA (Peng et al., 1995) for estimating the scaling properties of nonstationary time series. Package: r-cran-mverse Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4909 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multiverse, r-cran-rdpack, r-cran-magrittr, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-stringr, r-cran-broom, r-cran-igraph, r-cran-ggraph, r-cran-ggplot2, r-cran-ggupset Suggests: r-cran-tibble, r-cran-purrr, r-cran-scales, r-cran-mass, r-cran-testthat, r-cran-pkgdown, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-spelling Filename: pool/dists/noble/main/r-cran-mverse_0.2.3-1.ca2404.1_all.deb Size: 4228640 MD5sum: 1e62a0823c893f18e25f1aaf07cab38c SHA1: 90a752e38f14bcbf8ddcf22881c1b43cc04fee45 SHA256: c7355b009acfff8dc8a6cc77e031b45f3bdc50f8d4f124ed0ef07174853527b5 SHA512: b2bd58a4608782711c5ca44b6d7da6fd81d5e986f8ffadc1420daf6814bed23ce1979f25d38383c67d8b198c99079025c40344490cade1780e10621b366395db Homepage: https://cran.r-project.org/package=mverse Description: CRAN Package 'mverse' (Tidy Multiverse Analysis Made Simple) Extends 'multiverse' package (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021) , which allows users perform to create explorable multiverse analysis in R. This extension provides an additional level of abstraction to the 'multiverse' package with the aim of creating user friendly syntax to researchers, educators, and students in statistics. The 'mverse' syntax is designed to allow piping and takes hints from the 'tidyverse' grammar. The package allows users to define and inspect multiverse analysis using familiar syntax in R. Package: r-cran-mvet Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mvet_0.1.0-1.ca2404.1_all.deb Size: 93200 MD5sum: c3302087e8be3476bac536d4790e2a4e SHA1: 2cc7b450564b797b52c614faef08c620ed68ba53 SHA256: a62b949fdb658716a8f4bd9ea01562e9d51dcfa4b3a9956ef07e995c255b6c9a SHA512: 113aef88e4f95d2513c7099f79bbbffa8223f0404a4fce7cea0b31ae646e70b5be19432a811db374473d7aac12201d3e59c8d6ee65f8e366c3fb440ea2c81d31 Homepage: https://cran.r-project.org/package=MVET Description: CRAN Package 'MVET' (Multivariate Estimates and Tests) Multivariate estimation and testing, currently a package for testing parametric data. To deal with parametric data, various multivariate normality tests and outlier detection are performed and visualized using the 'ggplot2' package. Homogeneity tests for covariance matrices are also possible, as well as the Hotelling's T-square test and the multivariate analysis of variance test. We are exploring additional tests and visualization techniques, such as profile analysis and randomized complete block design, to be made available in the future and making them easily accessible to users. Package: r-cran-mvfmr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fdapace, r-cran-ggplot2, r-cran-doparallel, r-cran-foreach, r-cran-proc, r-cran-progress, r-cran-glmnet, r-cran-gridextra Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mvfmr_0.1.0-1.ca2404.1_all.deb Size: 266214 MD5sum: af6929e8b774fe8072d18306214b1dae SHA1: 90f609866f6a165b30e9529a5cec27d6ceb0796e SHA256: e3279655fc34f1017d90720fa88ef7f0432bcbb4154715a8954a9350624fe2d8 SHA512: 1f0a431caf5570c0619f308518c157329f93e079ec16964d60b2aa5dd8860288a8dce69e8f4e2fd3f75e127be2b86007f17613b7caf0763fb5d71489fbe2b02c Homepage: https://cran.r-project.org/package=mvfmr Description: CRAN Package 'mvfmr' (Functional Multivariable Mendelian Randomization) Implements Multivariable Functional Mendelian Randomization (MV-FMR) to estimate time-varying causal effects of multiple longitudinal exposures on health outcomes. Extends univariable functional Mendelian Randomisation (MR) (Tian et al., 2024 ) to the multivariable setting, enabling joint estimation of multiple time-varying exposures with pleiotropy and mediation scenarios. Key features include: (1) data-driven cross-validation for basis component selection, (2) handling of mediation pathways between exposures, (3) support for both continuous and binary outcomes using Generalized Method of Moments (GMM) and control function approaches, (4) one-sample and two-sample MR designs, (5) bootstrap inference and instrument diagnostics including Q-statistics for overidentification testing. Methods are described in Fontana et al. (2025) . Package: r-cran-mvglmmrank Architecture: all Version: 1.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-numderiv, r-cran-mass Filename: pool/dists/noble/main/r-cran-mvglmmrank_1.2-4-1.ca2404.1_all.deb Size: 406536 MD5sum: 1ed84d2dbc6bb8c2a3005fbfe2e5ab2f SHA1: 59f85e1375162963fdfeaa35587ef2cb0507b647 SHA256: 2dc9619aef6d5dde9c6393cd22decc734f901c51334c98aa7ba399c3c31ab4fe SHA512: 8387c7d01d26e154e0994ce6519a7f4cd401856083ed961cb073b690feef83d1b4b9edda16082365c758a89204a5e4946a2638a7dec08c4b5e4607713075fe00 Homepage: https://cran.r-project.org/package=mvglmmRank Description: CRAN Package 'mvglmmRank' (Multivariate Generalized Linear Mixed Models for Ranking SportsTeams) Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation as documented by Broatch and Karl (2018) , Karl, Yang, and Lohr (2014) , and by Karl (2012) . Karl and Zimmerman use this package to illustrate how the home field effect estimator from a mixed model can be biased under nonrandom scheduling. 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Package: r-cran-mvhtests Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-emplik, r-cran-foreach, r-cran-rangen, r-cran-rfast, r-cran-rfast2 Suggests: r-cran-highd2means Filename: pool/dists/noble/main/r-cran-mvhtests_1.2-1.ca2404.1_all.deb Size: 126820 MD5sum: 1014759a400fc5bd0bde86ecef12b50e SHA1: 95c783c6054efef96bdf8d08ee6064c9a58a9731 SHA256: d74e45d88c04fd8834f5b542fa042920b073ea67959de1cdb4448b8f00d53d55 SHA512: e091ee2bccd65a45f6229961f039d34481755bf1faab3b3bb84e4b924db6c2e2810009982425c95471c13be05ae59e8c1622bdaaa32c2780388e4ee67e1b4bef Homepage: https://cran.r-project.org/package=mvhtests Description: CRAN Package 'mvhtests' (Multivariate Hypothesis Tests) Hypothesis tests for multivariate data. Tests for one and two mean vectors, multivariate analysis of variance, tests for one, two or more covariance matrices. References include: Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press. 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The diagnostic measures include hat-values (leverages), generalized Cook's distance, and generalized squared 'studentized' residuals. Several types of plots to detect influential observations are provided. 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Package: r-cran-mvmise Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-mass Filename: pool/dists/noble/main/r-cran-mvmise_1.0-1.ca2404.1_all.deb Size: 67594 MD5sum: d39d7910db61554e52e910bb1ed023be SHA1: 6f2c2abd70f23860d33a4c05d7d0ba4cb742264f SHA256: bf31678c5c22ccc5ad0887ea9001502df5c59fa63bc5bd3a0c747acb8d22dc5b SHA512: 7291ef57918b0d51d4f7cf4bcc1e1495e0c8dc327c915588471634aa490b76f12419dd274b5ec194d0c09cfddd8ae11d03434dd2bbb7e0e528009322cd5c7ee7 Homepage: https://cran.r-project.org/package=mvMISE Description: CRAN Package 'mvMISE' (A General Framework of Multivariate Mixed-Effects SelectionModels) Offers a general framework of multivariate mixed-effects models for the joint analysis of multiple correlated outcomes with clustered data structures and potential missingness proposed by Wang et al. (2018) . The missingness of outcome values may depend on the values themselves (missing not at random and non-ignorable), or may depend on only the covariates (missing at random and ignorable), or both. This package provides functions for two models: 1) mvMISE_b() allows correlated outcome-specific random intercepts with a factor-analytic structure, and 2) mvMISE_e() allows the correlated outcome-specific error terms with a graphical lasso penalty on the error precision matrix. Both functions are motivated by the multivariate data analysis on data with clustered structures from labelling-based quantitative proteomic studies. These models and functions can also be applied to univariate and multivariate analyses of clustered data with balanced or unbalanced design and no missingness. Package: r-cran-mvmonitoring Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4574 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lazyeval, r-cran-plyr, r-cran-rlang, r-cran-xts, r-cran-zoo, r-cran-robustbase Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-mvmonitoring_0.2.4-1.ca2404.1_all.deb Size: 2815450 MD5sum: a5a1e4172b30b163f352a4d6e6695760 SHA1: c21eff026508c71a8c48946f4914390b143128f4 SHA256: ed08b6512ab5875f0e4f692f340ae7e9fe4a84852f48bf70bacd6d2c9e75695b SHA512: 0339758c756c8a24053d0c46c064a2c4284c3bfec1843da8e2ea6c1f01e1a55c12b814f8df7a3a7576f625efdd182350b4311bdaa2803c056f308ff2a9360926 Homepage: https://cran.r-project.org/package=mvMonitoring Description: CRAN Package 'mvMonitoring' (Multi-State Adaptive Dynamic Principal Component Analysis forMultivariate Process Monitoring) Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to data generated from a continuous-time multivariate industrial or natural process. Employ PCA-based dimension reduction to extract linear combinations of relevant features, reducing computational burdens. For a description of ADPCA, see , the 2016 paper from Kazor et al. The multi-state application of ADPCA is from a manuscript under current revision entitled "Multi-State Multivariate Statistical Process Control" by Odom, Newhart, Cath, and Hering, and is expected to appear in Q1 of 2018. Package: r-cran-mvn Architecture: all Version: 6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nortest, r-cran-moments, r-cran-mass, r-cran-boot, r-cran-car, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-ggplot2, r-cran-viridis, r-cran-cli, r-cran-energy, r-cran-plotly, r-cran-mice Suggests: r-cran-dt, r-cran-bslib, r-cran-future, r-cran-haven, r-cran-jsonlite, r-cran-readxl, r-cran-shiny, r-cran-yaml, r-cran-promises, r-cran-testthat, r-cran-zip Filename: pool/dists/noble/main/r-cran-mvn_6.3-1.ca2404.1_all.deb Size: 265954 MD5sum: 461afb2a3c924ecc76b5e6c6594895e9 SHA1: 9a2e6578ab6651a73eb3394c02bcea5e3404a7b7 SHA256: 2fafec9d89eebdf1b3905aab4356b0ca7db72b026a21d5635d854d97b73fd239 SHA512: f226c4ae342dae976797c67c6129650b3f9b3ec9f4ec12a83718a2c13e68d3eb5c6b1be70d87f906261e04ae21adf169b409eb337be34e3348236a17e025daad Homepage: https://cran.r-project.org/package=MVN Description: CRAN Package 'MVN' (Multivariate Normality Tests) A comprehensive suite for assessing multivariate normality using six statistical tests (Mardia, Henze–Zirkler, Henze–Wagner, Royston, Doornik–Hansen, Energy). Also includes univariate diagnostics, bivariate density visualization, robust outlier detection, power transformations (e.g., Box–Cox, Yeo–Johnson), and imputation strategies ("mean", "median", "mice") for handling missing data. Bootstrap resampling is supported for selected tests to improve p-value accuracy in small samples. Diagnostic plots are available via both 'ggplot2' and interactive 'plotly' visualizations. See Korkmaz et al. (2014) . Package: r-cran-mvnbayesian Architecture: all Version: 0.0.8-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-plyr Suggests: r-cran-rgl, r-cran-rfast Filename: pool/dists/noble/main/r-cran-mvnbayesian_0.0.8-11-1.ca2404.1_all.deb Size: 68136 MD5sum: cf027f96bb6fd67663085ac732303358 SHA1: 555b118f333e2e563d33dbbf2cde3e6cdda6af2d SHA256: 4c7188537046c876b47e3d183ad941c55fc8c1b1bb80aa9ddb33f5abfe02d0cb SHA512: c959458e9fbe2fd9312ca6e1f8f51edfe0642cac90738ab8015fc5f028004fb083a8b7728aa86a2cea7cf6ab3f4c3bbfd5cf57098e589823c7bdafcc72e7078e Homepage: https://cran.r-project.org/package=MVNBayesian Description: CRAN Package 'MVNBayesian' (Bayesian Analysis Framework for MVN (Mixture) Distribution) Tools of Bayesian analysis framework using the method suggested by Berger (1985) for multivariate normal (MVN) distribution and multivariate normal mixture (MixMVN) distribution: a) calculating Bayesian posteriori of (Mix)MVN distribution; b) generating random vectors of (Mix)MVN distribution; c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution. Package: r-cran-mvnggrad Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mvnggrad_0.1.6-1.ca2404.1_all.deb Size: 408232 MD5sum: df9921ee3df28271e90144d4b7030729 SHA1: e81cb5242873cc3d77ac17b69df53bab36943228 SHA256: 39a8e766c19be754a1d02775445e28dc1fca41ec2db0c3a033b404c8f2bd9903 SHA512: 0df7e965fe5e30d887e1342536953836ca5d8dbfab6dcf254c675e17e6d9d0d1dd4cd5c2f792221b16c23cb7f47288e419c784d802e1e7dc6f9a58060d893e19 Homepage: https://cran.r-project.org/package=mvngGrAd Description: CRAN Package 'mvngGrAd' (Moving Grid Adjustment in Plant Breeding Field Trials) Package for moving grid adjustment in plant breeding field trials. Package: r-cran-mvngmod Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bessel, r-cran-clustergeneration, r-cran-distributionutils, r-cran-matlib, r-cran-maxlik, r-cran-truncnorm, r-cran-pracma Filename: pool/dists/noble/main/r-cran-mvngmod_0.1.0-1.ca2404.1_all.deb Size: 146946 MD5sum: 0beaa39367b393c599939f8cb780f524 SHA1: fbe4f6496be8bd5eb6b3532a9b731c4ff037bcde SHA256: 01a7325a4f00e7f9ffbbeb915161233a0d42a3add5c55c4ff267eabe843bcd0d SHA512: c7d473bb019c276a343c2ed54a4368c0fb654578318bc6d88c777d2f548d432c7ab0a9f42165587d9df979c8510d97d2acea957bac07451f0a9438b48ee15cff Homepage: https://cran.r-project.org/package=MVNGmod Description: CRAN Package 'MVNGmod' (Matrix-Variate Non-Gaussian Linear Regression Models) An implementation of the expectation conditional maximization (ECM) algorithm for matrix-variate variance gamma (MVVG) and normal-inverse Gaussian (MVNIG) linear models. These models are designed for settings of multivariate analysis with clustered non-uniform observations and correlated responses. The package includes fitting and prediction functions for both models, and an example dataset from a periodontal on Gullah-speaking African Americans, with responses in 'gaad_res', and covariates in 'gaad_cov'. For more details on the matrix-variate distributions used, see Gallaugher & McNicholas (2019) . Package: r-cran-mvnma Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 927 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-meta, r-cran-netmeta, r-cran-r2jags, r-cran-coda, r-cran-dplyr, r-cran-magrittr, r-cran-matrixstats, r-cran-rlist, r-cran-ggplot2, r-cran-forcats Filename: pool/dists/noble/main/r-cran-mvnma_0.1-0-1.ca2404.1_all.deb Size: 910910 MD5sum: afdcb0dc8602ce6a1d884ccbc014e302 SHA1: 5fb2c37b254af3de8c277535c791b3a0df3f632b SHA256: 4e7aee111737547e36fd39d93e4c13c436e46d8177eb7fe8ea3a1c4a2ba7bce9 SHA512: 9cbbc842592fdd0f7a260d06176a52ac0333fbe8284ff4319bdc868d41602c9440a8084e281ef5d0eb6a8aa8f4e689a88dfaa439b9bcc21eb2cf0e8d5b356c71 Homepage: https://cran.r-project.org/package=mvnma Description: CRAN Package 'mvnma' (Multivariate Network Meta-Analysis using Bayesian Methods) Tools to conduct Bayesian multivariate network meta-analysis providing - the single correlation coefficient model by Efthimiou et al. (2015) ; - per-outcome treatment hierarchies using the surface under the cumulative ranking curve (SUCRA), the probability of best value, or median (or mean) ranks (Salanti et al., 2011) ; - across-outcomes benefit-risk assessment using the VišeKriterijumska Optimizacija I Kompromisno Rešenje (VIKOR) method (Opricovic & Tzeng, 2004) ; - convergence checks using trace plots, density plots, or the R-hat statistic; - forest plots of treatment estimates, scatter plots of per-outcome rankings, Hasse diagrams (Carlsen & Bruggemann, 2014) to visualize the partial order of the treatments across all outcomes. Package: r-cran-mvnormaltest Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nortest, r-cran-moments, r-cran-copula Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-mvnormaltest_1.0.1-1.ca2404.1_all.deb Size: 124246 MD5sum: f09c11d13a31c961608c2cfe22a86a03 SHA1: 7bc076bd1995fa933ba1f4d693699b0c597b893a SHA256: 70bbf609eabaeaf790cfffbe6350b366dbb0f7a026dad1554d197a06fbe13b82 SHA512: c3d27534cfeac051bf09e40a81b4bea718a797ffc7fbe46d49954efe57003d09fd44ef3d4582b186be84281568fb644c036f7d6e772e3e9fda04559e5f18c6db Homepage: https://cran.r-project.org/package=mvnormalTest Description: CRAN Package 'mvnormalTest' (Powerful Tests for Multivariate Normality) A simple informative powerful test (mvnTest()) for multivariate normality proposed by Zhou and Shao (2014) , which combines kurtosis with Shapiro-Wilk test that is easy for biomedical researchers to understand and easy to implement in all dimensions. This package also contains some other multivariate normality tests including Fattorini's FA test (faTest()), Mardia's skewness and kurtosis test (mardia()), Henze-Zirkler's test (mhz()), Bowman and Shenton's test (msk()), Royston’s H test (msw()), and Villasenor-Alva and Gonzalez-Estrada's test (msw()). Empirical power calculation functions for these tests are also provided. In addition, this package includes some functions to generate several types of multivariate distributions mentioned in Zhou and Shao (2014). Package: r-cran-mvnormtest Architecture: all Version: 0.1-9-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mvnormtest_0.1-9-3-1.ca2404.1_all.deb Size: 21848 MD5sum: b09c8c063d9e1564c1987dd1ec01f6c0 SHA1: fb02dc872d01127e4303048aa104c03c74cca678 SHA256: 6f1fc9d746cd9ef4ce1ec765e7bb3b011f82671954877d6d56fd57dd4a06e467 SHA512: c807a4852774651f2307e2cf0cdba6205891e1de43d4c82af0b807ffd066410ee7e49561f81c426cb78d619ca30bc0fa1c0e754852d53cc1b36f574bb70c4e42 Homepage: https://cran.r-project.org/package=mvnormtest Description: CRAN Package 'mvnormtest' (Normality Test for Multivariate Variables) Generalization of Shapiro-Wilk test for multivariate variables. Package: r-cran-mvnpermute Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mvnpermute_1.0.1-1.ca2404.1_all.deb Size: 13128 MD5sum: 5265de005596ac0b019d55a3c9b9460d SHA1: 23d79398ce6fc14a746484302936658405758465 SHA256: e0f5de6a69d7b6cd19d6588a8d69659882645f47fb02e5353f20fff43f058b95 SHA512: e4a187d0f9d5c91cc1cede3a9acb5a78bd6a2f25521f0259405a4cca026ef7e323640213ebac449cda56baba9668ca223983c4a42332db425658a701ce4ace0a Homepage: https://cran.r-project.org/package=mvnpermute Description: CRAN Package 'mvnpermute' (Generate New Multivariate Normal Samples from Permutations) Given a vector of multivariate normal data, a matrix of covariates and the data covariance matrix, generate new multivariate normal samples that have the same covariance matrix based on permutations of the transformed data residuals. Package: r-cran-mvntest Architecture: all Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-mass Filename: pool/dists/noble/main/r-cran-mvntest_1.1-0-1.ca2404.1_all.deb Size: 192920 MD5sum: fb8440d14a492d2325cd75327ec6d8c7 SHA1: b7e2bcb96ac9818f6bf55f55f46b93b848e4483a SHA256: 706afaa99e807468a2c67566ec4df73d8569fb8771b932fc65bf5dc43aae3d43 SHA512: 5aad5dacb3c5d8322d88b7dc34000992855a4ef3a9f3b7b864cb93f61486a5b18e0532424f47f4f88e826a80278486d1299c6fd74ce92701240da764619bb4bc Homepage: https://cran.r-project.org/package=mvnTest Description: CRAN Package 'mvnTest' (Goodness of Fit Tests for Multivariate Normality) Routines for assessing multivariate normality. Implements three Wald's type chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests; Doornik-Hansen test, Royston test and Henze-Zirkler test. Package: r-cran-mvntestchar Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-knitr, r-cran-ggplot2 Suggests: r-cran-markdown Filename: pool/dists/noble/main/r-cran-mvntestchar_1.1.3-1.ca2404.1_all.deb Size: 534038 MD5sum: 41c8cd04f75b7431e934b4973480dafd SHA1: e782a5361cfec54c4504c869c814dad03ce678ab SHA256: 7821fc90ebcca0fe23fc7f251e07a1d7ebaaa3de841ff14b813c7e1a4fcb481e SHA512: d08b755f3db1d9951dd299680fa4847b500fbe5ece85c33c10d1b4fa501b85a58173f954fa2442565747004958243703fcd8e92e192544822aa029597a6ebad1 Homepage: https://cran.r-project.org/package=MVNtestchar Description: CRAN Package 'MVNtestchar' (Test for Multivariate Normal Distribution Based on aCharacterization) Provides a test of multivariate normality of an unknown sample that does not require estimation of the nuisance parameters, the mean and covariance matrix. Rather, a sequence of transformations removes these nuisance parameters and results in a set of sample matrices that are positive definite. These matrices are uniformly distributed on the space of positive definite matrices in the unit hyper-rectangle if and only if the original data is multivariate normal (Fairweather, 1973, Doctoral dissertation, University of Washington). The package performs a goodness of fit test of this hypothesis. In addition to the test, functions in the package give visualizations of the support region of positive definite matrices for bivariate samples. Package: r-cran-mvopr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ncvreg, r-cran-rrpack Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mvopr_2.0.0-1.ca2404.1_all.deb Size: 23928 MD5sum: 219bc69a3501d416223b17de39398478 SHA1: 4b6346791869848176ce67eeb69fac8e1510e0dc SHA256: 8fe08337c43fc27bf0cfb2ba5c91dada37df5e8e399ab5c7a15e43fd04ffea67 SHA512: 7981fcd59678ad4c3678067c6678732e394898acc6cf88d65870020bac6a1f9244900765765e96029648e08e03facb3027faa6f0e160707ef0b90bc9a75b57d5 Homepage: https://cran.r-project.org/package=MVOPR Description: CRAN Package 'MVOPR' (Multi-View Orthogonal Projection Regression for Multi-ModalityIntegration) Implements the 'MVOPR' (Multi-View Orthogonal Projection Regression) method for robust variable selection and integration of multi-modality data. Package: r-cran-mvout Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-mvout_1.2-1.ca2404.1_all.deb Size: 78590 MD5sum: b351078d4540635eb7a630ae8d5ca351 SHA1: 69c51244635b4dbb75f847b3aab950a7f82a9b6f SHA256: 1c4be42062d0298c87ac60d822ea65e46b8294af72c3e119e26aa9cdd45811ac SHA512: 7dc3064038c496520e1fb06182fdb01913a2e544a1afaaca2b276d0fb4eb05287758f5fe80178a8393db4246a3a46fd275685387a2ac00b139c5570789a5ad55 Homepage: https://cran.r-project.org/package=mvout Description: CRAN Package 'mvout' (Robust Multivariate Outlier Detection) Detection of multivariate outliers using robust estimates of location and scale. The Minimum Covariance Determinant (MCD) estimator is used to calculate robust estimates of the mean vector and covariance matrix. Outliers are determined based on robust Mahalanobis distances using either an unstructured covariance matrix, a principal components structured covariance matrix, or a factor analysis structured covariance matrix. Includes options for specifying the direction of interest for outlier detection for each variable. Package: r-cran-mvoutlier Architecture: all Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sgeostat, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-mvoutlier_2.1.4-1.ca2404.1_all.deb Size: 795476 MD5sum: a76a6a385b69fc79f1e8f894b4277864 SHA1: a39ad589d55f12f0216baa8a7a3de2bff785a3fb SHA256: eda85826ba24e203e5b79d047c540ee44666e155353bd5dfd32c4d3fdb1fab2d SHA512: b1d9ac09e3d737c4745877777406e59ceb3ebe39ac314ab6e58807697b39169f5ff0838fbf4e7779dccf7345e2bf2f692c86e8c47ff15ff3ef1afdcf83d0afc3 Homepage: https://cran.r-project.org/package=mvoutlier Description: CRAN Package 'mvoutlier' (Multivariate Outlier Detection Based on Robust Methods) Various methods for multivariate outlier detection: arw, a Mahalanobis-type method with an adaptive outlier cutoff value; locout, a method incorporating local neighborhood; pcout, a method for high-dimensional data; mvoutlier.CoDa, a method for compositional data. References are provided in the corresponding help files. Package: r-cran-mvpbt Architecture: all Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-metafor, r-cran-mada, r-cran-mvmeta Filename: pool/dists/noble/main/r-cran-mvpbt_1.2-1-1.ca2404.1_all.deb Size: 41610 MD5sum: 8d532a47aa7812347a6a711255eab8d9 SHA1: 65b778e4e0d953bfe88fdc2a256228784c75b138 SHA256: 2df28789f2e8edaf54b839bc416e9ce6454c2113a1769132d1b2a5ab53913519 SHA512: a1178a7a1672dc32638ecd2b75e583b963eae3b5d57d277e8b7fef2102010001c83f028a30b4386466249d416931fe80c426349749737ae7d57a164810e897e9 Homepage: https://cran.r-project.org/package=MVPBT Description: CRAN Package 'MVPBT' (Publication Bias Tests for Meta-Analysis of Diagnostic AccuracyTest) Generalized Egger tests for detecting publication bias in meta-analysis for diagnostic accuracy test (Noma (2020) , Noma (2022) ). These publication bias tests are generally more powerful compared with the conventional univariate publication bias tests and can incorporate correlation information between the outcome variables. 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A function for estimating the parameters from data to fit a distribution to data is also provided, using the method from Nolan (2013) . 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The motivation for these functions was to support students learning multivariate concepts and R - there may be other functions and packages better-suited to practical data analysis. For details about the ellipse methods see Johnson and Wichern (2007, ISBN:9780131877153). 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Package: r-cran-mwmapdata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12940 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mwmapdata_1.0.0-1.ca2404.1_all.deb Size: 10726072 MD5sum: f1ea493cf03deb7030c4cbef2cf31d44 SHA1: 3a6127e7b429703864d5bf47c6a9fc85d618a395 SHA256: bb49f120b3aa98f5cb44b66629a86e3107bcd1257a2167ad2810b5970319598a SHA512: 745c528f93ceab28fb30a3acc9f25ed56913b7a79f4acfeb73fc82df5c6ccc5c7fa92d83be326561a6f91bab3e4df479dd68ed6dfc4589e631bfdea90b83fadb Homepage: https://cran.r-project.org/package=mwmapdata Description: CRAN Package 'mwmapdata' (Spatial Boundary Data for Malawi Administrative Levels) Provides official spatial boundary datasets for Malawi at multiple administrative levels: country (level 0), regions (level 1), districts (level 2), and traditional authorities (level 3). 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Package: r-cran-mwright Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature Filename: pool/dists/noble/main/r-cran-mwright_0.3.2-1.ca2404.1_all.deb Size: 51012 MD5sum: f303d063557aaf1991958ee6b960f3f5 SHA1: 128fad80c35877ae1748728c06fbb48744ce5944 SHA256: 13101546e13fb3086f0f844a3f307ade90992604bbeed5593fc83a3527cb3275 SHA512: 27bc1dbf3cb26ed9d33d12ddb75b9d2cdfb07645340cb6243ff2867d40ce5c5b18ada80d2684af972b74ce0009031df02dc3205c78550034cf00423651706c35 Homepage: https://cran.r-project.org/package=MWright Description: CRAN Package 'MWright' (Mainardi-Wright Family of Distributions) Implements random number generation, plotting, and estimation algorithms for the two-parameter one-sided and two-sided M-Wright (Mainardi-Wright) family. The M-Wright distributions naturally generalize the widely used one-sided (Airy and half-normal or half-Gaussian) and symmetric (Airy and Gaussian or normal) models. These are widely studied in time-fractional differential equations. References: Cahoy and Minkabo (2017) ; Cahoy (2012) ; Cahoy (2012) ; Cahoy (2011); Mainardi, Mura, and Pagnini (2010) . Package: r-cran-mwshiny Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-visnetwork, r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-mwshiny_2.1.0-1.ca2404.1_all.deb Size: 219368 MD5sum: bde5e7b5272b1f4325fde39df63edef5 SHA1: f8710fd27c2059355120f9c6053d1b0d30bcc78b SHA256: 8d7bc8694726b44d4518005083dcc109061a11ccbbd7dde7d23e95411764c102 SHA512: a1ecc4434d87e15390b9fe981ae7ed5c45e3ca38da3aaded2919e8f3b6714cd2010f4447957c10df95ea240183d5122f0dd171cbdd302ee47e175ecebbbde8b6 Homepage: https://cran.r-project.org/package=mwshiny Description: CRAN Package 'mwshiny' ('Shiny' for Multiple Windows) A simple function, mwsApp(), that runs a 'shiny' app spanning multiple, connected windows. 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Package: r-cran-mx.api Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-mx.api_0.2.0-1.ca2404.1_all.deb Size: 87000 MD5sum: fd6bce0a2737338126dd2d4dea51aabf SHA1: e7d49b96fb15eee61d08e304468a1ba20125bc69 SHA256: ad57c89fd9da011c8dfbf5e921250964333f6d5e2b3076ac0a17f93fd70d0b6c SHA512: 3367e3aa0861bbcd6639e587f153a993fff24adc1ebfcfa6a89e95d4de44b4cf75b3c4d172ad311eaf679226b0048c56b10588453fc4765ab23d1a1d74d82ec8 Homepage: https://cran.r-project.org/package=mx.api Description: CRAN Package 'mx.api' (Minimal Matrix Client-Server API) A minimal-dependency client for the 'Matrix' Client-Server HTTP API , suitable for talking to a 'Synapse' or 'Conduit' homeserver. 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Package: r-cran-mxcc Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-chi, r-cran-shotgroups Filename: pool/dists/noble/main/r-cran-mxcc_0.0.5-1.ca2404.1_all.deb Size: 91066 MD5sum: 06babad179d7e19a881bcfee74e0c300 SHA1: 7fc78473bdeddf1d3733c346d3fa10b91340209f SHA256: c7579fc874684c51c5b3e87c762c57db81894886f40fba4483c70e23e47377d8 SHA512: a51e84eecaf521d1a35adafc9879b8fd12ffb1632e03d30456807a04b62a57a1d85ebcfeb8511b260c6cc66eb9ceb1f079fc6b859990a1aa9210d80c6570de63 Homepage: https://cran.r-project.org/package=mxcc Description: CRAN Package 'mxcc' (Maxwell Control Charts) Computes Control limits, coefficients of control limits, various performance metrics and depicts control charts for monitoring Maxwell-distributed quality characteristics. Package: r-cran-mxfda Architecture: all Version: 0.2.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3583 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-tidyr, r-cran-purrr, r-cran-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-refund, r-cran-reshape2, r-cran-mgcv, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-spatentropy, r-cran-simdesign Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-survival, r-cran-ggpubr, r-cran-spatialtime, r-cran-tibble, r-cran-broom, r-cran-refund.shiny, r-cran-seurat, r-cran-seuratobject Filename: pool/dists/noble/main/r-cran-mxfda_0.2.2-1-1.ca2404.1_all.deb Size: 3135898 MD5sum: 1fdb68c36a0797182aa6f8019aba6b6b SHA1: 41a477fa6514a31b8c1005319f379f4ed420e77c SHA256: 92b3c97d2b49db121e14288d9ba451d2b1193f3a8b639e8852981dbb094eb1e4 SHA512: ec449d772c2cb06aca46e85dbe64882227b707cf070eb9289c1372a0db054ca87e24ccf54bd484ab6dc3a7a8850cd87f37d43d2b69722fb3332457c001f1c931 Homepage: https://cran.r-project.org/package=mxfda Description: CRAN Package 'mxfda' (A Functional Data Analysis Package for Spatial Single Cell Data) Methods and tools for deriving spatial summary functions from single-cell imaging data and performing functional data analyses. Functions can be applied to other single-cell technologies such as spatial transcriptomics. Functional regression and functional principal component analysis methods are in the 'refund' package while calculation of the spatial summary functions are from the 'spatstat' package . Package: r-cran-mxkssd Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mxkssd_1.2-1.ca2404.1_all.deb Size: 24148 MD5sum: 3b13f92e1da0c230b5c7cfe533967514 SHA1: 41147d88708214c0af039b3d79aad6a88192303d SHA256: d0e8a92856822446f806df02812b3fa74154ff682235da4e74a69d9009bafd39 SHA512: 6b01b65605f4597c343cab48f05fc263188c923be6f36c08f490b51131a5f2d2532b7c32d4da984a5109fc6e98a45be450425d5ef775e65642c88c658ab77609 Homepage: https://cran.r-project.org/package=mxkssd Description: CRAN Package 'mxkssd' (Efficient Mixed-Level k-Circulant Supersaturated Designs) Generates efficient balanced mixed-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has EfNOD efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient mixed-level k-circulant design through a progress bar. The progress of 100 per cent means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs. For more details, please see Mandal, B.N., Gupta V. K. and Parsad, R. (2011). Construction of Efficient Mixed-Level k-Circulant Supersaturated Designs, Journal of Statistical Theory and Practice, 5:4, 627-648, . Package: r-cran-mxm Architecture: all Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mass, r-cran-ordinal, r-cran-nnet, r-cran-quantreg, r-cran-lme4, r-cran-foreach, r-cran-doparallel, r-cran-relations, r-cran-rfast, r-cran-visnetwork, r-cran-energy, r-cran-geepack, r-cran-knitr, r-cran-dplyr, r-cran-bigmemory, r-cran-coxme, r-cran-rfast2, r-cran-hmisc Suggests: r-cran-markdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-mxm_1.5.5-1.ca2404.1_all.deb Size: 3751098 MD5sum: cc1242e90b00cc4879a455442be50e2b SHA1: b02ccde965179176dbc62500088331c2851d2e70 SHA256: 8d3225eed8dbf381e700f741d0dc435dd9939237b1537c9cbf52c6c9bd735ba5 SHA512: 158fb9d23546432708b93d257d2c47e5531378e4ec2be0b1c1f08f242c5bd50d73e445d406342ef935be9ef3b8ba33959d7c9f223b5764b97ac9fd37f950c987 Homepage: https://cran.r-project.org/package=MXM Description: CRAN Package 'MXM' (Feature Selection (Including Multiple Solutions) and BayesianNetworks) Many feature selection methods for a wide range of response variables, including minimal, statistically-equivalent and equally-predictive feature subsets. Bayesian network algorithms and related functions are also included. The package name 'MXM' stands for "Mens eX Machina", meaning "Mind from the Machine" in Latin. References: a) Lagani, V. and Athineou, G. and Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017). Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets. Journal of Statistical Software, 80(7). . b) Tsagris, M., Lagani, V. and Tsamardinos, I. (2018). Feature selection for high-dimensional temporal data. BMC Bioinformatics, 19:17. . c) Tsagris, M., Borboudakis, G., Lagani, V. and Tsamardinos, I. (2018). Constraint-based causal discovery with mixed data. International Journal of Data Science and Analytics, 6(1): 19-30. . d) Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2018). Efficient feature selection on gene expression data: Which algorithm to use? BioRxiv. . e) Tsagris, M. (2019). Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation. Applied Artificial Intelligence, 33(2):101-123. . f) Tsagris, M. and Tsamardinos, I. (2019). Feature selection with the R package MXM. F1000Research 7: 1505. . g) Borboudakis, G. and Tsamardinos, I. (2019). Forward-Backward Selection with Early Dropping. Journal of Machine Learning Research 20: 1-39. h) The gamma-OMP algorithm for feature selection with application to gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214-1224. . Package: r-cran-mxmmod Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-openmx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-mxmmod_1.1.0-1.ca2404.1_all.deb Size: 238022 MD5sum: b313d9e46125c1dbc224934083b69af1 SHA1: de596c57e0cfee29c6523c9fadc791877a5b5117 SHA256: 1d3ab93a464b11e5a76d6730496760ca81371ef719e0bfe553015f8215460d39 SHA512: e394e69b4037d40d0aeb53dfe654d3d58aada9fc551c2bfa8350b0ac5a5b9e56569a23ae1cbcefbab13b2d99160a020abfee9cfec39916a39517ac13c714a704 Homepage: https://cran.r-project.org/package=mxmmod Description: CRAN Package 'mxmmod' (Measurement Model of Derivatives in 'OpenMx') Provides a convenient interface in 'OpenMx' for building Estabrook's (2015) Measurement Model of Derivatives (MMOD). 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Reference for methods listed here: Harris, C., Wrobel, J., & Vandekar, S. (2022). mxnorm: An R Package to Normalize Multiplexed Imaging Data. Journal of Open Source Software, 7(71), 4180, . 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Package: r-cran-mycaas Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph, r-cran-rlang, r-cran-rpref, r-cran-shiny Suggests: r-cran-pks, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-mycaas_0.0.1-1.ca2404.1_all.deb Size: 67732 MD5sum: 8e11ce967dcf776c5daf82899d4c6f12 SHA1: 38c920f14ec09ea3f491acc1924adce33d7338c3 SHA256: 7c172a3d704832b90c31078382f307dbf87bb64891296d1a3d37906135a63565 SHA512: 3ff90ef370d290a0a0681c02b113dbad244681363094608dd3981b4770eeca7683e885bc56ec47cb2a527847b753b0d198b70a44a255ef88b7c797dc0d4a8ad0 Homepage: https://cran.r-project.org/package=mycaas Description: CRAN Package 'mycaas' (My Computerized Adaptive Assessment) Implementation of adaptive assessment procedures based on Knowledge Space Theory (KST, Doignon & Falmagne, 1999 ) and Formal Psychological Assessment (FPA, Spoto, Stefanutti & Vidotto, 2010 ) frameworks. 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Package: r-cran-mychisq Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-mychisq_0.1.3-1.ca2404.1_all.deb Size: 16850 MD5sum: 8f700f14179b00b9a7a865c0af19c773 SHA1: a10168cfdcd2d2016c25659ec119eb7c00c788fa SHA256: 8298740dd3d2aef79726e9c4be16c2ac07540ea7152c688924316f6d04015f40 SHA512: 291db6b0d5662f9e105121b8ee306fe375cb20271a1fa7cd78df444b56b5b3584efb3775c4e40d747f39ab51e9882c9790a9b3b6d833b84737ae2ff8cac946c8 Homepage: https://cran.r-project.org/package=Mychisq Description: CRAN Package 'Mychisq' (Chi-Squared Test for Goodness of Fit and Independence Test) The chi-squared test for goodness of fit and independence test. Package: r-cran-myclim Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2487 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-lubridate, r-cran-tibble, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-ggplot2, r-cran-ggforce, r-cran-viridis, r-cran-data.table, r-cran-plotly, r-cran-zoo, r-cran-vroom, r-cran-progress Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-myclim_1.5.1-1.ca2404.1_all.deb Size: 1933572 MD5sum: 22db6b4a94a14ee0937298e6f676a855 SHA1: 4629a443aa92fc0578c8873331ec735cec4d5c83 SHA256: 8db67d1de61bfb4bbe24d225399ac8ef1d77ad580ec33f690dab8bf81451514d SHA512: 60c0a0505c8bbfdf9d105083628dffe1bd93c718e6f2d06c40e1d90e7a80b353980ab516b32f59f557e71750d3deb90fe938e79e4397bc89b8ebf58f0edc2d80 Homepage: https://cran.r-project.org/package=myClim Description: CRAN Package 'myClim' (Microclimatic Data Processing) Handling the microclimatic data in R. The 'myClim' workflow begins at the reading data primary from microclimatic dataloggers, but can be also reading of meteorological station data from files. Cleaning time step, time zone settings and metadata collecting is the next step of the work flow. With 'myClim' tools one can crop, join, downscale, and convert microclimatic data formats, sort them into localities, request descriptive characteristics and compute microclimatic variables. Handy plotting functions are provided with smart defaults. Package: r-cran-mycobacrvr Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-mycobacrvr_1.1-1.ca2404.1_all.deb Size: 92766 MD5sum: 984a349a3fc38bc29620bb6b728b158b SHA1: afbbd526284f54bde6cc834d10472a41652f293e SHA256: 792334bdb147c31124432e3e1b177e96cbb7f5f11dc9fc0c34a39021f03e11d3 SHA512: 3b259f0e5454f26deeb334151d1e1e517ce918c072a8e34782892d70f17bb7cc2d0810a83c8205ea1aa91f1b380e0ae31eb1f428338df3bbff66530c12cd890f Homepage: https://cran.r-project.org/package=mycobacrvR Description: CRAN Package 'mycobacrvR' (Integrative Immunoinformatics for Mycobacterial Diseases in RPlatform) The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. 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Functions exist for detection, removal, replacement, imputation, recollection, etc. of 'NAs'. 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This package provides the function for implementing the novel community detection algorithm known as Network-Adjusted Covariates for Community Detection (NAC), which is designed to detect latent community structure in graphs with node-level information, i.e., covariates. This algorithm can handle models such as the degree-corrected stochastic block model (DCSBM) with covariates. NAC specifically addresses the discrepancy between the community structure inferred from the adjacency information and the community structure inferred from the covariates information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2023) . In addition to NAC, this package includes several other existing community detection algorithms that are compared to NAC in the reference paper. These algorithms are Spectral Clustering On Ratios-of Eigenvectors (SCORE), network-based regularized spectral clustering (Net-based), covariate-based spectral clustering (Cov-based), covariate-assisted spectral clustering (CAclustering) and semidefinite programming (SDP). 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(2018) ). Each barcode is assigned a messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its target can be counted. As a result each count of a specific barcode represents the presence of its target mRNA/miRNA. 'NACHO' (NAnoString quality Control dasHbOard) is able to analyse the exported NanoString nCounter data and facilitates the user in performing a quality control. 'NACHO' does this by visualising quality control metrics, expression of control genes, principal components and sample specific size factors in an interactive web application. 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If we enforce the exogeneity condition of the IV, it is likely that we end up with a large set of IVs without knowing which ones are good. Also, one could face the model uncertainty for structural equation, as large micro dataset is commonly available nowadays. This package uses adaptive group lasso and B-spline methods to select the nonparametric components of the IV function, with the linear function being a special case (naivereg). The package also incorporates two stage least squares estimator (2SLS), generalized method of moment (GMM), generalized empirical likelihood (GEL) methods post instrument selection, logistic-regression instrumental variables estimator (LIVE, for dummy endogenous variable problem), double-selection plus instrumental variable estimator (DS-IV) and double selection plus logistic regression instrumental variable estimator (DS-LIVE), where the double selection methods are useful for high-dimensional structural equation models. 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It includes the covariate balancing propensity score proposed by Imai and Ratkovic (2014) , which uses covariate balancing conditions in propensity score estimation. The point estimate of the parameter of interest as well as coefficients for propensity score estimation and their uncertainty are produced using the M-estimation. The same functions can be used to estimate average outcomes in missing outcome cases. Package: r-cran-nb.mclust Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-nb.mclust_1.1.1-1.ca2404.1_all.deb Size: 38814 MD5sum: fa1e2761c839508e0b0f206d560a56c9 SHA1: 405301be1e53709735ba7a09021532caa549216d SHA256: a8cb0be265d8e001e2d083c0321bc83f71634fdb47a86b1b5d50c83e85408c56 SHA512: fd3abb712e71aab6a47f94bff042f1b4035b9e03723c5426fc1b8849234b47bf49fbcf0f7801061c8fb8df5bc75f6cd7f9eda296ba33576b3554d9bfccbf6d8d Homepage: https://cran.r-project.org/package=NB.MClust Description: CRAN Package 'NB.MClust' (Negative Binomial Model-Based Clustering) Model-based clustering of high-dimensional non-negative data that follow Generalized Negative Binomial distribution. All functions in this package applies to either continuous or integer data. 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Package: r-cran-nbapalettes Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-nbapalettes_0.1.0-1.ca2404.1_all.deb Size: 118522 MD5sum: 326bc475f552b9ed3937e1a7fa970560 SHA1: f975a143a60150f83bf076b698329f328b2c5e1e SHA256: 772bc3ebaf1041a0cd36b7450926e9f481159e3b5906c3bd3845e2dd4b366191 SHA512: 0ad3d75eb1fd5fc1f1545e77039e27feef42e31bcd40d082339d61629345bc33e834b42898ea3bf8f3eb8c7113900741c11faa3558d2f609a3333b6399ab3b47 Homepage: https://cran.r-project.org/package=nbapalettes Description: CRAN Package 'nbapalettes' (An NBA Jersey Palette Generator) Palettes generated from NBA jersey colorways. Package: r-cran-nbbdesigns Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nbbdesigns_1.1.0-1.ca2404.1_all.deb Size: 66198 MD5sum: 7ca62055478f0c0fad2d19f444795051 SHA1: cebeccbae1b33f341d8660cedcf2fa97dc39aba8 SHA256: 3f5351a626ffab9cb8d116d80232e6160caf54456f9cd87b128dd739dfadfe16 SHA512: ff49e02723e7eefed03de68da6db52b1522b0d5f092c233f8299946e4ffdf105b140e07fdedb019a08286d19470a69a1a2dbefe7d6cc6c88a78e636692e0901a Homepage: https://cran.r-project.org/package=NBBDesigns Description: CRAN Package 'NBBDesigns' (Neighbour Balanced Block Designs (NBBDesigns)) Neighbour-balanced designs ensure that no treatment is disadvantaged unfairly by its surroundings. The treatment allocation in these designs is such that every treatment appears equally often as a neighbour with every other treatment. Neighbour Balanced Designs are employed when there is a possibility of neighbour effects from treatments used in adjacent experimental units. In the literature, a vast number of such designs have been developed. This package generates some efficient neighbour balanced block designs which are balanced and partially variance balanced for estimating the contrast pertaining to direct and neighbour effects, as well as provides a function for analysing the data obtained from such trials (Azais, J.M., Bailey, R.A. and Monod, H. (1993). "A catalogue of efficient neighbour designs with border plots". Biometrics, 49, 1252-1261 ; Tomar, J. S., Jaggi, Seema and Varghese, Cini (2005). "On totally balanced block designs for competition effects"). This package contains functions named nbbd1(),nbbd2(),nbbd3(),pnbbd1() and pnbbd2() which generates neighbour balanced block designs within a specified range of number of treatment (v). It contains another function named anlys()for performing the analysis of data generated from such trials. 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Functions are provided to calculate the density function, the distribution function and the quantile function of the convolutions in question given said evaluation methods. Functions for generating random deviates from negative binomial convolutions and for directly calculating the mean, variance, skewness, and excess kurtosis of said convolutions are also provided. 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The package provides tools to create, clean, process, and filter datasets and associated metadata. These utilities are intended to simplify reproducible data-preparation for future research. 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Also provides functions for making tables that compare observed and theoretical statistics. Package: r-cran-nbdesign Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pweall, r-cran-mass Filename: pool/dists/noble/main/r-cran-nbdesign_2.0.0-1.ca2404.1_all.deb Size: 54722 MD5sum: 47788fd964cbb3249b7593024083028c SHA1: 99c74997e8357fdd5812d5e58f962a031d558c6c SHA256: 13e686a2e7b5bbbd183276ba8436fd7ffa4907a30e40547b89cdcde362b0f264 SHA512: 6acb8f8a1d255dc2b49388dcbd4941c77bec61aef0de354df6ee96157449a8858f282bc1ba4d36c6ba47d628a18c4e5552db454c62f8c78242ecf90fd7e744aa Homepage: https://cran.r-project.org/package=NBDesign Description: CRAN Package 'NBDesign' (Design and Monitoring of Clinical Trials with Negative BinomialEndpoint) Calculate various functions needed for design and monitoring clinical trials with negative binomial endpoint with variable follow-up. This version has a few changes compared to the previous version 1.0.0, including (1) correct a typo in Type 1 censoring, mtbnull=bnull and (2) restructure the code to account for shape parameter equal to zero, i.e. Poisson scenario. 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Package: r-cran-nblda Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-poiclaclu, r-bioc-sseq Filename: pool/dists/noble/main/r-cran-nblda_1.0.1-1.ca2404.1_all.deb Size: 353052 MD5sum: ed27675ae1393a171fff763d13f61048 SHA1: 49ed396023f7b9b4d6f59aa733a33d003d905942 SHA256: 0edfd60b4c46bb4df49b320a058e5059261f342406bd241feffa6148dc97e547 SHA512: 1d46a18a0519f4e44c328cd554b446c177a522a6ab9a55fcb8214a968fa2527e2ffed818d81fc3c19e92b0ca33a6589fd2674239b4614e8d0c6fa200f7999084 Homepage: https://cran.r-project.org/package=NBLDA Description: CRAN Package 'NBLDA' (Negative Binomial Linear Discriminant Analysis) We proposed a package for the classification task which uses Negative Binomial distribution within Linear Discriminant Analysis (NBLDA). It is an extension of the 'PoiClaClu' package to Negative Binomial distribution. The classification algorithms are based on the papers Dong et al. (2016, ISSN: 1471-2105) and Witten, DM (2011, ISSN: 1932-6157) for NBLDA and PLDA, respectively. Although PLDA is a sparse algorithm and can be used for variable selection, the algorithm proposed by Dong et al. is not sparse. Therefore, it uses all variables in the classifier. Here, we extend Dong et al.'s algorithm to the sparse case by shrinking overdispersion towards 0 (Yu et al., 2013, ISSN: 1367-4803) and offset parameter towards 1 (as proposed by Witten DM, 2011). We support only the classification task with this version. 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The 'negligible' package provides functions that are useful for conducting negligible effect testing (also called equivalence testing). For example, there are functions for evaluating the equivalence of means or the presence of a negligible association (correlation or regression). Beribisky, N., Mara, C., & Cribbie, R. A. (2020) . Beribisky, N., Davidson, H., Cribbie, R. A. (2019) . Shiskina, T., Farmus, L., & Cribbie, R. A. (2018) . Mara, C. & Cribbie, R. A. (2017) . Counsell, A. & Cribbie, R. A. (2015) . van Wieringen, K. & Cribbie, R. A. (2014) . Goertzen, J. R. & Cribbie, R. A. (2010) . Cribbie, R. A., Gruman, J. & Arpin-Cribbie, C. (2004) . Package: r-cran-neighboot Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdstreeboot, r-cran-igraph, r-cran-rds, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-neighboot_1.0.1-1.ca2404.1_all.deb Size: 476256 MD5sum: b7742dcd8f41adf47edca7c8fd11c74e SHA1: 0b50231c58027f28a47867cf10cde66a8d4c1dd3 SHA256: a09dbdb627abbb93c1e0f9540c6e473be47c8cb65c05446ee567183e0c104919 SHA512: dd7fd1f4f930788cb5f50a8b590f281fc1ff5cd96272772045e1e5746717b8ef70c62d9405525d035c1bfd1b734e5197e39751c5a12718f80585c49dbc8dfc2f Homepage: https://cran.r-project.org/package=Neighboot Description: CRAN Package 'Neighboot' (Neighborhood Bootstrap Method for RDS) A bootstrap method for Respondent-Driven Sampling (RDS) that relies on the underlying structure of the RDS network to estimate uncertainty. 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Outputs ranked neighbors. Most features of this package are directly based on the PMML specification for KNN. Package: r-cran-neldermead Architecture: all Version: 1.0-13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2950 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optimbase, r-cran-optimsimplex Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-neldermead_1.0-13-1.ca2404.1_all.deb Size: 1522436 MD5sum: c2bc089439d60528fc6b3c34efb73b79 SHA1: 22071c43f624c4f8c918386042e9531fa96c5005 SHA256: a887a2e000e73d15d0735535fc6ee5394d31b1753761309fb7a88fbb419081e3 SHA512: 0bf1e444e76ad85be7e5de8acfa97669fb34b1f66d8f9e7b077a0f38a7f62bb01bb5b3f610984c0dabfcc0f8d0a45f8a391a1eb2eed0b16aca1eea9cf5adbb1b Homepage: https://cran.r-project.org/package=neldermead Description: CRAN Package 'neldermead' (R Port of the 'Scilab' Neldermead Module) Provides several direct search optimization algorithms based on the simplex method. The provided algorithms are direct search algorithms, i.e. algorithms which do not use the derivative of the cost function. They are based on the update of a simplex. The following algorithms are available: the fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained optimization with a fixed shape simplex, 1962) , the variable shape simplex method of Nelder and Mead (unconstrained optimization with a variable shape simplex made, 1965) , and Box's complex method (constrained optimization with a variable shape simplex, 1965) . 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The development of this package is financially supported the Slovenian Research Agency (www.arrs.gov.si) within the research program P5<96>0168 and the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks). Package: r-cran-nemsqar Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4960 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-hms, r-cran-lifecycle, r-cran-lubridate, r-cran-rlang, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-nemsqar_1.2.0-1.ca2404.1_all.deb Size: 4798028 MD5sum: 5912147cec37b5741d8413965b0efd17 SHA1: 28c4fbc40bb32d99d3cc54e129dc414f7506f19e SHA256: 9209f5f3a804c6e58de9abc9c706d12f9c6e033bc8cca2b5586be0fcbf7f3702 SHA512: b6a20b8ffaa87b97638115dac72eafe9a532e58e5651bc51b16176758a70b019bc2f43bb177b34f3df396f29c961678eb0ccc4f309920fd27e92ddda17f813b6 Homepage: https://cran.r-project.org/package=nemsqar Description: CRAN Package 'nemsqar' (National Emergency Medical Service Quality Alliance MeasureCalculations) Designed to automate the calculation of Emergency Medical Service (EMS) quality metrics, 'nemsqar' implements measures defined by the National EMS Quality Alliance (NEMSQA). 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Package: r-cran-nemtr Architecture: all Version: 0.0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-tidyr, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nemtr_0.0.1.0-1.ca2404.1_all.deb Size: 19960 MD5sum: bfd3688b8ae2ae438ca03fd25f4310a1 SHA1: c29b1efa392ede3c4d7aac26f4f87f03b93d2411 SHA256: 16809c7bede55614e5d635d1ed90b44f62cf19da1e8c26e5b55f376b6eadf15f SHA512: 9ed2719d2808604866f54fec23ae768e27f2405eba5cd5ad0a4b295d028f9328ff51c0d49255b7032fa4a13920be57102b8f00be81c2f309db33452e390e224a Homepage: https://cran.r-project.org/package=nemtr Description: CRAN Package 'nemtr' (Nonparametric Extended Median Test - Cumulative Summation Method) Calculates a cumulative summation nonparametric extended median test based on the work of Brown & Schaffer (2020) . It then generates a control chart to assess processes and determine if any streams are out of control. Package: r-cran-neo2r Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite, r-cran-httr2 Filename: pool/dists/noble/main/r-cran-neo2r_3.0.0-1.ca2404.1_all.deb Size: 52766 MD5sum: f378f0495d4ae01085145e7e61db33b4 SHA1: fd3c170e435ac9e04a6f2f24805da5bdc4fc27b5 SHA256: 5977226515c73a630ebe49703bf8d9af4a1de73230dc7fca384a454623645e5f SHA512: 5bd5813e3fdcbdd2c5d38d4b3b0100d4f153c7983dbf8341724d69e77ca44977b1d2240d5e6de27d8fbe82a7dfd38676df20f0b42a16a8a1c2761a2ee8fad67b Homepage: https://cran.r-project.org/package=neo2R Description: CRAN Package 'neo2R' (Neo4j to R) The aim of neo2R is to provide simple and low level connectors for querying neo4j graph databases (). 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Other useful functions for the importing and management of data on the 'Neo4J' server and basic local server admin. Package: r-cran-neo4r Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-attempt, r-cran-data.table, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-rstudioapi, r-cran-shiny, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-neo4r_0.1.4-1.ca2404.1_all.deb Size: 349920 MD5sum: daba638fa1ee118e44dae89da9a48f4f SHA1: f662b779c777955f1d9f7bcc91534a353f892f30 SHA256: 6acbea37dfbec80ed66e9b6085fabca702395ce2654124a03a946a26242a82a1 SHA512: bb714c52bf5adc927647add943a4feda59e94f22b49660aa5f33fa832cbd83bd5884cc57e3b7e6eaaadbc3fe83a4d1373ff7d49147c52fbece9c46fe2756c062 Homepage: https://cran.r-project.org/package=neo4r Description: CRAN Package 'neo4r' (A 'Neo4J' Driver) A Modern and Flexible 'Neo4J' Driver, allowing you to query data on a 'Neo4J' server and handle the results in R. It's modern in the sense it provides a driver that can be easily integrated in a data analysis workflow, especially by providing an API working smoothly with other data analysis and graph packages. It's flexible in the way it returns the results, by trying to stay as close as possible to the way 'Neo4J' returns data. That way, you have the control over the way you will compute the results. At the same time, the result is not too complex, so that the "heavy lifting" of data wrangling is not left to the user. Package: r-cran-neodistr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5055 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shinythemes, r-cran-plotly, r-cran-brms, r-cran-rmpfr, r-cran-ggplot2, r-cran-shiny Suggests: r-cran-spelling, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bayesplot, r-cran-loo Filename: pool/dists/noble/main/r-cran-neodistr_0.1.2-1.ca2404.1_all.deb Size: 4575878 MD5sum: 29bb2f0fb6ca21b866d189f8a92126ac SHA1: 140b597b46c6fdf4cc673b7741d00ca8fbfb62e0 SHA256: db17be3795b343bffa8c1b0e0d086eb3f9453a003ca92683da434e2d28bb5bbf SHA512: c1dcdcf516023e2ac02189f9ef160dd89dad92870a7e08a86194a3ce062d9dbe0e20ae34e93c64e67c0f291fb13a25fbee9e2927ec661225a3f9d1ddc226fc7c Homepage: https://cran.r-project.org/package=neodistr Description: CRAN Package 'neodistr' (Neo-Normal Distribution) Calculating the density, cumulative distribution, quantile, and random number of neo-normal distribution. It also interfaces with the 'brms' package, allowing the use of the neo-normal distribution as a custom family. This integration enables the application of various 'brms' formulas for neo-normal regression. Modified to be Stable as Normal from Burr (MSNBurr), Modified to be Stable as Normal from Burr-IIa (MSNBurr-IIa), Generalized of MSNBurr (GMSNBurr), Jones-Faddy Skew-t, Fernandez-Osiewalski-Steel Skew Exponential Power, and Jones Skew Exponential Power distributions are supported. References: Choir, A. S. (2020).Unpublished Dissertation, Iriawan, N. (2000).Unpublished Dissertation, Rigby, R. A., Stasinopoulos, M. D., Heller, G. Z., & Bastiani, F. D. (2019) . Package: r-cran-neoniso Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4679 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-zoo, r-cran-httr, r-cran-lubridate, r-cran-neonutilities, r-cran-magrittr, r-cran-r.utils, r-cran-tidyselect, r-cran-data.table, r-cran-rlang, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-hdf5r, r-bioc-rhdf5, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lifecycle, r-cran-fasttime Filename: pool/dists/noble/main/r-cran-neoniso_0.8.0-1.ca2404.1_all.deb Size: 2662036 MD5sum: 5569d5cf5fad17a95dfbfeb36b8da449 SHA1: e473dd525de1f0d1f9e21b469ea4c62c3bc52283 SHA256: dfdd6c0a130ad6684b36613650dcb19a7737de4828ac947556a7e2bd2c5c6a77 SHA512: f98062a08bc6db51635b2122696b451b014538aacfe6a65ff60353de9e23cdb8e1150550cff303485e968e4295913a50f6c7205bf2187ebe0242565e3be019a7 Homepage: https://cran.r-project.org/package=NEONiso Description: CRAN Package 'NEONiso' (Tools to Calibrate and Work with NEON Atmospheric Isotope Data) Functions for downloading, calibrating, and analyzing atmospheric isotope data bundled into the eddy covariance data products of the National Ecological Observatory Network (NEON) . 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Package: r-cran-neonos Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-curl, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-neonutilities Filename: pool/dists/noble/main/r-cran-neonos_1.1.0-1.ca2404.1_all.deb Size: 132834 MD5sum: 2824938c9190b0f5bd61e529a3979e5e SHA1: acdf5f2c8d397d5ed3d599e9b28ac84320cc3b3c SHA256: 32ed3711c6d2483f11088282e6fda848ed79b225c41745baa82407e20f9d5c49 SHA512: 094b2df7b6d61cb6977f6f901be9d46f6e8e7f30d59bea16a0d268042c0c4360b352ce730c91a08753e180d5b6bd5a6f226d7ff2cc0fbeea04f43bba02fe170e Homepage: https://cran.r-project.org/package=neonOS Description: CRAN Package 'neonOS' (Basic Data Wrangling for NEON Observational Data) NEON observational data are provided via the NEON Data Portal and NEON API, and can be downloaded and reformatted by the 'neonUtilities' package. NEON observational data (human-observed measurements, and analyses derived from human-collected samples, such as tree diameters and algal chemistry) are published in a format consisting of one or more tabular data files. This package provides tools for performing common operations on NEON observational data, including checking for duplicates and joining tables. Package: r-cran-neonplantecology Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4393 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-neonutilities, r-cran-vegan, r-cran-ggplot2, r-cran-data.table, r-cran-dtplyr, r-cran-dplyr, r-cran-lubridate, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-ggpubr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-neonplantecology_1.6.1-1.ca2404.1_all.deb Size: 4331722 MD5sum: fea8ca928307f43d4fe01f5233dd958f SHA1: 56751dd47a99aef04112319fcfbe217748acd27c SHA256: 89a84b68d57a0f50d543aedb100bfdbcf43a46de6a1094f55d33264dde389ef9 SHA512: f043f3d0481a6bc36439489721f621c645900d76be9c01a4719c1c1dc263757ae469892679db1ec9e24ce647cc82f7d2e922d09eeb004cfc28bccb2c60bf886f Homepage: https://cran.r-project.org/package=neonPlantEcology Description: CRAN Package 'neonPlantEcology' (Process NEON Plant Data for Ecological Analysis) Downloading and organizing plant presence and percent cover data from the National Ecological Observatory Network . 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This package provides a high-level user interface for downloading and storing NEON data products. Unlike 'neonUtilities', this package will avoid repeated downloading, provides persistent storage, and improves performance. 'neonstore' can also construct a local 'duckdb' database of stacked tables, making it possible to work with tables that are far to big to fit into memory. 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Data delivered from the Data Portal are provided as monthly zip files packaged within a parent zip file, while individual files can be accessed from the API. This package provides tools that aid in discovering, downloading, and reformatting data prior to use in analyses. This includes downloading data via the API, merging data tables by type, and converting formats. For more information, see the readme file at . 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Package: r-cran-nepic Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-paireddata Filename: pool/dists/noble/main/r-cran-nepic_1.0.1-1.ca2404.1_all.deb Size: 47212 MD5sum: cf113124d8a3eab98a523398fbbd71ee SHA1: 24f2beef5a90b797233cfbca3253cb180dec3635 SHA256: 53e754c6c9fdfc66417ba2099c9489ede2f4cf3f0916a303309ccef40abd0309 SHA512: 1d7bb1a866687524ee02af9ef3b0b4d3dc262a5d4ad9f09b26c37b30d33b4572a5905dd8ca5c3765dfece67b2874f32a67c9ac93f8829a7001d1c862d210c2dc Homepage: https://cran.r-project.org/package=NEpiC Description: CRAN Package 'NEpiC' (Network Assisted Algorithm for Epigenetic Studies Using Mean andVariance Combined Signals) Package for a Network assisted algorithm for Epigenetic studies using mean and variance Combined signals: NEpiC. NEpiC combines both signals in mean and variance differences in methylation level between case and control groups searching for differentially methylated sub-networks (modules) using the protein-protein interaction network. Package: r-cran-neptune Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-this.path, r-cran-rstudioapi, r-cran-ggplot2, r-cran-plotly, r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-neptune_0.2.3-1.ca2404.1_all.deb Size: 113908 MD5sum: 9d07d72c961d745140be8efa2a9909c1 SHA1: 1eafcfbbd3aff2a13d51cab69b17911049c6b5a9 SHA256: 159efb2dc7d8453d578eddca468ede6a834a9c531b1a34be392edfb208e90ffe SHA512: 2990f5704f36b3b03c976c85cfadd096c72f963b3b20a543d02ed5f100f7929068067a785510df844274199ba021b1d1cfd081205f6a684ce9d5d3f2520bf12c Homepage: https://cran.r-project.org/package=neptune Description: CRAN Package 'neptune' (MLOps Metadata Store - Experiment Tracking and Model Registryfor Production Teams) An interface to Neptune. A metadata store for MLOps, built for teams that run a lot of experiments. It gives you a single place to log, store, display, organize, compare, and query all your model-building metadata. Neptune is used for: • Experiment tracking: Log, display, organize, and compare ML experiments in a single place. • Model registry: Version, store, manage, and query trained models, and model building metadata. • Monitoring ML runs live: Record and monitor model training, evaluation, or production runs live For more information see . Package: r-cran-nesrdata Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rappdirs, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-dataone Filename: pool/dists/noble/main/r-cran-nesrdata_0.3.1-1.ca2404.1_all.deb Size: 166954 MD5sum: 0d448767d6754ac9beacde5cce2966df SHA1: 6341bdc83d9bdd77a71b3fd9b5432ad14652f59f SHA256: 542373c8569c76acb88491f34f5dceb03e2f6b41e86ea7ed1a8b5b9ca1b43a70 SHA512: f405cecfa27a7cdd29b6bb94dbe8a0a7d97d4502a4af1cc5ee198fffe6a676b44657f6c24d4c6e7d28edec00bf51fd521d6e4a535bc5627307b8864a28b515f2 Homepage: https://cran.r-project.org/package=nesRdata Description: CRAN Package 'nesRdata' (National Eutrophication Survey Data) Serves data from the United States Environmental Protection Agency (USEPA) National Eutrophication Survey . Package: r-cran-nestable Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools Suggests: r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nestable_0.1.0-1.ca2404.1_all.deb Size: 58168 MD5sum: 5334b6639afe5a3950a9c563131f4bca SHA1: d91e34478f8804a363385e145939b8f3b835123a SHA256: 3b8a263a9fff3a9d9df987e6f768c2ccf61d24db1ca1781f4e90f925feb6e45a SHA512: 66803eb0c4e45d56675dd01b254430c0a97691aeb221cddbd0935b01b0d7163755587ddc892d7edc1f4b256108359520c0ec892e1fe7f74cb58e7bc1fb395dee Homepage: https://cran.r-project.org/package=nestable Description: CRAN Package 'nestable' (Collapsible 'HTML' Tables from Hierarchical Data) Creates collapsible, expandable 'HTML' tables from hierarchical data. Supports data frame input with multi-level grouping, custom column formatters, bottom-up rollup aggregation, and CSS-variable-based theming. Works in 'Shiny' applications, R Markdown, 'Quarto', and the 'RStudio' Viewer. Package: r-cran-nestage Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1493 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-expm, r-cran-gt, r-cran-knitr, r-cran-popbio, r-cran-popdemo, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nestage_0.8.0-1.ca2404.1_all.deb Size: 636752 MD5sum: 2acca72350b57218480405c646ca2e66 SHA1: cd72a43924546b48669854ea7315f397f32dc36d SHA256: 51a41bb82af36f5bc1cb42fbae94e060180e6cf07ca0b637ea4e0d47c529dd13 SHA512: e2a5ba4f9fa143ad50cd0cd374c4e857500bd9488bf8b1cb236324c88e8fd1c6641bb8866db32273fef6fbe6b3a44c5cbc030c1c995b481268dce57564ca0d50 Homepage: https://cran.r-project.org/package=NeStage Description: CRAN Package 'NeStage' (Effective Population Size from Stage-Structured Populations) Computes effective population size (Ne) and the Ne/N ratio for stage-structured populations using the matrix population model framework of Yonezawa (2000) . Functions are provided for sexually reproducing, clonally reproducing, and mixed (sexual + clonal) populations. Includes sensitivity and elasticity analyses for Ne/N with respect to vital rates. Package: r-cran-nestcolor Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1190 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-ggplot2, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nestcolor_0.1.3-1.ca2404.1_all.deb Size: 403436 MD5sum: 0531f7373b19b83c46d37b9693d2359d SHA1: 0f3ba6ef236bece15808cb1df3c813397de8996c SHA256: dad782d6e05fc9ece72c2e31b4f3d44f22c72da300cc9227c71b9251485e553c SHA512: d208f595623b5cdbbf3abef0a5dee6b87bd0223b86587daeff8010409748acd57e3df8d65f67b605861cce7848bb141656f7f4b79d81c2378c0fd5fe17413ed3 Homepage: https://cran.r-project.org/package=nestcolor Description: CRAN Package 'nestcolor' (Colors for NEST Graphs) Clinical reporting figures require to use consistent colors and configurations. As a part of the Roche open-source clinical reporting project, namely the NEST project, the 'nestcolor' package specifies the color code and default theme with specifying 'ggplot2' theme parameters. Users can easily customize color and theme settings before using the reset of NEST packages to ensure consistent settings in both static and interactive output at the downstream. Package: r-cran-nestedcv Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4773 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-future.apply, r-cran-ggplot2, r-cran-glmnet, r-cran-matrixstats, r-cran-matrixtests, r-cran-proc, r-cran-rfast, r-cran-rhpcblasctl, r-cran-rlang, r-cran-rocr Suggests: r-cran-boruta, r-cran-corelearn, r-cran-fastshap, r-cran-gbm, r-cran-ggbeeswarm, r-cran-ggpubr, r-cran-hsstan, r-cran-mda, r-cran-mlbench, r-cran-pbapply, r-cran-pls, r-cran-randomforest, r-cran-ranger, r-cran-rcppeigen, r-cran-rmarkdown, r-cran-knitr, r-cran-superlearner Filename: pool/dists/noble/main/r-cran-nestedcv_0.8.2-1.ca2404.1_all.deb Size: 2106304 MD5sum: b4e7473abe64c6cdf66b2bb821509589 SHA1: cc9c5fb65f8e3c0f325eea51fcd17ec89a2ae176 SHA256: 411142c445b7644b8e06467657b79bc64cbba610c2f29ae6d9d728c0004d02e9 SHA512: 437d420166d4a0d1b0985118cd97314cd82d711729339c34b117cc1e12a5611eacfbfe9dcb7ecbbaf27dc33a58f2462199421c289f7fdff4f8445a112afe376d Homepage: https://cran.r-project.org/package=nestedcv Description: CRAN Package 'nestedcv' (Nested Cross-Validation with 'glmnet' and 'caret') Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package . Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) . Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) . Package: r-cran-nestedlogit Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1349 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-car, r-cran-dplyr, r-cran-effects, r-cran-stringr, r-cran-tibble, r-cran-scales Suggests: r-cran-aer, r-cran-cardata, r-cran-geomtextpath, r-cran-ggplot2, r-cran-ggeffects, r-cran-here, r-cran-insight, r-cran-lobstr, r-cran-knitr, r-cran-nnet, r-cran-parameters, r-cran-performance, r-cran-rmarkdown, r-cran-see, r-cran-spelling, r-cran-testthat, r-cran-tidyr, r-cran-mass, r-cran-vgam, r-cran-mlogit, r-cran-vcd Filename: pool/dists/noble/main/r-cran-nestedlogit_0.4.0-1.ca2404.1_all.deb Size: 719272 MD5sum: 5f53e57c087b3f7cd9458baaca9ad633 SHA1: 532d1c7ef7b10cf29848ba98882ffd56b9fb94d4 SHA256: d5cd6557af3a59f896845ed1d8484cb31818aca37c69a23d53156c7cad478d3c SHA512: d68aac8c18c62eba56746c17d3b14da52b38bd0aad2f3b2ffb84046acdffd8ef86374691cb6eba37fd88b9644b1ce1523673c2b7c9b3a5beb8284b343a90d380 Homepage: https://cran.r-project.org/package=nestedLogit Description: CRAN Package 'nestedLogit' (Nested Dichotomy Logistic Regression Models) Provides functions for specifying and fitting nested dichotomy logistic regression models for a multi-category response and methods for summarising and plotting those models. Nested dichotomies are statistically independent, and hence provide an additive decomposition of tests for the overall 'polytomous' response. When the dichotomies make sense substantively, this method can be a simpler alternative to the standard 'multinomial' logistic model which compares response categories to a reference level. See: J. Fox (2016), "Applied Regression Analysis and Generalized Linear Models", 3rd Ed., ISBN 1452205663. Package: r-cran-nestedmenu Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fontawesome, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jquerylib, r-cran-shiny Filename: pool/dists/noble/main/r-cran-nestedmenu_0.2.0-1.ca2404.1_all.deb Size: 61886 MD5sum: 3dce6a46664c2dc5605e972d96465ce1 SHA1: 964897f62d32dcf674f89f160e95590efb07bbf0 SHA256: 0f9f06e57e2547e3f64d543fd729bc16cf22c71822d52bc8f87cc98a52334c46 SHA512: e943439857f1fd87bb80e18be94f156262943f8da056ac407bcd2af795591ead9379c4308afd3c0e92a9e540e6f48752a300d7a582960e40a2214ec2ae665d82 Homepage: https://cran.r-project.org/package=NestedMenu Description: CRAN Package 'NestedMenu' (A Nested Menu Widget for 'Shiny' Applications) Provides a nested menu widget for usage in 'Shiny' applications. This is useful for hierarchical choices (e.g. continent, country, city). Package: r-cran-nestedmodels Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-foreach, r-cran-generics, r-cran-ggplot2, r-cran-lifecycle, r-cran-magrittr, r-cran-parsnip, r-cran-purrr, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-broom, r-cran-covr, r-cran-doparallel, r-cran-ggrepel, r-cran-glmnet, r-cran-glue, r-cran-hardhat, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-tidyselect, r-cran-tune, r-cran-vdiffr, r-cran-withr, r-cran-workflows Filename: pool/dists/noble/main/r-cran-nestedmodels_1.1.0-1.ca2404.1_all.deb Size: 207376 MD5sum: 52250e9a4356ba64e4cb22622c02f27a SHA1: 87bfacfcdb3fb718e03ffede8fc2bdd7c0200f14 SHA256: 719a7dda17d89544d86e12eeb39c7f078ee866afeb5057c35ed81996ec45d00d SHA512: dfe41dac056ea2122b06adaf7e22a2026acbb71daa93c2b96d44cfd4e27ca79a1bca2550d498fbe404c495d5b5c0a7826222da0dc74b6d62c8f5740a713ebafa Homepage: https://cran.r-project.org/package=nestedmodels Description: CRAN Package 'nestedmodels' (Tidy Modelling for Nested Data) A modelling framework for nested data using the 'tidymodels' ecosystem. Specify how to nest data using the 'recipes' package, create testing and training splits using 'rsample', and fit models to this data using the 'parsnip' and 'workflows' packages. Allows any model to be fit to nested data. Package: r-cran-nestedpp Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-xtable Filename: pool/dists/noble/main/r-cran-nestedpp_0.2.0-1.ca2404.1_all.deb Size: 39996 MD5sum: 725aa7f53a9e6d75fce70ace97557901 SHA1: 8a06dbe37ae22ca4bdaaa394003b298f185af79e SHA256: 15ac0e08b48b7bc8662ffe726859813d7a00f67296aeb0f033cdb6bdead627d1 SHA512: f3e64265d740f5c42c326c6ef0cce8d25f2590cafc8d652521983908d1c9bc41d361c3a8dda01e39cfdeccfcbd7eabce18704d27f70a166271e222d1b9eb9954 Homepage: https://cran.r-project.org/package=nestedpp Description: CRAN Package 'nestedpp' (Performance Profiles and Nested Performance Profiles) Library to plot performance profiles (Dolan and More (2002) ) and nested performance profiles (Hekmati and Mirhajianmoghadam (2019) ) for a given data frame. Package: r-cran-nestfs Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dgof, r-cran-proc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nestfs_1.0.3-1.ca2404.1_all.deb Size: 249332 MD5sum: 0091c02be5a6eeacf5ff785bd9c26096 SHA1: c73047fd9c857e721fea329415c436b5be49a416 SHA256: 23c66a4359b36cb0b5f44519cf806d793efd3c0562a2a6c7d64363150b916950 SHA512: 0c4a00f3029a9666a8b0e9626f03e2eedbdb28e7ba1f424b6a97a7e29c09fe68706b4f71a36a8d61bdfd15005616f46f57349214e96343aaf4f00bf2e2f674d1 Homepage: https://cran.r-project.org/package=nestfs Description: CRAN Package 'nestfs' (Cross-Validated (Nested) Forward Selection) Implementation of forward selection based on cross-validated linear and logistic regression. Package: r-cran-nestimate Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-glasso, r-cran-data.table, r-cran-cluster, r-cran-scales Suggests: r-cran-testthat, r-cran-markovchain, r-cran-tna, r-cran-cograph, r-cran-igraph, r-cran-glmnet, r-cran-lavaan, r-cran-stringdist, r-cran-nnet, r-cran-isingfit, r-cran-bootnet, r-cran-gimme, r-cran-qgraph, r-cran-reticulate, r-cran-gridextra, r-cran-lme4, r-cran-corpcor, r-cran-mlvar, r-cran-mgm, r-cran-matrix, r-cran-networkcomparisontest, r-cran-arules, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-nestimate_0.4.3-1.ca2404.1_all.deb Size: 2418892 MD5sum: 6862db78327cb59365d68773c763b2e3 SHA1: 97832f023f30c03faddc606115e72cfc00b8f0f9 SHA256: 11561508434c024e972e127222baac1ea4c5f23074ca61f1d29f3eb24597f688 SHA512: 0194d5847dd8b996b614f7a1afcae1326e611debbb47fd44459263455dc7561e7609d7ce5dab03ef7e9f65e34d9ed757d8f7e185748fa3d9559ac814893a0d3c Homepage: https://cran.r-project.org/package=Nestimate Description: CRAN Package 'Nestimate' (Network Estimation, Bootstrap, and Higher-Order Analysis) Estimate, compare, and analyze dynamic and psychological networks using a unified interface. Provides transition network analysis estimation (transition, frequency, co-occurrence, attention-weighted) Saqr et al. (2025) , psychological network methods (correlation, partial correlation, 'graphical lasso', 'Ising') Saqr, Beck, and Lopez-Pernas (2024) , and higher-order network methods including higher-order networks, higher-order network embedding, hyper-path anomaly, and multi-order generative model. Supports bootstrap inference, permutation testing, split-half reliability, centrality stability analysis, mixed Markov models, multi-cluster multi-layer networks and clustering. Package: r-cran-nestr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-vctrs, r-cran-tidyselect Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nestr_0.1.2-1.ca2404.1_all.deb Size: 31812 MD5sum: 35e7a50e041c7a1ca1ec0073efcaafb4 SHA1: 88d778171f7c26d19291dd64640d62c2583730f1 SHA256: dc657850de7e76d4f3af4c52721a660b10682fae0075edc5e9497f3728d29a89 SHA512: a5506d3f5ee78ec4642fa488856310f5b3b16b0c03e8db5db009397bfb3f4f472a82a8bc9cb5497d0306eb2728fea018ae634b10f5f78f877c89135bcb89e341 Homepage: https://cran.r-project.org/package=nestr Description: CRAN Package 'nestr' (Build Nesting or Hierarchical Structures) Facilitates building a nesting or hierarchical structure as a list or data frame by using a human friendly syntax. Package: r-cran-net4pg Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1091 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-bioc-graph, r-cran-magrittr, r-cran-matrix Suggests: r-bioc-biocstyle, r-cran-ggplot2, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-net4pg_0.1.2-1.ca2404.1_all.deb Size: 397384 MD5sum: bd5916226254d5739b5f9d2ee467854a SHA1: 1eebb74c791dc22c924eed75f1ffde0157abfd12 SHA256: 180eb26c350133f952e7c528e00be66811497542e1f34efa639f160b990554dd SHA512: 8d6a46a0994464b4a4832f3676f37c7a637bbb35df783efaf388f97bbf6eec0e3891555fccce49a1d4caaf4147c0c3d2393e8c52febb5264436f0e8857a800de Homepage: https://cran.r-project.org/package=net4pg Description: CRAN Package 'net4pg' (Handle Ambiguity of Protein Identifications from ShotgunProteomics) In shotgun proteomics, shared peptides (i.e., peptides that might originate from different proteins sharing homology, from different proteoforms due to alternative mRNA splicing, post-translational modifications, proteolytic cleavages, and/or allelic variants) represent a major source of ambiguity in protein identifications. The 'net4pg' package allows to assess and handle ambiguity of protein identifications. It implements methods for two main applications. First, it allows to represent and quantify ambiguity of protein identifications by means of graph connected components (CCs). In graph theory, CCs are defined as the largest subgraphs in which any two vertices are connected to each other by a path and not connected to any other of the vertices in the supergraph. Here, proteins sharing one or more peptides are thus gathered in the same CC (multi-protein CC), while unambiguous protein identifications constitute CCs with a single protein vertex (single-protein CCs). Therefore, the proportion of single-protein CCs and the size of multi-protein CCs can be used to measure the level of ambiguity of protein identifications. The package implements a strategy to efficiently calculate graph connected components on large datasets and allows to visually inspect them. Secondly, the 'net4pg' package allows to exploit the increasing availability of matched transcriptomic and proteomic datasets to reduce ambiguity of protein identifications. More precisely, it implement a transcriptome-based filtering strategy fundamentally consisting in the removal of those proteins whose corresponding transcript is not expressed in the sample-matched transcriptome. The underlying assumption is that, according to the central dogma of biology, there can be no proteins without the corresponding transcript. Most importantly, the package allows to visually inspect the effect of the filtering on protein identifications and quantify ambiguity before and after filtering by means of graph connected components. As such, it constitutes a reproducible and transparent method to exploit transcriptome information to enhance protein identifications. All methods implemented in the 'net4pg' package are fully described in Fancello and Burger (2022) . 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For example, it may be used to detect genes that co-occur across genomes. 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The full methodological details can be found in our publication by Ahn S and Oh EJ (2026) . 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The 'netgwas' package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is considerably larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The 'netgwas' implements the methodological developments in Behrouzi and Wit (2017) and Behrouzi and Wit (2017) . 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'NetLogoR' follows the same framework as the 'NetLogo' software (Wilensky (1999) ) and is a translation in R of the structure and functions of 'NetLogo'. 'NetLogoR' provides new R classes to define model agents and functions to implement spatially explicit agent-based models in the R environment. This package allows benefiting of the fast and easy coding phase from the highly developed 'NetLogo' framework, coupled with the versatility, power and massive resources of the R software. Examples of two models from the NetLogo software repository (Ants ) and Wolf-Sheep-Predation (), and a third, Butterfly, from Railsback and Grimm (2012) , all written using 'NetLogoR' are available. The 'NetLogo' code of the original version of these models is provided alongside. A programming guide inspired from the 'NetLogo' Programming Guide () and a dictionary of 'NetLogo' primitives () equivalences are also available. NOTE: To increment 'time', these functions can use a for loop or can be integrated with a discrete event simulator, such as 'SpaDES' (). 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Fits into 'ggplot2' grammar. 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URL: . BugReports: . Robins, Garry, Phillipa Pattison, and Jodie Woolcock (2005) . Snijders, Tom A. B., and Christian E. G. Steglich (2015) . Imai, Kosuke, Luke Keele, and Dustin Tingley (2010) . Duxbury, Scott (2023) . Duxbury, Scott (2024) . 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Implements functions for network centrality, cohesive subgroups, structural holes, similarity measures, path distances, signed networks, and random network generation. Supports ego-centric and whole-network analyses, including dyadic and triadic census, structural balance, and bipartite projections. Key references: Bonacich (1972) , Breiger (1974) , Kivelä et al. (2014) , Espinosa-Rada et al. (2024) . 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(2015) , Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) ; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) ; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) , or penalised logistic regression (Evrenoglou et al., 2022) ; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) ; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) ; - split direct and indirect evidence to check consistency (Dias et al., 2010) , (Efthimiou et al., 2019) ; - league table with network meta-analysis results; - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) ; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) ; - measures characterizing the flow of evidence between two treatments by König et al. (2013) ; - automated drawing of network graphs described in Rücker & Schwarzer (2016) ; - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) ; (Rücker & Schwarzer, 2017) ; - contribution matrix as described in Papakonstantinou et al. (2018) and Davies et al. (2022) ; - network meta-regression with a single continuous or binary covariate; - subgroup network meta-analysis. 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It builds a pathway model by exploring a pool of domain knowledge guided candidate statistical relationships between each of the variable pairs, selecting the 'best fit' on the basis of a specific criteria such as adjusted r-squared value. This material is based upon work supported by the U.S. National Science Foundation Award EEC-2052776 and EEC-2052662 for the MDS-Rely IUCRC Center, under the NSF Solicitation: NSF 20-570 Industry-University Cooperative Research Centers Program [1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013) . Package: r-cran-netshiny Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shinybs, r-cran-shiny, r-cran-shinydashboard, r-cran-colourpicker, r-cran-dt, r-cran-future, r-cran-future.callr, r-cran-ggplot2, r-cran-ggvenndiagram, r-cran-igraph, r-cran-ipc, r-cran-magrittr, r-cran-matrix, r-cran-netgwas, r-cran-plotly, r-cran-promises, r-cran-readxl, r-cran-shinycssloaders, r-cran-shinyjs, r-cran-shinyscreenshot, r-cran-shinywidgets, r-cran-visnetwork Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-netshiny_1.0-1.ca2404.1_all.deb Size: 201554 MD5sum: 8865fbfeafa0407000b2490655ebc7af SHA1: 89e1b7c9cb44b52670c0f123b2bcd7ce2901d163 SHA256: 1c925037d6d9462c886b22c1f27abda5db9b85bd35f460d26095a60b28a33319 SHA512: f28ad7acf05f4fae68b5355e52497894047d9d831255e88d0d1e7284e71ef1b9f2c28d4b8b754ab0102f5ed55f9dc0500af4c5460f6655139777c32b8cedd575 Homepage: https://cran.r-project.org/package=netShiny Description: CRAN Package 'netShiny' (Tool for Comparison and Visualization of Multiple Networks) We developed a comprehensive tool that helps with visualization and analysis of networks with the same variables across multiple factor levels. The 'netShiny' contains most of the popular network features such as centrality measures, modularity, and other summary statistics (e.g. clustering coefficient). It also contains known tools to look at the (dis)similarities between two networks, such as pairwise distance measures between networks, set operations on the nodes of the networks, distribution of the weights of the edges and a network representing the difference between two correlation matrices. The package 'netShiny' also contains tools to perform bootstrapping and find clusters in networks. See the 'netShiny' manual for more information, documentation and examples. Package: r-cran-netsimr Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 821 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmarkdown, r-cran-shiny, r-cran-shinybusy, r-cran-future.apply, r-cran-scales, r-cran-future, r-cran-dbi, r-cran-rmysql, r-cran-rodbc, r-cran-rpostgresql, r-cran-rsqlite, r-cran-plotly, r-cran-shinyjs, r-cran-mass, r-cran-fitdistrplus, r-cran-shinywidgets, r-cran-pareto Suggests: r-cran-knitr, r-cran-crch, r-cran-testthat Filename: pool/dists/noble/main/r-cran-netsimr_0.1.5-1.ca2404.1_all.deb Size: 352788 MD5sum: dc5e5cfe4fb623234a8cdf9cf60dc236 SHA1: ab1737a8af57ffad7ff042c1c0e851282ec7d654 SHA256: 028023fa15686a408aa9481d72fd02a9997e8271a0e4dc598d2434f57a7bda02 SHA512: dddc8bc0fe74209e668f5968809313528d67c60284e9e87b2d27134a0a3be9b3271ea0020665911718038c779d7dfb9eb4c08596add76cd7504e8ed2a900b74c Homepage: https://cran.r-project.org/package=NetSimR Description: CRAN Package 'NetSimR' (Actuarial Functions for Non-Life Insurance Modelling) Assists actuaries and other insurance modellers in pricing, reserving and capital modelling for non-life insurance and reinsurance modelling. Provides functions that help model excess levels, capping and pure Incurred but not reported claims (pure IBNR). Includes capped mean, exposure curves and increased limit factor curves (ILFs) for LogNormal, Gamma, Pareto, Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes mean, probability density function (pdf), cumulative probability function (cdf) and inverse cumulative probability function for Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes calculating pure IBNR exposure with LogNormal and Gamma distribution for reporting delay. Includes three shiny tools, one to simulate insurance claims applying reinsurance structures, fit generalised linear models and fit claims frequency or severity distributions. Methods used in the package refer to Free for All by Yiannis Parizas (2023) ; Escaping the triangle by Yiannis Parizas (2019) ; Take to excess by Yiannis Parizas (2019) . 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Primers offering technical background information on the 'netstat' command line utility are available in the "Linux System Administrator's Manual" by Michael Kerrisk (2014) , and on the Microsoft website (2017) . 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Built-in support for V-shape (2-star), triangle, and 3-star motifs, with a user-extensible interface for arbitrary additional motifs. Parallel execution is supported via 'doParallel' and 'foreach'. Based on Qi, Hua, Li and Zhou (2024) . 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The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided. This version includes performance optimizations with vectorized MCMC operations and modern ggplot2-based visualizations with colorblind-friendly palettes. Package: r-cran-networkcomparisontest Architecture: all Version: 2.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-reshape2, r-cran-qgraph, r-cran-networktools, r-cran-isingfit Suggests: r-cran-bootnet, r-cran-isingsampler, r-cran-testthat Filename: pool/dists/noble/main/r-cran-networkcomparisontest_2.2.3-1.ca2404.1_all.deb Size: 62884 MD5sum: 267d62a1842ca10f9872e471c4cb25e2 SHA1: 04c5755dca0519a4ff57744fb3255408c5d1cdd4 SHA256: 25362aab5774ad6397bdacc64d30713bca8e15f3148bbda6eb9c461d68027303 SHA512: defb856efc291859f0e22053f9b456b8ff411fa05f5dc63186974dda00c0886d3b8c800197c22a756ff29263c443628c88fdc313d27e15673742050accb3314d Homepage: https://cran.r-project.org/package=NetworkComparisonTest Description: CRAN Package 'NetworkComparisonTest' (Statistical Comparison of Two Networks Based on SeveralInvariance Measures) This permutation based hypothesis test, suited for several types of data supported by the estimateNetwork function of the bootnet package (Epskamp & Fried, 2018), assesses the difference between two networks based on several invariance measures (network structure invariance, global strength invariance, edge invariance, several centrality measures, etc.). Network structures are estimated with l1-regularization. The Network Comparison Test is suited for comparison of independent (e.g., two different groups) and dependent samples (e.g., one group that is measured twice). See van Borkulo et al. (2021), available from . 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(2022), : network structure invariance, global strength invariance, edge invariance, and various centrality measures. Edgelists from dependent or independent samples are used as input. These edgelists are generated from concept maps and summed into two comparable group networks. The networks can be directed or undirected. Package: r-cran-networkd3 Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.tree, r-cran-htmlwidgets, r-cran-igraph, r-cran-jsonlite, r-cran-magrittr Suggests: r-cran-htmltools, r-cran-tibble Filename: pool/dists/noble/main/r-cran-networkd3_0.4.1-1.ca2404.1_all.deb Size: 222850 MD5sum: 649228413fb7a185c5ba179fefa4e1ae SHA1: d50fb3c4c16e730e498c83dfa3ec0ca0c2005330 SHA256: c26c448428ad8eb083cd9eb59e1b9c9c5fe47dcbfabc1010f9541973bf8f331c SHA512: f6189d8a567593aa40111126048388f810ce1296a7789c487c7e5146ea4ab07ff43dfbe2ed83e1fed73d0443560cfa6d8f6c6922e1d147357642502f16b6ee4c Homepage: https://cran.r-project.org/package=networkD3 Description: CRAN Package 'networkD3' (D3 JavaScript Network Graphs from R) Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey graphs from 'R'. 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(2002) . Package: r-cran-networkgen Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-networkgen_0.1.1-1.ca2404.1_all.deb Size: 97016 MD5sum: 1421f097ea66ed9c7418a0cd7b6e312d SHA1: fe7db519707482cb68cd10835a16e519fa4a09bb SHA256: e0e86f23681d67b12c831d522fdf4aca0d17177da2d7ad83e256bc49c7b934bf SHA512: dd518b132f900fb029086b9c46c2ac336a3444410b1ab7fae5f118fe4c9947783a8161e63175465ccacc243190819d16e58330b36628b1a0429d5fbc4871adbd Homepage: https://cran.r-project.org/package=networkGen Description: CRAN Package 'networkGen' (Network Maze Generator) A network Maze generator that creates different types of network mazes. Package: r-cran-networklite Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-network, r-cran-statnet.common, r-cran-tibble, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-networklite_1.1.0-1.ca2404.1_all.deb Size: 118566 MD5sum: bb3281b496bf3e0e3115fe499d899b96 SHA1: a5c82ac6c380e60badee75a1b42a2682b439ae0a SHA256: cd4a51f7384c4cee20521bc92cedaee0f6e7c3b96e73095a4de09b2a0661422c SHA512: 76421626e07f7153213178bdfd3707e0dd89056274363d914b683e7dbcc8279512b986eb6762b412020c51a0db56b825d63d0a94c7e59d24277c684a528aeb71 Homepage: https://cran.r-project.org/package=networkLite Description: CRAN Package 'networkLite' (An Simplified Implementation of the 'network' PackageFunctionality) An implementation of some of the core 'network' package functionality based on a simplified data structure that is faster in many research applications. This package is designed for back-end use in the 'statnet' family of packages, including 'EpiModel'. Support is provided for binary and weighted, directed and undirected, bipartite and unipartite networks; no current support for multigraphs, hypergraphs, or loops. Package: r-cran-networkreg Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randnet, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-networkreg_2.0-1.ca2404.1_all.deb Size: 44860 MD5sum: 2048a1eb62fc7615357b542b92f944b4 SHA1: fd31b6e76531a06053ec7d2e86b2d5347722ae5e SHA256: cef1e14cf85ea1cd08d8254d8873a610ae1e3eeedc6e6591d53ec5e98489b6b8 SHA512: 1bc01f81fa22b5f64b1917d8b6e78cc178d5c28d31351df4fa17c5852a6426d23d8767073cb852f25ac33fa35195dadd7931b586e19c72762171c66441842ef7 Homepage: https://cran.r-project.org/package=NetworkReg Description: CRAN Package 'NetworkReg' (Generalized Linear Regression Models on Network-Linked Data withStatistical Inference) Linear regression model and generalized linear models with nonparametric network effects on network-linked observations. The model is originally proposed by Le and Li (2022) and is assumed on observations that are connected by a network or similar relational data structure. A more recent work by Wang, Le and Li (2024) further extends the framework to generalized linear models. All these models are implemented in the current package. The model does not assume that the relational data or network structure to be precisely observed; thus, the method is provably robust to a certain level of perturbation of the network structure. The package contains the estimation and inference function for the model. Package: r-cran-networkriskmeasures Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-expm, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-testthat, r-cran-igraph, r-cran-covr Filename: pool/dists/noble/main/r-cran-networkriskmeasures_0.1.7-1.ca2404.1_all.deb Size: 88432 MD5sum: 2c8b6598c5dc1a6c3f5f808f573a0d33 SHA1: dd4cccb870109acd84fc768c892103aa7ce4e228 SHA256: 6d4e8d0fb19f00d9245342309f3e5686576a982a909e9045b7bcf353fc9399c2 SHA512: 523c2f44d04d14d9287ccf8e9f9fc8d9d1f743f72fc5b307a46e5479737316c262667e1fbef7784f94234376c5ad6faa04f7c19c1512ce2eb53152dd56eb57cf Homepage: https://cran.r-project.org/package=NetworkRiskMeasures Description: CRAN Package 'NetworkRiskMeasures' (Risk Measures for (Financial) Networks) Implements some risk measures for (financial) networks, such as DebtRank, Impact Susceptibility, Impact Diffusion and Impact Fluidity. Package: r-cran-networksem Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-latentnet, r-cran-sna, r-cran-influential, r-cran-lavaan, r-cran-network, r-cran-igraph Filename: pool/dists/noble/main/r-cran-networksem_0.4-1.ca2404.1_all.deb Size: 80190 MD5sum: a3c5eea7ad8a455d1d610956fa4c2f67 SHA1: 67306b6756afe0af0f0c837efdf7554d00382c32 SHA256: 8d3b41cb9c7fcef5536f5ee754f92cf6028073f3c98aeac72f000e490d5532ff SHA512: a47048797e2903d02de3cdd126c18114e76d40ef39cb1e06a7c1975d085d0be70c7a28812b76f217173aa1f022824905d72882b8c6dee28db33202bc2fdbe960 Homepage: https://cran.r-project.org/package=networksem Description: CRAN Package 'networksem' (Network Structural Equation Modeling) Several methods have been developed to integrate structural equation modeling techniques with network data analysis to examine the relationship between network and non-network data. Both node-based and edge-based information can be extracted from the network data to be used as observed variables in structural equation modeling. To facilitate the application of these methods, model specification can be performed in the familiar syntax of the 'lavaan' package, ensuring ease of use for researchers. Technical details and examples can be found at . Package: r-cran-networktoolbox Architecture: all Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corrplot, r-cran-doparallel, r-cran-fdrtool, r-cran-foreach, r-cran-igraph, r-cran-isingfit, r-cran-mass, r-cran-pbapply, r-cran-ppcor, r-cran-psych, r-cran-pwr, r-cran-r.matlab, r-cran-qgraph Suggests: r-cran-googledrive Filename: pool/dists/noble/main/r-cran-networktoolbox_1.4.4-1.ca2404.1_all.deb Size: 524514 MD5sum: 7568f5dcafde321cbe737efb9801a816 SHA1: 6c57b72162dc8b95b173b29e53f7b3ee5a326aba SHA256: 93f383e49b1b39e87fbfa575efd6243507e514ab1f58e657077ae5340a12343d SHA512: fc0983b54999877ac39d9765ac365b4ef8af02c4d33c3bf58b01f790952c3c8497827720f0f599f72bdbf23bb9cafeb84471fbc2ad064b28f4e3580d28008ee8 Homepage: https://cran.r-project.org/package=NetworkToolbox Description: CRAN Package 'NetworkToolbox' (Methods and Measures for Brain, Cognitive, and PsychometricNetwork Analysis) Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 ), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 ), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 ). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis. Package: r-cran-networktools Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qgraph, r-cran-igraph, r-cran-reshape2, r-cran-ggplot2, r-cran-gridextra, r-cran-cocor, r-cran-rcolorbrewer, r-cran-r.utils, r-cran-eigenmodel, r-cran-psych, r-cran-smacof, r-cran-wordcloud Suggests: r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-networktools_1.6.0-1.ca2404.1_all.deb Size: 130148 MD5sum: f461caa68edfc6a823029d84bbfe387d SHA1: 118f20c8735a119e32a16d0ddc1a81cbd3188691 SHA256: 468ee6a11b987254ebf547242f2a6f7cc5d85eb6980e2278157e7f4855605379 SHA512: 3b6175c964551e71951d968e37caa27a698d78740eb93e6a490a9adc79bd4d5aa8bb54c61d746d89f71f6951d1efdc6ba857e91252ff1a650822582d3567115d Homepage: https://cran.r-project.org/package=networktools Description: CRAN Package 'networktools' (Tools for Identifying Important Nodes in Networks) Includes assorted tools for network analysis. Bridge centrality; goldbricker; MDS, PCA, & eigenmodel network plotting. Package: r-cran-networktree Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1538 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partykit, r-cran-qgraph, r-cran-matrix, r-cran-mvtnorm, r-cran-formula, r-cran-gridbase, r-cran-reshape2 Suggests: r-cran-r.rsp, r-cran-knitr, r-cran-rmarkdown, r-cran-fxregime, r-cran-zoo Filename: pool/dists/noble/main/r-cran-networktree_1.0.1-1.ca2404.1_all.deb Size: 1267018 MD5sum: 919a3a0dc8d5fe4bf16b25780e05d098 SHA1: 0be00f1f52dbdbcc9dae478152edf2d7c02bc7c9 SHA256: 9575223490e84133b8259c91f2768b7bcccdf63f4f3ce1184342da456c7ad8af SHA512: 149dc302ac7203aec303656a19f007883c4bc81af33d03f91a1a6a45e0f1669d16cccb51af08c5592ecde476f5670abdbf897f5bf8f76c7f1b8e4c39324ec0a3 Homepage: https://cran.r-project.org/package=networktree Description: CRAN Package 'networktree' (Recursive Partitioning of Network Models) Network trees recursively partition the data with respect to covariates. Two network tree algorithms are available: model-based trees based on a multivariate normal model and nonparametric trees based on covariance structures. After partitioning, correlation-based networks (psychometric networks) can be fit on the partitioned data. For details see Jones, Mair, Simon, & Zeileis (2020) . Package: r-cran-neudist Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-goftest Filename: pool/dists/noble/main/r-cran-neudist_1.0.1-1.ca2404.1_all.deb Size: 736938 MD5sum: 0444d1e15e1c1d3cf635607e966fb840 SHA1: f547c290008594cf737ae49297f01e7e84f7d055 SHA256: ab13811e7fee1978d68a58eb8fe0fc0e55d83191f8275f58205e3cf7a823752e SHA512: 458b37cac98721130f825e23cd2bace36cb94c500405526a0c7f5f09bb12dfe1ce9a78b7d80f4502049cf07b17cdb588b3b1c4b4542f2437d720cb2055deba62 Homepage: https://cran.r-project.org/package=NeuDist Description: CRAN Package 'NeuDist' (Univariate Continuous Distributions with Model Diagnostics) Implements univariate continuous probability distributions and associated model diagnostics based on the Lindley, Logistic, Half-Cauchy, Half-Logistic, and Poisson families. Provides functions for probability density, cumulative distribution, quantile, and hazard evaluation, random variate generation, and diagnostic procedures including Q-Q and P-P plots, goodness-of-fit tests, and model selection criteria. Package: r-cran-neuralestimators Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 833 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-juliaconnector, r-cran-magrittr Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-ggplotify, r-cran-ggpubr, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-r.rsp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-neuralestimators_0.2.1-1.ca2404.1_all.deb Size: 514824 MD5sum: a3c029284e459e6ae7ed159ee2204910 SHA1: 4aec703e6b4639bed4dd1d58b57d672e07667d46 SHA256: d40d20ff7bc4411a91181e10c204718ba01afa0af5efb81d9b72cf147795db39 SHA512: acb3a2abae5b5e4ab8acd4fd87f5068a639c20b24f1de85e94c42c0b00abfc025dac77ab1abb82f1aa534ba3dbe6af39e3568405652bff83f6445f40d49bcfd2 Homepage: https://cran.r-project.org/package=NeuralEstimators Description: CRAN Package 'NeuralEstimators' (Likelihood-Free Parameter Estimation using Neural Networks) An 'R' interface to the 'Julia' package 'NeuralEstimators.jl'. The package facilitates the user-friendly development of neural Bayes estimators, which are neural networks that map data to a point summary of the posterior distribution (Sainsbury-Dale et al., 2024, ). These estimators are likelihood-free and amortised, in the sense that, once the neural networks are trained on simulated data, inference from observed data can be made in a fraction of the time required by conventional approaches. The package also supports amortised Bayesian or frequentist inference using neural networks that approximate the posterior or likelihood-to-evidence ratio (Zammit-Mangion et al., 2025, Sec. 3.2, 5.2, ). The package accommodates any model for which simulation is feasible by allowing users to define models implicitly through simulated data. Package: r-cran-neuralgam Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensorflow, r-cran-keras, r-cran-ggplot2, r-cran-magrittr, r-cran-reticulate, r-cran-formula.tools, r-cran-matrixstats, r-cran-patchwork, r-cran-rlang Suggests: r-cran-covr, r-cran-testthat, r-cran-fs, r-cran-withr Filename: pool/dists/noble/main/r-cran-neuralgam_2.0.1-1.ca2404.1_all.deb Size: 225352 MD5sum: c2a51510ad6f1bb3c495ddfa271903fd SHA1: 22f65e2840f3cf9979f7d00d7c7b41ae7e5d11c5 SHA256: afa6e894600ed8a5f67563cfcecebfaacf7f1dd0248150f8186e042b473f5f37 SHA512: 84f7c81ff2682d4efaf68c4612ec1633b71f10b6471b7ac6edc11bc9d0ddf7294a195360e58e93df5a571fc8d442757f9bb5ab04cceaa1723248bd87ada4296c Homepage: https://cran.r-project.org/package=neuralGAM Description: CRAN Package 'neuralGAM' (Interpretable Neural Network Based on Generalized AdditiveModels) Neural Additive Model framework based on Generalized Additive Models from Hastie & Tibshirani (1990, ISBN:9780412343902), which trains a different neural network to estimate the contribution of each feature to the response variable. The networks are trained independently leveraging the local scoring and backfitting algorithms to ensure that the Generalized Additive Model converges and it is additive. The resultant Neural Network is a highly accurate and interpretable deep learning model, which can be used for high-risk AI practices where decision-making should be based on accountable and interpretable algorithms. Package: r-cran-neuralnet Architecture: all Version: 1.44.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-deriv Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-neuralnet_1.44.2-1.ca2404.1_all.deb Size: 122968 MD5sum: 6f0133ad0bc48149310fceb9f3908e11 SHA1: 5b2ba961b3e1dc339b664c05f59e8cf5cbe0de3e SHA256: 0adae10e2f58554493e0405e747733697ed56ee39f6d4b589193120eaa9d0675 SHA512: 4fc99ab3aae44d216f971532beeb98526de90dd1b67f506c0c300fa9b2d06e81c19494540bfed07d4041b9a5b16150ba92610ba2949d19a23982457a59f45611 Homepage: https://cran.r-project.org/package=neuralnet Description: CRAN Package 'neuralnet' (Training of Neural Networks) Training of neural networks using backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version by Anastasiadis et al. (2005). The package allows flexible settings through custom-choice of error and activation function. Furthermore, the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented. 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Functions are available for calculating and plotting the inputs importance and obtaining the activation function of each neuron layer and its derivatives. The importance of a given input is defined as the distribution of the derivatives of the output with respect to that input in each training data point . Package: r-cran-neurobase Architecture: all Version: 1.34.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2524 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-oro.nifti, r-cran-abind, r-cran-matrixstats, r-cran-r.utils, r-cran-rnifti Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-reshape2, r-cran-httr, r-cran-covr, r-cran-brainr Filename: pool/dists/noble/main/r-cran-neurobase_1.34.0-1.ca2404.1_all.deb Size: 1414002 MD5sum: 8f0ec8334d933ffbe52cb3e44f0ba84f SHA1: dfc530438b6c6f83b92a2415e903fbc71721557e SHA256: bd790134903a091db12f6d0d881af04a0e71c7864930f643ba6985cc053dd160 SHA512: 6c7a7e0816696b7e548337631112961ca34a00b9be5ede1bf68a4f200c01b9097f7c81d9d25fc25cf5f9b55f1eaa912e67b724a823d2abe8455113d67e9e1e53 Homepage: https://cran.r-project.org/package=neurobase Description: CRAN Package 'neurobase' ('Neuroconductor' Base Package with Helper Functions for 'nifti'Objects) Base package for 'Neuroconductor', which includes many helper functions that interact with objects of class 'nifti', implemented by package 'oro.nifti', for reading/writing and also other manipulation functions. Package: r-cran-neuroblastoma Architecture: all Version: 2023.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8058 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-neuroblastoma_2023.9.3-1.ca2404.1_all.deb Size: 8193250 MD5sum: 427f15ce0441e886eac148766489fa73 SHA1: 2930790ddcee2c918e382b88d9506783c1043ad1 SHA256: b568e0d75a9b8b3cbcc58f8e49b93e18dc3b8acbee6fd5a62ea582ea2f48cdf4 SHA512: a3b521b892ce9ba14e296b36a01dee8e73466790b5d7bc5cd83259d3159470287f5eba28eede054e65138621fee0ebb66dd7795715a8996f368c06c567388e7a Homepage: https://cran.r-project.org/package=neuroblastoma Description: CRAN Package 'neuroblastoma' (Neuroblastoma Copy Number Profiles) Annotated neuroblastoma copy number profiles, a benchmark data set for change-point detection algorithms, as described by Hocking et al. . 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Package: r-cran-neurodatasets Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1511 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-neurodatasets_0.3.0-1.ca2404.1_all.deb Size: 990296 MD5sum: 3a1e679635cccf041a8dfae183c030ea SHA1: 20e8d689dedacade7222f5c8801a5f8ac588de62 SHA256: 20e8c58de925cee60da67f3b0f7fa7508a207f069f04d78633a919121810b830 SHA512: c4544cc505c54100d7a7b38743a0b900ec17803c13de608ee0a89d168c7c0bd6ec506e7ada3315fc5d30a0e335477d8acbd2ac75cae769b4c7dad9f1d934daac Homepage: https://cran.r-project.org/package=NeuroDataSets Description: CRAN Package 'NeuroDataSets' (A Comprehensive Collection of Neuroscience and Brain-RelatedDatasets) Offers a rich and diverse collection of datasets focused on the brain, nervous system, and related disorders. The package includes clinical, experimental, neuroimaging, behavioral, cognitive, and simulated data on conditions such as Parkinson's disease, Alzheimer's disease, dementia, epilepsy, schizophrenia, autism spectrum disorder, attention deficit, hyperactivity disorder, Tourette's syndrome, traumatic brain injury, gliomas, migraines, headaches, sleep disorders, concussions, encephalitis, subarachnoid hemorrhage, and mental health conditions. Datasets cover structural and functional brain data, cross-sectional and longitudinal MRI imaging studies, neurotransmission, gene expression, cognitive performance, intelligence metrics, sleep deprivation effects, treatment outcomes, brain-body relationships across species, neurological injury patterns, and acupuncture interventions. Data sources include peer-reviewed studies, clinical trials, military health records, sports injury databases, and international comparative studies. Designed for researchers, neuroscientists, clinicians, psychologists, data scientists, and students, this package facilitates exploratory data analysis, statistical modeling, and hypothesis testing in neuroscience and neuroepidemiology. Package: r-cran-neurodecoder Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3461 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-dosnow, r-cran-e1071, r-cran-forcats, r-cran-foreach, r-cran-ggplot2, r-cran-gridextra, r-cran-magrittr, r-cran-purrr, r-cran-r.matlab, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tictoc, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-neurodecoder_0.2.0-1.ca2404.1_all.deb Size: 1400382 MD5sum: 8b1e91ef3b6bb7155e6d351439144b0c SHA1: ffb3da5510f4e76f0c0627d8eeba813aac9baef3 SHA256: 95b05bebd4c5d1d84cdf1764e2efc618c1a2fe324809a341cad55b859b6a2daa SHA512: f202cb29113ea616804c8b60a213d511f430f40a71201922152f85f6e39d20bac31052d7ab2875614d365955d05a61faab10d3059db033f67956417b09f332ff Homepage: https://cran.r-project.org/package=NeuroDecodeR Description: CRAN Package 'NeuroDecodeR' (Decode Information from Neural Activity) Neural decoding is method of analyzing neural data that uses a pattern classifiers to predict experimental conditions based on neural activity. 'NeuroDecodeR' is a system of objects that makes it easy to run neural decoding analyses. For more information on neural decoding see Meyers & Kreiman (2011) . Package: r-cran-neurohcp Architecture: all Version: 0.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 788 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-httr, r-cran-xml2, r-cran-base64enc, r-cran-aws.s3 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-neurohcp_0.11.0-1.ca2404.1_all.deb Size: 517412 MD5sum: 8e8831742ddec6991e73ef19908c2175 SHA1: 85979292994a800491a286f5b0a476c07eacdfe4 SHA256: c534f843d4269b4722220fa9ca24c5f7f8b26f7753924b5fc66caacc49fe211f SHA512: bbd01bddebc2693addb011cb024175a907bd11f0bcfb94f7b3051c47e87e73d57acdbaeca8d0007a5a5b92ce035ba9a1262a608042ceeff601884f812f264262 Homepage: https://cran.r-project.org/package=neurohcp Description: CRAN Package 'neurohcp' (Human 'Connectome' Project Interface) Downloads and reads data from Human 'Connectome' Project using Amazon Web Services ('AWS') 'S3' buckets. Package: r-cran-neuroimagene Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1080 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-dbi, r-cran-stringr, r-cran-ggseg, r-cran-sf, r-cran-rsqlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-neuroimagene_0.1.4-1.ca2404.1_all.deb Size: 814204 MD5sum: c72153976aa4df52d720580e137f26c9 SHA1: 9a84d7642057b0e74381d39707de69049856371f SHA256: 73a4cbda78b788e6835b94e4874133486812dfeb45489d7e158114b01edaa497 SHA512: b072151829eb51b6ad0c98e451ad9297fe55636730e0931ef14b0ebe01ddb52b6d966d6a061ada91eb1201778c318207e757d2fcb433768bbc640d142ee2e1be Homepage: https://cran.r-project.org/package=neuroimaGene Description: CRAN Package 'neuroimaGene' (Transcriptomic Atlas of Neuroimaging Derived Phenotypes) Contains functions to query and visualize the Neuroimaging features associated with genetically regulated gene expression (GReX). The primary utility, neuroimaGene(), relies on a list of user-defined genes and returns a table of neuroimaging features (NIDPs) associated with each gene. This resource is designed to assist in the interpretation of genome-wide and transcriptome-wide association studies that evaluate brain related traits. Bledsoe (2024) . In addition there are several visualization functions that generate summary plots and 2-dimensional visualizations of regional brain measures. Mowinckel (2020). Package: r-cran-neuromapr Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 875 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-gifti, r-cran-httr2, r-cran-igraph, r-cran-lifecycle, r-cran-rlang, r-cran-tibble, r-cran-withr Suggests: r-cran-ciftitools, r-cran-clue, r-cran-freesurferformats, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-rnifti, r-cran-rspectra, r-cran-testthat, r-cran-vcr Filename: pool/dists/noble/main/r-cran-neuromapr_0.2.2-1.ca2404.1_all.deb Size: 536812 MD5sum: 8c2aaeb26a9fa3a8dc3bbc6066768f1c SHA1: 55c42a875d0233b31bb039fde38ae9223fa43182 SHA256: 8d7ed3c8317a7ef6c308c9da25c2845b21f70358070540957186a3bea7aa266f SHA512: 58c434b4d79f96699eb3fa140ee1c08536f75a558cb12cb748b1250598445908c638f3c6d5d7ef23845240fbd64d5c8b7743120db53763efba91e6e049e57205 Homepage: https://cran.r-project.org/package=neuromapr Description: CRAN Package 'neuromapr' (Spatial Null Models and Transforms for Brain Map Comparison) Implements spatial null models and coordinate-space transformations for statistical comparison of brain maps, following the framework described in Markello et al. (2022) . Provides variogram-matching surrogates (Burt et al. 2020), Moran spectral randomization (Wagner & Dray 2015), and spin-based permutation tests (Alexander-Bloch et al. 2018). Includes an R interface to the 'neuromaps' annotation registry for browsing, downloading, and comparing brain map annotations from the Open Science Framework ('OSF'). Integrates with 'ciftiTools' for coordinate-space transforms. Package: r-cran-neuromplex Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayeslogit, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-neuromplex_1.0-1-1.ca2404.1_all.deb Size: 125232 MD5sum: 8ea9ffc5308eade94f20bd8bfcee7c7c SHA1: 0cd22277f0202d283a290304a613ca956c0bbf31 SHA256: f7619cb83046d92c5aed339dc07a705bbcabec1fcb86bed698320b375dc15cb1 SHA512: 4c8fde90b428a1664e2ed5b0c6d844718c5d17f6a6d6f08a55c8a77773400e923d97cdafd3ce273da09a75cd827248aab2190d8ea33fadb316237e5a17cb718c Homepage: https://cran.r-project.org/package=neuromplex Description: CRAN Package 'neuromplex' (Neural Multiplexing Analysis) Statistical methods for whole-trial and time-domain analysis of single cell neural response to multiple stimuli presented simultaneously. The package is based on the paper by C Glynn, ST Tokdar, A Zaman, VC Caruso, JT Mohl, SM Willett, and JM Groh (2021) "Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure", is in press for publication by the Annals of Applied Statistics. A preprint may be found at . Package: r-cran-neuronorm Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr, r-cran-oro.nifti, r-cran-fslr Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-neuronorm_1.0.2-1.ca2404.1_all.deb Size: 49806 MD5sum: 5d2ecd42dfa191aa596532a6e67c0edb SHA1: 2304992c2cae3d289575a7dcdc4fef00d9eba526 SHA256: 0212fdc298ded03f7e325fdb93de530f98ee6fda50053b90611021f566fb6e8d SHA512: 9287406d0334d442844d24a7e47bcaea033a74f036f1b12ae04b614b8703fe4a70941794de1324c7c114ac01ba1e88cfb392f2fb7062434f0fa17e9f22a2d0a5 Homepage: https://cran.r-project.org/package=neuronorm Description: CRAN Package 'neuronorm' (Preprocessing of Structural MRI for Multiple NeurodegenerativeDiseases) Preprocessing pipeline for normalizing and cleaning T1-weighted, T2-weighted and FLAIR MRI images coming from different sources, diseases, patients, scanners and sites. 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Package: r-cran-neuroup Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 952 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-ggplot2, r-cran-psychometric, r-cran-tibble, r-cran-magrittr, r-cran-bootstrap Suggests: r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-neuroup_0.3.1-1.ca2404.1_all.deb Size: 573726 MD5sum: 185a4fe9153429ac24384f3afe6e983d SHA1: 8bba411b9def6990a042d2dc1924bdbcd904d7cf SHA256: f94e5e78684b469a97353e548274b7702493e206000055ce7b33801caae693a7 SHA512: 9d09cbf8b48e976ed7ec0a8c01cf231aac779a6870c1f38b23e82d24f05c53de468c38d30da62f85995610214bd7a390ce0638a427749955e4be560264a48cd5 Homepage: https://cran.r-project.org/package=neuroUp Description: CRAN Package 'neuroUp' (Plan Sample Size for Task fMRI Research using Bayesian Updating) Calculate the precision in mean differences (raw or Cohen's D) and correlation coefficients for different sample sizes. 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The neverhpfilter package provides functions and data for reproducing his work. Hamilton (2017) . 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This package implements the following distributions: The Power Muth Distribution, a Bimodal Weibull Distribution, the Discrete Lindley Distribution, The Gamma-Lomax Distribution, Weighted Geometric Distribution, a Power Log-Dagum Distribution, Kumaraswamy Distribution, Lindley Distribution, the Unit-Inverse Gaussian Distribution, EP Distribution, Akash Distribution, Ishita Distribution, Maxwell Distribution, the Standard Omega Distribution, Slashed Generalized Rayleigh Distribution, Two-Parameter Rayleigh Distribution, Muth Distribution, Uniform-Geometric Distribution, Discrete Weibull Distribution. 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Package: r-cran-newfocus Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ctgt Filename: pool/dists/noble/main/r-cran-newfocus_1.1-1.ca2404.1_all.deb Size: 32276 MD5sum: fd5b9d626c586fa6e292dcdc65bdbf0d SHA1: 0c73ac7f92fdc02971099c3f7b029caeb71fc3dd SHA256: 158e4e231ae3af7e109b97d10870a874cf2a7f0d4a471fad82975ae7f3296441 SHA512: e70a469a99a24bd99622f0d329ad3d8c1484826891c8d2f3908d5d12269454c595bcd15ae3e081882e517712aff58b7dc54a0f8701dafbc33ee6ccacee56e42a Homepage: https://cran.r-project.org/package=newFocus Description: CRAN Package 'newFocus' (True Discovery Guarantee by Combining Partial Closed Testings) Closed testing has been proved powerful for true discovery guarantee. The computation of closed testing is, however, quite burdensome. A general way to reduce computational complexity is to combine partial closed testings for some prespecified feature sets of interest. Partial closed testings are performed at Bonferroni-corrected alpha level to guarantee the lower bounds for the number of true discoveries in prespecified sets are simultaneously valid. For any post hoc chosen sets of interest, coherence property is used to get the lower bound. In this package, we implement closed testing with globaltest to calculate the lower bound for number of true discoveries, see Ningning Xu et.al (2021) for detailed description. 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The package takes a uniform-hazard spectrum at multiple return periods as input (any source) and produces: (1) synthetic soil profile generation and fundamental period estimation from USCS classification via Ishihara's small-strain shear-modulus model and the inhomogeneous truncated shear-beam theory of Gazetas and Dakoulas; (2) nonlinear site amplification using the Seyhan & Stewart (2014) model , with inter-period correlation via Baker & Jayaram (2008) ; (3) Monte Carlo ensemble of six empirical Newmark sliding-block displacement models (Ambraseys & Menu (1988) , Jibson (2007) , Saygili & Rathje (2008) , Bray & Travasarou (2007) , Bray & Macedo (2017) , and the Bray and Macedo shallow-crustal update) with coherent correlated draws; (4) log-log inversion to the performance-based seismic coefficient kmax at user-specified displacement targets. All outputs are 'data.table' objects. 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Package: r-cran-nfcp Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 763 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fkf.sp, r-cran-lsmrealoptions, r-cran-mass, r-cran-numderiv, r-cran-rgenoud, r-cran-mathjaxr, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nfcp_1.2.2-1.ca2404.1_all.deb Size: 403022 MD5sum: b7a64777e20f56730aabbf98c58c405b SHA1: a0c7d9332c9f6bd54db5da63570369043cc6b2e0 SHA256: 8fcad33ed634eca9d44f52a9e8a53cb9f0e7f8479aee7d73fac7e58a09e8fd73 SHA512: 7c96991fa5de2881ca4250b71aa064ff13d51543adfac060ac349407d485c8c3a809f27272a295785a3ee733a9438e6b57fb3b85bc105ecf9bfa5f3205807b8a Homepage: https://cran.r-project.org/package=NFCP Description: CRAN Package 'NFCP' (N-Factor Commodity Pricing Through Term Structure Estimation) Commodity pricing models are (systems of) stochastic differential equations that are utilized for the valuation and hedging of commodity contingent claims (i.e. derivative products on the commodity) and other commodity related investments. Commodity pricing models that capture market dynamics are of great importance to commodity market participants in order to exercise sound investment and risk-management strategies. Parameters of commodity pricing models are estimated through maximum likelihood estimation, using available term structure futures data of a commodity. 'NFCP' (n-factor commodity pricing) provides a framework for the modeling, parameter estimation, probabilistic forecasting, option valuation and simulation of commodity prices through state space and Monte Carlo methods, risk-neutral valuation and Kalman filtering. 'NFCP' allows the commodity pricing model to consist of n correlated factors, with both random walk and mean-reverting elements. The n-factor commodity pricing model framework was first presented in the work of Cortazar and Naranjo (2006) . Examples presented in 'NFCP' replicate the two-factor crude oil commodity pricing model presented in the prolific work of Schwartz and Smith (2000) with the approximate term structure futures data applied within this study provided in the 'NFCP' package. 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Package: r-cran-nhanesdiva Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-curl, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-nhanesdiva_1.0.1-1.ca2404.1_all.deb Size: 48538 MD5sum: ca14bdbb633843b3471951b7408e3006 SHA1: 60cdad7b76a289b7c9a223d01e564012ff13a65b SHA256: 0881825ee4a352cef556c75d4bfd96654deab6255a65b15d08c8e6a3323f9bda SHA512: cf5935d054e8f1434538a87d21674148d2f4557b1c7f5f9f20eedc607b493686299013c43e141287e0d66e4b219f802864710fe5bb669abe0365c798dc5c872a Homepage: https://cran.r-project.org/package=nhanesdiva Description: CRAN Package 'nhanesdiva' (NHANES Data Search, Preview, and Download Tools) Search, preview, and download datasets from the National Health and Nutrition Examination Survey (NHANES) across survey cycles. The package provides functions to identify relevant datasets by keyword, inspect available .XPT files before downloading, and organize retrieved data locally. Data are retrieved from the NHANES web services available at . Package: r-cran-nhdplustools Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3018 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hydroloom, r-cran-dataretrieval, r-cran-dplyr, r-cran-sf, r-cran-units, r-cran-magrittr, r-cran-jsonlite, r-cran-httr, r-cran-xml2, r-cran-r.utils, r-cran-tidyr, r-cran-maptiles, r-cran-mapsf, r-cran-fst, r-cran-arrow, r-cran-zip, r-cran-pbapply, r-cran-memoise, r-cran-digest Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggmap, r-cran-ggplot2, r-cran-lwgeom, r-cran-gifski, r-cran-leaflet, r-cran-httptest, r-cran-future, r-cran-future.apply Filename: pool/dists/noble/main/r-cran-nhdplustools_1.4.2-1.ca2404.1_all.deb Size: 2474924 MD5sum: c4867df451e8b612df89f1eeb8b38b71 SHA1: 5bf154983d44a2af47c75841e162e1c5512d67b2 SHA256: d01677a311e9140fcf95c6a83ec7fbe49847000f07c3448423e2806d7ffd8565 SHA512: 798ddd89d7a65beaef211f8dc35886aa1f7722f7004cec366e3ebc274f3bf3a8ac86de94c74ceb530a652142dc29aceaa24fb5fb2906aa81d34bd5114eacfc26 Homepage: https://cran.r-project.org/package=nhdplusTools Description: CRAN Package 'nhdplusTools' (NHDPlus Tools) Tools for traversing and working with National Hydrography Dataset Plus (NHDPlus) data. 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Package: r-cran-nhlapi Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nhlapi_0.1.4-1.ca2404.1_all.deb Size: 557880 MD5sum: 0948fc00de2c22ac8e9366085bc9c849 SHA1: 28807a62d38720e4e6a574765c1dce84c406cfa1 SHA256: aa38a2b999da6a56a4640f5b0f56f4f3b7fe25513a7174fdd49d8e6318023bdb SHA512: b199da9f7dd107c0add1844ec43ffa9286fe9f5570e38d082db9bdfa243f3b326bfea12a50742642b494f3e4fb9ab34cfb2d73ec8f08f52f32ffaa12f3139942 Homepage: https://cran.r-project.org/package=nhlapi Description: CRAN Package 'nhlapi' (A Minimum-Dependency 'R' Interface to the 'NHL' API) Retrieves and processes the data exposed by the open 'NHL' API. This includes information on players, teams, games, tournaments, drafts, standings, schedules and other endpoints. A lower-level interface to access the data via URLs directly is also provided. Package: r-cran-nhldata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 703 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nhldata_1.0.0-1.ca2404.1_all.deb Size: 542766 MD5sum: c579b55d46916e6d40de387b4f88f8dd SHA1: d07f1a3207a66abeef54ee237fc241eee15ef9c0 SHA256: cab56938b36216938ef5ae8ca7285981d653e9466b5509540b0026461e1065d0 SHA512: 2c7343f8078e848eeed45cf1ff5be820feedd33fd0bff8d8d912c2dc9ac09a3ab03b547d6f68ea7d3ec254ec64afe817c9a8ab9dc083a69e756cc6f01ae9011b Homepage: https://cran.r-project.org/package=NHLData Description: CRAN Package 'NHLData' (Scores for Every Season Since the Founding of the NHL in 1917) Each dataset contains scores for every game during a specific season of the NHL. Package: r-cran-nhlscraper Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1533 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nhlscraper_0.5.0-1.ca2404.1_all.deb Size: 1237482 MD5sum: f4ee5aa3badd278f095c5416c17bf1e1 SHA1: 9d6a30e17f81a93b13fabd4f1a6ee22abc187149 SHA256: f3ae3614847425bd0243a4d7193e53193f283f3c74502944c11d1b1744a8d87d SHA512: ad39d3fdc7c98aa95c7cabb694cf4f152d6a10c5a9727e7a76ac0f7b15e03734aa869225bf644701f13d4238b4297136e22730f2438e2484dabd7eb51e836f89 Homepage: https://cran.r-project.org/package=nhlscraper Description: CRAN Package 'nhlscraper' (Scraper for National Hockey League Data) Scrapes and cleans data from the 'NHL' and 'ESPN' APIs into data.frames and lists. Wraps 125+ endpoints documented in from high-level multi-season summaries and award winners to low-level decisecond replays and bookmakers' odds, making them more accessible. Features cleaning and visualization tools, primarily for play-by-plays. Package: r-cran-nhpoisson Architecture: all Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car Filename: pool/dists/noble/main/r-cran-nhpoisson_3.4-1.ca2404.1_all.deb Size: 385024 MD5sum: b85cd43a624580fe0cf570a817ef37aa SHA1: 1d65b212c0be0cb1e7e1b44af4d80cca632b683a SHA256: cdd304a99909abe626149421d6884596cc30b9d4ffc16efc0b180cc28198b016 SHA512: 64a436c2a2c0dc649aa1304f442c9f54d3ff12783e48cf8a083d02b888fea281eaee01b07fcba138f7e483b0a8f90e00ccbee849ab1a1c159617ce251d3dd219 Homepage: https://cran.r-project.org/package=NHPoisson Description: CRAN Package 'NHPoisson' (Modelling and Validation of Non Homogeneous Poisson Processes) Tools for modelling, ML estimation, validation analysis and simulation of non homogeneous Poisson processes in time. Package: r-cran-nhs.predict Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nhs.predict_1.4.0-1.ca2404.1_all.deb Size: 55426 MD5sum: 39c4d5a6bba1eccecf09afba4d2c7121 SHA1: bbe0f5adca88613b8e152f58a275d320b19b7f4d SHA256: 2bb8ec0d7b38f32791a3289aae498c1a1aa3119bbac8c3b7f90c8b08bdc1d52f SHA512: 3aca3db40eab6d892bc861a2c3d7f525c6458c938116047a6ef76808dc7be8e80369a2ecf2ac8c04cd753dcf37841078cbc643d15a9f5aba34fa9035cc6b6fcf Homepage: https://cran.r-project.org/package=nhs.predict Description: CRAN Package 'nhs.predict' (Breast Cancer Survival and Therapy Benefits) Calculate Overall Survival or Recurrence-Free Survival for breast cancer patients, using 'NHS Predict'. The time interval for the estimation can be set up to 15 years, with default at 10. Incremental therapy benefits are estimated for hormone therapy, chemotherapy, trastuzumab, and bisphosphonates. An additional function, suited for SCAN audits, features a more user-friendly version of the code, with fewer inputs, but necessitates the correct standardised inputs. This work is not affiliated with the development of 'NHS Predict' and its underlying statistical model. Details on 'NHS Predict' can be found at: . The web version of 'NHS Predict': . A small dataset of 50 fictional patient observations is provided for the purpose of running examples with the main two functions, and an additional dataset is provided for running example with the dedicated SCAN function. Package: r-cran-nhsdatadictionary Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-dplyr, r-cran-magrittr, r-cran-rvest, r-cran-stringr, r-cran-purrr, r-cran-tibble, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-nhsdatadictionary_1.2.5-1.ca2404.1_all.deb Size: 149374 MD5sum: ee705c946d2d9bfe68c5fa3db17e80e1 SHA1: b27495d4a6649b8d679966a05bea318dbfff7f22 SHA256: e659c235df68378ebabc31dbfc2cfc00f934e3f0ce35089ad8fee171bd15ad81 SHA512: 60ef06a700701cb52b8f130a5911d47b28f063823db9e242398d279f422abf3e8350ca9b677d9daea73e0fa65af71f78e18ec82dc75fdc0e40293c42a8c7aaa2 Homepage: https://cran.r-project.org/package=NHSDataDictionaRy Description: CRAN Package 'NHSDataDictionaRy' (NHS Data Dictionary Toolset for NHS Lookups) Providing a common set of simplified web scraping tools for working with the NHS Data Dictionary . The intended usage is to access the data elements section of the NHS Data Dictionary to access key lookups. The benefits of having it in this package are that the lookups are the live lookups on the website and will not need to be maintained. This package was commissioned by the NHS-R community to provide this consistency of lookups. The OpenSafely lookups have now been added . Package: r-cran-nhsnumber Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nhsnumber_0.1.2-1.ca2404.1_all.deb Size: 15834 MD5sum: c4fd48500d09e10f81c9c3a8466e96f8 SHA1: 391cfb94b5151837a19082dca487fa013c9ee85f SHA256: ab1bd0ae6da11407edd5cca35e2a6400f5731948399a8000083463cd5b2e8f12 SHA512: eb3682d3e8293fa23941c3530de8be6c976c833639cf929a8844e3461b4b08582d306d0cadd93dc3c4b67d0ee739c593617752a4412c2a327dbca0580e5b2760 Homepage: https://cran.r-project.org/package=nhsnumber Description: CRAN Package 'nhsnumber' (Tools for Working with NHS Number Checksums) Provides functions for working with NHS number checksums. The UK's National Health Service issues NHS numbers to all users of its services and this package implements functions for verifying that the numbers are valid according to the checksum scheme the NHS use. Numbers can be validated and checksums created. 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This package contains synthetic data based on real healthcare datasets, or cuts of open-licenced official data. This package exists to support skills development in the NHS-R community: . 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This package supports the NHS 'Making Data Count' programme, and allows users to draw XmR charts, use change points and apply rules with summary indicators for when rules are breached. Package: r-cran-nhsrwaitinglist Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 601 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-rlang, r-cran-randomnames Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nhsrwaitinglist_0.1.2-1.ca2404.1_all.deb Size: 362858 MD5sum: eea4a9471f6638cde91028d627ae4be5 SHA1: 3fff8d1a12df98ee5221bebb3d2f48eb457ca183 SHA256: 81ddbd00c74487fcc4b7c2c67ad934816371260b15c05db56902bec8ade47c2b SHA512: c76a8b6581521db6c2d9bd95e32cfe95faa1e8b4e9633119a151da9a47e2fc6849fb017d46ad005d29a8a25cbcf96bd12d9ff456db42eac63cbbf4212546ab66 Homepage: https://cran.r-project.org/package=NHSRwaitinglist Description: CRAN Package 'NHSRwaitinglist' (Waiting List Metrics Using Queuing Theory) Waiting list management using queuing theory to analyse, predict and manage queues, based on the approach described in Fong et al. (2022) . Aimed at UK National Health Service (NHS) applications, waiting list summary statistics, target-value calculations, waiting list simulation, and scheduling functions are included. Package: r-cran-nhstplot Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1756 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nhstplot_1.4.2-1.ca2404.1_all.deb Size: 966220 MD5sum: fcec791cc10fbff2e35aee55eb162bc8 SHA1: 392a470a61f687335991e885d0f895e542491030 SHA256: 25761714857d5ba1b0f0c955365876ca5f0a79e4413a105997eb26b7f7d60781 SHA512: 64789b63668f366e9d2d7f1ffa8a1e99465cc54fbd6f9bf53aabdc87f724ff1f8c37d362903a2c83683b81139a88324ba4d7758e5d3cd211253d1e878c908405 Homepage: https://cran.r-project.org/package=nhstplot Description: CRAN Package 'nhstplot' (Plot Null Hypothesis Significance Tests) Illustrate graphically the most common Null Hypothesis Significance Testing procedures. More specifically, this package provides functions to plot Chi-Squared, F, t (one- and two-tailed) and z (one- and two-tailed) tests, by plotting the probability density under the null hypothesis as a function of the different test statistic values. Although highly flexible (color theme, fonts, etc.), only the minimal number of arguments (observed test statistic, degrees of freedom) are necessary for a clear and useful graph to be plotted, with the observed test statistic and the p value, as well as their corresponding value labels. The axes are automatically scaled to present the relevant part and the overall shape of the probability density function. This package is especially intended for education purposes, as it provides a helpful support to help explain the Null Hypothesis Significance Testing process, its use and/or shortcomings. Package: r-cran-niarules Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-niarules_0.2.0-1.ca2404.1_all.deb Size: 116400 MD5sum: e08e801b3059db995841a225d116cc25 SHA1: 4c39fcf84af125c7df30d144589a5ab3ebe36961 SHA256: 346a10c5c14175ff96e85d489e308132cedcd9469694de137daf2147821579e9 SHA512: a704bfa56950f48b4804819ba02b58fee677550e1d74bbf918784addf7773cbf4d3b16d36390d3f5e8d7a58ca91e8477b0721c9f915cab4317de58762fc9741d Homepage: https://cran.r-project.org/package=niarules Description: CRAN Package 'niarules' (Numerical Association Rule Mining using Population-BasedNature-Inspired Algorithms) Framework is devoted to mining numerical association rules through the utilization of nature-inspired algorithms for optimization. Drawing inspiration from the 'NiaARM' 'Python' and the 'NiaARM' 'Julia' packages, this repository introduces the capability to perform numerical association rule mining in the R programming language. Fister Jr., Iglesias, Galvez, Del Ser, Osaba and Fister (2018) . Package: r-cran-nic Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1712 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-palmerpenguins, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-nic_0.0.2-1.ca2404.1_all.deb Size: 1602328 MD5sum: dac10adc002b9daab64268f2fab1070c SHA1: 417ffce0383ce77f9719b8098d8edcc840dfc3eb SHA256: b6057ae71216651b575688581f69dc32c4e493adee8528bc02a41e64f9056419 SHA512: 2063b35fad46f1df968c7841dd3e086803aaf5f304e6b21f24b5e4511498cdc3c5dc96154cf99509d95877ca8899f88fabfed3f83b323d1b31b5ce02328ea5d6 Homepage: https://cran.r-project.org/package=nic Description: CRAN Package 'nic' (Nature Inspired Colours) Color palettes based on nature inspired colours in "Sri Lanka". Package: r-cran-nichebarcoding Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-dismo, r-cran-e1071, r-cran-maps, r-cran-proc, r-cran-randomforest, r-cran-raster, r-cran-rjava, r-cran-spider, r-cran-vegan Suggests: r-cran-rmarkdown, r-cran-covr, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-nichebarcoding_1.0-1.ca2404.1_all.deb Size: 1062198 MD5sum: a04d3ec777457e79c6387c5eecfd80a5 SHA1: cdc5f49388f32612e47274bd3150896dbd88d7b9 SHA256: fe7135c60610be08b6bef02be70dd2f6d33f6c089a32181b5535a3b72d90d98f SHA512: 6fa45f25cddc9be859725566dbc3b9cd7d95be54be08796e96d10d240c1dbb6db79ccf12eef411bd7170907d8728131afdb7f94c757a695b946a6cd9f18ed8eb Homepage: https://cran.r-project.org/package=NicheBarcoding Description: CRAN Package 'NicheBarcoding' (Niche-model-Based Species Identification) Species Identification using DNA Barcodes Integrated with Environmental Niche Models. Package: r-cran-nicherover Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nicherover_1.1.2-1.ca2404.1_all.deb Size: 572664 MD5sum: a7e163cee6daa14556c514718e7aa3d4 SHA1: 86fdf145341c4dae71a31c226185e02d4bcf485a SHA256: 97cc757977ab908a00f708b8cdcddcca4792e3bec89b9892a39d5f7775b61445 SHA512: 34af5dc80e334d77895cfe40d3c63567c3c87eebf8bc25d3926baa980421cc61c978b67bb3156ac6c66e14941d4e8fe1c3ee6190eaa449122a3671f5acf7aea3 Homepage: https://cran.r-project.org/package=nicheROVER Description: CRAN Package 'nicheROVER' (Niche Region and Niche Overlap Metrics for MultidimensionalEcological Niches) Implementation of a probabilistic method to calculate 'nicheROVER' (_niche_ _r_egion and niche _over_lap) metrics using multidimensional niche indicator data (e.g., stable isotopes, environmental variables, etc.). The niche region is defined as the joint probability density function of the multidimensional niche indicators at a user-defined probability alpha (e.g., 95%). Uncertainty is accounted for in a Bayesian framework, and the method can be extended to three or more indicator dimensions. It provides directional estimates of niche overlap, accounts for species-specific distributions in multivariate niche space, and produces unique and consistent bivariate projections of the multivariate niche region. The article by Swanson et al. (2015) provides a detailed description of the methodology. See the package vignette for a worked example using fish stable isotope data. Package: r-cran-nichetools Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2024 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ellipse, r-cran-lifecycle, r-cran-nicherover, r-cran-purrr, r-cran-rlang, r-cran-siber, r-cran-tibble, r-cran-tidyr Suggests: r-cran-bayestestr, r-cran-ggplot2, r-cran-ggdist, r-cran-ggtext, r-cran-ggh4x, r-cran-janitor, r-cran-knitr, r-cran-patchwork, r-cran-rjags, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-viridis Filename: pool/dists/noble/main/r-cran-nichetools_0.3.3-1.ca2404.1_all.deb Size: 1706190 MD5sum: 6d11c9792bf855a7bee42fadbcd11906 SHA1: c2e11399ae0e6db63ae3aab17924e7f81edc721e SHA256: e44472353353ce07565a1ae55296b1c45ca5180f87a2c8b89294a4b8ef48f728 SHA512: 173714deb38bd2b35c6ba0821962cebb3e8905eb36cca6d74ed58a20def8a89c06b157df922c796e18290be900e4f2e83e6c838471c5a9adf4ba166ae505bcb7 Homepage: https://cran.r-project.org/package=nichetools Description: CRAN Package 'nichetools' (Complementary Package to 'nicheROVER' and 'SIBER') Provides functions complementary to packages 'nicheROVER' and 'SIBER' allowing the user to extract Bayesian estimates from data objects created by the packages 'nicheROVER' and 'SIBER'. Please see the following publications for detailed methods on 'nicheROVER' and 'SIBER' Hansen et al. (2015) , Jackson et al. (2011) , and Layman et al. (2007) , respectfully. Package: r-cran-nichevol Architecture: all Version: 0.1.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1039 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-castor, r-cran-geiger, r-cran-stringr, r-cran-terra Suggests: r-cran-knitr, r-cran-phytools Filename: pool/dists/noble/main/r-cran-nichevol_0.1.20-1.ca2404.1_all.deb Size: 425542 MD5sum: 823d56a4dacba931aacc754f8936e6d7 SHA1: 807f4df05f93e941f769548705ba345885c787c4 SHA256: cb75b920b4d47342706d56c4fcaff5187761518d82beefb75a897e965db9fd19 SHA512: 4c7536deb094c4f4fb300eb751aa0b138e81667a208379a2244a391ce5c404cb0e46b5d6d5e0caf6a0bbb05a2f4c945a3b2a0277922bb3b23db5ee939b99fbf2 Homepage: https://cran.r-project.org/package=nichevol Description: CRAN Package 'nichevol' (Tools for Ecological Niche Evolution Assessment ConsideringUncertainty) A collection of tools that allow users to perform critical steps in the process of assessing ecological niche evolution over phylogenies, with uncertainty incorporated explicitly in reconstructions. The method proposed here for ancestral reconstruction of ecological niches characterizes species' niches using a bin-based approach that incorporates uncertainty in estimations. Compared to other existing methods, the approaches presented here reduce risk of overestimation of amounts and rates of ecological niche evolution. The main analyses include: initial exploration of environmental data in occurrence records and accessible areas, preparation of data for phylogenetic analyses, executing comparative phylogenetic analyses of ecological niches, and plotting for interpretations. Details on the theoretical background and methods used can be found in: Owens et al. (2020) , Peterson et al. (1999) , Soberón and Peterson (2005) , Peterson (2011) , Barve et al. (2011) , Machado-Stredel et al. (2021) , Owens et al. (2013) , Saupe et al. (2018) , and Cobos et al. (2021) . 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Scientific publication first using these tools Koscik TR, Man V, Jahn A, Lee CH, Cunningham WA (2020) "Decomposing the neural pathways in a simple, value-based choice." Neuroimage, 214, 116764. 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The package provides routines for solving 0-1, bounded and unbounded knapsack problems; 0-1, bounded and unbounded subset sum problems; additive partitioning of natural numbers; and one-dimensional bin-packing problem. 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Datasets document plant allometry, stem heights, nutrient and stable isotope content, and sediment denitrification enzyme assays. The data and analysis offer an examination of nitrogen uptake and allocation in two salt marsh plant species. 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Reference: Reddy T., Shkedy Z., van Rensburg C. J., Mwambi H., Debba P., Zuma K. and Manda, S. (2021). "Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach". BMC medical research methodology, 21(1), 1-11. . 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Package: r-cran-nlmixr2auto Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlmixr2data, r-cran-nlmixr2, r-cran-nlmixr2est, r-cran-nlmixr2autoinit, r-cran-rxode2, r-cran-dplyr, r-cran-progressr, r-cran-processx, r-cran-withr, r-cran-crayon Filename: pool/dists/noble/main/r-cran-nlmixr2auto_1.0.0-1.ca2404.1_all.deb Size: 418648 MD5sum: d3d8e775d06055ab3057e286395c1681 SHA1: 90e42e13b2f0e2fd385c1a487a0ec6d31603cc90 SHA256: 260140d996c6660f9e8d39ca807062add9dcd7c062765769cd6eaa6b1cc06e86 SHA512: b87cff1fa7196e3c291ec5883e5979f126195d78cd1c5808a4160d2c8b0296fb48cfe974c5b5b7b274dfec4a49865e9ddc22a5f94e0c45b75f52525410ae14b9 Homepage: https://cran.r-project.org/package=nlmixr2auto Description: CRAN Package 'nlmixr2auto' (Automated Population Pharmacokinetic Modeling) Automated population pharmacokinetic modeling framework for data-driven initialisation, model evaluation, and metaheuristic optimization. Supports genetic algorithms, ant colony optimization, tabu search, and stepwise procedures for automated model selection and parameter estimation within the nlmixr2 ecosystem. Package: r-cran-nlmixr2autoinit Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlmixr2data, r-cran-nlmixr2, r-cran-nlmixr2est, r-cran-rxode2, r-cran-dplyr, r-cran-tidyr, r-cran-crayon, r-cran-purrr, r-cran-knitr, r-cran-magrittr, r-cran-progressr, r-cran-tibble, r-cran-vpc Filename: pool/dists/noble/main/r-cran-nlmixr2autoinit_1.0.0-1.ca2404.1_all.deb Size: 400882 MD5sum: 6ff81650620f633acd361a45e5f4c8a8 SHA1: d638bcf24d27a2869f06b73289d9db677637e7b9 SHA256: cfd9fab12cce10098c6f5788cf9ead2472ca8f722561656d88b56fe82c206596 SHA512: 05c4942f0ce512cf7a1cc9dc549dc2e20f8427a0306394eefd10a8cf92bc00cc3897f452662ddadf79d2199288e5fe699b116806a4b6114f46a7bbeb49a79546 Homepage: https://cran.r-project.org/package=nlmixr2autoinit Description: CRAN Package 'nlmixr2autoinit' (Automatic Generation of Initial Estimates for PopulationPharmacokinetic Modeling) Provides automated methods for generating initial parameter estimates in population pharmacokinetic modeling. The pipeline integrates adaptive single-point methods, naive pooled graphic approaches, noncompartmental analysis methods, and parameter sweeping across pharmacokinetic models. It estimates residual unexplained variability using either data-driven or fixed-fraction approaches and assigns pragmatic initial values for inter-individual variability. These strategies are designed to improve model robustness and convergence in 'nlmixr2' workflows. For more details see Huang Z, Fidler M, Lan M, Cheng IL, Kloprogge F, Standing JF (2025) . Package: r-cran-nlmixr2data Architecture: all Version: 2.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nlmixr2data_2.0.9-1.ca2404.1_all.deb Size: 787706 MD5sum: 15778a67ab95efac9783619cff852ad0 SHA1: aa906e50266cf961ccb5d3d0da81b5e147dd964e SHA256: 5095ecd3cfa2ea69d5f28516963d85fde8cba4d013c73b826218be025f49bbb2 SHA512: 2c082224c3e87cb2fc40289ba9742c9171ffc33ea5a97758254adf613221dd78bcb3b2b372283b2b74e051dc2e4033808bb48b4120c60527cc7ba61202b94092 Homepage: https://cran.r-project.org/package=nlmixr2data Description: CRAN Package 'nlmixr2data' (Nonlinear Mixed Effects Models in Population PK/PD, Data) Datasets for 'nlmixr2' and 'rxode2'. 'nlmixr2' is used for fitting and comparing nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 ). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 ). 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The models include (and plan to include) pharmacokinetic, pharmacodynamic, and disease models used in pharmacometrics. Where applicable, references for each model are included in the meta-data for each individual model. The package also includes model composition and modification functions to make model updates easier. 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Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 ). This package is for 'ggplot2' plotting methods for 'nlmixr2' objects. 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Package: r-cran-nlmrt Architecture: all Version: 2016.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 581 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-minpack.lm, r-cran-optimx, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-nlmrt_2016.3.2-1.ca2404.1_all.deb Size: 371472 MD5sum: 374fb7e31754f0d2649e04055cfb73d1 SHA1: 96ff39fb3fa9d4fa34580291504fd5479a9b4109 SHA256: a8b85cbafa194f0a7bd9015e9f90b1af2271253c8e94a6f68680ff696ab5608e SHA512: c1c2d45f8b4f944c062ef5755c593862d47834ccebca0ad8779e4e9e86c558fe9a1e5cfa40cdff5005d0453600b6fcb2bf6bc916c995f4b1d6204078ae99217d Homepage: https://cran.r-project.org/package=nlmrt Description: CRAN Package 'nlmrt' (Functions for Nonlinear Least Squares Solutions) Replacement for nls() tools for working with nonlinear least squares problems. The calling structure is similar to, but much simpler than, that of the nls() function. 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References: Tianwei Yu (2018); Haodong Liu and others (2016); Kai Wang and others (2015); Tianwei Yu and others (2010). Package: r-cran-nlopt Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr Filename: pool/dists/noble/main/r-cran-nlopt_0.1.1-1.ca2404.1_all.deb Size: 31772 MD5sum: 85d1ca220cfd60532eef2b5ec8a3223a SHA1: 97f4ade01fdf5d1c6e1631ab896b90d9ec69552e SHA256: f7fe9da9d17a08f5e220d9f93570bcc5e5b3c855ba8e070d4fba34b0eedbb93d SHA512: 43835c0396a2f3b85358c94f2087199ebe33883d70d0a0a0ce832e6872332dbb1d5bbd1b5b50507816111060e200bfaeb7f34b290d7ca49161bceb199e16a8a5 Homepage: https://cran.r-project.org/package=nlopt Description: CRAN Package 'nlopt' (Call Optimization Solvers with .nl Files) The purpose of this library is to to call different optimization solvers (such as Gonzalez Rodriguez et al. 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In the biomedical and clinical settings, one challenge is the huge size of databases, e.g. when analyzing data of millions of patients over tens of years. To address this, this package provides functions to efficiently compute monthly co-occurrence matrices, which is the computational bottleneck of the analysis, by using the 'RcppAlgos' package and sparse matrices. Furthermore, the functions can be called on 'SQL' databases, enabling the computation of co-occurrence matrices of tens of gigabytes of data, representing millions of patients over tens of years. Partly based on Hong C. (2021) . 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Package: r-cran-nlpsem Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openmx, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-matrix, r-cran-nnet, r-cran-readr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nlpsem_0.4-1.ca2404.1_all.deb Size: 5513454 MD5sum: 729c03b2846f5bef6da3a1f732813b8d SHA1: 47600758d4a6149a80f4d0f9665bc2fa716a52aa SHA256: 185f21002af292791a60a270104b1f1028a76f9b00b6edea0b1a1846a45a6805 SHA512: 334c85d1cfb9d2ac2932c66a6a163c24eb06c95a926edac386d56e4bc9361ed03c8eb24c78043193fce78db994a9525ffa45e97683d6817ccd725fc782007cec Homepage: https://cran.r-project.org/package=nlpsem Description: CRAN Package 'nlpsem' (Nonlinear Longitudinal Process in Structural Equation Modeling) Provides computational tools for nonlinear longitudinal models, in particular the intrinsically nonlinear models, in four scenarios: (1) univariate longitudinal processes with growth factors, with or without covariates including time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal processes that facilitate the assessment of correlation or causation between multiple longitudinal variables; (3) multiple-group models for scenarios (1) and (2) to evaluate differences among manifested groups, and (4) longitudinal mixture models for scenarios (1) and (2), with an assumption that trajectories are from multiple latent classes. The methods implemented are introduced in Liu (2025) . Package: r-cran-nlputils Architecture: all Version: 0.0-5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlp, r-cran-snowballc, r-cran-qdap Filename: pool/dists/noble/main/r-cran-nlputils_0.0-5.1-1.ca2404.1_all.deb Size: 19052 MD5sum: b568772b8038271ac3173d26dbbaf01d SHA1: 9c94c01c9ef537f48a58a3a7accb0d5b148b7f5f SHA256: 0fb36e85e220b2753ca87e252dfb01bc4164466f0fb8f4b682cbfdea86b09b26 SHA512: 09e0619c383aa9f8d43873c3d0ed273bde75debe6578b17cfcbab9b1ea7bc18a87c047b21b618920af5324aae93776818d9c327b043f56d1c99d1d30211584d5 Homepage: https://cran.r-project.org/package=NLPutils Description: CRAN Package 'NLPutils' (Natural Language Processing Utilities) Utilities for Natural Language Processing. Package: r-cran-nlraa Architecture: all Version: 1.9.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4708 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-knitr, r-cran-mass, r-cran-matrix, r-cran-mgcv, r-cran-nlme Suggests: r-cran-bbmle, r-cran-car, r-cran-emmeans, r-cran-ggplot2, r-cran-lattice, r-cran-minpack.lm, r-cran-nistnls, r-cran-nlstools, r-cran-nls2, r-cran-rmarkdown, r-cran-segmented Filename: pool/dists/noble/main/r-cran-nlraa_1.9.10-1.ca2404.1_all.deb Size: 3489134 MD5sum: 8fcdadb6d62fa0b3c95b7c6816171547 SHA1: ed6fa661480d2971e3c9157e2ed91ae6d81e2f0d SHA256: 6322c9e644734bdc55a0cb48aebdedce982bcfa3dd3ed7004cb294a76c9cca5a SHA512: 2034b3fb5fa909e388054e14bd2433b2ac3cf0d0ba75aa3764997328f0cdcb800107c2747c56a575cc29cc1cfb2fe25164a1faaf7969d6d8661a2bd91c175933 Homepage: https://cran.r-project.org/package=nlraa Description: CRAN Package 'nlraa' (Nonlinear Regression for Agricultural Applications) Additional nonlinear regression functions using self-start (SS) algorithms. One of the functions is the Beta growth function proposed by Yin et al. (2003) . There are several other functions with breakpoints (e.g. linear-plateau, plateau-linear, exponential-plateau, plateau-exponential, quadratic-plateau, plateau-quadratic and bilinear), a non-rectangular hyperbola and a bell-shaped curve. Twenty eight (28) new self-start (SS) functions in total. This package also supports the publication 'Nonlinear regression Models and applications in agricultural research' by Archontoulis and Miguez (2015) , a book chapter with similar material and a publication by Oddi et. al. (2019) in Ecology and Evolution . The function 'nlsLMList' uses 'nlsLM' for fitting, but it is otherwise almost identical to 'nlme::nlsList'.In addition, this release of the package provides functions for conducting simulations for 'nlme' and 'gnls' objects as well as bootstrapping. These functions are intended to work with the modeling framework of the 'nlme' package. It also provides four vignettes with extended examples. Package: r-cran-nlreg Architecture: all Version: 1.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 748 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod, r-cran-survival Suggests: r-cran-boot, r-cran-cond, r-cran-csampling, r-cran-marg Filename: pool/dists/noble/main/r-cran-nlreg_1.2-4-1.ca2404.1_all.deb Size: 593540 MD5sum: 6d4441184d982dd2cbece0ffca804337 SHA1: 2eef9d15b0b6d50b20f75972f07ee3f3a2837bb5 SHA256: a2f796f5cb3f346d011e4f4019724ca334921f8bee0813a510fbc578cb7044f7 SHA512: 22a5bb8342ed07ce56c51347d120a55e199542d6882815e97bae9a3d5c5087a38501786ec3ab200a6d8a6a7924838800531b40d1881911865b62b5bcb13e0d71 Homepage: https://cran.r-project.org/package=nlreg Description: CRAN Package 'nlreg' (Higher Order Inference for Nonlinear Heteroscedastic Models) Implements likelihood inference based on higher order approximations for nonlinear models with possibly non constant variance. 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'nlrx' experiments use a similar structure as 'NetLogos' Behavior Space experiments. However, 'nlrx' offers more flexibility and additional tools for running and analyzing complex simulation designs and sensitivity analyses. The user defines all information that is needed in an intuitive framework, using class objects. Experiments are submitted from 'R' to 'NetLogo' via 'XML' files that are dynamically written, based on specifications defined by the user. By nesting model calls in future environments, large simulation design with many runs can be executed in parallel. This also enables simulating 'NetLogo' experiments on remote high performance computing machines. In order to use this package, 'Java' and 'NetLogo' (>= 5.3.1) need to be available on the executing system. Package: r-cran-nls.multstart Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-minpack.lm, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-lhs, r-cran-cli, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-broom, r-cran-nlstools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nls.multstart_2.0.0-1.ca2404.1_all.deb Size: 294542 MD5sum: 0a40d6e12fbcbf217dc27cb82066ca45 SHA1: 2bcdbed0f172f674acb63063d61ba47e5e99c701 SHA256: c06c49d2565a5113a391edea4eef445ec4c72223268de9b67cd8573043e466f3 SHA512: 6b8fa8848e6d8bd68265a11516b362ddf56351ff85c47bdc24524ec67a83bef132e3ddea5fac0f865906ca57beb02d8be56342eec0b7b60810d346782c67ad9d Homepage: https://cran.r-project.org/package=nls.multstart Description: CRAN Package 'nls.multstart' (Robust Non-Linear Regression using AIC Scores) Non-linear least squares regression with the Levenberg-Marquardt algorithm using multiple starting values for increasing the chance that the minimum found is the global minimum. Package: r-cran-nls2 Architecture: all Version: 0.3-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proto Suggests: r-cran-nlstools, r-cran-lhs, r-cran-cpoptim Filename: pool/dists/noble/main/r-cran-nls2_0.3-4-1.ca2404.1_all.deb Size: 71346 MD5sum: c509942b70d15d516ee9efae860f81ab SHA1: cacf6e502de0a65aeaf460f5401a5fbdd6acf9f9 SHA256: b868dc665d629ddc54e95f8bfb6f0b3af40c0f29e427e24bd7bd0d929096dbdc SHA512: 3c7d4ede1ecda7bf26693a690d71f0a55ec899162d9e1134f730d3fb83054a3b59736093263955bf3a631e7678109aa4bb27349c267a983753992446361cdc1c Homepage: https://cran.r-project.org/package=nls2 Description: CRAN Package 'nls2' (Non-Linear Regression with Brute Force) Adds brute force and multiple starting values to nls. Package: r-cran-nlsem Architecture: all Version: 0.8-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 674 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-orthopolynom, r-cran-nlme, r-cran-lavaan, r-cran-gaussquad, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-nlsem_0.8-1-1.ca2404.1_all.deb Size: 630866 MD5sum: 0779b3f69982a1e0adbb2b564029679a SHA1: 536ac0dbb2630033462e59b40a8d870826cb7167 SHA256: 67da965928c92b7870fed34e80156a958a28b981d4ab6522c39dfbbd01b8575e SHA512: 083d2cfaf72d6fcf8998d4bab6bbd37c367dd67208ccc13473ee98bf1b7ad8b653e178c8562ac67afcea77618f850f9e27a4d5e59c29eb7688b3e0eec0813163 Homepage: https://cran.r-project.org/package=nlsem Description: CRAN Package 'nlsem' (Fitting Structural Equation Mixture Models) Estimation of structural equation models with nonlinear effects and underlying nonnormal distributions. 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Linear problems are solved by dense QR decomposition from 'LAPACK' which can limit the size of treated problems. On the other side, we avoid condition number degradation which happens in classical quadratic programming approach. Inequality constraints treatment on each non linear iteration is based on 'NNLS' method (by Lawson and Hanson). We provide an original function 'lsi_ln' for solving linear least squares problem with inequality constraints in least norm sens. Thus if Jacobian of the problem is rank deficient a solution still can be provided. However, truncation errors are probable in this case. Equality constraints are treated by using a basis of Null-space. User defined function calculating residuals must return a list having residual vector (not their squared sum) and Jacobian. If Jacobian is not in the returned list, package 'numDeriv' is used to calculated finite difference version of Jacobian. The 'NLSIC' method was fist published in Sokol et al. (2012) . 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For the estimation of models reliable and robust tools than nls(), where the the Gauss-Newton method frequently stops with 'singular gradient' messages. This is accomplished by using, where possible, analytic derivatives to compute the matrix of derivatives and a stabilization of the solution of the estimation equations. Tools for approximate or externally supplied derivative matrices are included. Bounds and masks on parameters are handled properly. 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Package: r-cran-nmof Architecture: all Version: 2.11-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2923 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-pmwr, r-cran-runit, r-cran-rglpk, r-cran-datetimeutils, r-cran-openxlsx, r-cran-quadprog, r-cran-readxl, r-cran-tinytest, r-cran-zoo Filename: pool/dists/noble/main/r-cran-nmof_2.11-0-1.ca2404.1_all.deb Size: 2280012 MD5sum: e41c3d56c002b39b0c399db417426c71 SHA1: 13c2b7d243123ac32d901ed261024ec09aebbe06 SHA256: bc3ba720120ff77b0476d1642916b5012de4ea0615918b707c4889676d891a7b SHA512: fcb203bc6a1fe9d976298e28ea24bd2d6674dae596b25fe3f726ad80012e7c92eb7f8545ca4603936bc4f956f876d23cd99ab51a810129f5098acf7ea7e3cf8c Homepage: https://cran.r-project.org/package=NMOF Description: CRAN Package 'NMOF' (Numerical Methods and Optimization in Finance) Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. 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The methodology is based on our new algorithms and various references (Binczyk et al. (2015) ,Chen et al. (2002) , de Brouwer (2009) , Džakula (2000) , Ernst (1969) , Liland et al. (2010) ). 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Several functions are provided for data exploration, including functions for creating a subset of dataset, frequency tables and plots. Inference for order restricted dose- response data is performed by testing the significance of monotonic dose-response relationship, using Williams, Marcus, M, Modified M and Likelihood ratio tests. Several methods of multiplicity adjustment are also provided. Description of the methods can be found in . 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Additionally, provides functions for post-run processing of NONMEM output files, generating comprehensive PDF diagnostic reports including objective function value analysis, parameter estimates, prediction diagnostics, residual diagnostics, empirical Bayes estimate (EBE) analysis, input data summary, and individual pharmacokinetic parameter distributions. 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This is a methodology commonly used in phytosociology or marine benthos ecology to analyze the species' distribution (random, uniform or clumped patterns). Ludwig & Reynolds (1988, ISBN:0471832359). Package: r-cran-nnbenchmark Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1457 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-pkgload Suggests: r-cran-brnn, r-cran-validann Filename: pool/dists/noble/main/r-cran-nnbenchmark_3.2.0-1.ca2404.1_all.deb Size: 1394388 MD5sum: cb1ee451fb76dfb17c58c0e84d01de7d SHA1: f534fa9f6b9f69b08f837327daa8ba7f74a1f3a7 SHA256: af8fa6bad799567a3720f6d425a9bb95b8d4c05e9184af6c7b628513e7ae809a SHA512: 83e5c65a471fa24dd6c3cc77595c71e98d6f87ee421de78b79eb42048ea1dd2a083cc367fd26fc3a8ef46b625fc93f06f8dd592c1a31de782eeae097d1a9e21e Homepage: https://cran.r-project.org/package=NNbenchmark Description: CRAN Package 'NNbenchmark' (Datasets and Functions to Benchmark Neural Network Packages) Datasets and functions to benchmark (convergence, speed, ease of use) R packages dedicated to regression with neural networks (no classification in this version). The templates for the tested packages are available in the R, R Markdown and HTML formats at and . The submitted article to the R-Journal can be read at . 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Cui, Z., Marder, E. P., Click, E. S., Hoekstra, R. M., & Bruce, B. B. (2022) . Package: r-cran-nndiagram Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2965 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nndiagram_1.0.0-1.ca2404.1_all.deb Size: 2783604 MD5sum: 1b50d49bbc8f7750a3477ea0dacfc972 SHA1: 38bf1a78e519d0946721eb669b6681e45e9b0c3c SHA256: c30f2bd57d736cbfb947b68cc3c0576738a8e20e5bf83c98f0b682a6054333a3 SHA512: 9cec4d3db227550025397194ad25cba8cab2803f96a8eeaf1d9329061643e4723afd1ff62a5993865a9405d8b85d68e5669ba883979cb45c0d1185f9613be497 Homepage: https://cran.r-project.org/package=nndiagram Description: CRAN Package 'nndiagram' (Generator of 'LaTeX' Code for Drawing Neural Network Diagramswith 'TikZ') Generates 'LaTeX' code for drawing well-formatted neural network diagrams with 'TikZ'. Users have to define number of neurons on each layer, and optionally define neuron connections they would like to keep or omit, layers they consider to be oversized and neurons they would like to draw with lighter color. They can also specify the title of diagram, color, opacity of figure, labels of layers, input and output neurons. In addition, this package helps to produce 'LaTeX' code for drawing activation functions which are crucial in neural network analysis. To make the code work in a 'LaTeX' editor, users need to install and import some 'TeX' packages including 'TikZ' in the setting of 'TeX' file. 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Package: r-cran-nngarrote Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet Suggests: r-cran-testthat, r-cran-mvnfast Filename: pool/dists/noble/main/r-cran-nngarrote_1.0.4-1.ca2404.1_all.deb Size: 46170 MD5sum: bd59f39e85cec23abb6d8c90d3aec3c4 SHA1: 7f40bf1befc98bd1fbb9b5c60a0a314c8873d3a5 SHA256: 513c071d7dd3209aa9dc80e64e05700eeec24a3e3175e62baa4afa8af710c448 SHA512: 6fa948590b01fc20bf2fe336cab754342b009e1a8877d6682163b294329fde4add0b2e0ab5aa557c7ea0c8c18cfd397308d1dfdc539e9a985f13616e6aace6a0 Homepage: https://cran.r-project.org/package=nnGarrote Description: CRAN Package 'nnGarrote' (Non-Negative Garrote Estimation with Penalized InitialEstimators) Functions to compute the non-negative garrote estimator as proposed by Breiman (1995) with the penalized initial estimators extension as proposed by Yuan and Lin (2007) . Package: r-cran-nnlasso Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nnlasso_0.3-1.ca2404.1_all.deb Size: 119922 MD5sum: 88ad147669ca1f8cc81d3ef78043b081 SHA1: 08341bcf89c8f4fe09ca4bda458028cf473b0d51 SHA256: c0ee88d33fcc748ac948b980a71f89c7c8a23382076f04a731883aea6f8df5ed SHA512: ac02d80e2aaf04dcda1df601bd16a1ae7be84cb85359041cf0ec832884979367af6fa5b5623e228f5cb2182abd346b6814c11e27e41ca115da28cabed8d9a81d Homepage: https://cran.r-project.org/package=nnlasso Description: CRAN Package 'nnlasso' (Non-Negative Lasso and Elastic Net Penalized Generalized LinearModels) Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates. Package: r-cran-nnmf Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clusterr, r-cran-matrix, r-cran-osqp, r-cran-quadprog, r-cran-rfast, r-cran-rfast2, r-cran-rglpk, r-cran-sparcl Filename: pool/dists/noble/main/r-cran-nnmf_1.1-1.ca2404.1_all.deb Size: 52928 MD5sum: 4c21692b474b4a75f634988eeb0dac02 SHA1: 74dd37f87ced0319cad77e92c3bbea21c7e006b7 SHA256: 7853e392e0c4f7789237a8d110c7cd06da028a8c990c943b9b7802213c561795 SHA512: 379e8a8aec0f8e80696d3323e77a74c7063ef499e5188b445d4d9f7d06f8a4afd51f6107e502ed9d8a035517e50ecddf78f8ace634ced10a884dfebb415d9a07 Homepage: https://cran.r-project.org/package=nnmf Description: CRAN Package 'nnmf' (Nonnegative Matrix Factorization) Nonnegative matrix factorization (NMF) is a technique to factorize a matrix with nonnegative values into the product of two matrices. Covariates are also allowed. Parallel computing is an option to enhance the speed and high-dimensional and large scale (and/or sparse) data are allowed. Relevant papers include: Wang Y. X. and Zhang Y. J. (2012). Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering, 25(6), 1336-1353 and Kim H. and Park H. (2008). Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30(2), 713-730 . Package: r-cran-nnmis Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-nnmis_1.0.1-1.ca2404.1_all.deb Size: 50562 MD5sum: 1d690268ed5776648da5dab553003b7d SHA1: 5526ea5462ac177253c14d851a97a1aaf48c91c2 SHA256: 43c104b79d9e156f49e0f703ae6057d7c62f930c48116d252840e32b3a81d9c6 SHA512: 6ac3880ddabcb150bc4fbbb64b05fc355de34e53a5986c5de0424f2d363bdd3042691064de3be72351f79b125d68fadacedff267b36645aa9493384faa62e05d Homepage: https://cran.r-project.org/package=NNMIS Description: CRAN Package 'NNMIS' (Nearest Neighbor Based Multiple Imputation for Survival Datawith Missing Covariates) Imputation for both missing covariates and censored observations (optional) for survival data with missing covariates by the nearest neighbor based multiple imputation algorithm as described in Hsu et al. (2006) , and Hsu and Yu (2018) . Note that the current version can only impute for a situation with one missing covariate. Package: r-cran-nnmomo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1685 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-demography, r-cran-torch, r-cran-luz, r-cran-stmomo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nnmomo_0.1.0-1.ca2404.1_all.deb Size: 1626266 MD5sum: bb2e24de4e3dc4eb53e705bae3c52eeb SHA1: 351d5d557805e772731d3f76a7d2705600d95983 SHA256: bfaf492de97c021ba22c76b9789c375e27a235b09a89a600f4e91d5774f5efea SHA512: 40aaa958b2b8635b820bd73de31323f8883931922182b5be7d548314c27df9bd6fa4b0fa30ff2b7e99b6a2437baf65efea5912ec64f514503270eb2aa3475ec7 Homepage: https://cran.r-project.org/package=NNMoMo Description: CRAN Package 'NNMoMo' (Neural Network Extension to 'StMoMo' for Lee-Carter Modeling) Provides extensions to the 'StMoMo' package by incorporating neural network functionality for Lee-Carter and Poisson Lee-Carter mortality models. Includes tools for constructing mortality datasets from 'demogdata' objects and fitting neural network-based mortality models. Further analysis, such as plotting and forecasting, can be done with 'StMoMo' functions. Package: r-cran-nnr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nnr_0.1.0-1.ca2404.1_all.deb Size: 149860 MD5sum: 367b3c37993c5fb94980e43d7390e697 SHA1: ed9ade5f001f85a85f069688e14999d95c71ecb2 SHA256: 64da0b01bdaaef25561fc3de601dbdaac1b0bdc310374a9697d69d353631c253 SHA512: 0b056e3d063220acf8aebc772c509ca4172b48525d29fa70a4f895116fe6fd705cf5b59886524fff783f8b50ef9373e12d1053b628b194407be7cd1a77fc7ed4 Homepage: https://cran.r-project.org/package=nnR Description: CRAN Package 'nnR' (Neural Networks Made Algebraic) Do algebraic operations on neural networks. We seek here to implement in R, operations on neural networks and their resulting approximations. Our operations derive their descriptions mainly from Rafi S., Padgett, J.L., and Nakarmi, U. (2024), "Towards an Algebraic Framework For Approximating Functions Using Neural Network Polynomials", , Grohs P., Hornung, F., Jentzen, A. et al. (2023), "Space-time error estimates for deep neural network approximations for differential equations", , Jentzen A., Kuckuck B., von Wurstemberger, P. (2023), "Mathematical Introduction to Deep Learning Methods, Implementations, and Theory" . Our implementation is meant mainly as a pedagogical tool, and proof of concept. Faster implementations with deeper vectorizations may be made in future versions. Package: r-cran-nnspat Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1347 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-pcds, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-nnspat_0.1.2-1.ca2404.1_all.deb Size: 1248460 MD5sum: ca07f1e40fecc73836f5651042372f60 SHA1: 3c0aefe19cf5caf0b06819fe31c70b2c9b52b2b5 SHA256: 3c4a47b67c88266388ea00b835d1d3f616b2bcf5f885ae7375fc2cc8f66586af SHA512: 78471c656f0acb2e0207e7c59e97de8ae7fc5f14a617af509b60f3273882f1f91813ae60e75c35df43051767709e7b9734c76c209735b9af5a158a1d2e2db91d Homepage: https://cran.r-project.org/package=nnspat Description: CRAN Package 'nnspat' (Nearest Neighbor Methods for Spatial Patterns) Contains the functions for testing the spatial patterns (of segregation, spatial symmetry, association, disease clustering, species correspondence, and reflexivity) based on nearest neighbor relations, especially using contingency tables such as nearest neighbor contingency tables (Ceyhan (2010) and Ceyhan (2017) and references therein), nearest neighbor symmetry contingency tables (Ceyhan (2014) ), species correspondence contingency tables and reflexivity contingency tables (Ceyhan (2018) for two (or higher) dimensional data. The package also contains functions for generating patterns of segregation, association, uniformity in a multi-class setting (Ceyhan (2014) ), and various non-random labeling patterns for disease clustering in two dimensional cases (Ceyhan (2014) ), and for visualization of all these patterns for the two dimensional data. The tests are usually (asymptotic) normal z-tests or chi-square tests. Package: r-cran-nnt Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-survrm2 Filename: pool/dists/noble/main/r-cran-nnt_0.1.4-1.ca2404.1_all.deb Size: 28620 MD5sum: 9a845aae7e4f405e0936d3726d7de486 SHA1: 0c27e8e2d219f8deab48da58dbc102f87a971353 SHA256: 0024f4d43156d899b2a9da9ec0ba792200f61dc3da22a069fb77a48c005c222b SHA512: 216446ad5bd9dbdc8f274b67ca1e666145e2211379fa3918e43f70d5d7606dca253a1ed686ea584dc66012eb82ac80e97aeb051713d739d11b521ebad99743e1 Homepage: https://cran.r-project.org/package=nnt Description: CRAN Package 'nnt' (The Number Needed to Treat (NNT) for Survival Endpoint) Estimate the NNT using the proposed method in Yang and Yin's paper (2019) , in which the NNT-RMST (number needed to treat based on the restricted mean survival time) is defined as the RMST (restricted mean survival time) in the control group divided by the difference in RMSTs between the treatment and control groups up to a chosen time t. Package: r-cran-nntbiomarker Architecture: all Version: 0.29.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1254 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-xtable, r-cran-stringr, r-cran-magrittr, r-cran-mvbutils Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-plyr Filename: pool/dists/noble/main/r-cran-nntbiomarker_0.29.11-1.ca2404.1_all.deb Size: 373506 MD5sum: 8eb67489e3b3fe220dc043055e41756e SHA1: d04871b02f2ada9a7991879e5be44fb84473d7c2 SHA256: ae084d30a85b3c26d677baaf0e0b07ffe07ada8233ba8f1f4f87fd2106e121aa SHA512: 54164173e599f2be102fc827f61167b3ae84ab3cff5e4921adb408c724c3c2f4ab433dd33c59ba524e7934b3f1b7cc8097ba30b87fca594f662865bd82c2555c Homepage: https://cran.r-project.org/package=NNTbiomarker Description: CRAN Package 'NNTbiomarker' (Calculate Design Parameters for Biomarker Validation Studies) Helps a clinical trial team discuss the clinical goals of a well-defined biomarker with a diagnostic, staging, prognostic, or predictive purpose. From this discussion will come a statistical plan for a (non-randomized) validation trial. Both prospective and retrospective trials are supported. In a specific focused discussion, investigators should determine the range of "discomfort" for the NNT, number needed to treat. The meaning of the discomfort range, [NNTlower, NNTupper], is that within this range most physicians would feel discomfort either in treating or withholding treatment. A pair of NNT values bracketing that range, NNTpos and NNTneg, become the targets of the study's design. If the trial can demonstrate that a positive biomarker test yields an NNT less than NNTlower, and that a negative biomarker test yields an NNT less than NNTlower, then the biomarker may be useful for patients. A highlight of the package is visualization of a "contra-Bayes" theorem, which produces criteria for retrospective case-controls studies. 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See Andrzej Cichock et al (2009) and the reference section of GitHub README.md , for details of the methods. 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Node-based analysis of species distributions. Methods in Ecology and Evolution 5(11): 1225-1235. . Package for phylogenetic analysis of species distributions. The main function goes through each node in the phylogeny, compares the distributions of the two descendant nodes, and compares the result to a null model. This highlights nodes where major distributional divergence have occurred. The distributional divergence for these nodes is mapped. 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Package: r-cran-noia Architecture: all Version: 0.97.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-numderiv Filename: pool/dists/noble/main/r-cran-noia_0.97.3-1.ca2404.1_all.deb Size: 171030 MD5sum: c73bcf91b22577900aee05c0d92cd6d1 SHA1: 88fa30f577073d26788e9926ab14de7eba521da5 SHA256: bf8e6eff1b866a96f9546419f159e2012865ee55bc0551831e273b18c1e3feea SHA512: 6528f56b8fa0e0a2d97b5e72ce1aad17040fbe754f2220c1f2a22733df9cc1e753becd2071bc6a9a95970e3507c9e6874b9b0d9c7cd2dd4458f58211c68e255a Homepage: https://cran.r-project.org/package=noia Description: CRAN Package 'noia' (Implementation of the Natural and Orthogonal InterAction (NOIA)Model) The NOIA model, as described extensively in Alvarez-Castro & Carlborg (2007), is a framework facilitating the estimation of genetic effects and genotype-to-phenotype maps. This package provides the basic tools to perform linear and multilinear regressions from real populations (provided the phenotype and the genotype of every individuals), estimating the genetic effects from different reference points, the genotypic values, and the decomposition of genetic variances in a multi-locus, 2 alleles system. This package is presented in Le Rouzic & Alvarez-Castro (2008). Package: r-cran-noise Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 827 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-preprocesscore Filename: pool/dists/noble/main/r-cran-noise_1.0.2-1.ca2404.1_all.deb Size: 812638 MD5sum: 5758ec9192643e988b112ed0dca7dbfa SHA1: b181e635ad98cbb36368b6688dc4d7ec3a868c91 SHA256: 358eef3188fae1b23ff72bacafc86eb508dfb96976cd819a763ff23c6ab4f3c1 SHA512: c0dd91f475333336d1c31971f5b82db0f39bc7ad6fc746cec40d3e7648b78adbd93c327cfc0818793bf2ecbe95db0fe6119818448d5580a2ee810634c4a22af4 Homepage: https://cran.r-project.org/package=noise Description: CRAN Package 'noise' (Estimation of Intrinsic and Extrinsic Noise from Single-CellData) Functions to calculate estimates of intrinsic and extrinsic noise from the two-reporter single-cell experiment, as in Elowitz, M. B., A. J. Levine, E. D. Siggia, and P. S. Swain (2002) Stochastic gene expression in a single cell. Science, 297, 1183-1186. Functions implement multiple estimators developed for unbiasedness or min Mean Squared Error (MSE) in Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements. Statistical Applications in Genetics and Molecular Biology, 15(6), 447-471. Package: r-cran-noisemodel Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-nnet, r-cran-e1071, r-cran-fnn, r-cran-classint, r-cran-ggplot2, r-cran-extdist, r-cran-lsr, r-cran-stringr, r-cran-rcolorbrewer, r-cran-rsnns, r-cran-c50 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-noisemodel_1.0.2-1.ca2404.1_all.deb Size: 710382 MD5sum: 8be81b5f7039c3275bcadd0c38bd56ab SHA1: 9ce8d4cb9e69570acf5d2f129631a1ca335c5027 SHA256: 1529f5d9b9c5d1f8551ad114e3cc84909474f2e614c0011dfdd6697069df7d65 SHA512: eb0b0122a69da569cd9295a16963705f62499425bf40d31c118cb20cb9d2d6c714a5acb0bdab4c2ad434fb2abcb2dee0692741f3d771d9b254044bd8e3518677 Homepage: https://cran.r-project.org/package=noisemodel Description: CRAN Package 'noisemodel' (Noise Models for Classification Datasets) Implementation of models for the controlled introduction of errors in classification datasets. This package contains the noise models described in Saez (2022) that allow corrupting class labels, attributes and both simultaneously. Package: r-cran-noisyce2 Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-coda, r-cran-testthat Filename: pool/dists/noble/main/r-cran-noisyce2_1.1.0-1.ca2404.1_all.deb Size: 56824 MD5sum: 42b068da927847204d9869d13add72bd SHA1: 691f6e1038f2aa82660893106efa5d242051fcbb SHA256: da9ca9ca05fba39aaa99640d2a98f9d7189b49d16fe352be419f0f81e4a2d627 SHA512: 5b162c535dd59f3312e720b2ad25b188085cc3a9aa719d1c73a640fe0f47b06f518f04807083e6688907eecb530c0820d1cde77c1a2c6c36d942fc051d5d59e0 Homepage: https://cran.r-project.org/package=noisyCE2 Description: CRAN Package 'noisyCE2' (Cross-Entropy Optimisation of Noisy Functions) Cross-Entropy optimisation of unconstrained deterministic and noisy functions illustrated in Rubinstein and Kroese (2004, ISBN: 978-1-4419-1940-3) through a highly flexible and customisable function which allows user to define custom variable domains, sampling distributions, updating and smoothing rules, and stopping criteria. Several built-in methods and settings make the package very easy-to-use under standard optimisation problems. Package: r-cran-noisyr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4466 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-bioc-preprocesscore, r-bioc-iranges, r-bioc-genomicranges, r-bioc-rsamtools, r-cran-philentropy, r-cran-doparallel, r-cran-foreach Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-noisyr_1.0.0-1.ca2404.1_all.deb Size: 1876428 MD5sum: e077077e56b98ad0fc073968341ca3fc SHA1: c8a406084976cc0ffa2e7d56f872b5f5de109c99 SHA256: f1f37f2e5eb4d5e4a4540e24af15d3b4461b731cd4fce21be7eb306df44faf33 SHA512: 02a5df5e54474928f782b88371ab8a40c222dc847deec4d979fe6b84c53b5078d229e3fd7f2c3e1b7a078fad7e0e255f4cc97e5be5ea8241a951157b84ec2c89 Homepage: https://cran.r-project.org/package=noisyr Description: CRAN Package 'noisyr' (Noise Quantification in High Throughput Sequencing Output) Quantifies and removes technical noise from high-throughput sequencing data. Two approaches are used, one based on the count matrix, and one using the alignment BAM files directly. Contains several options for every step of the process, as well as tools to quality check and assess the stability of output. Package: r-cran-noisysbm Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1636 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-ggplot2, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-noisysbm_0.1.4-1.ca2404.1_all.deb Size: 1452274 MD5sum: 6c4448b0bd59cc184185d32d4caff884 SHA1: 8d0caf8e186ba96cd876bedb94efd75b638bcc83 SHA256: a0251503493f957afbf410be5a67cebc389e668309a19e483bab935d2fb4587e SHA512: 23925acaa276cad3265c10c4226a8b9b2735971f2f74e808f6f301c66ad0274da65af77bec2c19ae710abc263922de3213a0c6ac2e2590e60909babf41fe1a83 Homepage: https://cran.r-project.org/package=noisySBM Description: CRAN Package 'noisySBM' (Noisy Stochastic Block Mode: Graph Inference by Multiple Testing) Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying binary graph. This procedure comes with a control of the false discovery rate. The method is described in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, E. Roquain, F. Villers (2020) . Package: r-cran-nolock Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon, r-cran-rstudioapi, r-cran-stringr, r-cran-ncmisc Filename: pool/dists/noble/main/r-cran-nolock_1.1.0-1.ca2404.1_all.deb Size: 28530 MD5sum: fed0d105ecf610b4b35842c7ab4fb24f SHA1: df38ae269ee0427ff45fb19ab51df13577ba3b55 SHA256: 60293e3575c4327a301d0e86eadf308b6fd0b1d357efd1916169bb62ac5c7a74 SHA512: c7fbef3ed2bc9b2cf91fb287f122e31760703c7f668f06461f1f2f437a7632926cacde06051ebd3d1ccd2067e42ab602059fcd025c6a371cc992209d8bfebdbe Homepage: https://cran.r-project.org/package=nolock Description: CRAN Package 'nolock' (Append 'WITH (NOLOCK)' to 'SQL' Queries, Get Packages in ActiveScript) Provides a suite of tools that can assist in enhancing the processing efficiency of 'SQL' and 'R' scripts. - The 'libr_unused()' retrieves a vector of package names that are called within an 'R' script but are never actually used in the script. - The 'libr_used()' retrieves a vector of package names actively utilized within an 'R' script; packages loaded using 'library()' but not actually used in the script will not be included. - The 'libr_called()' retrieves a vector of all package names which are called within an 'R' script. - 'nolock()' appends 'WITH (nolock)' to all tables in 'SQL' queries. This facilitates reading from databases in scenarios where non-blocking reads are preferable, such as in high-transaction environments. Package: r-cran-nombre Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fracture Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nombre_0.4.1-1.ca2404.1_all.deb Size: 404454 MD5sum: 60bbb10dd95e7bc60ba8833bc657fca1 SHA1: 104d970d6884ed92c1b8a7cecd2920357ae4a222 SHA256: ae8819209e295711eadc027b4edc3423ba90d26419409b2f6810031c3809e957 SHA512: e28db000c2deaa6ff4866c167760eea0cc83a8641a74d6e848d0c4f13609c68535b1f7ff172e1feb63c34e8df34b91ff25ffca5eb1d676bca40ab5359a62b318 Homepage: https://cran.r-project.org/package=nombre Description: CRAN Package 'nombre' (Number Names) Converts numeric vectors to character vectors of English number names. Provides conversion to cardinals, ordinals, numerators, and denominators. Supports negative and non-integer numbers. Package: r-cran-nomesbr Architecture: all Version: 0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-httr2, r-cran-stringr, r-cran-tictoc Suggests: r-cran-testthat, r-cran-mockery, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nomesbr_0.0.9-1.ca2404.1_all.deb Size: 111176 MD5sum: 2bf88e2ecc80b1ec5fdbecf598eac80c SHA1: 7ab23c9c15f0c35039aac35241477d30c5e58c35 SHA256: 314181bfaabe8b889951bd76e94bd31ec796e36bb2269b49b188e37fc4465de2 SHA512: 486302a70f602f669dd6048f6ca00331d8fc09a7fa5d8b0c994d87515c93da8ee2e40e707ae8166ee8934b5396394d26a1d9e1edf15305939a15722ff35c70c8 Homepage: https://cran.r-project.org/package=nomesbr Description: CRAN Package 'nomesbr' (Limpa e Simplifica Nomes de Pessoas (Name Cleaner andSimplifier)) Limpa e simplifica nomes de pessoas para auxiliar no pareamento de banco de dados na ausência de chaves únicas não ambíguas. Detecta e corrige erros tipográficos mais comuns, simplifica opcionalmente termos sujeitos eventualmente a omissão em cadastros, e simplifica foneticamente suas palavras, aplicando variação própria do algoritmo metaphoneBR. (Cleans and simplifies person names to assist in database matching when unambiguous unique keys are unavailable. Detects and corrects common typos, optionally simplifies terms prone to omission in records, and applies phonetic simplification using a custom variation of the metaphoneBR algorithm.) Mation (2025) . Package: r-cran-nominatimlite Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-jsonlite, r-cran-sf, r-cran-testthat Suggests: r-cran-arcgeocoder, r-cran-ggplot2, r-cran-knitr, r-cran-lifecycle, r-cran-quarto, r-cran-rmarkdown, r-cran-tibble, r-cran-tidygeocoder Filename: pool/dists/noble/main/r-cran-nominatimlite_0.5.0-1.ca2404.1_all.deb Size: 238464 MD5sum: e5d03103c63f66f8323b770411c0b9ce SHA1: b550bd115694d9307459af840d3e666e2bdc77d2 SHA256: 70258f272542262d6284bf3aca7d870efc00d2b53692320aeb2a75925b75c237 SHA512: abe607be75b43b744c15f901fbb43a12a325beb9eeb93543fc108704f53fb6ce31e934bd8b2504a461db60d185aba86921be0d7daddb7084825e51fa9b9e6b53 Homepage: https://cran.r-project.org/package=nominatimlite Description: CRAN Package 'nominatimlite' (Interface with 'Nominatim' API Service) Lite interface for getting data from 'OSM' service 'Nominatim' . Extract coordinates from addresses, find places near a set of coordinates and return spatial objects on 'sf' format. Package: r-cran-nomine Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-rcurl, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nomine_1.0.2-1.ca2404.1_all.deb Size: 22334 MD5sum: 37ba58f1498b434d0752214d4e57040e SHA1: c2ae67c0c014a5d11bf4f5a0e386c81a92caea87 SHA256: f1ea2a4dd80cb6bcf5ae51328e842cd647be2b40792adbaf61cb1507472d8389 SHA512: 069ba854f8a8426ea802c4b4550b5c49c459bd5b6b6246c48925ad650c1f83a005c11563cd735026a608ae9566cffe3edb71c21e3d4a7ada8815156f75a66c14 Homepage: https://cran.r-project.org/package=nomine Description: CRAN Package 'nomine' (Classify Names by Gender, U.S. Ethnicity, and Leaf Nationality) Functions to use the 'NamePrism' API or 'NamSor' API v2 for classifying names based on gender, 6 U.S. ethnicities, or 39 leaf nationalities. Updated to work with current API endpoints. Package: r-cran-nomisdata Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3017 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-rlang, r-cran-tibble, r-cran-digest Suggests: r-cran-cachem, r-cran-ggplot2, r-cran-janitor, r-cran-knitr, r-cran-memoise, r-cran-rappdirs, r-cran-readr, r-cran-rmarkdown, r-cran-rsdmx, r-cran-scales, r-cran-sf, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-nomisdata_0.1.2-1.ca2404.1_all.deb Size: 2460736 MD5sum: ad6b5b842e05669fb95f357fe7fc397c SHA1: cabb3a586f5acdd4ac4f7a389e7228215699a468 SHA256: 5b9c92efd49f65185d36cb1eb488e28a49c7e0a7f76885b6934295c1794996f8 SHA512: 4f5cbed54f343ddae579b7c91526930d560480f6d5e77e2a9c1f592e2244f629c529aabd7607a11b9e16d0f1c618b72ef62efbf56c55b985fc5d09d3e41280f5 Homepage: https://cran.r-project.org/package=nomisdata Description: CRAN Package 'nomisdata' (Access 'Nomis' UK Labour Market Data and Statistics) Interface to the 'Nomis' database (), maintained by Durham University on behalf of the Office for National Statistics (ONS). Provides access to UK labour market statistics including census data, benefit claimant counts, and employment surveys. Supports automatic pagination, optional disk caching, spatial data via 'sf', and tidy data output. Independent implementation unaffiliated with ONS or Durham University. Package: r-cran-nomishape Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nomishape_1.0.2-1.ca2404.1_all.deb Size: 4001938 MD5sum: f8d46c0dd7328a3cbf2ca271f88f7af4 SHA1: bc2910265bdfcdffd3034beb31fac1e3c7b994a0 SHA256: 7635c1cf75336cc0a22cce07d287ea6ead388da44dd629b9018ac3723f384469 SHA512: 4782b7b69e22ed6cec6a5be45fbea63d4a3918ea056518b8d35c472c9de6c6371d0a172a2d2bdb3e055e3619cd2d644efe44e8c14ea602fc493f29f19cc4962a Homepage: https://cran.r-project.org/package=nomiShape Description: CRAN Package 'nomiShape' (Visualization and Analysis of Nominal Variable Distributions) Provides tools for visualizing and analyzing the shape of discrete nominal frequency distributions. The package introduces centered frequency plots, in which nominal categories are ordered from the most frequent category at the center toward less frequent categories on both sides, facilitating the detection of distributional patterns such as uniformity, dominance, symmetry, skewness, and long-tail behavior. In addition, the package supports Pareto charts for the study of dominance and cumulative frequency structure in nominal data. The package is designed for exploratory data analysis and statistical teaching, offering visualizations that emphasize distributional form rather than arbitrary category ordering. 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'Nomis' includes data from the Census, the Labour Force Survey, DWP benefit statistics and other economic and demographic data from the Office for National Statistics, based around statistical geographies. See for full API documentation. 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Supports styling, R Markdown and exporting diagrams in the PNG format. Note: you need a chromium based browser installed on your system. Package: r-cran-nomogramex Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-rms Filename: pool/dists/noble/main/r-cran-nomogramex_3.0-1.ca2404.1_all.deb Size: 14482 MD5sum: ed9473bf3f536805bc5a6db4c71696aa SHA1: b97cd76cba6bc317e1b5953f3914fea3e7c0e00e SHA256: 6c813cb1f958d195597e0435227f13348b8bc6209c96ecccdf58b25066c6b4fb SHA512: bdad834aea35e8d47930a187602ab485b090b949171799209c258f470ad60be098cf42700cb2797782dda7bfe87c2c47d05b5f477d336c88b782113d821db4cb Homepage: https://cran.r-project.org/package=nomogramEx Description: CRAN Package 'nomogramEx' (Extract Equations from a Nomogram) A nomogram can not be easily applied, because it is difficult to calculate the points or even the survival probability. The package, including a function of nomogramEx(), is to extract the polynomial equations to calculate the points of each variable, and the survival probability corresponding to the total points. 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However, it is not very easy to draw straight lines, read points and probabilities accurately. Even, it is hard for users to calculate total points and probabilities for all subjects. This package provides formula_rd() and formula_lp() functions to fit the formula of total points with raw data and linear predictors respectively by polynomial regression. Function points_cal() will help you calculate the total points. prob_cal() can be used to calculate the probabilities after lrm(), cph() or psm() regression. For more complex condition, interaction or restricted cubic spine, TotalPoints.rms() can be used. Package: r-cran-noncompart Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-noncompart_0.8.0-1.ca2404.1_all.deb Size: 244020 MD5sum: da622774ebd740a12da94a8087e8d595 SHA1: f755cd0ee67c51b664989a590dc05186ea078c10 SHA256: b6b0fe52b3118bf32acf8c3279219027584a93ff9980fcc12ec98e040b3f3cfe SHA512: a38c76730371db0c044e618ed8b570104f7e7b7b99365268d274e6c212a32ef144270a4c471b0bd69e060cd90d32bdf61a1a11e12f27507b8a648dc279d6b462 Homepage: https://cran.r-project.org/package=NonCompart Description: CRAN Package 'NonCompart' (Noncompartmental Analysis for Pharmacokinetic Data) Conduct a noncompartmental analysis with industrial strength. Some features are 1) Use of CDISC SDTM terms 2) Automatic or manual slope selection 3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method 4) Interval(partial) AUCs with 'linear' or 'log' interpolation method * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107). Package: r-cran-noncomplyr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-noncomplyr_1.0-1.ca2404.1_all.deb Size: 59318 MD5sum: 32bd83fecbb3d716f2d8b831b24cb8d5 SHA1: 09a812d7ce4d1d85240ffa38c624545d19d902fd SHA256: fc20df1d57fd63ba5379290adaf50af734fa086587fbbfd834a12e8b3cfae523 SHA512: 32bbfde91e59eae222008a5c9c8d28c78f82af4211bebeadc054daf04d9b3cce45e80983d2d68c2a415f7ff178ee80120cf447b82171d4ce99dcae0d57ef6c17 Homepage: https://cran.r-project.org/package=noncomplyR Description: CRAN Package 'noncomplyR' (Bayesian Analysis of Randomized Experiments with Non-Compliance) Functions for Bayesian analysis of data from randomized experiments with non-compliance. The functions are based on the models described in Imbens and Rubin (1997) . Currently only two types of outcome models are supported: binary outcomes and normally distributed outcomes. Models can be fit with and without the exclusion restriction and/or the strong access monotonicity assumption. Models are fit using the data augmentation algorithm as described in Tanner and Wong (1987) . 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It decides the relative weights of the different models predictions by using best models predictions as response variable and rest of the mo. User can decide the best model, therefore, It provides freedom to user to ensemble models based on their design solutions. Package: r-cran-nonlineardid Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-sandwich, r-cran-lmtest, r-cran-ggplot2 Suggests: r-cran-did, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-nonlineardid_0.2.0-1.ca2404.1_all.deb Size: 178794 MD5sum: a9a442f2377966cf9d0b35d0d83ec624 SHA1: 098a8ce5889cf94375c45f391a612fd2d9478972 SHA256: 0a3a2a9cecd1d82ac49ad006f99df198206ef733f46e06dd71b5751bd01643e5 SHA512: 7dac66bf4ba99205814fc92afb6699eb1b92d8a0ef4ff8e21c87f23d1d5c5a9232a76478f37b072d7140dfd85320bdecbe66b3e2d70cabf9be059857bf56bcd1 Homepage: https://cran.r-project.org/package=NonlinearDiD Description: CRAN Package 'NonlinearDiD' (Staggered Difference-in-Differences with Nonlinear Outcomes) Supports staggered difference-in-differences designs with nonlinear outcomes for both panel and repeated cross-section data. Implements estimators for staggered treatment adoption with binary, count, and other nonlinear outcomes, extending Callaway and Sant'Anna (2021) to settings with nonlinear outcome models such as logit, probit, and Poisson. For panel data, units are followed over time and 'idname' identifies repeated observations. For repeated cross-section data, observations are independent within each time period; 'idname' is optional and may identify survey records or households, but the estimator does not require the same units to appear across periods. Repeated cross-section estimation includes pooled quasi-maximum likelihood approaches motivated by Wooldridge (2023) , with optional weighting and clustered inference. Methods also draw on Roth and Sant'Anna (2023) and Sant'Anna and Zhao (2020) . Package: r-cran-nonlineardotplot Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nonlineardotplot_0.5.0-1.ca2404.1_all.deb Size: 50890 MD5sum: 95994f7f261cd42901b5d2b560e2c2e7 SHA1: 02be62149fa3adddee1543a29094b8465dbeded6 SHA256: 6ea15ca70db0e5a65242b6da13fc536ef1aed1b76ea4827867e3b7e48cc4d931 SHA512: b34d3afed37a51fe8df5931efbc23eab6ddb0904fc878533ef9a50b81f171d58dae998ec1407a9f00ed662eff746fcd5d478789b9c93322d462f4695c8dde487 Homepage: https://cran.r-project.org/package=nonLinearDotPlot Description: CRAN Package 'nonLinearDotPlot' (Non Linear Dot Plots) Non linear dot plots are diagrams that allow dots of varying size to be constructed, so that columns with a large number of samples are reduced in height. Implementation of algorithm described in: Nils Rodrigues and Daniel Weiskopf, "Nonlinear Dot Plots", IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 616-625, 2018. . Package: r-cran-nonlinearicp Architecture: all Version: 0.1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-condindtests, r-cran-data.tree, r-cran-catools, r-cran-randomforest Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nonlinearicp_0.1.2.1-1.ca2404.1_all.deb Size: 59038 MD5sum: dd5a53a97e7e4a01f5464e04b4dcb5be SHA1: ea73f8ab5bdbc90b143fa245c066abcf042a761a SHA256: dccf9c4d4fa7f644fe68cbbc39dd83fbb5d1050963362b195f587b9f2223d4ed SHA512: 5854fd7f2fde0ab2a7cf0b785e892bb9e67dcf3ce68b1cad78cd7566461f6e96477fe08b3791100a7b1d033e035a008e864f647b420154893264461ac3fb5b44 Homepage: https://cran.r-project.org/package=nonlinearICP Description: CRAN Package 'nonlinearICP' (Invariant Causal Prediction for Nonlinear Models) Performs 'nonlinear Invariant Causal Prediction' to estimate the causal parents of a given target variable from data collected in different experimental or environmental conditions, extending 'Invariant Causal Prediction' from Peters, Buehlmann and Meinshausen (2016), , to nonlinear settings. For more details, see C. Heinze-Deml, J. Peters and N. Meinshausen: 'Invariant Causal Prediction for Nonlinear Models', . Package: r-cran-nonlineartsa Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-tsdyn, r-cran-minpack.lm Filename: pool/dists/noble/main/r-cran-nonlineartsa_0.5.0-1.ca2404.1_all.deb Size: 146906 MD5sum: e4f7b137a5434077c52abf0748ec4724 SHA1: 6e204ccfd3381987b5d7cdcf924904fbd975608f SHA256: 64ad3ac8b9568d699b680b634500ebfa412325f87528f7357e9365ff273fcab9 SHA512: d62cb8ccf5f3414a463507fb63caeb245412e7bf713bdccaa14e0706bf29cb37412d4bb9198b0748f8d17e29f101664e438323611772600b320493d0067f5dba Homepage: https://cran.r-project.org/package=NonlinearTSA Description: CRAN Package 'NonlinearTSA' (Nonlinear Time Series Analysis) Function and data sets in the book entitled "Nonlinear Time Series Analysis with R Applications" B.Guris (2020). The book will be published in Turkish and the original name of this book will be "R Uygulamali Dogrusal Olmayan Zaman Serileri Analizi". It is possible to perform nonlinearity tests, nonlinear unit root tests, nonlinear cointegration tests and estimate nonlinear error correction models by using the functions written in this package. The Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) type unit root tests can be performed using the functions written. In addition, cointegration tests using the Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) models can be applied. It is possible to estimate nonlinear error correction models. The Granger causality test performed using nonlinear models can also be applied. Package: r-cran-nonmem2r Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3618 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-mvtnorm, r-cran-lattice, r-cran-latticeextra, r-cran-mass, r-cran-splines2, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nonmem2r_0.2.5-1.ca2404.1_all.deb Size: 2351890 MD5sum: 33c4603a19bcfd32266e0c87d2d02b75 SHA1: aae0ae25165b026b5a258d77d0a0f8fabd42d751 SHA256: 03c227a06a9619cb17c658139a75307b591133d5715206102636fc53eaea0728 SHA512: e1be05a2b746d9719e865743c610fb96af5a5552d3a110e8985b89a416d48b1ce36a99eeb0676822d880034e78dda009cd4867cb74c2ffa56be5cd2071c9473c Homepage: https://cran.r-project.org/package=nonmem2R Description: CRAN Package 'nonmem2R' (Loading NONMEM Output Files with Functions for Visual PredictiveChecks (VPC) and Goodness of Fit (GOF) Plots) Loading NONMEM (NONlinear Mixed-Effect Modeling, ) and PSN (Perl-speaks-NONMEM, ) output files to extract parameter estimates, provide visual predictive check (VPC) and goodness of fit (GOF) plots, and simulate with parameter uncertainty. 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Harvests NONMEM output, builds run logs, creates derivative data, generates diagnostics. NONMEM (ICON Development Solutions ) is software for nonlinear mixed effects modeling. See 'package?nonmemica'. 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Includes tests of model distinguishability and of model fit that can be applied to both nested and non-nested models. Also includes functionality to obtain confidence intervals associated with AIC and BIC. This material is partially based on work supported by the National Science Foundation under Grant Number SES-1061334. 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This package contains functions for measuring efficiency and productivity of decision making units (DMUs) under the framework of Data Envelopment Analysis (DEA) and its variations. 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This is developed as part of a postgraduate school project for an Advanced Bayesian Nonparametric course. It is inspired by Tamara Broderick's presentation on Nonparametric Bayesian statistics given at the Simons institute. Package: r-cran-nonpareil Architecture: all Version: 3.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nonpareil_3.5.3-1.ca2404.1_all.deb Size: 182910 MD5sum: c76cdd53532319033dea4702e7ec4248 SHA1: 348aa02b2ec70704d246ee17ceac774950c510ad SHA256: 30214e5a806802bb2b5e93e989b3c4550c46fc4a1e17d733bb83e54bccf16354 SHA512: bd2aef6c193c8ec0ca2d96a222daf18d4863990629c1c129f8035f9bad4039a9736a416472d64b46ae3783c3f67b7966c93553226a930e9c8ab158e2534c50b3 Homepage: https://cran.r-project.org/package=Nonpareil Description: CRAN Package 'Nonpareil' (Metagenome Coverage Estimation and Projections for 'Nonpareil') Plot, process, and analyze NPO files produced by 'Nonpareil' . Package: r-cran-nonparquantilecausality Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-quantreg, r-cran-kernsmooth Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nonparquantilecausality_0.1.0-1.ca2404.1_all.deb Size: 95244 MD5sum: 2d7486aca4f75b475e231d2d0ee4ba26 SHA1: 8e6ae72c83b39fdd7967c60bbdb01c7e9c113c18 SHA256: 182f7937d7fd12ac34f8dfb482f1923a99b14aa2bf8a4d50373eccbf40ca7714 SHA512: 5474ca2f5bb5fa6953661d1aef9e5f9a518398553612264b2f922a5f9418bc26f6ec17f56e14ec16e595480a022c9373c02f34e6370d66bbfb3faf04f362879e Homepage: https://cran.r-project.org/package=nonParQuantileCausality Description: CRAN Package 'nonParQuantileCausality' (Nonparametric Causality in Quantiles Test) Implements the nonparametric causality-in-quantiles test (in mean or variance), returning a test object with an S3 plot() method. The current implementation uses one lag of each series (first-order Granger causality setup). Methodology is based on Balcilar, Gupta, and Pierdzioch (2016a) and Balcilar et al. (2016) . Package: r-cran-nonparrolcor Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-pracma, r-cran-colorspace, r-cran-doparallel, r-cran-foreach, r-cran-scales Filename: pool/dists/noble/main/r-cran-nonparrolcor_0.8.0-1.ca2404.1_all.deb Size: 92936 MD5sum: 0510feb9eb97dc9fb09cf8c1eb1fcd15 SHA1: d87a85b2f6fdef80c8729cb5b514b3d259403112 SHA256: d526a84c1422face21e092f6dd70f91ede87feb19ce7032e81cffb27dbcd058d SHA512: 35635f40ed5b3abe4d4ed752cd679063c34c823e46f49e81211d14802a00e5d139a672150e93671dc6ca6645369ad044c404fc8fc3f12afe324f3e173203004f Homepage: https://cran.r-project.org/package=NonParRolCor Description: CRAN Package 'NonParRolCor' (a Non-Parametric Statistical Significance Test for RollingWindow Correlation) Estimates and plots (as a single plot and as a heat map) the rolling window correlation coefficients between two time series and computes their statistical significance, which is carried out through a non-parametric computing-intensive method. This method addresses the effects due to the multiple testing (inflation of the Type I error) when the statistical significance is estimated for the rolling window correlation coefficients. The method is based on Monte Carlo simulations by permuting one of the variables (e.g., the dependent) under analysis and keeping fixed the other variable (e.g., the independent). We improve the computational efficiency of this method to reduce the computation time through parallel computing. The 'NonParRolCor' package also provides examples with synthetic and real-life environmental time series to exemplify its use. Methods derived from R. Telford (2013) and J.M. Polanco-Martinez and J.L. Lopez-Martinez (2021) . Package: r-cran-nonpartrendr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nonpartrendr_0.1.0-1.ca2404.1_all.deb Size: 17762 MD5sum: 347e9bac1dfe565ad89eaa2b27a5146c SHA1: 313716843946f691b7dd21ec0e43f0b82aea22a4 SHA256: 8c3fbc95b92e82c40442107686c5c193018d6de398818a33353608e160be6e10 SHA512: 7d9aea564009466e2ce931de6b98614b51fce88af2cee2acc139c332daaaa85a3a3a5f629ac51207dc05e97269e6e9651d29b58e75669cf1a7445c54c60bb586 Homepage: https://cran.r-project.org/package=nonparTrendR Description: CRAN Package 'nonparTrendR' (A Nonparametric Trend Test for Independent and Dependent Samples) Implements the nonparametric trend test for one or several samples as proposed by Bathke (2009) . The method provides a unified framework for analyzing trends in both independent and dependent data samples, making it a versatile tool for various study designs. The package allows for the evaluation of different trend alternatives, including two-sided (general trend), monotonic increasing, and monotonic decreasing trends. As a nonparametric procedure, it does not require the assumption of data normality, offering a robust alternative to parametric tests. Package: r-cran-nonprobest Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-sampling, r-cran-e1071, r-cran-glmnet, r-cran-matrix Filename: pool/dists/noble/main/r-cran-nonprobest_0.2.4-1.ca2404.1_all.deb Size: 195592 MD5sum: 6c80095a64a4b49b4ced4d2db80c620a SHA1: 5204014eecb46d037da34d81b72c3fda471b7f8d SHA256: 2dc302e913e4ad437291e20ce583a2b5847a7bb5492e3c409c399a3b86283882 SHA512: 9905e4d1e57ea87e5b725348079b1d81ec2720b218d7f9a450a5fab885d21861adf6c7634662c0b77e0aed91e6ff4c8fe68edab270222d6292a18442d38e42bc Homepage: https://cran.r-project.org/package=NonProbEst Description: CRAN Package 'NonProbEst' (Estimation in Nonprobability Sampling) Different inference procedures are proposed in the literature to correct for selection bias that might be introduced with non-random selection mechanisms. A class of methods to correct for selection bias is to apply a statistical model to predict the units not in the sample (super-population modeling). Other studies use calibration or Statistical Matching (statistically match nonprobability and probability samples). To date, the more relevant methods are weighting by Propensity Score Adjustment (PSA). The Propensity Score Adjustment method was originally developed to construct weights by estimating response probabilities and using them in Horvitz–Thompson type estimators. This method is usually used by combining a non-probability sample with a reference sample to construct propensity models for the non-probability sample. Calibration can be used in a posterior way to adding information of auxiliary variables. Propensity scores in PSA are usually estimated using logistic regression models. Machine learning classification algorithms can be used as alternatives for logistic regression as a technique to estimate propensities. The package 'NonProbEst' implements some of these methods and thus provides a wide options to work with data coming from a non-probabilistic sample. Package: r-cran-nonsmooth Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3099 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-np, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-nonsmooth_1.0.0-1.ca2404.1_all.deb Size: 3137148 MD5sum: ab352dc2c7bd46bd0d279f3d3e365593 SHA1: 94c41961a021e84da146e97f2d3b035effa89dd1 SHA256: f58847d41fc04217bab5aeca632f9e51db03177d9b11899b87f694d16d13671f SHA512: a766e052aa3363fe73af5ae1f789e701e80b3970a3e6015abde1b910372f9ec6f103dc8c5d4728aea9c32948ca7f7171bda648f927729f19e2c7bb8f18b495f6 Homepage: https://cran.r-project.org/package=nonsmooth Description: CRAN Package 'nonsmooth' (Nonparametric Methods for Smoothing Nonsmooth Data) Nonparametric methods for smoothing regression function data with change-points, utilizing range kernels for iterative and anisotropic smoothing methods. For further details, see the paper by John R.J. Thompson (2024) . Package: r-cran-nonstat Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nonstat_0.0.6-1.ca2404.1_all.deb Size: 17286 MD5sum: 4f2438345e42f8e85779b1cfeb424ffd SHA1: e4e1f08e70c511e5ab8313e9042f33addfb375c6 SHA256: 02d885bffde1ad04cdcf3323ba817ae4a84c5a91fd6ccea6bf5870e86d4af895 SHA512: 10fdbde56f6051ee97f6e98fb0022a4359defaa76bf8045c631a3e6322af2153e20251faa6e8c0cdf1194f446a63d71ad2ab2b60f238286d856dac68efc9d742 Homepage: https://cran.r-project.org/package=nonstat Description: CRAN Package 'nonstat' (Detecting Nonstationarity in Time Series) Provides a nonvisual procedure for screening time series for nonstationarity in the context of intensive longitudinal designs, such as ecological momentary assessments. The method combines two diagnostics: one for detecting trends (based on the split R-hat statistic from Bayesian convergence diagnostics) and one for detecting changes in variance (a novel extension inspired by Levene's test). This approach allows researchers to efficiently and reproducibly detect violations of the stationarity assumption, especially when visual inspection of many individual time series is impractical. The procedure is suitable for use in all areas of research where time series analysis is central. For a detailed description of the method and its validation through simulations and empirical application, see Zitzmann, S., Lindner, C., Lohmann, J. F., & Hecht, M. (2024) "A Novel Nonvisual Procedure for Screening for Nonstationarity in Time Series as Obtained from Intensive Longitudinal Designs" . 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Package: r-cran-nor1mix Architecture: all Version: 1.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-cluster, r-cran-copula Filename: pool/dists/noble/main/r-cran-nor1mix_1.3-3-1.ca2404.1_all.deb Size: 130220 MD5sum: fc92f07360fc9d989bcb938efe39be17 SHA1: ff2e1ec905d1345288c481934147a801acec4e4a SHA256: 90d4cc8d37c8a646af1b5d0a32408e928123fba05ef4065f440cec31bb394180 SHA512: 8f268ea57cf51924b4566767cbfdd2f65c4a17925bc46005ec607c5ad3f64d9cd55fbb35622a5b43753debbd8b07ed1137e45d1b4c455e26480d26c4eb3b51e7 Homepage: https://cran.r-project.org/package=nor1mix Description: CRAN Package 'nor1mix' (Normal aka Gaussian 1-d Mixture Models) Onedimensional Normal (i.e. Gaussian) Mixture Models (S3) Classes, for, e.g., density estimation or clustering algorithms research and teaching; providing the widely used Marron-Wand densities. Efficient random number generation and graphics. Fitting to data by efficient ML (Maximum Likelihood) or traditional EM estimation. Package: r-cran-nord Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nord_1.0.0-1.ca2404.1_all.deb Size: 45562 MD5sum: c81ad364d105eb9c615cb49cd134a169 SHA1: 6c1384428ebaf532a2873929addb4b58cad647d1 SHA256: 6a9ea4ab83248592ff073f67c49b698ef62ab236e8584d3f60cb5608ee018d3d SHA512: 48e4305dbaf2b602b9a203c4eae4fb63adcb0c1896b19d9a667195e24c4a207544e4b19f5dbfa8473fd417066fc7634d88b8ede3a386c55946a5993cda3f5145 Homepage: https://cran.r-project.org/package=nord Description: CRAN Package 'nord' (Arctic Ice Studio's Nord and Group of Seven Inspired ColourPalettes for 'ggplot2') Provides the Arctic Ice Studio's Nord and Group of Seven inspired colour palettes for use with 'ggplot2' via custom functions. 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The project contractors were NORDKLIM/NORDMET on behalf of the National meteorological services in Denmark (DMI), Finland (FMI), Iceland (VI), Norway (DNMI) and Sweden (SMHI). Package: r-cran-nordstatextras Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rsqlite, r-cran-rlang, r-cran-tibble, r-cran-jsonlite, r-cran-digest Suggests: r-cran-mirai, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-nordstatextras_0.1.0-1.ca2404.1_all.deb Size: 96626 MD5sum: 0974a58a2698fd7ce59ad738bfd0510e SHA1: c02c32eccf0bd9058e6bdb0c39d308e36dc753d0 SHA256: 7cd98973418cb358a31ec9127415d9a0a0559833ee3a2f995e88a79d786af023 SHA512: 4dc9d0d2ff5deb76595575493bfe5f7d7f86e67b34dffe9555339102a442a17e9656eec0cfdc1c4ef19749e0fe58c369abb14210fd5a84403b4956d3f1146b36 Homepage: https://cran.r-project.org/package=nordstatExtras Description: CRAN Package 'nordstatExtras' (Shared 'SQLite' Cache Backend for the 'nordstat' Package Family) Provides a SQLite-backed cell-level cache that can be used as a drop-in backend by the nordstat family of packages ('rKolada', 'rTrafa', and 'pixieweb'). Designed for multi-user web applications where minimal fetch latency and asynchronous writes are required. Individual statistical values ("cells") are stored in a gatekeeper schema with a sidecar table for arbitrary metadata dimensions, enabling deduplication across overlapping queries. Package: r-cran-norma Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-norma_0.1-1.ca2404.1_all.deb Size: 47582 MD5sum: 2709cf5ba73173bd3ef1963945a472b0 SHA1: 15072967eb5855d9eb0a644ca69aecb51e3b790f SHA256: 1965cbb5d9e57a815adeb62acb6c3a549d828682ba5a365d20d5ba99d362794c SHA512: 12469ecf1a52581ec14234985aee535afdbc3e4c30e6afd8855b8ca1448bd3775ceab7f1465ce5f7b4d09d2f6f00d8decaac465a88043221673d2d90575f8ed5 Homepage: https://cran.r-project.org/package=NORMA Description: CRAN Package 'NORMA' (Builds General Noise SVRs) Builds general noise SVR models using Naive Online R Minimization Algorithm, NORMA, an optimization method based on classical stochastic gradient descent suitable for computing SVR models in an online setting. 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(2011) . Performs normalization of raw crossing threshold values (CT) and also calculates relative variability metrics that can be used to assess the impact of normalization on variance. 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Package: r-cran-normalp Architecture: all Version: 0.7.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-normalp_0.7.2.1-1.ca2404.1_all.deb Size: 102384 MD5sum: 9d0ae67f99546b412609ec8a117a9374 SHA1: 8bc17a2783d6752fa0ab5db63ef3dfe67fdb10c1 SHA256: 8dc6390619ab0f15d342e533119d6a68e74cef821652f5d70e8470665b101c93 SHA512: a555a2bac687c12512b933f3e62853efce15f59198fb6e8e4ec79e43c969a96f1fa1f00ca09aa52f12ab49fa0be5aeb4190c1c50b62c3b89e6ddc2ab3b0c9392 Homepage: https://cran.r-project.org/package=normalp Description: CRAN Package 'normalp' (Routines for Exponential Power Distribution) A collection of utilities referred to Exponential Power distribution, also known as General Error Distribution (see Mineo, A.M. and Ruggieri, M. (2005), A software Tool for the Exponential Power Distribution: The normalp package. In Journal of Statistical Software, Vol. 12, Issue 4). Package: r-cran-normalr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-purrr, r-cran-magrittr, r-cran-rlang, r-cran-shiny Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-normalr_1.0.0-1.ca2404.1_all.deb Size: 33060 MD5sum: 6b891422fdc88aabdd18c1b8c89bbd57 SHA1: ec7aad57970e2b33e55a0efaaacf6b97d4d52efc SHA256: ab54142428fe1d23b8d2f7a8d5a3a9bfc287561242177377e35a971a9567ed5f SHA512: b1b26388955d635858ddada30aaab58f55fbce55f4eb1928fff7fd172639f06ecdbe0bee91e3fda3cc8af6e312afa86f2df1c27c9dbb338df187718b4d493492 Homepage: https://cran.r-project.org/package=normalr Description: CRAN Package 'normalr' (Normalisation of Multiple Variables in Large-Scale Datasets) The robustness of many of the statistical techniques, such as factor analysis, applied in the social sciences rests upon the assumption of item-level normality. However, when dealing with real data, these assumptions are often not met. The Box-Cox transformation (Box & Cox, 1964) provides an optimal transformation for non-normal variables. Yet, for large datasets of continuous variables, its application in current software programs is cumbersome with analysts having to take several steps to normalise each variable. We present an R package 'normalr' that enables researchers to make convenient optimal transformations of multiple variables in datasets. This R package enables users to quickly and accurately: (1) anchor all of their variables at 1.00, (2) select the desired precision with which the optimal lambda is estimated, (3) apply each unique exponent to its variable, (4) rescale resultant values to within their original X1 and X(n) ranges, and (5) provide original and transformed estimates of skewness, kurtosis, and other inferential assessments of normality. Package: r-cran-normdata Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-doby, r-cran-mass, r-cran-lmtest, r-cran-dplyr, r-cran-sandwich, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-normdata_1.1-1.ca2404.1_all.deb Size: 371076 MD5sum: f498ab1bfb0d93f119986576139524ee SHA1: ba4921418bd44975279e894aaf1c96d3198218f6 SHA256: 1821c90609ebd788c1d9507b4df796eaa9176311f619931c09a982dec5aa9b55 SHA512: 7b7ef28afad0d6131b4844b5a95d435598f38d68483a445d324ba1445b4699201ba0821354b4deec7254522e98f4a7d77cfa64cd41f5ef836402c688cfb7f16f Homepage: https://cran.r-project.org/package=NormData Description: CRAN Package 'NormData' (Derivation of Regression-Based Normative Data) Normative data are often used to estimate the relative position of a raw test score in the population. This package allows for deriving regression-based normative data. It includes functions that enable the fitting of regression models for the mean and residual (or variance) structures, test the model assumptions, derive the normative data in the form of normative tables or automatic scoring sheets, and estimate confidence intervals for the norms. This package accompanies the book Van der Elst, W. (2024). Regression-based normative data for psychological assessment. A hands-on approach using R. Springer Nature. Package: r-cran-normexpression Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2271 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-normexpression_0.1.1-1.ca2404.1_all.deb Size: 1809000 MD5sum: 1e2532e0954eb85b354195de95b52e0d SHA1: d78a9ea64788d241307a4d7de7facf5447aa3185 SHA256: 2521a720dbbecdec68d68922a2bee7184fbbdf4aa0e2dfae2c1f3fc659cb7f41 SHA512: 3cdb10e55d872078ed988c0414dc409668aef97a0a0caca6bfa84033a64c53519befc2eba670c88d4399c0b99daee97bd3d812d525453da9172e02a1298b82da Homepage: https://cran.r-project.org/package=NormExpression Description: CRAN Package 'NormExpression' (Normalize Gene Expression Data using Evaluated Methods) It provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis, based on the consistency of metrics and the consistency of datasets. Zhenfeng Wu, Weixiang Liu, Xiufeng Jin, Deshui Yu, Hua Wang, Gustavo Glusman, Max Robinson, Lin Liu, Jishou Ruan and Shan Gao (2018) . 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Users should verify extended usage of the package on files from other assay types. 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Fitting models to data using 'MLE' (maximum likelihood estimation) for multivariate normal mixtures via smart parametrization using the 'LDL' (Cholesky) decomposition, see McLachlan and Peel (2000, ISBN:9780471006268), Celeux and Govaert (1995) . 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Norms are estimated using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), enabling flexible modelling of the full score distribution in a normative sample. The package supports applications in psychometrics and psychological testing, and includes functions for model selection, reliability estimation, norm calculation, including confidence intervals, and sample size planning. For more details, see Timmerman et al. (2021) . 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Developed and maintained for use at the Department of Forensic Sciences, Oslo, Norway. Package: r-cran-nortest Architecture: all Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nortest_1.0-4-1.ca2404.1_all.deb Size: 38434 MD5sum: 28bedd6dc5b496a70038d7061d282189 SHA1: 1df0ac5f357984a756c85b2ddb02f2483d2225f8 SHA256: f99b254bdb381d0134f433afd0d7335e1e5c0b505767627eb8294ed73c66984d SHA512: 221b9f92af130619633cbfde818b651b5ccf1a9099c32db1fc9a7b664af90f8fa971dc35d7851f2cbfd0e7524a55f3a284ecc956512a6973906d8628029c3dd3 Homepage: https://cran.r-project.org/package=nortest Description: CRAN Package 'nortest' (Tests for Normality) Five omnibus tests for testing the composite hypothesis of normality. Package: r-cran-nortstest Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-nortest, r-cran-ggplot2, r-cran-gridextra, r-cran-cowplot, r-cran-tseries, r-cran-uroot, r-cran-mass, r-cran-zoo Suggests: r-cran-ggfortify, r-cran-testthat Filename: pool/dists/noble/main/r-cran-nortstest_1.1.3-1.ca2404.1_all.deb Size: 399896 MD5sum: 2e0e8173966f821c031d59843dc1cb50 SHA1: bc2870444aebcf87512a0a4e7d05e85993cad761 SHA256: a246e07dda4a24076e25653f60c990f038b2e98e203df2bf4df5907568dd29af SHA512: c54a0bf82371cbf9d3a1d91385c33f76128f6df5c1da33de0aafa7a433a17fdb2c4a84454e01fac6ef3b7302b66b45e4717159e3f01a1f66d22d0eff6bb8a5b1 Homepage: https://cran.r-project.org/package=nortsTest Description: CRAN Package 'nortsTest' (Assessing Normality of Stationary Process) Despite that several tests for normality in stationary processes have been proposed in the literature, consistent implementations of these tests in programming languages are limited. Seven normality test are implemented. The asymptotic Lobato & Velasco's, asymptotic Epps, Psaradakis and Vávra, Lobato & Velasco's and Epps sieve bootstrap approximations, El bouch et al., and the random projections tests for univariate stationary process. Some other diagnostics such as, unit root test for stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. Additionally, the El bouch test performs normality tests for bivariate time series. The package also offers residual diagnostic for linear time series models developed in several packages. Package: r-cran-nos Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gmp, r-cran-bipartite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-nos_2.0.0-1.ca2404.1_all.deb Size: 69346 MD5sum: e35b355c3df311f84db96c7f7f94bd67 SHA1: a2d12ff06612358dedb5e3947e6b302264c0ea62 SHA256: abbdaa0f04c7ae011568fda984e6f93907ce22475eac3c470e2002d19c001e43 SHA512: ef71e95189185c6e7672794a69a9740fab7d271a59150279885915f821ff58ee0a3e9c58b3357c5ee3415a7b58da39549cbfa4e13c4396e67d7e55a20aa71e95 Homepage: https://cran.r-project.org/package=nos Description: CRAN Package 'nos' (Compute Node Overlap and Segregation in Ecological Networks) Calculate NOS (node overlap and segregation) and the associated metrics described in Strona and Veech (2015) and Strona et al. (2018) . The functions provided in the package enable assessment of structural patterns ranging from complete node segregation to perfect nestedness in a variety of network types. In addition, they provide a measure of network modularity. Package: r-cran-nose Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nose_1.0.5-1.ca2404.1_all.deb Size: 24782 MD5sum: b5255f509cf268072663bde06058f108 SHA1: 0730180fa71688e670c1657727fa1c0f06c58b3b SHA256: 7101aae4b172b01680c5c849e5b0e29ff083687258263259ef1f6846608dc8c0 SHA512: 3a658ac5485e92d7b40f5bcc00909cd006f03624b2fa902412790f45fe297e8d60a18bcdad94c8fc79123bebcd05554c50018954ea59ce067afcc6dfa083feca Homepage: https://cran.r-project.org/package=nose Description: CRAN Package 'nose' (Classification of Sparseness in 2-by-2 Categorical Data) Provides functions for classifying sparseness in 2 x 2 categorical data where one or more cells have zero counts. The classification uses three widely applied summary measures: Risk Difference (RD), Relative Risk (RR), and Odds Ratio (OR). Helps in selecting suitable continuity corrections for zero cells in multi-centre or meta-analysis studies. Also supports sensitivity analysis and can detect phenomena such as Simpson's paradox. The methodology is based on Subbiah and Srinivasan (2008) . 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Methods in Ecology and Evolution 11:1002-1007). It is named after the daimones of plague, sickness and disease that escaped Pandora's jar in the Greek mythology. 'nosoi' is able to take into account the influence of multiple variable on the transmission process (e.g. dual-host systems (such as arboviruses), within-host viral dynamics, transportation, population structure), alone or taken together, to create complex but relatively intuitive epidemiological simulations. Package: r-cran-nostalgir Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-txtplot Filename: pool/dists/noble/main/r-cran-nostalgir_1.0.2-1.ca2404.1_all.deb Size: 50004 MD5sum: 5384af5ff35112ec8dfb27afa21fba4c SHA1: 026748f965268ed00eb93363f0d65da94c324a00 SHA256: ac129c2d22a9b5ee6f529418766af4e57062054561a7b0244c75d37c52e09034 SHA512: 7d4e7d304ec2d69c959dde92f142e019dd80982a1bb24e22858b7ade413ee60ce31512b10bab51814c89dadb21d4951706d6010cd9dc73b603693b4036537cc8 Homepage: https://cran.r-project.org/package=NostalgiR Description: CRAN Package 'NostalgiR' (Advanced Text-Based Plots) Provides functions to produce advanced ascii graphics, directly to the terminal window. This package utilizes the txtplot() function from the 'txtplot' package, to produce text-based histograms, empirical cumulative distribution function plots, scatterplots with fitted and regression lines, quantile plots, density plots, image plots, and contour plots. 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Applied Vegetation Science, 24, e12548. 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Package: r-cran-nozzle.r1 Architecture: all Version: 1.1-1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 611 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nozzle.r1_1.1-1.1-1.ca2404.1_all.deb Size: 352182 MD5sum: 0f2e8b423784e14283ec11b8b349bd6f SHA1: 3b2c8b4c1114f8a338db4dafa646dac04c4f079d SHA256: 2681a3116158686a7301267a16eb415e7465b9ef9a6eefb1cabde5ad586a4440 SHA512: b47de1b198f814acfc4defe7b85df3d4be31dc3a3ed5febd3a666e4ba4b0e5166630917f8563862fa742717595a8029ea46227ec3aad1b5b351701ac603b412b Homepage: https://cran.r-project.org/package=Nozzle.R1 Description: CRAN Package 'Nozzle.R1' (Nozzle Reports) The Nozzle package provides an API to generate HTML reports with dynamic user interface elements based on JavaScript and CSS (Cascading Style Sheets). Nozzle was designed to facilitate summarization and rapid browsing of complex results in data analysis pipelines where multiple analyses are performed frequently on big data sets. The package can be applied to any project where user-friendly reports need to be created. Package: r-cran-npancova Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-npancova_0.1.1-1.ca2404.1_all.deb Size: 82928 MD5sum: a4b3d5376b7e4c4bbd84ae107626332b SHA1: cb641218c7afd23549a4c1da1bf670191d9e6f22 SHA256: 8f3dacb3d6981cc2b0995ed6d3e723a87013ea4a55f1e1a8c393ef0ad774d822 SHA512: 4dc56e58c1f35b419b98b164c22e22fa8a9c0954e07f78947d6137e3d0dae9e1f16c2acac44479f2c977b84da989f7c55d08d4fc3d8253b497d0c9c24db93d7d Homepage: https://cran.r-project.org/package=npANCOVA Description: CRAN Package 'npANCOVA' (Nonparametric ANCOVA Methods) Nonparametric methods for analysis of covariance (ANCOVA) are distribution-free and provide a flexible statistical framework for situations where the assumptions of parametric ANCOVA are violated or when the response variable is ordinal. 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Package: r-cran-nparact Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-stringr, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-nparact_0.9.1-1.ca2404.1_all.deb Size: 101578 MD5sum: 673be4737854dde47f3bd5c32e164ca9 SHA1: 47410dab07db896f0718d550d8c26770a3cf9884 SHA256: 9f50a9c7b6d8a8b407309343e2fb64e56a5805a3d3c1d193a1ba7491014120e0 SHA512: 8b822515f58fdba10fdb9e2c84be96beb6d4a91c2cd4e72e6cef2a0b9b4f5642475b0d63d7f27f6ee6d2afd1bbd2473916a1b1125eeab4c3d1b4cfb2cf2f6c34 Homepage: https://cran.r-project.org/package=nparACT Description: CRAN Package 'nparACT' (Non-Parametric Measures of Actigraphy Data) Computes interdaily stability (IS), intradaily variability (IV) & the relative amplitude (RA) from actigraphy data as described in Blume et al. (2016) and van Someren et al. (1999) . Additionally, it also computes L5 (i.e. the 5 hours with lowest average actigraphy amplitude) and M10 (the 10 hours with highest average amplitude) as well as the respective start times. The flex versions will also compute the L-value for a user-defined number of minutes. IS describes the strength of coupling of a rhythm to supposedly stable zeitgebers. It varies between 0 (Gaussian Noise) and 1 for perfect IS. IV describes the fragmentation of a rhythm, i.e. the frequency and extent of transitions between rest and activity. It is near 0 for a perfect sine wave, about 2 for Gaussian noise and may be even higher when a definite ultradian period of about 2 hrs is present. RA is the relative amplitude of a rhythm. Note that to obtain reliable results, actigraphy data should cover a reasonable number of days. 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The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers. See Konietschke et al. (2015) for details. Package: r-cran-nparld Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-nparld_2.2-1.ca2404.1_all.deb Size: 304536 MD5sum: 38ea04f02151867904fd659c090a54ec SHA1: 86e9d0fc00727bcd64f6d7630dbf4fd83718c8f3 SHA256: b010d2c776d29c5b4ecdadf826bedeaf18e9973f91590570b77a2af98cc45963 SHA512: 8b1b12a2fce4f7d4518dfd6e1f11022f162fbb2013772f2772ddecebc45eb1fd851d2bb212114d4f718c8070b402611be4013254dbf0a92bf544af2e9134fbe1 Homepage: https://cran.r-project.org/package=nparLD Description: CRAN Package 'nparLD' (Nonparametric Analysis of Longitudinal Data in FactorialExperiments) Performs nonparametric analysis of longitudinal data in factorial experiments. Longitudinal data are those which are collected from the same subjects over time, and they frequently arise in biological sciences. Nonparametric methods do not require distributional assumptions, and are applicable to a variety of data types (continuous, discrete, purely ordinal, and dichotomous). Such methods are also robust with respect to outliers and for small sample sizes. Package: r-cran-nparmd Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-matrixcalc, r-cran-mass, r-cran-gtools, r-cran-formula Filename: pool/dists/noble/main/r-cran-nparmd_0.2.3-1.ca2404.1_all.deb Size: 50128 MD5sum: 0a8ada95108791daa97a478fca8d18fc SHA1: e1261f5a8bb837dede75486be76ea842bdc72e63 SHA256: 3c6dd66823e07e947ae9816fdb07c1e3bc04716ca0f27b8ddd702b4ceaf8064b SHA512: 7d0bff7a5d2069b46ffe33050ad72fe2737ec34540f4424cc32a3a6a29fc520e8a584fb06a73b6f97355b65e3f4380fad56444f1350174d0bfa0e5212047fd13 Homepage: https://cran.r-project.org/package=nparMD Description: CRAN Package 'nparMD' (Nonparametric Analysis of Multivariate Data in Factorial Designs) Analysis of multivariate data with two-way completely randomized factorial design. The analysis is based on fully nonparametric, rank-based methods and uses test statistics based on the Dempster's ANOVA, Wilk's Lambda, Lawley-Hotelling and Bartlett-Nanda-Pillai criteria. The multivariate response is allowed to be ordinal, quantitative, binary or a mixture of the different variable types. The package offers two functions performing the analysis, one for small and the other for large sample sizes. The underlying methodology is largely described in Bathke and Harrar (2016) and in Munzel and Brunner (2000) and in Kiefel and Bathke (2022) . 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Package: r-cran-npbbbdaefficiency Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-npbbbdaefficiency_0.1.0-1.ca2404.1_all.deb Size: 16288 MD5sum: d19b79c89f385e0235932cac6eeac24f SHA1: a272cb43e364049c12a538e9c04d64aeb6dbe546 SHA256: c823bf28f92be08db2c337bae5224e3b72ae78ef1331198e553bb4a64b1b7ab0 SHA512: 8ab9a4994ce1aeb85ccb4d47208d2dab9ce153e5b4b718a3ce2c7f18c27b0f7db7ddda11422aae6b6a9d04185e66a41a7fb99881d20761fe308616d49cd14e11 Homepage: https://cran.r-project.org/package=NPBBBDAefficiency Description: CRAN Package 'NPBBBDAefficiency' (A-Efficiency for Nested Partially Balanced Bipartite Block(NPBBB) Designs) Nested Partially Balanced Bipartite Block (NPBBB) designs involve two levels of blocking: (i) The block design (ignoring sub-block classification) serves as a partially balanced bipartite block (PBBB) design, and (ii) The sub-block design (ignoring block classification) also serves as a PBBB design. More details on constructions of the PBBB designs and their characterization properties are available in Vinayaka et al.(2023) . This package calculates A-efficiency values for both block and sub-block structures, along with all parameters of a given NPBBB design. Package: r-cran-npboottprm Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-fgarch, r-cran-ggplot2, r-cran-lmperm, r-cran-mass, r-cran-mkinfer, r-cran-mmints, r-cran-shiny, r-cran-shinythemes, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-npboottprm_0.3.2-1.ca2404.1_all.deb Size: 280962 MD5sum: 9145365de98fd2aa3a95a74622d59d0d SHA1: cbd946a19756c611069c152122f10def8c2e5720 SHA256: c7799d3907a555eb80b719567b6c4d8d7811cc1b1b0f08ce650e1425fe9c6ae5 SHA512: b193244c3414eef25da1c43e0add6b54fc0edc12046f3f5a04aede383efff4142eabfdcc9e134786fc107ec3be8c07c7e374550f3f1f5ac321471f579c977c07 Homepage: https://cran.r-project.org/package=npboottprm Description: CRAN Package 'npboottprm' (Nonparametric Bootstrap Test with Pooled Resampling) Addressing crucial research questions often necessitates a small sample size due to factors such as distinctive target populations, rarity of the event under study, time and cost constraints, ethical concerns, or group-level unit of analysis. Many readily available analytic methods, however, do not accommodate small sample sizes, and the choice of the best method can be unclear. The 'npboottprm' package enables the execution of nonparametric bootstrap tests with pooled resampling to help fill this gap. Grounded in the statistical methods for small sample size studies detailed in Dwivedi, Mallawaarachchi, and Alvarado (2017) , the package facilitates a range of statistical tests, encompassing independent t-tests, paired t-tests, and one-way Analysis of Variance (ANOVA) F-tests. The nonparboot() function undertakes essential computations, yielding detailed outputs which include test statistics, effect sizes, confidence intervals, and bootstrap distributions. Further, 'npboottprm' incorporates an interactive 'shiny' web application, nonparboot_app(), offering intuitive, user-friendly data exploration. 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Two statistical methods with promising performance in small samples are the nonparametric bootstrap test with pooled resampling method, which is the focus of Dwivedi, Mallawaarachchi, and Alvarado (2017) , and informative hypothesis testing, which is implemented in the 'restriktor' package. The 'npboottprmFBar' package uses the nonparametric bootstrap test with pooled resampling method to implement informative hypothesis testing. The bootFbar() function can be used to analyze data with this method and the persimon() function can be used to conduct performance simulations on type-one error and statistical power. Package: r-cran-npcd Architecture: all Version: 1.0-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bb, r-cran-r.methodss3 Filename: pool/dists/noble/main/r-cran-npcd_1.0-11-1.ca2404.1_all.deb Size: 140506 MD5sum: e14020f3f5c7fe81265336efc2943569 SHA1: b56f87ddaff330123b4c155b0b23ee9731aeb392 SHA256: ecd9cfbcee04a23a1944de0b8e7c5f94dfd91416f9b4c58f0d07b1dd90662e72 SHA512: 20efed706362182143306ece139334e2e7118d8bc20b7d7bd467c91679099edc15fc0178d419088d7e083f7510bb2261ddce0a22b64efe0fa4c373481294764a Homepage: https://cran.r-project.org/package=NPCD Description: CRAN Package 'NPCD' (Nonparametric Methods for Cognitive Diagnosis) An array of nonparametric and parametric estimation methods for cognitive diagnostic models, including nonparametric classification of examinee attribute profiles, joint maximum likelihood estimation (JMLE) of examinee attribute profiles and item parameters, and nonparametric refinement of the Q-matrix, as well as conditional maximum likelihood estimation (CMLE) of examinee attribute profiles given item parameters and CMLE of item parameters given examinee attribute profiles. Currently the nonparametric methods in the package support both conjunctive and disjunctive models, and the parametric methods in the package support the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model. 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The core methods of the NPCD framework includes the nonparametric classification (NPC) method developed by Chiu and Douglas (2013) and the general NPC (GNPC) method developed by Chiu, Sun, and Bian (2018) and Chiu and Köhn (2019) . An extension of the NPCD framework included in the package is the nonparametric method for multiple-choice items (MC-NPC) developed by Wang, Chiu, and Koehn (2023) . Functions associated with various extensions concerning the evaluation, validation, and feasibility of the CD analysis are also provided. These topics include the completeness of Q-matrix, Q-matrix refinement method, as well as Q-matrix estimation. Package: r-cran-npclust Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-npclust_0.1.1-1.ca2404.1_all.deb Size: 69618 MD5sum: 96d836e747fc1d9a3e53cd338b360ea5 SHA1: a9b85a309a16206bf9837b8b327df955e6d3d064 SHA256: acf9d1529fd9c55e77bdd017ee368653bb61d6d38056c03e0b8963d698dfba85 SHA512: 49b68a071d385021835ef9b31aac57d92e464d58ef4940b4af116ace154c0ed50630fca3f48da904afcdb8107bb4781bbcf678bef6ece762f0aa9c1d5df0e49c Homepage: https://cran.r-project.org/package=npclust Description: CRAN Package 'npclust' (Nonparametric Tests for Incomplete Clustered Data) Nonparametric tests for clustered data in pre-post intervention design documented in Cui and Harrar (2021) and Harrar and Cui (2022) . Other than the main test results mentioned in the reference paper, this package also provides a function to calculate the sample size allocations for the input long format data set, and also a function for adjusted/unadjusted confidence intervals calculations. There are also functions to visualize the distribution of data across different intervention groups over time, and also the adjusted/unadjusted confidence intervals. 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Details of methodologies used in the package can be found in Sharma, A., Mehrotra, R. (2014). , Sharma, A., Mehrotra, R., Li, J., & Jha, S. (2016). , and Mehrotra, R., & Sharma, A. (2006). . 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Package: r-cran-nprobust Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-broom, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-nprobust_1.0.0-1.ca2404.1_all.deb Size: 215736 MD5sum: 0b59e0201cc23e2a202dc8dbcc353e88 SHA1: 81d8a3c7236bcaaf92aa90533ad2cfb18b88b439 SHA256: 0c8587425afc3a8536a43fbf2674e9cb8f819f88c87deec6c7cf810e1c1e48ee SHA512: fb03a7e581702983d06760259a4ab2d1e984faa08aabf76024f9486ab809a78a4e10f91b0bb8629f7e310e3d60938a22ccfdb78abb8aa2d818855ce4cd7ac619 Homepage: https://cran.r-project.org/package=nprobust Description: CRAN Package 'nprobust' (Kernel Density and Local Polynomial Regression Methods) Estimation, inference, bandwidth selection, and graphical procedures for kernel density and local polynomial regression methods, including robust bias-corrected confidence intervals as described in Calonico, Cattaneo and Farrell (2018, ). The package includes 'lprobust()' for local polynomial point estimation and robust bias-corrected inference, 'lpbwselect()' for local polynomial bandwidth selection, 'kdrobust()' for kernel density point estimation and robust bias-corrected inference, 'kdbwselect()' for kernel density bandwidth selection, and 'nprobust.plot()' for plotting results. The main methodological and numerical features are described in Calonico, Cattaneo and Farrell (2019, ). Package: r-cran-nproc Architecture: all Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-e1071, r-cran-randomforest, r-cran-naivebayes, r-cran-mass, r-cran-ada, r-cran-rocr, r-cran-tree Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nproc_2.1.5-1.ca2404.1_all.deb Size: 402664 MD5sum: 001c1c1d9e6e8e91022c7f30c17d01b9 SHA1: 0de91fd87642ff7b4424c98971a8e754ee3f0e69 SHA256: 378e727ec3e6a02a18ecc79177b8d350889ee65d46a409eec64a72a57efb346f SHA512: 380d5d9e5ee3b047b1cfeea5a45c0d1fa9ba8344c112912b7bb1a57f9c79349fd9040268720cba96ef1176d9294d37264bf7102022f8ed4b4f3cf431a4c66ef8 Homepage: https://cran.r-project.org/package=nproc Description: CRAN Package 'nproc' (Neyman-Pearson (NP) Classification Algorithms and NP ReceiverOperating Characteristic (NP-ROC) Curves) In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (i.e., the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (i.e., the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, alpha, on the type I error. Although the NP paradigm has a century-long history in hypothesis testing, it has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than alpha do not satisfy the type I error control objective because the resulting classifiers are still likely to have type I errors much larger than alpha. As a result, the NP paradigm has not been properly implemented for many classification scenarios in practice. In this work, we develop the first umbrella algorithm that implements the NP paradigm for all scoring-type classification methods, including popular methods such as logistic regression, support vector machines and random forests. Powered by this umbrella algorithm, we propose a novel graphical tool for NP classification methods: NP receiver operating characteristic (NP-ROC) bands, motivated by the popular receiver operating characteristic (ROC) curves. NP-ROC bands will help choose in a data adaptive way and compare different NP classifiers. 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Provides methods for bias reduction applying iterative procedures within a Newton-Raphson learning scheme. Cross-validation is exploited to select smoothing parameters. See Marco Di Marzio, Agnese Panzera & Charles C. Taylor (2018) . 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Includes methods, datasets, and random number generation useful for the study of robust and/or nonparametric statistics. Emphasizes classical nonparametric methods for a variety of designs --- especially one-sample and two-sample problems. Includes methods for general scores, including estimation and testing for the two-sample location problem as well as Hogg's adaptive method. 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Despite the multitude of options, the convention in survival studies is to assume proportional hazards and to use the unweighted log-rank test for design and analysis. This package provides sample size and power calculation for all of the above statistical tests with allowance for flexible accrual, censoring, and survival (eg. Weibull, piecewise-exponential, mixture cure). It is the companion R package to the paper by Yung and Liu (2020) . Specific to the weighted log-rank test, users may specify which approximations they wish to use to estimate the large-sample mean and variance. The default option has been shown to provide substantial improvement over the conventional sample size and power equations based on Schoenfeld (1981) . Package: r-cran-nptest Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-nptest_1.2-1.ca2404.1_all.deb Size: 275996 MD5sum: 66abecd3e64f6ea083cf62311f03e1ea SHA1: 5845a63ada3729a3ba7f3014b5d7cf10f3a6276e SHA256: b76f2867d207ca6a31edcbc16d7e50d9b6bb45bb1d6999323a0df80cdc74329f SHA512: 9586b271d399a96a265020b3722790f751b3df8084e6429d60ebbb74e28c03e54a5ebe25eb30f557b2c8d4bf55d8b4f333962fb54f6cea88cd39155ed477ab6c Homepage: https://cran.r-project.org/package=nptest Description: CRAN Package 'nptest' (Nonparametric Bootstrap and Permutation Tests) Robust nonparametric bootstrap and permutation tests for goodness of fit, distribution equivalence, location, correlation, and regression problems, as described in Helwig (2019a) and Helwig (2019b) . Univariate and multivariate tests are supported. For each problem, exact tests and Monte Carlo approximations are available. Five different nonparametric bootstrap confidence intervals are implemented. Parallel computing is implemented via the 'parallel' package. 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Such data may arise from a complex sampling design with features such as stratification, clustering, or unequal probabilities of selection. Multiple types of analyses may be conducted: comparisons of response rates across subgroups; comparisons of estimates before and after weighting adjustments; comparisons of sample-based estimates to external population totals; tests of systematic differences in covariate means between respondents and full samples; tests of independence between response status and covariates; and modeling of outcomes and response status as a function of covariates. Extensive documentation and references are provided for each type of analysis. Krenzke, Van de Kerckhove, and Mohadjer (2005) and Lohr and Riddles (2016) provide an overview of the methods implemented in this package. 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Package: r-cran-nrlr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-httr, r-cran-jsonlite, r-cran-rvest, r-cran-xml2, r-cran-stringr, r-cran-tibble, r-cran-glue, r-cran-cli, r-cran-lubridate Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-nrlr_0.1.2-1.ca2404.1_all.deb Size: 92484 MD5sum: 76efa0fadbb7cdae4469cd879a992cdf SHA1: a666cd820a6344d2e7414390d95026b826b032b9 SHA256: 1022b42eb53a648c7cee731a7a695dc9d0e2ea08d47bce3b9bc7c14cdb042f82 SHA512: 9fd47691d735793a5ffefd9fab6e245d2bf6b7859200ff5275386b1e78573cdbd156726e2a265cd57d6c91c53a57430ae619f0e38af89901af2359ee8e14dde5 Homepage: https://cran.r-project.org/package=nrlR Description: CRAN Package 'nrlR' (Functions to Scrape Rugby Data) Provides a set of functions to scrape and analyze rugby data. 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Package: r-cran-nrmsampling Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-sf, r-cran-terra, r-cran-ggplot2, r-cran-dplyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-nrmsampling_0.2.2-1.ca2404.1_all.deb Size: 144452 MD5sum: 6f2f56ad193cdba53b67e63a0b16023f SHA1: 675de6fc5874909bc8a0af5ff0c840a1d6ccef53 SHA256: 615cd247756a7fd62c1516ad140da849fd17037c1577a096bee65e045a4f72ad SHA512: 2d75d496e43d78a61fac8cfee5f5be3512f3ab8f745c083c3aa0aec60a9b3c4e309b0f2bd5aebf52eaa6f3e9b3f9c314238bca2c77719bf2c03dc7c47e227dbd Homepage: https://cran.r-project.org/package=NRMSampling Description: CRAN Package 'NRMSampling' (Sampling Design and Estimation Methods for Natural ResourceManagement) Provides functions for probability and non-probability sampling design, sample selection, and population estimation tailored to natural resource management. Probability methods include simple random sampling, stratified sampling, systematic sampling, cluster sampling, and probability-proportional-to-size sampling. Non-probability methods include convenience, judgement-based, and quota sampling. Estimation functions cover means, totals, ratio estimators, regression estimators, and the unequal-probability estimator of Horvitz and Thompson (1952, ) for unequal-probability designs. Utilities support biomass, soil-loss, and carbon-stock estimation from field plots. Spatial extensions provide random, systematic, stratified, and raster-weighted sampling within geographic polygons using the 'sf' and 'terra' packages, with extraction of remote-sensing covariates at sample locations. Applications include forest inventory, soil erosion monitoring, watershed studies, and ecological field surveys. Package: r-cran-nsae Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlist, r-cran-cluster, r-cran-mass, r-cran-lattice, r-cran-matrix, r-cran-numderiv, r-cran-nlme, r-cran-spgwr, r-cran-semipar Filename: pool/dists/noble/main/r-cran-nsae_0.4.0-1.ca2404.1_all.deb Size: 200224 MD5sum: 7dd160f13a8ed4c4ee903d665baf5684 SHA1: 5263cfc3ef5a60cc206baaf29eb5c1134adef233 SHA256: 41efac346b78201887cb320100143e57b7ebafea96e8f2b20ed496f4cfe3f318 SHA512: 98f2debbadf68f9c78b987decf4e350c271ffdc7a56fe851daeffef0a7f12e1cd3b7023053434dba2f842367bf8bfedb7c81d2f9008f27c09e5aa370dfc0f6a0 Homepage: https://cran.r-project.org/package=NSAE Description: CRAN Package 'NSAE' (Nonstationary Small Area Estimation) Executes nonstationary Fay-Herriot model and nonstationary generalized linear mixed model for small area estimation.The empirical best linear unbiased predictor (EBLUP) under stationary and nonstationary Fay-Herriot models and empirical best predictor (EBP) under nonstationary generalized linear mixed model along with the mean squared error estimation are included. EBLUP for prediction of non-sample area is also included under both stationary and nonstationary Fay-Herriot models. This extension to the Fay-Herriot model that accounts for the presence of spatial nonstationarity was developed by Hukum Chandra, Nicola Salvati and Ray Chambers (2015) and nonstationary generalized linear mixed model was developed by Hukum Chandra, Nicola Salvati and Ray Chambers (2017) . This package is dedicated to the memory of Dr. Hukum Chandra who passed away while the package creation was in progress. 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By choosing the appropriate regression algorithm for each data domain, it is possible to enforce sparsity, non-negativity or other kinds of constraints on the projection vectors. Multiple canonical variables are computed sequentially using a generalized deflation scheme, where the additional correlation not explained by previous variables is maximized. nscancor() is used to analyze paired data from two domains, and has the same interface as cancor() from the 'stats' package (plus some extra parameters). mcancor() is appropriate for analyzing data from three or more domains. See and Sigg et al. (2007) for more details. 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Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. The new index, normalized stochasticity ratio (NST), is to estimate ecological stochasticity, i.e. relative importance of stochastic processes, in community assembly. With functions in this package, NST can be calculated based on different similarity metrics and/or different null model algorithms, as well as some previous indexes, e.g. previous Stochasticity Ratio (ST), Standard Effect Size (SES), modified Raup-Crick metrics (RC). Functions for permutational test and bootstrapping analysis are also included. Previous ST is published by Zhou et al (2014) . NST is modified from ST by considering two alternative situations and normalizing the index to range from 0 to 1 (Ning et al 2019) . A modified version, MST, is a special case of NST, used in some recent or upcoming publications, e.g. Liang et al (2020) . SES is calculated as described in Kraft et al (2011) . RC is calculated as reported by Chase et al (2011) and Stegen et al (2013) . Version 3 added NST based on phylogenetic beta diversity, used by Ning et al (2020) . 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Specifically, the "effect-size distribution" that best quantifies a potentially causal relationship between a numeric y-Outcome variable and either a binary t-Treatment or continuous e-Exposure variable needs to consist of BLOCKS of relatively well-matched experimental units (e.g. patients) that have the most similar X-confounder characteristics. Since our NU Learning approach will form BLOCKS by "clustering" experimental units in confounder X-space, the implicit statistical model for learning is One-Way ANOVA. Within Block measures of effect-size are then either [a] LOCAL Treatment Differences (LTDs) between Within-Cluster y-Outcome Means ("new" minus "control") when treatment choice is Binary or else [b] LOCAL Rank Correlations (LRCs) when the e-Exposure variable is numeric with (hopefully many) more than two levels. An Instrumental Variable (IV) method is also provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute information for effect-size inferences when X-Covariates are assumed to influence Treatment choice or Exposure level but otherwise have no direct effects on y-Outcomes. Finally, a "Most-Like-Me" function provides histograms of effect-size distributions to aid Doctor-Patient (or Researcher-Society) communications about Heterogeneous Outcomes. Obenchain and Young (2013) ; Obenchain, Young and Krstic (2019) . 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The package automatically returns complete results on all 32 models, 25 charts and six tables. The user simply provides the tidy data, and answers a few questions (for example, how many times would you like to resample the data). From there the package randomly splits the data into train, test and validation sets as the user requests (for example, train = 0.60, test = 0.20, validation = 0.20), fits each of models on the training data, makes predictions on the test and validation sets, measures root mean squared error (RMSE), removes features above a user-set level of Variance Inflation Factor, and has several optional features including scaling all numeric data, four different ways to handle strings in the data. Perhaps the most significant feature is the package's ability to make predictions using the 32 pre trained models on totally new (untrained) data if the user selects that feature. This feature alone represents a very effective solution to the issue of reproducibility of models in data science. The package can also randomly resample the data as many times as the user sets, thus giving more accurate results than a single run. The graphs provide many results that are not typically found. For example, the package automatically calculates the Kolmogorov-Smirnov test for each of the 32 models and plots a bar chart of the results, a bias bar chart of each of the 32 models, as well as several plots for exploratory data analysis (automatic histograms of the numeric data, automatic histograms of the numeric data). The package also automatically creates a summary report that can be both sorted and searched for each of the 32 models, including RMSE, bias, train RMSE, test RMSE, validation RMSE, overfitting and duration. The best results on the holdout data typically beat the best results in data science competitions and published results for the same data set. 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Package: r-cran-obaspatial Architecture: all Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-modeest, r-cran-cubature, r-cran-truncdist, r-cran-invgamma, r-cran-laplacesdemon, r-cran-hdinterval, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-obaspatial_1.9-1.ca2404.1_all.deb Size: 222242 MD5sum: 33570d0d108cc59258523d5601d9a266 SHA1: 64fff145f87fe873709862331ca9bb014172160f SHA256: f05928c15d750ecf7afc0ba2203b3a9f3238f54707914205beec290031a8acd0 SHA512: db8cc99d24b75909814586df10d381b607b747482dcef6f4f0948e41d5dd48344bf709ecee51792a7e3a3724cae23c3d51a3c2c58df277a93915012d9b972ef5 Homepage: https://cran.r-project.org/package=OBASpatial Description: CRAN Package 'OBASpatial' (Objective Bayesian Analysis for Spatial Regression Models) It makes an objective Bayesian analysis of the spatial regression model using both the normal (NSR) and student-T (TSR) distributions. The functions provided give prior and posterior objective densities and allow default Bayesian estimation of the model regression parameters. Details can be found in Ordonez et al. (2020) . Package: r-cran-obcost Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-obcost_0.1.0-1.ca2404.1_all.deb Size: 428476 MD5sum: c99794e692b2d0124c18dd0f174f1119 SHA1: 1cfe02c312e3e2209b87e01aaf538e029a27d377 SHA256: d138191a2a46ead4fa4acfc3b169df08d8b6cd99e99cd8c789911d41eca86ed5 SHA512: 2d40458cb8f89967d32bdddbcd4195e31cb818731a14e9fbef6129bd8b13d404aef9a676f93047053203ac805b5886822680918e273518e686b7650d4c259a8f Homepage: https://cran.r-project.org/package=obcost Description: CRAN Package 'obcost' (Obesity Cost Database) This database contains necessary data relevant to medical costs on obesity throughout the United States. This database, in form of an R package, could output necessary data frames relevant to obesity costs, where the clients could easily manipulate the output using difference parameters, e.g. relative risks for each illnesses. This package contributes to parts of our published journal named "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis". Please use the following citation for the journal: Woods Thomas, Tatjana Miljkovic (2022) "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis" . The database is composed of the following main tables: 1. Relative_Risks: (constant) Relative risks for a given disease group with a risk factor of obesity; 2. Disease_Cost: (obesity_cost_disease) Supplementary output with all variables related to individual disease groups in a given state and year; 3. 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Blocks larger than pairs better distinguish effects caused by a treatment from unmeasured confounding in assignment of individuals to treatment. Somewhat counterintuitively, blocks larger than pairs can use more units while attaining better covariate balance and block homogeneity. A forthcoming manuscript by Brumberg and Rosenbaum details the design. 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The package 'oddnet' uses a feature-based method to identify anomalies. First, it computes many features for each network. Then it models the features using time series methods. Using time series residuals it detects anomalies. This way, the temporal dependencies are accounted for when identifying anomalies (Kandanaarachchi, Hyndman 2022) . Package: r-cran-odds.converter Architecture: all Version: 1.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-odds.converter_1.4.8-1.ca2404.1_all.deb Size: 74420 MD5sum: 6ab890f572e5fe9674958f96dafaafd5 SHA1: e9e9d40b24f2a82b84491b2d8dc68d0a9ffdd7bc SHA256: 945ceae57c3c5c71ff4beac2085de7c37d12da0389096f532e7b607b6049ff54 SHA512: 59d2d2bb1e5a49bdc6fb98199b0f98c67b4d827d3d75e28043170f77a6e12d99fcb81d6bd1a3a6bee2dcfe7cf542e8e7b7f095f11a9e6ca53dc3a2f1be5d0be8 Homepage: https://cran.r-project.org/package=odds.converter Description: CRAN Package 'odds.converter' (Betting Odds Conversion) Conversion between the most common odds types for sports betting. Hong Kong odds, US odds, Decimal odds, Indonesian odds, Malaysian odds, and raw Probability are covered in this package. Package: r-cran-odds.n.ends Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-odds.n.ends_0.1.4-1.ca2404.1_all.deb Size: 27564 MD5sum: 81d8434555339727fee23881b0448d1a SHA1: 12de45cc18729876e441b22ba1a42989802fd9e9 SHA256: 6b05b3037a31b3fa47b3908269f69b6a8e465d047f464ed417a9e9ce40ea9270 SHA512: 8a5bccf5dde21aca9999736c71ac8f323ab35677a2d72c7b5b33900a751b43fac9d5ca9b98bfde423941ab976823c1228f18110eba081efae44abb9c92bf10c3 Homepage: https://cran.r-project.org/package=odds.n.ends Description: CRAN Package 'odds.n.ends' (Odds Ratios, Contingency Table, and Model Significance from aGeneralized Linear Model Object) Computes odds ratios and 95% confidence intervals from a generalized linear model object. It also computes model significance with the chi-squared statistic and p-value and it computes model fit using a contingency table to determine the percent of observations for which the model correctly predicts the value of the outcome. Calculates model sensitivity and specificity. Package: r-cran-oddsapir Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-janitor, r-cran-jsonlite, r-cran-magrittr, r-cran-rlang, r-cran-rvest, r-cran-tidyr Suggests: r-cran-crayon, r-cran-curl, r-cran-dbi, r-cran-ggplot2, r-cran-ggrepel, r-cran-gt, r-cran-knitr, r-cran-progressr, r-cran-qs, r-cran-rcpp, r-cran-rcppparallel, r-cran-rmarkdown, r-cran-rsqlite, r-cran-stringi, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-tidyselect, r-cran-usethis, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-oddsapir_0.0.3-1.ca2404.1_all.deb Size: 66698 MD5sum: 44ae679b23446a9fddcdcc9ccebc4d31 SHA1: b573a31bd9f4c4487b821a2e31d3740ed8bc961f SHA256: 97b8aa870744ca64e669d07fdf3c2beb4224824d88f6304d5b91ed3b3b2160cb SHA512: e1963d13c64c85ec3330f0173b2f198d91c55348277dd5fd09cad59f5b405c8d6109940659aaf7e8e506b8dd50e63e67d51ae9eb8f2436b7bb0da49b911cda0d Homepage: https://cran.r-project.org/package=oddsapiR Description: CRAN Package 'oddsapiR' (Access Live Sports Odds from the Odds API) A utility to quickly obtain clean and tidy sports odds from The Odds API . Package: r-cran-oddsplotty Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-mlbench, r-cran-magrittr, r-cran-ggplot2, r-cran-tibble, r-cran-ggthemes, r-cran-e1071, r-cran-tidymodels, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-covr, r-cran-testthat, r-cran-markdown Filename: pool/dists/noble/main/r-cran-oddsplotty_1.0.2-1.ca2404.1_all.deb Size: 99118 MD5sum: 654586e205e0704b6728227f57c0790c SHA1: e16a30f8be1b059d95f1729626f4e661242ac591 SHA256: be9378bccaf7ab6a3a79803802f84a31bdb30d1831a348922309a3652eb7ade3 SHA512: fa4ddca27f911176094a77d610cafc9db35c756c9d532c969b22cb6dd54ee1f83f6f1450224f931acfc97c07b688707772e78bf9a6d2a4af29bc2ec50e9a1eb4 Homepage: https://cran.r-project.org/package=OddsPlotty Description: CRAN Package 'OddsPlotty' (Odds Plot to Visualise a Logistic Regression Model) Uses the outputs of a logistic regression model, from caret , to build an odds plot. This allows for the rapid visualisation of odds plot ratios and works best with the outputs of CARET's GLM model class, by returning the final trained model. Package: r-cran-oddsratio Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mgcv Suggests: r-cran-gam, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-oddsratio_2.0.2-1.ca2404.1_all.deb Size: 277186 MD5sum: dca2d87a0d5a8e06fded2ec03e06c02c SHA1: c7642cb86e9e2dcf493cbf59971f7e7925a53a0c SHA256: 2fa35dcfbe3c15b00d27045e088f2c63fd90e5131e59ba9c7482fd068c10ec7e SHA512: 91f2f5071f1ef714fdc66c71dd95fff87fab73f95960776dde3d1faaa16fec53c2395c65c2d034ff39074e8d53d876d1c6193111380de5ad6868a0987b05e7e2 Homepage: https://cran.r-project.org/package=oddsratio Description: CRAN Package 'oddsratio' (Odds Ratio Calculation for GAM(M)s & GLM(M)s) Simplified odds ratio calculation of GAM(M)s & GLM(M)s. Provides structured output (data frame) of all predictors and their corresponding odds ratios and confident intervals for further analyses. It helps to avoid false references of predictors and increments by specifying these parameters in a list instead of using 'exp(coef(model))' (standard approach of odds ratio calculation for GLMs) which just returns a plain numeric output. For GAM(M)s, odds ratio calculation is highly simplified with this package since it takes care of the multiple 'predict()' calls of the chosen predictor while holding other predictors constant. Also, this package allows odds ratio calculation of percentage steps across the whole predictor distribution range for GAM(M)s. In both cases, confident intervals are returned additionally. Calculated odds ratio of GAM(M)s can be inserted into the smooth function plot. Package: r-cran-oddstream Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3274 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcapp, r-cran-ggplot2, r-cran-ks, r-cran-mass, r-cran-rcpproll, r-cran-mgcv, r-cran-rcolorbrewer, r-cran-mvtsplot, r-cran-tibble, r-cran-reshape, r-cran-dplyr, r-cran-tidyr, r-cran-kernlab, r-cran-magrittr Suggests: r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-oddstream_0.5.0-1.ca2404.1_all.deb Size: 3278464 MD5sum: 477a1b973e75413ed6bc1cdc0523d1e8 SHA1: 6540fca38f8a5170b8380dadc44a67f8a2e232c2 SHA256: 4950c5d33b0bb28a815406ac0974510e885712287e59ada8c8a98a88aae1c620 SHA512: 09b370f203d0ff312a36f7a852ae94faa2b4630c8da0df10b3d3bec82eebbb175f13df97554379fe6293a96e9fdea94020aa3b6012c122db777569b48a1d0b97 Homepage: https://cran.r-project.org/package=oddstream Description: CRAN Package 'oddstream' (Outlier Detection in Data Streams) We proposes a framework that provides real time support for early detection of anomalous series within a large collection of streaming time series data. By definition, anomalies are rare in comparison to a system's typical behaviour. We define an anomaly as an observation that is very unlikely given the forecast distribution. The algorithm first forecasts a boundary for the system's typical behaviour using a representative sample of the typical behaviour of the system. An approach based on extreme value theory is used for this boundary prediction process. Then a sliding window is used to test for anomalous series within the newly arrived collection of series. Feature based representation of time series is used as the input to the model. To cope with concept drift, the forecast boundary for the system's typical behaviour is updated periodically. More details regarding the algorithm can be found in Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et al. (2019) . Package: r-cran-odenetwork Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-desolve Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-odenetwork_1.3.2-1.ca2404.1_all.deb Size: 130538 MD5sum: 2b4f198f98bf21137d6115d5ebdd8ef6 SHA1: 9bd3235c335a2dbca35e163e532c8ffa152741eb SHA256: 04b9bc964d9791208f8f14a0d4df0df485eb2771c3df8bdcc79ffe5641405947 SHA512: 6e52cb2d76ed289c2b8c7a82621ab67d9fb6d366652d65c0662394583c1427f1ad3080304d1ae2d71753b18215e23504f05e12cf6e55d25f0a78aa08fdeab021 Homepage: https://cran.r-project.org/package=ODEnetwork Description: CRAN Package 'ODEnetwork' (Network of Differential Equations) Simulates a network of ordinary differential equations of order two. The package provides an easy interface to construct networks. In addition you are able to define different external triggers to manipulate the trajectory. The method is described by Surmann, Ligges, and Weihs (2014) . Package: r-cran-odesensitivity Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-desolve, r-cran-odenetwork, r-cran-sensitivity Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-odesensitivity_1.1.2-1.ca2404.1_all.deb Size: 146408 MD5sum: dadcd96a5cebed31351f2c39e624fce6 SHA1: e2d8d931dd97b48ba6dc55130589fd88d137bc11 SHA256: 69145844119478232fc9e20c8a99892901d3766cce5137c1380dd8beb8033ced SHA512: 69cacb59ab79f0e1acf87bc3575a1fe2f59827e78509d0b63ab2bfce189c54e714b8480c93c81dcf3874ca1af499e442d3a5e693c43e46fbe935dd673692c201 Homepage: https://cran.r-project.org/package=ODEsensitivity Description: CRAN Package 'ODEsensitivity' (Sensitivity Analysis of Ordinary Differential Equations) Performs sensitivity analysis in ordinary differential equation (ode) models. The package utilize the ode interface from 'deSolve' and connects it with the sensitivity analysis from 'sensitivity'. Additionally we add a method to run the sensitivity analysis on variables with class 'ODEnetwork'. A detailed plotting function provides outputs on the calculations. The method is described by Weber, Theers, Surmann, Ligges, and Weihs (2018) . Package: r-cran-odetector Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ppclust Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-odetector_1.0.1-1.ca2404.1_all.deb Size: 226282 MD5sum: 837753a507c32d9d88a724e6a239fe5f SHA1: 91d8ee88e0a597b8f6c70b5b75f2958d7370203e SHA256: b4106fc6d9c4c6d7108435911b8d93ce07ad455c1134779d08794c4cf9179c7b SHA512: 9020616b0877803dcff16d59419bf66a8322fe77ad8a05d905d73bbcc004dcf6c576893179a3f56dd0bdb345acddeab6a251174e56433f0d99c2ab2bb6e47fee Homepage: https://cran.r-project.org/package=odetector Description: CRAN Package 'odetector' (Outlier Detection Using Partitioning Clustering Algorithms) An object is called "outlier" if it remarkably deviates from the other objects in a data set. Outlier detection is the process to find outliers by using the methods that are based on distance measures, clustering and spatial methods (Ben-Gal, 2005 ). It is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for outlier removing in data processing. This package provides the implementations of some novel approaches to detect the outliers based on typicality degrees that are obtained with the soft partitioning clustering algorithms such as Fuzzy C-means and its variants. Package: r-cran-odiffr Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-magick, r-cran-png, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-odiffr_0.5.1-1.ca2404.1_all.deb Size: 148428 MD5sum: ecc87ee880a808a26f4aa2a466b71b04 SHA1: cf32489e83fe615a3ad4cfe8c2f6cb681008aaeb SHA256: 8cbd7e1c4a13b0842a033661383fd7fbe0307a71f1071564658f9b89d874baee SHA512: 998cecbd60da5e8cc0d541fecdf6e302e767357f858344acb125928752c5b13f16da31f84f5219abb94940555b4d7e0483c8379ad810944fe479ad06f67717cf Homepage: https://cran.r-project.org/package=odiffr Description: CRAN Package 'odiffr' (Fast Pixel-by-Pixel Image Comparison Using 'odiff') R bindings to 'odiff', a blazing-fast pixel-by-pixel image comparison tool . Supports PNG, JPEG, WEBP, and TIFF with configurable thresholds, antialiasing detection, and region ignoring. Requires system installation of 'odiff'. Ideal for visual regression testing in automated workflows. Package: r-cran-odin Architecture: all Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2054 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-cinterpolate, r-cran-desolve, r-cran-digest, r-cran-glue, r-cran-jsonlite, r-cran-ring, r-cran-withr Suggests: r-cran-dde, r-cran-jsonvalidate, r-cran-knitr, r-cran-mockery, r-cran-pkgbuild, r-cran-pkgload, r-cran-rlang, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-odin_1.2.7-1.ca2404.1_all.deb Size: 1485162 MD5sum: 8d0b1b131ef4313eec03f8c7e8dca970 SHA1: fc12e555ce286fb5cb8c6e1cadf93334ec507995 SHA256: e34e4877cbbab241f74e0803d97e82643e113d1edae9598e0a9f301102cf737e SHA512: 816bdeeb1a32069a78ea1040d3e950ecedd7bf9ffe240111fcc9099c58b4a17dab8b9f1f1e8e2c1acacbd091f784400505406f6e857f04ed43b4770e54b160e8 Homepage: https://cran.r-project.org/package=odin Description: CRAN Package 'odin' (ODE Generation and Integration) Generate systems of ordinary differential equations (ODE) and integrate them, using a domain specific language (DSL). The DSL uses R's syntax, but compiles to C in order to efficiently solve the system. A solver is not provided, but instead interfaces to the packages 'deSolve' and 'dde' are generated. With these, while solving the differential equations, no allocations are done and the calculations remain entirely in compiled code. Alternatively, a model can be transpiled to R for use in contexts where a C compiler is not present. After compilation, models can be inspected to return information about parameters and outputs, or intermediate values after calculations. 'odin' is not targeted at any particular domain and is suitable for any system that can be expressed primarily as mathematical expressions. Additional support is provided for working with delays (delay differential equations, DDE), using interpolated functions during interpolation, and for integrating quantities that represent arrays. Package: r-cran-odk Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gsheet, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-odk_1.5-1.ca2404.1_all.deb Size: 153110 MD5sum: 6504845bdd88a2f1c70cbfa6b795b976 SHA1: c842aeed42d4709428cc5ece24c6f59191fe6298 SHA256: 77793d3a5de3368d9362680347765cec37080264ee9f515179c50c17a67691b1 SHA512: dde7932b7e8031be3101fc3a08a2672fa0ce24e8bf538ff3e804a7cedc39d2d52df16446c36839a506a46495376e5414bc2a4d0bbcb8fc919482423c766b8df1 Homepage: https://cran.r-project.org/package=odk Description: CRAN Package 'odk' (Convert 'ODK' or 'XLSForm' to 'SPSS' Data Frame) After develop a 'ODK' frame, we can link the frame to 'Google Sheets' and collect data through 'Android' . This data uploaded to a 'Google sheets'. odk2spss() function help to convert the 'odk' frame into 'SPSS' frame. Also able to add downloaded 'Google sheets' data or read data from 'Google sheets' by using 'ODK' frame 'submission_url'. Package: r-cran-odmeans Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geosphere, r-cran-ggplot2, r-cran-ggmap, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-odmeans_0.2.1-1.ca2404.1_all.deb Size: 3201312 MD5sum: f81615ec2e7ed6b5ca325664bb66d85d SHA1: 57ce9e1c647544a12ef612a009f4941f7d40f0e9 SHA256: d19009bb5280765334be5b0caa751ecc7bcb7a5f749a640d518551e0529be8ab SHA512: 9797d1ee4c67200296742ec8e20c81e417d467bb6cd66a135c917b817d70e68ff4f3ace8137389d3b980446f45ce649a5f4940ea2cfb4f71d5cc30b1e82a066f Homepage: https://cran.r-project.org/package=ODMeans Description: CRAN Package 'ODMeans' (OD-Means: k-Means for Origin-Destination) OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs. In the first layer of the hierarchy, the clusters are separated automatically based on the variation of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy corresponds to the sub clustering process of small clusters based on the distance between the origin and destination of each cluster. Package: r-cran-odr Architecture: all Version: 1.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-odr_1.8.3-1.ca2404.1_all.deb Size: 573630 MD5sum: e7a5e7b28d3f8b442806b7fa2dfb3081 SHA1: 245ac1a8a3746bd62fc512ad647c6438066259ef SHA256: 11c0c6d73607bf5b9a78330ef7fc79ffbbad2efc11a1ef1c067a0cb75355d5c5 SHA512: 9148b5c6ec01d26e028725a3c3bc637bc9e7459ba511b1bd2ae4dab2510b1087a71eefa373b1d5b23c845881e33404e54931a69bc34b46d07c1060d5456c0f7b Homepage: https://cran.r-project.org/package=odr Description: CRAN Package 'odr' (Optimal Design and Statistical Power for Experimental StudiesInvestigating Main, Mediation, and Moderation Effects) Calculate the optimal sample size allocation that uses the minimum resources to achieve targeted statistical power in experiments. Perform power analyses with and without accommodating costs and budget. The designs cover single-level and multilevel experiments detecting main, mediation, and moderation effects (and some combinations). The references for the proposed methods include: (1) Shen, Z., & Kelcey, B. (2020). Optimal sample allocation under unequal costs in cluster-randomized trials. Journal of Educational and Behavioral Statistics, 45(4): 446-474. . (2) Shen, Z., & Kelcey, B. (2022b). Optimal sample allocation for three-level multisite cluster-randomized trials. Journal of Research on Educational Effectiveness, 15 (1), 130-150. . (3) Shen, Z., & Kelcey, B. (2022a). Optimal sample allocation in multisite randomized trials. The Journal of Experimental Education, 90(3), 693-711. . (4) Shen, Z., Leite, W., Zhang, H., Quan, J., & Kuang, H. (2025). Using ant colony optimization to identify optimal sample allocations in cluster-randomized trials. The Journal of Experimental Education, 93(1), 167-185. . (5) Shen, Z., Li, W., & Leite, W. (in press). Statistical power and optimal design for randomized controlled trials investigating mediation effects. Psychological Methods. . (6) Champely, S. (2020). pwr: Basic functions for power analysis (Version 1.3-0) [Software]. Available from . Package: r-cran-ods Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature, r-cran-survival Filename: pool/dists/noble/main/r-cran-ods_0.2.0-1.ca2404.1_all.deb Size: 248518 MD5sum: 9b7bbab4a7c69a4ad10e598e230287c1 SHA1: 03825482da64959aa6103cad07117a10be089e2e SHA256: 48113596369bc3e212ca71d88694204411698d3b9316ab284099a1f4f01f5582 SHA512: cd0080aff134d59e8fe83332bb26b3977f075fc5fb36d586eb9c2631d4fa913b71b02dfc26189b6cc65fe7958129091cbb70a0bea88628d7129c481c6ab25ac7 Homepage: https://cran.r-project.org/package=ODS Description: CRAN Package 'ODS' (Statistical Methods for Outcome-Dependent Sampling Designs) Outcome-dependent sampling (ODS) schemes are cost-effective ways to enhance study efficiency. In ODS designs, one observes the exposure/covariates with a probability that depends on the outcome variable. Popular ODS designs include case-control for binary outcome, case-cohort for time-to-event outcome, and continuous outcome ODS design (Zhou et al. 2002) . Because ODS data has biased sampling nature, standard statistical analysis such as linear regression will lead to biases estimates of the population parameters. This package implements four statistical methods related to ODS designs: (1) An empirical likelihood method analyzing the primary continuous outcome with respect to exposure variables in continuous ODS design (Zhou et al., 2002). (2) A partial linear model analyzing the primary outcome in continuous ODS design (Zhou, Qin and Longnecker, 2011) . (3) Analyze a secondary outcome in continuous ODS design (Pan et al. 2018) . (4) An estimated likelihood method analyzing a secondary outcome in case-cohort data (Pan et al. 2017) . Package: r-cran-odt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1522 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-partykit, r-cran-data.tree, r-cran-magick, r-cran-diagrammersvg, r-cran-diagrammer, r-cran-rsvg Suggests: r-cran-runit, r-cran-matrix, r-cran-rmarkdown, r-cran-robustbase, r-cran-knitr Filename: pool/dists/noble/main/r-cran-odt_1.0.0-1.ca2404.1_all.deb Size: 1340754 MD5sum: ac4e5f92fc6b31f398fe6f5e56102f17 SHA1: a503d701bf4961449479ba840b1609b2afb1e091 SHA256: 6334df40ebee693e26314470fbc0708898f9fa4e7397b9968bf3735612e42205 SHA512: 01aeeb4b0bfe7260369f9c39d1406dab66cdfc7d45119fd597010ed7d575388041f999b1ae9583a084a196164729ee48128f74da1c4c28e7fb6fe52851dee342 Homepage: https://cran.r-project.org/package=ODT Description: CRAN Package 'ODT' (Optimal Decision Trees Algorithm) Implements a tree-based method specifically designed for personalized medicine applications. By using genomic and mutational data, 'ODT' efficiently identifies optimal drug recommendations tailored to individual patient profiles. The 'ODT' algorithm constructs decision trees that bifurcate at each node, selecting the most relevant markers (discrete or continuous) and corresponding treatments, thus ensuring that recommendations are both personalized and statistically robust. This iterative approach enhances therapeutic decision-making by refining treatment suggestions until a predefined group size is achieved. Moreover, the simplicity and interpretability of the resulting trees make the method accessible to healthcare professionals. Includes functions for training the decision tree, making predictions on new samples or patients, and visualizing the resulting tree. For detailed insights into the methodology, please refer to Gimeno et al. (2023) . Package: r-cran-odysseuscharacterizationmodule Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-databaseconnector, r-cran-jsonlite, r-cran-sqlrender Suggests: r-cran-andromeda, r-cran-eunomia, r-cran-featureextraction, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-odysseuscharacterizationmodule_0.0.1-1.ca2404.1_all.deb Size: 200340 MD5sum: f1529f1f570926f3346849a32504b429 SHA1: 0a02d90c8c72a2f0665450d08e9341a8135ed64f SHA256: 5081f77b255f120320bf08976fa471ba1b6819edac67305e7bfe5b7a8a2653ca SHA512: 2f8c0a05697460e05274aa0e89e0fff7c4473ec2948df3723b0c8f08cd05e2a6e0edde2f718d7ffa727d4748fccd2f6366fc99b190efee40a7fffe934ec7e271 Homepage: https://cran.r-project.org/package=OdysseusCharacterizationModule Description: CRAN Package 'OdysseusCharacterizationModule' (Handy and Minimalistic Common Data Model Characterization) Extracts covariates from Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) domains using an R-only pipeline. Supports configurable temporal windows, domain-specific covariates for drug exposure, drug era (including Anatomical Therapeutic Chemical (ATC) groupings), condition occurrence, condition era, concept sets and cohorts. Methods are based on the Observational Health Data Sciences and Informatics (OHDSI) framework described in Hripcsak et al. (2015) and "The Book of OHDSI" OHDSI (2019, ISBN:978-1-7923-0589-8). Package: r-cran-odysseuspathwaymodule Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-databaseconnector, r-cran-sqlrender Suggests: r-cran-eunomia, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-odysseuspathwaymodule_0.0.1-1.ca2404.1_all.deb Size: 78926 MD5sum: 72d70bc171c5686abb767817830bff97 SHA1: 986472be1dac9cde0597f6539f13e7ef8765a998 SHA256: 816850df317258363b386c9ecc267a8be6273fe34316b5e83067c4cd94ac485a SHA512: 62f0a6eca537bab91a7eeb3344eb1a98cd4a0299426308e10df20ad8aa065be0442941e4c7cb32725e1966dc580b0a030ca406b39fc9c272084a4c3904c3f1e2 Homepage: https://cran.r-project.org/package=OdysseusPathwayModule Description: CRAN Package 'OdysseusPathwayModule' (Cohort Pathway Analysis with Pre-Index Event Support) Provides cohort pathway analysis for Observational Medical Outcomes Partnership (OMOP) Common Data Model databases, including both standard (post-index) and pre-index pathway analyses. The pre-index analysis identifies sequences of events occurring in a lookback window before the target cohort index date. Built on the 'CohortPathways' analysis framework originally developed by Christopher Knoll and the Observational Health Data Sciences and Informatics community through 'WebAPI'. Methodological background and the originating implementation are described in . 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Package: r-cran-odyssey Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-rlang Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-odyssey_1.0.0-1.ca2404.1_all.deb Size: 78650 MD5sum: e4de6bea1ac9ab9b8e12eed4dbcd80c2 SHA1: 0cff3780ca96ff49a6951de56600ebbd55d19c18 SHA256: ac96d0abe4ef5181ca139b03a8c64a1cd0aea241de9171bdaf97c3ac8a870a3c SHA512: 50caa0beaaa0eedd8545d980ba2c27ea5af2b5fc62e616f4440689ad2cf7efb071438c42dc1808ba0cd5dc9cc5601fcf871c66a7e966feb17092f4794d6b2f0a Homepage: https://cran.r-project.org/package=odyssey Description: CRAN Package 'odyssey' (Interface to the HAL Open Archive API) An interface to the search API of 'HAL' , the French open archive for scholarly documents from all academic fields. This package provides programmatic access to the API and allows to search for records and download documents. 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The algorithm is a generalization of the procedure given in Köning, R., Wimmer, G. and Witkovský, V. (2014) . Package: r-cran-oenb Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-xml Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-oenb_0.0.2-1.ca2404.1_all.deb Size: 36092 MD5sum: 92554eec79a833019790a556e937ef99 SHA1: 54a68326ee5238a2e97e159688f22e355cb9c31e SHA256: 790a7a81a8885e33d9748a95bc06328913665b2b11f6bf1e6b817fd3e70cd6a4 SHA512: d8e7d1e8187f9fb5a03efe52f30cfccb33904127ae76e5b795644db5bf6cd37b74b17d81c7a2fe581233e2084d022b10cdcfce81a0d9eddae11caff47b21703c Homepage: https://cran.r-project.org/package=oenb Description: CRAN Package 'oenb' (Tools for the OeNB Data Web Service) Tools to access data from the data web service of the Oesterreichische Nationalbank (OeNB), . Package: r-cran-oenokpm Architecture: all Version: 2.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-openxlsx, r-cran-ggpubr, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-oenokpm_2.4.1-1.ca2404.1_all.deb Size: 88722 MD5sum: 9d34358113854c6c18ad20d446b468eb SHA1: 1bcfbdd68ad9c27b4ad72f68aa343033ff2ec9b5 SHA256: f6225df19cadff3a84167e18fd055d1981de968daa963260a3f41c1487de9813 SHA512: 6140fb84c13a2fa3ed94eba4a060cc7e6a446043fc4a07891c2f2a81ad651c6e3dfbc56a516857521b79314bdd56a7ff12bd5a4af8af4365f49426e7781497de Homepage: https://cran.r-project.org/package=OenoKPM Description: CRAN Package 'OenoKPM' (Modeling the Kinetics of Carbon Dioxide Production in AlcoholicFermentation) Developed to help researchers who need to model the kinetics of carbon dioxide (CO2) production in alcoholic fermentation of wines, beers and other fermented products. The following models are available for modeling the carbon dioxide production curve as a function of time: 5PL, Gompertz and 4PL. This package has different functions, which applied can: perform the modeling of the data obtained in the fermentation and return the coefficients, analyze the model fit and return different statistical metrics, and calculate the kinetic parameters: Maximum production of carbon dioxide; Maximum rate of production of carbon dioxide; Moment in which maximum fermentation rate occurs; Duration of the latency phase for carbon dioxide production; Carbon dioxide produced until maximum fermentation rate occurs. In addition, a function that generates graphs with the observed and predicted data from the models, isolated and combined, is available. Gava, A., Borsato, D., & Ficagna, E. (2020)."Effect of mixture of fining agents on the fermentation kinetics of base wine for sparkling wine production: Use of methodology for modeling". . 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This package prepares data from standard model output objects (such as from \code{lm()} and \code{estimatr::lm_robust()}) and creates visualizations of treatment effects from the prepared quantities, according to the standards of the US Office of Evaluation Sciences. 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Package: r-cran-ofgem Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform, r-cran-mass, r-cran-forestplot Filename: pool/dists/noble/main/r-cran-ofgem_1.0-1.ca2404.1_all.deb Size: 30538 MD5sum: c7d93c241774dbfe4436c3152a20774d SHA1: 89529ffa36ff61c8874529bb7d12a46f5e3ee383 SHA256: 8f154a6a19ac89fad21629adf65be91fe7a945bc328e090e7b29cfb240b47203 SHA512: c58612a2abcf3f3b8ce805b2876642b697183a2da2059058b128bc7d0d33d93382aed5d4633732601ccb30921160ae025bdbf6994ee350f1ec8f12f624ff70dd Homepage: https://cran.r-project.org/package=ofGEM Description: CRAN Package 'ofGEM' (A Meta-Analysis Approach with Filtering for IdentifyingGene-Level Gene-Environment Interactions with GeneticAssociation Data) Offers a gene-based meta-analysis test with filtering to detect gene-environment interactions (GxE) with association data, proposed by Wang et al. (2018) . 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Package: r-cran-ofpetrial Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3892 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-sf, r-cran-lwgeom, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-ggpubr, r-cran-ggextra, r-cran-terra, r-cran-zip, r-cran-rmarkdown, r-cran-tmap, r-cran-magrittr, r-cran-dplyr, r-cran-bookdown, r-cran-leaflet Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-ofpetrial_0.1.3-1.ca2404.1_all.deb Size: 1991016 MD5sum: a499deb3eac963747d589c10fcb20cd0 SHA1: 5c7f191f6bacbabffd14bb7eafa026f1ab7da75c SHA256: 38ffe9f47c1cc7b4ee4bdb62d7738714fa4b848fd629a75340d2aa8f053b049c SHA512: 92535e3d5dca2cfa4964dcd7cce64af0eef791d921f19da967704e9143a2f345cdb02b1280fdfc003af05636fbff9b492fa33f8790885fb373ff22b2248b136c Homepage: https://cran.r-project.org/package=ofpetrial Description: CRAN Package 'ofpetrial' (Design on-Farm Precision Field Agronomic Trials) A comprehensive system for designing and implementing on-farm precision field agronomic trials. 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Package: r-cran-oglmx Architecture: all Version: 3.0.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maxlik Suggests: r-cran-glmx, r-cran-lmtest Filename: pool/dists/noble/main/r-cran-oglmx_3.0.0.0-1.ca2404.1_all.deb Size: 496374 MD5sum: ddc98e612c975d06c8129899c4bbb25f SHA1: 1cdb163c4e5bfc22a11094d19375feab7db3d53d SHA256: 4df45242e554cfab6ff338d963d46fd225d887409cf529ea35e796158c5a6767 SHA512: d6b53fec4636645cf193997a152a07ae0316c99f1c8c16ede16b609b6d19f755f498fe06ca2d5985ef16065983e07e2ad4372a8d17d3af121224956abeba20cf Homepage: https://cran.r-project.org/package=oglmx Description: CRAN Package 'oglmx' (Estimation of Ordered Generalized Linear Models) Ordered models such as ordered probit and ordered logit presume that the error variance is constant across observations. In the case that this assumption does not hold estimates of marginal effects are typically biased (Weiss (1997)). This package allows for generalization of ordered probit and ordered logit models by allowing the user to specify a model for the variance. Furthermore, the package includes functions to calculate the marginal effects. Wrapper functions to estimate the standard limited dependent variable models are also included. 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Package: r-cran-ohcspackage Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-openxlsx, r-cran-readr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-ohcspackage_0.1.5-1.ca2404.1_all.deb Size: 48900 MD5sum: e10b9410c2c080bcce6352ce6c6ecdb8 SHA1: 13ab6454c35439bf98c8e97f3f44631b74f255ef SHA256: 5040f14329ff6fd00fd64a2238c409aba81f7787e36a45b92efcd12cbc0e1a5d SHA512: 0c24ef80cc2d7a94fbf567b36f6dd4f2ca088d3e0aec071e707f2197af7f451da8c1ae0729c6b23f4eb58aec7808019195f1e9651c8a1c34f1a58e07fa17fd1b Homepage: https://cran.r-project.org/package=OHCSpackage Description: CRAN Package 'OHCSpackage' (Prepare Housing Data for Analysis) Prepares census and core housing needs data, specifically designed for use with Statistics Canada data and standardized input data. 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Package: r-cran-ohdsireportgenerator Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2927 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-circer, r-cran-databaseconnector, r-cran-forestploter, r-cran-dplyr, r-cran-ggplot2, r-cran-gt, r-cran-gtextras, r-cran-kableextra, r-cran-parallellogger, r-cran-quarto, r-cran-rlang, r-cran-rmarkdown, r-cran-sqlrender, r-cran-tidyr Suggests: r-cran-htmltools, r-cran-knitr, r-cran-markdown, r-cran-resultmodelmanager, r-cran-rsqlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ohdsireportgenerator_2.2.0-1.ca2404.1_all.deb Size: 2462612 MD5sum: 044548949ae4d40965469a37da6c301a SHA1: 2b9d0755ec2d3d74bd583229414d7bf4a35a01cf SHA256: 68f25ddc355e58aae31846751d0c90617bf01c7fbccab06045b4597ba9b4a95e SHA512: 87de0aae923cb556e8618e8d47c6c0b23a933a7a6c0ccada996ec4f172d39ad8de6c619e0ceb0901fb183d266093cf9512be4f16bbfa4a0fe75d90850d762d1a Homepage: https://cran.r-project.org/package=OhdsiReportGenerator Description: CRAN Package 'OhdsiReportGenerator' (Observational Health Data Sciences and Informatics ReportGenerator) Extract results into R from the Observational Health Data Sciences and Informatics result database (see ) and generate reports/presentations via 'quarto' that summarize results in HTML format. 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Package: r-cran-ohdsishinyappbuilder Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-databaseconnector, r-cran-devtools, r-cran-dplyr, r-cran-parallellogger, r-cran-resultmodelmanager, r-cran-rlang, r-cran-shiny, r-cran-shinydashboard Suggests: r-cran-eunomia, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-markdown, r-cran-remotes Filename: pool/dists/noble/main/r-cran-ohdsishinyappbuilder_1.0.0-1.ca2404.1_all.deb Size: 145740 MD5sum: dce0692aaf27b9f363fa3b55cf507b7f SHA1: 854669e5d70173cad4a3d89c22c9c16087ef598a SHA256: 56e051762588117e3bd194236d92b77ac1e3041657a4dfa3fc8903d80f5906ee SHA512: f2e075f78a7047044a0a7bfecea8e096f23a45eed0d8acc064fe2208055f6b855290bccc736e5e832d5d1c781c33d470d6b5928cbf92b6d18a08af2b3251673a Homepage: https://cran.r-project.org/package=OhdsiShinyAppBuilder Description: CRAN Package 'OhdsiShinyAppBuilder' (Viewing Observational Health Data Sciences and InformaticsResults via 'shiny' Modules) Users can build a single 'shiny' app for exploring population characterization, population-level causal effect estimation, and patient-level prediction results generated via the R analyses packages in 'HADES' (see ). Learn more about 'OhdsiShinyAppBuilder' at . Package: r-cran-ohit Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ohit_1.0.0-1.ca2404.1_all.deb Size: 34062 MD5sum: 400f16a53912f0cededee187339db449 SHA1: 8e94c94bf7fd207e5747923988cbb0565fd73046 SHA256: acdf90402e3789f3d8954a1048be6c715265ce5bd518e325a189be9c7a0311bc SHA512: 14802b83d21c929952fc1372120c297b82611ab0e983c5c0b88362b801cabf4f82db4a6e71644be927788f99a067a24755d15a08581503e11f58410ea561a981 Homepage: https://cran.r-project.org/package=Ohit Description: CRAN Package 'Ohit' (OGA+HDIC+Trim and High-Dimensional Linear Regression Models) Ing and Lai (2011) proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. 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Package: r-cran-ohmmed Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cvms, r-cran-ggmcmc, r-cran-ggplot2, r-cran-gridextra, r-cran-mistr, r-cran-scales, r-cran-vcd Filename: pool/dists/noble/main/r-cran-ohmmed_1.0.2-1.ca2404.1_all.deb Size: 601300 MD5sum: d738911bf2342b419f68f038b9d8d5f8 SHA1: f70adf7bfffaf0192bb5308119d259388ff62483 SHA256: 717d95f774f95a86c78d4fee80195239a92fe189679e7b409ded54aef23b50fd SHA512: 3b6fe45eeed17d4c0a3fab07b9a72f5204bbf29a7c6da8886e49e0e0a66c401c388595833de86114f27520472549a435a30b699de16f29f1c4f704a68419790d Homepage: https://cran.r-project.org/package=oHMMed Description: CRAN Package 'oHMMed' (HMMs with Ordered Hidden States and Emission Densities) Inference using a class of Hidden Markov models (HMMs) called 'oHMMed'(ordered HMM with emission densities ): The 'oHMMed' algorithms identify the number of comparably homogeneous regions within observed sequences with autocorrelation patterns. 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Package: r-cran-ohun Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6932 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tuner, r-cran-warbler, r-cran-cli, r-cran-seewave, r-cran-fftw, r-cran-rlang, r-cran-sf, r-cran-igraph, r-cran-checkmate, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-viridis, r-cran-sim.diffproc, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ohun_1.0.4-1.ca2404.1_all.deb Size: 2795012 MD5sum: c40f076d0753a528d7c760ba087e4c5b SHA1: 5545b58b5ab149b63025bda18781395d08bf7ab7 SHA256: 55bb05231f646c3a1a0c7e5cd58b291a0410158883839d69c92d939950b71e45 SHA512: 7f47a9fae6b2f49e5fa745df12ffa14922d023fec5841927a46a34243161ab87bfc5407130cb86897611fe5a49f6150ff70f939812ac085038a4e5c3adbd0b86 Homepage: https://cran.r-project.org/package=ohun Description: CRAN Package 'ohun' (Optimizing Acoustic Signal Detection) Facilitates the automatic detection of acoustic signals, providing functions to diagnose and optimize the performance of detection routines. Detections from other software can also be explored and optimized. This package has been peer-reviewed by rOpenSci. Araya-Salas et al. (2022) . 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The package is used within the statistics component of the Masters of Science (MSc) in Social Science of the Internet at the Oxford Internet Institute (OII), University of Oxford, but the functions should be useful for general data analysis and especially for analysis of categorical and ordinal data. Package: r-cran-ojsr Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rvest, r-cran-tidyr, r-cran-urltools, r-cran-xml2, r-cran-purrr, r-cran-rlang, r-cran-rcurl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ojsr_0.1.5-1.ca2404.1_all.deb Size: 62048 MD5sum: f4068bb462e2fa546d069e6ecd72a482 SHA1: b383f0438f829a2d8996227627f9d5adaaab908e SHA256: de5dfd2a2551e74f448e059c9ffb90e865245d96d7fea4cfd93f6ff7b7a411fb SHA512: 865d9e26d16f4ba5361b7cf7e64182a45f0268cf27efdabd07ce2c7ba2308fb14ac7d33b5f8b3d536cb5051cf72229f82ece7e3b9cda0dd56894c267f78a48a1 Homepage: https://cran.r-project.org/package=ojsr Description: CRAN Package 'ojsr' (Crawler and Data Scraper for Open Journal System ('OJS')) Crawler for 'OJS' pages and scraper for meta-data from articles. 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The upstream API reference is available at . Package: r-cran-olcpm Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-laplacesdemon, r-cran-rspectra Filename: pool/dists/noble/main/r-cran-olcpm_0.1.2-1.ca2404.1_all.deb Size: 311016 MD5sum: 513bf2847a98926594c8716a889cfb5d SHA1: 39324a300c0ff49468e9792730577d8ed7a32dab SHA256: d8bb7afb008ab80f143ab69c2a2e615f72ccfe80701c9c79bc1a80c1e153fe0f SHA512: 2f5cbb072cc30fbea3a42be8549a0b936d9caacd63325a3a69fa4217c46d7d757959717a14c48d3bbfdc10221cbde5ad91082ca785577baa549ba9512d85d359 Homepage: https://cran.r-project.org/package=OLCPM Description: CRAN Package 'OLCPM' (Online Change Point Detection for Matrix-Valued Time Series) We provide two algorithms for monitoring change points with online matrix-valued time series, under the assumption of a two-way factor structure. The algorithms are based on different calculations of the second moment matrices. One is based on stacking the columns of matrix observations, while another is by a more delicate projected approach. A well-known fact is that, in the presence of a change point, a factor model can be rewritten as a model with a larger number of common factors. In turn, this entails that, in the presence of a change point, the number of spiked eigenvalues in the second moment matrix of the data increases. Based on this, we propose two families of procedures - one based on the fluctuations of partial sums, and one based on extreme value theory - to monitor whether the first non-spiked eigenvalue diverges after a point in time in the monitoring horizon, thereby indicating the presence of a change point. This package also provides some simple functions for detecting and removing outliers, imputing missing entries and testing moments. See more details in He et al. (2021). Package: r-cran-oldr Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2170 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbw, r-cran-car, r-cran-withr, r-cran-tibble, r-cran-rmarkdown, r-cran-cli, r-cran-tinytex Suggests: r-cran-testthat, r-cran-covr, r-cran-diagrammer, r-cran-knitr, r-cran-kableextra, r-cran-spelling Filename: pool/dists/noble/main/r-cran-oldr_0.2.4-1.ca2404.1_all.deb Size: 1684022 MD5sum: f15cf92561c7accded3f91dd9bf574b9 SHA1: 2c722c5da3664c9799a35c76117e1cb6d0f68357 SHA256: 4037948757385ff36f6abbfb58065925d3b0150864c19089825be3825656b99a SHA512: a263ade00c88a34fbaac6d9e64cde6edda09f3fdf0d563ea57a2084ab555a001505b8b5b4129356d9b87baf73d23e64f8190b67829fe5abbc89e75ea3a54c94e Homepage: https://cran.r-project.org/package=oldr Description: CRAN Package 'oldr' (An Implementation of Rapid Assessment Method for Older People) An implementation of the Rapid Assessment Method for Older People or RAM-OP . It provides various functions that allow the user to design and plan the assessment and analyse the collected data. RAM-OP provides accurate and reliable estimates of the needs of older people. Package: r-cran-olinkanalyze Architecture: all Version: 5.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3039 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-data.table, r-cran-dbplyr, r-cran-dplyr, r-cran-duckdb, r-cran-forcats, r-cran-ggplot2, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-broom, r-cran-car, r-bioc-clusterprofiler, r-cran-curl, r-cran-emmeans, r-cran-ggplotify, r-cran-ggpubr, r-cran-ggrepel, r-cran-lme4, r-cran-lmertest, r-cran-msigdbr, r-cran-ordinal, r-cran-readxl, r-cran-pheatmap, r-cran-scales, r-cran-showtext, r-cran-sysfonts, r-cran-systemfonts, r-cran-testthat, r-cran-vdiffr, r-cran-withr, r-cran-writexl, r-cran-zip, r-cran-umap, r-cran-rstatix, r-cran-fsa, r-cran-kableextra, r-cran-knitr Filename: pool/dists/noble/main/r-cran-olinkanalyze_5.0.0-1.ca2404.1_all.deb Size: 2600144 MD5sum: 97d98771c9a8cc12a74cc867a7a1af7e SHA1: 5e1a1ae94b9148549b4fad7662b49d2e9feecbac SHA256: 1bb637448dc5d04b1b7f23928567b5a3faf081478df058741075dab285241874 SHA512: aadacebdb7bde195692a0837b3cd8b20048625cca9edc031561b3e69ae5b00e39af722f895637a63b442739c4303cad1a0ceec6a3b58321847841ee777fb3f66 Homepage: https://cran.r-project.org/package=OlinkAnalyze Description: CRAN Package 'OlinkAnalyze' (Facilitate Analysis of Proteomic Data from Olink) A collection of functions to facilitate analysis of proteomic data from Olink, primarily NPX data that has been exported from Olink Software. The functions also work on QUANT data from Olink by log- transforming the QUANT data. The functions are focused on reading data, facilitating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to help users extract biological insights from proteomic data run on the Olink platform. Package: r-cran-ollamar Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 735 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64enc, r-cran-crayon, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ollamar_1.2.2-1.ca2404.1_all.deb Size: 615384 MD5sum: 0ccf483782ba333cb5ef6aba8f9af60e SHA1: 4d5b7c0d193e2171416f4bc6b9dcb83df1aa1ed6 SHA256: 9b01814df1f2856709ee58bcb10c0f8a11d43db8a06a5a63e094cea1280f6df5 SHA512: e634e9d1955ff4ed9420bcb19c7a5576a4832a7ccda8b050158f03ebd217ebba58c91905eee2b3b600d11a49ba789704a1f2f32b0f083fb753c1cfd260202a63 Homepage: https://cran.r-project.org/package=ollamar Description: CRAN Package 'ollamar' ('Ollama' Language Models) An interface to easily run local language models with 'Ollama' server and API endpoints (see for details). 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See Alizadeh (2019) for example. Package: r-cran-ollggamma Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggamma Filename: pool/dists/noble/main/r-cran-ollggamma_1.0.2-1.ca2404.1_all.deb Size: 18212 MD5sum: 039e879b7c3b7abf42007654a9a714a7 SHA1: 6028b5e49515eb05996f879e3aca21787a8c3f2a SHA256: d3f61887ea9bd3190fc043336b7f0db8b93753bb84a0779e215e81dc5bd8b66b SHA512: 947e137e2b15b4b4657d88213ce7d9c817dd9c8a584ceddf3de7ec613d206f52800ed8fbc2f7efa5fcf987bc7990a69deed4296a7e913da49e2176e49bd71ff0 Homepage: https://cran.r-project.org/package=ollggamma Description: CRAN Package 'ollggamma' (Odd Log-Logistic Generalized Gamma Probability Distribution) Density, distribution function, quantile function and random generation for the Odd Log-Logistic Generalized Gamma proposed in Prataviera, F. et al (2017) . Package: r-cran-olr Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-readxl, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-olr_1.2-1.ca2404.1_all.deb Size: 66540 MD5sum: 9893bdc17a5e88e4bef94a0188101c5c SHA1: 99e76d0fc681c60612979e4872c7d56293240387 SHA256: d74dd77b831e8ef28e24c1234ffe604abcf84f7c7d64c74bdea1afde7703da03 SHA512: 477a777f66989f5c038b978f08f64c868954904b0a06f82f2e578130b8cffcea5a6235ea5bd1088ef39b7dc366164f16bab72b27cdaf28ec15bcbbbe5134632b Homepage: https://cran.r-project.org/package=olr Description: CRAN Package 'olr' (Optimal Linear Regression) The olr function systematically evaluates multiple linear regression models by exhaustively fitting all possible combinations of independent variables against the specified dependent variable. It selects the model that yields the highest adjusted R-squared (by default) or R-squared, depending on user preference. In model evaluation, both R-squared and adjusted R-squared are key metrics: R-squared measures the proportion of variance explained but tends to increase with the addition of predictors—regardless of relevance—potentially leading to overfitting. Adjusted R-squared compensates for this by penalizing model complexity, providing a more balanced view of fit quality. The goal of olr is to identify the most suitable model that captures the underlying structure of the data while avoiding unnecessary complexity. By comparing both metrics, it offers a robust evaluation framework that balances predictive power with model parsimony. Example Analogy: Imagine a gardener trying to understand what influences plant growth (the dependent variable). They might consider variables like sunlight, watering frequency, soil type, and nutrients (independent variables). Instead of manually guessing which combination works best, the olr function automatically tests every possible combination of predictors and identifies the most effective model—based on either the highest R-squared or adjusted R-squared value. This saves the user from trial-and-error modeling and highlights only the most meaningful variables for explaining the outcome. A Python version is also available at . Package: r-cran-olsengine Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-olsengine_1.0.0-1.ca2404.1_all.deb Size: 54036 MD5sum: 8027dd285504102cc906f3b28105e8ee SHA1: bf0e00fb12746edf1ea64b8e6daecf734a40b40c SHA256: eec9d8160bd5df07c1a6095a019b287443da881e362ce5fcbc7458b12a72bdcb SHA512: 5639ba319706976b30ead078b365d80713343240f959b9605bfc725be3d52ec1cf8b6979a0d03163869b0e3cb8d730468efaf3b073382457e485c92a3266e7a5 Homepage: https://cran.r-project.org/package=OLSengine Description: CRAN Package 'OLSengine' (Transparent and Assisted Linear Modeling Engine) A transparent, modular, and base-R implemented statistical engine for linear regression (OLS), analysis of variance (ANOVA), and logistic regression (Logit). Designed under the principle of "assisted simplicity", it features an integrated methodological "customs" (Aduana) that automatically audits mathematical assumptions (e.g., multicollinearity, heteroskedasticity, normality, and perfect separation) and outputs publication-ready, APA-formatted tables. It deliberately avoids hidden heuristics and external dependencies, ensuring computational transparency and reproducibility for applied research. Package: r-cran-olsrr Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2545 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-ggplot2, r-cran-goftest, r-cran-gridextra, r-cran-nortest, r-cran-xplorerr Suggests: r-cran-covr, r-cran-descriptr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-olsrr_0.7.0-1.ca2404.1_all.deb Size: 2016994 MD5sum: 4f4f63e5b75d26e37060a9611b955577 SHA1: fa403ce00a5c6853048683b9397a8c85185b6893 SHA256: 4a3a92fa3b18fb3196f7fe00238ecd186a68497ecd4970f6e572aa1bc368da5b SHA512: c9d7158f5ef2eef0cfe359c663d5b4b35c21cacd2102766c95d03e3b7d87090693701bdb2a9a9477cb82e0560b6bd0f65a5d2cfd8ca47678f4fe32114631bd26 Homepage: https://cran.r-project.org/package=olsrr Description: CRAN Package 'olsrr' (Tools for Building OLS Regression Models) Tools designed to make it easier for users, particularly beginner/intermediate R users to build ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures. Package: r-cran-olstrajr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-broom, r-cran-ggplot2, r-cran-purrr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-olstrajr_0.1.0-1.ca2404.1_all.deb Size: 209958 MD5sum: 4ff1f29d38701ff70f628f2b94c31415 SHA1: f2b15f278208205f7ed08492bbae98340a11f826 SHA256: 2b17e310602d55161247cf54524763aa7b08301c7d11996cb33f741c24a6ccbb SHA512: 42796374f294755baab2ebb30deb929ca59280247ab5b1f5136c0e900cea3f3cf25e0638e923cb2873b0db27ac6b981e2584f4a3255807f8eea940bb804a3264 Homepage: https://cran.r-project.org/package=OLStrajr Description: CRAN Package 'OLStrajr' (Ordinary Least Squares Trajectory Analysis) The 'OLStrajr' package provides comprehensive functions for ordinary least squares (OLS) trajectory analysis and case-by-case OLS regression as outlined in Carrig, Wirth, and Curran (2004) and Rogosa and Saner (1995) . It encompasses two primary functions, OLStraj() and cbc_lm(). The OLStraj() function simplifies the estimation of individual growth curves over time via OLS regression, with options for visualizing both group-level and individual-level growth trajectories and support for linear and quadratic models. The cbc_lm() function facilitates case-by-case OLS estimates and provides unbiased mean population intercept and slope estimators by averaging OLS intercepts and slopes across cases. It further offers standard error calculations across bootstrap replicates and computation of 95% confidence intervals based on empirical distributions from the resampling processes. Package: r-cran-olympicrshiny Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-config, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-golem, r-cran-shiny, r-cran-shinybusy, r-cran-shinythemes, r-cran-summarytools Filename: pool/dists/noble/main/r-cran-olympicrshiny_1.0.2-1.ca2404.1_all.deb Size: 5138026 MD5sum: 7ecc197ce213cc9e5d3bca3ea3d29f02 SHA1: b2c38610cf3e6da9c40270b75507156bce179dd4 SHA256: 80d258ee1b92b717e775845dbcd45b3d1ebadd29a1c38a5adf3dc4c488d7d7d5 SHA512: f14c33b08d360562a06a589c3d839e26c1f8c8802232de42afecb43aab5cf9f7943f49433ef48706cb95c5017408854be7cc9c3af40bc67632b5a55a2b53bd58 Homepage: https://cran.r-project.org/package=OlympicRshiny Description: CRAN Package 'OlympicRshiny' ('Shiny' Application for Olympic Data) 'Shiny' Application to visualize Olympic Data. From 1896 to 2016. Even Winter Olympics events are included. Data is from Kaggle at . Package: r-cran-omegag Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-omegag_1.0.1-1.ca2404.1_all.deb Size: 39326 MD5sum: bc52c23ec80bbb349f666cd046c28eb9 SHA1: 4ef52cb72b9b0b5e658baf66643858695d8aad9b SHA256: 8a0745444b9aa236165581b703966efe2f67f93523ceb5a0499ab4ccc6ece58c SHA512: 07dea07ac1a795946652bbf8b7c17473496dca6ff5e158ef5957bf716ba759dc9ed8a14e4d5972c5e3082ca1029a69e6026dd8000de4dc56220189b14fb98d1c Homepage: https://cran.r-project.org/package=OmegaG Description: CRAN Package 'OmegaG' (Omega-Generic: Composite Reliability of MultidimensionalMeasures) It is a computer tool to estimate the item-sum score's reliability (composite reliability, CR) in multidimensional scales with overlapping items. An item that measures more than one domain construct is called an overlapping item. The estimation is based on factor models allowing unlimited cross-factor loadings such as exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM). The factor models include correlated-factor models and bi-factor models. Specifically for bi-factor models, a type of hierarchical factor model, the package estimates the CR hierarchical subscale/hierarchy and CR subscale/scale total. The CR estimator 'Omega-generic' was proposed by Mai, Srivastava, and Krull (2021) . The current version can only handle continuous data. Yujiao Mai contributes to the algorithms, R programming, and application example. Deo Kumar Srivastava contributes to the algorithms and the application example. Kevin R. Krull contributes to the application example. The package 'OmegaG' was sponsored by American Lebanese Syrian Associated Charities (ALSAC). However, the contents of 'OmegaG' do not necessarily represent the policy of the ALSAC. Package: r-cran-omicflow Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-matrix, r-cran-ape, r-cran-gghalves, r-cran-ggpubr, r-cran-ggrepel, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-magrittr, r-cran-patchwork, r-cran-purrr, r-cran-rbiom, r-cran-rcolorbrewer, r-bioc-rhdf5, r-cran-rstatix, r-cran-slam, r-cran-vegan, r-cran-viridis, r-cran-yyjsonr, r-cran-ggplot2 Suggests: r-cran-dt, r-cran-downloadthis, r-cran-rmarkdown, r-cran-cli, r-cran-testthat Filename: pool/dists/noble/main/r-cran-omicflow_1.3.2-1.ca2404.1_all.deb Size: 372880 MD5sum: b53621614d1ee49eae22df7f1753878e SHA1: 825a0252460ffb23a75cfa0235302c7028747882 SHA256: a9bb7fd01e933380650d816b401f08ab206bcfc9c4bc65ffff5b1ad9d9864883 SHA512: 5d7dbd25d55936cdcab7e166ea4374fdbad6fa47528b8e033bf9bbe46b0fd0b310f66ac4fb9d1964ed15388c438d6d5e8ad4fc6811e5d6305489f985bccb75d0 Homepage: https://cran.r-project.org/package=OmicFlow Description: CRAN Package 'OmicFlow' (Fast and Efficient (Automated) Analysis of Sparse Omics Data) A generalised data structure for fast and efficient loading and data munching of sparse omics data. The 'OmicFlow' requires an up-front validated metadata template from the user, which serves as a guide to connect all the pieces together by aligning them into a single object that is defined as an 'omics' class. Once this unified structure is established, users can perform manual subsetting, visualisation, and statistical analysis, or leverage the automated 'autoFlow' method to generate a comprehensive report. Package: r-cran-omickriging Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-rocr, r-cran-irlba, r-cran-foreach Filename: pool/dists/noble/main/r-cran-omickriging_1.4.0-1.ca2404.1_all.deb Size: 1815010 MD5sum: 7185901441e079576e8feb6b0482f6e9 SHA1: 583224ff210c5748f0fdc4bd9dc8770c69a59934 SHA256: e64eafc9b133da50a2c3d5eb6824f10e8af24196f2048b8122a013bba71c3778 SHA512: 24592451fe6977d7b8ec3ebc6368a7e3a873355a7888bd08f531ff89b2a030de9b6764a19ac6bf6e467b0cdfed7c006633e42cc7a73ba7eeb1f2181f993e3adc Homepage: https://cran.r-project.org/package=OmicKriging Description: CRAN Package 'OmicKriging' (Poly-Omic Prediction of Complex TRaits) It provides functions to generate a correlation matrix from a genetic dataset and to use this matrix to predict the phenotype of an individual by using the phenotypes of the remaining individuals through kriging. Kriging is a geostatistical method for optimal prediction or best unbiased linear prediction. It consists of predicting the value of a variable at an unobserved location as a weighted sum of the variable at observed locations. Intuitively, it works as a reverse linear regression: instead of computing correlation (univariate regression coefficients are simply scaled correlation) between a dependent variable Y and independent variables X, it uses known correlation between X and Y to predict Y. Package: r-cran-omicnavigator Architecture: all Version: 1.19.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1366 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite Suggests: r-cran-faviconplease, r-cran-ggplot2, r-cran-opencpu, r-cran-plotly, r-cran-tinytest, r-cran-ttdo, r-cran-upsetr Filename: pool/dists/noble/main/r-cran-omicnavigator_1.19.0-1.ca2404.1_all.deb Size: 866074 MD5sum: a9cafa85db587898fb739e2de610e2d1 SHA1: b6a996010f5dc6a7103182f350fbda06711b101b SHA256: 7e2dd0ac0ed16e9ef79e325721be6bd3cf20d1c99fe852b1ec529d69ea2f066e SHA512: 37871f582517fcf35ab9901a54857747b0fb66aaf9a80ab26d1380a610bbd41bde854af98bd186623b8edb7ee2153c798fd9660397ae257f61658b3bd106d6b0 Homepage: https://cran.r-project.org/package=OmicNavigator Description: CRAN Package 'OmicNavigator' (Open-Source Software for 'Omic' Data Analysis and Visualization) A tool for interactive exploration of the results from 'omics' experiments to facilitate novel discoveries from high-throughput biology. The software includes R functions for the 'bioinformatician' to deposit study metadata and the outputs from statistical analyses (e.g. differential expression, enrichment). These results are then exported to an interactive JavaScript dashboard that can be interrogated on the user's local machine or deployed online to be explored by collaborators. The dashboard includes 'sortable' tables, interactive plots including network visualization, and fine-grained filtering based on statistical significance. Package: r-cran-omicnetr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1288 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-mixomics Filename: pool/dists/noble/main/r-cran-omicnetr_0.1.1-1.ca2404.1_all.deb Size: 1096990 MD5sum: e743cf0356325e52b2187f3e3f5b19ab SHA1: 88500157eea758e9ea9220c92b28894d1bc001e1 SHA256: d9a02517f1060c443daf2742c17f27bde1cdb6acc2e972580df6a25ef1e25970 SHA512: 2e7665df8e371f490e7a453d75cc68987f13a13cbaba664218c0a682299b400ead2db98559ab166e4bdb2328f55f030e6a319155ad2bf1e8d9ef661cdc649ab8 Homepage: https://cran.r-project.org/package=OmicNetR Description: CRAN Package 'OmicNetR' (Network-Based Integration of Multi-Omics Data Using Sparse CCA) Provides an end-to-end workflow for integrative analysis of two omics layers using sparse canonical correlation analysis (sCCA), including sample alignment, feature selection, network edge construction, and visualization of gene-metabolite relationships. The underlying methods are based on penalized matrix decomposition and sparse CCA (Witten, Tibshirani and Hastie (2009) ), with design principles inspired by multivariate integrative frameworks such as mixOmics (Rohart et al. (2017) ). Package: r-cran-omics Architecture: all Version: 0.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-pheatmap Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-omics_0.1-5-1.ca2404.1_all.deb Size: 55754 MD5sum: b4aa3b9065a6becaedf8e701c1cb21af SHA1: 8025fc6bf3e0e1a1eb6a9a4bc639528739d224d8 SHA256: 6beb19b75d192d8545a514599d606ede69b8a38c1241cd57cfbcf5649c7dcc62 SHA512: b72019a62eb7dfec2f3f69044fb87c2a35eba383fab10f51775119882eeb31f732c77e867a5d240200be16ff4000563d63e755dd63f482f0c23f627ca83cf6d3 Homepage: https://cran.r-project.org/package=omics Description: CRAN Package 'omics' ('--omics' Data Analysis Toolbox) A collection of functions to analyse '--omics' datasets such as DNA methylation and gene expression profiles. Package: r-cran-omicsense Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-kernlab Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-omicsense_0.2.0-1.ca2404.1_all.deb Size: 128772 MD5sum: 06658e2412e52dbc21ff5ee153a2c0ff SHA1: 38dbe6e3e89e50ab50df0498fb842cc26da4ff53 SHA256: b8c69196f5c6a6f867649a29d573ae8d5987267c12c5988fbbc81ac060c1e106 SHA512: b35758b6ba6bc30d95b7eb2633b60e195cd3f252760fdf3b068c963d560d37b8fedd1cf087bbf6792fdd3dab64b2d13f975bd0a748914164bb3c7db4e09c0a73 Homepage: https://cran.r-project.org/package=OmicSense Description: CRAN Package 'OmicSense' (Biosensor Development using Omics Data) A method for the quantitative prediction using omics data. This package provides functions to construct the quantitative prediction model using omics data. Package: r-cran-omicspls Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-tibble, r-cran-softimpute, r-cran-withr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-gplots Filename: pool/dists/noble/main/r-cran-omicspls_2.1.0-1.ca2404.1_all.deb Size: 359340 MD5sum: 0d6c3a25b2e54b17f365c1533c95d44e SHA1: 6bbe2e66236da471272408a8c6818a834c4344e2 SHA256: a92b08647b8aea8d8ecab95a4acfafd8d8b3dda5488304846b959d47e457a182 SHA512: 5a72f6cf156035bb5e35dd967493daff0c86d046940b76c98abbe9cdc299f0e801868975a838ab168d4309131da58b3860b2a23495ce1bfcd3f7efbf0d000366 Homepage: https://cran.r-project.org/package=OmicsPLS Description: CRAN Package 'OmicsPLS' (Data Integration with Two-Way Orthogonal Partial Least Squares) Performs the O2PLS data integration method for two datasets, yielding joint and data-specific parts for each dataset. The algorithm automatically switches to a memory-efficient approach to fit O2PLS to high dimensional data. It provides a rigorous and a faster alternative cross-validation method to select the number of components, as well as functions to report proportions of explained variation and to construct plots of the results. See the software article by el Bouhaddani et al (2018) , and Trygg and Wold (2003) . It also performs Sparse Group (Penalized) O2PLS, see Gu et al (2020) and cross-validation for the degree of sparsity. Package: r-cran-omicsprepr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-omicsprepr_0.1.1-1.ca2404.1_all.deb Size: 39736 MD5sum: fedf60f7e56ccdf03f6fe6ae1353b197 SHA1: c81d5a1fc7ffc920df08bdd5bf69bed63fbe6896 SHA256: 2aff6dcce594ff4ef9707fb50446ff70e1c69026a72e787954182fb628b81596 SHA512: 7cb900777ffa6f10951bc9f80548900fda3f0a413879bbb3e68bd01b7054ca55037d1141763fee5710deba393fde3cc55a67b5f97b5a6da2494a51d39b13300b Homepage: https://cran.r-project.org/package=OmicsPrepR Description: CRAN Package 'OmicsPrepR' (Unified Preprocessing Toolkit for Proteomics and Metabolomics) Provides unified workflows for quality control, normalization, and visualization of proteomic and metabolomic data. The package simplifies preprocessing through automated imputation, scaling, and principal component analysis (PCA)-based exploratory analysis, enabling researchers to prepare omics datasets efficiently for downstream statistical and machine learning analyses. Package: r-cran-omicsqc Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1917 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fitdistrplus, r-cran-lsa, r-cran-boutroslab.plotting.general Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-omicsqc_1.1.1-1.ca2404.1_all.deb Size: 1026708 MD5sum: 316899ff3f22f3ad1adb978671bebf6c SHA1: 973b4cb9864b2fd9be3891c70da913a81642b31a SHA256: 2e35ba7f8955f9478c9a76fbfe5f5b809677fba02d49f7d1df306eb50594d9fd SHA512: 61cfea5fb6cb955acc7c5a67270aff33e8201690fc8ed7bb9f86a4222e631d79d4e5e44b320c08b976dbbb4c3217a60d7323f8b3e4f5fc7680b07a9bdbe9a61d Homepage: https://cran.r-project.org/package=OmicsQC Description: CRAN Package 'OmicsQC' (Nominating Quality Control Outliers in Genomic Profiling Studies) A method that analyzes quality control metrics from multi-sample genomic sequencing studies and nominates poor quality samples for exclusion. Per sample quality control data are transformed into z-scores and aggregated. The distribution of aggregated z-scores are modelled using parametric distributions. The parameters of the optimal model, selected either by goodness-of-fit statistics or user-designation, are used for outlier nomination. Two implementations of the Cosine Similarity Outlier Detection algorithm are provided with flexible parameters for dataset customization. Package: r-cran-omicstools Architecture: all Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bs4dash, r-cran-cli, r-cran-config, r-cran-dbscan, r-cran-dplyr, r-cran-dt, r-cran-forcats, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggrepel, r-cran-ggsci, r-cran-ggvenn, r-cran-golem, r-cran-janitor, r-cran-magrittr, r-cran-matrixstats, r-cran-moments, r-cran-outliers, r-cran-pheatmap, r-cran-purrr, r-cran-rcolorbrewer, r-cran-readxl, r-cran-readr, r-cran-rlang, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-viridis Suggests: r-bioc-biobase, r-bioc-pvca, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-boruta, r-cran-randomforest, r-cran-caret, r-cran-recipes, r-cran-parsnip, r-cran-workflows, r-cran-rsample, r-cran-tune, r-cran-dials, r-cran-doparallel, r-cran-vip, r-cran-goplot, r-cran-openxlsx, r-cran-upsetr, r-cran-zip Filename: pool/dists/noble/main/r-cran-omicstools_1.1.7-1.ca2404.1_all.deb Size: 608440 MD5sum: 89c2164927534ee46c4b616516855109 SHA1: 25af66cfc52d70f2e231e4fdea596201073b2d2c SHA256: 110a2799ba6332c9bd258e8dc6b3134801ebae5b08977e9220fd7d56e4c53fed SHA512: f981de93131c09af1fe50eea47d75b906351fbb9eadec6cdc59f47d537c0599b8701fd04a651a201d6df554f6fb5dd37a177c97b948d8925340cee7083609e5c Homepage: https://cran.r-project.org/package=omicsTools Description: CRAN Package 'omicsTools' (Omics Data Process Toolbox) Processing and analyzing omics data from genomics, transcriptomics, proteomics, and metabolomics platforms. It provides functions for preprocessing, normalization, visualization, and statistical analysis, as well as machine learning algorithms for predictive modeling. 'omicsTools' is an essential tool for researchers working with high-throughput omics data in fields such as biology, bioinformatics, and medicine.The QC-RLSC (quality control–based robust LOESS signal correction) algorithm is used for normalization. Dunn et al. (2011) . Package: r-cran-omicwas Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-data.table, r-cran-dplyr, r-cran-ff, r-cran-glmnet, r-cran-magrittr, r-cran-mass, r-cran-matrixstats, r-cran-purrr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-omicwas_0.8.0-1.ca2404.1_all.deb Size: 4148768 MD5sum: c923e8a2072276937c47118eda171eef SHA1: 1dfb4e7a536096cf94b12e7d5f997c064f34a269 SHA256: 210d6d5096ac7237c56fb6a8f9661bf56a50650b09d129c20bf94feb6537cc30 SHA512: 8ba304cdfce7c7a38baebea38118449d15eef9bf20c3f49cd72f33b164a65e0e3341af977f20cdcb37e4f7ce2e011c8c7f7c17e6abd5af1c55abd973c9e74ea7 Homepage: https://cran.r-project.org/package=omicwas Description: CRAN Package 'omicwas' (Cell-Type-Specific Association Testing in Bulk Omics Experiments) In bulk epigenome/transcriptome experiments, molecular expression is measured in a tissue, which is a mixture of multiple types of cells. This package tests association of a disease/phenotype with a molecular marker for each cell type. The proportion of cell types in each sample needs to be given as input. The package is applicable to epigenome-wide association study (EWAS) and differential gene expression analysis. Takeuchi and Kato (submitted) "omicwas: cell-type-specific epigenome-wide and transcriptome association study". Package: r-cran-omisc Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-psych, r-cran-copula Suggests: r-cran-lavaan Filename: pool/dists/noble/main/r-cran-omisc_0.1.5-1.ca2404.1_all.deb Size: 117356 MD5sum: 931c32d3325860aeb20e57ac858c8dc1 SHA1: 70dbdfb535ad945f91010b7d63d25d3f211eae31 SHA256: 00d38d4f167fec005158c0e4a3e5120af8e7479f13d3aa99b3f08ef10ec0863e SHA512: 8932cfd91c05725a21051a52d4e54e18e75f0734ffcd48ecbac071678045b0d98835abb22edd22aebd3fa49724d4b5a3fd0978674c841312ebfc6a1c0e2e1122 Homepage: https://cran.r-project.org/package=Omisc Description: CRAN Package 'Omisc' (Univariate Bootstrapping and Other Things) Primarily devoted to implementing the Univariate Bootstrap (as well as the Traditional Bootstrap). In addition there are multiple functions for DeFries-Fulker behavioral genetics models. The univariate bootstrapping functions, DeFries-Fulker functions, regression and traditional bootstrapping functions form the original core. Additional features may come online later, however this software is a work in progress. For more information about univariate bootstrapping see: Lee and Rodgers (1998) and Beasley et al (2007) . Package: r-cran-omixvizr Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-data.table, r-cran-dplyr, r-cran-genpwr, r-cran-ggbreak, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggsci, r-cran-ggtext, r-cran-gridextra, r-cran-gtable, r-cran-lulab.utils, r-cran-magrittr, r-cran-matrix, r-cran-patchwork, r-cran-pheatmap, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-showtext, r-cran-stringr, r-cran-sysfonts, r-cran-systemfonts, r-cran-tibble Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-omixvizr_1.4.0-1.ca2404.1_all.deb Size: 146398 MD5sum: 1b4d5099f3d559fb5996721d29cef820 SHA1: 9a69586d7b9e521309c976a6afbf56ce0bb08b55 SHA256: 558d53c99423dc1a99f8e58bba248b4936dae7f54d518b86d7d8827de9f5fe3c SHA512: 84f21e269b05aa8c1e84d597b2668ca3892261e60a88b32e893f892e5e5bc1cfa30919b9df940d0875a2d574b1adfebcc1f14f21ae8a5482a2d542360b7a5fda Homepage: https://cran.r-project.org/package=omixVizR Description: CRAN Package 'omixVizR' (A Toolkit for Omics Data Visualization) Provides a suite of tools for the comprehensive visualization of multi-omics data, including genomics, transcriptomics, and proteomics. Offers user-friendly functions to generate publication-quality plots, thereby facilitating the exploration and interpretation of complex biological datasets. Supports seamless integration with popular R visualization frameworks and is well-suited for both exploratory data analysis and the presentation of final results. Key formats and methods are presented in Huang, S., et al. (2024) "The Born in Guangzhou Cohort Study enables generational genetic discoveries" . Package: r-cran-omnibus Architecture: all Version: 1.2.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-omnibus_1.2.15-1.ca2404.1_all.deb Size: 271514 MD5sum: fc5269503c6220e742fc2d0ecbc02868 SHA1: 8833c4a480f19cbae1dd053de4e5889486fb46e1 SHA256: f1effd1ea3c8f9c6ad222b5f47c58c4c9c77c6a90052a84a1861b69461f9917f SHA512: 6bbfa649984f809d4753e1ff1ecc8359361cc60b122c6e0548dc03b4adf3808ac82dbc2d786189b5ccdc37811d0192097de07d3b35c81cd959480d7d669f5fed Homepage: https://cran.r-project.org/package=omnibus Description: CRAN Package 'omnibus' (Helper Tools for Managing Data, Dates, Missing Values, and Text) An assortment of helper functions for managing data (e.g., rotating values in matrices by a user-defined angle, switching from row- to column-indexing), dates (e.g., intuiting year from messy date strings), handling missing values (e.g., removing elements/rows across multiple vectors or matrices if any have an NA), text (e.g., flushing reports to the console in real-time); and combining data frames with different schema (copying, filling, or concatenating columns or applying functions before combining). Package: r-cran-omnibusfisher Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform, r-cran-stringr, r-cran-survey Filename: pool/dists/noble/main/r-cran-omnibusfisher_1.0-1.ca2404.1_all.deb Size: 93540 MD5sum: 19b2f2456ed67ac1657d18c7de207fef SHA1: 9330a30c80ffdcc53b2e2d44eac639c1952d372e SHA256: faaf1f540b859d85d996d15824e3c264270ef8f32620ba63a68c1443220a23c9 SHA512: 39506b5ac7d6bcec9dc60176f99bae442e423a5a773cb0e265dbf04080d985ab29f8d64281883b4282328313f66244c4f78874535033dcbeb0a5c3373bba8851 Homepage: https://cran.r-project.org/package=OmnibusFisher Description: CRAN Package 'OmnibusFisher' (A Modified Fisher’s Method to Test Overall Gene-Level Effect) The separate p-values of SNPs, RNA expressions and DNA methylations are calculated by KM regression. The correlation between different omics data are taken into account. This method can be applied to either samples with all three types of omics data or samples with two types. Package: r-cran-omock Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4207 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-clock, r-cran-omopgenerics, r-cran-purrr, r-cran-readr, r-cran-rlang Suggests: r-cran-cdmconnector, r-cran-dbi, r-cran-duckdb, r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-patientprofiles, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-omock_0.6.2-1.ca2404.1_all.deb Size: 4100046 MD5sum: 292ce5aea4da5efdc5176d5a0bbdd757 SHA1: edb6ef95c3a91a02f3de0215cb77a30d68b33f02 SHA256: 3b6112f35936d6f0c322f6b93bde246127fa08e08b9fd807c5402cb880275797 SHA512: d4dfabd52db3cbca9ef55bb2d05b51417d304a3c9bb6aac0987ccf53a5fa2f2ed6d2f9eec282fa8e0721a14669109559e34fe82091bf1878a4a208d08014fe3d Homepage: https://cran.r-project.org/package=omock Description: CRAN Package 'omock' (Creation of Mock Observational Medical Outcomes PartnershipCommon Data Model) Creates mock data for testing and package development for the Observational Medical Outcomes Partnership common data model. The package offers functions crafted with pipeline-friendly implementation, enabling users to effortlessly include only the necessary tables for their testing needs. Package: r-cran-omopconstructor Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2513 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang Suggests: r-cran-bookdown, r-cran-cdmconnector, r-cran-duckdb, r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-omopsketch, r-cran-rmarkdown, r-cran-rpostgres, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-visomopresults Filename: pool/dists/noble/main/r-cran-omopconstructor_0.3.0-1.ca2404.1_all.deb Size: 717598 MD5sum: b9386da3bd0ad5adaed8cde57a4a2bf4 SHA1: 48fc4e43b25e95f5f07c100e31a7beecc4cee229 SHA256: cff1cc4e647f4707e5a39163980999c4ceb81df7340d51a8c44df983f2d1dccb SHA512: 44bed8992d455329faee3af6798ae5a57caf6e659601c96835eac3db12c0559261716f2af672fef04e9879cdfa5e043bba46c9838a319f7b37c27e4a7c7bb404 Homepage: https://cran.r-project.org/package=OmopConstructor Description: CRAN Package 'OmopConstructor' (Build Tables in the OMOP Common Data Model) Provides functionality to construct standardised tables from health care data formatted according to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. The package includes tools to build key tables such as observation period and drug era, among others. Package: r-cran-omopgenerics Architecture: all Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1321 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbplyr, r-cran-dplyr, r-cran-generics, r-cran-glue, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-snakecase, r-cran-stringi, r-cran-stringr, r-cran-tidyr, r-cran-vctrs Suggests: r-cran-bit64, r-cran-cdmconnector, r-cran-covr, r-cran-duckdb, r-cran-gt, r-cran-here, r-cran-jsonlite, r-cran-knitr, r-cran-omock, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-omopgenerics_1.3.7-1.ca2404.1_all.deb Size: 713074 MD5sum: 275a113ea379b9062704467faeb98562 SHA1: 3d0423b5a8cecf1aa595fde1f174ba4e28484ea2 SHA256: 70bf5c2cacfb133ad816dcbe8bf4966852828a53aa9c2320b8c5660fb642d2b0 SHA512: d0a4991e1a641edff312188de4a0be9d297315afbeb614132ff61ea54a5cb636f3266d2053177427425447d9a01c792e37bb19dbf05e2863c1e8fdcdbcdb03db Homepage: https://cran.r-project.org/package=omopgenerics Description: CRAN Package 'omopgenerics' (Methods and Classes for the OMOP Common Data Model) Provides definitions of core classes and methods used by analytic pipelines that query the OMOP (Observational Medical Outcomes Partnership) common data model. Package: r-cran-omophub Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 665 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-r6, r-cran-rlang, r-cran-cli, r-cran-tibble, r-cran-purrr, r-cran-glue, r-cran-checkmate Suggests: r-cran-testthat, r-cran-httptest2, r-cran-webmockr, r-cran-knitr, r-cran-rmarkdown, r-cran-keyring, r-cran-withr Filename: pool/dists/noble/main/r-cran-omophub_1.7.0-1.ca2404.1_all.deb Size: 467038 MD5sum: d814a0126305c5715591bfdc2e4bea3b SHA1: 00d8d6b0f2a8acebc1d9603d877d8794eaf67874 SHA256: 48356446e7d63dfe3342158a650c59907c7f2f7b9a9cfb26c1eacee1a1c6e924 SHA512: ddc4e5d9ba179d319e78e6f71b14403ca75487284e4fe59fd74a4753b665330b9db2c6827fdf33207c21372de87961008c24ffe1beb3f0917ed110f606ee41b3 Homepage: https://cran.r-project.org/package=omophub Description: CRAN Package 'omophub' (R Client for the 'OMOPHub' Medical Vocabulary API) Provides an R interface to the 'OMOPHub' API for accessing 'OHDSI ATHENA' standardized medical vocabularies. Supports concept search, semantic search using neural embeddings, concept similarity, vocabulary exploration, hierarchy navigation, relationship queries, concept mappings, and FHIR-to-OMOP concept resolution with automatic pagination. Package: r-cran-omoponspark Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-glue, r-cran-omopgenerics, r-cran-purrr, r-cran-rlang, r-cran-stringr Suggests: r-cran-testthat, r-cran-omock, r-cran-knitr, r-cran-rmarkdown, r-cran-cdmconnector, r-cran-omopsketch, r-cran-odbc, r-cran-r6, r-cran-crayon, r-cran-sparklyr, r-cran-databaseconnector Filename: pool/dists/noble/main/r-cran-omoponspark_0.1.0-1.ca2404.1_all.deb Size: 262968 MD5sum: 70ed12706707fe3b11374c7b8cc87677 SHA1: db535e8c6e2aab01749f2e9c3926c443e009394a SHA256: 2f331ab28e97ed43d747836774b130fd9599b46705d8132695bbe51b79a91021 SHA512: c6648c51045a8d98c6320824fdb320b48d42da1a8ed358721575beef67411bb99f022ebe36b4fabe1f753829f74bfc0eba4fb61014840c5eb2700773c19ec6e7 Homepage: https://cran.r-project.org/package=OmopOnSpark Description: CRAN Package 'OmopOnSpark' (Using a Common Data Model on 'Spark') Use health data in the Observational Medical Outcomes Partnership Common Data Model format in 'Spark'. Functionality includes creating all required tables and fields and creation of a single reference to the data. Native 'Spark' functionality is supported. Package: r-cran-omopsketch Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2905 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-cohortconstructor, r-cran-dplyr, r-cran-lifecycle, r-cran-omopgenerics, r-cran-patientprofiles, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-cdmconnector, r-cran-codelistgenerator, r-cran-cohortcharacteristics, r-cran-dbi, r-cran-duckdb, r-cran-dt, r-cran-flextable, r-cran-gt, r-cran-here, r-cran-knitr, r-cran-lubridate, r-cran-odbc, r-cran-omopviewer, r-cran-sortable, r-cran-reactable, r-cran-remotes, r-cran-rmarkdown, r-cran-rpostgres, r-cran-shinywidgets, r-cran-testthat, r-cran-withr, r-cran-omock, r-cran-covr, r-cran-ggplot2, r-cran-visomopresults, r-cran-devtools, r-cran-usethis, r-cran-plotly Filename: pool/dists/noble/main/r-cran-omopsketch_1.0.1-1.ca2404.1_all.deb Size: 2102512 MD5sum: 30ad9f115bf0994d39aa44f41918965b SHA1: 7d31acca320806d91641075920089e63e8fb4135 SHA256: caf0ee0ac74f9550e7c4df2e74d4b6783bcd993dcf9c51905a156f26b591a757 SHA512: d4d20f9486546cc4ee3b5b2281ab91e3307a6b7b2d4c0fe64ed886c7d118cf5af39ca48cfd030d88976e6a5f43f3ec286d08faee44166ae28815bd7bfd6a4125 Homepage: https://cran.r-project.org/package=OmopSketch Description: CRAN Package 'OmopSketch' (Characterise Tables of an OMOP Common Data Model Instance) Summarises key information in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Assess suitability to perform specific epidemiological studies and explore the different domains to obtain feasibility counts and trends. Package: r-cran-omopstudybuilder Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-jsonlite, r-cran-omopgenerics, r-cran-rlang, r-cran-whisker Suggests: r-cran-dplyr, r-cran-gert, r-cran-getpass, r-cran-gh, r-cran-here, r-cran-knitr, r-cran-processx, r-cran-purrr, r-cran-renv, r-cran-rmarkdown, r-cran-scales, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-omopstudybuilder_0.1.1-1.ca2404.1_all.deb Size: 136138 MD5sum: c10f32c35d775dab50326ec79aea95ba SHA1: 99e60fc8d6b2d72d6a9962892f011a458246ab5f SHA256: 794bcd1b4a895748d831494293acc77b698ca96d55b4549b34595557459d058d SHA512: 5c92f33de9cb2dffa215b42967bd7f605dd5c683dd74458bcc8a7a868805aba9a32bffe2330d5c5d166e12b095dbc125be4282214d2aad83021f8107ddf9f524 Homepage: https://cran.r-project.org/package=OmopStudyBuilder Description: CRAN Package 'OmopStudyBuilder' (Build Reproducible Network Studies for OMOP Common Data Model) Streamlines the setup and execution of network studies using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Creates standardised project structures with template code, manages dependencies with 'renv', provides code review utilities, and supports containerised execution with 'Docker' for reproducible multi-site studies. Includes 'GitHub' integration for collaboration and version control. 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Designed for researchers, analysts, and bioinformatics practitioners, the package includes datasets on various cancer types such as melanoma, leukemia, breast, ovarian, and lung cancer, among others. It aims to facilitate advanced research, analysis, and understanding of cancer epidemiology, genetics, and treatment outcomes. Package: r-cran-oncofilterfast Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-survminer Filename: pool/dists/noble/main/r-cran-oncofilterfast_1.0.0-1.ca2404.1_all.deb Size: 167602 MD5sum: b836f2fdede3d36df94b93b2c673e7f4 SHA1: 37bf810afa41f1f677edc77cec95926edaa9500b SHA256: c1396089324ffa328e0e81dc0adc4123f3cc6efbdf1fb1f4653b644d57d924f8 SHA512: c8e71851429a95dec71772ce669e35d434b41e74c32ec5c887209fffe5be94aab8a266ae30599a5e5b63f225d2cfa8c6fcad611e4f92822297f7c3d5dfa59550 Homepage: https://cran.r-project.org/package=Oncofilterfast Description: CRAN Package 'Oncofilterfast' (Aids in the Analysis of Genes Influencing Cancer Survival) Aids in the analysis of genes influencing cancer survival by including a principal function, calculator(), which calculates the P-value for each provided gene under the optimal cutoff in cancer survival studies. Grounded in methodologies from significant works, this package references Therneau's 'survival' package (Therneau, 2024; ) and the survival analysis extensions by Therneau and Grambsch (2000, ISBN 0-387-98784-3). It also integrates the 'survminer' package by Kassambara et al. (2021; ), enhancing survival curve visualizations with 'ggplot2'. Package: r-cran-oncopredict Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ridge, r-cran-car, r-cran-glmnet, r-cran-pls, r-bioc-sva, r-bioc-preprocesscore, r-bioc-genomicfeatures, r-bioc-org.hs.eg.db, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-cran-tidyverse, r-bioc-tcgabiolinks, r-bioc-biocgenerics, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gdata, r-bioc-genefilter, r-bioc-maftools, r-cran-readxl, r-cran-testthat Filename: pool/dists/noble/main/r-cran-oncopredict_1.2-1.ca2404.1_all.deb Size: 109432 MD5sum: e0d258754b5b2e20e9fa356c9b84d77d SHA1: b095c783d7002ab47d3ffe1855b018bde52b2d5b SHA256: 8b7cbbed5ba24a92b232f98dc8398853463192c284fb60aafb642330735fe23e SHA512: f03c26846ceac0b58a4b36d07c4f87bb128e9eb3d12530e300b609aea222cfc2cd85da0e450bab26f4c67af94c019117c0e5adef16e95e2cea98478b9e74b832 Homepage: https://cran.r-project.org/package=oncoPredict Description: CRAN Package 'oncoPredict' (Drug Response Modeling and Biomarker Discovery) Allows for building drug response models using screening data between bulk RNA-Seq and a drug response metric and two additional tools for biomarker discovery that have been developed by the Huang Laboratory at University of Minnesota. There are 3 main functions within this package. (1) calcPhenotype is used to build drug response models on RNA-Seq data and impute them on any other RNA-Seq dataset given to the model. (2) GLDS is used to calculate the general level of drug sensitivity, which can improve biomarker discovery. (3) IDWAS can take the results from calcPhenotype and link the imputed response back to available genomic (mutation and CNV alterations) to identify biomarkers. Each of these functions comes from a paper from the Huang research laboratory. Below gives the relevant paper for each function. calcPhenotype - Geeleher et al, Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. GLDS - Geeleher et al, Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models. IDWAS - Geeleher et al, Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies. Package: r-cran-oncosubtype Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1479 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-summarizedexperiment, r-cran-caret, r-cran-randomforest, r-cran-e1071, r-cran-pheatmap, r-cran-tibble, r-cran-dplyr, r-bioc-limma, r-cran-rlang, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-oncosubtype_1.0.0-1.ca2404.1_all.deb Size: 1456290 MD5sum: 0673aeb4b05d3b12b4fdd57a2463dabc SHA1: 2eaf88172093b4e26aa9326fae714d113aa6804b SHA256: 6e5af122ae7174d995c02bb29a39c06ec2db2b75e48be2113afc162db3340cc8 SHA512: 2bde17ab7ebb01238179ddbed85d3e3b543acbcda105d4e87b4b5ec4b8a1e6400248ce072d128cf137be761948f07a95334a78025ed57b9ffe55d3efa34c297a Homepage: https://cran.r-project.org/package=OncoSubtype Description: CRAN Package 'OncoSubtype' (Predict Cancer Subtypes Based on TCGA Data using MachineLearning Method) Provide functionality for cancer subtyping using nearest centroids or machine learning methods based on TCGA data. Package: r-cran-oncotree Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-oncotree_0.3.5-1.ca2404.1_all.deb Size: 429358 MD5sum: b3588b98ff4c07602e62eb977b7453e3 SHA1: 9e09b4fdcc26ebff3103e0a7fd7d89b0cdc096f6 SHA256: 98e681c4e8d450dfab38022f4bdcf0262bc0fc32129ef5bd5c61711bda428a20 SHA512: 18d71d07d2b8a17470d329ea74a1b882abc3abef447d3bd06a685502f14332381a1ff814ef2fb5c925b47b2b06fc3963c1537c56b3dacf8f646afb26de7bd4f5 Homepage: https://cran.r-project.org/package=Oncotree Description: CRAN Package 'Oncotree' (Estimating Oncogenetic Trees) Construct and evaluate directed tree structures that model the process of occurrence of genetic alterations during carcinogenesis as described in Szabo, A. and Boucher, K (2002) . 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Package: r-cran-one4all Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4009 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-dplyr, r-cran-validate, r-cran-digest, r-cran-data.table, r-cran-ckanr, r-cran-openxlsx, r-cran-lexicon, r-cran-readr, r-cran-readxl, r-cran-tibble, r-cran-aws.s3, r-cran-rlang, r-cran-jsonlite, r-cran-mongolite, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-config, r-cran-dt, r-cran-shinythemes, r-cran-shinywidgets, r-cran-bs4dash, r-cran-shinyjs, r-cran-listviewer, r-cran-rcurl, r-cran-purrr, r-cran-stringr Filename: pool/dists/noble/main/r-cran-one4all_0.5-1.ca2404.1_all.deb Size: 2404694 MD5sum: af4a6c9b9abf7d6769070cbb8ad9e67b SHA1: a1dd0fc7bb93ef64cf6973dfd0d10c0c313f6ed5 SHA256: 3cab4dd2cd282e2140e96f8f536c512e871f306b26b8d89f08e9a13dbbfd3fe4 SHA512: 2c6f490d8e61ae70abe227e75a868417c594b110865345309a2a79d026a8f16889e9dc3ad4510d4a3214e4cd19c4c6aac253e1ee02cde1ba427fc0ca835df4b9 Homepage: https://cran.r-project.org/package=One4All Description: CRAN Package 'One4All' (Validate, Share, and Download Data) Designed to enhance data validation and management processes by employing a set of functions that read a set of rules from a 'CSV' or 'Excel' file and apply them to a dataset. Funded by the National Renewable Energy Laboratory and Possibility Lab, maintained by the Moore Institute for Plastic Pollution Research. Package: r-cran-onearm2stage Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-flexsurv, r-cran-ipdfromkm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-onearm2stage_1.2.1-1.ca2404.1_all.deb Size: 112746 MD5sum: 2c23395c584bb5af83459fbfd56f2d3f SHA1: 4eafaa8512fdfd6eef0a0b1d0badafa962d67a4f SHA256: 75bb8d0597fd0284034a7dcb61df322be638a35abb3f0d60a50b1e5d33d94c86 SHA512: 91b295e97d0b40a792a295587b72ca11c95dc86ad917d72116dd6f8d0c4336b2c7368394992b17312b40dd0a97a52541ea082757848e3591016522c1d5593a8f Homepage: https://cran.r-project.org/package=OneArm2stage Description: CRAN Package 'OneArm2stage' (Phase II Single-Arm Two-Stage Designs with Time-to-EventOutcomes) Two-stage design for single-arm phase II trials with time-to-event endpoints (e.g., clinical trials on immunotherapies among cancer patients) can be calculated using this package. Two notable advantages of the package: 1) It provides flexible choices from three design methods (optimal, minmax, and admissible), and 2) the power of the design is more accurately calculated using the exact variance in the one-sample log-rank test. The package can be used for 1) planning the sample sizes and other design parameters, and 2) conducting the interim and final analyses for the Go/No-go decisions. More details about the design method can be found in: Wu, J, Chen L, Wei J, Weiss H, Chauhan A. (2020). . Package: r-cran-onearmtte Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-survival Filename: pool/dists/noble/main/r-cran-onearmtte_1.0-1.ca2404.1_all.deb Size: 49864 MD5sum: f183a466d8a5c8bbb3109a4de36d356a SHA1: 2e2a00ea71416fa81b74960fe8e42583a0f226eb SHA256: 7f8942460f2e07a2f51dce6db5216204e5bdfcde44a5998a8a32eef169ce7503 SHA512: 7f814890bec0e6553a2034a0a90759f69c58d9666489d4bac2ab0b97597e6e62a837cbafcaebd12660dfca566c9842cff6f0db68d9682dc1ffa1857d5f3fbdb6 Homepage: https://cran.r-project.org/package=OneArmTTE Description: CRAN Package 'OneArmTTE' (One-Arm Clinical Trial Designs for Time-to-Event Endpoint) Get operating characteristics of one-arm clinical trial designs for time-to-event endpoint through simulation and perform analysis with time-to-event data. Package: r-cran-oneinfl Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-oneinfl_1.0.2-1.ca2404.1_all.deb Size: 137830 MD5sum: 180688437aaff4ddc9d75b2f7e631a2b SHA1: b11dd6c510e0acb923120c9472dabfb4192a4b38 SHA256: 2d7d988cc410418ff2e01d5f8e37dd35ee282bdec0fcfa162f2b9859cfacfbf3 SHA512: 35328b3bb034393389962c2e4456bf1a294f33c3a09a05721eb22c21b2b93c970f53bfd706f900740275ad23726f4c59818df16c01cb270ddf15567a32c51605 Homepage: https://cran.r-project.org/package=oneinfl Description: CRAN Package 'oneinfl' (Estimates OIPP and OIZTNB Regression Models) Estimates one-inflated positive Poisson (OIPP) and one-inflated zero-truncated negative binomial (OIZTNB) regression models. A suite of ancillary statistical tools are also provided, including: estimation of positive Poisson (PP) and zero-truncated negative binomial (ZTNB) models; marginal effects and their standard errors; diagnostic likelihood ratio and Wald tests; plotting; predicted counts and expected responses; and random variate generation. The models and tools, as well as four applications, are shown in Godwin, R. T. (2024). "One-inflated zero-truncated count regression models" arXiv preprint . Package: r-cran-onelogin Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-glue, r-cran-safer, r-cran-magrittr, r-cran-jsonlite, r-cran-tibble Filename: pool/dists/noble/main/r-cran-onelogin_0.2.0-1.ca2404.1_all.deb Size: 90252 MD5sum: 9e480d191e606c4a4a3003fcb9123c26 SHA1: 894299bd3dc7ac908c6758b16d083ef92bb5ab84 SHA256: b0bb314271fc0f0ee2d7b2f2f8dcfd931c7b157504f4e2f17559d7a0128a10ff SHA512: 43872bf47bbe642e58ac340ec3913a3bb79827b7822782927779dc2d8fdcb8c20043969df75e4eca77e9639b3789b0bc5bab6b7feae21e6d03221aadcc2d383d Homepage: https://cran.r-project.org/package=onelogin Description: CRAN Package 'onelogin' (Interact with the 'OneLogin' API) The identity provider ['OneLogin'] is used for authentication via Single Sign On (SSO). This package provides an R interface to their API. Package: r-cran-onemapsgapi Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-httr2, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-tidyr, r-cran-future, r-cran-furrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-googlepolylines Filename: pool/dists/noble/main/r-cran-onemapsgapi_2.0.0-1.ca2404.1_all.deb Size: 93302 MD5sum: 6ff4a5c05d732d17d3ab125013e828f2 SHA1: d15a7f915709a401a2660154cd1a513d12df49d6 SHA256: b3161341d82c12f54498de491ef7e3701ebfd655dc1bcd624afaf4b84d4851a4 SHA512: 9a29320e3b8e00ebf704e00ab6ecee2c59a114b1417575d1ea55939e0eba7a8d6bf27392b0ea6502a131878cf8a15bc2dee0f3c7ff02e8b33d5d6ede32f922bc Homepage: https://cran.r-project.org/package=onemapsgapi Description: CRAN Package 'onemapsgapi' (R Wrapper for the 'OneMap.Sg API') An R wrapper for the 'OneMap.Sg' API . 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Package: r-cran-oner Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-oner_2.2-1.ca2404.1_all.deb Size: 101306 MD5sum: e8e184dd7d29076df33fe8d15bdae1bf SHA1: dcd2ebc52e6403fd339a2ce8754c43ef1318189e SHA256: 5478125ae025e52ac25f466981ad0653cc4d31eb5f33d47c45af8666b369c950 SHA512: cb022199be3d5a84cf0d7c21231138e90689478951868d8541eebedb92a0f01d173594883947a24e850585cba56c89cf5d0bb511aa91a97de53bd6c6b1743f99 Homepage: https://cran.r-project.org/package=OneR Description: CRAN Package 'OneR' (One Rule Machine Learning Classification Algorithm withEnhancements) Implements the One Rule (OneR) Machine Learning classification algorithm (Holte, R.C. (1993) ) with enhancements for sophisticated handling of numeric data and missing values together with extensive diagnostic functions. It is useful as a baseline for machine learning models and the rules are often helpful heuristics. Package: r-cran-onesamplelogranktest Architecture: all Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-ggplot2, r-cran-survival, r-cran-survminer, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-onesamplelogranktest_0.9.2-1.ca2404.1_all.deb Size: 192036 MD5sum: 39baa344626939de8b0fe1e2743adb36 SHA1: 8124590407981dd22846750fcb1cdb88da87647f SHA256: babfd0e60476b7ff821be404d6a610fd5f877b0201e670c35e40a9146424394f SHA512: c9af1cd5f0b6ad981e535afafd7cc2b7db239534eb2be4aae12f03442ef45679c91da17b41b0d73bb1242435086d181c13d9b45c7d8d5a86dc986b10e055edfe Homepage: https://cran.r-project.org/package=OneSampleLogRankTest Description: CRAN Package 'OneSampleLogRankTest' (One-Sample Log-Rank Test) The log-rank test is performed to assess the survival outcomes between two group. When there is no proper control group or obtaining such data is cumbersome, one sample log-rank test can be applied. This package performs one sample log-rank test as described in Finkelstein et al. (2003) and variation of the test for small sample sizes which is detailed in FD Liddell (1984) paper. Visualization function in the package generates Kaplan-Meier Curve comparing survival curve of the general population against that of the population of interest. 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The package includes implementations of; the Sanderson and Windmeijer (2016) conditional F-statistic, the multiplicative structural mean model Hernán and Robins (2006) , and two-stage predictor substitution and two-stage residual inclusion estimators explained by Terza et al. (2008) . 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Input to the program should be binary(1/0) pathology data, where “0” may stand for negative and “1” for positive. Additional examples were given using the data from Rimm et al. (2017 ). Package: r-cran-onestep Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fitdistrplus, r-cran-numderiv, r-cran-extradistr Suggests: r-cran-actuar Filename: pool/dists/noble/main/r-cran-onestep_0.9.4-1.ca2404.1_all.deb Size: 70882 MD5sum: 8312259105bac69fb9e4a28602b1aee5 SHA1: cca0765ef98a762af1af1eff18d2181a3e87514a SHA256: fb8126e49c2911684c5a2b25f3259f2592046d4b834a0c05deaaf6269510e549 SHA512: 6802c00c242bbbc3859989dc50e4bca1f6474756ba6e7e9d7de359e773ea8a119a969a1ad3ab3dfa363243cf59f92b7eacaf71f48daeb12d29c096fd55784a08 Homepage: https://cran.r-project.org/package=OneStep Description: CRAN Package 'OneStep' (One-Step Estimation) Provide principally an eponymic function that numerically computes the Le Cam's one-step estimator for an independent and identically distributed sample. One-step estimation is asymptotically efficient (see L. Le Cam (1956) ) and can be computed faster than the maximum likelihood estimator for large observation samples, see e.g. Brouste et al. (2021) . 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For one normal sample x, the function reports descriptive statistics, plot, interval estimation and test of hypothesis of x. For two normal samples x and y, the function reports descriptive statistics, plot, interval estimation and test of hypothesis of x and y, respectively. It also reports interval estimation and test of hypothesis of mu1-mu2 (the difference of the means of x and y) and sigma1^2 / sigma2^2 (the ratio of the variances of x and y), tests whether x and y are from the same population, finds the correlation coefficient of x and y if x and y have the same length. Package: r-cran-onewaytests Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-moments, r-cran-car, r-cran-ggplot2, r-cran-nortest, r-cran-wesanderson Suggests: r-cran-aid, r-cran-tibble, r-cran-testthat Filename: pool/dists/noble/main/r-cran-onewaytests_3.1-1.ca2404.1_all.deb Size: 251994 MD5sum: 119239cd870e026768f0f1ecbbbdbc93 SHA1: ed10f29c06ca015dc6bd3a9c80ba0a162cd202b6 SHA256: 0121713b3abf72495c4a73db5bf32ddc67b67e2e8a9879733d331539fb48b1dc SHA512: f39b5cc3a61edaadbd9c1bbe22983a92b1c0e7b35afd9584407d0eca53f164caa18ccfe8782d2857868c90dda3af695e9cd0ababa4e835110fcd56f419681a78 Homepage: https://cran.r-project.org/package=onewaytests Description: CRAN Package 'onewaytests' (One-Way Tests in Independent Groups Designs) Performs one-way tests in independent groups designs including homoscedastic and heteroscedastic tests. 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The reference is Yigiter A, Chen J, An L, Danacioglu N (2015) . The link to the package is . Package: r-cran-onlineretail Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2829 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-onlineretail_0.1.2-1.ca2404.1_all.deb Size: 2861446 MD5sum: c9fe70b052db10197796b315eba67fac SHA1: 00fa7b15dafce69485ab764b671cde758ea2aa20 SHA256: 83510784b2edd0a18c4f4cd6ce68de85ef21ff5c71e4b98e21892b9c4f481fdc SHA512: 2c1612a1f3adc0c389800333594e6b02387ce2696d21a3369aad51650011c7747ab627895051598d61f3851152a1d3e87b98bec4b0e3f284bb0527619d46b64a Homepage: https://cran.r-project.org/package=onlineretail Description: CRAN Package 'onlineretail' (Online Retail Dataset) Transactions occurring for a UK-based and registered, non-store online retail between 01/12/2010 and 09/12/2011 (Chen et. al., 2012, ). 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The model consists of four dimensions: In 2021, these dimensions were updated to "Material Resources" (previously called "Material Deprivation"), "Households and Dwellings" (previously called "Residential Instability"), "Age and Labour Force" (previously called "Dependency"), and "Racialized and Newcomer Populations" (previously called "Ethnic Concentration"). This update reflects a movement away from deficit-based language. 2021 data will load with these new dimension names, wheras 2011 and 2016 data will load with the historical dimension names. Each of these dimensions are imported for a variety of geographic levels (DA, CD, etc.) for the 2021, 2011 and 2016 administrations of the census. These data sets contribute to community analysis of equity with respect to Ontario's Anti-Racism Act. The Ontario Marginalization Index data is retrieved from the Public Health Ontario website: . The shapefile data is retrieved from the Statistics Canada website: . 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It is inspired by the Food and Agrilculture Organizations (FAO) caliper platform and makes use of the Simple Knowledge Organisation System (SKOS). 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It expands on the previous work of Tarasov et al. (2019) . The PARAMO pipeline allows to reconstruct ancestral phenomes treating groups of morphological traits as a single complex character. The pipeline incorporates knowledge from ontologies during the amalgamation of individual character stochastic maps. Here we expand the current PARAMO functionality by adding new statistical methods for inferring evolutionary phenome dynamics using non-homogeneous Poisson process (NHPP). The new functionalities include: (1) reconstruction of evolutionary rate shifts of phenomes across lineages and time; (2) reconstruction of morphospace dynamics through time; and (3) estimation of rates of phenome evolution at different levels of anatomical hierarchy (e.g., entire body or specific regions only). The package also includes user-friendly tools for visualizing evolutionary rates of different anatomical regions using vector images of the organisms of interest. Package: r-cran-oobcurve Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlr, r-cran-randomforest, r-cran-ranger Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-oobcurve_0.3-1.ca2404.1_all.deb Size: 25166 MD5sum: 3bdced066c92f24f5517c48304690e1a SHA1: 97d02fb4e322b956b087e56082d62a52e99dcce8 SHA256: 95c6cc87361f4facfdc55c38d19d2a356184f24f371b89197d67a746a40dd3ac SHA512: 23752f10218be8ad782be7166d89cc19e3f46fbc94d75a10bbd70de4306e82ae53d558428b07952da0c1bd47f576783c476d7db09047f7c97cab68b2f694c020 Homepage: https://cran.r-project.org/package=OOBCurve Description: CRAN Package 'OOBCurve' (Out of Bag Learning Curve) Provides functions to calculate the out-of-bag learning curve for random forests for any measure that is available in the 'mlr' package. Supported random forest packages are 'randomForest' and 'ranger' and trained models of these packages with the train function of 'mlr'. The main function is OOBCurve() that calculates the out-of-bag curve depending on the number of trees. With the OOBCurvePars() function out-of-bag curves can also be calculated for 'mtry', 'sample.fraction' and 'min.node.size' for the 'ranger' package. 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This package provides a comprehensive toolbox for researchers and practitioners to design and analyze order-of-addition experiments. Detailed comparisons and summary of all statistical methods in this package can be found in Tsai (2026), "Order-of-addition experiments in R using OofAExp", Journal of Quality Technology (to appear). 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Package: r-cran-opencage Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-dplyr, r-cran-lifecycle, r-cran-jsonlite, r-cran-memoise, r-cran-progress, r-cran-purrr, r-cran-ratelimitr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-withr Suggests: r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-opencage_0.2.2-1.ca2404.1_all.deb Size: 148850 MD5sum: 0eae8355fd2e207b7bbbe62b2424ecc7 SHA1: a585ed25060485b21789b7dae057f29d1678b45d SHA256: 8c834d006298ff1face3537232f22787f8c918aba5f730c7d2405bea5fcc143c SHA512: ee4b9a4d5085f675d1f50a79eeeeb12413de16098bab3b674f66618b164c6ba1b48fdd0cd6310a1bf8e1dcb50ca15600db6e0b9af8a9d6f69fd3433354d04827 Homepage: https://cran.r-project.org/package=opencage Description: CRAN Package 'opencage' (Geocode with the OpenCage API) Geocode with the OpenCage API, either from place name to longitude and latitude (forward geocoding) or from longitude and latitude to the name and address of a location (reverse geocoding), see . Package: r-cran-opencast Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings, r-cran-seqinr, r-cran-stringr, r-cran-tibble, r-cran-entropy, r-cran-ftrcool Suggests: r-cran-caret, r-cran-kernlab, r-cran-ranger, r-cran-xgboost, r-cran-gbm Filename: pool/dists/noble/main/r-cran-opencast_0.1.1-1.ca2404.1_all.deb Size: 62696 MD5sum: db74c8992ebe28944be11ade8f4358c5 SHA1: 953b3435d0c36a038c77d9aaa57bc2b9099f3302 SHA256: 6a835e43d44780a5cb8421cdb10b1b831bb2af13e38e05e44be85456d25445e6 SHA512: 927ffdab205a34b3860e5c7ea3257af5032b6de2abb5ecf561d469832ca7c7b2370fe51f02fce4cd5f5094610c9e7578acdb17f262a87cc6d6d4911d97fe6ea0 Homepage: https://cran.r-project.org/package=OpEnCAST Description: CRAN Package 'OpEnCAST' (Optimized Ensemble Model for C and A Methylation Search in Plant) DNA methylation is an important epigenetic process that regulates gene activity through chemical modifications of DNA without changing its sequence. 'OpEnCAST' is a plant-specific ensemble-based prediction package that identifies 4mC, 5mC and 6mA methylation sites directly from DNA sequences. It combines multiple machine learning algorithms trained on monocot (Oryza sp.) and dicot (Arabidopsis sp.) reference models to deliver accurate predictions. This methodology is being inspired by the ensemble algorithm for methylation prediction developed by Wang et al. (2022) . Package: r-cran-opencis Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1191 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-tibble, r-cran-purrr, r-cran-haven, r-cran-magrittr, r-cran-rvest, r-cran-stringr, r-cran-memoise Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-opencis_0.1.1-1.ca2404.1_all.deb Size: 1152702 MD5sum: 86ad3105112b15cd213223cf16704d53 SHA1: 57b7dce34037e31c91e728bb54f874d32b0e8bd8 SHA256: e3402d400b169fe9a554369bd2dc40998ae98c921efc3bbad3f6a60a31026b78 SHA512: d49d14e355890ebbb646b94cdbcc6039f9112a884da70f99f8f51bc9a7aa16a4058f21b6813a7a59f632f6a210cc94a7c321907fa579a8ee8e7013ed2e52baa0 Homepage: https://cran.r-project.org/package=opencis Description: CRAN Package 'opencis' (Import Data from Spanish Sociological Research Center (CIS)) Search and import data directly to R from the Spanish Sociological Research Center (CIS) . The CIS is a public institution that conducts electoral and sociological research studies on the Spanish society. The CIS has a large database of surveys that can be accessed through its website. The package includes functions to search for surveys, survey questions and timeseries, and import the data directly to R. Package: r-cran-opencpu Architecture: all Version: 2.2.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 949 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-evaluate, r-cran-httpuv, r-cran-knitr, r-cran-jsonlite, r-cran-remotes, r-cran-sys, r-cran-webutils, r-cran-curl, r-cran-rappdirs, r-cran-rlang, r-cran-vctrs, r-cran-zip, r-cran-mime, r-cran-protolite, r-cran-brew, r-cran-openssl Suggests: r-cran-arrow, r-cran-unix, r-cran-haven, r-cran-pander, r-cran-r.rsp, r-cran-svglite Filename: pool/dists/noble/main/r-cran-opencpu_2.2.14-1.ca2404.1_all.deb Size: 520494 MD5sum: aa6c60c2229c5e4a452270a1699af85a SHA1: de33104b058943751e78d6e8462bae6d84e04ef5 SHA256: 5fc669aa01eeecf817faa18366ed7c55cd5045b5af664a8442cd43200f1d2d44 SHA512: 25f4c446dc972009df05f4552e7de0ac5f7d99267b51b8f0b531d22dce24bf5b81ce53c1e35e4add7f314ae5df5d2eee744dd9ae44f1ed83b4b5fffc58be3146 Homepage: https://cran.r-project.org/package=opencpu Description: CRAN Package 'opencpu' (Producing and Reproducing Results) A system for embedded scientific computing and reproducible research with R. The OpenCPU server exposes a simple but powerful HTTP api for RPC and data interchange with R. This provides a reliable and scalable foundation for statistical services or building R web applications. The OpenCPU server runs either as a single-user development server within the interactive R session, or as a multi-user Linux stack based on Apache2. The entire system is fully open source and permissively licensed. The OpenCPU website has detailed documentation and example apps. Package: r-cran-opendataformat Architecture: all Version: 2.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-zip, r-cran-magrittr, r-cran-xml2, r-cran-data.table, r-cran-tibble, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-islr, r-cran-dplyr, r-cran-haven Filename: pool/dists/noble/main/r-cran-opendataformat_2.2.2-1.ca2404.1_all.deb Size: 137944 MD5sum: ddcc33922d74c7a623ea80ce8684f776 SHA1: 24106473ca627de7ebebf242d38b0f6c57eebb8f SHA256: ec382f40103008bc01ca64ad3d034666394af6c86ab3e35052a03ffe783c49d4 SHA512: 86ffc00720342b9b5f30dd7992401ca97f89283f95701b720a47bba91916efb3bd9f260b26e62768dd07cf52621c9889f4a3b5964c81befc789f124f887b09d2 Homepage: https://cran.r-project.org/package=opendataformat Description: CRAN Package 'opendataformat' (Reading and Writing Open Data Format Files) The Open Data Format (ODF) is a new, non-proprietary, multilingual, metadata enriched, and zip-compressed data format with metadata structured in the Data Documentation Initiative (DDI) Codebook standard. This package allows reading and writing of data files in the Open Data Format (ODF) in R, and displaying metadata in different languages. For further information on the Open Data Format, see . Package: r-cran-opendatatoronto Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ckanr, r-cran-magrittr, r-cran-readxl, r-cran-sf, r-cran-tibble, r-cran-xml2, r-cran-curl Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggiraph, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-tidyr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-opendatatoronto_0.1.6-1.ca2404.1_all.deb Size: 71200 MD5sum: 29dbde6a3cf763339a719472bb74d32d SHA1: 207ab7c23f11859d3b03cddfb7c90c63cc985d07 SHA256: ddde61a4c1d3084c345493b6d0d9969683de6faf08846efcffcecf56346a61f9 SHA512: 336000781454e536bd7eced90f1f6fcb52106dbaf1546281bc9a923b62eb8cfa0936cbe51b3677fff753792b923fb7a4f878f38f46f655ad0d5b1fffd0cdbb90 Homepage: https://cran.r-project.org/package=opendatatoronto Description: CRAN Package 'opendatatoronto' (Access the City of Toronto Open Data Portal) Access data from the "City of Toronto Open Data Portal" () directly from R. Package: r-cran-opendotar Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-dplyr, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-opendotar_0.1.4-1.ca2404.1_all.deb Size: 21754 MD5sum: f9132e76a146a7ec7a9d174207d04778 SHA1: 8a30d181f724086ac98e4ccc8e07d2dd14379304 SHA256: 85ed7cfebbbeb02bf18f08c614045e3de6c33983cf3df858f4fa68f32ad6b804 SHA512: df77c63003f764f5f307d111c1779f51cf5fbe97f434bba318f1eba133ce8b8d483abe180333bde8b0e3724d6264b7f76c030cba217066fc26955150769602e1 Homepage: https://cran.r-project.org/package=opendotaR Description: CRAN Package 'opendotaR' (Interface for OpenDota API) Enables the usage of the OpenDota API from , get game lists, and download JSON's of parsed replays from the OpenDota API. Also has functionality to execute own code to extract the specific parts of the JSON file. Package: r-cran-openebgm Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 712 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-deoptim, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-openebgm_0.9.1-1.ca2404.1_all.deb Size: 428944 MD5sum: 76a9c78312ae7e87add6ed0649d58342 SHA1: 929dca681fc2903f53d2209b22580757d8f056d8 SHA256: 8076909b895a49dc100faa4ea2a8d2fc6dbc5ed79cdee055923fa3e2683965d9 SHA512: 1e536303ac3545fa2c3138e8e2cca551fcedf6079b83bbb38d6952e56bd91f6413ed6a7885f8ccf56bcdca83ff2b9af88fb054c0ca0df9087f2871adaea7e18c Homepage: https://cran.r-project.org/package=openEBGM Description: CRAN Package 'openEBGM' (EBGM Disproportionality Scores for Adverse Event Data Mining) An implementation of DuMouchel's (1999) Bayesian data mining method for the market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and posterior quantile scores using the Gamma-Poisson Shrinker (GPS) model to find unusually large cell counts in large, sparse contingency tables. Can be used to find unusually high reporting rates of adverse events associated with products. In general, can be used to mine any database where the co-occurrence of two variables or items is of interest. Also calculates relative and proportional reporting ratios. Builds on the work of the 'PhViD' package, from which much of the code is derived. Some of the added features include stratification to adjust for confounding variables and data squashing to improve computational efficiency. Includes an implementation of the EM algorithm for hyperparameter estimation loosely derived from the 'mederrRank' package. 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Package: r-cran-openesm Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-fs, r-cran-httr2, r-cran-jsonlite, r-cran-readr, r-cran-purrr, r-cran-dplyr, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-openesm_0.2.0-1.ca2404.1_all.deb Size: 71312 MD5sum: 4c21ccf523143b71deb996c81b150555 SHA1: 56accf598e637e24a62bcf974ea8a70386c92ae8 SHA256: 9af8442a6f568939d57d9fbc92b917a12304dbbc7805a8a6eea6be706276bb9b SHA512: 6936b3e19eade1678cf5068a415209eba5e9853cb3bc9ac37ac752c6f3a77051a674efa0cb541f42c35c4ee995ae4b7268514981fca05065bdfd19cf6b4b3a0b Homepage: https://cran.r-project.org/package=openesm Description: CRAN Package 'openesm' (Access the Open Experience Sampling Method Database) Provides programmatic access to the Open Experience Sampling Method ('openESM') database (), a collection of harmonized experience sampling datasets. The package enables researchers to discover, download, and work with the datasets while ensuring proper citation and license compliance. 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This package makes the API easily accessible, returning objects which the user can convert to JSON data and parse. Kass-Hout TA, Xu Z, Mohebbi M et al. (2016) . Package: r-cran-opengraph Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rvest Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-opengraph_0.0.4-1.ca2404.1_all.deb Size: 43986 MD5sum: 540e39770d56c88816d7416ac20ce7e4 SHA1: 6fe4ee52cda7dc8d14c1023deccc7bb0565e8c4a SHA256: a45d8e6dc69a6dd545e43dc55f6ada3b1d3eb851c623d9ffa96b81ca6d87f726 SHA512: d5d8311b9a449ba99b9bd0d085241e04611cccb055bd705d9d57555301e8240be4ef87fb1b67fb5d2b748f4f52740a5eb2a53e1ff4ca6bf3d799e3f1b5a0e23f Homepage: https://cran.r-project.org/package=opengraph Description: CRAN Package 'opengraph' (Process Metadata from the 'Open Graph Protocol') Social media sites often embed cards when links are shared, based on metadata in the 'Open Graph Protocol' (). This supports extracting that metadata from a website. 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Package: r-cran-openhimr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-cran-devtools, r-cran-tidyverse, r-cran-seqinr, r-cran-splitstackshape, r-cran-entropy, r-cran-party, r-cran-e1071, r-cran-caret, r-cran-randomforest, r-cran-gbm, r-cran-stringr, r-cran-ftrcool, r-cran-dplyr, r-cran-rcurl Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-openhimr_0.1.1-1.ca2404.1_all.deb Size: 43626 MD5sum: c782e7c00cf9a642ebd973408bc3b94c SHA1: 2cb46432b820bd638778360ad7ad2e4e7bde6961 SHA256: ee9b550ec78e8bd030db64dca42f85dd2def414c380fcf721089aeb71193d442 SHA512: e9a0a5294237ddac3b38a7c49f35aa88e02890ec98aef45d8c86100c849d1b86bb8908d722cb8c59999a93ccbcc3bc6199e1aadf35d272b59000dfbb15cba6a3 Homepage: https://cran.r-project.org/package=OpEnHiMR Description: CRAN Package 'OpEnHiMR' (Optimization Based Ensemble Model for Prediction of HistoneModifications in Rice) The comprehensive knowledge of epigenetic modifications in plants, encompassing histone modifications in regulating gene expression, is not completely ingrained. It is noteworthy that histone deacetylation and histone H3 lysine 27 trimethylation (H3K27me3) play a role in repressing transcription in eukaryotes. In contrast, histone acetylation (H3K9ac) and H3K4me3 have been inevitably linked to the stimulation of gene expression, which significantly influences plant development and plays a role in plant responses to biotic and abiotic stresses. To our knowledge this the first multiclass classifier for predicting histone modification in plants. . 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Package: r-cran-openrouteservice Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2335 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geojsonsf, r-cran-httr, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-keyring, r-cran-leaflet, r-cran-v8, r-cran-xml2 Suggests: r-cran-covr, r-cran-ggplot2, r-cran-googlepolylines, r-cran-lwgeom, r-cran-knitr, r-cran-mapview, r-cran-pkgdown, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-roxygen2, r-cran-sf, r-cran-testthat, r-cran-units Filename: pool/dists/noble/main/r-cran-openrouteservice_0.6.2-1.ca2404.1_all.deb Size: 846496 MD5sum: 32332e977966727465d27474d02744c4 SHA1: 01aee973130d046a9440fac66feca1dee5dbe84d SHA256: be1853abcc343599057f0a8a987638c5755008ecd8b655b8b8e46c6ce281bcee SHA512: fb802646b9f724cd5e86d6b522757f3f12352f3b9d5e626372a994f3255af59d5329b2dadd443c91803b0896aef5af03a5c35a07dc9123034c89c4d7e9490959 Homepage: https://cran.r-project.org/package=openrouteservice Description: CRAN Package 'openrouteservice' (An 'openrouteservice' API Client) The client streamlines access to the services provided by . 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Package: r-cran-openscoring Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-openscoring_1.1.0-1.ca2404.1_all.deb Size: 28094 MD5sum: 846ae712b1f01a8724d9af18980d399c SHA1: 18a5c155bbe939891f27172ff28209ec90a61638 SHA256: 47c5f388824b1b05f729ebc0087901f6259bcdba55f6dea17b1523d1c8f8ff22 SHA512: a47c2ee1edb16fddcaa1361b5f238cc3e36dd8434f9bbcf01c6aa4e8fd6edcdac7c328b9dff66d43914fcad55e92eeda8bc7e7d0a83448d2ed8dcc08ad0c6846 Homepage: https://cran.r-project.org/package=openscoring Description: CRAN Package 'openscoring' ('Open Scoring' API Client) Creativity research involves the need to score open-ended problems. 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Package: r-cran-opensourceap.downloadr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-rvest, r-cran-stringr, r-cran-jsonlite, r-cran-magrittr, r-cran-dplyr, r-cran-data.table, r-cran-r6, r-cran-withr, r-cran-lubridate, r-cran-dbi, r-cran-rpostgres, r-cran-getpass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-opensourceap.downloadr_0.1.0-1.ca2404.1_all.deb Size: 153732 MD5sum: 5ef78f85ea509bef1cc888bb53fa4c90 SHA1: 772f971b170853890e2ebc0eec7d4c3974ba98d0 SHA256: 2b84a738c919e92bd20b0bce87875133900b523f0a7fae9543ca8a5d329f448e SHA512: a60434ce2d99c958338657c0a862584ee9676dd635c35fee227ed0e7107db67b56749124cd5281261ce9c72c7ff8535c5716fd2a39b35bb11c26004e43110e70 Homepage: https://cran.r-project.org/package=OpenSourceAP.DownloadR Description: CRAN Package 'OpenSourceAP.DownloadR' (Download Open Source Asset Pricing (OpenAP) Data Directly) Convenient download functions enabling access Open Source Asset Pricing (OpenAP) data. 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Package: r-cran-openspecy Architecture: all Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1770 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite, r-cran-yaml, r-cran-catools, r-cran-hyperspec, r-cran-mmand, r-cran-plotly, r-cran-digest, r-cran-signal, r-cran-glmnet, r-cran-jpeg, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-shinyjs, r-cran-shinywidgets, r-cran-bs4dash, r-cran-dplyr, r-cran-dt, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-openspecy_1.5.3-1.ca2404.1_all.deb Size: 1416926 MD5sum: d4ff54058483bb6240bf2beca186b904 SHA1: a4fb7772a9119c365b4070a6cf7799dd78f8140d SHA256: 166117e55e5f4abb9dab7569b68e4df8edc91a10093f95b6c432d199b1a096bc SHA512: 4424c8c9eca5bad8cb38ff254cd894f544d18da5d11a3d4df7b2b143dd12845f3883c1b8dfe2249c92e51d4819a8e1afd4b8057fed27d3ed2c23826869085f09 Homepage: https://cran.r-project.org/package=OpenSpecy Description: CRAN Package 'OpenSpecy' (Analyze, Process, Identify, and Share Raman and (FT)IR Spectra) Raman and (FT)IR spectral analysis tool for plastic particles and other environmental samples (Cowger et al. 2021, ). 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Package: r-cran-openstreetmap Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rjava, r-cran-raster, r-cran-sp Filename: pool/dists/noble/main/r-cran-openstreetmap_0.4.1-1.ca2404.1_all.deb Size: 2264620 MD5sum: 8b2e2288f0069b34fe92da31018606fa SHA1: 850026e6fc8c67dc36759516e2347d3fcddcfce5 SHA256: 025b03795ea42acefe704438d536f8d08e1df135e44ef91cb26f5de9fd0abf6b SHA512: f7f097b96e3e3011581b0e5e36dbdb08590b4d4050539efb8aa91f710dea84da6b7cfb8ab173916decd7086e56f2c7ee9df9e918f9f89c10ae9d4908d094c521 Homepage: https://cran.r-project.org/package=OpenStreetMap Description: CRAN Package 'OpenStreetMap' (Access to Open Street Map Raster Images) Accesses high resolution raster maps using the OpenStreetMap protocol. Dozens of road, satellite, and topographic map servers are directly supported. 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Package: r-cran-opentreechronograms Architecture: all Version: 2022.1.28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2689 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-geiger, r-cran-knitcitations, r-cran-paleotree, r-cran-plyr, r-cran-rotl, r-cran-stringr, r-cran-taxize, r-cran-treebase, r-cran-usethis Filename: pool/dists/noble/main/r-cran-opentreechronograms_2022.1.28-1.ca2404.1_all.deb Size: 2702822 MD5sum: 76afb3be2ef3c42e10b7bc87b01d5edb SHA1: 38b30bf97ac09c353e3ec51c8b0687fcfbe3fdbc SHA256: fe903a96248b3262d9612ddca3332815089651b71456a9f1c16c6b9b6a07e334 SHA512: 5962605ab0992338d7c350c85c3e1c305f4dcd3a09cd0b619907af77b19f1a534e3eb885f6d7805b210e4d2825cdbe9d51a175af35e96d8388847095090cb8ca Homepage: https://cran.r-project.org/package=OpenTreeChronograms Description: CRAN Package 'OpenTreeChronograms' (Open Tree of Life Chronograms) Chronogram database constructed from Open Tree of Life's phylogenetic store. 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Package: r-cran-openva Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5386 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-interva5, r-cran-insilicova, r-cran-interva4, r-cran-tariff, r-cran-ggplot2, r-cran-crayon, r-cran-cli, r-cran-rlang Suggests: r-cran-covr, r-cran-eava, r-cran-knitr, r-cran-nbc4va, r-cran-r.rsp, r-cran-testthat, r-cran-vacalibration Filename: pool/dists/noble/main/r-cran-openva_1.2.0-1.ca2404.1_all.deb Size: 2289502 MD5sum: 6e612595080b1cf88c1d79ba7ffb9a21 SHA1: 1d7cb16f7c088fda712a574628686b7d9a7116ed SHA256: 6289fdb93ec0585c46d7a924d340419be9e16f83c4d65283e81b56abc0701ba8 SHA512: aab93413d4a31d9028111e96ab3d6bfec2ce0b86759615ad811e99131ea408e4e88d2574dedf5a8c727a68b8e329ff7a310d8dc09f1473b33401c566e24fe4d3 Homepage: https://cran.r-project.org/package=openVA Description: CRAN Package 'openVA' (Automated Method for Verbal Autopsy) Implements multiple existing open-source algorithms for coding cause of death from verbal autopsies. The methods implemented include 'InterVA4' by Byass et al (2012) , 'InterVA5' by Byass at al (2019) , 'InSilicoVA' by McCormick et al (2016) , 'NBC' by Miasnikof et al (2015) , and a replication of 'Tariff' method by James et al (2011) and Serina, et al. (2015) . It also provides tools for data manipulation tasks commonly used in Verbal Autopsy analysis and implements easy graphical visualization of individual and population level statistics. The 'NBC' method is implemented by the 'nbc4va' package that can be installed from . Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist in the implementation of the 'Tariff' method. Package: r-cran-operator.tools Architecture: all Version: 1.6.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-operators, r-cran-magrittr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-operator.tools_1.6.3.1-1.ca2404.1_all.deb Size: 53520 MD5sum: bf16f7703edb7ef23bd31d3b62ed361e SHA1: 3e3dfaade02667cce1b9e85174e572376a1659e9 SHA256: cd3641d4193de69e36820fe762443114d7ca9536f563f907347a62115ad3123a SHA512: c38b1b7b69ee1cbb00696d19852e907dbeb579e5b50cf1612e13aa89c5cb9ab22f4cd24363400bcf75069466518264bf8d536fcbc68d50792ab565c43ae1eb8c Homepage: https://cran.r-project.org/package=operator.tools Description: CRAN Package 'operator.tools' (Utilities for Working with R's Operators) Provides a collection of utilities that allow programming with R's operators. Routines allow classifying operators, translating to and from an operator and its underlying function, and inverting some operators (e.g. comparison operators), etc. All methods can be extended to custom infix operators. Package: r-cran-operators Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-operators_0.2.0-1.ca2404.1_all.deb Size: 61438 MD5sum: 31d203a1149eb738de0a6b5c5aa8f86a SHA1: 3bbf83ddf5fe9a9f84805b28f4d07cdcf703f787 SHA256: b012dc1d8f4022f124de8e4ee42eaa5bd876cc04a5a1169ee4da9e5b3c8ca535 SHA512: 2e2ed3aebc0ba7984a25e2022866eb3584e0a06132af53e6f1047056ee51c87db60f572f0fd5d20214c7dea06bf5e3cbd0dc1bd93d6603064ddcc56367fd8cf5 Homepage: https://cran.r-project.org/package=operators Description: CRAN Package 'operators' (Additional Binary Operators) A set of binary operators for common tasks such as regex manipulation. Package: r-cran-opgmmassessment Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adaptgauss, r-cran-datavisualizations, r-cran-distributionoptimization, r-cran-cluster, r-cran-mixtools, r-cran-foreach, r-cran-rlang, r-cran-ggplot2, r-cran-catools, r-cran-dplyr, r-cran-mclust, r-cran-mixak, r-cran-multimode, r-cran-nbclust, r-cran-clusterr, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-opgmmassessment_0.4.1-1.ca2404.1_all.deb Size: 65418 MD5sum: 5840dddc44dbadcc3ba9d2c855298a1e SHA1: 6949fb47d3c786d872e9e2b87f85dface8bc0e74 SHA256: 7eec3a9795ad47014fe8a9da16cd6fef8acc0f2fd6cc187090e98b1e197de8ef SHA512: 1e9a3dc5b05dc0833ed0a31e7e7938797e48db4494b61302a139e2ec971c26eb6e3945e9041186850f107834dd45726fd3767258c919cc4f1fcb9d29ef3816c3 Homepage: https://cran.r-project.org/package=opGMMassessment Description: CRAN Package 'opGMMassessment' (Optimized Automated Gaussian Mixture Assessment) Necessary functions for optimized automated evaluation of the number and parameters of Gaussian mixtures in one-dimensional data. Various methods are available for parameter estimation and for determining the number of modes in the mixture. A detailed description of the methods ca ben found in Lotsch, J., Malkusch, S. and A. Ultsch. (2022) . Package: r-cran-opi Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 792 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-rfast, r-cran-abind, r-cran-openssl Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-opi_3.1.0-1.ca2404.1_all.deb Size: 712042 MD5sum: fb1768abaaa304638459e566a6f1997e SHA1: 1d1c71326d1f9f60e87c8a7ebd68d9a5774f6d8b SHA256: 84961ffe910ee3dcfd228ead0d57a3f2cb1dd19542435feee3fde93d2218a513 SHA512: 276f9cd5fe2349ad55d9b0883b3b360836b6e824c9fba4047248a2902a715be3760b28dc5e65518241ba603f6341df501ed79f9cbc51e4af4c5ec7c19da437df Homepage: https://cran.r-project.org/package=OPI Description: CRAN Package 'OPI' (Open Perimetry Interface) Implementation of the Open Perimetry Interface (OPI) for simulating and controlling visual field machines using R. The OPI is a standard for interfacing with visual field testing machines (perimeters) first started as an open source project with support of Haag-Streit in 2010. It specifies basic functions that allow many visual field tests to be constructed. As of February 2022 it is fully implemented on the Haag-Streit Octopus 900 and 'CrewT ImoVifa' ('Topcon Tempo') with partial implementations on the Centervue Compass, Kowa AP 7000 and Android phones. It also has a cousin: the R package 'visualFields', which has tools for analysing and manipulating visual field data. Package: r-cran-opimputation Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfit, r-cran-caret, r-cran-abcanalysis, r-cran-ggplot2, r-cran-future, r-cran-future.apply, r-cran-progressr, r-cran-missforest, r-cran-mice, r-cran-miceranger, r-cran-multius, r-cran-amelia, r-cran-mi, r-cran-reshape2, r-cran-datavisualizations, r-cran-abind, r-cran-cowplot, r-cran-twosamples, r-cran-ggh4x, r-cran-ggrepel Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-opimputation_0.6-1.ca2404.1_all.deb Size: 143902 MD5sum: 4b0d563a2cdfd97f4f00616173a4153b SHA1: 4f97f94e1db8503fc2c18e96e39c1ee9e409358c SHA256: f58f90e3946111a99958cbeb0ea3d012fe2b98cb5ab5b1e050bc84d92fd163a5 SHA512: efb9471e5fb3e3c470a42a706d8594d426c6d1caacc0501f7d0768347668d4074ce6c4bff4d23981a0ca08f7002258b699ed3fc3f2c555773fcd30d05192e09f Homepage: https://cran.r-project.org/package=opImputation Description: CRAN Package 'opImputation' (Optimal Selection of Imputation Methods for Pain-RelatedNumerical Data) A model-agnostic framework for selecting dataset-specific imputation methods for missing values in numerical data related to pain. Lotsch J, Ultsch A (2025) "A model-agnostic framework for dataset-specific selection of missing value imputation methods in pain-related numerical data" Canadian Journal of Pain (in minor revision). Package: r-cran-opinar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-haven, r-cran-janitor, r-cran-rvest, r-cran-glue, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-magrittr, r-cran-assertthat, r-cran-rlang, r-cran-sjplot, r-cran-gt, r-cran-lubridate Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-opinar_1.0.0-1.ca2404.1_all.deb Size: 72154 MD5sum: 90a28ffe7212c16d766a42ae3c897cc1 SHA1: fc05d224eb1914ec16a8d5dc5d55afcd7442addd SHA256: 9384077dd76027b6d13b5b5793acb84385955f428ce68bbb4c12cdc390abd01d SHA512: 386ff5a7e13c5d2d022180a875e9d65f3414041581b1de1513f4eb8b2d7036bc3bdcaf8d997aa97a0ebf717a5d2b937607dd5fd17d744c7d24ee36db467dd3c8 Homepage: https://cran.r-project.org/package=opinAr Description: CRAN Package 'opinAr' (Argentina's Public Opinion Toolbox) A toolbox for working with public opinion data from Argentina. It facilitates access to microdata and the calculation of indicators of the Trust in Government Index (ICG), prepared by the Torcuato Di Tella University. Although we will try to document everything possible in English, by its very nature Spanish will be the main language. El paquete fue pensado como una caja de herramientas para el trabajo con datos de opinión pública de Argentina. El mismo facilita el acceso a los microdatos y el cálculos de indicadores del Índice de Confianza en el Gobierno (ICG), elaborado por la Universidad Torcuato Di Tella. Package: r-cran-opitools Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tibble, r-cran-tidytext, r-cran-magrittr, r-cran-dplyr, r-cran-stringr, r-cran-purrr, r-cran-tidyr, r-cran-likert, r-cran-tm, r-cran-wordcloud2, r-cran-forcats, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-rvest, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-opitools_1.8.0-1.ca2404.1_all.deb Size: 878616 MD5sum: e0e5de38ad671b6ec4b9656254c6772c SHA1: 084b8f20dce4a2ce3861a84f1f2ad77c4a8671e7 SHA256: 0a2a35f38375beade2dcc08a7bafaabb76ddb78d3f8175998b9cc80c6baaa8b7 SHA512: ecfd7859b1e2ae3a30d522e3988c9a3f8e0e12c6c4c5b087d9c2f6240015e9fea3687f72a6ba2b0530630993f737aec0724bffbf191607757ae31519a749ff07 Homepage: https://cran.r-project.org/package=opitools Description: CRAN Package 'opitools' (Analyzing the Opinions in a Big Text Document) Designed for performing impact analysis of opinions in a digital text document (DTD). The package allows a user to assess the extent to which a theme or subject within a document impacts the overall opinion expressed in the document. The package can be applied to a wide range of opinion-based DTD, including commentaries on social media platforms (such as 'Facebook', 'Twitter' and 'Youtube'), online products reviews, and so on. The utility of 'opitools' was originally demonstrated in Adepeju and Jimoh (2021) in the assessment of COVID-19 impacts on neighbourhood policing using Twitter data. Further examples can be found in the vignette of the package. Package: r-cran-opl Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-pander, r-cran-randomforest, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-opl_1.0.2-1.ca2404.1_all.deb Size: 70338 MD5sum: 4f896c706eee4b4aab2847e582cad6ae SHA1: 5374514ed8d6df79936faeeac3b1279718051afa SHA256: 452bd310c1a10998bc15bb27fb2ba27549c0d2cf26b455ec86c1bdeef6a17bc7 SHA512: d0ac173704ebd31b2552f7d9f8b12b3bb428eca8d9ceb4b961539c3fa7abc4ef553097c58ba838afb08b977ade6301ed2b759c8eaec91a13f720a2397bfe39ae Homepage: https://cran.r-project.org/package=OPL Description: CRAN Package 'OPL' (Optimal Policy Learning) Provides functions for optimal policy learning in socioeconomic applications helping users to learn the most effective policies based on data in order to maximize empirical welfare. Specifically, 'OPL' allows to find "treatment assignment rules" that maximize the overall welfare, defined as the sum of the policy effects estimated over all the policy beneficiaries. Documentation about 'OPL' is provided by several international articles via Athey et al (2021, ), Kitagawa et al (2018, ), Cerulli (2022, ), the paper by Cerulli (2021, ) and the book by Gareth et al (2013, ). Package: r-cran-opportunistic Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-hopbyhop, r-cran-endtoend Filename: pool/dists/noble/main/r-cran-opportunistic_1.2-1.ca2404.1_all.deb Size: 28960 MD5sum: 64cb05d5348a6c7add0f5196bfe143b0 SHA1: d519fdd39919c2320a6e36edab73336a136b07c6 SHA256: 84a2885497b7b17043aa5942b88564507cb189b38ff242740cb6d382d0cbc012 SHA512: 35e8d5187e239ed99f2653ebf8fdb2109f0fc222f97bdbaadf98810b80f50897614124743f601f9ae0c41db6c9e5ab6a489a79fba493583583923485cd216612 Homepage: https://cran.r-project.org/package=Opportunistic Description: CRAN Package 'Opportunistic' (Routing Distribution, Broadcasts, Transmissions and Receptionsin an Opportunistic Network) Computes the routing distribution, the expectation of the number of broadcasts, transmissions and receptions considering an Opportunistic transport model. It provides theoretical results and also estimated values based on Monte Carlo simulations. Package: r-cran-optband Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lambertw Suggests: r-cran-survival, r-cran-km.ci, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-optband_0.2.2-1.ca2404.1_all.deb Size: 210522 MD5sum: 0b4ae8b1d77021d70f06e177061d2525 SHA1: b7b576d3eb119c877bdcc78fe9cea833caf12dcd SHA256: 3c9bc33f0b123cb485a4213784828f27fb42b3dc9cf6def3ff192c31e8d5c55a SHA512: 9aac1b72a6b10e926ad95528c3ce3e94fbd7756372b0ed59a314597ab8a39475b1b211bf028423cfe7292cbec17cb2ddcbb79b066754b8e34a80b5375a8b6251 Homepage: https://cran.r-project.org/package=optband Description: CRAN Package 'optband' ('surv' Object Confidence Bands Optimized by Area) Given a certain coverage level, obtains simultaneous confidence bands for the survival and cumulative hazard functions such that the area between is minimized. Produces an approximate solution based on local time arguments. Package: r-cran-optbdmaeat Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-igraph Filename: pool/dists/noble/main/r-cran-optbdmaeat_1.0.2-1.ca2404.1_all.deb Size: 128348 MD5sum: 008e6c5832c9896727ef69c80aa57ebb SHA1: 3397d8a70fd9edd695278588d2b8bc26e9544ba4 SHA256: b39351b57fb75fe2d61c42944f75b9953e468b96e28b8ca37a2ebe8d9237b870 SHA512: b49c996dca2cd9ce944c4d5c6108266bd6008517ecf8ad5b045dffc3211063e97e47a3299b5dabb692ee6f5f890d0f65024504e4c2a2bbf4a60727c9d4ddf571 Homepage: https://cran.r-project.org/package=optbdmaeAT Description: CRAN Package 'optbdmaeAT' (Optimal Block Designs for Two-Colour cDNA Microarray Experiments) Computes A-, MV-, D- and E-optimal or near-optimal block designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all possible elementary treatment contrasts. The algorithms used in this package are based on the treatment exchange and array exchange algorithms of Debusho, Gemechu and Haines (2018) . The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly. Package: r-cran-optbinningr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1520 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-jsonlite, r-cran-lintr, r-cran-covr Filename: pool/dists/noble/main/r-cran-optbinningr_0.2.1-1.ca2404.1_all.deb Size: 805960 MD5sum: 5658a47cc83d0d5938fcd55bc1a756ef SHA1: f84747b40241b2262a5f3065311d7149ad89ac7f SHA256: 0b4753421c26dae3f1f46705c25182b2cb9929016c6dfcb529ff306c041044a0 SHA512: bc712279b591a46ed3e58389f4c5bab31a4108cda9abc2e628f44ee6a969f69b520faabea83b9abfc0f08e90426e2890fd4cdd17ded0764e64a7864d8a6b87d9 Homepage: https://cran.r-project.org/package=optbinningR Description: CRAN Package 'optbinningR' (Optimal Binning Methods for Predictive Modeling and Analytics) Native R tools for optimal binning workflows in predictive modeling. The package provides APIs for binary, multi-class and continuous targets, with multi-variable binning and scorecard workflows. Methods are informed by Navas-Palencia (2020) and Navas-Palencia (2021) . Package: r-cran-optbiomarker Architecture: all Version: 1.0-28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpanel, r-cran-mass, r-cran-randomforest, r-cran-e1071, r-cran-ipred, r-cran-msm, r-cran-rgl, r-cran-matrix Filename: pool/dists/noble/main/r-cran-optbiomarker_1.0-28-1.ca2404.1_all.deb Size: 443562 MD5sum: eb74ef8b2a2be20e92842f3b577c4cc7 SHA1: 68cbee1962da622de8d0a5f0411b0e8b56b9b141 SHA256: 65a04cd021cdabbad04078c0db3ce5c9e9909921b39b7448f9bcbbfbe35f792d SHA512: 712b0b678c0211e43eba6b4f7de996d8ea26cee92f56ad3b4a93b1b3ad536b413fa1711fe60b1d2c8c367fcc90e65e94f3d142e40c8704572202cea8c98cb5e2 Homepage: https://cran.r-project.org/package=optBiomarker Description: CRAN Package 'optBiomarker' (Estimation of Optimal Number of Biomarkers for Two-GroupMicroarray Based Classifications at a Given Error ToleranceLevel for Various Classification Rules) Estimates optimal number of biomarkers for two-group classification based on microarray data. Package: r-cran-optconerrf Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-optconerrf_1.0.2-1.ca2404.1_all.deb Size: 320994 MD5sum: 649c0ac730ac7b6f0a30e5e370279f21 SHA1: c5e5f12c5ba4eaf61462a979dadd5f5576411ac9 SHA256: afb7ef6ba73c919552076ba692705c2231a0e134585fc44404c8ac2674a8e418 SHA512: ed6805158542995f8f95eda9d85ea5ab5510d821464a1ee3f3a27a59be7bd44cf3b49d8acfb26586362a67cd413b4e83d66a5e1f374c6a19b13dc162bd32adcd Homepage: https://cran.r-project.org/package=optconerrf Description: CRAN Package 'optconerrf' (Optimal Monotone Conditional Error Functions) Design and analysis of confirmatory adaptive clinical trials using the optimal conditional error framework according to Brannath and Bauer (2004) . An extension to the optimal conditional error function using interim estimates as described in Brannath and Dreher (2024) and functions to ensure that the resulting conditional error function is non-increasing are also available. Package: r-cran-optdesignslopeint Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-optdesignslopeint_1.1.1-1.ca2404.1_all.deb Size: 50884 MD5sum: 1075d502311d00c8a3ea447fe4e7588b SHA1: ab64be606c0a1e7645d0fb551304bc66f312a114 SHA256: e8b3d6f897b693797ab23e302b4bded4d94fc55ed57fc25658e6e68dcd097ff3 SHA512: a0821704614d14e8ba4d6cfc43fdcff418a843f3cf070ef80a29b28135bf56c45de704e37c9c85a34748df296d57bd0c4254f3ae9f8453fff13dc67d730a4d08 Homepage: https://cran.r-project.org/package=optDesignSlopeInt Description: CRAN Package 'optDesignSlopeInt' (Optimal Designs for Estimating the Slope Divided by theIntercept) Aids practitioners to optimally design experiments that measure the slope divided by the intercept and provides confidence intervals for the ratio. Package: r-cran-optecd Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-optecd_1.0.0-1.ca2404.1_all.deb Size: 13900 MD5sum: 8433130165755b87658cace875d5c134 SHA1: 700a4f684af147c16120896d3a6ba4faeb673f14 SHA256: d791b6294d26c057074de18c81d43bc8da44920c92b961c539a28cee9d4c3c08 SHA512: 018733cfc44034ecdc5fc1314a3434215d36c0dd73e0c307d0d42bb77722203c557b861cd5a578bcb826a9570d95048177e9b81896a2b9b458d0298929c15c30 Homepage: https://cran.r-project.org/package=OPTeCD Description: CRAN Package 'OPTeCD' (Optimal Partial Tetra-Allele Cross Designs) Tetra-allele cross often referred as four-way cross or double cross or four-line cross are those type of mating designs in which every cross is obtained by mating amongst four inbred lines. A tetra-allele cross can be obtained by crossing the resultant of two unrelated diallel crosses. A common triallel cross involving four inbred lines A, B, C and D can be symbolically represented as (A X B) X (C X D) or (A, B, C, D) or (A B C D) etc. Tetra-allele cross can be broadly categorized as Complete Tetra-allele Cross (CTaC) and Partial Tetra-allele Crosses (PTaC). Rawlings and Cockerham (1962) firstly introduced and gave the method of analysis for tetra-allele cross hybrids using the analysis method of single cross hybrids under the assumption of no linkage. The set of all possible four-way mating between several genotypes (individuals, clones, homozygous lines, etc.) leads to a CTaC. If there are N number of inbred lines involved in a CTaC, the the total number of crosses, T = N*(N-1)*(N-2)*(N-3)/8. When more number of lines are to be considered, the total number of crosses in CTaC also increases. Thus, it is almost impossible for the investigator to carry out the experimentation with limited available resource material. This situation lies in taking a fraction of CTaC with certain underlying properties, known as PTaC. Package: r-cran-optedr Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1998 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-purrr, r-cran-rlang, r-cran-crayon, r-cran-cli, r-cran-dplyr, r-cran-nleqslv, r-cran-shiny Suggests: r-cran-testthat, r-cran-mockery, r-cran-markdown, r-cran-dt, r-cran-shinydashboard, r-cran-shinyalert, r-cran-plotly, r-cran-hrbrthemes, r-cran-shinyjs, r-cran-orthopolynom, r-cran-magrittr, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-optedr_2.2.0-1.ca2404.1_all.deb Size: 629186 MD5sum: f7d3a1b1a20d9ecae15df10a0b7a2fba SHA1: 41b9ea0d3a37bc07bcad9da4a8931b0ebfa45841 SHA256: c801039c3172546d7c6303812aae0786e67b710731b31661a569b7f4706d6314 SHA512: ed8b6e14365b8c0a539b1b8aada3854206e491f2610ca4f25a323ee441d671aa108ff3a07bddc0b4eb83b9817cbb78b5991e0c6fc75e7c4543508be5b0257d29 Homepage: https://cran.r-project.org/package=optedr Description: CRAN Package 'optedr' (Calculating Optimal and D-Augmented Designs) Calculates D-, Ds-, A-, I- and L-optimal designs for non-linear models, via an implementation of the cocktail algorithm (Yu, 2011, ). Compares designs via their efficiency, and augments any design with a controlled efficiency. An efficient rounding function has been provided to transform approximate designs to exact designs. Package: r-cran-optextras Architecture: all Version: 2019-12.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-optextras_2019-12.4-1.ca2404.1_all.deb Size: 119916 MD5sum: c9fc5b70ecd5e3dc01f2fe1f978cef05 SHA1: b86cf0aa7362133d5e20d8174b9d11b4644126b0 SHA256: e13cb1b8b079e5398bcbc705074c791809b13da8075e06337ed69054332d0b36 SHA512: a74a8ef8448be4f266d7b7195deb98211d5b3cbf28adf2f4e9567a141cbaa71262fbf38e297c2cfe028503e7d263c4f4684d17e52151e6bb0b3ffbbe5f46bebb Homepage: https://cran.r-project.org/package=optextras Description: CRAN Package 'optextras' (Tools to Support Optimization Possibly with Bounds and Masks) Tools to assist in safely applying user generated objective and derivative function to optimization programs. These are primarily function minimization methods with at most bounds and masks on the parameters. Provides a way to check the basic computation of objective functions that the user provides, along with proposed gradient and Hessian functions, as well as to wrap such functions to avoid failures when inadmissible parameters are provided. Check bounds and masks. Check scaling or optimality conditions. Perform an axial search to seek lower points on the objective function surface. Includes forward, central and backward gradient approximation codes. Package: r-cran-optholdoutsize Architecture: all Version: 0.1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3614 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-mnormt, r-cran-mvtnorm, r-cran-ranger Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-optholdoutsize_0.1.0.2-1.ca2404.1_all.deb Size: 3330282 MD5sum: f466473b33927b69b648c8197d12120f SHA1: 39ad99fb28838710b7398b5c190c7263a8992a0d SHA256: 2d3d19f539ee26a0c8e6cc11faec1e211576f90897c6f8bd7565eac7d395c987 SHA512: d0cafeeb1c0c5bd12fcfcf0ac70f7c61dff0492747db8112435ac771b0b91afb30079e7437c88e135a5042de75043f597bc28404fea37fda85c23b858acf2995 Homepage: https://cran.r-project.org/package=OptHoldoutSize Description: CRAN Package 'OptHoldoutSize' (Estimation of Optimal Size for a Holdout Set for Updating aPredictive Score) Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is 'held-out' from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) (in Annals of Applied Statistics) for details of methods. Package: r-cran-optic Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-did, r-cran-dplyr, r-cran-future.apply, r-cran-lmtest, r-cran-magrittr, r-cran-mass, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-sandwich, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-optic_1.0.1-1.ca2404.1_all.deb Size: 249316 MD5sum: 422b884c6918f717c9abe66b92566fc8 SHA1: 4d8d6be69f8c9deac4bfb2b8173746607728e3d6 SHA256: ac87a05bbd1310d41af96d3a1acbbb033178fe6aa013456a521717f04eb884aa SHA512: dbb6f9a15a7c25d34ee337cc78934417b056e4dc4925fa2590f301b4d187ca27ae6318512b64609b0fb458cb81006275aae8094994a30cfc441291f35c3ef493 Homepage: https://cran.r-project.org/package=optic Description: CRAN Package 'optic' (Simulation Tool for Causal Inference Using Longitudinal Data) Implements a simulation study to assess the strengths and weaknesses of causal inference methods for estimating policy effects using panel data. See Griffin et al. (2021) and Griffin et al. (2022) for a description of our methods. Package: r-cran-optical Architecture: all Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-optical_1.7.1-1.ca2404.1_all.deb Size: 468962 MD5sum: d4fa1f78ea0b3f9c594b42f0a118fca3 SHA1: 3b84d821bb1091b335954efc06c129335dd099fc SHA256: 97c861275b19f3ed3e07f41522761d2e1ce3b505437c83844f2d66e2cf903353 SHA512: 4434bd1778caf613a3102dae6f7395674f23aae40db3e4a8945eaaf86c79daa3630018514c21a064ed0f290b33be8fc4062bc7271a79b5670173f59e87837e2b Homepage: https://cran.r-project.org/package=optical Description: CRAN Package 'optical' (Optimal Item Calibration) The restricted optimal design method is implemented to optimally allocate a set of items that require calibration to a group of examinees. The optimization process is based on the method described in detail by Ul Hassan and Miller in their works published in (2019) and (2021) . To use the method, preliminary item characteristics must be provided as input. These characteristics can either be expert guesses or based on previous calibration with a small number of examinees. The item characteristics should be described in the form of parameters for an Item Response Theory (IRT) model. These models can include the Rasch model, the 2-parameter logistic model, the 3-parameter logistic model, or a mixture of these models. The output consists of a set of rules for each item that determine which examinees should be assigned to each item. The efficiency or gain achieved through the optimal design is quantified by comparing it to a random allocation. This comparison allows for an assessment of how much improvement or advantage is gained by using the optimal design approach. This work was supported by the Swedish Research Council (Vetenskapsrådet) Grant 2019-02706. Package: r-cran-opticskxi Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2904 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr, r-cran-matrix, r-cran-rlang Suggests: r-cran-amap, r-cran-dbscan, r-cran-cowplot, r-cran-fastica, r-cran-fpc, r-cran-ggrepel, r-cran-gtable, r-cran-knitr, r-cran-plyr, r-cran-reshape2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-opticskxi_1.2.1-1.ca2404.1_all.deb Size: 2610372 MD5sum: 9df821fb7142c0f9d44d5a293aef305a SHA1: 63d20537357aa24c860469e4d485807c4f4674e3 SHA256: f0a5f581b05640f03f15bf4953b5574dd37bc5fede0e54cef14ecd405db7d093 SHA512: c28e5d7af6550081ce5ae3cf62e76a755391875272c1932bddc60742ed9becfcc4ade21fb0679eefa7713f804fb98a36dbb768b7d6afecebc80db571dc88251e Homepage: https://cran.r-project.org/package=opticskxi Description: CRAN Package 'opticskxi' (OPTICS K-Xi Density-Based Clustering) Density-based clustering methods are well adapted to the clustering of high-dimensional data and enable the discovery of core groups of various shapes despite large amounts of noise. This package provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters. The vignette first introduces density-based algorithms with simulated datasets, then presents and evaluates the k-Xi cluster extraction method. Finally, the models comparison framework is described and experimented on 2 genetic datasets to identify groups and their discriminating features. The k-Xi algorithm is a novel OPTICS cluster extraction method that specifies directly the number of clusters and does not require fine-tuning of the steepness parameter as the OPTICS Xi method. Combined with a framework that compares models with varying parameters, the OPTICS k-Xi method can identify groups in noisy datasets with unknown number of clusters. Results on summarized genetic data of 1,200 patients are in Charlon T. (2019) . A short video tutorial can be found at . Package: r-cran-opticut Architecture: all Version: 0.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-mass, r-cran-pscl, r-cran-betareg, r-cran-resourceselection, r-cran-mefa4 Filename: pool/dists/noble/main/r-cran-opticut_0.1-4-1.ca2404.1_all.deb Size: 403406 MD5sum: fdac4c1e6845eefc2e0b8bba10d8f17a SHA1: 27f298ee70128fe8e93b80c5da2a6b182430e870 SHA256: 9cdf8c4d3039da707a652c1e521419670e585d52028e9f16d101bbf985db220e SHA512: b12608dd2b7ac4880e3df9630119cf67da5448197f153c102940f7db786fc7e1af7ae533d0d31f8c69b6d2b9be13346961cf232f884f653273f8b91891ff9d9e Homepage: https://cran.r-project.org/package=opticut Description: CRAN Package 'opticut' (Likelihood Based Optimal Partitioning and Indicator SpeciesAnalysis) Likelihood based optimal partitioning and indicator species analysis. Finding the best binary partition for each species based on model selection, with the possibility to take into account modifying/confounding variables as described in Kemencei et al. (2014) . The package implements binary and multi-level response models, various measures of uncertainty, Lorenz-curve based thresholding, with native support for parallel computations. Package: r-cran-optifunset Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-optifunset_1.0-1.ca2404.1_all.deb Size: 11128 MD5sum: 53976ab36fe28039243cdaa33b88af32 SHA1: 7eca2338a01729e1b9f74c4b9c73b1bcc5704e98 SHA256: 74e4aab21c770ae3b1ad7d769f98dc734fd37aedfb7bfe48e4e215696c074f87 SHA512: 8da22b0fe3d8951e5f92b800d900c4ba297ef00413678aa90ff5aa66a038d7cff08ce73db0ca59118308f2968c20b201647fe68e6dd01c068d13e321fab4fb50 Homepage: https://cran.r-project.org/package=optifunset Description: CRAN Package 'optifunset' (Set Options if Unset) A single function 'options.ifunset(...)' is contained herewith, which allows the user to set a global option ONLY if it is not already set. 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Package: r-cran-optigrab Architecture: all Version: 0.9.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-optigrab_0.9.2.1-1.ca2404.1_all.deb Size: 87272 MD5sum: 7e0db88fce73af72c329dcfb883aef1f SHA1: f2db3c3f4f00758413abec68d4823eb3aa027822 SHA256: e042fab75f54075e44716dccb6f29e577e077e6f0f4ab9e9850ecd903e2fa532 SHA512: a618323e93e5f783ec95bbc395338f314836ed143ab1ab7be139751f67c840dd4d8b492e126e8d34f1d0de99f65ef5548ad8de1184465eac22c91ec358b29b8a Homepage: https://cran.r-project.org/package=optigrab Description: CRAN Package 'optigrab' (Command-Line Parsing for an R World) Parse options from the command-line using a simple, clean syntax. It requires little or no specification and supports short and long options, GNU-, Java- or Microsoft- style syntaxes, verb commands and more. Package: r-cran-optim.functions Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lhs, r-cran-randtoolbox, r-cran-stringr Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-optim.functions_0.1-1.ca2404.1_all.deb Size: 25010 MD5sum: 221e12935eaf854f50ff8e981e145737 SHA1: beb2ebc3ddde32f5c93ceb13ca45b76374ad8610 SHA256: 7157d3ae91e1b9169ca79bddf96b32cb8bf3f57d5d734fe5d60c53c82d24532e SHA512: abfff9a289369a97b0e63fd099b124a5bacd3ad0a87d3a9ec601dbafc6d7aaab4edb3922be9ef5d87e9029ecc38ecccb7e1f6f714dc83e1767786a5effdaf86c Homepage: https://cran.r-project.org/package=optim.functions Description: CRAN Package 'optim.functions' (Standard Benchmark Optimization Functions) A set of standard benchmark optimization functions for R and a common interface to sample them. Package: r-cran-optimalcutpoints Architecture: all Version: 1.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-optimalcutpoints_1.1-5-1.ca2404.1_all.deb Size: 178222 MD5sum: 69b3a59be0aa5302ac9b4911acfb9a45 SHA1: ae517bd6f6a4777faf1570436ccdc5f3f689d7d1 SHA256: dd1dde9659122e754addcd4a2a818cb5f09a467385eb260befccfeb99312f670 SHA512: 01969f43a6df9e66ca025b2a2c3b101c87bbc6d13e7a868ad11d088f5dd2b3493b73a069c21a19f25a519ad1c0a81b54bfad143505543340d97abc20f38c0982 Homepage: https://cran.r-project.org/package=OptimalCutpoints Description: CRAN Package 'OptimalCutpoints' (Computing Optimal Cutpoints in Diagnostic Tests) Computes optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained. Package: r-cran-optimaldesign Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-lpsolve, r-cran-matrixstats, r-cran-matrixcalc, r-cran-plyr, r-cran-quadprog, r-cran-rgl Filename: pool/dists/noble/main/r-cran-optimaldesign_1.0.3-1.ca2404.1_all.deb Size: 378912 MD5sum: c122870ea689e980cae65bca8912e329 SHA1: 365143dabdcbe5cd70ab193f03208e1e940f2fd2 SHA256: 1b8de1a5e38bde3771cac6c9d4bcfcb54772bad4c9167b125dc5d4237f62fb57 SHA512: d01dc93a364c81effa0d1d40604bcd5e511ef0df13b21311a64f17d1cff5cf3242fc7498c8d26d104dd7ca19c33e99932e9622a054cd9d29a4243a0cb17fd947 Homepage: https://cran.r-project.org/package=OptimalDesign Description: CRAN Package 'OptimalDesign' (A Toolbox for Computing Efficient Designs of Experiments) Algorithms for D-, A-, I-, and c-optimal designs. For more details, see the package description. Some of the functions in this package require the 'gurobi' software and its accompanying R package. For their installation, please follow the instructions at and the file gurobi_inst.txt, respectively. Package: r-cran-optimalgoldstandarddesigns Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1427 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nloptr, r-cran-mvtnorm, r-cran-cli, r-cran-dplyr, r-cran-tibble, r-cran-rdpack Suggests: r-cran-testthat, r-cran-here, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-fpcompare, r-cran-mnormt, r-cran-future.apply Filename: pool/dists/noble/main/r-cran-optimalgoldstandarddesigns_1.0.1-1.ca2404.1_all.deb Size: 749878 MD5sum: 590c7607cd2e0f7535303c43cf73dfbf SHA1: dc4c233f98259c1caaaddcbd8f64cc8a4dc69213 SHA256: 1e9dc905dc405ea5d2ca63d90b5295559c8df4c4db189b4d5594f8cd22a4fad9 SHA512: 8906408162b18aa1527e50f893a4fba7111f7d09f51d95672cd924a91fb800eaaf38f2193c3b4cb6d8f595d579401fab12a1f6c4c78048351ec87d2da4b638a4 Homepage: https://cran.r-project.org/package=OptimalGoldstandardDesigns Description: CRAN Package 'OptimalGoldstandardDesigns' (Design Parameter Optimization for Gold-Standard Non-InferiorityTrials) Methods to calculate optimal design parameters for one- and two-stage three-arm group-sequential gold-standard non-inferiority trial designs with or without binding or nonbinding futility boundaries, as described in Meis et al. (2023) . Package: r-cran-optimall Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4574 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-rlang, r-cran-tibble Suggests: r-cran-bslib, r-cran-diagrammer, r-cran-dt, r-cran-globals, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-shiny, r-cran-shinytest, r-cran-survey, r-cran-tidyr, r-cran-testthat, r-cran-webshot Filename: pool/dists/noble/main/r-cran-optimall_1.4.0-1.ca2404.1_all.deb Size: 3180800 MD5sum: bd47dbf446926720362f327305630e92 SHA1: a1eba5fbe54dca268f7b2b82a949a166423b2b9d SHA256: 959cec0a370ecb32c579d347169d6babcb430c1432b8568d0f3ab0e208f2e3b1 SHA512: 19b001e4a68e3c99763bacdd52f383736ef99efbd70b1c8c18ee76d575a2d39a1217d1accb9b1f7da7907ab11b07e1189a70e53e5557a9386f8856b6f995b61a Homepage: https://cran.r-project.org/package=optimall Description: CRAN Package 'optimall' (Allocate Samples Among Strata) Functions for the design process of survey sampling, with specific tools for multi-wave and multi-phase designs. Perform optimum allocation using Neyman (1934) or Wright (2012) allocation, split strata based on quantiles or values of known variables, randomly select samples from strata, allocate sampling waves iteratively, and organize a complex survey design. Also includes a Shiny application for observing the effects of different strata splits. A paper on this package was published in the Journal of Statistical Software . Package: r-cran-optimalrerandexpdesigns Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-momentchi2, r-cran-greedyexperimentaldesign Filename: pool/dists/noble/main/r-cran-optimalrerandexpdesigns_1.1-1.ca2404.1_all.deb Size: 75880 MD5sum: 88c26e618355c2238b3b181a2a4bbb81 SHA1: c12c70ece874f4bb635cde6c76a33289520f3d71 SHA256: df7ffeee5414533ba8f148f3a1e650d16731d68f8b281c592dce79b54d93154e SHA512: e2f76b7914dd3939667ac345f816866b2bf9692856de96fb86cc7552d664ed4e9d576736ae10e6110ac4d218bb0506ba97fb54a50ee02b1ce5ec8bde658d5b30 Homepage: https://cran.r-project.org/package=OptimalRerandExpDesigns Description: CRAN Package 'OptimalRerandExpDesigns' (Optimal Rerandomization Experimental Designs) This is a tool to find the optimal rerandomization threshold in non-sequential experiments. We offer three procedures based on assumptions made on the residuals distribution: (1) normality assumed (2) excess kurtosis assumed (3) entire distribution assumed. Illustrations are included. Also included is a routine to unbiasedly estimate Frobenius norms of variance-covariance matrices. Details of the method can be found in "Optimal Rerandomization via a Criterion that Provides Insurance Against Failed Experiments" Adam Kapelner, Abba M. Krieger, Michael Sklar and David Azriel (2020) . Package: r-cran-optimalsurrogate Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-optimalsurrogate_1.0-1.ca2404.1_all.deb Size: 42836 MD5sum: d4c869aad161ed24089e09ff2774b53a SHA1: df2a96db9f9c0fff30bed743b97d729fc974bf3a SHA256: 9ae2937e3b04a18d0c243a659d54cd83bc8169af9c6038922e73aaeb2a930d17 SHA512: 75818ead2f19a57f21aea2b55314cab22ed4c6cb3656d00dde3569342401bba4806b97afc29ffb84b8191f29241f8b054dd17692895f7d9146deb0865592557c Homepage: https://cran.r-project.org/package=OptimalSurrogate Description: CRAN Package 'OptimalSurrogate' (Model Free Approach to Quantifying Surrogacy) Identifies an optimal transformation of a surrogate marker such that the proportion of treatment effect explained can be inferred based on the transformation of the surrogate and nonparametrically estimates two model-free quantities of this proportion. Details are described in Wang et al (2020) . Package: r-cran-optimalthreshold Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ars, r-cran-rjags, r-cran-hdinterval, r-cran-mgcv, r-cran-coda Filename: pool/dists/noble/main/r-cran-optimalthreshold_1.0-1.ca2404.1_all.deb Size: 396718 MD5sum: 2c532b25bdcf989da33425ea8fd0803b SHA1: ad890c8d4e98746c7c98764262c2e6569e3ca3c8 SHA256: e605bab0027cff073fe2f3bcf6e5ba411d0d9dceecf9e50fbc2ca303483f4b8c SHA512: 4fed3b87b3db64f9ce733758445e1aa3d4db3c554b53cfea6b10b32ab9d50e4fce5fbf715a970d7c88cc767c53718c878cd982c3bfade55cf558cbd4633115e7 Homepage: https://cran.r-project.org/package=optimalThreshold Description: CRAN Package 'optimalThreshold' (Bayesian Methods for Optimal Threshold Estimation) Functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) for diagnostic markers. Package: r-cran-optimaltiming Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mstate, r-cran-survival Filename: pool/dists/noble/main/r-cran-optimaltiming_0.1.0-1.ca2404.1_all.deb Size: 76446 MD5sum: 32faed1463b3d0e93427454dd5ff2689 SHA1: f54e04773ffd0a0b7b35f685b4cc11ef9829fd9f SHA256: ea298cb909bc623d1ca52dbaef07f71b300e6a16abe23e363bdfe85b491cbd82 SHA512: 21b78b10fbc91349279410dddea79fa1f6143e5393a7aa01ef8ecd6cbc507bc370d91189b13c847358b88969e0207538c65d8c060df412b2f4c5093f76a6f073 Homepage: https://cran.r-project.org/package=OptimalTiming Description: CRAN Package 'OptimalTiming' (Optimal Timing Identification) Identify the optimal timing for new treatment initiation during multiple state disease transition, including multistate model fitting, simulation of mean residual lifetime for a given transition state, and estimation of confidence interval. The method is referred to de Wreede, L., Fiocco, M., & Putter, H. (2011) . 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Provides functions to calculate the option premium and option greeks of European-style options. Package: r-cran-optiscale Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-optiscale_1.2.3-1.ca2404.1_all.deb Size: 62008 MD5sum: c89cc67636d4c896d4a87fe70b2d0000 SHA1: 3be0b1d4adaf872e18ca3227435978fc1afafea4 SHA256: bfe6ac4f359c24e5f041a7d066be8f2674ff7ea5d03b367838dff50f06c94c41 SHA512: 99b652e192222eacf934f644fc6f1f92fe4a5ff40157a32359807485e886a210b433a22ad64a0ae29faba4c9c0ae52b0c2d40d54bcb6acfea4d21ae12e8e23a6 Homepage: https://cran.r-project.org/package=optiscale Description: CRAN Package 'optiscale' (Optimal Scaling) Optimal scaling of a data vector, relative to a set of targets, is obtained through a least-squares transformation subject to appropriate measurement constraints. 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Package: r-cran-optisembleforecasting Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-keras, r-cran-tsutils, r-cran-readxl, r-cran-tibble, r-cran-tensorflow, r-cran-metrics, r-cran-forecast, r-cran-dplyr, r-cran-neuralnet, r-cran-mcs, r-cran-caretforecast, r-cran-kknn, r-cran-metaheuristicopt, r-cran-factominer, r-cran-factoextra Filename: pool/dists/noble/main/r-cran-optisembleforecasting_0.1.0-1.ca2404.1_all.deb Size: 53314 MD5sum: 6d1859dee83afaf8507ee44b3abc2dda SHA1: c56e6edb269d3b0ee93e0129ea08ce1a4ae3a1d4 SHA256: 012b80d51387902c559a23ce518cb13cb88f032cc492c1e7449e9a43862abdf1 SHA512: 9047a39b5c08aee002bfbb20aa28a885adc7aadee128cee10f6711729c21a587eb28b3792ca09a43a7f31a5e2f9b4466754abfc70bf7531dc92237c07a9b85ec Homepage: https://cran.r-project.org/package=OptiSembleForecasting Description: CRAN Package 'OptiSembleForecasting' (Optimization Based Ensemble Forecasting Using MCS Algorithm) The real-life data is complex in nature. No single model can capture all aspect of complex time series data. In this package, 14 models, namely Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Bidirectional LSTM, Deep LSTM, Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest (RF), k-Nearest Neighbour (KNN), XGBoost (XGB), Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonality (ETS) and TBATS models, have been implemented and their accuracy have been checked. An PCA based error index has been proposed to select a group of best models using MCS algorithms. After selecting the models, the forecasts from these models have been ensembled using optimization techniques. This package allows to implement 20 optimization techniques, namely, Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), Bat Algorithm (BA), Black Hole Optimization Algorithm (BHO), Clonal Selection Algorithm (CLONALG), Cuckoo Search (CS), Cat Swarm Optimization (CSO), Dragonfly Algorithm (DA), Differential Evolution (DE), Firefly Algorithm (FFA), Genetic Algorithm (GA), Gravitational Based Search Algorithm (GBS), Grasshopper Optimisation Algorithm (GOA), Grey Wolf Optimizer (GWO), Harmony Search Algorithm (HS), Krill-Herd Algorithm (KH), Moth Flame Optimizer (MFO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Shuffled Frog Leaping (SFL) and Whale Optimization Algorithm (WOA). This package has been developed using concept of Wang et al. (2022) , Qu et al. (2022) and Kriz (2019) . Package: r-cran-optisolve Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-shapes, r-cran-alabama, r-cran-cccp, r-cran-nloptr, r-cran-mass, r-cran-plyr, r-cran-stringr, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-optisolve_1.0-1.ca2404.1_all.deb Size: 799214 MD5sum: e12a275b74112183e5377f6e90262438 SHA1: 09dd82f822b779c65692df28cc2a8f5fa0f19dd5 SHA256: 7789ba5d99b20ab1e6ecdc184d24f0e779f9826182f26d80eba805419170f717 SHA512: 32f3204898dc0697f607a2e36b9bae19c54aac1545176d2ab9b50ea0fc508daf238897a8e23403842b358cefa2d6681fc412ec5a8890fb0ea15edc2062842a71 Homepage: https://cran.r-project.org/package=optiSolve Description: CRAN Package 'optiSolve' (Linear, Quadratic, and Rational Optimization) Solver for linear, quadratic, and rational programs with linear, quadratic, and rational constraints. 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Package: r-cran-optistock Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1208 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse, r-cran-shiny, r-cran-shinydashboard Filename: pool/dists/noble/main/r-cran-optistock_0.0.2-1.ca2404.1_all.deb Size: 654428 MD5sum: 1ac75c0158332590c5cb2246ca6048ce SHA1: b1e10b0ac82985de70b9b4009130c869c6cbd0f8 SHA256: b8625089ca102b5bf6a45608483bcd2b170c3ff1776bfe51af34b945a255d1df SHA512: e83b2bfd8d3be4af84398ae0db869bfcc4866158ce658f0fc15e3b22549816431df7480d822a525ee1cd7f8450b117096504c288355871d9aa7cb8cc45a4c0f2 Homepage: https://cran.r-project.org/package=optistock Description: CRAN Package 'optistock' (Determine Optimum Stocking Times Used in Fishery Enhancements) A collection of functions that aid in calculating the optimum time to stock hatchery reared fish into a body of water given the growth, mortality and cost of raising a particular number of individuals to a certain length. 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Package: r-cran-optr Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-optr_1.2.5-1.ca2404.1_all.deb Size: 88822 MD5sum: 2608c84e1dc3e86ed82f434f204d6651 SHA1: 4d751b37957fed671d122d59cc03267f8a90a235 SHA256: 56e9799eb07e18ba688ba680f83107fe1a2ac3d7f04f713c3b11541e780c53a0 SHA512: 58460a90ae84ba3d949e33ab53f6de1ca4f208b3ca9f3a0ed58210b8b7064f4031b66af496a481584da28b531cbc5523d70b8a4ffcca5e820eb54e7e542f059b Homepage: https://cran.r-project.org/package=optR Description: CRAN Package 'optR' (Optimization Toolbox for Solving Linear Systems) Solves linear systems of form Ax=b via Gauss elimination, LU decomposition, Gauss-Seidel, Conjugate Gradient Method (CGM) and Cholesky methods. 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To solve the linear program, the 'Gurobi' commercial optimization software is recommended, but not required. The 'gurobi' R package can be installed following the instructions at after claiming your free academic license at . 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Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, (UAI 2022), PMLR 180:1530–1540". 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These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose-limiting toxicity (DLT) rates, discrete or continuous dose levels, combining ordinal grades 0 and 1 into one category, and incorporate safety and/or stopping rules. For POM and CR model designs, ordinal toxicity grades are specified by common terminology criteria for adverse events (CTCAE) version 4.0. Function 'pseudodata' creates the necessary starting models for these 3 designs, and function 'nextdose' estimates the next dose to test in a cohort of patients for a target DLT rate. We also provide the function 'crmsimulations' to assess the performance of these 3 dose finding designs under various scenarios. 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These include the regularized elastic net ordinal regression of Wurm, Hanlon, and Rathouz (2021) in 'ordinalNet', the ordinal classification trees of Galimberti, Soffritti, and Di Maso (2012) in 'rpartScore', and the latent variable ordinal forests of Hornung (2020) in 'ordinalForest'. 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The 'is_[strictly_]*' family of functions test vectors for 'sorted', 'monotonic', 'increasing', 'decreasing' order; 'is_constant' and 'is_incremental' test for the degree of ordering. `ordering` provides a numeric indication of ordering -2 (strictly decreasing) to 2 (strictly increasing). 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By defining a lightweight interface around the inputs and outputs of an analysis, a lot of the repetitive work for reproducible research can be automated. We define a simple format for organising and describing work that facilitates collaborative reproducible research and acknowledges that all analyses are run multiple times over their lifespans. 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Kotz (2003) Statistical size distributions in economics and actuarial sciences; Gentle, J. (2009), Computational Statistics, Springer-Verlag; Naradajah, S. and Rocha, R. (2016), and Stasinopoulos, M. and Rigby, R. (2015), . The families of distributions are: Benini distributions, Burr distributions, Dagum distributions, Feller-Pareto distributions, Generalized Pareto distributions, Inverse Pareto distributions, The Inverse Paralogistic distributions, Marshall-Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, exponentiated exponential Poisson G distributions, Poisson-inverse gaussian distributions, Skew normal type 1 distributions, Skew student t distributions, Singh-Maddala distributions, Sinh-Arcsinh distributions, Sichel distributions, Zero inflated Poisson distributions. 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The combination of Laplace approximations and P-splines enable fast and flexible inference in a Bayesian framework. Specific approximations are proposed to account for the asymmetry in the marginal posterior distributions of non-penalized parameters. For more details, see Lambert and Gressani (2023) ; Preprint: ). 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These models have been described in Zhang and Archer (2021) . 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These scales measure subjects' perception of an intangible quantity, and cannot be handled as ratio variables because of their inherent non-linearity. We treat them as ordinal variables, measured on a continuous scale. A function (the g function) connects the scale with an underlying continuous latent variable. The link function is the inverse of the CDF of the assumed underlying distribution of the latent variable. A variety of link functions are currently implemented. Such models are described in Manuguerra et al (2020) . Package: r-cran-ordinalgof Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-mass, r-cran-vgam Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-ordinalgof_0.1.0-1.ca2404.1_all.deb Size: 23642 MD5sum: 5c195aebdc9e950ac525985b18898350 SHA1: 9efd161d4dbc2862a42a73d5fd6aac59a910097b SHA256: 01c96c0de648bcd63c4f09884d0274bb3fa362663ba0f8f1185c5762c0be1005 SHA512: 4ad3757dbfd81dc36c6b3cf2a396fe4a97378c35b4eddbe399a3750d0b3d180d59accc602ffef98006905825b886eed0e416257e5ea8141ba5a33463075c4283 Homepage: https://cran.r-project.org/package=ordinalGOF Description: CRAN Package 'ordinalGOF' (Goodness-of-Fit Tests for Ordinal Regression Models) Provides goodness-of-fit tests for ordinal regression models, including the Fagerland-Hosmer ordinal test, reproducing same output as 'Stata'. 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Package: r-cran-ordinalnet Architecture: all Version: 2.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-mass, r-cran-glmnet, r-cran-penalized, r-cran-vgam, r-cran-rms Filename: pool/dists/noble/main/r-cran-ordinalnet_2.14-1.ca2404.1_all.deb Size: 120256 MD5sum: 36d22847a7719e8ac47cc6d4bd9d28e1 SHA1: a68f60249b41d96342e4b1bd1b76764663810648 SHA256: e7d93bf81663fd2de9911d4e98e870898c9481da79b1d3b0df75e422973d9b5a SHA512: d7e6c6f37157da892a05959e2d648c118dc357073ebae3fc40f5c425c2bf8b1f8449cd1d9a7eb392cff0302d41c33c0341999ecbb94cc4317d7d0ec6fc673196 Homepage: https://cran.r-project.org/package=ordinalNet Description: CRAN Package 'ordinalNet' (Penalized Ordinal Regression) Fits ordinal regression models with elastic net penalty. Supported model families include cumulative probability, stopping ratio, continuation ratio, and adjacent category. These families are a subset of vector glm's which belong to a model class we call the elementwise link multinomial-ordinal (ELMO) class. Each family in this class links a vector of covariates to a vector of class probabilities. Each of these families has a parallel form, which is appropriate for ordinal response data, as well as a nonparallel form that is appropriate for an unordered categorical response, or as a more flexible model for ordinal data. The parallel model has a single set of coefficients, whereas the nonparallel model has a set of coefficients for each response category except the baseline category. It is also possible to fit a model with both parallel and nonparallel terms, which we call the semi-parallel model. The semi-parallel model has the flexibility of the nonparallel model, but the elastic net penalty shrinks it toward the parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2021) . Package: r-cran-ordinalrr Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags Filename: pool/dists/noble/main/r-cran-ordinalrr_1.1-1.ca2404.1_all.deb Size: 74636 MD5sum: 2244adab4cc589fc6fa35070d34fcf1e SHA1: 5658a5cec40b150ab29bb3fefa303a29bdb16a5d SHA256: eae8168d6a91261fd804524c14b95489b4e0b03fcd93e3f8ed9bfeeb3a8d6e9d SHA512: 234243998aadbd7a77daf5f54b5c71e2536c09c3500a4774f69952ff26c6ae7df3772baff82ca2a61ddcc437ad13076dffd504851a7568e2b639077cd201cded Homepage: https://cran.r-project.org/package=ordinalRR Description: CRAN Package 'ordinalRR' (Analysis of Repeatability and Reproducibility Studies withOrdinal Measurements) Implements Bayesian data analyses of balanced repeatability and reproducibility studies with ordinal measurements. Model fitting is based on MCMC posterior sampling with 'rjags'. Function ordinalRR() directly carries out the model fitting, and this function has the flexibility to allow the user to specify key aspects of the model, e.g., fixed versus random effects. Functions for preprocessing data and for the numerical and graphical display of a fitted model are also provided. There are also functions for displaying the model at fixed (user-specified) parameters and for simulating a hypothetical data set at a fixed (user-specified) set of parameters for a random-effects rater population. For additional technical details, refer to Culp, Ryan, Chen, and Hamada (2018) and cite this Technometrics paper when referencing any aspect of this work. The demo of this package reproduces results from the Technometrics paper. Package: r-cran-ordinalsimr Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-bslib, r-cran-callr, r-cran-coin, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-golem, r-cran-rhandsontable, r-cran-rlang, r-cran-rms, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-tidyr, r-cran-withr Suggests: r-cran-knitr, r-cran-pkgload, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-writexl Filename: pool/dists/noble/main/r-cran-ordinalsimr_0.2.4-1.ca2404.1_all.deb Size: 1993270 MD5sum: 0dfea1e94e8fff0cbf28939aa1f06957 SHA1: 5f4cb0ebac9c40a8c546d417cd9d9f85be020026 SHA256: 659ff2cf47d3ee369d997eca54017b801eac666e225645c66231d4c7290681e2 SHA512: 53ca746561a5c7dfd6431a54ec53a153090237e9157b81eddfc8365c6ca3c8da15f96adcb3c3b5507cce4aa3d1e7949905ce3b8eda2eb55eb446a28424d9dc92 Homepage: https://cran.r-project.org/package=ordinalsimr Description: CRAN Package 'ordinalsimr' (Compare Ordinal Endpoints Using Simulations) Simultaneously evaluate multiple ordinal outcome measures. Applied data analysts in particular are faced with uncertainty in choosing appropriate statistical tests for ordinal data. The included 'shiny' application allows users to simulate outcomes given different ordinal data distributions. Package: r-cran-ordinaltables Architecture: all Version: 1.0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ordinaltables_1.0.0.3-1.ca2404.1_all.deb Size: 992494 MD5sum: 85ff176490f63b9f689a86cd17840486 SHA1: 8563723de1377e9d955e7a217261ee0c3e59dbf1 SHA256: 54f620f04a16dfdf5efb6fff89b169b922ae4936310fadfae5b2e63e528d41fc SHA512: 63b2fca87c81dfb4fe73361c16ccad622e3678926833cc2fec933806eb18c872db04ab4b21dab117c18a89f6703b9674909a7692724d5b6214207521753578aa Homepage: https://cran.r-project.org/package=ordinalTables Description: CRAN Package 'ordinalTables' (Fit Models to Two-Way Tables with Correlated Ordered ResponseCategories) Fit a variety of models to two-way tables with ordered categories. Most of the models are appropriate to apply to tables of that have correlated ordered response categories. There is a particular interest in rater data and models for rescore tables. Some utility functions (e.g., Cohen's kappa and weighted kappa) support more general work on rater agreement. Because the names of the models are very similar, the functions that implement them are organized by last name of the primary author of the article or book that suggested the model, with the name of the function beginning with that author's name and an underscore. This may make some models more difficult to locate if one doesn't have the original sources. The vignettes and tests can help to locate models of interest. For more dertaiils see the following references: Agresti, A. (1983) "A Simple Diagonals-Parameter Symmetry And Quasi-Symmetry Model", Agrestim A. (1983) "Testing Marginal Homogeneity for Ordinal Categorical Variables", Agresti, A. (1988) "A Model For Agreement Between Ratings On An Ordinal Scale", Agresti, A. (1989) "An Agreement Model With Kappa As Parameter", Agresti, A. (2010 ISBN:978-0470082898) "Analysis Of Ordinal Categorical Data", Bhapkar, V. P. (1966) "A Note On The Equivalence Of Two Test Criteria For Hypotheses In Categorical Data", Bhapkar, V. P. (1979) "On Tests Of Marginal Symmetry And Quasi-Symmetry In Two And Three-Dimensional Contingency Tables", Bowker, A. H. (1948) "A Test For Symmetry In Contingency Tables", Clayton, D. G. (1974) "Some Odds Ratio Statistics For The Analysis Of Ordered Categorical Data", Cliff, N. (1993) "Dominance Statistics: Ordinal Analyses To Answer Ordinal Questions", Cliff, N. (1996 ISBN:978-0805813333) "Ordinal Methods For Behavioral Data Analysis", Goodman, L. A. (1979) "Simple Models For The Analysis Of Association In Cross-Classifications Having Ordered Categories", Goodman, L. A. (1979) "Multiplicative Models For Square Contingency Tables With Ordered Categories", Ireland, C. T., Ku, H. H., & Kullback, S. (1969) "Symmetry And Marginal Homogeneity Of An r × r Contingency Table", Ishi-kuntz, M. (1994 ISBN:978-0803943766) "Ordinal Log-linear Models", McCullah, P. (1977) "A Logistic Model For Paired Comparisons With Ordered Categorical Data", McCullagh, P. (1978) A Class Of Parametric Models For The Analysis Of Square Contingency Tables With Ordered Categories", McCullagh, P. (1980) "Regression Models For Ordinal Data", Penn State: Eberly College of Science (undated) "Stat 504: Analysis of Discrete Data, 11. Advanced Topics I", Schuster, C. (2001) "Kappa As A Parameter Of A Symmetry Model For Rater Agreement", Shoukri, M. M. (2004 ISBN:978-1584883210). "Measures Of Interobserver Agreement", Stuart, A. (1953) "The Estimation Of And Comparison Of Strengths Of Association In Contingency Tables", Stuart, A. (1955) "A Test For Homogeneity Of The Marginal Distributions In A Two-Way Classification", von Eye, A., & Mun, E. Y. (2005 ISBN:978-0805849677) "Analyzing Rater Agreement: Manifest Variable Methods". Package: r-cran-ordmonreg Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ordmonreg_1.0.4-1.ca2404.1_all.deb Size: 85142 MD5sum: 2b773790dd3a5f44712fd2efbe4bed71 SHA1: 0f293a6579abb44fadcbad75b1eb61dc73c11478 SHA256: fead639a22176e1a3134ac0d90abafaa0a75bce0c3e3fec03c3a7dbad5ba7fda SHA512: 910222fea66cfddaa0e83a0ea1ba38b5a19ed213bccf76f367d9a5caf3f863d9b25a190f889f6d073d43465d95425e712f195d4d2f0e5c4131d26139e5c8b8dc Homepage: https://cran.r-project.org/package=OrdMonReg Description: CRAN Package 'OrdMonReg' (Compute Least Squares Estimates of One Bounded or Two OrderedIsotonic Regression Curves) We consider the problem of estimating two isotonic regression curves g1* and g2* under the constraint that they are ordered, i.e. g1* <= g2*. Given two sets of n data points y_1, ..., y_n and z_1, ..., z_n that are observed at (the same) deterministic design points x_1, ..., x_n, the estimates are obtained by minimizing the Least Squares criterion L(a, b) = sum_{i=1}^n (y_i - a_i)^2 w1(x_i) + sum_{i=1}^n (z_i - b_i)^2 w2(x_i) over the class of pairs of vectors (a, b) such that a and b are isotonic and a_i <= b_i for all i = 1, ..., n. We offer two different approaches to compute the estimates: a projected subgradient algorithm where the projection is calculated using a PAVA as well as Dykstra's cyclical projection algorithm. Package: r-cran-ordnor Architecture: all Version: 2.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-corpcor, r-cran-matrix, r-cran-genord Filename: pool/dists/noble/main/r-cran-ordnor_2.2.3-1.ca2404.1_all.deb Size: 50648 MD5sum: 814faf739745feffde6fe465077dba30 SHA1: 63be282efd03f771e8466bfa00f8019a5cb833df SHA256: d9cf0f631b4b121cd6430d8d9309e30431f264d7d26b13a6285edcc88f77af4c SHA512: f11033c9d49c80e98bc0dfbe00b5a9143d4ad7230f85d2dff062794b9a1e45f672ac805c5eb815cb09f4f18d9b019f15234af0c51bbda7fd6808e983ff3a370c Homepage: https://cran.r-project.org/package=OrdNor Description: CRAN Package 'OrdNor' (Concurrent Generation of Ordinal and Normal Data with GivenCorrelation Matrix and Marginal Distributions) Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2015) . Package: r-cran-ordpanel Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-flextable, r-cran-ggplot2, r-cran-consort, r-cran-patchwork, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ordpanel_0.1.1-1.ca2404.1_all.deb Size: 413556 MD5sum: 9df4d87fab5902dc6c96f6f27eadb092 SHA1: 24c562f4e74381f9ca5d97685f0b81d89974419c SHA256: 9c7dc7dd63f3481f25cb9b137d06414a6330f85f5d63b46d3a5fe6af6d80c7dc SHA512: 72cf8240a64aa559072e6dce39450291dd15ba8b61b0550efd3dd1d80c6bc10d21e23f69cb01eafdf86cde95a4a89e28c70ddd160bc7d9baf9209cef386bfa76 Homepage: https://cran.r-project.org/package=ordPanel Description: CRAN Package 'ordPanel' (Ordered Panel) The ordered panel methodology (Zezulinski et al 2025 ) provides a structured framework for identifying and organizing sets of biomarkers, such as genetic variants, that distinguish between positive and negative subjects in a study when only a training cohort is available. This approach is particularly useful in situations where an independent validation cohort does not yet exist, rendering conventional performance metrics such as the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) inappropriate or potentially misleading. The methodology emphasizes transparent construction and evaluation of ordered signatures of biomarkers, allowing investigators to examine operating characteristics without establishing predictive performance. Package: r-cran-ordpens Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-grplasso, r-cran-mgcv, r-cran-rlrsim, r-cran-quadprog, r-cran-glmpath, r-cran-ordinalnet Suggests: r-cran-psy, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ordpens_1.1.0-1.ca2404.1_all.deb Size: 1464264 MD5sum: d114d7faec641abe7e808427d02d1647 SHA1: 8c3fb7a7f781edef2a430bfe020faa6e1683a24c SHA256: 0c54595988e51a278fc8e7967113fad0a525ee4a047f558c4dbb5985d4cf61a5 SHA512: e056c64821962f346e25d1dc8f85e87f441ccf3437e78629c5d9dcd808890ef21d17a193ce9d0101c0a66cea59d747cb3451eb4f3eaa9ae1dc3ed60b4da06f5f Homepage: https://cran.r-project.org/package=ordPens Description: CRAN Package 'ordPens' (Selection, Fusion, Smoothing and Principal Components Analysisfor Ordinal Variables) Selection, fusion, and/or smoothing of ordinally scaled independent variables using a group lasso, fused lasso or generalized ridge penalty, as well as non-linear principal components analysis for ordinal variables using a second-order difference/smoothing penalty. Package: r-cran-ordr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1834 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-cli, r-cran-mass, r-cran-stringr, r-cran-tidyselect, r-cran-scales, r-cran-generics, r-cran-magrittr, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-labeling, r-cran-ggrepel, r-cran-gggda Suggests: r-cran-testthat, r-cran-sessioninfo, r-cran-gridextra, r-cran-mlpack, r-cran-ddalpha, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ordr_0.2.0-1.ca2404.1_all.deb Size: 1281320 MD5sum: eb08209bb983e57b008f16803b99b84b SHA1: 4ab57d2bbe04ad7af932486e21f0449cdde26bdb SHA256: 86758d4973c185fdbf40e4be0d04f2799433194bcacfa30ee2f1a499e4477128 SHA512: 9eb478db6590e223ab8fc3c450b4265dbf38b0bbac63a5a82d1d07a48d5ba2c69fad1a8312000bbface8350dba769191f3bcf039c4c3ba41bf9065bc2958e264 Homepage: https://cran.r-project.org/package=ordr Description: CRAN Package 'ordr' (A 'Tidyverse' Extension for Ordinations and Biplots) Ordination comprises several multivariate exploratory and explanatory techniques with theoretical foundations in geometric data analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux & Rouanet (2005) for foundations. Greenacre (2010, ISBN:978-84-923846) shows how the most established of these, including principal components analysis, correspondence analysis, multidimensional scaling, factor analysis, and discriminant analysis, rely on eigen-decompositions or singular value decompositions of pre-processed numeric matrix data. These decompositions give rise to a set of shared coordinates along which the row and column elements can be measured. The overlay of their scatterplots on these axes, introduced by Gabriel (1971) , is called a biplot. 'ordr' provides inspection, extraction, manipulation, and visualization tools for several popular ordination classes supported by a set of recovery methods. It is inspired by and designed to integrate into 'Tidyverse' workflows provided by Wickham et al (2019) . Package: r-cran-oreo Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gridextra, r-cran-ggplot2, r-cran-openxlsx, r-cran-spectral, r-cran-pracma, r-cran-fftwtools, r-cran-scales Filename: pool/dists/noble/main/r-cran-oreo_1.0-1.ca2404.1_all.deb Size: 115468 MD5sum: 4a413b28d834ad4400d52d621ca025ce SHA1: b81c134e849b5f551a6deefcec3511643176b8fa SHA256: 6f8614bb0ac243f9e7974edb5dcbd54055f6df53d106dbd0f1d2948ec91f291c SHA512: 54bdc41a72f6c7d535642525564201b2f3cc25d2dd7656fe21fa5c74fbd263c54c9566a9fb8e009c533e74dc0dfff1d9e35839adc668c39f764fc32bcb3bd6ab Homepage: https://cran.r-project.org/package=oreo Description: CRAN Package 'oreo' (Large Amplitude Oscillatory Shear (LAOS)) The Sequence of Physical Processes (SPP) framework is a way of interpreting the transient data derived from oscillatory rheological tests. It is designed to allow both the linear and non-linear deformation regimes to be understood within a single unified framework. This code provides a convenient way to determine the SPP framework metrics for a given sample of oscillatory data. It will produce a text file containing the SPP metrics, which the user can then plot using their software of choice. It can also produce a second text file with additional derived data (components of tangent, normal, and binormal vectors), as well as pre-plotted figures if so desired. It is the R version of the Package SPP by Simon Rogers Group for Soft Matter (Simon A. Rogers, Brian M. Erwin, Dimitris Vlassopoulos, Michel Cloitre (2011) ). Package: r-cran-orfid Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-readr, r-cran-tidyr, r-cran-ggplot2, r-cran-stringr, r-cran-rlang, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-orfid_1.0.3-1.ca2404.1_all.deb Size: 167802 MD5sum: e0936be958b1f5797d891d944919363c SHA1: 1d13b45b984fcd19516a581b3cd86d6d43f52d42 SHA256: 685d43c4f013b54a5002b38d55d608c0935afad691debdbb4ee83f30970c3373 SHA512: 218adacdb5786b5656cb7d61644929bc312347e3b67e371c4f1b6256cfb80a5aba03ab916df973c2110d7dd290a8436cf192625518ce71b0065dce0842fd113b Homepage: https://cran.r-project.org/package=ORFID Description: CRAN Package 'ORFID' (Manage and Summarize Data from Oregon RFID ORMR and ORSR AntennaReaders) Automates and standardizes the import of raw data from Oregon RFID (radio-frequency identification) ORMR (Oregon RFID Multi-Reader) and ORSR (Oregon RFID Single Reader) antenna readers. Compiled data can then be combined within multi-reader arrays for further analysis, including summarizing tag and reader detections, determining tag direction, and calculating antenna efficiency. 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This package provides tools to set up and manage project directories, handle file paths consistently across operating systems, organize results using date-based structures, source code from specified directories, and perform file operations safely. It ensures consistency across projects while accommodating different requirements for various types of content. 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It supports 'rpart', 'glmnet', and 'kNN' engines with flexible residual distributions and heteroscedastic scale models, weighting variants by calibration-aware scores. A Gaussian/t copula couples the marginals to simulate joint forecast paths, returning quantiles, means, and step increments across horizons. Package: r-cran-organizr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fs, r-cran-here, r-cran-readr, r-cran-rlang, r-cran-rstudioapi, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-organizr_0.1.0-1.ca2404.1_all.deb Size: 21738 MD5sum: 2808531d9cafbd829c8cf6e15fcd8371 SHA1: c6a0a6b6a0a14f0dde91fbf59a9e04bd6ae9a297 SHA256: d02cdcc1bda1052d88b7bc9def1d9fa6dfa91faa89d7fc58916fc775f3c9c66e SHA512: 828b5e0e7861af0ac7c29f9bc1059571bd86c9161872e5b2da98cf2bf03415ac4e138dddcf6accd0a1e274728591d398af6ed7ca99a8ad68fb1dc7f61a08b41b Homepage: https://cran.r-project.org/package=organizr Description: CRAN Package 'organizr' (Shortcuts for File Creation with Informative Prefixes) Provides functions for quickly creating R and Python scripts, as well as 'Rmarkdown' or Quarto documents with automatically assigned name prefixes. Prefixes are either file counts (e.g. "001") or dates (e.g. "2022-09-26"). Package: r-cran-orgheatmap Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4792 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpolypath, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-stringr, r-cran-patchwork, r-cran-purrr, r-cran-stringdist, r-cran-sf, r-cran-data.table, r-cran-rcolorbrewer, r-cran-viridis Suggests: r-cran-knitr, r-cran-svglite, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-orgheatmap_0.3.4-1.ca2404.1_all.deb Size: 4063582 MD5sum: 172fef1b915cd843e14868cdec5fe9f8 SHA1: db77975ca2f6bef5d58f7dc727ca8719a8793864 SHA256: 2179e7347952310d5c1a9c505ddd97a6c20d857136cb97cf396a8069716b77b8 SHA512: b43e1d3b0ff17c5e187beb9d1aa66e6ea36f343db0e08c2fe741a8391208dd2963543c5b3cefdc88969ecb8ff1bed6e183a1ee6c3555defd7fa75d24734e0ac6 Homepage: https://cran.r-project.org/package=OrgHeatmap Description: CRAN Package 'OrgHeatmap' (Visualization Tool for Numerical Data on Human/Mouse Organs andOrganelles) A tool for visualizing numerical data (e.g., gene expression, protein abundance) on predefined anatomical maps of human/mouse organs and subcellular organelles. It supports customization of color schemes, filtering by organ systems (for organisms) or organelle types, and generation of optional bar charts for quantitative comparison. The package integrates coordinate data for organs and organelles to plot anatomical/subcellular contours, mapping data values to specific structures for intuitive visualization of biological data distribution.The underlying method was described in the preprint by Zhou et al. (2022) . Package: r-cran-orgmassspecr Architecture: all Version: 0.5-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1086 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-orgmassspecr_0.5-4-1.ca2404.1_all.deb Size: 495380 MD5sum: ddf1cff154e571515ddce0bc19617033 SHA1: 7fe202d14d9cd1604b045c88caa7a21940b35b9b SHA256: 1185a83d55c39013dbd76c08c4bd7cc113ff827de7f865e1e651fc0ea6fa9740 SHA512: 7076fc36301c7e3ce2c15fa45612a6d7e34516b576c7217da4a809c3cee72557e57319386eea76cdddfbe3206267aaef41469162a84d7338aa12beb947a13764 Homepage: https://cran.r-project.org/package=OrgMassSpecR Description: CRAN Package 'OrgMassSpecR' (Organic Mass Spectrometry) Organic/biological mass spectrometry data analysis. Package: r-cran-orgr Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggthemes, r-cran-ggplot2, r-cran-lubridate, r-cran-data.table, r-cran-stringr Filename: pool/dists/noble/main/r-cran-orgr_0.9.0-1.ca2404.1_all.deb Size: 34436 MD5sum: 775e0eb86061bf6ad044b67267748ad3 SHA1: 5fb8c105cadf0b6fc6ad0a478bcebd4307b6b34b SHA256: 7d8d772870e9c8cd714e574c1c1fef91e3f759585b9108f52b594f27c21ddf6c SHA512: 1c6db6c3ab45d16748766fd27947c0c0a381b0c0a9cfb237cd5ff13b3bc8b68e504901efac1e39222aa2df9d0c4a09fce36ed92ba60272bd45f3b1820b13ec5c Homepage: https://cran.r-project.org/package=orgR Description: CRAN Package 'orgR' (Analyse Text Files Created by Emacs' Org mode) Provides functionality to process text files created by Emacs' Org mode, and decompose the content to the smallest components (headlines, body, tag, clock entries etc). 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Package: r-cran-oriclust Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-oriclust_1.0-2-1.ca2404.1_all.deb Size: 263666 MD5sum: 54f786bc4c77305c8044177bd7390a57 SHA1: 8b6430ee8c1ab94b5f328ad972fc4a28519b1557 SHA256: 6fcd3428fc25bbbadd48d488a54b5a65bc8d913b8aacf807c14875fd5b3117a7 SHA512: 3af1aafa98c0405a03c0ee4fb2a41c43e02c5fde5a8059f3991013b38f4d7ef9dc1956310d10f4a931e8e6e8d4fc787fedb076660be319547b572ffe5a735ad3 Homepage: https://cran.r-project.org/package=ORIClust Description: CRAN Package 'ORIClust' (Order-Restricted Information Criterion-Based ClusteringAlgorithm) A user-friendly R-based software package for gene clustering. Clusters are given by genes matched to prespecified profiles across various ordered treatment groups. It is particularly useful for analyzing data obtained from short time-course or dose-response microarray experiments. Package: r-cran-orientlib Architecture: all Version: 0.10.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rgl, r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-orientlib_0.10.5-1.ca2404.1_all.deb Size: 333322 MD5sum: 20b18d27b5d7a928acf95bf8c30708a6 SHA1: a0370010b92afa7c27bca2991a68997f60a32df0 SHA256: 4f8919557c0f0773a9a75641024adf8da83eadfcd79d252a80cd96a2d7022db3 SHA512: 3331ffc5b7eba668791e45e90bac871afa7d373fa90509b408d3124b36f4c89057a82aa3c3d2cc2cc9714c2f1a5a57912eef7d21a2b2609d0a98e11e121601eb Homepage: https://cran.r-project.org/package=orientlib Description: CRAN Package 'orientlib' (Support for Orientation Data) Representations, conversions and display of orientation SO(3) data. See the orientlib help topic for details. Package: r-cran-origami Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-data.table, r-cran-assertthat, r-cran-future, r-cran-future.apply, r-cran-listenv Suggests: r-cran-testthat, r-cran-class, r-cran-rmarkdown, r-cran-knitr, r-cran-stringr, r-cran-glmnet, r-cran-forecast, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-origami_1.0.8-1.ca2404.1_all.deb Size: 93264 MD5sum: 9586b21a677e183c954c7fef46de9ba2 SHA1: d92b5dc137aa70d942410ba007d843bcee508d21 SHA256: ad28eb42297e1e3ac43b6228e072f38fcf5610e66c426f5efcac26de96e2cb72 SHA512: deea921e11d8345e324e1adeecf234ea1e0bfcf84222156bc1676537c7a4d6976ff91451f10a7ab76a815c6cd278a10c128445ac16c5905670b9792efc6c9218 Homepage: https://cran.r-project.org/package=origami Description: CRAN Package 'origami' (Generalized Framework for Cross-Validation) A general framework for the application of cross-validation schemes to particular functions. By allowing arbitrary lists of results, origami accommodates a range of cross-validation applications. This implementation was first described by Coyle and Hejazi (2018) . Package: r-cran-origamiplot Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fmsb, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-origamiplot_0.2.0-1.ca2404.1_all.deb Size: 88572 MD5sum: 8591f1e990066658ce882b4dec84e6a6 SHA1: 29c44827a77bd96e52d045555da337081a37e99a SHA256: fcaca11e5f1f2e5e1ba94e31ec9d97ce8b5e27b2cd772fa100b3c1439173efa7 SHA512: 6cd65cf4635221afe5807405f504448ca40abdc5d0fba20d81bd92046209558ebc5044c541561bfd30e96a8310b20be600efba350c92f616d39ba9de871e7024 Homepage: https://cran.r-project.org/package=OrigamiPlot Description: CRAN Package 'OrigamiPlot' (A Visualization Tool Enhancing Radar Plot Visualizations forMultivariate Data) A visualization tool for multivariate data. This package maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multi-criteria decision-making. Package: r-cran-origin Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1268 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rstudioapi Suggests: r-cran-data.table, r-cran-dplyr, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-origin_1.2.1-1.ca2404.1_all.deb Size: 457000 MD5sum: 12c4b981a7a08b21a5073f37380e83cb SHA1: 85a0e6bf1dbb753097ba0e3a575fd9100017525d SHA256: ab25f3e3d6966e84539be82ce762d30be33f8ce3cbd33969bd6d60f14204073d SHA512: 4805b7adeb4f1e31a4fcd9c6886963c8981ed47b48e1bf4f5f3faf0897fe01d244d1a3350b5cea146f3bd552b788bb68e880a16fe8886b740942ec7907525f7e Homepage: https://cran.r-project.org/package=origin Description: CRAN Package 'origin' (Explicitly Qualifying Namespaces by Automatically Adding 'pkg::'to Functions) Automatically adding 'pkg::' to a function, i.e. mutate() becomes dplyr::mutate(). It is up to the user to determine which packages should be used explicitly, whether to include base R packages or use the functionality on selected text, a file, or a complete directory. User friendly logging is provided in the 'RStudio' Markers pane. Lives in the spirit of 'lintr' and 'styler'. Can also be used for checking which packages are actually used in a project. Package: r-cran-orionz.g Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-orionz.g_1.0.1-1.ca2404.1_all.deb Size: 68474 MD5sum: b5b12cbe678f7f2723cd0aa061948388 SHA1: aea5f2d786fd65251360e2f4196a96735895b992 SHA256: 21ede53edf3b03fcfe92a7aad3732b2f7e975adac9d8f00398a95affbed17027 SHA512: e59d24ea1e25008cf9fdd68edd2ea1aa5f3ef85aa74c7c8689938477b2fed9a785c852d538dade3f2d14434b69de618f3a3737397a30f2981be5a192fe3d3109 Homepage: https://cran.r-project.org/package=ORIONZ.G Description: CRAN Package 'ORIONZ.G' (EAP Scoring in Exploratory FA Solutions with CorrelatedResiduals) Obtaining Bayes Expected A Posteriori (EAP) individual score estimates based on linear and non-linear extended Exploratoy Factor Analysis solutions that include a correlated-residual structure. Package: r-cran-orisma Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-digest, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-glue, r-cran-jsonlite, r-cran-magrittr, r-cran-pheatmap, r-cran-readr, r-cran-stringdist, r-cran-stringr, r-cran-synthesisr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rsvg, r-cran-testthat Filename: pool/dists/noble/main/r-cran-orisma_0.1.0-1.ca2404.1_all.deb Size: 390662 MD5sum: 4cab42ec7fa6e41bfd97d07a4f1e2a23 SHA1: 1bb1b305b39d5100a4de1b399880c4abbafcca6a SHA256: 72024175a894d9419d7ac3e860b91b940b218292fbef86794f0fa00c5e16b017 SHA512: 17929f080b4bbf08b9eb78a91992d9c71de49ce3f5bd6a7bbf14f3f77152ceff674ff14df83396f0a3f898cb30d51bc80f6bd70f4d6719522977053a90e212a2 Homepage: https://cran.r-project.org/package=orisma Description: CRAN Package 'orisma' (Occupational Risk Integrated Systematic Mapping and Analysis) A complete pipeline for systematic bibliometric mapping of occupational health and safety (OHS) evidence. Starting from reference files exported from major bibliographic databases such as Web of Science, Scopus, PubMed, Dimensions, EBSCO, and others, 'orisma' automates ingestion, deduplication, relevance filtering, occupational risk category extraction, bibliometric analysis, and report generation. The package is related to bibliometric science mapping and evidence synthesis workflows described by Aria and Cuccurullo (2017) , Westgate (2019) , and Lajeunesse (2016) , but adds a domain-specific occupational safety and health layer. The package implements three original bibliometric indicators: (1) the Worker-Risk Disconnection Index (WRDI), measuring the proportion of studies that characterise an occupational risk without including direct worker exposure data; (2) the Risk Category Saturation Index (RCS), measuring the relative over- or under-representation of each risk category relative to a uniform baseline; and (3) the Material-Gap Profile (MGP), measuring the ratio between a material's known hazard potential and its coverage in the occupational health literature. Two additional preventive intelligence indicators are provided: (4) the Abstract Sufficiency Score (ASS, 0-5), a cumulative hierarchical index of the preventively useful information contained in an abstract; and (5) the Bridge Article Score (0-5), identifying studies that simultaneously address technology, hazardous agent, worker population, exposure measurement, and preventive recommendations. Risk categories are extracted using a built-in occupational risk dictionary of 58 categories anchored in ISO 45001:2018, INSST, NIOSH, and EU-OSHA frameworks, organised in six blocks: Safety, Industrial Hygiene, Ergonomics, Psychosociology, Biological Hazards, and Emerging Technologies. The dictionary is user-extensible. Outputs include bilingual HTML reports, occupational risk sheets, priority reading rankings, guided extraction matrices for systematic review, and reproducibility certificates with MD5 hashes. Package: r-cran-orkm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-orkm_1.0.0-1.ca2404.1_all.deb Size: 419938 MD5sum: a7409e1543d95e16de4679270fa47aae SHA1: d6ed6a8b136590ef56411a9762901880e84f426d SHA256: 301192177a90ef4e1d757ad3c7a03f02b3a0de73ed7f306f158cf80023db9eb7 SHA512: bf1e2218b7d5b1a5d1ed18da34735f7c0270b832f343e84d4e136b9fb98f59278203dce2344edc03d278d3f3009ca02806ae078df7b046bff576bc07dcf850ee Homepage: https://cran.r-project.org/package=ORKM Description: CRAN Package 'ORKM' (The Online Regularized K-Means Clustering Algorithm) Algorithm of online regularized k-means to deal with online multi(single) view data. The philosophy of the package is described in Guo G. (2024) . Package: r-cran-orloca.es Architecture: all Version: 5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-orloca Filename: pool/dists/noble/main/r-cran-orloca.es_5.5-1.ca2404.1_all.deb Size: 92962 MD5sum: 2b9c24ae983be8959531265859c5b046 SHA1: 7362cd2ec57f1df80bb2d6dffbc4f415eda75d65 SHA256: f55563a89be7cf40da7a1523d9e82cd4cf52fb8848ec6c4724b4ee3cd32c3f93 SHA512: 13fa1bca9818bd5882f5ec6b6215155bc4a2e44cd336c91be545fff0efe05ce8def777bd052a981feaa2c55d58c4e573f4d9078ace535fa9a6e41f62ccea0e8e Homepage: https://cran.r-project.org/package=orloca.es Description: CRAN Package 'orloca.es' (Spanish version of orloca package. Modelos de localizacion eninvestigacion operativa) Help and demo in Spanish of the orloca package. Ayuda y demo en espanol del paquete orloca. Objetos y metodos para manejar y resolver el problema de localizacion de suma minima, tambien conocido como problema de Fermat-Weber. El problema de localizacion de suma minima busca un punto tal que la suma ponderada de las distancias a los puntos de demanda se minimice. Vease "The Fermat-Weber location problem revisited" por Brimberg, Mathematical Programming, 1, pag. 71-76, 1995. . Se usan algoritmos generales de optimizacion global para resolver el problema, junto con el metodo especifico Weiszfeld, vease "Sur le point pour lequel la Somme des distance de n points donnes est minimum", por Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pag. 355-386, 1937 o "On the point for which the sum of the distances to n given points is minimum", por E. Weiszfeld y F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. . Package: r-cran-orloca Architecture: all Version: 5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 727 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-png, r-cran-ucminf, r-cran-knitr, r-cran-rmarkdown Suggests: r-cran-orloca.es, r-cran-testthat Filename: pool/dists/noble/main/r-cran-orloca_5.6-1.ca2404.1_all.deb Size: 419800 MD5sum: be90ef8f15dff8573f59537f56317a21 SHA1: 049cafd71f9729213d7533e0f7bbe4e17fc791ae SHA256: 99f9122cf403befba2f51d8e6e601e368ac1b471dd94facf11044ed5110604af SHA512: 89e7f645754c57bc1f4f99bb910061537b718e8afa18e27c456cc4ce3f59dbf1e6e7b1380476a503933e0c6c30e6756f779beca9ebaf3bca1b87b8f9a6968703 Homepage: https://cran.r-project.org/package=orloca Description: CRAN Package 'orloca' (Operations Research LOCational Analysis Models) Objects and methods to handle and solve the min-sum location problem, also known as Fermat-Weber problem. The min-sum location problem search for a point such that the weighted sum of the distances to the demand points are minimized. See "The Fermat-Weber location problem revisited" by Brimberg, Mathematical Programming, 1, pg. 71-76, 1995. . General global optimization algorithms are used to solve the problem, along with the adhoc Weiszfeld method, see "Sur le point pour lequel la Somme des distances de n points donnes est minimum", by Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pg. 355-386, 1937 or "On the point for which the sum of the distances to n given points is minimum", by E. Weiszfeld and F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. . 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This package can convert microarray probe ID from GPL6864 , GPL8852 , and GPL2025 platforms to RAP-DB ID. RAP-DB "The Rice Annotation Project Database" is a well-known database for rice Oryza sativa, and the gene ID in this database is widely used in many areas related to rice research. For multiple probes representing a single gene, This package can merge them by taking the mean, max, or min value of these probes. Or we can keep multiple probes by appending sequence numbers to duplicate the RAP-DB ID. Package: r-cran-osbng Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5011 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geos Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-osbng_0.2.0-1.ca2404.1_all.deb Size: 4210130 MD5sum: c6fae17aa9326bb38516edaab1246c44 SHA1: 88a37e13f8f6c42c59f2e3839ae25e3c2e200605 SHA256: 82e0d7525add85504b1f5d2be4d725f01edc64a12099edf9741ebbc4ad6e4cb7 SHA512: 5569fe115fbd48565414aacf3fe194f2bafe80c37ccb128451a17f14122e49799857609ff5d5364747498828b3788a4cd96c446c5529f20c9ef05019c18fd77e Homepage: https://cran.r-project.org/package=osbng Description: CRAN Package 'osbng' (Geospatial Grid Indexing with the British National Grid) Offers a streamlined programmatic interface to Ordnance Survey's British National Grid (BNG) index system, enabling efficient spatial indexing and analysis based on grid references. 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Package: r-cran-oscars Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-oscars_0.1.2-1.ca2404.1_all.deb Size: 36536 MD5sum: 47d79fa2aeb2520478581d889d116663 SHA1: 917a6c9298b75c9b34b870dbf02363b0b51dab0f SHA256: 94a3d283d34d81a39fc3546fcc403530ce246ae0cfa42173e4af3ba2186eca7a SHA512: 45c452c30142ef525493b33dd6885020a28b3efadcfa202af324ef09fd746f565939c528696897e48402602f27c35c673d8361dc7ed7abb16fafcee5b706a71f Homepage: https://cran.r-project.org/package=OSCARS Description: CRAN Package 'OSCARS' (Global Bounded Optimization by the OSCARS-II Algorithm) A collection of general optimization routines based on variants of the One Side Cut Accelerated Random Search (OSCARS-II) algorithm (Price et al., 2020, ). The main function , 'oscars()', performs black-box optimization of a general (including nonsmooth or discontinuous) function subject to simple bounds on the unknowns. If all bounds are finite, oscars searches globally. The main method implements a stochastic direct search method and is derivative free. Testing shows the OSCARS-II algorithm usually finds extrema with fewer function evaluations than similar global derivative-free methods. Package: r-cran-oscillatorgenerator Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-oscillatorgenerator_0.1.0-1.ca2404.1_all.deb Size: 73348 MD5sum: 0790ed48c64f058b60386545426c179f SHA1: f63c9a34657ba9fcba1816e7aa592d75df33e425 SHA256: e77b23090f76627725263374c6a78222f51026ea791bdeb35d189ea46b336be3 SHA512: 9a04cf493cae792c033f65b6ff342ad2c37c005b3d5251784c4ffba3adc1668443d524b15b501069b85a9c77232f655c0ee418c5df3502407f00ab94f1905c99 Homepage: https://cran.r-project.org/package=OscillatorGenerator Description: CRAN Package 'OscillatorGenerator' (Generation of Customizable, Discretized Time Series ofOscillating Species) The supplied code allows for the generation of discrete time series of oscillating species. 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Package: r-cran-oscv Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mc2d Filename: pool/dists/noble/main/r-cran-oscv_1.0-1.ca2404.1_all.deb Size: 96516 MD5sum: 908b7623ac56efd313bc7c1c3da474ac SHA1: c3f2f47d3651443f71dde953615b81e88106067d SHA256: 0a77bb17badbc02e951b08c4c014b51a3095e92e82ee47af795ce142ff2934f9 SHA512: e512b62263f08235bce32e1473eb887d91be801a148afb8a27e63a4dc017f45e4c94fac71821e3809d3293f97df4d7111db38dee1d8f56e8bda661cb4fa3739f Homepage: https://cran.r-project.org/package=OSCV Description: CRAN Package 'OSCV' (One-Sided Cross-Validation) Functions for implementing different versions of the OSCV method in the kernel regression and density estimation frameworks. The package mainly supports the following articles: (1) Savchuk, O.Y., Hart, J.D. (2017). Fully robust one-sided cross-validation for regression functions. Computational Statistics, and (2) Savchuk, O.Y. (2017). One-sided cross-validation for nonsmooth density functions, . Package: r-cran-osd Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jade, r-cran-nnls Filename: pool/dists/noble/main/r-cran-osd_0.1-1.ca2404.1_all.deb Size: 211898 MD5sum: 1840afedad1124c5b537b84328ebc4c7 SHA1: 212b6d86fa8f640318ed9526d5af803bb95de0a0 SHA256: fe1b385cf3172bd12d74f6956644ea6f165987759310691f3f0df2d4af51f909 SHA512: 679eb29b353dd59c0b02c690106ec99b181b909525d3d4774567a89e9932dba8896f0e7be67cc38d04465056529a2be4d3744eb8db1fe64c529409755361704b Homepage: https://cran.r-project.org/package=osd Description: CRAN Package 'osd' (Orthogonal Signal Deconvolution for Spectra Deconvolution inGC-MS and GCxGC-MS Data) Compound deconvolution for chromatographic data, including gas chromatography - mass spectrometry (GC-MS) and comprehensive gas chromatography - mass spectrometry (GCxGC-MS). The package includes functions to perform independent component analysis - orthogonal signal deconvolution (ICA-OSD), independent component regression (ICR), multivariate curve resolution (MCR-ALS) and orthogonal signal deconvolution (OSD) alone. Package: r-cran-osdatahub Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1890 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geojsonsf, r-cran-geos, r-cran-httr, r-cran-jsonlite Suggests: r-cran-httptest, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-osdatahub_0.3.0-1.ca2404.1_all.deb Size: 1288714 MD5sum: 6efd03f3af7f804c81995931b124638e SHA1: 6c07126bafb6e93baef02290c4ecfdf26cfc99ae SHA256: fbad446265b3b05260bd661b90df4b6c40c80fcd51171615caeea0de454b7809 SHA512: a75f9736f89adf163e6eacd576ef358645d871904c8a87174f3a80cdce71482398ce48a3e1d1c9d6e3abebd6908df55adb8ec7611138ba263143bd44df6eb338 Homepage: https://cran.r-project.org/package=osdatahub Description: CRAN Package 'osdatahub' (Easier Interaction with the Ordnance Survey Data Hub) Ordnance Survey ('OS') is the national mapping agency for Great Britain and produces a large variety of mapping and geospatial products. 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Package: r-cran-osdesign Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1068 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-osdesign_1.8-1.ca2404.1_all.deb Size: 1058744 MD5sum: 86c92a88d15d7d6daec6dfe7f6626fa1 SHA1: 8f7e6146e6f97d1916ae89bf33d5962b83a91de8 SHA256: be2858ec3a25276d80f906d6769615d7296def32d8486dfeffe2241aaacaef96 SHA512: 26c1c2e4c661e647b7fd63fa2d717bdf171e3ef0b7c812102050e11c002fdba3900b42a06cf806c34a36489d88d712663735b088f2dcbf961436233c20147bcf Homepage: https://cran.r-project.org/package=osDesign Description: CRAN Package 'osDesign' (Design, Planning and Analysis of Observational Studies) A suite of functions for the design of case-control and two-phase studies, and the analysis of data that arise from them. Functions in this packages provides Monte Carlo based evaluation of operating characteristics such as powers for estimators of the components of a logistic regression model. For additional detail see: Haneuse S, Saegusa T and Lumley T (2011). Package: r-cran-osdr Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-osdr_1.1.4-1.ca2404.1_all.deb Size: 30828 MD5sum: ab1de5e3c412f77c221dfc9f75d12159 SHA1: ae3259c696b5fd7ef0cdcb95bb7170f446bb52b0 SHA256: baf980d4165018e06cc43d61130153e1ec6ae1173ac1ed98d3c66c49eb9ff608 SHA512: 9055725c524e851ade0162f74d565cea36ea5ba255bcd85fa24607c9ef207c2145019f11786c729042bc157a3d6de73c7b27c4b3fc26283ece285e25fb6d2dde Homepage: https://cran.r-project.org/package=OSDR Description: CRAN Package 'OSDR' (Finds an Optimal System of Distinct Representatives) Provides routines for finding an Optimal System of Distinct Representatives (OSDR), as defined by D.Gale (1968) . Package: r-cran-osfr Architecture: all Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-jsonlite, r-cran-stringi, r-cran-purrr, r-cran-tibble, r-cran-rlang, r-cran-fs, r-cran-memoise, r-cran-httr Suggests: r-cran-dplyr, r-cran-logger, r-cran-rprojroot, r-cran-brio, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-lintr, r-cran-covr, r-cran-spelling, r-cran-vcr Filename: pool/dists/noble/main/r-cran-osfr_0.2.9-1.ca2404.1_all.deb Size: 318172 MD5sum: 158c4b0c487713b356b03d1d2cbd2d9b SHA1: 76abd96be6ee0df1f3696693ed77a1b5a2d358b5 SHA256: b0f3524aa299830884b7db5842aef97934dbe54ebd150f17e4f0212912c6413d SHA512: 53b26f4b44a25ca25afdd7e10b15176e048aa281c326fb01e2625985889cdcc56a74ff3993303fc57230c2e8b25c26d55152763d972ab410791e626c88b86afa Homepage: https://cran.r-project.org/package=osfr Description: CRAN Package 'osfr' (Interface to the 'Open Science Framework' ('OSF')) An interface for interacting with 'OSF' (). 'osfr' enables you to access open research materials and data, or create and manage your own private or public projects. Package: r-cran-oshka Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-unitizer, r-cran-covr Filename: pool/dists/noble/main/r-cran-oshka_0.1.2-1.ca2404.1_all.deb Size: 35804 MD5sum: 73c1f24898983d495426fd97e4bcc326 SHA1: 3c503bccf3dca23c1cd99dc0b1592cb9f8a78e2d SHA256: c1fbaa4577de941249a11cc34136457ecc4361538febecfe35313ef22cebc8ab SHA512: 348e2b7ec2f42401c561c680cd98168cc94dfc63c4fceff8fddbdcf6db2e389008b57ca410c43c5839e78d4102079a41c124812699c5322a8074a42a82bbb4b3 Homepage: https://cran.r-project.org/package=oshka Description: CRAN Package 'oshka' (Recursive Quoted Language Expansion) Expands quoted language by recursively replacing any symbol that points to quoted language with the language it points to. The recursive process continues until only symbols that point to non-language objects remain. The resulting quoted language can then be evaluated normally. This differs from the traditional 'quote'/'eval' pattern because it resolves intermediate language objects that would interfere with evaluation. Package: r-cran-osldecomposition Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-luminescence, r-cran-deoptim, r-cran-minpack.lm, r-cran-gridextra, r-cran-ggplot2, r-cran-scales, r-cran-ggpubr, r-cran-rmarkdown Suggests: r-cran-kableextra, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-osldecomposition_1.2-1.ca2404.1_all.deb Size: 605274 MD5sum: 5ff5e3c74deb3eff67a2f5fddb43b7f5 SHA1: 8542ba30a93e7ab4f8d09f8a898371971afe0f43 SHA256: aa8ff663b648d843e96adb6f381df427e6731bf687b117ec422eac962961917c SHA512: 3d4a1cfdb52ebf07103b32f55c76dc08c1fe9767919d86213939415bb94167163eabc0c10af950adaff58a46ea6ab547a984de8274ad8d3c85f478185e05b68c Homepage: https://cran.r-project.org/package=OSLdecomposition Description: CRAN Package 'OSLdecomposition' (Signal Component Analysis for Optically Stimulated Luminescence) Function library for the identification and separation of exponentially decaying signal components in continuous-wave optically stimulated luminescence measurements. A special emphasis is laid on luminescence dating with quartz, which is known for systematic errors due to signal components with unequal physical behaviour. Also, this package enables an easy to use signal decomposition of data sets imported and analysed with the R package 'Luminescence'. This includes the optional automatic creation of HTML reports. Further information and tutorials can be found at . Package: r-cran-osmapir Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 736 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-xml2 Suggests: r-cran-httptest2, r-cran-httpuv, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-osmapir_0.2.5-1.ca2404.1_all.deb Size: 485454 MD5sum: 115e8e64628629a964b88aed39d76c0e SHA1: 08640edbd00045e9aebd3ffbb6899360138df80d SHA256: ea22a15ab33e0fd7a95b5db26b5fa88e9d7b6d62174393dac341e9333765b00c SHA512: 1cf7ca0264e9a95add6b166ed1b26905e64e25c99170256d6a7a94a3bdafe309ea1c8cb8910dc2fc360c78bdfcbb1b4678b03d3c9955de344fc296395872e6c0 Homepage: https://cran.r-project.org/package=osmapiR Description: CRAN Package 'osmapiR' ('OpenStreetMap' API) Interface to 'OpenStreetMap API' for fetching and saving data from/to the 'OpenStreetMap' database (). Package: r-cran-osmclass Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-collapse, r-cran-data.table, r-cran-stringi Filename: pool/dists/noble/main/r-cran-osmclass_0.1.5-1.ca2404.1_all.deb Size: 265048 MD5sum: 5b0f96e437f2b65a35f1c654a11568ae SHA1: 3b26612b78c1ccd165514aeae01d48753456258c SHA256: dd42c7fb73e22a8a7e5a3ab65c90d93f7e108dce567b039e48910a38eeccd98d SHA512: eb0daba155f4f5559e832de23267e1ac3909324e3ada2baf411e0c1aa101549495542363a3284608dfc8a22051084af30ddb0e6485a16d61cb94edb9a34e3b36 Homepage: https://cran.r-project.org/package=osmclass Description: CRAN Package 'osmclass' (Classify Open Street Map Features) Classify Open Street Map (OSM) features into meaningful functional or analytical categories. Designed for OSM PBF files, e.g. from imported as spatial data frames. 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Package: r-cran-osmextract Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-withr Filename: pool/dists/noble/main/r-cran-osmextract_0.6.0-1.ca2404.1_all.deb Size: 5030792 MD5sum: 4ce9616be06f4a6b8bc5d9ae496b48bd SHA1: 8f2daa97375464d47997d11967eeaa0cda8a8c2c SHA256: 224bd828beae88b05440ab8f474a1ad740a434f0259d7f39c694297c9a9418f8 SHA512: b7ff31124ce09af73681521e48acda7936255709c0c5379731cac672c62b590c2332408241b7c81c2ce7d6bc8ffb221e703435714d60e7b7be211faab86858cd Homepage: https://cran.r-project.org/package=osmextract Description: CRAN Package 'osmextract' (Download and Import Open Street Map Data Extracts) Match, download, convert and import Open Street Map data extracts obtained from several providers. Package: r-cran-osmscale Architecture: all Version: 0.5.23-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openstreetmap, r-cran-berryfunctions, r-cran-sf, r-cran-pbapply Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-osmscale_0.5.23-1.ca2404.1_all.deb Size: 96078 MD5sum: 94d44de2401964a58352000770a97efa SHA1: 60dcae16f68cbf76f3f1e7a86e35299788f81b44 SHA256: a31bf1f47d094b5c221d653867923d6f65a22d230fa77b3710b410c608a182f4 SHA512: 8bc7a900113614164ffbfbc1fc5029500799bc3077d445426b1a51a5922cc7a4ad3e6e1a2964840b6b589cc9a25fe5a78e7631c18eae7ea62bf41a1cde33eba5 Homepage: https://cran.r-project.org/package=OSMscale Description: CRAN Package 'OSMscale' (Add a Scale Bar to 'OpenStreetMap' Plots) Functionality to handle and project lat-long coordinates, easily download background maps and add a correct scale bar to 'OpenStreetMap' plots in any map projection. Package: r-cran-osnmtf Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-osnmtf_0.1.0-1.ca2404.1_all.deb Size: 215444 MD5sum: c4101f0dd2eaaf458cc2d993222b51f2 SHA1: df6e772b46bde57f6b74c7e792ba255e756ba289 SHA256: 4aac178c0258c7e8df8dd24b6334eaf8912562862d8c0e96099f19b34c932ca2 SHA512: 5e8435515f8c72561a491ea70b0a66895f7ef0bee0444d36ab0001b260a5d00c3ff24f9019339859dcef6a4ac65748ae00ada0c43aedea6f9492933833674997 Homepage: https://cran.r-project.org/package=OSNMTF Description: CRAN Package 'OSNMTF' (Orthogonal Sparse Non-Negative Matrix Tri-Factorization) A novel method to implement cancer subtyping and subtype specific drug targets identification via non-negative matrix tri-factorization. To improve the interpretability, we introduce orthogonal constraint to the row coefficient matrix and column coefficient matrix. To meet the prior knowledge that each subtype should be strongly associated with few gene sets, we introduce sparsity constraint to the association sub-matrix. The average residue was introduced to evaluate the row and column cluster numbers. This is part of the work "Liver Cancer Analysis via Orthogonal Sparse Non-Negative Matrix Tri- Factorization" which will be submitted to BBRC. Package: r-cran-osrm.backend Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1019 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-processx, r-cran-ps, r-cran-viridislite Suggests: r-cran-dt, r-cran-knitr, r-cran-mapgl, r-cran-osrm, r-cran-rmarkdown, r-cran-sf, r-cran-shiny, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-osrm.backend_0.3.1-1.ca2404.1_all.deb Size: 962234 MD5sum: f140b813b9c526f531933d119faea421 SHA1: 9664a5b33ae0875c9f69bc87b149ac4515a87dfe SHA256: 1a4a69182b524a9793a422f826296aec43932fe5fa536a0f3b3b12d39602cf09 SHA512: 0528863cd778130085958497c6d24fc47c52be8d0033560ddf87f5e4532176d5107d379155e7220ae41f3685a74fb2858c5630e630fb7932deb8247dd50e5933 Homepage: https://cran.r-project.org/package=osrm.backend Description: CRAN Package 'osrm.backend' (Bindings for 'Open Source Routing Machine') Install and control 'Open Source Routing Machine' ('OSRM') backend executables to prepare routing data and run/stop a local 'OSRM' server. For computations with the running server use the 'osrm' R package (). Package: r-cran-osrm Architecture: all Version: 5.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppsimdjson, r-cran-curl, r-cran-mapiso, r-cran-googlepolylines, r-cran-sf Suggests: r-cran-mapsf, r-cran-tinytest, r-cran-covr, r-cran-terra Filename: pool/dists/noble/main/r-cran-osrm_5.0.0-1.ca2404.1_all.deb Size: 473648 MD5sum: 3403384df3100389865366f21dba22b5 SHA1: 26f18e7903d10b2aba868adfbe8f19879f647225 SHA256: 5db128c75c0ba1d0f1a3063d87e61baa53accab13ccfe1fbc8488ece1de92048 SHA512: 129420ab71aa31ecd47279542240b57a0e135b69d940d668dba2f10199551e2e55ad1f37b04a39bb390271730a03581be2c46e1d590101f6a4daa067f1537c1f Homepage: https://cran.r-project.org/package=osrm Description: CRAN Package 'osrm' (Interface Between R and the OpenStreetMap-Based Routing ServiceOSRM) An interface between R and the 'OSRM' API. 'OSRM' is a routing service based on 'OpenStreetMap' data. See for more information. This package enables the computation of routes, trips, isochrones and travel distances matrices (travel time and kilometric distance). Package: r-cran-osrmr Architecture: all Version: 0.1.36-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-bitops, r-cran-rjson, r-cran-r.utils, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-osrmr_0.1.36-1.ca2404.1_all.deb Size: 88750 MD5sum: 15fb90db1984d54ad3aac2a64cb2e038 SHA1: 406df35cf7ddf02e6b5b40dfc08b07c89edfd836 SHA256: 6fadb14b6d4e66fe7a2252bf927c6b2c2f399afeccc9dd9de963eeb2d46bca50 SHA512: 669299d79816dc45a6e98aa1895fbb066180c5a4649d69c3bf5bb95e86d045ffe44c4f7b2778e7d0c3c739e0641d599156b1d8b073564028ba68c2fb3738822b Homepage: https://cran.r-project.org/package=osrmr Description: CRAN Package 'osrmr' (Wrapper for the 'OSRM' API) Wrapper around the 'Open Source Routing Machine (OSRM)' API . 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Package: r-cran-ossanma Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlcoptim, r-cran-deoptimr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-netmeta Filename: pool/dists/noble/main/r-cran-ossanma_0.1.2-1.ca2404.1_all.deb Size: 36382 MD5sum: aadb00db5d5f44e3ee253ad65d03e2bc SHA1: b9216b971a12a68e13298fa3d2357e5467682134 SHA256: 0800055fa1b926d8713a1b067c7209d2aa0933ba80f8e73a8451edb3ac87938c SHA512: a3a8e452ad50322f16bb3f510d46059c46ce7f57a35d851d38dc7216fef1f1d2534412e1b8c70867330cd66036a0e3e62cf5d07fb1bccdace9d312d333f8d490 Homepage: https://cran.r-project.org/package=OssaNMA Description: CRAN Package 'OssaNMA' (Optimal Sample Size and Allocation with a Network Meta-Analysis) A system for calculating the minimum total sample size needed to achieve a prespecified power or the optimal allocation for each treatment group with a fixed total sample size to maximize the power. Package: r-cran-ossurvival Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ossurvival_1.0-1.ca2404.1_all.deb Size: 85744 MD5sum: db0077ed7b45d6447a1317a5cd6d7f47 SHA1: b4d1f22b5362cb9dfaa6d9a4720d17a8a7bcfec1 SHA256: f7ea9234b90fbd589ebf126c0c486c2041c42bc4c2e98779331b1e6046c1bc29 SHA512: e7b3511676892a09d73629c4f94b94d71bc9428997531bf0bd27dbc7af779899b3d6c82f96a3442371c050caa5b1eed9ae7e63a38821ef5045439db0d0497fd8 Homepage: https://cran.r-project.org/package=OSsurvival Description: CRAN Package 'OSsurvival' (Assessing Surrogacy with a Censored Outcome) Identifies the optimal transformation of a surrogate marker and estimates the proportion of treatment explained (PTE) by the optimally-transformed surrogate at an earlier time point when the primary outcome of interest is a censored time-to-event outcome; details are described in Wang et al (2021) . Package: r-cran-ostats Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 870 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sfsmisc, r-cran-matrixstats, r-cran-circular, r-cran-hypervolume, r-cran-ggplot2, r-cran-gridextra, r-cran-viridis, r-cran-mass Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ostats_0.2.0-1.ca2404.1_all.deb Size: 577174 MD5sum: b2d8c1819c73da9105205c70daae31e2 SHA1: 104f014a0e63d3ff15786e85c80281c2a21bd8f4 SHA256: 435881e820e0a640861dea6d8574a65e692aafb45d723434256c1f441408a5e9 SHA512: be9775fba2982811917506b0729db547bd9c5fdf493c7d7b5324ee3fa31d13929426ccca6a596ee9980c60e54e4a3eec8fd27d29a21a45b8cc56f896b1de30f0 Homepage: https://cran.r-project.org/package=Ostats Description: CRAN Package 'Ostats' (O-Stats, or Pairwise Community-Level Niche Overlap Statistics) O-statistics, or overlap statistics, measure the degree of community-level trait overlap. They are estimated by fitting nonparametric kernel density functions to each species’ trait distribution and calculating their areas of overlap. For instance, the median pairwise overlap for a community is calculated by first determining the overlap of each species pair in trait space, and then taking the median overlap of each species pair in a community. This median overlap value is called the O-statistic (O for overlap). The Ostats() function calculates separate univariate overlap statistics for each trait, while the Ostats_multivariate() function calculates a single multivariate overlap statistic for all traits. O-statistics can be evaluated against null models to obtain standardized effect sizes. 'Ostats' is part of the collaborative Macrosystems Biodiversity Project "Local- to continental-scale drivers of biodiversity across the National Ecological Observatory Network (NEON)." For more information on this project, see the Macrosystems Biodiversity Website (). Calculation of O-statistics is described in Read et al. (2018) , and a teaching module for introducing the underlying biological concepts at an undergraduate level is described in Grady et al. (2018) . Package: r-cran-oste Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ranger, r-cran-pec, r-cran-survival, r-cran-prodlim Filename: pool/dists/noble/main/r-cran-oste_1.0-1.ca2404.1_all.deb Size: 36010 MD5sum: a6fb3572c86ab014bb460a816ce7d0ce SHA1: 0c05ab12ff2f4cb114390f02ea5e0729866a5e84 SHA256: ce366b1afa874fcf51d751dc5628062f749b6f216f4ce1b0bd4afce28107857f SHA512: 3b7a8b836a2fffdb11d3aea9498ba35b3ad2a356add30ce391f1b282c2e909330fc987c91ec5e0c1cb81dd26845fbc8bed2c4075031893dc8131f0cf0ff420e3 Homepage: https://cran.r-project.org/package=OSTE Description: CRAN Package 'OSTE' (Optimal Survival Trees Ensemble) Function for growing survival trees ensemble ('Naz Gul', 'Nosheen Faiz', 'Dan Brawn', 'Rafal Kulakowski', 'Zardad Khan', and 'Berthold Lausen' (2020) ) is given. The trees are grown by the method of random survival forest ('Marvin Wright', 'Andreas Ziegler' (2017) ). The survival trees grown are assessed for both individual and collective performances. The ensemble can give promising results on fewer survival trees selected in the final ensemble. Package: r-cran-osum Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1368 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-foreign, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-osum_0.1.0-1.ca2404.1_all.deb Size: 431676 MD5sum: 7edcb9066f87dad76f8d289d5d174cc4 SHA1: c3c92a3cacca88d9d72d6d08fc4c92d3af3d4860 SHA256: b1ee71e300d52120070ec4b6c46017aa137ba018ab284fdf1b5fd8c8136bd1e6 SHA512: ab3f40804b2a4590dab65c0539b45340e9a071b62374c7d1711e8e7bd863d90399af117241d401ae501f30868d2608c514e4fc003a431cae8bf3952d9f0b0505 Homepage: https://cran.r-project.org/package=osum Description: CRAN Package 'osum' (Provide Summary Information About R Objects) Inspired by 'S-PLUS' function objects.summary(), provides a function with the same name that returns data class, storage mode, mode, type, dimension, and size information for R objects in the specified environment. 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Package: r-cran-otargen Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 936 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ghql, r-cran-cli, r-cran-dplyr, r-cran-jsonlite, r-cran-magrittr, r-cran-tibble, r-cran-tidyr, r-cran-httr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-otargen_2.0.0-1.ca2404.1_all.deb Size: 902020 MD5sum: bfeb77ce6a3a2e7947269e5f586a9f0c SHA1: fbcc6f67cce608f2a74ab422d34c4cfa74ea16d9 SHA256: b0cafdab604981b1080aee72131e46a71d440e420bfde837f737fbf2b5cfcae0 SHA512: 5c2373e9b4d1a4f49b9c261b250e2981ad2615e1b3655f98aee50060eb78fa90b170b5d3f82ee088960ef72e97913d8819dc5a5017d423b6cafca8e6f86cf6da Homepage: https://cran.r-project.org/package=otargen Description: CRAN Package 'otargen' (Access Open Target) Interact seamlessly with Open Target GraphQL endpoint to query and retrieve tidy data tables, facilitating the analysis of gene, disease, drug, and genetic data. For more information about the Open Target API (). Package: r-cran-otbsegm Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1597 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-terra, r-cran-link2gi Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-otbsegm_0.1.2-1.ca2404.1_all.deb Size: 1577700 MD5sum: c57ccace4d95e926cfa3e7b7c7babac5 SHA1: e328cb30be777ea03bea80fe4545282f29f93c3d SHA256: ed177d85c7e2cf1991da20d9c5bee77fe2b3da37feb0eab140469f41dfd060c3 SHA512: c5dca3994b0c4f7b1cf671069cd21b29a259edc0b9523712f3aaecdd63c0f772d3f650ebc74d4860cd7d7402035314d411e10a3820c9e89c1770af6c5c00635b Homepage: https://cran.r-project.org/package=OTBsegm Description: CRAN Package 'OTBsegm' (Apply Unsupervised Segmentation Algorithms from 'OTB') Apply unsupervised segmentation algorithms included in 'Orfeo ToolBox' software (), such as mean shift or watershed segmentation. 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(2019) ) and class membership probability estimation (Khan, Z, Gul, A, Mahmoud, O, Miftahuddin, M, Perperoglou, A, Adler, W & Lausen, B (2016) ) are given. A few trees are selected from an initial set of trees grown by random forest for the ensemble on the basis of their individual and collective performance. Three different methods of tree selection for the case of classification are given. The prediction functions return estimates of the test responses and their class membership probabilities. Unexplained variations, error rates, confusion matrix, Brier scores, etc. are also returned for the test data. Package: r-cran-otel Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-callr, r-cran-cli, r-cran-glue, r-cran-jsonlite, r-cran-processx, r-cran-shiny, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-otel_0.2.0-1.ca2404.1_all.deb Size: 276782 MD5sum: 779826feec1886e3026f52a7537d15a6 SHA1: 00ebe19010f0f401efb2704799dbcc2aa38a8af0 SHA256: f781233b8826cd9a494bf6bccb409adff361bf53be1e626acdcaadcb225cbb03 SHA512: 27586e44b439f8a8520cb3cafacd74d43f5f91baa0f08d602ae84def7b78ca45c0d225671efb4b89a855ce1631c158ac1fdf5a42b96246b0a2ab569ee67db881 Homepage: https://cran.r-project.org/package=otel Description: CRAN Package 'otel' (OpenTelemetry R API) High-quality, ubiquitous, and portable telemetry to enable effective observability. 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Perform a two-sample test on multivariate data using these limiting distributions and binning. 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From two databases with no overlapping part except a subset of shared variables, the functions of the package assist users until obtaining a unique synthetic database, where the missing information is fully completed. 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Package: r-cran-otrkm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgenoud, r-cran-survival Filename: pool/dists/noble/main/r-cran-otrkm_0.2.1-1.ca2404.1_all.deb Size: 97942 MD5sum: fdaef45b5bcdb790a7fd11c7ba12e946 SHA1: f978708184b5ffedc6a5f38b1f013bf10010e641 SHA256: e0e27fddafdf9532fc040fec8a70ec04f4d949b2880e7c54c7e24eca988e3fc1 SHA512: 09c03b7989632fd16d63515193b5c7928a7fc24e911da340996a3bd6abf3e8d449cb2edd525fa57ea26d597cc28f4b0d666e8dca0da7b81301e13e46ebe24e62 Homepage: https://cran.r-project.org/package=otrKM Description: CRAN Package 'otrKM' (Optimal Treatment Regimes in Survival Contexts withKaplan-Meier-Like Estimators) Provide methods for estimating optimal treatment regimes in survival contexts with Kaplan-Meier-like estimators when no unmeasured confounding assumption is satisfied (Jiang, R., Lu, W., Song, R., and Davidian, M. (2017) ) and when no unmeasured confounding assumption fails to hold and a binary instrument is available (Xia, J., Zhan, Z., Zhang, J. (2022) ). 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(2015) , KNN-CAD see E. Burnaev et al. (2016) , KNN-LDCD see V. Ishimtsev et al. (2017) and CAD-OSE see M. Smirnov (2018) . The first three algorithms belong to prediction-based techniques and the last three belong to window-based techniques. In addition, the SD-EWMA and PEWMA algorithms are algorithms designed to work in stationary environments, while the other four are algorithms designed to work in non-stationary environments. 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Package: r-cran-outlierensembles Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 959 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-airt, r-cran-estcrm, r-cran-psych, r-cran-apcluster Suggests: r-cran-dbscan, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-outlierensembles_0.1.3-1.ca2404.1_all.deb Size: 784344 MD5sum: 5ccd53ed40880ad93ff9c3134025844f SHA1: 7605a964b0daf4303473b6964d26465932f73a0c SHA256: 016e6b4330e40265307198a2806281ae41b06b33665be6856c3ef8dfd16f1b26 SHA512: ae6a25aaa95673a898f09e31f2e458d7c6cefbb8bd3927966b0088d670cc7084010450eacab3a91b6e6061ee44c5e908d11330035d819e7705cd97c7370e6e6e Homepage: https://cran.r-project.org/package=outlierensembles Description: CRAN Package 'outlierensembles' (A Collection of Outlier Ensemble Algorithms) Ensemble functions for outlier/anomaly detection. There is a new ensemble method proposed using Item Response Theory. 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Package: r-cran-outliermbc Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clusterr, r-cran-dbscan, r-cran-flexcwm, r-cran-ggplot2, r-cran-mixture, r-cran-mvtnorm, r-cran-spatstat.univar Filename: pool/dists/noble/main/r-cran-outliermbc_0.0.1-1.ca2404.1_all.deb Size: 392634 MD5sum: 5783a6e25cb4321af29f5aabb405c4d8 SHA1: fecc007a70300986ff559c619409aa0c95e35a70 SHA256: fe65bd42b4d0673b874f67c220ba86bd45d5a260a0dc80be01d4de7be5658b05 SHA512: 6f24afacb076574770156fa12c5391f875d68499942854423fe231f5548bd4d4967237e0eab8ea713ce2663402e5d9a4b8bffbee3aa13081b1ec90edb94b03f3 Homepage: https://cran.r-project.org/package=outlierMBC Description: CRAN Package 'outlierMBC' (Sequential Outlier Identification for Model-Based Clustering) Sequential outlier identification for Gaussian mixture models using the distribution of Mahalanobis distances. The optimal number of outliers is chosen based on the dissimilarity between the theoretical and observed distributions of the scaled squared sample Mahalanobis distances. Also includes an extension for Gaussian linear cluster-weighted models using the distribution of studentized residuals. Doherty, McNicholas, and White (2025) . Package: r-cran-outliers.ts.oga Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 615 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-forecast, r-cran-future, r-cran-future.apply, r-cran-gsarima, r-cran-parallelly, r-cran-robust Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-outliers.ts.oga_1.1.2-1.ca2404.1_all.deb Size: 594104 MD5sum: e661a5cb9cb1d3091f7ef0f2e8e528a2 SHA1: 517b95bc9cbd2bfa8f83efabf76fa0a8c8128205 SHA256: 72deeeef56f313e78265773471c427d48173fd183e108a9242d1f546118e6fc6 SHA512: 7129a5efee3d6129fe9da8d417200cba07d589b2d9a182c2672c5b194b9c5db2d7d27bb59ccdadfc9a5ae43737bde74bdf3614cff7ab828de0864aba336f98d7 Homepage: https://cran.r-project.org/package=outliers.ts.oga Description: CRAN Package 'outliers.ts.oga' (Efficient Outlier Detection for Large Time Series Databases) Programs for detecting and cleaning outliers in single time series and in time series from homogeneous and heterogeneous databases using an Orthogonal Greedy Algorithm (OGA) for saturated linear regression models. The programs implement the procedures presented in the paper entitled "Efficient Outlier Detection for Large Time Series Databases" by Pedro Galeano, Daniel Peña and Ruey S. Tsay (2026), working paper, Universidad Carlos III de Madrid. Version 1.1.2 fixes one bug. Package: r-cran-outliers Architecture: all Version: 0.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-outliers_0.15-1.ca2404.1_all.deb Size: 83286 MD5sum: 4529bc55259dea1e2417ee4ed17d3f38 SHA1: 0816f9cadbcf35cec50c2b82f89d57e482848c3e SHA256: ce56a9a3e0145ce68d413acbf62fab9208f4c53b51835625833ae4de1ffb8f95 SHA512: 360b1f5b606712628c273af8b8396d05bd5fb40fc23785ad31ea5de15276075b9d18a73aca96900115140ffb95619a68ebb506f0172f8f2404360fe9b28e0672 Homepage: https://cran.r-project.org/package=outliers Description: CRAN Package 'outliers' (Tests for Outliers) A collection of some tests commonly used for identifying outliers. 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Includes a tutorial mode option that shows a description of each algorithm and provides a step-by-step execution explanation of how it identifies outliers from the given data with the specified input parameters. References include the works of Azzedine Boukerche, Lining Zheng, and Omar Alfandi (2020) , Abir Smiti (2020) , and Xiaogang Su, Chih-Ling Tsai (2011) . 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O3 plots are described in Unwin(2019) . The available methods are HDoutliers() from the package 'HDoutliers', FastPCS() from the package 'FastPCS', mvBACON() from 'robustX', adjOutlyingness() from 'robustbase', DectectDeviatingCells() from 'cellWise', covMcd() from 'robustbase'. Package: r-cran-outlierspinner Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-outlierspinner_0.1.0-1.ca2404.1_all.deb Size: 25478 MD5sum: cca46c42de1c7eb4d99fd0e1cf0109c5 SHA1: 3cce24805651dfeadf92cf6e553518720d94ddcf SHA256: b703e50575adec651505e29fc9941ac3920d9c337e5c7b4eeb84b8a8a5866729 SHA512: dd0f5e0aa5ba95fc2d7933123a79f6e2e49c24a6c7c8734b257c3d24c47040530ef3da746578c2883eb96eeeb6fecb91c6071b886f27e97225099627a87d2459 Homepage: https://cran.r-project.org/package=outlierspinner Description: CRAN Package 'outlierspinner' (Geometric Multivariate Outlier Detection via Random DirectionalProbing) Provides tools for multivariate outlier detection based on geometric properties of multivariate data using random directional projections. Observation-level outlier scores are computed by jointly probing radial magnitude and angular alignment through repeated projections onto random directions, with optional robust centering and covariance adjustment. In addition to global outlier scoring, the method produces dimension-level contribution measures to support interpretation of detected anomalies. Visualization utilities are included to summarize directional contributions for extreme observations. Package: r-cran-outlying Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-outlying_0.0.1-1.ca2404.1_all.deb Size: 17186 MD5sum: 5e60acb78fc8d3030e065ee82bdbf591 SHA1: 36ee98f8e6fb13f23386a04e67f932ede66ae384 SHA256: 28240f90a0c1020005e0489589dd7d99834659fba206f53a88f7ac56d726df3b SHA512: d815eed15d77b34fa329be1f8dc5102e997cf9a3e249be4addce49c9d0ed9f6d7cee3b49b63ce2aab81fe5d477694a425c6ffa3a65b5a21858c3707a5984169a Homepage: https://cran.r-project.org/package=outlying Description: CRAN Package 'outlying' (Outliers Detection) Provides functions for detecting outliers in datasets using statistical methods. The package supports identification of anomalous observations in numerical data and is intended for use in data cleaning, exploratory data analysis, and preprocessing workflows. Package: r-cran-outqrf Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 715 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ranger, r-cran-dplyr, r-cran-missranger, r-cran-ggpubr, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-renv, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-outqrf_1.0.0-1.ca2404.1_all.deb Size: 254380 MD5sum: 9379feda520afb8b196e4b5e2159c423 SHA1: 0990686b473d7da10c22699235c565f45b88c892 SHA256: 1df836b503ca9a272ab365d8c5c501225a2339708b2a8b93233d774dd8516a22 SHA512: 33b4fd07f044220739798afb11eeff6243121a3073dd5af58ce5e5d12d5b4bdbb84bdfce8b01c4ace0b4a38c876261313574110cb16facc895df7b983f7fe895 Homepage: https://cran.r-project.org/package=outqrf Description: CRAN Package 'outqrf' (Find the Outlier by Quantile Random Forests) Provides a method to find the outlier in custom data by quantile random forests method. Introduced by Meinshausen Nicolai (2006) . It directly calls the ranger() function of the 'ranger' package to perform data fitting and prediction. We also implement the evaluation of outlier prediction results. Compared with random forest detection of outliers, this method has higher accuracy and stability on large datasets. 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See Remiro‐Azócar A, Heath A, Baio G (2022) "Parametric G‐computation for compatible indirect treatment comparisons with limited individual patient data", Res. Synth. Methods, 1–31. ISSN 1759-2879, . 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Validating the library, packages and lock files. Exploring a complexity of a specific package like evaluating its size in bytes with all dependencies. The shiny app complexity could be explored too. Assessing the life duration of a specific package version. Checking a CRAN package check page status for any errors and warnings. Retrieving a DESCRIPTION or NAMESPACE file for any package version. Comparing DESCRIPTION or NAMESPACE files between different package versions. Getting a list of all releases for a specific package. The Bioconductor is partly supported. Package: r-cran-pacta.loanbook Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggrepel, r-cran-magrittr, r-cran-purrr, r-cran-r2dii.analysis, r-cran-r2dii.data, r-cran-r2dii.match, r-cran-r2dii.plot, r-cran-rlang, r-cran-rstudioapi, r-cran-scales, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-covr, r-cran-diagrammer, r-cran-ggplot2, r-cran-gt, r-cran-knitr, r-cran-readr, r-cran-readxl, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-tidyr, r-cran-writexl Filename: pool/dists/noble/main/r-cran-pacta.loanbook_0.1.1-1.ca2404.1_all.deb Size: 657566 MD5sum: 76a85a1c45ac1c89a50dd379b57c4c70 SHA1: 2e2e52f975c5712ca9a60800cc9d7fc0c75036d0 SHA256: e7faaa56fb57081ce517ce528c1cb38d7a6437ddf324de803220fc88015ba789 SHA512: 2b40d3382c1565e5465deae280ddbd2fa7971238eee4e338f6d74044e8db371665a7b6a4f9c2fdbc1ef2535623f3ae8614a7d44325ee13e58b5e9c761597c41e Homepage: https://cran.r-project.org/package=pacta.loanbook Description: CRAN Package 'pacta.loanbook' (Easily Install and Load PACTA for Banks Packages) PACTA (Paris Agreement Capital Transition Assessment) for Banks is a tool that allows banks to calculate the climate alignment of their corporate lending portfolios. 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Package: r-cran-pakpc Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1854 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dt, r-cran-htmltools, r-cran-magrittr, r-cran-pakpc2017, r-cran-pakpc2023, r-cran-rpivottable, r-cran-shiny, r-cran-shinydashboard, r-cran-shinydashboardplus Suggests: r-cran-shinytest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pakpc_0.3.0-1.ca2404.1_all.deb Size: 1169382 MD5sum: b10f63d2c973b0919bc05c579766aa32 SHA1: 29efceb745bd62ec6cacd6c166ad7823686f98fa SHA256: 139426a17e10543cbd8dcc6b1c49e31dd7d48a8bb52beff290b251183d62990b SHA512: b9d7d779b103bb8a6e3a49ec9456602288b78bafa3e24418d7ed306fc56241272dcdbc256293b1c6d84e802aaba07072e518c93026c979d4d41ef16ee3016213 Homepage: https://cran.r-project.org/package=PakPC Description: CRAN Package 'PakPC' ('shiny' App to Analyze Pakistan's Population Census Data) Provides tools for analyzing Pakistan's Population Censuses data via the 'PakPC2023' and 'PakPC2017' R packages. Designed for researchers, policymakers, and professionals, the app enables in-depth numerical and graphical analysis, including detailed cross-tabulations and insights. With diverse statistical models and visualization options, it supports informed decision-making in social and economic policy. This tool enhances users' ability to explore and interpret census data, providing valuable insights for effective planning and analysis across various fields. Package: r-cran-pakpmics2014ch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-pakpmics2014ch_0.1.0-1.ca2404.1_all.deb Size: 3198774 MD5sum: 852d316bd9873d66b25d6d148feab83f SHA1: b44b26ff91aa901b3635077b44ded00e80a507b6 SHA256: e7a4b0c1a0d22bdc69d718f6ff06d05723b7a7be0c43bf9386b0413e6375bafc SHA512: 6967b7377923b2ca88106770ac4cb9abe928b766e88a30e89d2a7cec632511be93eea91a39cf6040547344f1e92000b56f0cd28ed8d7b751f6100592b910a16a Homepage: https://cran.r-project.org/package=PakPMICS2014Ch Description: CRAN Package 'PakPMICS2014Ch' (Multiple Indicator Cluster Survey (MICS) 2014 ChildQuestionnaire Data for Punjab, Pakistan) Provides data set and functions for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Child questionnaire data for Punjab, Pakistan (). Package: r-cran-pakpmics2014hh Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2503 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-pakpmics2014hh_0.1.0-1.ca2404.1_all.deb Size: 2514080 MD5sum: eae5f7cdd792881392ec616739fc47f5 SHA1: 86fa5ef40b1288b37c048ddc5e8419585ffe5832 SHA256: adad084339a1230ae88b2f36e2a29a6f2fb6c0c5f6c200e4988df05299ccd16f SHA512: 158fed3a0872f2c689006fc0d8880d54ab10ff7a971f66c2dbdcdbc910f3f58417244bd0a3f1919dccfa7ff0cc70d031e74da3dfc4f62be3b8f496d0f6ebd658 Homepage: https://cran.r-project.org/package=PakPMICS2014HH Description: CRAN Package 'PakPMICS2014HH' (Multiple Indicator Cluster Survey (MICS) 2014 HouseholdQuestionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Household questionnaire data for Punjab, Pakistan (). Package: r-cran-pakpmics2014hl Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-pakpmics2014hl_0.1.1-1.ca2404.1_all.deb Size: 3321422 MD5sum: 32781064ffa230cdab362c394a6af886 SHA1: 457abbd8bed08629f45bfc8bd138e79c49889970 SHA256: 94e831b67f79c6a07237120b4fbb250a60fc3fcbce162f8bd084f1e373e51fae SHA512: 5aaf894c5f774f84bd377f57eb3d8dac3affd81fe13aa844af19f52af6ddba3a781a6847930ca83f95748ed470d0062d7c648ca8ca16fe64eb86952e59fc692e Homepage: https://cran.r-project.org/package=PakPMICS2014HL Description: CRAN Package 'PakPMICS2014HL' (Multiple Indicator Cluster Survey (MICS) 2014 Household ListingQuestionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Household Listing questionnaire data for Punjab, Pakistan. Package: r-cran-pakpmics2014wm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2888 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-pakpmics2014wm_0.1.1-1.ca2404.1_all.deb Size: 2922446 MD5sum: 7e482be2fec6bd1c07eb61695dfdcb57 SHA1: 330728ca7019f92bfd7279a507f6286b0ca26fcf SHA256: e35eeb4a8e4f62671e11f4b86ef2193f82dc99228f480d3195d7806756dd0466 SHA512: 3038084fbe7a8704d66a883bea8c2cdf9ef1e9a1dafdda7de36ccff85f364f9ce6fa2632b38b9b04262b76e90c378f394477144737ea3451562f9ae99963e74f Homepage: https://cran.r-project.org/package=PakPMICS2014Wm Description: CRAN Package 'PakPMICS2014Wm' (Multiple Indicator Cluster Survey (MICS) 2014 WomenQuestionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Women (age 15-49 years) questionnaire data for Punjab, Pakistan. Package: r-cran-pakpmics2018 Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1955 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pakpmics2018_1.2.0-1.ca2404.1_all.deb Size: 1804450 MD5sum: 2207d657665cf7fdfd6bf2a2a9ece888 SHA1: 5823bb6b9c1badb23f0c19077dcb0bcac9f6b583 SHA256: 9c7b28ed125556e07316c634ab2698f88f17089cf5848ac041932d7b0294dcc5 SHA512: 206af14c9c53f59821a6284ff0f6e04f20331825bf018b33eb76dd675fb76506863c4e54acb16d956f9561dcb856a7603a87ec3a4b83ea8d129fbea30cff73b3 Homepage: https://cran.r-project.org/package=PakPMICS2018 Description: CRAN Package 'PakPMICS2018' (Multiple Indicator Cluster Survey (MICS) 2017-18 Data forPunjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household. Package: r-cran-pakpmics2018bh Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3891 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pakpmics2018bh_0.1.0-1.ca2404.1_all.deb Size: 3814012 MD5sum: 92d88936b9b2782be6bcb9c2a68e0872 SHA1: bd0e92a686a9b0ac6ecc9915da5f03aca46434e4 SHA256: 239ec4cc68ed4e95a1d083cffab50435c15b16dc4f2d3212519141153cf71e2f SHA512: 14bdb046e9c58b3bd1f6eb30e6b2a1fa86ac3a6a6c94fafe3e54ba96007f6a859a479d54fb872b2ca0ce33824c5c460168c30fb6e17f964b1b5e476c8e541712 Homepage: https://cran.r-project.org/package=PakPMICS2018bh Description: CRAN Package 'PakPMICS2018bh' (Multiple Indicator Cluster Survey (MICS) 2017-18 Birth Historyof Children Questionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). Package: r-cran-pakpmics2018fs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3608 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pakpmics2018fs_0.1.0-1.ca2404.1_all.deb Size: 3539720 MD5sum: a31fb41640d4d4451a979fb725da9f1d SHA1: fc6623ebc0f140bf0e8662738542fd621dc278d7 SHA256: ef5ead9eca3217339661791a0c306cd90ac02dc022b27067c2112033e343f1aa SHA512: 07c7b843023cfc78bf6d33d4990b5bfe442c28599224627fbc4fa9088cbdfa0b8dc9d33aab90c82295816e92140166572932b5f222fee67a42bc9ce3456d4143 Homepage: https://cran.r-project.org/package=PakPMICS2018fs Description: CRAN Package 'PakPMICS2018fs' (Multiple Indicator Cluster Survey (MICS) 2017-18 Children Age5-17 Questionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Children Age 5-17 questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). Package: r-cran-pakpmics2018hh Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4780 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pakpmics2018hh_0.1.0-1.ca2404.1_all.deb Size: 4637986 MD5sum: a70b6f6239d2560b0190de8c383f7dc2 SHA1: 944baaba1af9984cf86387d0e4610c23870727f7 SHA256: c89c269f3f1f8aca78ea7f11b6c9122949825348099be8ffc2a7e29d703f2693 SHA512: b0b7c1b041987788f708c5760263bc37497b6c2dca3c013b7278f6583668b1495b2a2bdbc21e2dce97db226af8f2b140e2dbbd90974b6166c02ab071b60a9e81 Homepage: https://cran.r-project.org/package=PakPMICS2018hh Description: CRAN Package 'PakPMICS2018hh' (Multiple Indicator Cluster Survey (MICS) 2017-18 HouseholdQuestionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). Package: r-cran-pakpmics2018mm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3553 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pakpmics2018mm_0.1.0-1.ca2404.1_all.deb Size: 3433750 MD5sum: 35c8870ad00da560ab0f633af1c14a18 SHA1: 3afe34a070bd497d63e18b7fad1ed2969fef3588 SHA256: 9e74af4269c6b1cbd18b70db527d358040dce175704b7e14a36f21f60c0d8d9c SHA512: 79ae9565eea497da478568fb03403a652379cfd21c79a81b99e5154b802e7e86c5c951a4f2e96c8efaf625ce7671597f964e0048d30c72bf12fa1d9436ff5419 Homepage: https://cran.r-project.org/package=PakPMICS2018mm Description: CRAN Package 'PakPMICS2018mm' (Multiple Indicator Cluster Survey (MICS) 2017-18 MaternalMortality Questionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Maternal Mortality questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). Package: r-cran-pakpmics2018mn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2999 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pakpmics2018mn_0.1.0-1.ca2404.1_all.deb Size: 2919756 MD5sum: 7cd62be7e1046d63f9d169e8ff0d86b3 SHA1: e9d06cdb9ada25456513467ac61797843cf41803 SHA256: a9cea0582598fc02a47f1c4a7601452794e8a218e852018bde4863bec2fe0408 SHA512: 3f0772f6d3aa8ca1fa4ba77d9b34fc7afe2c1c8d15f3351791cfd85791f2c2e9facfcd8ebe501a268573bec0e13b68c66f6b3d391ffb43dca207cc89b8ee1933 Homepage: https://cran.r-project.org/package=PakPMICS2018mn Description: CRAN Package 'PakPMICS2018mn' (Multiple Indicator Cluster Survey (MICS) 2017-18 MenQuestionnaire Data for Punjab, Pakistan) Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Men questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (). Package: r-cran-pakret Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-readr, r-cran-rmarkdown Suggests: r-cran-callr, r-cran-pkgload, r-cran-testthat, r-cran-usethis, r-cran-withr Filename: pool/dists/noble/main/r-cran-pakret_0.3.1-1.ca2404.1_all.deb Size: 67090 MD5sum: e682f41efa1c21de6323d57d6eef1b67 SHA1: ae5ab7947d4d0fe1f541f5a476af55dc118ae999 SHA256: 28c887960af997b3061549b32b97b90b7c332349aff28385a57deaa76f763b52 SHA512: c9f58069856e297d2c1158fe8dad50f2130cd52bec4003bcbfa37b70436839ae050891d89cb2eec5e95b7860fd08976ea2d900fe2b228b2e9106bf8b4843921f Homepage: https://cran.r-project.org/package=pakret Description: CRAN Package 'pakret' (Cite 'R' Packages on the Fly in 'R Markdown' and 'Quarto') References and cites 'R' and 'R' packages on the fly in 'R Markdown' and 'Quarto'. 'pakret' provides a minimalist API that generates preformatted citations for 'R' and 'R' packages, and adds their references to a '.bib' file directly from within your document. Package: r-cran-palaeosig Architecture: all Version: 2.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-teachingdemos, r-cran-rioja, r-cran-mgcv, r-cran-mass, r-cran-tibble, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-cran-forcats, r-cran-assertr, r-cran-vegan, r-cran-rlang, r-cran-ggrepel, r-cran-tidyr, r-cran-purrr Suggests: r-cran-units, r-cran-knitr, r-cran-rmarkdown, r-cran-gstat, r-cran-sf, r-cran-analogue, r-cran-testthat Filename: pool/dists/noble/main/r-cran-palaeosig_2.1-4-1.ca2404.1_all.deb Size: 444048 MD5sum: 587098ec035b462dcc7d544669bd89da SHA1: 162125078ac54d881b3c57a2f2fe1eba770b34e2 SHA256: 5f20692feffa12be4ae7e1f35485bfba71d9bbde9622bbf606bfd5c624c4f811 SHA512: d56bdd3f9f8c004939e2c01a4190a6bfbf5bb69aa562f8f20deb31c5b6bb469623ef34e154f6f65281a9d582bdf41b954e4bd3863fcc27009604339dfcaf9591 Homepage: https://cran.r-project.org/package=palaeoSig Description: CRAN Package 'palaeoSig' (Significance Tests for Palaeoenvironmental Reconstructions) Several tests of quantitative palaeoenvironmental reconstructions from microfossil assemblages, including the null model tests of the statistically significant of reconstructions developed by Telford and Birks (2011) , and tests of the effect of spatial autocorrelation on transfer function model performance using methods from Telford and Birks (2009) and Trachsel and Telford (2016) . Age-depth models with generalized mixed-effect regression from Heegaard et al (2005) are also included. Package: r-cran-palaeoverse Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2788 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-ape, r-cran-sf, r-cran-stringdist, r-cran-geosphere, r-cran-h3jsr, r-cran-httr, r-cran-pbapply, r-cran-lifecycle Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-vdiffr, r-cran-paleotree, r-cran-phytools, r-cran-covr Filename: pool/dists/noble/main/r-cran-palaeoverse_1.4.0-1.ca2404.1_all.deb Size: 2074520 MD5sum: e3a3f2e59d269ff2996e1678cf0ab8bc SHA1: 1f3b22afd2c78f6dfb9fa4147d218237e8e592ec SHA256: 0452124391c9fb0238746385660af7b30b8501300b00f748f0a36e582c7db2ee SHA512: 89848abcb821175a60e5c266ff4859991cd2c333859d8b8ff828c1794c69eda52f4c7def0513cf635f40d40c1b84b9f2b69e38bf704392ad653de9414bde314e Homepage: https://cran.r-project.org/package=palaeoverse Description: CRAN Package 'palaeoverse' (Prepare and Explore Data for Palaeobiological Analyses) Provides functionality to support data preparation and exploration for palaeobiological analyses, improving code reproducibility and accessibility. The wider aim of 'palaeoverse' is to bring the palaeobiological community together to establish agreed standards. The package currently includes functionality for data cleaning, binning (time and space), exploration, summarisation and visualisation. Reference datasets (i.e. Geological Time Scales ) and auxiliary functions are also provided. Details can be found in: Jones et al., (2023) . Package: r-cran-palasso Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-survival Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-remotes, r-cran-proc, r-bioc-edger, r-cran-ashr Filename: pool/dists/noble/main/r-cran-palasso_1.0.0-1.ca2404.1_all.deb Size: 176046 MD5sum: c110cda0c7a302025ec74e6624cdf3dd SHA1: da191dd4d4c86e388321e5fc163a078e43451b41 SHA256: 8043a4fa93eff7acd51740c83e50875032a39e0c73edf1e3c9d23a7cef870be5 SHA512: 6361d69982dd777c447193e2b8b2723628669ea0c5489d975bf066527d9c7029ab11d5e2112422e3663e5ae327bbdaa9052b74babc8f5f367ed185f8fbdf95b0 Homepage: https://cran.r-project.org/package=palasso Description: CRAN Package 'palasso' (Sparse Regression with Paired Covariates) Implements sparse regression with paired covariates (). The paired lasso is designed for settings where each covariate in one set forms a pair with a covariate in the other set (one-to-one correspondence). For the optional correlation shrinkage, install ashr () and CorShrink () from GitHub (see README). Package: r-cran-pald Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 916 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-glue Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pald_0.0.5-1.ca2404.1_all.deb Size: 812510 MD5sum: 3d87ead1e346d68c6ab0a477540a6706 SHA1: e87b7353de5247701617bbeb99505b9934357843 SHA256: e2ee615bf458e513797dddddff948ddab52391c14ec3cf78c58de7e2d458533f SHA512: 58a3a8dd993153d5ae5942326474c80753940cf52a17e58c78457c66635efddd6871fe19a8cc88c242dbf000c0ffc6e3ccf3ea9ad91e9ba9aa027d645a4b3d1b Homepage: https://cran.r-project.org/package=pald Description: CRAN Package 'pald' (Partitioned Local Depth for Community Structure in Data) Implementation of the Partitioned Local Depth (PaLD) approach which provides a measure of local depth and the cohesion of a point to another which (together with a universal threshold for distinguishing strong and weak ties) may be used to reveal local and global structure in data, based on methods described in Berenhaut, Moore, and Melvin (2022) . No extraneous inputs, distributional assumptions, iterative procedures nor optimization criteria are employed. This package includes functions for computing local depths and cohesion as well as flexible functions for plotting community networks and displays of cohesion against distance. Package: r-cran-paleoam Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-paleoam_1.0.1-1.ca2404.1_all.deb Size: 156334 MD5sum: 034ad29e6120865509e341d45b969d5a SHA1: ea944de3faf405cb668aaac90c9f096ce48d4b2e SHA256: b556e7dc1a0fe78514bee1a92544445da497e00c5eadd397f69ef428b6437483 SHA512: 37e4aee2e9898101f56b69b7e521d82abe246ae00dc1c404dfd1ffbdd66c0ff202978fd4de848c680b057922ade04da4835388fd0f791d8efd62a7b1b55e049e Homepage: https://cran.r-project.org/package=paleoAM Description: CRAN Package 'paleoAM' (Simulating Assemblage Models of Abundance for the Fossil Record) Provides functions for fitting abundance distributions over environmental gradients to the species in ecological communities, and tools for simulating the fossil assemblages from those abundance models for such communities, as well as simulating assemblages across various patterns of sedimentary history and sampling. These tools are for particular use with fossil records with detailed age models and abundance distributions used for calculating environmental gradients from ordinations or other indices based on fossil assemblages. Package: r-cran-paleobiodb Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-jsonlite, r-cran-maps, r-cran-terra Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-paleobiodb_1.0.1-1.ca2404.1_all.deb Size: 446954 MD5sum: b37e9309871ab835c91407a14497abe5 SHA1: ca7cbb915547699483a253879c957155fb4028a0 SHA256: dd6314a3f8d2d715cb071b5d318eea56607bd33a643308f51e692220816abd82 SHA512: ed6c8689409a89bd8f9ee11f83b41d7a604f79b22d562358427da6e0515ab866fc5716fc7bcd1a2fd74ec597f04788459353bea927bcaa302935119930e3449b Homepage: https://cran.r-project.org/package=paleobioDB Description: CRAN Package 'paleobioDB' (Download and Process Data from the Paleobiology Database) Includes functions to wrap most endpoints of the 'PaleobioDB' API and to visualize and process the obtained fossil data. The API documentation for the Paleobiology Database can be found at . Package: r-cran-paleobuddy Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3395 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ape, r-cran-fitdistrplus, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-paleobuddy_1.1.0-1.ca2404.1_all.deb Size: 1706336 MD5sum: 8e9510edc714795453e2a5b2975e94b8 SHA1: b056c6227c10f39817118ff76037a9de35d3899c SHA256: cbc141dd79225b6230042f20d65e9132682d08ce10ff7684b6d0ee672aafb824 SHA512: 64ab027ad6e1d07e04cec4813a9138a273f82937c670821b4d63ae854e06d5dda7225da0171c7c5e6dc97bca1cbb84b8922da69ea5506e54c8bf26f002ee9974 Homepage: https://cran.r-project.org/package=paleobuddy Description: CRAN Package 'paleobuddy' (Simulating Diversification Dynamics) Simulation of species diversification, fossil records, and phylogenies. While the literature on species birth-death simulators is extensive, including important software like 'paleotree' and 'APE', we concluded there were interesting gaps to be filled regarding possible diversification scenarios. Here we strove for flexibility over focus, implementing a large array of regimens for users to experiment with and combine. In this way, 'paleobuddy' can be used in complement to other simulators as a flexible jack of all trades, or, in the case of scenarios implemented only here, can allow for robust and easy simulations for novel situations. Environmental data modified from that in 'RPANDA': Morlon H. et al (2016) . Package: r-cran-paleodiv Architecture: all Version: 0.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 974 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-stringr Suggests: r-cran-testthat, r-cran-strap, r-cran-divdyn, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-paleodiv_0.4.6-1.ca2404.1_all.deb Size: 750740 MD5sum: 9bf83a8d5a1435abce8752945e4a8cd8 SHA1: c558fc0f93b2091b02f6a008c8f293dacc2e1e02 SHA256: dda94752dadf8e2d0bbb8b4697fb59713a2ea22a4bfa94bcc473d07a2454662a SHA512: 98f3b60416fc788291e18862a32dd83914aeb664d9c764b270eee7078c506fcf4826083ac222cc46e3427d727eaae12117de8b6ea2593b786cfe76e309e0c4ba Homepage: https://cran.r-project.org/package=paleoDiv Description: CRAN Package 'paleoDiv' (Extracting and Visualizing Paleobiodiversity) Contains various tools for conveniently downloading and editing taxon-specific datasets from the Paleobiology Database , extracting information on abundance, temporal distribution of subtaxa and taxonomic diversity through deep time, and visualizing these data in relation to phylogeny and stratigraphy. Package: r-cran-paleomorph Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-testthat, r-cran-abind, r-cran-rgl Filename: pool/dists/noble/main/r-cran-paleomorph_0.1.4-1.ca2404.1_all.deb Size: 64482 MD5sum: aa1aec88c3fecfbd0dbe2bded0d41c4b SHA1: 4c2f009c80a3fc71386c9c7cc349e1e6f8141c2c SHA256: a10d001d536b480e4eff2fc11104e2131bf2ddf9d12611afec742442de47c906 SHA512: b1d941530f32b1e04a1a7f6246790c26e44e29b202e2e959e7acf072fe3a2edd7d61a26d99b15a119a76faa7768c0da9e34687215e2cc16e87611ceb948039b4 Homepage: https://cran.r-project.org/package=paleomorph Description: CRAN Package 'paleomorph' (Geometric Morphometric Tools for Paleobiology) Fill missing symmetrical data with mirroring, calculate Procrustes alignments with or without scaling, and compute standard or vector correlation and covariance matrices (congruence coefficients) of 3D landmarks. Tolerates missing data for all analyses. Package: r-cran-paleopop Architecture: all Version: 2.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4306 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-poems, r-cran-r6, r-cran-sf, r-cran-trend Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-dplyr, r-cran-raster Filename: pool/dists/noble/main/r-cran-paleopop_2.1.7-1.ca2404.1_all.deb Size: 3994952 MD5sum: 88cf7f3e002aef7628bef6a638f833a2 SHA1: 1bb071d0db866808be4457803f5c787622147536 SHA256: 210e5ed3907dd7299c9b9915e41b328a1511dc9f1cb05a13ccb9563291371888 SHA512: ac448ef01b977fddc233d8894d6f3779c40119ef2460fbadd79f8a9fb8c0ee5faae998309c046fabc543ac8c4534ff7daa1979397dcf3867e14233035446d3f3 Homepage: https://cran.r-project.org/package=paleopop Description: CRAN Package 'paleopop' (Pattern-Oriented Modeling Framework for Coupled Niche-PopulationPaleo-Climatic Models) This extension of the poems pattern-oriented modeling (POM) framework provides a collection of modules and functions customized for paleontological time-scales, and optimized for single-generation transitions and large populations, across multiple generations. Package: r-cran-paleotree Architecture: all Version: 3.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1587 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-phangorn, r-cran-phytools, r-cran-jsonlite, r-cran-png, r-cran-rcurl Suggests: r-cran-spelling, r-cran-testthat, r-cran-curl Filename: pool/dists/noble/main/r-cran-paleotree_3.4.7-1.ca2404.1_all.deb Size: 1531728 MD5sum: 494db3387d4603cfd5288f6c058e5fe4 SHA1: 98463a1b26bbaf5836b9da67daaf2772f6e63683 SHA256: 777dfb228838e348e8cf68e3b01a4397af39e7c94b3ac432fba710745c90c80c SHA512: 0599d864bfc4a36a904395c48260cbe0e223e949991f1446d5300846bd9868b483740643e1ab036fb41d9e0bea14f76723025ad00e590e1a1019686432f697bf Homepage: https://cran.r-project.org/package=paleotree Description: CRAN Package 'paleotree' (Paleontological and Phylogenetic Analyses of Evolution) Provides tools for transforming, a posteriori time-scaling, and modifying phylogenies containing extinct (i.e. fossil) lineages. In particular, most users are interested in the functions timePaleoPhy, bin_timePaleoPhy, cal3TimePaleoPhy and bin_cal3TimePaleoPhy, which date cladograms of fossil taxa using stratigraphic data. This package also contains a large number of likelihood functions for estimating sampling and diversification rates from different types of data available from the fossil record (e.g. range data, occurrence data, etc). paleotree users can also simulate diversification and sampling in the fossil record using the function simFossilRecord, which is a detailed simulator for branching birth-death-sampling processes composed of discrete taxonomic units arranged in ancestor-descendant relationships. Users can use simFossilRecord to simulate diversification in incompletely sampled fossil records, under various models of morphological differentiation (i.e. the various patterns by which morphotaxa originate from one another), and with time-dependent, longevity-dependent and/or diversity-dependent rates of diversification, extinction and sampling. Additional functions allow users to translate simulated ancestor-descendant data from simFossilRecord into standard time-scaled phylogenies or unscaled cladograms that reflect the relationships among taxon units. Package: r-cran-paleots Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mnormt, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-paleots_0.6.2-1.ca2404.1_all.deb Size: 581372 MD5sum: a34b6f18b5bd814b619211a74d251896 SHA1: 7ced0163a2c7f4c66df0c0e44e5fd42a44c70e8e SHA256: 0b5af141c0ad004f021142086811b255fdd2ca98b72f2017d1735048d09b538f SHA512: 7204dac4f7d445944fd18801caeb23f3a6aa41f82cf3e945b173feb54f848daa2356443d8909919633f3563edd32f198500b72468234c7db6674ad47401c89b2 Homepage: https://cran.r-project.org/package=paleoTS Description: CRAN Package 'paleoTS' (Analyze Paleontological Time-Series) Facilitates analysis of paleontological sequences of trait values. Functions are provided to fit, using maximum likelihood, simple evolutionary models (including unbiased random walks, directional evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and complex models (punctuation, mode shifts). Package: r-cran-palette Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-pillar, r-cran-vctrs Suggests: r-cran-ggplot2, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-palette_0.0.3-1.ca2404.1_all.deb Size: 121682 MD5sum: 0745d4a70be000a709cfa3b6187400be SHA1: ec1e711d8a97c03d78b306458fd75600058f3a0a SHA256: 50705f4c53f34433df71341d90f73cfeb29fc7068297721d4d40588b620a5200 SHA512: 9a4ea46bb7de102b004ccd70c0c9ebfc0325a1724efc685360cd571fae7b88aa0afabda9e2281b0a891c72fc523824248eba432f9d9f64f25a9ec2edaa617ce8 Homepage: https://cran.r-project.org/package=palette Description: CRAN Package 'palette' (Color Scheme Helpers) Hexadecimal codes are typically used to represent colors in R. Connecting these codes to their colors requires practice or memorization. 'palette' provides a 'vctrs' class for working with color palettes, including printing and plotting functions. The goal of the class is to place visual representations of color palettes directly on or, at least, next to their corresponding character representations. Palette extensions also are provided for data frames using 'pillar'. Package: r-cran-paletteer Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-prismatic, r-cran-rematch2, r-cran-rlang, r-cran-rstudioapi Suggests: r-cran-covr, r-cran-ggplot2, r-cran-ggthemes, r-cran-harrypotter, r-cran-knitr, r-cran-oompabase, r-cran-palr, r-cran-pals, r-cran-rmarkdown, r-cran-scico, r-cran-testthat, r-cran-vdiffr, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-paletteer_1.7.0-1.ca2404.1_all.deb Size: 473852 MD5sum: d7edb12606823794288a3f464eae30b8 SHA1: 220645330c6e692c152e54455225f0d4a6fa101a SHA256: e31d9867fb274c8f28dc5569fec8df05db5fc65e67b9e0f982c0be856955b975 SHA512: c0aa8099abd509eb4921e5eb92dc1f2de589dd9f89d7674ab858a96af8f37f866df778c799d0ed1d7ff193f06daad4da838fc9006a65477cc07fc659c3214227 Homepage: https://cran.r-project.org/package=paletteer Description: CRAN Package 'paletteer' (Comprehensive Collection of Color Palettes) The choices of color palettes in R can be quite overwhelming with palettes spread over many packages with many different API's. This packages aims to collect all color palettes across the R ecosystem under the same package with a streamlined API. Package: r-cran-paletteknife Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-paletteknife_0.4.2-1.ca2404.1_all.deb Size: 58004 MD5sum: a7fd4c1b187fa06413fde9788b415d7d SHA1: 67fd933253dd46f91f98d8a523f0a60dcd5081e1 SHA256: b91be94bfc0bb1c3ea08a5ee578e4d2b59c9107b3971393b4b3e6e4902637b52 SHA512: 62f0cfa83396549d3121cf9e4270bd82165b6e9ad996d29c46e80149dcac9bb22c9302fb4035c1cb34c444b371ce64ffe462fe4c007ed2b0472d03bba951973a Homepage: https://cran.r-project.org/package=paletteknife Description: CRAN Package 'paletteknife' (Create Colour Scales and Legend from Continuous or CategoricalVectors) Streamlines the steps for adding colour scales and associated legends when working with base R graphics, especially for interactive use. Popular palettes are included and pretty legends produced when mapping a large variety of vector classes to a colour scale. An additional helper for adding axes and grid lines complements the base::plot() work flow. Package: r-cran-palettephines Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-dplyr, r-cran-sfdep, r-cran-tidyr, r-cran-roroph, r-cran-rnaturalearth, r-cran-rnaturalearthdata Filename: pool/dists/noble/main/r-cran-palettephines_0.1.3-1.ca2404.1_all.deb Size: 54616 MD5sum: d7833d0164222a7e12c9e9deebbe2177 SHA1: c8efe58fb87b0dd7e3e8d20f8959ad1f7ad936da SHA256: ee008b82aa4ed5b8da27935268baa1906ea99d23f4ef5b99bcc212ba8ccacbfd SHA512: 0a2777ff2fa8abeb71dfa51d56e5adbb90acba14d939855e6117997e1672c116b3edd691d387a546916f7d56d7342b7c351a4ddd8b6e052307d04cc7024d2545 Homepage: https://cran.r-project.org/package=palettephines Description: CRAN Package 'palettephines' (Analytical Color Palettes for Philippine Phenology) Provides specialized color palettes representing phenological transitions and biological lifecycles within Philippine landscapes. Rather than abstract gradients, these scales are anchored to topologically grounded states such as agricultural maturity, seasonal vegetation shifts, and environmental readiness. Palettes are indexed against the Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale (Meier, 2023) for terrestrial vegetation and the Reef Health Index (RHI) framework (McField and Kramer, 2007) for marine ecosystems. This ensures scientific interoperability across archipelagic spatial models, aligning with global standards for ecological state-transition modeling (Schwartz, 2013) . Package: r-cran-palettes Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 989 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs, r-cran-cli, r-cran-pillar, r-cran-rlang, r-cran-purrr, r-cran-prismatic, r-cran-farver, r-cran-ggplot2, r-cran-scales, r-cran-tibble Suggests: r-cran-pkgdown, r-cran-testthat, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-colorspace, r-cran-gt, r-cran-biscale, r-cran-sf, r-cran-patchwork, r-cran-metbrewer, r-cran-nord, r-cran-pnwcolors, r-cran-viridislite, r-cran-covr, r-cran-withr Filename: pool/dists/noble/main/r-cran-palettes_0.2.2-1.ca2404.1_all.deb Size: 613296 MD5sum: 49f79b0529baf37e85553ca1743b7450 SHA1: 6d3c0cf69c961a71141d5aae934da4377cb37f82 SHA256: 1a1b2e0dd760fa740d9f9b6149976d7db0c9062c1d80fccaf1d69fde50d26e33 SHA512: 1c29d018011b915d8acd134ffc301c5e47eb8c0ac6b74487dad2b3c4563e82a081b42b0aa7b1ed370c07d2b525a832bb3ac48e341dad90281e2fcfe33575c886 Homepage: https://cran.r-project.org/package=palettes Description: CRAN Package 'palettes' (Methods for Colour Vectors and Colour Palettes) Provides a comprehensive library for colour vectors and colour palettes using a new family of colour classes (palettes_colour and palettes_palette) that always print as hex codes with colour previews. Capabilities include: formatting, casting and coercion, extraction and updating of components, plotting, colour mixing arithmetic, and colour interpolation. Package: r-cran-palettesforr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-palettesforr_0.1.2-1.ca2404.1_all.deb Size: 126704 MD5sum: 7e692dcaabf97514deb2d19d82deb0d0 SHA1: 319d81629f818f611dbf60d0ad5a5c511f72043f SHA256: abd6d6444881062d8b0b929324ad392caa42069af8a5fec0beec983be228bd65 SHA512: 6dabefa32644701cc98f5ebe1e1b586f767645c24a73528b7b202f739b7f9b09b62d5876c164f7f8f32fd2c5904239e39abdf82a8aeec77502a7c797ccb32f44 Homepage: https://cran.r-project.org/package=palettesForR Description: CRAN Package 'palettesForR' (GPL Palettes Copied from 'Gimp' and 'Inkscape') A set of palettes imported from 'Gimp' distributed under GPL3 (), and 'Inkscape' distributed under GPL2 (). Package: r-cran-palettetown Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-palettetown_0.1.1-1.ca2404.1_all.deb Size: 51624 MD5sum: 7c8eb923489a57807cbb082a60844222 SHA1: 60384632eedfc75a8f5e41fc61f99947a536031f SHA256: dcb0b081bcbc66c0c774e1047140b9714ded4c27ff537fe0764577e67d280e7e SHA512: a9fb7a8f2fa2af4197591cc77f9d2593c0aa9d996474853e2f4387aa9451a455b1767fe31a70a98f6e4c83d918b28705ab4788455d3f6496455c5c59ff0d7b8c Homepage: https://cran.r-project.org/package=palettetown Description: CRAN Package 'palettetown' (Use Pokemon Inspired Colour Palettes) Use Pokemon(R) inspired palettes with additional 'ggplot2' scales. Palettes are the colours in each Pokemon's sprite, ordered by how common they are in the image. The first 386 Pokemon are currently provided. Package: r-cran-palimpsestr Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2263 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-ggplot2, r-cran-viridis, r-cran-ggrepel, r-cran-plotly, r-cran-dbi, r-cran-rsqlite, r-cran-rpostgres Filename: pool/dists/noble/main/r-cran-palimpsestr_0.10.0-1.ca2404.1_all.deb Size: 1620730 MD5sum: f3b1e4d5f9169a2973d51e7dc8484534 SHA1: c977ec559eec8a06b03ddd8233e2a0c446eff00b SHA256: 0fc06b7b1555dac2ec6964a5cb9fbcac63f27aacc575f4e7558419f1d06228db SHA512: 410c4680e19bca87abc6ed88fa01e9b86e5334df2bde3049a797bea60e8760d8cd4f9e5801ff485a91a7742d4bf094d1a37670a2a4b615d325b98696ad3dcaee Homepage: https://cran.r-project.org/package=palimpsestr Description: CRAN Package 'palimpsestr' (Probabilistic Decomposition of Archaeological Palimpsests) Probabilistic framework for the analysis of archaeological palimpsests based on the Stratigraphic Entanglement Field (SEF). Integrates spatial proximity, stratigraphic depth, chronological overlap, and cultural similarity to estimate latent depositional phases via diagonal Gaussian mixture Expectation-Maximisation (EM). Provides the Stratigraphic Entanglement Index (SEI), Excavation Stratigraphic Energy (ESE), and Palimpsest Dissolution Index (PDI) for quantifying depositional coherence, detecting intrusive finds, and measuring palimpsest formation. Includes simulation, diagnostics, phase-count selection, publication-quality plots, and Geographic Information System (GIS) export via 'sf'. Methods are described in Cocca (2026) . Package: r-cran-palinsol Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4394 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-gsl Filename: pool/dists/noble/main/r-cran-palinsol_1.0-1.ca2404.1_all.deb Size: 4347878 MD5sum: 2dff09a5e6dc3f323c6e6be91b671455 SHA1: e70d147001209bdf8ab362dd7070befa2299d193 SHA256: 92dc7eb7d4f64e469a32590dd488f46f380aafedf5ed70120c1698834cbc0eed SHA512: 120f540fedc031d525877880cca2bd5f9e8db3565da99723e46deef94b2f8ed86086083baf846448cc017c0f113724f3f129f42567e5dd24b5c4cbda70d0bab0 Homepage: https://cran.r-project.org/package=palinsol Description: CRAN Package 'palinsol' (Insolation for Palaeoclimate Studies) R package to compute Incoming Solar Radiation (insolation) for palaeoclimate studies. Features three solutions: Berger (1978), Berger and Loutre (1991) and Laskar et al. (2004). Computes daily-mean, season-averaged and annual means and for all latitudes, and polar night dates. Package: r-cran-palmerpenguins Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3218 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-recipes Filename: pool/dists/noble/main/r-cran-palmerpenguins_0.1.1-1.ca2404.1_all.deb Size: 2971820 MD5sum: c338a5a9ede1d1a1d4e90e2d4afe82e9 SHA1: 69ccbdbe7c014fddd0833ffd74aa6ccccb6b9679 SHA256: 8e28cbccf253a99fc9b5e690af4166b92fe853ad4b9bfc7d1910651bd9160e9c SHA512: fee72689ebbef2d7cb2750f5c957ba8e72a9c3520a5db867730e3caed76189be6c6e80f6c2245a3fe23e74dbf6b2dcf35221825f3bbacba3b5750ff96afbee2c Homepage: https://cran.r-project.org/package=palmerpenguins Description: CRAN Package 'palmerpenguins' (Palmer Archipelago (Antarctica) Penguin Data) Size measurements, clutch observations, and blood isotope ratios for adult foraging Adélie, Chinstrap, and Gentoo penguins observed on islands in the Palmer Archipelago near Palmer Station, Antarctica. Data were collected and made available by Dr. Kristen Gorman and the Palmer Station Long Term Ecological Research (LTER) Program. Package: r-cran-palmr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-palmr_0.2.0-1.ca2404.1_all.deb Size: 57062 MD5sum: bb0ab0c5a24a7ed9e044502614efdf94 SHA1: e46e0b612c99036dcb5909231738ec6797aa6ded SHA256: d62356d3f376be7932344544bd3e630884140eced14961e3357e6ea9457f7998 SHA512: dc6d48b05d034065800c4b591e3198431cbfd07296ef86eb13a531c3df1c16e986712e3968e4a22172aad244ff8903bd5e28e5a8dc03bef8396d485a12fd4759 Homepage: https://cran.r-project.org/package=PaLMr Description: CRAN Package 'PaLMr' (Interface for 'Google Pathways Language Model 2 (PaLM 2)') 'Google Pathways Language Model 2 (PaLM 2)' as a coding and writing assistant designed for 'R'. 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Package: r-cran-palmtree Architecture: all Version: 0.9-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partykit, r-cran-formula Suggests: r-cran-mvtnorm, r-cran-psychotools Filename: pool/dists/noble/main/r-cran-palmtree_0.9-1-1.ca2404.1_all.deb Size: 33690 MD5sum: e181e9c425b765052da823aa016537c4 SHA1: 4b70e174c718e9b9ff0526b2a96d658fe5dc8592 SHA256: 334fc76f92b9d2833c032503610829b234d5aa85298255523366128202e1911e SHA512: 9c2ae97771bedd4c2172c995469677fcb39ce49459e2bf2bd6669f677ea6e6a8092d072c060521d48e3cda9ad9165a5df4c5c97b3963d56267cf2ab3b2d54b91 Homepage: https://cran.r-project.org/package=palmtree Description: CRAN Package 'palmtree' (Partially Additive (Generalized) Linear Model Trees) This is an implementation of model-based trees with global model parameters (PALM trees). The PALM tree algorithm is an extension to the MOB algorithm (implemented in the 'partykit' package), where some parameters are fixed across all groups. Details about the method can be found in Seibold, Hothorn, Zeileis (2016) . The package offers coef(), logLik(), plot(), and predict() functions for PALM trees. Package: r-cran-palr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-raster, r-cran-testthat, r-cran-covr, r-cran-stars, r-cran-viridis Filename: pool/dists/noble/main/r-cran-palr_0.4.0-1.ca2404.1_all.deb Size: 225480 MD5sum: 1ff6c8589e31643e04d78493e0e809d9 SHA1: 83563a44e505dd7c97bef6151ddd0633c041c5a3 SHA256: d47f8c1f25c27fd7d2fb1f71522b44aaa8ebfc803794a23f81a41ba7414e8157 SHA512: 6e4dfee1048d0faf1fddb58692d932f2b3513430a83fff0fa391875615b05af669931849e9d28ac2c01d458f3bc38c91a063420ec05bcf7f8111b2037b033872 Homepage: https://cran.r-project.org/package=palr Description: CRAN Package 'palr' (Colour Palettes for Data) Colour palettes for data, based on some well known public data sets. Includes helper functions to map absolute values to known palettes, and capture the work of image colour mapping as raster data sets. Package: r-cran-pals Architecture: all Version: 1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2278 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace, r-cran-dichromat, r-cran-mapproj, r-cran-maps Suggests: r-cran-classint, r-cran-ggplot2, r-cran-knitr, r-cran-latticeextra, r-cran-reshape2, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pals_1.10-1.ca2404.1_all.deb Size: 1845698 MD5sum: 739812c8c26b6a692de68af639e9254a SHA1: 14b141d662f0c23abcebe007e0bcab35f7f508dc SHA256: 29e0c3850af9620b112491c5f126d71bf45515904fe23d5fbb436a6d86d363bb SHA512: 0d400c9016f1d8861b48e39df2eecd158c48ff8e4a1243bca7462e5dde40e882766299a6ea96382a26d5e1a121a362311a93c9d2ff1fb6e210eb51cf44eb9cd2 Homepage: https://cran.r-project.org/package=pals Description: CRAN Package 'pals' (Color Palettes, Colormaps, and Tools to Evaluate Them) A comprehensive collection of color palettes, colormaps, and tools to evaluate them. See Kovesi (2015) . Package: r-cran-pam Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-data.table, r-cran-ggplot2, r-cran-minpack.lm, r-cran-cowplot, r-cran-gridextra, r-cran-ggthemes, r-cran-metrics Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pam_2.2.1-1.ca2404.1_all.deb Size: 187006 MD5sum: 2cf6be2cbd8327da9c48a41838dc509c SHA1: cff2d5601a657a687c83d4b70b2a5afab67b7ec9 SHA256: f0290955c4f1934e543a9af2fc9547c284b0ac6272da17f22e733973573dce49 SHA512: cd9d61cd1a11f2d330c6de0f535aa60f3ee1f30c8d0bb476ca30018f343be231e7983c26833bd9d713a54a23b60ff8b2f277d706be86307fd647f0f43d91dd97 Homepage: https://cran.r-project.org/package=pam Description: CRAN Package 'pam' (Fast and Efficient Processing of PAM Data) Processing Chlorophyll Fluorescence & P700 Absorbance data. Four models are provided for the regression of Pi curves, which can be compared with each other in order to select the most suitable model for the data set. Control plots ensure the successful verification of each regression. Bundled output of alpha, ETRmax, Ik etc. enables fast and reliable further processing of the data. Package: r-cran-pambinaries Architecture: all Version: 1.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2978 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pambinaries_1.9.3-1.ca2404.1_all.deb Size: 1489988 MD5sum: 493234880bc4602f66a6c5ca40ca0739 SHA1: 94d7af1fc2e58f9a99c460c7efe95ac8fe8ae9d4 SHA256: 3bcbcd1f15c8ecf50c0906bd75188d6fb35ac333c1ef0189e9a5b93ad067c35e SHA512: ca0cbc518dc3e877fd39ada16af86b820a8bf7f8a3fbcf6a8806649a0bf9afc3c563a47c9980af1c12bebf293b3dcf447a9326d364150efd5a359e92c85765e3 Homepage: https://cran.r-project.org/package=PamBinaries Description: CRAN Package 'PamBinaries' (Read and Process 'Pamguard' Binary Data) Functions for easily reading and processing binary data files created by 'Pamguard' (). All functions for directly reading the binary data files are based on 'MATLAB' code written by Michael Oswald. Package: r-cran-pameasures Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pameasures_0.1.0-1.ca2404.1_all.deb Size: 30416 MD5sum: bdbbc9b315e73888fe2065bf723c3837 SHA1: 2e4ca34e177ebe86c2f87be30db8b1695b03c373 SHA256: 9402d639d4ce12c04da898867dcb54bb6b9c8d4df68c77609dd0d64ecd18395c SHA512: a36dce698fecc44fb25665c1b7e0d50d8ca8db975cdfa2c6fcea7877dde8a3b8c868062e63d3e70d29615d90dd77f2ac7a6c7f05c28ec88ce959b68062566c78 Homepage: https://cran.r-project.org/package=PAmeasures Description: CRAN Package 'PAmeasures' (Prediction and Accuracy Measures for Nonlinear Models and forRight-Censored Time-to-Event Data) We propose a pair of summary measures for the predictive power of a prediction function based on a regression model. The regression model can be linear or nonlinear, parametric, semi-parametric, or nonparametric, and correctly specified or mis-specified. The first measure, R-squared, is an extension of the classical R-squared statistic for a linear model, quantifying the prediction function's ability to capture the variability of the response. The second measure, L-squared, quantifies the prediction function's bias for predicting the mean regression function. When used together, they give a complete summary of the predictive power of a prediction function. Please refer to Gang Li and Xiaoyan Wang (2016) for more details. Package: r-cran-pamhm Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-heatmapflex, r-cran-cluster, r-cran-rcolorbrewer, r-cran-r.utils, r-cran-readxl, r-cran-readmore, r-cran-plyr, r-cran-robusthd Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-pamhm_0.1.2-1.ca2404.1_all.deb Size: 262708 MD5sum: b3455bb155119f81f157ba024c513a3a SHA1: db564f1b4c4c982124c16688f235ca9d5fe80b99 SHA256: 56ab6a09e03a204c7741aa31fd5f9b3b6bd0e679dbbd2778669d9ce6942421be SHA512: 275a2020da34bcbd0faae3816ba5a0bb30ffc8d668017dfcbeb3980097e0ff0535ba663a1b0126dc3032790560f90bfecdcbfb6e22be6658877eed7d07a6883f Homepage: https://cran.r-project.org/package=PAMhm Description: CRAN Package 'PAMhm' (Generate Heatmaps Based on Partitioning Around Medoids (PAM)) Data are partitioned (clustered) into k clusters "around medoids", which is a more robust version of K-means implemented in the function pam() in the 'cluster' package. The PAM algorithm is described in Kaufman and Rousseeuw (1990) . Please refer to the pam() function documentation for more references. Clustered data is plotted as a split heatmap allowing visualisation of representative "group-clusters" (medoids) in the data as separated fractions of the graph while those "sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering. Package: r-cran-pamm Architecture: all Version: 1.122-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmertest, r-cran-lattice, r-cran-mvtnorm, r-cran-lme4 Suggests: r-cran-rgl Filename: pool/dists/noble/main/r-cran-pamm_1.122-1.ca2404.1_all.deb Size: 87228 MD5sum: 19c7b72252d9cd7e6f4748f3fd9cf7f0 SHA1: e45452a7e9b173a0aceffce4855adb7a386ece0c SHA256: d484bf78ba033886aba205a1f394e299b5c42b036783837fbf79769591b9c28d SHA512: 4dd430fd90db0eea6406274554d2204eb2518a4694ef8f5f2421e3af4e521538ad30c41fac7a7bfc3ea20becebc4102b010b147f0751f15742ea3bfb9abb7263 Homepage: https://cran.r-project.org/package=pamm Description: CRAN Package 'pamm' (Power Analysis for Random Effects in Mixed Models) Simulation functions to assess or explore the power of a dataset to estimates significant random effects (intercept or slope) in a mixed model. The functions are based on the "lme4" and "lmerTest" packages. Package: r-cran-pammtools Architecture: all Version: 0.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 826 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-survival, r-cran-checkmate, r-cran-magrittr, r-cran-rlang, r-cran-tidyr, r-cran-ggplot2, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-lazyeval, r-cran-formula, r-cran-mvtnorm, r-cran-pec, r-cran-vctrs, r-cran-scam Suggests: r-cran-testthat, r-cran-mstate, r-cran-broom Filename: pool/dists/noble/main/r-cran-pammtools_0.7.4-1.ca2404.1_all.deb Size: 773068 MD5sum: c000f8957f1aee05fbe2f6433d9070df SHA1: c92c4071f94b4619d9e2c64b322417f1b8e18063 SHA256: f15cf166fdfe6e6e60000036b0169d18e6231d3c63179e5a14015ecca5ffc449 SHA512: f78f51d2f2e36e497a34e86cda6514593136dacb2a7f1bf9af526e7c04e2d64d42b60ce8ee17f29362b0d3944774fe565a0ebf6dd1e66dfdbdb8726f6f125ab0 Homepage: https://cran.r-project.org/package=pammtools Description: CRAN Package 'pammtools' (Piece-Wise Exponential Additive Mixed Modeling Tools forSurvival Analysis) The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and others (2018) ) is a powerful model class for the analysis of survival (or time-to-event) data, based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2019)), as well as support for left-truncated data as well as competing risks, recurrent events and multi-state settings. pammtools provides tidy workflow for survival analysis with PAMMs, including data simulation, transformation and other functions for data preprocessing and model post-processing as well as visualization. Package: r-cran-pampal Architecture: all Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1879 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-pambinaries, r-cran-pammisc, r-cran-tuner, r-cran-seewave, r-cran-gam, r-cran-data.table, r-cran-rsqlite, r-cran-purrr, r-cran-signal, r-cran-tidyr, r-cran-ggplot2, r-cran-knitr, r-cran-xml2, r-cran-rlang, r-cran-reticulate, r-cran-lubridate, r-cran-geosphere, r-cran-shiny, r-cran-future.apply Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pampal_1.5.2-1.ca2404.1_all.deb Size: 1205294 MD5sum: 10db36b16f32dbd28925be4ea0432975 SHA1: f5c755cd9babe4f9848a4a0285f04d5a23e618f0 SHA256: fa4f00c15b0527a774a38574f44efb6ab848ad02273ebc980cbcf07973603e1a SHA512: d0edda000d8b209aa24688801e5fcf1b943d46d7142a61f117b0b66f26227d91276ca3cd76282d0d4e79a503a82ffbbfa38b8ed7a1d03a8d38043e3c348432cd Homepage: https://cran.r-project.org/package=PAMpal Description: CRAN Package 'PAMpal' (Load and Process Passive Acoustic Data) Tools for loading and processing passive acoustic data. Read in data that has been processed in 'Pamguard' (), apply a suite processing functions, and export data for reports or external modeling tools. Parameter calculations implement methods by Oswald et al (2007) , Griffiths et al (2020) and Baumann-Pickering et al (2010) . Package: r-cran-pampe Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-leaps Filename: pool/dists/noble/main/r-cran-pampe_1.1.2-1.ca2404.1_all.deb Size: 60848 MD5sum: 10e9f77258027ef405da3f3f61be6f32 SHA1: 37056a7bd3ec91da80edde1921b7794adfa2d3fa SHA256: 71d5cb569320c38583dc8eaafb3188b7dcfc0068bd1cf3384b528ca1ef4ee5fd SHA512: a3e4455d8f35fe63fbc03bb82f627ee57a6fc1fdbbe04384c7731904d0f8059dcf6e6acbe6aef31cba7d0061a0287d3588f93251b44ad870c6618d638c8623c3 Homepage: https://cran.r-project.org/package=pampe Description: CRAN Package 'pampe' (Implementation of the Panel Data Approach Method for ProgramEvaluation) Implements the Panel Data Approach Method for program evaluation as developed in Hsiao, Ching and Ki Wan (2012). pampe estimates the effect of an intervention by comparing the evolution of the outcome for a unit affected by an intervention or treatment to the evolution of the unit had it not been affected by the intervention. Package: r-cran-pamr Architecture: all Version: 1.57-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-survival Filename: pool/dists/noble/main/r-cran-pamr_1.57-1.ca2404.1_all.deb Size: 664280 MD5sum: aee0a1abf00e66113ea29b761d023359 SHA1: 62e41c60140bac4e39f475b3353fce4835b66ba0 SHA256: fff9b0eaba26b22c69fbbd247c9c1432e3e718d9f07944095bdef976632cb58d SHA512: 98b15394a5ea296d14610806f821be2f368438a3fecc64fde408595020c3305a149743dd389bd475a6e76c008a1fb98911754850baf7b6704865cd01d827cb51 Homepage: https://cran.r-project.org/package=pamr Description: CRAN Package 'pamr' (Pam: Prediction Analysis for Microarrays) Some functions for sample classification in microarrays. Package: r-cran-pams Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-smacof Suggests: r-cran-lmtest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pams_0.1.0-1.ca2404.1_all.deb Size: 44008 MD5sum: a8a00468b00a4f021436653ad2b48a3e SHA1: f16ebe340fab85b892078deb7056c5e0b4ff7ada SHA256: 4cddbe2ed8b10bccb28640a00bc96aaea1fe573780ffba809a39123ec6fd4bfe SHA512: 7756fd21a9802ca06f92bf57a3a052d9cdf53665a80eb877676992fd2ecc921342c70e9e5f79a8eb315de7391bb421261edb0465c30a228fc1d103f2d3a039b5 Homepage: https://cran.r-project.org/package=pams Description: CRAN Package 'pams' (Profile Analysis via Multidimensional Scaling) Implements Profile Analysis via Multidimensional Scaling (PAMS) for the identification of population-level core response profiles from cross-sectional and longitudinal person-score data. Each person profile is decomposed into a level component (the person mean) and a pattern component (ipsatized subscores). PAMS uses nonmetric multidimensional scaling via the SMACOF algorithm to identify a small number of core profiles that represent the central response patterns in a sample of any size. Bootstrap standard errors and bias-corrected and accelerated (BCa) confidence intervals for individual core profile coordinates are estimated, enabling significance testing of coordinates that is not available in other profile analysis methods such as cluster profile analysis or latent profile analysis. Person-level weights, R-squared values, and correlations with core profiles are also estimated, allowing individual profiles to be interpreted in terms of the core profile structure. PAMS can be applied to both cross-sectional data and longitudinal data, where core trajectory profiles describe how response patterns change over time. Methods are described in Kim and Kim (2024) , de Leeuw and Mair (2009) , and Kruskal (1964) . Package: r-cran-pamscapes Architecture: all Version: 0.15.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1391 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-ggplot2, r-cran-lubridate, r-cran-scales, r-cran-tidyr, r-cran-httr, r-cran-data.table, r-cran-geosphere, r-cran-sf, r-cran-pammisc, r-cran-ncdf4, r-cran-purrr, r-cran-shiny, r-cran-future.apply, r-cran-signal, r-cran-tuner, r-cran-dt Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pamscapes_0.15.0-1.ca2404.1_all.deb Size: 1039796 MD5sum: 6d1270c62becbe967e9c791d5214ba12 SHA1: 4745c7335154dd077ed145012a20e5bfd1ce772f SHA256: 10a260b12a7255d0184d060933195a352f26cd2092190c8e0acf746a649bbcd6 SHA512: a30b22204f3c7e351b6a674753d846b6cf833786b614fd109c0adb8b380ab121565e0af1a5138786d9f7e1b64b9f569777f8cd867163cf064de3e532611445ce Homepage: https://cran.r-project.org/package=PAMscapes Description: CRAN Package 'PAMscapes' (Tools for Summarising and Analysing Soundscape Data) A variety of tools relevant to the analysis of marine soundscape data. There are tools for downloading AIS (automatic identification system) data from Marine Cadastre , connecting AIS data to GPS coordinates, plotting summaries of various soundscape measurements, and downloading relevant environmental variables (wind, swell height) from the National Center for Atmospheric Research data server . Most tools were developed to work well with output from 'Triton' software, but can be adapted to work with any similar measurements. 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(2019) . Package: r-cran-pandemics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pandemics_0.1.0-1.ca2404.1_all.deb Size: 82150 MD5sum: 7b3eb901dda36e9b36b69de5a96b9ff6 SHA1: a781e940eb1084c3e555c25eba0e2f5a990800a4 SHA256: 207024ea343292fa9ea5eaa5ed1ec2dcb4fb71b5b2be3a07a9f862d7f49fddc3 SHA512: b98dedea759482e0089c2010793f0969d5299ee415c635c1ed96627ae44c77db0246c944683316c1bb26d2f33f5071dbc1d994f345aa61aaa3bb2036b063712a Homepage: https://cran.r-project.org/package=pandemics Description: CRAN Package 'pandemics' (Monitoring a Developing Pandemic with Available Data) Full dynamic system to describe and forecast the spread and the severity of a developing pandemic, based on available data. These data are number of infections, hospitalizations, deaths and recoveries notified each day. The system consists of three transitions, infection-infection, infection-hospital and hospital-death/recovery. The intensities of these transitions are dynamic and estimated using non-parametric local linear estimators. The package can be used to provide forecasts and survival indicators such as the median time spent in hospital and the probability that a patient who has been in hospital for a number of days can leave it alive. Methods are described in Gámiz, Mammen, Martínez-Miranda, and Nielsen (2024) and . 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This tool performs data preparation, clustering and visualisation within a dynamic GUI. With interactive methods allowing the user to change settings all without having to to leave the GUI. An earlier version of this package was described in Laa and Valencia (2022) . 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Package: r-cran-pandocfilters Architecture: all Version: 0.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-pandocfilters_0.1-6-1.ca2404.1_all.deb Size: 249432 MD5sum: e98f9747b741a3d83fc9da99b51097eb SHA1: 0ebe9f1c24a17687d3968b299ea261c7c53c8515 SHA256: 925e77a0ddd380f7e8c1924fcef1aed80f1f9b58ca2ebfe86de0c9e3b62c64f2 SHA512: 6ff09741b73552fefc4ecf29252210979655da65162c986cfea3e24a5a73b4389554e0f6904cdb13b8207dafa295832a2a8b1b297ca92fec002595d163ca5c77 Homepage: https://cran.r-project.org/package=pandocfilters Description: CRAN Package 'pandocfilters' (Pandoc Filters for R) The document converter 'pandoc' is widely used in the R community. One feature of 'pandoc' is that it can produce and consume JSON-formatted abstract syntax trees (AST). This allows to transform a given source document into JSON-formatted AST, alter it by so called filters and pass the altered JSON-formatted AST back to 'pandoc'. This package provides functions which allow to write such filters in native R code. Although this package is inspired by the Python package 'pandocfilters' , it provides additional convenience functions which make it simple to use the 'pandocfilters' package as a report generator. Since 'pandocfilters' inherits most of it's functionality from 'pandoc' it can create documents in many formats (for more information see ) but is also bound to the same limitations as 'pandoc'. Package: r-cran-pandora Architecture: all Version: 24.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-curl, r-cran-jsonlite, r-cran-magrittr, r-cran-openxlsx, r-cran-readods, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-yaml Suggests: r-cran-knitr, r-cran-qpdf, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pandora_24.2.0-1.ca2404.1_all.deb Size: 70892 MD5sum: 4a96aada0c14ae058b7577a2621cde2f SHA1: 72449e8290b0fef9de71c1f956e3091cf2bb1120 SHA256: 58591b9be79848d53b8c21b6318da502e9a03cec4c10be1f74710240f20b9f87 SHA512: 4111774f1e18b64f5f06cc206624e483348ebdc90e194a7c1b51a91a1685bc6b4bd159eb512c83942738f65b8b74066d16f6fd2b82efbf3c1a2db9dff1104302 Homepage: https://cran.r-project.org/package=Pandora Description: CRAN Package 'Pandora' (Retrieve Data using the API of the 'Pandora' Data Platform) API wrapper that contains functions to retrieve data from the 'Pandora' databases. Web services for API: . Package: r-cran-panelaggregation Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-panelaggregation_0.1.1-1.ca2404.1_all.deb Size: 154058 MD5sum: 4cb73adfdbce4b08e750e24ff907cd7b SHA1: 076ddf1fc21300ae23c3fbb058550176a5898264 SHA256: 7d194957915cb7704e4a1db92bf8ab663b43536f0e857bd0c6282d11733d827f SHA512: 664e0a89cb7f26755efc96d9df741c69eaee1cf3162b6cbd21ce02c77c6970efb37c8d06f8c0ca6f7a96c3c460ec206181cd274f16f8773bca5186c22289ce38 Homepage: https://cran.r-project.org/package=panelaggregation Description: CRAN Package 'panelaggregation' (Aggregate Longitudinal Survey Data) Aggregate Business Tendency Survey Data (and other qualitative surveys) to time series at various aggregation levels. Run aggregation of survey data in a speedy, re-traceable and a easily deployable way. Aggregation is substantially accelerated by use of data.table. This package intends to provide an interface that is less general and abstract than data.table but rather geared towards survey researchers. 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Package: r-cran-panelhetero Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-ggplot2, r-cran-kernsmooth, r-cran-rearrangement Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-panelhetero_1.0.1-1.ca2404.1_all.deb Size: 228790 MD5sum: 9af4d3ec475ac51093d88ee79dd942b2 SHA1: fafa97d7cc45c8699b70991be86ef65f33dba34e SHA256: dbc69d1beacc2e27e23457bcd825d73a488faf1171bc5542cf2491049e543df4 SHA512: 7470b1d6f42dddbef0d8f28e2e8ec08651e56349f36873d9e06cf19712c03a7e7706b6afbbdffe94f023edc37777da3115da6d67554bb9923c4fcc9bbf288c44 Homepage: https://cran.r-project.org/package=panelhetero Description: CRAN Package 'panelhetero' (Panel Data Analysis with Heterogeneous Dynamics) Understanding the dynamics of potentially heterogeneous variables is important in statistical applications. This package provides tools for estimating the degree of heterogeneity across cross-sectional units in the panel data analysis. The methods are developed by Okui and Yanagi (2019) and Okui and Yanagi (2020) . Package: r-cran-panelpomp Architecture: all Version: 1.5.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1960 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pomp, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-panelpomp_1.5.0.0-1.ca2404.1_all.deb Size: 1050166 MD5sum: 7eb33b472b6894e5be479fd44bd5fff5 SHA1: d75fa94ea7a5c02112a83304c40e9fdda1e51d96 SHA256: e97b4d85e0d3146dc04e99e53af21ca028f721754b90a83eadaa032722ae4ecd SHA512: 7c2c723f8ddcb594fb70284381ad2b3bd6dd938f637dc62d936e71d51e1bc6d7f2fd8a39e5174ccd572dfbd95e21039f39c12d68fb5242db340476c44bc157f8 Homepage: https://cran.r-project.org/package=panelPomp Description: CRAN Package 'panelPomp' (Inference for Panel Partially Observed Markov Processes) Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" . Package: r-cran-panelr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-crayon, r-cran-dplyr, r-cran-formula, r-cran-ggplot2, r-cran-jtools, r-cran-lmertest, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-reformulas, r-cran-vctrs Suggests: r-cran-aer, r-cran-brms, r-cran-broom.mixed, r-cran-car, r-cran-clubsandwich, r-cran-geepack, r-cran-generics, r-cran-nlme, r-cran-plm, r-cran-sandwich, r-cran-skimr, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-panelr_1.0.1-1.ca2404.1_all.deb Size: 943670 MD5sum: c7fa9154ed437ccb68fb707367a59e5b SHA1: cee18e912db52456f101a9c6ba3c04b875e953ea SHA256: 867806e4455d65ead269ebb694ebf2a1ea0e40216184708db4df4cdb91307695 SHA512: 94b886a9fe10aa5cb472a115c839120fb9ca2771345fe2e11ab4d7c6f982d259ad8991f678b58cc0db3b00131f485632feaa31d3141c22bb745da74f6c8fc59f Homepage: https://cran.r-project.org/package=panelr Description: CRAN Package 'panelr' (Regression Models and Utilities for Repeated Measures and PanelData) Provides an object type and associated tools for storing and wrangling panel data. Implements several methods for creating regression models that take advantage of the unique aspects of panel data. Among other capabilities, automates the "within-between" (also known as "between-within" and "hybrid") panel regression specification that combines the desirable aspects of both fixed effects and random effects econometric models and fits them as multilevel models (Allison, 2009 ; Bell & Jones, 2015 ). These models can also be estimated via generalized estimating equations (GEE; McNeish, 2019 ) and Bayesian estimation is (optionally) supported via 'Stan'. Supports estimation of asymmetric effects models via first differences (Allison, 2019 ) as well as a generalized linear model extension thereof using GEE. Package: r-cran-panelsummary Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 965 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fixest, r-cran-kableextra, r-cran-modelsummary, r-cran-rlang, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-covr, r-cran-gt, r-cran-knitr, r-cran-parameters, r-cran-performance, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-panelsummary_0.1.3-1.ca2404.1_all.deb Size: 680972 MD5sum: 32ee133283223b0710dd73789390aeb2 SHA1: f744a403f6931557ae3d5db2d22158f2e206f5b0 SHA256: 73535e0bd2119cab731530005e4dcefaef4db12a3c74245b2fa9bfd9a2765b64 SHA512: a4ad74f43fa9127b6a471430f27e0959d94d33072222e28d94130740776a683b6527fd9f2f51afad90dbf3a04e03bbfe1804684852734cc1420f807acd6652c6 Homepage: https://cran.r-project.org/package=panelsummary Description: CRAN Package 'panelsummary' (Create Publication-Ready Regression Tables with Panels) Create an automated regression table that is well-suited for models that are estimated with multiple dependent variables. 'panelsummary' extends 'modelsummary' (Arel-Bundock, V. (2022) ) by allowing regression tables to be split into multiple sections with a simple function call. Utilize familiar arguments such as fmt, estimate, statistic, vcov, conf_level, stars, coef_map, coef_omit, coef_rename, gof_map, and gof_omit from 'modelsummary' to clean the table, and additionally, add a row for the mean of the dependent variable without external manipulation. Package: r-cran-panelsur Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-formula.tools, r-cran-plm, r-cran-matlib, r-cran-fastmatrix Filename: pool/dists/noble/main/r-cran-panelsur_0.1.0-1.ca2404.1_all.deb Size: 73174 MD5sum: 6b230149f33dc4c18901ee488e6c613e SHA1: 6ece645b5d9f9b4e4609ece2c6ed7075e49e2cd4 SHA256: 31f2d671c196b56b8fb70bc1f34b63c1f2a77798af4e8fdd216130a3529deeb7 SHA512: 33318805e47e0171823fa96f865bbdd20512dafaa38e39c699f2def03463dae21c4ba6f11c1deaa85699b075385bb267e707928f66019f93a01e2fa9659f5d89 Homepage: https://cran.r-project.org/package=panelSUR Description: CRAN Package 'panelSUR' (Two-Way Error Component SUR Systems Estimation on UnbalancedPanel Data) Generalized Least Squares (GLS) estimation of Seemingly Unrelated Regression (SUR) systems on unbalanced panel in the one/two-way cases also taking into account the possibility of cross equation restrictions. Methodological details can be found in Biørn (2004) and Platoni, Sckokai, Moro (2012) . Package: r-cran-paneltests Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-plm, r-cran-zoo, r-cran-quantreg Filename: pool/dists/noble/main/r-cran-paneltests_1.0.5-1.ca2404.1_all.deb Size: 172376 MD5sum: def0a2d857f255d66969eccd75264b2c SHA1: 9600151e2337eac81cc7bd658eed7f4c561e0c30 SHA256: 081ca0707fc9652e55eccde74b90e4e905edd52eb924afe386b11045e2e598f6 SHA512: cbcbddccdb6eae6495e3589c4654c6e27c72b30213e2c91a2e06a1595d41c40151f957d045d78e5831f9c5512183cbd6bcfef32ae6745090b10440fe984d64ce Homepage: https://cran.r-project.org/package=paneltests Description: CRAN Package 'paneltests' (Panel Data Pre-Testing and Diagnostic Suite) Pre-testing and diagnostic tools for panel data analysis. Researchers should run these tests before any panel regression to verify modelling assumptions. The package implements: (1) the Hsiao (2014, ) homogeneity F-tests (F1/F2/F3), Swamy (1970) parameter heterogeneity test, and Pesaran (2004) cross-sectional dependence test via xtpretest(); (2) missing-data detection, mechanism testing, and imputation for unbalanced panels via xtmispanel(); (3) quantile-regression cross-sectional dependence tests (T_tau and T-tilde_tau statistics) of Demetrescu, Hosseinkouchack and Rodrigues (2023) via xtcsdq(); and (4) the panel quantile-regression slope homogeneity S-hat and D-hat statistics of Galvao, Juhl, Montes-Rojas and Olmo (2017) via xtqsh(). Together these tests address three fundamental pre-testing questions: (i) are slopes homogeneous? (ii) is there cross-sectional dependence? and (iii) is the panel balanced and is missingness ignorable? Package: r-cran-paneltm Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-mass Filename: pool/dists/noble/main/r-cran-paneltm_1.1-1.ca2404.1_all.deb Size: 142484 MD5sum: 37ad2f663d2c27d392b5c83dc5b1919d SHA1: bd5fd882ca44aaf9702d7bb6a5a23a6d0c775f5e SHA256: 77d4a13d8ec36068aeb3d632df3aa5266b33611c21a0f15da783e367cb08e221 SHA512: 9b8fcc1c6d1454313f8671b6582112c2c55d59da1c9b40acc294cb49723e5050dc6bf2fb08ff82549a8ddb12043cb1bbaddd285661ba20d4cde61db84b79dd81 Homepage: https://cran.r-project.org/package=PanelTM Description: CRAN Package 'PanelTM' (Two- And Three-Way Dynamic Panel Threshold Regression Model forChange Point Detection) Estimation of two- and three-way dynamic panel threshold regression models (Di Lascio and Perazzini (2024) ; Di Lascio and Perazzini (2022, ISBN:978-88-9193-231-0); Seo and Shin (2016) ) through the generalized method of moments based on the first difference transformation and the use of instrumental variables. The models can be used to find a change point detection in the time series. In addition, random number generation is also implemented. Package: r-cran-panelvar Architecture: all Version: 0.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2646 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr, r-cran-mass, r-cran-matrix, r-cran-progress, r-cran-matrixcalc, r-cran-texreg, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-panelvar_0.5.6-1.ca2404.1_all.deb Size: 2584556 MD5sum: 9b38dbc1a61b26270e6402c18f72a55c SHA1: 92d5492fcb5ad9b8138659709c38329bdf7a3c04 SHA256: 0b80b43d896fb28fb09d3681c24ad35727cf188f3d8a84c091e570c9f706efc8 SHA512: 304b225258f83c99e4fa08b59c69682999715be78fe0e5aaeaa542d6d444ec3fef9e3998dafaa7a31e62647f5b8a738e7736a74202d603796517d47e8dcd7c6c Homepage: https://cran.r-project.org/package=panelvar Description: CRAN Package 'panelvar' (Panel Vector Autoregression) We extend two general methods of moment estimators to panel vector autoregression models (PVAR) with p lags of endogenous variables, predetermined and strictly exogenous variables. This general PVAR model contains the first difference GMM estimator by Holtz-Eakin et al. (1988) , Arellano and Bond (1991) and the system GMM estimator by Blundell and Bond (1998) . We also provide specification tests (Hansen overidentification test, lag selection criterion and stability test of the PVAR polynomial) and classical structural analysis for PVAR models such as orthogonal and generalized impulse response functions, bootstrapped confidence intervals for impulse response analysis and forecast error variance decompositions. Package: r-cran-panelview Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-dplyr Suggests: r-cran-testthat, r-cran-igraph Filename: pool/dists/noble/main/r-cran-panelview_1.3.1-1.ca2404.1_all.deb Size: 234794 MD5sum: 7fd7b81b8fa15b7219ca91319d0f3c40 SHA1: 98810cc4a6a27525767492fd7b0b8261990da2c9 SHA256: 61f1c51032bfb0b49e6edee84c069a581b8bb5fe70ad85a5079c791894020dae SHA512: 0f0b933920456d0efbff0e087dd268fc973fd934eb0f770b9fcff3cbcdf075bce9f1b5bbc85f540f5c371a89749bf23cb36ed841ebb6c20837b6b9afa3dd6907 Homepage: https://cran.r-project.org/package=panelView Description: CRAN Package 'panelView' (Visualizing Panel Data) Visualizes panel data. It has four main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate; (4) it displays the network structure of multi-way fixed effects as a k-partite graph, identifying connected components, singletons, and duplicate observations. For details, see . Package: r-cran-panelwranglr Architecture: all Version: 1.2.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-hmisc, r-cran-caret Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-panelwranglr_1.2.13-1.ca2404.1_all.deb Size: 40210 MD5sum: 20e15a1cb12f0d9afe89f4b11ffb0a63 SHA1: 1ee4857aa9c69c43593b5fa15babc49abd8bd532 SHA256: fed9a4eb2551d263dd62d6bdbeadee02dc5e67726ac27499d51f4a24cf7456ec SHA512: f41662ed6799c9c0116c9f4e098ccd421673f08f1cc81d9aa8cd029a899458e4db69cd9c9fc740355016af5b732e419cd6740bc3bcaf34284676343ab54078b9 Homepage: https://cran.r-project.org/package=panelWranglR Description: CRAN Package 'panelWranglR' (Panel Data Wrangling Tools) Leading/lagging a panel, creating dummy variables, taking panel differences, looking for panel autocorrelations, and more. Implemented via a 'data.table' back end. Package: r-cran-pangaear Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-jsonlite, r-cran-xml2, r-cran-oai, r-cran-tibble, r-cran-hoardr, r-cran-png Suggests: r-cran-knitr, r-cran-testthat, r-cran-vcr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-pangaear_1.1.0-1.ca2404.1_all.deb Size: 89062 MD5sum: aac9c46866e9369b7b07442d9a266433 SHA1: 32abca851db78366004a273a4febec923946e435 SHA256: fc287eedcc7e9b184585f32d6cdf45a99090284e4b44029860a370b421c3421b SHA512: 3dac7f5faaf876cc6bf6eb372b70e8bfa2de9ebbd1621a56607cd6f22bc214d73049cd26357ff64ab3608df25fc799b4d7b98c0eec4e7a9c880155eb391c2ddf Homepage: https://cran.r-project.org/package=pangaear Description: CRAN Package 'pangaear' (Client for the 'Pangaea' Database) Tools to interact with the 'Pangaea' Database (), including functions for searching for data, fetching 'datasets' by 'dataset' 'ID', and working with the 'Pangaea' 'OAI-PMH' service. Package: r-cran-pangoling Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2670 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cachem, r-cran-data.table, r-cran-memoise, r-cran-reticulate, r-cran-rstudioapi, r-cran-tidyselect, r-cran-tidytable Suggests: r-cran-brms, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tictoc, r-cran-covr Filename: pool/dists/noble/main/r-cran-pangoling_1.0.3-1.ca2404.1_all.deb Size: 813060 MD5sum: 7b3eeec55cc0d59556fa8dfb04aebeab SHA1: 7ae9528d5570c33376580bcd40c206d9c36f6b58 SHA256: ffcab0529f9c0d2c47aae04351bb87b265056bb784d6aa1f4743597cf3fc1b2a SHA512: 9778e96b8b7e50419a55ddfba2b9572e7bbbfa9b0eccc210fedc40377d198a62c416e4ebd11b03d4813bcfd2af401c869fc2bef9f20bee6e861db002065af296 Homepage: https://cran.r-project.org/package=pangoling Description: CRAN Package 'pangoling' (Access to Large Language Model Predictions) Provides access to word predictability estimates using large language models (LLMs) based on 'transformer' architectures via integration with the 'Hugging Face' ecosystem . The package interfaces with pre-trained neural networks and supports both causal/auto-regressive LLMs (e.g., 'GPT-2') and masked/bidirectional LLMs (e.g., 'BERT') to compute the probability of words, phrases, or tokens given their linguistic context. For details on GPT-2 and causal models, see Radford et al. (2019) , for details on BERT and masked models, see Devlin et al. (2019) . By enabling a straightforward estimation of word predictability, the package facilitates research in psycholinguistics, computational linguistics, and natural language processing (NLP). Package: r-cran-panjen Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-panjen_1.6-1.ca2404.1_all.deb Size: 73480 MD5sum: 5a8e3823024ea2ce58f4b62b8a792db1 SHA1: b0bf26a11b7b645af434c3b3379777ce5d59a2eb SHA256: c7c4aaf5a0770197c266be19fa121542500a14fb3162a27014b4d2804fe17af3 SHA512: c20b69aa43635c36bf077990cbb04312e7a2b195eb7923a308a0bfb60da8e69543cfbbe70a9dcde9a6ea6d103dc24c05812975d2c8082b217de799e1833d7cf7 Homepage: https://cran.r-project.org/package=PanJen Description: CRAN Package 'PanJen' (A Semi-Parametric Test for Specifying Functional Form) A central decision in a parametric regression is how to specify the relation between an dependent variable and each explanatory variable. This package provides a semi-parametric tool for comparing different transformations of an explanatory variables in a parametric regression. The functions is relevant in a situation, where you would use a box-cox or Box-Tidwell transformations. In contrast to the classic power-transformations, the methods in this package allows for theoretical driven user input and the possibility to compare with a non-parametric transformation. Package: r-cran-pannotator Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-colourpicker, r-cran-config, r-cran-configr, r-cran-dplyr, r-cran-exiftoolr, r-cran-geojsonsf, r-cran-ggplot2, r-cran-golem, r-cran-htmlwidgets, r-cran-jpeg, r-cran-jsonify, r-cran-jsonlite, r-cran-leaflet, r-cran-leafpm, r-cran-magrittr, r-cran-readr, r-cran-scales, r-cran-sf, r-cran-shiny, r-cran-shinyfiles, r-cran-shinyhelper, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pannotator_1.0.1-1.ca2404.1_all.deb Size: 1842960 MD5sum: 1506743f672192fb95fb5efd7493288b SHA1: 6d4e8ab47c70f306ef804349e235c08c6dd8e87e SHA256: 1f468f0efd43ab985d6fd0a807a37692252582da985181e76951b1fb4f2584eb SHA512: 6a118aa3bfd37d551ad62e3063626bc33ff64b063bb2b0d9bd429fc98fe60d37c94d5ad8f05b5d356581fb0cf69b1177becb3c9659ac851d561b3cd958e855df Homepage: https://cran.r-project.org/package=pannotator Description: CRAN Package 'pannotator' (Visualisation and Annotation of 360 Degree Imagery) Provides a customisable R 'shiny' app for immersively visualising, mapping and annotating panospheric (360 degree) imagery. The flexible interface allows annotation of any geocoded images using up to 4 user specified drop-down menus. The app uses 'leaflet' to render maps that display the geo-locations of images and Panellum , a lightweight panorama viewer for the web, to render images in virtual 360 degree viewing mode. Key functions include the ability to draw on & export parts of 360 images for downstream applications. Users can also draw polygons and points on map imagery related to the panoramic images and export them for further analysis. Downstream applications include using annotations to train Artificial Intelligence/Machine Learning (AI/ML) models and geospatial modelling and analysis of camera based survey data. Package: r-cran-panstarrs Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bit64, r-cran-checkmate, r-cran-curl, r-cran-data.table, r-cran-httr, r-cran-jsonlite Suggests: r-cran-dplyr, r-cran-knitr, r-cran-magick, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-panstarrs_0.2.3-1.ca2404.1_all.deb Size: 357504 MD5sum: a390af966cb4a800c4469ed51c19a430 SHA1: 92ba6850398c5122f1709474a74a690315eaa4e9 SHA256: 3c3210a69ba77e549fd9966b8327be2b659a680bfb6dd36b099e6919101abae1 SHA512: d3df2f73dca90ab40fdc811dd3b7f36590af14a1b6ef9efd3b409111003e6da00ae3149c2e9fe76f7054ce07717d0d3ad87e393277f416a52d2aaba461656442 Homepage: https://cran.r-project.org/package=panstarrs Description: CRAN Package 'panstarrs' (Interface to the Pan-STARRS API) An interface to the API for 'Pan-STARRS1', a data archive of the PS1 wide-field astronomical survey. The package allows access to the PS1 catalog and to the PS1 images. (see for more information). You can use it to plan astronomical observations, make guidance pictures, find magnitudes in five broadband filters (g, r, i, z, y) and more. Package: r-cran-pantarhei Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1095 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-pantarhei_0.1.2-1.ca2404.1_all.deb Size: 553440 MD5sum: bff7903c3181e42b3e54346f66b59d12 SHA1: 03bef185e61daf69ba0da0aa1672e84d1469d293 SHA256: 24f289d996a5134d5bcdc1e71585e26ad44472781b6d9b4595926f2ab4cc0738 SHA512: 07e2be677a4b820d703b19e88fa5dc6a6a24fe5d718841ba9016203cfbd5bfb86def66ca7061376b4cac90ab1c2cb2c299f1961eca5f33e227b48a7ef8d1dfe7 Homepage: https://cran.r-project.org/package=PantaRhei Description: CRAN Package 'PantaRhei' (Plots Sankey Diagrams) Sankey diagrams are a powerfull and visually attractive way to visualize the flow of conservative substances through a system. They typically consists of a network of nodes, and fluxes between them, where the total balance in each internal node is 0, i.e. input equals output. Sankey diagrams are typically used to display energy systems, material flow accounts etc. Unlike so-called alluvial plots, Sankey diagrams also allow for cyclic flows: flows originating from a single node can, either direct or indirect, contribute to the input of that same node. This package, named after the Greek aphorism Panta Rhei (everything flows), provides functions to create publication-quality diagrams, using data in tables (or spread sheets) and a simple syntax. Package: r-cran-papaja Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2449 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tinylabels, r-cran-bookdown, r-cran-broom, r-cran-glue, r-cran-knitr, r-cran-rmarkdown, r-cran-rmdfiltr, r-cran-yaml, r-cran-zip Suggests: r-cran-afex, r-cran-bayesfactor, r-cran-beeswarm, r-cran-boot, r-cran-car, r-cran-dplyr, r-cran-effectsize, r-cran-emmeans, r-cran-ggforce, r-cran-ggplot2, r-cran-latex2exp, r-cran-lme4, r-cran-lmertest, r-cran-mass, r-cran-multcomp, r-cran-nlme, r-cran-nnet, r-cran-r.rsp, r-cran-skimr, r-cran-spelling, r-cran-testthat, r-cran-vgam Filename: pool/dists/noble/main/r-cran-papaja_0.1.4-1.ca2404.1_all.deb Size: 1213688 MD5sum: 613b8ac451e605b5d3471fa933ba9b73 SHA1: 0175e27ea3dcbb0245ff35afa84920321fe07836 SHA256: f1303666f5193bcde83ff3e103287296a743659f37794bec3cd95a5e2ff0cc04 SHA512: 0a128b7cb18783c0629adf34421e0e76b6a19913dad19c62db9294efb27c72fb55ca17909a18d7be75734617c95e517ccb56c8264968f68fb4895445539d0fbc Homepage: https://cran.r-project.org/package=papaja Description: CRAN Package 'papaja' (Prepare American Psychological Association Journal Articles withR Markdown) Tools to create dynamic, submission-ready manuscripts, which conform to American Psychological Association manuscript guidelines. We provide R Markdown document formats for manuscripts (PDF and Word) and revision letters (PDF). Helper functions facilitate reporting statistical analyses or create publication-ready tables and plots. Package: r-cran-papci Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyverse, r-cran-binom, r-cran-propcis, r-cran-ratesci, r-cran-hmisc, r-cran-shiny, r-cran-shinythemes, r-cran-readxl, r-cran-dt Filename: pool/dists/noble/main/r-cran-papci_0.1.0-1.ca2404.1_all.deb Size: 55022 MD5sum: c97be349a67c2c3cce64b9e2b2bedf53 SHA1: fbd8356028ccb306689d66b946d827aee4f84925 SHA256: f9eae2fed1e97515c7e3c376f9d50132457b85782368c1d42f864c696927b697 SHA512: d36127939ed517e05732550751adf5c081e8ed3882790817f6808499df661ae72b757cef8705fab36e8f3a80f4110ba90c920c9579d3cb7ad7687b781cc37e52 Homepage: https://cran.r-project.org/package=papci Description: CRAN Package 'papci' (Prevalence Adjusted PPV Confidence Interval) Positive predictive value (PPV) defined as the conditional probability of clinical trial assay (CTA) being positive given Companion diagnostic device (CDx) being positive is a key performance parameter for evaluating the clinical validity utility of a companion diagnostic test in clinical bridging studies. When bridging study patients are enrolled based on CTA assay results, Binomial-based confidence intervals (CI) may are not appropriate for PPV CI estimation. Bootstrap CIs which are not restricted by the Binomial assumption may be used for PPV CI estimation only when PPV is not 100%. Bootstrap CI is not valid when PPV is 100% and becomes a single value of [1, 1]. We proposed a risk ratio-based method for constructing CI for PPV. By simulation we illustrated that the coverage probability of the proposed CI is close to the nominal value even when PPV is high and negative percent agreement (NPA) is close to 100%. There is a lack of R package for PPV CI calculation. we developed a publicly available R package along with this shiny app to implement the proposed approach and some other existing methods. Package: r-cran-paper Architecture: all Version: 1.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-xtable, r-cran-gmodels Suggests: r-cran-nlme, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-foreign Filename: pool/dists/noble/main/r-cran-paper_1.0-6-1.ca2404.1_all.deb Size: 587352 MD5sum: c300afab60135a8aa5c41bf3fd5ec7f9 SHA1: 779adea2ada1faab94989c92b720512a37f325fe SHA256: 3421c83943478d8d903418ee2fec121022dacc2f3aa46aa1bab21e2ff48efd80 SHA512: 215e468cd9d24028e8b6511016a60d78533b611f622f897effb4fc224d0415ede1d3cff1ea5eb7764823e5daf628c94541ab8a8c9e58a392197d15348a36aff8 Homepage: https://cran.r-project.org/package=papeR Description: CRAN Package 'papeR' (A Toolbox for Writing Pretty Papers and Reports) A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'- or 'markdown'-based reports and to prettify the output of various estimated models. 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Package: r-cran-parabar Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2356 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-progress, r-cran-callr, r-cran-filelock Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-parabar_1.4.2-1.ca2404.1_all.deb Size: 1873268 MD5sum: 103f1d7c88b2e60c04ed28b26243e493 SHA1: cbac4ae18ea9c0a2cbf3a762356b2fa557ad82de SHA256: af56f916692d5c406ac4df8e504b97e58f199d30dc2d811ebf3f3e01729a1076 SHA512: dfddf4aa24e78e99733a15ae2558b7f7418336c5b9f53a3ab5204a8a946f8065f8a1bc4ced767401ed6743f4cba579f73da777eee4e45d4a1802ad832a247322 Homepage: https://cran.r-project.org/package=parabar Description: CRAN Package 'parabar' (Progress Bar for Parallel Tasks) A simple interface in the form of R6 classes for executing tasks in parallel, tracking their progress, and displaying accurate progress bars. Package: r-cran-parade Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-parade_0.1-1.ca2404.1_all.deb Size: 24394 MD5sum: 086fbef2d223faf8aef1c83308f6a1c4 SHA1: 8cbd52ac8fb5ef84e510ff6596e77de50759a411 SHA256: 5dd1bac8679b32259ea385d3e8860aabb86bdb560c18198f92c1f4eb57bef9cc SHA512: b8be7f68ea887a5defa1b8818ae35fbdbb3e81e200ec38771447570e68229037ece01300f723d66faea23dfb32390a914b8149d9a8ace297b7867ae0a001fbc0 Homepage: https://cran.r-project.org/package=parade Description: CRAN Package 'parade' (Pen's Income Parades) Tool for producing Pen's parade graphs, useful for visualizing inequalities in income, wages or other variables, as proposed by Pen (1971, ISBN: 978-0140212594). Income or another economic variable is captured by the vertical axis, while the population is arranged in ascending order of income along the horizontal axis. Pen's income parades provide an easy-to-interpret visualization of economic inequalities. Package: r-cran-paradox Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-backports, r-cran-checkmate, r-cran-data.table, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-rmarkdown, r-cran-mlr3learners, r-cran-e1071, r-cran-knitr, r-cran-lhs, r-cran-spacefillr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-paradox_1.0.1-1.ca2404.1_all.deb Size: 761088 MD5sum: dfc7335e64fbb779282023b433788a81 SHA1: af06aa69327f3934335aa63def655b6a55bce64b SHA256: 97db30013c9d3fbb349f5178c9c197ef128095e4d2dfbac6e418b2758c757c6c SHA512: 33f22c241c2465022d1526da876015caeebb91e28145c94a2f4f65c9c33d34f5b1f90139fd6e54aa1d350b8821461afd3bd343597beb8f94b1802fddf757d0c7 Homepage: https://cran.r-project.org/package=paradox Description: CRAN Package 'paradox' (Define and Work with Parameter Spaces for Complex Algorithms) Define parameter spaces, constraints and dependencies for arbitrary algorithms, to program on such spaces. 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Package: r-cran-parafac4microbiome Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3422 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-compositions, r-cran-cowplot, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-ggpubr, r-cran-lifecycle, r-cran-magrittr, r-cran-multiway, r-cran-pracma, r-cran-rlang, r-cran-rtensor, r-cran-tidyr Suggests: r-cran-knitr, r-bioc-phyloseq, r-cran-rmarkdown, r-bioc-treesummarizedexperiment, r-bioc-summarizedexperiment, r-cran-testthat, r-cran-withr, r-cran-nplstoolbox Filename: pool/dists/noble/main/r-cran-parafac4microbiome_1.3.2-1.ca2404.1_all.deb Size: 2888524 MD5sum: 1f269d9753c69d3793cb7bc2567e4f11 SHA1: 9122d991ad136220fbcc846e7e211a2633113af4 SHA256: 59341865294c74d8c07bf1fe9de57f256f73b8abb74a5f6c3d406b43752a495f SHA512: ced691169ea0910d4ef2ff4c43f460e0c70f5ecc3e80e6caf8d63955a96107cb0428c5244aa81aeb1154c1edff9da99aa4260d97ed68d2ff07144f85c45ed92d Homepage: https://cran.r-project.org/package=parafac4microbiome Description: CRAN Package 'parafac4microbiome' (Parallel Factor Analysis Modelling of Longitudinal MicrobiomeData) Creation and selection of PARAllel FACtor Analysis (PARAFAC) models of longitudinal microbiome data. You can import your own data with our import functions or use one of the example datasets to create your own PARAFAC models. Selection of the optimal number of components can be done using assessModelQuality() and assessModelStability(). The selected model can then be plotted using plotPARAFACmodel(). The Parallel Factor Analysis method was originally described by Caroll and Chang (1970) and Harshman (1970) . Package: r-cran-parallellogger Architecture: all Version: 3.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 700 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-snow, r-cran-xml2, r-cran-jsonlite, r-cran-rstudioapi, r-cran-memuse Suggests: r-cran-sendmailr, r-cran-testthat, r-cran-shiny, r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-parallellogger_3.5.1-1.ca2404.1_all.deb Size: 588386 MD5sum: e33472ddab37647554da7b03f0434724 SHA1: 6a216a6939f8c3928639dd43070936ac2158ed42 SHA256: 8c6d2f5061fe7973c4112a5229ba1ed0f1541d0673a6c119ffca5f31382aeb5a SHA512: 85337d84fe6f057f852aaf68d623856a174c0d452cd12f693808b7b51835b1e1abf60064bcd201ecd0576c531c7cf7626436fbc167520d1707d2d743c2ec80f0 Homepage: https://cran.r-project.org/package=ParallelLogger Description: CRAN Package 'ParallelLogger' (Support for Parallel Computation, Logging, and FunctionAutomation) Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk, and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in parallel). Package: r-cran-parallelmap Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bbmisc, r-cran-checkmate Suggests: r-cran-batchjobs, r-cran-batchtools, r-cran-data.table, r-cran-rmpi, r-cran-rpart, r-cran-snow, r-cran-testthat Filename: pool/dists/noble/main/r-cran-parallelmap_1.5.1-1.ca2404.1_all.deb Size: 107732 MD5sum: be726014439b7ee3c45c49e79787f989 SHA1: d8ced59bd882a31c6df751b5d7522d5ad931470a SHA256: 6a27841ae319e8e6232f6b610b286b0524f6a8559ff7a0a10bd05139fe56637a SHA512: 29f69abdb866cdf3e761506f0daeb154b92bc740dcddcd7cfc5f0d38641956b93822128d588642d1aefd65240dd489aa21eb7f08d8daea60abec108ec39cac02 Homepage: https://cran.r-project.org/package=parallelMap Description: CRAN Package 'parallelMap' (Unified Interface to Parallelization Back-Ends) Unified parallelization framework for multiple back-end, designed for internal package and interactive usage. The main operation is parallel mapping over lists. Supports 'local', 'multicore', 'mpi' and 'BatchJobs' mode. Allows tagging of the parallel operation with a level name that can be later selected by the user to switch on parallel execution for exactly this operation. Package: r-cran-parallelmcmccombine Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-parallelmcmccombine_2.0-1.ca2404.1_all.deb Size: 38498 MD5sum: 2120786ac645a1932080cbcb9c32b6af SHA1: 9f54f9b252964e0116c5354c57a31fcfa7698643 SHA256: 4da53d8fc43a3f6db8865b619396e099ff8f94c5b98acf801b350680237db8e5 SHA512: 6dc5fc3a728907b044a1feeff74127366f5dc814f80489aec017b54bbc30d9e2f3913185f0eb12c71830fd7e4c5c852d60faefc733681c375e982bd89c1ce759 Homepage: https://cran.r-project.org/package=parallelMCMCcombine Description: CRAN Package 'parallelMCMCcombine' (Combining Subset MCMC Samples to Estimate a Posterior Density) See Miroshnikov and Conlon (2014) . Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets. Package: r-cran-parallelpc Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bnlearn, r-cran-pcalg, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-parallelpc_1.2-1.ca2404.1_all.deb Size: 150578 MD5sum: 4ed3eb686174500b79f5f997330b6d07 SHA1: 52e7f1cb10db0d0b33ddf3e906ba164ecc400c84 SHA256: a16960070fc8c6099eabdc3b3ec803cdb26e932def19503d35b84f485d013bc9 SHA512: c40c6fdc3906b2d386ca95b8b113d1ea3796adf819dfcd3ffe3372a52912bddc4191b51d382566521144ee14e3598236807a03529f7d1ab590b7fdf69a97da9e Homepage: https://cran.r-project.org/package=ParallelPC Description: CRAN Package 'ParallelPC' (Paralellised Versions of Constraint Based Causal DiscoveryAlgorithms) Parallelise constraint based causality discovery and causal inference methods. The parallelised algorithms in the package will generate the same results as that of the 'pcalg' package but will be much more efficient. Package: r-cran-parallelplot Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1304 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-testthat, r-cran-shiny, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-parallelplot_0.4.0-1.ca2404.1_all.deb Size: 165934 MD5sum: 3f1c7626cf9f86f8e3096e3ba9b46cb4 SHA1: 7047c857ca1dd838690d70f4c79bf4ee82d98b7d SHA256: 8aec4e06c5682e9e8f0465e5e4080d5700e202ba48b94041f68658e99a096710 SHA512: 7c4d7397e25baa98ca661c2573882421a4bbe381463b8e822b557985775de68006ad737a57328175359b799dede87aa72290aaf9e12aaa69fbac53f7f8c8e6c5 Homepage: https://cran.r-project.org/package=parallelPlot Description: CRAN Package 'parallelPlot' (`htmlwidget` for a Parallel Coordinates Plot) Create a parallel coordinates plot, using `htmlwidgets` package and `d3.js`. 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Normal, skew-normal, skew-t and Tukey g-&-h distributions are supported, for now. Package: r-cran-paramanova Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-mlr3misc, r-cran-rlang, r-cran-rstatix, r-cran-tibble Filename: pool/dists/noble/main/r-cran-paramanova_0.1.4-1.ca2404.1_all.deb Size: 84340 MD5sum: 4a3c1d725623b197b6ae076ea6bc7f2f SHA1: 448cc0b74c6bce9ab8cbcc2072c63514c7bf7187 SHA256: 99570e9bd244b4ad9e9cb58e5bbb838172fdf8d3b43f0e9635125c489a3be9eb SHA512: 68aca54b3c5aea14dd453886a8e4b347f4093f82c2ca048b7ea7940ed28d7db3c29bfcf18b0cb24ff4925e1069c7d062b90c53f9d98bf130ce25dca615528b4c Homepage: https://cran.r-project.org/package=ParamANOVA Description: CRAN Package 'ParamANOVA' (Fully Parameterizable ANOVA Tests) Allows the user to perform ANOVA tests (in a strict sense: continuous and normally-distributed Y variable and 1 or more factorial/categorical X variable(s)), with the possibility to specify the type of sum of squares (1, 2 or 3), the types of variables (Fixed or Random) and their relationships (crossed or nested) with the sole function of the package (FullyParamANOVA()). The resulting outputs are the same as in 'SAS' software. A dataset (Butterfly) to test the function is also joined. 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Beyond computing p values, CIs, and other indices for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), this package implements features like bootstrapping or simulating of parameters and models, feature reduction (feature extraction and variable selection) as well as functions to describe data and variable characteristics (e.g. skewness, kurtosis, smoothness or distribution). 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Package: r-cran-paramhetero Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mass Filename: pool/dists/noble/main/r-cran-paramhetero_1.0.0-1.ca2404.1_all.deb Size: 30708 MD5sum: 4bb43f1d66c613f51a1281308afe4910 SHA1: 38f8744423452663a10ffd205fd4a4118ca4707a SHA256: c0100c275cfb84a10f64344c5058833a4ddf615e7069083dfd0e944509db58cf SHA512: 0e354800a78295d5890ad87bb694d5ddf1a186e586fee634eff067657a2bf1c0187d140212bf85a0451cc77cf32ee36a0ee67fa55242bccf5945c1b5d7a969f8 Homepage: https://cran.r-project.org/package=paramhetero Description: CRAN Package 'paramhetero' (Numeric and Visual Comparisons of Heterogeneity in ParametricModels) Performs statistical tests to compare coefficients and residual variance across models. Also provides graphical methods for assessing heterogeneity in coefficients and residuals. Currently supports linear and generalized linear models. 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Package: r-cran-paramlink2 Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pedtools, r-cran-pedprobr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-paramlink2_1.0.6-1.ca2404.1_all.deb Size: 149818 MD5sum: e0443bc5ff1b53ee9b26fc9e6969f6d4 SHA1: ca78697722d6c253b280f4912715d8996722b1dc SHA256: e921931a1f1b95909629574417684d5dc4deb9f2337dbc077243a36619938a1e SHA512: bc3401983e2b1737ee228c1167b0f611b7fcc93c7440277fcf2492fa22f2088509ae5e39164e61ad6a58c2dc4fab300b449c9a0d2c2477f96b6716b7028fdf36 Homepage: https://cran.r-project.org/package=paramlink2 Description: CRAN Package 'paramlink2' (Parametric Linkage Analysis) Parametric linkage analysis of monogenic traits in medical pedigrees. Features include singlepoint analysis, multipoint analysis via 'MERLIN' (Abecasis et al. (2002) ), visualisation of log of the odds (LOD) scores and summaries of linkage peaks. Disease models may be specified to accommodate phenocopies, reduced penetrance and liability classes. 'paramlink2' is part of the 'pedsuite' package ecosystem, presented in 'Pedigree Analysis in R' (Vigeland, 2021, ISBN:9780128244302). Package: r-cran-paramlink Architecture: all Version: 1.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-kinship2, r-cran-maxlik Suggests: r-cran-igraph Filename: pool/dists/noble/main/r-cran-paramlink_1.1-6-1.ca2404.1_all.deb Size: 643966 MD5sum: b70822c279bcc91cb58fce32c2ee6f7c SHA1: 59bee90e418fd907057d5471f011d1bdf5238774 SHA256: 6c6e959fc0c179d3ae5432679187815b830b9f454b4d9d809c9ef58045d269e9 SHA512: 155d846356feac69cc6005c07cd09c896856d0db9fc4b9817c1cf20ec0bdfb6daf7f1dc865be013a7e8ae5a5ba4f4716ebd29644f2cc5ae87522aad8eaf2cd10 Homepage: https://cran.r-project.org/package=paramlink Description: CRAN Package 'paramlink' (Parametric Linkage and Other Pedigree Analysis in R) NOTE: 'PARAMLINK' HAS BEEN SUPERSEDED BY THE 'PEDSUITE' PACKAGES (). 'PARAMLINK' IS MAINTAINED ONLY FOR LEGACY PURPOSES AND SHOULD NOT BE USED IN NEW PROJECTS. A suite of tools for analysing pedigrees with marker data, including parametric linkage analysis, forensic computations, relatedness analysis and marker simulations. The core of the package is an implementation of the Elston-Stewart algorithm for pedigree likelihoods, extended to allow mutations as well as complex inbreeding. Features for linkage analysis include singlepoint LOD scores, power analysis, and multipoint analysis (the latter through a wrapper to the 'MERLIN' software). Forensic applications include exclusion probabilities, genotype distributions and conditional simulations. Data from the 'Familias' software can be imported and analysed in 'paramlink'. Finally, 'paramlink' offers many utility functions for creating, manipulating and plotting pedigrees with or without marker data (the actual plotting is done by the 'kinship2' package). 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This attempts to provide a cleaner alternative to options(). Package: r-cran-paramsim Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-foreach, r-cran-doparallel, r-cran-future, r-cran-tibble Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-paramsim_0.1.0-1.ca2404.1_all.deb Size: 34590 MD5sum: 73c1173d16f6ed1155619d65bca406d1 SHA1: 24489df2bfea6c43494b9898bb1ba5eca60fc8d0 SHA256: 3f5dce96ae705682ca715886aa5d2c8b2e99a2e21618113985b6495b9d26ac79 SHA512: 2fe77f5821c3178f2ace4e9727faeb3f9dc932d23f8b6ffc54bac7aed5e3f84a599152d03c58ffedf75a49a639be14e6a4d6186bf330dbca894e5e464aca2ab4 Homepage: https://cran.r-project.org/package=paramsim Description: CRAN Package 'paramsim' (Parameterized Simulation) This function obtains a Random Number Generator (RNG) or collection of RNGs that replicate the required parameter(s) of a distribution for a time series of data. Consider the case of reproducing a time series data set of size 20 that uses an autoregressive (AR) model with phi = 0.8 and standard deviation equal to 1. When one checks the arima.sin() function's estimated parameters, it's possible that after a single trial or a few more, one won't find the precise parameters. This enables one to look for the ideal RNG setting for a simulation that will accurately duplicate the desired parameters. 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Horn's method contrasts eigenvalues produced through a PCA or FA on a number of random data sets of uncorrelated variables with the same number of variables and observations as the experimental or observational data set to produce eigenvalues for components or factors that are adjusted for the sample error-induced inflation. Components with adjusted eigenvalues greater than one are retained. paran may also be used to conduct parallel analysis following Glorfeld's (1995) suggestions to reduce the likelihood of over-retention. Package: r-cran-parasiter Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-ggplot2, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-blakerci, r-cran-boot, r-cran-readr Filename: pool/dists/noble/main/r-cran-parasiter_1.0-1.ca2404.1_all.deb Size: 94250 MD5sum: 6792e37f5e9bae72eafbc7b0a780cc6b SHA1: b70a68acdf498909a98e7abd2e14fa0ab371d4d7 SHA256: 757e0dc4a193576deeac43e42ab246248622d7f908957ca1871db8c6c72d4796 SHA512: c8a66f42f8a70f82bb1c2ad8781022c72c7b19b0781ad864a78818e93b72613524c697556cc686a0a722c2b82dd232d0bb3943f3a889a5849664b3babfce470b Homepage: https://cran.r-project.org/package=parasiteR Description: CRAN Package 'parasiteR' (A Theorical-Practical Approach to Parasitological Data Analysis) Standardizes and streamlines the processing of parasitological data by integrating descriptive analyses of parasite count distributions, automated calculation of parasitological indices and their dispersion measures, and intuitive visualizations for representing these metrics (Bush et al. 1997 , Reiczigel et al. 2019 ). 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Package: r-cran-pargasite Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4859 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-gstat, r-cran-leaflet, r-cran-leafsync, r-cran-raqs, r-cran-rlang, r-cran-sf, r-cran-shiny, r-cran-shinycssloaders, r-cran-stars Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pargasite_2.1.1-1.ca2404.1_all.deb Size: 4152636 MD5sum: 48cfcdbb60551ecc06421932c33ff72f SHA1: 173aa1393bb7309e145f05de8c727632be722376 SHA256: e6763a8f4686dc3e50cba0412ac3bc382edf51108819ed99e0e6743faae111e0 SHA512: e36d8d67073309a3fe12a4ab473bce713733834902fe9088260da0491be00116964ccd7fb2a6ed28122a429c30441802d49ea372fded2390514969064f130bda Homepage: https://cran.r-project.org/package=pargasite Description: CRAN Package 'pargasite' (Pollution-Associated Risk Geospatial Analysis Site) Offers tools to estimate and visualize levels of major pollutants (CO, NO2, SO2, Ozone, PM2.5 and PM10) across the conterminous United States for user-defined time ranges. 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The package also provides helper functions to manipulate data and execute common procedures such as finding the closest radiological exams considering a given timepoint, or creating a DICOM header database from the downloaded images. All functionalities are parallelized for fast and efficient analyses. 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The model belong to the semiparametric class, that including a parametric and nonparametric component. The error term considered belongs to the scale-mixture of normal (SMN) distribution, that includes well-known heavy tails distributions as the Student-t distribution, among others. To examine the performance of the fitted model, case-deletion and local influence techniques are provided to show its robust aspect against outlying and influential observations. This work is based in Ferreira, C. S., & Paula, G. A. (2017) but considering the SMN family. 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Package: r-cran-partitionbefsp Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-partitionbefsp_1.0-1.ca2404.1_all.deb Size: 30808 MD5sum: 288d127072a360b998d394b3cde30cb9 SHA1: 36202e8823616bf264e46d9b1f951adeab0502f8 SHA256: aaaf8e4cbbd87abea16c3d2fb32aec93de5857328e8127020fe79f48b1910137 SHA512: c6243c445d0ed6970643ad319ed5478b96dfbf19ddb0589d77a560ad6dacb43419c717230efe92c74dbcb7af29bc1adce2b2148a8df2467d3a0472d66450efa5 Homepage: https://cran.r-project.org/package=partitionBEFsp Description: CRAN Package 'partitionBEFsp' (Methods for Calculating the Loreau & Hector 2001 BEF Partition) A collection of functions that can be used to estimate selection and complementarity effects, sensu Loreau & Hector (2001) , even in cases where data are only available for a random subset of species (i.e. incomplete sample-level data). A full derivation and explanation of the statistical corrections used here is available in Clark et al. (2019) . 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Package: r-cran-patchwork Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gtable, r-cran-rlang, r-cran-cli, r-cran-farver Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridgraphics, r-cran-gridextra, r-cran-ragg, r-cran-testthat, r-cran-vdiffr, r-cran-covr, r-cran-png, r-cran-gt Filename: pool/dists/noble/main/r-cran-patchwork_1.3.2-1.ca2404.1_all.deb Size: 3298048 MD5sum: 115a2984585cb889c3e078d3a1572e73 SHA1: 1aacd3ac9fda601f8fa34ab615aa0946bd82093b SHA256: d45ce1a8b53de247d265e89238a5a7e3b1f1c5f2d77c9fc536165a8560673bd8 SHA512: 4e537ad76ad25deb6ee4ee5fe120a2d6b18df3c6e3c090d84291c93fc61069aa120c1470717ab232425d4bd7360489560a5651eb66c20727f19775c321e1b122 Homepage: https://cran.r-project.org/package=patchwork Description: CRAN Package 'patchwork' (The Composer of Plots) The 'ggplot2' package provides a strong API for sequentially building up a plot, but does not concern itself with composition of multiple plots. 'patchwork' is a package that expands the API to allow for arbitrarily complex composition of plots by, among others, providing mathematical operators for combining multiple plots. Other packages that try to address this need (but with a different approach) are 'gridExtra' and 'cowplot'. Package: r-cran-patentsview Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-lifecycle, r-cran-jsonlite Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-vcr Filename: pool/dists/noble/main/r-cran-patentsview_1.0.0-1.ca2404.1_all.deb Size: 85558 MD5sum: b2878a1543f96549d303bf13301ba184 SHA1: 48a6b2aa6aedc4ee60e1a6586ebab6db9056b24f SHA256: 93f145fbe6c06bba2d7e828c46d8fcbcccec009145c89ef52fe8822bf401e17e SHA512: 9b01303dd4c1727d92b8a85cd753700e6abb0336ca0c93ee18f80d4d9ec1e6a53186d7e5a9d06db2cab17f277ef6ed313d172aea57bbf74d9e4830d7ff9b13b9 Homepage: https://cran.r-project.org/package=patentsview Description: CRAN Package 'patentsview' (An R Client to the 'PatentsView' API) Provides functions to simplify the 'PatentsView' API () query language, send GET and POST requests to the API's twenty seven endpoints, and parse the data that comes back. Package: r-cran-pater Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-stringr Filename: pool/dists/noble/main/r-cran-pater_1.0.0-1.ca2404.1_all.deb Size: 46426 MD5sum: 0a0f8d28ec061d1eea80b4280ca58854 SHA1: c03e973d1c4e9507f9a3614d467e6710942f455e SHA256: 58b86344dad633f9cdf5da1b32a660d36e7d3818a44f4aab6c4f1f23a634e524 SHA512: 764a7abd234cd40d2f0b2850c50a7ab39a110bcb1d8b97b120c91bd345c78c9aded0cc89edb1b52b233979cb99813eb33052bd4e589662157aebc049387c68be Homepage: https://cran.r-project.org/package=pater Description: CRAN Package 'pater' (Turn a URL Pathname into a Regular Expression) R's implementation of the JavaScript library 'path-to-regexp', it aims to provide R web frameworks features such as parameter handling among other URL path utilities. Package: r-cran-path.analysis Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2688 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corrr, r-cran-corrplot, r-cran-hmisc, r-cran-gplots, r-cran-mathjaxr, r-cran-pastecs, r-cran-diagrammer, r-bioc-complexheatmap, r-cran-metan Suggests: r-cran-car, r-cran-ggplot2, r-cran-devtools, r-cran-usethis, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling Filename: pool/dists/noble/main/r-cran-path.analysis_0.1-1.ca2404.1_all.deb Size: 679890 MD5sum: f28413b346b81408461bc38b98345578 SHA1: 7b34525c8916b2e1b0f153bcd987f9bcaaa0fa9b SHA256: b3e9ded6754f05193a4aa9960e73fc91aeb33dab4f60a2ba30958a1e34481268 SHA512: ca78f2a9133b0767b7f4c01d833d25f31fe7bd6a0ef4225a47692a20c222fb5c166f1c34cc4d0eb1aeecdc5cda8b68e93fab0eacbd4fc78aabb4588431a5e860 Homepage: https://cran.r-project.org/package=Path.Analysis Description: CRAN Package 'Path.Analysis' (Path Coefficient Analysis) Facilitates the performance of several analyses, including simple and sequential path coefficient analysis, correlation estimate, drawing correlogram, Heatmap, and path diagram. When working with raw data, that includes one or more dependent variables along with one or more independent variables are available, the path coefficient analysis can be conducted. It allows for testing direct effects, which can be a vital indicator in path coefficient analysis. The process of preparing the dataset rule is explained in detail in the vignette file "Path.Analysis_manual.Rmd". You can find this in the folders labelled "data" and "~/inst/extdata". Also see: 1)the 'lavaan', 2)a sample of sequential path analysis in 'metan' suggested by Olivoto and Lúcio (2020) , 3)the simple 'PATHSAS' macro written in 'SAS' by Cramer et al. (1999) , and 4)the semPlot() function of 'OpenMx' as initial tools for conducting path coefficient analyses and SEM (Structural Equation Modeling). To gain a comprehensive understanding of path coefficient analysis, both in theory and practice, see a 'Minitab' macro developed by Arminian, A. in the paper by Arminian et al. (2008) . Package: r-cran-path.chain Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-stringi Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-config, r-cran-yaml, r-cran-fs, r-cran-magrittr, r-cran-logger Filename: pool/dists/noble/main/r-cran-path.chain_1.0.0-1.ca2404.1_all.deb Size: 194662 MD5sum: bc75fceaf50d4fc24a56ec6d02f8d5a3 SHA1: 9d9ef8c26995fc5e94f2c6f84b81567060460913 SHA256: 7ca1e98364468430a8379d4185d8a45e3045cdec2d34cb833e0eef27e117ecc1 SHA512: 87eb846ac8ec89346677e5826510d58207ac3d18b11924f48e3c98fd53472b8ec4da7722921f6741edaf7db27f6a11405721ecdd36c0b7c1086b032269f691b4 Homepage: https://cran.r-project.org/package=path.chain Description: CRAN Package 'path.chain' (Concise Structure for Chainable Paths) Provides path_chain class and functions, which facilitates loading and saving directory structure in YAML configuration files via 'config' package. The file structure you created during exploration can be transformed into legible section in the config file, and then easily loaded for further usage. Package: r-cran-pathfindr.data Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5188 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pathfindr.data_2.1.0-1.ca2404.1_all.deb Size: 5263790 MD5sum: 246c1d6695c79d47871720aeb9512b1b SHA1: ba7986e23d4cd3ed9634a31ba31c9ea73eb8647d SHA256: 758e0bc5c49b33886a4cc566c1370cc0c95dd76ce24d359c7ada7134262098db SHA512: 7923eba55e9cee09e40248ded18681cb8fb8d656fd728e703fe7cab4cd379dd1b66d389c840d7823b3df5ecb5d62d97ffdf493c93c4616fb63fc63c3df54ddd8 Homepage: https://cran.r-project.org/package=pathfindR.data Description: CRAN Package 'pathfindR.data' (Data Package for 'pathfindR') This is a data-only package, containing data needed to run the CRAN package 'pathfindR', a package for enrichment analysis utilizing active subnetworks. This package contains protein-protein interaction network data, data related to gene sets and example input/output data. Package: r-cran-pathfindr Architecture: all Version: 2.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pathfindr.data, r-cran-dbi, r-bioc-annotationdbi, r-cran-doparallel, r-cran-foreach, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggraph, r-cran-ggupset, r-cran-fpc, r-bioc-ggkegg, r-cran-httr, r-cran-igraph, r-cran-r.utils, r-cran-msigdbr, r-cran-knitr Suggests: r-bioc-org.hs.eg.db, r-cran-testthat, r-cran-covr, r-cran-mockery Filename: pool/dists/noble/main/r-cran-pathfindr_2.7.0-1.ca2404.1_all.deb Size: 1907152 MD5sum: de6b375cc40c605151480961bec2e0d4 SHA1: 9917a84107fd4888c576a89c80481eca3627360e SHA256: d4072a410690dc97479a9294fb78cf0b63ae00670263cca401582cf2d86e48a7 SHA512: a1e335dcc7789973cad306888eeeae93823585b45e89c510ae40de666c4047fda74c460c55528b95006a040c443ddc7e7b8a138f4b443c974e264ad4c4397298 Homepage: https://cran.r-project.org/package=pathfindR Description: CRAN Package 'pathfindR' (Enrichment Analysis Utilizing Active Subnetworks) Enrichment analysis enables researchers to uncover mechanisms underlying a phenotype. However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. 'pathfindR' is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. 'pathfindR' also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in 'pathfindR' are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. 'pathfindR': An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. . Package: r-cran-pathlit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-timeseries, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-pathlit_0.1.0-1.ca2404.1_all.deb Size: 31432 MD5sum: 24567942a06d967cca19ce767b350b49 SHA1: ff6e9e3ab1b3dadec9dcaf35327591e8da50493a SHA256: 43c0c8e3fde433a88524cd76c1b68f53ac965cc9203ed918c7582ba48fbce2b4 SHA512: f83b5639d6a83669ef98032bbcf55fc235a453998babc6c50ce2a486dfda4a230d5f88a63be0c495c5051857490dda312e38cba924f2b9e77af2e14fbb521a97 Homepage: https://cran.r-project.org/package=pathlit Description: CRAN Package 'pathlit' (An SDK for the PathLit Engine) This wrapper houses PathLit API endpoints for R. The usage of these endpoints require the use of an API key which can be obtained at . Package: r-cran-pathmodelfit Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan Filename: pool/dists/noble/main/r-cran-pathmodelfit_1.0.5-1.ca2404.1_all.deb Size: 26932 MD5sum: a6d96a8b99439e468663a8a8e36eed0a SHA1: 40b33b3eeae4e947ddb46335e7953137ab5110a6 SHA256: 074f8ed5e8e1f8fae0223f7d1a9e7c8bcf8e785ec17f4eb89151a9506cb52166 SHA512: 2a3d24c215f6ef195d927ae9d23985c94faa5b3c2da1c53ad468ca573c694de2836f2a544ce7d02c11cad1f551e8af5dd7caf71f7508b0c741848bdb53f6ad4d Homepage: https://cran.r-project.org/package=pathmodelfit Description: CRAN Package 'pathmodelfit' (Path Component Fit Indices for Latent Structural Equation Models) Functions for computing fit indices for evaluating the path component of latent variable structural equation models. Available fit indices include RMSEA-P and NSCI-P originally presented and evaluated by Williams and O'Boyle (2011) and demonstrated by O'Boyle and Williams (2011) and Williams, O'Boyle, & Yu (2020) . Also included are fit indices described by Hancock and Mueller (2011) . Package: r-cran-paths Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bart, r-cran-boot, r-cran-gbm, r-cran-ggplot2, r-cran-metr, r-cran-twang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-paths_0.1.2-1.ca2404.1_all.deb Size: 125044 MD5sum: 9d0349321f97eb270199bb469dc42e42 SHA1: 09bb19243332ebfb2f8cd64dfa7fba33a689d456 SHA256: edf8637438b14143272ecd74376043319e6361f932ea3b312f8ce8b779c9d00e SHA512: e4cb9240e4fd75fb517e7abde2b186141a96dc3649d0d06f623aae29881e07d10eef4df6848d6858475d979cbc809b38c17cf2d94e9ccf7eee4d5543b5560756 Homepage: https://cran.r-project.org/package=paths Description: CRAN Package 'paths' (An Imputation Approach to Estimating Path-Specific CausalEffects) In causal mediation analysis with multiple causally ordered mediators, a set of path-specific effects are identified under standard ignorability assumptions. This package implements an imputation approach to estimating these effects along with a set of bias formulas for conducting sensitivity analysis (Zhou and Yamamoto ). It contains two main functions: paths() for estimating path-specific effects and sens() for conducting sensitivity analysis. Estimation uncertainty is quantified using the nonparametric bootstrap. Package: r-cran-pathviewr Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3359 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.matlab, r-cran-data.table, r-cran-magrittr, r-cran-dplyr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-fancova, r-cran-purrr, r-cran-ggplot2, r-cran-tidyselect, r-cran-cowplot, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-anomalize, r-cran-covr Filename: pool/dists/noble/main/r-cran-pathviewr_1.1.8-1.ca2404.1_all.deb Size: 2245336 MD5sum: fe01b1c4681d164478fa6cd1f52bbdf8 SHA1: 68132934f2caa908c75279f12d0b573b8ebd3f8e SHA256: 2d2f78b38fbcbef7cef0eac080bee39e874ba57e22ec33b20241f14e73947f92 SHA512: cbe1f102749f538ee011527d72c3aefb10c90f8df622b01c772f2690b47bb8274c4852a23c8250acf8b8ed8c42d1a97dbd5f281d2db098238afbe481e9a5d634 Homepage: https://cran.r-project.org/package=pathviewr Description: CRAN Package 'pathviewr' (Wrangle, Analyze, and Visualize Animal Movement Data) Tools to import, clean, and visualize movement data, particularly from motion capture systems such as Optitrack's 'Motive', the Straw Lab's 'Flydra', or from other sources. We provide functions to remove artifacts, standardize tunnel position and tunnel axes, select a region of interest, isolate specific trajectories, fill gaps in trajectory data, and calculate 3D and per-axis velocity. For experiments of visual guidance, we also provide functions that use subject position to estimate perception of visual stimuli. Package: r-cran-pathwayspace Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3841 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgraphspace, r-cran-scales, r-cran-rann, r-cran-igraph, r-cran-ggplot2, r-cran-ggrepel, r-cran-colorspace, r-cran-patchwork, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-reder Filename: pool/dists/noble/main/r-cran-pathwayspace_1.2.0-1.ca2404.1_all.deb Size: 2794328 MD5sum: 8d2e6ab495735232c9bb15ded26a58a5 SHA1: fd2dffac691a374d8c35a07c70e219cde5c2c947 SHA256: 9988be3381e6683c2b226f7ef868505d275f7e8fd8ce80de8d105625f7d17429 SHA512: 7c0b92f26c3d65f80ee1368c8cb1c0d070472068bce6bc00ccde0a33f45ca0006edc92fafc2bc449b597f61316c828de49a7b2c8f547036b0a343e91c02f6a05 Homepage: https://cran.r-project.org/package=PathwaySpace Description: CRAN Package 'PathwaySpace' (Spatial Projection of Network Signals along Geodesic Paths) For a given graph containing vertices, edges, and a signal associated with the vertices, the 'PathwaySpace' package performs a convolution operation, which involves a weighted combination of neighboring vertices and their associated signals. The package uses a decay function to project these signals, creating geodesic paths on a 2D-image space. 'PathwaySpace' has various applications, such as visualizing network data in a graphical format that highlights the relationships and signal strengths between vertices. By combining graph theory, signal processing, and visualization, 'PathwaySpace' provides a way of representing graph data on a continuous projection space. Based on methods introduced in Tercan et al. (2025) and Ellrott et al. (2025) . Package: r-cran-pathwaytmb Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1936 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biocgenerics, r-cran-purrr, r-cran-glmnet, r-cran-randomforest, r-cran-survival, r-cran-survminer, r-cran-caret, r-cran-data.table, r-cran-rcolorbrewer, r-cran-proc, r-bioc-maftools, r-bioc-clusterprofiler Suggests: r-cran-stringi, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-biocmanager, r-cran-xfun, r-cran-e1071, r-cran-qpdf, r-cran-tinytex, r-cran-spelling Filename: pool/dists/noble/main/r-cran-pathwaytmb_0.1.3-1.ca2404.1_all.deb Size: 801670 MD5sum: 0f8f4eb336c7e5a72fc49cbfbfbbcd9a SHA1: 3c05bfe76bd8f5788536d894d1cba5836a0cba8b SHA256: eab51c3e71324e28e8ebb82dce8bc0a8562d6dd9fa2183f82d5c2f3788b50f95 SHA512: deafb8a76e8f12929ba0219cbbebc1cd58b80ca14af5196d5d7be29479dceec06775d5fd9ae16959c183e34b19955d64886dc7bd67c123a4f69f633af1b65a56 Homepage: https://cran.r-project.org/package=pathwayTMB Description: CRAN Package 'pathwayTMB' (Pathway Based Tumor Mutational Burden) A systematic bioinformatics tool to develop a new pathway-based gene panel for tumor mutational burden (TMB) assessment (pathway-based tumor mutational burden, PTMB), using somatic mutations files in an efficient manner from either The Cancer Genome Atlas sources or any in-house studies as long as the data is in mutation annotation file (MAF) format. Besides, we develop a multiple machine learning method using the sample's PTMB profiles to identify cancer-specific dysfunction pathways, which can be a biomarker of prognostic and predictive for cancer immunotherapy. Package: r-cran-pathwayvote Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-harmonicmeanp, r-bioc-annotationdbi, r-bioc-clusterprofiler, r-cran-future, r-cran-furrr, r-cran-parallelly Suggests: r-bioc-go.db, r-bioc-org.hs.eg.db, r-bioc-reactome.db, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-pathwayvote_0.1.3-1.ca2404.1_all.deb Size: 77594 MD5sum: 8f15dc6ae1a713508028201fe503d7ff SHA1: 1bb3ac6b75a98b3a5faa09d40d0645ebdf9fa1ea SHA256: 3fe604521bc6a5d58a0047ea2528a82f925f5e6eec6275d4d11a8c7ed52b85b1 SHA512: 6a816072270a436b01ff8dbfba79be3a58451fcf67b83aefd0b4e7612d19ea1c0610ffcfc5bc3832a2624bf252474e394568693a49f6e51cf61505e8ad291d15 Homepage: https://cran.r-project.org/package=PathwayVote Description: CRAN Package 'PathwayVote' (Robust Pathway Enrichment for DNA Methylation Studies UsingEnsemble Voting) Performs pathway enrichment analysis using a voting-based framework that integrates CpG–gene regulatory information from expression quantitative trait methylation (eQTM) data. For a grid of top-ranked CpGs and filtering thresholds, gene sets are generated and refined using an entropy-based pruning strategy that balances information richness, stability, and probe bias correction. In particular, gene lists dominated by genes with disproportionately high numbers of CpG mappings are penalized to mitigate active probe bias—a common artifact in methylation data analysis. Enrichment results across parameter combinations are then aggregated using a voting scheme, prioritizing pathways that are consistently recovered under diverse settings and robust to parameter perturbations. Package: r-cran-patientgenerator Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1026 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bslib, r-cran-checkmate, r-cran-data.table, r-cran-dbi, r-cran-dplyr, r-cran-dt, r-cran-duckdb, r-cran-ellmer, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-r2d3, r-cran-r6, r-cran-shiny, r-cran-stringr, r-cran-testthat Suggests: r-cran-cdmconnector, r-cran-cohortcharacteristics, r-cran-cohortconstructor, r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testgenerator Filename: pool/dists/noble/main/r-cran-patientgenerator_0.1.4-1.ca2404.1_all.deb Size: 637920 MD5sum: 310402dc26de23a3769d97acb4a3bd8f SHA1: dda286e48f80874922e601375618a73834d02481 SHA256: 9a3769949d9df024159f7446b4d7f398578dbd43cb246361c7970a68a354899a SHA512: 2e4f557b05ad040fd1b944d04b4865e48cabd16cf9b623f534d8a12cafb655d66158a566b5d2f234d0416c87ab9656f1d7bc4bc5946484aacb625d61b923c021 Homepage: https://cran.r-project.org/package=PatientGenerator Description: CRAN Package 'PatientGenerator' (Generator of Synthetic Patient Data for the OMOP Common DataModel) Tools to generate synthetic patient-level test datasets in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Includes a chat-driven generator backed by large language models and an interactive 'shiny' designer for editing CDM test sets. Package: r-cran-patientlevelprediction Architecture: all Version: 6.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-andromeda, r-cran-cyclops, r-cran-databaseconnector, r-cran-digest, r-cran-dplyr, r-cran-featureextraction, r-cran-matrix, r-cran-memuse, r-cran-parallellogger, r-cran-proc, r-cran-prroc, r-cran-rlang, r-cran-sqlrender, r-cran-tidyr Suggests: r-cran-curl, r-cran-eunomia, r-cran-glmnet, r-cran-ggplot2, r-cran-gridextra, r-cran-iterativehardthresholding, r-cran-knitr, r-cran-lightgbm, r-cran-metrics, r-cran-mgcv, r-cran-ohdsishinyappbuilder, r-cran-pkgload, r-cran-polspline, r-cran-readr, r-cran-resourceselection, r-cran-resultmodelmanager, r-cran-reticulate, r-cran-rmarkdown, r-cran-rsqlite, r-cran-scoring, r-cran-survival, r-cran-survminer, r-cran-testthat, r-cran-withr, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-patientlevelprediction_6.6.0-1.ca2404.1_all.deb Size: 2241656 MD5sum: 6202a3996092373edd9647b369ab98ba SHA1: 5c2ed074a7816c83c686d02e41f4e7118637b2c7 SHA256: 1b05a2e3303065eea7090e9799bbf615dd0370cbb73a429f58a13d7aedf0e2c8 SHA512: d22da195f8fcb03c5787a816cbb97f459ec2dbf8c9235f60837f2461481331c734a805ced7c1692977412661f2afbc2b42b7d5f87f6cf163337fd10cfdad0d93 Homepage: https://cran.r-project.org/package=PatientLevelPrediction Description: CRAN Package 'PatientLevelPrediction' (Develop Clinical Prediction Models Using the Common Data Model) A user friendly way to create patient level prediction models using the Observational Medical Outcomes Partnership Common Data Model. Given a cohort of interest and an outcome of interest, the package can use data in the Common Data Model to build a large set of features. These features can then be used to fit a predictive model with a number of machine learning algorithms. This is further described in Reps (2017) . Package: r-cran-patientprofiles Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3846 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-dplyr, r-cran-lifecycle, r-cran-omopgenerics, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-bit64, r-cran-cdmconnector, r-cran-codelistgenerator, r-cran-cohortconstructor, r-cran-covr, r-cran-dbi, r-cran-dbplyr, r-cran-dt, r-cran-duckdb, r-cran-ggplot2, r-cran-glue, r-cran-gt, r-cran-here, r-cran-hmisc, r-cran-knitr, r-cran-odbc, r-cran-omock, r-cran-patchwork, r-cran-rmarkdown, r-cran-rpostgres, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tictoc, r-cran-withr Filename: pool/dists/noble/main/r-cran-patientprofiles_1.5.0-1.ca2404.1_all.deb Size: 731326 MD5sum: b306370eaf9213a4d5e63fc170ecdbeb SHA1: 8fddc714c16ea7f84e3c9dd99a67700eb0e18e37 SHA256: 6cbb6e7059bf5ba8c825d90da70255d986a208c83b44eaad3c515aefa2ebeb1e SHA512: 66939164a48e1ea07673e08f08e1e9f58cd785ac8432dd3ae9ed7a0d3362c04cbc4d97aa18234a7261865f498e3c4bf2e220b89d24dd4a83aa5e05655ce053b3 Homepage: https://cran.r-project.org/package=PatientProfiles Description: CRAN Package 'PatientProfiles' (Identify Characteristics of Patients in the OMOP Common DataModel) Identify the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Package: r-cran-patientprofilesvis Architecture: all Version: 2.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clinutils, r-cran-ggplot2, r-cran-plyr, r-cran-cowplot, r-cran-reshape2, r-cran-knitr, r-cran-stringr, r-cran-gridextra, r-cran-scales Suggests: r-cran-pander, r-cran-shiny, r-cran-testthat, r-cran-rmarkdown, r-cran-gtable, r-cran-pdftools, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-patientprofilesvis_2.0.10-1.ca2404.1_all.deb Size: 3765338 MD5sum: a6615de2b6c9d39fd31b9b2caeffc2be SHA1: 0f98ada12b9c07d584f660c3ee5673e22efe1638 SHA256: 5cb004181b35f9040a6fa1c32ceb3e72ac0ba69f5f8c26d0ba4e03c12c970f54 SHA512: 5d12204cd00259f6b2eb4792425dfc18934038e08cf9f879d8741cd88a064c17e100b9f1d73887011459a6b7636e4f2322d3ee6d4f6017f8a54df440c2117223 Homepage: https://cran.r-project.org/package=patientProfilesVis Description: CRAN Package 'patientProfilesVis' (Visualization of Patient Profiles) Creation of patient profile visualizations for exploration, diagnostic or monitoring purposes during a clinical trial. These static visualizations display a patient-specific overview of the evolution during the trial time frame of parameters of interest (as laboratory, ECG, vital signs), presence of adverse events, exposure to a treatment; associated with metadata patient information, as demography, concomitant medication. The visualizations can be tailored for specific domain(s) or endpoint(s) of interest. Visualizations are exported into patient profile report(s) or can be embedded in custom report(s). Package: r-cran-patrick Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-patrick_0.3.1-1.ca2404.1_all.deb Size: 21776 MD5sum: 888ae6160a379a007d09ab26e237cb4e SHA1: 93665d13ea6178bafc9c440c9591e92f13a622c4 SHA256: 91422a09ced8c9bf9d49e47315481c0e28da8eb01400769353d7bc3fae632339 SHA512: e36f706c2b780b4eb5deacdf566cec7e173b0a4809726191196e5125a260d4a14e3234141b2a7bf602c0d3fdd92c05fcdc7c52397e778965e6493938af139cc1 Homepage: https://cran.r-project.org/package=patrick Description: CRAN Package 'patrick' (Parameterized Unit Testing) This is an extension of the 'testthat' package that lets you add parameters to your unit tests. Parameterized unit tests are often easier to read and more reliable, since they follow the DNRY (do not repeat yourself) rule. Package: r-cran-pattern.checks Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pattern.checks_0.1.0-1.ca2404.1_all.deb Size: 23930 MD5sum: 30eaab14c386101a2d67efbf2f918c85 SHA1: 6be34608fbdb451a6b0857128209af20622eadc0 SHA256: 34f1812aedf17e7bc379c085f91a2e811a3440a2adde341eeb64c22e39b99d9a SHA512: ade603ea0dd77bb34bb9c978c472338475e33f455841f0b68469598c7ab83255e60d58485a16103294e9bd10a96d436662f63a42c935d2c4d03c7c96fe8023dd Homepage: https://cran.r-project.org/package=pattern.checks Description: CRAN Package 'pattern.checks' (Identifies Patterned Responses in Scales) Identifies the entries with patterned responses for psychometric scales. The patterns included in the package are identical (a, a, a), ascending (a, b, c), descending (c, b, a), alternative (a, b, a, b / a, b, c, a, b, c). Package: r-cran-patternator Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-plyr Filename: pool/dists/noble/main/r-cran-patternator_0.1.0-1.ca2404.1_all.deb Size: 29808 MD5sum: f821dc392368f79682b517868cb48e49 SHA1: fe31058afa8f154b33560feb1897379737956eff SHA256: eb4ab49e5a865c52a2b26c4c2d27a7eebecf230a0fa60ac3913d219ecee6cc6b SHA512: e72e421081e6773fdd6af57f3192038583d456b791e43601c0197b18f7b10c06709d292e4a4bdb8415d4f922de4d5a77c84a5b2c8a262992fc17819e34c56e8a Homepage: https://cran.r-project.org/package=patternator Description: CRAN Package 'patternator' (Feature Extraction from Female Brown Anole Lizard DorsalPatterns) Provides a set of functions to efficiently recognize and clean the continuous dorsal pattern of a female brown anole lizard (Anolis sagrei) traced from 'ImageJ', an open platform for scientific image analysis (see for more information), and extract common features such as the pattern sinuosity indices, coefficient of variation, and max-min width. Package: r-cran-patterncausality Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plot3d, r-cran-ggplot2, r-cran-reshape2, r-cran-ggthemes, r-cran-tidyr, r-cran-statebins, r-cran-ggrepel, r-cran-rcolorbrewer, r-cran-scales, r-cran-gridextra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-lintr, r-cran-zoo Filename: pool/dists/noble/main/r-cran-patterncausality_0.2.4-1.ca2404.1_all.deb Size: 2955538 MD5sum: 6ab89e7d90f6381bb52f9c292e6d7b64 SHA1: c45d3bd8ce1784037b405488fd68f99a110850c3 SHA256: 4cc4b58cbf3272a6cab74cf17b524c8c70ee6a2af6348958da071209cf78d68d SHA512: e12769c4f68522eb865551e16dd9cd2fc5386244b77f4e2b11227fe2d915dd1167affadf8eb422a77d1c467b84530ff59585018fa7a058d3d627152f7bfa0a39 Homepage: https://cran.r-project.org/package=patterncausality Description: CRAN Package 'patterncausality' (Pattern Causality Algorithm) A comprehensive package for detecting and analyzing causal relationships in complex systems using pattern-based approaches. Key features include state space reconstruction, pattern identification, and causality strength evaluation. Package: r-cran-patternize Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 928 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-sf, r-cran-abind, r-cran-morpho, r-cran-dplyr, r-cran-imager, r-cran-magrittr, r-cran-purrr, r-cran-vegan, r-cran-rniftyreg, r-cran-geomorph, r-cran-clusterr Filename: pool/dists/noble/main/r-cran-patternize_0.0.5-1.ca2404.1_all.deb Size: 834936 MD5sum: 244809e380eb9c8140fb4e90dbecda86 SHA1: 81aa8e4a12b2f7b8a30dc3ffab196af3d93cfe12 SHA256: 90b996de21c7337d3c541c04538850716de85c3b5bdac5723787df53948ea502 SHA512: 548d7cd7ddf80e6df48b19f154cc031bc3e5f10e62e74f50cf7351b455fba7e953e86e34ca0ec3bbe361c714701cd4a69cfebbc084eb3f874159b2f00aa68880 Homepage: https://cran.r-project.org/package=patternize Description: CRAN Package 'patternize' (Quantification of Color Pattern Variation) Quantification of variation in organismal color patterns as obtained from image data. Patternize defines homology between pattern positions across images either through fixed landmarks or image registration. Pattern identification is performed by categorizing the distribution of colors using RGB thresholds or image segmentation. Package: r-cran-patterns Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1484 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-cluster, r-cran-e1071, r-cran-gplots, r-cran-igraph, r-cran-lars, r-cran-lattice, r-bioc-limma, r-bioc-mfuzz, r-cran-movmf, r-cran-nnls, r-cran-plotrix, r-cran-selectboost, r-cran-tnet, r-cran-vgam, r-cran-wgcna Suggests: r-cran-animation, r-bioc-biobase, r-bioc-biomart, r-cran-c060, r-cran-cascadedata, r-cran-elasticnet, r-cran-glmnet, r-cran-knitr, r-cran-pixmap, r-cran-r.rsp, r-cran-rmarkdown, r-cran-spls, r-cran-testthat Filename: pool/dists/noble/main/r-cran-patterns_1.7-1.ca2404.1_all.deb Size: 1082574 MD5sum: 73808245f528acad2578572c2b1415ef SHA1: 5538f7af11154f4cf26e08035c86499e4448a238 SHA256: c854176706ffc9ec633d4c988844af1019fa62569107cbfa7c6f1416e1dbb67c SHA512: 954668f7794af7df4e1cc4ed23f85010091d0790713a4c9ecdbaa3f57d242cecb3b783bc3541a89a2c99f974ff0596f961eb1e52f9e4af1d8d3b1db4b6e09dba Homepage: https://cran.r-project.org/package=Patterns Description: CRAN Package 'Patterns' (Deciphering Biological Networks with Patterned HeterogeneousMeasurements) A modeling tool dedicated to biological network modeling (Bertrand and others 2020, ). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, ). Example of use with microarray or RNA-Seq data are provided. Package: r-cran-pavo Architecture: all Version: 2.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2090 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-future.apply, r-cran-geometry, r-cran-lightr, r-cran-magick, r-cran-farver, r-cran-plot3d, r-cran-progressr, r-cran-sf, r-cran-viridislite Suggests: r-cran-alphashape3d, r-cran-imager, r-cran-knitr, r-cran-mapproj, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-digest Filename: pool/dists/noble/main/r-cran-pavo_2.9.0-1.ca2404.1_all.deb Size: 1896636 MD5sum: bb05c2956f04d78f0bd92c474c959b5e SHA1: 8b0f3bf5cf0b54ed8c282092fb2e249fdb533524 SHA256: 6e4ca4034994931153d9616b3c6fe89bd477b2d0d29164a08b1c13ff3cf5b923 SHA512: 784140ddfa125de53a4570df446685aa24b29e5ced849925ce46499ffa26e5bf4529cd977ce99454d106793977f01673bad3836c958502d5954f3a9a6cb851a0 Homepage: https://cran.r-project.org/package=pavo Description: CRAN Package 'pavo' (Perceptual Analysis, Visualization and Organization of SpectralColour Data) A cohesive framework for the spectral and spatial analysis of colour described in Maia, Eliason, Bitton, Doucet & Shawkey (2013) and Maia, Gruson, Endler & White (2019) . 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It also assists in calculating the efficiencies of PBIB designs with any number of associate classes. The package also constructs Youden-m square designs which are Row-Column designs for the two-way elimination of heterogeneity. The incomplete columns of these Youden-m square designs constitute PBIB designs. With the present functionality, the package will be of immense importance for the researchers as it will help them to construct PBIB designs, to check if their PBIB designs and association scheme satisfy various necessary conditions for the existence, to calculate the efficiencies of PBIB designs based on any association scheme and to construct Youden-m square designs for the two-way elimination of heterogeneity. R. C. Bose and K. R. Nair (1939) . Package: r-cran-pbimisc Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1784 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-matrix Suggests: r-cran-ggplot2, r-cran-ca, r-cran-lattice Filename: pool/dists/noble/main/r-cran-pbimisc_1.0-1.ca2404.1_all.deb Size: 1777886 MD5sum: 937558d688b6475229d8df1832df5d23 SHA1: 63870c010098725b6b59f80be40493d4d4ec3585 SHA256: 0562b05f0b751616b13589422b6d917d6da1fd6e7347447302b1e1dca6a86695 SHA512: d6e0fa0649e4c823199c61462d10d83c3872db7ab2f4840153f1de56409d1a9ab07555f0fc8556777947dcb0d03bd5464e08179a8fd4c7d6d8ddaf3c264707be Homepage: https://cran.r-project.org/package=PBImisc Description: CRAN Package 'PBImisc' (A Set of Datasets Used in My Classes or in the Book 'ModeleLiniowe i Mieszane w R, Wraz z Przykladami w Analizie Danych') A set of datasets and functions used in the book 'Modele liniowe i mieszane w R, wraz z przykladami w analizie danych'. 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Package: r-cran-pbs Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pbs_1.1-1.ca2404.1_all.deb Size: 19696 MD5sum: c8f1e14b95724e046c8499cc9f45ebff SHA1: c30ec0c24a489f81689561df118b832c29611f75 SHA256: 1780db0cbb5d182d87b32c386b749b5ab4172a3c5f7e01331c5cc177192ff226 SHA512: e741865c13b10619533e8c57b73d0f58ef79f6b991d0ee54fbe0dd0da66b7f3e073101dbe1ac19a5a74f904faa33db717cc43da96532e2a575b78ad7fc026067 Homepage: https://cran.r-project.org/package=pbs Description: CRAN Package 'pbs' (Periodic B Splines) Periodic B Splines Basis Package: r-cran-pbsadmb Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3091 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbsmodelling Filename: pool/dists/noble/main/r-cran-pbsadmb_1.1.6-1.ca2404.1_all.deb Size: 2836202 MD5sum: 378fc5f23052d58309a1ed86aa8baf28 SHA1: 62484f0697db4622673a5bc23bca9695195b2817 SHA256: 8a2e293dfc934ab97bc5581c64ce2b98be69c0cf2b0a3f80c164a5e058dca487 SHA512: e2a2f713d9b0c1c257b44462747d6b66117de257604166dbb2c16df58fb1f5fa57edeedf873efff6c0a38f8f7ad7ebae8df264897844173d3d1e440de0fe623f Homepage: https://cran.r-project.org/package=PBSadmb Description: CRAN Package 'PBSadmb' (ADMB for R Using Scripts or GUI) A collection of software provides R support for 'ADMB' (Automatic Differentiation Model Builder) and a 'GUI' interface facilitates the conversion of 'ADMB' template code to 'C code' followed by compilation to a binary executable. Stand-alone functions can also be run by users not interested in clicking a 'GUI'. Package: r-cran-pbtdesigns Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-pbtdesigns_1.0.0-1.ca2404.1_all.deb Size: 26478 MD5sum: 9a450dab7b586849c6de6a611354b6e1 SHA1: fdbb261591b89e873b0c726769b8ff9f3868e04b SHA256: c9c76e71704d1cbcaf4c490c15b0e3b2bc9ea21349e10a21bf385b836b648377 SHA512: d61d670c21b04a710e3bb5e78fdbb42e541e1f18e38ed8f22d9efc46a04e9ff18f4fcb6a51549bf7f9efb9b2fb2c368b01ec85823fc7ab09d06944527782f5a7 Homepage: https://cran.r-project.org/package=PBtDesigns Description: CRAN Package 'PBtDesigns' (Partially Balanced t-Designs (PBtDesigns)) The t-designs represent a generalized class of balanced incomplete block designs in which the number of blocks in which any t-tuple of treatments (t >= 2) occur together is a constant. When the focus of an experiment lies in grading and selecting treatment subgroups, t-designs would be preferred over the conventional ones, as they have the additional advantage of t-tuple balance. t-designs can be advantageously used in identifying the best crop-livestock combination for a particular location in Integrated Farming Systems that will help in generating maximum profit. But as the number of components increases, the number of possible t-component combinations will also increase. Most often, combinations derived from specific components are only practically feasible, for example, in a specific locality, farmers may not be interested in keeping a pig or goat and hence combinations involving these may not be of any use in that locality. In such situations partially balanced t-designs with few selected combinations appearing in a constant number of blocks (while others not at all appearing) may be useful (Sayantani Karmakar, Cini Varghese, Seema Jaggi & Mohd Harun (2021)). Further, every location may not have the resources to form equally sized homogeneous blocks. Partially balanced t-designs with unequal block sizes (Damaraju Raghavarao & Bei Zhou (1998). Sayantani Karmakar, Cini Varghese, Seema Jaggi & Mohd Harun (2022)." Partially Balanced t-designs with unequal block sizes") prove to be more suitable for such situations.This package generates three series of partially balanced t-designs namely Series 1, Series 2 and Series 3. Series 1 and Series 2 are designs having equal block sizes and with treatment structures 4(t + 1) and a prime number, respectively. Series 3 consists of designs with unequal block sizes and with treatment structure n(n-1)/2. This package is based on the function named PBtD() for generating partially balanced t-designs along with their parameters, information matrices, average variance factors and canonical efficiency factors. Package: r-cran-pcadsc Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-pander, r-cran-ggplot2, r-cran-matrix Filename: pool/dists/noble/main/r-cran-pcadsc_0.8.0-1.ca2404.1_all.deb Size: 59946 MD5sum: 834bb18cac6ab2ed424eb652d7d3c901 SHA1: 0b5bbd6a117a537ba0389a45496cd1030756c9f5 SHA256: a88b2d5b38c28d517d8e691532a8b0cf167390d1dbf6bf15a43d3f326f0e6682 SHA512: 623a2132fa3de74d603877d47f7720abcfcd34ff665497d8ad2fdc10fb9a2da5f450d3d2ea5697a2c331803a79eec8c65cc724d478cc28cb3d152e8b4d5bb159 Homepage: https://cran.r-project.org/package=PCADSC Description: CRAN Package 'PCADSC' (Tools for Principal Component Analysis-Based Data StructureComparisons) A suite of non-parametric, visual tools for assessing differences in data structures for two datasets that contain different observations of the same variables. These tools are all based on Principal Component Analysis (PCA) and thus effectively address differences in the structures of the covariance matrices of the two datasets. The PCASDC tools consist of easy-to-use, intuitive plots that each focus on different aspects of the PCA decompositions. The cumulative eigenvalue (CE) plot describes differences in the variance components (eigenvalues) of the deconstructed covariance matrices. The angle plot presents the information loss when moving from the PCA decomposition of one dataset to the PCA decomposition of the other. The chroma plot describes the loading patterns of the two datasets, thereby presenting the relative weighting and importance of the variables from the original dataset. Package: r-cran-pcal Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pcal_1.0.0-1.ca2404.1_all.deb Size: 65118 MD5sum: 080af9d23623a50e62cc2bbba7a3e534 SHA1: 8d6995d69811b3255c2e3d05e057fe5831a5ab85 SHA256: ff03d62ae4feaa018300a456974ebe76833d2938db60dee76beacb91591dd4a5 SHA512: 1eaf6bc08181aacacb2d4bc303e6b52d331f07bce7d0e002f69465d616a1a044c963d074030f0901ba54924d3882718c33629c968d30551c6a58feeaf8605063 Homepage: https://cran.r-project.org/package=pcal Description: CRAN Package 'pcal' (Calibration of P-Values for Point Null Hypothesis Testing) Calibrate p-values under a robust perspective using the methods developed by Sellke, Bayarri, and Berger (2001) and obtain measures of the evidence provided by the data in favor of point null hypotheses which are safer and more straightforward to interpret. Package: r-cran-pcalibrate Architecture: all Version: 0.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-exact2x2, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-pcalibrate_0.2-1-1.ca2404.1_all.deb Size: 92346 MD5sum: a6e1277eb39af7ad702ebff7929e1f1c SHA1: 9e7b19d13cc3c64987e96c043b310f1f9dc81764 SHA256: 89de5e81f343dddcede9634df3ff83737c668bd88103dae6b14e99a2193631e1 SHA512: f0c5cff9f5dcfc45f7ecfcefdff69876d69dca7170604883f275330c15cb9b0f297912ca59399b70571c9cdc94cf2140d119c64e46d402eb6b7f06a8d1bbadd5 Homepage: https://cran.r-project.org/package=pCalibrate Description: CRAN Package 'pCalibrate' (Bayesian Calibrations of p-Values) Implements transformations of p-values to the smallest possible Bayes factor within the specified class of alternative hypotheses, as described in Held & Ott (2018, ). Covers several common testing scenarios such as z-tests, t-tests, likelihood ratio tests and the F-test. Package: r-cran-pcalls Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pcalls_1.0-1.ca2404.1_all.deb Size: 25638 MD5sum: 124ca2cb70bde48348115308528d88f8 SHA1: 18cc8290400de53d43de80afbc1b25df93de696f SHA256: 1a80055202e8086db01c5c0a927991f3eea4c64a80c77ec935cd6a9f2c414c2d SHA512: c0953129330ed8d4a32c22afa36d6b0bf02ee363dc66c330ed4c40d4b59d5ecb2d2d89734def720241185f860c7be5780fe7fe11427f607ca309db355fa1402c Homepage: https://cran.r-project.org/package=pcalls Description: CRAN Package 'pcalls' (Pricing of Different Types of Call) Compute the price of different types of call using different methods. The types available are Vanilla European Calls, Vanilla American Calls and American Digital Calls. Available methods are Montecarlo Simulation, Montecarlo Simulation with Antithetic Variates, Black-Scholes and the Binary Tree. Package: r-cran-pcamatchr Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 547 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-optmatch, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pcamatchr_0.3.3-1.ca2404.1_all.deb Size: 487436 MD5sum: ebe17eb383217055dd1615a4639b2d90 SHA1: 8635d67299e67e5155f3c25fcd72eb47c49dc232 SHA256: c5dbd177dea9384ba607defc71a3f7fe91a818882335da3835bf3730e44d59bc SHA512: 2bc31d55022c43fad1d3f81deffeed143d8411b3603840d5c1971136b46c06e076a485d02e38bc53fc2a21ba6cd9d378188d3fb13ed0e9778ff58fc940f09a7b Homepage: https://cran.r-project.org/package=PCAmatchR Description: CRAN Package 'PCAmatchR' (Match Cases to Controls Based on Genotype Principal Components) Matches cases to controls based on genotype principal components (PC). In order to produce better results, matches are based on the weighted distance of PCs where the weights are equal to the % variance explained by that PC. A weighted Mahalanobis distance metric (Kidd et al. (1987) ) is used to determine matches. Package: r-cran-pcamixdata Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2585 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pcamixdata_3.1-1.ca2404.1_all.deb Size: 1824902 MD5sum: d17ec8bb2cee58b7773030210ccf9a10 SHA1: 527650769c5446e6ccda2b1e69924cb89dbdee84 SHA256: 68833c7f558a88a8441a224d10662ea7b7edd74859d5cfa0be570310178b5786 SHA512: 690eaa3c07b1816d18dd6f37c6223b0ec44d61c50027afda49550813aafd0c1cc08cf3c25c34f53c800afc1b7078187bbc4c76679f1efed381cf5c12de5cffdc Homepage: https://cran.r-project.org/package=PCAmixdata Description: CRAN Package 'PCAmixdata' (Multivariate Analysis of Mixed Data) Implements principal component analysis, orthogonal rotation and multiple factor analysis for a mixture of quantitative and qualitative variables. Package: r-cran-pcapam50 Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2097 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase, r-cran-lattice, r-bioc-complexheatmap, r-bioc-impute Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pcapam50_1.0.3-1.ca2404.1_all.deb Size: 892186 MD5sum: 667bd3b074f5e5f6ff7c09980bbdc035 SHA1: 42b6c0536fca57da44ee02e4a61359b349bb3c59 SHA256: ecf5f6d6479e745507f4ff757b18986c0e8864003fe0df46eb907e2266999ec6 SHA512: 74e2cd1f2701db6da4be0d149674c9023e97a5d27d0eea7746338a069b0dd0cd4079c2616bc8313e1afe2b724aeb5238791a6d810d8eb0a2bd1d55a6a616e705 Homepage: https://cran.r-project.org/package=PCAPAM50 Description: CRAN Package 'PCAPAM50' (Enhanced 'PAM50' Subtyping of Breast Cancer) Accurate classification of breast cancer tumors based on gene expression data is not a trivial task, and it lacks standard practices.The 'PAM50' classifier, which uses 50 gene centroid correlation distances to classify tumors, faces challenges with balancing estrogen receptor (ER) status and gene centering. The 'PCAPAM50' package leverages principal component analysis and iterative 'PAM50' calls to create a gene expression-based ER-balanced subset for gene centering, avoiding the use of protein expression-based ER data resulting into an enhanced Breast Cancer subtyping. Package: r-cran-pcatsapiclientr Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pcatsapiclientr_1.3.0-1.ca2404.1_all.deb Size: 53294 MD5sum: c2583deb6419641c9b40ab10b47bb3da SHA1: 4220ef512b9a2d1bbb2ca9766fa00d4b51245639 SHA256: 8c3afd10af98be9cc5b21ed91c4975c53aee58438000858fc1c5046aa061e234 SHA512: d58712b0cd54a85942a384f705f1156d7f2946326fb49158dc0131efc6829640154f3b7c41a780102090e630aa7cd50409bfa577959c6cee882945111fb8e46b Homepage: https://cran.r-project.org/package=pcatsAPIclientR Description: CRAN Package 'pcatsAPIclientR' ('PCATS' API Client) Provides an R interface to the 'PCATS' API , allowing R users to submit tasks and retrieve results. Package: r-cran-pcbn Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-igraph, r-cran-r2r, r-cran-vinecopula Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-data.tree, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-pcbn_0.1.1-1.ca2404.1_all.deb Size: 170620 MD5sum: a062b4165ab9aacd9c70a76d8b88169b SHA1: 63a0b6a3cbffcd0fee8ed5b122bc809f980220bb SHA256: 5bbe5a0c1576e4e4dbf14cffa17bdb902595cee5c5cd41ed55c010dcdd52d65a SHA512: 4706868f57b259374e34d767fe0e3c233bf0b1b95b33f2cdb724d1f0e7ff7ccdd54c9935c0d75b42b0319d0184b4f408a989ba93d4f03501f6a56b0c6ee7791d Homepage: https://cran.r-project.org/package=PCBN Description: CRAN Package 'PCBN' (Inference of Pair-Copula Bayesian Networks) Creates, fits and samples Pair-Copula Bayesian networks (PCBN) under some restrictions on the underlying Directed Acyclic Graph (DAG), that is, no active cycles nor interfering v-structures, following Derumigny, Horsman and Kurowicka (2025) . Package: r-cran-pcbs Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1567 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tibble, r-cran-ggrepel, r-cran-dplyr, r-cran-data.table Filename: pool/dists/noble/main/r-cran-pcbs_0.1.1-1.ca2404.1_all.deb Size: 1553636 MD5sum: 5d10ebaf74678b391d314cd511d4daea SHA1: b2b5b8d90bfe7c75e1b2eb93f2aa0cad55831063 SHA256: 1ffe5a59cc0419fec88925c4974db9fcdec8d0828dc1e27bf1145ad118a389f2 SHA512: 54572ea0591eee47bf8a826e81628e92e79affcdd21ba9db80b75a208558c3ed50fa6d08b5ea526e573e8e67be3997e33179f93c169cbbb0d9230440521cf6c9 Homepage: https://cran.r-project.org/package=PCBS Description: CRAN Package 'PCBS' (Principal Component BiSulfite) A system for fast, accurate, and flexible whole genome bisulfite sequencing (WGBS) data analysis of two-condition comparisons. Principal Component BiSulfite, 'PCBS', assigns methylated loci eigenvector values from the treatment-delineating principal component in lieu of running millions of pairwise statistical tests, which dramatically increases analysis flexibility and reduces computational requirements. Methods: . Package: r-cran-pcdid Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sandwich, r-cran-lmtest Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-pcdid_1.0.0-1.ca2404.1_all.deb Size: 139304 MD5sum: 3f0e4b8208baffec0dfdfef22d3e87c6 SHA1: c3a1f1ba55436a6e2287053475b4873bfbe64e54 SHA256: 065d6efddf2a297f0b887917039f7d6f778a67ed551b4dcd741a2857df93da90 SHA512: a6ea6044ce5c1143ecaba6c08d360f3aa8aa8e04fd105e0b978852b1495540823f61a255eea604460473310a04a31f6e4aeca9c63f693b8cffed59cfca2c2668 Homepage: https://cran.r-project.org/package=pcdid Description: CRAN Package 'pcdid' (Principal Components Difference-in-Differences) Implements the Principal Components Difference-in-Differences estimators as described in Chan, M. K., & Kwok, S. S. (2022) . 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Automation uses clustering, change points, or simple statistical models to distinguish "long" from "short" steps in a graph showing the posterior number of components as a function of a prior parameter. See . Package: r-cran-pcdpca Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-freqdom, r-cran-fda Filename: pool/dists/noble/main/r-cran-pcdpca_0.4-1.ca2404.1_all.deb Size: 21248 MD5sum: 494d677eea7742ce4964b241aaaa9489 SHA1: 26c078b38cca0325dc75d6236ed7b2450a5ed5a3 SHA256: 076635794d6baf6bf99c3cbe6654ab92d810ccb517af85327c0c34ca07d3b5c1 SHA512: 976d672416bb8025219bb9b2b87bd41b9718ba58705b4d7ec20a98f9e37d66b6e12dc7a877793f808e03096e921027ce95a9b4f4e537f8ab1ab8fd05e8b884e1 Homepage: https://cran.r-project.org/package=pcdpca Description: CRAN Package 'pcdpca' (Dynamic Principal Components for Periodically CorrelatedFunctional Time Series) Method extends multivariate and functional dynamic principal components to periodically correlated multivariate time series. This package allows you to compute true dynamic principal components in the presence of periodicity. We follow implementation guidelines as described in Kidzinski, Kokoszka and Jouzdani (2017), in Principal component analysis of periodically correlated functional time series . Package: r-cran-pcds.ugraph Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcds, r-cran-interp, r-cran-rdpack Suggests: r-cran-knitr, r-cran-scatterplot3d, r-cran-rmarkdown, r-cran-bookdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-pcds.ugraph_0.1.1-1.ca2404.1_all.deb Size: 527704 MD5sum: d858c56f4977aef2601cf181ea537de6 SHA1: 45a36cd8321361ba4392ecca8b4e83a72332aad7 SHA256: 358923c7270eded64bfaad50868e6cf04ec17768621ce7439546bea77ac55bcc SHA512: 1e805d0fda962a0a231ec8182b93ebf9b8bb7f8725a5f022de67405ed5a9e2352d05a1970a8727c7e2f274761d8f4d29791cdf06934422a3124c0bf0f8cb69be Homepage: https://cran.r-project.org/package=pcds.ugraph Description: CRAN Package 'pcds.ugraph' (Underlying Graphs of Proximity Catch Digraphs and TheirApplications) Contains the functions for construction and visualization of underlying and reflexivity graphs of the three families of the proximity catch digraphs (PCDs), see (Ceyhan (2005) ISBN:978-3-639-19063-2), and for computing the edge density of these PCD-based graphs which are then used for testing the patterns of segregation and association against complete spatial randomness (CSR)) or uniformity in one and two dimensional cases. The PCD families considered are Arc-Slice PCDs, Proportional-Edge (PE) PCDs (Ceyhan et al. (2006) ) and Central Similarity PCDs (Ceyhan et al. (2007) ). See also (Ceyhan (2016) ) for edge density of the underlying and reflexivity graphs of PE-PCDs. The package also has tools for visualization of PCD-based graphs for one, two, and three dimensional data. Package: r-cran-pcds Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4492 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-combinat, r-cran-interp, r-cran-gmoip, r-cran-plot3d, r-cran-plotrix, r-cran-rdpack Suggests: r-cran-knitr, r-cran-scatterplot3d, r-cran-spatstat.random, r-cran-rmarkdown, r-cran-bookdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-pcds_0.1.8-1.ca2404.1_all.deb Size: 3010676 MD5sum: 4b24ac3f716857c28e539254e47f1587 SHA1: 3935cd03ed59510cd603648340bd76a53b8a61e6 SHA256: d70f6aad363b3b48da2bfb888f320e3bcc6d179d90c3e2ee3114e77da53b6755 SHA512: 89207e74166c13a32cf460b81577d78bd03830832b85f4eb5007c46805d3261d2901971998c22986c4602b40b1e381899792eb1643d660406a6b963def8d6a7c Homepage: https://cran.r-project.org/package=pcds Description: CRAN Package 'pcds' (Proximity Catch Digraphs and Their Applications) Contains the functions for construction and visualization of various families of the proximity catch digraphs (PCDs), see (Ceyhan (2005) ISBN:978-3-639-19063-2), for computing the graph invariants for testing the patterns of segregation and association against complete spatial randomness (CSR) or uniformity in one, two and three dimensional cases. The package also has tools for generating points from these spatial patterns. The graph invariants used in testing spatial point data are the domination number (Ceyhan (2011) ) and arc density (Ceyhan et al. (2006) ; Ceyhan et al. (2007) ). The PCD families considered are Arc-Slice PCDs, Proportional-Edge PCDs, and Central Similarity PCDs. Package: r-cran-pcensmix Architecture: all Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pcensmix_1.2-1-1.ca2404.1_all.deb Size: 88074 MD5sum: 46a56e02805bc1395b582c05defbf9cc SHA1: 47f455e1e41efdeebadba25bfd9aa8b1e688b04a SHA256: 7455c0121c41aa984103ae543eac6c47814b6230402edaa7ae36299e225223af SHA512: 7f5a77515117ef55ee6c03b213e3153b62644850aefd0d4976c6650fbf1ce4154a6fb31446536d8dbea6e123c673871b61440a7dd706f052ece7714eaa4fe597 Homepage: https://cran.r-project.org/package=pcensmix Description: CRAN Package 'pcensmix' (Model Fitting to Progressively Censored Mixture Data) Functions for generating progressively Type-II censored data in a mixture structure and fitting models using a constrained EM algorithm. It can also create a progressive Type-II censored version of a given real dataset to be considered for model fitting. 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It is a dimension- reduction technique, similar to Principal component analysis (PCA), that seeks to maximize the proportion of variance (in the response vector) being explained by a set of covariates. Package: r-cran-pcfam Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pcfam_1.0-1.ca2404.1_all.deb Size: 122672 MD5sum: cd4499450169c4b5749522698e1e0d72 SHA1: f267548edd9e2c66c218c04cba138603f0eb7928 SHA256: af51cf8cedd0a75448ff48d7bdc1ebcd7442df043f0535c6d08866c953a8000c SHA512: 2bb482e76e796d705e0af0abd5a2dde5fbf4197dc008c276b92db6e311b26b96289ac963c84c3594745f690b56245bd1fb447f85aff43ecae3994034bc632902 Homepage: https://cran.r-project.org/package=PCFAM Description: CRAN Package 'PCFAM' (Computation of Ancestry Scores with Mixed Families and UnrelatedIndividuals) We provide several algorithms to compute the genotype ancestry scores (such as eigenvector projections) in the case where highly correlated individuals are involved. Package: r-cran-pcg Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pcg_1.1-1.ca2404.1_all.deb Size: 11658 MD5sum: 8a8472b8c16e8a275691693f8cdd1773 SHA1: 1a9682413e4a7d6efe2a72a788c33ddd7d799142 SHA256: b6acef0cdf085dc6b3c62f48937aa5dba5a51f052d749f51087689c46ca094fb SHA512: ec3005f8711353609c2b425c9a68342702c843b639a6c879d76d8fbc1d08d78aa0fb853cdc260b35397777ba76b732d9b494f09f322fb1344cf26e332d23e306 Homepage: https://cran.r-project.org/package=pcg Description: CRAN Package 'pcg' (Preconditioned Conjugate Gradient Algorithm for solving Ax=b) The package solves linear system of equations Ax=b by using Preconditioned Conjugate Gradient Algorithm where A is real symmetric positive definite matrix. A suitable preconditioner matrix may be provided by user. This can also be used to minimize quadratic function (x'Ax)/2-bx for unknown x. Package: r-cran-pcgen Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcalg, r-bioc-graph, r-cran-matrix, r-cran-mass, r-cran-hmisc, r-cran-lme4, r-cran-sommer, r-cran-ggm Filename: pool/dists/noble/main/r-cran-pcgen_0.2.0-1.ca2404.1_all.deb Size: 144612 MD5sum: 13fcad268b69b3b552e5bcd9bb5f3842 SHA1: b09b4136c9762ec3f6edc1a9b974bf1bccbae57d SHA256: f2c8dd62a1b43ffdf7b37d0cfe5a507b88757bb8336d0260c1a540244b9068a7 SHA512: 4c1e3e65151ef39baf36134a2d555af2bbc4ccccfab77e5a330ae9ad21cacd3e603eaf4f87fd0fab060089e70328094e8da729c2773eaab60eefc1fbb65f4c4d Homepage: https://cran.r-project.org/package=pcgen Description: CRAN Package 'pcgen' (Reconstruction of Causal Networks for Data with Random GeneticEffects) Implements the pcgen algorithm, which is a modified version of the standard pc-algorithm, with specific conditional independence tests and modified orientation rules. pcgen extends the approach of Valente et al. 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This package is developed for estimating and testing partial correlation graphs with prior information incorporated. Package: r-cran-pcgse Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmtstat, r-cran-mass Filename: pool/dists/noble/main/r-cran-pcgse_0.5.0-1.ca2404.1_all.deb Size: 43236 MD5sum: 421c403e6a78ea9c3d893310d32f1930 SHA1: 942d271678b514e92f9db4ba8fe881391596f036 SHA256: a6aba9564c0c4ced9f8cc8a9c900d4bddbdee3863515e410318e1f12d9d05e1e SHA512: c5117585bedb637724d98d2beee61b8b2d3ca6b9e0fe0854e8458348268cb5faf88d0d8370bffae8f0f98fbe472fc4edfb3a648a21851da5dcee29ab0fc50e41 Homepage: https://cran.r-project.org/package=PCGSE Description: CRAN Package 'PCGSE' (Principal Component Gene Set Enrichment) Contains logic for computing the statistical association of variable groups, i.e., gene sets, with respect to the principal components of genomic data. Package: r-cran-pch Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-pch_2.2-1.ca2404.1_all.deb Size: 108322 MD5sum: fac6d00ff5a6041febf4c5287f512cdd SHA1: 5b32f662b6f8f760c0776198a71fc15d04f06e7a SHA256: 7cb00ab70b53ee076771550ec398508874937d792502fd96ba59f16a5718ed2d SHA512: 95ed59d482eefa3405429001b11bf8bc9cc8ff5f144a950c95bb2988249767f32d663ce2bbea257641eb87134b8edfc941352905b0c8ad93a4e7a510a0cdbd74 Homepage: https://cran.r-project.org/package=pch Description: CRAN Package 'pch' (Piecewise Constant Hazard Models for Censored and Truncated Data) Piecewise constant hazard models for survival data. The package allows for right-censored, left-truncated, and interval-censored data. Package: r-cran-pchc Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigstatsr, r-cran-bnlearn, r-cran-dcov, r-cran-foreach, r-cran-doparallel, r-cran-rangen, r-cran-rfast, r-cran-rfast2, r-cran-robustbase Suggests: r-cran-bigreadr, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-pchc_1.4-1.ca2404.1_all.deb Size: 229788 MD5sum: cda99c3df998ed21ee18aa907e8a2e54 SHA1: a395476853a05cce10b5a5ec7aaba9d173728f61 SHA256: 188ee5489f2f3399d7d6c44dd70657b196e7e2488683eeb6bcf165a17f50b967 SHA512: f050cfc68ac88edd51cb8923c73bb128e32e3bc83de68db6c4a8704cf4aeaac942d87bc1bef78537930c28045e204309ebaba449d6f2e275feacb54403200d04 Homepage: https://cran.r-project.org/package=pchc Description: CRAN Package 'pchc' (Bayesian Network Learning with the PCHC and Related Algorithms) Bayesian network learning using the PCHC, FEDHC, MMHC and variants of these algorithms. PCHC stands for PC Hill-Climbing, a new hybrid algorithm that uses PC to construct the skeleton of the BN and then applies the Hill-Climbing greedy search. More algorithms and variants have been added, such as MMHC, FEDHC, and the Tabu search variants, PCTABU, MMTABU and FEDTABU. The relevant papers are: a) Tsagris M. (2021). "A new scalable Bayesian network learning algorithm with applications to economics". Computational Economics, 57(1): 341-367. . b) Tsagris M. (2022). "The FEDHC Bayesian Network Learning Algorithm". Mathematics 2022, 10(15): 2604. . c) Sevinc V. and Tsagris M. (2024). "On the Hyperparameters of PCTABU and PCHC Bayesian Network Learning Algorithms". . Package: r-cran-pci Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vek Suggests: r-cran-tinytest, r-cran-devtools Filename: pool/dists/noble/main/r-cran-pci_1.0.1-1.ca2404.1_all.deb Size: 43932 MD5sum: 369bd15d122baebb29575a8f7a758bfd SHA1: cad38bb2e80749ddaefeb2d1629e624a5cc79259 SHA256: 95ad415261b1042fd8d5cd1809c2ce5fccbf51623a4c61e9305ee19c5f58c06c SHA512: c2fd7593b641fa35e8acd826f5550b6c131cc52e3247e31fb18749c9d3a3590617c580ab4abb53209cda1923fe29234305c6fc720f6163473ee134c9a07b585d Homepage: https://cran.r-project.org/package=pci Description: CRAN Package 'pci' (A Collection of Process Capability Index Functions) A collection of process capability index functions, such as C_p(), C_pk(), C_pm(), and others, along with metadata about each, like 'LaTeX' equations and 'R' expressions. Its primary purpose is to form a foundation for other quality control packages to build on top of, by providing basic resources and functions. The indices belong to the field of statistical quality control, and quantify the degree to which a manufacturing process is able to create items that adhere to a certain standard of quality. For details see Montgomery, D. C. (2019, ISBN:978-1-119-39930-8). Package: r-cran-pcir Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3763 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-pcir_1.0.0-1.ca2404.1_all.deb Size: 3752280 MD5sum: 0f93aeea9f6b4346dd7d7aea26be0256 SHA1: 32bfdee426dae0a956d176043a8a171fafa67ec9 SHA256: 7487f2a2d82883e9953f483d2f5c224ea10f45efd1625c9543d587d914caeaa8 SHA512: b7f6132498899fe889a2ac9b45c9a19489f3133c3bda39c4cc71d1f53c3077685ddfdeb5b9c28bd7b1c6cb6620f8fe439ecbcc27b85c4232ca0da03d0b80e86f Homepage: https://cran.r-project.org/package=pciR Description: CRAN Package 'pciR' (Proactive Conservation Index) Calculates the Proactive Conservation Index, a new tool to prioritize species for conservation, which can incorporate information about future threats. Package: r-cran-pcl Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pcl_1.0-1.ca2404.1_all.deb Size: 18570 MD5sum: 4e81665e2dc84a0f4b2de0fd366faab0 SHA1: 77a39d0aadaee50e3e67202452861e491da21ff6 SHA256: 4c63f160f16b7ab0ca8f9f9d0a5b184527ff2eda090ba73b76185ce5d47f3c32 SHA512: e051c0206301cb571498038c005a62e8b795ac1cca7c8af7fc25cb1a3d2b7ef7af2e15d00c05c62b590b191493182c0d1f2a47d05d8f6e5c6de8233a251dd4d4 Homepage: https://cran.r-project.org/package=PCL Description: CRAN Package 'PCL' (Proximal Causal Learning) We fit causal models using proxies. We implement two stage proximal least squares estimator. E.J. Tchetgen Tchetgen, A. Ying, Y. Cui, X. Shi, and W. Miao. (2020). An Introduction to Proximal Causal Learning. arXiv e-prints, arXiv-2009 . Package: r-cran-pclassoreg Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-grpreg Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-pclassoreg_1.0.0-1.ca2404.1_all.deb Size: 895218 MD5sum: e15b0f2eb45d0367b69c50a9baf32b87 SHA1: bb45c4dc4c7260e1b4397a2cfef57ee31a2d2bbd SHA256: ba41aec135fbf02309be293ce798fc2a00bcfa220116b1265bf75377cebdf2bd SHA512: 7bab36ecc1cbbc1f12ca60efebaf18f2b943754304d3ef4e9afbcedf088b51da2a702eefab9494b6970fe7b086000c00785b832bfa3d2d9ee9b503dc281fa0ae Homepage: https://cran.r-project.org/package=PCLassoReg Description: CRAN Package 'PCLassoReg' (Group Regression Models for Risk Protein Complex Identification) Two protein complex-based group regression models (PCLasso and PCLasso2) for risk protein complex identification. PCLasso is a prognostic model that identifies risk protein complexes associated with survival. PCLasso2 is a classification model that identifies risk protein complexes associated with classes. For more information, see Wang and Liu (2021) . Package: r-cran-pcmabc Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-mvslouch, r-cran-phangorn, r-cran-yuima Suggests: r-cran-geiger, r-cran-distory Filename: pool/dists/noble/main/r-cran-pcmabc_1.1.3-1.ca2404.1_all.deb Size: 151684 MD5sum: 7c32f2f814a201e0489dfcb05f9d75cb SHA1: e396487a90c54d213b2c03e06fe223c192ae862c SHA256: 6d1a853757677ca4e4fbdad88634691af76bf479f054b5719fef5c286dcb2250 SHA512: 719f66e5a9cbba2527ed57242ac32a24c3141af00631a1e2c46cf4fe65f3d60c14f4cbe3e483d396e82f30d9e8827393699c9820cb7029b60f924b702da27629 Homepage: https://cran.r-project.org/package=pcmabc Description: CRAN Package 'pcmabc' (Approximate Bayesian Computations for Phylogenetic ComparativeMethods) Fits by ABC, the parameters of a stochastic process modelling the phylogeny and evolution of a suite of traits following the tree. The user may define an arbitrary Markov process for the trait and phylogeny. Importantly, trait-dependent speciation models are handled and fitted to data. See K. Bartoszek, P. Lio' (2019) . The suggested geiger package can be obtained from CRAN's archive , suggested to take latest version. Otherwise its required code is present in the pcmabc package. The suggested distory package can be obtained from CRAN's archive , suggested to take latest version. Package: r-cran-pcmbase Architecture: all Version: 1.2.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2222 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-abind, r-cran-expm, r-cran-mvtnorm, r-cran-data.table, r-cran-ggplot2, r-cran-xtable Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-ggtree, r-cran-cowplot, r-cran-covr, r-cran-mvslouch, r-cran-biocmanager Filename: pool/dists/noble/main/r-cran-pcmbase_1.2.15-1.ca2404.1_all.deb Size: 1172684 MD5sum: cf772cfa527d33a1be4b25a23fd16f19 SHA1: a04b53819a7385e8604bf3e15c0bc513f509d805 SHA256: 3a6c9a21f1b580c9234f93596e9391644ca673dfd64604df172f6c9a989b560c SHA512: 2601cbf95412a82e9049fb16fc29e0d1b8692de1031b1cb2ed44ac9367536e388a3526649ff57691d97baa921f59646afa067b2d47cfb46d8a8936e589963eb0 Homepage: https://cran.r-project.org/package=PCMBase Description: CRAN Package 'PCMBase' (Simulation and Likelihood Calculation of PhylogeneticComparative Models) Phylogenetic comparative methods represent models of continuous trait data associated with the tips of a phylogenetic tree. Examples of such models are Gaussian continuous time branching stochastic processes such as Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the tips of the tree as an observed (final) state of a Markov process starting from an initial state at the root and evolving along the branches of the tree. The PCMBase R package provides a general framework for manipulating such models. This framework consists of an application programming interface for specifying data and model parameters, and efficient algorithms for simulating trait evolution under a model and calculating the likelihood of model parameters for an assumed model and trait data. The package implements a growing collection of models, which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed Gaussian models, in which different types of the above models can be associated with different branches of the tree. The PCMBase package is limited to trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic models. The PCMFit package provides functionality for inference of these models to tree and trait data. The package web-site provides access to the documentation and other resources. 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Package: r-cran-pcts Architecture: all Version: 0.15.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2331 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sarima, r-cran-matrix, r-cran-bb, r-cran-polynomf, r-cran-gbutils, r-cran-zoo, r-cran-xts, r-cran-lagged, r-cran-mcompanion, r-cran-rdpack, r-cran-lubridate Suggests: r-cran-testthat, r-cran-funitroots, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pcts_0.15.8-1.ca2404.1_all.deb Size: 1794334 MD5sum: 4583fce3de42c88a23d683d807377063 SHA1: efe9122c5682be8c3059fd781d4307d9a3d6f00a SHA256: a046e72e29af507a15ff17bf80fcb72582ce6ae8ae6f1aec5d95adbe9ab3a72e SHA512: 82daf0f85187ef2fcdd303e02bddb4e8fc53da851b6ec0b878cace273fb3aa7bc0ed2582a7928ab32cfa31c6912010902cdecd39ee8fa92f2fb9d3c65c0a61d0 Homepage: https://cran.r-project.org/package=pcts Description: CRAN Package 'pcts' (Periodically Correlated and Periodically Integrated Time Series) Classes and methods for modelling and simulation of periodically correlated (PC) and periodically integrated time series. Compute theoretical periodic autocovariances and related properties of PC autoregressive moving average models. Some original methods including Boshnakov & Iqelan (2009) , Boshnakov (1996) . Package: r-cran-pcutils Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1853 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-ggplot2, r-cran-reshape2, r-cran-scales, r-cran-tidyr, r-cran-tibble, r-cran-rcolorbrewer Suggests: r-cran-agricolae, r-cran-clipr, r-cran-rlang, r-cran-biocmanager, r-cran-ggpubr, r-cran-kableextra, r-cran-htmlwidgets, r-cran-pagedown, r-cran-ggsci, r-cran-readr, r-cran-grimport2, r-cran-rsvg, r-cran-pmcmrplus, r-cran-nortest, r-cran-fitdistrplus, r-cran-ggalluvial, r-cran-gghalves, r-cran-ggspatial, r-cran-sf, r-cran-magick, r-cran-ggimage, r-cran-ggpmisc, r-cran-upsetr, r-cran-eulerr, r-cran-plotrix, r-cran-vegan, r-cran-circlize, r-cran-igraph, r-cran-knitr, r-cran-rmarkdown, r-cran-plotly, r-cran-htmltools, r-cran-leaflet, r-cran-relaimpo, r-cran-snow, r-cran-dosnow, r-cran-foreach, r-cran-stringr, r-cran-ggraph, r-cran-ggrepel, r-cran-treemap, r-cran-voronoitreemap, r-cran-devtools, r-cran-multcompview, r-cran-rio, r-cran-bookdown, r-cran-sysfonts, r-cran-showtext, r-cran-jsonlite, r-cran-httr, r-cran-r.proxy, r-cran-openssl, r-cran-styler, r-cran-lintr, r-cran-aplot, r-cran-ggbeeswarm, r-cran-ggvenndiagram, r-cran-gifski, r-cran-ggnewscale, r-cran-revtools Filename: pool/dists/noble/main/r-cran-pcutils_0.2.8-1.ca2404.1_all.deb Size: 1802640 MD5sum: 0b4f1ccb0472da767f314f9a1d4bb977 SHA1: b7a344d9d938bf3c4bf1dcb628148e4592c933b4 SHA256: d1eed2ddac58fa5ee38a8f499fcba1d0a571dea9dc13045ab7a527fb916e2da0 SHA512: 7efc0c45ddb10a849e547b883feaf77dad37c106a2a89ecc6e347dc183ec2f2e8778ee9c6033401e317ec46f3c946aa7801bcce7de5ef4b408785d272908487e Homepage: https://cran.r-project.org/package=pcutils Description: CRAN Package 'pcutils' (Some Useful Functions for Statistics and Visualization) Offers a range of utilities and functions for everyday programming tasks. 1.Data Manipulation. Such as grouping and merging, column splitting, and character expansion. 2.File Handling. Read and convert files in popular formats. 3.Plotting Assistance. Helpful utilities for generating color palettes, validating color formats, and adding transparency. 4.Statistical Analysis. Includes functions for pairwise comparisons and multiple testing corrections, enabling perform statistical analyses with ease. 5.Graph Plotting, Provides efficient tools for creating doughnut plot and multi-layered doughnut plot; Venn diagrams, including traditional Venn diagrams, upset plots, and flower plots; Simplified functions for creating stacked bar plots, or a box plot with alphabets group for multiple comparison group. Package: r-cran-pcv Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pcv_1.1.0-1.ca2404.1_all.deb Size: 478172 MD5sum: 782fce91948304b722e7b1c943c78fde SHA1: 12f7f626ba57933ef21a04f7bee35a37da88d84e SHA256: 1fb12a972c25426d48d14a791a4d3c1b43a33cd93ff56b4ccb87ff17023afb4b SHA512: 3edab7069bc0a2451b24a506f70f9bdcc1dc7e2c94cccbe975946a29a851328bd6879c555ff06140279732e073d6ec24ca8935430e3c5770c3659cf4b670a9de Homepage: https://cran.r-project.org/package=pcv Description: CRAN Package 'pcv' (Procrustes Cross-Validation) Implements Procrustes cross-validation method for Principal Component Analysis, Principal Component Regression and Partial Least Squares regression models. S. Kucheryavskiy (2023) . 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Package: r-cran-pdcor Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dcov, r-cran-rangen, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-pdcor_1.3-1.ca2404.1_all.deb Size: 27000 MD5sum: 03f8f4847421baa38912e633ad293718 SHA1: ed1193ff6bdeb6f125e2114260cdb414b520c880 SHA256: 12d959682260889339f921123510af53531ca16ca6005d9f2131b820f2f95db5 SHA512: 8caa9ac2bd8046904220df3d7126f08e53ca6f464ca4d254cfa2dd09dcc06789936018f33e36e879f40add20c84ac86df9c9649a690ddafcbeb5417e04383a66 Homepage: https://cran.r-project.org/package=pdcor Description: CRAN Package 'pdcor' (Fast and Light-Weight Partial Distance Correlation) Fast and memory-less computation of the partial distance correlation for vectors and matrices. Permutation-based and asymptotic hypothesis testing for zero partial distance correlation are also performed. References include: Szekely G. J. and Rizzo M. L. (2014). "Partial distance correlation with methods for dissimilarities". The Annals Statistics, 42(6): 2382--2412. . Shen C., Panda S. and Vogelstein J. T. (2022). "The Chi-Square Test of Distance Correlation". Journal of Computational and Graphical Statistics, 31(1): 254--262. . Szekely G. J. and Rizzo M. L. (2023). "The Energy of Data and Distance Correlation". Chapman and Hall/CRC. . Kontemeniotis N., Vargiakakis R. and Tsagris M. (2025). On independence testing using the (partial) distance correlation. . 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(2015) "A simple new test for slope homogeneity in panel data models with interactive effects" , Ando, T. and Bai, J. (2015) "Asset Pricing with a General Multifactor Structure" , Ando, T. and Bai, J. (2016) "Panel data models with grouped factor structure under unknown group membership" , Ando, T. and Bai, J. (2017) "Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures" , Ando, T. and Bai, J. (2020) "Quantile co-movement in financial markets" , Ando, T., Bai, J. and Li, K. (2021) "Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity" . 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This package provides two commonly used models for powering a design, linear mixed effects and Cox frailty model. Both models account for within-subject (cell line) correlation while holding different distributional assumptions about the outcome. Alternatively, the counterparts of fixed effects model are also available, which produces similar estimates of statistical power. 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Estimation of the model parameters relies on the Generalized Method of Moments (GMM) and instrumental variables (IV) estimation, numerical optimization (when nonlinear moment conditions are employed) and the computation of closed form solutions (when estimation is based on linear moment conditions). One-step, two-step and iterated estimation is available. For inference and specification testing, Windmeijer (2005) and doubly corrected standard errors (Hwang, Kang, Lee, 2021 ) are available. Additionally, serial correlation tests, tests for overidentification, and Wald tests are provided. Functions for visualizing panel data structures and modeling results obtained from GMM estimation are also available. The plot methods include functions to plot unbalanced panel structure, coefficient ranges and coefficient paths across GMM iterations (the latter is implemented according to the plot shown in Hansen and Lee, 2021 ). 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The goal for this package is to include tools and data sets for doing original research that mimics well what a user would have to previously get from a software package that may not be well-sourced or well-supported. Those software bundles were useful the extent to which they encourage replications of long-standing analyses by starting the data-generating process from scratch. However, a lot of the functionality can be done relatively quickly and more transparently in the R programming language. Package: r-cran-peach Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2586 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mnormt, r-cran-metap Filename: pool/dists/noble/main/r-cran-peach_0.1.1-1.ca2404.1_all.deb Size: 2611420 MD5sum: af76d75490c9d7a3e832cfad71282c1b SHA1: cae3015e075c6e500245efaebde305f074071fca SHA256: 0e154f83bff5420c7ae7101fdc0ebd2223756732f531fa4fa0cd4749d89a1ecc SHA512: cf541f83f46c9327a944d667e5d83e2f4ce0bd201a7e5ed42cd276ade7ae0b4995f3d14df90aefff412298e316bcdeb98cb969ac5d88c12489d7d51a1410bacd Homepage: https://cran.r-project.org/package=PEACH Description: CRAN Package 'PEACH' (Pareto Enrichment Analysis for Combining Heterogeneous Datasets) A meta gene set analysis tool developed based on principles of Pareto dominance (William B T Mock (2011) ). It is designed to combine gene set analysis p-values from multiple transcriptome datasets (e.g., microarray and RNA-Seq). The novel Pareto method for p-value combination allows PEACH to properly model heterogeneity and correlation in Omics datasets. Package: r-cran-peacock.test Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-peacock.test_1.0-1.ca2404.1_all.deb Size: 22574 MD5sum: fe946c7a56c778319379e0192e621437 SHA1: 9807b9e126f0a58f74afb72adcc1110a2f33d5d2 SHA256: cd5b48abe178f745b20096e574f1dbf5d9c7922fb56b888361d4865922f8518b SHA512: d2721b3ba08e7569c7fec6bf1985a3962d18754de7517762944e0c464ae68b7f1621f81f1fe8466981a3723ef5005471d590bbc703d1b87a3e3dab331d2fddd2 Homepage: https://cran.r-project.org/package=Peacock.test Description: CRAN Package 'Peacock.test' (Two and Three Dimensional Kolmogorov-Smirnov Two-Sample Tests) The original definition of the two and three dimensional Kolmogorov-Smirnov two-sample test statistics given by Peacock (1983) is implemented. Two R-functions: peacock2 and peacock3, are provided to compute the test statistics in two and three dimensional spaces, respectively. Note the Peacock test is different from the Fasano and Franceschini test (1987). The latter is a variant of the Peacock test. Package: r-cran-peacots Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-peacots_1.3.2-1.ca2404.1_all.deb Size: 152080 MD5sum: cd299132b6501f73d53073d0b2714672 SHA1: 1815141376ccc92926a11ad4b3bf8d4071be84c2 SHA256: f456a53aba4d919a8b17249bd8aed627802565b94ce9aec7c9d47481cfa22433 SHA512: 9ff04b637de89f776ab45630c6b6172a4dc48824ab8dca9c5b93bcf97c36b4c44d51ee862e2023f2f123ab88f90ab1d697be82c4227be09eed51d8ddc7ccc37d Homepage: https://cran.r-project.org/package=peacots Description: CRAN Package 'peacots' (Periodogram Peaks in Correlated Time Series) Calculates the periodogram of a time series, maximum-likelihood fits an Ornstein-Uhlenbeck state space (OUSS) null model and evaluates the statistical significance of periodogram peaks against the OUSS null hypothesis. The OUSS is a parsimonious model for stochastically fluctuating variables with linear stabilizing forces, subject to uncorrelated measurement errors. Contrary to the classical white noise null model for detecting cyclicity, the OUSS model can account for temporal correlations typically occurring in ecological and geological time series. Citation: Louca, Stilianos and Doebeli, Michael (2015) . Package: r-cran-peakram Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-peakram_1.0.2-1.ca2404.1_all.deb Size: 17538 MD5sum: 2a8659aaa9433261f7636283ae49f208 SHA1: d2bdafef9ee693dda07d0f11f99861b5202ffc20 SHA256: 2de131b964abadf051d48222bdfb82047c59513ce283469321a13d00e0fc60d2 SHA512: aac7e3fd3855409fcd41526bf215fe764c246ea63e91e19f88b1a01b5d9f8b011d4a75b52c16ab8ad4a065458838091da76a45035e90ecf1bfaa11ce73913971 Homepage: https://cran.r-project.org/package=peakRAM Description: CRAN Package 'peakRAM' (Monitor the Total and Peak RAM Used by an Expression or Function) When working with big data sets, RAM conservation is critically important. However, it is not always enough to just monitor the size of the objects created. So-called "copy-on-modify" behavior, characteristic of R, means that some expressions or functions may require an unexpectedly large amount of RAM overhead. For example, replacing a single value in a matrix duplicates that matrix in the back-end, making this task require twice as much RAM as that used by the matrix itself. This package makes it easy to monitor the total and peak RAM used so that developers can quickly identify and eliminate RAM hungry code. Package: r-cran-pearson7 Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pearson7_1.0-3-1.ca2404.1_all.deb Size: 35350 MD5sum: 27e04e76ab915c51fb2536bb93a080ef SHA1: 39739806fc0ea3453fb97587e7ffad7711c454b1 SHA256: 72f61d5a78d84fe570c9ab31bf215de3939f7874483fa92140cd6b3c546907f9 SHA512: c907f427911e32d2c919bfc077a6d4beb192f9b64ddea3e7a4148c21cf4733f152cc256b95deafb69f5a7399ed9584ce0e0685bef3643bc12eb7d1f596bbad5c Homepage: https://cran.r-project.org/package=pearson7 Description: CRAN Package 'pearson7' (Maximum Likelihood Inference for the Pearson VII Distributionwith Shape Parameter 3/2) Supports maximum likelihood inference for the Pearson VII distribution with shape parameter 3/2 and free location and scale parameters. This distribution is relevant when estimating the velocity of processive motor proteins with random detachment. Package: r-cran-pearsonica Architecture: all Version: 1.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pearsonica_1.2-5-1.ca2404.1_all.deb Size: 36444 MD5sum: a3e6f6c9d8fa84aa05b607b9683ee836 SHA1: 7c42fdae23a4a407d7a3386c52b28e89ab1c845e SHA256: b98934f17daa21d10caafdb9ac5679c375e6decb5935c4be13c2182bb81bb45f SHA512: f412cb61b088c3d2da71f6aec12cd3315b3e0d5a1f58301063847363ed08dc706a54e7a01fbfad818b14482f4cf2497560ae65d27af379e0b34ce53ff95b72c9 Homepage: https://cran.r-project.org/package=PearsonICA Description: CRAN Package 'PearsonICA' (Independent Component Analysis using Score Functions from thePearson System) The Pearson-ICA algorithm is a mutual information-based method for blind separation of statistically independent source signals. It has been shown that the minimization of mutual information leads to iterative use of score functions, i.e. derivatives of log densities. The Pearson system allows adaptive modeling of score functions. The flexibility of the Pearson system makes it possible to model a wide range of source distributions including asymmetric distributions. The algorithm is designed especially for problems with asymmetric sources but it works for symmetric sources as well. Package: r-cran-peaxai Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-benchmarking, r-cran-caret, r-cran-dear, r-cran-dplyr, r-cran-kernelshap, r-cran-iml, r-cran-isotone, r-cran-lime, r-cran-np, r-cran-prroc, r-cran-proc, r-cran-rminer, r-cran-rms Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-peaxai_1.0.1-1.ca2404.1_all.deb Size: 228642 MD5sum: c5fa3328f210902199c1db2dd3837def SHA1: 0b110d364aac021b266e1122e92d87d159c61a0b SHA256: 03ae8fe319a114337dc1f1a5812c849320ce4ecd9857cff6674b12385c9efd03 SHA512: 1bfd16896c83444099214a3783fd3bd09adbb557ba5ca067e61bce50b3462321607f4662e34b4b00728c2dba13eadbaddbbc14a43030733df60596cdc45538d0 Homepage: https://cran.r-project.org/package=PEAXAI Description: CRAN Package 'PEAXAI' (Probabilistic Efficiency Analysis Using Explainable ArtificialIntelligence) Provides a probabilistic framework that integrates Data Envelopment Analysis (DEA) (Banker et al., 1984) with machine learning classifiers (Kuhn, 2008) to estimate both the (in)efficiency status and the probability of efficiency for decision-making units. The approach trains predictive models on DEA-derived efficiency labels (Charnes et al., 1985) , enabling explainable artificial intelligence (XAI) workflows with global and local interpretability tools, including permutation importance (Molnar et al., 2018) , Shapley value explanations (Strumbelj & Kononenko, 2014) , and sensitivity analysis (Cortez, 2011) . The framework also supports probability-threshold peer selection and counterfactual improvement recommendations for benchmarking and policy evaluation. The probabilistic efficiency framework is detailed in González-Moyano et al. (2025) "Probability-based Technical Efficiency Analysis through Machine Learning", in review for publication. Package: r-cran-pecan Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-dplyr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pecan_0.1.0-1.ca2404.1_all.deb Size: 136656 MD5sum: 99b8ff5d0cf526aecfbaca44c8bd0470 SHA1: 481b9cb42d2aa909bf71ada23589ea72ef14ba6c SHA256: 661598583117a9575aafa4c9ae661ad13a80b883dde3d75eaed1f089ce3c832f SHA512: a40982f49d6e5cd321cef1c3e2b7595500f1ae6998f6da8e75933b8cfa9e4bd7d7544810bc86c4c7faed37aea89bbd3ecf1de23076078b0f28528c7f06eeddde Homepage: https://cran.r-project.org/package=pecan Description: CRAN Package 'pecan' (Portfolio for Economic Complexity Analysis and Navigation) A portfolio of tools for economic complexity analysis and industrial upgrading navigation. The package implements essential measures in international trade and development economics, including the relative comparative advantage (RCA), economic complexity index (ECI) and product complexity index (PCI). It enables users to analyze export structures, explore product relatedness, and identify potential upgrading paths grounded in economic theory, following the framework in Hausmann et al. (2014) . Package: r-cran-pecanr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pecanr_0.2.0-1.ca2404.1_all.deb Size: 76220 MD5sum: fd5cb2d650b997f3cf73e06846945f74 SHA1: 90f7991b3c5242f0f6884613699f9fdff1551362 SHA256: a33b3d396e0463412e721b65a22fe6f0ca32de30e53b8da95a081559d147ca3c SHA512: 52a0f67f222286842e9b282b1f828a6233007f6668cd5adb6cc262fa8b165f3f09fe69b79a652cb007b2dffbacf77b2fc1b1513bf0d77719d3e2f575ecd0bb94 Homepage: https://cran.r-project.org/package=pecanr Description: CRAN Package 'pecanr' (Partial Eta-Squared for Crossed, Nested, and Mixed Linear MixedModels) Computes partial eta-squared effect sizes for fixed effects in linear mixed models fitted with the 'lme4' package. Supports crossed, nested, and mixed (crossed-and-nested) random effects structures with any number of grouping factors. Mixed designs handle cases where grouping factors are simultaneously crossed with some variables and nested within others (e.g., photos nested within models, but both crossed with participants). Random slope variances are translated to the outcome scale using a variance decomposition approach, correctly accounting for predictor scaling and interaction terms. Both general and operative effect sizes are provided. Methods are based on Correll, Mellinger, McClelland, and Judd (2020) , Correll, Mellinger, and Pedersen (2022) , and Rights and Sterba (2019) . Package: r-cran-pedalfast.data Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1699 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-data.table, r-cran-digest, r-cran-dplyr, r-cran-ggplot2, r-cran-htmltools, r-cran-kableextra, r-cran-knitr, r-cran-lubridate, r-cran-qwraps2, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pedalfast.data_1.0.2-1.ca2404.1_all.deb Size: 637902 MD5sum: ccc85699467d24188411bc7826e11c9c SHA1: 37db5513c37fc0c9de5b54b43484e7738a205576 SHA256: 191c6baaf227f8aacd2e756de553ad77b17773a1bf7c0d29fde682dda9bdb212 SHA512: e3c7ea3d92ac275383512296f5d0fec32260b7d0b10a5768e86831678319b86abd515869eeb5986c4b7fb1de10b5e26f1daf639482d37cdfd42861db45ee5ce6 Homepage: https://cran.r-project.org/package=pedalfast.data Description: CRAN Package 'pedalfast.data' (PEDALFAST Data) Data files and documentation for PEDiatric vALidation oF vAriableS in TBI (PEDALFAST). The data was used in "Functional Status Scale in Children With Traumatic Brain Injury: A Prospective Cohort Study" by Bennett, Dixon, et al (2016) . Package: r-cran-pedbuildr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pedtools, r-cran-forrel, r-cran-glue, r-cran-pedmut, r-cran-pedprobr, r-cran-ribd Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pedbuildr_0.3.0-1.ca2404.1_all.deb Size: 220126 MD5sum: 03bcb79b7feefdccf08aedc1374020df SHA1: 9931c3dc67b25ed2072fe390f959549f5efd4e9b SHA256: 76d01662d5dc37e72ef5bdfff09b0090c6512bbce11949f94427222daf9a9373 SHA512: 92885621d777f91b6f2d56668ffa4982e5dd57d4cfe3535a0ca2ba82a5e6d28269cca255368d87db335febf47a6ecf12aedee2f9f2974e6ea8b42fd9b3bf1f55 Homepage: https://cran.r-project.org/package=pedbuildr Description: CRAN Package 'pedbuildr' (Pedigree Reconstruction) Reconstruct pedigrees from genotype data, by optimising the likelihood over all possible pedigrees subject to given restrictions. Tailor-made plots facilitate evaluation of the output. This package is part of the 'pedsuite' ecosystem for pedigree analysis. In particular, it imports 'pedprobr' for calculating pedigree likelihoods and 'forrel' for estimating pairwise relatedness. Package: r-cran-pedfamilias Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pedtools, r-cran-pedmut Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pedfamilias_0.2.5-1.ca2404.1_all.deb Size: 99240 MD5sum: 99fa88ca18e796f89885e8b71c942d8f SHA1: 493eaea672fad2bc13de13986104b3b8a29137a0 SHA256: 4c08e7b8a3b5fff97a343d10d55eac5ee2c78017ade837968a7e97e7bbe5ab99 SHA512: b93ee9df875b9b2595b68871da4ed3df7edbb3b6f38c062eef37f28f421e28eed3fb89d6a6a1d7310281a00d0466d362519db495060b577d80dd64c1f3c457c9 Homepage: https://cran.r-project.org/package=pedFamilias Description: CRAN Package 'pedFamilias' (Import and Export 'Familias' Files) Tools for exchanging pedigree data between the 'pedsuite' packages and the 'Familias' software for forensic kinship computations (Egeland et al. (2000) ). These functions were split out from the 'forrel' package to streamline maintenance and provide a lightweight alternative for packages otherwise independent of 'forrel'. Package: r-cran-pedgene Architecture: all Version: 4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-compquadform, r-cran-survey, r-bioc-pedixplorer Filename: pool/dists/noble/main/r-cran-pedgene_4.1-1.ca2404.1_all.deb Size: 281186 MD5sum: 95bc11233dc7a35829ad715b32f95ab9 SHA1: ff1dae404a6c39a335f36d19eaf1fd10feca090d SHA256: 60dc345dce2d7dc951cb6931153ac69608055287da39b0ef44ec45566bb525ba SHA512: 0aa9d56ff4f6b24192e2649687b090c53aa88dbf2c438160c3c15ca13c6fad53c36f308e4f8599ec57aaec741c88998f39d9acaaf5f15046a4194f365c30c41c Homepage: https://cran.r-project.org/package=pedgene Description: CRAN Package 'pedgene' (Gene-Level Variant Association Tests for Pedigree Data) Gene-level variant association tests with disease status for pedigree data: kernel and burden association statistics. Package: r-cran-pedmermaid Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 868 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-diagrammer, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pedmermaid_1.0.2-1.ca2404.1_all.deb Size: 263342 MD5sum: 70ac375f7a76eecb0e3316a9368953a4 SHA1: 70b1d536dfd5accfee3772708e91fe455c2d0961 SHA256: 3c564885b7fb77e465f7c7be44b00001226bb73076fb521c8923cb53e7e151df SHA512: afc1c464795c30e2da0d516f5a839a8596d9afd1230e674a577e29a9a063a73c01fea511d4f090428b6e89f99315920199b402452fa90a674843ff7c01e2eee8 Homepage: https://cran.r-project.org/package=pedMermaid Description: CRAN Package 'pedMermaid' (Pedigree Mermaid Syntax) Generate Mermaid syntax for a pedigree flowchart from a pedigree data frame. Mermaid syntax is commonly used to generate plots, charts, diagrams, and flowcharts. It is a textual syntax for creating reproducible illustrations. This package generates Mermaid syntax from a pedigree data frame to visualize a pedigree flowchart. The Mermaid syntax can be embedded in a Markdown or R Markdown file, or viewed on Mermaid editors and renderers. Links' shape, style, and orientation can be customized via function arguments, and nodes' shapes and styles can be customized via optional columns in the pedigree data frame. Package: r-cran-pedmut Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lpsolve Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pedmut_0.9.1-1.ca2404.1_all.deb Size: 147550 MD5sum: ff9da0612fa40ae202593e2279a21684 SHA1: 892d1ed01e260e5c30d653dd0c241c642c2daa97 SHA256: 5906cfb87723ee4d11c232234c98b9ebb0a74e755c3e3c290c5f638ed3d3d0e6 SHA512: 705d4f5374be7fac948b999763badaa553c0573d74e5ed4cd13165126caef0e66bc3705140c96bf1174432d7eb42aa84ae0d4c9d2acd2008a050d6a876f5da90 Homepage: https://cran.r-project.org/package=pedmut Description: CRAN Package 'pedmut' (Mutation Models for Pedigree Likelihood Computations) A collection of functions for modelling mutations in pedigrees with marker data, as used e.g. in likelihood computations with microsatellite data. Implemented models include equal, proportional and stepwise models, as well as random models for experimental work, and custom models allowing the user to apply any valid mutation matrix. Allele lumping is done following the lumpability criteria of Kemeny and Snell (1976), ISBN:0387901922. Package: r-cran-pedprobr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pedtools, r-cran-pedmut Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pedprobr_1.0.1-1.ca2404.1_all.deb Size: 194364 MD5sum: 3365cba6fe6975f985e3633efc0a180c SHA1: 0b659c67a96730da19d25776272ae3101cfe4cf6 SHA256: 5ebbd3af7fbfa92c196143a43547c8679597175eb511ff400e004711c360d8d7 SHA512: cbd2ea04387b7a48e0312d839c71ef0de91fc601bfcc662eaf99ae505e89b2f1e255d59f3fc10c96cb8f2cb19333edee87fa19c23a5c28837e7db0f845955fa1 Homepage: https://cran.r-project.org/package=pedprobr Description: CRAN Package 'pedprobr' (Probability Computations on Pedigrees) An implementation of the Elston-Stewart algorithm for calculating pedigree likelihoods given genetic marker data (Elston and Stewart (1971) ). The standard algorithm is extended to allow inbred founders. 'pedprobr' is part of the 'pedsuite', a collection of packages for pedigree analysis in R. In particular, 'pedprobr' depends on 'pedtools' for pedigree manipulations and 'pedmut' for mutation modelling. For more information, see 'Pedigree Analysis in R' (Vigeland, 2021, ISBN:9780128244302). 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The data sources including NBS, FRED, Sina, Eastmoney and etc. It also provides quantitative functions for trading strategies based on the 'data.table', 'TTR', 'PerformanceAnalytics' and etc packages. Package: r-cran-pedsimulate Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pedsimulate_1.4.3-1.ca2404.1_all.deb Size: 66808 MD5sum: bc6417aae15f0771dc62eb50a1ef03a1 SHA1: 5b4d261850edbbb84736fa6f417889ab6328bc92 SHA256: b8b00f8703a24c7fd726b58ffd56217a799d9bcc202bc7027390899a1ff6a0ff SHA512: 502f53058e98f90cefe50bda1f988318f1648108770e955ef25e30c4135d9410215d583dc76e3c542d20296f88f69aecd696ddf9cf66045d7be12b05854580f0 Homepage: https://cran.r-project.org/package=pedSimulate Description: CRAN Package 'pedSimulate' (Pedigree, Genetic Merit, Phenotype, and Genotype Simulation) Simulate pedigree, genetic merits and phenotypes with random/non-random matings followed by random/non-random selection with different intensities and patterns in males and females. Genotypes can be simulated for a given pedigree, or an appended pedigree to an existing pedigree with genotypes. Mrode, R. A. (2005) ; Nilforooshan, M.A. (2022) . 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A detailed presentation of the 'pedsuite' is given in the book 'Pedigree Analysis in R' (Vigeland, 2021, ISBN: 9780128244302). 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Payman Nickchi, Uladzislau Vadadokhau, Mehdi Mirzaie, Marc Baumann, Amir Ata Saei, Mohieddin Jafari (2025) . Package: r-cran-peip Architecture: all Version: 2.2-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bvls, r-cran-matrix, r-cran-rseis, r-cran-pracma, r-cran-geigen, r-cran-fields Filename: pool/dists/noble/main/r-cran-peip_2.2-5-1.ca2404.1_all.deb Size: 179634 MD5sum: 7e220ff65bf7aab833721235a6e539e3 SHA1: aaf433197bbbad38940255ab4a5b60552566d502 SHA256: 60a0d5a373e6ffbae2c76cf451e0932ea79057294ae2912b1b002492943ce07b SHA512: 2134289a8f27e8fd6f2aca2850637d27d756aadf1c320e0afbf38f6c701ac54d3f4f7706716944ee176e6ee3871096249005832387f0071363add763d7e3d969 Homepage: https://cran.r-project.org/package=PEIP Description: CRAN Package 'PEIP' (Geophysical Inverse Theory and Optimization) Several functions introduced in Aster et al.'s book on inverse theory. 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The two classifiers presented are the marginal classifier (that assumes test data is i.i.d.) next to a more computationally costly but accurate simultaneous classifier (that finds a labelling for the entire test dataset at once based on simultanous use of all the test data to predict each label). We also provide the Maximum Likelihood Estimation (MLE) of the only underlying parameter of the partition exchangeability generative model as well as hypothesis testing statistics for equality of this parameter with a single value, alternative, or multiple samples. We present functions to simulate the sequences from Ewens Sampling Formula as the realisation of the Poisson-Dirichlet distribution and their respective probabilities. 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For more details about the theory, please refer to Cuntrera, D., Augugliaro, L., & Muggeo, V. M. (2022) . 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The smoothly clipped absolute deviation (SCAD), 'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic SCAD' (SCAD and 'L2-norm') penalties are available. The tuning parameters can be found using either a fixed grid or a interval search. 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Package: r-cran-penguinr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-penguinr_0.1.0-1.ca2404.1_all.deb Size: 136304 MD5sum: 23bf2d631979f971c71fb0a3e4ad831a SHA1: 21c30dc56e3938c31469bfa4e2f403bd27e4a474 SHA256: ca5c0eb55880b62b215c7e433760d884cca8a44a0dc66e7da15747a0167d0386 SHA512: d503ee468eb14cf9123f8dea6a402bc0cc429dc1dc2529b2583892305f2df717106fc1efdb49944aef44b88c12636aba50a1f8ee8fbea1bda7e058895ee8a84a Homepage: https://cran.r-project.org/package=PenguinR Description: CRAN Package 'PenguinR' (A Comprehensive Collection of Penguin Datasets for StatisticalAnalysis and Experimental Design) Offers a comprehensive collection of penguin-related datasets suitable for descriptive statistics, hypothesis testing, and experimental design. 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More methods under other semiparametric models such as cure model or additive model will be included in future versions. For more details see Lu, M., Liu, Y., Li, C. and Sun, J. (2019) . 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Supports guided sessions through a 'Claude Code' skill bundle and autonomous research runs from R via autoresearch(). Results land in a structured vault of markdown pages with 'YAML' frontmatter and wikilinks, ready for hand-editing in your favourite editor alongside the LLM. Vaults are seeded with 'CLAUDE.md' and 'AGENTS.md' so 'Claude Code', 'Codex' , and other agents share the same operating instructions. Can adopt an existing 'Obsidian' vault in place via init_vault(adopt = TRUE). 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Package: r-cran-peopleanalytics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-peopleanalytics_0.1.0-1.ca2404.1_all.deb Size: 124116 MD5sum: a51101db8824c90ec28a5488625eaf01 SHA1: f3003b2fb168ed6c34af3a1ef7c7c98c5b2f121d SHA256: b882d2db5978bd2c7d2590c48c4444f959fa7c035852dcce3dceb6b469eaf1ac SHA512: 8bb874913059ba060a466e11178d63fe91cfaff95b96747bb3fb07d3672246bbfa31de8820083844939521f63a47804b29b01036b620daeaa7c0d6c49c129786 Homepage: https://cran.r-project.org/package=peopleanalytics Description: CRAN Package 'peopleanalytics' (Data Sets for Craig Starbuck's Book, "The Fundamentals of PeopleAnalytics: With Applications in R") Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R". 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(2018) ; Templ et al. (2026) ) and similar observation networks. Implements station-level data quality grading, outlier detection, phenological normals (climate baselines), anomaly detection, elevation and latitude gradient estimation with robust regression, spatial synchrony quantification, partial least squares (PLS) regression for identifying temperature-sensitive periods, and sequential Mann-Kendall trend analysis. Supports data import from PEP725 files, conversion of user-supplied data, and downloadable synthetic datasets for teaching without barriers of registration. All analysis outputs provide 'print', 'summary', and 'plot' methods. Interactive spatial visualization is available via 'leaflet'. Package: r-cran-pepa Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3195 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pepa_1.0-1.ca2404.1_all.deb Size: 3207920 MD5sum: 268d336557c46098537799e086ea1b5b SHA1: 407b5022cc730fa2daaefed6e0eebdbf7fd904c4 SHA256: 4754c31639a299ea1568b3ab90f5591b204e4d073cb6100d38bf7a8977705124 SHA512: c6241038464bbe8b9e146bc1e3f3cca2138d3dcb076f2fe20067bdc05f92ee991cd46f536c3adf704b495254666f0527f89816362d9cd44b6fc259e2f0ccdce4 Homepage: https://cran.r-project.org/package=pEPA Description: CRAN Package 'pEPA' (Tests of Equal Predictive Accuracy for Panels of Forecasts) Allows to perform the tests of equal predictive accuracy for panels of forecasts. Main references: Qu et al. (2024) and Akgun et al. (2024) . 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Implements S3 classes for data management and supports Generalized Linear Models (GLM; Nelder and Wedderburn (1972) ), Aligned Rank Transform (ART; Wobbrock et al. (2011) ), and pairwise test methods for statistical analysis. Includes visualization functions for Principal Component Analysis (PCA), volcano plots, and heatmaps. Package: r-cran-pepe Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1057 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-psych, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pepe_1.2.0-1.ca2404.1_all.deb Size: 877752 MD5sum: 444a9a457fd6064224b2d82da275d35c SHA1: 7503820073ec7fbebc68f5646d5a230c4541ba16 SHA256: 1d55cd6798aa57f78e4b536e7e8d1dfc40c5654e56cc02cf155b24c9e017cf06 SHA512: 5e201d151b3e0a78893b005980d32f101187ae88909e4d5357b5467adf377d60faa5b47318bb0a277b9aa487400c90b81c36ba5e2c537b3b398803aba3b713f0 Homepage: https://cran.r-project.org/package=pepe Description: CRAN Package 'pepe' (Data Manipulation) Is designed to make easier printing summary statistics (for continues and factor level) tables in Latex, and plotting by factor. 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Potential applications of 'PepMapViz' include the visualization of cross-software mass spectrometry results at the peptide level for specific protein and domain details in a linearized format and post-translational modification coverage across different experimental conditions; unraveling insights into disease mechanisms. It also enables visualization of Major histocompatibility complex-presented peptide clusters in different antibody regions predicting immunogenicity in antibody drug development. 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Package: r-cran-pepr Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-yaml, r-cran-stringr, r-cran-data.table, r-cran-rcurl, r-cran-httr2 Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-curl Filename: pool/dists/noble/main/r-cran-pepr_0.6.0-1.ca2404.1_all.deb Size: 284394 MD5sum: e67e59e2687b1e0a40f599dcfc24cc71 SHA1: 44e82d7de944e5e88d74ff1ed3195d2d8f4af00e SHA256: d219c026e424cf23aa2e418a16b8b02b704c70a0dde1031dc50c19af3771887e SHA512: 5743e19a56f542077b899375a8c97605cf14f74cdea35cf912eba03bb7f3cf3d443019cc8be357a5572c7e86f01cd6002bcf94a5a411f61090f8f79f0dff5db2 Homepage: https://cran.r-project.org/package=pepr Description: CRAN Package 'pepr' (Reading Portable Encapsulated Projects) A PEP, or Portable Encapsulated Project, is a dataset that subscribes to the PEP structure for organizing metadata. 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Package: r-cran-pepsavims Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3447 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-elasticnet Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-pepsavims_0.9.1-1.ca2404.1_all.deb Size: 3381028 MD5sum: 62fd57aecaed0f0afc4a992ce7bcf9a1 SHA1: 546c74a0bf6e15880c3a304d1a317f7945338c74 SHA256: 8fd971dd2e162660da0dc2f5a581502f3080be229c9029b73fcbdd29246f3f49 SHA512: ce250cf5def0a184c4b49406a59e635e1eb858faace5a505cd1912bc4ea0c63cd722380dc0e8eea6d880f852ba40792081edbba630dcf99b5e7816ef37872fe7 Homepage: https://cran.r-project.org/package=PepSAVIms Description: CRAN Package 'PepSAVIms' (PepSAVI-MS Data Analysis) An implementation of the data processing and data analysis portion of a pipeline named the PepSAVI-MS which is currently under development by the Hicks laboratory at the University of North Carolina. The statistical analysis package presented herein provides a collection of software tools used to facilitate the prioritization of putative bioactive peptides from a complex biological matrix. Tools are provided to deconvolute mass spectrometry features into a single representation for each peptide charge state, filter compounds to include only those possibly contributing to the observed bioactivity, and prioritize these remaining compounds for those most likely contributing to each bioactivity data set. Package: r-cran-peptoolkit Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-peptides, r-cran-dplyr, r-cran-stringr, r-cran-caret Filename: pool/dists/noble/main/r-cran-peptoolkit_0.0.1-1.ca2404.1_all.deb Size: 41488 MD5sum: 469c8eb6f3406cc6dca92e86fbe0f502 SHA1: 522b9f8abe8ac012bd00c9f6115c78f9f18119ce SHA256: 658fc8679b9af0143e9b81b796d2b5b512e2db7189761270eb0d6ae7092c9ef3 SHA512: 3ef6ceec79f5862b4b2f21cbb391973dba289b5631b282237fe2c6706ab56e9fe7cf99a60dc01b172e9d9368ef42d2a9bea650e98fcfcc505aade005aa424174 Homepage: https://cran.r-project.org/package=peptoolkit Description: CRAN Package 'peptoolkit' (A Toolkit for Using Peptide Sequences in Machine Learning) This toolkit is designed for manipulation and analysis of peptides. It provides functionalities to assist researchers in peptide engineering and proteomics. Users can manipulate peptides by adding amino acids at every position, count occurrences of each amino acid at each position, and transform amino acid counts based on probabilities. The package offers functionalities to select the best versus the worst peptides and analyze these peptides, which includes counting specific residues, reducing peptide sequences, extracting features through One Hot Encoding (OHE), and utilizing Quantitative Structure-Activity Relationship (QSAR) properties (based in the package 'Peptides' by Osorio et al. (2015) ). This package is intended for both researchers and bioinformatics enthusiasts working on peptide-based projects, especially for their use with machine learning. 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Provides the full Log base scale from warm paper (Log 50) to deep ink (Log 950), eight crew accent hues with light and dark variants, and 'ggplot2' scales for discrete and continuous mapping. Designed for long-form reading and code, with low saturation and a consistent earth- pigment register. Full design rationale and accessibility notes at . 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P., Jenkins, G. M., Reinsel, G. (1994) Brockwell, P. J., Davis, R. A. (1991, ISBN:978-1-4419-0319-8) Bretz, F., Hothorn, T., Westfall, P. (2010, ISBN: 9780429139543) Westfall, P. H., Young, S. S. (1993, ISBN:978-0-471-55761-6) Bloomfield, P., Hurd, H. L.,Lund, R. (1994) Dehay, D., Hurd, H. L. (1994, ISBN:0-7803-1023-3) Vecchia, A. (1985) Vecchia, A. (1985) Jones, R., Brelsford, W. (1967) Makagon, A. (1999) Sakai, H. (1989) Gladyshev, E. G. (1961) Ansley (1979) Hurd, H. L., Gerr, N. L. (1991) . 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These statistics allow assessing whether individual response patterns to tests or questionnaires are (im)plausible given the other respondents in the sample or given a specified item response theory model. Some PFSs apply to dichotomous data, such as the likelihood-based PFSs (lz, lz*) and the group-based PFSs (personal biserial correlation, caution index, (normed) number of Guttman errors, agreement/disagreement/dependability statistics, U3, ZU3, NCI, Ht). PFSs suitable to polytomous data include extensions of lz, U3, and (normed) number of Guttman errors. Package: r-cran-performance Architecture: all Version: 0.17.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3396 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bayestestr, r-cran-insight, r-cran-datawizard Suggests: r-cran-aer, r-cran-afex, r-cran-bayesfactor, r-cran-bayesplot, r-cran-betareg, r-cran-bigutilsr, r-cran-blavaan, r-cran-boot, r-cran-brms, r-cran-car, r-cran-cardata, r-cran-compquadform, r-cran-correlation, r-cran-cplm, r-cran-curl, r-cran-dagitty, r-cran-dbscan, r-cran-dharma, r-cran-discovr, r-cran-estimatr, r-cran-fixest, r-cran-flextable, r-cran-forecast, r-cran-ftextra, r-cran-gamm4, r-cran-ggdag, r-cran-glmmtmb, r-cran-gparotation, r-cran-hmisc, r-cran-httr2, r-cran-ics, r-cran-icsoutlier, r-cran-islr, r-cran-ivreg, r-cran-lavaan, r-cran-lme4, r-cran-lmtest, r-cran-loo, r-cran-mass, r-cran-matrix, r-cran-mclogit, r-cran-mclust, r-cran-metadat, r-cran-metafor, r-cran-mgcv, r-cran-mlogit, r-cran-modelbased, r-cran-multimode, r-cran-nestedlogit, r-cran-nlme, r-cran-nnet, r-cran-nonnest2, r-cran-ordinal, r-cran-parameters, r-cran-patchwork, r-cran-pscl, r-cran-psych, r-cran-psychtools, r-cran-quantreg, r-cran-qqplotr, r-cran-randomforest, r-cran-rcppeigen, r-cran-reformulas, r-cran-rempsyc, r-cran-rmarkdown, r-cran-rstanarm, r-cran-rstantools, r-cran-sandwich, r-cran-see, r-cran-survey, r-cran-survival, r-cran-testthat, r-cran-tweedie, r-cran-vgam, r-cran-withr Filename: pool/dists/noble/main/r-cran-performance_0.17.0-1.ca2404.1_all.deb Size: 2870650 MD5sum: 4e2a79782d5932712387dcc86c43915d SHA1: a9cdbbc87da1bca106f4b3e4c64c8989a75a6dd3 SHA256: 08e47299867486e53cce78b990c68e11ff6d3ebc0206fc0cc77c4f0f6baf5072 SHA512: 25bc6a4e85f7701bf044a424f0d0898b0370964425690840f6d636ec6eef40d426d53358725be06916ba04ae4839097630e4e722c13ebdc70908eaf983d209fe Homepage: https://cran.r-project.org/package=performance Description: CRAN Package 'performance' (Assessment of Regression Models Performance) Utilities for computing measures to assess model quality, which are not directly provided by R's 'base' or 'stats' packages. These include e.g. measures like r-squared, intraclass correlation coefficient (Nakagawa, Johnson & Schielzeth (2017) ), root mean squared error or functions to check models for overdispersion, singularity or zero-inflation and more. Functions apply to a large variety of regression models, including generalized linear models, mixed effects models and Bayesian models. References: Lüdecke et al. (2021) . Package: r-cran-performanceestimation Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1888 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-parallelmap, r-cran-tidyr, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-e1071, r-cran-randomforest, r-cran-quantmod, r-cran-nnet, r-cran-mlbench, r-cran-mass Filename: pool/dists/noble/main/r-cran-performanceestimation_1.1.0-1.ca2404.1_all.deb Size: 1460160 MD5sum: fea7e64def1e01326eb4cc4dd33a8c6d SHA1: 357fbc484b30edde00b848165cef8091e4061fdd SHA256: a943d3eb16a58b5b0fa75c4d3f58449ac02a808578b750b2318250b0e7349c88 SHA512: f19ed78e4de6ebc54a1e78bd2a1d9e1742e2aa418e65ebdd3b7d96016ff58b05277f1f478758e268ef9a45f7bf057fe2728da7cfe8184325499ea38c937f1c17 Homepage: https://cran.r-project.org/package=performanceEstimation Description: CRAN Package 'performanceEstimation' (An Infra-Structure for Performance Estimation of PredictiveModels) An infra-structure for estimating the predictive performance of predictive models. In this context, it can also be used to compare and/or select among different alternative ways of solving one or more predictive tasks. The main goal of the package is to provide a generic infra-structure to estimate the values of different metrics of predictive performance using different estimation procedures. These estimation tasks can be applied to any solutions (workflows) to the predictive tasks. The package provides easy to use standard workflows that allow the usage of any available R modeling algorithm together with some pre-defined data pre-processing steps and also prediction post- processing methods. It also provides means for addressing issues related with the statistical significance of the observed differences. Package: r-cran-periodics Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-rms Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-periodics_0.5.0-1.ca2404.1_all.deb Size: 180330 MD5sum: 90eeae80e39c37695fe024d7512b1a70 SHA1: e474e52d11b25c7cd81dd8d0b75e4829818dfb0b SHA256: 02c90a4b7d7ee33db904b8020672b68f0830fb0efb346c2410b44d145c08bfe3 SHA512: ebf13e9f5b5f4b7e931ef7b86e5be4ec06b384d501afba860258b1bb0ebf01ca5a0d9f3ea4db1da11ac663053a82eacb7fa0759b81a2754fc7bd13f6fea57f24 Homepage: https://cran.r-project.org/package=peRiodiCS Description: CRAN Package 'peRiodiCS' (Functions for Generating Periodic Curves) Functions for generating variants of curves: restricted cubic spline, periodic restricted cubic spline, periodic cubic spline. Periodic splines can be used to model data that has periodic nature / seasonality. Package: r-cran-periodictable Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-periodictable_0.1.2-1.ca2404.1_all.deb Size: 45138 MD5sum: ea99f4ff68de7304e132e27afeaf295c SHA1: 3faf157067d2fa21eeace4b0e9168bf8f3564b9a SHA256: 54b2bd150d1bc81a978c87c49718fc49ebbebb2e364f69af57d71dc1bf9e2a85 SHA512: 69f91c2f2c558a43db3b6ec8d17a8477ff955d78d3f41012d5901e7bda1f4c3071514ac263fc2e4f01f4301bde88682583a60e216b2218adf6fcef964e4b5a3f Homepage: https://cran.r-project.org/package=PeriodicTable Description: CRAN Package 'PeriodicTable' (Periodic Table of the Elements) Provides a dataset containing properties for chemical elements. Helper functions are also provided to access some atomic properties. Package: r-cran-periscope2 Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4394 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-bs4dash, r-cran-dt, r-cran-fresh, r-cran-lubridate, r-cran-reactable, r-cran-shinyfeedback, r-cran-shinywidgets, r-cran-writexl, r-cran-yaml, r-cran-lifecycle Suggests: r-cran-assertthat, r-cran-canvasxpress, r-cran-colourpicker, r-cran-ggplot2, r-cran-knitr, r-cran-lattice, r-cran-miniui, r-cran-openxlsx, r-cran-openxlsx2, r-cran-rmarkdown, r-cran-shinyjs, r-cran-spelling, r-cran-testthat, r-cran-waiter Filename: pool/dists/noble/main/r-cran-periscope2_0.3.0-1.ca2404.1_all.deb Size: 2180416 MD5sum: 7b7e028131cdc2fc59972cfdac3cad62 SHA1: 2c505df3d159bc839bdb68c3b915dfc3a6fefcad SHA256: 6a8c9c9e5192ca07f965f252453943bdcd80656baf4a02e1ae376ea3942564a3 SHA512: 83c9d45af47ebc8ad315cdf05fef2ea2cce7c5daa879971749c1eda9a3c64add07451d3764342946ebdb772332fe461e71b41925f40ba6db83cb51bb08cc8bac Homepage: https://cran.r-project.org/package=periscope2 Description: CRAN Package 'periscope2' (Enterprise Streamlined 'shiny' Application Framework Using'bs4Dash') A framework for building enterprise, scalable and UI-standardized 'shiny' applications. 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Package: r-cran-perk Architecture: all Version: 0.0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2206 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bs4dash, r-cran-dt, r-cran-shiny, r-cran-tibble, r-cran-colourpicker, r-cran-config, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-golem, r-cran-magrittr, r-cran-plotly, r-cran-readr, r-cran-shinyjs, r-cran-shinywidgets, r-cran-tidyr, r-cran-viridis, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-perk_0.0.9.2-1.ca2404.1_all.deb Size: 1615182 MD5sum: 4888f930582a98982a4244efe5c5f8fa SHA1: 83e8589715cd581ea4822d07b7aa04474963aa00 SHA256: 3edd672c3d91641b8bbf4bff789a3b2885363cad32c94091ca14fef4553e760c SHA512: eee1c4c082eca07d9e2ec8031833fd7368a894f9571f5aba0a6299e5e7070fc86fa458513d39f0231c1b8e5356232ec26fa00ce5999d9c1575147d7619d5515a Homepage: https://cran.r-project.org/package=PERK Description: CRAN Package 'PERK' (Predicting Environmental Concentration and Risk) A Shiny Web Application to predict and visualize concentrations of pharmaceuticals in the aqueous environment. Jagadeesan K., Barden R. and Kasprzyk-Hordern B. (2022) . Package: r-cran-perm Architecture: all Version: 1.0-0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-coin Filename: pool/dists/noble/main/r-cran-perm_1.0-0.4-1.ca2404.1_all.deb Size: 87458 MD5sum: 6f120c0c7044f7245c6b7e5ae745eea2 SHA1: 44abf05ac59f4235eb07e001af431deaecae615e SHA256: 8dbe058d3f1a48c2e5383e201447e1f0229c5d58a6d9d9ffb5634725b243a781 SHA512: 6b309582dc23442dc2209ef0e9c28872dda28e63bb8472addd49e308d306c9c1eeed67b17be23656c189ae1366b591441653da30a1bc8fd008cff5a2d37bcf45 Homepage: https://cran.r-project.org/package=perm Description: CRAN Package 'perm' (Exact or Asymptotic Permutation Tests) Perform Exact or Asymptotic permutation tests [see Fay and Shaw ]. Package: r-cran-permalgo Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-permalgo_1.2-1.ca2404.1_all.deb Size: 30744 MD5sum: 02e62805f63627543cf3a18262008e81 SHA1: 1722aa421046211662faa043fd2271eb263c1cd4 SHA256: 8f7abf987d1ff1c283b84e65c5b43f743b9308ef40d2cbb2c456e2b6dd084289 SHA512: a43550c967bba7856029e5148f119cdf529a09501322331175384e07ee480fd99a957960483bb94fcfd69d87283d3029113efd014c0a42c40f4622b69562815d Homepage: https://cran.r-project.org/package=PermAlgo Description: CRAN Package 'PermAlgo' (Permutational Algorithm to Simulate Survival Data) This version of the permutational algorithm generates a dataset in which event and censoring times are conditional on an user-specified list of covariates, some or all of which are time-dependent. 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The pictorial representation is based on the principal coordinates of the group means. There are some original results that will be published soon. Package: r-cran-permat Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-permat_0.1.0-1.ca2404.1_all.deb Size: 32666 MD5sum: 26d663ff048050b429e50da72a70878e SHA1: 863e669269bd58ab7135b8e4d6ec65bab5fba30a SHA256: 5b02b72310f85ffa6ca0190c0dcdbc6e5da15ed4cf36aa527847aeb57a31ef63 SHA512: ec4039e1fd84874812be56b2593bf55a7950a48f401983236eebd58e6f51b8d2b920c0e06ef9ca38266d93d9d0ce194dab5c76abd24209c8a46fdcf1e0c70c3d Homepage: https://cran.r-project.org/package=PerMat Description: CRAN Package 'PerMat' (Performance Metrics in Predictive Modeling) Performance metric provides different performance measures like mean squared error, root mean square error, mean absolute deviation, mean absolute percentage error etc. of a fitted model. These can provide a way for forecasters to quantitatively compare the performance of competing models. For method details see (i) Pankaj Das (2020) . Package: r-cran-permchacko Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-permchacko_1.0.1-1.ca2404.1_all.deb Size: 42134 MD5sum: af9086c28fec5086d127f0dbc0d73a63 SHA1: 9043d7a2187f64e4b5618b676655b6eb6a6e1019 SHA256: aedf5e6b6561ef103e0ec34f92e794234030789b540b1c399aec592c40480dcb SHA512: 2898d413b1b44688d49c943d6e51391d5e7117a76607e542a561f347d190f54ddf3f8be05eef6fb405bb24ea2d2a8eb6f30a5c63ebfb19694ff017f1d3d197a1 Homepage: https://cran.r-project.org/package=permChacko Description: CRAN Package 'permChacko' (Chacko Test for Order-Restriction with Permutation) Implements an extension of the Chacko chi-square test for ordered vectors (Chacko, 1966, ). Our extension brings the Chacko test to the computer age by implementing a permutation test to offer a numeric estimate of the p-value, which is particularly useful when the analytic solution is not available. Package: r-cran-permcor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-permcor_0.1.0-1.ca2404.1_all.deb Size: 43334 MD5sum: 06e3b907a5545801ace0f3c798fa6806 SHA1: f62da79912e446986892aae6acd9fa529591f724 SHA256: ea97e512b1e3e0b71209997acc512e3d8f3039af334a6637f63b4d8ff22c5e7c SHA512: 0c0a64582d1c9c857086f0a80f0e75ffbc73b488cf2ebba889595efebd390c21e6bac69059691736ed447c3952685d9597afa7b8bb2a3eea2339568fc28fd368 Homepage: https://cran.r-project.org/package=PermCor Description: CRAN Package 'PermCor' (Robust Permutation Tests of Correlation Coefficients) Provides tools for statistical testing of correlation coefficients through robust permutation method and large sample approximation method. Tailored to different types of correlation coefficients including Pearson correlation coefficient, weighted Pearson correlation coefficient, Spearman correlation coefficient, and Lin's concordance correlation coefficient.The robust permutation test controls type I error under general scenarios when sample size is small and two variables are dependent but uncorrelated. The large sample approximation test generally controls type I error when the sample size is large (>200). Package: r-cran-permgs Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-coin Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-permgs_0.2.5-1.ca2404.1_all.deb Size: 90778 MD5sum: 55c598a8e57fcad8cd84d52a6aca7577 SHA1: a56cc7b6442a046f6812a894b0384739a7253848 SHA256: 50b4aa7729627ba36fc8ccd5e866222b971da009b56d70e7e5c6c596558d1789 SHA512: a8328ff98c1e7a8547a482c27d178fa84c628780a62aee2652184e0e4fd39dec0257ac079648e06e3446b254bcd698ccc181d73491327e97a99c6bd0b2077129 Homepage: https://cran.r-project.org/package=permGS Description: CRAN Package 'permGS' (Permutational Group Sequential Test for Time-to-Event Data) Permutational group-sequential tests for time-to-event data based on the log-rank test statistic. Supports exact permutation test when the censoring distributions are equal in the treatment and the control group and approximate imputation-permutation methods when the censoring distributions are different. Package: r-cran-permimp Architecture: all Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ipred, r-cran-party, r-cran-pbapply, r-cran-randomforest, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-permimp_1.1-0-1.ca2404.1_all.deb Size: 146970 MD5sum: 75b123e2ce45f0559da1cefe84e3fd81 SHA1: 1908404eca791840ba9c38971e1d63018764308b SHA256: 3580d1933789ee8759c5709a265bc6a276dc4db8b95b3aa1a2fbba8b6871c7b8 SHA512: c0c2a66ab127473dc7e917fb023ab32800104ab5932caa9cc7eaba3ac328c55f6c1920399c8d66bee7c898b4fefe0e04ba1c891bf397c73e7f190ba2a49e841c Homepage: https://cran.r-project.org/package=permimp Description: CRAN Package 'permimp' (Conditional Permutation Importance) An add-on to the 'party' package, with a faster implementation of the partial-conditional permutation importance for random forests. The standard permutation importance is implemented exactly the same as in the 'party' package. The conditional permutation importance can be computed faster, with an option to be backward compatible to the 'party' implementation. The package is compatible with random forests fit using the 'party' and the 'randomForest' package. The methods are described in Strobl et al. (2007) and Debeer and Strobl (2020) . Package: r-cran-permrand Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-permrand_1.0.0-1.ca2404.1_all.deb Size: 387796 MD5sum: d1ac021195b1fd05f98c89103381e3f5 SHA1: 98e6142c88e78317c97733e634ddfc7982856f75 SHA256: f5c322eb0113d6428da5877625cf394f3bb64619042f46ceafacf425a03c842c SHA512: b8e3e12c12be5385f253137b6c01e60f176b0a3c8562d90376db6bdfbf2c720d0cbc916de4fcbb121948ae0bfda1efeff9214e4d85dfcf61b73f38a843a10097 Homepage: https://cran.r-project.org/package=permRand Description: CRAN Package 'permRand' (Permutation Randomization) Provides randomization using permutation for applications. To provide a Quality Control (QC) check, QC samples can be randomized within strata. A second function allows for the ability to ”switch” samples to meet set requirements and perform a certain amount of minimization on these switches. The functions are flexible for users by specifying strata size and number of QC samples per strata. The randomization meets the following requirements • QC sample requirements: QC samples not adjacent, QC samples from same mother must follow certain patterns. • Matched sample sets must be within a single strata, and next to each other. Package: r-cran-permubiome Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-dabestr, r-cran-gridextra, r-cran-matrix, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-permubiome_1.3.2-1.ca2404.1_all.deb Size: 59168 MD5sum: f3c29682989cb25d4840ee6e7267a052 SHA1: 01f6f652c70b91a291159c247e5d28e1cd6a658c SHA256: dc9b2a0ccef84f7db7a7d8f1234680305ed1e54162c725447d297e45bc4ed674 SHA512: 85686e416a87b9259c6ec5aa2480fce4be5b32154686851accfc901aa084ccaa4ace493f74d2d87a4a0534a7715baf17d0b784acf9b9bee269cffef4897eb83e Homepage: https://cran.r-project.org/package=permubiome Description: CRAN Package 'permubiome' (A Permutation Based Test for Biomarker Discovery in MicrobiomeData) The permubiome R package was created to perform a permutation-based non-parametric analysis on microbiome data for biomarker discovery aims. This test executes thousands of comparisons in a pairwise manner, after a random shuffling of data into the different groups of study with a prior selection of the microbiome features with the largest variation among groups. Previous to the permutation test itself, data can be normalized according to different methods proposed to handle microbiome data ('proportions' or 'Anders'). The median-based differences between groups resulting from the multiple simulations are fitted to a normal distribution with the aim to calculate their significance. A multiple testing correction based on Benjamini-Hochberg method (fdr) is finally applied to extract the differentially presented features between groups of your dataset. LATEST UPDATES: v1.1 and olders incorporates function to parse COLUMN format; v1.2 and olders incorporates -optimize- function to maximize evaluation of features with largest inter-class variation; v1.3 and olders includes the -size.effect- function to perform estimation statistics using the bootstrap-coupled approach implemented in the 'dabestr' (>=0.3.0) R package. Current v1.3.2 fixed bug with "Class" recognition and updated 'dabestr' functions. Package: r-cran-permutationr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-permutationr_0.1.0-1.ca2404.1_all.deb Size: 14180 MD5sum: fd2993c16c61ff78e3a067f90f649ae1 SHA1: e8415fd5f77fda50ad15f7965b3414439186a97c SHA256: 21a6bfc9d09d490508a1d8e29462b0cb62a41d05e4f17bb6d495dc63e24bedf9 SHA512: 03442a76ed1a11d43ede7c30f3404efe1b4c5a6f1183f8b864cc248a4585b37b57c1274eaddb07df11e6416334f9f88102c98c0c08a143ccd8be86ac4ea28a0b Homepage: https://cran.r-project.org/package=PermutationR Description: CRAN Package 'PermutationR' (Conduct Permutation Analysis of Variance in R) Conduct permutation One-Way or Two-Way Analysis of Variance in R. Use different permutation types for two-way designs. Package: r-cran-permutations Architecture: all Version: 1.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1862 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magic, r-cran-numbers, r-cran-partitions, r-cran-freealg Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-magrittr, r-cran-covr Filename: pool/dists/noble/main/r-cran-permutations_1.1-6-1.ca2404.1_all.deb Size: 587518 MD5sum: f2a0e7df1e0781d160cf58d0ac3a9365 SHA1: 3f901cd989921286eb6462256daa46f8ca942c4f SHA256: 9e07b0f20f7323a99964da6795bd15c89c52cb0ca1c6161584a5201b34c13c16 SHA512: c39a2596a290ad9961eef10dada3ae1ba228de40f0a5601e6a8a498c1e286ce024d7e5777b915c59e1d213a00d58016a186c436c1c405a77367dccc12c312d7b Homepage: https://cran.r-project.org/package=permutations Description: CRAN Package 'permutations' (The Symmetric Group: Permutations of a Finite Set) Manipulates invertible functions from a finite set to itself. Can transform from word form to cycle form and back. To cite the package in publications please use Hankin (2020) "Introducing the permutations R package", SoftwareX, volume 11 . Package: r-cran-permute Architecture: all Version: 0.9-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-vegan, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-sessioninfo Filename: pool/dists/noble/main/r-cran-permute_0.9-10-1.ca2404.1_all.deb Size: 219854 MD5sum: 0e4b377c899972887fff2c0ed3760472 SHA1: c5a43074ddc5095d5ec1aebe398c635c7f8e10a6 SHA256: bafc25c25baa0ed25c7d901d0c4a4264a3e2869dc199b8fd02d54e60fa218827 SHA512: 55ae2f9ad9d7dea048ba84fcdd80cefcb57edb9b9236dce6d5ecb3ec8612d93b20d0c5f47e4d9c32c6570c1f52bcbb66745ab2ae83992afce41d13343060d5a3 Homepage: https://cran.r-project.org/package=permute Description: CRAN Package 'permute' (Functions for Generating Restricted Permutations of Data) A set of restricted permutation designs for freely exchangeable, line transects (time series), and spatial grid designs plus permutation of blocks (groups of samples) is provided. 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Package: r-cran-permutes Architecture: all Version: 2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3316 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr Suggests: r-cran-buildmer, r-cran-car, r-cran-doparallel, r-cran-ggplot2, r-cran-glmmtmb, r-cran-knitr, r-cran-lme4, r-cran-lmperm, r-cran-permuco, r-cran-rmarkdown, r-cran-viridis Filename: pool/dists/noble/main/r-cran-permutes_2.8-1.ca2404.1_all.deb Size: 3002532 MD5sum: fcb451da910e50c649c7712ef89a4ef5 SHA1: 7511894d431350ece8c6111bee973f348ec90b94 SHA256: d14926809dcd75172adee8fa94e4af97d1e71356444073a9c46c5982922f8dea SHA512: decd4d6735201ab67a5b035bfd722b9bec71e322f972f02c164df86cfe07bf796c4e4f86b9b668802561916a560fe594be249c5c2403416477f18119e23999b9 Homepage: https://cran.r-project.org/package=permutes Description: CRAN Package 'permutes' (Permutation Tests for Time Series Data) Helps you determine the analysis window to use when analyzing densely-sampled time-series data, such as EEG data, using permutation testing (Maris & Oostenveld, 2007) . 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It provides the foundation for developing and running Perseus plugins implemented in R by providing all required input and output handling, including data and parameter parsing as described in Rudolph and Cox 2018 . Package: r-cran-persianstemmer Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-persianstemmer_1.0-1.ca2404.1_all.deb Size: 80106 MD5sum: ad635a7191c982aaa4cd3059dafda6b0 SHA1: 3cc0458c0b62f8943088a1c8481c9add560e464e SHA256: c5c2186bb1528f80a3e59d907ed81d9e2770a58c487429f012fe24ea9a129de3 SHA512: 2d4c834062ca950baabac8640f6656d15fe7ca252f2747a7cf1221036355ab21e8d4ad9e6718b9e511c3c83f93980d1f4a9fe7fe6df990f6832f77f2de9ba53b Homepage: https://cran.r-project.org/package=PersianStemmer Description: CRAN Package 'PersianStemmer' (Persian Stemmer for Text Analysis) Allows users to stem Persian texts for text analysis. 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(2017) . This package is intended for use for both randomized controlled trials and observational studies and is described in detail in Huling and Yu (2021) . Package: r-cran-personalr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desc, r-cran-devtools, r-cran-fs, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rprojroot, r-cran-rstudioapi, r-cran-usethis, r-cran-withr, r-cran-xfun Suggests: r-cran-dplyr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-personalr_1.0.3-1.ca2404.1_all.deb Size: 61708 MD5sum: f3bf378d903d005b728c55fb9b65bdc9 SHA1: ec8b087d76901e98aef367ab8e80ea979ade4e08 SHA256: 18105e9e898d7460432128b762422ba5eecca4d4eaaf9a084230b60806092b86 SHA512: 3c0f663fca1834ed08e322566f7c07a97566cafafcab98d491e3639e31dbc06e3ec74a02b809b7fb22c0b5601919988e8c3b445f7b34d389b42a1bc80c017f6d Homepage: https://cran.r-project.org/package=personalr Description: CRAN Package 'personalr' (Automated Personal Package Setup) Functions to setup a personal R package that attaches given libraries and exports personal helper functions. Package: r-cran-personnelselectionutility Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1346 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-personnelselectionutility_1.0.2-1.ca2404.1_all.deb Size: 880384 MD5sum: b47857f71fdff95a14d8554f8db4c314 SHA1: 26294263a90446976abcc00921b7e9cff92d5bd8 SHA256: 3492b44d2aca121d89d7a6633e9ab67447eb52e040c86c680ba0f34b41b5d404 SHA512: c18e811820fd24a2c6b83012e975fa9f53e19fb2b7428081804036594d88a84b88e538e03cb85ac76864d930711d5fe3c614d8320bcc98bdb8181b58e76d0749 Homepage: https://cran.r-project.org/package=personnelSelectionUtility Description: CRAN Package 'personnelSelectionUtility' (Utility Analysis Methods for Personnel Selection) Implements classical and contemporary utility-analysis methods for personnel selection, organised by criterion scale (classification or continuous/monetary) and selection structure (compensatory or multiple-hurdle). Methods include Taylor-Russell classification (Taylor and Russell, 1939, ), Brogden-Cronbach-Gleser monetary utility (Brogden, 1949, ), Schmidt-Hunter-Pearlman intervention utility (Schmidt and others, 1979, ), Sturman comprehensive cascade (Sturman, 2001, ), Thomas-Owen-Gunst multivariate classification (Thomas and others, 1977, ), compensatory versus multiple-hurdle simulation (Ock and Oswald, 2018, ), AUC-to-effect-size conversions (Salgado, 2018, ), Pareto frontiers for validity-diversity trade-offs, and Monte Carlo uncertainty propagation. Package: r-cran-personr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-rmarkdown, r-cran-shiny, r-cran-whisker Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-personr_1.0.0-1.ca2404.1_all.deb Size: 197844 MD5sum: 80698cc0f82a02b281ec0ceec9e853fa SHA1: 22ce89ce3d16720d8c7283450f2366c12ea1d997 SHA256: 38c2c7c790fd83b7013f7c6703a18df08424c106fe70d0c477b9eb35eeacfaa1 SHA512: b02ad3ff79c1c1c46033e2bb94ca1dfdb4497009f83fac32e37940844c4f012351fd7e2a3fb8f399871c469a00bbee8026bb1ceb0faaa9aea619866b7683744b Homepage: https://cran.r-project.org/package=personr Description: CRAN Package 'personr' (Test Your Personality) An R-package-version of an open online science-based personality test from , providing a better-designed interface and a more detailed report. The core command launch_test() opens a personality test in your browser, and generates a report after you click "Submit". In this report, your results are compared with other people's, to show what these results mean. Other people's data is from . Package: r-cran-perspectev Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-foreach, r-cran-ape, r-cran-doparallel, r-cran-mapproj, r-cran-boot, r-cran-sp Filename: pool/dists/noble/main/r-cran-perspectev_1.1-1.ca2404.1_all.deb Size: 98270 MD5sum: e9a0eae07c4de6bb3689f3227f25c9bd SHA1: f53767e4399b21cb50317e2089e2eddf82338eaf SHA256: 038033807f89687457b7ac0fd430f8d3a33c59c59a34e56ef1b4a77552ed1cb8 SHA512: e569275a5ad059484c5a510cb81d06e96990d39aab82392ef4c80923d00a6107d36a630cc77b810ece921df02c8a7ce50e5fe667055f3f6e944e68bd28c65b76 Homepage: https://cran.r-project.org/package=perspectev Description: CRAN Package 'perspectev' (Permutation of Species During Turnover Events) Provides a robust framework for analyzing the extent to which differential survival with respect to higher level trait variation is reducible to lower level variation. 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Package: r-cran-perspectiver Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6970 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools, r-cran-jsonlite Suggests: r-cran-arrow, r-cran-shiny, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-perspectiver_0.3.0-1.ca2404.1_all.deb Size: 4924764 MD5sum: 8233978e57f76dc0b7d41e0c98397f61 SHA1: 8b694c94a98b5e10c32a87d0f0037aaff2788b48 SHA256: c63f6757cdee88e571a2078098c7f2e365751709dfa6d921b78119db66d156db SHA512: a2ae3e258ee01aac739a8099622205af3a0a4ee4d0aef09394180641de14101dcafd6e8a712d795b1904fc82eda3beb6e23f15fb09762f74d6ad526868b10bbb Homepage: https://cran.r-project.org/package=perspectiveR Description: CRAN Package 'perspectiveR' (Interactive Pivot Tables and Visualizations with 'Perspective') An 'htmlwidgets' binding for the 'FINOS Perspective' library, a high-performance 'WebAssembly'-powered data visualization engine. 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Includes tools for visualizing survival data, checking proportional hazards assumptions (Grambsch and Therneau, 1994, ), comparing parametric (Ishak and colleagues, 2013, ), spline (Royston and Parmar, 2002, ) and cure models, examining hazard functions, and evaluating model extrapolation. Methods are consistent with recommendations in the NICE Decision Support Unit Technical Support Documents (14 and 21 ). Results are structured to facilitate integration into decision-analytic models, and reports can be generated with 'rmarkdown'. The package builds on existing tools including 'flexsurv' (Jackson, 2016, )) and 'flexsurvcure' for estimating cure models. Package: r-cran-persval Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmsb Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-persval_1.1.2-1.ca2404.1_all.deb Size: 72184 MD5sum: 11d555fb745d79e152aa2e7a7224d8ad SHA1: d3bd0f2962f00ac3c6ac04041a222eb3948cd6a6 SHA256: 3ecde6bc88ff2edc8d7711abf33f14449bd0187ff2763585e764caa2eb8c138c SHA512: f46b45fa8ee5e2261ac9893b6201e50b5a7b83d76e36270dc9306a5206cd56bb8e4610f3dcff4d821a1079227e7166529c916fd88b320bdac9f0ab6f37e54fbc Homepage: https://cran.r-project.org/package=persval Description: CRAN Package 'persval' (Computing Personal Values Scores) Compute personal values scores from various questionnaires based on the theoretical constructs proposed by professor Shalom H. Schwartz. 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Package: r-cran-peruapis Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-scales, r-cran-tibble Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-peruapis_0.1.0-1.ca2404.1_all.deb Size: 490842 MD5sum: f45a1346e444e8fb178ea03a46116f66 SHA1: 32ecaa1ce9272da20b96b409acad1d9a9e17ceb0 SHA256: 72c7575dddd3159ccc44db8ea6d3380ddfd45a698e6ca7522a05becce25af757 SHA512: 22d24747859f88c82141e4c7adcac8e8c0b4a173af4c260053eb96b3308b924523ea5faedf176f34022f778685438651dc7ad9c7b8c8a8fd2a2bedd323567b5d Homepage: https://cran.r-project.org/package=PeruAPIs Description: CRAN Package 'PeruAPIs' (Access Peruvian Data via Public APIs and Curated Datasets) Provides functions to access data from public RESTful APIs including 'Nager.Date', 'World Bank API', and 'REST Countries API', retrieving real-time or historical data related to Peru, such as holidays, economic indicators, and international demographic and geopolitical indicators. 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Package: r-cran-peruflorads43 Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1231 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-memoise, r-cran-progress, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-gt, r-cran-scales, r-cran-stringdist, r-cran-ggplot2, r-cran-forcats, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-peruflorads43_0.2.3-1.ca2404.1_all.deb Size: 1027166 MD5sum: bd61835982c87b817a8112c4d6247071 SHA1: e02c6c8295ed38e6ec13e144dc20517cfbdb8262 SHA256: 56f7763403a47f08f0420c911b8e3eb20dca74157efab4be5bedd054247c2185 SHA512: 4ae5a8c358cb637bff8e0c774f9bea3d8be7c5a009874b1792b2b531c091381abbf3affbc2855dd97935a98fd263fff075d28580c05751880beed35577ac1863 Homepage: https://cran.r-project.org/package=peruflorads43 Description: CRAN Package 'peruflorads43' (Check Threatened Plant Species Status Against Peru's SupremeDecree 043-2006-AG) Provides tools to match plant species names against the official threatened species list of Peru (Supreme Decree 043-2006-AG, 2006). 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Package: r-cran-peruse Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rlang Suggests: r-cran-testthat, r-cran-purrr Filename: pool/dists/noble/main/r-cran-peruse_0.3.1-1.ca2404.1_all.deb Size: 91914 MD5sum: 84cf4d82260976afccd6fd5fe47b82c2 SHA1: 59f31b35e3264000be44b6867ce8119e17389629 SHA256: 7857f729ddab6ae68776eccc6010286ff07fc16444ba7e111af2dd0cbf6a1c77 SHA512: d75789daeb82478f4e38cadae16c546f04209b17e1d81af10e224c8effef02dd1e07d2b565f6af5dc1c0fa935a415831ed76e61bc94c89c5aa76c5a2733a1dbe Homepage: https://cran.r-project.org/package=peruse Description: CRAN Package 'peruse' (A Tidy API for Sequence Iteration and Set Comprehension) A friendly API for sequence iteration and set comprehension. Package: r-cran-perutimber Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3946 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lifecycle Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-perutimber_0.1.0-1.ca2404.1_all.deb Size: 3655550 MD5sum: c4181272813ad418ed221d0785196320 SHA1: 1448b3f20310e69f3228a73f185e23bd3ed9dba5 SHA256: 56e163356de7d530b06dec50f4e85ec9eb1c389ce57068efc3cbb0f981763ddf SHA512: 862f229e7f90b1fafd4815573b597e771f24b3ec7dd2d77afd77fa56cfa6477c8feb30a5f9f1318435bce275525081449f436fc95b0f1da28529ad1f2e344025 Homepage: https://cran.r-project.org/package=perutimber Description: CRAN Package 'perutimber' (Catalogue of the Timber Forest Species of the Peruvian Amazon) Access the data of the 'Catalogue of the Timber Forest Species of the Peruvian Amazon' Vásquez Martínez, R., & Rojas Gonzáles, R.D.P.(2022). Package: r-cran-pervasive Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arules, r-cran-dplyr, r-cran-tibble, r-cran-psych Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-psychtools Filename: pool/dists/noble/main/r-cran-pervasive_1.0-1.ca2404.1_all.deb Size: 80198 MD5sum: 98bee344ffa9fa89c901f76fa0dd8973 SHA1: cba62947b931ae7764ee0c3e04a72cc9f77353be SHA256: ad7d66dcbbc3207f091b9673d618f6076ac3cf44ebdcaad61b3be542b6b06245 SHA512: 57074408377eecd0e542cb1a7148950c354c87c921443f0121059f5d443c796f0c77d47148ad73bc75660381d989878a38c4870fff5b8528648cce0cc0326a0c Homepage: https://cran.r-project.org/package=pervasive Description: CRAN Package 'pervasive' (Pervasiveness Functions for Correlational Data) Analysis of pervasiveness of effects in correlational data. The Observed Proportion (or Percentage) of Concordant Pairs (OPCP) is Kendall's Tau expressed on a 0 to 1 metric instead of the traditional -1 to 1 metric to facilitate interpretation. As its name implies, it represents the proportion of concordant pairs in a sample (with an adjustment for ties). Pairs are concordant when a participant who has a larger value on a variable than another participant also has a larger value on a second variable. The OPCP is therefore an easily interpretable indicator of monotonicity. The pervasive functions are essentially wrappers for the 'arules' package by Hahsler et al. (2025) and serve to count individuals who actually display the pattern(s) suggested by a regression. For more details, see the paper "Considering approaches to pervasiveness in the context of personality psychology" now accepted at the journal Personality Science. Package: r-cran-pesel Architecture: all Version: 0.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pesel_0.7.5-1.ca2404.1_all.deb Size: 27804 MD5sum: 0e69e98947ceeb2df65234357a40ab04 SHA1: efd1b39b666d78f5aff1d83b4cd448dff0144c81 SHA256: 94daf3af74e257fcf6ff85b40295371b51d99960c7e8d6443805a5ab9089a0fa SHA512: ac02ba4f51ddf376e921306d25e3e17d88f719ea27e0698764dd11d69ab11bb0fd9add00151f6a7c9979e6bc5d1754334518781f1cbf769ce491d29e44e6b07f Homepage: https://cran.r-project.org/package=pesel Description: CRAN Package 'pesel' (Automatic Estimation of Number of Principal Components in PCA) Automatic estimation of number of principal components in PCA with PEnalized SEmi-integrated Likelihood (PESEL). See Piotr Sobczyk, Malgorzata Bogdan, Julie Josse "Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood" (2017) . Package: r-cran-pesticideloadindicator Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-stringr, r-cran-magrittr, r-cran-rlang, r-cran-dplyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pesticideloadindicator_1.3.1-1.ca2404.1_all.deb Size: 91530 MD5sum: aad99efabb90446ba9632b5bcd8c7613 SHA1: 3608e04a96d9f8047857bc544c7367a196ead684 SHA256: 4fb43747c00da41dbbda9fd98247b1b1af5e4d99bdb4b24f6b5ae959b57abc1f SHA512: c4b0e679bc70c400f15ea352bdcb5107e5ef01c8987a484cd4f4437da9f5e728141fd3d219be40c39abcbb382c0339c31bcf23b1001cdda7b585b4358b293d80 Homepage: https://cran.r-project.org/package=PesticideLoadIndicator Description: CRAN Package 'PesticideLoadIndicator' (Computes Danish Pesticide Load Indicator) Computes the Danish Pesticide Load Indicator as described in Kudsk et al. (2018) and Moehring et al. (2019) for pesticide use data. Additionally offers the possibility to directly link pesticide use data to pesticide properties given access to the Pesticide properties database (Lewis et al., 2016) . Package: r-cran-pestr Architecture: all Version: 0.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-dbi, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-readr, r-cran-rlang, r-cran-rsqlite, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-mockr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pestr_0.8.4-1.ca2404.1_all.deb Size: 328200 MD5sum: e3704065263f22f1daf1cfb741ac3345 SHA1: 618b48bccad5f42c89f731a0258d713359dd3110 SHA256: 291640c942831f5b93bdd990490ad5f9fa3f1fb5da92cf273e37120d205e7f3c SHA512: edd8f0c58325370d7a2de11fea6a0da273c1ec19ecabc0af4b45e22890de79789c9dada68b84c2b8f50d2a0881c1834f81496ff0aa54af7849dfb0beafc46db7 Homepage: https://cran.r-project.org/package=pestr Description: CRAN Package 'pestr' (Interface to Download Data on Pests and Hosts from 'EPPO') Set of tools to automatize extraction of data on pests from 'EPPO Data Services' and 'EPPO Global Database' and to put them into tables with human readable format. Those function use 'EPPO database API', thus you first need to register on (free of charge). Additional helpers allow to download, check and connect to 'SQLite EPPO database'. Package: r-cran-petersen Architecture: all Version: 2025.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aiccmodavg, r-cran-bbmle, r-cran-btspas, r-cran-checkmate, r-cran-formula.tools, r-cran-ggplot2, r-cran-mass, r-cran-matrix, r-cran-msm, r-cran-numderiv, r-cran-plyr, r-cran-reshape2, r-cran-rlang, r-cran-spas, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-petersen_2025.3.1-1.ca2404.1_all.deb Size: 364020 MD5sum: f9e696a8787cfd29065556cfb6819aa5 SHA1: 9451e950c6ce47292414da0ac2ca3a6b9cde11db SHA256: 615e11e6766d6c94f85489d9ef78d4ea671e215b98c7b993f5c0e450e37efa88 SHA512: 14aa211c0f4fbd1058ba5391c941c8e2bfa95a4ea652787aff853701ea80078b773d15d3c87c6ce1fc21c4db58529ce4de1fb72accc5e86e72cdc1e1d1a18989 Homepage: https://cran.r-project.org/package=Petersen Description: CRAN Package 'Petersen' (Estimators for Two-Sample Capture-Recapture Studies) A comprehensive implementation of Petersen-type estimators and its many variants for two-sample capture-recapture studies. A conditional likelihood approach is used that allows for tag loss; non reporting of tags; reward tags; categorical, geographical and temporal stratification; partial stratification; reverse capture-recapture; and continuous variables in modeling the probability of capture. Many examples from fisheries management are presented. Package: r-cran-petersenlab Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1044 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-hmisc, r-cran-digest, r-cran-dplyr, r-cran-ggplot2, r-cran-lavaan, r-cran-mitools, r-cran-mix, r-cran-mvtnorm, r-cran-psych, r-cran-stringr, r-cran-xtable, r-cran-plyr, r-cran-reshape2, r-cran-rcolorbrewer, r-cran-viridislite, r-cran-tidyselect, r-cran-scales, r-cran-purrr, r-cran-lme4 Suggests: r-cran-testthat, r-cran-waldo, r-cran-withr Filename: pool/dists/noble/main/r-cran-petersenlab_1.2.0-1.ca2404.1_all.deb Size: 683710 MD5sum: 60cd06a3c655d28d1c781e3432e430d5 SHA1: a886d7cc960a0f43048152ce3b645a6e32673786 SHA256: 88eaecdc2d0143335e10e262a2cc01875f461ec25631bffde32477898bd00676 SHA512: 7ec77ec3f5a0fee26add4d8724a980b3fcc24fb931ee169c4e0ce97ebacf08d8d0ed1417d64228fef46954b718f128fc610c687594bdca6977d516f04d526e74 Homepage: https://cran.r-project.org/package=petersenlab Description: CRAN Package 'petersenlab' (A Collection of R Functions by the Petersen Lab) A collection of R functions that are widely used by the Petersen Lab. Included are functions for various purposes, including evaluating the accuracy of judgments and predictions, performing scoring of assessments, generating correlation matrices, conversion of data between various types, data management, psychometric evaluation, extensions related to latent variable modeling, various plotting capabilities, and other miscellaneous useful functions. By making the package available, we hope to make our methods reproducible and replicable by others and to help others perform their data processing and analysis methods more easily and efficiently. The codebase is provided in Petersen (2025) and on 'CRAN': . The package is described in "Principles of Psychological Assessment: With Applied Examples in R" (Petersen, 2024, 2025a) , , and in "Fantasy Football Analytics: Statistics, Prediction, and Empiricism Using R" (Petersen, 2025b). Package: r-cran-petests Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-petests_0.1.0-1.ca2404.1_all.deb Size: 134652 MD5sum: 70c5232c947049b004b8ba96bec2a57c SHA1: a36b07a96550d6db59fade999eceb59031ffe40d SHA256: fd681eb76f4e2aa3532fe9e47deee6734ef94cde35b2d38f0c78d4773ca84ebb SHA512: db559de3cc3b50e7372c3e83a32b30a593cf3f247162cb80cf3da83127009ffb190a30d5c058965c1104ccd887aea23420205ee2c908f139ef94f37699e64340 Homepage: https://cran.r-project.org/package=PEtests Description: CRAN Package 'PEtests' (Power-Enhanced (PE) Tests for High-Dimensional Data) Two-sample power-enhanced mean tests, covariance tests, and simultaneous tests on mean vectors and covariance matrices for high-dimensional data. Methods of these PE tests are presented in Yu, Li, and Xue (2022) ; Yu, Li, Xue, and Li (2022) . Package: r-cran-petfinder Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6 Suggests: r-cran-lubridate, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-httr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-petfinder_2.1.0-1.ca2404.1_all.deb Size: 81128 MD5sum: 8c1a0a1063ae32ee275e3aac8cd90136 SHA1: b401a4f251b21c78095b29be75b31eee05d13fbb SHA256: c6d72727ceeaa1cbc1494f22785963a28043ff094a9aaf5f0754b94f821b2bb9 SHA512: 8b81e3bca88e34d12e46022aee96760827500ee04bc85ab5ecac75a14e4f947dcc4dbb4058ab4a4ce5597d1a7b1d21eefb5899f57663b322a2808552a51c9214 Homepage: https://cran.r-project.org/package=PetfindeR Description: CRAN Package 'PetfindeR' ('Petfinder' API Wrapper) Wrapper of the 'Petfinder API' that implements methods for interacting with and extracting data from the 'Petfinder' database. 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Keeps community ecological and phylogenetic data matched up and comparable using 'comparative.comm' objects. Wrappers for common community phylogenetic indices ('pez.shape', 'pez.evenness', 'pez.dispersion', and 'pez.dissimilarity' metrics). Implementation of Cavender-Bares (2004) correlation of phylogenetic and ecological matrices ('fingerprint.regression'). Phylogenetic Generalised Linear Mixed Models (PGLMMs; 'pglmm') following Ives & Helmus (2011) and Rafferty & Ives (2013). Simulation of null assemblages, traits, and phylogenies ('scape', 'sim.meta.comm'). 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The package supports Minkowski distances, with the L1 case solved via iteratively re-weighted least squares and the case p > 1 solved via coordinate-wise root finding, as well as an adaptive, regularised Mahalanobis distance with per-cluster covariance matrices. Both plain fuzzy clustering and cluster-wise linear regression are provided. The corresponding paper can be found at Nguyen P.T., Tortora C., and Punzo A. (2026) . 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Package: r-cran-ph2mult Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clinfun Suggests: r-cran-gsdesign, r-cran-survival Filename: pool/dists/noble/main/r-cran-ph2mult_0.1.1-1.ca2404.1_all.deb Size: 58892 MD5sum: a599bba822ef640ab96b2106e42424f1 SHA1: 0d62d82fac8442a75bedbd6b08e5a5a11b6de2d3 SHA256: 31b37b09125c5e032b2e429da157e9f0f884251a6021ef24f7c2c7e56072e900 SHA512: 3b2393007c7b2a79473faa930858da20050e01f88e2042d84f62c360a52fc84d85d56f4b37e2ef75db7526918d3db4940963a0741662f34f39a93fc5844cd1c9 Homepage: https://cran.r-project.org/package=ph2mult Description: CRAN Package 'ph2mult' (Phase II Clinical Trial Design for Multinomial Endpoints) Provide multinomial design methods under intersection-union test (IUT) and union-intersection test (UIT) scheme for Phase II trial. The design types include : Minimax (minimize the maximum sample size), Optimal (minimize the expected sample size), Admissible (minimize the Bayesian risk) and Maxpower (maximize the exact power level). Package: r-cran-phagecocktail Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-stringr, r-cran-factoextra, r-cran-bipartite, r-cran-smerc, r-cran-rjsonio Filename: pool/dists/noble/main/r-cran-phagecocktail_1.0.3-1.ca2404.1_all.deb Size: 67978 MD5sum: 5e11add20981343f0056c4b3a05e0d5f SHA1: e6a6b00fa0310b77a02a8607b85fb9e23a2012ec SHA256: 074b4226ad46f1f958b0246080010885e71afde0b92cd47f3bd731d93f9390ef SHA512: 4905fa4138fc6895a33415ab4a78dbd47ba8f49025e22f7a8e425107bd3f10a5cf5fe288ecbc40edaceec6289615eba1fd6f695d56127073c0922b50d93fae1a Homepage: https://cran.r-project.org/package=PhageCocktail Description: CRAN Package 'PhageCocktail' (Design of the Best Phage Cocktail) There are 4 possible methods: "ExhaustiveSearch"; "ExhaustivePhi"; "ClusteringSearch"; and "ClusteringPhi". "ExhaustiveSearch"--> gives you the best phage cocktail from a phage-bacteria infection network. It checks different phage cocktail sizes from 1 to 7 and only stops before if it lyses all bacteria. Other option is when users have decided not to obtain a phage cocktail size higher than a limit value. "ExhaustivePhi"--> firstly, it finds Phi out. Phi is a formula indicating the necessary phage cocktail size. Phi needs nestedness temperature and fill, which are internally calculated. This function will only look for the best combination (phage cocktail) with a Phi size. "ClusteringSearch"--> firstly, an agglomerative hierarchical clustering using Ward's algorithm is calculated for phages. They will be clustered according to bacteria lysed by them. PhageCocktail() chooses how many clusters are needed in order to select 1 phage per cluster. Using the phages selected during the clustering, it checks different phage cocktail sizes from 1 to 7 and only stops before if it lyses all bacteria. Other option is when users have decided not to obtain a phage cocktail size higher than a limit value. "ClusteringPhi"--> firstly, an agglomerative hierarchical clustering using Ward's algorithm is calculated for phages. They will be clustered according to bacteria lysed by them. PhageCocktail() chooses how many clusters are needed in order to select 1 phage per cluster. Once the function has one phage per cluster, it calculates Phi. If the number of clusters is less than Phi number, it will be changed to obtain, as minimum, this quantity of candidates (phages). Then, it calculates the best combination of Phi phages using those selected during the clustering with Ward algorithm. If you use PhageCocktail, please cite it as: "PhageCocktail: An R Package to Design Phage Cocktails from Experimental Phage-Bacteria Infection Networks". María Victoria Díaz-Galián, Miguel A. Vega-Rodríguez, Felipe Molina. Computer Methods and Programs in Biomedicine, 221, 106865, Elsevier Ireland, Clare, Ireland, 2022, pp. 1-9, ISSN: 0169-2607. . Package: r-cran-phantsem Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3636 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr, r-cran-corpcor, r-cran-lavaan Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-phantsem_1.0.1-1.ca2404.1_all.deb Size: 3636470 MD5sum: eb4022e2494493913c0c11205a763045 SHA1: ad719c0cdfee53fbe34fbf3b7a0d7ae536ffd6f2 SHA256: 9d17e5baa6a496af565b0e9c77594bd4ca690b5d1082262f1bd1eb4ad80c1917 SHA512: 3fcf96527360952c42b2c278b443949250fcc2d9e363984561c0b46def2071f2e7031829b3bf3640e4d182d209130876c9be7c8e7ce514b88f8b6b3433362dde Homepage: https://cran.r-project.org/package=phantSEM Description: CRAN Package 'phantSEM' (Create Phantom Variables in Structural Equation Models forSensitivity Analyses) Create phantom variables, which are variables that were not observed, for the purpose of sensitivity analyses for structural equation models. The package makes it easier for a user to test different combinations of covariances between the phantom variable(s) and observed variables. The package may be used to assess a model's or effect's sensitivity to temporal bias (e.g., if cross-sectional data were collected) or confounding bias. Package: r-cran-pharmartf Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1334 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-stringr, r-cran-purrr, r-cran-huxtable Suggests: r-cran-testthat, r-cran-dplyr, r-cran-readr, r-cran-gt, r-cran-magrittr, r-cran-knitr, r-cran-rmarkdown, r-cran-readxl, r-cran-kableextra, r-cran-plyr, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-pharmartf_0.1.4-1.ca2404.1_all.deb Size: 790100 MD5sum: f791d1e282558e4bf4c726c5cb81737b SHA1: 05a79a584615ab2d0c10a2b4853d3e61425a6e5f SHA256: b8ef2e739cbce1e175217d3d071651b2bea11c81c8fabdb44942c374b3b399e4 SHA512: c5c9848e7bf298ed57699b826a76ef94f70866d1c92a8432d164cf8a0a3de3c910a28a07015905f59325f30788d0d18ee8ca08e7b28450630ed6b2eb4440b699 Homepage: https://cran.r-project.org/package=pharmaRTF Description: CRAN Package 'pharmaRTF' (Enhanced RTF Wrapper for Use with Existing Table Packages) Enhanced RTF wrapper written in R for use with existing R tables packages such as 'Huxtable' or 'GT'. This package fills a gap where tables in certain packages can be written out to RTF, but cannot add certain metadata or features to the document that are required/expected in a report for a regulatory submission, such as multiple levels of titles and footnotes, making the document landscape, and controlling properties such as margins. Package: r-cran-pharmaverse Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pharmaverse_0.0.2-1.ca2404.1_all.deb Size: 40128 MD5sum: cebb7f1d34909d4867dc279d47e9331f SHA1: 107980b9920745564673cc3f1cde9d5644b71903 SHA256: 1f98b421a3ede84eeecffcf01d18f513077f0d726f8d5ee36456fbe024a5b81e SHA512: bf406dfde0c668fb0c5394d2b23229ec9c55a76f647073940a88efce88f3c9d799111ff6ad7a10e909b5327ccff8d415fe9e95d2659d7da719500a9047a2963e Homepage: https://cran.r-project.org/package=pharmaverse Description: CRAN Package 'pharmaverse' (Navigate 'Pharmaverse') The 'pharmaverse' is a set of packages that compose multiple pathways through clinical data generation and reporting in the pharmaceutical industry. This package is designed to guide users to our work-spaces on 'GitHub', 'Slack' and 'LinkedIn' as well as our website and examples. Learn more about the 'pharmaverse' at . 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ADaM dataset specifications are described in the CDISC ADaM implementation guide, accessible by creating a free account on . 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Package: r-cran-pharmaverseraw Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-lintr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-usethis, r-cran-pharmaversesdtm Filename: pool/dists/noble/main/r-cran-pharmaverseraw_0.1.1-1.ca2404.1_all.deb Size: 257562 MD5sum: ff69a660cae102086b642dd123676886 SHA1: 8de1aa14a6adf3488cf592b509d817d50b85f6e7 SHA256: 8c0de381335638409259e13cb0ccc2c2e7307759ce1d3b86a44916d1be94a080 SHA512: b3c2f9cfd1c8f1089a42e6341ca92c76721feaeda4bab1340095cf9b3abd852f0b46bedec0dfb25eb95b43c4ff8c62c42b3427bc7bd8054e0039bec27beed297 Homepage: https://cran.r-project.org/package=pharmaverseraw Description: CRAN Package 'pharmaverseraw' (Raw Data for 'pharmaversesdtm' Package) A set of raw datasets used to create SDTM domains in 'pharmaversesdtm' package. 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SDTM dataset specifications are described in the CDISC SDTM implementation guide, accessible by creating a free account on . Package: r-cran-pharmaversesdtmjnj Architecture: all Version: 0.0.4-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-pharmaversesdtm, r-cran-random.cdisc.data Filename: pool/dists/noble/main/r-cran-pharmaversesdtmjnj_0.0.4-1.ca2404.2_all.deb Size: 148480 MD5sum: 17c3e57dc730ce285a68bb6af7c8cea8 SHA1: a0345be8d7110ecfc1ca05b12a6e62eb886a1d35 SHA256: 040b8e2574e25481c666a7d331b2d6c761f6d9c48dce21ff505f91e915e35ee8 SHA512: 6b8296af046657f50a41498a6e61e3c5d5d95990ffae184a4df05bc3d2afdedc2e451e416ff3dc01b8c50f58e99b6a7042585dc99272e5e93ed596f40d928432 Homepage: https://cran.r-project.org/package=pharmaversesdtmjnj Description: CRAN Package 'pharmaversesdtmjnj' (J&J Innovative Medicine SDTM Test Data) A set of Study Data Tabulation Model (SDTM) datasets constructed by modifying the 'pharmaversesdtm' package to meet J&J Innovative Medicine's standard data structure for Clinical and Statistical Programming. Package: r-cran-pharmr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 784 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-vegawidget, r-cran-reticulate, r-cran-cli Suggests: r-cran-testthat, r-cran-magrittr, r-cran-here, r-cran-knitr Filename: pool/dists/noble/main/r-cran-pharmr_2.1.0-1.ca2404.1_all.deb Size: 637914 MD5sum: 6f50c59de93a6219811a1a9fc9308342 SHA1: fdaab46f0a17b28849a13511ff1498765a55d62b SHA256: a3f226438dc5c5742e56e77cb79b2f0413deb3c64a7ec3428e8159acbd79f70c SHA512: fe565559169e69d26b00760aad4fcabe36160edd315705eaead48f46feb3071438a54e54b1fb8aad36fa9d29fcd78b9849a0559a7b1903073f8ec226ec551f41 Homepage: https://cran.r-project.org/package=pharmr Description: CRAN Package 'pharmr' (Interface to the 'Pharmpy' 'Pharmacometrics' Library) Interface to the 'Pharmpy' 'pharmacometrics' library. The 'Reticulate' package is used to interface Python from R. Package: r-cran-phase12designs Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-trialr, r-cran-iso Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phase12designs_0.3.1-1.ca2404.1_all.deb Size: 212254 MD5sum: d81481a666976b79c8ddf88db2e311a7 SHA1: 1788c6e18196882e501231f83356ce1f7337f8d3 SHA256: 93bdba5677b81024009163325bb6592c99edd93968458ee678d9ef126ada8e2a SHA512: 67f5f507d171b12f8abe9d138d60a0714b0c631141c68bb5cfaa6018b42c9c6863f540599d2f1bb9d632c2292343e60adb85d5b67fbbd18de36067051f9f5e40 Homepage: https://cran.r-project.org/package=phase12designs Description: CRAN Package 'phase12designs' (Comprehensive Tools for Running Model-Assisted Phase I/II TrialSimulations) Provides a comprehensive set of tools to simulate, evaluate, and compare model-assisted designs for early-phase (Phase I/II) clinical trials, including: - BOIN12 (Bayesian optimal interval phase 1/11 trial design; Lin et al. (2020) ), - BOIN-ET (Takeda, K., Taguri, M., & Morita, S. (2018) ), - EffTox (Thall, P. F., & Cook, J. D. (2004) ), - Ji3+3 (Joint i3+3 design; Lin, X., & Ji, Y. (2020) ), - PRINTE (probability intervals of toxicity and efficacy design; Lin, X., & Ji, Y. (2021) ), - STEIN (simple toxicity and efficacy interval design; Lin, R., & Yin, G. (2017) ), - TEPI (toxicity and efficacy probability interval design; Li, D. H., Whitmore, J. B., Guo, W., & Ji, Y. (2017) ), - uTPI (utility-based toxicity Probability interval design; Shi, H., Lin, R., & Lin, X. (2024) ). Includes flexible simulation parameters that allow researchers to efficiently compute operating characteristics under various fixed and random trial scenarios and export the results. Package: r-cran-phase1prmd Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-ggplot2, r-cran-rjags, r-cran-arrayhelpers, r-cran-mass, r-cran-reshape2, r-cran-plyr, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-gridextra, r-cran-kableextra, r-cran-knitr Filename: pool/dists/noble/main/r-cran-phase1prmd_1.0.2-1.ca2404.1_all.deb Size: 187188 MD5sum: 8856b6925c0d4b5cdada01081009221f SHA1: 2117dffee2b3cdc80a9d33d9aaf48ac0ec5bf02a SHA256: 50d5f841a9ab21d88e0cb83c2f6a15b1f3616b183d9762466265366e16315003 SHA512: 089825b35c96abb088903cc1a6cd1857701ec91f9c558cba48a7326fa0832b9728656e9b4b8b97550146edba5416d618a98846ffd8dbbe6f768aaff9c1a93cd9 Homepage: https://cran.r-project.org/package=phase1PRMD Description: CRAN Package 'phase1PRMD' (Personalized Repeated Measurement Design for Phase I ClinicalTrials) Implements Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints and longitudinal efficacy measure from multiple treatment cycles. The package provides flags to fit a variety of model-based phase I design, including 1 stage models with or without individualized dose modification, 3-stage models with or without individualized dose modification, etc. Functions are provided to recommend dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) . Package: r-cran-phase1rmd Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-boot, r-cran-rjags, r-cran-mvtnorm, r-cran-ggplot2, r-cran-arrayhelpers Suggests: r-bioc-biocparallel Filename: pool/dists/noble/main/r-cran-phase1rmd_1.0.9-1.ca2404.1_all.deb Size: 227638 MD5sum: 8d9049e0b597fc1df7a7a216fee5a964 SHA1: f97616711663b9b4a99f6ce18a40d1dbe9e57ebf SHA256: 2f0abe8abffbc39d84969b445a1035c3df810761d9f911c7f09dcee9396da8ff SHA512: 93e478c3b4923ee2ddd252a34dda60dee3b33c3078e25a11741caec090c9fd57a45352be4f70faaacafb43d507861a4f76d7749866d129e5911a747384c752d4 Homepage: https://cran.r-project.org/package=phase1RMD Description: CRAN Package 'phase1RMD' (Repeated Measurement Design for Phase I Clinical Trial) Implements our Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints from multiple treatment cycles. The package also provides a novel design to account for both multidimensional toxicity endpoints and early-stage efficacy endpoints in the phase I design. For both designs, functions are provided to recommend the next dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) . Package: r-cran-phase Architecture: all Version: 1.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circular, r-cran-lubridate, r-cran-plotly, r-cran-pracma, r-cran-signal, r-cran-zeitgebr, r-cran-zoo, r-cran-behavr, r-cran-wesanderson, r-cran-shiny, r-cran-shinythemes, r-cran-shinydashboard, r-cran-shinycssloaders, r-cran-shinyfiles Filename: pool/dists/noble/main/r-cran-phase_1.2.9-1.ca2404.1_all.deb Size: 395550 MD5sum: 656e27a9efac82ac404c2ff0c6328812 SHA1: ee68634e7cbd1469e145a47530ddd51f5c6a2418 SHA256: f018abd52137ad18014167b3eacec7fb53bcd75848c7a22e67efb90d65d8b436 SHA512: 3a3525dd224397b97f92655d4427a07d0411b4188dd5617b068523b476d3120b8dafe87e4fa3188aff2afbf26db6ff1b714be3b4332beda75c06424f19a9425f Homepage: https://cran.r-project.org/package=phase Description: CRAN Package 'phase' (Analyse Biological Time-Series Data) Compiles functions to trim, bin, visualise, and analyse activity/sleep time-series data collected from the Drosophila Activity Monitor (DAM) system (Trikinetics, USA). The following methods were used to compute periodograms - Chi-square periodogram: Sokolove and Bushell (1978) , Lomb-Scargle periodogram: Lomb (1976) , Scargle (1982) and Ruf (1999) , and Autocorrelation: Eijzenbach et al. (1986) . Identification of activity peaks is done after using a Savitzky-Golay filter (Savitzky and Golay (1964) ) to smooth raw activity data. Three methods to estimate anticipation of activity are used based on the following papers - Slope method: Fernandez et al. (2020) , Harrisingh method: Harrisingh et al. (2007) , and Stoleru method: Stoleru et al. (2004) . Rose plots and circular analysis are based on methods from - Batschelet (1981) and Zar (2010) . Package: r-cran-phasegmm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nleqslv Suggests: r-cran-extradistr Filename: pool/dists/noble/main/r-cran-phasegmm_0.1.1-1.ca2404.1_all.deb Size: 118648 MD5sum: fffafd367fa073b4b4ffd44b448e3e0c SHA1: 6a233959df0b7720df93a3169ca33c96468a9525 SHA256: 6af334dfd930bde23a7812d956b1a75eb9f7a88110f3aad1dbba5537e48d4a3e SHA512: 7390a47c6f7c4f0126ed58b9e822b4c6df3dc5c5eee42d40aedc11b7328d7f23a525434d5fa3dba67fccc4277853f705292691e1b41ed9d3a325a06d16089246 Homepage: https://cran.r-project.org/package=PhaseGMM Description: CRAN Package 'PhaseGMM' (Phase-Function Based Estimation and Inference for LinearErrors-in-Variables (EIV) Models) Estimation and inference for coefficients of linear EIV models with symmetric measurement errors. The measurement errors can be homoscedastic or heteroscedastic, for the latter, replication for at least some observations needs to be available. The estimation method and asymptotic inference are based on a generalised method of moments framework, where the estimating equations are formed from (1) minimising the distance between the empirical phase function (normalised characteristic function) of the response and that of the linear combination of all the covariates at the estimates, and (2) minimising a corrected least-square discrepancy function. Specifically, for a linear EIV model with p error-prone and q error-free covariates, if replicates are available, the GMM approach is based on a 2(p+q) estimating equations if some replicates are available and based on p+2q estimating equations if no replicate is available. The details of the method are described in Nghiem and Potgieter (2020) and Nghiem and Potgieter (2025) . Package: r-cran-phaser Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2223 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phaser_2.2.1-1.ca2404.1_all.deb Size: 1514690 MD5sum: 600fb6e15ffb8b3bc00d419617b74ec3 SHA1: 9d865f9842dffce7d5bd4ef0f7c7cfd0784e1a5d SHA256: d26a5d4d8a9cf0aa9ee25ceeb9bbe244236485718726a35c12fbdcd55c71380d SHA512: fc66e1b8cba539b4e8a86cbe185e6ca39b433decb47c92d3e854e41e2c3d5dbb2e97f86d091feb6b552059419146f21d92143b7c10dc54d6c3cc20f1db424f12 Homepage: https://cran.r-project.org/package=phaseR Description: CRAN Package 'phaseR' (Phase Plane Analysis of One- And Two-Dimensional Autonomous ODESystems) Performs a qualitative analysis of one- and two-dimensional autonomous ordinary differential equation systems, using phase plane methods. Programs are available to identify and classify equilibrium points, plot the direction field, and plot trajectories for multiple initial conditions. In the one-dimensional case, a program is also available to plot the phase portrait. Whilst in the two-dimensional case, programs are additionally available to plot nullclines and stable/unstable manifolds of saddle points. Many example systems are provided for the user. For further details can be found in Grayling (2014) . Package: r-cran-phasetyper Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1539 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-expm, r-cran-igraph Suggests: r-cran-knitr, r-cran-partitions, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phasetyper_1.0.4-1.ca2404.1_all.deb Size: 788712 MD5sum: ce3c5b2fc1cfb4b9030d312a86be1fe7 SHA1: 09dc9086d8540c6475064498da00659a0d5974ca SHA256: a0cc4fec2be2c13fbd0f317388b249a7f7c1202afb1cbfae2715533fe121add2 SHA512: e0596a3ffccb951251292aedeee0e16f64a9b9090e688d3ec314c02287fd31b6f12c928f290c7310b9000ea2491fd438d502708236b303789e4cad059bef0b6e Homepage: https://cran.r-project.org/package=PhaseTypeR Description: CRAN Package 'PhaseTypeR' (General-Purpose Phase-Type Functions) General implementation of core function from phase-type theory. 'PhaseTypeR' can be used to model continuous and discrete phase-type distributions, both univariate and multivariate. The package includes functions for outputting the mean and (co)variance of phase-type distributions; their density, probability and quantile functions; functions for random draws; functions for reward-transformation; and functions for plotting the distributions as networks. For more information on these functions please refer to Bladt and Nielsen (2017, ISBN: 978-1-4939-8377-3) and Campillo Navarro (2019) . Package: r-cran-phater Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4512 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-reticulate, r-cran-ggplot2, r-cran-memoise Suggests: r-cran-gridgraphics, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-phater_1.0.7-1.ca2404.1_all.deb Size: 3743066 MD5sum: e06adeccc40b365b8f79152ee0a2303f SHA1: 4ecbc28520f89cb9f681d14b971bd04294ba1702 SHA256: 14bc123d1b89ac738bead01b31a6acac1b04bcaae181101694f1f7a165965109 SHA512: 76164c3ad91e2f268d05986e795bce1d98a4f2d2eda67bb5c57e43a2a72453ae493f35dbaccd5ef78cee49610cf52e00448fcce2dc9db6a8a4389ce7c7a9a016 Homepage: https://cran.r-project.org/package=phateR Description: CRAN Package 'phateR' (PHATE - Potential of Heat-Diffusion for Affinity-BasedTransition Embedding) PHATE is a tool for visualizing high dimensional single-cell data with natural progressions or trajectories. PHATE uses a novel conceptual framework for learning and visualizing the manifold inherent to biological systems in which smooth transitions mark the progressions of cells from one state to another. To see how PHATE can be applied to single-cell RNA-seq datasets from hematopoietic stem cells, human embryonic stem cells, and bone marrow samples, check out our publication in Nature Biotechnology at . Package: r-cran-phclust Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phclust_0.1.0-1.ca2404.1_all.deb Size: 59568 MD5sum: fa2d86638855cdb4a22f9998347b956f SHA1: 9450db5032163df1a166413edc1d743704940626 SHA256: 3d81a752a58c53053ce804aa46965c9c2077abd7ce8434b36a50b514712af5e9 SHA512: a306f40f64e8ab39157db8492154efb6e34defd14b958f213b6fe2fa291a7fe1346c5f19f117c202b283e54fac88ee10474873aa2eafc76ce5002bb05d9d2cd8 Homepage: https://cran.r-project.org/package=PHclust Description: CRAN Package 'PHclust' (Poisson Hurdle Clustering for Sparse Microbiome Data) Clustering analysis for sparse microbiome data, based on a Poisson hurdle model. Package: r-cran-phd Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-phd_0.2-1.ca2404.1_all.deb Size: 22976 MD5sum: 896635ddddaecb65e8ebecad59a27fe4 SHA1: 158c2cd3bb74692931781227d8783e5d6ed76f11 SHA256: bd863529a8679d8be2c4fdea386966c092c6486401b0ecb305d9e1bcc463c111 SHA512: 5a26575289903bcf4e9f1381bbd317237d15a099dbc22adb095b5a8c09bc7db6de69f8ac8cae32d53a35025987e5b83924b7d531903c10ddc17d844e1b604085 Homepage: https://cran.r-project.org/package=phd Description: CRAN Package 'phd' (Permutation Testing in High-Dimensional Linear Models) Provides permutation methods for testing in high-dimensional linear models. The tests are often robust against heteroscedasticity and non-normality and usually perform well under anti-sparsity. See Hemerik, Thoresen and Finos (2021) . Package: r-cran-phdcocktail Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1359 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-here, r-cran-rcolorbrewer, r-cran-rstudioapi, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phdcocktail_0.1.0-1.ca2404.1_all.deb Size: 1243806 MD5sum: 95c8e704fa820703cdce7dcf96aaba50 SHA1: 8dd3a24c6a585d82e57f7fffd2aea763b1c5c8d8 SHA256: 93d6d6805dcfb36fe7222d72f681152e1775772864cac5f2da3b9c307203e70e SHA512: 0ef8e4049aa8df96870656e0423dbd9e84fd251e554dba414f3d5fadea67e57f0172d7497d36c2c0534df3b4f01ee62e4886bab3116bb66eb22f57c597468504 Homepage: https://cran.r-project.org/package=phdcocktail Description: CRAN Package 'phdcocktail' (Enhance the Ease of R Experience as an Emerging Researcher) A toolkit of functions to help: i) effortlessly transform collected data into a publication ready format, ii) generate insightful visualizations from clinical data, iii) report summary statistics in a publication-ready format, iv) efficiently export, save and reload R objects within the framework of R projects. Package: r-cran-pheatmap Architecture: all Version: 1.0.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-scales, r-cran-gtable Filename: pool/dists/noble/main/r-cran-pheatmap_1.0.13-1.ca2404.1_all.deb Size: 79042 MD5sum: 8f85d097b66198cc6277793edcf02b0e SHA1: 6e3782d3d43c2bddf6c3003f80f2b5396f3df65a SHA256: a59041b99a83cc6e053798a97d3b35b21ad49dd0aaece86be034ff2f45419bc5 SHA512: 095285fd47ee77d12f840da49c00eb9ceeabbd1cd8a06fcc4f93434125530bef94b0cc5cfd69a6351f26bb0f68646b8696c3409c3b7f174aca091a82b73ec4d5 Homepage: https://cran.r-project.org/package=pheatmap Description: CRAN Package 'pheatmap' (Pretty Heatmaps) Implementation of heatmaps that offers more control over dimensions and appearance. Package: r-cran-pheble Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 716 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-adabag, r-cran-c50, r-cran-caret, r-cran-catools, r-cran-data.table, r-cran-doparallel, r-cran-dplyr, r-cran-e1071, r-cran-earth, r-cran-evtree, r-cran-frbs, r-cran-glmnet, r-cran-gmodels, r-cran-hda, r-cran-hdclassif, r-cran-ipred, r-cran-kernlab, r-cran-kknn, r-cran-klar, r-cran-magrittr, r-cran-mass, r-cran-matrix, r-cran-mda, r-cran-mlmetrics, r-cran-nnet, r-cran-party, r-cran-pls, r-cran-randomforest, r-cran-rpartscore, r-cran-sparselda, r-cran-themis Suggests: r-cran-h2o Filename: pool/dists/noble/main/r-cran-pheble_0.1.0-1.ca2404.1_all.deb Size: 696668 MD5sum: 9305a077f441af2a673e85e95bd6a906 SHA1: 179162711b59c6e34859e67a14f3650e091dba72 SHA256: 8347488aa9638bec8d3407d077f08b9e569a71fa2b4b6adbd43844f22704298e SHA512: ab5c8970a754e74909b50fc06698a70c194f00605610ae9ff902f7a762f27bb5e4da4ba09b26667c17d5563aa2e82c3b0231c8a232fcd04cd0249a96cfcaac08 Homepage: https://cran.r-project.org/package=pheble Description: CRAN Package 'pheble' (Classifying High-Dimensional Phenotypes with Ensemble Learning) A system for binary and multi-class classification of high-dimensional phenotypic data using ensemble learning. By combining predictions from different classification models, this package attempts to improve performance over individual learners. The pre-processing, training, validation, and testing are performed end-to-end to minimize user input and simplify the process of classification. Package: r-cran-phecap Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4454 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-rmysql Suggests: r-cran-ggplot2, r-cran-e1071, r-cran-randomforestsrc, r-cran-xgboost, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phecap_1.2.1-1.ca2404.1_all.deb Size: 3915746 MD5sum: d0e751ec07de76604e0f9331d708f54f SHA1: 7a43728b3a9f75a61c3ca4073ddf41c6d1a61caf SHA256: 88156bda44388e6c99d7edc5dd11cdd61a9e0733e327d552f3f9f307e1592477 SHA512: 913b5de66f30c6a2a633cf254ce39cc71ffb9c047b7afaed627253d28ab5f267c3ffa7bfd55de74a4d3879c23dcc6b0cc00737ca37c0bdb22cb5c7d3a71ff7d1 Homepage: https://cran.r-project.org/package=PheCAP Description: CRAN Package 'PheCAP' (High-Throughput Phenotyping with EHR using a Common AutomatedPipeline) Implement surrogate-assisted feature extraction (SAFE) and common machine learning approaches to train and validate phenotyping models. Background and details about the methods can be found at Zhang et al. (2019) , Yu et al. (2017) , and Liao et al. (2015) . Package: r-cran-phecodemap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3001 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinybs, r-cran-collapsibletree, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-golem, r-cran-plotly, r-cran-purrr, r-cran-readr, r-cran-rintrojs, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinydashboardplus Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-shinytest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phecodemap_0.1.0-1.ca2404.1_all.deb Size: 2530650 MD5sum: 57c29622736e60cfef2eda283c40ab0c SHA1: e59bba0f5d96026eb66c11e4d1b52a2414615496 SHA256: d91e9c09159effcc66cd4d3caaeda3edcb22ac29262a94e0b1a3848a8bd9cf7e SHA512: 15b21336208e3a8877c39b144cdf966af89340944f0e970d68aaca6cbeec675954fbdbec4b57df163a0287d2ceef0fafe3239d5cf18f3e4a8403abedae88f5b0 Homepage: https://cran.r-project.org/package=phecodemap Description: CRAN Package 'phecodemap' (Visualization for PheCode Mapping with ICD-9 and ICD-10-CM Codes) To build a shiny app for visualization of the hierarchy of PheCode Mapping with International Classification of Diseases (ICD). The same PheCode hierarchy is displayed in two ways: as a sunburst plot and as a tree. Package: r-cran-pheindicatormethods Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 748 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-tidyr, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-pheindicatormethods_2.1.1-1.ca2404.1_all.deb Size: 492172 MD5sum: f88f2ca023cd7509c3fa5aed6049e132 SHA1: 0e854c014c2a4b9937939c3828d2f2d5eb026bd2 SHA256: 3988e492ba3fdc2b3b69897bc6a3f05eb2d0e52f334e7853905f04a638efff21 SHA512: 2d1fea180b51a8bdfc3efd9e11efcb0b93fb59278c55327deb44469974843dece473d95f46ae7fab9bb73c33e7fde15ce7e36dd693e9b1697b966d374ce09bf7 Homepage: https://cran.r-project.org/package=PHEindicatormethods Description: CRAN Package 'PHEindicatormethods' (Common Public Health Statistics and their Confidence Intervals) Functions to calculate commonly used public health statistics and their confidence intervals using methods approved for use in the production of Public Health England indicators such as those presented via Fingertips (). It provides functions for the generation of proportions, crude rates, means, directly standardised rates, indirectly standardised rates, standardised mortality ratios, slope and relative index of inequality and life expectancy. Statistical methods are referenced in the following publications. Breslow NE, Day NE (1987) . Dobson et al (1991) . Armitage P, Berry G (2002) . Wilson EB. (1927) . Altman DG et al (2000, ISBN: 978-0-727-91375-3). Chiang CL. (1968, ISBN: 978-0-882-75200-6). Newell C. (1994, ISBN: 978-0-898-62451-9). Eayres DP, Williams ES (2004) . Silcocks PBS et al (2001) . Low and Low (2004) . Fingertips Public Health Technical Guide: . Package: r-cran-phenesse Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-fitdistrplus Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phenesse_0.1.3-1.ca2404.1_all.deb Size: 47538 MD5sum: a33c638b024e96d1b77f4e8ea512fb74 SHA1: 22a6af8b87770ea1e3ae37da005bd1fdf454a1b9 SHA256: 11ad7c65178607428c6a3674b678ce3f545d1af5ea4045553c365f734c751ba1 SHA512: ff58da6723f7ab132be509fcafeac92bd5806ff3e8e7eb9a797a1fd91c139d6d40fbd4a0ef86415b83c7769c7fb79396368214b4596886df25bd1de3ff3cc002 Homepage: https://cran.r-project.org/package=phenesse Description: CRAN Package 'phenesse' (Estimate Phenological Metrics using Presence-Only Data) Generates Weibull-parameterized estimates of phenology for any percentile of a distribution using the framework established in Cooke (1979) . Extensive testing against other estimators suggest the weib_percentile() function is especially useful in generating more accurate and less biased estimates of onset and offset (Belitz et al. 2020) . Non-parametric bootstrapping can be used to generate confidence intervals around those estimates, although this is computationally expensive. Additionally, this package offers an easy way to perform non-parametric bootstrapping to generate confidence intervals for quantile estimates, mean estimates, or any statistical function of interest. Package: r-cran-phenix Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ppcor, r-cran-suppdists Filename: pool/dists/noble/main/r-cran-phenix_1.3.2-1.ca2404.1_all.deb Size: 85256 MD5sum: ce32f069e6fba5456c5fbf79d9d0e7ca SHA1: 5537b3d18b653e46115d0e107911a32c7419da38 SHA256: d4aa22efe75bdef6b258a92391ad91cb8d58cd5c971d2a9c108ea3515f039933 SHA512: afc63d21cc56aaa66e00cbbb3d20db3d1a006b70424d89d885b161ad0805275910d07db59dbf3802ebfaa1690e469d01b0ce80454e1364145a6bc6494067f677 Homepage: https://cran.r-project.org/package=PHENIX Description: CRAN Package 'PHENIX' (Phenotypic Integration Index) Provides functions to estimate the size-controlled phenotypic integration index, a novel method by Torices & Méndez (2014) to solve problems due to individual size when estimating integration (namely, larger individuals have larger components, which will drive a correlation between components only due to resource availability that might obscure the observed measures of integration). In addition, the package also provides the classical estimation by Wagner (1984) , bootstrapping and jackknife methods to calculate confidence intervals and a significance test for both integration indices. Further details can be found in Torices & Muñoz-Pajares . Package: r-cran-phenmodel Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-reshape Filename: pool/dists/noble/main/r-cran-phenmodel_1.0-1.ca2404.1_all.deb Size: 192806 MD5sum: 49cada5af196aaffa31be05d8d053d68 SHA1: 8aa525d2ee7b5b49d50c3c3819319e3b11cc5b98 SHA256: f826fd6fcf2879632156187f481a48a65c2dc00691856987919e2691af94e54b SHA512: d52b676d714ce8799ae119d1b3bb1a4fc6569152265a69ff08eb293f413666cadcbb5f70ea1f1af526388086ed7d682db27d59a48e57f57a1799b45d0fc166c9 Homepage: https://cran.r-project.org/package=phenModel Description: CRAN Package 'phenModel' (Insect Phenology Model Evaluation Based on Daily Temperatures) Generates predicted stage change days for an insect, based on daily temperatures and development rate parameters, as developed by Pollard (2014) . A few example datasets are included and implemented for P. vulgatissima, the blue willow beetle, but the approach can be readily applied to other species that display similar behaviour. Package: r-cran-phenocamr Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-changepoint, r-cran-httr, r-cran-zoo, r-cran-memoise, r-cran-daymetr, r-cran-modistools Suggests: r-cran-shiny, r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-shinydashboard, r-cran-leaflet, r-cran-plotly, r-cran-dt Filename: pool/dists/noble/main/r-cran-phenocamr_1.1.5-1.ca2404.1_all.deb Size: 249036 MD5sum: e120895fee8a52a88581c32c82276aea SHA1: 3bac18e694a3aa6583d0444bb9803211c814caca SHA256: 1b7fcca5789557186274f9592f7f35e5c0ba5f99a34ac8225b53eb95e849c8d9 SHA512: 8276bce7bd50962de806eec98dd2b2b72c000aee8207a5e9f730e23b9147177be9c41a67a4ed8d9e208c7a3a4499bab7f48813bc13e626b6dd300539bc0cbd57 Homepage: https://cran.r-project.org/package=phenocamr Description: CRAN Package 'phenocamr' (Facilitates 'PhenoCam' Data Access and Time SeriesPost-Processing) Programmatic interface to the 'PhenoCam' web services (). Allows for easy downloading of 'PhenoCam' data directly to your R workspace or your computer and provides post-processing routines for consistent and easy timeseries outlier detection, smoothing and estimation of phenological transition dates. Methods for this package are described in detail in Hufkens et. al (2018) . Package: r-cran-phenocdm Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phenocdm_0.1.3-1.ca2404.1_all.deb Size: 129082 MD5sum: fdece58e58ac3c477de610aeed2a0a03 SHA1: 76193f2693a9124799bce94b27410c77970d5a8c SHA256: e589aebf9c688fbc8f7ab524aa295f41f0d71ccf61ca225e9e198c7fcba8cb7f SHA512: 43297aa4f852dc7aa5ad64c810e33234d50baed9d68d3c14610c47303928147772dcdae2c457f0f68ecec072eb33652b9c6054f6e40733fa06607e3643cdb004 Homepage: https://cran.r-project.org/package=phenoCDM Description: CRAN Package 'phenoCDM' (Continuous Development Models for Incremental Time-SeriesAnalysis) Using the Bayesian state-space approach, we developed a continuous development model to quantify dynamic incremental changes in the response variable. While the model was originally developed for daily changes in forest green-up, the model can be used to predict any similar process. The CDM can capture both timing and rate of nonlinear processes. Unlike statics methods, which aggregate variations into a single metric, our dynamic model tracks the changing impacts over time. The CDM accommodates nonlinear responses to variation in predictors, which changes throughout development. Package: r-cran-phenolocrop Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr Suggests: r-cran-dplyr, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phenolocrop_0.0.4-1.ca2404.1_all.deb Size: 38368 MD5sum: 6a4b15e4ac15e7d41fad26926094d1ce SHA1: cf038a624086bad3a7f2b5d0884c83e0cc1610b1 SHA256: dead453da9049e915a25a9f5fb251e58f27f2cd7c16d27c7d1305d0bd7c9606e SHA512: 6c5755b96953fbc42a22df3b31e11829ef71ccbcf8a972b8859c7831b7c4620f1eeb39b63268f7e96163b7a31b84a302fd3e98bd4d5ec52f2dae464b3e7b3839 Homepage: https://cran.r-project.org/package=phenolocrop Description: CRAN Package 'phenolocrop' (Time-Series Models to the Crop Phenology) Fit a time-series model to a crop phenology data, such as time-series rice canopy height. This package returns the model parameters as the summary statistics of crop phenology, and these parameters will be useful to characterize the growth pattern of each cultivar and predict manually-measured traits, such as days to heading and biomass. Please see Taniguchi et al. (2022) and Taniguchi et al. (2025) for detail. This package has been designed for scientific use. Use for commercial purposes shall not be allowed. Package: r-cran-phenology Architecture: all Version: 2026.2.28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-optimx, r-cran-helpersmg Suggests: r-cran-shiny, r-cran-fields, r-cran-progress, r-cran-car, r-cran-multirng, r-cran-nlwaldtest, r-cran-pbapply, r-cran-cranlogs Filename: pool/dists/noble/main/r-cran-phenology_2026.2.28-1.ca2404.1_all.deb Size: 1296690 MD5sum: 9130951d224ae5416c61b05b579861ba SHA1: 363088781cca26859c8fd138280e9fa77554afb1 SHA256: b0ca981d3b43df2697dfe81d1aa7db04fb519cf2f72fba00d68ae665e2c0b736 SHA512: a6a2cb8b7e23e8eaffa42b47a1e912386876cf07c8dc34745c1cbe3447792942e827c6cde4f94f219b492eedb8f24c2ebad84e0e050287bc48c65cbf65297747 Homepage: https://cran.r-project.org/package=phenology Description: CRAN Package 'phenology' (Tools to Manage a Parametric Function that Describes Phenologyand More) Functions used to fit and test the phenology of species based on counts. Based on Girondot, M. (2010) for the phenology function, Girondot, M. (2017) for the convolution of negative binomial, Girondot, M. and Rizzo, A. (2015) for Bayesian estimate, Pfaller JB, ..., Girondot M (2019) for tag-loss estimate, Hancock J, ..., Girondot M (2019) for nesting history, Laloe J-O, ..., Girondot M, Hays GC (2020) for aggregating several seasons. Package: r-cran-phenomap Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 901 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-phenex, r-cran-plyr, r-cran-stringr, r-cran-terra, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-phenomap_2.0.1-1.ca2404.1_all.deb Size: 740584 MD5sum: 24da680fef0b1fac615f8740e856a2b3 SHA1: 16d690a6c4e96ebc5cb13de2f52edf0f8c7bc229 SHA256: d648ae9c6660c4e57b8791c35daa5653b7ed3aeb6636d60a70d6885cf4569eeb SHA512: 3d20fa372fa5d1ccdd75562df1529275af941c87cd5bfc964d07ba04d53cf0d1b2bbc88aafe96fc510d7609f49e0f351374cbeeafdb83be683cdda328991355d Homepage: https://cran.r-project.org/package=phenomap Description: CRAN Package 'phenomap' (Projecting Satellite-Derived Phenology in Space) This takes in a series of multi-layer raster files and returns a phenology projection raster, following methodologies described in John (2016) . Package: r-cran-phenopix Architecture: all Version: 2.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-plyr, r-cran-jpeg, r-cran-stringr, r-cran-strucchange, r-cran-foreach, r-cran-doparallel, r-cran-iterators, r-cran-gtools, r-cran-raster, r-cran-sp, r-cran-terra Filename: pool/dists/noble/main/r-cran-phenopix_2.4.5-1.ca2404.1_all.deb Size: 728530 MD5sum: f47b74101151352d7db388bd4e28dc11 SHA1: eb8228df4ec983d7399d3d8b04e6fb09d31f4287 SHA256: ca98011f8e5d338a77f4def17ecbbdd3dc175d554bb44cca0e64e666d3c8a2fb SHA512: e95a716054033aba08deed74965a612ffe72038f2740cb771d10ffaea74354c74dd2fd4f009e5f316271724c5299e9a92c64aa7cf702c6bd4be5e83a78993018 Homepage: https://cran.r-project.org/package=phenopix Description: CRAN Package 'phenopix' (Process Digital Images of a Vegetation Cover) A collection of functions to process digital images, depict greenness index trajectories and extract relevant phenological stages. Package: r-cran-phenorm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phenorm_0.1.0-1.ca2404.1_all.deb Size: 25178 MD5sum: 1bff228245ac0f1aa27b4b7a84eb0797 SHA1: afc6058a9e8a8a5cdff6f98c23c9f28c1714ab16 SHA256: 393449d8be71977a15a8c2948b9c7e76d7bf01e4f04c699ebfec97fcae8b40a0 SHA512: 18c4fae32506ff37311f0d95f874aefd9ffd3bff99c800d0faeb5fcfdaeb472fe10132caf6c7755192c7159b86df0d08f102efa0c3ae94858ec81ba21524447b Homepage: https://cran.r-project.org/package=PheNorm Description: CRAN Package 'PheNorm' (Unsupervised Gold-Standard Label Free Phenotyping Algorithm forEHR Data) The algorithm combines the most predictive variable, such as count of the main International Classification of Diseases (ICD) codes, and other Electronic Health Record (EHR) features (e.g. health utilization and processed clinical note data), to obtain a score for accurate risk prediction and disease classification. In particular, it normalizes the surrogate to resemble gaussian mixture and leverages the remaining features through random corruption denoising. Background and details about the method can be found at Yu et al. (2018) . Package: r-cran-phenospectra Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 525 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-writexl, r-cran-dplyr, r-cran-tidyr, r-cran-data.table, r-cran-lubridate, r-cran-openxlsx, r-cran-broom, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phenospectra_0.1.0-1.ca2404.1_all.deb Size: 483446 MD5sum: d8fdf7a486861956eda86bf986ddde99 SHA1: a9b9d41fa88e3115750dce6d63945c8557b13934 SHA256: 2aa840eb5759d1ede959d88eafc65dfbcc147d95348c58aeb1bc2f9f5ea47ba0 SHA512: 1ec93e38cdb0757a242fb79f2026e3ff056d14581d912d527340c1d939a1c9d9a532922d2c5f13da8f1e1bca0dca0dd60b61286a1bbd7ddddca67a915dfda645 Homepage: https://cran.r-project.org/package=PhenoSpectra Description: CRAN Package 'PhenoSpectra' (Multispectral Data Analysis and Visualization) Provides tools for processing, analyzing, and visualizing spectral data collected from 3D laser-based scanning systems. Supports applications in agriculture, forestry, environmental monitoring, industrial quality control, and biomedical research. Enables evaluation of plant growth, productivity, resource efficiency, disease management, and pest monitoring. Includes statistical methods for extracting insights from multispectral and hyperspectral data and generating publication-ready visualizations. See Zieschank & Junker (2023) and Saric et al. (2022) for related work. Package: r-cran-phenotype Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-phenotype_0.1.0-1.ca2404.1_all.deb Size: 37632 MD5sum: 576e4a5e3d2be770d2b87446fa24ca47 SHA1: a2b1c3c6cd48c05c480f09c9304c4dfa49a3f958 SHA256: 886529440ce1f7f340459609b4e8dbcae6d4deb5c0830c14251a4faa77a42a41 SHA512: aed67a66e3a7fbadaefd59046fb44cc09d3e816a89081039c401b8227159edba4e2738630cd5bca6a14713b3b769a29873e56e3330cd62bcc5f7e0a38f439ccd Homepage: https://cran.r-project.org/package=Phenotype Description: CRAN Package 'Phenotype' (A Tool for Phenotypic Data Processing) Large-scale phenotypic data processing is essential in research. Researchers need to eliminate outliers from the data in order to obtain true and reliable results. Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method can be used to process phenotypic data under different conditions and is widely used in animal and plant breeding. The 'Phenotype' can remove outliers from phenotypic data and performs the best linear unbiased prediction (BLUP), help researchers quickly complete phenotypic data analysis. H.P.Piepho. (2008) . Package: r-cran-phenotyper Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clock, r-cran-codelistgenerator, r-cran-cohortcharacteristics, r-cran-cohortconstructor, r-cran-dplyr, r-cran-drugutilisation, r-cran-incidenceprevalence, r-cran-measurementdiagnostics, r-cran-omopgenerics, r-cran-omopsketch, r-cran-patientprofiles, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-vctrs Suggests: r-cran-cdmconnector, r-cran-duckdb, r-cran-dbi, r-cran-gt, r-cran-omock, r-cran-testthat, r-cran-knitr, r-cran-glue, r-cran-rpostgres, r-cran-ggplot2, r-cran-stringr, r-cran-shiny, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-reactable, r-cran-rsvg, r-cran-sortable, r-cran-shinycssloaders, r-cran-here, r-cran-dt, r-cran-bslib, r-cran-shinywidgets, r-cran-plotly, r-cran-tidyr, r-cran-scales, r-cran-usethis, r-cran-rmarkdown, r-cran-cohortsurvival, r-cran-ellmer, r-cran-htmltools, r-cran-visomopresults, r-cran-rsconnect, r-cran-cpp11, r-cran-progress, r-cran-qs2, r-cran-lubridate, r-cran-systemfonts, r-cran-officer, r-cran-fs, r-cran-omopconstructor Filename: pool/dists/noble/main/r-cran-phenotyper_0.4.0-1.ca2404.1_all.deb Size: 607078 MD5sum: b6937b86d0b9da12f1f367b6e2bf61f8 SHA1: 1fbb301b0435145225683d9cb76d980e1969317d SHA256: 3b4ced89240247b29dd358e70e34669f5d4a51b447e44bb92fc3fd3ecf2cd5c5 SHA512: ef88f7cb579ab880a13362cfb274a15f3c4c8ac104545ba01220732de9395969ead6b9174ae053b93e4ecdb6cbda9e9ad123f0d184d3fb10dd23e40ee3d844b1 Homepage: https://cran.r-project.org/package=PhenotypeR Description: CRAN Package 'PhenotypeR' (Assess Study Cohorts Using a Common Data Model) Phenotype study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model. Diagnostics are run at the database, code list, cohort, and population level to assess whether study cohorts are ready for research. Package: r-cran-phenthauproc Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3326 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lubridate, r-cran-rlang, r-cran-terra Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyterra Filename: pool/dists/noble/main/r-cran-phenthauproc_1.1.2-1.ca2404.1_all.deb Size: 1216840 MD5sum: a02324a8e961e7ad49e8bfc7c0d690cb SHA1: da36eb3cb2e2538648cd5bba43d17eb82e0bde6a SHA256: 1ef461bc452cc3b3c12fe62349fbe6e1326cf67c2de53d1eb762bbd5291e8e52 SHA512: dfee28d073255ebb3917ad6954c4f06bdf4b6cc93b0cb956e0f75994047716ab10f24cd3c7d240df1cd68b5c73014113833ad2655a7725b02a7fe813d2809d17 Homepage: https://cran.r-project.org/package=PHENTHAUproc Description: CRAN Package 'PHENTHAUproc' (Phenology Modelling of Thaumetopoea Processionea) Methods to calculate and present 'PHENTHAUproc', an early warning and decision support system for hazard assessment and control of oak processionary moth (OPM) using local and spatial temperature data. It was created by Halbig et al. 2024 () at FVA () Forest Research Institute Baden-Wuerttemberg, Germany and at BOKU - University of Natural Ressources and Life Sciences, Vienna, Austria. 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See Bastarache et al. 2018 . 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Package: r-cran-phonenumber Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phonenumber_0.2.3-1.ca2404.1_all.deb Size: 186422 MD5sum: 0b876640d01e74ad6fb73640766c5b29 SHA1: 15d03bdef741bdca07569cb214a05bedfb8d2525 SHA256: e0d399b3684b31c5b08847015b0d9c1f57a88431db66e0c5064d1f0a5568d012 SHA512: 90586448f0b1a25073253d7f699c3c97eaf905381c078440bcd48ce899964e6579f436878174cee85fd87cef64ededc6d37b28ec30135dc087b1e828043dc891 Homepage: https://cran.r-project.org/package=phonenumber Description: CRAN Package 'phonenumber' (Convert Letters to Numbers and Back as on a Telephone Keypad) Convert English letters to numbers or numbers to English letters as on a telephone keypad. 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Package: r-cran-photobiology Architecture: all Version: 0.14.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4099 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-suncalcmeeus, r-cran-polynom, r-cran-tibble, r-cran-stringr, r-cran-lubridate, r-cran-catools, r-cran-plyr, r-cran-dplyr, r-cran-tidyr, r-cran-splus2r, r-cran-zoo, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-roxygen2, r-cran-lutz, r-cran-covr Filename: pool/dists/noble/main/r-cran-photobiology_0.14.2-1.ca2404.1_all.deb Size: 2997768 MD5sum: c772870f66305ee540583ec0d8c47b9a SHA1: 0a3d0b3df4267baf12872e1819de6b67884caf82 SHA256: 823a92399ea3729d470a2f47fd8b57c3d283a9622c6059f49d2c7622edf4b0cf SHA512: b2d37f9d03e2522bbc2de6ac06627faf0bcc0d3b09fc7e4983db75793bfcf6d65ef6e8733f0410a5bcdbf6132b25f4bc42972553f0ef387d84dc8a9e9ffe85e2 Homepage: https://cran.r-project.org/package=photobiology Description: CRAN Package 'photobiology' (Photobiological Calculations) Definitions of classes, methods, operators and functions for use in photobiology and radiation meteorology and climatology. Calculation of effective (weighted) and not-weighted irradiances/doses, fluence rates, transmittance, reflectance, absorptance, absorbance and diverse ratios and other derived quantities from spectral data. Local maxima and minima: peaks, valleys and spikes. Conversion between energy-and photon-based units. Wavelength interpolation. Colours and vision. This package is part of the 'r4photobiology' suite, Aphalo, P. J. (2015) . Package: r-cran-photobiologyfilters Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1515 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-photobiology Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggspectra, r-cran-photobiologywavebands Filename: pool/dists/noble/main/r-cran-photobiologyfilters_0.6.1-1.ca2404.1_all.deb Size: 1332780 MD5sum: 38db301993c5f7bf28dbcad813b8d3ce SHA1: 90b644c6cb4d237c7f74ec93c6f690e3cca053fc SHA256: 3b84e577ee88739d7799fe78b707fd1cf1054ed215bd6672426ec35f04331574 SHA512: c05c1504ab45f1a8050b3a34f1b7e4e5431dd9acf3ed8c1c04532d3ccef7cc4241907a36dce51b3b8d5cecfefb47960d363d3d54b2730dbe04f509fe969899f2 Homepage: https://cran.r-project.org/package=photobiologyFilters Description: CRAN Package 'photobiologyFilters' (Spectral Transmittance and Spectral Reflectance Data) Spectral 'transmittance' data for frequently used filters and similar materials. Plastic sheets and films; photography filters; theatrical gels; machine-vision filters; various types of window glass; optical glass and some laboratory plastics and glassware. Spectral reflectance data for frequently encountered materials. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . Package: r-cran-photobiologyinout Architecture: all Version: 0.4.33-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-photobiology, r-cran-suncalcmeeus, r-cran-stringr, r-cran-lubridate, r-cran-anytime, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-readr, r-cran-readxl, r-cran-colorspec, r-cran-jsonlite Suggests: r-cran-spacesxyz, r-cran-hyperspec, r-cran-pavo, r-cran-fda.usc, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggspectra, r-cran-photobiologywavebands, r-cran-testthat Filename: pool/dists/noble/main/r-cran-photobiologyinout_0.4.33-1.ca2404.1_all.deb Size: 1520564 MD5sum: ceb0287f142f303bfa4583d58b701688 SHA1: 3f4167f85250d0a7523be998c2e0a531ac431ce4 SHA256: 4a91d79faaf50339248ac85377f168973b18fd6dae838977ec39a540e568a49d SHA512: 9f04b9fe344fd45cc9990822924e7bc42fd86f1916d29d4315c5a3ac0df5f8c3cb0cf85f6f0c40d700ce219302b7ad82e0e73dcd69294973083efa960ddb55c2 Homepage: https://cran.r-project.org/package=photobiologyInOut Description: CRAN Package 'photobiologyInOut' (Read Spectral and Logged Data from Foreign Files) Functions for reading, and in some cases writing, foreign files containing spectral data from spectrometers and their associated software, output from daylight simulation models in common use, and some spectral data repositories. As well as functions for exchange of spectral data with other R packages. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . Package: r-cran-photobiologylamps Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-photobiology Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggspectra, r-cran-photobiologywavebands, r-cran-ggplot2, r-cran-photobiologyleds Filename: pool/dists/noble/main/r-cran-photobiologylamps_0.5.3-1.ca2404.1_all.deb Size: 1124670 MD5sum: d0d7dea330ebb417361cfc7a20b3a842 SHA1: 36b37af37df0038e2290465a07d2f5eb19dc8c7a SHA256: 767012c4718b5ac8dee601f9766800cfc639464ab71d7ddb2b4e4958c62c860e SHA512: 8cc5db0b042a13483ae08e1399bb9f2aab43151bcbc403a4decee3b537cccf5d6f26ec1ed548217f309474ed3d9b1e7ac66c254c19b3aa939bde85ec329b4a5f Homepage: https://cran.r-project.org/package=photobiologyLamps Description: CRAN Package 'photobiologyLamps' (Spectral Irradiance Data for Lamps) Spectral emission data for some frequently used lamps including bulbs and flashlights based on led emitting diodes (LEDs) but excluding LEDs available as electronic components. Original spectral irradiance data for incandescent-, LED- and discharge lamps are included. They are complemented by data on the effect of temperature on the emission by fluorescent tubes. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . Package: r-cran-photobiologyleds Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-photobiology Suggests: r-cran-photobiologywavebands, r-cran-photobiologylamps, r-cran-ggspectra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-photobiologyleds_0.5.3-1.ca2404.1_all.deb Size: 747446 MD5sum: 9ac8fabcc43f59ddec13f0df7e1f7cf0 SHA1: feb6612b31890c5e6c30fe86d8239f6aeba13f10 SHA256: b700e888100e61de736d146971a553657f8d0eddf788b79a6d4ccb1c11c731a7 SHA512: 8a8ebb0fe1f17f9de848b30eee6b168558e044590487eef77f99f58a101322b1d1dd99e641344eed1b69c2717ad0a63a26687ab40f5ee5bf43aae860c736370d Homepage: https://cran.r-project.org/package=photobiologyLEDs Description: CRAN Package 'photobiologyLEDs' (Spectral Data for Light-Emitting-Diodes) Spectral emission data for some frequently used light emitting diodes available as electronic components. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . Package: r-cran-photobiologyplants Architecture: all Version: 0.6.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 930 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-photobiology, r-cran-photobiologywavebands Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggspectra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-photobiologyplants_0.6.1-1-1.ca2404.1_all.deb Size: 694336 MD5sum: fda1dd14a6e057914c97f7272925ebd9 SHA1: 771f491b26c2315ec812e9f33eb3f7db0b18e6dc SHA256: aa076ce30c9cd30ea5fd11e088c9296b0effa47b310368a59c14007c6dff78cd SHA512: 77d4fd52fbe6629876234a3dbe6c53b5df5b0f982aadc9cbe3cb670daedada7a6c3518cbf7be0371d9fc375744b4d7455e90b9830c1313364097431e96ec3b2b Homepage: https://cran.r-project.org/package=photobiologyPlants Description: CRAN Package 'photobiologyPlants' (Plant Photobiology Related Functions and Data) Provides functions for quantifying visible (VIS) and ultraviolet (UV) radiation in relation to the photoreceptors Phytochromes, Cryptochromes, and UVR8 which are present in plants. It also includes data sets on the optical properties of plants. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . Package: r-cran-photobiologysensors Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-photobiology Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-photobiologywavebands, r-cran-ggspectra, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-photobiologysensors_0.5.3-1.ca2404.1_all.deb Size: 614316 MD5sum: ca6553a1e918a8ca16392f90ca4925d9 SHA1: c0da6476e6556d2fd13f9f376025d874b3724ed6 SHA256: 7537272c485a44edd0a6b52805693898d9e7c087a018a8cf91bb8c5a644be65f SHA512: 2c3e3c95e06e30d8609ad15ac7a4cdc654f957a62405f6daef751223e1bc36a5a4493ce265c0ae18f777aa7ac5533906ba63d00b327cb7cf78f48799cab04c82 Homepage: https://cran.r-project.org/package=photobiologySensors Description: CRAN Package 'photobiologySensors' (Response Data for Light Sensors) Spectral response data for broadband ultraviolet and visible radiation sensors. Angular response data for broadband ultraviolet and visible radiation sensors and diffusers used as entrance optics. Data obtained from multiple sources were used: author-supplied data from scientific research papers, sensor-manufacturer supplied data, and published sensor specifications. Part of the 'r4photobiology' suite Aphalo P. J. (2015) . Package: r-cran-photobiologysun Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4254 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-photobiology Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-photobiologywavebands, r-cran-ggspectra, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-photobiologysun_0.5.1-1.ca2404.1_all.deb Size: 3978138 MD5sum: 2ed39edb70fc63a2ed0da16c6486d6ab SHA1: 2888a1b8aaba891a93eecd0d15dffb6540304346 SHA256: 4995cdc72c4aaae7c4878fdbfa218e129fc3ef463aa2088f48b419c96a403916 SHA512: a969d90a8c2c6a9412ba54c6f4651577b9571d5c464f2a9f1403e915a4f02023adc18c9394e293ee783cd937ce2f8b4c2b2b3705000f4ddfda204854755e2234 Homepage: https://cran.r-project.org/package=photobiologySun Description: CRAN Package 'photobiologySun' (Data for Sunlight Spectra) Data for the extraterrestrial solar spectral irradiance and ground level solar spectral irradiance and irradiance. In addition data for shade light under vegetation and irradiance time series from different broadband sensors. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . Package: r-cran-photobiologywavebands Architecture: all Version: 0.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-photobiology Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-photobiologywavebands_0.5.4-1.ca2404.1_all.deb Size: 302868 MD5sum: 68fb4cf314dc6439fcb816f3907311c2 SHA1: a7aa208ec56487ad526fba5593c2107eb59c6817 SHA256: 6a054ae92df08138950811eab0d9381cb44f1fc1d96e7cd69f4bca2db4d8abcd SHA512: 9d37332c8728af059a7b2037b856a09e9de28a2ac3fef05aa14733eb7134893834c41ba4ef580a5c8b8596b180cb276c5ba40240eebdbd32abf3bb061e04f480 Homepage: https://cran.r-project.org/package=photobiologyWavebands Description: CRAN Package 'photobiologyWavebands' (Waveband Definitions for UV, VIS, and IR Radiation) Constructors of waveband objects for commonly used biological spectral weighting functions (BSWFs) and for different wavebands describing named ranges of wavelengths in the ultraviolet (UV), visible (VIS) and infrared (IR) regions of the electromagnetic spectrum. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) . Package: r-cran-photogea Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4289 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-openxlsx, r-cran-lattice, r-cran-dfoptim, r-cran-deoptim Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-plantecophys, r-cran-testthat Filename: pool/dists/noble/main/r-cran-photogea_1.4.0-1.ca2404.1_all.deb Size: 2522254 MD5sum: 340979b4551fa00fca6bde64cc00447b SHA1: 6d4397ff82e6a62bab65e6f6f7a1f3d3776cb7e5 SHA256: f7c04fd9823fb9446bde7361457a347bb8fd844463bec98c9d7d37d3cbb21f63 SHA512: d6729d6bdb78cc088f67395eddda9a47ad89f39c2b23882f2f32fcd40e8c6586de276870e3a1789ce7ed899efb662ce873e13d80031025a989788c3ffd668049 Homepage: https://cran.r-project.org/package=PhotoGEA Description: CRAN Package 'PhotoGEA' (Photosynthetic Gas Exchange Analysis) Read, process, fit, and analyze photosynthetic gas exchange measurements. Documentation is provided by several vignettes; also see Lochocki, Salesse-Smith, & McGrath (2025) . Package: r-cran-photon Architecture: all Version: 1.0.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-countrycode, r-cran-httr2, r-cran-r6, r-cran-sf, r-cran-processx, r-cran-rvest Suggests: r-cran-testthat, r-cran-tibble, r-cran-knitr, r-cran-rmarkdown, r-cran-webfakes, r-cran-ps Filename: pool/dists/noble/main/r-cran-photon_1.0.0-1-1.ca2404.1_all.deb Size: 280700 MD5sum: ff666bff50237489f2fbff2e8e5f6dde SHA1: 7e014afcc4f4ede5cc8ac707cf71b53bb8e5acf2 SHA256: fd11e91b23c2c3ed0e9c7e6f0ebbe436274fe9f3c3ab469cbdc9a99003bdc3c1 SHA512: 74b7c85247efb11992de99443bab0d87f21739c618c808bf71adb2d32f4d5740c60223e416886ac3063da3ff3bcf44264c80cb6993c25db8cedae8210e0d37c4 Homepage: https://cran.r-project.org/package=photon Description: CRAN Package 'photon' (High-Performance Geocoding using 'photon') Features unstructured, structured and reverse geocoding using the 'photon' geocoding API . Facilitates the setup of local 'photon' instances to enable offline geocoding. Package: r-cran-photosynq Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-getpass Filename: pool/dists/noble/main/r-cran-photosynq_0.2.3-1.ca2404.1_all.deb Size: 41942 MD5sum: db1bd3f23a91f69bdaefe88ca74090a8 SHA1: 90a9616d28c8ec71e1175597df84867fd44a7ddb SHA256: 2db623bd6ce799d057ea6b4152eda4abd584ec09287b0281e32d18951e7aaf86 SHA512: 420ab19a6066660ac1b8141697c84f1aa091e50fd9227e33e7462b3dbe8886fc4ff4a990910e615428e0b99a5d334f1390bbb67354535ce788b5d50887ca5523 Homepage: https://cran.r-project.org/package=PhotosynQ Description: CRAN Package 'PhotosynQ' (Connect to PhotosynQ) Connect R to the PhotosynQ platform (). It allows to login and logout, as well as receive project information and project data. Further it transforms the received JSON objects into a data frame, which can be used for the final data analysis. Package: r-cran-photosynthesis Architecture: all Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7376 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-units, r-cran-checkmate, r-cran-crayon, r-cran-dplyr, r-cran-furrr, r-cran-glue, r-cran-gunit, r-cran-lifecycle, r-cran-magrittr, r-cran-nlme, r-cran-progress, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tealeaves Suggests: r-cran-brms, r-cran-broom, r-cran-future, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Filename: pool/dists/noble/main/r-cran-photosynthesis_2.1.5-1.ca2404.1_all.deb Size: 1238552 MD5sum: 7f344f5f74e1c1f8a4fcf2d937e6a4cb SHA1: fa311a106d0304abf3743b408181ab4fc762f6dc SHA256: 5597f94dfa406c71e8cd5cf9910ec05836aef7afde882139c97fe4ee8307bcb8 SHA512: 877e47c7073288d0eccb6669c7b878268cf687e670de2d478cdef98c04639791c62e8ada1a338487fd38c19c2ad6b93d3895669809fb0a01afd0f85d1b817086 Homepage: https://cran.r-project.org/package=photosynthesis Description: CRAN Package 'photosynthesis' (Tools for Plant Ecophysiology & Modeling) Contains modeling and analytical tools for plant ecophysiology. MODELING: Simulate C3 photosynthesis using the Farquhar, von Caemmerer, Berry (1980) model as described in Buckley and Diaz-Espejo (2015) . It uses units to ensure that parameters are properly specified and transformed before calculations. Temperature response functions get automatically "baked" into all parameters based on leaf temperature following Bernacchi et al. (2002) . The package includes boundary layer, cuticular, stomatal, and mesophyll conductances to CO2, which each can vary on the upper and lower portions of the leaf. Use straightforward functions to simulate photosynthesis over environmental gradients such as Photosynthetic Photon Flux Density (PPFD) and leaf temperature, or over trait gradients such as CO2 conductance or photochemistry. ANALYTICAL TOOLS: Fit ACi (Farquhar et al. (1980) ) and AQ curves (Marshall & Biscoe (1980) ), temperature responses (Heskel et al. (2016) ; Kruse et al. (2008) , Medlyn et al. (2002) , Hobbs et al. (2013) ), respiration in the light (Kok (1956) , Walker & Ort (2015) , Yin et al. (2009) , Yin et al. (2011) ), mesophyll conductance (Harley et al. (1992) ), pressure-volume curves (Koide et al. (2000) , Sack et al. (2003) , Tyree et al. (1972) ), hydraulic vulnerability curves (Ogle et al. (2009) , Pammenter et al. (1998) ), and tools for running sensitivity analyses particularly for variables with uncertainty (e.g. g_mc(), gamma_star(), R_d()). Package: r-cran-photosynthesislrc Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-photosynthesislrc_1.0.6-1.ca2404.1_all.deb Size: 163932 MD5sum: 4e27d7ddf4b8a2265900309357e59811 SHA1: 389df08005e28f8bfbab989bd470fc7fce6621b9 SHA256: 710c9e295d18cef3c076872ca1defac63d529d26a70fa9d0ec2c449fafb150e4 SHA512: 4a068b010ec2e337158f58c2c69c828f2d75b2a447dd534333c43b6bca1e916b89eb0be2507f6a4252e014bcedd355f2d230f9e27cb4b6754fe37c785a2c9ac6 Homepage: https://cran.r-project.org/package=photosynthesisLRC Description: CRAN Package 'photosynthesisLRC' (Nonlinear Least Squares Models for Photosynthetic Light Response) Provides functions for modeling, comparing, and visualizing photosynthetic light response curves using established mechanistic and empirical models like the rectangular hyperbola Michaelis-Menton based models ((eq1 (Baly (1935) )) (eq2 (Kaipiainenn (2009) )) (eq3 (Smith (1936) ))), hyperbolic tangent based models ((eq4 (Jassby & Platt (1976) )) (eq5 (Abe et al. (2009) ))), the non-rectangular hyperbola model (eq6 (Prioul & Chartier (1977) )), exponential based models ((eq8 (Webb et al. (1974) )), (eq9 (Prado & de Moraes (1997) ))), and finally the Ye model (eq11 (Ye (2007) )). Each of these nonlinear least squares models are commonly used to express photosynthetic response under changing light conditions and has been well supported in the literature, but distinctions in each mathematical model represent moderately different assumptions about physiology and trait relationships which ultimately produce different calculated functional trait values. These models were all thoughtfully discussed and curated by Lobo et al. (2013) to express the importance of selecting an appropriate model for analysis, and methods were established in Davis et al. (in review) to evaluate the impact of analytical choice in phylogenetic analysis of the function-valued traits. 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Faith, D. P. (1992) Isaac, N. J. et al. (2007) Laffan, S. W. et al. (2016) Rosauer, D. et al. (2009) . Package: r-cran-phyloregion Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-phangorn, r-cran-matrix, r-cran-betapart, r-cran-colorspace, r-cran-igraph, r-cran-clustmixtype, r-cran-maptpx, r-cran-terra, r-cran-vegan, r-cran-predicts, r-cran-smoothr Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown, r-cran-mapproj, r-cran-survival, r-cran-rjava, r-bioc-phyloseq, r-cran-v8 Filename: pool/dists/noble/main/r-cran-phyloregion_1.0.9-1.ca2404.1_all.deb Size: 1507834 MD5sum: 028eeb3dc31386455b97cf31e03fceb4 SHA1: 6bacefe9e4a979e4bd5ae7ea7b3ddc381ecb55e0 SHA256: 1f9dff515daca0befed433a63fa3729585fa4b02c7c95fbe04664aed13463883 SHA512: 4ece671286924d22000f607261cf2d2913ca2aa86930e5bb65b3fad7e36083cd354271c02139923623026eecd2516f1d09a5af1a3be37dde3175f3e62db3d883 Homepage: https://cran.r-project.org/package=phyloregion Description: CRAN Package 'phyloregion' (Biogeographic Regionalization and Macroecology) Computational infrastructure for biogeography, community ecology, and biodiversity conservation (Daru et al. 2020) . It is based on the methods described in Daru et al. (2020) . The original conceptual work is described in Daru et al. (2017) on patterns and processes of biogeographical regionalization. Additionally, the package contains fast and efficient functions to compute more standard conservation measures such as phylogenetic diversity, phylogenetic endemism, evolutionary distinctiveness and global endangerment, as well as compositional turnover (e.g., beta diversity). Package: r-cran-phylosamp Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2510 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-lifecycle, r-cran-rlang Suggests: r-cran-cowplot, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-phylosamp_1.0.1-1.ca2404.1_all.deb Size: 1117226 MD5sum: 37ea2ce0c1304eb152f3965aed576b37 SHA1: e52529ab0442153120a498ac22090f047154c6fb SHA256: 4667d8396d7cf816fd7e61d1c618344b7fcf0c0e71172679028e5b51dc02bef1 SHA512: 507bf2af4441223aeb109a81d1a4a63d9f0a74f3ab81d159a69336da630383130e59453bf5bd44d7f03250b260fea8929f45105a016824e19550155726a21dc9 Homepage: https://cran.r-project.org/package=phylosamp Description: CRAN Package 'phylosamp' (Sample Size Calculations for Molecular and Phylogenetic Studies) Implements novel tools for estimating sample sizes needed for phylogenetic studies, including studies focused on estimating the probability of true pathogen transmission between two cases given phylogenetic linkage and studies focused on tracking pathogen variants at a population level. Methods described in Wohl, Giles, and Lessler (2021) and in Wohl, Lee, DiPrete, and Lessler (2023). Package: r-cran-phyloseqgraphtest Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1294 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-bioc-phyloseq, r-cran-ggnetwork, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-phyloseqgraphtest_0.1.1-1.ca2404.1_all.deb Size: 469816 MD5sum: 34a0ecf7789c6e3d97b8ebe7596cb7c1 SHA1: 72dcb89fe07cd92f9c64e659474625fc9474ef49 SHA256: 5a5021262b70bc9cb50321978a08b0d7cb3bffd42263e616ebbabd823f2b3813 SHA512: 66d1d733542a1524d35a207d5b727cd445cfd66703de58065455837d25b8a12ddc8e465305d75a378222eaa5c21d7192e149b3f6b0f13b89591fdc25c490358d Homepage: https://cran.r-project.org/package=phyloseqGraphTest Description: CRAN Package 'phyloseqGraphTest' (Graph-Based Permutation Tests for Microbiome Data) Provides functions for graph-based multiple-sample testing and visualization of microbiome data, in particular data stored in 'phyloseq' objects. The tests are based on those described in Friedman and Rafsky (1979) , and the tests are described in more detail in Callahan et al. (2016) . Package: r-cran-phylosignaldb Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-castor, r-cran-cluster, r-cran-doparallel, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-phylosignaldb_0.4.2-1.ca2404.1_all.deb Size: 66138 MD5sum: e3b0b28fdd8c9557878a94f43c4ab9fe SHA1: 334cecd7191f43e55a02ac6fab011cfdeebb8bc1 SHA256: 24b91a1e7f43447ad8f170ac428d859005dc3436aaff82c1351ffb97f4afe370 SHA512: 76c26befec73336edf7fabeb42f7dd7b29c32822fbf5387b59bb9c3ab3c6f93cbb9ff3979f015968e34e9ebbac0bdc8a13a76c5ff722ce5f0595eb19bdc0cef2 Homepage: https://cran.r-project.org/package=phylosignalDB Description: CRAN Package 'phylosignalDB' (Explore Phylogenetic Signals Using Distance-Based Methods) A unified method, called M statistic, is provided for detecting phylogenetic signals in continuous traits, discrete traits, and multi-trait combinations. Blomberg and Garland (2002) provided a widely accepted statistical definition of the phylogenetic signal, which is the "tendency for related species to resemble each other more than they resemble species drawn at random from the tree". The M statistic strictly adheres to the definition of phylogenetic signal, formulating an index and developing a method of testing in strict accordance with the definition, instead of relying on correlation analysis or evolutionary models. The novel method equivalently expressed the textual definition of the phylogenetic signal as an inequality equation of the phylogenetic and trait distances and constructed the M statistic. The M statistic implemented in this package is based on the methodology described in Yao and Yuan (2025) . If you use this method in your research, please cite the paper. Package: r-cran-phylospatial Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2770 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-sf, r-cran-terra, r-cran-vegan Suggests: r-cran-canaper, r-cran-furrr, r-cran-future, r-cran-testthat, r-cran-betapart, r-cran-knitr, r-cran-rmarkdown, r-cran-tmap, r-cran-magrittr, r-cran-hillr, r-cran-picante, r-cran-phytools, r-cran-nullcat, r-cran-paralleldist Filename: pool/dists/noble/main/r-cran-phylospatial_1.4.0-1.ca2404.1_all.deb Size: 2121926 MD5sum: c41b75076482b6295c6fa306667ae020 SHA1: 5042c40674902bcc87c3c491ddb5c5fe8e24d7fd SHA256: f5c6c2b54e4f212ef893706cdcba5f8ee3d57b0106332a11017f0eedb7618647 SHA512: b66e1059224dd6133beeb23cce0d245b46c05523544fe47cbaf1e58654fb4c3a0837a10b217aec71cf2750c384cdc2e00333dee7b410ebb4cd29cc1fff4011cf Homepage: https://cran.r-project.org/package=phylospatial Description: CRAN Package 'phylospatial' (Spatial Phylogenetic Analysis) Analyze spatial phylogenetic diversity patterns. Use your data on an evolutionary tree and geographic distributions of the terminal taxa to compute diversity and endemism metrics, test significance with null model randomization, analyze community turnover and biotic regionalization, and perform spatial conservation prioritizations. All functions support quantitative community data in addition to binary data. 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Package: r-cran-phylotop Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-igraph, r-cran-nhpoisson, r-cran-phylobase Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-phylotop_2.1.3-1.ca2404.1_all.deb Size: 133116 MD5sum: 96abbea86fc75b54ecf2872db8c4aec2 SHA1: d3d777bf447b1831d34e8a29ef31087fb4a7dde3 SHA256: 9d7187e6ea6d73d8dc916e324bd4e70f79351e39f3342114f1e942795c73abac SHA512: 420c0584404b48f853a04de4d4a8ba9d48a875d12d0f30b111a98bfd4085ce9e70d55a370ffab708fcef022eb58ee0dc36e125875b2979e2add1b2ff4fecd75a Homepage: https://cran.r-project.org/package=phyloTop Description: CRAN Package 'phyloTop' (Calculating Topological Properties of Phylogenies) Tools for calculating and viewing topological properties of phylogenetic trees. 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Package: r-cran-phyreg Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-phyreg_1.0.2-1.ca2404.1_all.deb Size: 133812 MD5sum: f82b540007006a674f86f0874507b47b SHA1: bacd7d5dbff269ac55bd1284829875d36809b1bf SHA256: a2a56be3722c34085c6806b5e722702d8585f95669674ef26c5885b676689ece SHA512: ccaeb7da75347b5a4f838efcd7fa02e5809089709ec86fe4592434450fc9b906c6ee9eb849e134426aa01f375c552ad13b41e5a068738ab086ee8d57ddbe664a Homepage: https://cran.r-project.org/package=phyreg Description: CRAN Package 'phyreg' (The Phylogenetic Regression of Grafen (1989)) Provides general linear model facilities (single y-variable, multiple x-variables with arbitrary mixture of continuous and categorical and arbitrary interactions) for cross-species data. The method is, however, based on the nowadays rather uncommon situation in which uncertainty about a phylogeny is well represented by adopting a single polytomous tree. The theory is in A. Grafen (1989, Proc. R. Soc. B 326, 119-157) and aims to cope with both recognised phylogeny (closely related species tend to be similar) and unrecognised phylogeny (a polytomy usually indicates ignorance about the true sequence of binary splits). Package: r-cran-physactbedrest Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chron, r-cran-stringr, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-physactbedrest_1.1-1.ca2404.1_all.deb Size: 42064 MD5sum: 4d9b1619ce9374ffee04f208ea9355a7 SHA1: c3928b977c77cac78c136974c4fe8c4d0b9b70ef SHA256: dadbb8e0ee7ef995b7e5ddfb025d7253074355b214d87f2210c181538ab4e51f SHA512: 2e8584d6b7c81fff419ca2057890bc63bc69770f735fe9c9602eb35df0e17ed281212c10eea7fce6b61d3e8106eabec99cf06c52145a7535c148bc49961db32f Homepage: https://cran.r-project.org/package=PhysActBedRest Description: CRAN Package 'PhysActBedRest' (Marks Periods of 'Bedrest' in Actigraph Accelerometer Data) Contains a function to categorize accelerometer readings collected in free-living (e.g., for 24 hours/day for 7 days), preprocessed and compressed as counts (unit-less value) in a specified time period termed epoch (e.g., 1 minute) as either bedrest (sleep) or active. The input is a matrix with a timestamp column and a column with number of counts per epoch. The output is the same dataframe with an additional column termed bedrest. In the bedrest column each line (epoch) contains a function-generated classification 'br' or 'a' denoting bedrest/sleep and activity, respectively. The package is designed to be used after wear/nonwear marking function in the 'PhysicalActivity' package. Version 1.1 adds preschool thresholds and corrects for possible errors in algorithm implementation. 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The package also contains functions for making plot for accelerometer data and obtaining the summary of various information including daily monitor wear time and the mean monitor wear time during valid days. "deliveryPred" and "markDelivery" can classify days for ActiGraph delivery by mail; "deliveryPreprocess" can process accelerometry data for analysis by zeropadding incomplete days and removing low activity days; "markPAI" can categorize physical activity intensity level based on user-defined cut-points of accelerometer counts. It also supports importing ActiGraph AGD files with "readActigraph" and "queryActigraph" functions. Package: r-cran-physioindexr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-physioindexr_0.1.0-1.ca2404.1_all.deb Size: 88846 MD5sum: a42b49389bed352637a18b66e6cc8a06 SHA1: 3c6325034031f341ce317d4b5a67570eb58afb48 SHA256: 9f1a6b3e7b58c92d7321f948e5e1d0cca04ab4b7432552f4d17d1f7209e6aad4 SHA512: dfc4f915b449db4f64d6aa5208353f0ba7bb123d235271d0cfe7ce4c102816768afdbc5fa262ada53a94b873e33228002ec468e46c6865ce9f27f1ba63037bfe Homepage: https://cran.r-project.org/package=PhysioIndexR Description: CRAN Package 'PhysioIndexR' (Physiological and Stress Indices for Crop Evaluation) Crop production systems are increasingly challenged by climate variability, resource limitations, and biotic–abiotic stresses. In this context, stress tolerance indices and physiological trait estimators are essential tools to identify stable and superior genotypes, quantify yield stability under stress versus non-stress conditions, and understand plant adaptive responses. The 'PhysioIndexR' package provides a unified framework to compute commonly used stress indices, physiological traits, and derived metrics that are critical in crop improvement, crop physiology, and other agricultural sciences. The package includes functions to calculate classical stress tolerance indices (See Lamba et al., 2023; ) such as Tolerance (TOL), Stress Tolerance Index (STI), Stress Susceptibility Percentage Index (SSPI), Yield Index (YI), Yield Stability Index (YSI), Relative Stress Index (RSI), Mean Productivity (MP), Geometric Mean Productivity (GMP), Harmonic Mean (HM), Mean Relative Performance (MRP), and Percent Yield Reduction (PYR), along with a convenience wrapper all_indices() that returns all indices simultaneously. The function mfvst_from_indices() integrates these indices into a composite stress score using direction-aware membership values (0–1 scaling) and also averaging, facilitating genotype ranking and selection (See Vinu et al., 2025; ). The package also implements two novel composite functions: WMFVST(), which computes the Weighted Mean Membership Function Value for Stress Tolerance, and WASI(), which computes the Weighted Average Stress Index, both derived from membership function values (MFV) and raw stress index values, respectively. Beyond stress indices, the package provides functions for key physiological traits relevant to sugarcane and other crops: bmap() computes biomass accumulation and partitioning between leaf, cane/shoot, and root fractions. chl() estimates total chlorophyll content from Soil-Plant Analysis Development (SPAD) and Chlorophyll Content Index (CCI) values using validated quadratic models particularly for sugarcane (See Krishnapriya et al., 2020; ). ctd() calculates canopy temperature depression (CTD) from ambient and canopy temperatures, an important indicator of transpiration efficiency. growth() computes key growth analysis parameters, including Leaf Area Index (LAI), Net Assimilation Rate (NAR), and Crop Growth Rate (CGR) across crop growth stages (See Watson, 1958; ). ranking() provides flexible ranking utilities for genotype performance with multiple tie-handling and NA-placement options. Through these tools, the package enables researchers to: (i) quantify crop responses to stress environments, (ii) partition physiological components of yield, (iii) integrate multiple indices into composite metrics for genotype evaluation, and (iv) facilitate informed decision making in breeding pipelines, and plant physiology experiments. By combining physiology-based traits with quantitative stress indices, 'PhysioIndexR' supports comprehensive crop evaluation and helps researchers identify multi-stress-resilient superior genotypes, thereby contributing to genetic improvement and ensuring sustainable production of food, fuel, and fibre in the era of limited resources and climate change. Package: r-cran-physortr Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-phytools, r-cran-ape Filename: pool/dists/noble/main/r-cran-physortr_1.0.9-1.ca2404.1_all.deb Size: 50524 MD5sum: 56879c889c2a17e84223ed2a83c54a89 SHA1: 33adff3ba54faa171bc5e1ca2e7d4868d1606767 SHA256: 9506fbd48e967be43ad7d7ad8c501addf085544ad055071e96801f5d458afc40 SHA512: 1028a6769326369763863dc8620ee31714970e0dda4d9e8e72761600af7649b56c9f33d9e98527c289b0a5d09ba82b81cc6cccf2114ca4a971208f7943b264f8 Homepage: https://cran.r-project.org/package=PhySortR Description: CRAN Package 'PhySortR' (A Fast, Flexible Tool for Sorting Phylogenetic Trees) Screens and sorts phylogenetic trees in both traditional and extended Newick format. Allows for the fast and flexible screening (within a tree) of Exclusive clades that comprise only the target taxa and/or Non- Exclusive clades that includes a defined portion of non-target taxa. Package: r-cran-phytoclass Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1273 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bestnormalize, r-cran-dynamictreecut, r-cran-ggplot2, r-cran-rcppml, r-cran-tidyr, r-cran-progress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-quarto, r-cran-here Filename: pool/dists/noble/main/r-cran-phytoclass_2.3.1-1.ca2404.1_all.deb Size: 425270 MD5sum: c1674087fd75b577bf1a338f10a83804 SHA1: 34b881443af9cad4ed49b3d5e79f0d12ab4c1466 SHA256: 264198632e4004e312997f29f76becb1cd1cf5cce4ae11abb06adb82c55f8e88 SHA512: 55e2ed126cae4c7d77949bb34d4975f342f1c6d78ba4afa71ab86898f2daf6258328e5804f11bcb3e25c319d19b4cb805e071e0a3ba383bc310bed247e2eb496 Homepage: https://cran.r-project.org/package=phytoclass Description: CRAN Package 'phytoclass' (Estimate Chla Concentrations of Phytoplankton Groups) Determine the chlorophyll a (Chl a) concentrations of different phytoplankton groups based on their pigment biomarkers. The method uses non-negative matrix factorisation and simulated annealing to minimise error between the observed and estimated values of pigment concentrations (Hayward et al. (2023) ). The approach is similar to the widely used 'CHEMTAX' program (Mackey et al. 1996) , but is more straightforward, accurate, and not reliant on initial guesses for the pigment to Chl a ratios for phytoplankton groups. Package: r-cran-phytoin Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggforce, r-cran-packcircles, r-cran-biomass, r-cran-scales Suggests: r-cran-httr2, r-cran-spelling Filename: pool/dists/noble/main/r-cran-phytoin_0.2.0-1.ca2404.1_all.deb Size: 144128 MD5sum: 555ddfc7d3f85e184795bf5b7a1368b6 SHA1: 1e0b80819fd92932d4d819406ad7eb5d5c8481f3 SHA256: 0d9b1cbb120d5ff4764e8e349eaf7c9f371339b2f4acaa6bcc65d0b27d79d456 SHA512: b4a1598efe354b3d90fb49fc5d9722f3f0c8d473390691d092cad2f94d7ebda51853447949af029a71dc51938a6cc3cde12622dd3947313db2ef812c3eed0d14 Homepage: https://cran.r-project.org/package=PhytoIn Description: CRAN Package 'PhytoIn' (Vegetation Analysis and Forest Inventory) Provides functions and example datasets for phytosociological analysis, forest inventory, biomass and carbon estimation, and visualization of vegetation data. Includes functions to compute structural parameters [phytoparam(), summary.param(), stats()], estimate above-ground biomass and carbon [AGB()], stratify wood volume by diameter at breast height (DBH) classes [stratvol()], generate collector and rarefaction curves [collector.curve(), rarefaction()], and visualize basal areas on quadrat maps [BAplot(), including rectangular plots and individual coordinates]. Several example datasets are provided to demonstrate the functionality of these tools. For more details see FAO (1981, ISBN:92-5-101132-X) "Manual of forest inventory", IBGE (2012, ISBN:9788524042720) "Manual técnico da vegetação brasileira" and Heringer et al. (2020) "Phytosociology in R: A routine to estimate phytosociological parameters" . Package: r-cran-phytools Architecture: all Version: 2.5-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2946 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-maps, r-cran-clustergeneration, r-cran-coda, r-cran-combinat, r-cran-deoptim, r-cran-doparallel, r-cran-expm, r-cran-foreach, r-cran-mass, r-cran-mnormt, r-cran-nlme, r-cran-numderiv, r-cran-optimparallel, r-cran-phangorn, r-cran-scatterplot3d Suggests: r-cran-animation, r-cran-geiger, r-cran-plotrix, r-cran-rcolorbrewer, r-cran-rgl Filename: pool/dists/noble/main/r-cran-phytools_2.5-2-1.ca2404.1_all.deb Size: 2848268 MD5sum: e293614985d3d2db11c206fd1674a0cc SHA1: 058bae8b3a2c56a9c4470b3412707ba05ff8fdf1 SHA256: 54746fe09a8d823a0a174d0511db3223c4ee75848a93d00d8dcf2f86f2d86bda SHA512: 0edc2971df90cf83a749f12f8047f61eafb66c521f2b2899d55130c030b7c48e03213d99a2e5a371ad641bd905b81625f9a6b64cde0a722b450750dfd98547e1 Homepage: https://cran.r-project.org/package=phytools Description: CRAN Package 'phytools' (Phylogenetic Tools for Comparative Biology (and Other Things)) A wide range of methods for phylogenetic analysis - concentrated in phylogenetic comparative biology, but also including numerous techniques for visualizing, analyzing, manipulating, reading or writing, and even inferring phylogenetic trees. Included among the functions in phylogenetic comparative biology are various for ancestral state reconstruction, model-fitting, and simulation of phylogenies and trait data. A broad range of plotting methods for phylogenies and comparative data include (but are not restricted to) methods for mapping trait evolution on trees, for projecting trees into phenotype space or a onto a geographic map, and for visualizing correlated speciation between trees. Lastly, numerous functions are designed for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data. For instance, there are functions for computing consensus phylogenies from a set, for simulating phylogenetic trees and data under a range of models, for randomly or non-randomly attaching species or clades to a tree, as well as for a wide range of other manipulations and analyses that phylogenetic biologists might find useful in their research. Package: r-cran-phytosanitarycalculator Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-acceptancesampling, r-cran-htmltools, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-devtools, r-cran-rlang Filename: pool/dists/noble/main/r-cran-phytosanitarycalculator_1.1.3-1.ca2404.1_all.deb Size: 40068 MD5sum: 4c0630d94688b0edba5c2850a23b9aec SHA1: 2e059e16bd867946d0e5b89c01849c38e287ae72 SHA256: d365ccf81f1e16beb6400ab48401102a6e4c52f3c7dbbc2ae0bf3a21239ed998 SHA512: 4d4fc9a1678f71fd6d744f44296107402cfb1818e892bee64996779602ec301daf0694cde5df4e8393f4ecf99a756375d0d48ee196b178234f9b0639ce134c23 Homepage: https://cran.r-project.org/package=PhytosanitaryCalculator Description: CRAN Package 'PhytosanitaryCalculator' (Phytosanitary Calculator for Inspection Plans Based on Risks) A 'Shiny' application for calculating phytosanitary inspection plans based on risks. It generates a diagram of pallets in a lot, highlights the units to be sampled, and documents them based on the selected sampling method (simple random or systematic sampling). Package: r-cran-piar Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-gpindex, r-cran-matrix Suggests: r-cran-data.tree, r-cran-knitr, r-cran-rmarkdown, r-cran-sps, r-cran-testthat, r-cran-treemap Filename: pool/dists/noble/main/r-cran-piar_0.9.0-1.ca2404.1_all.deb Size: 357216 MD5sum: b5021a3dae2f6a848c9884dfba97e14e SHA1: 3018e2d580c708251426a48705423378f1425a96 SHA256: 9481ad362d49a33ad6b1d8fbc3d53d419517268f841a8b331dd70993d89329f9 SHA512: 6540129e93e90147c7180a97a41312ea1ef4bf0192afbcd7c650ae26e64db525d576240096e5808a185349734b14f82a6716c3ed96f19e55a15625a66ced45ac Homepage: https://cran.r-project.org/package=piar Description: CRAN Package 'piar' (Price Index Aggregation) Most price indexes are made with a two-step procedure, where period-over-period elementary indexes are first calculated for a collection of elementary aggregates at each point in time, and then aggregated according to a price index aggregation structure. 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Package: r-cran-pic Architecture: all Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-collapse, r-cran-conicfit, r-cran-data.table, r-cran-dbscan, r-cran-dplyr, r-cran-foreach, r-cran-magrittr, r-cran-sf, r-cran-tictoc Suggests: r-cran-dt, r-cran-fs, r-cran-ggplot2, r-cran-later, r-cran-plotly, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinydashboard, r-cran-shinydashboardplus, r-cran-shinyfeedback, r-cran-shinyfiles, r-cran-shinyjs, r-cran-shinythemes, r-cran-shinywidgets, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-pic_1.2.7-1.ca2404.1_all.deb Size: 417800 MD5sum: abbb17c4639a968a52fbf16d88dec0d1 SHA1: 5b8f4da884a0760a36d0f30decbd47e136fa1e56 SHA256: 40d5fbd9415da55cf0370dcfb8a36edd10c96fc8102af043e2533b19d072e46b SHA512: 638b57f923dd0ad43219c6c0a6707cb9bf993477e5c54848d6bd832cada778ba3a57903ae3cac94da55f8575a5924d2535b7e54079abe5000de0401a4d7f54d9 Homepage: https://cran.r-project.org/package=PiC Description: CRAN Package 'PiC' (Pointcloud Interactive Computation) Provides advanced algorithms for analyzing pointcloud data from terrestrial laser scanner in forestry applications. 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Package: r-cran-picbayes Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 672 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-mcmcpack, r-cran-survival Filename: pool/dists/noble/main/r-cran-picbayes_1.0-1.ca2404.1_all.deb Size: 647906 MD5sum: d061bc30c55219cf58a2eb5059277947 SHA1: 84a5c0a0b20ce7bd44f51cebf86cada40cce9794 SHA256: 0c8fb1da70f2c74f9a5a78563a96d7a1b2e5819528388eddc760a36cb6d75464 SHA512: dba1dab5e432621896d608d3d0b9158fd5040c2d69396e154791dda74058f50335ec6cd01e44b68d316e23dc8a519f857c865aa7c437efd9fb7fa9bd262323eb Homepage: https://cran.r-project.org/package=PICBayes Description: CRAN Package 'PICBayes' (Bayesian Models for Partly Interval-Censored Data) Contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. (2020) ), PH model with spatial frailty to spatially dependent PIC data (Pan and Cai (2021) ), and mixed effects PH model to clustered PIC data. Each random intercept/random effect can follow both a normal prior and a Dirichlet process mixture prior. It also includes the corresponding functions for general interval-censored data. Package: r-cran-picclip Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-base64enc, r-cran-stringr, r-cran-htmltools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-picclip_0.1.0-1.ca2404.1_all.deb Size: 16202 MD5sum: 1145216ec1ea889bf329089a1dbc66d4 SHA1: 33ac4a0e3d6e94f031797cd84a94b146f72c45b5 SHA256: 35df84a7ec59d1c642c1555dae2c6f30722e1d3457e59ece135f083bf9323a74 SHA512: b165d4637aeac10ffdc36aebe8c43f4359b4e526880730277051a74988c346cad6eb67232ceb2f6bee38b49dfef342c44af8a86b23deb4f1496d98bf7eef28bc Homepage: https://cran.r-project.org/package=picClip Description: CRAN Package 'picClip' (Paste Box Input for 'Shiny') Provides a 'Shiny' input widget, pasteBoxInput, that allows users to paste images directly into a 'Shiny' application. 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Package: r-cran-pickmax Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-pickmax_0.1.0-1.ca2404.1_all.deb Size: 15278 MD5sum: 0f64b1886394c639a87d748b604bdc82 SHA1: 7ae637b5615e6a7aad42cb851e2857e4098bb87f SHA256: f03f077ace5f68189afe5ea3f729e819b5af892895cd7307acd27b5cb5a4a46c SHA512: 4211f4a814d9343d5682c339ab2467db59b6a8cf0b06b68ad016d2d43a0585edc2ed0d6d4d45144baf4158824a8ebce3e89be853f320df9a5c7e189b17422056 Homepage: https://cran.r-project.org/package=pickmax Description: CRAN Package 'pickmax' (Split and Coalesce Duplicated Records) Deduplicates datasets by retaining the most complete and informative records. Identifies duplicated entries based on a specified key column, calculates completeness scores for each row, and compares values within groups. When differences between duplicates exceed a user-defined threshold, records are split into unique IDs; otherwise, they are coalesced into a single, most complete entry. Returns a list containing the original duplicates, the split entries, and the final coalesced dataset. Useful for cleaning survey or administrative data where duplicated IDs may reflect minor data entry inconsistencies. Package: r-cran-picr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-picr_1.0.1-1.ca2404.1_all.deb Size: 43224 MD5sum: 939aca204ed5b6251a7ef351c2cc0303 SHA1: 0b2ebbab48b977c0b48856e5b05958b653b5d32b SHA256: a09209fc8e57e0ccaf25ac60701103fa5f5acfba1ca0c6370a9437d6cee2cee7 SHA512: 747e9a39c8585b5facf8bb6f1441bbd95534a1419b5397a61179deeba0621e0c66cf0ba99b32afb576fc8aee1dbd8866953e39f1742f9070992ee3baf4cc98d6 Homepage: https://cran.r-project.org/package=picR Description: CRAN Package 'picR' (Predictive Information Criteria for Model Selection) Computation of predictive information criteria (PIC) from select model object classes for model selection in predictive contexts. In contrast to the more widely used Akaike Information Criterion (AIC), which are derived under the assumption that target(s) of prediction (i.e. validation data) are independently and identically distributed to the fitting data, the PIC are derived under less restrictive assumptions and thus generalize AIC to the more practically relevant case of training/validation data heterogeneity. The methodology featured in this package is based on Flores (2021) "A new class of information criteria for improved prediction in the presence of training/validation data heterogeneity". Package: r-cran-pid Architecture: all Version: 0.65-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2008 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-png, r-cran-frf2, r-cran-doe.base, r-cran-frf2.catlg128 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-pid_0.65-1.ca2404.1_all.deb Size: 875094 MD5sum: 44b1329e0360bb8bb2859534d5ff50b4 SHA1: fd9a006c89c10269b19a32a8c2528a7c9fa51f2e SHA256: c86fd26f845960ff28830dcacf30fd8b068b67ac3617f8479dcab3edc5729e38 SHA512: 672aadb73f8a2facdeec4ab3ab3dd4ed88092ee51336cb8015644dfa109463fa3e0bfbccd5aff30dd6c057cdb4c5d9ca2acfcefce4f1c88af18aa64da8d21838 Homepage: https://cran.r-project.org/package=pid Description: CRAN Package 'pid' (Process Improvement using Data) A collection of scripts and data files for the statistics text: "Process Improvement using Data" and the online course "Experimentation for Improvement" found on Coursera. 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See the paper, Wang, Yang, Li, and Wang (2021) . Package: r-cran-piecemaker Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-rlang, r-cran-stringi, r-cran-stringr Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-piecemaker_1.0.2-1.ca2404.1_all.deb Size: 41478 MD5sum: 4ec9d387f56697b58cf56112fe65e054 SHA1: d5723a76318d1034a1e08161012677bc8077460e SHA256: 1104afe442bc6094ca04563df8411beff4f21cec1f97b8cf1e8a84d34c38a092 SHA512: 012097092b924c5e634622b7ba513c21f6d557a630d6757721afef5490f1f962099b7980eab3273984e74c9d878cacfd04761bbdef2994b485cfe79380f552a9 Homepage: https://cran.r-project.org/package=piecemaker Description: CRAN Package 'piecemaker' (Tools for Preparing Text for Tokenizers) Tokenizers break text into pieces that are more usable by machine learning models. 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Package: r-cran-pieglyph Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2678 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-ggiraph, r-cran-ggforce, r-cran-purrr, r-cran-forcats, r-cran-plyr, r-cran-scales, r-cran-cli Suggests: r-cran-spelling, r-cran-ranger, r-cran-maps, r-cran-cowplot, r-cran-mapproj, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-pieglyph_1.1.0-1.ca2404.1_all.deb Size: 769286 MD5sum: 98d245d8603a7cc2172c2f74e801b5c7 SHA1: 5c3cfd6510a48ce74ec39a5c986de8e37303ecf8 SHA256: 14519341ea5df5f28503f61bc4546e041affd44596ecf7c1f7c0ed857ffae105 SHA512: ab07bfee8b9cd3d7558f4fe13a996a0461cc33cc371326b1bbc53e3f3d42bd426dc0dc53cae47a675d4c1785ecaa05a3268cc0df28f3c91e88771401bc4acab4 Homepage: https://cran.r-project.org/package=PieGlyph Description: CRAN Package 'PieGlyph' (Axis Invariant Scatter Pie Plots) Extends 'ggplot2' to help replace points in a scatter plot with pie-chart glyphs showing the relative proportions of different categories. 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Package: r-cran-piggyback Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 767 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-gh, r-cran-httr, r-cran-jsonlite, r-cran-fs, r-cran-lubridate, r-cran-memoise Suggests: r-cran-spelling, r-cran-readr, r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-gert, r-cran-withr, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-piggyback_0.1.5-1.ca2404.1_all.deb Size: 558166 MD5sum: 097314a444d9e2b162b5049e759f031a SHA1: 5cebbb0a35b8d6486946b9803d01a43c33d79e17 SHA256: 3e74d076de887520e838739d0747a5b83a9566dbc663b5a819ab2f40c6579234 SHA512: 6ede43db71afd1b966c30b3e677eede69385e37a7bdffe581f3a86fa389c7748797c2b824eb7d22fff1a224b5cfdda059a140db264e667fa11ee1ce47c3ec3f8 Homepage: https://cran.r-project.org/package=piggyback Description: CRAN Package 'piggyback' (Managing Larger Data on a GitHub Repository) Because larger (> 50 MB) data files cannot easily be committed to git, a different approach is required to manage data associated with an analysis in a GitHub repository. 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Package: r-cran-pii Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-uuid Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pii_1.3.0-1.ca2404.1_all.deb Size: 20004 MD5sum: 512a54830ef5b58780fe3d885c59255c SHA1: 6248da0a33fe32c6541119f1886c31ee56c347b9 SHA256: d6b5d37c2214c006a80558051c37ae5832fb976b7a68fb9298bd2898bdc40328 SHA512: 747d6beb3b6b4571852c24c094c8cbc574d0552b73215f61612750df6d79456f6ba6defd442d28afd346a4808c06bd23483bc230a0303363841f8b8b6da1363a Homepage: https://cran.r-project.org/package=pii Description: CRAN Package 'pii' (Search Data Frames for Personally Identifiable Information) Check a data frame for personal information, including names, location, disability status, and geo-coordinates. 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Package: r-cran-pim Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 798 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nleqslv, r-cran-bb Suggests: r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-pim_2.0.4-1.ca2404.1_all.deb Size: 647594 MD5sum: 7f46effc1cda705a5b64273a7ed3b43c SHA1: 7e6f0205ceaf2137b0b7a3b47e86c276dd941778 SHA256: aaf9c44ae9c4f7e825e5b020a732a99413cddd04304f9aea9459d1484e0bfab9 SHA512: a36ac567e45590758bd44768f993bf20c6e37e7440db36e20d096337ea1f4964a6e9cf76f3be9ca5d42afa3fd914904aaea33a53498f646b968f130323850ab9 Homepage: https://cran.r-project.org/package=pim Description: CRAN Package 'pim' (Fit Probabilistic Index Models) Fit a probabilistic index model as described in Thas et al, 2012: . The interface to the modeling function has changed in this new version. The old version is still available at R-Forge. Package: r-cran-pinference Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pinference_0.2.6-1.ca2404.1_all.deb Size: 40132 MD5sum: 4c6a9943049977f33583662e42c2c272 SHA1: 20c7fc68dad83d9473f278ce9ef24ab54da45f5d SHA256: 55dd1cd10c2ba418b6dc85760264bed74d80029f5b8b8d4ec46902054530ec28 SHA512: 1166901c36050f8f2757d50041e729475971efa52848cad3b52e82a4b170dbb56699912b996eeb52abdb84e7684633292ac4cd31cb633a331fdcf96642aa68a1 Homepage: https://cran.r-project.org/package=Pinference Description: CRAN Package 'Pinference' (Probability Inference for Propositional Logic) Implementation of T. 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Package: r-cran-pinfsc50 Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4234 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pinfsc50_1.3.0-1.ca2404.1_all.deb Size: 3438004 MD5sum: 63775a894bea8627cfe6d9cd7d7a7c6d SHA1: b28c26e27df5160f696623bf8ff16dab4b689191 SHA256: 0fae9df9fd2245c21f96ed23978395dd13f982bc3b70e988936b8dc8f032de90 SHA512: db6bd1484fa13c27deb1252481a83598cc7a8fecc83b942fbeafc3f455d18fd2742782263fe49403e92e14afdd412aa67762c68729002f6d6ad0ff2c8f060ad3 Homepage: https://cran.r-project.org/package=pinfsc50 Description: CRAN Package 'pinfsc50' (Sequence ('FASTA'), Annotation ('GFF') and Variants ('VCF') for17 Samples of 'P. Infestans" and 1 'P. Mirabilis') Genomic data for the plant pathogen "Phytophthora infestans." It includes a variant file ('VCF'), a sequence file ('FASTA') and an annotation file ('GFF'). 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Package: r-cran-pingers Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-tibble, r-cran-tictoc, r-cran-tidyselect, r-cran-data.table, r-cran-lubridate, r-cran-plotly, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-pingers_0.1.1-1.ca2404.1_all.deb Size: 35362 MD5sum: d5d0fd77de679af9931fb12fbca1be80 SHA1: 056a1236b038c1f953dbadc8aabb7d1baedf4538 SHA256: aee3c0a22d72ddb6176b90941d2313ae3393112bf82dbd8bdc556ed26be351e8 SHA512: a5381bdfca041ec3b46c0f6e8b52f473683ed7b0cb8f60b1898fdc1497b18db3335f9d5134c9b9bf93691d87ee5b51639509b64e4d2ed48620abaec6e7a2783b Homepage: https://cran.r-project.org/package=pingers Description: CRAN Package 'pingers' (Identify, Ping, and Log Internet Provider Connection Data) To assist you with troubleshooting internet connection issues and assist in isolating packet loss on your network. 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Package: r-cran-pinnacle.data Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2309 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-odds.converter, r-cran-tidyverse, r-cran-lahman Filename: pool/dists/noble/main/r-cran-pinnacle.data_0.1.4-1.ca2404.1_all.deb Size: 2325402 MD5sum: 041c1f08a99e46e5d53fb48a4c0f6d8b SHA1: 8309bb30e2d6dac8c662d957d0a0c64cc78a91dd SHA256: f117ff4d39804fec746aba56d5fe7bafda5acaa1fc3876a46cc801ddbf80dcff SHA512: 9d03ce912636a6b52775755090b237caabfc69cb21bb6da3b00378da788e9eb44c6f0b0bb40b1edfc8915191f340e6561679386d0cf9fab334929bfcba16cd96 Homepage: https://cran.r-project.org/package=pinnacle.data Description: CRAN Package 'pinnacle.data' (Market Odds Data from Pinnacle) Market odds from from Pinnacle, an online sports betting bookmaker (see for more information). Included are datasets for the Major League Baseball (MLB) 2016 season and the USA election 2016. 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Package: r-cran-pinochet Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1132 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-devtools, r-cran-kableextra, r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-rnaturalearthdata, r-cran-sf, r-cran-tidyverse, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pinochet_0.1.0-1.ca2404.1_all.deb Size: 582678 MD5sum: 50551b819ed9384623f945aaa66e2cbf SHA1: 1b814432d925f28645a1f9a8e5b3bd75ab681e1a SHA256: 2e1b0f6bff8cbbb951440b018919597d2247af13d944ba79aa05f84afd5237cf SHA512: 9c74f27a9ee08b1aaa83aa0a0b39243baef53cc4bfa39b45db3c04700b2caae962000d9ecec06f759f74a5cad927142431af3512b56f28f3e934a44794eba357 Homepage: https://cran.r-project.org/package=pinochet Description: CRAN Package 'pinochet' (Data About the Victims of the Pinochet Regime, 1973-1990) Packages data about the victims of the Pinochet regime as compiled by the Chilean National Commission for Truth and Reconciliation Report (1991, ISBN:9780268016463). 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Package: r-cran-pins Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-digest, r-cran-fs, r-cran-generics, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-rappdirs, r-cran-rlang, r-cran-tibble, r-cran-whisker, r-cran-withr, r-cran-yaml Suggests: r-cran-archive, r-cran-arrow, r-cran-azurestor, r-cran-covr, r-cran-data.table, r-cran-filelock, r-cran-gitcreds, r-cran-googlecloudstorager, r-cran-googledrive, r-cran-httr2, r-cran-ids, r-cran-knitr, r-cran-microsoft365r, r-cran-mime, r-cran-mockery, r-cran-nanoparquet, r-cran-openssl, r-cran-paws.storage, r-cran-qs2, r-cran-r.utils, r-cran-rmarkdown, r-cran-rsconnect, r-cran-shiny, r-cran-sodium, r-cran-testthat, r-cran-webfakes, r-cran-xml2, r-cran-zip Filename: pool/dists/noble/main/r-cran-pins_1.4.2-1.ca2404.1_all.deb Size: 678552 MD5sum: df5e3ed3400d7b318adddade48ab36f3 SHA1: 6bb44e0ac685603550c58af70a2d233429e4bb8a SHA256: 56bc98cee32f74c1164690ece509d527f390b420304b26be9b978f4898834460 SHA512: 7e414cca6be68a32008cc2667557c48deea687b16d15cf7517007a3c3b70d9782a0f2837e01a9992a844aaed48073e5c8efa7dda14ec613834ab3bd922776b0d Homepage: https://cran.r-project.org/package=pins Description: CRAN Package 'pins' (Pin, Discover, and Share Resources) Publish data sets, models, and other R objects, making it easy to share them across projects and with your colleagues. 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Package: r-cran-pinstimation Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4132 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-knitr, r-cran-skellam, r-cran-nloptr, r-cran-furrr, r-cran-future, r-cran-dplyr, r-cran-rmarkdown, r-cran-coda, r-cran-magrittr, r-cran-tidyr Suggests: r-cran-fansi, r-cran-htmltools Filename: pool/dists/noble/main/r-cran-pinstimation_0.2.0-1.ca2404.1_all.deb Size: 2224950 MD5sum: d13ea07edcebbd536c6cc041537d43ae SHA1: fe34ff45fd91315637d3f9d62455e05994cf165f SHA256: ab79bb8ac87e14ac21280ef195003d1212d5cf642c45a19b84d16e88e7960679 SHA512: 2f409dc143ccb32dfc96fa2d89fffd144044fb5492ca9f81359d099fdaa38017321d12c464afd66ac7962f947a922454ba3bad6652b6a4fa73bcd85bcbc56746 Homepage: https://cran.r-project.org/package=PINstimation Description: CRAN Package 'PINstimation' (Estimation of the Probability of Informed Trading) A comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. 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Package: r-cran-pipeliner Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-modelr, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-pipeliner_0.1.1-1.ca2404.1_all.deb Size: 62220 MD5sum: 6c9522b5e7892dbf2491ceef312f7092 SHA1: f23cd8310f3846104fa0c49bf892cef7c6ffe0ed SHA256: eda863cb55bc81da0e341dc5825541768db92c3275b5af7ea0032fbfb7299f45 SHA512: da98123f7c873a381b36fb08dd0dda1cf752450b7e3a30b3c3b5d6492ed2fbe7a28747d41e747de4e1cd54835935e64e2f5e7ac76c2e7e1999defe0ba8b7f795 Homepage: https://cran.r-project.org/package=pipeliner Description: CRAN Package 'pipeliner' (Machine Learning Pipelines for R) A framework for defining 'pipelines' of functions for applying data transformations, model estimation and inverse-transformations, resulting in predicted value generation (or model-scoring) functions that automatically apply the entire pipeline of functions required to go from input to predicted output. 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Package: r-cran-piper Architecture: all Version: 0.6.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-piper_0.6.1.3-1.ca2404.1_all.deb Size: 49664 MD5sum: 1a74421a35e6481daa050a0c87761e75 SHA1: fac551c58b94769b3d7e39702e84be8132022113 SHA256: e662b8e717db9ad8a141143fbecc11d870a788307fcc251612901d5d708f7917 SHA512: ed8b5d5aac0ba5eaf66f164229d24dde165097c315c12e1de25bf680beb577f4a35fd57c08f04ebf3b6f7fedb619e4f033646b0ea976db2521e716adbba023de Homepage: https://cran.r-project.org/package=pipeR Description: CRAN Package 'pipeR' (Multi-Paradigm Pipeline Implementation) Provides various styles of function chaining methods: Pipe operator, Pipe object, and pipeline function, each representing a distinct pipeline model yet sharing almost a common set of features: A value can be piped to the first unnamed argument of a function and to dot symbol in an enclosed expression. 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The pharmacokinetics model explains that how the drug concentration change as the drug moves through the different compartments of the body. For pharmacokinetic modeling and analysis, it is essential to understand the basic pharmacokinetic parameters. All parameters are considered, but only some of parameters are used in the model. Therefore, we need to convert the estimated parameters to the other parameters after fitting the specific pharmacokinetic model. This package is developed to help this converting work. For more detailed explanation of pharmacokinetic parameters, see "Gabrielsson and Weiner" (2007), "ISBN-10: 9197651001"; "Benet and Zia-Amirhosseini" (1995) ; "Mould and Upton" (2012) ; "Mould and Upton" (2013) . 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Package: r-cran-pklmtest Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ranger Filename: pool/dists/noble/main/r-cran-pklmtest_1.0.1-1.ca2404.1_all.deb Size: 22210 MD5sum: a1aaec856b3ebeefe9379bd0e01286d1 SHA1: 5b848b390761bdc73cafca505a976813f25b0638 SHA256: 084aa5ae9029b854ee1d87d4f7adf0cee583e2ae26be29c1cba1fe18796233b8 SHA512: ccf3ad332df39f278ca150080d0904a95f2bd8eed549d23d5f813ceb0b3a67cdb74c93f8c1e9db02b716b8b0a78380a06f70723c395a820a5a4503f776319916 Homepage: https://cran.r-project.org/package=PKLMtest Description: CRAN Package 'PKLMtest' (Classification Based MCAR Test) Implementation of a KL-based (Kullback-Leibler) test for MCAR (Missing Completely At Random) in the context of missing data as introduced in Michel et al. (2021) . Package: r-cran-pkmapr Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1692 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-rlang, r-cran-cli, r-cran-spdep, r-cran-jsonlite Suggests: r-cran-ggplot2, r-cran-leaflet, r-cran-rmapshaper, r-cran-httptest2, r-cran-testthat, r-cran-tmap, r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-pkmapr_1.2.1-1.ca2404.1_all.deb Size: 564900 MD5sum: e693816e8d9e20bcf62deb515fcc2f6c SHA1: f1bb04e06bbb69439dfee9345a610f4d83328b2b SHA256: e83f1b0537cb609dfd2a56c68dee576f6eb218225409d9128d66b3a45d6a5715 SHA512: 23323fbb6a03bc8813c4adf44a0d7093183b0229a433c04673571dd6c6a9cee322b7719936a9293f08a3eaadc4546ba568db26e2860782a756ff94e6ba347f3b Homepage: https://cran.r-project.org/package=pkmapr Description: CRAN Package 'pkmapr' (Pakistan Spatial Data Toolkit) Provides a tidy interface to Pakistan's official administrative boundary data from the United Nations Office for the Coordination of Humanitarian Affairs (OCHA). Downloads and caches spatial data at country, province, district, and tehsil levels as 'sf' objects compatible with the 'tidyverse' and geospatial ecosystem. Includes utilities for geographic dictionary lookup, coordinate reference system selection, spatial measurement, and neighbour structure construction for use with 'spdep', 'ggplot2', 'leaflet', and related packages. Package: r-cran-pkmon Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pkmon_1.1-1.ca2404.1_all.deb Size: 68466 MD5sum: 9b1c8f470ef660ecb13be97bb7e8236d SHA1: 0c416f2db37897bcd70029bf28de93f16fdc9eec SHA256: dda5d1a8a223cc1b390ce78366b1320ea8c12bd679fc51e2d651f3188e76c578 SHA512: efa25f90c7de6e46e6864e7ff36f7fff47e88e82ef61180c98b4c4f97e4edde07b48a064d37ee6a35e81d88e994223362f8e15442b5fca2f108e996c0fdec940 Homepage: https://cran.r-project.org/package=pkmon Description: CRAN Package 'pkmon' (Least-Squares Estimator under k-Monotony Constraint for DiscreteFunctions) We implement two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008) . The first one is a projection estimator on the set of k-monotone discrete functions. The second one is a projection on the set of k-monotone discrete probabilities. This package provides functions to generate samples from the spline basis from Lefevre and Loisel (2013) , and from mixtures of splines. Package: r-cran-pknca Architecture: all Version: 0.12.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3060 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-digest, r-cran-nlme, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tibble, r-cran-lifecycle Suggests: r-cran-covr, r-cran-cowplot, r-cran-ggplot2, r-cran-knitr, r-cran-labeling, r-cran-pander, r-cran-pmxtools, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-units, r-cran-withr Filename: pool/dists/noble/main/r-cran-pknca_0.12.1-1.ca2404.1_all.deb Size: 1182068 MD5sum: bb6c5890907a709f60127ebc3076831d SHA1: 4a484fa2c50ea1495dce934abda67563c0258047 SHA256: 60347e6f29abed1cf551d35bf33c3a71e8babc1f5328cee8c78954193ea5e956 SHA512: bb1542192deb0aaf578c5bd96b765010fb54761f6b4448ab886f106ba877936c8e380a537997a35afea33c805404815adffdccc69f0f4edaf99f9f38cabb4062 Homepage: https://cran.r-project.org/package=PKNCA Description: CRAN Package 'PKNCA' (Perform Pharmacokinetic Non-Compartmental Analysis) Compute standard Non-Compartmental Analysis (NCA) parameters for typical pharmacokinetic analyses and summarize them. Package: r-cran-pkpd.release Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-gridextra, r-cran-scales Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pkpd.release_0.1.0-1.ca2404.1_all.deb Size: 268992 MD5sum: 45928141fd6a413e063ab04bad034b20 SHA1: 3397ff83b8f5c9015cb0dc06ebc22a515b0483d2 SHA256: 82fe66e1a439dce570f58f68a5380b1ae98b317deaddb818cdca54835df1efca SHA512: df8754d83176516b81a420998f9530391e6e4977c61041a1cdb18ab08d2b112f3111174595baf36d20f2a5c012753641f61916c46ff95552aba26f99060b668b Homepage: https://cran.r-project.org/package=pkpd.Release Description: CRAN Package 'pkpd.Release' (Model Fitting and Simulation for Drug Release Kinetics and PK/PD) Provides a comprehensive framework for model fitting and simulation of drug release kinetics, pharmacokinetics (PK), and pharmacodynamics (PD). The package implements widely used mechanistic and empirical models for in vitro drug release, including zero-order, first-order, Higuchi, Korsmeyer-Peppas, Hixson-Crowell, and Weibull models. Pharmacokinetic functionality includes linear and nonlinear functions for one- and two-compartment models for intravenous bolus and oral administration, Michaelis-Menten kinetics, and non-compartmental analysis (NCA). Pharmacodynamic and dose-response modeling is supported through Emax-based models, including stimulatory (sigmoid Emax) and inhibitory (sigmoid Imax) Hill models, four- and five-parameter logistic models, as well as median toxic dose (TD50) and lethal dose (LD50) models. The package is intended to support parameter estimation, simulation, and model comparison in pharmaceutical research, drug development, and pharmacometrics education. For more details, see Gabrielsson & Weiner (2000) , Holford & Sheiner (1981) , and Manlapaz (2025) . Package: r-cran-pkpdindex Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pkpdindex_0.2.0-1.ca2404.1_all.deb Size: 31632 MD5sum: 1ce9c401379e8d02ee6cb3c0dc1cd7e0 SHA1: 99a297800017f4c9a9293d6200937d8168a90b89 SHA256: 5ec22ab8430b691e4ef079e8f625c8b2e62b150c18627d0369b42819e1905e00 SHA512: 1c782eb6b21468823578fbba4a7ebb2d1b90c1ebe15f225f4d3ce70161516df1a829845b67a2e3e4e038434fdb73e85d5ac88c47990f7167eb9bf2e3203a54a9 Homepage: https://cran.r-project.org/package=PKPDindex Description: CRAN Package 'PKPDindex' (Optimal PK/PD Index Finder) Fits Emax models to pharmacokinetic/pharmacodynamic (PK/PD) data, estimate key parameters, and visualise model fits for multiple PK/PD indices. Methods are described in Macdougall J (2006) , Spiess AN, Neumeyer N (2010) , and Burnham KP, Anderson DR (2004) . Package: r-cran-pkr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreign, r-cran-binr, r-cran-forestplot, r-cran-rtf Filename: pool/dists/noble/main/r-cran-pkr_0.1.3-1.ca2404.1_all.deb Size: 370348 MD5sum: ea58ce67de97a41f4b52ba089573b90b SHA1: 28cfaf7b03c5de120b9f43ea763356d645816bd6 SHA256: dec13a7693acbbfecb93d2a84456c10f75b4c9ba3be5fa0832f5f24e933b56f8 SHA512: b6a82304ca343ab841828bdc9c47e7c95ea075a76529d57edbcbcfbfcea7e4058fab151258891c20553da5c203bea00740814e17e6c2ec2fd1bec58fe3ed8fb4 Homepage: https://cran.r-project.org/package=pkr Description: CRAN Package 'pkr' (Pharmacokinetics in R) Conduct a noncompartmental analysis as closely as possible to the most widely used commercial software. Some features are 1) CDISC SDTM terms 2) Automatic slope selection with the same criterion of WinNonlin(R) 3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method 4) Interval(partial) AUCs with 'linear' or 'log' interpolation method * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107). Package: r-cran-pks Architecture: all Version: 0.7-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sets Suggests: r-cran-relations, r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-pks_0.7-0-1.ca2404.1_all.deb Size: 483458 MD5sum: a6ddf147f23294f1d6531b7bfd743de6 SHA1: 31ce8bdaa004144f8da64378a744121a8332a544 SHA256: a2bd2fb9b071f0ddc68efd081c06b338723b43845f8adf9b0d8ad5b053acf0ce SHA512: b17f6160efce186bc34113f811a4a27e27a558fc458b332ad1a4a956819bea1eb8b0ebe3a705e3b18f64eea65b7444d29d8b754c06584d59c342d835eeb372b1 Homepage: https://cran.r-project.org/package=pks Description: CRAN Package 'pks' (Probabilistic Knowledge Structures) Fitting and testing probabilistic knowledge structures, especially the basic local independence model (BLIM, Doignon & Flamagne, 1999) and the simple learning model (SLM), using the minimum discrepancy maximum likelihood (MDML) method (Heller & Wickelmaier, 2013 ). Package: r-cran-pksea Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2875 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pksea_0.0.1-1.ca2404.1_all.deb Size: 2595616 MD5sum: 557f226d0003d3c95d3baa6f5c167326 SHA1: 8e6108fb092642e973078532d865154e5f5bb095 SHA256: df481e50d3c49975f97d773030bf9ebe34a790f872698d23bd9f807bcefd04ca SHA512: 430e96eb5ab3ea2f2b64cae1c5356b915ce32ee5e89a52d7607cc8a0d29c20efb5b77a287ff97de0325df5df4408161814e66e31f73ba87ff3bab36fd821a53b Homepage: https://cran.r-project.org/package=pKSEA Description: CRAN Package 'pKSEA' (Prediction-Based Kinase-Substrate Enrichment Analysis) A tool for inferring kinase activity changes from phosphoproteomics data. 'pKSEA' uses kinase-substrate prediction scores to weight observed changes in phosphopeptide abundance to calculate a phosphopeptide-level contribution score, then sums up these contribution scores by kinase to obtain a phosphoproteome-level kinase activity change score (KAC score). 'pKSEA' then assesses the significance of changes in predicted substrate abundances for each kinase using permutation testing. This results in a permutation score (pKSEA significance score) reflecting the likelihood of a similarly high or low KAC from random chance, which can then be interpreted in an analogous manner to an empirically calculated p-value. 'pKSEA' contains default databases of kinase-substrate predictions from 'NetworKIN' (NetworKINPred_db) Horn, et. al (2014) and of known kinase-substrate links from 'PhosphoSitePlus' (KSEAdb) Hornbeck PV, et. al (2015) . Package: r-cran-pksensi Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table, r-cran-desolve, r-cran-foreach, r-cran-doparallel Suggests: r-cran-covr, r-cran-knitr, r-cran-httk, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pksensi_1.2.3-1.ca2404.1_all.deb Size: 236660 MD5sum: 9c479cce3ef09c5d45051033019bc305 SHA1: 631ad15a135d63e71e86dabd388b6c2f40e143c1 SHA256: 7447ba562c7c1bbfe7812244152ef12ef0b58910954f770d4723aeb8b7169b79 SHA512: 79a56e07a681741c541b7b200383e58511166e961b6f5765275cedc0c140cc15854f85d2305f9cd97662f3523afe36a2035e329100dc254ef6e21f39ef71b6c1 Homepage: https://cran.r-project.org/package=pksensi Description: CRAN Package 'pksensi' (Global Sensitivity Analysis in Physiologically Based KineticModeling) Applying the global sensitivity analysis workflow to investigate the parameter uncertainty and sensitivity in physiologically based kinetic (PK) models, especially the physiologically based pharmacokinetic/toxicokinetic model with multivariate outputs. The package also provides some functions to check the convergence and sensitivity of model parameters. The workflow was first mentioned in Hsieh et al., (2018) , then further refined (Hsieh et al., 2020 ). Package: r-cran-pl94171 Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1060 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-stringr, r-cran-readr, r-cran-dplyr, r-cran-tinytiger, r-cran-sf, r-cran-withr, r-cran-foreign Suggests: r-cran-testthat, r-cran-lifecycle, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-pl94171_1.2.1-1.ca2404.1_all.deb Size: 319230 MD5sum: d62b7fd21d7eda25a52abdf206b1c562 SHA1: 004a14f947a88d971ea15588f427c494c7541282 SHA256: 9c8e8f31f8411da65a994937861c1821987179f8a73fa46247c2a08e1d200f86 SHA512: 9fdd415948007726ab05e92834d3d45e0ee5d3acdef6ccc9afcd24a0dd1ff015e91cef7bfded6169f7d0116232a29e0faef0201a3107363cd9593bfe5fda5cd3 Homepage: https://cran.r-project.org/package=PL94171 Description: CRAN Package 'PL94171' (Tabulate P.L. 94-171 Redistricting Data Summary Files) Tools to process legacy format summary redistricting data files produced by the United States Census Bureau pursuant to P.L. 94-171. These files are generally available earlier but are difficult to work with as-is. Package: r-cran-placematchr Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1624 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-stringdist, r-cran-data.table, r-cran-tidyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-readxl Filename: pool/dists/noble/main/r-cran-placematchr_0.2.4-1.ca2404.1_all.deb Size: 1557668 MD5sum: 5721d7a7b4802cd3ac8d668ca420ec70 SHA1: e8174a4d51cf68cc25139809299c136d8b18f65c SHA256: 5764cf7737085a0738600e88c8f4abc29f058fd13af641452c72fec82e423f35 SHA512: 627246318907ce2c2964ea8d3654ef6e0e3da94173177bec00871b64045c99b21f239357763ffc32ac2cb4da8d50526e941bd2c4d4f3b438eb0e9102d4995069 Homepage: https://cran.r-project.org/package=placematchr Description: CRAN Package 'placematchr' (Normalize and Match City Names to NUTS Regions) Normalizes city names for EEA countries and matches them to NUTS 3 regions using provided crosswalks. Features include comprehensive normalization rules, cascading matching logic (Exact NUTS -> Exact LAU -> Fuzzy), and single-source data synthesis. The package implements the NUTS classification as described in the NUTS methodology (Eurostat (2021) ). Package: r-cran-placer Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-placer_0.1.3-1.ca2404.1_all.deb Size: 49284 MD5sum: 0d06afefd334f6ca6faced9828d99c81 SHA1: 7cec3306f38859369fa066afbdccde0f314d15a4 SHA256: 532c96a37504a45069befdea7898c78721fc5b07aab56e4e522ea11fc2d690eb SHA512: 19f53e9fa75750b001d1d63475b97e0afa53eb1102ed3dd816a93f56aa5b272d5ddb0855f58a24092af899ef0464b5478015a2669c11ebf4e5a58d03860ddfa9 Homepage: https://cran.r-project.org/package=placer Description: CRAN Package 'placer' (PLastic ACcumulation Estimate using R (PLACER)) Assessment of the prevalence of plastic debris in bird nests based on bootstrap replicates. The package allows for calculating bootstrapped 95% confidence intervals for the estimated prevalence of debris. Combined with a Bayesian approach, the resampling simulations can be also used to define appropriate sample sizes to detect prevalence of plastics. The method has wide application, and can also be applied to estimate confidence intervals and define sample sizes for the prevalence of plastics ingested by any other organisms. The method is described in Tavares et al. (Submitted). Package: r-cran-plackettluce Architecture: all Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2530 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvxr, r-cran-igraph, r-cran-matrix, r-cran-matrixstats, r-cran-partykit, r-cran-psychotools, r-cran-psychotree, r-cran-r6, r-cran-rspectra, r-cran-qvcalc, r-cran-sandwich Suggests: r-cran-bayesmallows, r-cran-bradleyterry2, r-cran-plmix, r-cran-rologit, r-cran-statrank, r-cran-bookdown, r-cran-covr, r-cran-hyper2, r-cran-kableextra, r-cran-knitr, r-cran-lbfgs, r-cran-gnm, r-cran-pmr, r-cran-rmarkdown, r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plackettluce_0.4.5-1.ca2404.1_all.deb Size: 971686 MD5sum: 24b936d1031db0a21a337dd207cc2592 SHA1: c7d95798ac5f3f6d9a3abc299882e2597e82238a SHA256: 52269d5f9db5136ad4936bd2a0298eed2b8c04fb150eb4ee5dd4a3a5780b431e SHA512: 9ef9a315380c408c9e124297025e375844c4d166c2e7393f8d123079fe8a152413911169927304e5a01f73fe830eab17946c339c8473c3ee55289b38481763ae Homepage: https://cran.r-project.org/package=PlackettLuce Description: CRAN Package 'PlackettLuce' (Plackett-Luce Models for Rankings) Functions to prepare rankings data and fit the Plackett-Luce model jointly attributed to Plackett (1975) and Luce (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized to accommodate ties of any order in the ranking. Partial rankings, in which only a subset of items are ranked in each ranking, are also accommodated in the implementation. Disconnected/weakly connected networks implied by the rankings may be handled by adding pseudo-rankings with a hypothetical item. Optionally, a multivariate normal prior may be set on the log-worth parameters and ranker reliabilities may be incorporated as proposed by Raman and Joachims (2014) . Maximum a posteriori estimation is used when priors are set. Methods are provided to estimate standard errors or quasi-standard errors for inference as well as to fit Plackett-Luce trees. See the package website or vignette for further details. Package: r-cran-plainview Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 720 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-lattice, r-cran-png, r-cran-raster, r-cran-viridislite Suggests: r-cran-shiny, r-cran-sf, r-cran-sp Filename: pool/dists/noble/main/r-cran-plainview_0.2.2-1.ca2404.1_all.deb Size: 660866 MD5sum: 6382ada11fc9b9db39cd985b97d6e6ce SHA1: 1f2fee345dc6040ed22ddfc008bb8807c222f50f SHA256: 9d4952aeafda0d665b8d8a5b6e71837f1dff4b3ceed62a6812660cad6de0fb99 SHA512: aa1982d369ff888d014a2bfcb50805e713ff3ca6f6075297f9e4963b737b86a0c00956928d4e4d2ce4aea5202c69586c2116c181a743dcd3be37e1b30d352afa Homepage: https://cran.r-project.org/package=plainview Description: CRAN Package 'plainview' (Plot Raster Images Interactively on a Plain HTML Canvas) Provides methods for plotting potentially large (raster) images interactively on a plain HTML canvas. In contrast to package 'mapview' data are plotted without background map, but data can be projected to any spatial coordinate reference system. Supports plotting of classes 'RasterLayer', 'RasterStack', 'RasterBrick' (from package 'raster') as well as 'png' files located on disk. Interactivity includes zooming, panning, and mouse location information. In case of multi-layer 'RasterStacks' or 'RasterBricks', RGB image plots are created (similar to 'raster::plotRGB' - but interactive). Package: r-cran-plan Architecture: all Version: 0.4-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plan_0.4-5-1.ca2404.1_all.deb Size: 307466 MD5sum: 1360e6f7829b726999506021e0e66ac1 SHA1: 2ee5ed17c02b0898b16584cfc409f84e788f430d SHA256: 627cd33bb09192ee28287e102c61d58fa0ab09492d237d5fbde6cad10a509637 SHA512: 0bb0aacc687c20112e3b859a3fa4505617abf546cb404b728a25184e8b064e74c20873c5e72e1c0efb983aafce57e96b38771d24bed7af284977f0c06486da43 Homepage: https://cran.r-project.org/package=plan Description: CRAN Package 'plan' (Tools for Project Planning) Supports the creation of 'burndown' charts and 'gantt' diagrams. Package: r-cran-planegeometry Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-carlson, r-cran-cvxr, r-cran-fitconic, r-cran-r6, r-cran-rcdd, r-cran-sdpt3r, r-cran-stringr, r-cran-uniformly Suggests: r-cran-ellipse, r-cran-elliptic, r-cran-freegroup, r-cran-knitr, r-cran-rgl, r-cran-rmarkdown, r-cran-sets, r-cran-testthat, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-planegeometry_1.6.0-1.ca2404.1_all.deb Size: 5437152 MD5sum: 943aa0bbae72ec3bd6f873c5d9d38472 SHA1: 2a41224d358c4f900f2fbf9777e509b8347a22e0 SHA256: 19e6590eaabe3f3b16ce69b8845d0ccef6353f84b42785490f50a283a1ad132f SHA512: fdeca1652991b7cc5b5d7b36d004a1e02a707618d73c066986ca0f1aff9057c71a95a84298c5259d94dfda1ab1144cfef6824160b02092a0b9e6db62e71d65dc Homepage: https://cran.r-project.org/package=PlaneGeometry Description: CRAN Package 'PlaneGeometry' (Plane Geometry) An extensive set of plane geometry routines. Provides R6 classes representing triangles, circles, circular arcs, ellipses, elliptical arcs, lines, hyperbolae, and their plot methods. Also provides R6 classes representing transformations: rotations, reflections, homotheties, scalings, general affine transformations, inversions, Möbius transformations. Package: r-cran-planesmuestra Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-planesmuestra_0.1-1.ca2404.1_all.deb Size: 69424 MD5sum: e73d900bb69ca76c0fe657bfa5a1848f SHA1: e67541778f94cb2bdb294604fb77d11cf95b57f8 SHA256: 0f3bc0cfa3c85133ee981699efb8514884ae20f392b631535653c17394b78640 SHA512: ff7f2400db9d2862e120da69b02f6e1d2774759af50a6a1f2dc4d992e4c43950fb455ca4fbfa2eff78ba02798481ce562d2473ef866509192af477ee1dd5e42b Homepage: https://cran.r-project.org/package=Planesmuestra Description: CRAN Package 'Planesmuestra' (Functions for Calculating Dodge Romig, MIL STD 105E and MIL STD414 Acceptance Sampling Plan) Calculates an acceptance sampling plan, (sample size and acceptance number) based in MIL STD 105E, Dodge Romig and MIL STD 414 tables and procedures. The arguments for each function are related to lot size, inspection level and quality level. The specific plan operating curve (OC), is calculated by the binomial distribution. Package: r-cran-planetnicfi Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4233 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-sf, r-cran-data.table, r-cran-glue, r-cran-terra Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-planetnicfi_1.0.5-1.ca2404.1_all.deb Size: 2017048 MD5sum: fffa901ba439a6a5a664980e58c9d01b SHA1: d1ec626f4db4e55a5f1ea4553b0e1612b1bd56c0 SHA256: 1a949c8cd28d8998b7fdf8cd9d34a6d9500038a3398f2d063c0d9009effacbaa SHA512: f5746d15800ead7bfc0a359d6716eb1622574037ff7311b1fe689b5c18646a4fed0258166db1e7aaf1cac5196749c0ade35d0928abfee08c6e54e41f84b5ba3d Homepage: https://cran.r-project.org/package=PlanetNICFI Description: CRAN Package 'PlanetNICFI' (Processing of the 'Planet NICFI' Satellite Imagery) It includes functions to download and process the 'Planet NICFI' (Norway's International Climate and Forest Initiative) Satellite Imagery utilizing the Planet Mosaics API . 'GDAL' (library for raster and vector geospatial data formats) and 'aria2c' (paralleled download utility) must be installed and configured in the user's Operating System. Package: r-cran-planets Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-planets_0.1.0-1.ca2404.1_all.deb Size: 87550 MD5sum: 11d15ccd80bcf498dd391f00c652a33f SHA1: e7c60dac298244c84a617655c71913319e977ad5 SHA256: d8062f0e731ba4c514aa3653f5c56a852e54463729fd2d02006ac2d1ed03e778 SHA512: bec29ca520224deaa3501cf047704467ff6199e169a53f54d13ebcc4aaab47a29d0fe0f50f740fa04c889f1e2054b6ef8ebb0124757429d6f15296c73493489c Homepage: https://cran.r-project.org/package=planets Description: CRAN Package 'planets' (Simple and Accessible Data from all Known Planets) The goal of 'planets' is to provide of very simple and accessible data containing basic information from all known planets. Package: r-cran-planningml Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3032 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-caret, r-cran-lubridate, r-cran-matrix, r-cran-mess, r-cran-dplyr, r-cran-proc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-planningml_1.0.1-1.ca2404.1_all.deb Size: 1381694 MD5sum: 725f570515099d855d35259197408468 SHA1: d55628c319908e6e93ba9318bfb97ed5a8c9d510 SHA256: f18d01ea70f30652ac09e149d3d975d7f1076e4dc284f660b6281ac7a221ebdd SHA512: 97f0935346527aaebd166b9b0b03aefd2ee47de89f4964650e61c087ab342851997403a1366b3aa809d6b61bba5f943163377d466eefd59b0e591cdc152005a2 Homepage: https://cran.r-project.org/package=planningML Description: CRAN Package 'planningML' (A Sample Size Calculator for Machine Learning Applications inHealthcare) Advances in automated document classification has led to identifying massive numbers of clinical concepts from handwritten clinical notes. These high dimensional clinical concepts can serve as highly informative predictors in building classification algorithms for identifying patients with different clinical conditions, commonly referred to as patient phenotyping. However, from a planning perspective, it is critical to ensure that enough data is available for the phenotyping algorithm to obtain a desired classification performance. This challenge in sample size planning is further exacerbated by the high dimension of the feature space and the inherent imbalance of the response class. Currently available sample size planning methods can be categorized into: (i) model-based approaches that predict the sample size required for achieving a desired accuracy using a linear machine learning classifier and (ii) learning curve-based approaches (Figueroa et al. (2012) ) that fit an inverse power law curve to pilot data to extrapolate performance. We develop model-based approaches for imbalanced data with correlated features, deriving sample size formulas for performance metrics that are sensitive to class imbalance such as Area Under the receiver operating characteristic Curve (AUC) and Matthews Correlation Coefficient (MCC). This is done using a two-step approach where we first perform feature selection using the innovated High Criticism thresholding method (Hall and Jin (2010) ), then determine the sample size by optimizing the two performance metrics. Further, we develop software in the form of an R package named 'planningML' and an 'R' 'Shiny' app to facilitate the convenient implementation of the developed model-based approaches and learning curve approaches for imbalanced data. We apply our methods to the problem of phenotyping rare outcomes using the MIMIC-III electronic health record database. We show that our developed methods which relate training data size and performance on AUC and MCC, can predict the true or observed performance from linear ML classifiers such as LASSO and SVM at different training data sizes. Therefore, in high-dimensional classification analysis with imbalanced data and correlated features, our approach can efficiently and accurately determine the sample size needed for machine-learning based classification. Package: r-cran-planr Architecture: all Version: 0.6.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-lubridate, r-cran-magrittr, r-cran-rcpproll Suggests: r-cran-highcharter, r-cran-knitr, r-cran-reactable, r-cran-rmarkdown, r-cran-shiny, r-cran-tidyverse, r-cran-sparkline, r-cran-dt, r-cran-networkd3, r-cran-testthat Filename: pool/dists/noble/main/r-cran-planr_0.6.4-1.ca2404.1_all.deb Size: 148120 MD5sum: 92667905b06c00942f4339417be426d0 SHA1: 73b40bb5ddcb8ef312d10bec73d4169d0c16231b SHA256: 3554a3e8e6553c13502ad1206c23fe9a2abf4d70913d279e6f3dbcd987a9817c SHA512: d53fbe4e651edf83432c5c00e8b9333199af61d95b3e602aae216fe9a451efa2a0ed8cfa19cf0d2315bcd77f150e2b54605464b12437a4592f991b9120a53a80 Homepage: https://cran.r-project.org/package=planr Description: CRAN Package 'planr' (Tools for Supply Chain Management, Demand and Supply Planning) Perform flexible and quick calculations for Demand and Supply Planning, such as projected inventories and coverages, as well as replenishment plan. For any time bucket, daily, weekly or monthly, and any granularity level, product or group of products. Package: r-cran-planscorer Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-dplyr, r-cran-fs, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-webshot2 Suggests: r-cran-httptest2, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-planscorer_0.0.3-1.ca2404.1_all.deb Size: 380004 MD5sum: da4dfd707155ffba522800f270c12c49 SHA1: 6566288b6b8886e5a10f49a908f2c1a226e17674 SHA256: 240ce4b29302f2078b7adc9116facdb084dbdd956695cae67518c1a7c98385e2 SHA512: 27cc48f3e472b8504b8d444dcb1b9984a8432b6c7dc85da8f857ccd487edef9d853218d65decd2b3524b0b2a4e9bb958101faa7f8fafb5a3e3bef861bad7e0e9 Homepage: https://cran.r-project.org/package=planscorer Description: CRAN Package 'planscorer' (Score Redistricting Plans with 'PlanScore') Provides access to the 'PlanScore' Application Programming Interface () for scoring redistricting plans. Allows for upload of plans from block assignment files and shape files. For shapes in memory, such as from 'sf' or 'redist', it processes them to save and upload. Includes tools for tidying responses and saving output from the website. Package: r-cran-plantecophys Architecture: all Version: 1.4-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-nlstools, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dt Filename: pool/dists/noble/main/r-cran-plantecophys_1.4-6-1.ca2404.1_all.deb Size: 231728 MD5sum: 4a315a03345d045c8fa74c4441fc5b36 SHA1: d2412fb451cc09df9c6e95f71b20d0284c648d9f SHA256: 8a7d312be03537fb7cf9dae12375b8609112d1f8c1e20149e577647ea465d5a3 SHA512: caf6e60994727638fa34e6a092f8ea1f6c19a0f68ec4775bf129a3c7f0285710f079f631782a0c659315f7721d7e748d581b77aba13a721779f7e8656f8f9a7a Homepage: https://cran.r-project.org/package=plantecophys Description: CRAN Package 'plantecophys' (Modelling and Analysis of Leaf Gas Exchange Data) Coupled leaf gas exchange model, A-Ci curve simulation and fitting, Ball-Berry stomatal conductance models, leaf energy balance using Penman-Monteith, Cowan-Farquhar optimization, humidity unit conversions. See Duursma (2015) . Package: r-cran-plantecowrap Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1470 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-minpack.lm, r-cran-plantecophys, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plantecowrap_1.0.4-1.ca2404.1_all.deb Size: 336276 MD5sum: c966f6445b0b3290b6ec510b6b38fad9 SHA1: d0d7671314313e84c82019601ff211ab741e2474 SHA256: 6bacb1b997f5e6ab0c78787f190ea1913b4ab16438c79e75100e0b4d623790eb SHA512: f136b193f7f51f233efb6ecd622814fb57ac4482b0cb8cf8065865b8dc2cf06dc6bee363ae5a63003df9d5584eae773b4449e26b264d042d87e49f6c94f59fd6 Homepage: https://cran.r-project.org/package=plantecowrap Description: CRAN Package 'plantecowrap' (Enhancing Capabilities of 'plantecophys') Provides wrapping functions to add to capabilities to 'plantecophys' (Duursma, 2015, ). Key added capabilities include temperature responses of mesophyll conductance (gm, gmeso), apparent Michaelis-Menten constant for rubisco carboxylation in air (Km, Kcair),and photorespiratory CO2 compensation point (GammaStar) for fitting A-Ci or A-Cc curves for C3 plants (for temperature responses of gm, Km, & GammaStar, see Bernacchi et al., 2002, ; for theory on fitting A-Ci or A-Cc curves, see Farquhar et al., 1980; , von Caemmerer, 2000, ISBN:064306379X; Ethier & Livingston, 2004 ; and Gu et al., 2010, ). Includes the ability to fit the Arrhenius and modified Arrhenius temperature response functions (see Medlyn et al., 2002, ) for maximum rubisco carboxylation rates (Vcmax) and maximum electron transport rates (Jmax) (see Farquhar et al., 1980; ). Package: r-cran-plantphysior Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-plantphysior_1.0.0-1.ca2404.1_all.deb Size: 90688 MD5sum: 08ca0f78c401b001f81a622c60b55f99 SHA1: 754ec6358ba8d7a7b635a2369cb5faeba20f12fd SHA256: 48c1650627e70141617cbcb303e9c94c18956af65c8d06bfc2da7dbc0038413b SHA512: ced28c2b790dd74b2654a80e0d78d5927d6200e796e1fd82ba6f94b7170a24c37c99c46c2f8e610a2fe6e69bce4c6c301400e8fc018621e2316f112204eaa30a Homepage: https://cran.r-project.org/package=plantphysioR Description: CRAN Package 'plantphysioR' (Fundamental Formulas for Plant Physiology) Functions tailored for scientific and student communities involved in plant science research. Functionalities encompass estimation chlorophyll content according to Arnon (1949) , determination water potential of Polyethylene glycol(PEG)6000 as in Michel and Kaufmann (1973) and functions related to estimation of yield related indices like Abiotic tolerance index as given by Moosavi et al.(2008), Geometric mean productivity (GMP) by Fernandez (1992) , Golden Mean by Moradi et al.(2012), HAM by Schneider et al.(1997),MPI and TOL by Hossain etal., (1990), RDI by Fischer et al. (1979),SSI by Fisher et al.(1978), STI by Fernandez (1993),YSI by Bouslama & Schapaugh (1984), Yield index by Gavuzzi et al.(1997). Package: r-cran-planttracker Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-sf, r-cran-units Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-minidown, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-planttracker_1.2.1-1.ca2404.1_all.deb Size: 2884722 MD5sum: 4a30e191e3921d452a8f0aaeb01c05c9 SHA1: d97cc902617f812b1dbb3288af8e59169899b0d8 SHA256: f1000f911d1eac3fc5666881a467465d970a033902c2f61bf6899ae2a3c929b6 SHA512: 9ce13d062ffbdfcffbdebaabee61eda61b7c2f69c296e1522dc0dce6b1d23b263800181ac4de83519471ed6037d6f680c6995bde9b981523af6dff3b6a18be5b Homepage: https://cran.r-project.org/package=plantTracker Description: CRAN Package 'plantTracker' (Extract Demographic and Competition Data from Fine-Scale Maps) Extracts growth, survival, and local neighborhood density information from repeated, fine-scale maps of organism occurrence. Further information about this package can be found in our journal article, "plantTracker: An R package to translate maps of plant occurrence into demographic data" published in 2022 in Methods in Ecology and Evolution (Stears, et al., 2022) . Package: r-cran-plaqr Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg Filename: pool/dists/noble/main/r-cran-plaqr_2.0-1.ca2404.1_all.deb Size: 75988 MD5sum: f3c35ce9a08f4d4e48e31a25430b4d0a SHA1: 3f2d6e2734269c85da1e4e8e57a05ae736fbc48c SHA256: 0eaec77066dbb83f5dd8c477d181563d3047ec5a8482b005ce0effa1fa4c457d SHA512: be5799184b007987650387faaab8b06c352921b9b88ea69444e645af70daeff753285454e24147f777b4ffa1b70f08ad81f68d455cea0dcb5264a5dcbf82b776 Homepage: https://cran.r-project.org/package=plaqr Description: CRAN Package 'plaqr' (Partially Linear Additive Quantile Regression) Estimation, prediction, thresholding, transformation, and plotting for partially linear additive quantile regression. Intuitive functions for fitting and plotting partially linear additive quantile regression models. Uses and works with functions from the 'quantreg' package. Package: r-cran-plasma Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3363 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-pls, r-cran-plsrcox, r-cran-polychrome, r-cran-viridislite, r-cran-beanplot, r-cran-oompabase Suggests: r-cran-r.rsp, r-cran-tidyr, r-cran-classdiscovery Filename: pool/dists/noble/main/r-cran-plasma_1.1.5-1.ca2404.1_all.deb Size: 1848948 MD5sum: 9dc62733239fe3b3f8ea6f7a466fbdd5 SHA1: 9c81b5b078870156e76dc6be5dffc12ba03d7ee5 SHA256: 42406c389fe6394494d324c5b61b3fcfe07709b347d985a215e08f4387dbb4ea SHA512: 6730ef0959916633c97aa7aa9b54dc4086cc66bd67f6ae4d6828ed2776867ac7ed95bca80c90f2a1615cb14b155849a9c40a249ac2fe57144ed511095189ebed Homepage: https://cran.r-project.org/package=plasma Description: CRAN Package 'plasma' (Partial LeAst Squares for Multiomic Analysis) Contains tools for supervised analyses of incomplete, overlapping multiomics datasets. Applies partial least squares in multiple steps to find models that predict survival outcomes. See Yamaguchi et al. (2023) . Package: r-cran-plasmamutationdetector2 Architecture: all Version: 1.1.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4079 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-bioc-genomicranges, r-bioc-variantannotation, r-bioc-s4vectors, r-bioc-rsamtools, r-bioc-rtracklayer, r-cran-robustbase, r-bioc-summarizedexperiment Filename: pool/dists/noble/main/r-cran-plasmamutationdetector2_1.1.11-1.ca2404.1_all.deb Size: 3307808 MD5sum: 1abb82099a500cc56dfc72ba83be4988 SHA1: c99f228c948a0775412b424a7c2281f3f97945d9 SHA256: e490808d1fc2ae8af99eda49658c1bdaf67aa9fd37498c88e32defe394d0eb6f SHA512: e99b9ab4699adc2bd62dda107872223d27d8e766474f32fa3b2891a3a9833b7c00294f187c966d00c0561755b09d30d683e3a1a9aa48815a0d40e8da0073121b Homepage: https://cran.r-project.org/package=PlasmaMutationDetector2 Description: CRAN Package 'PlasmaMutationDetector2' (Tumor Mutation Detection in Plasma using Barcoding) Aims at detecting single nucleotide variation (SNV) and insertion/deletion (INDEL) in circulating tumor DNA (ctDNA), used as a surrogate marker for tumor, at each base position of an Next Generation Sequencing (NGS) analysis using barcoding. Mutations are assessed by comparing the minor-allele frequency at each position to the measured PER in control samples. This package has been used for Kjersti Tjensvoll, Morten Lapin, Bjørnar Gilje, Herish Garresori, Satu Oltedal, Rakel Brendsdal Forthun, Anders Molven, Yves Rozenholc and Oddmund N\o{o}rdgaard (2022) . Package: r-cran-plasmamutationdetector Architecture: all Version: 1.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4622 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-bioc-genomicranges, r-bioc-variantannotation, r-bioc-s4vectors, r-bioc-rsamtools, r-bioc-rtracklayer, r-cran-robustbase, r-bioc-summarizedexperiment Filename: pool/dists/noble/main/r-cran-plasmamutationdetector_1.7.2-1.ca2404.1_all.deb Size: 3865768 MD5sum: cc1a3a5b40758dc2d68c9036f28b0cde SHA1: a57c7a4897a74f3280a0e9f43ca53e8a6ac9ba20 SHA256: 6206a283a14b81215877d9e92f16c87c24564f50b0c00335f1a81f023c2df974 SHA512: cbcbf5c6463b96bbb54fe3aed1d19736e92e4b4c89c08d156df119ba3eda5602e53fa96466226c68f6626847c8de24e7b5e305a2eb313f6d96228105a8d5d477 Homepage: https://cran.r-project.org/package=PlasmaMutationDetector Description: CRAN Package 'PlasmaMutationDetector' (Tumor Mutation Detection in Plasma) Aims at detecting single nucleotide variation (SNV) and insertion/deletion (INDEL) in circulating tumor DNA (ctDNA), used as a surrogate marker for tumor, at each base position of an Next Generation Sequencing (NGS) analysis. Mutations are assessed by comparing the minor-allele frequency at each position to the measured PER in control samples. Package: r-cran-plasmidprofiler Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-gdata, r-cran-ggdendro, r-cran-ggplot2, r-cran-gridextra, r-cran-gtable, r-cran-htmlwidgets, r-cran-magrittr, r-cran-plotly, r-cran-plyr, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-stringr Suggests: r-cran-lintr Filename: pool/dists/noble/main/r-cran-plasmidprofiler_0.1.6-1.ca2404.1_all.deb Size: 98768 MD5sum: b965543764004acb98a1ee250866806b SHA1: 934b30cbfc20072ed100f2b1d5e3e3ce7c4a1d65 SHA256: c75051042716e7906dd84c44b958bccb211ebe25231291a3ca4300f70da60e5a SHA512: 34ea351eb1d7d2e46d2f663cf0db9470c219fa13e286070ec888fb3da65646f6ebfcc2ed3c80f11adea6724c3be2d9ee57a180a8d4b81444bf52f3052d6d097d Homepage: https://cran.r-project.org/package=Plasmidprofiler Description: CRAN Package 'Plasmidprofiler' (Visualization of Plasmid Profile Results) Contains functions developed to combine the results of querying a plasmid database using short-read sequence typing with the results of a blast analysis against the query results. Package: r-cran-plasso Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-iterators Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-xfun Filename: pool/dists/noble/main/r-cran-plasso_0.1.3-1.ca2404.1_all.deb Size: 203058 MD5sum: f6a24cf77e110c5e3aad3f7c2853a782 SHA1: 29d720b5fc5581472cc7aaef382c89f49f5b73ec SHA256: b762b0dd7f8eb1ecc271f5fba4d0718e45ed1222d5bf0528c4fa95443f70227f SHA512: 9062d1e635f7f4feace89d567df6303cd3add19ecedf69fed44fad6fce4979d281d03b3c007b57527522ac757277e5e3933fd14b35fac40490e5cd4c01072813 Homepage: https://cran.r-project.org/package=plasso Description: CRAN Package 'plasso' (Cross-Validated Post-Lasso) Provides tools for cross-validated Lasso and Post-Lasso estimation. Built on top of the 'glmnet' package by Friedman, Hastie and Tibshirani (2010) , the main function plasso() extends the standard 'glmnet' output with coefficient paths for Post-Lasso models, while cv.plasso() performs cross-validation for both Lasso and Post-Lasso models and different ways to select the penalty parameter lambda as discussed in Knaus (2021) . 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Easily read in plate-shaped data and convert it to tidy format, combine plate-shaped data with tidy data, and view tidy data in plate shape. Package: r-cran-platetools Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-viridis Filename: pool/dists/noble/main/r-cran-platetools_0.1.7-1.ca2404.1_all.deb Size: 147816 MD5sum: 0586813b7c19105578228968f5559932 SHA1: 1056ac9c25319c522daffcf38a3f2ebca163c222 SHA256: ff542db17de16c19bbe621719e8810003342dd4db0c37ce8a61ffa9c82a71db3 SHA512: e8df940c01bc13e4a12f477a1c3bb859eda4f16447301556f3a2c28e3183077c54495ca91775104c9bd1f77a27fa5d9c34bf18f830f3af21a836dd22d80a426f Homepage: https://cran.r-project.org/package=platetools Description: CRAN Package 'platetools' (Tools and Plots for Multi-Well Plates) Collection of functions for working with multi-well microtitre plates, mainly 96, 384 and 1536 well plates. Package: r-cran-platevision Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readxl, r-cran-ggplot2, r-cran-stringr, r-cran-plotly Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-platevision_0.1.0-1.ca2404.1_all.deb Size: 45624 MD5sum: bbf04ab7e3979cfbd06b3f6e7902a589 SHA1: e0659456c0553ba939e36b849bc91f6c23d0e22f SHA256: da7fc976791732cb8ca623a4ac280eeb7d726b3b845555191a9e33d2d44a02db SHA512: 592ac3cab423c04dc3f4b528d9fc46ad9387581bc56606d77e78ebdd604661b22851b0cc96204f8615403c267950fe506ec09ba4e078fd3d1527d9bcbc4c65a4 Homepage: https://cran.r-project.org/package=PlateVision Description: CRAN Package 'PlateVision' (Automated qPCR Analysis and Visual Quality Control) Directly pipes raw quantitative PCR (qPCR) machine outputs into downstream analyses using the comparative Ct (Delta-Delta Ct) method described by Livak and Schmittgen (2001) . Streamlines the workflow from 'Excel' export to publication-ready plots. Integrates unique visual quality control by reconstructing 96-well plate heatmaps, allowing users to instantly detect pipetting errors, edge effects, and outliers. Key features include automated error propagation, laboratory master mix calculations, and generation of bar charts and volcano plots. Package: r-cran-platformdesign Architecture: all Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1241 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-platformdesign_2.1.4-1.ca2404.1_all.deb Size: 560956 MD5sum: 52962d1d79bd59da7d9daba4bec5203a SHA1: bb6c922db93ac8c16ef2df76d60cba618f31fa5c SHA256: 9b4eead8dce81388a0b62c6b0d7de386958da9fab83196b5a566eb268c6ebc23 SHA512: 3ecb9c13a92f5762503d7056a1025df3deec72480dd30a4088f74af6fff02510e39a71599d2cccaf40ff842222f52552b71c1a51a1fd621a90457a41bf49b22f Homepage: https://cran.r-project.org/package=PlatformDesign Description: CRAN Package 'PlatformDesign' (Optimal Two-Period Multiarm Platform Design with NewExperimental Arms Added During the Trial) Design parameters of the optimal two-period multiarm platform design (controlling for either family-wise error rate or pair-wise error rate) can be calculated using this package, allowing pre-planned deferred arms to be added during the trial. More details about the design method can be found in the paper: Pan, H., Yuan, X. and Ye, J. (2022) "An optimal two-period multiarm platform design with new experimental arms added during the trial". Manuscript submitted for publication. For additional references: Dunnett, C. W. (1955) . Package: r-cran-platowork Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-platowork_0.0.1-1.ca2404.1_all.deb Size: 65126 MD5sum: 6840e1a059d0667f4af2cc437f58fe8b SHA1: 368c7933053b60ce5aa66f97171b787d65b4b9dd SHA256: 2dca450891989e03ac75f21e1f9ac43a32ba2350f55b64ad8cfdeac475abeeae SHA512: 8a5da2fdfd2e04e49b1f409b4045e29b81a3d55e75932ed13c4882bd024d845c0e67d921c9a9f08cafe5b6a59f9a4eb84f3a8f68f45f49fdb87056158c82157f Homepage: https://cran.r-project.org/package=platowork Description: CRAN Package 'platowork' (Data from a Test of the PlatoWork tDCS Headset) Data and analysis from an experiment with improving touch typing speed, using the tDCS PlatoWork headset produced by PlatoScience. Package: r-cran-plattice Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-plattice_1.1-1.ca2404.1_all.deb Size: 14424 MD5sum: e6438bca7c565bd667421bdb1b86f21a SHA1: 75dd3c7106e5a5b89da662daaca073a269f194de SHA256: f255a1d9cf700b6713c0ff65a6d2292f41a0fa4dbec1114c489e226987ed23fc SHA512: 3c278892ec701e4005fabfc7ddd184158ddec3868241e792a18afb49b0bc649a21741d567cbe8deecad799c70fb6fdd9561fc78baf17e5aae50f3a1841bf3baa Homepage: https://cran.r-project.org/package=plattice Description: CRAN Package 'plattice' (Lattice Plot for Panel Data) It creates a lattice plot to visualize panel or longitudinal data. The observed values are plotted as dots and the fitted values as lines, both against time. The plot is customizable and easy to edit, even if you do not know how to construct a lattice plot from scratch. Package: r-cran-platypus Architecture: all Version: 3.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1798 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-cran-cowplot, r-cran-dplyr, r-cran-ggalluvial, r-cran-ggplot2, r-cran-ggseqlogo, r-bioc-ggtree, r-cran-jsonlite, r-cran-knitr, r-cran-magrittr, r-cran-matrix, r-cran-plyr, r-cran-reshape2, r-cran-seqinr, r-cran-seurat, r-cran-seuratobject, r-cran-stringdist, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-useful Suggests: r-bioc-annotationdbi, r-cran-ape, r-bioc-biocgenerics, r-bioc-biomart, r-cran-circlize, r-cran-cluster, r-cran-doparallel, r-bioc-fgsea, r-cran-ggrepel, r-cran-ggridges, r-cran-gridextra, r-cran-harmony, r-cran-igraph, r-cran-inext, r-bioc-limma, r-cran-kmer, r-cran-msigdbr, r-cran-phangorn, r-cran-pheatmap, r-cran-phytools, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rstudioapi, r-cran-rtsne, r-cran-scales, r-cran-sf, r-bioc-singlecellexperiment, r-bioc-slingshot, r-cran-tidytree, r-cran-tidyselect, r-cran-tidyverse, r-cran-umap, r-cran-vegan, r-cran-viridis, r-cran-testthat Filename: pool/dists/noble/main/r-cran-platypus_3.6.0-1.ca2404.1_all.deb Size: 1499398 MD5sum: 7f3e678709f8d8edc1d1584a6f023753 SHA1: 790046b3c4ca2f027a87342ccaaecafdf5adb174 SHA256: 7958302e9eadb8b9c341bc38df076b4f8c2ee99ccee466d7c636a5e4dd43c573 SHA512: 9b1474fd36e3b2f635297c0794e55bd9e9328fe69ccd3aae90bf73801f0efb56b502c1c46e8dff63b092c1a9fd961533cb624bc61c2baf4b94b424a6684515ac Homepage: https://cran.r-project.org/package=Platypus Description: CRAN Package 'Platypus' (Single-Cell Immune Repertoire and Gene Expression Analysis) We present 'Platypus', an open-source software platform providing a user-friendly interface to investigate B-cell receptor and T-cell receptor repertoires from scSeq experiments. 'Platypus' provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression and gene ontology. This R version of 'Platypus' is part of the 'ePlatypus' ecosystem for computational analysis of immunogenomics data: Yermanos et al. (2021) , Cotet et al. (2023) . Package: r-cran-plausibounds Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-matrix, r-cran-mass, r-cran-magrittr, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-doparallel, r-cran-foreach, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plausibounds_1.0.1-1.ca2404.1_all.deb Size: 135212 MD5sum: 1e18c37d2a24c5634befa40e44b89265 SHA1: dc55061a5dc43f30314095a3772080dd38d91214 SHA256: 07a6ce7845abae920797cede40f71fed754608ef48ad3414a548719d5e76272f SHA512: e5fe7f9ed18429d07be1264737c3aa0dbddfb93c90ed3d102adf89a4a663431d8792bc2d6374e1c200bc6eda734f08e8025fcad82d9326393d7ae067e3eb5ae0 Homepage: https://cran.r-project.org/package=plausibounds Description: CRAN Package 'plausibounds' (Plausible Bounds for Treatment Path Estimates) Enhances dynamic effect plots as suggested in Freyaldenhoven and Hansen (2026) . Data-driven smoothing delivers a smooth estimated path with potentially improved point estimation properties and confidence regions covering a surrogate that can be substantially tighter than conventional pointwise or uniform bands. Package: r-cran-plavaan Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plavaan_0.0.1-1.ca2404.1_all.deb Size: 61046 MD5sum: 79548b19f2e0bf739003a0cc120d567e SHA1: c9b63945f6caf8c5de7b64cfd88bbd6063971a9e SHA256: dc3fdd01d65500c11a54a26377914cef7b26b498f8d35bcff520a6580e7e9d57 SHA512: 2352c18febd76458ca345cd01a4914612c6c26e085e254e6bca2c1776e50c9f1d8f9ab42a7135fdb47612a9c8bf94236ed00e67624ab2addca67b3ed5803b51b Homepage: https://cran.r-project.org/package=plavaan Description: CRAN Package 'plavaan' (Penalized Estimation for Latent Variable Models with 'lavaan') Extends the popular 'lavaan' package by adding penalized estimation capabilities. It supports penalty on individual parameters as well as the difference between parameters. Package: r-cran-play Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-worldfootballr Filename: pool/dists/noble/main/r-cran-play_0.1.3-1.ca2404.1_all.deb Size: 23682 MD5sum: 64025318c366cf50ceb03ea595a5dbec SHA1: 6ffe0ca579896a7e0042cc7c9933202bf3102be7 SHA256: 1e42a83838e36b8210b4c24203be0dfa9d20e610ee9a294934485100d5b0b5f9 SHA512: d1187016b3fc5de0ef78d7050b6588cf7dae6b9e7f5d13a07b4b249cbd457896cfe3b2bc9fdf432112b9a20007fbfbf174c7b7d49c6409bfd489c2208453bed3 Homepage: https://cran.r-project.org/package=play Description: CRAN Package 'play' (Visualize Sports Data) Provides functions to visualise sports data. Converts data into a format suitable for plotting charts. Helps to ease the process of working with messy sports data to a more user friendly format. Football data is accessed through 'worldfootballR' '' which gets data from 'FBref' , 'Transfermarkt' , 'Understat' , and 'fotmob' . 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Includes coin flip, hangman, jumble, magic 8 ball, poker, rock paper scissors, shut the box, spelling bee, and 2048. Package: r-cran-playerchart Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggtext, r-cran-magrittr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-playerchart_1.0.0-1.ca2404.1_all.deb Size: 18536 MD5sum: 57c170dafc0f7c12446b8cb71ccb3daa SHA1: 80a4545eb692e5ab5a700803f385f78758fdab78 SHA256: 1501b55967838dded385e43769620b7ae37fbd7b630f588633e2909998130874 SHA512: 187dd079a3f4c52586cec0185743a53c7789613fb8793cdd7723370af5d579fffb5dc501f9123e21400baa5cce4969ee9945ffa5b9db091ee26f1a2b927aaa09 Homepage: https://cran.r-project.org/package=PlayerChart Description: CRAN Package 'PlayerChart' (Generate Pizza Chart: Player Stats 0-100) Create an interactive pizza chart visualizing a specific player's statistics across various attributes in a sports dataset. The chart is constructed based on input parameters: 'data', a dataframe containing player data for any sports; 'player_stats_col', a vector specifying the names of the columns from the dataframe that will be used to create slices in the pizza chart, with statistics ranging between 0 and 100; 'name_col', specifying the name of the column in the dataframe that contains the player names; and 'player_name', representing the specific player whose statistics will be visualized in the chart, serving as the chart title. Package: r-cran-pldamixture Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-pldamixture_0.1.1-1.ca2404.1_all.deb Size: 125396 MD5sum: a110b3407a5b17eee64aba42ed343d73 SHA1: 15e60657a0043e499c9474e9b899e8937435b419 SHA256: 14bc2c068c24d69aa9db5354ac646d2fa262e855c13d9cf9dad388525f842333 SHA512: 7a8a4ebb601a6c77b51108b51743641b9ab080e2d98d69523a17599e21206669aca116f255782abb114636a5c3abe9a024ff13cbb4206630fe21c2bac9d47ff1 Homepage: https://cran.r-project.org/package=pldamixture Description: CRAN Package 'pldamixture' (Post-Linkage Data Analysis Based on Mixture Modelling) Perform inference in the secondary analysis setting with linked data potentially containing mismatch errors. Only the linked data file may be accessible and information about the record linkage process may be limited or unavailable. Implements the 'General Framework for Regression with Mismatched Data' developed by Slawski et al. (2023) . The framework uses a mixture model for pairs of linked records whose two components reflect distributions conditional on match status, i.e., correct match or mismatch. Inference is based on composite likelihood and the Expectation-Maximization (EM) algorithm. The package currently supports Cox Proportional Hazards Regression (right-censored data only) and Generalized Linear Regression Models (Gaussian, Gamma, Poisson, and Logistic (binary models only)). Information about the underlying record linkage process can be incorporated into the method if available (e.g., assumed overall mismatch rate, safe matches, predictors of match status, or predicted probabilities of correct matches). Package: r-cran-plde Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-plde_0.1.2-1.ca2404.1_all.deb Size: 44194 MD5sum: a901258b390b83662b14b3cd56816e3b SHA1: 0a0d5dd33f61dd52910c19c22ab3a43c34d3a6a4 SHA256: ef0ec94eee21e972f2bd4b0f830008e2a80181c3823a227eabd072ef9b1373af SHA512: c47917f4ee297033f390894869d6afdb90cc29bee1a7aa9804271a46852dec2f7f6568ffdc9e43e72dbd53f4309ab7591958ef60dde32ad418c5598071c2deab Homepage: https://cran.r-project.org/package=plde Description: CRAN Package 'plde' (Penalized Log-Density Estimation Using Legendre Polynomials) We present a penalized log-density estimation method using Legendre polynomials with lasso penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC). Package: r-cran-pleio Architecture: all Version: 1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rms, r-cran-matrix Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pleio_1.9-1.ca2404.1_all.deb Size: 434288 MD5sum: 41884a8d81391cd472262114013e6272 SHA1: 6a489da03a8ba4f2f260c9c282aaf46586dd2710 SHA256: 95364768ba82ed73c27a01a959feefe495760abe8e05aa1328184cca554836be SHA512: a5c0f1bf3b811ee693914ad85caf43698126aefcc20233990bae8922cf5b0d891083f7061c90d71ff1174433a9b9a4a2b321a3cf6b8d522c1b2d78c4758ceef7 Homepage: https://cran.r-project.org/package=pleio Description: CRAN Package 'pleio' (Pleiotropy Test for Multiple Traits on a Genetic Marker) Perform tests for pleiotropy of multiple traits of various variable types on genotypes for a genetic marker. Package: r-cran-pleioh2g Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-rlang, r-cran-mvtnorm, r-cran-fs, r-cran-arrow, r-cran-checkmate, r-cran-cli, r-cran-gdata, r-cran-glue, r-cran-purrr, r-cran-tibble, r-cran-vroom Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pleioh2g_0.1.2-1.ca2404.1_all.deb Size: 3444468 MD5sum: 8a31aac51fc0f7aac001705ae6779080 SHA1: 27e3b81e73b66d49d6952d072df810b4fa037dd9 SHA256: b85f2227cc13c8de9bc1599eeebc510009a2882de07a6ee743e16282d14f5572 SHA512: 3b536601c4a6e199f844a88860309b7bb0559244410883401e63b926dade8107b308a019ebdc4e2ddf5c2ed9f58de7669ae8bd07f4077b0c666d1fa0e44588fb Homepage: https://cran.r-project.org/package=pleioh2g Description: CRAN Package 'pleioh2g' (Estimation of Pleiotropic Heritability from Genome-WideAssociation Studies (GWAS) Summary Statistics) Provides tools to compute unbiased pleiotropic heritability estimates of complex diseases from genome-wide association studies (GWAS) summary statistics. We estimate pleiotropic heritability from GWAS summary statistics by estimating the proportion of variance explained from an estimated genetic correlation matrix (Bulik-Sullivan et al. 2015 ) and employing a Monte-Carlo bias correction procedure to account for sampling noise in genetic correlation estimates. Package: r-cran-plelma Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlogit, r-cran-dfidx Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plelma_0.2.2-1.ca2404.1_all.deb Size: 226686 MD5sum: e6e42a2064efe04035c287b4524e3fde SHA1: 031c5c21aaafc037c9ee5dae5f43db186ae5dfb3 SHA256: 07c989dd72eefdd9c24e152365a9323f237ebcd93cadfd38dfd24c382e3122ac SHA512: 70b394dfd4326cbe67d3ce0f57143df8c8c47b6693023b0bcfb8a31808088b2724bb6b78301e989e232c1b243368245dd11eecbe43c078f0ca5037da192ccf53 Homepage: https://cran.r-project.org/package=pleLMA Description: CRAN Package 'pleLMA' (Pseudo-Likelihood Estimation of Log-Multiplicative AssociationModels) Log-multiplicative association models (LMA) are models for cross-classifications of categorical variables where interactions are represented by products of category scale values and an association parameter. Maximum likelihood estimation (MLE) fails for moderate to large numbers of categorical variables. The 'pleLMA' package overcomes this limitation of MLE by using pseudo-likelihood estimation to fit the models to small or large cross-classifications dichotomous or multi-category variables. Originally proposed by Besag (1974, ), pseudo-likelihood estimation takes large complex models and breaks it down into smaller ones. Rather than maximizing the likelihood of the joint distribution of all the variables, a pseudo-likelihood function, which is the product likelihoods from conditional distributions, is maximized. LMA models can be derived from a number of different frameworks including (but not limited to) graphical models and uni-dimensional and multi-dimensional item response theory models. More details about the models and estimation can be found in the vignette. Package: r-cran-plexi Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-igraph, r-cran-keras, r-cran-ggraph, r-cran-ggplot2, r-cran-aggregation Filename: pool/dists/noble/main/r-cran-plexi_1.0.0-1.ca2404.1_all.deb Size: 90854 MD5sum: 94778a4213c986273db7fd8506634a75 SHA1: 8619e26373925da9b1040d77a2d3240cd9a68b9d SHA256: 64f303d59ac51bceb85edeec71e40d626e5de6f8bb9347d731ecd9c87e191870 SHA512: 95f84587a0eb32ff4c877f8f41f7ee8f5e443ef9da3b5a1884167916d52817d6c0a990ef5e8d57fce3e513e935da7ea55c1997a5fa08e0a678e43bf0817fe531 Homepage: https://cran.r-project.org/package=PLEXI Description: CRAN Package 'PLEXI' (Multiplex Network Analysis) Interactions between different biological entities are crucial for the function of biological systems. In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted. The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. However, such variations often occur locally and do not concern the whole network. To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation. Yousefi et al. (2023) . Package: r-cran-plfma Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gwidgets2, r-cran-gwidgets2tcltk, r-cran-tkrplot, r-bioc-limma, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-plfma_2.0-1.ca2404.1_all.deb Size: 133404 MD5sum: fc0e71e1047f59313c82ac750bf706f5 SHA1: d715b64ab1cd5139264cb86685699a6d8207638d SHA256: 13dbcffc91468884c6c8862b57449eb6688845939a8f08975a1ce21672ed44a2 SHA512: 57a7240923c9b3517b6ff4eec2a621c9bed27e497049847f218ec094d6c62e7ec2e3c241be1002d2a114218d0046bc3b47094e292f7b03c363eef5e839fde563 Homepage: https://cran.r-project.org/package=plfMA Description: CRAN Package 'plfMA' (A GUI to View, Design and Export Various Graphs of Data) Provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image. 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Package: r-cran-plindleyroc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tibble, r-cran-vctrs, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plindleyroc_0.1.2-1.ca2404.1_all.deb Size: 178924 MD5sum: d25b4abd9e03f3856d41bd85acf22d53 SHA1: a780a24b74796805ea9b03ac814f93b2a19a46b8 SHA256: 1f63243cc34a2fea3a6e96b1af1f230305c67d9eceb5b8ca2dad405d414b2a06 SHA512: 4f5c788bf708593fe1614f22e2d5e2b22066b32b31abea1205b9ab8733def685c538d0de0d8b5eb3e984496a9b185163c52af341cd6d3d8ef769524975f95596 Homepage: https://cran.r-project.org/package=PLindleyROC Description: CRAN Package 'PLindleyROC' (Receiver Operating Characteristic Based on Power LindleyDistribution) Receiver Operating Characteristic (ROC) analysis is performed assuming samples are from the Power Lindley distribution. Specificity, sensitivity, area under the curve and ROC curve are provided. Package: r-cran-plink Architecture: all Version: 1.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1762 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-mass, r-cran-statmod Filename: pool/dists/noble/main/r-cran-plink_1.5-1-1.ca2404.1_all.deb Size: 1205134 MD5sum: f206d1835fb2792ab1739ca1a2809d26 SHA1: d33b1b2564ffb4253d6af49abdb10e22e3b93c58 SHA256: e72714df88fd7a0dbdb86594b84ade4d820d18aeb7457c2f249513a7c29dc665 SHA512: 8ffecddc4cf013147d5f16cf1715c9303d88e6f079393fe7c32b9ce31b3776dce0309e8bd52b0242178c7daf185cb3899a3154dde8430d7f0ead4fa6e0af7cb0 Homepage: https://cran.r-project.org/package=plink Description: CRAN Package 'plink' (IRT Separate Calibration Linking Methods) Item response theory based methods are used to compute linking constants and conduct chain linking of unidimensional or multidimensional tests for multiple groups under a common item design. The unidimensional methods include the Mean/Mean, Mean/Sigma, Haebara, and Stocking-Lord methods for dichotomous (1PL, 2PL and 3PL) and/or polytomous (graded response, partial credit/generalized partial credit, nominal, and multiple-choice model) items. The multidimensional methods include the least squares method and extensions of the Haebara and Stocking-Lord method using single or multiple dilation parameters for multidimensional extensions of all the unidimensional dichotomous and polytomous item response models. The package also includes functions for importing item and/or ability parameters from common IRT software, conducting IRT true score and observed score equating, and plotting item response curves/surfaces, vector plots, information plots, and comparison plots for examining parameter drift. Package: r-cran-plinkfile Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2053 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-plinkfile_0.2.1-1.ca2404.1_all.deb Size: 607830 MD5sum: f6b55b7c3dc7b6e9d112f537cfd07572 SHA1: 30a49d266933cc07b0c3a35f98a6b56535568bdc SHA256: f07729848bcd98c876577bbb40547dee52c95cdd06aceb54336a234ee3862528 SHA512: bcef3a9c99c111b14b84abc7d09becd2d784a6741deea7ad225bda53e8bf011e51eeed2f3eb65c858ce05d2946fbcf492339148903acf21e71cf29fab27e7a6f Homepage: https://cran.r-project.org/package=plinkFile Description: CRAN Package 'plinkFile' ('PLINK' (and 'GCTA') File Helpers) Reads/write binary genotype file compatible with 'PLINK' into/from a R matrix; traverse genotype data one windows of variants at a time, like apply() or a for loop; reads/writes genotype relatedness/kinship matrices created by 'PLINK' or 'GCTA' into/from a R square matrix. It is best used for bringing data produced by 'PLINK' and 'GCTA' into R workflow. Package: r-cran-plinkqc Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4496 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optparse, r-cran-data.table, r-cran-r.utils, r-cran-ggplot2, r-cran-ggrepel, r-cran-cowplot, r-cran-upsetr, r-cran-dplyr, r-cran-igraph, r-cran-sys, r-cran-randomforest, r-cran-tidyr Suggests: r-cran-testthat, r-cran-mockery, r-cran-formatr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-plinkqc_1.1.0-1.ca2404.1_all.deb Size: 2321566 MD5sum: c2c2111192192e252ac007c53bfb88db SHA1: 752159a2dc9dacea35a03b5ccf440b1989d3a8c3 SHA256: 66095b61cd8a84fe61a01127bb3d9427cb8f4c2dcddfeda29bdf9910c6ba904b SHA512: ba3b5b2d85bc54feee5e36b3338b6083b252b4bcbaf01070faea12defda27382479707ee88df10a50794713fc06176e63c0e995d29b91df13d8acd98efa19504 Homepage: https://cran.r-project.org/package=plinkQC Description: CRAN Package 'plinkQC' (Genotype Quality Control with 'PLINK') Genotyping arrays enable the direct measurement of an individuals genotype at thousands of markers. 'plinkQC' facilitates genotype quality control for genetic association studies as described by Anderson and colleagues (2010) . It makes 'PLINK' basic statistics (e.g. missing genotyping rates per individual, allele frequencies per genetic marker) and relationship functions accessible from 'R' and generates a per-individual and per-marker quality control report. Individuals and markers that fail the quality control can subsequently be removed to generate a new, clean dataset. Removal of individuals based on relationship status is optimised to retain as many individuals as possible in the study. Additionally, there is a trained classifier to predict genomic ancestry of human samples. Package: r-cran-plis Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-plis_1.2-1.ca2404.1_all.deb Size: 53580 MD5sum: c51d93519c8c48d2326379910ad3cfa3 SHA1: b8485229836e4e8b23dd7de1678bfaf98b876514 SHA256: 3c2b64dfd9b548ed62e1a4a1a86a3aa12ba4543206669b04c66763296d884148 SHA512: fc8ab623b70ba23618714ca0e4132151db5d817672a98f538cde8138d565ba610745d64c8c339e10bfb12049282b61376c937b410c1834b07bb81ed1e279b4d3 Homepage: https://cran.r-project.org/package=PLIS Description: CRAN Package 'PLIS' (Multiplicity Control using Pooled LIS Statistic) A multiple testing procedure for testing several groups of hypotheses is implemented. Linear dependency among the hypotheses within the same group is modeled by using hidden Markov Models. It is noted that a smaller p value does not necessarily imply more significance due to the dependency. A typical application is to analyze genome wide association studies datasets, where SNPs from the same chromosome are treated as a group and exhibit strong linear genomic dependency. See Wei Z, Sun W, Wang K, Hakonarson H (2009) for more details. 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Test functions include model specification, serial correlation, cross-sectional dependence, panel unit root and panel Granger (non-)causality. Typical references are general econometrics text books such as Baltagi (2021), Econometric Analysis of Panel Data (), Hsiao (2014), Analysis of Panel Data (), and Croissant and Millo (2018), Panel Data Econometrics with R (). Package: r-cran-plmixed Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1275 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-matrix, r-cran-numderiv, r-cran-optimx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-irtoys Filename: pool/dists/noble/main/r-cran-plmixed_0.1.8-1.ca2404.1_all.deb Size: 1181944 MD5sum: 86407a5c2b80eef4bd5ec7716447edaf SHA1: 25f838fab431e0a6714c147ce63a12edc1a2b93f SHA256: 670cc4961513928bf771d8e5acfef143048647b65211a5480082553967fd1dad SHA512: 22b141af22be1e7b5380a610d774358be4fe5b83122a74332e61de9ec6c6fc9f810f729f32aad46647f433793abdfe074837e4a53144d60db2f144ef222e979b Homepage: https://cran.r-project.org/package=PLmixed Description: CRAN Package 'PLmixed' (Estimate (Generalized) Linear Mixed Models with FactorStructures) Utilizes the 'lme4' and 'optimx' packages (previously the optim() function from 'stats') to estimate (generalized) linear mixed models (GLMM) with factor structures using a profile likelihood approach, as outlined in Jeon and Rabe-Hesketh (2012) and Rockwood and Jeon (2019) . Factor analysis and item response models can be extended to allow for an arbitrary number of nested and crossed random effects, making it useful for multilevel and cross-classified models. 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Package: r-cran-plotftir Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2800 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-scales Suggests: r-cran-testthat, r-cran-withr, r-cran-ggplot2, r-cran-gghighlight, r-cran-ggthemes, r-cran-knitr, r-cran-rmarkdown, r-cran-ir, r-cran-chemospec, r-cran-r.utils, r-cran-readjdx Filename: pool/dists/noble/main/r-cran-plotftir_1.2.1-1.ca2404.1_all.deb Size: 1916534 MD5sum: 54000c00665fde1e00d88f35e5ce83b7 SHA1: e056cec4e323c326be6cdcd3d34cd22a534705a2 SHA256: 95476a747c5de5c2e4d083ed95481f013720b6dc4a13e87659fcd1a2b8ca93cb SHA512: c70b26c1eb5ed8571803a006f11d35401149d48fa92114c30e926cdae3843fb4e5089c6e3eba0e67274ff5653f9ffd71d73313471f89f95ebcf4a51e9e6cbcdc Homepage: https://cran.r-project.org/package=PlotFTIR Description: CRAN Package 'PlotFTIR' (Plot FTIR Spectra) The goal of 'PlotFTIR' is to easily and quickly kick-start the production of journal-quality Fourier Transform Infra-Red (FTIR) spectral plots in R using 'ggplot2'. The produced plots can be published directly or further modified by 'ggplot2' functions. L'objectif de 'PlotFTIR' est de démarrer facilement et rapidement la production des tracés spectraux de spectroscopie infrarouge à transformée de Fourier (IRTF) de qualité journal dans R à l'aide de 'ggplot2'. Les tracés produits peuvent être publiés directement ou modifiés davantage par les fonctions 'ggplot2'. Package: r-cran-plotfunctions Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1557 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-sp Filename: pool/dists/noble/main/r-cran-plotfunctions_1.5-1.ca2404.1_all.deb Size: 1149490 MD5sum: 29b770f93c6553b561a31d73cc4fc8a2 SHA1: 17db45f300ece8c51ab67cdde087ad795674df88 SHA256: cec4a8ea5253ce54c54e1387764bfccb317f6bf074c3a85bff514e32181edc03 SHA512: c62e0a85f68c4caf672dfd65479ea2a6f626c8b413232d0b1610b011e06b15b1eeabb6d27766b591c2a815fabce37adc702b25aaf15896830e4c2eaed71e8be8 Homepage: https://cran.r-project.org/package=plotfunctions Description: CRAN Package 'plotfunctions' (Various Functions to Facilitate Visualization of Data andAnalysis) When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions. Package: r-cran-plotgmm Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wesanderson, r-cran-amerika, r-cran-ggplot2 Suggests: r-cran-mixtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plotgmm_0.2.2-1.ca2404.1_all.deb Size: 21548 MD5sum: d102d669d80fae4d6852b85d88c885b5 SHA1: 3acb29c373c9e699a734b707b4ea1c80f3c6a01a SHA256: e46600df6eb1688ec174869551b0794e27add1bd497424ca80f387d13ac5b0dc SHA512: d0d8dd8bde6dbdcf671ea85ffd8fbd1a107fe040b6ea08cb9f5359126426105247ef385dda95c7f2cfcc437a7bab341e0c4f6652badeaaaf83f0396ef283f051 Homepage: https://cran.r-project.org/package=plotGMM Description: CRAN Package 'plotGMM' (Tools for Visualizing Gaussian Mixture Models) The main function, plot_GMM, is used for plotting output from Gaussian mixture models (GMMs), including both densities and overlaying mixture weight component curves from the fit GMM. The package also include the function, plot_cut_point, which plots the cutpoint (mu) from the GMM over a histogram of the distribution with several color options. Finally, the package includes the function, plot_mix_comps, which is used in the plot_GMM function, and can be used to create a custom plot for overlaying mixture component curves from GMMs. For the plot_mix_comps function, usage most often will be specifying the "fun" argument within "stat_function" in a ggplot2 object. Package: r-cran-plothelper Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-ggfittext, r-cran-magick, r-cran-gridextra, r-cran-scales, r-cran-farver Filename: pool/dists/noble/main/r-cran-plothelper_0.1.9-1.ca2404.1_all.deb Size: 304840 MD5sum: 0d44e3a4a1afe31c7635636e64d7c157 SHA1: 26e3b55f735252a88fb1fad91ef5ced83c93802d SHA256: 00c4888e080448aa16f05c7e12d31a1d6394f0e5bfc1f5b3355d652880d950a5 SHA512: ec09802f78c9e675cfe1b99e4f9c32447d573f44d23395ed63f38fda3e84c14a2c74761e2eb1000c8e423e58ff84e040aa005c9df93ace0f4102885de3134574 Homepage: https://cran.r-project.org/package=plothelper Description: CRAN Package 'plothelper' (New Plots Based on 'ggplot2' and Functions to Create RegularShapes) An extension to 'ggplot2' and 'magick'. It contains three groups of functions: Functions in the first group draw 'ggplot2' - based plots: geom_shading_bar() draws barplot with shading colors in each bar. geom_rect_cm(), geom_circle_cm() and geom_ellipse_cm() draw rectangles, circles and ellipses with centimeter as their unit. Thus their sizes do not change when the coordinate system or the aspect ratio changes. annotation_transparent_text() draws labels with transparent texts. annotation_shading_polygon() draws irregular polygons with shading colors. Functions in the second group generate coordinates for regular shapes and make linear transformations. Functions in the third group are 'magick' - based functions facilitating image processing. Package: r-cran-plotlsirm Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-patchwork, r-cran-scales Suggests: r-cran-plotly, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-plotlsirm_0.1.3-1.ca2404.1_all.deb Size: 164034 MD5sum: 3746bff44a8981e5b8f9c7e5c2701121 SHA1: 22acf94b28c041c7080b049ee56f61e859604a26 SHA256: 252c6c8121faed45a9432c41e380202a618c9e8cc0cdf8f611b0debd5b3b3c0e SHA512: 36e9262a6e4d87bf1568a6b2f098425573ac4c0f4935945ec8f6b9fd431c86fb8a18a7873d8868ff814fa0533cb35450ad9ef2d4120976fa6dbc708ae03d3f2a Homepage: https://cran.r-project.org/package=plotlsirm Description: CRAN Package 'plotlsirm' (Plot Toolkit for Latent Space Item Response Models) Provides publication‑quality and interactive plots for exploring the posterior output of Latent Space Item Response Models, including Posterior Interaction Profiles, radar charts, 2‑D latent maps, and item‑similarity heat maps. The methods implemented in this package are based on work by Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) . Package: r-cran-plotluck Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-quantreg, r-cran-scales, r-cran-plyr, r-cran-hexbin, r-cran-rcolorbrewer, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-ggplot2movies, r-cran-mgcv, r-cran-nlme, r-cran-knitr, r-cran-rmarkdown, r-cran-gapminder Filename: pool/dists/noble/main/r-cran-plotluck_1.1.1-1.ca2404.1_all.deb Size: 480754 MD5sum: 7a25f65da62125cf30fc2acc4093a88f SHA1: 12833b7e91a4d6a58d3a62eee41894a9ff0cbb75 SHA256: 8637928dd087387d3d498d78bfe698b0981dbf7c4f933ea419d191ac67d672f0 SHA512: c81d1f3faaa6d4c69012ff328c06d2976fbb43fad476db506377d2a73883b2567eeb4b537f95e517085d9e146db025b1a316a122ee8c3ac2b7d2a2071f3ce9f9 Homepage: https://cran.r-project.org/package=plotluck Description: CRAN Package 'plotluck' ('ggplot2' Version of "I'm Feeling Lucky!") Examines the characteristics of a data frame and a formula to automatically choose the most suitable type of plot out of the following supported options: scatter, violin, box, bar, density, hexagon bin, spine plot, and heat map. The aim of the package is to let the user focus on what to plot, rather than on the "how" during exploratory data analysis. It also automates handling of observation weights, logarithmic axis scaling, reordering of factor levels, and overlaying smoothing curves and median lines. Plots are drawn using 'ggplot2'. Package: r-cran-plotly Architecture: all Version: 4.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7379 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-digest, r-cran-viridislite, r-cran-base64enc, r-cran-htmltools, r-cran-htmlwidgets, r-cran-tidyr, r-cran-rcolorbrewer, r-cran-dplyr, r-cran-vctrs, r-cran-tibble, r-cran-lazyeval, r-cran-rlang, r-cran-crosstalk, r-cran-purrr, r-cran-data.table, r-cran-promises Suggests: r-cran-mass, r-cran-maps, r-cran-hexbin, r-cran-ggthemes, r-cran-ggally, r-cran-ggalluvial, r-cran-testthat, r-cran-knitr, r-cran-shiny, r-cran-shinytest2, r-cran-curl, r-cran-rmarkdown, r-cran-cairo, r-cran-broom, r-cran-webshot, r-cran-listviewer, r-cran-dendextend, r-cran-sf, r-cran-png, r-cran-irdisplay, r-cran-processx, r-cran-plotlygeoassets, r-cran-forcats, r-cran-withr, r-cran-palmerpenguins, r-cran-rversions, r-cran-reticulate, r-cran-rsvg, r-cran-ggridges Filename: pool/dists/noble/main/r-cran-plotly_4.12.0-1.ca2404.1_all.deb Size: 3563898 MD5sum: ceefefd230ea28d995c1a6bc101deefc SHA1: c34ce43140bc26f9ba0fd1dc46cf73996d4e5c6e SHA256: dc7ed944c9d0b779acb7ea87533061e770e61ea3f2301b648a07922e7c408a91 SHA512: d306ed11af54ab85e08401895abd08a1d73646f4aac5c6eef48c2b3ea19ace3aa1632c74c49c42581576c20eeda698b7cb3ba56aae3b53451b34f2f3d529d06a Homepage: https://cran.r-project.org/package=plotly Description: CRAN Package 'plotly' (Create Interactive Web Graphics via 'plotly.js') Create interactive web graphics from 'ggplot2' graphs and/or a custom interface to the (MIT-licensed) JavaScript library 'plotly.js' inspired by the grammar of graphics. Package: r-cran-plotlygeoassets Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4051 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-htmltools Filename: pool/dists/noble/main/r-cran-plotlygeoassets_0.0.2-1.ca2404.1_all.deb Size: 809470 MD5sum: b33a43838e298f7e94cb92f442e54c41 SHA1: 4ac68eef5cac763ac217d188b6ddefc1204003bd SHA256: 3725e192e3cb2e0e69a77fe71b96125bf38fb25b7569f7029a0de28a04c1361b SHA512: f9f1590ed1996ad9ecca271dec648871ed726b22b1d22470d5588c2ae18398c1f5b3a30cb7bacc6e229fd4a18969a300279f730acf3580e9b497e8e15b947c64 Homepage: https://cran.r-project.org/package=plotlyGeoAssets Description: CRAN Package 'plotlyGeoAssets' (Render 'Plotly' Maps without an Internet Connection) Includes 'JavaScript' files that allow 'plotly' maps to render without an internet connection. Package: r-cran-plotmcmc Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-gplots, r-cran-lattice Suggests: r-cran-gdata Filename: pool/dists/noble/main/r-cran-plotmcmc_2.0.1-1.ca2404.1_all.deb Size: 1027944 MD5sum: 631c9bfdf9becaab66d0ad057a9853c7 SHA1: 59c2dbe567091bba5e10c10aceab9117cb718a55 SHA256: bfda15688686360ce3e446665d1de6dd0b87a9e0d72d2343f27753673f3a38b6 SHA512: 6e29121205dcfbc151e4a128abbdad9c49ac94200c1f0d5620eb65525669676abee9be04570fec64701faf518754662cfaf53013de4e0d719167d45615c9feb1 Homepage: https://cran.r-project.org/package=plotMCMC Description: CRAN Package 'plotMCMC' (MCMC Diagnostic Plots) Markov chain Monte Carlo diagnostic plots. The purpose of the package is to combine existing tools from the 'coda' and 'lattice' packages, and make it easy to adjust graphical details. Package: r-cran-plotmelm Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-interactiontest Filename: pool/dists/noble/main/r-cran-plotmelm_0.1.5-1.ca2404.1_all.deb Size: 23492 MD5sum: 0434753cd53cd7b6d32240910ac202f5 SHA1: e4993257cffd28d2f382e260e024af07e701493f SHA256: c6449899dfdf972add560ee9aeefc90beb0962896bb72231ad63351659d972a3 SHA512: 47aa16f31b8125d384dfc8ff144cbb16b6106940931ce5eafdf0305b542fa70d165065170c01c01d15a9c9dc18dad2b984e9e8374b7d7c063dd99fc993085342 Homepage: https://cran.r-project.org/package=plotMElm Description: CRAN Package 'plotMElm' (Plot Marginal Effects from Linear Models) Plot marginal effects for interactions estimated from linear models. Package: r-cran-plotmm Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wesanderson, r-cran-amerika, r-cran-ggplot2, r-cran-mixtools, r-cran-emcluster, r-cran-flexmix Suggests: r-cran-testthat, r-cran-dplyr, r-cran-patchwork, r-cran-survival, r-cran-magrittr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-plotmm_0.1.2-1.ca2404.1_all.deb Size: 49544 MD5sum: 697bc01ade6312402e97c59c86f90369 SHA1: df41d167efc238b5e3551754f6f5c33a136dc75f SHA256: 475138db4caa9603ad17e3a05e28e306c476dc85dd751c961a51d3508882ca6d SHA512: 9e7992f192a88882a61fa0e1e9d78c5e8c231ca6298b751d99fa66ef2bd3821207390e0a87805a647966880213f20def6283b9d4610372609b953f428e88e20f Homepage: https://cran.r-project.org/package=plotmm Description: CRAN Package 'plotmm' (Tidy Tools for Visualizing Mixture Models) The main function, plot_mm(), is used for (gg)plotting output from mixture models, including both densities and overlaying mixture weight component curves from the fit models in line with the tidy principles. The package includes several additional functions for added plot customization. Supported model objects include: 'mixtools', 'EMCluster', and 'flexmix', with more from each in active dev. Supported mixture model specifications include mixtures of univariate Gaussians, multivariate Gaussians, Gammas, logistic regressions, linear regressions, and Poisson regressions. 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Also plot model residuals and other information on the model. Package: r-cran-plotnormtest Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1175 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-ggplot2, r-cran-matrix, r-cran-matrixextra, r-cran-mass, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plotnormtest_1.0.1-1.ca2404.1_all.deb Size: 833754 MD5sum: 35f10e03c3a67130857e87231e64d6c2 SHA1: 98781cec95ff58247effecb3bd6ee396f4a817c1 SHA256: b14d6a601826f9289e0ea71dadb59e9219def6e1e33dfae102ef6d13e9125372 SHA512: ca4ae62b0213e5ec224cf8e25e580ed5e5a2bd6a0c0e919f1ddcb54ea0999e93f2bb22c2f1a86f40274008466edfc990ee844d381606d0ec10eff0eb7a7769cc Homepage: https://cran.r-project.org/package=PlotNormTest Description: CRAN Package 'PlotNormTest' (Graphical Univariate/Multivariate Assessments for NormalityAssumption) Graphical methods testing multivariate normality assumption. Methods including assessing score function, and moment generating functions,independent transformations and linear transformations. For more details see Tran (2024),"Contributions to Multivariate Data Science: Assessment and Identification of Multivariate Distributions and Supervised Learning for Groups of Objects." , PhD thesis, . 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Package: r-cran-plrmodels Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-plrmodels_1.4-1.ca2404.1_all.deb Size: 298870 MD5sum: baa1b098cc4ca31aff70f1eef2f3765a SHA1: ee428e946f71a37ea30b2bc42f111ffc7c99b803 SHA256: 1a6b12ab7d20c69d37147e38211338b157a618130cc6cc008f3c84975203fd5c SHA512: 5f4cfc524bc583528b563cb68f46efdf90e60009191c10e47cbac987f3a0fdf3b4b5a4b3457a9f299219d001d32fd51206d447243b2497f1ed4167106904e802 Homepage: https://cran.r-project.org/package=PLRModels Description: CRAN Package 'PLRModels' (Statistical Inference in Partial Linear Regression Models) Contains statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. 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Package: r-cran-plsdepot Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-factominer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plsdepot_0.3.1-1.ca2404.1_all.deb Size: 199942 MD5sum: 46812a55ad383bd3d009b09641bd9066 SHA1: b745db9ce07a49612b393ef77aa252d671228641 SHA256: 39b5c64f935879e889e93d55ec503403e14cdac4d111ff7f6ef2a26ef1eaffdc SHA512: a5989c52aa0ef89afcba3b1a6af40cacb28537723215e5116f0932c1f2bf93163b6394573ad95f8f59080dc03aade85894bd4fc1be7a034255cd13d0344d647d Homepage: https://cran.r-project.org/package=plsdepot Description: CRAN Package 'plsdepot' (Partial Least Squares (PLS) Data Analysis Methods) Different methods for PLS analysis of one or two data tables such as Tucker's Inter-Battery, NIPALS, SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis. The main reference for this software is the awesome book (in French) 'La Regression PLS: Theorie et Pratique' by Michel Tenenhaus. Package: r-cran-plsdof Architecture: all Version: 0.5-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-plsdof_0.5-0-1.ca2404.1_all.deb Size: 208414 MD5sum: 436b89e2a22bc00ad34f9224d4d1a3b3 SHA1: d87e09e5f72770035877df98f847b66b6ca81395 SHA256: b92a59597054c84b407bd59de51e23e576609566412408b42951fe28ced3460a SHA512: 3974193b0af141bec5f9f2f0ffae62616e5c4dcdcce56d83c02686801ae6d7d746f86bdc98f9c4de2e2a650b1fcf8c3e4cd8da8153b3dcc8dee15de47823fd42 Homepage: https://cran.r-project.org/package=plsdof Description: CRAN Package 'plsdof' (Degrees of Freedom and Statistical Inference for Partial LeastSquares Regression) The plsdof package provides Degrees of Freedom estimates for Partial Least Squares (PLS) Regression. Model selection for PLS is based on various information criteria (aic, bic, gmdl) or on cross-validation. Estimates for the mean and covariance of the PLS regression coefficients are available. They allow the construction of approximate confidence intervals and the application of test procedures (Kramer and Sugiyama 2012 ). Further, cross-validation procedures for Ridge Regression and Principal Components Regression are available. Package: r-cran-plsgenomics Architecture: all Version: 1.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1686 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-boot, r-cran-reshape2, r-cran-plyr, r-cran-fields, r-cran-rhpcblasctl Filename: pool/dists/noble/main/r-cran-plsgenomics_1.5-3-1.ca2404.1_all.deb Size: 1657664 MD5sum: 59085ab76a5e86cae4d47e331eaa4bf4 SHA1: 4fdf8f9ab589076ac66d9ba07008161523fbded0 SHA256: ff7ca4c46b78153251384a95c3173ba656191dfdf88466f4e244442ed260f2dd SHA512: 3e17adaa0130dc7f70b3e80d92a9cbf075388dcc202253c1f3f01f1e2f2bd728cc815e5c8191cca457ce8e982a29376bd5d18e5289c86f9e177c57440a90ee54 Homepage: https://cran.r-project.org/package=plsgenomics Description: CRAN Package 'plsgenomics' (PLS Analyses for Genomics) Routines for PLS-based genomic analyses, implementing PLS methods for classification with microarray data and prediction of transcription factor activities from combined ChIP-chip analysis. The >=1.2-1 versions include two new classification methods for microarray data: GSIM and Ridge PLS. The >=1.3 versions includes a new classification method combining variable selection and compression in logistic regression context: logit-SPLS; and an adaptive version of the sparse PLS. Package: r-cran-plsmmlasso Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glmnet, r-cran-hdi, r-cran-mass, r-cran-mvtnorm, r-cran-rlang, r-cran-scalreg Filename: pool/dists/noble/main/r-cran-plsmmlasso_1.1.0-1.ca2404.1_all.deb Size: 695652 MD5sum: 42eda10b71dd06f244d169b0b16cebef SHA1: f04a09ff644edb826166db266324136d54c1d2e6 SHA256: ed5d2c854b2039ebe8aaaa729bef84dea5d888fcda5fb46a5b94821e8a020024 SHA512: 86f9a65486585721f7774362d22bc5b559ef5075c58af85cd7856fec01973ebb7abbf2f025b23db9c544310b477b691c7eb5e0041469134e00b1147f1c52f6c1 Homepage: https://cran.r-project.org/package=plsmmLasso Description: CRAN Package 'plsmmLasso' (Variable Selection and Inference for Partial SemiparametricLinear Mixed-Effects Model) Implements a partial linear semiparametric mixed-effects model (PLSMM) featuring a random intercept and applies a lasso penalty to both the fixed effects and the coefficients associated with the nonlinear function. The model also accommodates interactions between the nonlinear function and a grouping variable, allowing for the capture of group-specific nonlinearities. Nonlinear functions are modeled using a set of bases functions. Estimation is conducted using a penalized Expectation-Maximization algorithm, and the package offers flexibility in choosing between various information criteria for model selection. Post-selection inference is carried out using a debiasing method, while inference on the nonlinear functions employs a bootstrap approach. Package: r-cran-plsmod Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-parsnip, r-cran-dplyr, r-cran-generics, r-cran-magrittr, r-bioc-mixomics, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-covr, r-cran-modeldata, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plsmod_1.0.0-1.ca2404.1_all.deb Size: 39498 MD5sum: 0306c0e2fcab7c6387db2d591a5e967f SHA1: e1af0b185d708e518ee62c34ec5f80b22f9c7f57 SHA256: 3950b539d884704d2fa36d7b838d0665c55852453c5c9cebb4f54b936a4237ed SHA512: 6481fc3ad49c441892617c4c2fc005af401896606b87ef569077dc409a08e22a5e8a54780ddab9694c016dc01dca9472ed6fe012e588f52b0ee18389a73928b2 Homepage: https://cran.r-project.org/package=plsmod Description: CRAN Package 'plsmod' (Model Wrappers for Projection Methods) Bindings for additional regression models for use with the 'parsnip' package, including ordinary and spare partial least squares models for regression and classification (Rohart et al (2017) ). Package: r-cran-plsmselect Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glmnet, r-cran-mgcv, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-purrr Filename: pool/dists/noble/main/r-cran-plsmselect_0.2.0-1.ca2404.1_all.deb Size: 149676 MD5sum: 4184089cb79c0a99065b2731eaad92f0 SHA1: 287f64e1b250ff39b32d4b3ec19417a1f1af4e49 SHA256: 7f9832019808929e502537f946ba1fc7f3b231d2376a0e3b33278e52a9f77eac SHA512: 215595f2e89094a5bf9f3debb426ad46031fa0fb350985209fd1e1c17aad063442f32e544d19f25f78c902ca5cdd8b637451409dc5c0e60ab4b8b6476a2b1660 Homepage: https://cran.r-project.org/package=plsmselect Description: CRAN Package 'plsmselect' (Linear and Smooth Predictor Modelling with Penalisation andVariable Selection) Fit a model with potentially many linear and smooth predictors. Interaction effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties we use iterative steps alternating between using linear predictors (lasso) and smooth predictors (generalised additive model). Package: r-cran-plspm Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 778 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tester, r-cran-turner, r-cran-diagram, r-cran-shape, r-cran-amap Suggests: r-cran-factominer, r-cran-ggplot2, r-cran-reshape, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plspm_0.6.0-1.ca2404.1_all.deb Size: 691306 MD5sum: 74fddfa11d26a24cbf89218938a01bdd SHA1: c77f1eabb7f344e205386ff7000b4fb9dc196e30 SHA256: 0bfeca07c5e5cb678e8e2d482e716e02aaab954e91a300c6e3b243cfbfbc4d01 SHA512: f5a801834a52ff3b2d1213ef729421bfefb476c561bd7f0ddedd4a85014cc76d219c9c7efc58c0adb13e55b8c239abbb3ac50f3e1f110a8439ff4d1d423b8d98 Homepage: https://cran.r-project.org/package=plspm Description: CRAN Package 'plspm' (Partial Least Squares Path Modeling (PLS-PM)) Partial Least Squares Path Modeling (PLS-PM), Tenenhaus, Esposito Vinzi, Chatelin, Lauro (2005) , analysis for both metric and non-metric data, as well as REBUS analysis, Esposito Vinzi, Trinchera, Squillacciotti, and Tenenhaus (2008) . Package: r-cran-plsrbeta Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-boot, r-cran-formula, r-cran-mass, r-cran-plsrglm, r-cran-betareg Suggests: r-cran-bipartite, r-cran-knitr, r-cran-markdown, r-cran-plotrix, r-cran-pls, r-cran-plsdof, r-cran-prettydoc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plsrbeta_0.3.3-1.ca2404.1_all.deb Size: 3981238 MD5sum: ccbb6728dbcfc0ec653c2926081c642e SHA1: b1ebd34e9d68ecf178d4decd81b4871fd639ca89 SHA256: ae2e8db7919d3c5cb250444b5864c2e19c9dcca7f48e1cf1864704ad0d14f9e3 SHA512: 99df8cfddfbf7bbf5aa49d42e01c84a841f42374e81a27dde1fac4da14cde8fd14d82379491cdb3e65433ad1c7ec1cdbdc86d0e5b50d6a61d140efe96fed1748 Homepage: https://cran.r-project.org/package=plsRbeta Description: CRAN Package 'plsRbeta' (Partial Least Squares Regression for Beta Regression Models) Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013, ) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available. Package: r-cran-plsrcox Architecture: all Version: 1.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-plsrglm, r-cran-lars, r-cran-pls, r-cran-kernlab, r-bioc-mixomics, r-cran-risksetroc, r-bioc-survcomp, r-cran-survauc, r-cran-rms Suggests: r-cran-survivalroc, r-cran-plsdof, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plsrcox_1.8.2-1.ca2404.1_all.deb Size: 2307396 MD5sum: 400d2bddbd939e1f1659807f8baa2f01 SHA1: 25be0241faf2483e555081186fc5d1b45e6ecfb7 SHA256: 98e80cb62afce09a85c9f13ebe142acc618011b5900428b5659cba672915f15d SHA512: e15c0a4c552097560bbd6b9b3433479f0cf8ce18c8bd0bb779878b59eb267f28385b4e53023b3c3354cda4a18d486aab02a3368b58497e44fa4385fa77c9cacd Homepage: https://cran.r-project.org/package=plsRcox Description: CRAN Package 'plsRcox' (Partial Least Squares Regression for Cox Models and RelatedTechniques) Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings , Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in , Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data. Package: r-cran-plsrglm Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2274 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-boot, r-cran-bipartite, r-cran-car, r-cran-mass Suggests: r-cran-chemometrics, r-cran-plsdof, r-cran-plsdepot, r-cran-plspm, r-cran-plsrcox, r-cran-r.rsp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-plsrglm_1.6.0-1.ca2404.1_all.deb Size: 2130174 MD5sum: b0316e0e18f020a850bb5aff662cb66a SHA1: 37313e664c8c92a8187a091f0cc2d49111f45b28 SHA256: 62772ce049d76f2967bcb077cfc203b36140fff9388c8852ed25761d98f880ec SHA512: 512087b5b3f8b4e010b35343757a5c1661f4fb423a1be5868ba3cba1415144aa11ed220652aa7544e8adc58f0a0e327e14888e430f5b6800f7599806cd8a4382 Homepage: https://cran.r-project.org/package=plsRglm Description: CRAN Package 'plsRglm' (Partial Least Squares Regression for Generalized Linear Models) Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria . It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available. Package: r-cran-plssem Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-modsem, r-cran-lme4, r-cran-lavaan, r-cran-stringr, r-cran-purrr, r-cran-matrixstats, r-cran-rfast, r-cran-collapse, r-cran-mvnfast, r-cran-reformulas, r-cran-fnn Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mice, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-plssem_0.1.1-1.ca2404.1_all.deb Size: 1709280 MD5sum: 03490057fb82c1ae17fefe13f0fd3d88 SHA1: 527143b4a42e9c6b66fe4897aa0117e91ee4c29c SHA256: a1cd938b23d79243b7caaa68469f490a8a73054d90f6bc1a47690773b0fb686a SHA512: fb7fb4385fb3ecabb61e096a1bc68563c33bd066a967a8afd4d055e5c958bc2dcb70052898801d2e4fe08e111353e94877aba9ffd2ca3745353b546390c129b2 Homepage: https://cran.r-project.org/package=plssem Description: CRAN Package 'plssem' (Complex Partial Least Squares Structural Equation Modeling) Estimate complex Structural Equation Models (SEMs) by fitting Partial Least Squares Structural Equation Modeling (PLS-SEM) and Partial Least Squares consistent Structural Equation Modeling (PLSc-SEM) specifications that handle categorical data, non-linear relations, and multilevel structures. The implementation follows Lohmöller (1989) for the classic PLS-SEM algorithm, Dijkstra and Henseler (2015) for consistent PLSc-SEM, Dijkstra et al., (2014) for nonlinear PLSc-SEM, and Schuberth, Henseler, Dijkstra (2018) for ordinal PLS-SEM and PLSc-SEM. Additional extensions are under development. The MC-OrdPLSc algorithm, used to handle ordinal interaction models is detailed in Slupphaug et al., (2026). References: Lohmöller, J.-B. (1989, ISBN:9783790803002). "Latent Variable Path Modeling with Partial Least Squares." Dijkstra, T. K., & Henseler, J. (2015). . "Consistent partial least squares path modeling." Dijkstra, T. K., & Schermelleh-Engel, K. (2014). . "Consistent partial least squares for nonlinear structural equation models." Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018). . "Partial least squares path modeling using ordinal categorical indicators." Slupphaug, K. Mehmetoglu, M. & Mittner, M. (2026). . "Consistent Estimates from Biased Estimators: Monte-Carlo Consistent Partial Least Squares for Latent Interaction Models with Ordinal Indicators." Package: r-cran-plstests Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-harmonicmeanp, r-cran-mass, r-cran-psych Filename: pool/dists/noble/main/r-cran-plstests_0.1.1-1.ca2404.1_all.deb Size: 63838 MD5sum: 70aefbb10110089d08f1daa315031d8a SHA1: 38e3ec5cc232b05380d21668533e375e52f065f7 SHA256: 76c3851d5bb24f7108022613412a579a53b6dfd75c6794033f265c66faba2dbf SHA512: 70fedcc2bc4333f4aaa0c36ebe479cb43690b38c5cef5fdd3026aff6a1e51795eddfe55c2916bb338e0b3164a429944207182b362fe0099df9857085701a9b17 Homepage: https://cran.r-project.org/package=PLStests Description: CRAN Package 'PLStests' (Model Checking for High-Dimensional GLMs via Random Projections) Provides methods for testing the goodness-of-fit of generalized linear models (GLMs) using random projections. It is specifically designed for high-dimensional scenarios where the number of predictors substantially exceeds the sample size. The statistical methodologies implemented in this package are detailed in the paper by Wen Chen and Falong Tan (2024, ). Package: r-cran-plsvarsel Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pls, r-cran-genalg, r-cran-mvtnorm, r-cran-bdsmatrix, r-cran-mass, r-cran-progress, r-cran-praznik Suggests: r-cran-rmpi Filename: pool/dists/noble/main/r-cran-plsvarsel_0.10.0-1.ca2404.1_all.deb Size: 283716 MD5sum: 7f6c8c600dfdcd76cf8d4e7e0787e13a SHA1: 30e80738d3ab8384784a88e276821cfcfc19034d SHA256: 8e5678f59ec78b6ee48abb791e184e8bc29e3c79980640894024772c74776982 SHA512: dd44ff9ac0138c1f132391cdacdf5b813a196296ae4e53ae4eb11d251b96dbe690bf43d16124ff9614c4b8a3802e2124f87adeaa746ab2e3854b99acbf14230f Homepage: https://cran.r-project.org/package=plsVarSel Description: CRAN Package 'plsVarSel' (Variable Selection in Partial Least Squares) Interfaces and methods for variable selection in Partial Least Squares. The methods include filter methods, wrapper methods and embedded methods. 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Package: r-cran-plu Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 944 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lifecycle Suggests: r-cran-and, r-cran-covr, r-cran-crayon, r-cran-fracture, r-cran-glue, r-cran-knitr, r-cran-nombre, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-plu_0.3.0-1.ca2404.1_all.deb Size: 863684 MD5sum: ff7501d3f89563094df630c10371d1a5 SHA1: 451ad3371c1356183430625a644ed0e7dbeca1cd SHA256: dcfd314042abf85acd3df80c27f29ca1ba09dfa4d9a17eb1b737e33c03514b05 SHA512: 5ebf839ec44046bad6a5f7aeec98d2119759d42937d9b63e55b78499c81f764568502de69d59005fa433f141c2d385099a8e1ae27ccf7e63a633b953d38edeca Homepage: https://cran.r-project.org/package=plu Description: CRAN Package 'plu' (Dynamically Pluralize Phrases) Converts English phrases to singular or plural form based on the length of an associated vector. 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The 'PLUCR' package implements methods for constrained policy learning. Package: r-cran-plug Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2326 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-httr2, r-cran-glue, r-cran-keyring Filename: pool/dists/noble/main/r-cran-plug_0.1.0-1.ca2404.1_all.deb Size: 107238 MD5sum: e640c5c6158e9295f5db28659aeea36d SHA1: 083904ea2249d5574eb9023c50294c5b9f648cc7 SHA256: 7f0a573b26ca00b9d135c8d4fdf5a8b555b3ff47e3f578887197b18dcbe13cbb SHA512: 369fe7f827c67ede8ded71b53249a781d0c80817dfb796039bf33f561b5f5135b4f1395f480d0b33f7363696c8e99cd1c5be0667c0cf72b8eff7b65a48a2e101 Homepage: https://cran.r-project.org/package=plug Description: CRAN Package 'plug' (Secure and Intuitive Access to 'Plug' Interface) Provides a secure and user-friendly interface to interact with the 'Plug' 'API'. It enables developers to store and manage tokens securely using the 'keyring' package, retrieve data from 'API' endpoints with the 'httr2' package, and handle large datasets with chunked data fetching. Designed for simplicity and security, the package facilitates seamless integration with 'Plug' ecosystem. Package: r-cran-plumber2 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3624 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-cli, r-cran-fiery, r-cran-fireproof, r-cran-firesafety, r-cran-firesale, r-cran-firestorm, r-cran-fs, r-cran-jsonlite, r-cran-promises, r-cran-r6, r-cran-ragg, r-cran-rapidoc, r-cran-readr, r-cran-reqres, r-cran-rlang, r-cran-routr, r-cran-roxygen2, r-cran-stringi, r-cran-svglite, r-cran-webutils, r-cran-yaml Suggests: r-cran-arrow, r-cran-callr, r-cran-geojsonsf, r-cran-htmlwidgets, r-cran-later, r-cran-mirai, r-cran-nanoparquet, r-cran-quarto, r-cran-redoc, r-cran-rmarkdown, r-cran-shiny, r-cran-storr, r-cran-swagger, r-cran-testthat, r-cran-tomledit Filename: pool/dists/noble/main/r-cran-plumber2_0.2.0-1.ca2404.1_all.deb Size: 2413832 MD5sum: 8f199af493cbeba881de6cb86594c9a2 SHA1: f4999a392944e96af6a1757a5b0e1a6adc4eedd6 SHA256: 777d240367cc2da602eba7d6a1f5160f3515c3de050a7cf8ec0cb03759b43911 SHA512: 8ecd2a5a5a18be9dadd93f6b105ba791538da1df3afdfcab625ffe82fd11ffc6e9daebc1d1982530fa4776bbb3488c1bd924371d7bdd7ec5b8b89e8567e2a770 Homepage: https://cran.r-project.org/package=plumber2 Description: CRAN Package 'plumber2' (Easy and Powerful Web Servers) Automatically create a web server from annotated 'R' files or by building it up programmatically. Provides automatic 'OpenAPI' documentation, input handling, asynchronous evaluation, and plugin support. Package: r-cran-plumber Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1286 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-httpuv, r-cran-jsonlite, r-cran-lifecycle, r-cran-magrittr, r-cran-mime, r-cran-promises, r-cran-r6, r-cran-rlang, r-cran-sodium, r-cran-stringi, r-cran-swagger, r-cran-webutils Suggests: r-cran-arrow, r-cran-base64enc, r-cran-coro, r-cran-future, r-cran-geojsonsf, r-cran-htmlwidgets, r-cran-later, r-cran-ragg, r-cran-rapidoc, r-cran-readr, r-cran-readxl, r-cran-redoc, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-sf, r-cran-spelling, r-cran-svglite, r-cran-testthat, r-cran-visnetwork, r-cran-withr, r-cran-writexl, r-cran-yaml Filename: pool/dists/noble/main/r-cran-plumber_1.3.3-1.ca2404.1_all.deb Size: 1083416 MD5sum: 5516bec35eb9beb46e81efe4fe303f31 SHA1: ab38e7b686c66157f2e03ecd244c45cd928ba97c SHA256: fabe67f803bd6b2ed40f104ccdf89eb5ab34f5abb08eff05e69da2aaedc99584 SHA512: 103ddce8309c724eb00abd771c5daf2991bb6877026325b85328924e3e3a9fd6bc1a166c65a273e68b79eeb2392400ea2e75b6c0a4e4f1cf0a8e9318cba83870 Homepage: https://cran.r-project.org/package=plumber Description: CRAN Package 'plumber' (An API Generator for R) Gives the ability to automatically generate and serve an HTTP API from R functions using the annotations in the R documentation around your functions. Package: r-cran-plumberdeploy Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-analogsea, r-cran-ssh, r-cran-jsonlite, r-cran-lifecycle Filename: pool/dists/noble/main/r-cran-plumberdeploy_0.2.1-1.ca2404.1_all.deb Size: 52234 MD5sum: 5dd10ad79d62394edc858db8b1a5fd7c SHA1: 24805dd2069b61997d2091c5ca761ff9e563333a SHA256: c8d1aa67c000d48d5a96b015590ed45c0b496b7d8cafe8cba1e9a25b73c0aa77 SHA512: 0fef22ffed7bc00d2d4ecc0c72048272ea95585451507e1c2f835c34f3db4925ab804cba906a112202617d51b3436e6c704143e72983c631675b637074921b55 Homepage: https://cran.r-project.org/package=plumberDeploy Description: CRAN Package 'plumberDeploy' (Plumber Deployment) Gives the ability to automatically deploy a plumber API from R functions on 'DigitalOcean' and other cloud-based servers. Package: r-cran-plumbertableau Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2019 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plumber, r-cran-magrittr, r-cran-curl, r-cran-httpuv, r-cran-jsonlite, r-cran-later, r-cran-promises, r-cran-rlang, r-cran-htmltools, r-cran-debugme, r-cran-stringi, r-cran-markdown, r-cran-urltools, r-cran-httr, r-cran-knitr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-plumbertableau_0.1.1-1.ca2404.1_all.deb Size: 1393136 MD5sum: 1d495b51cde7b49f466cab1bb6660e70 SHA1: 43f608d9b05836569ee8e7d61c3d2a17550484ef SHA256: 63e9a5bfce83cd9821a3531ebf3fa751af5359b94ce0e9c9fecd0a3a6c94a9d0 SHA512: 943b02e184897b10ee07238ecf7a90dbab411b1453bca46cf75adf0e9df8fb8e5b54fcf66e7b7ac7858bcf07d3164bc43594503f48cc831c1da35920efa568c8 Homepage: https://cran.r-project.org/package=plumbertableau Description: CRAN Package 'plumbertableau' (Turn 'Plumber' APIs into 'Tableau' Extensions) Build 'Plumber' APIs that can be used in 'Tableau' workbooks. Annotations in R comments allow APIs to conform to the 'Tableau Analytics Extension' specification, so that R code can be used to power 'Tableau' workbooks. Package: r-cran-plumbr Architecture: all Version: 0.6.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-objectsignals Suggests: r-cran-plyr, r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-plumbr_0.6.10-1.ca2404.1_all.deb Size: 353278 MD5sum: a2fd5546ee1c1aacd8ee09c29273b396 SHA1: 290df165cdb065add13962ce6f215d2a761fa464 SHA256: b87d3029546e5f55db7238dcd451d0ac4b0c120a1d3dc8c5c33692ce3a9bd3b7 SHA512: 350fd8c4040cef7ac1bf9ae093bafea712d46f812b58e51a2f34b9b4530f8ec82b72e27cb5029f2f011ba308e21e8a956976e1dab8e1e834d0748fdc427c091a Homepage: https://cran.r-project.org/package=plumbr Description: CRAN Package 'plumbr' (Mutable and Dynamic Data Models) The base R data.frame, like any vector, is copied upon modification. This behavior is at odds with that of GUIs and interactive graphics. To rectify this, plumbr provides a mutable, dynamic tabular data model. Models may be chained together to form the complex plumbing necessary for sophisticated graphical interfaces. Also included is a general framework for linking datasets; an typical use case would be a linked brush. 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'plume' provides easy-to-use and flexible tools for inserting author data in 'YAML' as well as generating author and contribution lists (among others) as strings from tabular data. Package: r-cran-pluscode2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf Filename: pool/dists/noble/main/r-cran-pluscode2_0.1.0-1.ca2404.1_all.deb Size: 26162 MD5sum: 5ad495d4aa6bf2f3972fe74f3825f2ea SHA1: 81f1117d6726b4284504e1b6ba1c8ca3c29344d6 SHA256: f2bbae6f67cd8ccaaa2854dd298eb877bfb92e30de08531c2d65027bb66686fc SHA512: 8861fb11e9d8349d2ddedf849e5149546a37a0ce9e9ff2e352ece274cec4b3a0b691c3a10915b964edc7afc47ce48b5b998a2fab8ab3932a6cf5214a5b379347 Homepage: https://cran.r-project.org/package=plusCode2 Description: CRAN Package 'plusCode2' (Coordinates to 'Plus Code' Conversion Tool) Generates 'Plus Code' of geometric objects or data frames that contain them, giving the possibility to specify the precision of the area. The main feature of the package comes from the open-source code developed by 'Google Inc.' present in the repository . For details about 'Plus Code', visit or . Package: r-cran-pluscode Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-pluscode_0.1.0-1.ca2404.1_all.deb Size: 63030 MD5sum: d46a4318dc1d9d036dab2ab9cf6cc49f SHA1: 18a64b7cd303d1d3d92f4ca47ae4eecf26218b17 SHA256: 322eda76305548fde0552933b23d5f422839bffb05919eb6864a3f608c05037c SHA512: 40f287af4c7db2678f3d53ef5c9113cfd85f408b8b2871aadd4e835f4269ae1e107d4be99c338009e755f2ceecde0a90c2227a54d821e0a6d6bd2cb9a0ec3650 Homepage: https://cran.r-project.org/package=pluscode Description: CRAN Package 'pluscode' (Encoder for Google 'Pluscodes') Retrieves a 'pluscode' by inputting latitude and longitude. Includes additional functions to retrieve neighbouring 'pluscodes'. Package: r-cran-plutor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 616 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-baizer, r-cran-cli, r-cran-dplyr, r-cran-ggh4x, r-cran-ggplot2, r-cran-ggsci, r-cran-magrittr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-repr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-svglite, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-plutor_0.1.0-1.ca2404.1_all.deb Size: 510290 MD5sum: d4c7365df4ca93307c7e9c322125a00d SHA1: 2dd2701ee21881625d5046cb27da2699cc0a7f26 SHA256: 0ec0cb2087b8f1cec697a4463335381b352c9a1acfb5bc42ee5d578766023d40 SHA512: 7cb52ac41d44f1387083040d7689de89d5c8727efe16269bb7cc168d583d7c3d0af81c2cfc3978711b796bec885551fec9a1625aa8af43814b77b14a1133f802 Homepage: https://cran.r-project.org/package=plutor Description: CRAN Package 'plutor' (Useful Functions for Visualization) In ancient Roman mythology, 'Pluto' was the ruler of the underworld and presides over the afterlife. 'Pluto' was frequently conflated with 'Plutus', the god of wealth, because mineral wealth was found underground. When plotting with R, you try once, twice, practice again and again, and finally you get a pretty figure you want. It's a 'plot tour', a tour about repetition and reward. Hope 'plutor' helps you on the tour! Package: r-cran-pm3 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tableone Filename: pool/dists/noble/main/r-cran-pm3_0.2.0-1.ca2404.1_all.deb Size: 28266 MD5sum: b557c8534ccfcb3a8add1d12fcb2c3c4 SHA1: 1273b6696a622e39d686090026722914baf16dde SHA256: 1daa36cfb9c8b1c28bd4ce5f5f435f5f13b5b22d04c282113d77485926c3a61a SHA512: 424e101113c2e38030b957a07b56b1a57da5d25cab239a19185d4c75890776aeb69912341427260828df38ffccecaed9c6913a83e5f72ed06bbd9cca98e070cd Homepage: https://cran.r-project.org/package=pm3 Description: CRAN Package 'pm3' (Propensity Score Matching for Unordered 3-Group Data) You can use this program for 3 sets of categorical data for propensity score matching. Assume that the data has 3 different categorical variables. You can use it to perform propensity matching of baseline indicator groupings. The matching will make the differences in the baseline data smaller. This method was described by Alvaro Fuentes (2022) . Package: r-cran-pmapscore Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4288 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-clusterprofiler, r-cran-glmnet, r-bioc-maftools, r-bioc-org.hs.eg.db, r-cran-proc, r-cran-survival, r-cran-survminer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pmapscore_0.1.1-1.ca2404.1_all.deb Size: 2612342 MD5sum: cdfec540991b82948405bd06abcb680f SHA1: 82871cec169a1a369aa6f491998ae0741dd929b9 SHA256: 582f409c80be567ca5e756cd6ed91df3c0399f41c0a2ff351790c494e2621a3e SHA512: 4c21534ec09b992ac4d73dae0023cad74311ebb06978d67c2b009733319e790f827f9b8bf6087703cc6eddac9b16dce8d2f55465befb552ddb61b1536780ef56 Homepage: https://cran.r-project.org/package=PMAPscore Description: CRAN Package 'PMAPscore' (Identify Prognosis-Related Pathways Altered by Somatic Mutation) We innovatively defined a pathway mutation accumulate perturbation score (PMAPscore) to reflect the position and the cumulative effect of the genetic mutations at the pathway level. Based on the PMAPscore of pathways, identified prognosis-related pathways altered by somatic mutation and predict immunotherapy efficacy by constructing a multiple-pathway-based risk model (Tarca, Adi Laurentiu et al (2008) ). Package: r-cran-pmc Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-geiger, r-cran-ggplot2, r-cran-ouch, r-cran-tidyr, r-cran-phytools Suggests: r-cran-covr, r-cran-gridextra, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pmc_1.0.6-1.ca2404.1_all.deb Size: 126064 MD5sum: 6fdb6278e642c25e1924bacf82d93f89 SHA1: 67919a28532c22b5dadcff81c89a20df01ab85d3 SHA256: ac22be73548647d172a13f856d4c5f4fadf87e4534358d470942edfb80912ce1 SHA512: d94f7d12ba3a02fd155af02e148a99fc5ab1df349482d16f2e14060db104c0590e043e391da038475a85f4634ed28f24aaca60c5119b5a48f2ff144dcf66f628 Homepage: https://cran.r-project.org/package=pmc Description: CRAN Package 'pmc' (Phylogenetic Monte Carlo) Monte Carlo based model choice for applied phylogenetics of continuous traits. Method described in Carl Boettiger, Graham Coop, Peter Ralph (2012) Is your phylogeny informative? Measuring the power of comparative methods, Evolution 66 (7) 2240-51. . Package: r-cran-pmcalibration Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-mass, r-cran-mgcv, r-cran-survival, r-cran-pbapply Suggests: r-cran-rmarkdown, r-cran-data.table, r-cran-ggplot2, r-cran-rms, r-cran-simsurv Filename: pool/dists/noble/main/r-cran-pmcalibration_0.2.0-1.ca2404.1_all.deb Size: 363578 MD5sum: 98bcef234fe4cbcc9dbc6eeaabd1088b SHA1: 083cd9fe7743ba22a59a520d1f5a65fb18c9067a SHA256: 7d284b7c285f94be87f975bea078922367e4525da37fb015e3b820d08ddd6ff4 SHA512: 5e26ffdd5a2fd9f0e61a68800a36507cbeefa36058a26f302253e464c2a7154ebc3dd9f4ce5ce0ffc6d9a431002f617d6ee4977e2d84c8410dc753964d2bea39 Homepage: https://cran.r-project.org/package=pmcalibration Description: CRAN Package 'pmcalibration' (Calibration Curves for Clinical Prediction Models) Fit calibrations curves for clinical prediction models and calculate several associated metrics (Eavg, E50, E90, Emax). Ideally predicted probabilities from a prediction model should align with observed probabilities. Calibration curves relate predicted probabilities (or a transformation thereof) to observed outcomes via a flexible non-linear smoothing function. 'pmcalibration' allows users to choose between several smoothers (regression splines, generalized additive models/GAMs, lowess, loess). Both binary and time-to-event outcomes are supported. See Van Calster et al. (2016) ; Austin and Steyerberg (2019) ; Austin et al. (2020) . Package: r-cran-pmcmr Architecture: all Version: 4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-multcompview Filename: pool/dists/noble/main/r-cran-pmcmr_4.4-1.ca2404.1_all.deb Size: 53082 MD5sum: 9bc6678dd1a0ea2b3b9b54cee097a3e8 SHA1: c40fbbe44697de9084e59f5619a9c6cc37f8866f SHA256: e9d9b0f1704a729e7883277846201304d7266b8b15d9e3c8831af10c7ed51f2d SHA512: e8b1e42eca0d6f2a165942bcfb3690b39dbac299bc1620827f858430e596bd73b09ba064ae573c761f0af46fb999b7a244327766ec32d69d14114e75f52478d5 Homepage: https://cran.r-project.org/package=PMCMR Description: CRAN Package 'PMCMR' (Calculate Pairwise Multiple Comparisons of Mean Rank Sums) Note, that the 'PMCMR' package is superset by the novel 'PMCMRplus' package. The 'PMCMRplus' package contains all functions from 'PMCMR' and many more parametric and non-parametric multiple comparison procedures, one-factorial trend tests, as well as improved method functions, such as print, summary and plot. The 'PMCMR' package is no longer maintained, but kept for compatibility of reverse depending packages for some time. Package: r-cran-pmd Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4558 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-igraph, r-cran-envigcms Suggests: r-cran-knitr, r-cran-shiny, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pmd_0.2.7-1.ca2404.1_all.deb Size: 3723760 MD5sum: 26272653ec219191beecea8a939dad69 SHA1: 1c2e81ac759e04829b656e9a6f2343c9851cb58d SHA256: 63e718f7d0545b1115ae065e757a02b9c688fff99c269beadea3d61bfd992fc5 SHA512: fa7c71d80d7aa27e8ecf85971332eaf9a32c9e7fbe2bf9b0cfcf73a2f9eea4f848f9ccdb3e71a00b55e72e7c21c32fb579da926ed8c9e208343d19a7ef0eb6f6 Homepage: https://cran.r-project.org/package=pmd Description: CRAN Package 'pmd' (Paired Mass Distance Analysis for GC/LC-MS Based Non-TargetedAnalysis and Reactomics Analysis) Paired mass distance (PMD) analysis proposed in Yu, Olkowicz and Pawliszyn (2018) and PMD based reactomics analysis proposed in Yu and Petrick (2020) for gas/liquid chromatography–mass spectrometry (GC/LC-MS) based non-targeted analysis. PMD analysis including GlobalStd algorithm and structure/reaction directed analysis. GlobalStd algorithm could found independent peaks in m/z-retention time profiles based on retention time hierarchical cluster analysis and frequency analysis of paired mass distances within retention time groups. Structure directed analysis could be used to find potential relationship among those independent peaks in different retention time groups based on frequency of paired mass distances. Reactomics analysis could also be performed to build PMD network, assign sources and make biomarker reaction discovery. GUIs for PMD analysis is also included as 'shiny' applications. Package: r-cran-pmetar Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1546 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-httr2, r-cran-lubridate, r-cran-magrittr, r-cran-rcurl, r-cran-tidyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-pmetar_0.7.0-1.ca2404.1_all.deb Size: 1449612 MD5sum: 957ed9086eaffdafd26938376ab5380e SHA1: 1a50e256765c51060168683d72f9e07f953c3273 SHA256: 8d0aa9ffbbcfd30df704acbdd49eae1dcd5b6c99721be87063eb840f19c7597c SHA512: aab56619388aad72f394d915fb9d680fabd003cda1f372665a436cb2bd5fb0c0217f783b2c5dbf795f566e01760dee56eff938ee0068a5240fad0df6e9632633 Homepage: https://cran.r-project.org/package=pmetar Description: CRAN Package 'pmetar' (Processing METAR Weather Reports) Allows to download current and historical METAR weather reports extract and parse basic parameters and present main weather information. Current reports are downloaded from Aviation Weather Center and historical reports from Iowa Environmental Mesonet web page of Iowa State University ASOS-AWOS-METAR . Package: r-cran-pmev Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-zoo, r-cran-rlang, r-cran-ggplot2, r-cran-scales, r-cran-vdiffr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pmev_0.1.2-1.ca2404.1_all.deb Size: 112690 MD5sum: 6a1bb001f78bbfafdafb93aaa7effc0b SHA1: f744b3d671a0c0acdc8c7fc50ebbc778b9c04549 SHA256: ae453a0e07f7a20c4673dce5b1e43409af613937526f5d4edff25f588be19d2e SHA512: 10e182fcece935127b89efdcef4acd6d8b7ce7e24c527f4931aacd6c03335d5daedb703fdd13f347aabe2a580186e275716d91ffb651088c0b95713c7bd60cec Homepage: https://cran.r-project.org/package=pmev Description: CRAN Package 'pmev' (Calculates Earned Value for a Project Schedule) Given a project schedule and associated costs, this package calculates the earned value to date. It is an implementation of Project Management Body of Knowledge (PMBOK) methodologies (reference Project Management Institute. (2021). A guide to the Project Management Body of Knowledge (PMBOK guide) (7th ed.). Project Management Institute, Newtown Square, PA, ISBN 9781628256673 (pdf)). Package: r-cran-pmevapotranspiration Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pmevapotranspiration_0.1.0-1.ca2404.1_all.deb Size: 15842 MD5sum: 54fc25e3288358a947c2cc2b0d1a70e1 SHA1: 2d2aa5003bf0ca013a361a75c9c6c226b915e2e0 SHA256: f29d492cbc6a3b49974a449b57624d62f2ffbde941ef1985644139f9416215c4 SHA512: 934387cbafd2325e131abd1843d3a91fdcbcbff4575f78261b95eea59a61c232e7c1eab1a63ea963bf70ae99c55b3296ed945383fdab343f7aa380004f247a9a Homepage: https://cran.r-project.org/package=PMEvapotranspiration Description: CRAN Package 'PMEvapotranspiration' (Calculation of the Penman-Monteith Evapotranspiration usingWeather Variables) The Food and Agriculture Organization-56 Penman-Monteith is one of the important method for estimating evapotranspiration from vegetated land areas. This package helps to calculate reference evapotranspiration using the weather variables collected from weather station. Evapotranspiration is the process of water transfer from the land surface to the atmosphere through evaporation from soil and other surfaces and transpiration from plants. The package aims to support agricultural, hydrological, and environmental research by offering accurate and accessible reference evapotranspiration calculation. This package has been developed using concept of Córdova et al. (2015) and Debnath et al. (2015) . Package: r-cran-pmhtutorial Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-quandl Filename: pool/dists/noble/main/r-cran-pmhtutorial_1.5-1.ca2404.1_all.deb Size: 95586 MD5sum: 09c4eb51731d097f434e2c7048d2b994 SHA1: d28827687b53ef8246f6eb1211554ef1eb9feb25 SHA256: 8056736156e93d7646bd1efd336b0d334d127b461a96ee67dcf0d3afd3cf8864 SHA512: 63def5b73a7b9871a405f65a91dafa51f61a703df025f902bad14e202991545777b6504e333b1118b998122802303f4277df5ac5ce7e30e4fe6c6b4ac9ba2435 Homepage: https://cran.r-project.org/package=pmhtutorial Description: CRAN Package 'pmhtutorial' (Minimal Working Examples for Particle Metropolis-Hastings) Routines for state estimate in a linear Gaussian state space model and a simple stochastic volatility model using particle filtering. Parameter inference is also carried out in these models using the particle Metropolis-Hastings algorithm that includes the particle filter to provided an unbiased estimator of the likelihood. This package is a collection of minimal working examples of these algorithms and is only meant for educational use and as a start for learning to them on your own. Package: r-cran-pminternal Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1028 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dcurves, r-cran-insight, r-cran-marginaleffects, r-cran-pmcalibration, r-cran-proc, r-cran-pbapply, r-cran-purrr Suggests: r-cran-ggplot2, r-cran-glmnet, r-cran-hmisc, r-cran-knitr, r-cran-rmarkdown, r-cran-ranger, r-cran-gbm, r-cran-rms, r-cran-mgcv, r-cran-mice Filename: pool/dists/noble/main/r-cran-pminternal_0.1.0-1.ca2404.1_all.deb Size: 707790 MD5sum: a06ad8d09940b6528dd19f5ccdb1bdab SHA1: 85beec61ac3c0a7c9d978c7c6759eb0c1836ae60 SHA256: f71eafa4a7e80b19be97a7fbe9142f8ca1b70aa95297e7e8364f9716682d4ac3 SHA512: 0f98ee0318ef86b24db62765f7986b9018c2a47157b767f88221008889e97ae62e425cc66b462e440ff44f0c7db4d44df7b1b36e72926c3de67465972690a723 Homepage: https://cran.r-project.org/package=pminternal Description: CRAN Package 'pminternal' (Internal Validation of Clinical Prediction Models) Conduct internal validation of a clinical prediction model for a binary outcome. Produce bias corrected performance metrics (c-statistic, Brier score, calibration intercept/slope) via bootstrap (simple bootstrap, bootstrap optimism, .632 optimism) and cross-validation (CV optimism, CV average). Also includes functions to assess model stability via bootstrap resampling. See Steyerberg et al. (2001) ; Harrell (2015) ; Riley and Collins (2023) . Package: r-cran-pmlbr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pmlbr_0.3.0-1.ca2404.1_all.deb Size: 220086 MD5sum: 039083d35f21fb7f08365d7f1c3ef917 SHA1: 306d38621ac4dea7be53c9b1a8c93dcfccd6d022 SHA256: ccb9fc22768ed202d5935f4d17a67e2778c294f144259343e9d02f884f04e816 SHA512: fe46140621197ad717f4abe5c463f004945aff70adddbcf0971ba787b727d5289d800f859ef752c4390c29b391091dc6727209e496e496ca7722be941b31c1d6 Homepage: https://cran.r-project.org/package=pmlbr Description: CRAN Package 'pmlbr' (Interface to the Penn Machine Learning Benchmarks DataRepository) Check available classification and regression data sets from the PMLB repository and download them. The PMLB repository () contains a curated collection of data sets for evaluating and comparing machine learning algorithms. These data sets cover a range of applications, and include binary/multi-class classification problems and regression problems, as well as combinations of categorical, ordinal, and continuous features. There are currently over 150 datasets included in the PMLB repository. Package: r-cran-pmledecon Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-splitstackshape, r-cran-rmutil Filename: pool/dists/noble/main/r-cran-pmledecon_0.2.1-1.ca2404.1_all.deb Size: 42520 MD5sum: e986cc0cc6d0a13645bf5369739c4fc5 SHA1: 4ebe250fbcf4766e4e0a80877cea5bb5b24f9645 SHA256: 6b8512be3bde2da355a1e9a81a52f3e06938b81cd2b23c531d9b7190a963ffdc SHA512: 89c118d421237474792e29fd01b9061e2e6527d8b471530c30a331d8bc6dd3036d0221e750947432d9865bbcabf47dc34e2cb28ac148f96cc168916d4fefbebc Homepage: https://cran.r-project.org/package=pmledecon Description: CRAN Package 'pmledecon' (Deconvolution Density Estimation using Penalized MLE) Given a sample with additive measurement error, the package estimates the deconvolution density - that is, the density of the underlying distribution of the sample without measurement error. The method maximises the log-likelihood of the estimated density, plus a quadratic smoothness penalty. The distribution of the measurement error can be either a known family, or can be estimated from a "pure error" sample. For known error distributions, the package supports Normal, Laplace or Beta distributed error. For unknown error distribution, a pure error sample independent from the data is used. Package: r-cran-pmlsp Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-matrix, r-cran-matrixcalc, r-cran-maxlik, r-cran-minqa, r-cran-mvtnorm, r-cran-numderiv, r-cran-qrng, r-cran-spatialreg, r-cran-spdep Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pmlsp_1.0.1-1.ca2404.1_all.deb Size: 145398 MD5sum: f3867cb749c3bf9e1ef6e114427a96f8 SHA1: 6807b69bd4ce7c42e0fba9311102a3f96a6a2262 SHA256: b4599911cf0eadd23f9de75a3d08df5fad6b991703e4849079646026ce077d67 SHA512: 701e2114b48d28da4169c4326d0def63742c9efe951df47aea13f82937f413352b615e6ed46a35ab01e654f9ade4bf34588f680617722efa0c5aa44a34ac190e Homepage: https://cran.r-project.org/package=pmlsp Description: CRAN Package 'pmlsp' (Partial Maximum Likelihood Estimation of Spatial Probit Models) Estimate spatial autoregressive nonlinear probit models with and without autoregressive disturbances using partial maximum likelihood estimation. Estimation and inference regarding marginal effects is also possible. For more details see Bille and Leorato (2020) . 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More information about the PMML industry standard and the Data Mining Group can be found at . The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products. Package: r-cran-pmmltransformations Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-pmml Filename: pool/dists/noble/main/r-cran-pmmltransformations_1.3.3-1.ca2404.1_all.deb Size: 116454 MD5sum: 3a05d3e508bb8d4254439b60122404bc SHA1: 74c11876e6e849a51433b5905df93f4ea8120ee5 SHA256: 1380d79f025853e9391e316d6367cb3cf43e01ae971c3ebf4b57bd9bad6f69b8 SHA512: 85dfbe840adcdf6f892e9d8315d248894c79bdb63efc2dd61faca05bb4834c6c806b05c4e30167cbf4f8f0fba4609da2456461fd1da55f029e3ada4dfec71429 Homepage: https://cran.r-project.org/package=pmmlTransformations Description: CRAN Package 'pmmlTransformations' (Transforms Input Data from a PMML Perspective) Allows for data to be transformed before using it to construct models. Builds structures to allow functions in the PMML package to output transformation details in addition to the model in the resulting PMML file. The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at . The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms. Package: r-cran-pmparser Architecture: all Version: 1.0.25-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-data.table, r-cran-dbi, r-cran-foreach, r-cran-glue, r-cran-iterators, r-cran-jsonlite, r-cran-r.utils, r-cran-rcurl, r-cran-withr, r-cran-xml2 Suggests: r-cran-bigrquery, r-cran-doparallel, r-cran-rmariadb, r-cran-rpostgres, r-cran-rsqlite, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pmparser_1.0.25-1.ca2404.1_all.deb Size: 182804 MD5sum: 7c940e76eed457f2d9e38af7d456a27d SHA1: 7a574015e05d977507400f3ef487c21a6bfe120e SHA256: f3d048beec04ccb5a2ba660457a1b3c908f7d552a63e79f8cf71fc3f82be2caf SHA512: a23a3eb1825b4d07064fb8192e478ed456509b03c37d3661ad33393ee42b2c8248b53692bd30ed516c70295f38fd1a5f58c83096cf93d79cc644804835e71206 Homepage: https://cran.r-project.org/package=pmparser Description: CRAN Package 'pmparser' (Create and Maintain a Relational Database of Data fromPubMed/MEDLINE) Provides a simple interface for extracting various elements from the publicly available PubMed XML files, incorporating PubMed's regular updates, and combining the data with the NIH Open Citation Collection. See Schoenbachler and Hughey (2021) . Package: r-cran-pmr Architecture: all Version: 1.2.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pmr_1.2.5.1-1.ca2404.1_all.deb Size: 120520 MD5sum: aa6a2396d1f93563a1a0bb44c77bd934 SHA1: 9d635b3e23be272b4aa4df9a93a0d0271ede7ef4 SHA256: 2b6bc82f96d48760d854ee1b34b5088184b52b6691ff49e3a6292ae91267bd60 SHA512: 889232135716027a8193820ddae5e87083d740ae437b26589fb261f4c9f5b9e04faee88313812c75dbf1d100d6e33d8bb59775e4e545fb2505acbea87c1d9dcf Homepage: https://cran.r-project.org/package=pmr Description: CRAN Package 'pmr' (Probability Models for Ranking Data) Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package. Package: r-cran-pmrm Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1735 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-matrix, r-cran-nlme, r-cran-rtmb, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-readr, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pmrm_0.0.4-1.ca2404.1_all.deb Size: 1277782 MD5sum: 5a524834429130e9d1991063e384c2f5 SHA1: b641f8a3bbdd662da41a501fc9e38974d1b4a57d SHA256: e50567ace54f1388d47060cf9804719222ed1ba7270c29359dbd0e1a5aa22eff SHA512: 95dedb67961cf16604ab62f861014d279e193ed7153649de09f61c483fb03b76afcf6eed17e37f1ccd8299ff0291720ecbd70f2d763dfc157964cd3bee432847 Homepage: https://cran.r-project.org/package=pmrm Description: CRAN Package 'pmrm' (Progression Models for Repeated Measures) A progression model for repeated measures (PMRM) is a continuous-time nonlinear mixed-effects model for longitudinal clinical trials in progressive diseases. Unlike mixed models for repeated measures (MMRMs), which estimate treatment effects as linear combinations of additive effects on the outcome scale, PMRMs characterize treatment effects in terms of the underlying disease trajectory. This framing yields clinically interpretable quantities such as average time saved and percent reduction in decline due to treatment. This package implements frequentist PMRMs by Raket (2022) using 'RTMB' by Kristensen (2016) . Package: r-cran-pmsampsize Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pmsampsize_1.1.3-1.ca2404.1_all.deb Size: 46666 MD5sum: 656b2476d630ee19df75c7de91d71651 SHA1: 77ca40f14b5527273d1c3750eb5e7f24af32c14e SHA256: 7ec7983df28346ae1717f7ce77433e598d2236f0dd12e789fe291f81a0029846 SHA512: 39347d5d5cdb108139458c6ac5330fa48c87a6528653d4960504e7f4744c30b1724568d28a826f7d7de03804b02115fe1101a52c66d73d1398866264ef0e1560 Homepage: https://cran.r-project.org/package=pmsampsize Description: CRAN Package 'pmsampsize' (Sample Size for Development of a Prediction Model) Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) . pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. (2018) lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model. Package: r-cran-pmsesampling Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rootsolve Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pmsesampling_0.1.1-1.ca2404.1_all.deb Size: 21960 MD5sum: 09f9b8ff74148e5b00ab2e64230e2b98 SHA1: 7ed6f2664aebe7d4906b27c274da9e989d963e16 SHA256: 741ff5de77ecad382ea90fd282121f28ef41e84fab9d6e13a5da27c52cbca563 SHA512: f2a072b91a88b51f639a77a9047a05097b6edb9c95e7b3885e49f4f928134582db1729351030a6738e9b5776b7403761d8965b07612708ac84b2749d75d314e7 Homepage: https://cran.r-project.org/package=pmsesampling Description: CRAN Package 'pmsesampling' (Sample Size Determination for Accurate Predictive LinearRegression) Provides analytic and simulation tools to estimate the minimum sample size required for achieving a target prediction mean-squared error (PMSE) or a specified proportional PMSE reduction (pPMSEr) in linear regression models. Functions implement the criteria of Ma (2023) , support covariance-matrix handling, and include helpers for root-finding and diagnostic plotting. 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Package: r-cran-pmvalsampsize Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proc Filename: pool/dists/noble/main/r-cran-pmvalsampsize_0.1.0-1.ca2404.1_all.deb Size: 36642 MD5sum: b37f3e07f3d020c344506661f4c10837 SHA1: ff2be47df1bb359e3a851383b62c14f7800836ea SHA256: c1fd4c3fac98ec5a299965cd4d6d57377e402c1afcac5f4a8c4744faf57b48a4 SHA512: 3fcb989bcfe48003f3b36e14177257af67ade9f9b6d9016d305b17c0d4a03974f40ec77913cad895d1c9f6687496b71edb17d804ede684cff8c2e9a922e6ba25 Homepage: https://cran.r-project.org/package=pmvalsampsize Description: CRAN Package 'pmvalsampsize' (Sample Size for External Validation of a Prediction Model) Computes the minimum sample size required for the external validation of an existing multivariable prediction model using the criteria proposed by Archer (2020) and Riley (2021) . 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A more general implementation of the sampler based on the paper by Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D. (2020) . An HTML tutorial document describing the package is available at and includes several detailed examples, some background and troubleshooting steps. 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Package: r-cran-pmxnode Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1946 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-ggplot2, r-cran-checkmate Suggests: r-cran-rxode2, r-cran-nlmixr2, r-cran-withr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pmxnode_0.1.0-1.ca2404.1_all.deb Size: 1003522 MD5sum: 1fa51fc543e70a0b7f60899559a75ec3 SHA1: 10746094ad03df1819d8f311259f4179d7248353 SHA256: b81b18aa70c166b60ce9a3ff2b3211809621f3deb7cda1be0f7fcbff3589d071 SHA512: cb39d02ba01354df89187171ca8451463b5e5386ff25a926989be83a944cc681cd3187f26d5c39d720be42d53066c3e82c6dead1e26215676b428508d902d5ad Homepage: https://cran.r-project.org/package=pmxNODE Description: CRAN Package 'pmxNODE' (Application of NODEs in 'Monolix', 'NONMEM', and 'nlmixr2') An easy-to-use tool for implementing Neural Ordinary Differential Equations (NODEs) in pharmacometric software such as 'Monolix', 'NONMEM', and 'nlmixr2', see Bräm et al. (2024) and Bräm et al. (2025) . The main functionality is to automatically generate structural model code describing computations within a neural network. Additionally, parameters and software settings can be initialized automatically. For using these additional functionalities with 'Monolix', 'pmxNODE' interfaces with 'MonolixSuite' via the 'lixoftConnectors' package. The 'lixoftConnectors' package is distributed with 'MonolixSuite' () and is not available from public repositories. 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Package: r-cran-pmxtools Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1265 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-patchwork, r-cran-ggplot2, r-cran-chron, r-cran-xml2, r-cran-dplyr, r-cran-tibble, r-cran-ggdist, r-cran-scales, r-cran-mass, r-cran-stringr, r-cran-pknca, r-cran-magrittr, r-cran-data.tree Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pmxtools_1.5-1.ca2404.1_all.deb Size: 1028214 MD5sum: 34f7e00b5712fc5a65aa922c5d67327e SHA1: 017bee94f2eb2e31f2d9bcb989406b931b921456 SHA256: d08262901f3da3c402a4ed339d26a2d9672485d808e68e6a253573756b5eeef2 SHA512: 6249669d80d759404cb89e1960d3fe466d5e52e59f41f464126bd03d512bc9fa77f7369c5d1d15d4cbc327bd413b5bfa389c76e7d38c4fece2062f1f3fa1fdd3 Homepage: https://cran.r-project.org/package=pmxTools Description: CRAN Package 'pmxTools' (Pharmacometric and Pharmacokinetic Toolkit) Pharmacometric tools for common data analytical tasks; closed-form solutions for calculating concentrations at given times after dosing based on compartmental PK models (1-compartment, 2-compartment and 3-compartment, covering infusions, zero- and first-order absorption, and lag times, after single doses and at steady state, per Bertrand & Mentre (2008) ); parametric simulation from NONMEM-generated parameter estimates and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN). Package: r-cran-pnadcibge Architecture: all Version: 0.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-magrittr, r-cran-projmgr, r-cran-rcurl, r-cran-readr, r-cran-readxl, r-cran-survey, r-cran-tibble, r-cran-timedate Suggests: r-cran-convey, r-cran-sipdibge, r-cran-srvyr Filename: pool/dists/noble/main/r-cran-pnadcibge_0.7.5-1.ca2404.1_all.deb Size: 216526 MD5sum: ff729fcf5a7076ad44e6087a1665b04e SHA1: 85a70f9ac8c1af40f06e7818fae3fbafb574603e SHA256: 35870d146fffe9f384e3a5cc229d546cc475ed763548df815bc806515f96811a SHA512: 3eda87c44095408d4663870c0438326519af5e3057d4c445545d557ea3515c3a947384aab30767a7a6eceb2146749ee9a120205b1982c0292d95bd4ec38d4a0c Homepage: https://cran.r-project.org/package=PNADcIBGE Description: CRAN Package 'PNADcIBGE' (Downloading, Reading and Analyzing PNADC Microdata) Provides tools for downloading, reading and analyzing the Continuous National Household Sample Survey - PNADC, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website . Further analysis must be made using package 'survey'. Package: r-cran-pnadcperiods Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-checkmate, r-cran-sidrar, r-cran-lubridate Suggests: r-cran-dplyr, r-cran-fst, r-cran-haven, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-pnadcperiods_0.1.2-1.ca2404.1_all.deb Size: 2355278 MD5sum: 331e3359267519598c095107fc4162e7 SHA1: 808ba080ba8e876f5aa7ef633667cdba7ad2f9fa SHA256: e9af8605b4f66e770e2de67b71a9b31e3f3017ef3c45dad90c0b65c971398fea SHA512: 6093a56f9beab658d3b4f6231e28ee533f0302388d60a7c2a342c9a55cea0f39c3d429f0f2c9e6bfc3ba16f359399a2a9ac198c9b72e22c2f5c80ebe67aa7445 Homepage: https://cran.r-project.org/package=PNADCperiods Description: CRAN Package 'PNADCperiods' (Identify Reference Periods in Brazil's PNADC Survey Data) Identifies reference periods (months, fortnights, and weeks) in Brazil's quarterly PNADC (Pesquisa Nacional por Amostra de Domicilios Continua) survey data and computes calibrated weights for sub-quarterly analysis. The core algorithm uses IBGE (Instituto Brasileiro de Geografia e Estatistica) 'Parada Tecnica' (technical break) rules combined with respondent birthdates to determine which temporal period each survey observation refers to. Period identification follows a nested hierarchy enforced by construction: fortnights require months, weeks require fortnights. Achieves approximately 97% monthly determination rate with the full series (2012-2025). Strict fortnight and week rates are approximately 9% and 3% respectively, as they cannot leverage cross-quarter panel aggregation. Experimental strategies (probabilistic assignment and UPA (Primary Sampling Unit) aggregation) further improve these determination rates. The package provides adaptive hierarchical weight calibration (4/2/1 cell levels for month/fortnight/week) with period-specific smoothing to produce survey weights calibrated to SIDRA (Sistema IBGE de Recuperacao Automatica) population totals. Also includes a SIDRA mensalization module that converts 86+ official rolling quarter series from the IBGE SIDRA API (Application Programming Interface) into exact monthly estimates, without requiring access to microdata. Hecksher (2020) . Package: r-cran-pnar Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-cran-nloptr, r-cran-rangen, r-cran-rfast, r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-pnar_1.8-1.ca2404.1_all.deb Size: 279332 MD5sum: 56ea7087538b8f3e0aa95062d37c8010 SHA1: a41f8a7fd07c23d0f24d8a98417a35e86ca19cff SHA256: 631ca7a40b4dd157c24969d73117f96d28da323264e932cbd2960a97e6ea0927 SHA512: d69a2a139426bb99847a4715689465865c86c11dd0c38ebe1a56eac3b07d55fa6e2686875683180bd55ad65ea4804e99be712eb3d57ecde90a3c66ac85d1d21b Homepage: https://cran.r-project.org/package=PNAR Description: CRAN Package 'PNAR' (Poisson Network Autoregressive Models) Quasi likelihood-based methods for estimating linear and log-linear Poisson Network Autoregression models with p lags and covariates. Tools for testing the linearity versus several non-linear alternatives. Tools for simulation of multivariate count distributions, from linear and non-linear PNAR models, by using a specific copula construction. References include: Armillotta, M. and K. Fokianos (2023). "Nonlinear network autoregression". Annals of Statistics, 51(6): 2526--2552. . Armillotta, M. and K. Fokianos (2024). "Count network autoregression". Journal of Time Series Analysis, 45(4): 584--612. . Armillotta, M., Tsagris, M. and Fokianos, K. (2023). "Inference for Network Count Time Series with the R Package PNAR". The R Journal, 15/4: 255--269. . Package: r-cran-pnc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4469 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-phytools, r-cran-geiger Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pnc_0.1.0-1.ca2404.1_all.deb Size: 4136528 MD5sum: d79eb1cd16535961cb8296258b0729de SHA1: 2216f5c1e8da53968606e55d8c7ff49e95f73479 SHA256: 2373eeb78fbdd6fd98eaae548b617598c9ba381b1f6a44530a36b131565d6f44 SHA512: c9740b62699369294a90efc9c56c717f961be67319128554fe85ad3a211ced78e4f4d65cdd4e5a062222645eab80a1d1f333de6e290d066e6abc731a7f6f9495 Homepage: https://cran.r-project.org/package=PNC Description: CRAN Package 'PNC' (Phylogenetic Niche Conservatism Analysis for EcologicalCommunities) Provides functions for testing phylogenetic niche conservatism, a key prerequisite in community assembly studies. The package integrates global functional trait data across major taxonomic groups and implements methods such as Pagel's Lambda and Blomberg's K to quantify phylogenetic signals in ecological communities. Methods are described in Münkemüller et al. (2012) . 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The estimation uses the multiply robust method, allowing for the use of machine learning methods in model estimation (e.g., random forest, neural network, and the super learner ensemble). Partially nested designs (also known as partially clustered designs) are designs where individuals in the treatment arm are assigned to clusters (e.g., teachers, tutoring groups, therapists), whereas individuals in the control arm have no such clustering. 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Further attempts to match records from trades and general directories. Package: r-cran-podes Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 248 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl Filename: pool/dists/noble/main/r-cran-podes_0.1.0-1.ca2404.1_all.deb Size: 212328 MD5sum: 74131ca81c397e96d5e4a9fba15b6227 SHA1: d91139b42f8e20f83b2879b2d1214f6ea51256f5 SHA256: 06c0a27ed15f191004222e1b5cd3c72f78c8a86053d41e004d649050180ba461 SHA512: 980552c27609c1682dd4adfa085544a0de6f828237448f9cf0e21e56c74c50dc7c5d33229c9891e07e0e30efeed10ba29e2268dcabb85991d8382efa261aeda2 Homepage: https://cran.r-project.org/package=PODES Description: CRAN Package 'PODES' (Village Potential Statistics of Indonesia) Village potential statistics (PODES) collects various information on village potential and challenges faced by villages in Indonesia. 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The package includes classes for encapsulating and generating model parameters, and managing the POM workflow. The workflow includes: model setup; generating model parameters via Latin hyper-cube sampling (Iman & Conover, 1980, ); running multiple sampled model simulations; collating summary results; and validating and selecting an ensemble of models that best match known patterns. By default, model validation and selection utilizes an approximate Bayesian computation (ABC) approach (Beaumont et al., 2002, ), although alternative user-defined functionality could be employed. The package includes a spatially explicit demographic population model simulation engine, which incorporates default functionality for density dependence, correlated environmental stochasticity, stage-based transitions, and distance-based dispersal. The user may customize the simulator by defining functionality for translocations, harvesting, mortality, and other processes, as well as defining the sequence order for the simulator processes. The framework could also be adapted for use with other model simulators by utilizing its extendable (inheritable) base classes. 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Package: r-cran-pogromcydanych Architecture: all Version: 1.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-smarterpoland Filename: pool/dists/noble/main/r-cran-pogromcydanych_1.7.1-1.ca2404.1_all.deb Size: 4205474 MD5sum: 33b09e165dbbb2180ac514c731c69575 SHA1: 1500be130204e09a8880c829225e167fe601109c SHA256: bfae51cb92ae5afc70e0d673a1f3511cc575661c7506c6f03310ac1583aa9156 SHA512: a7c85588065f4a8934cd426512e68fddacb45912fd3c11297fca0a90ed0af14ee457075ed24f59376af688cf1f4f240de55fc6f6f39ec5e5f68ea3c574ec0b42 Homepage: https://cran.r-project.org/package=PogromcyDanych Description: CRAN Package 'PogromcyDanych' (DataCrunchers (PogromcyDanych) is the Massive Online Open Coursethat Brings R and Statistics to the People) The data sets used in the online course ,,PogromcyDanych''. You can process data in many ways. The course Data Crunchers will introduce you to this variety. For this reason we will work on datasets of different size (from several to several hundred thousand rows), with various level of complexity (from two to two thousand columns) and prepared in different formats (text data, quantitative data and qualitative data). All of these data sets were gathered in a single big package called PogromcyDanych to facilitate access to them. It contains all sorts of data sets such as data about offer prices of cars, results of opinion polls, information about changes in stock market indices, data about names given to newborn babies, ski jumping results or information about outcomes of breast cancer patients treatment. 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Performs structure-guided collapsing, which leads to local tests that borrow information from neighboring variants on a protein and that provide association information on a variant-specific level. For details of the implemented method see West, R. M., Lu, W., Rotroff, D. M., Kuenemann, M., Chang, S-M., Wagner M. J., Buse, J. B., Motsinger-Reif, A., Fourches, D., and Tzeng, J-Y. (2019) . 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Despite this, the general software to help ecologists construct such models in an easy-to-use framework is lacking. We therefore introduce the R package 'PointedSDMs': which provides the tools to help ecologists set up integrated models and perform inference on them. There are also functions within the package to help run spatial cross-validation for model selection, as well as generic plotting and predicting functions. An introduction to these methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and O’Hara (2020) . 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The data generation mechanism is a combination of the normal to anything principle and a connection between Poisson and normal correlations in the mixture. The details of the method are explained in Yahav et al. (2012) . 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This package allows researchers to easily visualize and quantify politeness between groups of documents. This package combines prior research on the linguistic markers of politeness. We thank the Spencer Foundation, the Hewlett Foundation, and Harvard's Institute for Quantitative Social Science for support. Package: r-cran-politicsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ineq Filename: pool/dists/noble/main/r-cran-politicsr_0.1.0-1.ca2404.1_all.deb Size: 260854 MD5sum: ffed887429102ba923acb63c84af61de SHA1: dd83eb90c8d50678cd027d2048c9d448aeb45fce SHA256: e7554abb6700d64476c0a980fe5fa1d6fbc90204848cc86c3595091e188deb0e SHA512: f5a0a1bd5f855f75766d0e8b312615c7a6f4f7c8211a0f7c331fd7ede5e4f34b6eaf46ed6ca967fe0b36ca95197a52892e7a18bc4d15cb6ed5a25160aa28b36c Homepage: https://cran.r-project.org/package=politicsR Description: CRAN Package 'politicsR' (Calculating Political System Metrics) A toolbox to facilitate the calculation of political system indicators for researchers. This package offers a variety of basic indicators related to electoral systems, party systems, elections, and parliamentary studies, as well as others. Main references are: Loosemore and Hanby (1971) ; Gallagher (1991) ; Laakso and Taagepera (1979) ; Rae (1968) ; Hirschmaņ (1945) ; Kesselman (1966) ; Jones and Mainwaring (2003) ; Rice (1925) ; Pedersen (1979) ; SANTOS (2002) . 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Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2026) for documentation and references. 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This package contains novel Bayesian predictive models of pollinator body size (for bees and hoverflies) as well as preexisting predictive models for pollinator body size (currently implemented for ants, bees, butterflies, flies, moths and wasps) as well as bee tongue length and foraging distance, total field nectar loads and wing loading. An additional GitHub repository provides model objects to use the bodysize function internally. All models are described in Kendall et al (2018) . 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Options include topline and (2-way and 3-way) crosstab tables of categorical or ordinal data as well as summary tables of weighted numeric variables. Optionally, include the margin of error at selected confidence intervals including the design effect. The design effect is calculated as described by Kish (1965) beginning on page 257. Output takes the form of tibbles (simple data frames). This package conveniently handles labelled data, such as that commonly used by 'Stata' and 'SPSS.' Complex survey design is not supported at this time. 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Reference: Voorrips and Tumino: PolyHaplotyper: haplotyping in polyploids based on bi-allelic marker dosage data. Submitted to BMC Bioinformatics (2021). 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It is based on the package 'polynom' and uses a lot of its methods to implement matrix operations. This package includes 3 methods of triangularization of polynomial matrices: Extended Euclidean algorithm which is most classical but numerically unstable; Sylvester algorithm based on LQ decomposition; Interpolation algorithm is based on LQ decomposition and Newton interpolation. Both methods are described in D. Henrion & M. Sebek, Reliable numerical methods for polynomial matrix triangularization, IEEE Transactions on Automatic Control (Volume 44, Issue 3, Mar 1999, Pages 497-508) and in Salah Labhalla, Henri Lombardi & Roger Marlin, Algorithmes de calcule de la reduction de Hermite d'une matrice a coefficients polynomeaux, Theoretical Computer Science (Volume 161, Issue 1-2, July 1996, Pages 69-92) . Package: r-cran-polymigr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-polymigr_0.1.0-1.ca2404.1_all.deb Size: 26922 MD5sum: df82d46987aa1b7c3ab31b9652094da1 SHA1: 95a454dd49a70d570d05e757b4280b1c84f9041f SHA256: a2a59f87e06f8be133df0ddaddf3597abc964af0027e7c0427b9383cd012fdb1 SHA512: 60f63701269aeb237e9c32b93ff32ff4c71c504a01ee216fb467642a996833fecddec6a6882ef8dc56dcf634931e0bba7b773a24be1b467c0ab2a88462e9e178 Homepage: https://cran.r-project.org/package=PolyMigR Description: CRAN Package 'PolyMigR' (Analysis of Polyphenol Migration from Packaging Films) The gradual release of active substances from packaging can enhance food preservation by maintaining high concentrations of polyphenols and antioxidants for a period of 72 hrs. To assess the effectiveness of packaging materials that serve as carriers for antioxidants, it is crucial to model the diffusivity of the active agents. Understanding this diffusivity helps evaluate the packaging's capacity to prolong the shelf life of food items. The process of migration, which encompasses diffusion, dissolution, and reaching equilibrium, facilitates the transfer of low molecular weight compounds from the packaging into food simulants. The rate at which these active compounds are released from the packaging is typically analysed using food simulants under conditions outlined in European food packaging regulations (Ramos et al., 2014). 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Package: r-cran-polypatex Architecture: all Version: 0.9.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 597 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools Filename: pool/dists/noble/main/r-cran-polypatex_0.9.2-1.ca2404.1_all.deb Size: 484984 MD5sum: 99ffafce049a532146fb532f8b3891e9 SHA1: 1540d1d8564eb98f40f0aa3cbab2d7cca8d87ae6 SHA256: 867fe732e682386929dbb129a7496e244bd091a71294a174f14a5a37d4a4fc1a SHA512: 81170f43750a4a2b00d1504511e72f558658ab0d310930ba91cdf1cac644edf459cb18cdc5a89850691b301456f888c13ec696a026f88c7d2e8f65ae28c3b1c7 Homepage: https://cran.r-project.org/package=PolyPatEx Description: CRAN Package 'PolyPatEx' (Paternity Exclusion in Autopolyploid Species) Functions to perform paternity exclusion via allele matching, in autopolyploid species having ploidy 4, 6, or 8. The marker data used can be genotype data (copy numbers known) or 'allelic phenotype data' (copy numbers not known). Package: r-cran-polypharmacy Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-itertools, r-cran-lubridate, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-polypharmacy_1.0.0-1.ca2404.1_all.deb Size: 230448 MD5sum: 5a1a44019f664506e8b4cf9d31944c05 SHA1: 53812fa8ce814459466739a9f5cfc183948640b5 SHA256: 3e325e801e327f4e749cd31bccc3414db8bbaa3ffea8bfcd206242aa63b622a6 SHA512: 027531dcb610f8d15a7c1a6ed7bee894909ed5ab29ddae107871e70b5d6a6747aa5bcfc03366b080b26c517f1588adf5ed2844f3e637db2cce77b8e2231d766b Homepage: https://cran.r-project.org/package=polypharmacy Description: CRAN Package 'polypharmacy' (Calculate Several Polypharmacy Indicators) Analyse prescription drug deliveries to calculate several indicators of polypharmacy corresponding to the various definitions found in the literature. Bjerrum, L., Rosholm, J. U., Hallas, J., & Kragstrup, J. (1997) . Chan, D.-C., Hao, Y.-T., & Wu, S.-C. (2009a) . Fincke, B. G., Snyder, K., Cantillon, C., Gaehde, S., Standring, P., Fiore, L., ... Gagnon, D.R. (2005) . Hovstadius, B., Astrand, B., & Petersson, G. (2009) . Hovstadius, B., Astrand, B., & Petersson, G. (2010) . Kennerfalk, A., Ruigómez, A., Wallander, M.-A., Wilhelmsen, L., & Johansson, S. (2002) . Masnoon, N., Shakib, S., Kalisch-Ellett, L., & Caughey, G. E. (2017) . Narayan, S. W., & Nishtala, P. S. (2015) . Nishtala, P. S., & Salahudeen, M. S. (2015) . Park, H. Y., Ryu, H. N., Shim, M. K., Sohn, H. S., & Kwon, J. W. (2016) . Veehof, L., Stewart, R., Haaijer-Ruskamp, F., & Jong, B. M. (2000) . 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Package: r-cran-pomodoro Architecture: all Version: 3.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 975 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-caret, r-cran-gbm, r-cran-randomforest, r-cran-proc, r-cran-ipred Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pomodoro_3.8.0-1.ca2404.1_all.deb Size: 803710 MD5sum: e89c23800883ea1061426c74295499b2 SHA1: 80fb57b89af9fc25edae6e83d92d162d41e1d860 SHA256: 92e532eb163c52181fc04a329e8e88fc9682493eead833e9a6e512aad48d1584 SHA512: 4b841c7efd4421345a413892fe2cbf47a8fe8c247516c6cb8c2718fa9614e745c3f0ca5c6a9bd657c1416d9cafa38f0a58bd831cc77d6cf5a8a611cbd46fc805 Homepage: https://cran.r-project.org/package=pomodoro Description: CRAN Package 'pomodoro' (Predictive Power of Linear and Tree Modeling) Runs generalized and multinominal logistic (GLM and MLM) models, as well as random forest (RF), Bagging (BAG), and Boosting (BOOST). This package prints out to predictive outcomes easy for the selected data and data splits. Package: r-cran-pompom Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1376 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan, r-cran-ggplot2, r-cran-reshape2, r-cran-qgraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pompom_0.2.1-1.ca2404.1_all.deb Size: 1273720 MD5sum: 3efb9b9baed4aa970571d28fc90fbb82 SHA1: 61bbf599d75df9bbb86c29c387cb7d248a95d843 SHA256: 826cb53c88c78dff25ed8ae4b80662301e9cdcd3ed7ce15d37d1c40823ffa24d SHA512: 2e9fbd4c6b6e507774d06c6b60b584a01207cbd783811ec41170fba7d2011d324868736c50ca2f140f66273723f830f7383b2de6471793de44c4d0d8f6dd9c3e Homepage: https://cran.r-project.org/package=pompom Description: CRAN Package 'pompom' (Person-Oriented Method and Perturbation on the Model) An implementation of a hybrid method of person-oriented method and perturbation on the model. Pompom is the initials of the two methods. The hybrid method will provide a multivariate intraindividual variability metric (iRAM). The person-oriented method used in this package refers to uSEM (unified structural equation modeling, see Kim et al., 2007, Gates et al., 2010 and Gates et al., 2012 for details). Perturbation on the model was conducted according to impulse response analysis introduced in Lutkepohl (2007). Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) . Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) . Gates, K. M., & Molenaar, P. C. M. (2012) . Lutkepohl, H. (2007, ISBN:3540262393). Package: r-cran-poms Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-data.table, r-cran-mass, r-cran-phangorn, r-cran-phylolm, r-cran-xnomial Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-poms_1.0.1-1.ca2404.1_all.deb Size: 109960 MD5sum: 6408cab606bb8344315df007599bc5c7 SHA1: 2b01ad97a5224feb04ed1392aaa328786be38dd8 SHA256: c3504a61308b27ceb6d6e5165bfb3db016f248ee30471cfe5f58e553b2193116 SHA512: 9244030f66438113f735e5f739091220769cf6b035c3388fa607b5613d72fd8ea83fe77e02de9a8365b66d7b8820b52327165eb738adea9484b34fe046ed5187 Homepage: https://cran.r-project.org/package=POMS Description: CRAN Package 'POMS' (Phylogenetic Organization of Metagenomic Signals) Code to identify functional enrichments across diverse taxa in phylogenetic tree, particularly where these taxa differ in abundance across samples in a non-random pattern. The motivation for this approach is to identify microbial functions encoded by diverse taxa that are at higher abundance in certain samples compared to others, which could indicate that such functions are broadly adaptive under certain conditions. See 'GitHub' repository for tutorial and examples: . Citation: Gavin M. Douglas, Molly G. Hayes, Morgan G. I. Langille, Elhanan Borenstein (2022) . Package: r-cran-poobly Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plm Suggests: r-cran-rfast, r-cran-rfast2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-poobly_0.1.2-1.ca2404.1_all.deb Size: 263340 MD5sum: 92b0403e64f7f8e768f6a17e34f326e2 SHA1: 772ba31f3a0df6ae38066f486e55ce0ddad9af67 SHA256: 18f6c0cfd699c6da2d1ee1dc49855049bb4ba568a8b76d710e48b813f5aadc20 SHA512: 8113e6c324758365995f9a553e99f6761295d3e9582f4f3ccd90995a8152589f77dc8b81598f560f55fd83a0bad7bced5778e26f1ba6f71ca98fce07c68014a8 Homepage: https://cran.r-project.org/package=poobly Description: CRAN Package 'poobly' (Poolability Tests in Panel Data) Homogeneity tests of the coefficients in panel data. Currently, only the Hsiao test for determining coefficient homogeneity between the panel data individuals is implemented, as described in Hsiao (2022), "Analysis of Panel Data" (). 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Currently the only supported pooled objects are 'DBI' connections. Package: r-cran-poolabc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7096 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-locfit, r-cran-metricsweighted, r-cran-nnet, r-cran-poolhelper, r-cran-rcolorbrewer, r-cran-rlang, r-cran-scrm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-poolabc_1.0.0-1.ca2404.1_all.deb Size: 4154216 MD5sum: ce62d2c729a59b32f56a22bcd256faec SHA1: da8bbd67dc339f2cbf9b2bd09db49a659b9b4ff1 SHA256: c2da75a1f7c49225365ca4d44132b84b428f107060b5c76dd4dfa138e4ee2d40 SHA512: 9da9880d6aec9ad94cb86ba30b404b02ae3285c01c2c8f068186ddcb63a2ea27f14eaaba8b599fbea061ea756001704332e91ce83bd4be0532c248b214182155 Homepage: https://cran.r-project.org/package=poolABC Description: CRAN Package 'poolABC' (Approximate Bayesian Computation with Pooled Sequencing Data) Provides functions to simulate Pool-seq data under models of demographic formation and to import Pool-seq data from real populations. Implements two ABC algorithms for performing parameter estimation and model selection using Pool-seq data. Cross-validation can also be performed to assess the accuracy of ABC estimates and model choice. Carvalho et al., (2022) . Package: r-cran-poolbal Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1170 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-poolbal_0.1-0-1.ca2404.1_all.deb Size: 1135502 MD5sum: 013f00067fba75052d1217af9da737cc SHA1: 4a4660cd247e6823dfe3cf0fd79ad5aa1699dfd4 SHA256: 9ce6070c6862287b23b54486c966982804166011162baedc50d4482d0ad3a572 SHA512: 906bcb1a61b521a46e20130de827a402e80288ed324b7d53eb0f39043f59a7e21b6863dc151412969af05a616c32c4b826e12ee83b3b21811a8ca774318867df Homepage: https://cran.r-project.org/package=PoolBal Description: CRAN Package 'PoolBal' (Balancing Central and Marginal Rejection of Pooled p-Values) When using pooled p-values to adjust for multiple testing, there is an inherent balance that must be struck between rejection based on weak evidence spread among many tests and strong evidence in a few, explored in Salahub and Olford (2023) . This package provides functionality to compute marginal and central rejection levels and the centrality quotient for p-value pooling functions and provides implementations of the chi-squared quantile pooled p-value (described in Salahub and Oldford (2023)) and a proposal from Heard and Rubin-Delanchy (2018) to control the quotient's value. Package: r-cran-pooldilutionr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-pooldilutionr_1.0.0-1.ca2404.1_all.deb Size: 105792 MD5sum: f5be91b1af426e1af13fbd64e54084f6 SHA1: d61733ab86961f2e4722f0953b19f1529c15b872 SHA256: fdceddd16820eac2d82798cd9693cfdfe4b69ef4eea9eb5ef1ae5c5c65a57262 SHA512: 15e974e02ad4d7b6869bbd10047958d03e92ea9f69c7cabe8e0a7f67d2dd9fa6aac6beffac4fda2d8fe8a0bc45ba709d62d7e68076199a6bb16f264089db515c Homepage: https://cran.r-project.org/package=PoolDilutionR Description: CRAN Package 'PoolDilutionR' (Calculate Gross Biogeochemical Flux Rates from Isotope PoolDilution Data) Pool dilution is a isotope tracer technique wherein a biogeochemical pool is artifically enriched with its heavy isotopologue and the gross productive and consumptive fluxes of that pool are quantified by the change in pool size and isotopic composition over time. This package calculates gross production and consumption rates from closed-system isotopic pool dilution time series data. Pool size concentrations and heavy isotope (e.g., 15N) content are measured over time and the model optimizes production rate (P) and the first order rate constant (k) by minimizing error in the model-predicted total pool size, as well as the isotopic signature. The model optimizes rates by weighting information against the signal:noise ratio of concentration and heavy- isotope signatures using measurement precision as well as the magnitude of change over time. The calculations used here are based on von Fischer and Hedin (2002) with some modifications. Package: r-cran-pooledcohort Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue Suggests: r-cran-testthat, r-cran-covr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-pooledcohort_0.0.2-1.ca2404.1_all.deb Size: 85214 MD5sum: caa1f0788d7efa2329cd58e2ece747e9 SHA1: 80209822dbf263138365e31400510e55bd36e0e1 SHA256: ba1315177ed92e650bc49c2a454eb9c8de3b137899532cdec654b38f0b416c1c SHA512: 8fd14c1df3a1b472969347a28600e6d92324bda0a78413d0daac864c252ff823957cea0331c528dbf75f826d012cf1f186ffabae372316d51ac45004c98529a4 Homepage: https://cran.r-project.org/package=PooledCohort Description: CRAN Package 'PooledCohort' (Predicted Risk for CVD using Pooled Cohort Equations, PREVENTEquations, and Other Contemporary CVD Risk Calculators) The 2017 American College of Cardiology and American Heart Association blood pressure guideline recommends using 10-year predicted atherosclerotic cardiovascular disease risk to guide the decision to initiate or intensify antihypertensive medication. The guideline recommends using the Pooled Cohort risk prediction equations to predict 10-year atherosclerotic cardiovascular disease risk. This package implements the original Pooled Cohort risk prediction equations and also incorporates updated versions based on more contemporary data and statistical methods. Package: r-cran-pooledmeangroup Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pooledmeangroup_1.0-1.ca2404.1_all.deb Size: 90778 MD5sum: 77fbdd0e2db45a25ceff628b31278d7b SHA1: 5a0b95e29134be07a9578b26ea8d9f651e79b0a8 SHA256: c852803f446bc1045751eb3d1517f035693403f4047e2941d34d3578977c641f SHA512: 8a626d85a9cdad11fbd4204eb2c493b047da57909085598a29cc500f7c666e2b3a895dfe7395d8050b40238f3661171d3426ddcfb0ee4facb63457efe2e28a56 Homepage: https://cran.r-project.org/package=PooledMeanGroup Description: CRAN Package 'PooledMeanGroup' (Pooled Mean Group Estimation of Dynamic Heterogenous Panels) Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) . Package: r-cran-pooledpeaks Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4447 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-fragman, r-cran-magrittr, r-cran-pdftools, r-cran-qpdf, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-plyr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pooledpeaks_1.2.2-1.ca2404.1_all.deb Size: 1826432 MD5sum: d6363b2f953211a1b4698c4c0c63bb81 SHA1: f05a0bfa03a6ab7b5be648d905e78b9e70caa356 SHA256: c89fcaf5e2ca69e02e067bd30ad3c0d1e246b8336d8c6caad9c926f43690837c SHA512: f9fc0b7d3a60b010ca3eb1bf1a48520ada55cef1877f7b29aa744da4b97563dbf52bee2d4e41a76d21fb49d817eaf241be31867782f7a5be10f1ca17b5d38070 Homepage: https://cran.r-project.org/package=pooledpeaks Description: CRAN Package 'pooledpeaks' (Genetic Analysis of Pooled Samples) Analyzing genetic data obtained from pooled samples. This package can read in Fragment Analysis output files, process the data, and score peaks, as well as facilitate various analyses, including cluster analysis, calculation of genetic distances and diversity indices, as well as bootstrap resampling for statistical inference. Specifically tailored to handle genetic data efficiently, researchers can explore population structure, genetic differentiation, and genetic relatedness among samples. We updated some functions from Covarrubias-Pazaran et al. (2016) to allow for the use of new file formats and referenced the following to write our genetic analysis functions: Long et al. (2022) , Jost (2008) , Nei (1973) , Foulley et al. (2006) , Chao et al. (2008) . Package: r-cran-poolhelper Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-metrics, r-cran-scrm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-poolhelper_1.1.0-1.ca2404.1_all.deb Size: 311626 MD5sum: ac4bf70a7bf9c2ab74ae16e11d2405a0 SHA1: 322891986827762b9bd47ed1e5b9239b5cf0870b SHA256: 230ed6753a6380932120ec65b347402ecfbea263dc656eb8242d2ccbbc085127 SHA512: 3301dea4349536b7a2d8bdde3885e2fabb1d482c5bc0d6ba86b17521e809bb1e89900b6509dfda5278088eaf94e04e9a6f417f305900ab5c573b17a74d1e3b54 Homepage: https://cran.r-project.org/package=poolHelper Description: CRAN Package 'poolHelper' (Simulates Pooled Sequencing Genetic Data) Simulates pooled sequencing data under a variety of conditions. Also allows for the evaluation of the average absolute difference between allele frequencies computed from genotypes and those computed from pooled data. Carvalho et al., (2022) . 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Approaches for handling measurement error follow the framework of Schisterman et al. (2010) . Package: r-cran-poolr Architecture: all Version: 1.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mathjaxr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-poolr_1.2-0-1.ca2404.1_all.deb Size: 256886 MD5sum: aff6a2159d7e581fb6a815cd3b0bc071 SHA1: 91572029a0340717c78d37cf626e7b1d9c86c4e6 SHA256: 466a2f8f32a448d77f5dc491c593f9868b874bb05f3acb5fe41e0b64c6f2db35 SHA512: 6cf5152c8dd429b34971197f5c5fba0bf1520169caf0132f6417d08510b87cb6c966b0e6fb3bc27c985794d8ab6f94ca7e16963481b81647b9a75aea9b02a965 Homepage: https://cran.r-project.org/package=poolr Description: CRAN Package 'poolr' (Methods for Pooling P-Values from (Dependent) Tests) Functions for pooling/combining the results (i.e., p-values) from (dependent) hypothesis tests. Included are Fisher's method, Stouffer's method, the inverse chi-square method, the Bonferroni method, Tippett's method, and the binomial test. Each method can be adjusted based on an estimate of the effective number of tests or using empirically derived null distribution using pseudo replicates. For Fisher's, Stouffer's, and the inverse chi-square method, direct generalizations based on multivariate theory are also available (leading to Brown's method, Strube's method, and the generalized inverse chi-square method). An introduction can be found in Cinar and Viechtbauer (2022) . 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The function aggregates AIR importance measures from a group of SNPs or probes and outputs a p-value for each gene. The procedures builds upon the method described in and will be published soon. Package: r-cran-poorman Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 593 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-poorman_0.2.7-1.ca2404.1_all.deb Size: 384824 MD5sum: a75a39fb5d63fa2fe4cbd0f26d074ec0 SHA1: 3a883f1f32f627a8430f714aea57a7bdf9d7e2dd SHA256: 354931e065f30cd7414f82d00803fb070b2fd727b2f0d59cac098f2886bbb95f SHA512: 79bc2d138abbebfe009c389810a353716233dc75f351731c10870bcba144ade9c8abd68ddf7f0fdbf55530a03ba391f8e45310e798bd48326fa902825bdef4ba Homepage: https://cran.r-project.org/package=poorman Description: CRAN Package 'poorman' (A Poor Man's Dependency Free Recreation of 'dplyr') A replication of key functionality from 'dplyr' and the wider 'tidyverse' using only 'base'. 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The various approaches for analysing population dynamics models (MPMs, IPMs, ODEs, POMPs, PVA) each require the model to be defined in a different way. This makes it difficult to combine different modelling approaches and data types to solve a given problem. 'pop' aims to provide a flexible and easy to use common interface for constructing population dynamic models and enabling to them to be fitted and analysed in lots of different ways. Package: r-cran-popbayes Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2003 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r2jags, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-popbayes_1.3-1.ca2404.1_all.deb Size: 1341694 MD5sum: 967e9769b2890155f83a70a2142e8c38 SHA1: cd43dadcb7fbd14186c77340f6408d78bfdab40f SHA256: c30bcc9aa263cdc5360c603a5f12b06c8ddd12b5bbcc6c6e965bdad6150ce06a SHA512: b431c073ced23c05cf424549593765d2e33f06e7153dbf00a702e08536d0bb2eab5a1b8823d3ee1f7c1ad1414119c237a918c4d827086fb2e1f063ddedcddd0d Homepage: https://cran.r-project.org/package=popbayes Description: CRAN Package 'popbayes' (Bayesian Model to Estimate Population Trends from Counts Series) Infers the trends of one or several animal populations over time from series of counts. It does so by accounting for count precision (provided or inferred based on expert knowledge, e.g. guesstimates), smoothing the population rate of increase over time, and accounting for the maximum demographic potential of species. Inference is carried out in a Bayesian framework. This work is part of the FRB-CESAB working group AfroBioDrivers . 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The package covers methods described in Matrix Population Models by Caswell (2001) and Quantitative Conservation Biology by Morris and Doak (2002). Package: r-cran-popcomm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2716 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seurat, r-cran-broom, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-matrix, r-cran-ggplot2, r-cran-ggpubr, r-cran-pheatmap, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-scales, r-cran-igraph, r-cran-pbmcapply, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-popcomm_1.0.0-1.ca2404.1_all.deb Size: 2709940 MD5sum: 886d7ae5908ecf1d251cdb7b6661e34d SHA1: 147f43a6627279b30d09b7623085aa7e09c20989 SHA256: f4246e8ef184e733950065956c57d98248eb3bc2ba97fa9a1869c42f021af488 SHA512: 6102ce8093ef81d55342cda392f195f0d43fa6ae18db8981f1c7e0044af8fd5efb827bf382d98cc51e60989c492eae815e48d5243e0c5b8497412395397c26a9 Homepage: https://cran.r-project.org/package=PopComm Description: CRAN Package 'PopComm' (Population-Level Cell-Cell Communication Analysis Tools) Facilitates population-level analysis of ligand-receptor (LR) interactions using large-scale single-cell transcriptomic data. Identifies significant LR pairs and quantifies their interactions through correlation-based filtering and projection score computations. Designed for large-sample single-cell studies, the package employs statistical modeling, including linear regression, to investigate LR relationships between cell types. It provides a systematic framework for understanding cell-cell communication, uncovering regulatory interactions and signaling mechanisms. Offers tools for LR pair-level, sample-level, and differential interaction analyses, with comprehensive visualization support to aid biological interpretation. The methodology is described in a manuscript currently under review and will be referenced here once published or publicly available. 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Package: r-cran-popdesign Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-iso, r-cran-knitr, r-cran-magick Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-popdesign_1.1.0-1.ca2404.1_all.deb Size: 378252 MD5sum: e3345d3486a2c6831b44ab588e10fd92 SHA1: bd39483eab62fff1893b1d6026a3794ce4eaa1a6 SHA256: a18604c0c37096bbcb1d13361dc3ca5e56d43210c29cdf915eac7a5fcbba28a9 SHA512: 960bcf67cfeefaef349c421c8a1116fec855f92586a3be31a4e730cc31876ba4b5865ffcc1289cbafb2fe53ff44d90aa01a427bc6ffb5ac754c7d60968972862 Homepage: https://cran.r-project.org/package=PoPdesign Description: CRAN Package 'PoPdesign' (Posterior Predictive (PoP) Design for Phase I Clinical Trials) The primary goal of phase I clinical trials is to find the maximum tolerated dose (MTD). 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Often this is based on a computation of the Fisher Information Matrix. This package was developed for pharmacometric problems, and examples and predefined models are available for these types of systems. The methods are described in Nyberg et al. (2012) , and Foracchia et al. (2004) . Package: r-cran-popepi Architecture: all Version: 0.4.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2681 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-epi, r-cran-survival Suggests: r-cran-covr, r-cran-knitr, r-cran-mstate, r-cran-relsurv, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-popepi_0.4.14-1.ca2404.1_all.deb Size: 1489446 MD5sum: 6ca0f02a1546481c08bcaa58206fdf75 SHA1: e3b164eb99c83dca027c6b2d81925f0f11ef10db SHA256: 28864a7b96684e07374ca711d48c348d497914021aa98eefb66a4b0c2fe2e561 SHA512: d7fa646f3b10b536d236bd840483365058becb86b04f348d13a50ef8fcd1f5a98c727608a18519a60005b4b3bc29b811a37a47c4d4a66929a54cfe8597af580f Homepage: https://cran.r-project.org/package=popEpi Description: CRAN Package 'popEpi' (Functions for Epidemiological Analysis using Population Data) Enables computation of epidemiological statistics, including those where counts or mortality rates of the reference population are used. Currently supported: excess hazard models (Dickman, Sloggett, Hills, and Hakulinen (2012) ), rates, mean survival times, relative/net survival (in particular the Ederer II (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme, Stare, and Esteve (2012) ) estimators), and standardized incidence and mortality ratios, all of which can be easily adjusted for by covariates such as age. Fast splitting and aggregation of 'Lexis' objects (from package 'Epi') and other computations achieved using 'data.table'. 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'PopGenHelpR' uses vcf, 'geno' (012), and csv files to generate output. Package: r-cran-popgenr Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-popgenr_0.2-1.ca2404.1_all.deb Size: 56502 MD5sum: 78dcd60a0d94f5acebefff9895713d28 SHA1: 35b2d39974eb3acb86fb26a0ce6a2a682a57c833 SHA256: 28621ef8abcad465408aa6362f5a8a4f7543136598ebfcfba8ccdff284f71704 SHA512: 44913a6f9f3d8cd8157b64ab862a739b55be316ea90f8190438518527f2f873b2b1b1b1c292ff361fc14451c9c198bdb9e81393467757c2e7487a760dc6cc6fe Homepage: https://cran.r-project.org/package=popgenr Description: CRAN Package 'popgenr' (Accompaniment to Population Genetics with R: An Introduction forLife Scientists) Provides several data sets and functions to accompany the book "Population Genetics with R: An Introduction for Life Scientists" (2021, ISBN:9780198829546). 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Method described in Ochoa and Storey (2021) . 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Package: r-cran-popvar Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bglr, r-cran-qtl, r-cran-rrblup Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-popvar_1.3.2-1.ca2404.1_all.deb Size: 318108 MD5sum: 60b1a6d5efd5956df2319eb7ff5ce832 SHA1: e0ddf719dcc3c73b5a2ea2e01f6cc2c678132bf1 SHA256: 512451a4f4a0bef3f9c84d798db059d1c107e93588d49fc37a837a88572fc0d6 SHA512: 37a68d9e7c267a8e1fe5175036831dd69195298e5c44012fece9e1126897ae8e708feb64825df63df15578622626886cf9ff68f36c2a92705f86a6a6190b3c1f Homepage: https://cran.r-project.org/package=PopVar Description: CRAN Package 'PopVar' (Genomic Breeding Tools: Genetic Variance Prediction andCross-Validation) The main attribute of 'PopVar' is the prediction of genetic variance in bi-parental populations, from which the package derives its name. 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Search, download and forecast time-series from the Ministry of Economy of Argentina. Forecasts are built with the 'forecast' package, Hyndman RJ, Khandakar Y (2008) . 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While most approaches and packages are rather complicated this one tries to simplify things and is agnostic regarding risk measures as well as optimization solvers. Some of the methods implemented are described by Konno and Yamazaki (1991) , Rockafellar and Uryasev (2001) and Markowitz (1952) . 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Intended for researchers and practitioners to backtest a set of different portfolios, as well as by a course instructor to assess the students in their portfolio design in a fully automated and convenient manner, with results conveniently formatted in tables and plots. Each portfolio design is easily defined as a function that takes as input a window of the stock prices and outputs the portfolio weights. Multiple portfolios can be easily specified as a list of functions or as files in a folder. Multiple datasets can be conveniently extracted randomly from different markets, different time periods, and different subsets of the stock universe. The results can be later assessed and ranked with tables based on a number of performance criteria (e.g., expected return, volatility, Sharpe ratio, drawdown, turnover rate, return on investment, computational time, etc.), as well as plotted in a number of ways with nice barplots and boxplots. See Chapter 8 (Portfolio Backtesting) of the book: Daniel P. Palomar, "Portfolio Optimization: Theory and Application", Cambridge University Press, 2025. 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Package: r-cran-posterdown Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pagedown, r-cran-rmarkdown, r-cran-yaml Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-posterdown_1.0-1.ca2404.1_all.deb Size: 27726 MD5sum: 21f2de6e8f7471fcb9ef437fee2e494f SHA1: 362de656c461a7342c39105302bce35e354d3411 SHA256: d7deedd91fb271aaa038f0c67ffcb43e8e8ab467fd054865f078de62131ff278 SHA512: 652bc163637a31a2fe41e35aec184a6cde49fb6577db99ababdd47d0356be8d5f845710bfea5e18686095055963fd170cedc38f8c9020044afc0ea01e1c28109 Homepage: https://cran.r-project.org/package=posterdown Description: CRAN Package 'posterdown' (Generate PDF Conference Posters Using R Markdown) Use 'rmarkdown' and 'pagedown' to generate HTML and PDF conference posters. Package: r-cran-posterior Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-checkmate, r-cran-rlang, r-cran-tibble, r-cran-vctrs, r-cran-tensora, r-cran-pillar, r-cran-distributional, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-caret, r-cran-gbm, r-cran-randomforest, r-cran-e1071, r-cran-dplyr, r-cran-tidyr, r-cran-knitr, r-cran-ggplot2, r-cran-ggdist, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-posterior_1.7.0-1.ca2404.1_all.deb Size: 979808 MD5sum: 75349998559fadcd979f11289545ba75 SHA1: 82b5dc3b58f33a0fc8c800dd50c3965b218fb5ef SHA256: 5824c1ab94d9199cb210181359d3a0547d56fedf90852ff24a208a82bfee2a33 SHA512: f5bee13b6d400ed3f81d580990dffcb877099cc935704bb825554c36ef52c93d12a6a6044b09d425c28148e912510cc822620297607e7b473f2a07e58f48e8ca Homepage: https://cran.r-project.org/package=posterior Description: CRAN Package 'posterior' (Tools for Working with Posterior Distributions) Provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to: (a) Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions. (b) Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws. (c) Provide various summaries of draws in convenient formats. (d) Provide lightweight implementations of state of the art posterior inference diagnostics. References: Vehtari et al. (2021) . Package: r-cran-posteriorbootstrap Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 747 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-mass Suggests: r-cran-bh, r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-lintr, r-cran-rcppeigen, r-cran-rcppparallel, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rstan, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-posteriorbootstrap_0.1.2-1.ca2404.1_all.deb Size: 518106 MD5sum: f65a5be00155d8dad712e9edcc7d55a8 SHA1: da4bbd626ecfffdf319b610be25bae54a4baebe4 SHA256: 264dec6d7d082b01d8e069c32573ff21536790a2f83cc7fb9ba5ece478133c59 SHA512: 14e426a6514873fd6da85246a8e2f3f3a43a74066abb6ac5691c189ccbc124d50c4c333ade09ce8222576caa4191ea054bdfd4c6a896eff83b883c9a9b65049b Homepage: https://cran.r-project.org/package=PosteriorBootstrap Description: CRAN Package 'PosteriorBootstrap' (Non-Parametric Sampling with Parallel Monte Carlo) An implementation of a non-parametric statistical model using a parallelised Monte Carlo sampling scheme. The method implemented in this package allows non-parametric inference to be regularized for small sample sizes, while also being more accurate than approximations such as variational Bayes. The concentration parameter is an effective sample size parameter, determining the faith we have in the model versus the data. When the concentration is low, the samples are close to the exact Bayesian logistic regression method; when the concentration is high, the samples are close to the simplified variational Bayes logistic regression. The method is described in full in the paper Lyddon, Walker, and Holmes (2018), "Nonparametric learning from Bayesian models with randomized objective functions" . Package: r-cran-postggir Architecture: all Version: 2.4.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1691 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-refund, r-cran-denseflmm, r-cran-dplyr, r-cran-xlsx, r-cran-survival, r-cran-tidyr, r-cran-zoo, r-cran-ineq, r-cran-cosinor, r-cran-cosinor2, r-cran-abind, r-cran-accelerometry, r-cran-actcr, r-cran-actfrag, r-cran-minpack.lm, r-cran-kableextra, r-cran-ggir Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-postggir_2.4.0.2-1.ca2404.1_all.deb Size: 1016252 MD5sum: e23388de405e183d9bb2267d3ec68b94 SHA1: f1349fb0f4a00ab7494567baff3ea98ab7924c6a SHA256: 3ca631a04cc3ff913420401457d56e3d66ef329bc4c21075e400f27a6c1c162d SHA512: 3e176fb79a71a012aca875836d2ad2daf6873828a6b2ce025308480e1d815f9de6c0dd2730fd7b8e7a1255949c6d00a1aa0ac99e8cace6e6176bfc84ca94313a Homepage: https://cran.r-project.org/package=postGGIR Description: CRAN Package 'postGGIR' (Data Processing after Running 'GGIR' for Accelerometer Data) Generate all necessary R/Rmd/shell files for data processing after running 'GGIR' (v2.4.0) for accelerometer data. In part 1, all csv files in the GGIR output directory were read, transformed and then merged. In part 2, the GGIR output files were checked and summarized in one excel sheet. In part 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In part 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data. Package: r-cran-posthoc Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multcomp Suggests: r-cran-xtable, r-cran-lme4, r-cran-nlme, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-posthoc_0.1.3-1.ca2404.1_all.deb Size: 207226 MD5sum: 8203cbb20efd593d3c04a169c0172680 SHA1: d872f3fa8e789a684be53b90fda719aa9aceb26b SHA256: 19b84386a619ed60b16faf54ac6ab235385589f1a7fe57bf7e5eb11d5c458b86 SHA512: 8321a1c38b3cdcf152090221529eb1e641725464865b114eb7a064f4d4ecb6faa37010318f008486a2921ec2a7d1fc850dae47b75eee930524269021c60db073 Homepage: https://cran.r-project.org/package=postHoc Description: CRAN Package 'postHoc' (Tools for Post-Hoc Analysis) Implements a range of facilities for post-hoc analysis and summarizing linear models, generalized linear models and generalized linear mixed models, including grouping and clustering via pairwise comparisons using graph representations and efficient algorithms for finding maximal cliques of a graph. 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Package: r-cran-postinfectious Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-postinfectious_0.1.0-1.ca2404.1_all.deb Size: 25252 MD5sum: e19fbdb7a3c98fcdb57de9136d9348f4 SHA1: 1880d99adf4304326e277af1a0ad3907b262de68 SHA256: 209fb38c10e6060b54aa3d56050a0c9f1eb3b3ff3a0b25b3429f5706273f34ae SHA512: 266331819c60049202f67d6e3d858cbdf806a4bcaf3fc2c532a4e128cb6ec19e21a91ead480ccd09d4f7c425176ff0765f70dde22128a29f1aee386eff8fd5d9 Homepage: https://cran.r-project.org/package=postinfectious Description: CRAN Package 'postinfectious' (Estimating the Incubation Period Distribution of Post-InfectiousSyndrome) Functions to estimate the incubation period distribution of post-infectious syndrome which is defined as the time between the symptom onset of the antecedent infection and that of the post-infectious syndrome. Package: r-cran-postlightmercury Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-crul, r-cran-purrr, r-cran-jsonlite, r-cran-rvest, r-cran-xml2 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-postlightmercury_1.2-1.ca2404.1_all.deb Size: 16056 MD5sum: 7554a85d634e718fe3de179f9c01b787 SHA1: c06d4d853a49c7c29b8f9ace6805ccecd7c37e11 SHA256: f6e52c8a27fac76d1af7c93d5ff5e48282a2fded6c080a232844a9d2dd716ec9 SHA512: 8deb40d5bb94b0433dcce072579a81f9300a3e89e2d329bff40074b4c6cf4bbb12874d3a8360f1b0428c84b245d61684b0a79e7bb63288fcd87c49f4a27a0495 Homepage: https://cran.r-project.org/package=postlightmercury Description: CRAN Package 'postlightmercury' (Parses Web Pages using Postlight Mercury) This is a wrapper for the Mercury Parser API. 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Package: r-cran-postm Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1639 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform, r-cran-ape Suggests: r-bioc-multtest, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-postm_1.4-1.ca2404.1_all.deb Size: 1398068 MD5sum: 4440e7c527380b42d87492f223652688 SHA1: 4bcf2fd77cd05595bc2e559b6833dd7ec262dc7b SHA256: ff19be065097949212551b2cbd72d34fc799c2951c52a8e9263b805cd66d6aa4 SHA512: be6f4a0279eeabedde2b8ac301e1bfba1a6388020ec6f1988027e6066e3fb3d9fc722d8fe015dab2dd0f7de3ea332f22068d32e9011072cff3db183479d194fe Homepage: https://cran.r-project.org/package=POSTm Description: CRAN Package 'POSTm' (Phylogeny-Guided OTU-Specific Association Test for MicrobiomeData) Implements the Phylogeny-Guided Microbiome OTU-Specific Association Test method, which boosts the testing power by adaptively borrowing information from phylogenetically close OTUs (operational taxonomic units) of the target OTU. This method is built on a kernel machine regression framework and allows for flexible modeling of complex microbiome effects, adjustments for covariates, and can accommodate both continuous and binary outcomes. 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These objects can be used to store output from models fitted with Bayesian inference using 'JAGS', 'WinBUGS', 'OpenBUGS', 'NIMBLE', 'Stan', or even custom MCMC algorithms. Although the 'coda' R package provides some methods for these objects, it is somewhat limited in easily performing post-processing tasks for specific nodes. Models are ever increasing in their complexity and the number of tracked nodes, and oftentimes a user may wish to summarize/diagnose sampling behavior for only a small subset of nodes at a time for a particular question or figure. Thus, many 'postpack' functions support performing tasks on a subset of nodes, where the subset is specified with regular expressions. The functions in 'postpack' streamline the extraction, summarization, and diagnostics of specific monitored nodes after model fitting. Further, because there is rarely only ever one model under consideration, 'postpack' scales efficiently to perform the same tasks on output from multiple models simultaneously, facilitating rapid assessment of model sensitivity to changes in assumptions. Package: r-cran-potential Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1688 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-mapiso, r-cran-doparallel, r-cran-foreach Suggests: r-cran-covr, r-cran-lwgeom, r-cran-eurostat, r-cran-giscor, r-cran-mapsf, r-cran-knitr, r-cran-tinytest, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-potential_0.2.0-1.ca2404.1_all.deb Size: 1306672 MD5sum: 04ab29078feaaa36e4ad4240a4b6dd49 SHA1: cbeee6d36708539c680151b600907246c144ffe9 SHA256: 68e3b454b9d4b2b6f7508e1be729cdc647599fe9051042ef4ae16b88dc96898c SHA512: 408b98283a11ac991446e09ee8cc912c7f34a2ecb3e573ea400ccc3a1a94d77a74d4ba964b5b8abbd2fa337066e01535e9fac828ca53f4e858035bd91669339c Homepage: https://cran.r-project.org/package=potential Description: CRAN Package 'potential' (Implementation of the Potential Model) Provides functions to compute the potential model as defined by Stewart (1941) . Several options are available to customize the model, such as the possibility to fine-tune the distance friction functions or to use custom distance matrices. Some computations are parallelized to improve their efficiency. Package: r-cran-poth Architecture: all Version: 0.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-stringr, r-cran-netmeta, r-cran-mass Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-poth_0.3-0-1.ca2404.1_all.deb Size: 100138 MD5sum: 731c0795716a21055b7e00bc6b12e5eb SHA1: 771959fc3889b2e3787df3c11aa3ac43f309aa82 SHA256: 99e12fd980b03b89c3b69329a7a567435b63fce3bc742ec6ba2406cbc0dd74a3 SHA512: fea6377c4e3b62c759e947a1ab891b94a2448b78455d97531756b6dcf38bd8e337642607069b1772ba01e04a0ddb09a36327d3317f8297959cea8d326c4f39d7 Homepage: https://cran.r-project.org/package=poth Description: CRAN Package 'poth' (Precision of Treatment Hierarchy (POTH)) Calculate POTH for treatment hierarchies from frequentist and Bayesian network meta-analysis. POTH quantifies the certainty in a treatment hierarchy. Subset POTH, POTH residuals, and best k treatments POTH can also be calculated to improve interpretation of treatment hierarchies. Package: r-cran-potions Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-lobstr, r-cran-purrr, r-cran-rrapply, r-cran-rlang, r-cran-stringi, r-cran-yaml Suggests: r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-potions_0.2.0-1.ca2404.1_all.deb Size: 446500 MD5sum: 17b453eb290c464143aaf708e5ab7df1 SHA1: 0c88245c8bcc333b00a1fbccb0f233712aebf3b5 SHA256: bee15d864bf79dde6d9277ab474e8f62f3cd645e20a4b0f49feaeef5901546f8 SHA512: 614b21b826f3547ac232e79f318df76b650fe9a172f921a3137dc1ede276391706a7fe147a9dd398587d3222be57bfde1e9f0fcf65b325c4c31bbae2aaa8785b Homepage: https://cran.r-project.org/package=potions Description: CRAN Package 'potions' (Easy Options Management) Store and retrieve data from options() using syntax derived from the 'here' package. 'potions' makes it straightforward to update and retrieve options, either in the workspace or during package development, without overwriting global options. 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This package provides some helper functions for building this support in R packages, e.g. common validation & I/O tasks. Package: r-cran-pould Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-haplo.stats, r-cran-gap, r-cran-ggplot2, r-cran-reshape2, r-cran-bigdawg, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pould_1.0.2-1.ca2404.1_all.deb Size: 107474 MD5sum: c3cc5edbda49da9737222b9a577cfe2e SHA1: 77aaf3c2e1ff8e9a52427e1f2bdc9a923263ff2d SHA256: 32527d2fef0e381c0aee23f2d217a5e8844d0a7debbda8a2fc14176f4a2a8518 SHA512: e2af8da215937af1780a14b12a1d5c3a550d8a3711c4488aa3b5febcf40ae65c09a35268fce5c65230802d4881335c047496f4db064e200c719f7b30e3eea2da Homepage: https://cran.r-project.org/package=pould Description: CRAN Package 'pould' (Phased or Unphased Linkage Disequilibrium) Computes the D', Wn, and conditional asymmetric linkage disequilibrium (ALD) measures for pairs of genetic loci. Performs these linkage disequilibrium (LD) calculations on phased genotype data recorded using Genotype List (GL) String or columnar formats. Alternatively, generates expectation-maximization (EM) estimated haplotypes from phased data, or performs LD calculations on EM estimated haplotypes. Performs sign tests comparing LD values for phased and unphased datasets, and generates heat-maps for each LD measure. Described by Osoegawa et al. (2019a) , and Osoegawa et. al. (2019b) . 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Package: r-cran-powdist Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmutil, r-cran-gamlss.dist, r-cran-normalp Filename: pool/dists/noble/main/r-cran-powdist_0.1.4-1.ca2404.1_all.deb Size: 107670 MD5sum: f009cab71961d9e558220dcddb3366be SHA1: 8feebc76fd02111fb45d19d8e18b8ab4c76fad3a SHA256: eeb4af9f2dec76069006a8196b5670189a4ca7cb6b0511f916267081ef9bf0d6 SHA512: 936d0b0856e791dd60a1fb52deef7fe76e6648a4713ef3f1a4ace992ae11211c65b655ed9f86278525fe0ee621c2fe1ca5aa7440af689135979b818779441cf7 Homepage: https://cran.r-project.org/package=powdist Description: CRAN Package 'powdist' (Power and Reversal Power Distributions) Density, distribution function, quantile function and random generation for the family of power and reversal power distributions. 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Derives quantitative estimates of crystalline and amorphous phase concentrations in complex mixtures. Package: r-cran-power.transform Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rlang, r-cran-nloptr Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-power.transform_1.0.4-1.ca2404.1_all.deb Size: 516676 MD5sum: a2a69ef568a2a0a836c11b8b6b0da913 SHA1: fc54111d0cb8c20e7d2958b8e0ee72e6e7fcdb1b SHA256: 0ab5875258a7e76633ed0994421d4bd0d3291c788a1f68261ddaabc9c62ce4f4 SHA512: 79c23526846f67a0fa7541c5397300c0ec3ffe86c3f25a83286259f80ed4d9c91448d6d2b2f5f13b70496fb9a9b9aa32afeabeed359b710e373f29bffe7e2ed1 Homepage: https://cran.r-project.org/package=power.transform Description: CRAN Package 'power.transform' (Location and Scale Invariant Power Transformations) Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations allow for transforming variables with distributions distant from 0 to normality. 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The package 'manymome' by Cheung and Cheung (2024) is used to specify the indirect paths or conditional indirect paths to be tested. Package: r-cran-powerbal Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-scales, r-cran-phytools, r-cran-treebalance, r-cran-r.utils, r-cran-memoise Suggests: r-cran-testthat, r-cran-diversitree Filename: pool/dists/noble/main/r-cran-powerbal_0.1.0-1.ca2404.1_all.deb Size: 261758 MD5sum: 35b7d9c0d4d8a031d2c6acc5634cd41b SHA1: 3b4931046641c59e46ae6b4ec312123ad0dc6167 SHA256: e80637235dc783e6ef7d7dab79ce7aa44a591f794f45d5b5b88a3c34d72c5800 SHA512: 3bb3112e502a08fc54f12dfe234276bc96595a6ab54f2533c52bd65ba9a061f23a90ad0f9b4d007c83e68e842a45b9dbf8f66e25f4cabf241f1990fd0e16e453 Homepage: https://cran.r-project.org/package=poweRbal Description: CRAN Package 'poweRbal' (Phylogenetic Tree Models and the Power of Tree Shape Statistics) The first goal of this package is to provide a multitude of tree models, i.e., functions that generate rooted binary trees with a given number of leaves. 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Package: r-cran-powerbir Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite, r-cran-httr, r-cran-azureauth Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-powerbir_0.1.0-1.ca2404.1_all.deb Size: 91462 MD5sum: a8fd3ddcf2804d4de1ee13235cac7214 SHA1: 463691db9457e3a4b88fced8d070cc0b193d1038 SHA256: 0b0e3458985a0b858cdd5fdae9f31e99c626153ac26549575722ebf1ae8d05f0 SHA512: 7095c25297a39cba6c934807f0d2df7f4dfad7bafea2c7da3911a06323dde86452fa54b6d64aba922c2a4ed41c43b2aac206babf6e52aaed537d60f2b00fe5e5 Homepage: https://cran.r-project.org/package=powerbiR Description: CRAN Package 'powerbiR' (An Interface to the 'Power BI REST APIs') Makes it easy to push data to 'Power BI' using R and the 'Power BI REST APIs' (see ). 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Package: r-cran-powerbrmsinla Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brms, r-cran-dplyr, r-cran-ggplot2, r-cran-pbapply, r-cran-rlang, r-cran-tibble, r-cran-scales, r-cran-viridislite, r-cran-magrittr Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-mass, r-cran-circular, r-cran-sn Filename: pool/dists/noble/main/r-cran-powerbrmsinla_1.1.1-1.ca2404.1_all.deb Size: 241216 MD5sum: 5c53ea1ffdda96ecfcf1bee0f3c355cd SHA1: be39a1086565bc7ec7c9b697b19c55f38120c1dd SHA256: 670509df39e56e1626e1e45eec01d80b0850cf25026cbd3373319a90c9c0a5da SHA512: e821ab504d37401c5cfb15c5f91516e7f7b311a2b03bc720d3a17be6dc44a681cce1217a31904a7e1e581a57946c84db30b9a2ea9c2c52e3f82aebafc8477113 Homepage: https://cran.r-project.org/package=powerbrmsINLA Description: CRAN Package 'powerbrmsINLA' (Bayesian Power Analysis Using 'brms' and 'INLA') Provides tools for Bayesian power analysis and assurance calculations using the statistical frameworks of 'brms' and 'INLA'. Includes simulation-based approaches, support for multiple decision rules (direction, threshold, ROPE), sequential designs, and visualisation helpers. Methods are based on Kruschke (2014, ISBN:9780124058880) "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan", O'Hagan & Stevens (2001) "Bayesian Assessment of Sample Size for Clinical Trials of Cost-Effectiveness", Kruschke (2018) "Rejecting or Accepting Parameter Values in Bayesian Estimation", Rue et al. (2009) "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations", and Bürkner (2017) "brms: An R Package for Bayesian Multilevel Models using Stan". Package: r-cran-powerbydesign Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-gdata, r-cran-mass, r-cran-reshape2, r-cran-stringr, r-cran-plyr, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-powerbydesign_1.0.5-1.ca2404.1_all.deb Size: 32102 MD5sum: 8f43e76001ca07576f23a6fedb263032 SHA1: 10a25379650d0f7098c8f1ea44e92704396796dd SHA256: d912c70e4e95983c386a7eeefdfebfa64be423f3549d5bf382d519519838e415 SHA512: 55311bc54f0517fb21ef5768b4fa18571d2786a4ef1a71a573613f53c697e0e0a9711d85e9423c025c8873c8916c4517f7aa9ddb4f9b03126f3fa21cf444a03c Homepage: https://cran.r-project.org/package=powerbydesign Description: CRAN Package 'powerbydesign' (Power Estimates for ANOVA Designs) Functions for bootstrapping the power of ANOVA designs based on estimated means and standard deviations of the conditions. 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It can also calculate power/sample size for testing the association of a SNP to a continuous type phenotype. Please see the reference: Dong X, Li X, Chang T-W, Scherzer CR, Weiss ST, Qiu W. (2021) . 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The package can handle any type of outcomes (binary, continuous, count, ordinal, time-to-event) and any number of such endpoints. It allows users to calculate sample size with a given power or to calculate power with a given sample size for hypothesis testing based on win ratios, win odds, net benefit, or DOOR (desirability of outcome ranking) as treatment effect between two groups for hierarchical endpoints. The methods of this package are described further in the paper by Barnhart, H. X. et al. (2024, ). 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(2023) . The implementation takes the form of a three-step recursive algorithm designed to find an optimal sample size given a model specification and a performance measure of interest. It starts with a Monte Carlo simulation step for computing the performance measure and a statistic at various sample sizes selected from an initial sample size range. It continues with a monotone curve-fitting step for interpolating the statistic across the entire sample size range. The final step employs stratified bootstrapping to quantify the uncertainty around the fitted curve. Package: r-cran-powermediation Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-powermediation_0.3.4-1.ca2404.1_all.deb Size: 208818 MD5sum: ffe413a32f41481d2cf9a3e7a1ebc88c SHA1: dac413ad25f843adbc15f1a8a395bc7252a6ee04 SHA256: b4661a76da2676ee17f465cf4529f73b764b0c6f2699114149e06427ea51732c SHA512: d1be2c9e962e8d351474f95ab061e25a3c3a83f45d6e0bdd6463a70352c3ee4c2cd77a52680084f5c8fef7581056095d6bf9cf28a97f6561a84e070f0f87ec15 Homepage: https://cran.r-project.org/package=powerMediation Description: CRAN Package 'powerMediation' (Power/Sample Size Calculation for Mediation Analysis) Functions to calculate power and sample size for testing (1) mediation effects; (2) the slope in a simple linear regression; (3) odds ratio in a simple logistic regression; (4) mean change for longitudinal study with 2 time points; (5) interaction effect in 2-way ANOVA; and (6) the slope in a simple Poisson regression. 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Package: r-cran-powerpkg Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-powerpkg_1.6-1.ca2404.1_all.deb Size: 56186 MD5sum: 7246818d1b3c7754210ac8eaf8265554 SHA1: 387f6fe8530bdf7c379a9115c664020999a2e6d8 SHA256: b4ad2bc5a790e762c46c6a7e8909d114cdb3e9f1cbc21ab09eafea96529a01c7 SHA512: a067da67e1f3d05e76033958e3527e7e28fb09c14ec837777bf17db801e3cb4b7cfb759bca4a619b3b27f878fa1c7c24b5e278c19f6e5770f0602c4ccf11a861 Homepage: https://cran.r-project.org/package=powerpkg Description: CRAN Package 'powerpkg' (Power Analyses for the Affected Sib Pair and the TDT Design) There are two main functions: (1) To estimate the power of testing for linkage using an affected sib pair design, as a function of the recurrence risk ratios. We will use analytical power formulae as implemented in R. 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Package: r-cran-powerprior Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-laplacesdemon, r-cran-ggplot2, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-dt, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-powerprior_1.0.0-1.ca2404.1_all.deb Size: 106148 MD5sum: a561d0abf8d3016a6fad02ff4e676a77 SHA1: 434ef0c29fcdebc4dafe16f86cb09f90801f6b42 SHA256: 38f488aa7f7a725d737fdbf211b544e4928edbe6065b0689d04075ed134e2c9c SHA512: d857a372eafcc17a619f8d3ad09109852255dd7cf0898c8cbc7531274f4be21c963a3236788bb321b31609739606f8f4e72fd62087227ca2d466f3d09e5ad904 Homepage: https://cran.r-project.org/package=powerprior Description: CRAN Package 'powerprior' (Conjugate Power Priors for Bayesian Analysis of Normal Data) Implements conjugate power priors for efficient Bayesian analysis of normal data. 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Package: r-cran-powersdi Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 606 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lmom, r-cran-lubridate, r-cran-nasapower Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vcr, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-powersdi_1.0.0-1.ca2404.1_all.deb Size: 479564 MD5sum: 2c4e6e49e8625760fadcaa08b0736639 SHA1: d07e1c9a2e0d965094e0e8b6fcd67155e3d787b2 SHA256: b4c18e65decac4aed26fd845c7dd2798ee02728f736dfa2b3339f8772537a226 SHA512: 41e2c8e86fab34c4514c8d3804d3e674afd2701bd9842868cb5707ea00785cea97310a2fcaaefb9d41774b55d1f58f10fbef4f1ea2ad20b2ba02136f20376f3b Homepage: https://cran.r-project.org/package=PowerSDI Description: CRAN Package 'PowerSDI' (Calculate Standardised Drought Indices Using NASA POWER Data) A set of functions designed to calculate the standardised precipitation and standardised precipitation evapotranspiration indices using NASA POWER data as described in Blain et al. 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Some calculations also take into account the competing risks and stratified analysis. This package also includes a set of functions to calculate power and sample size for testing main effect in the survival analysis of randomized clinical trials and conditional logistic regression for nested case-control study. Package: r-cran-powertools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-powertost, r-cran-hmisc, r-cran-knitr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-powertools_1.0.0-1.ca2404.1_all.deb Size: 341704 MD5sum: 3d0845d1c84c7ffa183bfcd20d7f8b07 SHA1: 6396c009d07d1ea9f11f9e1306048e890b72f381 SHA256: 07549706699f825e72ca5e768a00eeb84489fdf046ed5a9e01bfe8f5fd4f98eb SHA512: 15101b8b81cfaee948f6d329cca69f43304040ccf8cce397d3097f418d2ae84f2163918c6c9e43192c3d147d44778affb316972f6b555ce7b0baf7d25e904020 Homepage: https://cran.r-project.org/package=powertools Description: CRAN Package 'powertools' (Power and Sample Size Tools) Power and sample size calculations for a variety of study designs and outcomes. Methods include t tests, ANOVA (including tests for interactions, simple effects and contrasts), proportions, categorical data (chi-square tests and proportional odds), linear, logistic and Poisson regression, alternative and coprimary endpoints, power for confidence intervals, correlation coefficient tests, cluster randomized trials, individually randomized group treatment trials, multisite trials, treatment-by-covariate interaction effects and nonparametric tests of location. Utilities are provided for computing various effect sizes. Companion package to the book "Power and Sample Size in R", Crespi (2025, ISBN:9781138591622). Further resources available at . Package: r-cran-powertost Architecture: all Version: 1.5-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2365 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-cubature Suggests: r-cran-crossdes, r-cran-knitr, r-cran-rmarkdown, r-cran-tufte, r-cran-emmeans Filename: pool/dists/noble/main/r-cran-powertost_1.5-7-1.ca2404.1_all.deb Size: 1679376 MD5sum: 1bde85dc50cae0cae23533a0bdbab799 SHA1: 887a8cfef69473f34f7443d415253ae28fb8e174 SHA256: dbd4e69c4f67c4cc99301ade2816fdf11e857135c7e3c90f3d654c99f498d3be SHA512: 4f93b8910f175683720629ede573d6e9cb6201a91266af4210ef83006e32f8fda5e033a206743d6cad92a0e2901ab26472d5f222262b3df785426ce3a5af1f18 Homepage: https://cran.r-project.org/package=PowerTOST Description: CRAN Package 'PowerTOST' (Power and Sample Size for (Bio)Equivalence Studies) Contains functions to calculate power and sample size for various study designs used in bioequivalence studies. Use known.designs() to see the designs supported. Power and sample size can be obtained based on different methods, amongst them prominently the TOST procedure (two one-sided t-tests). See README and NEWS for further information. Package: r-cran-powerupr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-powerupr_1.1.0-1.ca2404.1_all.deb Size: 416510 MD5sum: 865d234b33d67eac389ce2f853ce0a75 SHA1: cfbf737f0e096ffd4701205b91e377c1313d620e SHA256: 5062e4a8d63798c6634c5bfacc6ddd7d10c38fef7cd7acc0237bf71d1993b45e SHA512: 72cedd5e34cab0864db5d6347fdfd2f679188a26d402cdd54f9d7cc385ed1383b15ae820d0a72a221dd8de7c7747e84ca52d52874b58c7f79a945328a375b6ef Homepage: https://cran.r-project.org/package=PowerUpR Description: CRAN Package 'PowerUpR' (Power Analysis Tools for Multilevel Randomized Experiments) Includes tools to calculate statistical power, minimum detectable effect size (MDES), MDES difference (MDESD), and minimum required sample size for various multilevel randomized experiments (MRE) with continuous outcomes. Accomodates 14 types of MRE designs to detect main treatment effect, seven types of MRE designs to detect moderated treatment effect (2-1-1, 2-1-2, 2-2-1, 2-2-2, 3-3-1, 3-3-2, and 3-3-3 designs; - - ), five types of MRE designs to detect mediated treatment effects (2-1-1, 2-2-1, 3-1-1, 3-2-1, and 3-3-1 designs; - - ), four types of partially nested (PN) design to detect main treatment effect, and three types of PN designs to detect mediated treatment effects (2/1, 3/1, 3/2; / ). See 'PowerUp!' Excel series at . Package: r-cran-powriclpm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-future.apply, r-cran-lavaan, r-cran-lifecycle, r-cran-progressr, r-cran-rlang, r-cran-ggplot2, r-cran-future Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-powriclpm_0.2.1-1.ca2404.1_all.deb Size: 237536 MD5sum: bee5b08d77d1d6026306442ce2731996 SHA1: 8f4dcb752eb5c99737647c95fe4287e83c64fae5 SHA256: 1d21d72ae7626bd789dd3da3d0620130807bf8e6c8ef6ddaf8037976c34b5c1e SHA512: 3df66e3e7de61ec0974e32cbd3742d6384c0efdc2f40a14993f34613631b06fcde6dcac66dbd88efa5532d57da9e06df67897e2158e347c85dfb4add5731a485 Homepage: https://cran.r-project.org/package=powRICLPM Description: CRAN Package 'powRICLPM' (Perform Power Analysis for the RI-CLPM and STARTS Model) Perform user-friendly power analyses for the random intercept cross-lagged panel model (RI-CLPM) and the bivariate stable trait autoregressive trait state (STARTS) model. The strategy as proposed by Mulder (2023) is implemented. Extensions include the use of parameter constraints over time, bounded estimation, generation of data with skewness and kurtosis, and the option to setup the power analysis for Mplus. Package: r-cran-powrpriori Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1040 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-ggplot2, r-cran-lme4, r-cran-lmertest, r-cran-magrittr, r-cran-mass, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-powrpriori_0.2.0-1.ca2404.1_all.deb Size: 673446 MD5sum: 95842ad6be53b0f383919b0a34abdd1f SHA1: 58efbd2c0ff95c8cf7bd047790f15da2827e61c8 SHA256: fa7d4972eb557efcd63cc26d04cfb047f0b1a229b8fb0fce566d3928fbbcdc5a SHA512: 67bfc8f2ee2e333343c9a3f06d1ec52e2837ce14bec62839a157b9afb2788d1c75cdd799c9f389518c4d35ba8931aada7ecd8a42393829a44fe8cd4338ae37b3 Homepage: https://cran.r-project.org/package=PowRPriori Description: CRAN Package 'PowRPriori' (Power Analysis via Data Simulation for (Generalized) LinearMixed Effects Models) Conduct a priori power analyses via Monte-Carlo style data simulation for linear and generalized linear mixed-effects models (LMMs/GLMMs). Provides a user-friendly workflow with helper functions to easily define fixed and random effects as well as diagnostic functions to evaluate the adequacy of the results of the power analysis. Package: r-cran-ppbigdata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-datanugget, r-cran-mass, r-cran-dplyr, r-cran-magrittr, r-cran-weights, r-cran-rstiefel, r-cran-gtools, r-cran-tourr, r-cran-mclust Filename: pool/dists/noble/main/r-cran-ppbigdata_1.0.0-1.ca2404.1_all.deb Size: 139950 MD5sum: 3940a9fa7546b75fb70e73a82bc33de7 SHA1: 625ec0159028b259d0a35ffa7d64788ce832469d SHA256: 89e18c85ee9e901713ff761da02641cdbf991e1f1cc89c68912398a0736f8f90 SHA512: 789b13ca92bd8f8feb7e8b9082632dc45bf095639699963529ab44ab35e2333b14e22511ef5e6fab130c49d157fa452644006c1b08cf4f0aab27a2d3e956f71e Homepage: https://cran.r-project.org/package=PPbigdata Description: CRAN Package 'PPbigdata' (Projection Pursuit for Big Data Based on Data Nuggets) Perform 1-dim/2-dim projection pursuit, grand tour and guided tour for big data based on data nuggets. Reference papers: [1] Beavers et al., (2024) . [2] Duan, Y., Cabrera, J., & Emir, B. (2023). "A New Projection Pursuit Index for Big Data." . Package: r-cran-ppcdt Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-ppcdt_0.2.0-1.ca2404.1_all.deb Size: 18672 MD5sum: d6f52719d2e060839cfca8ca015592d7 SHA1: e19b55ff876e6f67f6ff538ab2b740856eebf6e8 SHA256: 0508e2dada1d7f3132c99c826135b8c103f7fd5e1a501226734ed83084d762f0 SHA512: fd68b6667846ee446666bc757c9741380a8ac83063dd00fb7a5f294d76e42d53917d0be0aa0dc0976f713dc878debc4c4340bb84549d08a530946d61d09b2583 Homepage: https://cran.r-project.org/package=PPCDT Description: CRAN Package 'PPCDT' (An Optimal Subset Selection for Distributed Hypothesis Testing) In the era of big data, data redundancy and distributed characteristics present novel challenges to data analysis. This package introduces a method for estimating optimal subsets of redundant distributed data, based on PPCDT (Conjunction of Power and P-value in Distributed Settings). Leveraging PPC technology, this approach can efficiently extract valuable information from redundant distributed data and determine the optimal subset. Experimental results demonstrate that this method not only enhances data quality and utilization efficiency but also assesses its performance effectively. The philosophy of the package is described in Guo G. (2020) . Package: r-cran-ppci Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2568 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rarpack Filename: pool/dists/noble/main/r-cran-ppci_0.1.5-1.ca2404.1_all.deb Size: 2515120 MD5sum: bbbfaf190d4d753ea995c7a380d22123 SHA1: 65f04d2b663543cb870d7c7f37afa8d8a6a11d06 SHA256: a72d5340f6032d4fd946cf8a081ca9c5bfce22acd824c7eb23868befcf3fb5ea SHA512: 5d9fa3135128b82035a74fea8bc105800e79c3b433cd0ebec2976efe2f49eac48bd34091eb435239faf28d2574c50404f36ad3001a147f3b286aa5fdd0e65cf1 Homepage: https://cran.r-project.org/package=PPCI Description: CRAN Package 'PPCI' (Projection Pursuit for Cluster Identification) Implements recently developed projection pursuit algorithms for finding optimal linear cluster separators. The clustering algorithms use optimal hyperplane separators based on minimum density, Pavlidis et. al (2016) ; minimum normalised cut, Hofmeyr (2017) ; and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) . Package: r-cran-ppclust Architecture: all Version: 1.1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6754 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-inaparc, r-cran-mass Suggests: r-cran-cluster, r-cran-factoextra, r-cran-fclust, r-cran-knitr, r-cran-rmarkdown, r-cran-vegclust Filename: pool/dists/noble/main/r-cran-ppclust_1.1.0.1-1.ca2404.1_all.deb Size: 3413048 MD5sum: beb9344bc2c4133e4f038156027687b0 SHA1: 7d44c429cc4723cdf89b8fbddb03c3326c8d1cf8 SHA256: de33ec7d0c4ec0ba7c28bc8848aaec3ec3e6d6048fa7f5e050bf7c2372a0abda SHA512: 66da05813c6d880f0792784fd4cc2ab46065edb645c9cd8c762ec467c4e25850f7867dc2ef02f4916e00a350214cc8c869cea35978b0f43fcfa88fb5c6879d6f Homepage: https://cran.r-project.org/package=ppclust Description: CRAN Package 'ppclust' (Probabilistic and Possibilistic Cluster Analysis) Partitioning clustering divides the objects in a data set into non-overlapping subsets or clusters by using the prototype-based probabilistic and possibilistic clustering algorithms. This package covers a set of the functions for Fuzzy C-Means (Bezdek, 1974) , Possibilistic C-Means (Krishnapuram & Keller, 1993) , Possibilistic Fuzzy C-Means (Pal et al, 2005) , Possibilistic Clustering Algorithm (Yang et al, 2006) , Possibilistic C-Means with Repulsion (Wachs et al, 2006) and the other variants of hard and soft clustering algorithms. The cluster prototypes and membership matrices required by these partitioning algorithms are initialized with different initialization techniques that are available in the package 'inaparc'. As the distance metrics, not only the Euclidean distance but also a set of the commonly used distance metrics are available to use with some of the algorithms in the package. Package: r-cran-ppcor Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-ppcor_1.1-1.ca2404.1_all.deb Size: 29948 MD5sum: 11b32e5fe4f436189ef296a9d3048278 SHA1: c506910392b1ef493a1ff8f272109bc13ea251fd SHA256: 657f226ff847f8a54226c4c31128b789dd97ec5a8c1276df50758b11d474d091 SHA512: 633daed1aca507552277d796a6a28d6ee7fdd11c6bf2d4a245410555d9472153f0eb1d921000d56df2b73624f8e6410f793b1499f3db66a6c006cbeee9f284cf Homepage: https://cran.r-project.org/package=ppcor Description: CRAN Package 'ppcor' (Partial and Semi-Partial (Part) Correlation) Calculates partial and semi-partial (part) correlations along with p-value. 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This includes tools for simulating and fitting both common and more complex temporal point processes. We also include functions to visualise these point processes and collect existing diagnostic tools of Brown et al. (2002) and Wu et al. (2021) , which can be used to assess the fit of a chosen point process model. Package: r-cran-ppendemic Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3480 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-cli, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-memoise, r-cran-progress, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ppendemic_0.2.1-1.ca2404.1_all.deb Size: 3330452 MD5sum: be2870fa877581375ef6d30c3742938e SHA1: 02db35d6567267d5fe22dea6ca911078238a18ce SHA256: 94d52fa93752f03e5dd6e537b78e58cecd1aa0b129f42eea695900908539c7b2 SHA512: 06072a121c5cb8c935c181803b1d66754df4fd2676f541a1d2dec928a897ad6c6c56bfba4c770b568a839ff892e20abbe00ac20d30a6a79c34b9ca1352c740f8 Homepage: https://cran.r-project.org/package=ppendemic Description: CRAN Package 'ppendemic' (A Glimpse at the Diversity of Peru's Endemic Plants) Introducing a novel and updated database showcasing Peru's endemic plants. This meticulously compiled and revised botanical collection encompasses a remarkable assemblage of over 7,898 distinct species. The data for this resource was sourced from the work of Govaerts, R., Nic Lughadha, E., Black, N. et al., titled 'The World Checklist of Vascular Plants: A continuously updated resource for exploring global plant diversity', published in Sci Data 8, 215 (2021) . Package: r-cran-ppgam Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mgcv, r-cran-evgam Filename: pool/dists/noble/main/r-cran-ppgam_1.0.2-1.ca2404.1_all.deb Size: 130224 MD5sum: 31d7c473f83a8b4af6e669d6076d0403 SHA1: fcfd0e2c162d3c5f46ef4b706272d6c196424a50 SHA256: f00081e2cf0a831e7f59982afc619c7e13130d62a9bd349511ade80d0570bbca SHA512: 3fb601fd8830d6724678984fbd8dd1de6831645ee7efbea29fe367abbd2658606eb9a67761d50b70e41e91f6774a31fa45e60a6b862bf50ba7547b40bcf192e0 Homepage: https://cran.r-project.org/package=ppgam Description: CRAN Package 'ppgam' (Generalised Additive Point Process Models) Methods for fitting point processes with parameters of generalised additive model (GAM) form are provided. For an introduction to point processes see Cox, D.R & Isham, V. (Point Processes, 1980, CRC Press), GAMs see Wood, S.N. (2017) , and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) . 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The user can specify various models of trait evolution or estimate the best fit model, include fossils, use one or multiple phylogenies for inference, and make animations of shifting suitable habitat through time. This model was first used in Lawing and Polly (2011), and further implemented in Lawing et al (2016) and Rivera et al (2020). Lawing and Polly (2011) "Pleistocene climate, phylogeny and climate envelope models: An integrative approach to better understand species' response to climate change" Lawing et al (2016) "Including fossils in phylogenetic climate reconstructions: A deep time perspective on the climatic niche evolution and diversification of spiny lizards (Sceloporus)" Rivera et al (2020) "Reconstructing historical shifts in suitable habitat of Sceloporus lineages using phylogenetic niche modelling.". Package: r-cran-ppitables Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-covr, r-cran-spelling, r-cran-stringr, r-cran-readxl, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ppitables_0.6.1-1.ca2404.1_all.deb Size: 3137906 MD5sum: 78dcc714e15d4dd97794e173e7e62f78 SHA1: 9ec9f1a51d1f3fecb88a6bd649b501ef3112b89c SHA256: e678f3f4dfbef31f2a03fa3ab1acbbe1e1be496ad7c76dfb6993cb3848e255d6 SHA512: 421c970f37ff35a31aeded7e4d952249feedc1df0865b7424d6f2b34b05f3ac40b5fefedd9dc836673ecf72dad76e3597c96d49a660d119d8e91a2469e6bfc99 Homepage: https://cran.r-project.org/package=ppitables Description: CRAN Package 'ppitables' (Lookup Tables to Generate Poverty Likelihoods and Rates usingthe Poverty Probability Index (PPI)) The Poverty Probability Index (PPI) is a poverty measurement tool for organizations and businesses with a mission to serve the poor. The PPI is statistically-sound, yet simple to use: the answers to 10 questions about a household's characteristics and asset ownership are scored to compute the likelihood that the household is living below the poverty line - or above by only a narrow margin. This package contains country-specific lookup data tables used as reference to determine the poverty likelihood of a household based on their score from the country-specific PPI questionnaire. These lookup tables have been extracted from documentation of the PPI found at and managed by Innovations for Poverty Action . Package: r-cran-ppks Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rfast Suggests: r-cran-rfast2 Filename: pool/dists/noble/main/r-cran-ppks_1.0-1.ca2404.1_all.deb Size: 16974 MD5sum: 2670c4ab4a250b12baca856658136ef5 SHA1: 490eaaefee81dbc5c57dbb0e46f135364f0860d3 SHA256: 597c5fc0748239055600327d5e3da8eb8e726b69d537430ba65013c65fc8f249 SHA512: 3c4fb2c8e2167604ea7834f435d10ae8f61cbe46f4375442b0177cf93a2a8707dfc2dbd97db2e3b3c099c17a97dd92c9e8b1c04ded128657698d1b99f5a85a7e Homepage: https://cran.r-project.org/package=ppks Description: CRAN Package 'ppks' (Permutation Based Paired Kolmogorov-Smirnov Test) Permutation based Kolmogorov-Smirnov test for paired samples. The test was proposed by Wang W.S., Amsler C. and Schmidt, P. (2025) . Package: r-cran-pplasso Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 363 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-genlasso, r-cran-ggplot2, r-cran-cvcovest, r-cran-glmnet, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pplasso_2.0-1.ca2404.1_all.deb Size: 320084 MD5sum: 249611b557c836fb9ad1de78bcc46e2f SHA1: b6b40979a56cda55939df4c0d413702eed03dcd8 SHA256: 386b9779c3b728117aa8cb67d0b6bc6d65329d4882e147ef598b1e4bd75f457d SHA512: cc8db23e2e8f92ce93fb137635dc4fe6e5acb530d7ec6fa9aa136be0260b3eee759b7a65cad64f3416fa6b202b21f059b6473d3b30db4f235c8c645ca8074a8a Homepage: https://cran.r-project.org/package=PPLasso Description: CRAN Package 'PPLasso' (Prognostic Predictive Lasso for Biomarker Selection) We provide new tools for the identification of prognostic and predictive biomarkers. For further details we refer the reader to the paper: Zhu et al. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso. BMC Bioinformatics. 2023 Jan 23;24(1):25. Package: r-cran-pplot Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mbess Filename: pool/dists/noble/main/r-cran-pplot_0.9-1.ca2404.1_all.deb Size: 27206 MD5sum: fc9640d703a3e7ec761a4643168d4fc6 SHA1: 2128e720f31add9016e35d418a0c419f4ab530e6 SHA256: e34978750370bc62c88dd5bd48aef6c13f971f6f29058c7dc04421bd856adc1d SHA512: 92680e5afc4e6cbdd4cd50efe73f61c7107a71581f3ea04df42dbbb315dca989390d3fde21069f5f6478d673b14a862aea3fbae9bc84cffca9a7504ad3350c9c Homepage: https://cran.r-project.org/package=pplot Description: CRAN Package 'pplot' (Chronological and Ordered p-Plots for Empirical Data) Generates chronological and ordered p-plots for data vectors or vectors of p-values. The p-plot visualizes the evolution of the p-value of a significance test across the sampled data. It allows for assessing the consistency of the observed effects, for detecting the presence of potential moderator variables, and for estimating the influence of outlier values on the observed results. For non-significant findings, it can diagnose patterns indicative of underpowered study designs. The p-plot can thus either back the binary accept-vs-reject decision of common null-hypothesis significance tests, or it can qualify this decision and stimulate additional empirical work to arrive at more robust and replicable statistical inferences. Package: r-cran-ppls Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-ppls_2.0.0-1.ca2404.1_all.deb Size: 360006 MD5sum: f6df93149f0c29b845032371226cb46b SHA1: 81bc75dfe51ff2a2657255d9c39fa5d440bef057 SHA256: 7937db6cf5a54ea48e26440820ce8906753afd1ea4da70b52026324be4b41b39 SHA512: 099a19a1b854e1db6488b093119f1a4fff14d75f9a7d21fff624a80ad951aea22302aa1f48213a656fd21ed1161932b73bd2fdb8b5f1216157ebe417dfeb96e3 Homepage: https://cran.r-project.org/package=ppls Description: CRAN Package 'ppls' (Penalized Partial Least Squares) Linear and nonlinear regression methods based on Partial Least Squares and Penalization Techniques. Model parameters are selected via cross-validation, and confidence intervals ans tests for the regression coefficients can be conducted via jackknifing. The method is described and applied to simulated and experimental data in Kraemer et al. (2008) . Package: r-cran-ppmf Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-censable, r-cran-dplyr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-zip Suggests: r-cran-roxygen2, r-cran-spelling Filename: pool/dists/noble/main/r-cran-ppmf_0.2.1-1.ca2404.1_all.deb Size: 87908 MD5sum: adc122f5b1e9e51d364f3ecede70d44a SHA1: 698f9e47587509aa46e7e84b13aff46fcf1737a7 SHA256: ce984aebbfc368f036e882c86cb02295248fdc2de1f132f1319fcfc0da064e65 SHA512: 6cc2c0f53f4cbf9e75d4c0c6ad1147024db21c04b2dc376c80191538d6d6cb96fb233f34562db49a37d450ede0092811f3dba71a2fa687d3fab3e1e12d22371a Homepage: https://cran.r-project.org/package=ppmf Description: CRAN Package 'ppmf' (Read Census Privacy Protected Microdata Files) Implements data processing described in to align modern differentially private data with formatting of older US Census data releases. The primary goal is to read in Census Privacy Protected Microdata Files data in a reproducible way. This includes tools for aggregating to relevant levels of geography by creating geographic identifiers which match the US Census Bureau's numbering. Additionally, there are tools for grouping race numeric identifiers into categories, consistent with OMB (Office of Management and Budget) classifications. Functions exist for downloading and linking to existing sources of privacy protected microdata. Package: r-cran-ppmhr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nleqslv Filename: pool/dists/noble/main/r-cran-ppmhr_1.0-1.ca2404.1_all.deb Size: 147488 MD5sum: 3a5c9058915226c905fce6336d22ed2d SHA1: dbf1479899d83e101c6045ed6109fedae97643f1 SHA256: 84b4d22d1d17867eda49b857f5ba96192be17ed39157373f2957997bcfae2979 SHA512: 6e8aee05a3f2fc938ed883e8443030eaf4eef2abd0aa602e38ffd2276bc773a1c77013b0656bd42eb7251be7e3fb7c318b5de4d3ef528153a434e11c93d72cd5 Homepage: https://cran.r-project.org/package=ppmHR Description: CRAN Package 'ppmHR' (Privacy-Protecting Hazard Ratio Estimation in Distributed DataNetworks) An implementation of the one-step privacy-protecting method for estimating the overall and site-specific hazard ratios using inverse probability weighted Cox models in distributed data network studies, as proposed by Shu, Yoshida, Fireman, and Toh (2019) . This method only requires sharing of summary-level riskset tables instead of individual-level data. Both the conventional inverse probability weights and the stabilized weights are implemented. Package: r-cran-ppmlasso Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat, r-cran-data.table, r-cran-lattice, r-cran-plyr, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.geom Filename: pool/dists/noble/main/r-cran-ppmlasso_1.5-1.ca2404.1_all.deb Size: 518160 MD5sum: 9b378ef9fba28e4f487cfeda1352e4cc SHA1: d32102b4082461e59e226c3e564801d7df434a6a SHA256: 8e4595dedb37eac3d817bccffd0b1944ceced89554866fa474e218b58439b610 SHA512: d3bf31ead991032ca0719179bdd9286f0094ee73d7378b372b0c51ac134f64bd3219edf27462edef65355e1d5ad91cb97598b60712034c05db5c515bffc5239f Homepage: https://cran.r-project.org/package=ppmlasso Description: CRAN Package 'ppmlasso' (Point Process Models with LASSO-Type Penalties) Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"), as described in Renner, I.W. and Warton, D.I. (2013) . Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff. Package: r-cran-ppqplan Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12356 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-ppqplan_1.1.0-1.ca2404.1_all.deb Size: 2123376 MD5sum: 51f53e7e7b4b53d48674c8dbb8fa6663 SHA1: d5240a87f6d505d6c06369f19d5e56e3421231b0 SHA256: 4fd02dc1263fbdf7e7ddab324fc6941ab7a78b5305eb2294e84fd55b23eeb1d8 SHA512: 49b39aaba936db78e8b7bd9e0a0258c5f4f9a8cd18b4993af451717dbf9cd507a73bd9d4dcfbb80a6f30b485f284a5bb50e5d0d8d6ab6a31f187ce2150ebdd15 Homepage: https://cran.r-project.org/package=PPQplan Description: CRAN Package 'PPQplan' (Process Performance Qualification (PPQ) Plans in Chemistry,Manufacturing and Controls (CMC) Statistical Analysis) Assessment for statistically-based PPQ sampling plan, including calculating the passing probability, optimizing the baseline and high performance cutoff points, visualizing the PPQ plan and power dynamically. The analytical idea is based on the simulation methods from the textbook Burdick, R. K., LeBlond, D. J., Pfahler, L. B., Quiroz, J., Sidor, L., Vukovinsky, K., & Zhang, L. (2017). Statistical Methods for CMC Applications. In Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry (pp. 227-250). Springer, Cham. Package: r-cran-pprank Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pprank_0.1.1-1.ca2404.1_all.deb Size: 17494 MD5sum: b7b2a07a35f47884930e08b9b6a7c686 SHA1: 38c7463078754b240740c0bcbbf7c4623b0219c8 SHA256: b5a03a3e8cb86cd5a58a5626694eb87d1c8132a2070574a243e3d138cee8c2cb SHA512: 7d33a2cbf97873b0b0f9eca50ea3f3397195a4776cef7d6d629234a5a5611bb8150b8fb49a4d14239f78dfc71b9db85ccc7ea0a932e219850545ad7715a7ba5b Homepage: https://cran.r-project.org/package=ppRank Description: CRAN Package 'ppRank' (Classification of Algorithms) Implements the Bi-objective Lexicographical Classification method and Performance Assessment Ratio at 10% metric for algorithm classification. Constructs matrices representing algorithm performance under multiple criteria, facilitating decision-making in algorithm selection and evaluation. Analyzes and compares algorithm performance based on various metrics to identify the most suitable algorithms for specific tasks. This package includes methods for algorithm classification and evaluation, with examples provided in the documentation. Carvalho (2019) presents a statistical evaluation of algorithmic computational experimentation with infeasible solutions . Moreira and Carvalho (2023) analyze power in preprocessing methodologies for datasets with missing values . Package: r-cran-pprep Architecture: all Version: 0.42.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hypergeo Suggests: r-cran-roxygen2, r-cran-tinytest, r-cran-cubature Filename: pool/dists/noble/main/r-cran-pprep_0.42.3-1.ca2404.1_all.deb Size: 68664 MD5sum: 26c0f68b70dd837032d55a25c9c1da40 SHA1: 943716edf29629b1e8e1cc584ecf0ffb46c5fb2b SHA256: 5e137ac24536aa5f26e7e3e41af94bb75d5ed80fc4c93a546ecf3642514eecaa SHA512: 72b8f574a4a906a2a8bd1ca8d9bb7d027600b98609b1d576c80f3b3efa4dccbfd6205d32dc8c30cad4cf7040f108acb9241e206b6265e534402f916cb4885101 Homepage: https://cran.r-project.org/package=ppRep Description: CRAN Package 'ppRep' (Analysis of Replication Studies using Power Priors) Provides functionality for Bayesian analysis of replication studies using power prior approaches (Pawel et al., 2023) . Package: r-cran-pps Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pps_1.0-1.ca2404.1_all.deb Size: 202388 MD5sum: 7b609255faceb18ccb270277155fef87 SHA1: 9af24924b18ad6171732329e1468b3d9f976b806 SHA256: 8c7ec8d675ad5ab56614dbe647efa48c78754f8d44a9a862a0fa4d2d7ad31fc7 SHA512: 26bc97a8595a31505615b094993ac0493d161365d43064014c07f74c6f858b1b50ba8a957fea5484cc84c11b502a7b5e6933cf504e12fd64fe73cbb6ba9e9c53 Homepage: https://cran.r-project.org/package=pps Description: CRAN Package 'pps' (PPS Sampling) Functions to select samples using PPS (probability proportional to size) sampling. The package also includes a function for stratified simple random sampling, a function to compute joint inclusion probabilities for Sampford's method of PPS sampling, and a few utility functions. The user's guide pps-ug.pdf is included in the .../pps/doc directory. The methods are described in standard survey sampling theory books such as Cochran's "Sampling Techniques"; see the user's guide for references. Package: r-cran-ppsbm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfast, r-cran-clue, r-cran-gtools Filename: pool/dists/noble/main/r-cran-ppsbm_1.0.0-1.ca2404.1_all.deb Size: 206144 MD5sum: 53cea5215a10f7168b770aca3b871e74 SHA1: 73619c5c52bddd5ea9771c50aa98ea13ed03aa47 SHA256: 40f18895c250af08d7047c6bb03feb302c95659f4f759ffbb1b033efda4ab908 SHA512: 4ccdfd1c7aee7e4d202519d0cd3302e1b2e156f6c8624d21f923d589a2bd194ac22207bbf4d9d5c900d08e93c76aa7c4492969abbdf4fb2995258d062aecfc1c Homepage: https://cran.r-project.org/package=ppsbm Description: CRAN Package 'ppsbm' (Clustering in Longitudinal Networks) Stochastic block model used for dynamic graphs represented by Poisson processes. To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individuals’ latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach (with an adaptive choice of the partition size) or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion. Y. Baraud and L. Birgé (2009). . C. Biernacki, G. Celeux and G. Govaert (2000). . M. Corneli, P. Latouche and F. Rossi (2016). . J.-J. Daudin, F. Picard and S. Robin (2008). . A. P. Dempster, N. M. Laird and D. B. Rubin (1977). . G. Grégoire (1993). . L. Hubert and P. Arabie (1985). . M. Jordan, Z. Ghahramani, T. Jaakkola and L. Saul (1999). . C. Matias, T. Rebafka and F. Villers (2018). . C. Matias and S. Robin (2014). . H. Ramlau-Hansen (1983). . P. Reynaud-Bouret (2006). . Package: r-cran-ppseq Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11209 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-furrr, r-cran-ggplot2, r-cran-plotly, r-cran-purrr, r-cran-tibble, r-cran-patchwork, r-cran-tidyr Suggests: r-cran-covr, r-cran-gt, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ppseq_0.2.5-1.ca2404.1_all.deb Size: 1413240 MD5sum: f86374c1c5586453db8924d662cfaeff SHA1: 60698d2d5425dd46f972f8e85356c8c202f2a3da SHA256: 93693ce0bbe1f53b94c560831392cf4b878bb910585249f460f404bb0be6e3fe SHA512: 8f32e2abe8db26a5bd06c0e43d65499b2ffa8a22d1c96184b8dbf4168ee386ad36a0cdde86b2ff635866b3a39239f356b9e18cb9bbfb51f3db374a4d66a833dc Homepage: https://cran.r-project.org/package=ppseq Description: CRAN Package 'ppseq' (Design Clinical Trials using Sequential Predictive ProbabilityMonitoring) Functions are available to calibrate designs over a range of posterior and predictive thresholds, to plot the various design options, and to obtain the operating characteristics of optimal accuracy and optimal efficiency designs. Package: r-cran-ppsr Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-parsnip, r-cran-rpart, r-cran-withr, r-cran-gridextra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ppsr_0.0.5-1.ca2404.1_all.deb Size: 402512 MD5sum: 5dd87fba28e4f5f535635158562c7f78 SHA1: 5b5224ce7905fb362bf69f3f0c728031047593d5 SHA256: 2482fc8fe9bae1e71e741bbd26082de9869254c17448fffde41bac15ae2a4651 SHA512: 313872a0b44316adc830559d1ada35ad9bb74da3dfbacbeccc8e767d2d62aaede8550e15e2f71b3f5c9e8273ab34cb37d2b359ad55e582f302ebae89fe3da921 Homepage: https://cran.r-project.org/package=ppsr Description: CRAN Package 'ppsr' (Predictive Power Score) The Predictive Power Score (PPS) is an asymmetric, data-type-agnostic score that can detect linear or non-linear relationships between two variables. The score ranges from 0 (no predictive power) to 1 (perfect predictive power). PPS can be useful for data exploration purposes, in the same way correlation analysis is. For more information on PPS, see . Package: r-cran-pptcirc Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circular, r-cran-progress Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pptcirc_0.2.3-1.ca2404.1_all.deb Size: 148422 MD5sum: 859ee6bca4d1cecf401a6ee9c4effb99 SHA1: 10cd1030cd544d36becba66531ec8dcd22e037f0 SHA256: e7d58f8edd4516d492b963029e4e8467e7021fe98b0181b317f2bcdc11680f9a SHA512: 6b63435662bfa8a64354dc194d58b2b8c54070db8555b785dc6f549f45e4e839e4b6f17e374c4ec987f02540f654034da6573e5620416e3d6da36eb1b4dc59e8 Homepage: https://cran.r-project.org/package=PPTcirc Description: CRAN Package 'PPTcirc' (Projected Polya Tree for Circular Data) Provides functionality for the prior and posterior projected Polya tree for the analysis of circular data (Nieto-Barajas and Nunez-Antonio (2019) ). Package: r-cran-ppwdeming Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ppwdeming_2.1.0-1.ca2404.1_all.deb Size: 92554 MD5sum: bab3e50a0f78f402617bbbc650f7fe6a SHA1: 1982037e6f0b11862d94530798206f4cf1ed46ee SHA256: f348928b9854f168700dca3fe057802d08aeab62a2a7d949eb560e2f65d1a3fc SHA512: 1fa2ed2d47367ca60d3d3ae3b4ac7436c96b542fe13f405b666eba3374adf4285e933e6a60a05008f2db6606708eb1d583154d5a26a89965408399e538efd1f8 Homepage: https://cran.r-project.org/package=ppwdeming Description: CRAN Package 'ppwdeming' (Precision Profile Weighted Deming Regression) Weighted Deming regression, also known as 'errors-in-variable' regression, is applied with suitable weights. Weights are modeled via a precision profile; thus the methods implemented here are referred to as precision profile weighted Deming (PWD) regression. The package covers two settings – one where the precision profiles are known either from external studies or from adequate replication of the X and Y readings, and one in which there is a plausible functional form for the precision profiles but the exact (unknown) function must be estimated from the (generally singlicate) readings. The function set includes tools for: estimated standard errors (via jackknifing); standardized-residual analysis function with regression diagnostic tools for normality, linearity and constant variance; and an outlier analysis identifying significant outliers for closer investigation. The following reference provides further information on mathematical derivations and applications. Hawkins, D.M., and J.J. Kraker (2026). 'Precision Profile Weighted Deming Regression for Methods Comparison'. The Journal of Applied Laboratory Medicine 11, 379-392 . Package: r-cran-ppweibull Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-pracma, r-cran-segmented, r-cran-msm, r-cran-nloptr Filename: pool/dists/noble/main/r-cran-ppweibull_1.0-1.ca2404.1_all.deb Size: 74998 MD5sum: c36664394bf7d50413657786e89c3e2a SHA1: 39473737268b265c7c3eb4804fe2fb27df658cc7 SHA256: f7470ffde360066bc5cb2c45fcc38c10f43898efbeb1e8d441b460aab1b37d1b SHA512: 1026dd2024bb8e1867545fcd85e3c1b18b7c47d45e57a2766b30b4955729a8f5039ccfb6bff64a408d9ac7e69f4b24949da8a7ba6d8b245de4d215d4eb07c6fb Homepage: https://cran.r-project.org/package=ppweibull Description: CRAN Package 'ppweibull' (Piecewise Lifetime Models) Provides functions for estimation and data generation for several piecewise lifetime distributions. The package implements the power piecewise Weibull model, which includes the piecewise Rayleigh and piecewise exponential models as special cases. See Feigl and Zelen (1965) for methodological details. Package: r-cran-pqa Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pqa_1.0.0-1.ca2404.1_all.deb Size: 69252 MD5sum: 3465fe924860c90cdc018b16e9388631 SHA1: 9a697741f4240369425404a33b44d39534ef2d23 SHA256: 92a20dd171b26877db1608f94c6244bdd6abbf3825aeb9ac051c316a2bdcb350 SHA512: f9a02ff843401b2fce6f1f36dfefe5bc67f7d0c178fb92b0fc9e6a000b9cb1ec0122547075d92e291b692788ec0b60d04a2f62434312329fe3fb2e175c89f33a Homepage: https://cran.r-project.org/package=PQA Description: CRAN Package 'PQA' (Perform the Pearson-Quetelet Analysis on Two-Way ContingencyTables) Tools to perform Pearson-Quetelet analysis on two-way contingency tables. The package computes absolute and relative frequencies, Quetelet indices, Pearson-Quetelet decomposition, apex tables, and chi-square summaries for interpreting associations between categorical variables. Package: r-cran-pqantimalarials Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-rcolorbrewer, r-cran-plyr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-pqantimalarials_0.2-1.ca2404.1_all.deb Size: 43666 MD5sum: 0fb95dcddf1de38506dba57f6dc64b72 SHA1: 30309ed840509d3881c2730069d9f9e3c245acb8 SHA256: 07aaf2e4513e1cb66fe679592fb002bf13d05668c45fb94d2f33428b449d826f SHA512: 99bc2d1833c8641851c51cacfb737cfa95986b35c48a4866c4a502aac8931876cc2a4c2d59ac5f080387d87ee0ec3e7654ee444ca522f3c6fd167a5afe288dec Homepage: https://cran.r-project.org/package=pqantimalarials Description: CRAN Package 'pqantimalarials' (web tool for estimating under-five deaths caused by poor-qualityantimalarials in sub-Saharan Africa) This package allows users to calculate the number of under-five child deaths caused by consumption of poor quality antimalarials across 39 sub-Saharan nations. The package supports one function, that starts an interactive web tool created using the shiny R package. The web tool runs locally on the user's machine. The web tool allows users to set input parameters (prevalence of poor quality antimalarials, case fatality rate of children who take poor quality antimalarials, and sample size) which are then used to perform an uncertainty analysis following the Latin hypercube sampling scheme. Users can download the output figures as PDFs, and the output data as CSVs. Users can also download their input parameters for reference. This package was designed to accompany the analysis presented in: J. Patrick Renschler, Kelsey Walters, Paul Newton, Ramanan Laxminarayan "Estimated under-five deaths associated with poor-quality antimalarials in sub-Saharan Africa", 2014. Paper submitted. Package: r-cran-pql Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pql_0.1.0-1.ca2404.1_all.deb Size: 31898 MD5sum: 6413ed61e5db472dfc235312ebb50e1a SHA1: 40caf6f929bb11ad9917eba5c93dd6e0f5e0336e SHA256: 160a642d3a49bbb77d58f5ae80b01dc053ffe29ef1850c0f5d574b1b2b191505 SHA512: fa14335d5ffc3e6ac9d624232cda05c22ca2fd67c94d5a3f7ce761fa8f16037c9d029f0da3ff00ec7f140cd3852ca7193253daa35ada885fb659904d0d18a81d Homepage: https://cran.r-project.org/package=pql Description: CRAN Package 'pql' (A Partitioned Quasi-Likelihood for Distributed StatisticalInference) In the big data setting, working data sets are often distributed on multiple machines. However, classical statistical methods are often developed to solve the problems of single estimation or inference. We employ a novel parallel quasi-likelihood method in generalized linear models, to make the variances between different sub-estimators relatively similar. Estimates are obtained from projection subsets of data and later combined by suitably-chosen unknown weights. The philosophy of the package is described in Guo G. (2020) . Package: r-cran-pqtldata Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4032 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-rdpack Suggests: r-cran-dplyr, r-bioc-ensdb.hsapiens.v75, r-bioc-ensembldb, r-bioc-iranges, r-bioc-org.hs.eg.db, r-bioc-s4vectors, r-cran-venndiagram Filename: pool/dists/noble/main/r-cran-pqtldata_0.6-1.ca2404.1_all.deb Size: 2158450 MD5sum: c4fb67b1b8c7109c62173a180a1d0555 SHA1: 6bb29bd059ba7e758b3aef0eb313d696efbd37cf SHA256: b86b250fe294c6ef4cd9a96c25e6b893d2185d2635f2b285b983d684b40f62cf SHA512: 7093c26f246a074df64cdb18df8feb9b2ea12b221d7ab3dbff99fd1ad66b835febaeb1b6999144128cd1f101657578b1812e2b57e539edec937246e059f5aa6d Homepage: https://cran.r-project.org/package=pQTLdata Description: CRAN Package 'pQTLdata' (A Collection of Proteome Panels and Metadata) It aggregates protein panel data and metadata for protein quantitative trait locus (pQTL) analysis using 'pQTLtools' (). The package includes data from affinity-based panels such as 'Olink' () and 'SomaScan' (), as well as mass spectrometry-based panels from 'CellCarta' (), 'Seer' () and 'SWATH-MS' (). The metadata encompasses updated annotations and publication details. Package: r-cran-pra Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1927 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mc2d, r-cran-minpack.lm Suggests: r-cran-base64enc, r-cran-bslib, r-cran-ellmer, r-cran-ggplot2, r-cran-htmltools, r-cran-jsonlite, r-cran-knitr, r-cran-ragnar, r-cran-rmarkdown, r-cran-scales, r-cran-shiny, r-cran-shinychat, r-cran-mockr, r-cran-testthat, r-cran-vitals, r-cran-withr Filename: pool/dists/noble/main/r-cran-pra_0.4.0-1.ca2404.1_all.deb Size: 1341634 MD5sum: 65ab14b473316e1459e35541b9926d3a SHA1: 60a28b135cc5286854b36cb1b6c60c71c5174d73 SHA256: 02284e5cc699c9362c135a2e29714e07ee93503ae808c9a1cc167b98b64fb1ab SHA512: 380c1e6b684d4f646e5ba80e9241bf4ace51a1cf338e544a7cfe92fd1b4f0bab015f00a5ebf3864a5485b1c1c00fa8404ece2a19cbca1a3ae1cfbd8392847c3c Homepage: https://cran.r-project.org/package=PRA Description: CRAN Package 'PRA' (Project Risk Analysis) Data analysis for Project Risk Management via the Second Moment Method, Monte Carlo Simulation, Contingency Analysis, Sensitivity Analysis, Earned Value Management, Learning Curves, Bayesian Methods, and more. Package: r-cran-praatpicture Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1835 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-av, r-cran-bslib, r-cran-crayon, r-cran-emur, r-cran-gifski, r-cran-gsignal, r-cran-ipa, r-cran-multitaper, r-cran-phontools, r-cran-rpraat, r-cran-rstudioapi, r-cran-shiny, r-cran-shinyjs, r-cran-soundgen, r-cran-tuner, r-cran-wrassp, r-cran-zoo Filename: pool/dists/noble/main/r-cran-praatpicture_1.8.0-1.ca2404.1_all.deb Size: 1159826 MD5sum: f3a897362e2be01bee4636ff5c4e7d16 SHA1: 4b25d56480737f698ea59a47c12f801aeab269ce SHA256: 66d73fa556046698402836c1296c3d0ced59cfb4feb834dd9f72b53751cf2619 SHA512: 250ad4590992b8d80f86e20a83728b5a7e34d07d08d46c4a10f490ec9aae7f87a9812bfd05bc450fc775940024b9a9a1f1b4b2fc856b92fbd3002df9979d410a Homepage: https://cran.r-project.org/package=praatpicture Description: CRAN Package 'praatpicture' ('Praat Picture' Style Plots of Acoustic Data) Quickly and easily generate plots of acoustic data aligned with transcriptions similar to those made in 'Praat' using either derived signals generated directly in R with 'wrassp' or imported derived signals from 'Praat'. Provides easy and fast out-of-the-box solutions but also a high extent of flexibility. Also provides options for embedding audio in figures and animating figures. Package: r-cran-prabclus Architecture: all Version: 2.3-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 778 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mclust Suggests: r-cran-spdep, r-cran-spatialreg, r-cran-bootstrap, r-cran-foreign, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-prabclus_2.3-5-1.ca2404.1_all.deb Size: 472728 MD5sum: 39859e2c69f1adeb263a6d128e3a05a8 SHA1: 53d1e01cdfe4ab287dba3b12c1e009927347ea20 SHA256: 88123e919ae24057ae4e751a3737d8bea5d99de55475a16054f71f56b457f0b5 SHA512: 01407113718ed8d784fe069d64dde8d465e59515f77894a227767466a201a98f40b3b53d7b1526bd5bb49cd8dbeeb4e3cc5f826f87dd50b546d132286c0d4d8f Homepage: https://cran.r-project.org/package=prabclus Description: CRAN Package 'prabclus' (Functions for Clustering and Testing of Presence-Absence,Abundance and Multilocus Genetic Data) Distance-based parametric bootstrap tests for clustering with spatial neighborhood information. Some distance measures, Clustering of presence-absence, abundance and multilocus genetic data for species delimitation, nearest neighbor based noise detection. Genetic distances between communities. Tests whether various distance-based regressions are equal. Try package?prabclus for on overview. Package: r-cran-pracma Architecture: all Version: 2.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1904 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-nlcoptim, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-pracma_2.4.6-1.ca2404.1_all.deb Size: 1710094 MD5sum: ff43762974fafa298b22b2412cc9d426 SHA1: fd5ae13dace3cbd167739d9b4ec24ec70eabc65e SHA256: f788a4aa61f971b4196255e1e0f3ced4dcf30b621ea32a4acfae850cd2916512 SHA512: 8aad912028f4be8df73e1a6b7c7c24559ed5a5cff66eeeac214d131bc167f5eb6d152196fd0061f16405192dac7d0eed28f77a4628fa9af7ceab34d813c0d631 Homepage: https://cran.r-project.org/package=pracma Description: CRAN Package 'pracma' (Practical Numerical Math Functions) Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting. Package: r-cran-pracpac Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 509 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-glue, r-cran-fs, r-cran-rprojroot, r-cran-renv, r-cran-pkgbuild Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-pracpac_0.2.0-1.ca2404.1_all.deb Size: 323394 MD5sum: 0f1d2601e51211835493b3927240e765 SHA1: 6add0967f2b7e5ac0cd37c864671fa9a2525f911 SHA256: c4aa6a72acebceb0a56aeabf428a99d0fce0423e0d5e94926f7ce202d6b3c217 SHA512: 1e004b9dc1e755b2d9be140ae7438680c2f6fd1b0a3e5f56008cef9737840eba3f6df3701bcc44823d7d781e9516187197cf584216e109cd16fccb431f7390fc Homepage: https://cran.r-project.org/package=pracpac Description: CRAN Package 'pracpac' (Practical 'R' Packaging in 'Docker') Streamline the creation of 'Docker' images with 'R' packages and dependencies embedded. The 'pracpac' package provides a 'usethis'-like interface to creating Dockerfiles with dependencies managed by 'renv'. The 'pracpac' functionality is described in Nagraj and Turner (2023) . Package: r-cran-practicalequidesign Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-numderiv, r-cran-temporal, r-cran-tidyr Suggests: r-cran-r.rsp, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-practicalequidesign_0.0.3-1.ca2404.1_all.deb Size: 60888 MD5sum: 1a9f49fea97835bad7887b47615b5ada SHA1: 3243618e40710dc1569329c996c8c2b551b1e9a9 SHA256: d4ba07f8622de2c25fd37d146911ee1076bde6d038ec13ae953f3bf75c68ed3a SHA512: d54dd621f490ee64213f964085ae28d0f8b40b7af6703c0372769ea02dd98867f431c2b770c6f780dad845f1346a57f97bb2cf701e024f20954418fae20953b3 Homepage: https://cran.r-project.org/package=PracticalEquiDesign Description: CRAN Package 'PracticalEquiDesign' (Design of Practical Equivalence Trials) Sample size calculations for practical equivalence trial design with a time to event endpoint. Package: r-cran-practicalsigni Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-np, r-cran-generalcorr, r-cran-xtable, r-cran-shapleyvalue, r-cran-nns, r-cran-randomforest, r-cran-hypergeo Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-practicalsigni_0.1.2-1.ca2404.1_all.deb Size: 497090 MD5sum: fe419f59d3ea2a9a90feec0952bb8e57 SHA1: bd451c1b4a49f4943c78d0afb7f124cda0a0e928 SHA256: e45da535d2fd68740efdd8be1575b219c8f976fbe22249a59e81e91072ec33d2 SHA512: 26b2a5c2dc48f6ea1b48fd583526b0be516b98ea6a5a1d7d448c80d2ae5d5aaeeaaac5636473bb0cfe9856b8ae1a9ba1df0e8bfba4b93137876b38baca53fc6a Homepage: https://cran.r-project.org/package=practicalSigni Description: CRAN Package 'practicalSigni' (Practical Significance Ranking of Regressors and Exact t Density) Consider a possibly nonlinear nonparametric regression with p regressors. We provide evaluations by 13 methods to rank regressors by their practical significance or importance using various methods, including machine learning tools. Comprehensive methods are as follows. m6=Generalized partial correlation coefficient or GPCC by Vinod (2021) and Vinod (2022). m7= a generalization of psychologists' effect size incorporating nonlinearity and many variables. m8= local linear partial (dy/dxi) using the 'np' package for kernel regressions. m9= partial (dy/dxi) using the 'NNS' package. m10= importance measure using the 'NNS' boost function. m11= Shapley Value measure of importance (cooperative game theory). m12 and m13= two versions of the random forest algorithm. Taraldsen's exact density for sampling distribution of correlations added. Package: r-cran-practools Architecture: all Version: 1.7.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5004 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-geosphere, r-cran-ggplot2, r-cran-mass, r-cran-usmap Suggests: r-cran-doby, r-cran-foreign, r-cran-kableextra, r-cran-knitr, r-cran-lpsolve, r-cran-markdown, r-cran-plyr, r-cran-pps, r-cran-rcpp, r-cran-reshape, r-cran-rmarkdown, r-cran-roxygen2, r-cran-sampling, r-cran-samplingbook, r-cran-sp, r-cran-survey Filename: pool/dists/noble/main/r-cran-practools_1.7.5-1.ca2404.1_all.deb Size: 3883340 MD5sum: 951deaa5b0a8dc2c03c0ef670dc005c0 SHA1: 905baa0b5c426dcb7665e894d5ad9d59d7751ab3 SHA256: 4d975bad575ba29461f7cfd2c846cfdebb620c70552ab7362c8656f5de0430f1 SHA512: 794ec9cf7ebfe92a7991209a870153090e6dee685b63098b8c1c40f6a56a07016d0b29abb466e262ce309f112bea421377750b854c875e41f5d16e09cd8628d8 Homepage: https://cran.r-project.org/package=PracTools Description: CRAN Package 'PracTools' (Designing and Weighting Survey Samples) Functions and datasets to support Valliant, Dever, and Kreuter (2018), , "Practical Tools for Designing and Weighting Survey Samples". Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs, and single-stage audit sample designs. Functions are included that will group geographic units accounting for distances apart and measures of size. Other functions compute variance components for multistage designs, sample sizes in two-phase designs, and a stopping rule for ending data collection. A number of example data sets are included. Package: r-cran-prais Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sandwich, r-cran-pcse Filename: pool/dists/noble/main/r-cran-prais_1.1.4-1.ca2404.1_all.deb Size: 71468 MD5sum: e78636face8f65a87c225ba51895cd6f SHA1: 44899a5e3d5bd0dbb185e69d152a9cc2a46fdd38 SHA256: bfac02eeb3fde149379b8ee05737cfe8bc43f27fa9ecde546dc4162617bc1fac SHA512: 2c911c4b20b95ec91535036a372f39f0bca8de6db5e7e148321d77e055f448a7bd7ed15946962ab3a9f5ba4c80da4d40d7e3c76a05324cff11e6d1c9ef8b59ba Homepage: https://cran.r-project.org/package=prais Description: CRAN Package 'prais' (Prais-Winsten Estimator for AR(1) Serial Correlation) The Prais-Winsten estimator (Prais & Winsten, 1954) takes into account AR(1) serial correlation of the errors in a linear regression model. The procedure recursively estimates the coefficients and the error autocorrelation of the specified model until sufficient convergence of the AR(1) coefficient is attained. Package: r-cran-praise Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-praise_1.0.0-1.ca2404.1_all.deb Size: 17876 MD5sum: 3bb8f323208a8a91d95f03aaa7e262bf SHA1: 1cc51ee1af99b13d41c429787715de84f7a7deb2 SHA256: 660b10a6e8c11c22c39e7d8a6302fa9efcffee6e618142f844b33187c95bc519 SHA512: 5107900448d75a6e771740721c95208631f3ef86301f161a15dbd50de3d3bce510eb81870b75e2424af81dfb7b86bac9ff5c9be6431669173bda3a32870e6f0b Homepage: https://cran.r-project.org/package=praise Description: CRAN Package 'praise' (Praise Users) Build friendly R packages that praise their users if they have done something good, or they just need it to feel better. Package: r-cran-prakriti Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1967 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-prakriti_0.1.4-1.ca2404.1_all.deb Size: 1375376 MD5sum: 5607128810d4b8e861def552d1d86c15 SHA1: 94e8b316166bcb907efa761374886a579ba17504 SHA256: 3311ec0ea6117e95fd6e76b7747ee407bd4c517ce5c30da7123e884fe9dc846d SHA512: c2d1272c854a89c58ce76ee71f33fdd9398f16748903f06ee208455530438cb67f974984dadd6095c5e98a3fd72f4e82b05979670f3f6dee5be6da4d563b0674 Homepage: https://cran.r-project.org/package=prakriti Description: CRAN Package 'prakriti' (Color Palettes Inspired by India's Natural Landscapes) Curated color palettes drawn from India's natural beauty - Himalayan snow, Thar dunes, Kerala backwaters, Andaman reefs, Spiti's cold desert, Kashmir's autumn chinar, and more. 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Package: r-cran-precintcon Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-precintcon_2.3.0-1.ca2404.1_all.deb Size: 219394 MD5sum: 8ec4ba1be7cde6013b106a99c87ef275 SHA1: ae1d504b4a4a252941716b594c56e8586b47b11d SHA256: 268fbb2c005a8d203617da9dcaa47d4001e22e6498e9e99cf6b00b14f88615f2 SHA512: cbf9901a726d7fb1c0479fa47f2a2ea6d8c81dee1a1d6ae3ae132d12016b6a5ef6089c011454097d313d78173bed9ce842695e8bddc73a1330e35d4805c84c4d Homepage: https://cran.r-project.org/package=precintcon Description: CRAN Package 'precintcon' (Precipitation Intensity, Concentration and Anomaly Analysis) It contains functions to analyze the precipitation intensity, concentration and anomaly. 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Package: r-cran-preciseplacement Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-preciseplacement_0.2.0-1.ca2404.1_all.deb Size: 299778 MD5sum: 61a9b883af179d0e5b3b75c4a56cecc8 SHA1: d47817bab2be29c01e3e0146c2b0ef133d2d84d3 SHA256: 303bd1a37e57e278f11723026df17bc5e36d39f79e090703ff62918145e34274 SHA512: 177d27e46919858d54669a0594da653ab74b8016ab150b77b92b4b9e4d82d8d02545d8833daf6ad1773b24be938be347e46b3e21c9c6d7df4f90b2297572a7a2 Homepage: https://cran.r-project.org/package=precisePlacement Description: CRAN Package 'precisePlacement' (Suite of Functions to Help Get Plot Elements Exactly Where YouWant Them) Provides a selection of tools that make it easier to place elements onto a (base R) plot exactly where you want them. It allows users to identify points and distances on a plot in terms of inches, pixels, margin lines, data units, and proportions of the plotting space, all in a manner more simple than manipulating par(). Package: r-cran-precmed Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gbm, r-cran-gam, r-cran-ggplot2, r-cran-glmnet, r-cran-mass, r-cran-mgcv, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-survival, r-cran-randomforestsrc Filename: pool/dists/noble/main/r-cran-precmed_1.1.0-1.ca2404.1_all.deb Size: 1007820 MD5sum: 78fc3b14155a79479aae248926b46611 SHA1: e42ae737dae3017dfca0a47af2bcb175153842d4 SHA256: 2876262aafdd44a35a77f83bb5669f7e43adde4ceef00e7aad835cd558e07297 SHA512: 66407d60f2f2b72986a66cdf93483c7a68710c3648df14f5e814222e998c47984c865f75ade225d709af10e0ae98ea41099c08e26b8e6fa42b838d67bca9ea3e Homepage: https://cran.r-project.org/package=precmed Description: CRAN Package 'precmed' (Precision Medicine) A doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) . 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Package: r-cran-precviasbr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-arrow, r-cran-dplyr, r-cran-rlang Suggests: r-cran-sf Filename: pool/dists/noble/main/r-cran-precviasbr_0.1.0-1.ca2404.1_all.deb Size: 201730 MD5sum: 47947becd5da425679f4e7251e5194ae SHA1: 803d4086ec343092c41e2335ef83989daf178515 SHA256: dd6c87c849d40030df94f2e83597754fbde2b42a8c453d0febadf48a09c25ad5 SHA512: a2ed206c44f3f7a31444cf5f170c484b63f22185529dc2fc589893732c8db49773a973886cbcdddf2495f88a363845b270a7d4bdb236ddab6cd18bffd5d548c1 Homepage: https://cran.r-project.org/package=precviasBR Description: CRAN Package 'precviasBR' (Spatial Data of Road Precariousness in Brazil) Fornece acesso eficiente à malha espacial de precariedade viária brasileira. O pacote realiza o download em cache e a leitura otimizada (via Apache Arrow) de arquivos Parquet particionados, contendo o cruzamento de variáveis de infraestrutura do Entorno do Censo Demográfico 2022 (IBGE) com a malha viária aberta do Overture Maps. [English] Provides efficient access to the spatial network of road precariousness in Brazil. The package performs cached downloads and optimized reading (via Apache Arrow) of partitioned Parquet files. These files contain the intersection of infrastructure variables from the 2022 Demographic Census (IBGE) with the open street network from Overture Maps. Methodology and datasets are detailed in Passos (2026) . Package: r-cran-predcrg Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4694 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-cran-protr, r-cran-peptides, r-cran-kernlab, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-predcrg_1.0.2-1.ca2404.1_all.deb Size: 4752156 MD5sum: 917f5ad2fc0baabe8da7dea2047e9bc1 SHA1: 887fb1d29ae275bcc52ab35dab8e21556d2f24ca SHA256: 34fc6bdb2920297f9ef57c5467b68153b77b6df037a5ce3cf5b57112b6006794 SHA512: 12541442d9be556caa50668d9f8ea3575964db7f85ce94ffa9603bcbf8dfdb2ed0cb24385ab1778c5513f2912419257f27d20f2c3b7a0fca862cc40084d015e3 Homepage: https://cran.r-project.org/package=PredCRG Description: CRAN Package 'PredCRG' (Computational Prediction of Proteins Encoded by Circadian Genes) A computational model for predicting proteins encoded by circadian genes. The support vector machine has been employed with Laplace kernel for prediction of circadian proteins, where compositional, transitional and physico-chemical features were utilized as numeric features. User can predict for the test dataset using the proposed computational model. Besides, the user can also build their own training model using their training dataset, followed by prediction for the test set. Package: r-cran-predfairness Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-caret Filename: pool/dists/noble/main/r-cran-predfairness_0.1.0-1.ca2404.1_all.deb Size: 327484 MD5sum: 75eba62494ac5ad2ec0c4c0992a0254a SHA1: e67e6b8ef296661e8851ebdfcf6b4027ce7ef8ae SHA256: 5b73e5a30c6be2fa305c90bfce6d30fa36041ccad2dada5a3dced4d7a06b46af SHA512: 291b69647a860a869a6ad9b669cb817cd922ba32a4f1782ae79d1c9e6f639531b51b179206713e858cf9355d84067f749587e7cca5e7ff58a837fc5df49dec3b Homepage: https://cran.r-project.org/package=predfairness Description: CRAN Package 'predfairness' (Discrimination Mitigation for Machine Learning Models) Based on different statistical definitions of discrimination, several methods have been proposed to detect and mitigate social inequality in machine learning models. This package aims to provide an alternative to fairness treatment in predictive models. The ROC method implemented in this package is described by Kamiran, Karim and Zhang (2012) . Package: r-cran-predhy.gui Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2489 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-data.table, r-cran-dt, r-cran-predhy, r-cran-bglr, r-cran-pls, r-cran-glmnet, r-cran-xgboost, r-cran-lightgbm, r-cran-foreach, r-cran-doparallel, r-cran-htmltools Filename: pool/dists/noble/main/r-cran-predhy.gui_2.1.1-1.ca2404.1_all.deb Size: 2307380 MD5sum: d9d56fc1c4f94bb97757058b32f05976 SHA1: c7ab579e4eadde4541098cd1896a49bdab793cd8 SHA256: 7fd85c239293895be541632fabb0360fffba66d1b195c310b172687fcb1b7a8d SHA512: 93f1dbad4c9ad98807b661bdb8e555aa3b86734f5ff77cbb1f387e66bb6485175cad82fcb5ddd7160075640d3fb3cb51f7930d3a39ed21433fe54c8a903a7ea7 Homepage: https://cran.r-project.org/package=predhy.GUI Description: CRAN Package 'predhy.GUI' (Genomic Prediction of Hybrid Performance with Graphical UserInterface) Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, XGBoost and LightGBM. GBLUP: genomic best liner unbiased prediction, RKHS: reproducing kernel Hilbert space, PLS: partial least squares regression, LASSO: least absolute shrinkage and selection operator, XGBoost: extreme gradient boosting, LightGBM: light gradient boosting machine. It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) ; Xu S (2017) ). A complete manual for this package is provided in the manual folder of the package installation directory. You can locate the manual by running the following command in R: system.file("manual", package = "predhy.GUI"). Package: r-cran-predhy Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1272 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bglr, r-cran-pls, r-cran-glmnet, r-cran-xgboost, r-cran-lightgbm, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-predhy_2.1.2-1.ca2404.1_all.deb Size: 1263474 MD5sum: 127f5239f398a50741ebff7020316dbf SHA1: 9752f9cd97eca7d4be2a7a4889aeebc7cf809d6e SHA256: 7332ec4c44d32394e7f3bd0c7e91ec59396f4e140770fac894d709dcb4348ce4 SHA512: d20cc336c7416e741c2f842333f550fc9438bbee76004a656a11b1edd5d40a72c51a0133fc376a5a1b4a69571a6fdfa37a67397eccd9a2ea8ce0e1327553d1c4 Homepage: https://cran.r-project.org/package=predhy Description: CRAN Package 'predhy' (Genomic Prediction of Hybrid Performance) Performs genomic prediction of hybrid performance using eight statistical methods including GBLUP, BayesB, RKHS, PLS, LASSO, EN, LightGBM and XGBoost along with additive and additive-dominance models. Users are able to incorporate parental phenotypic information in all methods based on their specific needs. (Xu S et al(2017) ; Xu Y et al (2021) ). Package: r-cran-predict3d Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 997 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rgl, r-cran-dplyr, r-cran-ggiraphextra, r-cran-modelr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-magrittr, r-cran-reshape2, r-cran-plyr, r-cran-tidyr Suggests: r-cran-moonbook, r-cran-th.data, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-predict3d_0.1.6-1.ca2404.1_all.deb Size: 549576 MD5sum: d89f8de0844b4bc821fae328a4e9cfb0 SHA1: 35daa0055d154bb1c14b2b41bf38a5ea953dd0eb SHA256: 6c6857bd3f997322543525deb241275be859651f1403effedefa14c4dd7c06e0 SHA512: 143ee5a80a320ed4eb133f8d42c22479eef8d09ac4dd1a3f2492a1279689d6069eee2f160bc7b5b721c6d604f83edf8859c4b18e05184f909021a4e386293abc Homepage: https://cran.r-project.org/package=predict3d Description: CRAN Package 'predict3d' (Draw Three Dimensional Predict Plot Using Package 'rgl') Draw 2 dimensional and three dimensional plot for multiple regression models using package 'ggplot2' and 'rgl'. Supports linear models (lm), generalized linear models (glm) and local polynomial regression fittings (loess). Package: r-cran-predictabel Architecture: all Version: 1.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-rocr, r-cran-pbsmodelling, r-cran-lazyeval Filename: pool/dists/noble/main/r-cran-predictabel_1.2-4-1.ca2404.1_all.deb Size: 176968 MD5sum: 0b1105dbba5da80203bb8776c028710b SHA1: dc018e9ef0f4b5602a501d11b6b626e26bb93309 SHA256: 6d648a9cc1576a2a2f673414f936fa8ad1c207102839df9efae38ba379ea88cd SHA512: 08f02356696c48543428e1334531e57746c0ad82131285f24d22dc452966aa9895a7e71ef974433ba928f381d4c23ec5b251c2eb860c0b5358a0d837227f7a58 Homepage: https://cran.r-project.org/package=PredictABEL Description: CRAN Package 'PredictABEL' (Assessment of Risk Prediction Models) We included functions to assess the performance of risk models. The package contains functions for the various measures that are used in empirical studies, including univariate and multivariate odds ratios (OR) of the predictors, the c-statistic (or area under the receiver operating characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test, reclassification table, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Also included are functions to create plots, such as risk distributions, ROC curves, calibration plot, discrimination box plot and predictiveness curves. In addition to functions to assess the performance of risk models, the package includes functions to obtain weighted and unweighted risk scores as well as predicted risks using logistic regression analysis. These logistic regression functions are specifically written for models that include genetic variables, but they can also be applied to models that are based on non-genetic risk factors only. Finally, the package includes function to construct a simulated dataset with genotypes, genetic risks, and disease status for a hypothetical population, which is used for the evaluation of genetic risk models. Package: r-cran-prediction Architecture: all Version: 0.3.18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-prediction_0.3.18-1.ca2404.1_all.deb Size: 232876 MD5sum: 6e5cce65655b6ec4f1cbc273909d41d8 SHA1: 1da8fec02e23d20679f8594c8c3fc320d92d3fa4 SHA256: 78301af938c2ba05be01bfaaab235d301a4b75512f7e244251b54f227565107b SHA512: c39c3bb88c3f9e30b2d7dcc94021261f18c952fa8b2fb9318312dfea5e8403ff24cd355a92b3ed65bc2032177c51b19da322fa7eb8f19dd270890e27a9c6977d Homepage: https://cran.r-project.org/package=prediction Description: CRAN Package 'prediction' (Tidy, Type-Safe 'prediction()' Methods) A one-function package containing prediction(), a type-safe alternative to predict() that always returns a data frame. The summary() method provides a data frame with average predictions, possibly over counterfactual versions of the data (à la the margins command in 'Stata'). Marginal effect estimation is provided by the related package, 'margins' . The package currently supports common model types (e.g., lm, glm) from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README file or main package documentation page for a complete listing. Package: r-cran-predictioninterval Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mbess, r-cran-mass, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-predictioninterval_1.0.0-1.ca2404.1_all.deb Size: 60514 MD5sum: 09e6c3c2b736e705db65a2871d1e0b76 SHA1: 072be186a0c81acc2e30d1337f7a93f08954a3c5 SHA256: b1c743afa3a4e8cacf1c9cfbe86f799a6bc8eaaed4f91dbc783b7dbae2662871 SHA512: 393a0d707f523a46beeecb283cacd6cad47bd66243cb6a2164343fd55ec4239c7bcbbc45e12049ba6dc70453c977ee05b86dcb8946dcc71e6950b3f6bcc112b6 Homepage: https://cran.r-project.org/package=predictionInterval Description: CRAN Package 'predictionInterval' (Prediction Interval Functions for Assessing Replication StudyResults) A common problem faced by journal reviewers and authors is the question of whether the results of a replication study are consistent with the original published study. 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Methods include split conformal, 'CV+' and 'Jackknife+' (Barber et al. 2021) , 'Conformalized Quantile Regression' (Romano et al. 2019) , 'Adaptive Prediction Sets' (Romano, Sesia, Candes 2020) , 'Regularized Adaptive Prediction Sets' (Angelopoulos et al. 2021) , Mondrian conformal prediction for group-conditional coverage (Vovk et al. 2005), weighted conformal prediction for covariate shift (Tibshirani et al. 2019), and adaptive conformal inference for sequential prediction (Gibbs and Candes 2021). All methods are distribution-free and provide calibrated uncertainty quantification without parametric assumptions. Works with any model that can produce predictions from new data, including 'lm', 'glm', 'ranger', 'xgboost', and custom user-defined models. 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Data were collated from over 400 existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from sites around the world. These data are described in Hudson et al. (2013) . 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Package: r-cran-prepdat Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-reshape2, r-cran-psych Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-prepdat_1.0.8-1.ca2404.1_all.deb Size: 117278 MD5sum: 4f1d7175b005343202eae1a2acddaa2b SHA1: d847e263b6aa32acc8c142e3f7f2b27afd4b966c SHA256: c53c3bd7c2914125ef32bbec9999de0fc35a91468b5d3442e63eaace17164607 SHA512: 50515c115e040a0ea3252baef79950cfac27cad37ca254022d76a6dcfc4145db968b14491646432d7e2699a55b55ee98242e167d4e68a6d05d91d3ef81746f73 Homepage: https://cran.r-project.org/package=prepdat Description: CRAN Package 'prepdat' (Preparing Experimental Data for Statistical Analysis) Prepares data for statistical analysis (e.g., analysis of variance ;ANOVA) by enabling the user to easily and quickly merge (using the file_merge() function) raw data files into one merged table and then aggregate the merged table (using the prep() function) into a finalized table while keeping track and summarizing every step of the preparation. The finalized table contains several possibilities for dependent measures of the dependent variable. Most suitable when measuring variables in an interval or ratio scale (e.g., reaction-times) and/or discrete values such as accuracy. Main functions included are file_merge() and prep(). The file_merge() function vertically merges individual data files (in a long format) in which each line is a single observation to one single dataset. The prep() function aggregates the single dataset according to any combination of grouping variables (i.e., between-subjects and within-subjects independent variables, respectively), and returns a data frame with a number of dependent measures for further analysis for each cell according to the combination of provided grouping variables. Dependent measures for each cell include among others means before and after rejecting all values according to a flexible standard deviation criteria, number of rejected values according to the flexible standard deviation criteria, proportions of rejected values according to the flexible standard deviation criteria, number of values before rejection, means after rejecting values according to procedures described in Van Selst & Jolicoeur (1994; suitable when measuring reaction-times), standard deviations, medians, means according to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data frame prep() returns can also be exported as a txt file to be used for statistical analysis in other statistical programs. 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For such situations, partially replicated (p-Rep) designs have wide application potential as only a proportion of the test lines are replicated at each environment. A collection of several utility functions related to p-Rep designs have been developed. Here, the package contains six functions for a complete stepwise analytical study of these designs. Five functions pRep1(), pRep2(), pRep3(), pRep4() and pRep5(), are used to generate five new series of p-Rep designs and also compute average variance factors and canonical efficiency factors of generated designs. A fourth function NCEV() is used to generate incidence matrix (N), information matrix (C), canonical efficiency factor (E) and average variance factor (V). This function is general in nature and can be used for studying the characterization properties of any block design. A construction procedure for p-Rep designs was given by Williams et al.(2011) which was tedious and time consuming. Here, in this package, five different methods have been given to generate p-Rep designs easily. Package: r-cran-prepkit Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-prepkit_0.1.1-1.ca2404.1_all.deb Size: 458710 MD5sum: 84c243bd9f2d8e5bff0e5c5d55adc0a3 SHA1: ca3d840be9438a1a4b023966658e524b963a6cd0 SHA256: 7335e30b578209fb08ba4c1c7f0c143e12382eadd415ce08e8e0eb325d93626e SHA512: 22d66c5902d9168db94aa37c2d625372ef878c9a0fe9c54389663b6e84241368a3c1150fb217a4436c66a3e598c826c679d47e912c720bc9b4aa02d097dc80e7 Homepage: https://cran.r-project.org/package=prepkit Description: CRAN Package 'prepkit' (Data Normalization and Transformation) Provides functions for data normalization and transformation in preprocessing stages. Implements scaling methods (min-max, Z-score, L2 normalization) and power transformations (Box-Cox, Yeo-Johnson). Box-Cox transformation is described in Box and Cox (1964) , Yeo-Johnson transformation in Yeo and Johnson (2000) . 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The title, presentes, comes from present in spanish. 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Package: r-cran-presspurt Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-gridextra, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-presspurt_1.0.2-1.ca2404.1_all.deb Size: 185456 MD5sum: 858753f8f4a1ee387b8979d60c0f79f2 SHA1: d69927315acda1aaac5ae56b49675e1716731c36 SHA256: c50ae49d3c1d0c9ee8f5a593ddf7de919708e353abe940608a423bae38154371 SHA512: ee32d06c239a43692e963e878afc7a6d36b0af7fae8406e860739be6af4248a1b893ff19817638ba4732db7fd8d04ac04035bea7b8ea7a6aebb024325006114c Homepage: https://cran.r-project.org/package=PressPurt Description: CRAN Package 'PressPurt' (Indeterminacy of Networks via Press Perturbations) This is a computational package designed to identify the most sensitive interactions within a network which must be estimated most accurately in order to produce qualitatively robust predictions to a press perturbation. 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Package: r-cran-pretest Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pretest_0.2-1.ca2404.1_all.deb Size: 42070 MD5sum: c7da24b8b8bda56dc86da634e3506056 SHA1: a52c98a74db8e586cd5027a4289e431c236d5b3c SHA256: ea0548b8e1816b00163c7e5d8ed190d40161c8b3a587149185b1a44894089df7 SHA512: 0428ddeeb7eb0e4472cd4074db46d8b73b54cd3f384bc5faec96d30d3bfcff99bfa747f6b0b71db28c7adb6f413827b2096428c45a7e8c757842bbf4d255b843 Homepage: https://cran.r-project.org/package=pretest Description: CRAN Package 'pretest' (A Novel Approach to Predictive Accuracy Testing in NestedEnvironments) This repository contains the codes for using the predictive accuracy comparison tests developed in Pitarakis, J. (2023) . 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Includes extraction of relevant inflation and exchange rate data from World Bank API, data cleaning/parsing, and standardisation. Inflation adjustment calculations as found in Principles of Macroeconomics by Gregory Mankiw et al (2014). Current and historical end of day exchange rates for 171 currencies from the European Central Bank Statistical Data Warehouse (2020). 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Additionally we provide several functions and examples to facilitate the merging and aggregation of these tabular inputs. Package: r-cran-primefactr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-primefactr_0.1.1-1.ca2404.1_all.deb Size: 20390 MD5sum: e3ca7c3f431a9be53851a790d0ccbfef SHA1: 8c17010d0bd04dc310ebe043a3e7e9e49af484ee SHA256: 9d2e48f41bea8dbccd0d98fdaaee400500b8e121d4e22b9ed68190263ab660c6 SHA512: 93d159f358f61475c83b90901f2ecbaf64a859e757b86d0d0af1ba90f73b953a011432421fd174c119190be573c80252a61c570014431fa6b1be17f28ec3db70 Homepage: https://cran.r-project.org/package=primefactr Description: CRAN Package 'primefactr' (Use Prime Factorization for Computations) Use Prime Factorization for simplifying computations, for instance for ratios of large factorials. Package: r-cran-primepca Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-softimpute, r-cran-matrix, r-cran-mass Filename: pool/dists/noble/main/r-cran-primepca_1.2-1.ca2404.1_all.deb Size: 36004 MD5sum: 1518cb890292c5072c9810f0cf8618fb SHA1: 7606cf029685176cd15af62e0ff8408049c6e013 SHA256: 8733d6328dd28eb56f6b00969e41d2b211d777389c21559ec6ea2f5519e4ec6e SHA512: a5dc1e85ee5b3e916fdb4e3dc46cf91ceb3764254431d26cea1134f092797e0b1d6cd2eb3bb5b97f55577d4c5a338c11d51129c9992ce20fdc65fea93f613f17 Homepage: https://cran.r-project.org/package=primePCA Description: CRAN Package 'primePCA' (Projected Refinement for Imputation of Missing Entries in PCA) Implements the primePCA algorithm, developed and analysed in Zhu, Z., Wang, T. and Samworth, R. J. (2019) High-dimensional principal component analysis with heterogeneous missingness. . 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For optimization, the model space is searched for the best tree on the full sample by means of repeated subsampling. Restrictions are allowed so that only trees are accepted which do not include pre-specified uninterpretable split results (cf. Weihs & Buschfeld, 2021a). The function PrInDT() represents the basic resampling loop for 2-class classification (cf. Weihs & Buschfeld, 2021a). The function RePrInDT() (repeated PrInDT()) allows for repeated applications of PrInDT() for different percentages of the observations of the large and the small classes (cf. Weihs & Buschfeld, 2021c). The function NesPrInDT() (nested PrInDT()) allows for an extra layer of subsampling for a specific factor variable (cf. Weihs & Buschfeld, 2021b). The functions PrInDTMulev() and PrInDTMulab() deal with multilevel and multilabel classification. In addition to these PrInDT() variants for classification, the function PrInDTreg() has been developed for regression problems. Finally, the function PostPrInDT() allows for a posterior analysis of the distribution of a specified variable in the terminal nodes of a given tree. In version 2, additionally structured sampling is implemented in functions PrInDTCstruc() and PrInDTRstruc(). In these functions, repeated measurements data can be analyzed, too. Moreover, multilabel 2-stage versions of classification and regression trees are implemented in functions C2SPrInDT() and R2SPrInDT() as well as interdependent multilabel models in functions SimCPrInDT() and SimRPrInDT(). Finally, for mixtures of classification and regression models functions Mix2SPrInDT() and SimMixPrInDT() are implemented. Most of these extensions of PrInDT are described in Buschfeld & Weihs (2025Fc). References: -- Buschfeld, S., Weihs, C. (2025Fc) "Optimizing decision trees for the analysis of World Englishes and sociolinguistic data", Cambridge Elements. -- Weihs, C., Buschfeld, S. (2021a) "Combining Prediction and Interpretation in Decision Trees (PrInDT) - a Linguistic Example" ; -- Weihs, C., Buschfeld, S. (2021b) "NesPrInDT: Nested undersampling in PrInDT" ; -- Weihs, C., Buschfeld, S. (2021c) "Repeated undersampling in PrInDT (RePrInDT): Variation in undersampling and prediction, and ranking of predictors in ensembles" . 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Package: r-cran-projections Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 861 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-incidence, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-outbreaks, r-cran-magrittr, r-cran-distcrete, r-cran-svglite, r-cran-epiestim Filename: pool/dists/noble/main/r-cran-projections_0.6.1-1.ca2404.1_all.deb Size: 781926 MD5sum: f355014d7c5cfdcd72ac34a9ca7b6f58 SHA1: 0b5720db7325a8cbed85dcc267b45a069c1401ad SHA256: 161ef1cc9e2dac15943c7cdb589196d48398f97bc8c27a7a437bc590dab21155 SHA512: 8f6fa247cea7f26f2f65a3b0e21e4c27ae928ca5684c6cdd570087f5ba8c86e425c0f22038c5bf746005a5af2101f738fe49b289bbea0f4fa03e644c66ce0297 Homepage: https://cran.r-project.org/package=projections Description: CRAN Package 'projections' (Project Future Case Incidence) Provides functions and graphics for projecting daily incidence based on past incidence, and estimates of the serial interval and reproduction number. Projections are based on a branching process using a Poisson-distributed number of new cases per day, similar to the model used for estimating R in 'EpiEstim' or in 'earlyR', and described by Nouvellet et al. (2017) . The package provides the S3 class 'projections' which extends 'matrix', with accessors and additional helpers for handling, subsetting, merging, or adding these objects, as well as dedicated printing and plotting methods. Package: r-cran-projectlsa Architecture: all Version: 0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-colourpicker, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-flextable, r-cran-ggiraph, r-cran-ggplot2, r-cran-glca, r-cran-haven, r-cran-kableextra, r-cran-knitr, r-cran-lavaan, r-cran-magick, r-cran-mclust, r-cran-mirt, r-cran-officer, r-cran-plotly, r-cran-polca, r-cran-psych, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-scales, r-cran-semplot, r-cran-semptools, r-cran-shiny, r-cran-shinywidgets, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidylpa, r-cran-tidyverse, r-cran-viridislite Suggests: r-cran-pkgdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-projectlsa_0.0.9-1.ca2404.1_all.deb Size: 135986 MD5sum: 4f2d53b94ce2495713fd46a780b28109 SHA1: 3e4fbc34545acb19f48b88c34e54ab5fe690881a SHA256: 43db5f25a2cbbce5ff417f93cae780d0a990d222a5a83ab7b6ff5399e48597ea SHA512: 16f9d6dcc4dee2e665c33eb47f38c85275d4b5a5649df8ae288485fe2a12eed713da45a47c333223a18e4520e68b16a1440a57550005a30d1852bf547be1c388 Homepage: https://cran.r-project.org/package=projectLSA Description: CRAN Package 'projectLSA' (Shiny Application for Latent Structure Analysis with a GraphicalUser Interface) Provides an interactive Shiny-based toolkit for conducting latent structure analyses, including Latent Profile Analysis (LPA), Latent Class Analysis (LCA), Latent Trait Analysis (LTA/IRT), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). 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Package: r-cran-projectmanagement Architecture: all Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotly, r-cran-lpsolveapi, r-cran-triangle, r-cran-tuvalues, r-cran-igraph Filename: pool/dists/noble/main/r-cran-projectmanagement_2.1.4-1.ca2404.1_all.deb Size: 163776 MD5sum: 1aad4c1dcdbc9794167125d6c9b2da83 SHA1: 3f1d650e13bde9948a4c68cd6b50fcf92ed3b967 SHA256: 604f8452d303b48c046cba7434c03ebd560732d52d81f6db7d74147219e67776 SHA512: a52149fffb73eddff5a95797f93423a9e9c2d42677084a63276dabe29e9a95178819d3657c65c2053d8cc31f17cf6c594fc51234b15bf2537ff7609fc13670ec Homepage: https://cran.r-project.org/package=ProjectManagement Description: CRAN Package 'ProjectManagement' (Management of Deterministic and Stochastic Projects) Management problems of deterministic and stochastic projects. It obtains the duration of a project and the appropriate slack for each activity in a deterministic context. In addition it obtains a schedule of activities' time (Castro, Gómez & Tejada (2007) ). It also allows the management of resources. When the project is done, and the actual duration for each activity is known, then it can know how long the project is delayed and make a fair delivery of the delay between each activity (Bergantiños, Valencia-Toledo & Vidal-Puga (2018) ). In a stochastic context it can estimate the average duration of the project and plot the density of this duration, as well as, the density of the early and last times of the chosen activities. As in the deterministic case, it can make a distribution of the delay generated by observing the project already carried out. 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H., Schweinberger, M., Baugh, S. (2021) , and Andrew, D. M., Kevin M. Q., Jong Hee Park. (2011) . Package: r-cran-promethee123 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-promethee123_0.1.0-1.ca2404.1_all.deb Size: 27894 MD5sum: a0ed62c1c881c30e7d1e8ecd5cd43c6d SHA1: 2f8e39987ddfa2fe8e041593f083d0d5dd62144f SHA256: 052d278397b4c4a133857789cfd4a00b412340c36f8c6c9a750e01e4eff8853b SHA512: 73db7ef3914b69a0bc3d89079c78685f02c9717fcb66a59b7ac31f796e07c80fcb546cb0637ba7d8de43ea7a40d3ab2c47022f7c8d8c84e3bf3cd6434cbcc33c Homepage: https://cran.r-project.org/package=promethee123 Description: CRAN Package 'promethee123' (PROMETHEE I, II, and III Methods) The PROMETHEE method is a multi-criteria decision-making method addressing with outranking problems. 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Package: r-cran-promote Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-stringr Filename: pool/dists/noble/main/r-cran-promote_1.1.1-1.ca2404.1_all.deb Size: 60808 MD5sum: 930fddd0ac7e99c523e9b488d1e3d713 SHA1: a61e687e6f531439ae8aecc5e92fa2fed347e22a SHA256: 4111d094839721d9e9086c51109687f34a6d5a571865aae34fd5aeefd0a8ab23 SHA512: f2bce644c57f7d83014d419b7a13414f2d24e1aa76b77afd39ee8233e1315183746074365a976bf4104a4f20a785778fde65e9a9795258ca0321dfd0fe30b6d3 Homepage: https://cran.r-project.org/package=promote Description: CRAN Package 'promote' (Client for the 'Alteryx Promote' API) Deploy, maintain, and invoke predictive models using the 'Alteryx Promote' REST API. 'Alteryx Promote' is available at the URL: . 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It uses explicit specification of clusters, blocks and treatment allocations to furnish probability of assignment-based weights targeting any of several average treatment effect parameters, and for standard error calculations reflecting these design parameters. For covariance adjustment of its Hajek and (one-way) fixed effects estimates, it enables offsetting the outcome against predictions from a dedicated covariance model, with standard error calculations propagating error as appropriate from the covariance model. Package: r-cran-properties Architecture: all Version: 0.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-properties_0.0-9-1.ca2404.1_all.deb Size: 15186 MD5sum: b927e8ae95b70da5656978e18dc939ef SHA1: 7155703df21bfa6b5f677810a04c45c400e6093e SHA256: 26998938c0bc5273da6094cc3c8383b92d6d836002c8e88ea715db343c70c8c1 SHA512: 5a46700c974416c7c898afebb3a49c6de8a7bedde09f01a490f086ed5997764afeb344b6aad2d0476ad711908aed6f5c368f63a829f1debab4eb1de09db8ce87 Homepage: https://cran.r-project.org/package=properties Description: CRAN Package 'properties' (Parse 'Java' Properties Files for 'R Service Bus' Applications) Allows to parse 'Java' properties files in the context of 'R Service Bus' applications. Package: r-cran-proporz Architecture: all Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-shiny, r-cran-shinymatrix, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-proporz_1.5.2-1.ca2404.1_all.deb Size: 239764 MD5sum: 387b097c0431d03e871cb44bbd0112a1 SHA1: dabed7f9bd33987789507f02607f658e7556e785 SHA256: 9c66d3df4c69598181b4be79e6bb69b7900ca2f3387f8b2dc8fd9a7c9bfc9aa7 SHA512: 9f9245531a0e47f82c66c59bbe912c6a6a06de71abec01f3600a181ac0ffe2b058de225c267557a6bf76e28dc228cb8fa8d7339ea9a4abaee6a8d68bc74fcafe Homepage: https://cran.r-project.org/package=proporz Description: CRAN Package 'proporz' (Proportional Apportionment) Calculate seat apportionment for legislative bodies with various methods. The algorithms include divisor or highest averages methods (e.g. Jefferson, Webster or Adams), largest remainder methods and biproportional apportionment. Gaffke, N. & Pukelsheim, F. (2008) Oelbermann, K. F. (2016) . Package: r-cran-propoverlap Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4758 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase Filename: pool/dists/noble/main/r-cran-propoverlap_1.0-1.ca2404.1_all.deb Size: 4837700 MD5sum: d1e689ef71520f92ce63883238179973 SHA1: 1384522335cfd467e034fcaf563edf3ef23919ef SHA256: 0ab0ac36c00d8efc0f98bae2a479073ad786326b8f50a784aeb938988906d7b0 SHA512: f28bab3b759573a22b49540688ae666cf9b0583d67d8b6a4036d89e58447388c89ffa7a113aa5801929bf9a2d31f9e34865fdec1d7f2b2de95f61da9d2ef780d Homepage: https://cran.r-project.org/package=propOverlap Description: CRAN Package 'propOverlap' (Feature (gene) selection based on the Proportional OverlappingScores) A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced. Package: r-cran-propscrrand Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-propscrrand_1.1.2-1.ca2404.1_all.deb Size: 35658 MD5sum: 57ef3fe6a54fb6be9b3a19a8907631ee SHA1: 8a9009a1be0b22b961699f54380333b72234e2dd SHA256: aa9fe067740c63a6d5d60ce5e0a1e7e3fdc6aa3f7fe1753186b0591fe4dce025 SHA512: e69d8e9b899621a723a631c4d21299f8ad2898df861eb7bff2fb95a9439b096a5106a3833ad58f431b3f256b987b9f1b89519261d7fdffdc40c3c29450954591 Homepage: https://cran.r-project.org/package=PropScrRand Description: CRAN Package 'PropScrRand' (Propensity Score Methods for Assigning Treatment in RandomizedTrials) Contains functions to run propensity-biased allocation to balance covariate distributions in sequential trials and propensity-constrained randomization to balance covariate distributions in trials with known baseline covariates at time of randomization. Currently only supports trials comparing two groups. Package: r-cran-propubbills Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-propubbills_0.1-1.ca2404.1_all.deb Size: 23224 MD5sum: 3b1aa2fa290e455017a712134d68c6a7 SHA1: db6f3acc57cb74e09725eb2bb2117ee62400d546 SHA256: 3b245144c0d77e6c8b70f13721ca8186f775e075dfa5639e83eb0522b01caeb6 SHA512: b86e7234651b739bd0f56e26d8d19541a53306ec942f8653560850481811f7397e0230f665e74636f8dd29da29e7a4cd6ed04b430907587efd594f4be4f56872 Homepage: https://cran.r-project.org/package=proPubBills Description: CRAN Package 'proPubBills' ('ProPublica' U.S. Congress Bills API Wrapper) An API wrapper around the 'ProPublica' API for U.S. Congressional Bills. Users can include their API key, U.S. Congress, branch, and offset ranges, to return a dataframe of all results within those parameters. This package is different from the 'RPublica' package because it is for the 'ProPublica' U.S. Congress data API, and the 'RPublica' package is for the Nonprofit Explorer, Forensics, and Free the Files data APIs. Package: r-cran-propublicar Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-httr, r-cran-config, r-cran-lubridate Suggests: r-cran-testthat, r-cran-httptest, r-cran-knitr Filename: pool/dists/noble/main/r-cran-propublicar_1.1.4-1.ca2404.1_all.deb Size: 292136 MD5sum: 08f395c6332a8a25a06b20e40b85b182 SHA1: 2cfd8c1831b7da90810621bd2762eb6f32b16495 SHA256: ca4d278d510395e650cd8a1c0d7d7b3521cc3bac2c03826456558bd4925f9e5b SHA512: a1f1be7a310fd5ad7bb4384f3de3865e703c9aad717ba4c894a9f05a784f40b507d884a4228ff14a8e979dbf4b038311df44146d6dca18d93f10276ce5702cc6 Homepage: https://cran.r-project.org/package=ProPublicaR Description: CRAN Package 'ProPublicaR' (Access Functions for ProPublica's APIs) Provides wrapper functions to access the ProPublica's Congress and Campaign Finance APIs. The Congress API provides near real-time access to legislative data from the House of Representatives, the Senate and the Library of Congress. The Campaign Finance API provides data from United States Federal Election Commission filings and other sources. The API covers summary information for candidates and committees, as well as certain types of itemized data. For more information about these APIs go to: . Package: r-cran-proreg Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmsb, r-cran-car, r-cran-rcolorbrewer, r-cran-matrixcalc, r-cran-rootsolve, r-cran-numderiv, r-cran-matrix Filename: pool/dists/noble/main/r-cran-proreg_1.3.2-1.ca2404.1_all.deb Size: 171258 MD5sum: c7a04a65c3a15f7deaedd64708272a1d SHA1: 55e8a0065c4a5c417e5e638da1e1b540952f5a71 SHA256: 24d8843330078a8abf934b894a61bca6085e52436b11e74b41383a532e9a581c SHA512: 794f89bae5c54109c699e0a69f4b46e724cd03c826261edd6a1e0314c8a73b90cb8ac9a39d286411113d97f3391c23f2c2dd82ea5c9f5df87875ecf1aad42edf Homepage: https://cran.r-project.org/package=PROreg Description: CRAN Package 'PROreg' (Patient Reported Outcomes Regression Analysis) It offers a wide variety of techniques, such as graphics, recoding, or regression models, for a comprehensive analysis of patient-reported outcomes (PRO). Especially novel is the broad range of regression models based on the beta-binomial distribution useful for analyzing binomial data with over-dispersion in cross-sectional, longitudinal, or multidimensional response studies (see Najera-Zuloaga J., Lee D.-J. and Arostegui I. (2019) ). Package: r-cran-proscorer Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proscorertools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-proscorer_0.0.4-1.ca2404.1_all.deb Size: 105290 MD5sum: 9e639171229783d98ee12ab3a40ddbdb SHA1: 8e0b3f4ccf230f56c4db5a296a40dc5365f89e3b SHA256: 28551ba3775e6023aab6e6953fe048d8a8cd192137fd433d9966031fc1cf132b SHA512: 9bc14a11a0c7f6a644f6afe8f16d60dffba5af01ef35fe388d804ed1340eb18ba10f7716f0bec62ff9288b2794e365f62ee8ecf1a432809bb3ebdc7d29ddbd3e Homepage: https://cran.r-project.org/package=PROscorer Description: CRAN Package 'PROscorer' (Functions to Score Commonly-Used Patient-Reported Outcome (PRO)Measures and Other Psychometric Instruments) An extensible repository of accurate, up-to-date functions to score commonly used patient-reported outcome (PRO), quality of life (QOL), and other psychometric and psychological measures. 'PROscorer', together with the 'PROscorerTools' package, is a system to facilitate the incorporation of PRO measures into research studies and clinical settings in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote best practices for scoring PRO and PRO-like measures in research. The 'PROscorer' Instrument Descriptions vignette contains descriptions of each instrument scored by 'PROscorer', complete with references. These instrument descriptions are suitable for inclusion in formal study protocol documents, grant proposals, and manuscript Method sections. Each 'PROscorer' function is composed of helper functions from the 'PROscorerTools' package, and users are encouraged to contribute new functions to 'PROscorer'. More scoring functions are currently in development and will be added in future updates. Package: r-cran-proscorertools Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-proscorertools_0.0.4-1.ca2404.1_all.deb Size: 78930 MD5sum: 9d97f875f831e58ac440390e8d1c598a SHA1: 7258c879724d6b4f822d2cb2aaa9108e6e06607f SHA256: a687f145d0b951c54f8354312f0368d82a235eccf9359216b848f6825ea2d71a SHA512: c5237c69b35e35120b60f9d12fed638a500edf86061902e7a3a11cecefbeb64cd37d5c80b639d77ebf92d5fa5b49beba5a9de40d4c78b98d6fb30018537d4956 Homepage: https://cran.r-project.org/package=PROscorerTools Description: CRAN Package 'PROscorerTools' (Tools to Score Patient-Reported Outcome (PRO) and OtherPsychometric Measures) Provides a reliable and flexible toolbox to score patient-reported outcome (PRO), Quality of Life (QOL), and other psychometric measures. The guiding philosophy is that scoring errors can be eliminated by using a limited number of well-tested, well-behaved functions to score PRO-like measures. The workhorse of the package is the 'scoreScale' function, which can be used to score most single-scale measures. It can reverse code items that need to be reversed before scoring and pro-rate scores for missing item data. Currently, three different types of scores can be output: summed item scores, mean item scores, and scores scaled to range from 0 to 100. The 'PROscorerTools' functions can be used to write new functions that score more complex measures. In fact, 'PROscorerTools' functions are the building blocks of the scoring functions in the 'PROscorer' package (which is a repository of functions that score specific commonly-used instruments). Users are encouraged to use 'PROscorerTools' to write scoring functions for their favorite PRO-like instruments, and to submit these functions for inclusion in 'PROscorer' (a tutorial vignette will be added soon). The long-term vision for the 'PROscorerTools' and 'PROscorer' packages is to provide an easy-to-use system to facilitate the incorporation of PRO measures into research studies in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote "best practices" for scoring and describing PRO-like measures in research. Package: r-cran-prosgpv Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3716 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-brglm2, r-cran-mass, r-cran-survival Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-prosgpv_1.0.0-1.ca2404.1_all.deb Size: 2341858 MD5sum: 334835fc353fb2b31ac470671c2ac323 SHA1: aff5deac5f21d1af8b5025dfb2e76578c5990024 SHA256: 3c01a244c69c085c7cad5a4aab853387ea4b7b102db76ef19d72d2fd99f683a0 SHA512: 2ffed576f7f0c9ef6d1147dfd77693ad25316a98dc0f947a623db7a91ab599b6c28a9f6864dc2a6265f8d47f8df6a3e43d752277299d5a5fb0e4000a137c3215 Homepage: https://cran.r-project.org/package=ProSGPV Description: CRAN Package 'ProSGPV' (Penalized Regression with Second-Generation P-Values) Implementation of penalized regression with second-generation p-values for variable selection. The algorithm can handle linear regression, GLM, and Cox regression. S3 methods print(), summary(), coef(), predict(), and plot() are available for the algorithm. Technical details can be found at Zuo et al. (2021) . Package: r-cran-prosper Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Filename: pool/dists/noble/main/r-cran-prosper_0.3.3-1.ca2404.1_all.deb Size: 267788 MD5sum: 3da94e9b301c75c3100b39529d36087d SHA1: 91bc501761035c9a87d3a93da03e072b4d23db49 SHA256: bbce441328762e606d4959d3eab7e4609d2b32691d1e3775a6a740f7c054d9ef SHA512: 7dd9277fa3f8f19359896040cf6930863150e3ea60b8e98c6eca910df4b870a155cb3e13d25bb893688a0dc7e6d645bdf08faa29e2acd507bc947d3e7d241efe Homepage: https://cran.r-project.org/package=PROSPER Description: CRAN Package 'PROSPER' (Simulation of Weed Population Dynamics) An environment to simulate the development of annual plant populations with regard to population dynamics and genetics, especially herbicide resistance. It combines genetics on the individual level (Renton et al. 2011) with a stochastic development on the population level (Daedlow, 2015). Renton, M, Diggle, A, Manalil, S and Powles, S (2011) Daedlow, Daniel (2015, doctoral dissertation: University of Rostock, Faculty of Agriculture and Environmental Sciences.) Package: r-cran-prosportsdraftdata Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4989 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-prosportsdraftdata_1.0.3-1.ca2404.1_all.deb Size: 2559784 MD5sum: 291e7bafef3fca341dbb47b3464e4e0b SHA1: e8130a40e8981151f02cee72213942087d71ce39 SHA256: f0d29c60476f2c6b78dd6264af8ae21585e5d7cfbf5ddedf89ec7fbf136b8d76 SHA512: 373a97fed8cac766018bbab667bf8a8d8b226047cc810d53903815a51f6fdb5a32cdd3658d6e704ce9631793f2e79168f5cfc446b4a83d3de0961718edbb7446 Homepage: https://cran.r-project.org/package=ProSportsDraftData Description: CRAN Package 'ProSportsDraftData' (Professional Sports Draft Data) We provide comprehensive draft data for major professional sports leagues, including the National Football League (NFL), National Basketball Association (NBA), and National Hockey League (NHL). It offers access to both historical and current draft data, allowing for detailed analysis and research on player biases and player performance. The package is useful for sports fans and researchers interested in identifying biases and trends within scouting reports. Created by web scraping data from leading websites that cover professional sports player scouting reports, the package allows users to filter and summarize data for analytical purposes. For further details on the methods used, please refer to Wickham (2022) "rvest: Easily Harvest (Scrape) Web Pages" and Harrison (2023) "RSelenium: R Bindings for Selenium WebDriver" . Package: r-cran-protag Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-rcolorbrewer Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-protag_1.0.0-1.ca2404.1_all.deb Size: 58834 MD5sum: a55fe96e4bb9492dc34f3c58f0caf254 SHA1: 15260de2c85bfaa747c6107332737056512ee4ea SHA256: 13800989daa5bcf256a038a1d40b409ee7f85b7c7d9be14ff37ea7377f814b50 SHA512: 09dfc7c60b370649f6bb677e6485013655e3f3fad286c764d911785cb1ac506f6df07f869c79a1fa1307ac6361c1c1fe31dd0b8df335c6197a78e287309b1ef1 Homepage: https://cran.r-project.org/package=protag Description: CRAN Package 'protag' (Search Tagged Peptides & Draw Highlighted Mass Spectra) In a typical protein labelling procedure, proteins are chemically tagged with a functional group, usually at specific sites, then digested into peptides, which are then analyzed using matrix-assisted laser desorption ionization - time of flight mass spectrometry (MALDI-TOF MS) to generate peptide fingerprint. Relative to the control, peptides that are heavier by the mass of the labelling group are informative for sequence determination. Searching for peptides with such mass shifts, however, can be difficult. This package, designed to tackle this inconvenience, takes as input the mass list of two or multiple MALDI-TOF MS mass lists, and makes pairwise comparisons between the labeled groups vs. control, and restores centroid mass spectra with highlighted peaks of interest for easier visual examination. Particularly, peaks differentiated by the mass of the labelling group are defined as a “pair”, those with equal masses as a “match”, and all the other peaks as a “mismatch”.For more bioanalytical background information, refer to following publications: Jingjing Deng (2015) ; Elizabeth Chang (2016) . Package: r-cran-prote Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-vegan, r-cran-uniprotr, r-cran-stringr, r-cran-missranger, r-cran-car, r-cran-openxlsx, r-cran-tidyr, r-cran-broom, r-cran-reshape2, r-cran-ggpubr, r-cran-ggplot2, r-cran-vim, r-cran-forcats, r-bioc-limma, r-cran-pheatmap Suggests: r-cran-biocmanager, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-prote_1.0.3-1.ca2404.1_all.deb Size: 1917850 MD5sum: 59b347b3a575b20859c8ac692d9c5e60 SHA1: 0721e3e907d488dde0d352ca02771fb1700281d2 SHA256: 4e1131ddd0f83f4f55904344008d5e1ac94049b97c81236a81f335f8f2af5b7b SHA512: 5bba5bc44fa22cc2e5ca47b48cee3e2214f024c1adfca7e4adfe596c7f7f490a4d261567aea02b4cbc99b25596f8b45dc7f4e3f6f8431d447101859aaaebc59e Homepage: https://cran.r-project.org/package=ProtE Description: CRAN Package 'ProtE' (Processing Proteomics Data, Statistical Analysis andVisualization) The 'Proteomics Eye' ('ProtE') offers a comprehensive and intuitive framework for the univariate analysis of label-free proteomics data. By integrating essential data wrangling and processing steps into a single function, 'ProtE' streamlines pairwise statistical comparisons for categorical variables. It provides quality checks and generates publication-ready visualizations, enabling efficient and robust data analysis. 'ProtE' is compatible with proteomics data outputs from 'MaxQuant' (Cox & Mann, (2008) ), 'DIA-NN' (Demichev et al., (2020) ), and 'Proteome Discoverer' (Thermo Fisher Scientific, version 2.5). The package leverages 'ggplot2' for visualization (Wickham, (2016) ) and 'limma' for statistical analysis (Ritchie et al., (2015) ). Package: r-cran-protein8k Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4492 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-magick, r-cran-dplyr, r-cran-gridextra, r-cran-ggplot2, r-cran-rjson, r-cran-rlang, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-protein8k_0.0.2-1.ca2404.1_all.deb Size: 2214728 MD5sum: 92af5600df0da304b9b086ba3ba5d642 SHA1: 9edc287342c72f03f95d745abf425d797d1d786e SHA256: 39c162da76dffa2c9fc1a253a7e14bfbab0f504d4ae70b6a894bf2b57f38dc24 SHA512: 3dbbfbf156be919e9d487925ed2fb9502c237a4a72bfcd47d0ed5013e4f2bd171872899550b2d5a3a32c98e8b3033581d3b546abf78ae84d5070a4af898d2390 Homepage: https://cran.r-project.org/package=protein8k Description: CRAN Package 'protein8k' (Perform Analysis and Create Visualizations of Proteins) Read Protein Data Bank (PDB) files, performs its analysis, and presents the result using different visualization types including 3D. The package also has additional capability for handling Virus Report data from the National Center for Biotechnology Information (NCBI) database. Nature Structural Biology 10, 980 (2003) . US National Library of Medicine (2021) . Package: r-cran-proteinpca Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-proteinpca_0.1.1-1.ca2404.1_all.deb Size: 18360 MD5sum: 27b0789329615c70973f146e7d542cda SHA1: 6b978558ffaa8a258ad1a7138f26d16dff6aa613 SHA256: 152b83535eb13b96b50c494977f8052a9e037fcc1ca670473ba39f793fdefdb6 SHA512: 0c1eebfe2b142011708e738a22ef10dbd7346a2c890ec1a3a27c8c04c58310c60ed2e63be8144270d011660c5ddb62b83256db04b08eb8969ca2e6af72612d27 Homepage: https://cran.r-project.org/package=ProteinPCA Description: CRAN Package 'ProteinPCA' (Principal Component Analysis (PCA) Tool on Protein ExpressionData) Analysis of protein expression data can be done through Principal Component Analysis (PCA), and this R package is designed to streamline the analysis. This package enables users to perform PCA and it generates biplot and scree plot for advanced graphical visualization. Optionally, it supports grouping/clustering visualization with PCA loadings and confidence ellipses. With this R package, researchers can quickly explore complex protein datasets, interpret variance contributions, and visualize sample clustering through intuitive biplots. For more details, see Jolliffe (2001) , Gabriel (1971) , Zhang et al. (2024) , and Anandan et al. (2022) . Package: r-cran-proteobayes Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-mvtnorm, r-cran-tibble, r-cran-tidyr, r-cran-rlang, r-cran-extradistr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-proteobayes_1.0.0-1.ca2404.1_all.deb Size: 142640 MD5sum: be5066b80c3beb602eb147b1c687d9ab SHA1: abd6ff9b8552ff989e7574886a309ca73a6d2adc SHA256: 686619b37013e96223437fe9a5819968557b5295afb6aaa3396866804a8a2184 SHA512: 4d859e6e049d7a5d1acf3043e8f4b38aaf127447ffc4184b9fc5062244487ad1d48663729502081435f82c480dcedffb178069228d43f9ea9b32da29a860061b Homepage: https://cran.r-project.org/package=ProteoBayes Description: CRAN Package 'ProteoBayes' (Bayesian Statistical Tools for Quantitative Proteomics) Bayesian toolbox for quantitative proteomics. In particular, this package provides functions to generate synthetic datasets, execute Bayesian differential analysis methods, and display results as, described in the associated article Marie Chion and Arthur Leroy (2023) . Package: r-cran-proteomicscv Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-proteomicscv_0.4.0-1.ca2404.1_all.deb Size: 11778 MD5sum: 6e0a18bb33abfa1f0f2eba2c5391a829 SHA1: 983c356c9da82fb9dffc1343380f8af8ea357898 SHA256: 9cad6c5af943b6078be7fecff8577e59704ce488bb669dae1e97535e7e458091 SHA512: 8d2819395c845e362f8b677b6ad0482c81f16a1b8fac17131afe113ef29e5af7fe5647760a3ba09f5d3f71e4fb8467157f31fd060af50cf2ba8484ddf7cbfad1 Homepage: https://cran.r-project.org/package=proteomicsCV Description: CRAN Package 'proteomicsCV' (Calculates the Percentage CV for Mass Spectrometry-BasedProteomics Data) Calculates the percentage coefficient of variation (CV) for mass spectrometry-based proteomic data. The CV can be calculated with the traditional formula for raw (non log transformed) intensity data, or log transformed data. Package: r-cran-proteus Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-abind, r-cran-ggplot2, r-cran-ggthemes, r-cran-readr, r-cran-stringr, r-cran-lubridate, r-cran-narray, r-cran-fancova, r-cran-imputets, r-cran-modeest, r-cran-scales, r-cran-tictoc, r-cran-torch, r-cran-moments, r-cran-dplyr, r-cran-greybox, r-cran-furrr, r-cran-future Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-proteus_1.1.5-1.ca2404.1_all.deb Size: 229040 MD5sum: 09953883ef13407b8740f9f061fdc189 SHA1: cf3fddddd39eb4d2e9a2ff4a941f4f04390ec21c SHA256: 27190b4e83a08bfdc5e36f74465159dfca129f4d2e4856569023b7c669f0632e SHA512: 71233fc1df72d82dedcbb498d43f2134ddd5a7fc36b55a8cb22d1b52b1e25832776378dc3a6a09ef50c22743ee5e3c9c88997bee81a2f0fe600f7560a1d53418 Homepage: https://cran.r-project.org/package=proteus Description: CRAN Package 'proteus' (Multiform Seq2Seq Model for Time-Feature Analysis) Seq2seq time-feature analysis based on variational model, with a wide range of distributions available for the latent variable. Package: r-cran-prothmm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-phontools Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-prothmm_0.1.1-1.ca2404.1_all.deb Size: 181198 MD5sum: 2e0ad1bd6e0dca8d8e64fb5a443ae88b SHA1: d0e9f9420e42f7a37f18882cc7902d828908348e SHA256: dd2fd928d818cff9511806e56e586d55b68961efae9d81443b8932c986019d01 SHA512: 171194048647a81856e71203f8b69c3d77ec79f0e10da759c5bdc9bfe172c3724fe905bfd1bc66d67a6efb281a050dfb2b04eec441458c359ead4f1f25dc34b1 Homepage: https://cran.r-project.org/package=protHMM Description: CRAN Package 'protHMM' (Protein Feature Extraction from Profile Hidden Markov Models) Calculates a comprehensive list of features from profile hidden Markov models (HMMs) of proteins. Adapts and ports features for use with HMMs instead of Position Specific Scoring Matrices, in order to take advantage of more accurate multiple sequence alignment by programs such as 'HHBlits' Remmert et al. (2012) and 'HMMer' Eddy (2011) . Features calculated by this package can be used for protein fold classification, protein structural class prediction, sub-cellular localization and protein-protein interaction, among other tasks. Some examples of features extracted are found in Song et al. (2018) , Jin & Zhu (2021) , Lyons et al. (2015) and Saini et al. (2015) . Package: r-cran-proto Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-proto_1.0.0-1.ca2404.1_all.deb Size: 464840 MD5sum: fb6c95e0b2cbf9a8be0e5bb6782bc183 SHA1: a3d667584b35a7a157c8ba543ae9179878435e38 SHA256: 8095dd94eb7e983b0957f3bac2324793294472df7d379a8a1d8fba4499656278 SHA512: 082ed15be7443c6e3f48a05dc73a0619f7bc5f3870aeb8aa8351e7e5d1fdd7972cacdd1f62be7742ba22bc11d108768d040b689ef2bef296a4c922ba2b381881 Homepage: https://cran.r-project.org/package=proto Description: CRAN Package 'proto' (Prototype Object-Based Programming) An object oriented system using object-based, also called prototype-based, rather than class-based object oriented ideas. Package: r-cran-proton Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest Filename: pool/dists/noble/main/r-cran-proton_1.0-1.ca2404.1_all.deb Size: 447300 MD5sum: 3a6cfd05005f81e4ce8b2b1360684017 SHA1: 96f4505826333832b6c3829946c5c1dd4e3aa4df SHA256: 3ee1a7792d535860b3090b5837f643c5ed2c9f38eb9173c3d6c94faaeccbe2fb SHA512: 2cc7714538803755271a25ab2b06177679f1cf32b344430fd84df4b857666d0937abe9146cf8c19a78e34c31cd7dd75c243afe41a0cfa9f33cce860fafac6408 Homepage: https://cran.r-project.org/package=proton Description: CRAN Package 'proton' (The Proton Game) 'The Proton Game' is a console-based data-crunching game for younger and older data scientists. Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server. You have to solve four data-based puzzles to find the login and password. There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools. Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try. The knowledge of dplyr is not required but may be very helpful. This game is linked with the ,,Pietraszko's Cave'' story available at http://biecek.pl/BetaBit/Warsaw. It's a part of Beta and Bit series. You will find more about the Beta and Bit series at http://biecek.pl/BetaBit. Package: r-cran-protoshiny Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1687 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-dynamictreecut, r-cran-protoclust, r-cran-rare, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinythemes Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-protoshiny_0.1.1-1.ca2404.1_all.deb Size: 1431150 MD5sum: d278154f2896547016bb8a6d0327aaed SHA1: b70de28c7a363afa296335a1c60b6ab82c82fcdd SHA256: 54db928abde6b086af71ab17cd3f998d9d16967afa4349f03c1dc16151063abe SHA512: 56af961d9f9b54ea6cfcd45320f3aaf448079713e6d7a64f100e12e18ab0675a4a29e6d955025ef7f045bc209f566d49ad159fb478b98e050323f85b92925974 Homepage: https://cran.r-project.org/package=protoshiny Description: CRAN Package 'protoshiny' (Interactive Dendrograms for Visualizing Hierarchical Clusterswith Prototypes) Shiny app to interactively visualize hierarchical clustering with prototypes. For details on hierarchical clustering with prototypes, see Bien and Tibshirani (2011) . This package currently launches the application. Package: r-cran-protr Architecture: all Version: 1.7-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3218 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biostrings, r-bioc-gosemsim, r-cran-foreach, r-cran-doparallel, r-bioc-org.hs.eg.db Filename: pool/dists/noble/main/r-cran-protr_1.7-5-1.ca2404.1_all.deb Size: 1564558 MD5sum: 4a7cfe10526e3a21b728ccad7be7a1cc SHA1: 875e380f52cc2c22f14dd55cec8e8f3f42d557b1 SHA256: d7afc40a0c77a5ec3af3cecbfe5c03757ff16fe53d1df7c6df25b22872c6ee70 SHA512: fd442f5ae1ed635646611062fae5c3e34c0f499295af317e84e762258cd4fc92a0beb02f5c8e0ce7253e12e14b8954ec69276e2fd73db569b5d842ad58a89f3b Homepage: https://cran.r-project.org/package=protr Description: CRAN Package 'protr' (Generating Various Numerical Representation Schemes for ProteinSequences) Comprehensive toolkit for generating various numerical features of protein sequences described in Xiao et al. (2015) . For full functionality, the software 'ncbi-blast+' is needed, see for more information. Package: r-cran-protrackr Architecture: all Version: 0.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1240 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-audio, r-cran-lattice, r-cran-signal, r-cran-tune Suggests: r-cran-amigaffh Filename: pool/dists/noble/main/r-cran-protrackr_0.4.4-1.ca2404.1_all.deb Size: 912538 MD5sum: a3e31c9c0c7a622cc97b82f364a5e1f6 SHA1: 3902b37135a891cd17e01420d8c2ec1bae2a7e66 SHA256: 5f4ac7704951f5a0a912d6e39e0b6cba88feba8c8eb9d016ee953fddabd8aa0e SHA512: 07782d14d0fe3b7cce20986bed502e2231bae65d5f87c3855acb467b0ffee9d6a5388343783d9c2e5a15291bf83b20d35863b28ef4dcdd9a58d9bf5768cb5f2d Homepage: https://cran.r-project.org/package=ProTrackR Description: CRAN Package 'ProTrackR' (Manipulate and Play 'ProTracker' Modules) 'ProTracker' is a popular music tracker to sequence music on a Commodore Amiga machine. This package offers the opportunity to import, export, manipulate and play 'ProTracker' module files. Even though the file format could be considered archaic, it still remains popular to this date. This package intends to contribute to this popularity and therewith keeping the legacy of 'ProTracker' and the Commodore Amiga alive. Package: r-cran-protti Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2308 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-dplyr, r-cran-stringr, r-cran-magrittr, r-cran-data.table, r-cran-janitor, r-cran-progress, r-cran-purrr, r-cran-tidyr, r-cran-ggplot2, r-cran-forcats, r-cran-tibble, r-cran-plotly, r-cran-ggrepel, r-cran-curl, r-cran-readr, r-cran-lifecycle, r-cran-httr, r-cran-r.utils Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-r3dmol, r-bioc-proda, r-bioc-limma, r-cran-dendextend, r-cran-pheatmap, r-cran-heatmaply, r-cran-furrr, r-cran-future, r-cran-seriation, r-cran-drc, r-cran-igraph, r-cran-stringi, r-bioc-stringdb, r-cran-iq, r-cran-scales, r-cran-farver, r-cran-ggforce, r-cran-xml2, r-cran-jsonlite, r-cran-missforest Filename: pool/dists/noble/main/r-cran-protti_1.0.0-1.ca2404.1_all.deb Size: 1744390 MD5sum: 508d5f96ac79c2d23c015f50a070d7f9 SHA1: 45721966435e75aae3e6ee4e772d0bf6411badde SHA256: e14035831af642edaf75d9e4c05177b13da10f975e42674ea15fc6fb62a9bab0 SHA512: 367111ecda99a419dbb91fb84f06bf45cc25745b746bd9f6e6210412abc562579eb23f78c0ae1ecc4f2993179e88f815add3397a1f7b5d70e5e17de059f30028 Homepage: https://cran.r-project.org/package=protti Description: CRAN Package 'protti' (Bottom-Up Proteomics and LiP-MS Quality Control and DataAnalysis Tools) Useful functions and workflows for proteomics quality control and data analysis of both limited proteolysis-coupled mass spectrometry (LiP-MS) (Feng et. al. (2014) ) and regular bottom-up proteomics experiments. Data generated with search tools such as 'Spectronaut', 'MaxQuant' and 'Proteome Discover' can be easily used due to flexibility of functions. Package: r-cran-proustr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3028 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-rlang, r-cran-tidyr, r-cran-tokenizers, r-cran-snowballc, r-cran-attempt Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-proustr_0.4.0-1.ca2404.1_all.deb Size: 2970222 MD5sum: 79ce2c5cf34d5d4dbfb37649bc2f48a0 SHA1: b1096a342798e30279e72b2fce40cf2882d81c46 SHA256: 91ed87de73dec8eb853ab0512160c862a44855d2e78323c7b539e0416a7e5594 SHA512: 100e1f88acfe16da3af64d94d4241d62cb3414f1153bd2decb7fa586a7298bc083f859e3c335735d6dc5ad293babc5c8afb7803ee9a1283fc6bf85c5c86991ee Homepage: https://cran.r-project.org/package=proustr Description: CRAN Package 'proustr' (Tools for Natural Language Processing in French) Tools for Natural Language Processing in French and texts from Marcel Proust's collection "A La Recherche Du Temps Perdu". The novels contained in this collection are "Du cote de chez Swann ", "A l'ombre des jeunes filles en fleurs","Le Cote de Guermantes", "Sodome et Gomorrhe I et II", "La Prisonniere", "Albertine disparue", and "Le Temps retrouve". Package: r-cran-provdebugr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-provgraphr, r-cran-provparser, r-cran-textutils Suggests: r-cran-knitr, r-cran-rdtlite, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-provdebugr_1.0.1-1.ca2404.1_all.deb Size: 148504 MD5sum: 8d87c94d96b8a83f891b81e06e74d982 SHA1: 679238a36e923e80fd9478b97fd060c53de39407 SHA256: 01fc9e33f818e0add01c768e49000bfc4da2f16ac914864032de74a34f5d380d SHA512: cdf9076d83d720ab075f54a868b36887837cdd0720065067e344808e1d7f25dc93e560462db4003da7b87441ccc88a0d548111cce489083c629f32365bb69806 Homepage: https://cran.r-project.org/package=provDebugR Description: CRAN Package 'provDebugR' (A Time-Travelling Debugger) Uses provenance post-execution to help the user understand and debug their script by providing functions to look at intermediate steps and data values, their forwards and backwards lineage, and to understand the steps leading up to warning and error messages. 'provDebugR' uses provenance produced by 'rdtLite' (available on CRAN), stored in PROV-JSON format. 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Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper, Wasserstein-2 or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances,and count data with the chi-square distance. Varietal data can either be converted to one or more distributional datasets, or directly compared using the multivariate Wasserstein distance. Also included are tools to plot compositional and count data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R. Package: r-cran-proverbs Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-crayon, r-cran-httr2, r-cran-glue, r-cran-lubridate, r-cran-purrr, r-cran-rvest, r-cran-stringr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-proverbs_0.4.0-1.ca2404.1_all.deb Size: 321384 MD5sum: 536417360755f87f966c5604ae53b6fc SHA1: 9c4ea30b419fdaf1cc5a475f15f81fc9f2f3cbe5 SHA256: 8ead269e5332c84b4705718e0dc651abf814649e77a4f582317977cb8fc70807 SHA512: 153f5173b571308596ff9e7e307f9a4aab476136f603a568dacf71a067f33154c3f859397a79564ddc8abfe5abddf33fa82792e59a0aa5bac243aa50708b545c Homepage: https://cran.r-project.org/package=proverbs Description: CRAN Package 'proverbs' (Print a Daily Bible Proverb to Console) A simple package to grab a Bible proverb corresponding to the day of the month. Package: r-cran-provexplainr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-provparser, r-cran-diffobj, r-cran-digest, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-provexplainr_1.1.1-1.ca2404.1_all.deb Size: 69414 MD5sum: bd4909507918df0598ba699684f653e8 SHA1: 60c8831eac8582f931da1394a246c85c38d85542 SHA256: 54eed5e82ed53c0eb40f9736e58f121a928885362f16997b5d93559ed525f392 SHA512: f1966080e0a13b6145681109009022ea1f86e9f60ca3531217e3735416208e9f818aaef389d1c7c62530d0791ae2ff549f633e5bc4e438d92b4ad8f9efc3ab37 Homepage: https://cran.r-project.org/package=provExplainR Description: CRAN Package 'provExplainR' (Compare Provenance Collections to Explain Changed Script Outputs) Inspects provenance collected by the 'rdt' or 'rdtLite' packages, or other tools providing compatible PROV JSON output created by the execution of a script, and find differences between two provenance collections. Factors under examination included the hardware and software used to execute the script, versions of attached libraries, use of global variables, modified inputs and outputs, and changes in main and sourced scripts. Based on detected changes, 'provExplainR' can be used to study how these factors affect the behavior of the script and generate a promising diagnosis of the causes of different script results. More information about 'rdtLite' and associated tools is available at and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, . Package: r-cran-provgraphr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-matrix, r-cran-provparser Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-provgraphr_1.0.1-1.ca2404.1_all.deb Size: 112126 MD5sum: 53198ca616ded8625a3b7408121f88db SHA1: 5a89de31eec750739c79b1b90fac01605f8f4919 SHA256: 33559edb77da3350ae1f9ecae7f43a2ed684ef96340e0a0bea516c450bd49911 SHA512: 508bcf151cb154a147892b511fded4ed81778b1dbcbd417cf965fab98e5ed25a3894fb3e5adca49195303e1ea494e6fdcce63da2b2d40f51bfacad80761e1b6a Homepage: https://cran.r-project.org/package=provGraphR Description: CRAN Package 'provGraphR' (Creates Adjacency Matrices for Lineage Searches) Creates and manages a provenance graph corresponding to the provenance created by the 'rdtLite' package, which collects provenance from R scripts. 'rdtLite' is available on CRAN. The provenance format is an extension of the W3C PROV JSON format (). The extended JSON provenance format is described in . 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Package: r-cran-provparser Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-provparser_1.0-1.ca2404.1_all.deb Size: 152756 MD5sum: 46b566fbf10c7b2f34fb33c45a54145d SHA1: 51b9a2ee20e6ad77a72669b4f8b8cd355a445f1a SHA256: 2f4ce53bec1278c3610990c564d7e694d624f5b3ff38e0aca5dd6760bd236895 SHA512: 3bf12dd4c7ad8a79c5c7499b17afb4c253e7b1cce1bbacc0c67e9240faad1c3d897a9f8230fb0f71272a4a6a4e20755ad1328a76a654f45ba35db6b301eb9db6 Homepage: https://cran.r-project.org/package=provParseR Description: CRAN Package 'provParseR' (Pulls Information from Prov.Json Files) R functions to access provenance information collected by 'rdt' or 'rdtLite'. The information is stored inside a 'ProvInfo' object and can be accessed through a collection of functions that will return the requested data. The exact format of the JSON created by 'rdt' and 'rdtLite' is described in . 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It can also optionally package all the files into a zip file. The exact format of the PROV JSON file created by 'rdtLite' and 'rdt' is described in . More information about 'rdtLite' and associated tools is available at and Lerner, Boose, and Perez (2018), Using Introspection to Collect Provenance in R, Informatics, . 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Package: r-cran-provviz Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3481 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rdtlite Filename: pool/dists/noble/main/r-cran-provviz_1.0.9-1.ca2404.1_all.deb Size: 3127632 MD5sum: a48ab08802f9dcfb5896daf8bc2e78c5 SHA1: 7a2dca14656c2e8277f033b23452ea03e86c3ed7 SHA256: 4176ce0185c299a360ce7b3cb51d0093962b3f40cb6103b043c48fa6a4f8f62e SHA512: bc5dd7823234801ad055f87330b4cd8c05b898229229b51078ae9718b37ac217f7909711a3fa01c4346c5486d78db1f2535d7809da80e2a01c1ff0566769a5e1 Homepage: https://cran.r-project.org/package=provViz Description: CRAN Package 'provViz' (Provenance Visualizer) Displays provenance graphically for provenance collected by the 'rdt' or 'rdtLite' packages, or other tools providing compatible PROV JSON output. The exact format of the JSON created by 'rdt' and 'rdtLite' is described in . More information about rdtLite and associated tools is available at and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, . Package: r-cran-proxirr Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-proxirr_0.5-1.ca2404.1_all.deb Size: 55788 MD5sum: 3dfead5c9581f3b4080700c9517f24b9 SHA1: 4bf4dc81dcf45e65da55afe873ea4c2b3e3a03d3 SHA256: 56056888e48a4295734a56f6534b8098bb255589556d3c5bd137656d1c3c3204 SHA512: 5f785bbff697d92b5d91a6a7f9b8fd0e4b180ff943ac8b5e19b5b1a9e6d182a8c8e03ef53658221326a32daad1b41fff0895b76f73b089875eda39c79ccfa611 Homepage: https://cran.r-project.org/package=proxirr Description: CRAN Package 'proxirr' (Alpha, Beta and Gamma Proximity to Irreplaceability) Functions to measure Alpha, Beta and Gamma Proximity to Irreplaceability. The methods for Alpha and Beta irreplaceability were first described in: Baisero D., Schuster R. & Plumptre A.J. Redefining and Mapping Global Irreplaceability. Conservation Biology 2021;1-11. . 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The package is useful for high-dimensional regression problems and includes cross-validation procedures to select optimal penalty parameters. Package: r-cran-prozor Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3805 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ahocorasicktrie, r-cran-docopt, r-cran-matrix, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-seqinr, r-cran-stringr, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-prozor_0.3.1-1.ca2404.1_all.deb Size: 3020298 MD5sum: 4d4e732b9ad12de9c8452a0630fdd756 SHA1: 63daba8dd27e1d5ec82b220cfd5d807217865493 SHA256: 55c9e0e08e9c4aa351c11d3e260a8e64620438c7458c3fdf2a4c5d6cb5bfd0af SHA512: 32fb27214d85ca3944926b5f3deea3df9406d07b726f804f110b0df757d8f172024996ee35770d30556f42ba39bab4ba0607fb9bdbfb824b2bf34db00a93dc1b Homepage: https://cran.r-project.org/package=prozor Description: CRAN Package 'prozor' (Minimal Protein Set Explaining Peptide Spectrum Matches) Determine minimal protein set explaining peptide spectrum matches. Utility functions for creating fasta amino acid databases with decoys and contaminants. Peptide false discovery rate estimation for target decoy search results on psm, precursor, peptide and protein level. Computing dynamic swath window sizes based on MS1 or MS2 signal distributions. Package: r-cran-prp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-prp_0.1.1-1.ca2404.1_all.deb Size: 36464 MD5sum: 635b68b42180b73287c4cb14181f1f61 SHA1: 4d224f115ed9f07e54d9cefcfc3fec36ebe6f652 SHA256: bd3b76153f04ba6d882dd70e7b33951d66ca330efb5a94b9afd5413b97446521 SHA512: 63a538d27343e1c1767e819e5a86643ca2ff576f291e3b9c3ee02c581f44452708695c617a1fb1855402a7fbc56c87cf6d1355f751d931fb5b97f0178744a63e Homepage: https://cran.r-project.org/package=PRP Description: CRAN Package 'PRP' (Bayesian Prior and Posterior Predictive Replication Assessment) Utilize the Bayesian prior and posterior predictive checking approach to provide a statistical assessment of replication success and failure. The package is based on the methods proposed in Zhao,Y., Wen X.(2021) . 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Package: r-cran-prroc Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 688 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-rocr Filename: pool/dists/noble/main/r-cran-prroc_1.4-1.ca2404.1_all.deb Size: 575530 MD5sum: 4c4d0770eb03d2af097057c6bf3eb728 SHA1: 191a95d9b41a44986441c5f3d62b7e4b88f8ad77 SHA256: 82afb3e521530361b2ec9007e3a5cf15297c0f33a0921b5b44d13d6fb2308217 SHA512: aad4e0781038d7d85a2f211daccfbca3e12eda202c02409ab378fb203ebfe5046be0be6e43f195f248b0ebe9a7341809bea09baa85c5a18c2fbb74bde15313a3 Homepage: https://cran.r-project.org/package=PRROC Description: CRAN Package 'PRROC' (Precision-Recall and ROC Curves for Weighted and Unweighted Data) Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method. References: Davis and Goadrich (2006) ; Keilwagen et al. (2014) ; Grau et al. (2015) . 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Data queries, constructed as 'R' expressions, are evaluated using the non-standard evaluation framework provided by 'rlang' and file-backing is powered by the fast and efficient 'fst' package. 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(2020) . Working examples to decompose shape variation into small-scale and large-scale components, and to decompose the total shape variation into outline and residual shape components are provided. Two landmark datasets are provided, that quantify skull morphology in humans and papionin primates, respectively from Mitteroecker et al. (2020) and Grunstra et al. (2020) . Package: r-cran-przewodnik Architecture: all Version: 0.16.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1661 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pogromcydanych, r-cran-pbimisc Filename: pool/dists/noble/main/r-cran-przewodnik_0.16.12-1.ca2404.1_all.deb Size: 1583438 MD5sum: a2dab595c17f55bee902a60b01ab3eee SHA1: c7145fdc611a1b04fb16d4a56b906442f57e0e19 SHA256: e75489b91a94758781a7dbab13e660ad236adc76bc76dd6e6f51e6d41c65334f SHA512: d3d76ca0f8292ad274ad3dd054236473d74abd682bf4116d42b10b7a360e505a3858181f44a506170bc942466d4c678d2c7d112658017462a5c360ab52bb4e69 Homepage: https://cran.r-project.org/package=Przewodnik Description: CRAN Package 'Przewodnik' (Datasets and Functions Used in the Book 'Przewodnik po PakiecieR') Data sets and functions used in the polish book "Przewodnik po pakiecie R" (The Hitchhiker's Guide to the R). See more at . Among others you will find here data about housing prices, cancer patients, running times and many others. Package: r-cran-psaboot Architecture: all Version: 1.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6358 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-psagraphics, r-cran-ggthemes, r-cran-matching, r-cran-matchit, r-cran-modeltools, r-cran-party, r-cran-psych, r-cran-reshape2, r-cran-rpart, r-cran-trimatch Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-psaboot_1.3.9-1.ca2404.1_all.deb Size: 3544196 MD5sum: 0043c893aa5fe1d4616c4d9082617d9f SHA1: 0bec7d3745445b355ed6bc22869acfb480c124da SHA256: 4496f237268b91f6fa10dfb35dc313b061dbc9f5411e53467dba490dbca58a55 SHA512: 5265d35869063c61f56ad2f3ebf24876fabb43bec146daa7a18dbeadbe63c870273a58b894a2776d07f8fc1541a41757d97556aa6e413063e937578cb5fd2240 Homepage: https://cran.r-project.org/package=PSAboot Description: CRAN Package 'PSAboot' (Bootstrapping for Propensity Score Analysis) It is often advantageous to test a hypothesis more than once in the context of propensity score analysis (Rosenbaum, 2012) . The functions in this package facilitate bootstrapping for propensity score analysis (PSA). By default, bootstrapping using two classification tree methods (using 'rpart' and 'ctree' functions), two matching methods (using 'Matching' and 'MatchIt' packages), and stratification with logistic regression. A framework is described for users to implement additional propensity score methods. Visualizations are emphasized for diagnosing balance; exploring the correlation relationships between bootstrap samples and methods; and to summarize results. Package: r-cran-psagraphics Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpart Filename: pool/dists/noble/main/r-cran-psagraphics_2.1.3-1.ca2404.1_all.deb Size: 259734 MD5sum: 32c88abcde213e51dc3a57d902aabf4f SHA1: 091a611ee2f941d8e7d4d5fdc56358d9b8599efb SHA256: 9ebdb6485535553ac783e25212c07b494131b4ab94090a9429a5f0b483d69797 SHA512: 198de29e83afd7fc0aa9e77fb4d227bc7d70f6414f517262d6409ede5a12749de108879bb28efe1d9a0c7dad8687aeac8abe2cbd19fe53a81c54d7416a98c2e0 Homepage: https://cran.r-project.org/package=PSAgraphics Description: CRAN Package 'PSAgraphics' (Propensity Score Analysis Graphics) A collection of functions that primarily produce graphics to aid in a Propensity Score Analysis (PSA). Functions include: cat.psa and box.psa to test balance within strata of categorical and quantitative covariates, circ.psa for a representation of the estimated effect size by stratum, loess.psa that provides a graphic and loess based effect size estimate, and various balance functions that provide measures of the balance achieved via a PSA in a categorical covariate. Package: r-cran-psawr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat, r-cran-vcr Filename: pool/dists/noble/main/r-cran-psawr_0.1.0-1.ca2404.1_all.deb Size: 51838 MD5sum: 36801d1e1c713926b7430c6b9015b012 SHA1: d8c09ba2b43ac69518716d0c8a81d69ebb8fd23c SHA256: bf72132b6e394a5922ef8cee96fd4541758de6041bc22b01ec22ce38f8b2fd59 SHA512: 56cb5c130583fa27753fd71be05205cb5b5784a613e6cbcbd948437641811d595e9daa1a7321795eaefc002a18245dd2cde76225af657e18617f1a59792656de Homepage: https://cran.r-project.org/package=PSAWR Description: CRAN Package 'PSAWR' ('Pushshift' API Wrapper for 'Reddit' Submission and CommentSearch) Connects to the API of to search for 'Reddit' comments and submissions. Package: r-cran-psborrow2 Architecture: all Version: 0.0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4668 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-glue, r-cran-posterior, r-cran-generics, r-cran-matrix, r-cran-mvtnorm, r-cran-future, r-cran-simsurv Suggests: r-cran-survival, r-cran-flexsurv, r-cran-eha, r-cran-testthat, r-cran-usethis, r-cran-vdiffr, r-cran-tibble, r-cran-xml2, r-cran-knitr, r-cran-rmarkdown, r-cran-bayesplot, r-cran-matrixcalc, r-cran-weightit, r-cran-matchit, r-cran-bayesppd, r-cran-ggsurvfit, r-cran-gbm, r-cran-ggplot2, r-cran-cobalt, r-cran-table1, r-cran-gt, r-cran-gtsummary Filename: pool/dists/noble/main/r-cran-psborrow2_0.0.5.1-1.ca2404.1_all.deb Size: 2689926 MD5sum: 31f268884bee340da178c3c950db3054 SHA1: df8c9d8c57a11a718ebaf394cf299b581aa9e6dc SHA256: ab6df207a6e431bef4cd5caa423e23c4075ac07cf4252b1146c9ff24f40ce346 SHA512: 71de04a9e82be60e7ce542ea51991caa84a00d54771326f18ba0cd17f349c2314b6aa147af4d95d29c0b97bc1066eef5671161c873fd182c636c90114090919b Homepage: https://cran.r-project.org/package=psborrow2 Description: CRAN Package 'psborrow2' (Bayesian Dynamic Borrowing Analysis and Simulation) Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 . There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from 'cmdstanr' which can be installed from . 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Package: r-cran-pscore Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-lavaan, r-cran-jwileymisc Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pscore_0.4.1-1.ca2404.1_all.deb Size: 258424 MD5sum: c4a0daf480967079bccdfddd583c37a8 SHA1: aefe166bdf72d8ba4fd9b431b9bf302441c5c0ef SHA256: 43818e3e408d65703dc3120f70a81dd3772508ec3ed0ef20dbe73507f5aa6aaa SHA512: 104677edf1f42abbc4701fc42b460d4f264164eb84d3b6a9804672c856ab8e07b3ecee11daabc612abe46cb913ce57dd4e8697183d16fb54c6809d399499ed44 Homepage: https://cran.r-project.org/package=pscore Description: CRAN Package 'pscore' (Standardizing Physiological Composite Risk Endpoints) Provides a number of functions to simplify and automate the scoring, comparison, and evaluation of different ways of creating composites of data. 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For the distribution of the concurrent causes the alternative models are the Poisson, logarithmic, negative binomial and Bernoulli (which are includes in the original work), the polylogarithm model and the Flory-Schulz . The estimation procedure is based on the EM algorithm discussed in . For the distribution of the time-to-event the alternative models are slash half-normal, Weibull, gamma and Birnbaum-Saunders distributions. Package: r-cran-psdata Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-countrycode, r-cran-datacombine, r-cran-reshape2, r-cran-rio, r-cran-xlsx Filename: pool/dists/noble/main/r-cran-psdata_0.2.2-1.ca2404.1_all.deb Size: 73810 MD5sum: 2c754e8d097961e0754d628c3e33e2ea SHA1: 2f76291542c5538301c57272577a28350e6a2611 SHA256: 049aa2dd5d98c567e2d908551f2077f99bb5d9e196d1ba719000b51a28da6c72 SHA512: 6e977424c1c42218a10af7e02486b4b61885a8c4c26c9e9e05d56879fe22eea89fa56701444cd3f203294f2c829f70324a63bd3bd488aa23f9d3727cf62edfd1 Homepage: https://cran.r-project.org/package=psData Description: CRAN Package 'psData' (Download Regularly Maintained Political Science Data Sets) This R package includes functions for gathering commonly used and regularly maintained data set in political science. It also includes functions for combining components from these data sets into variables that have been suggested in the literature, but are not regularly maintained. Package: r-cran-psdistr Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-psdistr_0.0.1-1.ca2404.1_all.deb Size: 90228 MD5sum: 82d145433e800bf7437e8c0e394f754c SHA1: c04d6966d461636730491cfff269e425b03ea156 SHA256: a951333c87be0e6451dca704226bf9daf4470921ce4078475fb541d7aea20bb8 SHA512: d76839eb29bb38a3935fc827e722022b48ace9fe3b237a431f21f42113846b8a71539c533e88b17a4db61e78fd6e70b4b130f88f9c5844248ec38f25f66f11d1 Homepage: https://cran.r-project.org/package=PSDistr Description: CRAN Package 'PSDistr' (Distributions Derived from Normal Distribution) Presentation of distributions such as: two-piece power normal (TPPN), plasticizing component (PC), DS normal (DSN), expnormal (EN), Sulewski plasticizing component (SPC), easily changeable kurtosis (ECK) distributions. Density, distribution function, quantile function and random generation are presented. For details on this method see: Sulewski (2019) , Sulewski (2021) , Sulewski (2021) , Sulewski (2022) <"New members of the Johnson family of probability dis-tributions: properties and application">, Sulewski, Volodin (2022) , Sulewski (2023) . Package: r-cran-psdr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 880 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-devtools, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-psdr_1.0.3-1.ca2404.1_all.deb Size: 224416 MD5sum: f1de0fbe414c1eada078d259c1873cc4 SHA1: 3d539a663cd6449cd0058b200736284d893c4b04 SHA256: 9f0f6267e4f02db2b657eee6fb35d463de8d10807f497cc4dd310248886724ef SHA512: a6aa0d2e2804c534c7097ed323821eb604004bff4cba42ad257393697efef9cd15ae018e0a19efcc8f51eb07cd8138504514c6b5d164d66a7359e90758ab5d8a Homepage: https://cran.r-project.org/package=psdr Description: CRAN Package 'psdr' (Use Time Series to Generate and Compare Power Spectral Density) Functions that allow you to generate and compare power spectral density (PSD) plots given time series data. Fast Fourier Transform (FFT) is used to take a time series data, analyze the oscillations, and then output the frequencies of these oscillations in the time series in the form of a PSD plot.Thus given a time series, the dominant frequencies in the time series can be identified. Additional functions in this package allow the dominant frequencies of multiple groups of time series to be compared with each other. To see example usage with the main functions of this package, please visit this site: . The mathematical operations used to generate the PSDs are described in these sites: . . Package: r-cran-pseudo Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kmsurv, r-cran-geepack Filename: pool/dists/noble/main/r-cran-pseudo_1.4.3-1.ca2404.1_all.deb Size: 43346 MD5sum: 870a0243a12629939a92a31d81464701 SHA1: f289fd1ae445077d158683cfd81247b1b4034fef SHA256: 81bbcaab8312dd85164f34fcb7de73895e4549e3c610fcafef56bf1bebc6ab01 SHA512: 517e7942748d16c6792096a64e00708ba9d7225dbad64494a7a48f0b7ccf20f8ae5b845139c79df8535e3b7f04f6867062fd3f6f052e18620e9cba4edbed8ed7 Homepage: https://cran.r-project.org/package=pseudo Description: CRAN Package 'pseudo' (Computes Pseudo-Observations for Modeling) Various functions for computing pseudo-observations for censored data regression. Computes pseudo-observations for modeling: competing risks based on the cumulative incidence function, survival function based on the restricted mean, survival function based on the Kaplan-Meier estimator see Klein et al. (2008) . Package: r-cran-pseudobiber Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-quanteda, r-cran-quanteda.textstats, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-magrittr Suggests: r-cran-testthat, r-cran-udpipe Filename: pool/dists/noble/main/r-cran-pseudobiber_1.2-1.ca2404.1_all.deb Size: 97120 MD5sum: 4ca53fda6bb154026adce686d2534f8c SHA1: 8f60f151f9bb68a5fe31b31f4aac8d47e0ac63fa SHA256: 3e40fd32d6ace8c86e8c98261a590e5c299919f6f84882877b0d0d237c4ab174 SHA512: b14fef602b18feafcebeb1dcdb4b7839e6502fbf5a347427d1f674e5518eea55d8f80b93c7d581e5f7271d5e0da574d0b8614918ac202275ba0ed353d5509a87 Homepage: https://cran.r-project.org/package=pseudobibeR Description: CRAN Package 'pseudobibeR' (Aggregate Counts of Linguistic Features) Calculates the lexicogrammatical and functional features described by Biber (1985) and widely used for text-type, register, and genre classification tasks. Package: r-cran-pseudohouseholds Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4552 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-furrr, r-cran-sf Suggests: r-cran-covr, r-cran-future, r-cran-ggplot2, r-cran-ggspatial, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pseudohouseholds_0.1.1-1.ca2404.1_all.deb Size: 4542412 MD5sum: 6dc5f4caf25f85b2026b72551181a725 SHA1: d8cc684da5a632e9eb556950ceb9e7b5665be437 SHA256: 4ea2d3e2b8f3d6899b949106187e4adef8462de50180d1c089a45234f8c80280 SHA512: 460e65156b3bbff9f378a03f7a1081563cfd4bd79ef342c0a4ecab30f65c90dd978938c26077c5c0261bb9b8b0fec17849f99ac3e96e2bb6aeff5e422af01378 Homepage: https://cran.r-project.org/package=pseudohouseholds Description: CRAN Package 'pseudohouseholds' (Generate Pseudohouseholds on Road Networks in Regions) Given an arbitrary set of spatial regions and road networks, generate a set of representative points, or pseudohouseholds, that can be used for travel burden analysis. Parallel processing is supported. Package: r-cran-pseval Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-ggplot2, r-cran-testthat, r-cran-knitr, r-cran-printr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pseval_1.3.3-1.ca2404.1_all.deb Size: 268680 MD5sum: 675b49043657d3e0164baf563bc4a6a5 SHA1: 61ecafe68fb2af34ca30702cf1239ae246202b1a SHA256: d1db788445333dec977a28f05b6ff17359e9cf00b7d793cd082f5ddbd5aa955c SHA512: e56d51e6451b5341d941cc1308e25273284e224d5683a0b8a0873e191614701824cdf86fac1383ec8d921e697bae8c97762879d1e61e77c005e87ffcba7fd3ad Homepage: https://cran.r-project.org/package=pseval Description: CRAN Package 'pseval' (Methods for Evaluating Principal Surrogates of TreatmentResponse) Contains the core methods for the evaluation of principal surrogates in a single clinical trial. Provides a flexible interface for defining models for the risk given treatment and the surrogate, the models for integration over the missing counterfactual surrogate responses, and the estimation methods. Estimated maximum likelihood and pseudo-score can be used for estimation, and the bootstrap for inference. A variety of post-estimation summary methods are provided, including print, summary, plot, and testing. Package: r-cran-psf Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-cluster Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-forecast Filename: pool/dists/noble/main/r-cran-psf_0.5-1.ca2404.1_all.deb Size: 110250 MD5sum: 416566fe8e3415031f90466f42aca5fb SHA1: a7d8aa84c7229998c082c4b241a10a9f2471aabd SHA256: 84e74cc3ee86a65091eb2024aa92928f541c9348bfa8e2c8f18e096c5c66f07c SHA512: b265d9b9475e907a878a3886c83575a23339137f02c3d15fe7df80ee9953f25c05b4111832206e62f524d5fde010e7b2cf7d04595f8cd5753b8d2e62dde202d4 Homepage: https://cran.r-project.org/package=PSF Description: CRAN Package 'PSF' (Forecasting of Univariate Time Series Using the PatternSequence-Based Forecasting (PSF) Algorithm) Pattern Sequence Based Forecasting (PSF) takes univariate time series data as input and assist to forecast its future values. This algorithm forecasts the behavior of time series based on similarity of pattern sequences. Initially, clustering is done with the labeling of samples from database. The labels associated with samples are then used for forecasting the future behaviour of time series data. The further technical details and references regarding PSF are discussed in Vignette. Package: r-cran-psfmi Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-norm, r-cran-survival, r-cran-mitools, r-cran-proc, r-cran-rms, r-cran-magrittr, r-cran-rsample, r-cran-mice, r-cran-mitml, r-cran-cvauc, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-stringr, r-cran-lme4, r-cran-car Suggests: r-cran-foreign, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-bookdown, r-cran-readr, r-cran-gtools, r-cran-covr Filename: pool/dists/noble/main/r-cran-psfmi_1.4.0-1.ca2404.1_all.deb Size: 894016 MD5sum: 32578b5358d31bad4ffdcaad89346dcc SHA1: 9d3c3e9fe4f25366bbcfea24811a24fb1306d211 SHA256: ed765639383f645e3cc7265fa5aa042fa40b3730fa3e6c7fd6a45a1a6cd99faa SHA512: 3b31939fc274bf9df225223211423417778c8e56e5cd40635b91bbac1c50548ab5e4fad42e379ecbbd1bda878b006298631a0ab439da5fb3385f2f2aec0387ea Homepage: https://cran.r-project.org/package=psfmi Description: CRAN Package 'psfmi' (Prediction Model Pooling, Selection and Performance EvaluationAcross Multiply Imputed Datasets) Pooling, backward and forward selection of linear, logistic and Cox regression models in multiply imputed datasets. Backward and forward selection can be done from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and the median p-values method. This is also possible for Mixed models. The models can contain continuous, dichotomous, categorical and restricted cubic spline predictors and interaction terms between all these type of predictors. The stability of the models can be evaluated using (cluster) bootstrapping. The package further contains functions to pool model performance measures as ROC/AUC, Reclassification, R-squared, scaled Brier score, H&L test and calibration plots for logistic regression models. Internal validation can be done across multiply imputed datasets with cross-validation or bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients can be obtained. Backward and forward selection as part of internal validation is possible. A function to externally validate logistic prediction models in multiple imputed datasets is available and a function to compare models. For Cox models a strata variable can be included. Eekhout (2017) . Wiel (2009) . Marshall (2009) . Package: r-cran-psgc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11284 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli Suggests: r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-psgc_0.1.0-1.ca2404.1_all.deb Size: 6081478 MD5sum: d16c972a648bdbc7d6f476a36f588976 SHA1: 1855199f72206a67483a944b0cfae8bcb2b31c6b SHA256: c55f0f5ce987954308035dad8ea2e9f2876389e22b8f7afe7dd221f78a5acf88 SHA512: d8bcfa5248aa5c6196d1d48e45ba3e8da1dda5bb93e20d48e1d4593cd7714b673cd45e0d1ea475c3ada032b57c5cf881b32fb6e4ad0a1844652ef277bd884031 Homepage: https://cran.r-project.org/package=psgc Description: CRAN Package 'psgc' (Philippine Standard Geographic Code) Provides access to the Philippine Standard Geographic Code (PSGC), an official classification system for geographic areas in the Philippines published by the Philippine Statistics Authority (PSA). Includes area names, geographic levels (Region, Province, City, Municipality, Sub-Municipality, and Barangay), and census population figures across multiple PSA publication releases. Offers utilities to look up individual codes, filter by geographic level, track code changes across releases via a built-in crosswalk, and retrieve population data in long or wide format. Package: r-cran-psgoft Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-moments Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-psgoft_0.0.1-1.ca2404.1_all.deb Size: 27290 MD5sum: 0c11986fda2cbdab1726794b33784118 SHA1: 95628e83d3e63e5463e6e5971aa2f578071310ce SHA256: 58bdeeb0d0791867b5cdccea724e958b9dd513f24bf90b0fc5e300ca082ef536 SHA512: 4130e8511c6e631a8b22f9dce3edb892c3dd7b2604903f37cf32077d9621cc67bf55ca1e32374ac89e500fff15f45c8a2621507fa1f58a717407e32c77892fdb Homepage: https://cran.r-project.org/package=PSGoft Description: CRAN Package 'PSGoft' (Modified Lilliefors Goodness-of-Fit Normality Test) Presentation of a new goodness-of-fit normality test based on the Lilliefors method. For details on this method see: Sulewski (2019) . Package: r-cran-psharmonize Architecture: all Version: 0.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 992 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-rmarkdown, r-cran-stringr, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-psharmonize_0.3.6-1.ca2404.1_all.deb Size: 807280 MD5sum: bd293f9b9ff52eb1a9e1ca24ed92ad5f SHA1: 4eb09c100d434a70ca4cd2e5bdff8c8ff1a000a9 SHA256: dd1d9bdb5cf2975d0070be12fc213db6986c2f37f17914c3876076acf2e64742 SHA512: 9d9024cf2249d6026405ed080ac8cddf139015f8c61f492ba07116c89a3f3c5d51f143830dccb56ac5bc21d29e2c08437ef9e864ebc235b00f050a9949adea19 Homepage: https://cran.r-project.org/package=psHarmonize Description: CRAN Package 'psHarmonize' (Creates a Harmonized Dataset Based on a Set of Instructions) Functions which facilitate harmonization of data from multiple different datasets. Data harmonization involves taking data sources with differing values, creating coding instructions to create a harmonized set of values, then making those data modifications. 'psHarmonize' will assist with data modification once the harmonization instructions are written. Coding instructions are written by the user to create a "harmonization sheet". This sheet catalogs variable names, domains (e.g. clinical, behavioral, outcomes), provides R code instructions for mapping or conversion of data, specifies the variable name in the harmonized data set, and tracks notes. The package will then harmonize the source datasets according to the harmonization sheet to create a harmonized dataset. Once harmonization is finished, the package also has functions that will create descriptive statistics using 'RMarkdown'. Data Harmonization guidelines have been described by Fortier I, Raina P, Van den Heuvel ER, et al. (2017) . Additional details of our R package have been described by Stephen JJ, Carolan P, Krefman AE, et al. (2024) . Package: r-cran-psica Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-gridbase, r-cran-randomforest, r-cran-rpart, r-cran-partykit, r-cran-party, r-cran-bayestree Filename: pool/dists/noble/main/r-cran-psica_1.0.2-1.ca2404.1_all.deb Size: 59482 MD5sum: 84e9111dd2f0c3fe6a97c12531ddec2a SHA1: e7f7d2983eeac6237b4ee28b1f7c7995d8e94210 SHA256: 981dead2ae8dc5781c3c35a47129ac87cd0321e82df524dfe5fafb9e5ce2c117 SHA512: 38b9fe3d49286323fdfac6807bf10db32c217d08c35dc538a158db2333543ff4d613f84dfdd0ed4e82bd368b63eaa04e88fe68dfca21b9f7c382a1e6b322b095 Homepage: https://cran.r-project.org/package=psica Description: CRAN Package 'psica' (Decision Tree Analysis for Probabilistic Subgroup Identificationwith Multiple Treatments) In the situation when multiple alternative treatments or interventions available, different population groups may respond differently to different treatments. This package implements a method that discovers the population subgroups in which a certain treatment has a better effect than the other alternative treatments. This is done by first estimating the treatment effect for a given treatment and its uncertainty by computing random forests, and the resulting model is summarized by a decision tree in which the probabilities that the given treatment is best for a given subgroup is shown in the corresponding terminal node of the tree. Package: r-cran-psidr Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2864 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rcurl, r-cran-foreign, r-cran-sascii, r-cran-openxlsx, r-cran-futile.logger Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-psidr_2.3-1.ca2404.1_all.deb Size: 1637456 MD5sum: 7c2e4c4d50980685813fc3765cbda292 SHA1: 4abb1a17b2863d30e48a7f6ddf8dd926d2681ec2 SHA256: 48417270410b074e06d104043f27b62abf39d7a74b164c14aed101d0c4760d39 SHA512: 5d5b4b52be81654de9ef766b5e698d8f340a4afc24dc29e2927c62ae8d1b240a3603f6f08c57404be19a15f9ff462866d4ae3d66ca8ed5ce2515ea866fa08113 Homepage: https://cran.r-project.org/package=psidR Description: CRAN Package 'psidR' (Build Panel Data Sets from PSID Raw Data) Makes it easy to build panel data in wide format from Panel Survey of Income Dynamics (PSID) delivered raw data. Downloads data directly from the PSID server using the 'SAScii' package. 'psidR' takes care of merging data from each wave onto a cross-period index file, so that individuals can be followed over time. The user must specify which years they are interested in, and the 'PSID' variable names (e.g. ER21003) for each year (they differ in each year). The package offers helper functions to retrieve variable names from different waves. There are different panel data designs and sample subsetting criteria implemented ("SRC", "SEO", "immigrant" and "latino" samples). More information about the PSID can be obtained at . Package: r-cran-psidread Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4464 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-asciisetupreader, r-cran-dplyr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-diagrammer, r-cran-spelling Filename: pool/dists/noble/main/r-cran-psidread_1.0.6-1.ca2404.1_all.deb Size: 2414900 MD5sum: 7091c91fa275dffc681bafe923286130 SHA1: 6f28f1123990f0f30e341e0ed2f3eb086e8fef97 SHA256: 5b688d78bfb27faa2ee5d8bfdd92916b2e61292e6a37a06d6f6f709504c308af SHA512: dc5120eb177e97f065f09516b05514a5de2cab9ca2d27a47c942916708e8cecb2d3202b0199178cfdb89af6dc655e6a466a73b1cf0c5079a6d7aa82f09e73669 Homepage: https://cran.r-project.org/package=psidread Description: CRAN Package 'psidread' (Streamline Building Panel Data from Panel Study of IncomeDynamics ('PSID') Raw Files) Streamline the management, creation, and formatting of panel data from the Panel Study of Income Dynamics ('PSID') using this user-friendly tool. Simply define variable names and input code book details directly from the 'PSID' official website, and this toolbox will efficiently facilitate the data preparation process, transforming raw 'PSID' files into a well-organized format ready for further analysis. Package: r-cran-psim Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-matrixstats, r-cran-magrittr, r-cran-tidyverse Suggests: r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-psim_0.1.0-1.ca2404.1_all.deb Size: 13788 MD5sum: cec764639b1396ea5874311532df550c SHA1: ca301d4a33afaf08ad7c870b5fcd6c7ff8a5b827 SHA256: f2188841f0979965cc3b7e8b542c636ba7b8d00e5ec0d283b5d7070d343fe443 SHA512: 6f2d9a587780f634d14f9f8406c7554d00559a06a10ced058ef2bc7a49263ff826708d209658d745c71be7b4824ba21ddae4e44310ba9798ba2546e1c5e3972d Homepage: https://cran.r-project.org/package=PSIM Description: CRAN Package 'PSIM' (Preference Selection Index Method (PSIM)) The Preference Selection Index Method was created in (2010) and provides an innovative approach to determining the relative importance of criteria without pairwise comparisons, unlike the Analytic Hierarchy Process. The Preference Selection Index Method uses statistical methods to calculate the criteria weights and reflects their relative importance in the final decision-making process, offering an objective and non-subjective solution. This method is beneficial in multi-criteria decision analysis. The 'PSIM' package provides a practical and accessible tool for implementing the Preference Selection Index Method in R. It calculates the weights of criteria and makes the method available to researchers, analysts, and professionals without the need to develop complex calculations manually. More details about the Preference Selection Index Method can be found in Maniya K. and Bhatt M. G.(2010) . 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These tests are: the modular test and the logarithmic minimum test. For details on this method see: Sulewski (2017) , Sulewski (2018) , Sulewski (2019) , Sulewski (2021) . 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For more details see Klein et al. (2021) . Package: r-cran-pslm2015 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4374 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-pslm2015_0.2.0-1.ca2404.1_all.deb Size: 4350756 MD5sum: afc3f01433deacf31a7ac5cb0dd73fb1 SHA1: a11317e80f3455426199611334c4556e8e41bc6f SHA256: 66318c89f85a3d32eff2a5a2ae6becdb41ef03ed4ad17c4bf619292ccc0ee2e4 SHA512: be19dc0f590d6b8a8d6637b84b554bc9c12bf2dd25a40214210b6562264fe221315650c27141e211f408345e322564b74a7ec4672463040ec3ca00a83174176f Homepage: https://cran.r-project.org/package=PSLM2015 Description: CRAN Package 'PSLM2015' (Pakistan Social and Living Standards Measurement Survey 2014-15) Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (). 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These model structures are typically considered in cost-effectiveness modeling in advanced/metastatic cancer indications. Muston (2024). "Informing structural assumptions for three state oncology cost-effectiveness models through model efficiency and fit". Applied Health Economics and Health Policy. Package: r-cran-psme Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mgcv, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-psme_1.0.0-1.ca2404.1_all.deb Size: 35702 MD5sum: 67f53877906e024609abb9a59021a593 SHA1: a5786238bc894971a79515a384d1dd254ad8903d SHA256: fc955c296bb423f09a3664f9e2e0949e540dc9693cdd63d35d4cb90d01570d05 SHA512: ecd0fc46639b354fa7bc1dbb5d58854fef380620de50b0f95361d32fa4f19af5944ac28f00408833afe18bc42786a90e1d325c619a94588dfd6703304b50d01b Homepage: https://cran.r-project.org/package=psme Description: CRAN Package 'psme' (Penalized Splines Mixed-Effects Models) Fit penalized splines mixed-effects models (a special case of additive models) for large longitudinal datasets. The package includes a psme() function that (1) relies on package 'mgcv' for constructing population and subject smooth functions as penalized splines, (2) transforms the constructed additive model to a linear mixed-effects model, (3) exploits package 'lme4' for model estimation and (4) backtransforms the estimated linear mixed-effects model to the additive model for interpretation and visualization. See Pedersen et al. (2019) and Bates et al. (2015) for an introduction. Unlike the gamm() function in 'mgcv', the psme() function is fast and memory-efficient, able to handle datasets with millions of observations. Package: r-cran-psminer Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bupar, r-cran-dplyr, r-cran-data.table, r-cran-forcats, r-cran-ggplot2, r-cran-tidyr, r-cran-rlang, r-cran-cli, r-cran-glue, r-cran-stringi Suggests: r-cran-knitr, r-cran-eventdatar, r-cran-rmarkdown, r-cran-covr, r-cran-testthat, r-cran-edear Filename: pool/dists/noble/main/r-cran-psminer_0.1.1-1.ca2404.1_all.deb Size: 122052 MD5sum: af85688ef8b510bddfc2937b8ef612cb SHA1: 540c6dbac3eec48cb86208fb1625ab45dca59965 SHA256: 8586adc81eb2833d5cacf704e7e9aec86ec7fe661b30d1d5ef05cd73e8123dac SHA512: 0974d59ed6ccfe5113da72dd7beed10ed60dded881986cbc68826ab1fb7af7a14911fce9eb021f745b0e6bf1e0c6140ffdaeb56e4b391fdc7ee864f5dc7ccad7 Homepage: https://cran.r-project.org/package=psmineR Description: CRAN Package 'psmineR' (Performance Spectrum Miner for Event Data) Compute detailed and aggregated performance spectrum for event data. The detailed performance spectrum describes the event data in terms of segments, where the performance of each segment is measured and plotted for any occurrences of this segment over time and can be classified, e.g., regarding the overall population. The aggregated performance spectrum visualises the amount of cases of particular performance over time. Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2018) . Package: r-cran-pso Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-numderiv Filename: pool/dists/noble/main/r-cran-pso_1.0.4-1.ca2404.1_all.deb Size: 216448 MD5sum: ee16e43fce4e9e92da8e63ff1761b4ab SHA1: 74b0255d93304a57b683e38dc9235fe8b11c1d2e SHA256: d52d1a3ba31d51f08b138c6457d3317b2c6936df38cb742a7ffbf8fc5bbaba2c SHA512: 1357bae72d8f05c51ff442fd2fb304145c27d128a90406b52c8d4f7f029309f468ddafb3e6923bd7c851bbbc309ef5a5e48e023580e092c6d6c95b623b6af5b3 Homepage: https://cran.r-project.org/package=pso Description: CRAN Package 'pso' (Particle Swarm Optimization) Provides an implementation of particle swarm optimisation consistent with the standard PSO 2007/2011 by Maurice Clerc. Additionally a number of ancillary routines are provided for easy testing and graphics. Package: r-cran-psoptim Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-psoptim_1.0-1.ca2404.1_all.deb Size: 17864 MD5sum: b2f90ae91b73160bd7f91e8258ec168c SHA1: 3f1c5f7d6fbb66db85c0561089b1e2ab3ece64be SHA256: cfe2d34aef20bf70ce6653c02a1db7f4babd6e4be84052e9b6ff36cfc3d993c4 SHA512: f9ec6740f328b1c8375aa3ed155f6d0ea1cba48cde597f74f8f1943ad8fe60e1ed3a3d779ac30877f6c6d7f7a4e16f30b3f49539607acd92b6656c295f9a0324 Homepage: https://cran.r-project.org/package=psoptim Description: CRAN Package 'psoptim' (Particle Swarm Optimization) Particle swarm optimization - a basic variant. Package: r-cran-psor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-superlearner, r-cran-caret, r-cran-dplyr, r-cran-geex, r-cran-magrittr, r-cran-numderiv Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-psor_0.1.0-1.ca2404.1_all.deb Size: 126542 MD5sum: 9b694a07b8f1d23cc3429b069fd241be SHA1: 506982086eacaebb73dbfeb92504ee9a133a48ef SHA256: ea7d26796e165de8c89bd30f591e63de5fc9fe37663dee70622b8ef730611f70 SHA512: 90f06a3b3ccc00917509d4c8f422c6e58678620194b0d77b2bc06164a00a036e82a050167b89361cdb859e840a605ec7c474125859222cb575139b911364c6bb Homepage: https://cran.r-project.org/package=PSor Description: CRAN Package 'PSor' (Semiparametric Principal Stratification Analysis BeyondMonotonicity) Estimates principal causal effects under principal stratification using a margin-free, conditional odds ratio sensitivity parameter. This framework unifies the monotonicity assumption and the counterfactual intermediate independence assumption, allowing for robust analysis when monotonicity may not hold. Computes point estimates, standard errors, and confidence intervals for conditionally doubly robust and debiased machine learning estimators. The methodological details are described in Tong, Kahan, Harhay, and Li (2025) . Package: r-cran-pspatreg Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1610 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ameshousing, r-cran-dplyr, r-cran-fields, r-cran-ggplot2, r-cran-mba, r-cran-mass, r-cran-minqa, r-cran-matrix, r-cran-numderiv, r-cran-plm, r-cran-rdpack, r-cran-sf, r-cran-spatialreg, r-cran-spdep, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pspatreg_1.1.2-1.ca2404.1_all.deb Size: 1097942 MD5sum: cf5697d7537d1a177e3603fc263778e0 SHA1: 7e06772601e4907e8260289a221ec8945e06241a SHA256: 550d06778df154bf2067295a1571b48dc344841c359de22fe75f3ce9ecaab8d3 SHA512: 3bd1c42e69b47c81e5b8d50375ecf3a4303a35adca17891c378f3a4ecd0f4eee3a2c4e759c0f199c671054ab7dc23365031b39b20b86d624fdefd5bf86c40201 Homepage: https://cran.r-project.org/package=pspatreg Description: CRAN Package 'pspatreg' (Spatial and Spatio-Temporal Semiparametric Regression Modelswith Spatial Lags) Estimation and inference of spatial and spatio-temporal semiparametric models including spatial or spatio-temporal non-parametric trends, parametric and non-parametric covariates and, possibly, a spatial lag for the dependent variable and temporal correlation in the noise. The spatio-temporal trend can be decomposed in ANOVA way including main and interaction functional terms. Use of SAP algorithm to estimate the spatial or spatio-temporal trend and non-parametric covariates. The methodology of these models can be found in next references Basile, R. et al. (2014), ; Rodriguez-Alvarez, M.X. et al. (2015) and, particularly referred to the focus of the package, Minguez, R., Basile, R. and Durban, M. (2020) . Package: r-cran-pspower Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pspower_2.0.0-1.ca2404.1_all.deb Size: 257382 MD5sum: e06e70b67cc0275d33a504fb22d02d1b SHA1: 24be5d0d57c8ff20cae1930c0a3f5052283dac05 SHA256: 2fa203a71c586fbb82b4b997ebca8fc1239d8eabcbc9111f1a0d18049d441300 SHA512: eaa7c435dc1124091ca0cf8aac6b6939a6ef0c73c93bf02810eaf7433ff0724017d44f52d45719a19f4799f28bfee3381b54c6294e28e7bbf4f700dfe5a00f4c Homepage: https://cran.r-project.org/package=PSpower Description: CRAN Package 'PSpower' (Sample Size and Power for Propensity Score Weighted Estimators) Computes sample size and power for causal inference studies that use propensity score (PS) weighting. Supports continuous, binary, and time-to-event (survival) outcomes under four estimands: average treatment effect (ATE), average treatment effect on the treated (ATT), average treatment effect on the controls (ATC), and average treatment effect on the overlap population (ATO). For continuous and binary outcomes, the asymptotic variance of the Hajek inverse probability weighting estimator is derived under a logit-normal propensity score model, approximated by a Beta distribution matched through the Bhattacharyya overlap coefficient. For survival outcomes, the asymptotic variance of the propensity-score- weighted partial likelihood estimator is used for randomized trials and observational studies. The Schoenfeld formula is also available for randomized trial settings. Package: r-cran-psre Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1046 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-car, r-cran-nortest, r-cran-marginaleffects, r-cran-ggplot2, r-cran-boot, r-cran-cowplot, r-cran-fancova, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-tibble, r-cran-sm, r-cran-multcomp, r-cran-rlang, r-cran-ggrepel, r-cran-mgcv, r-cran-viztest Suggests: r-cran-ggeffects, r-cran-nnet Filename: pool/dists/noble/main/r-cran-psre_0.4-1.ca2404.1_all.deb Size: 629416 MD5sum: 84d5a5d6b1d9da95a826eba9a8bc870b SHA1: c7ede5651e9c21fec18068eebf3888a0612f1e69 SHA256: cb68520a2a26320aae9b5bcf38ad85545a473fb3696e16406069875189ac6dae SHA512: e4baae759168a0980855d29280a77f519fddadccdd60cdfefa2af4df498bad8c24aa475c72f4a28173683df6b798c460fd28539f2a479dc312d8466ee275d0f7 Homepage: https://cran.r-project.org/package=psre Description: CRAN Package 'psre' (Presenting Statistical Results Effectively) Includes functions and data used in the book "Presenting Statistical Results Effectively", Andersen and Armstrong (2022, ISBN: 978-1446269800). Several functions aid in data visualization - creating compact letter displays for simple slopes, kernel density estimates with normal density overlay. Other functions aid in post-model evaluation heatmap fit statistics for binary predictors, several variable importance measures, compact letter displays and simple-slope calculation. Finally, the package makes available the example datasets used in the book. Package: r-cran-psricalc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-psricalc_1.0.0-1.ca2404.1_all.deb Size: 15744 MD5sum: abceab0eed451e72a260eec7e2f56780 SHA1: 804a252fe39a28f5224056524e526deb7896fe76 SHA256: fd4a50ab28e34fd1935601775f9d40ef1e3c8b15d4cb6ebeca9cf2213d642104 SHA512: cb91c36e681aaea109e258dbc08c547e53ada6ba2632c93a059cd12ec91712bd2b5214f85b9633d1538b8a4d78c4ed429b5790e308ec5e8d0d6699d292615583 Homepage: https://cran.r-project.org/package=PSRICalc Description: CRAN Package 'PSRICalc' (Plant Stress Response Index Calculator) Calculate Plant Stress Response Index (PSRI) from time-series germination data with optional radicle vigor integration. Built on the methodological foundation of the Osmotic Stress Response Index (OSRI) framework developed by Walne et al. (2020) . Provides clean, direct PSRI calculations suitable for agricultural research and statistical analysis. Note: This package implements methodology currently under peer review. Please contact the author before publication using this approach. Package: r-cran-psricalcsm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-psricalcsm_1.0.0-1.ca2404.1_all.deb Size: 46566 MD5sum: 29911ad92ce645387fca8448e5c4dab1 SHA1: 7421e93b1a9683379974d73058a51ffa595dc191 SHA256: d6016a7341a9c18459a92673d5a3f62c4be741d23037dfbf04d4021e836d2a5d SHA512: 5ef912473411b302f117527e5e1393a576aec8823222ed3b6e2eaa35122f6fef59e4fdaeb9960e165e80bf4c4979254c870d6f895d345197fb944c6b1add32f6 Homepage: https://cran.r-project.org/package=PSRICalcSM Description: CRAN Package 'PSRICalcSM' (Plant Stress Response Index Calculator - Softmax Method) Implements the softmax aggregation method for calculating Plant Stress Response Index (PSRI) from time-series germination data under environmental stressors including prions, xenobiotics, osmotic stress, heavy metals, and chemical contaminants. Provides zero-robust PSRI computation through adaptive softmax weighting of germination components (Maximum Stress-adjusted Germination, Maximum Rate of Germination, complementary Mean Time to Germination, and Radicle Vigor Score), eliminating the zero-collapse failure mode of the geometric mean approach implemented in 'PSRICalc'. Includes perplexity-based temperature parameter calibration and modular component functions for transparent germination analysis. Built on the methodological foundation of the Osmotic Stress Response Index (OSRI) framework developed by Walne et al. (2020) . Note: This package implements methodology currently under peer review. Please contact the author before publication using this approach. Development followed an iterative human-machine collaboration where all algorithmic design, statistical methodologies, and biological validation logic were conceptualized, tested, and iteratively refined by Richard A. Feiss through repeated cycles of running experimental data, evaluating analytical outputs, and selecting among candidate algorithms and approaches. AI systems (Anthropic Claude and OpenAI GPT) served as coding assistants and analytical sounding boards under continuous human direction. The selection of statistical methods, evaluation of biological plausibility, and all final methodology decisions were made by the human author. AI systems did not independently originate algorithms, statistical approaches, or scientific methodologies. Package: r-cran-pss.health Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2271 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-easypower, r-cran-envstats, r-cran-epir, r-cran-ggplot2, r-cran-icc.sample.size, r-cran-kappasize, r-cran-longpower, r-cran-plotly, r-cran-powermediation, r-cran-powersurvepi, r-cran-presize, r-cran-proc, r-cran-pwr, r-cran-pwr2, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyfeedback, r-cran-shinyhelper, r-cran-writexl Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-mess, r-cran-webpower Filename: pool/dists/noble/main/r-cran-pss.health_1.1.5-1.ca2404.1_all.deb Size: 824366 MD5sum: bbb7186c789a0687d594d04bcaaf64cf SHA1: 615a8fadf7cfdf8c2e20afa7c8154192759379f8 SHA256: bd362befb93aa124f9e716490071189743610b3784c632d8b614d15192a8336a SHA512: 2d25844cf74611b01f5809e24ad28ad41eada1787c12e2d2e4fa2a1f4ab3a59fad7d14ace584b792a08fdc0ac04b30a36fdfb98e3fd3c3077247aea86a94f4ad Homepage: https://cran.r-project.org/package=PSS.Health Description: CRAN Package 'PSS.Health' (Power and Sample Size for Health Researchers via Shiny) Power and Sample Size for Health Researchers is a Shiny application that brings together a series of functions related to sample size and power calculations for common analysis in the healthcare field. There are functionalities to calculate the power, sample size to estimate or test hypotheses for means and proportions (including test for correlated groups, equivalence, non-inferiority and superiority), association, correlations coefficients, regression coefficients (linear, logistic, gamma, and Cox), linear mixed model, Cronbach's alpha, interobserver agreement, intraclass correlation coefficients, limit of agreement on Bland-Altman plots, area under the curve, sensitivity and specificity incorporating the prevalence of disease. You can also use the online version at . Package: r-cran-pssim Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pssim_0.1.0-1.ca2404.1_all.deb Size: 54474 MD5sum: bcb34c7b635f6f132a00dd1ed6be6c10 SHA1: 4aa8a3197b2e45c84e14662561ab2e68ed799e75 SHA256: de89cc0e4ac285e962a8bf02c37b6b5d8284b552e1391cda3ec01714de30dcb1 SHA512: f92a65cd684392ab9a03d75426fdd4f79aff3460f4dbd5d1ddb8b57b7b013e9a71b5501cc35de68a302552f1ed16b9787605cbd243e9f17c961dad7ab416fb37 Homepage: https://cran.r-project.org/package=PSSIM Description: CRAN Package 'PSSIM' (Test of Independence & Image Structural Similarity Measure PSSIM) Test-based Image structural similarity measure and test of independence. This package implements the key functions of two tasks: (1) computing image structural similarity measure PSSIM of Wang, Maldonado and Silwal (2011) ; and (2) test of independence between a response and a covariate in presence of heteroscedastic treatment effects proposed by Wang, Tolos, and Wang (2010) . Package: r-cran-pssmcool Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3078 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-infotheo, r-cran-phontools, r-cran-dtt Suggests: r-cran-testthat, r-cran-spelling, r-cran-waveslim, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pssmcool_0.2.4-1.ca2404.1_all.deb Size: 1544642 MD5sum: ff319878a975955e97c1e5fcef92a67f SHA1: 1c6711c89c058e6eec2e9607221143eec1cb4565 SHA256: 148793ee9cb8031b0582ce706ddab31584adef532491a113a26f7bf26caa6f7f SHA512: fe9f0fc63f1aa2d54907d5ab321d858dfa9bb0879b69ed9cc9a391c649f2fdeac609d9e5c786c42871ef0d290dc045f069e65fe51fe2aab4e8fccb8cb4cdd5c5 Homepage: https://cran.r-project.org/package=PSSMCOOL Description: CRAN Package 'PSSMCOOL' (Features Extracted from Position Specific Scoring Matrix (PSSM)) Returns almost all features that has been extracted from Position Specific Scoring Matrix (PSSM) so far, which is a matrix of L rows (L is protein length) and 20 columns produced by 'PSI-BLAST' which is a program to produce PSSM Matrix from multiple sequence alignment of proteins see for mor details. some of these features are described in Zahiri, J., et al.(2013) , Saini, H., et al.(2016) , Ding, S., et al.(2014) , Cheng, C.W., et al.(2008) , Juan, E.Y., et al.(2009) . Package: r-cran-pssmooth Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-osdesign, r-cran-np, r-cran-chngpt, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pssmooth_1.0.3-1.ca2404.1_all.deb Size: 110032 MD5sum: b2d23ad615cfb294246d1147c8fcb3b1 SHA1: bf65473886e2db8286ebf2937f535106603600ce SHA256: 21cca37bcfce561533a3cabc490729c15d9881acdd10724ea59fb36fcbe4a464 SHA512: 0b29ada2b9d2eb7cf04e1b6b54b5403aa2e0852e9065b7487ff3bdbae8c896196b81c56c79b1a4fcc2f8c70cfacf440b5b520f2f0e1acd358916a140b8afe55a Homepage: https://cran.r-project.org/package=pssmooth Description: CRAN Package 'pssmooth' (Flexible and Efficient Evaluation of PrincipalSurrogates/Treatment Effect Modifiers) Implements estimation and testing procedures for evaluating an intermediate biomarker response as a principal surrogate of a clinical response to treatment (i.e., principal stratification effect modification analysis), as described in Juraska M, Huang Y, and Gilbert PB (2020), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, 21(3): 545-560 . The methods avoid the restrictive 'placebo structural risk' modeling assumption common to past methods and further improve robustness by the use of nonparametric kernel smoothing for biomarker density estimation. A randomized controlled two-group clinical efficacy trial is assumed with an ordered categorical or continuous univariate biomarker response measured at a fixed timepoint post-randomization and with a univariate baseline surrogate measure allowed to be observed in only a subset of trial participants with an observed biomarker response (see the flexible three-phase sampling design in the paper for details). Bootstrap-based procedures are available for pointwise and simultaneous confidence intervals and testing of four relevant hypotheses. Summary and plotting functions are provided for estimation results. Package: r-cran-pssurvival Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2894 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-cowplot Suggests: r-cran-nnet, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pssurvival_0.2.0-1.ca2404.1_all.deb Size: 1712804 MD5sum: f1ab0429ffe881ac83d925d2b5e5085a SHA1: f2acc4cb0ae68b335548c1e0f8a0f877e33109bf SHA256: 8e4636418d74af19f1d9f162140b06c52236548d36be60245034e435827df78b SHA512: 3eb8b5860a617a4aeae373699371d90ef587c655b3b9fa912157cc7edc18380cbdb30a9265207144b6a154ba3191e5b18de0ffe794a40c8200101cd9a58f0dc0 Homepage: https://cran.r-project.org/package=PSsurvival Description: CRAN Package 'PSsurvival' (Propensity Score Methods for Survival Analysis) Implements propensity score weighting methods for estimating counterfactual survival functions, marginal hazard ratios, and weighted Kaplan-Meier and cumulative risk curves in observational studies with time-to-event outcomes. Supports binary and multiple treatment groups with inverse probability of treatment weighting (IPW), overlap weighting (OW), and average treatment effect on the treated (ATT). Includes symmetric trimming (Crump extension) for extreme propensity scores. Variance estimation via analytical M-estimation or bootstrap. Methods based on Li et al. (2018) , Li & Li (2019) , and Cheng et al. (2022) . Package: r-cran-pst Architecture: all Version: 0.94.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 690 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-traminer, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-pst_0.94.1-1.ca2404.1_all.deb Size: 481968 MD5sum: 0cca5c6f78b1fde0c58d7b83df43b26b SHA1: 121d8cfa0d6a53530d9e1393c44c04bbf1810ec3 SHA256: 95010ad5d91aede99baedc6d933d820c21fc8ab101eb12f35da5914a8f7123b3 SHA512: 6c915034e114b23404fea641644920003a3344b5295a84ca55bb476641e504bcfcb05bb9d2ad0eea201f35a32db588268ac6e05a632de716d20befb6ce239b55 Homepage: https://cran.r-project.org/package=PST Description: CRAN Package 'PST' (Probabilistic Suffix Trees and Variable Length Markov Chains) Provides a framework for analysing state sequences with probabilistic suffix trees (PST), the construction that stores variable length Markov chains (VLMC). Besides functions for learning and optimizing VLMC models, the PST library includes many additional tools to analyse sequence data with these models: visualization tools, functions for sequence prediction and artificial sequences generation, as well as for context and pattern mining. The package is specifically adapted to the field of social sciences by allowing to learn VLMC models from sets of individual sequences possibly containing missing values, and by accounting for case weights. The library also allows to compute probabilistic divergence between two models, and to fit segmented VLMC, where sub-models fitted to distinct strata of the learning sample are stored in a single PST. This software results from research work executed within the framework of the Swiss National Centre of Competence in Research LIVES, which is financed by the Swiss National Science Foundation. The authors are grateful to the Swiss National Science Foundation for its financial support. Package: r-cran-pstat Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pstat_1.2-1.ca2404.1_all.deb Size: 107620 MD5sum: 4c8d4158648b65bb6f17a36454bd666d SHA1: 69bfb915afd6ff81821aaa0d431ddd82ed4c982f SHA256: d034845d613c085524557306b2813633984c76cc72a734fdfd945054458e9059 SHA512: 329ca5026bd839cd3fcd6e6bb25abd2b1c55ae504758b44c23a3a0cbb11ce06b63edfadeb4c59a853bf63a5dcbc73e5115df4ab2bbe2a01ec08e7a2eb8f2d306 Homepage: https://cran.r-project.org/package=Pstat Description: CRAN Package 'Pstat' (Assessing Pst Statistics) Calculating Pst values to assess differentiation among populations from a set of quantitative traits is the primary purpose of such a package. The bootstrap method provides confidence intervals and distribution histograms of Pst. Variations of Pst in function of the parameter c/h^2 are studied as well. Finally, the package proposes different transformations especially to eliminate any variation resulting from allometric growth (calculation of residuals from linear regressions, Reist standardizations or Aitchison transformation). Package: r-cran-pstest Architecture: all Version: 0.1.3.900-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmx, r-cran-mass Filename: pool/dists/noble/main/r-cran-pstest_0.1.3.900-1.ca2404.1_all.deb Size: 30948 MD5sum: 3115c23bce37a88ec27fa2b224e889c4 SHA1: b65af0050eedcb7db27568ef37aa014f373c024b SHA256: 12cd900dc93a26d710164e696b96f3e2cc5763452dd57c33e66c2e8a943c4b82 SHA512: aab4faa162d8fd70f97bd3d38c7df37baa82914e189c54a0729ef77fbaae5ab885399fa97865bae2079ccbe76bb5e46a375906903adb69412ee3925ed76c4a43 Homepage: https://cran.r-project.org/package=pstest Description: CRAN Package 'pstest' (Specification Tests for Parametric Propensity Score Models) The propensity score is one of the most widely used tools in studying the causal effect of a treatment, intervention, or policy. Given that the propensity score is usually unknown, it has to be estimated, implying that the reliability of many treatment effect estimators depends on the correct specification of the (parametric) propensity score. This package implements the data-driven nonparametric diagnostic tools for detecting propensity score misspecification proposed by Sant'Anna and Song (2019) . Package: r-cran-pstr Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-r6, r-cran-knitr, r-cran-cli, r-cran-ggplot2, r-cran-plotly, r-cran-magrittr Suggests: r-cran-rmarkdown, r-cran-snowfall, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pstr_2.0.0-1.ca2404.1_all.deb Size: 492586 MD5sum: 16425025ddb78c0c15449421d4e73f9e SHA1: 03690785f6a2b502ef617451c7a76c4509b543e1 SHA256: 7ed13cc776afe5b3ce533b9d1678dce469a8231a935e54af8d5e01bc9b17945b SHA512: 6a0c1ffdbc804403c757f5cb7e7c2bc2db83c6ab3b00cdea9d4c039996df5876c09e84b33c24a6895eda420813a00e71eda04b0da4e7832d5776a861bcf51ee7 Homepage: https://cran.r-project.org/package=PSTR Description: CRAN Package 'PSTR' (Panel Smooth Transition Regression Modelling) Implements the Panel Smooth Transition Regression (PSTR) framework for nonlinear panel data modelling. The modelling procedure consists of three stages: Specification, Estimation and Evaluation. The package provides tools for model specification testing, to do PSTR model estimation, and to do model evaluation. The implemented tests allow for cluster dependence and are heteroskedasticity-consistent. The wild bootstrap and wild cluster bootstrap tests are also implemented. Parallel computation (as an option) is implemented in some functions, especially the bootstrap tests. The package supports parallel computation, which is useful for large-scale bootstrap procedures. Package: r-cran-pstrata Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rstan, r-cran-lme4, r-cran-reformulas, r-cran-purrr, r-cran-stringr Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-pstrata_1.0.1-1.ca2404.1_all.deb Size: 208004 MD5sum: b550f84d617120dfe31f09f9c8b8a1ac SHA1: 96adc95560aa34918b7abdd987cf3e48ca6667d1 SHA256: 17cf1d9a751c31accac5599c2227b57318ceb65e96ccd73d4be6718482d6b6b9 SHA512: 0c026226a5837136f308e04942c137b2ee69fb33a4042bba6e5875db32b09e44d6a22ca1c60534d5186030f7cdeb65413f696ce28dce8a20980bb53825a12cec Homepage: https://cran.r-project.org/package=PStrata Description: CRAN Package 'PStrata' (Principal Stratification Analysis in R) Estimating causal effects in the presence of post-treatment confounding using principal stratification. 'PStrata' allows for customized monotonicity assumptions and exclusion restriction assumptions, with automatic full Bayesian inference supported by 'Stan'. The main workflow is PStrataModel() to specify the model, fit() to run MCMC sampling, estimate() to extract potential outcomes, and contrast() to compute causal effects. Visualization tools are provided for diagnosis and interpretation. See Liu and Li (2023) for details. Package: r-cran-psvmsdr Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-psvmsdr_3.0.1-1.ca2404.1_all.deb Size: 126838 MD5sum: 9e082c1637c6ea8734da35c1c9519bd7 SHA1: ef76c2ab184ba41bcd6ad7434f2e877b265044d7 SHA256: a0f8d36f1fa82aa6d8e0450ec6ffaf7e1a85d4053161c2367ca0b22c44ea5bb1 SHA512: debdfd6dbf1b7cbd4f8be132dc1bd97c88c634965c4bf2fa1a21cf893431bc6ce98369586c05ecea71961251e7cd2d5d25adb04c5b311e325e128e2dad568bd8 Homepage: https://cran.r-project.org/package=psvmSDR Description: CRAN Package 'psvmSDR' (Unified Principal Sufficient Dimension Reduction Package) A unified and user-friendly framework for applying the principal sufficient dimension reduction methods for both linear and nonlinear cases. The package has an extendable power by varying loss functions for the support vector machine, even for an user-defined arbitrary function, unless those are convex and differentiable everywhere over the support (Li et al. (2011) ). Also, it provides a real-time sufficient dimension reduction update procedure using the principal least squares support vector machine (Artemiou et al. (2021) ). Package: r-cran-psw Architecture: all Version: 1.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hmisc, r-cran-gtools Filename: pool/dists/noble/main/r-cran-psw_1.1-3-1.ca2404.1_all.deb Size: 129090 MD5sum: 76ad65acc5081113c41a6c75cf11f968 SHA1: f426627b9f185c60d525b5924747156427278ca7 SHA256: 0cfa4ec6bb13e0c108ff22d6d3ed11310bb09ae746ac62e999413cd6db976202 SHA512: 90ac88a050e455dfc5a943bee00dc7c51abda10b622fc54c0519357c66dec232b6b40abb8868095ea5e73fb48590f454cca5386dc2f94e9b2eaaa1090ec05035 Homepage: https://cran.r-project.org/package=PSW Description: CRAN Package 'PSW' (Propensity Score Weighting Methods for Dichotomous Treatments) Provides propensity score weighting methods to control for confounding in causal inference with dichotomous treatments and continuous/binary outcomes. It includes the following functional modules: (1) visualization of the propensity score distribution in both treatment groups with mirror histogram, (2) covariate balance diagnosis, (3) propensity score model specification test, (4) weighted estimation of treatment effect, and (5) augmented estimation of treatment effect with outcome regression. The weighting methods include the inverse probability weight (IPW) for estimating the average treatment effect (ATE), the IPW for average treatment effect of the treated (ATT), the IPW for the average treatment effect of the controls (ATC), the matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich variance estimation is provided to adjust for the sampling variability of the estimated propensity score. These methods are discussed by Hirano et al (2003) , Lunceford and Davidian (2004) , Li and Greene (2013) , and Li et al (2016) . Package: r-cran-psweight Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-nnet, r-cran-mass, r-cran-ggplot2, r-cran-numderiv, r-cran-gbm, r-cran-superlearner, r-cran-survey Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-psweight_2.1.2-1.ca2404.1_all.deb Size: 1090732 MD5sum: ed6bab00e90827741ff576fca83fc03e SHA1: b4df5884b84fd5321fca19799316cb6d3326d71d SHA256: 7fbb92308783803aac5f584ab321d230ff4a58711105ee9be8c9f11a11b62e17 SHA512: 2115b168304703601b91e4170b320535c63dc06f325c70bf0751d191754c130d843b3078821f2884e8919147918f4db5c3c40bec143507a120755dee808c4002 Homepage: https://cran.r-project.org/package=PSweight Description: CRAN Package 'PSweight' (Propensity Score Weighting for Causal Inference withObservational Studies and Randomized Trials) Supports propensity score weighting analysis of observational studies and randomized trials. Enables the estimation and inference of average causal effects with binary and multiple treatments using overlap weights (ATO), inverse probability of treatment weights (ATE), average treatment effect among the treated weights (ATT), matching weights (ATM) and entropy weights (ATEN), with and without propensity score trimming. These weights are members of the family of balancing weights introduced in Li, Morgan and Zaslavsky (2018) and Li and Li (2019) . Package: r-cran-psy Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-psy_1.2-1.ca2404.1_all.deb Size: 155714 MD5sum: df4ed7f7f6179d4d94776f5af81d001a SHA1: 528555a634a8769c0c1101a914a0bd7d9ae26587 SHA256: b153fb9c7a8efba893666a755c627d2b9d1988efe8c24ff044ae9dcb4e59debe SHA512: 9863c63382204e5ef2afa06d7170d33e638403e275155753002a97aa32b9b2add4950a1491d2bf4a746f3c9b360f2c98952bb5adda028e281cffae444dc878cb Homepage: https://cran.r-project.org/package=psy Description: CRAN Package 'psy' (Various Procedures Used in Psychometrics) Kappa, ICC, reliability coefficient, parallel analysis, multi-traits multi-methods, spherical representation of a correlation matrix. 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Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the web page. Package: r-cran-psychmeta Architecture: all Version: 2.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3878 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-metafor, r-cran-ggplot2, r-cran-progress, r-cran-curl, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-rlang, r-cran-purrr Suggests: r-cran-mass, r-cran-mvtnorm, r-cran-nor1mix, r-cran-bib2df, r-cran-rmarkdown, r-cran-knitr, r-cran-stringi, r-cran-cli, r-cran-crayon, r-cran-testthat Filename: pool/dists/noble/main/r-cran-psychmeta_2.7.0-1.ca2404.1_all.deb Size: 2900806 MD5sum: 9cecc5c3707fe9713b3bbafcd2bbce1d SHA1: ca2fd20d5543064b894ef0cd9d24bb032eb39257 SHA256: d6c9c7cd4f09849159c4103a4f9b7ba7368427e4f29e2e749938ba8372ebd3da SHA512: 0e4b3085fb38adc14a41d7c572eca437154752056b1753b0aba1032447473b9649ddba81864e8c360454c5d95ee0cd148c1081249705197d67a072dabaf03578 Homepage: https://cran.r-project.org/package=psychmeta Description: CRAN Package 'psychmeta' (Psychometric Meta-Analysis Toolkit) Tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to or . 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Package: r-cran-psychometric Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-multilevel, r-cran-purrr, r-cran-nlme Filename: pool/dists/noble/main/r-cran-psychometric_2.4-1.ca2404.1_all.deb Size: 197938 MD5sum: c8602362a319d7633252153043f67574 SHA1: c5c1bdd42c4260cda07c592276a81d5f52e7d7d5 SHA256: eaf6a2f8df675ed799b2512da053b2695badd102407f70464cd1c164452f7038 SHA512: 7eb8158d228dddfc9d42c8f6783d75eec08e2d8593d7f26cbced2fa469043446026348075d551430b1099bd02350040fd8aae5d4589c69036b1b7e4176a22e01 Homepage: https://cran.r-project.org/package=psychometric Description: CRAN Package 'psychometric' (Applied Psychometric Theory) Contains functions useful for correlation theory, meta-analysis (validity-generalization), reliability, item analysis, inter-rater reliability, and classical utility. Package: r-cran-psychomix Architecture: all Version: 1.1-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1091 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-flexmix, r-cran-psychotools, r-cran-lattice, r-cran-formula, r-cran-modeltools Suggests: r-cran-effects, r-cran-lmtest, r-cran-nnet, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-psychomix_1.1-9-1.ca2404.1_all.deb Size: 828510 MD5sum: e9ca8492acbc8bfcd0174b16fbd21f21 SHA1: 5a071cde21c6ad4d6fc472c92c2d3151cffd7c78 SHA256: 509a598b0f78c1489e74f4592561b9ab2abcb5f35615243fa7a52efe04d8cbd5 SHA512: 821713b05d925f0d7164c983cd9b7d1d0a222e4e579b6420d3d636ee8c12fdc00da6f274063bd549db4f336f188dbc56c7068c2af4a0e8a564811bca0e825c9a Homepage: https://cran.r-project.org/package=psychomix Description: CRAN Package 'psychomix' (Psychometric Mixture Models) Psychometric mixture models based on 'flexmix' infrastructure. At the moment Rasch mixture models with different parameterizations of the score distribution (saturated vs. mean/variance specification), Bradley-Terry mixture models, and MPT mixture models are implemented. These mixture models can be estimated with or without concomitant variables. See Frick et al. (2012) and Frick et al. (2015) for details on the Rasch mixture models. Package: r-cran-psychotree Architecture: all Version: 0.16-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-partykit, r-cran-psychotools, r-cran-formula Suggests: r-cran-stablelearner, r-cran-strucchange, r-cran-tinytest, r-cran-mirt Filename: pool/dists/noble/main/r-cran-psychotree_0.16-2-1.ca2404.1_all.deb Size: 467702 MD5sum: 40cc1e3482a20d5256feae48dbc2d669 SHA1: ac56f9fb9210d1c971733862f048d9cdd8c920a3 SHA256: 6f31172364443dd5dd125341a49d95b02c40f4c0a16efe9581de6af7869c1a97 SHA512: cd8c534694b1f2c6ab3dc4df2c659cf6a51c30c77d538d686d82c725c3234d831cae1cb2427d39c31bb7d4f21b71c5a5accbf161b69092d4e773ff15b8488f9d Homepage: https://cran.r-project.org/package=psychotree Description: CRAN Package 'psychotree' (Recursive Partitioning Based on Psychometric Models) Recursive partitioning based on psychometric models, employing the general MOB algorithm (from package partykit) to obtain Bradley-Terry trees, Rasch trees, rating scale and partial credit trees, and MPT trees, trees for 1PL, 2PL, 3PL and 4PL models and generalized partial credit models. 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Package: r-cran-psycmodel Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1269 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-insight, r-cran-lavaan, r-cran-lifecycle, r-cran-lme4, r-cran-lmertest, r-cran-parameters, r-cran-patchwork, r-cran-performance, r-cran-psych, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-correlation, r-cran-covr, r-cran-cowplot, r-cran-fansi, r-cran-ggrepel, r-cran-gparotation, r-cran-gridextra, r-cran-interactions, r-cran-knitr, r-cran-nfactors, r-cran-nlme, r-cran-pagedown, r-cran-qqplotr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-sandwich, r-cran-see, r-cran-semplot, r-cran-spelling, r-cran-testthat, r-cran-lavasearch2 Filename: pool/dists/noble/main/r-cran-psycmodel_0.5.0-1.ca2404.1_all.deb Size: 869536 MD5sum: 925be4ea631dd79cc68fffea1ea8385c SHA1: 0f0cd251dd65473a188f113d99226a31c6e3169d SHA256: 0a688e983c1da508482f3ae4fb1c3ca7943997691c9fbf9222e0a9b5bd16aa7b SHA512: 24fe65a5e4656716c13a1d1159098011f5a2b22c5e1085b0822aa99e11f05f708b0ed91a2de3a97ee5b2dc397fbdfa2cad58cd59547e192b044d307782d08358 Homepage: https://cran.r-project.org/package=psycModel Description: CRAN Package 'psycModel' (Integrated Toolkit for Psychological Analysis and Modeling in R) A beginner-friendly R package for modeling in psychology or related field. 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It includes functions for calculating d' from several different experimental designs, links for m-alternative forced-choice (mafc) data to be used with the binomial family in glm (and possibly other contexts) and self-Start functions for estimating gamma values for CRT screen calibrations. 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The package reads downloaded study folders, parses questionnaire structure, optionally merges demographic exports from CloudResearch or Prolific, and produces summary overviews of responses and completion times. It also provides helper functions to extract and aggregate experiment measures and survey variables, and to export results to spreadsheet files for further analysis and archiving. See Stoet (2017) for the 'PsyToolkit' platform. 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Package: r-cran-ptable Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-flexdashboard, r-cran-ggplot2, r-cran-nloptr, r-cran-rcolorbrewer, r-cran-rlang, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ptable_1.0.0-1.ca2404.1_all.deb Size: 229012 MD5sum: 5def45e3fe9196eee5bc472e9b50265c SHA1: 82832b7c0917d0afdfc7e6b7c0b7a849dcaa7c2c SHA256: 7b7b7b9e4f3e118816c283749b4c4737fd0a819c66528f0cc4d4a8b1a602b6cb SHA512: 350de8fbc89b2c7e7d1ad25fe1c1104adc3f87b7c45992b34ec4294d47c07ad119b14d8ffb16062254972e5a861964bed6260d16a661d7a13cb1ebf0cbb5bd2d Homepage: https://cran.r-project.org/package=ptable Description: CRAN Package 'ptable' (Generation of Perturbation Tables for the Cell-Key Method) Tabular data from statistical institutes and agencies are mostly confidential and must be protected prior to publications. The cell-key method is a post-tabular Statistical Disclosure Control perturbation technique that adds random noise to tabular data. The statistical properties of the perturbations are defined by some noise probability distributions - also referred to as perturbation tables. This tool can be used to create the perturbation tables based on a maximum entropy approach as described for example in Giessing (2016) . The perturbation tables created can finally be used to apply a cell-key method to frequency count or magnitude tables. Package: r-cran-ptak Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1417 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tensor Filename: pool/dists/noble/main/r-cran-ptak_2.0.0-1.ca2404.1_all.deb Size: 1351882 MD5sum: 27f58aa9439dd2f3f37dd7edc120fe66 SHA1: 05749c4a6bf142a53bd72aab1802f89d37bfa945 SHA256: f821a11fe60be2931d1e9664d85bb6cc7c0c1c6eaabea87dbbda7aff1d7277eb SHA512: 28ada28650243943ce239f6819b194abde88f550ce61cd41952a05c0c4527963e1aa27bb3eae21394c6297d065486c5129f6753c7eb4f2f887caea3c603f6d02 Homepage: https://cran.r-project.org/package=PTAk Description: CRAN Package 'PTAk' (Principal Tensor Analysis on k Modes) A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions. Package: r-cran-pte Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-survival Filename: pool/dists/noble/main/r-cran-pte_1.7-1.ca2404.1_all.deb Size: 72570 MD5sum: f8807b90825f603ffccdae6ef51ae609 SHA1: f9b2687dfbef52b05600d3542eda333e7d9d19ab SHA256: e1fe456fca4e9c0789f17fc0f7744f0009e5286a6d9010e9e21545036e94c91c SHA512: 526ff899203b40f1f91f9a9e9efd74018dc59edd93d5de54cfa587ade470cfdc8f6f24f496921bbbfc7975570e0a32608d853d5e222e7e7a8061df672fd1ae5c Homepage: https://cran.r-project.org/package=PTE Description: CRAN Package 'PTE' (Personalized Treatment Evaluator) We provide inference for personalized medicine models. Namely, we answer the questions: (1) how much better does a purported personalized recommendation engine for treatments do over a business-as-usual approach and (2) is that difference statistically significant? Package: r-cran-pterp Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm, r-cran-survival Filename: pool/dists/noble/main/r-cran-pterp_1.0-1.ca2404.1_all.deb Size: 58570 MD5sum: 810644f20a513075c2c4cf2bec0c971f SHA1: 8bfa6f1389a1d327df06bd602cdb584b09ac87a2 SHA256: 245cdda4a50e4a8b42de0444d3860d02cbd734d09b991a33ab028befec17a410 SHA512: 0a1a1bfdbb97ae3a28cd3cd6c5622ccb82a0ae8b4fe8fc68ab51e1cd26ce20bfcc3988a522752e0b25453c42f76faaa26d7df67847a0d1ef260621bd6b72da49 Homepage: https://cran.r-project.org/package=PTERP Description: CRAN Package 'PTERP' (PTE and RP for Optimally-Transformed Surrogate) Evaluates the strength of a surrogate marker by estimating the proportion of treatment effect explained (PTE) and relative power(RP) for the optimally-transformed version of the surrogate. Details available in Wang et al (2022) . Package: r-cran-ptetools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bmisc, r-cran-matrix, r-cran-ggplot2, r-cran-drdid, r-cran-tidyr, r-cran-dplyr, r-cran-pbapply, r-cran-splines2 Suggests: r-cran-testthat, r-cran-did Filename: pool/dists/noble/main/r-cran-ptetools_1.0.0-1.ca2404.1_all.deb Size: 364356 MD5sum: 62d940597c6730498ed25dcda6f7c444 SHA1: e060e7086a7afe25e503812309829b24c9045cab SHA256: e6af4ff867f2e523c2d76c9713cf1f3d07c3439f416aa563b2a42f4046491b2e SHA512: 91deaeb0fd0de1a2a56ef75af97a88e913f13753857457bd84a49ebb0ee3f21e8a20cb372c27f9b68fc7005cdbdd705c9d827d2f6ef364738fccca7ef2eda490 Homepage: https://cran.r-project.org/package=ptetools Description: CRAN Package 'ptetools' (Panel Treatment Effects Tools) Generic code for estimating treatment effects with panel data. The idea is to break into separate steps organizing the data, looping over groups and time periods, computing group-time average treatment effects, and aggregating group-time average treatment effects. Often, one is able to implement a new identification/estimation procedure by simply replacing the step on estimating group-time average treatment effects. See several different examples of this approach in the package documentation. Package: r-cran-ptitan2 Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1472 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-bookdown, r-cran-diagrammer, r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-readr, r-cran-rmarkdown, r-cran-titan2, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-ptitan2_1.0.2-1.ca2404.1_all.deb Size: 400568 MD5sum: 7b3d800fc40735540259c782a0a48cf7 SHA1: d2657407865249f4fdc404bd0a8a52a49eff48f1 SHA256: 46e0b5ef680587408f4b959ddc3e944c3ed6b5196378794e090115c1f9de77f6 SHA512: 31f6c3e3dd1b2cc8cfea6f33f28e66e3b4feb2fbf856b051c98f3a5a56f36b97bff39e31a0c20f61cb1c259abd00e88402b640a058b24becb01cc78cb4fe9154 Homepage: https://cran.r-project.org/package=pTITAN2 Description: CRAN Package 'pTITAN2' (Permutations of Treatment Labels and TITAN2 Analysis) Permute treatment labels for taxa and environmental gradients to generate an empirical distribution of change points. 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Package: r-cran-ptm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1560 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bio3d, r-cran-curl, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ptm_1.0.1-1.ca2404.1_all.deb Size: 1418742 MD5sum: b834f738c1314077a8657701fbe836a1 SHA1: 05ec09af2728e26671e82a9bf51b23a97eba0cf5 SHA256: b85a4a0c7a4f0dad461a696c752d8c1945290d82ed81d59646d95b0f6ffaa8f9 SHA512: 81d551118c8a1766b28af2742e2cb266e39235761555420bfcea8002790209b40067eb12e6c35a33c2f9aeba23538eda19fb2328f8b3d925b009021a6b0b4eee Homepage: https://cran.r-project.org/package=ptm Description: CRAN Package 'ptm' (Analyses of Protein Post-Translational Modifications) Contains utilities for the analysis of post-translational modifications (PTMs) in proteins, with particular emphasis on the sulfoxidation of methionine residues. 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Likelihood approximation based on adaptive Gauss Hermite quadrature rule. 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Includes checking difference in two Poisson counts (e-test), checking the fit for a Poisson distribution, small sample tests for counts in bins, Weighted Displacement Difference test (Wheeler and Ratcliffe, 2018) , to evaluate crime changes over time in treated/control areas. Additionally includes functions for aggregating spatial data and spatial feature engineering. Package: r-cran-ptprocess Architecture: all Version: 3.3-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ptprocess_3.3-17-1.ca2404.1_all.deb Size: 278416 MD5sum: 3928c77207dc66f583c392bdb70b11b8 SHA1: 46a576c717106e5f3e45b59d767696bd765f1f81 SHA256: cf79aaf8b12ad26b7c682b4bda0d728e31e5ea4cb80b06187d7ecc855feec352 SHA512: ed4eac4e7b766226cdb556920b7d8d8b20f40c7b1333ebcaf2c720197b9d79717a5e2e65d7643bc582c2f44a866506b7008634889a0d0d0ad4d614c0080f9ec3 Homepage: https://cran.r-project.org/package=PtProcess Description: CRAN Package 'PtProcess' (Time Dependent Point Process Modelling) Fits and analyses time dependent marked point process models with an emphasis on earthquake modelling. For a more detailed introduction to the package, see the topic "PtProcess". A list of recent changes can be found in the topic "Change Log". Package: r-cran-ptsddiag Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 877 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-modelr Suggests: r-cran-dt, r-cran-knitr, r-cran-lattice, r-cran-psych, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ptsddiag_0.1.0-1.ca2404.1_all.deb Size: 367720 MD5sum: c44c52dfb0067840a4dc237272a05d2e SHA1: f066bc9e67ba6e80bcfc7bc16cd9504478d19789 SHA256: c7a025f50daac542b64b82d99b781383e4efc3d24ab2efce092cd6a13a703a35 SHA512: 86ba331377da7e749c20866ed2a3334f8244f943208af07dc277199bc3d8d5cba7882fd62f37f3528877724325f6f0409b2d3e76488e0652d2390fb6d4575603 Homepage: https://cran.r-project.org/package=PTSDdiag Description: CRAN Package 'PTSDdiag' (Optimize PTSD Diagnostic Criteria) Provides tools for analyzing and optimizing PTSD (Post-Traumatic Stress Disorder) diagnostic criteria using PCL-5 (PTSD Checklist for DSM-5) data. Functions identify optimal subsets of PCL-5 items that maintain diagnostic accuracy while reducing assessment burden. Includes tools for both hierarchical (cluster-based) and non-hierarchical symptom combinations, calculation of diagnostic metrics, and comparison with standard DSM-5 criteria. Model validation is conducted using holdout and cross-validation methods to assess robustness and generalizability of the results. For more details see Weidmann et al. (2025) . Package: r-cran-ptspotter Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-beepr, r-cran-log4r, r-cran-this.path, r-cran-pryr, r-cran-stringr Suggests: r-cran-covr, r-cran-knitr, r-cran-markdown, r-cran-projecttemplate, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ptspotter_1.0.2-1.ca2404.1_all.deb Size: 37352 MD5sum: 40ae31814d01258769cfadc70ed495ee SHA1: 5b42fba57d9a6082c546556e7ffc6e6fa501cd17 SHA256: 9841e2b5aa4657d4257af7ada2cf758b984f9b5cbca5bab3d507737afa921c61 SHA512: a6669b8bd9d4c737fdfafd2f87718a827a022caeaec06a0d1bb8842492b3a0314d8ddf186359437a9e6e62cedab8bca5f576e448e145d11f812542deb312dc1d Homepage: https://cran.r-project.org/package=ptspotter Description: CRAN Package 'ptspotter' (Helper Functions for Use with "ProjectTemplate") Utility functions produced specifically for (but not limited to) working with 'ProjectTemplate' data pipelines. 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(2025). "Positive time series regression models: theoretical and computational aspects". Computational Statistics 40, 1185–1215. . Package: r-cran-pttstability Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bayesiantools Filename: pool/dists/noble/main/r-cran-pttstability_1.4-1.ca2404.1_all.deb Size: 116602 MD5sum: dfb120ad577b41285e9517d0fd619b5f SHA1: bad11820c08349bc618f351da96ade50dd99fbb2 SHA256: 0a4ee6bff07869bf69adddba1d36b18f43941d6b44fc4135b02941b1ca841764 SHA512: 5a9e924ec21c9b3087130dcf837253abad5d9af0a2becbfcb853a45151d0aa470fa3f8ce6bc34e6112c05154eedd771e7668329a46444daaa81a3f746adf4e64 Homepage: https://cran.r-project.org/package=pttstability Description: CRAN Package 'pttstability' (Particle-Takens Stability) Includes a collection of functions presented in "Measuring stability in ecological systems without static equilibria" by Clark et al. (2022) in Ecosphere. These can be used to estimate the parameters of a stochastic state space model (i.e. a model where a time series is observed with error). The goal of this package is to estimate the variability around a deterministic process, both in terms of observation error - i.e. variability due to imperfect observations that does not influence system state - and in terms of process noise - i.e. stochastic variation in the actual state of the process. Unlike classical methods for estimating variability, this package does not necessarily assume that the deterministic state is fixed (i.e. a fixed-point equilibrium), meaning that variability around a dynamic trajectory can be estimated (e.g. stochastic fluctuations during predator-prey dynamics). 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Package: r-cran-public.ctn0094data Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1468 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-broom, r-cran-conflicted, r-cran-diagrammer, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-ggthemes, r-cran-gt, r-cran-haven, r-cran-infer, r-cran-janitor, r-cran-knitr, r-cran-kableextra, r-cran-magrittr, r-cran-pkgdown, r-cran-plyr, r-cran-psych, r-cran-purrr, r-cran-rlang, r-cran-rmarkdown, r-cran-roxygen2, r-cran-rum, r-cran-scales, r-cran-stringr, r-cran-table1, r-cran-tibble, r-cran-tidyr, r-cran-tidyverse, r-cran-testthat, r-cran-vcdextra, r-cran-xfun Filename: pool/dists/noble/main/r-cran-public.ctn0094data_1.1.0-1.ca2404.1_all.deb Size: 1269622 MD5sum: 0691b8c917b974592bc9fe64cd1b391a SHA1: 19ff1653f06cfc4d3aab0357cd31d20a58f05326 SHA256: c9a8c7783989a27d29c558a21fff11f033ccfc0455f22173e70ba15e3cbae562 SHA512: 748d1f459e8391197f6fb872fba510175c5381da536c01b513d33f5daec2c5fc7cbe57379f366f3757fac75ee1c8428723666b719b0ae2ba0fb3be01097ee8c8 Homepage: https://cran.r-project.org/package=public.ctn0094data Description: CRAN Package 'public.ctn0094data' (De-Identified Data from CTN-0094) These are harmonized datasets produced as part of the Clinical Trials Network (CTN) protocol number 0094. 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This 'public.ctn0094data' package contains harmonized datasets from some of the National Institute of Drug Abuse's Clinical Trials Network (NIDA's CTN) projects. Specifically, the CTN-0094 project is to harmonize and de-identify clinical trials data from the CTN-0027, CTN-0030, and CTN-51 studies for opioid use disorder. This current version is built from 'public.ctn0094data' v. 1.0.6. Package: r-cran-publicationbias Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-lifecycle, r-cran-metabias, r-cran-metafor, r-cran-rdpack, r-cran-rlang, r-cran-robumeta Suggests: r-cran-purrr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-publicationbias_2.4.0-1.ca2404.1_all.deb Size: 69696 MD5sum: 00428074a87de791c2dd26e296e23368 SHA1: 06d0fa1a73f4604ac25eb506518229cb28d106e4 SHA256: 8055d8e97a5f932c6903d931b5e212e16e2747f30411ab8a4fcde0d654325135 SHA512: c905f27e62b0cc9bac86cb8dd047da2133c8f620f30e6aec6e1c3d4b2b0572611f8711ed63b6d790a230c38a2d26f9719ef9a3bc8f0165cef140bc7fae94f29a Homepage: https://cran.r-project.org/package=PublicationBias Description: CRAN Package 'PublicationBias' (Sensitivity Analysis for Publication Bias in Meta-Analyses) Performs sensitivity analysis for publication bias in meta-analyses (per Mathur & VanderWeele, 2020 []). 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Package: r-cran-pubmatrixr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1321 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-pheatmap, r-cran-readods, r-cran-xml2 Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-pubmatrixr_1.0.0-1.ca2404.1_all.deb Size: 722194 MD5sum: ea554a5d5f570c3ab995bd8f9017b8cb SHA1: 4373f9d3324e650259a3700e3eb0df0a5e4ff12e SHA256: 70bbdaef660a74a47ce4ddfdea6586c49d6659d26b1005df8dabf9256ee4f335 SHA512: e46390ff39f1c6c2bde1b420a069fe507519b49304c77e44c69f996777f025f9fc6c869bc287bd593dc35f261aa9b282a997d11e6b3d2cbf4f70fccce0fb755b Homepage: https://cran.r-project.org/package=PubMatrixR Description: CRAN Package 'PubMatrixR' (PubMed Pairwise Co-Occurrence Matrix Construction andVisualization) Queries the 'NCBI' (National Center for Biotechnology Information) Entrez 'E-utilities' API to count pairwise co-occurrences between two sets of terms in 'PubMed' or 'PubMed Central'. It returns a matrix-like data frame of publication counts and can export hyperlink-enabled results in CSV or ODS format. The package also provides heatmap helpers for exploratory visualization of overlap patterns. Based on the method described in Becker et al. (2003) "PubMatrix: a tool for multiplex literature mining" . Package: r-cran-pubmed.miner Architecture: all Version: 1.0.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1394 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl, r-cran-xml, r-cran-boot, r-cran-r2html, r-cran-rjsonio Filename: pool/dists/noble/main/r-cran-pubmed.miner_1.0.21-1.ca2404.1_all.deb Size: 1255936 MD5sum: f912661610b961e018aa11ae8ed3d4d2 SHA1: eea1fe3cd2f9113284d9d749679bfe042e1144a5 SHA256: 067e14116f96716399267976672523886fd73df038e27ae224974479834d8ae3 SHA512: c7ae998e581d38ebeb9b675b43657a378512f462a322db54ca131dfdd5313d638e1a9583619d69e269e06a22899746e0e0921542da5d8befea54d1eb17b4af67 Homepage: https://cran.r-project.org/package=pubmed.mineR Description: CRAN Package 'pubmed.mineR' (Text Mining of PubMed Abstracts) Text mining of PubMed Abstracts (text and XML) from . Package: r-cran-pubmedmining Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-easypubmed, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pubmedmining_1.0.0-1.ca2404.1_all.deb Size: 21010 MD5sum: f5452a6455c0d598e659fe4db6c91f03 SHA1: 155838ac3d93849f3b4e0ebaf83b2dfc3452f37a SHA256: 125940cf8cbba17be216775488efbd5c8df0835299ffabb6ce7d415f5371c611 SHA512: 6e7695b3325d071d23ccf41818b9525e74281cd8be8e835b71d79b50b66f2a19324567106d5083af5d9ccfe105c7cdb6c872e78a10ac6325b4435b1953dd670b Homepage: https://cran.r-project.org/package=PubMedMining Description: CRAN Package 'PubMedMining' (Text-Mining of the 'PubMed' Repository) Easy function for text-mining the 'PubMed' repository based on defined sets of terms. The relationship between fix-terms (related to your research topic) and pub-terms (terms which pivot around your research focus) is calculated using the pointwise mutual information algorithm ('PMI'). Church, Kenneth Ward and Hanks, Patrick (1990) A text file is generated with the 'PMI'-scores for each fix-term. Then for each collocation pairs (a fix-term + a pub-term), a text file is generated with related article titles and publishing years. Additional Author section will follow in the next version updates. 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(2024) . Numerous visualization options, including static and animated, 2D and 3D, and a site map generator based on sensor and source coordinates. 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The methodologies are described in Cavicchia, Vichi, Zaccaria (2024) , (2022) and (2020) . 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The context is multilevel randomized experiments with multiple outcomes. The estimation takes into account the use of multiple testing procedures. Development of this package was supported by a grant from the Institute of Education Sciences (R305D170030). For a full package description, including a detailed technical appendix, see . 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Adsorption kinetics is used for characterizing the rate of solute adsorption and the time necessary for the adsorption process. Adsorption kinetics offers vital information on adsorption rate, adsorbent performance in response time, and mass transfer processes. In addition, diffusion models are included in the package as solute diffusion affects the adsorption kinetic experiments. This package consists of 20 adsorption and diffusion models, including Pseudo First Order (PFO), Pseudo Second Order (PSO), Elovich, and Weber-Morris model (commonly called the intraparticle model) stated by Plazinski et al. (2009) . This package also contains a summary function where the statistical errors of each model are ranked for a more straightforward determination of the best fit model. 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Compute per-person posterior probabilities that each Plasmodium vivax (Pv) recurrence is a recrudescence, relapse, or reinfection (3Rs) using per-person P. vivax genetic data on two or more episodes and a statistical model described in Taylor, Foo and White (2022) . Plot per-recurrence posterior probabilities. Package: r-cran-pvaclone Architecture: all Version: 0.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dcmle, r-cran-dclone, r-cran-coda Filename: pool/dists/noble/main/r-cran-pvaclone_0.1-8-1.ca2404.1_all.deb Size: 161884 MD5sum: 3800157fa53096772ef1626109224e90 SHA1: 510d6d7933150edda989859d902b23bd7ae87116 SHA256: 733f7384f310fcbfbce73e049fa18b5df373b35bf54dc0e9ac976016487aa0dd SHA512: d1998f43e4b157f236898776cb95e350749471a1d89591a6167abe36625ca8cbf80403f8da9617c857f7f4f3ee92bd1e9154504edc9d2ff290782dc38dd5e2e6 Homepage: https://cran.r-project.org/package=PVAClone Description: CRAN Package 'PVAClone' (Population Viability Analysis with Data Cloning) Likelihood based population viability analysis in the presence of observation error and missing data. The package can be used to fit, compare, predict, and forecast various growth model types using data cloning. Package: r-cran-pvaluefunctions Architecture: all Version: 1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2183 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-zipfr, r-cran-pracma, r-cran-gsl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-pvaluefunctions_1.6.3-1.ca2404.1_all.deb Size: 1342094 MD5sum: 16c7c1560b83042f5755a0be06c9a412 SHA1: fa0f1f7d8eb7c543943e0b407a1a5c86fd711bd4 SHA256: 5de42826f8b9a40d2a5819d4db57d50172ba91e3ed0a43500f9bbff36660c967 SHA512: c136e10358fa8fe01768190bf2ddb66403226525208d63730bd195a04a32ab0ec2f874b9d2f9ea5a629cb49b2d34a6a11f90a797c5f41ceb451a1d5cfc6561ab Homepage: https://cran.r-project.org/package=pvaluefunctions Description: CRAN Package 'pvaluefunctions' (Creates and Plots P-Value Functions, S-Value Functions,Confidence Distributions and Confidence Densities) Contains functions to compute and plot confidence distributions, confidence densities, p-value functions and s-value (surprisal) functions for several commonly used estimates. Instead of just calculating one p-value and one confidence interval, p-value functions display p-values and confidence intervals for many levels thereby allowing to gauge the compatibility of several parameter values with the data. These methods are discussed by Infanger D, Schmidt-Trucksäss A. (2019) ; Poole C. (1987) ; Schweder T, Hjort NL. (2002) ; Bender R, Berg G, Zeeb H. (2005) ; Singh K, Xie M, Strawderman WE. (2007) ; Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052); Amrhein V, Trafimow D, Greenland S. (2019) ; Greenland S. (2019) and Rafi Z, Greenland S. (2020) . Package: r-cran-pvars Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3530 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-svars, r-cran-clue, r-cran-copula, r-cran-deoptim, r-cran-expm, r-cran-ggplot2, r-cran-mass, r-cran-pbapply, r-cran-reshape2, r-cran-scales, r-cran-steadyica, r-cran-vars Suggests: r-cran-ggfortify, r-cran-ggpubr, r-cran-knitr, r-cran-plm, r-cran-rcolorbrewer, r-cran-testthat, r-cran-tikzdevice, r-cran-urca Filename: pool/dists/noble/main/r-cran-pvars_1.1.1-1.ca2404.1_all.deb Size: 3171950 MD5sum: 016c3871a22d945943cdaa2f54da0b2a SHA1: 468d2dc82afda411b3de19d8b2b85337d558dabf SHA256: fb49307226bb833f94e1fba6d0582b851db94f0c10266169f4b92e7b9a371aea SHA512: 333485eb65a7ab73f68e0881d69a5a1fa078840eb519d005378ad4c50426c95a2a1511ad7c2112adeed1cc4501a39e2309a4672aacb8b4ad3ee91462e296d4cb Homepage: https://cran.r-project.org/package=pvars Description: CRAN Package 'pvars' (VAR Modeling for Heterogeneous Panels) Implements (1) panel cointegration rank tests, (2) estimators for panel vector autoregressive (VAR) models, and (3) identification methods for panel structural vector autoregressive (SVAR) models as described in the accompanying vignette. 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From the progression of the curves turgor loss point, osmotic potential, apoplastic fraction as well as minimum conductance and stomatal closure can be derived. Methods adapted from Bartlett, Scoffoni, Sack (2012) and Sack, Scoffoni, PrometheusWikiContributors (2011) . 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The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project of the same name - 'PvSTATEM', which is an international project aiming for malaria elimination. 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This volatility is especially noticeable in agricultural commodities, adding uncertainty for farmers, traders, and others in the agricultural supply chain. Commonly and popularly used four volatility models viz, GARCH, Glosten Jagannatan Runkle-GARCH (GJR-GARCH) model, exponentially weighted moving average (EWMA) model and Multiplicative Error Model (MEM) are selected and implemented. PWAVE, weighted ensemble model based on particle swarm optimization (PSO) is proposed to combine the forecast obtained from all the candidate models. This package has been developed using algorithm of Paul et al. and Yeasin and Paul (2024) . Package: r-cran-pwexp Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-segmented, r-cran-foreach, r-cran-dosnow, r-cran-fastmatch Suggests: r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pwexp_0.5.1-1.ca2404.1_all.deb Size: 485358 MD5sum: b233acbfdd2034c2a4e5895bde5ccfe8 SHA1: c7712f883f60111abfd0dd9831141cb89de9435e SHA256: 3954bb557187dae0ca0012ec679f07c0362b86d3b399e278c8aef1d8b747904d SHA512: 4e15db58d6e7c4c226d2cbd0afe83f1a704f1d1140e326a5aa386ba92763168907d5d665f18f886104acc45c307f8cc24e6b65575930637cd08fffdf6b77620c Homepage: https://cran.r-project.org/package=PWEXP Description: CRAN Package 'PWEXP' (Piecewise Exponential Distribution Prediction Model) Build piecewise exponential survival model for study design (planning) and event/timeline prediction. Package: r-cran-pwir Architecture: all Version: 0.0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bibliometrix, r-cran-igraph, r-cran-progressr Filename: pool/dists/noble/main/r-cran-pwir_0.0.3.1-1.ca2404.1_all.deb Size: 33390 MD5sum: aa75fca5f7e1ada198a7d87986e78311 SHA1: 44e3247d4b2847b51fb718b75be034fcf56bc5a1 SHA256: a668359f13fabf1f57a8d04d76a6630184f956f551b76aff85d44947961aa0e5 SHA512: 7053b0a70d9e24316ac976236528c519ff89abe80b6fdcd80569bca395f2a01d59c0e00aa5f32187dbb7fa4eae7855c923d8ceb5c07a6e7f6dbebe378f8a5a54 Homepage: https://cran.r-project.org/package=PWIR Description: CRAN Package 'PWIR' (Provides a Function to Calculate Prize Winner Indices Based onBibliometric Data) A function 'PWI()' that calculates prize winner indices based on bibliometric data is provided. The default is the 'Derek de Solla Price Memorial Medal'. Users can provide recipients of other prizes. 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Methods based on Warwicker and Rebennack (2025) "Efficient continuous piecewise linear regression for linearising univariate non-linear functions" . Package: r-cran-pwr2 Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-pwr2_1.0-1.ca2404.1_all.deb Size: 34752 MD5sum: 42528c7f2d7d52171126b2415e6ed41e SHA1: dd7d1c0a7e128bdf4ec0776f5c2e0c1de7a7301a SHA256: ff38c3ce4fd36dd65b4c1caab2bdf16e1512d1e43d067c65309d68a06e0c4f40 SHA512: 00529fb58057c93610fae458bb87a54c327b0be539213b793293bee63ef807569879d4b37ac2a50a1fcabd546e80a55a24773745553cd925c918fb4f67483c03 Homepage: https://cran.r-project.org/package=pwr2 Description: CRAN Package 'pwr2' (Power and Sample Size Analysis for One-way and Two-way ANOVAModels) User friendly functions for power and sample size analysis at one-way and two-way ANOVA settings take either effect size or delta and sigma as arguments. They are designed for both one-way and two-way ANOVA settings. In addition, a function for plotting power curves is available for power comparison, which can be easily visualized by statisticians and clinical researchers. Package: r-cran-pwr2ppl Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 524 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-mass, r-cran-dplyr, r-cran-tidyr, r-cran-nlme, r-cran-phia, r-cran-afex, r-cran-mbess, r-cran-lavaan, r-cran-semtools, r-cran-quantreg, r-cran-broom, r-cran-lmtest, r-cran-lmperm, r-cran-pls Filename: pool/dists/noble/main/r-cran-pwr2ppl_0.6.0-1.ca2404.1_all.deb Size: 471458 MD5sum: dfa1a972e9f3377a17d8a130d5242b2e SHA1: 892293babd2aedc974e5eac4b01638fc93f0eda7 SHA256: 569e2119668848c83945cae8b922c91b633e32cb5abf0b5f735d787f3f2bccf7 SHA512: 79508c4090d1e928f1653d56d004a021d177b2a676a68fc67a56b379033774a0b9656523acb708254f9674e52722e4ddd6bec15d2d6297a8ff17ffeac5a318d7 Homepage: https://cran.r-project.org/package=pwr2ppl Description: CRAN Package 'pwr2ppl' (Power Analyses for Common Designs (Power to the People)) Statistical power analysis for designs including t-tests, correlations, multiple regression, ANOVA, mediation, and logistic regression. Functions accompany Aberson (2019) . Package: r-cran-pwr4exp Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emmeans, r-cran-mass, r-cran-matrix, r-cran-nlme, r-cran-numderiv Suggests: r-cran-agricolae, r-cran-algdesign, r-cran-crossdes, r-cran-frf2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pwr4exp_1.0.1-1.ca2404.1_all.deb Size: 252454 MD5sum: 5728ec45a5d7409303c75f5f94d0f2d8 SHA1: a5ee4c0055d8b843af069e9c3d14216f0c1d9b0b SHA256: d03edc5f4ad0842ea300036ee9dd4675a06a6434c1764287ca9979092dda5c48 SHA512: 36cf3dbbe80f3c15a21277cf884ea11ec70c2a0d5495747d26a4c154100035d9220daa87aa05d82d8005f38ffb9fde1590fba535a8e021411c62e70c0887fe0d Homepage: https://cran.r-project.org/package=pwr4exp Description: CRAN Package 'pwr4exp' (Power Analysis for Research Experiments) Provides tools for calculating statistical power for experiments analyzed using linear mixed models. It supports standard designs, including randomized block, split-plot, and Latin Square designs, while offering flexibility to accommodate a variety of other complex study designs. Package: r-cran-pwr Architecture: all Version: 1.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-scales, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-pwr_1.3-0-1.ca2404.1_all.deb Size: 149784 MD5sum: cc1b4cf0e0e1ace891196e119c68ba6f SHA1: ae958ffdf8624f656bd21d033729345d45befe6e SHA256: 1387a693aa097f047d29663a07d9078b6f58027ac1d38995a681e29939176e6e SHA512: 82b3aca3889c80de9b8cd056c2604ce336f433742ec61f0d745ff20d38d6f7be46d3f14a8e329d1c254ccd6cf5ea435e06004885787807e3a26abe3b32029a48 Homepage: https://cran.r-project.org/package=pwr Description: CRAN Package 'pwr' (Basic Functions for Power Analysis) Power analysis functions along the lines of Cohen (1988). Package: r-cran-pwrab Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pwrab_0.1.0-1.ca2404.1_all.deb Size: 24696 MD5sum: 988ee33e2f37a3989062e19a72eab1b3 SHA1: b73a9b41282b0abfddbc4aa8b79bb01016b488f3 SHA256: 4eebc07aae9a42fb2c510a4787e1b2720881ec28d87b5ee0beb10eef772153b9 SHA512: f684bf48c62415ee5014beba6edbc64c81d78c2c80177de3e862ef5c95831aa76ca72643606a1877e3ea238bbf3038c3889f1ec91d1dab5fb86da3963fb49f8f Homepage: https://cran.r-project.org/package=pwrAB Description: CRAN Package 'pwrAB' (Power Analysis for AB Testing) Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test, which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are unequal, which is frequently the case in AB testing. In such situations, the Student's t-test will give biased results due to using the pooled standard deviation, unlike the Welch's t-test. Package: r-cran-pwranova Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-pwranova_1.0.3-1.ca2404.1_all.deb Size: 88526 MD5sum: 0c5ff084e2681da336af030ee3a1323e SHA1: bc6e9a6ae36258ce5f055369bc9930d9c0d898ee SHA256: 392481a5789ca0d97dd4c4fe89adb37da9f98c951874cff3c8fa4281483bb129 SHA512: 12d5aefed9fef5ab574a44de28a885f3236d19ef8a94a2317a4aa87a5eee0ee636781d86348564a975186233323956cd3cf53fff139577a92d293ad674c170b5 Homepage: https://cran.r-project.org/package=pwranova Description: CRAN Package 'pwranova' (Power Analysis of Flexible ANOVA Designs and Related Tests) Provides functions for conducting power analysis in ANOVA designs, including between-, within-, and mixed-factor designs, with full support for both main effects and interactions. The package allows calculation of statistical power, required total sample size, significance level, and minimal detectable effect sizes expressed as partial eta squared or Cohen's f for ANOVA terms and planned contrasts. In addition, complementary functions are included for common related tests such as t-tests and correlation tests, making the package a convenient toolkit for power analysis in experimental psychology and related fields. Package: r-cran-pwrfdr Architecture: all Version: 3.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 677 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-flextable, r-cran-ggplot2, r-cran-mvtnorm, r-cran-stringr, r-cran-tablemonster Filename: pool/dists/noble/main/r-cran-pwrfdr_3.2.4-1.ca2404.1_all.deb Size: 603570 MD5sum: d692b234c682c9c4a38332a1931858ca SHA1: f7207535701e5c5d40e06cdb58f94e21800c9b9a SHA256: a7850c840c6c57dcbd12dd9b1ecce7e6d1bda5ec9636f9d30938189ef884221f SHA512: 02816be7d1302c4f475321a3336539f4c89ed9f2e39dec5fe942e6e07b09d452349bd300ad0df9eae4f1d69a32ae1424e7c2f2b4747418cfc661c8abf8df12a6 Homepage: https://cran.r-project.org/package=pwrFDR Description: CRAN Package 'pwrFDR' (FDR Power) Computing Average and TPX Power under various BHFDR type sequential procedures. All of these procedures involve control of some summary of the distribution of the FDP, e.g. the proportion of discoveries which are false in a given experiment. The most widely known of these, the BH-FDR procedure, controls the FDR which is the mean of the FDP. A lesser known procedure, due to Lehmann and Romano, controls the FDX, or probability that the FDP exceeds a user provided threshold. This is less conservative than FWE control procedures but much more conservative than the BH-FDR proceudre. This package and the references supporting it introduce a new procedure for controlling the FDX which we call the BH-FDX procedure. This procedure iteratively identifies, given alpha and lower threshold delta, an alpha* less than alpha at which BH-FDR guarantees FDX control. This uses asymptotic approximation and is only slightly more conservative than the BH-FDR procedure. Likewise, we can think of the power in multiple testing experiments in terms of a summary of the distribution of the True Positive Proportion (TPP), the portion of tests truly non-null distributed that are called significant. The package will compute power, sample size or any other missing parameter required for power defined as (i) the mean of the TPP (average power) or (ii) the probability that the TPP exceeds a given value, lambda, (TPX power) via asymptotic approximation. All supplied theoretical results are also obtainable via simulation. The suggested approach is to narrow in on a design via the theoretical approaches and then make final adjustments/verify the results by simulation. The theoretical results are described in Izmirlian, G (2020) Statistics and Probability letters, "", and an applied paper describing the methodology with a simulation study is in preparation. See citation("pwrFDR"). 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Also included are functions to do desp tests step-by-step,exploratory path analysis, and Monte Carlo X2 probabilities. This package accompanies Shipley, B, (2026).Cause and Correlation in Biology: A User's Guide to Path Analysis, StructuralEquations and Causal Inference (3rd edition). Cambridge University Press. 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Package: r-cran-qaensemble Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-coda, r-cran-diagram, r-cran-expm, r-cran-knitr, r-cran-rmarkdown, r-cran-svmisc Filename: pool/dists/noble/main/r-cran-qaensemble_1.0.0-1.ca2404.1_all.deb Size: 242608 MD5sum: 445e4935706dd59ef48094a52bc0d564 SHA1: 875543f1ab3aa0d54f09354e6616cdaa34f3deea SHA256: 386a7e4d4e9a5c96386f0a3efd138910c518fb52b9e1c0cefe9a7b3f3689f502 SHA512: 33db5606750160c233400033f060f4fef9b85a640da0bbd6627b0ffb4f6e15267fe4714ac4acc9b6e15f0b0be6efda3bb59c36ea2e3049c3583bfa246f19461b Homepage: https://cran.r-project.org/package=QAEnsemble Description: CRAN Package 'QAEnsemble' (Ensemble Quadratic and Affine Invariant Markov Chain Monte Carlo) The Ensemble Quadratic and Affine Invariant Markov chain Monte Carlo algorithms provide an efficient way to perform Bayesian inference in difficult parameter space geometries. The Ensemble Quadratic Monte Carlo algorithm was developed by Militzer (2023) . The Ensemble Affine Invariant algorithm was developed by Goodman and Weare (2010) and it was implemented in Python by Foreman-Mackey et al (2013) . The Quadratic Monte Carlo method was shown to perform better than the Affine Invariant method in the paper by Militzer (2023) and the Quadratic Monte Carlo method is the default method used. The Chen-Shao Highest Posterior Density Estimation algorithm is used for obtaining credible intervals and the potential scale reduction factor diagnostic is used for checking the convergence of the chains. Package: r-cran-qaig Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-formula Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qaig_0.1.7-1.ca2404.1_all.deb Size: 31294 MD5sum: dfbf2c7ce1b044cd3a6d66e81c0e5778 SHA1: b6dd621a9e9b5c055f5b52eb0cfe228967caeff9 SHA256: 31dd3716f8beb816d03c1782234d7621e67306f2e7bb5f25966bbfc746d47205 SHA512: 0d5da85a6dc0df48742b2f564cd0794a06cb39d928aa9fa7ea80d7240fa6eb5f12fd9a745ba4dee1e2f59496c26047510c773edcad4a312f92d21e807794c580 Homepage: https://cran.r-project.org/package=QAIG Description: CRAN Package 'QAIG' (Automatic Item Generator for Quantitative Multiple-Choice Items) A tool for automatic generation of sibling items from a parent item model defined by the user. It is an implementation of the process automatic item generation (AIG) focused on generating quantitative multiple-choice type of items (see Embretson, Kingston (2018) ). 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Package: r-cran-qapprox Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qapprox_0.2.0-1.ca2404.1_all.deb Size: 17314 MD5sum: d07500a8580ce07b7f16f5aa3ef99c81 SHA1: 1395c86b6568c0933fb97fe60bbde9e7202be640 SHA256: f788fc314b710414ca01dc43a177e2aac754aa883d0e8fd0b7ae756b5d0919c6 SHA512: ce7b0c3ea88d63d8885c7679960e01f8f856a7c3291abfb9917a36a28eca118a641fb5ad4b97a1f75da03f4b31f723409bfcedef60dcf84f2b5a70e03b706eae Homepage: https://cran.r-project.org/package=Qapprox Description: CRAN Package 'Qapprox' (Approximation to the Survival Functions of Quadratic Forms ofGaussian Variables) Calculates the right-tail probability of quadratic forms of Gaussian variables using the skewness-kurtosis ratio matching method, modified Liu-Tang-Zhang method and Satterthwaite-Welch method. The technical details can be found in Hong Zhang, Judong Shen and Zheyang Wu (2020) . Package: r-cran-qardlr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-qardlr_1.0.1-1.ca2404.1_all.deb Size: 159544 MD5sum: 0ba5fb96363561669e58dcd74c30b308 SHA1: f0fda3f8e32246a20e2213cbfc15d5215da19717 SHA256: 76152223d8ef0bfe3af780974fd8278f58c338ee9b13708e0e2300797d2b09ff SHA512: b7d20b819d794299611778f4b5b58273b81235c5dbc200db53802a9f3faf1fd234010da15d00361bb60993ecf0a6a67c84b0c9f7a3616978370525260cf0fe4e Homepage: https://cran.r-project.org/package=qardlr Description: CRAN Package 'qardlr' (Quantile Autoregressive Distributed Lag Model) Implements the Quantile Autoregressive Distributed Lag (QARDL) model of Cho, Kim and Shin (2015) . Estimates quantile-specific long-run (beta), short-run autoregressive (phi), and impact (gamma) parameters. Features include BIC-based automatic lag selection, Error Correction Model (ECM) parameterization, Wald tests for parameter constancy across quantiles, rolling/recursive QARDL estimation, Monte Carlo simulation, and publication-ready output tables. Package: r-cran-qarpi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg Filename: pool/dists/noble/main/r-cran-qarpi_0.1.0-1.ca2404.1_all.deb Size: 109000 MD5sum: 586db0e8391aa4ba408ebd27ef2dffc5 SHA1: 056bd69faf3b3eb03869918df3e1fee2d7a4c360 SHA256: c113c26a618c36520a2019aaca89cb16c22b5b93be9e7635dfdf34c4d1e46591 SHA512: 0b42d040d4fb6a5e628b6de57eadea8aa7ffd72c904857c2375c9658d320b5a583fdfaf1ba84d40ccab852b023ba6825c9255a424e2b48d73889049f613fd5f5 Homepage: https://cran.r-project.org/package=qarPI Description: CRAN Package 'qarPI' (Prediction Intervals for Quantile Autoregression) Provides prediction intervals for classical homoscedastic autoregressive models (AR(p)) and quantile autoregressive models (QAR(p)). 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Mirrors many of the columns in mtcars, but uses (1) non-US-centric makes and models, (2) 2025 prices, and (3) metric measurements, making it more appropriate for use as an example dataset outside the United States. For more details see Musgrave (2025) . 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It supports querying and retrieving phenotypic and genotypic data from systems like 'EBS' , 'BMS' , 'BreedBase' , 'GIGWA' (using 'BrAPI' calls), , and 'Germinate' . Extra helper functions support environmental data sources, including 'TerraClimate' and 'FAO' 'HWSDv2' soil database. Package: r-cran-qboxplot Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qboxplot_0.2-1.ca2404.1_all.deb Size: 19772 MD5sum: 0bae16e90f15c6a0a4db53bf561898a0 SHA1: 0bba12aeeaccb82a9da7792f475bae0afbd2b0f6 SHA256: a726df89150e28f41efcede091a45fdb1cc5e62ab05051d849fc130f00191e5a SHA512: 8b5f3672840ff83a0a7816a948a39d6c041ed6b81e0440097be3e4e8b04e63b16369c7e27644f29cc47c1944b075dc610cf0710686c6ac7270ffb5b64f1e227a Homepage: https://cran.r-project.org/package=qboxplot Description: CRAN Package 'qboxplot' (Quantile-Based Boxplot) Produce quantile-based box-and-whisker plot(s). 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Package: r-cran-qcacluster Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-plyr, r-cran-qca, r-cran-testit, r-cran-purrr, r-cran-upsetr, r-cran-magrittr, r-cran-stringi, r-cran-rlist Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-qcacluster_0.1.0-1.ca2404.1_all.deb Size: 132836 MD5sum: c88c8e31696ec503ac2e2920ea25bdf6 SHA1: 612fde9d472a6df780473b79ee5362bc66c6d736 SHA256: 107aebf26c82cfe4236219bc4ed2e5c6815dcb230c6fa25e865a429ad7371567 SHA512: ec84e298cb5486ee7cc5527f050754f8d35c35a18b11ff728c40490384e80dd9e6790888638533d68cdbf2f600836e53c5de3d6693708a81c5e7d997ff0fa633 Homepage: https://cran.r-project.org/package=QCAcluster Description: CRAN Package 'QCAcluster' (Tools for the Analysis of Clustered Data in QCA) Clustered set-relational data in Qualitative Comparative Analysis (QCA) can have a hierarchical structure, a panel structure or repeated cross sections. 'QCAcluster' allows QCA researchers to supplement the analysis of pooled the data with a disaggregated perspective focusing on selected partitions of the data. The pooled data can be partitioned along the dimensions of the clustered data (individual cross sections or time series) to perform partition-specific truth table minimizations. Empirical researchers can further calculate the weight that each partition has on the parameters of the pooled solution and the diversity of the cases under analysis within and across partitions (see ). Package: r-cran-qcapower Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 774 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggforce Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qcapower_0.2.0-1.ca2404.1_all.deb Size: 285266 MD5sum: 367405ea907801e571cf16ea1401caa5 SHA1: 30dd767508a72caebafa92f2580d0e732f99c065 SHA256: e532dabac048af679f918a8d469397dc17f03f3147c96cd17601434ec85d335f SHA512: 9c69c5776de2e44a45ae3ce6a2cca293d3ae2fbb590714e2ffc8d4fc62492d057a87e6f1463c179a2d171fecb78412c2be0134830fe66887646da381471a8ec2 Homepage: https://cran.r-project.org/package=qcapower Description: CRAN Package 'qcapower' (Estimate Power and Required Sample Size in QCA) Researchers working with Qualitative Comparative Analysis (QCA) can use the package to estimate power of a sufficient term using permutation tests. 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Package: r-cran-qcauchyreg Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg Filename: pool/dists/noble/main/r-cran-qcauchyreg_1.0-1.ca2404.1_all.deb Size: 99634 MD5sum: 376e666fa0e12c40ff5bfe99e3dc354b SHA1: 3a2ccb107af6219c6eaf60beae2d40e79ca481c5 SHA256: 12d54a5f69ea8e9c5cd7a751e474519a9d21549b3079899decb2c3f35ab6961e SHA512: fb81fca59142a730191b6145e1d8da681a854258618f669154bbf3790db528f5beea91e370de329645af3ee519433ba0cd6c68fd8e864fb894f9dc5e1c589880 Homepage: https://cran.r-project.org/package=qcauchyreg Description: CRAN Package 'qcauchyreg' (Quantile Regression Quasi-Cauchy) Quasi-Cauchy quantile regression, proposed by de Oliveira, Ospina, Leiva, Figueroa-Zuniga and Castro (2023) . This regression model is useful for the case where you want to model data of a nature limited to the intervals [0,1], (0,1], [0,1) or (0,1) and you want to use a quantile approach. 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QCBA postprocesses rule classification models making them typically smaller and in some cases more accurate. Supported are 'CBA' implementations from 'rCBA', 'arulesCBA' and 'arc' packages, and 'CPAR', 'CMAR', 'FOIL2' and 'PRM' implementations from 'arulesCBA' package and 'SBRL' implementation from the 'sbrl' package. The result of the post-processing is an ordered CBA-like rule list. 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Package: r-cran-qcewas Architecture: all Version: 1.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qcewas_1.2-3-1.ca2404.1_all.deb Size: 385944 MD5sum: cdebf34523e3f20ff9a387540c03b6aa SHA1: 5756593e39bf6e71189daf5fd605b3a64d37e95f SHA256: 6ae7f8dcf0737a6ff0cdcb434344681f73321d7906252d59502eef99bfc17832 SHA512: 32126f0b4fb1e9b6f6a44d3caa2d6f3bcdfe369c376eee589ff37f99575e66c925191b69b3c5af8c99269b58f22b578bff0da7849d2d5fda2c87099a5861d11b Homepage: https://cran.r-project.org/package=QCEWAS Description: CRAN Package 'QCEWAS' (Fast and Easy Quality Control of EWAS Results Files) Tools for (automated and manual) quality control of the results of Epigenome-Wide Association Studies. 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Package: r-cran-qcpm Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-csem, r-cran-broom Filename: pool/dists/noble/main/r-cran-qcpm_0.4-1.ca2404.1_all.deb Size: 112988 MD5sum: dcc2be6411af37ab4a679f325dfe5ca5 SHA1: f554f05e89769fef6c2e2bef05528d85e3af4453 SHA256: f6bec9fa50e62158e8530ae41c84cc308e3806ef02ee5097d7bcd902edb1f9c2 SHA512: e621ff64f8946c4ca22564b442da1685cf0046bfd8ec27f5f6d02523064426d541bc3179efb7e2fdf7b79286a976e869f5131c1e00b90a1dfc9d02a3e955fcc4 Homepage: https://cran.r-project.org/package=qcpm Description: CRAN Package 'qcpm' (Quantile Composite Path Modeling) Implements the Quantile Composite-based Path Modeling approach (Davino and Vinzi, 2016 ; Dolce et al., 2021 ). The method complements the traditional PLS Path Modeling approach, analyzing the entire distribution of outcome variables and, therefore, overcoming the classical exploration of only average effects. It exploits quantile regression to investigate changes in the relationships among constructs and between constructs and observed variables. Package: r-cran-qcqpcr Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-qcqpcr_1.5-1.ca2404.1_all.deb Size: 25098 MD5sum: 5bc85a1e3edf1740b072d2ac1251015d SHA1: 5582c8cff8472e564cd7475f770ecd5a6ea06322 SHA256: e46a794f41925503eb43dfc2012ead3e4df9275bda13f6cde7c534d54c644b1f SHA512: 025088bbe069d3f28edce717b69fcd26ee4172c517011a80c47fa976b6d7b1a5e77bf1b1c42d3fb673969ff8f3ade4a18aaf27f05d587d49a9c76197d48f368c Homepage: https://cran.r-project.org/package=qcQpcr Description: CRAN Package 'qcQpcr' (Histone ChIP-Seq qPCR Analyzer) Quality control of chromatin immunoprecipitation libraries (ChIP-seq) by quantitative polymerase chain reaction (qPCR). This function calculates Enrichment value with respect to reference for each histone modification (specific to 'Vii7' software ). This function is applicable to full panel of histone modifications described by International Human Epigenomic Consortium (IHEC). Package: r-cran-qcr Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qcc, r-cran-fda.usc, r-cran-mvtnorm, r-cran-mass Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-qcr_1.4-1.ca2404.1_all.deb Size: 393188 MD5sum: 31cf33e92ee9966d7962dc2411f5d7e3 SHA1: 4cfdaeba819db721a32776d3a2e02e5df04d7225 SHA256: 62471b0f120efa8e01ced80d6718fdbb317dd4004c057a46ef6c52c777f462b8 SHA512: 1556bebe01b95803d6cb18b6df23c85700b7aa1884c041e3a2c44012e58ed413b421f1c0c143326795a4f0faf05d13943e6afb145b1f3c70457aaab7b1c548a7 Homepage: https://cran.r-project.org/package=qcr Description: CRAN Package 'qcr' (Quality Control Review) Univariate and multivariate SQC tools that completes and increases the SQC techniques available in R. Apart from integrating different R packages devoted to SQC ('qcc','MSQC'), provides nonparametric tools that are highly useful when Gaussian assumption is not met. This package computes standard univariate control charts for individual measurements, 'X-bar', 'S', 'R', 'p', 'np', 'c', 'u', 'EWMA' and 'CUSUM'. In addition, it includes functions to perform multivariate control charts such as 'Hotelling T2', 'MEWMA' and 'MCUSUM'. As representative feature, multivariate nonparametric alternatives based on data depth are implemented in this package: 'r', 'Q' and 'S' control charts. In addition, Phase I and II control charts for functional data are included. This package also allows the estimation of the most complete set of capability indices from first to fourth generation, covering the nonparametric alternatives, and performing the corresponding capability analysis graphical outputs, including the process capability plots. See Flores et al. (2021) . Package: r-cran-qcrlscr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2901 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qcrlscr_0.1.3-1.ca2404.1_all.deb Size: 2863266 MD5sum: 4a02561853016d6fb664033bc2fb5842 SHA1: c9fde01964ffb0f0c5e7b611a01ce32b29b49694 SHA256: d4e405cb377c1c2a0ad7a16b5e1bdbedf18d2e42b11e23886ffbc49d2d197faf SHA512: 6906d40722725d1b8dce57a543f0e84f068741ef8da19262a0733ced0b075ad357297ff85f9eaad4399ec432e75399244f87d0f5f808845f4c4d5af85597466b Homepage: https://cran.r-project.org/package=qcrlscR Description: CRAN Package 'qcrlscR' (Quality Control–based Robust LOESS Signal Correction) An R implementation of quality control–based robust LOESS(local polynomial regression fitting) signal correction for metabolomics data analysis, described in Dunn, W., Broadhurst, D., Begley, P. et al. (2011) . The optimisation of LOESS's span parameter using generalized cross-validation (GCV) is provided as an option. In addition to signal correction, 'qcrlscR' includes some utility functions like batch shifting and data filtering. 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Package: r-cran-qcsis Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qcsis_0.1-1.ca2404.1_all.deb Size: 24768 MD5sum: 7b36fcccf4681478f5c8ba6271ecf041 SHA1: 505c0366b9443d54e8824d471f105c88a58d3e4a SHA256: b882ce5f6d5c051a57a84374a24c88b0b9d35dc68c628691386e1659f88cd66f SHA512: 3033dfa9482a576558e3a44dc3553356c92fbc5770ca7453cac5da3dd47f2fcc7e67506e73e1e9b79cb0e45aea7a866f02a7f58eefe188d1f2f23341bb38e193 Homepage: https://cran.r-project.org/package=QCSIS Description: CRAN Package 'QCSIS' (Sure Independence Screening via Quantile Correlation andComposite Quantile Correlation) Quantile correlation-sure independence screening (QC-SIS) and composite quantile correlation-sure independence screening (CQC-SIS) for ultrahigh-dimensional data. Package: r-cran-qcv Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qcv_1.0-1.ca2404.1_all.deb Size: 33632 MD5sum: 620aa9fddeca28ea4e2e9fee18e40334 SHA1: ac9026c8d665e2b6ed652bce572837a667599e8b SHA256: 9d4f07ad791cf19e56739d968bf1ce8e19a10d01ddf782f7975859202a084fde SHA512: f0699c2188b63fffe75065f3ebf1e877416d808fd4eb3ec2c1a6a3a7e99347c38bfdad023ade49df12a286564ed56dc5c45b4ebdc5971bcd27f85fa2f477f271 Homepage: https://cran.r-project.org/package=qcv Description: CRAN Package 'qcv' (Quantifying Construct Validity) Primarily, the 'qcv' package computes key indices related to the Quantifying Construct Validity procedure (QCV; Westen & Rosenthal, 2003 ; see also Furr & Heuckeroth, in press). 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The package provides parsing tools for preparing transcript data. Many functions enable the user to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R packages that undertake higher level analysis and visualization of text. This affords the user a more efficient and targeted analysis. 'qdap' is designed for transcript analysis, however, many functions are applicable to other areas of Text Mining/ Natural Language Processing. Package: r-cran-qdapdictionaries Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2201 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qdapdictionaries_1.0.7-1.ca2404.1_all.deb Size: 2153384 MD5sum: 431ac5b6c6210f66373d162418288933 SHA1: 85d42ac176a203cf78a9353d5ebcbf5c0f85e8fb SHA256: bbff9b128af3d2c4a90d87beee3ec0044349034167c128107fef97916a7907bc SHA512: b2ff2f8e971542df684669b55d32f3fcf884bd65873b37e4160ba10aa9fe74a1e54aaaf788940498c1d955b86c3fff7a66b5d8c67c95fac54a29f922e8c0fe2d Homepage: https://cran.r-project.org/package=qdapDictionaries Description: CRAN Package 'qdapDictionaries' (Dictionaries and Word Lists for the 'qdap' Package) A collection of text analysis dictionaries and word lists for use with the 'qdap' package. 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Tools include removal/extraction/replacement of abbreviations, dates, dollar amounts, email addresses, hash tags, numbers, percentages, citations, person tags, phone numbers, times, and zip codes. 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Package: r-cran-qdcomparison Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qdcomparison_3.0-1.ca2404.1_all.deb Size: 65054 MD5sum: b972a91b7f7990724b55da157d60e087 SHA1: 236823004da12e6b40ae6ae5861a391c978dd88f SHA256: 51809daf370531013cd058deaa42760a460a318ff02fb55e52696512df32e256 SHA512: ce6f316ade36858f5f3560c0c168dbbf8a6d202c07b4655b9caeefe0a8f4fe281aaa473a9892526ed6861875783b13bd8e2fc5246204d919240097fb7fa75d07 Homepage: https://cran.r-project.org/package=QDComparison Description: CRAN Package 'QDComparison' (Modern Nonparametric Tools for Two-Sample Quantile andDistribution Comparisons) Allows practitioners to determine (i) if two univariate distributions (which can be continuous, discrete, or even mixed) are equal, (ii) how two distributions differ (shape differences, e.g., location, scale, etc.), and (iii) where two distributions differ (at which quantiles), all using nonparametric LP statistics. The primary reference is Jungreis, D. (2019, Technical Report). Package: r-cran-qdea Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-doby, r-cran-highs, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qdea_1.0.0-1.ca2404.1_all.deb Size: 201412 MD5sum: 37456bc67be733361ce5aa543a473547 SHA1: 40cf73f4eff5036c4fe20a421ad013b9d7e5487d SHA256: c2f025cf76c0dde4d7c9b4def98a8f5a5aac53568e6a65a6089b31656d0cbdac SHA512: 4a87483b9b33f54771f4da11aac89ffaa7a95919a7e65adcaa3031d72464d0a9091785a106ab5f1748f73318b0de380b2132e56b2d43b8d1d6752639e5f2acdb Homepage: https://cran.r-project.org/package=qDEA Description: CRAN Package 'qDEA' (Quantile Data Envelopment Analysis) R implementation of Quantile Data Envelopment Analysis. The package 'qDEA' allows a user specified proportion of observations to lie external to a given Decision Making Units's (DMU's)reference hyperplane. 'qDEA' can be used to detect and address influential outliers or to implement quantile benchmarking, as discussed in Atwood and Shaik (2020). Quantile benchmarking is accomplished by using heuristic procedures to find a DMU's closest input-output projection point in a specified direction while allowing a specified proportion of observations to lie external to the projected point's hyperplane. The 'qDEA' package accommodates standard (DEA) and quantile DEA estimation, returns to scale CRS(constant),VRS(variable),DRS(decreasing) or IRS(increasing), the use of directional vectors, bias correction through subsample bootstrapping and subsample size selection procedures. The user can also recover each DMU's reference DMUs and external DMUs if desired. The implemented procedures are based on discussions in: Atwood and Shaik (2020) Atwood and Shaik (2018) Walden and Atwood (2023) Walden and Atwood (2025) . Package: r-cran-qdiabetes Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4467 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-qdiabetes_1.0-2-1.ca2404.1_all.deb Size: 4465088 MD5sum: d73f46d31ec5bb5607157892360d3e30 SHA1: a254a3002745bef06f987d24757b00e35e126b51 SHA256: f23ba94b285735a982462ee5c2b1948df9148a11d20aa5aa624c78a5dec3193b SHA512: d25bb42f433fc052267ef312f02701fe7eab4465a8a9def44c14fa454be42aebf5d33fa1e6252afb198f0bfc41f9472a3088191a088fda7e0fca0fb00620a25f Homepage: https://cran.r-project.org/package=QDiabetes Description: CRAN Package 'QDiabetes' (Type 2 Diabetes Risk Calculator) Calculate the risk of developing type 2 diabetes using risk prediction algorithms derived by 'ClinRisk'. Package: r-cran-qdm Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 796 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qdm_0.1-0-1.ca2404.1_all.deb Size: 781468 MD5sum: b73ff8d89b978e1f43d817800a4f52cf SHA1: b62d33b7dae40e236d3469273ffbc9cd19687393 SHA256: 0e55a3690c21e0fc96bb7fa38b6ee9239e22112d2322a5d68974145bef211a41 SHA512: f3f217ccabfe12bb48a55b25f2996cd71b7eb1931a1c434c2796568ee3622c8a1e9b6d4b19bfcfd2d4ec474814e46c6ac1e71282d5155d7958f872a0583bbc55 Homepage: https://cran.r-project.org/package=qdm Description: CRAN Package 'qdm' (Fitting a Quadrilateral Dissimilarity Model to Same-DifferentJudgments) This package provides different specifications of a Quadrilateral Dissimilarity Model which can be used to fit same-different judgments in order to get a predicted matrix that satisfies regular minimality [Colonius & Dzhafarov, 2006, Measurement and representations of sensations, Erlbaum]. From such a matrix, Fechnerian distances can be computed. Package: r-cran-qeml Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3783 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-regtools, r-cran-gtools, r-cran-rmarkdown, r-cran-tufte, r-cran-grf, r-cran-gbm, r-cran-toweranna, r-cran-tm, r-cran-rpart, r-cran-rpart.plot, r-cran-partools, r-cran-foci Suggests: r-cran-knitr, r-cran-partykit, r-cran-randomforest, r-cran-ranger, r-cran-e1071, r-cran-jousboost, r-cran-lightgbm, r-cran-keras, r-cran-neuralnet, r-cran-polyreg, r-cran-glmnet, r-cran-umap, r-cran-reticulate, r-cran-party, r-cran-proc, r-cran-xgboost, r-cran-rocr, r-cran-autoimage, r-cran-deepnet, r-cran-ncvreg, r-cran-uwot, r-cran-cdparcoord Filename: pool/dists/noble/main/r-cran-qeml_1.1-1.ca2404.1_all.deb Size: 1746884 MD5sum: 374d1476b35c2ba26b925635aa496219 SHA1: 2511842e55a12317ac4f53fb948705d48ec15170 SHA256: 999bb5f1f9cc45a10d86f11de6744c865b613389b230a045abe237884a1b8434 SHA512: 9db755d97b9fdacf5505b612177950e465b620067403bd871017e9ba234be94bff754afe973e1b5bbb9c2b0dd5dbb0e3912e2222292f061b79cb7337cb401116 Homepage: https://cran.r-project.org/package=qeML Description: CRAN Package 'qeML' (Quick and Easy Machine Learning Tools) The letters 'qe' in the package title stand for "quick and easy," alluding to the convenience goal of the package. We bring together a variety of machine learning (ML) tools from standard R packages, providing wrappers with a simple, convenient, and uniform interface. Package: r-cran-qfasar Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsolnp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qfasar_1.2.1-1.ca2404.1_all.deb Size: 284022 MD5sum: f7b2b6feb1280498d7b93e9629519b42 SHA1: 260208fa051bcb25a22839400df531da16df3338 SHA256: eb7cecb45bada9f819205dfb80f2acca6989b46a37ba33324446338b490624fe SHA512: ec8bd102faa99eea75c3e22b65dee29c7aac6946ea4d288e56cc0655de801a5e7c157759d4e6995fa86a57711f85dee0d1746a3a6c1331f788c34c9f47b3feda Homepage: https://cran.r-project.org/package=qfasar Description: CRAN Package 'qfasar' (Quantitative Fatty Acid Signature Analysis in R) An implementation of Quantitative Fatty Acid Signature Analysis (QFASA) in R. QFASA is a method of estimating the diet composition of predators. The fundamental unit of information in QFASA is a fatty acid signature (signature), which is a vector of proportions describing the composition of fatty acids within lipids. Signature data from at least one predator and from samples of all potential prey types are required. Calibration coefficients, which adjust for the differential metabolism of individual fatty acids by predators, are also required. Given those data inputs, a predator signature is modeled as a mixture of prey signatures and its diet estimate is obtained as the mixture that minimizes a measure of distance between the observed and modeled signatures. A variety of estimation options and simulation capabilities are implemented. Please refer to the vignette for additional details and references. Package: r-cran-qfrm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qfrm_1.0.1-1.ca2404.1_all.deb Size: 253958 MD5sum: 8bd20b5a9669a91a8df48940ca917797 SHA1: 0962532c4365fcff017a7f7b96347b67625cf5e2 SHA256: 995d05bb4e3dfb079a5fbc815db9b7036c8b371225fade103c037c333b9c00dd SHA512: f203f48d7276290f96f35e1f41459b8519a1f07a2c413c16bebb87656245f1f71651ff402a1fe5dc78477bebe7829f0d4089ea5bf1be57d7c4648ff10494ca8d Homepage: https://cran.r-project.org/package=QFRM Description: CRAN Package 'QFRM' (Pricing of Vanilla and Exotic Option Contracts) Option pricing (financial derivatives) techniques mainly following textbook 'Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc. This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015. Package: r-cran-qga Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1375 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-qga_1.0-1.ca2404.1_all.deb Size: 445868 MD5sum: 305ed09a1cef1b0aa3a35ebab7f29de6 SHA1: bdd9b536261707f19a821230b4e572727a47b78b SHA256: 699fa26babc0de797d5b0a002e4954ac9502f44e58505de9f5649d65cefaaf62 SHA512: 9947af8c57819390a6b257ca3a6a3247f0cb042cfab0411411d1ad0bd17b3c40230c050061e38365d2df6bfb01468dd65b1199ac5de63af3f538dc76171f8f2a Homepage: https://cran.r-project.org/package=QGA Description: CRAN Package 'QGA' (Quantum Genetic Algorithm) Function that implements the Quantum Genetic Algorithm, first proposed by Han and Kim in 2000. This is an R implementation of the 'python' application developed by Lahoz-Beltra (). Each optimization problem is represented as a maximization one, where each solution is a sequence of (qu)bits. Following the quantum paradigm, these qubits are in a superposition state: when measuring them, they collapse in a 0 or 1 state. After measurement, the fitness of the solution is calculated as in usual genetic algorithms. The evolution at each iteration is oriented by the application of two quantum gates to the amplitudes of the qubits: (1) a rotation gate (always); (2) a Pauli-X gate (optionally). The rotation is based on the theta angle values: higher values allow a quicker evolution, and lower values avoid local maxima. The Pauli-X gate is equivalent to the classical mutation operator and determines the swap between alfa and beta amplitudes of a given qubit. The package has been developed in such a way as to permit a complete separation between the engine, and the particular problem subject to combinatorial optimization. Package: r-cran-qgametheory Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-r.utils Filename: pool/dists/noble/main/r-cran-qgametheory_0.1.2-1.ca2404.1_all.deb Size: 156406 MD5sum: 8e3e80fd302a3f7c3b784dabc8804499 SHA1: a63aa05b30c30c79b824d93d292f3f3c609e2d3c SHA256: a44967c993a0b5163dd4bb9b3725c855bf09fc397608cf6a039bf03677daf2bc SHA512: 1dd0d0c3e0e51860a664dd7c9abce32c4c27389925fbee6c4a8620529b79dc9e115e5099427f310d02dfe217c309edcbb2a6ff024ea44e6fd2387fad91ed24aa Homepage: https://cran.r-project.org/package=QGameTheory Description: CRAN Package 'QGameTheory' (Quantum Game Theory Simulator) General purpose toolbox for simulating quantum versions of game theoretic models (Flitney and Abbott 2002) . Quantum (Nielsen and Chuang 2010, ISBN:978-1-107-00217-3) versions of models that have been handled are: Penny Flip Game (David A. Meyer 1998) , Prisoner's Dilemma (J. Orlin Grabbe 2005) , Two Person Duel (Flitney and Abbott 2004) , Battle of the Sexes (Nawaz and Toor 2004) , Hawk and Dove Game (Nawaz and Toor 2010) , Newcomb's Paradox (Piotrowski and Sladkowski 2002) and Monty Hall Problem (Flitney and Abbott 2002) . Package: r-cran-qgarch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-qgarch_0.1.0-1.ca2404.1_all.deb Size: 104626 MD5sum: d3431c7b70540599c60828761dc31acc SHA1: 29068934089f013b9f035d23b135c0b519c09a09 SHA256: c852fb5ee895aa5d13c1a40e30f12a89bd0d9a44934ca2e33e0e6db49ef82f5d SHA512: a23bd3ad153c5631b08947eb134eaf52e17a9b64b7c3a014e3e828dd7449dd44b71dff07a2d1a3c312921bd2c5d68f434b18dd3572eddbf921eb8777c830019e Homepage: https://cran.r-project.org/package=qgarch Description: CRAN Package 'qgarch' (Quadratic GARCH-in-Mean Models for Volatility Feedback) Fits quadratic generalized autoregressive conditional heteroskedasticity-in-mean (QGARCH-M) models motivated by Campbell and Hentschel (1992). 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Package: r-cran-qgcomp Architecture: all Version: 2.18.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1876 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aer, r-cran-arm, r-cran-future, r-cran-future.apply, r-cran-generics, r-cran-ggplot2, r-cran-gridextra, r-cran-nnet, r-cran-numderiv, r-cran-pscl, r-cran-rootsolve, r-cran-survival, r-cran-tibble Suggests: r-cran-broom, r-cran-devtools, r-cran-knitr, r-cran-markdown, r-cran-mass, r-cran-mice Filename: pool/dists/noble/main/r-cran-qgcomp_2.18.10-1.ca2404.1_all.deb Size: 1327198 MD5sum: 9294e56974fcc472d16c20e439d3401b SHA1: f408d6fdd2d8cf6b8149b52a8d573995893ae995 SHA256: 5293a2b92e1262ef61bd6ca0303027d8ed572e6b90698fcf2fc1ebc8f9ab2569 SHA512: 99d919c1404c22b9845245d6594d509f7bf022bca3914995feffae825dc0c48a157adb38aaa3aa434dd8c11cf85cd33ea09b32a540adf2b21629ba5909fd5dfc Homepage: https://cran.r-project.org/package=qgcomp Description: CRAN Package 'qgcomp' (Quantile G-Computation) G-computation for a set of time-fixed exposures with quantile-based basis functions, possibly under linearity and homogeneity assumptions. This approach estimates a regression line corresponding to the expected change in the outcome (on the link basis) given a simultaneous increase in the quantile-based category for all exposures. Works with continuous, binary, and right-censored time-to-event outcomes. Reference: Alexander P. Keil, Jessie P. Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and Alexandra J. White (2019) A quantile-based g-computation approach to addressing the effects of exposure mixtures; . 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Effect measure modification in this method is a way to assess how the effect of the mixture varies by a binary, categorical or continuous variable. Reference: Alexander P. Keil, Jessie P. Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and Alexandra J. White (2019) A quantile-based g-computation approach to addressing the effects of exposure mixtures; . Package: r-cran-qgglmm Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature Filename: pool/dists/noble/main/r-cran-qgglmm_0.8.0-1.ca2404.1_all.deb Size: 467152 MD5sum: 7cef125673c3466fd50ec9c9645aad74 SHA1: 0e01473d1aa0325074b4e92d2a53239d1d682b35 SHA256: a4bb11086db47bb9f2228d968a2675f2723e109dcce6ac58c8a80b2937229ee0 SHA512: cbf0ee7874866801bb729e788fc6af2b83689fc0978d12841bb1a2669b5ca8fd186d0bb8ffc6182e8cef24d9b89afc78e95d61eda94d0851da0717b2ed03c9be Homepage: https://cran.r-project.org/package=QGglmm Description: CRAN Package 'QGglmm' (Estimate Quantitative Genetics Parameters from GeneralisedLinear Mixed Models) Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance. Package: r-cran-qgisprocess Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4631 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-glue, r-cran-jsonlite, r-cran-processx, r-cran-rappdirs, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-vctrs, r-cran-withr Suggests: r-cran-dplyr, r-cran-knitr, r-cran-mapview, r-cran-raster, r-cran-rmarkdown, r-cran-rprojroot, r-cran-sf, r-cran-stars, r-cran-stringi, r-cran-terra, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-qgisprocess_0.4.2-1.ca2404.1_all.deb Size: 2612538 MD5sum: 25fed1952679488953a27999bb192069 SHA1: 3b3cb86ea2ac5a6d14bb2dc13e8f9c13b64b6a1f SHA256: f8e01da9528739a305eb4c5ac19b08cc671e11bbd60f4bf3958000f746d63c26 SHA512: 3379e8a236ad0e6b27c00e94a06574ef61f315cba05e99c3d4458dad1ee75953bfad89e24599478d766a8271798c28a127730e48d9b524ea4dfa0d26ea203133 Homepage: https://cran.r-project.org/package=qgisprocess Description: CRAN Package 'qgisprocess' (Use 'QGIS' Processing Algorithms) Provides seamless access to the 'QGIS' () processing toolbox using the standalone 'qgis_process' command-line utility. 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Package: r-cran-qgshiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 802 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qgshiny_0.1.0-1.ca2404.1_all.deb Size: 240346 MD5sum: c7dc657f9799bd6ae2ca6bc8c9d197f3 SHA1: 928f06c8e1f55a77222bb3991b3dd4047edda279 SHA256: 8c6fc606a98538ec4180e467e647f3b033a6d0ee8bd0e95468575bf54a353c8c SHA512: 825438587a8eb125d5f2f98acb4d48116a84093f989bc9bec0f15932d6df387fcbbbf03aa5ff94cd7bbc700d1c8d305f09c4f35e32131fb2d7fe828edb8ef0a6 Homepage: https://cran.r-project.org/package=qgshiny Description: CRAN Package 'qgshiny' (A 'shiny' Application for Active Learning Instruction inIntroductory Quantitative Genetics) A 'shiny' application for teaching introductory quantitative genetics and plant breeding through interactive simulations. The application relies on established plant breeding and quantitative genetic theory found in Falconer and Mackay (1996, ISBN:0582243025) and Bernardo (2010, ISBN:978-0972072427). Package: r-cran-qhot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-qhot_0.1.0-1.ca2404.1_all.deb Size: 31888 MD5sum: ae8a50a9e8575e86f98b92d3cdff20b9 SHA1: 76e1943002a527aa80b71e2784bbe4f5b8a0766d SHA256: 2c877b6629a634dbb14987a60a1a28a03c5c76a42650f34313d485dbb947033b SHA512: 226f7befb4795de40eaae2151f01ca4cfb541273e17ca51a486a8c5288d7447d9a7f8192e53e21dba9a1b8e743c77c02acbfbc1cfe3b713fd2388fcd9f442409 Homepage: https://cran.r-project.org/package=QHOT Description: CRAN Package 'QHOT' (QTL Hotspot Detection) This function produces both the numerical and graphical summaries of the QTL hotspot detection in the genomes that are available on the worldwide web including the flanking markers of QTLs. 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References: Pavía and Lledó (2022) . Pavía and Lledó (2023) . Pavía and Lledó (2025) . Acknowledgements: The authors wish to thank Conselleria de Educación, Universidades y Empleo, Generalitat Valenciana (grants AICO/2021/257; CIAICO/2024/031), Ministerio de Ciencia e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el calculo de productos de vida') for supporting this research. 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The package includes datasets for users who wish to view the most recently uploaded quality scores. It also provides tools to automatically gather relevant financials and stock price information, allowing users to update their data and customize their universe for further analysis. Package: r-cran-qmrparser Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 957 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-qmrparser_0.1.6-1.ca2404.1_all.deb Size: 637314 MD5sum: bd861f25a40baf21a79edb4eaa5746bd SHA1: 4936f66f8eaa01d765eb32924196ae0c51d958d4 SHA256: 563ff65fdf415d4575d5c938996a3f8fe3c7538a06bdc8d3f3cc426b3fc9ddc0 SHA512: 9c3ddc0b3ad425b8eea6e7f5a03f62dd8f57b4551171cd4580a4b1d81071f2b9fadd88ebfaad801d08f542af58327bb8c788a8951aa635aecaf0bfbb94bc1f56 Homepage: https://cran.r-project.org/package=qmrparser Description: CRAN Package 'qmrparser' (Parser Combinator in R) Basic functions for building parsers, with an application to PC-AXIS format files. Package: r-cran-qol Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1670 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-collapse, r-cran-openxlsx2, r-cran-fst Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-qol_1.3.1-1.ca2404.1_all.deb Size: 1278974 MD5sum: 22b485a0f62b5ff87632b1481d6db115 SHA1: 3fa63963eac062632c24481943484ed4ab615a2f SHA256: 84c589c9f1d06cbcd542f4b1a1cc4959d6c90410e3be6fec88e4be8481bf82a1 SHA512: ff04c54d1ff20bfc42cded8e5b0e7b97de378f7381d3c7a9e99cdc0c827fa1efb5f5b39a0c064afc91d2e6211658af02d889971bd4494156a37ad37524017db5 Homepage: https://cran.r-project.org/package=qol Description: CRAN Package 'qol' (Powerful 'SAS' Inspired Concepts for more Efficient BiggerOutputs) The main goal is to make descriptive evaluations easier to create bigger and more complex outputs in less time with less code. Introducing format containers with multilabels , a more powerful summarise which is capable to output every possible combination of the provided grouping variables in one go , tabulation functions which can create any table in different styles and other more readable functions. The code is optimized to work fast even with datasets of over a million observations. Package: r-cran-qolmiss Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-dplyr, r-cran-missmethods Filename: pool/dists/noble/main/r-cran-qolmiss_0.1.0-1.ca2404.1_all.deb Size: 139048 MD5sum: e4cf8bf4b35042adb60468830e8013c7 SHA1: 68cfb3df56ffac6e60e14d26edf5f7fef0ba6ab3 SHA256: cac01666b30eec763a8eb1b183e7fcedc87b7eedf5628bc1451f0eedf3e6b245 SHA512: 5f0600a1e00a1765aeed6d36f26c6cab8ffac426d1e50ddc76e5a571b3956ac077c1088a9e7a760e44c89750dac7ffb32dff8592adae0068e7e40f10f2c78b48 Homepage: https://cran.r-project.org/package=QoLMiss Description: CRAN Package 'QoLMiss' (Scales Score Calculation from Quality of Life Data) There are three functions: qol, miss_qol and miss_patient takes input of the data set containing the answers of QOL questionnaire. It will compute the three types of domain based scale scores: Global, Functional, and Symptoms. In case of missing data, the miss_qol and miss_patient functions will make the required changes and then calculate the domain-wise scale scores. Finally, provide an output replacing the question columns with the domain-based scale scores in the original data set. 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For the analysis of count data an implementation of the Closure Principle Computational Approach Test ("CPCAT") is provided (Lehmann, R et al. (2016) ), as well as an implementation of a "Dunnett GLM" approach using a Quasi-Poisson regression (Hothorn, L, Kluxen, F (2020) ). For the analysis of quantal data an implementation of the Closure Principle Fisher–Freeman–Halton test ("CPFISH") is provided (Lehmann, R et al. (2018) ). P-values and no/lowest observed (adverse) effect concentration values are calculated. All implemented methods include further functions to evaluate the power and the minimum detectable difference using a bootstrapping approach. Package: r-cran-qpcrhelper Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rstatix, r-cran-ggpubr, r-cran-ggplot2, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qpcrhelper_0.1.0-1.ca2404.1_all.deb Size: 22140 MD5sum: 4710f5d15b69e38d5d121a6285df9db1 SHA1: 1d917e4e71e0eddd3c27cdd37c3a1b2e939c562e SHA256: e7ca7bea665de485e169a20ff2d8cf934ed563a998956a75708ad23c00a6901a SHA512: 7916ef58bf614d4025fd76fcd50b64ff49355dc922e8ade9a0581a85b19971f42822bfed32e02d72687f21d08eae668db268388e7b7594e3ef7b75bde8e15f81 Homepage: https://cran.r-project.org/package=qPCRhelper Description: CRAN Package 'qPCRhelper' (qPCR Ct Values to Expression Values) Computes normalized cycle threshold (Ct) values (delta Ct) from raw quantitative polymerase chain reaction (qPCR) Ct values and conducts test of significance using t.test(). Plots expression values based from log2(2^(-1*delta delta Ct)) across groups per gene of interest. Methods for calculation of delta delta Ct and relative expression (2^(-1*delta delta Ct)) values are described in: Livak & Schmittgen, (2001) . 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Implements an extended version of the 'PennCNV' signal standardization method by Wang et al. (2007) for higher ploidy levels. Computes B-allele frequencies (BAF), z-scores, and identifies copy number variation patterns. Package: r-cran-qpnca Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1791 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-ggplot2, r-cran-knitr Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qpnca_1.1.6-1.ca2404.1_all.deb Size: 885852 MD5sum: bbd6aba896f70a3f94919fcb74982182 SHA1: 29a6165977f5f6bc8307c42f5e51741fa15e486c SHA256: fe1600a5872e88387fa22adc80b640ead304dcb68c5f1e4b9663c41370b328ea SHA512: 8d7185de19fcb1a8c3bd958a34f39a8df73dcbb073a4ec4817b1f7ce4c510d1be6f3988b988f3b6475034039af670ebc0f2f64b5043928997a5840de846e4b42 Homepage: https://cran.r-project.org/package=qpNCA Description: CRAN Package 'qpNCA' (Noncompartmental Pharmacokinetic Analysis by qPharmetra) Computes noncompartmental pharmacokinetic parameters for drug concentration profiles. For each profile, data imputations and adjustments are made as necessary and basic parameters are estimated. Supports single dose, multi-dose, and multi-subject data. Supports steady-state calculations and various routes of drug administration. See ?qpNCA and vignettes. Methodology follows Rowland and Tozer (2011, ISBN:978-0-683-07404-8), Gabrielsson and Weiner (1997, ISBN:978-91-9765-100-4), and Gibaldi and Perrier (1982, ISBN:978-0824710422). Package: r-cran-qpraentry Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 828 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bsplus, r-cran-dplyr, r-cran-dt, r-cran-eurostat, r-cran-ggiraph, r-cran-ggplot2, r-cran-giscor, r-cran-memoise, r-cran-purrr, r-cran-sf, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyjs, r-cran-shinywidgets, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qpraentry_0.1.1-1.ca2404.1_all.deb Size: 631508 MD5sum: 82ff448cfe34f92cc9cbcccccf8c32d2 SHA1: 8e4600f1ea5ee86fab75e367b0dfc87f3aeff5db SHA256: 1bdd54856335f79fcf78cbe73faa8e7915860b519a58745887652270ac7a0261 SHA512: 98c4a81200a71f4c50c6bbdd84cc69699aa32f6e1ef18f12069c7a83e747abc6bbc21d1b60db0a2d815827735d86a73f88298d36284d327cc0914ca3e78221f1 Homepage: https://cran.r-project.org/package=qPRAentry Description: CRAN Package 'qPRAentry' (Quantitative Pest Risk Assessment at the Entry Step) Supports risk assessors in performing the entry step of the quantitative Pest Risk Assessment. 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Package: r-cran-qqboxplot Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-gridextra, r-cran-testthat, r-cran-vdiffr, r-cran-scales Filename: pool/dists/noble/main/r-cran-qqboxplot_0.3.0-1.ca2404.1_all.deb Size: 458900 MD5sum: 2f815d53cd1f00cbc196a756ccc97542 SHA1: b2b9db8015715db4b8c77f5d2631e81baab3ba5e SHA256: 0a6e191e8b622a9f8daeb9877759d4ccdfc48dcd14892de80a6e1a64c8180259 SHA512: 2369b4834dc0939260109030acf50104810258006526d4b4acaa5b1c73e515680f256c658e98680235de78c0280c5bc6440954981c0ab7e00c7bc7e8f41c743d Homepage: https://cran.r-project.org/package=qqboxplot Description: CRAN Package 'qqboxplot' (Implementation of the Q-Q Boxplot) A system to implement the Q-Q boxplot. It is implemented as an extension to 'ggplot2'. The Q-Q boxplot is an amalgam of the boxplot and the Q-Q plot and allows the user to rapidly examine summary statistics and tail behavior for multiple distributions in the same pane. As an extension of the 'ggplot2' implementation of the boxplot, possible modifications to the boxplot extend to the Q-Q boxplot. Package: r-cran-qqman Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1557 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-calibrate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qqman_0.1.9-1.ca2404.1_all.deb Size: 1106976 MD5sum: fb8e528179df8355c249c32a095eda67 SHA1: 926f0dcca41f7bd9f7ae124fcf4864ebf2c286d8 SHA256: f81cd16c7f05d65fcbc4c0718d3bb489c0322c016dc91a5fc5e4135fd4462ecc SHA512: 4f43d022237a4e27e4f2237f4a3c56400fda7211956b4b3b98c6b3ea394eb6a36aa18107163b82be28d165edf58f70c6ae5b124f8647fdd4db5abb8aaf01e4d4 Homepage: https://cran.r-project.org/package=qqman Description: CRAN Package 'qqman' (Q-Q and Manhattan Plots for GWAS Data) Create Q-Q and manhattan plots for GWAS data from PLINK results. Package: r-cran-qqplotr Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1383 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-robustbase, r-cran-mass, r-cran-opdisdownsampling, r-cran-qqconf Suggests: r-cran-shiny, r-cran-devtools, r-cran-lattice, r-cran-shinybs, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qqplotr_0.0.7-1.ca2404.1_all.deb Size: 1074404 MD5sum: e1933fe6ef998e7eb8e8d3bc2ff37d7c SHA1: 84bff29a7901f1a28533eab76e4e64ef304d39a8 SHA256: 2ef2acbf70d68e7c7101143f957b439c7b7700ec1594238bb8cc53c1c2b125a7 SHA512: 7a6e41ecb38ea74725610d558f92e42a10757d0ac775fe274b70ac387dbd8e2b0297390227eea240a64ffb5255f4ec2f25ef092595c69917dccf2a7b9ff6708c Homepage: https://cran.r-project.org/package=qqplotr Description: CRAN Package 'qqplotr' (Quantile-Quantile Plot Extensions for 'ggplot2') Extensions of 'ggplot2' Q-Q plot functionalities. Package: r-cran-qqreflimits Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-qqreflimits_1.0.3-1.ca2404.1_all.deb Size: 58256 MD5sum: f0276d622d422912c629fe78ee99d32b SHA1: 735028a37b29c104b3826fac039d4d2ef069e0c2 SHA256: 36f6ded6e8d60202e7940fce6b6344ff16fe70c25388fa226aafbc37497afd5c SHA512: 83593df7ce8c21092fbaee42e0a825132a7a632b923810492e30096659ec72c0152b7d5eafd1bfe41c32967cfd347730fd878995595f03598f4fe5e70a611b4b Homepage: https://cran.r-project.org/package=QQreflimits Description: CRAN Package 'QQreflimits' (Reference Limits using QQ Methodology) A collection of routines for finding reference limits using, where appropriate, QQ methodology. All use a data vector X of cases from the reference population. The default is to get the central 95% reference range of the population, namely the 2.5 and 97.5 percentile, with optional adjustment of the range. Along with the reference limits, we want confidence intervals which, for historical reasons, are typically at 90% confidence. A full analysis provides six numbers: – the upper and the lower reference limits, and - each of their confidence intervals. For application details, see Hawkins and Esquivel (2024) . Package: r-cran-qqtest Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qqtest_1.2.0-1.ca2404.1_all.deb Size: 220118 MD5sum: a421dba7da94f74012cb451cd2110fb7 SHA1: a22e24dfcbf4c674d21cf4c38dd50113723c98e5 SHA256: b1b54d8d978d34307036b61d9af0003cb492f6d1aa850b5b660bba61aff8fbac SHA512: f54168441c0d3b449b7dc634846d912f03b32829302435f8709c2df75bc48481288c294de3715e78538bf59d9cb3a1814055375a7f448941e637c0d365a8d92e Homepage: https://cran.r-project.org/package=qqtest Description: CRAN Package 'qqtest' (Self Calibrating Quantile-Quantile Plots for Visual Testing) Provides the function qqtest which incorporates uncertainty in its qqplot display(s) so that the user might have a better sense of the evidence against the specified distributional hypothesis. qqtest draws a quantile quantile plot for visually assessing whether the data come from a test distribution that has been defined in one of many ways. The vertical axis plots the data quantiles, the horizontal those of a test distribution. The default behaviour generates 1000 samples from the test distribution and overlays the plot with shaded pointwise interval estimates for the ordered quantiles from the test distribution. A small number of independently generated exemplar quantile plots can also be overlaid. Both the interval estimates and the exemplars provide different comparative information to assess the evidence provided by the qqplot for or against the hypothesis that the data come from the test distribution (default is normal or gaussian). Finally, a visual test of significance (a lineup plot) can also be displayed to test the null hypothesis that the data come from the test distribution. Package: r-cran-qqvases Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-qqvases_1.0.0-1.ca2404.1_all.deb Size: 56898 MD5sum: 12b01f83a21dfab2cd50f8c58c935211 SHA1: d17a92213558ff147b7b45d711832c3127442a2b SHA256: 18834c95d9d97e510e434bbeaef301a32b02b9c576885472a6293741fb7ae7df SHA512: cb50e673801b7a5e6e5152e0505f1b44438ab57c32338f8ce1e0212ba9a77869ac0b4a698833257363ef26958b85329fb33c4794e801d9db636b26cb76ed679a Homepage: https://cran.r-project.org/package=qqvases Description: CRAN Package 'qqvases' (Animated Normal Quantile-Quantile Plots) Presents an explanatory animation of normal quantile-quantile plots based on a water-filling analogy. The animation presents a normal QQ plot as the parametric plot of the water levels in vases defined by two distributions. The distributions decorate the axes in the normal QQ plot and are optionally shown as vases adjacent to the plot. The package draws QQ plots for several distributions, either as samples or continuous functions. Package: r-cran-qr.break Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg Suggests: r-cran-sparsem Filename: pool/dists/noble/main/r-cran-qr.break_1.0.2-1.ca2404.1_all.deb Size: 179754 MD5sum: 9d6fb8108f00184336155ef8adcc74a4 SHA1: 4288d2a9648e7ef9ad367b95d43e4820ebd1fff4 SHA256: efe46aee7b3dbbd6c6b73f67061ff19c7288e76bd27d2a3387719520f6b0ff15 SHA512: 0038cec146173645c9b04e3f72aeb48f5dc469d075165b6412938b80f31f04fd3c44917d8d9a3400f5ff1d2e2cf61c692740ddbb50e2bb647939050c23e304da Homepage: https://cran.r-project.org/package=QR.break Description: CRAN Package 'QR.break' (Structural Breaks in Quantile Regression) Methods for detecting structural breaks, determining the number of breaks, and estimating break locations in linear quantile regression, using one or multiple quantiles, based on Qu (2008) and Oka and Qu (2011). Applicable to both time series and repeated cross-sectional data. The main function is rq.break(). References for detailed theoretical and empirical explanations: (1) Qu, Z. (2008). "Testing for Structural Change in Regression Quantiles." Journal of Econometrics, 146(1), 170-184 (2) Oka, T., and Qu, Z. (2011). "Estimating Structural Changes in Regression Quantiles." Journal of Econometrics, 162(2), 248-267 . 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It allows to define general single qubit gates and general controlled single qubit gates. For convenience, it currently provides the most common gates (X, Y, Z, H, Z, S, T, Rx, Ry, Rz, CNOT, SWAP, Toffoli or CCNOT, Fredkin or CSWAP). 'qsimulatR' also implements noise models. 'qsimulatR' supports plotting of circuits and is able to export circuits to 'Qiskit' , a python package which can be used to run on IBM's hardware . 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The 'qsort' package includes descriptions and scoring procedures for four different Q-sets commonly used in developmental psychology research: Attachment Q-set (version 3.0) (Waters, 1995, ); California Child Q-set (Block and Block, 1969, ); Maternal Behaviour Q-set (version 3.1) (Pederson et al., 1999, ); Preschool Q-set (Baumrind, 1968 revised by Wanda Bronson, ). 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These genotype probabilities are a central data object for mapping quantitative trait loci (QTL), but they can be quite large. The facilities in this package enable the genotype probabilities to be stored on disk, leading to reduced memory usage with only a modest increase in computation time. 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Useful in crosses with more than two alleles to identify how sets of alleles, genetically different strands at the same locus, have different response levels. Plots show profiles over a chromosome. Can handle multiple traits together. See . Package: r-cran-qtlbook Architecture: all Version: 0.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-qtl Filename: pool/dists/noble/main/r-cran-qtlbook_0.20-1.ca2404.1_all.deb Size: 187982 MD5sum: e2e1e238962d9292c35df4c311b9bab3 SHA1: 3694817d336ab937425e5a50c3ff8e61cfdcd8e9 SHA256: fbcf64a32104fc70d5b61aeb064bb04ee424b65f43e0e220eac0ccbdd0439a15 SHA512: 2517681748fe6681bc924d80dd72c429976301244c29c23a981fa671b274f6ddc9920df175fa9459fb39bc1b4eb1f242f0bc4d897c142d285a2e4d94a79c7ac4 Homepage: https://cran.r-project.org/package=qtlbook Description: CRAN Package 'qtlbook' (Datasets for the R/qtl Book) Datasets for the book, A Guide to QTL Mapping with R/qtl. Broman and Sen (2009) . Package: r-cran-qtlc Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tiff, r-cran-rgl, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-qtlc_1.0-1.ca2404.1_all.deb Size: 158838 MD5sum: e45e0dd9c82c6efdb91548b820529de1 SHA1: b83c037205cea7918254a6d3796fc82710dd6620 SHA256: 135808f6418c6167b627c0ab565a29870a66df7f7049e1628945eac4d616acef SHA512: 8b81450058f11484015ff0c8ca2dcc68f0fd087f11e1e1829ca949997c43cd2e4f0d6e018ca8f4509c0aae85f6405b51009e34d5419b8798e75d0d92aa1b940b Homepage: https://cran.r-project.org/package=qtlc Description: CRAN Package 'qtlc' (Densitometric Analysis of Thin-Layer Chromatography Plates) Densitometric evaluation of the photo-archived quantitative thin-layer chromatography (TLC) plates. Package: r-cran-qtlcharts Architecture: all Version: 0.18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1839 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qtl, r-cran-htmlwidgets Suggests: r-cran-htmltools, r-cran-jsonlite, r-cran-knitr, r-cran-devtools, r-cran-roxygen2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-qtlcharts_0.18-1.ca2404.1_all.deb Size: 701020 MD5sum: 88c67ba2b8353982c4fdce8762420200 SHA1: 628d0f1f67c027b8eacb182ada16ecdaad3301b1 SHA256: ab9f9de82a31eb0d547a317531090048147f6cb00c93aeed35042806227a65ca SHA512: 83f8bcd74dc52f2e0f8bf9063ecb132cd4ed9424754c0a2d45822d1899340c47407a8b8a5ed31021d0da461c91297c68fe2c2ee7235e66e0e0fdc2e53a245279 Homepage: https://cran.r-project.org/package=qtlcharts Description: CRAN Package 'qtlcharts' (Interactive Graphics for QTL Experiments) Web-based interactive charts (using D3.js) for the analysis of experimental crosses to identify genetic loci (quantitative trait loci, QTL) contributing to variation in quantitative traits. Broman (2015) . Package: r-cran-qtldesign Architecture: all Version: 0.953-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-qtl Filename: pool/dists/noble/main/r-cran-qtldesign_0.953-1.ca2404.1_all.deb Size: 99676 MD5sum: 7853ff650569de896478c04e28f30887 SHA1: 5197842cbfa5b3406acbfcd576440b20f73c5b89 SHA256: 2316e0fec8d455d02940e646980c65d3e109e280ede6745ef555fd9df930a8f3 SHA512: e41818543b803888bdb3d3e552edcc2f43424a85a50a6c6815faf7d327b5725bb01d8ca47de4a29a6575643cc0612544f2c4ae85094ec2084a0728027ec9cb08 Homepage: https://cran.r-project.org/package=qtlDesign Description: CRAN Package 'qtlDesign' (Design of QTL (Quantitative Trait Locus) Experiments) Design of QTL (quantitative trait locus) experiments involves choosing which strains to cross, the type of cross, genotyping strategies, phenotyping strategies, and the number of progeny to raise and phenotype. This package provides tools to help make such choices. Sen and others (2007) . Package: r-cran-qtlemm Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-gtools Filename: pool/dists/noble/main/r-cran-qtlemm_3.1.0-1.ca2404.1_all.deb Size: 2423236 MD5sum: 5fd2b3c98ac84445fc45de5acf23b917 SHA1: 73c0d80bfb1080e32cf9071067edd7b940a88dd1 SHA256: 3a5288499bee3517af060c7d1f85cdf13ccaf810045581985b2d9a08019e182b SHA512: 324d2b7ea816d29bc4773c925a4d289a216a2a39d3b12fac43da95fa8ff277a9a835d79e18b25f39ce41d22f5c527194e232fb13ea985734cfcf1faf129498c6 Homepage: https://cran.r-project.org/package=QTLEMM Description: CRAN Package 'QTLEMM' (QTL EM Algorithm Mapping and Hotspots Detection) For QTL mapping, this package comprises several functions designed to execute diverse tasks, such as simulating or analyzing data, calculating significance thresholds, and visualizing QTL mapping results. The single-QTL or multiple-QTL method, which enables the fitting and comparison of various statistical models, is employed to analyze the data for estimating QTL parameters. The models encompass linear regression, permutation tests, normal mixture models, and truncated normal mixture models. The Gaussian stochastic process is utilized to compute significance thresholds for QTL detection on a genetic linkage map within experimental populations. Two types of data, complete genotyping, and selective genotyping data from various experimental populations, including backcross, F2, recombinant inbred (RI) populations, and advanced intercrossed (AI) populations, are considered in the QTL mapping analysis. For QTL hotspot detection, statistical methods can be developed based on either utilizing individual-level data or summarized data. We have proposed a statistical framework capable of handling both individual-level data and summarized QTL data for QTL hotspot detection. Our statistical framework can overcome the underestimation of thresholds resulting from ignoring the correlation structure among traits. Additionally, it can identify different types of hotspots with minimal computational cost during the detection process. Here, we endeavor to furnish the R codes for our QTL mapping and hotspot detection methods, intended for general use in genes, genomics, and genetics studies. The QTL mapping methods for the complete and selective genotyping designs are based on the multiple interval mapping (MIM) model proposed by Kao, C.-H. , Z.-B. Zeng and R. D. Teasdale (1999) and H.-I Lee, H.-A. Ho and C.-H. Kao (2014) , respectively. The QTL hotspot detection analysis is based on the method by Wu, P.-Y., M.-.H. Yang, and C.-H. Kao (2021) . Package: r-cran-qtlhot Architecture: all Version: 1.2.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1987 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qtl, r-cran-mnormt, r-cran-corpcor, r-cran-broman Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qtlhot_1.2.10-1.ca2404.1_all.deb Size: 1742952 MD5sum: d0e416b12fdfb058f9f4f6b8c539bc01 SHA1: d8cbe59216b754f88529f858b31d0ef0ec164573 SHA256: ddb21e2bc0815f54ca690c7010824acfcacfaf8a6137039a6615e23b3ff69927 SHA512: aedd597b1c4edcd88718550b534bbc99b33a5405337817472ae85cad078d78438983c8aae3bbc1e58768f7dbb4e7b94b972aa4090e59a87e5351ec4852ad1d64 Homepage: https://cran.r-project.org/package=qtlhot Description: CRAN Package 'qtlhot' (Inference for QTL Hotspots) Functions to infer co-mapping trait hotspots and causal models. Chaibub Neto E, Keller MP, Broman AF, Attie AD, Jansen RC, Broman KW, Yandell BS (2012) Quantile-based permutation thresholds for QTL hotspots. Genetics 191 : 1355-1365. . Chaibub Neto E, Broman AT, Keller MP, Attie AD, Zhang B, Zhu J, Yandell BS (2013) Modeling causality for pairs of phenotypes in system genetics. Genetics 193 : 1003-1013. . Package: r-cran-qtlnet Architecture: all Version: 1.5.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4732 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qtl, r-cran-igraph, r-cran-sem, r-bioc-graph, r-cran-pcalg Filename: pool/dists/noble/main/r-cran-qtlnet_1.5.4-1.ca2404.1_all.deb Size: 3143308 MD5sum: 90d0b780f5414eae077fa0c3606c01d9 SHA1: 5cc225283d099c1f8e0dad9982e69c8bc1d75a27 SHA256: 0438b2464482629b2a6fb1f53a1edbb05e5f24d4a1f7b98348a58317a1e0de08 SHA512: 4da9e93a778a21e54700abb3e55e3c4fa94093100a8e82df5e6eb1e0153360f49658b9ef50aa528e610cd2f1fbad510e9711c0418eb7e478dd7dd3ce06d70919 Homepage: https://cran.r-project.org/package=qtlnet Description: CRAN Package 'qtlnet' (Causal Inference of QTL Networks) Functions to Simultaneously Infer Causal Graphs and Genetic Architecture. Includes acyclic and cyclic graphs for data from an experimental cross with a modest number (<10) of phenotypes driven by a few genetic loci (QTL). Chaibub Neto E, Keller MP, Attie AD, Yandell BS (2010) Causal Graphical Models in Systems Genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes. Annals of Applied Statistics 4: 320-339. . 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Specifically, we have developed distinct functions for three types of right censoring for static treatment using quantile criterion: (1) independent/random censoring, (2) treatment-dependent random censoring, and (3) covariates-dependent random censoring. It also includes a function to estimate quantile-optimal dynamic treatment regimes for independent censored data. Finally, this package also includes a simulation data generative model of a dynamic treatment experiment proposed in literature. 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Package: r-cran-quallmer.app Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quallmer, r-cran-shiny, r-cran-bslib, r-cran-dplyr, r-cran-tidyr, r-cran-irr, r-cran-htmltools, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-quallmer.app_0.1.0-1.ca2404.1_all.deb Size: 101936 MD5sum: 447bed5c9113686b50e5351fa028887f SHA1: 440e37f52daabc5a82b3d7780186b9ce6e932798 SHA256: 09777e3e37011262a091a99b080fe66fddb4653827c975d1316d97f505f144fa SHA512: 43221ee48bd2cd71c44021626b4081a6f1777314e0c5a806caa7d363958bc3940a15c5d67300c41eda78395ff958a8ac48ca11b706ea253c8f2e6207398448a0 Homepage: https://cran.r-project.org/package=quallmer.app Description: CRAN Package 'quallmer.app' (Interactive Validation App for 'quallmer') Companion package to 'quallmer' providing an interactive 'shiny' application for manual coding, reviewing large language model (LLM) generated annotations, and computing inter-rater reliability metrics. Supports three modes: blind manual coding, LLM output validation, and agreement calculation. Computes standard reliability metrics including Krippendorff's alpha (Krippendorff 2019 ), Cohen's kappa, Fleiss' kappa (Fleiss 1971 ), intraclass correlation coefficient (ICC), and percent agreement for nominal, ordinal, interval, and ratio data. Also computes gold-standard validation metrics including accuracy, precision, recall, and F1 scores following Sokolova and Lapalme (2009 ). Package: r-cran-quallmer Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1799 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ellmer, r-cran-cli, r-cran-digest, r-cran-irr, r-cran-lifecycle, r-cran-rlang, r-cran-tibble, r-cran-vctrs, r-cran-yardstick Suggests: r-cran-ggplot2, r-cran-janitor, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-kableextra, r-cran-mockery, r-cran-quanteda, r-cran-quanteda.tidy, r-cran-withr Filename: pool/dists/noble/main/r-cran-quallmer_0.4.0-1.ca2404.1_all.deb Size: 1751230 MD5sum: 6354b99bb4ede629ebd83df47b392d4f SHA1: 4488a6e5e02e7aed86cbc7a4cd71dd7f30b52643 SHA256: f1e7f25277d6d749cdf01a5f87fcfaabffa62e29c5bcdfa00fc916b8c95e39b3 SHA512: 4b1bd38ac942ee72cd87ef1bfbda472f751993bbf1ef975f40bcf49bfe79b5bc563369f3d51fdaae9016d9de94aa3f3c134c15245f3d52a9987cdd54d7c657c1 Homepage: https://cran.r-project.org/package=quallmer Description: CRAN Package 'quallmer' (Qualitative Analysis with Large Language Models) Tools for AI-assisted qualitative data coding using large language models ('LLMs') via the 'ellmer' package, supporting providers including 'OpenAI', 'Anthropic', 'Google', 'Azure', and local models via 'Ollama'. Provides a 'codebook'-based workflow for defining coding instructions and applying them to texts, images, and other data. Includes built-in 'codebooks' for common applications such as sentiment analysis and policy coding, and functions for creating custom 'codebooks' for specific research questions. Supports systematic replication across models and settings, computing inter-coder reliability statistics including Krippendorff's alpha (Krippendorff 2019, ) and Fleiss' kappa (Fleiss 1971, ), as well as gold-standard validation metrics including accuracy, precision, recall, and F1 scores following Sokolova and Lapalme (2009, ). Provides audit trail functionality for documenting coding workflows following Lincoln and Guba's (1985, ISBN:0803924313) framework for establishing trustworthiness in qualitative research. Package: r-cran-qualmap Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-leaflet, r-cran-purrr, r-cran-rlang, r-cran-sf Suggests: r-cran-covr, r-cran-ggplot2, r-cran-testthat, r-cran-tigris, r-cran-tidycensus, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-qualmap_0.2.2-1.ca2404.1_all.deb Size: 832390 MD5sum: 4998c9095bc44d683668cc39ce09a4fe SHA1: 7c1b16f630eb7ac64711d1fb183e728c6409040c SHA256: 6fc663872077a3d17dafa49de16f994ed3e600a5b6e04b189ec28e23b99f05b7 SHA512: f3d21532c7210ec7e4d8d271a0bd50d01400aba20ccd319db84fd995a26b64e1e50344f1200dfdd7ce6f8f6e6b808e4b8148e73e19117fa2100dda5b8bf3082d Homepage: https://cran.r-project.org/package=qualmap Description: CRAN Package 'qualmap' (Opinionated Approach for Digitizing Semi-Structured QualitativeGIS Data) Provides a set of functions for taking qualitative GIS data, hand drawn on a map, and converting it to a simple features object. These tools are focused on data that are drawn on a map that contains some type of polygon features. For each area identified on the map, the id numbers of these polygons can be entered as vectors and transformed using qualmap. Package: r-cran-qualtrics Architecture: all Version: 3.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 357 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-archive, r-cran-cli, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-sjlabelled, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-withr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vcr, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-qualtrics_3.2.2-1.ca2404.1_all.deb Size: 256400 MD5sum: 56ba5f144c3024aa7206dc25cacab3fe SHA1: 93a812213c46fc6e8f6febd232ec981e03b5f6ca SHA256: f523cc03a6812a40f87cf7a0e23378fb75499c9a45d44e9cafbe9fd7dade67cf SHA512: 2befdc9e834b90f00e1cba5b1893eb61284ccdc385dea4cf0e97ed68ac95ae3f1129ae21078dd6e4c1550a18747ba972373d1738e10264f6842fa09cd059acb1 Homepage: https://cran.r-project.org/package=qualtRics Description: CRAN Package 'qualtRics' (Download 'Qualtrics' Survey Data) Provides functions to access survey results directly into R using the 'Qualtrics' API. 'Qualtrics' is an online survey and data collection software platform. See for more information about the 'Qualtrics' API. This package is community-maintained and is not officially supported by 'Qualtrics'. Package: r-cran-qualvar Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-ggally, r-cran-dt Filename: pool/dists/noble/main/r-cran-qualvar_0.2.0-1.ca2404.1_all.deb Size: 198464 MD5sum: 5f288c54ab06377e643d13842567794d SHA1: 7ae1c85b1e38cd1766fceb1d750cb34d12a991a3 SHA256: eb2e3474751c24a34439e2a4eb8fd4f5c87f72d5eca8d8ddfafa11ec973d6bee SHA512: 09d731aa407f69e2cd89576eac747ebb266b4b00ee8c578a338e16db6af16712f3420ff04e9b6196de26cf5b0020930b442471b639b26b79b803cfa6c8c17125 Homepage: https://cran.r-project.org/package=qualvar Description: CRAN Package 'qualvar' (Implements Indices of Qualitative Variation Proposed by Wilcox(1973)) Implements indices of qualitative variation proposed by Wilcox (1973). Package: r-cran-qualypso Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-expm, r-cran-rfast, r-cran-gamlss, r-cran-gamlss.dist, r-cran-statmod Filename: pool/dists/noble/main/r-cran-qualypso_3.0-1.ca2404.1_all.deb Size: 143786 MD5sum: 25bd5341c6630f919ff8bbb2c8c6b1b5 SHA1: 111b8c6701a3ef85c714d0d52abccf84da2929e0 SHA256: ffff7b48ed9dd844249a503399785e57ae6e835fc7d3ad4a8e6803c0d46012d1 SHA512: 3cc103b2f5181af554e5157875df349e007d95242fd667bbbc11f1dabb22a444ebd0a95788d4e72cd337fb416bfc894d5acee75b2f2aef42cc6f80eae255aebc Homepage: https://cran.r-project.org/package=QUALYPSO Description: CRAN Package 'QUALYPSO' (Partitioning Uncertainty Components of an Incomplete Ensemble ofClimate Projections) These functions use data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates. - Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) . Package: r-cran-quandl Architecture: all Version: 2.11.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-httr, r-cran-zoo, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-timeseries Filename: pool/dists/noble/main/r-cran-quandl_2.11.0-1.ca2404.1_all.deb Size: 70362 MD5sum: 84fffd19d995d7d92ce2af590d63e6d3 SHA1: 2545d2992b09dcbf5b59532b2ef99553a411c9f9 SHA256: 72f97e377805d046d71009ab0aa029c2bd61343a0e21f5fc26025985c0369822 SHA512: ec8772d260c1f08b885b542f785d4b669258d7f3b82ca999271ce4bd07a6ad4fdd23069afa2b55fa22f93f8632722608c8db644716854268c2702ff56f5dceed Homepage: https://cran.r-project.org/package=Quandl Description: CRAN Package 'Quandl' (API Wrapper for Quandl.com) Functions for interacting directly with the Quandl API to offer data in a number of formats usable in R, downloading a zip with all data from a Quandl database, and the ability to search. This R package uses the Quandl API. For more information go to . For more help on the package itself go to . Package: r-cran-quantbayes Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1848 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-quantbayes_0.1.0-1.ca2404.1_all.deb Size: 1313108 MD5sum: b98bb83014ff8208275df7ec9bb162f8 SHA1: 81d9b7079ae1d58a1f9a32636cb172198080e44a SHA256: 35de7a12422d0dbf93d251dd60baa52731e0bc6ab345289df7967826eada7057 SHA512: 6eadd088cbf27e25cc956ac69a198338e8189144af1aed217959a12e3c5147f5ce28d177e115b65cc07e1a23a9c1e37772f16288942617e4674d58cb5a478224 Homepage: https://cran.r-project.org/package=quantbayes Description: CRAN Package 'quantbayes' (Bayesian Quantification of Evidence Sufficiency) Implements the Quantification Evidence Standard algorithm for computing Bayesian evidence sufficiency from binary evidence matrices. 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Package: r-cran-quantcurves Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kernsmooth, r-cran-locpol, r-cran-np, r-cran-quantreg, r-cran-quantreggrowth Filename: pool/dists/noble/main/r-cran-quantcurves_1.0.0-1.ca2404.1_all.deb Size: 60738 MD5sum: b0a9a83cf3aca0356347113e2bfb77b3 SHA1: 791a81715942b53e1cde93ebebd3b984f0807b53 SHA256: b0a21cc1dcdc2351439e6fd1e664ae8564338a956aab4400648611df747c97da SHA512: cb954e29364d9acfe902828c75db93f6f3a7e5b8728cd4ade40fda0a770a17fddcc7196ad8ba4be65b21215870ccfbcc6608b04111b35b6b277648ac0f4e0759 Homepage: https://cran.r-project.org/package=quantCurves Description: CRAN Package 'quantCurves' (Estimate Quantiles Curves) Non-parametric methods as local normal regression, polynomial local regression and penalized cubic B-splines regression are used to estimate quantiles curves. See Fan and Gijbels (1996) and Perperoglou et al.(2019) . Package: r-cran-quantdates Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-quantdates_2.0.4-1.ca2404.1_all.deb Size: 201780 MD5sum: d95610b8a256a94c8052979b7c5c38f0 SHA1: 994259a7db4256f90be67a82124cc17743fb67b6 SHA256: 90756356d0c48b36f9ead5caae5437a37fbaf1ccbffe919ba8718b82d05f56f7 SHA512: 408b65215ac450edd010061f4e1eeb2c2d847024c0d6a2ee1f0217c840ee80a490b08b94c702f65b7a31f8090ee1b311ba89a2af39e1947a13bc5aa4cccf906a Homepage: https://cran.r-project.org/package=quantdates Description: CRAN Package 'quantdates' (Manipulate Dates for Finance) Functions to manipulate dates and count days for quantitative finance analysis. 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Package: r-cran-quanteda.tidy Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quanteda, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-quanteda.tidy_0.4-1.ca2404.1_all.deb Size: 87222 MD5sum: 5bf05be0fb18c3493bfe8f99f3b31dae SHA1: 575e66d6358f9e27e8a2410a7533868b91548b2b SHA256: 755ad04f9f3c9eb1860ef331fb891a034a237aa7b7ae6eff7919c6dae2d16544 SHA512: 9feff8c677037db9d06bff5a42f73430bce9128c10fdb0c318427e2b0e429703362ab7511b8f3c0350ac0ac4e9f53fe3957df5c73ed5e751c768c782f6dcae42 Homepage: https://cran.r-project.org/package=quanteda.tidy Description: CRAN Package 'quanteda.tidy' ('tidyverse' Extensions for 'quanteda') Enables 'tidyverse' operations on 'quanteda' corpus objects by extending 'dplyr' verbs to work directly with corpus objects and their document-level variables ('docvars'). 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Methods implemented in the package draw from several sources, including Alexander (1976) , Batschelet (1981, ISBN:9780120810505), Benhamou (2004) , Bovet and Benhamou (1988) , Cheung et al. (2007) , Cheung et al. (2008) , Cleasby et al. (2019) , Farlow et al. (1981) , Ostrom (1972) , Rohlf (2008) , Rohlf (2009) , Ruiz and Torices (2013) , Scrucca et al. (2016) , Thulborn and Wade (1984) . Package: r-cran-quantification Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car Filename: pool/dists/noble/main/r-cran-quantification_0.2.0-1.ca2404.1_all.deb Size: 79690 MD5sum: 336b095ac9daeb73e0f8f1a92d100dc9 SHA1: 1a4f0b4fe08c5ffe3f9613d41dc23b2117e9d201 SHA256: 0355d089c2d1fc87d86ea0b56242b389380107c14ca3b80793c143cd41f59cac SHA512: 5eb459babd14984eee84e171868d6f5fe5bf2f6857747f3a8078792ced6f1e821df032926896038f912d1b3cd0e09ce2d7d909d502ae7477f2a89f0e894ba4e7 Homepage: https://cran.r-project.org/package=quantification Description: CRAN Package 'quantification' (Quantification of Qualitative Survey Data) Provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach, the balance approach and the conditional expectations method. Package: r-cran-quantileda Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-quantileda_1.2-1.ca2404.1_all.deb Size: 77306 MD5sum: 47491405ef7f5136199ab640ad5725cc SHA1: 8f1ac1785d56dbcfea3528a4af6bb1f6c73f9c22 SHA256: 8d7f3b975bc85adfbc331ff033435291b4e47d8a308eddaf718fd0a410ed4a6e SHA512: 0faf09dbb0d08c225d5d9b1e236a5381df7b38a5f6fc35ec70f0931aff4a027db7532ca1fee8704db08538283f43b0c37cb0ed8a10149beba745822495ae4d99 Homepage: https://cran.r-project.org/package=quantileDA Description: CRAN Package 'quantileDA' (Quantile Classifier) Code for centroid, median and quantile classifiers. Package: r-cran-quantilegh Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mixtools, r-cran-tclust, r-cran-fmx, r-cran-tukeygh77 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mixsmsn Filename: pool/dists/noble/main/r-cran-quantilegh_0.1.8-1.ca2404.1_all.deb Size: 102196 MD5sum: 28abe4574819b7f380c0dab667cbbc5a SHA1: 8e7be63e9532f6251a3ae7456299b9fb0dbf4503 SHA256: a02e29aff67e67920fbdea8e48d7c35d4e603b4350c45ede6275c79f8f99628f SHA512: ccc68b301598ca384be78635e13d7f4c12e52243e0d11766f7345852d011b8d4947c7368e091b5f32760cb5e4b79e7ab348e813d19b4241d4097dc12c11bbaac Homepage: https://cran.r-project.org/package=QuantileGH Description: CRAN Package 'QuantileGH' (Quantile Least Mahalanobis Distance Estimator for Tukey g-&-hMixture) Functions for simulation, estimation, and model selection of finite mixtures of Tukey g-and-h distributions. Package: r-cran-quantilegrader Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-quantilegrader_0.1.1-1.ca2404.1_all.deb Size: 72970 MD5sum: 0be40e50c60426570a797f643e42e041 SHA1: ea07c96895b8b89efb6629f4baaf82fcd1c0485e SHA256: 2f4b5416cd76701df89e2091e9254fbb3ebb853ef7d4b743103605d88f1f626d SHA512: 53af091b54626ea35ce2d4afbb7ae6506b93399ef635ff717c36de3e3d902e5523f5047287bbe47e541759f74ddaa17ad60724428af66853b4011d29f880797d Homepage: https://cran.r-project.org/package=QuantileGradeR Description: CRAN Package 'QuantileGradeR' (Quantile-Adjusted Restaurant Grading) Implementation of the food safety restaurant grading system adopted by Public Health - Seattle & King County (see Ashwood, Z.C., Elias, B., and Ho. D.E. "Improving the Reliability of Food Safety Disclosure: A Quantile Adjusted Restaurant Grading System for Seattle-King County" (working paper)). As reported in the accompanying paper, this package allows jurisdictions to easily implement refinements that address common challenges with unadjusted grading systems. First, in contrast to unadjusted grading, where the most recent single routine inspection is the primary determinant of a grade, grading inputs are allowed to be flexible. For instance, it is straightforward to base the grade on average inspection scores across multiple inspection cycles. Second, the package can identify quantile cutoffs by inputting substantively meaningful regulatory thresholds (e.g., the proportion of establishments receiving sufficient violation points to warrant a return visit). Third, the quantile adjustment equalizes the proportion of establishments in a flexible number of grading categories (e.g., A/B/C) across areas (e.g., ZIP codes, inspector areas) to account for inspector differences. Fourth, the package implements a refined quantile adjustment that addresses two limitations with the stats::quantile() function when applied to inspection score datasets with large numbers of score ties. The quantile adjustment algorithm iterates over quantiles until, over all restaurants in all areas, grading proportions are within a tolerance of desired global proportions. In addition the package allows a modified definition of "quantile" from "Nearest Rank". Instead of requiring that at least p[1]% of restaurants receive the top grade and at least (p[1]+p[2])% of restaurants receive the top or second best grade for quantiles p, the algorithm searches for cutoffs so that as close as possible p[1]% of restaurants receive the top grade, and as close as possible to p[2]% of restaurants receive the second top grade. Package: r-cran-quantilenpci Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-quantilenpci_0.9.0-1.ca2404.1_all.deb Size: 25398 MD5sum: 58156feace7a1d8d21584c7d68aaa57b SHA1: d57056a2b4568dcec407d8189ed670f24e58d52d SHA256: 682cb58de08d84b14bccebc91d92f14c32abda865694d7b1c21d7240c042f345 SHA512: 49d5d3ac26032fe0061313dfe3bcd91f8ea70b92c456951ef40c74935023341829c22c72afd054e53fa1ce343bfa81f6d87ea2c47bbc6133b8a8dea95d6acd6c Homepage: https://cran.r-project.org/package=QuantileNPCI Description: CRAN Package 'QuantileNPCI' (Nonparametric Confidence Intervals for Quantiles) Based on Alan D. Hutson (1999) , "Calculating nonparametric confidence intervals for quantiles using fractional order statistics", Journal of Applied Statistics, 26:3, 343-353. Package: r-cran-quantileonquantile Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-quantileonquantile_1.0.3-1.ca2404.1_all.deb Size: 87024 MD5sum: 9b20aa4d088e99f01e7f4e50f3c3d267 SHA1: 6b229cf72ca3a8f0033a8c005256299bb0c18647 SHA256: bffd52d8e5297ce90329b56e038a685ddad35e7a316811ec19bfcd2cde842238 SHA512: 2620db3c0f3be9274cd2b8af24fb607c669075599fc2f1864a3980024fa05e81031f9486b0a2b47af8778ba6f8405536b6f725d4a282a5e44c74dc0a0e22d738 Homepage: https://cran.r-project.org/package=QuantileOnQuantile Description: CRAN Package 'QuantileOnQuantile' (Quantile-on-Quantile Regression Analysis) Implements the Quantile-on-Quantile (QQ) regression methodology developed by Sim and Zhou (2015) . QQ regression estimates the effect that quantiles of one variable have on quantiles of another, capturing the dependence between distributions. The package provides functions for QQ regression estimation, 3D surface visualization with 'MATLAB'-style color schemes ('Jet', 'Viridis', 'Plasma'), heatmaps, contour plots, and quantile correlation analysis. Uses 'quantreg' for quantile regression and 'plotly' for interactive visualizations. Particularly useful for examining relationships between financial variables, oil prices, and stock returns under different market conditions. Package: r-cran-quantilogram Architecture: all Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-np, r-cran-quantreg, r-cran-rlang, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sparsem Filename: pool/dists/noble/main/r-cran-quantilogram_3.1.1-1.ca2404.1_all.deb Size: 319938 MD5sum: d58cd5625d3aadb69e50b022f71ea052 SHA1: 5e967e868d98278218a18fdced7c851769da0e83 SHA256: 6d0a269d0e5518959adab6002c0068526c4c67b206861ac76eff34036ee5a4a7 SHA512: b532c9c1db475076a084217eac9eb0152bd90d894029b85f6f882da6a4e77a772a3759fc73f0717da89dfc6717d3be3431bd1c93362ec163f36c84af64dc99ef Homepage: https://cran.r-project.org/package=quantilogram Description: CRAN Package 'quantilogram' (Cross-Quantilogram) Estimation and inference methods for the cross-quantilogram. The cross-quantilogram is a measure of nonlinear dependence between two variables, based on either unconditional or conditional quantile functions. It can be considered an extension of the correlogram, which is a correlation function over multiple lag periods that mainly focuses on linear dependency. One can use the cross-quantilogram to detect the presence of directional predictability from one time series to another. This package provides a statistical inference method based on the stationary bootstrap. For detailed theoretical and empirical explanations, see Linton and Whang (2007) for univariate time series analysis and Han, Linton, Oka and Whang (2016) for multivariate time series analysis. The full references for these key publications are as follows: (1) Linton, O., and Whang, Y. J. (2007). The quantilogram: with an application to evaluating directional predictability. Journal of Econometrics, 141(1), 250-282 ; (2) Han, H., Linton, O., Oka, T., and Whang, Y. J. (2016). The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series. Journal of Econometrics, 193(1), 251-270 . Package: r-cran-quantkriging Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hetgp, r-cran-matrix, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-quantkriging_0.1.0-1.ca2404.1_all.deb Size: 65396 MD5sum: 23d6518a9194cc24f21ae06935f9c35e SHA1: d4af09fcb4d9d2a0b1386ea1044a68b73ee15b21 SHA256: ec91e22d5d68e1fffc46b726298640c4847d2c1d08081d25f0e169e1abd70149 SHA512: dab5a73e52aebce255b5377fa13b596dab12393307dcd58f411cb84a1957b7990e6b03fce3677bc9944eb1bf3fbc4311ceb87905f95b03d42ad4331489f2f64c Homepage: https://cran.r-project.org/package=quantkriging Description: CRAN Package 'quantkriging' (Quantile Kriging for Stochastic Simulations with Replication) A re-implementation of quantile kriging. Quantile kriging was described by Plumlee and Tuo (2014) . With computational savings when dealing with replication from the recent paper by Binois, Gramacy, and Ludovski (2018) it is now possible to apply quantile kriging to a wider class of problems. In addition to fitting the model, other useful tools are provided such as the ability to automatically perform leave-one-out cross validation. Package: r-cran-quantmod Architecture: all Version: 0.4.28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1183 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-ttr, r-cran-curl, r-cran-jsonlite Suggests: r-cran-dbi, r-cran-rmysql, r-cran-rsqlite, r-cran-timeseries, r-cran-xml2, r-cran-downloader, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-quantmod_0.4.28-1.ca2404.1_all.deb Size: 1037728 MD5sum: 34801712260a64628fb67e476f9fcb55 SHA1: 98562df3c8b1435c924384e2ecf50d7dd7bc822c SHA256: 3e5bc0b32229e5708ae3f9ec43439cd4248e0867a4b99d5d7a03df8465f626fc SHA512: dbc11a8170df8abcdbbc5229853f39240332b333e307bd51d115f476e772276015619ec85be0da2fa691b40c16e641f65e583a190bb28fd9b269f745f313b853 Homepage: https://cran.r-project.org/package=quantmod Description: CRAN Package 'quantmod' (Quantitative Financial Modelling Framework) Specify, build, trade, and analyse quantitative financial trading strategies. Package: r-cran-quantnorm Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1940 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggally, r-cran-ggplot2, r-cran-network, r-cran-pheatmap, r-cran-rgl, r-cran-sna Filename: pool/dists/noble/main/r-cran-quantnorm_1.0.5-1.ca2404.1_all.deb Size: 1950072 MD5sum: 1fe3c2ed97578570d5ac2edaad0579f9 SHA1: d5cbaa0a0401936342172f42d4182c95997cfaf4 SHA256: 88a24c35e6c88df9a304f8c85949c7895f499b3d63a70c3ed4d56ad49cfa848d SHA512: 5b0714ff699b43a20e99ef0584614c22c63aba725279e4a1b2fefe0adddcf53b1f029afdf84965ec8ca723f021d09f5ba48bccf8836b8161b7c7e0c7ab4f4fce Homepage: https://cran.r-project.org/package=QuantNorm Description: CRAN Package 'QuantNorm' (Mitigating the Adverse Impact of Batch Effects in Sample PatternDetection) Modifies the distance matrix obtained from data with batch effects, so as to improve the performance of sample pattern detection, such as clustering, dimension reduction, and construction of networks between subjects. The method has been published in Bioinformatics (Fei et al, 2018, ). Also available on 'GitHub' . Package: r-cran-quantoptr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-rgenoud, r-cran-quantreg, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-quantoptr_0.1.3-1.ca2404.1_all.deb Size: 154612 MD5sum: 2e563d3c82b9c4243fe06102c9a47efc SHA1: 5f094437650f589d7dfabe6f4ee3f958756661fc SHA256: 78a91f3c968b4de13de076de30cc1c9843487e3e58473f345325ac1debd605b2 SHA512: 36d34c606f42b5fe88d0d4c0cba688f4f41abde4516c955fbc5da96197dd668959e69c3b0cd8039a90724cf2a5de8bc9f8f01387bbcf3ae29d0e0f848afe9bd7 Homepage: https://cran.r-project.org/package=quantoptr Description: CRAN Package 'quantoptr' (Algorithms for Quantile- And Mean-Optimal Treatment Regimes) Estimation methods for optimal treatment regimes under three different criteria, namely marginal quantile, marginal mean, and mean absolute difference. For the first two criteria, both one-stage and two-stage estimation method are implemented. A doubly robust estimator for estimating the quantile-optimal treatment regime is also included. Package: r-cran-quantpsyc Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-dplyr, r-cran-purrr, r-cran-mass Filename: pool/dists/noble/main/r-cran-quantpsyc_1.6-1.ca2404.1_all.deb Size: 123420 MD5sum: a33262f3f76d1f2a93008b061fe4e0af SHA1: 6ac4babf21e1b0f463f245b438aa955f03c6bcb0 SHA256: 4a328247e731c7daba4deb9aa1c48499873d1a102c87ea724a50ff681a29f8bb SHA512: 2757d79633ab0021eaa8b0aeec7fca89318343c53a65ab8ee175a50d37af15e6cc083b3fe24e949196643f0caf4b15b36e87c1938c5b5b77166bb5c9cd075f06 Homepage: https://cran.r-project.org/package=QuantPsyc Description: CRAN Package 'QuantPsyc' (Quantitative Psychology Tools) Contains tools useful for data screening, testing moderation (Cohen et. al. 2003), mediation (MacKinnon et. al. 2002) and estimating power (Murphy & Myors 2014). Package: r-cran-quantreg.nonpar Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-mnormt, r-cran-fda, r-cran-rearrangement Filename: pool/dists/noble/main/r-cran-quantreg.nonpar_1.0-1.ca2404.1_all.deb Size: 815492 MD5sum: f0e7b0b23112522b3cc0c3429244cefa SHA1: 270121ca57c8905465aaabfd1578727abae65596 SHA256: d1304b97f3a9b2b75dcca3a930e35ca2086312b01c7eb3bf058e6871ad41d4a3 SHA512: 1e58062ae67a963d28ddb2d67d8de5c299fb5831777806ed3cf123770bc9f6f7d8d829b542e5df88dafb266db225461b767123525a09eb1225d4f60f12e06b0d Homepage: https://cran.r-project.org/package=quantreg.nonpar Description: CRAN Package 'quantreg.nonpar' (Nonparametric Series Quantile Regression) Implements the nonparametric quantile regression method developed by Belloni, Chernozhukov, and Fernandez-Val (2011) to partially linear quantile models. Provides point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. Provides pointwise and uniform confidence intervals using analytic and resampling methods. Package: r-cran-quantreggrowth Architecture: all Version: 1.7-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-sparsem Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mgcv, r-cran-markdown Filename: pool/dists/noble/main/r-cran-quantreggrowth_1.7-2-1.ca2404.1_all.deb Size: 387078 MD5sum: 113dcde2c26c28f420a2d1cb476f6c28 SHA1: 8fae8e9e6a684e689caac14cdc86b90d8c932985 SHA256: 1e688630caab8503487172eb649051e16b779878a99052667a6c3c692dfc5821 SHA512: b5abcd6c8f990bd4a37e7b5a1474f6c479f05d0799aa46f3c86f36999f22f3b487b4f88832ad3185af26b5b3abd704852f595f98d61024c6125f3d31bd6d7633 Homepage: https://cran.r-project.org/package=quantregGrowth Description: CRAN Package 'quantregGrowth' (Non-Crossing Additive Regression Quantiles and Non-ParametricGrowth Charts) Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms, including varying coefficients, via B-splines with L1-norm difference penalties. Random intercepts and variable selection are allowed via the lasso penalties. The smoothing parameters are estimated as part of the model fitting, see Muggeo and others (2021) . Monotonicity and concavity constraints on the fitted curves are allowed, see Muggeo and others (2013) , and also or some code examples. Package: r-cran-quantsig Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-quantsig_0.1.0-1.ca2404.1_all.deb Size: 10594 MD5sum: 74a347b02c2060e47869fbfc03ae3bc1 SHA1: cd63d71eb46761938e110b177864de9a26d8977b SHA256: 8eb1e92795af59e20cf938506b582aae17b0ed6fcaf3d1c70c75c508accdc7c7 SHA512: cf04181593f4488acec2b1ef06e1cec50e7e51635e95fff199f39c5b7bbfd9185a52998dd3f9a9343ade73b015fe1f33f6dc955ab4e6f56141e8e27c67d1e673 Homepage: https://cran.r-project.org/package=quantsig Description: CRAN Package 'quantsig' (Sigmoidal Quantile Function Estimator) A sigmoidal quantile function estimator based on a newly defined generalized expectile function. The generalized sigmoidal quantile function can estimate quantiles beyond the range of the data, which is important for certain applications given smaller sample sizes. The package is based on the method introduced in Hutson (2024) . Package: r-cran-quantumops Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-quantumops_3.0.1-1.ca2404.1_all.deb Size: 268424 MD5sum: 163e4cf87733a22c2fde85482b885e0f SHA1: 2e06cf6d64ab2314a019f1360ee57f95bf249103 SHA256: c440779514328e4cce5c01ae22b80fb8759e6eabc923a6717beae4304e14e7a7 SHA512: d0fb94f89e53425cd5c2ea292c676281833b3cae86f98c220ad71f00f2025dbdb0ae7c1296c81036dac850c54a83d0361c6ad2480dbf24a7cc9a35b2888ad43a Homepage: https://cran.r-project.org/package=QuantumOps Description: CRAN Package 'QuantumOps' (Performs Common Linear Algebra Operations Used in QuantumComputing and Implements Quantum Algorithms) Contains basic structures and operations used frequently in quantum computing. Intended to be a convenient tool to help learn quantum mechanics and algorithms. Can create arbitrarily sized kets and bras and implements quantum gates, inner products, and tensor products. Creates arbitrarily controlled versions of all gates and can simulate complete or partial measurements of kets. Has functionality to convert functions into equivalent quantum gates and model quantum noise. Includes larger applications, such as Steane error correction , Quantum Fourier Transform and Shor's algorithm (Shor 1999), Grover's algorithm (1996), Quantum Approximation Optimization Algorithm (QAOA) (Farhi, Goldstone, and Gutmann 2014) , and a variational quantum classifier (Schuld 2018) . Can be used with the gridsynth algorithm to perform decomposition into the Clifford+T set. Package: r-cran-quaqcr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 986 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-quaqcr_1.0.4-1.ca2404.1_all.deb Size: 869330 MD5sum: f5756a9b7d199b4b305c68685ad78093 SHA1: dd3fdeecb41cf28f21af45cd84c624acb5957cea SHA256: 5df7dbc0e1901b4723163a5cbd4d5fd344bf83c29c00b32f51fb72ad43a2136c SHA512: bfe0d22ee5af01e880c8cfae97d531163d9756683352bb69a1091eaa801dde217cd91ad2798962924485b7026210b0521bcf819f5e0abaf315906c235517e0c0 Homepage: https://cran.r-project.org/package=quaqcr Description: CRAN Package 'quaqcr' (Quick ATAC-Seq QC) A wrapper around the 'quaqc' program described in Tremblay and Questa (2024) . 'quaqc' allows for assay for transposase-accessible chromatin using sequencing (ATAC-seq) specific quality control and read filtering of next-generation sequencing (NGS) data with minimal processing time and extremely low memory overhead. Any number of samples can be processed, using multiple threads if desired. 'quaqc' outputs a comprehensive set of aligned read metrics, including alignment size, fragment size, percent duplicates, mapq scores, read depth, GC content, and others. Although designed for ATAC-seq data, 'quaqc' can also be used for other unspliced DNA sequencing experiments (such as chromatin immunoprecipitation sequencing, or ChIP-seq) as many of the metrics are related to general sequencing quality. This R package also provides additional utilities for custom analyses and plotting of 'quaqc' results. Package: r-cran-quarks Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3361 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dygraphs, r-cran-ggplot2, r-cran-progress, r-cran-rugarch, r-cran-shiny, r-cran-shinyjs, r-cran-smoots, r-cran-yfr, r-cran-xts Filename: pool/dists/noble/main/r-cran-quarks_1.1.6-1.ca2404.1_all.deb Size: 3330628 MD5sum: 6f1d5096ffa3d198d30b8aee50cd3f3b SHA1: cbc5ea0c3b8edc60f33556c780d8784adf2e0f1f SHA256: 85620708b24a6cff905545c1eb0b879671abfb33a6f17e48eccb7e402d7ea7b1 SHA512: 6b099f6e0b7bd266940e2888ea0e40f00d343605c45418654c846836561d7621e38c58930d0c69a5da807938065a089b58019b4c089cd2e4df7d678d52c39d4f Homepage: https://cran.r-project.org/package=quarks Description: CRAN Package 'quarks' (Simple Methods for Calculating and Backtesting Value at Risk andExpected Shortfall) Enables the user to calculate Value at Risk (VaR) and Expected Shortfall (ES) by means of various types of historical simulation. Currently plain-, age-, volatility-weighted- and filtered historical simulation are implemented in this package. Volatility weighting can be carried out via an exponentially weighted moving average model (EWMA) or other GARCH-type models. The performance can be assessed via Traffic Light Test, Coverage Tests and Loss Functions. The methods of the package are described in Gurrola-Perez, P. and Murphy, D. (2015) as well as McNeil, J., Frey, R., and Embrechts, P. (2015) . Package: r-cran-quarrint Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3935 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-neuralnet Filename: pool/dists/noble/main/r-cran-quarrint_1.0.0-1.ca2404.1_all.deb Size: 3987844 MD5sum: 3efd6d184068d8a2c43367e315f8d3d5 SHA1: bbe61abfd56fba66c05748c06e8ca4552d5ef120 SHA256: 8f44e98a03cffa97b4149f6d30eaf5848e6a345a955cdde617a3922822d3baf7 SHA512: e5ab7582c43d37b25a90e130f2bd07d8f866911feaa58907700faadebbc14171346b431205c31dc9fcc912a4e73086e0fdfa410020aa827607f23758c92b0daf Homepage: https://cran.r-project.org/package=quarrint Description: CRAN Package 'quarrint' (Interaction Prediction Between Groundwater and Quarry ExtensionUsing Discrete Choice Models and Artificial Neural Networks) An implementation of two interaction indices between extractive activity and groundwater resources based on hazard and vulnerability parameters used in the assessment of natural hazards. 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Package: r-cran-quartets Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2265 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-quartets_0.1.1-1.ca2404.1_all.deb Size: 2192212 MD5sum: 8c7e5bf1d438661b097ad1896cb34efb SHA1: 010c4b2a9215c0227a4bd5cc272afa33e11f1e4f SHA256: 85de67a6c1b93c34179784fc0f206f6ef0be3b0344851aad4c9f3ac11b2b048e SHA512: 4f523e9aeec2daa19582b2d59d501dcde16ac5e79242ab787c5d6c488ec5d2329275c4da896a581c17337543efa4c235272350471ae3b558edbc2909a2e18ff1 Homepage: https://cran.r-project.org/package=quartets Description: CRAN Package 'quartets' (Datasets to Help Teach Statistics) In the spirit of Anscombe's quartet, this package includes datasets that demonstrate the importance of visualizing your data, the importance of not relying on statistical summary measures alone, and why additional assumptions about the data generating mechanism are needed when estimating causal effects. The package includes "Anscombe's Quartet" (Anscombe 1973) , D'Agostino McGowan & Barrett (2023) "Causal Quartet" , "Datasaurus Dozen" (Matejka & Fitzmaurice 2017), "Interaction Triptych" (Rohrer & Arslan 2021) , "Rashomon Quartet" (Biecek et al. 2023) , and Gelman "Variation and Heterogeneity Causal Quartets" (Gelman et al. 2023) . Package: r-cran-quartify Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3749 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstudioapi, r-cran-cli, r-cran-shiny, r-cran-miniui, r-cran-later, r-cran-base64enc, r-cran-shinyfiles, r-cran-quarto, r-cran-styler, r-cran-lintr Suggests: r-cran-testthat, r-cran-dplyr, r-cran-shinyalert Filename: pool/dists/noble/main/r-cran-quartify_1.1.1-1.ca2404.1_all.deb Size: 3702048 MD5sum: d2e9be3c47f9d2c535695f45d26c860d SHA1: 6fa071b931becb11037bcaa0620d535048e36cba SHA256: 2445c529780226959d561ff14400c8f3d5a7d48ad3dfbe6bd0011307cca55dfb SHA512: 4c58f07b128d4d72daa5b3eb683c0404c86f9b0738da6502b4360e171175845ef39723465942e4a45b98321bbe5cfdae4363f8556d77758815ad46e6be1aeb3b Homepage: https://cran.r-project.org/package=quartify Description: CRAN Package 'quartify' (Convert R Scripts to 'Quarto' Markdown Documents) Converts R scripts (.R) into 'Quarto' markdown documents (.qmd) with automatic formatting. 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Package: r-cran-quartose Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-knitr, r-cran-purrr, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-quarto, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-quartose_0.1.0-1.ca2404.1_all.deb Size: 41086 MD5sum: 7844ec174b85d84d9970c779c74a5064 SHA1: 7edd4fd7771b2993b332581b7f341f1686f67908 SHA256: b2733fbfc9ad2f1e52e11567eefe5e886bbfe939bbf5c6609bc04cf44c3be94c SHA512: b55c72ad10daa05a6f67ca466c405cf96a013359a1487c01d2eb84e58b936573dd0c932e2c254849ed6cfcd7eb90938b693fbe80e24efd530a1ddc521cb5c758 Homepage: https://cran.r-project.org/package=quartose Description: CRAN Package 'quartose' (Dynamically Generate Quarto Syntax) Provides helper functions to work programmatically within a quarto document. It allows the user to create section headers, tabsets, divs, and spans, and formats these objects into quarto syntax when printed into a document. Package: r-cran-quasar Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-matrix, r-cran-mass, r-cran-pracma, r-cran-sn Filename: pool/dists/noble/main/r-cran-quasar_0.2.1-1.ca2404.1_all.deb Size: 77598 MD5sum: a1789ee44c6173161cdcb687d42db1ed SHA1: d9072d268cd152b3e27b675fc12b7d7de20805e7 SHA256: 4923b98de499d963edca73b5ffe04cb4e1ea57aa59541fc7b4c7470e9fb22c7c SHA512: 6ae21a467ae3a36189e630482a594ed5b6f5a4b466870a86bd8435295b5900f52219294870b158cc0ee7960baee2a731333de38867f923520ef9051db4343379 Homepage: https://cran.r-project.org/package=quasar Description: CRAN Package 'quasar' (Valid Inference on Multiple Quantile Regressions) The approach is based on the closed testing procedure to control familywise error rate in a strong sense. The local tests implemented are Wald-type and rank-score. The method is described in De Santis, et al., (2026), . Package: r-cran-quaxnat Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1873 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra Filename: pool/dists/noble/main/r-cran-quaxnat_1.0.1-1.ca2404.1_all.deb Size: 1847490 MD5sum: dcc4a414ddcf332d6a7a20b20452868d SHA1: 69fd1562b96d953fa9cd58abb2d245b9ec418421 SHA256: 53b5df7e8762a7b2375e16e8a813d829ca346f2e666183de28b6031e02db2382 SHA512: 21ce1d2fa30d7c7afe75774ec2bd8f7b262fd691f969ef4697945b21311b2a185ef0c48041d427ff2759f1b980f4838e80c0ab6b5b20a38486d099dff6c02222 Homepage: https://cran.r-project.org/package=quaxnat Description: CRAN Package 'quaxnat' (Estimation of Natural Regeneration Potential) Functions for estimating the potential dispersal of tree species using regeneration densities and dispersal distances to nearest seed trees. A quantile regression is implemented to determine the dispersal potential. Spatial prediction can be used to identify natural regeneration potential for forest restoration as described in Axer et al (2021) . Package: r-cran-quclu Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-quclu_1.0.1-1.ca2404.1_all.deb Size: 50066 MD5sum: e93acae81b38a4af3b81456bb04354bd SHA1: 79ba761505e8b2e9eee1b1edcd2d93647d63d841 SHA256: 43d897b8325e7b50d6f0c89e1b6007ffe6a100cae0272ceed0000a9bb184dd30 SHA512: 58997726e29d244231b4e9c5d0ec9250cafbb79cca18b2e73bbcc3e0da8619a7a1d54ed110c31da2e9f7ab09b0d5551665b5ee4ca17fd21d561dd805e6bdc1cc Homepage: https://cran.r-project.org/package=QuClu Description: CRAN Package 'QuClu' (Quantile-Based Clustering Algorithms) Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, C., Viroli, C., Anderlucci, L. (2019) "Quantile-based clustering." Electronic Journal of Statistics. 13 (2) 4849 - 4883 . Package: r-cran-querybuilder Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-dplyr, r-cran-glue, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-querybuilder_0.1.0-1.ca2404.1_all.deb Size: 112178 MD5sum: c97834565b8d293ca7966708ad108745 SHA1: 6a8d92e933ad26fe41755bf9b96fd99835d0490b SHA256: cd891efbaf765eed33b59cc296bd1189eb40eae477c554b8158bfac78ed443d6 SHA512: a51e8bacc6af7afe8089d1cdd6c20439827c67063653b025be1bf52cfac2f70f3b26f14818686db6654c9e449462d6c8401b579b28e499449cd65b515543b176 Homepage: https://cran.r-project.org/package=queryBuilder Description: CRAN Package 'queryBuilder' (Programmatic Way to Construct Complex Filtering Queries) Syntax for defining complex filtering expressions in a programmatic way. 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Use it to drive reactive calculations, visualizations, downloads, and more. Package: r-cran-queryparser Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-queryparser_0.3.2-1.ca2404.1_all.deb Size: 214010 MD5sum: 2f67301c8c58be0d3e48ff4085c0361a SHA1: 4926daf55d845421b756ecf30fc1753949ac6313 SHA256: 4a52f1278b3432676ab09d9341fe16ef25ed83ce6f1f3908d0ef1b6a1c064339 SHA512: a3374f0455cc028ada926651977afe7b58691e7b7c1ed53f44c3fbf75f11214856860bca3c18394b79abba12e6ffadaf0d5ddd19de3139ce71464daf376b20ed Homepage: https://cran.r-project.org/package=queryparser Description: CRAN Package 'queryparser' (Translate 'SQL' Queries into 'R' Expressions) Translate 'SQL' 'SELECT' statements into lists of 'R' expressions. Package: r-cran-queryup Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-covr, r-cran-knitr, r-cran-xml2, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-queryup_1.0.5-1.ca2404.1_all.deb Size: 70186 MD5sum: 825a5169ad3d2e5dbc5f14f34ca7412f SHA1: 65d035ad42ddedb605baee0caf81a7d4e447955d SHA256: 16681f476566f8a7761be7aef321342c3518f3691ec2f7726016d94fc5e3ee1e SHA512: d67f85d3e3b294cf1c4e61a77494cf4977a9c9741f626a41f0ca30fb916aedb4c3509ade054a3889a84ebb7c9f1eb13eeb79dea817fc3f759f22aa831b593e21 Homepage: https://cran.r-project.org/package=queryup Description: CRAN Package 'queryup' (Query the 'UniProtKB' REST API) Retrieve protein information from the 'UniProtKB' REST API (see ). Package: r-cran-quest Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 778 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-str2str, r-cran-abind, r-cran-checkmate, r-cran-plyr, r-cran-car, r-cran-psych, r-cran-boot, r-cran-mbess, r-cran-nlme, r-cran-lme4, r-cran-multilevel, r-cran-lavaan Suggests: r-cran-reshape, r-cran-psychtools, r-cran-lmeinfo, r-cran-semtools Filename: pool/dists/noble/main/r-cran-quest_0.2.1-1.ca2404.1_all.deb Size: 731298 MD5sum: 1f08afca4a4c4ac0bc56707dca5c6a7d SHA1: 7f1b5f578feabb4ed3317aed9651c5e53afee7cd SHA256: 849b588459a3ee3694cb23913c5a022553ca865c66e2124f754eb2da3784f897 SHA512: d212761193e72293728cd5986dd671e016dbf2ce739a2f3b3e5d38c9a01aaa2fbe8bf988bf0bd4d9991bbcf961f9d4ae73a2a76c7f09511a5ea7fcfd065d1fca Homepage: https://cran.r-project.org/package=quest Description: CRAN Package 'quest' (Prepare Questionnaire Data for Analysis) Offers a suite of functions to prepare questionnaire data for analysis (perhaps other types of data as well). By data preparation, I mean data analytic tasks to get your raw data ready for statistical modeling (e.g., regression). There are functions to investigate missing data, reshape data, validate responses, recode variables, score questionnaires, center variables, aggregate by groups, shift scores (i.e., leads or lags), etc. It provides functions for both single level and multilevel (i.e., grouped) data. With a few exceptions (e.g., ncases()), functions without an "s" at the end of their primary word (e.g., center_by()) act on atomic vectors, while functions with an "s" at the end of their primary word (e.g., centers_by()) act on multiple columns of a data.frame. Package: r-cran-questionr Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4205 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-miniui, r-cran-rstudioapi, r-cran-highr, r-cran-styler, r-cran-classint, r-cran-htmltools, r-cran-rlang, r-cran-labelled Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-janitor, r-cran-forcats, r-cran-knitr, r-cran-rmarkdown, r-cran-survey, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-questionr_0.8.2-1.ca2404.1_all.deb Size: 3869388 MD5sum: dd9381625eedb317d18139dbc11581b1 SHA1: 171312c2a3f7393ac2ec275befc3351ebb4d3f71 SHA256: e191cb170631b6d60e72bc6ac22456a8c7566c55ec13d4d9ed13f68151d66780 SHA512: a74fcb880fe8d42e7079dcde4a3dac3ae5009ceedc54f04fed8337f88c0f7272c8a36b3a1498759f29f0d533857d4f37e7c2843654481c0f242e250e41e8043b Homepage: https://cran.r-project.org/package=questionr Description: CRAN Package 'questionr' (Functions to Make Surveys Processing Easier) Set of functions to make the processing and analysis of surveys easier : interactive shiny apps and addins for data recoding, contingency tables, dataset metadata handling, and several convenience functions. Package: r-cran-questr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-kernlab Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-questr_0.1.1-1.ca2404.1_all.deb Size: 136804 MD5sum: 39952f53f2c486f4159c65580bd74d04 SHA1: c3762fad0aea29bc2c20bcb876080a4a7c36de99 SHA256: e49718d2adf91235f5c1b5742ae020834c67b444b8b1a3a7d8563cdaaa008b48 SHA512: 051efff1fdef2eaa73956c3903deb34a7efc236db473b4867128f4b1e1b84681472fa45171a6b636aeab6b7667db0efdbc6e2aeaf3466bc9c280a7edd4aac1f6 Homepage: https://cran.r-project.org/package=QuESTr Description: CRAN Package 'QuESTr' (Constructing Quantitative Environment Sensor usingTranscriptomes) A method for prediction of environmental conditions based on transcriptome data linked with the environmental gradients. This package provides functions to overview gene-environment relationships, to construct the prediction model, and to predict environmental conditions where the transcriptomes were generated. This package can quest for candidate genes for the model construction even in non-model organisms' transcriptomes without any genetic information. Package: r-cran-queueing Architecture: all Version: 0.2.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1217 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-queueing_0.2.12-1.ca2404.1_all.deb Size: 963162 MD5sum: 5a8c5167c267fc612e919d56713d1c15 SHA1: 98999a2a2b64f4bee6e2d986464f8c39315a2c4a SHA256: 09a3f0dcedcd64d9d417dff88e4bad5db871ff6b5f7091d7ed2dae3381d712d5 SHA512: 35a60cfba0b5b66659daba4580c558467b59994d7887f94b95117848f95e5701aa3f4d737e23b3507474283fc1463ab72bcdb9060d7b2171f09465d57189e53e Homepage: https://cran.r-project.org/package=queueing Description: CRAN Package 'queueing' (Analysis of Queueing Networks and Models) It provides versatile tools for analysis of birth and death based Markovian Queueing Models and Single and Multiclass Product-Form Queueing Networks. It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K, Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks, Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks and Single Channel Multiple Class Mixed Networks. Also it provides a B-Erlang, C-Erlang and Engset calculators. This work is dedicated to the memory of D. Sixto Rios Insua. Package: r-cran-quickcheck Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-testthat, r-cran-hedgehog, r-cran-purrr, r-cran-tibble, r-cran-data.table, r-cran-hms, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-quickcheck_0.1.3-1.ca2404.1_all.deb Size: 632054 MD5sum: 488fe08e753e575d40bd91b0eca52772 SHA1: 9bb623d46b40f9a8feaa41038b078327d4273c04 SHA256: d033567a8102310625afc5b3219c7efb5ad5bbb7a39f301c6089e1cfcb2b34d4 SHA512: faf77e3f31fb8d9de4b57ccf3516b47bf82146d6f5a6c56eedfb6ccf6326f928082a03583ea562631bbd61e520fb8b6b8aad9369f42b39f83a1d72498c3aa668 Homepage: https://cran.r-project.org/package=quickcheck Description: CRAN Package 'quickcheck' (Property Based Testing) Property based testing, inspired by the original 'QuickCheck'. This package builds on the property based testing framework provided by 'hedgehog' and is designed to seamlessly integrate with 'testthat'. Package: r-cran-quickcode Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstudioapi, r-cran-fitdistrplus Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-qpdf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-quickcode_1.0.9-1.ca2404.1_all.deb Size: 1148560 MD5sum: 1bd4dd2aab5bd90a04778e6d9dc828f5 SHA1: 3ae09bd02d379ea76c2bfe33b0013182e5ea6007 SHA256: 0dc2ee814c96d02d0936768a8bd8d0a384bbc02421300d7db317140afa76a81f SHA512: c61b94876021f20b01e90c5c9648e8df119c60fb638e30a7cb860934bbcdd98c40ebeb08130c386b832655ba18ac685398b7336676d3aa706e4adc0d92bd495e Homepage: https://cran.r-project.org/package=quickcode Description: CRAN Package 'quickcode' (Quick and Essential 'R' Tricks for Better Scripts) The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to improve your scripts. Improve the quality and reproducibility of 'R' scripts. Package: r-cran-quickexplore Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-bslib, r-cran-dt, r-cran-dplyr, r-cran-haven, r-cran-readr, r-cran-jsonlite, r-cran-writexl, r-cran-rlang, r-cran-digest Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-quickexplore_0.1.0-1.ca2404.1_all.deb Size: 157916 MD5sum: d87a4ceb14061747a0ffb5c7f5eec709 SHA1: e3d48b33f6cd4f3f53586f14c3e2c945d0b409f8 SHA256: ee28a21eef71241d9ecf0c1e6b7dcdd51243dc869551d803c73b6838b04f100c SHA512: 75343d53ded621805f6390a37ba4a32663e34604bb204c5a60c948b472339614fad58db79d4bc3d8bc66af9462c8b716dac67a39d455785c6dda58708b765ad2 Homepage: https://cran.r-project.org/package=QuickExplore Description: CRAN Package 'QuickExplore' (Interactive Dataset Explorer for 'R' and 'SAS' and Other DataFormats) A 'Shiny' application that provides nice interface for browsing, exploring, summarising, and converting datasets stored in 'SAS' (.sas7bdat, .xpt), CSV (.csv), and 'R' (.rds) formats. Users can register multiple directory-based libraries, interactively filter data using 'dplyr' expressions, inspect per-variable statistics, and export datasets to Excel, JSON, CSV, 'R' data, or 'SAS' transport formats. Package: r-cran-quicknmix Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-optimparallel, r-cran-doparallel Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-quicknmix_1.1.1-1.ca2404.1_all.deb Size: 101746 MD5sum: d0b04957c64d1404b6f7a8c6d66f3eb3 SHA1: a40eaa81722986107164b33aa4b5e104d4d4d5c4 SHA256: a6c446d97327f82d49701ea323815fc2f8375db9b15881a8fbfcd73eab8a62e3 SHA512: d9c1b3f56f0a0973a47cbde95996160d457dc26e5a5cf5db52887ba730a9e384c5d765b8294b4ae069e3a717b66f8259d51afddbdb00dfc0762d76879aca975b Homepage: https://cran.r-project.org/package=quickNmix Description: CRAN Package 'quickNmix' (Asymptotic N-Mixture Model Fitting) For fitting N-mixture models using either FFT or asymptotic approaches. FFT N-mixture models extend the work of Cowen et al. (2017) . Asymptotic N-mixture models extend the work of Dail and Madsen (2011) , to consider asymptotic solutions to the open population N-mixture models. The FFT models are derived and described in "Parker, M.R.P., Elliott, L., Cowen, L.L.E. (2022). Computational efficiency and precision for replicated-count and batch-marked hidden population models [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.". The asymptotic models are derived and described in: "Parker, M.R.P., Elliott, L., Cowen, L.L.E., Cao, J. (2022). Fast asymptotic solutions for N-mixtures on large populations [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.". Package: r-cran-quickoutlier Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3841 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbscan, r-cran-ggplot2, r-cran-isotree, r-cran-plotly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-quickoutlier_0.1.5-1.ca2404.1_all.deb Size: 1026402 MD5sum: fa6e9a91931e868dbfae17fa23987f73 SHA1: caff6797edc105b1bf8010b16e3bba2365afeaed SHA256: ec30d5ab8bdba7f77a2fa2c34238f8706e9c809a9e2554b24092170c7d9efd88 SHA512: 2db00caff08169fd6281cd5b82960fa07776e002a86ca69212f74ca6ed26123e39daf59e80d909d7843696d2c0fee39e83b84ea8a10a82b3f27a4d886cbe768f Homepage: https://cran.r-project.org/package=quickOutlier Description: CRAN Package 'quickOutlier' (Detect and Treat Outliers in Data Mining) Implements a suite of tools for outlier detection and treatment in data mining. 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It unifies the interface for multiple algorithms (including 'glmnet', 'ranger', 'xgboost', and 'naivebayes') and memory-efficient sparse matrix vectorization methods (Bag-of-Words, Term Frequency, TF-IDF, and Binary). Users can go from raw text to a fully evaluated sentiment model, complete with ROC-optimized thresholds, in just a few function calls. The resulting model artifact automatically aligns the vocabulary of new datasets during the prediction phase, safely appending predicted classes and probability matrices directly to the user's original dataframe to preserve metadata. 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E. Farhi (1997): ; C. Godsil (2011) . 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Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. Because both financial institutions and market data providers keep their data private, this package provides fake, public data to enable the development and use of 'PACTA' in R. 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Financial institutions use 'PACTA' to study how their capital allocation decisions align with climate change mitigation goals. This package matches data from corporate lending portfolios to asset level data from market-intelligence databases (e.g. power plant capacities, emission factors, etc.). This is the first step to assess if a financial portfolio aligns with climate goals. 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Toolkit to easily incorporate celebratory splashes in 'Rmarkdown' and 'shiny' apps. Package: r-cran-r2glmm Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-matrix, r-cran-pbkrtest, r-cran-ggplot2, r-cran-afex, r-cran-mass, r-cran-gridextra Suggests: r-cran-lme4, r-cran-nlme, r-cran-testthat, r-cran-data.table, r-cran-dplyr, r-cran-lmertest Filename: pool/dists/noble/main/r-cran-r2glmm_0.1.3-1.ca2404.1_all.deb Size: 91838 MD5sum: ca8058c0f97f9f44f4452d5eecb55621 SHA1: cdcb1e86699db12965b2645ac86dce8cf71ab497 SHA256: e2ddc4cb454b837692561dfd053bd64c418aaa86c10ac78ace442719eb266fa9 SHA512: 332e73114d2939b30c99b777a394f19b90f05040705be9876eb3903d209903e87199e5517773e153fba035786a7178a540a61516fdcde76469f84963303add4a Homepage: https://cran.r-project.org/package=r2glmm Description: CRAN Package 'r2glmm' (Computes R Squared for Mixed (Multilevel) Models) The model R squared and semi-partial R squared for the linear and generalized linear mixed model (LMM and GLMM) are computed with confidence limits. The R squared measure from Edwards et.al (2008) is extended to the GLMM using penalized quasi-likelihood (PQL) estimation (see Jaeger et al. 2016 ). Three methods of computation are provided and described as follows. First, The Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest' package and the 'glmmPQL' function, the Kenward-Roger approach in the 'r2glmm' package is limited to the LMM. Second, The method introduced by Nakagawa and Schielzeth (2013) and later extended by Johnson (2014) . The 'r2glmm' package only computes marginal R squared for the LMM and does not generalize the statistic to the GLMM; however, confidence limits and semi-partial R squared for fixed effects are useful additions. Lastly, an approach using standardized generalized variance (SGV) can be used for covariance model selection. Package installation instructions can be found in the readme file. Package: r-cran-r2html Architecture: all Version: 2.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 912 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-boot, r-cran-survival, r-cran-cluster, r-cran-nlme, r-cran-rpart, r-cran-nnet Filename: pool/dists/noble/main/r-cran-r2html_2.3.4-1.ca2404.1_all.deb Size: 583312 MD5sum: e55bfab4f2093412433d774d3b8defda SHA1: 3538ae1ef3d6a9f34493e0da018b5022e4eddaa2 SHA256: ce5bd83b111dd788a279ed56a7f2b125ee1d625bb6476964db1273e299fcb6a4 SHA512: 03c3d38f6cc68366b41b79791e0c38c4a30f04930fdb4ecb4de8aea0cb15c9431c4b94e003e7b846dc348da77c20a0820722b8962628e6230e8dba6b23c9f828 Homepage: https://cran.r-project.org/package=R2HTML Description: CRAN Package 'R2HTML' (HTML Exportation for R Objects) Includes HTML function and methods to write in an HTML file. Thus, making HTML reports is easy. Includes a function that allows redirection on the fly, which appears to be very useful for teaching purpose, as the student can keep a copy of the produced output to keep all that he did during the course. Package comes with a vignette describing how to write HTML reports for statistical analysis. Finally, a driver for 'Sweave' allows to parse HTML flat files containing R code and to automatically write the corresponding outputs (tables and graphs). Package: r-cran-r2jags Architecture: all Version: 0.8-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-abind, r-cran-coda, r-cran-r2winbugs, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-r2jags_0.8-9-1.ca2404.1_all.deb Size: 107280 MD5sum: 6ed4a80c58dc5a90ced39e4a13e3f4c0 SHA1: 68d1b984b384adcdffba17e99493a64d52a8cf15 SHA256: a15ac5d82e0c3e738e107361f79487ad9f849b1d393345da3abdaadab6d9a10b SHA512: 27bae691c461888e9501b1a89791cb33588c97ae81e985ff7d6c7dc382423d54e30d655814b9d437875731ab2ef9cbaab85bd01e2cbffb393603824a522259d4 Homepage: https://cran.r-project.org/package=R2jags Description: CRAN Package 'R2jags' (Using R to Run 'JAGS') Providing wrapper functions to implement Bayesian analysis in JAGS. Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains. Package: r-cran-r2mlm Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-nlme, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-rockchalk, r-cran-stringr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-matrix Filename: pool/dists/noble/main/r-cran-r2mlm_0.3.8-1.ca2404.1_all.deb Size: 407462 MD5sum: 9b0c63fae4f812ccc75162a45de2284e SHA1: 4e060d8940a7bfe16c6e5e6f66b4bf5e90224ce2 SHA256: 767353b5979c640a949f2ac8a2c3fc7d1d2410b93c208e51fa32823b386309a0 SHA512: b935161a074ded6360b3ab50371fa94400c85637b14b95a5b8576b248d67acc4eaf18bd2b586071facf3dee800cc82c2956da586352558a354977773980bd05d Homepage: https://cran.r-project.org/package=r2mlm Description: CRAN Package 'r2mlm' (R-Squared Measures for Multilevel Models) Generates both total- and level-specific R-squared measures from Rights and Sterba’s (2019) framework of R-squared measures for multilevel models with random intercepts and/or slopes, which is based on a complete decomposition of variance. 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There are many possible uses for this new tool, such as to examine mathematical expressions with very irregular shapes, to aid teaching people with impaired vision, to create raised relief maps from digital elevation maps (DEMs), to bridge the gap between mathematical tools and rapid prototyping, and many more. Ian Walker created the function r2stl() and Jose' Gama assembled the package. 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Use tools and methods that are selected carefully to align with academic consensus, bridging the gap between theoretical knowledge and practical application. They help you find your own personalized optimal discretionary spending or optimal asset allocation, and prepare you for retirement or financial independence. The optimal solution to this problems is extremely complex, and we only have a single lifetime to get it right. Fortunately, we now have the user-friendly tools implemented, that integrate life-cycle models with single-period net-worth mean-variance optimization models. Those tools can be used by anyone who wants to see what highly-personalized optimal decisions can look like. For more details see: Idzorek T., Kaplan P. (2024, ISBN:9781952927379), Haghani V., White J. (2023, ISBN:9781119747918). 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Package: r-cran-r4lineups Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-here, r-cran-magick, r-cran-magrittr, r-cran-proc, r-cran-psych, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pander Filename: pool/dists/noble/main/r-cran-r4lineups_0.1.1-1.ca2404.1_all.deb Size: 531338 MD5sum: 79834f63b6695f69e2d53555cca348c8 SHA1: 60ce9edf4ed2b176d27ad889ca128fcc12c0c3a3 SHA256: 41b90db9f021ad4b5fa47078dd48ae2f7e533368fe3329a12b86dae9114c5e7c SHA512: 718a159b887512e64545b5820eec666b0e5ff36b85c82693f39842a97380b38e125dc004a01882ebba53dbdf5512359002ad6fdb8d00bf6982abc3dbf55cf867 Homepage: https://cran.r-project.org/package=r4lineups Description: CRAN Package 'r4lineups' (Statistical Inference on Lineup Fairness) Since the early 1970s eyewitness testimony researchers have recognised the importance of estimating properties such as lineup bias (is the lineup biased against the suspect, leading to a rate of choosing higher than one would expect by chance?), and lineup size (how many reasonable choices are in fact available to the witness? 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Package: r-cran-r4pde Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 506 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-car, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-igraph, r-cran-interval, r-cran-lubridate, r-cran-nasapower, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-survival, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-r4pde_0.1.0-1.ca2404.1_all.deb Size: 473000 MD5sum: 83ebdd28522cc9d80a36f2866fbc5a45 SHA1: 8e76f62878b638d1d82935cc5b4930091d75c4c4 SHA256: 5595a66a998a4ebba51633179f25dc54893e1cd98e9b6176d7dbd48e073185b5 SHA512: 82d391a0abad6c1716d99f36e8d25c41d4eedeedb0112964eb00cfec6d8a5d6e53248fd99d08fed5d807758883df6620e59634f3f383a7bceef67249276ab187 Homepage: https://cran.r-project.org/package=r4pde Description: CRAN Package 'r4pde' (Companion to R for Plant Disease Epidemiology Book) Datasets and utility functions to support the book "R for Plant Disease Epidemiology" (R4PDE). It includes functions for quantifying disease, assessing spatial patterns, and modeling plant disease epidemics based on weather predictors. These tools are intended for teaching and research in plant disease epidemiology. Several functions are based on classical and contemporary methods, including those discussed in Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) . Package: r-cran-r4ss Architecture: all Version: 1.44.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2167 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-corpcor, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-lifecycle, r-cran-stringr, r-cran-kableextra Suggests: r-cran-gtools, r-cran-gplots, r-cran-knitr, r-cran-maps, r-cran-pso, r-cran-testthat, r-cran-truncnorm, r-cran-rmarkdown, r-cran-shiny, r-cran-flextable, r-cran-reshape2, r-cran-ggpubr Filename: pool/dists/noble/main/r-cran-r4ss_1.44.0-1.ca2404.1_all.deb Size: 2108458 MD5sum: 78b574e6fe9fe04f8d9c8b88278ba0d7 SHA1: 9b92f49209a66ab4729ee46da5cd3d6a5f73b551 SHA256: cf350f67058e95957af3c685da3938a8eac56040f6940556ad28650c9b9cc4fc SHA512: 402c2c7b5ce7127aca97cd085e7fd4aaeffcce72f65015af7bef82856f84f3ae326deb0fb07bc0c1dd0ef8c7123b12df1a0050eb90676400bffed6bd99a98612 Homepage: https://cran.r-project.org/package=r4ss Description: CRAN Package 'r4ss' (R Code for Stock Synthesis) A collection of R functions for use with Stock Synthesis, a fisheries stock assessment modeling platform written in ADMB by Dr. Richard D. Methot at the NOAA Northwest Fisheries Science Center. The functions include tools for summarizing and plotting results, manipulating files, visualizing model parameterizations, and various other common stock assessment tasks. This version of '{r4ss}' is compatible with Stock Synthesis versions 3.24 through 3.30 (specifically version 3.30.19.01, from April 2022). Package: r-cran-r4sub Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-r4subcore, r-cran-r4subdata, r-cran-r4subprofile, r-cran-r4subrisk, r-cran-r4subscore, r-cran-r4subtrace, r-cran-rlang, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-r4sub_0.1.0-1.ca2404.1_all.deb Size: 29498 MD5sum: 103926c99d4499f5f6960e686d7811ff SHA1: 352fad628303fbf1c5cf3040a9ecd6c8eb0e6998 SHA256: cc04fb0369448b276780e7de2ed1e6a7c1ea1441c00a70f50120202ee693338f SHA512: a4b88a0b913d605919959c5ba4b8060df96c9cecf510909fdd94ca04c3d1bf34a8135d24b7926819f2ef74e5b72d88f0f29fb36f7e9096c14be7a1c5a69a56a7 Homepage: https://cran.r-project.org/package=r4sub Description: CRAN Package 'r4sub' (Easily Install and Load the R4SUB Ecosystem) The 'r4sub' package is a meta-package that installs and loads core packages of the R4SUB (R for Regulatory Submission) clinical submission readiness ecosystem. Loading 'r4sub' attaches 'r4subcore', 'r4subtrace', 'r4subscore', 'r4subrisk', 'r4subdata', and 'r4subprofile'. Package: r-cran-r4subcore Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-r4subcore_0.1.0-1.ca2404.1_all.deb Size: 72016 MD5sum: 69a205fb495a4b1f9e02ebebd3344e9b SHA1: 1b1002f8c262920353d6d5a718aefe131a3b691e SHA256: aed4fac770fcf4c3e59cc2197566da61e1c02e370b2f3dd733604170c28056e6 SHA512: abb848d25eb8b61eba8720a088fb6e757896e67abc02b7536f78e10f04615f45848a2088227eb1fcb0daafcfde9f32b7c6e49400cfe11a147a67ef50f5eea0e9 Homepage: https://cran.r-project.org/package=r4subcore Description: CRAN Package 'r4subcore' (Core Data Contracts, Parsers, and Scoring Primitives forClinical Submission Readiness) Foundational package in the R4SUB (R for Regulatory Submission) ecosystem. Defines the core evidence table schema, parsers, indicator abstractions, and scoring primitives needed to quantify clinical submission readiness. Provides a standardized contract for ingesting heterogeneous sources (validation outputs, metadata, traceability) into a single evidence framework. Package: r-cran-r4subdata Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-r4subdata_0.1.1-1.ca2404.1_all.deb Size: 45446 MD5sum: 02d4d84683b2bb8ff8754b87464ad0a1 SHA1: cf616cb3e226528355b9afdcad7e8610df188c6f SHA256: 9234ba68165c7ae93bc1190af2adf02cf64c680011560d625cc0557f0ed4490c SHA512: 55f26f82fe743f63f362b2f16f18ddd34004d4b27867c14319115774491b3aa1ff6ee3514799799239dff80e3d77bc3eca8a1b30800e974cd31139ec5559dd54 Homepage: https://cran.r-project.org/package=r4subdata Description: CRAN Package 'r4subdata' (Example Datasets for Clinical Submission Readiness) Provides realistic synthetic example datasets for the R4SUB (R for Regulatory Submission) ecosystem. Includes a pharma study evidence table, ADaM (Analysis Data Model) and SDTM (Study Data Tabulation Model) metadata following CDISC (Clinical Data Interchange Standards Consortium) conventions (), traceability mappings, a risk register based on ICH (International Council for Harmonisation) Q9 quality risk management principles (), and regulatory indicator definitions. Designed for demos, vignettes, and package testing. Package: r-cran-r4subprofile Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-r4subcore, r-cran-rlang, r-cran-tibble Suggests: r-cran-r4subscore, r-cran-r4subrisk, r-cran-testthat Filename: pool/dists/noble/main/r-cran-r4subprofile_0.1.0-1.ca2404.1_all.deb Size: 43002 MD5sum: 07485080ee9b5bce8dc1de808dd50bde SHA1: d672ae7986a224cdadd0e3a89c1069ad2f524674 SHA256: 7679f154462828745dcbf2171bb3cf757e0e56a34362f53373405cac7951fe45 SHA512: 702830f18cee3e6b1e3cf213dac5d27d10ca035bdf717c9b7ecd56bdbef8dd5c7a28f71ef47785e070b217121bf76d402710bff985c588e65a5a067431935a0a Homepage: https://cran.r-project.org/package=r4subprofile Description: CRAN Package 'r4subprofile' (Regulatory Submission Profiles for Clinical Submission Readiness) Defines submission profiles per regulatory authority with authority-specific pillar weights, decision thresholds, indicator requirements, and risk configuration. Supports the U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), Pharmaceuticals and Medical Devices Agency (PMDA), Health Canada, Therapeutic Goods Administration (TGA), and Medicines and Healthcare products Regulatory Agency (MHRA). Integrates with 'r4subscore' and 'r4subrisk' configuration systems. Package: r-cran-r4subrisk Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-r4subcore, r-cran-rlang, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-r4subrisk_0.1.0-1.ca2404.1_all.deb Size: 62418 MD5sum: 59c112d2e57d1039446d2cbd90304228 SHA1: 4dd2c1b4f13b0383b87d23daace3d05145d3a2aa SHA256: 5368f21a2a0f572db18dd40d16fccf61b014c4d0e02f95a977f7c9a892bcce84 SHA512: 901de21c5d5bd95d124596c11727e3b833c6bf94fc2fa3675953298cf52b7f2d726ee6c619f867c97a2d412836539f5dc04dd8e826abbfddcdd3e6231b27e59f Homepage: https://cran.r-project.org/package=r4subrisk Description: CRAN Package 'r4subrisk' (Risk Quantification Engine for Clinical Submission Readiness) Quantifies submission risk using a Failure Modes and Effects Analysis (FMEA)-inspired framework (probability, impact, detectability). Builds risk registers from evidence, computes Risk Priority Numbers (RPN), classifies risk levels, and emits standardized R4SUB (R for Regulatory Submission) evidence table rows via 'r4subcore'. Supports risk mitigation tracking and trend analysis across submission milestones. Package: r-cran-r4subscore Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-r4subcore, r-cran-rlang, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-r4subscore_0.1.0-1.ca2404.1_all.deb Size: 43938 MD5sum: b26116333dc40eebfb2175b022797591 SHA1: 61cbe056ee730cd90350da4d56d4c66a48d84669 SHA256: e6ff85cde24016842a510f9294132a9fca04e5a4dedeb3e1e9b4434e5b5ca9ac SHA512: 4aa4dee8113c7514db2bd8f874c179a53f49ed3644482d902aa27b925e09e853ea7f28dfd7e367ec41d6a87d961a12195818b0fd383afd367d70832bbbfe3fb7 Homepage: https://cran.r-project.org/package=r4subscore Description: CRAN Package 'r4subscore' (Submission Confidence Index Engine) Converts standardized R4SUB (R for Regulatory Submission) evidence into indicator scores, pillar scores, and a Submission Confidence Index (SCI). Provides sensitivity analysis, explainability tables, and decision band classification to answer the question: are we ready for regulatory submission. Package: r-cran-r4subtrace Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-r4subcore, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-igraph, r-cran-testthat Filename: pool/dists/noble/main/r-cran-r4subtrace_0.1.0-1.ca2404.1_all.deb Size: 51648 MD5sum: d915a4c971a3f78eb50b1c16cfce0032 SHA1: 291d080fd1d727fe70de898942045248ad583a2b SHA256: 89e4c034d49410df39e5658e14ac745e38b7ea9b5313982b11fac254d859112c SHA512: c7e0908ed0e7452c19fbf1fd473e5c3286b118afe8615c3107cc447d6bcc78849ca15bc5c9fe6045a848ce1eb4374334382fd5db4bd64d70822f19936bb25d41 Homepage: https://cran.r-project.org/package=r4subtrace Description: CRAN Package 'r4subtrace' (Traceability Engine for Clinical Submission Readiness) Quantifies and explains end-to-end traceability between clinical submission artifacts (ADaM (Analysis Data Model) outputs, derivations, SDTM (Study Data Tabulation Model) sources, specs, code). Builds trace models from metadata and mapping sheets, computes trace levels, and emits standardized R4SUB (R for Regulatory Submission) evidence table rows via 'r4subcore'. Package: r-cran-r5r Architecture: all Version: 2.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6155 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-isoband, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-rjava, r-cran-rlang, r-cran-sf, r-cran-sfheaders, r-cran-zip Suggests: r-cran-accessibility, r-cran-covr, r-cran-dplyr, r-cran-fs, r-cran-ggplot2, r-cran-gtfstools, r-cran-h3jsr, r-cran-interp, r-cran-knitr, r-cran-mapview, r-cran-patchwork, r-cran-rjavaenv, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-r5r_2.4.0-1.ca2404.1_all.deb Size: 3282896 MD5sum: da679227a44ca81c8c44c712d3ec823e SHA1: f9f87b83adb119f7d808083775ea2e40ab3596df SHA256: 2b1caa84bd41c1e6bd1f0f0f9a285276b3cd72ded334695d84c2f405077571ca SHA512: b6a85bca0d079072a0ba43264e51f8ccae52f7f45fcfd41d558bb314d9c0f286a4f7500c13cb9f512514e03c901fea3b0edad543ff664363db860b6255977dbf Homepage: https://cran.r-project.org/package=r5r Description: CRAN Package 'r5r' (Rapid Realistic Routing with 'R5') Rapid realistic routing on multimodal transport networks (walk, bike, public transport and car) using 'R5', the Rapid Realistic Routing on Real-world and Reimagined networks engine . The package allows users to generate detailed routing analysis or calculate travel time and monetary cost matrices using seamless parallel computing on top of the R5 Java machine. While R5 is developed by Conveyal, the package r5r is independently developed by a team at the Institute for Applied Economic Research (Ipea) with contributions from collaborators. Apart from the documentation in this package, users will find additional information on R5 documentation at . Although we try to keep new releases of r5r in synchrony with R5, the development of R5 follows Conveyal's independent update process. Hence, users should confirm the R5 version implied by the Conveyal user manual (see ) corresponds with the R5 version that r5r depends on. This version of r5r depends on R5 v7.1. Package: r-cran-r5rgui Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dt, r-cran-mapgl, r-cran-r5r, r-cran-sf, r-cran-shiny Suggests: r-cran-mockery, r-cran-quarto, r-cran-rlang, r-cran-shinytest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-r5rgui_0.2.0-1.ca2404.1_all.deb Size: 125788 MD5sum: eaefa1b05f1c4fd52f46559cf306717d SHA1: ab188c9614c88d23948fc7fdd62f5ac99988e9fc SHA256: 5aff0c5c912b169e2ab42481b84bf268b63b3b9614582c8d3e31c4765a527851 SHA512: 02df2388ab3796cd60c69cf081cdf3d1a2ddf2a3d28db51a51e4194253d2908e0997ba11a2c698b0a9a5a09dbacd28f86478d6dd40a5838d64f78a2635fb2daf Homepage: https://cran.r-project.org/package=r5rgui Description: CRAN Package 'r5rgui' (Graphical User Interface for 'r5r' Router) Interactively build and explore public transit routes with 'r5r' package via a graphical user interface in a 'shiny' app. The underlying routing methods are described in Pereira et al. (2021) . Package: r-cran-r62s3 Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-pkgdown, r-cran-testthat, r-cran-r6 Filename: pool/dists/noble/main/r-cran-r62s3_1.4.1-1.ca2404.1_all.deb Size: 88094 MD5sum: 4fdf33c7015c420cf954a2b89226f7de SHA1: 82b09287b7ec47f6fcbcf0365e5db12fcbe61ee4 SHA256: ed52e185c726ccd550e1839be2654f5948bb72948b6d558c18f2b65f123e5050 SHA512: ef4270eb0816c2012a6ed089b887bf49bd9d7dd3d175860a3c5ea7cbfc7687df6a7490d536fe850d705c747f8ca9440ad9f6eef8cf04beda82b2bd05b6f097f5 Homepage: https://cran.r-project.org/package=R62S3 Description: CRAN Package 'R62S3' (Automatic Method Generation from R6) After defining an R6 class, R62S3 is used to automatically generate optional S3/S4 generics and methods for dispatch. Also allows piping for R6 objects. Package: r-cran-r6 Architecture: all Version: 2.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-lobstr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-r6_2.6.1-1.ca2404.1_all.deb Size: 86876 MD5sum: bc64defd53cc3a140be11f77067504f4 SHA1: dfa95955e572ce36ca2a13321ff53d07bb0632d3 SHA256: 1cd484e6532ee84bd84f30184996b9b55089541b41cb1c9572c10a435d2a3aa2 SHA512: 44fd217def2cb174f68c6d70e9a93a2e5375df5babe39a799b56247e18364c2006921442510fcdfd4547d0237ebe7c7ed49d5f4e4c2cac2920e1c8832d904d89 Homepage: https://cran.r-project.org/package=R6 Description: CRAN Package 'R6' (Encapsulated Classes with Reference Semantics) Creates classes with reference semantics, similar to R's built-in reference classes. Compared to reference classes, R6 classes are simpler and lighter-weight, and they are not built on S4 classes so they do not require the methods package. These classes allow public and private members, and they support inheritance, even when the classes are defined in different packages. Package: r-cran-r6causal Architecture: all Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 882 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-causaleffect, r-cran-cfid, r-cran-data.table, r-cran-dosearch, r-cran-glue, r-cran-igraph, r-cran-r6, r-cran-mass Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-qgraph, r-cran-sqldf Filename: pool/dists/noble/main/r-cran-r6causal_0.8.3-1.ca2404.1_all.deb Size: 735130 MD5sum: be5ce80ce989dd1c4a3bcb17a3aae4d8 SHA1: 0e36a2ece61582f02000ab2b758d47fa152f2453 SHA256: 203e8ee3ee2f57e1f0e84048717b4945035e8d4a90fb4ab4ab5cc9ce4930c318 SHA512: dd6d21553a5197832adf4b811aadaff00e8e1f68db111d11b448d4d6884ff16dbe7372bcf1997951f2d8fd55cb16b31be847e67859cd323b17f9e5b642b2424d Homepage: https://cran.r-project.org/package=R6causal Description: CRAN Package 'R6causal' (R6 Class for Structural Causal Models) The implemented R6 class 'SCM' aims to simplify working with structural causal models. The missing data mechanism can be defined as a part of the structural model. The class contains methods for 1) defining a structural causal model via functions, text or conditional probability tables, 2) printing basic information on the model, 3) plotting the graph for the model using packages 'igraph' or 'qgraph', 4) simulating data from the model, 5) applying an intervention, 6) checking the identifiability of a query using the R packages 'causaleffect' and 'dosearch', 7) defining the missing data mechanism, 8) simulating incomplete data from the model according to the specified missing data mechanism and 9) checking the identifiability in a missing data problem using the R package 'dosearch'. In addition, there are functions for running experiments and doing counterfactual inference using simulation. Package: r-cran-r6ds Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 411 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-r6ds_1.2.0-1.ca2404.1_all.deb Size: 308668 MD5sum: 56742995203c201f5805eebd22e95a8a SHA1: a80e15c3c28cd3ee146c5bef004771450b1df7d9 SHA256: 43f38e877bf37b8721dd1a133567b73c0af24c0d08ec2cd42e1d533af9842102 SHA512: 4ad4b5ea2c4b8e4317e9949ca0946f8ce16c73d212360a03e395dd37c1211803bd370f692f18a61b15ad1b68b0ef387a420bfc825efcd6b04984f97822e7de05 Homepage: https://cran.r-project.org/package=R6DS Description: CRAN Package 'R6DS' (R6 Reference Class Based Data Structures) Provides reference classes implementing some useful data structures. The package implements these data structures by using the reference class R6. Therefore, the classes of the data structures are also reference classes which means that their instances are passed by reference. The implemented data structures include stack, queue, double-ended queue, doubly linked list, set, dictionary and binary search tree. See for example for more information about the data structures. 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Rank abundance distributions are widely used in biology and ecology to describe species abundances, and are mathematically equivalent to complementary cumulative distribution functions (CCDFs) used in physics, linguistics, sociology, and other fields. The method is described in Saeedghalati et al. (2017) . Package: r-cran-radar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-radar_1.0.0-1.ca2404.1_all.deb Size: 101522 MD5sum: 811330f429db14c97a0875c0e61559ad SHA1: f49a768fcd3cedab928c0b92f4b91e073b617135 SHA256: 5c915af7a36b85224ab814184f9be0887983bb9d27a3085db826f11e0df608bc SHA512: 0a5335661b2117d377adde3f3ad694a955096c58862779991c91079c90dad291cad12f46b4b02175d843f8d951982f49ee4b0ee3380d857c7df803c254d73099 Homepage: https://cran.r-project.org/package=radar Description: CRAN Package 'radar' (Fundamental Formulas for Radar) Fundamental formulas for Radar, for attenuation, range, velocity, effectiveness, power, scatter, doppler, geometry, radar equations, etc. Based on Nick Guy's Python package PyRadarMet Package: r-cran-radarboxplot Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-radarboxplot_1.0.5-1.ca2404.1_all.deb Size: 196952 MD5sum: 72fb2447996ac2b1c3da198b21e976a9 SHA1: 71295d30e43d810dd11ffd432dfd1e81a90e72b3 SHA256: 548b63195fd2567f37d8688f2a1d681c3c8a622e34c231bc3e4b7ce37f90f891 SHA512: a1691a109ec11ef3f0aae86ad57f137981793cfa8a34d7a4a998b943d75cd47bd77e36c95ba6e197f8c332bbc4214a5152ca6927422f8f3e819f1f55c2c3f0e2 Homepage: https://cran.r-project.org/package=radarBoxplot Description: CRAN Package 'radarBoxplot' (Implementation of the Radar-Boxplot) Creates the radar-boxplot, a plot that was created by the author during his Ph.D. in forest resources. The radar-boxplot is a visualization feature suited for multivariate classification/clustering. It provides an intuitive deep understanding of the data. Package: r-cran-radarchart Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1408 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-radarchart_0.3.1-1.ca2404.1_all.deb Size: 324388 MD5sum: 5d08a97d8347b68657ed16c7bfd2a081 SHA1: 0042b651e27fade933ad233218477927cbf83b55 SHA256: 1ca281e9f07bb4f679a8639340cc824c48fd0dbcb485fd48ec7e3d9eeab0aebc SHA512: 91aceb9260e587533f29427e8d893ef95fed2e74996a7ca12eef872be17b7e98ba55e23bdb6f65955990b6b054d793c5a19a0659b6adcdf233f4f08c0d9e0580 Homepage: https://cran.r-project.org/package=radarchart Description: CRAN Package 'radarchart' (Radar Chart from 'Chart.js') Create interactive radar charts using the 'Chart.js' 'JavaScript' library and the 'htmlwidgets' package. 'Chart.js' is a lightweight library that supports several types of simple chart using the 'HTML5' canvas element. This package provides an R interface specifically to the radar chart, sometimes called a spider chart, for visualising multivariate data. Package: r-cran-raddata Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4451 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-radsafer Filename: pool/dists/noble/main/r-cran-raddata_1.0.2-1.ca2404.1_all.deb Size: 4517782 MD5sum: ccc75eff57491a25a88096a05bcf3ee1 SHA1: 873bfbe99e1ec6966f963304b218dd254c8532d7 SHA256: 0778abe86b3a28f493d51ba9b1159c2720083c6191e8a746b1e350f74870e53e SHA512: 9c48a853e2c5da85e3942ecea65499c3ed29462929b287b7c002e4d6a7d9fc60bf105cc3d7379108c89c10069341298803e9f68e1a426bc6b3552bf03904e7fb Homepage: https://cran.r-project.org/package=RadData Description: CRAN Package 'RadData' (Nuclear Decay Data for Dosimetric Calculations - ICRP 107) Nuclear Decay Data for Dosimetric Calculations from the International Commission on Radiological Protection from ICRP Publication 107. Ann. ICRP 38 (3). Eckerman, Keith and Endo, Akira 2008 . This is a database of the physical data needed in calculations of radionuclide-specific protection and operational quantities. The data is prescribed by the ICRP, the international authority on radiation dose standards, for estimating dose from the intake of or exposure to radionuclides in the workplace and the environment. The database contains information on the half-lives, decay chains, and yields and energies of radiations emitted in nuclear transformations of 1252 radionuclides of 97 elements. 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Package: r-cran-radialvisgadgets Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 593 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-import, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-miniui, r-cran-shiny, r-cran-shinyjs, r-cran-caret, r-cran-rlang, r-cran-shinyscreenshot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-clvalid Filename: pool/dists/noble/main/r-cran-radialvisgadgets_0.2.0-1.ca2404.1_all.deb Size: 406092 MD5sum: 582eac07a98bf010cdaa1a783d0adbb4 SHA1: cfd3934aee39622b3834bd0f56a298fd05a4ac6a SHA256: a6c1a822852b8f786abb002649431c15c0880e7f0a9baeccbe0ea943607629b7 SHA512: 7a31f54ac49dabf2f38584e575f7a67f40eba19e6fa854393421152a95ba69dab20419463d7b9d000f46006c6597156d867b8d179da087a6a8da7c3c788a3752 Homepage: https://cran.r-project.org/package=RadialVisGadgets Description: CRAN Package 'RadialVisGadgets' (Interactive Gadgets for Radial Visualization Approaches) Shiny-based interactive gadgets of radial visualization methods and extensions thereof. 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The application extends the functionality in 'radiant.data'. Package: r-cran-radiant.data Architecture: all Version: 1.6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3320 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-ggplot2, r-cran-lubridate, r-cran-tidyr, r-cran-dplyr, r-cran-tibble, r-cran-rlang, r-cran-broom, r-cran-car, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-shiny, r-cran-jsonlite, r-cran-shinyace, r-cran-psych, r-cran-dt, r-cran-readr, r-cran-readxl, r-cran-writexl, r-cran-scales, r-cran-curl, r-cran-rstudioapi, r-cran-import, r-cran-plotly, r-cran-glue, r-cran-shinyfiles, r-cran-stringi, r-cran-randomizr, r-cran-patchwork, r-cran-bslib, r-cran-png, r-cran-mass, r-cran-base64enc Suggests: r-cran-arrow, r-cran-dbplyr, r-cran-dbi, r-cran-rsqlite, r-cran-rpostgres, r-cran-webshot, r-cran-testthat, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-radiant.data_1.6.8-1.ca2404.1_all.deb Size: 2062316 MD5sum: 63c0d7edaf3af690034dd4f0e86be910 SHA1: 14ba11c8bcbbea7d4d0bdb3fcdc243d69f6e593c SHA256: 6b42da6f0554fa2eb9bc857dabbefe93075e98c3cce3a454de3af0ebbf5fc33f SHA512: bc6701037da9d5c6b138fc691c302bbab8b60da199184c9d65f50c9c942e0ee6d0a28b700215d8bf009739900f2e8c2ee23605b5d306ae3911ceaa100db3e77a Homepage: https://cran.r-project.org/package=radiant.data Description: CRAN Package 'radiant.data' (Data Menu for Radiant: Business Analytics using R and Shiny) The Radiant Data menu includes interfaces for loading, saving, viewing, visualizing, summarizing, transforming, and combining data. 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Package: r-cran-radiant.design Architecture: all Version: 1.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1623 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-radiant.data, r-cran-dplyr, r-cran-magrittr, r-cran-shiny, r-cran-algdesign, r-cran-import, r-cran-pwr, r-cran-randomizr, r-cran-mvtnorm, r-cran-polycor Suggests: r-cran-testthat, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-radiant.design_1.6.6-1.ca2404.1_all.deb Size: 1368330 MD5sum: 904108a7fa128070e3f0a346d9ad4c9a SHA1: 4fba41e23bb6b1a57255602cf24d884039ef6118 SHA256: ac116eb2d9adb7f09ee2ef52092b0a38604bca38eb64c4266b918b88d3adb1b7 SHA512: d45b897b9a141a9e1a02f7215ad9a8894e92599813c304ad02a0b1d67d26f0dcb135e1a380e7095684de88e9ff4924208c100c7315510df89bec31057835bb64 Homepage: https://cran.r-project.org/package=radiant.design Description: CRAN Package 'radiant.design' (Design Menu for Radiant: Business Analytics using R and Shiny) The Radiant Design menu includes interfaces for design of experiments, sampling, and sample size calculation. The application extends the functionality in 'radiant.data'. 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The application extends the functionality in 'radiant.data'. 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The application extends the functionality in 'radiant.data'. 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The application combines the functionality of 'radiant.data', 'radiant.design', 'radiant.basics', 'radiant.model', and 'radiant.multivariate'. Package: r-cran-radiosonde Architecture: all Version: 4.2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 893 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields Filename: pool/dists/noble/main/r-cran-radiosonde_4.2-1.ca2404.2_all.deb Size: 345726 MD5sum: 6f9cb4668faadfa5222fca7a562a9029 SHA1: 5dc72b59faab787fec982aa4845fc9931a594934 SHA256: 71ed53411e971111a8e1e95a42dc3402d195ba56df27cd34098056144b54e111 SHA512: 5ffb8fd2e5f89242e1466be9d44152b4f36a85a24d2cf7f8f25c1f88ea21914bb99711bbdb16c94e64679454eccc14f434986b80897bbe9283a681bad666e50b Homepage: https://cran.r-project.org/package=RadioSonde Description: CRAN Package 'RadioSonde' (Tools for Plotting Skew-T Diagrams and Wind Profiles) A collection of programs for plotting SKEW-T,log p diagrams and wind profiles for data collected by radiosondes (the typical weather balloon-borne instrument). The format of this plot with companion lines to assess atmospheric stability are both standard in meteorology and difficult to create from basic graphics functions. Hence this package. One novel feature is being able add several profiles to the same plot for comparison. Use "help(ExampleSonde)" for an explanation of the variables needed and how they should be named in a data frame. See for the package home page. 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These tools also enable subsequent visualization and statistical analysis of these data. Package: r-cran-radous Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-httr, r-cran-readr, r-cran-checkmate, r-cran-curl Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-radous_0.1.3-1.ca2404.1_all.deb Size: 10898 MD5sum: 8facfb52877f7fecf2955748e71b7e04 SHA1: 2686c2f892550c04352683f0465053a78e7d902d SHA256: 1896939c92ad1d93c98ba9709ff64ff01a314f8a3fa31481f8208a9ab354ba31 SHA512: a64d363723577d60cb2d497c606e96570a0de0c30bb207ac9d1d6881b1894f91ec31afd8686e5bf9b627580e5e1fc492f2b9ec90778b1e0af6accb62c38b52c6 Homepage: https://cran.r-project.org/package=radous Description: CRAN Package 'radous' (Query Random User Data from the Random User Generator API) Generate random user data from the Random User Generator API. 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Supports 'OpenAI'- and 'Ollama'-compatible embedding models, full-text and 'HNSW' (Hierarchical Navigable Small World) indexing, and modular large language model (LLM) invocation. Designed for advanced question-answering, chat-based applications, and production-ready AI pipelines. This package is the R equivalent of the 'python' package 'RAGFlowChain' available at . 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Package: r-cran-rai Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-readr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rai_1.0.0-1.ca2404.1_all.deb Size: 83352 MD5sum: d7d45a037240ff339e2fb0e503e19e88 SHA1: 262b4b63823a240045f6431466dfcc71fe4e2fc7 SHA256: 6becf4d96b5db067aef4e40002f3c206ace06b21ce1c3f1d1fe05d6a396c35ec SHA512: 76b6232d4072f72d9d1d60b8180bf4348c153a8a5c379746b491bcc91bd9ef0df8d7326a56f02f41a31bdff5ee2c16a8f6c624b78cd53ca4dec2d096a31e6d79 Homepage: https://cran.r-project.org/package=rai Description: CRAN Package 'rai' (Revisiting-Alpha-Investing for Polynomial Regression) A modified implementation of stepwise regression that greedily searches the space of interactions among features in order to build polynomial regression models. Furthermore, the hypothesis tests conducted are valid-post model selection due to the use of a revisiting procedure that implements an alpha-investing rule. As a result, the set of rejected sequential hypotheses is proven to control the marginal false discover rate. When not searching for polynomials, the package provides a statistically valid algorithm to run and terminate stepwise regression. For more information, see Johnson, Stine, and Foster (2019) . 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This package is initially designed for the scoring system in a high school project showcase to rank student research projects, where each judge can only evaluate a set of projects in a limited time period. See Langville, A. N. and Meyer, C. D. (2012), Who is Number 1: The Science of Rating and Ranking, Princeton University Press , and Gou, J. and Wu, S. (2020), A Judging System for Project Showcase: Rating and Ranking with Incomplete Information, Technical Report. Package: r-cran-rainerosr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rainerosr_0.1.1-1.ca2404.1_all.deb Size: 82054 MD5sum: a3725089ea4aa840a5a3c5cb9ff11002 SHA1: b950e30fe42ce30e07571ebb935a682b3591f207 SHA256: 817b93d038782348ce1129a85f401d9603144487d770412854a75a6b20eb7cc1 SHA512: ecc806eee90e5ffbed2ff9144041f8bd16af4a2b91f280df334b8d0f18c3cc89529921884bd1a58b6b97c64181c6ceb0c146a21126727ece5dfc0952b7ac79e0 Homepage: https://cran.r-project.org/package=rainerosr Description: CRAN Package 'rainerosr' (Calculate Rainfall Intensity and Erosivity Indices) Calculates I30 (maximum 30-minute rainfall intensity) and EI30 (erosivity index) from rainfall breakpoint data. 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Package: r-cran-rainfarmr Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rainfarmr_0.1-1.ca2404.1_all.deb Size: 70672 MD5sum: 82630e738d0696d9c0834175417c1ec8 SHA1: a1234af0cd9788ef13ff70897949e833c60ba7ef SHA256: 32ab724990598ae66e427710962cada2fadb681f2c558fa0afdb58dac93e847e SHA512: a172c23bd0cb608ff3b5cf477e73111cab9fe496b9c1c5ed5202b22500e687c1357208ffe9a4cfdaaaca6a7723ad67259efb9f6d465af9be936a250102968949 Homepage: https://cran.r-project.org/package=rainfarmr Description: CRAN Package 'rainfarmr' (Stochastic Precipitation Downscaling with the RainFARM Method) An implementation of the RainFARM (Rainfall Filtered Autoregressive Model) stochastic precipitation downscaling method (Rebora et al. (2006) ). Adapted for climate downscaling according to D'Onofrio et al. (2018) and for complex topography as in Terzago et al. 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Such studies are, for example, international assessments like 'TIMSS', 'PIRLS' and 'PISA'. A graphical interface is available for the non-technical user.The package includes functions to covert the original data from 'SPSS' into 'R' data sets keeping the user-defined missing values, merge data from different respondents and/or countries, generate variable dictionaries, modify data, produce descriptive statistics (percentages, means, percentiles, benchmarks) and multivariate statistics (correlations, linear regression, binary logistic regression). The number of supported studies and analysis types will increase in future. For a general presentation of the package, see 'Mirazchiyski', 2021a (). For detailed technical aspects of the package, see 'Mirazchiyski', 2021b (). 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Pre-processing includes normalisation functions, peak identification based on local maxima, smoothing process and removal of spectral region of no interest. Polymer identification can be performed using Pearson correlation coefficient or Euclidean distance (Renner et al. (2019), ), and the comparison can be done with a user-defined database or with the database already implemented in the package, which currently includes 356 spectra, with several spectra of plastic colorants. 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Package: r-cran-ramcharts4 Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10933 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-reactr, r-cran-shiny, r-cran-jsonlite, r-cran-lubridate, r-cran-minpack.lm, r-cran-base64enc, r-cran-xml2, r-cran-stringr Suggests: r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-ramcharts4_1.6.0-1.ca2404.1_all.deb Size: 1733328 MD5sum: f319eed18e57d2e53b840dda50c64a18 SHA1: ca34a083d2db477cd2184fabcee717e5d9c10d14 SHA256: b1d53525469759ba977b7b872587ffd5fe1a4fd9432de92ceb91714721e9c34a SHA512: 93fa0d4b5636dd12d6fd9f411f0151cba51de7862912d61c7f239995c98c80d7c50463d05d30e22e07a0de887d5761ae85575904aadde274a43c4565a9ce3258 Homepage: https://cran.r-project.org/package=rAmCharts4 Description: CRAN Package 'rAmCharts4' (Interface to the JavaScript Library 'amCharts 4') Creates JavaScript charts. 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Package: r-cran-ramchoice Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-ramchoice_2.2-1.ca2404.1_all.deb Size: 146720 MD5sum: d315784622dcd5ffdc2ff7ff1bdff79f SHA1: 604a6a05549fea4902490711db699016623465d5 SHA256: 575b2bec7d90de5d0ee648b1cee94fd15b33a01b4564730a34f9e9effa273a76 SHA512: 976dcc1babc512ad87255a4f64c21fb05509bf6a64c9b0dc2e365d4354fe642cca98e62a3f5ffbdb776a765c7d41046696407766030f43419a84531309fae79d Homepage: https://cran.r-project.org/package=ramchoice Description: CRAN Package 'ramchoice' (Revealed Preference and Attention Analysis in Random LimitedAttention Models) It is widely documented in psychology, economics and other disciplines that socio-economic agent may not pay full attention to all available alternatives, rendering standard revealed preference theory invalid. This package implements the estimation and inference procedures of Cattaneo, Ma, Masatlioglu and Suleymanov (2020) and Cattaneo, Cheung, Ma, and Masatlioglu (2022) , which utilizes standard choice data to partially identify and estimate a decision maker's preference and attention. For inference, several simulation-based critical values are provided. 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The database is named after Dr. Ransom A. Myers whose original stock-recruitment database, is no longer being updated. More information about the database can be found at . Ricard, D., Minto, C., Jensen, O.P. and Baum, J.K. (2012) . 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The model selection can be done by a general cross-validation framework called ECV from Li et. al. (2016) . Several other model-based and task-specific methods are also included, such as NCV from Chen and Lei (2016) , likelihood ratio method from Wang and Bickel (2015) , spectral methods from Le and Levina (2015) . Many network analysis methods are also implemented, such as the regularized spectral clustering (Amini et. al. 2013 ) and its degree corrected version and graphon neighborhood smoothing (Zhang et. al. 2015 ). It also includes the consensus clustering of Gao et. al. (2014) , the method of moments estimation of nomination SBM of Li et. al. (2020) , and the network mixing method of Li and Le (2021) . It also includes the informative core-periphery data processing method of Miao and Li (2021) . The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134. 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The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis. Also weighted correlation matrices may be considered for PA. 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Metrics such as partial correlations and variance inflation factors are tabulated as well as plotted for the user. A function is available for tuning the main Random Forest hyper-parameter based on model performance and variable importance metrics. This grid-search technique provides tables and plots showing the effect of the main hyper-parameter on each of the assessment metrics. It also returns each of the evaluated models to the user. The package also provides superior variable importance plots for individual models. All of the plots are developed so that the user has the ability to edit and improve further upon the plots. Derivations and methodology are described in Bladen (2022) . 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Brunner, E., Bathke, A. and Konietschke, F. (2018) . Package: r-cran-rankhazard Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-rms Filename: pool/dists/noble/main/r-cran-rankhazard_1.1.1-1.ca2404.1_all.deb Size: 75942 MD5sum: cf84791070aee5ffd1f134d942708c9a SHA1: 4ea4bfc4c45654d6985d95ff5a788074e0f6d392 SHA256: d3f15d6dfc4f28828e947852ae29650be6abb61fb98f56c6fd09bbef7a014d92 SHA512: 7968974a157539577a518093454f7282595ddcc7ce3c3d718323e6aad8059406211a87b6c9bd594073cf8819cb807ccdd6a7f870fabb1e33d5658971a883fbe9 Homepage: https://cran.r-project.org/package=rankhazard Description: CRAN Package 'rankhazard' (Rank-Hazard Plots) Rank-hazard plots Karvanen and Harrell (2009) visualize the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0,1]. The relative hazard is plotted in respect to the reference hazard, which can bee.g. the hazard related to the median of the covariate. 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Companion package to "A Primer on Visualizations for Comparing Populations, Including the Issue of Overlapping Confidence Intervals" by Wright, Klein, and Wieczorek (2019) and "A Joint Confidence Region for an Overall Ranking of Populations" by Klein, Wright, and Wieczorek (2020) . 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Package: r-cran-rankpca Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret Filename: pool/dists/noble/main/r-cran-rankpca_0.1.0-1.ca2404.1_all.deb Size: 16924 MD5sum: 1b28ce20064d59e3fc2b0e1168656ebc SHA1: ef1a05cad42439feda8a24368734e89c086e1b77 SHA256: 1a186349e842918d437c5ba6ae304b04a044054a0531fda1080a4b940c7ffdb4 SHA512: 72151600507ff9ff8a05602d3b3ba23d5e5c390b9b9a27968ae57cdf93f941dd00cf828e5389cb5fa897fb1ac7edae0cce51a11266e96635fc09cafbfe98f15d Homepage: https://cran.r-project.org/package=RankPCA Description: CRAN Package 'RankPCA' (Rank of Variables Based on Principal Component Analysis forMixed Data Types) Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while preserving as much variability as possible. By transforming the original variables into a new set of uncorrelated variables called principal components, PCA helps in identifying patterns and simplifying the complexity of high-dimensional data. The 'RankPCA' package provides a streamlined workflow for performing PCA on datasets containing both categorical and continuous variables. It facilitates data preprocessing, encoding of categorical variables, and computes PCA to determine the optimal number of principal components based on a specified variance threshold. The package also computes composite indices for ranking observations, which can be useful for various analytical purposes. Garai, S., & Paul, R. K. (2023) . 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You can also determine the ("special") card of highest rank of the remaining cards for that suit. At some point, you notice that a certain opponent discards that special card. What can you infer about his holding in that suit? A series of simulation studies are reported here that allows a quantitative inference based on the conditional probability, given that the opponent has the special card. The same procedure is also used for the conditional probability, given that the opponent does not have the special card. 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This project was supported by the National Science Foundation under Grant No. 2019901. Package: r-cran-rankresponse Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rankresponse_4.0.0-1.ca2404.1_all.deb Size: 42606 MD5sum: 8aea0477a3820ac56da2bfd76276f1be SHA1: f59c18a6b016af476da4d8c24cf3db7fad0c29c2 SHA256: d9dfee8c422876fde841e4e10d9b57374e5249af1fa49cc591130852c7397001 SHA512: 7f9ab66ad3e2dd72c2d5cb2c88991538eb87b1f9e7c90eba1f46914cf8006c125074d55ccc68bf14c7f7acb8437281ff604dd33f04b048ce1a472d956d6cb305 Homepage: https://cran.r-project.org/package=RankResponse Description: CRAN Package 'RankResponse' (Ranking Responses in a Single Response Question or a MultipleResponse Question) Methods for ranking responses of a single response question or a multiple response question are described in the two papers: 1. Wang, H. (2008). Ranking Responses in Multiple-Choice Questions. Journal of Applied Statistics, 35, 465-474. 2. Wang, H. and Huang, W. H. (2014). Bayesian Ranking Responses in Multiple Response Questions. Journal of the Royal Statistical Society: Series A (Statistics in Society), 177, 191-208. . 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Reales,G., Vigorito, E., Kelemen,M., Wallace,C. (2021) "RápidoPGS: A rapid polygenic score calculator for summary GWAS data without a test dataset". 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Van Everdingen, A. F. and Hurst, W. (1949) . Fetkovich, M. J. (1971) . Yildiz, T. and Khosravi, A. (2007) . 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Version 1.1 includes a minor adjustment to the number of combinations to be considered for multiple testing correction. This updated version is more conservative in its approach and hence more selective. . 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The package provides tools for rare-event inference, minimum-action paths, and quasi-potential computation in stochastic dynamical systems. Methods are based on Rezende and Mohamed (2015) , Girsanov (1960) , and Freidlin and Wentzell (2012, ISBN:978-0387955477). Package: r-cran-rarenmtests Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan Filename: pool/dists/noble/main/r-cran-rarenmtests_1.2-1.ca2404.1_all.deb Size: 116702 MD5sum: f1e080afc63eb232d34e355ccc5b8e66 SHA1: f5b63c4575d0eb9cead43a9b88a69ffc607ffe77 SHA256: 74af9e3190eac33a60cbbc30a9af69d247c0c51fd4199172448fe06097310393 SHA512: fccb3212a86c50d212750cb9e481ab9c3ef80cfabb47b83f55a274b8b92f5d49a298c83f6ec412653967eb9fff2de0bb9801faac785f5a4702967d30256cb49c Homepage: https://cran.r-project.org/package=rareNMtests Description: CRAN Package 'rareNMtests' (Ecological and Biogeographical Null Model Tests for ComparingRarefaction Curves) Randomization tests for the statistical comparison of i = two or more individual-based, sample-based or coverage-based rarefaction curves. The ecological null hypothesis is that the i samples were all drawn randomly from a single assemblage, with (necessarily) a single underlying species abundance distribution. The biogeographic null hypothesis is that the i samples were all drawn from different assemblages that, nonetheless, share similar species richness and species abundance distributions. Functions are described in L. Cayuela, N.J. Gotelli & R.K. Colwell (2015) . Package: r-cran-rarestr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rarestr_1.1.1-1.ca2404.1_all.deb Size: 65002 MD5sum: 90020e89d9d325452fd2209b3aebabe8 SHA1: 8fa6de21f508d26789b2788cb195094dd761026c SHA256: b259f4f3966eda295e02dd5545593b5854915a017bd36b598ad129f704e22720 SHA512: 8d0b1f42fc1d1965f38f14839286abcbf9cecffbfa6674c2515e44da991b002d6ed3095b5729bbb675bb14a74d6299dfa78d563319ed3bce48aa67f1af19b944 Homepage: https://cran.r-project.org/package=rarestR Description: CRAN Package 'rarestR' (Rarefaction-Based Species Richness Estimator) Calculate rarefaction-based alpha- and beta-diversity. Offer parametric extrapolation to estimate the total expected species in a single community and the total expected shared species between two communities. Visualize the curve-fitting for these estimators. Package: r-cran-raretrans Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1332 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-bslib, r-cran-dplyr, r-cran-formatr, r-cran-gridextra, r-cran-huxtable, r-cran-knitr, r-cran-popdemo, r-cran-popbio, r-cran-purrr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-shiny, r-cran-testthat, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-raretrans_1.0.5-1.ca2404.1_all.deb Size: 640608 MD5sum: f95316655eabe581489d82f5ea90b71d SHA1: 470d6ab82ddeb42d4cb092eb87f602daddf25f66 SHA256: 721aa82095ac5ecd50b0d73cc8f35083fdc00a7bad5c8b04443c81941d785cae SHA512: fff61a085c2dfe565f1b4b8ce9265d340bd683414e77f6d8efd8ff8d57a92a333a6468c53a8ebde71f882026c9c368d5c81a02be1969408655e72544337e6e24 Homepage: https://cran.r-project.org/package=raretrans Description: CRAN Package 'raretrans' (Bayesian Priors for Matrix Population Models) Provides functions to correct biased transition and fertility estimates in population projection matrices caused by small sample sizes. Small or short-term studies frequently produce structural zeros (biologically possible transitions never observed) and structural ones (transitions estimated at 100% survival, stasis, or mortality that are biologically implausible). Both distort matrix structure and bias estimates of population growth. Implements a multinomial-Dirichlet Bayesian prior for transition probabilities and a Gamma-Poisson prior for reproduction, allowing analysts to incorporate prior biological knowledge and regularise estimates from rare or unobserved events. Includes functions to compute marginal posterior credible intervals for all transition probabilities (transition_CrI()), visualise those intervals as point-range plots (plot_transition_CrI()), and display the full posterior beta density for each matrix entry (plot_transition_density()). Methods are described in Tremblay et al. (2021) . Package: r-cran-rarfreq Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-latex2exp, r-cran-magrittr, r-cran-patchwork, r-cran-tidyr, r-cran-data.table, r-cran-reshape2, r-cran-rdpack Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rarfreq_0.1.5-1.ca2404.1_all.deb Size: 201714 MD5sum: b1ce956afe0c0badd977635f9acd831c SHA1: 7e00f61b6e6291e946417002e3718d63fce9b9b8 SHA256: f6c5dbc33ff55071d19b346f707b6b49e9e20c085ba68e414a4440c1558eba78 SHA512: 5b0c650fcea7af2f0cef33b75e304a9d960ae8582de10b7c77fbc49e6158dd175b72f953d73ba80a211913012b22da03aaaf8fe45eb474051e4d86fe9235c814 Homepage: https://cran.r-project.org/package=RARfreq Description: CRAN Package 'RARfreq' (Response Adaptive Randomization with 'Frequentist' Approaches) Provides functions and command-line user interface to generate allocation sequence by response-adaptive randomization for clinical trials. The package currently supports two families of frequentist response-adaptive randomization procedures, Doubly Adaptive Biased Coin Design ('DBCD') and Sequential Estimation-adjusted Urn Model ('SEU'), for binary and normal endpoints. One-sided proportion (or mean) difference and Chi-square (or 'ANOVA') hypothesis testing methods are also available in the package to facilitate the inference for treatment effect. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. For example, plots for relationship among assumed treatment effects, sample size, and power are provided. Five allocation functions for 'DBCD' and six addition rule functions for 'SEU' are implemented to target allocations such as 'Neyman', 'Rosenberger' Rosenberger et al. (2001) and 'Urn' allocations. Package: r-cran-rarity Architecture: all Version: 1.3-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rarity_1.3-8-1.ca2404.1_all.deb Size: 92438 MD5sum: 65dcf0f152b24bca8a0ae62a65d72068 SHA1: 95c97ad5bdf13dd79fd74718032faa870d9eca81 SHA256: 0a1280e6c627b98d63caae062570e6ff74d85bf253d4a7f1c6da9fcb2b846e2c SHA512: b8c423591c52222b68d0eeeb99261be0775b98291e3dcec21080cc6461ae2efd7856cad70deb0469c9a3fb20aaa87e5b7874652a6274a6ecdfd8682edc0e01a5 Homepage: https://cran.r-project.org/package=Rarity Description: CRAN Package 'Rarity' (Calculation of Rarity Indices for Species and Assemblages ofSpecies) Allows calculation of rarity weights for species and indices of rarity for assemblages of species according to different methods (Leroy et al. 2012, Insect. Conserv. Divers. 5:159-168 ; Leroy et al. 2013, Divers. Distrib. 19:794-803 ). 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Also provide relevant information and metadata for each of the input variables needed for sending the data inquery. Package: r-cran-rarpack Architecture: all Version: 0.11-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rspectra Suggests: r-cran-matrix Filename: pool/dists/noble/main/r-cran-rarpack_0.11-0-1.ca2404.1_all.deb Size: 30032 MD5sum: f86c4aeb0f3bb03e271390e8cae028e4 SHA1: 1b46335fa53386a9ddf01dafef532729a9661dda SHA256: 3a600a5f2a7f2d6fa53d810c58e430ded1522ee4a7406667f6e26d4211963527 SHA512: 9aa290abaf45cfd6228c87cdf7846a03147b0b0f3800969229a531072a29bafcc9f80473e9016d36cff749d844280b409fd4b819345960ac158eda84c2851d84 Homepage: https://cran.r-project.org/package=rARPACK Description: CRAN Package 'rARPACK' (Solvers for Large Scale Eigenvalue and SVD Problems) Previously an R wrapper of the 'ARPACK' library , and now a shell of the R package 'RSpectra', an R interface to the 'Spectra' library for solving large scale eigenvalue/vector problems. The current version of 'rARPACK' simply imports and exports the functions provided by 'RSpectra'. New users of 'rARPACK' are advised to switch to the 'RSpectra' package. Package: r-cran-rartrials Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 733 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pins, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rartrials_0.0.2-1.ca2404.1_all.deb Size: 641402 MD5sum: 9a7a8ec54bd445794fc0bcefffcaf741 SHA1: 2f65d0d40a5d59ff83154812e5bcb64984f119ec SHA256: 81fe5b8adeae7d72378fcde877678cfed6aef463040a55ebbee8c2161bc397db SHA512: 7fcbb23265806258537a3ac5de7870a3d42842aae85c53330302f52eecd064da2f68bf5fbffa28eb1ae2b85da1e688b8f8242b85939c1667fd66261fae2578fc Homepage: https://cran.r-project.org/package=RARtrials Description: CRAN Package 'RARtrials' (Response-Adaptive Randomization in Clinical Trials) Some response-adaptive randomization methods commonly found in literature are included in this package. These methods include the randomized play-the-winner rule for binary endpoint (Wei and Durham (1978) ), the doubly adaptive biased coin design with minimal variance strategy for binary endpoint (Atkinson and Biswas (2013) , Rosenberger and Lachin (2015) ) and maximal power strategy targeting Neyman allocation for binary endpoint (Tymofyeyev, Rosenberger, and Hu (2007) ) and RSIHR allocation with each letter representing the first character of the names of the individuals who first proposed this rule (Youngsook and Hu (2010) , Bello and Sabo (2016) ), A-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) ), Aa-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) ), generalized RSIHR allocation for continuous endpoint (Atkinson and Biswas (2013) ), Bayesian response-adaptive randomization with a control group using the Thall \& Wathen method for binary and continuous endpoints (Thall and Wathen (2007) ) and the forward-looking Gittins index rule for binary and continuous endpoints (Villar, Wason, and Bowden (2015) , Williamson and Villar (2019) ). Package: r-cran-rasciidoc Architecture: all Version: 4.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 554 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-fritools, r-cran-gert, r-cran-highr, r-cran-knitr, r-cran-reticulate Suggests: r-cran-devtools, r-cran-pkgload, r-cran-rmarkdown, r-cran-rprojroot, r-cran-runit, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-rasciidoc_4.1.1-1.ca2404.1_all.deb Size: 103188 MD5sum: dab4ecf5acaff058d016d183bfb30173 SHA1: b2f45f7c722220a863349251cf184f2ea8ac4ee9 SHA256: bfb6f7de36afd54f2070e968964aabd003198665cc699233c5973b033996d49f SHA512: 78273ea1b8503bd9c66eb2294e5c3170d5f970e0d42b6a19ea0b5a407a3b9029df4bd887f354dfad75411c535f93f0eb796f8b8bd1f45b0105da60aaf2c825b7 Homepage: https://cran.r-project.org/package=rasciidoc Description: CRAN Package 'rasciidoc' (Create Reports Using R and 'asciidoc') Inspired by Karl Broman`s reader on using 'knitr' with 'asciidoc' (), this is merely a wrapper to 'knitr' and 'asciidoc'. Package: r-cran-rasclass Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-nnet, r-cran-rsnns, r-cran-e1071, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-rasclass_0.2.2-1.ca2404.1_all.deb Size: 113134 MD5sum: d7bfbe850027ff3d8e523ba472746814 SHA1: 5230ff738e34858b5b442125d3c43ea6f8f7ba8d SHA256: b2e7bdf6395fc75de5ecaeff407fd0b4ee648d0b782db076311b1f29a129ebc9 SHA512: 30421a62958a3cc90b68a4c94ac213c557dc7911077f014cf1204f73bfad7ca6aeb10d7f715df21c999d64a75059a50d753cdfaa16debb6fcf8aa18b67a1b8b5 Homepage: https://cran.r-project.org/package=rasclass Description: CRAN Package 'rasclass' (Supervised Raster Image Classification) Software to perform supervised and pixel based raster image classification. It has been designed to facilitate land-cover analysis. Five classification algorithms can be used: Maximum Likelihood Classification, Multinomial Logistic Regression, Neural Networks, Random Forests and Support Vector Machines. The output includes the classified raster and standard classification accuracy assessment such as the accuracy matrix, the overall accuracy and the kappa coefficient. An option for in-sample verification is available. 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In RaSE algorithm, for each weak learner, some random subspaces are generated and the optimal one is chosen to train the model on the basis of some criterion. To be adapted to the problem, a novel criterion, ratio information criterion (RIC) is put up with based on Kullback-Leibler divergence. Besides minimizing RIC, multiple criteria can be applied, for instance, minimizing extended Bayesian information criterion (eBIC), minimizing training error, minimizing the validation error, minimizing the cross-validation error, minimizing leave-one-out error. There are various choices of base classifier, for instance, linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbour, logistic regression, decision trees, random forest, support vector machines. RaSE algorithm can also be applied to do feature ranking, providing us the importance of each feature based on the selected percentage in multiple subspaces. RaSE framework can be extended to the general prediction framework, including both classification and regression. We can use the selected percentages of variables for variable screening. The latest version added the variable screening function for both regression and classification problems. 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Supports survival analysis and various hypothesis testing frameworks. Package: r-cran-rassta Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4136 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-foreach, r-cran-ggally, r-cran-ggplot2, r-cran-histogram, r-cran-kernsmooth, r-cran-kohonen, r-cran-plotly, r-cran-rlang, r-cran-scales, r-cran-shiny, r-cran-stringdist, r-cran-stringi, r-cran-terra Suggests: r-cran-testthat, r-cran-tinytest, r-cran-doparallel, r-cran-mgcv, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rassta_1.0.6-1.ca2404.1_all.deb Size: 3575142 MD5sum: 180229d2a6ff732355c1035afe4297c3 SHA1: 197fdf0fb6fc8b17e0d120e09d54ee8932802dc3 SHA256: 161b7775b1d31ae0dc4065763d0047d939ced895a613dc1c9925015ece746580 SHA512: 191c0cf40bd3fdd33e83a0b6fc57f5dfbf540152a9dd56d2b61b1861115daa59c31c045b9e3c7a4fbbb6a68082d4fc463a21f029dd603776b38b68e679cad548 Homepage: https://cran.r-project.org/package=rassta Description: CRAN Package 'rassta' (Raster-Based Spatial Stratification Algorithms) Algorithms for the spatial stratification of landscapes, sampling and modeling of spatially-varying phenomena. These algorithms offer a simple framework for the stratification of geographic space based on raster layers representing landscape factors and/or factor scales. The stratification process follows a hierarchical approach, which is based on first level units (i.e., classification units) and second-level units (i.e., stratification units). Nonparametric techniques allow to measure the correspondence between the geographic space and the landscape configuration represented by the units. These correspondence metrics are useful to define sampling schemes and to model the spatial variability of environmental phenomena. The theoretical background of the algorithms and code examples are presented in Fuentes et al. (2022). . 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Including the rate (or risk) difference ('RD') or rate ratio (or relative risk, 'RR') for binomial proportions or Poisson rates, and odds ratio ('OR', binomial only). Also confidence intervals for RD, RR or OR for paired binomial data, and estimation of a proportion from clustered binomial data. Includes skewness-corrected asymptotic score ('SCAS') methods, which have been developed in Laud (2017) from Miettinen and Nurminen (1985) and Gart and Nam (1988) , and in Laud (2025, under review) for paired proportions. The same score produces hypothesis tests that are improved versions of the non-inferiority test for binomial RD and RR by Farrington and Manning (1990) , or a generalisation of the McNemar test for paired data. The package also includes MOVER methods (Method Of Variance Estimates Recovery) for all contrasts, derived from the Newcombe method but with options to use equal-tailed intervals in place of the Wilson score method, and generalised for Bayesian applications incorporating prior information. So-called 'exact' methods for strictly conservative coverage are approximated using continuity adjustments, and the amount of adjustment can be selected to avoid over-conservative coverage. Also includes methods for stratified calculations (e.g. meta-analysis), either with fixed effect assumption (matching the CMH test) or incorporating stratum heterogeneity. Package: r-cran-ratest Architecture: all Version: 0.1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-gridextra, r-cran-quantreg Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ratest_0.1.10-1.ca2404.1_all.deb Size: 406506 MD5sum: 0c19e614e59c40139bda5874d52fcb75 SHA1: 84fe6b3ca93aec1d3f849aa48bff0dbbbc42c172 SHA256: fd40e1f443e3481d8001e935c1e7c859ad7102854ea8b9fd1388dec1a0708da8 SHA512: 6e8bb8b3fc5968759732b7ca6ae142cbf48579a00b2d236cd8df93ea1c623fdf1ce2e9f7bec4e2706be8ab112b89e00ab7ca3c632d9b3183b4481b43e6b33287 Homepage: https://cran.r-project.org/package=RATest Description: CRAN Package 'RATest' (Randomization Tests) A collection of randomization tests, data sets and examples. The current version focuses on five testing problems and their implementation in empirical work. First, it facilitates the empirical researcher to test for particular hypotheses, such as comparisons of means, medians, and variances from k populations using robust permutation tests, which asymptotic validity holds under very weak assumptions, while retaining the exact rejection probability in finite samples when the underlying distributions are identical. Second, the description and implementation of a permutation test for testing the continuity assumption of the baseline covariates in the sharp regression discontinuity design (RDD) as in Canay and Kamat (2018) . More specifically, it allows the user to select a set of covariates and test the aforementioned hypothesis using a permutation test based on the Cramer-von Misses test statistic. Graphical inspection of the empirical CDF and histograms for the variables of interest is also supported in the package. Third, it provides the practitioner with an effortless implementation of a permutation test based on the martingale decomposition of the empirical process for testing for heterogeneous treatment effects in the presence of an estimated nuisance parameter as in Chung and Olivares (2021) . Fourth, this version considers the two-sample goodness-of-fit testing problem under covariate adaptive randomization and implements a permutation test based on a prepivoted Kolmogorov-Smirnov test statistic. Lastly, it implements an asymptotically valid permutation test based on the quantile process for the hypothesis of constant quantile treatment effects in the presence of an estimated nuisance parameter. Package: r-cran-rathena Architecture: all Version: 2.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 977 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dbi, r-cran-reticulate, r-cran-uuid Suggests: r-cran-arrow, r-cran-bit64, r-cran-dplyr, r-cran-dbplyr, r-cran-testthat, r-cran-tibble, r-cran-vroom, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-jsonify, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-rathena_2.6.3-1.ca2404.1_all.deb Size: 613988 MD5sum: c3e81da970d43f78dbbc7c74a878aa94 SHA1: 4865b1e8d3c7f44b4fc402e46a7e36e792761f9c SHA256: 56108ac23e92ac187724089f2e3ce1d613c8412ad036fea1540fe28906a6017c SHA512: fbb9e0aff2b6a06814b4085de46834a8f2b58e73f4abd1a58d9b48ca4a0b132f4dbf9393fb50ba1ad5850dd7601761cbe7ad65eee39634513a3e0a240ea3a2f8 Homepage: https://cran.r-project.org/package=RAthena Description: CRAN Package 'RAthena' (Connect to 'AWS Athena' using 'Boto3' ('DBI' Interface)) Designed to be compatible with the R package 'DBI' (Database Interface) when connecting to Amazon Web Service ('AWS') Athena . To do this 'Python' 'Boto3' Software Development Kit ('SDK') is used as a driver. Package: r-cran-ratingscalereduction Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proc, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-ratingscalereduction_1.4-1.ca2404.1_all.deb Size: 44054 MD5sum: 8c957fbd34c3277f3a8bca112a669d8c SHA1: d883670995b342703ba1ab61309ad077f7f985c2 SHA256: 10346fd620aa06b0bdb9fad3f03e7dc9b1da5e8332b76f21b6e4cac4e57e8182 SHA512: 2dd5fa5faaa038f6c4c797c9c37b58a4745925d6680f50780505b01c497752d04eb2e4944165f258b8fa80a2e5fed60df09662c2e3581c31c621004841b0c678 Homepage: https://cran.r-project.org/package=RatingScaleReduction Description: CRAN Package 'RatingScaleReduction' (Rating Scale Reduction Procedure) Describes a new procedure of reducing items in a rating scale called Rating Scale Reduction (RSR). The new stop criterion in RSR procedure is added (stop global max). The function order is replaced by sort.list. Package: r-cran-rationalexp Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-snowfall Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rationalexp_0.2.2-1.ca2404.1_all.deb Size: 185164 MD5sum: f8eb2a3da1f51b87abfe86edaef28c81 SHA1: 834cacfaf7827b878d630d1e01cc2f55006b3bf5 SHA256: 9212d66d316c99237afe5dad68972701396f4ed00e495d4c95a631e973b98325 SHA512: 93e208555f88f33387b0de924cee9422d69b8db18d8b826b9f85dc98e3a635ce7faef7bb4a92d35b4aae1e1bd1215dc8c5689833b944d51fa9aeb2ff95061d19 Homepage: https://cran.r-project.org/package=RationalExp Description: CRAN Package 'RationalExp' (Rationalizing Rational Expectations. 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Package: r-cran-ratios Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-data.table Filename: pool/dists/noble/main/r-cran-ratios_1.2.0-1.ca2404.1_all.deb Size: 124380 MD5sum: 29693ee86c58376e2a83a917dc2333e3 SHA1: e9328a0f913ba8363bc25bf2e27beae530495b06 SHA256: 43de71a76a48a6b8f272afd342a9a861b77f273f986614bec96d80e926883221 SHA512: 1427d06ecd9c9463787ccdfbd77d84458ea2849b0185a901cb0f9c6e55066b01cb15e5a7aee32807ca870ad5a6ba157deb250c2abbcf5f3b09e9d9103171cafc Homepage: https://cran.r-project.org/package=ratios Description: CRAN Package 'ratios' (Calculating Ratios Between Two Data Sets and Correction forAdhering Particles on Plants) Calculation of ratios between two data sets containing environmental data like element concentrations by different methods. 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Package: r-cran-rato Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-igraph Suggests: r-cran-animation, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-rato_0.1.0-1.ca2404.1_all.deb Size: 54608 MD5sum: 6c28675dab3db3f7d38f8706b4916bcb SHA1: e0801b6b7b1ae0dc1eb0de9392e1040a3c0694e6 SHA256: 2410732b5319c8deb2f7a45719ca272565bacdda116b535e12867743543ca702 SHA512: 11c4c53c57c17f9d187c5ba13b862d8b4641fa2324cc53f73caadbc73cf197c872418baa92c28ec2fa51940d1989e2aa9895938428c8e34269016b2e5f49429f Homepage: https://cran.r-project.org/package=Rato Description: CRAN Package 'Rato' (Resilience Analysis Toolkit (RATO)) Collection of tools for the analysis of the resilience of dynamic networks. Created as a classroom project. Package: r-cran-rattains Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-crul, r-cran-curl, r-cran-dplyr, r-cran-fauxpas, r-cran-fs, r-cran-jsonlite, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-rlist, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-vcr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-rattains_1.1.0-1.ca2404.1_all.deb Size: 148528 MD5sum: c67604310d0c429d4ff11de908e3a85a SHA1: 3aa85705c23c87a553bef4ba98e02d84ee29c090 SHA256: 3d6b25bee1a817d50671cb8c04fa1d53b06a7aa3eea51cdd93c5672d2d69f5fa SHA512: e338bb8965cfce6a45e538aa6029084cf5558beb8289c286ec31b7feef1d482db7d5aea11b9565629d83d2d2dc6db081878e81edf53660ee23b359f6a249af45 Homepage: https://cran.r-project.org/package=rATTAINS Description: CRAN Package 'rATTAINS' (Access EPA 'ATTAINS' Data) An R interface to United States Environmental Protection Agency (EPA) Assessment, Total Maximum Daily Load (TMDL) Tracking and Implementation System ('ATTAINS') data. 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Package: r-cran-rattle Architecture: all Version: 5.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10802 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-bitops, r-cran-ggplot2, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-stringi, r-cran-stringr, r-cran-tidyr, r-cran-dplyr, r-cran-xml, r-cran-rpart.plot Suggests: r-cran-ada, r-cran-amap, r-cran-arules, r-cran-arulesviz, r-cran-cba, r-cran-cluster, r-cran-colorspace, r-cran-corrplot, r-cran-descr, r-cran-doby, r-cran-e1071, r-cran-ellipse, r-cran-fbasics, r-cran-foreign, r-cran-fpc, r-cran-gdata, r-cran-ggdendro, r-cran-gplots, r-cran-gridextra, r-cran-gtools, r-cran-hmisc, r-cran-janitor, r-cran-kernlab, r-cran-matrix, r-cran-mice, r-cran-nnet, r-cran-party, r-cran-plyr, r-cran-psych, r-cran-randomforest, r-cran-rcolorbrewer, r-cran-readxl, r-cran-reshape, r-cran-rocr, r-cran-rpart, r-cran-scales, r-cran-snowballc, r-cran-survival, r-cran-tidyselect, r-cran-timedate, r-cran-tm, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-rattle_5.6.2-1.ca2404.1_all.deb Size: 7033194 MD5sum: aca9ddff069a315d4ba480e2f42c9e41 SHA1: b95e79c911b9ca126737cbc3dbdb01ea52dca588 SHA256: 8196c15b24329d955af741f4c11b749a39a2362a76cf51ba81712490a7f175b5 SHA512: 3978fbd4dfb6de477768ddbe55002872b1a6181b7ecb82454b5e36af7ab6c9bfc2041ae2f51a15fb991d36afa9fc35f3f7177be06d36daadf87fbc79f9eaa1c3 Homepage: https://cran.r-project.org/package=rattle Description: CRAN Package 'rattle' (R Data Science Supporting Rattle) The R Analytic Tool To Learn Easily (Rattle) provides a collection of utilities functions for the data scientist. This package (v5.6.0) supports the companion graphical interface with the aim to provide a simple and intuitive introduction to R for data science, allowing a user to quickly load data from a CSV file transform and explore the data, and to build and evaluate models. A key aspect of the GUI is that all R commands are logged and commented through the log tab. This can be saved as a standalone R script file and as an aid for the user to learn R or to copy-and-paste directly into R itself. If you want to use the older Rattle implementing the GUI in RGtk2 (which is no longer available from CRAN) then please install the Rattle package v5.5.1. See rattle.togaware.com for instructions on installing the modern Rattle graphical user interface. Package: r-cran-ravecore Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2320 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bidsr, r-cran-data.table, r-cran-filearray, r-cran-fs, r-cran-ieegio, r-cran-jsonlite, r-cran-r6, r-cran-ravepipeline, r-cran-ravetools, r-cran-s7, r-cran-threebrain Suggests: r-cran-rpyants, r-cran-rpymat, r-cran-htmltools, r-cran-httpuv, r-cran-knitr, r-cran-plotly, r-cran-rnifti, r-cran-rniftyreg, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ravecore_0.1.1-1.ca2404.1_all.deb Size: 2126652 MD5sum: 36dda86850d2c07556d097458322d24d SHA1: dbe5e4f5b3b975144a8d765519bde466e032fa9e SHA256: 03ae7ad073921c576d1014728701ffe12d187c916296a6974eaea58c49d9b2fe SHA512: c81eccbb592c297debcdb812d0355ceae972534b436d7907b5d17337ca6bfabb2bc9051968bc180fbbaacfd9534b44b313b3033ce7bd224db87cc6228c8e0802 Homepage: https://cran.r-project.org/package=ravecore Description: CRAN Package 'ravecore' (Core File Structures and Workflows for 'RAVE') Defines storage standard for Read, process, and analyze intracranial electroencephalography and deep-brain stimulation in 'RAVE', a reproducible framework for analysis and visualization of iEEG by Magnotti, Wang, and Beauchamp, (2020, ). Supports brain imaging data structure (BIDS) and native file structure to ingest signals from 'Matlab' data files, hierarchical data format 5 (HDF5), European data format (EDF), BrainVision core data format (BVCDF), or BlackRock Microsystem (NEV/NSx); process images in Neuroimaging informatics technology initiative (NIfTI) and 'FreeSurfer' formats, providing brain imaging normalization to template brain, facilitating 'threeBrain' package for comprehensive electrode localization via 'YAEL' (your advanced electrode localizer) by Wang, Magnotti, Zhang, and Beauchamp (2023, ). Package: r-cran-ravel Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 293 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-digest, r-cran-httr2, r-cran-jsonlite, r-cran-miniui, r-cran-rstudioapi, r-cran-shiny, r-cran-tibble Suggests: r-cran-keyring, r-cran-knitr, r-cran-lintr, r-cran-pkgdown, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ravel_0.1.1-1.ca2404.1_all.deb Size: 229186 MD5sum: 4e0f8792be2187c247e6ddd0a8435fde SHA1: 8110b8b51823cb3fc4094059fff7a2c24b625e96 SHA256: 0c44950e7c11fda48f375128ea47dc108c0b85b1445b23b08da8162c203b7d8a SHA512: 680f3b6e52c9af39e09dacde19055384abe919d8616237cd95c47d184a1d0427834a0124a8370b78524296ec43bf62d230555baed9502ac0caad3972afe99ab1 Homepage: https://cran.r-project.org/package=ravel Description: CRAN Package 'ravel' (AI Copilot for R Analysis Workflows in 'RStudio') An AI copilot for R users in 'RStudio' and Posit workflows with active-editor, workspace, object, console, plot, and git-aware context. Provides statistical helpers for interpreting lm() and glm() models, stages code and file actions before execution, drafts reproducible 'Quarto' content, and connects to official provider APIs or CLIs for 'OpenAI', 'GitHub Copilot', 'Gemini', and 'Anthropic'. Package: r-cran-ravelry Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-tidyr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ravelry_0.1.0-1.ca2404.1_all.deb Size: 102492 MD5sum: c5a64ce52afa6428e7a99be5ef7975bc SHA1: df44da1dac6fecd4d63065b2cbf5c87c06d830b4 SHA256: 96aa20e69c577230c31b85b5a9cdf18a8efdb82f68b994c30840b9e13ea0657e SHA512: 4d36c78b6c261e736dca4ffff989addb24477596b736ef71cc7c2ffe4f9542e1486523f9d7974670f8761a5b7ece976598f588de87cbaeca8af2af8125acc951 Homepage: https://cran.r-project.org/package=ravelRy Description: CRAN Package 'ravelRy' (An Interface to the 'Ravelry' API) Provides access to the 'Ravelry' API . An R wrapper for pulling data from 'Ravelry.com', an organizational tool for crocheters, knitters, spinners, and weavers. You can retrieve pattern, yarn, author, and shop information by search or by a given id. Package: r-cran-raven.rdf Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-raven.rdf_0.2.0-1.ca2404.1_all.deb Size: 58634 MD5sum: a6724cd66b4c1b756079e9650cd00644 SHA1: 57c31f2ddba9d4308d0a09c4cc7e720a18616992 SHA256: df04ea240b46773ed427477583bbcedee6dea44d20282775c805dff4ac64f129 SHA512: 2e6f98f912e34c3754154dfeed14d985d915baaa23af5b236657a579d426993d49132705d8f3e71966a5996ee7a74fddcc7a2a1547d17998217de090ef5f35b1 Homepage: https://cran.r-project.org/package=raven.rdf Description: CRAN Package 'raven.rdf' (An R Interface for Raven DataFrames (Beta0)) Provides an I/O interface between R data.frames and Raven DataFrames. Defines functions to both read and write DataFrame files, as well as serialize/deserialize data.frames/DataFrames. 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Both R and 'Python' are supported, with 'Markdown' and 'shiny' dashboard templates for extending and building customized pipelines. See the full documentations at ; to cite us, check out our paper by Magnotti, Wang, and Beauchamp (2020, ), or run citation("ravepipeline") for details. 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Package: r-cran-rbiodatacr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-purrr Suggests: r-cran-coordinatecleaner, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rbiodatacr_0.1.1-1.ca2404.1_all.deb Size: 577950 MD5sum: 1b25cc5f379f406ef0b9880da2a477c9 SHA1: 26a04a7e633a88354d3f4603ffa9afcb61ff9dcb SHA256: 5ba007f037470d38ceda448c9e78e5917428a82440e54dd6d71ec0e57dcc5c10 SHA512: ffc8dc9aaeff3e9d42d7bf012c13a4a91863b5a0a8bb02f0e9cbc4aec85e7af886414afd5eb34771a5562db73c801f2af654ce6450f4bc45777d22657e136fce Homepage: https://cran.r-project.org/package=rbiodatacr Description: CRAN Package 'rbiodatacr' (R Client for the BIODATACR Biodiversity Data Platform of CostaRica) Provides functions to query occurrence records, species information, and datasets from BIODATACR , the national biodiversity information platform of Costa Rica managed by the Technical Office of CONAGEBIO, Costa Rica. Built on the Atlas of Living Australia (ALA) API infrastructure. 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It is possible to link behavioral labels extracted from 'BORIS' software or manually written in a csv file. Package: r-cran-rbmf Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rcpp Filename: pool/dists/noble/main/r-cran-rbmf_1.1-1.ca2404.1_all.deb Size: 79334 MD5sum: d9f4aa1a26ae941ebeb54a356578de10 SHA1: bdf1ed6248c172b1aa5a4a8d99165043166ca776 SHA256: 459ab4d858d918943336403cc67b1da9ceeda4334fb5dcefadcce783d8684940 SHA512: 6d540fa5a76caf55a384bac55d87bc569e53acb1191fcfaaaaec17e2420168d95c163638b5a64b3616fc5159f1905eac2055952230d38d445023ac956a32766b Homepage: https://cran.r-project.org/package=rBMF Description: CRAN Package 'rBMF' (Boolean Matrix Factorization) Provides four boolean matrix factorization (BMF) methods. BMF has many applications like data mining and categorical data analysis. BMF is also known as boolean matrix decomposition (BMD) and was found to be an NP-hard (non-deterministic polynomial-time) problem. Currently implemented methods are 'Asso' Miettinen, Pauli and others (2008) , 'GreConD' R. Belohlavek, V. Vychodil (2010) , 'GreConDPlus' R. Belohlavek, V. Vychodil (2010) , 'topFiberM' A. Desouki, M. Roeder, A. Ngonga (2019) . 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It includes functions not only for basic calibration, uncalibration, and plotting of one or more dates, but also a statistical framework for building demographic and related longitudinal inferences from aggregate radiocarbon date lists, including: Monte-Carlo simulation test (Timpson et al 2014 ), random mark permutation test (Crema et al 2016 ) and spatial permutation tests (Crema, Bevan, and Shennan 2017 ). Package: r-cran-rcartocolor Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales Suggests: r-cran-covr, r-cran-mass, r-cran-testthat, r-cran-vdiffr, r-cran-sf, r-cran-spdata Filename: pool/dists/noble/main/r-cran-rcartocolor_2.1.2-1.ca2404.1_all.deb Size: 348860 MD5sum: 7154c50dc0ccf3ce67c5c44e0d3026a2 SHA1: 6363f6da1c5a5ad72e675415bba1a7edf6f52ea1 SHA256: 81c127c864d169b71bc4f0505040cd4df3e3e5a402cc7fb22e7936b2d4b056fc SHA512: 78814cc1d622bf03ec6362d06f48b64d8f24c67c0116e300f9706a9d7e87d746f1008a1b2d9f7bbfa3a04b0eee8cce6f9c59f3ee2b4e14d7eb0d6f0d53a26376 Homepage: https://cran.r-project.org/package=rcartocolor Description: CRAN Package 'rcartocolor' ('CARTOColors' Palettes) Provides color schemes for maps and other graphics designed by 'CARTO' as described at . It includes four types of palettes: aggregation, diverging, qualitative, and quantitative. Package: r-cran-rcatfish Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 868 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-qdapregex, r-cran-rcurl, r-cran-rfishbase, r-cran-rvest, r-cran-stringr, r-cran-tidyr, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcatfish_1.0.2-1.ca2404.1_all.deb Size: 374714 MD5sum: c192325159d4034921cca6dc5a65e94a SHA1: 983d86ee4a36ac6fe60eaaefb14460300e2126f1 SHA256: d4ff9e6db56726134260a3a42ee67a45e32feaf31599feea910d71cb606f2426 SHA512: 684c7518f7faef4e60451a5bd5c571d5c6350a7f2a3c63be61edefa855973fbb74cbbb2f6ee2c687cd753aff032260055e5dba3fe74d443461ecf70eb898a9f7 Homepage: https://cran.r-project.org/package=rcatfish Description: CRAN Package 'rcatfish' (An R Interface to the California Academy of Sciences Eschmeyer'sCatalog of Fishes) Accesses the California Academy of Sciences Eschmeyer's Catalog of Fishes in R using web requests. The Catalog of fishes is the leading authority in fish taxonomy. Functions in the package allow users to search for fish taxa and valid names, retrieve taxonomic references, retrieve monthly taxonomic changes, obtain natural history collection information, and see the number of species by taxonomic group. For more information on the Catalog: Fricke, R., Eschmeyer, W. N. & R. van der Laan (eds) 2025. ESCHMEYER'S CATALOG OF FISHES . Package: r-cran-rcauctile Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-rcauctile_1.0.4-1.ca2404.1_all.deb Size: 35054 MD5sum: 84eafc392590cee941640f0f9cbe9b20 SHA1: c8b7e07d1c9e4759ca56ad26b26ad20056025779 SHA256: aae1b7feb061bd23a86fd1ed0d1c5aaf7f8416d8b3eceeb849e988d4c52b670d SHA512: 4b052c4c17ded7be31b74ceaf54e224eee30490ca91bfe693bc32a34e820e9d6f712564924b93378006c13555664d83f5be514eee05ee434caa4502e58f54c7b Homepage: https://cran.r-project.org/package=RCaucTile Description: CRAN Package 'RCaucTile' (Tile Grid Maps for East Caucasian Languages) Generates tile maps for the East Caucasian language family, inspired by the Typological Atlas of the Languages of Daghestan (TALD, ). It leverages 'ggplot2' to create visually informative maps, displaying rectangles for each language and allowing for color-coding based on linguistic features. The package includes a built-in dataset of 56 languages and the template for their distribution and provides flexibility to customize the tile map's appearance. The default template can be modified via the ability to hide or rename languages. It's designed to be used with external data tables containing language information and features, offering a tool for visualizing the geographic distribution and linguistic characteristics of East Caucasian languages. Package: r-cran-rcausalegm Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcausalegm_0.3.3-1.ca2404.1_all.deb Size: 179592 MD5sum: 6f66b2af790b56c47fe6784808d11b8c SHA1: 3073ebe2406abf4b8c5ed833ac283e9c65a22463 SHA256: b3f85c4a5f85147d666e2ca9b883202aa0fa3f3a79aea4947bd4233d07367e84 SHA512: 0915e91c66047f874e2028822d44f065bea7bb46fd9604fac1e43efc8c5bdf52597e1e12987b7021ea3535eddf5e7c4941194ebda2e17975ed43422a5529b851 Homepage: https://cran.r-project.org/package=RcausalEGM Description: CRAN Package 'RcausalEGM' (A General Causal Inference Framework by Encoding GenerativeModeling) CausalEGM is a general causal inference framework for estimating causal effects by encoding generative modeling, which can be applied in both discrete and continuous treatment settings. A description of the methods is given in Liu (2022) . Package: r-cran-rcausim Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4206 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-tidyr, r-cran-igraph Suggests: r-cran-broom, r-cran-ggnetwork, r-cran-ggpubr, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-rcausim_0.1.1-1.ca2404.1_all.deb Size: 1612482 MD5sum: 744326836d3ee5c6aada601583e817ae SHA1: f417fc0917e5d661731b6313a2fcc95230ddb8ff SHA256: 51f8cbf3a75be7f08a130ccd7d48b302884dd5ffc50c775399dea4e1731c7e70 SHA512: 7e8862056190104476c6fa28b015add44f793affc5096081006309f5fa4639f82d79eaa3d19d9f20be4a9093389dc8dd6a23bf7a96ee21e06aa3c2ab55b542ac Homepage: https://cran.r-project.org/package=rcausim Description: CRAN Package 'rcausim' (Generate Causally-Simulated Data) Generate causally-simulated data to serve as ground truth for evaluating methods in causal discovery and effect estimation. The package provides tools to assist in defining functions based on specified edges, and conversely, defining edges based on functions. It enables the generation of data according to these predefined functions and causal structures. This is particularly useful for researchers in fields such as artificial intelligence, statistics, biology, medicine, epidemiology, economics, and social sciences, who are developing a general or a domain-specific methods to discover causal structures and estimate causal effects. Data simulation adheres to principles of structural causal modeling. Detailed methodologies and examples are documented in our vignette, available at . Package: r-cran-rcba Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-arules, r-cran-r.utils, r-cran-tunepareto Filename: pool/dists/noble/main/r-cran-rcba_0.4.3-1.ca2404.1_all.deb Size: 99712 MD5sum: c8e32ad8e92fae95e5ccb3b495d5a984 SHA1: 91a8827a0eff042f62c5adf5e3feea42471502df SHA256: 8c880614478d56a371dabc5317acf21b7f83bfb759f0af3d2a250130753dbb2d SHA512: 5bfecb9105d2cd2ba4a4dc03df610701b672fb16cd4a45aace2ccddac6e8673aafad3c144d68fe0722eba1f219aa4056c91e149098dad5bbe932aef8c83be514 Homepage: https://cran.r-project.org/package=rCBA Description: CRAN Package 'rCBA' (CBA Classifier) Provides implementations of a classifier based on the "Classification Based on Associations" (CBA). It can be used for building classification models from association rules. Rules are pruned in the order of precedence given by the sort criteria and a default rule is added. The final classifier labels provided instances. CBA was originally proposed by Liu, B. Hsu, W. and Ma, Y. Integrating Classification and Association Rule Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp80-86 (1998, ISBN:1-57735-070-7). Package: r-cran-rcbalance Architecture: all Version: 1.8.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-plyr, r-cran-rlemon Suggests: r-cran-optmatch, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcbalance_1.8.8-1.ca2404.1_all.deb Size: 75940 MD5sum: 6a6a6f8ef1fdb919f8baf0a91c7222b5 SHA1: 8df82a4e52464af44405cfb2e904c0454d8b4e94 SHA256: 263fa7c4ee31f32d3dece4bc6d56d6db4da1392765e10ce8c67693d1927561b3 SHA512: 103a2a4b84b36c6433f72b9a5ed5e988478e16bc5d619d628454a724481aa0b9f428d392a1afab5b20061f6061fc737b3ce9633eb3e5dc0a562dd6846bedf152 Homepage: https://cran.r-project.org/package=rcbalance Description: CRAN Package 'rcbalance' (Large, Sparse Optimal Matching with Refined Covariate Balance) Tools for large, sparse optimal matching of treated units and control units in observational studies. Provisions are made for refined covariate balance constraints, which include fine and near-fine balance as special cases. Matches are optimal in the sense that they are computed as solutions to network optimization problems rather than greedy algorithms. See Pimentel, et al.(2015) and Pimentel (2016), Obs. Studies 2(1):4-23. The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at . Package: r-cran-rcbr Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rmosek, r-cran-rebayes, r-cran-orthopolynom, r-cran-formula, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-digest Filename: pool/dists/noble/main/r-cran-rcbr_0.6.2-1.ca2404.1_all.deb Size: 156430 MD5sum: 6f945c8fb3fb87d5da7c0be13397bf06 SHA1: 1e99b9f626193e818c7933d51c188c5f3e6d73ed SHA256: c9db8984f325cac489e25617693522aed5dfdeba09f8f71015741a8989caeb85 SHA512: a0d3557d57c5f31da57a0d97107c8cfd5ab94ea9b436ef5b963d511bc8d70bd4089ba9ac4ca9026af5f3fe0d3c878138f8e510326876b3d06429707afb784330 Homepage: https://cran.r-project.org/package=RCBR Description: CRAN Package 'RCBR' (Random Coefficient Binary Response Estimation) Nonparametric maximum likelihood estimation methods for random coefficient binary response models and some related functionality for sequential processing of hyperplane arrangements. See J. Gu and R. Koenker (2020) . 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A close relative is the 'rcbalance' package. See Pimentel, et al.(2015) and Pimentel and Kelz (2020) . The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at . 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Package: r-cran-rcd3 Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rcd3_0.1.1-1.ca2404.1_all.deb Size: 28780 MD5sum: d1100c01e5c945084efd098f86ec7c29 SHA1: dea884ab4143d6e9c1d650946032dcea96d200f9 SHA256: 85df1ab1fccc6461372e1d63246c31e6517941c27e052798de71ab3e61d350b6 SHA512: 8611301858fa60fdfdfc5e6a00bc7fa16c662a5deb4b3ee4645cc1691d45c4b6f5cae72c01f2d0b4b8d00edad758023fcaf09c60548ec7bca3d01071d63143a4 Homepage: https://cran.r-project.org/package=rcd3 Description: CRAN Package 'rcd3' (Efficient Row-Column Designs for 3 Level Factorial Experimentsin 3 Rows) Provides functions to construct efficient row-column designs for 3-level factorial experiments in 3 rows. The designs ensure the estimation of all main effects (full efficiency) and two factor interactions in minimum replications. For more details, see Dey, A. and Mukerjee, R. (2012) and Dash, S., Parsad, R., and Gupta, V. K. (2013) . 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The conditional approach is based on Daraio and Simar (2007) . Besides we provide graphs to help with the choice of m. We relay on the 'Benchmarking' package to compute the efficiency scores and on the 'np' package to compute non parametric estimation of similarity among units. 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It offers a flexible interface for handling data stored in encrypted Parquet format, along with metadata extraction, key management, and secure operations using AES and RSA encryptions. Package: r-cran-rcdk Architecture: all Version: 3.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 867 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcdklibs, r-cran-fingerprint, r-cran-rjava, r-cran-png, r-cran-iterators, r-cran-itertools Suggests: r-cran-xtable, r-cran-runit, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-rcdk_3.8.2-1.ca2404.1_all.deb Size: 560360 MD5sum: 6beb94f44c7588188719be68a9ff9e42 SHA1: 759a72485d2a553ca330ba10298a09c6002c4769 SHA256: 4d505609f9dc06b22ad0838d4b41b319af8c004ba90ae0dd38427495bbe895e2 SHA512: 3a9e7cce4ad9d7418b25c0fe87ec4b8c2699e6bc2a11cad912eddd37070b5e914123e3e8f1cb79af33adf22fb38b026189e0d8c211b4d45fc03dfb8c47aa3837 Homepage: https://cran.r-project.org/package=rcdk Description: CRAN Package 'rcdk' (Interface to the 'CDK' Libraries) Allows the user to access functionality in the 'CDK', a Java framework for cheminformatics. This allows the user to load molecules, evaluate fingerprints, calculate molecular descriptors and so on. In addition, the 'CDK' API allows the user to view structures in 2D. Package: r-cran-rcdklibs Architecture: all Version: 2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 20540 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava Filename: pool/dists/noble/main/r-cran-rcdklibs_2.9-1.ca2404.1_all.deb Size: 19255090 MD5sum: 469af198e93b0fca2656a4e01721fb3f SHA1: 650559c9ff7da0ad5b2ffc1f96ea88a48e03cf8d SHA256: be68cfe540c074c665be5d1e6262486eea017647bbeec42848f1869744de5284 SHA512: 37b9bd78a45de00d8a3278d0c108c84d316900100f86d880b3be140328a3f15aeb20947446c16d9f85c28794ccf08e7d2aaf9ad8bf50d0d502f594bc92f2b825 Homepage: https://cran.r-project.org/package=rcdklibs Description: CRAN Package 'rcdklibs' (The CDK Libraries Packaged for R) An R interface to the Chemistry Development Kit, a Java library for chemoinformatics. Given the size of the library itself, this package is not expected to change very frequently. To make use of the CDK within R, it is suggested that you use the 'rcdk' package. Note that it is possible to directly interact with the CDK using 'rJava'. However 'rcdk' exposes functionality in a more idiomatic way. The CDK library itself is released as LGPL and the sources can be obtained from . Package: r-cran-rcdo Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4228 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-r6, r-cran-rlang Suggests: r-cran-glue, r-cran-knitr, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcdo_0.3.2-1.ca2404.1_all.deb Size: 1454100 MD5sum: 98408bbeb0741b0876b324b9e787a053 SHA1: f548355e6316056151309fb9767d8302cb51c2a0 SHA256: 07041e6ebe82e60dd4f0d07f415ca6be8a95227a719acd1512cba60bc17f2660 SHA512: 1ef21084453b937638f0c4d1c05bf6fbbddbb3436a2b9e83ce4ffc5e72b7a38b2f40d1264a9192c12e3334c6e729d53e617c1b4c923ff43e560ca6d8a3884c3d Homepage: https://cran.r-project.org/package=rcdo Description: CRAN Package 'rcdo' (Wrapper of 'CDO' Operators) Provides a translation layer between 'R' and 'CDO' operators. Each operator is it's own function with documentation. Nested or piped functions will be translated into 'CDO' chains. Package: r-cran-rceim Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1352 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rceim_0.3-1.ca2404.1_all.deb Size: 1309164 MD5sum: 6ec7c7dd3e03db8ab9aa7c3ba6ab96be SHA1: 70b0ecd75963a7ea6f3f083c8fb8180c397dbb4e SHA256: 36e11cc60be72b4b35e48ff3a61da93b6ccaebd954b40ce27882971fc9340ad2 SHA512: 13929e125aac9af92bd4e2a297058f5b3dcde78fd1f42b0ed3cf5500ae60984a488331924a25347042f533a186ef7cf5297abc720b4a354aed1c5b182e732042 Homepage: https://cran.r-project.org/package=RCEIM Description: CRAN Package 'RCEIM' (R Cross Entropy Inspired Method for Optimization) An implementation of a stochastic heuristic method for performing multidimensional function optimization. The method is inspired in the Cross-Entropy Method. It does not relies on derivatives, neither imposes particularly strong requirements into the function to be optimized. Additionally, it takes profit from multi-core processing to enable optimization of time-consuming functions. Package: r-cran-rcens Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rcens_0.1.1-1.ca2404.1_all.deb Size: 48672 MD5sum: 4535de04657dfb51380c01f9acc9fd50 SHA1: 6a128acb33d230e568b2a81b49db72d22b6bbf7d SHA256: ccddc1ede37c3c5c08950f65f766ed56fa883cac5d0c26ec4a9df0a12048fc39 SHA512: 671ad326d161c36b20547290894b232a2ce5710158eef6575b7f92333267baabc9d91c9229d4e615ddba4e2a8f31a350ebd6269cace49b1c864836166b157a90 Homepage: https://cran.r-project.org/package=rcens Description: CRAN Package 'rcens' (Generate Sample Censoring) Provides functions to generate censored samples of type I, II and III, from any random sample generator. It also supplies the option to create left and right censorship. Along with this, the generation of samples with interval censoring is in the testing phase, with two options of fixed length intervals and random lengths. Package: r-cran-rcensuspkg Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table, r-cran-httr2, r-cran-downloader, r-cran-jsonlite, r-cran-stringr, r-cran-purrr, r-cran-sf, r-cran-ggplotify, r-cran-gtable, r-cran-rplotterpkg Suggests: r-cran-knitr, r-cran-testthat, r-cran-usethis, r-cran-usmap, r-cran-vdiffr, r-cran-classint, r-cran-rcolorbrewer, r-cran-here, r-cran-withr Filename: pool/dists/noble/main/r-cran-rcensuspkg_0.1.5-1.ca2404.1_all.deb Size: 240030 MD5sum: 07178d17a451c55fc046f61ac026b81e SHA1: f633dae6941220a18c16a8e1be345bf3c4e64b62 SHA256: 70d3b0f74ff298324e8563a381c81f4b041f3efa9ef3289acdd659b79044bbc8 SHA512: 738a1d742ad2d429ea1717d2ef6002c91b075406b335cc7d9997e2dda928c2e24e4e71b38b93918f32ea89e1975c41c55525cae4aff4c872c37a05a2760cb951 Homepage: https://cran.r-project.org/package=RcensusPkg Description: CRAN Package 'RcensusPkg' (Easily Access US Census Bureau Survey and Geographic Data) The key function 'get_vintage_data()' returns a dataframe and is the window into the Census Bureau API requiring just a dataset name, vintage(year), and vector of variable names for survey estimates/percentages. 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Package: r-cran-rcereal Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-cpp11, r-cran-rcpp, r-cran-decor, r-cran-git2r, r-cran-httr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rcereal_1.3.2-1.ca2404.1_all.deb Size: 212740 MD5sum: 6f026fb23a0690e4f9cce6f845c884b7 SHA1: 63add5be5b4aed34cc89aef014051d8bf2a92a83 SHA256: 233231d70ece2c344f8cb23ec98ec8c01fd246bbf926e634540a9e58d2ad2a76 SHA512: 8228860f5e8c2033af66a2eae5d411a8fd0f3a0fdcaf658c7d4d80c6d5798fe83878b150bc653553e2d2ce732c90fd262a1e9be6f3f01327600cd6cec7de9350 Homepage: https://cran.r-project.org/package=Rcereal Description: CRAN Package 'Rcereal' ("Cereal Headers for R and C++ Serialization") To facilitate using 'cereal' with R via 'cpp11' or 'Rcpp'. 'cereal' is a header-only C++11 serialization library. 'cereal' takes arbitrary data types and reversibly turns them into different representations, such as compact binary encodings, 'XML', or 'JSON'. 'cereal' was designed to be fast, light-weight, and easy to extend - it has no external dependencies and can be easily bundled with other code or used standalone. Please see for more information. Package: r-cran-rcexttools Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1359 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-igraph, r-cran-sqldf, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-rcexttools_0.1.1-1.ca2404.1_all.deb Size: 1215180 MD5sum: 08410b60353c08378084a2910bf3c08f SHA1: 77f0c9152dbe7121168c84158fe9724266087985 SHA256: 07aa0b57d6a5af2aea7f03580ba464e5f4a1c451e807f5231f9829e5d872cb62 SHA512: 0643d4c9943aa794c2c9927e3a945603c92bc93e9bea0f947c41f34d83078f7342a761408ec70f658834a192173ad549b760f43c7b53a0ca402f37cdc7b9980a Homepage: https://cran.r-project.org/package=RcextTools Description: CRAN Package 'RcextTools' (Analytical Procedures in Support of Brazilian Public SectorExternal Auditing) Set of analytical procedures based on advanced data analysis in support of Brazil's public sector external control activity. Package: r-cran-rcgls Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl, r-cran-ncdf4, r-cran-raster, r-cran-sp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcgls_1.0.3-1.ca2404.1_all.deb Size: 24540 MD5sum: 7c02f9e25583a6a6af2a1e962502bb33 SHA1: 08a131535f4663f311a284143fcef2c7b517b271 SHA256: 91cb08eafcbbc5a23279c2d23513154f557698aa71671e2637107bf1ddc71d3f SHA512: bcab8f8d68fc98afe5ed4ab47484c48c5151848301a854b8acef89e765d504caa7b7a183f276c45de05cb40ad117eeb37ab62c4e6ade9660399c1e21660f80ec Homepage: https://cran.r-project.org/package=RCGLS Description: CRAN Package 'RCGLS' (Download and Open Data Provided by the Copernicus Global LandService) Download and open manifest files provided by the Copernicus Global Land Service data . The manifest files are available at: . Also see: . Before you can download the data, you will first need to register to create a username and password. Package: r-cran-rchallenge Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmarkdown, r-cran-knitr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rchallenge_1.3.4-1.ca2404.1_all.deb Size: 112696 MD5sum: 341ba4143b69f22b76f9279ae81c1289 SHA1: a453870a7371f118abc898b1b2a56d2f8efc9df3 SHA256: 37e50d2962a6f5058df5a47a01cc78e8abaf15abbd999353eae7d1739426ee3c SHA512: 93cca39ff5457fe02b286926a07337e5614cfeb007ac49591cb4eb9f287ffe3a7c86169c2caff3a1105e0c707841753e527087798a4ae37bcd72eb47e6f5d974 Homepage: https://cran.r-project.org/package=rchallenge Description: CRAN Package 'rchallenge' (A Simple Data Science Challenge System) A simple data science challenge system using R Markdown and 'Dropbox' . It requires no network configuration, does not depend on external platforms like e.g. 'Kaggle' and can be easily installed on a personal computer. Package: r-cran-rchasm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 800 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cowplot, r-cran-dplyr, r-cran-envstats, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggsci, r-cran-ggstar, r-cran-magrittr, r-cran-matrixstats, r-cran-mclust, r-cran-rstatix, r-cran-sirt, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-readr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-extradistr Filename: pool/dists/noble/main/r-cran-rchasm_1.0.1-1.ca2404.1_all.deb Size: 559132 MD5sum: af9f59dd35c0294b19b241f4ebd06283 SHA1: 2c223f2d2aa9558c05998a7e7c9af2682649bd14 SHA256: b2548db1ec5b3a63ccb53a5fa3c60dabfab2df29c61c701ef57d45c48a2df529 SHA512: bca0fa1f553b7e66f3f24d66ac82a856d556e1a99dcabe34048e66d03a216c0d2161d8814c714352f58ff9fe7110ff267f800435a760cee9b38f5d27626b56fa Homepage: https://cran.r-project.org/package=RChASM Description: CRAN Package 'RChASM' (Detection of Chromosomal Aneuploidies in Ancient DNA Studies) An R implementation of ChASM (Chromosomal Aneuploidy Screening Methodology): a statistically rigorous Bayesian approach for screening data sets for autosomal and sex chromosomal aneuploidies. This package takes as input the number of (deduplicated) reads mapping to chromosomes 1-22 and the X and Y chromosomes, and models these using a Dirichlet-multinomial distribution. From this, This package returns posterior probabilities of sex chromosomal karyotypes (XX, XY, XXY, XYY, XXX and X) and full autosomal aneuploidies (trisomy 13, trisomy 18 and trisomy 21). This package also returns two diagnostic statistics: (i) a posterior probability addressing whether contamination between XX and XY may explain the observed sex chromosomal aneuploidy, and (ii) a chi-squared statistic measuring whether the observed read counts are too divergent from the underlying distribution (and may represent abnormal sequencing/quality issues). Package: r-cran-rchea3 Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1807 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-crayon, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-rlang, r-cran-tidyselect, r-cran-writexl Suggests: r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rchea3_0.2.0-1.ca2404.1_all.deb Size: 1054266 MD5sum: 46d7809c5d6072c8c0efb4d22ad2216b SHA1: be66d93aba2000677c5d83312b758111ef3cea08 SHA256: 5aeade960fab4ad47c6a8ded7f783bd93c3a545f57c787e493d0a88664b20777 SHA512: c7a628e9bd5efa34f59a81931146ed762c116e9c9501e8ac38c786bdb003a269e9f2ed9e8147ced35cd576248159cf2b333651530a3ad355371e956a8c9e827e Homepage: https://cran.r-project.org/package=rChEA3 Description: CRAN Package 'rChEA3' (R Client for the 'ChEA3' Transcription Factor Enrichment API) Interface to the 'ChEA3' transcription factor enrichment API. 'ChEA3' integrates evidence from ChIP-seq, co-expression, and literature resources to prioritize transcription factors regulating a given set of genes. This package provides convenient R functions to query the API, retrieve ranked results across collections (including integrated scores), and standardize output for downstream analysis in R/Bioconductor workflows. See or Keenan (2019) for further details. Package: r-cran-rchemo Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5220 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-fnn, r-cran-signal, r-cran-e1071 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rchemo_0.1-3-1.ca2404.1_all.deb Size: 5254634 MD5sum: af95fc6906c344896580c93c404a58f8 SHA1: 6814e85a60d418152b95723d32d6fe616e6f08b3 SHA256: c1d66bb0e4a5f92c2ea8ba9ba5fada8a3c971cd78a8df7fd268778ef850c0915 SHA512: e49fddf35abf24843e853bf48c7bc5ba0406638a65aa915f6f7a1492d77788102f7a1c0f9a2dda161e3d20e03185e48387f6bd968bca4a16db7b5d382060f5b5 Homepage: https://cran.r-project.org/package=rchemo Description: CRAN Package 'rchemo' (Dimension Reduction, Regression and Discrimination forChemometrics) Data exploration and prediction with focus on high dimensional data and chemometrics. The package was initially designed about partial least squares regression and discrimination models and variants, in particular locally weighted PLS models (LWPLS). Then, it has been expanded to many other methods for analyzing high dimensional data. The name 'rchemo' comes from the fact that the package is orientated to chemometrics, but most of the provided methods are fully generic to other domains. Functions such as transform(), predict(), coef() and summary() are available. Tuning the predictive models is facilitated by generic functions gridscore() (validation dataset) and gridcv() (cross-validation). Faster versions are also available for models based on latent variables (LVs) (gridscorelv() and gridcvlv()) and ridge regularization (gridscorelb() and gridcvlb()). Package: r-cran-rcheology Architecture: all Version: 4.6.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1010 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rcheology_4.6.0.0-1.ca2404.1_all.deb Size: 814948 MD5sum: 4ddb2024083394e9e0937438e001eb1d SHA1: af1b968b24c18a0d58c88307bec7dd2dc31cddbb SHA256: 7f4a2731134cbaf000d26eec6b1966abf9589f6ad2157b688b67e1155eef8800 SHA512: 6d972be37b6ef4f77aef1546c2598b0e21e4aee0ea360461c30f9759e719b40bed38e4ece92bdc9c6b1fdd4662f9386ef4d659819f42f619cbf255e917e00ef9 Homepage: https://cran.r-project.org/package=rcheology Description: CRAN Package 'rcheology' (Data on Base and Recommended Packages for Current and PreviousVersions of R) Provides a dataset of functions in all base and recommended packages of R versions 0.50 onwards. 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Knowledge of the number of change-points is not required. The code is written in Go and interfaced with R. 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This package provides a convenient interface to interact with the REST API of 'ChromaDB' . 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Package: r-cran-rciplot Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tibble Filename: pool/dists/noble/main/r-cran-rciplot_0.1.1-1.ca2404.1_all.deb Size: 30274 MD5sum: e1a52bf93ca9bfb80880c8726fa72f2e SHA1: b048504a6d5285eca432c6afd2570737685bac89 SHA256: 3708bc278b2ac81b3fa2db20c872867b6557d44d6fb2bb87fe6201b45ab50ba7 SHA512: 10353c4a20173d8acf180a8bc145555e3df3479087daefa88460dc426fa8738e06b4b63bc0f0bcec955479783963075fbb307f1f41fdf7f8010e15cd8ff9634e Homepage: https://cran.r-project.org/package=rciplot Description: CRAN Package 'rciplot' (Plot Jacobson-Truax Reliable Change Indices) The concept of reliable and clinically significant change (Jacobson & Truax, 1991) helps you answer the following questions for a sample with two measurements at different points in time (pre & post): Which proportion of my sample has a (considering the reliability of the instrument) probably not-just-by-chance difference in pre- vs. post-scores? 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Package: r-cran-rcircos Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1728 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rcircos_1.2.2-1.ca2404.1_all.deb Size: 1570324 MD5sum: baf802ca25bec8560216bf79e3041164 SHA1: 94190190f7ed83a05054d6c2579c126d2d26435b SHA256: 4cbbe0e41f97dc1e7ee52e0bb256aa28290a2bc69f9643b030f8762e6d951d69 SHA512: 1daeb6b95fc846d485e8868f0ec73e5b62b8db53a69ad8c62b8a4ab344b03ca4efc977650b230d079efd96c4f8501067f064b6167f64af78d905ded8f7277a00 Homepage: https://cran.r-project.org/package=RCircos Description: CRAN Package 'RCircos' (Circos 2D Track Plot) A simple and flexible way to generate Circos 2D track plot images for genomic data visualization is implemented in this package. 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(2018) . The 'rcldf' package is designed to facilitate the manipulation and analysis of these datasets by simplifying the loading, querying, and visualisation of CLDF datasets making it easier to conduct comparative linguistic analyses, manage language data, and apply statistical methods directly within R. 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Package: r-cran-rcloner Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fs, r-cran-jsonlite, r-cran-processx Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-rcloner_0.0.1-1.ca2404.1_all.deb Size: 75360 MD5sum: 3268b3a67c51314d76f40e903d419744 SHA1: d80e7d5bb23e3f9489135f2dfb2b099a4e0be25b SHA256: 1498a450cb6d16d2019350de106dfdbc91703ed06ce169c37c11783e395400aa SHA512: 61687eedcd28452475b4dfca394b252bc6a2c6be8b02cbcd57b41801292e6f985a82ab582126146c7fddc6a5e6db290522613b2275143a168e99a7468879e5bb Homepage: https://cran.r-project.org/package=rcloner Description: CRAN Package 'rcloner' (Interface to 'rclone' Cloud Storage Utility) Provides an R interface to 'rclone' , a command-line program for managing files on cloud storage. 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Package: r-cran-rclsp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-cvxr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rclsp_1.0.0-1.ca2404.1_all.deb Size: 77232 MD5sum: d4d9f817ecf552ed12c53261e4254d3f SHA1: 4b9e362dcaaa77a25d44b225627a6ab5a82eeafc SHA256: c1ce69996fe281bc196d73f872c9f68b132696a84c0a0fe23922af362ea40ccc SHA512: 8e5d885b769a37403a0b8d3354fae437c4435c80669a6029122ac1bedf776d8e0a361e0f7ad0255154c4086f8775d4f4ace01281d0142e5c205c3eee9832ac75 Homepage: https://cran.r-project.org/package=rclsp Description: CRAN Package 'rclsp' (A Modular Two-Step Convex Optimization Estimator for Ill-PosedProblems) Convex Least Squares Programming (CLSP) is a two-step estimator for solving underdetermined, ill-posed, or structurally constrained least-squares problems. It combines pseudoinverse-based estimation with convex-programming correction methods inspired by Lasso, Ridge, and Elastic Net to ensure numerical stability, constraint enforcement, and interpretability. The package also provides numerical stability analysis and CLSP-specific diagnostics, including partial R^2, normalized RMSE (NRMSE), Monte Carlo t-tests for mean NRMSE, and condition-number-based confidence bands. Package: r-cran-rclustool Architecture: all Version: 0.91.61-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tcltk2, r-cran-tkrplot, r-cran-class, r-cran-cluster, r-cran-conclust, r-cran-corrplot, r-cran-e1071, r-cran-factoextra, r-cran-factominer, r-cran-ggplot2, r-cran-jpeg, r-cran-knitr, r-cran-mass, r-cran-mclust, r-cran-mda, r-cran-mmand, r-cran-nnet, r-cran-png, r-cran-randomforest, r-cran-reshape, r-cran-rlang, r-cran-searchtrees, r-cran-sp, r-cran-stringi, r-cran-stringr Filename: pool/dists/noble/main/r-cran-rclustool_0.91.61-1.ca2404.1_all.deb Size: 1471610 MD5sum: c91908cdbe9ee51e874fda477657ecb7 SHA1: 3f30eee4682f934150f0b60cb1851f5f98568adb SHA256: f1c58da4ef13c4ae6836c4956fb6fc4586eaf19e75fb78ec73d6ebe3eaef1b86 SHA512: ee2e865bc70ad9c2a067117fe86f0870801c2faa1bb34e22bd0db1bef629c1b346fb61566e7491977642224f1ba1841a0db42d438329449877dfafea0e233084 Homepage: https://cran.r-project.org/package=RclusTool Description: CRAN Package 'RclusTool' (Graphical Toolbox for Clustering and Classification of DataFrames) Graphical toolbox for clustering and classification of data frames. It proposes a graphical interface to process clustering and classification methods on features data-frames, and to view initial data as well as resulted cluster or classes. According to the level of available labels, different approaches are proposed: unsupervised clustering, semi-supervised clustering and supervised classification. To assess the processed clusters or classes, the toolbox can import and show some supplementary data formats: either profile/time series, or images. These added information can help the expert to label clusters (clustering), or to constrain data frame rows (semi-supervised clustering), using Constrained spectral embedding algorithm by Wacquet et al. (2013) and the methodology provided by Wacquet et al. (2013) . Package: r-cran-rcma Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rjava Filename: pool/dists/noble/main/r-cran-rcma_1.1.1-1.ca2404.1_all.deb Size: 339510 MD5sum: 3b8bce84e8a2a733daeb9792b0581183 SHA1: 8c89b9276553e08a4e1441ccf3411235eb9e6882 SHA256: a6821e86da23f444b50a5575c4a53fdb745064e3138dcb858e5d101a084c659c SHA512: e8123757b6bb8740c3904b8e223042445dd4994982fddb7651bdc631458b315c5f8b870ca3d1370a8a0d18676186e75ecbb44b1e86f9690eaacedd8278699459 Homepage: https://cran.r-project.org/package=rCMA Description: CRAN Package 'rCMA' (R-to-Java Interface for 'CMA-ES') Tool for providing access to the Java version 'CMAEvolutionStrategy' of Nikolaus Hansen. 'CMA-ES' is the Covariance Matrix Adaptation Evolution Strategy, see . Package: r-cran-rcmdcheck Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-callr, r-cran-cli, r-cran-curl, r-cran-desc, r-cran-digest, r-cran-pkgbuild, r-cran-prettyunits, r-cran-r6, r-cran-rprojroot, r-cran-sessioninfo, r-cran-withr, r-cran-xopen Suggests: r-cran-covr, r-cran-knitr, r-cran-mockery, r-cran-processx, r-cran-ps, r-cran-rmarkdown, r-cran-svglite, r-cran-testthat, r-cran-webfakes Filename: pool/dists/noble/main/r-cran-rcmdcheck_1.4.0-1.ca2404.1_all.deb Size: 170250 MD5sum: c37115246225bbcd105ff4af0f1c9fec SHA1: 046e9bd89c80874ea9ceb0ef3c14b4a174c44db1 SHA256: d2008d7c62be90cf4c30c9a944da28d3acf1b9c82feea5a7a3f7d6697299cb94 SHA512: 8b5b992c5fe9f7ea6e430214d513a14cc87a8d706320acdd277a626f711c983e28369008eef8e56e1296ac154f79244a52f09f4c6ac6e9ae2c90455633135c41 Homepage: https://cran.r-project.org/package=rcmdcheck Description: CRAN Package 'rcmdcheck' (Run 'R CMD check' from 'R' and Capture Results) Run 'R CMD check' from 'R' and capture the results of the individual checks. Supports running checks in the background, timeouts, pretty printing and comparing check results. Package: r-cran-rcmdr Architecture: all Version: 2.12.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8968 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcmdrmisc, r-cran-car, r-cran-effects, r-cran-tcltk2, r-cran-abind, r-cran-relimp, r-cran-lme4 Suggests: r-cran-aplpack, r-cran-boot, r-cran-colorspace, r-cran-e1071, r-cran-foreign, r-cran-hmisc, r-cran-knitr, r-cran-lattice, r-cran-leaps, r-cran-lmtest, r-cran-markdown, r-cran-mass, r-cran-mgcv, r-cran-multcomp, r-cran-nlme, r-cran-nnet, r-cran-nortest, r-cran-readxl, r-cran-rgl, r-cran-rmarkdown, r-cran-sem Filename: pool/dists/noble/main/r-cran-rcmdr_2.12.3-1.ca2404.1_all.deb Size: 5609124 MD5sum: c54a01ebc9e59f72ed5dc7c423a09b25 SHA1: aa0ea9493f4493b7a5aad3ea91b375de3b09c022 SHA256: de428f4a3a1ce207e0160ec0cec23c769127cddb64fd2ae36aab3e7b5fed93c1 SHA512: 4c678cefe9e5cf27af8061772a1d7188fd8ae42f3d45350c9f98e5d9fe2f51d4f52fbdc1891b0c9b63ba0d8ca02ea3437d471941e92beb9396aacdaac0592966 Homepage: https://cran.r-project.org/package=Rcmdr Description: CRAN Package 'Rcmdr' (R Commander) A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package. Package: r-cran-rcmdrmisc Architecture: all Version: 2.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-sandwich, r-cran-abind, r-cran-colorspace, r-cran-hmisc, r-cran-mass, r-cran-e1071, r-cran-foreign, r-cran-haven, r-cran-readstata13, r-cran-readxl, r-cran-nortest, r-cran-lattice Suggests: r-cran-boot, r-cran-cardata Filename: pool/dists/noble/main/r-cran-rcmdrmisc_2.10.1-1.ca2404.1_all.deb Size: 223122 MD5sum: af6e09d74ec0ca317afd4514f9197de9 SHA1: 97057c258c3d757bc8c1db501cdce7c1f8255323 SHA256: 2493be77f642b80aef0bfb04be22cc8ca3c6c2e39c791002e12b0eba7aa51839 SHA512: 67183407e51584cc49382106bc0029192c0d8474ae1b72342773e0c420f49ca539b574708e6b693fd644a32728a9da3b4c110f85cc106302c9573902b8f9ab4e Homepage: https://cran.r-project.org/package=RcmdrMisc Description: CRAN Package 'RcmdrMisc' (R Commander Miscellaneous Functions) Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R. Package: r-cran-rcmdrplugin.arnova Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2548 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcmdr Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.arnova_0.0.6-1.ca2404.1_all.deb Size: 1921052 MD5sum: 570cc89fc02e68bf28928704f7c2e4b2 SHA1: d3d62439129178346b4aebbfa748617e3a797c45 SHA256: cb7e27eab1c518c5b7d2a8a403f9e720dce7306183482253e732d48ebd503221 SHA512: 1b00d42efbd53d93e2e0da604fea8fc49ab35fa1e25705f27afc5ac30a5272befb06199dfee0ab28ce0f1575764c692bc596e72bb73741db43225916959fabf4 Homepage: https://cran.r-project.org/package=RcmdrPlugin.aRnova Description: CRAN Package 'RcmdrPlugin.aRnova' (R Commander Plug-in for Repeated-Measures ANOVA) R Commander plug-in for repeated-measures and mixed-design ('split-plot') ANOVA. It adds a new menu entry for repeated measures that allows to deal with up to three within-subject factors and optionally with one or several between-subject factors. It also provides supplementary options to oneWayAnova() and multiWayAnova() functions, such as choice of ANOVA type, display of effect sizes and post hoc analysis for multiWayAnova(). Package: r-cran-rcmdrplugin.biclustgui Architecture: all Version: 1.1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biclust, r-bioc-fabia, r-bioc-ibbig, r-cran-superbiclust, r-cran-bcdiag, r-bioc-bicare, r-cran-s4vd, r-cran-bibitr, r-cran-rcmdr, r-cran-gplots, r-cran-viridis Suggests: r-cran-knitr, r-bioc-rqubic, r-cran-biocmanager Filename: pool/dists/noble/main/r-cran-rcmdrplugin.biclustgui_1.1.3.1-1.ca2404.1_all.deb Size: 2771874 MD5sum: 3b774b23ccb1d88491e791a8b73b43b1 SHA1: b2344624d15d2977a0d7a13191f739b41e0fb547 SHA256: 890b78b8467d891c5ec58e0f96ecefa3b8a278ef7c41180eababef986fee33e7 SHA512: f10bbba521f3063896e53ac3d8003a0e7f716ad2d8920a77ea80b2a3d7224781d9a4a2ed56446b0fa8870de349cc1024ececa147444882abcf916f5fb9741770 Homepage: https://cran.r-project.org/package=RcmdrPlugin.BiclustGUI Description: CRAN Package 'RcmdrPlugin.BiclustGUI' ('Rcmdr' Plug-in GUI for Biclustering) A plug-in for R Commander ('Rcmdr'). The package is a Graphical User Interface (GUI) in which several biclustering methods can be executed, followed by diagnostics and plots of the results. Further, the GUI also has the possibility to connect the methods to more general diagnostic packages for biclustering. Biclustering methods from 'biclust', 'fabia', 's4vd', 'iBBiG', 'isa2', 'BiBitR', 'rqubic' and 'BicARE' are implemented. Additionally, 'superbiclust' and 'BcDiag' are also implemented to be able to further investigate results. The GUI also provides a couple of extra utilities to export, save, search through and plot the results. 'RcmdrPlugin.BiclustGUI' also provides a very specific framework for biclustering in which new methods, diagnostics and plots can be added. Scripts were prepared so that R-package developers can freely design their own dialogs in the GUI which can then be added by the maintainer of 'RcmdrPlugin.BiclustGUI'. These scripts do not required any knowledge of 'tcltk' and 'Rcmdr' and are easy to fill in. (Note: rqubic currently requires manual installation through BiocManager::install('rqubic').) Package: r-cran-rcmdrplugin.bws1 Architecture: all Version: 0.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crossdes, r-cran-support.bws, r-cran-support.ces, r-cran-survival, r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.bws1_0.3-0-1.ca2404.1_all.deb Size: 116226 MD5sum: d02bfdda484d963bbd61caf6f3d0bd06 SHA1: 40133a1d68568498b66bf009bb4e3def8d4293fa SHA256: f30729700625f7543f582ab586172e9e7a35de39678fc513116f6ff488d5fe18 SHA512: ea75b7fbe05f5ada094730d8402088bb25508056bb919184031b808585a8961836363798e97682dc97f25fd0337ef097bafe45d4d9fa70880a182b06a34ac052 Homepage: https://cran.r-project.org/package=RcmdrPlugin.BWS1 Description: CRAN Package 'RcmdrPlugin.BWS1' (R Commander Plug-in for Case 1 Best-Worst Scaling) Adds menu items to the R Commander for implementing case 1 (object case) best-worst scaling (BWS1) from designing choice sets to measuring preferences for items. BWS1 is a question-based survey method that constructs various combinations of items (choice sets) using the experimental designs, asks respondents to select the best and worst items in each choice set, and then measures preferences for the items by analyzing the responses. For details, refer to Aizaki and Fogarty (2023) . Package: r-cran-rcmdrplugin.bws2 Architecture: all Version: 0.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-support.bws2, r-cran-support.ces, r-cran-survival, r-cran-rcmdr, r-cran-doe.base Filename: pool/dists/noble/main/r-cran-rcmdrplugin.bws2_0.3-0-1.ca2404.1_all.deb Size: 98740 MD5sum: 1d067a93c9ad5c5ed493d46188b88187 SHA1: 80aa767a06f199c58018d858d3c343753e6e11c0 SHA256: e2eb94c214656f8563acad3404193ca08a59f767fb2d7ce429c69a91cce48457 SHA512: 7903cda202184e19caf622fd6a226a51f58e700e40ad928fbbb8416b954604ab03d7112719a73cc7572283e2690bae9df87c9ea548ac8f4184e104b10bfaf32a Homepage: https://cran.r-project.org/package=RcmdrPlugin.BWS2 Description: CRAN Package 'RcmdrPlugin.BWS2' (R Commander Plug-in for Case 2 Best-Worst Scaling) Adds menu items for case 2 (profile case) best-worst scaling (BWS2) to the R Commander. BWS2 is a question-based survey method that constructs profiles (combinations of attribute levels) using an orthogonal array, asks respondents to select the best and worst levels in each profile, and measures preferences for attribute levels by analyzing the responses. For details, see Aizaki and Fogarty (2019) . Package: r-cran-rcmdrplugin.bws3 Architecture: all Version: 0.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-support.bws3, r-cran-support.ces, r-cran-survival, r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.bws3_0.3-1-1.ca2404.1_all.deb Size: 103840 MD5sum: 16a5f51c65edbb11914bcd0e350eb57b SHA1: 37486f120c50222fc2f140c65c549899add11768 SHA256: b5feda01cf418daf31c2984185398b40022f4bc3d32812d98f09c6d1742da9b5 SHA512: f84ad291be42349141b2c778a108e4da373822b03bb0d0e687bf0b208ac61d83dba312c064b00cd064db4a62124395489b240872e49ce84c9ce46a10fe338604 Homepage: https://cran.r-project.org/package=RcmdrPlugin.BWS3 Description: CRAN Package 'RcmdrPlugin.BWS3' (R Commander Plug-in for Case 3 Best-Worst Scaling) Adds menu items for case 3 (multi-profile) best-worst scaling (BWS3) to the R Commander. BWS3 is a question-based survey method that designs various combinations of attribute levels (profiles), asks respondents to select the best and worst profiles in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on BWS3, refer to Louviere et al. (2015) . Package: r-cran-rcmdrplugin.cpd Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cpd, r-cran-rcmdrmisc, r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.cpd_0.2.0-1.ca2404.1_all.deb Size: 90322 MD5sum: cbf600cfb4899296539a5b17e4d32042 SHA1: 7d4bd414bf314e41cb385b3c6010861b8cda8dc4 SHA256: 6c5d185ef879ce15a2a9e52303e83d20dbe39620784ac0ac1e7bc230f6c5a5f3 SHA512: 34b149c718e4d624f4a681e7272e1677dc5b3fa9263e547f770ab67e35cf413ed7d1977e40d5c1b080d538706e1d317669e92c492137aa6dbeec2240c156fea9 Homepage: https://cran.r-project.org/package=RcmdrPlugin.cpd Description: CRAN Package 'RcmdrPlugin.cpd' (R Commander Plug-in for Complex Pearson Distributions) Provides an 'Rcmdr' plug-in based on the 'cpd' package. Package: r-cran-rcmdrplugin.dccv Architecture: all Version: 0.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dcchoice, r-cran-rcmdr, r-cran-isocodes Filename: pool/dists/noble/main/r-cran-rcmdrplugin.dccv_0.2-0-1.ca2404.1_all.deb Size: 66486 MD5sum: 54d9914228fc6dda08d1983306a3b832 SHA1: 105f98fb41764ab484214b69f83b4a74cd4e00fd SHA256: 354e369ce8dd44dff82c0906e2e01175f3f74238d6093017043526165f394927 SHA512: b4d044b95a2e58cff378c9c6c4b5658209359d6b2b452c3a899ec0bbd3f13f3251f05275cc8bac002eb631910c26543bbad3a04449270483bae88817e38da39c Homepage: https://cran.r-project.org/package=RcmdrPlugin.DCCV Description: CRAN Package 'RcmdrPlugin.DCCV' (R Commander Plug-in for Dichotomous Choice Contingent Valuation) Adds menu items to the R Commander for parametric analysis of dichotomous choice contingent valuation (DCCV) data. CV is a question-based survey method to elicit individuals' preferences for goods and services. This package depends on functions regarding parametric DCCV analysis in the package DCchoice. See Carson and Hanemann (2005) for DCCV. Package: r-cran-rcmdrplugin.dce Architecture: all Version: 0.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-support.ces, r-cran-survival, r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.dce_0.3-1-1.ca2404.1_all.deb Size: 93450 MD5sum: 9bdfc4ba07272efbbba79ba372c9c422 SHA1: 153df9e1ac6a7653d75a68ca164ae9f9102f2903 SHA256: 84cd797bcb3e9c25b219e6bc87d3774b058258e25f9fa4b71338e9f0fca9f604 SHA512: a00a333ae2eb5d204321d886279785edd8a0da5eec65a8747cbcf7cd3bec67b44578e8cc23af57355563f9a0a8208d991121f623c06d4479572cbd567bdf9a32 Homepage: https://cran.r-project.org/package=RcmdrPlugin.DCE Description: CRAN Package 'RcmdrPlugin.DCE' (R Commander Plug-in for Discrete Choice Experiments) Adds menu items for discrete choice experiments (DCEs) to the R Commander. DCE is a question-based survey method that designs various combinations (profiles) of attribute levels using the experimental designs, asks respondents to select the most preferred profile in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on DCEs, refer to Louviere et al. (2000) . Package: r-cran-rcmdrplugin.depthtools Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-depthtools Filename: pool/dists/noble/main/r-cran-rcmdrplugin.depthtools_1.4-1.ca2404.1_all.deb Size: 71918 MD5sum: 4856b60108232fbe8ac166b60dd51a00 SHA1: 7b90ebfa12e25d1322fc7392d0ed7fcefb3b82b2 SHA256: b781a747b159325c8c0099a2d9f426ee23908727d63e5983d57b9188daee2a44 SHA512: f75cfd0f0a11d6168107100effe8fd2fd5b1ef48cc99a09eaf6da2ad1e687d28b8517bc89ddd331d80c1e4c6d6d3e691f4d8925674261e7145e4efd2dad96096 Homepage: https://cran.r-project.org/package=RcmdrPlugin.depthTools Description: CRAN Package 'RcmdrPlugin.depthTools' (R Commander Depth Tools Plug-in) We provide an Rcmdr plug-in based on the depthTools package, which implements different robust statistical tools for the description and analysis of gene expression data based on the Modified Band Depth, namely, the scale curves for visualizing the dispersion of one or various groups of samples (e.g. types of tumors), a rank test to decide whether two groups of samples come from a single distribution and two methods of supervised classification techniques, the DS and TAD methods. Package: r-cran-rcmdrplugin.doe Architecture: all Version: 0.12-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2358 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doe.base, r-cran-frf2, r-cran-doe.wrapper, r-cran-rcmdr, r-cran-rcmdrmisc Suggests: r-cran-frf2.catlg128 Filename: pool/dists/noble/main/r-cran-rcmdrplugin.doe_0.12-6-1.ca2404.1_all.deb Size: 2163842 MD5sum: 6524459ca3c31e80526e57476e948037 SHA1: 7a96e5645a0fc71622fdb554f57e060b8604e643 SHA256: 57ee616dd7d6d529367f5107880f7fa8bca89f954942c5c76fac26ddb3a931fd SHA512: 0ba127c57820873605c049c2f02b5f90e24e06b3285c8021412922a82b4533016fbe049eac2cfd1ea22e935113ce5a99445277469e5e727c8d439e3932832a33 Homepage: https://cran.r-project.org/package=RcmdrPlugin.DoE Description: CRAN Package 'RcmdrPlugin.DoE' (R Commander Plugin for (Industrial) Design of Experiments) Provides a platform-independent GUI for design of experiments. The package is implemented as a plugin to the R-Commander, which is a more general graphical user interface for statistics in R based on tcl/tk. DoE functionality can be accessed through the menu Design that is added to the R-Commander menus. Package: r-cran-rcmdrplugin.eacspir Architecture: all Version: 0.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 441 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r2html, r-cran-abind, r-cran-ez, r-cran-nortest, r-cran-reshape, r-cran-rcmdr, r-cran-rcmdrmisc Filename: pool/dists/noble/main/r-cran-rcmdrplugin.eacspir_0.2-3-1.ca2404.1_all.deb Size: 406328 MD5sum: 7a65e911ea18dae285a8031a28ee851f SHA1: 981e7c1438fbeaf23a782a366e8707907f6542de SHA256: f5677307540b6d7d024e1e266303fecf3ac614d4bba42b52d5dec1d0b5ede978 SHA512: 00779dc59532fae9a46d2df4598a898e46322a2ef6636650c44df7b25467577474e466d6195b7ba8267ec493ef1853d6eda0402e09db886d5255fb18351bbeeb Homepage: https://cran.r-project.org/package=RcmdrPlugin.EACSPIR Description: CRAN Package 'RcmdrPlugin.EACSPIR' (Plugin de R-Commander para el Manual 'EACSPIR') Este paquete proporciona una interfaz grafica de usuario (GUI) para algunos de los procedimientos estadisticos detallados en un curso de 'Estadistica aplicada a las Ciencias Sociales mediante el programa informatico R' (EACSPIR). LA GUI se ha desarrollado como un Plugin del programa R-Commander. Package: r-cran-rcmdrplugin.ebm Architecture: all Version: 1.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-epir, r-cran-abind Filename: pool/dists/noble/main/r-cran-rcmdrplugin.ebm_1.0-10-1.ca2404.1_all.deb Size: 60594 MD5sum: d739f0ac68b4b43bdb188bb85336637b SHA1: bfdf0c42dce166c2139d9d5c2fc0611a1ee33d79 SHA256: cbd2c8bf01630c50fef23a9c9c75ac08f791c70b2c8771ca996f33fbd19a2126 SHA512: 528b94e526b63dd6cfc521c840d8d5cf43e7a12c4fba6f43a6741071b2e2fec2e11b9141973cba14d04658d6a359638078716b11dfad4286a42779fef5235653 Homepage: https://cran.r-project.org/package=RcmdrPlugin.EBM Description: CRAN Package 'RcmdrPlugin.EBM' (Rcmdr Evidence Based Medicine Plug-in Package) Rcmdr plug-in GUI extension for Evidence Based Medicine medical indicators calculations (Sensitivity, specificity, absolute risk reduction, relative risk, ...). Package: r-cran-rcmdrplugin.ecovirtual Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-ecovirtual Filename: pool/dists/noble/main/r-cran-rcmdrplugin.ecovirtual_1.0-1.ca2404.1_all.deb Size: 148908 MD5sum: fdd37a5d9ddb18244dacede8d471697b SHA1: d636b737982b0a68275471dc24b6e6cbba052623 SHA256: 0b4f14318f191f95b5febe5c771a8d88583b25e72caa03ecad704c445421861c SHA512: 02c547117ea6f62aff6fb65726f74357a6cb5f53877c3cd36891643b9d4d2b23b51f44a5070f3bb1b656aea7fd6e3f3d3163c5589e304c424923082b73949801 Homepage: https://cran.r-project.org/package=RcmdrPlugin.EcoVirtual Description: CRAN Package 'RcmdrPlugin.EcoVirtual' (Rcmdr EcoVirtual Plugin) A Rcmdr "plug-in" for the EcoVirtual package, designed primarily for teaching ecological models using simulations. Package: r-cran-rcmdrplugin.export Architecture: all Version: 0.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-xtable, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-rcmdrplugin.export_0.3-1-1.ca2404.1_all.deb Size: 70706 MD5sum: 80808e6680143e943e312a2ea1f838cb SHA1: 78ff8a04a38f8710ae363822e8c56841ce84fd81 SHA256: 9227642fbb48b97bc267917e81f3d4d94101e49eaca9a0ef6f24bc6cb78f6f15 SHA512: 20b6a88f77966168dd5d196c83c8badb5d74693660c4b700af7b1cd2f6728f304d3472460d0dbfc85895660aa65a943c43ced0ab60f5f58e825fa24d17b633cb Homepage: https://cran.r-project.org/package=RcmdrPlugin.Export Description: CRAN Package 'RcmdrPlugin.Export' (Export R Output to LaTeX or HTML) Export Rcmdr output to LaTeX or HTML code. The plug-in was originally intended to facilitate exporting Rcmdr output to formats other than ASCII text and to provide R novices with an easy-to-use, easy-to-access reference on exporting R objects to formats suited for printed output. The package documentation contains several pointers on creating reports, either by using conventional word processors or LaTeX/LyX. Package: r-cran-rcmdrplugin.ezr Architecture: all Version: 1.70-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1699 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcmdr, r-cran-readstata13 Suggests: r-cran-abind, r-cran-aod, r-cran-aplpack, r-cran-brant, r-cran-car, r-cran-clinfun, r-cran-cmprsk, r-cran-consort, r-cran-foreign, r-cran-ggplot2, r-cran-lawstat, r-cran-meta, r-cran-metatest, r-cran-netmeta, r-cran-multcomp, r-cran-mvtnorm, r-cran-matching, r-cran-plotly, r-cran-proc, r-cran-survivalroc, r-cran-survrm2, r-cran-tableone, r-cran-readxl, r-cran-lmertest, r-cran-swimplot, r-cran-currentsurvival, r-cran-rstatix Filename: pool/dists/noble/main/r-cran-rcmdrplugin.ezr_1.70-1.ca2404.1_all.deb Size: 1428316 MD5sum: 33dbcaf02433d4a0e3dced3bcc3125a4 SHA1: 224c2691962d450a87c4b29c9a315af634f26655 SHA256: 949326f728a6f23b279b79f0e5e4e98fd4a1de1f068f8c0a831da016480234da SHA512: a3662202165edc45d47466362914df9c5a0b0bfc97b62e20b23ea71403bc37b7a9d13011493376082b32e7ee8b1a8cf63e6d5cae3d87f349fd33802f1575993e Homepage: https://cran.r-project.org/package=RcmdrPlugin.EZR Description: CRAN Package 'RcmdrPlugin.EZR' (R Commander Plug-in for the EZR (Easy R) Package) EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10918-6, Nankodo, ISBN: 978-4-524-21861-5, Ohmsha, ISBN: 978-4-274-22632-8), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below. This report has been cited in more than 14,000 scientific articles. Package: r-cran-rcmdrplugin.factominer Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 556 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-factominer, r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.factominer_1.8-1.ca2404.1_all.deb Size: 492216 MD5sum: 45713d6e539b2621a0a7ff104fb71c8c SHA1: e8e7f3665f873f760ce4ded07f8b58d6f66bc8ed SHA256: 04f837751c40d71c7404677fb8871a44fee146089d09d3a5362d29f3c05b69d4 SHA512: e5eddb0a7d300b20d008a50ad5b6fd31b739ff0cb4db2e1cbc62d091847655d11265fad3cb0260106b9ef150ef08cb286e76d04b3b78a049d68a935b100d03e0 Homepage: https://cran.r-project.org/package=RcmdrPlugin.FactoMineR Description: CRAN Package 'RcmdrPlugin.FactoMineR' (Graphical User Interface for FactoMineR) Rcmdr Plugin for the 'FactoMineR' package. Package: r-cran-rcmdrplugin.gwrm Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gwrm, r-cran-rcmdrmisc, r-cran-rcmdr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.gwrm_1.0.2-1.ca2404.1_all.deb Size: 45728 MD5sum: 536cac6c787c4f7d9bd97a629cdc8c59 SHA1: b1985ec3cdd2491b337b08f2805409222a73c9dd SHA256: f74cd455abf6962b27b0e532cdd357df9973f01ab3b9d3a7beb8f459322306de SHA512: ba71b266f08a9aab663387a535dc23939ececc46d4502b0eba1abd4a53e6028bcd54cc1938d1843c0d9a8ecb3af8102d5e2f6ad975af2409e0a9c06e708a917d Homepage: https://cran.r-project.org/package=RcmdrPlugin.GWRM Description: CRAN Package 'RcmdrPlugin.GWRM' (R Commander Plug-in for Fitting Generalized Waring RegressionModels) Provides an Rcmdr "plug-in" based on the GWRM package. Package: r-cran-rcmdrplugin.hh Architecture: all Version: 1.1-51-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hh, r-cran-rcmdr, r-cran-lattice, r-cran-mgcv Suggests: r-cran-car, r-cran-leaps, r-cran-latticeextra, r-cran-rgl Filename: pool/dists/noble/main/r-cran-rcmdrplugin.hh_1.1-51-1.ca2404.1_all.deb Size: 254370 MD5sum: 6dc767232ae3bb384b2e1e853325d14e SHA1: 2abcd7cb6f0d3c661628c2a3e083f6cc713b276d SHA256: c6810499d08a503452ce676ad64820b849e78423353acc758ee0f6d7d1f237bb SHA512: 7489500a09e82913ea1979b6f4398206fff8bb0a87d5311d3376b908a069c3b4c1b64141796f96fc8b2806f6d479548fb24bd896d64d9eec1f5a6b67f5dc4adb Homepage: https://cran.r-project.org/package=RcmdrPlugin.HH Description: CRAN Package 'RcmdrPlugin.HH' (Rcmdr Support for the HH Package) Rcmdr menu support for many of the functions in the HH package. The focus is on menu items for functions we use in our introductory courses. Package: r-cran-rcmdrplugin.kmggplot2 Architecture: all Version: 0.2-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 919 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-ggplot2, r-cran-rlang, r-cran-ggthemes, r-cran-plyr, r-cran-rcolorbrewer, r-cran-scales, r-cran-survival, r-cran-tcltk2 Suggests: r-cran-extrafont, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rcmdrplugin.kmggplot2_0.2-7-1.ca2404.1_all.deb Size: 710204 MD5sum: e91e2327e8cd80e6dcce1d93dc20aba1 SHA1: 3d757d19896556b32a6dec6143c0c1391d972b3c SHA256: 55c2c5fcfc6940af3f2d63fcf372bf95026566f02cc742c4e68f0a1c10efafe8 SHA512: 17130f02dc347ff9f36702f8825ecbfaaaef6971f8a602f24227acbe189d0071a7b6ee27c6b94695ea205bbc4dd83b477b860b6f91b94ebc77ca24c95feb97d0 Homepage: https://cran.r-project.org/package=RcmdrPlugin.KMggplot2 Description: CRAN Package 'RcmdrPlugin.KMggplot2' (R Commander Plug-in for Data Visualization with 'ggplot2') A GUI front-end for 'ggplot2' supports Kaplan-Meier plot, histogram, Q-Q plot, box plot, errorbar plot, scatter plot, line chart, pie chart, bar chart, contour plot, and distribution plot. Package: r-cran-rcmdrplugin.ma Architecture: all Version: 0.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-mad, r-cran-metafor Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-compute.es, r-cran-ggplot2, r-cran-gridextra, r-cran-scales Filename: pool/dists/noble/main/r-cran-rcmdrplugin.ma_0.0-2-1.ca2404.1_all.deb Size: 213096 MD5sum: 41463812a14b07508191ca950d1e4084 SHA1: b147e95c08385724511789abb0592880ce1a4ea8 SHA256: c01fbebcfbdef38c6e2303a4f97bbe967054971e04bf917128462bdfa5a6f73c SHA512: 140917b8348e6cee978afe5bedcd649781f57816d60ad86aa8455db19e0248209889db0ba8fa37d6dfe3c92e4c38af7ce3300727e71d34342abd73ac35e21db5 Homepage: https://cran.r-project.org/package=RcmdrPlugin.MA Description: CRAN Package 'RcmdrPlugin.MA' (Graphical User Interface for Conducting Meta-Analyses in R) Easy to use interface for conducting meta-analysis in R. This package is an Rcmdr-plugin, which allows the user to conduct analyses in a menu-driven, graphical user interface environment (e.g., CMA, SPSS). It uses recommended procedures as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009). Package: r-cran-rcmdrplugin.mpastats Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-ordinal Filename: pool/dists/noble/main/r-cran-rcmdrplugin.mpastats_1.2.2-1.ca2404.1_all.deb Size: 165298 MD5sum: 665c5f1d5ff0cdd555da36e6264b50ca SHA1: e1a4dee68293fe497ad25d8f43c18041b9a73925 SHA256: ac62de8733d6c775aa8b66a1368746585e6184a61e3c4a38286ccccf78e3511c SHA512: 458d3964f056b5d8311ebf125d949e572bcfec03fb178063ffb9600b95648efc43ef204d0b5e5dcf20ac0da9f021be0728f7297b978fe39b0c33168290a8f6bd Homepage: https://cran.r-project.org/package=RcmdrPlugin.MPAStats Description: CRAN Package 'RcmdrPlugin.MPAStats' (R Commander Plug-in for MPA Statistics) Extends R Commander with a unified menu of new and pre-existing statistical functions related to public management and policy analysis statistics. Functions and menus have been renamed according to the usage in PMGT 630 in the Master of Public Administration program at Brigham Young University. Package: r-cran-rcmdrplugin.nmbu Architecture: all Version: 1.8.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mixlm, r-cran-mass, r-cran-pls, r-cran-xtable, r-cran-phia, r-cran-rcmdr, r-cran-car Suggests: r-cran-lme4, r-cran-leaps, r-cran-mvtnorm, r-cran-gmodels, r-cran-abind, r-cran-lattice, r-cran-pbkrtest, r-cran-vcd, r-cran-multcomp, r-cran-e1071, r-cran-nnet Filename: pool/dists/noble/main/r-cran-rcmdrplugin.nmbu_1.8.15-1.ca2404.1_all.deb Size: 486276 MD5sum: 71a0322e8981cb7aeb97c039efa1df82 SHA1: f4466cf3ed3ebfdee4256eea49b6555be91781f7 SHA256: b0a3cbc456a551eec8a35dda42e3666701c409106da700f5adb5b0635c2f8054 SHA512: 1862ca24be519e069f8043b3af343fb19f61fe85c5526eba5fd27f09bf1cb1fcd989cac0a519935d7e9536835f01979fcbdc2b929d0d98761c9d0201c15a8b6d Homepage: https://cran.r-project.org/package=RcmdrPlugin.NMBU Description: CRAN Package 'RcmdrPlugin.NMBU' (R Commander Plug-in for University Level Applied Statistics) An R Commander "plug-in" extending functionality of linear models and providing an interface to Partial Least Squares Regression and Linear and Quadratic Discriminant analysis. Several statistical summaries are extended, predictions are offered for additional types of analyses, and extra plots, tests and mixed models are available. Package: r-cran-rcmdrplugin.orloca Architecture: all Version: 4.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-orloca, r-cran-orloca.es, r-cran-rcmdr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rcmdrplugin.orloca_4.8.2-1.ca2404.1_all.deb Size: 365598 MD5sum: b92c4d36ab317a0ad14cf952ac752b8b SHA1: ed11a1887f0ec79d869cb6f7b32230d364ec6933 SHA256: 191630515b391c1bb0a4b8be88c7ed8b4a96a47ad589d4cfa987e096b0122b69 SHA512: 7cde5375257372f250995491064fa0ecf4aa1c8b80f1e9b1faa35e2e4739dcd192f01b6c96b7e86cb1c72a6a09f6e339781677301f01b494decfbbb217f1c031 Homepage: https://cran.r-project.org/package=RcmdrPlugin.orloca Description: CRAN Package 'RcmdrPlugin.orloca' (A GUI for Planar Location Problems) A GUI for the orloca package is provided as a Rcmdr plug-in. The package deals with continuos planar location problems. Package: r-cran-rcmdrplugin.pcarobust Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rrcov, r-cran-tkrplot, r-cran-rcmdr, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-rcmdrplugin.pcarobust_1.1.4-1.ca2404.1_all.deb Size: 28764 MD5sum: db7a3d9d19c38fd6e9665d1be010402c SHA1: 00285ef5d2432b2b79c8406f51c511c9f7017197 SHA256: 0800e8ffa5946d2f606214c1320d1ac5c6130a76b395563f7a742def68dd73a5 SHA512: 60af0bbc238b559da9a641cdb02b546d38604a2e6bdc896ec9d50dea833936ffebee3129e902cafd6a37cf8f4050e5a9c784a7bacde2997fd20e019a35dcfbc1 Homepage: https://cran.r-project.org/package=RcmdrPlugin.PcaRobust Description: CRAN Package 'RcmdrPlugin.PcaRobust' (R Commander Plug-in for Robust Principal Component Analysis) The R commander plug-in for robust principal component analysis. The Graphical User Interface for Principal Component Analysis (PCA) with Hubert Algorithm method. Package: r-cran-rcmdrplugin.riskdemo Architecture: all Version: 3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4438 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcmdr, r-cran-demography, r-cran-forecast, r-cran-ftsa, r-cran-ggplot2, r-cran-dplyr, r-cran-scales, r-cran-zoo, r-cran-data.table Suggests: r-cran-tkrplot, r-cran-rgl Filename: pool/dists/noble/main/r-cran-rcmdrplugin.riskdemo_3.3-1.ca2404.1_all.deb Size: 4421954 MD5sum: 8fdda3947359ebfeda9ee18e0c76dd7e SHA1: ee121976547d5bb0b8e5eaf8daef8e08d319baef SHA256: 524e2b34fbecceec35e80e668be3c1a4f81109bf9c3243b2719082b515f55e9a SHA512: a641f525b4c3f1f7cd56d57fc9f84b91997f1c601ca0fee53ca119297931bafeaa20e2bb73b1c1d465c9754cd9fce97f371865bb84ec07bac516ae0d9544277d Homepage: https://cran.r-project.org/package=RcmdrPlugin.RiskDemo Description: CRAN Package 'RcmdrPlugin.RiskDemo' (R Commander Plug-in for Risk Demonstration) R Commander plug-in to demonstrate various actuarial and financial risks. It includes valuation of bonds and stocks, portfolio optimization, classical ruin theory, demography and epidemic. Package: r-cran-rcmdrplugin.rmtcjags Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-runjags, r-cran-rmeta, r-cran-igraph, r-cran-coda, r-cran-rjags Filename: pool/dists/noble/main/r-cran-rcmdrplugin.rmtcjags_1.0-2-1.ca2404.1_all.deb Size: 53886 MD5sum: 0a72d94f94ad95c87e990ad6e86384b3 SHA1: 22851e7089549804abecb5d36910c21d404b59c7 SHA256: 422d79438960333e30fb86abf3ea930e7f816004702bf6f44ee8be0bd170ee73 SHA512: 62e3415ed7e6f2f609dc86e585f2b2d656bcddbd2af2931f51c41ea37667ee2c6ca9be7aa6a47d633bbaf0b42132528f0101618c6223682b78a6d90e941340ca Homepage: https://cran.r-project.org/package=RcmdrPlugin.RMTCJags Description: CRAN Package 'RcmdrPlugin.RMTCJags' (R MTC Jags 'Rcmdr' Plugin) Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). This package provides an 'Rcmdr' plugin to perform Mixed Treatment Comparison for binary outcome using BUGS code from Bristol University (Lu and Ades). Package: r-cran-rcmdrplugin.roc Architecture: all Version: 1.0-19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcmdr, r-cran-proc, r-cran-resourceselection Filename: pool/dists/noble/main/r-cran-rcmdrplugin.roc_1.0-19-1.ca2404.1_all.deb Size: 170982 MD5sum: e8dffc52484ce427f2e188f1b9362abd SHA1: 108e7a61d9b4ef5a4150e73adec705b179c236a0 SHA256: 0069044731142eeab184c8ced83c1876dc121a60de3b1f0dce19586a96c3a61e SHA512: daa003495318f72afea3cc8ae00ef5cff746a84090f45ea79b8ed1ad61bb7023eedaf7a821f294034833204ecfbb92b7cf0a6301ef3681769290ab71c4a27f8e Homepage: https://cran.r-project.org/package=RcmdrPlugin.ROC Description: CRAN Package 'RcmdrPlugin.ROC' (Rcmdr Receiver Operator Characteristic Plug-in Package) Rcmdr GUI extension plug-in for Receiver Operator Characteristic tools from pROC package. Also it ads a Rcmdr GUI extension for Hosmer and Lemeshow GOF test from the package ResourceSelection. Package: r-cran-rcmdrplugin.sos Architecture: all Version: 0.3-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sos, r-cran-rcmdr, r-cran-tcltk2 Filename: pool/dists/noble/main/r-cran-rcmdrplugin.sos_0.3-0-1.ca2404.1_all.deb Size: 30214 MD5sum: e1aac69f6df6d42bc7df479fccf843e9 SHA1: fb406ebebad4d642b758ce1e628d441abefd2c4c SHA256: 209015381391fddce02c56edaed7aa5606cdde124186d86766887ed1aa815b3f SHA512: 3966c3247678829acc9ffd2f9e08e8c6028168e0ddd7ccd74c696901993acb37c393b96c9e37593b6d03621affbd34e1847e1827799a9cd7575de2d467525a46 Homepage: https://cran.r-project.org/package=RcmdrPlugin.sos Description: CRAN Package 'RcmdrPlugin.sos' (Efficiently search the R help pages) Rcmdr interface to the 'sos' package. The plug-in renders the 'sos' searching functionality easily accessible via the Rcmdr menus. It also simplifies the task of performing multiple searches and subsequently obtaining the union or the intersection of the results. Package: r-cran-rcmdrplugin.survival Architecture: all Version: 1.3-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1242 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-date, r-cran-rcmdr, r-cran-car Filename: pool/dists/noble/main/r-cran-rcmdrplugin.survival_1.3-2-1.ca2404.1_all.deb Size: 1108504 MD5sum: 83f19740dae0cf6faf21bb838dc6fd36 SHA1: dc23ebec7a70f4d3931cac9df6752ff9b11df6b3 SHA256: 327bcd6afffeed52b04fab029920c502b29d44882c0dbeff553db0fa231a1832 SHA512: aa6257df99e1bd7728c37de83b1c74db6ce3bbd8426981ba965f10ab23b7ef9ffd758cdfed796b17a51a9fd4aef7a09e53e8c728c5c58127c517466c9e894576 Homepage: https://cran.r-project.org/package=RcmdrPlugin.survival Description: CRAN Package 'RcmdrPlugin.survival' (R Commander Plug-in for the 'survival' Package) An R Commander plug-in for the survival package, with dialogs for Cox models, parametric survival regression models, estimation of survival curves, and testing for differences in survival curves, along with data-management facilities and a variety of tests, diagnostics and graphs. Package: r-cran-rcmdrplugin.teachingdemos Architecture: all Version: 1.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-teachingdemos, r-cran-rcmdr Suggests: r-cran-rgl, r-cran-tkrplot Filename: pool/dists/noble/main/r-cran-rcmdrplugin.teachingdemos_1.2-0-1.ca2404.1_all.deb Size: 35722 MD5sum: 11020c727df0409bb33f0e5b589c909e SHA1: 9e197d38b1ad8a2a20a70e4c3554e6f102998bef SHA256: d54bd159bba1a3b68b659f0806c410519a03296fb1914192f52952ba9ee6e10d SHA512: 410a1f4d97cd4b28558121625dcd82ed810fcb88c4901eb5e9f5c373f258d0d377d22ed7f677ab3762d2030acdf2d3820775aa59f7eba61de2f8a3bb304a79b2 Homepage: https://cran.r-project.org/package=RcmdrPlugin.TeachingDemos Description: CRAN Package 'RcmdrPlugin.TeachingDemos' (Rcmdr Teaching Demos Plug-in) Provides an Rcmdr "plug-in" based on the TeachingDemos package, and is primarily for illustrative purposes. 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Package: r-cran-rcmdrplugin.uca Architecture: all Version: 5.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-iqcc, r-cran-qcc, r-cran-qicharts2, r-cran-randtests, r-cran-rmarkdown, r-cran-teachingdemos, r-cran-tseries, r-cran-rcmdr Suggests: r-cran-car, r-cran-cardata, r-cran-knitr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.uca_5.1-4-1.ca2404.1_all.deb Size: 121560 MD5sum: 3b39fdcfb9397360d76a4e179971a56e SHA1: 0503f9034ea0894dd80ed88d67604f16e01c93ea SHA256: a75f8448b5e7a696ae9fa353aa218d26299a359a1874413d9f16ef571a6a33e1 SHA512: 7b9423fbb66f99f8d63632dd5237fc36852b5c79de5c754855931879940ab8a1d6ca9c577894fa295af72f76eec89eacff98d86459f7c6f344d1df14d68b4aa8 Homepage: https://cran.r-project.org/package=RcmdrPlugin.UCA Description: CRAN Package 'RcmdrPlugin.UCA' (UCA Rcmdr Plug-in) Some extensions to Rcmdr (R Commander), randomness test, variance test for one normal sample and predictions using active model, made by R-UCA project and used in teaching statistics at University of Cadiz (UCA). Package: r-cran-rcmdrplugin.worldflora Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-worldflora, r-cran-rcmdr Suggests: r-cran-data.table, r-cran-stringr Filename: pool/dists/noble/main/r-cran-rcmdrplugin.worldflora_1.3-1.ca2404.1_all.deb Size: 57112 MD5sum: 03a1c07a0ef3b84aed52009a7740b331 SHA1: 204100e45baa9ff8902e5a9f6206cd292bb862b6 SHA256: cb6030f7aedb513770afb6e25da2a27e0008eb170dda4a377b42e8bec9ed4cd7 SHA512: 51b3cd5f73d4acd9964d8152fe4bfeb967afbfeb280956d1429acae487ea372321b369a03e1da8f5035a29ce9644aee3dbe43afc531fef9a50bd1d4968cb4ab9 Homepage: https://cran.r-project.org/package=RcmdrPlugin.WorldFlora Description: CRAN Package 'RcmdrPlugin.WorldFlora' (R Commander Plug-in for the 'WorldFlora' Package) An R Commander plug-in for the 'WorldFlora' package. It was mainly developed to show work flows and scripts for first-time users. Package: r-cran-rcminification Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernsmooth, r-cran-locpol Filename: pool/dists/noble/main/r-cran-rcminification_1.4-1.ca2404.1_all.deb Size: 98798 MD5sum: 09c29347ffe04e4de579077b9219ea1a SHA1: ecc52e6770503d8fc39c7b4bcd3e678e59e078b6 SHA256: cb6fcf9c3b372ca0e18f7db3780f46d4bf827088e84ba4850e33b945943b6ab4 SHA512: 437f533f5de690bd3a77f2fe261b87376f9273aab2c83e76d5d86e2d7d7c7c500616c30d7f8db939f3ed15ca2a1542dbdc56e66ede15a172fc3e113c0424c152 Homepage: https://cran.r-project.org/package=RCMinification Description: CRAN Package 'RCMinification' (Random Coefficient Minification Time Series Models) Data sets, and functions for simulating and fitting nonlinear time series with minification and nonparametric models. Package: r-cran-rcmsize Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-binom Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rcmsize_1.0.1-1.ca2404.1_all.deb Size: 71096 MD5sum: d324d2e17753cb437028b51af932e030 SHA1: 765053564b3c5705cd823b7ef17ccd2eeddcbe37 SHA256: 8677772733779f51e5da8caea1babc827249a24d3724d543473148c936c2673a SHA512: f5bedca0abbb9530d194be2492bb54ebaa89332db5d1e63d07c7f8bb2782c1e103aa6d80af6ddde7a37afed620b1b324249ea9aef2c1c70239239b3a61d9b7a2 Homepage: https://cran.r-project.org/package=RCMsize Description: CRAN Package 'RCMsize' (Sample Size Calculation in Reversible Catalytic Models) Sample size and confidence interval calculations in reversible catalytic models, with applications in malaria research. Further details can be found in the paper by Sepúlveda and Drakeley (2015, ). 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Here is the webpage of 'NanoString nCounter' where you can find detailed information about this platform . It has great clinical application, such as diagnosis and prognosis of cancer. Implements integrated system of random-coefficient hierarchical regression model to normalize data from 'NanoString nCounter' platform so that noise from various sources can be removed. 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Robust treatment assignment by strata/blocks, that handles misfits; Power calculations of the minimum detectable treatment effect or minimum populations; Balance tables of T-test of covariates; Balance Regression: (treatment ~ all x variables) with F-test of null model; Impact_evaluation: Impact evaluation regressions. This function gives you the option to include control_vars, fixed effect variables, cluster variables (for robust SE), multiple endogenous variables and multiple heterogeneous variables (to test treatment effect heterogeneity) summary_statistics: Function that creates a summary statistics table with statistics rank observations in n groups: Creates a factor variable with n groups. Each group has a min and max label attach to each category. Athey, Susan, and Guido W. Imbens (2017) . 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Distance-sampling is popular in ecology, especially when survey targets are observed from aerial platforms (e.g., airplane or drone), surface vessels (e.g., boat or truck), or along walking transects. Analysis involves fitting smooth (parametric) curves to histograms of observation distances and using those functions to adjust density estimates for missed targets. Routines included here fit curves to observation distance histograms, estimate effective sampling area, density of targets in surveyed areas, and the abundance of targets in a surrounding study area. Confidence interval estimation uses built-in bootstrap resampling. Help files are extensive and have been vetted by multiple authors. Many tutorials are available on the package's website (URL below). Package: r-cran-rdiversity Architecture: all Version: 2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 773 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-stringdist Suggests: r-cran-ape, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-rdiversity_2.2.0-1.ca2404.1_all.deb Size: 573928 MD5sum: 6df0e7de1abe09db4831191d9c10db5c SHA1: 80ff462c02af9d6d05212f5bfed8fe1b2102b30e SHA256: fc7e20fd2e6540eee93636c9ca1c6ffdafc86307320ccc419ffdf7b7e05c08bf SHA512: 6f75da9af791561aef1d63acabb4241b4c8263471ddabe625652d9e9bf4780dfa340f9fde62a606263b8b0442f2663e752ea6804d2c261c68be71665538161cd Homepage: https://cran.r-project.org/package=rdiversity Description: CRAN Package 'rdiversity' (Measurement and Partitioning of Similarity-SensitiveBiodiversity) Provides a framework for the measurement and partitioning of the (similarity-sensitive) biodiversity of a metacommunity and its constituent subcommunities. Richard Reeve, et al. (2016) . Package: r-cran-rdkitpyr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rdkitpyr_0.2.1-1.ca2404.1_all.deb Size: 79754 MD5sum: 982391dc4c6e7723f3fe148f14a71b4b SHA1: 40685cc4cd46486053255bb9c8048f6333e111ad SHA256: 675f684717bb76f26e2a889193ed82a3ad9b7600f81a12c640bf2b122fc94bad SHA512: ff053b4b548acb4bb18bdc518a0b3d0269deade0a866777ddc78f4baae3454cf1ed8cde5c304be90f0b242fb08d587b566238cf603a2b7b5346750178054f7fa Homepage: https://cran.r-project.org/package=rdkitpyr Description: CRAN Package 'rdkitpyr' (Task-Oriented Cheminformatics in R Using 'RDKit' via 'Python') A task-oriented R interface to the 'RDKit' library through its 'Python' API via 'reticulate'. The package offers high-level cheminformatics functionality, including molecule parsing, descriptor calculation, and fingerprint generation without replicating the native structure of 'RDKit'. 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(2022) . The learned cutoffs are guaranteed to perform no worse than the existing cutoffs in terms of overall outcomes. The 'rdlearn' package also includes features for visualizing the learned cutoffs relative to the baseline and conducting sensitivity analyses. 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See Cattaneo, Titiunik and Vazquez-Bare (2016) for further methodological details, and references. 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Package: r-cran-rdmulti Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-rdrobust, r-cran-rlang Filename: pool/dists/noble/main/r-cran-rdmulti_2.0.0-1.ca2404.1_all.deb Size: 85670 MD5sum: e92c76a58c900cab54b8000e5a30bda7 SHA1: dcaddcdd4111ed209957ce5847e6b47cb6a31c2c SHA256: c1ff10898bf0727156a3de6eeb100353fd23d63a0fd4cb5bf8761f50536063ea SHA512: 9b12cab156cc0f899dc328227b9f03ec2b393ccdafb1f3f55d8ef3ed18feb561863fcd6f5eae19a3ef4afc049928056c719ff96ddde4002e79e87b529c25df80 Homepage: https://cran.r-project.org/package=rdmulti Description: CRAN Package 'rdmulti' (Robust Local Polynomial Methods for RD Designs with MultipleCutoffs or Multiple Scores) The 'rdmulti' package implements estimation, inference, and graphical procedures for regression discontinuity (RD) designs with multiple cutoffs or multiple scores. rdmc() provides point estimation and robust bias-corrected inference for multi-cutoff designs, rdmcplot() provides data-driven RD plots for multi-cutoff designs, and rdms() provides point estimation and robust bias-corrected inference for multi-score designs. See Cattaneo, Titiunik and Vazquez-Bare (2020) for further methodological details. Package: r-cran-rdnase Architecture: all Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3087 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-bioc-biostrings, r-bioc-gosemsim, r-cran-foreach, r-cran-doparallel, r-cran-rcurl, r-bioc-org.hs.eg.db Filename: pool/dists/noble/main/r-cran-rdnase_1.1-1-1.ca2404.1_all.deb Size: 1243236 MD5sum: 52008dc1e0a2aef609c29a5a232e8a9a SHA1: 0536ce130e066b76ef61e7f3ff07d3803ce3ec70 SHA256: 01950e5ee34f000a117bc29c34e2209a02095c282b4e50471db942066917626a SHA512: a3ec7f672379aff0d9120d2dbad2b70028769ca22bb09db4fa726c7368397abe5783a7a8c4519857c7f5439cf2d3f26b58d22d0fcfa6660f486cfbd36801738c Homepage: https://cran.r-project.org/package=rDNAse Description: CRAN Package 'rDNAse' (Generating Various Numerical Representation Schemes of DNASequences) Comprehensive toolkit for generating various numerical representation schemes of DNA sequence. 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The German National Library is the German central archival library, collecting, archiving, bibliographically classifying all German and German-language publications, foreign publications about Germany, translations of German works, and the works of German-speaking emigrants published abroad between 1933 and 1945. 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The Bulut's statistic is not sensitive to outliers in high-dimensional data, unlike one of Najarzadeh (2021) . So, the Bulut's statistic can be performed robustly by using RDnp function. 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Use Shallalist (service discontinued), 'VirusTotal' (which provides access to lots of services) , 'DMOZ' , University Domain list , 'OpenAI' 'GPT' models, 'Anthropic' 'Claude' models, or validated machine learning classifiers based on 'Shallalist' data to learn about the kind of content hosted by a domain. Package: r-cran-rdota2 Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rdota2_0.1.6-1.ca2404.1_all.deb Size: 104182 MD5sum: e628eeff12764126ddb808588fe8cc50 SHA1: 8ba4b82c76169fcc31f5ddecae505203cb2fc6e4 SHA256: d10e74928189211b8d05fb30759ab71e91c14dd1546ddf869c4d9853131cee40 SHA512: 612889a61417c216c28db82eae43c3c4834ddd3ec802f58bb45535793c3c68139abb61129e22901a666c5acc3eb1a9aecd79d7dddf5fd4ea1fb82100533d08a8 Homepage: https://cran.r-project.org/package=RDota2 Description: CRAN Package 'RDota2' (An R Steam API Client for Valve's Dota2) An R API Client for Valve's Dota2. RDota2 can be easily used to connect to the Steam API and retrieve data for Valve's popular video game Dota2. You can find out more about Dota2 at . 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Package: r-cran-rdpower Architecture: all Version: 3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rdrobust Filename: pool/dists/noble/main/r-cran-rdpower_3.0-1.ca2404.1_all.deb Size: 98964 MD5sum: 6a48d5a133091b6b6b3c111172b54e43 SHA1: e804175671c1b938665c7c4b347d77d6f1157ea9 SHA256: 1febb4df1285be42c6f5e3fe2987f671d6783fc1c45ee1f82ce4b22f60cabfa0 SHA512: 19442996f823017fd1bbe83ec167222b9ca9f23062f27823889b2d9bc8b9f07efa147d2f2d3a358000ee35a4c43fd6e6744be84f7ef3c1db062893b7bfb1307c Homepage: https://cran.r-project.org/package=rdpower Description: CRAN Package 'rdpower' (Power, Sample Size, and Minimum Detectable Effect Calculationsfor RD Designs) The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The 'rdpower' package provides tools to perform power, sample size, and minimum detectable effect calculations in RD designs: rdpower() calculates the power of an RD design, rdsampsi() calculates the required sample size to achieve a desired power, and rdmde() calculates minimum detectable effects. See Cattaneo, Titiunik and Vazquez-Bare (2019) for further methodological details. Package: r-cran-rdracor Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite, r-cran-data.table, r-cran-xml2, r-cran-igraph, r-cran-httr, r-cran-tibble, r-cran-purrr, r-cran-stringr, r-cran-tidyr, r-cran-rdpack Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rdracor_1.0.6-1.ca2404.1_all.deb Size: 447344 MD5sum: f56cb80546cf80a16bed07d6e9041938 SHA1: 043c988ec9531caac52ef9a91446a892213da1cd SHA256: ce5d254b7934e7dc1452ef61a5c09a70e634744be0d97620bad34a10e99c4b6e SHA512: bbefbb375a59a4596ab50cf31517932c8bb95a87cbd4780935c001c6547c72bd9a43665ac9d0ba07eecec89d5e129eb147c78448c1b903fdaedd8c9da6d0971b Homepage: https://cran.r-project.org/package=rdracor Description: CRAN Package 'rdracor' (Access to the 'DraCor' API) Provide an interface for 'Drama Corpora Project' ('DraCor') API: . Package: r-cran-rdrobust Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-mass Suggests: r-cran-broom, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rdrobust_4.0.0-1.ca2404.1_all.deb Size: 383454 MD5sum: f56bb2239b5dc87c47807118fc8792a9 SHA1: da40e0740e897de511e6e06aac1d234ddcedabd9 SHA256: a186cba085b0f75318b0bafbae36d7a70fd1d8b149fe9ebc65fa119f3824bc8a SHA512: 4abddd430cb93ace07ada875ea446c9fbe0285844258bd7de1619e01db0403723c0efa9436edee82802e9e34b57790462df126e7ba85c840b13879698768783b Homepage: https://cran.r-project.org/package=rdrobust Description: CRAN Package 'rdrobust' (Robust Data-Driven Statistical Inference inRegression-Discontinuity Designs) Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots). Package: r-cran-rdrw Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-rdrw_1.0.2-1.ca2404.1_all.deb Size: 62744 MD5sum: 4872baaee940c0bf08cea18afce58d9f SHA1: 76166c521e0af2b5f0408d2b0ada4c10bf01e84c SHA256: 67c13337ffc036c581df159edba19b7f1f88181c4c8ce0cbccec6ab0813cff3e SHA512: da9f956757f60992e5dc5c19bf8a348d7173a0a3394a8fe0857a19a6091451959c6f9249ac069c78b01247b2f6d0290c2ccf92842682a3964107cf47084daa12 Homepage: https://cran.r-project.org/package=Rdrw Description: CRAN Package 'Rdrw' (Univariate and Multivariate Damped Random Walk Processes) We provide a toolbox to fit and simulate a univariate or multivariate damped random walk process that is also known as an Ornstein-Uhlenbeck process or a continuous-time autoregressive model of the first order, i.e., CAR(1) or CARMA(1, 0). This process is suitable for analyzing univariate or multivariate time series data with irregularly-spaced observation times and heteroscedastic measurement errors. When it comes to the multivariate case, the number of data points (measurements/observations) available at each observation time does not need to be the same, and the length of each time series can vary. The number of time series data sets that can be modeled simultaneously is limited to ten in this version of the package. We use Kalman-filtering to evaluate the resulting likelihood function, which leads to a scalable and efficient computation in finding maximum likelihood estimates of the model parameters or in drawing their posterior samples. Please pay attention to loading the data if this package is used for astronomical data analyses; see the details in the manual. Also see Hu and Tak (2020) . Package: r-cran-rdryad Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-curl, r-cran-tibble, r-cran-hoardr, r-cran-zip, r-cran-jsonlite, r-cran-mime Suggests: r-cran-testthat, r-cran-vcr Filename: pool/dists/noble/main/r-cran-rdryad_1.0.0-1.ca2404.1_all.deb Size: 83490 MD5sum: 301bc043f2fca0f1fdfaa8f3563eec97 SHA1: 59829f7ad7bee2f7d8846c7ed93b01f7105e53b2 SHA256: a393c27e0c024c45aad3cc169c5063154f4a772685e44dfaadb201d36e9fc1d9 SHA512: 19893a267f038aab0bb43972a1a41569e969b96fe04d84c52d20fcf3daf7b87de5c9f94493f9c20c25464811453dae07e6b03b458e23a7e97241a30b403cacdd Homepage: https://cran.r-project.org/package=rdryad Description: CRAN Package 'rdryad' (Access for Dryad Web Services) Interface to the Dryad "Solr" API, their "OAI-PMH" service, and fetch datasets. Dryad () is a curated host of data underlying scientific publications. 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'rdss' includes datasets, helper functions, and plotting components to enable use and replication of the book. Package: r-cran-rdstagger Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sandwich, r-cran-ggplot2, r-cran-rdrobust Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-covr Filename: pool/dists/noble/main/r-cran-rdstagger_0.1.0-1.ca2404.1_all.deb Size: 101220 MD5sum: c770db820fa5a939a7e2a34e11375ed0 SHA1: 56c27b461df60d5726f712ba9d0ef025ae588a5d SHA256: 75e3ea4182618bed866b18002e12c398b4a4328d322d3317358e8588585dc829 SHA512: 2894977b38d42761464ab728782c5e7643c112f9fc9c66691def2d3e8a98fa3fc29b941034f93f7e814260ae09d59531eedbffa85978a52c2717e0a1dbcea8a6 Homepage: https://cran.r-project.org/package=rdstagger Description: CRAN Package 'rdstagger' (Staggered Regression Discontinuity with Network Interference) Implements a unified framework combining staggered difference-in-differences with regression discontinuity designs and network interference. Extends Callaway and Sant'Anna (2021) to settings where treatment assignment is determined by a running variable crossing a cutoff, adoption timing is heterogeneous across units, and spillover effects operate through a known network structure. Provides group-time average treatment effects (direct and spillover), aggregation schemes, bandwidth selection, and pre-treatment falsification tests. Package: r-cran-rdstreeboot Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rdstreeboot_1.0-1.ca2404.1_all.deb Size: 60876 MD5sum: 1b4139ca671603b29cb203794f67d317 SHA1: 0f223b8d95c970356077b1c8f864565d280ae3c3 SHA256: e06f2acd3df8cadfc0d4cc9e6a2a2ca257c5e99fb9400e6678895196b4d015f4 SHA512: ec5797272fb3802c0aca2592597485badc91b476c624671e551f4de47583a22c63a4241ce5512ba11003b5a2e05385c4635a1c38f5806133457800db097cffdd Homepage: https://cran.r-project.org/package=RDStreeboot Description: CRAN Package 'RDStreeboot' (RDS Tree Bootstrap Method) A tree bootstrap method for estimating uncertainty in respondent-driven samples (RDS). Quantiles are estimated by multilevel resampling in such a way that preserves the dependencies of and accounts for the high variability of the RDS process. Package: r-cran-rdta Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-mvtnorm, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-rdta_1.0.1-1.ca2404.1_all.deb Size: 38322 MD5sum: 5f926ab4e32891db24be7c19605a1973 SHA1: 82a31b556a209c704eb4f52545221a2c559af105 SHA256: 5008214ba947bdeb596d10a9a8ff1b650eb1405317dfd5d8381edbaac6e0f090 SHA512: f55da151595e323b6ba7de39ac1d71feb94e352bc3c468538647e2c5fce091611021958494ad7a7ecda3229e76ee49f2e8b6152b751c79d35a2a601a06e208b4 Homepage: https://cran.r-project.org/package=Rdta Description: CRAN Package 'Rdta' (Data Transforming Augmentation for Linear Mixed Models) We provide a toolbox to fit univariate and multivariate linear mixed models via data transforming augmentation. Users can also fit these models via typical data augmentation for a comparison. 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The output is a text file in 'PROV-JSON' format. Package: r-cran-rduckhts Architecture: all Version: 1.2.1-0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 26005 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dbi, r-cran-duckdb Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-rduckhts_1.2.1-0.1.0-1.ca2404.1_all.deb Size: 6042490 MD5sum: a7e986f564bb05e0d3fa5f4bb59366e4 SHA1: 656ca5a6c3ef80f14bba5d0cc54f70173fc42a47 SHA256: 225c906105627fffe870c0baf5b9130ee4414c5add0de7b1f3adbe68acf0610c SHA512: 3f6161e3100ce07566509b751a6b4a8ce2cd079ad4e55e5adb537336fa89c2c5e19fff493885416581892381296aa76476c464df9f6612939e00dad1559f9ddf Homepage: https://cran.r-project.org/package=Rduckhts Description: CRAN Package 'Rduckhts' ('DuckDB' High Throughput Sequencing File Formats ReaderExtension) Bundles the 'duckhts' 'DuckDB' extension for reading High Throughput Sequencing file formats with 'DuckDB'. The 'DuckDB' C extension API and its 'htslib' dependency are compiled from vendored sources during package installation. James K Bonfield and co-authors (2021) . Package: r-cran-rduino Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-serial Filename: pool/dists/noble/main/r-cran-rduino_0.1-1.ca2404.1_all.deb Size: 26036 MD5sum: a85228f2257734792cca1f048776b0df SHA1: d3c27700a2945359479283f84e1704ebfc2bc5bc SHA256: 87b1bda9e62c057f48b67ace9f90ed72e2c2e237adea48515596105fc0762777 SHA512: dde0d1069c4cca30bb88c1a73d86de5b5f1155415f3ac00536918c7822471140f817304f359adefe2fdd4fc26bf2050ee910ed70d0d39fe536b787e794e550cd Homepage: https://cran.r-project.org/package=Rduino Description: CRAN Package 'Rduino' (A Microcontroller Interface) Functions for connecting to and interfacing with an 'Arduino' or similar device. Functionality includes uploading of sketches, setting and reading digital and analog pins, and rudimentary servo control. This project is not affiliated with the 'Arduino' company, . 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Package: r-cran-rdune Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4851 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rdune_1.1.1-1.ca2404.1_all.deb Size: 4810030 MD5sum: dd48cdee0e9ddf136f8fef03bfab7c4b SHA1: 2fdaca863cd8da0e0f17d8639e208d1261cb49a3 SHA256: 5a59d2f53b7bc66b5f27eae940635c607dbd83b34009b8b6b600f1c08248576c SHA512: 3e50c8d1ed6f452cb3359369f418a01151a8afa3def9630cb0da7b664f01cbc2211757fb6988e93a7111dfb94016efd4e55eeeb5fbcda5206fc39b29ca02b491 Homepage: https://cran.r-project.org/package=Rdune Description: CRAN Package 'Rdune' ('Creates Color Palettes Inspired by Dune') Enables the use of color palettes inspired by the 'Dune' movies. These palettes are compatible with 'ggplot2'. See Wickham (2016) for more details on 'ggplot2'. 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The package includes functions for preprocessing digital elevation and streams data, and one function to compute all the spatially explicit land use metrics described in Peterson et al. (2011) and previously implemented by Peterson and Pearse (2017) in ArcGIS-Python as IDW-PLUS. 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It provides functions that intend to (1) make it easier for users familiar with 'Python' to work with regular expressions, (2) reduce the complexity often associated with regular expressions code, (3) and enable users to write more readable and maintainable code that relies on regular expression-based pattern matching. 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Package: r-cran-react Architecture: all Version: 2024.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-rlang Filename: pool/dists/noble/main/r-cran-react_2024.1.0-1.ca2404.1_all.deb Size: 12040 MD5sum: affc73ce11d2a992915a59db5b6c7fbf SHA1: b846f612c2bbc83e4700738bc45dedbe8d967624 SHA256: e7c4c5e71bc0b4788ce6ee91b5c4206d10f8d001ef2ba7f20db0b4e11d51ce5d SHA512: dc52f383bbec67ed18ac2d3c036a30f6f794f62d4f02d9db55cf3a00bd567e345698e34f0b40f6341f4c7f393e6262ad473d8f00ea68f59f90d38354ac8ef926 Homepage: https://cran.r-project.org/package=react Description: CRAN Package 'react' (Reactivity Helper for 'shiny') Tools to help with 'shiny' reactivity. The 'react' object offers an alternative way to call reactive expressions to better identify them in the server code. 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Apply conditional formatting to cells with data bars, color scales, color tiles, and icon sets. Utilize custom table themes inspired by popular websites such and bootstrap themes. Apply sparkline line & bar charts (note this feature requires the 'dataui' package which can be downloaded from ). Increase the portability and reproducibility of reactable tables by embedding images from the web directly into cells. Save the final table output as a static image or interactive file. Package: r-cran-reactcheckbox Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-reactr Suggests: r-cran-shiny Filename: pool/dists/noble/main/r-cran-reactcheckbox_1.0.0-1.ca2404.1_all.deb Size: 103602 MD5sum: bd6222a493968b241348dab9f6d82592 SHA1: 3d6bdc978b1a44893c70d89ea8c19005f35d8ddd SHA256: 7e5ac2941f4e1e3460d8428c3f36e34db3a03b2769f7f1a30f16b42d5b46688f SHA512: b527fc59feaa8a743a5ee8aeadd8f0f2c7d6a678ed6970103d185af5c0c52cc9e0ab0d4d95e0b40c98efd20593595777e4e1e79ad7fbf48a9a2f7a591657b06c Homepage: https://cran.r-project.org/package=reactCheckbox Description: CRAN Package 'reactCheckbox' (Checkbox Group Input for 'Shiny') Provides a checkbox group input for usage in a 'Shiny' application. The checkbox group has a head checkbox allowing to check or uncheck all the checkboxes in the group. The checkboxes are customizable. 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References (citations in PubMed format in details of each function): Bolland MJ, Avenell A, Gamble GD, Grey A. (2016) . Bolland MJ, Gamble GD, Avenell A, Grey A, Lumley T. (2019) . Bolland MJ, Gamble GD, Avenell A, Grey A. (2019) . Bolland MJ, Gamble GD, Grey A, Avenell A. (2020) . Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2021) . Bolland MJ, Gamble GD, Avenell A, Grey A. (2021) . Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2023) . Carlisle JB, Loadsman JA. (2017) . Carlisle JB. (2017) . 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Package: r-cran-recalibratinn Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3044 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-rann, r-cran-tidyr, r-cran-tibble, r-cran-glue, r-cran-magrittr, r-cran-hmisc, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-recalibratinn_0.3.2-1.ca2404.1_all.deb Size: 2810472 MD5sum: 42d58862f9acecf3c3bbd00d717f6516 SHA1: 3c6693f79b38fa059d73a55765713bc97cd2ff84 SHA256: 461747577febec4d68802c6cb0ad95b1c5afc5a2ae37852a4e37e4ec55a45ac1 SHA512: 4a39cb32e9edc854b699e12119a028b4d9798ddf4f4c42ec5086f0cae808885cce8024458820c606390c2eec4ededcc2c3203d9e54bb5630a4749b64b9cd10a4 Homepage: https://cran.r-project.org/package=recalibratiNN Description: CRAN Package 'recalibratiNN' (Quantile Recalibration for Regression Models) Enables the diagnostics and enhancement of regression model calibration.It offers both global and local visualization tools for calibration diagnostics and provides one recalibration method: Torres R, Nott DJ, Sisson SA, Rodrigues T, Reis JG, Rodrigues GS (2024) . The method leverages on Probabilistic Integral Transform (PIT) values to both evaluate and perform the calibration of statistical models. For a more detailed description of the package, please refer to the bachelor's thesis available bellow. 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However, the principal utility is a set of functions to simulate random draws from these estimators, and use these to conduct hypothesis tests and power calculations. Additionally, a set of functions are provided for generating confidence intervals via bootstrapping. Functions are also provided to test abundance estimator consistency under complete or partial stratification, and to calculate stratified or partially stratified estimators. Functions are also provided to calculate recommended sample sizes. Referenced methods can be found in Arnason et al. (1996) , Bailey (1951) , Bailey (1952) , Chapman (1951) NAID:20001644490, Cohen (1988) ISBN:0-12-179060-6, Darroch (1961) , and Robson and Regier (1964) . 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This package can be used to access artist's data including songs, blogs, news, reviews etc. Song's data including audio summary, style, danceability, tempo etc can also be accessed. Package: r-cran-recipes Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2088 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-cli, r-cran-clock, r-cran-generics, r-cran-glue, r-cran-gower, r-cran-hardhat, r-cran-ipred, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-matrix, r-cran-purrr, r-cran-rlang, r-cran-sparsevctrs, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-timedate, r-cran-vctrs, r-cran-withr Suggests: r-cran-covr, r-cran-ddalpha, r-cran-dials, r-cran-dimred, r-cran-fastica, r-cran-ggplot2, r-cran-igraph, r-cran-kernlab, r-cran-knitr, r-cran-modeldata, r-cran-parsnip, r-cran-rann, r-cran-rcpproll, r-cran-rmarkdown, r-cran-rpart, r-cran-rsample, r-cran-rspectra, r-cran-splines2, r-cran-testthat, r-cran-workflows, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-recipes_1.3.2-1.ca2404.1_all.deb Size: 1672326 MD5sum: 18a6f07c264bd9766af47d0ee6484a2c SHA1: fcf106cefa76d034d091125219faef2828ab1426 SHA256: 004ce4ab4c32d4da530e73a09fe464731241341118686cd42f241f4200c74f06 SHA512: 4b162e2124a07a2f65f527cebaabcf618076b055d33f7cabc8b28c397e1943288f5eb4bcf5e99146395e7a75f34cdaa7dc2b77c66c3dd7ae18eaca752ebb25dd Homepage: https://cran.r-project.org/package=recipes Description: CRAN Package 'recipes' (Preprocessing and Feature Engineering Steps for Modeling) A recipe prepares your data for modeling. We provide an extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data. Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting processed output can then be used as inputs for statistical or machine learning models. Package: r-cran-recluster Architecture: all Version: 3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-alphahull, r-cran-ape, r-cran-sf, r-cran-sp, r-cran-vegan, r-cran-phytools, r-cran-phangorn, r-cran-picante, r-cran-cluster, r-cran-plotrix Suggests: r-cran-betapart Filename: pool/dists/noble/main/r-cran-recluster_3.5-1.ca2404.1_all.deb Size: 237480 MD5sum: bfe6b8d6617ec2a03e790ea9b4abe37e SHA1: 2d9c4ba61443b3cdbcea6ef19405c980d4acf20d SHA256: 9a94dffa48e6427e4098b975c5b260da0d2f42c0f6542d737d3e8729d24ff4eb SHA512: 76aa74c18677bc8b2ec1594b67808520d04a4c7d8a53009523bdbad848e8725848704f76f72fc53c56602064397be288e046d80d4d47ce0f58f31a8796428aa1 Homepage: https://cran.r-project.org/package=recluster Description: CRAN Package 'recluster' (Ordination Methods for the Analysis of Beta-Diversity Indices) The analysis of different aspects of biodiversity requires specific algorithms. 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These methods are discussed in detail in VanderWeele and Padgett (2024) . The relative excess correlation matrices will, generally, have numerous negative entries even if all of the raw correlations between each pair of indicators are positive. The positive deviations of the relative excess correlation matrix entries help identify clusters of indicators that are more strongly related to one another, providing insights somewhat analogous to factor analysis, but without the need for rotations or decisions concerning the number of factors. A goal similar to exploratory/confirmatory factor analysis, but 'recmetrics' uses novel methods that do not rely on assumptions of latent variables or latent variable structures. 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Package: r-cran-reconstructkm Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-survival, r-cran-rlang, r-cran-survminer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-reconstructkm_0.4.0-1.ca2404.1_all.deb Size: 375374 MD5sum: 3400a9ef8bca9c163e688c48cb389f2c SHA1: 8e29181caef7a9e3919992063d33bb2e8255766e SHA256: 1b87fd697fa449fc38805aa453ff19369d0b3cc30ed7ee5a73c708bcd5ff8d61 SHA512: c37a5039613426180f9069ba2e04d002873fd2aaeda2e16592641e8cb618dd8cf557a138c416613a73df5d51f3af36698366f2a1df7aca7e6009113d6b9a4f3b Homepage: https://cran.r-project.org/package=reconstructKM Description: CRAN Package 'reconstructKM' (Reconstruct Individual-Level Data from Published KM Plots) Functions for reconstructing individual-level data (time, status, arm) from Kaplan-MEIER curves published in academic journals (e.g. NEJM, JCO, JAMA). 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Package: r-cran-recurrentpseudo Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-survival, r-cran-geepack, r-cran-stringr, r-cran-prodlim Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-recurrentpseudo_1.0.0-1.ca2404.1_all.deb Size: 65010 MD5sum: 34ec9b8f3f0dc0b76afb085d33d04ad5 SHA1: b7696fd16af8e6dbdc568775dfab58113beed65c SHA256: 48ebecb1f9112b30e4048c9d8ec8b421dbd2b50e1b9c5bb9462c97c8f39e5332 SHA512: d53269c0534a7c8cb61b475904dc6402210a19d30bc2d871b4d0d6a9a55ee31084e163121056a547b26e68492afa070c29af3c2f16e490c5ca6f57ca1f672808 Homepage: https://cran.r-project.org/package=recurrentpseudo Description: CRAN Package 'recurrentpseudo' (Creates Pseudo-Observations and Analysis for Recurrent EventData) Computation of one-, two- and three-dimensional pseudo-observations based on recurrent events and terminal events. 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It provides functions to create, modify and export data models in json format. It also allows importing models created with 'MySQL Workbench' (). These functions are accessible through a graphical user interface made with 'shiny'. Constraints such as types, keys, uniqueness and mandatory fields are automatically checked and corrected when editing a model. Finally, real data can be confronted to a model to check their compatibility. 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Package: r-cran-redbookperu Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 917 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-redbookperu_0.0.3-1.ca2404.1_all.deb Size: 874910 MD5sum: e0284e6f0bba1dcc71a9bf6333d29074 SHA1: e8e16524c14f1f2af0546399f0a70fac2eb1f352 SHA256: 16fe9aebe8a205497edb0039d5c5c8e013a118374c9809d366f41ac123d57083 SHA512: aae82c280a154b5eb077a4417a0649cc4ab4e1452ad0c3c46c031aae08248938486292649e0ebbe999b61cdf187ebaf6673e2f18ebf8cb95267fd6a6327e3178 Homepage: https://cran.r-project.org/package=redbookperu Description: CRAN Package 'redbookperu' (Access and Analyze Data from the Red Book of Endemic Plants ofPeru) Provides access to and analysis of data from "The Red Book of Endemic Plants of Peru" (León, B., Roque, J., Ulloa, C., Jorgensen, P.M., Pitman, N., Cano, A. 2006) . This package offers comprehensive taxonomic, geographic, and conservation information about Peru's endemic plant species. It includes functions to verify species inclusion, obtain updated taxonomic details, and explore the dataset. Package: r-cran-redcapapi Architecture: all Version: 2.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3715 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-chron, r-cran-curl, r-cran-jsonlite, r-cran-labelvector, r-cran-lubridate, r-cran-mime, r-cran-shelter Suggests: r-cran-testthat, r-cran-hmisc, r-cran-mockery, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-redcapapi_2.12.0-1.ca2404.1_all.deb Size: 3266922 MD5sum: 78e6560d1b7af91f195f9ffa51bbbddc SHA1: 03db1b61a17071e851a7073495e5dc0c5c266bed SHA256: f8dba4fb8d8d537a188f10626d5123b505143bb5f527813bf24a05104688a623 SHA512: b483bb0021e5f95be2fdf89b4f6756dc9a3aa16bb35669ddc8c200308f25955563690ea605f0486c50c7e6de4341bd6aea8f35880431c2c791b5f9bdc4a14f4d Homepage: https://cran.r-project.org/package=redcapAPI Description: CRAN Package 'redcapAPI' (Interface to 'REDCap') Access data stored in 'REDCap' databases using the Application Programming Interface (API). 'REDCap' (Research Electronic Data CAPture; , Harris, et al. (2009) , Harris, et al. (2019) ) is a web application for building and managing online surveys and databases developed at Vanderbilt University. The API allows users to access data and project meta data (such as the data dictionary) from the web programmatically. The 'redcapAPI' package facilitates the process of accessing data with options to prepare an analysis-ready data set consistent with the definitions in a database's data dictionary. Package: r-cran-redcapcast Architecture: all Version: 26.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-redcapr, r-cran-tidyr, r-cran-tidyselect, r-cran-keyring, r-cran-purrr, r-cran-readr, r-cran-zip, r-cran-assertthat, r-cran-forcats, r-cran-vctrs, r-cran-gt, r-cran-bslib, r-cran-here, r-cran-glue, r-cran-gtsummary, r-cran-shiny, r-cran-haven, r-cran-openxlsx2, r-cran-readods Suggests: r-cran-hmisc, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-roxygen2, r-cran-spelling, r-cran-jsonlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-redcapcast_26.1.1-1.ca2404.1_all.deb Size: 265086 MD5sum: 51fc89b480cb1873126f162d18b46aac SHA1: 18874150e3113a9bb1582a87a328b49338bf4362 SHA256: 6c10dd55c8cdd240cb76ad6c40366ca34dbcf58661a2fcffd716576b312a13ba SHA512: 01e1cee55e4f1c5c7e340d85a28c1bd69cae1ee27d1d2fb6ebc4fd9baf843c6527fa16e04765812762d85bf0ff333caa6b68c9f98afc1b2565c007ced0d87536 Homepage: https://cran.r-project.org/package=REDCapCAST Description: CRAN Package 'REDCapCAST' (REDCap Metadata Casting and Castellated Data Handling) Casting metadata for REDCap database creation and handling of castellated data using repeated instruments and longitudinal projects in 'REDCap'. Keeps a focused data export approach, by allowing to only export required data from the database. Also for casting new REDCap databases based on datasets from other sources. Originally forked from the R part of 'REDCapRITS' by Paul Egeler. See . 'REDCap' (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources (Harris et al (2009) ; Harris et al (2019) ). Package: r-cran-redcapdm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-janitor, r-cran-openxlsx, r-cran-purrr, r-cran-redcapr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-labelled, r-cran-stringi, r-cran-cli, r-cran-forcats, r-cran-lifecycle, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-testthat, r-cran-mockery Filename: pool/dists/noble/main/r-cran-redcapdm_1.0.1-1.ca2404.1_all.deb Size: 414656 MD5sum: a840154f7f8d36c9b7780a258ef78595 SHA1: 2210da79096104879b1e960741cf6fc6c595f63f SHA256: 2cb8d425bc224a44d0cff77c5e6732d9f945be57c8c6f7c9af4ba6cc6dcc2676 SHA512: e784cd38423bcdf3c50573823e6eba03364d77bb06852cf722ce23f0c4b423dbd50c7a1a906917d1357579a32bbabcd0c68f119e130a512fcc5feb2cb92c4236 Homepage: https://cran.r-project.org/package=REDCapDM Description: CRAN Package 'REDCapDM' ('REDCap' Data Management) REDCap Data Management - 'REDCap' (Research Electronic Data CAPture; ) is a web application developed at Vanderbilt University, designed for creating and managing online surveys and databases and the REDCap API is an interface that allows external applications to connect to REDCap remotely, and is used to programmatically retrieve or modify project data or settings within REDCap, such as importing or exporting data. REDCapDM is an R package that allows users to manage data exported directly from REDCap or using an API connection. This package includes several functions designed for pre-processing data, generating reports of queries such as outliers or missing values, and following up on previously identified queries. Package: r-cran-redcapexporter Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-keyring, r-cran-lubridate, r-cran-rjson Suggests: r-cran-devtools, r-cran-fs, r-cran-getpass, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-qwraps2, r-cran-rmarkdown, r-cran-secret Filename: pool/dists/noble/main/r-cran-redcapexporter_0.3.4-1.ca2404.1_all.deb Size: 122758 MD5sum: acc9231b94bd3020f4ac632d0c92351e SHA1: 027abac03dfbf2ec44f33d102863a784ea598fab SHA256: d818076e888f9d7aa5f735f72e0365fb3f5092cb014c02411e63b8dc97b380e5 SHA512: 7a5ef93c64639e814fab94f326d87a57d6e8244e44510e72e2331550c4e7e753f9734d913d78b7e8e936351b9166b7f7f10c66c523cc34b87b688397da47e513 Homepage: https://cran.r-project.org/package=REDCapExporter Description: CRAN Package 'REDCapExporter' (Automated Construction of R Data Packages from REDCap Projects) Export all data, including metadata, from a REDCap (Research Electronic Data Capture) Project via the REDCap API . The exported (meta)data will be processed and formatted into a stand alone R data package which can be installed and shared between researchers. Several default reports are generated as vignettes in the resulting package. Package: r-cran-redcapr Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2468 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-readr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-spelling, r-cran-covr, r-cran-dbi, r-cran-kableextra, r-cran-knitr, r-cran-odbc, r-cran-purrr, r-cran-rmarkdown, r-cran-sessioninfo, r-cran-testthat, r-cran-tidyselect, r-cran-yaml Filename: pool/dists/noble/main/r-cran-redcapr_1.6.0-1.ca2404.1_all.deb Size: 1243104 MD5sum: cf29aea962b3f86c87d659923e20aa54 SHA1: 47e84df537d8b3868adc11d54a2ffcb41328d729 SHA256: b842df79ef7df97edd4fbfd8bccf34828dc6696fbfa0abc3c09880ac1cc41eea SHA512: a84983e97fd77fd567697945ee46d6dd7e66be36b353972da20afd754bd2e7f2dcc563e9c74a4b65861fa2ed964e9af801eef7d48052cc0850e59981c77b92b8 Homepage: https://cran.r-project.org/package=REDCapR Description: CRAN Package 'REDCapR' (Interaction Between R and REDCap) Encapsulates functions to streamline calls from R to the REDCap API. REDCap (Research Electronic Data CAPture) is a web application for building and managing online surveys and databases developed at Vanderbilt University. The Application Programming Interface (API) offers an avenue to access and modify data programmatically, improving the capacity for literate and reproducible programming. Package: r-cran-redcapsync Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3746 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-hoardr, r-cran-lubridate, r-cran-openxlsx, r-cran-r6, r-cran-readxl, r-cran-redcapapi, r-cran-redcapr, r-cran-skimr, r-cran-stringr Suggests: r-cran-keyring, r-cran-knitr, r-cran-listviewer, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-redcapsync_0.1.0-1.ca2404.1_all.deb Size: 2186042 MD5sum: a2f03c9341d70dd418951efe6d5bfac8 SHA1: c9b387a0d40b621de3694f5db50d75ef49827d7a SHA256: 9980f370535ed24cdc5b5bc16e365f8963969fccda2b817e1fbd2c4397af996f SHA512: 381d911cc54a076a07f743509c1b8170df2fe8e06c8a416b4f97fe8bd4e3db69d83ba01b6a54f479ae7d23a19bc1329755312ebe4fd5695b13128a758fe1942a Homepage: https://cran.r-project.org/package=REDCapSync Description: CRAN Package 'REDCapSync' (Encapsulated 'REDCap' Projects for Synchronized Data Pipelines) Wraps dozens of 'REDCap' API endpoints into a standardized R6 object. Research Electronic Data Capture ('REDCap') is a survey and database web application software maintained by Vanderbilt University. It has a robust application programming interface (API) utilized by several R packages. 'REDCapSync' uses 'redcapAPI' and 'REDCapR' behind-the-scenes to retrieve all metadata, data, and log details for a project. To minimize unnecessary server calls, the interim 'REDCap' log is analyzed and used to only update necessary records. Furthermore, the user can define custom datasets that save to a directory. Those datasets continue to refresh when projects are synced. Having a secure, standardized, API-efficient, project-agnostic R object for 'REDCap' projects, streamlines downstream use in scripts, functions, and shiny applications. Package: r-cran-redcaptidier Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2978 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-lobstr, r-cran-lubridate, r-cran-purrr, r-cran-redcapr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-formattable, r-cran-pillar, r-cran-vctrs, r-cran-readr, r-cran-forcats Suggests: r-cran-covr, r-cran-knitr, r-cran-labelled, r-cran-lintr, r-cran-openxlsx2, r-cran-prettyunits, r-cran-rmarkdown, r-cran-skimr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-redcaptidier_1.2.4-1.ca2404.1_all.deb Size: 1996838 MD5sum: 9a40519dd8411ae95fbfb9f562c206a6 SHA1: 23b8f963366cde883f12424fbebb3685efd9239b SHA256: f56c9680d257895812ee31b3f8edd7d7275889f4b52441ceafc05e534767ae89 SHA512: fe8d9fdae2db5f38fd0cdecfab0add284a17d3e8791bea8761d6c30a2ef0c08e0aa58849411bfe431b75c72060e772888b779e54706c863e0d0a5cb123e78535 Homepage: https://cran.r-project.org/package=REDCapTidieR Description: CRAN Package 'REDCapTidieR' (Extract 'REDCap' Databases into Tidy 'Tibble's) Convert 'REDCap' exports into tidy tables for easy handling of 'REDCap' repeat instruments and event arms. Package: r-cran-redcas Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 761 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-redcas_0.1.1-1.ca2404.1_all.deb Size: 368622 MD5sum: 698458d3ced645c953fb3079da8f7398 SHA1: f6b65d94dcd2845338b410ed9c71ac4aa8af0ee9 SHA256: 800148bf6c7d6ec6f45577f2aff1a7627e1d6deb72f6868f47b8fe51a698e6a2 SHA512: 6679cbe1bf36c03126ccc6bcf6ea169ce28a48e9b41cf8e8e5e64479de4d1ed78a9cfbe399ab71c4eac3cfd8aa4ae4668ef435046e4355244082cd3109546218 Homepage: https://cran.r-project.org/package=redcas Description: CRAN Package 'redcas' (An Interface to the Computer Algebra System 'REDUCE') 'REDUCE' is a portable general-purpose computer algebra system supporting scalar, vector, matrix and tensor algebra, symbolic differential and integral calculus, arbitrary precision numerical calculations and output in 'LaTeX' format. 'REDUCE' is based on 'Lisp' and is available on the two dialects 'Portable Standard Lisp' ('PSL') and 'Codemist Standard Lisp' ('CSL'). The 'redcas' package provides an interface for executing arbitrary 'REDUCE' code interactively from 'R', returning output as character vectors. 'R' code and 'REDUCE' code can be interspersed. It also provides a specialized function for calling the 'REDUCE' feature for solving systems of equations, returning the output as an 'R' object designed for the purpose. A further specialized function uses 'REDUCE' features to generate 'LaTeX' output and post-processes this for direct use in 'LaTeX' documents, e.g. using 'Sweave'. 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Package: r-cran-redeagroradar Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-magick, r-cran-shiny, r-cran-bslib Filename: pool/dists/noble/main/r-cran-redeagroradar_0.1.1-1.ca2404.1_all.deb Size: 59692 MD5sum: 9665d906662c3431e7a3e6edc8869a4a SHA1: b374df4e3f8e2aca0b305d1ff0b223ee47914457 SHA256: 616b3d9391b80495fa8b058d6cf57a61de6621ed2825290578bd77f993ce66d2 SHA512: 75852fcdcb934743496a57a2e88334e1a772446df87cb8231de63c082cc2b489d7dade73a607ad85004fb2f6f4a12cbe08b29997868d261905e3d13222cf7c77 Homepage: https://cran.r-project.org/package=RedeAgroRadar Description: CRAN Package 'RedeAgroRadar' (Weather Radar Monitoring and 'Telegram' Alerts for AgricultureResearch) Provides tools to download, process, and analyze real-time meteorological radar images from Simepar (Paraná, Brazil) . Designed to support the 'Rede Agropesquisa' hydrological monitoring, it includes functions to detect rainfall intensity based on Red, Green, and Blue (RGB) color values within predefined circular study areas. Features automated integration with the 'Telegram Bot API' to send spatialized image alerts and an interactive 'shiny' dashboard for easy configuration and continuous weather tracking. Package: r-cran-redi Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-tibble, r-cran-magrittr, r-cran-rlang, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-redi_1.0.0-1.ca2404.1_all.deb Size: 140062 MD5sum: 2fcd95ae9c35545eb194e0aed2c1d33a SHA1: 3ab7d137b5a1444cd0c3f628040c150d0a527d5c SHA256: 93f2ec36d4ccca9269ef5be8a8b9fc1738ad1e27376392b847036daeba2ce874 SHA512: 39185dcbc7baf61cb1b86fe49e851b6faea55e380e12ef174b58f29d7e5ba9a7d9661caa5255a9847aa074917db59ac63feb76daa56c8673056279ff383fe8d1 Homepage: https://cran.r-project.org/package=REDI Description: CRAN Package 'REDI' (Robust Exponential Decreasing Index) Implementation of the Robust Exponential Decreasing Index (REDI), proposed in the article by Issa Moussa, Arthur Leroy et al. (2019) . The REDI represents a measure of cumulated workload, robust to missing data, providing control of the decreasing influence of workload over time. Various functions are provided to format data, compute REDI, and visualise results in a simple and convenient way. Package: r-cran-redirection Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-pracma, r-cran-gtools Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-redirection_1.0.1-1.ca2404.1_all.deb Size: 34068 MD5sum: af844bc023cd3da5e9db97b1b7ff153e SHA1: 12af0b0410d40792645faf34032a613466aa55c1 SHA256: 036b94b4eb440444143ff9bc1a57aa7f67aa6ea89e5743caeb43558269c76af4 SHA512: 8abbcc9727aa3bf3e78d1bebbef105cd1ace060c6503c0426c9b83e03c7d10f2aab02fb1c6ea53be0feefe5dd74e4e7be7ca6064b319539fbb7b247f1b376ae2 Homepage: https://cran.r-project.org/package=ReDirection Description: CRAN Package 'ReDirection' (Predict Dominant Direction of Reactions of a Biochemical Network) Biologically relevant, yet mathematically sound constraints are used to compute the propensity and thence infer the dominant direction of reactions of a generic biochemical network. The reactions must be unique and their number must exceed that of the reactants,i.e., reactions >= reactants + 2. 'ReDirection', computes the null space of a user-defined stoichiometry matrix. The spanning non-zero and unique reaction vectors (RVs) are combinatorially summed to generate one or more subspaces recursively. Every reaction is represented as a sequence of identical components across all RVs of a particular subspace. The terms are evaluated with (biologically relevant bounds, linear maps, tests of convergence, descriptive statistics, vector norms) and the terms are classified into forward-, reverse- and equivalent-subsets. Since, these are mutually exclusive the probability of occurrence is binary (all, 1; none, 0). The combined propensity of a reaction is the p1-norm of the sub-propensities, i.e., sum of the products of the probability and maximum numeric value of a subset (least upper bound, greatest lower bound). This, if strictly positive is the probable rate constant, is used to infer dominant direction and annotate a reaction as "Forward (f)", "Reverse (b)" or "Equivalent (e)". The inherent computational complexity (NP-hard) per iteration suggests that a suitable value for the number of reactions is around 20. Three functions comprise ReDirection. These are check_matrix() and reaction_vector() which are internal, and calculate_reaction_vector() which is external. Package: r-cran-redisbasecontainer Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dockerparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-redisbasecontainer_1.0.1-1.ca2404.1_all.deb Size: 265986 MD5sum: 75536bb99c22a42c86a81b2ebb159400 SHA1: 6187b7de160062a2f7fc255079ab40895f0d845f SHA256: a18c98852677aeebe04d9aa9b2a2efb7ac8017eb125c4ecf814b603125dc4a9a SHA512: 33a8a20f02b8b45281fa258cc6c818f9913d4d8f4a93388f7ffcbf08184e9c74138b19c9f85d464f49a1b7335888e7f829a133058b45649d5aaeb74da1b0e2a2 Homepage: https://cran.r-project.org/package=RedisBaseContainer Description: CRAN Package 'RedisBaseContainer' (The Container for the DockerParallel Package) Providing the container for the DockerParallel package. Package: r-cran-rediscover Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3761 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-poissonbinomial, r-cran-shiftconvolvepoibin, r-cran-matrixstats, r-bioc-maftools, r-cran-data.table, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-runit, r-bioc-biocstyle, r-cran-dplyr, r-cran-kableextra, r-cran-magick, r-bioc-qvalue, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rediscover_0.3.3-1.ca2404.1_all.deb Size: 2759164 MD5sum: 2828f124ba8a98bcf2b8b8c2b3e5ec89 SHA1: de69b5221870891bfab20b01298d91eb97abacd8 SHA256: b95b95e270ff30f29a4a366d8da192015051a47787f673f138e357383613f735 SHA512: a1029213d5a67c21ea686720f49be6421d5fc9e3a3cb4866b1603565b1cdfba0ab60187c8f65942f2aba9c1e874392be46f9de966017c664e3d6b15ef5b80ec0 Homepage: https://cran.r-project.org/package=Rediscover Description: CRAN Package 'Rediscover' (Identify Mutually Exclusive Mutations) An optimized method for identifying mutually exclusive genomic events. Its main contribution is a statistical analysis based on the Poisson-Binomial distribution that takes into account that some samples are more mutated than others. See [Canisius, Sander, John WM Martens, and Lodewyk FA Wessels. (2016) "A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence." Genome biology 17.1 : 1-17. ]. The mutations matrices are sparse matrices. The method developed takes advantage of the advantages of this type of matrix to save time and computing resources. 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This package makes it easy to install and load multiple 'redistverse' packages at once. The 'redistverse' is developed and maintained by the Algorithm-Assisted Redistricting Methodology (ALARM) Project. For more details see . Package: r-cran-redlist Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2114 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-magrittr, r-cran-rvest Suggests: r-cran-ggplot2, r-cran-httptest2, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-redlist_0.2.0-1.ca2404.1_all.deb Size: 1030448 MD5sum: 7234d4028e565973808a1dbb9b4a8879 SHA1: b93360265f859a1ebdd9b07727aaaffbf6bff2ed SHA256: cecbc929a99d208205ed10e74ce8088b85de4400236d6f055ac245f0246e8685 SHA512: 49ab3c7028ee7f4ba11071fe79bc67d7d4fa5537543c76525447eba635f1cd9ba7dedaadb169e5b74f426712acce5ce3efa980eec2a11314988b6ed944f4ebfa Homepage: https://cran.r-project.org/package=redlist Description: CRAN Package 'redlist' (Interface to the IUCN Red List Data) Provides an interface to access data from the International Union for Conservation of Nature (IUCN) Red List . 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Package: r-cran-redlistr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1229 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-plyr, r-cran-sf, r-cran-terra Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-redlistr_1.0.4-1.ca2404.1_all.deb Size: 380066 MD5sum: 50003dd5e3c1055735b014e81024c16f SHA1: a6193a4ffff1a9ca2bcd5f94bc454e4d8e2a2dd3 SHA256: 267752db3533727a9fac4a73dd21f97a910ed1b970c1536a5f0acbae265f4aa7 SHA512: 5471cc3f2c6604dfa0702c02705ffe043067dc342e606f83c12d87db0fc340f9572c416cc436e310b0264e4b7199ff2ad40ed962d2b7d7fbbd7f619ec9466f96 Homepage: https://cran.r-project.org/package=redlistr Description: CRAN Package 'redlistr' (Tools for the IUCN Red List of Ecosystems and Species) A toolbox created by members of the International Union for Conservation of Nature (IUCN) Red List of Ecosystems Committee for Scientific Standards. Primarily, it is a set of tools suitable for calculating the metrics required for making assessments of species and ecosystems against the IUCN Red List of Threatened Species and the IUCN Red List of Ecosystems categories and criteria. See the IUCN website for detailed guidelines, the criteria, publications and other information. Package: r-cran-redmonder Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-redmonder_0.2.0-1.ca2404.1_all.deb Size: 49158 MD5sum: 2b6720957c4146b09899723e4be0ba1b SHA1: 37c3351768977076f4028320635e74b58710164f SHA256: 7e9c3b49a63a98b0cb6b31afebe11c5d1527159c0877ea6583dd60455e54f135 SHA512: 319135ef9615db3520e8c7bdb73f0a9881e3ec80bfb3b038f80b7499218e78d6cf6619eff93ac33b02bdc89a823478f1b15aea6344ab4c3049b768469ab00edf Homepage: https://cran.r-project.org/package=Redmonder Description: CRAN Package 'Redmonder' (Microsoft(r)-Inspired Color Palettes) Provide color schemes for maps (and other graphics) based on the color palettes of several Microsoft(r) products. Forked from 'RColorBrewer' v1.1-2. 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Processes data in chunks to handle large datasets without exceeding available memory. Features include data labeling, coded value conversion, and hearing a "quack" sound on success. Package: r-cran-reemtree Architecture: all Version: 0.90.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-rpart, r-cran-aer Filename: pool/dists/noble/main/r-cran-reemtree_0.90.6-1.ca2404.1_all.deb Size: 69272 MD5sum: 91200a7d6adc213d7ade08dbaa0a08b3 SHA1: 040c8f6fca4dc5c198ce564745c67884138ac1b4 SHA256: 42f37fd68052ce6248ea84f83f68fcf1d2b82cbbe3143694017a969e7bdf2ee3 SHA512: 95252eeaf829da6bbbd93874d034c8bcd1e3f2aa3d9a916e0a5ccbd77411bdfe56ae026079b031a6f2b5b7cb5c25e9a4e82b2aeae16a9761f46d6d6cf82db1e9 Homepage: https://cran.r-project.org/package=REEMtree Description: CRAN Package 'REEMtree' (Regression Trees with Random Effects for Longitudinal (Panel)Data) A data mining approach for longitudinal and clustered data, which combines the structure of mixed effects model with tree-based estimation methods. See Sela, R.J. and Simonoff, J.S. (2012) RE-EM trees: a data mining approach for longitudinal and clustered data . Package: r-cran-ref.icar Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-sp, r-cran-spdep, r-cran-mvtnorm, r-cran-coda, r-cran-mcmcglmm, r-cran-rdpack, r-cran-pracma, r-cran-classint, r-cran-dplyr, r-cran-ggplot2, r-cran-gtools Suggests: r-cran-maps, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-rcrossref, r-cran-spdata, r-cran-formatr Filename: pool/dists/noble/main/r-cran-ref.icar_2.0.2-1.ca2404.1_all.deb Size: 595544 MD5sum: b69e50928fba43730797d8d4b46a0a54 SHA1: 3cc307701e1c98392cc070ea54a9a0f8e76010ac SHA256: 702bbe972cb90b66a0e51ec26b5d2dd11cd83e84f02e41f60d6aa115cced83e2 SHA512: cfa731821d37cea82e9df8223fbf7ac1860df2a29628b893ed97f1de4be9bc3dc755472e0ce41d9b499a448b60fb53751046904522a66af7c0ccfa5002a949c1 Homepage: https://cran.r-project.org/package=ref.ICAR Description: CRAN Package 'ref.ICAR' (Objective Bayes Intrinsic Conditional Autoregressive Model forAreal Data) Implements an objective Bayes intrinsic conditional autoregressive prior. This model provides an objective Bayesian approach for modeling spatially correlated areal data using an intrinsic conditional autoregressive prior on a vector of spatial random effects. 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The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance. For more detail of Robust Exponential Factor Analysis, please refer to Hu et al. (2026) . Package: r-cran-refdb Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1397 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-readr, r-cran-dplyr, r-cran-stringr, r-cran-tidyr, r-cran-rentrez, r-cran-taxize, r-cran-xml2, r-cran-bioseq, r-cran-ape, r-cran-igraph, r-cran-ggplot2, r-cran-ggraph, r-cran-yaml, r-cran-rlang, r-cran-rmarkdown, r-cran-leaflet Suggests: r-cran-testthat, r-cran-covr, r-cran-dt, r-cran-knitr, r-cran-forcats Filename: pool/dists/noble/main/r-cran-refdb_0.1.3-1.ca2404.1_all.deb Size: 689178 MD5sum: a5c6bdf7aff6623bd585c35a6b58e7fe SHA1: f5097472c4d4af8a55d9c6e16fad9a521086687e SHA256: 4df544abf080f7f56a3a0c527226cb4f80a927a6fa3ddf72a67ea4a6d7000d2b SHA512: 273532a8cb9a032bd0a29e742b1fa9c5292517fcbb81276e01e56d91a93ac9f0ced1091e421340b5b41d917d04cf04db7d45d93f3478eace8960d58e0c4935dc Homepage: https://cran.r-project.org/package=refdb Description: CRAN Package 'refdb' (A DNA Reference Library Manager) Reference database manager offering a set of functions to import, organize, clean, filter, audit and export reference genetic data. Provide functions to download sequence data from NCBI GenBank . Designed as an environment for semi-automatic and assisted construction of reference databases and to improve standardization and repeatability in barcoding and metabarcoding studies. Package: r-cran-refer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-elist, r-cran-matchr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-refer_0.1.0-1.ca2404.1_all.deb Size: 132352 MD5sum: 79d378c8a1d1e62aba1ac24eecebf966 SHA1: 73af567f17d1e1d5e81973d3ac9a26739556ba1f SHA256: 4d69bbb41bacf54aa7af744ec4e46eac88f8736a8eb61f4feaa3d791b8f2b0d6 SHA512: 451f0cf74257aeb42bdacb0f8a07c29937fa08577fd4168e136a69653fb4304ab4088964da74a26d1f95d5c7084a94dd97b70900ddb05fc920d79a9927b77a86 Homepage: https://cran.r-project.org/package=refer Description: CRAN Package 'refer' (Create Object References) Allows users to easily create references to R objects then 'dereference' when needed or modify in place without using reference classes, environments, or active bindings as workarounds. Users can also create expression references that allow subsets of any object to be referenced or expressions containing references to multiple objects. Package: r-cran-referenceintervals Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-extremevalues, r-cran-mass, r-cran-outliers Filename: pool/dists/noble/main/r-cran-referenceintervals_1.3.1-1.ca2404.1_all.deb Size: 77454 MD5sum: 07ea38528425281950b4729b82276b64 SHA1: a037c1ec9f71b092ab60e2043de420c582d7b237 SHA256: 28efcf4334e1ed8be7f99560e2e4cf6234364e97f9fdd838ff012316ae318f9e SHA512: 628833293ba5594738a4710e0c66708aac1744a8b711d5015d419c19b057becc96ca6540da7ecdf453303f9072ad09632829d5b5d5534246aaf02aeb644833aa Homepage: https://cran.r-project.org/package=referenceIntervals Description: CRAN Package 'referenceIntervals' (Reference Intervals) This is a collection of tools to allow the medical professional to calculate appropriate reference ranges (intervals) with confidence intervals around the limits for diagnostic purposes. Package: r-cran-reffectivepred Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-yaml, r-cran-config, r-cran-zoo Filename: pool/dists/noble/main/r-cran-reffectivepred_1.0.1-1.ca2404.1_all.deb Size: 110198 MD5sum: 37d7752976bb9116ab4561de7357392e SHA1: 1dd493a990e221a8d8a3ef47b1b9f26519591adc SHA256: 322cacb433cdaa7231fe318932203dd315a8ba39cc6bf2b99f5dd6ede98b6665 SHA512: 53bf1734f06a699339602d677a8dd964935ea17eadd38f3bb22abae463b2d39cab23d335967dd2a5c3335c4811ea373fc243d7aded723c2df456374abd5a4b6a Homepage: https://cran.r-project.org/package=REffectivePred Description: CRAN Package 'REffectivePred' (Pandemic Prediction Model in an SIRS Framework) A suite of methods to fit and predict case count data using a compartmental SIRS (Susceptible – Infectious – Recovered – Susceptible) model, based on an assumed specification of the effective reproduction number. The significance of this approach is that it relates epidemic progression to the average number of contacts of infected individuals, which decays as a function of the total susceptible fraction remaining in the population. The main functions are pred.curve(), which computes the epidemic curve for a set of parameters, and estimate.mle(), which finds the best fitting curve to observed data. The easiest way to pass arguments to the functions is via a config file, which contains input settings required for prediction, and the package offers two methods, navigate_to_config() which points the user to the configuration file, and re_predict() for starting the fit-predict process. The main model was published in Razvan G. Romanescu et al. . Package: r-cran-refiner Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5693 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ash, r-cran-future, r-cran-future.apply Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-refiner_2.0.0-1.ca2404.1_all.deb Size: 4475352 MD5sum: 8388cd593adbe9dbf15c9fe9dc4dcb40 SHA1: 9563c065d570d00d632f1840f399eb789491bc21 SHA256: e4f3345c035a6847e80881e141a6d7ac68992982b17a1258d45dae7bc712fead SHA512: c5e5d706fdb8b3fd3eef4a23589fdbdfb2b0d56f2a5a40c6cae624dc7837cc0e4698427a634d0b137feaa4b480cccbdc206a4a4934b5779f7c989df072137c0f Homepage: https://cran.r-project.org/package=refineR Description: CRAN Package 'refineR' (Reference Interval Estimation using Real-World Data) Indirect method for the estimation of reference intervals (RIs) using Real-World Data ('RWD') and methods for comparing and verifying RIs. Estimates RIs by applying advanced statistical methods to routine diagnostic test measurements, which include both pathological and non-pathological samples, to model the distribution of non-pathological samples. This distribution is then used to derive reference intervals and support RI verification, i.e., deciding if a specific RI is suitable for the local population. The package also provides functions for printing and plotting algorithm results. See ?refineR for a detailed description of features. Version 1.0 of the algorithm is described in 'Ammer et al. (2021)' . Additional guidance is in 'Ammer et al. (2023)' . The verification method is described in 'Beck et al. (2025)' . 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It supports including effect measure modification by covariates(treatment-covariate and mediator-covariate product terms in mediator and outcome regression models) as proposed by Li et al (2023) . It also accommodates the original 'SAS' macro and 'PROC CAUSALMED' procedure in 'SAS' when there is no effect measure modification. Linear and logistic models are supported for the mediator model. Linear, logistic, loglinear, Poisson, negative binomial, Cox, and accelerated failure time (exponential and Weibull) models are supported for the outcome model. 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The package can be used to generate synthetic or hybrid continuous microdata, and the relationship to the original data can be controlled in several ways. A function for replacing suppressed tabular cell frequencies with decimal numbers is included. Package: r-cran-regspec Architecture: all Version: 2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-regspec_2.7-1.ca2404.1_all.deb Size: 78402 MD5sum: fdf155d4cc4c105dca4a0efb4f122c5b SHA1: d78981a29fa2ec22c06c8b6274339b10d9c9520c SHA256: bae0c871c2531588da5d7e093822af62b1df13abfbcf1acc5781f3365fa21758 SHA512: bd1155ca8d5fc22c9904d5533e8c81c7cd6296c8d72631a04b1115a8fc561cfcd19d5024e9abe86db4b69544437039fc9dbb4b5f594dbf79c0bb241ce473fd73 Homepage: https://cran.r-project.org/package=regspec Description: CRAN Package 'regspec' (Non-Parametric Bayesian Spectrum Estimation for Multirate Data) Computes linear Bayesian spectral estimates from multirate data for second-order stationary time series. Provides credible intervals and methods for plotting various spectral estimates. Please see the paper `Should we sample a time series more frequently?' (doi below) for a full description of and motivation for the methodology. Package: r-cran-regsubseq Architecture: all Version: 0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1742 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-regsubseq_0.12-1.ca2404.1_all.deb Size: 1736352 MD5sum: 102c71ccbed7f1f738a5e59d35435ed7 SHA1: 5ac515aa57c14c1a1e0c3be99bad668ef25f2ac0 SHA256: 0f6e994d1240907e9b65228ddad449d47e0a30d08acff88be28fc3cf129e195c SHA512: 9ff1f9c3f5d95c1f1c4d09c203cdb5a7662c0eff1efd7275d4410f4a41f1fafc15aa9e3f4c7b16f247b29ab02047f36b124f0900c8b6726f16b2a725e607ba18 Homepage: https://cran.r-project.org/package=regsubseq Description: CRAN Package 'regsubseq' (Detect and Test Regular Sequences and Subsequences) For a sequence of event occurence times, we are interested in finding subsequences in it that are too "regular". We define regular as being significantly different from a homogeneous Poisson process. The departure from the Poisson process is measured using a L1 distance. See Di and Perlman 2007 for more details. 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Interpreting possible changes in the mean in such situations can lead to biased results since the values were not randomly selected, they come from truncated sampling. This method allows to estimate the range of means where treatment effects are likely to occur when regression toward the mean is present. Ostermann, T., Willich, Stefan N. & Luedtke, Rainer. (2008). Regression toward the mean - a detection method for unknown population mean based on Mee and Chua's algorithm. BMC Medical Research Methodology.. Acknowledgments: We would like to acknowledge "Lena Roth" and "Nico Steckhan" for the package's initial updates (Q3 2024) and continued supervision and guidance. Both have contributed to discussing and integrating these methods into the package, ensuring they are up-to-date and contextually relevant. 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Novel graphical methods for assessment of parametric models using nonparametric methods. One vs. All and All vs. All multiclass classification, optional class probabilities adjustment. Nonparametric regression (k-NN) for general dimension, local-linear option. Nonlinear regression with Eickert-White method for dealing with heteroscedasticity. Utilities for converting time series to rectangular form. Utilities for conversion between factors and indicator variables. Some code related to "Statistical Regression and Classification: from Linear Models to Machine Learning", N. Matloff, 2017, CRC, ISBN 9781498710916. 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Implementation of a clustering approach based on the k-means algorithm that can be used with any distance. In addition, implementation of the Hartigan and Wong method to accommodate alternative distance metrics. Both methods can operate with any distance measure, provided a suitable method is available to compute cluster centers under the chosen metric. Additionally, the k-medoids algorithm is implemented, offering a robust alternative for clustering without the need of computing cluster centers under the chosen metric. All three methods are designed to support Relative distances, Euclidean distances, and any user-defined distance functions. The Hartigan and Wong method is described in Hartigan and Wong (1979) and an explanation of the k-medoids algorithm can be found in Reynolds et al (2006) . Package: r-cran-relcircle Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-relcircle_1.0-1.ca2404.1_all.deb Size: 46738 MD5sum: 5aac6fa112fa1bbb59ff7d9a2e15a8bf SHA1: 1cb962bb258b1548d1d34f737fbb1aef4377282e SHA256: 51316645e65de0f941889d4f2a026ef205b68629e194d9560fe7717e5f70e121 SHA512: b6e0d917b7c6edabaeb60335ca260d803ae3e53882e59f2b5836ef57da1fc45255c1773b7d5fd70fb52861c29426d92f44f6df419c5e1bb07678c3390ef5a3c7 Homepage: https://cran.r-project.org/package=relcircle Description: CRAN Package 'relcircle' (Draw Regulatory Relationships Between Genes) According to the order of the loci on the chromosome, the loci can be connected according to the interrelationship between them and classified according to different locus types. Package: r-cran-reldist Architecture: all Version: 1.7-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-densestbayes Suggests: r-cran-locfit Filename: pool/dists/noble/main/r-cran-reldist_1.7-2-1.ca2404.1_all.deb Size: 180992 MD5sum: df8c9e4497fa0726ef7de0301336c438 SHA1: 89f95467aa295d3334f9419480bd9d2579d97587 SHA256: 0ee94fb6e6d04e82c15f848fb2e9fe2996b5609164734bee2299e1add6f80ccd SHA512: bc3a7bedebfd009767955840bfc6d86835e4ccfbd5bd7e2670da9cbf255414c96a7c5eee4be89683f86564e730ca2049ae519f16502fdee288e6b8bb1ec58e36 Homepage: https://cran.r-project.org/package=reldist Description: CRAN Package 'reldist' (Relative Distribution Methods) Tools for the comparison of distributions. This includes nonparametric estimation of the relative distribution PDF and CDF and numerical summaries as described in "Relative Distribution Methods in the Social Sciences" by Mark S. Handcock and Martina Morris, Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789. 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Package: r-cran-releaser Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gh, r-cran-desc, r-cran-base64enc, r-cran-roxygen2 Suggests: r-cran-rlang, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-releaser_1.1.0-1.ca2404.1_all.deb Size: 58792 MD5sum: d2cc0cad2918dde8936ae9209b8f9099 SHA1: f93343325907e2fb7729b6589e9e7a5bdaaab711 SHA256: 435e6b5525392ace2edc2167512a293954e036abcd1bcb443ee43fbf3b44e1f4 SHA512: 3884bbd209b318d8ff91aefd281d1a46dc32a605df04793ca0682409eadf2852654884c297c29bfe86f3d47f222018b32b81c193afa8f491d2d06b6bf9254cf9 Homepage: https://cran.r-project.org/package=releaser Description: CRAN Package 'releaser' (Help with Preparing a New Version of an R Package) Helps to prepare a release. Before releasing an R package it is important to update the DESCRIPTION file and the changelog. 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All the above will allow the users to carry out deep studies on the results obtained in any type of electoral process. The methods are described in: Oñate, Pablo and Ocaña, Francisco A. (1999, ISBN:9788474762815); Ruiz Rodríguez, Leticia M. and Otero Felipe, Patricia (2011, ISBN:9788474766226). Package: r-cran-relevance Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass, r-cran-survival, r-cran-knitr Filename: pool/dists/noble/main/r-cran-relevance_2.1-1.ca2404.1_all.deb Size: 617986 MD5sum: 357f315affea1e9ffbd4497d672644c9 SHA1: d5f7edddc6bd2dc52903d140fb8c103519939ec8 SHA256: e98129b43f4fc3f820a73f50d1ab9d446f172ca88f2963b437ea1a691cedcfca SHA512: e7886c489a749e638ddce1fa621cd117b51d53b17dac3cdda2ee656452519e66d737b3af2f4438f67d94028cdd861c1163abc50e383a4d3e28c6ffef2193b393 Homepage: https://cran.r-project.org/package=relevance Description: CRAN Package 'relevance' (Calculate Relevance and Significance Measures) Calculates relevance and significance values for simple models and for many types of regression models. These are introduced in 'Stahel, Werner A.' (2021) "Measuring Significance and Relevance instead of p-values." . 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See Dimitriadis, Gneiting, Jordan (2021) . Package: r-cran-reliabilitytheory Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-actuar, r-cran-combinat, r-cran-fraction, r-cran-igraph, r-cran-mcmc, r-cran-phasetype, r-cran-sfsmisc Suggests: r-cran-testthat, r-cran-reshape2, r-cran-ggplot2, r-cran-xtable Filename: pool/dists/noble/main/r-cran-reliabilitytheory_0.3.1-1.ca2404.1_all.deb Size: 273772 MD5sum: c9096f4f0e88c7263fc3dd19c0794250 SHA1: 6d3bf607775c1d192fbb0788e5a8ea67a56758e5 SHA256: 74d337b4d1207376dd01ea1f151cc17cd8ba5f90f3cfa7a8c3bfecc9cbc0eccc SHA512: 153ca5eb561e0e8d37f24d24742936a5739f00426f6539c4142e40dbf0a34752ce619c306dcccfd12bf65cf42f01c333519183a03854c736682080d3844a409c Homepage: https://cran.r-project.org/package=ReliabilityTheory Description: CRAN Package 'ReliabilityTheory' (Structural Reliability Analysis) Perform structural reliability analysis, including computation and simulation with system signatures, Samaniego (2007) , and survival signatures, Coolen and Coolen-Maturi (2013) . 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Package: r-cran-reliacoef Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-psych, r-cran-matrixcalc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rcsdp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-reliacoef_1.0.1-1.ca2404.1_all.deb Size: 134328 MD5sum: 0a4c09decccd16fd16b871d348df6d4b SHA1: fc2a90f53671dbc2cf0adce0b01566cd11a854df SHA256: 0e1933ec22b4125fde06db54570e40f0a482038475418dc1d6b3159a31f3ce88 SHA512: 44eb0d62d259282884dd91584f722f464b2b0c09371f3dd3bbee802f74330049a00cda6bbf46c9c30553cad584ad61af575efd33b2d4d7167107c2cdd5cd525e Homepage: https://cran.r-project.org/package=reliacoef Description: CRAN Package 'reliacoef' (Unidimensional and Multidimensional Reliability Coefficients) Calculates and compares various reliability coefficients for unidimensional and multidimensional scales. Supported unidimensional estimators include coefficient alpha, congeneric reliability, the Gilmer-Feldt coefficient, Feldt's classical congeneric reliability, Hancock's H, Heise-Bohrnstedt's omega, Kaiser-Caffrey's alpha, and Ten Berge and Zegers' mu series. Multidimensional estimators include stratified alpha, maximal reliability, correlated factors reliability, second-order factor reliability, and bifactor reliability. See Cho (2021) , Cho (2024) , Cho (2025) . Package: r-cran-reliagrowr Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2253 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-plumber, r-cran-segmented Suggests: r-cran-ellmer, r-cran-knitr, r-cran-mcptools, r-cran-pkgload, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr, r-cran-weibullr Filename: pool/dists/noble/main/r-cran-reliagrowr_0.7-1.ca2404.1_all.deb Size: 1496576 MD5sum: 6c51c4f56dee5b2d2157d4523bdf6e84 SHA1: 612d6f4f9e51770285288974a79dbc111a49e14e SHA256: ce88fc72b0049fd3e954a1433393cbe5a078f73c18c49f66e60e7eb071311ea5 SHA512: 796b0ad873a0d503439fc0663a39ec020080520e84be7d469c13a3cf7cf4aad1246868ca40047228af19459f3af1e06a60b407b12ba6739b38c06a47f38df6a1 Homepage: https://cran.r-project.org/package=ReliaGrowR Description: CRAN Package 'ReliaGrowR' (Reliability Growth Analysis and Repairable Systems Modeling) Modeling and plotting functions for Reliability Growth Analysis (RGA) and Non-Homogeneous Poisson Process (NHPP) models for repairable systems. RGA models include the Duane (1962) , NHPP by Crow (1975) (No. AMSAATR138), Piecewise Weibull NHPP by Guo et al. (2010) , and Piecewise Weibull NHPP with Change Point Detection based on the 'segmented' package by Muggeo (2024) . Repairable systems functions include the Mean Cumulative Function (MCF) using the Nelson-Aalen estimator, parametric Power Law and Log-Linear NHPP models, and forecasting. Package: r-cran-relialearnr Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-learnr, r-cran-reliagrowr, r-cran-weibullr, r-cran-weibullr.alt Suggests: r-cran-knitr, r-cran-mockery, r-cran-reliaplotr, r-cran-reliashiny, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-relialearnr_0.3-1.ca2404.1_all.deb Size: 501394 MD5sum: ba4d3207bec6e55e13d2f3f58defe8b6 SHA1: 677ee7a730a0ec7a63b19034eba1b59f31979fe1 SHA256: 14c97bbd43fd88ca88910b064b9d5a267c0e6499c07ae371bdb33ad51169e956 SHA512: 527846af4f1835ac05d75db53310ff214b0bce34179495f8706055d989b3f381affc5c2f8760adc27fdee7d2aa533217578e28678d49556c79ea6b36c10e84c7 Homepage: https://cran.r-project.org/package=ReliaLearnR Description: CRAN Package 'ReliaLearnR' (Learning Modules for Reliability Analysis) Learning modules for reliability analysis including modules for Reliability, Availability, and Maintainability (RAM) Analysis, Life Data Analysis, and Reliability Testing. 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Package: r-cran-reliar Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-reliar_0.2-1.ca2404.1_all.deb Size: 585754 MD5sum: 69cfb0f33988fd85abf73225a5d85bd8 SHA1: fdd6ce8182e13a00a2090c715a21292e3e885dc8 SHA256: 58f1e31ad5fc8e28227421219921f1b56a9bbcc8a1ed94b403e8d7335d2f26eb SHA512: 1a6972c72809362a2fa2c09bce2e81c9443aaea332fa2a6d07aa2a42856b158c804764c9536664c52a23dd189c9127ddf591f0c60cd505fff8c6a5c880d529cc Homepage: https://cran.r-project.org/package=reliaR Description: CRAN Package 'reliaR' (Comprehensive Tools for some Probability Distributions) Provides a comprehensive suite of utilities for univariate continuous probability distributions and reliability models. Includes functions to compute the probability density, cumulative distribution, quantile, reliability, and hazard functions, along with random variate generation. Also offers diagnostic and model assessment tools such as Quantile-Quantile (Q-Q) and Probability-Probability (P-P) plots, the Kolmogorov-Smirnov goodness-of-fit test, and model selection criteria including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Currently implements the following distributions: Burr X, Chen, Exponential Extension, Exponentiated Logistic, Exponentiated Weibull, Exponential Power, Flexible Weibull, Generalized Exponential, Gompertz, Generalized Power Weibull, Gumbel, Inverse Generalized Exponential, Linear Failure Rate, Log-Gamma, Logistic-Exponential, Logistic-Rayleigh, Log-log, Marshall-Olkin Extended Exponential, Marshall-Olkin Extended Weibull, and Weibull Extension distributions. Serves as a valuable resource for teaching and research in probability theory, reliability analysis, and applied statistical modeling. Package: r-cran-reliashiny Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-reliagrowr, r-cran-reliaplotr, r-cran-shinycssloaders, r-cran-weibullr, r-cran-weibullr.alt, r-cran-shiny, r-cran-shinydashboard, r-cran-shinywidgets Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shinytest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-reliashiny_0.5-1.ca2404.1_all.deb Size: 106466 MD5sum: 09f407289f83155718dd35a15e459e2d SHA1: 18430a24f0f01bec7d2a9a537d335fe64ecaa30c SHA256: af5b27c5d6a31b2af38b5435792a7c9e6eb2504b2257e250c0e3c66b0cd33e35 SHA512: 1de0f3a1f09a0cfca35fc9f170976744b958c7d92c56ea5d2c26d03743e75e1ba639b6f1a434e95a44340e952fa133b29b09d9d10a2a4c6e0a7d01d87cec3d97 Homepage: https://cran.r-project.org/package=ReliaShiny Description: CRAN Package 'ReliaShiny' (A 'Shiny' App for Reliability Analysis) An interactive web application for reliability analysis using the 'shiny' framework. The app provides an easy-to-use interface for performing reliability analysis using 'WeibullR' and 'ReliaGrowR' . 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In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Yuri Balasanov (2017) . 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This package uses 'Monolix' software (), which provide robust statistical method for non-linear mixed effects modeling. 'Monolix' must have been installed prior to use. Package: r-cran-remla Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gparotation, r-cran-geex Suggests: r-cran-knitr, r-cran-lavaan, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-remla_1.2.0-1.ca2404.1_all.deb Size: 95760 MD5sum: 77119cd45423290815c025f9659c951f SHA1: 1f8456c748b716cf6e26c5f4010128d666dc786f SHA256: fec29733c3cca61d268877b5e0022af9d68d23c9892e3ebd1e6828d3ede3d539 SHA512: 3e4f0218cfa204154802520d594d620a58b34959473d314380c8f8e81ddd8ce4219175a9ca3d6cf7d124c1c3a6c6e1439fc23d0623d9b9b0e437798ce9dfd2b2 Homepage: https://cran.r-project.org/package=REMLA Description: CRAN Package 'REMLA' (Robust Expectation-Maximization Estimation for Latent VariableModels) Traditional latent variable models assume that the population is homogeneous, meaning that all individuals in the population are assumed to have the same latent structure. 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Package: r-cran-remm Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stream, r-cran-cluster, r-cran-clustergeneration, r-cran-mass, r-cran-proxy, r-cran-igraph Suggests: r-bioc-graph, r-bioc-rgraphviz, r-cran-testthat Filename: pool/dists/noble/main/r-cran-remm_1.2.1-1.ca2404.1_all.deb Size: 911804 MD5sum: 157000d84cddb50bd0db41e2989a9fd3 SHA1: c4cd1b1bc7d82e7ccb56fa207273fbb39a05b028 SHA256: 604c111c4792e4beee14c6e5ccf99eb3723a8b1e0cd4a88a233d88fa5da537c7 SHA512: 4dd3db25b25f01c2dd5d78301bbc8d5bf6770ff896e5e4d9e2cc1ad942707f51d58e753baeb45c8b2dc4ae0a785ec19a984b5f4e047bcd00e2d503987ff0cf07 Homepage: https://cran.r-project.org/package=rEMM Description: CRAN Package 'rEMM' (Extensible Markov Model for Modelling Temporal RelationshipsBetween Clusters) Implements TRACDS (Temporal Relationships between Clusters for Data Streams), a generalization of Extensible Markov Model (EMM). 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This package provides the 'install_*' functions in 'devtools'. Indeed most of the code was copied over from 'devtools'. Package: r-cran-rempsyc Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3704 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-dplyr Suggests: r-cran-flextable, r-cran-ggplot2, r-cran-effectsize, r-cran-performance, r-cran-insight, r-cran-correlation, r-cran-datawizard, r-cran-report, r-cran-modelbased, r-cran-see, r-cran-lmtest, r-cran-ggrepel, r-cran-boot, r-cran-bootes, r-cran-ggsignif, r-cran-qqplotr, r-cran-broom, r-cran-emmeans, r-cran-ggpubr, r-cran-interactions, r-cran-openxlsx2, r-cran-patchwork, r-cran-psych, r-cran-venndiagram, r-cran-rmisc, r-cran-tidyr, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rempsyc_0.2.0-1.ca2404.1_all.deb Size: 2178314 MD5sum: 810e11ce81f000bc499f588a161d0943 SHA1: 2cff6d61068bf02388ba9392c0dde24bbab0717b SHA256: d194bfd05311ce4366ef63e6e51ff405e545a58f803c358d42c79954611cfa56 SHA512: 348205cfb1e34bd15ec63c4b1e97d275c8edb20004992634b11658849f2f91998e264af20328ca9725e26d475686e4c0bdb74621576d0a79cf6ff2384be9928b Homepage: https://cran.r-project.org/package=rempsyc Description: CRAN Package 'rempsyc' (Convenience Functions for Psychology) Make your workflow faster and easier. Easily customizable plots (via 'ggplot2'), nice APA tables (following the style of the *American Psychological Association*) exportable to Word (via 'flextable'), easily run statistical tests or check assumptions, and automatize various other tasks. 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TNM stage is important in treatment decision-making and outcome predicting. The existing oropharyngeal Cancer (OPC) TNM stages have not made distinction of the two sub sites of Human papillomavirus positive (HPV+) and Human papillomavirus negative (HPV-) diseases. We developed novel criteria to assess performance of the TNM stage grouping schemes based on parametric modeling adjusting on important clinical factors. These criteria evaluate the TNM stage grouping scheme in five different measures: hazard consistency, hazard discrimination, explained variation, likelihood difference, and balance. The methods are described in Xu, W., et al. (2015) . Package: r-cran-ren Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2003 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-glmnet, r-cran-quadprog, r-cran-doparallel, r-cran-matrix, r-cran-tictoc, r-cran-corpcor, r-cran-ggplot2, r-cran-reshape2, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-kernsmooth, r-cran-cluster, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ren_0.1.0-1.ca2404.1_all.deb Size: 1714162 MD5sum: 506006e851c8e227e11074b0a03015c5 SHA1: 270a8f61304a83479f2aef87aee3a7b94a0a4a1f SHA256: 5ad16f8bdfb6f0457c27a078ba6efa0e9427bd00b5f1985499672edb48da45ed SHA512: 1cadf46c9f702a161f02c1a3e941672cb01ed3760caf32a25597e1296bd2df850ae30299eab13621da398899d01cb0b309e5c2bca0533896264d75cb68d29ee5 Homepage: https://cran.r-project.org/package=REN Description: CRAN Package 'REN' (Regularization Ensemble for Robust Portfolio Optimization) Portfolio optimization is achieved through a combination of regularization techniques and ensemble methods that are designed to generate stable out-of-sample return predictions, particularly in the presence of strong correlations among assets. 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Analysis and graphical tools for DC and AC circuits and their use in electric power systems. Analysis and graphical tools for thermodynamic cycles and heat engines, supporting efficiency calculations in coal-fired power plants, gas-fired power plants. Calculations of carbon emissions and atmospheric CO2 dynamics. Analysis of power flow and demand for the grid, as well as power models for microgrids and off-grid systems. Provides resource and power generation for hydro power, wind power, and solar power. Package: r-cran-rentrez Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rentrez_1.2.4-1.ca2404.1_all.deb Size: 121898 MD5sum: 3d39a0f72208c123d5b388b5a1697011 SHA1: 91c3e24c04c2028fd5fbdbaae8764dbe4ab6e5a3 SHA256: 9d61f2efa966e6b6c3b0a5311ce8cac896c04377f71f0b49aa1834fecf433a40 SHA512: 4ce4b9058cc7699095ba287509f608bba78df8aef3b709ccd7c61f6603d00448b4c1cbfa9d5e4863b814b3b9da66e3969e1f7ef64791c9bd49e06e3c489be196 Homepage: https://cran.r-project.org/package=rentrez Description: CRAN Package 'rentrez' ('Entrez' in R) Provides an R interface to the NCBI's 'EUtils' API, allowing users to search databases like 'GenBank' and 'PubMed' , process the results of those searches and pull data into their R sessions. 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Using 'renv', you can create and manage project-local R libraries, save the state of these libraries to a 'lockfile', and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible. Package: r-cran-renvlp Architecture: all Version: 3.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 879 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsolnp, r-cran-orthogonalsplinebasis, r-cran-pls, r-cran-matrixcalc, r-cran-matrix Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-renvlp_3.4.5-1.ca2404.1_all.deb Size: 809614 MD5sum: 71f40c90aed0918409649abd12266a0f SHA1: 2229dbaa74f432966f08af4db79162c059e00ab2 SHA256: c47921bd4c3fffa22f5806c50c0e4fd97bde44f604842a866568c8c272dec54d SHA512: 2f54aa815522ab75c2fe2f8f35825b7433476cd7e7cb0cdced8a56518dccc8afb1107887a1413985541a3bacfda1d5740c3221516205aa61f858a55840411932 Homepage: https://cran.r-project.org/package=Renvlp Description: CRAN Package 'Renvlp' (Computing Envelope Estimators) Provides a general routine, envMU, which allows estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model. This package implements response envelopes, partial response envelopes, envelopes in the predictor space, heteroscedastic envelopes, simultaneous envelopes, scaled response envelopes, scaled envelopes in the predictor space, groupwise envelopes, weighted envelopes, envelopes in logistic regression, envelopes in Poisson regression envelopes in function-on-function linear regression, envelope-based Partial Partial Least Squares, envelopes with non-constant error covariance, envelopes with t-distributed errors, reduced rank envelopes and reduced rank envelopes with non-constant error covariance. For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing. Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) . 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"bash -l") will be used to initialize the current session. The module function can also; load or unload specific software, list all the loaded software within the current session, and list all the applications available for loading from the module system. Lastly, the module function can remove all loaded software from the current session. Package: r-cran-renyiextropy Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-renyiextropy_0.4.0-1.ca2404.1_all.deb Size: 176612 MD5sum: 842729809cf6860c3a6680b852f308fb SHA1: a50673697ff50a430796ee072589224649beb622 SHA256: c26e8996151ee57eab5fc5226f83bc1006e6d0175495c9fb83b3f4996a3e390e SHA512: 63068ebee07fdf9fba7464ce689236877ea5195406765cd56e07b40c5edabbb5e12be8264a23261acf2bcec9cad21b35a9a3ffa072a777845b396a97501c992b Homepage: https://cran.r-project.org/package=RenyiExtropy Description: CRAN Package 'RenyiExtropy' (Entropy and Extropy Measures for Probability Distributions) Provides functions to compute Shannon entropy, Renyi entropy, Tsallis entropy, and related extropy measures for discrete probability distributions. Includes joint and conditional entropy, KL divergence, Jensen-Shannon divergence, cross-entropy, normalized entropy, and Renyi extropy (including the conditional and maximum forms). All measures use the natural logarithm (nats). Useful for information theory, statistics, and machine learning applications. Package: r-cran-renz Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 971 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vgam Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-renz_0.2.1-1.ca2404.1_all.deb Size: 544282 MD5sum: bef3a908ebbc2a6423a4c4e70d535fc8 SHA1: 1f13193cabf970ea1ca6a59dc1c8047501198d6d SHA256: c2852ad8a63b007979156640773a2348610f33bbcf22bb82c41cbe9d18f82635 SHA512: c9c71550bcda87731d0039a874d1e187e697430a9e3454ba152d8ed4bd06a094a026fa8f78607f267324869abe1910b75b7c29ab14a3b7f73305773c7a839eab Homepage: https://cran.r-project.org/package=renz Description: CRAN Package 'renz' (R-Enzymology) Contains utilities for the analysis of Michaelian kinetic data. 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Package: r-cran-repairdata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1267 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-repairdata_0.1.0-1.ca2404.1_all.deb Size: 1207834 MD5sum: f469eba87f62b16ce4e41a40b7a78235 SHA1: 5e2d63d5236194688260e20581cc9552238740df SHA256: 00210ede8b07e99c86fd7ede69c0454ebad46e1cce6e9e6ae9a0ec5850035d8c SHA512: 439b835d0a556f88c63c732611ebd79f645f6be27ffe44ba34fd1b1524e81ebe1c15e33b8484a81665e0243f03ef44eb3931e77b803ed67d872bfc00f10a2eea Homepage: https://cran.r-project.org/package=repairData Description: CRAN Package 'repairData' (Open Repair Alliance Datasets 2021) The complete data set of open repair data, full compliant with the Open Repair Data Standards (ORDS). It combines the datasets contributed by partner organizations of the Open Repair Alliance (ORA). Last updated: 2021-02-22. The package also contains via quests enriched datasets on batteries, printer, mobiles, and tablets. Package: r-cran-repana Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-config, r-cran-dplyr, r-cran-magrittr, r-cran-lubridate, r-cran-rmarkdown, r-cran-processx, r-cran-readr, r-cran-rstudioapi, r-cran-pool, r-cran-openxlsx, r-cran-yaml, r-cran-digest Suggests: r-cran-spelling, r-cran-testthat, r-cran-knitr, r-cran-rsqlite, r-cran-rpostgres, r-cran-duckdb, r-cran-pacman, r-cran-targets Filename: pool/dists/noble/main/r-cran-repana_2.2.1-1.ca2404.1_all.deb Size: 150876 MD5sum: 99f589910b361bdaf14277876b534e09 SHA1: 199ee54f2fe3cf1df4e4daeb0bd0c3d21dd89d70 SHA256: c169478973e96918925d0159eb43ab69df966855cf9a902d741882ac2718789d SHA512: 5d0f24e4773c58201d0766bc1361db391ab7cdbed4cf7ffb9f31844c3a53b91ed656172412746aacf67a12b6f9236f508f7a364b9511b5d884654faa08efc2ae Homepage: https://cran.r-project.org/package=repana Description: CRAN Package 'repana' (Repeatable Analysis in R) Set of utilities to facilitate the reproduction of analysis in R. 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Package: r-cran-repeatabel Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hglm Filename: pool/dists/noble/main/r-cran-repeatabel_2.0-1.ca2404.1_all.deb Size: 85816 MD5sum: 0a376b3606d932ca20e0c3552b3de560 SHA1: d3faddc428a2fd974809bc1eb4854f396542214c SHA256: 86fda51e26f40cc393b86c58d2557410ff4f9a2b48b5833a451060d01738836f SHA512: 317d4f93a552f3ce91c4b5dba52eba6de245773b238ebb78ec93e51f3af526df5e6bc4f0a625a95e037983d5367613cc7d7f681d382edc4f5e25b558a4e2d25f Homepage: https://cran.r-project.org/package=RepeatABEL Description: CRAN Package 'RepeatABEL' (GWAS for Multiple Observations on Related Individuals) Performs genome-wide association studies (GWAS) on individuals that are both related and have repeated measurements. For each Single Nucleotide Polymorphism (SNP), it computes score statistic based p-values for a linear mixed model including random polygenic effects and a random effect for repeated measurements. The computed p-values can be visualized in a Manhattan plot. For more details see Ronnegard et al. (2016) and for more examples see . 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Package: r-cran-repello Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-repello_1.0.1-1.ca2404.1_all.deb Size: 47070 MD5sum: 0537ca592ba2a0e0edb561c88c0fb156 SHA1: cee239fb85bf93dd8b18c4dbf1fb695091ca7194 SHA256: db495a50b110b9cc195d8b1a2490a07d05ab34774b06d5358790cddae35fadd5 SHA512: c91fd5d00571e94b4525720728419d5664cdce1ca15b646d1b820e294ce8fa5428c152a43c91ee99feeb79237c73dfd224abdd6b23b2e7fceffb6218d46104a6 Homepage: https://cran.r-project.org/package=repello Description: CRAN Package 'repello' (Reports from Trello in R) Creates reports from Trello, a collaborative, project organization and list-making application. 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Package: r-cran-repertoir Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circlize, r-cran-igraph, r-cran-reshape2, r-cran-stringdist, r-cran-stringi, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-repertoir_0.0.1-1.ca2404.1_all.deb Size: 51356 MD5sum: b9d51c84bcd4383b2a99f27a64cd84a5 SHA1: 075e5f9dbc4b38d4b86265df5f97b9f1dfa3347a SHA256: 40fd2d0a3519b5e6e9c9175a1a6a58c269301aaf25fc0b050139513de0f01ef6 SHA512: ab0770efcae797335cb1c4b2fbc63c3e1ad78c11f454cae14c80cfe9f79aaf69f0e30278dcb013a03216bd828d0ef8da781584e464c0eb08e69166b6cd0b8555 Homepage: https://cran.r-project.org/package=RepertoiR Description: CRAN Package 'RepertoiR' (Repertoire Graphical Visualization) Visualization platform for T cell receptor repertoire analysis output results. It includes comparison of sequence frequency among samples, network of similar sequences and convergent recombination source between species. Currently repertoire analysis is in early stage of development and requires new approaches for repertoire data examination and assessment as we intend to develop. No publication is available yet (will be available in the near future), Efroni (2021) . Package: r-cran-repfun Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4643 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-data.table, r-cran-glue, r-cran-jsonlite, r-cran-rlang, r-cran-xportr, r-cran-hmisc, r-cran-r2rtf, r-cran-haven, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-rprojroot, r-cran-dt, r-cran-kableextra, r-cran-admiral, r-cran-tibble, r-cran-lubridate, r-cran-ggplot2, r-cran-devtools, r-cran-spelling Filename: pool/dists/noble/main/r-cran-repfun_0.1.2-1.ca2404.1_all.deb Size: 2053960 MD5sum: 0a0ae8ad729d39f540b1eea609ae686b SHA1: 1d27177ff7761521cdc9a19ae92118a24536998d SHA256: 55bad564fd488f389450bae11aed8378ec831391bd695d2855b130f6be2937c0 SHA512: 72729761a8b5498e5ee7dc5fbb5bb0cfc2da2f910a0211ef1ff3b580b3545d0a5132c811565a6e1d77a770c2c3a597bfcb864185794b60fce51ccd9ad708ce04 Homepage: https://cran.r-project.org/package=repfun Description: CRAN Package 'repfun' (Create Tables, Listings and Figures using Functions Styled afterSAS™ Macros) Mimic the style of traditional reporting macros for clinical trials. The purpose is to generate tables, listings and figures that support clinical research. This package is well suited for firms or individuals who wish to incorporate R without changing their ways of working as it follows a traditional clinical research workflow. Invoke functions (instead of macros) to summarize data and produce formatted reports. This package differs from others in that it includes tools (wrappers) for both analyzing and reporting data. Package: r-cran-replacer Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown, r-cran-cardata, r-cran-testthat, r-cran-checkmate, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-replacer_1.0.2-1.ca2404.1_all.deb Size: 78830 MD5sum: a7184a6e93ff074ad55cf13cf95b0e0c SHA1: 9e02c913eedad1105656d5f36c2228b6c9f0ddc5 SHA256: 311acbf847e8bb36488d60ba6c578f7cbfe85072d22bb56a6eedbc74a5621a98 SHA512: f184e6f8c53faedc9f94d7852a75876287fe27ce261fd084f7779af786ef9c3df6f84dc6b9445e84c9da8dc10742fec19dabfe1415317172466d27badae54b46 Homepage: https://cran.r-project.org/package=replacer Description: CRAN Package 'replacer' (A Value Replacement Utility) Updates values within csv format data files using a custom, User-built csv format lookup file. Based on 'data.table' package. Package: r-cran-replesentr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-modules, r-cran-dat, r-cran-knitr Suggests: r-cran-testthat, r-cran-covr, r-cran-txtplot Filename: pool/dists/noble/main/r-cran-replesentr_0.4.1-1.ca2404.1_all.deb Size: 34732 MD5sum: 857eadd7e5abb81b37ee00ddb3164e3d SHA1: 8ee903fd56dc1c8973ec62657d28c92b34aac161 SHA256: 19cc68a56841763d1ee0c678a72bc5929a2e151db5c121331ae92d0b7cd60454 SHA512: c068a04168ff27605aec8ec3969eba938f26a59a2372966e6707fd09510ca399e7924e3194c935656267b9cdfa5d72cda14754dee5dfe0aa239e0bdf4b5e1c02 Homepage: https://cran.r-project.org/package=REPLesentR Description: CRAN Package 'REPLesentR' (Presentations in the REPL) Create presentations and display them inside the R 'REPL' (Read-Eval-Print loop), aka the R console. Presentations can be written in 'RMarkdown' or any other text format. A set of convenient navigation options as well as code evaluation during a presentation is provided. It is great for tech talks with live coding examples and tutorials. While this is not a replacement for standard presentation formats, it's old-school looks might just be what sets it apart. This project has been inspired by the 'REPLesent' project for presentations in the 'Scala' 'REPL'. Package: r-cran-replicate Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metafor, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-replicate_1.2.0-1.ca2404.1_all.deb Size: 37188 MD5sum: 9596b56865d3e80a066e2043085c8d33 SHA1: 477156998169611166068c2e47f39ff8909532b2 SHA256: 53b5b0cdd6efc826865ff3ce8eb8cd157cf016e67a6eea6c895341767ccc39a4 SHA512: 673e47b0bf968019c9db41445267ac94fb0173f355f930e360bc5d69a6c4385a102d5acfc042545bb5f7467a8baaa423f66b187dc656f2c012330abbf7f80a16 Homepage: https://cran.r-project.org/package=Replicate Description: CRAN Package 'Replicate' (Statistical Metrics for Multisite Replication Studies) For a multisite replication project, computes the consistency metric P_orig, which is the probability that the original study would observe an estimated effect size as extreme or more extreme than it actually did, if in fact the original study were statistically consistent with the replications. Other recommended metrics are: (1) the probability of a true effect of scientifically meaningful size in the same direction as the estimate the original study; and (2) the probability of a true effect of meaningful size in the direction opposite the original study's estimate. These two can be computed using the package \code{MetaUtility::prop_stronger}. Additionally computes older metrics used in replication projects (namely expected agreement in "statistical significance" between an original study and replication studies as well as prediction intervals for the replication estimates). See Mathur and VanderWeele (under review; ) for details. 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Implemented are 'Method A' / 'Method B' and the detection of outliers. If the design allows, assessment of the empiric Type I Error and iteratively adjusting alpha to control the consumer risk. Average Bioequivalence - optionally with a tighter (narrow therapeutic index drugs) or wider acceptance range (South Africa: Cmax) - is implemented as well. 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(2016) . Package: r-cran-replicationsuccess Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-replicationsuccess_1.3.3-1.ca2404.1_all.deb Size: 509270 MD5sum: 030ff0af3f849c1d20c0654fc5ca7212 SHA1: 21949e13a4f68a4bbb5d74f5127a38f6b2ce4193 SHA256: 7c33fe776594db1a679ae0e0423e1e882eaab68e7cce5a294cdea0f7bd8b06cc SHA512: bc6e38406947b6ce15ffd8f8e805074cc5b8811e09e69811de7d50f00209b46b07c5e39d4f1facf321ead7d3bf69c62f5a0c95c8af3ba5bdb96bbf4648bb85be Homepage: https://cran.r-project.org/package=ReplicationSuccess Description: CRAN Package 'ReplicationSuccess' (Design and Analysis of Replication Studies) Provides utilities for the design and analysis of replication studies. Features both traditional methods based on statistical significance and more recent methods such as the sceptical p-value; Held L. (2020) , Held et al. (2022) , Micheloud et al. (2023) . Also provides related methods including the harmonic mean chi-squared test; Held, L. (2020) , and intrinsic credibility; Held, L. (2019) . Contains datasets from five large-scale replication projects. Package: r-cran-repliscope Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1423 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-shiny, r-cran-colourpicker Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-repliscope_1.1.1-1.ca2404.1_all.deb Size: 922840 MD5sum: 78a5a823615d36577b7fa562e5aa9583 SHA1: 15bf72bc7d7f84326fed484cbd9f5e0e59b62380 SHA256: 0efebbc17b68edc339febbf7d50a94b3110b11f3de6eea89e6e64fa601949232 SHA512: d2e5a2140978539b7e959b12d75e7282554518697eb1ca71540e55290d82631e53ec79857b4fc75256294b151151bc5e0ec0550634273287481d181a919f9fd8 Homepage: https://cran.r-project.org/package=Repliscope Description: CRAN Package 'Repliscope' (Replication Timing Profiling using DNA Copy Number) Create, Plot and Compare Replication Timing Profiles. The method is described in Muller et al., (2014) . 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Originally designed to create tables, listings, and figures for the pharmaceutical, biotechnology, and medical device industries, these reports are generalized enough that they could be used in any industry. Generates text, rich-text, PDF, HTML, and Microsoft Word file formats. The package specializes in printing wide and long tables with automatic page wrapping and splitting. Reports can be produced with a minimum of function calls, and without relying on other table packages. The package supports titles, footnotes, page header, page footers, spanning headers, page by variables, and automatic page numbering. 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'GRSA' is a threshold-free method that works well with all types of biomedical features, such as genes, chemical compounds, and microbial species. Importantly, the 'GRSA' supports multi-group and longitudinal experimental designs, because of the included multi-group-compatible statistical methods. 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Larabi Marie-Sainte and is included in the package. 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Package: r-cran-represent Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-represent_1.0.1-1.ca2404.1_all.deb Size: 48048 MD5sum: 7e0592892e9b4d0a99e7c15970dde771 SHA1: 0f4806a4f4b0514d617a6cd57ae2b42f4cf20255 SHA256: bf408beeca6c2f798e5991facf76d6b839e8066e83d49fed296671cb4deb4f82 SHA512: d4ac5c36de3f94dbd91ee27b6e6c9738012764b57641b3a86374d89dd7e97fcbb6c56e4e742c1b1a165cc79f0b9da0f76552a4b40ec9cbad4badb0c2b84fde93 Homepage: https://cran.r-project.org/package=represent Description: CRAN Package 'represent' (Determine How Representative Two Multidimensional Data Sets are) Compute the values of various parameters evaluating how similar two multidimensional datasets' structures are in multidimensional space, as described in: Jouan-Rimbaud, D., Massart, D. L., Saby, C. A., Puel, C. (1998), . 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Package: r-cran-reproducible Architecture: all Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1694 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-digest, r-cran-filelock, r-cran-fpcompare, r-cran-fs, r-cran-lobstr Suggests: r-cran-archive, r-cran-covr, r-cran-curl, r-cran-dbi, r-cran-future, r-cran-geodata, r-cran-glue, r-cran-googledrive, r-cran-httr, r-cran-httr2, r-cran-knitr, r-cran-parallelly, r-cran-qs2, r-cran-raster, r-cran-rcurl, r-cran-rlang, r-cran-rmarkdown, r-cran-rsqlite, r-cran-r.utils, r-cran-rvest, r-cran-sf, r-cran-sp, r-cran-terra, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-reproducible_3.1.1-1.ca2404.1_all.deb Size: 1303752 MD5sum: 9971a980f956c0c2b1f156686573de79 SHA1: 109a7a1ab47a4b5d6cbc3d5da20a42c2acdcc0ee SHA256: 961f7bfff94aa2136feca9ff50cd15ae29baa98283d531b157ad03fcfa63a536 SHA512: 3a9339339c05a0bd1b7b24f870d6cdd04c46e251fcaafa05fd80bbcf7e975f8eecee85d4da8dd2574fcb9faf58be3f6e3afd0c7bec9b3ac3b5bde414a4cafe19 Homepage: https://cran.r-project.org/package=reproducible Description: CRAN Package 'reproducible' (Enhance Reproducibility of R Code) A collection of high-level, machine- and OS-independent tools for making reproducible and reusable content in R. The two workhorse functions are 'Cache()' and 'prepInputs()'. 'Cache()' allows for nested caching, is robust to environments and objects with environments (like functions), and deals with some classes of file-backed R objects e.g., from 'terra' and 'raster' packages. Both functions have been developed to be foundational components of data retrieval and processing in continuous workflow situations. In both functions, efforts are made to make the first and subsequent calls of functions have the same result, but faster at subsequent times by way of checksums and digesting. Several features are still under development, including cloud storage of cached objects allowing for sharing between users. Several advanced options are available, see '?reproducibleOptions()'. 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Package: r-cran-reproresearchr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-reproresearchr_0.1.1-1.ca2404.1_all.deb Size: 73108 MD5sum: 18d2e46b8c678f7323f6dbce78198707 SHA1: 27ee1e6ac328f89b7bc370cc298540c8b2fc2884 SHA256: 0bbb272e15531d4ee04b10b8ff8dfae303ce735569600b0998fdf521850eaa9b SHA512: bad59457406a1b2c584cf7a418d13f29a7b0abf557882392b412013e596de029acf94165f3f06724687de5228f67018444b3a984c20ccff29202d82abdeaf414 Homepage: https://cran.r-project.org/package=reproresearchR Description: CRAN Package 'reproresearchR' (Companion Package for 'Reproducible Research Using R') Provides teaching datasets and helper functions to support the open educational resource Martinez (2026) "Reproducible Research Using R" . 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Package: r-cran-reprostat Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-glmnet, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-reprostat_0.1.2-1.ca2404.1_all.deb Size: 127520 MD5sum: e6893824279a1c5cf5677568736e835b SHA1: 67e48532e145df31810c16d57beb44581820f2c8 SHA256: b67785716054809e8ae4c15d0d9fc022b05885b7651fde6fde4ecb344722d166 SHA512: 0da0670675f7ace36aedad9c45c6de91b29ad324901e13125353a91aa1f7b438cd8d064780ea781b79b30bd90315703d96197a21e12d6520a87d83378b0ee9cf Homepage: https://cran.r-project.org/package=ReproStat Description: CRAN Package 'ReproStat' (Reproducibility Diagnostics for Statistical Modeling) Tools for diagnosing the reproducibility of statistical model outputs under data perturbations. Implements bootstrap, subsampling, and noise-based perturbation schemes and computes coefficient stability, p-value stability, selection stability, prediction stability, and a composite reproducibility index on a 0 to 100 scale. Includes cross-validation ranking stability for model comparison and visualization utilities. Optional 'backends' support robust M-estimation ('MASS') and penalized regression ('glmnet'). Bootstrap perturbation follows 'Efron' and 'Tibshirani' (1993, ISBN:9780412042317); selection stability follows 'Meinshausen' and 'Buhlmann' (2010) ; reproducibility framework follows 'Peng' (2011) . Package: r-cran-reps Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-kfas, r-cran-stringr, r-cran-lmtest Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-reps_1.1.1-1.ca2404.1_all.deb Size: 493536 MD5sum: b9cab010c2046e3fac2d1bb8583f3dfb SHA1: a2efbe8af9f3946373a56fc2e83cafbad347b70b SHA256: 9b95ca3963e631f7abd243d4276e5f49cd6f728a702a1b39f1da00a6b0a477eb SHA512: d12ecd03fc2c663c1c7672a85f347802c648be438f049d33dc10fbea1ff1049f520f6360e3c3bdb9472a7d13c4e7945937e6afba4ee0fd070596c69dc48fd587 Homepage: https://cran.r-project.org/package=REPS Description: CRAN Package 'REPS' (Hedonic and Multilateral Index Methods for Real Estate PriceStatistics) Compute price indices using various Hedonic and multilateral methods, including Laspeyres, Paasche, Fisher, and HMTS (Hedonic Multilateral Time series re-estimation with splicing). The central function calculate_hedonic_index() offers a unified interface for running these methods on structured datasets. This package is designed to support index construction workflows across a wide range of domains — including but not limited to real estate — where quality-adjusted price comparisons over time are essential. The development of this package was funded by Eurostat and Statistics Netherlands (CBS), and carried out by Statistics Netherlands. The HMTS method implemented here is described in Ishaak, Ouwehand and Remøy (2024) . For broader methodological context, see Eurostat (2013, ISBN:978-92-79-25984-5, ). Package: r-cran-repsd Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-progress Suggests: r-cran-colordf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-repsd_1.0.1-1.ca2404.1_all.deb Size: 52274 MD5sum: cd42744651f46a11896a5f755133e7f5 SHA1: abaf2cd00bdac150b6026e1f1e70efd57343c2f9 SHA256: 21580bacbe2d431f35913e1fdaff6d7eb79a919a96d945b552c35fed076b23ee SHA512: 35f932c716c73768a0b503af4c04a30fc4bcf2f4fc5f28ca2fad1b398ea91a438b3c9e74cdd9e41477ae8a324931e505b21c783d79505d3478b74c5a3e0f6370 Homepage: https://cran.r-project.org/package=repsd Description: CRAN Package 'repsd' (Root Expected Proportion Squared Difference for Detecting DIF) Root Expected Proportion Squared Difference (REPSD) is a nonparametric differential item functioning (DIF) method that (a) allows practitioners to explore for DIF related to small, fine-grained focal groups of examinees, and (b) compares the focal group directly to the composite group that will be used to develop the reported test score scale. Using your provided response matrix with a column that identifies focal group membership, this package provides the REPSD values, a simulated null distribution of possible REPSD values, and the simulated p-values identifying items possibly displaying DIF without requiring enormous sample sizes. Package: r-cran-reptile Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 932 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-optparse, r-cran-randomforest, r-cran-flux Filename: pool/dists/noble/main/r-cran-reptile_1.0-1.ca2404.1_all.deb Size: 919672 MD5sum: 3b5791a0ccd72f2c2f3bb7a9acce9e57 SHA1: 07b0b49a3c8dce81194bbd4acab56115fcfe5f7a SHA256: 09e2e9e16f9270e4275350eb8c8d067df4ef9346964609ac108e7c4792bc9881 SHA512: 1a4713ef94e03306cfdb3244e736c7d92b3e15920e62c25f54ed9ae34b88042cff6ca07226d591f10db77e82643463cb4baf61187ee3ed0cf49e63bbb518d1d2 Homepage: https://cran.r-project.org/package=REPTILE Description: CRAN Package 'REPTILE' (Regulatory DNA Element Prediction) Predicting regulatory DNA elements based on epigenomic signatures. This package is more of a set of building blocks than a direct solution. REPTILE regulatory prediction pipeline is built on this R package. See for more information. Package: r-cran-reptiledb.data Architecture: all Version: 0.0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 786 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr, r-cran-rvest, r-cran-stringr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-reptiledb.data_0.0.0.2-1.ca2404.1_all.deb Size: 764002 MD5sum: efec5a01f1b4e380c201dc2b2b989cc0 SHA1: d13c0e9662d9a06fb78abd0dd8af650d714c61e7 SHA256: b9c2fecfdbc0026c020fdb9a213d9129591825060b6c8653e91b9a1d13a3ab6b SHA512: d5ecb6aab31b27a929a452a1aa74600c33afad161d2bdcf51aefc1d16502147f53e47079f0ba0e12d46dbd80fb20bf6d46b85435a20ed35e2cb3cc3f56082086 Homepage: https://cran.r-project.org/package=reptiledb.data Description: CRAN Package 'reptiledb.data' (Reptile Database Data) Provides easy access to 'The Reptile Database', a comprehensive catalogue of all living reptile species and their classification. This package includes taxonomic data for over 10,000 reptile species, approximately 2,800 of which are subspecies, covering all extant reptiles. The dataset features taxonomic names, synonyms, distribution data, type specimens, and literature references, making it ready for research and analysis. Data is sourced from 'The Reptile Database' . 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Users can retrieve species-level data, distribution, etymology, synonyms, common names, and other relevant information for reptiles. Designed for taxonomists, ecologists, and biodiversity researchers. 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Package: r-cran-rerandpower Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-runuran Filename: pool/dists/noble/main/r-cran-rerandpower_0.0.1-1.ca2404.1_all.deb Size: 31514 MD5sum: ab04d2f70cadfd15f5893c8e2b39d063 SHA1: 41a0de236b303768104672475327c3ee1b4fc4b9 SHA256: d6219a376beb3ce93639988da8bab56d76c1db56d80e57ada5f0383bbb281668 SHA512: 3ebe29c5137b104a214eb57fac68a531000fa4c9d8ddae65eaf9bf1bb8bcd5176978347e80464f524225bf7e9a925d798e74b5a7faaaf5efa0177367c03efc7f Homepage: https://cran.r-project.org/package=rerandPower Description: CRAN Package 'rerandPower' (Power and Sample Size Calculations for Completely Randomized andRerandomized Experiments) Computes the power resulting from completely randomized and rerandomized experiments with two groups. 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Input information about the distribution, the given 'life' value, the percentile, and the type of residual life, and the function will return your desired values. For the 'flexsurv' option, the function allows the user to input their own data for making predictions. This function is based on Jackson (2016) . Package: r-cran-reslik Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-reslik_0.1.2-1.ca2404.1_all.deb Size: 53922 MD5sum: 806304e0b9b139ba14e8eb67cd196409 SHA1: 75a4084786afc5720cf3701d67e28f52f56d193c SHA256: 0851ce107df106229bebb8b92c6dde965d8c80e5f6402c4daac2288e69416055 SHA512: 148513f226abb576318dbc119b55067a1ccbb914031b277ec9c9aab0770cc8caf457bf74baf18bcb06613ee6e2580fb94fccd48216ee1b4c90652fe0d494485e Homepage: https://cran.r-project.org/package=resLIK Description: CRAN Package 'resLIK' (Representation-Level Control Surfaces for Reliability Sensing) Implements the Representation-Level Control Surfaces (RLCS) paradigm for ensuring the reliability of autonomous systems and AI models. It provides three deterministic sensors: Residual Likelihood (ResLik) for population-level anomaly detection, Temporal Consistency Sensor (TCS) for drift and shock detection, and Agreement Sensor for multi-modal redundancy checks. These sensors feed into a standardized control surface that issues 'PROCEED', 'DEFER', or 'ABSTAIN' signals based on strict safety invariants, allowing systems to detect and react to out-of-distribution states, sensor failures, and environmental shifts before they propagate to decision-making layers. Package: r-cran-reslr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4090 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-fastdummies, r-cran-fields, r-cran-geosphere, r-cran-ggplot2, r-cran-magrittr, r-cran-ncdf4, r-cran-plyr, r-cran-posterior, r-cran-purrr, r-cran-r2jags, r-cran-stringr, r-cran-tidybayes, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-reslr_0.1.1-1.ca2404.1_all.deb Size: 2647012 MD5sum: c2576339bd84d763a88f8a9703f02fa3 SHA1: 7144da5d4f2f01861884f2db107cf3bf85654b62 SHA256: 628bec7b0511696a20bc9207f6e0fdbf1d2de1042980b0fea410279627079725 SHA512: 725c3a4c196de1cb6b0c33afdfe7c997428fd2345aae71177c20226bd2ccf9bb5fcdab01b31bef3fe49faf6eecb136af94f10ab278deb5ee0d5c39cfe3c4c384 Homepage: https://cran.r-project.org/package=reslr Description: CRAN Package 'reslr' (Modelling Relative Sea Level Data) The Bayesian modelling of relative sea-level data using a comprehensive approach that incorporates various statistical models within a unifying framework. Details regarding each statistical models; linear regression (Ashe et al 2019) , change point models (Cahill et al 2015) , integrated Gaussian process models (Cahill et al 2015) , temporal splines (Upton et al 2023) , spatio-temporal splines (Upton et al 2023) and generalised additive models (Upton et al 2023) . This package facilitates data loading, model fitting and result summarisation. Notably, it accommodates the inherent measurement errors found in relative sea-level data across multiple dimensions, allowing for their inclusion in the statistical models. Package: r-cran-resmush Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-httr2 Suggests: r-cran-knitr, r-cran-png, r-cran-quarto, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-resmush_1.0.0-1.ca2404.1_all.deb Size: 796794 MD5sum: 643724a9f7fcab39b54fd7f9feacf1da SHA1: f9822665eb244a2dff749ee081ddd8a8c2f6cdc8 SHA256: a5af0e8973f70c2a593817d872d73a4f0cdcabb5c045113fa7ebbf9bab1109e3 SHA512: 6ab4177101b0982e5e42c5c8369bb2b90be0135c48e57ecb97a49207a2501399dad11d19123465b1cbb72eaf75a154bfc1b830a264ff5c32f060f115a60d5f87 Homepage: https://cran.r-project.org/package=resmush Description: CRAN Package 'resmush' (Optimize and Compress Image Files with 'reSmush.it') Compress local and online images using the 'reSmush.it' API service . Package: r-cran-resourcecodedata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7523 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-resourcecodedata_1.0.0-1.ca2404.1_all.deb Size: 7664680 MD5sum: b95b1ea0328bc60b8254acea42373144 SHA1: 969bf506b75430a2fc0760a95cc0cef05028be9d SHA256: 18b005e2860c9578fe7c51caf153f40c7f2e314a584e4743a21fe1971d38a626 SHA512: 47622cadc67649b79dfa7e5391a0fc7e4b8fdd105053b82122271fa7cd357321127521721884d064817b859911fd8db4cfc498b8599ce7ebb7642b2e44298db2 Homepage: https://cran.r-project.org/package=resourcecodedata Description: CRAN Package 'resourcecodedata' (Resourcecode Database Configuration Data) Includes Resourcecode hindcast database (see ) configuration data: nodes locations for both the sea-state parameters and the spectra data; examples of time series of 1D and 2D surface elevation variance spectral density. Package: r-cran-resourcer Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1185 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-haven, r-cran-readr, r-cran-readxl, r-cran-ssh, r-cran-sys, r-cran-mongolite, r-cran-dplyr, r-cran-dbplyr, r-cran-dbi, r-cran-rmariadb, r-cran-rpostgres, r-cran-sparklyr, r-cran-rpresto, r-cran-nodbi, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-resourcer_1.5.1-1.ca2404.1_all.deb Size: 828108 MD5sum: 3a5045c947063a945aa86eec64ee738e SHA1: 9176d03f93426256dbba4d1a03b85fff9f09f78a SHA256: b6da3b80be8958bef4e521674c8d453f1d9495b27492abe08b08c71c6b740202 SHA512: 8af1fc71b387344686f0729e6d035889c59aab06b23239081b636c75cb82310a05a307e1bbc20e69ff8b2d9e884e3d0b515af56541791190932296bc57669385 Homepage: https://cran.r-project.org/package=resourcer Description: CRAN Package 'resourcer' (Resource Resolver) A resource represents some data or a computation unit. It is described by a URL and credentials. This package proposes a Resource model with "resolver" and "client" classes to facilitate the access and the usage of the resources. Package: r-cran-resourceselection Architecture: all Version: 0.3-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 532 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-pbapply, r-cran-matrix Filename: pool/dists/noble/main/r-cran-resourceselection_0.3-6-1.ca2404.1_all.deb Size: 505652 MD5sum: ccd630a39be361460a9878d4443ceac8 SHA1: 3530bedbf54f7122ac81fc9cfc4337d3f0c9758d SHA256: 0b9b9f848048192b0bcc4d9283aec427cb7f72073762166c7a0fe91052f6e0f7 SHA512: d4b94b485b041282b3fbb7d22974ef16dcf85209d19378fdebbfbe43202d59ca8003a5acd4e0f7fa5ddbe2ec4d54027aa27f7a2c2f53d03e1b19875f7d611597 Homepage: https://cran.r-project.org/package=ResourceSelection Description: CRAN Package 'ResourceSelection' (Resource Selection (Probability) Functions for Use-AvailabilityData) Resource Selection (Probability) Functions for use-availability wildlife data based on weighted distributions as described in Lele and Keim (2006) , Lele (2009) , and Solymos & Lele (2016) . Package: r-cran-respbibd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-respbibd_0.1.0-1.ca2404.1_all.deb Size: 31416 MD5sum: f4fad8005f5bd80eec191d239ed76fb1 SHA1: 9b7ec1bb6d83017a77c4fab21dbf9a6963a7432f SHA256: a1ea9866a6ff1e31bc2e5d929fe0d6f337c7481ca5a7b3f20ee8b16d14879365 SHA512: 24cef773733bfa255c64f676d5be5111210daceab07dfb056239ca2beb8d3d4d8035a3f35561f3b082236806a6ce15b260fb48352a7cfee9da158c306bd11cb7 Homepage: https://cran.r-project.org/package=ResPBIBD Description: CRAN Package 'ResPBIBD' ("Resolvable Partially Balanced Incomplete Block Designs(PBIBDs)") A collection of several utility functions related to resolvable and affine resolvable Partially Balanced Incomplete Block Designs (PBIBDs), have been developed. In the class of resolvable designs, affine resolvable designs are said to be optimal, Bailey (1995) . Here, the package contains three functions to generate and study the characterization properties of these designs. Developed functions are named as PBIBD1(), PBIBD2() and PBIBD3(), in which first two functions are used to generate two new series of affine resolvable PBIBDs and last one is used to generate a new series of resolvable PBIBDs, respectively. In addition, these functions can also be used to generate design parameters (v, b, r and k), canonical efficiency factors, variance factor between associates and average variance factors of the generated designs. Here v is the number of treatments, b (= b1 + b2, in case of non-proper design) is the number of blocks, r is the number of replications and k (= k1 + k2; k1 is the size of b1 and k2 is the size of b2) is the block size. Package: r-cran-respirometry Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-birk, r-cran-dplyr, r-cran-lubridate, r-cran-marelac, r-cran-measurements, r-cran-minpack.lm, r-cran-plyr, r-cran-rlang, r-cran-seacarb, r-cran-segmented Filename: pool/dists/noble/main/r-cran-respirometry_2.0.2-1.ca2404.1_all.deb Size: 534138 MD5sum: bdcd656e0313f3b550711c06fe615e1a SHA1: 6506e9b1d24540005eba3652b2ccaf320e225c6b SHA256: 476242c50aa09da987e506fa24954b28b5326c53a4749cbe5596ba2721f6adf7 SHA512: 5a005788781527426dc7345588ca24c7ef3f7786fad0d27343a8bfcd00edd9f7ad17b7bb228ac21f096531c82039214b2ce9df75eb91e6b80e3638397d63f8b8 Homepage: https://cran.r-project.org/package=respirometry Description: CRAN Package 'respirometry' (Tools for Conducting and Analyzing Respirometry Experiments) Provides tools to enable the researcher to more precisely conduct respirometry experiments. Strong emphasis is on aquatic respirometry. Tools focus on helping the researcher setup and conduct experiments. Functions for analysis of resulting respirometry data are also provided. This package provides tools for intermittent, flow-through, and closed respirometry techniques. Package: r-cran-responsepatterns Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-magrittr Filename: pool/dists/noble/main/r-cran-responsepatterns_0.1.1-1.ca2404.1_all.deb Size: 111602 MD5sum: ebaa350fb12b64acc4f94957ad35d1b5 SHA1: 27e733833a9738c5d9648720720f5f6dee487f0a SHA256: 70b258162fff83780114ca1deb59e590386779e4520d32530708ee217328a26a SHA512: 6d5ed47b51d9863ab8a1a4943949140140fb97e64dc991ac1620e6c7db9865572d1e74b424f41e584f3c3f293b80592d41d367832c76542bc9cd3bd6768492af Homepage: https://cran.r-project.org/package=responsePatterns Description: CRAN Package 'responsePatterns' (Screening for Careless Responding Patterns) Some survey participants tend to respond carelessly which complicates data analysis. This package provides functions that make it easier to explore responses and identify those that may be problematic. See Gottfried et al. (2022) for more information. Package: r-cran-respr Architecture: all Version: 2.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2409 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-magrittr, r-cran-marelac, r-cran-purrr, r-cran-roll, r-cran-segmented, r-cran-stringr, r-cran-xml2 Suggests: r-cran-respirometry, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-respr_2.3.4-1.ca2404.1_all.deb Size: 2115664 MD5sum: 8d1d27e60affd782a4f823c2f70c833b SHA1: 9886cbae7959c730312b3f9b9c389d1518780e71 SHA256: 45001f4962652b6ed7b24b48d574301ee15af58f78b1db7c2ae7b900f2737ead SHA512: c79f88e49c2b2417e91599a65bc68fbaa4a248ee26423e7dba7ad32940b97a627700a34baa695562bbfd967ab920c98bfaf18cf05a4affa7c87eecfa902c51a1 Homepage: https://cran.r-project.org/package=respR Description: CRAN Package 'respR' (Import, Process, Analyse, and Calculate Rates from RespirometryData) Provides a structural, reproducible workflow for the processing and analysis of respirometry data. It contains analytical functions and utilities for working with oxygen time-series to determine respiration or oxygen production rates, and to make it easier to report and share analyses. See Harianto et al. 2019 . Package: r-cran-resquin Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-purrr, r-cran-rlang, r-cran-slider, r-cran-stringi, r-cran-tibble, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-resquin_0.1.1-1.ca2404.1_all.deb Size: 138926 MD5sum: 487961ab102a221b1cb2ae0ba0f2c299 SHA1: 0ae754461c7beb7fbaf3795b28134c9265f7e629 SHA256: c7d3bc13f30059635f424c95182b028509373eecbb430484c9b93156c061535d SHA512: 3470613009b92b09a3cd7e96342313664d0ec692ea6cd23c3f647a49e45d0bd9d99f1d94b50283b88ec31c4e59a6e9dcfbef553461c52a79218942927d02d910 Homepage: https://cran.r-project.org/package=resquin Description: CRAN Package 'resquin' (Response Quality Indicators for Survey Research) Calculate common survey data quality indicators for multi-item scales and matrix questions. Currently supports the calculation of response style indicators and response distribution indicators. For an overview on response quality indicators see Bhaktha N, Henning S, Clemens L (2024). 'Characterizing response quality in surveys with multi-item scales: A unified framework' . Package: r-cran-ress Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ress_1.3-1.ca2404.1_all.deb Size: 40320 MD5sum: f3deb0bef4c06caffdd5c2b22ace9106 SHA1: 3e1bb1580fec6ef82ce2bc24e3360766ebd4763f SHA256: 1e3ba771fe2f72498456e7758a7075f30072aa53ed383f6c6f1ba11d88b28ef6 SHA512: 1cf6622bbc09678b606083cf5e2d508ba901574ff0f1fb3fd0d49856d4ecdc7150aba2f6ae646c171d85b159652d138267b43274f3c856119c562b555f539724 Homepage: https://cran.r-project.org/package=RESS Description: CRAN Package 'RESS' (Integrates R and Essentia) Contains three functions that query AuriQ Systems' Essentia Database and return the results in R. 'essQuery' takes a single Essentia command and captures the output in R, where you can save the output to a dataframe or stream it directly into additional analysis. 'read.essentia' takes an Essentia script and captures the output csv data into R, where you can save the output to a dataframe or stream it directly into additional analysis. 'capture.essentia' takes a file containing any number of Essentia commands and captures the output of the specified statements into R dataframes. Essentia can be downloaded for free at http://www.auriq.com/documentation/source/install/index.html. Package: r-cran-restatapi Architecture: all Version: 0.25.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-rjson, r-cran-xml2 Suggests: r-cran-chron, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-remotes Filename: pool/dists/noble/main/r-cran-restatapi_0.25.0-1.ca2404.1_all.deb Size: 233782 MD5sum: 23169da3b98f2f58040a57a3dc95d687 SHA1: 3c4d66ca6666b56a37fe6b92ef4676eec7905ebd SHA256: e4617cac9ef3f7233cd151ef7a7c4c387e1025fdd126e460e1f31eed0ccadbb7 SHA512: 64f64621a0844b15e7643fee082e847efd33e6e2ba36126a69a6a55aa9b4d378fa3ac622d6d2946a93c91b3f54a99dd5e7024f0e517428741ab42f3bd9e51c0c Homepage: https://cran.r-project.org/package=restatapi Description: CRAN Package 'restatapi' (Search and Retrieve Data from Eurostat Database) Eurostat is the statistical office of the European Union and provides high quality statistics for Europe. Large set of the data is disseminated through the Eurostat database (). The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX) Web Services () to search and download data from the Eurostat database using the SDMX standard. Package: r-cran-restatis Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-askpass, r-cran-httr2, r-cran-memoise, r-cran-readr, r-cran-tibble, r-cran-vctrs, r-cran-purrr Suggests: r-cran-httptest2, r-cran-quarto, r-cran-rmarkdown, r-cran-testthat, r-cran-rvest, r-cran-usethis, r-cran-withr Filename: pool/dists/noble/main/r-cran-restatis_0.4.0-1.ca2404.1_all.deb Size: 530252 MD5sum: 1ee5688baa7f79b1b62ff0db8710bead SHA1: c09c314c99fa7e46c9640be7ac239d33cbefe762 SHA256: 53ad3d03292c8830119782891c6ee0fabb6213f8edc69d77d8ee3ac035566708 SHA512: a99f9b680f8a08d64c64b19c5bfe068635044035cae89a4cf2afb445e73134c9c305a476a0203812b8077dd6f58580cbeb14e9cb31282db83be01df99d5253cb Homepage: https://cran.r-project.org/package=restatis Description: CRAN Package 'restatis' (R Wrapper to Access a Wide Range of Germany's FederalStatistical System Databases Based on the GENESIS Web ServiceRESTful API of the German Federal Statistical Office(Statistisches Bundesamt/Destatis)) A RESTful API wrapper for accessing the main databases of Germany's Federal Statistical System. Supports data search functions, credential management, result caching, and handling remote background jobs for large datasets. Package: r-cran-restaurant Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-restaurant_0.1.0-1.ca2404.1_all.deb Size: 35724 MD5sum: 658191e0d9d543898b337ebea1b8faa1 SHA1: 2111ab18492b7405bfc991d78f48c863097e668d SHA256: e77d8a3c23cad355b2638f119b77a0cae115d39c0a183aa399ba91974cdc59e0 SHA512: 21445b5375852c60231aad4482cb8cf3b7121068bf43ed2ef7810af750c6726f8f21f7de145e14dd65e10123ddfd52850b9e1d0c908c5d9ad20ad505e5aae63f Homepage: https://cran.r-project.org/package=restaurant Description: CRAN Package 'restaurant' (Restaurant Data for Entity Resolution) Duplicated restaurant data (pre-processed and formatted) for entity resolution. This package contains formatted data from a data set that contains information about different restaurants, with the Zagats portion containing 331 records and the Fodors portion containing 533 records. The following variables are included in the data set: id, name, address, city, phone, type. The data set has a respective gold data set that provides information on which records match based on id. Package: r-cran-restez Architecture: all Version: 2.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rentrez, r-cran-dbi, r-cran-curl, r-cran-cli, r-cran-crayon, r-cran-stringi, r-cran-duckdb, r-cran-fs, r-cran-assertthat, r-cran-ape Suggests: r-cran-sessioninfo, r-cran-testthat, r-cran-knitr, r-cran-r.utils, r-cran-rmarkdown, r-cran-mockery Filename: pool/dists/noble/main/r-cran-restez_2.1.5-1.ca2404.1_all.deb Size: 425690 MD5sum: a8dc13085173ae40a8b64c571095f100 SHA1: fb98bf054d629a26805609d896b8dda89e13deeb SHA256: 096416d591986fe01484d15296ed5297d248a188c1fd109a8a5d9649ff7a46bf SHA512: 48a3c88eca6245b54ea43a5fe70935e2ab99ce5d7d5dd77b1adbab4725e466ca7b12bce43fc6037dd99b33286fb2c1e42a0fcd7d4e68c62139aceca9c108ebb5 Homepage: https://cran.r-project.org/package=restez Description: CRAN Package 'restez' (Create and Query a Local Copy of 'GenBank' in R) Download large sections of 'GenBank' and generate a local SQL-based database. A user can then query this database using 'restez' functions or through 'rentrez' wrappers. Package: r-cran-restimizeapi Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl, r-cran-rjsonio Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-restimizeapi_1.0.0-1.ca2404.1_all.deb Size: 40800 MD5sum: e33472b80b3de66698aa2c4ee5cef26c SHA1: 76c8c94e1dcc70d2b56c1c593d7988df16360632 SHA256: bf725ac375df04209a54efd994d37ccdc6035532bd8f568c42646db0d6a02ae5 SHA512: 774a80bc066acfb3c1da4fa566ebe0edb74b565ab0665b9b76da11fae5a403ad81d8cb7e727d2a53c40b2a451185cf98f14ed266f2ca15a390da0c8190722caf Homepage: https://cran.r-project.org/package=restimizeapi Description: CRAN Package 'restimizeapi' (Functions for Working with the 'www.estimize.com' Web Services) Provides the user with functions to develop their trading strategy, uncover actionable trading ideas, and monitor consensus shifts with crowdsourced earnings and economic estimate data directly from . Further information regarding the web services this package invokes can be found at . Package: r-cran-restk Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-restk_1.0.2-1.ca2404.1_all.deb Size: 33518 MD5sum: 2de9a233e4602384ef03e69f150b596b SHA1: b2be3c6184baa33b9562480905a24b5aa92ea9b1 SHA256: e4df5f79ff9de5faad6f308f4376fc311e3d6725a377988925b6de600e0d4fc9 SHA512: 7ba612d5799578e2974660cdbc9441c0aa10903c4bbf01a13489de8d98f61e88b9f2a5a7ddde85933311e9b4b2c8b2c02d8eebabe0218dc31120783a1f392f0f Homepage: https://cran.r-project.org/package=RESTK Description: CRAN Package 'RESTK' (An Implementation of the RESTK Algorithm) Implementation of the RESTK algorithm based on Markov's Inequality from Vilardell, Sergi, Serra, Isabel, Mezzetti, Enrico, Abella, Jaume, Cazorla, Francisco J. and Del Castillo, J. (2022). "Using Markov's Inequality with Power-Of-k Function for Probabilistic WCET Estimation". In 34th Euromicro Conference on Real-Time Systems (ECRTS 2022). Leibniz International Proceedings in Informatics (LIPIcs) 231 20:1-20:24. . This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773). Package: r-cran-restoptr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4998 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-rjava, r-cran-units, r-cran-assertthat, r-cran-magrittr, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr, r-cran-roxygen2, r-cran-rmarkdown, r-cran-landscapemetrics, r-cran-vegan, r-cran-cluster, r-cran-ggthemes, r-cran-paletteer Filename: pool/dists/noble/main/r-cran-restoptr_1.1.1-1.ca2404.1_all.deb Size: 3911124 MD5sum: 4518418fbd33afa2738150d762703e51 SHA1: 40111dd3e594f4f71a1447951e6454b614681c99 SHA256: a82ab90a8f5a8eca96369d7372e8b0bcd9cfc4481f77009b3b7a9dfc34c0711f SHA512: 1734129de5f34716406c69bb195f5885ec753093a56b21d68e91526511711f6584f9894a3a1de6639c4b332980544e3f9968c96267b045150b3efb63f747147d Homepage: https://cran.r-project.org/package=restoptr Description: CRAN Package 'restoptr' (Ecological Restoration Planning) Flexible framework for ecological restoration planning. It aims to identify priority areas for restoration efforts using optimization algorithms (based on Justeau-Allaire et al. 2021 ). Priority areas can be identified by maximizing landscape indices, such as the effective mesh size (Jaeger 2000 ), or the integral index of connectivity (Pascual-Hortal & Saura 2006 ). Additionally, constraints can be used to ensure that priority areas exhibit particular characteristics (e.g., ensure that particular places are not selected for restoration, ensure that priority areas form a single contiguous network). Furthermore, multiple near-optimal solutions can be generated to explore multiple options in restoration planning. The package leverages the 'Choco-solver' software to perform optimization using constraint programming (CP) techniques (). Package: r-cran-restorenet Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 590 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-xtable, r-cran-scales, r-cran-stringr, r-cran-ggplot2, r-cran-scatterpie, r-cran-rcolorbrewer Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-restorenet_1.0.1-1.ca2404.1_all.deb Size: 540674 MD5sum: 165381a0d7755f66b205ea4b35ac3262 SHA1: 30d087b39ca99f45a1b79008b2d12bb2689209a9 SHA256: c560ae6001ce84ad0b0d56576b6a978a049a70fbed07f115e407a0ed03d992d4 SHA512: 82a1eb9f9611761b7f124a491e3c53edb486b9f595dfdd1b2136804211ede838f331b9b38bd726fd22d9a3da8b3bcd7e59df9019839e4a53249e409ac3973d1d Homepage: https://cran.r-project.org/package=RestoreNet Description: CRAN Package 'RestoreNet' (Random-Effects Stochastic Reaction Networks) A random-effects stochastic model that allows quick detection of clonal dominance events from clonal tracking data collected in gene therapy studies. Starting from the Ito-type equation describing the dynamics of cells duplication, death and differentiation at clonal level, we first considered its local linear approximation as the base model. The parameters of the base model, which are inferred using a maximum likelihood approach, are assumed to be shared across the clones. Although this assumption makes inference easier, in some cases it can be too restrictive and does not take into account possible scenarios of clonal dominance. Therefore we extended the base model by introducing random effects for the clones. In this extended formulation the dynamic parameters are estimated using a tailor-made expectation maximization algorithm. Further details on the methods can be found in L. Del Core et al., (2022) . 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'ResultModelManager' provides utility functions to allow package maintainers to migrate existing SQL database models, export and import results in consistent patterns. Package: r-cran-resumer Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-useful, r-cran-dplyr, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-resumer_0.0.5-1.ca2404.1_all.deb Size: 38980 MD5sum: eb21b7897bf1260d14067f49cb656d64 SHA1: 05641e3efe89c6679c79bcda638488da41a426cb SHA256: 24609edc6b01d6c77df0ef4b01b509b8d40a1cf4c13364baadced355b565286b SHA512: 9607350469fe0a02dc07454c1b8f1efb83a43b49419cb922e35dc83a4c58bc33a84ac393e655fad412b19991c83f3f5f37616b2b6110c8a0bd2dbac9b2760ca2 Homepage: https://cran.r-project.org/package=resumer Description: CRAN Package 'resumer' (Build Resumes with R) Using a CSV, LaTeX and R to easily build attractive resumes. 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También permite obtener tablas de frecuencia clásicas y gráficos cuando se desea realizar un análisis de series agrupadas. Su objetivo es de aplicación didáctica para un curso introductorio de Bioestadística utilizando el software R, para las carreras de grado las carreras de grado y otras ofertas educativas de la Facultad de Ciencias Agrarias de la UNJu / It generates summary measures and graphs for discrete or continuous numerical data in simple series. It also enables the creation of classic frequency tables and graphs when analyzing grouped series. Its purpose is for educational application in an introductory Biostatistics course using the R software, aimed at undergraduate programs and other educational offerings of the Faculty of Agricultural Sciences at the National University of Jujuy (UNJu). 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(2023) . Implementation of Neural Networks, Extreme Gradient Boosting, and Cox model with splines to optimise the partial log-likelihood of proportional hazard models. 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This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552. 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The challenge therefore is to infer what the original data could have been given summarized information. We present an R package that reverse engineers decoupled and/or censored count data with two main functions. The cnbinom.pars function estimates the average and dispersion parameter of a censored univariate frequency table. The rec function reverse engineers summarized data into an uncensored bivariate table of probabilities. 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Package: r-cran-revsd Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-revsd_0.1.1-1.ca2404.1_all.deb Size: 25322 MD5sum: 0b94fd4a8a25a54bb27eb8f4b0d27d0d SHA1: cc0357cdd8a2fc9db20f1fac97ea7ff724f22855 SHA256: 56e7b58fc9d1392ea972e266471ebcfee54b19bc9a58d030ffb8a50a07bd27c3 SHA512: bdf2f577daa83eeee0b68f10346fc440a83669d592748dcfa7bf9281515df4084a6c1ed1ba81ed0b1e9802dd0714e100153db817056efe29d3bb137659a78ec5 Homepage: https://cran.r-project.org/package=RevSD Description: CRAN Package 'RevSD' (Visualizing the Standard Deviation via Revolution) We visualize the standard deviation of a data set as the radius of a cylinder whose volume equals the total volume of several cylinders made by revolving the empirical cumulative distribution function about the vertical line through the mean. For more details see Sarkar and Rashid (2016) . 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Package: r-cran-revulyticsr Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-jsonlite, r-cran-purrr, r-cran-httr, r-cran-tidyselect, r-cran-tidyr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-revulyticsr_0.0.3-1.ca2404.1_all.deb Size: 62530 MD5sum: b6c76b1b1e9655884fe084d00c915ec4 SHA1: 42579b194549b6a6267c8449e4270ce015747575 SHA256: c640a0f11c16f50edba7ba4d47a1054848b08526298d6a23e90922d3db131baf SHA512: 0201cba5a75cc58db438e4dbf9d9585655e567ffa092fd7c26bd753d6fde2da66b4d90d8fcf7630ef71b8123f5a1d383824195d838a481c8f8317fe5bcd67705 Homepage: https://cran.r-project.org/package=revulyticsR Description: CRAN Package 'revulyticsR' (Connect to Your 'Revulytics' Data) Facilitates making a connection to the 'Revulytics' API and executing various queries. 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Hausman, Jeffrey (1978) . Allison, Paul (2009) . Neuhaus, J.M., and J. D. Kalbfleisch (1998) . 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Exercises can be defined in a table, based on text and figures, and may contain gaps to be filled with provided options. Exam documents can be generated in various formats. It allows us to generate a version for conducting the assessment and another version that facilitates correction, linked through a code. Package: r-cran-rexoplanets Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-checkmate, r-cran-purrr, r-cran-httr2, r-cran-readr, r-cran-ggplot2, r-cran-jsonlite, r-cran-logger Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-lintr, r-cran-devtools, r-cran-spelling, r-cran-shiny, r-cran-shinyjs, r-cran-bslib, r-cran-shinytest2, r-cran-bsicons, r-cran-htmltools Filename: pool/dists/noble/main/r-cran-rexoplanets_0.1.2-1.ca2404.1_all.deb Size: 191670 MD5sum: f1584aa0af38447a11894482dee1a00e SHA1: 5c74238368fe61909125d5606041d0a3925ad877 SHA256: 31a1f34f9a70ceea2e1059207e2d7c298379ac3ab52d25554bc866dee8a0fa86 SHA512: 287e56739a3c4269740561679f0744046a5f873b60098145d433cfc7caebc5c0047a0aa6fb73633bf23ee190d9173c0911b6930245a0d14ac66c592318e4d0e6 Homepage: https://cran.r-project.org/package=REXoplanets Description: CRAN Package 'REXoplanets' (Creates Interface with NASA 'Exoplanets Archive API') Provides a user-friendly interface to NASA 'Exoplanets Archive API' , enabling retrieval and analysis of exoplanetary and stellar data. 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Package: r-cran-rextendr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 996 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brio, r-cran-cli, r-cran-desc, r-cran-dplyr, r-cran-glue, r-cran-jsonlite, r-cran-lifecycle, r-cran-pkgbuild, r-cran-processx, r-cran-rlang, r-cran-rprojroot, r-cran-stringi, r-cran-vctrs, r-cran-withr Suggests: r-cran-devtools, r-cran-knitr, r-cran-lintr, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-rextendr_0.5.0-1.ca2404.1_all.deb Size: 472602 MD5sum: 45c59decb3d2e951f69d5aa12cdd6c5a SHA1: 7cc4bd4dee874ad80c5bcacf479cfd9e01c35701 SHA256: d0d1d7493ad6a68c08edcd2366b1ed608c6cc6138927b910355129fb7eb6c2fa SHA512: efbdb2e0b687b52eca5b604d0f5c9f50912dad4533be0e145d39ea10092184132924749954bd2df91279de90ba2bc27679e4bc7c2888f64dbe6ab7472f0c1ab7 Homepage: https://cran.r-project.org/package=rextendr Description: CRAN Package 'rextendr' (Build 'Rust' Powered 'R' Packages) Provides a framework for creating high-performance 'R' packages powered by the 'Rust' programming language using the 'extendr' Rust crate. 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Package: r-cran-rextor Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rlang, r-cran-scales, r-cran-tibble Filename: pool/dists/noble/main/r-cran-rextor_1.1.0-1.ca2404.1_all.deb Size: 176858 MD5sum: 8b5b6df21c1b35017f33683fcff8faba SHA1: 2e1aab2f887a25619e3a5e6f76691b24fdbc055a SHA256: 95b7d3aa90b804962374bbb9ac9e5bac0277224d99eea03cd6cbec1adeabcfdb SHA512: bde9c4a7d214ad278bd554c419770a9890e40a6b50b245b1212c05fa2c2f8deea4c0f763399618c521ceb08623b5380949ae4ea2a94cb84c95d086f452b9d65e Homepage: https://cran.r-project.org/package=rextor Description: CRAN Package 'rextor' (Prepare 'WEXTOR' Data) Facilitate data preparation for data collected on 'WEXTOR' , created by Reips and Neuhaus (2002) . Perform plausibility and other checks and make use of cool color palettes and themes for data visualization. Package: r-cran-rfacebook Architecture: all Version: 0.6.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-rjson, r-cran-httpuv Filename: pool/dists/noble/main/r-cran-rfacebook_0.6.15-1.ca2404.1_all.deb Size: 148640 MD5sum: b48eeec64f0959374d8f0d9182e2173f SHA1: 77b088b69e2e74fe94ce6aa3c367bf24ded09ada SHA256: dcbb6be420bba3018147d41d89b43b0dd1a6c64a9d20d80378ab27810db7b227 SHA512: d0dd8ed0662cf508daa7ecd00347385006b9e4a3f2a9a4a0c0e1c355267395ab684ec2c794f077eb89e3520aab90f741f1964933219b6f0f9d713cfe835b6ebc Homepage: https://cran.r-project.org/package=Rfacebook Description: CRAN Package 'Rfacebook' (Access to Facebook API via R) Provides an interface to the Facebook API. Package: r-cran-rfacebookstat Architecture: all Version: 2.14.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 741 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-tidyselect, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rfacebookstat_2.14.0-1.ca2404.1_all.deb Size: 457846 MD5sum: 5f07ad0a4a4311cb3b7e4b414cb317a6 SHA1: 56b102fb7e19412bbd09d8b391f43a6b39687203 SHA256: 7c6d50e193bdc8bf5cabf2a4afff344481c70499b7839df185c20fe6be939ec8 SHA512: d916d0b3d76e0bc71f7beeae76c5e4825ab71d172b9435cdf51fc9b20df7d9eafdf353756226f36a98fef527a366a8a4c8cda861d32c31e12fc328d15f9495b6 Homepage: https://cran.r-project.org/package=rfacebookstat Description: CRAN Package 'rfacebookstat' (Load Data from Facebook API Marketing) Load data by campaigns, ads, ad sets and insights, ad account and business manager from Facebook Marketing API into R. 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Package: r-cran-rfacts Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 692 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-fs, r-cran-tibble, r-cran-xml2 Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-rfacts_0.2.1-1.ca2404.1_all.deb Size: 174438 MD5sum: 1217750992edf458a39c9f48ade07ba9 SHA1: 54a70dc2c0b32b8562401ac894ec4d6aa7283b5f SHA256: 4931f41fbbc5fdd10639581c40fd353169cf091b26f44764671cf0cfffe3fa5d SHA512: df2ae529265c6f6ca13813f20c7107d5d90b60cc28c9da658dc1377ee2177a2144c455a94576d75f84f6064408fe7c8bd0aaba1b9632b24577f7be0fc2a375d0 Homepage: https://cran.r-project.org/package=rfacts Description: CRAN Package 'rfacts' (R Interface to 'FACTS' on Unix-Like Systems) The 'rfacts' package is an R interface to the Fixed and Adaptive Clinical Trial Simulator ('FACTS') on Unix-like systems. 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Package: r-cran-rfae Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-data.table, r-cran-foreach, r-cran-matrix, r-cran-mgcv, r-cran-ranger, r-cran-rann, r-cran-rspectra, r-cran-tibble Suggests: r-cran-arf, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rfae_0.1.0-1.ca2404.1_all.deb Size: 98364 MD5sum: 6189b40cef9d94015bc453440918baff SHA1: 58f5515ea02899fc036ee5881dd16442875e6602 SHA256: 806292ce5bbdc5c9f8a19616cca3a522ab0f8d29903e542486472566e5871149 SHA512: 18c6dab82ee39b93de3531f141b3e226f8f9627192dc9434d38c7ff124159f2c75b5c83c6bea08b9b4e3681c52798d2545856afa7dc2577364e4f2f665e609b5 Homepage: https://cran.r-project.org/package=RFAE Description: CRAN Package 'RFAE' (Autoencoding Random Forests) Autoencoding Random Forests ('RFAE') provide a method to autoencode mixed-type tabular data using Random Forests ('RF'), which involves projecting the data to a latent feature space of user-chosen dimensionality (usually a lower dimension), and then decoding the latent representations back into the input space. The encoding stage is useful for feature engineering and data visualisation tasks, akin to how principal component analysis ('PCA') is used, and the decoding stage is useful for compression and denoising tasks. At its core, 'RFAE' is a post-processing pipeline on a trained random forest model. This means that it can accept any trained RF of 'ranger' object type: 'RF', 'URF' or 'ARF'. Because of this, it inherits Random Forests' robust performance and capacity to seamlessly handle mixed-type tabular data. For more details, see Vu et al. (2025) . Package: r-cran-rfams Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-fsa Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-metr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-zipfr, r-cran-quarto, r-cran-fsadata, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-rfams_0.0.3-1.ca2404.1_all.deb Size: 225434 MD5sum: 2f9c426c3b5077c130b48ab371035d1f SHA1: bde9c95717d0cb70426f35a1d5c330452fb1741b SHA256: 6972fe7a43e049ce0a51b49c76654a5fbfaefaf8a64ded25caf1589b8234d5fa SHA512: 96015d28adc0a1f4cee018bcca92bf20885d23a8773877c94ebae28adb072bfcae697c86aef3b00128c3f57c7ea9268e3352a951f7bc22f9198676c6e517570b Homepage: https://cran.r-project.org/package=rFAMS Description: CRAN Package 'rFAMS' (Fisheries Analysis and Modeling Simulator) Simulates the dynamics of exploited fish populations using the Jones modification of the Beverton-Holt equilibrium yield equation to compute yield-per-recruit and dynamic pool models (Ricker 1975) . Allows users to evaluate minimum, slot, and inverted length limits on exploited fisheries using specified life history parameters. Users can simulate population under a variety of conditional fishing mortality and conditional natural mortality. Calculated quantities include number of fish harvested and dying naturally, mean weight and length of fish harvested, number of fish that reach specified lengths of interest, total number of fish and biomass in the population, and stock density indices. Package: r-cran-rfars Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3342 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-downloader, r-cran-dplyr, r-cran-haven, r-cran-janitor, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-leaflet, r-cran-leaflet.extras, r-cran-ggplot2, r-cran-scales, r-cran-stargazer, r-cran-viridis, r-cran-lme4, r-cran-tidyverse, r-cran-tidytext, r-cran-dt, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rfars_2.0.4-1.ca2404.1_all.deb Size: 2080824 MD5sum: e614d632192171b37f4a1d3e0b74901b SHA1: 5f5e63d5ed252f33e7bfdaac49070bcb283e843f SHA256: 14f96fb50a60d13b098a7d68233e5af47cad97530d16855fda05bad7c545a94b SHA512: df8de252297ab55fefbb2a20f0152b88bd2e675dcfa6b64081075e59e755e72f85153156ed5449b1ce52a482e2ba357ad321d8cf1483baf7b8d66493445faa73 Homepage: https://cran.r-project.org/package=rfars Description: CRAN Package 'rfars' (Download and Analyze Crash Data) Easily Download Analysis-Ready Crash Data from the U.S. National Highway Traffic Safety Administration. Package: r-cran-rfclust Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1763 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-consensusclusterplus, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-rfclust_0.1.2-1.ca2404.1_all.deb Size: 1771124 MD5sum: 8b3d5b6237fda9deeca15a1e41741180 SHA1: ac7439ec7e65b53009617061430ec033d44ebcfd SHA256: a533d4d1c6be5a5bdf960ea664d53ec8a2c2e22ec61ea8a48528bf4105741d28 SHA512: d50165c3786d16ba57b2f6dee86a1549a1c9c90fcc5bbb190647ef5d47743e556ffe4a3c342e22353bb4eb90ff5938c27c2a74732f41fd65c8e54122d4992f26 Homepage: https://cran.r-project.org/package=RFclust Description: CRAN Package 'RFclust' (Random Forest Cluster Analysis) Tools to perform random forest consensus clustering of different data types. The package is designed to accept a list of matrices from different assays, typically from high-throughput molecular profiling so that class discovery may be jointly performed. For references, please see Tao Shi & Steve Horvath (2006) & Monti et al (2003) . Package: r-cran-rfdp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rfdp_0.1.1-1.ca2404.1_all.deb Size: 14444 MD5sum: c35129e710a93ce9a2a13e7b65774236 SHA1: ee61eb8c7ac6e1cb3f0d6fb0981230d6807f74d5 SHA256: 7000496bfa54e4ac27fae88e0589578f0d72b89e6e3adc3450774b0bcc684154 SHA512: 36f7bd16fa57b1f1f0844d2aa9eeb48c5f418559caa7e1b99b2acc13e0667fcd3d4e12db19b2a5b8c245b56b7088717cf8c926fb10510c357926ad8343d745d0 Homepage: https://cran.r-project.org/package=rFDP Description: CRAN Package 'rFDP' (Resampling-Based False Discovery Proportion Control) Methods for Resampling-based False Discovery Proportion control. A function is provided that provides simultaneous, multi-resolution False Discovery Exceedance (FDX) control as described in Hemerik (2025) . Package: r-cran-rfempimp Architecture: all Version: 2.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mice, r-cran-ranger Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rfempimp_2.1.8-1.ca2404.1_all.deb Size: 208992 MD5sum: aa65a23f9489b22bef242c558073834d SHA1: c4f7568fb399c64ceae2e5100aa3c4f48f86b341 SHA256: 874f2c98a786f021b2e779e3cb936d8ce86ea44ace0f104103e44b02aa04868c SHA512: 592190729ab146d32fe6edba69bc5a42785739b3d258dcc936b00dcc3fb688eeda562107277a1493c17f9220119371e47ad6992a2fa44afbeed75485981528d2 Homepage: https://cran.r-project.org/package=RfEmpImp Description: CRAN Package 'RfEmpImp' (Multiple Imputation using Chained Random Forests) An R package for multiple imputation using chained random forests. Implemented methods can handle missing data in mixed types of variables by using prediction-based or node-based conditional distributions constructed using random forests. For prediction-based imputation, the method based on the empirical distribution of out-of-bag prediction errors of random forests and the method based on normality assumption for prediction errors of random forests are provided for imputing continuous variables. And the method based on predicted probabilities is provided for imputing categorical variables. For node-based imputation, the method based on the conditional distribution formed by the predicting nodes of random forests, and the method based on proximity measures of random forests are provided. More details of the statistical methods can be found in Hong et al. (2020) . Package: r-cran-rfia Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7358 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dtplyr, r-cran-tidyr, r-cran-stringr, r-cran-sf, r-cran-data.table, r-cran-tidyselect, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gganimate, r-cran-r2jags, r-cran-coda, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rfia_1.1.3-1.ca2404.1_all.deb Size: 7225794 MD5sum: 99003b59e197c4c9369fc7ab9b11aa5d SHA1: 5e4aef3c5f68ebeb6225458fd6288d6cd3be49cb SHA256: 6594ccb3752bf03fc561345ed25d2f45c03390dcfe19258c4548b13fd5b0fddc SHA512: 629226ff8a8936c0a7a848bb499f4e9fb33870217100f08391a44f0f23654dc28a9c620d7158c461c7dd029e7c70f68d58006e3c436b9fdf9ac512921019b6a3 Homepage: https://cran.r-project.org/package=rFIA Description: CRAN Package 'rFIA' (Estimation of Forest Variables using the FIA Database) The goal of 'rFIA' is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, 'rFIA' simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, 'rFIA' improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., 'dplyr', 'tidyr', and 'sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) , and has been validated against estimates and sampling errors produced by FIA 'EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted and model-based estimators to improve population, change, and ratio estimates. 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In essence, the package contains functions which are sophisticated wrappers around existing R functions that are called by using 'f_' (user f_riendly) prefix followed by the normal function name. This third version of the 'rfriend' package focuses primarily on data exploration, including tools for creating summary tables, f_summary(), summary figures, f_scan(), outlier detection and removal, f_outlier() and f_remove_outliers(), performing data transformations, f_boxcox() in part based on 'MASS/boxcox' and 'rcompanion', and f_bestNormalize() which wraps and extends functionality from the 'bestNormalize' package. Furthermore, 'rfriend' can automatically (or on request) generate visualizations such as boxplots, f_boxplot(), QQ-plots, f_qqnorm(), histograms f_hist(), and density plots f_density(). Additionally, the package includes several statistical test functions: f_aov(), f_chisq_test(), f_corplot(), f_kruskal_test(), f_lmer(), f_glm(), f_t_test(), f_wilcox_test(), for sequential testing and visualisation of the similar named 'stats' functions. These functions, except for f_chisq_test(), support testing multiple response variables and predictors, while also handling assumption checks, data transformations, and post hoc tests. Post hoc results are automatically summarized in a table using the compact letter display (cld) format for easy interpretation. The package also provides a function to do model comparison, f_model_comparison(), and several utility functions to simplify common R tasks. For example, f_clear() clears the workspace and restarts R with a single command; f_setwd() sets the working directory to match the directory of the current script; f_theme() quickly changes 'RStudio' themes; and f_factors() converts multiple columns of a data frame to factors, and much more. If you encounter any issues or have feature requests, please feel free to contact me via email. 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The main function of the package is rfvimptest(), which allows to test for the statistical significance of predictors in random forests using different (sequential) permutation test strategies [1]. The advantage of sequential over conventional permutation tests is that they are computationally considerably less intensive, as the sequential procedure is stopped as soon as there is sufficient evidence for either the null or the alternative hypothesis. Reference: [1] Hapfelmeier, A., Hornung, R. & Haller, B. (2023) Efficient permutation testing of variable importance measures by the example of random forests. Computational Statistics & Data Analysis 181:107689, . 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Package: r-cran-rgabriel Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rgabriel_0.9-1.ca2404.1_all.deb Size: 20808 MD5sum: 11cc74acba5232471b6f4532326b25cb SHA1: 8990ca0bb8295941080c937b9bb55a079381834d SHA256: fc0a1a988a990a8c26ec48ccf51ecab992a179951e3a46e6769d6ee0e9b7ffdf SHA512: 6ce67d0ecfa6d0299a189edb6c61640c8b412dcfdc4a6d3b3444a15036f9238dbf7f56df45c298b492bf2dbff039df13f056890a9c15c92bcc3a337c6a8b4be3 Homepage: https://cran.r-project.org/package=rgabriel Description: CRAN Package 'rgabriel' (Gabriel Multiple Comparison Test and Plot the ConfidenceInterval on Barplot) Analyze multi-level one-way experimental designs where there are unequal sample sizes and population variance homogeneity can not be assumed. To conduct the Gabriel test , create two vectors: one for your observations and one for the factor level of each observation. The function, rgabriel, conduct the test and save the output as a vector to input into the gabriel.plot function, which produces a confidence interval plot for Multiple Comparison. Package: r-cran-rgan Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-torch, r-cran-viridis Filename: pool/dists/noble/main/r-cran-rgan_0.1.1-1.ca2404.1_all.deb Size: 248412 MD5sum: 0e8e639f64f52de105e2ec1fb0687273 SHA1: 8608fe47422c407afa68d5282ba689209caff60b SHA256: b6e30956eaea162c186647317fee2467e7377af725c3054703f7d622cc3b0b44 SHA512: 6f023bd7715a808ce5b7e57cf2b100086081b8b4d95e2c5096e46da5c1ac85c8f44c5705055fcb90e4ab39331cd5d89be3eef3472d2d96dcb2d2d91f25d9a581 Homepage: https://cran.r-project.org/package=RGAN Description: CRAN Package 'RGAN' (Generative Adversarial Nets (GAN) in R) An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially described by Goodfellow et al. 2014 . A GAN can be used to learn the joint distribution of complex data by comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two neural networks play an adversarial minimax game. Built-in GAN models make the training of GANs in R possible in one line and make it easy to experiment with different design choices (e.g. different network architectures, value functions, optimizers). The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data. Methods to post-process the output of GAN models to enhance the quality of samples are available. Package: r-cran-rgap Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1817 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kfas, r-cran-zoo, r-cran-dlm, r-cran-openxlsx, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-rgap_0.1.1-1.ca2404.1_all.deb Size: 1690704 MD5sum: 5d39abc69c6e5a65fcd97747d2b5bc8c SHA1: d97627a1e0cbaf5a6172218c6f4d3a95ff057f41 SHA256: 6f2401bb01a21e55b88db5761a28a5d560e352898acd70b6c63becbcd8fc7a99 SHA512: 8b220285a2cd52e6cddb236238dfec47ef63445b48130e43741818fef2888a3f171bc0c843131b8730c6a6842af8ef6d597c512f512c7f5d5216081fb8060354 Homepage: https://cran.r-project.org/package=RGAP Description: CRAN Package 'RGAP' (Production Function Output Gap Estimation) The output gap indicates the percentage difference between the actual output of an economy and its potential. Since potential output is a latent process, the estimation of the output gap poses a challenge and numerous filtering techniques have been proposed. 'RGAP' facilitates the estimation of a Cobb-Douglas production function type output gap, as suggested by the European Commission (Havik et al. 2014) . To that end, the non-accelerating wage rate of unemployment (NAWRU) and the trend of total factor productivity (TFP) can be estimated in two bivariate unobserved component models by means of Kalman filtering and smoothing. 'RGAP' features a flexible modeling framework for the appropriate state-space models and offers frequentist as well as Bayesian estimation techniques. Additional functionalities include direct access to the 'AMECO' database and automated model selection procedures. See the paper by Streicher (2022) for details. Package: r-cran-rgbif Architecture: all Version: 3.8.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1655 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-ggplot2, r-cran-crul, r-cran-data.table, r-cran-whisker, r-cran-magrittr, r-cran-jsonlite, r-cran-oai, r-cran-tibble, r-cran-lazyeval, r-cran-r6 Suggests: r-cran-testthat, r-cran-png, r-cran-terra, r-cran-magick, r-cran-protolite, r-cran-sf, r-cran-vcr, r-cran-knitr, r-cran-rmarkdown, r-cran-bit64 Filename: pool/dists/noble/main/r-cran-rgbif_3.8.5-1.ca2404.1_all.deb Size: 1515104 MD5sum: 6a8be9bdcf8aaf8d4fb25530a7e4ee2c SHA1: 28c8a190a8af5a2d4b5067546ee2e8f4e1bf14ad SHA256: dcb7cfdb36dba16457aec86dfea3599c20134d4e67ed7dfe746e945f682fa94e SHA512: ea9d6c85ff8ac0ab8bd718ee1e8944deca053af899e12734fa122e6e5176fa0f7d4f571c9476ee9c3792ad51c6570cbfe939a93725fefb09c6191f8182016b4d Homepage: https://cran.r-project.org/package=rgbif Description: CRAN Package 'rgbif' (Interface to the Global Biodiversity Information Facility API) A programmatic interface to the Web Service methods provided by the Global Biodiversity Information Facility (GBIF; ). GBIF is a database of species occurrence records from sources all over the globe. rgbif includes functions for searching for taxonomic names, retrieving information on data providers, getting species occurrence records, getting counts of occurrence records, and using the GBIF tile map service to make rasters summarizing huge amounts of data. Package: r-cran-rgbindices Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1545 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rgbindices_0.1.1-1.ca2404.1_all.deb Size: 1102390 MD5sum: 9d9e07c09a0ceb5a563ff2694ac436e7 SHA1: e1054b705b36427575088d4963fef4a108bedafd SHA256: f1ef7cf7655a71466d864a7987c4e000ac13c64e3e31f066d449deb63eae0ca8 SHA512: 3911212e633206d9b213751b18f02cf21f2a065b932fb26bfd9ec75ada0e405b4d53ebc7a09ca3c4f87d989c3716035fb629073a7a0fd956c93312ee27e30590 Homepage: https://cran.r-project.org/package=rgbIndices Description: CRAN Package 'rgbIndices' (RGB Visible Indices for Image Analysis) Computes RGB-based vegetation, color, and spectral indices from digital images for applications in agriculture, crop phenotyping, and remote sensing. The methods are based on digital image processing and plant phenotyping approaches (Singh et al. (2023) ). Package: r-cran-rgbp Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sn, r-cran-mnormt Filename: pool/dists/noble/main/r-cran-rgbp_1.1.4-1.ca2404.1_all.deb Size: 198230 MD5sum: b9e06f6d450bcff4ea529d5a1988066b SHA1: bf28c46818124052a7c512e89be6a231a4f6b040 SHA256: 20c9d267dc307b29169d4f5659d7f937d75280d5ceb11a0ceb1d5c244c91cf2f SHA512: ac076bb30c6b73e6537ba1b3106817948b6ed1be4d0f1169844e5c2071c45f9f59de9d1041fa12fffb11396f441c1867fe729d238ba4d42e547c676ca753e3ca Homepage: https://cran.r-project.org/package=Rgbp Description: CRAN Package 'Rgbp' (Hierarchical Modeling and Frequency Method Checking onOverdispersed Gaussian, Poisson, and Binomial Data) We utilize approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data. The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking. Package: r-cran-rgcca Architecture: all Version: 3.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 728 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-deriv, r-cran-ggplot2, r-cran-ggrepel, r-cran-gridextra, r-cran-mass, r-cran-matrixstats, r-cran-pbapply, r-cran-rlang Suggests: r-cran-devtools, r-cran-factominer, r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-rticles, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-rgcca_3.0.3-1.ca2404.1_all.deb Size: 617630 MD5sum: a719573f8617633ff95bc150fc5532b2 SHA1: d7485a6799d68091443c6f01ea1318e63732c26f SHA256: 80826d12cd71f6393a7e06155df018244c922d1627cf25d52f521787b3400ac0 SHA512: 33c43878f4164573e0465869e21696b17bdfa00fb6780a5b58cf0795ee9d0efc35c6e7d3e9cbfa59282b4c7b2835bfdb71f8b37c141f8551981f7f5b61a614ff Homepage: https://cran.r-project.org/package=RGCCA Description: CRAN Package 'RGCCA' (Regularized and Sparse Generalized Canonical CorrelationAnalysis for Multiblock Data) Multi-block data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: to study the relationships between blocks and to identify subsets of variables of each block which are active in their relationships with the other blocks. This package allows to (i) run R/SGCCA and related methods, (ii) help the user to find out the optimal parameters for R/SGCCA such as regularization parameters (tau or sparsity), (iii) evaluate the stability of the RGCCA results and their significance, (iv) build predictive models from the R/SGCCA. (v) Generic print() and plot() functions apply to all these functionalities. Package: r-cran-rgcxgc Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5436 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rnetcdf, r-cran-ptw, r-cran-colorramps, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lattice, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-rgcxgc_1.2.0-1.ca2404.1_all.deb Size: 3189782 MD5sum: 5a57a677077e7d64b70397d168a26161 SHA1: f01a3ca8a8958ddd77fa4f9fef03d01c76b2802d SHA256: 18d4627aa298971340c721c0a4d542bb052690eda4cc570edfbeba9e2c068d1f SHA512: f04149e4cf3a7963635fd10041548745d20aff70e16e74e10464ccc971690a7759a5232f793ed42604a8507fa8fb8d16301c1a9650181250eb1075f6a4eda922 Homepage: https://cran.r-project.org/package=RGCxGC Description: CRAN Package 'RGCxGC' (Preprocessing and Multivariate Analysis of Bidimensional GasChromatography Data) Toolbox for chemometrics analysis of bidimensional gas chromatography data. This package import data for common scientific data format (NetCDF) and fold it to 2D chromatogram. Then, it can perform preprocessing and multivariate analysis. In the preprocessing algorithms, baseline correction, smoothing, and peak alignment are available. While in multivariate analysis, multiway principal component analysis is incorporated. Package: r-cran-rgdax Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-digest, r-cran-jsonlite, r-cran-rcurl, r-cran-httr, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rgdax_1.2.1-1.ca2404.1_all.deb Size: 66266 MD5sum: 16cc838d49e00b8fbe0cf60e8af02595 SHA1: 728930c7ec77f7f6b3ed656c4870f8030a711320 SHA256: 06ab27bb3066888f4d566f6a72a6dcd97e500b9a90d6e74ebe1a0d3b6d8792ab SHA512: a8a50f076901a80f5c7c484a49e3884cc7c5a96ffd3ca43041515995f6c3025bf007619a42dd51fbbc785de0e3f5f4bb0fac1318563aad9b8696457bb46e3d1c Homepage: https://cran.r-project.org/package=rgdax Description: CRAN Package 'rgdax' (Wrapper for 'Coinbase Pro (erstwhile GDAX)' CryptocurrencyExchange) Allow access to both public and private end points to Coinbase Pro (erstwhile GDAX) cryptocurrency exchange. For authenticated flow, users must have valid api, secret and passphrase to be able to connect. Package: r-cran-rge Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1582 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-matrixmodels, r-cran-coda Filename: pool/dists/noble/main/r-cran-rge_1.0-1.ca2404.1_all.deb Size: 1585884 MD5sum: a0b87ceaeb77ed3c3383c9cee15734d9 SHA1: 2d5125b4f412b40925fea092d43c80f12f2d15e0 SHA256: be7f11ff293c67d3745e5e7506d6d709a377c92ced6f99bc776e049ba6ba6526 SHA512: c186e065c1fc5d6b84a8d596ba4ac2753e4109dc098bd7a570039d9c4fb13e3e181f32dde06da990b8ba95e14448515343bcb28ad2152eb000de48c3be47ff94 Homepage: https://cran.r-project.org/package=RGE Description: CRAN Package 'RGE' (Response from Genotype to Environment) Compute yield-stability index based on Bayesian methodology, which is useful for analyze multi-environment trials in plant breeding programs. References: Cotes Torres JM, Gonzalez Jaimes EP, and Cotes Torres A (2016) Seleccion de Genotipos con Alta Respuesta y Estabilidad Fenotipica en Pruebas Regionales: Recuperando el Concepto Biologico. Package: r-cran-rgee Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8493 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-rstudioapi, r-cran-leaflet, r-cran-magrittr, r-cran-jsonlite, r-cran-processx, r-cran-leafem, r-cran-crayon, r-cran-r6, r-cran-cli Suggests: r-cran-magick, r-cran-geojsonio, r-cran-sf, r-cran-stars, r-cran-googledrive, r-cran-gargle, r-cran-httr, r-cran-digest, r-cran-testthat, r-cran-future, r-cran-terra, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-png, r-cran-googlecloudstorager, r-cran-leaflet.extras2, r-cran-spelling, r-cran-raster Filename: pool/dists/noble/main/r-cran-rgee_1.1.8-1.ca2404.1_all.deb Size: 2699660 MD5sum: cc91eb69988895cbcbd99aaaff6b7c3e SHA1: 58793fef84756d5abb44a66cf82a1ba7ddec5db0 SHA256: c9701fc8a47c1660894ed6c88028da15d8344579897a0747c48cc93f4cd48c7e SHA512: 313e452f9a913d14b0a8182b9083ce17a0a44f344349d028f70e26ccc348e666fc89fe89d82af83e5f3d0b51ca2b1e512ab1614d2f12d2d795518bff690e1b2a Homepage: https://cran.r-project.org/package=rgee Description: CRAN Package 'rgee' (R Bindings for Calling the 'Earth Engine' API) Earth Engine client library for R. All of the 'Earth Engine' API classes, modules, and functions are made available. Additional functions implemented include importing (exporting) of Earth Engine spatial objects, extraction of time series, interactive map display, assets management interface, and metadata display. See for further details. Package: r-cran-rgeedim Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-terra, r-cran-raster, r-cran-tinytest, r-cran-litedown Filename: pool/dists/noble/main/r-cran-rgeedim_0.4.0-1.ca2404.1_all.deb Size: 362660 MD5sum: 78e6f05de576053423d6ee0c9fe2a0bf SHA1: 7e4a53d3a90037b1a9836e5b372130f72b3ec0bc SHA256: 54321f4b3f0ddec31cc75d85de22528fad08612edf66d572bdea18b39b26c870 SHA512: 559e2a008983897a9448e50188cacb701afba7f0ca61f61e7dfe4d01ad8bffe38c40bba218913c192b2fc2b762c07b5bdc9624dfc641ea79458a5cd0f9d7637f Homepage: https://cran.r-project.org/package=rgeedim Description: CRAN Package 'rgeedim' (Search, Composite, and Download 'Google Earth Engine' Imagerywith the 'Python' Module 'geedim') Search, composite, and download 'Google Earth Engine' imagery with 'reticulate' bindings for the 'Python' module 'geedim' by Dugal Harris. Read the 'geedim' documentation here: . Wrapper functions are provided to make it more convenient to use 'geedim' to download images larger than the 'Google Earth Engine' size limit . By default the "High Volume" API endpoint is used to download data and this URL can be customized during initialization of the package. Package: r-cran-rgen Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rgen_0.0.1-1.ca2404.1_all.deb Size: 16412 MD5sum: 4bf92a5ff2a1178e5ae42af6196b3753 SHA1: 3fe138788be21bef226974e8b3e3eb65367d36a6 SHA256: b5bfdd8181d56eaceb9eb3544ee89b174223c1f2186313ebcb9052dab62cf718 SHA512: 6e321d3d4f893c8a843a6abe78c36199b7e6cb4ed8c69d15cd140f8e1eb5a14aaf1ab826dc4faa92f8018d789f75a923678236f3ee6eedccc0bf667b283fce60 Homepage: https://cran.r-project.org/package=rgen Description: CRAN Package 'rgen' (Random Sampling Distribution C++ Routines for Armadillo) Provides popular sampling distributions C++ routines based in armadillo through a header file approach. Package: r-cran-rgendata Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rgendata_1.0-1.ca2404.1_all.deb Size: 33110 MD5sum: 3b172b06075bb2555f4d54417dc95c6a SHA1: 2e78682e2edba0b1b923bcada57bc1673858d3df SHA256: e585599313b8ba3cd09b5d70b03643858e49dde3ec4b45e4934b10f206a42f63 SHA512: 9d21fbf3d127bf5107f2c76eb1cca2106f487995ea45caaafcc7fa22a4ce3fe434d00f0c2678757ce387956d6954e51bf69b091aec840182da8fa12b64b7127d Homepage: https://cran.r-project.org/package=RGenData Description: CRAN Package 'RGenData' (Generates Multivariate Nonnormal Data and Determines How ManyFactors to Retain) The GenDataSample() and GenDataPopulation() functions create, respectively, a sample or population of multivariate nonnormal data using methods described in Ruscio and Kaczetow (2008). Both of these functions call a FactorAnalysis() function to reproduce a correlation matrix. The EFACompData() function allows users to determine how many factors to retain in an exploratory factor analysis of an empirical data set using a method described in Ruscio and Roche (2012). The latter function uses populations of comparison data created by calling the GenDataPopulation() function. . . Package: r-cran-rgenerate Architecture: all Version: 1.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3572 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmawgen, r-cran-magrittr, r-cran-vars Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rgenerate_1.3.8-1.ca2404.1_all.deb Size: 3144324 MD5sum: 467165c96ee3becb4e253a78292e0f6c SHA1: 920ea17b1511b87bd9236b6df20776e8f3cf092b SHA256: 6478670d9a7d027b54984351e5a90177600f4cb78d19c4091b1e5472bd141c5c SHA512: 47d0f1124d23f1c8d10a0796c58f91a20e774c35cdafccdb8ba43f9afe947b7d480c7236c14ac1f77df1789f9d8c0f5191752a8603b4471b38d7c879cc253aa4 Homepage: https://cran.r-project.org/package=RGENERATE Description: CRAN Package 'RGENERATE' (Tools to Generate Vector Time Series) A method 'generate()' is implemented in this package for the random generation of vector time series according to models obtained by 'RMAWGEN', 'vars' or other packages. This package was created to generalize the algorithms of the 'RMAWGEN' package for the analysis and generation of any environmental vector time series. 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It generates precipitation occurrence in several sites using logit regression (Generalized Linear Models) and the approach by D.S. Wilks (1998) . 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The online method (GPlates Web Service) makes the rotation of static plates, coastlines, and a low number of geographic coordinates available using nothing but an internet connection. The offline method requires an external installation of the GPlates Desktop Application, but allows the efficient batch rotation of thousands of coordinates, Simple Features (sf) and Spatial (sp) objects with custom reconstruction trees and partitioning polygons. Examples of such plate tectonic models are accessible via the chronosphere . This R extension is developed under the umbrella of the DFG (Deutsche Forschungsgemeinschaft) Research Unit TERSANE2 (For 2332, TEmperature Related Stressors in ANcient Extinctions). 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The method is provided in Yichuan Bai and Lynna Chu (2023) . Package: r-cran-rgtmx Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-rgtmx_0.1.4-1.ca2404.1_all.deb Size: 52398 MD5sum: f0aa0bae8367774a96b351690a72bafc SHA1: 7d8cae3925082d7a554747b91eddd766c129e0d4 SHA256: 2f5ecf2e6ff859f132283ba62f874ac603123e58807aff6df98c6fbb1fbce344 SHA512: cc9e5805f812ae248c250b3f6b84a4d70727bbc4aaac22a78fe37174a9019a372f020c83dabfc64b632f64a8cb7038f2e26a22e6842b5660c56392101b0dab8e Homepage: https://cran.r-project.org/package=rgtmx Description: CRAN Package 'rgtmx' (Manage GTmetrix Tests in R) This is a library to access the current API of the web speed test service 'GTmetrix'. 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See for details. Package: r-cran-rhawkes Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1019 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ihsep Filename: pool/dists/noble/main/r-cran-rhawkes_1.0-1.ca2404.1_all.deb Size: 1006450 MD5sum: 3d38623c7d82ad7228c407968e6ab309 SHA1: f1f204c1820c93b087bf248568e4fe67930b4c62 SHA256: 6746bf36ac983c6571a87aabe746fcc62a31c75edf8316059390104540b63a8f SHA512: 42d1abfdff6999181751e0992b50cb4868c652e67ae2be1d196096d6229b00deb4b9c0d2814b0e556bb36678360126bc8f6354b2988a5f4a26696fbf45563084 Homepage: https://cran.r-project.org/package=RHawkes Description: CRAN Package 'RHawkes' (Renewal Hawkes Process) The renewal Hawkes (RHawkes) process (Wheatley, Filimonov, and Sornette, 2016 ) is an extension to the classical Hawkes self-exciting point process widely used in the modelling of clustered event sequence data. This package provides functions to simulate the RHawkes process with a given immigrant hazard rate function and offspring birth time density function, to compute the exact likelihood of a RHawkes process using the recursive algorithm proposed by Chen and Stindl (2018) , to compute the Rosenblatt residuals for goodness-of-fit assessment, and to predict future event times based on observed event times up to a given time. A function implementing the linear time RHawkes process likelihood approximation algorithm proposed in Stindl and Chen (2021) is also included. Package: r-cran-rhc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2190 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-permute, r-cran-lattice, r-cran-ade4, r-cran-geometry, r-cran-vegan, r-cran-fd, r-cran-randomforest, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-rhc_0.1.0-1.ca2404.1_all.deb Size: 2190806 MD5sum: d01dd94fa831b11ccb8d0627807f4f49 SHA1: a8b0cf51de877918ba383c41c2bab9fcc6c07450 SHA256: e206a8d706a58f8202fa93eeb91f2b5ad9cc61967e44a0cd4a9ff0ff5423d9ee SHA512: 7a649710e861a2797ae09a130f7b1c81b8b44550d4bebe466424165d7611465bb57450ffeadff8eaf9f33a7adf75e67046a64f0e185b96d18133b0c0ee4a5c70 Homepage: https://cran.r-project.org/package=RHC Description: CRAN Package 'RHC' (Rangeland Health and Condition) The evaluation criteria of rangeland health, condition and landscape function analysis based on species diversity and functional diversity of rangeland plant communities. 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This package contains functions designed to cluster subjects based on gene features including single nucleotide polymorphisms (SNPs), DNA methylation (CPG), gene expression (GE), and covariate data. The novel concept follows the general K-means (Hartigan and Wong (1979) framework but uses weighted Euclidean distances across the gene features to cluster subjects. This approach is unique in that it attempts to capture all pairwise interactions in an effort to cluster based on their complex biological interactions. Package: r-cran-rhcoclust Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-igraph Filename: pool/dists/noble/main/r-cran-rhcoclust_2.0.0-1.ca2404.1_all.deb Size: 212476 MD5sum: 0faeef1bf1d8500ece0fb750748c2ec6 SHA1: 40dba1a6a957b580ee196b59fca67fac45583b69 SHA256: 7d812e03cb1194e8ce377a6d46608a034532778a4016547c1d9b89b49a5bca65 SHA512: 3c9e06d1d5d677bbab0f01b0f0eee44ee410a47b313752c736a1fdff5dacebf33004972428a54c021c97da144510f8682b4e9780c239527a0bfdf9deb3888032 Homepage: https://cran.r-project.org/package=rhcoclust Description: CRAN Package 'rhcoclust' (Robust Hierarchical Co-Clustering to Identify SignificantCo-Cluster) Here we performs robust hierarchical co-clustering between row and column entities of a data matrix in absence and presence of outlying observations. 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(2013) for clustering DNA sequences from multiple sequence alignments in FASTA format. The implementation includes improved defaults and plotting capabilities and unlike the original 'MATLAB' version removes singleton SNPs by default. 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This package focuses on finding the ridge parameter value k which makes the variance inflation factors closest to 1, while keeping them above 1 as addressed by Michael Kutner, Christopher Nachtsheim, John Neter, William Li (2004, ISBN:978-0073108742). Moreover, the package offers end-to-end functionality to find optimum k value and presents the detailed ridge regression results. Finally it shows three sets of graphs consisting k versus variance inflation factors, regression coefficients and standard errors of them. Package: r-cran-ridigbio Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4096 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-httr, r-cran-jsonlite, r-cran-leaflet, r-cran-kableextra, r-cran-tidyverse, r-cran-cowplot Suggests: r-cran-testthat, r-cran-markdown, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ridigbio_0.4.1-1.ca2404.1_all.deb Size: 1509674 MD5sum: 88b44c2b73214e01977e85bb88bce34e SHA1: 87fe64e263b8de6cd27cc21f29291bb667e061d2 SHA256: 0430682ff4bcdc755eb9ec4174f2b29a1a465b233cb0e31b4f730563ca2cab79 SHA512: 03a6e9bb237d95acb155e03b96cdb2b952dc05c5c883595c54eddf54889a86a84605e67dd3dad1228241d3ae721e7e7ad88b8c46beb5cd0074ee90a2a0755b20 Homepage: https://cran.r-project.org/package=ridigbio Description: CRAN Package 'ridigbio' (Interface to the iDigBio Data API) An interface to iDigBio's search API that allows downloading specimen records. Searches are returned as a data.frame. Other functions such as the metadata end points return lists of information. iDigBio is a US project focused on digitizing and serving museum specimen collections on the web. See for information on iDigBio. Package: r-cran-ridittools Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ridittools_0.1-1.ca2404.1_all.deb Size: 39254 MD5sum: 86631dae5be2bed4aed15455b139c499 SHA1: 58a4a868e90453db27750d102800265d4202ed70 SHA256: 2c61abd6b564c8e3bf026d4b8e562d37a4ec4ee71a520d19aee0f78fa6e2fbed SHA512: c3e0b458d9eddcdd254575f7a0e9db7030dbe728b62fe8f265643ff2fc17718569cffd8054b03428bba40d2950c8f1d376110ae2e5573356566401bd034e68ae Homepage: https://cran.r-project.org/package=ridittools Description: CRAN Package 'ridittools' (Useful Functions for Ridit Analysis) Functions to compute ridit scores of vectors, compute mean ridits and their standard errors for vectors compared to a reference vector, as described in Fleiss (1981, ISBN:0-471-06428-9), and compute means/SEs for multiple groups in matrices. Data can be either counts or proportions. 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Package: r-cran-riem Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tibble Suggests: r-cran-dplyr, r-cran-forecast, r-cran-ggplot2, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-weathermetrics, r-cran-xts Filename: pool/dists/noble/main/r-cran-riem_1.0.0-1.ca2404.1_all.deb Size: 25430 MD5sum: 8f935ba84b0330204520000668dd10d8 SHA1: 341293a2659291eea889deebbe6e7d0d09bbdba9 SHA256: 87ab4bc218bb0a5987f870cfe2397f4db93894bde2c742e331c21d6a061f6f9f SHA512: 4f292741a620738ea3d303290224ba38b981251cac3ca329a6e4a8b0a56fa020f4c553de95f68b66e1845bdafc4a7bf618ef3a508c1338c718cc5e00aeb00a8f Homepage: https://cran.r-project.org/package=riem Description: CRAN Package 'riem' (Accesses Weather Data from the Iowa Environment Mesonet) Allows to get weather data from Automated Surface Observing System (ASOS) stations (airports) in the whole world thanks to the Iowa Environment Mesonet website. Package: r-cran-riemstats Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-covtools, r-bioc-sva, r-cran-purrr, r-cran-riemtan Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-riemstats_0.2.0-1.ca2404.1_all.deb Size: 52440 MD5sum: 8c61c15fd50ed2760936ac72959a56f4 SHA1: 36409884e714f98edb993eb7435932375ee87b25 SHA256: 75f70092af8ff3d7311f8db42af11a8d7b302b8deb992798da9e780ef271a6f3 SHA512: 78a72261b1e5fbb35e622155f72f3694e829f80c663d56f98733b40474f1b2e6657c73e9cff68df31f63dadf27121b0089d6b6171e088b9ccdec0ad3ca42e6e2 Homepage: https://cran.r-project.org/package=riemstats Description: CRAN Package 'riemstats' (Riemannian ANOVA Statistics) Provides statistical methods for analyzing samples of symmetric positive definite (SPD) matrices, particularly functional connectivity matrices from neuroimaging data. Implements Fréchet ANOVA (Dubey and Müller (2019) ) for testing differences between groups in metric spaces, and Riemannian ANOVA methods that leverage tangent space geometry with classic multivariate test statistics including Wilks' Lambda and Pillai's trace. Also includes harmonization techniques for removing batch effects in multi-site studies: ComBat-based harmonization (Honnorat et al. (2024) ) and rigid harmonization (Simeon et al. (2022) ). Builds on 'riemtan' package infrastructure for efficient computation with multiple Riemannian metrics. Package: r-cran-riemtan Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-expm, r-cran-r6, r-cran-purrr, r-cran-mass, r-cran-furrr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-riemtan_0.1.0-1.ca2404.1_all.deb Size: 210224 MD5sum: 90952c078d8562ca99fadd8a656f3bd6 SHA1: 0d3d3e45bf6cf3c2b75995ae967e6a16fc43da67 SHA256: 1e71340bbde661bfecb3f49447639cf4dd7d8fadcdfec5837b8fd3746d667f09 SHA512: b2dae67e1d0d212e15f8a6c3659627987c09092f51d530408a3c685252ad923a58f2d3998feea0489fc248283807efdc88b0a9a4be3d519ad7a2f0a3f5f50a27 Homepage: https://cran.r-project.org/package=riemtan Description: CRAN Package 'riemtan' (Riemannian Metrics for Symmetric Positive Definite Matrices) Implements various Riemannian metrics for symmetric positive definite matrices, including AIRM (Affine Invariant Riemannian Metric, see Pennec, Fillard, and Ayache (2006) ), Log-Euclidean (see Arsigny, Fillard, Pennec, and Ayache (2006) ), Euclidean, Log-Cholesky (see Lin (2019) ), and Bures-Wasserstein metrics (see Bhatia, Jain, and Lim (2019) ). Provides functions for computing logarithmic and exponential maps, vectorization, and statistical operations on the manifold of positive definite matrices. Package: r-cran-riex Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-ttr, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-purrr, r-cran-rjson, r-cran-stringr, r-cran-urltools, r-cran-quantmod Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-riex_1.0.2-1.ca2404.1_all.deb Size: 127822 MD5sum: 5110d82278603d0de96d1861b06f0711 SHA1: d32c0bde6d906c190ba655f569fc4abc4dd9c3f6 SHA256: e1d7457876ad9d67860f966c161141733cca488bdea55399258b2cf4700a2123 SHA512: b5ec465ac8e13e237f4f0009cafa08997da3820fb5e86990a3ad5cce695e0e047749105571fb61da6dd1b127652c5b24eb470bc8de16db1b3fa0e69bba0106be Homepage: https://cran.r-project.org/package=Riex Description: CRAN Package 'Riex' (IEX Stocks and Market Data) Retrieves efficiently and reliably Investors Exchange ('IEX') stock and market data using 'IEX Cloud API'. The platform is offered by Investors Exchange Group (IEX Group). Main goal is to leverage 'R' capabilities including existing packages to effectively provide financial and statistical analysis as well as visualization in support of fact-based decisions. In addition, continuously improve and enhance 'Riex' by applying best practices and being in tune with users' feedback and requirements. Please, make sure to review and acknowledge Investors Exchange Group (IEX Group) terms and conditions before using 'Riex' (). Package: r-cran-rifle Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-rifle_1.0-1.ca2404.1_all.deb Size: 23562 MD5sum: d694c56ea6c3fb52b282877200209251 SHA1: bbc7c44361afb8a3a147ab58cec2cfe4e7a226c1 SHA256: ef2d505f9334305fc726b8858938297fd86f68480448676bb6134c9d481a94fa SHA512: 67e95e1961a4557f7e45b51b2656953fda3c5837aa5124a3a2898d09b5f6c59da9ffa0b1037f77b396f4fde0f4348973866baa1e1288c00d41f73023468dfc6b Homepage: https://cran.r-project.org/package=rifle Description: CRAN Package 'rifle' (Sparse Generalized Eigenvalue Problem) Implements the algorithms for solving sparse generalized eigenvalue problem by Tan, et. al. (2018). Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow. To appear in Journal of the Royal Statistical Society: Series B. . 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The package allows to regress the RIF on any number of covariates. Generic print, plot and summary functions are also provided. Reference: Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. (2009) . "Unconditional Quantile Regressions.". Package: r-cran-rifs Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rifs_0.1.6-1.ca2404.1_all.deb Size: 27746 MD5sum: 80048c18e29ddb4a87f2287379f573cf SHA1: 7f8a18375d8c5054a648d864c7609253625d3ec2 SHA256: 6e2bee3f26d232966c0adb83d6a20ce4f4b2a4e52238406b1ae6cd824f28101e SHA512: 1f7e12273c9715f7fe2081bb811d1e2f2aecbce127160c101ca5ec85a087f762679bf05f51ecd61e02e98c4d615d4d1788fe3baee6f55197128db6ed66708ce3 Homepage: https://cran.r-project.org/package=RIFS Description: CRAN Package 'RIFS' (Random Iterated Function System) Pointwise generation and display of attractors (prefractals) of the random iterated function system (RIFS) for various combinations of probabilistic and geometric parameters of some fixed point sets (protofractals), described by Bukhovets A.G. (2012) . Package: r-cran-rifttable Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-purrr, r-cran-risks, r-cran-rlang, r-cran-survival, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-gt, r-cran-knitr, r-cran-markdown, r-cran-quantreg, r-cran-rmarkdown, r-cran-sandwich, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rifttable_0.7.2-1.ca2404.1_all.deb Size: 392312 MD5sum: 9b0f36bd5f83a672c191d40fad8be174 SHA1: 6dc61211fbb62404d7234a2161a2b69f433c0502 SHA256: ebfc7397c094fc837cd9bff5439ef53f02ee133a65efaf1bf4bea43d595d16c1 SHA512: bfc3f89dce672227329f25b55e45fc7420a89ebf906a056e0667cfdb8248eea3202d5b873dc3bf91f0546936b0b5e14ba9b370e0e5fd76e8cd222a54f95e4ef2 Homepage: https://cran.r-project.org/package=rifttable Description: CRAN Package 'rifttable' (Results Tables to Bridge the Rift Between Epidemiologists andTheir Data) Presentation-ready results tables for epidemiologists in an automated, reproducible fashion. The user provides the final analytical dataset and specifies the design of the table, with rows and/or columns defined by exposure(s), effect modifier(s), and estimands as desired, allowing to show descriptors and inferential estimates in one table -- bridging the rift between epidemiologists and their data, one table at a time. See Rothman (2017) . Package: r-cran-rigma Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bslib, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-lubridate, r-cran-magrittr, r-cran-png, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-withr, r-cran-xml2 Suggests: r-cran-covr, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rigma_0.3.0-1.ca2404.1_all.deb Size: 132622 MD5sum: 19548b9555ed200a43b6bc60e8b6ed38 SHA1: 93bbc75a28a28390349ad07055b809190770d90d SHA256: d7c0fc2b111695d6fc1a4b2e56299b57c824c34645b5a120bb5f08b1d485cdc8 SHA512: 74eb0bf3186977d17bc601c130c8dfdaae1311550d62153b279a4476bd1ea045dea1f57db8907f5da1876a7a43a4d8ce534e7e8070353efa1552b9b4e93a3c8f Homepage: https://cran.r-project.org/package=Rigma Description: CRAN Package 'Rigma' (Access to the 'Figma' API) The goal of Rigma is to provide a user friendly client to the 'Figma' API . It uses the latest `httr2` for a stable interface with the REST API. More than 20 methods are provided to interact with 'Figma' files, and teams. Get design data into R by reading published components and styles, converting and downloading images, getting access to the full 'Figma' file as a hierarchical data structure, and much more. Enhance your creativity and streamline the application development by automating the extraction, transformation, and loading of design data to your applications and 'HTML' documents. Package: r-cran-rigr Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sandwich, r-cran-survival Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-car Filename: pool/dists/noble/main/r-cran-rigr_1.0.9-1.ca2404.1_all.deb Size: 512164 MD5sum: 560fa7cd83985ad4e85a90fd11cbdf8c SHA1: 87f6da6a010dbdc0a3848468ff987c4cb699eb57 SHA256: c777fcb85048310564ae9a5e78355ea6c1ddf2e33f31dd63c8dda4f8600c20e8 SHA512: 78d67ebaaaf31cf3072aa6fdedbec2f30e5e4113ae7506d5cad1e3bccdfcf851ae3746963b73e39b4c6e7c7f8f17c9caa5a542717bab450cd35332db98f9b40d Homepage: https://cran.r-project.org/package=rigr Description: CRAN Package 'rigr' (Regression, Inference, and General Data Analysis Tools in R) A set of tools to streamline data analysis. Learning both R and introductory statistics at the same time can be challenging, and so we created 'rigr' to facilitate common data analysis tasks and enable learners to focus on statistical concepts. We provide easy-to-use interfaces for descriptive statistics, one- and two-sample inference, and regression analyses. 'rigr' output includes key information while omitting unnecessary details that can be confusing to beginners. Heteroscedasticity-robust ("sandwich") standard errors are returned by default, and multiple partial F-tests and tests for contrasts are easy to specify. A single regression function can fit both linear and generalized linear models, allowing students to more easily make connections between different classes of models. Package: r-cran-riim Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-xgboost, r-cran-optmatch Suggests: r-cran-vgam, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-riim_2.0.0-1.ca2404.1_all.deb Size: 52442 MD5sum: 49782a1d96ac30dce046b6bd5eb305f8 SHA1: 09b77318159d6737b3ef5ee1d4811cdb6136b589 SHA256: 7e611fd499cba02578496aa8297c3c4c0e221385402438fd8d0a6122ede96784 SHA512: 0853b26d603a89f100099ad15e9e156f7e192f3017bcc5661012a796b274c88cff5d31e11e386fba4e6504337e78714b0fb7c0f354fc6c686ef320fe66337cf4 Homepage: https://cran.r-project.org/package=RIIM Description: CRAN Package 'RIIM' (Randomization-Based Inference Under Inexact Matching) Randomization-based inference for average treatment effects in potentially inexactly matched observational studies. It implements the inverse post-matching probability weighting framework proposed by the authors. The post-matching probability calculation follows the approach of Pimentel and Huang (2024) . The optimal full matching method is based on Hansen (2004) . The variance estimator extends the method proposed in Fogarty (2018) from the perfect randomization settings to the potentially inexact matching case. Comparisons are made with conventional methods, as described in Rosenbaum (2002) , Fogarty (2018) , and Kang et al. (2016) . 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Package: r-cran-ripc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-countrycode, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-ripc_1.0.0-1.ca2404.1_all.deb Size: 156936 MD5sum: 811989d27edd16721b731a7c09179623 SHA1: f5d7f945a017e2bc6b7a75ddf0199bff073ca464 SHA256: e1a3fea5f383a2e40c75b3fcbc4e17eb437e51cd57af836e95ea74e8e05577e4 SHA512: 5aa59166780cc6b6fa248b2c88c3bcf5718069e950a2e1a6a0155d2e854c559bfe4fec382926662a45a70654a9925301769771236204637757bcc0949f2f179e Homepage: https://cran.r-project.org/package=ripc Description: CRAN Package 'ripc' (Download and Tidy IPC and CH Data) Utilities to access Integrated Food Security Phase Classification (IPC) and Cadre Harmonisé (CH) food security data. Wrapper functions are available for all of the 'IPC-CH' Public API () simplified and advanced endpoints to easily download the data in a clean and tidy format. Package: r-cran-rirods Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-jsonlite, r-cran-rappdirs, r-cran-testthat, r-cran-withr Suggests: r-cran-httptest2, r-cran-kableextra, r-cran-knitr, r-cran-purrr, r-cran-readr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-rirods_0.2.0-1.ca2404.1_all.deb Size: 203880 MD5sum: 1b9eeaf2e898b497f427e38751a214c5 SHA1: bb8f0dd0fa04c467c55afe1d2ca725610f9b440b SHA256: bd40072b5efe70114bb46df1de04408f246b05afbf36d077da426039ab1fcdf9 SHA512: b5829fd8f950d349d990c1fc865517e5098e1c684b3f4b3e6bd75ddbc4e38a26d3da764ce5d3bc30c0d63f46d750f9ec7e16d4e7f625b708d3636a009b51cb7f Homepage: https://cran.r-project.org/package=rirods Description: CRAN Package 'rirods' (R Client for 'iRODS') The open sourced data management software 'Integrated Rule-Oriented Data System' ('iRODS') offers solutions for the whole data life cycle (). The loosely constructed and highly configurable architecture of 'iRODS' frees the user from strict formatting constraints and single-vendor solutions. This package provides an interface to the 'iRODS' HTTP API, allowing you to manage your data and metadata in 'iRODS' with R. Storage of annotated files and R objects in 'iRODS' ensures findability, accessibility, interoperability, and reusability of data. Package: r-cran-risca Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1392 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-relsurv, r-cran-reticulate, r-cran-tune, r-cran-nlme, r-cran-mass, r-cran-mvtnorm, r-cran-statmod, r-cran-doparallel, r-cran-foreach, r-cran-nnet, r-cran-kernlab, r-cran-glmnet, r-cran-caret, r-cran-superlearner, r-cran-rpart, r-cran-mosaic, r-cran-cubature Filename: pool/dists/noble/main/r-cran-risca_1.0.7-1.ca2404.1_all.deb Size: 1365174 MD5sum: daebd9b43564a23e1ffc498150e21b7b SHA1: 68a20482c76b70e97c5e21696a8a7e1fd25cb98e SHA256: e5d2cd8b58437620dbc5823e7c432c9a6b445b8b290c4039f6ebd072e0b6015a SHA512: 036eaadedbda227f06f5444e771838e054e7818e449ac56f4e02217c6e4927085145b8a8aaaab269ce8e7410f77fffc2c945a9407d86c38acdca5d264b644861 Homepage: https://cran.r-project.org/package=RISCA Description: CRAN Package 'RISCA' (Causal Inference and Prediction in Cohort-Based Analyses) Numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events, competing events, and multi-state data. For multistate data, semi-Markov model with interval censoring may be considered, and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables. For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders. Finally, several functions are available to assess time-dependent ROC curves or survival curves from aggregated data. Package: r-cran-rise Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rise_1.0.4-1.ca2404.1_all.deb Size: 18648 MD5sum: 826382a6ff1e9bc046a4e9599735f1f9 SHA1: 77fe5302b54d0c9efcfa1c5f8edcc063a0bd9cf3 SHA256: 8bc3e4a0f5cbb23f710cfb3a4f0ed4177dcce28bedba47c1b090a796278d82a1 SHA512: 48655bfc6aad5538140d868ec9cff31d176a592edfd1885683ff8866a2a380499f70f75bc3736b7e571a22fe7da88b7bacb96c875781e1c1d12ae467e9af07a5 Homepage: https://cran.r-project.org/package=rise Description: CRAN Package 'rise' (Conduct RISE Analysis) Implements techniques for educational resource inspection, selection, and evaluation (RISE) described in Bodily, Nyland, and Wiley (2017) . Automates the process of identifying learning materials that are not effectively supporting student learning in technology-mediated courses by synthesizing information about access to course content and performance on assessments. Package: r-cran-risk.assessr Architecture: all Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3173 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-remotes, r-cran-callr, r-cran-checkmate, r-cran-covr, r-cran-desc, r-cran-dplyr, r-cran-fs, r-cran-pkgload, r-cran-purrr, r-cran-rmarkdown, r-cran-rcmdcheck, r-cran-rlang, r-cran-xml2, r-cran-stringr, r-cran-tidyr, r-cran-curl, r-cran-jsonlite, r-cran-memoise, r-cran-testthat, r-cran-biocmanager, r-cran-glue Suggests: r-cran-devtools, r-cran-forcats, r-cran-here, r-cran-htmltools, r-cran-htmlwidgets, r-cran-kableextra, r-cran-knitr, r-cran-magrittr, r-cran-openxlsx, r-cran-r6, r-cran-s7, r-cran-readr, r-cran-roxygen2, r-cran-testit, r-cran-tibble, r-cran-tidyselect, r-cran-withr, r-cran-mockery, r-cran-ggplot2, r-cran-dt Filename: pool/dists/noble/main/r-cran-risk.assessr_3.1.1-1.ca2404.1_all.deb Size: 1922294 MD5sum: f1c3b32b46a008a359fcca39a706488e SHA1: 27d36dedb94efb10f52ea5a0f70370fe6b496a0a SHA256: 6fe23ef5dff0734ff2ab92790bf24de84c94b3a5e525fc8037775308ec660604 SHA512: 8a18b4632b58142deec10d2c2b9d64a3b585a28d99855be1c3f4b150b9669edfe695432b7548f65266e7b8493b3e855d545091c0199dca6b58ac4726e8a53a7d Homepage: https://cran.r-project.org/package=risk.assessr Description: CRAN Package 'risk.assessr' (Assessing Package Risk Metrics) A reliable and validated tool that captures detailed risk metrics such as R 'CMD' check, test coverage, traceability matrix, documentation, dependencies, reverse dependencies, suggested dependency analysis, repository data, and enhanced reporting for R packages that are local or stored on remote repositories such as GitHub, CRAN, and Bioconductor. Package: r-cran-risk Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-risk_1.0-1.ca2404.1_all.deb Size: 86658 MD5sum: b49d9dadcc41d9ef367b6eeb44b276fd SHA1: e240f804d9f771938cfb73c170358895f54c6040 SHA256: d9c294cafc765ad0ec46a8956229c117ac22ef4574d48255aeeb47aa9a7cfef0 SHA512: de2e8dc20ba3349bef28714f8f671b2b0d92a057708b6e1c485d73f7f10437acf3229bf95d074f3cb4a4a6114e0e30e15416366cf1c83019307fbf93fdd22d1c Homepage: https://cran.r-project.org/package=Risk Description: CRAN Package 'Risk' (Computes 26 Financial Risk Measures for Any ContinuousDistribution) Computes 26 financial risk measures for any continuous distribution. The 26 financial risk measures include value at risk, expected shortfall due to Artzner et al. (1999) , tail conditional median due to Kou et al. (2013) , expectiles due to Newey and Powell (1987) , beyond value at risk due to Longin (2001) , expected proportional shortfall due to Belzunce et al. (2012) , elementary risk measure due to Ahmadi-Javid (2012) , omega due to Shadwick and Keating (2002), sortino ratio due to Rollinger and Hoffman (2013), kappa due to Kaplan and Knowles (2004), Wang (1998)'s risk measures, Stone (1973)'s risk measures, Luce (1980)'s risk measures, Sarin (1987)'s risk measures, Bronshtein and Kurelenkova (2009)'s risk measures. Package: r-cran-riskclustr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlogit, r-cran-stringr, r-cran-matrix Suggests: r-cran-testthat, r-cran-covr, r-cran-rmarkdown, r-cran-dplyr, r-cran-knitr, r-cran-usethis, r-cran-spelling Filename: pool/dists/noble/main/r-cran-riskclustr_0.4.1-1.ca2404.1_all.deb Size: 386072 MD5sum: 86b186b8c5e29dec65b240f0287efa78 SHA1: b5d07ce33ca4362bf9fed46ced0bb46e882b95a4 SHA256: bde645f08c09176aaed2771905ccc40150bf2520d3232df77a4b96d7ec66abbf SHA512: b5969bb35ae313747a5e2f7113968b281c3b3cf1b3d1beb5b2dddfb8d1f270affd5a510d3b0d2003dac1b2b5de4ec6fd0e0a3bab9b192bfed2a0ec4d88b01d58 Homepage: https://cran.r-project.org/package=riskclustr Description: CRAN Package 'riskclustr' (Functions to Study Etiologic Heterogeneity) A collection of functions related to the study of etiologic heterogeneity both across disease subtypes and across individual disease markers. The included functions allow one to quantify the extent of etiologic heterogeneity in the context of a case-control study, and provide p-values to test for etiologic heterogeneity across individual risk factors. Begg CB, Zabor EC, Bernstein JL, Bernstein L, Press MF, Seshan VE (2013) . Package: r-cran-riskcommunicator Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1528 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-magrittr, r-cran-mass, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-printr, r-cran-stringr, r-cran-formatr, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-riskcommunicator_1.0.1-1.ca2404.1_all.deb Size: 1093346 MD5sum: a3834f1d8d8cee00fb4e353f11e3fde3 SHA1: c7d149059d65342ba4c9bfb72b92801acc442562 SHA256: 9a5c94641758eb6c71b9c287d14aa1321af13ee7d22566efbcb624e9081a1528 SHA512: 3a16dc6bf8738a0aad844258eb98763261aa9c8fb6b4276c0c44336d68bf1cb0d2b182dc2d9d03a57d04158a7458b03f8e851d7b7a9da99427044a012c147cf9 Homepage: https://cran.r-project.org/package=riskCommunicator Description: CRAN Package 'riskCommunicator' (G-Computation to Estimate Interpretable Epidemiological Effects) Estimates flexible epidemiological effect measures including both differences and ratios using the parametric G-formula developed as an alternative to inverse probability weighting. It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback. G-computation was originally described by Robbins (1986) and has been described in detail by Ahern, Hubbard, and Galea (2009) ; Snowden, Rose, and Mortimer (2011) ; and Westreich et al. (2012) . Package: r-cran-riskdiff Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 732 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-ggplot2 Suggests: r-cran-kableextra, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-mockery Filename: pool/dists/noble/main/r-cran-riskdiff_0.3.0-1.ca2404.1_all.deb Size: 412010 MD5sum: c1a94cb26472dbbab741b23259c1167d SHA1: 1893aa6e666790bd3db14484dfa0da5d0d480d6b SHA256: bcbabcae4388d5a54b91fec6bd443d9fe28925aec24069669510458629a60dc3 SHA512: 175fdb4e9d703bfa621e8bfa60a645f1645c4ba9f4af430dd9498b54f72b5b98e5c2378bfc60990f5dae33d5c6c52e5e4c8d5c460e93138bbdfefaeb87d6804e Homepage: https://cran.r-project.org/package=riskdiff Description: CRAN Package 'riskdiff' (Risk Difference Estimation with Multiple Link Functions andInverse Probability of Treatment Weighting) Calculates risk differences (or prevalence differences for cross-sectional data) and Number Needed to Treat (NNT) using generalized linear models with automatic link function selection. Provides robust model fitting with fallback methods, support for stratification and adjustment variables, inverse probability of treatment weighting (IPTW) for causal inference with NNT calculations, and publication-ready output formatting. Handles model convergence issues gracefully and provides confidence intervals using multiple approaches. Methods are based on approaches described in Mark W. Donoghoe and Ian C. Marschner (2018) "logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model" for robust GLM fitting, Peter C. Austin (2011) "An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies" for IPTW methods, and standard epidemiological methods for risk difference estimation as described in Kenneth J. Rothman, Sander Greenland and Timothy L. Lash (2008, ISBN:9780781755641) "Modern Epidemiology". Package: r-cran-riskmap Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1107 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-terra, r-cran-xtable, r-cran-spatialeco, r-cran-spatialsample, r-cran-deriv, r-cran-numderiv, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-gridextra, r-cran-sf, r-cran-sns, r-cran-stars Filename: pool/dists/noble/main/r-cran-riskmap_1.0.0-1.ca2404.1_all.deb Size: 1068004 MD5sum: 899e154ea24fa0ba058f2ae3d4f600b3 SHA1: b001db6f9b4d3128d83aead698308be966765aa6 SHA256: c272b86993c609d32331acb3717cf0e5d0d2592109a25a4b1e394ee130743086 SHA512: 161531a1844ca12ebe7e2c786ea57696688cb6d98da471f5f0b87866b09389d0f8b0187ea9ca74871bb3d2094ce55d0fa3140b72da63bcf95f63d6a34dbf2640 Homepage: https://cran.r-project.org/package=RiskMap Description: CRAN Package 'RiskMap' (Geostatistical Modeling of Spatially Referenced Data) Geostatistical analysis of continuous and count data. Implements stationary Gaussian processes with Matérn correlation for spatial prediction, as described in Diggle and Giorgi (2019, ISBN: 978-1-138-06102-7). Package: r-cran-riskmetric Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-xml2, r-cran-httr, r-cran-curl, r-cran-urltools, r-cran-memoise, r-cran-biocmanager, r-cran-cranlogs, r-cran-covr, r-cran-vctrs, r-cran-pillar, r-cran-tibble, r-cran-pkgload, r-cran-devtools Suggests: r-cran-dplyr, r-cran-jsonlite, r-cran-knitr, r-cran-magrittr, r-cran-pkgbuild, r-cran-rmarkdown, r-cran-testthat, r-cran-webmockr, r-cran-withr Filename: pool/dists/noble/main/r-cran-riskmetric_0.2.7-1.ca2404.1_all.deb Size: 760938 MD5sum: 9a681abf2412a84ae95a2565bef12c10 SHA1: 8fbcb33ce489f316b4f5f79a6522ca95874a3855 SHA256: e706e363f180634ddc8f4bf0d3a1451be78031ce803b420a301da413d4d49fc4 SHA512: 9927c317dc20fa5c12de64f21e304584a4f0170010637cde307af6745ef917246a5855aa81151a1d9d265d51f7b1ee1c484ec6244df19aa6ffc02a78c27b47d1 Homepage: https://cran.r-project.org/package=riskmetric Description: CRAN Package 'riskmetric' (Risk Metrics to Evaluating R Packages) Facilities for assessing R packages against a number of metrics to help quantify their robustness. Package: r-cran-riskportfolios Architecture: all Version: 2.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-quadprog, r-cran-nloptr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-riskportfolios_2.1.7-1.ca2404.1_all.deb Size: 98434 MD5sum: ee043525e73152a31ea64899ba3c1a38 SHA1: 1b65ce200abea37ed9e6cd062ad8c6edf291f635 SHA256: 75e8fc353adfba908f5f6041ef18a2f3a00931196e21062cf8ace45ae14f706d SHA512: 22017e965cb1e9bfbc712e2e55015c3377379d8a19475600e5467657007f68b9694abd2bca8d44c19712721b67773ac42cd7d0b131302ad6072a6b582dd154c0 Homepage: https://cran.r-project.org/package=RiskPortfolios Description: CRAN Package 'RiskPortfolios' (Computation of Risk-Based Portfolios) Collection of functions designed to compute risk-based portfolios as described in Ardia et al. (2017) and Ardia et al. (2017) . Package: r-cran-riskpredictclustdata Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-gee, r-cran-hmisc, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-riskpredictclustdata_0.2.6-1.ca2404.1_all.deb Size: 87530 MD5sum: 9bb6e37b00d45385c951a86f13874c32 SHA1: a3b731d815a23e7e1e0e923edeb4e98a31f8f3c4 SHA256: 624c7ce987321074f03589d079afccd39533fd115768a31a5efbcbd38bfbd5d2 SHA512: 1eb721daa908f4b1bab36f34cf7b3abaad35f456f2b0d1bdc1c2667ed06b880a7d105770eeeb76814c1890708ec2f4f17725946372615f92b966a69014903a7b Homepage: https://cran.r-project.org/package=riskPredictClustData Description: CRAN Package 'riskPredictClustData' (Assessing Risk Predictions for Clustered Data) Assessing and comparing risk prediction rules for clustered data. The method is based on the paper: Rosner B, Qiu W, and Lee MLT.(2013) . Package: r-cran-risks Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-bcaboot, r-cran-broom, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-sandwich, r-cran-tibble, r-cran-tidyr Suggests: r-cran-addreg, r-cran-covr, r-cran-knitr, r-cran-logbin, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-risks_0.4.3-1.ca2404.1_all.deb Size: 1387748 MD5sum: f7efcb0e176dd92e93fef8079d9ebfdd SHA1: dbccc466a6b274c5ee204b5caafc41c95d966c21 SHA256: 0704f4db0eb1014748e6068c42965f712c1d6c0508703b901431af6afcf13bf4 SHA512: 3127c8c9448fe7f33de538fbecb97e6dc64d24eacce992fcf3658afe57c9fd84043f9bd5b7bd9afc8d52c437e608f5a2504df07df4b88d266b6177acff161f4c Homepage: https://cran.r-project.org/package=risks Description: CRAN Package 'risks' (Estimate Risk Ratios and Risk Differences using Regression) Risk ratios and risk differences are estimated using regression models that allow for binary, categorical, and continuous exposures and confounders. Implemented are marginal standardization after fitting logistic models (g-computation) with delta-method and bootstrap standard errors, Miettinen's case-duplication approach (Schouten et al. 1993, ), log-binomial (Poisson) models with empirical variance (Zou 2004, ), binomial models with starting values from Poisson models (Spiegelman and Hertzmark 2005, ), and others. Package: r-cran-riskscores Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-magrittr, r-cran-proc Suggests: r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-riskscores_1.2.3-1.ca2404.1_all.deb Size: 168144 MD5sum: a5d82560154f71c867d201742007ae72 SHA1: 578dc885df535a101a03c5ba9bb2ceb91e99f54b SHA256: ee77b64f6489a38253609f6225d2aeb56c04feb44097fadb20539e0fa9bdabd9 SHA512: 4769ab66e7303d336a2ed8ebeb9e5e12cde5535e5484a24b5dbb3b5f306cf0d6a00e6caa2ae9ce19227cd74687387ea661adf2f1b4a3c732a0eb79408a6a6a88 Homepage: https://cran.r-project.org/package=riskscores Description: CRAN Package 'riskscores' (Optimized Integer Risk Score Models) Implements an optimized approach to learning risk score models, where sparsity and integer constraints are integrated into the model-fitting process. Package: r-cran-riskscorescvd Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-pooledcohort Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-purrr, r-cran-testthat, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-riskscorescvd_0.3.1-1.ca2404.1_all.deb Size: 545384 MD5sum: f9f739ac2ee09cdc48a52f5d85adbaf7 SHA1: daca85ea24fe6e666f55c230b60944257113ee94 SHA256: cb67c237e1609b04bff014b6b299183ad8b9f65f26645bd72751386f532af850 SHA512: f3103d9984c82e0ee178981ddeedcb0fb883aae784c07dd5b438d251bd2f5f1ea9c995fa0ff186e09f8c4a882b3b2be8617ae71ea1d7c7723dd256e761be5e51 Homepage: https://cran.r-project.org/package=RiskScorescvd Description: CRAN Package 'RiskScorescvd' (Cardiovascular Risk Scores Calculator) A tool to calculate Cardiovascular Risk Scores in large data frames as published in Perez-Vicencio, et al (2024) . Cardiovascular risk scores are statistical tools used to assess an individual's likelihood of developing a cardiovascular disease based on various risk factors, such as age, gender, blood pressure, cholesterol levels, and smoking. Here we bring together the six most commonly used in the emergency department. Using 'RiskScorescvd', you can calculate all the risk scores in an extended dataset in seconds. PCE (ASCVD) described in Goff, et al (2013) . EDACS described in Mark DG, et al (2016) . GRACE described in Fox KA, et al (2006) . HEART is described in Mahler SA, et al (2017) . SCORE2/OP described in SCORE2 working group and ESC Cardiovascular risk collaboration (2021) . TIMI described in Antman EM, et al (2000) . SCORE2-Diabetes described in SCORE2-Diabetes working group and ESC Cardiovascular risk collaboration (2023) . SCORE2/OP with CKD add-on described in Kunihiro M et al (2022) . 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By providing a toolbox of corresponding metrics and representations, 'riskyr' computes, translates, and visualizes risk-related information in a variety of ways. Adopting multiple complementary perspectives provides insights into the interplay between key parameters and renders teaching and training programs on risk literacy more transparent (see , for details). 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Package: r-cran-rita Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Filename: pool/dists/noble/main/r-cran-rita_1.2.0-1.ca2404.1_all.deb Size: 84182 MD5sum: 18124121cbac3d4fb9d69a108b0daa7e SHA1: dd5f982fd33f1dc5862872d318cc90f93f70ad69 SHA256: 316cc3a961c35d041dc46cd569f1d1465397b4ab883450364fec1d8f152b0dfa SHA512: d4766caf44be191b3f97f3e1580defd565826777bbadd04c9df3650d802f1123211d90bf40a8a41d7cb9c9b94ac187c90a4d64f1ef1b6baa47fbbe8947908c2d Homepage: https://cran.r-project.org/package=Rita Description: CRAN Package 'Rita' (Automated Transformations, Normality Testing, and Reporting) Automated performance of common transformations used to fulfill parametric assumptions of normality and identification of the best performing method for the user. Output for various normality tests (Thode, 2002) corresponding to the best performing method and a descriptive statistical report of the input data in its original units (5-number summary and mathematical moments) are also presented. Lastly, the Rankit, an empirical normal quantile transformation (ENQT) (Soloman & Sawilowsky, 2009), is provided to accommodate non-standard use cases and facilitate adoption. . . 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'ritalic' includes functions for retrieving information about lichen scientific names, geographic distribution, ecological data, morpho-functional traits and identification keys. More information about the data is available at . The API documentation is available at . 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Package: r-cran-ritis Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-solrium, r-cran-crul, r-cran-jsonlite, r-cran-data.table, r-cran-tibble Suggests: r-cran-testthat, r-cran-webmockr, r-cran-vcr Filename: pool/dists/noble/main/r-cran-ritis_1.0.0-1.ca2404.1_all.deb Size: 153656 MD5sum: 024d73106c5f3a8dadf95e2fea55b180 SHA1: 281c4b84812f5c5f3031bc41a47c601df24606cb SHA256: 76b82776fb1357ecf79ae0090c92e0d53e4c5d0e61c0994f9b14e846adb26c59 SHA512: c109078c974136bab8bb3bf8d5ab085945eba1305b250300d91d18c08861b3ca69d2b9f0d7db4f37f0b076ae7820939c4f72ab7bb8823ef597ab2cfdb2563804 Homepage: https://cran.r-project.org/package=ritis Description: CRAN Package 'ritis' (Integrated Taxonomic Information System Client) An interface to the Integrated Taxonomic Information System ('ITIS') (). 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Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008) . This test is useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments. 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For a review, see Jumani et al. (2020) and Baldan et al. (2022) Functions to calculate temporal indices improvement when fragmentation due to barriers is reduced are also included. 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Also included are a variety of computation and graphical tools designed for fisheries telemetry research, such as minimum home range, kernel density estimation, and clustering analysis using empirical k-functions with a bootstrap envelope. Tools are also provided for editing the river networks, meaning there is no reliance on external software. 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The package provides functions to read this data from text files, to analyse the network structure and network paths and regions consisting of sections and nodes that fulfill prescribed criteria, and to plot the river network and associated properties. 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Application domains include pharamaceutical industry QA/QC and R&D together with academic research. 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Define derivations in 'R', 'Python' or 'Julia', chain them into a composition of pure functions and build the resulting pipeline using 'Nix' as the underlying end-to-end build tool. Functions to plot the pipeline as a directed acyclic graph are included, as well as functions to load and inspect intermediary results for interactive analysis. User experience heavily inspired by the 'targets' package. 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Streamline your data analysis workflows by importing datasets effortlessly and focusing on insights rather than manual data handling. Perfect for data enthusiasts and professionals looking to integrate Kaggle datasets into their R projects with minimal hassle. 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Package: r-cran-rkeel Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 28697 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-xml, r-cran-doparallel, r-cran-foreach, r-cran-gdata, r-cran-rkeeldata, r-cran-pmml, r-cran-arules, r-cran-matrix, r-cran-rjava, r-cran-openssl, r-cran-downloader Filename: pool/dists/noble/main/r-cran-rkeel_1.3.4-1.ca2404.1_all.deb Size: 13312850 MD5sum: b6a24e9ce3c914b16359d4361f1caf06 SHA1: 78750cce1c52f2d1e6baae2beaccceb29cf4d1a2 SHA256: d975e382153c95394c0c21cfc52fe75aaeff2d452b3c71442fca61e9684e5815 SHA512: 1e15e9127a3bf8e648364888ef4200248529a2546311d3f9dff8be5c73717988da45ceda3df418341b4075ca5607e348a40f792f5430cf808b5aae9c90f42f83 Homepage: https://cran.r-project.org/package=RKEEL Description: CRAN Package 'RKEEL' (Using 'KEEL' in R Code) 'KEEL' is a popular 'Java' software for a large number of different knowledge data discovery tasks. This package takes the advantages of 'KEEL' and R, allowing to use 'KEEL' algorithms in simple R code. The implemented R code layer between R and 'KEEL' makes easy both using 'KEEL' algorithms in R as implementing new algorithms for 'RKEEL' in a very simple way. It includes more than 100 algorithms for classification, regression, preprocess, association rules and imbalance learning, which allows a more complete experimentation process. For more information about 'KEEL', see . 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This package includes the datasets from 'KEEL' in .dat format for its use in 'RKEEL' package. For more information about 'KEEL', see . Package: r-cran-rkelly Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rkelly_1.0-1.ca2404.1_all.deb Size: 23024 MD5sum: 1a17ec2bbf27ef1093fcd49040058e69 SHA1: 78c26e3ae82509999cf9785850d98415ab4ad895 SHA256: f07b145fe9a48c7dd68119cd2118f8ec921f2f7f48dc7aec788bda19eefc64da SHA512: 542b89447feebf28df0030de1544340dc45ba9ed1f02b5a63c5f2c9c93b28dbb1bbf54e80bed8cabc4d2ba5c75cb59b8f5bd32b5393a40bc0fb5c31bad4ec2a4 Homepage: https://cran.r-project.org/package=RKelly Description: CRAN Package 'RKelly' (Translate Odds and Probabilities) Calculates the Kelly criterion (Kelly, J.L. (1956) ) for bets given quoted prices, model predictions and commissions. Additionally it contains helper functions to calculate the probabilities for wins and draws in multi-leg games. 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Package: r-cran-rkin Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ks, r-cran-sf, r-cran-ggplot2, r-cran-mass, r-cran-rcolorbrewer, r-cran-randomcolor, r-cran-shades, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rkin_1.0.4-1.ca2404.1_all.deb Size: 100948 MD5sum: 1af2306f7aa76aae79d76377ae3239fd SHA1: 49ed0cc0e0f7f36048b9ee4b27a5e4c48c584c1f SHA256: ceefefea3753e914893d5aeba40a60ef11bb3f089e091feaef74843e5620c5c6 SHA512: 8709cacb3ffd7a56ca9a1a012942c9263d437206a4982b0b18ae83ba2366b1706f1609872962c5393ab3480238945085f3f5810afa264b41edcc31309b44ac46 Homepage: https://cran.r-project.org/package=rKIN Description: CRAN Package 'rKIN' ((Kernel) Isotope Niche Estimation) Applies methods used to estimate animal homerange, but instead of geospatial coordinates, we use isotopic coordinates. The estimation methods include: 1) 2-dimensional bivariate normal kernel utilization density estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon estimator, all applied to stable isotope data. Additionally, functions to determine niche area, polygon overlap between groups and levels (confidence contours) and plotting capabilities. Package: r-cran-rkmetrics Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rkmetrics_1.3-1.ca2404.1_all.deb Size: 97118 MD5sum: f52d449cfc67a1716b5dec6a2246ce4e SHA1: 595cf68b32b445c4fb4e1633dc190a6d22f9ad70 SHA256: 8ba4ef5b752aaf852399af5873cdbabc8f3bcbd041922d5514817d57ec152135 SHA512: 2ca4ff2241d058520845f86b3ee4456aa177ead532886a28700c1b62ca5dcfd4237e92be39a3ea595aaad4903b10b185d4b45ad64c0d281bdc61a3866685cdbc Homepage: https://cran.r-project.org/package=RkMetrics Description: CRAN Package 'RkMetrics' (Hybrid Mortality Estimation) Hybrid Mortality Modelling (HMM) provides a framework in which mortality around "the accident hump" and at very old ages can be modelled under a single model. The graphics' codes necessary for visualization of the models' output are included here. Specifically, the graphics are based on the assumption that, the mortality rates can be expressed as a function of the area under the curve between the crude mortality rates plots and the tangential transform of the force of mortality. Package: r-cran-rkolada Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-jsonlite, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-stringi, r-cran-ggplot2, r-cran-scales, r-cran-progress Filename: pool/dists/noble/main/r-cran-rkolada_0.3.1-1.ca2404.1_all.deb Size: 689670 MD5sum: 9e6c623ffa14baadf425c4b87f3e8c1c SHA1: 3b872a42e243b73e14bc98c4fccd0a64c4e025fe SHA256: 814681df0c63675b86a252778ccdee47402d7e924eb31c0296ef49a13d59f2d4 SHA512: 7a9d032cfa8ab0e39d582df5ed801d89a3995432a8ae22038fd7fd21bd59c36adcf51ab956c528424576ea05d7bb4616e97a4b3cbd794b27842f49bf92310160 Homepage: https://cran.r-project.org/package=rKolada Description: CRAN Package 'rKolada' (Access Data from the 'Kolada' Database) Methods for downloading and processing data and metadata from 'Kolada', the official Swedish regions and municipalities database . Package: r-cran-rkomics Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 24870 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ape, r-cran-circlize, r-bioc-complexheatmap, r-cran-reshape2, r-cran-dplyr, r-cran-factoextra, r-cran-factominer, r-cran-ggpubr, r-cran-magrittr, r-cran-stringr Suggests: r-cran-viridis Filename: pool/dists/noble/main/r-cran-rkomics_1.3-1.ca2404.1_all.deb Size: 1770044 MD5sum: fb53cd803e4daaabe38fa4d5e8f216e4 SHA1: 83da1d4744b9426491f87aae01c5b8d6f7b9fc78 SHA256: 29e9bde29d07ef9562077b66c9b38696a507ee7891131da5d91a9d63e9968bee SHA512: 9368e82bebaa41163087a5bd0d5a709afbd6be46dab9c8d3943ab90d8bd7a9c32d4d5d678deaf84d02a3a78837a011518895dd0801b7d6b671bf05c2e1382ffe Homepage: https://cran.r-project.org/package=rKOMICS Description: CRAN Package 'rKOMICS' (Minicircle Sequence Classes (MSC) Analyses) This is a analysis toolkit to streamline the analyses of minicircle sequence diversity in population-scale genome projects. rKOMICS is a user-friendly R package that has simple installation requirements and that is applicable to all 27 trypanosomatid genera. Once minicircle sequence alignments are generated, rKOMICS allows to examine, summarize and visualize minicircle sequence diversity within and between samples through the analyses of minicircle sequence clusters. We showcase the functionalities of the (r)KOMICS tool suite using a whole-genome sequencing dataset from a recently published study on the history of diversification of the Leishmania braziliensis species complex in Peru. Analyses of population diversity and structure highlighted differences in minicircle sequence richness and composition between Leishmania subspecies, and between subpopulations within subspecies. The rKOMICS package establishes a critical framework to manipulate, explore and extract biologically relevant information from mitochondrial minicircle assemblies in tens to hundreds of samples simultaneously and efficiently. This should facilitate research that aims to develop new molecular markers for identifying species-specific minicircles, or to study the ancestry of parasites for complementary insights into their evolutionary history. ***** !! WARNING: this package relies on dependencies from Bioconductor. For Mac users, this can generate errors when installing rKOMICS. Install Bioconductor and ComplexHeatmap at advance: install.packages("BiocManager"); BiocManager::install("ComplexHeatmap") *****. Package: r-cran-rkorapclient Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 834 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.cache, r-cran-broom, r-cran-ggplot2, r-cran-tibble, r-cran-magrittr, r-cran-tidyr, r-cran-dplyr, r-cran-lubridate, r-cran-highcharter, r-cran-jsonlite, r-cran-keyring, r-cran-httr2, r-cran-curl, r-cran-ptxqc, r-cran-purrr, r-cran-stringr, r-cran-urltools, r-cran-xml2 Suggests: r-cran-lifecycle, r-cran-testthat, r-cran-tidyllm, r-cran-htmlwidgets, r-cran-rmarkdown, r-cran-shiny, r-cran-vcd, r-cran-kableextra, r-cran-knitr, r-cran-purrrlyr, r-cran-raster, r-cran-tidyverse, r-cran-sf, r-cran-geojsonsf, r-cran-viridislite Filename: pool/dists/noble/main/r-cran-rkorapclient_1.2.1-1.ca2404.1_all.deb Size: 636904 MD5sum: 9c410589776f22e1a361f154d34e4036 SHA1: 90753452aa5714f3fd29c1b5ef5fd732b24cb110 SHA256: 96372f37a534c4c470c58839e340f4c90e6cf95b39ebb8a7fba32036d66aca12 SHA512: f0c866f247c4104b0c97da6f5c2b85246280753c7bc6c8ad15bd337606ace3cbbf68088c7718079af4e43421c317b79d66d566a2a68f84314744372d332916e7 Homepage: https://cran.r-project.org/package=RKorAPClient Description: CRAN Package 'RKorAPClient' ('KorAP' Web Service Client Package) A client package that makes the 'KorAP' web service API accessible from R. The corpus analysis platform 'KorAP' has been developed as a scientific tool to make potentially large, stratified and multiply annotated corpora, such as the 'German Reference Corpus DeReKo' or the 'Corpus of the Contemporary Romanian Language CoRoLa', accessible for linguists to let them verify hypotheses and to find interesting patterns in real language use. The 'RKorAPClient' package provides access to 'KorAP' and the corpora behind it for user-created R code, as a programmatic alternative to the 'KorAP' web user-interface. You can learn more about 'KorAP' and use it directly on 'DeReKo' at . Package: r-cran-rkt Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rkt_1.7-1.ca2404.1_all.deb Size: 46624 MD5sum: 55c32a0aeee3248fee60649f5a202ae1 SHA1: 276e30f5118e7c39c60644fddbc3823e8a3b41a0 SHA256: 508bf7b3d80070e7b003494392a6d366b5fe1d24d1b1f9db87a3e021b3b761a6 SHA512: 13bb8d22a5484b9a00381cf9c678fc0f029323c2699f271950c2da17da2b822163c8f1dea721682fc1b8c36f4c0104d40624bdc6ed19d322861f9125e604a943 Homepage: https://cran.r-project.org/package=rkt Description: CRAN Package 'rkt' (Mann-Kendall Test, Seasonal and Regional Kendall Tests) Contains function rkt which computes the Mann-Kendall test (MK) and the Seasonal and the Regional Kendall Tests for trend (SKT and RKT) and Theil-Sen's slope estimator. 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A, Calhoun, C. D., Wang Y.-P. (2018) . Package: r-cran-rlab Architecture: all Version: 4.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rlab_4.5.1-1.ca2404.1_all.deb Size: 265548 MD5sum: 8430c8b3d3651ca28a8c38b64f535529 SHA1: 282ce30e3edca7c7533c3b7225796fdce91c155b SHA256: a76c8a90ad835fdff30f377ada94d965dc6155c9a4d238899b42112fc0e07ea1 SHA512: b12c6af53b2821e2b01e13cdc1f4b35f44c302d6cd931841780e5611bf7f4556e5a98a029e9e09090fdd829b4be531a65056a1efdaf57940d38b0246381a8f06 Homepage: https://cran.r-project.org/package=Rlab Description: CRAN Package 'Rlab' (Functions and Datasets Required for ST370 Class) Provides functions and datasets required for the ST 370 course at North Carolina State University. Package: r-cran-rlakeanalyzer Architecture: all Version: 1.11.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2014 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rlakeanalyzer_1.11.4.1-1.ca2404.1_all.deb Size: 528162 MD5sum: 639df2e1a8662a909a9e7b8265757a97 SHA1: c5444b1fad12d1e7bd7a2c083b808d779debf655 SHA256: c7b2636153292881827b45e1f6a0c7d8e89d40ee87d1a4e104053cbe102c47fb SHA512: eea05f470cd4c0e1215e0cc2500d1895b13b093cc0ab1a54f76250c2de42fd97e2177dcd5a73f55520ca1a6cad3b69061ec9cdce89968be0b31b10e5def4d8cb Homepage: https://cran.r-project.org/package=rLakeAnalyzer Description: CRAN Package 'rLakeAnalyzer' (Lake Physics Tools) Standardized methods for calculating common important derived physical features of lakes including water density based based on temperature, thermal layers, thermocline depth, lake number, Wedderburn number, Schmidt stability and others. 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Other functions focus on quantifying physical aquatic habitats (e.g., littoral, epliminion, metalimnion, hypolimnion) from interpolated digital elevation models (DEMs). Functions were designed to calculate these metrics across water levels for use in reservoirs but can be applied to any DEM and will provide values for fixed conditions. Parameters like Secchi disk depth or estimated photic zone, thermocline depth, and water level fluctuation depth are included in most functions. 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Package: r-cran-rldcp Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rldcp_1.0.2-1.ca2404.1_all.deb Size: 101138 MD5sum: db09cf08044da3c01574e6ad5797b4bf SHA1: 96d786892370087956bf9be0c154a5abc2458d44 SHA256: 6d376b14d8ca0bbef0d7b526bb7e008092550d0da5bfad669ed84d727a0b002e SHA512: ba8007eccdebbd295b976cdacf94379977a951294610968e2e223c89be27cabe53da112aaf87f26e7c7c23937813e47aba71d9a4873577013944409276849fc3 Homepage: https://cran.r-project.org/package=rLDCP Description: CRAN Package 'rLDCP' (Text Generation from Data) Linguistic Descriptions of Complex Phenomena (LDCP) is an architecture and methodology that allows us to model complex phenomena, interpreting input data, and generating automatic text reports customized to the user needs (see and ). 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Package: r-cran-rleafangle Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rleafangle_1.0-1.ca2404.1_all.deb Size: 215226 MD5sum: 6d3dcf6df3da325895ba892b3bf07dcd SHA1: 509988fafa176b8bdd817c3fc6435b42f40b5319 SHA256: bcf88aae2b4e91fc7feca3ba994717b71777d225d14506716cbb52648f721341 SHA512: 616736306d9de196be073427586e373d44ea4979281cfade3b5aaae596828ceca9d507c8639a790c5d2812e2e6f7da12baa2c8ca171bff9bfb2ce46cb8c6d146 Homepage: https://cran.r-project.org/package=RLeafAngle Description: CRAN Package 'RLeafAngle' (Estimates, Plots and Evaluates Leaf Angle DistributionFunctions, Calculates Extinction Coefficients) Leaf angle distribution is described by a number of functions (e.g. ellipsoidal, Beta and rotated ellipsoidal). The parameters of leaf angle distributions functions are estimated through different empirical relationship. 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Package: r-cran-rlescalation Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-r6, r-cran-nleqslv, r-cran-reticulate, r-cran-zip Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rlescalation_1.0.3-1.ca2404.1_all.deb Size: 176538 MD5sum: c5aac12d84f6883be4ad2ce64860f77b SHA1: c16881d5ac5340824a37bc39685f5fc0990cb7ce SHA256: daedb86fb7603f20ac572b09d03a1c8ff62586174e8c00f8d5caa9c2b82117f4 SHA512: 972015cec60e90603538282020a6198e7d2f4b1e373e15d5a9cdd1c32468d992ab27ef687f6bbb308750c30bf42aeaf7acc4070c6906b4f4078cf23f156a4d04 Homepage: https://cran.r-project.org/package=RLescalation Description: CRAN Package 'RLescalation' (Optimal Dose Escalation Using Deep Reinforcement Learning) An implementation to compute an optimal dose escalation rule using deep reinforcement learning in phase I oncology trials (Matsuura et al. 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Package: r-cran-rmass2 Architecture: all Version: 0.0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmass2_0.0.0.2-1.ca2404.1_all.deb Size: 44030 MD5sum: fb17a7441a3313d0f55bdef23b1c65db SHA1: fd96544f75bcef30b575fb29eb8097094c6063e3 SHA256: 26e67b07974bcd551c43bb570b1ed9606c845a48301a30b2af30d867f11458e9 SHA512: c8421c7d7741f9f870e37e5ad5bcf51ed3a8ff9cdc1a784221506dee870f93bcdfa4a6ab774383fe19528877fd11bd6690475b823366ff0c1f8e1a3b48580a08 Homepage: https://cran.r-project.org/package=rmass2 Description: CRAN Package 'rmass2' (Repeated Measures with Attrition: Sample Sizes and Power Levelsfor 2 Groups) For the calculation of sample size or power in a two-group repeated measures design, accounting for attrition and accommodating a variety of correlation structures for the repeated measures; details of the method can be found in the scientific paper: Donald Hedeker, Robert D. Gibbons, Christine Waternaux (1999) . Package: r-cran-rmat Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-rmat_0.2.0-1.ca2404.1_all.deb Size: 54276 MD5sum: b783869e9e624baadeadb11893d7a840 SHA1: 91c60c73aae37fd484f4b52f12303a4e583ac0b9 SHA256: c5b3ba0b3151827361a11fd5c5a9da57cc46581da989bde6f8f5cf5b8c2a150f SHA512: 67b66f073b9fe4d3e7ac9c415423c13f660de8131664c30c5d7a14e128a240b724598ba7ad65a1f3828a6832aabd4d7655b3a78a304f07b53e379116dc39cb8c Homepage: https://cran.r-project.org/package=RMAT Description: CRAN Package 'RMAT' (Random Matrix Analysis Toolkit) Simulate random matrices and ensembles and compute their eigenvalue spectra and dispersions. Package: r-cran-rmawgen Architecture: all Version: 1.3.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2007 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chron, r-cran-date, r-cran-vars, r-cran-matrix Suggests: r-cran-lubridate Filename: pool/dists/noble/main/r-cran-rmawgen_1.3.9.3-1.ca2404.1_all.deb Size: 1986580 MD5sum: 4847aa4555ac695140e95c773385155a SHA1: 4fdcb15e554b998098551977f7687f0c6ba49ca5 SHA256: b029c3cc5afd910e0010907986753d7d2f9858f2f7a0b2f46b044f83fdcd4f19 SHA512: d3c978f3ef1163ff0feec3d5968a38eda5028e7c12a8a6ea56519e16cf091425086cc065119c993c6ed5b3ca7a422c6c63bd349237775ede9bd3866b346468fc Homepage: https://cran.r-project.org/package=RMAWGEN Description: CRAN Package 'RMAWGEN' (Multi-Site Auto-Regressive Weather GENerator) S3 and S4 functions are implemented for spatial multi-site stochastic generation of daily time series of temperature and precipitation. These tools make use of Vector AutoRegressive models (VARs). The weather generator model is then saved as an object and is calibrated by daily instrumental "Gaussianized" time series through the 'vars' package tools. Once obtained this model, it can it can be used for weather generations and be adapted to work with several climatic monthly time series. Package: r-cran-rmbc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ktaucenters, r-cran-mvtnorm, r-cran-mass Suggests: r-cran-tclust, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmbc_0.1.0-1.ca2404.1_all.deb Size: 56462 MD5sum: 461cf48742bb318e1cfe258f5da86243 SHA1: bd64b55fc55bbaa5dc4076ea70fbc2e827594162 SHA256: fed00dd26af83c31692a214e156708dc6893e8a8bdd5f4c1b740aa58705bd8c8 SHA512: 5fb2b713f75ef44dd45a53589e41a27aee41815c34f64d545bd30fce3f52df7f6bc22f9e0c75c34f07fdbc6e921f3302e6eedbafab967c6114991822d3f60cff Homepage: https://cran.r-project.org/package=RMBC Description: CRAN Package 'RMBC' (Robust Model Based Clustering) A robust clustering algorithm (Model-Based) similar to Expectation Maximization for finite mixture normal distributions is implemented, its main advantage is that the estimator is resistant to outliers, that means that results of parameter estimation are still correct when there are atypical values in the sample (see Gonzalez, Maronna, Yohai and Zamar (2021) ). Package: r-cran-rmcda Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-igraph, r-cran-fmsb, r-cran-lpsolve, r-cran-matlib, r-cran-nloptr, r-cran-matrixstats, r-cran-pracma Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmcda_0.3-1.ca2404.1_all.deb Size: 249744 MD5sum: 54df3cab48ef5999974bcd47a89eaba9 SHA1: a474cf457a48753ae9072e7d6d4f7e7b2443d4ba SHA256: ad611ad839f4d76c36745aa81bc9688214ad257fc33876fa10c466f89bf09424 SHA512: 1903dd5714f01a4af4c00f852db8869dde7d0aa378222ab9bfe5fa3bc707a8867750b7912251a89d02e7f0bfde03efb253b092955e632560fb3e430b3511a826 Homepage: https://cran.r-project.org/package=RMCDA Description: CRAN Package 'RMCDA' (Multi-Criteria Decision Analysis) Supporting decision making involving multiple criteria. Annice Najafi, Shokoufeh Mirzaei (2025) RMCDA: The Comprehensive R Library for applying multi-criteria decision analysis methods, Volume 24, e100762 . Package: r-cran-rmcfs Architecture: all Version: 1.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4940 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-yaml, r-cran-ggplot2, r-cran-gridextra, r-cran-reshape2, r-cran-dplyr, r-cran-stringi, r-cran-igraph, r-cran-data.table Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-rmcfs_1.3.6-1.ca2404.1_all.deb Size: 4683468 MD5sum: 501b1fb7a7d726c60772136bd349531f SHA1: 9a1596370c0f6675fd238c2f24d7b3ac38764b82 SHA256: 02dbb168953b31e3655d100e612be1d53e4a0f6e2ef6d18e376ed748a046b009 SHA512: 2738fe184c42fe1e07938359abc5aa626edfdcf4a62b72ec4372bac071e9e9d516c4835232cc7a4e9b04efbd82b5d02e1b1e08b33201e7aeaac419cf367bc907 Homepage: https://cran.r-project.org/package=rmcfs Description: CRAN Package 'rmcfs' (The MCFS-ID Algorithm for Feature Selection and InterdependencyDiscovery) MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data. M. Draminski, J. Koronacki (2018) . Package: r-cran-rmcmc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rlang, r-cran-withr Suggests: r-cran-knitr, r-cran-posterior, r-cran-progress, r-cran-ramcmc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmcmc_0.1.2-1.ca2404.1_all.deb Size: 288416 MD5sum: b0ab88e2ecc16c533e9aca38ea2b66fa SHA1: 96a5bc03d93a54de72edbdd9ba346af977285eb0 SHA256: 6841913003d6978dd84a9ca8f3310c8044241adff0fc34e4279bf277db97b30a SHA512: 3a749671b49a24431ead839957801df1af3688a9c11e0d3d24012bafca1f05d65c2dff312e371b9a2431f49c1314419c82d5ae74c64975cf372e4f3b18252004 Homepage: https://cran.r-project.org/package=rmcmc Description: CRAN Package 'rmcmc' (Robust Markov Chain Monte Carlo Methods) Functions for simulating Markov chains using the Barker proposal to compute Markov chain Monte Carlo (MCMC) estimates of expectations with respect to a target distribution on a real-valued vector space. The Barker proposal, described in Livingstone and Zanella (2022) , is a gradient-based MCMC algorithm inspired by the Barker accept-reject rule. It combines the robustness of simpler MCMC schemes, such as random-walk Metropolis, with the efficiency of gradient-based methods, such as the Metropolis adjusted Langevin algorithm. The key function provided by the package is sample_chain(), which allows sampling a Markov chain with a specified target distribution as its stationary distribution. The chain is sampled by generating proposals and accepting or rejecting them using a Metropolis-Hasting acceptance rule. During an initial warm-up stage, the parameters of the proposal distribution can be adapted, with adapters available to both: tune the scale of the proposals by coercing the average acceptance rate to a target value; tune the shape of the proposals to match covariance estimates under the target distribution. As well as the default Barker proposal, the package also provides implementations of alternative proposal distributions, such as (Gaussian) random walk and Langevin proposals. Optionally, if 'BridgeStan's R interface , available on GitHub , is installed, then 'BridgeStan' can be used to specify the target distribution to sample from. Package: r-cran-rmcorr Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2496 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-plotrix, r-cran-lme4, r-cran-mertools, r-cran-pwr, r-cran-aiccmodavg, r-cran-pals, r-cran-testthat, r-cran-vdiffr, r-cran-corrplot, r-cran-cocor, r-cran-covr, r-cran-ggextra, r-cran-gglm, r-cran-dplyr, r-cran-esc, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-rmcorr_0.7.0-1.ca2404.1_all.deb Size: 1470460 MD5sum: 8e6dea3c364eaa51d89ab1436718bec6 SHA1: 50e75c8c55159a418c2273d765b14c34ec1fc0fe SHA256: 911631135dc0d2187f0755df8fb0278ef9dbaea8e5b3b5b501ec3d91f03e4957 SHA512: 0d4f96c49041aadef1e150cdb24ddf8dafeca07fa902e697fbb55bc1f6ac5f426135b852fa7663a2ddedb0acae583fd45194fc0c14c28051efe6d34240878b12 Homepage: https://cran.r-project.org/package=rmcorr Description: CRAN Package 'rmcorr' (Repeated Measures Correlation) Compute the repeated measures correlation, a statistical technique for determining the overall within-individual relationship among paired measures assessed on two or more occasions, first introduced by Bland and Altman (1995). 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Given one or more risk prediction instruments (risk models) that estimate the probability of a binary outcome, rmda provides functions to estimate and display decision curves and other figures that help assess the population impact of using a risk model for clinical decision making. Here, "population" refers to the relevant patient population. Decision curves display estimates of the (standardized) net benefit over a range of probability thresholds used to categorize observations as 'high risk'. The curves help evaluate a treatment policy that recommends treatment for patients who are estimated to be 'high risk' by comparing the population impact of a risk-based policy to "treat all" and "treat none" intervention policies. Curves can be estimated using data from a prospective cohort. In addition, rmda can estimate decision curves using data from a case-control study if an estimate of the population outcome prevalence is available. 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Package: r-cran-rmdhelpers Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-rmdhelpers_1.3.1-1.ca2404.1_all.deb Size: 38050 MD5sum: 56a6d1d838e20437210b9e873ea801f7 SHA1: 14dd97949eeeed940e3c80504de0b34c5bfdea2c SHA256: 7d1c7a6c49729f40c13b84f65ac8d0fef21db6f772ab3d99337d950753c06aef SHA512: ef5f2deff70665c35f2082b0647596bcc186e2362cb01bc3f3d21c6f251807ff9c2022512056fbed5e2800c001022382cdb52f6e843ad64cae0db8ddc661f832 Homepage: https://cran.r-project.org/package=rmdHelpers Description: CRAN Package 'rmdHelpers' (Helper Functions for Rmd Documents) A series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. I will continue updating with new functions as I add utility functions for myself. Package: r-cran-rmdl Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vctrs, r-cran-tibble, r-cran-generics, r-cran-dplyr, r-cran-broom, r-cran-tidyr, r-cran-rlang, r-cran-pillar, r-cran-purrr, r-cran-janitor Suggests: r-cran-testthat, r-cran-covr, r-cran-cli, r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-gt, r-cran-survival, r-cran-cmprsk, r-cran-tidycmprsk Filename: pool/dists/noble/main/r-cran-rmdl_0.1.0-1.ca2404.1_all.deb Size: 257062 MD5sum: 33eb00ec0488bbf5ce78944a15d56bee SHA1: 2ccf050b9ac1a865f50ede21200b0ac40719e462 SHA256: 57e14338777e1e197f11ab54f4df87acc631eb5a112c559f9242b67aaed77fc8 SHA512: 6cd5f9cc3cf6d6105b8fffde309a56a7736afad62336a14c083908173fd2e1b4763cef31a593071c9c7ea98e81dd58cadef5bf8048e4286640f2a0529c65d121 Homepage: https://cran.r-project.org/package=rmdl Description: CRAN Package 'rmdl' (Language to Manage Many Models) A system for describing and manipulating the many models that are generated in causal inference and data analysis projects, as based on the causal theory and criteria of Austin Bradford Hill (1965) . This system includes the addition of formal attributes that modify base `R` objects, including terms and formulas, with a focus on variable roles in the "do-calculus" of modeling, as described in Pearl (2010) . For example, the definition of exposure, outcome, and interaction are implicit in the roles variables take in a formula. These premises allow for a more fluent modeling approach focusing on variable relationships, and assessing effect modification, as described by VanderWeele and Robins (2007) . The essential goal is to help contextualize formulas and models in causality-oriented workflows. Package: r-cran-rmdpartials Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-knitr, r-cran-rlang Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-covr, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-pkgdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-rmdpartials_0.6.5-1.ca2404.1_all.deb Size: 48728 MD5sum: 06814ea3446e1fa928f06dcf79da1bc3 SHA1: a6ed54fefbc582e5814850a9d57c344878a07f02 SHA256: 699342a4b238c68575439b28057273b5051692f395cf49827e1fe4ac2b67d5e7 SHA512: d37711d906fae4caad27df2f924ed10e86b9dec00be446537a7e28b208ff9de97f95be722a9630237b6c3c3d0086b7f98e4c60a1237c091c2212ab54386b6840 Homepage: https://cran.r-project.org/package=rmdpartials Description: CRAN Package 'rmdpartials' (Partial 'rmarkdown' Documents to Prettify your Reports) Use 'rmarkdown' partials, also know as child documents in 'knitr', so you can make components for HTML, PDF, and Word documents. The package provides various helper functions to make certain functions easier. You may want to use this package, if you want to flexibly summarise objects using a combination of figures, tables, text, and HTML widgets. Unlike HTML widgets, the output is Markdown and can hence be turn into other output formats than HTML. Currently does not play well with 'rmarkdown' notebooks, not tested with Quarto. Package: r-cran-rmdplugr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rmarkdown, r-cran-bookdown Suggests: r-cran-testthat, r-cran-covr, r-cran-spelling, r-cran-knitr Filename: pool/dists/noble/main/r-cran-rmdplugr_0.4.1-1.ca2404.1_all.deb Size: 295390 MD5sum: 5a2b9fb693184284c0e5e30252b04983 SHA1: 3647abe5022f5dfbe9ced8adcacf0855ecd7dac3 SHA256: a88834ffdc5d097b03b22f311fba20dedb9cd18eec96aca4922f06588de39870 SHA512: 821ebdf046f1943ea1ef56e66728158f95a2582e6cfbaece176fd4479c99ad3c10bea1f1b09db9cf66c358849e7ab896863ad130908d62afc1c3e6a24e583528 Homepage: https://cran.r-project.org/package=rmdplugr Description: CRAN Package 'rmdplugr' (Plugins for R Markdown Formats) Formats for R Markdown that undo modifications by 'pandoc' and 'rmarkdown' to original 'latex' templates, such as smaller margins, paragraph spacing, and compact titles. In addition, enhancements such as author blocks with affiliations and headers and footers are introduced. All of this functionality is built around plugins that modify the default 'pandoc' template without relying on custom templates. 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Package: r-cran-rmea Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2410 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rmea_1.2.2-1.ca2404.1_all.deb Size: 938434 MD5sum: 6d04d2cdfa8f5833ceff06be64db0c5f SHA1: 9c1481b4a41144c85cbbc68e33afcef46f5ee4e3 SHA256: b51f15821539171b21e9d3f9e9ca5d47ea1a66f8cd9bf07a98ed72315bc8b39f SHA512: 422c1d98d4b8e1dc17cd3e7328df5e4138ca873c51d477896276b13376b536f2a38e6d162563c93aeffa0b4503197639a83e1a82d3b4d89cb502b310ae23dfe5 Homepage: https://cran.r-project.org/package=rMEA Description: CRAN Package 'rMEA' (Synchrony in Motion Energy Analysis (MEA) Time-Series) A suite of tools useful to read, visualize and export bivariate motion energy time-series. Lagged synchrony between subjects can be analyzed through windowed cross-correlation. Surrogate data generation allows an estimation of pseudosynchrony that helps to estimate the effect size of the observed synchronization. Kleinbub, J. R., & Ramseyer, F. T. (2020). rMEA: An R package to assess nonverbal synchronization in motion energy analysis time-series. Psychotherapy research, 1-14. . Package: r-cran-rmediation Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1845 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-e1071, r-cran-openmx, r-cran-mass, r-cran-modelr, r-cran-generics, r-cran-checkmate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmediation_1.3.0-1.ca2404.1_all.deb Size: 1248300 MD5sum: 47e1348e48ecd54e8dfe899b0f1e938e SHA1: 36050faf4da0dc99e758f36bee23694f08c262ca SHA256: 311d737f0db4499508806ac1ae0681a1371ea099a7ae5b1247f25e2cc54a8ee7 SHA512: df94a5920ee1ccce526727070247b42f3e9e08b2b04660e11978945a879f56eef0452ade873491f0c2b994ea2fae1bd3139d76c4bb5c1268c824d53855f1f873 Homepage: https://cran.r-project.org/package=RMediation Description: CRAN Package 'RMediation' (Mediation Analysis Confidence Intervals) Computes confidence intervals for nonlinear functions of model parameters (e.g., product of k coefficients) in single-level and multilevel structural equation models. Methods include the distribution of the product, Monte Carlo simulation, and bootstrap methods. It also performs the Model-Based Constrained Optimization (MBCO) procedure for hypothesis testing of indirect effects. References: Tofighi, D., and MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692-700. ; Tofighi, D., and Kelley, K. (2020). Improved inference in mediation analysis: Introducing the model-based constrained optimization procedure. Psychological Methods, 25(4), 496-515. ; Tofighi, D. (2020). Bootstrap Model-Based Constrained Optimization Tests of Indirect Effects. Frontiers in Psychology, 10, 2989. . Package: r-cran-rmedpower2 Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8604 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lme4, r-cran-dplyr, r-cran-simr, r-cran-magrittr, r-cran-ggplot2, r-cran-ggtext, r-cran-quantreg, r-cran-tibble, r-cran-lmertest, r-cran-dharma, r-cran-influence.me, r-cran-envstats, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmedpower2_1.0.1-1.ca2404.1_all.deb Size: 5396362 MD5sum: 86978138181d5d4309e9d9a636252b94 SHA1: b79f90ea904fbc4ecebed67aa4ed43ceac3c7425 SHA256: 873aae571a9161017dd0f5a244681e58970d7c649c35e159a4a8f981c070a2cd SHA512: ed987bddcb4c0165997b5a04d7168418c4aaf19df0ec7f1fcb02727f64e45c18d7721fdc0fba03a5669d78f9d36771b60feabd917f50aef295bfdfbd88642c1e Homepage: https://cran.r-project.org/package=RMeDPower2 Description: CRAN Package 'RMeDPower2' (Design and Modeling for Repeated Measures Studies) Provides complete functionality to analyse data from repeated measures experiments with hierarchical or crossed experimental designs. Supports testing modeling assumptions, identifying outlier observations and experimental units, estimating statistical power, and performing sample size calculations. Uses linear mixed effects models via 'lme4' and simulation-based power analysis via 'simr'. Handles both normal and non-normal error distributions including binomial and Poisson families. For more details see Shin et al. (2022) , Bates et al. (2015) , Green and MacLeod (2016) , Hartig (2024) , Nieuwenhuis et al. (2012) , Millard (2013) and Kuznetsova et al. (2017) . Package: r-cran-rmedsem Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-mvtnorm, r-cran-ggplot2, r-cran-dplyr, r-cran-purrr Suggests: r-cran-blavaan, r-cran-boot, r-cran-csem, r-cran-hdinterval, r-cran-modsem, r-cran-semplot, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmedsem_1.0.0-1.ca2404.1_all.deb Size: 286280 MD5sum: 11eaffcc69d9ca8a9b11b631688cfbb1 SHA1: d7dfed48b84576c8edd70903d88fe9a6e79601c3 SHA256: 98c897f6f5ef9cc3b8526416a0104adc555be4794e2600c55aa1b2e71610fdff SHA512: b1f219c72f3c162546d12f5e82f4577ea8232216f64f873fd5360640425122fe708186c06ca0cc2e421319e99040cb8d90af15e8b8e25c56e8452feffaea3785 Homepage: https://cran.r-project.org/package=rmedsem Description: CRAN Package 'rmedsem' (Statistical Mediation Analysis for SEMs) Conducts mediation analysis for structural equation models (SEM) estimated with 'lavaan', 'blavaan', 'cSEM', or 'modsem'. Implements the Baron and Kenny (1986) and Zhao, Lynch & Chen (2010) approaches to determine the presence and type of mediation. Supports covariance-based SEM, partial least squares SEM, Bayesian SEM, and moderated mediation models. Reports indirect effects with standard errors from Sobel, Delta, Monte-Carlo, and bootstrap methods, along with effect size measures (RIT, RID). Package: r-cran-rmerec Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmerec_0.1.1-1.ca2404.1_all.deb Size: 14010 MD5sum: e754ef7d4c3ee61a58eec80cae92955f SHA1: 3b0a61867ceb483cb08aaf15ff34e9721c3e8c33 SHA256: 22938d74336fe03af7b6ec9807057b9b28e3119ef84144ab1359fd006c4cc0e4 SHA512: efd4ce03f73ba566943a0bc501c3b602326ebc756f97c819ae7e1583a3888a83ae45c2c6cd21c975f103fa6ae61ceabf72cbcaec32bf728ed7944744332a5c36 Homepage: https://cran.r-project.org/package=rmerec Description: CRAN Package 'rmerec' (MEREC - Method Based on the Removal Effects of Criteria) Implementation of the MEthod based on the Removal Effects of Criteria - MEREC- a new objective weighting method for determining criteria weights for Multiple Criteria Decision Making problems, created by Mehdi Keshavarz-Ghorabaee (2021) . Given a decision matrix, the function return the Merec´s weight vector and all intermediate matrix/vectors used to calculate it. Package: r-cran-rmet Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl Suggests: r-cran-spelling, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmet_0.1.0-1.ca2404.1_all.deb Size: 326342 MD5sum: 1ebc803a98bf4ea4384d323e1888fcc2 SHA1: cc2982acf3f28d671d680ecb41719786ec118dba SHA256: ebeb373f18eba3512798e9eaa5814712c34e842178e3c0be4499d75ff5fcb5a1 SHA512: 1a32b745f3198699e0107a371a7ec05611c6493733100d6cbd419d1a324dfb996723e7e7b7538384dfaf80ea02e54cc6927dbc07e4083814cb73c893e0c200c5 Homepage: https://cran.r-project.org/package=rmet Description: CRAN Package 'rmet' (Download and Read Brazilian Meteorological Data from INMET) Automates the download and processing of historical weather data from the Brazilian National Institute of Meteorology (INMET). It resolves formatting inconsistencies in raw CSV files across different years, removes structural artifacts, standardizes column names, converts timestamps to local Brazilian time zones, and outputs tidy data frames ready for analysis. Data are retrieved from . 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This package provides functions to build these distributions from raw data. Resulting metalog objects are then useful for exploratory and probabilistic analysis. Package: r-cran-rmfanova Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-mass, r-cran-refund Filename: pool/dists/noble/main/r-cran-rmfanova_0.1.0-1.ca2404.1_all.deb Size: 61246 MD5sum: 7ed811d299d2a54e14206f5127c6bdca SHA1: e44b1ec22fadc2fe66d576445f5014bafd83221a SHA256: 7643ab81d8465449b250ab9d327070ea9695e3032a5a235c9233d640aee44d4b SHA512: 21c3a6a7bc207eec91089b197d496b6394df108c288831dae0a0afefc1bb18e5b4d081cc46f7b26bbbc09bcfba4176c48eb768fd2e09c53127b5f62f1d1eec45 Homepage: https://cran.r-project.org/package=rmfanova Description: CRAN Package 'rmfanova' (Repeated Measures Functional Analysis of Variance) The provided package implements the statistical tests for the functional repeated measures analysis problem (Kurylo and Smaga, 2023, ). 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Package: r-cran-rmfrac Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-parallelly, r-cran-ggplot2, r-cran-zoo, r-cran-matrixstats, r-cran-proxy, r-cran-rlang, r-cran-foreach, r-cran-doparallel, r-cran-plotly, r-cran-shiny, r-cran-shinycssloaders, r-cran-fields Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmfrac_1.0.0-1.ca2404.1_all.deb Size: 316062 MD5sum: 49d1fdff75ffb135cedfd7fb90fe0486 SHA1: 5b74b4c611d0f52b07fc0dcf1dec9061bafbd940 SHA256: 398951b1ce3a3cdf0696880b15f78f2cd51b69bbf49877a2886978a518c0f4ff SHA512: 7a980fc24b84b07e8f70a08995926d8e4875f805993599f2ee0d154f4135f6ebfa41b52942e0d0a5164e24fc035e9d489d0797e6c044bbaad30e711a3448cf55 Homepage: https://cran.r-project.org/package=Rmfrac Description: CRAN Package 'Rmfrac' (Simulation and Statistical Analysis of Multifractional Processes) Simulation of several fractional and multifractional processes. Includes Brownian and fractional Brownian motions, bridges and Gaussian Haar-based multifractional processes (GHBMP). Implements the methods from Ayache, Olenko and Samarakoon (2026) for simulation of GHBMP. Estimation of Hurst functions and local fractal dimension. Clustering realisations based on the Hurst functions. Several functions to estimate and plot geometric statistics of the processes and time series. Provides a 'shiny' application for interactive use of the functions from the package. Package: r-cran-rmidas2 Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-httr2, r-cran-processx, r-cran-rlang Suggests: r-cran-arrow, r-cran-jsonlite, r-cran-reticulate, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmidas2_0.1.1-1.ca2404.1_all.deb Size: 90536 MD5sum: 6e13266a149353d3153e97d24f4918f9 SHA1: 8196d71955dcaa0cea9b77f42a2bd20cd4c683f6 SHA256: b93919063a55d60e5a31fe4c50806ea89002a1f2f9d030e6ed1db7b9427fc9e8 SHA512: f30196e892036f094bf2abe3e33527cf3fdb89090d20b6c9145a05742bbc946dc1a97a5091b5429e43d9ee3e926677dbf857b23396ce75b2e53921a6732238b8 Homepage: https://cran.r-project.org/package=rMIDAS2 Description: CRAN Package 'rMIDAS2' (Multiple Imputation with 'MIDAS2' Denoising Autoencoders) Fits 'MIDAS' denoising autoencoder models for multiple imputation of missing data, generates multiply-imputed datasets, computes imputation means, and runs Rubin's rules regression analysis. Wraps the 'MIDAS2' 'Python' engine via a local 'FastAPI' server over 'HTTP', so no 'reticulate' dependency is needed at runtime. Methods are described in Lall and Robinson (2022) and Lall and Robinson (2023) . Package: r-cran-rmidas Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-mltools, r-cran-reticulate, r-cran-rappdirs, r-cran-rdpack Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmidas_1.0.1-1.ca2404.1_all.deb Size: 160040 MD5sum: d3921f873c2a0cb46e7958358c357593 SHA1: d1f86ccd0996f49140fe6c74d8ce7f5a6fa970e4 SHA256: 1c670a2a78af0b03150c44755ec32e212c996da394a6a1778a10c750d5d98450 SHA512: 794c86270d30e04c55edd476a4ed4867c5b3a5b05670b6f3341f1969e4511f1a5828dfac2ec9e0e9d4ddd7d9a9a436cb8de1fb39d43b212050945145b28beaf8 Homepage: https://cran.r-project.org/package=rMIDAS Description: CRAN Package 'rMIDAS' (Multiple Imputation with Denoising Autoencoders) A tool for multiply imputing missing data using 'MIDAS', a deep learning method based on denoising autoencoder neural networks (see Lall and Robinson, 2022; ). This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with 'Python' to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets. For more information see Lall and Robinson (2023) . This package is deprecated in favor of 'rMIDAS2'; it remains available for existing workflows but will receive only compatibility and documentation updates. Package: r-cran-rminer Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1052 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotrix, r-cran-lattice, r-cran-nnet, r-cran-kknn, r-cran-pls, r-cran-mass, r-cran-mda, r-cran-rpart, r-cran-randomforest, r-cran-adabag, r-cran-party, r-cran-cubist, r-cran-kernlab, r-cran-e1071, r-cran-glmnet, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-rminer_1.5.0-1.ca2404.1_all.deb Size: 928524 MD5sum: 3b9883e0b296d829eced1500b824fe5e SHA1: 2937a514409633c3e87d6ba82a66d57b6486d351 SHA256: ea283abcc46c9b79b6220a044787fc4532e1b13b76d4dacf80cd2eb423be58c8 SHA512: 9d45384301be24bb5664b554f44ff15848548195ee446ad41e636bba7169f35181f36e6a934e1293562056db584097cb2505eb0d12369a9ac097632a79eab651 Homepage: https://cran.r-project.org/package=rminer Description: CRAN Package 'rminer' (Machine Learning Classification and Regression Methods) Facilitates the use of machine learning algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.5.0 improved mparheuristic function (new hyperparameter heuristics); 1.4.9 / 1.4.8 improved help, several warning and error code fixes (more stable version, all examples run correctly); 1.4.7 - improved Importance function and examples, minor error fixes; 1.4.6 / 1.4.5 / 1.4.4 new automated machine learning (AutoML) and ensembles, via improved fit(), mining() and mparheuristic() functions, and new categorical preprocessing, via improved delevels() function; 1.4.3 new metrics (e.g., macro precision, explained variance), new "lssvm" model and improved mparheuristic() function; 1.4.2 new "NMAE" metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics; 1.2 - new input importance methods via improved Importance() function; 1.0 - first version. 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Mixture models are fitted using a SEM algorithm. It includes 8 models for real, categorical, counting, functional and ranking data. 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Package: r-cran-rmlnomogram Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2645 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-broom, r-cran-ggplot2, r-cran-ggpubr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-tidyverse, r-cran-knitr, r-cran-caret, r-cran-randomforest, r-cran-iml, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmlnomogram_0.1.2-1.ca2404.1_all.deb Size: 1472176 MD5sum: 55f0b9b638831f90e0a95f122288e1c7 SHA1: 1fc697ae6fe77b8d6d6606d0bf27dff34043897f SHA256: 299451481d511524dafd53dcc0d3f104d61a84dd466bd54e1238d03f63f9e7d8 SHA512: 189ccdeb2b70057a4edb50671a3495decab232b3ec8cbe9793a9af24c64c0d2d55368cea581633292dab64efd9716697ff7562736565943cf0c9eb79928b653f Homepage: https://cran.r-project.org/package=rmlnomogram Description: CRAN Package 'rmlnomogram' (Construct Explainable Nomogram for a Machine Learning Model) Construct an explainable nomogram for a machine learning (ML) model to improve availability of an ML prediction model in addition to a computer application, particularly in a situation where a computer, a mobile phone, an internet connection, or the application accessibility are unreliable. 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Package: r-cran-rmlpca Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-pracma, r-cran-rspectra Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmlpca_0.0.1-1.ca2404.1_all.deb Size: 556514 MD5sum: 3eeda2ce7875036a464f8c22fd087fc7 SHA1: a017cdf03cb553728c36bbf4dd319edbbe9e36b0 SHA256: ceda5972a690f3c149bdb2530cce3345f56bd401e7643779544feacc83c421e6 SHA512: 2c2a38675874ab4646c2562aecba3bc0b33787e24158ffe6f66d1b205cd910ed2a3b7390aea30cd17decdcb270ff2993598e276200dec052a878348e378af521 Homepage: https://cran.r-project.org/package=RMLPCA Description: CRAN Package 'RMLPCA' (Maximum Likelihood Principal Component Analysis) R implementation of Maximum Likelihood Principal Component Analysis The main idea of this package is to have an alternative way of PCA for subspace modeling that considers measurement errors. 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The jar files are put in this R package, the modelling logic can be found in the RMOA package. 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It allows users to search, insert, and manage emojis by keyword, category, or through an interactive 'shiny'-based drop-down. The package enables integration of emojis into 'R' scripts, 'R Markdown', 'Quarto', 'shiny' apps, and 'ggplot2' plots. Also includes built-in mappings for commit messages, useful for version control. It builds on established emoji libraries and Unicode standards, adding expressiveness and visual cues to documentation, user interfaces, and reports. For more details see 'Emojipedia' (2024) and GitHub Emoji Cheat Sheet . Package: r-cran-rmolt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmolt_1.0.0-1.ca2404.1_all.deb Size: 56564 MD5sum: fe78fd0206441acdb61d91ef08a867b9 SHA1: bf0886db888519ece84662b688577912e1fc5ed3 SHA256: df883d3720bd1b937f9a30b186ac4c99ba96cdfff339523bc39f920baf2ab021 SHA512: 959d1ddc4b2ab74751032106c0cdba8f2e6a1830aa9ad8ca372542320a84490e583122b2944f791a63393c4aa366613cbab8ab1e366920a3d8612d3590f756ec Homepage: https://cran.r-project.org/package=Rmolt Description: CRAN Package 'Rmolt' (Graphic Visualization of the Birds' Molt) Graphical visualization of the birds' molt to facilitate the creation of molting graph for passerines having 9 (Rmolt(data,9)) or 10 primaries (Rmolt(data,10)), and also only for the 10 first primaries (Rmolt(data,"10_0")). Package: r-cran-rmon Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-processx Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmon_1.1.0-1.ca2404.1_all.deb Size: 19572 MD5sum: 7f524587881478f4c71b08d5c272b19a SHA1: a4201dc5e928b4ec98c2e0563f3d05541704ae63 SHA256: 1fde7b41f9c0254c191cf4da9167a54ae4fc505e5cef8e5f1f4394771eb61697 SHA512: 80d9801edcff9b662802de164b1a5b427d62a467ea36906f5db4587c6a8085140e25d7da3ed4620031b8e97a7e802cddbbec6e6adcb880c2afdae530f20b96be Homepage: https://cran.r-project.org/package=rmon Description: CRAN Package 'rmon' (Monitor Changes in Source Code and Auto-Restart Your Server) The 'R' equivalent of 'nodemon'. Watches specified directories for file changes and reruns a designated 'R' script when changes are detected. 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Package: r-cran-rmonize Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2059 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-crayon, r-cran-haven, r-cran-fs, r-cran-fabr, r-cran-madshapr Suggests: r-cran-janitor, r-cran-car, r-cran-lubridate, r-cran-knitr Filename: pool/dists/noble/main/r-cran-rmonize_2.0.0-1.ca2404.1_all.deb Size: 1593796 MD5sum: e02d2d8cb5858a4f51ef8a99609fa5b6 SHA1: 363f69d435b068f1574e42215a4cc8647662fef1 SHA256: 30c47a848c50d54f9c944531635f1747796951f1ce6b02330056c7447049440c SHA512: b6786f90b19a432aa234ccbf45aa47cb4be2afdd14df0371b1a7e131af609d4ec447a35decdd6f4d8c44b0c8a3901112d85760d3ed59a05ce2ab3e24ad5516b3 Homepage: https://cran.r-project.org/package=Rmonize Description: CRAN Package 'Rmonize' (Tools for Data Harmonization) Integrated tools to support rigorous and well documented data harmonization based on Maelstrom Research guidelines. The package includes functions to assess and prepare input elements, apply specified processing rules to generate harmonized datasets, validate data processing and identify processing errors, and document and summarize harmonized outputs. The harmonization process is defined and structured by two key user-generated documents: the DataSchema (specifying the list of harmonized variables to generate across datasets) and the Data Processing Elements (specifying the input elements and processing algorithms to generate harmonized variables in DataSchema formats). The package was developed to address key challenges of retrospective data harmonization in epidemiology (as described in Fortier I and al. (2017) ) but can be used for any data harmonization initiative. Package: r-cran-rmoo Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 776 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-foreach, r-cran-ga, r-cran-plotly, r-cran-ggplot2, r-cran-bbmisc Suggests: r-cran-testthat, r-cran-covr, r-cran-rgl, r-cran-ecr, r-cran-emoa, r-cran-cdata, r-cran-dplyr, r-cran-reshape2, r-cran-doparallel, r-cran-dorng Filename: pool/dists/noble/main/r-cran-rmoo_0.3.2-1.ca2404.1_all.deb Size: 609206 MD5sum: 438f88213fc23b453ba150f68b40a3e3 SHA1: 81ea0d809c2e9f517222ad30859fa8f3527a2ccf SHA256: 82c8eb639dd83005ec2515e470996fd10d021ee1bdbed079489bb61e74da46b4 SHA512: db76434ad1e9096e1b3b9f555096a95bad392b8089a5c53d1ac1aa8a75b9633e15d5db96855167d88bcabbcfbef499d22302e3a798a4be4ee7cd9500f8088839 Homepage: https://cran.r-project.org/package=rmoo Description: CRAN Package 'rmoo' (Multi-Objective Optimization in R) The 'rmoo' package is a framework for multi- and many-objective optimization, which allows researchers and users versatility in parameter configuration, as well as tools for analysis, replication and visualization of results. The 'rmoo' package was built as a fork of the 'GA' package by Luca Scrucca(2017) and implementing the Non-Dominated Sorting Genetic Algorithms proposed by K. Deb's. Package: r-cran-rmopi Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tibble, r-cran-lubridate, r-cran-timeseries, r-cran-xts, r-cran-mass, r-cran-performanceanalytics, r-cran-ttr, r-cran-fportfolio, r-cran-rugarch, r-cran-timedate Filename: pool/dists/noble/main/r-cran-rmopi_1.1-1.ca2404.1_all.deb Size: 98068 MD5sum: 0ea98e577948d0c6ae4d79bfc87a7009 SHA1: cb50f3b2b2194e0fba0adf804ea18b290c4ed091 SHA256: 6568965159f86b1ea7e8046dbe18b2849af4a9110a3d43bdd0f4eff4fee8a0e3 SHA512: c0fb785351634f8913dcc877ebd9dd1f232cf8a1c41d2ca877c50ef559f4f437a080b393c0760c7d4557f439f606d420b2425e9ff77ecf71588ae3ea3fa4cbbc Homepage: https://cran.r-project.org/package=RMOPI Description: CRAN Package 'RMOPI' (Risk Management and Optimization for Portfolio Investment) Provides functions for risk management and portfolio investment of securities with practical tools for data processing and plotting. Moreover, it contains functions which perform the COS Method, an option pricing method based on the Fourier-cosine series (Fang, F. (2008) ). Package: r-cran-rmosek Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-pkgbuild Filename: pool/dists/noble/main/r-cran-rmosek_1.3.5-1.ca2404.1_all.deb Size: 34974 MD5sum: d886b57713293027888a0f52d5ea3d6d SHA1: 2224591ea26cf978cae0b987772f36520f8e9e27 SHA256: 4f354a4e1e60f6b73c2586fc5a1a0adcf219b59fc3c37d36866c09e015ff6f51 SHA512: ff3069fd6be1b764ddc22448c6338eb43662d353c9faa9e170789361a7f849f339f0a4da444fef622548b278d62fbafd20e5318533a8815d7247e23c06d61b0b Homepage: https://cran.r-project.org/package=Rmosek Description: CRAN Package 'Rmosek' (The R to MOSEK Optimization Interface) This is a meta-package designed to support the installation of Rmosek (>= 6.0) and bring the optimization facilities of MOSEK (>= 6.0) to the R-language. The interface supports large-scale optimization of many kinds: Mixed-integer and continuous linear, second-order cone, exponential cone and power cone optimization, as well as continuous semidefinite optimization. Rmosek and the R-language are open-source projects. MOSEK is a proprietary product, but unrestricted trial and academic licenses are available. Package: r-cran-rmost Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nloptr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmost_1.0.1-1.ca2404.1_all.deb Size: 190092 MD5sum: 6ca0bd1bef2ba81623fc6deaa77817ee SHA1: f78e8fbd6af6ec4d07f827cfd8cff1d1f5dd153e SHA256: c5c6c753927ad618ee2817bfe988ab12b4b43c130599cb1b35849ba75fed6e92 SHA512: 0a577dbea8529ea61332ad1d96d49c30ab0561d61bf2c7ec7925518e3abaf301fd2d67c0b11a6a62431e6a8669f1bce46c7a2c93381f3b08a0d64022f012a04b Homepage: https://cran.r-project.org/package=rMOST Description: CRAN Package 'rMOST' (Estimates Pareto-Optimal Solution for Hiring with 3 Objectives) Estimates Pareto-optimal solution for personnel selection with 3 objectives using Normal Boundary Intersection (NBI) algorithm introduced by Das and Dennis (1998) . Takes predictor intercorrelations and predictor-objective relations as input and generates a series of solutions containing predictor weights as output. Accepts between 3 and 10 selection predictors. Maximum 2 objectives could be adverse impact objectives. Partially modeled after De Corte (2006) TROFSS Fortran program and updated from 'ParetoR' package described in Song et al. (2017) . For details, see Study 3 of Zhang et al. (2023). Package: r-cran-rmpw Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-mass Filename: pool/dists/noble/main/r-cran-rmpw_0.0.6-1.ca2404.1_all.deb Size: 93224 MD5sum: 284cbdfb672db5739401d44a459e9511 SHA1: 7bbc1eacfeed9f64344d6a248cc92ab757429a17 SHA256: c919d47d4eb84e34f7d8b0887fd7360377b6d32dfea0f13fe8263fcc0475b54f SHA512: 28298d9827bfec4900daac8f23da38d3aa7d96c6e163f9fe760627341fea3e7f48e6a4c5ac93a97b34fca212cdf4a17f2eff9c3dbd208c4d1c990f64120f211b Homepage: https://cran.r-project.org/package=rmpw Description: CRAN Package 'rmpw' (Causal Mediation Analysis Using Weighting Approach) We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) . It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) . Package: r-cran-rmr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biglm Filename: pool/dists/noble/main/r-cran-rmr_1.1.0-1.ca2404.1_all.deb Size: 274736 MD5sum: 35bc91f8456fdd137b7103c5645ff234 SHA1: eb00825f700b4fe39d0ae30373f6d742d016a44c SHA256: 1efd6d26801af75efcef1620fe74012b386b45f57d52cd3aa4e7d403e2cfa590 SHA512: 769f59d36b583761989d9665bab3daa67afc50eaa9facac46e0b39bd59b924c7d12ed90de17f5a8f71fd21537b4ce7e52e0e1fa81433c45a29f783673d996d08 Homepage: https://cran.r-project.org/package=rMR Description: CRAN Package 'rMR' (Importing Data from Loligo Systems Software, CalculatingMetabolic Rates and Critical Tensions) Analysis of oxygen consumption data generated by Loligo (R) Systems respirometry equipment. The package includes a function for loading data output by Loligo's 'AutoResp' software (get.witrox.data()), functions for calculating metabolic rates over user-specified time intervals, extracting critical points from data using broken stick regressions based on Yeager and Ultsch (), and easy functions for converting between different units of barometric pressure. Package: r-cran-rmsbma Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4489 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpubr, r-cran-gridextra, r-cran-matrix, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmsbma_0.1.2-1.ca2404.1_all.deb Size: 3247440 MD5sum: abdf2af63285ce931e3a22583343e076 SHA1: af156dfca7585312c58d8cd8dd0166c257af25b7 SHA256: a36ccac1b42cb8b34f900788edf5891649c92f1ab38b750fb55d325dc0d215c7 SHA512: 835dabd913c95269e78e6620ea471e0c465a4e5d357aa9b227e42f2dd4c95b3419cc14487b551c81594205cb228301cd4c16804b587bbd82c71f87fbeb3ca307 Homepage: https://cran.r-project.org/package=rmsBMA Description: CRAN Package 'rmsBMA' (Reduced Model Space Bayesian Model Averaging) Implements Bayesian model averaging for settings with many candidate regressors relative to the available sample size, including cases where the number of regressors exceeds the number of observations. By restricting attention to models with at most M regressors, the package supports reduced model space inference, thereby preserving degrees of freedom for estimation. It provides posterior summaries, Extreme Bounds Analysis, model selection procedures, joint inclusion measures, and graphical tools for exploring model probabilities, model size distributions, and coefficient distributions. The methodological approach follows Doppelhofer and Weeks (2009) . Package: r-cran-rmsd Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmsd_0.1.1-1.ca2404.1_all.deb Size: 16988 MD5sum: 48f4f025d9f80e3f6eca92d4f4e69163 SHA1: 430d5261a2946f017859367dc43582c8a404a62a SHA256: 4cbdbd5a0f6a4d518572e54468119e2aa88dcf3b1ca4b5add7c4a99be2e440be SHA512: c62e577f93f54a54c685528c92e0c5c713066dc8fb73a2f81372aaecbf8ceb99c2e393c8a2275f327b2644f280c2ca17b7529758fe8797c8eb8519ffadc6ffb8 Homepage: https://cran.r-project.org/package=RMSD Description: CRAN Package 'RMSD' (Refined Modified Stahel-Donoho Estimators for Outlier Detection) A function for multivariate outlier detection named Modified Stahel-Donoho (MSD) estimators is contained. The function is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. The function is called the single core version in Wada & Tsubaki (2013) and evaluated with other methods in Wada, Kawano & Tsubaki (2020) . Package: r-cran-rmsdp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmsdp_0.1.1-1.ca2404.1_all.deb Size: 27296 MD5sum: b9853da32c1f9a88b448a45d38adac25 SHA1: 5d2b96d49060430ac8d950fe55edb0c4b11be8c6 SHA256: 5338f839f1e163534a8efae5cf882c831eaaf0df2ea22ea117be0f8290a2a7d4 SHA512: 577c5a83ba3fc32947a33c73df8d54eec289b25cf0f4d9e19af56e73947b0e0a52e7beff07f0fac7c935fa3c008801ae1207d53608a6087ee114fa365473f31a Homepage: https://cran.r-project.org/package=RMSDp Description: CRAN Package 'RMSDp' (Refined Modified Stahel-Donoho (MSD) Estimators for OutlierDetection (Parallel Version)) A parallel function for multivariate outlier detection named modified Stahel-Donoho estimators is contained in this package. The function RMSDp() is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. This function is for higher dimensional datasets that cannot be handled by a single core function RMSD() included in 'RMSD' package. See Wada and Tsubaki (2013) for the detail of the algorithm. Package: r-cran-rmsfact Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rmsfact_0.0.3-1.ca2404.1_all.deb Size: 20204 MD5sum: 076b3abf0b772f8a570475fc787e9c16 SHA1: 07307552dee306a6fd3580d79e9d9d5df420f4fb SHA256: 4f68401b458608d89b16224045fbcd657b85537f0e78ce1fed238dd98ca3c08b SHA512: 5fbc3d0a733d3efbba28bb166df6600e7c0d2b2fc7fff2be7bd62063ed73b54a98f2bec186f9ac2bfc2099e3ecc98e1697b7b8031f95cefa918b548486d5b57e Homepage: https://cran.r-project.org/package=rmsfact Description: CRAN Package 'rmsfact' (Amazing Random Facts About the World's Greatest Hacker) Display a randomly selected quote about Richard M. Stallman based on the collection in the 'GNU Octave' function 'fact()' which was aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site stallmanfacts.com (which is accessible only via ). Package: r-cran-rmsmd Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 780 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rms, r-cran-ggplot2, r-cran-rlang, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-officer, r-cran-flextable, r-cran-dplyr, r-cran-testthat, r-cran-vdiffr, r-cran-hmisc Filename: pool/dists/noble/main/r-cran-rmsmd_1.0.1-1.ca2404.1_all.deb Size: 395190 MD5sum: 6569fd773150f6e1f90a7da41390138d SHA1: dd43a7a392ba1e33ab4d6be917dfe7e5f5211ce7 SHA256: 604bba718d662a7166c8d1ca90b05389b3128780e35b8041b75be8a48496382a SHA512: bb1c1660f9bd449d341c015492a60b3000473f34bd6a339796c8b6b4661b8dc9abc2959a1b8980924f255d4aaafe96b7b356c7d46da13b21b3713222642d06c6 Homepage: https://cran.r-project.org/package=rmsMD Description: CRAN Package 'rmsMD' (Output Results from 'rms' Models for Medical Journals) Provides streamlined functions for summarising and visualising regression models fitted with the 'rms' package, in the preferred format for medical journals. The 'modelsummary_rms()' function produces concise summaries for linear, logistic, and Cox regression models, including automatic handling of models containing restricted cubic spline (RCS) terms. The resulting summary dataframe can be easily converted into publication-ready documents using the 'flextable' and 'officer' packages. The 'ggrmsMD()' function creates clear and customizable plots ('ggplot2' objects) to visualise RCS terms. Package: r-cran-rmst Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rata, r-cran-reshape2, r-cran-rirt Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmst_0.0.3-1.ca2404.1_all.deb Size: 61822 MD5sum: 51b13e94b010bc5d96383c91427fd5fb SHA1: cb9725af55faf0cc3e356e124fd5d5962abce351 SHA256: f00f737cee91be33a1bb85f1bfaacea2b18dc286b1c6173a073bc9a40f3057a9 SHA512: 99797a0e060b29eb1d11e728b93d56cc0046abe3cf9e81b994a19d936facf9abe66f6cc1359c6eeb5190bba6c320450ff365c11ca81a17e243b52a99364261ef Homepage: https://cran.r-project.org/package=Rmst Description: CRAN Package 'Rmst' (Computerized Adaptive Multistage Testing) Assemble the panels of computerized adaptive multistage testing by the bottom-up and the top-down approach, and simulate the administration of the assembled panels. The full documentation and tutorials are at . Reference: Luo and Kim (2018) . Package: r-cran-rmstbayespara Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-brms, r-cran-crayon, r-cran-loo, r-cran-rstan, r-cran-zipfr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rmstbayespara_0.1.0-1.ca2404.1_all.deb Size: 35676 MD5sum: 92e54883b2ec979f2841fb3ba2bb6efb SHA1: b3b72659c4f66899751d8513c7579366c694fbce SHA256: 9eb319bd873418ac38ce718cd2a68d60513900509b63309a3305bc107b51494e SHA512: fb989ad50916f0360c812088de8347f89eed13b46f5da25e4b3d74c6ed704c42005c437dd26e7359406b89280cb9cfad6c798c7e28d7a84dac2daf9217016964 Homepage: https://cran.r-project.org/package=rmstBayespara Description: CRAN Package 'rmstBayespara' (Bayesian Restricted Mean Survival Time for Cluster Effect) The parametric Bayes analysis for the restricted mean survival time (RMST) with cluster effect, as described in Hanada and Kojima (2024) . Bayes estimation with random-effect and frailty-effect can be applied to several parametric models useful in survival time analysis. The RMST under these parametric models can be computed from the obtained posterior samples. Package: r-cran-rmstcompsens Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-rmstcompsens_0.1.5-1.ca2404.1_all.deb Size: 36254 MD5sum: b37fef13317ffe8834ab8658c753e9ba SHA1: 118dc2b8023083d97a6bcb5cfd90101bd8bf9643 SHA256: 832c752a2a97164632b14da50e4b95d648dfddf5e9925d03c79ceccaa4295424 SHA512: 5cc8d649ea5ce0a89c0d15cfdab808cffbc9a2a8130bb1dda14088ec8addfca221370eebd8ca641425c3fbaeee12023b38fdc97f2a5b0c007129059a19c696c8 Homepage: https://cran.r-project.org/package=rmstcompsens Description: CRAN Package 'rmstcompsens' (Comparing Restricted Mean Survival Time as Sensitivity Analysis) Performs two-sample comparisons using the restricted mean survival time (RMST) when survival curves end at different time points between groups. This package implements a sensitivity approach that allows the threshold timepoint tau to be specified after the longest survival time in the shorter survival group. Two kinds of between-group contrast estimators (the difference in RMST and the ratio of RMST) are computed: Uno et al(2014), Uno et al(2022), Ueno and Morita(2023). 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Provides power and sample size calculations for two-arm studies using direct modeling approaches from the literature, including semiparametric additive models, linear Inverse Probability Weighting based models from Wei (2014) , multiplicative stratified models from Wang (2019) , and covariate-dependent censoring methods from Wang (2018) . Package: r-cran-rmt4ds Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rmtstat, r-cran-lpsolve, r-cran-mpoly, r-cran-nleqslv, r-cran-pracma, r-cran-rarpack, r-cran-rootsolve, r-cran-quadprog Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmt4ds_0.0.1-1.ca2404.1_all.deb Size: 105888 MD5sum: ad89a1a046d15cd2ac507990da4db3e6 SHA1: 1c50d0a1df6584b7b0bf2f50f5aec80abc7f3f6f SHA256: c768090bcceaa2e3b932e4b54f30125200bb924a98fc05450e7bdcf81ad28589 SHA512: cce9a4d3255d4694ba4258d1791d3d3025f814ccc0b15fc848c09d24daabc60322dcc91467d408682ec27a64effe54ad293f48da42dab750b264b8544496ac9a Homepage: https://cran.r-project.org/package=RMT4DS Description: CRAN Package 'RMT4DS' (Computation of Random Matrix Models) We generate random variables following general Marchenko-Pastur distribution and Tracy-Widom distribution. We compute limits and distributions of eigenvalues and generalized components of spiked covariance matrices. We give estimation of all population eigenvalues of spiked covariance matrix model. We give tests of population covariance matrix. We also perform matrix denoising for signal-plus-noise model. Package: r-cran-rmt Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rmt_1.0-1.ca2404.1_all.deb Size: 120410 MD5sum: d86b5b862541aba0f70a80bae511285d SHA1: b5d25c5ba5a078dcce0714fbfdb022acc06c878d SHA256: 8845fdb99c61fc4c8eb861caaa4830263c15ddf9970b16039830803478faba18 SHA512: 95e4833e4e0404d6161aab84ce4126a78ed049154d492fcc5d3f05265b5819ad2d2ff397cf32cb5135779707fa231708fec2bd5963d5e74f4baa94ccd229c972 Homepage: https://cran.r-project.org/package=rmt Description: CRAN Package 'rmt' (Restricted Mean Time in Favor of Treatment) Contains inferential and graphical routines for comparing two treatment arms in terms of the restricted mean time in favor of treatment. 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The reference paper can be found from the URL mentioned below. Ting Li, Zhongyuan Lyu, Chenyu Ren, Dong Xia (2023) . 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See the R Journal publication of Ieva et al. (2019) for an in-depth presentation of the 'roahd' package. See Aleman-Gomez et al. (2021) for details about the concept of depthgram. 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Package: r-cran-robcbi Architecture: all Version: 1.1-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-robeth Filename: pool/dists/noble/main/r-cran-robcbi_1.1-4-1.ca2404.1_all.deb Size: 466036 MD5sum: 3749228a000a927ad1de4f6893be87d9 SHA1: 957e07d1683441edb6393569e6565b8c65e66193 SHA256: ef5e1310c4b812795aa2c45031f95e686a0822dab67e0e2ead7cfd4c4e52821c SHA512: 137dddfb962c03e7dc47578e51c6b92937f05463f860e4a5350621cfffa9d64d0302e9d51027a9407b8d0cd7f69b8d977751d9cad763e3e7299a668b381c82d5 Homepage: https://cran.r-project.org/package=robcbi Description: CRAN Package 'robcbi' (Conditionally Unbiased Bounded Influence Estimates) Conditionally unbiased bounded influence estimates as described in Kuensch et al. (1989) in three special cases of the generalized linear model: Bernoulli, Binomial, and Poisson distributed responses. 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It detects if the community structure found by a set of algorithms is statistically significant and compares the different selected detection algorithms on the same network. robin helps to choose among different community detection algorithms the one that better fits the network of interest. Reference in Policastro V., Righelli D., Carissimo A., Cutillo L., De Feis I. (2021) . Package: r-cran-robincar2 Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-numderiv, r-cran-mass, r-cran-sandwich, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-robincar2_0.2.2-1.ca2404.1_all.deb Size: 224732 MD5sum: 1fa0d4d848719492d2cabe7861a99ad4 SHA1: 0171ce5a8149fc732be3bca4de95e113a78c70a9 SHA256: 099a649eaa5093a3f21e7dfbb007c2eb5e2f331437429e266592b2ced96007c9 SHA512: 23222d0b2279e23929fcda91a937e0d48fd2b1b17b9601ff67bd2c0132820e6ef37ab39c6bc4d0b625d9e9e20e2910802a300542255faadb7926f829eaad1c39 Homepage: https://cran.r-project.org/package=RobinCar2 Description: CRAN Package 'RobinCar2' (ROBust INference for Covariate Adjustment in Randomized ClinicalTrials) Performs robust estimation and inference when using covariate adjustment and/or covariate-adaptive randomization in randomized controlled trials. This package is trimmed to reduce the dependencies and validated to be used across industry. See "FDA's final guidance on covariate adjustment", Tsiatis (2008) , Bugni et al. (2018) , Ye, Shao, Yi, and Zhao (2023), Ye, Shao, and Yi (2022), Rosenblum and van der Laan (2010), Wang et al. (2021), Ye, Bannick, Yi, and Shao (2023), and Bannick, Shao, Liu, Du, Yi, and Ye (2024). 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Ting Ye, Jun Shao, Yanyao Yi, Qinyuan Zhao (2023) . Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao (2023) . Ting Ye, Jun Shao, Yanyao Yi (2023) . Marlena Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi, Ting Ye (2024) . Xiaoyu Qiu, Yuhan Qian, Jaehwan Yi, Jinqiu Wang, Yu Du, Yanyao Yi, Ting Ye (2025) . Package: r-cran-robincid Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-numderiv, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-robincid_1.0.0-1.ca2404.1_all.deb Size: 93764 MD5sum: 56bc93b7b40088473e4160375130d278 SHA1: bd67b9c91e84d2b10c0cfc709cf6abfb88ef0af5 SHA256: 245b98afbba484ec7f87091bd979cb0767fb0192f0bb174e4b377b468ab9764d SHA512: c1eac02c6976357c0eba9e7834888095dbab561c079c7d401ebe5d01bee7d53b6eaab2be55e61635977529a45522be1b1c5997cca147f23e4edcf5be986a7f92 Homepage: https://cran.r-project.org/package=RobinCID Description: CRAN Package 'RobinCID' (Robust Inference in Complex Innovative Trial Design) Perform robust estimation and inference in platform trials and other master protocol trials. Yuhan Qian, Yifan Yi, Jun Shao, Yanyao Yi, Gregory Levin, Nicole Mayer-Hamblett, Patrick J. Heagerty, Ting Ye (2025) . Package: r-cran-robinhood Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-lubridate, r-cran-profvis, r-cran-magrittr, r-cran-dplyr, r-cran-httr, r-cran-uuid Filename: pool/dists/noble/main/r-cran-robinhood_1.7.0-1.ca2404.1_all.deb Size: 219348 MD5sum: 829346ab744419271b6b8e1824800eba SHA1: 46e812713525ac025995502e14930034a348ea72 SHA256: 8481c3608e48aeda6d8c1561c072056df40144475760f8daeb46330d1b3b7507 SHA512: abe3b2edbfed939ab7582c5e5627eed7b87e47a1fae6a0c9bf0f65349ee05208c686d9f7956ef44cafa8b08afbbebfadf4ae6c62d0e5c0bb42eadc1b56de8b61 Homepage: https://cran.r-project.org/package=RobinHood Description: CRAN Package 'RobinHood' (Interface for the RobinHood.com No Commission Investing Platform) Execute API calls to the RobinHood investing platform. 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Package: r-cran-roblox Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 478 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-distrmod, r-cran-robastbase, r-cran-lattice, r-cran-rcolorbrewer, r-bioc-biobase, r-cran-randvar, r-cran-distr Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-roblox_1.2.3-1.ca2404.1_all.deb Size: 425884 MD5sum: 82538a33f75324606ad5865e48c13875 SHA1: f4704d944e3385fb48e4d9c0406af15fe476b362 SHA256: 65d7f7b563e4dfef170a531d4823e2d97bdb7044087a9d0061d07838bf08980c SHA512: 74d3a5602b5f68266c25cc5dd7bc5a89fa328981fe42569335b4078a8d069b53feef8608e9b605c126fd5a40fc3f34fa806d5d430a00302672bbc52e757c8d76 Homepage: https://cran.r-project.org/package=RobLox Description: CRAN Package 'RobLox' (Optimally Robust Influence Curves and Estimators for Locationand Scale) Functions for the determination of optimally robust influence curves and estimators in case of normal location and/or scale (see Chapter 8 in Kohl (2005) ). 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Package: r-cran-robmed Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-robustbase, r-cran-boot, r-cran-quantreg, r-cran-sn Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-robmed_1.3.0-1.ca2404.1_all.deb Size: 969868 MD5sum: e39d3d90d25381f3163808a5ac0f12b4 SHA1: b22108c626bf1fdccff94d12a4cc6db613d12e60 SHA256: 2c12cf000a32779e1071cdaeb00666005f74d05d60d4953fb714064f7b4840f4 SHA512: 71f03af3ba6a01414381e133e89b378561d3c12d56352a445d2b4837ac8e97b3b1f0d4f4cffc7371216c702d4838baa50c3fc5c702297c8b67e894d19f7b049d Homepage: https://cran.r-project.org/package=robmed Description: CRAN Package 'robmed' ((Robust) Mediation Analysis) Perform mediation analysis via the fast-and-robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; ), as well as various other methods. Details on the implementation and code examples can be found in Alfons, Ates, and Groenen (2022b) . Further discussion on robust mediation analysis can be found in Alfons & Schley (2025) . 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Most notably, it provides a graphical user interface for the robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; ) to make the method more accessible to less proficient 'R' users, as well as functions to export the results as a table in a 'Microsoft Word' or 'Microsoft Powerpoint' document, or as a 'LaTeX' table. Furthermore, the package contains a 'shiny' app to compare various bootstrap procedures for mediation analysis on simulated data. Package: r-cran-robmixreg Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-flexmix, r-cran-robustbase, r-cran-gtools, r-cran-mass, r-cran-robust, r-cran-lars, r-cran-dplyr, r-cran-rlang, r-cran-scales, r-cran-gplots, r-cran-rcolorbrewer, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-robmixreg_1.1.0-1.ca2404.1_all.deb Size: 2084342 MD5sum: b911b9bd000a1bc32f65b1fc65cd2e9f SHA1: 5f3fc04371d0533256228d9682fe215d27ae1347 SHA256: 9e62cccf72a0ff4b930bd88d6a500bbc9d60e81f839b408275ca23178c9a637d SHA512: f04a20270a60747a21a6685b6405b9fa69faa2fabb1fbc1c693dec8f828a76e869c550c2067e5cb3690022fddaca13a583e5854636e41342ccfc020c2957ba71 Homepage: https://cran.r-project.org/package=RobMixReg Description: CRAN Package 'RobMixReg' (Robust Mixture Regression) Finite mixture models are a popular technique for modelling unobserved heterogeneity or to approximate general distribution functions in a semi-parametric way. They are used in a lot of different areas such as astronomy, biology, economics, marketing or medicine. This package is the implementation of popular robust mixture regression methods based on different algorithms including: fleximix, finite mixture models and latent class regression; CTLERob, component-wise adaptive trimming likelihood estimation; mixbi, bi-square estimation; mixL, Laplacian distribution; mixt, t-distribution; TLE, trimmed likelihood estimation. The implemented algorithms includes: CTLERob stands for Component-wise adaptive Trimming Likelihood Estimation based mixture regression; mixbi stands for mixture regression based on bi-square estimation; mixLstands for mixture regression based on Laplacian distribution; TLE stands for Trimmed Likelihood Estimation based mixture regression. For more detail of the algorithms, please refer to below references. Reference: Chun Yu, Weixin Yao, Kun Chen (2017) . NeyKov N, Filzmoser P, Dimova R et al. (2007) . Bai X, Yao W. Boyer JE (2012) . Wennan Chang, Xinyu Zhou, Yong Zang, Chi Zhang, Sha Cao (2020) . Package: r-cran-robnptests Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1676 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-gtools, r-cran-robustbase, r-cran-statmod, r-cran-checkmate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-usethis, r-cran-covr Filename: pool/dists/noble/main/r-cran-robnptests_1.1.0-1.ca2404.1_all.deb Size: 489088 MD5sum: f9f8e3a2af996c60d8a3871d36a1edc1 SHA1: 54bafa40a43f4ebef01078fe97ee226b95f6a332 SHA256: d0baac31e994f1c65766ac8e513b37d69300a544d5cb4f03bcc182ca7cbee607 SHA512: d8366ef15995e8cf7ab27d21f89f3e313c91fe238ee2e2fdffee1505df1ed05b5dfdbc7cf3073f80c89d810a239f61020480edc120b9f5f9a0b78e43ec5c332f Homepage: https://cran.r-project.org/package=robnptests Description: CRAN Package 'robnptests' (Robust Nonparametric Two-Sample Tests for Location/Scale) Implementations of several robust nonparametric two-sample tests for location or scale differences. The test statistics are based on robust location and scale estimators, e.g. the sample median or the Hodges-Lehmann estimators as described in Fried & Dehling (2011) . The p-values can be computed via the permutation principle, the randomization principle, or by using the asymptotic distributions of the test statistics under the null hypothesis, which ensures (approximate) distribution independence of the test decision. To test for a difference in scale, we apply the tests for location difference to transformed observations; see Fried (2012) . Random noise on a small range can be added to the original observations in order to hold the significance level on data from discrete distributions. The location tests assume homoscedasticity and the scale tests require the location parameters to be zero. Package: r-cran-robomit Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plm, r-cran-dplyr, r-cran-ggplot2, r-cran-broom, r-cran-tidyr, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-robomit_1.0.7-1.ca2404.1_all.deb Size: 102530 MD5sum: 54f74942455180ccf6a1ee76dc1a7d61 SHA1: 9a640f6e0e25303fa3ffd9cd29cd90bb33246c45 SHA256: 6a81b092d8819088faf44e75cc33b9174ef75eb6e2dac498c3522652b75c357e SHA512: abbcac6ab44b53e9414b24af96fe10b1fe8e4d2518190250820e14f10d88e2067ad68743841743c2c47d97aab4aa41c91bed7efe139a0ce468d1210e026f4133 Homepage: https://cran.r-project.org/package=robomit Description: CRAN Package 'robomit' (Robustness Checks for Omitted Variable Bias) Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oster (2019). The 'robomit' package computes i) the bias-adjusted treatment correlation or effect and ii) the degree of selection on unobservables relative to observables (with respect to the treatment variable) that would be necessary to eliminate the result based on the framework by Oster (2019). The code is based on the 'psacalc' command in 'Stata'. Additionally, 'robomit' offers a set of sensitivity analysis and visualization functions. See Oster, E. 2019. . Additionally, see Diegert, P., Masten, M. A., & Poirier, A. (2022) for a recent discussion of the topic: . 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This package extends Mahalanobis distance-based oversampling techniques by integrating robust covariance estimators to better handle outliers and complex data distributions. The implemented methodology builds upon and significantly expands the RM-SMOTE algorithm originally proposed by Taban et al. (2025) . 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Package: r-cran-robpc Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-trimcluster Filename: pool/dists/noble/main/r-cran-robpc_1.4-1.ca2404.1_all.deb Size: 13200 MD5sum: cf33aa4b9e6ea9fc8b8b6e511e55ce1f SHA1: 589c91c5a0347effc456fb47c1a85c3042cac967 SHA256: fcedd385701eaee2de1c4a78c3f38235403fd8f59704351f496e29b9044a91f4 SHA512: 8d35782784dfc8a747c69604d53a13e591d98c69f0ce159c4514688aa2e5ef19e71e5dc9f059f1f58b45ba860b655dfe33029561a3065a717af024863bd9d8be Homepage: https://cran.r-project.org/package=RobPC Description: CRAN Package 'RobPC' (Robust Panel Clustering Algorithm) Performs both classical and robust panel clustering by applying Principal Component Analysis (PCA) for dimensionality reduction and clustering via standard K-Means or Trimmed K-Means. The method is designed to ensure stable and reliable clustering, even in the presence of outliers. Suitable for analyzing panel data in domains such as economic research, financial time-series, healthcare analytics, and social sciences. The package allows users to choose between classical K-Means for standard clustering and Trimmed K-Means for robust clustering, making it a flexible tool for various applications. For this package, we have benefited from the studies Rencher (2003), Wang and Lu (2021) , Cuesta-Albertos et al. (1997) . Package: r-cran-robper Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 925 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-robustbase, r-cran-quantreg, r-cran-bb, r-cran-rgenoud Filename: pool/dists/noble/main/r-cran-robper_1.2.3-1.ca2404.1_all.deb Size: 785472 MD5sum: b4d2118df8a984f8a3bf1e5a616653c4 SHA1: a84c4d114892b799dccc5345b7583d7d238f3f9f SHA256: c9ee3d24d34c72d046053309d3e6c00a5f59f0f9ed15b954c7cf48a7bcfea938 SHA512: 245ef146276df8f6b9029c5a4e06f181bc8a12e5641baa3071becaa1e4b9a6ad293957699ac9cef47f8b757697524ab7890f50750f62d4be9bbdba3a7d2d0855 Homepage: https://cran.r-project.org/package=RobPer Description: CRAN Package 'RobPer' (Robust Periodogram and Periodicity Detection Methods) Calculates periodograms based on (robustly) fitting periodic functions to light curves (irregularly observed time series, possibly with measurement accuracies, occurring in astroparticle physics). Three main functions are included: RobPer() calculates the periodogram. Outlying periodogram bars (indicating a period) can be detected with betaCvMfit(). Artificial light curves can be generated using the function tsgen(). For more details see the corresponding article: Thieler, Fried and Rathjens (2016), Journal of Statistical Software 69(9), 1-36, . Package: r-cran-robqda Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rfast, r-cran-rfast2 Suggests: r-cran-mvcauchy Filename: pool/dists/noble/main/r-cran-robqda_1.0-1.ca2404.1_all.deb Size: 24128 MD5sum: 950f34a9d8501d40bf83451e777816ca SHA1: 824f0856d8f54e9850790afb1315b008a765f420 SHA256: 8f3de171f3b4e7222039db227fc0b271b826d9dbba43e1b5e3e79c7a46da30e0 SHA512: db8885160369d6f5b7458245173046a7cb26d15296244161fff5dc8fa49b5474dd643ad73b311b8b5cd389e3e8e94243fd8ea0aa68ad70f50d1736b787d7336f Homepage: https://cran.r-project.org/package=robqda Description: CRAN Package 'robqda' (Robust Quadratic Discriminant Analysis) The minimum covariance determinant estimator is used to perform robust quadratic discriminant analysis, including cross-validation. References: Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. . Package: r-cran-robratio Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat, r-cran-latex2exp, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-robratio_0.1.0-1.ca2404.1_all.deb Size: 73538 MD5sum: 7f70b17f57da9badbe926fb9ff455c6f SHA1: 32b537fa26700e8e9abe0964d60b11682cec644b SHA256: 803d3059a59a00153b8fa35b31c772a74f344d02a4a2e21ada235673ed96be4d SHA512: b4b115023d76d9eedc0e7d5c01244e5484644e3992c3a7f47e0ac32a424cbde87043237f75bef8158715958b326ab08f92e2b10777d9a3b04c1a7173efeb0afb Homepage: https://cran.r-project.org/package=robRatio Description: CRAN Package 'robRatio' (M-Estimators for Generalized Ratio and Linear Regression Models) Robust estimators for generalized ratio model (Wada, Sakashita and Tsubaki, 2021) and linear regression model by the IRLS(iterative reweighted least squares) algorithm are contained. Package: r-cran-robreg3s Architecture: all Version: 0.3-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gse, r-cran-mass, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-robreg3s_0.3-1-1.ca2404.1_all.deb Size: 47630 MD5sum: b7ba9c4b4ffedeed93e1dbf12a9e713a SHA1: 98ad91a18480070aa3f43f5666f568868a2709db SHA256: 1981ef731b887c35eb6974a8cab1778684c613b91150e803b9df5e70c556aaff SHA512: a021481f1d1d113d6e37ece4f9b7264ebf1f8d22f32484421413bdd3b88ccab3b43de31b28b46be04152d739dbf6edbb791dc343b6cfadea6cf163f4ced04df6 Homepage: https://cran.r-project.org/package=robreg3S Description: CRAN Package 'robreg3S' (Three-Step Regression and Inference for Cellwise and CasewiseContamination) Three-step regression and inference for cellwise and casewise contamination. Package: r-cran-robservable Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1089 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-jsonlite Suggests: r-cran-gapminder, r-cran-palmerpenguins, r-cran-knitr, r-cran-rmarkdown, r-cran-readr, r-cran-dplyr, r-cran-tidyr, r-cran-lubridate, r-cran-stringr Filename: pool/dists/noble/main/r-cran-robservable_0.2.2-1.ca2404.1_all.deb Size: 135324 MD5sum: 0fe1a6422a403d5d011ebf41aa39f42f SHA1: 4338503b7ac9eba944d4282206dd1343a6e5ff06 SHA256: 6f2207b41f06c7972e12f67b5dda2ce9c71e5c59723b0de5a0e17d8d68cca7c4 SHA512: 02d72b15be2012856e3db787c2baa7833f61a0e24e954d746c49f48374a26df4ec34c359730cdd38b04c5b8afe74bfdc3b7642580cc3a02906e5dcd5ff8bc6a2 Homepage: https://cran.r-project.org/package=robservable Description: CRAN Package 'robservable' (Import an Observable Notebook as HTML Widget) Allows loading and displaying an Observable notebook (online JavaScript notebooks powered by ) as an HTML Widget in an R session, 'shiny' application or 'rmarkdown' document. Package: r-cran-robumeta Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-clubsandwich, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-robumeta_2.1-1.ca2404.1_all.deb Size: 518854 MD5sum: 4308c9bddcac6b080fb1fa741400059e SHA1: b98dea353adbee2fed981fac7f4d642e16ba24d3 SHA256: d4a3293a3e0adccf6948a4d7c58dbcee76a3c5e02e0e607dd93c28547443b392 SHA512: 17c8daa399f7ac7964988ecdd3c4aa34e6b25824597c40b3d8656bb595dff465ecd6f9f4d90d9dbd6fac9816c078f38f5b789989f163b0c5231926a0b49226c4 Homepage: https://cran.r-project.org/package=robumeta Description: CRAN Package 'robumeta' (Robust Variance Meta-Regression) Functions for conducting robust variance estimation (RVE) meta-regression using both large and small sample RVE estimators under various weighting schemes. 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Package: r-cran-robust2sls Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 625 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-exactci, r-cran-foreach, r-cran-ivreg, r-cran-mass, r-cran-mathjaxr, r-cran-pracma Suggests: r-cran-covr, r-cran-dofuture, r-cran-doparallel, r-cran-dorng, r-cran-future, r-cran-ggplot2, r-cran-ivgets, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-robust2sls_0.2.3-1.ca2404.1_all.deb Size: 350668 MD5sum: b5261a8f5716549624f23993e62a598f SHA1: 56a03faeada97a88aa13dd9556b8f13db1a1a7f5 SHA256: 0f87713f311806b5ab7f938db8d038599b9d34a36b72b8007568654a48959026 SHA512: 8e2a1f05ce00d53586e4d581224a1ca5ed3f4a1541c7a560442e9c4a5156c35371fa3fed638a1c0d5ca920c7b4cad0e17d806e54dd3c9e0ad5833ccbaf0a9dec Homepage: https://cran.r-project.org/package=robust2sls Description: CRAN Package 'robust2sls' (Outlier Robust Two-Stage Least Squares Inference and Testing) An implementation of easy tools for outlier robust inference in two-stage least squares (2SLS) models. The user specifies a reference distribution against which observations are classified as outliers or not. After removing the outliers, adjusted standard errors are automatically provided. Furthermore, several statistical tests for the false outlier detection rate can be calculated. The outlier removing algorithm can be iterated a fixed number of times or until the procedure converges. The algorithms and robust inference are described in more detail in Jiao (2019) . Package: r-cran-robustadaptivedecomposition Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-robustadaptivedecomposition_0.1.0-1.ca2404.1_all.deb Size: 12678 MD5sum: 5052bf76f396702ca9116dac72268d71 SHA1: be225d65d310356d40bf7b6049633e09845865d6 SHA256: a52d5f94d3a0f53d6f48171e5616c59faebbcd0c1cfb0187efe6288d88a498d8 SHA512: df063d8b6fd58754bc89beeaab14ea90210a98bf1057a3b163c33f36271686a3b47d86a9e90e32b38a4af9ea3aeab362762e1c50e39d3993d0c5cef77a3f12b2 Homepage: https://cran.r-project.org/package=RobustAdaptiveDecomposition Description: CRAN Package 'RobustAdaptiveDecomposition' (Decomposes a Univariate Time Series into Subcomponents) Provides a method to decompose a univariate time series into meaningful subcomponents for analysis and denoising. Package: r-cran-robustanova Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-peip, r-cran-optimbase Filename: pool/dists/noble/main/r-cran-robustanova_0.3.0-1.ca2404.1_all.deb Size: 37344 MD5sum: cd65d05e07aa103392595d7ea8e4bbed SHA1: f2449dc079f72f61829df540ff7ce7d8e25b33ab SHA256: e06fb5c987fdf7dd1eaa7d345aaf677248809d5a0e1a3743bdb55826e311f54a SHA512: 52b49d72df2e80e21678cb0f525cac6d5c574019def7efbf87628127d07ffd70a8a6dd5cef172eb4f3dc6cc11244adc0b4da56a1b09697a0ad3d302eb759414a Homepage: https://cran.r-project.org/package=RobustANOVA Description: CRAN Package 'RobustANOVA' (Robust One-Way ANOVA Tests under Heteroscedasticity andNonnormality) Robust tests (RW, RPB and RGF) are provided for testing the equality of several long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW, RPB and RGF tests are robust versions of Welch's F test proposed by Welch (1951) , parametric bootstrap test proposed by Krishnamoorthy et. al (2007) ; and generalized F test proposed by Weerahandi (1995) ;, respectively. These tests are based on the modified maximum likelihood (MML) estimators proposed by Tiku(1967, 1968) , . Package: r-cran-robustbetareg Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-betareg, r-cran-rmpfr, r-cran-rstudioapi, r-cran-crayon, r-cran-pracma, r-cran-numderiv, r-cran-formula, r-cran-robustbase, r-cran-zoo, r-cran-bbmisc, r-cran-mass, r-cran-misctools, r-cran-matrix Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-robustbetareg_0.3.1-1.ca2404.1_all.deb Size: 229392 MD5sum: 69845e9e3d40c311fa04637972188f11 SHA1: 15e50848a9d790408c0329805de6b181c75a6574 SHA256: da662299e2b0995ad4f4f35b564dee3d82627fc579bdb1ff22d3eed96fb7a4b7 SHA512: df0760cfa6bca7cdc1f0c79aa72ba2cbee64ca9c9412f30a9a15d54fb58c663a1a22a751a0461d2154f6814ab3a13dd9de867a46784c759fd0749b8d5201777a Homepage: https://cran.r-project.org/package=robustbetareg Description: CRAN Package 'robustbetareg' (Robust Beta Regression) Robust estimators for the beta regression, useful for modeling bounded continuous data. Currently, four types of robust estimators are supported. They depend on a tuning constant which may be fixed or selected by a data-driven algorithm also implemented in the package. Diagnostic tools associated with the fitted model, such as the residuals and goodness-of-fit statistics, are implemented. Robust Wald-type tests are available. More details about robust beta regression are described in Maluf et al. (2025) . Package: r-cran-robustbf Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-robustbf_0.2.0-1.ca2404.1_all.deb Size: 24992 MD5sum: 3d81efd7a2e30b25c2740a560b427d50 SHA1: b997d4d111a72c759860265836115bf588029c35 SHA256: 681502ec19cb2ef81424c6cb81b1f27d6919d32cb627e7b9ba3a21a726241124 SHA512: 7c1c06b84196bcad4bc8e03f6412dead5f3dbecef51cdc9b245c06e5a43ac976dc4e667b90b9bd744fb96acde9090ce98da9623cbb0eb603bc1b9cc20cd8cb48 Homepage: https://cran.r-project.org/package=RobustBF Description: CRAN Package 'RobustBF' (Robust Solution to the Behrens-Fisher Problem) Robust tests (RW and RF) are provided for testing the equality of two long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW test is a robust version of Welch's two sample t test and the RF is a robust fiducial based test. The RW and RF tests are proposed using the adaptive modified maximum likelihood (AMML) estimators derived by Tiku and Surucu (2009) and Donmez (2010) . Package: r-cran-robustda Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mclust, r-cran-rsolnp Filename: pool/dists/noble/main/r-cran-robustda_1.2-1.ca2404.1_all.deb Size: 24032 MD5sum: 13b33a1610ab8c19dd14f6a727bef587 SHA1: 6bb7af7d95be8ce36cf2b8feb350ea3d9a03cdea SHA256: a60994b55351376700dac1789b1cac89bea60040dc4851c169524697ac1399f3 SHA512: f50d66bb6a84b314fd5cbf14c4bcb72f8732d2b234a4363ae7c500a6b3e30f7f5100893fe96d37e72eab0edcb66f712e1871440d2efdfa0a8ee697d58ea6ef54 Homepage: https://cran.r-project.org/package=robustDA Description: CRAN Package 'robustDA' (Robust Mixture Discriminant Analysis) Robust mixture discriminant analysis (RMDA), proposed in Bouveyron & Girard, 2009 , allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels. Package: r-cran-robustdif Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-mirt, r-cran-lavaan Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-robustdif_0.2.0-1.ca2404.1_all.deb Size: 108874 MD5sum: 4828298068d64de394b57e089e6b7244 SHA1: 36fe95a8e1127c424fbacfbf65c615dce7349588 SHA256: 14b4d3c5370c4aaacfaa93915446933477845fe54cf0720c0dc9cb221ee182fc SHA512: 991fd497684e9da9ee9dd502d0f628f3df2b9a95d991713065aab82529dfdb86f09a1836e8a26a5992f43734c0782b4a67ab8fd140265f0526e4e6164261276d Homepage: https://cran.r-project.org/package=robustDIF Description: CRAN Package 'robustDIF' (Differential Item Functioning Using Robust Scaling) Provides tools for testing differential item functioning (DIF) and differential test functioning (DTF) in two-group item response theory models. The package estimates robust scaling parameters via iteratively reweighted least squares with Tukey's bisquare loss, and supports Wald-type tests of item-level and test-level differences from robust scaling parameters. Inputs can be supplied directly from model parameter/covariance objects or extracted from fitted 'mirt' and 'lavaan' models. Methods are described in Halpin (2022) . Package: r-cran-robustfa Architecture: all Version: 1.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1756 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rrcov Suggests: r-cran-lattice, r-cran-cluster, r-cran-mclust, r-cran-mass, r-cran-ellipse, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-robustfa_1.2-0-1.ca2404.1_all.deb Size: 1379662 MD5sum: f2cb321aaee791f7c09419889b33e23e SHA1: ea54047c3213b6023bfab8cc2c1e1986c9e01c65 SHA256: f9bf84b44a04c72d169b7dd0633203598bda97a52b06ab3ab2f07adb939268cf SHA512: bcdba74d90052ea58ecf5694c64f7192d7b4e6a6231aaec9e54068d9b791084efe2392d0ab215446ed36791f5749ae5be30961150e3b8728f1fc97ea4cffe5aa Homepage: https://cran.r-project.org/package=robustfa Description: CRAN Package 'robustfa' (Object Oriented Solution for Robust Factor Analysis) Outliers virtually exist in any datasets of any application field. To avoid the impact of outliers, we need to use robust estimators. Classical estimators of multivariate mean and covariance matrix are the sample mean and the sample covariance matrix. Outliers will affect the sample mean and the sample covariance matrix, and thus they will affect the classical factor analysis which depends on the classical estimators (Pison, G., Rousseeuw, P.J., Filzmoser, P. and Croux, C. (2003) ). So it is necessary to use the robust estimators of the sample mean and the sample covariance matrix. There are several robust estimators in the literature: Minimum Covariance Determinant estimator, Orthogonalized Gnanadesikan-Kettenring, Minimum Volume Ellipsoid, M, S, and Stahel-Donoho. The most direct way to make multivariate analysis more robust is to replace the sample mean and the sample covariance matrix of the classical estimators to robust estimators (Maronna, R.A., Martin, D. and Yohai, V. (2006) ) (Todorov, V. and Filzmoser, P. (2009) ), which is our choice of robust factor analysis. We created an object oriented solution for robust factor analysis based on new S4 classes. Package: r-cran-robustflow Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-golem, r-cran-bslib, r-cran-ggplot2, r-cran-dt, r-cran-plotly, r-cran-scales, r-cran-htmltools, r-cran-rmarkdown Suggests: r-cran-testthat, r-cran-knitr, r-cran-covr, r-cran-withr Filename: pool/dists/noble/main/r-cran-robustflow_0.1.1-1.ca2404.1_all.deb Size: 129672 MD5sum: b53efa40341b5364082c6d7568ee90a5 SHA1: 0818cdc700adeee5248c4caa744dffa381dcb9da SHA256: 5665858fcd130c0815825c769e1206417d4270ba63354bf8da693a8185de3516 SHA512: ec319e03fd5f02c39a39b3960132e9a7aa2d980af58dbee0642fb5d82e7e7a5e4b8be921be3717729d182c72938285399503db2f9e8fcff142b97a05d2d7ea80 Homepage: https://cran.r-project.org/package=RobustFlow Description: CRAN Package 'RobustFlow' (Robustness and Drift Auditing for Longitudinal Decision Systems) Provides tools for constructing longitudinal decision paths, quantifying temporal drift, tracking subgroup disparity trajectories, and stress-testing longitudinal conclusions under hidden bias. Implements three signature metrics: the Drift Intensity Index (DII), which measures structural instability in transition dynamics using the Frobenius norm of consecutive transition matrix differences; the Bias Amplification Index (BAI), which quantifies whether group disparities widen or converge over time; and the Temporal Fragility Index (TFI), which estimates the minimum hidden-bias perturbation required to nullify a longitudinal trend conclusion. An interactive 'shiny' application supports exploratory analysis, visualization, and reproducible reporting. Methods are motivated by applications in educational and social science research, including the Early Childhood Longitudinal Study (ECLS). The DII is based on the Frobenius norm as described in Golub and Van Loan (2013, ISBN:9781421407944). The TFI extends the hidden-bias sensitivity framework of Rosenbaum (2002, ISBN:9781441912633). The BAI draws on disparity-trajectory methods discussed in Duncan and Murnane (2011, ISBN:9780871542731). Package: r-cran-robustgarch Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsolnp, r-cran-nloptr, r-cran-rugarch, r-cran-zoo, r-cran-xts Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-pcra Filename: pool/dists/noble/main/r-cran-robustgarch_0.4.2-1.ca2404.1_all.deb Size: 78416 MD5sum: a46384414e6f1173f76f5425b9c7cbe7 SHA1: de01bd126cae989c7768ff828d27fcd83051957b SHA256: 5e281af51cd83d26adbe1b94af09470495bd3a54c38bae19562b486718ac99aa SHA512: cdc0fa6d79f35575ef674c82b0fc71e5d1de66fc315b81b3bc2ca07fc214bcf33bb7a711ca81d82cef083b1cc97f639952dad797ced8295ac9bf904c23fdfdd2 Homepage: https://cran.r-project.org/package=robustGarch Description: CRAN Package 'robustGarch' (Robust Garch(1,1) Model) A method for modeling robust generalized autoregressive conditional heteroskedasticity (Garch) (1,1) processes, providing robustness toward additive outliers instead of innovation outliers. 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(2018) ) and SearchingSampling ('Guo' (2023) ), which are effective for both low- and high-dimensional covariates and instrumental variables; test of endogeneity in high dimensions ('Guo et al.' (2018) ). Package: r-cran-robustlinearreg Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-robustlinearreg_1.2.0-1.ca2404.1_all.deb Size: 18172 MD5sum: c7f6330f7c6d53fe299f29fa1d9a37b7 SHA1: 23c5fc361c1bcb1b83e266782bd3799da6e886e4 SHA256: 9f06eeff2378eb4d71b3cee3d4c4b6677bd1b3dd9cb5300baf4d5af92ac32b6f SHA512: de3e124855f6fc2031673fdbdb89deb2191ebfaf8d073e357f94f76081ba0c5ee7da56346d0892109004bd7420e3bda2cac44b8e96d632e2ec57a7316b6bc14f Homepage: https://cran.r-project.org/package=RobustLinearReg Description: CRAN Package 'RobustLinearReg' (Robust Linear Regressions) Provides an easy way to compute the Theil Sehn Regression method and also the Siegel Regression Method which are both robust methods base on the median of slopes between all pairs of data. In contrast with the least squared linear regression, these methods are not sensitive to outliers. Theil, H. (1992) , Sen, P. K. (1968) . 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(2013) . Users can enjoy the near optimal, consistent, and oracle properties of the procedures. 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Estimates natural direct and indirect effects over a user-defined treatment grid and supports flexible dose-response mediation analysis. Includes diagnostic procedures for assessing covariate balance in both treatment and mediator models using standardized mean differences. Implements pathway-specific extensions of the impact threshold for a confounding variable (ITCV; Frank, 2000 ) adapted to mediation settings. Provides joint sensitivity analysis combining E-values (VanderWeele and Ding, 2017 ) and violations of sequential ignorability (Imai, Keele, and Yamamoto, 2010 ). Additional utilities include visualization of dose-response mediation functions, robustness profiles, fragility summaries, and formatted outputs for applied research. Supports clustered data structures and multiple outcome families. Package: r-cran-robustmeta Architecture: all Version: 1.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metafor Filename: pool/dists/noble/main/r-cran-robustmeta_1.2-1-1.ca2404.1_all.deb Size: 34412 MD5sum: 37cb97a5b0868b9eef9f13578054676d SHA1: 5111f5594dc202761b6bd55432522ced5f30df6f SHA256: eb4528996620a4da348e354be0116d9701302d8db279d88f0bc3254c97894980 SHA512: 91c4ff364949ad47bfcdc2de62870b2681c1d67ad7aaa02e30a279a619552a293a17040411f0c86c305cc075fece59be55ca3c639a7b1bcea2dea19f78c7bfbe Homepage: https://cran.r-project.org/package=robustmeta Description: CRAN Package 'robustmeta' (Robust Inference for Meta-Analysis with Influential OutlyingStudies) Robust inference methods for fixed-effect and random-effects models of meta-analysis are implementable. The robust methods are developed using the density power divergence that is a robust estimating criterion developed in machine learning theory, and can effectively circumvent biases and misleading results caused by influential outliers. The density power divergence is originally introduced by Basu et al. (1998) , and the meta-analysis methods are developed by Noma et al. (2022) . 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Plots the receiver operating characteristic curve (ROC curve) together with the recall / 1-precision curve. Package: r-cran-robustprediction Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mboost, r-cran-mlr, r-cran-ranger, r-cran-e1071, r-cran-proc Filename: pool/dists/noble/main/r-cran-robustprediction_0.1.7-1.ca2404.1_all.deb Size: 301496 MD5sum: 5fd53355b05472e94ff3c11bdfca3550 SHA1: c00df8ce90f3e67b1fc9b96353ed9c649d55c84c SHA256: b7784603bfc013a2ca2b4e41954d5d06b4dc72412108152ad9ac77b1271ef9d9 SHA512: ea4dbaf078042fbc34f9675b2d4d33f949c2f71c3b0327dbfa70eba279300f12fec0ba3d64a0ebba87f3c3a25e82825e11a1ea878e18f7d89f18b7dc4cc1fe3c Homepage: https://cran.r-project.org/package=RobustPrediction Description: CRAN Package 'RobustPrediction' (Robust Tuning and Training for Cross-Source Prediction) Provides robust parameter tuning and model training for predictive models applied across data sources where the data distribution varies slightly from source to source. This package implements three primary tuning methods: cross-validation-based internal tuning, external tuning, and the 'RobustTuneC' method. External tuning includes a conservative option where parameters are tuned internally on the training data and validating on an external dataset, providing a slightly pessimistic estimate. It supports Lasso, Ridge, Random Forest, Boosting, and Support Vector Machine classifiers. Currently, only binary classification is supported. The response variable must be the first column of the dataset and a factor with exactly two levels. The tuning methods are based on the paper by Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, and Roman Hornung (2021) "Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning" . Package: r-cran-robustrankaggreg Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-robustrankaggreg_1.2.1-1.ca2404.1_all.deb Size: 50028 MD5sum: fc69bcd42ad95f6ef545fc7f728d2bf8 SHA1: a93e5f6e75f41de015cbb1fab5b04bde81105af7 SHA256: 50cbf38fc0f34bbd5240804b4c23b2776d71ea351396a0afa334afdc65d20d50 SHA512: 52efa6a4695a458c2435d94012f42cd6b6df8871d9df0e962b8a908e5cb3503e2d61f9810ea023b5ea1bf5ca51bf3175991fa1d1183026c6cb6ca002e4259dd2 Homepage: https://cran.r-project.org/package=RobustRankAggreg Description: CRAN Package 'RobustRankAggreg' (Methods for Robust Rank Aggregation) Methods for aggregating ranked lists, especially lists of genes. It implements the Robust Rank Aggregation Kolde et al (2012) and some other simple algorithms for the task. RRA method uses a probabilistic model for aggregation that is robust to noise and also facilitates the calculation of significance probabilities for all the elements in the final ranking. Package: r-cran-robustrao Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-gmp, r-cran-iterpc, r-cran-quadprog, r-cran-igraph, r-cran-foreach Filename: pool/dists/noble/main/r-cran-robustrao_1.0-5-1.ca2404.1_all.deb Size: 194790 MD5sum: 2e6bb94df393a8ce3d31f03cfb92ffeb SHA1: 54d25ff55240c08563f32992076428eaab13817f SHA256: 02276ecaa34ba24f5cbbb2427b6a9753c6038a59827043079f47f9a183f464ab SHA512: ff9dfe13a58ff6710fe48bccc424836e6043480521072082d1dc674e74dcaee4c7889f4ca95791b0138d18ac69019924cc812bb78c2de523103115b477e755ff Homepage: https://cran.r-project.org/package=robustrao Description: CRAN Package 'robustrao' (An Extended Rao-Stirling Diversity Index to Handle Missing Data) A collection of functions to compute the Rao-Stirling diversity index (Porter and Rafols, 2009) and its extension to acknowledge missing data (i.e., uncategorized references) by calculating its interval of uncertainty using mathematical optimization as proposed in Calatrava et al. (2016) . The Rao-Stirling diversity index is a well-established bibliometric indicator to measure the interdisciplinarity of scientific publications. Apart from the obligatory dataset of publications with their respective references and a taxonomy of disciplines that categorizes references as well as a measure of similarity between the disciplines, the Rao-Stirling diversity index requires a complete categorization of all references of a publication into disciplines. Thus, it fails for a incomplete categorization; in this case, the robust extension has to be used, which encodes the uncertainty caused by missing bibliographic data as an uncertainty interval. Classification / ACM - 2012: Information systems ~ Similarity measures, Theory of computation ~ Quadratic programming, Applied computing ~ Digital libraries and archives. Package: r-cran-robustsur Architecture: all Version: 0.0-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase, r-cran-robreg3s, r-cran-matrix, r-cran-gse Suggests: r-cran-systemfit Filename: pool/dists/noble/main/r-cran-robustsur_0.0-8-1.ca2404.1_all.deb Size: 46736 MD5sum: d288fe401a7a1f441a797c8c7686b806 SHA1: 9947625da70125cdfebe155e5e0fcf88190ba50d SHA256: 642b165f7e978be0763f3f95c31efcf7a855a8decaeae4d2c18fcd2975a64738 SHA512: 188d85bf6c1beb5b5f361f903352495b1f22cfc4875e1c5cab1f4a9e467af8af27b837796aadefd3b898ff4dec632285171ebbb8d9abb836d5486ce386923f17 Homepage: https://cran.r-project.org/package=robustsur Description: CRAN Package 'robustsur' (Robust Estimation for Seemingly Unrelated Regression Models) Data sets are often corrupted by outliers. When data are multivariate outliers can be classified as case-wise or cell-wise. The latters are particularly challenge to handle. We implement a robust estimation procedure for Seemingly Unrelated Regression Models which is able to cope well with both type of outliers. Giovanni Saraceno, Fatemah Alqallaf, Claudio Agostinelli (2021) . Package: r-cran-robustt2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-mass, r-cran-ggplot2, r-cran-rrcov, r-cran-shiny, r-cran-ggrepel, r-cran-forcats, r-cran-matrix Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-robustt2_0.1.0-1.ca2404.1_all.deb Size: 94448 MD5sum: c9186a1b89badf793811c0a90cb57979 SHA1: f3760185aff6f7ce9fddcb9e3c0e6cfb7bb8b53c SHA256: 22337be4cdbc28da0dd6089abf83bdf3971191be4dd3d0a2c15fc5799ddd6b1d SHA512: 1b0c0fb15f5403ef26ce13e1bf76025079fcc295c75dc04e5d274bcd3e654161ed15bfc9b256f1cfd258fc6c1d90cdffffbbdd6df8224327c5e21115d58831c8 Homepage: https://cran.r-project.org/package=robustT2 Description: CRAN Package 'robustT2' (Robust Hotelling-Type T² Control Chart Based on the Dual STATISApproach) Implements a robust multivariate control-chart methodology for batch-based industrial processes with multiple correlated variables using the Dual STATIS (Structuration des Tableaux A Trois Indices de la Statistique) framework. A robust compromise covariance matrix is constructed from Phase I batches with the Minimum Covariance Determinant (MCD) estimator, and a Hotelling-type T² statistic is applied for anomaly detection in Phase II. The package includes functions to simulate clean and contaminated batches, to compute both robust and classical Hotelling T² control charts, to visualize results via robust biplots, and to launch an interactive 'shiny' dashboard. An internal dataset (pharma_data) is provided for reproducibility. See Lavit, Escoufier, Sabatier and Traissac (1994) for the original STATIS methodology, and Rousseeuw and Van Driessen (1999) for the MCD estimator. Package: r-cran-robustx Architecture: all Version: 1.2-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-robustbase Suggests: r-cran-mass, r-cran-lattice, r-cran-pcapp Filename: pool/dists/noble/main/r-cran-robustx_1.2-8-1.ca2404.1_all.deb Size: 128878 MD5sum: 77c065f67cc64399143f1b6c79c742ae SHA1: 349862c45c012070cde7cf9d33404943dbf741e5 SHA256: 5543f8c3347c97a98c5e1668d6476e852e418fc17ffee0a122cc54a200a6edf3 SHA512: 2670a09992ba5073ac3a0c02a6b889df6684f15aae1975a14e9f8c0d6e0cb1a0bea40ec87bf4eefddbd4e3c334c342e066b1c31b97a6b51a706116b74983da6c Homepage: https://cran.r-project.org/package=robustX Description: CRAN Package 'robustX' ('eXtra' / 'eXperimental' Functionality for Robust Statistics) Robustness -- 'eXperimental', 'eXtraneous', or 'eXtraordinary' Functionality for Robust Statistics. Hence methods which are not well established, often related to methods in package 'robustbase'. Amazingly, 'BACON()', originally by Billor, Hadi, and Velleman (2000) has become established in places. The "barrow wheel" `rbwheel()` is from Stahel and Mächler (2009) . Package: r-cran-robvis Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2097 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-robvis_0.3.0-1.ca2404.1_all.deb Size: 1252034 MD5sum: 68810cc5e049d8e772170cf5b85997cc SHA1: 9e2ad450593843f703d9f85341d52ff6cceeaf56 SHA256: cd2bc4374a2efbc41bdb276e91be382bbb1046d57719476f961f10e6116c6d7d SHA512: 33cec45e83df33fd32f42b1fba25f0479904b2318ef14884e675e8ed998c2d493d9395ad440488523c6abd425b581adc2a822baa221ed24cd0753fdbc1ed21bc Homepage: https://cran.r-project.org/package=robvis Description: CRAN Package 'robvis' (Visualize the Results of Risk-of-Bias (ROB) Assessments) Helps users in quickly visualizing risk-of-bias assessments performed as part of a systematic review. 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Package: r-cran-rocaggregator Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-testthat, r-cran-mockery, r-cran-mockr, r-cran-knitr, r-cran-rmarkdown, r-cran-rocr, r-cran-proc, r-cran-pracma Filename: pool/dists/noble/main/r-cran-rocaggregator_1.0.1-1.ca2404.1_all.deb Size: 44132 MD5sum: 921c6f28b2c181943915d0d65d6e8a7e SHA1: cac3b6f7f9621d38eb273bb2473412937a477a41 SHA256: 037b823b93e177826d13c5a64281aba41ba595f567a19af89a86c6a164b69f96 SHA512: 3f30c1c4bec396f61c81c4772196dc03bb8a4f8b5ab13c0cf6367ce5c7da9f524eafeba285af2475f270821645d898f55725f32d24432dfa88b0a04255353289 Homepage: https://cran.r-project.org/package=ROCaggregator Description: CRAN Package 'ROCaggregator' (Aggregate Multiple ROC Curves into One Global ROC) Aggregates multiple Receiver Operating Characteristic (ROC) curves obtained from different sources into one global ROC. Additionally, it’s also possible to calculate the aggregated precision-recall (PR) curve. Package: r-cran-rocbc Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1779 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-clinfun, r-cran-splancs, r-cran-mvtnorm, r-cran-formattable, r-cran-mrmcaov, r-cran-proc Suggests: r-cran-knitr, r-cran-markdown, r-cran-matlab Filename: pool/dists/noble/main/r-cran-rocbc_3.2.0-1.ca2404.1_all.deb Size: 994802 MD5sum: c58b732af0c982dcb4abe705ecff8f9c SHA1: fb2de72b041def01f0e743274bb8b25cab8260bc SHA256: 8497fc04bfc28456d61d89be25e76c8311c379ec411b890579a21c1204a645a2 SHA512: 22f92f8d6e56ddb0fba3e85c661e22d1fd66fff25d28bcab536b198cb92688e17ab04d230dd6ef2287c5612dd51782c4bf1b357eb5d50a873eb07b24532fb7e6 Homepage: https://cran.r-project.org/package=rocbc Description: CRAN Package 'rocbc' (Statistical Inference for Box-Cox Based Receiver OperatingCharacteristic Curves) Generation of Box-Cox based ROC curves and several aspects of inferences and hypothesis testing. Can be used when inferences for one biomarker (Bantis LE, Nakas CT, Reiser B. (2018)) are of interest or when comparisons of two correlated biomarkers (Bantis LE, Nakas CT, Reiser B. (2021)) are of interest. Provides inferences and comparisons around the AUC, the Youden index, the sensitivity at a given specificity level (and vice versa), the optimal operating point of the ROC curve (in the Youden sense), and the Youden based cutoff. Package: r-cran-rocc Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rocr Filename: pool/dists/noble/main/r-cran-rocc_1.3-1.ca2404.1_all.deb Size: 37950 MD5sum: 6a38ddff58b9ee5390b7222137e8af4e SHA1: 83596575b6362854981e13ef158151cb0fccd27b SHA256: 9335edcb05cb07b325d81f98b0ad2127a5f763088151cc773ad0717b0845f473 SHA512: e71f59af85010427ff4ef23594e2c8053a081196c31904cbde92a41ceff4f0798f9212e29a0ad6801cf1f13284bf5f81cc94e78c2b59aafd318f2a2c37bcfb01 Homepage: https://cran.r-project.org/package=rocc Description: CRAN Package 'rocc' (ROC Based Classification) Functions for a classification method based on receiver operating characteristics (ROC). Briefly, features are selected according to their ranked AUC value in the training set. The selected features are merged by the mean value to form a meta-gene. The samples are ranked by their meta-gene value and the meta-gene threshold that has the highest accuracy in splitting the training samples is determined. A new sample is classified by its meta-gene value relative to the threshold. In the first place, the package is aimed at two class problems in gene expression data, but might also apply to other problems. Package: r-cran-roccv Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-proc Filename: pool/dists/noble/main/r-cran-roccv_1.2-1.ca2404.1_all.deb Size: 24858 MD5sum: 336dbed899f152478a7834ea55c070ee SHA1: 7c0f292582cb094838969b59ed26eecad86b2494 SHA256: c7bb41e2a8278f8ce48c2bf997897bb5b55b9c5726c268d853ec0f0d8c14799c SHA512: d19d60b2b145342d5b828d528291b8d97b08c3ba1e709161f44abc1f1e8f72900208423eaf1a3a19a169e93ec25bac8d17ead4d2d195e75436410493e3b08aa8 Homepage: https://cran.r-project.org/package=roccv Description: CRAN Package 'roccv' (ROC for Cross Validation Results) Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models. Framework was built to handle genetic data, but works for any data. Package: r-cran-rocean Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ff Filename: pool/dists/noble/main/r-cran-rocean_1.0-1.ca2404.1_all.deb Size: 45402 MD5sum: 7ea66be44bbc4522898f24ec12a93ca5 SHA1: 6d9232b341ef4af4d7f1842d6bf78fdea637ffe9 SHA256: 2e6f5bc684c64512ffa4bc47b8cb107ee822aa746605987d52298d23f557c45d SHA512: c80515fd555672d1fe0c189fa9e627b932437e634505343481327f2c70fd72b900a7c132978d7864ea2e1a408a832be072bef4f9379d5d0880cf89688a680336 Homepage: https://cran.r-project.org/package=rOCEAN Description: CRAN Package 'rOCEAN' (Two-Way Feature Set Testing for Multi-Omics) For any two way feature-set from a pair of pre-processed omics data, 3 different true discovery proportions (TDP), namely pairwise-TDP, column-TDP and row-TDP are calculated. Due to embedded closed testing procedure, the choice of feature-sets can be changed infinite times and even after seeing the data without any change in type I error rate. For more details refer to Ebrahimpoor et al., (2024) . Package: r-cran-rocftp.mms Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rocftp.mms_1.0.1-1.ca2404.1_all.deb Size: 20974 MD5sum: ddaaca87f6ede383b624493c333052e6 SHA1: cae07159d6422a02e8a5208355b9fe0b3246cc2b SHA256: 1ed0ff5889a1a99a534c251b0900585b5cb3f6c12bb883b83833f0cc4ab009ac SHA512: 9d5136956ddeac8cc57d3458b07abd1d64bb97e9ba7b513bca06930e8e13977c4dc5a70461f57fbc9241be1d83014a8152b2b85c237577225833ada7d35d3d41 Homepage: https://cran.r-project.org/package=ROCFTP.MMS Description: CRAN Package 'ROCFTP.MMS' (Perfect Sampling) The algorithm provided in this package generates perfect sample for unimodal or multimodal posteriors. Read Once Coupling From The Past, with Metropolis-Multishift is used to generate a perfect sample for a given posterior density based on the two extreme starting paths, minimum and maximum of the most interest range of the posterior. It uses the monotone random operation of multishift coupler which allows to sandwich all of the state space in one point. It means both Markov Chains starting from the maximum and minimum will be coalesced. The generated sample is independent from the starting points. It is useful for mixture distributions too. The output of this function is a real value as an exact draw from the posterior distribution. Package: r-cran-rocit Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rocit_2.1.2-1.ca2404.1_all.deb Size: 280634 MD5sum: 8fdb77a2a578a60cfea4f9bc9fdbe8f8 SHA1: 97e8dac7c333478aecadf56956b66e029d52eb27 SHA256: 6ab254daa16b12f85e54ad2c621a262672b114eadb0d290fde215dff32cdcce5 SHA512: f3df650bb8e9cae80de3c557f95d22ea82ee3f4c13bffac4c62fb96ffe97bc0fe2322e93c68a6b9b49f15fe92d4ba2500499f9caad98f30dc58fb5ebe449dce9 Homepage: https://cran.r-project.org/package=ROCit Description: CRAN Package 'ROCit' (Performance Assessment of Binary Classifier with Visualization) Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use. Package: r-cran-rock Architecture: all Version: 0.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1689 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.tree, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-ggplot2, r-cran-glue, r-cran-htmltools, r-cran-markdown, r-cran-purrr, r-cran-squids, r-cran-yaml, r-cran-yum Suggests: r-cran-covr, r-cran-googlesheets4, r-cran-haven, r-cran-justifier, r-cran-knitr, r-cran-limonaid, r-cran-openxlsx, r-cran-pdftools, r-cran-pkgdown, r-cran-preregr, r-cran-readxl, r-cran-rmarkdown, r-cran-rvest, r-cran-rsvg, r-cran-rstudioapi, r-cran-striprtf, r-cran-testthat, r-cran-writexl, r-cran-xlconnect, r-cran-xml2, r-cran-zip Filename: pool/dists/noble/main/r-cran-rock_0.9.6-1.ca2404.1_all.deb Size: 1214310 MD5sum: 829ba88f42e22734e4c10e00eb42dd38 SHA1: 4b3b5e8ecaa00fd1b723870a1542ccaba395919d SHA256: de71fa91ec325713ecd954735b5539afa2305c2ff211847e9db87d6744de16be SHA512: f5b099ef34742015cca4d23d2cb3f48df69b6be7411c0c36e0acb3cae99218250ba27e328fff47f8fb46d1dd94b2b10cd30d03de857d7d9f0ec5a1031e037ab6 Homepage: https://cran.r-project.org/package=rock Description: CRAN Package 'rock' (Reproducible Open Coding Kit) The Reproducible Open Coding Kit ('ROCK', and this package, 'rock') was developed to facilitate reproducible and open coding, specifically geared towards qualitative research methods. It was developed to be both human- and machine-readable, in the spirit of MarkDown and 'YAML'. The idea is that this makes it relatively easy to write other functions and packages to process 'ROCK' files. The 'rock' package contains functions for basic coding and analysis, such as collecting and showing coded fragments and prettifying sources, as well as a number of advanced analyses such as the Qualitative Network Approach and Qualitative/Unified Exploration of State Transitions. The 'ROCK' and this 'rock' package are described in the ROCK book (Zörgő & Peters, 2022; ), in Zörgő & Peters (2024) and Peters, Zörgő and van der Maas (2022) , and more information and tutorials are available at . Package: r-cran-rockchalk Architecture: all Version: 1.8.164-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2827 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-cardata, r-cran-mass, r-cran-kutils, r-cran-reformulas Suggests: r-cran-tables, r-cran-hmisc, r-cran-car, r-cran-mvtnorm, r-cran-scatterplot3d, r-cran-hh Filename: pool/dists/noble/main/r-cran-rockchalk_1.8.164-1.ca2404.1_all.deb Size: 2379608 MD5sum: d1b6d22e0d25ef1df67dae116d25dc4f SHA1: 602d1f6305c43eb1696b7c0a8882417509e76bb7 SHA256: 68772fa610a538b1b762365e66654755f4083c222b5feff0ff4bc79ec5c78670 SHA512: cdcb5cd7be252d4b5b86e47398bff175e5555bb1a769a6ed3dc482e63cb864dbe5d9d1ceea8dc1309da287923f6198f5f4203316065857c11fea78457c94f396 Homepage: https://cran.r-project.org/package=rockchalk Description: CRAN Package 'rockchalk' (Regression Estimation and Presentation) A collection of functions for interpretation and presentation of regression analysis. These functions are used to produce the statistics lectures in . Includes regression diagnostics, regression tables, and plots of interactions and "moderator" variables. The emphasis is on "mean-centered" and "residual-centered" predictors. The vignette 'rockchalk' offers a fairly comprehensive overview. The vignette 'Rstyle' has advice about coding in R. The package title 'rockchalk' refers to our school motto, 'Rock Chalk Jayhawk, Go K.U.'. Package: r-cran-rocker Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 581 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-r6, r-cran-sodium Suggests: r-cran-covr, r-cran-crayon, r-cran-knitr, r-cran-rmariadb, r-cran-testthat, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rsqlite Filename: pool/dists/noble/main/r-cran-rocker_0.3.2-1.ca2404.1_all.deb Size: 350420 MD5sum: 95f77aa1312698280232c88689670ebd SHA1: 5944413dba7be921110815977f14fdf4311cf13c SHA256: 53d054a9206ab9980b455ec92e19ad3320ed72a12afc6f77a205de45cdae185e SHA512: 7aac69dee34fdda5b3eb17af14423c19e37f03b4e996ebbb79492dd7a7e25b5fb5a1a17b2949259f93f700bfdb0119b40439d86c72c9f0496952bbb6f4955151 Homepage: https://cran.r-project.org/package=rocker Description: CRAN Package 'rocker' (Database Interface Class) 'R6' class interface for handling relational database connections using 'DBI' package as backend. The class allows handling of connections to e.g. PostgreSQL, MariaDB and SQLite. The purpose is having an intuitive object allowing straightforward handling of SQL databases. Package: r-cran-rocket Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rocket_1.0.3-1.ca2404.1_all.deb Size: 128596 MD5sum: 0a6a19373251a5ac4916b65a4d38cb62 SHA1: e51ba334a4621e82995df184b05b0bc3fbbcde53 SHA256: d1a2660e4d31abd4bea326c6c89007538716da0e75d6892d86723a842919aec6 SHA512: e5551af45a64f3b7c012390cca9bcc580d14a5d2ebcdaefaca58483111d00e9d42f405e810628da19c18e689855b6284a6aded049c7e64d1d166d3dcdef69fd8 Homepage: https://cran.r-project.org/package=ROCket Description: CRAN Package 'ROCket' (Simple and Fast ROC Curves) A set of functions for receiver operating characteristic (ROC) curve estimation and area under the curve (AUC) calculation. All functions are designed to work with aggregated data; nevertheless, they can also handle raw samples. In 'ROCket', we distinguish two types of ROC curve representations: 1) parametric curves - the true positive rate (TPR) and the false positive rate (FPR) are functions of a parameter (the score), 2) functions - TPR is a function of FPR. There are several ROC curve estimation methods available. An introduction to the mathematical background of the implemented methods (and much more) can be found in de Zea Bermudez, Gonçalves, Oliveira & Subtil (2014) and Cai & Pepe (2004). Package: r-cran-rockfab Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-bioc-ebimage Filename: pool/dists/noble/main/r-cran-rockfab_1.2.1-1.ca2404.1_all.deb Size: 119082 MD5sum: 1cd2b99d6c581ca6f793110545e70e5b SHA1: f680b74e033f5a02609d1eeb659d47b658e542b4 SHA256: 2606eee8c2777759a9b1989b48a38b523b402e3f5e18071e9c725b1f68b3c0e6 SHA512: f0d7a690563942421484fb3a403bf6b18280db3b91c09a3ced793daa1b5e8a48fddf40adf849d1fdd6455f037fefb3ca0f7d4d398571f6f4c247c96837b957b0 Homepage: https://cran.r-project.org/package=RockFab Description: CRAN Package 'RockFab' (Rock Fabric and Strain Analysis Tools) Provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality. Package: r-cran-rockr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-mime, r-cran-progress Suggests: r-cran-knitr, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-rockr_1.0.0-1.ca2404.1_all.deb Size: 129006 MD5sum: 44a98e626fe30aceb0e5992404c53efb SHA1: 8d22d38ed50da5cb7e5cd31a7d8b95e2119731a9 SHA256: 9189ae48f55ff97b33135e35f7daf01e081a13f16ec08d4728d848f947713aa2 SHA512: 9cbdd551545e2aeaa02a0d4ff3c2717e7d6022fe560eb725e5aabb3df923ccb303444af2693d27680edd75a77a84b93756824f17a8ff21835f07f3b9c255f238 Homepage: https://cran.r-project.org/package=rockr Description: CRAN Package 'rockr' ('Rock' R Server Client) Connector to the REST API of a 'Rock' R server, to perform operations on a remote R server session, or administration tasks. See 'Rock' documentation at . Package: r-cran-rockx Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-purrr, r-cran-tidyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse, r-cran-magrittr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rockx_0.1.0-1.ca2404.1_all.deb Size: 43260 MD5sum: f4d1b0c44026430a891008e37f6c660d SHA1: 28e25fc2975d871ebd264c21664077a349dc04a2 SHA256: bc68a42883297d753d46faa711cf81894cecd6e4908e2ac2b0ad72abdde68c80 SHA512: f92a934b6774729924ec0848e379467eb8ddd0de8a865c1582b5db49e57c846cd8669d8c2e213a4cd2a952e5c28b986374268aa53d9c06348a7980ed08a2efc8 Homepage: https://cran.r-project.org/package=rockx Description: CRAN Package 'rockx' (Easily Import Data from Your 'ODK-X Sync Endpoint') Provides helper functions for authenticating and retrieving data from your 'ODK-X Sync Endpoint'. This is an early release intended for testing and feedback. Package: r-cran-roclab Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-fastdummies, r-cran-kernlab, r-cran-pracma, r-cran-rsample, r-cran-dplyr, r-cran-caret, r-cran-proc Suggests: r-cran-mlbench, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-roclab_0.1.4-1.ca2404.1_all.deb Size: 244158 MD5sum: c550062ecb3776da6f5182af149c321f SHA1: 219cd974f34433467d0783e80d2c9e0b49510e4c SHA256: 3a1c6481d9f4c3a2bf5cbc2c3d1f04384a86336e350ad3276cc313e46a639517 SHA512: bf0d621ffb5262d125b31237b5a1980dc2456240ee4fb7e3fa8b8179377624e6474a5fdeea51dd6bebb77406e2ed1a364824a295fac2380ede1873a65397f8d2 Homepage: https://cran.r-project.org/package=roclab Description: CRAN Package 'roclab' (ROC-Optimizing Binary Classifiers) Implements ROC (Receiver Operating Characteristic)–Optimizing Binary Classifiers, supporting both linear and kernel models. Both model types provide a variety of surrogate loss functions. In addition, linear models offer multiple regularization penalties, whereas kernel models support a range of kernel functions. Scalability for large datasets is achieved through approximation-based options, which accelerate training and make fitting feasible on large data. Utilities are provided for model training, prediction, and cross-validation. The implementation builds on the ROC-Optimizing Support Vector Machines. For more information, see Hernàndez-Orallo, José, et al. (2004) , presented in the ROC Analysis in AI Workshop (ROCAI-2004). Package: r-cran-roclang Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-stringr, r-cran-magrittr, r-cran-rlang, r-cran-roxygen2, r-cran-rex Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-roclang_0.2.3-1.ca2404.1_all.deb Size: 36684 MD5sum: ea9acc8c3cabdfbd83384a4506154f49 SHA1: bcf4bb1424b3c9c1c2021c1a9f563b01266f7171 SHA256: ba37e33486aa337061f4730743c57c21ff3a31b8f900d2c7f0f64394a3bb3cf7 SHA512: e02961257068700d6c4d7988eae0a882360049e6030bb3c8c2720dddc798a6f7ed863b43427251891d51c0f48d56d8e13013bc5df053347dca8d41fd69a4f0ab Homepage: https://cran.r-project.org/package=roclang Description: CRAN Package 'roclang' (Functions for Diffusing Function Documentations into 'Roxygen'Comments) Efficient diffusing of content across function documentations. Sections, parameters or dot parameters are extracted from function documentations and turned into valid Rd character strings, which are ready to diffuse into the 'roxygen' comments of another function by inserting inline code. Package: r-cran-rocmodels Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-kedd, r-cran-dplyr, r-cran-survival, r-cran-nleqslv, r-cran-hdinterval, r-cran-rocit, r-cran-doparallel, r-cran-foreach, r-cran-pbivnorm, r-cran-nor1mix, r-cran-readr, r-cran-mass, r-cran-dorng Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rocmodels_1.0.0-1.ca2404.1_all.deb Size: 174378 MD5sum: fe48b5ef0724eb33642fabaada50ea6d SHA1: 513e02ed5f32e077ec694d6e82ae7f29b0bda95e SHA256: 22561f9dfef2563f7cf784d571399181d1b36fab2067f933da1c55f5686e2604 SHA512: b038e47e17ed605ec6b8adc1c10897cefbdea951dd7fabb3bcbc09bb23c515959b2bfbb162bf4b7c36c8c07698cef210acb0534cbedcf945ba858851e28642ba Homepage: https://cran.r-project.org/package=ROCModels Description: CRAN Package 'ROCModels' (ROC Models and AUC Estimation) The receiver operating characteristic (ROC) curve is one of the most widely used tools for evaluating diagnostic and prognostic biomarkers across diverse scientific fields, particularly in medicine. Despite its ubiquity, ROC estimation and testing methods differ substantially in their assumptions and resulting curve properties. This package provides a unified framework for constructing, visualizing, and comparing parametric, nonparametric, semiparametric, and Bayesian ROC curves. 'ROCModels' helps researchers identify and implement ROC inference methods most suitable for their data. See the accompanying vignette 'ROCModels_Package_Doc' for a detailed introduction. Alonzo, T. A., and Pepe, M. S. (2002) , Andrews, D. F., and Herzberg, A. M. (1985) , Bamber, D. (1975) , Cox, D. R. (1972) , Cox, D. R. (1975) , DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L. (1988) , Dorfman, D. D., and Alf, E. (1969) , Dorfman, D. D., Berbaum, K. S., and Metz, C. E. (1997) , Erkanli, A., Sung, L., and Stamey, J. D. (2006) , Faraggi, D., and Reiser, B. (2002) , Ghebremichael, M., and Habtemicael, S. (2018) , Ghebremichael, M., and Michael, H. (2024) , Ghebremichael, M., Michael, H., Tubbs, J., and Paintsil, E. (2019) , Gönen, M., and Heller, G. (2010) , Gopalakrishnan, V., Bose, E., Nair, U., Cheng, Y., and Ghebremichael, M. (2020) , Green, D. M., and Swets, J. A. (1966, ISBN:0471324205), Gu, J., and Ghosal, S. (2009) , Gu, Y., Ghosal, S., and Roy, A. (2008) , Guidoum, A. C. (2020) , , Guo, B. (2015) , Hanley, J. A., and McNeil, B. J. (1982) , Hsieh, F., and Turnbull, B. W. (1996) , Hussain, E. (2012) , Ishwaran, H., and James, L. F. (2002) , Jokiel-Rokita, A., and Topolnicki, R. (2020) , Krzanowski, W. J., and Hand, D. J. (2009) , Kundu, D., and Gupta, R. D. (2006) , Lloyd, C. J. (1998) , Lehmann, E. L. (1953) , Metz, C. E., Herman, B. A., and Shen, J. H. (1998) , Pepe, M. S. (2003) , Pundir, S., and Amala, R. (2014) , Silverman, B. W. (2018) , Yeo, I. K., and Johnson, R. A. (2000) , Zhou, X. H., McClish, D. K., and Obuchowski, N. A. (2009) , Zou, K. H., Hall, W. J., and Shapiro, D. E. (1997) . Package: r-cran-rocngo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 372 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-bioc-summarizedexperiment, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rocngo_0.1.0-1.ca2404.1_all.deb Size: 236520 MD5sum: d33e89a49a1e5209d5136cdaa13fb6c5 SHA1: f3591085857c8a62c27b034156b19947c0e50a6d SHA256: 6296ffebb57898a6b3ac91d4ab6960837ece58288e9ede6dbc2fd418760a4066 SHA512: 73b358b2e1496295c4c626ed876aa8cff52c97213d42f2984f6dbf9338f9717a8f5869caea214698cf7e30c5f9e94e402cfc013394df2aa6e9c165e52368547a Homepage: https://cran.r-project.org/package=ROCnGO Description: CRAN Package 'ROCnGO' (Fast Analysis of ROC Curves) A toolkit for analyzing classifier performance by using receiver operating characteristic (ROC) curves. Performance may be assessed on a single classifier or multiple ones simultaneously, making it suitable for comparisons. In addition, different metrics allow the evaluation of local performance when working within restricted ranges of sensitivity and specificity. For details on the different implementations, see McClish D. K. (1989) , Vivo J.-M., Franco M. and Vicari D. (2018) , Jiang Y., et al (1996) , Franco M. and Vivo J.-M. (2021) and Carrington, André M., et al (2020) . Package: r-cran-rocnit Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rocnit_1.0-1.ca2404.1_all.deb Size: 14312 MD5sum: b9f3a5720a7aeddcbb7e4538da544003 SHA1: f53c6ea8521fbdd218397d74a64fa536f36c5ea3 SHA256: 7967782919b8d3dcdc563aa7839ed7b8a92f1ac9ebf0c2b99d5b2b23224af3e0 SHA512: 1300c61e0806e47fcdccaac13ffc6789bbaccfb4d8c11edeb247de365da050aeda602b3d3a667e2e58b915a4c7ab65ce2331f2d757b460649227801e257d6f07 Homepage: https://cran.r-project.org/package=rocNIT Description: CRAN Package 'rocNIT' (Non-Inferiority Test for Paired ROC Curves) Non-inferiority test and diagnostic test are very important in clinical trails. This package is to get a p value from the non-inferiority test for ROC curves from diagnostic test. Package: r-cran-rocnp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rocnp_0.1.0-1.ca2404.1_all.deb Size: 38210 MD5sum: 054b9881b79eaff10c81732043371de5 SHA1: 3b463cf45b08c8258d5844e8b3eb0fce0b9bd689 SHA256: ba5cd14d33e6153dee0ead749f1ce18053619fe3b228b19e4175b940e8b9d499 SHA512: a9eea58d5a5d3c70aff6cc120c404c1780d060bc727962f50dd9376ce32acb1cbacfe6a4f952cfe106a54a59a04d4f97b6ad8fea03cda7fced1ef2f8b6fae0bd Homepage: https://cran.r-project.org/package=rocnp Description: CRAN Package 'rocnp' (Work with Romanian Personal Numeric Codes PNC / CNP) A set of tools for working with Romanian personal numeric codes. The core is a validation function which applies several verification criteria to assess the validity of numeric codes. This is accompanied by functionality for extracting the different components of a personal numeric code. A personal numeric code is issued to all Romanian residents either at birth or when they obtain a residence permit. Package: r-cran-rocnreg Architecture: all Version: 1.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 728 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-np, r-cran-matrix, r-cran-moments, r-cran-nor1mix, r-cran-spatstat.univar, r-cran-lattice, r-cran-mass, r-cran-pbivnorm Filename: pool/dists/noble/main/r-cran-rocnreg_1.0-9-1.ca2404.1_all.deb Size: 700872 MD5sum: 82246a4454bab625ef977832b312c003 SHA1: 05c87673ffb5da9b32f7c6221588aa6b2440c065 SHA256: a1b161a853083d467d4ac5d1c1646e3260a6188078e8cfd960064aefe07056eb SHA512: fa6370712fc2eb34e550863e56b34642e307e4973683afefd49d3efd9663d8518109af0ee3faae021f4f74271acea0c0fab2015c6ae0e9d71a4c995cac33c263 Homepage: https://cran.r-project.org/package=ROCnReg Description: CRAN Package 'ROCnReg' (ROC Curve Inference with and without Covariates) Estimates the pooled (unadjusted) Receiver Operating Characteristic (ROC) curve, the covariate-adjusted ROC (AROC) curve, and the covariate-specific/conditional ROC (cROC) curve by different methods, both Bayesian and frequentist. Also, it provides functions to obtain ROC-based optimal cutpoints utilizing several criteria. Based on Erkanli, A. et al. (2006) ; Faraggi, D. (2003) ; Gu, J. et al. (2008) ; Inacio de Carvalho, V. et al. (2013) ; Inacio de Carvalho, V., and Rodriguez-Alvarez, M.X. (2022) ; Janes, H., and Pepe, M.S. (2009) ; Pepe, M.S. (1998) ; Rodriguez-Alvarez, M.X. et al. (2011a) ; Rodriguez-Alvarez, M.X. et al. (2011a) . Please see Rodriguez-Alvarez, M.X. and Inacio, V. (2021) for more details. Package: r-cran-rocpsych Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reportroc, r-cran-proc Filename: pool/dists/noble/main/r-cran-rocpsych_1.4-1.ca2404.1_all.deb Size: 48026 MD5sum: e0e6ae66338b351cc11fa1cba44fe69f SHA1: fc35215c8c6bcc6b3a1a3f66ad0bc3a1f8ea5645 SHA256: 51a88f70b1c5c484667eb447943fbb89e9d03cc86623d99c136348b02895f47e SHA512: 9aecd4d28089f473c8ebddb0eeda9df789cdec03a7c93b26f74c742dc6f9eccd0996ca21b44a28656cc92d0247d19305cbb2b516e128273664293eebcd96b996 Homepage: https://cran.r-project.org/package=ROCpsych Description: CRAN Package 'ROCpsych' (Compute and Compare Diagnostic Test Statistics Across Groups) Functions for (1) computing diagnostic test statistics (sensitivity, specificity, etc.) from confusion matrices with adjustment for various base rates or known prevalence based on McCaffrey et al (2003) , (2) computing optimal cut-off scores with different criteria including maximizing sensitivity, maximizing specificity, and maximizing the Youden Index from Youden (1950) , and (3) displaying and comparing classification statistics and area under the receiver operating characteristic (ROC) curves or area under the curves (AUC) across consecutive categories for ordinal variables. Package: r-cran-rocr Architecture: all Version: 1.0-12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 617 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gplots Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rocr_1.0-12-1.ca2404.1_all.deb Size: 462352 MD5sum: 115413768824b5923734fe242c59c4d6 SHA1: 66face9d17cba0d265b6a3e815b0618510f9294f SHA256: 644756cdf6d0a82d1c0a8473264835335f59e33cadf7e83b5322f7bcc70384b0 SHA512: 33a9ca6b1408024393fb1f77c24887a2da64d075bdfa235f6cdd64bd7d26eba111d7780b7f45768a8af272657d6c555d8cc54197f3e301daec34fbe653b24e56 Homepage: https://cran.r-project.org/package=ROCR Description: CRAN Package 'ROCR' (Visualizing the Performance of Scoring Classifiers) ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are popular examples of trade-off visualizations for specific pairs of performance measures. ROCR is a flexible tool for creating cutoff-parameterized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parameterization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism. Despite its flexibility, ROCR is easy to use, with only three commands and reasonable default values for all optional parameters. Package: r-cran-rocrater Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-jsonlite, r-cran-lifecycle, r-cran-zip Suggests: r-cran-fs, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rocrater_0.1.0-1.ca2404.1_all.deb Size: 208774 MD5sum: 4a50b30bfa331a626f0b199c27f7a2d2 SHA1: 1bf1cea04342105d96b05b83d1695457beedfc53 SHA256: 27a6634436bb486eba004ec08d97dbc70c90cca0c3875b3a903b9d53e98933b2 SHA512: 25cdcbe36d1098cb34f69d845147cae223e5c2fb3cad7219980593776fe970eacbf89fc839998effe1397924715ece56070c46b393661484b844c49f9e57205b Homepage: https://cran.r-project.org/package=rocrateR Description: CRAN Package 'rocrateR' (Tools for Creating and Manipulating RO-Crates) Provides tools for creating, manipulating and reading Research Object Crates (RO-Crates), a lightweight approach to packaging research data with structured metadata. 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Computes eROC curve and the corresponding AUC for imperfect reference standard. Package: r-cran-rocsi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mass Filename: pool/dists/noble/main/r-cran-rocsi_0.1.0-1.ca2404.1_all.deb Size: 62798 MD5sum: 3220b288c171d78af03468abafee234c SHA1: 1501b6be6a2f9b5189ef62b7f0ac011b6d726ecd SHA256: 4fdb90d235eddd5de5d06da37e24b9fc0605d8c5ab0aaa55e7fa13ce5ae4bcfd SHA512: 1ceca6e7c4c03fbee97477b2bdae30a397c5460d995ffa24ca43c62bfc463338265bf736553a321c2ae4aa04396627b16114c2c65fd565e8a0d15124c7d889d8 Homepage: https://cran.r-project.org/package=ROCSI Description: CRAN Package 'ROCSI' (Receiver Operating Characteristic Based Signature Identification) Optimal linear combination predictive signatures for maximizing the area between two Receiver Operating Characteristic (ROC) curves (treatment vs. control). 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In addition, the volume under the ROC surface and true positive fractions values are evaluated by ROC surface analysis. 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The methods are based on the Receiver Operating Characteristic (ROC) curve length (Bantis et al. (2021) ) and the overlap coefficient (OVL) (Franco-Pereira et al. (2021) ), as well as a joint ROC length-OVL-based approach. These methods do not require prior knowledge of the underlying non-PH pattern and can accommodate right, left, and doubly censored data. 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The ROC-SVM solution path algorithm greatly facilitates the tuning procedure for regularization parameter, lambda in ROC-SVM by avoiding grid search algorithm which may be computationally too intensive. For more information on the ROC-SVM, see the report in the ROC Analysis in AI workshop(ROCAI-2004) : Hernàndez-Orallo, José, et al. (2004) . 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Package: r-cran-rollout Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom.mixed, r-cran-dplyr, r-cran-glue, r-cran-lifecycle, r-cran-pbapply, r-cran-purrr, r-cran-rlang, r-cran-tidyr Suggests: r-cran-lme4, r-cran-lmertest, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-rollout_0.1.0-1.ca2404.1_all.deb Size: 87774 MD5sum: 1c6682dede8356cca027e1a5935bb784 SHA1: 71d4d1d700b8d15cec82b3e672bd0ff62d229062 SHA256: 29c51c73a0b7ee268961ef1fa6a95687a72c3e98d309f3f7589840abb40bb5dc SHA512: 7e4ba6fba1f3647f79d3a8d03282350202a5f3102261943ba15ebd9daa935849eeac29593dec8234807c01729c9f7ef4d18a4f36a24bf7f4fe297ffc3ea90e2e Homepage: https://cran.r-project.org/package=rollout Description: CRAN Package 'rollout' (Tools for Designing, Simulating, and Analyzing ImplementationRollout Trials) Provides a unified framework for designing, simulating, and analyzing implementation rollout trials, including stepped wedge, sequential rollout, head-to-head, multi-condition, and rollout implementation optimization designs. The package enables users to flexibly specify rollout schedules, incorporate site-level and nested data structures, generate outcomes under rich hierarchical models, and evaluate analytic strategies through simulation-based power analysis. By separating data generation from model fitting, the tools support assessment of bias, Type I error, and robustness to model misspecification. The workflow integrates with standard mixed-effects modeling approaches and the tidyverse ecosystem, offering transparent and reproducible tools for implementation scientists and applied statisticians. Package: r-cran-rollup Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 795 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-magrittr, r-cran-sparklyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rollup_0.1.0-1.ca2404.1_all.deb Size: 323420 MD5sum: cb590a97d95aae773411c1b3a248bfab SHA1: 6de38ae324e390ad23b1814b0fd4f1694aee9a22 SHA256: 13765366b31effab120625d84be58a9eb7138a839dda7361e690084ad3118ea9 SHA512: c86f9ceecb0e2a190de837f1b09c86ec6985ec02e67dea2fa4bdc4717f06a67f488fff2d1cb3e726474ce30baabf29b89c3a4b69fbc013bc6cf51d3bac568a9a Homepage: https://cran.r-project.org/package=rollup Description: CRAN Package 'rollup' (A Tidy Grouping Set Aggregation) A Tidy implementation of 'grouping sets', 'rollup' and 'cube' - extensions of the 'group_by' clause that allow for computing multiple 'group_by' clauses in a single statement. For more detailed information on these functions, please refer to "Enhanced Aggregation, Cube, Grouping and Rollup" . Package: r-cran-rolluptree Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rolluptree_0.4.1-1.ca2404.1_all.deb Size: 267328 MD5sum: 87a52ace0818c1f5468fabab2f032a29 SHA1: ae0ec4ff044ba257d5373abc29b0a2c72f8c2d12 SHA256: affbf733901c78f1c80f25d612d57d320f1add0faefa46cc3c991b71a4a860c2 SHA512: 9b7dd4bce3528e7bfdd95b9f08a5c1893fa53c186ba104e19740bbf78364fe30302a237c9442deead17b8c7dd94c60da07c0edb73e5a5a42c5104e7a0e2fda97 Homepage: https://cran.r-project.org/package=rollupTree Description: CRAN Package 'rollupTree' (Perform Recursive Computations) Mass rollup for a Bill of Materials is an example of a class of computations in which elements are arranged in a tree structure and some property of each element is a computed function of the corresponding values of its child elements. Leaf elements, i.e., those with no children, have values assigned. In many cases, the combining function is simple arithmetic sum; in other cases (e.g., mass properties), the combiner may involve other information such as the geometric relationship between parent and child, or statistical relations such as root-sum-of-squares (RSS). This package implements a general function for such problems. It is adapted to specific recursive computations by functional programming techniques; the caller passes a function as the update parameter to rollup() (or, at a lower level, passes functions as the get, set, combine, and override parameters to update_prop()) at runtime to specify the desired operations. The implementation relies on graph-theoretic algorithms from the 'igraph' package of Csárdi, et al. (2006 ). Package: r-cran-roloc Architecture: all Version: 0.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-roloc_0.1-2-1.ca2404.1_all.deb Size: 115580 MD5sum: a347df80aa50c0e41ea3d324e01f9f63 SHA1: b8e8b988baf350d18ef8c6ba0d764257b77fe65d SHA256: adaf5a2e8c7302c69ace2bed1643a133f69688982040cd175ed41912b9b8ee34 SHA512: c2ac62edf12f57fb2c75ea440a9e4a040c8df53694924b1e0b4cf6c483c8d5e2b4ac2b4429e1a13f279eb48c2026f8330043306f4750659ae6ac3350b0361801 Homepage: https://cran.r-project.org/package=roloc Description: CRAN Package 'roloc' (Convert Colour Specification to Colour Name) Functions to convert an R colour specification to a colour name. The user can select and create different lists of colour names and different colour metrics for the conversion. Package: r-cran-rolocisccnbs Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6090 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-roloc, r-cran-colorspace Filename: pool/dists/noble/main/r-cran-rolocisccnbs_0.1-1.ca2404.1_all.deb Size: 5300736 MD5sum: 3454636121c75bd809253ba5fb310382 SHA1: 3e6152ef03919e5f245f4f18d9f6654daa8d2431 SHA256: c261bea5eebffc8636a0ce9fd9e36a778ac22b1af4e76ec6b41bf4db6b03697a SHA512: 9379d1770243e60698d5df919d6a0a054d0cd2f8f684cc51cd79b23ce4818cf14d5601d9fb01e6d8213c8e76462dd3dca6cda10063f06c10a9d7d870ec294d75 Homepage: https://cran.r-project.org/package=rolocISCCNBS Description: CRAN Package 'rolocISCCNBS' (A Colour List and Colour Metric Based on the ISCC-NBS System ofColor Designation) A colour list and colour metric based on the ISCC-NBS System of Color Designation for use with the 'roloc' package for converting colour specifications to colour names. Package: r-cran-rologit Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-evd Filename: pool/dists/noble/main/r-cran-rologit_0.1.3-1.ca2404.1_all.deb Size: 125690 MD5sum: 4962668099d574744f41bee1fa1979f8 SHA1: 4ebd34546f9c0a859d8321ab7dc5f592220be37f SHA256: 170e980b3684c01e494585f8496d0fded4fd7bd3256cb6db8a7665ab61c6a5f7 SHA512: 9153315f8d18193f5ad0dfea2fc6c8811b33a2c96a98b8c2ec0d32eb91cf2166a6579767882bc1ceead6bcc1dcf9a1d4adb92e3ea302d35692568c63e8657bf7 Homepage: https://cran.r-project.org/package=ROlogit Description: CRAN Package 'ROlogit' (Fit Rank-Ordered Logit (RO-Logit) Model) Implements the rank-ordered logit (RO-logit) model for stratified analysis of continuous outcomes introduced by Tan et al. (2017) . Model diagnostics based on the heuristic residuals and estimates in linear scales are available from the package, and outcomes with ties are supported. Package: r-cran-rolr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rolr_1.0.0-1.ca2404.1_all.deb Size: 47558 MD5sum: de93b33ab53a659293fde451ba700ce8 SHA1: 893fdf081f09864468ea0ac5aab3d9915c05e768 SHA256: dc37670c6efcbffd3f9b247f83f9dbe16f40fdced50d5c45927fedfb361cdc05 SHA512: 032aa7bba0611fa6b2b088652eacdc0c7d15d5728058f8e5a911130541f4c8ab7e2be3d47092ccc159311d82928502722c37460081294b8ef805c682207706d4 Homepage: https://cran.r-project.org/package=rolr Description: CRAN Package 'rolr' (Finding Optimal Three-Group Splits Based on a Survival Outcome) Provides fast procedures for exploring all pairs of cutpoints of a single covariate with respect to survival and determining optimal cutpoints using a hierarchical method and various ordered logrank tests. Package: r-cran-rolwinmulcor Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools, r-cran-zoo, r-cran-pracma, r-cran-colorspace, r-cran-scales Filename: pool/dists/noble/main/r-cran-rolwinmulcor_1.2.0-1.ca2404.1_all.deb Size: 127394 MD5sum: 58c04840a3aa3882b7ea046863d6f7af SHA1: 0f28d3a3242fa39927eb8d6c9d24a80e45ff4602 SHA256: a3d7de4708957b187ad89d4cac85238fb559c49ccfb8ddd671d7907cd7ebac18 SHA512: 7a2c3975859643cd5f7a909a337b2383f1e22ef71d258372b3d834f9b6b5c3cc85525c523a8a739ba3bd031ff0932f41c73764deb69fcf627e48be42dc7311f0 Homepage: https://cran.r-project.org/package=RolWinMulCor Description: CRAN Package 'RolWinMulCor' (Subroutines to Estimate Rolling Window Multiple Correlation) Rolling Window Multiple Correlation ('RolWinMulCor') estimates the rolling (running) window correlation for the bi- and multi-variate cases between regular (sampled on identical time points) time series, with especial emphasis to ecological data although this can be applied to other kinds of data sets. 'RolWinMulCor' is based on the concept of rolling, running or sliding window and is useful to evaluate the evolution of correlation through time and time-scales. 'RolWinMulCor' contains six functions. The first two focus on the bi-variate case: (1) rolwincor_1win() and (2) rolwincor_heatmap(), which estimate the correlation coefficients and the their respective p-values for only one window-length (time-scale) and considering all possible window-lengths or a band of window-lengths, respectively. The second two functions: (3) rolwinmulcor_1win() and (4) rolwinmulcor_heatmap() are designed to analyze the multi-variate case, following the bi-variate case to visually display the results, but these two approaches are methodologically different. That is, the multi-variate case estimates the adjusted coefficients of determination instead of the correlation coefficients. The last two functions: (5) plot_1win() and (6) plot_heatmap() are used to represent graphically the outputs of the four aforementioned functions as simple plots or as heat maps. The functions contained in 'RolWinMulCor' are highly flexible since these contains several parameters to control the estimation of correlation and the features of the plot output, e.g. to remove the (linear) trend contained in the time series under analysis, to choose different p-value correction methods (which are used to address the multiple comparison problem) or to personalise the plot outputs. The 'RolWinMulCor' package also provides examples with synthetic and real-life ecological time series to exemplify its use. Methods derived from H. Abdi. (2007) , R. Telford (2013) , and J. M. Polanco-Martinez (2020) . Package: r-cran-rolwinwavcor Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-waveslim Filename: pool/dists/noble/main/r-cran-rolwinwavcor_0.4.0-1.ca2404.1_all.deb Size: 95418 MD5sum: ef3e3e76accdc42279bebe1872b9f0a0 SHA1: d37e3bcfb6ef1f89d5e53892fc3e6fba1614d9e2 SHA256: 9110b6915e4d1f6eb87a03b462ed7f4cf6b0daca2bd4083df31de1b123b5b37b SHA512: d7ca65da54412ee56118ab4d5cedd06d0f2bd81a4b778446728d06b5a8f3913783d64cb21fc35a6bcc617b3495b2cbd698988deacf9fa380ecf00860b8662e65 Homepage: https://cran.r-project.org/package=RolWinWavCor Description: CRAN Package 'RolWinWavCor' (Estimate Rolling Window Wavelet Correlation Between Two TimeSeries) Estimates and plots as a heat map the rolling window wavelet correlation (RWWC) coefficients statistically significant (within the 95% CI) between two regular (evenly spaced) time series. 'RolWinWavCor' also plots at the same graphic the time series under study. The 'RolWinWavCor' was designed for financial time series, but this software can be used with other kinds of data (e.g., climatic, ecological, geological, etc). The functions contained in 'RolWinWavCor' are highly flexible since these contains some parameters to personalize the time series under analysis and the heat maps of the rolling window wavelet correlation coefficients. Moreover, we have also included a data set (named EU_stock_markets) that contains nine European stock market indices to exemplify the use of the functions contained in 'RolWinWavCor'. Methods derived from Polanco-Martínez et al (2018) ). Package: r-cran-romdb Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-httr, r-cran-stringr, r-cran-rodbc, r-cran-magick Filename: pool/dists/noble/main/r-cran-romdb_0.1.0-1.ca2404.1_all.deb Size: 130740 MD5sum: 7087e036e05cd112e7d3bce10b7a816d SHA1: 7f041e749dd98e7ae862c74c388b4fea9da54a4b SHA256: b708827d1bafc2597ca9d3f547e071a5668ec57496a14743cccec1b341dd5d6a SHA512: 4161ab3d5bf88355d10dc32aaa851fa0681e664606188060c6c895b7dfd5b107409d97bd84f710c060a48be5b7a4a60a65b65045e6f1664a649b53103721e645 Homepage: https://cran.r-project.org/package=ROMDB Description: CRAN Package 'ROMDB' (Get 'OMDB' API Multiple Information) Load multiple movies, series, actors, directors etc from 'OMDB' API. More information in: . Package: r-cran-rome Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4030 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-timedate, r-cran-stringr, r-cran-ggplot2, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-zip, r-cran-maps, r-cran-sp, r-cran-dplyr, r-cran-ggrepel, r-cran-magrittr, r-cran-geosphere, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-svdialogs, r-cran-shinyjs Filename: pool/dists/noble/main/r-cran-rome_0.2.3-1.ca2404.1_all.deb Size: 3981732 MD5sum: ef31ba78d8293bb4fe308fb2050c044b SHA1: 4c7d54f63caabfd546c73e5cb6f8ae240272ab32 SHA256: e95743ebfc5aed02fbfede35574331126b90fc25cbd107677bd193720ec0c8b6 SHA512: 6a5c4f331eb348e97dd6de8a43f7498ea736f7c293669e00747beace172e23c6502e4bed17f78fdfa3ca05ac486ffa0af0ec38be80f1e18359623f0cd52368ae Homepage: https://cran.r-project.org/package=RoME Description: CRAN Package 'RoME' (Multiple Checks on MEDITS Trawl Survey Data) Provides quality checks for MEDITS (International Bottom Trawl Survey in the Mediterranean) trawl survey exchange data tables (TA (Haul data), TB (Catch data), TC (Biological data), TE (Biological individual data), TL (Litter data)). The main function RoME() calls all check functions in a defined sequence to perform a complete quality control of TX (Generic exchange data) data, including header validation, controlled-vocabulary checks, cross-table consistency tests, and biological plausibility checks. No automatic correction is applied: the package detects errors, warns the user, and specifies the type of error to ease data correction. Checks can be run simultaneously on multi-year datasets. An embedded 'shiny' application is also provided via run_RoME_app(). References describing the methods: MEDITS Working Group (2017) . Package: r-cran-romeb Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjags, r-cran-coda Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-romeb_0.1.2-1.ca2404.1_all.deb Size: 58492 MD5sum: 122bb6ce10eca9f68a314f1818c14503 SHA1: 5b67f5dcf8908a6a8cff5b0b3648148f96d06375 SHA256: 10ec7d139c2426c4187a3992a9feb844b469058aa74128946e96d27912685977 SHA512: d54b4d39af082ab38ee9683a6925798329642a5c0af48919121450dbe6518a72365965f732e2d8eeffaa4a44485a529233d892ce72beb22b03728fb2f3edecb4 Homepage: https://cran.r-project.org/package=Romeb Description: CRAN Package 'Romeb' (Robust Median-Based Bayesian Growth Curve Modeling) Implements robust median-based Bayesian growth curve models that handle Missing Completely at Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR) missing-data mechanisms, and allow auxiliary variables. Models are fitted via 'rjags' (interface to 'JAGS') and summarized with 'coda'. Package: r-cran-romic Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1745 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-purrr, r-cran-readr, r-cran-reshape2, r-cran-rlang, r-cran-shiny, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-bioc-impute, r-cran-lazyeval, r-cran-rmarkdown, r-cran-usethis, r-cran-testthat Filename: pool/dists/noble/main/r-cran-romic_1.3.3-1.ca2404.1_all.deb Size: 896112 MD5sum: 0b50d9307080c1041dab89d42e855ec5 SHA1: 252715c415e922e0b0aee3ca153ba4bd0b254685 SHA256: aa1b116277745579c1b8af67c414c034e54212d4f632a7964edf7a083787adb9 SHA512: a93627bfb4b6781da56c0357e05cfcfc47e6fcc28c6e9e83269279cd9091ee5dad13a42308705418ba35d8d8fa0f8cde407bcc0da8c6d0c1e2215b1101a65bf8 Homepage: https://cran.r-project.org/package=romic Description: CRAN Package 'romic' (R for High-Dimensional Molecular Data) Represents high-dimensional data as tables of features, samples and measurements, and a design list for tracking the meaning of individual variables. Using this format, filtering, normalization, and other transformations of a dataset can be carried out in a flexible manner. 'romic' takes advantage of these transformations to create interactive 'shiny' apps for exploratory data analysis such as an interactive heatmap. Package: r-cran-romney Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-psych Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-romney_0.1.0-1.ca2404.1_all.deb Size: 60510 MD5sum: 281aea87f7bc105d233e43f8b63907f3 SHA1: 3b335dcbb326a2ec32b9b1cbb13476c86aed15c1 SHA256: c94d992f12937dd66746809a1524da4d5ff21a4233097aec3ba16a425a2dd3a2 SHA512: 155e6dfee9dbcfa083ea2b00fa79544b02665a103313d497658027292aeab5ac1041bb659d10b1449575f2a9843cacc2e51c16ca9950f6eac3c6996203154dc9 Homepage: https://cran.r-project.org/package=Romney Description: CRAN Package 'Romney' (Classical Cultural Consensus Analysis) Implements classical cultural consensus analysis with formal, informal, and covariance agreement models, 'UCINET'-aligned minimum-residual factor extraction, competence estimation, and answer-key estimation. Based on the classical framework of Romney, Weller, and Batchelder (1986) , Romney, Batchelder, and Weller (1987) , and Weller (2007) . Package: r-cran-ronfhir Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-jsonlite, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ronfhir_0.4.0-1.ca2404.1_all.deb Size: 147412 MD5sum: bc9690128603a13527da1c7de30cd15a SHA1: 7c2cece4d73bf6d3b77533e7045c2e555c088165 SHA256: cf0c0ef649b04e4259504c33a3901cdb6234d63a949cd9ac1c0c0a782836bc25 SHA512: 51a291de151bb7afba7356894930a7e338a70b6030ec1e996e7b23eeaffe1dae91332cd23432a2ca5accc683eaacdc3735aa093ea213090a1ca9a5ca1e062e0a Homepage: https://cran.r-project.org/package=RonFHIR Description: CRAN Package 'RonFHIR' (Read and Search Interface to the 'HL7 FHIR' REST API) R on FHIR is an easy to use wrapper around the 'HL7 FHIR' REST API (STU 3 and R4). It provides tools to easily read and search resources on a FHIR server and brings the results into the R environment. R on FHIR is based on the FhirClient of the official 'HL7 FHIR .NET API', also made by Firely. Package: r-cran-ronfig Architecture: all Version: 0.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-carrier, r-cran-cli Suggests: r-cran-litedown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ronfig_0.0.10-1.ca2404.1_all.deb Size: 29246 MD5sum: 20b7a073312f6909c6c369e6f130e6da SHA1: e104bd82ece474178c58587d60305779a9971b44 SHA256: c265962e1463a3e2b08ce032c67a43b406b496bd911e3b114335fb40002d7028 SHA512: 5fc3a89e7a60edd66e205c2aa165ee44042d236c060aebf0e8c88f2f2e8af3f4e7fefc7f34533dfb98976595de139b3e2a03820a2d78539db1695349a50c50a5 Homepage: https://cran.r-project.org/package=ronfig Description: CRAN Package 'ronfig' (Load Configuration Values) A simple approach to configuring R projects with different parameter values. Configurations are specified using a reduced subset of base R and parsed accordingly. Package: r-cran-roopsd Architecture: all Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-lmoments, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-roopsd_0.3.9-1.ca2404.1_all.deb Size: 459568 MD5sum: 0983d59d0305fcf4ccf44ce2f9609d19 SHA1: aacd3c59d1deb456fc4fdb54a020e314c98aeb76 SHA256: 3af2ff16e0755499dd21cff9609f21562b914db2f29689e07b8f4f6c413e9443 SHA512: 07b0163703dc275fb891cf537d62284340f1529e9cc2960b012d65b274064008e61beffe9093a2a8f24b0d3e6b100c550885223b7223f08f3e8db0c4290c3e03 Homepage: https://cran.r-project.org/package=ROOPSD Description: CRAN Package 'ROOPSD' (R Object Oriented Programming for Statistical Distribution) Statistical distribution in OOP (Object Oriented Programming) way. This package proposes a R6 class interface to classic statistical distribution, and new distributions can be easily added with the class AbstractDist. A useful point is the generic fit() method for each class, which uses a maximum likelihood estimation to find the parameters of a dataset, see, e.g. Hastie, T. and al (2009) . Furthermore, the rv_histogram class gives a non-parametric fit, with the same accessors that for the classic distribution. Finally, three random generators useful to build synthetic data are given: a multivariate normal generator, an orthogonal matrix generator, and a symmetric positive definite matrix generator, see Mezzadri, F. (2007) . Package: r-cran-root Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 791 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rpart, r-cran-gbm, r-cran-withr, r-cran-rpart.plot Suggests: r-cran-mlbench, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ragg Filename: pool/dists/noble/main/r-cran-root_0.1.1-1.ca2404.1_all.deb Size: 572190 MD5sum: 8ae884a0e55011cec464b55cbb7ea544 SHA1: 205d4eb3b9e0f84621a2160c57150c42624722f7 SHA256: 8281e09477c1edfbab31e904352cddb225146040309d30483b6f81993a31957a SHA512: 3e875e66828cedd043a29564d70a4b7fcbe9d4fad3cfa1404d466516aa8d25e14437a8c07a6e82a8de5a40f9e8410a3685b9e4015f6659f9229da9bfc8ebc1a4 Homepage: https://cran.r-project.org/package=ROOT Description: CRAN Package 'ROOT' (Rashomon Set of Optimal Trees) Implements a general framework for globally optimizing user-specified objective functionals over interpretable binary weight functions represented as sparse decision trees, called ROOT (Rashomon Set of Optimal Trees). It searches over candidate trees to construct a Rashomon set of near-optimal solutions and derives a summary tree highlighting stable patterns in the optimized weights. ROOT includes a built-in generalizability mode for identifying subgroups in trial settings for transportability analyses (Parikh et al. (2025) ). Package: r-cran-roots Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-animation, r-cran-rarpack, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-roots_1.0-1.ca2404.1_all.deb Size: 123154 MD5sum: deb36b64e0f314c7df54cd6dcde8a4e3 SHA1: f74bec022c930b5542dd54af71a82d4e13497e0f SHA256: 2ce19ce0b5401df2c1ceffe5fa749adefb64a54cdb7e2598adf25aba1b9a2767 SHA512: b9f5ed4106809d9f15cbf274231a9030a79ad4b6066871eb8909c68025e8ef8c3722687e9ada8f4a27f9c08f1476d1f2bc9705d5909ff9f568b6af7e7a8e18ad Homepage: https://cran.r-project.org/package=roots Description: CRAN Package 'roots' (Reconstructing Ordered Ontogenic Trajectories) A set of tools to reconstruct ordered ontogenic trajectories from single cell RNAseq data. Package: r-cran-rootscanr Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-png, r-cran-abind Filename: pool/dists/noble/main/r-cran-rootscanr_0.0.1-1.ca2404.1_all.deb Size: 182700 MD5sum: 5390aabc70a8673c921dd34b2785973b SHA1: 3be28a934b1460e71dbfe7c6ab1622693efc4637 SHA256: 54131681480a09b79f552b1051f1eb23869c75053319f10cd3891e8ec6efb17f SHA512: a9c63cad14ea2e917e8d3896f45d092d0f82fe5c053459c0aeacd5d4ee5e9c36d9d89fa6a6536fbf0d220010b8e8b7f572fe00278ac71bae373bbb928bf1146b Homepage: https://cran.r-project.org/package=RootscanR Description: CRAN Package 'RootscanR' (Stitching and Analyzing Root Scans) Minirhizotrons are widely used to observe and explore roots and their growth. This package provides the means to stitch images and divide them into depth layers. Please note that this R package was developed alongside the following manuscript: Stitching root scans and extracting depth layer information -- a workflow and practical examples, S. Kersting, L. Knüver, and M. Fischer. The manuscript is currently in preparation and should be citet as soon as it is available. This project was supported by the project ArtIGROW, which is a part of the WIR!-Alliance ArtIFARM – Artificial Intelligence in Farming funded by the German Federal Ministry of Research, Technology and Space (No. 03WIR4805). Package: r-cran-rootsextremainflections Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-iterators, r-cran-foreach, r-cran-doparallel, r-cran-inflection Filename: pool/dists/noble/main/r-cran-rootsextremainflections_1.2.5-1.ca2404.1_all.deb Size: 176224 MD5sum: 88a26b4f20b4248571a5892e83d071bb SHA1: 501193761bbbf4ad1abe71042445ec34b5ba8b18 SHA256: 2b445872314d7c5ed033179aadcfd803b00e09ab4ed8b526aec54294b5df0415 SHA512: 4dcaf9d1d396a9637a6839e117eb0076a8dce1f8b1badc4019161937956fbfb4ff427328f7be42160f8ce36f707865f2f5106720d5f5f9a882fd520621b6c7ff Homepage: https://cran.r-project.org/package=RootsExtremaInflections Description: CRAN Package 'RootsExtremaInflections' (Finds Roots, Extrema and Inflection Points of a Curve) Implementation of Taylor Regression Estimator (TRE), Tulip Extreme Finding Estimator (TEFE), Bell Extreme Finding Estimator (BEFE), Integration Extreme Finding Estimator (IEFE) and Integration Root Finding Estimator (IRFE) for roots, extrema and inflections of a curve . Christopoulos, DT (2019) . Christopoulos, DT (2016) . Christopoulos, DT (2016) . Christopoulos, DT (2014) . Package: r-cran-rope Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1009 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-matrix, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rope_1.0-1.ca2404.1_all.deb Size: 963602 MD5sum: 629a026f3ca8803a4268751c3b765c11 SHA1: bcb7b354d555c3f9c3c2c6f7f41a288bb39901cd SHA256: 6ea4fd91e54869f37d43c7e528812828facbc452209c2a02513265f5a508d9f0 SHA512: e618b3b1f8595c8e09f4109f2f48e57643d687d239462465fe59b84712cc757d32ef9aceb73fe664917126a1be67930d54d4a606cad2cc44c1034e9ff17e61ce Homepage: https://cran.r-project.org/package=rope Description: CRAN Package 'rope' (Model Selection with FDR Control of Selected Variables) Selects one model with variable selection FDR controlled at a specified level. A q-value for each potential variable is also returned. The input, variable selection counts over many bootstraps for several levels of penalization, is modeled as coming from a beta-binomial mixture distribution. 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Package: r-cran-ropencvlite Architecture: all Version: 4.130.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ropencvlite_4.130.0-1.ca2404.1_all.deb Size: 63678 MD5sum: f7f5f827c774dc930b0e4eb93b59268a SHA1: 9cca7f91ffe3cd11090143a92f797e07697575de SHA256: c374155c040dbbd6a646d9cfc5e91ec3953fb2db4fa79dccc83dbd5d4789a995 SHA512: 0eb73772dafb6402706357244cd05752f3fd2770beb7f9c6ca402f18716cb14531689bc723180e1543c34061be8aed303206e28e71b348a94a60de1af4330eed Homepage: https://cran.r-project.org/package=ROpenCVLite Description: CRAN Package 'ROpenCVLite' (Helper Package for Installing 'OpenCV') Installs 'OpenCV' for use by other packages. 'OpenCV' is library of programming functions mainly aimed at real-time computer vision. 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Package: r-cran-ropendata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-ropendata_0.1.0-1.ca2404.1_all.deb Size: 26406 MD5sum: 13ed55a70f7f677c4219eb390234a26d SHA1: a71d1415728e8b727ce0df8c098daaf51680a5fe SHA256: a9c67211113ed9f667aa376bc1b59df95abc0d42055ecdd913b42eaa8132a60b SHA512: 411a2eb7f15f6ac352fd9cef5da2c651e61b15b1d28f3b6199791992f7c2d7dcc6fb9874bd792f3360ae21cc03d079f2e4a538232ca48ebbf13b9875b01221cf Homepage: https://cran.r-project.org/package=ropendata Description: CRAN Package 'ropendata' (Query and Download 'Rapid7' 'Cybersecurity' Data Sets) 'Rapid7' collects 'cybersecurity' data and makes it available via their 'Open Data' portal which has an API. Tools are provided to assist in querying for available data sets and downloading any data set authorized to a free, registered account. Package: r-cran-ropendota Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-ropendota_0.1.2-1.ca2404.1_all.deb Size: 47582 MD5sum: 22bdcf42908560fb85fa720c58a61fc0 SHA1: 6830c9ced4234c065cc37a5e7912d39bda767342 SHA256: e494d42dc030dde15b92898ea6805790a1e548e9785512aea8c5d096868fd0dd SHA512: b6052391469d1753aa56e53e996b1aa3e752de7f680264253484c77f2784cc62d66c18e9b22ebd560131372d2b05509b3953a33e19d37e12ec3ac7bab8544000 Homepage: https://cran.r-project.org/package=ROpenDota Description: CRAN Package 'ROpenDota' (Access OpenDota Services in R) Provides a client for the API of OpenDota. OpenDota is a web service which is provide DOTA2 real time data. Data is collected through the Steam WebAPI. With ROpenDota you can easily grab the latest DOTA2 statistics in R programming such as latest match on official international competition, analyzing your or enemy performance to learn their strategies,etc. Please see for more information. Package: r-cran-ropenfigi Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-ropenfigi_0.2.8-1.ca2404.1_all.deb Size: 22290 MD5sum: 5d1b546d185ca236dd61b734dce25727 SHA1: 14ad4f9b713c7bb271064e626870e5b51d49662f SHA256: de82539dcd17fbc4507726ec0b65b7fee972dafc324e3b9fd560d182141b28ab SHA512: 2e2f128ccb49385decde0bda7bf5e8f23b077d3c243106926cb567b4fb939c79a3553f313475ce542ce4db1b55ec216fec052cdbaf27488bc44b5803d15c92f9 Homepage: https://cran.r-project.org/package=ROpenFIGI Description: CRAN Package 'ROpenFIGI' (R Interface to OpenFIGI) Provide a simple interface to Bloomberg's OpenFIGI API. Please see for API details and registration. You may be eligible to have an API key to accelerate your loading process. Package: r-cran-ropenmeteo Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4905 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-lubridate, r-cran-glue, r-cran-imputets, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-readr, r-cran-tibble, r-cran-spelling Filename: pool/dists/noble/main/r-cran-ropenmeteo_0.1.1-1.ca2404.1_all.deb Size: 535054 MD5sum: 4b74f4de0e0cb8b3d797c0142531d0a1 SHA1: 792de603b4d1625c7c375fe7aea5955fbf9fa4cb SHA256: 8fb27a6de4ba9062267342c240231f91a7f24c0f465bc4037c1da022cdcfcf6b SHA512: 45aa6de4c92cfac8c7906800ccbaf3865d5a43ffc2665cdd6ab5194db57b9fd64df50225827fdbd6966c0b8128e382265856c56b83275ea1694e8faf118630d9 Homepage: https://cran.r-project.org/package=ropenmeteo Description: CRAN Package 'ropenmeteo' (Wrappers for 'Open-Meteo' API) Wrappers for the Application Programming Interface from the project along with helper functions. The project streamlines access to a range of publicly historical and forecast meteorology data from agencies across the world. Package: r-cran-ropensecretsapi Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjsonio, r-cran-rcurl Filename: pool/dists/noble/main/r-cran-ropensecretsapi_1.0.1-1.ca2404.1_all.deb Size: 33936 MD5sum: 682d57e83b487053bb952c110874920d SHA1: ba44aee7d762bd590d5f562fdf40794b2b3752e8 SHA256: ca4fd5833430857387298cd536362fcbee50c9c691d3590d710eff391d55395a SHA512: b65e7ba4829e3688a0c59103408b84421a2718961ec6706077b9492d060ec8c3dafcf7c283edf7dc73cb9152d4fe4d0fa5df365767f50cf9c54606eda5ee29c9 Homepage: https://cran.r-project.org/package=ropensecretsapi Description: CRAN Package 'ropensecretsapi' (R Package for the OpenSecrets.org API) An R package for the OpenSecrets.org web services API. Package: r-cran-ropenweathermap Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-rcurl, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-ropenweathermap_1.1-1.ca2404.1_all.deb Size: 19336 MD5sum: b0f631895d79ef8a90a3db539ae41665 SHA1: 2f67925e70965d4c93067155f50d05bb78dd711b SHA256: d252fb8ca8cb41988d110d19e96835c48cb74856d84cdb50c865dde6c79636eb SHA512: ed779fcf6ae16a2b68b5b5eff8b58eada0c6ec336988889d3edf848e317575c5b17de9604b09aee7598815124618daf6a0d8d2becb071553552c1fdca0aa5c0f Homepage: https://cran.r-project.org/package=ROpenWeatherMap Description: CRAN Package 'ROpenWeatherMap' (R Interface to OpenWeatherMap API) OpenWeatherMap (OWM) is a service providing weather related data. This package can be used to access current weather data for one location or several locations. 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Package: r-cran-ropercenter Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1519 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rselenium, r-cran-dplyr, r-cran-foreign, r-cran-haven, r-cran-magrittr, r-cran-netstat, r-cran-purrr, r-cran-readr, r-cran-rio, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ropercenter_0.3.2-1.ca2404.1_all.deb Size: 1009632 MD5sum: 9acbe79999e4669983940132d19f67c5 SHA1: 0052247524be6bcdd523501163080867c8b3d198 SHA256: 18099f5f3dfb243c183ec72104214e80f063ab1af0c044c591d2f4509237c44a SHA512: a08b5668652dff987b35b51b1186787b7a40e58d13b320673bc05be08aae2316866a97d69a825e89a061bb16e884e4ecb222e5a03a3cd1c81f4e79f9d240a83c Homepage: https://cran.r-project.org/package=ropercenter Description: CRAN Package 'ropercenter' (Reproducible Data Retrieval from the Roper Center Data Archive) Reproducible, programmatic retrieval of datasets from the Roper Center data archive. The Roper Center for Public Opinion Research maintains the largest archive of public opinion data in existence, but researchers using these datasets are caught in a bind. The Center's terms and conditions bar redistribution of downloaded datasets, but to ensure that one's work can be reproduced, assessed, and built upon by others, one must provide access to the raw data one employed. The `ropercenter` package cuts this knot by providing registered users with programmatic, reproducible access to Roper Center datasets from within R. Package: r-cran-roptest Architecture: all Version: 1.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1910 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-distr, r-cran-distrex, r-cran-distrmod, r-cran-randvar, r-cran-robastbase, r-cran-startupmsg, r-cran-mass Suggests: r-cran-roblox Filename: pool/dists/noble/main/r-cran-roptest_1.3.5-1.ca2404.1_all.deb Size: 1186038 MD5sum: 39ec320f192a6ce405865d7174c5a04f SHA1: 0bb28ad342bba8e3467fa33895db5199f1c9f645 SHA256: b3a92d87d9af88d1fdb07bd4f8d97365cc169d852cdf1280e5a5531b99ce134a SHA512: 7d8a73aeb4dc67504f6dd7e9e0fe29190ab48279c796dd70e14ccabba2930b670844fa25276a016301910e0a8a282c9218dc01a0cc322cac838e8c13031e8b55 Homepage: https://cran.r-project.org/package=ROptEst Description: CRAN Package 'ROptEst' (Optimally Robust Estimation) R infrastructure for optimally robust estimation in general smoothly parameterized models using S4 classes and methods as described Kohl, M., Ruckdeschel, P., and Rieder, H. (2010), , and in Rieder, H., Kohl, M., and Ruckdeschel, P. (2008), . Package: r-cran-roptimus Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3635 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-iterators Filename: pool/dists/noble/main/r-cran-roptimus_3.0.0-1.ca2404.1_all.deb Size: 1113002 MD5sum: 62fa8ad888b9bbca10c414ec40b335d2 SHA1: 42c6b1bbc3a56da4eb13d6301d35c29a2b79bdeb SHA256: 92137c921c9921e500549b2ec8a4b06dd18944c75876e90928e1d24d737b10c9 SHA512: cad67d79fc1fb8ea08f965681538b26d0ab103c193f51b4cbb6f1625da66a966c524bb2abd276ac85a224a33aaf4a6ac7a6651cab0b0b862471a44b6985ef926 Homepage: https://cran.r-project.org/package=ROptimus Description: CRAN Package 'ROptimus' (A Parallel General-Purpose Adaptive Optimisation Engine) A general-purpose optimisation engine that supports i) Monte Carlo optimisation with Metropolis criterion [Metropolis et al. (1953) , Hastings (1970) ] and Acceptance Ratio Simulated Annealing [Kirkpatrick et al. (1983) , Černý (1985) ] on multiple cores, and ii) Acceptance Ratio Replica Exchange Monte Carlo Optimisation. In each case, the system pseudo-temperature is dynamically adjusted such that the observed acceptance ratio is kept near to the desired (fixed or changing) acceptance ratio. Package: r-cran-roptions Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-ggplot2, r-cran-plotly Filename: pool/dists/noble/main/r-cran-roptions_1.0.3-1.ca2404.1_all.deb Size: 104990 MD5sum: c89490ebc49c08f314977d3f474db23b SHA1: aa57387b5524aa3a77e7c24425e6ab804478cebf SHA256: 8eb266d9e54e06f5db09c5d8cfef8815183cf3ced646a8e4fb2e58e52a1dfad1 SHA512: 59ca96f0b3cbb4710dcc9fcf60bbf986794bd666172de66f3e3c2783b3af6e594557cc0327309aacb7c9b41b4a2af6327ccc096c01783d0b278680071d37ae9d Homepage: https://cran.r-project.org/package=roptions Description: CRAN Package 'roptions' (Option Strategies and Valuation) Collection of tools to develop options strategies, value option contracts using the Black-Scholes-Merten option pricing model and calculate the option Greeks. Hull, John C. "Options, Futures, and Other Derivatives" (1997, ISBN:0-13-601589-1). Fischer Black, Myron Scholes (1973) "The Pricing of Options and Corporate Liabilities" . Package: r-cran-rorcid Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1343 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crul, r-cran-httr, r-cran-fauxpas, r-cran-jsonlite, r-cran-xml2, r-cran-tibble, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rcrossref, r-cran-handlr, r-cran-httpuv, r-cran-vcr Filename: pool/dists/noble/main/r-cran-rorcid_0.7.0-1.ca2404.1_all.deb Size: 1321478 MD5sum: 6c3b89d08b01d063787f709c57f0847d SHA1: 5e834c0601a3f5cb58fe031cba2cf5303ef76e35 SHA256: ceea5816d0d4a2a472febb4e7e817e91f7ee15b494f0588c5425606e832fb16b SHA512: f8903e6f8c7936a886c7095476d2f1836c49aedb82565a459206ee967f16d257f03d700e53eae384bf174c484fac294cb012a688ee733ed0e2b1158509cedcf9 Homepage: https://cran.r-project.org/package=rorcid Description: CRAN Package 'rorcid' (Interface to the 'Orcid.org' API) Client for the 'Orcid.org' API (). Functions included for searching for people, searching by 'DOI', and searching by 'Orcid' 'ID'. Package: r-cran-roroph Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-archipelagoengine, r-cran-dplyr, r-cran-ggplot2, r-cran-sf, r-cran-spdep Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-ggrepel, r-cran-ggspatial Filename: pool/dists/noble/main/r-cran-roroph_0.1.1-1.ca2404.1_all.deb Size: 101716 MD5sum: 05e1e37975b6e3be6b00cb9ca0dbabbe SHA1: aae3964fe9c6adad2950ea4b2fab39b0f8067301 SHA256: 2c6f4073abac823dc342c4d7ca281f19d1b9ca74075edbbffb44f29717bc5b79 SHA512: e63eceef7904698f857ee58173918eab8677a2295164910484863a91617b60e1351f25785b23c8b0b0c6fbeaaf3742d66b51dffc7d341f69416341ec6339f585 Homepage: https://cran.r-project.org/package=roroph Description: CRAN Package 'roroph' (Philippine Roll-on/Roll-Off (RoRo) Connectivity and TransportData) Provides the first standardized dataset of the Philippines' Roll-on/Roll-off (RoRo) shipping network, reflecting the 2024-2026 operational state. It digitizes fragmented records from the Maritime Industry Authority (MARINA) and Philippine Ports Authority (PPA) into a unified framework for transport modeling. The package includes 108 bidirectional provincial links across the Western, Central, and Eastern Nautical Highways, complete with GADM-standardized naming, geospatial coordinates, and metrics such as distance, travel time, and vessel frequency. Methodology follows Anselin (1988, ISBN:9024737354) and LeSage and Pace (2009) for spatial weight construction. Data sources include "MARINA Inventory of RoRo Routes" and "PPA Port Statistics" . Designed to support research in economic geography and disaster-response logistics. Package: r-cran-rorqual.morpho Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-rorqual.morpho_0.1.1-1.ca2404.1_all.deb Size: 33000 MD5sum: b3790a72ea23866fe0a0629867705763 SHA1: 54c361f6f2ed66c16dd63a4b3daf3fe216452514 SHA256: 2256260c971905370021c3e78b9e7f931bbd07619abf7738cdcf674a8597768b SHA512: 341cc424218ecf68c4bd50deb065f5b0c8464d425b446ff874267a561a4ff26fe49b1cb54e8cdcbd458350ed78ec804d70378c0f0aad6ea7ccc4f3b702e1777b Homepage: https://cran.r-project.org/package=rorqual.morpho Description: CRAN Package 'rorqual.morpho' (Morphological Allometry of Rorquals) Predicts morphological parameters of rorquals (e.g. body mass, flipper length, maximum engulfment capacity) from body length using allometric equations from Kahane-Rapport and Goldbogen (2018) . Package: r-cran-rosario Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-magrittr, r-cran-purrr, r-cran-furrr, r-cran-future, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-ggplot2, r-cran-spelling, r-cran-knitr, r-cran-lubridate, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-rosario_0.1.1-1.ca2404.1_all.deb Size: 103836 MD5sum: 1c93a7361ff67cd02c580cf946cb14fe SHA1: 2d6f777497ccb590b890119449bc0a29ce8e03ff SHA256: 8c994060ff56cb05287fead8cfacde211b708491aff8726e6fe78ad66d0454a3 SHA512: be3523a1cb5a651fe746a7143312de568383fe2b2f0b00ff9f7559bc9a431b0e2d8a8add4c2fc76d60b8ff9fe9f3001e2be42278a3b85dca8adacdef390ad39c Homepage: https://cran.r-project.org/package=rosario Description: CRAN Package 'rosario' (A Null Model Algorithm to Analyze Cyclical Data in Ecology) Implements a null model analysis to quantify concurrent temporal niche overlap (i.e., activity or phenology) among biological identities (e.g., individuals, populations, species) using the Rosario randomization algorithm (Castro-Arellano et al. 2010) . Package: r-cran-rose Architecture: all Version: 0.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass, r-cran-nnet, r-cran-rpart, r-cran-tree Filename: pool/dists/noble/main/r-cran-rose_0.0-4-1.ca2404.1_all.deb Size: 113840 MD5sum: 991b1dc9888d135991eee1793feab754 SHA1: 8bbabd8a079ab56047c09a6a3d219c2f12adc874 SHA256: f8dbb6c3511a1ba68160bcd2bf66406136fe2571d2be83a66d218d7e9d2dc109 SHA512: 100e68bf23658927682bd5bb5e99f04cc4897175c0097e579561b35efd3b9aeff5f0d3725d216166699799f8ec03111d51d8fad673a28e2bc48e33c5282d3a82 Homepage: https://cran.r-project.org/package=ROSE Description: CRAN Package 'ROSE' (Random Over-Sampling Examples) Functions to deal with binary classification problems in the presence of imbalanced classes. Synthetic balanced samples are generated according to ROSE (Menardi and Torelli, 2013). 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Package: r-cran-rosenbrock Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-rosenbrock_0.1.0-1.ca2404.1_all.deb Size: 23200 MD5sum: d22854ff14c8d5c6a81225b733f7958d SHA1: c4402a0e593bb083b8370cfbd75a82323c4c16aa SHA256: 5259f9a17d343ccece814d7a46616554e36b9632528e3832a48858a6b6d62178 SHA512: e794747e419780bb75d57516f8eda3177bb7d25b0ddd557e416d40c8b7ee579cdcb81db21f7e8b6ce6744e8a134b9f7388dea315743e29bc9fc490652ccafb33 Homepage: https://cran.r-project.org/package=Rosenbrock Description: CRAN Package 'Rosenbrock' (Extended Rosenbrock-Type Densities for Markov Chain Monte Carlo(MCMC) Sampler Benchmarking) New Markov chain Monte Carlo (MCMC) samplers new to be thoroughly tested and their performance accurately assessed. This requires densities that offer challenging properties to the novel sampling algorithms. One such popular problem is the Rosenbrock function. However, while its shape lends itself well to a benchmark problem, no codified multivariate expansion of the density exists. We have developed an extension to this class of distributions and supplied densities and direct sampler functions to assess the performance of novel MCMC algorithms. The functions are introduced in "An n-dimensional Rosenbrock Distribution for MCMC Testing" by Pagani, Wiegand and Nadarajah (2019) . 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Details can be found in the paper by Young and Shah (2024) . 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To ease such efforts, the Rosetta Stats website () allows comparing analyses in different packages. This package is the companion to the Rosetta Stats website, aiming to provide functions that produce output that is similar to output from other statistical packages, thereby facilitating 'software-agnostic' teaching of statistics. 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'Osmium' is a multipurpose command line tool that enables one to manipulate and analyze OpenStreetMap files through several different commands. Currently, this package does not aim to offer functions that cover the entire 'Osmium' API, instead making available functions that wrap only a very limited set of its features. 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Package: r-cran-roxylint Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-roxylint_0.1.0-1.ca2404.1_all.deb Size: 51784 MD5sum: 86948873f823e6378458b6c00dcd3938 SHA1: 731519597609d8b661e27e4c40183f7ef99cd37d SHA256: 59aff51839722c819166ad6b254752cfd0ddf3ab0dcc54813355054c1c2602be SHA512: 6a42d20413752c307aa4334deea385b36681be24cfd68947d4ca3747ea3a324bcdc165bf143d2393a34b2647e684497b852ea6534a6821b04e5945259545c3bb Homepage: https://cran.r-project.org/package=roxylint Description: CRAN Package 'roxylint' (Lint 'roxygen2'-Generated Documentation) Provides formatting linting to 'roxygen2' tags. Linters report 'roxygen2' tags that do not conform to a standard style. These linters can be a helpful check for building more consistent documentation and to provide reminders about best practices or checks for typos. Default linting suites are provided for common style guides such as the one followed by the 'tidyverse', though custom linters can be registered by other packages or be custom-tailored to a specific package. 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Package: r-cran-rpaci Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2600 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bnlearn, r-cran-ggplot2, r-cran-tidyr, r-cran-ggpubr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rpaci_0.2.2-1.ca2404.1_all.deb Size: 1024666 MD5sum: cfb94ff024c42875a95c4e2ac148d4bc SHA1: 5b820afee4ffc24b7f9f60c05d749e4855552ac2 SHA256: 696e0133443f53d908f739203e3584fa949e82ec41eb4440a516b54ae68db560 SHA512: 1a7458332f7f54b201074173e05a64946162b209b9bb0c1173e8bec1d993b4426a52797dab29d3c95567a4fc3312930e3cd2de94f699d1af928a1456e6787cd3 Homepage: https://cran.r-project.org/package=rPACI Description: CRAN Package 'rPACI' (Placido Analysis of Corneal Irregularity) Analysis of corneal data obtained from a Placido disk corneal topographer with calculation of irregularity indices. This package performs analyses of corneal data obtained from a Placido disk corneal topographer, with the calculation of the Placido irregularity indices and the posterior analysis. The package is intended to be easy to use by a practitioner, providing a simple interface and yielding easily interpretable results. A corneal topographer is an ophthalmic clinical device that obtains measurements in the cornea (the anterior part of the eye). A Placido disk corneal topographer makes use of the Placido disk [Rowsey et al. (1981)], which produce a circular pattern of measurement nodes. The raw information measured by such a topographer is used by practitioners to analyze curvatures, to study optical aberrations, or to diagnose specific conditions of the eye (e.g. keratoconus, an important corneal disease). The rPACI package allows the calculation of the corneal irregularity indices described in [Castro-Luna et al. (2020)], [Ramos-Lopez et al. (2013)], and [Ramos-Lopez et al. (2011)]. It provides a simple interface to read corneal topography data files as exported by a typical Placido disk topographer, to compute the irregularity indices mentioned before, and to display summary plots that are easy to interpret for a clinician. Package: r-cran-rpackedbar Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotly, r-cran-data.table, r-cran-scales, r-cran-shiny Suggests: r-cran-covr, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-rpackedbar_0.2.2-1.ca2404.1_all.deb Size: 1105650 MD5sum: b48368481ca462f032e980f3ff734eb3 SHA1: 8d162010e96bb4f871c84f251b4e71d95bb02e66 SHA256: f64f26a857e75dddc78d0bf3b12718dfe0f732be8b6d6efcc2eb5d3f7dde552e SHA512: 52574a8e22a1e75bdc5db650f5780d582d83c207e2694995a6002fb54b416d1737dfd9b91b2f038c57155613ecfacefa3d3744620966cbfbbcaade48756507b6 Homepage: https://cran.r-project.org/package=rPackedBar Description: CRAN Package 'rPackedBar' (Packed Bar Charts with 'plotly') Packed bar charts are a variation of treemaps for visualizing skewed data. The concept was introduced by Xan Gregg at 'JMP'. Package: r-cran-rpadrino Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 639 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ipmr, r-cran-curl, r-cran-ggplot2, r-cran-magrittr, r-cran-mvtnorm, r-cran-purrr, r-cran-rlang, r-cran-rmarkdown, r-cran-truncdist Suggests: r-cran-covr, r-cran-knitr, r-cran-maps, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rpadrino_0.0.5-1.ca2404.1_all.deb Size: 323582 MD5sum: 2523ebe8ac990da3921c9cbff1425799 SHA1: 3dc4f2a095185c3c19c4c696398d74a7ffad5dd0 SHA256: fdff016bea2313386e3d07297c3bf7ecd67aa0f7985e478118aee52596c7644e SHA512: 9b9be5b7d095e152e51124365e1dd60dd78589d1a34c39431a726b005f50543495062d24d928daa33caac6118c314b1ccc1d0a94e1971d8a486113f5597f07cf Homepage: https://cran.r-project.org/package=Rpadrino Description: CRAN Package 'Rpadrino' (Interact with the 'PADRINO' IPM Database) 'PADRINO' houses textual representations of Integral Projection Models which can be converted from their table format into full kernels to reproduce or extend an already published analysis. 'Rpadrino' is an R interface to this database. For more information on Integral Projection Models, see Easterling et al. (2000) , Merow et al. (2013) , Rees et al. (2014) , and Metcalf et al. (2015) . See Levin et al. (2021) for more information on 'ipmr', the engine that powers model reconstruction . Package: r-cran-rpaex Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 969 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-agricolae Filename: pool/dists/noble/main/r-cran-rpaex_1.0.5-1.ca2404.1_all.deb Size: 946240 MD5sum: 092a0d9bbfef9ec869ba6a79218cb2ae SHA1: fd78045a1cfc5b9025919b08afacf69998e134b0 SHA256: db004e06f45742d175c638ca8fb232406b27891a81c02a07304288423b538729 SHA512: 134c492babef65b1347cc4100c1a43542ee24924173dc10deb7ec6e563bbb2ed9c8866f9bb5f61ee73bcd9c815c4c993b4cf7f08ff756f7e53a8a525a1562025 Homepage: https://cran.r-project.org/package=rPAex Description: CRAN Package 'rPAex' (Automatic Detection of Experimental Unit in PrecisionAgriculture) A part of precision agriculture is linked to the spectral image obtained from the cameras. With the image information of the agricultural experiment, the included functions facilitate the collection of spectral data associated with the experimental units. Some designs generated in R are linked to the images, which allows the use of the information of each pixel of the image in the experimental unit and the treatment. Tables and images are generated for the analysis of the precision agriculture experiment during the entire vegetative period of the crop. Package: r-cran-rpaleoclim Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-fs, r-cran-httr, r-cran-rlang, r-cran-terra Suggests: r-cran-knitr, r-cran-covr, r-cran-raster, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rpaleoclim_1.1.0-1.ca2404.1_all.deb Size: 491920 MD5sum: 71947cbb7afe0cdb4edc0725456938f2 SHA1: e666a9c4d45e697cf4247284790495a03d2e1e5c SHA256: a7e2d29c16c38e28d686f67d767da9c193f66b571cb0a97fa366fad6e53b3c9d SHA512: 40b9821301be984b528fa3ecfe88673579555a1dc571a1fc02d3e75566be61ccb43cc4ee88a8e68578452916228076d6c39fdc69059eae326ece728e10de6a25 Homepage: https://cran.r-project.org/package=rpaleoclim Description: CRAN Package 'rpaleoclim' (Download Paleoclimate Data from 'PaleoClim') 'PaleoClim' (Brown et al. 2019, ) is a set of free, high resolution paleoclimate surfaces covering the whole globe. It includes data on surface temperature, precipitation and the standard bioclimatic variables commonly used in ecological modelling, derived from the 'HadCM3' general circulation model and downscaled to a spatial resolution of up to 2.5 minutes. Simulations are available for key time periods from the Late Holocene to mid-Pliocene. Data on current and Last Glacial Maximum climate is derived from 'CHELSA' (Karger et al. 2017, ) and reprocessed by 'PaleoClim' to match their format; it is available at up to 30 seconds resolution. This package provides a simple interface for downloading 'PaleoClim' data in R, with support for caching and filtering retrieved data by period, resolution, and geographic extent. Package: r-cran-rpandas Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rpandas_0.1.4-1.ca2404.1_all.deb Size: 97816 MD5sum: ad0592b08b55be041972950bf168b9d1 SHA1: b91b3d3576a551c346951e4746f0c6fd9b134101 SHA256: 2994a6138713d97297a91ace53d9e29f302d7be837b4586626a5827dda90ef79 SHA512: c91d87e9a27502943771420bf1b6730e0db93d66a1fbb0509ec45d19959fb738ca94a95c1f87b48f896345881a003b7ae5c6c2e159df1bde28799a64732ef15c Homepage: https://cran.r-project.org/package=rPandas Description: CRAN Package 'rPandas' (Translating from R to Python's Pandas Package) Provides an R interface to Python's 'pandas' library using non-standard evaluation. 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Package: r-cran-rplec Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3966 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-pbapply, r-cran-purrr, r-cran-rpmm, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-tidyverse, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rplec_0.1.3-1.ca2404.1_all.deb Size: 3248464 MD5sum: 678b424b7b497ecba8147e42e35c2aa5 SHA1: 4bbe2eb2856da20a58c314d8895bca4578594ca6 SHA256: a97d97ce39c232b9f22c6443ff2f89dc3303b6085fdd7a32965fb433c0892d28 SHA512: a6d9694b7955f68e505c8768772361f495c923d37da60fd2ee2fe627f8aec066ee725f9b4b6b4ef1bec36959bf9b40e71a61408290ee669694e9fe1c01e90a41 Homepage: https://cran.r-project.org/package=rplec Description: CRAN Package 'rplec' (Placental Epigenetic Clock to Estimate Aging by DNA Methylation) Placental epigenetic clock to estimate aging based on gestational age using DNA methylation levels, so called placental epigenetic clock (PlEC). We developed a PlEC for the 2024 Placental Clock DREAM Challenge (). Our PlEC achieved the top performance based on an independent test set. PlEC can be used to identify accelerated/decelerated aging of placenta for understanding placental dysfunction-related conditions, e.g., great obstetrical syndromes including preeclampsia, fetal growth restriction, preterm labor, preterm premature rupture of the membranes, late spontaneous abortion, and placental abruption. Detailed methodologies and examples are documented in our vignette, available at . 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The package includes over 15 functions for the production and arrangement of basic graphing. Package: r-cran-rpls Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcapp, r-cran-robustbase Filename: pool/dists/noble/main/r-cran-rpls_0.6.0-1.ca2404.1_all.deb Size: 43638 MD5sum: 4c658098b1ddb3b743f363e101d1bf08 SHA1: 3cf98208f63f8475642fd275ce8f3d83a7970f70 SHA256: 45d76405ac35c32ec097e30dc8c99211d3ca669308c7845f506ca72d7429db8c SHA512: b94a3162c21496bda2d72d7816236a7e6361e3072c1b823c5991a91df817805c671cde83ba311509093d1b9ef3b0ff3841006613ca8ed5d58c7ba5558aae0252 Homepage: https://cran.r-project.org/package=rpls Description: CRAN Package 'rpls' (Robust Partial Least Squares) A robust Partial Least-Squares (PLS) method is implemented that is robust to outliers in the residuals as well as to leverage points. 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This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) . 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This R-package provide the estimation method for replication rate which makes use of the summary statistics from the primary study. We can use the estimated RR to determine the sample size of the replication study, and to check the consistency between the results of the primary study and those of the replication study. 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This includes the RRBoost method proposed in the paper "Robust Boosting for Regression Problems" (Ju X and Salibian-Barrera M. 2020) . It also implements previously proposed boosting algorithms in the simulation section of the paper: L2Boost, LADBoost, MBoost (Friedman, J. H. (2001) ) and Robloss (Lutz et al. (2008) ). 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Robust versions (Engelen and Hubert (2011) ) and versions for compositional data are also provided (Gallo (2015) , Di Palma et al. (2018) ). Several optimization methods alternative to ALS are available (Simonacci and Gallo (2019) , Simonacci and Gallo (2020) ). 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Package: r-cran-rrepast Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lhs, r-cran-sensitivity, r-cran-ggplot2, r-cran-digest, r-cran-xlsx, r-cran-dosnow, r-cran-rjava, r-cran-gridextra, r-cran-foreach Filename: pool/dists/noble/main/r-cran-rrepast_0.8.0-1.ca2404.1_all.deb Size: 295652 MD5sum: 25a9d57cafab191cad29e14087c2fdaf SHA1: cf60beb81e51473a44aeab241652b482781c480e SHA256: 9d4e74a318f638834f09c1b682ba1ac27573eb1214ab95cb9fad570b011cd86a SHA512: b947515742db16b9c68370970d352153731805fee3ff2ec8923f9ec29bd7cf5b83f084c2a330a015b8b8a09131d0b578abf344b9bea8a836c2375dbd4a0e4726 Homepage: https://cran.r-project.org/package=rrepast Description: CRAN Package 'rrepast' (Invoke 'Repast Simphony' Simulation Models) An R and Repast integration tool for running individual-based (IbM) simulation models developed using 'Repast Simphony' Agent-Based framework directly from R code supporting multicore execution. 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For more details see Saluja, Parlak, and Mejia (2026+) . 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The functions perform the estimation of phenotypic evolutionary rates, identification of phenotypic evolutionary rate shifts, quantification of direction and size of evolutionary change in multivariate traits, the computation of ontogenetic shape vectors and test for morphological convergence. Package: r-cran-rrpp Architecture: all Version: 2.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1830 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-ggplot2, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-rrpp_2.1.2-1.ca2404.1_all.deb Size: 1543700 MD5sum: 2687e659a75c7b4ec44051c1a2533ec1 SHA1: 749a548fd7c797625a6a0f8436667b4c93aeb066 SHA256: a29ec3a679a8cbbfa097e2010a10773f04c888996c4c358a48a3577c81f48184 SHA512: 0ae3e2e0e0c2e4a9414fc4d903a54ca3b26a9efb499da421176e641bcc05c64b7f63cffc4a475cda1e0e515db30a4cb057f95f47186035ea85304956b5378d2f Homepage: https://cran.r-project.org/package=RRPP Description: CRAN Package 'RRPP' (Linear Model Evaluation with Randomized Residuals in aPermutation Procedure) Linear model calculations are made for many random versions of data. Using residual randomization in a permutation procedure, sums of squares are calculated over many permutations to generate empirical probability distributions for evaluating model effects. Additionally, coefficients, statistics, fitted values, and residuals generated over many permutations can be used for various procedures including pairwise tests, prediction, classification, and model comparison. This package should provide most tools one could need for the analysis of high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well. Package: r-cran-rrr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-cran-magrittr, r-cran-dplyr, r-cran-ggplot2, r-cran-plotly, r-cran-ggally Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-readr Filename: pool/dists/noble/main/r-cran-rrr_1.0.0-1.ca2404.1_all.deb Size: 1936258 MD5sum: 37b02df7d2386ed18cfaed535f8064d0 SHA1: 95db558c8eb701b711d586092a2483d8b536b3be SHA256: b47e5ea2aeebf8ee883628477e84f9eeba01e6911ca3f9413e3c5991813d59a3 SHA512: a266be81dca060151e8586f0a370af6da27555fe5c2441a7b9446f73de2964131af69a31d0af608a60bfa56946aeb817118fab3311951f384c6ccab4d07ebf97 Homepage: https://cran.r-project.org/package=rrr Description: CRAN Package 'rrr' (Reduced-Rank Regression) Reduced-rank regression, diagnostics and graphics. Package: r-cran-rrreg Architecture: all Version: 0.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-lme4 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-rrreg_0.7.6-1.ca2404.1_all.deb Size: 880420 MD5sum: 31f2c88561a9ee3bf4a73f2da99340d8 SHA1: bd708d442e7716a39717441912c758c9457da48c SHA256: 4cccc72811ead1f4906e5e712d656048052655359b233c91a1706589834777b2 SHA512: 851e0d19e3725dd85715e6cbc0c6e1ff77e46669fa608046a368d65616c2cee2714b79774e7ba674c6bb62ee24e6da3559cc3d9578f2516b164a26f6adc4ff2e Homepage: https://cran.r-project.org/package=RRreg Description: CRAN Package 'RRreg' (Correlation and Regression Analyses for Randomized Response Data) Univariate and multivariate methods to analyze randomized response (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 63–69, ). Besides univariate estimates of true proportions, RR variables can be used for correlations, as dependent variable in a logistic regression (with or without random effects), or as predictors in a linear regression (Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and regression analyses of randomized response data. Journal of Statistical Software, 85(2), 1–29, ). For simulations and the estimation of statistical power, RR data can be generated according to several models. The implemented methods also allow to test the link between continuous covariates and dishonesty in cheating paradigms such as the coin-toss or dice-roll task (Moshagen, M., & Hilbig, B. E. (2017). The statistical analysis of cheating paradigms. Behavior Research Methods, 49, 724–732, ). Package: r-cran-rrrr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixcalc, r-cran-expm, r-cran-ggplot2, r-cran-magrittr, r-cran-mvtnorm Suggests: r-cran-lazybar, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rrrr_1.1.1-1.ca2404.1_all.deb Size: 180258 MD5sum: 17141d36f4959fa317c189592ce7635d SHA1: 4cdf35307d61b54277e354fe797f1aee9e5a8f91 SHA256: 594f745dbeeca220768634163eb67ce519540ee2050602951846e8f1e1fad58e SHA512: bac7fbb36085ecb9bbb61ca4863952cd46f48dbe5ea341783fb4fdc8eac7b84aca68c74f6105f2c97a9033aaa10f94484cd02f0d02ae72053c08fa250a599a5e Homepage: https://cran.r-project.org/package=RRRR Description: CRAN Package 'RRRR' (Online Robust Reduced-Rank Regression Estimation) Methods for estimating online robust reduced-rank regression. The Gaussian maximum likelihood estimation method is described in Johansen, S. (1991) . The majorisation-minimisation estimation method is partly described in Zhao, Z., & Palomar, D. P. (2017) . The description of the generic stochastic successive upper-bound minimisation method and the sample average approximation can be found in Razaviyayn, M., Sanjabi, M., & Luo, Z. Q. (2016) . Package: r-cran-rrscale Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-deoptim, r-cran-nloptr, r-cran-abind Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-rrscale_1.0-1.ca2404.1_all.deb Size: 209238 MD5sum: c2c41e9cb10cd6005c4acc4ed3ef8e32 SHA1: cb7fc9f82e1dccb03ff9bd0e917cbed29462eec1 SHA256: 9fde4cee61b4fa410716a9de1669d32570d35849a42e412379232db5482d9841 SHA512: 1a452b14aaeee07b594b28faff3cc2fc573aec70f4dcee3239dda272c7490c76550c98233194453d702fbd1bc4e977cfd79f58d333bbf8eb400e29c5a50f6cd6 Homepage: https://cran.r-project.org/package=rrscale Description: CRAN Package 'rrscale' (Robust Re-Scaling to Better Recover Latent Effects in Data) Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation. A publication describing the method has the following citation: Gregory J. Hunt, Mark A. Dane, James E. Korkola, Laura M. Heiser & Johann A. Gagnon-Bartsch (2020) "Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data", Journal of Computational and Graphical Statistics, . 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The randomized response technique was developed to obtain estimates that are more valid when studying sensitive topics. Estimators and variances for 14 randomized response methods for qualitative variables and 7 randomized response methods for quantitative variables are also implemented. In addition, some data sets from surveys with these randomization methods are included in the package. Package: r-cran-rsa Architecture: all Version: 0.10.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 509 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-ggplot2, r-cran-lattice, r-cran-plyr, r-cran-rcolorbrewer, r-cran-aplpack Suggests: r-cran-fields, r-cran-rgl, r-cran-qgraph, r-cran-tkrplot, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-rsa_0.10.8-1.ca2404.1_all.deb Size: 410952 MD5sum: b90ec8d13a5404ce9f875636cd3d2ccb SHA1: 421bbc244766d9ebf4d7c3e78e27767fca5a98a5 SHA256: 4e4d8057d7c88dd86ca74dfb64c92fa8a3f4414823a766fd208fba02ccf79c54 SHA512: c47859b85e2890bd2890ac6e24a5312d8335c7aae59e10bbe9d258d38a2768b966af6bb8e745ba689173c212f50fc0ca13875289045da3ab9062659a6ceb31a3 Homepage: https://cran.r-project.org/package=RSA Description: CRAN Package 'RSA' (Response Surface Analysis) Advanced response surface analysis. The main function RSA computes and compares several nested polynomial regression models (full second- or third-order polynomial, shifted and rotated squared difference model, rising ridge surfaces, basic squared difference model, asymmetric or level-dependent congruence effect models). The package provides plotting functions for 3d wireframe surfaces, interactive 3d plots, and contour plots. Calculates many surface parameters (a1 to a5, principal axes, stationary point, eigenvalues) and provides standard, robust, or bootstrapped standard errors and confidence intervals for them. Package: r-cran-rsadbe Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rsadbe_1.0-1.ca2404.1_all.deb Size: 114290 MD5sum: 4b6ab73bf028d1fbdbeef956b71603c9 SHA1: 37663878cf5217ce2713d0f51379247bfbd95ba0 SHA256: 0d8416fd6ea296832941eaef5e3a8130b2b659df3e4efe96e782d01eff3808af SHA512: 3e2fb200cfd3340444acecd3dc8f5769d286039031b5235704d9602d98ccb40a0bed085cd5cd8c1e66faeee2fba9eb316b32a1518b8af9a43417463dc19756ee Homepage: https://cran.r-project.org/package=RSADBE Description: CRAN Package 'RSADBE' (Data related to the book "R Statistical Application Developmentby Example") The package contains all the data sets related to the book written by the maintainer of the package. Package: r-cran-rsafe Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dalex, r-cran-dendextend, r-cran-ggplot2, r-cran-ggpubr, r-cran-ingredients, r-cran-sets Suggests: r-cran-gbm, r-cran-knitr, r-cran-pander, r-cran-randomforest, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-rsafe_0.1.4-1.ca2404.1_all.deb Size: 287394 MD5sum: a8c2e5946c1f197184c298fda5fe280a SHA1: 3f2a574e19edaa7df27607ac307053fc88a9f291 SHA256: 4bc41a6a5e565ef0746a1f05874be36e93df1f8f16504a4c3b4909775654838d SHA512: 5a28e8bf7362b2bdab6ecbe80083515666e072417458e61ef3b357658f943f73b1401cd8656e67b24187f76b868655e5dd6f1bd79d90ce4e68804cb59b787d9c Homepage: https://cran.r-project.org/package=rSAFE Description: CRAN Package 'rSAFE' (Surrogate-Assisted Feature Extraction) Provides a model agnostic tool for white-box model trained on features extracted from a black-box model. For more information see: Gosiewska et al. (2020) . 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This package furthermore provides several R functions for handling ASCII grids, including a flexible framework for applying local functions (including predict methods of fitted models) and focal functions to multiple grids. SAGA GIS is available under GPL-2 / LGPL-2 licences from . Package: r-cran-rsagacmd Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 874 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-generics, r-cran-sf, r-cran-terra, r-cran-stars, r-cran-foreign, r-cran-stringr, r-cran-rlang, r-cran-tibble, r-cran-processx, r-cran-rvest Suggests: r-cran-dplyr, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-rsagacmd_0.4.3-1.ca2404.1_all.deb Size: 824922 MD5sum: 833266aed995706ab7c733e3d4963699 SHA1: db0f950c2da072ea0980a5284301a6af26149c2f SHA256: 62da8f7408d56ab2ac844a986c50b5442d46b15de5a6e903ef251bf7c7a71ccc SHA512: 5e23a38a5966e6e17214ae707988889666f7d8a2334392d208af15e63397b50f40077e4d5499352f7e969cb0ea7b05dd90644550d296dbc07bacea326f7a97e1 Homepage: https://cran.r-project.org/package=Rsagacmd Description: CRAN Package 'Rsagacmd' (Linking R with the Open-Source 'SAGA-GIS' Software) Provides an R scripting interface to the open-source 'SAGA-GIS' (System for Automated Geoscientific Analyses Geographical Information System) software. 'Rsagacmd' dynamically generates R functions for every 'SAGA-GIS' geoprocessing tool based on the user's currently installed 'SAGA-GIS' version. These functions are contained within an S3 object and are accessed as a named list of libraries and tools. This structure facilitates an easier scripting experience by organizing the large number of 'SAGA-GIS' geoprocessing tools (>700) by their respective library. Interactive scripting can fully take advantage of code autocompletion tools (e.g. in 'RStudio'), allowing for each tools syntax to be quickly recognized. Furthermore, the most common types of spatial data (via the 'terra', 'sp', and 'sf' packages) along with non-spatial data are automatically passed from R to the 'SAGA-GIS' command line tool for geoprocessing operations, and the results are loaded as the appropriate R object. Outputs from individual 'SAGA-GIS' tools can also be chained using pipes from the 'magrittr' and 'dplyr' packages to combine complex geoprocessing operations together in a single statement. 'SAGA-GIS' is available under a GPLv2 / LGPLv2 licence from including Windows x86/x64 and macOS binaries. SAGA-GIS is also included in Debian/Ubuntu default software repositories. Rsagacmd has currently been tested on 'SAGA-GIS' versions from 2.3.1 to 9.5.1 on Windows, Linux and macOS. Package: r-cran-rsahmi Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-blit, r-cran-cli, r-cran-rlang, r-bioc-shortread Filename: pool/dists/noble/main/r-cran-rsahmi_0.0.2-1.ca2404.1_all.deb Size: 4716940 MD5sum: f1b1703ec032303fc3b0a7f0d647aecd SHA1: 705cc5f7b2e4e4ae4fb8873f273eeed9372f4767 SHA256: ee71f9d1acdf6c81a606d164b1422e7d9dac1d86bb5e7665be4a895c5bb275a3 SHA512: 5a00e5f1ff56f0bda669c444910116a6d8b1f675c98359be6928ae037fedd21b803cb7c5373da5a94e45242f255274c889cc6a72c72d81ad372020e6209db7e8 Homepage: https://cran.r-project.org/package=rsahmi Description: CRAN Package 'rsahmi' (Single-Cell Analysis of Host-Microbiome Interactions) A computational resource designed to accurately detect microbial nucleic acids while filtering out contaminants and false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues. For more details, see Ghaddar (2023) . This implementation leverages the 'polars' package for fast and systematic microbial signal recovery and denoising from host tissue genomic sequencing. Package: r-cran-rsample Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2668 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-furrr, r-cran-generics, r-cran-glue, r-cran-lifecycle, r-cran-pillar, r-cran-purrr, r-cran-rlang, r-cran-slider, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-broom, r-cran-covr, r-cran-ggplot2, r-cran-knitr, r-cran-modeldata, r-cran-recipes, r-cran-rmarkdown, r-cran-testthat, r-cran-whisker, r-cran-withr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-rsample_1.3.2-1.ca2404.1_all.deb Size: 1664014 MD5sum: 94396a7add45380ceed44cbfc599d3d7 SHA1: 9b42a2478960d9c3ffeaf501571702c4291aca52 SHA256: c50c4d20848055a8f4302b635593853268b93235ef28c0c090ede1b238127d75 SHA512: c8b9421db50b30f8dc02906f6f95cd862ede97765ebdb3649117d0670c19474e9bb563349fcefe919f6a7cfcca98da585be2febeb32bc2c388198b89541d9434 Homepage: https://cran.r-project.org/package=rsample Description: CRAN Package 'rsample' (General Resampling Infrastructure) Classes and functions to create and summarize different types of resampling objects (e.g. bootstrap, cross-validation). Package: r-cran-rsampling Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 863 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rsampling_0.1.1-1.ca2404.1_all.deb Size: 410496 MD5sum: 2f7f5a6ac9faf1923c8e00564bfe0ce6 SHA1: 3ae19b57fd627402eb3f1d33a826d5e6c55910ab SHA256: 70732477759916a5f61b2a350b0c060725fe4e1dd0f531d5793f6e9a23e3f2b1 SHA512: 9d42e6e3b77123adc23f0c9e07e544df35dd671e63d5d00fb78fc22ba0e221c09c368236cdd1f69a8fcfbbe10bc9568e2c6cb5e9b03e5dc0cd24eae0372532a0 Homepage: https://cran.r-project.org/package=Rsampling Description: CRAN Package 'Rsampling' (Ports the Workflow of "Resampling Stats" Add-in to R) Resampling Stats (http://www.resample.com) is an add-in for running randomization tests in Excel worksheets. The workflow is (1) to define a statistic of interest that can be calculated from a data table, (2) to randomize rows ad/or columns of a data table to simulate a null hypothesis and (3) and to score the value of the statistic from many randomizations. The relative frequency distribution of the statistic in the simulations is then used to infer the probability of the observed value be generated by the null process (probability of Type I error). This package intends to translate this logic for R for teaching purposes. Keeping the original workflow is favored over performance. Package: r-cran-rsat Architecture: all Version: 0.1.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3754 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml, r-cran-curl, r-cran-httr, r-cran-leafem, r-cran-leaflet, r-cran-rjson, r-cran-rvest, r-cran-tmap, r-cran-xml2, r-cran-zip, r-cran-rdpack, r-cran-fields, r-cran-calendr, r-cran-sf, r-cran-stars, r-cran-terra, r-cran-sp, r-cran-raster Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rsat_0.1.21-1.ca2404.1_all.deb Size: 2912346 MD5sum: 434cb6d38f178ec9df1938fe1c3d463a SHA1: a797e0bbe22386b9803d6b1294d500b352105de4 SHA256: 5cc45f7365a3fef07c67a4dc5bf0ae22005892f96019eb4ad8207d80e3e1aca9 SHA512: b4bfe08d7ab6f0dd4f058cc4bef0acc8a7f7a32112ddfdb6fbc8fc683a404f0a1b86aab244f9d6a8826d6479d2f30b559c018f343ea18f31a2e6dd80f3962a57 Homepage: https://cran.r-project.org/package=rsat Description: CRAN Package 'rsat' (Dealing with Multiplatform Satellite Images) Downloading, customizing, and processing time series of satellite images for a region of interest. 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Package: r-cran-rsatools Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4526 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-semtools, r-cran-rsa, r-cran-ggplot2, r-cran-lattice, r-cran-plyr, r-cran-rcolorbrewer, r-cran-aplpack, r-cran-mass Suggests: r-cran-fields, r-cran-rgl, r-cran-qgraph, r-cran-tkrplot, r-cran-testthat, r-cran-covr, r-cran-psych, r-cran-ggpubr Filename: pool/dists/noble/main/r-cran-rsatools_0.1.2-1.ca2404.1_all.deb Size: 2698718 MD5sum: e07e60ae75effabc0630550819d7b832 SHA1: e706c958b9b78250b8cdafdd7aa9f29b4a86dd90 SHA256: c9ef3adf38625108716164a4ceae992ca7d1dbeb6ecbdb7447ce81d9b8e1557e SHA512: c40bdffa7e076eb77a1ccbb6f1c4faada9e033e0515b4eefd9279075abdd55d4a73b4038a30c524b0b77f527358f3c4ddde6382055a8ce9424d18223a7ef2194 Homepage: https://cran.r-project.org/package=RSAtools Description: CRAN Package 'RSAtools' (Advanced Response Surface Analysis) Provides tools for response surface analysis, using a comparative framework that identifies best-fitting solutions across 37 families of polynomials. 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Package: r-cran-rsatscan Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreign Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/noble/main/r-cran-rsatscan_1.0.10-1.ca2404.1_all.deb Size: 135752 MD5sum: f5e54fc4a7a484546595c13d77112b21 SHA1: b3c4ceb844ad8601d14c30bceb1364494397654d SHA256: 4b0b80bbd0019202d25700ce60b2de2506c8c57931cfb1e1a01cafec43d8c366 SHA512: 3526d77176039572a6bc38ea636f9fd784d080b75bcedb07152627e1536bd13722ca9bf7a54e61cddcc1269d6e743c0b9f35b59160536cbd1ea8a92b07912486 Homepage: https://cran.r-project.org/package=rsatscan Description: CRAN Package 'rsatscan' (Tools for Running the External 'SaTScan' Software using RClasses and Methods) The 'SaTScan'(TM) software uses spatial and space-time scan statistics to detect and evaluate spatial and space-time clusters. 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Package: r-cran-rsaucelabs Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-xml2, r-cran-whisker, r-cran-data.table Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rsaucelabs_0.1.6-1.ca2404.1_all.deb Size: 114076 MD5sum: 7bb34d767ee156837521a6f1852a9a6f SHA1: 1fe7dfbf9706373a2972064d2ec584ed51cafd7e SHA256: 6a13ac0c0162295e779c157e260d362015a263e55b7c6b8cd8720e04e87ef18b SHA512: f2531d22bfcf3f814a284926c41a00525de67e7672917f617f2719d23749bee7e44dd52c742cc12261aab3de18c6ff6c1f0dd55d14e6729c7f632441d8c8dd80 Homepage: https://cran.r-project.org/package=RSauceLabs Description: CRAN Package 'RSauceLabs' (R Wrapper for 'SauceLabs' REST API) Retrieve, update, delete job information from . Poll the 'SauceLabs' services current status and access supported platforms. Send and retrieve files from 'SauceLabs' and manage tunnels associated with 'SauceConnect'. Package: r-cran-rsbjson Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-rsbjson_1.1.2-1.ca2404.1_all.deb Size: 12190 MD5sum: 3945c548db80e29881acdcd8ddad818b SHA1: 00c1e50cd558cc1a25c8e0f3d32b887c69889b9b SHA256: d945d2290baedbfde246506f52556bf9ec83408e5d058e3b7bd7675e0f46eea6 SHA512: 671bd4781ec24d9a9884d673522bd07962b88622c2126dbef7a424f5c79913763df0162e337f692dafbef65f6979b66712523e0e728e8f205e75b7b298d4d1fc Homepage: https://cran.r-project.org/package=RSBJson Description: CRAN Package 'RSBJson' (Handle R Requests from R Service Bus Applications with JSONPayloads) Package to Handle R Requests from R Service Bus Applications with JSON Payloads in a generic way. The incoming request is encoded as a string (character vector of length one) containing the JSON file passed through by the client. Package: r-cran-rsca Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rsca_3.1-1.ca2404.1_all.deb Size: 83744 MD5sum: 7110061592f57dc22dc6c86ad9291a8a SHA1: 4d56a80edb1681fbd89cf88ba7f05c1fd4a57ddb SHA256: 6c7162e665d5169eb892bfe41a1c8789122c0b65d57ff685cde4ea68ef13b53f SHA512: bdd6f36cf5076f50d8eafb30fdae8521dea3f02db8a1493da41002b652343fd37609f4e1db86956da3312fe9bc8582d213c033b2590313fbc35006e53b87bede Homepage: https://cran.r-project.org/package=rSCA Description: CRAN Package 'rSCA' (An R Package for Stepwise Cluster Analysis) A statistical tool for multivariate modeling and clustering using stepwise cluster analysis. 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Package: r-cran-rscat Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyjs, r-cran-metrics, r-cran-ggplot2, r-cran-gridextra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rscat_1.1.3-1.ca2404.1_all.deb Size: 339474 MD5sum: 9ffa22fa1034779baecf3b5da0c68ffd SHA1: a4e4956798ba1ea1cce999d25da3deae8a9fcf34 SHA256: 6b8d0003ad766d22f0977b1c544e8132e55a04bbd9118364c21cf0b81defaab4 SHA512: ce588cb41a94b6c6f22200758f6c776c5762a931915c616cfbcc059ccf6ecfe54491188d7801408f91c9a52ff68b5e8a5b435c4a3c5443b41d60cc108211b00a Homepage: https://cran.r-project.org/package=RSCAT Description: CRAN Package 'RSCAT' (Shadow-Test Approach to Computerized Adaptive Testing) As an advanced approach to computerized adaptive testing (CAT), shadow testing (van der Linden(2005) ) dynamically assembles entire shadow tests as a part of selecting items throughout the testing process. 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It can classify sentences to the following categories of sentiments:- Positive, Negative, very Positive, very negative, Neutral. For a vector of sentences, it counts the number of sentences in each category of sentiment.In calculating the score, negation and various degrees of adjectives are taken into consideration. It deals only with English sentences. Package: r-cran-rsetse Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-matrix, r-cran-rlang, r-cran-igraph, r-cran-purrr, r-cran-tibble, r-cran-minpack.lm, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-ggplot2, r-cran-ggraph, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-rsetse_0.5.0-1.ca2404.1_all.deb Size: 429110 MD5sum: 62339895937f38090cdf6a3d968d68cc SHA1: ae8253a394e89aa6ed6378416f0ca026e63de4bb SHA256: 22e5229bbbcf289a36da33887c3d5b37c851e9222912f368bdf58b036cd9d1ee SHA512: 45d9326e1079544686845251a9d0469ecb0b9f09ca16727fb33c3f549c752182d8baa43308d0467519506d10c7b8569492ce86d3c61b9b9ea46e0b54627a6a6d Homepage: https://cran.r-project.org/package=rsetse Description: CRAN Package 'rsetse' (Strain Elevation Tension Spring Embedding) An R implementation for the Strain Elevation and Tension embedding algorithm from Bourne (2020) . The package embeds graphs and networks using the Strain Elevation and Tension embedding (SETSe) algorithm. SETSe represents the network as a physical system, where edges are elastic, and nodes exert a force either up or down based on node features. SETSe positions the nodes vertically such that the tension in the edges of a node is equal and opposite to the force it exerts for all nodes in the network. The resultant structure can then be analysed by looking at the node elevation and the edge strain and tension. This algorithm works on weighted and unweighted networks as well as networks with or without explicit node features. Edge elasticity can be created from existing edge weights or kept as a constant. Package: r-cran-rsf Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bookdown, r-cran-gert, r-cran-here, r-cran-knitr, r-cran-magrittr, r-cran-renv, r-cran-rlang, r-cran-usethis, r-cran-yaml, r-cran-ymlthis Filename: pool/dists/noble/main/r-cran-rsf_0.3.0-1.ca2404.1_all.deb Size: 33414 MD5sum: 8a6e6967889ee86077660105b7a185ce SHA1: e8986344c385eb3369b6f70b12b75b63bd2c4c33 SHA256: fdb4b89698aad5e65d1788eabf3f32b8012e60e8f89b5499f5eddacabb876360 SHA512: 31e3dcb995d3aa808ff68f866273c712ed1a45d095ae2efd5935e99410eda01135d0f024a200e2b2d20ba92eeeca53052165692aa21888d728085ee3dcbd826f Homepage: https://cran.r-project.org/package=rsf Description: CRAN Package 'rsf' (Report of Statistical Findings in 'bookdown') A report of statistical findings (RSF) project template is generated using a 'bookdown' format. 'YAML' fields can be further customized. Additional helper functions provide extra features to the RSF. Package: r-cran-rsfa Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-rsfa_1.5-1.ca2404.1_all.deb Size: 150800 MD5sum: dfb5aad9a05efbd0f90e385a4f17083d SHA1: cb4b6e2d3b14b5b3468363c63fd0956673fd7f0d SHA256: 443d674de03cf222b97791fd4f497f2b86d3acd145e9f06a97523437c11e7dc1 SHA512: 9a34915f96f5986f04aa58e5be03c6c52325928d3f803a523392297ba13b2eb370d15ac03b805dc2a29aca307f0e660379b6b18c4cc5b205d6dd2418ac42748f Homepage: https://cran.r-project.org/package=rSFA Description: CRAN Package 'rSFA' (Slow Feature Analysis) Slow Feature Analysis (SFA), ported to R based on 'matlab' implementations of SFA: 'SFA toolkit' 1.0 by Pietro Berkes and 'SFA toolkit' 2.8 by Wolfgang Konen. Package: r-cran-rsfar Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda Filename: pool/dists/noble/main/r-cran-rsfar_0.0.1-1.ca2404.1_all.deb Size: 30052 MD5sum: 9c469ba4f0af022b990dda2353aa4627 SHA1: 8e74b1da031e17e002992bcbe82685a338fb1e94 SHA256: 4a9fee49c383d729fea525627053302dcbd1a5186bc836f49eebb8feb0665d59 SHA512: 6c971330f383b032b9c704778a7bdc096a6aeb1c9cda6dcacbde7152d5f9173459a30fae5449990562b6e95817051b7c2f2b33ef0757c37cf4ff3b387549f0aa Homepage: https://cran.r-project.org/package=Rsfar Description: CRAN Package 'Rsfar' (Seasonal Functional Autoregressive Models) This is a collection of functions designed for simulating, estimating and forecasting seasonal functional autoregressive time series of order one. These methods are addressed in the manuscript: . Package: r-cran-rsgf Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-png, r-cran-stringi, r-cran-stringr Filename: pool/dists/noble/main/r-cran-rsgf_1.0.0-1.ca2404.1_all.deb Size: 143298 MD5sum: ee8cd7f977c8ade1e5203907fd052da3 SHA1: 30dd6531fba007620b60073c4edfeb224b5c01dc SHA256: 26a586123164804589efef57151ff383b887dc076a4a4cd8f529fadd13fb75f7 SHA512: fde91bd6f816cd30be6ed1f6694c1a0f9ad88952afc0a62ce4208b6254b30bbf071832840cd35714ef4686462a04ddf11a830f044906f4dd71da7b760c62ee2f Homepage: https://cran.r-project.org/package=Rsgf Description: CRAN Package 'Rsgf' (SGF (Smart Game File) File Format Import) Import SGF (Smart Game File) into R. Package: r-cran-rshape Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-sn, r-cran-vgam, r-cran-evd, r-cran-rsqlite, r-cran-dbi, r-cran-foreach, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-rshape_0.3.2-1.ca2404.1_all.deb Size: 335054 MD5sum: 44bb4b04baa1b07a475c3debbfe43359 SHA1: a233cf2add50f3902ea0cffc25e7c66749365ae5 SHA256: 1435f78f6addb5cb76123c4e53b67a8699da3c4a61f5a8f11ca1b4360b9377cf SHA512: cf8ed910fe156b853ad2323c3a23d1600ae49536ecb3e44227e879b91c59a53891405e765033c69567140758aa3c2421c979146a84d17bd9e5a5a9c7bdd91b51 Homepage: https://cran.r-project.org/package=rSHAPE Description: CRAN Package 'rSHAPE' (Simulated Haploid Asexual Population Evolution) In silico experimental evolution offers a cost-and-time effective means to test evolutionary hypotheses. Existing evolutionary simulation tools focus on simulations in a limited experimental framework, and tend to report on only the results presumed of interest by the tools designer. The R-package for Simulated Haploid Asexual Population Evolution ('rSHAPE') addresses these concerns by implementing a robust simulation framework that outputs complete population demographic and genomic information for in silico evolving communities. Allowing more than 60 parameters to be specified, 'rSHAPE' simulates evolution across discrete time-steps for an evolving community of haploid asexual populations with binary state genomes. These settings are for the current state of 'rSHAPE' and future steps will be to increase the breadth of evolutionary conditions permitted. At present, most effort was placed into permitting varied growth models to be simulated (such as constant size, exponential growth, and logistic growth) as well as various fitness landscape models to reflect the evolutionary landscape (e.g.: Additive, House of Cards - Stuart Kauffman and Simon Levin (1987) , NK - Stuart A. Kauffman and Edward D. Weinberger (1989) , Rough Mount Fuji - Neidhart, Johannes and Szendro, Ivan G and Krug, Joachim (2014) ). This package includes numerous functions though users will only need defineSHAPE(), runSHAPE(), shapeExperiment() and summariseExperiment(). All other functions are called by these main functions and are likely only to be on interest for someone wishing to develop 'rSHAPE'. Simulation results will be stored in files which are exported to the directory referenced by the shape_workDir option (defaults to tempdir() but do change this by passing a folderpath argument for workDir when calling defineSHAPE() if you plan to make use of your results beyond your current session). 'rSHAPE' will generate numerous replicate simulations for your defined range of experimental parameters. The experiment will be built under the experimental working directory (i.e.: referenced by the option shape_workDir set using defineSHAPE() ) where individual replicate simulation results will be stored as well as processed results which I have made in an effort to facilitate analyses by automating collection and processing of the potentially thousands of files which will be created. On that note, 'rSHAPE' implements a robust and flexible framework with highly detailed output at the cost of computational efficiency and potentially requiring significant disk space (generally gigabytes but up to tera-bytes for very large simulation efforts). So, while 'rSHAPE' offers a single framework in which we can simulate evolution and directly compare the impacts of a wide range of parameters, it is not as quick to run as other in silico simulation tools which focus on a single scenario with limited output. There you have it, 'rSHAPE' offers you a less restrictive in silico evolutionary playground than other tools and I hope you enjoy testing your hypotheses. Package: r-cran-rsi Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1278 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future.apply, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-proceduralnames, r-cran-rlang, r-cran-rstac, r-cran-sf, r-cran-terra, r-cran-tibble Suggests: r-cran-curl, r-cran-knitr, r-cran-progressr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-rsi_0.3.3-1.ca2404.1_all.deb Size: 1200690 MD5sum: 001fb253eab467d61602800cb82fd395 SHA1: fecfd4e5f6094dbafcdfb1e88742d62da170e661 SHA256: e9cc4dc7d1c2dd5ea3d93e2a4669be2d3edaa04bcc0e6db3524943665eb6ae77 SHA512: 7f6b73b80d142d00c77648fd1d6328fbe9b1db7d670dd8729c4af384cce13e58dfdcf0b4fdf382b2e865f93f7a1c74a1d057c33642206302c2eb48043f3da30d Homepage: https://cran.r-project.org/package=rsi Description: CRAN Package 'rsi' (Efficiently Retrieve and Process Satellite Imagery) Downloads spatial data from spatiotemporal asset catalogs ('STAC'), computes standard spectral indices from the Awesome Spectral Indices project (Montero et al. (2023) ) against raster data, and glues the outputs together into predictor bricks. Methods focus on interoperability with the broader spatial ecosystem; function arguments and outputs use classes from 'sf' and 'terra', and data downloading functions support complex 'CQL2' queries using 'rstac'. Package: r-cran-rsimmosaic Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1216 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jpeg, r-cran-fields, r-cran-rann Filename: pool/dists/noble/main/r-cran-rsimmosaic_1.0.3-1.ca2404.1_all.deb Size: 1084634 MD5sum: f0dd1e85edf758f00319bc4e238adfc4 SHA1: 192f2f7fec098de79b2fcc42cd5f46ba938b5882 SHA256: 1a66cfb27fb4a633192766272b3d8ec5c434e7458fae0dd7df2d866328883a01 SHA512: d55f61846f19a924d7cf91081a002330c28ae0f4e86e5a89acd421a61175803d3fa5e84a4763ed5df4d26dab1cb2aa40cae3830df502c7482a8a3500fb4c78bb Homepage: https://cran.r-project.org/package=RsimMosaic Description: CRAN Package 'RsimMosaic' (R Simple Image Mosaic Creation Library) Provides a way to transform an image into a mosaic composed from a set of smaller images (tiles). It also contains a simple function for creating the tiles from a folder of images directly through R, without the need of any external code. At this moment only the JPEG format is supported, either as input (image and tiles) or output (mosaic transformed image). Package: r-cran-rsimsum Architecture: all Version: 0.13.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4549 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-generics, r-cran-ggplot2, r-cran-ggridges, r-cran-knitr, r-cran-lifecycle, r-cran-rlang, r-cran-scales Suggests: r-cran-covr, r-cran-devtools, r-cran-dplyr, r-cran-eha, r-cran-rmarkdown, r-cran-rstpm2, r-cran-survival, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-rsimsum_0.13.1-1.ca2404.1_all.deb Size: 3348496 MD5sum: 1f734880d2250ab4d57eca1c265b99ef SHA1: d1914543ebefb73b94c55aa3bd3f40c40d4f4e14 SHA256: 8c4212bc6ac20ae078fbfeb296fa14d93d7db700544a68c1d1edb57d136218c0 SHA512: 56965794ef34578dd0780faa40f605659e009c472a0f78e6bd6efb5a132a2e0d00c8bc4f9ee51ce8dae07164b5e167d7047d3b966ca550c039721cfd81c4d1d2 Homepage: https://cran.r-project.org/package=rsimsum Description: CRAN Package 'rsimsum' (Analysis of Simulation Studies Including Monte Carlo Error) Summarise results from simulation studies and compute Monte Carlo standard errors of commonly used summary statistics. This package is modelled on the 'simsum' user-written command in 'Stata' (White I.R., 2010 ), further extending it with additional performance measures and functionality. Package: r-cran-rsinaica Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rsinaica_1.1.1-1.ca2404.1_all.deb Size: 237648 MD5sum: 29768f378e39f04e72d26fb81bec43d7 SHA1: 141bb213fb97c2e40dd675ab45af81919b4ac511 SHA256: 6fa2ac515b95ada92fbed368655e12c4973cc39b2ff76058d3905f2ee5e5edce SHA512: 765dbef5585daadd851871315622a3714eaf7a759b2f636af8af1a421441384646e8a0447b05df02f61af911818edc01d3f7cc2ec2252d3d12d704a5a6ca5f8d Homepage: https://cran.r-project.org/package=rsinaica Description: CRAN Package 'rsinaica' (Download Data from Mexico's Air Quality Information System) Easy-to-use functions for downloading air quality data from the Mexican National Air Quality Information System (SINAICA). Allows you to query pollution and meteorological parameters from more than a hundred monitoring stations located throughout Mexico. See for more information. Package: r-cran-rsizebiased Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-rsizebiased_0.1.0-1.ca2404.1_all.deb Size: 61230 MD5sum: 57010ada485f91314af747817bdbbeb5 SHA1: 2d26185a5591fc4a510506ed71010362b898fd31 SHA256: eb08332945d73934b1c9b98ef8d40f7d82f46c81a2d4d160c5fa8a6f3556e246 SHA512: 3fd9c8c6d3810e627f0b30a3b415065da1bb68cad3d83e43ddbe69ff6085a03b4e32cbcb1fe2bad80f8aaad68d74249ed73617d31022c32f79e430ab58a0d053 Homepage: https://cran.r-project.org/package=RSizeBiased Description: CRAN Package 'RSizeBiased' (Hypothesis Testing Based on R-Size Biased Samples) Provides functions and examples for testing hypothesis about the population mean and variance on samples drawn by r-size biased sampling schemes. Package: r-cran-rskey Architecture: all Version: 0.4.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstudioapi, r-cran-berryfunctions Suggests: r-cran-knitr, r-cran-pillar Filename: pool/dists/noble/main/r-cran-rskey_0.4.19-1.ca2404.1_all.deb Size: 51790 MD5sum: 215e63eb20909194fc3de99993e3491f SHA1: 2900d1d743e26c84d18b283e50649e2fd5298166 SHA256: 54344918ed1843da8b45db6b50df649e63ca405ecce3c56145fcf8dc11406bfb SHA512: 11763fd13efdd55683088d7607d9c49a97296e544a3bcf5fe167e95ec45bb2f80ce604747f5c0cca2df252a1f27fc53c58d068a648cb5d4087ff4309d9e3373f Homepage: https://cran.r-project.org/package=rskey Description: CRAN Package 'rskey' (Create Custom 'Rstudio' Keyboard Shortcuts) Create custom keyboard shortcuts to examine code selected in the 'Rstudio' editor. F3 can for example yield 'str(selection)' and F7 open the source code of CRAN and base package functions on 'github'. Package: r-cran-rsleep Architecture: all Version: 1.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-dplyr, r-cran-edfreader, r-cran-ggplot2, r-cran-jsonlite, r-cran-psd, r-cran-signal, r-cran-xml2, r-cran-readr, r-cran-xts Suggests: r-cran-caret, r-cran-gridextra, r-cran-keras, r-cran-knitr, r-cran-reshape2, r-cran-rmarkdown, r-cran-testthat, r-cran-rsqlite, r-cran-dbi, r-cran-sleepcycles, r-cran-devtools Filename: pool/dists/noble/main/r-cran-rsleep_1.0.12-1.ca2404.1_all.deb Size: 276688 MD5sum: 303ccbd0d02fdb980a9a44db06b82e3b SHA1: afd37c05f84c37e2802f54c3a6b84c277ea85dd3 SHA256: 6e0fca5163f6221f3489da4419832ebb553f0e7b9ef373245486c608d773ca30 SHA512: aa51aff9c92142fdde1b368d702a60ae6e9a84737081bb461507dc02fac1e440d8d6c45573042f56959dd18793a8a6b1fa06f91f43738415db5b60e4089bd710 Homepage: https://cran.r-project.org/package=rsleep Description: CRAN Package 'rsleep' (Analysis of Sleep Data) A toolbox for sleep data processing, visualization and analysis. Tools for state of the art automatic sleep stages scoring. Package: r-cran-rslp Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-stringi, r-cran-plyr, r-cran-magrittr, r-cran-tokenizers Suggests: r-cran-dplyr, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-rslp_0.2.0-1.ca2404.1_all.deb Size: 39616 MD5sum: 4d5c934de03908dff919a93c42e3db6d SHA1: a240f3b76123cf3ee328e2af9f9ebffdb26c809d SHA256: 3a63c2c4fa054ce3ab741e4938b2a7f7bbb53d52340651d6fa70c9ce1dc759c7 SHA512: 822d0dc9ab0fd84060986f5576899d9cf8d4c9c20fde140bfea0ed61094a3d1979efb2808c20ce280b3d79d5d2ec4385bf8588b954699ebd46873eb06b4c5220 Homepage: https://cran.r-project.org/package=rslp Description: CRAN Package 'rslp' (A Stemming Algorithm for the Portuguese Language) Implements the "Stemming Algorithm for the Portuguese Language" . Package: r-cran-rslurm Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-whisker Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-rslurm_0.6.2-1.ca2404.1_all.deb Size: 155736 MD5sum: d8096e5146a4aabcaee520f9ee5cb19a SHA1: 65908a565aa8ecbaa0e55304b39eaa5d6a18965c SHA256: 6f7dbc5833f136db944d0083adc1622a83572ff168050804e344a962e63d3b3b SHA512: e881f094beb1a31c6f0caa11293835f3b483df6ef17f75ef1802926f025f3cbaa0a522dbd2c309c086715e1b4893fbb0a514dedd132a51a286984cca69006cd0 Homepage: https://cran.r-project.org/package=rslurm Description: CRAN Package 'rslurm' (Submit R Calculations to a 'Slurm' Cluster) Functions that simplify submitting R scripts to a 'Slurm' workload manager, in part by automating the division of embarrassingly parallel calculations across cluster nodes. Package: r-cran-rsm Architecture: all Version: 2.10.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1291 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-estimability Suggests: r-cran-emmeans, r-cran-vdg, r-cran-conf.design, r-cran-doe.base, r-cran-frf2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-rsm_2.10.6-1.ca2404.1_all.deb Size: 860532 MD5sum: 9a7359917bb986b4818520d9b1383e7c SHA1: 1acd1d8b680234ea248b7594ea02df700ef22021 SHA256: 92cd8fa4489d4b80804471faf293dfd415c7b4dca881d9ec36fd9863f512ab21 SHA512: 331becaa0c59cc457d597e977f805fd3d9845e122eb61d10dc3afd1f8dd63fa2c5cc785aa9a81eac21a65c7b04ecfa551ffc5bf6cb0281465d178fa3f92f2a01 Homepage: https://cran.r-project.org/package=rsm Description: CRAN Package 'rsm' (Response-Surface Analysis) Provides functions to generate response-surface designs, fit first- and second-order response-surface models, make surface plots, obtain the path of steepest ascent, and do canonical analysis. A good reference on these methods is Chapter 10 of Wu, C-F J and Hamada, M (2009) "Experiments: Planning, Analysis, and Parameter Design Optimization" ISBN 978-0-471-69946-0. An early version of the package is documented in Journal of Statistical Software . 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For more information on small telescopes analysis see Uri Simonsohn (2015) . Package: r-cran-rsmartlyio Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl Filename: pool/dists/noble/main/r-cran-rsmartlyio_0.1.3-1.ca2404.1_all.deb Size: 15388 MD5sum: b6f389467f476511d2334feaa8026c42 SHA1: 560cdb12bf8ee038d1c3e178700314df3a8bfd51 SHA256: a22e78dceb12e01142478d13583df15e927fda1ee19150cc21656dc160ceef19 SHA512: 9822d11a041699a561b0cff7b00841954f65f7001cd3ba3dfe6343484cc752e3b570883dfd1b03237c6cdee4bdb4475956d18ea5f780006983324a30ea2523b5 Homepage: https://cran.r-project.org/package=RSmartlyIO Description: CRAN Package 'RSmartlyIO' (Loading Facebook and Instagram Advertising Data from'Smartly.io') Aims at loading Facebook and Instagram advertising data from 'Smartly.io' into R. 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(2013) Introduction to statistical quality control. Hoboken, NJ: Wiley.). Typical workflow is taking the time series, specify the control limits, and list of Nelson rules you want to evaluate. There are several options how to modify the rules (one sided limits, numerical parameters of rules, etc.). Package is also capable of calculating the control limits from the data (so far only for i-chart and c-chart are implemented). Package: r-cran-rspde Architecture: all Version: 2.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4553 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-fmesher, r-cran-lifecycle, r-cran-broom Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-lattice, r-cran-splancs, r-cran-optimparallel, r-cran-rspectra, r-cran-numderiv, r-cran-inlabru, r-cran-sn, r-cran-viridis, r-cran-doparallel, r-cran-foreach, r-cran-tidyr, r-cran-dplyr, r-cran-generalizedhyperbolic, r-cran-gridextra, r-cran-metricgraph, r-cran-sf Filename: pool/dists/noble/main/r-cran-rspde_2.5.2-1.ca2404.1_all.deb Size: 4457464 MD5sum: 545bfdd8c5be86c65a216120391d0b2a SHA1: 574068fc4e7c3116460c955683704151f39d197e SHA256: ab925e8f3ce4b56bdb88ac1ce507ce739d665cfc0bb8bccb3026f20dfa6b574a SHA512: 767c78a3bdb77df857a33f61b25cb652a36a50025b39f6d9db98f9ef61d3b611035e409bfcd2b4f14d43adfa75ca96eac810d4c5c48583bb3207bcb325451fdd Homepage: https://cran.r-project.org/package=rSPDE Description: CRAN Package 'rSPDE' (Rational Approximations of Fractional Stochastic PartialDifferential Equations) Functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) for the covariance-based method and Bolin and Kirchner (2020) for the operator-based rational approximation. These can be generated by the citation function in R. Package: r-cran-rspincalc Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rspincalc_1.0.2-1.ca2404.1_all.deb Size: 148642 MD5sum: 01b69f6867faa1b1e548019bf43f24d6 SHA1: 1a4f1817f261e33d3c794cfd1f45a1e987a32142 SHA256: ffc505f139adf5cf87d7166d018847c546305a43cad96800aa610c0c711dc5db SHA512: 22523465a8edac046087150c5c446b3092bb9aed0169b8bc18c2a1f7d61d73308081e7c6c038745ed5dfdc83c5b0400f824b581c431697f89520f8b5c8b94902 Homepage: https://cran.r-project.org/package=RSpincalc Description: CRAN Package 'RSpincalc' (Conversion Between Attitude Representations of DCM, EulerAngles, Quaternions, and Euler Vectors) Conversion between attitude representations: DCM, Euler angles, Quaternions, and Euler vectors. Plus conversion between 2 Euler angle set types (xyx, yzy, zxz, xzx, yxy, zyz, xyz, yzx, zxy, xzy, yxz, zyx). 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Package: r-cran-rspiro Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rspiro_0.5-1.ca2404.1_all.deb Size: 314086 MD5sum: 05b5f5384e5c0904651a727fb5425b45 SHA1: 825b5f772c64f259618fd2e60b0a0c3478fc8000 SHA256: f60e6e5d78b1be0fa544755dc3acbc593cbf523b083b0a5b95fa8d816e6a4956 SHA512: c44e5288e7d3088793e679194c2dca38ac69b25150b3ce0f136630014f14b160a69a14b8a8cd158bc2faa2b3cb9776595dad0f0d9d951bedd36f865b2e512eb1 Homepage: https://cran.r-project.org/package=rspiro Description: CRAN Package 'rspiro' (Implementation of Spirometry Equations) Implementation of various spirometry equations in R, currently the GLI-2012 (Global Lung Initiative; Quanjer et al. 2012 ), the race-neutral GLI global 2022 (Global Lung Initiative; Bowerman et al. 2023 ), the NHANES3 (National Health and Nutrition Examination Survey; Hankinson et al. 1999 ) and the JRS 2014 (Japanese Respiratory Society; Kubota et al. 2014 ) equations. Also the GLI-2017 diffusing capacity equations are implemented. Contains user-friendly functions to calculate predicted and LLN (Lower Limit of Normal) values for different spirometric parameters such as FEV1 (Forced Expiratory Volume in 1 second), FVC (Forced Vital Capacity), etc, and to convert absolute spirometry measurements to percent (%) predicted and z-scores. 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Provides functions to convert between 'TextMate' and Visual Studio Code or Positron themes, as well as ports of several Visual Studio Code themes. Package: r-cran-rsubgroup Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2982 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjava, r-cran-foreign Filename: pool/dists/noble/main/r-cran-rsubgroup_1.1-1.ca2404.1_all.deb Size: 2657702 MD5sum: ce2c3e86f597690713f54db379d8f528 SHA1: b83753214561873f2b26f0eaa526a883daadd94a SHA256: 17b19e6a7eb54f7b1e9229c37c5b333433c7452cd63f3ad901d12e26b5383648 SHA512: c587a368845fb7ebb384eb16003f37adca1fdd7131ad429f527f7388fee62abfe7c3da312d4ac68fd31723f7e48b20a17f08e3dab3c562516fc85ede3ad1f8da Homepage: https://cran.r-project.org/package=rsubgroup Description: CRAN Package 'rsubgroup' (Subgroup Discovery and Analytics) A collection of efficient and effective tools and algorithms for subgroup discovery and analytics. The package integrates an R interface to the org.vikamine.kernel library of the VIKAMINE system implementing subgroup discovery, pattern mining and analytics in Java. Package: r-cran-rsurface Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plotly, r-cran-rsm Filename: pool/dists/noble/main/r-cran-rsurface_1.1.0-1.ca2404.1_all.deb Size: 31874 MD5sum: 48bc5ec25985739e787fc891855facc9 SHA1: 2071ac0c06e5bba817e55d23e6f6888322e36fc5 SHA256: 1d89a9b63ecc34706ecd8e8e8d145df2b0e7a18c04cdb1bc6f448cb48faa0e4f SHA512: 2125a619ddc9f7ab5f399b726c294222caea1e2d164b8fd0ec96db60f7c7f2046efee626a214081c704020f43d6b9a94e62032dfd26ead7a759ea287edecd83f Homepage: https://cran.r-project.org/package=rsurface Description: CRAN Package 'rsurface' (Design of Rotatable Central Composite Experiments and ResponseSurface Analysis) Produces tables with the level of replication (number of replicates) and the experimental uncoded values of the quantitative factors to be used for rotatable Central Composite Design (CCD) experimentation and a 2-D contour plot of the corresponding variance of the predicted response according to Mead et al. (2012) design_ccd(), and analyzes CCD data with response surface methodology ccd_analysis(). A rotatable CCD provides values of the variance of the predicted response that are concentrically distributed around the average treatment combination used in the experimentation, which with uniform precision (implied by the use of several replicates at the average treatment combination) improves greatly the search and finding of an optimum response. These properties of a rotatable CCD represent undeniable advantages over the classical factorial design, as discussed by Panneton et al. (1999) and Mead et al. (2012) among others. 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The package 'rsurv' also stands out by its ability to generate survival data from an unlimited number of baseline distributions provided that an implementation of the quantile function of the chosen baseline distribution is available in R. Another nice feature of the package 'rsurv' lies in the fact that linear predictors are specified via a formula-based approach, facilitating the inclusion of categorical variables and interaction terms. The functions implemented in the package 'rsurv' can also be employed to simulate survival data with more complex structures, such as survival data with different types of censoring mechanisms, survival data with cure fraction, survival data with random effects (frailties), multivariate survival data, and competing risks survival data. Details about the R package 'rsurv' can be found in Demarqui (2024) . Package: r-cran-rsurveillance Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-epitools, r-cran-epir, r-cran-mc2d Filename: pool/dists/noble/main/r-cran-rsurveillance_0.2.1-1.ca2404.1_all.deb Size: 173338 MD5sum: 26cf3705380a29eb79b93f3b4b51bdd2 SHA1: 05cd038847e8c6f0148104da78187bccc7cdac19 SHA256: d431c524d831d0d0a3d43a61557f443201abf64fd5ff7fe885fa52486c3f0289 SHA512: edb27d97ceb79316dfc09fe88f694afe2938962cf7fb7c2b195c36739bae29d22c9b51fd7d82834a0268531e5aa5ad7e33d7985e9bb8228728de45c4b4445087 Homepage: https://cran.r-project.org/package=RSurveillance Description: CRAN Package 'RSurveillance' (Design and Analysis of Disease Surveillance Activities) A range of functions for the design and analysis of disease surveillance activities. These functions were originally developed for animal health surveillance activities but can be equally applied to aquatic animal, wildlife, plant and human health surveillance activities. Utilities are included for sample size calculation and analysis of representative surveys for disease freedom, risk-based studies for disease freedom and for prevalence estimation. This package is based on Cameron A., Conraths F., Frohlich A., Schauer B., Schulz K., Sergeant E., Sonnenburg J., Staubach C. (2015). R package of functions for risk-based surveillance. Deliverable 6.24, WP 6 - Decision making tools for implementing risk-based surveillance, Grant Number no. 310806, RISKSUR (). Many of the 'RSurveillance' functions are incorporated into the 'epitools' website: Sergeant, ESG, 2019. Epitools epidemiological calculators. Ausvet Pty Ltd. Available at: . 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Package: r-cran-rtables Architecture: all Version: 0.6.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8896 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formatters, r-cran-magrittr, r-cran-checkmate, r-cran-htmltools, r-cran-lifecycle, r-cran-stringi Suggests: r-cran-broom, r-cran-car, r-cran-dplyr, r-cran-knitr, r-cran-lme4, r-cran-r2rtf, r-cran-rmarkdown, r-cran-survival, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-withr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-rtables_0.6.16-1.ca2404.1_all.deb Size: 2362386 MD5sum: 41c266d2947d587f4a514cc31b07a8eb SHA1: 64bbceefdfca54e4c26ed647041c855424396472 SHA256: 9874692553b45d67831dda95aa57671a40f58c34e5b283c0779d37f0fe64190f SHA512: 3f4951b5c707ac0c79b8305795479d4eb9d011b735a1794efc781e61e2631397f4631653d5634b0d881b362ee0be838071d254f83225fc929cb67996f358d061 Homepage: https://cran.r-project.org/package=rtables Description: CRAN Package 'rtables' (Reporting Tables) Reporting tables often have structure that goes beyond simple rectangular data. 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Package: r-cran-rtabulator Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 958 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-htmltools, r-cran-htmlwidgets, r-cran-purrr, r-cran-readr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-rtabulator_0.1.2-1.ca2404.1_all.deb Size: 216556 MD5sum: 5613f519c2b628c470af9194f2777cde SHA1: bf83a5571bc53c821fd1acf0a29b7106c92d3373 SHA256: 2876de08588cd537e21b5c53c459816a39b70ce0fe8326de593f82d05fd7cab8 SHA512: 1c8d70afae6ff7ac6c17e2e772673aa344fd86550bacc1fbb2aee761961f9fdd25d81f59d2d6b056c45da79bfe4b2f799f14f72055bcb57d439c37cbef95a172 Homepage: https://cran.r-project.org/package=rtabulator Description: CRAN Package 'rtabulator' (R Bindings for 'Tabulator JS') Provides R bindings for 'Tabulator JS' . Makes it a breeze to create highly customizable interactive tables in 'rmarkdown' documents and 'shiny' applications. 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Package: r-cran-rtapas Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1602 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-phytools, r-cran-ape, r-cran-distory, r-cran-paco, r-cran-parallelly, r-cran-stringr, r-cran-vegan Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rtapas_1.2-1.ca2404.1_all.deb Size: 1594830 MD5sum: 7a6f31abdbd3da8132d52b8a9bb261d6 SHA1: b8b572692388505df2152860ac268f142413d40d SHA256: 4a24dadd05978f26553f635322077b6ac27aefc6b805a983520596c00a8cb3a5 SHA512: f2a04e2ce027bdcf1a3581819b6e09f5148cad7ff087e314ec5dce9b27ddeb29f45ccee52b032522d3c249736291b058915a92d891b1baf07c0ab58af1fa7500 Homepage: https://cran.r-project.org/package=Rtapas Description: CRAN Package 'Rtapas' (Random Tanglegram Partitions) Applies a given global-fit method to random partial tanglegrams of a fixed size to identify the associations, terminals, and nodes that maximize phylogenetic (in)congruence. It also includes functions to compute more easily the confidence intervals of classification metrics and plot results, reducing computational time. See Llaberia-Robledillo et al., (2023) . Package: r-cran-rtape Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rtape_2.2-1.ca2404.1_all.deb Size: 35076 MD5sum: 6eb6958af41eb5d192d8ef0554601c5f SHA1: 78019e1b938703bad76836edae9b6ffde91397fc SHA256: 3516dba65a41607a545195c7ebfc679364f1663c54884c499a6b73e1d7025b6b SHA512: d8e08b8f59a609e5a2ef7d5c77b1f4d3d04476623996ca6c9040a0bd1bdf7f846b8d9b4681334d957ba11765bbc39c5f3ae56309cf82cec995b824e0acbff1eb Homepage: https://cran.r-project.org/package=rtape Description: CRAN Package 'rtape' (Manage and manipulate large collections of R objects stored astape-like files) Storing huge data in RData format causes problems because of the necessity to load the whole file to the memory in order to access and manipulate objects inside such file; rtape is a simple solution to this problem. 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A discrete Markov chain that approximates in the sense of weak convergence a continuous-valued univariate Autoregressive process of first order is generated. It is a popular method used in economics and in finance. Package: r-cran-rtaxometrics Architecture: all Version: 3.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-rtaxometrics_3.2.1-1.ca2404.1_all.deb Size: 181754 MD5sum: b4c9b2b1b877bfaef5d2f1ca74fe5c1c SHA1: c5abccaa3a2f7e36c69b8597517a1e7bf0e6d837 SHA256: 179b47c8db5c5366b8ada84970a5f4153b8e31a6536afcd2e51dc91d9d2117f1 SHA512: 1df911d35c3578a98a92ac70441d3950257ca826d996fc7268811faff076e871a0dd1f0c5394d12aecd67fcd76d215c45c58e81e54c9f7002930254669fdc41b Homepage: https://cran.r-project.org/package=RTaxometrics Description: CRAN Package 'RTaxometrics' (Taxometric Analysis) We provide functions to perform taxometric analyses. This package contains 46 functions, but only 5 should be called directly by users. CheckData() should be run prior to any taxometric analysis to ensure that the data are appropriate for taxometric analysis. RunTaxometrics() performs taxometric analyses for a sample of data. RunCCFIProfile() performs a series of taxometric analyses to generate a CCFI profile. CreateData() generates a sample of categorical or dimensional data. ClassifyCases() assigns cases to groups using the base-rate classification method. 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Package: r-cran-rtemis.a3 Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-rtemis.core, r-cran-s7 Suggests: r-bioc-biomart, r-cran-httr, r-cran-jsonlite, r-cran-seqinr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rtemis.a3_0.5.3-1.ca2404.1_all.deb Size: 140622 MD5sum: 62a21b1f760d1931b731522e29efd657 SHA1: 247df5ad0cb813ec0a5f559371d157ba85be661d SHA256: 0c89e429353e174a0d0a4c275c010983f64bb4b1732e34305b8d486abcbe7ae0 SHA512: b21398bf0b698e2cfaab8bc37a539a37f765e6e83873941fb6a586e42e38a151dafdf23639f621eb5749c4a1fc486d1d2c9e0553640ebf911b7da79d063c1ddb Homepage: https://cran.r-project.org/package=rtemis.a3 Description: CRAN Package 'rtemis.a3' (Amino Acid Annotation (A3) Format) Implements the Amino Acid Annotation (A3) format using 'S7' classes. 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Package: r-cran-rtemis.core Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-s7 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rtemis.core_0.1.0-1.ca2404.1_all.deb Size: 277028 MD5sum: 5a7bb51b36e94d25b88350b680ec7f5d SHA1: 5b3bf53ff150b295307214738bdd8a66a19a9e2e SHA256: 1a612b72e80b0c346dea855df8c2a1fdb7ea01793715b7ebb634d897c21613b9 SHA512: 9cb076bc8a4145aebf72b2f9a05abc613e2325baabca4ac37f1ba31cbe0ea63573bb2fa24ca28b6a8d4c19bf84a8269990dc3897350393dd2bcd7cb06cea88fb Homepage: https://cran.r-project.org/package=rtemis.core Description: CRAN Package 'rtemis.core' (Core Utilities for the 'rtemis' Ecosystem) Utilities used across packages of the 'rtemis' ecosystem. Includes the msg() messaging system and the fmt() formatting system. 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Package: r-cran-rtemis.llm Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-digest, r-cran-httr2, r-cran-jsonlite, r-cran-rtemis.core, r-cran-s7 Suggests: r-cran-keyring, r-cran-testthat, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-rtemis.llm_0.8.1-1.ca2404.1_all.deb Size: 515950 MD5sum: 6f7044f76cdecbb4588e0a42c8fb9a9c SHA1: 3fb1e4ae921f8f73118027b5a166b7c0592de660 SHA256: 4748c1b9abc459f6255601dc079ef011865975830850414948a743740e462c3d SHA512: 12497c4de2d4ab9a99a3a31bcc2611ec96648e6ca0ae97502d7b3014b9a9c91881c43c60fc2ec30888d8a5251a9ecb27378b273807e6fa65828518a167bc5aa2 Homepage: https://cran.r-project.org/package=rtemis.llm Description: CRAN Package 'rtemis.llm' (Large Language Models and Agentic AI) Unified interface for creating LLM and Agent objects, generating responses and performing batch inference based on a type-checked and validated 'S7' backend. Features reasoning, structured output, memory management, and tool use. Supports 'Ollama' , 'OpenAI'-compatible , and 'Anthropic'-compatible endpoints. Package: r-cran-rtemis Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3322 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-s7, r-cran-data.table, r-cran-future, r-cran-htmltools, r-cran-cli Suggests: r-cran-arrow, r-cran-bit64, r-cran-car, r-cran-colorspace, r-cran-dbi, r-cran-dbscan, r-cran-dendextend, r-cran-duckdb, r-cran-e1071, r-cran-farff, r-cran-fastica, r-cran-flexclust, r-cran-future.apply, r-cran-future.mirai, r-cran-futurize, r-cran-geosphere, r-cran-ggplot2, r-cran-glmnet, r-cran-geojsonio, r-cran-glue, r-cran-gsubfn, r-cran-haven, r-cran-heatmaply, r-cran-htmlwidgets, r-cran-igraph, r-cran-jsonlite, r-cran-leaflet, r-cran-leaps, r-cran-lightauc, r-cran-lightgbm, r-cran-matrixstats, r-cran-mgcv, r-cran-mice, r-cran-mirai, r-cran-missranger, r-cran-networkd3, r-cran-nmf, r-cran-openxlsx, r-cran-parallelly, r-cran-partykit, r-cran-plotly, r-cran-proc, r-cran-progressr, r-cran-psych, r-cran-pvclust, r-cran-ranger, r-cran-reactable, r-cran-readxl, r-cran-reticulate, r-cran-rocr, r-cran-rpart, r-cran-rtsne, r-cran-seqinr, r-cran-sf, r-cran-shapr, r-cran-survival, r-cran-tabnet, r-cran-threejs, r-cran-testthat, r-cran-tibble, r-cran-timedate, r-cran-toml, r-cran-torch, r-cran-uwot, r-cran-vegan, r-cran-vroom, r-cran-withr Filename: pool/dists/noble/main/r-cran-rtemis_1.0.0-1.ca2404.1_all.deb Size: 3116084 MD5sum: c307df30072b8af67522b5052ea4b553 SHA1: 88ac29ce91da73b802bfcfe8b1008ce2047259ba SHA256: 10f97c10f14aa28216be63ebff39eadce8f00382a337129fab7fe58adebd26ee SHA512: 0e5d1722b533552eb043de27a150b6ec403c331fcf8787a7e67b4e861b465afb273e4291de12e6f85e6d6312e3ed3857ef32ace613c3ec2eadd6a0e199ddd327 Homepage: https://cran.r-project.org/package=rtemis Description: CRAN Package 'rtemis' (Machine Learning and Visualization) Machine learning and visualization package with an 'S7' backend featuring comprehensive type checking and validation, paired with an efficient functional user-facing API. train(), cluster(), and decomp() provide one-call access to supervised and unsupervised learning. All configuration steps are performed using setup functions and validated. A single call to train() handles preprocessing, hyperparameter tuning, and testing with nested resampling. Supports 'data.frame', 'data.table', and 'tibble' inputs, parallel execution, and interactive visualizations. The package first appeared in E.D. Gennatas (2017) . Package: r-cran-rtemps Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-dt, r-cran-ggplot2, r-cran-xfun Filename: pool/dists/noble/main/r-cran-rtemps_0.8.0-1.ca2404.1_all.deb Size: 38678 MD5sum: 7e558702fddad768f5a831cddd57865c SHA1: 0cd8ea4514e96e34d7204f81974fad589f89726f SHA256: 58a145af628d7139a0af31db3e5f3b4b86d5175157b1db5e4954f9141767d79e SHA512: fefd7414393aa8acfbc68f349ba8cba06d06e6b51de5da42efd6a23f24b8d77a4b216b1de430b8fd8867e4dcfe9929520955c0ca09ef42cc1a3ba397bfc18ef2 Homepage: https://cran.r-project.org/package=rtemps Description: CRAN Package 'rtemps' (R Templates for Reproducible Data Analyses) A collection of R Markdown templates for nicely structured, reproducible data analyses in R. 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It provides tools for using a set of models to investigate temporal processes in bivariate (e.g., dyadic) systems. The general approach is to model, one dyad at a time, the dynamics of a variable that is assessed repeatedly from both partners, extract the parameter estimates for each dyad, and then use those parameter estimates as input to a latent profile analysis to extract groups of dyads with qualitatively distinct dynamics. Finally, the profile memberships can be used to either predict, or be predicted by, another variable of interest. Currently, 2 models are supported: 1) inertia-coordination, and 2) a coupled-oscillator. Extended documentation is provided in vignettes. Theoretical background can be found in Butler (2011) and Butler & Barnard (2019) . 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Package: r-cran-rtmpinv Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rclsp Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-rtmpinv_1.0.0-1.ca2404.1_all.deb Size: 40548 MD5sum: da24c862b2cbe2e73135c4947a5f3488 SHA1: 9129f3043a86305b3c8311a8094cb083ef6e77f1 SHA256: 742e179c5f53397d375f220c127d3e240e13a5a714c540f5376a3b3c4079d13e SHA512: 5fc8d7965a2d243567d0401aeab1c68562f36ab3d37ec2305d6a2b76275955a01dfd4d3835bbffc60a64d77ef925a06d4335b7c291e54c565e4e472208ba5fc2 Homepage: https://cran.r-project.org/package=rtmpinv Description: CRAN Package 'rtmpinv' (Tabular Matrix Problems via Pseudoinverse Estimation) The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv) is a two-stage estimation method that reformulates structured table-based systems - such as allocation problems, transaction matrices, and input-output tables - as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, TMPinv solves systems with row and column constraints, block structure, and optionally reduced dimensionality by (1) constructing a canonical constraint form and applying a pseudoinverse-based projection, followed by (2) a convex-programming refinement stage to improve fit, coherence, and regularization (e.g., via Lasso, Ridge, or Elastic Net). 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The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv) is a two-stage estimation method that reformulates structured table-based systems - such as allocation problems, transaction matrices, and input-output tables - as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, TMPinv solves systems with row and column constraints, block structure, and optionally reduced dimensionality by (1) constructing a canonical constraint form and applying a pseudoinverse-based projection, followed by (2) a convex-programming refinement stage to improve fit, coherence, and regularization (e.g., via Lasso, Ridge, or Elastic Net). 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It contains an implementation of the Transformed-Stationary (TS) methodology for non-stationary extreme value Analysis (EVA) as described in Mentaschi et al. (2016) . In synthesis this approach consists in: (i) transforming a non-stationary time series into a stationary one to which the stationary extreme value theory can be applied; and (ii) reverse-transforming the result into a non-stationary extreme value distribution. 'RtsEva' offers several options for trend estimation (mean, extremes, seasonal) and contains multiple plotting functions displaying different aspects of the non-stationarity of extremes. 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Package: r-cran-rucm Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kfas Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-rucm_0.6-1.ca2404.1_all.deb Size: 42812 MD5sum: 996268a67510f4f15cfee98a5b50cc41 SHA1: 2a5cc80a08956cddb409262d94e2d43019abb240 SHA256: ec7eb5d0ef6755347bb6d11038325d0f3307349200efa086783d0325c07ff6aa SHA512: 62c3764c6e65e959342ca6bd2582d3725bc115c2b9e78fd8f200135121df0c5ff6068c4d99a233a094599d7b13b89d079f60e1f6373388004b436ec10aa821ba Homepage: https://cran.r-project.org/package=rucm Description: CRAN Package 'rucm' (Implementation of Unobserved Components Model (UCM)) Unobserved Components Models (introduced in Harvey, A. 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The functions were design to accommodate a variety of sampling designs. Users can tailor calculations by specifying spectrogram time bin size, amplitude thresholds and normality tests. By simplifying computation and standardizing reproducible methods, the package aims to support ecoacoustics studies. For more details about the indices read Towsey (2017) and Burivalova (2017) . Package: r-cran-ruijter Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Filename: pool/dists/noble/main/r-cran-ruijter_0.1.3-1.ca2404.1_all.deb Size: 2464142 MD5sum: 9f2be65bd9487fb0bc6c4fbe4eb6a8b6 SHA1: 14d7bda656cfb6df4c8fef5f44ca8344460897e8 SHA256: c9dac15c6a7a5796d920d3408e2012355d87661e0213e2cdbe819197e1bac485 SHA512: 6dd6a287fb6f4fd7576ff1c8a9d79b5adfb23dfe4519191bfd528bea2aa2d78ca0d6c83caa11208e98a35c4d2269b108763aea2f5d228cc4617852ab3e4a82ee Homepage: https://cran.r-project.org/package=ruijter Description: CRAN Package 'ruijter' (Technical Data Sets by Ruijter et al. (2013)) The real-time quantitative polymerase chain reaction (qPCR) technical data sets by Ruijter et al. (2013) : (i) the four-point 10-fold dilution series; (ii) 380 replicates; and (iii) the competimer data set. These three data sets can be used to benchmark qPCR methods. Original data set is available at . This package fixes incorrect annotations in the original data sets. Package: r-cran-ruin Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-actuar, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ruin_0.1.1-1.ca2404.1_all.deb Size: 123422 MD5sum: 1e22b5b09d68f564602fcf82e43f066c SHA1: 3b49cf2da019fd7e0c52c4ad7243c596f851099c SHA256: 977d1b8bf5362620425320016ccb087dffdfa1a3f8ae8baf95cbfa352c655da4 SHA512: 859705d69f9c9dfee4023ac839cf1ad540986eb6ffa63a2848805338ceac9eec363a4fdb11eb6f29fcf235061ffc191803a93962936974d299f1391db097d552 Homepage: https://cran.r-project.org/package=ruin Description: CRAN Package 'ruin' (Simulation of Various Risk Processes) A (not yet exhaustive) collection of common models of risk processes in actuarial science, represented as formal S4 classes. Each class (risk model) has a simulator of its path, and a plotting function. 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Package: r-cran-rusk Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-reshape2, r-cran-shiny, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-rusk_0.1.1-1.ca2404.1_all.deb Size: 23880 MD5sum: a3aa9a99ea73c174112e951166f4dc8e SHA1: 45d4461663fe124f59de746ea4439107c116cc2f SHA256: 2b033513e92614f9dca808bbd5915679579babc7b401dfda91a0eac66730d162 SHA512: 3fbfea4a7de2d064c1f7cb2cfc189d0f12b09e5ec06b3f18343da13fb9cdbdc58fbfe00e5411ee9ef61ed86103aa32a286649d052f3b51911cc4cb12fe5937fe Homepage: https://cran.r-project.org/package=rusk Description: CRAN Package 'rusk' (Beautiful Graphical Representation of Multiplication Tables on aModular Circle) By placing on a circle 10 points numbered from 1 to 10, and connecting them by a straight line to the point corresponding to its multiplication by 2. (1 must be connected to 1 * 2 = 2, point 2 must be set to 2 * 2 = 4, point 3 to 3 * 2 = 6 and so on). You will obtain an amazing geometric figure that complicates and beautifies itself by varying the number of points and the multiplication table you use. Package: r-cran-rusquant Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantmod, r-cran-data.table, r-cran-jsonlite, r-cran-httr, r-cran-xts, r-cran-xml, r-cran-stringr, r-cran-jose, r-cran-rvest, r-cran-base64enc Filename: pool/dists/noble/main/r-cran-rusquant_1.1.4-1.ca2404.1_all.deb Size: 201194 MD5sum: f9bf4396bb593bbe5d7933561b59629f SHA1: 669933db3b5aaa481918bbc791110c61eaf30743 SHA256: 50d8432df1aab93d6b173a3c57c77bc6215f3a7785a138bf6c4d56c37bbd3921 SHA512: c94d531b90555362b0e70dfb233a56bfaee4ca54367cce4bc337497c686d45d9e20af6225da11288923d61ed689df4d87cd08de4f009c6fb8493c1f56044f380 Homepage: https://cran.r-project.org/package=rusquant Description: CRAN Package 'rusquant' (Quantitative Trading Framework) Collection of functions to retrieve financial data from various sources, including brokerage and exchange platforms, financial websites, and data providers. 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The library is written in C++ and supports 'CUDA', 'OpenCL', and 'OpenMP' (including switches at runtime). I have placed these libraries in this package as a more efficient distribution system for CRAN. The idea is that you can write a package that depends on the 'ViennaCL' library and yet you do not need to distribute a copy of this code with your package. 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Package: r-cran-rvif Architecture: all Version: 3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3630 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-multicoll, r-cran-car Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-rvif_3.2-1.ca2404.1_all.deb Size: 1271738 MD5sum: e36e170f26986df14a06f05ef05eb097 SHA1: a05e162b1d590204a81bac802f21b0884226db29 SHA256: 4c476418425bad7d7ee16d203e38c233456cf011b8b88e1f263284ca2a49d66f SHA512: abbc4ac5f0e1df586955bcff204638309faf5b8b7509ca980b924b0b0e7ecb8e4898869f392b9cef81ca77c3248fc4e8cc0b7c36b556a27034a82d6a9c2a9f83 Homepage: https://cran.r-project.org/package=rvif Description: CRAN Package 'rvif' (Collinearity Detection using Redefined Variance Inflation Factorand Graphical Methods) The detection of troubling approximate collinearity in a multiple linear regression model is a classical problem in Econometrics. This package is focused on determining whether or not the degree of approximate multicollinearity in a multiple linear regression model is of concern, meaning that it affects the statistical analysis (i.e. individual significance tests) of the model. This objective is achieved by using the variance inflation factor redefined and the scatterplot between the variance inflation factor and the coefficient of variation. For more details see Salmerón R., García C.B. and García J. (2018) , Salmerón, R., Rodríguez, A. and García C. (2020) , Salmerón, R., García, C.B, Rodríguez, A. and García, C. (2022) , Salmerón, R., García, C.B. and García, J. (2025) and Salmerón, R., García, C.B, García J. (2023, working paper) . You can also view the package vignette using 'browseVignettes("rvif")', the package website () using 'browseURL(system.file("docs/index.html", package = "rvif"))' or version control on GitHub (). 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The overall approach consists of four sequential steps, each of which has a function in this package: (1) estimate the template spectrum with the function estimate_template(), (2) find absorption features in the estimated template with the function findabsorptionfeatures(), (3) fit Gaussians to the absorption features with the function Gaussfit(), (4) apply the HGRV with simple linear regression by calling the function hgrv(). This package is meant to be open source. But please cite the paper Holzer et al. (2020) when publishing results that use this package. 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On all platforms the package first honours a user-supplied 'VTK_DIR' environment variable. On macOS it then tries 'Homebrew', followed by 'pkg-config'. On Linux it tries 'pkg-config' and well-known system prefixes ('/usr', '/usr/local'). If no suitable system installation is found on macOS or Linux, pre-built static libraries are downloaded automatically from the package's GitHub releases. On Windows the package tries 'VTK_DIR', then 'Rtools45' 'pacman', then common 'MSYS2' prefixes, accepting both static ('.a') and shared ('.dll.a' import libs + DLLs) installations. When shared libraries are used, the VTK DLLs are staged in 'inst/vtk-dlls/' and an '.onLoad' hook prepends that directory to PATH via 'Sys.setenv()' when the package is loaded, and restored in '.onUnload()'. The pre-built fallback downloads static libraries by default; set 'VTK_LINK_TYPE=shared' before installation to download the DLL build instead. 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Visualizing MCMC Convergence in Phylogenetics) Implements various tests, visualizations, and metrics for diagnosing convergence of MCMC chains in phylogenetics. It implements and automates many of the functions of the AWTY package in the R environment, as well as a host of other functions. Warren, Geneva, and Lanfear (2017), . 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While all methods focus on Maximum Likelihood estimation of unknown true effects under normal distribution-theory, some estimates are modified to be Unbiased or to have "Correct Range" when estimating either [1] the noncentrality of the F-ratio for testing that true Beta coefficients are Zeros or [2] the "relative" MSE Risk (i.e. MSE divided by true sigma-square, where the "relative" variance of OLS is known.) The eff.ridge() function implements the "Efficient Shrinkage Path" introduced in Obenchain (2022) . This "p-Parameter" Shrinkage-Path always passes through the vector of regression coefficient estimates Most-Likely to achieve the overall Optimal Variance-Bias Trade-Off and is the shortest Path with this property. Functions eff.aug() and eff.biv() augment the calculations made by eff.ridge() to provide plots of the bivariate confidence ellipses corresponding to any of the p*(p-1) possible ordered pairs of shrunken regression coefficients. Functions for plotting TRACE Diagnostics now have more options. 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YouTheria is an online database of mammalian trait data . 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Package: r-cran-rywaasb Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 884 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-factoextra, r-cran-factominer, r-cran-lifecycle, r-cran-mathjaxr Suggests: r-cran-car, r-cran-metan, r-cran-devtools, r-cran-usethis, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling Filename: pool/dists/noble/main/r-cran-rywaasb_0.3-1.ca2404.1_all.deb Size: 288770 MD5sum: 30735e88d8aba0464e142f3b776ff719 SHA1: d2f71e1fffae05c933fac21c13f2ed4eb8713429 SHA256: 184961d73d2511464d4e084ec41e302ea0563909f5433cced57a8b0dca6e7196 SHA512: 159b365dabcfe9bf25fafb5d2158572f90028bc6ee93b9dcb2557b83da6750b2e1327fe6edae6c79f1bf584d4542215cbecbbdc2165f6cc0aa69bd6a055d3303 Homepage: https://cran.r-project.org/package=rYWAASB Description: CRAN Package 'rYWAASB' (Simultaneous Selection by Trait and WAASB Index) This tool proposes a new ranking algorithm that utilizes a "Y*WAASB" biplot generated by the 'metan'. The aim of the current package is to effectively distinguish the top-ranked genotypes in MET (Multi-Environmental Trials). For a detailed explanation of the process of obtaining "WAASB", "WAASBY" indices, and a "Y*WAASB" biplot, refer to the manual included in this package as well as the study by Olivoto & Lúcio (2020) . In this context, "WAASB" refers to the "Weighted Average of Absolute Scores" provided by Olivoto et al. (2019) , which quantifies the stability of genotypes across different environments using linear mixed-effect models. To run the package, you need to extract the "WAASB" and "WAASBY" coefficients using the 'metan' and apply them. This tool utilizes PCA (Principal Component Analysis) and differentiates the entries which may be genotypes, hybrids, varieties, etc using "WAASB", "WAASBY", and a combination of the specified trait and WAASB index. Package: r-cran-rzabbix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-rzabbix_0.1.0-1.ca2404.1_all.deb Size: 16846 MD5sum: a031399459406a6710db48d8a47e0d9b SHA1: 6e4012fea1645e52373cfb1e19deed469c7e62fe SHA256: 61d9afe34cb7e6eacc86c1431e17d9957311a7343537602bae9a4d4bee11f3a7 SHA512: 69ca62e42dd9733cead1e52ecb91b63d5aa8bfd2c787170a319c5c77cbda14cda4912d238b9194d08eaa32835a9e5d6cbcb139092b87ef829802404063218789 Homepage: https://cran.r-project.org/package=RZabbix Description: CRAN Package 'RZabbix' (R Module for Working with the 'Zabbix API') R interface to the 'Zabbix API' data . Enables easy and direct communication with 'Zabbix API' from 'R'. Package: r-cran-rzentra Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-dplyr, r-cran-lubridate, r-cran-purrr, r-cran-data.table Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-rzentra_0.1.0-1.ca2404.1_all.deb Size: 28096 MD5sum: 39d14011738a953c59974ff8d1a25e39 SHA1: a5da2ec841a39e6f53bbdda4488b81bdd9587265 SHA256: 3b7bbf7400862df629b6acc5d9751ff850e10d4a044e8ba06aa87c4aa0f841ed SHA512: 6a75dce0f5fb327b3e3f24eadd110875a4db0c96301b1e051dc3c94a575441960b644b25d3bd323509214c9720caae605b38f67d7d5316771139af0449e7444c Homepage: https://cran.r-project.org/package=rzentra Description: CRAN Package 'rzentra' (Client for the 'ZENTRA Cloud' API) Provides functionality to read settings, statuses and readings of weather stations from the 'ZENTRA Cloud' API . Package: r-cran-s20x Architecture: all Version: 3.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 893 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bootstrap, r-cran-dafs, r-cran-emmeans, r-cran-formatr, r-cran-ggplot2, r-cran-knitr, r-cran-markdown, r-cran-nlme, r-cran-rmarkdown, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-s20x_3.2.2-1.ca2404.1_all.deb Size: 518104 MD5sum: 60a7eaefa2a87921accd8baf41d763be SHA1: 0cc8b53071a01dfbe1483092c732a64594d93f8d SHA256: e8686a4612418c17ef6255dbb6405d8680dd7ec3c848963e9a3e5bf60e376009 SHA512: 3acd68da30917e4c17220249d0d2c53bb1886e8a36fcd1bd1ac3f8a63e6ca1f69af2702360343289c8eae9290e21fcab627033760e9fd425bf5b6cc8005e0295 Homepage: https://cran.r-project.org/package=s20x Description: CRAN Package 's20x' (Functions for University of Auckland Course STATS 201/208 DataAnalysis) A set of functions used in teaching STATS 201/208 Data Analysis at the University of Auckland. 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Package: r-cran-s2dv Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2337 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-bigmemory, r-cran-maps, r-cran-mapproj, r-cran-climprojdiags, r-cran-plyr, r-cran-ncdf4, r-cran-nbclust, r-cran-multiapply, r-cran-specsverification, r-cran-easyncdf, r-cran-easyverification, r-cran-signal, r-cran-zoo Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-s2dv_2.3.0-1.ca2404.1_all.deb Size: 2306366 MD5sum: d7b384203937120ff021e5a0df028e76 SHA1: 8318802c0bb5e6a4e21e507568967de4d964c20f SHA256: 85955d1701eb5c144f0d7b77bd2a0e8d84323055b4513b6cf127d97f8e5999d5 SHA512: 7b8397bc86de2c661713e88054e023f1a7de9a67b9e3ace0e8ad0489ffb83f3b9e239b6a7f3590172f34775422860f79436ea3822b7bc2daa7243423cbf70668 Homepage: https://cran.r-project.org/package=s2dv Description: CRAN Package 's2dv' (Seasonal to Decadal Verification) An advanced version of package 's2dverification'. Intended for seasonal to decadal (s2d) climate forecast verification, but also applicable to other types of forecasts or general climate analysis. This package is specifically designed for comparing experimental and observational datasets. It provides functionality for data retrieval, post-processing, skill score computation against observations, and visualization. Compared to 's2dverification', 's2dv' is more compatible with the package 'startR', able to use multiple cores for computation and handle multi-dimensional arrays with a higher flexibility. The Climate Data Operators (CDO) version used in development is 1.9.8. Implements methods described in Wilks (2011) , DelSole and Tippett (2016) , Kharin et al. (2012) , Doblas-Reyes et al. (2003) . Package: r-cran-s3.resourcer Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-httr, r-cran-resourcer, r-cran-aws.s3, r-cran-sparklyr Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-s3.resourcer_1.1.2-1.ca2404.1_all.deb Size: 96406 MD5sum: f3e00ab937e0d49644dfad3479a24fac SHA1: 24bb57c37e7b72d7315417c7aab3b26805373e3a SHA256: 5025dd5009fcd58546a978ddf09cfe6a2fa8a2e727be150d07d8cfbccfa16356 SHA512: 7ba45489759c9acc1cf3b0f39e4ce2ffaacf030b359750172dce9372d6f22e8d20b4f46feaed4801138670de366b88d0d989a5996063e81216270651c558ba56 Homepage: https://cran.r-project.org/package=s3.resourcer Description: CRAN Package 's3.resourcer' (S3 Resource Resolver) A S3 resource is provided by Amazon Web Services S3 or a S3-compatible object store (such as Minio). 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Package: r-cran-s3 Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-prettyunits, r-cran-fs, r-cran-rlang, r-cran-httr, r-cran-glue, r-cran-purrr, r-cran-dplyr Suggests: r-cran-aws.signature, r-cran-testthat, r-cran-digest, r-cran-withr Filename: pool/dists/noble/main/r-cran-s3_1.1.0-1.ca2404.1_all.deb Size: 29492 MD5sum: 7ae93bc85537b59cf83da9e146b8977f SHA1: ba0ee56c9101d7f690077b4354e2fa24996c9e34 SHA256: a695f77334e51f207a8bb6c5f0599f25451da550bd03a77262bbeaa3521937d1 SHA512: 3ee0cde34229d275c87b558e7a5ab12961b6be9e4412cd62528d41643a8c3fa9fc2d7c8a6211f7747533e67c553e05cd9d95134a3ba4570b1ca8c1d4af3dca13 Homepage: https://cran.r-project.org/package=s3 Description: CRAN Package 's3' (Download Files from 'AWS S3') Download files hosted on 'AWS S3' (Amazon Web Services Simple Storage Service; ) to a local directory based on their URI. 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Package: r-cran-s3vs Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-ncvreg, r-cran-survival, r-cran-mombf, r-cran-future.apply, r-cran-eha, r-cran-pec, r-cran-aftgee, r-cran-afthd Suggests: r-cran-rjags, r-cran-knitr, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-s3vs_1.0-1.ca2404.1_all.deb Size: 461428 MD5sum: c8c6e84fd00788d55e62b06568beacd7 SHA1: a9bef5f08f9a32117fa286b3d64eb8a804cdf690 SHA256: b71055860f5aacd241f54a8eed89df3c99167e5833c72597cee6cb65ebe03b06 SHA512: 02d24d458a8435adda971a28f390f403a13c48c7a1271619ce841781afc8fc5fdf2b61ca8d3552c54d484073111403a38d2ca72b81ffb4f0b0647a8c6be54a23 Homepage: https://cran.r-project.org/package=S3VS Description: CRAN Package 'S3VS' (Structured Screen-and-Select Variable Selection in Linear,Generalized Linear, and Survival Models) Performs variable selection using the structured screen-and-select (S3VS) framework in linear models, generalized linear models with binary data, and survival models such as the Cox model and accelerated failure time (AFT) model. Package: r-cran-s4dm Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-corpcor, r-cran-densratio, r-cran-flexclust, r-cran-geometry, r-cran-kernlab, r-cran-maxnet, r-cran-mvtnorm, r-cran-np, r-cran-proc, r-cran-robust, r-cran-rvinecopulib, r-cran-sf, r-cran-terra, r-cran-dplyr, r-cran-rdpack Suggests: r-cran-geodata, r-cran-bien, r-cran-ggplot2, r-cran-tidyterra, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-s4dm_0.0.2-1.ca2404.1_all.deb Size: 241076 MD5sum: 0a80fa078fcf25835b2c00c6401bc730 SHA1: 3ea0f04216025b2c14f8eb37d405d0532c5839dc SHA256: 953c708acdae9a130cddd164070c84db588cbcace06851d49e7cca19860fa746 SHA512: 9fdf12f9345d6ba9d3a0a4feea4912dd6a38bc506f246a81069476bf1e54facd6be5ece3f91b7747e405d74b5eaadf0d1c071814b696f819a83094e1b39a8202 Homepage: https://cran.r-project.org/package=S4DM Description: CRAN Package 'S4DM' (Small Sample Size Species Distribution Modeling) Implements a set of distribution modeling methods that are suited to species with small sample sizes (e.g., poorly sampled species or rare species). While these methods can also be used on well-sampled taxa, they are united by the fact that they can be utilized with relatively few data points. More details on the currently implemented methodologies can be found in Maitner et al. (2026) , Drake and Richards (2018) , Drake (2015) , and Drake (2014) . Package: r-cran-s4vd Architecture: all Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biclust, r-cran-irlba, r-cran-foreach Filename: pool/dists/noble/main/r-cran-s4vd_1.1-1-1.ca2404.1_all.deb Size: 173124 MD5sum: 553502eafcfa3bb5a2fcfb689fd79eda SHA1: dae834ed14eb518f62fb46bab59f49b4c11cfa3e SHA256: 89c08d47bc0cf8f1da620dc4b3c80ac2f035a2c88e3f3c0506bd43e2635c1497 SHA512: 2e53c964bf8e86acd2e4485d150261fc6105de8140971647745daa5b97458d7d5b7c08c92481a6f4abfb99d36e7b5af228709404ac41154973d6049f4720479a Homepage: https://cran.r-project.org/package=s4vd Description: CRAN Package 's4vd' (Biclustering via Sparse Singular Value DecompositionIncorporating Stability Selection) The main function s4vd() performs a biclustering via sparse singular value decomposition with a nested stability selection. The results is an biclust object and thus all methods of the biclust package can be applied. Package: r-cran-s7contract Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-s7contract_0.1.0-1.ca2404.1_all.deb Size: 121628 MD5sum: f729ed7490a98b20209bf655937ace1a SHA1: 2825ac9805e8dcdbc4f0e13aeaebf77a03bf5c8d SHA256: 5381cb3b326b2184799cdb90d7956c9436f338fe7707634ef3e73a53399409c4 SHA512: 79832abb9f9ae004cf4c6a6de1df80281ad58c1e52d7693112955853f5fb7c2dde80c496db1ddd6d857e1852f6556dce0ee2a7d0db5a37ce28bceb90fa50071e Homepage: https://cran.r-project.org/package=s7contract Description: CRAN Package 's7contract' ('Go'-Like Interfaces and 'Rust'-Like Traits with 'S7') Contract helpers built with 'S7' for expressing runtime protocols around ordinary 'S7' dispatch. 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Package: r-cran-s7schema Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-s7, r-cran-v8, r-cran-yaml Suggests: r-cran-jsonlite, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-withr Filename: pool/dists/noble/main/r-cran-s7schema_0.1.1-1.ca2404.1_all.deb Size: 203042 MD5sum: a572e69963c663957e6813d60f58f262 SHA1: 118d337330ec7050b56fd07fca2c8bb89bbed8b5 SHA256: fa122b948c5d41446a5f86ef9ff70c6ae1f74418437769f29b6a3f164f8d5afc SHA512: 6a1920341dc34e8744660bac25ae5bd2ee86e2bc2117ce26d2075c707cf318cdc5a38322936ef1d5c5f7528340ce28b72092e701f3f153eb2e6d2060ea10f063 Homepage: https://cran.r-project.org/package=S7schema Description: CRAN Package 'S7schema' ('S7' Framework for Schema-Validated YAML Configuration) Provides a generic framework for working with YAML (YAML Ain't Markup Language) configuration files. 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Package: r-cran-saascnv Architecture: all Version: 0.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1063 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rann, r-bioc-dnacopy Filename: pool/dists/noble/main/r-cran-saascnv_0.3.4-1.ca2404.1_all.deb Size: 992670 MD5sum: 2964caa22f7fde2df064227dfbb1dd0f SHA1: 21c1a56450a3a786d9d6f1abfe1218ba7d40022c SHA256: 64baa01d294595a7e418e9cb71a5afd46bf4e1eb13a376013c486dd156cac122 SHA512: d27316833468e38a8bcbc1b39caf5e11543c503ce50cb66ba86a3347f0d5d3b4db370a6f74f8e31f15c2b114d89aa5b81754f30625c70150cafd751cd538a74a Homepage: https://cran.r-project.org/package=saasCNV Description: CRAN Package 'saasCNV' (Somatic Copy Number Alteration Analysis Using Sequencing and SNPArray Data) Perform joint segmentation on two signal dimensions derived from total read depth (intensity) and allele specific read depth (intensity) for whole genome sequencing (WGS), whole exome sequencing (WES) and SNP array data. Package: r-cran-saber Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-saber_0.7.1-1.ca2404.1_all.deb Size: 130498 MD5sum: 4dfdb9f5ed90eba6e1706f17e7e2110b SHA1: bd3cb2b448308e5ec903cd75856b8fbac0d93c92 SHA256: 930dc6ed6cc9d4e59a43a0c149b74de00dd1eb161dde74a170fc400372f66fc4 SHA512: e6fa76ff16fd038287cc1ad360e8749d904e2b657764b8e627ba4900a9aa998d0a3cd3c49efc6ab7db5e2ff7418702ac2194c827a4a7b8cffd561c5127a25ae6 Homepage: https://cran.r-project.org/package=saber Description: CRAN Package 'saber' (Context Engineering for Large Language Model Agents) Context-engineering primitives for Artificial Intelligence (AI) coding agents working in R. Assembles agent context from memory and instruction files ('AGENTS.md', 'CLAUDE.md'), traces function call blast radius across projects, generates project briefings, parses source into Abstract Syntax Tree (AST) symbol indices, discovers dependency graphs, and introspects installed packages. Zero dependencies. Package: r-cran-sabre Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 745 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-entropy, r-cran-raster, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sabre_0.4.3-1.ca2404.1_all.deb Size: 631936 MD5sum: da6ad983e65bee47f503dd203333af6d SHA1: 670d378237425468e33cf3b7da8027a3351ae472 SHA256: bbfdaf90f537d67127fd98b197a59af472a9c07258f919e64e5d1dd87f1765e8 SHA512: df05772fd425523eed5d7b8ef9b1682fe3fb0d5fd7f669400a241616fcdd7bf891b63837673c96b35e52bfee5532b29313cb327a7548a71f90f9015eb549965b Homepage: https://cran.r-project.org/package=sabre Description: CRAN Package 'sabre' (Spatial Association Between Regionalizations) Calculates a degree of spatial association between regionalizations or categorical maps using the information-theoretical V-measure (Nowosad and Stepinski (2018) ). It also offers an R implementation of the MapCurve method (Hargrove et al. (2006) ). Package: r-cran-sac Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sac_1.0.2-1.ca2404.1_all.deb Size: 92218 MD5sum: 80422e65d6f4a73fa1c1323615b003a0 SHA1: ddb45fc5145ee3fa50bceec226dced62ca4fcd31 SHA256: ec9f916665ea5a616be83de6aead5d8c41b8d31020b7e95e1e3ae399ef9b289a SHA512: 92558d709b79ab76828b4b0715973b85d4fd1624578085118f42a3957b06075a7c537d1faed7739be4e8aab376728a252d92a8fa773b61b9e6c75ffa76d00d9d Homepage: https://cran.r-project.org/package=sac Description: CRAN Package 'sac' (Semiparametric Analysis of Change-Point) Semiparametric empirical likelihood ratio based tests of change-point with one-change or epidemic alternatives with data-based model diagnostic are contained. Package: r-cran-saccr Architecture: all Version: 3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.tree, r-cran-jsonlite, r-cran-trading Filename: pool/dists/noble/main/r-cran-saccr_3.4-1.ca2404.1_all.deb Size: 112012 MD5sum: 5948c0ece6a860acdd8024e00f819d28 SHA1: 556b20b2cefae308c1a425827acce649cfc91ab7 SHA256: 1acfe2f70424f5f2a4483c612de9347d08a75c4b4199c7e2e7982cffd1001abb SHA512: 0fa08c72466d9a73c86937bbdcd5d2ca7bd9df5ffc93c9ace36f40c78abc229dcdaffbc4bc7f80d4e89e92c13ffb913a73832788629e98b2b0616bb6d0f1d65d Homepage: https://cran.r-project.org/package=SACCR Description: CRAN Package 'SACCR' (SA Counterparty Credit Risk under CRR2) Computes the Exposure-At-Default based on the standardized approach of CRR2 (SA-CCR). The simplified version of SA-CCR has been included, as well as the OEM methodology. 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Package: r-cran-sadeg Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sadeg_1.0.0-1.ca2404.1_all.deb Size: 50382 MD5sum: 69430742120e5ae9a31a595269721d0a SHA1: 757b344a204ecb4a56f135095cf68c5f93c61389 SHA256: 62c8e07d894c7a5d8ea989b4ff5af1972dde030c48f842f4b61ec611cfe81227 SHA512: 3f8903f40cec8b6729c162da0ad99211ed21b5ad4eb494554883a9d6c7eecfac5a4b154275506cf84d67d4c07fc0059c0c7bff2ceec80a386e796fe66f215b19 Homepage: https://cran.r-project.org/package=SADEG Description: CRAN Package 'SADEG' (Stability Analysis in Differentially Expressed Genes) We analyzed the nucleotide composition of genes with a special emphasis on stability of DNA sequences. Besides, in a variety of different organisms unequal use of synonymous codons, or codon usage bias, occurs which also show variation among genes in the same genome. Seemingly, codon usage bias is affected by both selective constraints and mutation bias which allows and enables us to examine and detect changes in these two evolutionary forces between genomes or along one genome. Therefore, we determined the codon adaptation index (CAI), effective number of codons (ENC) and codon usage analysis with calculation of the relative synonymous codon usage (RSCU), and subsequently predicted the translation efficiency and accuracy through GC-rich codon usages. Furthermore, we estimated the relative stability of the DNA sequence following calculation of the average free energy (Delta G) and Dimer base-stacking energy level. Package: r-cran-sadisa Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-ddd Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sadisa_1.2-1.ca2404.1_all.deb Size: 129898 MD5sum: 1c98725427cb43602cc14ef0af4c3141 SHA1: 8ca1a419e6dc6f7875971a87fbea6b203653081e SHA256: 9513f3b11de993f95447dce2db8efe50f83180c38d56f27d9ca3220ecd538ae3 SHA512: e770da40b0ce87cdccb1f2a0cfbdbf230bb4f7ad636601c0e349b539c3f07401df5666af24ca51804dd9d1aa23e50919a0f7930ea85f39ad3e46de04abe9aa90 Homepage: https://cran.r-project.org/package=SADISA Description: CRAN Package 'SADISA' (Species Abundance Distributions with Independent-SpeciesAssumption) Computes the probability of a set of species abundances of a single or multiple samples of individuals with one or more guilds under a mainland-island model. One must specify the mainland (metacommunity) model and the island (local) community model. It assumes that species fluctuate independently. The package also contains functions to simulate under this model. See Haegeman, B. & R.S. Etienne (2017). A general sampling formula for community structure data. Methods in Ecology & Evolution 8: 1506-1519 . Package: r-cran-sadists Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 603 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pdqutils, r-cran-hypergeo, r-cran-orthopolynom Suggests: r-cran-sharper, r-cran-shiny, r-cran-testthat, r-cran-ggplot2, r-cran-xtable, r-cran-formatr, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sadists_0.2.5-1.ca2404.1_all.deb Size: 474348 MD5sum: ce9f953ab921313e437c4753fe579b53 SHA1: 25b52c446d3f5c81ab364ba6e65393f24006fb6b SHA256: f02d255dfc2bfd7d420627a76e0c8462b781d53af81cbb0db376c1dd4c8df53f SHA512: cc5c5e56d4dc5124f374d0d234551360d8488ec263f8ed0a2c1c97065f470db64f1e7064482a3811711a37d8f556b743a45e393558dc690984ac76f7554885f4 Homepage: https://cran.r-project.org/package=sadists Description: CRAN Package 'sadists' (Some Additional Distributions) Provides the density, distribution, quantile and generation functions of some obscure probability distributions, including the doubly non-central t, F, Beta, and Eta distributions; the lambda-prime and K-prime; the upsilon distribution; the (weighted) sum of non-central chi-squares to a power; the (weighted) sum of log non-central chi-squares; the product of non-central chi-squares to powers; the product of doubly non-central F variables; the product of independent normals. Package: r-cran-sae.projection Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1494 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidymodels, r-cran-survey, r-cran-cli, r-cran-doparallel, r-cran-dplyr, r-cran-parsnip, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-tune, r-cran-workflows, r-cran-yardstick, r-cran-bonsai, r-cran-ranger, r-cran-randomforest, r-cran-themis, r-cran-lightgbm, r-cran-caret Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sae.projection_0.1.5-1.ca2404.1_all.deb Size: 1450742 MD5sum: ebbd120c7ec8f6d37fc00167e18f6f1b SHA1: fa4341357eb6cae7809444e24b599d3d5320c180 SHA256: 6bcfb86c1dcad9492fbc192e3add6da10c38b7414341a619c8bbdf2dfdd35252 SHA512: 37063c16b167cf4c9643ac6a25beac7db17932589d4f42f3ec74e1897824103a470916c144ccbfd774186d2b2bd394a9c27e88ba6cd9083f1f04b0ca9bd2bd0f Homepage: https://cran.r-project.org/package=sae.projection Description: CRAN Package 'sae.projection' (Small Area Estimation Using Model-Assisted Projection Method) Combines information from two independent surveys using a model-assisted projection method. Designed for survey sampling scenarios where a large sample collects only auxiliary information (Survey 1) and a smaller sample provides data on both variables of interest and auxiliary variables (Survey 2). Implements a working model to generate synthetic values of the variable of interest by fitting the model to Survey 2 data and predicting values for Survey 1 based on its auxiliary variables (Kim & Rao, 2012) . Package: r-cran-sae.prop Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magic, r-cran-mass, r-cran-corpcor, r-cran-progress, r-cran-fpc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sae.prop_0.1.2-1.ca2404.1_all.deb Size: 350224 MD5sum: 43f789d78838debb42b0885443de460a SHA1: 7a68f2e4754be1c4b77489a2f32ad2bcbd85b0e4 SHA256: 14ec9523651dc4a93c91e4438f8683f35c35dfabb45eb44c69734d3d9a53ef17 SHA512: 9f440e5a607857eae302d660092ba83c83cc6c3bcae1dec2855f16524a7faca0cf8814d7c06be4a7262d0f556e7382070dfb6d80493aff71a18f66aac964919d Homepage: https://cran.r-project.org/package=sae.prop Description: CRAN Package 'sae.prop' (Small Area Estimation using Fay-Herriot Models with AdditiveLogistic Transformation) Implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based on covariance matrix used by Esteban M, Lombardía M, López-Vizcaíno E, Morales D, and Pérez A . The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I , with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A . Package: r-cran-sae2 Architecture: all Version: 1.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-survey Suggests: r-cran-sae Filename: pool/dists/noble/main/r-cran-sae2_1.2-2-1.ca2404.1_all.deb Size: 146732 MD5sum: 875866b61e71b591553a9d3a102a091a SHA1: 68f8b56f547d1837178757c77ef28589098fbcc8 SHA256: 55feb4021d0a0666f46ce7e0163062392b05aa10a24eba8f69c9f0c318290416 SHA512: 60231642ae442176e44c1cc25ec229ff09af3dbfc07776e76b45076cad69ce739758317d20c1bf53c0afe6d9d2d88f666baebb7b75958c6e052935204b15a753 Homepage: https://cran.r-project.org/package=sae2 Description: CRAN Package 'sae2' (Small Area Estimation: Time-Series Models) Time series area-level models for small area estimation. The package supplements the functionality of the sae package. Specifically, it includes EBLUP fitting of the Rao-Yu model in the original form without a spatial component. The package also offers a modified ("dynamic") version of the Rao-Yu model, replacing the assumption of stationarity. Both univariate and multivariate applications are supported. Of particular note is the allowance for covariance of the area-level sample estimates over time, as encountered in rotating panel designs such as the U.S. National Crime Victimization Survey or present in a time-series of 5-year estimates from the American Community Survey. Key references to the methods include J.N.K. Rao and I. Molina (2015, ISBN:9781118735787), J.N.K. Rao and M. Yu (1994) , and R.E. Fay and R.A. Herriot (1979) . Package: r-cran-sae4health Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1131 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-golem, r-cran-gridextra, r-cran-htmltools, r-cran-htmlwidgets, r-cran-leaflet, r-cran-r6, r-cran-sf, r-cran-sp, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboard, r-cran-shinyfeedback, r-cran-shinyjs, r-cran-shinywidgets, r-cran-summer, r-cran-surveyprev, r-cran-survey, r-cran-geodata, r-cran-bookdown, r-cran-markdown, r-cran-haven, r-cran-ggridges, r-cran-ggthemes, r-cran-rcolorbrewer, r-cran-viridislite, r-cran-scales, r-cran-patchwork, r-cran-leaflegend, r-cran-leafsync, r-cran-plotly, r-cran-readr, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sae4health_1.2.3-1.ca2404.1_all.deb Size: 1074646 MD5sum: 560fe61e6315379124bc410ada45777a SHA1: e002ebd8c07cc4a9d1fad7090c6f0163f6117083 SHA256: 9b042aa3cd11584778c576f90c93dc03c0389633675deb72e70912270ce7463b SHA512: 2bf8a0ff566527d5a0972b5d4fa99ca23fa9b730e79161bb49bb46d57fafb5c15188cd1d85ba6f7b34f018eaae0bdc1032d7f9aa78071142a186642df1ffc3f5 Homepage: https://cran.r-project.org/package=sae4health Description: CRAN Package 'sae4health' (Small Area Estimation for Key Health and Demographic Indicatorsfrom Household Surveys) Enables small area estimation (SAE) of health and demographic indicators in low- and middle-income countries (LMICs). It powers an R 'shiny' application for generating subnational estimates and prevalence maps of 150+ binary indicators from Demographic and Health Surveys (DHS). It builds on the SAE analysis workflow from the 'surveyPrev' package. For documentation, visit . Methodological details can be found at Wu et al. (2025) . Package: r-cran-sae Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1369 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-sae_1.3-1.ca2404.1_all.deb Size: 1178688 MD5sum: 13777c15d42b6ebcad07db535a0053c6 SHA1: 9fad5e9280ced0f0f0f53ac6222f209c9820f5ec SHA256: 1621a1ac9ead904a18f9ba678d0d6c51cd2eaab7be00c9b30e291be90f0fc016 SHA512: 63bf874a1d412cc29a2bc2fd36660682975a548608d4cb8f7907428962737e2f1a0ffdd0d1e868ebb3da4ff2b1d7f9cedda8503c81ddc9bc1362e1cb4807ae75 Homepage: https://cran.r-project.org/package=sae Description: CRAN Package 'sae' (Small Area Estimation) Functions for small area estimation. Package: r-cran-saebest Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sae Filename: pool/dists/noble/main/r-cran-saebest_0.1.0-1.ca2404.1_all.deb Size: 19040 MD5sum: b4102a1be16367d097230684db430b3e SHA1: 5134bf70a1b045dda53b2a2d9a60833b37640d4e SHA256: a67934b51f7a7b73d22c567a6d48de8eb857333fd58a4a181618af88f25e863e SHA512: 4b6be2d9860b8d38779f2c30f20e506b616b61e5ae4474f0d56fba799eb4bd3bb57098a07180ce6c0a3bdbe65f6cffa3c756095e3719f5a27ad169efbde4e215 Homepage: https://cran.r-project.org/package=saeBest Description: CRAN Package 'saeBest' (Selecting Auxiliary Variables in Small Area Estimation (SAE)Model) Select best combination of auxiliary variables with certain criterion. Package: r-cran-saebnocov Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-descr, r-cran-dplyr, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-saebnocov_0.1.0-1.ca2404.1_all.deb Size: 81060 MD5sum: ccd13066cab8779f0633472db69768c0 SHA1: da3d8c1f5435bed91400850bf96a5c64226f2f98 SHA256: 5214362069359c4f0467969f909feb2f6172a0d185cc350a8f6ae3d9d142748a SHA512: f65bb9413a5ac6111d2e6556d4f1d964af3bb417cde26072e4bb10b46d14557528e61c9af2cc2fbfc630c7e3256768cacab7d12952f2326c79e871448e929f41 Homepage: https://cran.r-project.org/package=saebnocov Description: CRAN Package 'saebnocov' (Small Area Estimation using Empirical Bayes without AuxiliaryVariable) Estimates the parameter of small area in binary data without auxiliary variable using Empirical Bayes technique, mainly from Rao and Molina (2015,ISBN:9781118735787) with book entitled "Small Area Estimation Second Edition". This package provides another option of direct estimation using weight. This package also features alpha and beta parameter estimation on calculating process of small area. Those methods are Newton-Raphson and Moment which based on Wilcox (1979) and Kleinman (1973) . Package: r-cran-saeeb Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-count, r-cran-mass Filename: pool/dists/noble/main/r-cran-saeeb_0.1.0-1.ca2404.1_all.deb Size: 33078 MD5sum: 8d3d6be3980a5f8c15ac780cab3ad890 SHA1: 358f88174abad17a87379700e932cf59e328f1f1 SHA256: a60023af767ed8e017bda77dffec74349c644ea4d8058302e5bcb2c49349a0ce SHA512: a49f774b5b8ab50cd41edbf73f0fb0f620f58992700ae3e1f325856906f536f50c30e4b8df0ea9f2afe93cbe628e5d44d99ad332e1d35298d0ae356db92234a2 Homepage: https://cran.r-project.org/package=saeeb Description: CRAN Package 'saeeb' (Small Area Estimation for Count Data) Provides small area estimation for count data type and gives option whether to use covariates in the estimation or not. By implementing Empirical Bayes (EB) Poisson-Gamma model, each function returns EB estimators and mean squared error (MSE) estimators for each area. The EB estimators without covariates are obtained using the model proposed by Clayton & Kaldor (1987) , the EB estimators with covariates are obtained using the model proposed by Wakefield (2006) and the MSE estimators are obtained using Jackknife method by Jiang et. al. (2002) . Package: r-cran-saehb.me.beta Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-rjags, r-cran-stringr Suggests: r-cran-covr, r-cran-knitr, r-cran-r.rsp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-saehb.me.beta_1.1.0-1.ca2404.1_all.deb Size: 49008 MD5sum: 25280adcc2392a9c446b0cf932b89686 SHA1: e61376a814b4e4b4937644e7c3bb36f81c745a94 SHA256: 92dc7e812bbc93ebcd8204ac3afb237329bf64a26ed3b6949211732c649141b1 SHA512: f268faec8713c321e10abf9557a8a01188c258440889e9029f09c1830d2f6b29dd866541c9835dc20f22b135eed988559d0309fef0a5a269a7eba8d86dceeaf1 Homepage: https://cran.r-project.org/package=saeHB.ME.beta Description: CRAN Package 'saeHB.ME.beta' (SAE with Measurement Error using HB under Beta Distribution) Implementation of Small Area Estimation (SAE) using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error under Beta Distribution. The 'rjags' package is employed to obtain parameter estimates. For the references, see J.N.K & Molina (2015) , Ybarra and Sharon (2008) , and Ntzoufras (2009, ISBN-10: 1118210352). Package: r-cran-saehb.me Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-rjags, r-cran-stringr Filename: pool/dists/noble/main/r-cran-saehb.me_1.0.1-1.ca2404.1_all.deb Size: 58608 MD5sum: 0a43cef969ab6ed26f911e5e23b9a11f SHA1: 2130e5359169a4ed55a446c2ede21342006b740a SHA256: c0899181d0f5de7aad16cd7ba021b8c56aa3109089e956f8dd72b9c377f5710c SHA512: 9eb1932f25101a9c243dc13ff6652a05498c142957a4f3b2da3c4c38c279e9b34653a0ac63d93ac0499fa5a7f32208d8999857ddd044de071a193bb10060c1bc Homepage: https://cran.r-project.org/package=saeHB.ME Description: CRAN Package 'saeHB.ME' (Small Area Estimation with Measurement Error using HierarchicalBayesian Method) Implementation of small area estimation using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error. The 'rjags' package is employed to obtain parameter estimates. For the references, see Rao and Molina (2015) , Ybarra and Lohr (2008) , and Ntzoufras (2009, ISBN-10: 1118210352). Package: r-cran-saehb.panel.beta Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-dplyr, r-cran-rjags, r-cran-stringr Suggests: r-cran-knitr, r-cran-r.rsp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-saehb.panel.beta_0.1.5-1.ca2404.1_all.deb Size: 80768 MD5sum: aa627015ef590796454aae0f70ce151e SHA1: f46fd2ded7649a2d3ca7e7bbe962b79c1151c9b5 SHA256: 1473d5662fed1aacbbd532d0f042b68345c3b2454ab962d69570f276c299ca34 SHA512: 1c0d93f091c8a503c951f8ce735baf8fd1ef33a61eedbd58dcda86c321a9defc31206cbf0446162807f16024a89e4c338cb5cac6c6a252d635886d91a41190d7 Homepage: https://cran.r-project.org/package=saeHB.panel.beta Description: CRAN Package 'saeHB.panel.beta' (Small Area Estimation using HB for Rao Yu Model under BetaDistribution) Several functions are provided for small area estimation at the area level using the hierarchical bayesian (HB) method with panel data under beta distribution for variable interest. This package also provides a dataset produced by data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators, which include the mean and the variation of the mean. For the reference, see Rao and Molina (2015, ISBN: 978-1-118-73578-7). Package: r-cran-saehb.panel Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-coda, r-cran-rjags Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-saehb.panel_0.1.1-1.ca2404.1_all.deb Size: 90130 MD5sum: 7b3e25788612d9d50cb67ecfbceee1ff SHA1: 1c57987117c6fbf1ea41eeaf720d55da16b22b9f SHA256: b40cd4732b3d0a40e332e2a7af43637fcc40d407ee8bbc332fc4afb1bfc9aa57 SHA512: f8eb975c8737edc9bc80e74c9e7a60b4ceff76f7c5d965dec1cfc359cdf1569b10e2b6c524a5844f27e6d99b217cd25085620014dd9e28aa67047ee6438e0a64 Homepage: https://cran.r-project.org/package=saeHB.panel Description: CRAN Package 'saeHB.panel' (Small Area Estimation using Hierarchical Bayesian Method for RaoYu Model) We designed this package to provide several functions for area level of small area estimation using hierarchical Bayesian (HB) method. This package provides model using panel data for variable interest.This package also provides a dataset produced by a data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015). Package: r-cran-saehb.spatial Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-coda, r-cran-rjags Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-saehb.spatial_0.1.1-1.ca2404.1_all.deb Size: 44190 MD5sum: f59720c42de8e5cbbb6b3e6a06d892d4 SHA1: 1089a21e51d6481da234501378dce78a1f6cd796 SHA256: 6d5cbdb3566a96c347c3a3d800cf62f2de1195d3081f10f6310c47df7055b357 SHA512: 6fda5e7164553304f923cde80543dc28acd558692411f251e7bb82ca1c2d75e9900fead35c87841f356fc522d838ddc6d0234d4ef966a2f5d4ce7c895ddc1cff Homepage: https://cran.r-project.org/package=saeHB.spatial Description: CRAN Package 'saeHB.spatial' (Small Area Estimation Hierarchical Bayes For Spatial Model) Provides several functions and datasets for area level of Small Area Estimation under Spatial Model using Hierarchical Bayesian (HB) Method. Model-based estimators include the HB estimators based on a Spatial Fay-Herriot model with univariate normal distribution for variable of interest.The 'rjags' package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) . Package: r-cran-saehb.twofold Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-coda, r-cran-stringr, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-saehb.twofold_0.1.2-1.ca2404.1_all.deb Size: 108204 MD5sum: 51eaf4cfbc45d330951b4deee842665c SHA1: 93ca9c6b2d5d59f15d32999924669d465deb5f52 SHA256: 3a2653c5e299903b8c275d9b22e395553e0d62934c3ca667d527383d9528f9ed SHA512: 9ab1479bf11988891157781a1dcbba3cb836562c0424d90e8a2adde56c903ab544a5a98f207483efd52553601154be8590c1a82e78d3ede02bb4895a4acc50fd Homepage: https://cran.r-project.org/package=saeHB.twofold Description: CRAN Package 'saeHB.twofold' (Hierarchical Bayes Twofold Subarea Level Model SAE) We designed this package to provides several functions for area and subarea level of small area estimation under Twofold Subarea Level Model using hierarchical Bayesian (HB) method with Univariate Normal distribution for variables of interest. Some dataset simulated by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates using Gibbs Sampling algorithm. Model-based estimators involves the HB estimators which include the mean, the variation of mean, and the quantile. For the reference, see Rao and Molina (2015) , Torabi and Rao (2014) , Leyla Mohadjer et al.(2007) , and Erciulescu et al.(2019) . Package: r-cran-saehb.unit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-coda, r-cran-dplyr, r-cran-rjags Filename: pool/dists/noble/main/r-cran-saehb.unit_0.1.0-1.ca2404.1_all.deb Size: 352974 MD5sum: f1ae816f0a4478d18b5cde51fcb6cf93 SHA1: bb8db0ee7045b91114f3a2aff44451b551dd2f3f SHA256: 6c8c33239c6e116ef19a97f7473823aad8019ac0260d63801835bc914fef1295 SHA512: dcdf434c40a32b47cfbe79eae9060a8dcc5b14aa3192f273e4bd7079c111c6e6ef9373453498bea5213588bbe3bed5833f5e977bc69872494be3eb82cbe2984e Homepage: https://cran.r-project.org/package=saeHB.unit Description: CRAN Package 'saeHB.unit' (Basic Unit Level Model using Hierarchical Bayesian Approach) Small area estimation unit level models (Battese-Harter-Fuller model) with a Bayesian Hierarchical approach. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Battese et al. (1988) . Package: r-cran-saehb.zib Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-coda, r-cran-rjags Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-saehb.zib_0.1.1-1.ca2404.1_all.deb Size: 37372 MD5sum: 0991f2c4026f030180096c6cef315bd3 SHA1: b5389246acf82eb66af644c8ef30e5ef14c0c7bb SHA256: f46ec9ac67e1b7f5d8c50d9cf0b8e59aef162958b3e20e604dea53763bde9e99 SHA512: 114bec1c1141123a5221e883e9d924820b26e5578b619a240b0a7b78432b4cac6b4181ec08a64d8349174d5f7a8b46e0e9cadd9dab789fdfa1398bb12e3d44d8 Homepage: https://cran.r-project.org/package=saeHB.ZIB Description: CRAN Package 'saeHB.ZIB' (Small Area Estimation using Hierarchical Bayesian under ZeroInflated Binomial Distribution) Provides function for area level of small area estimation using hierarchical Bayesian (HB) method with Zero-Inflated Binomial distribution for variables of interest. Some dataset produced by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. Package: r-cran-saehb Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-coda, r-cran-rjags, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-nimble, r-cran-mass, r-cran-carbayesdata Filename: pool/dists/noble/main/r-cran-saehb_0.2.3-1.ca2404.1_all.deb Size: 237920 MD5sum: 3ba27266f6d6c517d7f53623dfd49031 SHA1: 661c259d70b0bb37789d8c0ebe508ce267a1b8c1 SHA256: 846f9cddde59617511194876bc25488222b86e9c2975e9237bb2f2aa8e3e2414 SHA512: 25b18324d719daf0063b5e4f710d8a66d74d6f35fd570550f72ffea3e21897edc92e47faafa4ce85b7760db8e0df563c883c5ee27605a31054db4ea224b3a94b Homepage: https://cran.r-project.org/package=saeHB Description: CRAN Package 'saeHB' (Small Area Estimation using Hierarchical Bayesian Method) Provides several functions for area level of small area estimation using hierarchical Bayesian (HB) methods with several univariate distributions for variables of interest. The dataset that is used in every function is generated accordingly in the Example. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015) . Package: r-cran-saekernel Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-saekernel_0.1.1-1.ca2404.1_all.deb Size: 25566 MD5sum: 96bde9754222001c46ce3530b10573da SHA1: 0b133e2e55b277f7d6dc10848f491cdac78ff8d7 SHA256: d58d0e95fa0740f9d58e32dcd91c11b4072c706337846844d7d20631844dfb77 SHA512: 07463af2d750b3da117f9bf79cd8caad1f2f644af5badf0546870d46e7b2990547298aed4036df45d77419d392b45eb83d29dd39c2c1ef27e1b99b7eba305d94 Homepage: https://cran.r-project.org/package=saekernel Description: CRAN Package 'saekernel' (Small Area Estimation Non-Parametric Based Nadaraya-WatsonKernel) Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) . Package: r-cran-saeme Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-saeme_1.3.1-1.ca2404.1_all.deb Size: 56208 MD5sum: 0d26facf8d9617f3ea2ab8e32ce6b9f9 SHA1: 201777a23ba9f58e22391c95bb2a7e9bd7027a95 SHA256: d14bce3b246590faf2fda7dd98b0ae0fce1b49f4b3e552eb7dab9798d9c2e5a9 SHA512: 2390391c986adab9ce0748a22931921358669816d0aeafd6feeeb5bfe77eb898b00a9bccb0eff4bbed334b17b918ddf0c598d173364364253bc7731ba7496ecb Homepage: https://cran.r-project.org/package=saeME Description: CRAN Package 'saeME' (Small Area Estimation with Measurement Error) A set of functions and datasets implementation of small area estimation when auxiliary variable is measured with error. These functions provide a empirical best linear unbiased prediction (EBLUP) estimator and mean squared error (MSE) estimator of the EBLUP. These models were developed by Ybarra and Lohr (2008) . Package: r-cran-saemix Architecture: all Version: 3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4174 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-npde, r-cran-gridextra, r-cran-ggplot2, r-cran-rlang, r-cran-mclust, r-cran-scales, r-cran-mass Suggests: r-cran-testthat, r-cran-survival Filename: pool/dists/noble/main/r-cran-saemix_3.5-1.ca2404.1_all.deb Size: 3279194 MD5sum: 8a8a27d46ad6437b06a67ade7f77d6bb SHA1: 2b3f05c8360cb90d9a5a59dc3b83ed24f6597504 SHA256: 20f08c1dcf681510ed6e7298024605b0af8db7b259d35a4b387db32c851d4102 SHA512: 2020870f6a786e8c6684f75f4b348f6b3281356888342c62e3ac62b2d3518877c57e3e67c2e5c7a50facd80f1bcb305c8f600fa9896046ede692175e654f15e5 Homepage: https://cran.r-project.org/package=saemix Description: CRAN Package 'saemix' (Stochastic Approximation Expectation Maximization (SAEM)Algorithm) The 'saemix' package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. It (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) ). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for 'saemix': . Package: r-cran-saens Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-saens_0.1.2-1.ca2404.1_all.deb Size: 285096 MD5sum: 094cada7bf6ecd4b4d1e947b3bc7dd0d SHA1: 0db9b889fa0fff9aa747e67c7bdf10072fc827b7 SHA256: 6c095e8d6d418aff728cf05323bf7c2abb8d82790bfdb9b2f90a3c79c67a5488 SHA512: 532ee48e3c4c6df519f7c29db3339b1f952f6de8b17db867da69cd222c97a904f762b383ce015c564dc1799ab1da947c5f3dc482c4c872e3b52fd4eb1d37a314 Homepage: https://cran.r-project.org/package=saens Description: CRAN Package 'saens' (Small Area Estimation with Cluster Information for Estimation ofNon-Sampled Areas) Implementation of small area estimation (Fay-Herriot model) with EBLUP (Empirical Best Linear Unbiased Prediction) Approach for non-sampled area estimation by adding cluster information and assuming that there are similarities among particular areas. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Anisa et al. (2013) . Package: r-cran-saepseudo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-sae Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-saepseudo_0.1.0-1.ca2404.1_all.deb Size: 28438 MD5sum: 430a9b3f667f9188786939adcd50a356 SHA1: f4e7c2e75782c4f2e9bf3fc03e3e6a1bb38f172a SHA256: 8795bfd0a0844d01c52a8f98d66543e351baca98a01775602fb48af484dc173a SHA512: 535a7b9af9134d9a510c7da1dbc7b0566b284a7107890b3907f53d065af528537d0b2a5a731406732b1da1155603f9c02260ce6e1c81d77a7a9f946bc11c70e7 Homepage: https://cran.r-project.org/package=saePseudo Description: CRAN Package 'saePseudo' (Small Area Estimation using Averaging Pseudo Area Level Model) Provides function for small area estimation at area level using averaging pseudo area level model for variables of interest. A dataset produced by data generation is also provided. This package estimates small areas at the village level and then aggregates them to the sub-district, region, and provincial levels. Package: r-cran-saery Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-saery_2.0-1.ca2404.1_all.deb Size: 163886 MD5sum: 713edf5a51fceebf8b9d20c69b0a8b20 SHA1: f6b3e46f32c263caab86f0eab77178487ea8f278 SHA256: 7213496bb10a3e001d409fa3f59ef4151de9d2c15028ab3cab6aeb9082564449 SHA512: bf7fd357e3ccae2f5ddc2625f237527dbaa33382d2ebb9e70f2d43ba6302e07849da858e3262293d53caa2b2e41785df081167168d26e3e516b745bd523ca263 Homepage: https://cran.r-project.org/package=saery Description: CRAN Package 'saery' (Small Area Estimation for Rao and Yu Model) Functions to calculate EBLUPs (Empirical Best Linear Unbiased Predictor) estimators and their MSEs (Mean Squared Errors). Estimators are based on an area-level linear mixed model introduced by Rao and Yu (1994) . The REML (Residual Maximum Likelihood) method is used for fitting the model. Package: r-cran-saesim Architecture: all Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-functional, r-cran-ggplot2, r-cran-mass, r-cran-spdep, r-cran-parallelmap Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-saesim_0.13.0-1.ca2404.1_all.deb Size: 265200 MD5sum: cad7caf2dbe3cbd5f09e285ed63144c2 SHA1: bdf7483ba44849d8c6fcf8328a8c5ba63c9f3b8e SHA256: 11c9e5544f3a59e34b669cb2f9955b6cf0ad6f65fc175643d5bc669e46b9f1fd SHA512: f2764225984c53e60206bdc23cc8d0023eeb905e6fb61075af3273d325056154b5203bfbf3a64adfe7f25bb13307de936ebb72ee0291174a5802ff212191b027 Homepage: https://cran.r-project.org/package=saeSim Description: CRAN Package 'saeSim' (Simulation Tools for Small Area Estimation) Tools for the simulation of data in the context of small area estimation. Combine all steps of your simulation - from data generation over drawing samples to model fitting - in one object. This enables easy modification and combination of different scenarios. You can store your results in a folder or start the simulation in parallel. Package: r-cran-saetrafo Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2002 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-emdi, r-cran-sfsmisc, r-cran-parallelmap, r-cran-ggplot2, r-cran-moments, r-cran-openxlsx, r-cran-reshape2, r-cran-hlmdiag, r-cran-gridextra, r-cran-stringr, r-cran-readods, r-cran-rlang Suggests: r-cran-simframe, r-cran-sf, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-saetrafo_1.0.6-1.ca2404.1_all.deb Size: 1962884 MD5sum: 9f4e64a29a8e361bea3130196d6ddbeb SHA1: f66193c7a00ed7e8fea37de04852f2dd6e91855a SHA256: 3b0511222a9d334dbc55a4c6547df5ef1acb01acdd31efdae144597da3664fdd SHA512: 8511245f86d87f47c0039896c5a01eca512481dbcc8e138a016b4f035855a7fa9aa75be6a2f20df4d872979af3fcc9b17544bf2700d089f45c708a7b65a02219 Homepage: https://cran.r-project.org/package=saeTrafo Description: CRAN Package 'saeTrafo' (Transformations for Unit-Level Small Area Models) The aim of this package is to offer new methodology for unit-level small area models under transformations and limited population auxiliary information. In addition to this new methodology, the widely used nested error regression model without transformations (see "An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data" by Battese, Harter and Fuller (1988) ) and its well-known uncertainty estimate (see "The estimation of the mean squared error of small-area estimators" by Prasad and Rao (1990) ) are provided. In this package, the log transformation and the data-driven log-shift transformation are provided. If a transformation is selected, an appropriate method is chosen depending on the respective input of the population data: Individual population data (see "Empirical best prediction under a nested error model with log transformation" by Molina and Martín (2018) ) but also aggregated population data (see "Estimating regional income indicators under transformations and access to limited population auxiliary information" by Würz, Schmid and Tzavidis ) can be entered. Especially under limited data access, new methodologies are provided in saeTrafo. Several options are available to assess the used model and to judge, present and export its results. For a detailed description of the package and the methods used see the corresponding vignette. Package: r-cran-saeval Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1040 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-ggplot2, r-cran-ggspatial Filename: pool/dists/noble/main/r-cran-saeval_1.0.0-1.ca2404.1_all.deb Size: 1032694 MD5sum: faf87c7774c2b399d534ad24d1e0582f SHA1: 9e8c8661323ecedea1c9d70f9ad0aa7049eeb2f6 SHA256: d4cd649e0b29cfef4b4c59b3239f3fe8a72e3645b748fb6182f30139e8124e5a SHA512: aaad79f994b62fdabde6bb23e85e3de8488e244f3c2b9abc62c3beefcf3937eb95efab9b0e016d95e272b56f25332a314519dee84302f77f8597fd4a1d84868d Homepage: https://cran.r-project.org/package=SAEval Description: CRAN Package 'SAEval' (Small Area Estimation Evaluation) Allows users to produce diagnostic procedures and graphic tools for the evaluation of Small Area estimators. 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(2021) , with additional support for stochastic experimentation. Package: r-cran-sager Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-markdown, r-cran-prettydoc, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-learnr, r-cran-spelling, r-cran-lattice, r-cran-spatstat.data, r-cran-matrix, r-cran-mgcv, r-cran-pmcmrplus, r-cran-devtools, r-cran-desctools, r-cran-ggally, r-cran-vgam, r-cran-car, r-cran-dplyr, r-cran-tibble, r-cran-ggiraphextra, r-cran-scales, r-cran-spatstat, r-cran-vcd, r-cran-factominer, r-cran-cluster, r-cran-caret, r-cran-corrplot, r-cran-actuar Filename: pool/dists/noble/main/r-cran-sager_0.7.0-1.ca2404.1_all.deb Size: 501678 MD5sum: b7669cede4c5885ae9b30406e6fee9a7 SHA1: 5c8f178246b60714dc14091c5f4a7ef94173874d SHA256: 9a389036aac462a930375ffd335cc07f99224a7c788f8a57a096f47c20eefb96 SHA512: 746816adca2e52e61ec706980c2d99c6501acc693ec9e3ffae15a8572835245c95941d83c9c72ee3b70ddbfe2f720d1740a9920b0bd3860322da769ec6215354 Homepage: https://cran.r-project.org/package=sageR Description: CRAN Package 'sageR' (Applied Statistics for Economics and Management with R) Datasets and functions for the book "Statistiques pour l’économie et la gestion", "Théorie et applications en entreprise", F. Bertrand, Ch. Derquenne, G. Dufrénot, F. Jawadi and M. Maumy, C. Borsenberger editor, (2021, ISBN:9782807319448, De Boeck Supérieur, Louvain-la-Neuve). The first chapter of the book is dedicated to an introduction to statistics and their world. The second chapter deals with univariate exploratory statistics and graphics. The third chapter deals with bivariate and multivariate exploratory statistics and graphics. The fourth chapter is dedicated to data exploration with Principal Component Analysis. The fifth chapter is dedicated to data exploration with Correspondance Analysis. The sixth chapter is dedicated to data exploration with Multiple Correspondance Analysis. The seventh chapter is dedicated to data exploration with automatic clustering. The eighth chapter is dedicated to an introduction to probability theory and classical probability distributions. The ninth chapter is dedicated to an estimation theory, one-sample and two-sample tests. The tenth chapter is dedicated to an Gaussian linear model. The eleventh chapter is dedicated to an introduction to time series. The twelfth chapter is dedicated to an introduction to probit and logit models. Various example datasets are shipped with the package as well as some new functions. Package: r-cran-sagm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fastmatrix, r-cran-gigrvg, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-sagm_1.0.0-1.ca2404.1_all.deb Size: 36214 MD5sum: ecb7d51931d17b7d80ee28dd1ea9ab05 SHA1: c373c9c2b1677f9f4a890527a96171be55620b41 SHA256: 69e43e0b93a2f14fd1e0b942e93e493431b0f71b2207f8eac0aaf6f4820bb36f SHA512: 9c9faed165896983ac31f0267df387fc0443af68e77ce58647a5dae0e77d6ab7f2c3237b43213c2a5b6debbe97025879935d35663159bfebda8b15c6bc4bbd6d Homepage: https://cran.r-project.org/package=SAGM Description: CRAN Package 'SAGM' (Spatial Autoregressive Graphical Model) Implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2023) , and allows for the statistical modeling of asymmetric between-location effects, as well as within-location effects using spatial autoregressive graphical models. The package allows for the generation of spatial weight matrices to capture asymmetric effects for strip-type intercropping designs, although it can handle any type of spatial data commonly found in other sciences. 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Package: r-cran-saive Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1871 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-crayon, r-cran-doparallel, r-cran-proxy, r-cran-rlang, r-cran-terra, r-cran-vsurf Suggests: r-cran-ranger, r-cran-testthat, r-cran-vdiffr, r-cran-whitebox Filename: pool/dists/noble/main/r-cran-saive_1.0.6-1.ca2404.1_all.deb Size: 1337996 MD5sum: 737170c7fe323188cc4178e38006f145 SHA1: 8f147fb49c2f048c79f98d7095cee3fca18029d5 SHA256: 143e585cd2d6e788f65a4b9aa0126d65a3ce5d85958cebebcf479e0821086292 SHA512: ce556228dbd2420bbbcbc548fa39c01c28d0c3b7ecc6ff83957ee2c6c9150ec63bf61b1dbaf3e069104d6bcc0c4aab035ed53a4bb2e6cc82462b7aa2d9d1e9d9 Homepage: https://cran.r-project.org/package=SAiVE Description: CRAN Package 'SAiVE' (Functions Used for SAiVE Group Research, Collaborations, andPublications) Holds functions developed by the University of Ottawa's SAiVE (Spatio-temporal Analysis of isotope Variations in the Environment) research group with the intention of facilitating the re-use of code, foster good code writing practices, and to allow others to benefit from the work done by the SAiVE group. Contributions are welcome via the 'GitHub' repository by group members as well as non-members. Package: r-cran-sakernas Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 767 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl Filename: pool/dists/noble/main/r-cran-sakernas_0.1.0-1.ca2404.1_all.deb Size: 714724 MD5sum: c6acfb5cab4c90514c48aec3376def1e SHA1: 935944c573cbd986bd1ff025278794201be5353b SHA256: 12f29f18c31eb7a18b76db5c42b7db19d1dafe3f7cb5aabc36ef70223f9c5ec1 SHA512: 35c60bab1efe1e471c519589e45538c696e917d658e0cb34d98012a65703b56d629f82f70d3df6edc39ef2fcef0a2ba97a5039cdad37658b3e84a86ada3b7250 Homepage: https://cran.r-project.org/package=SAKERNAS Description: CRAN Package 'SAKERNAS' (A National Labor Force Survey of Indonesia) Surveys to collect employment data so as to obtain data estimates on the number of employed people, the number of unemployed, and other employment indicators. 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Package: r-cran-salmonmse Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 908 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-msetool, r-cran-rtmb, r-cran-dplyr, r-cran-ggplot2, r-cran-gsl, r-cran-reshape2, r-cran-rlang, r-cran-rmarkdown Suggests: r-cran-ggrepel, r-cran-rstan, r-cran-scales, r-cran-testthat, r-cran-tmbstan Filename: pool/dists/noble/main/r-cran-salmonmse_2.1.0-1.ca2404.1_all.deb Size: 858556 MD5sum: 5f13964bafd8fe5fb1754f39f1b2b095 SHA1: 85a9d90e455c517c479ca2af523fb06e4a294643 SHA256: 726554daf960799b6a1ec8dfa73e1b0746bb5ede24e9d051fec9a49ce1ad430f SHA512: a5fcd69c4b887bc0956c61e4590f965f1143685dbb03fa2f44fa310a9c302cacb8b147f3d44c005d847e16b151da1eba6bbfa599e0d59734b4168e1be67ee67d Homepage: https://cran.r-project.org/package=salmonMSE Description: CRAN Package 'salmonMSE' (Management Strategy Evaluation for Salmon Species) Simulation tools to evaluate the long-term effects of salmon management strategies, including a combination of habitat, harvest, and habitat actions. The stochastic age-structured operating model accommodates complex life histories, including freshwater survival across early life stages, juvenile survival and fishery exploitation in the marine life stage, partial maturity by age class, and fitness impacts of hatchery programs on natural spawning populations. 'salmonMSE' also provides an age-structured conditioning model to develop operating models fitted to data. Package: r-cran-saltsampler Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Suggests: r-cran-knitr, r-cran-coda Filename: pool/dists/noble/main/r-cran-saltsampler_1.1.0-1.ca2404.1_all.deb Size: 68424 MD5sum: 4cff1b27fc0566362c6b7214d1178829 SHA1: cc754864d2ebb27217f2d8f0d97fdc01f83bc32d SHA256: e50d7173f5e6ab066080e9e9dbe60c44ca3603b8f187fd85911776b2753ccda0 SHA512: 134e51a53db59a992a553883d5727f92f440edf77e42f3202e65bab99ce52ec15e413d3ec049668707e10f4b5f9c0ceb28b4873a561474df6b3dba931eb22cc8 Homepage: https://cran.r-project.org/package=SALTSampler Description: CRAN Package 'SALTSampler' (Efficient Sampling on the Simplex) The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC) sampling of random variables on a simplex. 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Package: r-cran-salty Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-purrr, r-cran-stringr Suggests: r-cran-charlatan, r-cran-testthat, r-cran-tibble, r-cran-covr Filename: pool/dists/noble/main/r-cran-salty_0.1.2-1.ca2404.1_all.deb Size: 91150 MD5sum: d89ec29805b4232a441fa5b143627c88 SHA1: f42be3ba21a8eac5894c51c9b15919efb7d01920 SHA256: 022f54fff43369fc19871e63eef30ddbef2d236b42cf1b86317bdf086db19b0b SHA512: fc235eb2a620e8ca3f77b0aa27aa10cb1587e78ed46c40c9f1f2a39dc613ee918268f8ff67df26c10178c4271767d09bd21bf97cd2f9ca71342eb4c8e8e1d2a3 Homepage: https://cran.r-project.org/package=salty Description: CRAN Package 'salty' (Turn Clean Data into Messy Data) Take real or simulated data and salt it with errors commonly found in the wild, such as pseudo-OCR errors, Unicode problems, numeric fields with nonsensical punctuation, bad dates, etc. Package: r-cran-samadb Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rmysql, r-cran-writexl, r-cran-data.table, r-cran-collapse Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-samadb_0.3.1-1.ca2404.1_all.deb Size: 69264 MD5sum: e894c2be21801a3932344d1dd74e8343 SHA1: 177f8db03d902526f79b6f4e796c6b98b68f2a62 SHA256: 32f8ec12e5e17582bf445f747f74324b52503a810f9cb213ce25c3e053412bcf SHA512: 2b85ab2044ca9609c2cc60827ffcdbf5c0ceace0fcc05f29340eac42db950dcd044337d7184f41446d6ca27cc238d656e6958738bd3d598c8a5dcbe0abcf6be3 Homepage: https://cran.r-project.org/package=samadb Description: CRAN Package 'samadb' (South Africa Macroeconomic Database API) An R API providing access to a relational database with macroeconomic time series data for South Africa, obtained from the South African Reserve Bank (SARB) and Statistics South Africa (STATSSA), and updated on a weekly basis via the EconData platform and automated scraping of the SARB and STATSSA websites. The database is maintained at the Department of Economics at Stellenbosch University. Package: r-cran-samba Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-optimx, r-cran-survey Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-scales, r-cran-mass Filename: pool/dists/noble/main/r-cran-samba_0.9.0-1.ca2404.1_all.deb Size: 224214 MD5sum: 441d8184249eeee487803e7bd1c26433 SHA1: e9f49b97e5105c17dafe01b6fecad85bc52f76d8 SHA256: 3c5178a73b244df23b870a07b1e1eb2ca71b92aaa1161fc2f79b21330025da3c SHA512: 4f85edf73b5f59cbb437bdfc92f35c37341508b3343e9a720f281d0a9d3f96c600ed3ac18cabb902c2538c6d59d12d1595241d540734f4e369562b5b7bcc4006 Homepage: https://cran.r-project.org/package=SAMBA Description: CRAN Package 'SAMBA' (Selection and Misclassification Bias Adjustment for LogisticRegression Models) Health research using data from electronic health records (EHR) has gained popularity, but misclassification of EHR-derived disease status and lack of representativeness of the study sample can result in substantial bias in effect estimates and can impact power and type I error for association tests. Here, the assumed target of inference is the relationship between binary disease status and predictors modeled using a logistic regression model. 'SAMBA' implements several methods for obtaining bias-corrected point estimates along with valid standard errors as proposed in Beesley and Mukherjee (2020) , currently under review. Package: r-cran-sambia Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-dplyr, r-cran-smotefamily, r-cran-e1071, r-cran-ranger, r-cran-proc, r-cran-fnn Filename: pool/dists/noble/main/r-cran-sambia_0.1.0-1.ca2404.1_all.deb Size: 60108 MD5sum: ac57779bf669b4c8b9195c033b55d238 SHA1: 3d3fe11eda9b6b1f7dd33f1a586aec810fd237d2 SHA256: 5c54d8553dad5df5dbb4a9282333a2ed1a5ecef91d782a473fd4838c81907e0c SHA512: c2ec1cb59ac5d5c1c443095c0d547e588823597435999fecb1f5f1c24b8381b1827c438ef44452d238c2cc59345ba0742a6114fd45b466cde3715fe1a643e0d2 Homepage: https://cran.r-project.org/package=sambia Description: CRAN Package 'sambia' (A Collection of Techniques Correcting for Sample Selection Bias) A collection of various techniques correcting statistical models for sample selection bias is provided. In particular, the resampling-based methods "stochastic inverse-probability oversampling" and "parametric inverse-probability bagging" are placed at the disposal which generate synthetic observations for correcting classifiers for biased samples resulting from stratified random sampling. For further information, see the article Krautenbacher, Theis, and Fuchs (2017) . The methods may be used for further purposes where weighting and generation of new observations is needed. Package: r-cran-same Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-rjags, r-cran-coda, r-cran-extradistr, r-cran-survival, r-cran-ggplot2, r-cran-expint Suggests: r-cran-testthat, r-cran-mockery, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-same_0.1.0-1.ca2404.1_all.deb Size: 87464 MD5sum: fe167dfa9df9870c4638e747e9337152 SHA1: 974efecca0951e21f7e0dd1ee37528010388dfa9 SHA256: bfdd404111693968db5f39f49f3a0ea32fb854940593c69f7f9fe8c7d9aacf75 SHA512: 6ddf0868388436d0298608bd928eb3668fa1519237e75b5d9861b3e5b1714bee711c0704151246cf323a4c6d3a8847ad75fbb3f2f5c3e249881d6591dea2ca5d Homepage: https://cran.r-project.org/package=SAME Description: CRAN Package 'SAME' (Seamless Adaptive Multi-Arm Multi-Stage Enrichment) Design a Bayesian seamless multi-arm biomarker-enriched phase II/III design with the survival endpoint with allowing sample size re-estimation. James M S Wason, Jean E Abraham, Richard D Baird, Ioannis Gournaris, Anne-Laure Vallier, James D Brenton, Helena M Earl, Adrian P Mander (2015) . Guosheng Yin, Nan Chen, J. Jack Lee (2018) . Ying Yuan, Beibei Guo, Mark Munsell, Karen Lu, Amir Jazaeri (2016) . Package: r-cran-sameplot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-ragg Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sameplot_0.1.0-1.ca2404.1_all.deb Size: 51822 MD5sum: 6ebb7c341794255810e05bdc18a341e6 SHA1: 6495dd282c5a5d36bdbbd1d08022cfcbae19e4de SHA256: f9daa222db61319c37a84dced58fc57b841d81fccf46b9f94bcbee6cea834734 SHA512: cdf75c39d065a179f1c1840bdb06755d965860526c324e83d9fb2dddb469addf0b48f843f382c5d78817437c76a279d543849a662964acf59d54ceebd52cff2d Homepage: https://cran.r-project.org/package=sameplot Description: CRAN Package 'sameplot' (Consistent Plot Rendering and Saving Across Interactive Sessionsand Reports) Renders plots to a temporary image using the ragg graphics device and returns knitr::include_graphics() output. Optionally saves the image to a specified path. This helps ensure consistent appearance across interactive sessions, saved files, and knitted documents. For more details see Pedersen and Shemanarev (2025) . Package: r-cran-samesies Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggbeeswarm, r-cran-ggplot2, r-cran-purrr, r-cran-scales, r-cran-stringdist Suggests: r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-samesies_0.1.0-1.ca2404.1_all.deb Size: 416454 MD5sum: 651f1cf078e17d0abb3a0e438d369266 SHA1: 686a780e7ee39ced68d1c28d004eb6f503e92390 SHA256: 05d0ebd179cc4d2ce325e3c7db6bd6b3142fee199ad952f0d807518d83e3e6a6 SHA512: f43387fa8984c532e355579987f83480724797bdde1fe6072c4b7442831f8ee7892e4e2603bca0a0e118900aa033fcbdbd8603fdc3b731e2ee105ba81852dfe4 Homepage: https://cran.r-project.org/package=samesies Description: CRAN Package 'samesies' (Compare Similarity Across Text, Factors, or Numbers) Compare lists of texts, factors, or numerical values to measure their similarity. The motivating use case is evaluating the similarity of large language model responses across models, providers, or prompts. Approximate string matching is implemented using 'stringdist'. Package: r-cran-sampbias Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-forcats, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang, r-cran-tidyr, r-cran-viridis, r-cran-terra, r-cran-sf, r-cran-rnaturalearth Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sampbias_2.0.0-1.ca2404.1_all.deb Size: 694252 MD5sum: 5811b157c7c3d5181626782fd19e3815 SHA1: f52e5fdee3c36d0ae03d9227237d68142bbc68bc SHA256: 4252014b5a3622f0f4002ef55e0d01ea44dde72d116421327cac236bd08b868b SHA512: defd3ab0169ea2df33eeeffa585f26d5d7f3a664b68dac6fd1524b8b1f4276787248f7f0a3c0f747982e5e88197c252175d2fbdd1ad44f383ff2f90003a7f908 Homepage: https://cran.r-project.org/package=sampbias Description: CRAN Package 'sampbias' (Evaluating Geographic Sampling Bias in Biological Collections) Evaluating the biasing impact of geographic features such as airports, cities, roads, rivers in datasets of coordinates based biological collection datasets, by Bayesian estimation of the parameters of a Poisson process. Enables also spatial visualization of sampling bias and includes a set of convenience functions for publication level plotting. Also available as 'shiny' app. The reference for the methodology is: Zizka et al. (2020) . Package: r-cran-sampcompr Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 644 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-data.table, r-cran-dplyr, r-cran-forcats, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-hmisc, r-cran-lmtest, r-cran-magrittr, r-cran-psych, r-cran-purrr, r-cran-readr, r-cran-reshape2, r-cran-sandwich, r-cran-survey, r-cran-svrep, r-cran-tibble, r-cran-tidyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-jtools, r-cran-stargazer Filename: pool/dists/noble/main/r-cran-sampcompr_0.3.2-1.ca2404.1_all.deb Size: 606304 MD5sum: f1c07c25e6f48a034829b32e36d979e1 SHA1: f2d217bfdf9ddd936f762cf63953376f3a08caf3 SHA256: 52eb16f3eb3281b012932c246f3f99e51de3de67f363d6d18c8e157d76f7325c SHA512: 27a74cc98bbcee0be7bb4ef621be9d71bf9b44f02d13b69f7e70b80a11d09638153f760e8152812de2feb2e15c7abbb45ff2b25b70aeb06873cdcaafae9d8b54 Homepage: https://cran.r-project.org/package=sampcompR Description: CRAN Package 'sampcompR' (Comparing and Visualizing Differences Between Surveys) Easily analyze and visualize differences between samples (e.g., benchmark comparisons, nonresponse comparisons in surveys) on three levels. The comparisons can be univariate, bivariate or multivariate. On univariate level the variables of interest of a survey and a comparison survey (i.e. benchmark) are compared, by calculating one of several difference measures (e.g., relative difference in mean), and an average difference between the surveys. On bivariate level a function can calculate significant differences in correlations for the surveys. And on multivariate levels a function can calculate significant differences in model coefficients between the surveys of comparison. All of those differences can be easily plotted and outputted as a table. For more detailed information on the methods and example use see Rohr, B., Silber, H., & Felderer, B. (2024). Comparing the Accuracy of Univariate, Bivariate, and Multivariate Estimates across Probability and Nonprobability Surveys with Population Benchmarks. Sociological Methodology . Package: r-cran-sampledatasets Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sampledatasets_0.1.0-1.ca2404.1_all.deb Size: 89402 MD5sum: 809a769d12a6e91c1c1706bab89be572 SHA1: 077cdf9670cadb100de8acb6374f359896386c07 SHA256: 5c6af87b63328811263a806a5d1c821cee8f8b683083b6f08f11da8233f4cf4e SHA512: 681027147ff0e15a9289d1f2533448db1e4cca33145a403be9729f6c15e898ae2eae5246964ba4d58a460d0ec5168b0bbb738fbec506307eaad78977ede9329d Homepage: https://cran.r-project.org/package=sampledatasets Description: CRAN Package 'sampledatasets' (A Collection of Sample Datasets) Provides a collection of sample datasets on various fields such as automotive performance and safety data to historical demographics and socioeconomic indicators, as well as recreational data. It serves as a resource for researchers and analysts seeking to perform analyses and derive insights from classic data sets in R. Package: r-cran-sampler Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-reshape, r-cran-purrr Filename: pool/dists/noble/main/r-cran-sampler_0.2.4-1.ca2404.1_all.deb Size: 260918 MD5sum: 58fe56e7517f995a48b31fcde672fa58 SHA1: 645826ea01313dc0fcc135a50d66e3ba17352bc0 SHA256: cb5b52cd4cf87cde4c355a1a3b31ff23fa37528e5078ff93f1b969ef2099cc72 SHA512: dbc67429b26b449c86cf35e927ebb00bcb1dcb8bbca3110266e0f1144ab6c6022edc7c96987595de13f15eaf36f610c3021890f931d63cd9566db5afb2e5a005 Homepage: https://cran.r-project.org/package=sampler Description: CRAN Package 'sampler' (Sample Design, Drawing & Data Analysis Using Data Frames) Determine sample sizes, draw samples, and conduct data analysis using data frames. It specifically enables you to determine simple random sample sizes, stratified sample sizes, and complex stratified sample sizes using a secondary variable such as population; draw simple random samples and stratified random samples from sampling data frames; determine which observations are missing from a random sample, missing by strata, duplicated within a dataset; and perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs. Package: r-cran-sampleselection Architecture: all Version: 1.2-14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1742 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-maxlik, r-cran-misctools, r-cran-systemfit, r-cran-formula, r-cran-vgam, r-cran-mvtnorm Suggests: r-cran-lmtest, r-cran-ecdat Filename: pool/dists/noble/main/r-cran-sampleselection_1.2-14-1.ca2404.1_all.deb Size: 1593372 MD5sum: 828acade2a32d3b775b91d944b55d35f SHA1: d7b3a8afc9c4567768ac4ed747dc09ce9d3f6850 SHA256: eb9dea42cddec9cba38530c921337ceaca9bd2a19825a002e3c0407c69a87048 SHA512: 795510292fcd3042ec1a304e805438ce3e821f2d6210e0253fc48ffb9ee0aab137ba895f67c8c038cee02e0665f86bf0f85340324c51176b58c0d7cfa5c8b78a Homepage: https://cran.r-project.org/package=sampleSelection Description: CRAN Package 'sampleSelection' (Sample Selection Models) Two-step and maximum likelihood estimation of Heckman-type sample selection models: standard sample selection models (Tobit-2), endogenous switching regression models (Tobit-5), sample selection models with binary dependent outcome variable, interval regression with sample selection (only ML estimation), and endogenous treatment effects models. These methods are described in the three vignettes that are included in this package and in econometric textbooks such as Greene (2011, Econometric Analysis, 7th edition, Pearson). Package: r-cran-samplesize4clinicaltrials Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-samplesize4clinicaltrials_0.2.3-1.ca2404.1_all.deb Size: 18030 MD5sum: aad7039e68c2acde27c27cc757e20240 SHA1: ad7df033f073c3a83618b391ce1c1b43259240d7 SHA256: 9ad1cd712b38c822c74628900f5a8d40f1db250ee85a31db3348490f8b4f6cab SHA512: 2a4995049f65bec18d960341e5a57c3c762bf594e50553fec2c64c5f7c8eb505b70b0be67727f4fa9b9366e43d1ba90d06b6c31a19e473b10a7e154312887b16 Homepage: https://cran.r-project.org/package=SampleSize4ClinicalTrials Description: CRAN Package 'SampleSize4ClinicalTrials' (Sample Size Calculation for the Comparison of Means orProportions in Phase III Clinical Trials) There are four categories of Phase III clinical trials according to different research goals, including (1) Testing for equality, (2) Superiority trial, (3) Non-inferiority trial, and (4) Equivalence trial. This package aims to help researchers to calculate sample size when comparing means or proportions in Phase III clinical trials with different research goals. Package: r-cran-samplesize4surveys Architecture: all Version: 4.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2630 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-teachingsampling, r-cran-timedate, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-samplesize4surveys_4.1.1-1.ca2404.1_all.deb Size: 1741608 MD5sum: b4d3e383451776fe42972163fc7cd56a SHA1: b6717f64d01c4d4f116dcfc5bc6efab8e52156c5 SHA256: 3747808ea93298641278b97344198b3cc8e1e3f5dbe711094b28acc508bf245f SHA512: 7f2f8b1070866b90ea4197577839ad5b9c47856111480d7d86386117f127a5f5c542c5754fc5e834ddce0b1c4a47fb2ebb5ca02d083e0069674213031fbd9761 Homepage: https://cran.r-project.org/package=samplesize4surveys Description: CRAN Package 'samplesize4surveys' (Sample Size Calculations for Complex Surveys) Computes the required sample size for estimation of totals, means and proportions under complex sampling designs. Package: r-cran-samplesize Architecture: all Version: 0.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-samplesize_0.2-4-1.ca2404.1_all.deb Size: 23566 MD5sum: 89f6558f1544e6f5adaaf8bd3ef4dc74 SHA1: b0300143be1b8a32abf0192841378bbeeb518e4a SHA256: f394b35cf14ece553cdf9715aee3b6d2f2cc8375164fbc86762f96f8b2e99dd9 SHA512: cf3f89c8029dad4e7d2810b28c91b529d1a0414ead27ddf233fd6897c8f751a5001cf0fc98ec307f2e4120cffe9244dd4fdb27419b34640e608888b0c7ee0e0f Homepage: https://cran.r-project.org/package=samplesize Description: CRAN Package 'samplesize' (Sample Size Calculation for Various t-Tests and Wilcoxon-Test) Computes sample size for Student's t-test and for the Wilcoxon-Mann-Whitney test for categorical data. The t-test function allows paired and unpaired (balanced / unbalanced) designs as well as homogeneous and heterogeneous variances. The Wilcoxon function allows for ties. Package: r-cran-samplesizecalculator Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinythemes, r-cran-dt, r-cran-bslib Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-samplesizecalculator_0.1.0-1.ca2404.1_all.deb Size: 197006 MD5sum: a742c7fa1455ae0fbc192971f397c09b SHA1: 77593e86c95de4377419d760944c95454d51a35b SHA256: 45e31473ea3649c7a1f683545867e148d889e6d52c13f5645fecad27eb342548 SHA512: 11f0920410499c7f28be1b060d5a71cd95e1dd363f77fb98836a143c1ea4bba2a0d305fbd60efdabdd1e3fc296f2b8ef40c1aed7899506e38182ed0072f8aa5a Homepage: https://cran.r-project.org/package=SampleSizeCalculator Description: CRAN Package 'SampleSizeCalculator' (Sample Size Calculator under Complex Survey Design) It helps in determination of sample size for estimating population mean or proportion under simple random sampling with or without replacement and stratified random sampling without replacement. When prior information on the population coefficient of variation (CV) is unavailable, then a preliminary sample is drawn to estimate the CV which is used to compute the final sample size. If the final size exceeds the preliminary sample size, then additional units are drawn; otherwise, the preliminary sample size is considered as final sample size. For stratified random sampling without replacement design, it also calculates the sample size in each stratum under different allocation methods for estimation of population mean and proportion based upon the availability of prior information on sizes of the strata, standard deviations of the strata and costs of drawing a sampling unit in the strata.For details on sampling methodology, see, Cochran (1977) "Sampling Techniques" . Package: r-cran-samplesizecmh Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-desctools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-samplesizecmh_0.0.3-1.ca2404.1_all.deb Size: 74258 MD5sum: e65810b8aa57998e57c540d60108eabe SHA1: 5061c1feae8584c7b29651c6753fae8ff1d29e15 SHA256: aba301e64e8143b6d8bf776216807770a78fa63a0e9947a1e00ebf16f4a7933a SHA512: 3aba9a7f64d71c59a80d499ed88db4a2f8962de0d94e17f31fcf47d426edc12aacc5cca968e24e78746d723421d9274c3b1bb680b5c65213892838a6d0d266f8 Homepage: https://cran.r-project.org/package=samplesizeCMH Description: CRAN Package 'samplesizeCMH' (Power and Sample Size Calculation for theCochran-Mantel-Haenszel Test) Calculates the power and sample size for Cochran-Mantel-Haenszel tests. There are also several helper functions for working with probability, odds, relative risk, and odds ratio values. Package: r-cran-samplesizediagnostics Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-samplesizediagnostics_0.1.1-1.ca2404.1_all.deb Size: 17598 MD5sum: 24ee8ff6672e0b7efb4446efc561c63e SHA1: 227f445f68c30b01326d3e13591c40703b4b4e04 SHA256: edf4b7bb745144613a12da93139a7d575dc9dad38f7f20b713d354de146faaa5 SHA512: a3a42a719212672d275d1e1bfeea6faac424ebe935057224f7fe2d4f16d5d3371c0417587595c16ab6ed73efb2f12dce79ce60bedbc9207e157c0cf678ba7cf7 Homepage: https://cran.r-project.org/package=SampleSizeDiagnostics Description: CRAN Package 'SampleSizeDiagnostics' (Choosing Sample Size for Evaluating a Diagnostic Test) Calculates the sample size needed for evaluating a diagnostic test based on sensitivity, specificity, prevalence, and desired precision. Based on Buderer (1996) . Package: r-cran-samplesizeestimator Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi Filename: pool/dists/noble/main/r-cran-samplesizeestimator_1.0.0-1.ca2404.1_all.deb Size: 67348 MD5sum: d7a582ddc01926893809cd76c7f6d9a2 SHA1: c804eab121827754bdcfd4f45ba80e869bbbb10d SHA256: 6587d9d1b98ebfd375690a948bf63d21b2da49e3d550ab2662bc38fb9d0bfa02 SHA512: 11b4e7b52460b2985ba245190875f662f931be213ff3b41369ea05bd649a8be39b4e5feb3570645363461d23d4b5b762ab2e863d7de6a06ddc437b9b86c865f4 Homepage: https://cran.r-project.org/package=samplesizeestimator Description: CRAN Package 'samplesizeestimator' (Calculate Sample Size for Various Scenarios) Calculates sample size for various scenarios, such as sample size to estimate population proportion with stated absolute or relative precision, testing a single proportion with a reference value, to estimate the population mean with stated absolute or relative precision, testing single mean with a reference value and sample size for comparing two unpaired or independent means, comparing two paired means, the sample size For case control studies, estimating the odds ratio with stated precision, testing the odds ratio with a reference value, estimating relative risk with stated precision, testing relative risk with a reference value, testing a correlation coefficient with a specified value, etc. . Package: r-cran-samplesizelogisticcasecontrol Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-samplesizelogisticcasecontrol_2.0.2-1.ca2404.1_all.deb Size: 511466 MD5sum: b7c3bb06a9f1657de7d888509415178a SHA1: 5e8dc3830cad130fd57dbfb4f277c1b6c7ad25ac SHA256: c6d54a802da947515c955c2bac4e07907269adf47340a3758b9d76f377e977cb SHA512: 4bc093d92615526976c21bbe0cba295162edc89f2ee2cded003541aecf135f57237311e95be0ae7b58fc208f4d6502fdf0bd734b2c345c3078f2b9e6805b2925 Homepage: https://cran.r-project.org/package=samplesizelogisticcasecontrol Description: CRAN Package 'samplesizelogisticcasecontrol' (Sample Size and Power Calculations for Case-Control Studies) To determine sample size or power for case-control studies to be analyzed using logistic regression. Package: r-cran-samplesizemeans Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-samplesizemeans_1.2.3-1.ca2404.1_all.deb Size: 194740 MD5sum: ac91160b80bad62b1138c9baf6cdec86 SHA1: 476902667d69ae0af93d953fae27ed8f9a7f86c8 SHA256: 0bd8a63ee260ad7f5cb338eb248c2cd55af9beb97bba6ac5707c40a656f30d91 SHA512: 04358f3a7f4b93b6b31e045e9de2b160fafcbcd2279c49aceb261b4d0efb65dca747a23f5011af650d84b40395116ea4647515193e660a4702be7dddef8a52d4 Homepage: https://cran.r-project.org/package=SampleSizeMeans Description: CRAN Package 'SampleSizeMeans' (Sample Size Calculations for Normal Means) Sample size requirements calculation using three different Bayesian criteria in the context of designing an experiment to estimate a normal mean or the difference between two normal means. Functions for calculation of required sample sizes for the Average Length Criterion, the Average Coverage Criterion and the Worst Outcome Criterion in the context of normal means are provided. Functions for both the fully Bayesian and the mixed Bayesian/likelihood approaches are provided. For reference see Joseph L. and Bélisle P. (1997) . Package: r-cran-samplesizeproportions Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-samplesizeproportions_1.1.3-1.ca2404.1_all.deb Size: 91028 MD5sum: 422660c8b8b0a1ea5ac8f44c0e7971dc SHA1: bafbf1fe703b77acd7b0b74b73524ab9d5482d20 SHA256: 21c79349016ddd0c145831ddd8f1a0fe8fba0789c9ea4d86375036f1e450a70e SHA512: 09dcaa35f0e5f4ebfff07b95cc60fb6526146a456418da657d5850515c958eda1117a00202f032c07f2ff8783e1f1c745adb0336b6f3067cc8cb20a8325b2b5f Homepage: https://cran.r-project.org/package=SampleSizeProportions Description: CRAN Package 'SampleSizeProportions' (Calculating Sample Size Requirements when Estimating theDifference Between Two Binomial Proportions) Sample size requirements calculation using three different Bayesian criteria in the context of designing an experiment to estimate the difference between two binomial proportions. Functions for calculation of required sample sizes for the Average Length Criterion, the Average Coverage Criterion and the Worst Outcome Criterion in the context of binomial observations are provided. In all cases, estimation of the difference between two binomial proportions is considered. Functions for both the fully Bayesian and the mixed Bayesian/likelihood approaches are provided. For reference see Joseph L., du Berger R. and Bélisle P. (1997) . Package: r-cran-samplesizesinglearmsurvival Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-samplesizesinglearmsurvival_0.1.0-1.ca2404.1_all.deb Size: 21164 MD5sum: 798adc5c612e68fcfab773b8557c20ef SHA1: cced8ecc874c0e704165beef75fe87536df51048 SHA256: 6a71fef4b281e544bbe5db169535e29b21a2ce28dc0825be88e4281d89a9fedd SHA512: db5f98bad3b72493aeeca69fe367a697849dfc8a56e1319be1d1ebf2f41585888419e54dc08a09727fa56d5d5f805a175cf58f1bbccd57376ff89a34c9d6b363 Homepage: https://cran.r-project.org/package=SampleSizeSingleArmSurvival Description: CRAN Package 'SampleSizeSingleArmSurvival' (Calculate Sample Size for Single-Arm Survival Studies) Provides methods to calculate sample size for single-arm survival studies using the arcsine transformation, incorporating uniform accrual and exponential survival assumptions. Includes functionality for detailed numerical integration and simulation. This method is based on Nagashima et al. (2021) . Package: r-cran-samplevadir Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2589 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-splitstackshape Suggests: r-cran-haven, r-cran-rio Filename: pool/dists/noble/main/r-cran-samplevadir_1.0.0-1.ca2404.1_all.deb Size: 2614850 MD5sum: 879d294b9cb4a951547599241775152c SHA1: cc95ab421620048b7a5c9f0ae7a5bcbacc154e86 SHA256: ae2a6cc6d522353ca6870e4d7779eaeb1d697feac2a4e794b147e0033869ec24 SHA512: d1887fd1b17437fdb871899a0f78079f2b752589ad1926b5a1f425310bdb8aad768907d660a41281f8563d4bd31ea38fe83d78e0f2dd5e807847f95549b145d7 Homepage: https://cran.r-project.org/package=sampleVADIR Description: CRAN Package 'sampleVADIR' (Draw Stratified Samples from the VADIR Database) Affords researchers the ability to draw stratified samples from the U.S. Department of Veteran's Affairs/Department of Defense Identity Repository (VADIR) database according to a variety of population characteristics. The VADIR database contains information for all veterans who were separated from the military after 1980. The central utility of the present package is to integrate data cleaning and formatting for the VADIR database with the stratification methods described by Mahto (2019) . Data from VADIR are not provided as part of this package. Package: r-cran-samplex Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dt, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-samplex_0.3.0-1.ca2404.1_all.deb Size: 31800 MD5sum: 6189193850cd31725cd387e188574a27 SHA1: b4eed40308e2a03cc3e3db77c091b3d66c7a4874 SHA256: 45d1ab40290c50e1956b84541f2ec1ff047624a7567eac964415c1f34dafe06b SHA512: a96dd8524ec1b429bbafe38a7afde41246b5f74a98b157bfdc2d6dd9a88881757735cff7c3f66a88f228e953f46e76bf3305c1d668db65d32613f3e488a39088 Homepage: https://cran.r-project.org/package=samplex Description: CRAN Package 'samplex' (Shiny Tool for Sample Size Calculation) An interactive 'shiny' application to assist in determining sample sizes for common survey designs such as 'simple random sampling', 'stratified sampling', and 'cluster sampling'. It includes formulas, helper calculators, and illustrative examples. Package: r-cran-samplezoo Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-samplezoo_1.2.1-1.ca2404.1_all.deb Size: 255538 MD5sum: 93625ec71d3bd1fa1342c0ae0226aa04 SHA1: 34099891c26acd7c0e08df6bb8ee0c6b4c55a4b2 SHA256: 547c7b401f3b9157547cc2a5cd8c107fb127ed1e0faa5617f2d840c4ba30cffd SHA512: f278aa1d67504849abe1a33df6c4bf2e6e1f1acfeabe41865b0b59276ee46db4dd7add6f644161bf71eb70d0bb7594affd4b93fc019194f0c89240786fac375f Homepage: https://cran.r-project.org/package=samplezoo Description: CRAN Package 'samplezoo' (Generate Samples with a Variety of Probability Distributions) Simplifies the process of generating samples from a variety of probability distributions, allowing users to quickly create data frames for demonstrations, troubleshooting, or teaching purposes. Data is available in multiple sizes—small, medium, and large. For more information, refer to the package documentation. Package: r-cran-samplingbook Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pps, r-cran-sampling, r-cran-survey Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-samplingbook_1.2.4-1.ca2404.1_all.deb Size: 263930 MD5sum: d384fc0162dd331fa5b69527abbb5e8a SHA1: 5f3f6b76566ce8d12ccc38bc83bce8b461347503 SHA256: 9ca5c934ac98d0f519eef74e03fc4bbd4f12a7d3a6dcc2d6a29508ad7557574c SHA512: bfac2e459bf2e0197cc120deb518f82dac82ad6be6f20d2c1f66b90f7dcead1862b4a1669ed5f65fe3e4a3fec0ce40b8debe02363d6944c499144b64a29a5971 Homepage: https://cran.r-project.org/package=samplingbook Description: CRAN Package 'samplingbook' (Survey Sampling Procedures) Sampling procedures from the book 'Stichproben - Methoden und praktische Umsetzung mit R' by Goeran Kauermann and Helmut Kuechenhoff (2010). 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Package: r-cran-samplingin Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 513 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-magrittr, r-cran-rlang, r-cran-sampling Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-samplingin_1.1.1-1.ca2404.1_all.deb Size: 278162 MD5sum: ec7e3a66d7db07151c52d1957f790cc1 SHA1: baaf2e5fd0e269decd976fd2f6960e791980a9fc SHA256: ae07d8491bfbfa1a320bf55150cbec0252a25b198fc9e4f06ea10fcd6a5eddb4 SHA512: c34571055ad71875cbbec0eca2c25b7126ea57d5dc58c6d4d80d8d348b5d2d562b5eeaf7e39c73df16cbf8df61f96ae609ad1ac077a07dd6054681937f9f05d7 Homepage: https://cran.r-project.org/package=samplingin Description: CRAN Package 'samplingin' (Dynamic Survey Sampling Solutions) A robust solution employing the SRS (Simple Random Sampling), systematic and PPS (Probability Proportional to Size) sampling methods, ensuring a methodical and representative selection of data. Seamlessly allocate predetermined allocations to smaller levels. Package: r-cran-samplingr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-samplingr_1.0.1-1.ca2404.1_all.deb Size: 107398 MD5sum: b4712f1f4b2ec3f290e27b21df22242a SHA1: 55cbefe7a13942af76079db45f52548fafe7f9f9 SHA256: b17320f36b616822fe555614621a21a99d728d77586664e3537ef9cb22307192 SHA512: cb549af2b4f60c3eb3aaf94a299ce5a1ecf2040eaef16b83c2920afc7e71a18bc01455289ce86214ca2aea4deccecc7df7cca909216876f95b4151de2d92e48f Homepage: https://cran.r-project.org/package=samplingR Description: CRAN Package 'samplingR' (Sampling and Estimation Methods) Functions to take samples of data, sample size estimation and getting useful estimators such as total, mean, proportion about its population using simple random, stratified, systematic and cluster sampling. 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It determines a stratification of a sampling frame that minimizes sample cost while satisfying precision constraints in a multivariate and multidomain context. The approach relies on a genetic algorithm; each candidate partition of the frame is an individual whose fitness is evaluated via the Bethel-Chromy allocation to meet target precisions. Functions support analysis of optimization results, labeling of the frame with new strata, and drawing a sample according to the optimal allocation. Algorithmic components adapt code from the 'genalg' package. See M. Ballin and G. Barcaroli (2020) "R package SamplingStrata: new developments and extension to Spatial Sampling" . Package: r-cran-samplrdata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2749 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-samplrdata_1.0.0-1.ca2404.1_all.deb Size: 2715720 MD5sum: 13509ea1b746d965c06952be34c7ac49 SHA1: 1d98df583648ca6a46fb5516e32bcc6b66b82865 SHA256: f3d28993349a02bb8fade8a9faa6ec6aaea7743b7ed6b3542e0a0b57622306fc SHA512: a35db944386f9b4e8db2c4b2063a6a77e760fedb0515831c3014473072bc1e40ab83c9c6f709f62d482a0e561b2a8eaa812a2b05ed8907327c264ba849809614 Homepage: https://cran.r-project.org/package=samplrData Description: CRAN Package 'samplrData' (Datasets from the SAMPLING Project) Contains human behaviour datasets collected by the SAMPLING project (). Package: r-cran-samprior Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rbest, r-cran-matchit, r-cran-metrics, r-cran-assertthat, r-cran-checkmate, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-foreach, r-cran-purrr, r-cran-rstanarm, r-cran-scales, r-cran-broom, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-samprior_3.0.0-1.ca2404.1_all.deb Size: 382454 MD5sum: 7af67f42a5c3830a98e34c4b46599eb7 SHA1: 4ebc85239a857e30e83cadeb861a17a359f0f905 SHA256: 0d77833855ac32e140a27f739a9a00097326c8a7b649f9f316db01a6f4a74c18 SHA512: 8569a4c2de0a17c22b76004a631b03571b4c8a2b33def5ed653eda327f923c755c621076833b83777a67006f7701b24c74074e4b76c8b476ac0a0ca6452692a1 Homepage: https://cran.r-project.org/package=SAMprior Description: CRAN Package 'SAMprior' (Self-Adapting Mixture (SAM) Priors) Implementation of the SAM prior and generation of its operating characteristics for dynamically borrowing information from historical data. For details, please refer to Yang et al. (2023) . Package: r-cran-sampsizeval Architecture: all Version: 1.0.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sn, r-cran-pracma, r-cran-dplyr, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sampsizeval_1.0.0.0-1.ca2404.1_all.deb Size: 29086 MD5sum: 49e0088379ec7fed61fcc02b62c18347 SHA1: 7aa203f5fb95c67e561ee66cc885c3eead5a891b SHA256: 12464440e53e718f8d112ccc3f25058772f70e1dfc27c08190f966f2592d30d4 SHA512: 27f541f5a180dbd8957a3502797ed4f5cdf0dc7661524bfbc36887a15361f4f3e118ec1cf3779e3d599036850a7481db4dbca15aa9b22d9aee553e56ccaa7da9 Homepage: https://cran.r-project.org/package=sampsizeval Description: CRAN Package 'sampsizeval' (Sample Size for Validation of Risk Models with Binary Outcomes) Estimation of the required sample size to validate a risk model for binary outcomes, based on the sample size equations proposed by Pavlou et al. (2021) . For precision-based sample size calculations, the user is required to enter the anticipated values of the C-statistic and outcome prevalence, which can be obtained from a previous study. The user also needs to specify the required precision (standard error) for the C-statistic, the calibration slope and the calibration in the large. The calculations are valid under the assumption of marginal normality for the distribution of the linear predictor. 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Functionality is provided for Gibbs sampling as in Algorithm 3 of Neal (2000) , restricted Gibbs merge-split sampling as described in Jain & Neal (2004) , and sequentially-allocated merge-split sampling , as well as summary and utility functions. 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Blodorn Kim , this package brings Wada's color combinations to R for easy use in data visualizations. This package honors 60 of Wada's color combinations: 20 duos, 20 trios, and 20 quads. Package: r-cran-saotd Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2455 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-widyr, r-cran-stringr, r-cran-tidytext, r-cran-rtweet, r-cran-tidyr, r-cran-igraph, r-cran-ggplot2, r-cran-ggraph, r-cran-scales, r-cran-reshape2, r-cran-lubridate, r-cran-magrittr, r-cran-ldatuning, r-cran-topicmodels Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-httr, r-cran-base64enc, r-cran-tibble, r-cran-covr Filename: pool/dists/noble/main/r-cran-saotd_0.3.1-1.ca2404.1_all.deb Size: 1948188 MD5sum: a3714a4c8591754ea0d94d3d4e2cfd2d SHA1: 399350240ff96396d4407829a5809d9c77e1f21f SHA256: 0f35261b35872b403d3c704fa09e86debe1b1414507580792afa28a21b142367 SHA512: 58a6566e7c17fa379bc83d892ff64db9e13ae5eab92d30461c1f2aaed3bc2867e849173d2ca0253df00463f4ab666f4d1e008ddeb84bd967c22dd7a46f8e309d Homepage: https://cran.r-project.org/package=saotd Description: CRAN Package 'saotd' (Sentiment Analysis of Twitter Data) This analytic is an in initial foray into sentiment analysis. 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Package: r-cran-sap Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bsda Filename: pool/dists/noble/main/r-cran-sap_1.0-1.ca2404.1_all.deb Size: 14052 MD5sum: b0e285ddcf7eb697e81870a57336cbba SHA1: 338d175c402ee07efe649852c9907a69edb297a3 SHA256: 2833434da7670584540ff2eb1ed12e4d2fde56621e790ebaea91e171621b2782 SHA512: 66a1d25ef37b901a0a24e6bf763c8ed9dc1efd76d3dfb945b5841fb59d1e56740783cea25d3184fc920fec12078734dc064f1e7783f801679b70a51347bd61cc Homepage: https://cran.r-project.org/package=SAP Description: CRAN Package 'SAP' (Statistical Analysis and Programming) The Hypothesis tests for the means of independent or paired groups. This package investigates the normality assumption automatically. Then, it tests the hypothesis tests for two independent or paired group means by using parametric or non-parametric tests. 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Package: r-cran-sapevom Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sapevom_0.2.0-1.ca2404.1_all.deb Size: 21456 MD5sum: cde7fe80ca950312a9b13b3ac28ca55e SHA1: afadc6c5be75cfe9d0850d98487aae167b2e221e SHA256: 593d671d58470cbac2338d1857243c00cf8f5e9d8110f1523f46f9c9a4874ecb SHA512: e7aad164b1c876bb75d62b5fc7d36641f042d7092addc44fc69b4ee424e2931608d839ab870fe0e3d6c25970c382f35f8d919908c5d81d93c67055a86992c9c1 Homepage: https://cran.r-project.org/package=sapevom Description: CRAN Package 'sapevom' (Group Ordinal Method for Multiple Criteria Decision-Making) Implementation of SAPEVO-M, a Group Ordinal Method for Multiple Criteria Decision-Making (MCDM). SAPEVO-M is an acronym for Simple Aggregation of Preferences Expressed by Ordinal Vectors Group Decision Making. 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Package: r-cran-sapo Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sapo_0.8.0-1.ca2404.1_all.deb Size: 98072 MD5sum: e1ab9ae07a131be1595075de017eaa2d SHA1: ec4c79c3888fdfac7e30f244b981c8a0e5fecbf2 SHA256: 95bea1fdc206c1f331fa75465822dc3ceed97a66825ddcc794206a2baee30008 SHA512: a27137a909034f549a86662cdbff0c43c7699db18638191fa9115d217f6dad806614016277a492ef4ca4c5905243372aead07d4d55875116c43206bbbb1e054e Homepage: https://cran.r-project.org/package=sapo Description: CRAN Package 'sapo' (Spatial Association of Different Types of Polygon) In ecology, spatial data is often represented using polygons. These polygons can represent a variety of spatial entities, such as ecological patches, animal home ranges, or gaps in the forest canopy. Researchers often need to determine if two spatial processes, represented by these polygons, are independent of each other. For instance, they might want to test if the home range of a particular animal species is influenced by the presence of a certain type of vegetation. To address this, Godoy et al. (2022) () developed conditional Monte Carlo tests. These tests are designed to assess spatial independence while taking into account the shape and size of the polygons. Package: r-cran-saqgetr Architecture: all Version: 0.2.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-lubridate, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-tidyr, r-cran-magrittr, r-cran-httr Suggests: r-cran-openair Filename: pool/dists/noble/main/r-cran-saqgetr_0.2.21-1.ca2404.1_all.deb Size: 77820 MD5sum: 10eeeba6dc3a14454a7fbf22ea007428 SHA1: 40a14d11f35df6d5a5609eab6443d878a8952421 SHA256: 09200d991fef30853e1ed6580910d09c24ed88bfabb691b5378cd16e4e9381fd SHA512: 024319bd577f39fe9ec07a14b476b4fbfea258595aee90e94d3bf8f5a16341094a3eca3185ff83260a083f7828b5a914aba9b09dee787f27de61a40e9653b3f7 Homepage: https://cran.r-project.org/package=saqgetr Description: CRAN Package 'saqgetr' (Import Air Quality Monitoring Data in a Fast and Easy Way) A collection of tools to access prepared air quality monitoring data files from web servers with ease and speed. 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Package: r-cran-sara4r Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2555 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tcltk2, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sara4r_0.1.0-1.ca2404.1_all.deb Size: 1986246 MD5sum: bb4e33a01d5759db2b88304edf14099f SHA1: 0ba27b423d9ff5d12023e1140cfecd431489f9f9 SHA256: 4d1c9e57f0d9dc543dfe01a73d36fde0f18ab37e9efd7f56dff00bce0a145e30 SHA512: 15d49659ff401df3b87cbdbb7c140c6134afc0a5f0b43227fcb2da6866bf43509508d4296146c9668ad7a9eb1002cc80f4a8a223bf5a28721d11115bb9fa7897 Homepage: https://cran.r-project.org/package=sara4r Description: CRAN Package 'sara4r' (An R-GUI for Spatial Analysis of Surface Runoff using theNRCS-CN Method) A Graphical user interface to calculate the rainfall-runoff relation using the Natural Resources Conservation Service - Curve Number method (NRCS-CN method) but include modifications by Hawkins et al., (2002) about the Initial Abstraction. This GUI follows the programming logic of a previously published software (Hernandez-Guzman et al., 2011). It is a raster-based GIS tool that outputs runoff estimates from Land use/land cover and hydrologic soil group maps. This package has already been published in Journal of Hydroinformatics (Hernandez-Guzman et al., 2021) but it is under constant development at the Institute about Natural Resources Research (INIRENA) from the Universidad Michoacana de San Nicolas de Hidalgo and represents a collaborative effort between the Hydro-Geomatic Lab (INIRENA) with the Environmental Management Lab (CIAD, A.C.). Package: r-cran-saros.base Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vctrs, r-cran-rlang, r-cran-cli, r-cran-tidyselect, r-cran-dplyr, r-cran-tidyr, r-cran-glue, r-cran-stringi, r-cran-forcats, r-cran-fs, r-cran-yaml, r-cran-zip, r-cran-rstudioapi, r-cran-bcrypt Suggests: r-cran-covr, r-cran-haven, r-cran-srvyr, r-cran-readr, r-cran-qs, r-cran-purrr, r-cran-writexl, r-cran-webshot, r-cran-usethis, r-cran-quarto, r-cran-labelled, r-cran-testthat, r-cran-tibble, r-cran-withr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-saros.base_1.2.0-1.ca2404.1_all.deb Size: 516212 MD5sum: 89302ff3a1f6df648f7f8e81f0265df0 SHA1: 5e6af95182a451d874a8a1acb317bf1677366ec2 SHA256: 6602a3d63ba27dfc6cf61e1ed79682cf358a1ee3ba1c39baeb64d594a19e9388 SHA512: fa28f91c7dd9e183b765eb1e479ab785aa0105d1728ad2dd165995d825dbc38137513292e0df1d80642d379fe59865011e4d7bb7fd1151f46496e80759ff1cbb Homepage: https://cran.r-project.org/package=saros.base Description: CRAN Package 'saros.base' (Base Tools for Semi-Automatic Reporting of Ordinary Surveys) Scaffold an entire web-based report using template chunks, based on a small chapter overview and a dataset. Highly adaptable with prefixes, suffixes, translations, etc. Also contains tools for password-protecting, e.g. for each organization's report on a website. Developed for the common case of a survey across multiple organizations/sites where each organization wants to obtain results for their organization compared with everyone else. See 'saros' () for tools used for authors in the drafted reports. Package: r-cran-saros Architecture: all Version: 1.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 836 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-fs, r-cran-ggiraph, r-cran-ggplot2, r-cran-glue, r-cran-mschart, r-cran-officer, r-cran-rlang, r-cran-stringi, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-covr, r-cran-haven, r-cran-labelled, r-cran-pdftools, r-cran-quarto, r-cran-knitr, r-cran-readr, r-cran-scales, r-cran-spelling, r-cran-srvyr, r-cran-survey, r-cran-testthat, r-cran-tibble, r-cran-vdiffr, r-cran-withr, r-cran-writexl, r-cran-readxl Filename: pool/dists/noble/main/r-cran-saros_1.6.2-1.ca2404.1_all.deb Size: 676500 MD5sum: 8a8cea29d96e5b001e38104d13e3ac6e SHA1: 50af15394993dae9eda5ae73fd3e8e149c9debf9 SHA256: f7226d2e98fbd24241025b69c2fe03a947a222d1383cf0a36fa99d82be7aa841 SHA512: 9a552e91e542a7587587cca76fc554abea455aff69c39d261046b0043dd89d6ad2bfabba6660f5238d313e753b8ebbaed7af10591783d534cc2b53b66f73ee22 Homepage: https://cran.r-project.org/package=saros Description: CRAN Package 'saros' (Semi-Automatic Reporting of Ordinary Surveys) Offers a systematic way for conditional reporting of figures and tables for many (and bivariate combinations of) variables, typically from survey data. Contains interactive 'ggiraph'-based () plotting functions and data frame-based summary tables (bivariate significance tests, frequencies/proportions, unique open ended responses, etc) with many arguments for customization, and extensions possible. Uses a global options() system for neatly reducing redundant code. Also contains tools for immediate saving of objects and returning a hashed link to the object, useful for creating download links to high resolution images upon rendering in 'Quarto'. Suitable for highly customized reports, primarily intended for survey research. Package: r-cran-sarp.compo Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-car Suggests: r-cran-lme4 Filename: pool/dists/noble/main/r-cran-sarp.compo_0.1.8-1.ca2404.1_all.deb Size: 247070 MD5sum: 1e43d3072c20a129232e447b7ea96c7b SHA1: 56408c10effa0ac9ff4ee8dfa0dc0af905c1acab SHA256: d189b07cb83a901571a09d94bf5a1c926cb7dce5ffe1f59d28435e371bb54fb2 SHA512: e0923ca0b92611376d297b7cee80a114f4cd284ef38a164953dfd4819bcac7c646a678bb08b645230fbff6d6bc117c2297bc27efc5c6369bfc662d0f24f15a51 Homepage: https://cran.r-project.org/package=SARP.compo Description: CRAN Package 'SARP.compo' (Network-Based Interpretation of Changes in Compositional Data) Provides a set of functions to interpret changes in compositional data based on a network representation of all pairwise ratio comparisons: computation of all pairwise ratio, construction of a p-value matrix of all pairwise tests of these ratios between conditions, conversion of this matrix to a network. Package: r-cran-sarp.moodle Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-magick Suggests: r-cran-readods, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-sarp.moodle_1.2.3-1.ca2404.1_all.deb Size: 391808 MD5sum: 7ab1b76c6ba9c295568276bca573d0c4 SHA1: 4c4c2ea40a21f2b3f7973b24d8e17cb30b4cd5a4 SHA256: 36afc9e90fbf1540fcbed768916bfc07cb36f7aa151d149187c789f1786b1030 SHA512: 3e27db0c9079a1ed43281e9637f2114831346f6854afab4299f50d1b8d7a5771af40907422e42efb6183c0842e424b3665a713e600c442276ae309919460a6f0 Homepage: https://cran.r-project.org/package=SARP.moodle Description: CRAN Package 'SARP.moodle' (XML Output Functions for Easy Creation of Moodle Questions) Provides a set of basic functions for creating Moodle XML output files suited for importing questions in Moodle (a learning management system, see for more information). Package: r-cran-sarp.snowprofile.alignment Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3850 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sarp.snowprofile, r-cran-cluster, r-cran-dtw, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-dendextend, r-cran-smacof, r-cran-testthat, r-cran-progress Filename: pool/dists/noble/main/r-cran-sarp.snowprofile.alignment_2.0.2-1.ca2404.1_all.deb Size: 2824942 MD5sum: 6c57643055fa2c9c7017f1b4cee63723 SHA1: 166f54e5124fb5e770f93ae940eb1622fb8c53f0 SHA256: 4f44e0734acf3dbc4591168d96dd7b3303fcbcdb661e2cf306774c4aa9f82219 SHA512: f95218414d09a7e7debb394d641577d9b97129c9fe7491cd309ea145bb3dbc5c5648ae76cba960d1b6c6939a6419a05a663e36b78ecb99ce5809686a5875be92 Homepage: https://cran.r-project.org/package=sarp.snowprofile.alignment Description: CRAN Package 'sarp.snowprofile.alignment' (Snow Profile Alignment, Aggregation, and Clustering) Snow profiles describe the vertical (1D) stratigraphy of layered snow with different layer characteristics, such as grain type, hardness, deposition date, and many more. Hence, they represent a data format similar to multivariate time series containing categorical, ordinal, and numerical data types. Use this package to align snow profiles by matching their individual layers based on Dynamic Time Warping (DTW). The aligned profiles can then be assessed with an independent, global similarity measure that is geared towards avalanche hazard assessment. Finally, through exploiting data aggregation and clustering methods, the similarity measure provides the foundation for grouping and summarizing snow profiles according to similar hazard conditions. In particular, this package allows for averaging large numbers of snow profiles with DTW Barycenter Averaging and thereby facilitates the computation of individual layer distributions and summary statistics that are relevant for avalanche forecasting purposes. For more background information refer to Herla, Horton, Mair, and Haegeli (2021) , Herla, Mair, and Haegeli (2022) , and Horton, Herla, and Haegeli (2024) . Package: r-cran-sarp.snowprofile.pyface Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3988 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sarp.snowprofile, r-cran-reticulate, r-cran-data.table Filename: pool/dists/noble/main/r-cran-sarp.snowprofile.pyface_0.4.3-1.ca2404.1_all.deb Size: 459368 MD5sum: e76d48a3e6a427a8aadf71d65b3281b3 SHA1: dad44e4ae252c996529d9201fcd4525c8a24e4a4 SHA256: c6df8033be21fac46a69014fca3a103444136dc3dfda978e9b8fdb052e76428a SHA512: 88359ccd4e7b457d0c4471cab6bc4d469a95d99af4309063c8b48c22f1d67d28fcc1ca5964b9c6c63a4929abd2a550c200a96fc67e77361422bcca6200e6f264 Homepage: https://cran.r-project.org/package=sarp.snowprofile.pyface Description: CRAN Package 'sarp.snowprofile.pyface' ('python' Modules from Snowpack and Avalanche Research) The development of post-processing functionality for simulated snow profiles by the snow and avalanche community is often done in 'python'. This package aims to make some of these tools accessible to 'R' users. Currently integrated modules contain functions to calculate dry snow layer instabilities in support of avalache hazard assessments following the publications of Richter, Schweizer, Rotach, and Van Herwijnen (2019) , and Mayer, Van Herwijnen, Techel, and Schweizer (2022) . Package: r-cran-sarp.snowprofile Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 858 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-sarp.snowprofile.alignment Filename: pool/dists/noble/main/r-cran-sarp.snowprofile_1.4.1-1.ca2404.1_all.deb Size: 559332 MD5sum: 81831ecff74cfbd3dc060e95b22951db SHA1: c12377e5c360d05d310b5fbf1b5a650c4baf91cb SHA256: 9bf964b9804f2408c91d589adc903872a4fb0fbe8d53fb629ce5a86c3d72097e SHA512: 452324b8e6acfb532a9ff9a3d4f28ae0643fd7ec5d80f4a442f60a06a516d4897ece06face9da654c47f697eb6c02d58c9b4d523636f690f043268b76409c3bf Homepage: https://cran.r-project.org/package=sarp.snowprofile Description: CRAN Package 'sarp.snowprofile' (Snow Profile Analysis for Snowpack and Avalanche Research) Analysis and plotting tools for snow profile data produced from manual snowpack observations and physical snowpack models. The functions in this package support snowpack and avalanche research by reading various formats of data (including CAAML, SMET, generic csv, and outputs from the snow cover model SNOWPACK), manipulate the data, and produce graphics such as stratigraphy and time series profiles. Package developed by the Simon Fraser University Avalanche Research Program . Graphics apply visualization concepts from Horton, Nowak, and Haegeli (2020, ). Package: r-cran-sars Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1082 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nortest, r-cran-crayon, r-cran-cli, r-cran-numderiv, r-cran-doparallel, r-cran-foreach, r-cran-aiccmodavg, r-cran-minpack.lm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-sars_2.1.1-1.ca2404.1_all.deb Size: 757918 MD5sum: 171fcbb47c8685b6b1c90c465e354ca8 SHA1: 57c778b44273d357359e5de2049e86510b3c09e1 SHA256: 0853051aedddb077ddb67318d7006d115f90e745b991a02bccbf404f6a6df56a SHA512: d8b9561937e84c99a4f5fe521a0fb8092fcab73ec9ef3a7ee90c3f4f42e035e84b33d73bc09650c48b0427e376b452b62a5334ce3355dd48f502814bbc4bfdff Homepage: https://cran.r-project.org/package=sars Description: CRAN Package 'sars' (Fit and Compare Species-Area Relationship Models UsingMultimodel Inference) Implements the basic elements of the multi-model inference paradigm for up to twenty species-area relationship models (SAR), using simple R list-objects and functions, as in Triantis et al. 2012 . The package is scalable and users can easily create their own model and data objects. Additional SAR related functions are provided. Package: r-cran-sas7bdat Architecture: all Version: 0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sas7bdat_0.8-1.ca2404.1_all.deb Size: 202784 MD5sum: f37c88ce60d86edbafb8e6c7fcbfba28 SHA1: b328f24e149ff2e121b62966e520caf724e6f5ff SHA256: ac168bb2893c066575ad723d8885772ea887c3344dee1c35b5d1fec842487589 SHA512: d089cfdbdd635966b22674a36187f14181c7c4c1dd231a0fd1f923dda175151ae1b356c3e1f16875aa6f27632a4357dd4aaa4ec0b318ca4ea99e657aae502419 Homepage: https://cran.r-project.org/package=sas7bdat Description: CRAN Package 'sas7bdat' (sas7bdat Reverse Engineering Documentation) Documentation and prototypes for the earliest (circa 2010) open-source effort to reverse engineer the sas7bdat file format. The package includes a prototype reader for sas7bdat files. However, newer packages may contain more robust readers for sas7bdat files. Package: r-cran-sascii Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sascii_1.0.2-1.ca2404.1_all.deb Size: 43318 MD5sum: 45fb367284d6e1e8f3a7b50e1fca10ba SHA1: 0fecd174bbceb6c71c4d75677804dc59d6bc13ac SHA256: 087775603aa59636d24aa54f0f19d44d21fa7ba22b77c7f204fb1442f3251a8f SHA512: f75dfa6c0397d70d3a6db2e9768cadf35a1485250b616d6932f280bf5f1408498c32f4f3f37627037465953d6896b0341771f3f4b275ff9d721bc1d1d83e6d2c Homepage: https://cran.r-project.org/package=SAScii Description: CRAN Package 'SAScii' (Import ASCII Files Directly into R using Only a 'SAS' InputScript) Using any importation code designed for 'SAS' users to read ASCII files into 'sas7bdat' files, this package parses through the INPUT block of a '.sas' syntax file to design the parameters needed for a 'read.fwf()' function call. This allows the user to specify the location of the ASCII (often a '.dat') file and the location of the 'SAS' syntax file, and then load the data frame directly into R in just one step. Package: r-cran-sasdates Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sasdates_0.1.0-1.ca2404.1_all.deb Size: 19518 MD5sum: b3225a3493115861173f250301a0619c SHA1: e7564241e25ceef3ea785df19ab98da57ba14673 SHA256: b41f21e550e50c83ca7247eed276a66d04bccc827c7bf9521485ac5a09f1b769 SHA512: c21661e473211ca8af7a07be6b1ae8540e055f5e37fbbefb54db41ee6081a9642f169b030ad1e34f9960bca42e1016fd86dc02167ab84362c4525ca4c0da343a Homepage: https://cran.r-project.org/package=SASdates Description: CRAN Package 'SASdates' (Convert the Dates to 'SAS' Formats) Converts the dates to different 'SAS' date formats. In 'SAS' dates are a special case of numeric values. Each day is assigned a specific numeric value, starting from January 1, 1960. This date is assigned the date value 0, and the next date has a date value of 1 and so on. The previous days to this date are represented by -1 , -2 and so on. With this approach, 'SAS' can represent any date in the future or any date in the past. There are many date formats used in 'SAS' to represent date-time. Here, we try to develop functions which will convert the date to different 'SAS' date formats. Package: r-cran-sasif Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sasif_0.1.3-1.ca2404.1_all.deb Size: 33958 MD5sum: a5676a295bd6d1ff6fbd160e34539716 SHA1: 1e6f53a05b7d30dd9f962b6ccf996b5d58cd0bd1 SHA256: 8f51fc86fd3c9943dbf702b247afa4270e7d236c5dd688b8e5655548393c5fc4 SHA512: 054bcea9448d918cd8ae05ab3456bd8a7b14ac01f16e0890a0e49549010bd9e91461b17432bb788360ea3930155b5bf0b7cb5361283d60380c63b37dccbd5221 Homepage: https://cran.r-project.org/package=sasif Description: CRAN Package 'sasif' ('SAS' IF Style Data Step Logic for Data Tables) Provides 'SAS'-style IF/ELSE chains, independent IF rules, and DELETE logic for 'data.table', enabling clinical programmers to express Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM)-style derivations in familiar SAS-like syntax. Methods are informed by clinical data standards described in CDISC SDTM and ADaM implementation guides. See and . Package: r-cran-saslm Architecture: all Version: 0.10.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1328 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-saslm_0.10.8-1.ca2404.1_all.deb Size: 1183608 MD5sum: 7653b7685b7b0c551bc566be34116c6b SHA1: 00701cd8edaacbf8017b589c644febfae4ddb982 SHA256: 74adf30f33ec1f6283af087ae30c837544d0f4b6203306a5415877478dad2252 SHA512: de67039bb0f134817fc5a9b6e23308816d6f3f09a77487bd7547cde5a115b343ec57bef60d447a8db71ac9abedc3172638099b2decd6293f4e937c8769510ac7 Homepage: https://cran.r-project.org/package=sasLM Description: CRAN Package 'sasLM' ('SAS' Linear Model) This is a core implementation of 'SAS' procedures for linear models - GLM, REG, ANOVA, TTEST, FREQ, and UNIVARIATE. Some R packages provide type II and type III SS. However, the results of nested and complex designs are often different from those of 'SAS.' Different results does not necessarily mean incorrectness. However, many wants the same results to SAS. This package aims to achieve that. Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0). Package: r-cran-sasmarkdown Architecture: all Version: 0.8.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-xfun Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sasmarkdown_0.8.7-1.ca2404.1_all.deb Size: 100376 MD5sum: 8a8826dcfeea46953565d838c20cc435 SHA1: f6d7c5a7404fdc4840e1a2c7dbe824378d44266f SHA256: f98a2d8892562fb1b13e926639e1c5ed7af90c9e4b3819d4719d78e509735129 SHA512: 07662bb4e9dd67b3ccfedbb0dd5726e1f431c4f6bfb251af64637b34d7a406ea10ca85997a5ff23a69f3ef1813bf0d0eeca2e0b7be17ded9463b741c62efb664 Homepage: https://cran.r-project.org/package=SASmarkdown Description: CRAN Package 'SASmarkdown' ('SAS' Markdown) Settings and functions to extend the 'knitr' 'SAS' engine. Package: r-cran-sasmixed Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-lme4, r-cran-lattice Filename: pool/dists/noble/main/r-cran-sasmixed_1.0-5-1.ca2404.1_all.deb Size: 330846 MD5sum: 26945b1536ecfe5d8896cfe0a8d3106c SHA1: 0e760c3cf1ba4e44071ada701dd2cb5b98f563f8 SHA256: 423b048d578b697596901a72fbac178ec2136c05d5ac39e85d8266e1390dfd1d SHA512: d839774e88a1726674c1436892d7d8fdc5b4cf4385439e39f10a1dcabd2fbca8bff47272d8676c26b0a311f2494ede3a79e05038a656e514d167786d264b0e6e Homepage: https://cran.r-project.org/package=SASmixed Description: CRAN Package 'SASmixed' (Data Sets from "SAS System for Mixed Models) Data sets and sample lmer analyses corresponding to the examples in Littell, Milliken, Stroup and Wolfinger (1996), "SAS System for Mixed Models", SAS Institute. Package: r-cran-sasquatch Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-evaluate, r-cran-htmlwidgets, r-cran-knitr, r-cran-reticulate, r-cran-rlang, r-cran-rstudioapi Suggests: r-cran-curl, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-sasquatch_0.1.3-1.ca2404.1_all.deb Size: 1393556 MD5sum: 16eaab07e46d435866ae567b50c7b35e SHA1: 157f4790c6c57336ae32470a5b5ab2e8dd445e50 SHA256: dc81c7f34741828dcd9e4b38dfa8da04ca110d3f5dcecfa2c656ae2ec86eca99 SHA512: 018010be25e5b8362bf48dfa8a4b09efba3d417b9e4cebdc242e4aa3e98e0ed0123460b97e0b941510b7e195fc8b167f082ea6b6bb3de366dc330708ee6ac071 Homepage: https://cran.r-project.org/package=sasquatch Description: CRAN Package 'sasquatch' (Use 'SAS', R, and 'quarto' Together) Use R and 'SAS' within reproducible multilingual 'quarto' documents. Run 'SAS' code blocks interactively, send data back and forth between 'SAS' and R, and render 'SAS' output within 'quarto' documents. 'SAS' connections are established through a combination of 'SASPy' and 'reticulate'. Package: r-cran-sasr Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-lifecycle, r-cran-reticulate Suggests: r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sasr_0.1.5-1.ca2404.1_all.deb Size: 365090 MD5sum: 69960f26fba3d09cec58764e5d8ad4a8 SHA1: be267d9446dd321695c6baa6178c46ddfd8a774f SHA256: f23b8fa1287d5158275ff178f4261a72cf26f6bf69591337b53c0790404bab60 SHA512: 045548f9d5dc753edd7e1db04b3c5a1ba8997c1e1099e0031817fd5f04531e479411835af89d8dadf1e6b909639ddcb6e0296fad22574c75076f7b693d2fca72 Homepage: https://cran.r-project.org/package=sasr Description: CRAN Package 'sasr' ('SAS' Interface) Provides a 'SAS' interface, through 'SASPy'() and 'reticulate'(). This package helps you create 'SAS' sessions, execute 'SAS' code in remote 'SAS' servers, retrieve execution results and log, and exchange datasets between 'SAS' and 'R'. It also helps you to install 'SASPy' and create a configuration file for the connection. Please review the 'SASPy' license file as instructed so that you comply with its separate and independent license. Package: r-cran-sassy Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmtr, r-cran-common, r-cran-logr, r-cran-libr, r-cran-reporter, r-cran-procs, r-cran-macro Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidylog, r-cran-magrittr, r-cran-covr Filename: pool/dists/noble/main/r-cran-sassy_1.3.0-1.ca2404.1_all.deb Size: 1698066 MD5sum: 6b4e411a834b80e9632a352a4686821d SHA1: 6bc8fc6ce7d45d42aba59096b0e54dbda085cac2 SHA256: d586f1a6acb58f32b5ca5a6b9e4f9f9f103cc29e1eaea74470b64189e6369062 SHA512: 8d7d9d003f161fa17de83c96fcbfcb0354d80ec8827c558cac1dce0516200b9cf1a2fe3da2a75d2d117187cd7cc73eae3ed6b1ecf4a2edbfad2a99f2857d8ec2 Homepage: https://cran.r-project.org/package=sassy Description: CRAN Package 'sassy' (Makes 'R' Easier for Everyone) A meta-package that aims to make 'R' easier for everyone, especially programmers who have a background in 'SAS®' software. This set of packages brings many useful concepts to 'R', including data libraries, data dictionaries, formats and format catalogs, a data step, and a traceable log. The system also includes a package that replicates several commonly-used 'SAS®' procedures, like 'PROC FREQ', 'PROC MEANS', and 'PROC REG'. Package: r-cran-sate Architecture: all Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ellipse, r-cran-mass, r-cran-survey Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sate_3.1.1-1.ca2404.1_all.deb Size: 159280 MD5sum: ea3fa453ef6f95f4f9a1fbbebfda1ee2 SHA1: dc2ec5096707ffccbef44e754299e680c0348121 SHA256: b925cd5cfb200d070b588a5ecfadb1bf40e1ba607e085fc99257edd960f7d6ed SHA512: 511f188fe21931ea1b415159c7f51a34b0bcfaceddcf7f077b9eb6f9fa222ce225581a34565406fa7b3f4cc80d725ad3c8641a3947a26cfe8015990af51755a8 Homepage: https://cran.r-project.org/package=sate Description: CRAN Package 'sate' (Scientific Analysis of Trial Errors (SATE)) Bundles functions used to analyze the harmfulness of trial errors in criminal trials. Functions in the Scientific Analysis of Trial Errors ('sate') package help users estimate the probability that a jury will find a defendant guilty given jurors' preferences for a guilty verdict and the uncertainty of that estimate. Users can also compare actual and hypothetical trial conditions to conduct harmful error analysis. The conceptual framework is discussed by Barry Edwards, A Scientific Framework for Analyzing the Harmfulness of Trial Errors, UCLA Criminal Justice Law Review (2024) and Barry Edwards, If The Jury Only Knew: The Effect Of Omitted Mitigation Evidence On The Probability Of A Death Sentence, Virginia Journal of Social Policy & the Law (2025) . The relationship between individual jurors' verdict preferences and the probability that a jury returns a guilty verdict has been studied by Davis (1973) ; MacCoun & Kerr (1988) , and Devine et el. (2001) , among others. Package: r-cran-satin Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1071 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ncdf4, r-cran-maps, r-cran-pbsmapping, r-cran-splancs, r-cran-sp, r-cran-geosphere Filename: pool/dists/noble/main/r-cran-satin_1.2.0-1.ca2404.1_all.deb Size: 1034310 MD5sum: 8f43f6f96f0e187bc8c81bc549e02ed0 SHA1: 21e28f142ec88d8b81e08c2a18688f2a42fb865c SHA256: 41d5e5e618430ed4db6516233e387d7cb9f0c96b66770e2fecf919806098891f SHA512: 2df5a783bd74f9791d968323f236148c82e7fe2174972df20e4473b7beacf12dca047808617e6d129df69491ae1ebaa21a608effaa603b4ef521862038712728 Homepage: https://cran.r-project.org/package=satin Description: CRAN Package 'satin' (Visualisation and Analysis of Ocean Data Derived from Satellites) With 'satin' functions, visualisation, data extraction and further analysis like producing climatologies from several images, and anomalies of satellite derived ocean data can be easily done. Reading functions can import a user defined geographical extent of data stored in netCDF files. Currently supported ocean data sources include NASA's Oceancolor web page , sensors VIIRS-SNPP; MODIS-Terra; MODIS-Aqua; and SeaWiFS. Available variables from this source includes chlorophyll concentration, sea surface temperature (SST), and several others. Data sources specific for SST that can be imported too includes Pathfinder AVHRR and GHRSST . In addition, ocean productivity data produced by Oregon State University can also be handled previous conversion from HDF4 to HDF5 format. Many other ocean variables can be processed by importing netCDF data files from two European Union's Copernicus Marine Service databases , namely Global Ocean Physical Reanalysis and Global Ocean Biogeochemistry Hindcast. Package: r-cran-satres Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2432 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-snakecase, r-cran-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-satres_1.1.1-1.ca2404.1_all.deb Size: 1767358 MD5sum: 79a9074490735b65dc9b710640d914d2 SHA1: f1a8725ca45714c2f7b992abe6c7ae6e0c455a32 SHA256: 1f563072bcfd582e18effe98ed2310530ba64caebdca074ca12e65105cc8b1b0 SHA512: 04c1e7df2fcf88f4ef7fa2fd1c02f35bc96bcd6049c979f02bb7e69d7decd17f03a847ff5d5ecc279c5dcd37bfea62f3d2baf721d92860032c904213e3cb07cd Homepage: https://cran.r-project.org/package=satres Description: CRAN Package 'satres' (Grouping Satellite Bands by Spectral and Spatial Resolution) Given raster files directly downloaded from various websites, it generates a raster structure where it merges them if they are tiles of the same scene and classifies them according to their spectral and spatial resolution for easy access by name. Package: r-cran-saturncoefficient Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixcorrelation, r-cran-projectionbasedclustering, r-cran-umap Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-saturncoefficient_1.6-1.ca2404.1_all.deb Size: 29832 MD5sum: 1ffff188087dd48c57dd1a1b9b232a2e SHA1: e81353563f60a287e2685de972646807a77c7d5c SHA256: a4b98c32c468b8fd615030e41f37d7c62a520193e6c1e61f2d7a25e0d6d62bb5 SHA512: 433d6e57bcdf6e484d6bbf41317106bf15f572badcf1b7bcf8b18d682f10ec76065e41a1cf261c835680b929f93550b9b0e52ed813ee409261407fdd0cb8f183 Homepage: https://cran.r-project.org/package=SaturnCoefficient Description: CRAN Package 'SaturnCoefficient' (Statistical Evaluation of UMAP Dimensionality Reductions) A metric expressing the quality of a UMAP layout. This is a package that contains the Saturn_coefficient() function that reads an input matrix, its dimensionality reduction produced by UMAP, and evaluates the quality of this dimensionality reduction by producing a real value in the [0; 1] interval. We call this real value Saturn coefficient. A higher value means better dimensionality reduction; a lower value means worse dimensionality reduction. Reference: Davide Chicco et al. (February 2026), "The advantages of our proposed Saturn coefficient over continuity and trustworthiness for UMAP dimensionality reduction evaluation", PeerJ Computer Science 12:e3424 (pp. 1-30), . Package: r-cran-sautomata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sautomata_0.1.0-1.ca2404.1_all.deb Size: 39816 MD5sum: 9d53942cfd524fcf5071a5a32e359f4a SHA1: e48934405931372815c2cd974b48f6b86246ff59 SHA256: 4dab1042b1a941740616f0fbc0de75b9d960676b0c0213e521a07392776fa9e2 SHA512: 5923b83f91a2ad565d7e2ff8cff327b6236e2a6abfaca9e34098d0c1cc477a2cb783156167d5bffc6813ed66f64d2df3089a50cea3150c5c29f15eace5485b32 Homepage: https://cran.r-project.org/package=SAutomata Description: CRAN Package 'SAutomata' (Inference and Learning in Stochastic Automata) Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) . 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See Huang M, et al (2018) for more details. Package: r-cran-savonliquide Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-htmltools, r-cran-httr, r-cran-crayon Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-savonliquide_0.2.0-1.ca2404.1_all.deb Size: 37642 MD5sum: 81728882970a092da2dd31e85d08d932 SHA1: c62600ff51f020242c87cabede9cf3e58f2ac86d SHA256: 730bee60c1733d5b3b108915ebcf1839a59da8a9ba50cb6625a702035f20a760 SHA512: d15086ad255d849489a364861ecd72e8c901b1be2eb6319d4e9c7c353e400d17170d5737be84729e820a4d2bb7f615e2ce8e6f6651b4493202c619b63db02714 Homepage: https://cran.r-project.org/package=savonliquide Description: CRAN Package 'savonliquide' (Accessibility Toolbox for 'R' Users) Provides a toolbox that allows the user to implement accessibility related concepts. 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Methods include the Stein estimator (St), Diagonal Shrinkage (DSh), Simple Slab Regression (SR), Generalized Slab Regression (GSR), Ledoit-Wolf Linear Shrinkage (LW), Quadratic-Inverse Shrinkage (QIS), and Shrinkage (Sh), all integrated into the iteratively reweighted least squares (IRLS) algorithm. This approach enhances estimation accuracy, convergence, and robustness in the presence of multicollinearity. The best-fitting model is selected based on the Akaike Information Criterion (AIC). Methods are related to methods described in Marschner (2011) , Asimit et al. (2025) , Ledoit and Wolf (2004) , and Ledoit and Wolf (2022) . 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The package solves an optimization problem that balances the contribution of each predictor variable to ensure estimation stability in the presence of multicollinearity. It supports two distinct parameterization methods, a Budget-based approach that allocates a fixed loss contribution to each predictor, and a Target-based approach (t-tuning) that utilizes a relative elasticity weight for the response variable. The package provides comprehensive tools for model estimation, risk distribution analysis, and parameter tuning via cross-validation (PR1, PR2, and PR3 model types) to optimize predictive accuracy. Methods are based on Asimit, Chen, Ichim and Millossovich (2026) . 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(2021) . Although statistical methodologies have advanced, in AE analyses often the incidence proportion, the incidence density or a non-parametric Kaplan-Meier estimator are used, which either ignore censoring or competing events. This package contains functions to easily conduct the proposed improved AE analyses. 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Comparing finite sequences of discrete events with non-uniform time intervals, Sequential Analysis, 40(3), 291-313. . Package: r-cran-saws Architecture: all Version: 0.9-7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gee Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-saws_0.9-7.0-1.ca2404.1_all.deb Size: 340994 MD5sum: 0195b41e84a19cb3dd8cb23317617d3a SHA1: bb8f184af9027805603ad784e4c1e8f6e933e56d SHA256: 79d851dbec68bc53c09bcf165704f60ad565f885b2ed79a1349b3bfcc641d2aa SHA512: 98d831934f07edb813d2b86e137a2a5fc5f28839f4ec10cb5261bcd95fe435ff26bed08c95893b03351a7e68724da85dda847fce987dc19ad9fd087e62282269 Homepage: https://cran.r-project.org/package=saws Description: CRAN Package 'saws' (Small-Sample Adjustments for Wald Tests Using SandwichEstimators) Tests coefficients with sandwich estimator of variance and with small samples. Regression types supported are gee, linear regression, and conditional logistic regression. 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"Quantifying the Bias due to Observed Individual Confounders in Causal Treatment Effect Estimates". Statistics in Medicine, 39(18): 2447- 2476 . 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In this method, the prior structure is modeled and incorporated into the Bayesian information criterion framework. Additionally, we also provide the implementation of a two-step algorithm to generate the candidate model pool. 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Multi-phase and multi-baseline designs are supported. Analysing methods include regression models (multilevel, multivariate, bayesian), between case standardised mean difference, overlap indices ('PND', 'PEM', 'PAND', 'NAP', 'PET', 'tau-u', 'IRD', 'baseline corrected tau', 'CDC'), and randomization tests. Data preparation functions support outlier detection, handling missing values, scaling, and custom transformations. An export function helps to generate html, word, and latex tables in a publication friendly style. A shiny app allows to use scan in a graphical user interface. More details can be found in the online book 'Analyzing single-case data with R and scan', Juergen Wilbert (2026) . Package: r-cran-scannotate Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-seurat, r-cran-harmony, r-cran-seuratobject Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-scannotate_0.3-1.ca2404.1_all.deb Size: 847850 MD5sum: cc0d2b2e3b8e30af2f5b74a6a86d1659 SHA1: c815af20be12183c3ca740469ae6cc8474674482 SHA256: 4fe340b2826de4cdd9f18d31cb791913a2d45ab1d08459ed3618897c3f319f20 SHA512: 72e5d959458b79e894a3e7a83b8317329e9382a1a8d04d87d51d6972ad97314a7069f1426221d172d0398183d0db47d02d321156d1cc3980cc4c66264ff7a782 Homepage: https://cran.r-project.org/package=scAnnotate Description: CRAN Package 'scAnnotate' (An Automated Cell Type Annotation Tool for Single-CellRNA-Sequencing Data) An entirely data-driven cell type annotation tools, which requires training data to learn the classifier, but not biological knowledge to make subjective decisions. It consists of three steps: preprocessing training and test data, model fitting on training data, and cell classification on test data. See Xiangling Ji,Danielle Tsao, Kailun Bai, Min Tsao, Li Xing, Xuekui Zhang.(2022) for more details. Package: r-cran-scape Architecture: all Version: 2.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4476 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-hmisc, r-cran-lattice Suggests: r-cran-gdata Filename: pool/dists/noble/main/r-cran-scape_2.3.5-1.ca2404.1_all.deb Size: 2744168 MD5sum: 813d6477ac1964933043712365aca89b SHA1: 335af0cbecf354b5fa6c6cb4e4e780ae06cdc113 SHA256: 66f1bf8d3c6900177c92addf052b546c7395802b19cdf4cd3f70b60482363e82 SHA512: 0a03707df691a197a400c58882482265a9d47e8cce99967cc397e2d8d2a64bdec169676abcb609dde4f1cea02988a1dfd0b0b635bfaade43203560c1a95d32ec Homepage: https://cran.r-project.org/package=scape Description: CRAN Package 'scape' (Statistical Catch-at-Age Plotting Environment) Import, plot, and diagnose results from statistical catch-at-age models, used in fisheries stock assessment. 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'CytoSig' database is described in: Jiang at al., (2021) . 'Reactome' database is described in: Gillespie et al., (2021) . The 'VAM' method is outlined in: Frost (2020) . Package: r-cran-scapesclassification Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1971 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra Suggests: r-cran-gifski, r-cran-knitr, r-cran-leafem, r-cran-leaflet, r-cran-leafpop, r-cran-mapview, r-cran-raster, r-cran-spelling, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scapesclassification_1.0.0-1.ca2404.1_all.deb Size: 1205494 MD5sum: 6eb01ac19022ddebf347af904ce69e51 SHA1: d2d40c6c9d828000d1c577b7c099210d47490194 SHA256: f730ccf6f1f7992fc36c6dfae1178019cf89640ee8fa60d0521d6427e3cd474b SHA512: be110e3c641ac8b30de03ace8769fe1f0a9fb19ee82a96f9971d4a704a614585c2503cdd37f7e04fc6845afc33c554c565e8dd4488288b4edd85e6cd1ab9f908 Homepage: https://cran.r-project.org/package=scapesClassification Description: CRAN Package 'scapesClassification' (User-Defined Classification of Raster Surfaces) Series of algorithms to translate users' mental models of seascapes, landscapes and, more generally, of geographic features into computer representations (classifications). 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Package: r-cran-scapgnn Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4924 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-activepathways, r-cran-adaptgauss, r-cran-coop, r-cran-igraph, r-cran-mixtools, r-cran-reticulate Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-scapgnn_0.1.4-1.ca2404.1_all.deb Size: 3049398 MD5sum: 2c68dd77526a5e73a10f57f7eaf19da8 SHA1: f7b900860812993d9e78a3ba6327c2dea115af37 SHA256: eb5a3af70d54c8a7c5ff49be3bd71ffbd544eb8d06d9c4a91c4e1c42a2372201 SHA512: 452e2dc652efc218d94c2451c66b03615185f27f182899f33671263e05ee20f0699abb13838d975777593b04e896d33aac83a0b6cfe2f7149b708e5750092a2e Homepage: https://cran.r-project.org/package=scapGNN Description: CRAN Package 'scapGNN' (Graph Neural Network-Based Framework for Single Cell ActivePathways and Gene Modules Analysis) It is a single cell active pathway analysis tool based on the graph neural network (F. 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Package: r-cran-scarabee Architecture: all Version: 1.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2167 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-neldermead, r-cran-optimsimplex, r-cran-optimbase, r-cran-lattice, r-cran-desolve Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-scarabee_1.1-5-1.ca2404.1_all.deb Size: 842906 MD5sum: d9ba19a1892ff95272f9ba0275b299e6 SHA1: 4c9ed110d2ca1ceb8d75cef293b66cd3ed46ac60 SHA256: 2bf4be8da48651d370975152e3d5a8a500d3f728cc83a75c25a89570e393f4f0 SHA512: 350b0bfd6b0ca55afbc50ba8c8cb78479e5744b4fcc1584c8477baa4ace3960ca1b85ae414a39302504a40cd35f16d6c7074218b822e008fcc73b2519a07fe1b Homepage: https://cran.r-project.org/package=scaRabee Description: CRAN Package 'scaRabee' (Optimization Toolkit for Pharmacokinetic-Pharmacodynamic Models) A port of the Scarabee toolkit originally written as a Matlab-based application. scaRabee provides a framework for simulation and optimization of pharmacokinetic-pharmacodynamic models at the individual and population level. 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The methods include simultaneous confidence bands, local polynomial fitting, bandwidth selection by plug-in and cross-validation, goodness-of-fit tests for parametric models, equality tests for two-sample problems, and plotting functions. Package: r-cran-scbsp Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-bioc-sparsematrixstats, r-cran-fitdistrplus, r-cran-rann, r-cran-spam Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-scbsp_1.1.0-1.ca2404.1_all.deb Size: 106876 MD5sum: e726de57211c36f06305baa35ad84f9b SHA1: c1d91a195a50cfd0b06e7ca621adc62b61c62750 SHA256: 59111aed6a86dde59e0830b17c03b16a509f78c6415e40ed92725a5c36db2875 SHA512: 071a3e2c13a74e6446f279bcede233bad005502a5b1bf255dba8d0928f93bb3777fc1b02df6bed8c9c715d70f63044dde056ef11104012d994a8ba6073252b8a Homepage: https://cran.r-project.org/package=scBSP Description: CRAN Package 'scBSP' (A Fast Tool for Single-Cell Spatially Variable GenesIdentifications on Large-Scale Data) Identifying spatially variable genes is critical in linking molecular cell functions with tissue phenotypes. This package utilizes a granularity-based dimension-agnostic tool, single-cell big-small patch (scBSP), implementing sparse matrix operation and KD tree methods for distance calculation, for the identification of spatially variable genes on large-scale data. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), ). Package: r-cran-scbursts Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1175 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytex Filename: pool/dists/noble/main/r-cran-scbursts_1.6-1.ca2404.1_all.deb Size: 657950 MD5sum: 87867004534b184f13a0eef5fa7aa8d1 SHA1: 78c29e0ddc5b461700b7bfe1c40031c8ff1eb994 SHA256: b5bc4bca7ccd69acdacb4d6bd783b1f160ddcb3c5251d2708cb3f1c32ec977ed SHA512: 20d2d048b1d591ec8a885e8db9db8baf475757a7bd7f73b93acfd63f01a630c27e1948cb606f2de264239cf8c1e232219dd984671189d14a53c4ebd0ab84c3cb Homepage: https://cran.r-project.org/package=scbursts Description: CRAN Package 'scbursts' (Single Channel Bursts Analysis) Provides tools to import and export from several existing pieces of ion-channel analysis software such as 'TAC', 'QUB', 'SCAN', and 'Clampfit', implements procedures such as dwell-time correction and defining bursts with a critical time, and provides tools for analysis of bursts, such as tools for sorting and plotting. Package: r-cran-sccan Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4250 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scdha, r-cran-fnn, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sccan_1.0.5-1.ca2404.1_all.deb Size: 3359654 MD5sum: 7d6f957199aba6461263591180ddd008 SHA1: 2f1f2cc3f3c16951a80f07af1b7eef375a082154 SHA256: e37f158516655a9fdb65ab7d7f6ce7d57515459082820afde445773cd897aadb SHA512: 22982bb9a614fb6e1c82ca1c282a8dd474a4959c39f76d5178e616ed6d1e8cd3b0ff9fd6b8a55f5eefca1a48a16049616379c68efa68ff0e0c2837669862897e Homepage: https://cran.r-project.org/package=scCAN Description: CRAN Package 'scCAN' (Single-Cell Clustering using Autoencoder and Network Fusion) A single-cell Clustering method using 'Autoencoder' and Network fusion ('scCAN') Bang Tran (2022) for segregating the cells from the high-dimensional 'scRNA-Seq' data. The software automatically determines the optimal number of clusters and then partitions the cells in a way such that the results are robust to noise and dropouts. 'scCAN' is fast and it supports Windows, Linux, and Mac OS. Package: r-cran-sccatch Architecture: all Version: 3.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2484 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-progress, r-cran-reshape2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-sccatch_3.2.2-1.ca2404.1_all.deb Size: 2329690 MD5sum: 933463aa84c2eeac4e35506868097ae4 SHA1: 300dc4524a306e6b55e96391bce250bbd4f351e3 SHA256: fbae1bb196dec0bb11324ed6e48fd9670db45e212f7426ce95666c2819f599bd SHA512: 2622a7ccbd29e5c952eb1c393d3e8cedc3329647f2516ecb88eb700c8ca605d5bbb1fe6a66f9b569e0d89ac151723514db34008bba160081794e7bbb2d0be615 Homepage: https://cran.r-project.org/package=scCATCH Description: CRAN Package 'scCATCH' (Single Cell Cluster-Based Annotation Toolkit for CellularHeterogeneity) An automatic cluster-based annotation pipeline based on evidence-based score by matching the marker genes with known cell markers in tissue-specific cell taxonomy reference database for single-cell RNA-seq data. See Shao X, et al (2020) for more details. Package: r-cran-sccca Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seurat, r-cran-dplyr, r-cran-plyr, r-cran-scales, r-cran-hgnchelper, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-sccca_0.1.1-1.ca2404.1_all.deb Size: 60580 MD5sum: a0bb0376d113047af8552b4355ed61ad SHA1: ad49cc2e986df0afd2508bf845a2e91b2a2bb1ac SHA256: 5562100d79545f0b7cb2e503efc092dddcf2c4ce7f65b190512987f6f0f4a198 SHA512: 5e734b86d122a8622ddb95054733da02f87ba7ce9274faa1695f95c0b48203bee6f74ae1bbfe34b74f81e6e9a2f780e7f2020c19ed555e8a52479205cad2ca72 Homepage: https://cran.r-project.org/package=sccca Description: CRAN Package 'sccca' (Single-Cell Correlation Based Cell Type Annotation) Performing cell type annotation based on cell markers from a unified database. The approach utilizes correlation-based approach combined with association analysis using Fisher-exact and phyper statistical tests (Upton, Graham JG. (1992) ). Package: r-cran-sccic Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-wooldridge, r-cran-qte, r-cran-synth Filename: pool/dists/noble/main/r-cran-sccic_0.1.1-1.ca2404.1_all.deb Size: 115624 MD5sum: f1ba9df42b80d431b11aa3ce1304012e SHA1: a95d8fe8207bf5b0d28988849914751ac0458f91 SHA256: 001274eb7b85e4f74b2379c5e329e280437760bacf2567931b49529296e14cb8 SHA512: 81a260e33a72a16e5e8c32899b0e9cdc74f7a6cd66d95999faf729f86e30f3536c74521997d50763fe9d815f54b601c82ad1a0ceb5f15d704fe7a48044af9fbb Homepage: https://cran.r-project.org/package=sccic Description: CRAN Package 'sccic' (Synthetic Control Changes-in-Changes Estimator) Implements the Changes-in-Changes (CIC) estimator of Athey and Imbens (2006) combined with synthetic control methods. Provides both the continuous CIC estimator (Theorem 3.1) and the discrete CIC estimator (Theorem 4.1) for integer-valued outcomes, with analytic and bootstrap inference. Also provides nonparametric estimation of the entire counterfactual distribution of outcomes for a treated group, allowing evaluation of average, quantile, and distributional treatment effects. Synthetic control weights are constructed via elastic net regularization to handle settings with many potential control units. Package: r-cran-sccr Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-sccr_2.1-1.ca2404.1_all.deb Size: 25930 MD5sum: c9da3a893aa0564eec4af14f8e40c429 SHA1: 6262c092cac025796ca6d11563714a3baa067d30 SHA256: 60f6fc4d9c52aab32a092449b6b9cd7ee20481a86410aacf3c9bee18d2da560d SHA512: 71f3ad14db121c8f7c1cc6ec0c02e371f6ebb5abbc88d68b76ddc6788044124ac208745be7cd5d31dae72db41dcac8077e6fa8e64cc35d4ab05c93c33c5cae53 Homepage: https://cran.r-project.org/package=sccr Description: CRAN Package 'sccr' (The Self-Consistent, Competing Risks (SC-CR) Algorithms) The SC-SR Algorithm is used to calculate fully non-parametric and self-consistent estimators of the cause-specific failure probabilities in the presence of interval-censoring and possible making of the failure cause in a competing risks environment. In the version 2.0 the function creating the probability matrix from double-censored data is added. Package: r-cran-sccs Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-corpcor, r-cran-fda, r-cran-r.methodss3, r-cran-gnm Filename: pool/dists/noble/main/r-cran-sccs_1.7-1.ca2404.1_all.deb Size: 604450 MD5sum: 6ffc209d0e021b571f8fc515e49fda11 SHA1: 97db9d8f2e02bd128fbbfb7292f4cc7c621154f5 SHA256: 2b8d8869d9b9c55d3cde486af3facb4ab130a1adad9f002b815c4938fe3fa205 SHA512: eddfe971ae2af853f67cc2257e0bee7122d25ce42167ee99731058f6cc86aa5b37d204930e75ee67b889858a86a9b39817876980e2fe3539557fbf6a18ae0ef5 Homepage: https://cran.r-project.org/package=SCCS Description: CRAN Package 'SCCS' (The Self-Controlled Case Series Method) Various self-controlled case series models used to investigate associations between time-varying exposures such as vaccines or other drugs or non drug exposures and an adverse event can be fitted. Detailed information on the self-controlled case series method and its extensions with more examples can be found in Farrington, P., Whitaker, H., and Ghebremichael Weldeselassie, Y. (2018, ISBN: 978-1-4987-8159-6. Self-controlled Case Series studies: A modelling Guide with R. Boca Raton: Chapman & Hall/CRC Press) and . Package: r-cran-sccustomize Architecture: all Version: 3.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2375 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-seurat, r-cran-circlize, r-cran-cli, r-cran-cowplot, r-cran-data.table, r-cran-dplyr, r-cran-forcats, r-cran-ggbeeswarm, r-cran-ggplot2, r-cran-ggprism, r-cran-ggrastr, r-cran-ggrepel, r-cran-glue, r-cran-janitor, r-cran-lifecycle, r-cran-magrittr, r-cran-matrix, r-cran-mcprogress, r-cran-paletteer, r-cran-patchwork, r-cran-pbapply, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-scattermore, r-cran-seuratobject, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-bioc-biocfilecache, r-bioc-complexheatmap, r-bioc-dittoseq, r-bioc-dropletutils, r-cran-ggpubr, r-cran-hdf5r, r-cran-knitr, r-bioc-nebulosa, r-cran-remotes, r-cran-reticulate, r-cran-rliger, r-cran-rmarkdown, r-bioc-scuttle, r-cran-tidyselect, r-cran-qs2, r-cran-viridis Filename: pool/dists/noble/main/r-cran-sccustomize_3.3.0-1.ca2404.1_all.deb Size: 1772534 MD5sum: 94ca1c507423db8f7ff6e58d3d46f691 SHA1: 248bf3ea512d72d9eda6a5ce5babbbd98c8dd903 SHA256: 1a344b7dad7d396d0fad8240a6e8e98024a31d6cb1a9b578153ffed752f4ab67 SHA512: e49725ee42fdd3dfcd81164d0ee0134aaf19e508d35c161aed47d719d56a41671dabe9ea1f7c27c7804bfdc75fc514d09b5ab5495cd4e7a03bf6d0db42f0e124 Homepage: https://cran.r-project.org/package=scCustomize Description: CRAN Package 'scCustomize' (Custom Visualizations & Functions for Streamlined Analyses ofSingle Cell Sequencing) Collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using 'R'. 'scCustomize' aims to provide 1) Customized visualizations for aid in ease of use and to create more aesthetic and functional visuals. 2) Improve speed/reproducibility of common tasks/pieces of code in scRNA-seq analysis with a single or group of functions. For citation please use: Marsh SE (2021) "Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing" RRID:SCR_024675. Package: r-cran-scda Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 974 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatialreg, r-cran-sp, r-cran-spdep, r-cran-rlang, r-cran-performance, r-cran-dplyr, r-cran-sf, r-cran-nbclust, r-cran-ggplot2, r-cran-ggspatial Suggests: r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-scda_0.0.2-1.ca2404.1_all.deb Size: 952652 MD5sum: afd3c879efc453324293fbd56206e448 SHA1: f5e2a2e33c2683c9b9d44a6c85a47e9c33a218b0 SHA256: 300f2a72723891ee6aa777ecbe5bdc1cdab31b4dfec4c7fb405828ea85fcd50f SHA512: 60f4a6888049a0fdf5d76eec8c66aa4b82b519535c5094ef30db3f9a34c51a8528f111b1f6b459062579e69bdce7b224e5b15700144785a6bd77c94bab2498c5 Homepage: https://cran.r-project.org/package=SCDA Description: CRAN Package 'SCDA' (Spatially-Clustered Data Analysis) Contains functions for statistical data analysis based on spatially-clustered techniques. The package allows estimating the spatially-clustered spatial regression models presented in Cerqueti, Maranzano \& Mattera (2024), "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe", arXiv preprint 2407.15874 . Specifically, the current release allows the estimation of the spatially-clustered linear regression model (SCLM), the spatially-clustered spatial autoregressive model (SCSAR), the spatially-clustered spatial Durbin model (SCSEM), and the spatially-clustered linear regression model with spatially-lagged exogenous covariates (SCSLX). From release 0.0.2, the library contains functions to estimate spatial clustering based on Adiajacent Matrix K-Means (AMKM) as described in Zhou, Liu \& Zhu (2019), "Weighted adjacent matrix for K-means clustering", Multimedia Tools and Applications, 78 (23) . Package: r-cran-scdb Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-checkmate, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-glue, r-cran-lubridate, r-cran-openssl, r-cran-parallelly, r-cran-purrr, r-cran-rlang, r-cran-r6, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect, r-cran-magrittr Suggests: r-cran-callr, r-cran-conflicted, r-cran-devtools, r-cran-duckdb, r-cran-ggplot2, r-cran-here, r-cran-jsonlite, r-cran-knitr, r-cran-lintr, r-cran-microbenchmark, r-cran-odbc, r-cran-pak, r-cran-rmarkdown, r-cran-roxygen2, r-cran-pkgdown, r-cran-rpostgres, r-cran-rsqlite, r-cran-spelling, r-cran-testthat, r-cran-tibble, r-cran-tidyverse, r-cran-withr Filename: pool/dists/noble/main/r-cran-scdb_0.6.1-1.ca2404.1_all.deb Size: 374996 MD5sum: 6d00e4eaf72c97b4df7fcf0862151124 SHA1: 41469a224362c82a1cc1ffcb58f8306f36591cc1 SHA256: 7211e846eb24ae431e1ee4e4f9dff17590aa7f43528443229915e61727d8dc2e SHA512: ff5aed1116fc593d84fd5681e9400962f1d1a642595ef1a5741766387e0655c221c4db7e241b9f2d4575f65d944cc29adec3ac63b85b4cad37307af4528fbd6c Homepage: https://cran.r-project.org/package=SCDB Description: CRAN Package 'SCDB' (Easily Access and Maintain Time-Based Versioned Data(Slowly-Changing-Dimension)) A collection of functions that enable easy access and updating of a database of data over time. More specifically, the package facilitates type-2 history for data-warehouses and provides a number of Quality of life improvements for working on SQL databases with R. For reference see Ralph Kimball and Margy Ross (2013, ISBN 9781118530801). Package: r-cran-scdeco Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rjags, r-cran-msm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-scdeco_0.1.1-1.ca2404.1_all.deb Size: 83336 MD5sum: f7cfaaf4001b5cd891b29a02ed3710d4 SHA1: b2b94e987e1a2c303df5b2a66db67073f8c9ffc4 SHA256: 810583e6673350ce43fb3565a987730bf0dd210d45654155474074411ebe359a SHA512: d2c31770c3bed7367d47914170e615d7874ffbe0817be9041690d78e430e212870410fdc3722e75c8461452f3c66c1e54786bd27d135facb31ce34ee81bbec58 Homepage: https://cran.r-project.org/package=scDECO Description: CRAN Package 'scDECO' (Estimating Dynamic Correlation) Implementations for two different Bayesian models of differential co-expression. scdeco.cop() fits the bivariate Gaussian copula model from Zichen Ma, Shannon W. Davis, Yen-Yi Ho (2023) , while scdeco.pg() fits the bivariate Poisson-Gamma model from Zhen Yang, Yen-Yi Ho (2022) . 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Provide various visualization functions. Package: r-cran-scdensity Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2884 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog, r-cran-lpsolve Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-scdensity_1.0.3-1.ca2404.1_all.deb Size: 1062960 MD5sum: 11ccedb25bfb55897f1c8a03d7675553 SHA1: d526ffc48e9f61d57a7a5cd8c62b53c73054c235 SHA256: f2a95aed0231481816eb3930bba9fd43e5135979c684b686b403b084b55947ab SHA512: 58a655c75a2202289b5b9df571fc7328544c7a2ccde362232f05604c59d1cf1a2048f32760f4c418c860246bbcf7cabe07fa01ff0f030d2a2c03416f3ff60ff7 Homepage: https://cran.r-project.org/package=scdensity Description: CRAN Package 'scdensity' (Shape-Constrained Kernel Density Estimation) Implements methods for obtaining kernel density estimates subject to a variety of shape constraints (unimodality, bimodality, symmetry, tail monotonicity, bounds, and constraints on the number of inflection points). Enforcing constraints can eliminate unwanted waves or kinks in the estimate, which improves its subjective appearance and can also improve statistical performance. The main function scdensity() is very similar to the density() function in 'stats', allowing shape-restricted estimates to be obtained with little effort. The methods implemented in this package are described in Wolters and Braun (2017) , Wolters (2012) , and Hall and Huang (2002) . See the scdensity() help for for full citations. Package: r-cran-scdhlm Architecture: all Version: 0.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-readxl, r-cran-rlang, r-cran-dplyr, r-cran-tidyselect, r-cran-magrittr, r-cran-lmeinfo Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-ggplot2, r-cran-plyr, r-cran-boot, r-cran-shiny, r-cran-shinytest, r-cran-glue, r-cran-janitor, r-cran-rclipboard, r-cran-rvest, r-cran-testthat, r-cran-snow, r-cran-dosnow, r-cran-foreach, r-cran-iterators, r-cran-reshape, r-cran-rlecuyer Filename: pool/dists/noble/main/r-cran-scdhlm_0.7.4-1.ca2404.1_all.deb Size: 844792 MD5sum: 61e019bf111b922e7ddb26ea0ba88d95 SHA1: fbc5758ffd678928c69572059741ad37d274692b SHA256: d04495be4d93a69527490bf462b51f47a55c7c3e994fde2fe57304d12fa64abf SHA512: ec7557db7574667c23fd2fab8d44ea5ee93b798c8935924aeed86c3ad8d99fe5c0de90100339c45e17f5e8528f6b3cb1edf4d0ba387f15fbf1225073a310feb0 Homepage: https://cran.r-project.org/package=scdhlm Description: CRAN Package 'scdhlm' (Estimating Hierarchical Linear Models for Single-Case Designs) Provides a set of tools for estimating hierarchical linear models and effect sizes based on data from single-case designs. Functions are provided for calculating standardized mean difference effect sizes that are directly comparable to standardized mean differences estimated from between-subjects randomized experiments, as described in Hedges, Pustejovsky, and Shadish (2012) ; Hedges, Pustejovsky, and Shadish (2013) ; Pustejovsky, Hedges, and Shadish (2014) ; and Chen, Pustejovsky, Klingbeil, and Van Norman (2023) . Includes an interactive web interface. Package: r-cran-scdiffcom Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3439 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-bioc-delayedarray, r-cran-future, r-cran-future.apply, r-cran-lifecycle, r-cran-magrittr, r-cran-seurat Suggests: r-bioc-biomart, r-cran-covr, r-cran-dt, r-cran-ggplot2, r-bioc-gosemsim, r-cran-igraph, r-cran-kableextra, r-bioc-keggrest, r-cran-knitr, r-cran-ontologyindex, r-bioc-ontoproc, r-cran-pkgdown, r-cran-plotly, r-cran-rcolorbrewer, r-cran-rmarkdown, r-bioc-rrvgo, r-cran-spelling, r-cran-shiny, r-cran-shinythemes, r-cran-shinywidgets, r-cran-testthat, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-scdiffcom_1.2.0-1.ca2404.1_all.deb Size: 3291246 MD5sum: e649cc802a7b2dcbb983fefc9cc8402b SHA1: 97179e4d4875c7cb85f4211133f07bd960e485b8 SHA256: e3b17ad7f284b3c76544147420b8fb3bde539a71488a0442a8b61974fecec344 SHA512: b8d68e6307a8dcdde5bd4a2a8c44ae0bd52a7e014843d172367464ca9ce5707e43e6509d0249a55056b97a11f94cf648378c1f1b25161b6323d37a74e8475c54 Homepage: https://cran.r-project.org/package=scDiffCom Description: CRAN Package 'scDiffCom' (Differential Analysis of Intercellular Communication fromscRNA-Seq Data) Analysis tools to investigate changes in intercellular communication from scRNA-seq data. Using a Seurat object as input, the package infers which cell-cell interactions are present in the dataset and how these interactions change between two conditions of interest (e.g. young vs old). It relies on an internal database of ligand-receptor interactions (available for human, mouse and rat) that have been gathered from several published studies. Detection and differential analyses rely on permutation tests. The package also contains several tools to perform over-representation analysis and visualize the results. See Lagger, C. et al. (2023) for a full description of the methodology. Package: r-cran-scdiftest Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich, r-cran-strucchange, r-cran-mirt, r-cran-zoo Suggests: r-cran-mvtnorm, r-cran-psychotree, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scdiftest_0.1.1-1.ca2404.1_all.deb Size: 43430 MD5sum: 589496e71cf636b3506f454b5590c8ab SHA1: 08bbe17da644d84e09fb9a17c058e8463164c238 SHA256: 196f6497975805cc759dc75d0d68f5f3e905ab4a1950b6f3e1641ad6d2ec95f6 SHA512: 8198be553ece50a0eecd98a55df12ec63c8197e37a01398715aa0c552cf08887af25c8018b84233ed831b27ae8386d2bfa4c258e2691296e8f4cad61630e6484 Homepage: https://cran.r-project.org/package=scDIFtest Description: CRAN Package 'scDIFtest' (Item-Wise Score-Based DIF Detection) Detection of item-wise Differential Item Functioning (DIF) in fitted 'mirt', 'multipleGroup' or 'bfactor' models using score-based structural change tests. Under the hood the sctest() function from the 'strucchange' package is used. Package: r-cran-scdtb Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom.mixed, r-cran-dt, r-cran-ggplot2, r-cran-mass, r-cran-mmcards, r-cran-mmints, r-cran-nlme, r-cran-shiny, r-cran-shinythemes, r-cran-sn Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-scdtb_0.2.0-1.ca2404.1_all.deb Size: 1883952 MD5sum: 4523b073ebdfb6649f9dbfec62bb2b66 SHA1: 18cfa49fcb73ab7f1894a2c6accae3b68b477975 SHA256: de355270eafea50fb5918922596b37b5d8a13afe76369e557d3a81dab4952d7f SHA512: e7013c30087d1e930c4bf8244744e83e8e44dd7c253abfc512483a576ac4d5dc917e21b68828159665e93bafbf683393bb4ebaa51b462e6dc9bdc379bf685286 Homepage: https://cran.r-project.org/package=scdtb Description: CRAN Package 'scdtb' (Single Case Design Tools) In some situations where researchers would like to demonstrate causal effects, it is hard to obtain a sample size that would allow for a well-powered randomized controlled trial. Single case designs are experimental designs that can be used to demonstrate causal effects with only one participant or with only a few participants. The 'scdtb' package provides a suite of tools for analyzing data from studies that use single case designs. The nap() function can be used to compute the nonoverlap of all pairs as outlined by the What Works Clearinghouse (2022) . The package also offers the mixed_model_analysis() and cross_lagged() functions which implement mixed effects models and cross lagged analyses as described in Maric & van der Werff (2020) . The randomization_test() function implements randomization tests based on methods presented in Onghena (2020) . The scdtb() 'shiny' application can be used to upload single case design data and access various 'scdtb' tools for plotting and analysis. Package: r-cran-sce Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sce_1.1.4-1.ca2404.1_all.deb Size: 219474 MD5sum: 01c031e14bfd875b588317a656e7e8d5 SHA1: c7bab4d2c25b30567da02914d48bb0e76515b280 SHA256: dbf9e089c8f615e1826c26bbe2280e4527aa073c50e077ab67081b8c84376491 SHA512: cf4338954526b4dc52164203d050aff3defd39db862ec4125e5059576122788ef16b6cc345c11ae1ffd4711c9da9d06934bf3a89a384bf91e314616068b3da5c Homepage: https://cran.r-project.org/package=SCE Description: CRAN Package 'SCE' (Stepwise Clustered Ensemble) Implementation of Stepwise Clustered Ensemble (SCE) and Stepwise Cluster Analysis (SCA) for multivariate data analysis. The package provides comprehensive tools for feature selection, model training, prediction, and evaluation in hydrological and environmental modeling applications. Key functionalities include recursive feature elimination (RFE), Wilks feature importance analysis, model validation through out-of-bag (OOB) validation, and ensemble prediction capabilities. The package supports both single and multivariate response variables, making it suitable for complex environmental modeling scenarios. For more details see Li et al. (2021) . Package: r-cran-scem Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-mathjaxr Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scem_1.1.0-1.ca2404.1_all.deb Size: 189614 MD5sum: fc5af5f48661a215e5639f89de044588 SHA1: 407ec42dc9dc7fb5b50eee7e42122c0a01edde61 SHA256: 34e293528cbd3160b699669947572f56bd8f88b039ac3a23f6e6b97309c0c2f5 SHA512: ab9deed9e98c1065d1d1b8f8f52bf0835e0d002722c8bd1a03fcde359a778538871b951e888cae0dcc98b796cbb441eee5e18d385e9c0117c6ca9d3f170edee1 Homepage: https://cran.r-project.org/package=SCEM Description: CRAN Package 'SCEM' (Splitting-Coalescence-Estimation Method) We introduce improved methods for statistically assessing birth seasonality and intra-annual variation. The first method we propose is a new idea that uses a nonparametric clustering procedure to group individuals with similar time series data and estimate birth seasonality based on the clusters. One can use the function SCEM() to implement this method. The second method estimates input parameters for use with a previously-developed parametric approach (Tornero et al., 2013). The relevant code for this approach is makeFits_OLS(), while makeFits_initial() is the code to implement the same method but with given initial conditions for two parameters. The latter can be used to show the disadvantage of the existing approach. One can use the function makeFits() to generate parametric birth seasonality estimates using either initialization. Detailed description can be found here: Chazin Hannah, Soudeep Deb, Joshua Falk, and Arun Srinivasan. (2019) "New Statistical Approaches to Intra-Individual Isotopic Analysis and Modeling Birth Seasonality in Studies of Herd Animals." . Package: r-cran-scenes Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-cookies, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-shiny Suggests: r-cran-covr, r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scenes_0.1.0-1.ca2404.1_all.deb Size: 252666 MD5sum: 57599ae389c4c3a1d6e90e0f5f3602d9 SHA1: 5f9a7f95c5a7a82bc7908295b3e1d62f9a6b8589 SHA256: 40d5439495dbeebc8786a61569d1fb59f3467105a9d30842f04eb7eb0e2b6c84 SHA512: 3852d8128776c9f376acb381d0b053ceb77830cadc6b30fe73c9329cd62ec73177355136ebd6145811931b653c9e042aa789f10ca93937237563ff96902bd9df Homepage: https://cran.r-project.org/package=scenes Description: CRAN Package 'scenes' (Switch Between Alternative 'shiny' UIs) Sometimes it is useful to serve up alternative 'shiny' UIs depending on information passed in the request object, such as the value of a cookie or a query parameter. This packages facilitates such switches. Package: r-cran-scenfire Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-foreach, r-cran-readr, r-cran-sf, r-cran-terra, r-cran-tidyr, r-cran-rlang, r-cran-stringr, r-cran-doparallel, r-cran-tidyselect Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-scenfire_0.1.0-1.ca2404.1_all.deb Size: 68310 MD5sum: 9984cdbc52cd8f76b1ec24f0922ed58c SHA1: 05773e53af4334c05259ebf77193a40bfc060a22 SHA256: 9300859065b5c2620357da593ac13cd14caeb73f57f5d874f80d4f479ec47471 SHA512: 452cd0b2f6f228b4c9aa8558cc9738a104c49e2d729cd9acbd9d1513ebdaa63c70e6b5b397b7de35ed76b5def358f6716986d42b92850f7fd123547f9e4c88cb Homepage: https://cran.r-project.org/package=scenfire Description: CRAN Package 'scenfire' (Post-Processing Algorithm for Integrating Wildfire Simulations) A specialized selection algorithm designed to align simulated fire perimeters with specific fire size distribution scenarios. The foundation of this approach lies in generating a vast collection of plausible simulated fires across a wide range of conditions, assuming a random pattern of ignition. The algorithm then assembles individual fire perimeters based on their specific probabilities of occurrence, e.g., determined by (i) the likelihood of ignition and (ii) the probability of particular fire-weather scenarios, including wind speed and direction. Implements the method presented in Rodrigues (2025a) . Demo data and code examples can be found in Rodrigues (2025b) . Package: r-cran-scent Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-entropy, r-cran-tibble Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scent_0.0.1-1.ca2404.1_all.deb Size: 31726 MD5sum: b1883a06fd0588765cc96375c7488976 SHA1: 4a20f810ae38298a5df5fb78c5b14416be2ac866 SHA256: 24142ec6e8c4377077eb9bf137f818d310ce4acfd840dd60bdead00d44ccbc95 SHA512: 9012e4a61c8b7ff7dfbeada7069bd514e8dcecc071986c314da80a723cec6366a728b97c3fbb079baf8e227a3d17ddb5ac31020086f807c2554f57a963528fb7 Homepage: https://cran.r-project.org/package=SCEnt Description: CRAN Package 'SCEnt' (Single Cell Entropy Analysis of Gene Heterogeneity in CellPopulations) Analyse single cell RNA sequencing data using entropy to calculate heterogeneity and homogeneity of genes amongst the cell population. From the work of Michael J. Casey, Ruben J. Sanchez-Garcia and Ben D. MacArthur. Package: r-cran-scf Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1616 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-haven, r-cran-httr, r-cran-rlang, r-cran-survey, r-cran-quantreg Suggests: r-cran-dplyr, r-cran-hexbin, r-cran-kableextra, r-cran-knitr, r-cran-mitools, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scf_1.0.10-1.ca2404.1_all.deb Size: 1517772 MD5sum: d6c7f5c7b550008292e13002e2e98b7c SHA1: 3b25f3768c73c668a1db0976ec5073cd5925601d SHA256: cab584f88125bba2718ffc1f2cda8ced5e2042dc0b4f50c262dbd7f31b00de89 SHA512: 751eec5be8e550d963b787b2539b15e83a66c8aead4d85761fe9206acc78e5b57e05cd64642641c2e3906e7cb963933098cb396189cf57aaa5ad496303a8ce04 Homepage: https://cran.r-project.org/package=scf Description: CRAN Package 'scf' (Analyzing the Survey of Consumer Finances) Analyze public-use micro data from the Survey of Consumer Finances. Provides tools to download prepared data files, construct replicate-weighted multiply imputed survey designs, compute descriptive statistics and model estimates, and produce plots and tables. Methods follow design-based inference for complex surveys and pooling across multiple imputations. See the package website and the code book for background. Package: r-cran-scfmonitor Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1386 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readr, r-cran-stringr, r-cran-tidyselect, r-cran-dplyr, r-cran-tibble, r-cran-ggplot2, r-cran-tidyr, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-scfmonitor_0.3.5-1.ca2404.1_all.deb Size: 397302 MD5sum: 18aa326337a4777f3b99ca9216280202 SHA1: 1f486e02f362d5d7ebd3959329b00e186637118e SHA256: 35fbbb092b2ebb551ff881555ad791eb830a5d802d81448d3bff1b8adcb44248 SHA512: 87f8d1e80c7cc26398be94f40dc1fadb95b20db9eb03e0ea38ef9ed57c6875b1866f5130c6ade3231ff34c979a3e306dc50b9dbbf0fc15e7bf86dfe0d9096087 Homepage: https://cran.r-project.org/package=SCFMonitor Description: CRAN Package 'SCFMonitor' (Clear Monitor and Graphing Software Processing Gaussian .logFile) Self-Consistent Field(SCF) calculation method is one of the most important steps in the calculation methods of quantum chemistry. Ehrenreich, H., & Cohen, M. H. (1959). However, the most prevailing software in this area, 'Gaussian''s SCF convergence process is hard to monitor, especially while the job is still running, causing researchers difficulty in knowing whether the oscillation has started or not, wasting time and energy on useless configurations or abandoning the jobs that can actually work. M.J. Frisch, G.W. Trucks, H.B. Schlegel et al. (2016). 'SCFMonitor' enables 'Gaussian' quantum chemistry calculation software users to easily read the 'Gaussian' .log files and monitor the SCF convergence and geometry optimization process with little effort and clear, beautiful, and clean outputs. It can generate graphs using 'tidyverse' to let users check SCF convergence and geometry optimization processes in real-time. The software supports processing .log files remotely using with rbase::url(). This software is a suitcase for saving time and energy for the researchers, supporting multiple versions of 'Gaussian'. Package: r-cran-scgate Architecture: all Version: 1.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1943 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seurat, r-bioc-ucell, r-cran-dplyr, r-cran-patchwork, r-cran-ggridges, r-cran-colorspace, r-cran-reshape2, r-cran-ggplot2, r-bioc-biocparallel Suggests: r-cran-ggparty, r-cran-partykit, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-scgate_1.7.2-1.ca2404.1_all.deb Size: 1940832 MD5sum: 627947a280b3194db49c0b4b66612472 SHA1: 780ab44af3629afc884ba3f4012fa3b59c4022d0 SHA256: 60185b73a911246ecf1ee2a3ac68bb502faab7a7547ce6707d34c1295eab0a83 SHA512: ad811105b779ba627d94c1ba5dd1af7a0e82a21015112f882347ac52287c1fdbb9638b03207c4fa6628fc8504c10c1e643c444ba93aea86966451cfcdea38d98 Homepage: https://cran.r-project.org/package=scGate Description: CRAN Package 'scGate' (Marker-Based Cell Type Purification for Single-Cell SequencingData) A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. 'scGate' automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. Briefly, 'scGate' takes as input: i) a gene expression matrix stored in a 'Seurat' object and ii) a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry. 'scGate' evaluates the strength of signature marker expression in each cell using the rank-based method 'UCell', and then performs k-nearest neighbor (kNN) smoothing by calculating the mean 'UCell' score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest. See the related publication Andreatta et al. (2022) . 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Covariates can also be grouped in themes. 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The functions "tissue_by_celltype_enrichment", "tissue_scMappR_internal", and "tissue_scMappR_custom" combine these consistently processed scRNAseq count data with gene-set enrichment tools to allow for cell-type marker enrichment of a generic gene list (e.g. GWAS hits). Reference: Sokolowski,D.J., Faykoo-Martinez,M., Erdman,L., Hou,H., Chan,C., Zhu,H., Holmes,M.M., Goldenberg,A. and Wilson,M.D. (2021) Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes. NAR Genomics and Bioinformatics. 3(1). Iqab011. . 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Package: r-cran-scorecard Architecture: all Version: 0.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-gridextra, r-cran-foreach, r-cran-doparallel, r-cran-openxlsx, r-cran-stringi, r-cran-cli, r-cran-xml2, r-cran-xefun Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scorecard_0.4.6-1.ca2404.1_all.deb Size: 367744 MD5sum: fab3522600abce86a8d00acc0f6341ec SHA1: 371049b714389333542bce4a2036952213afd018 SHA256: d57127aae0ef9b87ed14a2a78ae797dd135d3cb96e83b32fb39ea2a41e0ae428 SHA512: 3a535e39b2c8574b6f71e0f8993486687ed021282ec6d8eff6152e25b06b13c25e708a412b1bedd04222f251554e4c325d176f82715f387924f7e38e83ff40c0 Homepage: https://cran.r-project.org/package=scorecard Description: CRAN Package 'scorecard' (Credit Risk Scorecard) The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring. Package: r-cran-scorecardmodelutils Architecture: all Version: 0.0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-e1071, r-cran-gbm, r-cran-partykit, r-cran-randomforest, r-cran-reshape2, r-cran-sqldf, r-cran-stringr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-scorecardmodelutils_0.0.1.0-1.ca2404.1_all.deb Size: 142482 MD5sum: c4b62af7c52dccbac6c9e9c89eee6bfb SHA1: f89561a3dc6699a66bcfbc9062564f8df0ff45ab SHA256: 75d8085751e4ab4dcc14cbbf27de3130a6c32aeea36ab78235af74e684d97165 SHA512: 5d0394e59a79b1efe76e9d1a5322939379e2c8b3eb0922b8e5f7ea75e8c432df95af3ddad5b79528bbd74a27f83dfc9f9451e7e43e21b5180d84256222be8593 Homepage: https://cran.r-project.org/package=scorecardModelUtils Description: CRAN Package 'scorecardModelUtils' (Credit Scorecard Modelling Utils) Provides infrastructure functionalities such as missing value treatment, information value calculation, GINI calculation etc. which are used for developing a traditional credit scorecard as well as a machine learning based model. 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Package: r-cran-scoreplus Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-combinat, r-cran-limsolve, r-cran-rspectra, r-cran-igraph, r-cran-igraphdata Filename: pool/dists/noble/main/r-cran-scoreplus_0.1-1.ca2404.1_all.deb Size: 38898 MD5sum: 03b2decdff54c46e178f5d57b8bbf96f SHA1: 0c36bc166acb687ac932032d3134bda85f189e54 SHA256: 1c2bf3741ccbfabd69751649e6c93ef2834029f4db24636037ae58024cab991d SHA512: 56cb97289fdfbec13fff3c96cec2faa976c9d37d6601cc436bfd5662cb79f8528dd855aa32c327a7d6d9e4f87272649dafcf238189a7df9a30a2c79ecb4aed31 Homepage: https://cran.r-project.org/package=ScorePlus Description: CRAN Package 'ScorePlus' (Implementation of SCORE, SCORE+ and Mixed-SCORE) Implementation of community detection algorithm SCORE in the paper J. Jin (2015) , and SCORE+ in J. Jin, Z. Ke and S. Luo (2018) . Membership estimation algorithm called Mixed-SCORE in J. Jin, Z. 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Unlike conventional pairwise co-expression analyses that rely on a single correlation metric, scPairs integrates 14 complementary metrics across five orthogonal evidence layers to compute a composite synergy score with optional permutation-based significance testing. The five evidence layers span cell-level co-expression (Pearson, Spearman, biweight midcorrelation, mutual information, ratio consistency), neighbourhood-aware smoothing (KNN-smoothed correlation, neighbourhood co-expression, cluster pseudo-bulk, cross-cell-type, neighbourhood synergy), prior biological knowledge (GO/KEGG co-annotation Jaccard, pathway bridge score), trans-cellular interaction, and spatial co-variation (Lee's L, co-location quotient). This multi-scale design enables researchers to move beyond simple co-expression towards a comprehensive characterisation of cooperative gene regulation at transcriptomic and spatial resolution. For more information, see the package documentation at . 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Package: r-cran-screenot Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-screenot_0.1.0-1.ca2404.1_all.deb Size: 17886 MD5sum: a9a2d3ae823ba261a028b3d642410c49 SHA1: c3b94375f5b230234536dc75b0eddbe0d9351032 SHA256: b70d1ca16140fb2cb814d1fe7e83aab295331a784272e97c99706000f677f03f SHA512: a78eb47e0262c8b5a943b0ca73497d577d5687875ee6c56709a1aa2a8a4a53718297c85964f688cc3e595247585ec90504ce29bcbc86af588e9488f2a62979ca Homepage: https://cran.r-project.org/package=ScreeNOT Description: CRAN Package 'ScreeNOT' ('ScreeNOT': MSE-Optimal Singular Value Thresholding inCorrelated Noise) Optimal hard thresholding of singular values. The procedure adaptively estimates the best singular value threshold under unknown noise characteristics. The threshold chosen by 'ScreeNOT' is optimal (asymptotically, in the sense of minimum Frobenius error) under the the so-called "Spiked model" of a low-rank matrix observed in additive noise. In contrast to previous works, the noise is not assumed to be i.i.d. or white; it can have an essentially arbitrary and unknown correlation structure, across either rows, columns or both. 'ScreeNOT' is proposed to practitioners as a mathematically solid alternative to Cattell's ever-popular but vague Scree Plot heuristic from 1966. If you use this package, please cite our paper: David L. Donoho, Matan Gavish and Elad Romanov (2023). "ScreeNOT: Exact MSE-optimal singular value thresholding in correlated noise." Annals of Statistics, 2023 (To appear). . 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'SCRIP' proposed and compared two frameworks with Gamma-Poisson and Beta-Gamma-Poisson models for simulating Single Cell RNA Sequencing data. Other reference is available in Zappia et al. (2017) . 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Package: r-cran-scriptloc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-scriptloc_1.0.0-1.ca2404.1_all.deb Size: 66082 MD5sum: 45aaf239bddadda4164130e2c78af108 SHA1: d73b96daae325e99b8822c3aab8cec2000346a6e SHA256: 5dc5c6c3aebe7c68314e4685d6496cb756b0a216ee41b49c0b7f9172cb8f7f1d SHA512: 622b9b32c22a48d50af46bb15d145220df6373b62fb81e327e0233903f5be38d2b03a1312bace9c3b3206b5ef010635ba849f5e77674870bd0b260d01ee3f110 Homepage: https://cran.r-project.org/package=scriptloc Description: CRAN Package 'scriptloc' (Get the Location of the R Script that is Being Sourced/Executed) Provides functions to retrieve the location of R scripts loaded through the source() function or run from the command line using the Rscript command. 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The package clusters the input data to do imputation for each cluster, and do a distribution check using the Anderson-Darling normality test to impute dropouts using mean or median (Yazici, B., & Yolacan, S. (2007) ). 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Meanwhile we use common cell type marker genes for T cells, B cells, Myeloid cells, Epithelial cells, and stromal cells (Fiboblast, Endothelial cells, Pericyte, Smooth muscle cells) to visualize the Seurat clusters, to facilitate labeling them by biological names. Once users named each cluster, they can evaluate the quality of them again and find the de novo marker genes also. 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Package: r-cran-scrobbler Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-scrobbler_1.0.3-1.ca2404.1_all.deb Size: 35198 MD5sum: 9a5a8db5a1467828c9e392f8d63bf888 SHA1: 3c55daac3f061fddd269e900414614a230b7a7c8 SHA256: bb797c6548a5978961ae58582eca48aa7f7bb27236abeb502477fff540d3c8e1 SHA512: 8e9be2a03f75f82745d10b68e3e57d72347e828e662a18319c3342c8a4f8dfd907c95a2337ac66261ea77ca907aa5b0b647507a26c4590d0ade59a2297c5248c Homepage: https://cran.r-project.org/package=scrobbler Description: CRAN Package 'scrobbler' (Download 'Scrobbles' from 'Last.fm') 'Last.fm' is a music platform focussed on building a detailed profile of a users listening habits. It does this by 'scrobbling' (recording) every track you listen to on other platforms ('spotify', 'youtube', 'soundcloud' etc) and transferring them to your 'Last.fm' database. This allows 'Last.fm' to act as a complete record of your entire listening history. 'scrobbler' provides helper functions to download and analyse your listening history in R. 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See for more information. Package: r-cran-scroshi Architecture: all Version: 1.0.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 778 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-limma, r-bioc-s4vectors, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-uwot Filename: pool/dists/noble/main/r-cran-scroshi_1.0.0.0-1.ca2404.1_all.deb Size: 761058 MD5sum: 47e0cf4e064f02f2aca3b208890ce7ca SHA1: a20a8af21a50b097115ec5b0aed18b8438b22d37 SHA256: c53a3c9e582dd8579eb442a079cfc5f3c4c030c9fc1f667012bd7250e42bff63 SHA512: 1b605c82fe13b805240802dca09dfad436fcc9d74a6fd610c9796bad4d217b1030c0d07edcaaaa802721a4a5b351b5a9b6bb41ce956d66b0ce088dca99cfc42a Homepage: https://cran.r-project.org/package=scROSHI Description: CRAN Package 'scROSHI' (Robust Supervised Hierarchical Identification of Single Cells) Identifying cell types based on expression profiles is a pillar of single cell analysis. 'scROSHI' identifies cell types based on expression profiles of single cell analysis by utilizing previously obtained cell type specific gene sets. It takes into account the hierarchical nature of cell type relationship and does not require training or annotated data. A detailed description of the method can be found at: Prummer, Bertolini, Bosshard, Barkmann, Yates, Boeva, The Tumor Profiler Consortium, Stekhoven, and Singer (2022) . Package: r-cran-scrt Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-scrt_1.3.1-1.ca2404.1_all.deb Size: 190086 MD5sum: 6568766e41c3cff0515654f81f1c01cf SHA1: ba8df0f3126d44d7f3bf4373feb655789355dacd SHA256: 10c6381b3981a88611467a2ae449cb630866c21413aedf8dd1be80cb556ec1e5 SHA512: ef4d42da07011eb5807664f9c7eca77d0ec4178b0931e6969979702159617ed3aaad2448ae877093eb2c6410c30dc354c68983e16cc1e8d6792c1ff405e12fc9 Homepage: https://cran.r-project.org/package=SCRT Description: CRAN Package 'SCRT' (Single-Case Randomization Tests) Design single-case phase, alternation and multiple-baseline experiments, and conduct randomization tests on data gathered by means of such designs, as discussed in Bulte and Onghena (2013) . Package: r-cran-scrutiny Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-corrr, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-glue, r-cran-lifecycle, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scrutiny_0.6.1-1.ca2404.1_all.deb Size: 1538344 MD5sum: d342bd33ae7ea82b6f82374295f5b4e4 SHA1: 733d0d5b07c282e6781a9ec64a4e538d493847d7 SHA256: 856c5bbcd42e164a9500c1ad23295db0f2108ca54728a5f836393fe19aae8b0b SHA512: 6c0847d29bd9df1a638197ccea29d2952d5f91b60a58563577c09028584758a43511050605c1eadb4b0c52c4e44dfa104e7d81613a9eb21a7b7e67f79447ea7c Homepage: https://cran.r-project.org/package=scrutiny Description: CRAN Package 'scrutiny' (Error Detection in Science) Test published summary statistics for consistency (Brown and Heathers, 2017, ; Allard, 2018, ; Heathers and Brown, 2019, ). The package also provides infrastructure for implementing new error detection techniques. 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Complements single-dataset inspection tools by operating across an entire collection at once. Also includes lightweight utilities for related file and folder management tasks. Package: r-cran-scryr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 646 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-purrr, r-cran-tibble Suggests: r-cran-covr, r-cran-httptest, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scryr_1.0.0-1.ca2404.1_all.deb Size: 197054 MD5sum: bc6b18ce29282e220ac795d4ac3cc7c4 SHA1: 13578ed64487aa9fd3889607e3d24c0adfa905c6 SHA256: 5f57ca7f2497b5f7bb193169fa8925b410274d8d2ab41f849dd1f8d4c8d1cdbd SHA512: 10875ea137a26e4b6765d0e7c8e7d113cbbeb99c2e6f1445b4bf977764a6d04dd0aef180ab6efd09aaf9187d79c274b4cc0af6fb47639c7913009500b337d7bc Homepage: https://cran.r-project.org/package=scryr Description: CRAN Package 'scryr' (An Interface to the 'Scryfall' API) A simple, light, and robust interface between R and the 'Scryfall' card data API . Package: r-cran-scsorter Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-scsorter_0.0.2-1.ca2404.1_all.deb Size: 336920 MD5sum: de741d973ec697c0f7126f8db932deba SHA1: 7a9a9c29695f6a68219e7c928a6440667ff6cda3 SHA256: 859ea26abcc57cf3636998cd8216a6e7810686189b12e3a427914545fb0653d0 SHA512: 1d1589fa2f7bcf7a7920712b9d4f42f60ce604744470ac02a485cb4f66d6d7bf676ec31e6722794c7d2791e454d636edcd3b421775bf4af2948e43e5b3d6a253 Homepage: https://cran.r-project.org/package=scSorter Description: CRAN Package 'scSorter' (Implementation of 'scSorter' Algorithm) Implements the algorithm described in Guo, H., and Li, J., "scSorter: assigning cells to known cell types according to known marker genes". Cluster cells to known cell types based on marker genes specified for each cell type. Package: r-cran-scspatialsim Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-spatstat.geom, r-cran-crayon, r-cran-ggpubr, r-cran-pbmcapply, r-cran-spatstat.random, r-cran-tidyr, r-cran-proxy Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-spatialtime, r-cran-spdep Filename: pool/dists/noble/main/r-cran-scspatialsim_0.1.4-1.ca2404.1_all.deb Size: 1515918 MD5sum: 8b625a481e8654f670494d1aca0a17de SHA1: cf62e66aac3b22155d609af9eebc33f53f744fb2 SHA256: f92fdc261a61748fed3a0d3e58e28ae116d65b2685308e5baa64953d6249de5c SHA512: 0e427d11e2b845bc370db6662fb676c952566e742ccd621639e8e6128aff969c23773bd1d2dd4ba69b14a047887c2af3e9d685f76369385e452903b23a0e878f Homepage: https://cran.r-project.org/package=scSpatialSIM Description: CRAN Package 'scSpatialSIM' (A Point Pattern Simulator for Spatial Cellular Data) Single cell resolution data has been valuable in learning about tissue microenvironments and interactions between cells or spots. This package allows for the simulation of this level of data, be it single cell or ‘spots’, in both a univariate (single metric or cell type) and bivariate (2 or more metrics or cell types) ways. As more technologies come to marker, more methods will be developed to derive spatial metrics from the data which will require a way to benchmark methods against each other. Additionally, as the field currently stands, there is not a gold standard method to be compared against. We set out to develop an R package that will allow users to simulate point patterns that can be biologically informed from different tissue domains, holes, and varying degrees of clustering/colocalization. The data can be exported as spatial files and a summary file (like 'HALO'). . Package: r-cran-scstability Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 298 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-aricode, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-magrittr, r-cran-pcapp, r-cran-rlang, r-cran-rtsne, r-cran-seurat, r-cran-uwot, r-cran-vegan Suggests: r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-bioc-scrnaseq, r-bioc-summarizedexperiment, r-cran-biocmanager, r-cran-testthat Filename: pool/dists/noble/main/r-cran-scstability_1.0.3-1.ca2404.1_all.deb Size: 138776 MD5sum: aa3082bb006957333e80809da4c367da SHA1: 744ac21f4d641f1f97fb84c51f6ada8ef791e22d SHA256: e6de77a40cc671f4370bcb73e9a01ab4524381c8863260bee21a4e9aa73dfa09 SHA512: 6cd27fee4c3d874bc48ac9362c531cccb702900b7436df7c5ed3a376ed9504ae697a7ef077d6679a51858a699018d905b6bf2f5015839247956e410431fff8d0 Homepage: https://cran.r-project.org/package=scStability Description: CRAN Package 'scStability' (Measuring the Stability of Dimension Reduction and ClusterAssignment in scRNA-Seq Experiments) Provides functions for evaluating the stability of low-dimensional embeddings and cluster assignments in single‑cell RNA sequencing (scRNA‑seq) datasets. Starting from a principal component analysis (PCA) object, users can generate multiple replicates of t‑Distributed Stochastic Neighbor Embedding (t‑SNE) or Uniform Manifold Approximation and Projection (UMAP) embeddings. Embedding stability is quantified by computing pairwise Kendall’s Tau correlations across replicates and summarizing the distribution of correlation coefficients. In addition to dimensionality reduction, 'scStability' assesses clustering consistency using either Louvain or Leiden algorithms and calculating the Normalized Mutual Information (NMI) between all pairs of cluster assignments. For background on UMAP and t-SNE algorithms, see McInnes et al. (2020, ) and van der Maaten & Hinton (2008, ), respectively. Package: r-cran-sctenifoldknk Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 682 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-matrix, r-cran-mass, r-cran-sctenifoldnet, r-cran-cli, r-cran-enrichr, r-cran-igraph, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sctenifoldknk_1.0.3-1.ca2404.1_all.deb Size: 114556 MD5sum: 79f7a9758a00cf0e57cdc6f7ba54804c SHA1: f5e91a88788927023f59ac3bb4b5cf88c37c3953 SHA256: 5f00ae2667de36acf6278fb45119192f25b78654d37f2d318e0b68819eb2c51e SHA512: bf486e88ba684bb0f9c28c7a00c15e0b74ef4edc139aeae1abaf8568d163ce45ca7e865d067c3b9ceb55022a7ed18c53ffc3418bb7790eb646fb478da989caa3 Homepage: https://cran.r-project.org/package=scTenifoldKnk Description: CRAN Package 'scTenifoldKnk' (In-Silico Knockout Experiments from Single-Cell Gene RegulatoryNetworks) A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells. Package: r-cran-sctenifoldnet Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pbapply, r-cran-rspectra, r-cran-matrix, r-cran-mass, r-cran-rhpcblasctl Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sctenifoldnet_1.3-1.ca2404.1_all.deb Size: 83628 MD5sum: ac192c3e36470ccba569f5b8d171f2a7 SHA1: 5243863b72dc374db60756500f71680ce902c55a SHA256: e3be002f1ad86028294da5f86ea9bbe2c2d3093f19f04a0863b5b7e897ea2072 SHA512: dda083349f5ec365c5e517b3a4f2875955ce6c94d37952fd16ba5ba81c1d2f7baf35144e82795209eb2fe9562191a442470e8604079c93c5a02a118f673dfd8e Homepage: https://cran.r-project.org/package=scTenifoldNet Description: CRAN Package 'scTenifoldNet' (Construct and Compare scGRN from Single-Cell Transcriptomic Data) A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See for more details. Package: r-cran-sctools Architecture: all Version: 0.3.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2304 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-future, r-cran-ggplot2, r-cran-synth, r-cran-stringr, r-cran-cvtools, r-cran-furrr, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sctools_0.3.3.1-1.ca2404.1_all.deb Size: 1362558 MD5sum: c5e4bced48aac1d28cd402cad9dedda6 SHA1: 1be91945698dbec40b3fa21dba2072989ce6b7a8 SHA256: aadfdafec44b947a5c531a514094063c6f1b3245cb509a7634b4a2d2ac8b4dc0 SHA512: 31c67a47240db3f04d7abc49abf00c39bd68090dc2644f60f39b7921241ac19f5a0e038c682dbab5d2ff43681f518ef195d8a036c569ebe58398a6f08dcec65f Homepage: https://cran.r-project.org/package=SCtools Description: CRAN Package 'SCtools' (Extensions for Synthetic Controls Analysis) Extends the functionality of the package 'Synth' as detailed in Abadie, Diamond, and Hainmueller (2011) . Includes generating and plotting placebos, post/pre-MSPE (Mean Squared Prediction Error) significance tests and plots, and calculating average treatment effects for multiple treated units. Package: r-cran-scutils Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-scales, r-cran-assertthat, r-cran-dplyr, r-cran-viridis, r-cran-viridislite Suggests: r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-scutils_0.1.0-1.ca2404.1_all.deb Size: 89128 MD5sum: c86814f16b11344e942534a231ccfa53 SHA1: 0077a8972bd8680c75e2f5defde20b80d58cf317 SHA256: 48bba93ab34732b52d0a1a5fbb388b711396f8d563189aa060b400fe839e1015 SHA512: 2cc9f5fb4d7f9236d57418f2b093cf9a382cea2fa2f45ed8b73cf9aeef2d8ed3c9a3f6defdd466e956fb821349fffbfe4aa5750539570497f43074625ac4221a Homepage: https://cran.r-project.org/package=scUtils Description: CRAN Package 'scUtils' (Utility Functions for Single-Cell RNA Sequencing Data) Analysis of single-cell RNA sequencing data can be simple and clear with the right utility functions. This package collects such functions, aiming to fulfill the following criteria: code clarity over performance (i.e. plain R code instead of C code), most important analysis steps over completeness (analysis 'by hand', not automated integration etc.), emphasis on quantitative visualization (intensity-coded color scale, etc.). Package: r-cran-scutr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-smotefamily, r-cran-mclust Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-scutr_0.2.0-1.ca2404.1_all.deb Size: 244082 MD5sum: 294deae890c3790dac927452b2eb45d5 SHA1: 74f8faa4accd93c343704820e63aa87348dce97e SHA256: 1ea5e9c55334e03c43ff999e9b2f679d22e6dda65ce2868eefcf0657924ff19a SHA512: c23c9fec7cb95bb12676abcfdbc233849614b7a677e2dddfbf664751c037610dca64e416bfe6d4fe9733b9c51db69018fedda148eda589bade3020b58a767d6a Homepage: https://cran.r-project.org/package=scutr Description: CRAN Package 'scutr' (Balancing Multiclass Datasets for Classification Tasks) Imbalanced training datasets impede many popular classifiers. To balance training data, a combination of oversampling minority classes and undersampling majority classes is useful. This package implements the SCUT (SMOTE and Cluster-based Undersampling Technique) algorithm as described in Agrawal et. al. (2015) . Their paper uses model-based clustering and synthetic oversampling to balance multiclass training datasets, although other resampling methods are provided in this package. Package: r-cran-scva Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggextra, r-cran-ggplot2, r-cran-plotly, r-cran-scales Filename: pool/dists/noble/main/r-cran-scva_1.3.1-1.ca2404.1_all.deb Size: 229892 MD5sum: b427ff98fc476be38ecbafcb1f8a0eb5 SHA1: f81fcbbfc3c577d62e7c25fe2bdff12a332dcaee SHA256: 6c4744abdec8760ce32fe259214ec99fde5083bb8b7dfee5519f814c5697e845 SHA512: 0c366c5aa3a8825b176258a4f41c197c0c6405fc1c53170a2b19212fdbe73387c225cc7e324e895f081cda603496b8228930c129759cce66721d08905e1edfcc Homepage: https://cran.r-project.org/package=SCVA Description: CRAN Package 'SCVA' (Single-Case Visual Analysis) Make graphical representations of single case data and transform graphical displays back to raw data, as discussed in Bulte and Onghena (2013) . The package also includes tools for visually analyzing single-case data, by displaying central location, variability and trend. 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The classifier is trained using James-Stein-type shrinkage estimators and predictor variables are ranked using correlation-adjusted t-scores (CAT scores). Variable selection error is controlled using false non-discovery rates or higher criticism. Package: r-cran-sdaa Architecture: all Version: 0.1-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-survey, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-sdaa_0.1-5-1.ca2404.1_all.deb Size: 567122 MD5sum: a074e9712d4ec21c86e5577d2880b89f SHA1: 0e5a052328defa0906a925b27f0897d9d29257c0 SHA256: 2be137401637c0c38e6580c5cdf5dada372695aaa333a6de31c461f9d1d0dd9a SHA512: b2ac06de832524fa7ce6ddec8c2b997fb1ae3725d573695d943a014dfba30baae9cd317dcb63e9ec367d8aeecdeb1a1b157f68b52bc4ac1fada3c56fd3657490 Homepage: https://cran.r-project.org/package=SDaA Description: CRAN Package 'SDaA' (Sampling: Design and Analysis) Functions and Datasets from Lohr, S. (1999), Sampling: Design and Analysis, Duxbury. Package: r-cran-sdafilter Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-glasso, r-cran-poet, r-cran-selectiveinference Suggests: r-cran-testthat, r-cran-mass Filename: pool/dists/noble/main/r-cran-sdafilter_1.0.1-1.ca2404.1_all.deb Size: 43310 MD5sum: fde19b030613ac29bf6d1d586730e90e SHA1: d99a9c1171228b6758698c33cfaf23fa2ab05c80 SHA256: def949ef2ef98402526394c8005cce4a203c66a7a4b67c3db76ebcf751b99cad SHA512: cb4334d50ff3ee5fa4cf1e40817d95b2c52c6e5358a95bfea5bceadd965ba6f805ec69c656c8eeaa47c4c18bbee331b2a97b4e2bc231c4d9562a4df3cb5bafd2 Homepage: https://cran.r-project.org/package=sdafilter Description: CRAN Package 'sdafilter' (Symmetrized Data Aggregation) We develop a new class of distribution free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data driven threshold along the ranking to control the FDR. For more information, see the website below and the accompanying paper: Du et al. (2023), "False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation", . Some optional functionality uses the archived R packages ‘huge’ and ‘pfa’, which are not available from CRAN’s main repositories. Users who need this optional functionality can obtain them from the CRAN Archive as follows: ‘huge’ at ; ‘pfa’ at . Package: r-cran-sdam Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-grimport2, r-cran-multiplex Suggests: r-cran-httr, r-cran-rjson, r-cran-knitr, r-cran-rmarkdown, r-cran-multigraph Filename: pool/dists/noble/main/r-cran-sdam_1.1.4-1.ca2404.1_all.deb Size: 2557614 MD5sum: 185c9e4728505262e4b7a5dbcf08617a SHA1: 56295e4797c1e7d9865807c5467b0b7d65f5f304 SHA256: 30b63555a1fbdb281acc0a257c9f0b42efa61c97fb26405fb9f0a6998600fc88 SHA512: 227829728d2f0f6d82e7cfcd85da86881d1dbeadd7cd8b9cf8493af76ea7f70df9506b8d8531513f462056f22ea499b8ce462627278fb5f5b818da35abf4ddfd Homepage: https://cran.r-project.org/package=sdam Description: CRAN Package 'sdam' (Social Dynamics and Complexity in the Ancient Mediterranean) Provides digital tools for performing analyses within Social Dynamics and complexity in the Ancient Mediterranean (SDAM), which is a research group based at the Department of History and Classical Studies at Aarhus University. Package: r-cran-sdamr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-car Filename: pool/dists/noble/main/r-cran-sdamr_0.2.0-1.ca2404.1_all.deb Size: 226470 MD5sum: 9c717184e0210c1bb88aa3845ff3cac5 SHA1: 61f6ed4d657488e0e55ba6fb6739b1452c2cb8f0 SHA256: 7f37d01bdb6f91240286a22a7e6724431888923dfc144da1977e8c5e0f5deb73 SHA512: c51abe00233c853f976513ea5e595e0d618ef4323866042908e40ca221752e69e3be403c5920df4d4d2a481a919e0d0c551a8145684e9f204af2b5080646bcf6 Homepage: https://cran.r-project.org/package=sdamr Description: CRAN Package 'sdamr' (Statistics: Data Analysis and Modelling) Data sets and functions to support the books "Statistics: Data analysis and modelling" by Speekenbrink, M. (2021) and "An R companion to Statistics: data analysis and modelling" by Speekenbrink, M. (2021) . All datasets analysed in these books are provided in this package. In addition, the package provides functions to compute sample statistics (variance, standard deviation, mode), create raincloud and enhanced Q-Q plots, and expand Anova results into omnibus tests and tests of individual contrasts. Package: r-cran-sdar Architecture: all Version: 0.9-55-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4656 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-linbin, r-cran-grimport2, r-cran-readxl Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sdar_0.9-55-1.ca2404.1_all.deb Size: 1480786 MD5sum: 1678ade2f654f1008db73e52378c437c SHA1: d7fa049b2b1204fa3a497c7f6f772fcd1a7b9ece SHA256: a3401a0a373b531071e3dc92e32820ab5cd6f4a1955e89c95ef5f29c40e4431e SHA512: 46e9a67d05171502f7589bdda48ab1a3815094b7cddbd7f8de740d20a030ee1eade7dbd6dc2baf85b637b3c0caf3e0ace199802bd32531dc4753b4ec8192c3bb Homepage: https://cran.r-project.org/package=SDAR Description: CRAN Package 'SDAR' (Stratigraphic Data Analysis) A fast, consistent tool for plotting and facilitating the analysis of stratigraphic and sedimentological data. Taking advantage of the flexible plotting tools available in R, 'SDAR' uses stratigraphic and sedimentological data to produce detailed graphic logs for outcrop sections and borehole logs. These logs can include multiple features (e.g., bed thickness, lithology, samples, sedimentary structures, colors, fossil content, bioturbation index, gamma ray logs) (Johnson, 1992, ). Package: r-cran-sdaresources Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3166 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sdaresources_0.1.1-1.ca2404.1_all.deb Size: 3110816 MD5sum: ad77816194c1c9de50baad3c7c6c0d34 SHA1: f9ca4276e714125ae57dcbe465fdfe1dbcc64559 SHA256: 7921f59564beb87d0e16003a3b5b7a5f79a581351041ca488684fdbf4d622939 SHA512: fcee9b8a05e42f18b3d1bd40f4b0bb69e403b3488f4d5e6ee93d84c09a0c51afd4d99d28d09fdae9630295cc2b5726ce572bdbe6f035864d3b0eee2221668184 Homepage: https://cran.r-project.org/package=SDAResources Description: CRAN Package 'SDAResources' (Datasets and Functions for 'Sampling: Design and Analysis, 3rdEdition') Includes all the datasets of 'Sampling: Design and Analysis' (3rd edition by Sharon Lohr) in R format and additional functions for analyzing and graphing probability samples. 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They represent how systems change over time and are mathematically equivalent to ordinary differential equations. 'sdbuildR' (system dynamics builder) provides an intuitive interface for constructing stock-and-flow models without requiring extensive domain knowledge. Models can quickly be simulated and revised, supporting iterative development. 'sdbuildR' simulates models in 'R' and 'Julia', where 'Julia' offers unit support and large-scale ensemble simulations. Additionally, 'sdbuildR' can import models created in 'Insight Maker' (). Package: r-cran-sdclog Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-mathjaxr Suggests: r-cran-cffr, r-cran-knitr, r-cran-lfe, r-cran-rmarkdown, r-cran-skimr, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-sdclog_0.5.1-1.ca2404.1_all.deb Size: 99258 MD5sum: 538572a218604b6288bf4546b2efeda6 SHA1: 8865faff91910f8495ebaa0b41c8b35a88d9a0d8 SHA256: c235d2bb2aff9978059cbf044f7861b5f7399477d2cfdb477364dd3ce8c428ea SHA512: f6e4f9aa2c1530061578f76e841a2cffbf4b25ba8b95f44aeddbb51cc5637496491ba0eb11f1ab2da8cd7b8fe6d34a1d7dcbc8413331ff8f83f35b84f206552e Homepage: https://cran.r-project.org/package=sdcLog Description: CRAN Package 'sdcLog' (Tools for Statistical Disclosure Control in Research DataCenters) Tools for researchers to explicitly show that their results comply to rules for statistical disclosure control imposed by research data centers. These tools help in checking descriptive statistics and models and in calculating extreme values that are not individual data. Also included is a simple function to create log files. The methods used here are described in the "Guidelines for the checking of output based on microdata research" by Bond, Brandt, and de Wolf (2015) . Package: r-cran-sdcnway Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 967 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-plyr, r-cran-dplyr, r-cran-ggplot2, r-cran-mass Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-sdcnway_1.0.1-1.ca2404.1_all.deb Size: 805382 MD5sum: 34e74c343ab0dd345c3d7851c3977dc3 SHA1: c8cf9744459c64435fe53b1f21de88f0427c02dd SHA256: 0bd02aeb71388c6e734f3c2035f2a4069cd7b44780644d3effbdd6dd0ad45f37 SHA512: 5ace2716f702e20d8bcaef9e25097befd9e48c613685b343458a97f4a1131e9f54efb50f463b2768eaa14466feef0582c86ed163eb0353206cd7ee0b61eb05ff Homepage: https://cran.r-project.org/package=SDCNway Description: CRAN Package 'SDCNway' (Tools to Evaluate Disclosure Risk) Tools for calculating disclosure risk measures for microdata, including record-level and file-level measures. The record-level disclosure risk is estimated primarily using exhaustive tabulation. The file-level disclosure risk is estimated by fitting loglinear models on the observed sample counts in cells formed by key variables and their interactions. Funded by the National Center for Education Statistics. See Skinner and Shlomo (2008) for a description of the file-level risk measures and the loglinear model approach. 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Protection methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) , continuous variables by de Wolf and de Jonge (2018) , suppressing revealing values and a generalization of the quad tree method by Suñé, Rovira, Ibáñez and Farré (2017) . 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(2002). , Bolós, V.J.; Benítez, R.; Coll-Serrano, V. (2024) . Package: r-cran-sdefsr Architecture: all Version: 0.7.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1050 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreign, r-cran-ggplot2, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sdefsr_0.7.22-1.ca2404.1_all.deb Size: 773244 MD5sum: 02e3d104670a9ba8c986464816787a6f SHA1: 0fab4ac3988ea29caa92e3acbe051f5ff556f202 SHA256: 0d746e9fe6579e3ecb4c90ff4b99a24a6b7bc29da3f5aa8f2f63ff7daf385e85 SHA512: 3a6d4f97d870d856d4ac8c794d6f1e6ba55d5fcb367c050021554ee32df1aef8a59761a256e933acfacb4b4742dc1a1181e08d26a1ab328786f0c18e7000954d Homepage: https://cran.r-project.org/package=SDEFSR Description: CRAN Package 'SDEFSR' (Subgroup Discovery with Evolutionary Fuzzy Systems) Implementation of evolutionary fuzzy systems for the data mining task called "subgroup discovery". 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Package: r-cran-sdgdetector Architecture: all Version: 2.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1989 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-stringr, r-cran-ggplot2, r-cran-tidyr, r-cran-rnaturalearth, r-cran-scales, r-cran-magick Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sdgdetector_2.7.3-1.ca2404.1_all.deb Size: 1776724 MD5sum: 15ddb294f3ee047cf322de758d8f74a4 SHA1: 4e69174213c33c81a0299225a55f30d069b06130 SHA256: 850952b8947edd74a44ff010ac823cc72a36396dd17f8548fba3a74f35df8882 SHA512: c7ceb0f57dc4202415713c5f51840fecda3fb265afd44bef8d410762f883716ca8c3fc85a61b4b8d6cef5a555284add77c6036aa5edfa39c17c5431064ce2e31 Homepage: https://cran.r-project.org/package=SDGdetector Description: CRAN Package 'SDGdetector' (Detect SDGs and Targets in Text) Identify 17 Sustainable Development Goals and associated 169 targets in text. 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The package supports Pareto-type and Gamma-type DGLMs, which are suitable for modeling heavy-tailed phenomena such as wealth allocation and financial returns. It provides simulation tools for synthetic DGLM data, adaptive mutation-rate strategies (ScaI, ScaII, ScaIII), geometric temperature ladders for parallel tempering, and posterior predictive evaluation metrics (e.g., R2, RMSE). The methodology is based on the scalable MCMC framework described in Guo et al. (2025). Package: r-cran-sdi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-topsis, r-cran-liver Filename: pool/dists/noble/main/r-cran-sdi_0.1.0-1.ca2404.1_all.deb Size: 39254 MD5sum: 8c442146fcacf9ad118b2fd6ab28d22c SHA1: 027f8347341f3893c0dd46fa552257def750a3ee SHA256: a1a6edeee612cddf8daa333eec3bb72573102b80caa540c75285084299ceb8dc SHA512: ac77dfd6fbce85308b838d354ca6110bcd4388d853f6a40e66175e38039e1325f6fd11faa08578fe86794db22fb68868ddeb4b397f6e8353a993c4e2443e11a8 Homepage: https://cran.r-project.org/package=SDI Description: CRAN Package 'SDI' (Slow Digestibility Index) The Slow Digestibility Index (SDI) is a tool that helps users evaluate the slow-digestion properties of crops or food matrices by combining multiple factors into a single score. It considers parameters related to starch composition [total starch (TS), amylose/amylopectin ratio (Aratio), total amylose content (TAC), and total amylopectin content (TAPC)], starch digestibility [rapidly digestible starch (RDS), slowly digestible starch (SDS) and resistant starch (RS)], structural properties [relative crystallinity (RC)], non-starch components [total protein, total oil content (TOC), and total phenolic content (TPC)], and pasting behaviour [peak viscosity (PV), pasting temperature (PT), holding strength (HS), and final viscosity (FV)].The SDI is flexible and allows users to calculate the index using all parameters or only selected ones, depending on the data available. Users can also compute a starch-based SDI (using only starch-related parameters) or a principal component analysis (PCA)-based SDI, where weights are determined automatically from the data. Thus, the SDI provides a simple way to compare and rank crops or food samples based on their slow digestion potential. The package implements SDI(),starchSDI(), genSDI(), scoreSDI(), and pcaSDI() for estimating slow digestibility index using predefined weighted TOPSIS, starch-specific TOPSIS, user-defined weighted TOPSIS, score-based normalization, and PCA based approaches, respectively. The package has been developed using the algorithm of Pandey et al. (2026) . Package: r-cran-sdlfilter Architecture: all Version: 2.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-geosphere, r-cran-data.table, r-cran-gridextra, r-cran-ggmap, r-cran-maps, r-cran-pracma, r-cran-lubridate, r-cran-dplyr, r-cran-emmeans, r-cran-sf, r-cran-stars, r-cran-ggspatial Filename: pool/dists/noble/main/r-cran-sdlfilter_2.3.3-1.ca2404.1_all.deb Size: 1111350 MD5sum: d213f6bda9ef6aae6f1e2b54c3fc4ae7 SHA1: 142f3a411389eecd600d630bdd89e429a51712b5 SHA256: 3a066836c63eb10d1e35350a57e5646a1bec7f082f19e2f57b5a8c1e1d24e23a SHA512: 4315ba7d6375a15a6455bbb7f3d61ebc5ee046fba1a8720965e3f8a17f4152fb5035695a6de2cedaf46b74c1f2c5be8b77ba7437392173fabef484b12c178b28 Homepage: https://cran.r-project.org/package=SDLfilter Description: CRAN Package 'SDLfilter' (Filtering and Assessing the Sample Size of Tracking Data) Functions to filter GPS/Argos locations, as well as assessing the sample size for the analysis of animal distributions. 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The package provides a set of functions for a complete analysis of integer-valued data, where the dependent variable is assumed to follow a modified SDL distribution. This regression model is useful for the analysis of integer-valued data and experimental studies in which paired discrete observations are collected. Package: r-cran-sdm Architecture: all Version: 1.2-59-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4295 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-sp, r-cran-raster Suggests: r-cran-r.rsp, r-cran-shinybs, r-cran-shiny, r-cran-dismo Filename: pool/dists/noble/main/r-cran-sdm_1.2-59-1.ca2404.1_all.deb Size: 3073362 MD5sum: 2c49afef1879b32542eb6b0162dc1d7a SHA1: a5da5a7bc660ed6ce7dfd201b40730463ddade8e SHA256: 7bd9cfba31ac29947d9e591a3508e524d436ebc9c6b27b44b27a421bf494b353 SHA512: 477a99e2e8757043ba2968570fa128ffc1c9bc6753a3b621088ded468aba398cca1ec00e0546dc4ee48db2f57c99aee31f8c21ec2220b169e419128ab1a2827a Homepage: https://cran.r-project.org/package=sdm Description: CRAN Package 'sdm' (Species Distribution Modelling) An extensible framework for developing species distribution models using individual and community-based approaches, generate ensembles of models, evaluate the models, and predict species potential distributions in space and time. 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These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. 'SDModels' provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) ) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) ). 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The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. Hyperpriors for all effects can be elicitated within the package. Including complex tensor product interaction terms and variable selection priors. The basic model is explained in in Klein and Kneib (2016) . 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The reusable algorithms concept in 'sdtm.oak' provides a framework for modular programming and can potentially automate the conversion of raw clinical data to SDTM through standardized SDTM specifications. SDTM is one of the required standards for data submission to the Food and Drug Administration (FDA) in the United States and Pharmaceuticals and Medical Devices Agency (PMDA) in Japan. SDTM standards are implemented following the SDTM Implementation Guide as defined by CDISC . Package: r-cran-sdtm.terminology Architecture: all Version: 2025-3-25-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2067 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-sdtm.terminology_2025-3-25-1.ca2404.1_all.deb Size: 1148104 MD5sum: 8b2080f0d38e129ebea99e733f09b2b6 SHA1: cb16cb4f58bb0943d80270bff38edcd9f6d2474e SHA256: 62e4be7662cc99c08a56a833b7ede83d4da4f4ca38d3737ec6aebaf4d97566c6 SHA512: b990bfc587239015990f14470c7b393671f811ef8cac4980a9ba618866c3aa09464d894574185b9f94baba489da3ae976adda80b4e736db0d118b605a65a5ce2 Homepage: https://cran.r-project.org/package=sdtm.terminology Description: CRAN Package 'sdtm.terminology' (CDISC SDTM Controlled Terminology) Clinical Data Interchange Standards Consortium (CDISC) Standard Data Tabulation Model (SDTM) controlled terminology, 2025-03-25. Source: . Package: r-cran-sdtmchecks Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1332 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyselect, r-cran-openxlsx Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dt Filename: pool/dists/noble/main/r-cran-sdtmchecks_1.0.0-1.ca2404.1_all.deb Size: 767058 MD5sum: c89ea0822a8819583ec5c3c323a04faa SHA1: b3cf08ed7ec8c69faab4f1b6bd32c04f21b296c0 SHA256: db1d86943a431e4f2c3407554e2b3284e708845db6df0d7e44c9dbe0cc482694 SHA512: cae33c3721cb315328f9acf03a6288da7f90d473b593c4f76c91d4903d249d740e2081a8771ca8292475dfa0f6c79c448f1cc18d55912eeb42e85c96cf76a1f7 Homepage: https://cran.r-project.org/package=sdtmchecks Description: CRAN Package 'sdtmchecks' (Data Quality Checks for Study Data Tabulation Model (SDTM)Datasets) A series of checks to identify common issues in Study Data Tabulation Model (SDTM) datasets. These checks are intended to be generalizable, actionable, and meaningful for analysis. Package: r-cran-sdtmval Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-haven, r-cran-knitr, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-readxl, r-cran-rlang, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sdtmval_0.4.1-1.ca2404.1_all.deb Size: 144050 MD5sum: 714829c567540d5261d3b43c110820ab SHA1: 76dafcca258c3b7e862738800d2eca4e05b18bd4 SHA256: 1b897906b2c68e3e87dde7f977a4ffa58da93c6abaaba6e671e3d54f3e752d83 SHA512: cbb98fda4e9f1636f86cf060277c3334d8d6b1d33b5e8e7a748d8e4a9136182b23d399699b5cc7302f8e8bca69902768fd87ea26390f271aeffc3bc69a808aa3 Homepage: https://cran.r-project.org/package=sdtmval Description: CRAN Package 'sdtmval' (Validate SDTM Domains) Provides a set of tools to assist statistical programmers in validating Study Data Tabulation Model (SDTM) domain data sets. Statistical programmers are required to validate that a SDTM data set domain has been programmed correctly, per the SDTM Implementation Guide (SDTMIG) by 'CDISC' (), study specification, and study protocol using a process called double programming. Double programming involves two different programmers independently converting the raw electronic data cut (EDC) data into a SDTM domain data table and comparing their results to ensure accurate standardization of the data. One of these attempts is termed 'production' and the other 'validation'. Generally, production runs are the official programs for submittals and these are written in 'SAS'. Validation runs can be programmed in another language, in this case 'R'. Package: r-cran-se.eq Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-se.eq_1.0-1.ca2404.1_all.deb Size: 40190 MD5sum: 31a8cfc99a61e72696c96a29a51418f2 SHA1: 69173a90910e2f5af15b4898c2e73613d94b5a7c SHA256: 54eeb2a5ddb886268956df2268706136f5a7544d3c7800cbff7d870e7a2d867b SHA512: 44fa042942a390d04112b368d3e778e1cc9c95b6d104a358d8aa96aa47d63370940c9eb573c0751022d5cf5323107d094a8ca2de8501423d2c33e09f13ba763e Homepage: https://cran.r-project.org/package=SE.EQ Description: CRAN Package 'SE.EQ' (SE-Test for Equivalence) Implements the SE-test for equivalence according to Hoffelder et al. (2015) . The SE-test for equivalence is a multivariate two-sample equivalence test. Distance measure of the test is the sum of standardized differences between the expected values or in other words: the sum of effect sizes (SE) of all components of the two multivariate samples. The test is an asymptotically valid test for normally distributed data (see Hoffelder et al.,2015). The function SE.EQ() implements the SE-test for equivalence according to Hoffelder et al. (2015). The function SE.EQ.dissolution.profiles() implements a variant of the SE-test for equivalence for similarity analyses of dissolution profiles as mentioned in Suarez-Sharp et al.(2020) ). The equivalence margin used in SE.EQ.dissolution.profiles() is analogically defined as for the T2EQ approach according to Hoffelder (2019) ) by means of a systematic shift in location of 10 [\% of label claim] of both dissolution profile populations. SE.EQ.dissolution.profiles() checks whether the weighted mean of the differences of the expected values of both dissolution profile populations is statistically significantly smaller than 10 [\% of label claim]. The weights are built up by the inverse variances. Package: r-cran-sea Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8586 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-mass, r-cran-doparallel, r-cran-foreach, r-cran-kscorrect, r-cran-data.table Filename: pool/dists/noble/main/r-cran-sea_2.0.1-1.ca2404.1_all.deb Size: 7493754 MD5sum: 07a5a576d1747ac1d60be4520395f130 SHA1: eaeb493b9237bf3f2b5eca5576db2e6b813eecdc SHA256: 444b83d7f758dda13c4a674f53d873637174087e32252052055c5ee5a00eb517 SHA512: 4da49e32906a199a1cdcc54bdd491b9492d1e815df930b20c51ee19c17620cf34db0d83233f571563438699121329af84faf8d03174ab2bf8bc3d64d37a229c4 Homepage: https://cran.r-project.org/package=SEA Description: CRAN Package 'SEA' (Segregation Analysis) A few major genes and a series of polygene are responsive for each quantitative trait. Major genes are individually identified while polygene is collectively detected. This is mixed major genes plus polygene inheritance analysis or segregation analysis (SEA). In the SEA, phenotypes from a single or multiple bi-parental segregation populations along with their parents are used to fit all the possible models and the best model of the trait for population phenotypic distributions is viewed as the model of the trait. There are fourteen types of population combinations available. Zhang Yuan-Ming, Gai Jun-Yi, Yang Yong-Hua (2003, ). Package: r-cran-seacarb Architecture: all Version: 3.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-oce, r-cran-gsw, r-cran-solvesaphe Filename: pool/dists/noble/main/r-cran-seacarb_3.3.4-1.ca2404.1_all.deb Size: 703462 MD5sum: ed3a7ec67d4a6bfba93eb0e4d892db42 SHA1: a6ab1c73e1300fc2772ba4c0eaf55cbb60379959 SHA256: dafe8cefc55d2aa4021d4dbb6accbacb35790322bca1d702773295b9db109a14 SHA512: 9f19dda86998e4d447ce4eb8ac1fc75a2ff6e9a40e5902eb91681a5f2ba7d7cb457dc5d1369ed845a670039a331d087d58ae22457cbcb659324f669eafcc7f7a Homepage: https://cran.r-project.org/package=seacarb Description: CRAN Package 'seacarb' (Seawater Carbonate Chemistry) Calculates parameters of the seawater carbonate system and assists in design of ocean acidification perturbation experiments. Package: r-cran-seagle Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3012 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-compquadform Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-seagle_1.0.1-1.ca2404.1_all.deb Size: 401550 MD5sum: b96ef7796038ad014c912bbbfbb7da93 SHA1: 1a206ae19ceb948386d2e1bb25ce5bc3060a044e SHA256: e02d51aa4e53afa6d8af83afe42947db006563b6d45ad481968eaa6d7f7c8862 SHA512: 300bafb4472e40b6e06b402826ea9e164edec29a26d10c46b4a64d5d56a806a82a2a82bba11016e5f446764d47c95378079be3c24b0449b8b26ba28afe9dd05c Homepage: https://cran.r-project.org/package=SEAGLE Description: CRAN Package 'SEAGLE' (Scalable Exact Algorithm for Large-Scale Set-BasedGene-Environment Interaction Tests) The explosion of biobank data offers immediate opportunities for gene-environment (GxE) interaction studies of complex diseases because of the large sample sizes and rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in GxE assessment, especially for set-based GxE variance component (VC) tests, a widely used strategy to boost overall GxE signals and to evaluate the joint GxE effect of multiple variants from a biologically meaningful unit (e.g., gene). We present 'SEAGLE', a Scalable Exact AlGorithm for Large-scale Set-based GxE tests, to permit GxE VC test scalable to biobank data. 'SEAGLE' employs modern matrix computations to achieve the same “exact” results as the original GxE VC tests, and does not impose additional assumptions nor relies on approximations. 'SEAGLE' can easily accommodate sample sizes in the order of 10^5, is implementable on standard laptops, and does not require specialized equipment. The accompanying manuscript for this package can be found at Chi, Ipsen, Hsiao, Lin, Wang, Lee, Lu, and Tzeng. (2021+) . Package: r-cran-seagraphs Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2369 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfnetworks, r-cran-sf, r-cran-terra, r-cran-leaflet, r-cran-leaflet.minicharts, r-cran-leaflet.extras2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-seagraphs_0.1.3-1.ca2404.1_all.deb Size: 1507722 MD5sum: 52a9c63c9e214f1f8e960a56169b2ddb SHA1: d2a3b7a022449efa22f7b631743c5ac1e131356d SHA256: d7a2cbace50ced5e9f13698cc6f9ed1503abc43e91df898785a8352d03e0cc43 SHA512: 656afdd4023576688005c885bdd683701c04b6bf0de65b6273103ba7495f530c46fe13bf75e5bd7fbd61bbe374c967530ba96d1c7827b48d86631910fb8cd4ed Homepage: https://cran.r-project.org/package=SeaGraphs Description: CRAN Package 'SeaGraphs' (Sea Currents to Connectivity Transformation) Transformation of sea currents to connectivity data. Two files of horizontal and vertical currents flows are transformed into connectivity data in the form of 'sfnetwork', shapefile, edge list and adjacency matrix. An application example is shown at Nagkoulis et al. (2025) . Package: r-cran-seahors Architecture: all Version: 1.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinybs, r-cran-shinythemes, r-cran-shinyjs, r-cran-shinywidgets, r-cran-ggplot2, r-cran-gridextra, r-cran-dplyr, r-cran-plotly, r-cran-dt, r-cran-mass, r-cran-readxl, r-cran-raster, r-cran-stringr, r-cran-viridis, r-cran-rmarkdown, r-cran-htmlwidgets Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-seahors_1.9.0-1.ca2404.1_all.deb Size: 418394 MD5sum: f21e7c412cf62116e371d9f15d78a09f SHA1: e0c790e5469d7133e123cca0b28a1f6710acc425 SHA256: 66617d83027c02cc7c9f859a12680f516b2c34ef428fd28c5944ce95f9651d89 SHA512: e9806d4baa94e86d9ed22eb48f76ebab3bdf152bef1cd85c4cc5b915c1d9a8360c7f64c45ceb4b6ed77d52d159e54a2c085fb8051dfc6ff5a1a291f048e8f78a Homepage: https://cran.r-project.org/package=SEAHORS Description: CRAN Package 'SEAHORS' (Spatial Exploration of ArcHaeological Objects in R Shiny) An R 'Shiny' application dedicated to the intra-site spatial analysis of piece-plotted archaeological remains, making the two and three-dimensional spatial exploration of archaeological data as user-friendly as possible. Documentation about 'SEAHORS' is provided by the vignette included in this package and by the companion scientific paper: Royer, Discamps, Plutniak, Thomas (2023, PCI Archaeology, ). Package: r-cran-seairmobility Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-seairmobility_0.1.0-1.ca2404.1_all.deb Size: 70734 MD5sum: 409ebcc4fb2b03b177b519dc6afe9f0e SHA1: 442c6347e2d678a60a6f442c50e9f5fd5998049a SHA256: 46da8894404f0ad948e67d53b3e14161bcea05589cb58fded770542808e11733 SHA512: f12a7e016e6e50828432a0d98bf40ebeaf0cdd7a9fa8612d2675eceb9f2274801c8e6cb883e37e60fe0b7afc8a1762db6eb36d3a23618eb3a12d57aca6b04411 Homepage: https://cran.r-project.org/package=seairmobility Description: CRAN Package 'seairmobility' (Mobility-Based SEAIR Epidemic Models) Tools for simulating, analysing, and fitting mobility-based SEAIR (Susceptible-Exposed-Asymptomatic-Infectious-Recovered) compartmental epidemic models with heterogeneous individual mobility. Each individual carries a fixed mobility trait that scales susceptibility and infectiousness through a rank-one kernel, extending the mobility-based compartmental framework of Jiang et al. (2025) by adding a latent stage and a behavioural split between asymptomatic and symptomatic infectiousness. Provides a numerical solver for the underlying partial differential equation system, closed-form computation of the basic reproduction number R0 and the final epidemic size, and a parametric least-squares routine for recovering the mobility distribution from an observed aggregate symptomatic time series. Package: r-cran-sealasso Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lars Filename: pool/dists/noble/main/r-cran-sealasso_0.1-3-1.ca2404.1_all.deb Size: 158294 MD5sum: 1b5319d30a20f790863e48df0f9ece27 SHA1: 2fea6c7ff2d6bf4c6d09620bd3636a9615fc9187 SHA256: 3450a66e37695b8e8a8ae6e85a0b4707649673ae352fa8eb7875af67d1eb7843 SHA512: 87624369bc27683d8af7676e0ff0cd7fc04df9e223a7cf33ac7fa13e5b09f95689833e3a9b75cb60b4c62d23b84fb927d21311c8c8d331bf14f94a94be1fdb2d Homepage: https://cran.r-project.org/package=sealasso Description: CRAN Package 'sealasso' (Standard Error Adjusted Adaptive Lasso) Standard error adjusted adaptive lasso (SEA-lasso) is a version of the adaptive lasso, which incorporates OLS standard error to the L1 penalty weight. This method is intended for variable selection under linear regression settings (n > p). This new weight assignment strategy is especially useful when the collinearity of the design matrix is a concern. Package: r-cran-seamless Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3005 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biobase, r-cran-ggplot2, r-cran-optparse, r-cran-data.table Suggests: r-cran-ggtern, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-seamless_0.1.1-1.ca2404.1_all.deb Size: 3022362 MD5sum: 59917d2f13c438c27e6859b1bae3783d SHA1: a44db784ce94d2d463c0b2ea56d27ae6836fe269 SHA256: f2d66450ca8b6e4e4af2bdf247a6ae335450b26c55f48a8b07d3250a066f730a SHA512: 0ba0c8b25dd4801e0d596bb1a7547eec02bc75d023a513ec0d257e6901ea556829d2ee7e5b4e44a39e3eae9eff84da623664545fb3e0e9a865763665cc358578 Homepage: https://cran.r-project.org/package=seAMLess Description: CRAN Package 'seAMLess' (A Single Cell Transcriptomics Based Deconvolution Pipeline forLeukemia) Given a bulk transcriptomic (RNA-seq) sample of an Myeloid Leukemia patient calculates immune composition and drug resistance for different small-molecule inhibitors. Published in . Package: r-cran-searchanalyzer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-ggplot2, r-cran-openxlsx, r-cran-stringdist, r-cran-lubridate, r-cran-digest Suggests: r-cran-testthat, r-cran-covr, r-cran-dplyr, r-cran-shiny, r-cran-shinydashboard, r-cran-flexdashboard, r-cran-plotly, r-cran-dt, r-cran-boot, r-cran-pwr, r-cran-progress, r-cran-rmarkdown, r-cran-knitr, r-cran-glue, r-cran-rentrez, r-cran-xml2, r-cran-readr, r-cran-jsonlite, r-cran-revtools, r-cran-tidyr, r-cran-patchwork, r-cran-metagear Filename: pool/dists/noble/main/r-cran-searchanalyzer_0.1.0-1.ca2404.1_all.deb Size: 844204 MD5sum: d25f265e8ef1ab7cd2b4305713ed7e46 SHA1: 1d00efb1fc8417cdcae961e426bac3a261bfe0ce SHA256: 3a988a5000d16098a88367484a4fb142e5f3affe4e005e030d5c7848d62ea4fc SHA512: 4a6ee104347a63f5a38010ca31bd32974a1edfa972ee32fb66f0bc7d54c005b3b37184308ff54879a9b1ece38dbec5a30ddd930bb7ab74810f4e4aad42c948a7 Homepage: https://cran.r-project.org/package=searchAnalyzeR Description: CRAN Package 'searchAnalyzeR' (Advanced Analytics and Testing Framework for Systematic ReviewSearch Strategies) Provides comprehensive analytics, reporting, and testing capabilities for systematic review search strategies. The package focuses on validating search performance, generating standardized 'PRISMA'-compliant reports, and ensuring reproducibility in evidence synthesis. Features include precision-recall analysis, cross-database performance comparison, benchmark validation against gold standards, sensitivity analysis, temporal coverage assessment, automated report generation, and statistical comparison of search strategies. Supports multiple export formats including 'CSV', 'Excel', 'RIS', 'BibTeX', and 'EndNote'. Includes tools for duplicate detection, search strategy optimization, cross-validation frameworks, meta-analysis of benchmark results, power analysis for study design, and reproducibility package creation. Optionally connects to 'PubMed' for direct database searching and real-time strategy comparison using the 'E-utilities' 'API'. Enhanced with bootstrap comparison methods, 'McNemar' test for strategy evaluation, and comprehensive visualization tools for performance assessment. Methods based on Manning et al. (2008) for information retrieval metrics, Moher et al. (2009) for 'PRISMA' guidelines, and Sampson et al. (2006) for systematic review search methodology. Package: r-cran-searcher Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-searcher_0.0.7-1.ca2404.1_all.deb Size: 52340 MD5sum: eba070eee96433c92bf5c54c2b92b7c0 SHA1: 44a12a91222d2add931e258d11d883e639106326 SHA256: e8904a344fbb911858a1ea71819bf7d192d2a5b493fec82f01a7ce66b8cbd407 SHA512: bf3466c58aa1dbc7be5f35ee5f2c47b9d71ae52b816da920f1c5e7d2023957282730df91d35797ee85d9306c6868aa4c7c2387dba5b5c75ea30f2a4d3c62cea7 Homepage: https://cran.r-project.org/package=searcher Description: CRAN Package 'searcher' (Query Search Interfaces) Provides a search interface to look up terms on 'Google', 'Bing', 'DuckDuckGo', 'Startpage', 'Ecosia', 'rseek', 'Twitter', 'StackOverflow', 'RStudio Community', 'GitHub', and 'BitBucket'. Upon searching, a browser window will open with the aforementioned search results. Package: r-cran-sears Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boin Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sears_0.1.0-1.ca2404.1_all.deb Size: 43460 MD5sum: 3e75303dfdc754fc934e4b13d4cfb9be SHA1: 41ebb62908735880412e3d2b95799eb9c4de07ad SHA256: fca95a4709f0f70ac1a5e6bb1ef47acfcc8a4d4e7326ec483146684b041e20a4 SHA512: 50f6f4fe044c5a20b5ea6528da3fe3237892bc3c312a432160fd17106705772a9bcddb5f14f864486007ccf4a5f13c818e1677c4ecb00e41438b49ee4861e833 Homepage: https://cran.r-project.org/package=SEARS Description: CRAN Package 'SEARS' (Seamless Dose Escalation/Expansion with Adaptive RandomizationScheme) A seamless design that combines phase I dose escalation based on toxicity with phase II dose expansion and dose comparison based on efficacy. Package: r-cran-seasepi Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm, r-cran-ngspatial Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-seasepi_0.0.3-1.ca2404.1_all.deb Size: 132564 MD5sum: bb88c5e8ae4534479fbb9343330f0990 SHA1: d6615424d39fea200e33e47522ad81edabd84d3a SHA256: 3a566cb8046d851f49bf944a50211680b77c6856988fcd4a5d9f5945975be405 SHA512: b923a293993a7347fa33e2a9be31349d4c6a96147518509adcf7f0b652798011412f8b1fc32f60d33422a03e5155f20e0e0f973958b5045da0c102d3d48b5585 Homepage: https://cran.r-project.org/package=SeasEpi Description: CRAN Package 'SeasEpi' (Spatiotemporal Modeling of Seasonal Infectious Disease) Spatiotemporal individual-level model of seasonal infectious disease transmission within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model seasonal infectious disease transmission. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. In addition to model fitting and parameter estimation, the package offers functions for calculating AIC using real pandemic data and conducting simulation studies customized to user-specified model configurations. Package: r-cran-season Architecture: all Version: 0.3.16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 632 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mass, r-cran-survival, r-cran-coda, r-cran-stringr Suggests: r-cran-dlnm, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-season_0.3.16-1.ca2404.1_all.deb Size: 513214 MD5sum: f25b3d3af1318f0d073f54360faf360a SHA1: ea91d3c4bb5f01b6a8a3e3f95978c67d94a75e15 SHA256: f7912484dbcff7b54a9acf69ca1c1b2dc05cbb0e4f2a8adab88d03e996630e57 SHA512: 25b225fa3bb5dc44ea17872a3f42d3617613c871fbc4067c245b344454fa90765524025bfc98fd79493b556c0aae45fb89ef89074838a1f481134db490fe0807 Homepage: https://cran.r-project.org/package=season Description: CRAN Package 'season' (Seasonal Analysis of Health Data) Routines for the seasonal analysis of health data, including regression models, time-stratified case-crossover, plotting functions and residual checks, see Barnett and Dobson (2010) ISBN 978-3-642-10748-1. Thanks to Yuming Guo for checking the case-crossover code. Package: r-cran-seasonal Architecture: all Version: 1.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 706 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-x13binary Suggests: r-cran-seasonalview, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-seasonal_1.10.0-1.ca2404.1_all.deb Size: 554424 MD5sum: 4d65838124f8f666a8745320cc288d24 SHA1: 6f73a8bd90b306d4f2a7cf5bdf0d6df3629ea1e4 SHA256: 181cfe321bf7ee24b895b4eea65f286e996ca9b857bc97b461d4085707f55312 SHA512: 838952affb05ffb3e2fa768b5da41053e68c049a275cebfc384a67fa332ef5ba0b30ccaab649773d2665d4b9998a458260b83a8f1dcdf190cafdcd0a4a867326 Homepage: https://cran.r-project.org/package=seasonal Description: CRAN Package 'seasonal' (R Interface to X-13-ARIMA-SEATS) Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment software by the US Census Bureau. 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Although there is no established principle, over a long period of time, things often move with a certain periodicity. This is sometimes referred to as anomaly. The seasonPlot() function in this package calculates and visualizes the average value of price movements over a year for any given period. In addition, the monthly increase or decrease in price movement is represented with a colored background. This seasonPlot() function can use the same symbols as the 'quantmod' package (e.g. ^IXIC, ^DJI, SPY, BTC-USD, and ETH-USD etc). Package: r-cran-seasonalview Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 148 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seasonal, r-cran-shinydashboard, r-cran-shiny, r-cran-dygraphs, r-cran-htmlwidgets, r-cran-openxlsx, r-cran-xtable, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-seasonalview_1.0.0-1.ca2404.1_all.deb Size: 88278 MD5sum: aa99bd4b2abf1d6a2b86271bbde1eb2b SHA1: d3fc8a44e1dcebd60ad2f728d082532f0d229db2 SHA256: 379ea569c2eadefc5e375202ea9a852a2920f9f42fdf20014a28bfaa6d9a9295 SHA512: 2d7a14f3ef1e972d5f810919419645dcec4687bd797d50daa552e222198926cdd0579ada5be97f3f3f8fce897d4d08f2a0a9e92d983e7c6092418b2ee039aeb5 Homepage: https://cran.r-project.org/package=seasonalview Description: CRAN Package 'seasonalview' (Graphical User Interface for Seasonal Adjustment) A graphical user interface to the 'seasonal' package and 'X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software. Package: r-cran-seasonalytics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seastests Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-seasonalytics_0.1.0-1.ca2404.1_all.deb Size: 16022 MD5sum: 56e41e99c6cac4b7bd241045cd4235bd SHA1: 454276139b182f48ddf856a359063b83d86bcb11 SHA256: ade88daedeb49ff82893a2b4b4c75b575cf50b1666c4c3701e1a49eb3c214529 SHA512: 8d6761d4840e2f9a88a9e992d43c416c8159c12b460a7599360a05a52cedaac4b00a1a892738b62c91f0cae75e243b93171f6848eabd26c2b52206be2296cc95 Homepage: https://cran.r-project.org/package=seasonalytics Description: CRAN Package 'seasonalytics' (Compute Seasonality Index, Seasonalized and Deseaonalised theTime Series Data) The computation of a seasonal index is a fundamental step in time-series forecasting when the data exhibits seasonality. Specifically, a seasonal index quantifies — for each season (e.g. month, quarter, week) — the relative magnitude of the seasonal effect compared to the overall average level of the series. This package has been developed to compute seasonal index for time series data and it also seasonalise and desesaonalise the time series data. Package: r-cran-seasonder Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10217 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bit64, r-cran-bitops, r-cran-constants, r-cran-data.table, r-cran-dplyr, r-cran-geosphere, r-cran-ggplot2, r-cran-glue, r-cran-lubridate, r-cran-magrittr, r-cran-pracma, r-cran-purrr, r-cran-rlang, r-cran-slider, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-uuid, r-cran-whisker, r-cran-yaml, r-cran-zoo Suggests: r-cran-here, r-cran-mockthat, r-cran-testthat, r-cran-openssl, r-cran-jsonlite, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-seasonder_0.2.8-1.ca2404.1_all.deb Size: 4811640 MD5sum: dac10ca47be5b59180493c9b29944ece SHA1: 0db54958342886f2ec41148da6f3cbdda1d2110a SHA256: 823f79256732d0547b63fcd1cc0369a3952d446a17f4ce307777179625607755 SHA512: f6cff0e5c64b8992a54fbe8391738d63908ee3b6a79be24e1dfe602dbab6eebacc746cc764bb49f787c4746f74afbb1cfc45486ed08c4c029096e4c20df99000 Homepage: https://cran.r-project.org/package=SeaSondeR Description: CRAN Package 'SeaSondeR' (Radial Metrics from SeaSonde HF-Radar Data) Read CODAR's SeaSonde High-Frequency Radar spectra files, compute radial metrics, and generate plots for spectra and antenna pattern data. Implementation is based in technical manuals, publications and patents, please refer to the following documents for more information: Barrick and Lipa (1999) ; CODAR Ocean Sensors (2002) ; Lipa et al. (2006) ; Paolo et al. (2007) ; CODAR Ocean Sensors (2009a) ; CODAR Ocean Sensors (2009b) ; CODAR Ocean Sensors (2016a) ; CODAR Ocean Sensors (2016b) ; CODAR Ocean Sensors (2016c) ; Bushnell and Worthington (2022) . Package: r-cran-seastests Architecture: all Version: 0.15.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-forecast Filename: pool/dists/noble/main/r-cran-seastests_0.15.4-1.ca2404.1_all.deb Size: 74252 MD5sum: ce1ac1bc647b43a4699ea3880a8748f8 SHA1: 79520b5e5fca8914186ead728615c1b5472066b6 SHA256: 3cbd77f88816eb47d5274a54c7ec6ded89b6c0cae25d8a6c56ee7c109090e65f SHA512: e6114b9195cdde4f89b34d0bf4a4b54e62387f70ffb2ddd1ba44cb809cf029575fe0ced094f88c319e3621c3af24382f0fef46d5d09f4553014a4de9ec415a88 Homepage: https://cran.r-project.org/package=seastests Description: CRAN Package 'seastests' (Seasonality Tests) An overall test for seasonality of a given time series in addition to a set of individual seasonality tests as described by Ollech and Webel (forthcoming): An overall seasonality test. Bundesbank Discussion Paper. Package: r-cran-seaval Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 19674 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-ggnewscale, r-cran-ggplotify, r-cran-lifecycle, r-cran-maps, r-cran-ncdf4, r-cran-patchwork, r-cran-rcolorbrewer, r-cran-scales, r-cran-stringr Filename: pool/dists/noble/main/r-cran-seaval_1.2.0-1.ca2404.1_all.deb Size: 3260004 MD5sum: 5e3e939d089b29ce3c02aaf3a4fb8777 SHA1: 69d40ea6fc7bd5f7e9aacb592f13058f676c022e SHA256: ba2385616a3474edd747ca547df452d52e652c09995717aab0af4259aae8c64b SHA512: 613f4dd99a1009a1f40a7c037ae3aabe32dff9423f722d6fa3a7b4cdb747933d1eaac29104ec868cd76b87241c586016438c0055e1f2cf157d803441b0f09b52 Homepage: https://cran.r-project.org/package=SeaVal Description: CRAN Package 'SeaVal' (Validation of Seasonal Weather Forecasts) Provides tools for processing and evaluating seasonal weather forecasts, with an emphasis on tercile forecasts. 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Package: r-cran-seawaveq Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rms, r-cran-lubridate, r-cran-reshape2, r-cran-plyr Suggests: r-cran-waterdata Filename: pool/dists/noble/main/r-cran-seawaveq_2.0.2-1.ca2404.1_all.deb Size: 1003502 MD5sum: a49c7530ed5808859b9514c8701e56e9 SHA1: 5453ab1341f936b8ab001bb8107306ff8167809a SHA256: 992836a9c1e00d1118712aafd40e71a6b85d8df3da2fae23a3a6e12cfd486a64 SHA512: d089b53d1f97a41a58b038fe3d6d39dbffc9dfc26f417f4b7e6ada71c93d7c0cc418d3a170cde7afd85a43b77d813b7cc15b245ae3fb95595bd652e0d96f9ce0 Homepage: https://cran.r-project.org/package=seawaveQ Description: CRAN Package 'seawaveQ' (SEAWAVE-Q Model) A model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables. 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Package: r-cran-secretsprovider Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-getpass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-secretsprovider_1.0.1-1.ca2404.1_all.deb Size: 14830 MD5sum: f69e5f1dcce7c17f69403c59c8c68491 SHA1: 17d36480e0e169a5fb430be807eb56b99633c7d4 SHA256: 316ce22bf8d4d1aace5f8cf34d9e783baded676b9e57a550c816648080e2b48d SHA512: d48314627e7aa4f2310c31b121ca3c42c71719f65b0bdaeab5fc1def7331e9b6f3d94c1c6777fe046306c0d5387ac7ed741c65940458184ce76da2e2439cab29 Homepage: https://cran.r-project.org/package=SecretsProvider Description: CRAN Package 'SecretsProvider' (Save and Retrieve Name-Value Pairs to and from a File) Facilitates secret management by storing credentials in a dedicated file, keeping them out of your code base. The secrets are stored without encryption. This package is compatible with secrets stored by the 'SecretsProvider' 'Python' package . Package: r-cran-secrettext Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr, r-cran-testthat, r-cran-rlang Filename: pool/dists/noble/main/r-cran-secrettext_0.1.0-1.ca2404.1_all.deb Size: 15484 MD5sum: 00654e8a0e92d464c46d87be51bd8f68 SHA1: d89df5c4930e45ccf80192a5dc81aa199622973f SHA256: ac42936a8bff0b984ae62e789da995d34d3aba036ea83ff73e4a7a4fea16be93 SHA512: d334e9c6999f10f4b15c62e0c2100593e2c26fcd83e648d108ac9f1717950ddaef2b26a29acfc38a97d91a01c669603a3e8b8c5c3ec504d89f2dbd0854697f07 Homepage: https://cran.r-project.org/package=secrettext Description: CRAN Package 'secrettext' (Encrypt Text Using a Shifting Substitution Cipher) Encrypt text using a simple shifting substitution cipher with setcode(), providing two numeric keys used to define the encryption algorithm. The resulting text can be decoded using decode() function and the two numeric keys specified during encryption. Package: r-cran-secrlinear Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 881 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-secr, r-cran-mass, r-cran-sp, r-cran-igraph, r-cran-sf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-secrdesign, r-cran-spatstat, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-testthat Filename: pool/dists/noble/main/r-cran-secrlinear_1.2.5-1.ca2404.1_all.deb Size: 736476 MD5sum: 592ec1836c77af1c5e42bfd4b98579f9 SHA1: 6f2baababdc5a6d347c360d4368a1c3e96658236 SHA256: be4778da2df6d7965a5017c3b5b1fa3c16b7238af39b26b3489ac256976c3972 SHA512: 7f7fad3a6bb852b61650acdb7be77d460ffc790caa74b8da1f17996de1b9f64fa3c1f8555bfc0897be34a089d4629ded5849299485325b94d03264df257935cc Homepage: https://cran.r-project.org/package=secrlinear Description: CRAN Package 'secrlinear' (Spatially Explicit Capture-Recapture for Linear Habitats) Tools for spatially explicit capture-recapture analysis of animal populations in linear habitats, extending package 'secr'. Package: r-cran-sectorgap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kfas, r-cran-zoo, r-cran-ggplot2, r-cran-mcmcpack, r-cran-dplyr, r-cran-tidyr, r-cran-tempdisagg Filename: pool/dists/noble/main/r-cran-sectorgap_0.1.0-1.ca2404.1_all.deb Size: 317332 MD5sum: 5436036b6973ba3f8145072180f8457d SHA1: 2d792d098cd9698175c316f9209e693ed16d2a59 SHA256: 39a1f63bdf344dd09bb94749514679f5f9d9bbb751deed57166ba36b1ac54f43 SHA512: 732c1130d391366adefc4819e161812a926d87ddd6288add6761b29175d048954f7d0712a1bb2b845c299acf1a77cca6db5e8eaad3d14327bc4d65f93fb138f5 Homepage: https://cran.r-project.org/package=sectorgap Description: CRAN Package 'sectorgap' (Consistent Economic Trend Cycle Decomposition) Determining potential output and the output gap - two inherently unobservable variables - is a major challenge for macroeconomists. 'sectorgap' features a flexible modeling and estimation framework for a multivariate Bayesian state space model identifying economic output fluctuations consistent with subsectors of the economy. The proposed model is able to capture various correlations between output and a set of aggregate as well as subsector indicators. Estimation of the latent states and parameters is achieved using a simple Gibbs sampling procedure and various plotting options facilitate the assessment of the results. For details on the methodology and an illustrative example, see Streicher (2024) . Package: r-cran-secutrialr Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2333 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-haven, r-cran-readr, r-cran-readxl, r-cran-stringr, r-cran-tibble, r-cran-magrittr, r-cran-purrr, r-cran-tidyr, r-cran-dplyr, r-cran-rlang, r-cran-lubridate Suggests: r-cran-knitr, r-cran-lintr, r-cran-rmarkdown, r-cran-testthat, r-cran-tufte, r-cran-igraph Filename: pool/dists/noble/main/r-cran-secutrialr_1.3.3-1.ca2404.1_all.deb Size: 1329250 MD5sum: beb506b71e94fc03102931df9893a5da SHA1: 8dc654f33c30f5fbe171ebad5ac6b835de75f947 SHA256: 6c02a41b53a9ae0da2bb28c2865a7c483cabcaccfc498e539c6151eaf24b28f7 SHA512: d9726a450701df70ab5cd77b648c3a5920f6ca328f1da3de42f543e7ad1390307bd4db30c7e1be6235afa35976b153620c053c906799721313262e158da3e439 Homepage: https://cran.r-project.org/package=secuTrialR Description: CRAN Package 'secuTrialR' (Handling of Data from the Clinical Data Management System'secuTrial') Seamless and standardized interaction with data exported from the clinical data management system (CDMS) 'secuTrial'. The primary data export the package works with is a standard non-rectangular export. Package: r-cran-sedproxy Architecture: all Version: 0.7.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2968 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sedproxy_0.7.6-1.ca2404.1_all.deb Size: 2392072 MD5sum: dc8ece874faf1d035cd3c8b0700e25bc SHA1: aa8e491061296ca4ea7c373ffa973a4d971a823e SHA256: bc16081add1a9618c317f35134d95e714965c74d8cdd2c8d0ae6c173431bee44 SHA512: a879d1bda82e4d2ad1dc4df9af4587efeb90fd859ca47b5658478fdf12577455c549fff55418b495eef71c4bb01d045c76e7632ccd3d3ac66ae71b53655b1c2f Homepage: https://cran.r-project.org/package=sedproxy Description: CRAN Package 'sedproxy' (Simulation of Sediment Archived Climate Proxy Records) Proxy forward modelling for sediment archived climate proxies such as Mg/Ca, d18O or Alkenones. The user provides a hypothesised "true" past climate, such as output from a climate model, and details of the sedimentation rate and sampling scheme of a sediment core. Sedproxy returns simulated proxy records. Implements the methods described in Dolman and Laepple (2018) . Package: r-cran-see Architecture: all Version: 0.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 803 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayestestr, r-cran-correlation, r-cran-datawizard, r-cran-effectsize, r-cran-ggplot2, r-cran-insight, r-cran-modelbased, r-cran-patchwork, r-cran-parameters, r-cran-performance Suggests: r-cran-bh, r-cran-brms, r-cran-collapse, r-cran-curl, r-cran-dharma, r-cran-discovr, r-cran-emmeans, r-cran-factoextra, r-cran-formula, r-cran-ggdag, r-cran-ggdist, r-cran-ggraph, r-cran-ggrepel, r-cran-ggridges, r-cran-ggside, r-cran-glmmtmb, r-cran-httr2, r-cran-lavaan, r-cran-lme4, r-cran-logspline, r-cran-marginaleffects, r-cran-mass, r-cran-mclogit, r-cran-mclust, r-cran-merderiv, r-cran-mgcv, r-cran-metafor, r-cran-nbclust, r-cran-nfactors, r-cran-psych, r-cran-qqplotr, r-cran-randomforest, r-cran-rcppeigen, r-cran-rlang, r-cran-rmarkdown, r-cran-rstanarm, r-cran-scales, r-cran-testthat, r-cran-tidygraph, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-see_0.13.0-1.ca2404.1_all.deb Size: 680652 MD5sum: 05e1fd303816ad0e7583ff2b25c2f136 SHA1: 63a82ef9396d4b96d61592c5d326a374ac794dea SHA256: 6395cd494e656a4addd69505ca8e33cf77d4a5011bb8d4b3ab5e118b5211c33c SHA512: a708859854b191d1d781aad21d396bac3c33ba6428e978e3ba05c41ba8ab97d96b2ed0607c572aff454ee62aebcbe38d8ac3c12cc6c72d19cfb132a6d5e75cc1 Homepage: https://cran.r-project.org/package=see Description: CRAN Package 'see' (Model Visualisation Toolbox for 'easystats' and 'ggplot2') Provides plotting utilities supporting packages in the 'easystats' ecosystem () and some extra themes, geoms, and scales for 'ggplot2'. Color scales are based on . References: Lüdecke et al. (2021) . Package: r-cran-seeclickfixr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-rcurl Filename: pool/dists/noble/main/r-cran-seeclickfixr_1.1.0-1.ca2404.1_all.deb Size: 51178 MD5sum: 39ca24d72f09c56b955211362f282942 SHA1: a2e53e1e5eddbaf4f1093c50c195dbf27e996a32 SHA256: e6673e6860b26f73118238cf9d4a4c4718d14ae365f566d80af21537185a6d2c SHA512: eeeed35b25e2cfc7e1b4a2e0aa70adb635df83616a75c8edd881297b74647ceba3131be0ce51ca8be75812caa8fbe876699cd2dca738410b99ba6e1ca02e6660 Homepage: https://cran.r-project.org/package=seeclickfixr Description: CRAN Package 'seeclickfixr' (Access Data from the SeeClickFix Web API) Provides a wrapper to access data from the SeeClickFix web API for R. SeeClickFix is a central platform employed by many cities that allows citizens to request their city's services. This package creates several functions to work with all the built-in calls to the SeeClickFix API. Allows users to download service request data from numerous locations in easy-to-use dataframe format manipulable in standard R functions. Package: r-cran-seecolor Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1677 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-crayon, r-cran-purrr, r-cran-stringr, r-cran-rstudioapi, r-cran-magrittr, r-cran-ggplot2, r-cran-fansi Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-seecolor_0.2.0-1.ca2404.1_all.deb Size: 1143632 MD5sum: 39d8d7bcde78dc5cad1997ff61c3cd18 SHA1: 10ffebd0c6f5d02fc7a008996581753426edf6b4 SHA256: fb1c471dc127c21d8cd9f738b0dd9b94273a4313bc2f5487a923e569f8fa8f5f SHA512: 3dc2ff5f66718b8c14511ab23adb0497828451fad6428da58b7178499b0b598b11331e2ccc09fb5097bcef40ab5f0a406a1148b421cff0c163c926e874a5c1eb Homepage: https://cran.r-project.org/package=seecolor Description: CRAN Package 'seecolor' (View Colors Used in R Objects in the Console) Output colors used in literal vectors, palettes and plot objects (ggplot). Package: r-cran-seedcalc Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-seedcalc_1.0.0-1.ca2404.1_all.deb Size: 73644 MD5sum: abe31ea15b20e4033823723d2a4bf943 SHA1: 0618a7183c7ff2a12a8d4c6b1c07ccc2c4e2bb3e SHA256: 435f326830ca2f05cb73c7183f6c9686e261808baf0c25c0b476d77dc0edca77 SHA512: 4f205cbbdee9605c2cdde1727659e8996922e7e813f8da86fdcab12e088a2e352c2eeba5d863fe1ca424d97ed2da48aa7f9bb12bcd77d9943f75cc374105d826 Homepage: https://cran.r-project.org/package=SeedCalc Description: CRAN Package 'SeedCalc' (Seed Germination and Seedling Growth Indexes) Functions to calculate seed germination and seedling emergence and growth indexes. The main indexes for germination and seedling emergence, considering the time for seed germinate are: T10, T50 and T90, in Farooq et al. (2005) <10.1111/j.1744-7909.2005.00031.x>; and MGT, in Labouriau (1983). Considering the germination speed are: Germination Speed Index, in Maguire (1962), Mean Germination Rate, in Labouriau (1983); considering the homogeneity of germination are: Coefficient of Variation of the Germination Time, in Carvalho et al. (2005) <10.1590/S0100-84042005000300018>, and Variance of Germination, in Labouriau (1983); Uncertainty, in Labouriau and Valadares (1976) ; and Synchrony, in Primack (1980). The main seedling indexes are Growth, in Sako (2001), Uniformity, in Sako (2001) and Castan et al. (2018) ; and Vigour, in Medeiros and Pereira (2018) . Package: r-cran-seedcca Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cca, r-cran-corpcor Filename: pool/dists/noble/main/r-cran-seedcca_3.1-1.ca2404.1_all.deb Size: 358100 MD5sum: ec1e7a51e01bf36aad31b587d49a9552 SHA1: b2ae7b2b79b2fa9df8c78735a505c9e545b0bc6c SHA256: f25a83f925a635b679bd79d93c0deb41e545b557693898c45ef7d2b32087c70e SHA512: 2e1ff787c362933f02baf607b79dd6ec1e3fd57556041682e4307d80caf3d731b4022ed29d984b6acd1fe703aee19c5a53cfc14d478a24e0fec6705675abed33 Homepage: https://cran.r-project.org/package=seedCCA Description: CRAN Package 'seedCCA' (Seeded Canonical Correlation Analysis) Functions for dimension reduction through the seeded canonical correlation analysis are provided. A classical canonical correlation analysis (CCA) is one of useful statistical methods in multivariate data analysis, but it is limited in use due to the matrix inversion for large p small n data. To overcome this, a seeded CCA has been proposed in Im, Gang and Yoo (2015) \doi{10.1002/cem.2691}. The seeded CCA is a two-step procedure. The sets of variables are initially reduced by successively projecting cov(X,Y) or cov(Y,X) onto cov(X) and cov(Y), respectively, without loss of information on canonical correlation analysis, following Cook, Li and Chiaromonte (2007) \doi{10.1093/biomet/asm038} and Lee and Yoo (2014) \doi{10.1111/anzs.12057}. Then, the canonical correlation is finalized with the initially-reduced two sets of variables. Package: r-cran-seedimbibition Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 40 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-seedimbibition_0.1.0-1.ca2404.1_all.deb Size: 9914 MD5sum: 9179b7ab5842e3b8492b73295458bb67 SHA1: 97f5db181271e21e6e61c80de73f796a18854e37 SHA256: 4ca8c48a5f3a985b672b8fd699f89f23795de215ed572b8d29df036f3dc10705 SHA512: faf72ab287ae9d2b3d755eab625b0de0c0fab1603873ce8bd7b6bce3c431496ebc98b673aaa9116f4448ae00ce3aac39efa88e2a79117315fafcb6fb6df31ec9 Homepage: https://cran.r-project.org/package=SeedImbibition Description: CRAN Package 'SeedImbibition' (Seed Imbibition Percentage) Imbibition causes seeds to expand, which results in the seed coat or testa being broken. Seed germination begins with imbibition. Imbibition aids in the transport of water into the developing ovules. Imbibition is required during the first stages of root water absorption. Package: r-cran-seedmaker Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-seedmaker_1.0.0-1.ca2404.1_all.deb Size: 45062 MD5sum: 12534746334c0311c5e3899125684208 SHA1: bbec6880d08563da12fd964a43514e257aedd8ec SHA256: 6d233d095c5ec6ebf8b0be84589e2b1e59244026a27e7c457e7278677c9f0cb6 SHA512: 6a54380b6dff79e408dbef7a923c3a74ca692e42c2ed05f905d7ed03c7b519bf72e2b269709d5bde017f58e5b39e9f85643d529265021852502ee5cc7471aed4 Homepage: https://cran.r-project.org/package=SeedMaker Description: CRAN Package 'SeedMaker' (Generate a Collection of Seeds from a Single Seed) A mechanism for easily generating and organizing a collection of seeds from a single seed, which may be subsequently used to ensure reproducibility in processes/pipelines that utilize multiple random components (e.g., trial simulation). Package: r-cran-seedmatchr Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-bioc-biostrings, r-bioc-ggmsa, r-bioc-msa, r-cran-ggplot2, r-bioc-annotationhub, r-bioc-genomeinfodb, r-bioc-genomicfeatures, r-cran-cowplot, r-cran-testit, r-cran-lifecycle, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-org.rn.eg.db Filename: pool/dists/noble/main/r-cran-seedmatchr_1.1.1-1.ca2404.1_all.deb Size: 590712 MD5sum: dd553a922d63a76aee5d65e5b0224e24 SHA1: e9a2ed5052222fb00b6729544db20477fee8755c SHA256: fa653c2e806c94395380bf458bc2d70640ef5609427ed086fad96907248a06ea SHA512: 99eb590f412f28df70fa2d7c5609f7a49d67f05bbb89fa69d9ac855296bcdd74ebcf36c2d99ce9017f5b38992bc09d60430a517bf2acede4c244ec0db5eb80df Homepage: https://cran.r-project.org/package=SeedMatchR Description: CRAN Package 'SeedMatchR' (Find Matches to Canonical SiRNA Seeds in Genomic Features) On-target gene knockdown using siRNA ideally results from binding fully complementary regions in mRNA transcripts to induce cleavage. Off-target siRNA gene knockdown can occur through several modes, one being a seed-mediated mechanism mimicking miRNA gene regulation. Seed-mediated off-target effects occur when the ~8 nucleotides at the 5’ end of the guide strand, called a seed region, bind the 3’ untranslated regions of mRNA, causing reduced translation. Experiments using siRNA knockdown paired with RNA-seq can be used to detect siRNA sequences with potential off-target effects driven by the seed region. 'SeedMatchR' provides tools for exploring and detecting potential seed-mediated off-target effects of siRNA in RNA-seq experiments. 'SeedMatchR' is designed to extend current differential expression analysis tools, such as 'DESeq2', by annotating results with predicted seed matches. Using publicly available data, we demonstrate the ability of 'SeedMatchR' to detect cumulative changes in differential gene expression attributed to siRNA seed regions. Package: r-cran-seedr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-binom Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-seedr_0.3.0-1.ca2404.1_all.deb Size: 146058 MD5sum: 6839a8282019fc958f3f9a61ddee4164 SHA1: 3934c68eeebf918819f5586cf7159b7c343ebe4e SHA256: 47f9aba376288e0bf9d25939ef58fac93d6d27bfebccf10a3e0ed41ae5b06e8c SHA512: 2cf43ff27a20b6852386fc9b3f99d3e389d7719d42986acf30f03d5043cfc1ae09f7709d5064a796702d4200a4b3a461417e965ae041f7b6f122918265b3a59b Homepage: https://cran.r-project.org/package=seedr Description: CRAN Package 'seedr' (Hydro and Thermal Time Seed Germination Models in R) Analysis of seed germination data using the physiological time modelling approach. Includes functions to fit hydrotime and thermal-time models with the traditional approaches of Bradford (1990) and Garcia-Huidobro (1982) . Allows to fit models to grouped datasets, i.e. datasets containing multiple species, seedlots or experiments. Package: r-cran-seedreg Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-drc, r-cran-ggplot2, r-cran-car, r-cran-crayon, r-cran-emmeans, r-cran-multcomp, r-cran-hnp, r-cran-boot, r-cran-multcompview, r-cran-stringr, r-cran-sf, r-cran-gridextra, r-cran-dplyr Suggests: r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-seedreg_1.0.3-1.ca2404.1_all.deb Size: 292058 MD5sum: 3ad4ac948b5054c899c09909b71ef7ec SHA1: 8416eb692632b3752d6c6da69cff0b28edafcde7 SHA256: 7d5f8e6b943b472b88febad33ed0632b7e654294f488622efff13dc234eae6e3 SHA512: 2d5636d09c737dda8daa3a548947a42e384a0ca6919a556e30349771a87bdb6e415c138bbdb2d2fbacf3ec2ff8e25778f57308fc0f86323759ffd737b22d1fa0 Homepage: https://cran.r-project.org/package=seedreg Description: CRAN Package 'seedreg' (Regression Analysis for Seed Germination as a Function ofTemperature) Regression analysis using common models in seed temperature studies, such as the Gaussian model (Martins, JF, Barroso, AAM, & Alves, PLCA (2017) ), quadratic (Nunes, AL, Sossmeier, S, Gotz, AP, & Bispo, NB (2018) ) and others with potential for use, such as those implemented in the 'drc' package (Ritz, C, Baty, F, Streibig, JC, & Gerhard, D (2015). ), in the estimation of the ideal and cardinal temperature for the occurrence of plant seed germination. The functions return graphs with the equations automatically. Package: r-cran-seeds Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 637 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-pracma, r-cran-deriv, r-cran-ryacas, r-cran-mvtnorm, r-cran-matrixstats, r-cran-statmod, r-cran-coda, r-cran-mass, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-hmisc, r-cran-r.utils, r-cran-callr Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-rsbml Filename: pool/dists/noble/main/r-cran-seeds_0.9.1-1.ca2404.1_all.deb Size: 440578 MD5sum: 322b3a80a8a69b93ad0c5a8a87433c4b SHA1: ca29503c8a36af65b426ee8ed8a7ae4fd5be59a6 SHA256: 90b6b22cf76e46387fb4e0f8d8055fb46005512397bca99ed29ce7644e9d4cf4 SHA512: e937fab02bec197dc71bba60c1d7822f82e10279eb52b548fdb3b54ca018552fd8797ae51e025ea565925e5e5189e2d94e5377a9cb112975b82a64d2bdbf50c4 Homepage: https://cran.r-project.org/package=seeds Description: CRAN Package 'seeds' (Estimate Hidden Inputs using the Dynamic Elastic Net) Algorithms to calculate the hidden inputs of systems of differential equations. These hidden inputs can be interpreted as a control that tries to minimize the discrepancies between a given model and taken measurements. The idea is also called the Dynamic Elastic Net, as proposed in the paper "Learning (from) the errors of a systems biology model" (Engelhardt, Froelich, Kschischo 2016) . To use the experimental SBML import function, the 'rsbml' package is required. For installation I refer to the official 'rsbml' page: . Package: r-cran-seedvigorindex Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 40 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-seedvigorindex_0.1.0-1.ca2404.1_all.deb Size: 10116 MD5sum: 44b7cd9f1c8e8a45dbf337be8daaa43b SHA1: 5bcf5206691115b44a1d7ed636e06b74463ceb66 SHA256: 269baa8acf27cfc7c88e9ab3248b800ed219f31606acf2f096af36009b9f3442 SHA512: 32634fcee6bdf4997c164ad4ad371e863816aa4c40482a120a46f24c6c4df8f976ef8ae85c3bd3d7f2e8125bd286c1eeb9a331ad0306ba34efa9e0564dd8cffd Homepage: https://cran.r-project.org/package=SeedVigorIndex Description: CRAN Package 'SeedVigorIndex' (Seed Vigor Index) Seed vigor is defined as the sum total of those properties of the seed which determine the level of activity and performance of the seed or seed lot during germination and seedling emergence. Testing for vigor becomes more important for carryover seeds, especially if seeds were stored under unknown conditions or under unfavorable storage conditions. Seed vigor testing is also used as indicator of the storage potential of a seed lot and in ranking various seed lots with different qualities. The vigour index is calculated using the equation given by (Ling et al. 2014) . Package: r-cran-seeker Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-affy, r-bioc-annotationdbi, r-cran-biocmanager, r-bioc-biomart, r-cran-checkmate, r-cran-curl, r-cran-data.table, r-cran-foreach, r-bioc-geoquery, r-cran-glue, r-cran-jsonlite, r-cran-qs, r-cran-r.utils, r-cran-rcurl, r-cran-readr, r-cran-sessioninfo, r-bioc-tximport, r-cran-withr, r-cran-yaml Suggests: r-bioc-arrayexpress, r-bioc-biobase, r-cran-doparallel, r-cran-knitr, r-bioc-org.mm.eg.db, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-seeker_1.1.6-1.ca2404.1_all.deb Size: 162710 MD5sum: 152a2eb3c7f934b3b6c2acb029f51fa3 SHA1: 9ff14daa8c35a1602aa9a31ca3c5721ffd9ab954 SHA256: fe60e29f59f779351fd38ae479b98c8ff339ecb1fe298fdb3bf31a9a83cdbfdd SHA512: 23616cb07f2b85a0a3152f2d6b12eab590c43ecba924d95e4f20233405b669f63ef2f68972cfe03a3bc3a2ee29bddb1302bcfdf2ec935c3b81e855dce0b661e8 Homepage: https://cran.r-project.org/package=seeker Description: CRAN Package 'seeker' (Simplified Fetching and Processing of Microarray and RNA-SeqData) Wrapper around various existing tools and command-line interfaces, providing a standard interface, simple parallelization, and detailed logging. For microarray data, maps probe sets to standard gene IDs, building on 'GEOquery' Davis and Meltzer (2007) , 'ArrayExpress' Kauffmann et al. (2009) , Robust multi-array average 'RMA' Irizarry et al. (2003) , and 'BrainArray' Dai et al. (2005) . For RNA-seq data, fetches metadata and raw reads from National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA), performs standard adapter and quality trimming using 'TrimGalore' Krueger , performs quality control checks using 'FastQC' Andrews , quantifies transcript abundances using 'salmon' Patro et al. (2017) and potentially 'refgenie' Stolarczyk et al. (2020) , aggregates the results using 'MultiQC' Ewels et al. (2016) , maps transcripts to genes using 'biomaRt' Durinkck et al. (2009) , and summarizes transcript-level quantifications for gene-level analyses using 'tximport' Soneson et al. (2015) . Package: r-cran-seekr Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-fs, r-cran-lifecycle, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-seekr_0.1.4-1.ca2404.1_all.deb Size: 95922 MD5sum: 9a1e373b1619e2b7ff6cb99c70705559 SHA1: 7a8dc09bff09c99866ca52d49ebe648906a70fee SHA256: db789544ed8cc9db3cb6de1c8654d52a2a9648d921300a1844ac456dabbeb674 SHA512: 63ef52ab5feb2282ba0e5fa2941361dfca581738213b8ea43987f8115f572afce5eb0c406e80f5e9bc479558fc2db58235c5a006bc0687e55882ace49d5dd096 Homepage: https://cran.r-project.org/package=seekr Description: CRAN Package 'seekr' (Extract Matching Lines from Matching Files) Provides a simple interface to recursively list files from a directory, filter them using a regular expression, read their contents, and extract lines that match a user-defined pattern. The package returns a dataframe containing the matched lines, their line numbers, file paths, and the corresponding matched substrings. Designed for quick code base exploration, log inspection, or any use case involving pattern-based file and line filtering. Package: r-cran-seer Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-urca, r-cran-forecast, r-cran-dplyr, r-cran-magrittr, r-cran-randomforest, r-cran-forectheta, r-cran-stringr, r-cran-tibble, r-cran-purrr, r-cran-future, r-cran-furrr, r-cran-tsfeatures Suggests: r-cran-testthat, r-cran-covr, r-cran-repmis, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tidyr, r-cran-mcomp, r-cran-ggally Filename: pool/dists/noble/main/r-cran-seer_1.1.8-1.ca2404.1_all.deb Size: 134406 MD5sum: 4afd2188d3b3935cb9b41af4cc613a50 SHA1: 3fbd24c54f445df6b29a3402787185778a4e765c SHA256: 73b0a98c15f133d497cea43d31acdab1456383b7c48be2377df31a322541b002 SHA512: b518734dd1c451b4d1a3cf038e2ea56ab35dc22333ca542f6c2a047ead30d9281063570e237a9a286aa176ad273a7dffde31ee7bed773f73f55286154e545fa0 Homepage: https://cran.r-project.org/package=seer Description: CRAN Package 'seer' (Feature-Based Forecast Model Selection) A novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details see our paper at . Package: r-cran-seewave Architecture: all Version: 2.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3808 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tuner Suggests: r-cran-audio, r-cran-circlize, r-cran-factominer, r-cran-fftw, r-cran-ggplot2, r-cran-rgl, r-cran-rpanel, r-cran-phontools, r-cran-signal Filename: pool/dists/noble/main/r-cran-seewave_2.2.4-1.ca2404.1_all.deb Size: 3549332 MD5sum: ec420e13f624ee276c491a8424e299b4 SHA1: a5396813cfe1b10f1a02f277afa6c3cf23ec4e6a SHA256: 590e20fc7dbc6027fcc03e378675e4658719c35a05a9e71d4fe406ec89cd4027 SHA512: 839bf4b7034239205770339ad9662ea1875599a140da69ef1a8cd6bc7ab1143a948b7ea04d58fd37c19c524a15fd053e267d68013ad918d75e3e8e9c42544c6c Homepage: https://cran.r-project.org/package=seewave Description: CRAN Package 'seewave' (Sound Analysis and Synthesis) Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound). This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses. See Sueur et al. (2008) and Sueur (2018) . Package: r-cran-segcorr Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jointseg Filename: pool/dists/noble/main/r-cran-segcorr_1.2-1.ca2404.1_all.deb Size: 308278 MD5sum: ffc2bb512de6e6ceda3594c2a4724086 SHA1: b77481912af5cdc73c2662c3384a22c76bf9cf5e SHA256: 208ee6f6d542633252cd5ee93613748a3627fe39083c4277abe395346b96bd78 SHA512: 0e3d0b263ac50f5e50111eb809cfc92eecb1ebcb2a80b9105d81f22db723e95891582131b00d8a1cb58c8311fffd4878d91604340f57e25e202f30f9ee96d024 Homepage: https://cran.r-project.org/package=SegCorr Description: CRAN Package 'SegCorr' (Detecting Correlated Genomic Regions) Performs correlation matrix segmentation and applies a test procedure to detect highly correlated regions in gene expression. Package: r-cran-segen Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-ggplot2, r-cran-readr, r-cran-lubridate, r-cran-imputets, r-cran-fancova, r-cran-scales, r-cran-tictoc, r-cran-modeest, r-cran-moments, r-cran-greybox, r-cran-philentropy, r-cran-entropy, r-cran-rfast, r-cran-narray, r-cran-fastdummies, r-cran-dtw, r-cran-digest, r-cran-furrr, r-cran-future Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-segen_2.0.0-1.ca2404.1_all.deb Size: 71194 MD5sum: e2b2d6e1bf3b70d7a34ef409d6cbc8cc SHA1: 4c9a8fce09e1c19534ef93b04ef7be02ba8e3bae SHA256: 81a31ac0a6f65fe9e449356c2aea642d626e7db60a0065369f6a2afa2e4c4148 SHA512: 53a7d0201b9389605e7265c4b042a9200a2ed44ec7c1e4a726c0ca963b43496dcca26eb4f147cd0d0c5839c3a5c908100960580e2eb75ae91808fee55a8b9f1c Homepage: https://cran.r-project.org/package=segen Description: CRAN Package 'segen' (Sequence Generalization Through Similarity Network) Proposes an application for sequence prediction generalizing the similarity within the network of previous sequences. Package: r-cran-segenvineq Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-spdep, r-cran-oasisr, r-cran-outliers Filename: pool/dists/noble/main/r-cran-segenvineq_1.2-1.ca2404.1_all.deb Size: 52854 MD5sum: 51d20486f4496fe7abc3e8824c6bf29e SHA1: aa2e4b3faa4c032ea20e9c76eee0a9743d269088 SHA256: c5d00a406526ee57e0e49e42bcf94ff72156ce45a12bb4a09a3234e83e53db3a SHA512: e3f2d19e9f5b4476b795492018992c7b8fae6f5deb2f462129d4e0a23262363d431ecee6b4b383ad38dfaad4fd64e722fd4f370f4b33686f4159d98dcdaa0a46 Homepage: https://cran.r-project.org/package=SegEnvIneq Description: CRAN Package 'SegEnvIneq' (Environmental Inequality Indices Based on Segregation Measures) A set of segregation-based indices and randomization methods to make robust environmental inequality assessments, as described in Schaeffer and Tivadar (2019) "Measuring Environmental Inequalities: Insights from the Residential Segregation Literature" . Package: r-cran-segmented Architecture: all Version: 2.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1468 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-nlme Filename: pool/dists/noble/main/r-cran-segmented_2.2-1-1.ca2404.1_all.deb Size: 1427274 MD5sum: 14e13f63827f951f6230fb1e709bea1a SHA1: ec4f2893bd924aa084ee1272bb67cad7ba2edb53 SHA256: d7d0d9d08a31eea2fc2a9826b32b871aeb97b6a87e1310be7452ba7ab3dbd156 SHA512: dee7e4ac008c6d484f08ad116f9b461cae9dfa6f773e29394453cca06c3b657bf8dacda1839a66b76c10a10c538a58f0c97b1cb747921e25e32d2b08b265ab77 Homepage: https://cran.r-project.org/package=segmented Description: CRAN Package 'segmented' (Regression Models with Break-Points / Change-Points Estimation(with Possibly Random Effects)) Fitting regression models where, in addition to possible linear terms, one or more covariates have segmented (i.e., broken-line or piece-wise linear) or stepmented (i.e. piece-wise constant) effects. Multiple breakpoints for the same variable are allowed. The estimation method is discussed in Muggeo (2003, ) and illustrated in Muggeo (2008, ). An approach for hypothesis testing is presented in Muggeo (2016, ), and interval estimation for the breakpoint is discussed in Muggeo (2017, ). Segmented mixed models, i.e. random effects in the change point, are discussed in Muggeo (2014, ). Estimation of piecewise-constant relationships and changepoints (mean-shift models) is discussed in Fasola et al. (2018, ). Package: r-cran-segmetric Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1394 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-magrittr, r-cran-units Suggests: r-cran-classint, r-cran-dplyr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-segmetric_0.3.0-1.ca2404.1_all.deb Size: 1370786 MD5sum: 85be14dbdcb93dbfe52dc86f6e5aecd6 SHA1: 6215383789d4fde4dfedb4e8d5f10ddc05015ab8 SHA256: f6e2d611c79fa91b37d368e45524146c74a27bb750ab4d11636f7999ff985fa6 SHA512: 07103d9398abedb336e315f7b296bbd50383fcd625719e3f52728fc2b2228ac65dccdc9761c08b68a76fec772f380db1660edc3b4b7ea40ec3daaf9263bd81da Homepage: https://cran.r-project.org/package=segmetric Description: CRAN Package 'segmetric' (Metrics for Assessing Segmentation Accuracy for Geospatial Data) A system that computes metrics to assess the segmentation accuracy of geospatial data. These metrics calculate the discrepancy between segmented and reference objects, and indicate the segmentation accuracy. For more details on choosing evaluation metrics, we suggest seeing Costa et al. (2018) and Jozdani et al. (2020) . Package: r-cran-segrda Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-segrda_1.0.2-1.ca2404.1_all.deb Size: 168114 MD5sum: e356e44578657388b7d46d4f6a6b6614 SHA1: a4d74b7143bc0110b9b82d125ae77ae14722e755 SHA256: dccbef26c93bcdb58444f3de71e8342328801c1d12f76f9f4d32b4de5837e2ff SHA512: 345316dbf7c97b6b3c4a5c7344ec1d7b62067214408ff1dd945300fa23d1d677c1860049b871b301da6ae59b1ea7da2e9561a40915acea458e717b8a9520e265 Homepage: https://cran.r-project.org/package=segRDA Description: CRAN Package 'segRDA' (Modeling Non-Continuous Linear Responses of Ecological Data) Tools for modeling non-continuous linear responses of ecological communities to environmental data. The package is straightforward through three steps: (1) data ordering (function OrdData()), (2) split-moving-window analysis (function SMW()) and (3) piecewise redundancy analysis (function pwRDA()). Relevant references include Cornelius and Reynolds (1991) and Legendre and Legendre (2012, ISBN: 9780444538697). Package: r-cran-segregatr Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pedtools, r-cran-pedprobr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-segregatr_0.5.0-1.ca2404.1_all.deb Size: 67030 MD5sum: 5fa116109cd5e71501be7004af395b25 SHA1: f2c32c38959f6f0efe82ed278c7755e5e8ea024e SHA256: 34f4dc477ccc3b0d844425d792aba42ffd303604ba5828134ed9021d236dd3cc SHA512: bbe8e50ce3b14a781f834e06f060ec309da3e6f08275c8de60c093952514e61186ca9eb61ab082d9e64a6de04d7cf6dee50cac39fba480aa8b5a6b10635ee903 Homepage: https://cran.r-project.org/package=segregatr Description: CRAN Package 'segregatr' (Segregation Analysis for Variant Interpretation) An implementation of the full-likelihood Bayes factor (FLB) for evaluating segregation evidence in clinical medical genetics. The method was introduced by Thompson et al. (2003) . This implementation supports custom penetrance values and liability classes, and allows visualisations and robustness analysis as presented in Ratajska et al. (2023) . See also the online app 'shinyseg', , which offers interactive segregation analysis with many additional features (Carrizosa et al. (2024) ). 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The package was developed as a result of an internship in MI2 Group - , Faculty of Mathematics and Information Science, Warsaw University of Technology. Package: r-cran-sejong Architecture: all Version: 0.01-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1612 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sejong_0.01-1.ca2404.1_all.deb Size: 1617082 MD5sum: 53cd64787ed2972b6eb0148de0a3d4f9 SHA1: 414b87b867ebb16e944e40f25dd5e08a2adf6598 SHA256: cb7727c28838a70db0513e6f05c787a4117ca7585665a28f69a16eec12604b1c SHA512: c3dd3b1a99f0a06dc403c41d6b53ea38361653846e5731ac2f7473f6e58d1478cdcf4e3efcecd1cbbe2133d7963b137022b6ca9fe979cdfadce34e6f081a2d9f Homepage: https://cran.r-project.org/package=Sejong Description: CRAN Package 'Sejong' (KoNLP static dictionaries and Sejong project resources) Sejong(http://www.sejong.or.kr/) corpus and Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum) dictionaries for KoNLP Package: r-cran-selcorr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-selcorr_1.0-1.ca2404.1_all.deb Size: 20616 MD5sum: 2141e4822c0b9f6617e4de3b70944b49 SHA1: 00cf4c384b323a935484586b2d2e2a4ffbe125a2 SHA256: 30bb7d5ac7d599adb566325e28d2387bae5a2768087cf7d36a7bc9a2d4b242a8 SHA512: 7fd4fa7bab3106216ba8ba40fc0652c861497139b6b9ddb6490660dba960652d084ba2434e223f99d61b0dd144a55a2338546ad98dde0fce790d099e19342533 Homepage: https://cran.r-project.org/package=selcorr Description: CRAN Package 'selcorr' (Post-Selection Inference for Generalized Linear Models) Calculates (unconditional) post-selection confidence intervals and p-values for the coefficients of (generalized) linear models. Package: r-cran-select Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsolnp, r-cran-latticeextra, r-cran-lattice, r-cran-ade4 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-fd Filename: pool/dists/noble/main/r-cran-select_1.6-1.ca2404.1_all.deb Size: 166376 MD5sum: 3c6b922fdd7409af2ec150b5bd770d2c SHA1: d4253aeac58dc7679b8f7b7331b416480977e9db SHA256: f54e704e130e93c2b78c4d62346ee05ef6199d78c69be720fae7b9f68223bc26 SHA512: 023f08029c25f90b2731f48350b84935cb94cf4256abf4e9a8d33eca57ae089f4eab204008ab7ed3d11e5d2ea02d1a1f2769c297383652c6dfa3ef117b67fd14 Homepage: https://cran.r-project.org/package=Select Description: CRAN Package 'Select' (Determines Species Probabilities Based on Functional Traits) The objective of these functions is to derive a species assemblage that satisfies a functional trait profile. Restoring resilient ecosystems requires a flexible framework for selecting assemblages that are based on the functional traits of species. However, current trait-based models have been limited to algorithms that can only select species by optimising specific trait values, and could not elegantly accommodate the common desire among restoration ecologists to produce functionally diverse assemblages. We have solved this problem by applying a non-linear optimisation algorithm that optimises Rao Q, a closed-form functional trait diversity index that incorporates species abundances, subject to other linear constraints. This framework generalises previous models that only optimised the entropy of the community, and can optimise both functional diversity and entropy simultaneously. This package can also be used to generate experimental assemblages to test the effects of community-level traits on community dynamics and ecosystem function. The method is based on theory discussed in Laughlin (2014, Ecology Letters) and Laughlin et al. (2018, Methods in Ecology and Evolution). Package: r-cran-selectapref Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-selectapref_0.1.2-1.ca2404.1_all.deb Size: 15440 MD5sum: 1092034437102499f9e5575dc3502968 SHA1: 2132887340d0086386de633bd4e9cacea4aaac40 SHA256: e106a57435f2f32138c8922e11098e5049e75ba51c242da1669b7a6c068dc377 SHA512: dd120676db1a3c91794a44e889eaf59928db180fc8c0975fb710454e7b948d688fa446734a8650bd5b886d8cada4bf638f3f6ea0a6aebc1bf46ee5b5a8a1ef85 Homepage: https://cran.r-project.org/package=selectapref Description: CRAN Package 'selectapref' (Analysis of Field and Laboratory Foraging) Provides indices such as Manly's alpha, foraging ratio, and Ivlev's selectivity to allow for analysis of dietary selectivity and preference. Can accommodate multiple experimental designs such as constant prey number of prey depletion. Please contact the package maintainer with any publications making use of this package in an effort to maintain a repository of dietary selections studies. 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The package provides FDA-native design and preprocessing objects for raw curves, spline-basis expansions, Functional principal component analysis scores, and scalar covariates; grouped stability-selection routines based on repeated subject-level subsampling; multiple selector backends including lasso, group lasso, and sparse-group lasso; FDA-aware grouping functions and calibration helpers for 'SelectBoost'; method-comparison utilities; a formula interface; simulation, benchmarking, and validation helpers with mapped ground truth; targeted sensitivity-study utilities and shipped benchmark summaries for mean 'F1' comparisons between FDA-aware and plain 'SelectBoost' workflows; small example datasets; and an optional adapter to the native stability-selection interface from the 'FDboost' package. Package: r-cran-selectboost.quantile Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 717 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-movmf, r-cran-quantreg, r-cran-withr Suggests: r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-selectboost.quantile_0.3.1-1.ca2404.1_all.deb Size: 465376 MD5sum: 5774b9238d3024d5823872795255ca10 SHA1: 1b23caff1358084f6d50c0498ed26db615d1d3cd SHA256: 2495a0ab9cd4a4673119d7726ba2861a94af37801a97b15115ff24db8d4aad6e SHA512: 5a36da46e616e5145cb0b8e0f3daec2fbce5952488184971eb76cf038cf1d2e0d129f7ebd4231ed68fa3392383d5bbf7d3edae75dc871fbaec197cad914ccc74 Homepage: https://cran.r-project.org/package=SelectBoost.quantile Description: CRAN Package 'SelectBoost.quantile' ('SelectBoost'-Style Variable Selection for Quantile Regression) A 'SelectBoost'-inspired workflow for sparse quantile regression. The package builds correlation neighborhoods, perturbs correlated predictors with a directional sampler inspired by the original 'SelectBoost' internals, refits penalized quantile regression models on the perturbed designs, and aggregates variable-selection frequencies across a path of correlation thresholds. 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This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective. Package: r-cran-selection.index Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-selection.index_1.2.1-1.ca2404.1_all.deb Size: 51234 MD5sum: d97720ac3112e3666123022fbeba6aa3 SHA1: ed15ae909c93341a09d65f0b46089679a897d6a0 SHA256: 62d4031b216716b004f4ef7168b8e2ed44c9000b17e74112d03297712d681b6a SHA512: 67571f78ef01defe94d2020277892137955219157323a180f9803290f2f957c84537649e37bd2bc2f4523b630b6fea44128b275269080eab749c467a9ebdb2a7 Homepage: https://cran.r-project.org/package=selection.index Description: CRAN Package 'selection.index' (Analysis of Selection Index in Plant Breeding) The aim of most plant breeding programmes is simultaneous improvement of several characters. An objective method involving simultaneous selection for several attributes then becomes necessary. It has been recognised that most rapid improvements in the economic value is expected from selection applied simultaneously to all the characters which determine the economic value of a plant, and appropriate assigned weights to each character according to their economic importance, heritability and correlations between characters. So the selection for economic value is a complex matter. If the component characters are combined together into an index in such a way that when selection is applied to the index, as if index is the character to be improved, most rapid improvement of economic value is expected. Such an index was first proposed by Smith (1937 ) based on the Fisher's (1936 ) "discriminant function" Dabholkar (1999 ). In this package selection index is calculated based on the Smith (1937) selection index method. Package: r-cran-selectionbias Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arm, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-table1, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-selectionbias_2.1.0-1.ca2404.1_all.deb Size: 234998 MD5sum: e26af6f2c69eca69d00513b9063561e3 SHA1: 2082ba6a9bc2b16ae26fdf6069e73a1d6aab3903 SHA256: 7f6a14ada96de1e1a52fd8c96986aa15ca7999291f90a851a7624ed33e4d9982 SHA512: ddd23e11f512208d27192c781a9bdbc8079cde5f9dc47e282a1fdb88d0bdf0e72014a548894e62e5500622c0729ba8b9cc7f999815bb55e7d7f06b6c5eab3a68 Homepage: https://cran.r-project.org/package=SelectionBias Description: CRAN Package 'SelectionBias' (Calculates Bounds for the Selection Bias for Binary Treatmentand Outcome Variables) Computes bounds and sensitivity parameters as part of sensitivity analysis for selection bias. Different bounds are provided: the SV (Smith and VanderWeele), sharp bounds, AF (assumption-free) bound, GAF (generalized AF), and CAF (counterfactual AF) bounds. The calculation of the sensitivity parameters for the SV, sharp, and GAF bounds assume an additional dependence structure in form of a generalized M-structure. The bounds can be calculated for any structure as long as the necessary assumptions hold. See Smith and VanderWeele (2019) , Zetterstrom, Sjölander, and Waernabum (2025) , Zetterstrom and Waernbaum (2022) , and Zetterstrom (2024) . Package: r-cran-selectiongain Architecture: all Version: 2.0.710-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-selectiongain_2.0.710-1.ca2404.1_all.deb Size: 229928 MD5sum: 0f47bc8ca93aa6a03f28fca4587d7680 SHA1: 1752fbc53675565731ead064b26a6a9a5605e5b5 SHA256: 2de27d92f39dfa96c3eaa84d42f22f5648faf0afd9327ed45ddfec1b808ef4c4 SHA512: 3dc61d326d1a30cf5c986f0d0893b06ee0641ce2bdd9e983a9c0c8a3c45b079d3b0a07a36266e3894bdbcfc82a104955ece58c969ddd15be42b1c24754d8aa76 Homepage: https://cran.r-project.org/package=selectiongain Description: CRAN Package 'selectiongain' (A Tool for Calculation and Optimization of the Expected Gainfrom Multi-Stage Selection) Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage. Package: r-cran-selectmeta Architecture: all Version: 1.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-deoptim Filename: pool/dists/noble/main/r-cran-selectmeta_1.0.9-1.ca2404.1_all.deb Size: 89872 MD5sum: 9a3ff0cf1fab452855b61324f7cf6653 SHA1: e5bc6f99ba36e7ac48860a86ff5878e07bb1c13c SHA256: d454d02a95660446ef50a7d6b162b1daad27c25d481c5ee03dece2b0478b677c SHA512: 7c680ff555276af38eb29eace51e1cec84b02764690e163af221fd6a3828920432c8c3e95ec9affafcf361bd1e35232eeb996fca51a7d4fb4480d8f2434bc2fd Homepage: https://cran.r-project.org/package=selectMeta Description: CRAN Package 'selectMeta' (Estimation of Weight Functions in Meta Analysis) Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6). Package: r-cran-selectr Architecture: all Version: 0.5-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 651 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-r6 Suggests: r-cran-testthat, r-cran-xml, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-selectr_0.5-1-1.ca2404.1_all.deb Size: 478928 MD5sum: 1bdfc78746b61ddc097279c14628275e SHA1: 99db1144d612a432bfdef74aa55ebaedfbfb54d2 SHA256: 1be3fca3c701801eb1bebba1f48f4ff7ea39711e6de29754c4774f6e5fc86b37 SHA512: 69b5d26c906aa6f9da20c4a18141daa063a6d42d0935c3ea517b96545f2b6bef0375114b51c925038d8d899a0a7d37701014ef42304eae2b2c4cd97762076eda Homepage: https://cran.r-project.org/package=selectr Description: CRAN Package 'selectr' (Translate CSS Selectors to XPath Expressions) Translates a CSS selector into an equivalent XPath expression. This allows us to use CSS selectors when working with the XML package as it can only evaluate XPath expressions. Also provided are convenience functions useful for using CSS selectors on XML nodes. This package is a port of the Python package 'cssselect' (). Package: r-cran-selectspm Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat, r-cran-ecespa, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.random Filename: pool/dists/noble/main/r-cran-selectspm_0.7-1.ca2404.1_all.deb Size: 76398 MD5sum: 6d399f4a88d8e59489d62f887a1dd258 SHA1: b1889a480a234f6899a563615c555b2e1cd125f9 SHA256: 34c2a72908478cac2d111aa975bcf66f30d8dbe12638ca51d58b34cd17f37738 SHA512: 79907594e1c2bd326890875cc0d32058f8897ff3b511d2a6c59aaa56a7290370c528d31e8a292492b22412ed0210eeab0cc3bceb02b1330ab1852c009a9a6896 Homepage: https://cran.r-project.org/package=selectspm Description: CRAN Package 'selectspm' (Select Point Pattern Models Based on Minimum Contrast, AIC andGoodness of Fit) Fit and selects point pattern models based on minimum contrast, AIC and and goodness of fit. Package: r-cran-selemix Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-ecdat, r-cran-xtable Filename: pool/dists/noble/main/r-cran-selemix_1.0.4-1.ca2404.1_all.deb Size: 85738 MD5sum: dd7a2cf13c805e021b9c6feca942e663 SHA1: 5bcf9f1d31438bf56b889f6bca4790c45dc729d8 SHA256: 1519a5020de84f20d59e1a1267f09f1040ba34eb63aa49a0132535b703f4ac76 SHA512: f9d8ba6e796b59b28241c7c4ae2d68ce036c02b85df2ebdb52cf503e42a26e777606a57e364871db296fb53fddf2fb9ea6a10acf159f9d142676860f5ffde955 Homepage: https://cran.r-project.org/package=SeleMix Description: CRAN Package 'SeleMix' (Selective Editing via Mixture Models) Detection of outliers and influential errors using a latent variable model. Package: r-cran-selenider Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 812 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-coro, r-cran-curl, r-cran-lifecycle, r-cran-prettyunits, r-cran-rlang, r-cran-vctrs, r-cran-withr Suggests: r-cran-chromote, r-cran-jsonlite, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-rselenium, r-cran-rvest, r-cran-selenium, r-cran-shiny, r-cran-shinytest2, r-cran-showimage, r-cran-testthat, r-cran-wdman, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-selenider_0.4.1-1.ca2404.1_all.deb Size: 592170 MD5sum: cadad90d35247ff413f62de46103229c SHA1: 86035594e810035c855333cb2293e1e8e391ac7d SHA256: 917bc178b35bebc5eeb67abbcc6bfea26dd1685e29dcb1b026d6c33694a35d98 SHA512: 7cc1edb2efa9469b59e8bfa373bee24e6246aaeb9fa1b93a95899f688498facb9465a77c06a31c3ad148c1c36aa0b41953f744da32c3db752fe5fb300046e96f Homepage: https://cran.r-project.org/package=selenider Description: CRAN Package 'selenider' (Concise, Lazy and Reliable Wrapper for 'chromote' and 'selenium') A user-friendly wrapper for web automation, using either 'chromote' or 'selenium'. Provides a simple and consistent API to make web scraping and testing scripts easy to write and understand. Elements are lazy, and automatically wait for the website to be valid, resulting in reliable and reproducible code, with no visible impact on the experience of the programmer. Package: r-cran-selenium Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-processx, r-cran-r6, r-cran-rappdirs, r-cran-rlang Suggests: r-cran-gitcreds, r-cran-testthat, r-cran-withr, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-selenium_0.2.0-1.ca2404.1_all.deb Size: 367384 MD5sum: 08cb9beb56f9eb7de87aebf2118c5a0d SHA1: f7b4a20f417de644d568ff4cd3685dee2b173130 SHA256: 40474676422ebf103626b681a025b36d0d7ce4d09b00e5b7192bacd57720b2d8 SHA512: d2c0ba131a87863a9a3e0bc46139a6e65425e7358500424917ebc4ac0cf6af0416d0f758cd283b3ef5f6e6dc3639ec2efc21382eb4f24bb91ef8949ec715139a Homepage: https://cran.r-project.org/package=selenium Description: CRAN Package 'selenium' (Low-Level Browser Automation Interface) An implementation of 'W3C WebDriver 2.0' (), allowing interaction with a 'Selenium Server' () instance from 'R'. Allows a web browser to be automated from 'R'. Package: r-cran-seleniumpipes Architecture: all Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2010 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-xml2, r-cran-magrittr, r-cran-whisker Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-data.table, r-cran-testthat, r-cran-covr, r-cran-rsaucelabs Filename: pool/dists/noble/main/r-cran-seleniumpipes_0.3.7-1.ca2404.1_all.deb Size: 1121298 MD5sum: 3040a720696bd76db4219a504cdd1f22 SHA1: b792f9291b1e195c8ae60f04045703aab3def7f0 SHA256: 90a29d7d3c269571e814811dc0d1ab9130b1909742ebdf6490caab91f92bd18e SHA512: 5b50931e9340e70039e6a6d1c5bd817f060f137d0172413ac7e1907b6660d8cf1be170d9e020cfc11fcd062d708718ac1151d6cf4f91ca80b8e44ca2d1709953 Homepage: https://cran.r-project.org/package=seleniumPipes Description: CRAN Package 'seleniumPipes' (R Client Implementing the W3C WebDriver Specification) The W3C WebDriver specification defines a way for out-of-process programs to remotely instruct the behaviour of web browsers. It is detailed at . This package provides an R client implementing the W3C specification. Package: r-cran-selfingtree Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1290 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach Filename: pool/dists/noble/main/r-cran-selfingtree_0.2-1.ca2404.1_all.deb Size: 1267276 MD5sum: 5ed84f93f49768fd66573c46fe28a65e SHA1: 4bbe7f551c9883e5ad5b68f778fa024d147890cb SHA256: 347b84694df0c2d95f47c54d224d444da9f2be72d9eb85b752e23a667ee37f87 SHA512: 415793c046ef60a27f326d879fae0a20b8f638f357308906733a960aa1ce4fa43b5ecbdca4c981c5164ebcba6296fa2f35b7c34159353c3963a17aee0b91c453 Homepage: https://cran.r-project.org/package=selfingTree Description: CRAN Package 'selfingTree' (Genotype Probabilities in Intermediate Generations of InbreedingThrough Selfing) A probability tree allows to compute probabilities of complex events, such as genotype probabilities in intermediate generations of inbreeding through recurrent self-fertilization (selfing). This package implements functionality to compute probability trees for two- and three-marker genotypes in the F2 to F7 selfing generations. The conditional probabilities are derived automatically and in symbolic form. The package also provides functionality to extract and evaluate the relevant probabilities. Package: r-cran-selindrix Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-psych Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-selindrix_0.1.2-1.ca2404.1_all.deb Size: 67110 MD5sum: 42f920df7a9e4ba14eacbea86400352a SHA1: 77222a016100ec0c2ac5a0e2279ac909d87dfde2 SHA256: 71f099c30bd18b15b6c5d4b401e70745efd896dc8c5daaa661f643e472cb16bb SHA512: d73e4bc7ac5596de3f00841d098cec85eb7a82e13ca01dc1868b127bb5ac018ed15d84518f8115c93b2e7dcbf84d984c73f1ca8a9a691d05d938aac95740eb7b Homepage: https://cran.r-project.org/package=seliNDRIx Description: CRAN Package 'seliNDRIx' (Construction of Selection Index) Selection index is one of the efficient and acurrate method for selection of animals. This package is useful for construction of selection indices. It uses mixed and random model least squares analysis to estimate the heritability of traits and genetic correlation between traits. The package uses the sire model as it is considered as random effect. The genetic and phenotypic (co)variances along with the relative economic values are used to construct the selection index for any number of traits. It also estimates the accuracy of the index and the genetic gain expected for different traits. Fisher (1936) . Package: r-cran-semantic.assets Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 35187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-shiny, r-cran-shiny.semantic Filename: pool/dists/noble/main/r-cran-semantic.assets_1.1.0-1.ca2404.1_all.deb Size: 2252258 MD5sum: eadb1ae825d84804ed19cf1f55e27bb7 SHA1: de23fd2c352cc1befa4a499939412415273fd7d0 SHA256: d1d1ca06f19b824d3c08685270091f28e98b3f0b8f00b872cf3d52b9b22fda16 SHA512: 8fd3e6bbba33116ed1139f5d229db433d89c496d9e886324191bddaad95505e4770152fccbf993ff011866a0c218772fc63a81afa7cce982b2a694e8dfceb5f2 Homepage: https://cran.r-project.org/package=semantic.assets Description: CRAN Package 'semantic.assets' (Assets for 'shiny.semantic') Style sheets and JavaScript assets for 'shiny.semantic' package. Package: r-cran-semantic.dashboard Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shiny.semantic, r-cran-htmltools, r-cran-glue, r-cran-checkmate Suggests: r-cran-testthat, r-cran-lintr, r-cran-shinydashboard, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-semantic.dashboard_0.2.1-1.ca2404.1_all.deb Size: 229864 MD5sum: d7bfb9e7d054b8a9617b720e41b3342d SHA1: 7528a3c35f26ba92bd30499bc609d13d9a8ab0d2 SHA256: 560b9f44880ea9c1a758645f05ce8463abba0683745fecacc4be0e7e4e610b48 SHA512: 10d97a5a977629a8b20fe61c3245b28cdd09f5f5f401ca967566d94ae9343b253e7cd0084f4cbdaac75d3305928dcb73a6f30ed0e325324c63f44b5c460fed64 Homepage: https://cran.r-project.org/package=semantic.dashboard Description: CRAN Package 'semantic.dashboard' (Dashboard with Fomantic UI Support for Shiny) It offers functions for creating dashboard with Fomantic UI. Package: r-cran-semanticdistance Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1567 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-cluster, r-cran-dendextend, r-cran-dplyr, r-cran-httr, r-cran-igraph, r-cran-lsa, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-textstem, r-cran-tidyselect, r-cran-tm, r-cran-tidyr, r-cran-textclean, r-cran-wesanderson Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-semanticdistance_0.1.1-1.ca2404.1_all.deb Size: 1222746 MD5sum: 67585b73c745db46981cfac99cdfd662 SHA1: 3b20ace4febd54850aef292fd4cbc865c84964b4 SHA256: ca8d976adfa3ef88e6c21744a01e1576c9523900682c62e9cee46c5b9fc95034 SHA512: 404793e3302a6985b90c838134ca1af493c62b82665219c40b8d2055adc363d67080062cc837c4f1a19a57443eada0625e1249789ead9249e1e0b8a22a4db47c Homepage: https://cran.r-project.org/package=SemanticDistance Description: CRAN Package 'SemanticDistance' (Compute Semantic Distance Between Text Constituents) Cleans and formats language transcripts guided by a series of transformation options (e.g., lemmatize words, omit stopwords, split strings across rows). 'SemanticDistance' computes two distinct metrics of cosine semantic distance (experiential and embedding). These values reflect pairwise cosine distance between different elements or chunks of a language sample. 'SemanticDistance' can process monologues (e.g., stories, ordered text), dialogues (e.g., conversation transcripts), word pairs arrayed in columns, and unordered word lists. Users specify options for how they wish to chunk distance calculations. These options include: rolling ngram-to-word distance (window of n-words to each new word), ngram-to-ngram distance (2-word chunk to the next 2-word chunk), pairwise distance between words arrayed in columns, matrix comparisons (i.e., all possible pairwise distances between words in an unordered list), turn-by-turn distance (talker to talker in a dialogue transcript). 'SemanticDistance' includes visualization options for analyzing distances as time series data and simple semantic network dynamics (e.g., clustering, undirected graph network). Package: r-cran-semblance Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fields, r-cran-performanceanalytics, r-cran-desctools, r-cran-msos Suggests: r-cran-kernlab Filename: pool/dists/noble/main/r-cran-semblance_1.1.0-1.ca2404.1_all.deb Size: 33914 MD5sum: 8aad9c9dc98b4c3a49ef3ca7a478d498 SHA1: b60c1dcf918401784cb5e2af7a9fc625e6cf139f SHA256: b7493baa399641086b2ef1363ad8f72363399c74b7bdd3cba2cdf67da9a6e0e3 SHA512: bd956d2d4895169eca8da045224e6be370433e218340385385856e3edc03aa16046127aa2d6ebfd72fb0930ad367b3a5561f1fdbc97a63cd0623c35db85d227b Homepage: https://cran.r-project.org/package=Semblance Description: CRAN Package 'Semblance' (A Data-Driven Similarity Kernel on Probability Spaces) We present a rank-based Mercer kernel to compute a pair-wise similarity metric corresponding to informative representation of data. We tailor the development of a kernel to encode our prior knowledge about the data distribution over a probability space. The philosophical concept behind our construction is that objects whose feature values fall on the extreme of that feature’s probability mass distribution are more similar to each other, than objects whose feature values lie closer to the mean. Semblance emphasizes features whose values lie far away from the mean of their probability distribution. The kernel relies on properties empirically determined from the data and does not assume an underlying distribution. The use of feature ranks on a probability space ensures that Semblance is computational efficacious, robust to outliers, and statistically stable, thus making it widely applicable algorithm for pattern analysis. The output from the kernel is a square, symmetric matrix that gives proximity values between pairs of observations. Package: r-cran-semboottools Architecture: all Version: 0.1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 572 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-lavaan, r-cran-psych, r-cran-lavaan.printer, r-cran-ggplot2, r-cran-ggally, r-cran-rlang, r-cran-patchwork Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-semboottools_0.1.2.1-1.ca2404.1_all.deb Size: 320248 MD5sum: 87ac749be3c790ead8a898cee97360fe SHA1: f9a90dcf31a5aafd5cc961563b9eb726e860ed65 SHA256: 3b626719c654f6669b6a6d64ab88cc409924c4eea73071da0e73ef758f806eda SHA512: 2ca588ea900c60a8a53843c7eb99a0081cdd719a9895777ba29f5edef29c5db664b1d71ad09fb66e15e39bc727615b3dd16b3d25d4e7c154fb7cee2572891806 Homepage: https://cran.r-project.org/package=semboottools Description: CRAN Package 'semboottools' (Bootstrapping Helpers for Structural Equation Modelling) A collection of helper functions for forming bootstrapping confidence intervals and examining bootstrap estimates in structural equation modelling, introduced in Yang and Cheung (2026) The function currently support models fitted by the 'lavaan' package by Rosseel (2012) . Package: r-cran-semdeep Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-semgraph, r-cran-igraph, r-cran-coro, r-cran-corpcor, r-cran-kernelshap, r-cran-lavaan, r-cran-neuralnettools, r-cran-parabar, r-cran-progress, r-cran-ranger, r-cran-rpart, r-cran-torch, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-semdeep_1.1.1-1.ca2404.1_all.deb Size: 297608 MD5sum: 36b2c4b3e1abd9a3b5ca750fb1c79299 SHA1: d697839cce4ab631ac6976a74676b7ffa081763f SHA256: e910a419fe759113cc10ad914af9cd18c611a29dc32642c7470512a34a196a07 SHA512: c4e244823738ec54bfd52a8d2616e7690c4a02fb4c0a07f112da7c436d32acdf81a3fc4752195db7a3acbde42a9675ecd1b0df7c86eb89191da46ec2af5b9c8c Homepage: https://cran.r-project.org/package=SEMdeep Description: CRAN Package 'SEMdeep' (Structural Equation Modeling with Deep Neural Network andMachine Learning Algorithms) Training and validation of a custom (or data-driven) Structural Equation Models using Deep Neural Networks or Machine Learning algorithms, which extend the fitting procedures of the 'SEMgraph' R package . Package: r-cran-semdrw Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinyace, r-cran-lavaan, r-cran-semplot, r-cran-dplyr, r-cran-semtools, r-cran-psych Filename: pool/dists/noble/main/r-cran-semdrw_0.1.0-1.ca2404.1_all.deb Size: 73912 MD5sum: 0bc04d672525ba3469ae994b6642058f SHA1: 4a34d3f6ec1062f4a709f5d46de33d10c6b46402 SHA256: 427926f0d1bc538b2836ec66915d70fd9c22a3901a11f1d73ee4df8f5aabae88 SHA512: 9226b86928f1e05b69169282131200a9a90ef6ab53dc0c5e4e99b1b1257225d4125447f5b1e104cdf0de0d551538d6dafe8d7fa03088baedd7b7f517291537e2 Homepage: https://cran.r-project.org/package=semdrw Description: CRAN Package 'semdrw' ('SEM Shiny') Interactive 'shiny' application for working with Structural Equation Modelling technique. Runtime examples are provided in the package function as well as at . Package: r-cran-semds Architecture: all Version: 0.9-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-minpack.lm Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-semds_0.9-7-1.ca2404.1_all.deb Size: 93960 MD5sum: 762ffb39c9b2f27cc147f6eef62f0b19 SHA1: d01d3bfd04cb68cc45a3595826d54bf38daa1bc1 SHA256: 98b60ef93f8406121a6859bc8b3950a4e0089603c024ffd65e606c9a60ba7bde SHA512: c24057143ccc4e680be78808b6e617c978a6cc1b02e455ea30e0b2a83bcfacfb7ccd35a1a7017329f9ad9613d8ea49050e189b6f1796dd9408a7621f76282ad2 Homepage: https://cran.r-project.org/package=semds Description: CRAN Package 'semds' (Structural Equation Multidimensional Scaling) Fits a structural equation multidimensional scaling (SEMDS) model for asymmetric and three-way input dissimilarities. It assumes that the dissimilarities are measured with errors. The latent dissimilarities are estimated as factor scores within an SEM framework while the objects are represented in a low-dimensional space as in MDS. Package: r-cran-semeff Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-gsl, r-cran-lme4 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-markdown, r-cran-piecewisesem, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-semeff_0.7.2-1.ca2404.1_all.deb Size: 2242986 MD5sum: d889ed42fa1f46acb928ab91ab89c2df SHA1: df090689ec2211a2d525fcb00c37c3806a7dba1c SHA256: b10a323c9e162c2f777cb04247257439756d89725e9069ef6fec4ef157cf6471 SHA512: 1b0777f90e3638e2b26901ecc86965b41ae7f699cf789c1a3a8e30d9f03fa634970d274095faa5ca084c7565baa6645252547c2141d5058b7ba6c854712c3b0f Homepage: https://cran.r-project.org/package=semEff Description: CRAN Package 'semEff' (Automatic Calculation of Effects for Piecewise StructuralEquation Models) Automatically calculate direct, indirect, and total effects for piecewise structural equation models, comprising lists of fitted models representing structured equations (Lefcheck, 2016 ). Confidence intervals are provided via bootstrapping. Package: r-cran-semeffect Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-piecewisesem, r-cran-plspm, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-checkmate, r-cran-rcolorbrewer Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-semeffect_1.2.3-1.ca2404.1_all.deb Size: 26992 MD5sum: 5f7b6701d35bf1c6ecb3dc88676497d5 SHA1: 41def64201f779ec756c833c160ebc4e3e245339 SHA256: 6a57121b267fa71bacd02914cbb225c703ae230ced45a91a7ae1881af87ae79b SHA512: e445cb5d67575d4d8e92368b77f7f3531f0bf2eacbe70f3c12160a404b91d6e2f8ab6f1ba4b48b8af46c41549bac52d0fa5e9d63e4d66845070a856b7b99e477 Homepage: https://cran.r-project.org/package=semEffect Description: CRAN Package 'semEffect' (Structural Equation Model Effect Analysis and Visualization) Provides standardized effect decomposition (direct, indirect, and total effects) for three major structural equation modeling frameworks: 'lavaan', 'piecewiseSEM', and 'plspm'. Automatically handles zero-effect variables, generates publication-ready 'ggplot2' visualizations, and returns both wide-format and long-format effect tables. Supports effect filtering, multi-model object inputs, and customizable visualization parameters. For a general overview of the methods used in this package, see Rosseel (2012) and Lefcheck (2016) . Package: r-cran-semfindr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-ggplot2, r-cran-ggrepel, r-cran-rlang, r-cran-matrix, r-cran-pbapply Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-modi, r-cran-mass Filename: pool/dists/noble/main/r-cran-semfindr_0.2.0-1.ca2404.1_all.deb Size: 950300 MD5sum: 0eb77a86db11f0df2ffd1dfd0ac0c886 SHA1: 6055a74517184172986ba5df439c61e9a5a54953 SHA256: 637246c73317f2c48391c1b1f30a9541123f2f7177ac6ba0e1eb07e4a90459a1 SHA512: c73b7e904f1ccb817a7fa33448fd65fded5c063bb6f37339370c5221d96570d93ebb9455e18785fe44504f26dbd36220fba86ead5ae701af5e37ed7aecf64345 Homepage: https://cran.r-project.org/package=semfindr Description: CRAN Package 'semfindr' (Influential Cases in Structural Equation Modeling) Sensitivity analysis in structural equation modeling using influence measures and diagnostic plots. Support leave-one-out casewise sensitivity analysis presented by Pek and MacCallum (2011) and approximate casewise influence using scores and casewise likelihood. An introduction to the package can be found in Cheung and Lai (2026) . Package: r-cran-semgram Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-rsyntax, r-cran-stringr Filename: pool/dists/noble/main/r-cran-semgram_0.1.0-1.ca2404.1_all.deb Size: 116372 MD5sum: 337cf5b29fefb6739e23a84fdacf3529 SHA1: f79f071425a19d7d2ddc339d9d489160839e3b3f SHA256: a4ecb290f4e39288b71f862fc0bbae71315074b77f796451872e1f782d088365 SHA512: b3694bf69410ee49b63f46b1c8a7b6fd962b88b864f30df4b53de26cc771fbc9cf65d229454e6e4d1cd8c97588a82d3b85599ec0b8a32b9cac9c3cc938b0830f Homepage: https://cran.r-project.org/package=semgram Description: CRAN Package 'semgram' (Extracting Semantic Motifs from Textual Data) A framework for extracting semantic motifs around entities in textual data. It implements an entity-centered semantic grammar that distinguishes six classes of motifs: actions of an entity, treatments of an entity, agents acting upon an entity, patients acted upon by an entity, characterizations of an entity, and possessions of an entity. Motifs are identified by applying a set of extraction rules to a parsed text object that includes part-of-speech tags and dependency annotations - such as those generated by 'spacyr'. For further reference, see: Stuhler (2022) . Package: r-cran-semgraph Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4813 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-lavaan, r-cran-aspect, r-cran-boot, r-cran-corpcor, r-cran-dagitty, r-cran-flip, r-cran-gdata, r-cran-ggm, r-cran-glasso, r-cran-glmnet, r-bioc-graph, r-cran-mgcv, r-cran-mvtnorm, r-cran-pbapply, r-cran-protoclust, r-bioc-rbgl, r-bioc-rgraphviz, r-bioc-graphite, r-bioc-annotationdbi Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-org.hs.eg.db Filename: pool/dists/noble/main/r-cran-semgraph_1.2.4-1.ca2404.1_all.deb Size: 3922094 MD5sum: 3d3024709ca6cc42ef6271c72b53d2ce SHA1: c9b912124bebfbacc75aa754861ea6d3fac6b3f7 SHA256: 6d1df0f390f511995212b54cb526e466476e35599445de3b54fad482a5a3e462 SHA512: 2411ad7731c94e8f4b480a3567ad8ce2731e8e714d8a6d22e97cad9e1a8ec285a8fc0c8efbe6afae0feccfc9c7d1834e0e7e3cf2db800e0234150dc79f746a12 Homepage: https://cran.r-project.org/package=SEMgraph Description: CRAN Package 'SEMgraph' (Network Analysis and Causal Inference Through StructuralEquation Modeling) Estimate networks and causal relationships in complex systems through Structural Equation Modeling. This package also includes functions for importing, weight, manipulate, and fit biological network models within the Structural Equation Modeling framework as outlined in the Supplementary Material of Grassi M, Palluzzi F, Tarantino B (2022) . Package: r-cran-semhelpinghands Architecture: all Version: 0.1.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1339 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-boot, r-cran-rlang, r-cran-ggplot2, r-cran-ggrepel Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-semtools, r-cran-testthat Filename: pool/dists/noble/main/r-cran-semhelpinghands_0.1.14-1.ca2404.1_all.deb Size: 1175370 MD5sum: 4683aed1ac0d01270227b3e90d1a8904 SHA1: 693f8254e66d7356318e9707202eb916a0d4d38e SHA256: fa2854b28c015cd1cfffea69173aaeb28f96810b452bc87d7a38c6dfbdcf93ca SHA512: 9e0ae655766cdd421fbd51fc0a96aa496aefcc2a733694a68674eb7888c82f4c0545f671a5a4e8491cce9593f9d5cd507f27cc64b040401be847269133cebc96 Homepage: https://cran.r-project.org/package=semhelpinghands Description: CRAN Package 'semhelpinghands' (Helper Functions for Structural Equation Modeling) An assortment of helper functions for doing structural equation modeling, mainly by 'lavaan' for now. Most of them are time-saving functions for common tasks in doing structural equation modeling and reading the output. This package is not for functions that implement advanced statistical procedures. It is a light-weight package for simple functions that do simple tasks conveniently, with as few dependencies as possible. Package: r-cran-semiartificial Architecture: all Version: 2.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corelearn, r-cran-rsnns, r-cran-mass, r-cran-nnet, r-cran-cluster, r-cran-fpc, r-cran-timedate, r-cran-robustbase, r-cran-ks, r-cran-logspline, r-cran-mcclust, r-cran-flexclust, r-cran-statmatch Filename: pool/dists/noble/main/r-cran-semiartificial_2.4.1-1.ca2404.1_all.deb Size: 206944 MD5sum: 9477ae66e6d0af1ec985665f33ffb1a3 SHA1: 3dbf21e95ea1fee665ccff2e9598c22906cdc3a1 SHA256: 3aa08ae97f17e9da18b989e0ef330b74b97939269d255507bfc942f49dd54da1 SHA512: a1f2869044f951d99f9313f6bdd27c147a3b0a1769de97cddcbf7e62aab10045ad465bb745456e637cbd5029160e41050a86b83bf44e5ff97e3df3f7f74dd2ef Homepage: https://cran.r-project.org/package=semiArtificial Description: CRAN Package 'semiArtificial' (Generator of Semi-Artificial Data) Contains methods to generate and evaluate semi-artificial data sets. Based on a given data set different methods learn data properties using machine learning algorithms and generate new data with the same properties. The package currently includes the following data generators: i) a RBF network based generator using rbfDDA() from package 'RSNNS', ii) a Random Forest based generator for both classification and regression problems iii) a density forest based generator for unsupervised data Data evaluation support tools include: a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM c) evaluation based on classification performance with various learning models, e.g., random forests. Package: r-cran-semicmprskcoxmsm Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-survival, r-cran-twang, r-cran-fastghquad, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-semicmprskcoxmsm_0.2.0-1.ca2404.1_all.deb Size: 116704 MD5sum: cb973c1f5f2bbd01829c6593daeda79c SHA1: 3546fc2737630b2d6627c7d7f342c20c2be70e6a SHA256: cb6787ddb9f8e929f890be54f289593126aa0b04c26656565f9dcb9e0b596489 SHA512: f92b01e0ba171a4947ddde4bce2b508f2805f5e5b750b1b568fc09d4124c507b97b980ee1015e370a44686a71a075b6e9b9e8e4349056ca7f0d6268a60887860 Homepage: https://cran.r-project.org/package=semicmprskcoxmsm Description: CRAN Package 'semicmprskcoxmsm' (Use Inverse Probability Weighting to Estimate Treatment Effectfor Semi Competing Risks Data) Use inverse probability weighting methods to estimate treatment effect under marginal structure model (MSM) for the transition hazard of semi competing risk data, i.e. illness death model. We implement two specific such models, the usual Markov illness death structural model and the general Markov illness death structural model. We also provide the predicted three risks functions from the marginal structure models. Zhang, Y. and Xu, R. (2022) . Package: r-cran-semicontmanova Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixcalc, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-semicontmanova_0.2-1.ca2404.1_all.deb Size: 80202 MD5sum: 1382b43a49cb6ee7add5b78a7b4221d3 SHA1: 4202f927b8df33add9596f30903fa64f9be41de2 SHA256: 19e4f3403fb1c3ea49cb6487045b7cf78bed32067ab7737f8df4b250a1c25481 SHA512: 35aeee69cf6f169e215e17bd568d99133605147fbb30d917e1ad0f2a8965e445431b2adc65d0f9106363b773b60b09471e8ab9ab9fcee567780e156f7a3f2708 Homepage: https://cran.r-project.org/package=semicontMANOVA Description: CRAN Package 'semicontMANOVA' (Multivariate ANalysis of VAriance with Ridge Regularization forSemicontinuous High-Dimensional Data) Implements Multivariate ANalysis Of VAriance (MANOVA) parameters' inference and test with regularization for semicontinuous high-dimensional data. The method can be applied also in presence of low-dimensional data. The p-value can be obtained through asymptotic distribution or using a permutation procedure. The package gives also the possibility to simulate this type of data. Method is described in Elena Sabbioni, Claudio Agostinelli and Alessio Farcomeni (2025) A regularized MANOVA test for semicontinuous high-dimensional data. Biometrical Journal, 67:e70054. DOI , arXiv DOI . Package: r-cran-semid Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.oo, r-cran-r.methodss3, r-cran-igraph, r-cran-r.utils, r-cran-rje Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-semid_0.5.1-1.ca2404.1_all.deb Size: 380078 MD5sum: dcf7e4a0470ead96f128a1d33dbd8f3c SHA1: f934ff230a36a04929548e7b7ef26b9953fac50f SHA256: 16c19464721214a9e88680d84b1a2a6472cd6ca36c01521f52f389814570c924 SHA512: 73c109c6218ed17ac29428dfa1c463eebd9287f51ce4215f00c9446e58d8fc2b6f838d8f5236c01b299464b9cf6a831226e302228304b300a120d21695a6be83 Homepage: https://cran.r-project.org/package=SEMID Description: CRAN Package 'SEMID' (Identifiability of Linear Structural Equation Models) Provides routines to check identifiability of linear structural equation models and factor analysis models. The routines are based on the graphical representation of structural equation models. Package: r-cran-semiestimate Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-numderiv, r-cran-purrr, r-cran-rlang, r-cran-testthat, r-cran-bb, r-cran-nleqslv, r-cran-splines2 Filename: pool/dists/noble/main/r-cran-semiestimate_1.1.3-1.ca2404.1_all.deb Size: 60462 MD5sum: 53455c27baa454245ee2399777510b02 SHA1: fb8c4565f29ba4af9a1c6af596c888b7f068181e SHA256: 2a379463fcc0745f66325083eb3a219d8ecaba2fc0bfe2284e3ab0a25d49fe5f SHA512: 8c1e818ce2c56446f9e7efaf497b1698ab934a3ed9f14abf4b34330bea2914a9e64d94a8a8f9c1d10812970a707df2c43b2561d91fd300399dd2cbbbf8950726 Homepage: https://cran.r-project.org/package=SemiEstimate Description: CRAN Package 'SemiEstimate' (Solve Semi-Parametric Estimation by Implicit Profiling) Semi-parametric estimation problem can be solved by two-step Newton-Raphson iteration. The implicit profiling method is an improved method of two-step NR iteration especially for the implicit-bundled type of the parametric part and non-parametric part. This package provides a function semislv() supporting the above two methods and numeric derivative approximation for unprovided Jacobian matrix. Package: r-cran-semimarkov Architecture: all Version: 1.4.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-mass, r-cran-rsolnp Filename: pool/dists/noble/main/r-cran-semimarkov_1.4.6-1.ca2404.1_all.deb Size: 238402 MD5sum: cffc927f096025747b0ea4aa7b39d07c SHA1: 30b4849c8d4d074ab42ee1016871b2e685709b10 SHA256: 9807b9d7dfa47b8f0e9c778371ebf96abc9fc17f4e99ff61124b32774dac55b2 SHA512: ce24611c11cfaf48522c9fc16983ae74c76988fbd2b4f9f1dc38bb0ed3c155bdc98f3abd2be1d5feccf8a8554d11fd7a233428dc6391853064052370dbd82075 Homepage: https://cran.r-project.org/package=SemiMarkov Description: CRAN Package 'SemiMarkov' (Multi-States Semi-Markov Models) Functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model. Reference: A. Krol and P. Saint-Pierre (2015) . Package: r-cran-seminr Architecture: all Version: 2.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-glue, r-cran-diagrammer, r-cran-diagrammersvg Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-webp Filename: pool/dists/noble/main/r-cran-seminr_2.4.2-1.ca2404.1_all.deb Size: 1540568 MD5sum: 1fb8fc1a9dbaca13945280778f64cf3a SHA1: 817bd75d54ec7050ca8e79e2db27099e52ca8e28 SHA256: abbc4c9324bd05145c1012469a2056303e9744781298117063adab63749cf9ad SHA512: 9e9b7d83992861904e68b39082e0783ab36a39295b3de2d2153435f9cb91f5bc4c81667193f5c110ea4ae51f6b3f3cd8d8f82d3abd236bccfb554f9df041b9de Homepage: https://cran.r-project.org/package=seminr Description: CRAN Package 'seminr' (Building and Estimating Structural Equation Models) A powerful, easy to use syntax for specifying and estimating complex Structural Equation Models. Models can be estimated using Partial Least Squares Path Modeling or Covariance-Based Structural Equation Modeling or covariance based Confirmatory Factor Analysis (Ray, Danks, and Valdez 2021 ). Package: r-cran-seminrextras Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2694 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-seminr, r-cran-rpart Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-paran, r-cran-psych, r-cran-learnr Filename: pool/dists/noble/main/r-cran-seminrextras_1.0.1-1.ca2404.1_all.deb Size: 832450 MD5sum: cc2eacec3be86ce201372a22b081aeaf SHA1: e4902c06ca532f546db85f7c8a28b8829942829b SHA256: ac402afea6940585fb481550ae1b0e3ed51be94126c0acc0cb4e31172850a654 SHA512: ef3f0eee88ff7f196880f6525baa2c97fe195dbcd416cc60df556b5e0da1db47b5f1b954b33a08814219a63bea6dc97d3a32586ac81560fa269803cfa62e77cc Homepage: https://cran.r-project.org/package=seminrExtras Description: CRAN Package 'seminrExtras' (Conduct Additional Modeling and Analysis for 'seminr') Supplementary tools for evaluating and validating partial least squares structural equation models estimated with 'seminr'. Provides methods for predictive model assessment, importance-performance analysis with necessary condition testing, overfitting diagnostics, measurement model verification, mediator contribution analysis, unobserved heterogeneity detection via latent class and prediction-oriented segmentation, and congruence coefficient testing. All functions accept estimated 'seminr' model objects and return results with print, summary, and plot methods. Package: r-cran-semipar.depcens Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-copula, r-cran-foreach, r-cran-doparallel, r-cran-pbivnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-semipar.depcens_0.1.3-1.ca2404.1_all.deb Size: 105682 MD5sum: 8e33b713201b18cc53bdb7adc9d2dbae SHA1: cf9dd277382ee689576d7ffa09340502cf9b90a4 SHA256: cf0cc030a1267f26abb2a2ff214d142ea9acc23d57ed7edc78f7a11f444593ed SHA512: 1023d7b76fe27e053bd88d5f2186bbf508acb190d633b62e6f70f758ecff3105af50fb5d53122395f0220b57b4a805b395c0d781cc141be9cfb77a6e6b418c36 Homepage: https://cran.r-project.org/package=SemiPar.depCens Description: CRAN Package 'SemiPar.depCens' (Copula Based Cox Proportional Hazards Models for DependentCensoring) Copula based Cox proportional hazards models for survival data subject to dependent censoring. This approach does not assume that the parameter defining the copula is known. The dependency parameter is estimated with other finite model parameters by maximizing a Pseudo likelihood function. The cumulative hazard function is estimated via estimating equations derived based on martingale ideas. Available copula functions include Frank, Gumbel and Normal copulas. Only Weibull and lognormal models are allowed for the censoring model, even though any parametric model that satisfies certain identifiability conditions could be used. Implemented methods are described in the article "Copula based Cox proportional hazards models for dependent censoring" by Deresa and Van Keilegom (2024) . Package: r-cran-semipar Architecture: all Version: 1.0-4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-cluster, r-cran-nlme Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-semipar_1.0-4.2-1.ca2404.1_all.deb Size: 316222 MD5sum: 30efc171e7873db8e26657d32816b334 SHA1: 02a1ef7bb17dac2152268d844ad2163d3bc57e63 SHA256: 91154fa2cee8a223d2da10be5b63887a577693ff77121c6fe5ed79b493305e00 SHA512: 2532cd96e8af16bff768ac06a59d3a8984a8bc8f3d12a6a1663c555a005ce0966f7139cf05996725f7eefd010f21ef4702b93cc48bace6d8499db955b2dbfca7 Homepage: https://cran.r-project.org/package=SemiPar Description: CRAN Package 'SemiPar' (Semiparametic Regression) Functions for semiparametric regression analysis, to complement the book: Ruppert, D., Wand, M.P. and Carroll, R.J. (2003). Semiparametric Regression. Cambridge University Press. Package: r-cran-semiparmf Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-spdep Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-semiparmf_1.0.0-1.ca2404.1_all.deb Size: 64702 MD5sum: be1200922ddd94f5e4923a21aa576cd4 SHA1: efa667f510713b730515f70d61cf8d5e003b7301 SHA256: cc41363a627a2bae5f3782e16610e7755dca10f94a814a6873e978756f55896d SHA512: 6c3d1df5b71bcae3b7596c37bff1af4dac29d87c090375ce1941e2562b62cbd7ff0664434a50bbb20d05f6936a90f5c4451f05d98e6bff8c653850c6381e11d9 Homepage: https://cran.r-project.org/package=SemiparMF Description: CRAN Package 'SemiparMF' (Semiparametric Spatiotemporal Model with Mixed Frequencies) Fits a semiparametric spatiotemporal model for data with mixed frequencies, specifically where the response variable is observed at a lower frequency than some covariates. The estimation uses an iterative backfitting algorithm that combines a non-parametric smoothing spline for high-frequency data, parametric estimation for low-frequency and spatial neighborhood effects, and an autoregressive error structure. Methodology based on Malabanan, Lansangan, and Barrios (2022) . Package: r-cran-semlbci Architecture: all Version: 0.11.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 979 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-nloptr, r-cran-mass, r-cran-ggplot2, r-cran-ggrepel, r-cran-rlang, r-cran-pbapply, r-cran-callr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-semlbci_0.11.5-1.ca2404.1_all.deb Size: 775960 MD5sum: 78163eb4ca5e922df2e1c0e5ce27e68e SHA1: fe53fbefddfbc0d326873322d87d933344dbc56f SHA256: 23014bd4737bdbef62e1930f129c0be790d4844eb8fa07261a7ca89a8606c62f SHA512: 2c5d64af156ee7ac97e8b7c3b186ac0e885510b0846b069980584c34c835b1bac7820d6c5f800118b69dfd4b0250094b006f66cbe74882d3451d5cf0a5d906a3 Homepage: https://cran.r-project.org/package=semlbci Description: CRAN Package 'semlbci' (Likelihood-Based Confidence Interval in Structural EquationModels) Forms likelihood-based confidence intervals (LBCIs) for parameters in structural equation modeling, introduced in Cheung and Pesigan (2023) . Currently implements the algorithm illustrated by Pek and Wu (2018) , and supports the robust LBCI proposed by Falk (2018) . Package: r-cran-semlrtp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 602 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan, r-cran-pbapply Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-semlrtp_0.1.1-1.ca2404.1_all.deb Size: 537510 MD5sum: 3cb0bbc7f0362c94aed19e4b2c5458f8 SHA1: 0845b2d6db346621218439b6fe8141f4a562305f SHA256: c71ce66411c65218a67b382a91bc6da57f25b48dff6deb21dbedf2c69aebd060 SHA512: 97829ad0c5c6c206a070f3bc98ae5fd784ccfd02389ff21d3ec87774641731f668a25eb6e8ef2272d132a7b4672364becab51396c33ac07329c94dfb3f5b9225 Homepage: https://cran.r-project.org/package=semlrtp Description: CRAN Package 'semlrtp' (Likelihood Ratio Test P-Values for Structural Equation Models) Computes likelihood ratio test (LRT) p-values for free parameters in a structural equation model. Currently supports models fitted by the 'lavaan' package by Rosseel (2012) . Package: r-cran-semmcci Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-mice Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-mass, r-cran-psych, r-cran-amelia, r-cran-bmemlavaan Filename: pool/dists/noble/main/r-cran-semmcci_1.1.5-1.ca2404.1_all.deb Size: 105188 MD5sum: 43571aabb437b8fab05765eefeff0aca SHA1: 2d4f36097dfa9b9373df895d68a340f8331eab6b SHA256: 4a434a4ee02aaf5f33979fa8dd8cabd26a4dc9657bb9b6e7c6194656f4160fed SHA512: 89b4d6791d344fd52844dbfd118b7be05ba3cdc91e58410c40d2c91b0eddbbd179eab090005ba7f5e6a039f5f58f883874bf7049ddd5c874e96e9e768f1f17d5 Homepage: https://cran.r-project.org/package=semmcci Description: CRAN Package 'semmcci' (Monte Carlo Confidence Intervals in Structural Equation Modeling) Monte Carlo confidence intervals for free and defined parameters in models fitted in the structural equation modeling package 'lavaan' can be generated using the 'semmcci' package. 'semmcci' has three main functions, namely, MC(), MCMI(), and MCStd(). The output of 'lavaan' is passed as the first argument to the MC() function or the MCMI() function to generate Monte Carlo confidence intervals. Monte Carlo confidence intervals for the standardized estimates can also be generated by passing the output of the MC() function or the MCMI() function to the MCStd() function. A description of the package and code examples are presented in Pesigan and Cheung (2024) . Package: r-cran-semmcmc Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-msm Filename: pool/dists/noble/main/r-cran-semmcmc_0.0.6-1.ca2404.1_all.deb Size: 27006 MD5sum: fa848d1735627a7bc83ea995e64800fa SHA1: 31e3fd43751b4ce8a39ab308099fde3cd7339194 SHA256: 429bc77c0e3dd995bf7f4ad906a507124df4ccd8efa81257f197abc89150186c SHA512: 58e6f66e78e53a8b83950c67f7ed2e9b7356e966dabdf64d506b883b3c02efd5fece8b118a9cea38c5239c020dab027746d87eb311efa5fdff8d1ced9e6b5495 Homepage: https://cran.r-project.org/package=semmcmc Description: CRAN Package 'semmcmc' (Bayesian Structural Equation Modeling in Multiple Omics DataIntegration) Provides Markov Chain Monte Carlo (MCMC) routine for the structural equation modelling described in Maity et. al. (2020) . This MCMC sampler is useful when one attempts to perform an integrative survival analysis for multiple platforms of the Omics data where the response is time to event and the predictors are different omics expressions for different platforms. Package: r-cran-semnar Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-lubridate, r-cran-parsedate, r-cran-leaflet, r-cran-urlshortener Suggests: r-cran-curl, r-cran-covr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-semnar_0.8.2-1.ca2404.1_all.deb Size: 81614 MD5sum: b49cc9af1b1847b7b147c406258ef490 SHA1: d3bbac2fc01bdca58bdead4159ca685ff005ef3f SHA256: c0da130dd4f09cebd6efa95fc8f458a3d8343b64fb0af8d74d0fca0a533233d7 SHA512: ff7476815714684bcf00a07e046a5440ddcd24c009d83c9d39d5e2b0e5c5ceba18e0eeef62e6596d92a447e558f8ad691a47f59fe52c4b8d7f4fcd0f3cb2cecf Homepage: https://cran.r-project.org/package=semnar Description: CRAN Package 'semnar' (Constructing and Interacting with Databases of Presentations) Provides methods for constructing and maintaining a database of presentations in R. The presentations are either ones that the user gives or gave or presentations at a particular event or event series. The package also provides a plot method for the interactive mapping of the presentations using 'leaflet' by grouping them according to country, city, year and other presentation attributes. The markers on the map come with popups providing presentation details (title, institution, event, links to materials and events, and so on). Package: r-cran-semnet Architecture: all Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2911 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pbapply, r-cran-dplyr, r-cran-plyr, r-cran-magrittr, r-cran-ggplot2, r-cran-igraph, r-cran-qgraph, r-cran-scales, r-cran-car, r-cran-broom, r-cran-effects, r-cran-philentropy Suggests: r-cran-shiny, r-cran-shinyjs, r-cran-shinyalert, r-cran-shinymatrix, r-cran-shinybs, r-cran-animation, r-cran-r.matlab, r-cran-foreign, r-cran-readxl, r-cran-data.table, r-cran-networktoolbox, r-cran-semnetcleaner, r-cran-semnetdictionaries Filename: pool/dists/noble/main/r-cran-semnet_1.4.5-1.ca2404.1_all.deb Size: 2620198 MD5sum: 50af3cb226e0031aa61bdb7a004cc116 SHA1: cbbfb4b0c6bc32b97fd85c76aec4b0044c3ddecb SHA256: eed5e7724d1197a750cdf572334a9f8cf51b13fd102db3d6b48e24bdad45250b SHA512: e5f4c9eea88efe2d8d9db1969996d673ede781d58bd63b37e7dbe114a806294ad99a0dcff1c919e644930fe32631a229a68d25f2394e2e8558435891dfa6d8dc Homepage: https://cran.r-project.org/package=SemNeT Description: CRAN Package 'SemNeT' (Methods and Measures for Semantic Network Analysis) Implements several functions for the analysis of semantic networks including different network estimation algorithms, partial node bootstrapping (Kenett, Anaki, & Faust, 2014 ), random walk simulation (Kenett & Austerweil, 2016 ), and a function to compute global network measures. Significance tests and plotting features are also implemented. Package: r-cran-semnetcleaner Architecture: all Version: 1.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1759 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-semnetdictionaries, r-cran-foreign, r-cran-pbapply, r-cran-r.matlab, r-cran-readxl, r-cran-rstudioapi, r-cran-searcher, r-cran-shiny, r-cran-stringi, r-cran-stringdist Suggests: r-cran-dt, r-cran-hunspell, r-cran-easycsv, r-cran-htmltable, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-semnetcleaner_1.3.7-1.ca2404.1_all.deb Size: 597044 MD5sum: 93044ce7f2432ebcdcf039350e1f6787 SHA1: 8487670a65f3179d470db159e57845760cecf3a1 SHA256: 1df9a91ef120f73da8f191c8df007fc42f66c4e3c4f5ea577f005c1aec0b8487 SHA512: 5d5790851d72792bdd1c67db756d0a5ed3a76dad80280ab3fabe6c8d0e7b3e71aa19e0137e99c6d719f38ee4b323773a3c47c9fc9d619aba14d0d8266c63ecb6 Homepage: https://cran.r-project.org/package=SemNetCleaner Description: CRAN Package 'SemNetCleaner' (An Automated Cleaning Tool for Semantic and Linguistic Data) Implements several functions that automates the cleaning and spell-checking of text data. Also converges, finalizes, removes plurals and continuous strings, and puts text data in binary format for semantic network analysis. Uses the 'SemNetDictionaries' package to make the cleaning process more accurate, efficient, and reproducible. Package: r-cran-semnetdictionaries Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3927 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-easycsv, r-cran-ggplot2, r-cran-gridextra, r-cran-htmltable, r-cran-knitr, r-cran-markdown, r-cran-patchwork, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyalert, r-cran-shinyjs Filename: pool/dists/noble/main/r-cran-semnetdictionaries_0.2.1-1.ca2404.1_all.deb Size: 3511892 MD5sum: 624cba43fdb0bfecf078d809f22078cb SHA1: 4f37b52b929069bcdf41f35dc9c069f9c1c4b12d SHA256: d761d5ff4ae57b8658bbea1c8763dbc1c55097a054a42da258ed2ceeb6f1a404 SHA512: 100a02e44d19a7178918273f8344692331b446ce4a65e2e06e45c6f011f99e8982416e2b69e994de8b82e38ad5a90d4ebdabe978d1f5cbb85a8e835b26fa3b16 Homepage: https://cran.r-project.org/package=SemNetDictionaries Description: CRAN Package 'SemNetDictionaries' (Dictionaries for the 'SemNetCleaner' Package) Implements dictionaries that can be used in the 'SemNetCleaner' package. Also includes several functions aimed at facilitating the text cleaning analysis in the 'SemNetCleaner' package. This package is designed to integrate and update word lists and dictionaries based on each user's individual needs by allowing users to store and save their own dictionaries. Dictionaries can be added to the 'SemNetDictionaries' package by submitting user-defined dictionaries to . Package: r-cran-semnova Architecture: all Version: 0.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan, r-cran-matrix, r-cran-mass Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-semnova_0.1-6-1.ca2404.1_all.deb Size: 113880 MD5sum: b49bcab0538a0c97a15d1a2691d1e718 SHA1: 74343aa295d4e8c771ba75dddb08adc9c019c379 SHA256: 791129d9d25c35454b1b1f58f760776d338018a1bbed155d3ab8338353430788 SHA512: 6ed4531922d0fde96c3e19b8d0183bdaf43077e00cede81d61196891cb0985bdf74ca6ca4bab6161f68d300d95abf5ff54f05af62f24df2191e47d4a5316d4d2 Homepage: https://cran.r-project.org/package=semnova Description: CRAN Package 'semnova' (Latent Repeated Measures ANOVA) Latent repeated measures ANOVA (L-RM-ANOVA) is a structural equation modeling based alternative to traditional repeated measures ANOVA. L-RM-ANOVA extends the latent growth components approach by Mayer et al. (2012) and introduces latent variables to repeated measures analysis. Package: r-cran-semplot Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qgraph, r-cran-lavaan, r-cran-sem, r-cran-plyr, r-cran-xml, r-cran-igraph, r-cran-lisreltor, r-cran-rockchalk, r-cran-colorspace, r-cran-corpcor, r-cran-openmx Suggests: r-cran-mplusautomation Filename: pool/dists/noble/main/r-cran-semplot_1.1.8-1.ca2404.1_all.deb Size: 347020 MD5sum: ee69004f300ac5dc70b3a0a70dfa99b5 SHA1: bb0b5005c9f8c60b27cdd3daa5e5da78cd30e09e SHA256: b8884e2e5b328da083a323783771a022a89a4db51819881d1db716fcc4c10f9d SHA512: 8fbe24ac9a1fc8de8b33030d4afbe5812f499b3efa142652f9935d4c9cc75552f96b464c59cc01951549be794a5ce76454627c48f5d4b99f53033097cad6ea43 Homepage: https://cran.r-project.org/package=semPlot Description: CRAN Package 'semPlot' (Path Diagrams and Visual Analysis of Various SEM Packages'Output) Path diagrams and visual analysis of various SEM packages' output. Package: r-cran-sempower Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 960 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bookdown, r-cran-covsim, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-knitr, r-cran-lavaan, r-cran-mnonr, r-cran-progressr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sempower_2.1.3-1.ca2404.1_all.deb Size: 920672 MD5sum: cec5156b74a01ce7b6ad1ba74a34e906 SHA1: 0345a28ffa8fed320b4df42df093873d9e22e732 SHA256: b56d4964d7150df7fca623396c9990b934de46e1bb9d8623809467678516c205 SHA512: b7055acadac948ff03d8674ecf9929c8ede88ce6347518d305e0890c1a1aea368bb597ac1a0838280eb2d2c1b2302b45b799cd4ff95102a7cce91849f3993b67 Homepage: https://cran.r-project.org/package=semPower Description: CRAN Package 'semPower' (Power Analyses for SEM) Provides a-priori, post-hoc, and compromise power-analyses for structural equation models (SEM). Package: r-cran-semptools Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1580 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-rlang, r-cran-semplot Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-semptools_0.3.3-1.ca2404.1_all.deb Size: 1086240 MD5sum: ad6c041f55bb3076a37310fbbe3b733c SHA1: adf62b4718ef4ca3a3195427244199beda52104f SHA256: 85c7d6949394b5bbd64e3d19529ac220697817862208310c10dbedd856b91bc2 SHA512: ed846960f88b4cba9e61b1710c448a831acfac7155bfb011270610da6ee9ebed31cd54d66c4bcf630185acab915ced1cd3c0b14fb3595fc750089e60daec6ece Homepage: https://cran.r-project.org/package=semptools Description: CRAN Package 'semptools' (Customizing Structural Equation Modelling Plots) Most function focus on specific ways to customize a graph. They use a 'qgraph' output as the first argument, and return a modified 'qgraph' object. This allows the functions to be chained by a pipe operator. Package: r-cran-semsens Architecture: all Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-semsens_1.5.5-1.ca2404.1_all.deb Size: 192382 MD5sum: 8e87be571f012edd5c231720c345ebe6 SHA1: 9dd8054f13e181c10cee6e75f80dd536f813dda1 SHA256: 56113e65e30072118573b11243deec9ce1fd589eb707011545a32f99fb348571 SHA512: 3241caf0a5e29ba06dbaabf53a75d6038e53279fc86b07a1c14dc43f79c1ed51f0cfc14aac9adb38f8e5904b0c9ef01b6c3e6656beac56bce5fff68e02fbe812 Homepage: https://cran.r-project.org/package=SEMsens Description: CRAN Package 'SEMsens' (A Tool for Sensitivity Analysis in Structural Equation Modeling) Perform sensitivity analysis in structural equation modeling using meta-heuristic optimization methods (e.g., ant colony optimization and others). The references for the proposed methods are: (1) Leite, W., & Shen, Z., Marcoulides, K., Fish, C., & Harring, J. (2022). (2) Harring, J. R., McNeish, D. M., & Hancock, G. R. (2017) ; (3) Fisk, C., Harring, J., Shen, Z., Leite, W., Suen, K., & Marcoulides, K. (2022). ; (4) Socha, K., & Dorigo, M. (2008) . We also thank Dr. Krzysztof Socha for sharing his research on ant colony optimization algorithm with continuous domains and associated R code, which provided the base for the development of this package. Package: r-cran-semsensitivity Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-dplyr, r-cran-r.utils, r-cran-semfindr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-semsensitivity_0.1.0-1.ca2404.1_all.deb Size: 167894 MD5sum: 964d1844b2d116aae3dc66f988180bb7 SHA1: 4764e08672c244cf352e6e34f583505389ae8e60 SHA256: a2f1684c74449fed561f8fe12a7edd6e8a55352237e5cfb68ac8ba91af42b334 SHA512: cd0b65bcc99b51e85fab75f0dd09b5134c03ab182cad8df72b62592142d48fccbaaafe344c4766771e42e871f5986e5c6c82f57df22f4c2854bda4cc2e4608fb Homepage: https://cran.r-project.org/package=SEMsensitivity Description: CRAN Package 'SEMsensitivity' (SEM Sensitivity Analysis) Performs sensitivity analysis for Structural Equation Modeling (SEM). It determines which sample points need to be removed for the sign of a specific path in the SEM model to change, thus assessing the robustness of the model. Methodological manuscript in preparation. Package: r-cran-semsfa Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-np, r-cran-gamlss, r-cran-moments, r-cran-doparallel, r-cran-foreach, r-cran-iterators Filename: pool/dists/noble/main/r-cran-semsfa_1.2-1.ca2404.1_all.deb Size: 47544 MD5sum: ef22fb96303cab3c31cf094761a15742 SHA1: 052f4f0412a459f52307a40c145acc6c628fedd0 SHA256: 5e9fec96b5d53f3a053765ff6b450bf897d26f243abc642cb0ccd6c3846f8227 SHA512: 78e0f04aabf55f5dfeb95913cff6f73406713c8878408fe73c3c38430d92f24d14687f1c05feb4c2cf268119297311af8cfdecc30d7953e6041ba7fcb3138cfc Homepage: https://cran.r-project.org/package=semsfa Description: CRAN Package 'semsfa' (Semiparametric Estimation of Stochastic Frontier Models) Semiparametric Estimation of Stochastic Frontier Models following a two step procedure: in the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions of the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood estimators or by method of moments. Package: r-cran-semtests Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-compquadform, r-cran-rspectra, r-cran-mass, r-cran-matrix Suggests: r-cran-covr, r-cran-testthat, r-cran-psych Filename: pool/dists/noble/main/r-cran-semtests_0.7.1-1.ca2404.1_all.deb Size: 139600 MD5sum: 1c1f2350a1db6612dbbc4bdd85681011 SHA1: 66788609e1b86cd550213c7997aa2e64a0ac6679 SHA256: 99e5ead309d50139051c5d0fd8bf8bee29af010078ba87c3fee9d9391ad99870 SHA512: 54d7abba8cb22a650186c604e04d76ec452f8d12a290639211157a28a2d485864002f9969065a37bb8c52aea0c29aa568587110f0153713c4e5d614163e550f9 Homepage: https://cran.r-project.org/package=semTests Description: CRAN Package 'semTests' (Goodness-of-Fit Testing for Structural Equation Models) Supports eigenvalue block-averaging p-values (Foldnes, Grønneberg, 2018) , penalized eigenvalue block-averaging p-values (Foldnes, Moss, Grønneberg, 2024) , penalized regression p-values (Foldnes, Moss, Grønneberg, 2024) , as well as traditional p-values such as Satorra-Bentler. 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Package: r-cran-semtools Architecture: all Version: 0.5-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2596 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lavaan, r-cran-pbivnorm Suggests: r-cran-blavaan, r-cran-emmeans, r-cran-lavaan.mi, r-cran-mass, r-cran-mice, r-cran-mnormt, r-cran-restriktor, r-cran-testthat Filename: pool/dists/noble/main/r-cran-semtools_0.5-8-1.ca2404.1_all.deb Size: 2484878 MD5sum: 19c81c47b50a266ccb38d880a7db7984 SHA1: 40e60d7a1c9385bdd75b5bbd866c5c4a1b74a16a SHA256: 08595d2b07d0fccfc745fc0b3eaf68ab6413af0bc0d549a463825fb784ec084b SHA512: c46a04d7bb91c80eaa89992eb6c5a5ca0bf221292c2ec063c19482346759dddb09668b5dc1d5d53b7fd19accf64c795729126c1a42a989b91608bb61fdc666f1 Homepage: https://cran.r-project.org/package=semTools Description: CRAN Package 'semTools' (Useful Tools for Structural Equation Modeling) Provides miscellaneous tools for structural equation modeling, many of which extend the 'lavaan' package. 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SEM trees hierarchically split empirical data into homogeneous groups each sharing similar data patterns with respect to a SEM by recursively selecting optimal predictors of these differences. SEM forests are an extension of SEM trees. They are ensembles of SEM trees each built on a random sample of the original data. By aggregating over a forest, we obtain measures of variable importance that are more robust than measures from single trees. A description of the method was published by Brandmaier, von Oertzen, McArdle, & Lindenberger (2013) and Arnold, Voelkle, & Brandmaier (2020) . 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SMTP Authentication and SSL/STARTTLS is implemented using curl. Package: r-cran-sense Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlr3, r-cran-mlr3learners, r-cran-mlr3filters, r-cran-mlr3pipelines, r-cran-mlr3viz, r-cran-paradox, r-cran-mlr3tuning, r-cran-bbotk, r-cran-tictoc, r-cran-forcats, r-cran-readr, r-cran-lubridate, r-cran-purrr, r-cran-metrics, r-cran-data.table, r-cran-visnetwork Suggests: r-cran-xgboost, r-cran-rpart, r-cran-ranger, r-cran-kknn, r-cran-glmnet, r-cran-e1071, r-cran-mlr3misc, r-cran-fselectorrcpp, r-cran-care, r-cran-praznik, r-cran-lme4, r-cran-nloptr Filename: pool/dists/noble/main/r-cran-sense_1.1.0-1.ca2404.1_all.deb Size: 48628 MD5sum: 422ea67bca083d1a01cb62fb478a7620 SHA1: 33ff51467d2c97ca12a060c506c93cf8a2c1fc2e SHA256: 8e79026bc5a52cd154f4952f394c27768d8f84b4e99cf2170242962a038e4a95 SHA512: 731a931f54b85b16fbf563b44ac3bd511556556c43e90c5125ee98954de507dceae6814e99fd91ee99274630a874955fc3f01b26d69c66afa1c5e560cc4b24a2 Homepage: https://cran.r-project.org/package=sense Description: CRAN Package 'sense' (Automatic Stacked Ensemble for Regression Tasks) Stacked ensemble for regression tasks based on 'mlr3' framework with a pipeline for preprocessing numeric and factor features and hyper-parameter tuning using grid or random search. Package: r-cran-sensemakr Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1724 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-png, r-cran-stargazer, r-cran-fixest Filename: pool/dists/noble/main/r-cran-sensemakr_0.1.6-1.ca2404.1_all.deb Size: 1275412 MD5sum: b6838f6f9a0ba02238469eefdde6addf SHA1: 0efe91e555aabfc48e7ce0be5c1762888687ef84 SHA256: db35f73832320959df704429245f13b9dc5210d585478fa68fc164a26dd9c8e0 SHA512: bc4587073a019c7c96a3a7e3b40fe613dc3ec0578141fba9a2e2c0498655243e1cfa2a3bb2e64706e815691c2d0acd7a6b1c7e2d983712d35ecf046ba9c72f1b Homepage: https://cran.r-project.org/package=sensemakr Description: CRAN Package 'sensemakr' (Sensitivity Analysis Tools for Regression Models) Implements a suite of sensitivity analysis tools that extends the traditional omitted variable bias framework and makes it easier to understand the impact of omitted variables in regression models, as discussed in Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology) . Package: r-cran-senser Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-senser_0.1.0-1.ca2404.1_all.deb Size: 24888 MD5sum: 669935cdc8d1cde38824da217b985bc7 SHA1: 6d1a5d10b2a058090a3644003a7c3cf57a7b3a65 SHA256: c0c36b129bd4c4e7032f66fed1eb3bd49edd8592ef51c14587577c47607116ca SHA512: 3d0819d8c46b53240a21e607bb85cd20babf918ec230f4edb997b41d38b81e81e48a0186ca7e86af3f5f34012f01f6a17a263ea6e2d223e5facc35972f98b951 Homepage: https://cran.r-project.org/package=senseR Description: CRAN Package 'senseR' (Proxy Indicator Diagnostic Tool for Analytical and Policy Use) Provides statistical diagnostics to evaluate whether proxy indicators reliably represent an unobservable target construct. The main function 'senser()' assesses proxies across multiple dimensions including monotonicity, information content, stability, distributional alignment, and potential bias risk. It prints a concise, interpretable summary suitable for analytical and policy-oriented assessment, without claiming causal inference. Package: r-cran-senseweight Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 747 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-estimatr, r-cran-ggplot2, r-cran-ggrepel, r-cran-kableextra, r-cran-metr, r-cran-rlang, r-cran-survey, r-cran-weightit Suggests: r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-senseweight_0.0.1-1.ca2404.1_all.deb Size: 595408 MD5sum: f3c20d3c40937159d77ac45c45ed4b93 SHA1: 75b3aa473c72ead935ce7f2d13b864103dc2d81a SHA256: 70d5bef1dc6e78dc7fc1ed702c93e57259953035abdd108091a1df49087aecd6 SHA512: d7be10061907996e692dcc9e74bf5ace057d915b87397c7030ce24b3dc89ff761c1e38bd014bebc1597cd16143f98fa6182f3a5811f08e2a8590c4093968ecaf Homepage: https://cran.r-project.org/package=senseweight Description: CRAN Package 'senseweight' (Sensitivity Analysis for Weighted Estimators) Provides tools to conduct interpretable sensitivity analyses for weighted estimators, introduced in Huang (2024) and Hartman and Huang (2024) . The package allows researchers to generate the set of recommended sensitivity summaries to evaluate the sensitivity in their underlying weighting estimators to omitted moderators or confounders. The tools can be flexibly applied in causal inference settings (i.e., in external and internal validity contexts) or survey contexts. Package: r-cran-sensibo.sky Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-glue Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sensibo.sky_1.0.0-1.ca2404.1_all.deb Size: 37868 MD5sum: 5be5cf43407f2d94ec2acff3aaa37a4b SHA1: 31172cbda104b8a4b9627f2b560cc56d707e84b2 SHA256: 613985f71a9c06e4576c1f20ca6f3c5d86952f3cace4c3d94bfb4cd177d11980 SHA512: 71a91806d286a6e9efa243b043a45cd2c7952c004833b31f1d0109de32c392636b74f8ca76bfb9bbe662994b7a0665a8baf1ecf49bea78a082150b741638623d Homepage: https://cran.r-project.org/package=sensibo.sky Description: CRAN Package 'sensibo.sky' (Access to 'Sensibo Sky' API V2 for Air Conditioners RemoteControl) Provides an interface to the 'Sensibo Sky' API which allows to remotely control non-smart air conditioning units. See for more informations. Package: r-cran-sensiphy Architecture: all Version: 0.8.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1360 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-phylolm, r-cran-ggplot2, r-cran-caper, r-cran-phytools, r-cran-geiger Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sensiphy_0.8.5-1.ca2404.1_all.deb Size: 1125036 MD5sum: 2cc1135afe36d6143517d57f67012ecd SHA1: 75772d0b59da92afe78cbb982bb07f7969e2c6db SHA256: 2bd78e8e24bb3a53d321c7dfbdbe6555b0339245ceb5744c3298b37555294012 SHA512: a0d68297f60b25f3090a8be9f114ab05cea171c618806f6fa284f80f8277d00bdb3c20e0eb53e698bb0d550b49cdc171962b825055da4776af2ef9bc3418ef49 Homepage: https://cran.r-project.org/package=sensiPhy Description: CRAN Package 'sensiPhy' (Sensitivity Analysis for Comparative Methods) An implementation of sensitivity analysis for phylogenetic comparative methods. The package is an umbrella of statistical and graphical methods that estimate and report different types of uncertainty in PCM: (i) Species Sampling uncertainty (sample size; influential species and clades). (ii) Phylogenetic uncertainty (different topologies and/or branch lengths). (iii) Data uncertainty (intraspecific variation and measurement error). Package: r-cran-sensitivity2x2xk Architecture: all Version: 1.01-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-biasedurn, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-sensitivity2x2xk_1.01-1.ca2404.1_all.deb Size: 66626 MD5sum: e28bd307d72325533aa5b99cb7f42e51 SHA1: 177c496bbee4a4ee6b63297137b81f6cf092d866 SHA256: 67274a87a62b381a841bef3a7896434de07b9f0369aacaecbf2b16b6b856e035 SHA512: 958fb2b225452fbc8796b226f15a28666bf62ae9ae98db14d87d7a384ff2f73386ebc4389bc601aac3c472185c204d9c2c92cb6f866703dd7d33bef7c0549f3e Homepage: https://cran.r-project.org/package=sensitivity2x2xk Description: CRAN Package 'sensitivity2x2xk' (Sensitivity Analysis for 2x2xk Tables in Observational Studies) Performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies. Package: r-cran-sensitivitycalibration Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-relaimpo, r-cran-splitstackshape, r-cran-ggrepel, r-cran-stringi, r-cran-plotly Filename: pool/dists/noble/main/r-cran-sensitivitycalibration_0.0.1-1.ca2404.1_all.deb Size: 115892 MD5sum: f9274226c8a2810643fd65925f233422 SHA1: f5dda41d12216adae105edc337d4320f89fa33be SHA256: 8b944c68cebc64ce7db4d833c891f9d970413cdad15e812b440e98d2ac7ff0ac SHA512: a44d5891cccc92b4bb6b986600c74ad2f283651f21f8f8a3fab25b2b46c1002fba1b28565121eca2330a138c0541a53d2f85047a306dc4dd22393fd8b2927038 Homepage: https://cran.r-project.org/package=sensitivityCalibration Description: CRAN Package 'sensitivityCalibration' (A Calibrated Sensitivity Analysis for Matched ObservationalStudies) Implements the calibrated sensitivity analysis approach for matched observational studies. Our sensitivity analysis framework views matched sets as drawn from a super-population. The unmeasured confounder is modeled as a random variable. We combine matching and model-based covariate-adjustment methods to estimate the treatment effect. The hypothesized unmeasured confounder enters the picture as a missing covariate. We adopt a state-of-art Expectation Maximization (EM) algorithm to handle this missing covariate problem in generalized linear models (GLMs). As our method also estimates the effect of each observed covariate on the outcome and treatment assignment, we are able to calibrate the unmeasured confounder to observed covariates. Zhang, B., Small, D. S. (2018). . Package: r-cran-sensitivitycasecontrol Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sensitivitycasecontrol_2.2-1.ca2404.1_all.deb Size: 51248 MD5sum: 9ff56c811ae8f3dfc8aec28b308a331f SHA1: a0b8935aa6cad81e03059318e33c0f997ad21f88 SHA256: 3661f013a61088389f1360e6d76936adbf43298a6eaa32f7eb00169bd8e100d2 SHA512: bd87f7da64cd1e53204f15cfbe2d7b264bca4c60964fc0340c6ecdde9453de9028ce13deeed80d4d5bcc1e719f766314e545436c45e9b2735caab67efd7c8e48 Homepage: https://cran.r-project.org/package=SensitivityCaseControl Description: CRAN Package 'SensitivityCaseControl' (Sensitivity Analysis for Case-Control Studies) Sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition. The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468). The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al. The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al. The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test. The other functions in the package are internal functions. Package: r-cran-sensitivityfull Architecture: all Version: 1.5.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sensitivityfull_1.5.6-1.ca2404.1_all.deb Size: 46380 MD5sum: fd8bb3009312110bb0a35614b1c68df7 SHA1: 4b1b7d20532a2a18196fa3b682329cf534887ad8 SHA256: 6849ce85dc2962b05850c1c143e608d3b79f883b1b25083a3ac50e6762c50365 SHA512: 524a0b05bd6d845b2665837319e986c8118c292dc091ad0884b43f38fa055d6c0a959824ac00eaec2223ebcbbd2c5f34f03f5bb23ebb8212c000dc60324fe31a Homepage: https://cran.r-project.org/package=sensitivityfull Description: CRAN Package 'sensitivityfull' (Sensitivity Analysis for Full Matching in Observational Studies) Sensitivity to unmeasured biases in an observational study that is a full match. Function senfm() performs tests and function senfmCI() creates confidence intervals. The method uses Huber's M-statistics, including least squares, and is described in Rosenbaum (2007, Biometrics) . Package: r-cran-sensitivitymult Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sensitivitymult_1.0.2-1.ca2404.1_all.deb Size: 125596 MD5sum: f82ebba0d587535d9593bceede7c02b6 SHA1: 51ec21df2fc5a30faea32181933a6b1edf163ff2 SHA256: 2c37e612ec6a64f8e028dc46200b2fadb4ff44ac1689036122808348ce104b98 SHA512: 9c90c8847b3c4a52812fdfa43caab856a62c173f553dab6a944cd3a90a6492c712e986399f228286f5bacb48c8e8e5413eb9891ece6ff7f471975006000dc79b Homepage: https://cran.r-project.org/package=sensitivitymult Description: CRAN Package 'sensitivitymult' (Sensitivity Analysis for Observational Studies with MultipleOutcomes) Sensitivity analysis for multiple outcomes in observational studies. For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) . Alternatively, attention may focus on a few principal components in the principal() function. The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function. Package: r-cran-sensitivitymv Architecture: all Version: 1.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sensitivitymv_1.4.4-1.ca2404.1_all.deb Size: 76536 MD5sum: 5916c3eee23b117b510455a2cc57210d SHA1: 385c32addfe82ea208a092a27ef504636fb8b99c SHA256: 3f761db3b2c0b008d7e2894dfd1bc103da6859d953ca7a34a38c594dcc78d4e3 SHA512: f20f864fd5c2bd63edc6ce868c57d0312d6760133cc5de41ca2a2421de0005f9c04365aaf053fc1738e2814595bfd8d79571f1df175386ef16aaa3deea68d39a Homepage: https://cran.r-project.org/package=sensitivitymv Description: CRAN Package 'sensitivitymv' (Sensitivity Analysis in Observational Studies) The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean. The main function in the package is senmv(), but amplify() and truncatedP() are also useful. The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, . Package: r-cran-sensitivitymw Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sensitivitymw_2.1-1.ca2404.1_all.deb Size: 60126 MD5sum: 9f213e8ad9dd6d1dc693a53f60c6679d SHA1: 24538339ce0231048618a3e5951520bf5ce59c88 SHA256: cbe48af6db9773bdcbb17577346c0d74fe15ed1bd9bb3985ad8955d726429f12 SHA512: 17706e505c2a71f3499d75f064adfe07ac633fbe816209181a0a1f9fe3379409482cca30e208a5a06d9daa6faf5c9f77ba0390f9dd18314996e1d99c29fc7fd7 Homepage: https://cran.r-project.org/package=sensitivitymw Description: CRAN Package 'sensitivitymw' (Sensitivity Analysis for Observational Studies Using WeightedM-Statistics) Sensitivity analysis for tests, confidence intervals and estimates in matched observational studies with one or more controls using weighted or unweighted Huber-Maritz M-tests (including the permutational t-test). The method is from Rosenbaum (2014) Weighted M-statistics with superior design sensitivity in matched observational studies with multiple controls JASA, 109(507), 1145-1158 . Package: r-cran-sensmap Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doby, r-cran-lattice, r-cran-factominer, r-cran-mcmcpack, r-cran-factoextra, r-cran-fields, r-cran-ggdendro, r-cran-glmulti, r-cran-mgcv, r-cran-plotly, r-cran-shiny, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sensmap_0.7-1.ca2404.1_all.deb Size: 120468 MD5sum: 911205b5b67a3911ed3dd3f5dfa9fe73 SHA1: e1dba6831ba31c81683fd3fcda366dde8e485944 SHA256: 646e49ba99073a52de639f7c250181e787695082e06f9211ba039f099a943418 SHA512: d5ef25e316c63af74e982a424af178b5c11d97b83a3a1bdd41acd923cc92dc512ac181e627d3997c643a016205eb39b1e46ea03976fece43f25d2916f341cb36 Homepage: https://cran.r-project.org/package=SensMap Description: CRAN Package 'SensMap' (Sensory and Consumer Data Mapping) Provides Sensory and Consumer Data mapping and analysis . The mapping visualization is made available from several features : options in dimension reduction methods and prediction models ranging from linear to non linear regressions. A smoothed version of the map performed using locally weighted regression algorithm is available. A selection process of map stability is provided. A 'shiny' application is included. It presents an easy GUI for the implemented functions as well as a comparative tool of fit models using several criteria. Basic analysis such as characterization of products, panelists and sessions likewise consumer segmentation are also made available. Package: r-cran-sensmediation Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-maxlik, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sensmediation_0.3.1-1.ca2404.1_all.deb Size: 233830 MD5sum: 466242e1f84eec2191af0ad0a4307518 SHA1: 573aa9e5018a3508d43560cadd2b2aebaccaa9bc SHA256: ad6aef5b1c8dd02b484c208d185020650ca4f32c978fe1822d4937bd56302423 SHA512: 72a2e83b5c6e25cf18de46bf27b68a3dd0099f3c9fce25326ec6f91ae20b64ba33b667e2ecfbdb556e8e5a0d59b738da29238c4ef267cc17a105eada96c709c0 Homepage: https://cran.r-project.org/package=sensmediation Description: CRAN Package 'sensmediation' (Parametric Estimation and Sensitivity Analysis of Direct andIndirect Effects) We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) and Lindmark (2022) . The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships. Package: r-cran-sensominer Architecture: all Version: 1.28-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1060 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-kernsmooth, r-cran-ggplot2, r-cran-reshape2, r-cran-algdesign, r-cran-gtools, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-sensominer_1.28-1.ca2404.1_all.deb Size: 999178 MD5sum: a8fa5b744210dc06d88a45845df78ff8 SHA1: db15d2c772a82e40396fe3d8073c42fcb3d87c0a SHA256: 423cc18f1d39136aa9c8b7ee5f4a70e970b92c2623674c6ff8e45be32f5f72cc SHA512: cef2093cb97181f74b7fe7897189f52d4f3b6d36a3e3788fc14e8413c8f98fe3802839609be9e672fa37af33ab412ce9c512dcdefd7500217594c8b9abf78cda Homepage: https://cran.r-project.org/package=SensoMineR Description: CRAN Package 'SensoMineR' (Sensory Data Analysis) Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008). Package: r-cran-sensortowerr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 922 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-httr, r-cran-httr2, r-cran-jsonlite, r-cran-lubridate, r-cran-openssl, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-gt, r-cran-gtextras, r-cran-knitr, r-cran-pkgbuild, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-rhub, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sensortowerr_1.0.1-1.ca2404.1_all.deb Size: 745438 MD5sum: 938be259e13a83a37dfcd486e2cc9e2f SHA1: 556553141b32a955878b243661369d4875eecb30 SHA256: d2656e1dfd98c30603432d2e3e9bdd48da252c8ebf8576fc9d8cc119a4ba08bb SHA512: 975742e3744c4a679fb780b0616474ea21743cb97216b6247917a151571b76115587c7a5d80f9ea04ff12804f74032864b5a5f012ebf162dcdcbc76d4e8c1ffa Homepage: https://cran.r-project.org/package=sensortowerR Description: CRAN Package 'sensortowerR' (Interface to 'Sensor Tower' Mobile App Intelligence API) Interface to the 'Sensor Tower' API for mobile app analytics and market intelligence. Provides a small, consistent set of functions to retrieve app metadata, publisher information, download and revenue estimates, active user metrics, category rankings, aggregate game market denominators, and market trends. Four core verbs ('st_metrics', 'st_rankings', 'st_app'/'st_apps', 'st_filter') cover the common workflows with standardized parameters and tidyverse-friendly output. Supports both iOS and Android app ecosystems with unified data structures for cross-platform analysis. 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Package: r-cran-sensr Architecture: all Version: 1.5-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1299 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multcomp, r-cran-mass, r-cran-numderiv Suggests: r-cran-ordinal, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sensr_1.5-3-1.ca2404.1_all.deb Size: 996164 MD5sum: 3b37d8f2957f4c191db58517d6c6809d SHA1: 5ff3fb8cd243a9ae7198d723543494bc34d0d020 SHA256: 0f78bb1d87f74c636d31969307b0654cdf94a0ac7cea92236722547eb8fff4fe SHA512: 59b1a96992bfbd5ce61dfaf71d800b01f00b293b2c3aaadf5e6ce410749ffc6223796966e3c4f484328ddc1974bfb09afaa705da56f04487edcf8127a4bb8206 Homepage: https://cran.r-project.org/package=sensR Description: CRAN Package 'sensR' (Thurstonian Models for Sensory Discrimination) Provides methods for sensory discrimination methods; duotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different, 2-AC and degree-of-difference. This enables the calculation of d-primes, standard errors of d-primes, sample size and power computations, and comparisons of different d-primes. Methods for profile likelihood confidence intervals and plotting are included. Most methods are described in Brockhoff, P.B. and Christensen, R.H.B. (2010) . Package: r-cran-sensrivastava Architecture: all Version: 2015.6.25.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-leaps, r-cran-car Filename: pool/dists/noble/main/r-cran-sensrivastava_2015.6.25.1-1.ca2404.1_all.deb Size: 151762 MD5sum: dc5bb2539e6e2ba99bc32813b0606e66 SHA1: 8cfa4f15d8e2c3073043d70b24138bd96759a960 SHA256: b7a0b1723121eebdd5ea929fde07d74b138993ed5782e87a8f86268a457aa8a5 SHA512: 2782df9e3e520a90ee3a0d250987452a6360da38f99a58d440274190401a22d0891e6d25a7c274323e42ede57445192951274a87cdfab38a1b677dfbecfa198f Homepage: https://cran.r-project.org/package=SenSrivastava Description: CRAN Package 'SenSrivastava' (Datasets from Sen & Srivastava) Collection of datasets from Sen & Srivastava: "Regression Analysis, Theory, Methods and Applications", Springer. Sources for individual data files are more fully documented in the book. 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Package: r-cran-sentiment.ai Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 970 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite, r-cran-reticulate, r-cran-roperators, r-cran-tensorflow, r-cran-tfhub, r-cran-xgboost Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-magrittr, r-cran-microbenchmark, r-cran-prettydoc, r-cran-rappdirs, r-cran-rstudioapi, r-cran-text2vec Filename: pool/dists/noble/main/r-cran-sentiment.ai_0.1.1-1.ca2404.1_all.deb Size: 874168 MD5sum: e4d760ef65a693b311e0282705b488fc SHA1: 12ef90d4e0156c40bf6fa7b0ac64a2f86f011827 SHA256: f5195584fd8cd8a71492b7c603f95e047895d599dbaadb2acc34587a3db00add SHA512: 7c7e820411ead8025140d0d2946c2701177378723e71bce15fbbb601005d28e8c98970d29d929909acd2fd13be721862ff3d53bb77fe748a1980dbb2a12c629e Homepage: https://cran.r-project.org/package=sentiment.ai Description: CRAN Package 'sentiment.ai' (Simple Sentiment Analysis Using Deep Learning) Sentiment Analysis via deep learning and gradient boosting models with a lot of the underlying hassle taken care of to make the process as simple as possible. 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Package: r-cran-sentinmixt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dosnow, r-cran-foreach, r-cran-snow, r-cran-tsdist, r-cran-tidyr, r-cran-data.table, r-cran-expint, r-cran-zipfr, r-cran-mclust, r-cran-rlist, r-cran-withr Filename: pool/dists/noble/main/r-cran-sentinmixt_1.0.0-1.ca2404.1_all.deb Size: 126648 MD5sum: ada52b57e98ba919656756e3c7b235bd SHA1: 9012e649dbfe5972c7fedca56e5d1817907fe596 SHA256: a12a86b88d223a5a7c5470b15a0851b39808076cd9d0395b45867823ce5af9d8 SHA512: f680fec5b9ef344cdc7f54e9b7c0c8350b8afbc9300080e4abd4c6cb1f0ea6761ad70c137bdaa2f96d645f6877af3ce190acb3a181380b85b716befdebbe6e6d Homepage: https://cran.r-project.org/package=SenTinMixt Description: CRAN Package 'SenTinMixt' (Parsimonious Mixtures of MSEN and MTIN Distributions) Implements parsimonious mixtures of MSEN and MTIN distributions via expectation- maximization based algorithms for model-based clustering. 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Package: r-cran-sentryr Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-stringr, r-cran-tibble, r-cran-uuid Suggests: r-cran-httptest, r-cran-mockery, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sentryr_1.1.2-1.ca2404.1_all.deb Size: 317074 MD5sum: 8177d588e48c455c2cd57a4e8acbed19 SHA1: bd3135cf6169c4cd635afd121681f8fd6c8efc78 SHA256: 7b7fd8e535178c5753ec7d89428c32b81494beb94c38bafb465205670ed6fc5f SHA512: 4bc6ca807b33f3eb008043ea4ceaca79f5c6b9d17a30f3da9a752dcaaf62f42e227105dd008c25f6d430e7b7d26a3f21e19460ee5c9dd6ac0a2c8f87beda91b8 Homepage: https://cran.r-project.org/package=sentryR Description: CRAN Package 'sentryR' (Send Errors and Messages to Sentry) Unofficial client for 'Sentry' , a self-hosted or cloud-based error-monitoring service. It will inform about errors in real-time, and includes integration with the 'Plumber' package. Package: r-cran-seofm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-seofm_0.1.0-1.ca2404.1_all.deb Size: 13296 MD5sum: 348c06b54ffc2068069abfd253b88aaa SHA1: 63e0a42ccd52c5771debc7276b3c311bf5d08a3a SHA256: 324f937582c04c97419e2ea5f1d9121b0d5b9f2f3bd5696ad0d23f528f2fe78d SHA512: 16389d354eeb88dbad8cec2083086ceb95f506694bd21dededb53309866d696786db53629ba8d3abaad55651d797353d44716911f2b4a845f21afd9271c2c4a6 Homepage: https://cran.r-project.org/package=SEofM Description: CRAN Package 'SEofM' (Standard Error of Measurement) To calculate the standard error of measurement (SEM) to assess the observer variability (inter- and intra-observer variation). The methods used in this package are referenced from Zoran B. Popović (2017) . Package: r-cran-sepa Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1924 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Suggests: r-cran-writexl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sepa_0.1.0-1.ca2404.1_all.deb Size: 1757126 MD5sum: 20c7e473243547a69a72458b8a7dbf49 SHA1: 33e3a3aa4c7d03cffa1b538ee24eae1806eb4876 SHA256: 2f350e11bfe4b82b4a7eb35708b47c0f57dd8aaac829794207ef4b17c3443e00 SHA512: f45dde6dd292d8b14ede350bf120ee2692e2b57f74f5ebc4a88e5240c965c105e48220f87b628f89ddc32508e23aa7f979fc8b07d0b6e5a68393fc3d86628eeb Homepage: https://cran.r-project.org/package=SEPA Description: CRAN Package 'SEPA' (Segment Profile Extraction via Pattern Analysis) Implements the Segment Profile Extraction via Pattern Analysis method for row-mean-centered multivariate data. Core capabilities include SVD-based row-isometric biplot construction, bias-corrected and accelerated, and percentile bootstrap confidence intervals for domain coordinates and per-person direction cosines, Procrustes alignment of bootstrap replicates across planes, parallel analysis for dimensionality selection, and segment profile reconstruction in planes defined by pairs of singular dimensions. A synthetic Woodcock-Johnson IV look-alike dataset is provided for examples and testing. The method is described in Kim and Grochowalski (2019) . Package: r-cran-sepals Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sepals_0.1.0-1.ca2404.1_all.deb Size: 302556 MD5sum: be6b437feaeb29bb16abdc70bf28cbac SHA1: 51d3af51937eeb052d2e87decbe0cb2e1936a487 SHA256: eaff9e9aed8971c7341d2c457e2369d9b1997a467ce8a359d7f831c83173857d SHA512: f44b8edd7220dfcfcae78894dc8a8fa45c3ed89500b6bc0181bddc28d8242413d4a652b5ebaee2d75748957e9c208306a0b91f84583e017588f0c8fe1d0f25ed Homepage: https://cran.r-project.org/package=SEPaLS Description: CRAN Package 'SEPaLS' (Shrinkage for Extreme Partial Least-Squares (SEPaLS)) Regression context for the Partial Least Squares framework for Extreme values. Estimations of the Shrinkage for Extreme Partial Least-Squares (SEPaLS) estimators, an adaptation of the original Partial Least Squares (PLS) method tailored to the extreme-value framework. The SEPaLS project is a joint work by Stephane Girard, Hadrien Lorenzo and Julyan Arbel. R code to replicate the results of the paper is available at . Extremes within PLS was already studied by one of the authors, see M Bousebeta, G Enjolras, S Girard (2023) . Package: r-cran-separate Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-separate_0.3.2-1.ca2404.1_all.deb Size: 68874 MD5sum: 0e129f0bcd06ce11abff28db7f3a01a7 SHA1: a72d911dc233ab06f92c329d02a58c4c86e845e0 SHA256: 262130b59c9dce2b6a1e019780f7d585b65546a1fc389ebea4c1e6003b28d3d1 SHA512: 32a30d91ff33ef763b6799b26ae6c24a1e7b9429b2abf95580a3193ef4c84ec796a2260177b51d0aac423f4c589a2adfcbb6098405cc0b0e5af2bc1238cd4c30 Homepage: https://cran.r-project.org/package=sEparaTe Description: CRAN Package 'sEparaTe' (Maximum Likelihood Estimation and Likelihood Ratio TestFunctions for Separable Variance-Covariance Structures) Maximum likelihood estimation of the parameters of matrix and 3rd-order tensor normal distributions with unstructured factor variance covariance matrices, two procedures, and for unbiased modified likelihood ratio testing of simple and double separability for variance-covariance structures, two procedures. References: Dutilleul P. (1999) , Manceur AM, Dutilleul P. (2013) , and Manceur AM, Dutilleul P. (2013) . Package: r-cran-separationplot Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-hmisc, r-cran-mass, r-cran-foreign Filename: pool/dists/noble/main/r-cran-separationplot_1.4-1.ca2404.1_all.deb Size: 35104 MD5sum: cd99daeed7de4e74a7dc7ece51099b7b SHA1: cf300e14116784fff6c956f42f52d66ca90bfe5d SHA256: c555fa7ebb8a9409a201255b781d218d02fa373e22d63e2ab0d0c1888d7a2feb SHA512: f8bb5387e66a761fd4ba25f9497dd95c7b472993b2d5d1b11d2fd36f601d3496a83394d77fd3757ed4b4e25008accfc39072d71e0133bc034377c2df56171bb6 Homepage: https://cran.r-project.org/package=separationplot Description: CRAN Package 'separationplot' (Separation Plots) Visual representations of model fit or predictive success in the form of "separation plots." See Greenhill, Brian, Michael D. Ward, and Audrey Sacks. "The separation plot: A new visual method for evaluating the fit of binary models." American Journal of Political Science 55.4 (2011): 991-1002. Package: r-cran-sephora Architecture: all Version: 0.1.31-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-geots, r-cran-ebsc, r-cran-rootsolve, r-cran-dtwclust, r-cran-foreach, r-cran-doparallel, r-cran-dplyr, r-cran-nlme, r-cran-mass, r-cran-ggnewscale, r-cran-spiralize Suggests: r-cran-tsclust, r-cran-bigmemory, r-cran-vcd Filename: pool/dists/noble/main/r-cran-sephora_0.1.31-1.ca2404.1_all.deb Size: 361538 MD5sum: 17a0ada0da38314f961d8fd9748ba732 SHA1: 1e520416411d52ced01498af2a199ec7347d708d SHA256: fee5134a2765fcbcf805b7abf9c8346bd18c566e41f8da2faba6bb22af399e56 SHA512: 7b432b8b1af038d3e32e90ff56e22d3154aafed84519e426431f985675a3ec9fa9726095b3e2bdf164fa8aad2038ba774307302e9a8c6bf9a8a94c645810b911 Homepage: https://cran.r-project.org/package=sephora Description: CRAN Package 'sephora' (Statistical Estimation of Phenological Parameters) Provides functions and methods for estimating phenological dates (green up, start of a season, maturity, senescence, end of a season and dormancy) from (nearly) periodic Earth Observation time series. These dates are critical points of some derivatives of an idealized curve which, in turn, is obtained through a functional principal component analysis-based regression model. Some of the methods implemented here are based on T. Krivobokova, P. Serra and F. Rosales (2022) . Methods for handling and plotting Earth observation time series are also provided. Package: r-cran-sepkoski Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1476 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-sepkoski_0.0.1-1.ca2404.1_all.deb Size: 1420862 MD5sum: 978f86c3c2d4230478404738cee9cde4 SHA1: 51927094623316b65af773d1d24fb8ed1e37611c SHA256: 9f91ea2cc34e2f21852259a9a12ed9dc28b8ec8a8ceb6060d204c8488c06e291 SHA512: 3a3b638e374e819d1a7e9968f541e018d827e9f178efe4785724789b7abe0265762dff61aaa0e285727619dba208fd2c1cd4b54ca7750a8f3b2c6d6aafa5974e Homepage: https://cran.r-project.org/package=sepkoski Description: CRAN Package 'sepkoski' (Sepkoski's Fossil Marine Animal Genera Compendium) Stratigraphic ranges of fossil marine animal genera from Sepkoski's (2002) published compendium. No changes have been made to any taxonomic names. However, first and last appearance intervals have been updated to be consistent with stages of the International Geological Timescale. Functionality for generating a plot of Sepkoski's evolutionary fauna is also included. For specific details on the compendium see: Sepkoski, J. J. (2002). A compendium of fossil marine animal genera. Bulletins of American Paleontology, 363, pp. 1–560 (ISBN 0-87710-450-6). Access: . Package: r-cran-septest Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-combinat, r-cran-dhsic, r-cran-fields, r-cran-get, r-cran-ggplot2, r-cran-mass, r-cran-patchwork, r-cran-reshape2, r-cran-scatterplot3d, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.model, r-cran-splancs Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-pkgdown, r-cran-knitr, r-cran-spatstat, r-cran-stpp, r-cran-rcolorbrewer, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-septest_0.0.1-1.ca2404.1_all.deb Size: 224522 MD5sum: d6cb4b385276f985ecddb87cfaaa0bab SHA1: b987b4f5ff5026d502ea56ef2f748b83843f0474 SHA256: 424ae820a3b9dac9ee285672233be8f6bd98b7774e14bbf7605b7562971c2a17 SHA512: 104800e2082c4a783cca5080ce041609f59a3aca7eb7e73ff2dd6b6670b69d897ace4368e5d6a0681ded54b1e499fd6f902e14ec9b3ec3c56787f911d7f2df91 Homepage: https://cran.r-project.org/package=SepTest Description: CRAN Package 'SepTest' (Tests for First-Order Separability in Spatio-Temporal PointProcesses) Provides statistical tools for testing first-order separability in spatio-temporal point processes, that is, assessing whether the spatio-temporal intensity function can be expressed as the product of spatial and temporal components. The package implements several hypothesis tests, including exact and asymptotic methods for Poisson and non-Poisson processes. Methods include global envelope tests, chi-squared type statistics, and a novel Hilbert-Schmidt independence criterion (HSIC) test, all with both block and pure permutation procedures. Simulation studies and real world examples, including the 2001 UK foot and mouth disease outbreak data, illustrate the utility of the proposed methods. The package contains all simulation studies and applications presented in Ghorbani et al. (2021) and Ghorbani et al. (2025) . Package: r-cran-seqalignr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 66 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plot.matrix Filename: pool/dists/noble/main/r-cran-seqalignr_0.1.1-1.ca2404.1_all.deb Size: 36210 MD5sum: c984d73f048be066e1818ebc6707c5ac SHA1: 800389c413eb615bd73dd2e64e440afbe92091a3 SHA256: 8d1d0f664df91e224bbe0eef27625301249427001165e65b6b59f5522390211b SHA512: 76fa5e10f45b9da761adf8c91d1d4271d73723322102ef440190576b0bd6977f1855a956b894587fdd6eb49fbdca1eb015b8576750c119597d54f9871303a8c3 Homepage: https://cran.r-project.org/package=SeqAlignR Description: CRAN Package 'SeqAlignR' (Sequence Alignment and Visualization Tool) Computes the optimal alignment of two character sequences. Visualizes the result of the alignment in a matrix plot. Needleman, Saul B.; Wunsch, Christian D. (1970) "A general method applicable to the search for similarities in the amino acid sequence of two proteins" . Package: r-cran-seqalloc Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-seqalloc_1.0-1.ca2404.1_all.deb Size: 58132 MD5sum: 26252179aa1129a9f45b8067a3fe6ba7 SHA1: 0ec35ececd1ac57faa60e3f2a66e37cbb7dbf606 SHA256: d4407107448c4e2a5842ec1baeabf9cde7c8e1d4c074a170e7ccf1b7b57156ed SHA512: 0e54aecfd68e5e28064130554b64e01c10122bfbe50c919164bac4debc7693d6cbf54e5b72a791a1bbdbcedb9b7f9726ceefad58c9080e7683cff163f7e6af9a Homepage: https://cran.r-project.org/package=SeqAlloc Description: CRAN Package 'SeqAlloc' (Sequential Allocation for Prospective Experiments) Potential randomization schemes are prospectively evaluated when units are assigned to treatment arms upon entry into the experiment. The schemes are evaluated for balance on covariates and on predictability (i.e., how well could a site worker guess the treatment of the next unit enrolled). Package: r-cran-seqdesign Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 517 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-seqdesign_1.2-1.ca2404.1_all.deb Size: 424436 MD5sum: 4313072dee70dca9f9d55ef0f3744ce1 SHA1: 6a360bfbbd01cfc0b56374f02852bb1ef0f8b874 SHA256: d9312332244a21aed4fe26663d46b64036bd2f4bc4d24039646886954904aaa9 SHA512: a2e371b6b9698269556c850171d20dbb88e398491db32ab49e1017a24086637c8a080052944bf1002660981377a263580f75db53681acb253f58d4bed2444064 Homepage: https://cran.r-project.org/package=seqDesign Description: CRAN Package 'seqDesign' (Simulation and Group Sequential Monitoring of RandomizedTwo-Stage Treatment Efficacy Trials with Time-to-EventEndpoints) A modification of the preventive vaccine efficacy trial design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases) is implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accounts for the issues that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given; there is interest in assessing the durability of treatment efficacy over time; and group sequential monitoring of each treatment group for potential harm, non-efficacy/efficacy futility, and high efficacy is warranted. The design divides the trial into two stages of time periods, where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including an unconditional power for intention-to-treat and per-protocol/as-treated analyses; trial duration; probabilities of the different possible trial monitoring outcomes (e.g., stopping early for non-efficacy); unconditional power for comparing treatment efficacies; and distributions of numbers of endpoint events occurring after the treatments/vaccinations are given, useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design. Package: r-cran-seqexpmatch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-checkmate, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-seqexpmatch_0.1.0-1.ca2404.1_all.deb Size: 133612 MD5sum: 854871b58bfb1fc3d6c1bcdde04f8137 SHA1: 98bfaed6f35c1ebdbdd95e4e790fb24d1d8da121 SHA256: e3ad4bdf9f28dafd6c58876311e48277bcce8cea6edffb8e93f4ce18b4a94007 SHA512: 8c8fa2922bfdd2079cd48c5425701308eb0af82ae1fcfe68de7448a77540484abedd1bd6492af7d0226b27e5026654d3334ac815de8840b403a95e32ca5aff3e Homepage: https://cran.r-project.org/package=SeqExpMatch Description: CRAN Package 'SeqExpMatch' (Sequential Experimental Design via Matching on-the-Fly) Generates the following sequential two-arm experimental designs: (1) completely randomized (Bernoulli) (2) balanced completely randomized (3) Efron's (1971) Biased Coin (4) Atkinson's (1982) Covariate-Adjusted Biased Coin (5) Kapelner and Krieger's (2014) Covariate-Adjusted Matching on the Fly (6) Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights (Naive) (7) Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights (Stepwise) and also provides the following types of inference: (1) estimation (with both Z-style estimators and OLS estimators), (2) frequentist testing (via asymptotic distribution results and via employing the nonparametric randomization test) and (3) frequentist confidence intervals (only under the superpopulation sampling assumption currently). Details can be found in our publication: Kapelner and Krieger "A Matching Procedure for Sequential Experiments that Iteratively Learns which Covariates Improve Power" (2020) . Package: r-cran-seqfeatr Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1514 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tcltk2, r-bioc-biostrings, r-cran-plotrix, r-cran-plyr, r-cran-phangorn, r-bioc-qvalue, r-bioc-widgettools, r-cran-calibrate, r-cran-ggplot2, r-cran-r2jags, r-cran-coda, r-cran-scales, r-cran-ape Suggests: r-cran-xtable Filename: pool/dists/noble/main/r-cran-seqfeatr_0.3.3-1.ca2404.1_all.deb Size: 910152 MD5sum: d0c4eb27dc87b769b8a1c97df4d47bd3 SHA1: ace3cb477999ce18b8cdf617d533cb5b0069d4fb SHA256: cb81f6ec3c871bc5e23e531e2d1e1d1cdee161bf03942d5d6cca53c4ff9c5886 SHA512: c4405fe7113fc7f91ff8d07ae1f6dea98139129f86e8c1929ee21ad11aca3dd13c93e181af5bba2dafb3a30b68c66e93c60bb6afa2ee7913c685ae288846e4c5 Homepage: https://cran.r-project.org/package=SeqFeatR Description: CRAN Package 'SeqFeatR' (A Tool to Associate FASTA Sequences and Features) Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) . Package: r-cran-seqgendiff Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-irlba, r-bioc-sva, r-cran-pdist, r-cran-matchingr, r-cran-clue Suggests: r-cran-covr, r-cran-testthat, r-bioc-summarizedexperiment, r-bioc-deseq2, r-cran-knitr, r-cran-rmarkdown, r-bioc-airway, r-bioc-limma, r-bioc-qvalue, r-bioc-edger Filename: pool/dists/noble/main/r-cran-seqgendiff_1.2.4-1.ca2404.1_all.deb Size: 397122 MD5sum: a44b95a951f4a0d913f5f87bccf609d8 SHA1: 5b7573bdc4821a235f4ea88b5bd295ce04ba2f46 SHA256: 7301714c3d562f638766d4c33edbf8cfc63bfa781e59f1d2618d7c8634cd629b SHA512: 02d458ca7a7342b81607b933046abf3a37fb4ca5a416356814fc429e82693bb174dd2f0131ae482f8858602b50ee1522b164a812c8883bf13f00aca2a99e3e56 Homepage: https://cran.r-project.org/package=seqgendiff Description: CRAN Package 'seqgendiff' (RNA-Seq Generation/Modification for Simulation) Generates/modifies RNA-seq data for use in simulations. We provide a suite of functions that will add a known amount of signal to a real RNA-seq dataset. The advantage of using this approach over simulating under a theoretical distribution is that common/annoying aspects of the data are more preserved, giving a more realistic evaluation of your method. The main functions are select_counts(), thin_diff(), thin_lib(), thin_gene(), thin_2group(), thin_all(), and effective_cor(). See Gerard (2020) for details on the implemented methods. Package: r-cran-seqhandbook Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-traminer Suggests: r-cran-factominer, r-cran-descriptio, r-cran-rcolorbrewer, r-cran-traminerextras, r-cran-weightedcluster, r-cran-ade4, r-cran-cluster, r-cran-questionr, r-cran-rmdformats, r-cran-dplyr, r-cran-purrr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-seqhandbook_0.1.2-1.ca2404.1_all.deb Size: 78834 MD5sum: 6a140fb89b938d353e001445b2e23fbb SHA1: c78691a21caa529913edeb6b542eba4d41288fd6 SHA256: 495fe2b222a26320a9fde3d9a90cfb62c7f739aee834cf22aa184df5d9b087f5 SHA512: e88912405bfae4e3a78d16ad47d34a710ccc35c6efff20653578ec718f9ae1cd9d6f2880b9aaa7ac1f24f03517d55e3626ee4b68dbbf0f91723f4c989a1284bc Homepage: https://cran.r-project.org/package=seqhandbook Description: CRAN Package 'seqhandbook' (Miscellaneous Tools for Sequence Analysis) It provides miscellaneous sequence analysis functions for describing episodes in individual sequences, measuring association between domains in multidimensional sequence analysis (see Piccarreta (2017) ), heat maps of sequence data, Globally Interdependent Multidimensional Sequence Analysis (see Robette et al (2015) ), smoothing sequences for index plots (see Piccarreta (2012) ), coding sequences for Qualitative Harmonic Analysis (see Deville (1982)), measuring stress from multidimensional scaling factors (see Piccarreta and Lior (2010) ), symmetrical (or canonical) Partial Least Squares (see Bry (1996)). Package: r-cran-seqicp Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dhsic, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-seqicp_1.1-1.ca2404.1_all.deb Size: 98838 MD5sum: f20411df10e04743f320aaff3488b978 SHA1: 9870a3621f72e096125312b0c3693c880c112215 SHA256: 32934a16c93431b8d2aea95ca419389dc6f960d082ae231bc0e109522734b874 SHA512: cb00bc86214b7744963fa868dfb002a8a84d2aa56c9daa1bfdb8f7c71da9f78b423c985b353e404974b008e4e1779e29a0f2a59d6f06b49995b850751ff97d73 Homepage: https://cran.r-project.org/package=seqICP Description: CRAN Package 'seqICP' (Sequential Invariant Causal Prediction) Contains an implementation of invariant causal prediction for sequential data. The main function in the package is 'seqICP', which performs linear sequential invariant causal prediction and has guaranteed type I error control. For non-linear dependencies the package also contains a non-linear method 'seqICPnl', which allows to input any regression procedure and performs tests based on a permutation approach that is only approximately correct. In order to test whether an individual set S is invariant the package contains the subroutines 'seqICP.s' and 'seqICPnl.s' corresponding to the respective main methods. Package: r-cran-seqimpute Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1671 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-amelia, r-cran-cluster, r-cran-dfidx, r-cran-dorng, r-cran-dosnow, r-cran-dplyr, r-cran-foreach, r-cran-mlr, r-cran-nnet, r-cran-plyr, r-cran-ranger, r-cran-rms, r-cran-stringr, r-cran-traminer, r-cran-traminerextras, r-cran-mice, r-cran-parallelly Suggests: r-cran-r.rsp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-seqimpute_2.2.1-1.ca2404.1_all.deb Size: 1024836 MD5sum: a3840fac080005cc602b845256609ede SHA1: d21251e754dbb5050980632d00bab10732c31b2e SHA256: 1c92217b2faed5be39ef8ebaead9b1d636332502d991613c866e07b8ca7e54a6 SHA512: 52e15d11c89cc17e2bb1458f50cee82092bdf571cacbf41a68352d1d6013f2a09fb74d335da321557323dfd447e76ab2bfd36fe4895c9292bcf91d505359c464 Homepage: https://cran.r-project.org/package=seqimpute Description: CRAN Package 'seqimpute' (Imputation of Missing Data in Sequence Analysis) Multiple imputation of missing data in a dataset using MICT or MICT-timing methods. The core idea of the algorithms is to fill gaps of missing data, which is the typical form of missing data in a longitudinal setting, recursively from their edges. Prediction is based on either a multinomial or random forest regression model. Covariates and time-dependent covariates can be included in the model. Package: r-cran-seqmade Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-seqmade_1.0-1.ca2404.1_all.deb Size: 41584 MD5sum: 659a0446e076ba1547a79c374473d0b5 SHA1: 0cf598942dc4ef94032f54ba49d1a00c1cd50f91 SHA256: 1d7c91fdc6b9b0e1c1180e5d45a3cea1fa831feb703c8948bfe47525cd8f4c9b SHA512: 0d2e881b32e0ac15a9237678641ac03e5fd1b366e5cb2f1453202f1b8c56df9d14f6c61fdf6e9ceed3936503dfb7f17c5fed2392bf705d02888c9dbebb1c8af4 Homepage: https://cran.r-project.org/package=SeqMADE Description: CRAN Package 'SeqMADE' (Network Module-Based Model in the Differential ExpressionAnalysis for RNA-Seq) A network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq. Package: r-cran-seqmagick Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings, r-cran-magrittr, r-cran-yulab.utils Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-genomicalignments, r-bioc-genomicranges, r-bioc-iranges, r-bioc-muscle, r-bioc-rsamtools, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-seqmagick_0.1.8-1.ca2404.1_all.deb Size: 204730 MD5sum: 4b5e2c26275108c522f11a59b70acec8 SHA1: f89a7c6dbdb60f34c395276fdc883b863985d988 SHA256: 8819f1310e227ffc8075ec4449b04affac5b6211f7a5e1d7e7c76aae60ee465c SHA512: c09b791e55cfa1b540da72778a1dd645b46cd0889c12c772852cd6dd91312dd918e02e7bd85f87bc9a25faf6bb7d12c4c49ec0bb59735225b84048fc2429299b Homepage: https://cran.r-project.org/package=seqmagick Description: CRAN Package 'seqmagick' (Sequence Manipulation Utilities) Tools for reading and writing biological sequences in multiple formats, including 'FASTA', 'PHYLIP', 'CLUSTAL', 'STOCKHOLM', 'MEGA' and 'GenBank'. Supports interleaved and sequential layouts where applicable, converts between formats, and manipulates sequence sets (e.g., filtering by patterns and computing consensus sequences from alignments). Also includes functions to download nucleotide records from NCBI by accession. Package: r-cran-seqmon Architecture: all Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-seqmon_2.5-1.ca2404.1_all.deb Size: 99028 MD5sum: d37e8b3374577b101f3b3e0ee6435a3a SHA1: e69751cc6cb15f10c4666f953822108a5e68ab27 SHA256: a8fb548621206e57a9b45a6c59a79bb231e34684c810d3f8cd9540f56215c6aa SHA512: d99a856ab1648dbe7d748502c79cb4793a1069c124a663587125a25ab2193f1102265937c6967ce7ae528d31f05258fbae167b23ef6fdc3289e08956db467ff3 Homepage: https://cran.r-project.org/package=seqmon Description: CRAN Package 'seqmon' (Group Sequential Design Class for Clinical Trials) S4 class object for creating and managing group sequential designs. It calculates the efficacy and futility boundaries at each look. It allows modifying the design and tracking the design update history. 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Package: r-cran-sfarrow Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 762 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-arrow, r-cran-jsonlite, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sfarrow_0.4.1-1.ca2404.1_all.deb Size: 263638 MD5sum: 16ec8c350593368c78a07590ac0677f5 SHA1: 627af5f3d9b09791db43b39bede1393c7a05bcf5 SHA256: 69b90cf4543be3bc4f86f837fe71c306ee472fded8449a9b074d446462453732 SHA512: 4c0bfc7c4199219b6b8cf2f99a58ada414acefedc20f238029b988a157248548b9c2272a78e88f2091ea401c538ded14f265e8667476541b7623a9a8c5617e0b Homepage: https://cran.r-project.org/package=sfarrow Description: CRAN Package 'sfarrow' (Read/Write Simple Feature Objects ('sf') with 'Apache' 'Arrow') Support for reading/writing simple feature ('sf') spatial objects from/to 'Parquet' files. 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Package: r-cran-sfd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2569 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-cli, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfd_0.1.0-1.ca2404.1_all.deb Size: 2450210 MD5sum: 55744fea5c7106e2f93c4e1c6d479c38 SHA1: 876e3610b7d05789697b76c9a51f28f9ee798615 SHA256: 3e308cb9cb7acf9a1db56ab059632301f4131a72dfb6bf265f336de1c5c5e36f SHA512: 8ce4060304dd97c05baaecc183b0033a4dda947fb59622b37edfb028b9d6e0ea29baba83544a3f8ddb6184ebc6913a8a3583999652cc05a0515858f81221b6aa Homepage: https://cran.r-project.org/package=sfd Description: CRAN Package 'sfd' (Space-Filling Design Library) A collection of pre-optimized space-filling designs, for up to ten parameters, is contained here. Functions are provided to access designs described by Husslage et al (2011) and Wang and Fang (2005) . 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The sfFDR framework adapts the fFDR to leverage informative data from multiple sets of GWAS summary statistics to increase power in study while accommodating for linkage disequilibrium. sfFDR provides estimates of key FDR quantities in a significance analysis such as the functional local FDR and $q$-value, and uses these estimates to derive a functional $p$-value for type I error rate control and a functional local Bayes' factor for post-GWAS analyses (e.g., fine mapping and colocalization). Package: r-cran-sfflhd Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doe.base, r-cran-conf.design, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfflhd_0.1.2-1.ca2404.1_all.deb Size: 194660 MD5sum: 5ff1664ad48dc8d694c48e4fd46ccdf2 SHA1: 0010e9e1e75a903933a0a54562ea413102521c86 SHA256: 4604313ed2f4887b6fce43e9c58a8af4a258181d8a8735ae6a0283f3326e5769 SHA512: 5b61c79f3d781bc26b2bececd6c5d5691daaaf8d9ec3d4adc463ad868fc12a7123b3a886ea816ea80fed89f465c112f6d2bab21ac7581775425da7c83f3c01fb Homepage: https://cran.r-project.org/package=sFFLHD Description: CRAN Package 'sFFLHD' (Sequential Full Factorial-Based Latin Hypercube Design) Gives design points from a sequential full factorial-based Latin hypercube design, as described in Duan, Ankenman, Sanchez, and Sanchez (2015, Technometrics, ). Package: r-cran-sfhelper Architecture: all Version: 0.2.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl, r-cran-rjson, r-cran-mapview, r-cran-sf, r-cran-stringr, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfhelper_0.2.2.0-1.ca2404.1_all.deb Size: 35180 MD5sum: 7a0b11e46b502548dcf4e00c1b4673cc SHA1: 28d59cf31b9385ef63909c4b7dcc1d7fbf77297c SHA256: d7edfbad930cba0fbd6b4be2116edd8896e13b6091521d383d52d3d84ac4bb57 SHA512: 611685358915f0a8871beab6a6bbb8f7305989cadfe9de0e088e36ab54003448cd01942f3a43a3fdfdb623b592cc963a2b16d70ab1de6cad2d85e629dd9b5525 Homepage: https://cran.r-project.org/package=sfhelper Description: CRAN Package 'sfhelper' (Repair Functions for 'sf' Package Objects) A group of functions that support the 'sf' package, focused primarily on repairing polygons that break when re-projected. Package: r-cran-sfhnv Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-future.apply, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfhnv_0.1.0-1.ca2404.1_all.deb Size: 54120 MD5sum: cf98dbd738175d7f02b89bc0e6a826bd SHA1: d208f117972c750a62169156fbcbd0674922eece SHA256: c88a17107e9eb56e124cedbe975abc3fe0e10eed82eccbeb69c4688d0fa2b23e SHA512: 017ce1cad31f5dce15f946588de719dec30888d52788aa6ee6f0021b913c1dfa2bc4dda7c8aae87fb02f8d620f2440f9525a2fcae88bb11216a49c8ea1ab32e8 Homepage: https://cran.r-project.org/package=SFHNV Description: CRAN Package 'SFHNV' (Structural Forest for the Heterogeneous Newsvendor Model) Implements the structural forest methodology for the heterogeneous newsvendor model. The package provides tools to prepare data, fit honest newsvendor trees and forests, and obtain point and distributional predictions for demand decisions under uncertainty. Package: r-cran-sfhotspot Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1476 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-rlang, r-cran-sf, r-cran-spatialkde, r-cran-spdep, r-cran-tibble Suggests: r-cran-testthat, r-cran-knitr, r-cran-lubridate, r-cran-quarto, r-cran-ggspatial Filename: pool/dists/noble/main/r-cran-sfhotspot_1.0.0-1.ca2404.1_all.deb Size: 1261484 MD5sum: 2b65268678969f8c3262711b0ad8b149 SHA1: 5f26d70bb2e12728766cd0553fa45a7da1403169 SHA256: 91b034edfd1a878b8efe346692445fddf1bb513b604fbd1b0d27c8c80dc7d8ef SHA512: 2a09944aae1af923c1b2a81cb35f8c69bb55c4752fdeeb1015385a1b2901479967ad4d746d7471229cdcdb6f98f9bec800904a3adeba0a2a12e57dd1e53442f2 Homepage: https://cran.r-project.org/package=sfhotspot Description: CRAN Package 'sfhotspot' (Hot-Spot Analysis with Simple Features) Identify and understand clusters of points (typically representing the locations of places or events) stored in simple-features (SF) objects. 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Package: r-cran-sfinx Architecture: all Version: 1.7.99-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfinx_1.7.99-1.ca2404.1_all.deb Size: 70802 MD5sum: 7cedb741bd0e530d6131087e70a0eec0 SHA1: f6ca8b866296674e8638fc8ad5e23236d791f5dd SHA256: e777c7f718e4d68b9e97657e4df0f796bb932782d4130829455abbbcd20909b4 SHA512: 8e2cdb543c14b486fbb925a68772d57ce4f4a50e8429a721c03b454554414035201f5a9e78e1e823a63f29ff4081ac6f208956b3bf9baf5a60068fab28836b5a Homepage: https://cran.r-project.org/package=sfinx Description: CRAN Package 'sfinx' (Straightforward Filtering Index for AP-MS Data Analysis (SFINX)) The straightforward filtering index (SFINX) identifies true positive protein interactions in a fast, user-friendly, and highly accurate way. It is not only useful for the filtering of affinity purification - mass spectrometry (AP-MS) data, but also for similar types of data resulting from other co-complex interactomics technologies, such as TAP-MS, Virotrap and BioID. SFINX can also be used via the website interface at . Package: r-cran-sfislands Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4714 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-sf, r-cran-spdep, r-cran-stringr, r-cran-tidyr, r-cran-broom.mixed, r-cran-lifecycle Suggests: r-cran-mgcv, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfislands_1.1.2-1.ca2404.1_all.deb Size: 2835844 MD5sum: 07c736cddce35b7a579c21bed845bfc6 SHA1: 1612eb6bc2fbf7e6e3c893c665acc0c3301b40ff SHA256: 1af989fb3f0a3105bbe4e461a06d2f4416b553cf899975f6a7428ee99999b648 SHA512: 435c7a7cef0492051845b9cb73ab962a2c8ad75acce18e3d40ed15b8bce06f450f2546e7a88def6be1513ed44a79a11e37fafe1f0dbd98c6a5a6e4f7e53fd16b Homepage: https://cran.r-project.org/package=sfislands Description: CRAN Package 'sfislands' (Streamlines the Process of Fitting Areal Spatial Models) Helpers for addressing the issue of disconnected spatial units. It allows for convenient adding and removal of neighbourhood connectivity between areal units prior to modelling, with the visual aid of maps. Post-modelling, it reduces the human workload for extracting, tidying and mapping predictions from areal models. Package: r-cran-sfm Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1486 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-sopc, r-cran-matrixcalc, r-cran-sn, r-cran-psych Suggests: r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-sfm_0.2.1-1.ca2404.1_all.deb Size: 1485132 MD5sum: 2946c7c867cf6b6107205c41b9199e88 SHA1: af3b45648ef1abb32cccf6e70697ae0ddb534126 SHA256: 2fba7ae2926fc0cacab5773740ef15baef4cb2e82bb1a94c0fb7c4dce31b3c7c SHA512: c8fa0d595579a8e868e30202feb5841cba44ae598ef4ed4c76ec5b879e2f49373aea3ce1f5d87cdef223feddd4e34c22b0bdd16e9e69c44273fffe376d3ff30d Homepage: https://cran.r-project.org/package=SFM Description: CRAN Package 'SFM' (A Package for Analyzing Skew Factor Models) Generates Skew Factor Models data and applies Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Projected Principal Component (PPC), Perturbation Principal Component (PPC), Stochastic Approximation Principal Component (SAPC), Sparse Principal Component (SPC) and other PC methods to estimate model parameters. It includes capabilities for calculating mean squared error, relative error, and sparsity of the loading matrix.The philosophy of the package is described in Guo G. (2023) . Package: r-cran-sfnetworks Architecture: all Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4320 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-crayon, r-cran-dplyr, r-cran-igraph, r-cran-lwgeom, r-cran-rlang, r-cran-sf, r-cran-sfheaders, r-cran-tibble, r-cran-tidygraph, r-cran-units Suggests: r-cran-dbscan, r-cran-fansi, r-cran-ggplot2, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown, r-cran-s2, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-testthat, r-cran-tsp Filename: pool/dists/noble/main/r-cran-sfnetworks_0.6.6-1.ca2404.1_all.deb Size: 2797802 MD5sum: 90d68e267c29f6eb9bb541457469ffdb SHA1: ffedfc20a2bae1a6522b44dd4ee73fcd289e8928 SHA256: 98364f2d0843de1cdeec168606ededd544a9dc47bcd0c4f95b9154cc3a29650d SHA512: 7b0ec6c06a16304224a186bde444f2d914e61da5687e91334c0913cf1fcfb815904f1daecb5dcc96ea00ece6b3aa8014f965cf08434fe0270f2d4ff2747388aa Homepage: https://cran.r-project.org/package=sfnetworks Description: CRAN Package 'sfnetworks' (Tidy Geospatial Networks) Provides a tidy approach to spatial network analysis, in the form of classes and functions that enable a seamless interaction between the network analysis package 'tidygraph' and the spatial analysis package 'sf'. Package: r-cran-sfo Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5908 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-magrittr, r-cran-plotly, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-sfo_0.1.2-1.ca2404.1_all.deb Size: 1990572 MD5sum: bda9e29875f74ee0d95794cae2c0b159 SHA1: 619f4f1332a696f1e332101efe04cb4b84bd0f81 SHA256: 660e73d52ff44bdfc9d6d2615aa8b633c0264af04fa8ac6dfc8d06253b479003 SHA512: 3c40cbe9051e11b9d9da665576feda575592423f3232019769e7adf1277a5cd316d1bf8db58cec3fe01196d3d4d4ccf8869d32ba7bfc0bf9556ca2a69c25b503 Homepage: https://cran.r-project.org/package=sfo Description: CRAN Package 'sfo' (San Francisco International Airport Monthly Air Passengers) Provides monthly statistics on the number of monthly air passengers at SFO airport such as operating airline, terminal, geo, etc. Data source: San Francisco data portal (DataSF) . Package: r-cran-sfocds Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-gtools Filename: pool/dists/noble/main/r-cran-sfocds_1.2.0-1.ca2404.1_all.deb Size: 53098 MD5sum: c30a1cba8e5a8e271fbd4559c28ccfe2 SHA1: db59d066a0dd157770e150e78317e75bfc3ce069 SHA256: a4a48304df395cc7b54cced779b9a69a61db5dffad251aa9cdbb4c27623506a0 SHA512: 753a82e1fc893bbe947338776d2e0aac9e4974bb1e96026613ab32e6b2f92576c44c17c0968423c92b84fb633559f042623478a02d6990751f09577bf0b7f31c Homepage: https://cran.r-project.org/package=SFOCDs Description: CRAN Package 'SFOCDs' (Space Filling Optimal Covariate Designs) We have designed this package to address experimental scenarios involving multiple covariates. It focuses on construction of Optimal Covariate Designs (OCDs), checking space filling property of the developed design. The primary objective of the package is to generate OCDs using four methods viz., M array method, Juxtapose method, Orthogonal Integer Array and Hadamard method. The package also evaluates space filling properties of both the base design and OCDs using the MaxPro criterion, providing a meaningful basis for comparison. In addition, it includes tool to visualize the spread offered by the design points in the form of scatterplot, which help users to assess distribution and coverage of design points. 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Package: r-cran-sfreapportion Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-sp Suggests: r-cran-areal, r-cran-populr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sfreapportion_0.2.0-1.ca2404.1_all.deb Size: 587270 MD5sum: dca8023da0ea959f72bacc42952f3c3f SHA1: e518f4802c80688d7b51b9b6e3c06617c237b5fb SHA256: 683faddfbecef7e137d38b01d5545c7fa68b0996e1b683b105e71fc6ecfa99b7 SHA512: 54fd5a96cf5c29bc8fe7993d529208c31aa233dc15f8ea7f62ff2e683debceb600ddf4ab3076a7fcce8a49091f0256ac80a11391a938d389aefaba41773d4b09 Homepage: https://cran.r-project.org/package=sfReapportion Description: CRAN Package 'sfReapportion' (Reapportion Data from One Geography to Another) A port of the 'spReapportion' package, using Simple Features in order to lose the dependencies to the retired 'maptools' and 'rgeos' packages. 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For graphics, have pretty (Log-scale) axes eaxis(), an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole(). Package: r-cran-sft Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 990 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-suppdists Filename: pool/dists/noble/main/r-cran-sft_2.4-1.ca2404.1_all.deb Size: 927988 MD5sum: 5e5714c6481c890cfe144f4f7d05d73d SHA1: 7b796884d48e8746897475c17489b2382c02a28e SHA256: 80c4818f8a37467668c4165cf6c7d90d7ad1859b15d37105c3d31a945a79c2c1 SHA512: c6900e2fb8535b0896f20d3057dc9b4ca984c6d183cc554839a3f4c75c53e24f3e802343f6bc1edeb2a49cae225b07b58fe8d8179eba8499b4ec19574c0ceaca Homepage: https://cran.r-project.org/package=sft Description: CRAN Package 'sft' (Functions for Systems Factorial Technology Analysis of Data) A series of tools for analyzing Systems Factorial Technology data. This includes functions for plotting and statistically testing capacity coefficient functions and survivor interaction contrast functions. Houpt, Blaha, McIntire, Havig, and Townsend (2013) provide a basic introduction to Systems Factorial Technology along with examples using the sft R package. 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It is a new distribution on the simplex (i.e. on the space of compositions or positive vectors with sum of components equal to 1). The Dirichlet distribution can be constructed from a random vector of independent Gamma variables divided by their sum. The SGB follows the same construction with generalized Gamma instead of Gamma variables. The Dirichlet exponents are supplemented by an overall shape parameter and a vector of scales. The scale vector is itself a composition and can be modeled with auxiliary variables through a log-ratio transformation. Graf, M. (2017, ISBN: 978-84-947240-0-8). See also the vignette enclosed in the package. Package: r-cran-sgbj Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gbj, r-cran-survival Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sgbj_0.1.1-1.ca2404.1_all.deb Size: 29474 MD5sum: 17c777ff8de483ac8d0f4c25eb1ea0ad SHA1: 16945f34da7f89a70bde1bea019009c7a40d2d7f SHA256: 5f7df9cb507db5fd7fe8ea65132fa2afca97d19ac801212878896e15f51678d9 SHA512: 0cfa7aa4bc4b93b2841bd53604911fb5c560314893d7cde5f7f04f0baa24e80a511f81083108d43a270d942200dbcfbffe935a1a9910653c4d5f38dc0dfb5e55 Homepage: https://cran.r-project.org/package=sGBJ Description: CRAN Package 'sGBJ' (Survival Extension of the Generalized Berk-Jones Test) Implements an extension of the Generalized Berk-Jones (GBJ) statistic for survival data, sGBJ. 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Version of PLS Discriminant analysis is also provided. Package: r-cran-sgpv Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sgpv_1.1.0-1.ca2404.1_all.deb Size: 414498 MD5sum: fb53183bd66633926e6572d46e772943 SHA1: f3254faf3e3adcc3cf5dded8672a429cefa9c869 SHA256: a10a8f9b163b7a115d92547bdc95d26ab32f501d9cb14f30f89bd618c03d37fb SHA512: c361d284a5368abc4b109762f11e721629f39c5e95f9f99bf759615ff3734bc2adbd75ef48f94505acd550133788531058b038eac0532d8a6e00d7e25db7e0dc Homepage: https://cran.r-project.org/package=sgpv Description: CRAN Package 'sgpv' (Calculate Second-Generation p-Values and Associated Measures) Computation of second-generation p-values as described in Blume et al. (2018) and Blume et al. (2019) . 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While several 'R' packages have been developed to interface 'sigma.js', all were developed for v1.x.x and none have migrated to v2.4.0 nor are they planning to. This package builds upon the 'sigmaNet' package, and users familiar with it will recognize the similar design approach. Two extensions have been added to the classic 'sigma.js' visualizations by overriding the underlying 'JavaScript' code, enabling to draw a frame around node labels, and to display labels on multiple lines by parsing line breaks. Other additional functionalities that did not require overriding 'sigma.js' code include toggling node visibility when clicked using a node attribute and highlighting specific edges. 'sigma.js' is currently preparing a stable release v3.0.0, and this package plans to update to it when it is available. 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Primary functions provide means to generate data-driven stratifications & methods for allocating samples. Intermediate functions for calculating and extracting important information about input covariates and samples are also included. Processing outcomes are intended to help forest and environmental management practitioners better optimize field sample placement as well as assess and augment existing sample networks in the context of data distributions and conditions. ALS data is the primary intended use case, however any rasterized remote sensing data can be used, enabling data-driven stratifications and sampling approaches. 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By analysing temperature-dependent equilibrium data, thermodynamic interpretation expands adsorption studies beyond conventional isotherm fitting, offering deeper insight into underlying mechanisms and surface–solute interactions. Such an approach typically involves evaluating equilibrium coefficients across multiple temperatures and non-temperature treatments, deriving thermodynamic parameters using established thermodynamic relationships, and determining delta G as a temperature-specific indicator of adsorption favourability. This analytical pathway is widely applicable across environmental science, soil science, chemistry, materials science, and engineering, where reliable assessment of sorption behaviour is critical for examining contaminant retention, nutrient dynamics, and the behaviour of natural and engineered surfaces. By focusing specifically on thermodynamic inference, this framework complements existing adsorption isotherm-fitting packages such as “AdIsMF” , and strengthens the scientific basis for interpreting adsorption energetics in both research and applied contexts. Details can be found in Roy et al. (2025) . Package: r-cran-shannon Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vares, r-cran-extradistr Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-shannon_0.2.0-1.ca2404.1_all.deb Size: 372312 MD5sum: da4f357ac36832f3da1ea3ddad505998 SHA1: 921fec04560f8de64d0f2fbb8a0a15f380248453 SHA256: a137ee50080c0601eb2a08c48d0016a1b1a946464c833ada9b792c39737b46c9 SHA512: 929e33c9c9fd99528e6678ff1a8a4731b58efed990542b1fffe494adf30088d96ecb64ff8050d000b7908e4444fe0f276eecd09418c6fb7924c71e5026118ee0 Homepage: https://cran.r-project.org/package=shannon Description: CRAN Package 'shannon' (Computation of Entropy Measures and Relative Loss) The functions allow for the numerical evaluation of some commonly used entropy measures, such as Shannon entropy, Rényi entropy, Havrda and Charvat entropy, and Arimoto entropy, at selected parametric values from several well-known and widely used probability distributions. Moreover, the functions also compute the relative loss of these entropies using the truncated distributions. Related works include: Awad, A. M., & Alawneh, A. J. (1987). Application of entropy to a life-time model. IMA Journal of Mathematical Control and Information, 4(2), 143-148. . Package: r-cran-shapboost Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xgboost, r-cran-shapforxgboost, r-cran-caret, r-cran-matrix Suggests: r-cran-flare, r-cran-survival Filename: pool/dists/noble/main/r-cran-shapboost_1.0.3-1.ca2404.1_all.deb Size: 178700 MD5sum: 998e789ceff0655aa59e0e22ac7e51e0 SHA1: 5df1c2b3185ef366246210e0142f455297e41656 SHA256: 547601c33bf3b94a0af4e1152a2d86b30fc11a5f129fcd913f78baffeb314178 SHA512: eb8f9af7f00417cc4bbc9c62dc6dd05646ee8ba131f8711b81f13bf1788455c71ce8db3f85cacf6863e4e0ab0c15cecb07bb114700c12695ee126fd8d42f7d12 Homepage: https://cran.r-project.org/package=SHAPBoost Description: CRAN Package 'SHAPBoost' (The SHAPBoost Feature Selection Algorithm) The implementation of SHAPBoost, a boosting-based feature selection technique that ranks features iteratively based on Shapley values. Package: r-cran-shapdoe Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtools Filename: pool/dists/noble/main/r-cran-shapdoe_1.0.0-1.ca2404.1_all.deb Size: 55714 MD5sum: 97c6939a51d9617cadc5cc1db613bd4c SHA1: 3ff1326ba6effdf391110f7bc0dff6453a9fc75e SHA256: b854a2868db19f89bc498b185b62b347646d6f41de883732a138432af85fcda8 SHA512: 7b4422a023f7a01f41eaa3c64393b86f9165766bf0c715f67373c2a4a9c1426fb6985ae58fcc36d238976e07215870fc0e8a351fca105c3663ee2a1c39662775 Homepage: https://cran.r-project.org/package=ShapDoE Description: CRAN Package 'ShapDoE' (Approximation of the Shapley Values Based on ExperimentalDesigns) Estimating the Shapley values using the algorithm in the paper Liuqing Yang, Yongdao Zhou, Haoda Fu, Min-Qian Liu and Wei Zheng (2024) "Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs". You provide the data and define the value function, it retures the estimated Shapley values based on sampling methods or experimental designs. Package: r-cran-shape Architecture: all Version: 1.4.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-shape_1.4.6.1-1.ca2404.1_all.deb Size: 747218 MD5sum: a5bec55064e249abbbdeac8f21e4e64a SHA1: 76c297f95c8ebac66eee80e230d9b6c03dc0a948 SHA256: ca2c5d4815525f3972a41d64fd7c2d9cae2b6deaaa61d22af3b876ca2edb231e SHA512: 9615d301508297fa6c51726c014f7f098d7465e22160a58e47146a1ae1215725160ec81acc3fc5ee563a5150a38682c3387309360bef279ca0556fddcbccc8a0 Homepage: https://cran.r-project.org/package=shape Description: CRAN Package 'shape' (Functions for Plotting Graphical Shapes, Colors) Functions for plotting graphical shapes such as ellipses, circles, cylinders, arrows, ... Package: r-cran-shapechange Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coneproj, r-cran-quadprog Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-shapechange_1.5-1.ca2404.1_all.deb Size: 203496 MD5sum: 7d794f74dbb58482ce87616814b2db5c SHA1: 1bb1d9a3f72d4e5cc5bfefa0b5927ea19b41c99a SHA256: 1701a04a9515df602705d59a7d0974c8c750df8b61cb9b1e1e32a44d18d30a12 SHA512: 0553305c7d4d728efc9478ea02c9819a222946e618af6471dd1ab0f38518ea19b9d61d563a4b38349e5bab1f4010285f0f1dfe8b07d5105ef2081dd94f1fba62 Homepage: https://cran.r-project.org/package=ShapeChange Description: CRAN Package 'ShapeChange' (Change-Point Estimation using Shape-Restricted Splines) In a scatterplot where the response variable is Gaussian, Poisson or binomial, we consider the case in which the mean function is smooth with a change-point, which is a mode, an inflection point or a jump point. The main routine estimates the mean curve and the change-point as well using shape-restricted B-splines. An optional subroutine delivering a bootstrap confidence interval for the change-point is incorporated in the main routine. Package: r-cran-shapefiles Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreign Filename: pool/dists/noble/main/r-cran-shapefiles_0.7.2-1.ca2404.1_all.deb Size: 52030 MD5sum: 8b25ed57eb171ef67255e3803ff3691a SHA1: ab8ff214b0edd2f3feadaa153da85b65f9cc2b24 SHA256: c69cd09c143b5c87e2cf4d3bc143f40bc95fdc9d43bbf302a39942e4ca2899d9 SHA512: 80581d9eebf876528dfb17c67c3a75a332ec733358da0f589571663df09569632cce3840a6719a9d5c53b86dd485c8a00283acbaff87cf6545360c8f5d5d163d Homepage: https://cran.r-project.org/package=shapefiles Description: CRAN Package 'shapefiles' (Read and Write ESRI Shapefiles) Functions to read and write ESRI shapefiles. Package: r-cran-shapena Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm, r-cran-mice Filename: pool/dists/noble/main/r-cran-shapena_0.0.2-1.ca2404.1_all.deb Size: 84072 MD5sum: afb1a23a90026761706222845a79cdfd SHA1: 7ea822848ad7d8cab42f823733252e922ba0e2f4 SHA256: 5264a12255ead14f1234efd7b518f97a95da906c57c1cf1ebd0cb08f1c1c314b SHA512: 44574cee65becac5e851f12861ac31a1c0a2e1ca40a6e173f7f8da9cc58d0da78a6ab058b6082f76bb32cc32ca1f6544fd4285b929b0b3a280f92f46ab690d9a Homepage: https://cran.r-project.org/package=shapeNA Description: CRAN Package 'shapeNA' (M-Estimation of Shape for Data with Missing Values) M-estimators of location and shape following the power family (Frahm, Nordhausen, Oja (2020) ) are provided in the case of complete data and also when observations have missing values together with functions aiding their visualization. Package: r-cran-shapepattern Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-igraph, r-cran-terra, r-cran-landscapemetrics, r-cran-raster Filename: pool/dists/noble/main/r-cran-shapepattern_3.1.0-1.ca2404.1_all.deb Size: 196426 MD5sum: 9576ee2ea847cc37f74858b3dd4201f2 SHA1: 93341b4ec9d17db2d53bf9f486e49f5c75ca1729 SHA256: 3831c7651512fbfc53d86f2c11caa9c51683c8e65d5e0542c159e0bfb3bd868f SHA512: 25d7a3acc852bc30d18f8f1f6adad79747e3427251d505655b8b8c0215bd577c461ca2f6ac7fcc71d409005a3e85f5bc8e3ac4126fb503ab79a2658f6582b4bf Homepage: https://cran.r-project.org/package=ShapePattern Description: CRAN Package 'ShapePattern' (Tools for Analyzing Shapes and Patterns) This is an evolving and growing collection of tools for the quantification, assessment, and comparison of shape and pattern. This collection provides tools for: (1) the spatial decomposition of planar shapes using 'ShrinkShape' to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking; the spectra of results are returned in graphic and tabular formats (Remmel 2015) , (2) simulating landscape patterns, (3) provision of tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes. Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made. The code permits the analysis of single maps or pairs of maps (Remmel and Fortin 2013) , (4) counting the number of each first-order pattern element and converting that information into both frequency and empirical probability vectors (Remmel 2020) , and (5) computing the porosity of raster patches . NOTE: This is a consolidation of existing packages ('PatternClass', 'ShapePattern') to begin warehousing all shape and pattern code in a common package. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented. Note that all future developments will appear in this package and that 'PatternClass' will eventually be archived. Package: r-cran-shaper Architecture: all Version: 1.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3611 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plotrix, r-cran-jpeg, r-cran-pixmap, r-cran-wavethresh, r-cran-vegan, r-cran-mass Filename: pool/dists/noble/main/r-cran-shaper_1.0-2-1.ca2404.1_all.deb Size: 3623264 MD5sum: 8a0b4b1bd20f04900e1a7ff30da88edb SHA1: 39d9cfc8b938f5a7e1f996fa9b3f42d29cc891d0 SHA256: 70c8fc46236c5f8a42ffec9f638951e922a5e1dc00ca31c3cad6a811b4f5af3b SHA512: d2d209018ecf0ba10266d50776b59bc9c6fc9703f87afa967640200062c7ff9708cacfd9c137ee167f9690ac6f3d71b4d30f37821212fa1b70c3cc241dd0441f Homepage: https://cran.r-project.org/package=shapeR Description: CRAN Package 'shapeR' (Collection and Analysis of Otolith Shape Data) Studies otolith shape variation among fish populations. Otoliths are calcified structures found in the inner ear of teleost fish and their shape has been known to vary among several fish populations and stocks, making them very useful in taxonomy, species identification and to study geographic variations. The package extends previously described software used for otolith shape analysis by allowing the user to automatically extract closed contour outlines from a large number of images, perform smoothing to eliminate pixel noise described in Haines and Crampton (2000) , choose from conducting either a Fourier or wavelet see Gençay et al (2001) transform to the outlines and visualize the mean shape. The output of the package are independent Fourier or wavelet coefficients which can be directly imported into a wide range of statistical packages in R. The package might prove useful in studies of any two dimensional objects. Package: r-cran-shaperotator Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plot3d Suggests: r-cran-geomorph Filename: pool/dists/noble/main/r-cran-shaperotator_0.1.0-1.ca2404.1_all.deb Size: 91006 MD5sum: f5de2c14e4bfef50a6628bd774ea3f18 SHA1: 2a05aa799ea3829b095172e031f9e7f08cc10964 SHA256: bde2511cb66d794282c637d80cf36f1af1ef8980809cf7e24de238e7603ab09c SHA512: 891ed6dca5ea59d83e29633ac7d2106b98dd2b425eeab45e42797dfa10d243a27d57ab9ebd81821dbfd05c6d6a10b05142532f638bcdc49b69c173e310412d42 Homepage: https://cran.r-project.org/package=ShapeRotator Description: CRAN Package 'ShapeRotator' (Standardised Rigid Rotations of Articulated Three-DimensionalStructures) Here we describe a simple geometric rigid rotation approach that removes the effect of random translation and rotation, enabling the morphological analysis of 3D articulated structures. Our method is based on Cartesian coordinates in 3D space so it can be applied to any morphometric problem that also uses 3D coordinates. See Vidal-García, M., Bandara, L., Keogh, J.S. (2018) . Package: r-cran-shapes Architecture: all Version: 1.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 995 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-minpack.lm, r-cran-scatterplot3d, r-cran-rgl, r-cran-mass, r-cran-fitdistrplus Filename: pool/dists/noble/main/r-cran-shapes_1.2.8-1.ca2404.1_all.deb Size: 899970 MD5sum: 5a2f4b92da1f8cd8d8a2026b5809dfe1 SHA1: 376b414234f0debfc2748b96ca4c5dd1c6e00556 SHA256: 5f5f4c8b2cbada54c90708bd66c97c06ce688db1386a7dda832318ada62b6ff4 SHA512: eb27b3f2052ba4506276fd795b9b48d9aedff9de49306433549371c0b7c4c0bcef0d4de614a652244fdd0c242151dfb98bf02d7f7cd3c0f2d2e58ce8287debc1 Homepage: https://cran.r-project.org/package=shapes Description: CRAN Package 'shapes' (Statistical Shape Analysis) Routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate spline transformation grids and comparing covariance matrices. See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, with Applications in R (2nd Edition), John Wiley and Sons. Package: r-cran-shapeselectforest Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1495 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coneproj, r-cran-raster Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-shapeselectforest_1.7-1.ca2404.1_all.deb Size: 179500 MD5sum: 1f3679e89c73d05fb1117afe10d3602b SHA1: ac4132a216a9c8d121c9ccd64eeac9a3c99ec357 SHA256: e3f8f8f59d077c56ea69839c371074a1440adf364205b274605c7879a63f1b10 SHA512: 5422524e91320a2c20316221bb775e23f19e532485f2d5d812a8665de37c846f2799933337d57ec113ab774f376b27595330f43ae50325283a840925016ecb66 Homepage: https://cran.r-project.org/package=ShapeSelectForest Description: CRAN Package 'ShapeSelectForest' (Shape Selection for Landsat Time Series of Forest Dynamics) Landsat satellites collect important data about global forest conditions. Documentation about Landsat's role in forest disturbance estimation is available at the site . By constrained quadratic B-splines, this package delivers an optimal shape-restricted trajectory to a time series of Landsat imagery for the purpose of modeling annual forest disturbance dynamics to behave in an ecologically sensible manner assuming one of seven possible "shapes", namely, flat, decreasing, one-jump (decreasing, jump up, decreasing), inverted vee (increasing then decreasing), vee (decreasing then increasing), linear increasing, and double-jump (decreasing, jump up, decreasing, jump up, decreasing). The main routine selects the best shape according to the minimum Bayes information criterion (BIC) or the cone information criterion (CIC), which is defined as the log of the estimated predictive squared error. The package also provides parameters summarizing the temporal pattern including year(s) of inflection, magnitude of change, pre- and post-inflection rates of growth or recovery. In addition, it contains routines for converting a flat map of disturbance agents to time-series disturbance maps and a graphical routine displaying the fitted trajectory of Landsat imagery. Package: r-cran-shapforxgboost Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 874 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-xgboost, r-cran-data.table, r-cran-ggforce, r-cran-ggextra, r-cran-rcolorbrewer, r-cran-ggpubr, r-cran-bbmisc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-here, r-cran-lightgbm Filename: pool/dists/noble/main/r-cran-shapforxgboost_0.2.0-1.ca2404.1_all.deb Size: 804640 MD5sum: 3494a5b2e7d808a85a73d0b5a2b75929 SHA1: dbcb35480adbc2ed9f516abe263ca4253f8d7e93 SHA256: c730a6d90dc8b5dec559e1c690f8276c13540347062d739c07b00a4d7bf58cc6 SHA512: f0a49f78e5166e73b868520f44238537cb8ab5858970b7240dc5f47b5ca80ee3d2a531692612bd3ab910f1017d3aa2dbac1541cdeff0a30c820f8d7f8dd4a318 Homepage: https://cran.r-project.org/package=SHAPforxgboost Description: CRAN Package 'SHAPforxgboost' (SHAP Plots for 'XGBoost') Aid in visual data investigations using SHAP (SHapley Additive exPlanation) visualization plots for 'XGBoost' and 'LightGBM'. It provides summary plot, dependence plot, interaction plot, and force plot and relies on the SHAP implementation provided by 'XGBoost' and 'LightGBM'. Package: r-cran-shapley Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1035 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-h2o, r-cran-curl, r-cran-pander Filename: pool/dists/noble/main/r-cran-shapley_0.7.0-1.ca2404.1_all.deb Size: 805864 MD5sum: 07f9571dd440f466e9672b416ff95f83 SHA1: 95699aea61fd95532adc3bbf1502c00e80fdbac6 SHA256: d6ff06cc0fb82feb950a0d60e76ee88f80469ab7e6c89e2ad41c4c1474042320 SHA512: 17f76991fe7db935171ee28f7285ddff76aaa4ad44cd180d81b01e1bb021f225962cf986204b34c2b8eef5fc20cbc5684e3faa78086c6601814ed6fa1015684e Homepage: https://cran.r-project.org/package=shapley Description: CRAN Package 'shapley' (Weighted Mean SHAP and CI for Robust Feature Assessment in MLGrid) This R package introduces Weighted Mean SHapley Additive exPlanations (WMSHAP), an innovative method for calculating SHAP values for a grid of fine-tuned base-learner machine learning models as well as stacked ensembles, a method not previously available due to the common reliance on single best-performing models. By integrating the weighted mean SHAP values from individual base-learners comprising the ensemble or individual base-learners in a tuning grid search, the package weights SHAP contributions according to each model's performance, assessed by multiple either R squared (for both regression and classification models). alternatively, this software also offers weighting SHAP values based on the area under the precision-recall curve (AUCPR), the area under the curve (AUC), and F2 measures for binary classifiers. It further extends this framework to implement weighted confidence intervals for weighted mean SHAP values, offering a more comprehensive and robust feature importance evaluation over a grid of machine learning models, instead of solely computing SHAP values for the best model. This methodology is particularly beneficial for addressing the severe class imbalance (class rarity) problem by providing a transparent, generalized measure of feature importance that mitigates the risk of reporting SHAP values for an overfitted or biased model and maintains robustness under severe class imbalance, where there is no universal criteria of identifying the absolute best model. Furthermore, the package implements hypothesis testing to ascertain the statistical significance of SHAP values for individual features, as well as comparative significance testing of SHAP contributions between features. Additionally, it tackles a critical gap in feature selection literature by presenting criteria for the automatic feature selection of the most important features across a grid of models or stacked ensembles, eliminating the need for arbitrary determination of the number of top features to be extracted. This utility is invaluable for researchers analyzing feature significance, particularly within severely imbalanced outcomes where conventional methods fall short. Moreover, it is also expected to report democratic feature importance across a grid of models, resulting in a more comprehensive and generalizable feature selection. The package further implements a novel method for visualizing SHAP values both at subject level and feature level as well as a plot for feature selection based on the weighted mean SHAP ratios. Package: r-cran-shapleyoutlier Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rdpack, r-cran-tibble, r-cran-tidyr, r-cran-robustbase, r-cran-forcats, r-cran-egg, r-cran-ggplot2, r-cran-gridextra, r-cran-rcolorbrewer, r-cran-magrittr Suggests: r-cran-cellwise, r-cran-robusthd, r-cran-knitr, r-cran-mass, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-shapleyoutlier_0.1.2-1.ca2404.1_all.deb Size: 790656 MD5sum: 903dc7e32172b1f2e28f76aa8ebe8a8e SHA1: 493b7a1a1c0f0880d639e8ac93580565a629559e SHA256: 79c8d070b5748d33a689bd4f727adace1cc0db64f8411b462c6d8e1afbd84dc2 SHA512: f5d2fcbf233def431b59946f3dc5722ad0d8937a6e404689915443508ceb5853731f29e580d50fc5b5c08405f6de02ae739e6d4e549394e174e28ef6d6347727 Homepage: https://cran.r-project.org/package=ShapleyOutlier Description: CRAN Package 'ShapleyOutlier' (Multivariate Outlier Explanations using Shapley Values andMahalanobis Distances) Based on Shapley values to explain multivariate outlyingness and to detect and impute cellwise outliers. Includes implementations of methods described in Mayrhofer and Filzmoser (2023) . Package: r-cran-shapleyvalue Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyverse, r-cran-kableextra, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-shapleyvalue_0.2.0-1.ca2404.1_all.deb Size: 21154 MD5sum: d8f5b4eb39398ea7ba22aa43d1b60f4a SHA1: 261b649cda30680e8d1858f665e5af3267b917a3 SHA256: 50383c237029fe26f3c3c3607a0b75ed0dc5d047a176bb2844ba765b15d5052e SHA512: 94b09e8b6fa13d7388c78ff64ee9562512e2e93b2b12e5f488b7586baa572309083b84a30abb682e17b0a857d29c542aafa10fbb563c9534371ae8d7cabd3cbf Homepage: https://cran.r-project.org/package=ShapleyValue Description: CRAN Package 'ShapleyValue' (Shapley Value Regression for Relative Importance of Attributes) Shapley Value Regression for calculating the relative importance of independent variables in linear regression with avoiding the collinearity. Package: r-cran-shapper Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate, r-cran-dalex, r-cran-ggplot2 Suggests: r-cran-covr, r-cran-knitr, r-cran-randomforest, r-cran-rpart, r-cran-testthat, r-cran-markdown, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-shapper_0.1.3-1.ca2404.1_all.deb Size: 67540 MD5sum: b614716d377bc593a992b80c3554f34b SHA1: 4257f72b1ecc43ccc5049d924ed486e01b2e670a SHA256: 805adb17b67ae25a648cf2976bd45f7a71ecaf6c7b3f9379d8fcf6e249cc2747 SHA512: 539df21643c1a1c6791d4c36c9f5a2e631c62767c618afba0ec562f66de6edb227c6fefc8ebbf208abf5d43b3f0bcfefceb89e85ce71a69ed651a7f9d5111ff4 Homepage: https://cran.r-project.org/package=shapper Description: CRAN Package 'shapper' (Wrapper of Python Library 'shap') Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'. Package: r-cran-shapviz Architecture: all Version: 0.10.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3202 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggfittext, r-cran-gggenes, r-cran-ggplot2, r-cran-ggrepel, r-cran-patchwork, r-cran-rlang, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shapviz_0.10.3-1.ca2404.1_all.deb Size: 1915728 MD5sum: 69728df74f2daa3561abe051693954b1 SHA1: 9e022770852f2815e8bfc8e0c029fc01be02f344 SHA256: 14a733d5e55e5872794fab42607fb3ecae0de4da14e50357011301f1ee153d3f SHA512: 9fa9fbc9fff49580388cc3331e3b7ba149452dd90e6d3f6f0068d9bb3400c1b9f77c9fd232f0a8b0668c9d861a252608f160fddf0b26af076207ce7830739932 Homepage: https://cran.r-project.org/package=shapviz Description: CRAN Package 'shapviz' (SHAP Visualizations) Visualizations for SHAP (SHapley Additive exPlanations), such as waterfall plots, force plots, various types of importance plots, dependence plots, and interaction plots. These plots act on a 'shapviz' object created from a matrix of SHAP values and a corresponding feature dataset. Wrappers for the R packages 'xgboost', 'lightgbm', 'fastshap', 'shapr', 'h2o', 'treeshap', 'DALEX', and 'kernelshap' are added for convenience. By separating visualization and computation, it is possible to display factor variables in graphs, even if the SHAP values are calculated by a model that requires numerical features. The plots are inspired by those provided by the 'shap' package in Python, but there is no dependency on it. Package: r-cran-shar Architecture: all Version: 2.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2294 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-classint, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.model, r-cran-spatstat.random, r-cran-terra Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-spatstat, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shar_2.3.1-1.ca2404.1_all.deb Size: 1236908 MD5sum: 04e22d92b014f0ff4ae81bfd0ff274b4 SHA1: a9ecd3287c6b480faa7ea79f913ea59fcc8fc091 SHA256: 36a5d97377b6990048a983f9f2f57e740c235a8e2c491c3bab8de80331ca25a4 SHA512: 7a4b398c8f08692a18eacd07bacee38f9d0a79ee471d977b70d75c676cf4f3caa7ff4a287176e89093f0cde4311f7d722531c4880bc447992556a101f754fd28 Homepage: https://cran.r-project.org/package=shar Description: CRAN Package 'shar' (Species-Habitat Associations) Analyse species-habitat associations in R. Therefore, information about the location of the species (as a point pattern) is needed together with environmental conditions (as a categorical raster). To test for significance habitat associations, one of the two components is randomized. Methods are mainly based on Plotkin et al. (2000) and Harms et al. (2001) . Package: r-cran-shark4r Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1618 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-httr, r-cran-jsonlite, r-cran-leaflet, r-cran-lifecycle, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rlang, r-cran-sf, r-cran-sp, r-cran-stringi, r-cran-terra, r-cran-tidyr, r-cran-vroom, r-cran-worrms Suggests: r-cran-htmltools, r-cran-irfcb, r-cran-knitr, r-cran-plotly, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-skimr, r-cran-spelling, r-cran-shiny, r-cran-shinythemes, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shark4r_1.1.1-1.ca2404.1_all.deb Size: 1254586 MD5sum: 6fb525c10b1353ac8e2bbd7878fed192 SHA1: a671a73543aee2c8c0547771431777e2e6ed86ad SHA256: 3a3be7de6af21184d3bfc56e885ea714d5eecc8b18249b7e8777681b31ffbadb SHA512: 77d94edb71aac0e59b93cd438b2d861e42755fdadcf5784795cdcfaa9a37a0c3ddb4b864b840cbcf397d6ef8534f6e03905433dfa3dc5e021bef2c00f13a4fd6 Homepage: https://cran.r-project.org/package=SHARK4R Description: CRAN Package 'SHARK4R' (Accessing and Validating Marine Environmental Data from 'SHARK'and Related Databases) Provides functions to retrieve, process, analyze, and quality-control marine physical, chemical, and biological data. The main focus is on Swedish monitoring data available through the 'SHARK' database , with additional API support for 'Nordic Microalgae' , 'Dyntaxa' , World Register of Marine Species ('WoRMS') , 'AlgaeBase' , OBIS 'xylookup' web service and Intergovernmental Oceanographic Commission (IOC) - UNESCO databases on harmful algae and toxins . Package: r-cran-sharkdemography Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-interp, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-popbio, r-cran-iterators, r-cran-dofuture, r-cran-doparallel, r-cran-foreach, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sharkdemography_1.1.0-1.ca2404.1_all.deb Size: 115968 MD5sum: b78a1ca12faa16cbe33c050ae4be5c05 SHA1: 426ff8bd9ab93b782970cd17d989267c3cb6cb1a SHA256: 7f3d5737d34214c746be13eadd6cdf98925a8fdef8f3320b157bd0d2a32d8fb9 SHA512: 529b2916a21aa0541fc4acd49c10d3a77694f88789cc1c3cd099e73220aa5f6d9bcc85c693931a20056d750cb4f2966b7539bc66dd53d01822a4f1bc197db4e8 Homepage: https://cran.r-project.org/package=SharkDemography Description: CRAN Package 'SharkDemography' (Shark Demographic Analyses Using Leslie Matrix Models) Run Leslie Matrix models using Monte Carlo simulations for any specified shark species. This package was developed during the publication of Smart, JJ, White, WT, Baje, L, et al. (2020) "Can multi-species shark longline fisheries be managed sustainably using size limits? Theoretically, yes. Realistically, no".J Appl Ecol. 2020; 57; 1847–1860. . Package: r-cran-sharp Architecture: all Version: 1.4.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1987 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fake, r-cran-abind, r-cran-beepr, r-cran-future, r-cran-future.apply, r-cran-glassofast, r-cran-glmnet, r-cran-igraph, r-cran-mclust, r-cran-nloptr, r-cran-plotrix, r-cran-rdpack, r-cran-withr Suggests: r-cran-cluster, r-cran-corpcor, r-cran-dbscan, r-cran-elasticnet, r-cran-gglasso, r-bioc-mixomics, r-cran-nnet, r-cran-openmx, r-bioc-rcy3, r-cran-randomcolor, r-cran-rmarkdown, r-cran-rpart, r-cran-sgpls, r-cran-sparcl, r-cran-survival, r-cran-testthat, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-sharp_1.4.8-1.ca2404.1_all.deb Size: 1461288 MD5sum: 6d121f2a9ec0ef8656d3d7779ff42980 SHA1: cb86f02921873c98e5d9cc51ab8bd409d06d7a16 SHA256: 6f88b6dbabe48570c6ff9ca2d1c523596f693afc2f1a56f774ce11a56320801f SHA512: 397f93a75d5345a4abb82b8e660ce616c9e37bb25f5ec49bb7caf45356472abd0e31dba4eda643744688279a55d5df9260d5e7d7124a56bed10271a8c99c6be9 Homepage: https://cran.r-project.org/package=sharp Description: CRAN Package 'sharp' (Stability-enHanced Approaches using Resampling Procedures) In stability selection (N Meinshausen, P Bühlmann (2010) ) and consensus clustering (S Monti et al (2003) ), resampling techniques are used to enhance the reliability of the results. In this package (B Bodinier et al (2025) ), hyper-parameters are calibrated by maximising model stability, which is measured under the null hypothesis that all selection (or co-membership) probabilities are identical (B Bodinier et al (2023a) and B Bodinier et al (2023b) ). Functions are readily implemented for the use of LASSO regression, sparse PCA, sparse (group) PLS or graphical LASSO in stability selection, and hierarchical clustering, partitioning around medoids, K means or Gaussian mixture models in consensus clustering. Package: r-cran-sharper Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3094 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixcalc, r-cran-zoo, r-cran-epsiwal Suggests: r-cran-xtable, r-cran-xts, r-cran-timeseries, r-cran-quantmod, r-cran-mass, r-cran-ttr, r-cran-testthat, r-cran-sandwich, r-cran-txtplot, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sharper_1.4.0-1.ca2404.1_all.deb Size: 2647784 MD5sum: 00716c6bf545f5ee032b36464e77e064 SHA1: 31e983fc40bae40297860661e10b38d126582faf SHA256: 6689e259c1c490af5b6bc512e3244ef5f308c23245bf30c0940d3bc95f1509af SHA512: f1ffe5d7ec800f626f64a52a9c8922db5c288065c39b5fd799089adbd9d7be9903ae80a1d92b565ccccd7054e5b04e84b23903d1806c49645774491165948c2f Homepage: https://cran.r-project.org/package=SharpeR Description: CRAN Package 'SharpeR' (Statistical Significance of the Sharpe Ratio) A collection of tools for analyzing significance of assets, funds, and trading strategies, based on the Sharpe ratio and overfit of the same. Provides density, distribution, quantile and random generation of the Sharpe ratio distribution based on normal returns, as well as the optimal Sharpe ratio over multiple assets. Computes confidence intervals on the Sharpe and provides a test of equality of Sharpe ratios based on the Delta method. The statistical foundations of the Sharpe can be found in the author's Short Sharpe Course . Package: r-cran-sharpr2 Architecture: all Version: 1.1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-matrix Filename: pool/dists/noble/main/r-cran-sharpr2_1.1.1.0-1.ca2404.1_all.deb Size: 184016 MD5sum: 9faef414a8108ed1ef93bcf65e248e31 SHA1: 7b519b0c50f6e5f31af8949a46794b00cb6c4068 SHA256: b758faddeb968e57b502b1086e4a9176116342de0120852569875544878d193e SHA512: 630aff21b68afe4fafc7cb4f4dc7464a940a61c996499f2ffe4d12fec82a92e3ea73776eddbe34d58244d0ede7059d082222f47452b9bb40e78afbdd2d21143e Homepage: https://cran.r-project.org/package=sharpr2 Description: CRAN Package 'sharpr2' (Estimating Regulatory Scores and Identifying ATAC-STARR Data) An algorithm for identifying high-resolution driver elements for datasets from a high-definition reporter assay library. Xinchen Wang, Liang He, Sarah Goggin, Alham Saadat, Li Wang, Melina Claussnitzer, Manolis Kellis (2017) . Package: r-cran-sharpshootr Architecture: all Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-aqp, r-cran-ape, r-cran-soildb, r-cran-cluster, r-cran-lattice, r-cran-reshape2, r-cran-scales, r-cran-circular, r-cran-rcolorbrewer, r-cran-plyr, r-cran-digest, r-cran-e1071, r-cran-stringi, r-cran-curl Suggests: r-cran-mass, r-cran-spdep, r-cran-circlize, r-cran-rvest, r-cran-xml2, r-cran-terra, r-cran-raster, r-cran-exactextractr, r-cran-httr, r-cran-jsonlite, r-cran-igraph, r-cran-dendextend, r-cran-testthat, r-cran-latticeextra, r-cran-farver, r-cran-venn, r-cran-gower, r-cran-daymetr, r-cran-elevatr, r-cran-evapotranspiration, r-cran-zoo, r-cran-soiltaxonomy, r-cran-sf, r-cran-hmisc, r-cran-knitr, r-cran-rmarkdown, r-cran-vegan Filename: pool/dists/noble/main/r-cran-sharpshootr_2.5-1.ca2404.1_all.deb Size: 532502 MD5sum: 5497047b1d186f26c428175c1fccb1e2 SHA1: 3d83a426afa78af38dc617d618ee2cc066a6b8d4 SHA256: d6604dba7b7c999c64f85c22d393c5206fd444df85ff8ad64172950a579cfe45 SHA512: 17462b72084cd61f6027fbf5f7877c2cebd966704dcc9db2678a81071e25c72d217d8b73cf74b5cc196975b6853926bd8ea422540ff1966aa5cec51ed799a1c8 Homepage: https://cran.r-project.org/package=sharpshootR Description: CRAN Package 'sharpshootR' (A Soil Survey Toolkit) A collection of data processing, visualization, and export functions to support soil survey operations. Many of the functions build on the `SoilProfileCollection` S4 class provided by the aqp package, extending baseline visualization to more elaborate depictions in the context of spatial and taxonomic data. While this package is primarily developed by and for the USDA-NRCS, in support of the National Cooperative Soil Survey, the authors strive for generalization sufficient to support any soil survey operation. Many of the included functions are used by the SoilWeb suite of websites and movile applications. These functions are provided here, with additional documentation, to enable others to replicate high quality versions of these figures for their own purposes. Package: r-cran-sharx Architecture: all Version: 1.0-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-dcmle, r-cran-dclone Filename: pool/dists/noble/main/r-cran-sharx_1.0-7-1.ca2404.1_all.deb Size: 201222 MD5sum: 4820a0446545ce06c35a03966aebf880 SHA1: a1ae79863db4055da9a27e377d18a8c05db906e8 SHA256: af7312da8e5867df4fa1954a79256aa608676665216c51f505167bf6b17eb193 SHA512: eacc315a866f5afcdab4c51923f1b0cb3151b8dffb2f920144577671efede04e17b50fe76268106d806e33bbba1c2e7510741a9f11928caef515a7ce4859402c Homepage: https://cran.r-project.org/package=sharx Description: CRAN Package 'sharx' (Models and Data Sets for the Study of Species-Area Relationships) Hierarchical models for the analysis of species-area relationships (SARs) by combining several data sets and covariates; with a global data set combining individual SAR studies; as described in Solymos and Lele (2012) . Package: r-cran-shattering Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fnn, r-cran-pdist, r-cran-slam, r-cran-ryacas, r-cran-rmarkdown, r-cran-pracma, r-cran-e1071, r-cran-nmf Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-shattering_1.0.7-1.ca2404.1_all.deb Size: 71692 MD5sum: 80de2c5263f87c6bd6cfac27e64c2914 SHA1: 15832b5a072fe977dfc30dfd5512b93d31065de7 SHA256: 4bbdae64f24f2fa462a54a1619ac925e7e3bb11e663b706ae68fa79d5cb16930 SHA512: 2ae36880a569afefc743947ea870c225dd32497de920e11e773f0be6d9fc21c91a68e6c3c6fa7e1a91ff7595045a9f80cba33b25114ca93951c3edbd476f76f6 Homepage: https://cran.r-project.org/package=shattering Description: CRAN Package 'shattering' (Estimate the Shattering Coefficient for a Particular Dataset) The Statistical Learning Theory (SLT) provides the theoretical background to ensure that a supervised algorithm generalizes the mapping f:X -> Y given f is selected from its search space bias F. This formal result depends on the Shattering coefficient function N(F,2n) to upper bound the empirical risk minimization principle, from which one can estimate the necessary training sample size to ensure the probabilistic learning convergence and, most importantly, the characterization of the capacity of F, including its under and overfitting abilities while addressing specific target problems. In this context, we propose a new approach to estimate the maximal number of hyperplanes required to shatter a given sample, i.e., to separate every pair of points from one another, based on the recent contributions by Har-Peled and Jones in the dataset partitioning scenario, and use such foundation to analytically compute the Shattering coefficient function for both binary and multi-class problems. As main contributions, one can use our approach to study the complexity of the search space bias F, estimate training sample sizes, and parametrize the number of hyperplanes a learning algorithm needs to address some supervised task, what is specially appealing to deep neural networks. Reference: de Mello, R.F. (2019) "On the Shattering Coefficient of Supervised Learning Algorithms" ; de Mello, R.F., Ponti, M.A. (2018, ISBN: 978-3319949888) "Machine Learning: A Practical Approach on the Statistical Learning Theory". Package: r-cran-shazam Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2619 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-alakazam, r-cran-ape, r-cran-diptest, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-igraph, r-cran-iterators, r-cran-kernsmooth, r-cran-lazyeval, r-cran-mass, r-cran-progress, r-cran-rlang, r-cran-scales, r-cran-seqinr, r-cran-stringi, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-shazam_1.3.2-1.ca2404.1_all.deb Size: 2188946 MD5sum: a7aecc718cb31e5a7f302829292e9264 SHA1: d0f4068d9a4c889a1aa3bc87207976dc7fbf6661 SHA256: 973b7d909ed286de6f01f0b37f4a239754b04aee3923e44d16d50000a6d6d221 SHA512: 0c3416c4131f813fa585abfe5ab60bf79aafbf842e17a654685dd6925e9d20c8cf98b8a0814d84dc29a06bd74e53727b4ad1e53c58b05b8291d2a9ec93d7fc49 Homepage: https://cran.r-project.org/package=shazam Description: CRAN Package 'shazam' (Immunoglobulin Somatic Hypermutation Analysis) Provides a computational framework for analyzing mutations in immunoglobulin (Ig) sequences. Includes methods for Bayesian estimation of antigen-driven selection pressure, mutational load quantification, building of somatic hypermutation (SHM) models, and model-dependent distance calculations. Also includes empirically derived models of SHM for both mice and humans. Citations: Gupta and Vander Heiden, et al (2015) , Yaari, et al (2012) , Yaari, et al (2013) , Cui, et al (2016) . Package: r-cran-shelf Architecture: all Version: 1.13.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 902 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexsurv, r-cran-ggextra, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggridges, r-cran-hmisc, r-cran-rmarkdown, r-cran-scales, r-cran-shiny, r-cran-shinymatrix, r-cran-sn, r-cran-survival, r-cran-survminer, r-cran-tidyr Suggests: r-cran-ggally, r-cran-knitr, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-shelf_1.13.0-1.ca2404.1_all.deb Size: 703844 MD5sum: 6445ee678ec8cb65300e6bc5ce9fb9bb SHA1: ca38fd2f8491eaed9fdf91715483a4e0fb57c3be SHA256: b72dae0de8ee7e6afcc1d3a67757c49cc34aed296b7f06be4066f1a5e7106c46 SHA512: 5a45137db93f7a40a6a1c4cd9c1b48517f0605e2ae1f7b2982217fd61a2c55385012b710177246e81c085ea14efa3638565230d347b98f47f1c6ef7c649ecf54 Homepage: https://cran.r-project.org/package=SHELF Description: CRAN Package 'SHELF' (Tools to Support the Sheffield Elicitation Framework) Implements various methods for eliciting a probability distribution for a single parameter from an expert or a group of experts. The expert provides a small number of probability judgements, corresponding to points on his or her cumulative distribution function. A range of parametric distributions can then be fitted and displayed, with feedback provided in the form of fitted probabilities and percentiles. For multiple experts, a weighted linear pool can be calculated. Also includes functions for eliciting beliefs about population distributions; eliciting multivariate distributions using a Gaussian copula; eliciting a Dirichlet distribution; eliciting distributions for variance parameters in a random effects meta-analysis model; survival extrapolation. R Shiny apps for most of the methods are included. Package: r-cran-shellchron Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 579 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rtop, r-cran-zoo, r-cran-ggplot2, r-cran-ggpubr, r-cran-tidyr, r-cran-scales, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-shellchron_0.4.0-1.ca2404.1_all.deb Size: 522716 MD5sum: c0899b34dff846178b17002dc37266c1 SHA1: cc18ed4aa6d754f42ae72c62e778b700f39dca03 SHA256: e3011081a19673e00d851771eec1c3aaf54c829919f093d2dad1975caaec03f3 SHA512: 91520901ec840bd8c2e639c0816ea815255712ff6f45d4cf33b1d34199c26081a6ee84cde3a9445fff6fb4737a5a4318a43177d7f44340d1c854a2184c36b7a7 Homepage: https://cran.r-project.org/package=ShellChron Description: CRAN Package 'ShellChron' (Builds Chronologies from Oxygen Isotope Profiles in Shells) Takes as input a stable oxygen isotope (d18O) profile measured in growth direction (D) through a shell + uncertainties in both variables (d18O_err & D_err). It then models the seasonality in the d18O record by fitting a combination of a growth and temperature sine wave to year-length chunks of the data (see Judd et al., (2018) ). This modeling is carried out along a sliding window through the data and yields estimates of the day of the year (Julian Day) and local growth rate for each data point. Uncertainties in both modeling routine and the data itself are propagated and pooled to obtain a confidence envelope around the age of each data point in the shell. The end result is a shell chronology consisting of estimated ages of shell formation relative to the annual cycle with their uncertainties. All formulae in the package serve this purpose, but the user can customize the model (e.g. number of days in a year and the mineralogy of the shell carbonate) through input parameters. Package: r-cran-shelltrace Architecture: all Version: 3.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2798 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xlsx, r-cran-bmp, r-cran-tiff Filename: pool/dists/noble/main/r-cran-shelltrace_3.5.1-1.ca2404.1_all.deb Size: 2536652 MD5sum: c50573a2719ed5ec6769e44a429838d5 SHA1: 1cc0004f052e0e0238795d731031f115f36f1dde SHA256: 872c9d77ed8ec25df2e715e0246531b11ff3037866da24dba89966cd3141008f SHA512: 47742ccfcbc984722ae31321431a6eba727cc2ffe829193f48f90f26ade2e0e80374edc03d5f4f66582aa229c52d9ac9c40c078dc8d52e169801037b4870fcd3 Homepage: https://cran.r-project.org/package=shelltrace Description: CRAN Package 'shelltrace' (Bivalve Growth and Trace Element Accumulation Model) Contains all the formulae of the growth and trace element uptake model described in the equally-named Geoscientific Model Development paper (de Winter, 2017, ). The model takes as input a file with X- and Y-coordinates of digitized growth increments recognized on a longitudinal cross section through the bivalve shell, as well as a BMP file of an elemental map of the cross section surface with chemically distinct phases separated by phase analysis. It proceeds by a step-by-step process described in the paper, by which digitized growth increments are used to calculate changes in shell height, shell thickness, shell volume, shell mass and shell growth rate through the bivalve's life time. Then, results of this growth modelling are combined with the trace element mapping results to trace the incorporation of trace elements into the bivalve shell. Results of various modelling parameters can be exported in the form of XLSX files. Package: r-cran-shelter Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-getpass, r-cran-yaml, r-cran-filelock, r-cran-rappdirs, r-cran-sodium Suggests: r-cran-testthat, r-cran-rstudioapi, r-cran-mockery, r-cran-keyring Filename: pool/dists/noble/main/r-cran-shelter_0.2.1-1.ca2404.1_all.deb Size: 75348 MD5sum: 640e1823644055ef7fa51e68a5487d1c SHA1: b8c585b286d8a9a064d824481480bc5054c1ce88 SHA256: 7972172050f6cfbc8d7a7aa48ed7c4f24ff5c2196239179520231e90c4b4e143 SHA512: a3bb3785b9f12e0ea50b067d73d0853835dc844d92b570e6ab33fe20da1cba7dc332ec4076665a8efa4c0c8b512284d337e2a35d027b43d790a3724d802a05c2 Homepage: https://cran.r-project.org/package=shelter Description: CRAN Package 'shelter' (Support for Secure API Key Management) Secure handling of API keys can be difficult. This package provides secure convenience functions for entering / handling API keys and opening connections via inversion of control on those keys. Works seamlessly between production and developer environments. Package: r-cran-sherlock Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 627 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rlang, r-cran-forcats, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-cowplot, r-cran-scales, r-cran-ggh4x, r-cran-stringr, r-cran-plotly, r-cran-readr, r-cran-openxlsx, r-cran-purrr, r-cran-fs, r-cran-rstudioapi, r-cran-tidytext Suggests: r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-sherlock_0.7.0-1.ca2404.1_all.deb Size: 494990 MD5sum: d1c447d0fc9d5dfd3efb511aeb78085d SHA1: a7462192feb3b8ceee9a3fa9288130fb2f8a2594 SHA256: 9a8a1f51fb3046d39314ea9ac454830c595a11a28359fbd352d9ed836ef94294 SHA512: b63ca466e9480fc00112c9578b32c5ae9d4e44ba0f05eb5fa2ddebbcce3b93a11c5fce9b7e979d1bc477dcd3ed4bbb8ed13830f16989e5fa4942aacc3c3cb06e Homepage: https://cran.r-project.org/package=sherlock Description: CRAN Package 'sherlock' (Graphical Displays for Structured Problem Solving and Diagnosis) Powerful graphical displays and statistical tools for structured problem solving and diagnosis. The functions of the 'sherlock' package are especially useful for applying the process of elimination as a problem diagnosis technique. The 'sherlock' package was designed to seamlessly work with the 'tidyverse' set of packages and provides a collection of graphical displays built on top of the 'ggplot' and 'plotly' packages, such as different kinds of small multiple plots as well as helper functions such as adding reference lines, normalizing observations, reading in data or saving analysis results in an Excel file. References: David Hartshorne (2019, ISBN: 978-1-5272-5139-7). Stefan H. Steiner, R. Jock MacKay (2005, ISBN: 0873896467). 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For example, Arthur Conan Doyle wrote a total of 60 Sherlock Holmes stories, comprised of 54 short stories and 4 longer novels. I wanted to test my own subjective impression that, in many of the stories, Sherlock Holmes' popularity was used as bait to induce the reader to read a story that is essentially not primarily a Sherlock Holmes story. I used the term "Holmes" as a search pattern, since Watson would frequently address him by name, or use his name to describe something that he was doing. My hypothesis is that the frequency distribution of the search pattern "Holmes" is a good proxy for the degree to which a story is or is not truly a Sherlock Holmes story. The results are presented in a manuscript that is available as a vignette and online at . Package: r-cran-shewhartr Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1985 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rlang, r-cran-cli, r-cran-dplyr, r-cran-purrr, r-cran-tibble, r-cran-tidyselect, r-cran-ggplot2, r-cran-broom, r-cran-slider Suggests: r-cran-patchwork, r-cran-plotly, r-cran-qcc, r-cran-lubridate, r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-mass Filename: pool/dists/noble/main/r-cran-shewhartr_1.3.0-1.ca2404.1_all.deb Size: 1337376 MD5sum: 5b223323ecc0c576bc2755e77d8bda8a SHA1: 20965c46b1d0fce4884d303094d92b45f1d04b57 SHA256: c4840c78953d61474e6d706c412a70c85d75b813716820df5c25f4a031e152cc SHA512: 790d3b426694fe6955d72e5f5089d6fb6186176655dadd873d3a8c2647a5842310b1b81d4b4d507662e6b8ffe426cc5f908bfa688c9551267e666f799ac40670 Homepage: https://cran.r-project.org/package=shewhartr Description: CRAN Package 'shewhartr' (Statistical Process Control with Tidyverse-Native Workflows) A comprehensive toolkit for Statistical Process Control (SPC) that combines the rigor of classical Shewhart methodology with modern tidyverse-native interfaces. Provides classical control charts for variables (I-MR, Xbar-R, Xbar-S) and attributes (p, np, c, u), as well as regression-based control charts for processes with trend. Includes Nelson runs tests, Average Run Length (ARL) simulation, process capability indices with bootstrap confidence intervals, Box-Cox transformation guidance, and a clean Phase I / Phase II workflow. All chart objects integrate with broom via 'tidy', 'glance' and 'augment' methods. References: Shewhart (1931, ISBN:0-87389-076-0); Montgomery (2019, ISBN:978-1-119-39930-8); Nelson (1984) ; Woodall (2000) ; Box & Cox (1964) . Package: r-cran-shidashi Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4841 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-digest, r-cran-ellmer, r-cran-fastmap, r-cran-formatr, r-cran-httr2, r-cran-s7, r-cran-shiny, r-cran-shinychat, r-cran-yaml, r-cran-jsonlite, r-cran-htmlwidgets Suggests: r-cran-cli, r-cran-coro, r-cran-logger, r-cran-processx, r-cran-promises, r-cran-later, r-cran-rstudioapi, r-cran-htmltools, r-cran-httpuv, r-cran-ggplot2, r-cran-ggextra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shidashi_0.2.0-1.ca2404.1_all.deb Size: 1293104 MD5sum: 47d110afb5f13d891ff61cc2f39f32e2 SHA1: 6390d15a1a1c088a5764ed5eb59a69179c8c143d SHA256: 2c201521d66a7d0e2d30146037034253811199e4522d80fb65f69c7cbc536862 SHA512: b44043b801b4fa768dd5c4ff9fd296d5832073b3be5e80abcba243d0bf3f7ddefcbd8676cf1c052d648a45a57f5d9fa9331ac95c1327562269395ae212fc8fd8 Homepage: https://cran.r-project.org/package=shidashi Description: CRAN Package 'shidashi' (A Shiny Dashboard Template Modular System with Chat Bot Support) A template dashboard system with AI agent integrated. 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Package: r-cran-shiftsharese Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1522 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-aer, r-cran-spelling, r-cran-formatr Filename: pool/dists/noble/main/r-cran-shiftsharese_1.1.0-1.ca2404.1_all.deb Size: 1480810 MD5sum: c17990f0e4a5026c0aa1ac82f0b1377b SHA1: 51814fbbf9ab5f3c6bede093a13f6abbb4b3f0b9 SHA256: 77872c31eb0a2b19029e5c4af107458b5d093a14536c3f64f4a30be6c47eb2b0 SHA512: d2a107ab7b9c00b8c23d0936591ff0048454a71b4860a272e022fe09811f9cffdf4a335052aaf3481d52bbf0e1a5d2d86d343ce164136db81d2e9bd172527db3 Homepage: https://cran.r-project.org/package=ShiftShareSE Description: CRAN Package 'ShiftShareSE' (Inference in Regressions with Shift-Share Structure) Provides confidence intervals in least-squares regressions when the variable of interest has a shift-share structure, and in instrumental variables regressions when the instrument has a shift-share structure. 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Package: r-cran-shinipsum Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-attempt, r-cran-dt, r-cran-dygraphs, r-cran-ggplot2, r-cran-magrittr, r-cran-plotly Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinipsum_0.1.1-1.ca2404.1_all.deb Size: 355646 MD5sum: 951cb136b1f2c77eb0caeb2c9b087d6c SHA1: c86978ce34880a7ac706cb18407fad69a2f8ba60 SHA256: 83eda302ed12f25604f030903af914ba0a1a5d8200c81f9941e58ac6c1626a91 SHA512: 19dc5bfd4fe38a962c024c50393df8b3956a58ffbc156ec698455a284258b392731745406ea6e3e74ecc04924930807ea5bb20994cb4dccec16e7953afcbf2e2 Homepage: https://cran.r-project.org/package=shinipsum Description: CRAN Package 'shinipsum' (Lorem-Ipsum-Like Helpers for Fast Shiny Prototyping) Prototype your shiny apps quickly with these Lorem-Ipsum-like Helpers. 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Package: r-cran-shiny.blueprint Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-htmltools, r-cran-shiny, r-cran-shiny.react Suggests: r-cran-covr, r-cran-knitr, r-cran-lintr, r-cran-purrr, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-shiny.router Filename: pool/dists/noble/main/r-cran-shiny.blueprint_0.3.0-1.ca2404.1_all.deb Size: 658206 MD5sum: 6fb6229bfa7bbca3c330c903f0713fae SHA1: e616571878f38c305a56d60791f95eebb0daa910 SHA256: 0b0ca4e81ebddd6b739161bd957c8729acfae97472dbaa08b46d0206fdb2ed16 SHA512: 17e0c70f5d17a011a085380472d8be57c4f9aead8108754e5badeb3d716b3d1460a4aa3d4ec59d51bdaeb60a7cc1ef28072f2b5f3afcb0f8e08b7dc4d2f52260 Homepage: https://cran.r-project.org/package=shiny.blueprint Description: CRAN Package 'shiny.blueprint' (Palantir's 'Blueprint' for 'Shiny' Apps) Easily use 'Blueprint', the popular 'React' library from Palantir, in your 'Shiny' app. 'Blueprint' provides a rich set of UI components for creating visually appealing applications and is optimized for building complex, data-dense web interfaces. This package provides most components from the underlying library, as well as special wrappers for some components to make it easy to use them in 'R' without writing 'JavaScript' code. Package: r-cran-shiny.destroy Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-rlang, r-cran-shiny Suggests: r-cran-bslib, r-cran-htmltools, r-cran-knitr, r-cran-rmarkdown, r-cran-shinytest2, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shiny.destroy_0.1.0-1.ca2404.1_all.deb Size: 309352 MD5sum: 38ee803f97bcab8c903281e45b234bad SHA1: 29171ab093c7df09aa16c96ca2a3b02ce1590a34 SHA256: cc7d43e6d98f91cf7a3aec0791d3f8d64ddf59c874a6c115ed9629ab383edc67 SHA512: bb8f66462060095a2f9cf46195199543de9399c2b5bf1203cbf85413ac44014ac41f108ede8361d01acba46937ce870cdaaf9edc58923196370524135cc7700b Homepage: https://cran.r-project.org/package=shiny.destroy Description: CRAN Package 'shiny.destroy' (Create Destroyable Modules in 'Shiny') Enables the complete removal of various 'Shiny' components, such as inputs, outputs and modules. It also aids in the removal of observers that have been created in dynamically created modules. Package: r-cran-shiny.emptystate Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fontawesome, r-cran-htmltools, r-cran-r6, r-cran-shiny Suggests: r-cran-bsicons, r-cran-chromote, r-cran-covr, r-cran-knitr, r-cran-lintr, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-shinytest2, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-shiny.emptystate_0.1.0-1.ca2404.1_all.deb Size: 126238 MD5sum: 1a119bb31886d17e1ee12f85561eaf51 SHA1: e195aa9fcc0753b500c6b8664c6c063537a5f2b4 SHA256: 11b46738a512f60a2620b563fffe6d654eb89cb6600d98d6489a60fd7f54f7c6 SHA512: d9fe98b02f2ef8640e1a8f98680433713f49bc51fd5e02462a0340642432956baadac7859c26054ca93e77ac9dcbf9897b59e99349054aa49ad9cea8db5fe44c Homepage: https://cran.r-project.org/package=shiny.emptystate Description: CRAN Package 'shiny.emptystate' (Empty State Components for 'Shiny') Offers a comprehensive solution for managing 'empty states' in 'Shiny' applications. It provides tools to create both default and customizable components for scenarios where data is absent or doesn't match user-defined filters. The package prioritizes user experience, ensuring clarity and consistency even when data is not available to display. 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Package: r-cran-shiny.fluent Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3434 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-jsonlite, r-cran-purrr, r-cran-shiny, r-cran-shiny.react Suggests: r-cran-chromote, r-cran-covr, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-glue, r-cran-imola, r-cran-knitr, r-cran-leaflet, r-cran-mockery, r-cran-plotly, r-cran-rcmdcheck, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-sass, r-cran-shiny.i18n, r-cran-shiny.router, r-cran-shinyjs, r-cran-shinytest2, r-cran-sortable, r-cran-stringi, r-cran-testthat, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-shiny.fluent_0.4.0-1.ca2404.1_all.deb Size: 1257100 MD5sum: 95186c071faef9d02ae80cf68b09057e SHA1: 9eb7282057e851f62fd08d22da7e3598aa1b9c6c SHA256: 64ff179b752f8eee80851be3a1aa6f0b53154fb8a40d4e1c1f0af9d0ae1caa3a SHA512: c831d8fe05a6d916b818f864040dfce0881b9250f81e16b2a6eae5f0d5da602cdcf46a7f3fd14f4e1015e1637a625624abedadb43fb1ff962fa3530392fc57bb Homepage: https://cran.r-project.org/package=shiny.fluent Description: CRAN Package 'shiny.fluent' (Microsoft Fluent UI for Shiny Apps) A rich set of UI components for building Shiny applications, including inputs, containers, overlays, menus, and various utilities. 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Package: r-cran-shiny.info Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-git2r, r-cran-glue, r-cran-shiny Suggests: r-cran-testthat, r-cran-lintr, r-cran-covr Filename: pool/dists/noble/main/r-cran-shiny.info_0.2.0-1.ca2404.1_all.deb Size: 108142 MD5sum: 46bf8d44dbc057a19ea7bbc7fe60ea5e SHA1: 1f29db47148aee2c688aa458e3a190be9145b891 SHA256: 1f74af4cfda50d845fea0795c323b60b66a48051a0989920114653bbc77d672d SHA512: 7eb2215d4b820315dd9f895cd4d6d8148198f76496f03a0ba84a58952055732b586f900253835237f7498fb805c4b46bf1f3d82d8d018678e48a3b3b9bd631ca Homepage: https://cran.r-project.org/package=shiny.info Description: CRAN Package 'shiny.info' ('shiny' Info) Displays simple diagnostic information of the 'shiny' project in the user interface of the app. Package: r-cran-shiny.ollama Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-bslib, r-cran-httr, r-cran-jsonlite, r-cran-markdown, r-cran-mockery Suggests: r-cran-testthat, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-shiny.ollama_0.1.1-1.ca2404.1_all.deb Size: 29250 MD5sum: 69846631c5d5e87642aa4dd2ef9a0fe7 SHA1: ecd810ae49dd72e946739ebefbb109615c921c95 SHA256: d569fd54a633cb140212d52c4636d8840e7fcd69eb8227564ef396c4afb3306b SHA512: a16127a9ec1af1a49848d98b40a0d34e94bdb746bd9bc7d8dbca981110ae97a8dc6aba5005a2b3dc268040baeb78359438862ce49f42b4c0fcee03d28dd37977 Homepage: https://cran.r-project.org/package=shiny.ollama Description: CRAN Package 'shiny.ollama' (R 'shiny' Interface for Chatting with Large Language ModelsOffline on Local with 'ollama') Chat with large language models on your machine without internet with complete privacy via 'ollama', powered by R 'shiny' interface. 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Package: r-cran-shiny.react Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3348 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-htmltools, r-cran-jsonlite, r-cran-logger, r-cran-purrr, r-cran-rlang, r-cran-shiny, r-cran-stringi Suggests: r-cran-chromote, r-cran-covr, r-cran-knitr, r-cran-leaflet, r-cran-lintr, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-shinytest2, r-cran-styler, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-shiny.react_0.4.0-1.ca2404.1_all.deb Size: 681056 MD5sum: 0a900b99d0aa6af6f161b083c634a783 SHA1: 5af139daf846ea952af1ed649e8efcdd0a3ca390 SHA256: c1cbeff2055d90a52de0eeb36feaab77cb156639c536b89e07b957883ebc1d53 SHA512: ed0e5eb2a62a73366dbabe7bcffa241813f7ce29ed4a3ced45d44f97c4df0da47fd0ffa7bba6d1bc9d09a960e4c178ed8c7b4f389bb153995c9e07433061610f Homepage: https://cran.r-project.org/package=shiny.react Description: CRAN Package 'shiny.react' (Tools for Using React in Shiny) A toolbox for defining React component wrappers which can be used seamlessly in Shiny apps. Package: r-cran-shiny.reglog Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 926 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-shiny, r-cran-dplyr, r-cran-lubridate, r-cran-lifecycle, r-cran-scrypt, r-cran-shinyjs, r-cran-stringi, r-cran-uuid Suggests: r-cran-covr, r-cran-dbi, r-cran-dt, r-cran-devtools, r-cran-emayili, r-cran-gmailr, r-cran-googledrive, r-cran-googlesheets4, r-cran-jsonlite, r-cran-knitr, r-cran-mongolite, r-cran-rmarkdown, r-cran-rsqlite, r-cran-shinytest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shiny.reglog_0.5.2-1.ca2404.1_all.deb Size: 714814 MD5sum: 6d6e5ee179e72806a82aef8d9be20f59 SHA1: 44bb825873af68fb3f8309eb9b5deeca163bb2cb SHA256: 841ba7a4226e39eca8ac075c134cf91d8764219cbd06db1a9ec96537dfeb413e SHA512: 97163a3c325be0253753b7f98c9b3741bdb3e2feabbe86c60cfa91c24352a85e6329588c34f5c4b6180e5a78bfe4efd4bb96fc528fc5256770c988718491166a Homepage: https://cran.r-project.org/package=shiny.reglog Description: CRAN Package 'shiny.reglog' (Optional Login and Registration Module System for ShinyApps) RegLog system provides a set of shiny modules to handle register procedure for your users, alongside with login, edit credentials and password reset functionality. It provides support for popular SQL databases and optionally googlesheet-based database for easy setup. For email sending it provides support for 'emayili' and 'gmailr' backends. Architecture makes customizing usability pretty straightforward. The authentication system created with shiny.reglog is designed to be optional: user don't need to be logged-in to access your application, but when logged-in the user data can be used to read from and write to relational databases. Package: r-cran-shiny.router Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-glue, r-cran-rlang, r-cran-shiny Suggests: r-cran-covr, r-cran-lintr, r-cran-rcmdcheck, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shiny.router_0.3.1-1.ca2404.1_all.deb Size: 84580 MD5sum: ebfb1d9fec7592aca45c2299fba2a8e7 SHA1: 3d6c8235e608d7ba349a993c1bea4533b733c924 SHA256: c42b015354f6889ff4930401925690ca8c8cf61acda67817288d37daf33bc78c SHA512: 6fb576b816b99689aa4080065c676db08195fe8708ddd818694af3da170d7516b1410754b0d9d4fc7e584afb082eae71a679a1b5cc1d10c0bc4c6ae9d8801a62 Homepage: https://cran.r-project.org/package=shiny.router Description: CRAN Package 'shiny.router' (Basic Routing for Shiny Web Applications) It is a simple router for your Shiny apps. The router allows you to create dynamic web applications with real-time User Interface and easily share url to pages within your Shiny apps. Package: r-cran-shiny.semantic Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3222 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-semantic.assets, r-cran-shiny Suggests: r-cran-covr, r-cran-chromote, r-cran-dplyr, r-cran-dt, r-cran-gapminder, r-cran-knitr, r-cran-leaflet, r-cran-lintr, r-cran-markdown, r-cran-mockery, r-cran-plotly, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-testthat, r-cran-shinytest2, r-cran-tibble, r-cran-withr Filename: pool/dists/noble/main/r-cran-shiny.semantic_0.5.1-1.ca2404.1_all.deb Size: 2869400 MD5sum: 21854796da77cd4789dd4a3fedbee3ca SHA1: 433420ccbfac684ca63434562e143a0a0bbde67f SHA256: f1449f27ace980b211f1f901003f0b1bbe87b7bfaed4933f41a387fcf6f4fa07 SHA512: a613fd3722660a419109cd00e9875166939e71f9a701cdcf36df962800fc1be1175863bac1e54f9b21e4d608c14692ca9514c143c4ec35bc36b918c863d2e606 Homepage: https://cran.r-project.org/package=shiny.semantic Description: CRAN Package 'shiny.semantic' (Semantic UI Support for Shiny) Creating a great user interface for your Shiny apps can be a hassle, especially if you want to work purely in R and don't want to use, for instance HTML templates. 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Package: r-cran-shiny.tailwind Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-shiny Suggests: r-cran-fontawesome, r-cran-palmerpenguins, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-shiny.tailwind_0.2.2-1.ca2404.1_all.deb Size: 176788 MD5sum: 31ba1c57e6d3a7df48d64781242051ca SHA1: 18af59e503d625ce9f38dea3c1364fcaa39a5532 SHA256: 9b438eb0f15198f91c84803f9bd4b852f78cae4c4f38319af0dfa3201723c24f SHA512: 72489a600ed6759d5c626ceb936b4788ab556abb26d1eeee7c10256ada6c4d4f0862df10fa55add5915df3fd5923472dd07b5850ad31812afb36d0bba8d35d6c Homepage: https://cran.r-project.org/package=shiny.tailwind Description: CRAN Package 'shiny.tailwind' ('TailwindCSS' for Shiny Apps) Allows 'TailwindCSS' to be used in Shiny apps with just-in-time compiling, custom css with '@apply' directive, and custom tailwind configurations. Package: r-cran-shiny.telemetry Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2300 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-digest, r-cran-dplyr, r-cran-glue, r-cran-htmltools, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-logger, r-cran-lubridate, r-cran-odbc, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-rsqlite, r-cran-shiny, r-cran-stringr, r-cran-tidyr Suggests: r-cran-box, r-cran-config, r-cran-dt, r-cran-knitr, r-cran-mongolite, r-cran-plotly, r-cran-plumber, r-cran-rcolorbrewer, r-cran-rmariadb, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rpostgresql, r-cran-scales, r-cran-semantic.dashboard, r-cran-shiny.semantic, r-cran-shinyjs, r-cran-testthat, r-cran-timevis, r-cran-withr Filename: pool/dists/noble/main/r-cran-shiny.telemetry_0.3.2-1.ca2404.1_all.deb Size: 1565532 MD5sum: d6fe845cc39f5d1b9e53c2bb53ffb9aa SHA1: 19758b5dcc8a91948e3a8ced1b2f0905a758404a SHA256: 9ea2712df4581d5be5dbd711b34eef3b43bf0e8d6cf891ca558be36f88990a2e SHA512: 7e58da492ef37e4b99e2e1da996c3b33cccee1ffc74d281ba6eeb524cff6b38758d6ac0b4a64b48eefab80fccdde3d3f6dabb26158772a3337add9adee14f49c Homepage: https://cran.r-project.org/package=shiny.telemetry Description: CRAN Package 'shiny.telemetry' ('Shiny' App Usage Telemetry) Enables instrumentation of 'Shiny' apps for tracking user session events such as input changes, browser type, and session duration. 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Designed to work particularly well with the 'ellmer' R package for calling LLMs. Package: r-cran-shinychatr Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dbi, r-cran-purrr, r-cran-r6, r-cran-shiny Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-rsqlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinychatr_1.2.0-1.ca2404.1_all.deb Size: 126436 MD5sum: 83405a519fab03acc624b07b7ba2fea2 SHA1: fc29fff2efde083130396dde38adcf6477541a4a SHA256: eee371f3b804ccfd1a528305751885f7f3c1b3f39043292bdda6eb5a4a75c540 SHA512: 5ac32b98c3d37b6c30f71b6dfd0b2a121eb14077767f28fcb5883f638cc7d6055b4ea882e31520406f6e74797cadc62189c2e87b7b1e01a19998321f05b710d2 Homepage: https://cran.r-project.org/package=shinyChatR Description: CRAN Package 'shinyChatR' (R Shiny Chat Module) Provides an easy-to-use module for adding a chat to a Shiny app. Allows users to send messages and view messages from other users. Messages can be stored in a database or a .rds file. Package: r-cran-shinyclt Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-testthat, r-cran-purrr, r-cran-shiny, r-cran-gamlss, r-cran-dplyr, r-cran-plotly, r-cran-future, r-cran-shinycssloaders, r-cran-waiter, r-cran-shinythemes, r-cran-shinywidgets, r-cran-cachem, r-cran-knitr Filename: pool/dists/noble/main/r-cran-shinyclt_0.9.4-1.ca2404.1_all.deb Size: 41350 MD5sum: 2c38c71e2a97fcd8acb0f573a72596d0 SHA1: 17aedad6ff90557709ef90ed6f08f390e369d447 SHA256: 91bffb9dca46305b379bbdfbcc98ea9de1661ef7304e7edf1f140e7a54da3c20 SHA512: 3b38189421e923511c8f81fba6f578cc9952eb899128d0eab145a14e8400d65b20cd60d9005e0af9b1024c72cb5d60a59556e86b459e1f758bfd977833b5c532 Homepage: https://cran.r-project.org/package=shinyCLT Description: CRAN Package 'shinyCLT' (Central Limit Theorem 'shiny' Application) A 'shiny' application estimating the operating characteristics of the Student's t-test by Student (1908) , Welch's t-test by Welch (1947) , and Wilcoxon test by Wilcoxon (1945) in one-sample or two-sample cases, in settings defined by the user (conditional distribution, sample size per group, location parameter per group, nuisance parameter per group), using Monte Carlo simulations Malvin H. Kalos, Paula A. Whitlock (2008) . Package: r-cran-shinycohortbuilder Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2932 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-glue, r-cran-bslib, r-cran-jsonlite, r-cran-purrr, r-cran-ggplot2, r-cran-ggiraph, r-cran-htmltools, r-cran-shiny, r-cran-shinywidgets, r-cran-htmlwidgets, r-cran-dplyr, r-cran-cohortbuilder, r-cran-trycatchlog, r-cran-highr, r-cran-shinygizmo, r-cran-rlang, r-cran-tibble, r-cran-lifecycle Suggests: r-cran-querybuilder, r-cran-shinyquerybuilder, r-cran-pkgload, r-cran-packer, r-cran-sass, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinycohortbuilder_0.4.0-1.ca2404.1_all.deb Size: 1872048 MD5sum: e366201499daed077f0ec1b0d69120f4 SHA1: 32d32c924b1ad64a0a6301d2089c0c043a9ae640 SHA256: d581e7a59cffe4b21406e73ef3a1303195e667fe0c5ad1c40a66e255959e034c SHA512: f0782f2163e9f77edada0a928fc2dd9a53eb051cbc1d33878d153496f988bcaf8575721ee721a1a18fc68ff304c9756e5486ceb14a5cb0a0aab90847cf877041 Homepage: https://cran.r-project.org/package=shinyCohortBuilder Description: CRAN Package 'shinyCohortBuilder' (Modular Cohort-Building Framework for Analytical Dashboards) You can easily add advanced cohort-building component to your analytical dashboard or simple 'Shiny' app. Then you can instantly start building cohorts using multiple filters of different types, filtering datasets, and filtering steps. Filters can be complex and data-specific, and together with multiple filtering steps you can use complex filtering rules. The cohort-building sidebar panel allows you to easily work with filters, add and remove filtering steps. It helps you with handling missing values during filtering, and provides instant filtering feedback with filter feedback plots. The GUI panel is not only compatible with native shiny bookmarking, but also provides reproducible R code. Package: r-cran-shinycox Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-survival Suggests: r-cran-dt, r-cran-knitr, r-cran-rmarkdown, r-cran-shinydashboard Filename: pool/dists/noble/main/r-cran-shinycox_1.1.3-1.ca2404.1_all.deb Size: 111530 MD5sum: 3ec5143b4c08f26c91e9d010308a6051 SHA1: 801bf7c7669500acf0cac8c5bd8b9175e224af46 SHA256: 6cb060fb24028ee49ad642b279c5d0fd58beeb71d7fe6a9a70164f0a978ee2ae SHA512: ca4af697923ff5fdff532be12e631fe152c88c800212b5964359c00eb58ca4e1c6ed9fda9c2ce3226ccad31305dcc0be74a5cecc6499e5175e478c822cb496c9 Homepage: https://cran.r-project.org/package=shinyCox Description: CRAN Package 'shinyCox' (Create 'shiny' Applications for Cox Proportional Hazards Models) Takes one or more fitted Cox proportional hazards models and writes a 'shiny' application to a directory specified by the user. 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Package: r-cran-shinycroneditor Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets Suggests: r-cran-shiny Filename: pool/dists/noble/main/r-cran-shinycroneditor_1.0.0-1.ca2404.1_all.deb Size: 34872 MD5sum: c2c90c96e4de2fb6908a4a720a7bbb54 SHA1: 99d2d36fcec2d741be03ee77e0d918b47364d3ba SHA256: 9706f3ccdc1eb65ec4acca1c7395224ded6baa623e74c1f9be9783048148d7b3 SHA512: 3c46cb86243cd2c0ec39d70e7db428dab89cd7086ba107e693c7b0c8174c5879a371b4d9ab29cdb59fe580684f2bc53a23baece9b995f7c7fa39311c4ae3df65 Homepage: https://cran.r-project.org/package=shinycroneditor Description: CRAN Package 'shinycroneditor' ('shiny' Cron Expression Input Widget) A widget for 'shiny' apps to handle schedule expression input, using the 'cron-expression-input' JavaScript component. 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Package: r-cran-shinydataviewer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-htmltools, r-cran-reactable, r-cran-shiny Suggests: r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinydataviewer_0.1.0-1.ca2404.1_all.deb Size: 391376 MD5sum: 18ea0c77be1817d5e1c8de0999960c02 SHA1: 35e6e32b0da493c8782d4a081fa9b64b05b9ebcd SHA256: 426cd3f2e86be9307d0ef43cd869d150426748d706a53dab6c0174ec3dfd015f SHA512: 9e83c898044e64aa74b4b7db00438042a16b35ed9d6377a2ea025bf397ec363486705493f95ffee208882f680186b359c8aa70136aaca63dacb94210b2f08fff Homepage: https://cran.r-project.org/package=shinydataviewer Description: CRAN Package 'shinydataviewer' (Reusable Data Viewer Module for 'shiny') Provides a reusable 'shiny' module for viewing tabular data with a searchable 'reactable' table and a variable summary sidebar built with 'bslib'. Package: r-cran-shinydatetimepickers Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7835 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-lubridate, r-cran-reactr, r-cran-shiny Filename: pool/dists/noble/main/r-cran-shinydatetimepickers_1.2.0-1.ca2404.1_all.deb Size: 876722 MD5sum: 6cca09bd91465716e49470e13a4c2531 SHA1: 8aad50713521e744c4d278d7c8de5495ae297752 SHA256: 4c06a63ab400d1f0364c18fb66cd7ff5bba1fcd1c39cf517fdf1f695926ea006 SHA512: cdef7adefe7379801a5a97159cb29fab81dd70c56b586faa3f02c253c0f774fdb7205cdb5dd6303c83db46ee7daa50b581e6280f0eeaea015fdd23b7430b7ee5 Homepage: https://cran.r-project.org/package=shinyDatetimePickers Description: CRAN Package 'shinyDatetimePickers' (Some Datetime Pickers for 'Shiny') Provides three types of datetime pickers for usage in a 'Shiny' UI. A datetime picker is an input field for selecting both a date and a time. 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Package: r-cran-shinydisconnect Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-glue, r-cran-htmltools, r-cran-shiny Suggests: r-cran-colourpicker Filename: pool/dists/noble/main/r-cran-shinydisconnect_0.1.1-1.ca2404.1_all.deb Size: 246670 MD5sum: c0078e3389b9f9f95d95075df7b740e5 SHA1: 77947b7949e47f5dd3c6fb9592b0e81e30f88f77 SHA256: 3bfae04db770c3856ef7f0a71dfa7cb5f1c3384bbaedfb35f9301f257b1b52e7 SHA512: 08a9bfcdca5d357539b8d7ea00da2438564d0f8c16f92c24a4d96ac594ada02228c06da87d200147679cdcb8ab00646f935f0d87408805de408af5df1ae05e02 Homepage: https://cran.r-project.org/package=shinydisconnect Description: CRAN Package 'shinydisconnect' (Show a Nice Message When a 'Shiny' App Disconnects or Errors) A 'Shiny' app can disconnect for a variety of reasons: an unrecoverable error occurred in the app, the server went down, the user lost internet connection, or any other reason that might cause the 'Shiny' app to lose connection to its server. With 'shinydisconnect', you can call disonnectMessage() anywhere in a Shiny app's UI to add a nice message when this happens. Works locally (running Shiny apps within 'RStudio') and on Shiny servers (such as shinyapps.io, 'RStudio Connect', 'Shiny Server Open Source', 'Shiny Server Pro'). See demo online at . Package: r-cran-shinydlplot Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shinybs, r-cran-shiny, r-cran-shinyjs, r-cran-plotly, r-cran-htmlwidgets, r-cran-htmltools Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-shinydlplot_0.1.4-1.ca2404.1_all.deb Size: 32386 MD5sum: 10ba65ef5795fae9880923a86fd3c65c SHA1: 82135af3dfbda1b0a26d19707208911e31eab120 SHA256: fc71e9b9496235044338ae328da3ed4e6f1dc0f4eb9c6d96dbc5bf8866cd2785 SHA512: 389b9459886ca791c720d24f28cd189e988cfe855ea474cc60293ef32eeed26b56506f223c8401925a9fe9b3af83851eb100a1c8751745565f3ac124169dadd5 Homepage: https://cran.r-project.org/package=shinydlplot Description: CRAN Package 'shinydlplot' (Add a Download Button to a 'shiny' Plot or 'plotly') Add a download button to a 'shiny' plot or 'plotly' that appears when the plot is hovered. 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Package: r-cran-shinylive Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-archive, r-cran-brio, r-cran-cli, r-cran-fs, r-cran-gh, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-pkgdepends, r-cran-rappdirs, r-cran-renv, r-cran-rlang, r-cran-whisker, r-cran-withr Suggests: r-cran-httpuv, r-cran-pkgcache, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinylive_0.4.1-1.ca2404.1_all.deb Size: 100912 MD5sum: c5ab9b7bf8be51584424010c688db5e5 SHA1: cae32a0cfa5619c55a90c415945edfe7919a69ed SHA256: 85ef12d0ffa0ee02f120797c89b4cef2ca5ebdbf61e9ff9ee6a5bab6120713f9 SHA512: d465ec7ba8755cc719f3cc5c6a4c66f20e228a5beaf2a26d3a1b864c45d838f22e9de216b9de6507c69b3c46316898b97aeb9038c22b204a77ce18f0b68fa62e Homepage: https://cran.r-project.org/package=shinylive Description: CRAN Package 'shinylive' (Run 'shiny' Applications in the Browser) Exporting 'shiny' applications with 'shinylive' allows you to run them entirely in a web browser, without the need for a separate R server. 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Package: r-cran-shinylogs Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-shiny, r-cran-jsonlite, r-cran-data.table, r-cran-bit64, r-cran-nanotime, r-cran-digest, r-cran-anytime Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-dbi, r-cran-rsqlite, r-cran-googledrive Filename: pool/dists/noble/main/r-cran-shinylogs_0.2.1-1.ca2404.1_all.deb Size: 119524 MD5sum: 6fe762af59ffc82a94836a28b9277c1b SHA1: 7f3c94d170eea7d7f7de7e9be8466aa71d76f2c4 SHA256: 539accf5460d09771eab2957b13a72d4666e1f4aa768c8132b7544912274b87d SHA512: ae744ec7192d63f9c6aa0e8925500fbe0b78ee979ad82dbdc02f4cf320e14ee2b970c50ed2a03d588a7fad7d021e9139bcf19ef153cab1d5362d4e64be3569b0 Homepage: https://cran.r-project.org/package=shinylogs Description: CRAN Package 'shinylogs' (Record Everything that Happens in a 'Shiny' Application) Track and record the use of applications and the user's interactions with 'Shiny' inputs. 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Package: r-cran-shinylottie Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-jsonlite, r-cran-glue, r-cran-htmltools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-shinylottie_1.0.0-1.ca2404.1_all.deb Size: 91402 MD5sum: f2670ae9df473c95a772a80f242a2fff SHA1: 18ea91b92b87225384ef8b75694aa3572781166f SHA256: f51329691a7370861baebec7c708294b545db7cbd2b32ec1edab8474eda5a5a0 SHA512: df696e3a3261be0d481417a83d0828d2c8ef6b0b055720c8c18c4ead19053556ec6d500449aa1ad38a66ea47f5365d6ef56eef4c45e664b861de41c70c4417c5 Homepage: https://cran.r-project.org/package=shinyLottie Description: CRAN Package 'shinyLottie' (Seamlessly Integrate 'Lottie' Animations into 'shiny'Applications) Easily integrate and control 'Lottie' animations within 'shiny' applications', without the need for idiosyncratic expression or use of 'JavaScript'. 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Package: r-cran-shinylp Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinylp_1.1.3-1.ca2404.1_all.deb Size: 36130 MD5sum: 42ec24afc7403d12dd45d2789f03c15e SHA1: 7d2828d5e120016cd2fcd67ff9d2190e41c66932 SHA256: 48c4126fd77f4195f0536deee554350269c8b767dab38a40e2c835d7d1a3e9b0 SHA512: 04bdabb09c83a52862956920a71b03dbbe969219ec917fb3c08928aba637973d9a05a3cc7551951cda39a594419f584a62c705598fe4aaffdededc5b6de41626 Homepage: https://cran.r-project.org/package=shinyLP Description: CRAN Package 'shinyLP' (Bootstrap Landing Home Pages for Shiny Applications) Provides functions that wrap HTML Bootstrap components code to enable the design and layout of informative landing home pages for Shiny applications. 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Package: r-cran-shinymanager Architecture: all Version: 1.0.410-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3510 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-shiny, r-cran-htmltools, r-cran-dt, r-cran-dbi, r-cran-rsqlite, r-cran-openssl, r-cran-r.utils, r-cran-billboarder, r-cran-scrypt Suggests: r-cran-keyring, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-shinymanager_1.0.410-1.ca2404.1_all.deb Size: 1674994 MD5sum: 937b78863e6068df3bc5cc79bac47686 SHA1: cb33be7ed5b5fc794a458610cd35f4aef71bbad0 SHA256: 0399be46f728333d7ce069571eec23640b4b55ec4e85215d401c75e89aa3645a SHA512: 11467942a05b4912d944577cfd804b8c96f226bb20ef963d31c36e9abc296acebef905ec80cb4bf774da963c57b9daf83e3eea87d9346fc7c9bef913c2fc32b3 Homepage: https://cran.r-project.org/package=shinymanager Description: CRAN Package 'shinymanager' (Authentication Management for 'Shiny' Applications) Simple and secure authentification mechanism for single 'Shiny' applications. 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Package: r-cran-shinymaterial Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1653 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-jsonlite, r-cran-sass Filename: pool/dists/noble/main/r-cran-shinymaterial_1.2.0-1.ca2404.1_all.deb Size: 1152450 MD5sum: 42b0d93ea2a02b703f763b5dcd54f1b1 SHA1: 1461d1c52c26172d06334cd13cb3bf5a6709fa8f SHA256: 9cf80b25a605fac55f4fb575852191965a9589b0769a0cc4f2bfb10ffcf1affe SHA512: 44cb9298a7c53b792ac0eb62d6c8ccce40098bfd290b15b49d0f7b710082a0f6ab766ca1187fbdb81d9a051d3cf4631403e3018a432526353e4a4bd11f69eba6 Homepage: https://cran.r-project.org/package=shinymaterial Description: CRAN Package 'shinymaterial' (Implement Material Design in Shiny Applications) Allows shiny developers to incorporate UI elements based on Google's Material design. See for more information. Package: r-cran-shinymatrix Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-shinymatrix_0.8.1-1.ca2404.1_all.deb Size: 132352 MD5sum: f843a3841ef65a6927e42ce86d4b22f9 SHA1: 38ec0cb70fc9cf62aed839d86c9843d471bfdeb6 SHA256: a18e1da4a28fa0435e491003f1e0ce3a6f1b89800dde618eaa53eebd189a5e0d SHA512: d8e63bc32e5cf1319e3af595b24853801b99fe548475545a9d2e595914c6f05afae5f93719e2fd7cc00483e26cf455fc8585685c302e466d891744d8220cd950 Homepage: https://cran.r-project.org/package=shinyMatrix Description: CRAN Package 'shinyMatrix' (Shiny Matrix Input Field) Implements a custom matrix input field. Package: r-cran-shinymergely Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Suggests: r-cran-shinyjqui, r-cran-shinythemes Filename: pool/dists/noble/main/r-cran-shinymergely_0.2.0-1.ca2404.1_all.deb Size: 590844 MD5sum: e087e56087373bbc16ac43641ad68669 SHA1: 7217e6014e2669e3a8f0502a36fe021094ef7ed8 SHA256: ced6aa620c2c8f72ebba8fe8b9af35d613de0631463be30c20d928d5a3d96d14 SHA512: a4f68e98055762704ada691375d02553affc492015460dbc49252fc12cd7773fd38d430098c2385107ab58e083ad95d17fab9c505a39a7d76aa6259225b60834 Homepage: https://cran.r-project.org/package=shinyMergely Description: CRAN Package 'shinyMergely' (Compare and Merge Two Files with a 'Shiny' App) A 'Shiny' app allowing to compare and merge two files, with syntax highlighting for several coding languages. Package: r-cran-shinymeta Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-fastmap, r-cran-fs, r-cran-htmltools, r-cran-rlang, r-cran-shiny, r-cran-sourcetools, r-cran-styler Suggests: r-cran-clipr, r-cran-cranlogs, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-shinyace, r-cran-stringr, r-cran-testthat, r-cran-xfun, r-cran-zoo Filename: pool/dists/noble/main/r-cran-shinymeta_0.2.2-1.ca2404.1_all.deb Size: 133740 MD5sum: bd0ea03d0363db2d02bae5d4ef64e01a SHA1: 7a60251a76569ca55d65904188efe286436c3466 SHA256: a8a0486ba5b251e7d4fafff0dd8f64deed722f32461a0937aea0a80d3a6e9c72 SHA512: 3b2246dfa2b935b7356bba977e00ba5fed4ec9176c67dde8d73a4cc0792ab3b526edde9babcca47d0723c049657fd931c055550bf9cd082aa1f9b5f1e456404c Homepage: https://cran.r-project.org/package=shinymeta Description: CRAN Package 'shinymeta' (Export Domain Logic from Shiny using Meta-Programming) Provides tools for capturing logic in a Shiny app and exposing it as code that can be run outside of Shiny (e.g., from an R console). It also provides tools for bundling both the code and results to the end user. Package: r-cran-shinymgr Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5383 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-dbi, r-cran-reactable, r-cran-renv, r-cran-rsqlite, r-cran-shinyjs, r-cran-shinydashboard Suggests: r-cran-fs, r-cran-learnr, r-cran-shinytest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinymgr_1.1.0-1.ca2404.1_all.deb Size: 2435376 MD5sum: 50502d82038a5c1e054f6e856ea6a24f SHA1: 3f9fd4adb1c25de8572273df35e6f2ca0ef9bd43 SHA256: 2fb6590594eaf3b3c4a988a7fd71c74de0e70cf713e0f82ddbd0e0d424e77fe5 SHA512: 134d0701361c62e431b87fb416944cda981ee035ed2a96e641e0a3912f4cb85433137f3c5e2312c6fb5b9dd821ce9995c3c588d81480202556c2e6f00ebbf7bb Homepage: https://cran.r-project.org/package=shinymgr Description: CRAN Package 'shinymgr' (A Framework for Building, Managing, and Stitching 'shiny'Modules into Reproducible Workflows) A unifying framework for managing and deploying 'shiny' applications that consist of modules, where an "app" is a tab-based workflow that guides a user step-by-step through an analysis. The 'shinymgr' app builder "stitches" 'shiny' modules together so that outputs from one module serve as inputs to the next, creating an analysis pipeline that is easy to implement and maintain. Users of 'shinymgr' apps can save analyses as an RDS file that fully reproduces the analytic steps and can be ingested into an R Markdown report for rapid reporting. In short, developers use the 'shinymgr' framework to write modules and seamlessly combine them into 'shiny' apps, and users of these apps can execute reproducible analyses that can be incorporated into reports for rapid dissemination. Package: r-cran-shinymixr Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1097 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-ggplot2, r-cran-gridextra, r-cran-collapsibletree, r-cran-shinyace, r-cran-dt, r-cran-bs4dash, r-cran-shinywidgets, r-cran-stringi, r-cran-r3port, r-cran-whisker, r-cran-plotly, r-cran-patchwork, r-cran-shinyjs, r-cran-ps, r-cran-xfun, r-cran-fresh, r-cran-nlmixr2est, r-cran-magrittr, r-cran-rxode2, r-cran-cli Suggests: r-cran-xpose, r-cran-nlmixr2, r-cran-nlmixr2plot, r-cran-xpose.nlmixr2, r-cran-nlme, r-cran-testthat, r-cran-shinytest2, r-cran-knitr, r-cran-rmarkdown, r-cran-rlang, r-cran-miniui, r-cran-shinyfiles, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-shinymixr_0.5.3-1.ca2404.1_all.deb Size: 954978 MD5sum: 6c7a7227a8d4f7d47c8e8cec01382df7 SHA1: e24e862499e6568bb09e79437269f735dd69ccfb SHA256: 150de0f9a43a84445ac454250e287da8f4cc795e554334deaaefcac3151a03d7 SHA512: e41bc3548677cf3edc185c1eb872895740a205ce20f961fc280b903b0e677b2cdf903e9599c73ddc357d72bd128e2d0e43657e308716ba1ffdfd361ab2b3d6f8 Homepage: https://cran.r-project.org/package=shinyMixR Description: CRAN Package 'shinyMixR' (Interactive 'shiny' Dashboard for 'nlmixr2') An R shiny user interface for the 'nlmixr2' (Fidler et al (2019) ) package, designed to simplify the modeling process for users. Additionally, this package includes supplementary functions to further enhances the usage of 'nlmixr2'. Package: r-cran-shinyml Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-data.table, r-cran-shiny, r-cran-argondash, r-cran-argonr, r-cran-shinyjs, r-cran-h2o, r-cran-shinywidgets, r-cran-dygraphs, r-cran-plotly, r-cran-sparklyr, r-cran-tidyr, r-cran-dt, r-cran-ggplot2, r-cran-shinycssloaders, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinyml_1.0.1-1.ca2404.1_all.deb Size: 158488 MD5sum: 3fb98cad71803b352aef07b75f7ab5e6 SHA1: f5c8e2c1307a2819f6ef60ec737b53c077611870 SHA256: 27577567d204f3ca6978c83e22e0a02bcffe536452f8de766daf6b85dddbe87b SHA512: 1d6340a9fdba8a868717cb704a2e49353484fa29a1ef91f5574659d451d7596f9affdb9b6e552cc7ee25159786c2c4e3e9627144e32179e95a3d569debc77c9e Homepage: https://cran.r-project.org/package=shinyML Description: CRAN Package 'shinyML' (Compare Supervised Machine Learning Models Using Shiny App) Implementation of a shiny app to easily compare supervised machine learning model performances. You provide the data and configure each model parameter directly on the shiny app. Different supervised learning algorithms can be tested either on Spark or H2O frameworks to suit your regression and classification tasks. Implementation of available machine learning models on R has been done by Lantz (2013, ISBN:9781782162148). Package: r-cran-shinymobile Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4511 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-htmltools, r-cran-jsonlite, r-cran-magrittr, r-cran-gplots, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cli, r-cran-testthat, r-cran-rstudioapi, r-cran-shinywidgets, r-cran-apexcharter, r-cran-ggplot2, r-cran-dplyr, r-cran-bslib, r-cran-shinytest2, r-cran-thematic Filename: pool/dists/noble/main/r-cran-shinymobile_2.0.1-1.ca2404.1_all.deb Size: 1634218 MD5sum: 47ea46785d11352f8298b32ebaef6a58 SHA1: fa58888d0e10c26712d43ff4683a43fd3d0eab2a SHA256: 3d5cd26791f8defe6129bba5f04c01bf04657360c57c6d161a1f384a52657a78 SHA512: 30bd6f617de19ffaa785f481b97ab3f979c59ca7f298d5c7e7be8cbcb44ef6940a27e4c7d7c71df55183763aa4d0a29cd0b1e550b7e4ab0b64ea5a446d73299f Homepage: https://cran.r-project.org/package=shinyMobile Description: CRAN Package 'shinyMobile' (Mobile Ready 'shiny' Apps with Standalone Capabilities) Develop outstanding 'shiny' apps for 'iOS' and 'Android' as well as beautiful 'shiny' gadgets. 'shinyMobile' is built on top of the latest 'Framework7' template . Discover 14 new input widgets (sliders, vertical sliders, stepper, grouped action buttons, toggles, picker, smart select, ...), 2 themes (light and dark), 12 new widgets (expandable cards, badges, chips, timelines, gauges, progress bars, ...) combined with the power of server-side notifications such as alerts, modals, toasts, action sheets, sheets (and more) as well as 3 layouts (single, tabs and split). Package: r-cran-shinymodels Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1607 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-dt, r-cran-generics, r-cran-glue, r-cran-htmltools, r-cran-magrittr, r-cran-parsnip, r-cran-plotly, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-shiny, r-cran-shinydashboard, r-cran-tidyr, r-cran-tidyselect, r-cran-tune, r-cran-yardstick Suggests: r-cran-covr, r-cran-finetune, r-cran-knitr, r-cran-markdown, r-cran-modeldata, r-cran-rmarkdown, r-cran-shinytest, r-cran-spelling, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-shinymodels_0.1.1-1.ca2404.1_all.deb Size: 1529692 MD5sum: 7b7d5b7596f4aa673a9b5c11e5fc017a SHA1: f43eb07022d4d5f56e03d5f89857b050b3bd9787 SHA256: 5058321c25fd80cfb26978d4f6233442602d3ceb976372065b866496be7bce51 SHA512: 0c0522d0b90afce25b03bb2e6c36d0b5dd226030a56f72266523689124f25d6070c5317df57e6d3971d903d27ed7d0f92d2b0815611f55d7fce489f4b63765d5 Homepage: https://cran.r-project.org/package=shinymodels Description: CRAN Package 'shinymodels' (Interactive Assessments of Models) Launch a 'shiny' application for 'tidymodels' results. For classification or regression models, the app can be used to determine if there is lack of fit or poorly predicted points. 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It is particularly well developed for 'JavaScript'. In addition to the 'Monaco' editor features, the app provides prettifiers and minifiers for multiple languages, 'SCSS' and 'TypeScript' compilers, code checking for 'C' and 'C++' (requires 'cppcheck'). Package: r-cran-shinymrp Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3853 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bsicons, r-cran-bslib, r-cran-checkmate, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-golem, r-cran-highcharter, r-cran-htmlwidgets, r-cran-httr2, r-cran-loo, r-cran-lubridate, r-cran-magrittr, r-cran-matrix, r-cran-patchwork, r-cran-posterior, r-cran-purrr, r-cran-qs2, r-cran-r6, r-cran-rcolorbrewer, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-stringr, r-cran-tidyr, r-cran-waiter Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinymrp_0.10.0-1.ca2404.1_all.deb Size: 2145540 MD5sum: b199e3a8fda459855c732cdae53c8dba SHA1: 45a5558877c6216314e20dffc7a7f4b007807869 SHA256: 193156fa2b3ff23ce78e054efcb38feab60891b7e1eebd7790a646a5f62df684 SHA512: fcfb2a6fe5cc7692616d72c9ee4b6773ad772901e619224aa25264d0a779103f5b3ad71fae1cf70eed65b2315390735d49d08db421050b50c165c78fbb56f95f Homepage: https://cran.r-project.org/package=shinymrp Description: CRAN Package 'shinymrp' (Interface for Multilevel Regression and Poststratification) Dual interfaces, graphical and programmatic, designed for intuitive applications of Multilevel Regression and Poststratification (MRP). 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Package: r-cran-shinynextui Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4692 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-shiny, r-cran-shiny.react, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-shinytest2, r-cran-purrr, r-cran-thematic, r-cran-shiny.router, r-cran-roxy.shinylive Filename: pool/dists/noble/main/r-cran-shinynextui_0.1.0-1.ca2404.1_all.deb Size: 1565244 MD5sum: 35099b96cf99efdc4ac6c895f03f2683 SHA1: bdab7988d3c70543aea593fa7db8d1c4f6f3b8a0 SHA256: aa304715eadb296451f49b4e716cf65e61289d38e4920518bd558e745ad9f0f8 SHA512: bc788ad88dc5892a54e41aaf86622e5988a56c72ee90ade8b629ebb51e91e724b06164ac5f1c82bb9b092014b21a2c6082e49c0bdc4b80e8c2d6783b0238c9eb Homepage: https://cran.r-project.org/package=shinyNextUI Description: CRAN Package 'shinyNextUI' ('HeroUI' 'React' Template for 'shiny' Apps) A set of user interface components to create outstanding 'shiny' apps , with the power of 'React' 'JavaScript' . 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Defines providers, clients, and tokens, as well as various supporting functions and a 'shiny' module. Features include cross-site request forgery (CSRF) protection, state encryption, 'Proof Key for Code Exchange' (PKCE) handling, validation of OIDC identity tokens (nonces, signatures, claims), automatic user info retrieval, asynchronous flows, and hooks for audit logging. 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Package: r-cran-shinyquerybuilder Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1250 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-querybuilder, r-cran-rlang, r-cran-r6, r-cran-magrittr, r-cran-jsonlite, r-cran-htmltools, r-cran-shiny, r-cran-glue, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-shinyquerybuilder_0.1.0-1.ca2404.1_all.deb Size: 565006 MD5sum: 48aabf63c8da03c4c577b193111dcaea SHA1: c625d2a2ca718e819bfe399d429b210e588225e1 SHA256: 8390dd584f16b29c65942ea40606242a53f91e9f9d637875e1146cb5c6aa3bfb SHA512: 18a46396587db97f3160ece9abbe243eededbc9a2b3614e6d594c88beb6779bd27cd2e99496202685d0b0b06b52f584bcc25687e9dea39fad0f6f9270750a03e Homepage: https://cran.r-project.org/package=shinyQueryBuilder Description: CRAN Package 'shinyQueryBuilder' (Construct Complex Filtering Queries in 'Shiny') Input widget that allows to construct complex filtering queries in 'Shiny'. It's a wrapper for 'JavaScript' library 'jQuery-QueryBuilder', check . 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Build your 'recipe' by dragging the variables, visually analyze your data to decide which steps to use, add those steps and preprocess your data. 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Package: r-cran-shinyrgl Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinyrgl_0.1.0-1.ca2404.1_all.deb Size: 22824 MD5sum: cca49cad4610818f0da79b1341283ca3 SHA1: 800e660a113251cf74d653f53f3731cc7d1389a2 SHA256: 690e2ef10a6deed169267a1974e69eda316ed3a159ea07c27851047eaea32349 SHA512: 76a3c8272f39bc1fd28f160efd69777130704a4d843b5094bfe724a86c9799cdc4252ef80a45d6d2bf2fd2e2fd47746354277f61b534021a4eb45e24210bf00b Homepage: https://cran.r-project.org/package=shinyRGL Description: CRAN Package 'shinyRGL' (Shiny Wrappers for RGL) Shiny wrappers for the RGL package. This package exposes RGL's ability to export WebGL visualization in a shiny-friendly format. 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Forked from 'wallace'. Code is split into modules that are loaded and linked together automatically and each call one function. Guidance pages explain modules to users and flexible logging informs them of any errors. Options enable asynchronous operations, viewing of source code, interactive maps and data tables. Use to create complex analytical applications, following best practices in open science and software development. Includes functions for automating repetitive development tasks and an example application at run_shinyscholar() that requires install.packages("shinyscholar", dependencies = TRUE). A guide to developing applications can be found on the package website. Package: r-cran-shinyscreenshot Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-base64enc, r-cran-htmltools, r-cran-jsonlite, r-cran-shiny, r-cran-uuid Suggests: r-cran-timevis Filename: pool/dists/noble/main/r-cran-shinyscreenshot_0.2.1-1.ca2404.1_all.deb Size: 284898 MD5sum: 50e594bfa8f5c8c6166147b5456ced09 SHA1: 38f268d92e75be6b04d961cebdbfea6e78ab3d9a SHA256: 506602cbcb351f05460fefa170e9f9512fd40c719dfb9d54b256f4b992861375 SHA512: 17d122c1ac4f602cce012f4759cb76d23ba6381bd9a2410848cbb7853b549a949e2b8b4b6f877f78a28cd6791872508a750b12a4c2c18b46ad2cab403dda683c Homepage: https://cran.r-project.org/package=shinyscreenshot Description: CRAN Package 'shinyscreenshot' (Capture Screenshots of Entire Pages or Parts of Pages in 'Shiny') Capture screenshots in 'Shiny' applications. 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Package: r-cran-shinyshortcut Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-shinyshortcut_0.1.0-1.ca2404.1_all.deb Size: 24110 MD5sum: 7bea092beadb820c3c3c0c867827ca3b SHA1: 3545534913be3203daeedb94c515dffc36b03016 SHA256: 9212eb1ed0f09cafa20f9ddc86998f6e561faaa4b8c88ebe4d3fe3dc22de4001 SHA512: 09e8def4a2369ef435d266c3a0d1cc2fb17ae3ba9419d33ff267bc7b034ec638d722532e09be99635c1bd74a0c665b307d06df718a2149026a5fb0e7ce1f543c Homepage: https://cran.r-project.org/package=shinyShortcut Description: CRAN Package 'shinyShortcut' (Creates an Executable Shortcut for Shiny Applications) Provides function shinyShortcut() that, when given the base directory of a shiny application, will produce an executable file that runs the shiny app directly in the user's default browser. Tested on both windows and unix machines. Inspired by and borrowing from . Package: r-cran-shinysir Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1086 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-shiny, r-cran-desolve Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinysir_0.1.2-1.ca2404.1_all.deb Size: 644092 MD5sum: 847e1ae7aaf49c2933d862ffd0b8b291 SHA1: a97d125ba629c5a6af159b86313f7f97a56a2cdb SHA256: 1e7a6797ab3e3c016b9269f4710968cfe59675d6e8b4f59cdde37bde1c4c0173 SHA512: fcda5577c2b767291d491c1c14c942de2da77f9f07d4af3ba14e552bbfa98f4a8c081ed29655432c53ef23a1eeac58027db25f73397fcde212bd78ad8693fd8b Homepage: https://cran.r-project.org/package=shinySIR Description: CRAN Package 'shinySIR' (Interactive Plotting for Mathematical Models of InfectiousDisease Spread) Provides interactive plotting for mathematical models of infectious disease spread. Users can choose from a variety of common built-in ordinary differential equation (ODE) models (such as the SIR, SIRS, and SIS models), or create their own. This latter flexibility allows 'shinySIR' to be applied to simple ODEs from any discipline. The package is a useful teaching tool as students can visualize how changing different parameters can impact model dynamics, with minimal knowledge of coding in R. The built-in models are inspired by those featured in Keeling and Rohani (2008) and Bjornstad (2018) . 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The interface is powered by the 'Shiny' web application framework from 'RStudio' and works with the output of MCMC programs written in any programming language (and has extended functionality for 'Stan' models fit using the 'rstan' and 'rstanarm' packages). 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Package: r-cran-shinystoreplus Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 513 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-jsonlite, r-cran-htmltools, r-cran-shinywidgets Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-qpdf Filename: pool/dists/noble/main/r-cran-shinystoreplus_1.6-1.ca2404.1_all.deb Size: 111982 MD5sum: 4e2074817080205b81113350e7f5e7d8 SHA1: 16b19335b6f4bf639b2924e5eb4bc8b67a37f343 SHA256: 364205b3846ccd85e07a007b7c5e4c5e0033354239d16fbba6c973a172384549 SHA512: caa9fe7105c2bb55547e56cce62ecbb59a628c306e7a9078e3546a9674be70acadd833bb60d3d7e4e3dc270f7cb044f8942fe5aa3c081cc19815ed7ccf48591f Homepage: https://cran.r-project.org/package=shinyStorePlus Description: CRAN Package 'shinyStorePlus' (Secure in-Browser and Database Storage for 'shiny' Inputs,Outputs, Views and User Likes) Store persistent and synchronized data from 'shiny' inputs within the browser. Refresh 'shiny' applications and preserve user-inputs over multiple sessions. A database-like storage format is implemented using 'Dexie.js' , a minimal wrapper for 'IndexedDB'. Transfer browser link parameters to 'shiny' input or output values. Store app visitor views, likes and followers. Package: r-cran-shinysurveys Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 922 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-sass, r-cran-htmltools, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-shinysurveys_0.2.0-1.ca2404.1_all.deb Size: 539280 MD5sum: dd0a73908905687ee3ffc3aa7fe51cfa SHA1: 6c1b4cf83733b2420553dc96b7593c14c27d806d SHA256: 568ebbf0d4fa6a9076c9f4848ffbaeca0a476da3ba8704633830409f61857a89 SHA512: 1a36d6abc9d6416da7053776c58a88c2e98272e3cf6081dbf0498e5a9a34dd332b71bce544e172f2612b6c06e7697d9cf686547a22d8739b6158eeaea45e4c5f Homepage: https://cran.r-project.org/package=shinysurveys Description: CRAN Package 'shinysurveys' (Create and Deploy Surveys in 'Shiny') Easily create and deploy surveys in 'Shiny'. This package includes a minimalistic framework similar to 'Google Forms' that allows for url-based user tracking, customizable submit actions, easy survey-theming, and more. 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This shiny application helps exploring temporal characteristics of the evolutionary trees through linear regression analysis and with the ability to identify and remove incorrect labels. The method was extended to support exploring other phylogenetic signals under strict and relaxed models. 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Package: r-cran-shinytester Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-stringr, r-cran-tidyr, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-shinytester_0.1.0-1.ca2404.1_all.deb Size: 24418 MD5sum: 2370cc64d83d21faba8e98b811afc9a7 SHA1: 06a105b5391d63619ba9d7adfe8e4b9126f25782 SHA256: 063ea31c514814e83319b775683b1dc1add9c7d7ee9883c1efbdad35dc21eb40 SHA512: 21c13608c121e188b3808c72c2ad103ca9b16a35d5ce08bf182ad6aa643e60ba98d9c9ff34d854c1b2c09c33c2a69c6c00ef55e00b77e42591dc4ba77b787fa3 Homepage: https://cran.r-project.org/package=ShinyTester Description: CRAN Package 'ShinyTester' (Functions to Minimize Bonehead Moves While Working with 'shiny') It's my experience that working with 'shiny' is intuitive once you're into it, but can be quite daunting at first. Several common mistakes are fairly predictable, and therefore we can control for these. The functions in this package help match up the assets listed in the UI and the SERVER files, and Visualize the ad hoc structure of the 'shiny' App. Package: r-cran-shinytesters Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinytesters_0.1.0-1.ca2404.1_all.deb Size: 23912 MD5sum: 3b4051fde33b0d40112a43c04a1d3826 SHA1: e3d82b9c1ba2acef8fec3e0967666d605f09727d SHA256: 985d578d647a2f7033c5bb5df97ecf8842319785856377668cac5b1f466f219a SHA512: 87528ed9b4da557ea331b041fafb9aef2baca20cbb8e1c1d838b7a163fad89bfd822cd9ff46aed7c849f8296771d65c75a7d7c651513e60357dfc969b526b746 Homepage: https://cran.r-project.org/package=shinytesters Description: CRAN Package 'shinytesters' (Update 'Shiny' Inputs when using testServer()) Create mocked bindings to 'Shiny' update functions within test function calls to automatically update input values. 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Package: r-cran-shinytime Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-shiny Suggests: r-cran-testthat, r-cran-spelling, r-cran-hms Filename: pool/dists/noble/main/r-cran-shinytime_1.0.3-1.ca2404.1_all.deb Size: 42344 MD5sum: 2ae8c9f6f364299b9d8b30eb147a5fb4 SHA1: 6a80758cb34bcec4c2ff54d6c534b7049fce08bc SHA256: 8cca891fbc970f620fa6825818df07ed10530bd1c503de1a5bc9506fe3f10f00 SHA512: ef84f5c4094800292160c535aa97b5cf534cebaf35bcd6b1bcfc3b913b8ae3993cd0f81c26084e05f6ee58346a8138674434968aed93706f9af1329971d2b21f Homepage: https://cran.r-project.org/package=shinyTime Description: CRAN Package 'shinyTime' (A Time Input Widget for Shiny) Provides a time input widget for Shiny. This widget allows intuitive time input in the '[hh]:[mm]:[ss]' or '[hh]:[mm]' (24H) format by using a separate numeric input for each time component. 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Key features include countdown and count-up mode, multiple display formats (including simple seconds, minutes-seconds, hours-minutes-seconds, and minutes-seconds-centiseconds), ability to pause, resume, and reset the timer. 'shinytimer' widget can be particularly useful for creating interactive and time-sensitive applications, tracking session times, setting time limits for tasks or quizzes, and more. 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Package: r-cran-shinywgd Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3016 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinyalert, r-cran-stringr, r-cran-vroom, r-cran-fs, r-cran-tidyr, r-cran-data.table, r-cran-dplyr, r-cran-ape, r-cran-ks, r-cran-mclust, r-cran-htmltools, r-cran-seqinr, r-cran-httr, r-cran-jsonlite Suggests: r-cran-tidyverse, r-cran-knitr, r-cran-rmarkdown, r-cran-dt, r-cran-argparse, r-cran-bslib, r-cran-bsplus, r-cran-english, r-cran-fontawesome, r-cran-igraph, r-cran-shinybs, r-cran-shinyfiles, r-cran-shinywidgets, r-cran-shinyjs, r-cran-stringi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shinywgd_1.0.0-1.ca2404.1_all.deb Size: 893570 MD5sum: 2f30b0a43fbf4c82c1c6e1ed777a773e SHA1: 51c490dbd8d89dad8ee7b192bbf45e75b5f3c76a SHA256: ea55de09740be6a6ab7088ddbc67c2ab8d703f4b65064918200004c652893337 SHA512: 56278b0709fd4b03d87cda95a6ca4c85394cdede2f61e099e65d3420a3568498c0ec2983c9121923f8fd362937f3a454b8ee49763254d40405e9315f6e575286 Homepage: https://cran.r-project.org/package=shinyWGD Description: CRAN Package 'shinyWGD' ('Shiny' Application for Whole Genome Duplication Analysis) Provides a comprehensive 'Shiny' application for analyzing Whole Genome Duplication ('WGD') events. This package provides a user-friendly 'Shiny' web application for non-experienced researchers to prepare input data and execute command lines for several well-known 'WGD' analysis tools, including 'wgd', 'ksrates', 'i-ADHoRe', 'OrthoFinder', and 'Whale'. This package also provides the source code for experienced researchers to adjust and install the package to their own server. Key Features 1) Input Data Preparation This package allows users to conveniently upload and format their data, making it compatible with various 'WGD' analysis tools. 2) Command Line Generation This package automatically generates the necessary command lines for selected 'WGD' analysis tools, reducing manual errors and saving time. 3) Visualization This package offers interactive visualizations to explore and interpret 'WGD' results, facilitating in-depth 'WGD' analysis. 4) Comparative Genomics Users can study and compare 'WGD' events across different species, aiding in evolutionary and comparative genomics studies. 5) User-Friendly Interface This 'Shiny' web application provides an intuitive and accessible interface, making 'WGD' analysis accessible to researchers and 'bioinformaticians' of all levels. 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Give your applications a unique and colorful style ! 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Package: r-cran-shoppingwords Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringi, r-cran-stopwords, r-cran-stringdist, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-shoppingwords_0.1.0-1.ca2404.1_all.deb Size: 4172836 MD5sum: 793750a1b7c6d1283af97f4b561135c3 SHA1: 7997236b23986a0bb719efc9dee914e83ce4db30 SHA256: ba98211995d91598bb11590167b518d93660448b847a8dbe4c206dfa3ce6f6f3 SHA512: a1b3e7f8452c81d643d9f069bc04f2d54e18100bd3f29e28d5c3fbe8df5998c9b5b48ab775ffd83db1e53f93a8186dc5e26a6a2f4496a56d068616638ce0cc83 Homepage: https://cran.r-project.org/package=shoppingwords Description: CRAN Package 'shoppingwords' (Text Processing Tools for Turkish E-Commerce Data) Provides several datasets useful for processing and analysis of text in Turkish from an online shopping platform. Package: r-cran-shoredate Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2308 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggridges, r-cran-ggspatial, r-cran-sf, r-cran-terra Suggests: r-cran-covr, r-cran-elevatr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-rnaturalearth Filename: pool/dists/noble/main/r-cran-shoredate_1.1.1-1.ca2404.1_all.deb Size: 1565420 MD5sum: 24985a5ab65f2a7368ba5f1fb842fad0 SHA1: b5c946e6606e995f3301f7d7bc04a3f43744cd9e SHA256: c1851b03ff05dcb15cb82bdf2440e0a442a6cb46bd9366cda6c8e771763aae2a SHA512: 674d97b71aa36ffd482648753fc09465b63b54a2cca458e832b94c733220bd98fa56bed382c11d2b1dc0cdb94a18aba797cc7bc1bec786c726b2fd28a10506f0 Homepage: https://cran.r-project.org/package=shoredate Description: CRAN Package 'shoredate' (Shoreline Dating Coastal Stone Age Sites) Provides tools for shoreline dating coastal Stone Age sites. The implemented method was developed in Roalkvam (2023) for the Norwegian Skagerrak coast. Although it can be extended to other areas, this also forms the core area for application of the package. Shoreline dating is based on the present-day elevation of a site, a reconstruction of past relative sea-level change, and empirically derived estimates of the likely elevation of the sites above the contemporaneous sea-level when they were in use. The geographical and temporal coverage of the method thus follows from the availability of local geological reconstructions of shoreline displacement and the degree to which the settlements to be dated have been located on or close to the shoreline when they were in use. Methods for numerical treatment and visualisation of the dates are provided, along with basic tools for visualising and evaluating the location of sites. Package: r-cran-shorm Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-srmers, r-cran-ggplot2, r-cran-scales, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-shorm_0.1.3-1.ca2404.1_all.deb Size: 21866 MD5sum: e3c50fa8994c44ea40af535191ba1397 SHA1: 0412f639b65ce5f7d631711e218dfe1abb4c475d SHA256: 5347154cf4226cfe303c1629e6ffdb5fd7f1bb799b6b2bc882814ea9c54c8c94 SHA512: 7c351b9b44f4a86ccbbe6719e9f0d3dac636edd936b3653818352c45cab5172de1a026e5fb87914d6c7c378b15a5b4a8c6a046085d9e1e22ccf9c43b95e7b937 Homepage: https://cran.r-project.org/package=shorm Description: CRAN Package 'shorm' (Detect the Shape of Dose-Response Curves) Provides functions for hormesis screening by classifying the shapes of dose-response curves based on semiparametric tests. The shapes are indications of different potential toxicology effect. It also offers a scalable visualization scheme to present testing conclusions for large-scale dataset with a large number of dose-response curves. For more information, see Jin et al. (2026) . 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Package: r-cran-shortform Architecture: all Version: 0.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lavaan, r-cran-stringr, r-cran-dosnow, r-cran-foreach, r-cran-rlang Suggests: r-cran-knitr, r-cran-mplusautomation, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-shortform_0.5.8-1.ca2404.1_all.deb Size: 383190 MD5sum: 40ed5fcfc77379849501e5b39d34de28 SHA1: 2728e7f9fc145a899737a21790e74d17e2f01fe2 SHA256: b50c3c98f668dd6a9fb5edf042c36b694597635fba80a3280c8088f4b6e4dcb5 SHA512: 1c1d657aee81aac450fa75a169b77f63eea4add4d082534cc744b206d0369d983ca4d62556593ef473a3258fcaf86880c8027c6c0789420a6de74d5d99906b51 Homepage: https://cran.r-project.org/package=ShortForm Description: CRAN Package 'ShortForm' (Automatic Short Form Creation) Performs automatic creation of short forms of scales with an ant colony optimization algorithm and a Tabu search. As implemented in the package, the ant colony algorithm randomly selects items to build a model of a specified length, then updates the probability of item selection according to the fit of the best model within each set of searches. The algorithm continues until the same items are selected by multiple ants a given number of times in a row. On the other hand, the Tabu search changes one parameter at a time to be either free, constrained, or fixed while keeping track of the changes made and putting changes that result in worse fit in a "tabu" list so that the algorithm does not revisit them for some number of searches. See Leite, Huang, & Marcoulides (2008) for an applied example of the ant colony algorithm, and Marcoulides & Falk (2018) for an applied example of the Tabu search. Package: r-cran-shortirt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-mass, r-cran-rmarkdown, r-cran-sirt, r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-shortirt_1.0.0-1.ca2404.1_all.deb Size: 103860 MD5sum: 2566aa680e8aa5bfb1453e3a8d2c0980 SHA1: bacdc32000639d8fddfc7bde808b952fb9b2cd06 SHA256: 2ba7b4399860e5e4fab20c76a4728903617f794b188b41b4f74b271f9d63f8e0 SHA512: f7cb4b7fc5907b69c7202734b5dd5c3f988225b049534f53145a1c1ba6daf37279ab615c8781d80a2956a8ddb4054bb8a6e23c56632c84df49d4d334c2e9fe4d Homepage: https://cran.r-project.org/package=shortIRT Description: CRAN Package 'shortIRT' (Procedures Based on Item Response Theory Models for theDevelopment of Short Test Forms) Implement different Item Response Theory (IRT) based procedures for the development of static short test forms (STFs) from a test. Two main procedures are considered (Epifania, Anselmi & Robusto, 2022 ). The procedures differ in how the most informative items are selected for the inclusion in the STF, either by considering their item information functions without any reference to any specific latent trait level (benchmark procedure) or by considering their information with respect to specific latent trait levels, denoted as theta targets (theta target procedure). Three methods are implemented for the definition of the theta targets: (i) as the midpoints of equal intervals on the latent trait, (ii) as the centroids of the clusters obtained by clustering the latent trait, and (iii) as user-defined values. Importantly, the number of theta targets defines the number of items included in the STF. For further details on the procedure, please refer to Epifania, Anselmi & Robusto (2022) . Package: r-cran-shortr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-shortr_1.0.3-1.ca2404.1_all.deb Size: 31828 MD5sum: abe1ee22366d8acd5407e6a535988881 SHA1: d6f2481ef1ab293b0f592530e5df7ad5abf39585 SHA256: 5dc99c7e1d8c7db9b0a9ca1a48d145fd2e499ab362768a29386a2bedb72c9e33 SHA512: ac7024b328b325c356c7e914460b6172af6d5a20c2dd161e9154a6bc3b447b0accf3ebd361383ebb8c52f5dceb6e2b3b1cd00f347d6b3673a15d0ccfb769c989 Homepage: https://cran.r-project.org/package=shortr Description: CRAN Package 'shortr' (Develop Concise but Comprehensive Shortened Versions ofPsychometric Instruments) Operationalizes the identification problem of which subset of items should be kept in the shortened version of a said psychometric instrument to best represent the set of items comprised in the original version of the said psychometric instrument. Package: r-cran-shorts Architecture: all Version: 3.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4679 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lambertw, r-cran-tidyr, r-cran-ggplot2, r-cran-minpack.lm, r-cran-purrr Filename: pool/dists/noble/main/r-cran-shorts_3.2.0-1.ca2404.1_all.deb Size: 3797256 MD5sum: c1bcf0de27dd63b6d795c3f119dc1b53 SHA1: 680c9455f073d589fd60024cff61b3f15722dbbc SHA256: f977471c227230763505ef684c3b5b5b86f6888c4ddcf00f59e5c501c7a07931 SHA512: 8d124c907cda3f60f90a49e69d6ffa27c382e502b11d58b2e5b09d3ea448b914a7ab85df002e0fdcef8de93ce1c64d1511b37436a6309895af80b67209b103dd Homepage: https://cran.r-project.org/package=shorts Description: CRAN Package 'shorts' (Short Sprints) Create short sprint acceleration-velocity (AVP) and force-velocity (FVP) profiles and predict kinematic and kinetic variables using the timing-gate split times, laser or radar gun data, tether devices data, as well as the data provided by the GPS and LPS monitoring systems. The modeling method utilized in this package is based on the works of Furusawa K, Hill AV, Parkinson JL (1927) , Greene PR. (1986) , Chelly SM, Denis C. (2001) , Clark KP, Rieger RH, Bruno RF, Stearne DJ. (2017) , Samozino P. (2018) , Samozino P. and Peyrot N., et al (2022) , Clavel, P., et al (2023) , Jovanovic M. (2023) , and Jovanovic M., et al (2024) . Package: r-cran-shotgroups Architecture: all Version: 0.8.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2798 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-compquadform, r-cran-kernsmooth Suggests: r-cran-knitr, r-cran-coin, r-cran-robustbase, r-cran-energy, r-cran-mvoutlier, r-cran-shiny, r-cran-jsonlite, r-cran-interp, r-cran-mba, r-cran-bs4dash Filename: pool/dists/noble/main/r-cran-shotgroups_0.8.4-1.ca2404.1_all.deb Size: 2069000 MD5sum: 5f2ef279a4d1c60d837a3c1ba5a6938b SHA1: 4bce33c251beb45b7b74bac506f3140b271014e9 SHA256: da327d966a9887fb53c84f121934abb30d10b3af260fcc12770d810c5597dd0b SHA512: 7cfe7743952308cfd5f64a8d0e96f6488ef207c02443871a236530de3d351d9fb0d85d631102616999d8d5cd17d7a12f274f079211b8a25ca753d4678bf6bc23 Homepage: https://cran.r-project.org/package=shotGroups Description: CRAN Package 'shotGroups' (Analyze Shot Group Data) Analyzes shooting data with respect to group shape, precision, and accuracy. This includes graphical methods, descriptive statistics, and inference tests using standard, but also non-parametric and robust statistical methods. Implements distributions for radial error in bivariate normal variables. Works with files exported by 'OnTarget PC/TDS', 'Silver Mountain' e-target, 'ShotMarker' e-target, 'SIUS' e-target, or 'Taran', as well as with custom data files in text format. Supports inference from range statistics such as extreme spread. Includes a set of web-based graphical user interfaces. 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Package: r-cran-shred Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clustofvar Filename: pool/dists/noble/main/r-cran-shred_1.0.0-1.ca2404.1_all.deb Size: 44678 MD5sum: dbc89b6e47af21beb43c6c527971711f SHA1: 635a7ef6858f25a9fd2a714f5f2aeb2d0390ec98 SHA256: bec8920f524a5310eefc2b95b74de1666edab46aeb26ae6c56ece75713cc830d SHA512: 22ad117bc3b8ae31d192bc94df5712645bea96beb3604a2cfd8f508146e04c3f2274defebdd9fca16eba783db9222d5266a8311009e1271ed8de5b2d4c557d8d Homepage: https://cran.r-project.org/package=SHRED Description: CRAN Package 'SHRED' (Setwise Hierarchical Rate of Erroneous Discovery) Setwise Hierarchical Rate of Erroneous Discovery (SHRED) methods for setwise variable selection with false discovery rate (FDR) control. Setwise variable selection means that sets of variables may be selected when the true variable cannot be identified. This allows us to maintain FDR control but increase power. Details of the SHRED methods are in Organ, Kenney & Gu (2026) . 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Package: r-cran-shrinkem Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-extradistr, r-cran-brms, r-cran-cholwishart, r-cran-matrixcalc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-shrinkem_0.2.0-1.ca2404.1_all.deb Size: 44692 MD5sum: ff62ff08e326fbf9555ad0f9c6869480 SHA1: cf44ec3f7e05d9689c898b92b19b97573695b629 SHA256: 8f21ec9b158ca289a88ff8c8eab3de4251d58c86bb573c218dafc00ece8a037b SHA512: 2dea7e3112e8f2bc3f9dd3979f2457aa33084adb6dc7240aab09872a6bb8fb34176e063dea3138ee78dca471bfc85c085f3008647aeeab18cb3997ae1241b15a Homepage: https://cran.r-project.org/package=shrinkem Description: CRAN Package 'shrinkem' (Approximate Bayesian Regularization for Parsimonious Estimates) Approximate Bayesian regularization using Gaussian approximations. 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The package leverages normalizing flows to approximate complex posterior distributions. For details on implementation, see Knaus (2025) . Package: r-cran-shroomdk Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-httr Filename: pool/dists/noble/main/r-cran-shroomdk_0.3.0-1.ca2404.1_all.deb Size: 34084 MD5sum: a0dc2eff849d3c8bfd7c8feac29d1174 SHA1: 4c4c93d09d73d19371412337806ec4dfe92bc556 SHA256: 8bade166df4513e7d2c37855b64b6ca348bc3354f03f1dc7f907cf1abd86433d SHA512: 051b662cf0a8f7883e2619026799dfc6c99e6e751da700a7eb9661f15db6a30ee101a2eb28b90f2bef80509bf3a3fc856c473fb27ef6fab0d60f7868c63a90b1 Homepage: https://cran.r-project.org/package=shroomDK Description: CRAN Package 'shroomDK' (Accessing the Flipside Crypto ShroomDK API) Programmatic access to Flipside Crypto data via the Compass RPC API: . 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Package: r-cran-signaturesurvival Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4626 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forestplot, r-cran-gplots, r-cran-gtools, r-cran-survival, r-cran-survminer, r-cran-ggplot2 Suggests: r-cran-rmisc Filename: pool/dists/noble/main/r-cran-signaturesurvival_1.0.0-1.ca2404.1_all.deb Size: 4632416 MD5sum: a2e8b6919acb34ee829606bc72e62a10 SHA1: eb753c70b0c42234b5764c130cba926acb9a73c2 SHA256: 03df08e2b892c96e90dd1d41729329c4d98697578db962a0e6adfcd5cec263a3 SHA512: b90449cfe104991adf020e372ba4e4ac0cc31ec92d282e9326afc1c262d0f75bf740fad62cb477f6c3be0a576ec557a22a0723e69328f201a06a2adfe8891721 Homepage: https://cran.r-project.org/package=signatureSurvival Description: CRAN Package 'signatureSurvival' (Signature Survival Analysis) When multiple Cox proportional hazard models are performed on clinical data (month or year and status) and a set of differential expressions of genes, the results (Hazard risks, z-scores and p-values) can be used to create gene-expression signatures. 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Furkan Gursoy and Bertan Badur. "Extracting the signed backbone of intrinsically dense weighted networks." Journal of Complex Networks. . Package: r-cran-signibox Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-signibox_1.0-1.ca2404.1_all.deb Size: 10140 MD5sum: 50252ac7776ff901e6508b4865d3559b SHA1: 16282fed626d86e6fae752cf53b17413ab75ffe1 SHA256: 4135e8ee98cd2895cb0ab5fee321fa5357d01ff829b9ec237cafef06e3caf9e9 SHA512: 10b6e0d0b4c1f1b90b4742f2490e7cde5586047c727f819f9024d9015dfae79eed6530144cc4b52865fa07785075641ebd3e2b78d74c14970de196f0dd4d8e6b Homepage: https://cran.r-project.org/package=signibox Description: CRAN Package 'signibox' (Statistical Significance Marks on Boxplots) Add significance marks to any R Boxplot, including a given significance niveau. 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The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) . 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Traditionally, pathway analysis methods regard pathways as collections of single genes and treat all genes in a pathway as equally informative. However, this can lead to identifying spurious pathways as statistically significant since components are often shared amongst pathways. SIGORA seeks to avoid this pitfall by focusing on genes or gene pairs that are (as a combination) specific to a single pathway. In relying on such pathway gene-pair signatures (Pathway-GPS), SIGORA inherently uses the status of other genes in the experimental context to identify the most relevant pathways. The current version allows for pathway analysis of human and mouse datasets. In addition, it contains pre-computed Pathway-GPS data for pathways in the KEGG and Reactome pathway repositories and mechanisms for extracting GPS for user-supplied repositories. 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Package: r-cran-sihr Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 986 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvxr, r-cran-glmnet Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-sihr_2.1.1-1.ca2404.1_all.deb Size: 809462 MD5sum: 873eb84cedbfd7f53a28d9f00c4b9d5a SHA1: 3ea5979940c8deeb0145df524e45ae6711231bd0 SHA256: 14de48e252175579b58b5b55c8cac8234cd5f06a89929605bc548fc8e685d41a SHA512: 76b9c62d84248e8f257990020640d10f100124d8964bca7ae7a38e89e336324fa289cf5e8ca39b6133c49364ac66a72c7863681e233fe1de8650b31e7d52db63 Homepage: https://cran.r-project.org/package=SIHR Description: CRAN Package 'SIHR' (Statistical Inference in High Dimensional Regression) The goal of SIHR is to provide inference procedures in the high-dimensional generalized linear regression setting for: (1) linear functionals , (2) conditional average treatment effects, (3) quadratic functionals , (4) inner product, (5) distance. Package: r-cran-siie Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyfst Filename: pool/dists/noble/main/r-cran-siie_0.4.0-1.ca2404.1_all.deb Size: 35010 MD5sum: 2ac66b629de8ecc22eaa29229b65e803 SHA1: 63db26260c6da6e33ecda6234cc81370d228e384 SHA256: 90689d1365e7abb6e6680e36ec1fd6e552455250a6325217e05201b8a4d5f207 SHA512: e2bf4d02b6ff30c206967b4fbf9898ed06662d19a2034b4fe5d91694213527f69d97095b53122c3a6947aae726e93545b32a00a04e323688374853d5dea93853 Homepage: https://cran.r-project.org/package=siie Description: CRAN Package 'siie' (Superior Identification Index and Its Extensions) Calculate superior identification index and its extensions. Measure the performance of journals based on how well they could identify the top papers by any index (e.g. citation indices) according to Huang & Yang. (2022) . These methods could be extended to evaluate other entities such as institutes, countries, etc. Package: r-cran-siland Architecture: all Version: 3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1827 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-lme4, r-cran-sp, r-cran-raster, r-cran-ggplot2, r-cran-ggforce, r-cran-fasterize, r-cran-reshape2, r-cran-fields Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-siland_3.0.2-1.ca2404.1_all.deb Size: 1716108 MD5sum: 81899cca1f6ed83181692ab216f8a724 SHA1: 66b97a928ab01c76615525fcdc714bc63c166dd4 SHA256: fc349e6af8fadd7d3a1f9ad428c2643174e6467c123ab2320b161b308383860c SHA512: fe40fd0089df9870877a167165fb941959f75f1c4512b416004ccb955f79ad175572ced9bd30d49fcaafb2475489ae230a65a0f05b8a5b5ac443a4d09a977999 Homepage: https://cran.r-project.org/package=siland Description: CRAN Package 'siland' (Spatial Influence of Landscape) Method to estimate the spatial influence scales of landscape variables on a response variable. The method is based on Chandler and Hepinstall-Cymerman (2016) Estimating the spatial scales of landscape effects on abundance, Landscape ecology, 31: 1383-1394, . Package: r-cran-silfs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-glmnet, r-cran-ckmeans.1d.dp Filename: pool/dists/noble/main/r-cran-silfs_0.1.0-1.ca2404.1_all.deb Size: 98772 MD5sum: 8285f3acd9b6e1162d3d43a68a318b0b SHA1: 13daa14bcd1bd00ff310868d565660122358c4d3 SHA256: c9f5527ef8d7940c8a8fd6516d4f5e883c86a8dd3242c5e4e2971e4018e6669a SHA512: 31c745457c27f897a25e41dafb356d86059fb31e61052f11e6f70a08a439466b6f1e4e9aef4effef740ecea5d505c54716132db9b320485611f648697e34755e Homepage: https://cran.r-project.org/package=SILFS Description: CRAN Package 'SILFS' (Subgroup Identification with Latent Factor Structure) In various domains, many datasets exhibit both high variable dependency and group structures, which necessitates their simultaneous estimation. This package provides functions for two subgroup identification methods based on penalized functions, both of which utilize factor model structures to adapt to data with cross-sectional dependency. The first method is the Subgroup Identification with Latent Factor Structure Method (SILFSM) we proposed. By employing Center-Augmented Regularization and factor structures, the SILFSM effectively eliminates data dependencies while identifying subgroups within datasets. For this model, we offer optimization functions based on two different methods: Coordinate Descent and our newly developed Difference of Convex-Alternating Direction Method of Multipliers (DC-ADMM) algorithms; the latter can be applied to cases where the distance function in Center-Augmented Regularization takes L1 and L2 forms. The other method is the Factor-Adjusted Pairwise Fusion Penalty (FA-PFP) model, which incorporates factor augmentation into the Pairwise Fusion Penalty (PFP) developed by Ma, S. and Huang, J. (2017) . Additionally, we provide a function for the Standard CAR (S-CAR) method, which does not consider the dependency and is for comparative analysis with other approaches. Furthermore, functions based on the Bayesian Information Criterion (BIC) of the SILFSM and the FA-PFP method are also included in 'SILFS' for selecting tuning parameters. For more details of Subgroup Identification with Latent Factor Structure Method, please refer to He et al. (2024) . Package: r-cran-silhouette Architecture: all Version: 0.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 728 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-lifecycle Suggests: r-cran-proxy, r-cran-ppclust, r-cran-blockcluster, r-cran-cluster, r-cran-factoextra, r-cran-drclust, r-cran-tidyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-silhouette_0.9.6-1.ca2404.1_all.deb Size: 412328 MD5sum: c2fa4a4bb52e7c1c5c43449f20c09749 SHA1: 1f66f49e465313e362672f3bc47026114959d0ac SHA256: 4b19a78f0da1b63fd74afc485b58d6ebcce1782daf67c95cdb5d6bd065cce936 SHA512: d8410e2b342c666b4c4369ec1c050fad254b4489e457b5c18ea56dff19ffdfd85eef2cee47b54284c789a790bcb577c51af4a44b2df5f3197f70404cca26eb30 Homepage: https://cran.r-project.org/package=Silhouette Description: CRAN Package 'Silhouette' (Proximity Measure Based Diagnostics for Standard, Soft, andMulti-Way Clustering) Quantifies clustering quality by measuring both cohesion within clusters and separation between clusters. Implements advanced silhouette width computations for diverse clustering structures, including: simplified silhouette (Van der Laan et al., 2003) , Probability of Alternative Cluster normalization methods (Raymaekers & Rousseeuw, 2022) , fuzzy clustering and silhouette diagnostics using membership probabilities (Campello & Hruschka, 2006; Menardi, 2011; Bhat & Kiruthika, 2024) , , , and multi-way clustering extensions such as block and tensor clustering (Schepers et al., 2008; Bhat & Kiruthika, 2025) , . Provides tools for computation and visualization (Rousseeuw, 1987) to support robust and reproducible cluster diagnostics across standard, soft, and multi-way clustering settings. Package: r-cran-silicate Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3220 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gibble, r-cran-purrr, r-cran-rlang, r-cran-decido, r-cran-tibble, r-cran-unjoin, r-cran-magrittr, r-cran-gridbase, r-cran-crsmeta Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-sp, r-cran-testthat, r-cran-trip, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-silicate_0.7.1-1.ca2404.1_all.deb Size: 1986140 MD5sum: 98959966641503e1e95015fd99020eb0 SHA1: 654ac768e062a73074a36007b1ed79dd6f9f8b20 SHA256: 33e4eac049bc589f5de3235858e492494e0572149bac2ee89cd86074da446272 SHA512: 8f4251cf12a36311dfa6846152308e0531b08ea22b9b3fbed2ef72693bd164bbd006b7516a863fbcc2df91c3a9bdaefd635975be9f1e00bf3b3839e6c41eb3b8 Homepage: https://cran.r-project.org/package=silicate Description: CRAN Package 'silicate' (Common Forms for Complex Hierarchical and Relational DataStructures) Generate common data forms for complex data suitable for conversions and transmission by decomposition as paths or primitives. Paths are sequentially-linked records, primitives are basic atomic elements and both can model many forms and be grouped into hierarchical structures. The universal models 'SC0' (structural) and 'SC' (labelled, relational) are composed of edges and can represent any hierarchical form. Specialist models 'PATH', 'ARC' and 'TRI' provide the most common intermediate forms used for converting from one form to another. The methods are inspired by the simplicial complex and provide intermediate forms that relate spatial data structures to this mathematical construct. Package: r-cran-sillyputty Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1950 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster, r-cran-thresher, r-cran-oompabase, r-cran-polychrome Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mercator, r-cran-umpire, r-cran-mclust Filename: pool/dists/noble/main/r-cran-sillyputty_0.4.2-1.ca2404.1_all.deb Size: 1175380 MD5sum: c71c363a8153822f861877aa17c146ba SHA1: 9fb5e548f7b1907d8351f3a7f1e864e4dc0be179 SHA256: ffcf120f462641c631c782107b520445e466fe31281a25d92a4f3782ef2207da SHA512: ce34c0bad611e735864b5a0f7dae5758b23ba77164958acf410d9fb8a6b9ff3465add1dc92f5fedfe593a9c3706d2172604a18f581254451589ccc574e8e3f63 Homepage: https://cran.r-project.org/package=SillyPutty Description: CRAN Package 'SillyPutty' (Silly Putty Clustering) Implements a simple, novel clustering algorithm based on optimizing the silhouette width. See for details. Package: r-cran-silm Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scalreg, r-cran-glmnet, r-cran-hdi, r-cran-sis Filename: pool/dists/noble/main/r-cran-silm_1.0.0-1.ca2404.1_all.deb Size: 30892 MD5sum: 246fefeb7e254edaa341b1ca9158b1f9 SHA1: d0b184acfc29c76e9559c472e5243d7d02dc0d88 SHA256: 7ceb52489d6746b69530559dc3a1f32f52f0ce0e67729269bc48534ff0b41edd SHA512: c79e625be5fe4b2fb93ba93c25a8ea9e43c6f4f1e279e8fb055a5b96e951b85cc2a5b8d189635b773e27558cf447defe76c686c4f5238587a33bdaa87f1cde41 Homepage: https://cran.r-project.org/package=SILM Description: CRAN Package 'SILM' (Simultaneous Inference for Linear Models) Simultaneous inference procedures for high-dimensional linear models as described by Zhang, X., and Cheng, G. (2017) . Package: r-cran-silp Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-lavaan, r-cran-mass, r-cran-purrr, r-cran-semtools, r-cran-stringr Filename: pool/dists/noble/main/r-cran-silp_1.0.3-1.ca2404.1_all.deb Size: 82618 MD5sum: 86f3da4018baaa447cd23d10a9ea539e SHA1: 42d041136451f5d2522b577ebbd483b1d10f70f5 SHA256: 2136b14abb899f195dadf94b65d9d04600bbeb0a57efd7f63c9fe7e7e7d86f42 SHA512: 9d9f6537ee17b6304c1db125d67725362faa8335ce620a991166b7c20c309c0243672b35e2a01ec02ff840b6e4c0bff11c17dda622a4f43b51e261aa767991a1 Homepage: https://cran.r-project.org/package=silp Description: CRAN Package 'silp' (Conditional Process Analysis (CPA) via SEM Approach) Utilizes the Reliability-Adjusted Product Indicator (RAPI) method to estimate effects among latent variables, thus allowing for more precise definition and analysis of mediation and moderation models. Our simulation studies reveal that while 'silp' may exhibit instability with smaller sample sizes and lower reliability scores (e.g., N = 100, 'omega' = 0.7), implementing nearest positive definite matrix correction and bootstrap confidence interval estimation can significantly ameliorate this volatility. When these adjustments are applied, 'silp' achieves estimations akin in quality to those derived from LMS. In conclusion, the 'silp' package is a valuable tool for researchers seeking to explore complex relational structures between variables without resorting to commercial software. Cheung et al.(2021) Hsiao et al.(2018). Package: r-cran-silviculture Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-rlang, r-cran-s7 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-silviculture_0.2.0-1.ca2404.1_all.deb Size: 471452 MD5sum: af68b5acecae9c5a700128563335c28c SHA1: 54881e315ad23e75d4194eded4da05773e186190 SHA256: 19880493e2298e8f3202f4f59d124071f18b2d92eb0ef93c95cbcacd698a2f08 SHA512: 8148ddf183449842cd513720c544587edf4bfeb89dd5c00afe3c199464a16a35d746f628f83d7fa694813e71fb241b6807ffc0e8882970453cd0b02eaf6b3aee Homepage: https://cran.r-project.org/package=silviculture Description: CRAN Package 'silviculture' (Utility Functions for Forest Inventory and Silviculture) Perform common dendrometry operations such as inventory preparing, and inventory data analysis. Package: r-cran-sim.ba Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chk, r-cran-cobalt, r-cran-ggplot2, r-cran-scales, r-cran-pbapply, r-cran-rlang, r-cran-survival Suggests: r-cran-matchit, r-cran-weightit, r-cran-openxlsx, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sim.ba_0.1.0-1.ca2404.1_all.deb Size: 226994 MD5sum: 54e06b7af16d721ff2ba8d18a3ed03ad SHA1: 8e2ff23662fde252c5b41bec496488a9aa626140 SHA256: 3a538eb2b819d939b7448f68d4796de278fc6eb86eb2f8c142835b9669946111 SHA512: 3c8a63d9cd97e05b7f1b441eaccdcbbe10686e47d638e687e92840f4cdab23e56de547d5431db2d1e2796f0bf198f76c980cc0d0d3fedbdd5a122c794899c83b Homepage: https://cran.r-project.org/package=sim.BA Description: CRAN Package 'sim.BA' (Simulation-Based Bias Analysis for Observational Studies) Allows user to conduct a simulation based quantitative bias analysis using covariate structures generated with individual-level data to characterize the bias arising from unmeasured confounding. Users can specify their desired data generating mechanisms to simulate data and quantitatively summarize findings in an end-to-end application using this package. Package: r-cran-sim.diffproc Architecture: all Version: 5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1997 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-deriv, r-cran-mass Suggests: r-cran-desolve, r-cran-knitr, r-cran-rgl, r-cran-rmarkdown, r-cran-scatterplot3d, r-cran-sm Filename: pool/dists/noble/main/r-cran-sim.diffproc_5.0-1.ca2404.1_all.deb Size: 1468832 MD5sum: d29a50757341e11c95e29165d06df1c7 SHA1: 1ee824da429ee2fbd46683411583b3a07a8ccd97 SHA256: f09b5f8624eca79711c9b11ea289ebbaeafe90faca76e996392f3efea8062d58 SHA512: 34fb1d2c3bf9d14cd71fbca1155c0a21d8e62d817f73d312b97b0631a2e6aca74c46c27859b850680f4d55343a767d9d60bda3e2e4e3327584941fff6ac5722b Homepage: https://cran.r-project.org/package=Sim.DiffProc Description: CRAN Package 'Sim.DiffProc' (Simulation of Diffusion Processes) It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs . Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2. Package: r-cran-sim.plfn Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fuzzynumbers, r-cran-distrib Filename: pool/dists/noble/main/r-cran-sim.plfn_1.0-1.ca2404.1_all.deb Size: 50592 MD5sum: 1622edd22bafc84f8c23103a61500ce8 SHA1: 228f2b124301d28cd9ba57f410bdefc2767f523e SHA256: 04d0380fd1ee0df19936e01288c67b9ef5f69907777e6dd40dfb4dd99c36046d SHA512: 20940d4550ad9dfd96b80781e0da553409f591fc8c185c87db4769d35aa59a1b19c5e56522375661ea66ea1d60b47b5271f8af37d59b5bc1cac4282b18fd632f Homepage: https://cran.r-project.org/package=Sim.PLFN Description: CRAN Package 'Sim.PLFN' (Simulation of Piecewise Linear Fuzzy Numbers) The definition of fuzzy random variable and the methods of simulation from fuzzy random variables are two challenging statistical problems in three recent decades. This package is organized based on a special definition of fuzzy random variable and simulate fuzzy random variable by Piecewise Linear Fuzzy Numbers (PLFNs); see Coroianua et al. (2013) for details about PLFNs. Some important statistical functions are considered for obtaining the membership function of main statistics, such as mean, variance, summation, standard deviation and coefficient of variance. Some of applied advantages of 'Sim.PLFN' package are: (1) Easily generating / simulation a random sample of PLFN, (2) drawing the membership functions of the simulated PLFNs or the membership function of the statistical result, and (3) Considering the simulated PLFNs for arithmetic operation or importing into some statistical computation. Finally, it must be mentioned that 'Sim.PLFN' package works on the basis of 'FuzzyNumbers' package. Package: r-cran-sim1000g Architecture: all Version: 1.40-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1737 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hapsim, r-cran-mass, r-cran-stringr, r-cran-readr Suggests: r-cran-knitr, r-cran-prettydoc, r-cran-testthat, r-cran-gplots, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sim1000g_1.40-1.ca2404.1_all.deb Size: 1150272 MD5sum: 5398d77a4097ef03380de4c4353c7e91 SHA1: 3f3ee0359328e2dbf1c8a4ec7f72e6b2462ebe92 SHA256: 30f8428dacdaf4c74932fb742196cbd9d96302d7d67d4d3700c108ca2fd28959 SHA512: 2c9a5539f3bf258f58c00f3ba4fb96c4f0de39bccec91e7a3d5cfaf0c0f1517792b3a5d12420c604e1662df162436232b8dee1919c2380f71ec901bbf382be04 Homepage: https://cran.r-project.org/package=sim1000G Description: CRAN Package 'sim1000G' (Genotype Simulations for Rare or Common Variants UsingHaplotypes from 1000 Genomes) Generates realistic simulated genetic data in families or unrelated individuals. Package: r-cran-sim2dpredictr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-rdpack, r-cran-spam, r-cran-tibble, r-cran-dplyr, r-cran-matrixcalc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-sim2dpredictr_0.1.1-1.ca2404.1_all.deb Size: 198922 MD5sum: bc0ef7961f2bcef04bd99e781db081bc SHA1: 076fd5a43977f390e03ff0706b38a8dc71768363 SHA256: 32be35f14ad9368ecbd5071f3b827c279e87c7e524cf1288d78b924b4d9f2d7d SHA512: e33384e1c8cc5b72f91c5e0705411c69c0f0d2d844d001064f1183d2a4a99ed8cc8350eac0e9eca7a8d4ac613d51c1ccc2fc7e046df8f6d37aea3bc22c218d2b Homepage: https://cran.r-project.org/package=sim2Dpredictr Description: CRAN Package 'sim2Dpredictr' (Simulate Outcomes Using Spatially Dependent Design Matrices) Provides tools for simulating spatially dependent predictors (continuous or binary), which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous predictors are generated using traditional multivariate normal distributions or Gauss Markov random fields with several correlation function approaches (e.g., see Rue (2001) and Furrer and Sain (2010) ), while binary predictors are generated using a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors exhibiting spatial clustering can also be easily specified by the user. Package: r-cran-simaerep Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 681 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-forcats, r-cran-cowplot, r-cran-rcolorbrewer, r-cran-furrr, r-cran-progressr, r-cran-knitr, r-cran-tibble, r-cran-dbplyr, r-cran-glue Suggests: r-cran-testthat, r-cran-devtools, r-cran-pkgdown, r-cran-spelling, r-cran-haven, r-cran-vdiffr, r-cran-lintr, r-cran-dbi, r-cran-duckdb, r-cran-ggextra Filename: pool/dists/noble/main/r-cran-simaerep_1.0.0-1.ca2404.1_all.deb Size: 619250 MD5sum: b42bfa4f10c744d544dc8a73b9d19019 SHA1: 746fec7e2b7042be50b712f523aab8a63184047b SHA256: 21b0451385f3eb4ef890bcf26cee5e025b4e3b74d4800caa0e315468afab88a3 SHA512: dccfa380e98ea956a5c3270ed0c20f086a44f3e446887388961f25a773ef677cc77785079dc070dc8e9f2a7efab8f5710aa7f0ce45be4d20320a0f5416b73585 Homepage: https://cran.r-project.org/package=simaerep Description: CRAN Package 'simaerep' (Detect Clinical Trial Sites Over- or Under-Reporting ClinicalEvents) Monitoring reporting rates of subject-level clinical events (e.g. adverse events, protocol deviations) reported by clinical trial sites is an important aspect of risk-based quality monitoring strategy. Sites that are under-reporting or over-reporting events can be detected using bootstrap simulations during which patients are redistributed between sites. Site-specific distributions of event reporting rates are generated that are used to assign probabilities to the observed reporting rates. (Koneswarakantha 2024 ). Package: r-cran-simbarepro Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ddalpha, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-simbarepro_0.1.0-1.ca2404.1_all.deb Size: 233470 MD5sum: 6ca1a3b4f71a21fd1a061d71c9a89e14 SHA1: 5be9a4fdace379b161971605b4b2e616d05cbb34 SHA256: edec1ef393035479c9151a8a987b410aa250eaac71134ed8e0f543a23edaa3ea SHA512: 4785534c414b6813508b5fd6d19ca2980b6eaf330f32fed63f8fca70e50a95c1834e4b43e5f1151dc06fe22e29825238a7cf707d5b612039d20bca15a565018f Homepage: https://cran.r-project.org/package=SimBaRepro Description: CRAN Package 'SimBaRepro' (Simulation-Based, Finite-Sample Inference via Repro Samples) Functions for obtaining p-values (for hypothesis tests), confidence intervals, and multivariate confidence sets. In particular, the method is compatible with differentially private dataset, as long as the privacy mechanism is known. For more details, see Awan and Wang (2024), "Simulation-based, Finite-sample Inference for Privatized Data", . Package: r-cran-simbkmrdata Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-bkmr, r-cran-fields, r-cran-gt, r-cran-quarto, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-simbkmrdata_0.2.1-1.ca2404.1_all.deb Size: 586214 MD5sum: f0d445be2f3c04e547fa97d9d9ff5bc7 SHA1: cb5fd039f5fdeef23bccfeec59dedbf4cbdc6069 SHA256: 5853691e242a737ba451819009f1421068d44dda06a1916ae3347e8b5e294a90 SHA512: e4efd6367742d45055781fddf93c9e02cda9f31da4fbe17819aeaf6aa86d3bced73221b672eb9fca98b8448500385764d2d4c5549c22d4680920ada0c0472d80 Homepage: https://cran.r-project.org/package=simBKMRdata Description: CRAN Package 'simBKMRdata' (Helper Functions for Bayesian Kernel Machine Regression) Provides a suite of helper functions to support Bayesian Kernel Machine Regression (BKMR) analyses in environmental health research. 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Package: r-cran-simboot Architecture: all Version: 0.2-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-simboot_0.2-8-1.ca2404.1_all.deb Size: 120640 MD5sum: 66639ebb075043e5e3dfa732d611cd47 SHA1: 8f1cf732dcf968c15b08bd1bcb42f1009dbc99b9 SHA256: 8693188a8a661f79e9d8a20f935f161dcd1b23c06524f25a0b57fce66b62b338 SHA512: c71d71b52a601c72861e939551e7414884c9c0707fa5cbf93cf62aa73dc549e12ef5bc5c878df23a90ff203f2d4e41b18dc3f0df8df9de22d8963e10ec883d6a Homepage: https://cran.r-project.org/package=simboot Description: CRAN Package 'simboot' (Simultaneous Inference for Diversity Indices) Provides estimation of simultaneous bootstrap and asymptotic confidence intervals for diversity indices, namely the Shannon and the Simpson index. Several pre--specified multiple comparison types are available to choose. Further user--defined contrast matrices are applicable. In addition, simboot estimates adjusted as well as unadjusted p--values for two of the three proposed bootstrap methods. Further simboot allows for comparing biological diversities of two or more groups while simultaneously testing a user-defined selection of Hill numbers of orders q, which are considered as appropriate and useful indices for measuring diversity. 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Selects items with Maximum Fisher Information method or randomly, with or without constraints (content balancing and item exposure control). Evaluates the simulation results in terms of precision, item exposure, and test length. Inspired on Magis & Barrada (2017) . Package: r-cran-simcausal Architecture: all Version: 0.5.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-igraph, r-cran-stringr, r-cran-r6, r-cran-assertthat, r-cran-matrix Suggests: r-cran-copula, r-cran-runit, r-cran-ltmle, r-cran-knitr, r-cran-ggplot2, r-cran-hmisc, r-cran-mvtnorm, r-cran-bindata Filename: pool/dists/noble/main/r-cran-simcausal_0.5.7-1.ca2404.1_all.deb Size: 957322 MD5sum: d96ee569ba4dc39fa0e89a1a0c0b6d64 SHA1: f5842780aecdc73ef231e2fd9e3d59b7a41fc3d0 SHA256: 0bacc643104b8595ec03c3431e7a162eaa38baea5d25e70695cb1bb331d5350d SHA512: e92e30b990ce887f39d4fbf658627130f20629f81dbc2de113915dc590202b36592825532608d9c654186cff241b4004c6a6e4bcaddf0b342101912b2713c3db Homepage: https://cran.r-project.org/package=simcausal Description: CRAN Package 'simcausal' (Simulating Longitudinal Data with Causal Inference Applications) A flexible tool for simulating complex longitudinal data using structural equations, with emphasis on problems in causal inference. Specify interventions and simulate from intervened data generating distributions. Define and evaluate treatment-specific means, the average treatment effects and coefficients from working marginal structural models. User interface designed to facilitate the conduct of transparent and reproducible simulation studies, and allows concise expression of complex functional dependencies for a large number of time-varying nodes. See the package vignette for more information, documentation and examples. Package: r-cran-simcomp Architecture: all Version: 3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-multcomp, r-cran-mratios Filename: pool/dists/noble/main/r-cran-simcomp_3.6-1.ca2404.1_all.deb Size: 186078 MD5sum: d6cda1290141d7e5440e54a33b05910c SHA1: 937a711dcc587f9e6a871e63f5800a7d1fc2d290 SHA256: 8aafa36f19f417af48237225779a7257f003dabc99fef82cb5ef0cc2c44d7d3c SHA512: e0407dc5f5a9b40c155225d9ccdd2076ea3575078ce5ef2fa2b63db05275d53978b82f131ff2d6250719bb7e998bf6a4795e5023e6bf9deadbe8a400fc2e3198 Homepage: https://cran.r-project.org/package=SimComp Description: CRAN Package 'SimComp' (Simultaneous Comparisons for Multiple Endpoints) Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011 ) or ratios (Hasler and Hothorn, 2012 ) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008 ) or covariance matrices (Hasler, 2014 ). Package: r-cran-simcop Architecture: all Version: 0.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog, r-cran-rgl Filename: pool/dists/noble/main/r-cran-simcop_0.7.4-1.ca2404.1_all.deb Size: 374606 MD5sum: 4119ad6fc55b2d0ec83df647231e1424 SHA1: ae8d83e292aa80d609fff0235a1cbc04ad88c15d SHA256: efe4780b0c3b9f08cb76bee37ccce72eef6885355eb147030f74529f1db9b687 SHA512: 03ae326869a5021ef9c0f3f123800f40d4fe5bf66cc0dc2d634c09c27ede571c752adffd5a1aae8844d0a8a8de154013d8c9b777eeab491d5b78831baf20e693 Homepage: https://cran.r-project.org/package=SimCop Description: CRAN Package 'SimCop' (Simulate from Arbitrary Copulae) Provides a framework to generating random variates from arbitrary multivariate copulae, while concentrating on (bivariate) extreme value copulae. Particularly useful if the multivariate copulae are not available in closed form. Detailed discussion of the methodologies used can be found in Tajvidi and Turlach (2018) . 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This package can be used to simulate data sets that mimic real-world clinical or genetic data sets (i.e., plasmodes, as in Vaughan et al., 2009 ). The methods extend those found in the 'SimMultiCorrData' R package. Standard normal variables with an imposed intermediate correlation matrix are transformed to generate the desired distributions. Continuous variables are simulated using either Fleishman (1978)'s third order or Headrick (2002)'s fifth order polynomial transformation method (the power method transformation, PMT). Non-mixture distributions require the user to specify mean, variance, skewness, standardized kurtosis, and standardized fifth and sixth cumulants. Mixture distributions require these inputs for the component distributions plus the mixing probabilities. Simulation occurs at the component level for continuous mixture distributions. The target correlation matrix is specified in terms of correlations with components of continuous mixture variables. These components are transformed into the desired mixture variables using random multinomial variables based on the mixing probabilities. However, the package provides functions to approximate expected correlations with continuous mixture variables given target correlations with the components. Binary and ordinal variables are simulated using a modification of ordsample() in package 'GenOrd'. Count variables are simulated using the inverse CDF method. There are two simulation pathways which calculate intermediate correlations involving count variables differently. Correlation Method 1 adapts Yahav and Shmueli's 2012 method and performs best with large count variable means and positive correlations or small means and negative correlations. Correlation Method 2 adapts Barbiero and Ferrari's 2015 modification of the 'GenOrd' package and performs best under the opposite scenarios. The optional error loop may be used to improve the accuracy of the final correlation matrix. The package also contains functions to calculate the standardized cumulants of continuous mixture distributions, check parameter inputs, calculate feasible correlation boundaries, and summarize and plot simulated variables. 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The methods use an acceptance/rejection simulation algorithm for the Bingham distribution and are described fully by Kent, Ganeiber and Mardia (2018) . These methods supersede earlier MCMC simulation methods and are more general than earlier simulation methods. The methods can be slower in specific situations where there are existing non-MCMC simulation methods (see Section 8 of Kent, Ganeiber and Mardia (2018) for further details). 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Package: r-cran-simdissolution Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-alabama, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-simdissolution_0.1.0-1.ca2404.1_all.deb Size: 40530 MD5sum: 7ad2defb24d7d89562ce6b2b9cd2355d SHA1: 24ac382a24eba9cdbbadc9f16937dc69276f7dc5 SHA256: 68622528973cdc76bf2bfe86a67a717f8ac95cca150c1fcb0fcda2fffc372d5d SHA512: 7a849f0916183596df59b6dca8d89e82aaff4421636973ce29403fc14893ff3e72cd4b04637b2a31286540c9069ba8a2eb6a1420db02bf57d89eb9962be4bdff Homepage: https://cran.r-project.org/package=SimDissolution Description: CRAN Package 'SimDissolution' (Modeling and Assessing Similarity of Drug Dissolutions Profiles) Implementation of a model-based bootstrap approach for testing whether two formulations are similar. The package provides a function for fitting a pharmacokinetic model to time-concentration data and comparing the results for all five candidate models regarding the Residual Sum of Squares (RSS). The candidate set contains a First order, Hixson-Crowell, Higuchi, Weibull and a logistic model. The assessment of similarity implemented in this package is performed regarding the maximum deviation of the profiles. See Moellenhoff et al. (2018) for details. 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Also contains functions for visualizing: event-driven details of a single-server queue model; a Lehmer random number generator; variate generation via acceptance-rejection; and of generating a non-homogeneous Poisson process via thinning. Also contains two queueing data sets (one fabricated, one real-world) to facilitate input modeling. More details on the use of these functions can be found in Lawson and Leemis (2015) , in Kudlay, Lawson, and Leemis (2020) , and in Lawson and Leemis (2021) . 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These calculations main refer to Allen et al.(1998, ISBN:92-5-104219-5), Teh (2006, ISBN:1-58-112-998-X), and Liu et al.(2006) . 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It also combines the tests and returns a properly adjusted family-wise p value. Details can be found in . Package: r-cran-simhaz Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-simhaz_0.1-1.ca2404.1_all.deb Size: 86416 MD5sum: 3b5dca8b1dfe1b369a01f4bbd81fc57e SHA1: 6d7203b0184dd9325227aecff2de81bca82c3583 SHA256: 0f7d5bc71edca0754de9357e166ecb81af293b11f9e7c64b0a4fe91d01afd324 SHA512: b28fe6698f629ec98a1dd54d5fd3e265a75f1b967a26254e3c157fd46c60a4b979941465d8acfffd9e82b9ba5a26792f36a92883267a5cd4ae8cc781511f0787 Homepage: https://cran.r-project.org/package=SimHaz Description: CRAN Package 'SimHaz' (Simulated Survival and Hazard Analysis for Time-DependentExposure) Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. 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Package: r-cran-simico Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bindata, r-cran-fastghquad, r-cran-compquadform, r-cran-icskat Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-simico_0.2.0-1.ca2404.1_all.deb Size: 147728 MD5sum: 7be947897453fb58495f96ffa3de19cd SHA1: 45e7e698da5b6b26886455f5cf02e6cb4440a3a6 SHA256: 7064b5dcfb3ee20e0a09cd0ad23bcc006b74d72d7ece9e075f9dd9a254e051e0 SHA512: f5fa5be1e007e77b2d3fd51892a6fbf7d22959e90dd9e879cae04abf669b9a4bb60ab68de480fe71774876241c791deb7eadd3867a9100efac2f3cdc25ddb2d7 Homepage: https://cran.r-project.org/package=SIMICO Description: CRAN Package 'SIMICO' (Set-Based Inference for Multiple Interval-Censored Outcomes) Contains tests for association between a set of genetic variants and multiple correlated outcomes that are interval censored. 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Package: r-cran-simindep Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fnn Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-simindep_0.1.2-1.ca2404.1_all.deb Size: 19554 MD5sum: 2548c75bf2e55e4b29448c160671423e SHA1: c959f8a7a2d7581985e0f9d731cc60aeb8903644 SHA256: d8b4dbd667cefa5e9e7d920673cabc5f3826494e9462a8ff860e6d672f6aaf48 SHA512: 49aa6162197457cbeab692a6a81e61d3336df583df99a5c9abaa8c492932eeefa07f1fa0dc822ea941428937c005f232c3f32b0ba9fc511c91f8b1f010e705ca Homepage: https://cran.r-project.org/package=SimIndep Description: CRAN Package 'SimIndep' (WISE: a Weighted Similarity Aggregation Test for SerialIndependence) A fast implementation of the weighted information similarity aggregation (WISE) test for detecting serial dependence, particularly suited for high-dimensional and non-Euclidean time series. 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These tools include: fitting, selection and plotting distributions to model system effectiveness, transformation towards a prespecified expected value, proxy to fitting of copula models based on these distributions, and simulation of new evaluation data from these distributions and copula models. Package: r-cran-simitation Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-devtools, r-cran-markdown, r-cran-formatr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-simitation_0.0.7-1.ca2404.1_all.deb Size: 234628 MD5sum: 4f13f04dd8825e908d6ed5ee97def188 SHA1: bde040973eb20dadb0ba17753a2780425d5d95ae SHA256: 0ed9f4f00ae3161aa7ae0d61e0f10fd12ded6551074adfde1ae931f7667ed4f9 SHA512: 2ff1c0f96d45466b06e73d87f736c1375a52b2e843f99fec1ad59eec468eecf871644804f9b84c6911dd5a4533368a40751e86fbe62f8d57ac96201e4beb9e6c Homepage: https://cran.r-project.org/package=simitation Description: CRAN Package 'simitation' (Simplified Simulations) Provides tools for generating and analyzing simulation studies. Users may easily specify all terms of a simulation study, often in a single line of code. Common univariate and bivariate methods, such as t tests, proportions tests, and chi squared tests, are integrated. Multivariate studies involving linear or logistic regression may also be specified with symbolic inputs. The simulation studies generate data for n observations in each of B experiments. Analyses of each experiment are integrated, and empirical results across the experiments are also provided. Package: r-cran-simits Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-arm, r-cran-ggplot2, r-cran-knitr, r-cran-plyr, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-simits_0.1.1-1.ca2404.1_all.deb Size: 329608 MD5sum: 744b980d5ca44838f1af4189f9238e21 SHA1: 7c3164d76fce6d0ff1471c07c41dd0a79bd534c1 SHA256: 05c747b676d4f6f46e0c9216e4c5f7e41955f37b27f1092204ab3128f82da313 SHA512: 6b5eced9ea9239cf712847749839a6e1dacff9d551bd7fa9bf928ff57f5caac90dcfcc3d131cc7b4f7c33d4864fc2516afeed967ae97e3f10dc8ffb5326ec238 Homepage: https://cran.r-project.org/package=simITS Description: CRAN Package 'simITS' (Analysis via Simulation of Interrupted Time Series (ITS) Data) Uses simulation to create prediction intervals for post-policy outcomes in interrupted time series (ITS) designs, following Miratrix (2020) . This package provides methods for fitting ITS models with lagged outcomes and variables to account for temporal dependencies. It then conducts inference via simulation, simulating a set of plausible counterfactual post-policy series to compare to the observed post-policy series. This package also provides methods to visualize such data, and also to incorporate seasonality models and smoothing and aggregation/summarization. This work partially funded by Arnold Ventures in collaboration with MDRC. 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The method is based on the potential landscape definition by Wang et al. (2008) (also see Zhou & Li, 2016 for further mathematical discussions) and can be used for a large variety of models. Package: r-cran-simle Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-matrix, r-cran-plotly, r-cran-stringr, r-cran-rcurl, r-cran-sie2nts Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-simle_0.1.0-1.ca2404.1_all.deb Size: 220946 MD5sum: 3be0f7aec48cfe30ac68768d30507e60 SHA1: bf27322cc377e8b356de47b33c434cf7197c887b SHA256: cecc940bfc5cb9177bb4a831950d8d4c5254c143c5bb70fa9d53c16c8ceb48c2 SHA512: 4308b125fcd17db53ab484acf7e531b98e91b5201011e268e52aa9fea79756443f4f06a48174fd11aa2c5f6a2693ec4c7a85ac942586974a962d1bb6b09acbf4 Homepage: https://cran.r-project.org/package=SIMle Description: CRAN Package 'SIMle' (Estimation and Inference for General Time Series Regression) We provide functions for estimation and inference of nonlinear and non-stationary time series regression using the sieve methods and bootstrapping procedure. 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Package: r-cran-simmer.plot Architecture: all Version: 0.1.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-simmer, r-cran-ggplot2, r-cran-diagrammer, r-cran-dplyr, r-cran-tidyr, r-cran-scales Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-simmer.plot_0.1.19-1.ca2404.1_all.deb Size: 112512 MD5sum: 146fe575432f963e3fa636d9026aa3e4 SHA1: 578af92192fcd421a549936be1f58b0b60e0839a SHA256: a9ff833b81cc2a82100310546b59ef891a60a46314d3b7367e3afcd81e94d399 SHA512: 2e333e488e8db4261fe4dacd1ef5430d4487a2c6917a53d55f08a6d7206042292f1323e5c5bb3c5addf5a60abd1a41490ceb279fcdae2689818f1ed75602c72e Homepage: https://cran.r-project.org/package=simmer.plot Description: CRAN Package 'simmer.plot' (Plotting Methods for 'simmer') A set of plotting methods for 'simmer' trajectories and simulations. Package: r-cran-simmetric Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-simmetric_0.1.1-1.ca2404.1_all.deb Size: 381988 MD5sum: 4adfcee7b88736da27b91720c0b8eefa SHA1: 8e1b56814f619345a4a4719c3802ca5fe498f2a0 SHA256: b4d77e422364886f4db50de6f7e04e3fb8e08d7ef2a1b573009b3b5a073a6c47 SHA512: ed77a45e91c596be3ce0d02f60ad521b83489608bf3b466940540f6fe9c2ce9af59360bb75b98cafbd781daf95624c625c7c8fa0d53ccbfd534558077a3013fb Homepage: https://cran.r-project.org/package=simMetric Description: CRAN Package 'simMetric' (Metrics (with Uncertainty) for Simulation Studies that EvaluateStatistical Methods) Allows users to quickly apply individual or multiple metrics to evaluate Monte Carlo simulation studies. Package: r-cran-simml Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-simml_0.3.0-1.ca2404.1_all.deb Size: 68988 MD5sum: 82c74cb01e5fb98dc9b44eb9eb1b6782 SHA1: 839e6c7374aae9e16c4e8f61c7034688ed0a90ec SHA256: 0cb711ce44df2a875fa3cd3daa7e2c5a50e2f8a42f4c42def10741130e5f2ee4 SHA512: 8746769294ad374bee2d8d5a64456099110c15398a082922e1afd26eb7e157319709dc994f12a4002c7fce7642cf02eb9591bb2facfe695548e104d2c04d2938 Homepage: https://cran.r-project.org/package=simml Description: CRAN Package 'simml' (Single-Index Models with Multiple-Links) A major challenge in estimating treatment decision rules from a randomized clinical trial dataset with covariates measured at baseline lies in detecting relatively small treatment effect modification-related variability (i.e., the treatment-by-covariates interaction effects on treatment outcomes) against a relatively large non-treatment-related variability (i.e., the main effects of covariates on treatment outcomes). The class of Single-Index Models with Multiple-Links is a novel single-index model specifically designed to estimate a single-index (a linear combination) of the covariates associated with the treatment effect modification-related variability, while allowing a nonlinear association with the treatment outcomes via flexible link functions. The models provide a flexible regression approach to developing treatment decision rules based on patients' data measured at baseline. We refer to Park, Petkova, Tarpey, and Ogden (2020) and Park, Petkova, Tarpey, and Ogden (2020) (that allows an unspecified X main effect) for detail of the method. The main function of this package is simml(). Package: r-cran-simmp Architecture: all Version: 0.17.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-bioc-biostrings, r-bioc-bsgenome, r-bioc-genomeinfodb, r-bioc-genomicranges, r-bioc-iranges, r-bioc-xvector Suggests: r-bioc-bsgenome.hsapiens.ucsc.hg38 Filename: pool/dists/noble/main/r-cran-simmp_0.17.3-1.ca2404.1_all.deb Size: 41632 MD5sum: 4a0620d507858328a49f9af162bd7676 SHA1: d39691cf55509100b9cb03c71cd0c336c01695ab SHA256: 5a08b246c7a2e370a42aa28f9d37abbd363e288385ac7683aebe2aabb9a5e17d SHA512: e2a32f879143cde5cbd35ca9741b6cd56730746803cb4a24344d5f23f4bfa05bcca643ef8e4a54ab49b18e2146dedbf8b73dddb9acc2fa91ab5b18444c6c87b9 Homepage: https://cran.r-project.org/package=simMP Description: CRAN Package 'simMP' (Simulate Somatic Mutations in Cancer Genomes from MutationalProcesses) Simulates somatic single base substitutions carried in cancer genomes. By only providing a human reference genome, substitutions that result from mutational processes operative in every cancer genome can be generated. Package: r-cran-simms Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1418 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mass, r-cran-glmnet, r-cran-doparallel, r-cran-foreach, r-cran-randomforestsrc Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-xtable Filename: pool/dists/noble/main/r-cran-simms_1.3.2-1.ca2404.1_all.deb Size: 359262 MD5sum: c1f0629f71c1f9b2c659e10485c9c60b SHA1: 39e554842a1e36b412261dec3c1e772854fd9a55 SHA256: 9d4846590c017c8ba521c594bb28f5b5e16a6902a5653712986736b0062be16c SHA512: d9cd44498d81c07f1b2af4d5d3da4e012271a9f866dfe07d78b5967ee37b153d588a305516aceb4920ec760f2dad883c56c431183eca745a5281645dc241afd2 Homepage: https://cran.r-project.org/package=SIMMS Description: CRAN Package 'SIMMS' (Subnetwork Integration for Multi-Modal Signatures) Algorithms to create prognostic biomarkers using biological genesets or networks. Package: r-cran-simmsm Architecture: all Version: 1.1.42-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mvna Filename: pool/dists/noble/main/r-cran-simmsm_1.1.42-1.ca2404.1_all.deb Size: 87112 MD5sum: bba1c9850d95d1641a6cb0ac28e08ffd SHA1: cc922f3d1dc1c4680e6e9f0b5ee9b8746b425232 SHA256: 40b663ab8d180e9a14718f158ba1529f7b44461fc10a72853565beaa7657c3ab SHA512: 324e780425ca072ca4e54252bad968de69375b84ce2d3e057d8b85beebd2b46b0354fc0e1c392175e8e6a34372fc1912a27c0c576debbecd3514387175b37238 Homepage: https://cran.r-project.org/package=simMSM Description: CRAN Package 'simMSM' (Simulation of Event Histories for Multi-State Models) Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions. Package: r-cran-simmulticorrdata Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1595 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bb, r-cran-nleqslv, r-cran-genord, r-cran-psych, r-cran-matrix, r-cran-vgam, r-cran-triangle, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-printr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-simmulticorrdata_0.2.2-1.ca2404.1_all.deb Size: 1041736 MD5sum: 6bb3a63ba40de80cf97d069c08cb0bc5 SHA1: 22a0a9d33f0f7942a111b6c15104d00b270489e0 SHA256: 493d07bd30cbb4f6a815d98600b9d8ab672638f2c13417c75127b27568c66c5e SHA512: e9ea9ad1f4d873d4db049b545573a724944a3523259b4c7dcfaded6ce458a413a2b2e1f1f47637536aa52401f4f6bab33d4b3d7e3f14ee86899c3d894634f749 Homepage: https://cran.r-project.org/package=SimMultiCorrData Description: CRAN Package 'SimMultiCorrData' (Simulation of Correlated Data with Multiple Variable Types) Generate continuous (normal or non-normal), binary, ordinal, and count (Poisson or Negative Binomial) variables with a specified correlation matrix. It can also produce a single continuous variable. This package can be used to simulate data sets that mimic real-world situations (i.e. clinical or genetic data sets, plasmodes). All variables are generated from standard normal variables with an imposed intermediate correlation matrix. Continuous variables are simulated by specifying mean, variance, skewness, standardized kurtosis, and fifth and sixth standardized cumulants using either Fleishman's third-order () or Headrick's fifth-order () polynomial transformation. Binary and ordinal variables are simulated using a modification of the ordsample() function from 'GenOrd'. Count variables are simulated using the inverse cdf method. There are two simulation pathways which differ primarily according to the calculation of the intermediate correlation matrix. In Correlation Method 1, the intercorrelations involving count variables are determined using a simulation based, logarithmic correlation correction (adapting Yahav and Shmueli's 2012 method, ). In Correlation Method 2, the count variables are treated as ordinal (adapting Barbiero and Ferrari's 2015 modification of GenOrd, ). There is an optional error loop that corrects the final correlation matrix to be within a user-specified precision value of the target matrix. The package also includes functions to calculate standardized cumulants for theoretical distributions or from real data sets, check if a target correlation matrix is within the possible correlation bounds (given the distributions of the simulated variables), summarize results (numerically or graphically), to verify valid power method pdfs, and to calculate lower standardized kurtosis bounds. Package: r-cran-simnph Architecture: all Version: 0.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-simdesign, r-cran-survival, r-cran-minipch, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-nph, r-cran-nphrct, r-cran-car, r-cran-dplyr, r-cran-stringr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-withr, r-cran-ldbounds, r-cran-ggplot2, r-cran-patchwork, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-simnph_0.5.8-1.ca2404.1_all.deb Size: 297350 MD5sum: b060b688559342f87878a8d0aa63680e SHA1: 74b775fdba7ac6503a4555e9628431cb5266ed31 SHA256: 7b3085d6dcee6813fca26c916a155b26f705f7446604b3a92cdf3519bc943192 SHA512: dde1e9ecbdc802e98e1316100340cd1bd0f66ff726c0f6d91c31e41ec09c757b2ee3c47f6b916fd5ec06c1005e4e6d04944e61635881ab9eb83014639aa2155f Homepage: https://cran.r-project.org/package=SimNPH Description: CRAN Package 'SimNPH' (Simulate Non-Proportional Hazards) A toolkit for simulation studies concerning time-to-event endpoints with non-proportional hazards. 'SimNPH' encompasses functions for simulating time-to-event data in various scenarios, simulating different trial designs like fixed-followup, event-driven, and group sequential designs. The package provides functions to calculate the true values of common summary statistics for the implemented scenarios and offers common analysis methods for time-to-event data. Helper functions for running simulations with the 'SimDesign' package and for aggregating and presenting the results are also included. Results of the conducted simulation study are available in the paper: "A Comparison of Statistical Methods for Time-To-Event Analyses in Randomized Controlled Trials Under Non-Proportional Hazards", Klinglmüller et al. (2025) . Package: r-cran-simode Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-pracma, r-cran-quadprog, r-cran-glmnet, r-cran-ncvreg Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-simode_1.2.2-1.ca2404.1_all.deb Size: 355406 MD5sum: da042a4879fa124f4a3e3ba93f29838c SHA1: 10589c743346f2d890513514a7151bb99b89a0bf SHA256: 07c8069d589bb04b47e20dadd0e8449adc07cc1c561570f0c01edac21726aacb SHA512: 2d4cad25639381731d84488c7b4e89aa60b203e54f9fbb65fe87e4510add364064377802bda1487546d2ba96d1160de3ad9851ea9d4c9ccb750374151aa2f453 Homepage: https://cran.r-project.org/package=simode Description: CRAN Package 'simode' (Statistical Inference for Systems of Ordinary DifferentialEquations using Separable Integral-Matching) Implements statistical inference for systems of ordinary differential equations, that uses the integral-matching criterion and takes advantage of the separability of parameters, in order to obtain initial parameter estimates for nonlinear least squares optimization. Dattner & Yaari (2018) . Dattner et al. (2017) . Dattner & Klaassen (2015) . 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SIMs predict the amount of interaction, for example number of trips per day, between geographic entities representing trip origins and destinations. Contains functions for creating origin-destination datasets from geographic input datasets and calculating movement between origin-destination pairs with constrained, production-constrained, and attraction-constrained models (Wilson 1979) . 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Package: r-cran-simrel Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1935 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-frf2, r-cran-ggplot2, r-cran-gridextra, r-cran-jsonlite, r-cran-magrittr, r-cran-miniui, r-cran-purrr, r-cran-reshape2, r-cran-rstudioapi, r-cran-scales, r-cran-sfsmisc, r-cran-shiny, r-cran-tibble, r-cran-tidyr, r-cran-rlang, r-cran-testthat Suggests: r-cran-covr, r-cran-knitr, r-cran-pls, r-cran-markdown, r-cran-doe.base Filename: pool/dists/noble/main/r-cran-simrel_2.1.0-1.ca2404.1_all.deb Size: 355158 MD5sum: 8244d630a0237aefbf965dbfce473137 SHA1: 099dfdc4b30fdfcde22d79d18fa88e00d2003e76 SHA256: 5001f60654405cc804daafcb6be7133baa89ed798a5a07ed14eed52377b76493 SHA512: 00cb804ee0683b5a147a681cd2c3865422668f194db876843e4b012004710b93f93522616066cea3b5e0b06a85fa314a89892fdd7ff1b7ed558178d8333c01f6 Homepage: https://cran.r-project.org/package=simrel Description: CRAN Package 'simrel' (Simulation of Multivariate Linear Model Data) Researchers have been using simulated data from a multivariate linear model to compare and evaluate different methods, ideas and models. Additionally, teachers and educators have been using a simulation tool to demonstrate and teach various statistical and machine learning concepts. This package helps users to simulate linear model data with a wide range of properties by tuning few parameters such as relevant latent components. In addition, a shiny app as an 'RStudio' gadget gives users a simple interface for using the simulation function. See more on: Sæbø, S., Almøy, T., Helland, I.S. (2015) and Rimal, R., Almøy, T., Sæbø, S. (2018) . Package: r-cran-sims Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chk, r-cran-future.apply, r-cran-nlist, r-cran-yesno Suggests: r-cran-covr, r-cran-future, r-cran-knitr, r-cran-progressr, r-cran-rjags, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-sims_0.0.4-1.ca2404.1_all.deb Size: 63082 MD5sum: 8f2b89134c14c8636211c8fe8ade085f SHA1: 6d3592c03769421713fb55a1ae33241bb51e19e9 SHA256: 90ef9ab70461f3ff86611401348e4240564b354fc2cf5e53eb404a0c767b875f SHA512: 52205e6b643137b5328d7516799f57969957e79c3daa6794c0130deb89574ac958d784bab3809fd8a7b86ce3e27f87d13b9b85c74056389383fa628043978dd8 Homepage: https://cran.r-project.org/package=sims Description: CRAN Package 'sims' (Simulate Data from R or 'JAGS' Code) Generates data from R or 'JAGS' code for use in simulation studies. The data are returned as an 'nlist::nlists' object and/or saved to file as individual '.rds' files. Parallelization is implemented using the 'future' package. Progress is reported using the 'progressr' package. Package: r-cran-simsalapar Architecture: all Version: 1.0-13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfsmisc, r-cran-gridbase, r-cran-colorspace Suggests: r-cran-lattice, r-cran-rmpi, r-cran-hmisc, r-cran-copula, r-cran-foreach, r-cran-doparallel, r-cran-fgarch, r-cran-robustbase, r-cran-codetools Filename: pool/dists/noble/main/r-cran-simsalapar_1.0-13-1.ca2404.1_all.deb Size: 464474 MD5sum: 6a99b87121dfdffd2cff78181201261d SHA1: a5d61be562cafd8e72d0f6ec39eab2bb1cdf30e9 SHA256: ecefd97df0715dfb9fffc308ef220ca1502145942e9e42028f06319a0282613b SHA512: df8bfc8395f3a9a55d85fed2421c356b92220af3bded75350b303d3fccd2ac5afa9e6e148cfb5af1d0e322f984d10959a97b41ce928e196ceffb057d032b6254 Homepage: https://cran.r-project.org/package=simsalapar Description: CRAN Package 'simsalapar' (Tools for Simulation Studies in Parallel) Tools for setting up ("design"), conducting, and evaluating large-scale simulation studies with graphics and tables, including parallel computations. Package: r-cran-simsem Architecture: all Version: 0.5-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1252 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan Suggests: r-cran-quantreg, r-cran-kernsmooth, r-cran-semtools, r-cran-lavaan.mi, r-cran-openmx, r-cran-copula Filename: pool/dists/noble/main/r-cran-simsem_0.5-17-1.ca2404.1_all.deb Size: 1147278 MD5sum: 5ab69adf6738fdb711088cbcea2a07b3 SHA1: 90f4e3ab33dad3040213fdfd216e9f7c8aa2e804 SHA256: 67530b0e5b5b1cc8e805722786816cb83ef5478d41a82ff5501d18792ef077bd SHA512: 3f6f62f361e8c418dfbbebc2f7fa8eec17479c42d029791c95ffa60cb1f4b7551edac2a50640215c2c9adda0a1ce9dbd3f418757f44c06fc141190a00f7371fd Homepage: https://cran.r-project.org/package=simsem Description: CRAN Package 'simsem' (SIMulated Structural Equation Modeling) Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning. Package: r-cran-simseq Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4383 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fdrtool Filename: pool/dists/noble/main/r-cran-simseq_1.4.0-1.ca2404.1_all.deb Size: 4443118 MD5sum: 54be1180c5bed287aaa13403e9caaca4 SHA1: 06cf6b45e7d394725f0d1122ba6720f2613deb6f SHA256: 63cc0b2db9970ac2ea56ba0ad5bb9077242bebcf024418c8469c62bb7f0b01fe SHA512: a6a4af08e1852b89934879aaed64638336a51b4358a2c8643826b675751f5c6a24a900976603c75e7c60da85c2f51b0c0182aa9e6c0351a7558917f31c6ebfd6 Homepage: https://cran.r-project.org/package=SimSeq Description: CRAN Package 'SimSeq' (Nonparametric Simulation of RNA-Seq Data) RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the assumed model. This testing strategy can result in an overly optimistic view of the performance of an RNA-seq analysis method. We develop a data-based simulation algorithm for RNA-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source RNA-seq dataset provided by the user. Users control the proportion of genes simulated to be differentially expressed (DE) and can provide a vector of weights to control the distribution of effect sizes. The algorithm requires a matrix of RNA-seq read counts with large sample sizes in at least two treatment groups. Many datasets are available that fit this standard. Package: r-cran-simsl Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-simsl_0.2.1-1.ca2404.1_all.deb Size: 216676 MD5sum: 34904b6685e646bf8b1a918cfd6c64cd SHA1: e6f01f089532bfa08ac32d42f81e1453567c3794 SHA256: 88ad89a335bd7434c4da38b78e6cd4b02e183d9376dc2f36a28b277bcdc305b8 SHA512: ec1ad95d3c2b827fd0651ab5163caa2e6453b5a17e900bb020dddeba1d0eb090b69594acde5a39a9291ae25836637de1cf738265792f3b8ca2998825f9cd141a Homepage: https://cran.r-project.org/package=simsl Description: CRAN Package 'simsl' (Single-Index Models with a Surface-Link) An implementation of a single-index regression for optimizing individualized dose rules from an observational study. To model interaction effects between baseline covariates and a treatment variable defined on a continuum, we employ two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear combination of the covariates (a single-index). An unspecified main effect for the covariates is allowed, which can also be modeled through a parametric model. A unique contribution of this work is in the parsimonious single-index parametrization specifically defined for the interaction effect term. We refer to Park, Petkova, Tarpey, and Ogden (2020) (for the case of a discrete treatment) and Park, Petkova, Tarpey, and Ogden (2021) "A single-index model with a surface-link for optimizing individualized dose rules" for detail of the method. The model can take a member of the exponential family as a response variable and can also take an ordinal categorical response. The main function of this package is simsl(). Package: r-cran-simsst Architecture: all Version: 0.0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-gamlss.dist, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-simsst_0.0.5.2-1.ca2404.1_all.deb Size: 56160 MD5sum: 9af6f1928b97bf3b5e8f3571ba1d375f SHA1: 033691fcf2a75d7b37992677b154a4c3a974dc7d SHA256: a7462303bc86765a376513f303b9245b4e85a5dfa024b5daf3221ff9447bafff SHA512: f355c3bfe60dde27593b12cbdc3eac146aaf6f33cb02ffede3148a71fd44c1db662af0a06d0660906a86507d1924329c79ad88b286c13d5a3f2209427d24c768 Homepage: https://cran.r-project.org/package=SimSST Description: CRAN Package 'SimSST' (Simulated Stop Signal Task Data) Stop signal task data of go and stop trials is generated per participant. The simulation process is based on the generally non-independent horse race model and fixed stop signal delay or tracking method. Each of go and stop process is assumed having exponentially modified Gaussian(ExG) or Shifted Wald (SW) distributions. The output data can be converted to 'BEESTS' software input data enabling researchers to test and evaluate various brain stopping processes manifested by ExG or SW distributional parameters of interest. Methods are described in: Soltanifar M (2020) , Matzke D, Love J, Wiecki TV, Brown SD, Logan GD and Wagenmakers E-J (2013) , Logan GD, Van Zandt T, Verbruggen F, Wagenmakers EJ. (2014) . Package: r-cran-simstandard Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1259 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lavaan, r-cran-mvtnorm, r-cran-tibble, r-cran-magrittr, r-cran-rlang, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-forcats, r-cran-stringr, r-cran-testthat, r-cran-covr, r-cran-badger Filename: pool/dists/noble/main/r-cran-simstandard_0.6.3-1.ca2404.1_all.deb Size: 931542 MD5sum: b81b88e108b96a8e10c9b17354a5c22f SHA1: a819fc9e75977db05177256f4d5db52c43f761d8 SHA256: 855236c33f799c77fca5596d91ce2a700dac4428437a70af5e518f54bb53b604 SHA512: 96902200a1aeda13b4dc83589b4157610a16f252eb7e1b5e6c3ce82458262ce7d30b1784ec56e645bf2dbbde90eca668507dee90b5435cde9fd6ca978ccdf45c Homepage: https://cran.r-project.org/package=simstandard Description: CRAN Package 'simstandard' (Generate Standardized Data) Creates simulated data from structural equation models with standardized loading. Data generation methods are described in Schneider (2013) . Package: r-cran-simsurv Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bb, r-cran-eha, r-cran-flexsurv, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-rstpm2, r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-simsurv_1.0.1-1.ca2404.1_all.deb Size: 148974 MD5sum: c6df0fc883267b72bf0017f4cc7109ca SHA1: 81ffe762ac9b7a70af3cf275ffe31e43fc649712 SHA256: cfa3bc1c400ec1e87c56744562e3066291d5f08b2cacdb72b2f03f12fd30f376 SHA512: 91a244b859613fc7d7a6bd9e726793bdcd8fb584d117f6eadadce1a55a9ba6fc7fa475cd319ff80882d6da013e54790246fce25744eb2e1291c6a01f65ad502f Homepage: https://cran.r-project.org/package=simsurv Description: CRAN Package 'simsurv' (Simulate Survival Data) Simulate survival times from standard parametric survival distributions (exponential, Weibull, Gompertz), 2-component mixture distributions, or a user-defined hazard, log hazard, cumulative hazard, or log cumulative hazard function. Baseline covariates can be included under a proportional hazards assumption. Time dependent effects (i.e. non-proportional hazards) can be included by interacting covariates with linear time or a user-defined function of time. Clustered event times are also accommodated. The 2-component mixture distributions can allow for a variety of flexible baseline hazard functions reflecting those seen in practice. If the user wishes to provide a user-defined hazard or log hazard function then this is possible, and the resulting cumulative hazard function does not need to have a closed-form solution. For details see the supporting paper . Note that this package is modelled on the 'survsim' package available in the 'Stata' software (see Crowther and Lambert (2012) or Crowther and Lambert (2013) ). Package: r-cran-simsurvey Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3629 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-stars, r-cran-data.table, r-cran-progress, r-cran-doparallel, r-cran-foreach, r-cran-plotly, r-cran-rlang, r-cran-lifecycle, r-cran-matrix Suggests: r-cran-fields, r-cran-rmarkdown, r-cran-flexdashboard, r-cran-shiny, r-cran-crosstalk, r-cran-htmltools, r-cran-viridis, r-cran-lme4, r-cran-ggplot2, r-cran-inlaspacetime, r-cran-knitr, r-cran-bezier Filename: pool/dists/noble/main/r-cran-simsurvey_0.1.8-1.ca2404.1_all.deb Size: 3627692 MD5sum: 740fd68f04bf50557e1077b52c1eb2ef SHA1: 5875eab900f5d0addf73b23512d040cf592a0627 SHA256: cfd24316e56af0f9193b7799b97568c70f41168c0db1242e8f0f4612e6c3e3a1 SHA512: 646586ba8b6d65ff7eb8cebd6c6a733c823cc23afd9c9dd848fa626e79cbc77d88a159e49885b6e01ce467687630dbd965b8611c534ef482c9fc52dc80b77afd Homepage: https://cran.r-project.org/package=SimSurvey Description: CRAN Package 'SimSurvey' (Test Surveys by Simulating Spatially-Correlated Populations) Simulate age-structured populations that vary in space and time and explore the efficacy of a range of built-in or user-defined sampling protocols to reproduce the population parameters of the known population. (See Regular et al. (2020) for more details). Package: r-cran-simtablr Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1347 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-flextable, r-cran-lmtest, r-cran-openxlsx, r-cran-sandwich, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-simtablr_1.2.0-1.ca2404.1_all.deb Size: 1222932 MD5sum: f9d7ee26604bb35de628a040ed593265 SHA1: 5369bc9ed5d4e757d5642343191e1917fad9d2c0 SHA256: acff1bb52b37308d123c43e57cc471effc93d133d867b1b3834cf79263aec545 SHA512: 83931105877a685c1f0e7038698bf8701a5da6a66adbba71d3a492e0068914ff1ab4ae15bf7786fe7f7975084428f25814fff1ef98007471f6262e9fa74fcc25 Homepage: https://cran.r-project.org/package=SimtablR Description: CRAN Package 'SimtablR' (Easy Publication-Ready Tables and Regression Analysis) Streamlines the creation of descriptive frequency tables ('Table 1'), diagnostic test accuracy evaluations (sensitivity, specificity, predictive values), and multi-outcome regression summaries. Features automatic tables, prevalence and odds ratio calculations, and seamless integration with 'flextable' for exporting results to 'Microsoft Word' and 'PowerPoint'. Package: r-cran-simtargetcov Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-simtargetcov_1.0.1-1.ca2404.1_all.deb Size: 15510 MD5sum: 43749a3cbb14378a4c9d0849ad76d8e3 SHA1: 6a632360455145b0a775f8247eb1a4e3a3269e96 SHA256: 72047549077bfa4364debcc88c80f76318a40a25912d6d23bb077dc99e5fb755 SHA512: 84701393c4aee32d5bfb36fe409a4df5da02dcd65581acf11cbeb5977a452ed3968df10d7bbc1fd1ed61daf743b4b4044bf0d34cce50c28bd587e24c940dcc9c Homepage: https://cran.r-project.org/package=simTargetCov Description: CRAN Package 'simTargetCov' (Data Transformation or Simulation with Empirical CovarianceMatrix) Transforms or simulates data with a target empirical covariance matrix supplied by the user. The method to obtain the data with the target empirical covariance matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019) . Package: r-cran-simtimer Architecture: all Version: 4.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark Filename: pool/dists/noble/main/r-cran-simtimer_4.0.0-1.ca2404.1_all.deb Size: 207710 MD5sum: ece5dd99e181eab337cd94d29fd06411 SHA1: 518567f6643905fffcee4862efa6b51573011bca SHA256: 72bb482103272295ba2f259dc64eae5767f794922c298df4ae5d6c9efa331c53 SHA512: a3fb46b000421a1210de0a610b05fd15cdcb9468277e4b2cf0a2067bdfb1de99b84df209960c85e4f8ab02498b8e865d9b4d0a53fcafe8011133cc91b9d23366 Homepage: https://cran.r-project.org/package=simtimer Description: CRAN Package 'simtimer' (Datetimes as Integers for Discrete-Event Simulations) Handles datetimes as integers for the usage inside Discrete-Event Simulations (DES). The conversion is made using the internally generic function as.numeric() of the base package. DES is described in Simulation Modeling and Analysis by Averill Law and David Kelton (1999) . Package: r-cran-simtimevar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-metafor, r-cran-mvtnorm, r-cran-icc, r-cran-misctools, r-cran-car, r-cran-plyr, r-cran-corpcor, r-cran-psych Filename: pool/dists/noble/main/r-cran-simtimevar_1.0.0-1.ca2404.1_all.deb Size: 77942 MD5sum: 70cb113e820708230c443441ecadbb33 SHA1: d5b8045b6146184957df5eb2643722cf3f820fe1 SHA256: db25e0a6ac6a15053347c8f9a8afda0cdb429bfdd2c51daf00d05b3fb957cf8a SHA512: 3af86f5132d732560391ef0a4d8e1165320a4d65ca90623ee708c6046cbfd6a3e16732f237b851c639d46a22fec23d625eb2d20beb895921e7b041c818f1d13e Homepage: https://cran.r-project.org/package=SimTimeVar Description: CRAN Package 'SimTimeVar' (Simulate Longitudinal Dataset with Time-Varying CorrelatedCovariates) Flexibly simulates a dataset with time-varying covariates with user-specified exchangeable correlation structures across and within clusters. Covariates can be normal or binary and can be static within a cluster or time-varying. Time-varying normal variables can optionally have linear trajectories within each cluster. See ?make_one_dataset for the main wrapper function. See Montez-Rath et al. for methodological details. Package: r-cran-simtool Architecture: all Version: 1.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1085 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-tibble, r-cran-vctrs Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-boot, r-cran-broom, r-cran-rmarkdown, r-cran-tinytest, r-cran-lintr, r-cran-roxygen2, r-cran-covr Filename: pool/dists/noble/main/r-cran-simtool_1.1.9-1.ca2404.1_all.deb Size: 322124 MD5sum: 64e535da19d0a8cae58dede76db9dc98 SHA1: fc5429985da92d3d62ebfde3df76bfe9e80d87d0 SHA256: 45c5a9ca61e58b087977c2117076bd4be20063811634dde1542dae5df8de7a25 SHA512: cd101cb49b6177309cee5ef3cd9c2808f4e20bd220e5ba7a1a031455880774b4dbd8ca6b114b7ad5de2fd374980594b137cc1e2ac6f33a7b3ca70cd43b640675 Homepage: https://cran.r-project.org/package=simTool Description: CRAN Package 'simTool' (Conduct Simulation Studies with a Minimal Amount of Source Code) Tool for statistical simulations that have two components. One component generates the data and the other one analyzes the data. The main aims of the package are the reduction of the administrative source code (mainly loops and management code for the results) and a simple applicability of the package that allows the user to quickly learn how to work with it. Parallel computing is also supported. Finally, convenient functions are provided to summarize the simulation results. Package: r-cran-simtrait Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-prroc Suggests: r-cran-popkin, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bnpsd, r-cran-bedmatrix Filename: pool/dists/noble/main/r-cran-simtrait_1.1.3-1.ca2404.1_all.deb Size: 346660 MD5sum: 9f002be58bac16eec9bc78173b48baa1 SHA1: 8f867a14d80e424428bcb15a6c19c2fb8d35f3ea SHA256: 8c98e92faf37f7131be0fd724042b13cec5325125e6955979aee665e505b87dd SHA512: 403fe45430c6f019dc29d8ded834e2bb53901753ec4632bb964cbd26130bd8d6b4b82b494e5dd4672d516c6f9133561a2fcda0a19e02039c3542be46d8c92d42 Homepage: https://cran.r-project.org/package=simtrait Description: CRAN Package 'simtrait' (Simulate Complex Traits from Genotypes) Simulate complex traits given a SNP genotype matrix and model parameters (the desired heritability, number of causal loci, and either the true ancestral allele frequencies used to generate the genotypes or the mean kinship for a real dataset). Emphasis on avoiding common biases due to the use of estimated allele frequencies. The code selects random loci to be causal, constructs coefficients for these loci and random independent non-genetic effects, and can optionally generate random group effects. Traits can follow three models: random coefficients, fixed effect sizes, and infinitesimal (multivariate normal). GWAS method benchmarking functions are also provided. Described in Yao and Ochoa (2022) . Package: r-cran-simuclustfactor Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-simuclustfactor_0.0.3-1.ca2404.1_all.deb Size: 150138 MD5sum: a2e79278568e2479c6472df3897f2bd6 SHA1: de1ba556f66be702706b9f39d49219dfd8f0577e SHA256: f94efc98737ab76212eed14b6935423e47d07f90d82551ea6cb06d3d8ec24fdc SHA512: 2bee3d2c44f71bc677531144010e2849296de01c5c235f078066a8f93f7126812e65c40d704e5dad5955347c64a64a9d7988e5c8bc1c956365d0231cd055537c Homepage: https://cran.r-project.org/package=simuclustfactor Description: CRAN Package 'simuclustfactor' (Simultaneous Clustering and Factorial Decomposition of Three-WayDatasets) Implements two iterative techniques called T3Clus and 3Fkmeans, aimed at simultaneously clustering objects and a factorial dimensionality reduction of variables and occasions on three-mode datasets developed by Vichi et al. (2007) . Also, we provide a convex combination of these two simultaneous procedures called CT3Clus and based on a hyperparameter alpha (alpha in [0,1], with 3FKMeans for alpha=0 and T3Clus for alpha= 1) also developed by Vichi et al. (2007) . Furthermore, we implemented the traditional tandem procedures of T3Clus (TWCFTA) and 3FKMeans (TWFCTA) for sequential clustering-factorial decomposition (TWCFTA), and vice-versa (TWFCTA) proposed by P. Arabie and L. Hubert (1996) . Package: r-cran-simukde Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ks, r-cran-mvtnorm, r-cran-mass Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-simukde_1.3.0-1.ca2404.1_all.deb Size: 47042 MD5sum: bd918f0dbade6425554786aabb4732c9 SHA1: 4e74b180fb5af6c52f33cf559ea41be104f44e9e SHA256: cd341b074a2cd38f48fa84547a6139e47fb681d61f627bb8162ef72d923fa03e SHA512: faf6887a762ef14a367ace00e157a2fb8d5599fe830f869a4be964e4324003dd9b3775159eeb799a5e0c8ba6d57f1d7caac111c5cf5f49138b7970e7b3272c3c Homepage: https://cran.r-project.org/package=simukde Description: CRAN Package 'simukde' (Simulation with Kernel Density Estimation) Generates random values from a univariate and multivariate continuous distribution by using kernel density estimation based on a sample. Duong (2017) , Christian P. Robert and George Casella (2010 ISBN:978-1-4419-1575-7) . Package: r-cran-simulariatools Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2267 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate, r-cran-reticulate, r-cran-scales, r-cran-terra Suggests: r-cran-magick, r-cran-testthat, r-cran-openair, r-cran-sf Filename: pool/dists/noble/main/r-cran-simulariatools_3.1.0-1.ca2404.1_all.deb Size: 968492 MD5sum: ce3a4e8412db79902ad84eae4a76722f SHA1: 21a8810fbb1f85e2eb38813dea237e40c0f95f62 SHA256: ca9e4d7d8597210d20da1ef736e24b854ce9d247c154359df8fc38025225a953 SHA512: 87f48fc685daa7b12ba763518c9677a2dc46bfd31a9b325cc229ec067dc671849796e77d269b7ffd32a6265359cc38a7a76219a83503419db1325ed48408f185 Homepage: https://cran.r-project.org/package=simulariatools Description: CRAN Package 'simulariatools' (Simularia Tools for the Analysis of Air Pollution Data) A set of tools developed at Simularia for Simularia, to help preprocessing and post-processing of meteorological and air quality data. Package: r-cran-simulatedce Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 784 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-evd, r-cran-formula.tools, r-cran-ggplot2, r-cran-kableextra, r-cran-magrittr, r-cran-mixl, r-cran-psych, r-cran-purrr, r-cran-readr, r-cran-rmarkdown, r-cran-stringr, r-cran-tibble, r-cran-tictoc, r-cran-tidyr, r-cran-future, r-cran-furrr, r-cran-qs2, r-cran-data.table, r-cran-glue Suggests: r-cran-knitr, r-cran-testthat, r-cran-rlang Filename: pool/dists/noble/main/r-cran-simulatedce_0.3.2-1.ca2404.1_all.deb Size: 388164 MD5sum: 7d77e68b552f0a98e408e6c2e1bd3c02 SHA1: b6af47d8d785cb0c33cc1583f9c6ff36eca03181 SHA256: 067b428489a99d0ffba4318211a5cc8594fadaa8795fffcf8665d4257db1dba2 SHA512: f5b4f88a45d94e7358ae86116e1ecf0e23830760865925eca6cab469e4e4f6d0f8811d47cd1f5e1c1e66e89bffcaa2e40b871d966b8be687de862454e17ddc6a Homepage: https://cran.r-project.org/package=simulateDCE Description: CRAN Package 'simulateDCE' (Simulate Data for Discrete Choice Experiments) Supports simulating choice experiment data for given designs. It helps to quickly test different designs against each other and compare the performance of new models. The goal of 'simulateDCE' is to make it easy to simulate choice experiment datasets using designs from 'NGENE', 'idefix' or 'spdesign'. You have to store the design file(s) in a sub-directory and need to specify certain parameters and the utility functions for the data generating process. For more details on choice experiments see Mariel et al. (2021) . Package: r-cran-simulator Architecture: all Version: 0.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2026 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-digest, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-glmnet, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-simulator_0.2.5-1.ca2404.1_all.deb Size: 1372594 MD5sum: bab27f51e8744648a788c9e0fd27a9e8 SHA1: 0f5c2d276671bed4c9192856cd0bc3161da33eb1 SHA256: 3e1ee936022f3f55459c2401727fbe32fd202757a23f1d4d9fffabc71f2a0782 SHA512: a127ad4bbdd676529ec4203006dee9e367ae5e85a2e07eb9f05262e9f6a9dd6f4069d7f87788644c6d9f2d7390774f2bde7f01fd813a7cd274cbfa37eb865e79 Homepage: https://cran.r-project.org/package=simulator Description: CRAN Package 'simulator' (An Engine for Running Simulations) A framework for performing simulations such as those common in methodological statistics papers. The design principles of this package are described in greater depth in Bien, J. (2016) "The simulator: An Engine to Streamline Simulations," which is available at . Package: r-cran-simule Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-pcapp, r-cran-igraph Filename: pool/dists/noble/main/r-cran-simule_1.3.0-1.ca2404.1_all.deb Size: 376800 MD5sum: c490e2dacc13cff5914c459c639b37b2 SHA1: fe74b36b15c93287b3dfa2130eec3d841ee0c89d SHA256: 655054c9039ab7e4a8b4f0071b1c8f9aabb32b6fcd6f52cb64ea5fb93b33f2e7 SHA512: 4dabc4ce09435dcc108f32740abcd4444684cfc39be24214d04819421e28e673c7287afe7f9f1fb917e24313a2cd9ea725f4db5d710e4ee64b24da18e8a51bf3 Homepage: https://cran.r-project.org/package=simule Description: CRAN Package 'simule' (A Constrained L1 Minimization Approach for Estimating MultipleSparse Gaussian or Nonparanormal Graphical Models) This is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogenous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) . Package: r-cran-simulist Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8046 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-english, r-cran-epiparameter, r-cran-grates, r-cran-randomnames, r-cran-rlang Suggests: r-cran-dplyr, r-cran-epicontacts, r-cran-ggplot2, r-cran-incidence2, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-simulist_0.7.0-1.ca2404.1_all.deb Size: 1768254 MD5sum: 827922a764dffcba67141c4eacebb773 SHA1: 3b9322f57dd1cb1176772905b38e8fabc8423a31 SHA256: d9436881d4910dab0b746a076dd935d9025a145aa3112d7dc4cbe0d745f968d6 SHA512: 65b1b104772aa2c5e190277d3d744f0ea302e8ba585ffd2dce799080056aef22518ccfad05b62c12591b435badfd8f81a3edc64b63218ab8dd697db1598ae72a Homepage: https://cran.r-project.org/package=simulist Description: CRAN Package 'simulist' (Simulate Disease Outbreak Line List and Contacts Data) Tools to simulate realistic raw case data for an epidemic in the form of line lists and contacts using a branching process. Simulated outbreaks are parameterised with epidemiological parameters and can have age-structured populations, age-stratified hospitalisation and death risk and time-varying case fatality risk. Package: r-cran-simulmgf Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-simulmgf_0.1.1-1.ca2404.1_all.deb Size: 31976 MD5sum: 30665ca2f22e52f1ad81666336998192 SHA1: c1838a4b93ee992bc1fc61075d23c4a62b2231dd SHA256: 8474394023fd708f8652ef2003f576ea9158fd0ae586b829c1d29bb7c024964f SHA512: be3ffef28eddb46d26de489a28b41970d2504eea65aeda91adf988a8121708453a3c0ea910064a2900339852d11ecc536267bc3a1cd72ba6838f7424e9374996 Homepage: https://cran.r-project.org/package=simulMGF Description: CRAN Package 'simulMGF' (Simulate SNP Matrix, Phenotype and Genotypic Effects) Simulate genotypes in SNP (single nucleotide polymorphisms) Matrix as random numbers from an uniform distribution, for diploid organisms (coded by 0, 1, 2), Sikorska et al., (2013) , or half-sib/full-sib SNP matrix from real or simulated parents SNP data, assuming mendelian segregation. Simulate phenotypic traits for real or simulated SNP data, controlled by a specific number of quantitative trait loci and their effects, sampled from a Normal or an Uniform distributions, assuming a pure additive model. This is useful for testing association and genomic prediction models or for educational purposes. 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Supports generation of estimation scenarios and control files for external engines (e.g., 'Monolix'), simulation of models using 'rxode2', and creation of goodness-of-fit diagnostics. Includes tools for covariate modeling, virtual population design, and local and global sensitivity analyses. Package: r-cran-simvitd Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-simpleboot Filename: pool/dists/noble/main/r-cran-simvitd_1.0.3-1.ca2404.1_all.deb Size: 588356 MD5sum: 01aa17a35db35b7b372e2b4c020df188 SHA1: aaf7d0ced0151f24b75fac7dbbf18864885e43b4 SHA256: 054639313bdf4af35c90ba04324ac0ce2fd43c7c5ae5bb28d2790e19919571fd SHA512: 629cd38550f9bb784f131631d06597e68928dc013c1f4b6309a2bbffb7e8a51b6bf6f61a2952b7e1a95d432452386567098e3857ab9de2a50cb3bfe748925862 Homepage: https://cran.r-project.org/package=SimVitD Description: CRAN Package 'SimVitD' (Simulation Tools for Planning Vitamin D Studies) Simulation tools for planning Vitamin D studies. Individual vitamin D status profiles are simulated, modelling population heterogeneity in trial arms. Exposures to infectious agents are generated, with infection depending on vitamin D status. Package: r-cran-sinaplot Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1536 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-sinaplot_1.1.0-1.ca2404.1_all.deb Size: 1156888 MD5sum: 7c491cdb263968de52b3c5b316dd2944 SHA1: 1ece9b2561d63e5dae849ac7266450bbc40f18fc SHA256: 6d2c997797fae50db7e72459e3f34b36def3172d16b6c5950ac18df2dfe14aac SHA512: 7ae5fcdfcc69aecf403a27b3e0be5df41a495082ef7cc39597d1fba939035ffda28b7484654ea4aa7e4a387d4fe33609d5c8ff4e30d0403f5f4326b708782025 Homepage: https://cran.r-project.org/package=sinaplot Description: CRAN Package 'sinaplot' (An Enhanced Chart for Simple and Truthful Representation ofSingle Observations over Multiple Classes) The sinaplot is a data visualization chart suitable for plotting any single variable in a multiclass data set. It is an enhanced jitter strip chart, where the width of the jitter is controlled by the density distribution of the data within each class. 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Programmatically attach namespaces in R and Rmd script, populates 'Roxygen2' skeletons with information scraped from within functions and populate the Imports field of the DESCRIPTION file. Package: r-cran-singcar Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cholwishart, r-cran-mass, r-cran-withr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-lme4, r-cran-lmertest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-singcar_0.1.5-1.ca2404.1_all.deb Size: 231840 MD5sum: 00f202b8e7b7c74828344ff3f189f16d SHA1: 0986b9a31730c8c0a3e5c2338200c2207bf36b51 SHA256: ed65e59774195668d4a3bb60dba559b3d32916138ec244008737bb22165d9367 SHA512: 2d31884b68afc4c633bdd2c7fd16286c1c3306e8ecb77ae0007a722d2efbd4807ff8ca3cf3fce68ac67476e669c19fe7687f07408f18a117cec3f671eb170fbe Homepage: https://cran.r-project.org/package=singcar Description: CRAN Package 'singcar' (Comparing Single Cases to Small Samples) When comparing single cases to control populations and no parameters are known researchers and clinicians must estimate these with a control sample. This is often done when testing a case's abnormality on some variable or testing abnormality of the discrepancy between two variables. Appropriate frequentist and Bayesian methods for doing this are here implemented, including tests allowing for the inclusion of covariates. These have been developed first and foremost by John Crawford and Paul Garthwaite, e.g. in Crawford and Howell (1998) , Crawford and Garthwaite (2005) , Crawford and Garthwaite (2007) and Crawford, Garthwaite and Ryan (2011) . The package is also equipped with power calculators for each method. Package: r-cran-singlearmmrct Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-singlearmmrct_0.1.1-1.ca2404.1_all.deb Size: 270380 MD5sum: e88ab82383e904b84029584847ce0f43 SHA1: 345c6dc983200330c351756981454daf4ece7519 SHA256: 8021d2d022b649f633bf440c730e61493f1616ef4340473ff3dd0bbe2bf9ce58 SHA512: 239521f4ed39d581e8237dece329d711344d356ea60dbc3fbc22fac416b8a42309c3aaf4a85e813d483f8cdb9f23e29bc98b3291890f2cf2c5db72916902f106 Homepage: https://cran.r-project.org/package=SingleArmMRCT Description: CRAN Package 'SingleArmMRCT' (Regional Consistency Probability for Single-Arm Multi-RegionalClinical Trials) Provides functions to calculate and visualise the Regional Consistency Probability (RCP) for single-arm multi-regional clinical trials (MRCTs) using the Effect Retention Approach (ERA). Six endpoint types are supported: continuous, binary, count (negative binomial), time-to-event via hazard ratio, milestone survival, and restricted mean survival time (RMST). For each endpoint, both a closed-form (or semi-analytical) solution and a Monte Carlo simulation approach are implemented. Two consistency evaluation methods are available: Method 1 (effect retention in Region 1 relative to the overall population) and Method 2 (simultaneous positive effect across all regions). Plotting functions generate faceted visualisations of RCP as a function of the regional allocation proportion, overlaying formula and simulation results for direct comparison. The methodology follows the Japanese MHLW guidelines for MRCTs. Abbreviations used: RCP (Regional Consistency Probability), MRCT (Multi-Regional Clinical Trial), RMST (Restricted Mean Survival Time), MHLW (Ministry of Health, Labour and Welfare). Package: r-cran-singlecasees Architecture: all Version: 0.7.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 854 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-tidyr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-spelling, r-cran-shiny, r-cran-shinytest2, r-cran-stringr, r-cran-rvest, r-cran-ggplot2, r-cran-purrrlyr, r-cran-testthat, r-cran-markdown, r-cran-knitr, r-cran-rmarkdown, r-cran-cleanrmd, r-cran-kendall, r-cran-kableextra, r-cran-covr, r-cran-readxl, r-cran-glue, r-cran-janitor, r-cran-rclipboard Filename: pool/dists/noble/main/r-cran-singlecasees_0.7.4-1.ca2404.1_all.deb Size: 295542 MD5sum: f89f7bc369f1ce915f3920335b123431 SHA1: 39b8cca8833f0f52b63097d83992f7b0d3a40b96 SHA256: aa0510c43ab428682c5b8b7e56b87e1286b2c89f34f9934af9d435e3baac83d6 SHA512: 0fc9987cc409f31c1bce3babdf8418ce9a8508e413372edc0f71529849d8f099cc0a2dd7fc9ec048151e8013a97860b9a3d4d2c4550634357e191dbb79f075b3 Homepage: https://cran.r-project.org/package=SingleCaseES Description: CRAN Package 'SingleCaseES' (A Calculator for Single-Case Effect Sizes) Provides R functions for calculating basic effect size indices for single-case designs, including several non-overlap measures and parametric effect size measures, and for estimating the gradual effects model developed by Swan and Pustejovsky (2018) . Standard errors and confidence intervals (based on the assumption that the outcome measurements are mutually independent) are provided for the subset of effect sizes indices with known sampling distributions. Package: r-cran-singlecellcomplexheatmap Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-complexheatmap, r-cran-seurat, r-cran-dplyr, r-cran-tidyr, r-cran-rcolorbrewer, r-cran-circlize, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-viridis, r-cran-devtools, r-cran-biocmanager, r-cran-ggsci, r-cran-seuratobject Filename: pool/dists/noble/main/r-cran-singlecellcomplexheatmap_0.1.2-1.ca2404.1_all.deb Size: 656684 MD5sum: d14be2dbd8f8ac0a949cf6cc5b604413 SHA1: 01d695f933efc05dfbbbc7e3a9fc6e6f37bbc59f SHA256: 25c82a4da95e02d86bff1c51856c39873b5ba6d865473970a7eb5f01454cd7e0 SHA512: b408eb8d64da9667bb99ce2d2db4118eb14ff7cf99d26064d33c281305d8d8cf9f89aaee8170a5e1b6cc24e8624a9f674ae5d1260226c2cb185f41be42085338 Homepage: https://cran.r-project.org/package=SingleCellComplexHeatMap Description: CRAN Package 'SingleCellComplexHeatMap' (Complex Heatmaps for Single Cell Expression Data with DualInformation Display) Creates complex heatmaps for single cell RNA-seq data that simultaneously display gene expression levels (as color intensity) and expression percentages (as circle sizes). Supports gene grouping, cell type annotations, and time point comparisons. Built on top of 'ComplexHeatmap' and integrates with 'Seurat' objects. For more details see Gu (2022) and Hao (2024) . Package: r-cran-singlecellhaystack Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 886 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-cran-seuratobject, r-cran-cowplot, r-cran-wrswor, r-bioc-sparsematrixstats, r-bioc-complexheatmap, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-singlecellhaystack_1.0.3-1.ca2404.1_all.deb Size: 732896 MD5sum: e80298f525f39fe986a7440ff5bc72ed SHA1: 6d91fb3bc494c001eabc949edddf4a6bfaf7348e SHA256: 0f9ad74444b034b8443a74f213cbb24014c9d227e0f3ec4c7f8c66994f7c7214 SHA512: b5302c15ffd7397e4ff59870d811de21917fdc91e80a460a75c3d8a4a434a2092e2b6612794b969ea7b4a827e32f96c2f51d4fbc523941e277cec1b5dc2317ca Homepage: https://cran.r-project.org/package=singleCellHaystack Description: CRAN Package 'singleCellHaystack' (A Universal Differential Expression Prediction Tool forSingle-Cell and Spatial Genomics Data) One key exploratory analysis step in single-cell genomics data analysis is the prediction of features with different activity levels. For example, we want to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, spatial DEGs in spatial transcriptomics data, or differentially accessible regions (DARs) in single-cell ATAC-seq data. 'singleCellHaystack' predicts differentially active features in single cell omics datasets without relying on the clustering of cells into arbitrary clusters. 'singleCellHaystack' uses Kullback-Leibler divergence to find features (e.g., genes, genomic regions, etc) that are active in subsets of cells that are non-randomly positioned inside an input space (such as 1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For the theoretical background of 'singleCellHaystack' we refer to our original paper Vandenbon and Diez (Nature Communications, 2020) and our update Vandenbon and Diez (Scientific Reports, 2023) . Package: r-cran-singlecellstat Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2569 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-cran-matrix, r-cran-vegan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-singlecellstat_0.3.1-1.ca2404.1_all.deb Size: 2595048 MD5sum: 3703b10e31ea3215c0903541290cce83 SHA1: ca67eba489d70d841c5e455a6dea201ef90947e2 SHA256: 0c25d8529e1ed1b35279a85d190dc523a996b673e393afd1d7569f5300dbcc2f SHA512: 9f7c83ed1eac502a9125bbe7188fe2117e5e5512a5c45f4f4f787013a9344d5eb7cc381f7a359f910be243a6583ba361cf5a9893877ca1dc3f1cabc0f851dda1 Homepage: https://cran.r-project.org/package=SingleCellStat Description: CRAN Package 'SingleCellStat' (A Toolkit for Statistical Analysis of Single-Cell Omics Data) A suite of statistical methods for analysis of single-cell omics data including linear model-based methods for differential abundance analysis for individual level single-cell RNA-seq data. For more details see Zhang, et al. (Submitted to Bioinformatics). Package: r-cran-singlercapture Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2633 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lamw, r-cran-mathjaxr, r-cran-sandwich, r-cran-doparallel, r-cran-foreach Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-tinytest, r-cran-covr, r-cran-vgam, r-cran-lmtest Filename: pool/dists/noble/main/r-cran-singlercapture_1.1.0-1.ca2404.1_all.deb Size: 1909820 MD5sum: 0f7dc84a36a3482a77960f79432685a9 SHA1: c8a8951c1fe17aaf486855c9268cf56421c6fd14 SHA256: 90b155388e0dc8cb8582123b93845ff4e62b445c9142a928b95eab2cdd3f386c SHA512: b58f07e83ed14f583d50684a2eea53ee3b4845f774cefb49271148591ff673b2a33aff9fa509582734c4491fc3306c617ba599bbe122f3ae8942a70c53d4267f Homepage: https://cran.r-project.org/package=singleRcapture Description: CRAN Package 'singleRcapture' (Single-Source Capture-Recapture Models) Implementation of single-source capture-recapture methods for population size estimation using zero-truncated, zero-one truncated and zero-truncated one-inflated Poisson, Geometric and Negative Binomial regression as well as Zelterman's, Chao's and ratio-regression estimators. Package includes point and interval estimators for the population size with variances estimated using analytical or bootstrap method. Details can be found in: van der Heijden et all. (2003) , Böhning and van der Heijden (2019) , Böhning et al. (2020) Capture-Recapture Methods for the Social and Medical Sciences or Böhning and Friedl (2021) . Package: r-cran-sinib Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sinib_1.0.0-1.ca2404.1_all.deb Size: 25212 MD5sum: 8c95f1a1fbc9446b1e50a81bf4fce619 SHA1: fbae88df7442baf88a763535514d9ccc1f9360a9 SHA256: 8f1090cc95e6ea915ad3a0fb1592ca7a56d558678b0ab8c13e6e67e166e66719 SHA512: 2a97b9c5102b3f53dd1ce788768e3af881f4e09b689b541b9326d152f97ba5dfb0d890a6cb344577716dfed68d8c24eb01b71dabfb8fbf8de56b645932a07c2f Homepage: https://cran.r-project.org/package=sinib Description: CRAN Package 'sinib' (Sum of Independent Non-Identical Binomial Random Variables) Density, distribution function, quantile function and random generation for the sum of independent non-identical binomial distribution with parameters \code{size} and \code{prob}. Package: r-cran-sinrelef.ld Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinyjs Filename: pool/dists/noble/main/r-cran-sinrelef.ld_1.1.0-1.ca2404.1_all.deb Size: 86562 MD5sum: 8cabb2cf1ccf5a8b5e8922bf0844200d SHA1: 17c82f5e16ace8102c40aada30a44d036ac03a65 SHA256: 8ac642ef95495270902d4ca699dcacf806ac0717b66beac0d73ab2ed518a77c6 SHA512: b05fb1ff13eb57685cf9646ab228db2ecf54e5fd3cd53eac5a7d7cc476b3e7808b109f9dde5103b4c81239657c333af0451c4d5358e8670b372a343e9702b7c8 Homepage: https://cran.r-project.org/package=SINRELEF.LD Description: CRAN Package 'SINRELEF.LD' (Reliability and Relative Efficiency in Locally-DependentMeasures) Implements an approach aimed at assessing the accuracy and effectiveness of raw scores obtained in scales that contain locally dependent items. The program uses as input the calibration (structural) item estimates obtained from fitting extended unidimensional factor-analytic solutions in which the existing local dependencies are included. Measures of reliability (Omega) and information are proposed at three levels: (a) total score, (b) bivariate-doublet, and (c) item-by-item deletion, and are compared to those that would be obtained if all the items had been locally independent. All the implemented procedures can be obtained from: (a) linear factor-analytic solutions in which the item scores are treated as approximately continuous, and (b) non-linear solutions in which the item scores are treated as ordered-categorical. A detailed guide can be obtained at the following url. Package: r-cran-sip Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sip_0.1.0-1.ca2404.1_all.deb Size: 2444768 MD5sum: f27b0e5c6f0689be9071cf465af386aa SHA1: d8cdaf2df7de0341cd32c296810ab40047d6b3d8 SHA256: 7b9dd207fc270441f007fb4ebb5582bd474106b4f08cfd41d4fe04a99ba43035 SHA512: acbd80443d4b97eb4d2ac84bf7269c131639885fc7ae556fb2f9c1d8e02c1e50b5f169eef313a443d463bc39685deafb18d0503715e32b26fbc8c97aed9945f7 Homepage: https://cran.r-project.org/package=SIP Description: CRAN Package 'SIP' (Single-Iteration Permutation for Large-Scale Biobank Data) A single, phenome-wide permutation of large-scale biobank data. When a large number of phenotypes are analyzed in parallel, a single permutation across all phenotypes followed by genetic association analyses of the permuted data enables estimation of false discovery rates (FDRs) across the phenome. These FDR estimates provide a significance criterion for interpreting genetic associations in a biobank context. For the basic permutation of unrelated samples, this package takes a sample-by-variable file with ID, genotypic covariates, phenotypic covariates, and phenotypes as input. For data with related samples, it also takes a file with sample pair-wise identity-by-descent information. The function outputs a permuted sample-by-variable file ready for genome-wide association analysis. See Annis et al. (2021) for details. Package: r-cran-sipdibge Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-covidibge, r-cran-pnadcibge, r-cran-pndsibge, r-cran-pnsibge, r-cran-cli, r-cran-png, r-cran-purrr, r-cran-rstudioapi, r-cran-tibble Suggests: r-cran-convey, r-cran-dplyr, r-cran-httr, r-cran-magrittr, r-cran-projmgr, r-cran-rcurl, r-cran-readr, r-cran-readxl, r-cran-srvyr, r-cran-survey, r-cran-timedate Filename: pool/dists/noble/main/r-cran-sipdibge_0.2.1-1.ca2404.1_all.deb Size: 362708 MD5sum: efdef9755fbff4e06d6a473a98b1661e SHA1: 7527265dec6c0b8a254efedd0ce6e55a478b3dfb SHA256: 00729609a76d32eaaba6bd43f780f9742cce9e38907f7881ff430134044e9829 SHA512: c5a179d56e8c6b6d2634d0191520e2289af423ed9aea643066726b5883a1cb6b161bc4c70d8feda17b537587e5cf289c2edcfba1c5f2c8411c612da1cdf60986 Homepage: https://cran.r-project.org/package=SIPDIBGE Description: CRAN Package 'SIPDIBGE' (Collection of Household Survey Packages Conducted by IBGE) Provides access to packages developed for downloading, reading and analyzing microdata from household surveys in Integrated System of Household Surveys - SIPD conducted by Brazilian Institute of Geography and Statistics - IBGE. More information can be obtained from the official website . Package: r-cran-sipetool Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-convolutioner Filename: pool/dists/noble/main/r-cran-sipetool_0.1.0-1.ca2404.1_all.deb Size: 236502 MD5sum: a4ff1d0ea4ea981ba940193304002ec7 SHA1: 92b2365060d0c338f940f80556da27414d03d515 SHA256: 28f0a2cea066b78817cc6827ebe8b6bf06c0903fb589d37263efbbed397c5c15 SHA512: f6c57c41da8013daece22846bc42bde37c2b92dabd2ae07b9dfbb09b48fc5dc22339ed9dfdf44acae451feadf4ef722a644065a5d22c65bd2a15fa8f3bd2e5d6 Homepage: https://cran.r-project.org/package=SIPETool Description: CRAN Package 'SIPETool' (SIFT-MS and CPET Data Processor) Processor for selected ion flow tube mass spectrometer (SIFT-MS) output file from breath analysis. It allows the filtering of the SIFT output file (i.e., variation over time of the target analyte concentration) and the following analysis for the determination of: maximum, average, and standard deviation value of target concentration measured at each exhalation, and the respiratory rate over the measurement. Additionally, it is possible to align the SIFT-MS data with other on-line techniques such as cardio pulmonary exercise test (CPET) for a comprehensive characterization of breath samples. Package: r-cran-siplab Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.geom Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-siplab_1.6-1.ca2404.1_all.deb Size: 481274 MD5sum: c3381c8e42eb410005a7838062b4ca70 SHA1: 29db34f3dda451f732081bfd554092c07253d4c6 SHA256: 0c008afebec6da97871ff8d287addbf1b44c7c9f33f35d8f155bd3694702a950 SHA512: a412aacf76b34e8f09025c282025edf842a55daa461cedc3f8f73d0a88b2e8a44adf91949501e4817e9beff548ad0f65879e53c0aac83dbd53c74031b36e5793 Homepage: https://cran.r-project.org/package=siplab Description: CRAN Package 'siplab' (Spatial Individual-Plant Modelling) A platform for computing competition indices and experimenting with spatially explicit individual-based vegetation models. Package: r-cran-siqr Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-kernsmooth Filename: pool/dists/noble/main/r-cran-siqr_0.8.1-1.ca2404.1_all.deb Size: 29144 MD5sum: 97b58d017c06dc58986ad161bc0e91af SHA1: 36135e55b536a7e8f9dce58888aaebd845985239 SHA256: a5bd1b07ebe4cf6b6eeb56ac1c6f6f9b72ecdc1c9ed148775a92d11666e51012 SHA512: 805181aca7a76871d92bc46da5b029afb6aa79fd993f8138ef00356b1c0eaa2533ed27faf16ac6f309eb132873496786033ff96d483cc42c2f8245f5799eea76 Homepage: https://cran.r-project.org/package=siqr Description: CRAN Package 'siqr' (An R Package for Single-Index Quantile Regression) Single-Index Quantile Regression is effective in some scenarios. We provides functions that allow users to fit Single-Index Quantile Regression model. It also provides functions to do prediction, estimate standard errors of the single-index coefficients via bootstrap, and visualize the estimated univariate function. Please see W., Y., Y. (2010) for details. Package: r-cran-sirad Architecture: all Version: 2.3-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3061 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-raster Filename: pool/dists/noble/main/r-cran-sirad_2.3-3-1.ca2404.1_all.deb Size: 3092636 MD5sum: be425f196207e730a7fe661796f08194 SHA1: 978827da502d932ff3ecfa4309ce68ba76788f47 SHA256: 50fafb9902ca649e1b7ff7a8006ff3c4ddd8b7054ff7e48bef5fe3ece4a39bd6 SHA512: ffe2c8df3488e814267b90c85c970df113d41d7b835130c34fd59d2a6d94972af0bb515b468c3644101b56f73657b49d2fe7c619cc8867f5431d652d45ecad29 Homepage: https://cran.r-project.org/package=sirad Description: CRAN Package 'sirad' (Functions for Calculating Daily Solar Radiation andEvapotranspiration) Calculating daily global solar radiation at horizontal surface using several well-known models (i.e. Angstrom-Prescott, Supit-Van Kappel, Hargreaves, Bristow and Campbell, and Mahmood-Hubbard), and model calibration based on ground-truth data, and (3) model auto-calibration. The FAO Penmann-Monteith equation to calculate evapotranspiration is also included. Package: r-cran-sire Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-systemfit, r-cran-psych, r-cran-igraph, r-cran-matrixcalc, r-cran-mass, r-cran-numderiv, r-cran-matrix, r-cran-stringr, r-cran-rsolnp, r-cran-dplyr, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-sire_1.1.0-1.ca2404.1_all.deb Size: 53348 MD5sum: b97d7c664667e73040248e7bb2ddb18f SHA1: 7e7229f1bb4353d695e9eba4faca7429a7d52cf0 SHA256: 8110e584ca0aace4051e00231aace1dad2a78f17dc4e1d2024b82cc0c756cb69 SHA512: 111601e8addd914b1fb593386e2595c1a0fcc270ae674b7ff7d8951d93e29f7f11c320c7fcb802524f260398b8fa54c4839127db1a4a3dcee7153257fa5f7344 Homepage: https://cran.r-project.org/package=SIRE Description: CRAN Package 'SIRE' (Finding Feedback Effects in SEM and Testing for TheirSignificance) Provides two main functionalities. 1 - Given a system of simultaneous equation, it decomposes the matrix of coefficients weighting the endogenous variables into three submatrices: one includes the subset of coefficients that have a causal nature in the model, two include the subset of coefficients that have a interdependent nature in the model, either at systematic level or induced by the correlation between error terms. 2 - Given a decomposed model, it tests for the significance of the interdependent relationships acting in the system, via Maximum likelihood and Wald test, which can be built starting from the function output. For theoretical reference see Faliva (1992) and Faliva and Zoia (1994) . Package: r-cran-siren Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-psych, r-cran-efa.mrfa, r-cran-lavaan Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-siren_1.0.6-1.ca2404.1_all.deb Size: 63440 MD5sum: c1578ad9241dc8d2426a38003de10cc4 SHA1: 054a5f69f46e888e95fd423f11892abcbfa3b51e SHA256: 28afd583afd77da5741b7dbb7df045e01876a30563bf909c015bbb10ee921829 SHA512: a2d4389b7f0145d749c8402bf6bdcf7255616c40f5f04d0f59d3cb5145b8c342b23b75463721c513bc6591f0cad1d711c7f0876b2d16eec3c357df8e57be499a Homepage: https://cran.r-project.org/package=siren Description: CRAN Package 'siren' (Hybrid FA-CFA for Controlling Acquiescence in RestrictedFactorial Solutions) Performs hybrid multi-stage factor analytic procedure for controlling acquiescence in restricted solutions (Ferrando & Lorenzo-Seva, 2000 ). Package: r-cran-sirthresholded Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1032 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-strucchange Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-sirthresholded_1.0.2-1.ca2404.1_all.deb Size: 666126 MD5sum: ebc2867c4581545a7e607f0edb63de37 SHA1: dc93073475524fcc58cb3d4dce5e83593687784f SHA256: fa014a05fdc70b71d9e1e388029a1e626dc9e8b1e690309b527c169c8c215f0f SHA512: d545d761972967da636ba5f627eaabf541421d1b65f490841049873c77bbb67ca6117d195414b7a985a26df40f9354afd8a0d96207854b2b1a719cab3f16ae21 Homepage: https://cran.r-project.org/package=SIRthresholded Description: CRAN Package 'SIRthresholded' (Sliced Inverse Regression with Thresholding) Implements a thresholded version of the Sliced Inverse Regression method (Li, K. C. (1991) ), which allows to do variable selection. Package: r-cran-sis Architecture: all Version: 0.8-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2580 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-ncvreg, r-cran-survival Filename: pool/dists/noble/main/r-cran-sis_0.8-8-1.ca2404.1_all.deb Size: 2606564 MD5sum: f82fe5a135561edd9301e4c30b75a574 SHA1: fe15ecb56a329a910ba019b66718abb881916c9e SHA256: 5f0bde6a8a8b9521072860653c9cc6058c66b6ec6662e558dadd406383b6c89a SHA512: 57ca7aca649dd3525283387bb07e100a9f80a27d02e435ca161fe95525501af5cb31eeaf9cec8e7dccf2c2705f211a50bcf49e448e65138d80912c48ff6af621 Homepage: https://cran.r-project.org/package=SIS Description: CRAN Package 'SIS' (Sure Independence Screening) Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)) and all of its variants in generalized linear models (Fan and Song (2009)) and the Cox proportional hazards model (Fan, Feng and Wu (2010)). Package: r-cran-sisal Architecture: all Version: 0.49-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-lattice, r-cran-mgcv, r-cran-digest, r-cran-r.matlab, r-cran-r.methodss3 Suggests: r-bioc-graph, r-bioc-rgraphviz, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sisal_0.49-1.ca2404.1_all.deb Size: 639368 MD5sum: bb0696335b3732895451e5006230b49c SHA1: 1c7e6fc7923723fd07705b96296fcfb80174f6d6 SHA256: 2f450acf0a449e348704c5900b2d4de879b08e68495f6e066e896a2c7436b3db SHA512: cdd0826e70fe119748818966d16ee6c55520cfa16f7877c8cdc26a05436926df9f9b83040ef4132b50c3c3a97049bdafbc91cff02a9bf0a3153f75372a5ba6b0 Homepage: https://cran.r-project.org/package=sisal Description: CRAN Package 'sisal' (Sequential Input Selection Algorithm) Implements the SISAL algorithm by Tikka and Hollmén. 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Methods include a semiparametric approach based on Sliced Inverse Regression (SIR), as described in (standard ridge and sparse SIR are also included in the package) and a random forest based approach, as described in . Package: r-cran-sistec Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 943 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-openxlsx, r-cran-rlang, r-cran-shiny, r-cran-stringi, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sistec_0.2.0-1.ca2404.1_all.deb Size: 369814 MD5sum: a29942ef80b41cfbf9ba9100f84d5600 SHA1: 26919661c024b402a82b49bdbfd5d5a4ab765be4 SHA256: 4a864e44c51b0ae3537aad02f7a894f86d6ee768d396ef8461e14c79456334da SHA512: bdf58b1f65c6482657f37e714cc68559c1cc6c9611fa97ab3a9bdc7bb41b8287ff0013e8f2ead1f47a39b2aa5a546dd3c1a7d4934e3c91db73ff681385458249 Homepage: https://cran.r-project.org/package=sistec Description: CRAN Package 'sistec' (Tools to Analyze 'Sistec' Datasets) The Brazilian system for diploma registration and validation on technical and superior courses are managing by 'Sistec' platform, see . This package provides tools for Brazilian institutions to update the student's registration and make data analysis about their situation, retention and drop out. Package: r-cran-sisti Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4786 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Filename: pool/dists/noble/main/r-cran-sisti_0.0.1-1.ca2404.1_all.deb Size: 2973516 MD5sum: 135e36114b8de8977aab7ed4af86487c SHA1: 216fd6743ee0a57794026bc4c802c310a88deab9 SHA256: 9a641af6134115216bce8b078499da7aee575bbcbe25b593a416a5c9890b773b SHA512: 7bf823cc8c011d22ef0836dc0982d4a9f99ef090eeff22d9c4e27da4780e9371fe2c1ef7dcff6f2a1eb5bd4a4735f8f7241b24b8c0c123ca2c30dbdf8ffa1b23 Homepage: https://cran.r-project.org/package=sisti Description: CRAN Package 'sisti' (Real-Time PCR Data Sets by Sisti et al. (2010)) This data package contains four datasets of quantitative PCR (qPCR) amplification curves that were used as supplementary data in the research article by Sisti et al. (2010), . The primary dataset comprises a ten-fold dilution series spanning copy numbers from 3.14 × 10^7 to 3.14 × 10^2, with twelve replicates per concentration. These samples are based on a pGEM-T Promega plasmid containing a 104 bp fragment of the mitochondrial gene NADH dehydrogenase 1 (MT-ND1), amplified using the ND1/ND2 primer pair. The remaining three datasets contain qPCR results in the presence of specific PCR inhibitors: tannic acid, immunoglobulin G (IgG), and quercetin, respectively, to assess their effects on the amplification process. These datasets are useful for researchers interested in PCR kinetics. The original raw data file is available as Additional File 1: . 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Package: r-cran-sisvive Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lars Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-sisvive_1.4-1.ca2404.1_all.deb Size: 33502 MD5sum: b35f10c6c4daa50c948e78ecbb5dbd0e SHA1: 1eaf58d20d160a649a30af3856cd418cf9faef24 SHA256: 68b2938d82f39ac991a30e0cceba986eb4b4fe9d4c838d021a81b28ca455aa7e SHA512: 83dec03f18a37a0ffca40e778ecd8b8c45fe4396234f912996c64b877e112b9880917bdcc3b2065072bf3948964968f82ee1bb20368314d25ce29792a1418deb Homepage: https://cran.r-project.org/package=sisVIVE Description: CRAN Package 'sisVIVE' (Some Invalid Some Valid Instrumental Variables Estimator) Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome. 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Package: r-cran-sitrep Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-apyramid, r-cran-epidict, r-cran-epikit, r-cran-epitabulate Suggests: r-cran-anthro, r-cran-binom, r-cran-broom, r-cran-clipr, r-cran-covr, r-cran-dplyr, r-cran-flextable, r-cran-forcats, r-cran-ggalluvial, r-cran-ggplot2, r-cran-ggspatial, r-cran-glue, r-cran-gtsummary, r-cran-here, r-cran-janitor, r-cran-knitr, r-cran-labelled, r-cran-lubridate, r-cran-matchmaker, r-cran-pacman, r-cran-parsedate, r-cran-patchwork, r-cran-purrr, r-cran-rio, r-cran-rlang, r-cran-rmarkdown, r-cran-scales, r-cran-sessioninfo, r-cran-sf, r-cran-slider, r-cran-srvyr, r-cran-stringr, r-cran-summarytools, r-cran-survey, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-tsibble, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-sitrep_0.4.1-1.ca2404.1_all.deb Size: 951990 MD5sum: f07433898a75163ed027781f45a59536 SHA1: fdcfebc878605e580fa3d3f7844753733e18c6a1 SHA256: 0c0582bcf3caeb8518e27857fe789375e65396b4e5a3ac3fefbb5d68b7aac282 SHA512: 35215c4a8d1923d948bc24e77dd0c4b2db65e13b7f89256d6129782a1a9271b0b81035120aa092ea52188d6e90b8d7690bb2f4bc7337372e6bc27be60dcaad4d Homepage: https://cran.r-project.org/package=sitrep Description: CRAN Package 'sitrep' (Report Templates and Helper Functions for Applied Epidemiology) A meta-package that loads the complete sitrep ecosystem for applied epidemiology analysis. 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Package: r-cran-sivirep Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1915 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-config, r-cran-dplyr, r-cran-epitrix, r-cran-ggplot2, r-cran-httr2, r-cran-kableextra, r-cran-readxl, r-cran-rlang, r-cran-sf, r-cran-showtext, r-cran-stringr, r-cran-sysfonts, r-cran-xml2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sivirep_1.0.1-1.ca2404.1_all.deb Size: 1558132 MD5sum: 1715e2225d3a620c483d12821b3f8aba SHA1: 3459840b2776bc7a6d3841bbc1431fbf316020c5 SHA256: 66985ddfcc69f0c5dd9293510191420a9d13e783a1bbc642626988fadcf0668b SHA512: a90eee8bafb4ec33f3aeae2a1a47526e1eb00fc70fe98372c2ac6bcce1897d1de21fbaca538d22d4ea119bb318f341831b07ebfdec413e44af25beabd0ddabc5 Homepage: https://cran.r-project.org/package=sivirep Description: CRAN Package 'sivirep' (Data Wrangling and Automated Reports from 'SIVIGILA' Source) Data wrangling, pre-processing, and generating automated reports from Colombia's epidemiological surveillance system, 'SIVIGILA' . It provides a customizable R Markdown template for analysis and automatic generation of epidemiological reports that can be adapted to local, regional, and national contexts. This tool offers a standardized and reproducible workflow that helps to reduce manual labor and potential errors in report generation, improving their efficiency and consistency. Package: r-cran-sivs Architecture: all Version: 0.2.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-glmnet, r-cran-proc, r-cran-varhandle Suggests: r-cran-markdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sivs_0.2.11-1.ca2404.1_all.deb Size: 204146 MD5sum: 78305d48c9b2dad60ad6997280600456 SHA1: b3a3c55d9cb7473c59928f35845e3aa5ab7bb0f0 SHA256: 11d8df5f1c6e19cbf9ba516b71350a8af3098f9837ae02560dfcc4e5b0862781 SHA512: bae8e6693c3ba6ff421fdd1df54d616bfe3408040767bd274aefd23d78ee704ccd5ce191427addd099eef08eba539c4dad7882bdfa7273cf54e56aec8a67779a Homepage: https://cran.r-project.org/package=sivs Description: CRAN Package 'sivs' (Stable Iterative Variable Selection) An iterative feature selection method that internally utilizes various Machine Learning methods that have embedded feature reduction in order to shrink down the feature space into a small and yet robust set. Package: r-cran-sixsigma Architecture: all Version: 0.11.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 669 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-ggplot2, r-cran-reshape2, r-cran-nortest, r-cran-e1071, r-cran-scales, r-cran-testthat, r-cran-xtable Suggests: r-cran-covr Filename: pool/dists/noble/main/r-cran-sixsigma_0.11.1-1.ca2404.1_all.deb Size: 605936 MD5sum: be6f9794501a1b84dbe5c6c7bb3ce141 SHA1: a7872b4c9262edfe16edcd78d5eba3625406ddb1 SHA256: d9a5d1be2ab7d2f515ee2d994b03e44439b684e7277af7b6a98cdb9788b6baa0 SHA512: 7d9979297a06b06f231a4a6202ebb6d6fc230d03cc01af4de4a5d7f3506c5b27fccb4cbe7e9fbb177a87d3e068a8e7ba99d7dbce379df3565a9639cb8831f7a8 Homepage: https://cran.r-project.org/package=SixSigma Description: CRAN Package 'SixSigma' (Six Sigma Tools for Quality Control and Improvement) Functions and utilities to perform Statistical Analyses in the Six Sigma way. Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control), you can manage several Quality Management studies: Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis, etc. Data frames used in the books "Six Sigma with R" [ISBN 978-1-4614-3652-2] and "Quality Control with R" [ISBN 978-3-319-24046-6], are also included in the package. Package: r-cran-sixtyfour Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1015 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-paws, r-cran-paws.common, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-fs, r-cran-s3fs, r-cran-cli, r-cran-glue, r-cran-memoise, r-cran-uuid, r-cran-jsonlite, r-cran-curl, r-cran-tidyr, r-cran-clipr, r-cran-withr, r-cran-ipaddress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-dbi, r-cran-rpostgres, r-cran-rmariadb, r-cran-ggplot2, r-cran-lubridate, r-cran-testthat, r-cran-vcr, r-cran-webmockr Filename: pool/dists/noble/main/r-cran-sixtyfour_0.2.0-1.ca2404.1_all.deb Size: 681284 MD5sum: 87b0427b7181b324ff698b058dd4666b SHA1: 8154ef2bc4f4a1c11eacb976cc0abe09d1623de5 SHA256: 62574b8d41d3163d23cc6c2e63bdb847fa746117daa2cef6f84a32b7bbc9a511 SHA512: 26a759bdcd7435296c96774ae4e618eefdc9845ca68a4a9399e2c562f8a5d25c82b178fd113831a83fe34640ed3f837691eacc408d708214fbb66c3b2020db0b Homepage: https://cran.r-project.org/package=sixtyfour Description: CRAN Package 'sixtyfour' (Humane Interface to Amazon Web Services) An opinionated interface to Amazon Web Services , with functions for interacting with 'IAM' (Identity and Access Management), 'S3' (Simple Storage Service), 'RDS' (Relational Data Service), Redshift, and Billing. Lower level functions ('aws_' prefix) are for do it yourself workflows, while higher level functions ('six_' prefix) automate common tasks. Package: r-cran-sizeestimation Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-msm Filename: pool/dists/noble/main/r-cran-sizeestimation_1.1.1-1.ca2404.1_all.deb Size: 37532 MD5sum: 5091f88810308d676f653fddc367f5fa SHA1: 84edfb19ea7e4407e8215c9c5a2104375a0835fa SHA256: bac193a83c8bad03f448437c66f3f0ef79f1cc618650ec717c544480c2ae3ca5 SHA512: 1bc507c63c7480e13d0b038d5f4d0f83d6ba985c0349c5f06d6476db8b9b31b45b2e750b46e6ad6319b91b2eabab37951cf42fcb3b5458cb8dd2dcb5a5044bf6 Homepage: https://cran.r-project.org/package=SizeEstimation Description: CRAN Package 'SizeEstimation' (Estimating the Sizes of Populations at Risk of HIV Infectionfrom Multiple Data Sources Using a Bayesian Hierarchical Model) This function develops an algorithm for presenting a Bayesian hierarchical model for estimating the sizes of local and national drug injected populations in Bangladesh. 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Package: r-cran-sizemat Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1181 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack, r-cran-matrixstats, r-cran-mass Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sizemat_1.1.2-1.ca2404.1_all.deb Size: 550382 MD5sum: 248d96bae69550a8c0c72a9d0e231957 SHA1: 40ac9700ee43034fd91f1022616197c6acf7aca0 SHA256: 20a0dbe6e052740c4038f92d8880c895b4824833d3aa7c07907eb16dc893886a SHA512: a52bb510e47bf1ec76240b83e4f5e63a215ee200383864d6b4191a592fb96f5deb54eb9c36ae5a6ad6c9fe81eaa52f1c6d9a2bfc5689dbf3635be548fea3ebdc Homepage: https://cran.r-project.org/package=sizeMat Description: CRAN Package 'sizeMat' (Estimate Size at Sexual Maturity) Estimate morphometric and gonadal size at sexual maturity for organisms, usually fish and invertebrates. 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Package: r-cran-sizer Architecture: all Version: 0.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-sizer_0.1-8-1.ca2404.1_all.deb Size: 81824 MD5sum: 04b290729c94620418dd718f15b45abe SHA1: 5998b9bfb1c423997bf501411fce1fcfc6c696ce SHA256: deb3d44c6729e9c2618bc4bae2907ba6d3d2498cca2fea0b948a91343a374dfd SHA512: e818192e450a32bae318a45a699ef42cc3a2bf0fe9474fba79d7f901d9e268dbc8cebc7e2ad78558fde2c7bd8203ef2cafcdce1b3dcd8099963717537e0ef334 Homepage: https://cran.r-project.org/package=SiZer Description: CRAN Package 'SiZer' (Significant Zero Crossings) Calculates and plots the SiZer map for scatterplot data. 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Package: r-cran-sjlabelled Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-insight, r-cran-datawizard Suggests: r-cran-dplyr, r-cran-haven, r-cran-magrittr, r-cran-sjmisc, r-cran-sjplot, r-cran-knitr, r-cran-rlang, r-cran-rmarkdown, r-cran-snakecase, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sjlabelled_1.2.0-1.ca2404.1_all.deb Size: 293536 MD5sum: 5bc6907328226f896fc4c4b0898bcd7e SHA1: df2fa6e20901e91230e07a699a5762af6d98245b SHA256: 7428c67ba3c0f55a699f8caf9244ec22e7c56d8f4305bd8b01b352afb0633219 SHA512: a2d60e88abd13851e33ac8d0f54714d32e9007f258713d04699f6a2a5be93daff65f6b5dc66e6237e2b69f42bb7990d043d00d367e4b15cbc09fcdc671c29bd2 Homepage: https://cran.r-project.org/package=sjlabelled Description: CRAN Package 'sjlabelled' (Labelled Data Utility Functions) Collection of functions dealing with labelled data, like reading and writing data between R and other statistical software packages like 'SPSS', 'SAS' or 'Stata', and working with labelled data. This includes easy ways to get, set or change value and variable label attributes, to convert labelled vectors into factors or numeric (and vice versa), or to deal with multiple declared missing values. Package: r-cran-sjmisc Architecture: all Version: 2.8.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-insight, r-cran-datawizard, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-sjlabelled, r-cran-tidyselect Suggests: r-cran-ggplot2, r-cran-haven, r-cran-mice, r-cran-nnet, r-cran-sjplot, r-cran-sjstats, r-cran-knitr, r-cran-rmarkdown, r-cran-stringdist, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-sjmisc_2.8.11-1.ca2404.1_all.deb Size: 516036 MD5sum: 0a9484be0841c299ed4c25d7582f2367 SHA1: 19fd97f820d02cc9b59bf7b0bae00b81dea28807 SHA256: f0ee33befe3784dc115d0f19270872aeefe321850f855c8b442d0d05e28c3131 SHA512: 84e9bfbeedd2be2c91ff8c01b2e6741c548e051dc2f68745e711ca08335fb967d867c24e258656058a3de1639fc5866b3ff6aae3c66a8e501ff5c9d647c98158 Homepage: https://cran.r-project.org/package=sjmisc Description: CRAN Package 'sjmisc' (Data and Variable Transformation Functions) Collection of miscellaneous utility functions, supporting data transformation tasks like recoding, dichotomizing or grouping variables, setting and replacing missing values. 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Package: r-cran-sjplot Architecture: all Version: 2.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayestestr, r-cran-datawizard, r-cran-dplyr, r-cran-ggeffects, r-cran-ggplot2, r-cran-knitr, r-cran-insight, r-cran-parameters, r-cran-performance, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-sjlabelled, r-cran-sjmisc, r-cran-sjstats, r-cran-tidyr Suggests: r-cran-brms, r-cran-car, r-cran-clubsandwich, r-cran-cluster, r-cran-cowplot, r-cran-effects, r-cran-haven, r-cran-gparotation, r-cran-ggrepel, r-cran-glmmtmb, r-cran-gridextra, r-cran-ggridges, r-cran-httr, r-cran-lme4, r-cran-mass, r-cran-nfactors, r-cran-pscl, r-cran-psych, r-cran-rmarkdown, r-cran-rstanarm, r-cran-sandwich, r-cran-survey, r-cran-tmb, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sjplot_2.9.0-1.ca2404.1_all.deb Size: 1397940 MD5sum: 2f2957e77498958245881e991ada7165 SHA1: 527d5d1d6fdd443933aac53d757a317daebb914f SHA256: 07ed58bec316f8f4ba2ebd9440c3a0a07ac0c68d64beb2ee8ec0bf895a804ef3 SHA512: 154e7f9e220e1633cf496e6df099c724f2b0b48e4c9554e5300809c5d79b51cf4d2b5311f1666f9b3022fad1f0cfbdfb06e3971643053fa04459940c999ee5e8 Homepage: https://cran.r-project.org/package=sjPlot Description: CRAN Package 'sjPlot' (Data Visualization for Statistics in Social Science) Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data. Package: r-cran-sjsdm Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1822 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate, r-cran-mvtnorm, r-cran-rstudioapi, r-cran-abind, r-cran-metrics, r-cran-mgcv, r-cran-cli, r-cran-crayon, r-cran-ggplot2, r-cran-checkmate, r-cran-mathjaxr, r-cran-beeswarm, r-cran-qgam, r-cran-scales, r-cran-viridis Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-iml, r-cran-fields Filename: pool/dists/noble/main/r-cran-sjsdm_1.0.7-1.ca2404.1_all.deb Size: 1084536 MD5sum: e8c4d06725291bb275acbac4140fe77a SHA1: a0bcc581c7d3ac57ddd554a26c9d0d162311b557 SHA256: cd7e729ec89cfa8d708087a7d003648c6034abd0494d4076b84b1c2b861179d4 SHA512: 4df082fdd264bf4e6db5070dfc59e6c12b2919c5e85f785e99ea279990141aa857edee23e9ad988592859eae8c6f1c718a1c7b88e7141fba9d614ff50e109b34 Homepage: https://cran.r-project.org/package=sjSDM Description: CRAN Package 'sjSDM' (Scalable Joint Species Distribution Modeling) A scalable and fast method for estimating joint Species Distribution Models (jSDMs) for big community data, including eDNA data. The package estimates a full (i.e. non-latent) jSDM with different response distributions (including the traditional multivariate probit model). The package allows to perform variation partitioning (VP) / ANOVA on the fitted models to separate the contribution of environmental, spatial, and biotic associations. In addition, the total R-squared can be further partitioned per species and site to reveal the internal metacommunity structure, see Leibold et al., . The internal structure can then be regressed against environmental and spatial distinctiveness, richness, and traits to analyze metacommunity assembly processes. The package includes support for accounting for spatial autocorrelation and the option to fit responses using deep neural networks instead of a standard linear predictor. As described in Pichler & Hartig (2021) , scalability is achieved by using a Monte Carlo approximation of the joint likelihood implemented via 'PyTorch' and 'reticulate', which can be run on CPUs or GPUs. Package: r-cran-sjstats Architecture: all Version: 0.19.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 430 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-datawizard, r-cran-effectsize, r-cran-insight, r-cran-parameters, r-cran-performance Suggests: r-cran-brms, r-cran-car, r-cran-coin, r-cran-ggplot2, r-cran-lme4, r-cran-mass, r-cran-pscl, r-cran-pwr, r-cran-survey, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sjstats_0.19.1-1.ca2404.1_all.deb Size: 376686 MD5sum: c600c3d40cb54c9349d05f6d4b01141a SHA1: 55935093883e77a6de35ef5cabd58bb316499043 SHA256: 1fb4e998771d334fa8fdc9acc1f24f265a6fab1e9646ec8cd1406afe0fd9f1a7 SHA512: 942f254eec02a2d02a55a5b5fb2cccd13c303e60713a11c355ab7f03cce2d37cdda1b9664eee941c0f458bd8f05e4d698bcc07e6055d3f1c117ff45175d583da Homepage: https://cran.r-project.org/package=sjstats Description: CRAN Package 'sjstats' (Collection of Convenient Functions for Common StatisticalComputations) Collection of convenient functions for common statistical computations, which are not directly provided by R's base or stats packages. This package aims at providing, first, shortcuts for statistical measures, which otherwise could only be calculated with additional effort (like Cramer's V, Phi, or effect size statistics like Eta or Omega squared), or for which currently no functions available. Second, another focus lies on weighted variants of common statistical measures and tests like weighted standard error, mean, t-test, correlation, and more. Package: r-cran-sjtable2df Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-kableextra, r-cran-rlang, r-cran-rvest, r-cran-xml2 Suggests: r-cran-lintr, r-cran-lme4, r-cran-mlbench, r-cran-quarto, r-cran-sjplot, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sjtable2df_0.0.5-1.ca2404.1_all.deb Size: 37770 MD5sum: 679d9995054a15edd791f4b9bad6879d SHA1: e540e60f6c084fa72f4c08c7f894883e61c3a770 SHA256: dbad5e3588a86320e474e24256b934fb78bf1a8fb8589aa283a2f9c2ce388759 SHA512: 36013eba36546799abf41cc77d80ba9c6392d30296d48af167f988569bb05b43923bbf197fa317aa9e0a75623cdbf633cd38fd9ef03b67d62902c159b6b6dbe0 Homepage: https://cran.r-project.org/package=sjtable2df Description: CRAN Package 'sjtable2df' (Convert 'sjPlot' HTML-Tables to R 'data.frame') A small set of helper functions to convert 'sjPlot' HTML-tables to R data.frame objects / knitr::kable-tables. Package: r-cran-skater Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1111 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-purrr, r-cran-kinship2, r-cran-corrr, r-cran-tibble, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-skater_0.2.0-1.ca2404.1_all.deb Size: 455364 MD5sum: 485bee43041ed1c38a28e137dedfdc82 SHA1: bdc39e4d19dc71beeeec8f6e5be48a4cd9de36da SHA256: 27f3fe1be7da931dcb9dff1803ee01448f598425519551d9a5ac5b941b279efe SHA512: 1360b4aa2236b88bf707e5b45e16ed0243836419349a48bd227f219d8b0d720e90b62a47fafea061c3ba7f3a1bf4381ff2612261082e1016bc1bdda986a6d76c Homepage: https://cran.r-project.org/package=skater Description: CRAN Package 'skater' (Utilities for SNP-Based Kinship Analysis) Utilities for single nucleotide polymorphism (SNP) based kinship analysis testing and evaluation. The 'skater' package contains functions for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing identity by descent (IBD) segment data. Package functions and methods are described in Turner et al. (2021) "skater: An R package for SNP-based Kinship Analysis, Testing, and Evaluation" . Package: r-cran-skedastic Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-broom, r-cran-pracma, r-cran-compquadform, r-cran-mass, r-cran-quadprog, r-cran-inflection, r-cran-rfast, r-cran-caret, r-cran-matrix, r-cran-quadprogxt, r-cran-slam, r-cran-roi, r-cran-osqp, r-cran-mgcv, r-cran-roi.plugin.qpoases Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-lmtest, r-cran-car, r-cran-tseries, r-cran-tibble, r-cran-testthat, r-cran-mlbench, r-cran-expm, r-cran-arrangements, r-cran-quantreg, r-cran-gmp, r-cran-rmpfr, r-cran-cubature, r-cran-mvtnorm, r-cran-lmboot, r-cran-sandwich, r-cran-cmna Filename: pool/dists/noble/main/r-cran-skedastic_2.0.3-1.ca2404.1_all.deb Size: 4184452 MD5sum: 964b653e06f80cb9ab2ca9802a3a194a SHA1: 8a7e6601200061f64a5082fbcbbd24eca5731229 SHA256: 751f596f8fee53bbcfc785a84899f750b91c71310e4b17512366e66096baf02c SHA512: 69872e6d7b427d061b974bc016f1faa472706abc76550bf5a65d3477aaf929f63892f6e8b7c515253234b99828d2527626f41d632ef606bea7c114dc7a37d193 Homepage: https://cran.r-project.org/package=skedastic Description: CRAN Package 'skedastic' (Handling Heteroskedasticity in the Linear Regression Model) Implements numerous methods for testing for, modelling, and correcting for heteroskedasticity in the classical linear regression model. The most novel contribution of the package is found in the functions that implement the as-yet-unpublished auxiliary linear variance models and auxiliary nonlinear variance models that are designed to estimate error variances in a heteroskedastic linear regression model. These models follow principles of statistical learning described in Hastie (2009) . The nonlinear version of the model is estimated using quasi-likelihood methods as described in Seber and Wild (2003, ISBN: 0-471-47135-6). Bootstrap methods for approximate confidence intervals for error variances are implemented as described in Efron and Tibshirani (1993, ISBN: 978-1-4899-4541-9), including also the expansion technique described in Hesterberg (2014) . The wild bootstrap employed here follows the description in Davidson and Flachaire (2008) . Tuning of hyper-parameters makes use of a golden section search function that is modelled after the MATLAB function of Zarnowiec (2022) . A methodological description of the algorithm can be found in Fox (2021, ISBN: 978-1-003-00957-3). There are 25 different functions that implement hypothesis tests for heteroskedasticity. These include a test based on Anscombe (1961) , Ramsey's (1969) BAMSET Test , the tests of Bickel (1978) , Breusch and Pagan (1979) with and without the modification proposed by Koenker (1981) , Carapeto and Holt (2003) , Cook and Weisberg (1983) (including their graphical methods), Diblasi and Bowman (1997) , Dufour, Khalaf, Bernard, and Genest (2004) , Evans and King (1985) and Evans and King (1988) , Glejser (1969) as formulated by Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), Godfrey and Orme (1999) , Goldfeld and Quandt (1965) , Harrison and McCabe (1979) , Harvey (1976) , Honda (1989) , Horn (1981) , Li and Yao (2019) with and without the modification of Bai, Pan, and Yin (2016) , Rackauskas and Zuokas (2007) , Simonoff and Tsai (1994) with and without the modification of Ferrari, Cysneiros, and Cribari-Neto (2004) , Szroeter (1978) , Verbyla (1993) , White (1980) , Wilcox and Keselman (2006) , Yuce (2008) , and Zhou, Song, and Thompson (2015) . Besides these heteroskedasticity tests, there are supporting functions that compute the BLUS residuals of Theil (1965) , the conditional two-sided p-values of Kulinskaya (2008) , and probabilities for the nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1). For handling heteroskedasticity, in addition to the new auxiliary variance model methods, there is a function to implement various existing Heteroskedasticity-Consistent Covariance Matrix Estimators from the literature, such as those of White (1980) , MacKinnon and White (1985) , Cribari-Neto (2004) , Cribari-Neto et al. (2007) , Cribari-Neto and da Silva (2011) , Aftab and Chang (2016) , and Li et al. (2017) . Package: r-cran-skeletalvis Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2243 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-cowplot, r-cran-dplyr, r-cran-dt, r-cran-ggplot2, r-cran-ggrepel, r-cran-httr, r-cran-igraph, r-cran-miniui, r-cran-pbapply, r-cran-plotly, r-cran-rlang, r-cran-shiny, r-cran-tidyr, r-cran-visnetwork Suggests: r-cran-mockery, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-skeletalvis_0.1.2-1.ca2404.1_all.deb Size: 1296330 MD5sum: 6b88c1c34790d93f12d9b11f8f30fc8d SHA1: ba0563e424ce05f257951658816c2e198f203c54 SHA256: ead37954ba63ef92638cea1078457d2b7861c56315c17ded133858a6109725a6 SHA512: 1a28a827ed4600f3055ed79c48dc984c766a8e13ce7709f13e827850dc6cc4f3a7f65ad29923d6776ff2800ac8c5ffc4040a907e493a781e2a7805f7cc0512cd Homepage: https://cran.r-project.org/package=SkeletalVis Description: CRAN Package 'SkeletalVis' (Exploration and Visualisation of Skeletal Transcriptomics Data) Allows search and visualisation of a collection of uniformly processed skeletal transcriptomic datasets. Includes methods to identify datasets where genes of interest are differentially expressed and find datasets with a similar gene expression pattern to a query dataset Soul J, Hardingham TE, Boot-Handford RP, Schwartz JM (2019) . Package: r-cran-skeletor Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-skeletor_1.0.4-1.ca2404.1_all.deb Size: 21596 MD5sum: 80f21920a27a5940e9951e61ed4693e7 SHA1: db42856e87948662ce63a05467fd33e069416420 SHA256: ddde941caa19bb2ea35f145d4497738dc2a7109a8e5d2b210018ebdc931a5add SHA512: ff9ca70aba882989284ce32b61dd818689ddc2da2782873ba15d5b551a48080986bb828d41ec523fb2823aecbb632da530284ff366f9c9fe5ffa5ac690a59d3e Homepage: https://cran.r-project.org/package=skeletor Description: CRAN Package 'skeletor' (An R Package Skeleton Generator) A tool for bootstrapping new packages with useful defaults, including a test suite outline that passes checks and helpers for running tests, checking test coverage, building vignettes, and more. Package skeletons it creates are set up for pushing your package to 'GitHub' and using other hosted services for building and test automation. 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(2020). sketcher: An R package for converting a photo into a sketch style image. . Package: r-cran-sketchy Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-knitr, r-cran-stringi, r-cran-crayon, r-cran-packrat, r-cran-git2r, r-cran-xaringanextra, r-cran-rmarkdown, r-cran-remotes, r-cran-cli, r-cran-urlchecker, r-cran-stringr Suggests: r-cran-testthat, r-cran-formatr Filename: pool/dists/noble/main/r-cran-sketchy_1.0.5-1.ca2404.1_all.deb Size: 93016 MD5sum: e498741879be213f513d6c4096220997 SHA1: 446dbf712c958ccdef99ce6d11e0db732f2be718 SHA256: 156453c9cf722d2cee519655c9c04218fd6f58d1676a630907b968dfef438b0d SHA512: 56abc6eafb5eca2ed6c02d0c07ca202c774c91171a5fa250dbe2ecc1916fb7fed887247b46622ee4f761d5c9193e7c50fd60b7f129cba7f185ce32dfa3848638 Homepage: https://cran.r-project.org/package=sketchy Description: CRAN Package 'sketchy' (Create Custom Research Compendiums) Provides functions to create and manage research compendiums for data analysis. Research compendiums are a standard and intuitive folder structure for organizing the digital materials of a research project, which can significantly improve reproducibility. The package offers several compendium structure options that fit different research project as well as the ability of duplicating the folder structure of existing projects or implementing custom structures. It also simplifies the use of version control. Package: r-cran-skewhyperbolic Architecture: all Version: 0.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-distributionutils, r-cran-generalizedhyperbolic Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-skewhyperbolic_0.4-2-1.ca2404.1_all.deb Size: 172652 MD5sum: 9054bd1ab6ad22d36c487983ce74a53f SHA1: 707340865cfbf64bac35c60fbb48326dd4f6dacd SHA256: 97519e97a65abaf4005974202da587f47b775cff540c1c69fd5329ff22f30439 SHA512: cb61ac5ce4272b59d35c071d0f058f7db5e3fe2ba6bceeab4cd53e1d5aca419e22bff2f220e3712c009a6f2bcaa5a1b7c4c8e29bca5ddec596daaacfefcc81ea Homepage: https://cran.r-project.org/package=SkewHyperbolic Description: CRAN Package 'SkewHyperbolic' (The Skew Hyperbolic Student t-Distribution) Functions are provided for the density function, distribution function, quantiles and random number generation for the skew hyperbolic t-distribution. There are also functions that fit the distribution to data. There are functions for the mean, variance, skewness, kurtosis and mode of a given distribution and to calculate moments of any order about any centre. To assess goodness of fit, there are functions to generate a Q-Q plot, a P-P plot and a tail plot. Package: r-cran-skewlmm Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-optimparallel, r-cran-dplyr, r-cran-ggplot2, r-cran-future, r-cran-ggrepel, r-cran-haven, r-cran-mvtnorm, r-cran-nlme, r-cran-purrr, r-cran-furrr, r-cran-matrixcalc, r-cran-moments, r-cran-numderiv, r-cran-momtrunc, r-cran-truncatednormal Filename: pool/dists/noble/main/r-cran-skewlmm_1.1.3-1.ca2404.1_all.deb Size: 1183826 MD5sum: 7db07e668a5277961660e62b719215e6 SHA1: b91abe4b7846eef2604790638e8a2a553ba251ee SHA256: d9cf78f2ced7b2f2bc9ca4f028d7286ec7fa63dc92ae6a2e247784b36770b4ae SHA512: 0318c858e587b8c7cf3572861b3fe14f9df8ff5e2f2ceb846fbd8fe4d86fe059765e4b8e267990846295ead606a1c5adebc20948dbe9f2e2db37375bc77dd75f Homepage: https://cran.r-project.org/package=skewlmm Description: CRAN Package 'skewlmm' (Scale Mixture of Skew-Normal Linear Mixed Models) It fits scale mixture of skew-normal linear mixed models using either an expectation–maximization (EM) type algorithm or its accelerated version (Damped Anderson Acceleration with Epsilon Monotonicity, DAAREM), including some possibilities for modeling the within-subject dependence . Package: r-cran-skewmlrm Architecture: all Version: 1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-moments, r-cran-clustergeneration, r-cran-doparallel, r-cran-mass, r-cran-mvtnorm, r-cran-matrixcalc Suggests: r-cran-sn Filename: pool/dists/noble/main/r-cran-skewmlrm_1.7-1.ca2404.1_all.deb Size: 672732 MD5sum: dea06c3273e4c14ea2ea39d9a8153535 SHA1: 96b4bb718d26f9e6f7dd5a63bb898e50973de9ee SHA256: e3959430e90fa7578fe46fe8472e31735d3a8491d6e9a7bb7f5a444ca0761829 SHA512: 62ecc0f27c98f03820dec4f04684043edb973f4d3b01eeefa8ac412a2d076bd537e44f7207112fbaf3deeefacb4055d0348668a7eb2268de30d4df0678d14fd9 Homepage: https://cran.r-project.org/package=skewMLRM Description: CRAN Package 'skewMLRM' (Estimation for Scale-Shape Mixtures of Skew-Normal Distributions) Provide data generation and estimation tools for the multivariate scale mixtures of normal presented in Lange and Sinsheimer (1993) , the multivariate scale mixtures of skew-normal presented in Zeller, Lachos and Vilca (2011) , the multivariate skew scale mixtures of normal presented in Louredo, Zeller and Ferreira (2021) and the multivariate scale mixtures of skew-normal-Cauchy presented in Kahrari et al. (2020) . Package: r-cran-skewsamp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-skewsamp_1.0.0-1.ca2404.1_all.deb Size: 74412 MD5sum: f56f7a5b344a65138c8438f75e9ca1cd SHA1: dd5413bf40151ac9cc843e332478d683e7f2da8b SHA256: 67a083e1abee98363f5de768d5fb20e1f88b1581fce88de0a7392a56582a6004 SHA512: 9e1e4981de93ce1b25c6208e601598c9554a54fc0a035ac64089ca1c52668bb4c6bc27a077f82993e0852c046b44e69307887fd8191e97dd89d3d578b84afa48 Homepage: https://cran.r-project.org/package=skewsamp Description: CRAN Package 'skewsamp' (Estimate Sample Sizes for Group Comparisons with SkewedDistributions) Estimate necessary sample sizes for comparing the location of data from two groups or categories when the distribution of the data is skewed. 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Functions are provided that make it possible to interact with the 'Slack' platform 'API'. When you need to share information or data from R, rather than resort to copy/ paste in e-mails or other services like 'Skype' , you can use this package to send well-formatted output from multiple R objects and expressions to all teammates at the same time with little effort. You can also send images from the current graphics device, R objects, and upload files. Package: r-cran-slanter Architecture: all Version: 0.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 927 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-pheatmap, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-slanter_0.2-0-1.ca2404.1_all.deb Size: 745932 MD5sum: df566ef01f77e0d8d829fa17d262f533 SHA1: 95a90ef89eed78544bd281721aaf07836f168763 SHA256: 93d9c954bff7d25caad480f06fe75fe12d14936c15bb7dac4b2981a25848e526 SHA512: e146f8b2263f14962f31e4b5cba44f8bd6f089fab05ce5239f60ad9b1657041f65fb7d91d9911c560a6463c664eca2ead8e5033f986ae08c019897761fbaf91f Homepage: https://cran.r-project.org/package=slanter Description: CRAN Package 'slanter' (Slanted Matrices and Ordered Clustering) Slanted matrices and ordered clustering for better visualization of similarity data. Package: r-cran-slap Architecture: all Version: 2024.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-slap_2024.4.1-1.ca2404.1_all.deb Size: 435756 MD5sum: 5c50b9a0b54206192169e759f2e79074 SHA1: 23661b813f1f8924d78edbd376950ec2bf4921ef SHA256: 1163887ef99f390d990d03a0b2d4e751ed040a9b2dfcac93ea6f565f0439ef41 SHA512: aa809dc4ead60a50c7ff011d6f9480e501f4a471571bb40de01d7e5aece963fdc0587b39cdd238f96f09afc387bfebed16d61ccf195895addc3e99b74cdf6b2a Homepage: https://cran.r-project.org/package=slap Description: CRAN Package 'slap' (Simplified Error Handling) Alternative to using withCallingHandlers() in the simple case of catch and rethrow. The `%!%` operator evaluates the expression on its left hand side, and if an error occurs, the right hand side is used to construct a new error that embeds the original error. Package: r-cran-slapmeg Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-lcmm, r-bioc-globaltest, r-cran-magic, r-cran-reshape2, r-cran-ggplot2, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-slapmeg_1.0.2-1.ca2404.1_all.deb Size: 93168 MD5sum: 94c89142555c7b78f6c035304ba6b652 SHA1: f341073407c0b7837ecd86a401032d9d8a4b77de SHA256: 1aa9daab1d426a03eae8b511c411bb15eaf863fdcc3a867c469cb058935ea6e3 SHA512: 029303299508c13da9781ea88e299e70ea5eb0c523277848e9fcc0631910971760b66844eb8faff82ce903969baf13ba0018db2ca035f47c02ccbd6d76d15e27 Homepage: https://cran.r-project.org/package=SlaPMEG Description: CRAN Package 'SlaPMEG' (Pathway Testing for Longitudinal Omics) A self-contained hypothesis is tested for a given pathway of longitudinal omics. 'SlaPMEG' is a two-step procedure. First, a shared latent process mixed model is fitted over the longitudinal measures of omics in a pathway. This shared model allows deviation from the shared process at subject level (a random intercept, slope, or both per subject) and also at omic level (a random effect per omic). These random effects summarize the longitudinal trend of the observations which can be used to test for group differences using 'Globaltest' in the second step. If the pathway is large or the shared effect is small, the package fits a series of pairwise models and estimates the shared random effects based on them. Package: r-cran-slash Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1814 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-slash_0.2.0-1.ca2404.1_all.deb Size: 1793678 MD5sum: 6045cb50be33d65bb476bab38be88cb4 SHA1: df94cb7dcd6ccace390804417ddefe9fe9c3abfd SHA256: 39d89b6fe8c126092883b268ec4baba80276958cf6521b7da45e57841481ce1d SHA512: 83704fb33caa7aa36083098a61e7906f1c500d74d909aac42838cf70f58ecd018855bce6386352a8d90afedc8177fe5468577f38770e8a54ec8f4a28bd35506f Homepage: https://cran.r-project.org/package=slash Description: CRAN Package 'slash' (Path-Based Access and Manipulation of Nested Lists) Allows users to list data structures using path-based navigation. Provides intuitive methods for storing, accessing, and manipulating nested data through simple path strings. 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Package: r-cran-slcare Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-nnet, r-cran-reda, r-cran-rereg, r-cran-rlang, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-slcare_1.2.0-1.ca2404.1_all.deb Size: 77094 MD5sum: e53f34c4a795ce456b3e856f05e3ac5e SHA1: c3da7a7bf2ee7ccb4e7dd801e63904b6748d3917 SHA256: 503248b14329b16208450013bc7861f1fe660fee0ade748db1dbd6ca8b01b64f SHA512: 34eabb1caaf7c22700105714c83aaf16769553cd5987a39dd8cb8f31b346795ef016101ff05865bf428f46459998c4408e9beac0d49d53af78b8077257c8a840 Homepage: https://cran.r-project.org/package=SLCARE Description: CRAN Package 'SLCARE' (Semiparametric Latent Class Analysis of Recurrent Events) Efficient R package for latent class analysis of recurrent events, based on the semiparametric multiplicative intensity model by Zhao et al. (2022) . SLCARE returns estimates for non-functional model parameters along with the associated variance estimates and p-values. Visualization tools are provided to depict the estimated functional model parameters and related functional quantities of interest. SLCARE also delivers a model checking plot to help assess the adequacy of the fitted model. Package: r-cran-sldassay Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sldassay_1.8-1.ca2404.1_all.deb Size: 30616 MD5sum: faae8f43c6c49845b27fd7bc7ebdabf8 SHA1: d2b315ed727255dba170660dec82848ba1062a64 SHA256: 6eb14f1d1782ab4242f9c9a50ddd01ff348f259513ee4f271e28069d7ad44988 SHA512: c0153c33faa4e22954391521974aad8d26fbe1ee069eac8cdee7abed1cdb28f706561e80a3980108070305db4b65d91aa9afbfaa56e4fd70fb37779e3818949f Homepage: https://cran.r-project.org/package=SLDAssay Description: CRAN Package 'SLDAssay' (Software for Analyzing Limiting Dilution Assays) Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and exact and asymptotic goodness of fit p-values for concentration of infectious units from serial limiting dilution assays. 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Package: r-cran-sleacr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2478 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-parallelly Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-sleacr_0.1.3-1.ca2404.1_all.deb Size: 2474516 MD5sum: 58b2a29d3767953fae7384792b490037 SHA1: 7b419cadceeca420e330cbe2b9ea52699b15adee SHA256: 913e70d4f0f09b4dd47213d962fb7d4b49979c55b014ddf0736ae6bf74e5a511 SHA512: a42c31d31c44be37face3d9ad939633383892acd3e8aca21d0f6d6e101587071770dc8140d89b76a9c29c20b0a89c2d0c26d6aa5ccb1cf7948bc7179f84690e9 Homepage: https://cran.r-project.org/package=sleacr Description: CRAN Package 'sleacr' (Simplified Lot Quality Assurance Sampling Evaluation of Accessand Coverage (SLEAC) Tools) In the recent past, measurement of coverage has been mainly through two-stage cluster sampled surveys either as part of a nutrition assessment or through a specific coverage survey known as Centric Systematic Area Sampling (CSAS). 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Package: r-cran-sleekts Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sleekts_1.0.2-1.ca2404.1_all.deb Size: 19946 MD5sum: 8da6fa0db8b9ea72d0cc5d15d1e7f4e1 SHA1: 406ab4db5df7d8d4ebb2a325de3591335a2d0b18 SHA256: 7a410bba25ef8a6e011b23bfcbfee8e53c067f1fde48374ed956eaeec761bbc4 SHA512: 9911ca4cf9183943aec13ce500298a58ba9b6442a766c40ca961c0655a130c3c46234959e12d26f3936768af419b6dd33430c24192ab9d8bb805f34b21eadd72 Homepage: https://cran.r-project.org/package=sleekts Description: CRAN Package 'sleekts' (4253H, Twice Smoothing) Compute Time series Resistant Smooth 4253H, twice smoothing method. Package: r-cran-sleepcycles Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-plyr, r-cran-stringr, r-cran-viridis Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sleepcycles_1.1.4-1.ca2404.1_all.deb Size: 99142 MD5sum: d0c7e9712c68008632c835c0b1f2f9f8 SHA1: 3eba2636cfc7ef468b9620f3de96d7a23153f008 SHA256: e4651acbc2a786e3fc2a120c6b8d54f2d42b7f1e35a0435d482520bc1a0bc736 SHA512: 8e42f7250502ff58c342571d3b9324acc98b054ea42f2505203376147f2a49e947d2048ed57a0b9a40b40c42af4648e9d1de96026d35a69142eafcbcca8d70a2 Homepage: https://cran.r-project.org/package=SleepCycles Description: CRAN Package 'SleepCycles' (Sleep Cycle Detection) Sleep cycles are largely detected according to the originally proposed criteria by Feinberg & Floyd (1979) as described in Blume & Cajochen (2021) . 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Package: r-cran-sleepr Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-behavr, r-cran-data.table Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sleepr_0.3.1-1.ca2404.1_all.deb Size: 43000 MD5sum: 4530424dad9d9688ed88c4c6afc00a90 SHA1: 8c4ee7fd2b5018cb9dd706f40f2b467a96804ef2 SHA256: c762bf2b615adaba9499c3d2600f35bfea4908390e83c3536a7640d1d27200ef SHA512: 23132541f3c02cfd29b81b1e7c2755566facd1aa0ec5ecdf193dd111d4af16de226b99b9c30b938ad031a048d2346ff3ea4ca78cb92b36324cb7ae8c26e6d7d2 Homepage: https://cran.r-project.org/package=sleepr Description: CRAN Package 'sleepr' (Analyse Activity and Sleep Behaviour) Use behavioural variables to score activity and infer sleep from bouts of immobility. It is primarily designed to score sleep in fruit flies from Drosophila Activity Monitor (TriKinetics) and Ethoscope data. It implements sleep scoring using the "five-minute rule" (Hendricks et al. (2000) ), activity classification for Ethoscopes (Geissmann et al. (2017) ) and a new algorithm to detect when animals are dead. Package: r-cran-sleepwalk Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jrc, r-cran-cowplot, r-cran-httpuv, r-cran-jsonlite, r-cran-scales, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-sleepwalk_0.3.2-1.ca2404.1_all.deb Size: 121726 MD5sum: c22102030ba4cf91c42d50868e4d3842 SHA1: e05800fa09b43f341fb584a0ad7af6bb41a1bbb9 SHA256: 52ff300ba17fefdf818eddd4e579c4f5067bb928b07ada6e9822522518f57e32 SHA512: 69ae0c9167510b72a2bc62d3326a7faec079f7dc6f242fc4b9e0efd5b9c9cf396cb79968ff07588861f838bce53ec13ecf94453601b10edadcb2ec7641f66570 Homepage: https://cran.r-project.org/package=sleepwalk Description: CRAN Package 'sleepwalk' (Interactively Explore Dimension-Reduced Embeddings) A tool to interactively explore the embeddings created by dimension reduction methods such as Principal Components Analysis (PCA), Multidimensional Scaling (MDS), T-distributed Stochastic Neighbour Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP) or any other. Package: r-cran-slemi Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1929 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-e1071, r-cran-ggplot2, r-cran-gridextra, r-cran-nnet, r-cran-hmisc, r-cran-reshape2, r-cran-stringr, r-cran-doparallel, r-cran-caret, r-cran-corrplot, r-cran-foreach Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-data.table, r-cran-covr Filename: pool/dists/noble/main/r-cran-slemi_1.0.2-1.ca2404.1_all.deb Size: 1465914 MD5sum: 2cd223d45d74999ff1053d96920edb31 SHA1: 4b48e7dfabb555dbc3a4cfa717c13c0f48ab1946 SHA256: 88c39d1313abc2eab7e4c1b7e6ae2c04a2d8a0db6d5fcea97374d060c7941291 SHA512: 021dc21804b92f057b427c2753145ee132451f8be2d583e4e24f7738976a08e93449c99e9ed793ea8e6e1aad12f5746ac8d970fabbabed1e6358f1989939f939 Homepage: https://cran.r-project.org/package=SLEMI Description: CRAN Package 'SLEMI' (Statistical Learning Based Estimation of Mutual Information) The implementation of the algorithm for estimation of mutual information and channel capacity from experimental data by classification procedures (logistic regression). 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Package: r-cran-slendr Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4640 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-magrittr, r-cran-reticulate, r-cran-tidyr, r-cran-png, r-cran-ijtiff, r-cran-ape, r-cran-scales, r-cran-digest, r-cran-ggrepel Suggests: r-cran-testthat, r-cran-sf, r-cran-stars, r-cran-rnaturalearth, r-cran-gganimate, r-cran-knitr, r-cran-rmarkdown, r-cran-admixr, r-cran-units, r-cran-magick, r-cran-cowplot, r-cran-forcats, r-cran-shinywidgets, r-cran-shiny, r-cran-rsvg Filename: pool/dists/noble/main/r-cran-slendr_1.4.0-1.ca2404.1_all.deb Size: 2518888 MD5sum: 6cf4b0a5afcda83f10ae309f85556657 SHA1: b7015af985f95490fb8670b1b75ef5d95111d131 SHA256: 465b007bd7d77d3cb4aceafd16fc01684d2c79b26318587e5b6c1ad2fc0aa079 SHA512: df861b8d91fe01fffe0967fec11a1d0d2cd4260dc98ba414116c5f12df56d63de8804f7a0dffe3a9bdb152fad3803e19a640ce73725a63c6c6e644abca96f85d Homepage: https://cran.r-project.org/package=slendr Description: CRAN Package 'slendr' (A Simulation Framework for Spatiotemporal Population Genetics) A framework for simulating spatially explicit genomic data which leverages real cartographic information for programmatic and visual encoding of spatiotemporal population dynamics on real geographic landscapes. Population genetic models are then automatically executed by the 'SLiM' software by Haller et al. (2019) behind the scenes, using a custom built-in simulation 'SLiM' script. Additionally, fully abstract spatial models not tied to a specific geographic location are supported, and users can also simulate data from standard, non-spatial, random-mating models. These can be simulated either with the 'SLiM' built-in back-end script, or using an efficient coalescent population genetics simulator 'msprime' by Baumdicker et al. (2022) with a custom-built 'Python' script bundled with the R package. Simulated genomic data is saved in a tree-sequence format and can be loaded, manipulated, and summarised using tree-sequence functionality via an R interface to the 'Python' module 'tskit' by Kelleher et al. (2019) . Complete model configuration, simulation and analysis pipelines can be therefore constructed without a need to leave the R environment, eliminating friction between disparate tools for population genetic simulations and data analysis. Package: r-cran-sleuth2 Architecture: all Version: 2.0-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5072 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-lattice, r-cran-knitr, r-cran-mass, r-cran-agricolae, r-cran-car, r-cran-gmodels, r-cran-leaps, r-cran-mosaic Filename: pool/dists/noble/main/r-cran-sleuth2_2.0-7-1.ca2404.1_all.deb Size: 3899320 MD5sum: bdbdeca932d472ea12a4317b545969a4 SHA1: 0f181d90e43b26db83071439efcd40ab29311257 SHA256: 579b0f60726ef39c497671af9896ba194e028217accd462b2003143999c8c5d3 SHA512: 445c3fd3a242e7f40ba66631b57479744b1571db286e21c64c4b7351f380ccf78ae79f75aaab251cf69ec45c8bb47a7a876972a97052615bbf3e30b1e47199f7 Homepage: https://cran.r-project.org/package=Sleuth2 Description: CRAN Package 'Sleuth2' (Data Sets from Ramsey and Schafer's "Statistical Sleuth (2ndEd)") Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The Statistical Sleuth: A Course in Methods of Data Analysis (2nd ed)", Duxbury. 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Statistica Sinica, 1117-1130, . 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Package: r-cran-smacofx Architecture: all Version: 1.22-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 928 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-smacof, r-cran-mass, r-cran-minqa, r-cran-plotrix, r-cran-projectionbasedclustering, r-cran-weights, r-cran-vegan Filename: pool/dists/noble/main/r-cran-smacofx_1.22-0-1.ca2404.1_all.deb Size: 739430 MD5sum: 3a626533c396cf3566f7d1c8881956d5 SHA1: 0a25d5c5391ced436b5a47c62d0fd81f57b2ea53 SHA256: 4ca9010a1008d6edc559400b4b199e0e7ef801337ab94b19deaad9dff412989f SHA512: 5d335726285e069101bd2028a3159ab37a99da09bd31d177cc0ae2e1df0a25945eff26af2d3579afbcedb53b2b4122cfef70b4b3bbfb3be8787c2556c2c531b5 Homepage: https://cran.r-project.org/package=smacofx Description: CRAN Package 'smacofx' (Flexible Multidimensional Scaling and 'smacof' Extensions) Flexible multidimensional scaling (MDS) methods and extensions to the package 'smacof'. This package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different flexible MDS models. These are: Torgerson scaling (Torgerson, 1958, ISBN:978-0471879459) with powers, Sammon mapping (Sammon, 1969, ) with ratio and interval optimal scaling, Multiscale MDS (Ramsay, 1977, ) with ratio and interval optimal scaling, s-stress MDS (ALSCAL; Takane, Young & De Leeuw, 1977, ) with ratio and interval optimal scaling, elastic scaling (McGee, 1966, ) with ratio and interval optimal scaling, r-stress MDS (De Leeuw, Groenen & Mair, 2016, ) with ratio, interval, splines and nonmetric optimal scaling, power-stress MDS (POST-MDS; Buja & Swayne, 2002 ) with ratio and interval optimal scaling, restricted power-stress (Rusch, Mair & Hornik, 2021, ) with ratio and interval optimal scaling, approximate power-stress with ratio optimal scaling (Rusch, Mair & Hornik, 2021, ), Box-Cox MDS (Chen & Buja, 2013, ), local MDS (Chen & Buja, 2009, ), curvilinear component analysis (Demartines & Herault, 1997, ), curvilinear distance analysis (Lee, Lendasse & Verleysen, 2004, ), nonlinear MDS with optimal dissimilarity powers functions (De Leeuw, 2024, ), sparsified (power) MDS and sparsified multidimensional (power) distance analysis aka extended curvilinear (power) component analysis and extended curvilinear (power) distance analysis (Rusch, 2024, ). Some functions are suitably flexible to allow any other sensible combination of explicit power transformations for weights, distances and input proximities with implicit ratio, interval, splines or nonmetric optimal scaling of the input proximities. Most functions use a Majorization-Minimization algorithm. Currently the methods are only available for one-mode two-way data (symmetric dissimilarity matrices). 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Package: r-cran-smcrm Architecture: all Version: 0.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-smcrm_0.0-3-1.ca2404.1_all.deb Size: 126434 MD5sum: 3c01bc3a2a4a382c86a7a6e6a3fb2732 SHA1: 7f7d6b9b6976d89e60efb11be32d526644a2d227 SHA256: 267a29d1b82f51c545a6d1103523e5bb50ccd56eb7bd374ea47fc086ff0af123 SHA512: 0fef7f281b35780c8bd011abce73c5baf52f7c65306c25f5e9b8e632f709dd25440af51dc0402641b4f4e3fd14450947db6e2968d1c5469d046a698c2496435d Homepage: https://cran.r-project.org/package=SMCRM Description: CRAN Package 'SMCRM' (Data Sets for Statistical Methods in Customer RelationshipManagement by Kumar and Petersen (2012)) Data Sets for Kumar and Petersen (2012). Statistical Methods in Customer Relationship Management, Wiley: New York. 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Package: r-cran-smdi Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3584 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-caret, r-cran-dplyr, r-cran-fastdummies, r-cran-forcats, r-cran-ggplot2, r-cran-glue, r-cran-gt, r-cran-hotelling, r-cran-lifecycle, r-cran-magrittr, r-cran-mice, r-cran-naniar, r-cran-proc, r-cran-randomforest, r-cran-stringr, r-cran-survival, r-cran-tableone, r-cran-tibble, r-cran-tidyr Suggests: r-cran-broom.helpers, r-cran-cardx, r-cran-gridextra, r-cran-gtsummary, r-cran-here, r-cran-knitr, r-cran-reactr, r-cran-reactable, r-cran-rmarkdown, r-cran-simsurv, r-cran-smd, r-cran-survminer, r-cran-usethis, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-smdi_0.3.2-1.ca2404.1_all.deb Size: 2244520 MD5sum: 64cf52194951aa0e8eb417878b7d1758 SHA1: 796de86c8c6a23f3e70fafb78447cab39d732163 SHA256: af5df81b68d5ba680728380d9d298d4b2f218f3ffe0aa5841a29f8759cc31c77 SHA512: f65343bad3107a829d1aeea73178bba36a8c001ae188d35d3646daf25eb8d40e07652083706b0a8d1e9c2bf4e1f6eca8ff3b0e29a87d401e04d7ce8187ed3d3f Homepage: https://cran.r-project.org/package=smdi Description: CRAN Package 'smdi' (Perform Structural Missing Data Investigations) An easy to use implementation of routine structural missing data diagnostics with functions to visualize the proportions of missing observations, investigate missing data patterns and conduct various empirical missing data diagnostic tests. Reference: Weberpals J, Raman SR, Shaw PA, Lee H, Hammill BG, Toh S, Connolly JG, Dandreo KJ, Tian F, Liu W, Li J, Hernández-Muñoz JJ, Glynn RJ, Desai RJ. smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies. JAMIA Open. 2024 Jan 31;7(1):ooae008. . 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The operation modes including: i) inferring the relative abundance matrix of tumor-infiltrating immune cells and integrating it with a particular gene mutation status, ii) detecting differential immune cells with respect to the gene mutation status and converting the abundance matrix of significant differential immune cell into two binary matrices (one for up-regulated and one for down-regulated), iii) identifying somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and iv) visualization of immune cell abundance of samples in different mutation status.. Package: r-cran-smdocker Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-paws.compute, r-cran-paws.developer.tools, r-cran-paws.machine.learning, r-cran-paws.management, r-cran-paws.storage, r-cran-paws.security.identity, r-cran-zip, r-cran-uuid Suggests: r-cran-covr, r-cran-crayon, r-cran-mockery, r-cran-testthat Filename: pool/dists/noble/main/r-cran-smdocker_0.1.4-1.ca2404.1_all.deb Size: 67886 MD5sum: 0963926eac9c6dccc8cf903e61b496c1 SHA1: 77688fbe95b3023a7314cfb8e07aa01ada985455 SHA256: 5672add972874f4a2c8e488da143d38e24d45ce7748994d658efde6b894095fb SHA512: 7c9ce161fb7e3b802836d6a7087f7e22a21e3c2853e98b2357596fd9706195b428a100b881484f1867639ea0bb461b02a659bf977d5f70f3e5eaed2a839b8eef Homepage: https://cran.r-project.org/package=smdocker Description: CRAN Package 'smdocker' (Build 'Docker Images' in 'Amazon SageMaker Studio' using 'AmazonWeb Service CodeBuild') Allows users to easily build custom 'docker images' from 'Amazon Web Service Sagemaker' using 'Amazon Web Service CodeBuild' . 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Contains routines for the deterministic metafrontier envelope of O'Donnell et al. (2008) via linear and quadratic programming, and the stochastic metafrontier of Huang et al. (2014) . Also supports latent class stochastic metafrontier analysis and sample selection correction stochastic metafrontier models. Depends on the 'sfaR' package by Dakpo et al. (2023) . Package: r-cran-smfilter Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-smfilter_1.0.3-1.ca2404.1_all.deb Size: 170088 MD5sum: b7b442b6530f1d099e1fc3af4c256fb7 SHA1: bfaeae6c4ca4e51f93edc71a6b69241bf5ca77bf SHA256: 0c45ea43c0b0aa0efe2067c01a743387ee64d04dcfde306659340d47494f4e9a SHA512: ee92d248afadd71bbf9cb55b6da39c2521cb8fe7ea6a18511f64e45b56a74b78acc23e187d2ea8a1004c79de91236d9715f7e1d10be6943d12e0b1ac6a0403cf Homepage: https://cran.r-project.org/package=SMFilter Description: CRAN Package 'SMFilter' (Filtering Algorithms for the State Space Models on the StiefelManifold) Provides the filtering algorithms for the state space models on the Stiefel manifold as well as the corresponding sampling algorithms for uniform, vector Langevin-Bingham and matrix Langevin-Bingham distributions on the Stiefel manifold. Package: r-cran-smicd Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ineq, r-cran-truncnorm, r-cran-lme4, r-cran-formula.tools, r-cran-mvtnorm, r-cran-hmisc, r-cran-laeken, r-cran-weights Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mlmrev, r-cran-r.rsp, r-cran-kernelheaping Filename: pool/dists/noble/main/r-cran-smicd_1.1.5-1.ca2404.1_all.deb Size: 623268 MD5sum: 1ceb21942800ddd35b617e1987917a7b SHA1: 333b980b6b6f80d54c76c17017d4bb963440915c SHA256: 4378130f5ded896098c14f0342f617287aa1874d78d9442a373ea85543b26092 SHA512: 129d5cada7210ace28ae5dc0f1715e836f963b819f2233a86fc7c23ae27488087b3fef221f6b8c01fb0f05d8a58916c278bcb2a3b63cc0236610afe7c95cdf19 Homepage: https://cran.r-project.org/package=smicd Description: CRAN Package 'smicd' (Statistical Methods for Interval-Censored Data) Functions that provide statistical methods for interval-censored (grouped) data. The package supports the estimation of linear and linear mixed regression models with interval-censored dependent variables. Parameter estimates are obtained by a stochastic expectation maximization algorithm. Furthermore, the package enables the direct (without covariates) estimation of statistical indicators from interval-censored data via an iterative kernel density algorithm. Survey and Organisation for Economic Co-operation and Development (OECD) weights can be included into the direct estimation (see, Walter, P. (2019) ). Package: r-cran-smidm Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-extradistr Suggests: r-cran-testthat, r-cran-covr, r-cran-xml2, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-hdrcde, r-cran-ggplot2, r-cran-scales Filename: pool/dists/noble/main/r-cran-smidm_1.0-1.ca2404.1_all.deb Size: 155008 MD5sum: 8e0329879e2d51689056e7b8ed2f67e1 SHA1: 93be4b22151325dbf9c90afb9b8a07e34971d76f SHA256: 17d2e28a6ebd381e0097bb10b9479d606be68eaf5515252d83bdecfe348a1c28 SHA512: 9fef76beaef08b523ba29fd42019455c304c119f16ac8869e836a14723169f39b65e8767520be877f90aefbd1057edfe4c4b52fd4542957b8ede02da1cf395c7 Homepage: https://cran.r-project.org/package=smidm Description: CRAN Package 'smidm' (Statistical Modelling for Infectious Disease Management) Statistical models for specific coronavirus disease 2019 use cases at German local health authorities. All models of Statistical modelling for infectious disease management 'smidm' are part of the decision support toolkit in the 'EsteR' project. More information is published in Sonja Jäckle, Rieke Alpers, Lisa Kühne, Jakob Schumacher, Benjamin Geisler, Max Westphal "'EsteR' – A Digital Toolkit for COVID-19 Decision Support in Local Health Authorities" (2022) and Sonja Jäckle, Elias Röger, Volker Dicken, Benjamin Geisler, Jakob Schumacher, Max Westphal "A Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions" (2021) . Package: r-cran-smiles Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-meta Suggests: r-cran-bookdown, r-cran-diagrammer, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-smiles_0.1-0-1.ca2404.1_all.deb Size: 155786 MD5sum: 57534c89b9efd6cc7a39e45abe17040d SHA1: 628b885ed775ad8c1e02c09cf4067e5a190ad9e6 SHA256: 224c246f5e09d1395410471748eb2a746778bb48102e3e7251a902a8fd5573f4 SHA512: 46fd77fb0da127c932e09bfbe0e8eab1fbc57688cc6f419067e1d0b2e9234b7064e4fb22035c32b25abe0d25abb26ee8d9540bd18c2b0539f0357932fde7feeb Homepage: https://cran.r-project.org/package=smiles Description: CRAN Package 'smiles' (Sequential Method in Leading Evidence Synthesis) Trial sequential analysis emerges as an important method in data synthesis realm. It is necessary to integrate pooling methods and sequential analysis coherently, as discussed in the Chapter by Thomas, J., Askie, L.M., Berlin, J.A., Elliott, J.H., Ghersi, D., Simmonds, M., Takwoingi, Y., Tierney, J.F. and Higgins, J.P. (2019). "Prospective approaches to accumulating evidence". In Cochrane Handbook for Systematic Reviews of Interventions (eds J.P.T. Higgins, J. Thomas, J. Chandler, M. Cumpston, T. Li, M.J. Page and V.A. Welch). . Package: r-cran-smimodel Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 699 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cgaim, r-cran-conformalforecast, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-generics, r-cran-ggplot2, r-cran-gratia, r-cran-gtools, r-cran-matrix, r-cran-mgcv, r-cran-purrr, r-cran-roi, r-cran-tibble, r-cran-tidyselect, r-cran-tidyr, r-cran-tsibble Suggests: r-cran-knitr, r-cran-lubridate, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-smimodel_0.1.3-1.ca2404.1_all.deb Size: 572024 MD5sum: 8cf43a8537c5641de654f8b06d13770b SHA1: 08d1ed40ba8fd0c65685abcf0e9ad4559c9b1f5b SHA256: 6b43517bc1a5ac69232e61084562ad137547e63ed863a526bb7a2ca88c618e7f SHA512: 43cfc2daad939b7649ca78f9fb9e1e09289aee497d07b178d1914e7cf1523ed1fe78c53c61d57bf38e2380a2b5412a46c87fc1a52abbff12f26dbb9881b25672 Homepage: https://cran.r-project.org/package=smimodel Description: CRAN Package 'smimodel' (Sparse Multiple Index Models for Nonparametric Forecasting) Implements a general algorithm for estimating Sparse Multiple Index (SMI) models for nonparametric forecasting and prediction. Estimation of SMI models requires the Gurobi mixed integer programming (MIP) solver via the gurobi R package. To use this functionality, the Gurobi Optimizer must be installed, and a valid license obtained and activated from . The gurobi R package must then be installed and configured following the instructions at . The package also includes functions for fitting nonparametric additive models with backward elimination, group-wise additive index models, and projection pursuit regression models as benchmark comparison methods. In addition, it provides tools for generating prediction intervals to quantify uncertainty in point forecasts produced by the SMI model and benchmark models, using the classical block bootstrap and a new method called conformal bootstrap, which integrates block bootstrap with split conformal prediction. Package: r-cran-smirnov Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 41 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-smirnov_1.0-1-1.ca2404.1_all.deb Size: 11546 MD5sum: 19cb311987e3f29a27e9d6ab3d5a1ffd SHA1: 7319ef5d8d391394ebb3a648c50e0c0c2cf8f396 SHA256: 7aefe9aade3825db5bf0a2bb191d5f52b2c0b94d76b8f13d962aab15b5b673ec SHA512: 068db08e5c7d5f107437a851435f5740c10f48240c53687d65122d1f0e14b332688819554f562ef1f2dbaef62430da45f84feb73ed2bfa73edc93688548c682d Homepage: https://cran.r-project.org/package=smirnov Description: CRAN Package 'smirnov' (Provides two taxonomic coefficients from E. S. Smirnov"Taxonomic analysis" (1969) book) This tiny package contains one function smirnov() which calculates two scaled taxonomic coefficients, Txy (coefficient of similarity) and Txx (coefficient of originality). 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Package: r-cran-smithwilsonyieldcurve Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-smithwilsonyieldcurve_1.1.1-1.ca2404.1_all.deb Size: 189474 MD5sum: 70439e8bf3a6875fb5dc7d863a8e6982 SHA1: 9d3f1127cdb19555248a2b3182b80ee7d3603908 SHA256: ad8549f3c055d649e39840d2e0295f01f81d5d02e988b3929ef6b331e55b8777 SHA512: 6ed7de3cf487633d6e6ad93687f48444198b553a6cb8b48e22f8ca6860c907da2587caa9993c9d9120d65629f052f2b8326ec81aa445d5b0559fda3d9bb5c2c2 Homepage: https://cran.r-project.org/package=SmithWilsonYieldCurve Description: CRAN Package 'SmithWilsonYieldCurve' (Smith-Wilson Yield Curve Construction) Constructs a yield curve by the Smith-Wilson method from a table of libor and swap rates. Now updated to take bond coupons and prices in the same table. Package: r-cran-smitidstruct Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-sf, r-bioc-biostrings Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-smitidstruct_0.0.5-1.ca2404.1_all.deb Size: 216292 MD5sum: f624bb846c519324ffe0d42d274b12c6 SHA1: 7d1338f6ad9b51bba6d95264f0652aa77e2810fb SHA256: 77e30eb54712bff983a77f37c97691a2d528c82cb39613165c3b4288054a0831 SHA512: 28349e09cb572f98d565b85bb93a64e2e76e135b8f48bcd350bcb2748a8e0b47c69d93ec28d374f50504205b0e8fff384b2496dc922ebb29920c43f00a4e1083 Homepage: https://cran.r-project.org/package=SMITIDstruct Description: CRAN Package 'SMITIDstruct' (Data Structure and Manipulations Tool for Host and ViralPopulation) Statistical Methods for Inferring Transmissions of Infectious Diseases from deep sequencing data (SMITID). It allow sequence-space-time host and viral population data storage, indexation and querying. Package: r-cran-smle Architecture: all Version: 2.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3372 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-matrixcalc, r-cran-mvnfast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-smle_2.2-3-1.ca2404.1_all.deb Size: 3215416 MD5sum: 6d92b5cda572561c4aa01f2e5f52fcec SHA1: d2d17420cc46865598554721319333e2b9fefe6f SHA256: c6af27d8bcf79f80144586720a326f813ca0760cfb8908513a201c731c1cc997 SHA512: 7e19b34c8de8b8b00d537ab59a2a8dcb38f7ab99e7027129080a17609cbfec6e46c8affe9032ef60410bb792b234f0d93afcb0af7c725c5f6b1b88c34479ac46 Homepage: https://cran.r-project.org/package=SMLE Description: CRAN Package 'SMLE' (Joint Feature Screening via Sparse MLE) Feature screening is a powerful tool in processing ultrahigh dimensional data. 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Package: r-cran-smleph Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-splines2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-smleph_0.1.1-1.ca2404.1_all.deb Size: 26982 MD5sum: 4c3624bc6eeef24a6fb9e1f81ebbdbb3 SHA1: 61e897f3ed816a9029360cf0748dc6fa223035ee SHA256: 1cbaf0d04c25b8e12231251ccb60ba52ef2e89687b9ad7a3dfc958cf17509a87 SHA512: 36cdee61342858c2040c084c09b73c6ce58f94cc8b5399558b2eda5889a7c2e85f473672e7e9b3dab88b03317038ea0520f89a70ba99550f8cac4457b95fe405 Homepage: https://cran.r-project.org/package=smlePH Description: CRAN Package 'smlePH' (Sieve Maximum Full Likelihood Estimation for the Right-CensoredProportional Hazards Model) Fitting the full likelihood proportional hazards model and extracting the residuals. Package: r-cran-smlmkalman Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3013 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spdep, r-cran-pracma, r-cran-scales, r-cran-truncnorm Filename: pool/dists/noble/main/r-cran-smlmkalman_0.1.1-1.ca2404.1_all.deb Size: 3017314 MD5sum: 2333e1201d9ee079a6b33e9dcb9b9ce5 SHA1: 661e0d7d052c086d961bfbc35361f7ddead7a130 SHA256: 643d91078eed54d4ef265c1c9237386b4f4ad560c5eace29f7f297cb495258d7 SHA512: af2c2eafdb6e2898fd9604d9b54f3104f87350651d45d21d207068e01c623b9e85390d310a71b694f5ff456d1bfd2d283f1441d39b29267e7e59b9e15c313fc8 Homepage: https://cran.r-project.org/package=smlmkalman Description: CRAN Package 'smlmkalman' (Generation and Tracking of Super-Resolution Filamentous Datasets) A pair of functions that allow for the generation and tracking of coordinate data clouds without a time dimension, primarily for use in super-resolution plant micro-tubule image segmentation. Package: r-cran-smloutliers Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-smloutliers_0.1-1.ca2404.1_all.deb Size: 17512 MD5sum: 7e6cc2e342a346771d94b4bccfe7250b SHA1: 454683a06d96ebaecf1298691a72ea59674a1f0f SHA256: 7b779f640b9595d9be7bb8365feb1717e0544609713528f1aa940a0a91fb2144 SHA512: 8d492a7873792aac8fdd0c1988011507f6cf36197c65dbf274161e63dce3ea2b8697d632cf1e07562a964ad906415fd006abc66a474fb9f7db91a134618feb0a Homepage: https://cran.r-project.org/package=SMLoutliers Description: CRAN Package 'SMLoutliers' (Outlier Detection Using Statistical and Machine Learning Methods) Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see . Package: r-cran-smm Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1034 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-seqinr, r-cran-discreteweibull Filename: pool/dists/noble/main/r-cran-smm_1.0.2-1.ca2404.1_all.deb Size: 946858 MD5sum: 5944f6ce87839b8b4fdca400df6954f9 SHA1: d86e61b377582c4dfbe2802edd1128c2fd30bcc6 SHA256: 54affe20dc37552a226bf27fa2bcba01fb807df79bf698dcaa9cece6a6ce2eb8 SHA512: d5bbbfaa0de70033d130af06f803790d03902a2e4f0c60940d3e0b73a7e76efd1ef4a960a6fcff76fe18586fd1d974d89cfbe6a1cc1a35c3e7e4827e0b471b07 Homepage: https://cran.r-project.org/package=SMM Description: CRAN Package 'SMM' (Simulation and Estimation of Multi-State Discrete-TimeSemi-Markov and Markov Models) Performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) , Barbu, V.S., Limnios, N. (2008) and Trevezas, S., Limnios, N. (2011) . Estimation and simulation of discrete-time k-th order Markov chains are also considered. Package: r-cran-smmal Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1620 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-randomforest, r-cran-splines2, r-cran-xgboost Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-smmal_0.0.5-1.ca2404.1_all.deb Size: 1520416 MD5sum: 6de645dcba9015d38606e77539a59791 SHA1: 9434da8ed645cefd85fa221017b69f160319a5c4 SHA256: 531d7f70a6a87d47210fe286724ae7b3d6a454d3e10203f81415b82539b82ae3 SHA512: e996685fb5004695ac4d66874a5c17eda04839040a632541450fc0b1b654de9a2bea0ad54f630e412f6cdf9441c9b4c95a448759d40689e14ac6009736af22e2 Homepage: https://cran.r-project.org/package=SMMAL Description: CRAN Package 'SMMAL' (Semi-Supervised Estimation of Average Treatment Effects) Provides a pipeline for estimating the average treatment effect via semi-supervised learning. Outcome regression is fit with cross-fitting using various machine learning method or user customized function. Doubly robust ATE estimation leverages both labeled and unlabeled data under a semi-supervised missing-data framework. For more details see Hou et al. (2021) . A detailed vignette is included. Package: r-cran-smmt Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-xml, r-cran-tibble, r-cran-curl, r-cran-rvest, r-cran-xml2 Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-smmt_1.2.0-1.ca2404.1_all.deb Size: 206682 MD5sum: 94add33196af7e49b4365f751f20d8c0 SHA1: 9cee04dbaef95cbc3f2845fd995d2584362f670b SHA256: 7aac8f10ca1fe6bb36adfec850b636c9232424219bf327d8e638ddb4c2ca81e7 SHA512: 4e1b85557f8bf458ccf6aeeb97d104bf6fc328e378782e567c1be764ef9c56282e17954f4fb546555c86bbc2875ab069b5a737689351eed4f9e4baa4985aeac2 Homepage: https://cran.r-project.org/package=SMMT Description: CRAN Package 'SMMT' (The Swiss Municipal Data Merger Tool Maps Municipalities OverTime) In Switzerland, the landscape of municipalities is changing rapidly mainly due to mergers. The Swiss Municipal Data Merger Tool automatically detects these mutations and maps municipalities over time, i.e. municipalities of an old state to municipalities of a new state. This functionality is helpful when working with datasets that are based on different spatial references. The package's idea and use case is discussed in the following article: . Package: r-cran-smncensreg Architecture: all Version: 3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-performanceanalytics Filename: pool/dists/noble/main/r-cran-smncensreg_3.1-1.ca2404.1_all.deb Size: 88404 MD5sum: 527dcef331d7708c7ecc1eb8437697bf SHA1: 8a0a6a37e3ef7bacd276023849113c27b5daa617 SHA256: 643507c5ae29cb0a1cda797fca75e5ddbdbc98040d19d25949cc2a8556a8438b SHA512: 33346b7d39f8330833eca1f8a736e732312a9931d13beeee0df8da0b08ccc5859d43f0df7f66232de8785c2e6d99dadd61f3f2ae1825ca9651a463702ed1eb95 Homepage: https://cran.r-project.org/package=SMNCensReg Description: CRAN Package 'SMNCensReg' (Fitting Univariate Censored Regression Model Under the Family ofScale Mixture of Normal Distributions) Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions. Package: r-cran-smnlmec Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rstan, r-cran-stanheaders, r-cran-mass, r-cran-tmvtnorm, r-cran-mvtnorm, r-cran-mnormt, r-cran-laplacesdemon, r-cran-truncatednormal, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-smnlmec_1.0.2-1.ca2404.1_all.deb Size: 268506 MD5sum: 1a1d6c61da083c5c4e20199b0b073667 SHA1: bb9a1f728ecb3df2d11b6fa27a0dc20e0c79a82e SHA256: 3aaf130cace02982f49d01eb331e4de53dab4035a4303801f8dbb569a260b48d SHA512: cff715de9f73237c5cb1968c47062bf7b7b3b358383af3793ec7f97e03a7e7bdfdf3a4dc9eeba9c3625feebfd52d86a1b7c36627c94980b9349218c325d279fa Homepage: https://cran.r-project.org/package=SMNlmec Description: CRAN Package 'SMNlmec' (Scale Mixture of Normal Distribution in Linear Mixed-EffectsModel) Bayesian analysis of censored linear mixed-effects models that replace Gaussian assumptions with a flexible class of distributions, such as the scale mixture of normal family distributions, considering a damped exponential correlation structure which was employed to account for within-subject autocorrelation among irregularly observed measures. For more details, see Kelin Zhong, Fernanda L. Schumacher, Luis M. Castro, Victor H. Lachos (2025) . 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Two variants of the present approach have been developed, one in each of the next references: Azzalini (2023) , (2024) . Package: r-cran-smoke Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1697 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-smoke_2.0.1-1.ca2404.1_all.deb Size: 1604646 MD5sum: e8ee02fa54b234d859914a89cd697dc9 SHA1: b08690a83591152f0d43eb180c781297913e1c31 SHA256: 676542e76d62b2576ebc6ae37ba53415307048451db4966ec153a8fb83b04d5c SHA512: a52ae1bca8732b3d8d18ce1aebdf03f26b40b09e75856c56f456ba364d9abade776a9e62253a887328e27ff59ed6c06c2c3db8ad49757997582b1aee6ba51636 Homepage: https://cran.r-project.org/package=smoke Description: CRAN Package 'smoke' (Small Molecule Octet/BLI Kinetics Experiment) Bio-Layer Interferometry (BLI) is a technology to determine the binding kinetics between biomolecules. BLI signals are small and noisy when small molecules are investigated as ligands (analytes). We develop this package to process and analyze the BLI data acquired on Octet Red96 from Fortebio more accurately. Sun Q., Li X., et al (2020) . In this new version, we organize the BLI experiment data and analysis methods into a S4 class with self-explaining structure. Package: r-cran-smoothapc Architecture: all Version: 0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quantreg, r-cran-sparsem, r-cran-lmtest, r-cran-rgl, r-cran-colorspace Suggests: r-cran-testthat, r-cran-demography Filename: pool/dists/noble/main/r-cran-smoothapc_0.3-1.ca2404.1_all.deb Size: 84726 MD5sum: 45e7a3ebed17e257885ca83caf0bf69f SHA1: 8603d2f813f5d921f066211d934719de964f1fe7 SHA256: 1017adb61778a097955a501c83c6c45ee3c14337c28a1f1ea4e7b55920339d5f SHA512: 58aeaad000b2215682fbff103bcd1880f0bc4ccb5165db86ea45513e2de7ccb3d786d785fff5df9fbc66edc758e44b2466a60df3701b181d681d87c65c737af9 Homepage: https://cran.r-project.org/package=smoothAPC Description: CRAN Package 'smoothAPC' (Smoothing of Two-Dimensional Demographic Data, Optionally Takinginto Account Period and Cohort Effects) The implemented method uses for smoothing bivariate thin plate splines, bivariate lasso-type regularization, and allows for both period and cohort effects. Thus the mortality rates are modelled as the sum of four components: a smooth bivariate function of age and time, smooth one-dimensional cohort effects, smooth one-dimensional period effects and random errors. Package: r-cran-smoothedipw Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 318 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-progress Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-smoothedipw_0.1.0-1.ca2404.1_all.deb Size: 285460 MD5sum: 9970b2431cec014a2809dad14983c326 SHA1: a6837d4dec61b202f59367d053c13df3c37ef1f6 SHA256: 96b3a29386df405be2e716f8a28b6c551b5f003cdc1ea59d11d703887a017210 SHA512: 585442d6e98aba024a7c3b36f33c7c8118debf41941700aa745db90e0ef95271ab9061e6eabdfc308ca7dc92543b91a1489f22e805ee065df887764a332b818b Homepage: https://cran.r-project.org/package=smoothedIPW Description: CRAN Package 'smoothedIPW' (Time-Smoothed Inverse Probability Weighting for RepeatedlyMeasured Outcomes) Implements several methods to estimate effects of generalized time-varying treatment strategies on the mean of an outcome at one or more selected follow-up times of interest. Specifically, the package implements the time-smoothed inverse probability weighted estimators described in McGrath et al. (2025) . Outcomes may be repeatedly, non-monotonically, informatively, and sparsely measured in the data source. The package also supports settings where outcomes are truncated by death, i.e. some individuals die during follow-up which renders the outcome of interest undefined at the follow-up time of interest. 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For this, the objective function of a user-specified regression operator is first smoothed using Nesterov smoothing (see Y. Nesterov (2005) ), resulting in a modified objective function with explicit gradients everywhere. The smoothed objective function and its gradient are minimized via BFGS, and the obtained minimizer is returned. Using Nesterov smoothing, the smoothed objective function can be made arbitrarily close to the original (unsmoothed) one. In particular, the Nesterov approach has the advantage that it comes with explicit accuracy bounds, both on the L1/L2 difference of the unsmoothed to the smoothed objective functions as well as on their respective minimizers (see G. Hahn, S.M. Lutz, N. Laha, C. Lange (2020) ). A progressive smoothing approach is provided which iteratively smoothes the objective function, resulting in more stable regression estimates. A function to perform cross validation for selection of the regularization parameter is provided. Package: r-cran-smoother Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ttr Filename: pool/dists/noble/main/r-cran-smoother_1.3-1.ca2404.1_all.deb Size: 24080 MD5sum: 5c2162e91b72ae069a5be5c101071ae3 SHA1: 532f7720b3eb03c1c72fa8d2e73f73611e8b0a86 SHA256: 2ea97e70211dde95a01df0f2ae04be3c0d994e2160cabfda984b37389484b3ec SHA512: 093568ba7486a62ab2795eff96d866a8980ba240b8f936fc19a5c11ede2b6031a389d2d9e5f06793bacf9788d9f979811e961f92d399f61a69758926598e99db Homepage: https://cran.r-project.org/package=smoother Description: CRAN Package 'smoother' (Functions Relating to the Smoothing of Numerical Data) A collection of methods for smoothing numerical data, commencing with a port of the Matlab gaussian window smoothing function. In addition, several functions typically used in smoothing of financial data are included. 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To better understand the effects that each continuous covariate has on the outcome, results are expressed in terms of hazard ratio curves, taking a specific covariate value as reference. Confidence bands for these curves are also derived. 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Includes classical regression with normally distributed errors and robust regression, where the errors are from the Laplace distribution. The "smooth generalized normal distribution" is used, where the estimation of an additional shape parameter allows the user to move smoothly between both types of regression. See O'Neill and Burke (2022) "Robust Distributional Regression with Automatic Variable Selection" for more details. . This package also contains the data analyses from O'Neill and Burke (2023). "Variable selection using a smooth information criterion for distributional regression models". . 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Package: r-cran-smoothmest Architecture: all Version: 0.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-smoothmest_0.1-3-1.ca2404.1_all.deb Size: 59320 MD5sum: 76c1e1e26de9ed782a0ee2bd0a2134d6 SHA1: a89ea56378d7b8fb5b6d6b0c26f8b7ab954059a4 SHA256: 2a1c4b2ba918daee5e99e3ed937b720c16071451e86899d2db69c6898d68c055 SHA512: e7b96de17651e4276067bcce6e5a782382edb7dd819cc9f30ebc4fb31d7af7dd1b6400b684f6366dfbb0b3e514a99ca3cc677323dedf5b3c961985866f0b1fa3 Homepage: https://cran.r-project.org/package=smoothmest Description: CRAN Package 'smoothmest' (Smoothed M-Estimators for 1-Dimensional Location) Some M-estimators for 1-dimensional location (Bisquare, ML for the Cauchy distribution, and the estimators from application of the smoothing principle introduced in Hampel, Hennig and Ronchetti (2011) to the above, the Huber M-estimator, and the median, main function is smoothm), and Pitman estimator. 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This approach builds upon the basis expansion methods for functional 'PLS' regression described in Aguilera et al. (2010) . The package seamlessly handles both Scalar Functional Data ('SFD') and Categorical Functional Data ('CFD'), providing interpretable regression curves even for discrete state changes. It was developed during a PhD thesis between 'DECATHLON' and French research institute 'INRIA' 2022-2026. The 'SmoothPLS' method does not directly decompose the data into a basis; rather, it assumes the data is known as precisely as desired, and for every 'PLS' component, the weight functions are decomposed into the basis. For both single-state and multi-state 'CFD' as well as 'SFD', the algorithm is implemented for a scalar response. To provide a baseline, a naive 'PLS' method on time-value functions and standard Functional 'PLS' are also implemented. Package: r-cran-smoothr Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1143 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-units Suggests: r-cran-knitr, r-cran-lwgeom, r-cran-rmarkdown, r-cran-sp, r-cran-testthat, r-cran-terra Filename: pool/dists/noble/main/r-cran-smoothr_1.3.0-1.ca2404.1_all.deb Size: 899016 MD5sum: cfcb6f9aab8ff9d532871560027c687c SHA1: c6626d7fab26f2baee60080885fcf77d6bcac90f SHA256: e2a7572c98d596cf23edc53483da7a69d77f9be44bfcffc0a9e41efc66ad4fff SHA512: de79418266525c8fb07a9b4032f1aababc831a766d0604d51331b17aa694817cef92a4fbcf36afadd22cbfb644cd8e3818f38f9c6efcd223b374cb58cbd589bc Homepage: https://cran.r-project.org/package=smoothr Description: CRAN Package 'smoothr' (Smooth and Tidy Spatial Features) Tools for smoothing and tidying spatial features (i.e. lines and polygons) to make them more aesthetically pleasing. 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The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log--concave density function. This procedure is justified by the fact that the GPD density is log--concave for gamma in [-1,0]. 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The package can be used for visualizing multivariate data similar to Principal Component Analysis or Multidimensional Scaling using a ranking approach. In contrast to 'MDS' and 'PCA', 'SNHA' uses a network approach to explore interacting variables. For details see 'Hermanussen et. al. 2021', . 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Package: r-cran-snn Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-snn_1.1-1.ca2404.1_all.deb Size: 45228 MD5sum: b462b1e39a124e846f4281627c12c2c2 SHA1: a449bdd648469cd46356d3aeba461e92608dbf8c SHA256: 5aa495b767aa36d70576749671afaf0d3df907c7af30312128c0ac1b402742c0 SHA512: aaa176174eacf70d1035969738fd52e87c0ad286c97910d74033d7371e4dcf47a8b1f509b6908675770b37428eb4c45c1f18ff05783f373e502259b16f367c30 Homepage: https://cran.r-project.org/package=snn Description: CRAN Package 'snn' (Stabilized Nearest Neighbor Classifier) Implement K-nearest neighbor classifier, weighted nearest neighbor classifier, bagged nearest neighbor classifier, optimal weighted nearest neighbor classifier and stabilized nearest neighbor classifier, and perform model selection via 5 fold cross-validation for them. 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Package: r-cran-snowdata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra Filename: pool/dists/noble/main/r-cran-snowdata_1.0.0-1.ca2404.1_all.deb Size: 29666 MD5sum: 5ebf34239154ec7c7baa3b80b626a0a2 SHA1: cb6e9619e2580a1c750e1a4d017d713026ba0000 SHA256: 15078c64cf9d86c8d1197659e77e8b16e5131788f750ac6b7a2f8862fc086736 SHA512: 9f39ef412653cf9aa258b0f8c77c31c9cf9fa7caf9e9a199e9cf42caa72fb880f6c944cd782b3e8dc56215ec88ec96c9072f2bde6c3792e711d4fc549a51338f Homepage: https://cran.r-project.org/package=SnowData Description: CRAN Package 'SnowData' (Historical Data from John Snow's 1854 Cholera Outbreak Map) Provides historical datasets related to John Snow's 1854 cholera outbreak study in London. Includes data on cholera cases, water pump locations, and the street layout, enabling analysis and visualisation of the outbreak. Package: r-cran-snowfall Architecture: all Version: 1.84-6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-snow Suggests: r-cran-rmpi Filename: pool/dists/noble/main/r-cran-snowfall_1.84-6.3-1.ca2404.1_all.deb Size: 248898 MD5sum: 488982a5d653694a71c8318be22fe5a2 SHA1: 4aead979c627faadadd8f441986d6958e23ee6ae SHA256: f1596efdcabe42dd75f6dc661b0b893dd577700d04f92759cd8fe269a49ae94a SHA512: f5d3eef18a54bbfac74bd649f592bfbfb4424b2608bc66cb23fc0c6d2a902c71edab45a6e2d4ec007e44c5497637894274933a840100cf48f9303e233de57603 Homepage: https://cran.r-project.org/package=snowfall Description: CRAN Package 'snowfall' (Easier Cluster Computing (Based on 'snow')) Usability wrapper around snow for easier development of parallel R programs. This package offers e.g. extended error checks, and additional functions. All functions work in sequential mode, too, if no cluster is present or wished. 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Package: r-cran-snowflakeauth Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-curl, r-cran-jsonlite, r-cran-rcpptoml, r-cran-rlang, r-cran-jose, r-cran-openssl Suggests: r-cran-httpuv, r-cran-keyring, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-snowflakeauth_0.2.2-1.ca2404.1_all.deb Size: 67540 MD5sum: 182c197ae8a2c987bcfb9213f85dc3c8 SHA1: 710eb25bd50b418f76b27d771b0ab5715d638e4f SHA256: 787c0ef55325deb658e715a289fa4724596494a7bf8e5ae0bc607d54d513c9d2 SHA512: 03125ac7d79a28fc026730278b02a71b49c476c90b6de32e575f734eff37c6064c8be84fce8d076286f59f8289a846c89514ca79d05b064f7e8c2ffb269ddacd Homepage: https://cran.r-project.org/package=snowflakeauth Description: CRAN Package 'snowflakeauth' (Authentication Helpers for 'Snowflake') Authentication helpers for 'Snowflake'. 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Each snowflake is defined by a given diameter, width of the crystal, color, and random seed. Snowflakes are plotted in such way that they always remain round, no matter what the aspect ratio of the plot is. Snowflakes can be created using transparent colors, which creates a more interesting, somewhat realistic, image. Images of the snowflakes can be separately saved as svg files and used in websites as static or animated images. Package: r-cran-snowft Architecture: all Version: 1.6-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlecuyer, r-cran-snow Suggests: r-cran-rmpi Filename: pool/dists/noble/main/r-cran-snowft_1.6-1-1.ca2404.1_all.deb Size: 76902 MD5sum: bacb85768064c96a7bb8910ed0b5a14f SHA1: 10e461ccc1f18610d7d425b7583185eb44170df0 SHA256: 605901177bdf4728ce4a409678aad33e77186284b03fa2dd32fb4d88c7ea4481 SHA512: ad7779af2facb22b15862de4476e184977782c2086fa267bf2791bf9f8ec7acf44dcb685680f760df97a558831c08fa92cb5d7f578e77ca5baadc3545c1425b0 Homepage: https://cran.r-project.org/package=snowFT Description: CRAN Package 'snowFT' (Fault Tolerant Simple Network of Workstations) Extension of the snow package supporting fault tolerant and reproducible applications, as well as supporting easy-to-use parallel programming - only one function is needed. Dynamic cluster size is also available. Package: r-cran-snowquery Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-yaml, r-cran-reticulate, r-cran-rpostgres, r-cran-rsqlite, r-cran-dbi, r-cran-duckdb, r-cran-dplyr, r-cran-dbplyr Filename: pool/dists/noble/main/r-cran-snowquery_1.3.0-1.ca2404.1_all.deb Size: 31694 MD5sum: a65fee5a57f49431d79dc6c5af863efe SHA1: 716089ac9b5042460d99eb8acfdb38c3ca8d29d8 SHA256: a3f9f502a5205152417c9fa3e7b11a3c4453ed931882dd9e11ddb2899ded6a78 SHA512: dd70c3568d9ccb54cc75733274cd8f21bc909d393c8ad507d5cdb850cd3bf9d525b0daf3971fcaa35a9038a3b0e1b89ed8fc513dfc26027afcc9be33447676df Homepage: https://cran.r-project.org/package=snowquery Description: CRAN Package 'snowquery' (SQL Interface to 'Snowflake', 'Redshift', 'Postgres', 'SQLite',and 'DuckDB') Run 'SQL' queries across 'Snowflake', 'Amazon Redshift', 'PostgreSQL', 'SQLite', and 'DuckDB' from R with a single function. Optionally stream and cache large query results to a local 'DuckDB' database for efficient work with larger-than-memory datasets. Package: r-cran-snpaimer Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-adegenet, r-cran-doparallel, r-cran-dplyr, r-cran-forcats, r-cran-foreach, r-cran-ggplot2, r-cran-magrittr, r-cran-readr, r-cran-tidyr, r-cran-withr, r-cran-yaml Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-snpaimer_2.1.1-1.ca2404.1_all.deb Size: 24994 MD5sum: bea0831f19fa5e09eb46381cf9f09aba SHA1: 4db2694f41b8f1bc847559a1871b7836427adc1d SHA256: 3d31f25df347879c6aa806dd795d3663eb0cbfd0b64f16c588e68cc233c70570 SHA512: 33f38cf3a2b04912483a40a0576c5261c5a73630fd389dae3ca98f683167d42769931b0c809a25bd8c4e2c1d03dff7a4ce38fbdffc186d6effc1252ab85351a2 Homepage: https://cran.r-project.org/package=snpAIMeR Description: CRAN Package 'snpAIMeR' (Assess the Diagnostic Power of Genomic Marker Combinations) Population genetics package for designing diagnostic panels. 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Package: r-cran-snpassoc Architecture: all Version: 2.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1336 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-haplo.stats, r-cran-mvtnorm, r-cran-survival, r-cran-tidyr, r-cran-plyr, r-cran-ggplot2, r-cran-poisbinom Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biomart, r-bioc-variantannotation, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-org.hs.eg.db, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene Filename: pool/dists/noble/main/r-cran-snpassoc_2.1-2-1.ca2404.1_all.deb Size: 1189176 MD5sum: cc97751e88ed96c72e6c241f795ce614 SHA1: 736537bd4f4ed6b59207f49649f2b5b6369a9646 SHA256: 3aff44d8c7dd83d6f64c0351554728570f524affc73c797be8a2b114b13e7fba SHA512: b1497b808c4145eb3bfbf54b32be14614691d9d09e00a4539a81a35f84a6ed5c89f203f045e0b5cdbb38abbf3844844193bc36f9a99bbbfcc9302ffdbd7a3e3d Homepage: https://cran.r-project.org/package=SNPassoc Description: CRAN Package 'SNPassoc' (SNPs-Based Whole Genome Association Studies) Functions to perform most of the common analysis in genome association studies are implemented. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated. The methods are described in Gonzalez JR et al., 2007 . Package: r-cran-snpfiltr Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 985 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vcfr, r-cran-ggplot2, r-cran-rtsne, r-cran-cluster, r-cran-adegenet, r-cran-gridextra, r-cran-ggridges Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-snpfiltr_1.0.7-1.ca2404.1_all.deb Size: 661470 MD5sum: 477eda7911185ab8e9cbcf4624e79566 SHA1: 0a4d3a33494f14a91ec3b0da8b839c95e02b4e73 SHA256: f992e9a52c254767cddebf20032821e7f3ebf2ee2d51cc59ae1d45ac60b6a18f SHA512: 77ec2baa9a782c6126f0982a43a00306faa9e86e1989bc1be97727d8f091ebf1588126775aa10ec5ec5cff09556c0a14deb06d4a05eecd152fd4f5fe01733d68 Homepage: https://cran.r-project.org/package=SNPfiltR Description: CRAN Package 'SNPfiltR' (Interactively Filter SNP Datasets) Is designed to interactively and reproducibly visualize and filter SNP (single-nucleotide polymorphism) datasets. This R-based implementation of SNP and genotype filters facilitates an interactive and iterative SNP filtering pipeline, which can be documented reproducibly via 'rmarkdown'. 'SNPfiltR' contains functions for visualizing various quality and missing data metrics for a SNP dataset, and then filtering the dataset based on user specified cutoffs. All functions take 'vcfR' objects as input, which can easily be generated by reading standard vcf (variant call format) files into R using the R package 'vcfR' authored by Knaus and Grünwald (2017) . Each 'SNPfiltR' function can return a newly filtered 'vcfR' object, which can then be written to a local directory in standard vcf format using the 'vcfR' package, for downstream population genetic and phylogenetic analyses. Package: r-cran-snplinkage Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5671 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-gwastools, r-bioc-biomart, r-cran-cowplot, r-cran-data.table, r-bioc-gdsfmt, r-cran-ggplot2, r-cran-ggrepel, r-cran-gtable, r-cran-knitr, r-cran-magrittr, r-cran-reshape2, r-bioc-snprelate Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-snplinkage_1.2.0-1.ca2404.1_all.deb Size: 4720654 MD5sum: a2e96c13456740be81d031a76808b797 SHA1: 22b995831b17c531b7043127430d939729af7508 SHA256: 22860b0ec37e23cf0f59d5680da8912e206e4dcd2f0153fb6bfb6cfc0f420391 SHA512: 00da913fedcf05ffd12b6a9b2c638ae03742f030e4b56ded1e2ca0a928bbaaa8ea6659c26fcc07ddd0093ff3d76fe585582cc3f0f4c16076026a246bcd2302c3 Homepage: https://cran.r-project.org/package=snplinkage Description: CRAN Package 'snplinkage' (Single Nucleotide Polymorphisms Linkage DisequilibriumVisualizations) Linkage disequilibrium visualizations of up to several hundreds of single nucleotide polymorphisms (SNPs), annotated with chromosomic positions and gene names. Two types of plots are available for small numbers of SNPs (<40) and for large numbers (tested up to 500). Both can be extended by combining other ggplots, e.g. association studies results, and functions enable to directly visualize the effect of SNP selection methods, as minor allele frequency filtering and TagSNP selection, with a second correlation heatmap. The SNPs correlations are computed on Genotype Data objects from the 'GWASTools' package using the 'SNPRelate' package, and the plots are customizable 'ggplot2' and 'gtable' objects and are annotated using the 'biomaRt' package. Usage is detailed in the vignette with example data and results from up to 500 SNPs of 1,200 scans are in Charlon T. (2019) . Package: r-cran-snpls Architecture: all Version: 1.0.27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-clickr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-ggrepel, r-cran-ks, r-cran-mass, r-cran-matrix, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-snpls_1.0.27-1.ca2404.1_all.deb Size: 92364 MD5sum: 3ca07e90b3b0a2ae764a17be4f6e67ad SHA1: 60ece0ac4747741bbe6e8a04e802270319ba8f33 SHA256: fbffc49ce82ae7cfae244ca19c114829125b28d456fa9baa70e783b9ba0c3d02 SHA512: 53c66f2be0806a663544a0639adc5392cdc044d7b47a6adb3bfbaf52b84ba059c007e64067a55fb594e0c51419722e837c6e027d638a9d6b82bb8d7c75f6f6d3 Homepage: https://cran.r-project.org/package=sNPLS Description: CRAN Package 'sNPLS' (NPLS Regression with L1 Penalization) Tools for performing variable selection in three-way data using N-PLS in combination with L1 penalization, Selectivity Ratio and VIP scores. The N-PLS model (Rasmus Bro, 1996 ) is the natural extension of PLS (Partial Least Squares) to N-way structures, and tries to maximize the covariance between X and Y data arrays. The package also adds variable selection through L1 penalization, Selectivity Ratio and VIP scores. Package: r-cran-snpready Architecture: all Version: 0.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 911 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-matrixcalc, r-cran-stringr, r-cran-rgl, r-bioc-impute Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-snpready_0.9.6-1.ca2404.1_all.deb Size: 361372 MD5sum: d6b0a4d46e6b454a74446d7803c83f0f SHA1: 59c7a1bce67b07a7cd9bf297ba27eb508a6c0de6 SHA256: a27e3db9e7b46e3953ae3423480e967a03aa91d0a4fd87ba718d3f4604593196 SHA512: a5f2dde1805cd55be1aca3d0cceebc2174b7810682aec482ace25d5a8a9a8bb83533c86d8f8e9bc9aff0426770ea677f716a41c52930d3202297cf0fcc713772 Homepage: https://cran.r-project.org/package=snpReady Description: CRAN Package 'snpReady' (Preparing Genotypic Datasets in Order to Run Genomic Analysis) Three functions to clean, summarize and prepare genomic datasets to Genome Selection and Genome Association analysis and to estimate population genetic parameters. Package: r-cran-snqtl Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rarpack, r-cran-mass Filename: pool/dists/noble/main/r-cran-snqtl_0.2-1.ca2404.1_all.deb Size: 200868 MD5sum: c3243f9fa048fd0f8965885928c058f5 SHA1: 9b9c3305a70413cf4ef2edaf7d598988cf106aac SHA256: b96b68838a78d8af57826a3bc874816deca8c3585da3d2c5e148fd7f369c0d07 SHA512: 88f90cbe98de3be75a8ebb7b063367749f66be40430aa99ef59cc518bd4ea9b03bd8a2c8072f050fca651830af6b55abd404932fb6c8437d605b0be5388aacce Homepage: https://cran.r-project.org/package=snQTL Description: CRAN Package 'snQTL' (Spectral Network Quantitative Trait Loci (snQTL) Analysis) A spectral framework to map quantitative trait loci (QTLs) affecting joint differential networks of gene co-Expression. Test the equivalence among multiple biological networks via spectral statistics. See reference Hu, J., Weber, J. N., Fuess, L. E., Steinel, N. C., Bolnick, D. I., & Wang, M. (2025) . Package: r-cran-sns Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-coda, r-cran-numderiv Suggests: r-cran-regressionfactory, r-cran-mfusampler Filename: pool/dists/noble/main/r-cran-sns_1.2.2-1.ca2404.1_all.deb Size: 542706 MD5sum: db5ca1f40fca500b26ff7e11a2c51a51 SHA1: 75b35ff7cbcdfa2d8ba3dbe21f8534de3a16b074 SHA256: 4f99adb1f7fb5c8ab878a44720c4b7eba73d9bd7db14a214a68bfb342b9d3d28 SHA512: 3ee2e1fe6042570b28946741799681562a581332f2b4a421a0c7973d46a60b0312550b516f62ce5dfe9d18bdf090eb224ca5bbd73d84de5a7f0bd2826b0a8c9d Homepage: https://cran.r-project.org/package=sns Description: CRAN Package 'sns' (Stochastic Newton Sampler (SNS)) Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided. Note: Formerly available versions of the MfUSampler can be obtained from the archive . Package: r-cran-snschart Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 330 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-snschart_1.4.0-1.ca2404.1_all.deb Size: 236944 MD5sum: 608ed38f928065743372b7e128dec4a2 SHA1: 6af7ce42ea744adff5f7031098d73ecd83d98be9 SHA256: 453f053228afd7443e48e0beacb55250241187579b036fbf1f401d2d2fd40c69 SHA512: 31f13489a7401e49f97df1656f7bd69289c60b0c8f136ff63e048c888902c0d577291f93351deb123b12c946e6dd98fc51e64bc6d3951171bed2542b4ebc8b74 Homepage: https://cran.r-project.org/package=SNSchart Description: CRAN Package 'SNSchart' (Sequential Normal Scores in Statistical Process Management) The methods discussed in this package are new non-parametric methods based on sequential normal scores 'SNS' (Conover et al (2017) ), designed for sequences of observations, usually time series data, which may occur singly or in batches, and may be univariate or multivariate. These methods are designed to detect changes in the process, which may occur as changes in location (mean or median), changes in scale (standard deviation, or variance), or other changes of interest in the distribution of the observations, over the time observed. They usually apply to large data sets, so computations need to be simple enough to be done in a reasonable time on a computer, and easily updated as each new observation (or batch of observations) becomes available. Some examples and more detail in 'SNS' is presented in the work by Conover et al (2019) . Package: r-cran-snsequate Architecture: all Version: 1.3-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magic, r-cran-ake, r-cran-equate, r-cran-moments, r-cran-emdbook, r-cran-plyr, r-cran-statmod, r-cran-knitr, r-cran-progress Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-snsequate_1.3-5-1.ca2404.1_all.deb Size: 320616 MD5sum: 787a14e38160726942c0f968f30bfb95 SHA1: 885055fde9344893907fb7fcfa0e32274d543d3b SHA256: 3c0e7c6a0138a40ed106189f16cedc37340c7f0e2cc9b8aaa5258bac2ab93a3f SHA512: 615189cb9b2b93657e578a6a9637ec043c58ba7276ad4fedd76f723d8d435816c9eabc519cf03348425efd126d35f6161a550f7ad7620b33082bc26d0274aca2 Homepage: https://cran.r-project.org/package=SNSequate Description: CRAN Package 'SNSequate' (Standard and Nonstandard Statistical Models and Methods for TestEquating) Contains functions to perform various models and methods for test equating (Kolen and Brennan, 2014 ; Gonzalez and Wiberg, 2017 ; von Davier et. al, 2004 ). It currently implements the traditional mean, linear and equipercentile equating methods. Both IRT observed-score and true-score equating are also supported, as well as the mean-mean, mean-sigma, Haebara and Stocking-Lord IRT linking methods. It also supports newest methods such that local equating, kernel equating (using Gaussian, logistic, Epanechnikov, uniform and adaptive kernels) with presmoothing, and IRT parameter linking methods based on asymmetric item characteristic functions. Functions to obtain both standard error of equating (SEE) and standard error of equating differences between two equating functions (SEED) are also implemented for the kernel method of equating. Package: r-cran-snsfdatasets Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-snsfdatasets_0.1.1-1.ca2404.1_all.deb Size: 22430 MD5sum: 4d043ec1bbd50bc73571a2e0256a0ad1 SHA1: d6fd761fedb54c85d71a675bbc1e00aa79e7db01 SHA256: 281f2bd971e445cd99fea9dd5fda49b5ff127ad2e6d34f707300411f6307896c SHA512: 1b961e783dee0a120025ee0b9431923484d4c1daab6a5685c567b926f35880802fdce8f000e931575281be67fa4a1d85ded5c9332df0adfb2eb69fd7d6b9e852 Homepage: https://cran.r-project.org/package=SNSFdatasets Description: CRAN Package 'SNSFdatasets' (Download Datasets from the Swiss National Science Foundation(SNF, FNS, SNSF)) Download and read datasets from the Swiss National Science Foundation (SNF, FNS, SNSF; ). The package is lightweight and without dependencies. Downloaded data can optionally be cached, to avoid repeated downloads of the same files. There are also utilities for comparing different versions of datasets, i.e. to report added, removed and changed entries. Package: r-cran-snsmart Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-envstats, r-cran-bayestestr, r-cran-condmvnorm, r-cran-cubature, r-cran-geepack, r-cran-hdinterval, r-cran-pracma, r-cran-rjags, r-cran-tidyr, r-cran-truncdist Suggests: r-cran-coda, r-cran-testthat Filename: pool/dists/noble/main/r-cran-snsmart_0.2.4-1.ca2404.1_all.deb Size: 155902 MD5sum: a8f87d025cec31884cb37b875b88dbdf SHA1: 8d6507f55182fff22cc07f07aa37f0125407b052 SHA256: fbe259edf6864d97f764833b9a9dd9a477efe871bbc81c35b2f88c608d50a927 SHA512: ea5d1a4f458a7c885668ec970a64531fb99ddf9de4780ef23e8528d2855b8645ecc3db442c91883b22190cf25b390e8765b3e170832eeeeeb91b3559a6c02790 Homepage: https://cran.r-project.org/package=snSMART Description: CRAN Package 'snSMART' (Small N Sequential Multiple Assignment Randomized Trial Methods) Consolidated data simulation, sample size calculation and analysis functions for several snSMART (small sample sequential, multiple assignment, randomized trial) designs under one library. See Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732 . Package: r-cran-snvecr Architecture: all Version: 3.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1782 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-cli, r-cran-dplyr, r-cran-tibble, r-cran-purrr, r-cran-readr, r-cran-tidyselect, r-cran-rlang, r-cran-glue, r-cran-backports, r-cran-stringr Suggests: r-cran-astrochron, r-cran-ggplot2, r-cran-tidyr, r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-withr, r-cran-curl, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-snvecr_3.10.1-1.ca2404.1_all.deb Size: 1330442 MD5sum: 6e95cea908fc4d5e01ab96f0b8433011 SHA1: a4ac23b12f3fb71718be41e035941833369e9c9f SHA256: 4e64e8080bb2d35c77ae056ee86920aa74590121e8e6ecac395d0d2f87daf74b SHA512: 09d731df9a42b1b8e5768c6d7554d43e686e0fe2d9eff3a41171cf4023fe54db7a215b846957f0cfe2d1a01b167e809c488027c9e32a029e4659121e19cd12b3 Homepage: https://cran.r-project.org/package=snvecR Description: CRAN Package 'snvecR' (Calculate Earth’s Obliquity and Precession in the Past) Easily calculate precession and obliquity from an orbital solution (defaults to ZB18a from Zeebe and Lourens (2019) ) and assumed or reconstructed values for tidal dissipation (Td) and dynamical ellipticity (Ed). This is a translation and adaptation of the 'C'-code in the supplementary material to Zeebe and Lourens (2022) , with further details on the methodology described in Zeebe (2022) . The name of the 'C'-routine is 'snvec', which refers to the key units of computation: spin vector s and orbit normal vector n. Package: r-cran-snvlfdr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-snvlfdr_1.0.1-1.ca2404.1_all.deb Size: 109406 MD5sum: 87f02dad7abf76063e752e46b099ba2d SHA1: 5675581a02a9e3d543ec4f3a316522acc06be381 SHA256: be015b2deb6faac8da108e51599e0c07aefd581caf5dbd139abb26a9b61f507c SHA512: 0571d24b599b49856eed8931dbaf9b745a668925ea99f2bcb690fe7a79d60f82c9ce6616790671866c7dcddd2543f076d71f083bf3e91dae6bcbe5ecfd635d89 Homepage: https://cran.r-project.org/package=SNVLFDR Description: CRAN Package 'SNVLFDR' (Empirical Bayes Single Nucleotide Variant Calling) Identifies single nucleotide variants in next-generation sequencing data by estimating their local false discovery rates. For more details, see Karimnezhad, A. and Perkins, T. J. (2024) . Package: r-cran-soar Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-soar_1.0-1-1.ca2404.1_all.deb Size: 225324 MD5sum: 0b8a9c52a4ff79955af71cf4587afbdc SHA1: 7c3586353b379f11c6439f90ca1d266785efa2ab SHA256: 9c317b0b37a46284c240c11dbd8294dab7f5e49313f8156f2dc71fc406db2630 SHA512: 1ade49031996a760701459c3fee50a64a62507de82083398319f80c189869a8bf11a78b7caf0cff4c72c6f4c859395594ba6fc6a2c265ba78824f22fb7d492aa Homepage: https://cran.r-project.org/package=SOAR Description: CRAN Package 'SOAR' (Memory Management in R by Delayed Assignments) Allows objects to be stored on disc and automatically recalled into memory, as required, by delayed assignment. Package: r-cran-soas Architecture: all Version: 1.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-doe.base, r-cran-combinat, r-cran-frf2, r-cran-igraph, r-cran-lhs, r-cran-conf.design, r-cran-sfsmisc, r-cran-partitions Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-soas_1.4-1-1.ca2404.1_all.deb Size: 367634 MD5sum: 0fd67a231d44a646641de4a3af2b8038 SHA1: a11502e595a9ba232ff457afe233fceaa342ad13 SHA256: 76d1a12403f0bd36cbeea206647eb9d7f48ba163c80511d53a61114eb6297fef SHA512: 3133a06857eb8f773a7e0bddea3be02dc1ebf2cc0735266dab3bf980077eab6b5e0f39f104bfe9c4c5496db5ed4b32a13ff5a2925b3608b97f5319311bba1823 Homepage: https://cran.r-project.org/package=SOAs Description: CRAN Package 'SOAs' (Creation of Stratum Orthogonal Arrays) Creates stratum orthogonal arrays (also known as strong orthogonal arrays). These are arrays with more levels per column than the typical orthogonal array, and whose low order projections behave like orthogonal arrays, when collapsing levels to coarser strata. Details are described in Groemping (2022) "A unifying implementation of stratum (aka strong) orthogonal arrays" . 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Package: r-cran-socialdrift Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-purrr, r-cran-ggplot2, r-cran-igraph Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-patchwork Filename: pool/dists/noble/main/r-cran-socialdrift_0.1.0-1.ca2404.1_all.deb Size: 171010 MD5sum: b113f591bea5e407c1779a036fc28669 SHA1: 95b015f06dda88d74f2ad0519be25dbaa54712f8 SHA256: b0e27d47747d6702f0b026f421503a8b525be0eab98b6b161bf20adb3f7c34d0 SHA512: 990708be53647b4ee8ead199b426a6cedf992c8cb189db04460344b4a715695eaa8885298e75539389241a2cd71362efb18eebd9eed172f99dc4ccd6577496d2 Homepage: https://cran.r-project.org/package=socialdrift Description: CRAN Package 'socialdrift' (Temporal Auditing of Social Interaction Networks) Tools for constructing, auditing, and visualizing temporal social interaction networks from event-log data. 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Package: r-cran-socialh Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circlize, r-cran-dplyr, r-cran-data.table, r-cran-ggplot2, r-cran-magrittr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-socialh_0.1.1-1.ca2404.1_all.deb Size: 1002698 MD5sum: 4b8830bdf5e6c1406bfc97e389c0e0eb SHA1: 8f9e95811d7837799341366dbd7e246db9b11237 SHA256: 87182e331581be55ed970f1a79cbf66421ce7e704dca267a86f76388d189ecfb SHA512: 5a8a9705a6ab7174f8c027e2076a562ba5bc7b7ff23fa191119431892e88ea3dd6487e5cd23eb5328d42d3bfa37fab1eb8cd27991d00d37724477f108391c3c8 Homepage: https://cran.r-project.org/package=socialh Description: CRAN Package 'socialh' (Rank and Social Hierarchy for Gregarious Animals) Tools developed to facilitate the establishment of the rank and social hierarchy for gregarious animals by the Si method developed by Kondo & Hurnik (1990). It is also possible to determine the number of agonistic interactions between two individuals, sociometric and dyadics matrix from dataset obtained through electronic bins. In addition, it is possible plotting the results using a bar plot, box plot, and sociogram. Package: r-cran-socialmixr Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1231 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-countrycode, r-cran-curl, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-memoise, r-cran-purrr, r-cran-oai, r-cran-wpp2017, r-cran-xml2, r-cran-cli, r-cran-rlang Suggests: r-cran-contactsurveys, r-cran-ggplot2, r-cran-here, r-cran-knitr, r-cran-quarto, r-cran-reshape2, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-socialmixr_0.6.0-1.ca2404.1_all.deb Size: 896698 MD5sum: 57beaac957686585c52d088a3411078a SHA1: 56dfca4583b027206742b27be68dc891ff7b4d54 SHA256: f7133f731da24d555e0e388901a90988ed567033d2556264124cf9700c6b2143 SHA512: 2929a98a44d8b954d89cf43e499141a74ca59fb3d79fe1e033c5f9058d6870cebb3401cff63c069e8ad23f29aa7e05baf2bd15161a71c4389c35c30939512205 Homepage: https://cran.r-project.org/package=socialmixr Description: CRAN Package 'socialmixr' (Social Mixing Matrices for Infectious Disease Modelling) Methods for sampling contact matrices from diary data for use in infectious disease modelling, as discussed in Mossong et al. (2008) . 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Traditionally this ranking is determined through numerical evaluation. More often than not however only ordinal data between coalitions is known. The package 'socialranking' offers a set of solutions to rank players based on a transitive ranking between coalitions, including through CP-Majority, ordinal Banzhaf or lexicographic excellence solution summarized by Tahar Allouche, Bruno Escoffier, Stefano Moretti and Meltem Öztürk (2020, ). Package: r-cran-socialrisk Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-stringr, r-cran-rlang, r-cran-tidyselect, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-socialrisk_0.5.1-1.ca2404.1_all.deb Size: 36398 MD5sum: 8319d6817a69870c9b8b371360e17e9d SHA1: 4bab1feed8ba3fb921dc60e62892d095798bfbca SHA256: 7ed00f5fc39532a62b835cc87382cee6d3e954d4615e61a943995150731e3589 SHA512: 5573278386341f1f2eae425b0f96d4c95ae689763019db7ff8b82c4caea249453be42d58e9727ad81f4d916fa20068066174eddcfb2c4e29882832b5d8fc4039 Homepage: https://cran.r-project.org/package=socialrisk Description: CRAN Package 'socialrisk' (Identifying Patient Social Risk from Administrative Health CareData) Social risks are increasingly becoming a critical component of health care research. One of the most common ways to identify social needs is by using ICD-10-CM "Z-codes." This package identifies social risks using varying taxonomies of ICD-10-CM Z-codes from administrative health care data. The conceptual taxonomies come from: Centers for Medicare and Medicaid Services (2021) , Reidhead (2018) , A Arons, S DeSilvey, C Fichtenberg, L Gottlieb (2018) . Package: r-cran-socialsim Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-future, r-cran-future.apply Suggests: r-cran-rstan, r-cran-devtools, r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-socialsim_0.1.9-1.ca2404.1_all.deb Size: 40454 MD5sum: f80b65e56fc3daf681704960030e7c58 SHA1: eaf14930d1875e8b2678296b0589c3cbe884f983 SHA256: b6f680c6d296414e3ad7dce79132e58cb929fa0df6fac4870ac04c3ca2ba8571 SHA512: bb309c82db147ab3344feadaf26f193a3c2fc845131c40364fd4e8a1f2de70856a251599103d19f8468ed333125f4bef945e274da414513b532fc682d69b66c2 Homepage: https://cran.r-project.org/package=socialSim Description: CRAN Package 'socialSim' (Simulate and Analyse Social Interaction Data) Provides tools to simulate and analyse datasets of social interactions between individuals using hierarchical Bayesian models implemented in Stan. Model fitting is performed via the 'rstan' package. Users can generate realistic interaction data where individual phenotypes influence and respond to those of their partners, with control over sampling design parameters such as the number of individuals, partners, and repeated dyads. The simulation framework allows flexible control over variation and correlation in mean trait values, social responsiveness, and social impact, making it suitable for research on interacting phenotypes and on direct and indirect genetic effects ('DGEs' and 'IGEs'). The package also includes functions to fit and compare alternative models of social effects, including impact–responsiveness, variance–partitioning, and trait-based models, and to summarise model performance in terms of bias and dispersion. For a more detailed description of the available models and impact–responsiveness, see the accompanying article Wijnhorst et al. (2026) . 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Apuntes de clase interactivos. Package: r-cran-sofia Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-png Filename: pool/dists/noble/main/r-cran-sofia_1.0-1.ca2404.1_all.deb Size: 186078 MD5sum: 0182c637ba0e1b8209cea482cbe69968 SHA1: ab185ff35045c2b0dcd1e9ed6284125347b11c53 SHA256: 5e0e9f3ddcfa35291679c4b7f3536e4bd70135a73a709395c04b6d16d0c22750 SHA512: af88eb7e25d046935568227d200ef700833d14d875747dba72edd7680f1b39a546b1a8f03813debc85f563cd966fbe3cd824096e342314c88d897607ca246a79 Homepage: https://cran.r-project.org/package=SOFIA Description: CRAN Package 'SOFIA' (Making Sophisticated and Aesthetical Figures in R) Software that leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. Circos is a novel software that addresses the challenges in visualizing genetic data by creating circular ideograms composed of tracks of heatmaps, scatter plots, line plots, histograms, links between common markers, glyphs, text, and etc. Please see . Package: r-cran-softbib Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-renv, r-cran-rmarkdown, r-cran-bibtex Filename: pool/dists/noble/main/r-cran-softbib_0.0.2-1.ca2404.1_all.deb Size: 19428 MD5sum: 8d1904edd521f11b60a0e209e5f608f9 SHA1: 804488c201849fc95a945472439315c79a2aa551 SHA256: 1a5f538e839d3932dd20c4ea768ac47ffbaa1d2be71c8da53b2ca4d9d3f047b1 SHA512: 6be249d29a90e4faad6b820d09a62a6713a25d80c49258a7394ad7a6b6123c0fa6a7744affda9f91005ae2aaf1e430ab6397360f57d59abbd45b8186c6ec0a30 Homepage: https://cran.r-project.org/package=softbib Description: CRAN Package 'softbib' (Software Bibliographies for R Projects) Detect libraries used in a project and automatically create software bibliographies in 'PDF', 'Word', 'Rmarkdown', and 'BibTeX' formats. Package: r-cran-softclassval Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrayhelpers, r-cran-svunit Filename: pool/dists/noble/main/r-cran-softclassval_1.1.0-1.ca2404.1_all.deb Size: 69876 MD5sum: a6aba03eefdee5bd0486c1da7c8e39b3 SHA1: 6fcd2a0de6766835278cf279d8c99214921ca69e SHA256: b8370e0a6355234716a2524c1ad4b4d050bce5f242af01a1aa8b07b3ff24c60b SHA512: 025db7b26f732ea6b4659944d1255e62213091bd90b6b25cc26f91fe4bd909e3274499ef6ba1e21f59470bbc65c5df5de68a652eb3b4c414912fb0a8a9230b8a Homepage: https://cran.r-project.org/package=softclassval Description: CRAN Package 'softclassval' (Soft Classification Performance Measures) An extension of sensitivity, specificity, positive and negative predictive value to continuous predicted and reference memberships in [0, 1]. Package: r-cran-softclustering Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-softclustering_2.1.3-1.ca2404.1_all.deb Size: 79608 MD5sum: d628deb5667459a7c4708594e9c36f7f SHA1: 28432c087bcecbadb9af47dea05e550935d21a9c SHA256: f5c5831a37b38dea7b47cdbe06e3dbe5a46d17577009bfae01385d0c5ab8d358 SHA512: 18fbcd766a7e85cf6d6b7bd8c434b96c111c172238d5d9dbacd0f36417d90ba71193af99102d44773a8053d463e32bb12dbf91bc3b62b65c0174ae9394fbad06 Homepage: https://cran.r-project.org/package=SoftClustering Description: CRAN Package 'SoftClustering' (Soft Clustering Algorithms) It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters' refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo. Package: r-cran-softwarerisk Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1678 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggraph, r-cran-igraph, r-cran-ineq, r-cran-purrr, r-cran-scales, r-cran-tibble, r-cran-tidygraph, r-cran-rlang, r-cran-sensobol Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-softwarerisk_0.2.0-1.ca2404.1_all.deb Size: 1233416 MD5sum: 43d22c2d9ac1cd206c4e6cb23fb405ce SHA1: ed4726df0f54bbb4673929b2473519df2e8d85ac SHA256: fcebaac78725807594fc7dee8bbfd972f2847b3fd7ca8e7dadedaf99c44a79c6 SHA512: 96ab1abc2297b2215476ff941509a69a143651c5abb52f0c281478ec39957d643078fd7235a2b59c9c7801646380496086794a0b91a15ee05ccd1312cf2e278e Homepage: https://cran.r-project.org/package=softwareRisk Description: CRAN Package 'softwareRisk' (Computation of Node and Path-Level Risk Scores in ScientificModels) It leverages the network-like architecture of scientific models together with software quality metrics to identify chains of function calls that are more prone to generating and propagating errors. It operates on tbl_graph objects representing call dependencies between functions (callers and callees) and computes risk scores for individual functions and for paths (sequences of function calls) based on cyclomatic complexity, in-degree and betweenness centrality. The package supports variance-based uncertainty and sensitivity analyses after Puy et al. (2022) to assess how risk scores change under alternative risk definitions. Package: r-cran-sohpie Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-robustbase, r-cran-dplyr, r-cran-fdrtool, r-cran-gtools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sohpie_1.0.6-1.ca2404.1_all.deb Size: 153008 MD5sum: 56b5e9d33084b190870fbdad957b481c SHA1: e5e3c9bc181de8854a3954bfd4348604188a23df SHA256: 82469ec1462469c800189256ce6012d24dea6329546c6e8927688055ab3c64b3 SHA512: 8bc90ac96c88eef5a4312f90d0d4c8a27eeb35025c96cdc530850e5f966d7afa36cfb4416ad412cf0299d413c86f73795bba6e5ebaaf5cdba68b40e90a22821b Homepage: https://cran.r-project.org/package=SOHPIE Description: CRAN Package 'SOHPIE' (Statistical Approach via Pseudo-Value Information and Estimation) 'SOHPIE' (pronounced as SOFIE) is a novel pseudo-value regression approach for differential co-abundance network analysis of microbiome data, which can include additional clinical covariate in the model. The full methodological details can be found in Ahn S and Datta S (2023) . Package: r-cran-soil Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-ncvreg, r-cran-mass, r-cran-brglm2 Filename: pool/dists/noble/main/r-cran-soil_1.1-1.ca2404.1_all.deb Size: 41146 MD5sum: f6406e2dc46142b97fbcfe23b392e0ae SHA1: 807a7fa5940be42bc34bbc73673bb0a2a1dd6173 SHA256: 64e43355557f195fc922002bb6c114a57dac9357daa3aaee1b51f6023ae3ad4e SHA512: c3d8fd9521b2bf17a06e1066109671132eaf391ba9eba0c6ceac1a20c7e0080231843776b7de4d10dcf318ec0a5fa5a46562544012ad6fbd0c53eacf0c5f80f7 Homepage: https://cran.r-project.org/package=SOIL Description: CRAN Package 'SOIL' (Sparsity Oriented Importance Learning) Sparsity Oriented Importance Learning (SOIL) provides a new variable importance measure for high dimensional linear regression and logistic regression from a sparse penalization perspective, by taking into account the variable selection uncertainty via the use of a sensible model weighting. The package is an implementation of Ye, C., Yang, Y., and Yang, Y. (2017+). Package: r-cran-soilassessment Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2309 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-desolve, r-cran-e1071, r-cran-fuzzyahp, r-cran-googledrive, r-cran-hmisc, r-cran-nnet, r-cran-png, r-cran-randomforest, r-cran-raster, r-cran-soiltexture, r-cran-sp, r-cran-sf, r-cran-terra, r-cran-curl, r-cran-httr, r-cran-withr Suggests: r-cran-arm, r-cran-cubist, r-cran-aqp, r-cran-kernlab, r-cran-nlme, r-cran-ranger, r-cran-rpart, r-cran-plyr, r-cran-qrnn, r-cran-quantregforest Filename: pool/dists/noble/main/r-cran-soilassessment_1.3.0-1.ca2404.1_all.deb Size: 2264056 MD5sum: 554368bada471aa5d0e4a0963978e1b3 SHA1: 5f6a98cb04954a955a54c5372ddab6ef86dc6636 SHA256: 29445467e66e857b804cdf1172eb263c4eeb6096be9c38d6e728394a44b6c547 SHA512: 122909c1b664d252004320e389a5eae9b42a19a428897d480dba62c225a46842a452f8404caa701b847d84d830a3dc9e3e7c89960f7e43140c56605d7b21f67e Homepage: https://cran.r-project.org/package=soilassessment Description: CRAN Package 'soilassessment' (Soil Health Assessment Models for Assessing Soil Conditions andSuitability) Soil health assessment builds information to improve decision in soil management. It facilitates assessment of soil conditions for crop suitability [such as those given by FAO ], groundwater recharge, fertility, erosion, salinization [], carbon sequestration, irrigation potential, and status of soil resources. Package: r-cran-soilchemistry Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-soilchemistry_0.1.0-1.ca2404.1_all.deb Size: 29864 MD5sum: 3f1a268e5c01e2d020cbc13b455b2eed SHA1: 624d0dfcd252c3522c278e294c162968e330ab5b SHA256: 26f2f7379df9b8c8181d61f3266cdd375c3a2eea0a0cc18e92d25ed08381559d SHA512: a868f0072c4a03643fa66690458511a8665f80b4550a76b653247f518d0c51454567ac6764f34a3718566e48695882c0d895e859648d672c9fc7e864bad89527 Homepage: https://cran.r-project.org/package=soilchemistry Description: CRAN Package 'soilchemistry' (Computation of Properties Related to Soil Chemical Environmentand Nutrient Availability) Facilitates basic and equation-based analyses of some important soil properties related to soil chemical environment and nutrient availability to plants. Freundlich H (1907). . Datta SP, Bhadoria PBS (1999). ."Boron adsorption and desorption in some acid soils of West Bengal, India". Langmuir I (1918). "The adsorption of gases on plane surfaces of glass, mica, and platinum". Khasawneh FE (1971). "Solution ion activity and plant growth". Package: r-cran-soilconservation Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-soilconservation_1.0.1-1.ca2404.1_all.deb Size: 53022 MD5sum: 02b77877e1ef956f932cfd46af8eb949 SHA1: 438131d2b47b7a5e6dd3d933be38c50fa28480d0 SHA256: da4deb25c492d01904e4705257599ce2ea284af866b2bf7df90e1d03db1c6f8b SHA512: e0608f4e628868490773b5c31aa1531fb063575c470a2147b5d72f5ef35c2ab1abbdc006892ed62d001e2aa96e71985f2f94bbe54f207334f94ad4c9cac82267 Homepage: https://cran.r-project.org/package=SoilConservation Description: CRAN Package 'SoilConservation' (Soil and Water Conservation) Includes four functions: RFactor_calc(), RFactor_est(), KFactor() and SoilLoss(). The rainfall erosivity factors can be calculated or estimated, and soil erodibility will be estimated by the equation extracted from the monograph. Soil loss will be estimated by the product of five factors (rainfall erosivity, soil erodibility, length and steepness slope, cover-management factor and support practice factor. In the future, additional functions can be included. This efforts to advance research in soil and water conservation, with fast and accurate results. Package: r-cran-soildb Architecture: all Version: 2.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2258 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dbi, r-cran-curl, r-cran-jsonlite Suggests: r-cran-aqp, r-cran-xml2, r-cran-httr, r-cran-rvest, r-cran-odbc, r-cran-rsqlite, r-cran-sf, r-cran-wk, r-cran-terra, r-cran-raster, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-soildb_2.9.1-1.ca2404.1_all.deb Size: 1533692 MD5sum: ab7f0454d04f196d83c5ff82334e9ed2 SHA1: 61230752a85816c74d07b3b6f363d991734e0eb9 SHA256: 2b58b59c1daa3af0bc39d9b2266fc979b8417b284e8ca474a10b34afaaf56e2e SHA512: f879c409f17489c07f477eeb682c1e8832d5aab80f11a151519d37146ead119e6aa8c9394df39eb798905b790c498c72701d1be8d98d3528a95a0cdd8049b35c Homepage: https://cran.r-project.org/package=soilDB Description: CRAN Package 'soilDB' (Soil Database Interface) A collection of functions for reading soil data from U.S. Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) and National Cooperative Soil Survey (NCSS) databases. Package: r-cran-soilfda Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-soilfda_0.1.0-1.ca2404.1_all.deb Size: 17790 MD5sum: 39e62fddff84da2b24cbd6f5ab1e79e5 SHA1: 2980f23759f2e150b6e4bb942098089bf70cdf41 SHA256: 86b768acffd0898eda5cf6243fafad71315ff3891aaded733d4c7da9b1efa0f1 SHA512: 18d3a3d09d0b4f93946280d1886b0ecc15065001bf1e823ce1bf230e953b07dd85387a5ad1f5e4ab64a8eb29b15b32022a6630df9be67488c6c0524ffe150dc9 Homepage: https://cran.r-project.org/package=SoilFDA Description: CRAN Package 'SoilFDA' (Fractal Dimension Analysis of Soil Particle Size Distribution) Function for the computation of fractal dimension based on mass of soil particle size distribution by Tyler & Wheatcraft (1992) . It also provides functions for calculation of mean weight and geometric mean diameter of particle size distribution by Perfect et al. (1992) . Package: r-cran-soilflux Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3708 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-purrr, r-cran-tibble, r-cran-stringr, r-cran-reticulate, r-cran-tensorflow, r-cran-rlang Suggests: r-cran-keras3, r-cran-ggtern, r-cran-readxl, r-cran-scales, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-soilflux_0.1.5-1.ca2404.1_all.deb Size: 3668218 MD5sum: 62b5b460ab8f4fcf56f41324f259c70b SHA1: f14e9b24033daa9dc67ffc3dd34e6c01b27bf1d2 SHA256: 117500d757951ce83c6f9edb728207177b15cca03c8daad3950303de5ae077d0 SHA512: f70c876fee020a1be71daf654b8082a74ea1fdebe68bb95e2bd03274b1796e793c1df69c5cb082af9836e51950e41e17cdf9dcec39503bfd005d7fe70bc26213 Homepage: https://cran.r-project.org/package=soilFlux Description: CRAN Package 'soilFlux' (Physics-Informed Neural Networks for Soil Water Retention Curves) Implements a physics-informed one-dimensional convolutional neural network (CNN1D-PINN) for estimating the complete soil water retention curve (SWRC) as a continuous function of matric potential, from soil texture, organic carbon, bulk density, and depth. The network architecture ensures strict monotonic decrease of volumetric water content with increasing suction by construction, through cumulative integration of non-negative slope outputs (monotone integral architecture). Four physics-based residual constraints adapted from Norouzi et al. (2025) are embedded in the loss function: (S1) linearity at the dry end (pF in [5, 7.6]); (S2) non-negativity at pF = 6.2; (S3) non-positivity at pF = 7.6; and (S4) a near-zero derivative in the saturated plateau region (pF in [-2, -0.3]). Includes tools for data preparation, model training, dense prediction, performance metrics, texture classification, and publication-quality visualisation. Package: r-cran-soilfoodwebs Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-diagram, r-cran-quadprog, r-cran-lpsolve, r-cran-rootsolve, r-cran-desolve Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-soilfoodwebs_1.0.2-1.ca2404.1_all.deb Size: 262090 MD5sum: 5e92361765269be380c42b223561f934 SHA1: d34971670a8e0b02c2320e074df202a7e6ae39d5 SHA256: c60317fbcc77a18131b6bee8ea46959275753e2f172e8acc8b8aac0393e1809f SHA512: a4e04cfb0f9b005e72ef8ca9c83940be8da1787b3688668fce4d67b8e4a2c71d084563cdf9cae53272552e9ad21dd98771ef10ca73eca6dfba6c22e15d639d3e Homepage: https://cran.r-project.org/package=soilfoodwebs Description: CRAN Package 'soilfoodwebs' (Soil Food Web Analysis) Analyzing soil food webs or any food web measured at equilibrium. The package calculates carbon and nitrogen fluxes and stability properties using methods described by Hunt et al. (1987) , de Ruiter et al. (1995) , Holtkamp et al. (2011) , and Buchkowski and Lindo (2021) . The package can also manipulate the structure of the food web as well as simulate food webs away from equilibrium and run decomposition experiments. Package: r-cran-soilfunctionality Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-soilfunctionality_0.1.0-1.ca2404.1_all.deb Size: 15270 MD5sum: ff029eb970a86a5b50d2f656c19a3bed SHA1: f45c7a4e91f6a6efbb56581325f2045c9f476049 SHA256: 472973b9a69c36f578feea452f9a5e0bfd86456dfa6bf58e7c508d20a8b02d09 SHA512: f163602e16c4e79926e6b23485832ad3a3f0118fd348917485204dd163896039dfd07855d9dcaff6fe1920edeac1013f09037dee7685e89f7457a8d828822c2d Homepage: https://cran.r-project.org/package=SoilFunctionality Description: CRAN Package 'SoilFunctionality' (Soil Functionality Measurement) Generally, soil functionality is characterized by its capability to sustain microbial activity, nutritional element supply, structural stability and aid for crop production. Since soil functions can be linked to 80% of ecosystem services, conservation of degraded land should strive to restore not only the capacity of soil to sustain flora but also ecosystem provisions. The primary ecosystem services of soil are carbon sequestration, food or biomass production, provision of microbial habitat, nutrient recycling. However, the actual magnitude of soil functions provided by agricultural land uses has never been quantified. Nutrient supply capacity (NSC) is a measure of nutrient dynamics in restored land uses. Carbon accumulation proficiency (CAP) is a measure of ecosystem carbon sequestration. Biological activity index (BAI) is the average of responses of all enzyme activities in treated land over control/reference land. The CAP parameter investigates how land uses may affect carbon flows, retention, and sequestration. The CAP provides a signal for C cycles, flows, and the systems' relative operational supremacy. Package: r-cran-soilhyp Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-lubridate Filename: pool/dists/noble/main/r-cran-soilhyp_0.1.7-1.ca2404.1_all.deb Size: 230112 MD5sum: cb4ba109a505856ec7aaea3197428480 SHA1: 859250a8bc36f7478c07012e1766bfdbf32806d3 SHA256: b99410f3566b2af8b9ee1c2e446b7817c30e9885299cbd5440943bcc57e902d7 SHA512: de7510bf6cebf542946b3b7f99fcb4e168362e835e5591c5470fea70b08ab9717ad9a07c52ecb6548b02bbff1a3c1e5a40cfa8805cfdeea05501864bd5ca782c Homepage: https://cran.r-project.org/package=SoilHyP Description: CRAN Package 'SoilHyP' (Soil Hydraulic Properties) Provides functions for (1) soil water retention (SWC) and unsaturated hydraulic conductivity (Ku) (van Genuchten-Mualem (vGM or vG) [1, 2], Peters-Durner-Iden (PDI) [3, 4, 5], Brooks and Corey (bc) [8]), (2) fitting of parameter for SWC and/or Ku using Shuffled Complex Evolution (SCE) optimisation and (3) calculation of soil hydraulic properties (Ku and soil water contents) based on the simplified evaporation method (SEM) [6, 7]. Main references: [1] van Genuchten (1980) , [2] Mualem (1976) , [3] Peters (2013) , [4] Iden and Durner (2013) , [5] Peters (2014) , [6] Wind G. P. (1966), [7] Peters and Durner (2008) and [8] Brooks and Corey (1964). Package: r-cran-soilhypfit Architecture: all Version: 0.1-8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr, r-cran-snowfall, r-cran-mgcv, r-cran-quadprog, r-cran-rmpfr, r-cran-soilhyp Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-soilhypfit_0.1-8-1.ca2404.1_all.deb Size: 370756 MD5sum: 17248ece36809b13656e2e89ded8e298 SHA1: 5dd2ccd1694f8334186e9aaf6c0a7d3ee8145b02 SHA256: 2e0043c3938324d846980e3ccfef695bcb8011cc17fe2d2d9b33abc43481f466 SHA512: e8699877f4c2eb848d36cc004fe4944c081828bc87e73aea96ff70fefb5dc5c2ec7db0a4f6cd1a1f445690f54251d041f2b4b6d48d6395f30bd70cff00c1fbb1 Homepage: https://cran.r-project.org/package=soilhypfit Description: CRAN Package 'soilhypfit' (Modelling of Soil Water Retention and Hydraulic ConductivityData) Provides functions for efficiently estimating properties of the Van Genuchten-Mualem model for soil hydraulic parameters from possibly sparse soil water retention and hydraulic conductivity data by multi-response parameter estimation methods (Stewart, W.E., Caracotsios, M. Soerensen, J.P. (1992) "Parameter estimation from multi-response data" ). Parameter estimation is simplified by exploiting the fact that residual and saturated water contents and saturated conductivity are conditionally linear parameters (Bates, D. M. and Watts, D. G. (1988) "Nonlinear Regression Analysis and Its Applications" ). Estimated parameters are optionally constrained by the evaporation characteristic length (Lehmann, P., Bickel, S., Wei, Z. and Or, D. (2020) "Physical Constraints for Improved Soil Hydraulic Parameter Estimation by Pedotransfer Functions" ) to ensure that the estimated parameters are physically valid. Common S3 methods and further utility functions allow to process, explore and visualise estimation results. Package: r-cran-soilkey Architecture: all Version: 0.9.97-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14153 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-yaml, r-cran-cli, r-cran-rlang, r-cran-withr Suggests: r-cran-aqp, r-cran-soiltaxonomy, r-cran-mpspline2, r-cran-terra, r-cran-foreign, r-cran-sf, r-cran-chromote, r-cran-munsellinterpol, r-cran-pls, r-cran-prospectr, r-cran-resemble, r-cran-ellmer, r-cran-httr, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-pdftools, r-cran-magick, r-cran-shiny, r-cran-dt, r-cran-dbi, r-cran-rsqlite, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-soilkey_0.9.97-1.ca2404.1_all.deb Size: 7957330 MD5sum: 5f08a6b8b9bd1f3550dd069a1c633555 SHA1: 92bee474477e37889967124bd525ea98fbe9ccce SHA256: d6df325686ba8945f1dd3d3bc566bec3f796ac3639c0bdf28836caebdd34e6f8 SHA512: 2955db905af638026d56efc416f3f378866eccc216224afad8d9530add409c8f0fde6008fc455f90250d1a7ec37178f70cc5b0499c1531fca5c7d8f48db5bde4 Homepage: https://cran.r-project.org/package=soilKey Description: CRAN Package 'soilKey' (Automated Soil Profile Classification per 'WRB' 2022, 'SiBCS' 5and 'USDA' Soil Taxonomy 13) Implements deterministic classification keys for the World Reference Base for Soil Resources ('WRB') 2022, 4th edition (IUSS Working Group WRB, 2022, ISBN:979-8-9862451-1-9), the Brazilian System of Soil Classification ('SiBCS') 5th edition (Santos et al., 2018, ISBN:978-85-7035-800-4) and the United States Department of Agriculture ('USDA') Soil Taxonomy 13th edition (Soil Survey Staff, 2022, ). Provides a unified profile representation with explicit per-attribute provenance, multimodal extraction from field reports and photos via vision-language models (VLM), spatial priors from 'SoilGrids' (Poggio et al., 2021, ) and national soil maps, and gap-filling of soil attributes from visible-near-infrared (Vis-NIR) or mid-infrared (MIR) spectra via the Open Soil Spectral Library ('OSSL'; Safanelli et al., 2025, ). The taxonomic key itself is never delegated to a large language model (LLM); LLMs are restricted to schema-validated extraction. Each classification result reports a key trace, a provenance-aware evidence grade, and ambiguities that further measurement would resolve. Package: r-cran-soilmanager Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-lubridate, r-cran-ggplot2, r-cran-ggthemes, r-cran-tidyr, r-cran-tibble, r-cran-readxl, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-soilmanager_1.1.0-1.ca2404.1_all.deb Size: 861000 MD5sum: 9d4efb8584dc9fc22a292a3f1fc5c9b7 SHA1: 923e27c2f261b72fae196c303d45afb094ea2c2a SHA256: c7cabcc8e086c96ab2a748e3102ac3e205532f0868c53f9f0e0c6ab52ee79bd8 SHA512: b3a78a09a636ff64fad54c34d967fec5e7c8461fdbaac1d4da2b8b007bf2c1a2d0759241813f3032f438f001f7df891421fd5b5d04381b6724af884936d15b87 Homepage: https://cran.r-project.org/package=SoilManageR Description: CRAN Package 'SoilManageR' (Calculate Soil Management Indicators for Agricultural PracticeAssessment) Calculate numerical agricultural soil management indicators from on a management timeline of an arable field. Currently, indicators for carbon (C) input into the soil system, soil tillage intensity rating (STIR), number of soil cover and living plant cover days, N fertilization and livestock intensity, and plant diversity are implemented. The functions can also be used independently of the management timeline to calculate some indicators. The package contains tables with reference information for the functions, as well as a '*.xlsx' template to collect the management data. Package: r-cran-soilphysics Architecture: all Version: 5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-mass, r-cran-shiny, r-cran-rhandsontable, r-cran-shinydashboard, r-cran-fields Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rpanel Filename: pool/dists/noble/main/r-cran-soilphysics_5.1-1.ca2404.1_all.deb Size: 1767650 MD5sum: 8390b36f8d338eace48d092dd4f96f42 SHA1: 69763f4ce1764e5fd1caf1dc64ea526be1d9a5d1 SHA256: fbe20b8d2d3d46b3eac7f0001b95d0de571ebaf904ac56f6e98059ca4b3e1c9d SHA512: 36c870aa07652e6041cf5a26006c894fc4d718d5ad29a893ea47e3e5a729ea2c4a26f9340371b661fda91240b33e22321785033c5c94c5c35a76ed81709967d9 Homepage: https://cran.r-project.org/package=soilphysics Description: CRAN Package 'soilphysics' (Soil Physical Analysis) Basic and model-based soil physical analyses. Package: r-cran-soilr Architecture: all Version: 1.2.107-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3705 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-igraph, r-cran-assertthat, r-cran-expm, r-cran-sets, r-cran-purrr Suggests: r-cran-fme, r-cran-lattice, r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-getopt, r-cran-tinytex Filename: pool/dists/noble/main/r-cran-soilr_1.2.107-1.ca2404.1_all.deb Size: 2000576 MD5sum: e24d353b3f5267de098ac4d616a84de4 SHA1: 3bc136890e414b5d6811b631bc88090337b6ead0 SHA256: ab886278ce8a0297c5feb700fec10dc7af7ad115761a1efae7aa2d6d306d237d SHA512: c55fb57c947b8b0ea199417c0dd187ce619e3a82650a824157c7c1bbb8d73e95fd586485bd1517bc2be576fb2938c091c1a62d4ebeb6e044c1170c89fa8a923b Homepage: https://cran.r-project.org/package=SoilR Description: CRAN Package 'SoilR' (Models of Soil Organic Matter Decomposition) Functions for modeling Soil Organic Matter decomposition in terrestrial ecosystems with linear and nonlinear systems of differential equations. The package implements models according to the compartmental system representation described in Sierra and others (2012) and Sierra and others (2014) . Package: r-cran-soilsaltindex Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4777 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-soilsaltindex_0.1.0-1.ca2404.1_all.deb Size: 4400100 MD5sum: f7b72ed8a85e1545b406f2cd4ff232b7 SHA1: 31c495a180dc5dab4b876b5348d4243af77ed0b0 SHA256: df37207e5cf24d677085b6aa31b652dd45fc0e5f11ac5a3abc3630d119fa3a15 SHA512: 55e2fa39d3a8ffc1a8b196cdd75ed5d3b288bc11d2c4f5926fd0addf361d4d1d0ff727383422f7b6d66564861feb0798c342e11c567055ac85ad0782724a2f55 Homepage: https://cran.r-project.org/package=SoilSaltIndex Description: CRAN Package 'SoilSaltIndex' (Soil Salinity Indices Generation using Satellite Data) The developed function generates soil salinity indices using satellite data, utilizing multiple spectral bands such as Blue, Green, Red, Near-Infrared (NIR), and Shortwave Infrared (SWIR1, SWIR2). It computes 24 different salinity indices crucial for monitoring and analyzing salt-affected soils efficiently. For more details see, Rani, et al. (2022). . One of the key features of the developed function is its flexibility. Users can provide any combination of the required spectral bands, and the function will automatically calculate only the relevant indices based on the available data. This dynamic capability ensures that users can maximize the utility of their data without the need for all spectral bands, making the package versatile and user-friendly. Outputs are provided as GeoTIFF file format, facilitating easy integration with GIS workflows. Package: r-cran-soiltaxonomy Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-data.table Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-soildb, r-cran-ape, r-cran-data.tree Filename: pool/dists/noble/main/r-cran-soiltaxonomy_0.2.8-1.ca2404.1_all.deb Size: 410554 MD5sum: 2b157f69c4afb57670b87de3ed8f9982 SHA1: e96db760403bcddc86c73a0769c1eaa052db8adf SHA256: f785d573ba280a0d544c336ac85801a388129c42d63f00c655b20a01b37566bd SHA512: 78f483d7b8ffdc41f851dfe606d4cfdfd7e589b7e537dbc8b98933a995e749c2b37282b3526721c8e786b26a5cd7ad5ab196c58320fa2ed7a28c4d08d5c4216e Homepage: https://cran.r-project.org/package=SoilTaxonomy Description: CRAN Package 'SoilTaxonomy' (A System of Soil Classification for Making and Interpreting SoilSurveys) Taxonomic dictionaries, formative element lists, and functions related to the maintenance, development and application of U.S. Soil Taxonomy. Data and functionality are based on official U.S. Department of Agriculture sources including the latest edition of the Keys to Soil Taxonomy. Descriptions and metadata are obtained from the National Soil Information System or Soil Survey Geographic databases. Other sources are referenced in the data documentation. Provides tools for understanding and interacting with concepts in the U.S. Soil Taxonomic System. Most of the current utilities are for working with taxonomic concepts at the "higher" taxonomic levels: Order, Suborder, Great Group, and Subgroup. Package: r-cran-soiltestcorr Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3019 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-tidyr, r-cran-purrr, r-cran-data.table, r-cran-ggplot2, r-cran-ggpp, r-cran-nlstools, r-cran-minpack.lm, r-cran-modelr, r-cran-nlraa, r-cran-aiccmodavg, r-cran-smatr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-soiltestcorr_2.2.1-1.ca2404.1_all.deb Size: 1508630 MD5sum: c0abb0f63c60732d38074687ca9e9f84 SHA1: 05e726b5c4e612e1dd43045f818ef6ed75dc35f9 SHA256: dcc9d8132ed9fa30544d450a609243c984c9b6cfb1871ca9abcae5aa21274999 SHA512: e90b193cb43b773ef291b0072ef89f6e6a57baf02970e3834e4fc357ebde3cce294b149e28bd149c2a6d87f935e7a074b76b7a2101766e6b06c6d650f416a65d Homepage: https://cran.r-project.org/package=soiltestcorr Description: CRAN Package 'soiltestcorr' (Soil Test Correlation and Calibration) A compilation of functions designed to assist users on the correlation analysis of crop yield and soil test values. Functions to estimate crop response patterns to soil nutrient availability and critical soil test values using various approaches such as: 1) the modified arcsine-log calibration curve (Correndo et al. (2017) ); 2) the graphical Cate-Nelson quadrants analysis (Cate & Nelson (1965)), 3) the statistical Cate-Nelson quadrants analysis (Cate & Nelson (1971) ), 4) the linear-plateau regression (Anderson & Nelson (1975) ), 5) the quadratic-plateau regression (Bullock & Bullock (1994) ), and 6) the Mitscherlich-type exponential regression (Melsted & Peck (1977) ). The package development stemmed from ongoing work with the Fertilizer Recommendation Support Tool (FRST) and Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification (SIIL) projects. Package: r-cran-soiltesting Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-soiltesting_0.1.0-1.ca2404.1_all.deb Size: 48880 MD5sum: bfcbc4304c1de27d24e85ef05f58b684 SHA1: cfdbbe187db58baf67fffcf726f28804ab93764b SHA256: 45d9621d7373dc749553a2a5b17f512c0cd9a593b58949653d4316b61546379a SHA512: 44ef46033694ea40c1a101a218b3e9b1c99b8288cc54a45087c414f2e48f5f244f01b356e8476cdf50858a728a944953b2f4421b6af84707fb9421e37ffa8c07 Homepage: https://cran.r-project.org/package=SoilTesting Description: CRAN Package 'SoilTesting' (Organic Carbon and Plant Available Nutrient Contents in Soil) Testing of soil for the contents of organic carbon, and available macro- and micro-nutrients is a crucial part of soil fertility assessment. This package computes some routinely tested soil properties viz. organic carbon (C), total nitrogen (N), available N, mineral N, available phosphorus (P), available potassium (K), available iron (Fe), available zinc (Zn), available manganese (Mn), available copper (Cu), and available nickel (Ni) in soil based on laboratory analysis data obtained by most commonly followed protocols. Besides, it can also draw standard curves based on absorption/emission vs. concentration data, and give out unknown concentrations from absorption/emission readings. Package: r-cran-soiltexture Architecture: all Version: 1.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 976 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sp, r-cran-mass Suggests: r-cran-xtable Filename: pool/dists/noble/main/r-cran-soiltexture_1.5.3-1.ca2404.1_all.deb Size: 764168 MD5sum: 2282b810d3ee811bb304ecfb6a5725a4 SHA1: 3eb8b553543107962d0ca7e46633fb6192f04c09 SHA256: 28212256c6da4b418911a7f18fdcb53bc9312ff8dbbaeab4d6fc2a4a845fc274 SHA512: 85672de31c1172c2fedfa996fc271518a3c5544073cc07e5b6f39652de20b13ff4df63402d24dac2517488d451ef06685f222f0e1e224fb19baf8dea6fa87d6a Homepage: https://cran.r-project.org/package=soiltexture Description: CRAN Package 'soiltexture' (Functions for Soil Texture Plot, Classification andTransformation) "The Soil Texture Wizard" is a set of R functions designed to produce texture triangles (also called texture plots, texture diagrams, texture ternary plots), classify and transform soil textures data. These functions virtually allows to plot any soil texture triangle (classification) into any triangle geometry (isosceles, right-angled triangles, etc.). This set of function is expected to be useful to people using soil textures data from different soil texture classification or different particle size systems. Many (> 15) texture triangles from all around the world are predefined in the package. A simple text based graphical user interface is provided: soiltexture_gui(). Package: r-cran-soiltillr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-soiltillr_0.1.0-1.ca2404.1_all.deb Size: 112790 MD5sum: 338b01c7f475ed23c54b058b0b033e17 SHA1: c00a9dc1cd9c21f5e342644bd721ad631ee00726 SHA256: ce8d85639b1047eaae714d1957e9cf1f06662def1c64fea095f5609efa6197bd SHA512: 9c78d93c23dfee08677de220350e7681deb13d90f20f4e318b88cb54c4106b599d274877dd83c3538eca8fb6f277418d30f7226c0d3a485cf90f53526081fd09 Homepage: https://cran.r-project.org/package=soiltillr Description: CRAN Package 'soiltillr' (Analyse Soil Tillage Depth and Erosion Over Time) Provides tools to record, validate, and analyse soil tillage depth and erosion across years and field treatments. Includes functions for year-wise tillage operation summaries, erosion depth tracking, compaction detection, soil loss estimation, and visualisation of temporal changes in tillage and erosion profiles. Methods follow Lal (2001) and Renard et al. (1997) "Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE)" . Package: r-cran-soilvae Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prospectr, r-cran-pls Filename: pool/dists/noble/main/r-cran-soilvae_0.1.9-1.ca2404.1_all.deb Size: 2310368 MD5sum: d79c36c83d3de46be41e50448ea88adc SHA1: e1170a2b18193991c36ba20ee4d00d9d20e19913 SHA256: 4c6b2836a8648481026da5a8614a35cd1249233f40587307d9bb0ceeffcf530b SHA512: 680c799507bd77243293b7f02af82f603405ab0c699cae4c3dda086361664583b99941ca126c90abe7451ff4e94c740814737f7889c6e67ccea970c69c541643 Homepage: https://cran.r-project.org/package=soilVAE Description: CRAN Package 'soilVAE' (Supervised Variational Autoencoder Regression via 'reticulate') Supervised latent-variable regression for high-dimensional predictors such as soil reflectance spectra. The model uses an encoder-decoder neural network with a stochastic Gaussian latent representation regularized by a Kullback-Leibler term, and a supervised prediction head trained jointly with the reconstruction objective. The implementation interfaces R with a 'Python' deep-learning backend and provides utilities for training, tuning, and prediction. Package: r-cran-soilwater Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-raster Filename: pool/dists/noble/main/r-cran-soilwater_1.0.5-1.ca2404.1_all.deb Size: 35690 MD5sum: 61e1ce25f053fed90b43d4fcee9a2cc1 SHA1: 0f2051807b7e50ceac31449290847b70edff4d38 SHA256: 4f85c4d4654fb8580448edf4809124869f98f09e40dec34838cfcc8d461630f6 SHA512: 5472b173765e363bda3788f3e748fddb9e54354f4de5e9f7c601bf18753ba035b5cd9d67dfba7e7745525a8380fefd05c9446e89373234f5169111c592dcca56 Homepage: https://cran.r-project.org/package=soilwater Description: CRAN Package 'soilwater' (Implementation of Parametric Formulas for Soil Water Retentionor Conductivity Curve) It implements parametric formulas of soil water retention or conductivity curve. At the moment, only Van Genuchten (for soil water retention curve) and Mualem (for hydraulic conductivity) were implemented. See reference (). Package: r-cran-sojourn.data Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-nnet Filename: pool/dists/noble/main/r-cran-sojourn.data_0.3.0-1.ca2404.1_all.deb Size: 47856 MD5sum: 41ec5c0a44aad16c5d94e341197fa512 SHA1: e0c05130d6f270d748a041661f69120d7e4dc1d5 SHA256: a35dea7199892902dd5bc6b437599f05fb053a53d21dd0ed3fe9df2ff401b35b SHA512: 675d4db1bf417e4e44de7081c16c8e551866ab0b04e87bbfa6cd1d26b71b45a4cc7a617c6ccfa7de6b0e689740a6b84607d455b6e2df927497f319b0f80140a7 Homepage: https://cran.r-project.org/package=Sojourn.Data Description: CRAN Package 'Sojourn.Data' (Supporting Objects for Sojourn Accelerometer Methods) Stores objects (e.g. neural networks) that are needed for using Sojourn accelerometer methods. For more information, see Lyden K, Keadle S, Staudenmayer J, & Freedson P (2014) , Ellingson LD, Schwabacher IJ, Kim Y, Welk GJ, & Cook DB (2016) , and Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018) . 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Different versions of the algorithm are implemented, for numeric and relational data and for contingency tables as described, respectively, in Kohonen (2001) , Olteanu & Villa-Vialaneix (2005) and Cottrell et al (2004) . The package also contains many plotting features (to help the user interpret the results), can handle (and impute) missing values and is delivered with a graphical user interface based on 'shiny'. Package: r-cran-somemtp Architecture: all Version: 1.4.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-somemtp_1.4.1.1-1.ca2404.1_all.deb Size: 200650 MD5sum: cde817975618ac31cdd2c1a4f3d27a1d SHA1: 4f894f177d91a7640a6b93141d7358fe67481e5d SHA256: e0862252c6be0ccdde1a792d8a81954e8ff01839fd28c2b32a7e851e9494c2c9 SHA512: 047ef5e2e252233d9807fa2563a994a19b84db8405bee2ecc99afa6d1f6dc9f480ea0ce7ddb24f6af7258d746aa307bcb90b11b375591d2c51fd206ff16bfb54 Homepage: https://cran.r-project.org/package=someMTP Description: CRAN Package 'someMTP' (Some Multiple Testing Procedures) It's a collection of functions for Multiplicity Correction and Multiple Testing. Package: r-cran-somenv Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlist, r-cran-kohonen, r-cran-shiny, r-cran-dplyr, r-cran-plyr, r-cran-openair, r-cran-colourpicker, r-cran-shinycssloaders, r-cran-shinycustomloader Filename: pool/dists/noble/main/r-cran-somenv_1.1.2-1.ca2404.1_all.deb Size: 151816 MD5sum: 402f2958d2c2d60201669357576b68e4 SHA1: 75b6ea4eab21d0d6d27633dbb8f32eb8c168633d SHA256: 867f375bd74751030d0b137ad29935627d09c9d4ab31a354cf2ca9439c1488be SHA512: cd333890ea77a5bc463d2363f83baade14bae83d9cefba5280ed633476911739b94f4046da1cab23cfcc65e949493f4566960641a4236954bf06dc1a9d551226 Homepage: https://cran.r-project.org/package=SOMEnv Description: CRAN Package 'SOMEnv' (SOM Algorithm for the Analysis of Multivariate EnvironmentalData) Analysis of multivariate environmental high frequency data by Self-Organizing Map and k-means clustering algorithms. By means of the graphical user interface it provides a comfortable way to elaborate by self-organizing map algorithm rather big datasets (txt files up to 100 MB ) obtained by environmental high-frequency monitoring by sensors/instruments. The functions present in the package are based on 'kohonen' and 'openair' packages implemented by functions embedding Vesanto et al. (2001) heuristic rules for map initialization parameters, k-means clustering algorithm and map features visualization. Cluster profiles visualization as well as graphs dedicated to the visualization of time-dependent variables Licen et al. (2020) are provided. Package: r-cran-somhca Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-kohonen, r-cran-awesom, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-maptree, r-cran-fpc Filename: pool/dists/noble/main/r-cran-somhca_0.4.0-1.ca2404.1_all.deb Size: 60648 MD5sum: e96e7b7eeec57d65089b410fa1fdc79a SHA1: 8658c7ceb58896b04ffc1adbd0f084121a849f8d SHA256: a1cb060c6955e66d2a95452ebdac31aa70e06a349871dc9e76cfb56013c9a135 SHA512: 80d5e1579b54a1b26d73d0a0b8126895653fc1d83fc1a7f66847f7e3f4708307e1087b23bc09824222595ec51c0b22989e70b9997e1a8919e5403fab6734370c Homepage: https://cran.r-project.org/package=somhca Description: CRAN Package 'somhca' (Self-Organising Maps Coupled with Hierarchical Cluster Analysis) Implements self-organising maps combined with hierarchical cluster analysis (SOM-HCA) for clustering and visualization of high-dimensional data. The package includes functions to estimate the optimal map size based on various quality measures and to generate a model using the selected dimensions. It also performs hierarchical clustering on the map nodes or other data to group similar units. Documentation about the SOM-HCA method is provided in Pastorelli et al. (2024) . Package: r-cran-somnmr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3614 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-pracma, r-cran-minpack.lm, r-cran-quadprog, r-cran-intervalsurgeon, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-somnmr_0.3.0-1.ca2404.1_all.deb Size: 3634884 MD5sum: a3a0bed189cb5552d2b58041f7c6d872 SHA1: 1f2a982df6139cdfa972cb9b2848d66517894ef9 SHA256: 70b1aecac100fdc471341190b772635b96a583031cd1b09e4b440b412604bd66 SHA512: 7c2a9e36b4433d82964ff898911dbf7db3fe8cab14dc89d85c8be38d93c43bbd7152b4556587dabf5e63216a6f3eba629609871b66eb1770079f390b1dc9c19b Homepage: https://cran.r-project.org/package=SOMnmR Description: CRAN Package 'SOMnmR' (Analysis of Soil Organic Matter using Nuclear Magnetic Resonance) Integrates the 13C nuclear magnetic resonance spectra using different integration ranges. Output depends on the method chosen. For the Molecular Mixing Model, a measurement of the fitting quality is given by its R-factor. For more details see: . Package: r-cran-somspace Architecture: all Version: 1.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3994 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-data.table, r-cran-kohonen, r-cran-maps, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-somspace_1.2.4-1.ca2404.1_all.deb Size: 3568690 MD5sum: a6be6b5263bba0319fb0cdf9087e3ef8 SHA1: 7f17dbc0ade0840e6768de92018532b888d01c91 SHA256: 427e0aafed408bb3876c43e631faab581c042a995a17a9593d7bd09cb74ce0cc SHA512: 9d86dc7b253de2b23107903fe1d32ad9fc221abe321f10544ace4437d9f7f50de294afbef359ea6ddf434c20e04ce51c552536d656b44fa9467deb2d08afaedc Homepage: https://cran.r-project.org/package=somspace Description: CRAN Package 'somspace' (Spatial Analysis with Self-Organizing Maps) Application of the Self-Organizing Maps technique for spatial classification of time series. The package uses spatial data, point or gridded, to create clusters with similar characteristics. The clusters can be further refined to a smaller number of regions by hierarchical clustering and their spatial dependencies can be presented as complex networks. Thus, meaningful maps can be created, representing the regional heterogeneity of a single variable. More information and an example of implementation can be found in Markonis and Strnad (2020, ). 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Package: r-cran-songevo Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-geosphere, r-cran-lattice, r-cran-sp Suggests: r-cran-reshape2, r-cran-hmisc, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-songevo_1.0.0-1.ca2404.1_all.deb Size: 619844 MD5sum: 20880f460f19b44231e6e9a50f83cccc SHA1: 480cd5cff7b280b370d344d67670477c4fd78564 SHA256: 8ffe74c71bed6857002bd40c3676dd5e287ba519b23b3c913b2db1034bbb520c SHA512: d6dba48f075765a4c7dd066b9ef26eeecc38208c4137e0cb091620f4ce2dc7c0b54c9e746f8b3f28b5acdc284cc87725cad3174fec34e732423d6bb2f90d9831 Homepage: https://cran.r-project.org/package=SongEvo Description: CRAN Package 'SongEvo' (An Individual-Based Model of Bird Song Evolution) Simulates the cultural evolution of quantitative traits of bird song. 'SongEvo' is an individual- (agent-) based model. 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Package: r-cran-soniclength Architecture: all Version: 1.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-soniclength_1.4.7-1.ca2404.1_all.deb Size: 387272 MD5sum: 6aea6b9ae930f3e9687e43fde736cf3c SHA1: 55679e1e412e311f2a01598e8198bf1c7eb54bc3 SHA256: 692061f9a4b6d4932b4a03fb98915f2129070162b926cad0530f7248a9b80fb1 SHA512: 2d2916b4a5871b4164693c806e88665a246645b06c1701be39d4598cfb0f7c89df800bc97bd4fceed884852597307b809e82e26ec079a7349cdc3a63a13d58a5 Homepage: https://cran.r-project.org/package=sonicLength Description: CRAN Package 'sonicLength' (Estimating Abundance of Clones from DNA Fragmentation Data) Estimate the abundance of cell clones from the distribution of lengths of DNA fragments (as created by sonication, whence `sonicLength'). The algorithm in "Estimating abundances of retroviral insertion sites from DNA fragment length data" by Berry CC, Gillet NA, Melamed A, Gormley N, Bangham CR, Bushman FD. Bioinformatics; 2012 Mar 15;28(6):755-62 is implemented. The experimental setting and estimation details are described in detail there. Briefly, integration of new DNA in a host genome (due to retroviral infection or gene therapy) can be tracked using DNA sequencing, potentially allowing characterization of the abundance of individual cell clones bearing distinct integration sites. The locations of integration sites can be determined by fragmenting the host DNA (via sonication or fragmentase), breaking the newly integrated DNA at a known sequence, amplifying the fragments containing both host and integrated DNA, sequencing those amplicons, then mapping the host sequences to positions on the reference genome. The relative number of fragments containing a given position in the host genome estimates the relative abundance of cells hosting the corresponding integration site, but that number is not available and the count of amplicons per fragment varies widely. However, the expected number of distinct fragment lengths is a function of the abundance of cells hosting an integration site at a given position and a certain nuisance parameter. The algorithm implicitly estimates that function to estimate the relative abundance. Package: r-cran-sonicscrewdriver Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2716 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-hms, r-cran-jsonlite, r-cran-mime, r-cran-rdpack, r-cran-seewave, r-cran-stringi, r-cran-suncalc, r-cran-tuner Suggests: r-cran-av, r-cran-covr, r-cran-devtools, r-cran-googlecloudstorager, r-cran-googlelanguager, r-cran-knitr, r-cran-pbapply, r-cran-plotrix, r-cran-reticulate, r-cran-rmarkdown, r-cran-soundecology, r-cran-spelling, r-cran-testthat, r-cran-waveletcomp Filename: pool/dists/noble/main/r-cran-sonicscrewdriver_0.0.7-1.ca2404.1_all.deb Size: 2080618 MD5sum: 91ad5f810c2d0f4a4c6188647a911bcf SHA1: 9d11904109ff6ef9c61a3c41a4e3b27c520c2f33 SHA256: 421663bd2cdd7fe6534f185ba42123a45dc5253bd83f103d4f7f59d2cc7f0ce6 SHA512: a97f84e6a8fb1d767bebd6cc8211e7b056722f9dc5233599b024b31d1bc10994c95575f8d390d599e9e48cfe4fa99d6f449ad5a519d35af2a055de6ec64848b3 Homepage: https://cran.r-project.org/package=sonicscrewdriver Description: CRAN Package 'sonicscrewdriver' (Bioacoustic Analysis and Publication Tools) Provides tools for manipulating sound files for bioacoustic analysis, and preparing analyses these for publication. The package validates that values are physically possible wherever feasible. Package: r-cran-sonify Architecture: all Version: 0.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tune Filename: pool/dists/noble/main/r-cran-sonify_0.0-1-1.ca2404.1_all.deb Size: 20544 MD5sum: e642dd10a57c3b1222f5b0972bafde9f SHA1: 47466b9fc9e9feafa1d34ed675237c17258ccaa6 SHA256: 244749f4408aca849fc742c3da20785d8edc66199c7c01578520018c469a189f SHA512: 4e95b59362f91cf490517be8b76c646c0127bf1b89f38d01c5d1e879864dad48c8eababc6b24be148661a17941ce4c7fc602fd1b8dfdedbabeffe2664c6046ff Homepage: https://cran.r-project.org/package=sonify Description: CRAN Package 'sonify' (Data Sonification - Turning Data into Sound) Sonification (or audification) is the process of representing data by sounds in the audible range. This package provides the R function sonify() that transforms univariate data, sampled at regular or irregular intervals, into a continuous sound with time-varying frequency. The ups and downs in frequency represent the ups and downs in the data. Sonify provides a substitute for R's plot function to simplify data analysis for the visually impaired. Package: r-cran-sono Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-desctools, r-cran-ggplot2, r-cran-rdpack, r-cran-rje Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sono_1.2-1.ca2404.1_all.deb Size: 170842 MD5sum: 7fd8f8a06fd497311fe974115a32b4d1 SHA1: 3eab439e2a32681b72449d097e8d9159797e0434 SHA256: e5726351d8fce6458fdbe8edad193490ecdd5b0b846e31e78c47cb05e47d18ca SHA512: a7c58178432fe028812ae086573567e325f83bef991f82018c451ed2dcad96f1f3245c8b9341b0c300d4307688d5424a39decbcde4cebfcee20594d3c9c7198a Homepage: https://cran.r-project.org/package=SONO Description: CRAN Package 'SONO' (Scores of Nominal Outlyingness (SONO)) Computes scores of outlyingness for data sets consisting of nominal variables and includes various evaluation metrics for assessing performance of outlier identification algorithms producing scores of outlyingness. The scores of nominal outlyingness are computed based on the framework of Costa and Papatsouma (2025) . Package: r-cran-sooty Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2914 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-curl, r-cran-s7, r-cran-tibble Suggests: r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-sooty_0.6.1-1.ca2404.1_all.deb Size: 1450284 MD5sum: 9ae0d018f83f3a7d60b394ee228bd411 SHA1: d3478ca03a4cc046a1187f7a1db4e85a6c916398 SHA256: 59a4e59495a4b10087af7b3e7fc200a8e980bd5d1d0a4f2358dbc06ca15e009b SHA512: d136aa9d18d2cb7ff4fc50f01e5f1dfa39b893bd41bf35e60788a06984448455dfd300eaa3141f402d47856bfdbbbd6f5e0a045d56a8bd0e0ae4d5ce6f8454c9 Homepage: https://cran.r-project.org/package=sooty Description: CRAN Package 'sooty' (Data Source Catalogues Online for Southern Ocean EcosystemResearch) Obtains lists of files of remote sensing collections for Southern Ocean surface properties. Commonly used data sources of sea surface temperature, sea ice concentration, and altimetry products such as sea surface height and sea surface currents are cached in object storage on the Pawsey Supercomputing Research Centre facility. Patterns of working to retrieve data from these object storage catalogues are described. The catalogues include complete collections of datasets Reynolds et al. (2008) "NOAA Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1" , Spreen et al. (2008) "Artist Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sea ice concentration" . In future releases helpers will be added to identify particular data collections and target specific dates for earth observation data for reading, as well as helpers to retrieve data set citation and provenance details. This work was supported by resources provided by the Pawsey Supercomputing Research Centre with funding from the Australian Government and the Government of Western Australia. This software was developed by the Integrated Digital East Antarctica program of the Australian Antarctic Division. Package: r-cran-sop Architecture: all Version: 1.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-spats Filename: pool/dists/noble/main/r-cran-sop_1.0-1-1.ca2404.1_all.deb Size: 172384 MD5sum: de62b5422242abb3824398ff5a1f7024 SHA1: 35437f97d33ee616cc5d70cd13223de0a1dcb8e7 SHA256: df458784ca93890e133132081e4e93bf2b95bdfdca66545e62f66a397ef85d2d SHA512: f9ab3e6cbd2b8877cc9b71fc54969da416492aab157754f5f1148207b3757034ebb58243ca317c605d5f5aa017777758e5ecf5b0b4b3811bcf7272baad4168ac Homepage: https://cran.r-project.org/package=SOP Description: CRAN Package 'SOP' (Generalised Additive P-Spline Regression Models Estimation) Generalised additive P-spline regression models estimation using the separation of overlapping precision matrices (SOP) method. Estimation is based on the equivalence between P-splines and linear mixed models, and variance/smoothing parameters are estimated based on restricted maximum likelihood (REML). The package enables users to estimate P-spline models with overlapping penalties. Based on the work described in Rodriguez-Alvarez et al. (2015) ; Rodriguez-Alvarez et al. (2019) , and Eilers and Marx (1996) . 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Package: r-cran-sotu Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3954 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sotu_1.0.4-1.ca2404.1_all.deb Size: 4014258 MD5sum: 1e88f57b58f6bc6adafbb5b856d89655 SHA1: 5ed376cfa2e4e506b64eebf5acad582baca2e1b8 SHA256: 45e63388fcaccc34a3d8bd35c10b80a519b8a259faeb28db05f5a29e6884b570 SHA512: 5a520ab42bb950c6f2604a9865b270d840202a71dd80620c9fe7eef319ae056bfa60b17e22f48a46bf3f387cad22aa0ad6ffab7840420a2922084797b4e056f6 Homepage: https://cran.r-project.org/package=sotu Description: CRAN Package 'sotu' (United States Presidential State of the Union Addresses) The President of the United States is constitutionally obligated to provide a report known as the 'State of the Union'. The report summarizes the current challenges facing the country and the president's upcoming legislative agenda. 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The main purpose is to provide a sound classification workflow, from annotating sound events in recordings to training and automating model usage in real-life situations. Using the package requires a pre-compiled collection of recordings with sound events of interest and it can be employed for: 1) Annotation: create a database of annotated recordings, 2) Training: prepare train data from annotated recordings and fit CNN models, 3) Classification: automate the use of the fitted model for classifying new recordings. By using automatic feature selection and a user-friendly GUI for managing data and training/deploying models, this package is intended to be used by a broad audience as it does not require specific expertise in statistics, programming or sound analysis. Please refer to the vignette for further information. Gibb, R., et al. (2019) Mac Aodha, O., et al. (2018) Stowell, D., et al. (2019) LeCun, Y., et al. (2012) . Package: r-cran-soundecology Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-oce, r-cran-ineq, r-cran-vegan, r-cran-tuner, r-cran-seewave Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-soundecology_1.3.3-1.ca2404.1_all.deb Size: 824350 MD5sum: 1b646d37b3b1332fc282ab127648fa17 SHA1: 7f14e113f97595d40d213d083c4a3fcf0d27fd5d SHA256: 54ea82cb885d65da40a91b8635246fcc3cb34fb8111bce3df5ae4668d6a8d229 SHA512: 9a85fde3876f9d86790bacae9811ba8dc37bef81257072af6e0235760f4682bcadcd24fad433d379c20c97509aa9c661f435cf48145803afd220983cd0f44b52 Homepage: https://cran.r-project.org/package=soundecology Description: CRAN Package 'soundecology' (Soundscape Ecology) Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings. 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Also offers tools for audio manipulation and acoustic analysis, including pitch tracking, spectral analysis, audio segmentation, pitch and formant shifting, etc. Includes four interactive web apps for synthesizing and annotating audio, manually correcting pitch contours, and measuring formant frequencies. Reference: Anikin (2019) . 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Eigensound is a multidisciplinary method focused on the direct comparison between stereotyped sounds from different species. 'SoundShape', in turn, provide the tools required for anyone to go from sound waves to Principal Components Analysis, using tools extracted from traditional bioacoustics (i.e. 'tuneR' and 'seewave' packages), geometric morphometrics (i.e. 'geomorph' package) and multivariate analysis (e.g. 'stats' package). For more information, please see Rocha and Romano (2021) and check 'SoundShape' repository on GitHub for news and updates . 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Package: r-cran-soyurt Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3985 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-soyurt_1.0.0-1.ca2404.1_all.deb Size: 4043540 MD5sum: d4a07d15886afc8b601620a338c73f84 SHA1: 2a9eb1d4967a7ab28af8b8413f01c8daf61422b8 SHA256: 952c80c3aedee239ca0cb088b9a1274cc91939c0b7ba703636f1155c6812abf8 SHA512: b9f91d60d6c40551ea0ffc2c845eb3df3284fca64b10a0a201a1ff0909975def17bf4fd050bd413767eb1b17088210df214b70cfe1f031a5648dd1481264f427 Homepage: https://cran.r-project.org/package=SoyURT Description: CRAN Package 'SoyURT' (USDA Northern Region Uniform Soybean Tests Dataset) Data sets used by 'Krause et al. (2022)' . It comprises phenotypic records obtained from the USDA Northern Region Uniform Soybean Tests from 1989 to 2019 for maturity groups II and III. 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Access tables describing catalogue of the Chinese known species of animals, plants, fungi, micro-organisms, and more. This package also supports access to catalogue of life global , China animal scientific database and catalogue of life Taiwan . The development of 'SP2000' package were supported by Biodiversity Survey and Assessment Project of the Ministry of Ecology and Environment, China <2019HJ2096001006>,Yunnan University's "Double First Class" Project and Yunnan University's Research Innovation Fund for Graduate Students <2019227>. Package: r-cran-sp23design Architecture: all Version: 0.9-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 595 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-survival Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-sp23design_0.9-1-1.ca2404.1_all.deb Size: 430098 MD5sum: 3a8c42a399595f525fd23ce23f31e992 SHA1: a2bedc71f0c52dd3df87c7e1f014311c2cc83318 SHA256: 1f4a7e784d5e706665ebdafe0f8b3e15e765e03733ca0e5679bcc03e1baedb24 SHA512: f757082ea66a0aa5504aa6bd99fe93201fbd3f1567393884a363cf35624651c0dee78a0944d048eaa308a2d94226775f24e7008df03f0803375418bfb495b8eb Homepage: https://cran.r-project.org/package=sp23design Description: CRAN Package 'sp23design' (Design and Simulation of Seamless Phase II-III Clinical Trials) Provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. 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Package: r-cran-spanishoddata Architecture: all Version: 0.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3817 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dbi, r-cran-digest, r-cran-dplyr, r-cran-duckdb, r-cran-fs, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lifecycle, r-cran-lubridate, r-cran-memoise, r-cran-openssl, r-cran-parallelly, r-cran-paws.storage, r-cran-purrr, r-cran-rdpack, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-stringr, r-cran-tibble, r-cran-xml2 Suggests: r-cran-cli, r-cran-flowmapblue, r-cran-flowmapper, r-cran-furrr, r-cran-future, r-cran-future.mirai, r-cran-hexsticker, r-cran-mapspain, r-cran-quarto, r-cran-remotes, r-cran-scales, r-cran-testthat, r-cran-tidyverse, r-cran-withr Filename: pool/dists/noble/main/r-cran-spanishoddata_0.2.4-1.ca2404.1_all.deb Size: 2740796 MD5sum: 0042cbbc35f6afe17c97bc44948464ba SHA1: a79641383c461de02ba80dc4bdba1338706959eb SHA256: b42eaa5ee284501a7d1d0e9716dfdf1b6a383500d0e4f4f03f74f77322db2fc5 SHA512: 355b59cf3032da3ba7a9efcb6648ff3420a967aea3991d824369c54baaada85b1a5adf5f495b61648c70d699e5941198b60de3abef8402464c81d61cb4f4da48 Homepage: https://cran.r-project.org/package=spanishoddata Description: CRAN Package 'spanishoddata' (Get Spanish Origin-Destination Data) Gain seamless access to origin-destination (OD) data from the Spanish Ministry of Transport, hosted at . This package simplifies the management of these large datasets by providing tools to download zone boundaries, handle associated origin-destination data, and process it efficiently with the 'duckdb' database interface. Local caching minimizes repeated downloads, streamlining workflows for researchers and analysts. Methods described in Kotov et al. (2026) . Extensive documentation is available at , offering guides on creating static and dynamic mobility flow visualizations and transforming large datasets into analysis-ready formats. Package: r-cran-spanova Architecture: all Version: 0.99.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geor, r-cran-shiny, r-cran-mass, r-cran-matrix, r-cran-scottknott, r-cran-car, r-cran-gtools, r-cran-multcomp, r-cran-multcompview, r-cran-mvtnorm, r-cran-dt, r-cran-shinybs, r-cran-xtable, r-cran-shinythemes, r-cran-rmarkdown, r-cran-knitr, r-cran-spdep, r-cran-ape, r-cran-spatialreg, r-cran-shinycssloaders Filename: pool/dists/noble/main/r-cran-spanova_0.99.4-1.ca2404.1_all.deb Size: 166474 MD5sum: 7f587066d02ef1f92d75417884c30de4 SHA1: 376044a2b476ed8f7469cd861239e0b77c13f7f8 SHA256: 7518c1ef861cbaa4e3a60c3061529c71790701935bf76fcc31a149bf47544137 SHA512: 7d4884912e44ea7a1b8528204bdb343c2adb39f763ee87523199326f8a3eae626c2cd3efc391bd8ed3a7008e7392fef7a535cd212e94f3f94c6105a727452b77 Homepage: https://cran.r-project.org/package=spANOVA Description: CRAN Package 'spANOVA' (Analysis of Field Trials with Geostatistics & Spatial AR Models) Perform analysis of variance when the experimental units are spatially correlated. There are two methods to deal with spatial dependence: Spatial autoregressive models (see Rossoni, D. F., & Lima, R. R. (2019) ) and geostatistics (see Pontes, J. M., & Oliveira, M. S. D. (2004) ). For both methods, there are three multicomparison procedure available: Tukey, multivariate T, and Scott-Knott. Package: r-cran-spantest Architecture: all Version: 1.1-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spantest_1.1-3-1.ca2404.1_all.deb Size: 79674 MD5sum: b2e859a9b3ada92c03164db11c81f210 SHA1: bc1591316cda6005e77b025e66f8f1f980ab4bce SHA256: 01f655eb48f3bf66215675e76f9a1a781adb0f1e2d447a4dc872267af531eca3 SHA512: c134ca3501c04e8e492a2b1f4d177f95fdc0dd3c09878aa81d59d43a40b1acabde436eda12cbd8769fbabada49555b1489895e457c54c15623921e473a88d88a Homepage: https://cran.r-project.org/package=spantest Description: CRAN Package 'spantest' (Mean-Variance Spanning Tests) Provides a comprehensive suite of portfolio spanning tests for asset pricing, such as Huberman and Kandel (1987) , Gibbons et al. (1989) , Kempf and Memmel (2006) , Pesaran and Yamagata (2024) , and Gungor and Luger (2016) . 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Package: r-cran-spark.sas7bdat Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparklyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spark.sas7bdat_1.4-1.ca2404.1_all.deb Size: 21022 MD5sum: 13397b67d0be546f1a615e4f747a3dda SHA1: 1657e2aae7c121265a8e1ca74f86940072caa4f5 SHA256: 43400fedbdd9b976f74c9b4c00037d72047a9c2e41f9eb7b3b0af838540a59aa SHA512: a8b8e13e9ca7bd89f0130455735092235f9aa5009802ed7d57362e49c30291a5bbee5da60509b093e97569a04fd4e272a816acf66fc82646a67d5a86180077eb Homepage: https://cran.r-project.org/package=spark.sas7bdat Description: CRAN Package 'spark.sas7bdat' (Read in 'SAS' Data ('.sas7bdat' Files) into 'Apache Spark') Read in 'SAS' Data ('.sas7bdat' Files) into 'Apache Spark' from R. 'Apache Spark' is an open source cluster computing framework available at . This R package uses the 'spark-sas7bdat' 'Spark' package () to import and process 'SAS' data in parallel using 'Spark'. Hereby allowing to execute 'dplyr' statements in parallel on top of 'SAS' data. Package: r-cran-sparkavro Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparklyr, r-cran-dplyr, r-cran-dbi Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparkavro_0.3.0-1.ca2404.1_all.deb Size: 28254 MD5sum: afe17ffecefdec6b36e2c62f6b7a9cd2 SHA1: 1b93a43c5047bb6ee1c7c5396c7b98f2c49724ea SHA256: f6c4af89a98a8d6fd76ca641eb49fd61ec33eb14a2f841227f1b42b9ca9f2292 SHA512: 60741559fccae3d7d633bb498af5136bf35d6ef59ea17f880709172c37d83b1ce18de76646dd248cbfa7a9f282211d9804fac381e96930d3b9d54389983d6e0e Homepage: https://cran.r-project.org/package=sparkavro Description: CRAN Package 'sparkavro' (Load Avro file into 'Apache Spark') Load Avro Files into 'Apache Spark' using 'sparklyr'. This allows to read files from 'Apache Avro' . 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Package: r-cran-sparkhail Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-sparklyr, r-cran-sparklyr.nested Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparkhail_0.1.1-1.ca2404.1_all.deb Size: 105584 MD5sum: 84d141b7a5d167014d0bb5ec63e96e17 SHA1: afe77a277b365733c9a728617e940d7736865796 SHA256: 8ace67a7de071dc1b2b7664424cbdcf3bdac765c543738e2214ed470d4df4ce0 SHA512: 498690f54f4714266e11527f05a523c9c91761b27c8a096448864573e15e25d84b6573cf74d2d13ee0f04c7a276c32a5ac211ac8a97e6328f379a247ad1a49e2 Homepage: https://cran.r-project.org/package=sparkhail Description: CRAN Package 'sparkhail' (A 'Sparklyr' Extension for 'Hail') 'Hail' is an open-source, general-purpose, 'python' based data analysis tool with additional data types and methods for working with genomic data, see . 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Package: r-cran-sparkline Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 762 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets Suggests: r-cran-formattable, r-cran-knitr, r-cran-rmarkdown, r-cran-shiny Filename: pool/dists/noble/main/r-cran-sparkline_2.0-1.ca2404.1_all.deb Size: 203670 MD5sum: 53761c55d11976f020b467edced30ce2 SHA1: 81c01234e209bb03591399c3c5582fb5553af8ac SHA256: 910230a526443a90b6ed32257ee8eef9a75f478f21cc329a3560f9564f052f67 SHA512: cbb35164f6b3af648c9323512fddf2db2b582c4bd988ce9d83a9812bd11736cc39c96e371bcbd4dc90664a4c2726a4332dd99c63c1f9c227a0912b4d2f56b115 Homepage: https://cran.r-project.org/package=sparkline Description: CRAN Package 'sparkline' ('jQuery' Sparkline 'htmlwidget') Include interactive sparkline charts in all R contexts with the convenience of 'htmlwidgets'. 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Package: r-cran-sparklyr Architecture: all Version: 1.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4749 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-generics, r-cran-globals, r-cran-glue, r-cran-httr, r-cran-jsonlite, r-cran-openssl, r-cran-purrr, r-cran-rlang, r-cran-rstudioapi, r-cran-tidyr, r-cran-tidyselect, r-cran-uuid, r-cran-vctrs, r-cran-withr, r-cran-xml2 Suggests: r-cran-arrow, r-cran-broom, r-cran-diffobj, r-cran-foreach, r-cran-ggplot2, r-cran-iterators, r-cran-janeaustenr, r-cran-lahman, r-cran-mlbench, r-cran-nnet, r-cran-nycflights13, r-cran-r6, r-cran-r2d3, r-cran-rcurl, r-cran-reshape2, r-cran-shiny, r-cran-parsnip, r-cran-testthat, r-cran-rprojroot Filename: pool/dists/noble/main/r-cran-sparklyr_1.9.4-1.ca2404.1_all.deb Size: 3801338 MD5sum: 6a5569f0cb44b0b7da2ac13587a692ad SHA1: 2c78ad695c056458c4e6913481f057471569d2d2 SHA256: e732d5c2be2bd2f44b4c54a56ce165dc75623b9df31523869fdf703bc835d396 SHA512: 597a3acf0d05a3faa6641c91e80ae1869a632f3c1744c61c853daaf75864ed16bd958e52f714040b56cc79025178793b269ef46b05d52e908ab40cc75d6729af Homepage: https://cran.r-project.org/package=sparklyr Description: CRAN Package 'sparklyr' (R Interface to Apache Spark) R interface to Apache Spark, a fast and general engine for big data processing, see . 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Package: r-cran-sparktf Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparklyr Suggests: r-cran-testthat, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-sparktf_0.1.0-1.ca2404.1_all.deb Size: 19404 MD5sum: 95f17aa7c6b64ea98222333c8a785a0f SHA1: e54f85ebeeaa7619abf5eace8c218490a2627273 SHA256: 48dc114e78e7cead0165f3355c1ac6d92ed386f08258fc9fd6194bd92cac05f1 SHA512: bbcef499b23ed47277f8214565a613af534c62513d73be09edcda0700348542edbfc296202f5a3cae188e595420a7b606b2d426fed3091a36222ced43c383d93 Homepage: https://cran.r-project.org/package=sparktf Description: CRAN Package 'sparktf' (Interface for 'TensorFlow' 'TFRecord' Files with 'Apache Spark') A 'sparklyr' extension that enables reading and writing 'TensorFlow' TFRecord files via 'Apache Spark'. 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Package: r-cran-sparqlr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-lintr, r-cran-rcmdcheck, r-cran-rmarkdown, r-cran-styler, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparqlr_0.1.0-1.ca2404.1_all.deb Size: 64024 MD5sum: ad35ad20782a38bcd2893e336dcfef61 SHA1: 280035e422936357bf1659834bc5e4cfbb769d45 SHA256: a963ad265add785e74629e36e5bef62a031b5dab24066290cbe32bf9adf020d6 SHA512: 299c1cccaff6500a8b07323aaeb1535eba1b613e9988801f75ddf30b3f11a951c302491aad36739b81fc6878fe7cbd810b2483e111cd8f3f4a857c1dcad5e119 Homepage: https://cran.r-project.org/package=sparqlr Description: CRAN Package 'sparqlr' (A SPARQL Client for R) Provides a client for running SPARQL queries directly from R. SPARQL (short for SPARQL Protocol and RDF Query Language) is a query language used to retrieve and manipulate data stored in RDF (Resource Description Framework) format. Package: r-cran-sparr Architecture: all Version: 2.3-16-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 655 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.utils, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-spatstat.random, r-cran-spatstat.univar, r-cran-doparallel, r-cran-foreach, r-cran-misc3d Suggests: r-cran-fftwtools Filename: pool/dists/noble/main/r-cran-sparr_2.3-16-1.ca2404.1_all.deb Size: 623084 MD5sum: e155d42912a7d492b22b205ef46bdbbf SHA1: d5d8e702d369215a29d8f99083ebeffe89015d21 SHA256: 08838e7b83074897715508e2aa1ea854f4a22ad1a55ab40923fa1b574ebafc11 SHA512: a210f36926a55edcbc222439bb7825409fa711ce6bea6bb9ba8ee0d285a2e8c74ccd61775d9aec06de02c26203f7c37f38d9653df321f0e2e25597445993025e Homepage: https://cran.r-project.org/package=sparr Description: CRAN Package 'sparr' (Spatial and Spatiotemporal Relative Risk) Provides functions to estimate kernel-smoothed spatial and spatio-temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) . Package: r-cran-sparrafairness Architecture: all Version: 0.1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1551 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-ranger, r-cran-mvtnorm, r-cran-cvauc, r-cran-ggplot2, r-cran-ggrepel, r-cran-patchwork, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparrafairness_0.1.0.0-1.ca2404.1_all.deb Size: 1479624 MD5sum: 21c7c03f33a062e5c7c435c48516a7a2 SHA1: 3a186e466e366077442798350b3ee8a35fcf9f09 SHA256: ebf7994ccb613215048756be3989c3ea862dc69a0ad73c1b0432002d1fbacb7c SHA512: 413a482c4ad503c37a4bad9b515819530fb607b2c01913cfd5f7b2ab03b8311a04b72dfad17d7d6a2d7e64b08e309da126f4fe6abdd7d791298b82591bddd9d8 Homepage: https://cran.r-project.org/package=SPARRAfairness Description: CRAN Package 'SPARRAfairness' (Analysis of Differential Behaviour of SPARRA Score AcrossDemographic Groups) The SPARRA risk score (Scottish Patients At Risk of admission and Re-Admission) estimates yearly risk of emergency hospital admission using electronic health records on a monthly basis for most of the Scottish population. This package implements a suite of functions used to analyse the behaviour and performance of the score, focusing particularly on differential performance over demographically-defined groups. It includes useful utility functions to plot receiver-operator-characteristic, precision-recall and calibration curves, draw stock human figures, estimate counterfactual quantities without the need to re-compute risk scores, to simulate a semi-realistic dataset. Our manuscript can be found at: . Package: r-cran-sparrpowr Architecture: all Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1249 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dofuture, r-cran-dorng, r-cran-fields, r-cran-foreach, r-cran-future, r-cran-iterators, r-cran-lifecycle, r-cran-sparr, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-terra Suggests: r-cran-geojsonsf, r-cran-ggmap, r-cran-ggplot2, r-cran-sf, r-cran-spatstat.data, r-cran-spelling, r-cran-testthat, r-cran-tidyterra, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-sparrpowr_0.2.9-1.ca2404.1_all.deb Size: 918844 MD5sum: bbc98ee94c3a9769c175bd37f6461e96 SHA1: f89d628cb1c743e624568cccb3cd2412bcd7b926 SHA256: 5000ac7530d5e9e7c9d439bb9740ad869178ea5a24a4feef128578fc3ee19ab6 SHA512: a50661e1a43c292e31e21ffdd740d9a1d1a4ac678e925594923f38fcbfbd5ca32e9790d535bc85394d69bf55020b189659bf9771ffbe1178dfa5040d3c547ec7 Homepage: https://cran.r-project.org/package=sparrpowR Description: CRAN Package 'sparrpowR' (Power Analysis to Detect Spatial Relative Risk Clusters) Calculate the statistical power to detect clusters using kernel-based spatial relative risk functions that are estimated using the 'sparr' package. Details about the 'sparr' package methods can be found in the tutorial: Davies et al. (2018) . Details about kernel density estimation can be found in J. F. Bithell (1990) . More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) . Package: r-cran-sparsebc Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2227 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glasso, r-cran-fields Filename: pool/dists/noble/main/r-cran-sparsebc_1.2-1.ca2404.1_all.deb Size: 2246428 MD5sum: fc0ea119837b0890f7f8c1d1cea37095 SHA1: aa585161844fe14e0983069d60fad87ea621ebe9 SHA256: e0fbb79d5fb6f8fdcd3ffa21372fe8622bf12177ea30899067bfcbb70759d55b SHA512: a405ba5550ff8825cfa147c41e12b988abd1b1754a94695948e11a896e22534f740d46803580cd82a76924898c9eb9436b6968ff998fc5f3949caac9b68f3613 Homepage: https://cran.r-project.org/package=sparseBC Description: CRAN Package 'sparseBC' (Sparse Biclustering of Transposable Data) Implements the sparse biclustering proposal of Tan and Witten (2014), Sparse biclustering of transposable data. Journal of Computational and Graphical Statistics 23(4):985-1008. Package: r-cran-sparsebiplots Architecture: all Version: 4.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-sparsepca Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparsebiplots_4.1.1-1.ca2404.1_all.deb Size: 50116 MD5sum: 0ff2e1ebec9454cbf0e4340808d9dbd4 SHA1: 7d068592314c2481309f83ca54dcd3cef607c4f0 SHA256: 04fd10204f7c78dedbe5ab99d9871f0c2760f6e5ce11758e075f0036a874e85a SHA512: 09f288d6a0618c35a25d83bedb07467f87262e69359497fdabbbc941f338b78dba0d9f77aa8899d679457feca30499661cd475343684a0262e0172447a390dbd Homepage: https://cran.r-project.org/package=SparseBiplots Description: CRAN Package 'SparseBiplots' ('HJ-Biplot' using Different Ways of Penalization Plotting with'ggplot2') The 'HJ-Biplot' is a multivariate method that represents high-dimensional data in a low-dimensional subspace, capturing most of the information’s variability in just a few dimensions. This package implements three new regularized versions of the HJ-Biplot: Ridge, LASSO, and Elastic Net. These versions introduce restrictions that shrink or zero-out variable weights to improve interpretability based on regularization theory. All methods provide graphical representations using 'ggplot2'. Package: r-cran-sparsecommunity Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rspectra Suggests: r-cran-irlba, r-cran-clue, r-cran-igraph, r-cran-igraphdata, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparsecommunity_0.1.1-1.ca2404.1_all.deb Size: 112292 MD5sum: 265e6793788f94911d88a5bfb583f897 SHA1: 6fbe0acad0b50aafdf7adae69f795b71efbbf341 SHA256: 2c3da27494f0e707b74234de31460228b8ddaf72335c3c411817e880ce1a1a7d SHA512: d9ac3426946f54fc029f571d4b2c83ee84d43bb707a4aa06317909f68cad301e15dba7d95f77af824dec4628571812bcfa1d1ee386d3096c62398d9af3f08f52 Homepage: https://cran.r-project.org/package=sparsecommunity Description: CRAN Package 'sparsecommunity' (Spectral Community Detection for Sparse Networks) Implements spectral clustering algorithms for community detection in sparse networks under the stochastic block model ('SBM') and degree-corrected stochastic block model ('DCSBM'), following the methods of Lei and Rinaldo (2015) . Provides a regularized normalized Laplacian embedding, spherical k-median clustering for 'DCSBM', standard k-means for 'SBM', simulation utilities for both models, and a misclustering rate evaluation metric. Also includes the 'NCAA' college football network of Girvan and Newman (2002) as a benchmark dataset, and the Bethe-Hessian community number estimator of Hwang (2023) . Package: r-cran-sparsecov Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-mvnfast, r-cran-rfast, r-cran-sparsemvn Filename: pool/dists/noble/main/r-cran-sparsecov_0.0.1-1.ca2404.1_all.deb Size: 39002 MD5sum: fb64dcb9c4be87e09648278098e521a4 SHA1: 84b4d6b9c8f0051e6bf8ebbce4ffad7eb3ba131a SHA256: 1ffa995fd54db7ccd0a12a8cecc23abf6bf0f715622150a568cc41d80bbae961 SHA512: 9dfd49f25bb4b66ae0392383933ef8d0702ce2816cefce30b2b9b09919eba2f987f05529c5344e09ffc6e729138cd5c6fef805fb7b8f1dcb353cf26cbf9dbb7e Homepage: https://cran.r-project.org/package=sparseCov Description: CRAN Package 'sparseCov' (Sparse Covariance Estimation Based on Thresholding) A sparse covariance estimator based on different thresholding operators. Package: r-cran-sparsedc Architecture: all Version: 0.1.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4785 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparsedc_0.1.17-1.ca2404.1_all.deb Size: 4818408 MD5sum: bb5102c9df55b87902bd55d3c4dc8914 SHA1: 6fbdfbc417eafdcc0fcaf47d7a45359dca8d1309 SHA256: 1099ece67e35950f04259ce966b1d36df3528290e0efbbf754ca6a971dd362a9 SHA512: 92c5511c15a0f136cb28e43769aebcff8eef33b87cfd727c03a2e71eeb9404ee17c5f6c3baf65e396fb3594355f83cb8f1f474702ace80f09a1a5eeca364c07b Homepage: https://cran.r-project.org/package=SparseDC Description: CRAN Package 'SparseDC' (Implementation of SparseDC Algorithm) Implements the algorithm described in Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data", Nucleic Acids Research, gkx1113, . This algorithm clusters samples from two different populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers. Package: r-cran-sparsediscrim Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bdsmatrix, r-cran-corpcor, r-cran-dplyr, r-cran-ggplot2, r-cran-mvtnorm, r-cran-rlang Suggests: r-cran-testthat, r-cran-mass, r-cran-covr, r-cran-modeldata, r-cran-spelling Filename: pool/dists/noble/main/r-cran-sparsediscrim_0.3.0-1.ca2404.1_all.deb Size: 340212 MD5sum: bb02a678803be4bb50968871ca33a2bd SHA1: bb456a5df3e83799205c15218628ca7558bc3dd1 SHA256: f657141ccf7d5401c7abe9ec65627d5b3a06ac78a8e7ee59b1c0299020e8e925 SHA512: 89f1f95dd5f1b5314a7598256348bd0c52a857a373538719222c28d595356147b4110738aa9a0b7b630c530be0599f272757021a480c58a403878e1fdf9d8c93 Homepage: https://cran.r-project.org/package=sparsediscrim Description: CRAN Package 'sparsediscrim' (Sparse and Regularized Discriminant Analysis) A collection of sparse and regularized discriminant analysis methods intended for small-sample, high-dimensional data sets. The package features the High-Dimensional Regularized Discriminant Analysis classifier from Ramey et al. (2017) . Other classifiers include those from Dudoit et al. (2002) , Pang et al. (2009) , and Tong et al. (2012) . Package: r-cran-sparseeigen Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparseeigen_0.1.0-1.ca2404.1_all.deb Size: 326586 MD5sum: 6c5f7a05466833ac9259e31a0e80759e SHA1: d64401ce5bc292e3242e3c0d68db1b951b76f4fe SHA256: 0d3caa638493ea3c1fe3fb6569d919b86284d7396769c7f6e98c452034b66d72 SHA512: 0ae813b89eeb26e5df3f60901676f7c8c530cc07bc7ba743f962ded53d43b012b45cf00887c578397cfe907cd3bf8fd8c4e0c68dc74abe6a3a5a3aeaef5a01b4 Homepage: https://cran.r-project.org/package=sparseEigen Description: CRAN Package 'sparseEigen' (Computation of Sparse Eigenvectors of a Matrix) Computation of sparse eigenvectors of a matrix (aka sparse PCA) with running time 2-3 orders of magnitude lower than existing methods and better final performance in terms of recovery of sparsity pattern and estimation of numerical values. Can handle covariance matrices as well as data matrices with real or complex-valued entries. Different levels of sparsity can be specified for each individual ordered eigenvector and the method is robust in parameter selection. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). "Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on Signal Processing . Package: r-cran-sparseflmm Architecture: all Version: 0.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-refund, r-cran-mass, r-cran-matrix, r-cran-data.table Filename: pool/dists/noble/main/r-cran-sparseflmm_0.4.2-1.ca2404.1_all.deb Size: 385016 MD5sum: b6cd1d62458c4fea21a180636f59c35c SHA1: 3ccd8d5827207a9fdd96b927969120590653cd0d SHA256: aeb8fc39cd8c97974ecc74a294aaef6af0f1e57d46908cef29d1ae10e4b9f01d SHA512: 20e092268867e84bc12b2a313696323f938380388a6dd11eebac1e9ef04d864fbf099c51ab5168b02be2ad2638072796a224bb716e96d07b236db780a030462a Homepage: https://cran.r-project.org/package=sparseFLMM Description: CRAN Package 'sparseFLMM' (Functional Linear Mixed Models for Irregularly or SparselySampled Data) Estimation of functional linear mixed models for irregularly or sparsely sampled data based on functional principal component analysis. Package: r-cran-sparsefunclust Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cluster Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparsefunclust_1.0.0-1.ca2404.1_all.deb Size: 129832 MD5sum: 67de88999a3db5d550ae7c2cd98480f5 SHA1: a742a4e4b02f70cbd002e285dd557b40c0b96e12 SHA256: a758c46755776b401c9136c1c1ce7ea1b1c9896df0b407dc38869c76430d7924 SHA512: 295fd6744ee9c3e4df92b28e31d2cb7d33b9c4864db20b3c41b673344cd4b85f3565a3635d5282a509a7d434fa4e35f1a48ddb82991a04e30f972ce98000ccb4 Homepage: https://cran.r-project.org/package=SparseFunClust Description: CRAN Package 'SparseFunClust' (Sparse Functional Clustering) Provides a general framework for performing sparse functional clustering as originally described in Floriello and Vitelli (2017) , with the possibility of jointly handling data misalignment (see Vitelli, 2019, ). Package: r-cran-sparsegfm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gfm, r-cran-mass, r-cran-irlba Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparsegfm_0.1.0-1.ca2404.1_all.deb Size: 61646 MD5sum: 3c2121e9b64c1588bcf3ef0e75691a89 SHA1: 0080421aa54f07b8028d8aaf4886a876e6909b87 SHA256: c482d7bfb2d3fb80e4ea5eeb5ab4ef3cd76c265bbc3028265f476f67a6176b58 SHA512: 146e9f622b36bc6a79693e9db0f75e3c4319d9f88f44543f11d83e6dd8b728ae5f6bf96654a31d78938051e961fd74126167e76e2c71a290bca6925c0fd0922f Homepage: https://cran.r-project.org/package=sparseGFM Description: CRAN Package 'sparseGFM' (Sparse Generalized Factor Models with Multiple Penalty Functions) Implements sparse generalized factor models (sparseGFM) for dimension reduction and variable selection in high-dimensional data with automatic adaptation to weak factor scenarios. The package supports multiple data types (continuous, count, binary) through generalized linear model frameworks and handles missing values automatically. It provides 12 different penalty functions including Least Absolute Shrinkage and Selection Operator (Lasso), adaptive Lasso, Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP), group Lasso, and their adaptive versions for inducing row-wise sparsity in factor loadings. Key features include cross-validation for regularization parameter selection using Sparsity Information Criterion (SIC), automatic determination of the number of factors via multiple information criteria, and specialized algorithms for row-sparse loading structures. The methodology employs alternating minimization with Singular Value Decomposition (SVD)-based identifiability constraints and is particularly effective for high-dimensional applications in genomics, economics, and social sciences where interpretable sparse dimension reduction is crucial. For penalty functions, see Tibshirani (1996) , Fan and Li (2001) , and Zhang (2010) . Package: r-cran-sparsegrid Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-statmod, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-sparsegrid_0.8.2-1.ca2404.1_all.deb Size: 174080 MD5sum: 28b6fac7263632c9eff7829c16dc7841 SHA1: 352705dd835ae425f86f309c65e412054e3f8956 SHA256: 6a1501504997a3022c9e42cdadaa33329b10d515ae9ec9a49bfb816e59be7794 SHA512: a664fbc6b53b01e000af1ccb1f5de90d5a8412177a218c766d45abda1b879dcaa349d7dd764b926b9a2130710897a8db1c916908c3360d1cf49368f1ada83fd3 Homepage: https://cran.r-project.org/package=SparseGrid Description: CRAN Package 'SparseGrid' (Sparse grid integration in R) SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de Package: r-cran-sparseindextracking Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2962 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bookdown, r-cran-knitr, r-cran-prettydoc, r-cran-rmarkdown, r-cran-r.rsp, r-cran-xts Filename: pool/dists/noble/main/r-cran-sparseindextracking_0.1.1-1.ca2404.1_all.deb Size: 2383010 MD5sum: a429f984de804a9b8378d06ea5f78ab7 SHA1: 8256d4dd955bb234a9227bf4c89ada8529f82bc6 SHA256: 34429394b8ba051050de933cbb3f95de91dc5ab466b03a824daa85a6fb3a471f SHA512: 5a571b5972d0cc30d1cb6b2683c05dbfb8ab2b450e37d0e7e00f719e360d0345612db57a122e1af5e1612502fdb61b9bd7d33d7cb041e458b717de069e5fe358 Homepage: https://cran.r-project.org/package=sparseIndexTracking Description: CRAN Package 'sparseIndexTracking' (Design of Portfolio of Stocks to Track an Index) Computation of sparse portfolios for financial index tracking, i.e., joint selection of a subset of the assets that compose the index and computation of their relative weights (capital allocation). The level of sparsity of the portfolios, i.e., the number of selected assets, is controlled through a regularization parameter. Different tracking measures are available, namely, the empirical tracking error (ETE), downside risk (DR), Huber empirical tracking error (HETE), and Huber downside risk (HDR). See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Feng, and D. P. Palomar, "Sparse Portfolios for High-Dimensional Financial Index Tracking," IEEE Trans. on Signal Processing, vol. 66, no. 1, pp. 155-170, Jan. 2018. . Package: r-cran-sparselda Architecture: all Version: 0.1-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 403 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-elasticnet, r-cran-mass, r-cran-mda Filename: pool/dists/noble/main/r-cran-sparselda_0.1-9-1.ca2404.1_all.deb Size: 380286 MD5sum: 2e3c513c848e30487f20f0d80c4cb361 SHA1: a27b1e3e490be6fddfdc214c177c2195407f0b9e SHA256: 7f04dff69e0d40e18b667dde1bba17a0e3c6295e7d0a4ae771b8f8ab385ee3fc SHA512: 53eff85e4347a1a54d3c3fd4b125c4e915899a3849701fc5c3a5f2e517ab3abc6afdd29784e36fd4c3f0c61d267b036bfc12188747657a2185674416ad78c57d Homepage: https://cran.r-project.org/package=sparseLDA Description: CRAN Package 'sparseLDA' (Sparse Discriminant Analysis) Performs sparse linear discriminant analysis for Gaussians and mixture of Gaussian models. Package: r-cran-sparselink Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 488 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-proc, r-cran-mvtnorm, r-cran-spls, r-cran-xrnet Suggests: r-cran-knitr, r-cran-testthat, r-cran-remotes, r-cran-glmtrans, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparselink_1.0.0-1.ca2404.1_all.deb Size: 171886 MD5sum: afcb5b439bd3dc76c2b571be58214ee5 SHA1: da2a0565cb4d6717d49bbcf86a791021d697507f SHA256: 21f2004d17bf09779fd059776093eda438a1cfb45bd361a645551846b570b857 SHA512: 406b0223d5443cf0c423ca2cb0b56e5c02d3ba13bbab8a491496c2a59042031119544c9332b2227684f9c0ee920d40f1a8fd80edb3aa859fa0fb08c7fd96ae55 Homepage: https://cran.r-project.org/package=sparselink Description: CRAN Package 'sparselink' (Sparse Regression for Related Problems) Estimates sparse regression models (i.e., with few non-zero coefficients) in high-dimensional multi-task learning and transfer learning settings, as proposed by Rauschenberger et al. (2025) . Package: r-cran-sparselrmatrix Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-rspectra Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparselrmatrix_0.1.0-1.ca2404.1_all.deb Size: 27404 MD5sum: 92d2c80095f5a2731778e58ce0e29b7b SHA1: 7ae0aee31ec8bf4360393cd26ebde1688111bc3a SHA256: a16f25e72a75bf2fab65e09baf79a9e3e64ec54431e3e2332fed1d4a655aa190 SHA512: 90d0a8ec5a7ebfe4e3d336da0d6ca581e68b517ea930fd8593555dafc9346b783e3cde5baf05d61cbbc6a3c7a9cdd33704a3b0ba75f348a79d92c42a1414ced4 Homepage: https://cran.r-project.org/package=sparseLRMatrix Description: CRAN Package 'sparseLRMatrix' (Represent and Use Sparse + Low Rank Matrices) Provides an S4 class for representing and interacting with sparse plus rank matrices. At the moment the implementation is quite spare, but the plan is eventually subclass Matrix objects. Package: r-cran-sparsematest Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glasso Filename: pool/dists/noble/main/r-cran-sparsematest_1.0.0-1.ca2404.1_all.deb Size: 41376 MD5sum: 0d3753198c70e443bb4bebed958b007b SHA1: d2b2730820d32478571f9eb2336d78053449b991 SHA256: 2c6f9eac8c6db43bb5af23673d4851dff835414d82d8b72f6abf25470e8f5d2f SHA512: 674e27de3ba8a1e19036af1712038a2d861edd6ea803c734aa22ff9e443ad990afc1460ed9fadb84c612fac5822f86bb67d3b2f0d5f1088b5e354d52e02e88ae Homepage: https://cran.r-project.org/package=sparseMatEst Description: CRAN Package 'sparseMatEst' (Sparse Matrix Estimation and Inference) The 'sparseMatEst' package provides functions for estimating sparse covariance and precision matrices with error control. A false positive rate is fixed corresponding to the probability of falsely including a matrix entry in the support of the estimator. It uses the binary search method outlined in Kashlak and Kong (2019) and in Kashlak (2019) . Package: r-cran-sparsemdc Architecture: all Version: 0.99.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4800 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dorng, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparsemdc_0.99.5-1.ca2404.1_all.deb Size: 4829744 MD5sum: e1de8e9be36b16abdc0b149a0bdf8170 SHA1: 4f89f79748b2f1a2a12aaf6da0d635570bd69d1c SHA256: 92279ca5d426cf010caea62a8f8311f75cbd6615fc6ce39e9b412f80fef4acde SHA512: 878fa897d8fe2a7b7bc2d1799717fd5100e605cb5f52fe32584e6634ad458b0bf83ef5057807e8b2fd5befecc19ce4590df61747897be22118074d205ba4eed8 Homepage: https://cran.r-project.org/package=SparseMDC Description: CRAN Package 'SparseMDC' (Implementation of SparseMDC Algorithm) Implements the algorithm described in Barron, M., and Li, J. (Not yet published). This algorithm clusters samples from multiple ordered populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseMDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers. Package: r-cran-sparsemse Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lpsolve, r-cran-rcapture Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sparsemse_2.0.1-1.ca2404.1_all.deb Size: 116136 MD5sum: 625e476eafd5d1a69883ee54bb1982a2 SHA1: fc8ca79f39a66823d214992f7d0802c73b4b0f01 SHA256: 50a9b2c68145bd8182c1a01e3333ef00beea2e52917e5e796377d86e5881603b SHA512: 274acbf74d6d7944071a3395b5d7789c19e4443c444bae318bf0e56c47eaa5b354116614afe5e244d91db2e031ecf793b8c27b88a592a75ba6d380c197171911 Homepage: https://cran.r-project.org/package=SparseMSE Description: CRAN Package 'SparseMSE' ('Multiple Systems Estimation for Sparse Capture Data') Implements the routines and algorithms developed and analysed in "Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges when there are Non-Overlapping Lists" Chan, L, Silverman, B. W., Vincent, K (2019) . This package explicitly handles situations where there are pairs of lists which have no observed individuals in common. It deals correctly with parameters whose estimated values can be considered as being negative infinity. It also addresses other possible issues of non-existence and non-identifiability of maximum likelihood estimates. Package: r-cran-sparsemvn Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-forcats, r-cran-knitr, r-cran-bookdown, r-cran-kableextra, r-cran-testthat, r-cran-scales, r-cran-trustoptim Filename: pool/dists/noble/main/r-cran-sparsemvn_0.2.2-1.ca2404.1_all.deb Size: 275256 MD5sum: 39d27c0ab1f00f4e1c6fc842bdb71b42 SHA1: 4ce75acadee2fe78a8ae23bee404a3129362316e SHA256: 3b71c4e8d64ef829da45f10781917c3e8fd530c3cd9d5765565c40d9fa0d0a62 SHA512: 787d5d72c918d6e7b147c83adb93d2b9a7e1fe37180335cef94568be38d3d6b29e84cc2a5cecb06727f605710ac00ddec5612a2187af9aa9b5262dd5b1b444ae Homepage: https://cran.r-project.org/package=sparseMVN Description: CRAN Package 'sparseMVN' (Multivariate Normal Functions for Sparse Covariance andPrecision Matrices) Computes multivariate normal (MVN) densities, and samples from MVN distributions, when the covariance or precision matrix is sparse. Package: r-cran-sparsepca Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsvd Filename: pool/dists/noble/main/r-cran-sparsepca_0.1.2-1.ca2404.1_all.deb Size: 49750 MD5sum: d51d102de703a0d5d784a0527b16cae3 SHA1: e9924f82327070d559fedcbd68cf07f532fda9d0 SHA256: da2dfdae9626b5af308cac0d5b4190e9668e6ed83d5dd9a38c13b881e5eec7b9 SHA512: 588548d632585c12bd4bac61d5b2f628c98623c3fc355cf5deb2a41ea1cfbc371825d41e91dbce6d32d5c5889b9ba47b14b3a8f56b619e985e6f7ce5ecd213ea Homepage: https://cran.r-project.org/package=sparsepca Description: CRAN Package 'sparsepca' (Sparse Principal Component Analysis (SPCA)) Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few 'active' (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data. The methods are discussed in detail by N. Benjamin Erichson et al. (2018) . Package: r-cran-sparsepp Architecture: all Version: 1.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-rcpp Filename: pool/dists/noble/main/r-cran-sparsepp_1.22-1.ca2404.1_all.deb Size: 111924 MD5sum: 66a220891d0526d45ce5212c660a741f SHA1: 10b8a786b25cb1f4cb5b158e8d80d8d7916aae0f SHA256: 4774cb8cd241136886423e867f1bd67123ccf9273be8c72f2550ba3f24bf028b SHA512: 2082a4b9a06d58b8403e55e6953646737c29dfa87eabc1aae0f554fd484003fbda16b9ad3c7deaa8732f22f26858730583a741af43eece275ea65d18d89ea1bb Homepage: https://cran.r-project.org/package=sparsepp Description: CRAN Package 'sparsepp' ('Rcpp' Interface to 'sparsepp') Provides interface to 'sparsepp' - fast, memory efficient hash map. It is derived from Google's excellent 'sparsehash' implementation. We believe 'sparsepp' provides an unparalleled combination of performance and memory usage, and will outperform your compiler's unordered_map on both counts. Only Google's 'dense_hash_map' is consistently faster, at the cost of much greater memory usage (especially when the final size of the map is not known in advance). Package: r-cran-sparser Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2376 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ncvreg, r-cran-rlang, r-cran-magrittr, r-cran-dplyr, r-cran-recipes Suggests: r-cran-survival, r-cran-knitr, r-cran-rmarkdown, r-cran-kableextra, r-cran-testthat, r-cran-covr, r-cran-modeldata, r-cran-mass Filename: pool/dists/noble/main/r-cran-sparser_0.3.2-1.ca2404.1_all.deb Size: 1666232 MD5sum: 5fdfb43b067ba5b5e5ce9c2f1489bcd8 SHA1: 52617405a31463da0cd6915d02c18533f7cdab6d SHA256: d1864eec14a1ef3b9c3bf0cd469e046d18b67af56b13cd8db7937cbc03544bc2 SHA512: 94fac4e74676d264f4c21ef07c9d33083e94d3e72c4c618ef7c09245a13d5418d0f118f38b7d1ad33a600abb995a9a0fc8cd0a3366878182644255c95bd53416 Homepage: https://cran.r-project.org/package=sparseR Description: CRAN Package 'sparseR' (Variable Selection under Ranked Sparsity Principles forInteractions and Polynomials) An implementation of ranked sparsity methods, including penalized regression methods such as the sparsity-ranked lasso, its non-convex alternatives, and elastic net, as well as the sparsity-ranked Bayesian Information Criterion. As described in Peterson and Cavanaugh (2022) , ranked sparsity is a philosophy with methods primarily useful for variable selection in the presence of prior informational asymmetry, which occurs in the context of trying to perform variable selection in the presence of interactions and/or polynomials. Ultimately, this package attempts to facilitate dealing with cumbersome interactions and polynomials while not avoiding them entirely. Typically, models selected under ranked sparsity principles will also be more transparent, having fewer falsely selected interactions and polynomials than other methods. Package: r-cran-sparsestep Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Filename: pool/dists/noble/main/r-cran-sparsestep_1.0.1-1.ca2404.1_all.deb Size: 49830 MD5sum: cdb4aed50f6ffdeaafecd3def14c41af SHA1: 5e6ae31d65aa20f5b74062136158e33bd74312fc SHA256: 094eac900457d2a3dd7ecff650b064c71483482bf1ba156450539cd7ddf4f701 SHA512: 25d2ab0ea1a482f6409dfb85fc3b2f3e6f84ccfb109cc64afe4037e3045903dd84ceb67a5ae1538c924a1f0f22ba49e0b3a563f2a67fb66c511115def35952c7 Homepage: https://cran.r-project.org/package=sparsestep Description: CRAN Package 'sparsestep' (SparseStep Regression) Implements the SparseStep model for solving regression problems with a sparsity constraint on the parameters. The SparseStep regression model was proposed in Van den Burg, Groenen, and Alfons (2017) . In the model, a regularization term is added to the regression problem which approximates the counting norm of the parameters. By iteratively improving the approximation a sparse solution to the regression problem can be obtained. In this package both the standard SparseStep algorithm is implemented as well as a path algorithm which uses golden section search to determine solutions with different values for the regularization parameter. Package: r-cran-sparsesurv Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r2jags, r-cran-coda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rjags, r-cran-rmarkdown, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-sparsesurv_0.1.1-1.ca2404.1_all.deb Size: 130536 MD5sum: c06b2f30743ff853aa9291683adea2ac SHA1: b225808d9e13e9f2d20c7ed424a6b2a72d83d9d9 SHA256: 626c713a7975a38c4b0b34ce6608dd9c7de2663d6397ac036c3946fc8640c710 SHA512: 6ba138aee16cd2ace974051641aee8b95b7e26471f50755b30941894b3cb640c04d9194fdce647627a31d02434b6b35ad349ec0c797ee0d421d905d0096d1175 Homepage: https://cran.r-project.org/package=sparsesurv Description: CRAN Package 'sparsesurv' (Forecasting and Early Outbreak Detection for Sparse Count Data) Functions for fitting, forecasting, and early detection of outbreaks in sparse surveillance count time series. Supports negative binomial (NB), self-exciting NB, generalise autoregressive moving average (GARMA) NB , zero-inflated NB (ZINB), self-exciting ZINB, generalise autoregressive moving average ZINB, and hurdle formulations. Climatic and environmental covariates can be included in the regression component and/or the zero-modified components. Includes outbreak-detection algorithms for NB, ZINB, and hurdle models, with utilities for prediction and diagnostics. Package: r-cran-sparsevar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-ncvreg, r-cran-doparallel, r-cran-glmnet, r-cran-ggplot2, r-cran-reshape2, r-cran-mvtnorm, r-cran-corpcor, r-cran-checkmate, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sparsevar_1.0.0-1.ca2404.1_all.deb Size: 230348 MD5sum: a3948610d5d828b0aadf6ad636fc8c4c SHA1: b876e879a9d9ce88adb0d4092a1078f6db0287cc SHA256: f11661311f7040cda9bac3623cddf95d8eba15904cd17210360bf99e3b0b3293 SHA512: 051d6c7fd1785d3d0b02433b8f37ed9154f8aae9c3de7c6e83ebcb7dea01314057b0fb19aa646449bdd55a68f707848fd32b885d1499090910dbcff1e816fc05 Homepage: https://cran.r-project.org/package=sparsevar Description: CRAN Package 'sparsevar' (Sparse VAR (Vector Autoregression) / VECM (Vector ErrorCorrection Model) Estimation) A wrapper for sparse VAR (Vector Autoregression) and VECM (Vector Error Correction Model) time series models estimation using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped Absolute Deviation) and MCP (Minimax Concave Penalty). Based on the work of Basu and Michailidis (2015) . Package: r-cran-sparsevfc Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2878 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pdist, r-cran-purrr Suggests: r-cran-ggplot2, r-cran-dplyr, r-cran-tibble, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sparsevfc_0.1.2-1.ca2404.1_all.deb Size: 2782434 MD5sum: 6cf92b711063925c3c58e7f64337e3dd SHA1: 8646ce3239f4cd8c09b0731c19ab356c93e96d8f SHA256: c4be31945e34c6ebc9c1ea3ad5304e0440b32fb4197ec400b627d9ae574feea8 SHA512: 0b73e2291163aadd9d57ca364b32a91fa60d3939602cb73ad6a43151230d0c6156e7c556abb5899cfccba01803da2c0d39b5eb4b651db883d6e15719875b1f66 Homepage: https://cran.r-project.org/package=SparseVFC Description: CRAN Package 'SparseVFC' (Sparse Vector Field Consensus for Vector Field Learning) The sparse vector field consensus (SparseVFC) algorithm (Ma et al., 2013 ) for robust vector field learning. Largely translated from the Matlab functions in . Package: r-cran-spartaas Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-factominer, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-plotly, r-cran-stringr, r-cran-colorspace, r-cran-shiny, r-cran-shinydashboard, r-cran-shinyjs, r-cran-shinyjqui, r-cran-fpc, r-cran-ggdendro, r-cran-htmltools, r-cran-htmlwidgets, r-cran-shinythemes, r-cran-explor, r-cran-shinywidgets, r-cran-scatterd3, r-cran-ks, r-cran-cluster, r-cran-leaflet, r-cran-ape, r-cran-mass, r-cran-ade4, r-cran-lmtest, r-cran-nor1mix, r-cran-shinycssloaders, r-cran-scales, r-cran-fastcluster, r-cran-nloptr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/noble/main/r-cran-spartaas_1.2.5-1.ca2404.1_all.deb Size: 2493122 MD5sum: eb8e28d518660cf50c2fd7f62f3493f2 SHA1: 5be84951a54430c7797c4591cadfeee606fd7d01 SHA256: 830a29bddf9cd8b5ee092f756411dba5923dafcf9e0bb6cbf9aa4c26df3a0f4e SHA512: f6c88d3fbc509913716dd3f5f81bb37d696125342f066aea76c40659f21cc033ed92ed971e648c732622e32dde9b76f73d5bf32ca35dda6a6664c5477c3186a5 Homepage: https://cran.r-project.org/package=SPARTAAS Description: CRAN Package 'SPARTAAS' (Statistical Pattern Recognition and daTing using ArchaeologicalArtefacts assemblageS) Statistical pattern recognition and dating using archaeological artefacts assemblages. Package of statistical tools for archaeology. hclustcompro()/perioclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021, ISBN:978-3-030-60103-4). mapclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021) . seriograph(): Desachy Bruno (2004) . cerardat(): Bellanger Lise, Husi Philippe (2012) . Package: r-cran-spatemr Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist, r-cran-sphet, r-cran-matrix Suggests: r-cran-testthat, r-cran-sp, r-cran-spdep Filename: pool/dists/noble/main/r-cran-spatemr_1.3.0-1.ca2404.1_all.deb Size: 69092 MD5sum: 940f1d2bc9532932b24554a8c7c7416b SHA1: 1e1f383dc6ecdd576c8567646fecebe148b6dd34 SHA256: b86f5fe7e1021a5441283d365533388cfc3e02e3fc9c27b721149baf2ed35416 SHA512: 598b9a9ea33c4a3d464351e3c6b44b39ee8ff195b10c44eccd73443bdd11cbf01380a51349bc1f7fe3cdffe452e1cbb8fe15209fccff26239812b97f297a41a2 Homepage: https://cran.r-project.org/package=spatemR Description: CRAN Package 'spatemR' (Generalized Spatial Autoregresive Models for Mean and Variance) Modeling spatial dependencies in dependent variables, extending traditional spatial regression approaches. It allows for the joint modeling of both the mean and the variance of the dependent variable, incorporating semiparametric effects in both models. Based on generalized additive models (GAM), the package enables the inclusion of non-parametric terms while maintaining the classical theoretical framework of spatial regression. Additionally, it implements the Generalized Spatial Autoregression (GSAR) model, which extends classical methods like logistic Spatial Autoregresive Models (SAR), probit Spatial Autoregresive Models (SAR), and Poisson Spatial Autoregresive Models (SAR), offering greater flexibility in modeling spatial dependencies and significantly improving computational efficiency and the statistical properties of the estimators. Related work includes: a) J.D. Toloza-Delgado, Melo O.O., Cruz N.A. (2024). "Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices". . b) Cruz, N. A., Toloza-Delgado, J. D., Melo, O. O. (2024). "Generalized spatial autoregressive model". . Package: r-cran-spatentropy Architecture: all Version: 2.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat, r-cran-spatstat.geom, r-cran-spatstat.random Filename: pool/dists/noble/main/r-cran-spatentropy_2.2-4-1.ca2404.1_all.deb Size: 453024 MD5sum: acd0807315f74426d0ecb2a12f5f27c3 SHA1: 42afa9a0a3ea2ba658f457b1b36a13a82335d631 SHA256: 3621482db8c575c2d7c20fd9c802f0de0979760b8bd439460ed4b8956ee39124 SHA512: 4e13358f19cc51beff0ff943967ee45382463ff50c39092b8c9483767a3208783b61fb8cd74cb6128601c60df69e10dfdf8adbd176f036ac29360efb4816708f Homepage: https://cran.r-project.org/package=SpatEntropy Description: CRAN Package 'SpatEntropy' (Spatial Entropy Measures) The heterogeneity of spatial data presenting a finite number of categories can be measured via computation of spatial entropy. Functions are available for the computation of the main entropy and spatial entropy measures in the literature. They include the traditional version of Shannon's entropy (Shannon, 1948 ), Batty's spatial entropy (Batty, 1974 ), O'Neill's entropy (O'Neill et al., 1998 ), Li and Reynolds' contagion index (Li and Reynolds, 1993 ), Karlstrom and Ceccato's entropy (Karlstrom and Ceccato, 2002 ), Leibovici's entropy (Leibovici, 2009 ), Parresol and Edwards' entropy (Parresol and Edwards, 2014 ) and Altieri's entropy (Altieri et al., 2018, ). Full references for all measures can be found under the topic 'SpatEntropy'. The package is able to work with lattice and point data. The updated version works with the updated 'spatstat' package (>= 3.0-2). Package: r-cran-spatest Architecture: all Version: 3.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spatest_3.1.2-1.ca2404.1_all.deb Size: 120430 MD5sum: 79003167263154e63694eec72cdd1f65 SHA1: 3a7aba3f259175e59ee329b8dbab4b61d09c1b7b SHA256: f52be1983e223d8aa635cec05765ab536299eb53b2601078239fa8144c497b71 SHA512: 6f8137ac6ec7812a1b14a64d1e44de1a36c43ecaf9341a523e8fa8dac4a4929aed6125d6528907cb30208223a3aa6d52aa9494339501763ea7fe2e8708fb59d8 Homepage: https://cran.r-project.org/package=SPAtest Description: CRAN Package 'SPAtest' (Score Test and Meta-Analysis Based on Saddlepoint Approximation) Performs score test using saddlepoint approximation to estimate the null distribution. Also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results. For the latest version, please check . Package: r-cran-spatfd Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3720 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plotly, r-cran-dplyr, r-cran-proxy, r-cran-reshape, r-cran-gstat, r-cran-sp, r-cran-fda, r-cran-sf, r-cran-mass, r-cran-geor, r-cran-tidyr, r-cran-fda.usc Filename: pool/dists/noble/main/r-cran-spatfd_0.0.1-1.ca2404.1_all.deb Size: 3741456 MD5sum: 18241d94973a8427972db506f3881744 SHA1: d05812121765701c3bd6f87d178a7a91f3498ca6 SHA256: 82d2c5f4422006a5f09efca3629dd05fe55f16efbfa32436f7559f68f636fbe4 SHA512: de42fa4db1bcd1a591c7b805e25dd96d285f7b62e09320cc33e403f59e321180e8e391d4c8cafb0440bec5dfc41ac32352a03c04ce775ddad7fb99d7fa2d8a14 Homepage: https://cran.r-project.org/package=SpatFD Description: CRAN Package 'SpatFD' (Functional Geostatistics: Univariate and Multivariate FunctionalSpatial Prediction) Performance of functional kriging, cokriging, optimal sampling and simulation for spatial prediction of functional data. The framework of spatial prediction, optimal sampling and simulation are extended from scalar to functional data. 'SpatFD' is based on the Karhunen-Loève expansion that allows to represent the observed functions in terms of its empirical functional principal components. Based on this approach, the functional auto-covariances and cross-covariances required for spatial functional predictions and optimal sampling, are completely determined by the sum of the spatial auto-covariances and cross-covariances of the respective score components. The package provides new classes of data and functions for modeling spatial dependence structure among curves. The spatial prediction of curves at unsampled locations can be carried out using two types of predictors, and both of them report, the respective variances of the prediction error. In addition, there is a function for the determination of spatial locations sampling configuration that ensures minimum variance of spatial functional prediction. There are also two functions for plotting predicted curves at each location and mapping the surface at each time point, respectively. References Bohorquez, M., Giraldo, R., and Mateu, J. (2016) , Bohorquez, M., Giraldo, R., and Mateu, J. (2016) , Bohorquez M., Giraldo R. and Mateu J. (2021) . Package: r-cran-spatgc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 87 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-spdep, r-cran-sf Filename: pool/dists/noble/main/r-cran-spatgc_0.1.0-1.ca2404.1_all.deb Size: 56634 MD5sum: ff09d6e9e98cf36047f28b26823cea9d SHA1: 646201aa8598185dc641c3be054976f47df92b18 SHA256: c8ac312436988376e0416bdccd25eb6d4a212fc58179c6a0e9c9bf297f53d22b SHA512: eef2a9e6ec75c0a8b16864a8ada1b51d8473b36bd3585a18dce5a5c536f665e4c994401aa6a644a3a738edad17f1672846db632cd6671c3318c94e005a0e63ac Homepage: https://cran.r-project.org/package=SpatGC Description: CRAN Package 'SpatGC' (Bayesian Modeling of Spatial Count Data) Provides a collection of functions for preparing data and fitting Bayesian count spatial regression models, with a specific focus on the Gamma-Count (GC) model. The GC model is well-suited for modeling dispersed count data, including under-dispersed or over-dispersed counts, or counts with equivalent dispersion, using Integrated Nested Laplace Approximations (INLA). The package includes functions for generating data from the GC model, as well as spatially correlated versions of the model. See Nadifar, Baghishani, Fallah (2023) . Package: r-cran-spatgeom Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-sf, r-cran-dplyr, r-cran-lwgeom, r-cran-cowplot, r-cran-purrr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatgeom_0.3.0-1.ca2404.1_all.deb Size: 46618 MD5sum: e0f4d0441141fd6b513036c937e5dea1 SHA1: bad962eb3ec9cd2c3b3322f459036b14338c2d29 SHA256: 3ee03deb7069649e36a64c0c66821e6515c66ad0589e2c132bcf34629f765bf6 SHA512: f167115561e5da755675cbfb65c6af396ec2caa22cb37a022a0fa88f424e4f6ca7ec23e436f33fffce9d7ad13ac11e3594f4fdf80530fde5ecbcfcc545184df7 Homepage: https://cran.r-project.org/package=spatgeom Description: CRAN Package 'spatgeom' (Geometric Spatial Point Analysis) The implementation to perform the geometric spatial point analysis developed in Hernández & Solís (2022) . It estimates the geometric goodness-of-fit index for a set of variables against a response one based on the 'sf' package. The package has methods to print and plot the results. Package: r-cran-spatgrid Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-sf, r-cran-qpdf Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatgrid_0.1.0-1.ca2404.1_all.deb Size: 25204 MD5sum: 736dcb6e2f9162422116bc11b2886578 SHA1: 3bd38783f33321e92f0a34c32b4f1aabf0dbec5a SHA256: cc3b41adec188dd2486b371c45e872f7b00aa930a6159372ad9fafce10ee8b70 SHA512: dd3bec0bfd909d2e1e1aa426bafb3af6c8e723ef7c7b611d3bd1ef5ff5a88763921c1bb1ae809d8a59dc285c03163630707fdab304a1c75ade041a923ba02f10 Homepage: https://cran.r-project.org/package=SpatGRID Description: CRAN Package 'SpatGRID' (Spatial Grid Generation from Longitude and Latitude List) The developed function is designed for the generation of spatial grids based on user-specified longitude and latitude coordinates. The function first validates the input longitude and latitude values, ensuring they fall within the appropriate geographic ranges. It then creates a polygon from the coordinates and determines the appropriate Universal Transverse Mercator zone based on the provided hemisphere and longitude values. Subsequently, transforming the input Shapefile to the Universal Transverse Mercator projection when necessary. Finally, a spatial grid is generated with the specified interval and saved as a Shapefile. For method details see, Brus,D.J.(2022).. The function takes into account crucial parameters such as the hemisphere (north or south), desired grid interval, and the output Shapefile path. The developed function is an efficient tool, simplifying the process of empty spatial grid generation for applications such as, geo-statistical analysis, digital soil mapping product generation, etc. Whether for environmental studies, urban planning, or any other geo-spatial analysis, this package caters to the diverse needs of users working with spatial data, enhancing the accessibility and ease of spatial data processing and visualization. Package: r-cran-spathial Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2534 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-igraph, r-cran-matrixstats, r-cran-mass, r-cran-rtsne, r-cran-class, r-cran-knitr, r-cran-rmarkdown, r-cran-irlba Filename: pool/dists/noble/main/r-cran-spathial_0.1.2-1.ca2404.1_all.deb Size: 1891776 MD5sum: dcfc59db46553256159482e4f26f1c24 SHA1: 72304d3f4b682eb4645d5c4a96f81edf94835af3 SHA256: ec470252d52853bdacd185d2981af62f53b6941dc2fb166148898643808b1ede SHA512: d0a918b94fadec867c1c491a36e2ac3e7be57d7400bb4eeaaa980c7d853a87742ed4e69a289f39a6bf2ed4b2cbb9304221719850b7a37819aa028275976a3cc0 Homepage: https://cran.r-project.org/package=spathial Description: CRAN Package 'spathial' (Evolutionary Analysis) A generic tool for manifold analysis. It allows to infer a relevant transition or evolutionary path which can highlights the features involved in a specific process. 'spathial' can be useful in all the scenarios where the temporal (or pseudo-temporal) evolution is the main problem (e.g. tumor progression). The algorithm for finding the principal path is described in: Ferrarotti et al., (2019) ." Package: r-cran-spatialacc Architecture: all Version: 0.1-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Filename: pool/dists/noble/main/r-cran-spatialacc_0.1-6-1.ca2404.1_all.deb Size: 51132 MD5sum: 1c3593dbca2c3deb02f2808de853a755 SHA1: 82f6fdf2ba0901887f48698e588cced07bf1512a SHA256: 7e6d075a5b86e6a0aa754950ad37258794dda646240138193377d32acdc6a110 SHA512: 672a8df5a599234f53f75db2dde7ee1c593960808f15dfb4e8ceb3de1ae007dd5effaf0be16adb5c31dbebce5552cdac78996a6271d9ed6802660069e87e6be9 Homepage: https://cran.r-project.org/package=SpatialAcc Description: CRAN Package 'SpatialAcc' (Spatial Accessibility Measures) Provides a set of spatial accessibility measures from a set of locations (demand) to another set of locations (supply). It aims, among others, to support research on spatial accessibility to health care facilities. Includes the locations and some characteristics of major public hospitals in Greece. Package: r-cran-spatialatomizer Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5225 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nimble, r-cran-sp, r-cran-sf, r-cran-spdep, r-cran-mass, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-reshape2, r-cran-coda, r-cran-biasedurn, r-cran-raster Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tigris Filename: pool/dists/noble/main/r-cran-spatialatomizer_0.2.8-1.ca2404.1_all.deb Size: 4712414 MD5sum: b9ce3fa8520f4ba8e39fcbc3999f7f67 SHA1: ff7d29ae8aca157f765768b6b70c7e7e5c4e2348 SHA256: d1ff5b586a7f72a1fb06e269e3353edf86db50753d4283233522f7012a87450c SHA512: 54f2019fdc743d2edd576197c9316143531b173a46d52993f96eceac62efc29440277f1f981845af2783d1fe3bf5d773d4657c92a1df42a16dd9ff439bef6d09 Homepage: https://cran.r-project.org/package=spatialAtomizeR Description: CRAN Package 'spatialAtomizeR' (Spatial Analysis with Misaligned Data Using Atom-BasedRegression Models) Implements atom-based regression models (ABRM) for analyzing spatially misaligned data. Provides functions for simulating misaligned spatial data, preparing NIMBLE model inputs, running MCMC diagnostics, and providing results. All main functions return S3 objects with print(), summary(), and plot() methods for intuitive result exploration. Methods originally described in Mugglin et al. (2000) , further investigated in Trevisani & Gelfand (2013), and applied in Nethery et al. (2023) . Package: r-cran-spatialcatalogueviewer Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-leaflet, r-cran-leaflet.extras, r-cran-shiny, r-cran-shinythemes, r-cran-dt Filename: pool/dists/noble/main/r-cran-spatialcatalogueviewer_0.2.1-1.ca2404.1_all.deb Size: 38296 MD5sum: 7880ba47148062cb6802b3e2c3e61592 SHA1: e01fe772331bd273d50a006c5f1f9fa817664c0c SHA256: 9160ce32c24518962c2149395c0aaf980ad524ae6ae65b6310e28531f39b290b SHA512: bcc5c2381b1cbd0393f2de2d5595dcac3142d0ea14cfe8e0bfe79baf4f5686e492fb8cb51e48c575a730c7d9f1c3e25d09aa7cd5be3b66d7a031e274f09a506f Homepage: https://cran.r-project.org/package=spatialCatalogueViewer Description: CRAN Package 'spatialCatalogueViewer' (A 'Shiny' Tool to Create Interactive Catalogues for GeospatialData) Seamlessly create interactive online catalogues for geospatial data. Items can be mapped as points or areas and retrieved using either a map or a dynamic table with search form and optional column filters. Package: r-cran-spatialcovariance Architecture: all Version: 0.6-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spatialcovariance_0.6-9-1.ca2404.1_all.deb Size: 95260 MD5sum: 84f6663245bc94778db13e50937baae2 SHA1: 1d143dba433a31986821d23974ed90f9e16c6e4a SHA256: 4aab47a57d35ce803061f9335ecdb056b49fb24bc6dc4ff7241fefe33124d68c SHA512: 8d90edb280be4500da506fcece55b6d2dd64e537458b225a194fdb5b3fb0315e59004f38988ddd742056ecc7848cc3d57e01c3e8e770a75daf89b5c706e75be2 Homepage: https://cran.r-project.org/package=spatialCovariance Description: CRAN Package 'spatialCovariance' (Computation of Spatial Covariance Matrices for Data onRectangles) Functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units. This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration. Package: r-cran-spatialdata Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4366 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-terra Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatialdata_1.0.1-1.ca2404.1_all.deb Size: 4419880 MD5sum: 40d3c456ac51027a6500dfdb71eab5a5 SHA1: 5f7eb8a400e657afbb5f2c38117b5bf5806ae1c6 SHA256: fcd975e0a61cf846282a5077f78f0eef417b7fda461f47b03d45eb4ef5e2c832 SHA512: 07d17a0099f93bd8e888bedd211874ea52d03f78baaeeb2d839ab5bc3886ad638a53e2487eba6ce9cb8b053a014d670e5f693ca972dddff554b07e886ffea2ac Homepage: https://cran.r-project.org/package=spatialData Description: CRAN Package 'spatialData' (Spatial Datasets for Ecological Modeling) Provides spatial datasets ready to use for ecological modelling and raster companion data for prediction: Neanderthal presence during the Last Interglacial (Benito et al. 2017 ); Plant diversity metrics for the World's Ecoregions (Maestre et al. 2021 ); tree richness across the Americas (Benito et al. 2013 ); plant communities from the Sierra Nevada (Spain) with future climate scenarios (Benito et al. 2013 ); butterfly-plant interaction data from Sierra Nevada (Spain) (Benito et al. 2011 ); plant species occurrences in Andalusia (Spain) (Benito et al. 2014 ); presence of the plant Linaria nigricans and greenhouses (Benito et al. 2009 ); global NDVI and environmental predictors, and European oak species occurrences. All datasets include pre-processed environmental predictors ready for statistical modelling. Package: r-cran-spatialddls Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3835 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang, r-cran-grr, r-cran-matrix, r-bioc-spatialexperiment, r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-bioc-zinbwave, r-cran-pbapply, r-bioc-s4vectors, r-cran-dplyr, r-cran-reshape2, r-cran-gtools, r-cran-reticulate, r-cran-keras, r-cran-tensorflow, r-cran-fnn, r-cran-ggplot2, r-cran-ggpubr, r-bioc-scran, r-bioc-scuttle Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocparallel, r-bioc-rhdf5, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-hdf5array, r-cran-testthat, r-bioc-complexheatmap, r-bioc-bluster, r-cran-lsa, r-cran-irlba Filename: pool/dists/noble/main/r-cran-spatialddls_1.0.3-1.ca2404.1_all.deb Size: 3264814 MD5sum: 844aababa46ed2cfd06e46c43c777f35 SHA1: 066d3597c53773bd1e0ea96dd5f2c49f6852cc10 SHA256: 6a08585d0541903ec8f2aedce95a3bf81daf3baf90bc725754b73924b816b01d SHA512: d2338cd2e3b835d75eb4ded5a9b968af64e211f0943d69d49bdff8150a93ef5ef518705632c53bbeef3c2d64c1cd2bc39f679fc5dfb2cfa2f9f9b40ea9143c3d Homepage: https://cran.r-project.org/package=SpatialDDLS Description: CRAN Package 'SpatialDDLS' (Deconvolution of Spatial Transcriptomics Data Based on NeuralNetworks) Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) and Mañanes et al. (2024) to get an overview of the method and see some examples of its performance. Package: r-cran-spatialdownscaling Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1777 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensorflow, r-cran-keras3, r-cran-magrittr, r-cran-rdpack, r-cran-raster, r-cran-abind Filename: pool/dists/noble/main/r-cran-spatialdownscaling_0.1.2-1.ca2404.1_all.deb Size: 1781060 MD5sum: 1965a040afa0ea81b45dc069d8d50047 SHA1: 8e4f843fe12aeb5adbf86461bfa062319140302c SHA256: 3ea7a53abe7a3a3749d4b6b8786e7c8575bded171169cd2cdd6322e8677255ee SHA512: ae556e3659e0f1c6c22ceb96233224a94e792ba7c811922dec09c1c1faba4b8046dd965c6465496795a169b6dc7b55f3ca6eed166f2e183c30359f78b7f03488 Homepage: https://cran.r-project.org/package=SpatialDownscaling Description: CRAN Package 'SpatialDownscaling' (Methods for Spatial Downscaling Using Deep Learning) The aim of the spatial downscaling is to increase the spatial resolution of the gridded geospatial input data. This package contains two deep learning based spatial downscaling methods, super-resolution deep residual network (SRDRN) (Wang et al., 2021 ) and UNet (Ronneberger et al., 2015 ), along with a statistical baseline method bias correction and spatial disaggregation (Wood et al., 2004 ). The SRDRN and UNet methods are implemented to optionally account for cyclical temporal patterns in case of spatio-temporal data. For more details of the methods, see Sipilä et al. (2025) . Package: r-cran-spatialeco Architecture: all Version: 2.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2320 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-terra Suggests: r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-spdep, r-cran-ks, r-cran-cluster, r-cran-readr, r-cran-rcurl, r-cran-rann, r-cran-rms, r-cran-yaimpute, r-cran-mgcv, r-cran-zyp, r-cran-spatialpack, r-cran-mass, r-cran-caret, r-cran-dplyr, r-cran-earth, r-cran-matrix, r-cran-gstat, r-cran-spatstat.data, r-cran-units, r-cran-sp, r-cran-stringr, r-cran-lwgeom, r-cran-geodata Filename: pool/dists/noble/main/r-cran-spatialeco_2.0-5-1.ca2404.1_all.deb Size: 2158002 MD5sum: 9a99457ece0d8d4c027fe54885882b76 SHA1: 2547332178c01b40a498612ed59b1f652d945a27 SHA256: 925270c8e171a448d1fc8b88cfbd6eb527a6ebdfba63af84f2b568676a4356b0 SHA512: 19e8c4855ce07180e1338fe1be2e814f67519a39b78447d8f66ed0f387360d9da3daf4906fcb3c6e6cd5dc6581fa8d75db6f0b8e71cc65b144b344b6876228f3 Homepage: https://cran.r-project.org/package=spatialEco Description: CRAN Package 'spatialEco' (Spatial Analysis and Modelling Utilities) Utilities to support spatial data manipulation, query, sampling and modelling in ecological applications. Functions include models for species population density, spatial smoothing, multivariate separability, point process model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and analysis, auto-logistic modeling, sampling models, cluster optimization, statistical exploratory tools and raster-based metrics. Package: r-cran-spatialfdar Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fda, r-cran-rgl, r-cran-geometry, r-cran-knitr, r-cran-rmarkdown Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-spatialfdar_1.0.0-1.ca2404.1_all.deb Size: 201158 MD5sum: 85a33ba850dfe76233b1b4bf3f11adf3 SHA1: 2bc6cf9b1b4b93426b8a9f7dd78ef7e679c43f90 SHA256: d9cfcb8ea3500a14e79add8661aa4c0799a98149caf88618fad4c9de57a867af SHA512: c93e941f40b6e136ee2561675e767e94e30980a9f883e516307eb2a1d17b5a8b4b582259fa3b0e568085ea22e0d1e999406313394a0e5412d3b52913eba87ec7 Homepage: https://cran.r-project.org/package=SpatialfdaR Description: CRAN Package 'SpatialfdaR' (Spatial Functional Data Analysis) Finite element modeling (FEM) uses meshes of triangles to define surfaces. A surface within a triangle may be either linear or quadratic. In the order one case each node in the mesh is associated with a basis function and the basis is called the order one finite element basis. In the order two case each edge mid-point is also associated with a basis function. Functions are provided for smoothing, density function estimation point evaluation and plotting results. Two papers illustrating the finite element data analysis are Sangalli, L.M., Ramsay, J.O., Ramsay, T.O. (2013) and Bernardi, M.S, Carey, M., Ramsay, J. O., Sangalli, L. (2018). Modelling spatial anisotropy via regression with partial differential regularization Journal of Multivariate Analysis, 167, 15-30. Package: r-cran-spatialfusion Architecture: all Version: 0.7-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 565 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-sp, r-cran-sf, r-cran-fields, r-cran-spam, r-cran-deldir Suggests: r-cran-testthat, r-cran-tmap, r-cran-r.rsp, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spatialfusion_0.7-2-1.ca2404.1_all.deb Size: 423590 MD5sum: da2e147e3e5446cb82006dc5ef51e00b SHA1: 01a17a1c69f0b97bea73bba43c1bd19247c0ec34 SHA256: 805372d9199976486cce171d4120b9d0b5c1c2933083a875664fde622fc6108a SHA512: f764fc06e184c7cfac6dc98c4a86f597fa21b991ac824d274069df3fa9eed097a0a61efc09840f34eab5088c47da28ac2914a9d1999c38b21bb7b0ad1ea3d87a Homepage: https://cran.r-project.org/package=spatialfusion Description: CRAN Package 'spatialfusion' (Multivariate Analysis of Spatial Data Using a Unifying SpatialFusion Framework) Multivariate modelling of geostatistical (point), lattice (areal) and point pattern data in a unifying spatial fusion framework. Details are given in Wang and Furrer (2021) . Model inference is done using either 'Stan' or 'INLA' . Package: r-cran-spatialgraph Architecture: all Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-cran-pracma, r-cran-sf, r-cran-shape, r-cran-sp, r-cran-splancs Filename: pool/dists/noble/main/r-cran-spatialgraph_1.0-4-1.ca2404.1_all.deb Size: 124950 MD5sum: 35d6d64674aaf4703fe885a344ca4188 SHA1: 54f58c8924cacfaee33331ca5485843a609a1e44 SHA256: 6eb0ba58b2493df20ae24b94a5d8634b3d8ff073295a7948e6797d5c9b468b6b SHA512: 3fdbbccd3b11f63f3873f34f34fad06faf1817dc2cbac90a05aaf03387d2d7ebe60df1084e1064aec6ef1282ccf1783011cd5d9e89e4c62d609bf2ba4e49370a Homepage: https://cran.r-project.org/package=SpatialGraph Description: CRAN Package 'SpatialGraph' (The SpatialGraph Class and Utilities) Provision of the S4 SpatialGraph class built on top of objects provided by 'igraph' and 'sp' packages, and associated utilities. See the documentation of the SpatialGraph-class within this package for further description. An example of how from a few points one can arrive to a SpatialGraph is provided in the function sl2sg(). Package: r-cran-spatialml Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ranger, r-cran-caret, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-spatialml_0.1.7-1.ca2404.1_all.deb Size: 65628 MD5sum: 93b4b132f262a9c834aadf060997e5be SHA1: 30828b4b65d3792a008794482e60b13b53890529 SHA256: f9fcec3d6a76bbc423a51517ce341983acaf536c6e45a977f04232b45399b445 SHA512: 0b3df7dceeff258432f4eb95b2a4ff97812edc3f0e174f2b62a627d19fe4e27f8d398d5c7dc2e2fa29599a4832e9722db8c0f948d88a124b5f6c39725c3a6b7f Homepage: https://cran.r-project.org/package=SpatialML Description: CRAN Package 'SpatialML' (Spatial Machine Learning) Implements a spatial extension of the random forest algorithm (Georganos et al. (2019) ). Allows for a geographically weighted random forest regression including a function to find the optical bandwidth. (Georganos and Kalogirou (2022) ). Package: r-cran-spatialpop Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-qpdf, r-cran-numbers Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatialpop_0.1.0-1.ca2404.1_all.deb Size: 22508 MD5sum: 6042638675bf1157ca72474910c7cde5 SHA1: 08b70312a637636f9155468a2a742c3d4c9faff9 SHA256: a99fc468d5616f556767536f8469bf4459c21247a2d61fcaec9ca49775486dc0 SHA512: a7f593c285f453c1e9f8f9e2aad51a92239376312359976bfc7f6523a9a688bf9698febe69943d18ed4684f6eef66ecb59f6c600eb6b0288ac76bda0d8989f5d Homepage: https://cran.r-project.org/package=SpatialPOP Description: CRAN Package 'SpatialPOP' (Generation of Spatial Data with Spatially Varying ModelParameter) A spatial population can be generated based on spatially varying regression model under the assumption that observations are collected from a uniform two-dimensional grid consist of (m * m) lattice points with unit distance between any two neighbouring points. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).. This spatially generated data can be used to test different issues related to the statistical analysis of spatial data. This generated spatial data can be utilized in geographically weighted regression analysis for studying the spatially varying relationships among the variables. Package: r-cran-spatialposition Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-sp, r-cran-isoband, r-cran-raster Suggests: r-cran-lwgeom, r-cran-doparallel, r-cran-foreach, r-cran-cartography, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spatialposition_2.1.3-1.ca2404.1_all.deb Size: 478804 MD5sum: 6a9d4e537a34051df337abfd7c4f3d51 SHA1: 2e71a8bc2697d313f162f20a9ea783dc08c10776 SHA256: dd1090683fec001fd783521e66a50332c2f0b04559a0a7ba3f950b2261c613ef SHA512: f6dee7c8b80e71a1ce01bd6c001a6b5d20cfed647371a30c24915c89ffc31ea39573856bdb73403037b4150118a84ab969298bab58750337c82fc3668d743717 Homepage: https://cran.r-project.org/package=SpatialPosition Description: CRAN Package 'SpatialPosition' (Spatial Position Models) Computes spatial position models: the potential model as defined by Stewart (1941) and catchment areas as defined by Reilly (1931) or Huff (1964) . Package: r-cran-spatialprobit Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-spdep, r-cran-spatialreg, r-cran-mvtnorm, r-cran-tmvtnorm Suggests: r-cran-runit, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatialprobit_1.0.4-1.ca2404.1_all.deb Size: 271992 MD5sum: bf8a2c19c2db8302961677c9c457fe63 SHA1: 9e872c20df6a945941c9e7afdcb86e27dde034ea SHA256: fbbafbf9c3e6eb1e4f50dd43abc30a58b2fc6c6e240b6eed535efee0b77355ee SHA512: 17bff52d1d856b2fdfcb70a2998c71e5396acb349474cbcf9e15bce0d4c8645096684d982a2dba904fd124a12f8b1884837444f0ddbba469348cca0dfaaa1dab Homepage: https://cran.r-project.org/package=spatialprobit Description: CRAN Package 'spatialprobit' (Spatial Probit Models) A collection of methods for the Bayesian estimation of Spatial Probit, Spatial Ordered Probit and Spatial Tobit Models. Original implementations from the works of 'LeSage and Pace' (2009, ISBN: 1420064258) were ported and adjusted for R, as described in 'Wilhelm and de Matos' (2013) . Package: r-cran-spatialrdd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2225 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-ggplot2, r-cran-rdrobust, r-cran-lmtest, r-cran-sandwich, r-cran-cowplot, r-cran-magrittr, r-cran-rlang, r-cran-broom Suggests: r-cran-knitr, r-cran-tmap, r-cran-rmarkdown, r-cran-testthat, r-cran-kableextra, r-cran-lfe, r-cran-stargazer Filename: pool/dists/noble/main/r-cran-spatialrdd_0.1.0-1.ca2404.1_all.deb Size: 1678414 MD5sum: 24c9d108c4113abf663705d4c9e786f6 SHA1: 94d411c470f7949aabe6f2659cd8198387457e47 SHA256: 0765f284812479a3f01eb176f210f7a6419f8116227e4607aa210c2088e326ce SHA512: 3942b23522b5a71abc070f4299796079568a7677c9c3237154364bc200fedff17294a3c21ef68e20a222330cde08038b2f0ee508ba1f65c49c8099cc16a1a611 Homepage: https://cran.r-project.org/package=SpatialRDD Description: CRAN Package 'SpatialRDD' (Conduct Multiple Types of Geographic Regression DiscontinuityDesigns) Spatial versions of Regression Discontinuity Designs (RDDs) are becoming increasingly popular as tools for causal inference. However, conducting state-of-the-art analyses often involves tedious and time-consuming steps. This package offers comprehensive functionalities for executing all required spatial and econometric tasks in a streamlined manner. Moreover, it equips researchers with tools for performing essential placebo and balancing checks comprehensively. The fact that researchers do not have to rely on 'APIs' of external 'GIS' software ensures replicability and raises the standard for spatial RDDs. Package: r-cran-spatialreg.hp Architecture: all Version: 0.0-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatialreg, r-cran-ggplot2, r-cran-vegan, r-cran-spdep Filename: pool/dists/noble/main/r-cran-spatialreg.hp_0.0-2-1.ca2404.1_all.deb Size: 45384 MD5sum: 82c485cfd1976dec57ecdbc0861b63ff SHA1: 59b98ab72d87be69019292f331f6fefc8c9a34f7 SHA256: 992a0de5622b8db860af7ab321bb17f05f65489b41567a1574fabbe5656fd1eb SHA512: c586bf058625d076e3b4153925483f9683d021566e678c7441d7efc047479474ebbde314e39e5f0ac94380b86b4a13a073f62fd827b3fc8a747e01a0a70010eb Homepage: https://cran.r-project.org/package=spatialreg.hp Description: CRAN Package 'spatialreg.hp' (Hierarchical Partitioning of R2 for Spatial SimultaneousAutoregressive Model) Conducts hierarchical partitioning to calculate individual contributions of spatial and predictors (groups) towards total R2 for spatial simultaneous autoregressive model. Package: r-cran-spatialregimes Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spdep, r-cran-quantreg, r-cran-gwmodel, r-cran-plm, r-cran-spatialreg Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-spatialregimes_1.2-1.ca2404.1_all.deb Size: 454198 MD5sum: 4f4edc0374e7c177e073540d2e7fe963 SHA1: aabe221b9338b40cdcb7b5e6561a43bd5c9b77e7 SHA256: 987a4d79347f800069404191d5db19f39ce1db3ebe344e66d0377f06c27c83c1 SHA512: a413477b43a406eba69859f7d357d6e171b3bcb1d3a6298580275b84f415ea5967100fa81fe30e79b5a1b74dbaf13b9d8c2ba23cfa88eb3a9e56050b8bc6d757 Homepage: https://cran.r-project.org/package=SpatialRegimes Description: CRAN Package 'SpatialRegimes' (Spatial Constrained Clusterwise Regression) A collection of functions for estimating spatial regimes, aggregations of neighboring spatial units that are homogeneous in functional terms. The term spatial regime, therefore, should not be understood as a synonym for cluster. More precisely, the term cluster does not presuppose any functional relationship between the variables considered, while the term regime is linked to a regressive relationship underlying the spatial process. Package: r-cran-spatialregroup Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-sf, r-cran-spdep, r-cran-igraph, r-cran-rlang Filename: pool/dists/noble/main/r-cran-spatialregroup_0.1.0-1.ca2404.1_all.deb Size: 54812 MD5sum: 423d8a9fec319e0f987ef6372e2d15d6 SHA1: 9ff38835ac5a5417b78a1798ebe6a81b053a851e SHA256: 39122a3b55d008296f92598387668402ce6c0137476a82dcc16ce13bbc3f1f3c SHA512: 5d5b2f51db6881dfe0dfdedb8303ec540459d6e86305123e8eabefc81c2bf4d5044a144a7bda90701389416dce57cae3e0e78dd02cf780a748920e0ae885f531 Homepage: https://cran.r-project.org/package=spatialRegroup Description: CRAN Package 'spatialRegroup' (Iterative Spatial Regrouping of Administrative Units byAttributive Affinity) Evaluates the statistical coherence of existing administrative partitions (e.g. inter-municipal groupings, districts) by identifying spatial units whose attributive profile is more similar to a neighbouring group than to their own. Border units are iteratively reassigned to the group they are most affine with, based on Euclidean or Mahalanobis distance computed on user-supplied numeric variables, with optional per-variable weighting and standardisation. Spatial contiguity is enforced throughout: isolated candidates are reintegrated into their original group, disconnected fragments are resolved, and empty groups are restored. Convergence is monitored via an eta-squared cohesion criterion. The resulting partition can be compared to the original administrative delineation using multilevel models, providing a quantitative measure of boundary inefficiency. Package: r-cran-spatialrf Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-tibble, r-cran-foreach, r-cran-doparallel, r-cran-ranger, r-cran-rlang, r-cran-tidyr, r-cran-tidyselect, r-cran-huxtable, r-cran-patchwork, r-cran-viridis Suggests: r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-spatialrf_1.1.5-1.ca2404.1_all.deb Size: 6976754 MD5sum: 09e0cf3c340d40527aca9069f35da32a SHA1: 0493772ace91a1fe5400f53a062ab543f8c785dc SHA256: 99571e3a7f97400e1a84c7c4c516207931f2aeb67693d6151acf4e1ec2d6bc79 SHA512: 909e2fc52f53633b37ecbe2591c4e69eed18a6e45e91e25125c7544925297dadd83ba5fd4d16f99cfaa0cfce8f07b6813a90191fd2bb6c88b295fa515ea8778b Homepage: https://cran.r-project.org/package=spatialRF Description: CRAN Package 'spatialRF' (Easy Spatial Modeling with Random Forest) Automatic generation and selection of spatial predictors for Random Forest models fitted to spatially structured data. Spatial predictors are constructed from a distance matrix among training samples using Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 ) or the RFsp approach (Hengl et al. ). These predictors are used alongside user-supplied explanatory variables in Random Forest models. The package provides functions for model fitting, multicollinearity reduction, interaction identification, hyperparameter tuning, evaluation via spatial cross-validation, and result visualization using partial dependence and interaction plots. Model fitting relies on the 'ranger' package (Wright and Ziegler 2017 ). Package: r-cran-spatialromle Architecture: all Version: 0.1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spatialromle_0.1.1.1-1.ca2404.1_all.deb Size: 42318 MD5sum: 60aadc6a548e8f2d83df39781c67b26c SHA1: 47e679963bbfd9d563a10536a148f026a2a5816d SHA256: f7aa31c64964c83ba05b988c82ec611604247a40febe8b738e7257fb2fbf1f88 SHA512: 5039223e198db8f48ac7893062a8bf5532e88bb6c02868019aea5876a920a9138c5f19f58bb5f3207441cc2ff2e28df14eecfc0bff3099e129009d4a41bad0c7 Homepage: https://cran.r-project.org/package=SpatialRoMLE Description: CRAN Package 'SpatialRoMLE' (Robust Maximum Likelihood Estimation for Spatial Error Model) Provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, ). Package: r-cran-spatialtime Architecture: all Version: 1.3.4-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-scales, r-cran-purrr, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-rcolorbrewer, r-cran-furrr, r-cran-future, r-cran-tidyselect, r-cran-crayon, r-cran-pbmcapply, r-cran-dixon, r-cran-tibble, r-cran-stringr Suggests: r-cran-knitr, r-cran-devtools, r-cran-rmarkdown, r-cran-testthat, r-cran-gridextra, r-cran-pheatmap Filename: pool/dists/noble/main/r-cran-spatialtime_1.3.4-5-1.ca2404.1_all.deb Size: 3400578 MD5sum: bab67bdf8bbfaca0c4e78e4787b12416 SHA1: d6547063f8e90940c1837c7c93d6ad205a9ec42a SHA256: 09847dcc613d255ff9f3a38a146d74843346df8a8287562b2e1979b427cc05a8 SHA512: 47b34187c436e36005cb332d0278cf644f836915210795278cc16319899ead75f6c6e010281ac66a35d68ec33cce47919c5b158fd3a10251195c64e4c0bc1cdd Homepage: https://cran.r-project.org/package=spatialTIME Description: CRAN Package 'spatialTIME' (Spatial Analysis of Vectra Immunoflourescent Data) Visualization and analysis of Vectra Immunoflourescent data. Options for calculating both the univariate and bivariate Ripley's K are included. Calculations are performed using a permutation-based approach presented by Wilson et al. . Package: r-cran-spatialvs Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-nlme, r-cran-fields Filename: pool/dists/noble/main/r-cran-spatialvs_1.1-1.ca2404.1_all.deb Size: 343172 MD5sum: 8ac35bf656805a90eeff1c5ea25a6796 SHA1: e8b0f28a6a4da27245079ca81efc4573e59568f3 SHA256: 2f26eab7d98ce28511da98ff0fb5cc505b923e294eda227cf96b1efe7e7d3518 SHA512: ccc4f0d614073648c532bbeb98f6dfb83a091a024d6ba12c105a45fdc3fb459b68891dcd209c03f0e7f5c2b762a0249892cd34ad1a027d802d321a97ab02886d Homepage: https://cran.r-project.org/package=SpatialVS Description: CRAN Package 'SpatialVS' (Spatial Variable Selection) Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the input. We use a spatial Poisson regression model to link the spatial counts and covariates. For maximization of the likelihood under adaptive elastic net penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate penalized loglikelihood (APL) methods. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations among the responses. More details are available in Xie et al. (2018, ). The package also contains the Lyme disease dataset, which consists of the disease case data from 2006 to 2011, and demographic data and land cover data in Virginia. The Lyme disease case data were collected by the Virginia Department of Health. The demographic data (e.g., population density, median income, and average age) are from the 2010 census. Land cover data were obtained from the Multi-Resolution Land Cover Consortium for 2006. Package: r-cran-spatialvx Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3884 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat, r-cran-fields, r-cran-smoothie, r-cran-smatr, r-cran-turboem, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-spatstat.model, r-cran-distillery, r-cran-maps, r-cran-boot, r-cran-circstats, r-cran-fastcluster, r-cran-waveslim Filename: pool/dists/noble/main/r-cran-spatialvx_1.0-3-1.ca2404.1_all.deb Size: 3888912 MD5sum: 0fcb91660c2ded21742756dc0565f78e SHA1: 93dfb9a38c06db8a787250a7b6b33a7f8f8c6f7b SHA256: ebaf8fb6f3e7229487dd5602a40f9fbefeb3de03cb23fde0d0130c68446e54c2 SHA512: e49b9d9c29d214807e8354e8ea2c1081a2b82a8ecb203bd45f1ba496b956cbdf71d69f074364e020114861d33c7726c8b1d390f3b78d3bfd9b4c6f367f316c3c Homepage: https://cran.r-project.org/package=SpatialVx Description: CRAN Package 'SpatialVx' (Spatial Forecast Verification) Spatial forecast verification refers to verifying weather forecasts when the verification set (forecast and observations) is on a spatial field, usually a high-resolution gridded spatial field. Most of the functions here require the forecast and observed fields to be gridded and on the same grid. For a thorough review of most of the methods in this package, please see Gilleland et al. (2009) and for a tutorial on some of the main functions available here, see Gilleland (2022) . Package: r-cran-spats Architecture: all Version: 1.0-20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fields, r-cran-spam, r-cran-data.table Filename: pool/dists/noble/main/r-cran-spats_1.0-20-1.ca2404.1_all.deb Size: 184390 MD5sum: e8aa112b09dd37a3ee71b99fbe695a85 SHA1: 9106d429303c69fe4a7afd3690a6850a0211ff9f SHA256: 189c93302a7cdcfd5897aa959180cabf8bf57edb46fd5061858dd610d950f489 SHA512: 4ac0e29fdf118a2a58bc9f3a47e4055f4d6e692d88ded246f36f03a9127084288a53afcec937c5abc582cb109f4391a2b863e2b18a356b9a16482d5588959e97 Homepage: https://cran.r-project.org/package=SpATS Description: CRAN Package 'SpATS' (Spatial Analysis of Field Trials with Splines) Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models. Package: r-cran-spatsoc Architecture: all Version: 0.2.13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2310 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adehabitathr, r-cran-data.table, r-cran-igraph, r-cran-sf, r-cran-lwgeom, r-cran-circstats, r-cran-units, r-cran-rlang Suggests: r-cran-asnipe, r-cran-knitr, r-cran-markdown, r-cran-move2, r-cran-rmarkdown, r-cran-s2, r-cran-sftrack, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spatsoc_0.2.13-1.ca2404.1_all.deb Size: 1022678 MD5sum: ddfdccdf1458b08d58bc8c98450dd991 SHA1: 5960df1e8d164feb4d105b18e23494465595a3c8 SHA256: dd2b5da692b1b6521f185d4e0626a422e07921c3515a4d95d3a17c3ffa94edec SHA512: 83fb3836b95ebd548163bf919096840bc09540d5cf5169dd01a4d2a9c781febe380b6fb87e45a494d6243cdd4f141d4f0a66a554643eda5046b54853ecb0c9d9 Homepage: https://cran.r-project.org/package=spatsoc Description: CRAN Package 'spatsoc' (Group Animal Relocation Data by Spatial and TemporalRelationship and Measure Intragroup Social Dynamics) Detects spatial and temporal groups in GPS relocations (Robitaille et al. (2019) ). It can be used to convert GPS relocations to gambit-of-the-group format to build proximity-based social networks, and perform data-stream randomization methods suitable for GPS data. Also provides measures of intragroup social dynamics including distance and direction to leaders, centroids and nearest neighbours. Package: r-cran-spatstat.data Architecture: all Version: 3.1-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4277 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.utils, r-cran-matrix Suggests: r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet Filename: pool/dists/noble/main/r-cran-spatstat.data_3.1-9-1.ca2404.1_all.deb Size: 4154170 MD5sum: e855e9f78c76da030fba655ba2d410fb SHA1: 85283b45c19c68e9be6ee9ed61315af55d84ef8e SHA256: db3ff88e125d9cf64c3fcf9fda8bf10fe70dcbbb7e4e07a419e252940def202a SHA512: 446421069c4fa956f4bcf22b14f62de6106ab56dee5b2d6d478119026cb687532e092bbc9540f0f3b264a51a6aec6c536e840b6cd9f05b6e9f63671fd814f116 Homepage: https://cran.r-project.org/package=spatstat.data Description: CRAN Package 'spatstat.data' (Datasets for 'spatstat' Family) Contains all the datasets for the 'spatstat' family of packages. Package: r-cran-spatstat.gui Architecture: all Version: 3.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatstat, r-cran-rpanel, r-cran-spatstat.utils Filename: pool/dists/noble/main/r-cran-spatstat.gui_3.1-0-1.ca2404.1_all.deb Size: 79134 MD5sum: beca6983dea6c38d7f0a70ab453a38f7 SHA1: 64f2074cdb7d8f683a0dc02a670a507b67249696 SHA256: 57a9b60f2d463b124b273bbbfca8dab0d6fea9188690aa6d456a163103edab4e SHA512: 3703c26603aaf94edac7645124c405634ecfa66a0db8e890aaabb09a19411a125c05f358d8252e04cacf286668de27f0d809818a1cb377c3b717fb61ab071e06 Homepage: https://cran.r-project.org/package=spatstat.gui Description: CRAN Package 'spatstat.gui' (Interactive Graphics Functions for the 'spatstat' Package) Extension to the 'spatstat' package, containing interactive graphics capabilities. Package: r-cran-spatstat.local Architecture: all Version: 5.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.sparse, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat, r-cran-tensor, r-cran-spatstat.utils Filename: pool/dists/noble/main/r-cran-spatstat.local_5.1-0-1.ca2404.1_all.deb Size: 349182 MD5sum: d36ff32d3099b5f242fd6269a501ffff SHA1: b2c7ac9162e7727a80bfc98584accaacdb7d9dd6 SHA256: db72389765b1abe43fc6207673ffcee1a94fc14223f6a758455d77d5f70fbdba SHA512: 9bac8f3b6578a448af12746a4d9fe4de23493ac78a12999992cdc32c7b457cae76f83ad376142cbfb1af19a1f6063d62fa5f370768466a46802936e3ff5a2edc Homepage: https://cran.r-project.org/package=spatstat.local Description: CRAN Package 'spatstat.local' (Extension to 'spatstat' for Local Composite Likelihood) Extension to the 'spatstat' package, enabling the user to fit point process models to point pattern data by local composite likelihood ('geographically weighted regression'). Package: r-cran-spatstat Architecture: all Version: 3.6-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5477 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatstat.utils Filename: pool/dists/noble/main/r-cran-spatstat_3.6-0-1.ca2404.1_all.deb Size: 4333340 MD5sum: d5f6ed793b9df00a4c49e7138e5bab3d SHA1: 6694c50ae5a6513095d1340c7aa517645ae70394 SHA256: e0d6b25edccf569bc7ecc5b76a80491c75f72add1dba527840a2f8010a856fbe SHA512: ccd340660cedaafd78689cddaaed724b63311f3b0bd32a366ae8a69352755e65bb9c435ad3de0710b15ed62e4898147949586255758c8df0460d5ae8dc1e0738 Homepage: https://cran.r-project.org/package=spatstat Description: CRAN Package 'spatstat' (Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests) Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 3000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Package: r-cran-spatsurv Architecture: all Version: 2.0-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 995 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-sp, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-raster, r-cran-iterators, r-cran-fields, r-cran-matrix, r-cran-stringr, r-cran-sf, r-cran-rcolorbrewer, r-cran-lubridate Suggests: r-cran-rgl Filename: pool/dists/noble/main/r-cran-spatsurv_2.0-1-1.ca2404.1_all.deb Size: 927936 MD5sum: 55ebe27e08ba0610a2fc3faea3f5ca20 SHA1: 13ea97f7d6d3d0e2419ba208a0ac9bbd93b6d5ca SHA256: 1229d3218e406cc6a0e12e4d00218263a66a508a8f04178b786d1f821dd8173a SHA512: 6e2337b27fa29e69bb0e0f59f0c7b2917c025fbd8fd3a90e9d0f37556baa972c0f8c6916becd749429883443f243a6feeba1adf66d3dd54170ea0443323a0b52 Homepage: https://cran.r-project.org/package=spatsurv Description: CRAN Package 'spatsurv' (Bayesian Spatial Survival Analysis with Parametric ProportionalHazards Models) Bayesian inference for parametric proportional hazards spatial survival models; flexible spatial survival models. See Benjamin M. Taylor, Barry S. Rowlingson (2017) . Package: r-cran-spb Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon, r-cran-knitr Filename: pool/dists/noble/main/r-cran-spb_1.0-1.ca2404.1_all.deb Size: 36414 MD5sum: f9d8c9909e251f02d6cc09f7156d26fd SHA1: 49c1730f2562a46c4a992c0c4f80ccd9adeac839 SHA256: b1c6f901f1c6ae919a6dfd512709527db0a92d2e2edaee7ede6bbefd577404a9 SHA512: 00671de77be34232929d812b6451755f825b2936f83cf63350d4a7d49f52e713b48be5f4a288bd948dd9e762ec1a6e92d15d51747304a9dc4a92324eda60bfe2 Homepage: https://cran.r-project.org/package=SPB Description: CRAN Package 'SPB' (Simple Progress Bars for Procedural Coding) Provides a simple progress bar to use for basic and advanced users that suits all those who prefer procedural programming. It is especially useful for integration into markdown files thanks to the progress bar's customisable appearance. 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Package: r-cran-spdynmod Architecture: all Version: 1.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1881 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-sp, r-cran-desolve, r-cran-animation Suggests: r-cran-knitr, r-cran-roxygen2, r-cran-igraph, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spdynmod_1.1.6-1.ca2404.1_all.deb Size: 457410 MD5sum: aa75e4a8dd09fbc670f92abb31c366fc SHA1: f61fc6a385eb77d208913e385fc1773a61e4103e SHA256: 7aee65a233f9fa8944846ee11dbb39ed3a634d6303ead159e76dc9957f1a8f81 SHA512: 7e877a830799b8ae647b7ba42cf08276d7c8fb58417c6cb538d3b3da8e3d75a34fbfda3b07296e56655be19102a53717c06769f9af642f639e17636716346d6e Homepage: https://cran.r-project.org/package=spdynmod Description: CRAN Package 'spdynmod' (Spatio-Dynamic Wetland Plant Communities Model) A spatio-dynamic modelling package that focuses on three characteristic wetland plant communities in a semiarid Mediterranean wetland in response to hydrological pressures from the catchment. 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Package: r-cran-speakr Architecture: all Version: 3.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 885 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-ggplot2, r-cran-lifecycle, r-cran-quarto, r-cran-readr, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-speakr_3.2.4-1.ca2404.1_all.deb Size: 321438 MD5sum: 001701e90a8be3f3fb63fc50bcbb14e8 SHA1: ae943ec1737ea05a8e5212b3991987ad43265939 SHA256: 07266b1ef332e2ee8dc6eb632ab01afe6d271e3d42db087fe5fa59f41f119877 SHA512: 6dab5a3022398c44ff9b1ae431d87cf3fa595b2a619e7503d21c90e5cdaff5608d7255a1c07f27562db1b41b76dc8d68f186afe231b3dfcf39861cbf749af1c8 Homepage: https://cran.r-project.org/package=speakr Description: CRAN Package 'speakr' (A Wrapper for the Phonetic Software 'Praat') It allows running 'Praat' scripts from R and it provides some wrappers for basic plotting. It also adds support for literate markdown tangling. 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Package: r-cran-spec Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-encode, r-cran-csv, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-spec_0.1.9-1.ca2404.1_all.deb Size: 106370 MD5sum: 90f9e25cf9d6ca869b9fff6e79eeb1e6 SHA1: f37de608a82c8d7bb8f77029fc53900bf37b1982 SHA256: 7cbd713aee28606d7b721c1896d75d1240c17e33456ae9815e9d25eaa44376dc SHA512: 4a74c1abba64366d25856fb0127fdc0f4cea279acc218c0023dd3c5278d9d7c0496a702cf0d1d42e63f2ae5c5ca3bb2028938694bb5cc7d8297c1177e1272d45 Homepage: https://cran.r-project.org/package=spec Description: CRAN Package 'spec' (A Data Specification Format and Interface) Creates a data specification that describes the columns of a table (data.frame). Provides methods to read, write, and update the specification. Checks whether a table matches its specification. 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It improves upon existing offerings with additional features and 'tidyverse' integration. Users can easily visualize and evaluate how their models behave under different specifications with a high degree of customization. For a description and applications of specification curve analysis see Simonsohn, Simmons, and Nelson (2020) . 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Package: r-cran-specieschrom Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorramps, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-abind Filename: pool/dists/noble/main/r-cran-specieschrom_1.0.0-1.ca2404.1_all.deb Size: 102080 MD5sum: 90e0dad017ac014ad17bd5ad18cc5ee8 SHA1: f22b87bd27620ec2d58dd1ff51cc4205c4dd04ab SHA256: e0b51debc3a72ed2bdc756b9e5eeabf5dbc3c2b31122fc54a6f9757824e9f4c4 SHA512: f3127a5297d0d5a494b4e995d72ae819b93d376997e4da5fd33edf4f17e7526aa0c654ff836e5e2a68340bdaa388b270cd841c9dd46dd955f68796ecd20238e8 Homepage: https://cran.r-project.org/package=specieschrom Description: CRAN Package 'specieschrom' (The Species Chromatogram) A simple method to display and characterise the multidimensional ecological niche of a species. The method also estimates the optimums and amplitudes along each niche dimension. Give also an estimation of the degree of niche overlapping between species. See Kleparski and Beaugrand (2022) for further details. Package: r-cran-speck Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4079 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ckmeans.1d.dp, r-cran-magrittr, r-cran-matrix, r-cran-rsvd, r-cran-seurat Suggests: r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-rmarkdown, r-cran-seuratobject, r-cran-usethis Filename: pool/dists/noble/main/r-cran-speck_1.0.1-1.ca2404.1_all.deb Size: 4086420 MD5sum: fd91d378f7ca790a1f8df47511520971 SHA1: 8f2da9374870ae619bfe8996e71986e1d868c54c SHA256: ff3fed0dde8d84202fe606117c967c91e8ec80758aa09fc872c5a15db230b030 SHA512: 72f6663e6c91d55f682177b38df9e965cd67a6a5c68c5894b81b244172ed05fc12f6ff4cd96221a0f41e0dcf8b4dc69108f0d363181f8c291ecb3347a1df2dbb Homepage: https://cran.r-project.org/package=SPECK Description: CRAN Package 'SPECK' (Receptor Abundance Estimation using Reduced Rank Reconstructionand Clustered Thresholding) Surface Protein abundance Estimation using CKmeans-based clustered thresholding ('SPECK') is an unsupervised learning-based method that performs receptor abundance estimation for single cell RNA-sequencing data based on reduced rank reconstruction (RRR) and a clustered thresholding mechanism. Seurat's normalization method is described in: Hao et al., (2021) , Stuart et al., (2019) , Butler et al., (2018) and Satija et al., (2015) . Method for the RRR is further detailed in: Erichson et al., (2019) and Halko et al., (2009) . Clustering method is outlined in: Song et al., (2020) and Wang et al., (2011) . Package: r-cran-specleanr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2705 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-dbscan, r-cran-e1071, r-cran-isotree, r-cran-robust, r-cran-robustbase, r-cran-usdm, r-cran-mgcv Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-ggpmisc, r-cran-tibble, r-cran-rinat, r-cran-rvertnet, r-cran-rgbif, r-cran-curl, r-cran-rfishbase, r-cran-sf, r-cran-terra, r-cran-tidytext, r-cran-scatterplot3d Filename: pool/dists/noble/main/r-cran-specleanr_1.0.0-1.ca2404.1_all.deb Size: 2137332 MD5sum: 7d58e5d028d1976b996142af735440ac SHA1: b91edd3ebe0445f49c4b558bf965f01f98910d25 SHA256: 86508dba976ed7bb9c5ebc119569d74d66a467bfabf0bbb47ebb559f308415c8 SHA512: 9ab2cc6ea84c3d1cb9335c9743ae8971a85721b3d7d2be085906b76766e2cbb7f24e6d283971fe8cc204d4ae04e0c08adc0c19f63d3a8bea438c5dae7f58d419 Homepage: https://cran.r-project.org/package=specleanr Description: CRAN Package 'specleanr' (Detecting Environmental Outliers in Data Analysis Pipelines) A framework used to detect and handle outliers during data analysis workflows. Outlier detection is a statistical concept with applications in data analysis workflows, highlighting records that are suspiciously high or low. Outlier detection in distribution models was initiated by Chapman (1991) (available at ), who developed the reverse jackknifing method. The concept was further developed and incorporated into different R packages, including 'flexsdm' (Velazco et al., 2022, ) and 'biogeo' (Robertson et al., 2016 ). We compiled various outlier detection methods obtained from the literature, including those elaborated in Dastjerdy et al. (2023) and Liu et al. (2008) . In this package, we introduced the ensembling aspect, where multiple outlier detection methods are used to flag the record as either an absolute outlier. The concept can also be applied in general data analysis, as well as during the development of species distribution models. Package: r-cran-specr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3782 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-cowplot, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-ggraph, r-cran-glue, r-cran-igraph, r-cran-lifecycle, r-cran-lme4, r-cran-magrittr, r-cran-parallelly, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-broom.mixed, r-cran-gapminder, r-cran-ggridges, r-cran-knitr, r-cran-lavaan, r-cran-testthat, r-cran-tidyverse, r-cran-performance, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-specr_1.0.0-1.ca2404.1_all.deb Size: 2345790 MD5sum: 7c83f8e7c3936d6dbd238fe84920caaa SHA1: e92bf71e30152378e4ec3477d2e8d1a46f5f4776 SHA256: 7553722bc75c0da75051cce7cf273a6954fdb2b94119604a8ff4565fe66143b7 SHA512: 12d663f7c263bc9be7c0f480d77e05a57ccdc2fde10683ef18108593f874ae332ca3cf0b75d162cb8bf9c2d0de7c8d38881754ff50512f740d62697f44417c2b Homepage: https://cran.r-project.org/package=specr Description: CRAN Package 'specr' (Conducting and Visualizing Specification Curve Analyses) Provides utilities for conducting specification curve analyses (Simonsohn, Simmons & Nelson (2020, ) or multiverse analyses (Steegen, Tuerlinckx, Gelman & Vanpaemel, 2016, ) including functions to setup, run, evaluate, and plot all specifications. 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'spect' provides a simple interface consistent with commonly used R data analysis packages, such as 'caret', a variety of parameter options to help facilitate search automation, a high degree of transparency to the end-user - all intermediate data sets and parameters are made available for further analysis and useful, out-of-the-box visualizations of model performance. Methods for transforming survival data into discrete-time are adapted from the 'autosurv' package by Suresh et al., (2022) . Package: r-cran-spectacles Architecture: all Version: 0.5-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2819 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-reshape2, r-cran-stringr, r-cran-baseline, r-cran-signal, r-cran-epir Suggests: r-cran-caret, r-cran-pls, r-cran-rcolorbrewer, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-spectacles_0.5-5-1.ca2404.1_all.deb Size: 2465024 MD5sum: 44187c3c59c9efe715ecd34b2be5031f SHA1: fe1cd7667309714434cba6ed77a9281fdea6c87b SHA256: 736f7a45859bf17a0ea5438931f0548d0f9bbea5b7bd064904746948a85b8596 SHA512: ed352b5e5e7278b2f58c3095aed120c29812164d817bfc0e30fd5f085e480eb4c954d44024a26bc2fe0017565f6c40cc5304162cf868ef5d8594bc9c68d29977 Homepage: https://cran.r-project.org/package=spectacles Description: CRAN Package 'spectacles' (Storing, Manipulating and Analysis Spectroscopy and AssociatedData) Stores and eases the manipulation of spectra and associated data, with dedicated classes for spatial and soil-related data. Package: r-cran-spectator Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geojsonsf, r-cran-httr, r-cran-sf Suggests: r-cran-calendar, r-cran-calendr, r-cran-httptest, r-cran-knitr, r-cran-lubridate, r-cran-lutz, r-cran-maps, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spectator_0.2.0-1.ca2404.1_all.deb Size: 115302 MD5sum: 4ad29061d852986a564b14b2766fc0cf SHA1: dae358124da8f39217446819624ab69ed55c923a SHA256: e03ac5c1a1cf84982d39581e151d78c1aa3d3fb5375aeae7db27e375bfbff7b2 SHA512: 86cae8fa27d2d4fa1857a93cbb90b979819fc34f283880e2006eb124a20dbab90af95fa3ac821979a4b6d3ec330903403eb326d42548af71fd055b5aba15b7b4 Homepage: https://cran.r-project.org/package=spectator Description: CRAN Package 'spectator' (Interface to the 'Spectator Earth' API) Provides interface to the 'Spectator Earth' API , mainly for obtaining the acquisition plans and satellite overpasses for Sentinel-1, Sentinel-2, Landsat-8 and Landsat-9 satellites. Current position and trajectory can also be obtained for a much larger set of satellites. It is also possible to search the archive for available images over the area of interest for a given (past) period, get the URL links to download the whole image tiles, or alternatively to download the image for just the area of interest based on selected spectral bands. Package: r-cran-spectr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-foreach, r-cran-lomb Suggests: r-cran-doparallel, r-cran-knitr, r-cran-qs, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spectr_1.0.1-1.ca2404.1_all.deb Size: 22722 MD5sum: 23f8f2680a5afd21a70e024fc37db0b6 SHA1: 37cda96c291df8f86fe6d9cb549d77495a9def0e SHA256: e18ad1121c7bb92bb2cf4faf79ac49cf756a84711c417dbc33f5eef05f59e281 SHA512: 3da5877be13d7c6d0206a6dea3d7ac5f381f4f095afbb9ee93337f214aca1e034cdc926bca4f3f0b131b7672817b92a5576c1cc4e9c6325fd7609e5347348c69 Homepage: https://cran.r-project.org/package=spectr Description: CRAN Package 'spectr' (Calculate the Periodogram of a Time-Course) Provides a consistent interface to use various methods to calculate the periodogram and estimate the period of a rhythmic time-course. Methods include Lomb-Scargle, fast Fourier transform, and three versions of the chi-square periodogram. See Tackenberg and Hughey (2021) . Package: r-cran-spectrakit Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-ggplot2, r-cran-ggrepel, r-cran-tibble, r-cran-purrr, r-cran-rlang, r-cran-magick, r-cran-glue, r-cran-data.table, r-cran-scales Suggests: r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-spectrakit_0.2.0-1.ca2404.1_all.deb Size: 65286 MD5sum: 51ba70e217d1569c8791cd79b8bda80c SHA1: bc3216d58099588a77e9a715abb05d2e1892cb90 SHA256: 494cce2b527e402eb31662e51763793664ea787cac768ed04f23e33bb94a2cdc SHA512: dde2b5776cae6dae062b3f4a62ad80cfbeabb877654a0fa4a99c1a209b20cab67effc4bbdf7d4202fdad495506216a3c93a68f8e20b35d185875111134028211 Homepage: https://cran.r-project.org/package=spectrakit Description: CRAN Package 'spectrakit' (Spectral Data Handling and Visualization) Provides functions to combine, normalize and visualize spectral data, perform principal component analysis (PCA), and assemble customizable image grids suitable for publication-quality scientific figures. Package: r-cran-spectral Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rasterimage, r-cran-lattice, r-cran-rhpcblasctl, r-cran-pbapply Filename: pool/dists/noble/main/r-cran-spectral_2.0-1.ca2404.1_all.deb Size: 159082 MD5sum: e693f3d461e1ceb6c943a4400825a8a3 SHA1: 76bfb560b80d5a6b6f68b41ae2ac32a7a1df0acc SHA256: 13499a99caf011e3b4be3593ce5ed556a7dff37fef063be201dad9c4c325eaac SHA512: 96a3934fac052a2612424df09c91effb89cc5424fcf48688d1b957768a4c42da2383921ade10cbd8083cf1b8ef6c8fb769bcdfa4e44f25ba600859a6aaca3707 Homepage: https://cran.r-project.org/package=spectral Description: CRAN Package 'spectral' (Common Methods of Spectral Data Analysis) On discrete data spectral analysis is performed by Fourier and Hilbert transforms as well as with model based analysis called Lomb-Scargle method. Fragmented and irregularly spaced data can be processed in almost all methods. Both, FFT as well as LOMB methods take multivariate data and return standardized PSD. For didactic reasons an analytical approach for deconvolution of noise spectra and sampling function is provided. A user friendly interface helps to interpret the results. Package: r-cran-spectralanalysis Architecture: all Version: 4.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6132 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-baseline, r-bioc-biocgenerics, r-cran-data.table, r-cran-ggplot2, r-cran-jsonlite, r-cran-magrittr, r-cran-nnls, r-cran-nmf, r-cran-plotly, r-cran-plyr, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-signal, r-cran-viridis, r-cran-hnmf, r-cran-zoo, r-cran-pls Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-webshot, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-spectralanalysis_4.3.3-1.ca2404.1_all.deb Size: 2146062 MD5sum: 3695351167be738c58689089800991a3 SHA1: 80b6ab9be24f0c7b307b47fb515c377e69709ef0 SHA256: b15d1077ea3eed359b35550bb7d473e87fa72dce84193c5d82d25ebaf696b175 SHA512: b1787d740ea175938ccc57b1665a6bb904154279f09fc8354134d9e33ab8fe8c782424fdb5d0cb87e1b20db02a0d34093b327896632d53fd5ca92365524ee42d Homepage: https://cran.r-project.org/package=spectralAnalysis Description: CRAN Package 'spectralAnalysis' (Pre-Process, Visualize and Analyse Spectral Data) Infrared, near-infrared and Raman spectroscopic data measured during chemical reactions, provide structural fingerprints by which molecules can be identified and quantified. The application of these spectroscopic techniques as inline process analytical tools (PAT), provides the pharmaceutical and chemical industry with novel tools, allowing to monitor their chemical processes, resulting in a better process understanding through insight in reaction rates, mechanistics, stability, etc. Data can be read into R via the generic spc-format, which is generally supported by spectrometer vendor software. Versatile pre-processing functions are available to perform baseline correction by linking to the 'baseline' package; noise reduction via the 'signal' package; as well as time alignment, normalization, differentiation, integration and interpolation. Implementation based on the S4 object system allows storing a pre-processing pipeline as part of a spectral data object, and easily transferring it to other datasets. Interactive plotting tools are provided based on the 'plotly' package. Non-negative matrix factorization (NMF) has been implemented to perform multivariate analyses on individual spectral datasets or on multiple datasets at once. NMF provides a parts-based representation of the spectral data in terms of spectral signatures of the chemical compounds and their relative proportions. See 'hNMF'-package for references on available methods. The functionality to read in spc-files was adapted from the 'hyperSpec' package. Package: r-cran-spectralanomaly Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-spectralanomaly_0.1.1-1.ca2404.1_all.deb Size: 85436 MD5sum: 52a1ca2a29c3b803a18b797307b662df SHA1: 80d201e19aa20306a054804f78614035db3239c2 SHA256: 0f0bad1fc916388227b29cc804412c67950d51be7bc6adc0672fa54c93d5b25c SHA512: e6e07a0915ed8056e81117c1ae85d40024dba160b21b85934b54a7abe0d7aaf98282d8b7d9c902a4958056de3adfef586aad057f8be0ca5c663ad38476a516f4 Homepage: https://cran.r-project.org/package=spectralAnomaly Description: CRAN Package 'spectralAnomaly' (Detect Anomalies Using the Spectral Residual Algorithm) Apply the spectral residual algorithm to data, such as a time series, to detect anomalies. Anomaly scores can be used to determine outliers based upon a threshold or fed into more sophisticated prediction models. Methods are based upon "Time-Series Anomaly Detection Service at Microsoft", Ren, H., Xu, B., Wang, Y., et al., (2019) . Package: r-cran-spectralclmixed Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rspectra, r-cran-cluster, r-cran-ggplot2, r-cran-ggally Filename: pool/dists/noble/main/r-cran-spectralclmixed_1.0.2-1.ca2404.1_all.deb Size: 25338 MD5sum: f7e0b5ae7f12461f232c1b66d5eb6a33 SHA1: 9b31ccedbc81b8ca47160f059a38557002561e52 SHA256: bcbcf28377af5b8eb13e7e183565227e3bb5cf91877aa924fab607663510edb0 SHA512: 969aead12349e51533ff55eead00fad1d5525ff620f52ab3435b0e674cb645edbe4d98677cdab5206ad17e2e589a27368ad26e7167b5076d1aedc10ce8529311 Homepage: https://cran.r-project.org/package=SpectralClMixed Description: CRAN Package 'SpectralClMixed' (Spectral Clustering for Mixed Type Data) Performs cluster analysis of mixed-type data using Spectral Clustering, see F. Mbuga and, C. Tortora (2022) . Package: r-cran-spectralgp Architecture: all Version: 1.3.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spectralgp_1.3.4-1.ca2404.1_all.deb Size: 138522 MD5sum: b114e5934e7ab02cb1e15501c0321dc9 SHA1: b70de69214ad42194602c526af46fee8ef3be357 SHA256: 62b5a171f0d1d39e07837a9256e5ceee5ebe464fd7362bbba638e1d480602ec9 SHA512: ca8528a22cdb812c61958cf9397aed53d885a8aa84a32e5068ce1b4ec491b1c73554ce699368610488209bf0fdc448a8381fd414aaa153f13a0890cf43723db9 Homepage: https://cran.r-project.org/package=spectralGP Description: CRAN Package 'spectralGP' (Approximate Gaussian Processes Using the Fourier Basis) Routines for creating, manipulating, and performing Bayesian inference about Gaussian processes in one and two dimensions using the Fourier basis approximation: simulation and plotting of processes, calculation of coefficient variances, calculation of process density, coefficient proposals (for use in MCMC). It uses R environments to store GP objects as references/pointers. Package: r-cran-spectralmap Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-scatterplot3d, r-cran-fields Filename: pool/dists/noble/main/r-cran-spectralmap_1.0-1.ca2404.1_all.deb Size: 14628 MD5sum: 29fd4187e0bf6c3609525c1380d39a4b SHA1: 33f5a301070a47fb6a049b0a6b545542f21a061e SHA256: 7f01a523063123442bbf816a3f2dc0d3c27460130effce393f7ad221fe1657cf SHA512: 2819a6547840ae972baa4e99a96d15c3b1d133303df3524bd738841537d95457bc32e1f515e3df7ef889749245bf99cf8062c732ce89285fd12831e7c9d567b1 Homepage: https://cran.r-project.org/package=SpectralMap Description: CRAN Package 'SpectralMap' (Diffusion Map and Spectral Map) Implements the diffusion map method of dimensionality reduction and spectral method of combining multiple diffusion maps, including creation of the spectra and visualization of maps. Package: r-cran-spectralr Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgee, r-cran-geojsonio, r-cran-sf, r-cran-dplyr, r-cran-ggplot2, r-cran-reshape2, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-spectralr_0.1.4-1.ca2404.1_all.deb Size: 955130 MD5sum: 3547111d08fe059dd9f5b3941868b92a SHA1: 88a6ac8a63cf5b2318ec0410e5f70378cddbf21e SHA256: f6fda336db51640f7f59bf524beef0204f863ccfdb7d487aee1f617a8970d2fc SHA512: b6adc2cad159e097817873dc3eaab03c23365f5b2095293440972fcd6b6628022ec62ce9a699260e845bebbcf2968d83f50135f1db26a1a812aea2b960f1756b Homepage: https://cran.r-project.org/package=spectralR Description: CRAN Package 'spectralR' (Obtain and Visualize Spectral Reflectance Data for Earth SurfacePolygons) Tools for obtaining, processing, and visualizing spectral reflectance data for the user-defined land or water surface classes for visual exploring in which wavelength the classes differ. Input should be a shapefile with polygons of surface classes (it might be different habitat types, crops, vegetation, etc.). The Sentinel-2 L2A satellite mission optical bands pixel data are obtained through the Google Earth Engine service () and used as a source of spectral data. Package: r-cran-spectran Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3193 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chromote, r-cran-colorspec, r-cran-cowplot, r-cran-dplyr, r-cran-gghighlight, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggridges, r-cran-ggtext, r-cran-gt, r-cran-htmltools, r-cran-magrittr, r-cran-openxlsx, r-cran-pagedown, r-cran-patchwork, r-cran-png, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-shiny, r-cran-shinyalert, r-cran-shinydashboard, r-cran-shinyfeedback, r-cran-shinyjs, r-cran-shinywidgets, r-cran-spacesxyz, r-cran-spscomps, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-waiter, r-cran-webshot2, r-cran-withr Suggests: r-cran-config, r-cran-here, r-cran-magick, r-cran-pkgload, r-cran-readxl, r-cran-rhub, r-cran-rmarkdown, r-cran-rsconnect Filename: pool/dists/noble/main/r-cran-spectran_1.0.6-1.ca2404.1_all.deb Size: 2725456 MD5sum: 37ba7efe1f699bb733cbe3fcff0b04ae SHA1: 2f466a221c5cbece083de1756e186bf6974ea68c SHA256: 9e610902f6e94849346cac7b35064760c00bf16fb8199f5c836e91ed663790ae SHA512: 6b824ef7ffcdceb384024f3354405cd3578c2029366a9a5c29ffc3d9ac5ad4cf54c65d1143547aabc6cd876f17f3ca4efda41d5ea670758ada2df2954a1037f3 Homepage: https://cran.r-project.org/package=Spectran Description: CRAN Package 'Spectran' (Visual and Non-Visual Spectral Analysis of Light) Analyse light spectra for visual and non-visual (often called melanopic) needs, wrapped up in a Shiny App. 'Spectran' allows for the import of spectra in various CSV forms but also provides a wide range of example spectra and even the creation of own spectral power distributions. The goal of the app is to provide easy access and a visual overview of the spectral calculations underlying common parameters used in the field. It is thus ideal for educational purposes or the creation of presentation ready graphs in lighting research and application. 'Spectran' uses equations and action spectra described in CIE S026 (2018) , DIN/TS 5031-100 (2021) , and ISO/CIE 23539 (2023) . Package: r-cran-spectrolab Architecture: all Version: 0.0.19-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3438 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-shiny, r-cran-shinyjs Suggests: r-cran-covr, r-cran-tinytex, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spectrolab_0.0.19-1.ca2404.1_all.deb Size: 1727278 MD5sum: b348e5d593ea937fd566054e17227f3b SHA1: affd733424e83cafc63e773b7a02ed36d56a23c5 SHA256: 77703312dacb39571fd2776e2f2b39eabe2f2e9850060914d358935d0d47bc1c SHA512: 984ea0ebec84df73dd8fdbf9f267291b070755866722f9b7bb732aa6a756fc9eb235ee34cb938def887803e027b6181800b3e351d4dcd8872e974939ec3d6c1d Homepage: https://cran.r-project.org/package=spectrolab Description: CRAN Package 'spectrolab' (Class and Methods for Spectral Data) Input/Output, processing and visualization of spectra taken with different spectrometers, including SVC (Spectra Vista), ASD and PSR (Spectral Evolution). Implements an S3 class spectra that other packages can build on. Provides methods to access, plot, manipulate, splice sensor overlap, vector normalize and smooth spectra. Package: r-cran-spectrum Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3913 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-clusterr, r-cran-rfast, r-cran-diptest Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-spectrum_1.1-1.ca2404.1_all.deb Size: 3535992 MD5sum: a1e91b3a1793a6144422690f14d11e50 SHA1: fed64eb7aad9b02798cba685b6466e5b9a84f06f SHA256: 29b04eb6d4689662c6d503b83044e89540a0ead7e7153387c0e11e8a8c64ad2f SHA512: 8b89e98f46eb0a5a8fc8f603c611eb58740d3ab5aa0399ab0286a013f51afac4ffceea45c7d45e59d2a6f3eae846c14efc469dea132efd21cb88638963cf2c10 Homepage: https://cran.r-project.org/package=Spectrum Description: CRAN Package 'Spectrum' (Fast Adaptive Spectral Clustering for Single and Multi-View Data) A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens connections in the graph based on common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K. Package: r-cran-spedecon Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-spedecon_0.1-1.ca2404.1_all.deb Size: 49656 MD5sum: 6fee69291fd107c7faace08234aceadc SHA1: 74268b0f54f2bd3246a49c8b99e9ed09dcdf1b13 SHA256: cb514466ab6b6f37ad21c6a106d8b7c9590dccbba2842093d0922350619a6bbf SHA512: b94f79190e86cfeb957a266f232653d2b56093b12b9efdcfbdc4ac0fe00f6364caeb5fd8e4f8a36d4062693297f8786a79feda88090899444c505f096bdb9c58 Homepage: https://cran.r-project.org/package=spedecon Description: CRAN Package 'spedecon' (Smoothness-Penalized Deconvolution for Density Estimation UnderMeasurement Error) Implements the Smoothness-Penalized Deconvolution method for estimating a probability density under measurement error of Kent and Ruppert (2023) . The estimator is formed by computing a histogram of the error-contaminated data, and then finding an estimate that minimizes a reconstruction error plus a smoothness-inducing penalty term. The primary function, sped(), takes the data and error distribution, and returns the estimator as a function. Package: r-cran-spedinstabr Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2939 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-beanplot, r-cran-raster, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-spedinstabr_2.2-1.ca2404.1_all.deb Size: 2520456 MD5sum: 3a1a7b438398ed442cc585137003394c SHA1: 4c9f915d7f3fac12f6b909b3f95d08b07cc1db3f SHA256: a68b4ec226b4e98ddd45468b4488c3e3934ddc7bd0f08e6a5520f97eca792fb3 SHA512: 966903cf4c7035eebac7380c34ab3ac2bd7b5c9ba8d42432ed41c34ec2b9480a3aeecd1a47bbb5c2eda5503d95295ee41f8ec41073bf058d601b35b44c3c96e3 Homepage: https://cran.r-project.org/package=SPEDInstabR Description: CRAN Package 'SPEDInstabR' (Estimation of the Relative Importance of Factors AffectingSpecies Distribution Based on Stability Concept) From output files obtained from the software 'ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent. 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The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models. Package: r-cran-spfsr Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlr3, r-cran-future, r-cran-tictoc, r-cran-mlr3pipelines, r-cran-mlr3learners, r-cran-ranger, r-cran-ggplot2, r-cran-lgr Suggests: r-cran-caret, r-cran-mass Filename: pool/dists/noble/main/r-cran-spfsr_2.0.4-1.ca2404.1_all.deb Size: 74922 MD5sum: 2f04f2544f4d4b1377d4e89604a15324 SHA1: cbd4f7b1f7c7fc1663afaab501054a24c8eb7539 SHA256: f977f9b438cf8cc577386e530e5514b7430a49a99f8315c45631f761e16b307e SHA512: 6bca15e3d2719ee2da44063ed4ea972c22bd6b7b8f6406f6eb58777b3b0af48f80ed31ebeed35d1cca9e6c4d4e7066d146fddb49c1baf6147b79842cd6229b63 Homepage: https://cran.r-project.org/package=spFSR Description: CRAN Package 'spFSR' (Feature Selection and Ranking via Simultaneous PerturbationStochastic Approximation) An implementation of feature selection, weighting and ranking via simultaneous perturbation stochastic approximation (SPSA). The SPSA-FSR algorithm searches for a locally optimal set of features that yield the best predictive performance using some error measures such as mean squared error (for regression problems) and accuracy rate (for classification problems). Package: r-cran-spheredata Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spheredata_0.1.3-1.ca2404.1_all.deb Size: 184816 MD5sum: c79d3ed64575ab2c469f26ecb8ab3623 SHA1: 4b9682342cd6c95b9202be5357ddd58553e1a68d SHA256: 915a2d27cdc98a1435f7caad2b9f49e3214c8d090fe8b02e3686ac8540483f22 SHA512: e8780707300b7ec2a722e63ffc1f0cb53057e01d01777f6a03f6fa0885357a4bf6e06ed9a9df91caf8726449bc64974f0dd656170c60fef867ee7e3aae876e4d Homepage: https://cran.r-project.org/package=spheredata Description: CRAN Package 'spheredata' (Students' Performance Dataset in Physics Education Research(SPHERE)) A multidimensional dataset of students' performance assessment in high school physics. The SPHERE dataset was collected from 497 students in four public high schools specifically measuring their conceptual understanding, scientific ability, and attitude toward physics [see Santoso et al. (2024) ]. The data collection was conducted using some research based assessments established by the physics education research community. They include the Force Concept Inventory, the Force and Motion Conceptual Evaluation, the Rotational and Rolling Motion Conceptual Survey, the Fluid Mechanics Concept Inventory, the Mechanical Waves Conceptual Survey, the Thermal Concept Evaluation, the Survey of Thermodynamic Processes and First and Second Laws, the Scientific Abilities Assessment Rubrics, and the Colorado Learning Attitudes about Science Survey. Students' attributes related to gender, age, socioeconomic status, domicile, literacy, physics identity, and test results administered using teachers' developed items are also reported in this dataset. Package: r-cran-sphereml Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-shinydashboard, r-cran-spheredata, r-cran-lavaan, r-cran-semplot, r-cran-ctt, r-cran-mirt, r-cran-shinycssloaders, r-cran-fselectorrcpp, r-cran-randomforest, r-cran-caret, r-cran-catools, r-cran-proc, r-cran-ga, r-cran-readxl Filename: pool/dists/noble/main/r-cran-sphereml_0.1.1-1.ca2404.1_all.deb Size: 194950 MD5sum: 95322c0528d74b24ba0ba230338d8f48 SHA1: 6b891d95d835004ebdf1b7389da7efcf79b69d50 SHA256: c445ed1b43fab4dd420370239797d1816462430a734317aadc905afe1150614d SHA512: f564b2a8063da6d7ee59160643c0d56b881fc8b25e3ad900fb528ca1ccd5f7baeda34b0b591d2e4ef9c97a23195fe6d65e339d486be34c1117fecee6db50adf0 Homepage: https://cran.r-project.org/package=sphereML Description: CRAN Package 'sphereML' (Analyzing Students' Performance Dataset in Physics EducationResearch (SPHERE) using Machine Learning (ML)) A simple package facilitating ML based analysis for physics education research (PER) purposes. The implemented machine learning technique is random forest optimized by item response theory (IRT) for feature selection and genetic algorithm (GA) for hyperparameter tuning. The data analyzed here has been made available in the CRAN repository through the 'spheredata' package. The SPHERE stands for Students' Performance in Physics Education Research (PER). The students are the eleventh graders learning physics at the high school curriculum. We follow the stream of multidimensional students' assessment as probed by some research based assessments in PER. The goal is to predict the students' performance at the end of the learning process. Three learning domains are measured including conceptual understanding, scientific ability, and scientific attitude. Furthermore, demographic backgrounds and potential variables predicting students' performance on physics are also demonstrated. Package: r-cran-sphereoptimize Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sphereoptimize_0.1.1-1.ca2404.1_all.deb Size: 17296 MD5sum: 5c90e8c537447f21f54fe4c4266c8036 SHA1: efe3580d5e04494fe1bb5fdbe37918d70b3b7ca7 SHA256: d259f4dcaf2b7cc8c0288bbf16535d23f96acb0512ee9c92defda2205f23f2a2 SHA512: c44ef018481aa0ae257a7a226e622d1589c3791fb449d881daae97723ecc25657c1e845040b84ccf9ad5189459d214bd6503aafe3755d8944533fb31226b5d9d Homepage: https://cran.r-project.org/package=SphereOptimize Description: CRAN Package 'SphereOptimize' (Optimization on a Unit Sphere) A simple tool for numerical optimization on the unit sphere. This is achieved by combining the spherical coordinating system with L-BFGS-B optimization. This algorithm is implemented in Kolkiewicz, A., Rice, G., & Xie, Y. (2020) . Package: r-cran-spherepc Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geosphere, r-cran-rgl, r-cran-sphereplot Filename: pool/dists/noble/main/r-cran-spherepc_0.1.7-1.ca2404.1_all.deb Size: 114444 MD5sum: 32e2173f6baaa2a24c5a3c14704c9626 SHA1: 8b33553606a0f706466860334ecc44a3717e0c1e SHA256: 2fe958d7ddbc80ac52aa27813e99d72efbd9b452d173815930027fcd9c00407e SHA512: 3dda0c88defff193a3be23182698912614fbe1b35a70128ff240913337a697a4540e5acba63aa2bcf5d2ac8a4d1c18cdc685e57474e89a4a3702951bbf8d4441 Homepage: https://cran.r-project.org/package=spherepc Description: CRAN Package 'spherepc' (Spherical Principal Curves) Fitting dimension reduction methods to data lying on two-dimensional sphere. This package provides principal geodesic analysis, principal circle, principal curves proposed by Hauberg, and spherical principal curves. Moreover, it offers the method of locally defined principal geodesics which is underway. The detailed procedures are described in Lee, J., Kim, J.-H. and Oh, H.-S. (2021) . Also see Kim, J.-H., Lee, J. and Oh, H.-S. (2020) . Package: r-cran-sphereplot Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgl Filename: pool/dists/noble/main/r-cran-sphereplot_1.5.1-1.ca2404.1_all.deb Size: 42200 MD5sum: 7a10f4010090c58bca69f14805d7d85e SHA1: 47cbdb53749b55fd2b819000f00b08f54e1c2ec3 SHA256: b7326fc708473cdb79fbf0f5608e27354545ef14d68c652cf325f110a1ffc44d SHA512: aec3de417684296caef7d5758a1036869210816d11f712623d3f7a5117be1b25288efa46c2d5dcb0bdede82c16e55accde919e9c90de2aa8cbd4f99f652522fb Homepage: https://cran.r-project.org/package=sphereplot Description: CRAN Package 'sphereplot' (Spherical Plotting) Various functions for creating spherical coordinate system plots via extensions to rgl. Package: r-cran-spheresmooth Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sphereplot, r-cran-rgl, r-cran-ggplot2, r-cran-rworldmap, r-cran-sf Filename: pool/dists/noble/main/r-cran-spheresmooth_0.1.3-1.ca2404.1_all.deb Size: 218872 MD5sum: 991451b412404275277dc6c64d755d51 SHA1: 39a2052e3d736cc47f4143a92451d2938af6bf06 SHA256: 4cd179b0c2c9d6879823ceec57b7246871dd8b8e5b148e29aed940d13fb275b3 SHA512: e1b206f3ac38161e54a8f09ef649ce0d760c04a52fd357def9da1043b0124183bb49efeafb7033d760fdadcd4e4d197231935ff55cd1499cfacd89a54633c9b9 Homepage: https://cran.r-project.org/package=spheresmooth Description: CRAN Package 'spheresmooth' (Piecewise Geodesic Smoothing for Spherical Data) Fitting a smooth path to a given set of noisy spherical data observed at known time points. It implements a piecewise geodesic curve fitting method on the unit sphere based on a velocity-based penalization scheme. The proposed approach is implemented using the Riemannian block coordinate descent algorithm. To understand the method and algorithm, one can refer to Bak, K. Y., Shin, J. K., & Koo, J. Y. (2023) for the case of order 1. Additionally, this package includes various functions necessary for handling spherical data. Package: r-cran-sphericalcubature Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubature, r-cran-simplicialcubature, r-cran-mvmesh, r-cran-abind Filename: pool/dists/noble/main/r-cran-sphericalcubature_1.5-1.ca2404.1_all.deb Size: 102014 MD5sum: 6403512883632f9034e2215205324db9 SHA1: 4c0e30c0f632393f2168d067079c9f28257a6c81 SHA256: 4ce43777374de913cdfaeaa11327f873f53e1fcd3158744a3ce9cd81ca6c9d16 SHA512: 8bf81c62d21f897f31cf298b0bfd31ba4c7a559a2115ecdcdb85e41087fabd2130150c2a17d561eb5e2d0edaaf5fa925c058d8e9e2523606d72bb1a1d7afe487 Homepage: https://cran.r-project.org/package=SphericalCubature Description: CRAN Package 'SphericalCubature' (Numerical Integration over Spheres and Balls in n-Dimensions;Multivariate Polar Coordinates) Provides several methods to integrate functions over the unit sphere and ball in n-dimensional Euclidean space. Routines for converting to/from multivariate polar/spherical coordinates are also provided. Package: r-cran-sphet Architecture: all Version: 2.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 627 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-spatialreg, r-cran-spdep, r-cran-matrix, r-cran-sp, r-cran-mvtnorm, r-cran-stringr, r-cran-coda, r-cran-spdata, r-cran-sf Filename: pool/dists/noble/main/r-cran-sphet_2.1-1-1.ca2404.1_all.deb Size: 543588 MD5sum: 41517fa1224f227cb6002f50410bb1a0 SHA1: 79eb0d0b1010703d7ae9b6789732aba1a77f30a1 SHA256: 1da55a7f6300de54158d91cbdd57e655a8a9b8b7a8123b6060fa2c5778492045 SHA512: 20fb97f4dbf75cae0f3cfba2171cc0ff4f71e248cf30cd33990c3b5a5c34a0e6c16005bdfdb322fb135caeabb0daf106a1dc774ca442af2cb88fc6a9e3a8fdbe Homepage: https://cran.r-project.org/package=sphet Description: CRAN Package 'sphet' (Estimation of Spatial Autoregressive Models with and withoutHeteroskedastic Innovations) Functions for fitting Cliff-Ord-type spatial autoregressive models with and without heteroskedastic innovations using Generalized Method of Moments estimation are provided. Some support is available for fitting spatial HAC models, and for fitting with non-spatial endogeneous variables using instrumental variables. Package: r-cran-spicefp Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 873 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-stringr, r-cran-tidyr, r-cran-matrix, r-cran-genlasso, r-cran-purrr Suggests: r-cran-fields Filename: pool/dists/noble/main/r-cran-spicefp_0.1.2-1.ca2404.1_all.deb Size: 800632 MD5sum: 3eb6d98856484979c1c53485b9860cc1 SHA1: 1bf3064218605446ca52dad2e9d3bc0212976c35 SHA256: c305c5c57d1fadcf67815d34e435ed55df5b16302a776a3967d3973a1035715c SHA512: c579250c225a752133eecf6b76b989ad2a14eee4587f7c062296da8d8508f571b4d0ce6f1cbc90fd1fb141981bb2a355ac214d7937d83b36af35e0028c6b45fe Homepage: https://cran.r-project.org/package=SpiceFP Description: CRAN Package 'SpiceFP' (Sparse Method to Identify Joint Effects of Functional Predictors) A set of functions allowing to implement the 'SpiceFP' approach which is iterative. It involves transformation of functional predictors into several candidate explanatory matrices (based on contingency tables), to which relative edge matrices with contiguity constraints are associated. Generalized Fused Lasso regression are performed in order to identify the best candidate matrix, the best class intervals and related coefficients at each iteration. The approach is stopped when the maximal number of iterations is reached or when retained coefficients are zeros. Supplementary functions allow to get coefficients of any candidate matrix or mean of coefficients of many candidates. The methods in this package are describing in Girault Gnanguenon Guesse, Patrice Loisel, Bénedicte Fontez, Thierry Simonneau, Nadine Hilgert (2021) "An exploratory penalized regression to identify combined effects of functional variables -Application to agri-environmental issues" . Package: r-cran-spichanges Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 814 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist, r-cran-lubridate, r-cran-mumin, r-cran-rlang, r-cran-spsutil, r-cran-brglm2, r-cran-zoo Suggests: r-cran-archive, r-cran-curl, r-cran-doparallel, r-cran-dplyr, r-cran-foreach, r-cran-ggplot2, r-cran-knitr, r-cran-ncdf4, r-cran-patchwork, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-sf, r-cran-purrr, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-spichanges_0.2.1-1.ca2404.1_all.deb Size: 404978 MD5sum: 28a1d9818bf12da32f4644f1bfab98c9 SHA1: f5125a50abafe8e0f1ca5900045766759433cc0b SHA256: ea68f28df4f5e16cbf2c6e1f566c34b9c2579df7dfbe5e4b2f058229c78ea227 SHA512: 2c8117f33bdd64c68060457e442dbd45fe0f37b467c0c18881ca63a2e11a01fe8931f26b8f9fc2eca98dee0e7d2fa94d9b69537e8d8a7f89b6bd5ff4edde6900 Homepage: https://cran.r-project.org/package=SPIChanges Description: CRAN Package 'SPIChanges' (Improves the Interpretation of the Standardized PrecipitationIndex Under Changing Climate Conditions) Improves the interpretation of the Standardized Precipitation Index under changing climate conditions. The package uses the nonstationary approach proposed in Blain et al. (2022) to detect trends in rainfall quantities and to quantify the effect of such trends on the probability of a drought event occurring. Package: r-cran-spicy Architecture: all Version: 0.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2567 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-crayon, r-cran-dplyr, r-cran-labelled, r-cran-rlang, r-cran-sandwich, r-cran-stringr, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-broom, r-cran-clipr, r-cran-clubsandwich, r-cran-dt, r-cran-effectsize, r-cran-emmeans, r-cran-flextable, r-cran-gt, r-cran-haven, r-cran-htmltools, r-cran-knitr, r-cran-marginaleffects, r-cran-officer, r-cran-openxlsx2, r-cran-parameters, r-cran-performance, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tinytable, r-cran-withr Filename: pool/dists/noble/main/r-cran-spicy_0.12.0-1.ca2404.1_all.deb Size: 1747450 MD5sum: 1976d4f3bc13ef8ce71ca954f659fb64 SHA1: e6187c28fc6d6010073a1b426b3719f8e7658b88 SHA256: db6424c46081a04158d5db48fd2766cc634694999d2fc3ebf906a389a2ee8f0f SHA512: 2c8153de1814fbf9cdab0216c080fb8289f678ed130b33bb1bc42538cf954e0fa4657fd2bc140676c15eb81a430239a1654af873eb94c954d8250d3c1a69adc6 Homepage: https://cran.r-project.org/package=spicy Description: CRAN Package 'spicy' (Descriptive Statistics, Summary Tables, and Data ManagementTools) Provides tabulation, descriptive-summary, and variable-inspection tools for applied data analysis. Frequency tables and cross-tabulations with contingency-table association measures (Cramer's V, Phi, Goodman-Kruskal Gamma, Kendall's Tau-b, Somers' D, and others); categorical and continuous summary tables; regression coefficient tables for one or more 'lm' or 'glm' fits side by side; and outcome-by-group comparison tables from linear models with optional additive covariate adjustment. All table outputs follow APA conventions and expose 'broom'-compatible 'tidy()' / 'glance()' methods for downstream pipelines. Helpers cover interactive codebooks, variable-label extraction, clipboard export, and row-wise descriptive summaries. Package: r-cran-spider Architecture: all Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-pegas Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-spider_1.5.2-1.ca2404.1_all.deb Size: 257458 MD5sum: a928dc73677fa976b622de629e226e11 SHA1: 161602fe9ef57f95de2a89cd7f7b1a7310be3fd1 SHA256: cdf5c36e472b9c4a232d097f715af428a59e8052e408e4f940dec61e839e0c18 SHA512: eaad20583ac13e83325fcf05105a073eb709a9c16ca302f8ecd457b79e926015fc3a50b15c11e3bf459cd3b6ceaeb923163c11b2ec3fbce49b98bd7abac141dd Homepage: https://cran.r-project.org/package=spider Description: CRAN Package 'spider' (Species Identity and Evolution in R) Analysis of species limits and DNA barcoding data. Included are functions for generating important summary statistics from DNA barcode data, assessing specimen identification efficacy, testing and optimizing divergence threshold limits, assessment of diagnostic nucleotides, and calculation of the probability of reciprocal monophyly. Additionally, a sliding window function offers opportunities to analyse information across a gene, often used for marker design in degraded DNA studies. Further information on the package has been published in Brown et al (2012) . Package: r-cran-spidr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-rgbif, r-cran-rworldmap, r-cran-rworldxtra Filename: pool/dists/noble/main/r-cran-spidr_1.0.2-1.ca2404.1_all.deb Size: 72810 MD5sum: f743d12929bf44ac97c4481c8a5decf9 SHA1: 947611fd128a813e84b86f19f094ff1d19f6be2a SHA256: 14083292afbb27cf05425882828fbd003eec9b1ef7209b4294193b15f2a17b0b SHA512: 7c1241c82cf7dad9b35c3bfac20348c4b8e9d86c462db4d5c8c633a327f6664548d80abdc13395054784a69392d10f8669c29a1f61afd19440e4e42b4d6aa887 Homepage: https://cran.r-project.org/package=spidR Description: CRAN Package 'spidR' (Spider Knowledge Online) Allows the user to connect with the World Spider Catalogue (WSC; ) and the World Spider Trait (WST; ) databases. Also performs several basic functions such as checking names validity, retrieving coordinate data from the Global Biodiversity Information Facility (GBIF; ), and mapping. Package: r-cran-spiga Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ga Filename: pool/dists/noble/main/r-cran-spiga_1.0.0-1.ca2404.1_all.deb Size: 68396 MD5sum: 47d30aa7be6b70f36b0f461b7d404353 SHA1: 84b45afabb5244a6a4dbaa7b6ee296473cbec8f3 SHA256: a0b7de7ff9c0cc21bdf26db7342c93656b70a3944a6305836a7fac16ad11236a SHA512: 870e1cf3e5be706077fae4a302276c868030643e3cf66e9a4c42cdd9b80d607cf19e405d4ba0625e624ccf84ee94af2905eef751094653a0a5fa24b458708bc4 Homepage: https://cran.r-project.org/package=SPIGA Description: CRAN Package 'SPIGA' (Compute SPI Index using the Methods Genetic Algorithm andMaximum Likelihood) Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML). For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx. Package: r-cran-spikes Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 703 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-emdbook Filename: pool/dists/noble/main/r-cran-spikes_1.1-1.ca2404.1_all.deb Size: 687360 MD5sum: 48dc89d2fcc66ce6309a970d4b29cb32 SHA1: da4d87ffdaaa2ff30a0df9c21f8238eeb7de30cd SHA256: b7db9621f7d358359f49c8819ec3d7ecc27fe2f8ed1d2e08d7facd54d6fad8d4 SHA512: d1b83d6de6ea9b3b607f2cd72f64f968fde1807f7582693b1c257a5d2f10f4b0fa61014eb6146e6b1627e2b7353b10dddc88d12226763049586c0500fdceb0fa Homepage: https://cran.r-project.org/package=spikes Description: CRAN Package 'spikes' (Detecting Election Fraud from Irregularities in Vote-ShareDistributions) Applies re-sampled kernel density method to detect vote fraud. It estimates the proportion of coarse vote-shares in the observed data relative to the null hypothesis of no fraud. Package: r-cran-spillover Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 697 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vars, r-cran-dplyr, r-cran-ggplot2, r-cran-fastsom, r-cran-tidyr, r-cran-zoo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-spillover_0.1.1-1.ca2404.1_all.deb Size: 587122 MD5sum: 7233f15383c0ea1e87b3e4fc92acd572 SHA1: b7aba118be33a090ff02445051e7758546ac3767 SHA256: d6026b93150e5e4210eeb5469eac6185b6b9f088431445c7e74797e66202126e SHA512: bcb405a39d76c6a026dde8729c28302fb78aa172178e07bfca1a5e410eccd59512a98215edf6f74d571a57af2c6ee5bfa449c6296edc339665eed26cbb91825e Homepage: https://cran.r-project.org/package=Spillover Description: CRAN Package 'Spillover' (Spillover/Connectedness Index Based on VAR Modelling) A user-friendly tool for estimating both total and directional connectedness spillovers based on Diebold and Yilmaz (2009, 2012). It also provides the user with rolling estimation for total and net indices. User can find both orthogonalized and generalized versions for each kind of measures. See Diebold and Yilmaz (2009, 2012) find them at and . Package: r-cran-spina Architecture: all Version: 4.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spina_4.1.0-1.ca2404.1_all.deb Size: 61628 MD5sum: 26fd32bcbe1e50ce38ec0bd65d702e2f SHA1: 7189a70f2179dcf8e0021e69096d8d3a1ade780b SHA256: 9c2ee3036723bfa797c1698ce1782d7c109953bcff8117382711bf75a2de83cb SHA512: ea2922a1c9092e27ce3693711d534c720018a33b4a2f5ff6ff279007f4a7675e626079bee09c15d011b4a2e14adadd2ce0a8e0f5643007448cf0a64fc78628eb Homepage: https://cran.r-project.org/package=SPINA Description: CRAN Package 'SPINA' (Structure Parameter Inference Approach) Calculates constant structure parameters of endocrine homeostatic systems from equilibrium hormone concentrations. Methods and equations have been described in Dietrich et al. (2012) and Dietrich et al. (2016) . Package: r-cran-spinar Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-progress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-spinar_0.2.0-1.ca2404.1_all.deb Size: 130630 MD5sum: dfa4c3771d134b46dda4b5fccff9cd2e SHA1: 18e279607594be8c115201b2b9c4e32bbcb6b15d SHA256: d84789a0e61d88724238065fb0624711390edf3893755544b6d72a896d217384 SHA512: 3605ecbfdf98ef527d89698fd9c236fe3cafb56411a79198257f614dfac21f2c4e7f6cce39dc031d13f65c3c9bda479fa8a24d07c88574f6d0d695189f8f5aae Homepage: https://cran.r-project.org/package=spINAR Description: CRAN Package 'spINAR' ((Semi)Parametric Estimation and Bootstrapping of INAR Models) Semiparametric and parametric estimation of INAR models including a finite sample refinement (Faymonville et al. (2022) ) for the semiparametric setting introduced in Drost et al. (2009) , different procedures to bootstrap INAR data (Jentsch, C. and Weiß, C.H. (2017) ) and flexible simulation of INAR data. Package: r-cran-spind Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gee, r-cran-geepack, r-cran-mass, r-cran-splancs, r-cran-lattice, r-cran-waveslim, r-cran-rje, r-cran-stringr, r-cran-ggplot2, r-cran-rcolorbrewer, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra, r-cran-testthat, r-cran-presenceabsence, r-cran-sp, r-cran-covr Filename: pool/dists/noble/main/r-cran-spind_2.2.1-1.ca2404.1_all.deb Size: 337090 MD5sum: ee1fd2c2823d63002b5ff4e9c23bca0f SHA1: 08a131c526b3528684c8eb5f0e7201cfce184edb SHA256: 0aa08bd4ee5caa7a511056ee5bf11fb5b46ea0614a40cc237411d90b2f1bd7a4 SHA512: 569de53a821fbce2d7b7805216ae438af64357187abdbd0677068f63a50ccd1bbf55b0eca71bff152476f309d7e9668f2230a29c6bf89734ad4aeffbc444ad9c Homepage: https://cran.r-project.org/package=spind Description: CRAN Package 'spind' (Spatial Methods and Indices) Functions for spatial methods based on generalized estimating equations (GEE) and wavelet-revised methods (WRM), functions for scaling by wavelet multiresolution regression (WMRR), conducting multi-model inference, and stepwise model selection. Further, contains functions for spatially corrected model accuracy measures. Package: r-cran-spinebil Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2979 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tourr, r-cran-ggplot2, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-tictoc, r-cran-cassowaryr, r-cran-rlang Suggests: r-cran-minerva, r-cran-testthat, r-cran-purrr, r-cran-furrr, r-cran-future, r-cran-quarto, r-cran-knitr Filename: pool/dists/noble/main/r-cran-spinebil_1.0.5-1.ca2404.1_all.deb Size: 2240194 MD5sum: a9b280648c1370bdd071f9d83205d6f8 SHA1: ef96b92bc4ba7ee596db3e97670a7b654681e631 SHA256: 583187c8c59d1ba3396ac543eecb8bf9304db2a7fe615f043d25e7c7c12ab548 SHA512: d0adfa46aabd5ac905b6e22310919cf4fe98f11e85e549148981bb4198259e7429f9aa302fceb893548a2d614019f6645143871361f0be1b06cf3a534f6d45eb Homepage: https://cran.r-project.org/package=spinebil Description: CRAN Package 'spinebil' (Investigating New Projection Pursuit Index Functions) Projection pursuit is used to find interesting low-dimensional projections of high-dimensional data by optimizing an index over all possible projections. 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Package: r-cran-spinifex Architecture: all Version: 0.3.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1972 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tourr, r-cran-ggplot2, r-cran-gganimate, r-cran-plotly, r-cran-shiny, r-cran-rdimtools, r-cran-magrittr Suggests: r-cran-mass, r-cran-ggrepel, r-cran-patchwork, r-cran-gifski, r-cran-hexbin, r-cran-htmlwidgets, r-cran-png, r-cran-dplyr, r-cran-knitr, r-cran-ggally, r-cran-testthat, r-cran-lifecycle, r-cran-covr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-spinifex_0.3.10-1.ca2404.1_all.deb Size: 1188346 MD5sum: c545352f6e1a7fafe0b8fdea5bb81a62 SHA1: d35e6684e4e36a875393e33849a0013142e97263 SHA256: 52295bd2f1db5c20d72bf80028c2435e4effdec6e16eb6cee707e1ac39c27dd2 SHA512: b139dce95aa291038783e32e78e62c03640fe715bd4632c0e46794697a0e5ef382337f01ae9ab7ce0fd9b32567c116ee6d3faf545e445cafcfcee43738639d22 Homepage: https://cran.r-project.org/package=spinifex Description: CRAN Package 'spinifex' (Manual Tours, Manual Control of Dynamic Projections of NumericMultivariate Data) Data visualization tours animates linear projection of multivariate data as its basis (ie. orientation) changes. The 'spinifex' packages generates paths for manual tours by manipulating the contribution of a single variable at a time Cook & Buja (1997) . Other types of tours, such as grand (random walk) and guided (optimizing some objective function) are available in the 'tourr' package Wickham et al. . 'spinifex' builds on 'tourr' and can render tours with 'gganimate' and 'plotly' graphics, and allows for exporting as an .html widget and as an .gif, respectively. This work is fully discussed in Spyrison & Cook (2020) . Package: r-cran-spinner Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-torch, r-cran-purrr, r-cran-ggplot2, r-cran-ggthemes, r-cran-tictoc, r-cran-readr, r-cran-lubridate, r-cran-rlist, r-cran-fastdummies, r-cran-entropy, r-cran-abind Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-spinner_1.1.1-1.ca2404.1_all.deb Size: 186572 MD5sum: 88cfb85b5c320748ca5ace05599c95c2 SHA1: 96ca6f5d09c6b973a1f2675c736086b9431a52d6 SHA256: e73da94aa3200152aa114ac4e8c96e5f883c7839035946d00188a803fd502d7a SHA512: 5360e4e66be1a95b4165321cc621db949bb233dc950bbce0d0501203b357a9b341d90d743d0c2f908e33b2fcd2c40c46b1d4633bb9529fcf9d9658a7ccbbd5ec Homepage: https://cran.r-project.org/package=spinner Description: CRAN Package 'spinner' (An Implementation of Graph Net Architecture Based on 'torch') Proposes a 'torch' implementation of Graph Net architecture allowing different options for message passing and feature embedding. Package: r-cran-spinyreg Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spinyreg_0.1-0-1.ca2404.1_all.deb Size: 146024 MD5sum: b4826cd41c73e26d9bb27efbb2bbb5fd SHA1: fdb5a0dfa9647ab147030e59e169c3dc7da81bba SHA256: 803e6e6fee590ee4a7be53b92fb1560a7b9f2d70a80c30ea64cc94d59db4175a SHA512: e6e5ad7e9edc00d6340bf7213d767798320e21cac126142c1847af7131f95950b53f64c62bce3d2175dce94591a0c3153136414d1b58fd387cce61dc70ea8b56 Homepage: https://cran.r-project.org/package=spinyReg Description: CRAN Package 'spinyReg' (Sparse Generative Model and Its EM Algorithm) Implements a generative model that uses a spike-and-slab like prior distribution obtained by multiplying a deterministic binary vector. Such a model allows an EM algorithm, optimizing a type-II log-likelihood. Package: r-cran-spiralize Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-globaloptions, r-cran-getoptlong, r-cran-circlize, r-cran-lubridate, r-bioc-complexheatmap Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-grimport, r-cran-grimport2, r-cran-jpeg, r-cran-png, r-cran-tiff, r-cran-cranlogs, r-cran-cowplot, r-cran-dendextend, r-cran-bezier, r-cran-magick, r-cran-ape Filename: pool/dists/noble/main/r-cran-spiralize_1.1.1-1.ca2404.1_all.deb Size: 995304 MD5sum: 9d3855e383eb5e40d766693246310384 SHA1: cb89e55b793bcc3a9de1bb405325c518262e8846 SHA256: f0f3479db056b94f7ce584cf78d7233c8441099db85339628143305a84c6f6b2 SHA512: 0bbd60955fa20e0ab270bf7298fb6931f89a267f71bf087d7cb4acc15645ca3b29e8861c0e8f755b788d1448b6dbdefc008efaaa858a0b2e66d9c0d53ba99f40 Homepage: https://cran.r-project.org/package=spiralize Description: CRAN Package 'spiralize' (Visualize Data on Spirals) It visualizes data along an Archimedean spiral , makes so-called spiral graph or spiral chart. It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high resolution. 2. It is efficient for time series data to reveal periodic patterns. Package: r-cran-spiritr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-httr, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-spiritr_0.1.1-1.ca2404.1_all.deb Size: 61642 MD5sum: da8cf81fcc7af4e93a2726888c6a3eba SHA1: 24abd56eba9aaf2ca61a97edc3fbf1cdc9e1097e SHA256: 57ae487cd485b61995b8f58ac8391ffc63bf1162fad847b770a1e4179571da69 SHA512: 838f8bee3979a2ff4597eb81a27cbe912eb4d254760d5b33f2f4948b59cb32a613ab983f61078a0a51c0cfa6887a45db1aa311e5d26533733692522174f4b070 Homepage: https://cran.r-project.org/package=spiritR Description: CRAN Package 'spiritR' (Template for Clinical Trial Protocol) Contains an R Markdown template for a clinical trial protocol adhering to the SPIRIT statement. 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Package: r-cran-spiro Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2331 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-xml2, r-cran-readxl, r-cran-knitr, r-cran-cowplot, r-cran-digest, r-cran-signal Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-rmarkdown, r-cran-ggborderline Filename: pool/dists/noble/main/r-cran-spiro_0.2.3-1.ca2404.1_all.deb Size: 1111726 MD5sum: bc5193e54251de1ddfc48abe346b097a SHA1: b2450f5fbeb964e9372dd671ae0bcbd684624565 SHA256: b019ced32a37217302f86b4392aa8acd9dac2f24b00eeaf4cb2b5f667836a07d SHA512: 5899cc8fb371f7d367094e4e32035ffb0a30e3fc3dd758292a087808a611d26779c62d64a2d5718b734cf37acc0850787e88749f3e74c4198da8ee6f3b14a13f Homepage: https://cran.r-project.org/package=spiro Description: CRAN Package 'spiro' (Manage Data from Cardiopulmonary Exercise Testing) Import, process, summarize and visualize raw data from metabolic carts. See Robergs, Dwyer, and Astorino (2010) for more details on data processing. Package: r-cran-splash Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-splash_1.0.2-1.ca2404.1_all.deb Size: 86926 MD5sum: c3c5c276e3a5e074bddfaeb66d75edfd SHA1: 2491a6d18c016b917764b956aa402ec849e41fc6 SHA256: a686fcd02d87d9b14daafb504d6927acbdd72578e5707ee5d1f636d5b80caa78 SHA512: ec0df744698b43f1f60598a5b22314718c32ca4f9d078304d6593dfd24d28acfdfd12753d93e3275ce61e41c9353607646d49986614f2cedd2a7a51400ed354a Homepage: https://cran.r-project.org/package=splash Description: CRAN Package 'splash' (Simple Process-Led Algorithms for Simulating Habitats) This program calculates bioclimatic indices and fluxes (radiation, evapotranspiration, soil moisture) for use in studies of ecosystem function, species distribution, and vegetation dynamics under changing climate scenarios. Predictions are based on a minimum of required inputs: latitude, precipitation, air temperature, and cloudiness. Davis et al. (2017) . Package: r-cran-spldv Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-formula, r-cran-matrix, r-cran-maxlik, r-cran-sphet, r-cran-memisc, r-cran-car, r-cran-numderiv, r-cran-mass, r-cran-spatialreg Suggests: r-cran-spdep Filename: pool/dists/noble/main/r-cran-spldv_0.1.3-1.ca2404.1_all.deb Size: 112166 MD5sum: 7dbd2bc264c35e482ca4f19df1b0eaed SHA1: 2c94e90cbbc6e0d3b76938def065730c181a2c96 SHA256: 32496f8bd90c7277311f7b32ffafd78a1152328f993f7bb3c5c5c39008244805 SHA512: 69b8be166d3cbc684121c80551e334d1aaf3703de2043ae459e186253ebcfe76b57a2bb87e3f8b9124e6b71ce7d54e219b18b207e45ba29b7b045c8e1308486e Homepage: https://cran.r-project.org/package=spldv Description: CRAN Package 'spldv' (Spatial Models for Limited Dependent Variables) The current version of this package estimates spatial autoregressive models for binary dependent variables using GMM estimators . It supports one-step (Pinkse and Slade, 1998) and two-step GMM estimator along with the linearized GMM estimator proposed by Klier and McMillen (2008) . It also allows for either Probit or Logit model and compute the average marginal effects. All these models are presented in Sarrias and Piras (2023) . Package: r-cran-splice Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2711 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-synthetic, r-cran-zoo, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-rcolorbrewer, r-cran-actuar Filename: pool/dists/noble/main/r-cran-splice_1.1.2-1.ca2404.1_all.deb Size: 2059012 MD5sum: fce01b3ef634f5b7ae7c5e344bb7eb8c SHA1: 707ec15eda5fce6e0de2ee9098af771c056a7388 SHA256: ec22c459425a1f164a37a0623d48f33877121097fe4d390a965be1197f753758 SHA512: d4b5b7537a660c9f7fcb4600f761bfc95defafa44ef91bf3686a687261520d0f3c2ae77b54e59e6d052add1bd65aafefb97f6bc384029f1e59b80a28e6e8ae79 Homepage: https://cran.r-project.org/package=SPLICE Description: CRAN Package 'SPLICE' (Synthetic Paid Loss and Incurred Cost Experience (SPLICE)Simulator) An extension to the individual claim simulator called 'SynthETIC' (on CRAN), to simulate the evolution of case estimates of incurred losses through the lifetime of an insurance claim. The transactional simulation output now comprises key dates, and both claim payments and revisions of estimated incurred losses. An initial set of test parameters, designed to mirror the experience of a real insurance portfolio, were set up and applied by default to generate a realistic test data set of incurred histories (see vignette). However, the distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M (2021) "SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator" . Package: r-cran-splinecox Architecture: all Version: 0.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-joint.cox Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-splinecox_0.0.8-1.ca2404.1_all.deb Size: 83616 MD5sum: 8ed725ed5cab6d38898a4fcecb483455 SHA1: 764822b5d4242b168b961b12ccf220bb8f6e1c54 SHA256: baa27e4ae1e75e07c7da87497a00cc378e77f1137d20465fec874b4c34dd2b4c SHA512: 98edb7a49547fa375a40284d2153401b963ba133a978ff8f04f1e2f5c987f04de052d91e031412a288691eecfe7245f307bb373c489f29f948aa64d20b3466e8 Homepage: https://cran.r-project.org/package=splineCox Description: CRAN Package 'splineCox' (A Two-Stage Estimation Approach to Cox Regression Using M-SplineFunction) Implements a two-stage estimation approach for Cox regression using five-parameter M-spline functions to model the baseline hazard. It allows for flexible hazard shapes and model selection based on log-likelihood criteria as described in Teranishi et al.(2025). In addition, the package provides functions for constructing and evaluating B-spline copulas based on five M-spline or I-spline basis functions, allowing users to flexibly model and compute bivariate dependence structures. Both the copula function and its density can be evaluated. Furthermore, the package supports computation of dependence measures such as Kendall's tau and Spearman's rho, derived analytically from the copula parameters. Package: r-cran-splinemixmeta Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-mgcv, r-cran-mixmeta Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-splinemixmeta_1.0.1-1.ca2404.1_all.deb Size: 92560 MD5sum: ce97fbb2895d6bd248a3b41777710aa1 SHA1: 9dc485d6b16a9538d7625290858f4a5f01d0fe18 SHA256: 0afa4d22bff75aa9389cf31b4b175c036515330ec279bdeb8a0ba9ca4484ee9e SHA512: 9a1eb303a86adcd18bfe864a71422d32482b2526cbe643673e8c8a9c8797aa89cc9f9e5b6eeb770549f684de0e4d3e2f0599f6b54e0fe36939c919695f4248c8 Homepage: https://cran.r-project.org/package=splinemixmeta Description: CRAN Package 'splinemixmeta' (Additive Mixed Meta-Analysis with Spline Meta-Regression) Fit additive mixed meta-analysis (AMMA) models, extending the 'mixmeta' package to allow for spline-based meta-regression. Functions combine features of 'mgcv' for building spline components and 'mixmeta' for estimating general mixed-effects meta-analysis models. Package: r-cran-splineplot Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 366 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-mgcv, r-cran-survival, r-cran-survey, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-splineplot_0.3.0-1.ca2404.1_all.deb Size: 231512 MD5sum: 0515cbc98ff99457ad442bdca0e3b362 SHA1: d4cb108fd11232e7546a8cf1c325f4564010f2ff SHA256: 769b162209ac4e3201aba5beac7f9349e693700227b07c1eb20261d2239d6713 SHA512: f1722b01ed7eed0f9806c8c5df9b7e1b7150a1d3096e18c836ae7c264973ce536fbe3b4b26b71edeadabd8ffe8d05ba3a7e948b2e919b71f5ad809254a4fe039 Homepage: https://cran.r-project.org/package=splineplot Description: CRAN Package 'splineplot' (Visualization of Spline Effects in GAM and GLM Models) Creates 'ggplot2'-based visualizations of smooth effects from GAM (Generalized Additive Models) fitted with 'mgcv' and spline effects from GLM (Generalized Linear Models). Supports survey-weighted models ('svyglm', 'svycoxph') from the 'survey' package, interaction terms, and provides hazard ratio plots with histograms for survival analysis. Wood (2017, ISBN:9781498728331) provides comprehensive methodology for generalized additive models. Package: r-cran-splinetree Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpart, r-cran-nlme, r-cran-mosaic, r-cran-ggplot2, r-cran-treeclust, r-cran-mclust Suggests: r-cran-r.rsp, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-splinetree_0.2.0-1.ca2404.1_all.deb Size: 2434240 MD5sum: d6e7a99cb8ea3a70011d381ad4eb4ebc SHA1: 38ff442bc287da234c4e16d24ee54e6a1fb048d8 SHA256: ed79dc98e199e9a85050b83fdfa6505cef5be228547fa8e4ef7b26de75dbbdc3 SHA512: 316bf81ba2cb79a456e803e7e018fd40385beeb3890853ee2f4c5abb8dc776d8eb54d116e1f0d2df326a983f56072d23e400ece2f49030bafb8b22954f3bff99 Homepage: https://cran.r-project.org/package=splinetree Description: CRAN Package 'splinetree' (Longitudinal Regression Trees and Forests) Builds regression trees and random forests for longitudinal or functional data using a spline projection method. 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Package: r-cran-splinetrials Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-cli, r-cran-dplyr, r-cran-emmeans, r-cran-mmrm, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-survival, r-cran-testthat Filename: pool/dists/noble/main/r-cran-splinetrials_0.1.1-1.ca2404.1_all.deb Size: 331822 MD5sum: 34b503fa6fc5c1e984147f843cf82608 SHA1: 25fa05bbad173c84c5e45ef5b1e4d42cbe2cd7cc SHA256: 2536f2a36a5270f0a209375b6207fec69f3098e89737f4806ac0c404ea363252 SHA512: e06b4f5175ef875117052ca0fe0003d48336bb65721b6d99998ff20b5511c60995839ce4d33ca32048fe2e58cd56f05965a99de4cdbd458cc59aad03445ad67e Homepage: https://cran.r-project.org/package=splinetrials Description: CRAN Package 'splinetrials' (Facilitate Clinical Trials Analysis Using Natural Cubic Splines) Create mixed models with repeated measures using natural cubic splines applied to an observed continuous time variable, as described by Donohue et al. 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Package: r-cran-splinets Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4113 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-splinets_1.5.1-1.ca2404.1_all.deb Size: 4097690 MD5sum: 295f748ebc83263bf3c1abfd4cecf2ba SHA1: e5c64041241837951e5e16db1ce13e2b894fdf20 SHA256: b93b9ed777c88fcf052863a2408108ac2c28e7ad46968b21b1d45640e3358f97 SHA512: 3c8046ba55fd7e12fd43277a51475428f02eec63ce33c2addd54a41205dff1d3eb7a2758aa03972a1c5b342f4a1ae9b242f050f382498a80e0261872805df626 Homepage: https://cran.r-project.org/package=Splinets Description: CRAN Package 'Splinets' (Functional Data Analysis using Splines and Orthogonal SplineBases) Splines are efficiently represented through their Taylor expansion at the knots. The representation accounts for the support sets and is thus suitable for sparse functional data. Two cases of boundary conditions are considered: zero-boundary or periodic-boundary for all derivatives except the last. The periodical splines are represented graphically using polar coordinates. The B-splines and orthogonal bases of splines that reside on small total support are implemented. The orthogonal bases are referred to as 'splinets' and are utilized for functional data analysis. Random spline generator is implemented as well as all fundamental algebraic and calculus operations on splines. The optimal, in the least square sense, functional fit by 'splinets' to data consisting of sampled values of functions as well as splines build over another set of knots is obtained and used for functional data analysis. The S4-version of the object oriented R is used. , , . Package: r-cran-splitfngr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lbfgs Filename: pool/dists/noble/main/r-cran-splitfngr_0.1.2-1.ca2404.1_all.deb Size: 24916 MD5sum: 1b3ed6b25528315f371f5954dbe5a67c SHA1: 00b47691aed58c36239a7528b3389f6160fba2cc SHA256: 1d7805b907648c9c721572d556c35fe30135f8802a0f8ff3c30a0ab9dc600602 SHA512: d557b6754e7a2c6489aec7118270370b5e395e230e1c7b9be94ca7cc502e5e1994e23bff4a23c2e497746a9b2db2fac6edebaff2c3a25b10ec409f30b1d73248 Homepage: https://cran.r-project.org/package=splitfngr Description: CRAN Package 'splitfngr' (Combined Evaluation and Split Access of Functions) Some R functions, such as optim(), require a function its gradient passed as separate arguments. 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The 'splithalfr' supports researcher-provided scoring algorithms, with six vignettes illustrating how on included datasets. The package provides four splitting methods (first-second, odd-even, permutated, Monte Carlo), the option to stratify splits by task design, a number of reliability coefficients, the option to sub-sample data, and bootstrapped confidence intervals. Package: r-cran-splitknockoff Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mass, r-cran-latex2exp, r-cran-rspectra, r-cran-ggplot2, r-cran-matrix, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-splitknockoff_2.1-1.ca2404.1_all.deb Size: 79856 MD5sum: a90def7fd0d6cd5844d5dd2f9a14a90b SHA1: d1731926691968d20714212debb0f2b130e43873 SHA256: 73598e6cd854094d3efe490c7efae40152d2771fbbcbdee7bf9bb1dbcd9ee02f SHA512: a90277eee8077da715bed151a7c5cd766750016b8e571a5a6bbd4c4d331023e59189425ee072d4a2451fd74189e404a624405d1551bb08859cbccad98162e7c5 Homepage: https://cran.r-project.org/package=SplitKnockoff Description: CRAN Package 'SplitKnockoff' (Split Knockoffs for Structural Sparsity) Split Knockoff is a data adaptive variable selection framework for controlling the (directional) false discovery rate (FDR) in structural sparsity, where variable selection on linear transformation of parameters is of concern. This proposed scheme relaxes the linear subspace constraint to its neighborhood, often known as variable splitting in optimization. Simulation experiments can be reproduced following the Vignette. 'Split Knockoffs' is first defined in Cao et al. (2021) . 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Package: r-cran-splitselect Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-multicool, r-cran-glmnet, r-cran-doparallel, r-cran-foreach Suggests: r-cran-testthat, r-cran-mvnfast Filename: pool/dists/noble/main/r-cran-splitselect_1.0.3-1.ca2404.1_all.deb Size: 84000 MD5sum: acadafcc611cda2edf23cedc286a256d SHA1: e1e9b06ccc3d213d34e12c7d0b347aac9e412a07 SHA256: 064e796020648ff15bba4bc07947af0795bad3d852ad9f1b50da82d09ef0cfa7 SHA512: 2ffa33de57baa12f6c1806c832655d214236ad80678fc2b885a2a9473fef9cef56d374edfdb5f819ee04e1e0215f5d963392d78f0dfa2f1ee96e59610c7c0050 Homepage: https://cran.r-project.org/package=splitSelect Description: CRAN Package 'splitSelect' (Best Split Selection Modeling for Low-Dimensional Data) Functions to generate or sample from all possible splits of features or variables into a number of specified groups. 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The package focuses exclusively on Monte Carlo simulation experiment variants of (expected) prospective power analyses, criterion analyses, compromise analyses, sensitivity analyses, and a priori/post-hoc analyses. The default simulation experiment functions defined within the package provide stochastic variants of the power analysis subroutines in G*Power 3.1 (Faul, Erdfelder, Buchner, and Lang, 2009) , along with various other parametric and non-parametric power analysis applications (e.g., mediation analyses) and support for Bayesian power analysis by way of Bayes factors or posterior probability evaluations. Additional functions for building empirical power curves, reanalyzing simulation information, and for increasing the precision of the resulting power estimates are also included, each of which utilize similar API structures. For further details see the associated publication in Chalmers (2025) . 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See Hui Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford, Rong Wang, Harvey Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical Association, 109:505, 11-23, . Package: r-cran-spphpr Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spphpr_1.1.5-1.ca2404.1_all.deb Size: 342720 MD5sum: f72e34e87efa238b847490a66c47b203 SHA1: 05fbb7d3eafbfc4dee6b9c9b8a6b2e5f995d714d SHA256: f750c679dd645b4b720206a702ea21313e317585c48624a37256c545b26e51cb SHA512: cd2dcbf2e3fc246b0fcc3e5d35b2254bf2a0583a2a16094fec387e70917eb5607a09fca1c59a50ae0d6da92527507d3307762bbfb3578802b259eda62f551449 Homepage: https://cran.r-project.org/package=spphpr Description: CRAN Package 'spphpr' (Spring Phenological Prediction) Predicts the occurrence times (in day-of-year) of spring phenological events. Three methods, including the accumulated degree days (ADD) method, the accumulated days transferred to a standardized temperature (ADTS) method, and the accumulated developmental progress (ADP) method, were used. See Shi et al. (2017a) and Shi et al. (2017b) for details. Package: r-cran-sppop Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qpdf, r-cran-numbers Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sppop_0.1.0-1.ca2404.1_all.deb Size: 59762 MD5sum: 09d931efe80aed22d7f1b01c5f8808ab SHA1: e7d8fb971462618a74b0486ec764b308ef04a843 SHA256: 6fe398ad8806b0f1d63985f9f50a63c96174087c6e343e5603866dfccb6ef8a0 SHA512: 010451b968d09ea72440977ae00c076bc1f4b0b708e018656cf02f7f251afdbe101b280c11c9d777ca8e61fc5a2b0401bd7ee79007847b33ac52cbd1b3cefc0d Homepage: https://cran.r-project.org/package=SpPOP Description: CRAN Package 'SpPOP' (Generation of Spatial Population under Different Levels ofRelationships among Variables) The developed package can be used to generate a spatial population for different levels of relationships among the dependent and auxiliary variables along with spatially varying model parameters. A spatial layout is designed as a [0,k-1]x[0,k-1] square region on which observations are collected at (k x k) lattice points with a unit distance between any two neighbouring points along the horizontal and vertical axes. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).. The generated spatial population can be utilized in Geographically Weighted Regression model based analysis for studying the spatially varying relationships among the variables. Furthermore, various statistical analysis can be performed on this spatially generated data. Package: r-cran-spptrend Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3920 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-terra, r-cran-stringr, r-cran-tidyselect, r-cran-ggplot2, r-cran-patchwork, r-cran-sf Suggests: r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-spptrend_0.4-1.ca2404.1_all.deb Size: 2875760 MD5sum: 34e01b4dabeaa14a6c3fc2b9e42fa1ab SHA1: 204f2df06e558ee0169089b6ab4b6185eb16cb45 SHA256: ffce552f3dea5149cce8379cb2cb310ec2b2e03498e249725613e25a3d3ace71 SHA512: 21334f35d26e0fa4f6a94a3c97ff65812bc5b74a5aaaafe2c8650b71fdcbfde092633b2b63708ec9c6916279d860342842fa9a81d4ad88d86713880880a10675 Homepage: https://cran.r-project.org/package=SppTrend Description: CRAN Package 'SppTrend' (Analyzing Linear Trends in Species Occurrence Data) Provides a methodology to analyze how species occurrences change over time, particularly in relation to spatial and thermal factors. It facilitates the development of explanatory hypotheses about the impact of environmental shifts on species by analyzing historical presence data that includes temporal and geographic information. Approach described in Lobo et al., 2023 . Package: r-cran-spqdep Architecture: all Version: 0.1.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1601 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-ggplot2, r-cran-gt, r-cran-gtools, r-cran-igraph, r-cran-magrittr, r-cran-matrix, r-cran-purrr, r-cran-rsample, r-cran-sf, r-cran-sp, r-cran-spatialreg, r-cran-spdep, r-cran-tidyr, r-cran-units Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spqdep_0.1.3.6-1.ca2404.1_all.deb Size: 1318258 MD5sum: 4960022b1eed4cb3e8a2c3c7a97a9738 SHA1: dfeca9b0469bbaeda95c50e960b14ed0267b8c60 SHA256: e4b5d24480b44e6d876f02aa21bcb955898b5287fc2c8f8a8794e792c66dc3d1 SHA512: a75b23d4ab840ab5b906e7d436d343ced6a669a6449d96f15dbadb893a0b9bf446101f13ae1a403a14317375f9822b3f4c20ecb97904ce1f55b7b7df9f60a468 Homepage: https://cran.r-project.org/package=spqdep Description: CRAN Package 'spqdep' (Testing for Spatial Independence of Cross-Sectional QualitativeData) Testing for Spatial Dependence of Qualitative Data in Cross Section. The list of functions includes join-count tests, Q test, spatial scan test, similarity test and spatial runs test. The methodology of these models can be found in and . Package: r-cran-spreda Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 896 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-nlme Filename: pool/dists/noble/main/r-cran-spreda_1.2-1.ca2404.1_all.deb Size: 858618 MD5sum: e2cf9cf0b8c7b1dff0bbfb2eb873b38a SHA1: 207998734505413729df451dcb2a2a83981f0463 SHA256: a03ead52b128e8286e66f553c56fa9906aeecdd0e2400265a51c28bd3bcc4181 SHA512: 1e636c57343984cc375a3a47a05ff56e58cca98116cc2416b08eb8d353f1666bfdc118e5405e52be5cb6882235791bde877e01d92e6ad8ef761d08e13c13d423 Homepage: https://cran.r-project.org/package=SPREDA Description: CRAN Package 'SPREDA' (Statistical Package for Reliability Data Analysis) The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged. Package: r-cran-spreg Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sn, r-cran-ucminf Filename: pool/dists/noble/main/r-cran-spreg_1.0-1.ca2404.1_all.deb Size: 31190 MD5sum: 10d8c165c2a3bd4ad0665c4db8bad009 SHA1: 954b7ab34b0e814b7ae398d10ed3732b45da4e65 SHA256: ce2dbdf3ecb7884093e9541343621eb2e676b128692d3a54858da3d9fac9044a SHA512: 135716a1a79110fc6389112370b3292d877432ff3cf3b84eb5303c5617c6a993a6b953475fb812b23bc4b8c772b75ef52932e9fb978b869411454201c6d68882 Homepage: https://cran.r-project.org/package=SPreg Description: CRAN Package 'SPreg' (Bias Reduction in the Skew-Probit Model for a Binary Response) Provides a function for the estimation of parameters in a binary regression with the skew-probit link function. Naive MLE, Jeffrey type of prior and Cauchy prior type of penalization are implemented, as described in DongHyuk Lee and Samiran Sinha (2019+) . Package: r-cran-spreval Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1103 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-timedate, r-cran-interp Suggests: r-cran-fields, r-cran-knitr, r-cran-rmarkdown, r-cran-plotrix Filename: pool/dists/noble/main/r-cran-spreval_1.1.0-1.ca2404.1_all.deb Size: 687742 MD5sum: 069008bb48cb07b09b6d6b941f83922e SHA1: 4169f2d19858b60e7f8398269d3809238e449cf5 SHA256: 997a7573700d7ab4f43864572c0da2571daf17a50fdb9d9c1c640ec69c5c285e SHA512: 3c43c92e55f287aac895ecd48344b9ce32eed697a17a493f2928e2c63fde46c6e52c29861a4614e3ce269c2bd4de65638602408b47761bfad2cfae77ad5e2855 Homepage: https://cran.r-project.org/package=spreval Description: CRAN Package 'spreval' (Evaluation of Sprinkler Irrigation Uniformity and Efficiency) Processing and analysis of field collected or simulated sprinkler system catch data (depths) to characterize irrigation uniformity and efficiency using standard and other measures. Standard measures include the Christiansen coefficient of uniformity (CU) as found in Christiansen, J.E.(1942, ISBN:0138779295, "Irrigation by Sprinkling"); and distribution uniformity (DU), potential efficiency of the low quarter (PELQ), and application efficiency of the low quarter (AELQ) that are implementations of measures of the same notation in Keller, J. and Merriam, J.L. (1978) "Farm Irrigation System Evaluation: A Guide for Management" . spreval::DU.lh is similar to spreval::DU but is the distribution uniformity of the low half instead of low quarter as in DU. spreval::PELQT is a version of spreval::PELQ adapted for traveling systems instead of lateral move or solid-set sprinkler systems. The function spreval::eff is analogous to the method used to compute application efficiency for furrow irrigation presented in Walker, W. and Skogerboe, G.V. (1987,ISBN:0138779295, "Surface Irrigation: Theory and Practice"),that uses piecewise integration of infiltrated depth compared against soil-moisture deficit (SMD), when the argument "target" is set equal to SMD. The other functions contained in the package provide graphical representation of sprinkler system uniformity, and other standard univariate parametric and non-parametric statistical measures as applied to sprinkler system catch depths. A sample data set of field test data spreval::catchcan (catch depths) is provided and is used in examples and vignettes. Agricultural systems emphasized, but this package can be used for landscape irrigation evaluation, and a landscape (turf) vignette is included as an example application. Package: r-cran-sprex Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-swfscmisc Filename: pool/dists/noble/main/r-cran-sprex_1.4.3-1.ca2404.1_all.deb Size: 73138 MD5sum: 2f02dcc8caca2a846dce0f86f1118d26 SHA1: 706c8a2ce938ee999a59adfc35eb307c6b5f6bb0 SHA256: c9fbb8c5f6d0c0dfb5b7d534584dfa5a77b6d2e395394a0d78d20bfd7ea649e3 SHA512: f714e1b99618f67a01eb5057e303a723e873aa6d4cb7a8408c35bc3e2ff838e93c4062dd4478c4857db3ec0590c48975df4d30b7ce758f2e9161423980104e2c Homepage: https://cran.r-project.org/package=sprex Description: CRAN Package 'sprex' (Species Richness and Extrapolation) Functions for calculating species richness for rarefaction and extrapolation, primarily non-parametric species richness such as jackknife, Chao1, and ACE. Also available are functions for plotting species richness and extrapolation curves, and computing standard diversity and entropy indices. Package: r-cran-springpheno Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-springpheno_0.5.0-1.ca2404.1_all.deb Size: 149806 MD5sum: 25c629c68d97566e06d1e10f8ff7a342 SHA1: da7fd6daf8426bcc97d6d9f135e93ca06216fe7f SHA256: b853879fa9faf113470bd7ec35b386ea24856534c1117b785d0be04d84e483bf SHA512: c49effbdeb210eee0186e9b0ac1e2992cf684d2522c05b394f7f128da5393ccd31033b30053fb66c9776ca2881bfb25c5ced0bf96d150e07ef6e5db1268e4b71 Homepage: https://cran.r-project.org/package=springpheno Description: CRAN Package 'springpheno' (Spring Phenological Indices) Computes the extended spring indices (SI-x) and false spring exposure indices (FSEI). The SI-x indices are standard indices used for analysis in spring phenology studies. In addition, the FSEI is also from research on the climatology of false springs and adjusted to include an early and late false spring exposure index. The indices include the first leaf index, first bloom index, and false spring exposure indices, along with all calculations for all functions needed to calculate each index. The main function returns all indices, but each function can also be run separately. Allstadt et al. (2015) Ault et al. (2015) Peterson and Abatzoglou (2014) Schwarz et al. (2006) Schwarz et al. (2013) . Package: r-cran-springsteen Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 992 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-devtools, r-cran-rlang Suggests: r-cran-tidytext, r-cran-dplyr, r-cran-lubridate, r-cran-readr, r-cran-stringr, r-cran-magrittr, r-cran-tidyselect Filename: pool/dists/noble/main/r-cran-springsteen_0.1.0-1.ca2404.1_all.deb Size: 728000 MD5sum: 5ecd31d472e395cb8457c432342a62cc SHA1: 48fcea4f034f62da86e855d7cdd3cca382e13215 SHA256: 56d7cfd900366070a3c370a593e49851aedb757eacaf11ba56121e40ac51ed74 SHA512: 07a73d122e6f4d00aee07c9c13c03ed909ad96404d8f3329390806700b12573105aae5b02e9ac3dbd3b91a9aa8faa65aac1747466e70e0ea9da2ce2c5bb522d6 Homepage: https://cran.r-project.org/package=spRingsteen Description: CRAN Package 'spRingsteen' (All Things Data and Springsteen) An R data package containing setlists from all Bruce Springsteen concerts over 1973-2021. Also includes all his song details such as lyrics and albums. Data extracted from: . Package: r-cran-sprt Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sprt_1.1.0-1.ca2404.1_all.deb Size: 34014 MD5sum: e0f436b10013c0976d754cd6b882f15b SHA1: c6cfc118b6c285438b45b60487ce8936251631dc SHA256: af23b34caaefe060682b636aed9f21158f62a4bd29d5b753660616686d8690a2 SHA512: f079f77faa3b79fa241297a4317838a1de0ae04eeb1a61c2c7489f938c2130f56b5133b01d398b9d6b027cc71a9d9438a28b8231bfa3d1cb387b067cf0aab19d Homepage: https://cran.r-project.org/package=SPRT Description: CRAN Package 'SPRT' (Sequential Probability Ratio Test (SPRT) Method) Provides functions to perform the Sequential Probability Ratio Test (SPRT) for hypothesis testing in Binomial, Poisson and Normal distributions. The package allows users to specify Type I and Type II error probabilities, decision thresholds, and compare null and alternative hypotheses sequentially as data accumulate. It includes visualization tools for plotting the likelihood ratio path and decision boundaries, making it easier to interpret results. The methods are based on Wald (1945) , who introduced the SPRT as one of the earliest and most powerful sequential analysis techniques. This package is useful in quality control, clinical trials, and other applications requiring early decision-making.The term 'SPRT' is an abbreviation and used intentionally. Package: r-cran-sprtt Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6298 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-rappdirs, r-cran-dplyr, r-cran-mbess, r-cran-purrr, r-cran-glue, r-cran-ggplot2, r-cran-lifecycle, r-cran-knitr, r-cran-rmarkdown, r-cran-gt, r-cran-ggtext, r-cran-scales, r-cran-piggyback Suggests: r-cran-testthat, r-cran-testthis, r-cran-withr, r-cran-xml2, r-cran-effsize, r-cran-effectsize, r-cran-vdiffr, r-cran-diffviewer Filename: pool/dists/noble/main/r-cran-sprtt_0.3.1-1.ca2404.1_all.deb Size: 1648986 MD5sum: 9614bd5018e6f80c33501bc5950ecebe SHA1: a3540c3e6a66a98fa994a2a00365dda22eb7812f SHA256: 51f3c10d77fd5cfd491420f84ebafdcb32a01340fa0d1723d76da587c9b3599a SHA512: b1026d630be87c49f12d66efd8f7ce98895a03932d4483fcb141f0d4b4e55d999bd495902c461933904a161d63c08dbd1cb7bf9ae7b8f3bedb6f0a1e226126b8 Homepage: https://cran.r-project.org/package=sprtt Description: CRAN Package 'sprtt' (Sequential Probability Ratio Tests Toolbox) A toolbox for Sequential Probability Ratio Tests (SPRT) based on Wald (1945) . SPRTs are applied during the sampling process, ideally after each observation, and at every stage return a decision to either continue sampling or terminate and accept one of the specified hypotheses. The `seq_ttest()` function performs one-sample, two-sample, and paired t-tests for one- and two-sided hypotheses (Schnuerch & Erdfelder (2019) ). The `seq_anova()` function performs a sequential one-way fixed effects ANOVA (Steinhilber et al. (2024) ). The `plan_sample_size()` function helps plan sequential studies by simulating required sample sizes across a range of effect sizes. For more information, see the vignettes browseVignettes(package = "sprtt") or the package website . Package: r-cran-sps Architecture: all Version: 0.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-kit, r-cran-knitr, r-cran-quarto, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sps_0.6.3-1.ca2404.1_all.deb Size: 262284 MD5sum: 53242078f0dcbe691ab8d9e446ea6cd1 SHA1: 6f552fb12a64ee581fdec31d6c7267d2f6c6c24c SHA256: e7c3826c2036ed4cfe4a961d08111098cde1d71d82d57344f594d45fb4d903c3 SHA512: 1ab761f7a52162230ea808d256808fc8b3627f7003bccf95234fee2267f831056c03cd843b75a1b8a8a1bb0e09180c215ad955fe2053e7425a1854978f82eda5 Homepage: https://cran.r-project.org/package=sps Description: CRAN Package 'sps' (Sequential Poisson Sampling) Sequential Poisson sampling is a variation of Poisson sampling for drawing probability-proportional-to-size samples with a given number of units, and is commonly used for price-index surveys. This package gives functions to draw stratified sequential Poisson samples according to the method by Ohlsson (1998, ISSN:0282-423X), as well as other order sample designs by Rosén (1997, ), and generate approximate bootstrap replicate weights according to the generalized bootstrap method by Beaumont and Patak (2012, ). Package: r-cran-spscomps Architecture: all Version: 0.3.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 453 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-assertthat, r-cran-stringr, r-cran-glue, r-cran-magrittr, r-cran-shinytoastr, r-cran-shinyace, r-cran-htmltools, r-cran-r6, r-cran-crayon Suggests: r-cran-testthat, r-cran-shinyjqui, r-cran-spsutil Filename: pool/dists/noble/main/r-cran-spscomps_0.3.4.0-1.ca2404.1_all.deb Size: 286238 MD5sum: 16962e4b14cad85cd5614ce2d43ffdc5 SHA1: 480d1f408fb6e35e349a37cd2194eb8899bd0585 SHA256: 599d4f086302c6629287e331c3e14c85386f5569a2137443f7f0adeb31a59a7f SHA512: 205efc5a7d4c83002a050af469698b6a7bd2fa29ab93f5e0c858d9503de6acacc8b74511007503940f0f13a3699431d3ef921aec596496861c8c97435bc35cbd Homepage: https://cran.r-project.org/package=spsComps Description: CRAN Package 'spsComps' ('systemPipeShiny' UI and Server Components) The systemPipeShiny (SPS) framework comes with many UI and server components. However, installing the whole framework is heavy and takes some time. If you would like to use UI and server components from SPS in your own Shiny apps, do not hesitate to try this package. Package: r-cran-spselect Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tester, r-cran-magic, r-cran-pracma Filename: pool/dists/noble/main/r-cran-spselect_0.0.1-1.ca2404.1_all.deb Size: 92756 MD5sum: 255649a2bc83dc1567c3ff13f7c36ff4 SHA1: 88919a0554edf1ec4a5f53b4d942bce3859a20a7 SHA256: cfbae475c99c85e17e309d499384e762f0600ea38efa0dc31e3fd8476f42382f SHA512: 5e02c3be78f106f9f7efa94c80e103162919f361c6e7289d131fe6ef623ec76b92322b16cb2a1d44da9f0c9f4fb4e97e390a603ba8ec828b14347ce13d995f52 Homepage: https://cran.r-project.org/package=spselect Description: CRAN Package 'spselect' (Selecting Spatial Scale of Covariates in Regression Models) Fits spatial scale (SS) forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso models. All area-level covariates are considered at all available scales to enter a model, but the SS algorithms are constrained to select each area-level covariate at a single spatial scale. Package: r-cran-spsh Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-deoptim, r-cran-lhs, r-cran-pracma, r-cran-fme, r-cran-hypergeo, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-spsh_1.1.0-1.ca2404.1_all.deb Size: 189918 MD5sum: f86c0ca733f719c08a7eb6ead80fcb0d SHA1: d0d3fbb2bc709b842764c2e6720650ccc444a28d SHA256: a09aa40c3a86f700b6754bb0a79762ae6b0f0edd6f6abd98b267d01d32193be1 SHA512: 2c9b4d5260a04f3c13992d052bcae45ab8ce2cef7910c90a55384a872be274497dc34c8374279e8fc6f9f725c2f5947f6efdf75588ac2b54ec5731e6d4cad57c Homepage: https://cran.r-project.org/package=spsh Description: CRAN Package 'spsh' (Estimation and Prediction of Parameters of Various SoilHydraulic Property Models) Estimates model parameters of soil hydraulic property functions by inverting measured data. A wide range of hydraulic models, weighting schemes, global optimization algorithms, Markov chain Monte Carlo samplers, and extended statistical analyses of results are provided. Prediction of soil hydraulic property model parameters and common soil properties using pedotransfer functions is facilitated. Parameter estimation is based on identically and independentally distributed (weighted) model residuals, and simple model selection criteria (Hoege, M., Woehling, T., and Nowak, W. (2018) ) can be calculated. The included models are the van Genuchten-Mualem in its unimodal, bimodal and trimodal form, the the Kosugi 2 parametric-Mualem model, and the Fredlund-Xing model. All models can be extended to account for non-capillary water storage and conductivity (Weber, T.K.D., Durner, W., Streck, T. and Diamantopoulos, E. (2019) . The isothermal vapour conductivity (Saito, H., Simunek, J. and Mohanty, B.P. (2006) ) is calculated based on volumetric air space and a selection of different tortuosity models: (Grable, A.R., Siemer, E.G. (1968) , Lai, S.H., Tiedje J.M., Erickson, E. (1976) , Moldrup, P., Olesen, T., Rolston, D.E., and Yamaguchi, T. (1997) , Moldrup, P., Olesen, T., Yoshikawa, S., Komatsu, T., and Rolston, D.E. (2004) , Moldrup, P., Olesen, T., Yoshikawa, S., Komatsu, T., and Rolston, D.E. (2005) , Millington, R.J., Quirk, J.P. (1961) , Penman, H.L. (1940) , and Xu, X, Nieber, J.L. Gupta, S.C. (1992) ). Package: r-cran-spsl Architecture: all Version: 0.1-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/noble/main/r-cran-spsl_0.1-9-1.ca2404.1_all.deb Size: 115376 MD5sum: e57b1554adc96bbd4ea0a7e0d6b2018f SHA1: d8ae3631362dd740d7144e194518830534661f71 SHA256: c6d91674e81bf027e186c4e7ac6f4cd1194ec131dcbc26c17feb8180e378196c SHA512: 05d884f057775abf16289ccc6b4d3508281b7d66978abd337311e5c8fa00d7e6135ef56372d8a5d7990c82c4bef7d33b237a968685cd525262a27cf33dffe36b Homepage: https://cran.r-project.org/package=SPSL Description: CRAN Package 'SPSL' (Site Percolation on Square Lattices (SPSL)) Provides basic functionality for labeling iso- & anisotropic percolation clusters on 2D & 3D square lattices with various lattice sizes, occupation probabilities, von Neumann & Moore (1,d)-neighborhoods, and random variables weighting the percolation lattice sites. Package: r-cran-spsur Architecture: all Version: 1.0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6913 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-ggplot2, r-cran-gmodels, r-cran-gridextra, r-cran-mass, r-cran-matrix, r-cran-minqa, r-cran-numderiv, r-cran-rdpack, r-cran-rlang, r-cran-sparsemvn, r-cran-spatialreg, r-cran-spdep, r-cran-sphet Suggests: r-cran-bookdown, r-cran-dplyr, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/noble/main/r-cran-spsur_1.0.2.6-1.ca2404.1_all.deb Size: 1893442 MD5sum: a928721cc38bee5fa4a66f4b6c75b369 SHA1: 4694a264843d11e86cf2ee6da51ac43218aa34ab SHA256: 40e15d900b453cf759b6a9a093236cdafc72d1aa2bcffa5bbbebb5a8f246b9e2 SHA512: cea044b9f101b67061a392fe927336839e1bdd082ff3d7c768d228751f592ab83374ed9500d5eb509234fd46b784d450c37a4b8af6545af62194827fcd478ed7 Homepage: https://cran.r-project.org/package=spsur Description: CRAN Package 'spsur' (Spatial Seemingly Unrelated Regression Models) A collection of functions to test and estimate Seemingly Unrelated Regression (usually called SUR) models, with spatial structure, by maximum likelihood and three-stage least squares. The package estimates the most common spatial specifications, that is, SUR with Spatial Lag of X regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), SUR with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called SUR-SDM), SUR with Spatial Durbin Error Model (called SUR-SDEM), SUR with Spatial Autoregressive terms and Spatial Autoregressive Disturbances (called SUR-SARAR), SUR-SARAR with Spatial Lag of X regressors (called SUR-GNM) and SUR with Spatially Independent Model (called SUR-SIM). The methodology of these models can be found in next references Minguez, R., Lopez, F.A., and Mur, J. (2022) Mur, J., Lopez, F.A., and Herrera, M. (2010) Lopez, F.A., Mur, J., and Angulo, A. (2014) . Package: r-cran-spsurvey Architecture: all Version: 5.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2822 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-survey, r-cran-boot, r-cran-crossdes, r-cran-deldir, r-cran-lme4, r-cran-mass, r-cran-sampling, r-cran-units Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spsurvey_5.6.1-1.ca2404.1_all.deb Size: 1900276 MD5sum: 77f001dab5538c830bfe33c54a5ea40f SHA1: d62ff867cd1ae03e7eddd5d346dd5d98d6b1ae8a SHA256: d2a9f18c9c316b6c5605d533f501d35ab98c56f55e0b39f167a5ffae39fe243f SHA512: e867be9bb3ed2218d84db5f3d0b2ca0e0054dd118a6e6aa292966a0b4a1361081fe9b3aa1999f33f9da8e63273ea870e5f076f57249b2ae408a0d03b0cbef108 Homepage: https://cran.r-project.org/package=spsurvey Description: CRAN Package 'spsurvey' (Spatial Sampling Design and Analysis) A design-based approach to statistical inference, with a focus on spatial data. Spatially balanced samples are selected using the Generalized Random Tessellation Stratified (GRTS) algorithm. The GRTS algorithm can be applied to finite resources (point geometries) and infinite resources (linear / linestring and areal / polygon geometries) and flexibly accommodates a diverse set of sampling design features, including stratification, unequal inclusion probabilities, proportional (to size) inclusion probabilities, legacy (historical) sites, a minimum distance between sites, and two options for replacement sites (reverse hierarchical order and nearest neighbor). Data are analyzed using a wide range of analysis functions that perform categorical variable analysis, continuous variable analysis, attributable risk analysis, risk difference analysis, relative risk analysis, change analysis, and trend analysis. spsurvey can also be used to summarize objects, visualize objects, select samples that are not spatially balanced, select panel samples, measure the amount of spatial balance in a sample, adjust design weights, and more. For additional details, see Dumelle et al. (2023) . Package: r-cran-spsutil Architecture: all Version: 0.2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-assertthat, r-cran-stringr, r-cran-glue, r-cran-magrittr, r-cran-crayon, r-cran-r6 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-spsutil_0.2.2.1-1.ca2404.1_all.deb Size: 126952 MD5sum: 9e593d28cac2f5768c4945ec987438fd SHA1: f712bdaf1fd270d60d64d33b3d14e6abd1d061dd SHA256: c8d301b0aff3b0426de10b79392e773020ab579f198d24dbaff587337c1128f8 SHA512: 04902e9016e9092dab8875c99d20347b97ac7af5d30713e8b8f93c9ce6228b8111fe090c9bb734f759e898681fac1909a8af85e8dadaa001d5e00d4c3d0c78a5 Homepage: https://cran.r-project.org/package=spsUtil Description: CRAN Package 'spsUtil' ('systemPipeShiny' Utility Functions) The systemPipeShiny (SPS) framework comes with many useful utility functions. However, installing the whole framework is heavy and takes some time. If you like only a few useful utility functions from SPS, install this package is enough. Package: r-cran-spthin Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spam, r-cran-fields, r-cran-knitr Filename: pool/dists/noble/main/r-cran-spthin_0.2.0-1.ca2404.1_all.deb Size: 94326 MD5sum: be115abc8d2e53a0b2d98de6bfea3b10 SHA1: 4ad5026673899827ad7f7b20c1f629d69c017867 SHA256: c66a92fde2a94c95a1c59d5875077f6fc492d9abb8b79417cde975345b38173d SHA512: 42bd1f897df8c4ca8d5c5a2300716c753de5afd3cf6586d618aea97a110d42e2c66598be5432f40c62b3de194c046b16410547d9e5054129c34e1b91701a1751 Homepage: https://cran.r-project.org/package=spThin Description: CRAN Package 'spThin' (Functions for Spatial Thinning of Species Occurrence Records forUse in Ecological Models) A set of functions that can be used to spatially thin species occurrence data. The resulting thinned data can be used in ecological modeling, such as ecological niche modeling. Package: r-cran-sptotal Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1769 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-viridis Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-prettydoc, r-cran-tibble, r-cran-gstat Filename: pool/dists/noble/main/r-cran-sptotal_1.0.1-1.ca2404.1_all.deb Size: 1157848 MD5sum: 993b98c7bd857d75cc99033dac399bb3 SHA1: df54d7f2c4056ea5ec3b4b65add1f20e0719beae SHA256: c047f3b142b8ef214a77ee2d134e9d828a31316b39aa6412a57ccf56e95cb7be SHA512: 9f6ef74dfae4a29e86aedcef27d9d4b1ba39657a7553d43cde960907922152c4d44f734e33e0e330674569e47fff91a4285ff64bcf88260ccc2fbdabae70284d Homepage: https://cran.r-project.org/package=sptotal Description: CRAN Package 'sptotal' (Predicting Totals and Weighted Sums from Spatial Data) Performs predictions of totals and weighted sums, or finite population block kriging, on spatial data using the methods in Ver Hoef (2008) . The primary outputs are an estimate of the total, mean, or weighted sum in the region, an estimated prediction variance, and a plot of the predicted and observed values. This is useful primarily to users with ecological data that are counts or densities measured on some sites in a finite area of interest. Spatial prediction for the total count or average density in the entire region can then be done using the functions in this package. 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(2007) and Brown and Heuvelink (2007) . The package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model outputs. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for. The MC realizations may be used as input to the environmental models called from R, or externally. Package: r-cran-spuriouscorrelations Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spuriouscorrelations_0.1-1.ca2404.1_all.deb Size: 84020 MD5sum: 44efd684b3b6c44b7289472f8129c4ee SHA1: 235f73a13d34a336cec1edd1763d5b64334b5dba SHA256: a0136bacde3900ac05064e653f450afc00a137cd51f340a3f43604360d44104f SHA512: 65e4de92dde85f610043415287cddd0ea3b3d27f01a6ba2670ba1dc1a20ef01c4040308c3887dfe94b48abcdc7029421c7ab19e1687fd67f886602157e4db7ab Homepage: https://cran.r-project.org/package=spuriouscorrelations Description: CRAN Package 'spuriouscorrelations' (Datasets with Strong and Spurious Correlations) Provides datasets from Vigen (2015) rescued from the Internet Wayback Machine. These should be preserved for statistics introductory courses as these make it very clear that correlation is not causation. Package: r-cran-spuriousmemory Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fracdiff Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-spuriousmemory_1.0.0-1.ca2404.1_all.deb Size: 74828 MD5sum: f10ebd447c57fab3aa805a20ae9b978c SHA1: 0482b29d746c9ade13b4bdc8c591d92f880b7333 SHA256: 759884b4b37d7fed7336940e1365b225599e31d80c620dd59363cd6df4217422 SHA512: 94d352758b3be1ae3d0b612af22b96e156c3fe55196bddeaa5aa6cc6cf827203f110cc6e16da80595fa6479d0108340a02a1f6222a96947c7bf39e1066bcce01 Homepage: https://cran.r-project.org/package=SpuriousMemory Description: CRAN Package 'SpuriousMemory' (Testing True Long Memory Against Spurious Long Memory) Implements a test for distinguishing between true long memory and spurious long memory. Reference: Qu, Z. (2011). "A Test Against Spurious Long Memory." Journal of Business & Economic Statistics, 29(3), 423–438. . Package: r-cran-spurs Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-lattice Filename: pool/dists/noble/main/r-cran-spurs_2.0.3-1.ca2404.1_all.deb Size: 198424 MD5sum: 7ab76682ade3a7eacccfdddeb5c541eb SHA1: 1504564d97d38c159b1e5fd42bbfd21de8e696ed SHA256: b23544cd23d71f4f730917eb6c3d4b2ad8364e929acec759c3a4949800dd686d SHA512: 137f651964432823bb42c2490a73d6f361063b38832ad787f926c0b9221254d8797aa13e149a4f1f1e670f81e587dfbd89bd0768ab32408d3cb4043d465c0f6c Homepage: https://cran.r-project.org/package=spuRs Description: CRAN Package 'spuRs' (Functions and Datasets for "Introduction to ScientificProgramming and Simulation Using R") Provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson. 2014. An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC. Package: r-cran-sputnik Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-bioc-edger, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-viridis, r-cran-ggplot2, r-cran-reshape, r-cran-imager, r-cran-infotheo, r-cran-irlba, r-cran-dosnow, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sputnik_1.4.3-1.ca2404.1_all.deb Size: 222284 MD5sum: 0dc578cba825bae2c1329bc0d527243c SHA1: 8a1dbc605144612a3e3c0649719eceea0724b76e SHA256: 1c159d19802d08bcbb6be324f2c0f4fafe52c8eb6d90544166cc39139a7ef262 SHA512: 5ce46fae409a6d8ccc12fd3535d0a33674e4c43b4b86cd476862e8eef8ea3b1b3bd3ccd18612d3c4838503260a35683002be4eeca0aa8e08c5ce502b98b43c7d Homepage: https://cran.r-project.org/package=SPUTNIK Description: CRAN Package 'SPUTNIK' (Spatially Automatic Denoising for Imaging Mass SpectrometryToolkit) Set of tools for peak filtering of mass spectrometry imaging data based on spatial distribution of signal. Given a region-of-interest, representing the spatial region where the informative signal is expected to be localized, a series of filters determine which peak signals are characterized by an implausible spatial distribution. The filters reduce the dataset dimension and increase its information vs noise ratio, improving the quality of the unsupervised analysis results, reducing data dimension and simplifying the chemical interpretation. The methods are described in Inglese P. et al (2019) . Package: r-cran-spyvsspy Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-spyvsspy_0.1.1-1.ca2404.1_all.deb Size: 13562 MD5sum: a80f30e06ac7a89d12ab509611fde54c SHA1: 79892d6e5fc18b52a463df71918f21a8c5260b31 SHA256: c2614e89362b446974d8bd70d829ac581c877f9a66c8eadf4a9366a092d04163 SHA512: 0924c984cc082a44cd5a9bc7c2fef299f9fed5a256be5c0ff7a0fc35b49888e63d8974a89b64410b12ec4b4aac80633dc15e6cc35497717e7568bf9706d70bb6 Homepage: https://cran.r-project.org/package=SPYvsSPY Description: CRAN Package 'SPYvsSPY' (Spy vs. Spy Data) Data on the Spy vs. Spy comic strip of Mad magazine, created and written by Antonio Prohias. Package: r-cran-sqi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-dplyr, r-cran-matrixstats, r-cran-olsrr, r-cran-factominer Filename: pool/dists/noble/main/r-cran-sqi_0.1.0-1.ca2404.1_all.deb Size: 86638 MD5sum: 4cca5bbd57771c678ae107ece34b9bb7 SHA1: cf68f4720e3a2d505aefca8988e42d71d9d8e54f SHA256: a55a44b61b084ae642f8e458e960068d216ecb9a7242a93e001603e274f253ea SHA512: 171ca95996ec01399115716e0a58c7511ba74bd3395073cc0875d0f156e4193b6e2d11e5d21a5ac56797a22dc204b1f3450420f9fc1029b3fad3481f2ed755a6 Homepage: https://cran.r-project.org/package=SQI Description: CRAN Package 'SQI' (Soil Quality Index) The overall performance of soil ecosystem services and productivity greatly relies on soil health, making it a crucial indicator. The evaluation of soil physical, chemical, and biological parameters is necessary to determine the overall soil quality index. In our package, three commonly used methods, including linear scoring, regression-based, and principal component-based soil quality indexing, are employed to calculate the soil quality index. This package has been developed using concept of Bastida et al. (2008) and Doran and Parkin (1994) . 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Implements four variable scoring functions: more-is-better, less-is-better, optimum-value, and trapezoidal, following Karlen and Stott (1994, ). Includes automated Minimum Data Set selection via Principal Component Analysis with Variance Inflation Factor filtering (Kaiser, 1960, ), one-way ANOVA with Tukey HSD post-hoc tests, leave-one-out sensitivity analysis, and publication-quality visualization using 'ggplot2'. Package: r-cran-sql Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-duckdb, r-cran-arrow, r-cran-stringr Filename: pool/dists/noble/main/r-cran-sql_0.1.1-1.ca2404.1_all.deb Size: 16756 MD5sum: 1fafdf5a63b6c8f8c4d2934558c8c1bd SHA1: 0e47a4099653c8b854c22023055aaa037e526e04 SHA256: 7412079168e9347de177bb08f4a0f737b1bc8de85912512b95cd56868f3a2e71 SHA512: b5a3c3c9ce114c6861e2a914d1a5175783c134f2f450d0646f82c2811f5787c1abbded9f1cea91f8df21a6eeb3bd675103f8071ff51ef457587553bb6e67fea2 Homepage: https://cran.r-project.org/package=SQL Description: CRAN Package 'SQL' (Executes 'SQL' Statements) Runs 'SQL' statements on in-memory data frames within a temporary in-memory 'duckdb' data base. Package: r-cran-sqlcaser Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sqlcaser_0.2.1-1.ca2404.1_all.deb Size: 41266 MD5sum: 67b05745f164278de91b66f35ff7d956 SHA1: ba335d693508127e47349d49b04b469c3cfb0eff SHA256: 9ade53193224adf3fd33c72c57f4a68a3bcda91d6991feead42a353ee9c68539 SHA512: 24f38cdbd9c773a09cf6e6476764e62da969706401ae03419ea38bcfe3133f0a781bc9a5524cb6a6d52f26f165b7b72986a8f7a76eda5f9a9271eb15ec007b16 Homepage: https://cran.r-project.org/package=sqlcaser Description: CRAN Package 'sqlcaser' ('SQL' Case Statement Generator) Includes built-in methods for generating long 'SQL' CASE statements, and other 'SQL' statements that may otherwise be arduous to construct by hand.The generated statement can easily be concatenated to string literals to form queries to 'SQL'-like databases, such as when using the 'RODBC' package. The current methods include casewhen() for building CASE statements, inlist() for building IN statements, and updatetable() for building UPDATE statements. 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Package: r-cran-sqltargets Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-fs, r-cran-glue, r-cran-jinjar, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tarchetypes, r-cran-targets, r-cran-withr Suggests: r-cran-testthat, r-cran-rsqlite Filename: pool/dists/noble/main/r-cran-sqltargets_0.3.0-1.ca2404.1_all.deb Size: 112834 MD5sum: 37bcb1b71e4b2f8346301473fbd925a4 SHA1: b5cf230406eddf00bab525354d1330aa024419e5 SHA256: 14f079d1bb78b51c0493ba2dff844a5ac8809aed89fd640f651ec85f4fadf228 SHA512: a302c3f0092beca0c208301308df8ebe565a5eaa3c16aa1f06b035d07a4348eaf541fd224a3f8d23dd830334bed48b417d49348fc70ae4ba22a0c7bf99461a0f Homepage: https://cran.r-project.org/package=sqltargets Description: CRAN Package 'sqltargets' ('Targets' Extension for 'SQL' Queries) Provides an extension for 'SQL' queries as separate file within 'targets' pipelines. The shorthand creates two targets, the query file and the query result. Package: r-cran-sqmtools Architecture: all Version: 1.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3615 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zip, r-cran-reshape2, r-cran-ggplot2, r-bioc-pathview, r-cran-data.table, r-bioc-biostrings Suggests: r-cran-vegan, r-cran-microeco, r-bioc-phyloseq Filename: pool/dists/noble/main/r-cran-sqmtools_1.7.2-1.ca2404.1_all.deb Size: 3654050 MD5sum: bd6509010c9946d369bf090850eaf54c SHA1: 89071dadf8d4ae4ccbab39d665f04a1efa658150 SHA256: b1821053d06c7d46dd8782a3259f58ed3bff0bf5be975510ca70df9e1215d448 SHA512: 2f1f10f727d6b23f1a4c6388e7da28212e0b9ae215d9b1c454cfd46424ec5c4ebef344dcc9f1ec58f8c503544dd120ba5db7c19c736936fdbcde801fa68b6fc2 Homepage: https://cran.r-project.org/package=SQMtools Description: CRAN Package 'SQMtools' (Analyze Results Generated by the 'SqueezeMeta' Pipeline) 'SqueezeMeta' is a versatile pipeline for the automated analysis of metagenomics/metatranscriptomics data (). This package provides functions loading 'SqueezeMeta' results into R, filtering them based on different criteria, and visualizing the results using basic plots. The 'SqueezeMeta' project (and any subsets of it generated by the different filtering functions) is parsed into a single object, whose different components (e.g. tables with the taxonomic or functional composition across samples, contig/gene abundance profiles) can be easily analyzed using other R packages such as 'vegan' or 'DESeq2'. The methods in this package are further described in Puente-Sánchez et al., (2020) . Package: r-cran-sqn Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mclust, r-cran-nor1mix Filename: pool/dists/noble/main/r-cran-sqn_1.0.6-1.ca2404.1_all.deb Size: 106866 MD5sum: 7b39df364bd04218bd2e1eb516744656 SHA1: 8728668e564dfc5041bb70aa2c81d649ae6d9ec2 SHA256: 76d0f839cfaf9e3f1cf980f6a6cad6e8c3675be1594c36cd2f71874e6ab3ce20 SHA512: 9047c91df2606258d840624a8df14353b885b7f1c5fc4bd3d9a22551db08b206fcca25a441ee99a579937941f438d3033e7e6655e92ddbf11906ba963a48975b Homepage: https://cran.r-project.org/package=SQN Description: CRAN Package 'SQN' (Subset Quantile Normalization) Normalization based a subset of negative control probes as described in 'Subset quantile normalization using negative control features'. Wu Z, Aryee MJ, J Comput Biol. 2010 Oct;17(10):1385-95 [PMID 20976876]. Package: r-cran-sqrl Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rodbc Filename: pool/dists/noble/main/r-cran-sqrl_1.0.3-1.ca2404.1_all.deb Size: 396268 MD5sum: 5645faf35b64ad72a9b17cf9a7c8ec3a SHA1: 9cf24a9014b7bd07ec7c68d922a035796bcbc2e6 SHA256: e00da87b7880c3e5e702276a8865702883948620d32a6ec60bd3457559082d66 SHA512: 7b487506c75de3712acdd1449e9850d680f0eadbf00bd2493aea5e4f1489dac56f3311c532df7bdbb3f46d6868db5fb2152b883dc7d13b08efda192411566495 Homepage: https://cran.r-project.org/package=SQRL Description: CRAN Package 'SQRL' (Enhances Interaction with 'ODBC' Databases) Provides simple and powerful interfaces that facilitate interaction with 'ODBC' data sources. Each data source gets its own unique and dedicated interface, wrapped around 'RODBC'. Communication settings are remembered between queries, and are managed silently in the background. 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Package: r-cran-squant Architecture: all Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-survival, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-squant_1.1.7-1.ca2404.1_all.deb Size: 127978 MD5sum: 1edf1bb5710e69cbb81360971b02e1a7 SHA1: c85c3965d234c893c5c7c0ef7beeb63374b85aba SHA256: 4504e065e971381c229ea9cabbdea78d1116d67ea14f052b466194c7b4f7bb04 SHA512: a9dbbebb742e4a73b6e2f02f3c3da4ff49fa74f4eb28591384fa0e98bff3b21e7d7866e2d7da6f19331f22de1b00e72e7da6ebb00b6b072f961c09b981624353 Homepage: https://cran.r-project.org/package=squant Description: CRAN Package 'squant' (Subgroup Identification Based on Quantitative Objectives) A subgroup identification method for precision medicine based on quantitative objectives. This method can handle continuous, binary and survival endpoint for both prognostic and predictive case. For the predictive case, the method aims at identifying a subgroup for which treatment is better than control by at least a pre-specified or auto-selected constant. For the prognostic case, the method aims at identifying a subgroup that is at least better than a pre-specified/auto-selected constant. The derived signature is a linear combination of predictors, and the selected subgroup are subjects with the signature > 0. The false discover rate when no true subgroup exists is controlled at a user-specified level. Package: r-cran-squarem Architecture: all Version: 2026.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-setrng, r-cran-interval Filename: pool/dists/noble/main/r-cran-squarem_2026.1-1.ca2404.1_all.deb Size: 176960 MD5sum: 585b502d13c9f43343db39a0f4ded847 SHA1: f46eafb4e8b262dbd41751db6aff9cb8b90a04db SHA256: 0291c0ee35678780323b48fae48451cfc372e0b4e4e188429896592ae9e0657a SHA512: 50e51ab76c5ed98a2ce1ead49b53e4faa8f069954a9c7d7f863959cc97ad49374ca4fa0fe92b36e45efb08339b421602f8889940d018884f48de9e36faaa4ea3 Homepage: https://cran.r-project.org/package=SQUAREM Description: CRAN Package 'SQUAREM' (Squared Extrapolation Methods for Accelerating EM-Like MonotoneAlgorithms) Algorithms for accelerating the convergence of slow, monotone sequences from smooth, contraction mappings such as the EM algorithm. It can be used to accelerate any smooth, linearly convergent scheme. 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Package: r-cran-squeezy Architecture: all Version: 1.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-matrix, r-cran-multiridge, r-cran-mvtnorm Suggests: r-cran-ggplot2, r-cran-ecpc Filename: pool/dists/noble/main/r-cran-squeezy_1.1-1-1.ca2404.1_all.deb Size: 68702 MD5sum: fc89fa889bf232da2382a52e23e49487 SHA1: 25d24f72e8f676055f12e18ef6f33a7030b6373d SHA256: 95018b26989129eba08a78de839a9597fcada5d8ee8c13542a372ce67a6f0ebe SHA512: 3cbf2b62145ae6784f9353f92fb3acae889ba5282b2fa81f4ba43b19c4ac8c7c3448b7e118784309c5ec3f4797522068591f4fb164111570675bdcb66cd58039 Homepage: https://cran.r-project.org/package=squeezy Description: CRAN Package 'squeezy' (Group-Adaptive Elastic Net Penalised Generalised Linear Models) Fit linear and logistic regression models penalised with group-adaptive elastic net penalties. The group penalties correspond to groups of covariates defined by a co-data group set. The method accommodates inclusion of unpenalised covariates and overlapping groups. See Van Nee et al. (2021) . Package: r-cran-squid Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5338 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-shinymatrix, r-cran-ggplot2, r-cran-plotly, r-cran-mass, r-cran-lme4, r-cran-arm, r-cran-data.table, r-cran-brms Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-squid_0.2.1-1.ca2404.1_all.deb Size: 3850522 MD5sum: 0b5d09c6435b53fdf5d886e8806e9f7a SHA1: c7a39b91db19be314f6e0a8873347da0922df764 SHA256: d5fd8dd191949eac8b2306af24b3e1410fc6cfc39f84ead6908da989b518cc97 SHA512: 469c56d183ed3bf3aa77a7c6c6b8df921cb08a8d8b7d244861066d04de60436b4a9c8ed82c38d496f972ed757386832b78c743b958ff0f1655068243b38c806a Homepage: https://cran.r-project.org/package=squid Description: CRAN Package 'squid' (Statistical Quantification of Individual Differences) A simulation-based tool made to help researchers to become familiar with multilevel variations, and to build up sampling designs for their study. This tool has two main objectives: First, it provides an educational tool useful for students, teachers and researchers who want to learn to use mixed-effects models. Users can experience how the mixed-effects model framework can be used to understand distinct biological phenomena by interactively exploring simulated multilevel data. Second, it offers research opportunities to those who are already familiar with mixed-effects models, as it enables the generation of data sets that users may download and use for a range of simulation-based statistical analyses such as power and sensitivity analysis of multilevel and multivariate data [Allegue, H., Araya-Ajoy, Y.G., Dingemanse, N.J., Dochtermann N.A., Garamszegi, L.Z., Nakagawa, S., Reale, D., Schielzeth, H. and Westneat, D.F. (2016) ]. Package: r-cran-squids Architecture: all Version: 25.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-squids_25.6.1-1.ca2404.1_all.deb Size: 228642 MD5sum: 95900f0fb996df6516ce5ab8ce8e7fc6 SHA1: 968def3520a6781b3f0d7048744795ff9cce1ff6 SHA256: afb577a986409388102a5d10a505a7bcb744c4bb6664088058dc40cbb3be56fc SHA512: bdc6bbd951d79b267e07066de618b1bab3ad89b06228ee8b86f13752fb2b4cfa298762aeb27c13deb611042b01a66166660e1674494b37148a1bc3406ca464cb Homepage: https://cran.r-project.org/package=squids Description: CRAN Package 'squids' (Short Quasi-Unique Identifiers (SQUIDs)) It is often useful to produce short, quasi-unique identifiers (SQUIDs) without the benefit of a central authority to prevent duplication. Although Universally Unique Identifiers (UUIDs) provide for this, these are also unwieldy; for example, the most used UUID, version 4, is 36 characters long. SQUIDs are short (8 characters) at the expense of having more collisions, which can be mitigated by combining them with human-produced suffixes, yielding relatively brief, half human-readable, almost-unique identifiers (see for example the identifiers used for Decentralized Construct Taxonomies; Peters & Crutzen, 2024 ). SQUIDs are the number of centiseconds elapsed since the beginning of 1970 converted to a base 30 system. This package contains functions to produce SQUIDs as well as convert them back into dates and times. Package: r-cran-squire Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-galahad, r-cran-knitr Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-squire_1.0.1-1.ca2404.1_all.deb Size: 44184 MD5sum: 644165221f27ccf32d40d96231cb2ffe SHA1: dfa6fc5ab3b6b991ce541446a17cf5d556f4ac6c SHA256: 53479d7800f6c586e483be37d6da8b88b7964af3ad1731d9c20e612307e57959 SHA512: 80b3aa2cfc4a493f77145fe27f8aac42147eebc50b82a90259670ef4e2163adf515508a3d627382f5e54e187efab9251847de60ff79bcfb26bb8a0809e293101 Homepage: https://cran.r-project.org/package=SQUIRE Description: CRAN Package 'SQUIRE' (Statistical Quality-Assured Integrated Response Estimation) Provides systematic geometry-adaptive parameter optimization with statistical validation for experimental biological data. Combines ANOVA-based validation with systematic constraint configuration testing (log-scale, positive domain, Euclidean) through T,P,E testing. Only proceeds with parameter optimization when statistically significant biological effects are detected, preventing over-fitting to noise. Uses 'GALAHAD' trust region methods with constraint projection from Conn et al. (2000) , ANOVA-based validation following Fisher (1925) , and effect size calculations per Cohen (1988, ISBN:0805802835). Designed for structured experimental data including kinetic curves, dose-response studies, and treatment comparisons where appropriate parameter constraints and statistical justification are important for meaningful biological interpretation. Developed at the Minnesota Center for Prion Research and Outreach at the University of Minnesota. Package: r-cran-sr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-progress, r-cran-rann, r-cran-vdiffr Suggests: r-cran-knitr, r-cran-magrittr, r-cran-nnet, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sr_0.1.0-1.ca2404.1_all.deb Size: 231440 MD5sum: 261824dfe57fe8995436f5d65ed2e9ce SHA1: 79ebc8f81e610f4c0b213aa047b808bd7f8133e2 SHA256: 3faf35985ab79352796d7db77b212d68727898e09c3f5ca88de10d523df56ac0 SHA512: ec2e7651fe27eb14bc0359936e4b6e021b593b36d6bcbb283aa6ff89e126ab01cb824db2e4e593055f48e3eda809affc770d5f3d3809a35a952f4fa458255faa Homepage: https://cran.r-project.org/package=sr Description: CRAN Package 'sr' (Smooth Regression - The Gamma Test and Tools) Finds causal connections in precision data, finds lags and embeddings in time series, guides training of neural networks and other smooth models, evaluates their performance, gives a mathematically grounded answer to the over-training problem. Smooth regression is based on the Gamma test, which measures smoothness in a multivariate relationship. Causal relations are smooth, noise is not. 'sr' includes the Gamma test and search techniques that use it. References: Evans & Jones (2002) , AJ Jones (2004) . Package: r-cran-sra Architecture: all Version: 0.1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sra_0.1.4.1-1.ca2404.1_all.deb Size: 142602 MD5sum: 722ebdcb9d183e5095c6ce0be88c4809 SHA1: 53e5166bbb96a6924aba29f2ecf569a2b728c82b SHA256: 247f37c87290725d42773503ba7cffe6a8601c7a276526ea8eded1b207cc2771 SHA512: 2316fab48a6d8844406ad6b43f7664053052ac44c74dd93e92f659416fcad81ef54f8698d4860430e8e6726a10f0124b10bb2afcdc15e8711eee748eee934027 Homepage: https://cran.r-project.org/package=sra Description: CRAN Package 'sra' (Selection Response Analysis) Artificial selection through selective breeding is an efficient way to induce changes in traits of interest in experimental populations. This package (sra) provides a set of tools to analyse artificial-selection response datasets. The data typically feature for several generations the average value of a trait in a population, the variance of the trait, the population size and the average value of the parents that were chosen to breed. Sra implements two families of models aiming at describing the dynamics of the genetic architecture of the trait during the selection response. The first family relies on purely descriptive (phenomenological) models, based on an autoregressive framework. The second family provides different mechanistic models, accounting e.g. for inbreeding, mutations, genetic and environmental canalization, or epistasis. The parameters underlying the dynamics of the time series are estimated by maximum likelihood. The sra package thus provides (i) a wrapper for the R functions mle() and optim() aiming at fitting in a convenient way a predetermined set of models, and (ii) some functions to plot and analyze the output of the models. Package: r-cran-srcpkgs Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clitable, r-cran-devtools, r-cran-pkgload, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-srcpkgs_0.2.1-1.ca2404.1_all.deb Size: 191196 MD5sum: 8e77dc4b14a7b5d4116c13053be9b233 SHA1: 108fcbd3990f44e7384290431bd50fbdfd008f6e SHA256: 75fcb17a12d20853f790a4a30cd5a3354cb6d82798f827a259f8a187e38b24e3 SHA512: 085a336c8af7c1234360587aa64325a5560aeb53306b252e490c7603d85881833901a75e49ac3598dfc05cd95ffc85fed01e7d33f724f07f2eb6ba92ea9ad7c4 Homepage: https://cran.r-project.org/package=srcpkgs Description: CRAN Package 'srcpkgs' (R Source Packages Manager) Manage a collection/library of R source packages. Discover, document, load, test source packages. Enable to use those packages as if they were actually installed. 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This package provides a simple tool to fill two purposes: abstracting connection details, including secret credentials, out of your source code and managing configuration for frequently-used database connections in a persistent and flexible way, while minimizing requirements on the runtime environment. Package: r-cran-srcs Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4301 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-srcs_1.1-1.ca2404.1_all.deb Size: 4183422 MD5sum: f9a9444f67f03b8ba4741a301444a34d SHA1: 39414abf66c36f0fbe7cbdfcceb995267668c7f1 SHA256: 60d07514e85c8442d48b7f50fd005bbad1160b69997cd6d9d37ef182b47f9727 SHA512: 56441a27c7ffb2df207f484bd4ec920b1bb78c21d4d1c11af22edfba2b9ae7d68ec68a6a15b037ff75b9cfdfda59b5f060ed950967dd352d1763b13cd5016471 Homepage: https://cran.r-project.org/package=SRCS Description: CRAN Package 'SRCS' (Statistical Ranking Color Scheme for Multiple PairwiseComparisons) Implementation of the SRCS method for a color-based visualization of the results of multiple pairwise tests on a large number of problem configurations, proposed in: I.G. del Amo, D.A. Pelta. SRCS: a technique for comparing multiple algorithms under several factors in dynamic optimization problems. In: E. Alba, A. Nakib, P. Siarry (Eds.), Metaheuristics for Dynamic Optimization. Series: Studies in Computational Intelligence 433, Springer, Berlin/Heidelberg, 2012. 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Implemented in a partial least squares framework, for more details see Csala et al. (2017) . Package: r-cran-srdpdata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-magrittr, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-srdpdata_0.1.0-1.ca2404.1_all.deb Size: 119876 MD5sum: a4032b6d3f96a0351001fd4a7b86376b SHA1: 1b696afb00d30f4330304a2676817e9900b92dfb SHA256: 1ff09595b3e87f4cca55465b775a4408887aa7bccf738c4849c0c3b8589c3903 SHA512: ccd788cbdbec9ec171fbf6c396ac552c18c11ac338ea656ebefb11b3db67a68c0896c9c1d0a4cf00ed1774e2ceacf10e630082056fd3f5eceb8130b13b7f8494 Homepage: https://cran.r-project.org/package=sRdpData Description: CRAN Package 'sRdpData' (Strategies of Resistance Data Project) Provides you with easy, programmatic access to SRDP data. 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Package: r-cran-sregsurvey Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss, r-cran-gamlss.dist, r-cran-teachingsampling, r-cran-dplyr, r-cran-caret, r-cran-magrittr Suggests: r-cran-survey Filename: pool/dists/noble/main/r-cran-sregsurvey_0.1.3-1.ca2404.1_all.deb Size: 44944 MD5sum: ad36ac5b5ff06a313a534410d338900e SHA1: 6bc0e8fec6d6528edf395993fdc5929100e48d39 SHA256: 750c493d3d5d559df00034f3f4c8bed4a39166f5436cc7c8ff509a5c39535646 SHA512: 72324d2e7facc00487187dfccc12b11655906b2aca7c0a85a29d6e908347fb2065076e864083615b229c10a1ba13f6fd01aed6cc3fbaa9c79af26487de02a360 Homepage: https://cran.r-project.org/package=sregsurvey Description: CRAN Package 'sregsurvey' (Semiparametric Model-Assisted Estimation in Finite Populations) It is a framework to fit semiparametric regression estimators for the total parameter of a finite population when the interest variable is asymmetric distributed. The main references for this package are Sarndal C.E., Swensson B., and Wretman J. (2003,ISBN: 978-0-387-40620-6, "Model Assisted Survey Sampling." Springer-Verlag) Cardozo C.A, Paula G.A. and Vanegas L.H. (2022) "Generalized log-gamma additive partial linear mdoels with P-spline smoothing", Statistical Papers. Cardozo C.A and Alonso-Malaver C.E. (2022). "Semi-parametric model assisted estimation in finite populations." In preparation. Package: r-cran-srlars Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cellwise, r-cran-robustbase, r-cran-mvnfast Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-srlars_2.0.1-1.ca2404.1_all.deb Size: 51292 MD5sum: 0111d2b287b515ab40fdfc416b26c1cc SHA1: 2c28bae434e3cc3ce151c62a54ee155c94d13ace SHA256: d041778f07d4248584e2db42922a797fd12f15f33484e0c4d39887869141ff56 SHA512: 5b7d53d352cd230a763aa0728dca6a782c47b45754dbd7da462a01f59fabb879679b7e6839f3c22f7c16f144487a4d9a5c5f43480752d30589cefd80c3b01209 Homepage: https://cran.r-project.org/package=srlars Description: CRAN Package 'srlars' (Fast and Scalable Cellwise-Robust Ensemble) Functions to perform robust variable selection and regression using the Fast and Scalable Cellwise-Robust Ensemble (FSCRE) algorithm. 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This methodology is especially useful for large time series with exogenous features and/or complex seasonality. Originally described in Peterson and Cavanaugh (2022) in the context of variable selection with interactions and/or polynomials, ranked sparsity is a philosophy with methods useful for variable selection in the presence of prior informational asymmetry. This situation exists for time series data with complex seasonality, as shown in Peterson and Cavanaugh (2023+) , which also describes this package in greater detail. The Sparsity-Ranked Lasso (SRL) for Time Series implemented in 'srlTS' can fit large/complex/high-frequency time series quickly, even with a high-dimensional exogenous feature set. The SRL is considerably faster than its competitors, while often producing more accurate predictions. Also included is a long hourly series of arrivals into the University of Iowa Emergency Department with concurrent local temperature. Package: r-cran-srmdata Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-srmdata_1.0.2-1.ca2404.1_all.deb Size: 392946 MD5sum: e10f84109eb553e40b8c8e7507f4b358 SHA1: bad6e1325d99fc7d9267a7cf5297fa78f2e965b8 SHA256: 8c8206c8835f9966791c06ca2eb7b7a1912c69f817f4bd8d41c5f30acca93c6f SHA512: efee03601a7283aa44f72249468ac8390b7d4e767451a14cdf9d5c46c96e1bdd37c2cf84c1232400c8fc590fd878cfb57cdd6306bd6987fd079c6e41755a0087 Homepage: https://cran.r-project.org/package=SRMData Description: CRAN Package 'SRMData' (Data Files Supporting "Scientific Research and Methodology" byPeter K. Dunn (2025)) Provides most of the data files used in the textbook "Scientific Research and Methodology" by Dunn (2025, ISBN: 9781032496726). Package: r-cran-srmers Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-splines2, r-cran-lme4, r-cran-nloptr, r-cran-dplyr, r-cran-mass, r-cran-coneproj, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-srmers_0.1.1-1.ca2404.1_all.deb Size: 234036 MD5sum: 4ac4b6ec777df66a22107bce9d9205f9 SHA1: 82607aeec46293ba7f981548f6bf4b8c459ee67d SHA256: 4c654132f3a1f89179f0cd178ed474d73436c3874047950956ad3c1b01712035 SHA512: 874c6a28904cf8c55566e1f4b5effaa5c6abad6ac0e16cfcd85ab04664f776ec68f163e3490ab002e0f962fd11184c973abf47c8aa90ec8b0ff0c5987558985c Homepage: https://cran.r-project.org/package=SRMERS Description: CRAN Package 'SRMERS' (Semi-Parametric Shape-Restricted Fixed/Mixed Effect(s)Regression Spline) Select the most suitable shape to describe the relationship between the exposure and the outcome among increasing, decreasing, convex, and concave shapes (Yin et al. (2021) ); estimate the direct and indirect effects with prior knowledge on the relationship between the mediator and the outcome with binary exposure (Yin et al. (2024) ); estimate the direct and indirect effects using linear regression-based approach (VanderWeele (2015, ISBN:9780199325870)). Package: r-cran-srnagenetic Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-deseq2, r-cran-futile.logger, r-cran-ggplot2, r-cran-ggsci, r-cran-plyr, r-cran-venndiagram Filename: pool/dists/noble/main/r-cran-srnagenetic_0.1.0-1.ca2404.1_all.deb Size: 104732 MD5sum: cc2d23e8fd3de065363878f6bb628339 SHA1: c4ee4244833b3882aedc91b02635624017052aa8 SHA256: 1cd05f1fa1c77d02e10ac61909249e68a7cd2e9a0b99f9149b8d2047430f899e SHA512: 6ff551e7438dcc252d6693cfa6e271a7573cc94332de4aad50261cf1c4b7dfab4412eb7b8dc3d27ee8b7dc89f887932ca025c113dd42a875ad1098d218fbccf5 Homepage: https://cran.r-project.org/package=sRNAGenetic Description: CRAN Package 'sRNAGenetic' (Analysis of Small RNA Expression Changes in Hybrid Plants) The most important function of the R package is the genetic effects analysis of small RNA in hybrid plants via two methods, and at the same time, it provides various forms of graph related to data characteristics and expression analysis. In terms of two classification methods, one is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the small RNA in progeny with the expression level in the parent species. 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Package: r-cran-srpi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-srpi_0.1.0-1.ca2404.1_all.deb Size: 21014 MD5sum: 2503e408f678a33129c323f0efc8e038 SHA1: 16412b2f6e5c6fe60883732162aef3e67d097b94 SHA256: 1c428f56b592ecf17258acb16592671dd32300ce9b4d6f868fefa7afb120303c SHA512: f8925c8c327a6e711ac8d71d5dd9134e7c8c8af3cb2acfc278de1bbcad5b5a0e5deb3128859e65e5d8d27b1ee53f7da09a9147a4d0421ee85df88f178fae1199 Homepage: https://cran.r-project.org/package=srpi Description: CRAN Package 'srpi' (Standardized Ranking Performance Index for Model Selection) Flexible implementation of the Standardized Ranking Performance Index (sRPI) for model selection based on multiple evaluation criteria. The package combines multiple statistical measures into a single index to provide an objective and robust ranking of models across calibration, validation, and combined scenarios. It supports evaluation of statistical, machine learning, and other predictive models using user-defined performance criteria. For more details see Aschonitis et al. (2019) and Singh et al. (2023) . Package: r-cran-srppp Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4499 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dm, r-cran-xml2, r-cran-tibble, r-cran-stringr, r-cran-dplyr, r-cran-cli, r-cran-data.tree, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-here, r-cran-diagrammer, r-cran-diagrammersvg, r-cran-testthat, r-cran-waldo Filename: pool/dists/noble/main/r-cran-srppp_2.0.3-1.ca2404.1_all.deb Size: 2555276 MD5sum: b1075fb63d2138a04f9b4309e49685b8 SHA1: 18f558dbea3771d1126c1b5746143f425170b767 SHA256: 65846b19e014ff9c025fd06303f0027df34bf87ff223c8f671f2a48f96d519e1 SHA512: 48c9f87c0d472f0474db62c7159bd84b452fda4849d10fb606604370e6d1d74ed92e21a37c1a6db5f8223c1ef30df77815a79b568f940296870f4b08579f328d Homepage: https://cran.r-project.org/package=srppp Description: CRAN Package 'srppp' (Read the Swiss Register of Plant Protection Products) Generate data objects from XML versions of the Swiss Register of Plant Protection Products. An online version of the register can be accessed at . There is no guarantee of correspondence of the data read in using this package with that online version, or with the original registration documents. Also, the Federal Food Safety and Veterinary Office, coordinating the authorisation of plant protection products in Switzerland, does not answer requests regarding this package. Package: r-cran-srs Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vegan, r-cran-shiny, r-cran-dt, r-cran-shinycssloaders, r-cran-shinybusy Filename: pool/dists/noble/main/r-cran-srs_0.2.3-1.ca2404.1_all.deb Size: 60576 MD5sum: b5652993f4e27bc21becf6a5b9f0a249 SHA1: fbe3b04bd2265ef58f0be0f4e3792e8709303319 SHA256: 6a78a63c0adfee7dfba3d89cfc2c72265fae81a5fc038d22c3a31fbbfe8c9a61 SHA512: cb5082091c9ca4cfeef4aa2b35251d001e3e25387c6d81cb53b5b585acefd0fa71add47886274be642b4e9f51ba474e085fcfe80ab4dbe550151e4c7b1fe9708 Homepage: https://cran.r-project.org/package=SRS Description: CRAN Package 'SRS' (Scaling with Ranked Subsampling) Analysis of species count data in ecology often requires normalization to an identical sample size. Rarefying (random subsampling without replacement), which is a popular method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. An alternative to rarefying is scaling with ranked subsampling (SRS). SRS consists of two steps. In the first step, the total counts for all OTUs (operational taxonomic units) or species in each sample are divided by a scaling factor chosen in such a way that the sum of the scaled counts Cscaled equals Cmin. In the second step, the non-integer Cscaled values are converted into integers by an algorithm that we dub ranked subsampling. The Cscaled value for each OTU or species is split into the integer part Cint (Cint = floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cints). Since the sum of Cint is smaller or equal to Cmin, additional delta C = Cmin - the sum of Cint counts have to be added to the library to reach the total count of Cmin. This is achieved as follows. OTUs are ranked in the descending order of their Cfrac values. Beginning with the OTU of the highest rank, single count per OTU is added to the normalized library until the total number of added counts reaches delta C and the sum of all counts in the normalized library equals Cmin. When the lowest Cfrag involved in picking delta C counts is shared by several OTUs, the OTUs used for adding a single count to the library are selected in the order of their Cint values. This selection minimizes the effect of normalization on the relative frequencies of OTUs. OTUs with identical Cfrag as well as Cint are sampled randomly without replacement. See Beule & Karlovsky (2020) for details. 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Package: r-cran-sscsrs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sscsrs_0.1.0-1.ca2404.1_all.deb Size: 17218 MD5sum: adcb56850e849475de00b263b3f96a07 SHA1: 492fae74458d0f04f11f578b3f79edf8bfb8f14e SHA256: 403f325df1d905e2faf3167029da18599e0f7cb6fc27b84dd918973cb17d15ba SHA512: cbdbceff7ed01aab3316a61b23949c427ef50e8363e26bb289ab9a8c1af6bc8ccb04748458205fcf1e29041cd41da2ac7cf800751cfff97a02f527045556a851 Homepage: https://cran.r-project.org/package=SscSrs Description: CRAN Package 'SscSrs' (Sample Size Calculator for Estimation of Population Mean andProportion under SRS) It helps in determination of sample size for estimation of population mean and proportion based upon the availability of prior information on coefficient of variation (CV) of the population under Simple Random Sampling (SRS) with or without replacement sampling design. 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It consists of 17 primary data sets from four different Australian and Canadian organizations as well as five datasets from anonymous sources. It also includes a data set of the results of fitting various distributions using different software. Package: r-cran-ssdforr Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-psych, r-cran-ttr, r-cran-mass, r-cran-mad, r-cran-metafor, r-cran-singlecasees, r-cran-modifiedmk, r-cran-retrodesign Filename: pool/dists/noble/main/r-cran-ssdforr_2.4-1.ca2404.1_all.deb Size: 503654 MD5sum: 31a6c34398b7c0936fe6a8fcc5992210 SHA1: 699b6c832fe7c72db92d8b60e8c5af2e42901c37 SHA256: 4474e6692dfb0ad70bdebc0c23dd5b9af9debb40e1e5aa5c28c8bda3490376e5 SHA512: 12b3551679cc2065ff50f0a56adc8005bfda2368fb1b72691ffc29980fb586365f47b955adf73918084998e821e2e7030d71f623c34b8dc60d9987a17b410a12 Homepage: https://cran.r-project.org/package=SSDforR Description: CRAN Package 'SSDforR' (Functions to Analyze Single System Data) Functions to visually and statistically analyze single system data. Package: r-cran-ssdgsa Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-gsva, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-vctrs, r-cran-tidyselect, r-bioc-clusterprofiler, r-bioc-org.hs.eg.db Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ssdgsa_0.1.1-1.ca2404.1_all.deb Size: 193552 MD5sum: 6dbd88fac5ae0ab90e7ec6b3add5d084 SHA1: 32f3b9cae7369447b34f4173ec8066f0ad7c19e8 SHA256: f630a9eb55c2b4b2abc296c39b699c60474db9d285a07660fcd2666ea9cf9274 SHA512: d1176f0fb3c799a1dc4bdfe2e830a9676a7c9bbe309f66a9088ef6258c6ab7fd8a4f76970c388ee9da5de97ec5f45568052c71522dddbe659d3f2b12c073ab71 Homepage: https://cran.r-project.org/package=ssdGSA Description: CRAN Package 'ssdGSA' (Single Sample Directional Gene Set Analysis) A method that inherits the standard gene set variation analysis (GSVA) method and also provides the option to use summary statistics from any analysis (disease vs healthy, lesional side vs nonlesional side, etc..) input to define the direction of gene sets used for directional gene set score calculation for a given disease. Note to use this package, GSVA(>= 1.52.1) is needed to pre-installed. Hanzelmann, S., Castelo, R., and Guinney, J. (2013) . Package: r-cran-ssdm Architecture: all Version: 0.2.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4523 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-raster, r-cran-mgcv, r-cran-earth, r-cran-rpart, r-cran-gbm, r-cran-randomforest, r-cran-dismo, r-cran-nnet, r-cran-e1071, r-cran-ggplot2, r-cran-reshape2, r-cran-scales, r-cran-shiny, r-cran-shinydashboard, r-cran-spthin, r-cran-poibin, r-cran-foreach, r-cran-doparallel, r-cran-iterators, r-cran-itertools, r-cran-leaflet, r-cran-magrittr, r-cran-sdm Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-shinyfiles Filename: pool/dists/noble/main/r-cran-ssdm_0.2.11-1.ca2404.1_all.deb Size: 1926090 MD5sum: 23050cdeafe36f2a51f1330b1cb3d4f5 SHA1: df0d7f4173e5e55716b79042da1b4ff9a13a7f56 SHA256: 99b10f3c6a07328b29d7c7e767bfbb1455732ee2e371ea4c9f83c8196a0f98fb SHA512: 6cb28301d9eda86bbd3903f26d13c583988a548c57020b5cc8e1a227a39b0b8c50c2b473d0b59cad8630bc1c0c4efd419e429152fe9d3dd9473cde11ab903d68 Homepage: https://cran.r-project.org/package=SSDM Description: CRAN Package 'SSDM' (Stacked Species Distribution Modelling) Allows to map species richness and endemism based on stacked species distribution models (SSDM). Individuals SDMs can be created using a single or multiple algorithms (ensemble SDMs). For each species, an SDM can yield a habitat suitability map, a binary map, a between-algorithm variance map, and can assess variable importance, algorithm accuracy, and between- algorithm correlation. Methods to stack individual SDMs include summing individual probabilities and thresholding then summing. Thresholding can be based on a specific evaluation metric or by drawing repeatedly from a Bernoulli distribution. The SSDM package also provides a user-friendly interface. Package: r-cran-ssdr Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/noble/main/r-cran-ssdr_1.2.0-1.ca2404.1_all.deb Size: 48034 MD5sum: 5c4cc95c428846e5f7d68597fcdea7c3 SHA1: cdc994b024e1ee5959a7681e23309135e1781975 SHA256: d91d9086f3b3b84d6d44ee7e347f1c2e4bbfb24bc8dbef0db6ec6c7225df1eeb SHA512: f73de2c111a746e9ddbfb658972d23d2ab09b6f69e14d5ff943879580bc849a7da57837b09297204c79351e1c6fcbf5f694c5ddce397b1a7671ea561e02e9817 Homepage: https://cran.r-project.org/package=sSDR Description: CRAN Package 'sSDR' (Tools Developed for Structured Sufficient Dimension Reduction(sSDR)) Performs structured OLS (sOLS) and structured SIR (sSIR). Package: r-cran-sse Architecture: all Version: 0.7-17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 578 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-sse_0.7-17-1.ca2404.1_all.deb Size: 436868 MD5sum: b1a5c3314737f217741b2a5e2ea82860 SHA1: 50f493df8117579f982b8a894a9963929832a699 SHA256: d4cbe1fb99b3e4d204339d2ce499f2fff5bde898624d45ea418eebec64aa992e SHA512: 93841b179a31164b8ad8384d46dc81ad37dfbbbce3b5cbf6c60cffccfb4c9289758c26af37d44a854f5e77fe8303d142814a06f4c9b467b12eed27c55b502c47 Homepage: https://cran.r-project.org/package=sse Description: CRAN Package 'sse' (Sample Size Estimation) Provides functions to evaluate user-defined power functions for a parameter range, and draws a sensitivity plot. It also provides a resampling procedure for semi-parametric sample size estimation and methods for adding information to a Sweave report. 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The SSEBI is computed from albedo and TS–TA to estimate surface moisture and evaporative dynamics, providing a robust assessment of surface dryness while accounting for atmospheric variations. Based on Roerink, Su, and Menenti (2000) . Package: r-cran-sseparser Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-stringr Suggests: r-cran-jsonlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sseparser_0.1.0-1.ca2404.1_all.deb Size: 54848 MD5sum: 7531b8aec747ec62523ede0a926f4dff SHA1: 64e9f64a717dfa0249a38364fa5dce31928b0d39 SHA256: d56c6eba9db63fc9f5d2289e552b2992b62f355ca0b46fef983c61347cce8020 SHA512: 353db9655c2cdfc3a79e49733275c6c2188bd90cb0f3da4d97b5147caccf296394ba8354b7c9ec317d459ca9803c046701bd251ba29ee3edabc38eff23a9f9fb Homepage: https://cran.r-project.org/package=SSEparser Description: CRAN Package 'SSEparser' (Parse Server-Sent Events) Functionality to parse server-sent events with a high-level interface that can be extended for custom applications. Package: r-cran-ssev Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pwr, r-cran-mess Filename: pool/dists/noble/main/r-cran-ssev_0.1.0-1.ca2404.1_all.deb Size: 28844 MD5sum: 38eb5fb852864cdda866f6ed02a4c182 SHA1: 52e05479393e69dce6813d634f510f9863057d5f SHA256: add2b4aabf64aa3dcd7a63b2a3876808a29a4079809fb8c6b9a5dcecfc2706d4 SHA512: 5a8e42446457d728161d3dc94fe73afc794ace47263d7415e19289a533f67bbbc8211a82a2e6d3e57009d7898a1b877b15902b90941bebbd717c1db88d4f9708 Homepage: https://cran.r-project.org/package=ssev Description: CRAN Package 'ssev' (Sample Size Computation for Fixed N with Optimal Reward) Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. Sample sizes and expected rewards for standard t- and z- tests are also provided. Package: r-cran-ssfa Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1577 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-maxlik, r-cran-spdep, r-cran-sp, r-cran-spatialreg Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-ssfa_1.2.3-1.ca2404.1_all.deb Size: 1461816 MD5sum: f19b730ba6c2d6bf9f55cf69562147de SHA1: 7790952eb55254a7ad8ae75f9ad066d7e1b638f2 SHA256: a832d350cc079ba2e476b92d29a73d5f0338ea553e86d43735af20c5c6367efe SHA512: 8f88d7120717d3addd3e1fdc012908c018ac1b49e860264ea35e5a59a80aa5204daf543e03e8e4ddf3b0d0ac200480ac750870c4c4b489272912475371132c2e Homepage: https://cran.r-project.org/package=ssfa Description: CRAN Package 'ssfa' (Spatial Stochastic Frontier Analysis) Spatial Stochastic Frontier Analysis (SSFA) is an original method for controlling the spatial heterogeneity in Stochastic Frontier Analysis (SFA) models, for cross-sectional data, by splitting the inefficiency term into three terms: the first one related to spatial peculiarities of the territory in which each single unit operates, the second one related to the specific production features and the third one representing the error term. Package: r-cran-ssfit Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survey Filename: pool/dists/noble/main/r-cran-ssfit_1.2-1.ca2404.1_all.deb Size: 20206 MD5sum: 1bc100d5474aaa831e03b3e04032863b SHA1: 5df96e83a00b33b12c78aef87233c359fe9469e5 SHA256: fae634bde835cf88fa5524064a3d7c80343454db1cf5fc5611e0e6ecf1476881 SHA512: f585b104a0641450d1a65e3354a34634797793aae1698f34ed2bcdad59b6c4f31009fb90b891cbb2fd96add69adfd128217db68966c01cd42c1a84fde3f26cf8 Homepage: https://cran.r-project.org/package=ssfit Description: CRAN Package 'ssfit' (Fitting of Parametric Models using Summary Statistics) Fits complex parametric models using the method proposed by Cox and Kartsonaki (2012) without likelihoods. Package: r-cran-sshaarp Architecture: all Version: 2.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2891 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-stringr, r-cran-gtools, r-cran-gmt, r-cran-desctools, r-cran-dplyr, r-cran-filesstrings, r-cran-purrr, r-cran-stringi, r-cran-hlatools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sshaarp_2.0.8-1.ca2404.1_all.deb Size: 1857912 MD5sum: 74cdded921eb4e418d7852535c84d0db SHA1: 75e8eae2e6c277b27d068fdc592923609bf8343a SHA256: 9873c9b29a25311801f46b1cb80e85c2ebfb51560d99935c1b891ee93adb0a7e SHA512: 20d14b6c081c8ed09a5aeec4ac77333e0f6c93d1761e9e51affa782d1d0e5f128b969d3bb73dfa91318f4974dd323b80abf7f2ca953e16f46c6652956c50fec3 Homepage: https://cran.r-project.org/package=SSHAARP Description: CRAN Package 'SSHAARP' (Searching Shared HLA Amino Acid Residue Prevalence) Processes amino acid alignments produced by the 'IPD-IMGT/HLA (Immuno Polymorphism-ImMunoGeneTics/Human Leukocyte Antigen) Database' to identify user-defined amino acid residue motifs shared across HLA alleles, HLA alleles, or HLA haplotypes, and calculates frequencies based on HLA allele frequency data. 'SSHAARP' (Searching Shared HLA Amino Acid Residue Prevalence) uses 'Generic Mapping Tools (GMT)' software and the 'GMT' R package to generate global frequency heat maps that illustrate the distribution of each user-defined map around the globe. 'SSHAARP' analyzes the allele frequency data described by Solberg et al. (2008) , a global set of 497 population samples from 185 published datasets, representing 66,800 individuals total. Users may also specify their own datasets, but file conventions must follow the prebundled Solberg dataset, or the mock haplotype dataset. 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Package: r-cran-ssifs Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gtools, r-cran-igraph, r-cran-meta, r-cran-netmeta, r-cran-plyr, r-cran-r2jags, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ssifs_1.0.5-1.ca2404.1_all.deb Size: 251336 MD5sum: a398e0cba2fcbadbbe9138bf37f68b86 SHA1: 8b4e4d99e2373560d85a4ba5cb6208d08792b373 SHA256: c3daeca398dafebc979dacddfd04b3059984466eb2eeb36db25aec940e8e1ca2 SHA512: 93bb19d137803596fe89b97d658e772bb30a1b75e8de06af636c8c579e30495992cd00513528db379ca389579ac28b383de710b19a9248a26085e717c0bf8ffe Homepage: https://cran.r-project.org/package=ssifs Description: CRAN Package 'ssifs' (Stochastic Search Inconsistency Factor Selection) Evaluating the consistency assumption of Network Meta-Analysis both globally and locally in the Bayesian framework. 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The geographical SSIM method incorporates well-developed 'geographically weighted summary statistics'('Brunsdon', 'Fotheringham' and 'Charlton' 2002) with an adaptive bandwidth kernel function for irregular lattice-based maps. 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Package: r-cran-ssize.fdr Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ssize.fdr_1.3-1.ca2404.1_all.deb Size: 83434 MD5sum: b2f70d1ca866f210be79c357f7987e4a SHA1: 65ae8aa15aad18ea12232aa836db9da453be1a87 SHA256: 333c0d91cd7093dcf1a99939b0bd102ac9dbd4a512fd5480ab2458a981d541ee SHA512: a913fa09e022e0d2e8660301093612e2b6473c79bf5f4deba431bf5a826cbc1e47efcdd1cc6529c7130bde40e974eafd8c7c8b6762475daa9b4aef83dc01a708 Homepage: https://cran.r-project.org/package=ssize.fdr Description: CRAN Package 'ssize.fdr' (Sample Size Calculations for Microarray Experiments) Functions that calculate appropriate sample sizes for one-sample t-tests, two-sample t-tests, and F-tests for microarray experiments based on desired power while controlling for false discovery rates. For all tests, the standard deviations (variances) among genes can be assumed fixed or random. This is also true for effect sizes among genes in one-sample and two sample experiments. Functions also output a chart of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes. Package: r-cran-ssizerna Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-bioc-biobase, r-bioc-edger, r-bioc-limma, r-bioc-qvalue, r-cran-ssize.fdr Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-ssizerna_1.3.3-1.ca2404.1_all.deb Size: 454886 MD5sum: db93deda9f2de4390d3307fb8c0a1bec SHA1: 470a744dcc7f453fc6d502c7a28da12e43c11d2f SHA256: 57493bfa2309200e0384468f891778ccf8d40007f289e22ad453916776a4b3c4 SHA512: 122157150f2705d84e3f258ec97df29d406ed0ae4abb0869c1c85758cbea293f0adbcd983ccbd283a54dcccca988eff325e151dff29fb81124ef0a8b44b47910 Homepage: https://cran.r-project.org/package=ssizeRNA Description: CRAN Package 'ssizeRNA' (Sample Size Calculation for RNA-Seq Experimental Design) We propose a procedure for sample size calculation while controlling false discovery rate for RNA-seq experimental design. Our procedure depends on the Voom method proposed for RNA-seq data analysis by Law et al. (2014) and the sample size calculation method proposed for microarray experiments by Liu and Hwang (2007) . We develop a set of functions that calculates appropriate sample sizes for two-sample t-test for RNA-seq experiments with fixed or varied set of parameters. The outputs also contain a plot of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes. To install this package, please use 'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'. For R version 3.5 or greater, please use 'if(!requireNamespace("BiocManager", quietly = TRUE)){install.packages("BiocManager")}; BiocManager::install("ssizeRNA")'. Package: r-cran-sslfmm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-matrixstats Filename: pool/dists/noble/main/r-cran-sslfmm_0.1.0-1.ca2404.1_all.deb Size: 111236 MD5sum: cabf2092df47744f97160c1b16a1facc SHA1: ef327c1048720b21e05c604f4b1841ba0aba64c2 SHA256: 8357da8b7347d2974214e5bd2583a1edc0268014f33634e8f5f99465000f60ce SHA512: 32922e5f890145652f93889318904054739ccd90ddddf7eac13e1dd0f36d8547a1a70d5b795ce7dae6b3beeb366675999c6178141bb427640281e16412595706 Homepage: https://cran.r-project.org/package=SSLfmm Description: CRAN Package 'SSLfmm' (Semi-Supervised Learning under a Mixed-Missingness Mechanism inFinite Mixture Models) Implements a semi-supervised learning framework for finite mixture models under a mixed-missingness mechanism. The approach models both missing completely at random (MCAR) and entropy-based missing at random (MAR) processes using a logistic–entropy formulation. Estimation is carried out via an Expectation–-Conditional Maximisation (ECM) algorithm with robust initialisation routines for stable convergence. The methodology relates to the statistical perspective and informative missingness behaviour discussed in Ahfock and McLachlan (2020) and Ahfock and McLachlan (2023) . The package provides functions for data simulation, model estimation, prediction, and theoretical Bayes error evaluation for analysing partially labelled data under a mixed-missingness mechanism. Package: r-cran-ssm Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ssm_1.0.1-1.ca2404.1_all.deb Size: 275442 MD5sum: a3ccf3a626e1671d3cfff64d82196c39 SHA1: 1bc48a6a74426fd22a139cc7c8e954b5796f9331 SHA256: 317579584d931a15fff444d4a8b9dca5893e3161bcd989b18893461f7b1f9b2a SHA512: 23593ae43942af10b275287c75fe1add0676848ce8d31ea4f052d716b8d3244e27a441e5e143d9581cccc7d4a8474dbc59ab2a6c861ed93e9b5cfb74324d5850 Homepage: https://cran.r-project.org/package=SSM Description: CRAN Package 'SSM' (Fit and Analyze Smooth Supersaturated Models) Creates an S4 class "SSM" and defines functions for fitting smooth supersaturated models, a polynomial model with spline-like behaviour. Functions are defined for the computation of Sobol indices for sensitivity analysis and plotting the main effects using FANOVA methods. It also implements the estimation of the SSM metamodel error using a GP model with a variety of defined correlation functions. Package: r-cran-ssmn Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mnormt, r-cran-moments, r-cran-truncdist, r-cran-sn Filename: pool/dists/noble/main/r-cran-ssmn_1.1-1.ca2404.1_all.deb Size: 125326 MD5sum: 03364d1904c16b9f68e4bc36fbde5897 SHA1: 14e6a02d8a8118077d79c4ddc7cc3ce9d015623f SHA256: 8d676b7737fda614cbd0e7705ce808c46391e44070350a827ed60c9fbab770ce SHA512: e8fa00b31c7bee2b398d56e4021d88e4f955988edfb104d403ceeeb196a4e6f7ee3009a4adc6e5174870157fb66ba44ccd79ea1929a474a0d873e072f72d8c30 Homepage: https://cran.r-project.org/package=ssmn Description: CRAN Package 'ssmn' (Skew Scale Mixtures of Normal Distributions) Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions. Package: r-cran-ssmodels Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1294 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sn, r-cran-numderiv, r-cran-pracma, r-cran-misctools, r-cran-rdpack Suggests: r-cran-knitr, r-cran-testthat, r-cran-maxlik, r-cran-mvtnorm, r-cran-sampleselection, r-cran-kableextra, r-cran-kfigr, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-ssmodels_2.0.1-1.ca2404.1_all.deb Size: 1118990 MD5sum: fb4743a467db59b759b98dcb9327640e SHA1: a737e4b822bd2e08ae14f5e76652bf420d606c99 SHA256: 740f1920ae93cf8de746f5c7d2cffbf2da7e508748e319747c0ea00b370c7a4e SHA512: 585e40fb032007da7edad3d3fe38fe8c627142513cfe8284a7deace4a2299c02883c6b809044a5d3e7af5e462be78926844f155d2a31875abaec8e68419167af Homepage: https://cran.r-project.org/package=ssmodels Description: CRAN Package 'ssmodels' (Sample Selection Models) In order to facilitate the adjustment of the sample selection models existing in the literature, we created the 'ssmodels' package. Our package allows the adjustment of the classic Heckman model (Heckman (1976), Heckman (1979) ), and the estimation of the parameters of this model via the maximum likelihood method and two-step method, in addition to the adjustment of the Heckman-t models introduced in the literature by Marchenko and Genton (2012) and the Heckman-Skew model introduced in the literature by Ogundimu and Hutton (2016) . We also implemented functions to adjust the generalized version of the Heckman model, introduced by Bastos, Barreto-Souza, and Genton (2021) , that allows the inclusion of covariables to the dispersion and correlation parameters, and a function to adjust the Heckman-BS model introduced by Bastos and Barreto-Souza (2020) that uses the Birnbaum-Saunders distribution as a joint distribution of the selection and primary regression variables. This package extends and complements existing R packages such as 'sampleSelection' (Toomet and Henningsen, 2008) and 'ssmrob' (Zhelonkin et al., 2016), providing additional robust and flexible sample selection models. Package: r-cran-ssmrcd Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1099 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-robustbase, r-cran-scales, r-cran-car, r-cran-dbscan, r-cran-plot3d, r-cran-dplyr, r-cran-ggplot2, r-cran-expm, r-cran-foreach, r-cran-doparallel, r-cran-rrcov, r-cran-desctools, r-cran-rootsolve, r-cran-matrix, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ssmrcd_1.1.0-1.ca2404.1_all.deb Size: 951618 MD5sum: 115d281bf54a075811e216e6e4ff65eb SHA1: 4e89efc53f0025a0fc3971cee2dbb82eb4bc65ad SHA256: b4f914b9de1ac2829597646179bdc5566a56685304a39b35ad8ac5d02436d6af SHA512: 5990a082767b325bb798fe53033aa5c68f7ed989735c8f3b99599b95fd575e90be064fccc8709ffaa6771320d268e47c1b41407a3edf69408f6ff43d914a30bb Homepage: https://cran.r-project.org/package=ssMRCD Description: CRAN Package 'ssMRCD' (Spatially Smoothed MRCD Estimator) Estimation of the Spatially Smoothed Minimum Regularized Determinant (ssMRCD) estimator and its usage in an ssMRCD-based outlier detection method as described in Puchhammer and Filzmoser (2023) and for sparse robust PCA for multi-source data described in Puchhammer, Wilms and Filzmoser (2024) . Included are also complementary visualization and parameter tuning tools. Package: r-cran-ssmrob Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sampleselection, r-cran-robustbase, r-cran-mass Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-ssmrob_1.0-1.ca2404.1_all.deb Size: 253466 MD5sum: ce69aa51ec673d4558536efff77ebed8 SHA1: 4a233830fcb51cf3301dc5e8a11c228bbd99b169 SHA256: ef1a7a3ad5d834a957a7634ff337ceaec044c605ef35caca9696c89bf0323c90 SHA512: 6da4c3adaead536ebf11e3e9a9c89aeab550ed3e53970bf1a251d4b1c84a042e1cc686678e3faef3d37edf537ce00b00f5e278af80a17d7bf3e2434f63ca1a4c Homepage: https://cran.r-project.org/package=ssmrob Description: CRAN Package 'ssmrob' (Robust Estimation and Inference in Sample Selection Models) Package provides a set of tools for robust estimation and inference for models with sample selectivity and endogenous treatment model. For details, see Zhelonkin and Ronchetti (2021) . Package: r-cran-ssmsn Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcpack Filename: pool/dists/noble/main/r-cran-ssmsn_0.2.0-1.ca2404.1_all.deb Size: 16482 MD5sum: dee94f11e2dc0e915b7022af9bb5e629 SHA1: 8b9e5cf74dccc1425c14f685551e84e6c77cec6d SHA256: f06f9d08d9ad8ae31168dd0b31c6e6db7173935c9a4d338f6cf2bb9806d39dac SHA512: 047a5279dd5b63f561fda131eb66c48b128c93901ada2b565a0389377d3a174cfaa7684faed359babe795b743665067d6d1361b943f7be27d65d5e117a1234ee Homepage: https://cran.r-project.org/package=ssmsn Description: CRAN Package 'ssmsn' (Scale-Shape Mixtures of Skew-Normal Distributions) It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016) . Package: r-cran-ssmutpa Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3312 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ggridges, r-cran-igraph, r-cran-kernlab, r-bioc-maftools, r-cran-matrix, r-cran-nbclust, r-cran-pheatmap, r-cran-rcolorbrewer, r-cran-survival Suggests: r-cran-knitr, r-cran-qpdf, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ssmutpa_0.1.2-1.ca2404.1_all.deb Size: 1974780 MD5sum: c522ba85a7db6f9e15e5a5f06d8deb49 SHA1: 4389e00b02af17c0a1fb8758d769439144014cf1 SHA256: 70de09375d1780077bfe2fc76981f82287420ebcb055972b2e4c64bb38a2ebe3 SHA512: e6c8d9929fa8b288b6be1bb51fc0481fb906d4b3c198dd106753a646735ff50d5d0d571a41b3cbd1160e388c202c56acd27e375899e5c48c5ae7bf64fdcc7294 Homepage: https://cran.r-project.org/package=ssMutPA Description: CRAN Package 'ssMutPA' (Single-Sample Mutation-Based Pathway Analysis) A systematic bioinformatics tool to perform single-sample mutation-based pathway analysis by integrating somatic mutation data with the Protein-Protein Interaction (PPI) network. In this method, we use local and global weighted strategies to evaluate the effects of network genes from mutations according to the network topology and then calculate the mutation-based pathway enrichment score (ssMutPES) to reflect the accumulated effect of mutations of each pathway. Subsequently, the ssMutPES profiles are used for unsupervised spectral clustering to identify cancer subtypes. Package: r-cran-ssnbayes Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4961 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-dplyr, r-cran-rstan, r-cran-sf, r-cran-ssn2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ssnbayes_0.0.3-1.ca2404.1_all.deb Size: 816030 MD5sum: 806b6ae648331daaa6c4ce9bef8251bc SHA1: 857b545da9179388547151bc277344a834d98bc7 SHA256: ed5c391a85a4d35af2ce94a9d1cc3165a83934767665234fec44a53f2cd81319 SHA512: 78fb365614185c20313f227f07e19590d7956ce0e0aa5fa5fe240636b39ac36db844fc6f17874e19769c10e0db08c6c651379e5fe036d17a30bc6aac38443667 Homepage: https://cran.r-project.org/package=SSNbayes Description: CRAN Package 'SSNbayes' (Bayesian Spatio-Temporal Analysis in Stream Networks) Fits Bayesian spatio-temporal models and makes predictions on stream networks using the approach by Santos-Fernandez, Edgar, et al. (2022)."Bayesian spatio-temporal models for stream networks". . In these models, spatial dependence is captured using stream distance and flow connectivity, while temporal autocorrelation is modelled using vector autoregression methods. Package: r-cran-ssnbler Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2557 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-pdist, r-cran-igraph, r-cran-rsqlite, r-cran-withr, r-cran-ssn2, r-cran-doparallel, r-cran-foreach Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ssnbler_1.1.1-1.ca2404.1_all.deb Size: 884918 MD5sum: ffd0f1090a6e90668e006a10e3962b48 SHA1: b825f5d1bcb0169824097190bbf264a65b7c6312 SHA256: 963adce687c2fdf07dbd88136a9070eb62ed332caa7888599f78d46096facff3 SHA512: 45fe8f6c729a87eeecdf12a2107a7ae1cf51641caabdd45c3fa6f559a2885888618fcbe7a9958c84c31606aee6a17b0a0c0efdf730838c98c96be1b7e9e13133 Homepage: https://cran.r-project.org/package=SSNbler Description: CRAN Package 'SSNbler' (Assemble 'SSN' Objects) Import, create and assemble data needed to fit spatial-statistical stream-network models using the 'SSN2' package for 'R'. Streams, observations, and prediction locations are represented as simple features and specific tools provided to define topological relationships between features; calculate the hydrologic distances (with flow-direction preserved) and the spatial additive function used to weight converging stream segments; and export the topological, spatial, and attribute information to an `SSN` (spatial stream network) object, which can be efficiently stored, accessed and analysed in 'R'. A detailed description of methods used to calculate and format the spatial data can be found in Peterson, E.E. and Ver Hoef, J.M., (2014) . Package: r-cran-ssp Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-sampling, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-ssp_1.1.0-1.ca2404.1_all.deb Size: 216116 MD5sum: c7721be9197252362483713c6dc92a19 SHA1: afd98de95b06f8016e4680590eda817ff2ceac36 SHA256: f42f332dfe133647bb3c89aa1ed1c8e5a4bc2159bc450a99c9e45d1d6501c5f1 SHA512: 0115344e5caa70a646ed710fb6b77b5c085a0b42685c61187362175a8d4ba86b8afa745047d6754e1da78491a84e0c279167ca9274b8c5a3ad16cf35d4052e3b Homepage: https://cran.r-project.org/package=SSP Description: CRAN Package 'SSP' (Simulated Sampling Procedure for Community Ecology) The Simulation-based Sampling Protocol (SSP) is an R package designed to estimate sampling effort in studies of ecological communities. It is based on the concept of pseudo-multivariate standard error (MultSE) (Anderson & Santana-Garcon, 2015, ) and the simulation of ecological data. The theoretical background is described in Guerra-Castro et al. (2020, ). Package: r-cran-ssplots Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-zoo Filename: pool/dists/noble/main/r-cran-ssplots_0.1.2-1.ca2404.1_all.deb Size: 52880 MD5sum: 72898e0480661e190c5b7f5e5a1857a5 SHA1: ca9b24f6a5915a9025b4005d19434dc332d6ef21 SHA256: 1fe135074633d6787ad4f927b2f00bc064190632157a2136858108987577e21c SHA512: f0502139dd34c5fb3bc277a964f511c43070a58af720601ec8b45f33b53eddd0f2af8bb2e2babfd6e8aef9b36302df2bbda08f46e7f7c9784157b2ba06f79941 Homepage: https://cran.r-project.org/package=SSplots Description: CRAN Package 'SSplots' (Stock Status Plots (SSPs)) Pauly et al. (2008) created (and coined the name) 'Stock Status Plots' for a UNEP compendium on Large Marine Ecosystems(LMEs, Sherman and Hempel (2009)). Stock status plots are bivariate graphs summarizing the status (e.g., developing, fully exploited, overexploited, etc.), through time, of the multispecies fisheries of a fished area or ecosystem. This package contains three functions to generate stock status plots viz., SSplots_pauly() (as per the criteria proposed by Pauly et al.,2008), SSplots_kleisner() (as per the criteria proposed by Kleisner and Pauly (2011) and Kleisner et al. (2013) )and SSplots_EPI() (as per the criteria proposed by Jayasankar et al.,2021 ). Package: r-cran-sspm Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3591 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-mgcv, r-cran-units, r-cran-checkmate, r-cran-cli, r-cran-tibble, r-cran-magrittr, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-stringr, r-cran-rlang Suggests: r-cran-testthat, r-cran-covr, r-cran-ggplot2, r-cran-ggforce, r-cran-lwgeom, r-cran-tweedie, r-cran-sfdct, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-sspm_1.1.0-1.ca2404.1_all.deb Size: 2803538 MD5sum: 77b4c92e9b133a54e2693619dc2577fa SHA1: a00d0d32b1cc5290d69095355680eb091be11274 SHA256: c11d7f83e2086577de91073b8d58c7ae5ab770bdc45531b8035a5938130977fe SHA512: 633715ea2a8e8a3691f5d8e437f95cb620b205cc0420ad50820381055dac0b2774afd2e5fd0754a656a573a3dfa9b8c34e5a6a2404dd1fc391e3ace52c2c5cd0 Homepage: https://cran.r-project.org/package=sspm Description: CRAN Package 'sspm' (Spatial Surplus Production Model Framework for Northern ShrimpPopulations) Implement a GAM-based (Generalized Additive Models) spatial surplus production model (spatial SPM), aimed at modeling northern shrimp population in Atlantic Canada but potentially to any stock in any location. The package is opinionated in its implementation of SPMs as it internally makes the choice to use penalized spatial gams with time lags. However, it also aims to provide options for the user to customize their model. The methods are described in Pedersen et al. (2022, ). Package: r-cran-ssr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-e1071 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tgp Filename: pool/dists/noble/main/r-cran-ssr_0.1.1-1.ca2404.1_all.deb Size: 192706 MD5sum: ec5da00eb58d3a418cb7912e2b80a76a SHA1: a7f9909071baf153b6f5a39ba9cb1a24aa054203 SHA256: 0d5d9f1534abe6ee4f4200a4c75d8ad84a935c4005dca9308df41edb491bd016 SHA512: c01e3818a1aad4ec8da9c724c28fb7357deeaa821b5d0181cfd5c9368bc25a1e06896b3b5ce126102afc68638bb314cd85716b6d22ab6a3f988b17bdd085e03a Homepage: https://cran.r-project.org/package=ssr Description: CRAN Package 'ssr' (Semi-Supervised Regression Methods) An implementation of semi-supervised regression methods including self-learning and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G. (2009) . 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Package includes functions for generating a sequential relation table and a treegram to visualize the sequential relations between pairs of items. Package: r-cran-ssrat Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-sna Filename: pool/dists/noble/main/r-cran-ssrat_1.1-1.ca2404.1_all.deb Size: 128124 MD5sum: 8c6433485803fb6613d64a0a59a9a87d SHA1: 266a86ea07d26a1680df1a48fab78020340a7586 SHA256: cd5383881afe223450ccddbbd51e672a252fffa893b93375a6bdb82abd059309 SHA512: a9f78885778e46efe9ec47b45711f786cb6e078e166a6f4a807163be29c28df4f65100ce9816441c799c65eddd0567117c9adac92abc0e4c968cdb81f2a3aab4 Homepage: https://cran.r-project.org/package=SSrat Description: CRAN Package 'SSrat' (Two-Dimensional Sociometric Status Determination with RatingScales) A set of functions for two-dimensional sociometric status determination with rating scales. 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Kapur K, Bhaumik R, Tang XC, Hur K, Reda DJ, Bhaumik D (2014) . Package: r-cran-ssrmst Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-survrm2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ssrmst_0.1.1-1.ca2404.1_all.deb Size: 36016 MD5sum: f9b62d038b3dd9935ce44e9de88b7ef8 SHA1: 3bf326ea3d38e0ec95b44a00c27260efedf151df SHA256: e4b84aef02791513f07f9527983ea73a67a674e1ab2876870cdeb4a049c41e57 SHA512: 0c0e47e871f9d23503acf25a8f60e8e3995f62dd520e69837155303771c6f72ae3731cf873adeb6d55be5d3f34298b453fd9cd8d21e3abf41b12e25fe9ee58e6 Homepage: https://cran.r-project.org/package=SSRMST Description: CRAN Package 'SSRMST' (Sample Size Calculation using Restricted Mean Survival Time) Calculates the power and sample size based on the difference in Restricted Mean Survival Time. Package: r-cran-ssrn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 641 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat, r-cran-scanstatistics Filename: pool/dists/noble/main/r-cran-ssrn_0.1.0-1.ca2404.1_all.deb Size: 618332 MD5sum: cd5b9c471f9aa2ba7b385f6a4d18ffab SHA1: fdbca5394d5ac5acef3795b1a8cd7b499079766e SHA256: bfe4d6800cb17d85030eaa4f9bd44ac8c08546f72d0876efa1e1c3c98be799dc SHA512: 3bf3387ad2a741c0a2c527ee70c5af134c267511c1d206fb23833051e9f7a71266db49c0568213429d9b8d20eceb592cd6d6d1fff078b3d49d99206c2a2d3c08 Homepage: https://cran.r-project.org/package=ssrn Description: CRAN Package 'ssrn' (Scan Statistics for Railway Network) Implement the algorithm provided in scan for estimating the transmission route on railway network using passenger volume. 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It can detect whether a sample contains contaminant from the same species. In the first stage of the approach, a change-point detection method is used to identify copy number variations for filtering. Next, features are extracted from the data for a support vector machine model. For log-likelihood calculation, the deviation parameter is estimated by maximum likelihood method. Using a radial basis function kernel support vector machine, the contamination of a sample can be detected. Package: r-cran-sssimple Architecture: all Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-maps Filename: pool/dists/noble/main/r-cran-sssimple_0.6.6-1.ca2404.1_all.deb Size: 357164 MD5sum: 78135fd53a93ef825165fb17a4716257 SHA1: 5d7558f5064027da8ec5a896f828d85b4d19420d SHA256: b7370a2121daf49d488901092684974f6964d973b1427dc6e21a5ff5e2899a64 SHA512: 68fd8875018849caedcd3225cdf839751a4355baa161c718548cf017f7e36f1b49b952139a6189b6eb50673c81ce5cdbed99b188e5f14be97c27f71b076941e2 Homepage: https://cran.r-project.org/package=SSsimple Description: CRAN Package 'SSsimple' (State Space Models) Simulate, solve state space models. Package: r-cran-sssvcqr Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1686 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-fnn, r-cran-igraph, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sssvcqr_0.0.4-1.ca2404.1_all.deb Size: 1434742 MD5sum: 18517e458088f36a1191bfd892dd3ee2 SHA1: 8390b3b43f81773ad4fe3625b8b96295c358d2cb SHA256: 0c1adb486dd7926ce41244f0a29495ca56e6a9c344afe628193b6f142890bd78 SHA512: 47463120a6e588dfee81e626d7e38cac5dfeb0c7b2386c9e2b44403fb0ae4ac654847cb801159f3a8584b89f786bc55877f248d86c165f31f51573e5e63120fa Homepage: https://cran.r-project.org/package=sssvcqr Description: CRAN Package 'sssvcqr' (Sparse-Smooth Spatially Varying Coefficient Quantile Regression) Implements sparse-smooth spatially varying coefficient quantile regression (SS-SVCQR), combining quantile regression of Koenker and Bassett (1978) , grouped variable selection of Yuan and Lin (2006) , graph regularization, and the alternating direction method of multipliers of Boyd et al. (2011) . The package provides graph-regularized estimation, spatially blocked cross-validation, prediction, diagnostics, and simulation helpers for global-local spatial quantile regression. Package: r-cran-sstack Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest, r-cran-foreach, r-cran-dplyr, r-cran-doparallel Filename: pool/dists/noble/main/r-cran-sstack_1.0.1-1.ca2404.1_all.deb Size: 659674 MD5sum: 5e77264cf0af6594c24bea0f82539d6d SHA1: 85fe9911c88baf846d99bf9fbcbce37aba60f850 SHA256: 5b89e8f3138997e0561fa1fa7e847135a5c1de34e846fa45193c04ff3639ecb3 SHA512: 5e1de5f7db386299f54551b3efa374fe405d8a7cd0004509c374d404053a5feccd8feb67f7495e66d530268071bbc6cb47983072822eb79b6dba6f618d84881f Homepage: https://cran.r-project.org/package=Sstack Description: CRAN Package 'Sstack' (Bootstrap Stacking of Random Forest Models for HeterogeneousData) Generates and predicts a set of linearly stacked Random Forest models using bootstrap sampling. Individual datasets may be heterogeneous (not all samples have full sets of features). 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Package: r-cran-ssutil Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 542 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-broom, r-cran-mass, r-cran-gsdesign, r-cran-mvtnorm, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ssutil_1.0.0-1.ca2404.1_all.deb Size: 396584 MD5sum: 31cf638a83034dbdccba5fc0bdac4f0d SHA1: 56df2498da829173c8fc467ef55c7a515f0303fd SHA256: 3076973e2eb8fc4c2295f43e895880ea2d0917c77e2fea58549042e672041b2b SHA512: e2129c50f205af4cbad91d085843d8c17fbd691669b665de105c4ba4bf2934e0f2a06e51bcb3fe0758313b1a071603f8424547f7e3b18256d2bfc4e95855e6d2 Homepage: https://cran.r-project.org/package=ssutil Description: CRAN Package 'ssutil' (Sample Size Calculation Tools) Functions for sample size estimation and simulation in clinical trials. Includes methods for selecting the best group using the Indifference-zone approach, as well as designs for non-inferiority, equivalence, and negative binomial models. For the sample size calculation for non-inferiority of vaccines, the approach is based on Fleming, Powers, and Huang (2021) . The Indifference-zone approach is based on Sobel and Huyett (1957) and Bechhofer, Santner, and Goldsman (1995, ISBN:978-0-471-57427-9). 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SSVS is a Bayesian variable selection method used to estimate the probability that individual predictors should be included in a regression model. Using MCMC estimation, the method samples thousands of regression models in order to characterize the model uncertainty regarding both the predictor set and the regression parameters. For details see Bainter, McCauley, Wager, and Losin (2020) Improving practices for selecting a subset of important predictors in psychology: An application to predicting pain, Advances in Methods and Practices in Psychological Science 3(1), 66-80 . Package: r-cran-ssw Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 731 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reticulate Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ssw_0.2.1-1.ca2404.1_all.deb Size: 265574 MD5sum: ff854a46017d30a811f1a911c591e1aa SHA1: 03ea0fc4e2ecb107a2652da95b82e103561d4edf SHA256: a96494f5a631e381e1da5b3c8ca6c5bdaa4555a5d5b0ca60c0128306df9fa732 SHA512: 111d916bf76f15a86dd0a559d9c28a71765b0bfb63e4cd2f1378ec970087fc9464f622d1f5d9b9194cc7e1b2333eece9ea17c221c755e649b15f9730d5a14e14 Homepage: https://cran.r-project.org/package=ssw Description: CRAN Package 'ssw' (Striped Smith-Waterman Algorithm for Sequence Alignment usingSIMD) Provides an R interface for 'SSW' (Striped Smith-Waterman) via its 'Python' binding 'ssw-py'. 'SSW' is a fast 'C' and 'C++' implementation of the Smith-Waterman algorithm for pairwise sequence alignment using Single-Instruction-Multiple-Data (SIMD) instructions. 'SSW' enhances the standard algorithm by efficiently returning alignment information and suboptimal alignment scores. The core 'SSW' library offers performance improvements for various bioinformatics tasks, including protein database searches, short-read alignments, primary and split-read mapping, structural variant detection, and read-overlap graph generation. These features make 'SSW' particularly useful for genomic applications. Zhao et al. (2013) developed the original 'C' and 'C++' implementation. Package: r-cran-ssym Architecture: all Version: 1.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gigrvg, r-cran-numderiv, r-cran-normalp, r-cran-formula, r-cran-survival, r-cran-sandwich Suggests: r-cran-nistnls, r-cran-gam, r-cran-sn, r-cran-mass Filename: pool/dists/noble/main/r-cran-ssym_1.5.8-1.ca2404.1_all.deb Size: 345242 MD5sum: 900947ca2f69ae0c41448fbb5e4fe729 SHA1: 953dc71642e0bf2b93da1b63da89b6b6ffcbf116 SHA256: ef5b720d33988c13f70fa971845fb62140e672cc568ecfbf13264c6839a45930 SHA512: c16e7029ce8294a08a006abc247e46756889c8c15d58ea840854d2028d464a5694336b5f16dab61769850520848bfeecafed6d184d62afe427c5fb41cfe48c7b Homepage: https://cran.r-project.org/package=ssym Description: CRAN Package 'ssym' (Fitting Semi-Parametric log-Symmetric Regression Models) Set of tools to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, asymmetric and possibly, censored. Under this setup, both the median and the skewness of the response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components may be approximated by natural cubic splines or P-splines. Supported distributions for the model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t distributions. Package: r-cran-st Architecture: all Version: 1.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sda, r-cran-fdrtool, r-cran-corpcor Suggests: r-bioc-limma, r-cran-samr Filename: pool/dists/noble/main/r-cran-st_1.2.7-1.ca2404.1_all.deb Size: 586686 MD5sum: 9805213b169101050f5c768573284281 SHA1: 0ca4e7f474fc00ff6773df3e138b25e6dd0c15f8 SHA256: 81a6bf770fbe9d06b04d53e36358977298d494fe97c67d0559002d288a2a9cf7 SHA512: dd923cea8fa5394d9c01505052e82c380e1e2960adaa74ed3762a3e7d217ca613ed4574dd5e0a4a22c867cf1f03e9e8962453acdccf0edb7d11561661dd0babc Homepage: https://cran.r-project.org/package=st Description: CRAN Package 'st' (Shrinkage t Statistic and Correlation-Adjusted t-Score) Implements the "shrinkage t" statistic introduced in Opgen-Rhein and Strimmer (2007) and a shrinkage estimate of the "correlation-adjusted t-score" (CAT score) described in Zuber and Strimmer (2009) . It also offers a convenient interface to a number of other regularized t-statistics commonly employed in high-dimensional case-control studies. Package: r-cran-sta Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-raster, r-cran-geots, r-cran-foreach, r-cran-trend, r-cran-doparallel, r-cran-mapview, r-cran-rcolorbrewer Suggests: r-cran-sp Filename: pool/dists/noble/main/r-cran-sta_0.1.7-1.ca2404.1_all.deb Size: 2045964 MD5sum: 998c7f7793cbc21d23e6259918c60901 SHA1: 2a3b987eb5de9281f24cc9cfdd73614a8bb4a70e SHA256: 6d3ecd116410522c69fb460ea3640b8a328ede605fede52c1abab29675cd0355 SHA512: 50f38e57e12836c8530d0307ee2c87d00ede7ece3256c4f1c80430599e7fec499b5ad0b2e0ce2b1fd218d46f99cbc2a79279d41f69e6e7d282a4c239ad19e839 Homepage: https://cran.r-project.org/package=sta Description: CRAN Package 'sta' (Seasonal Trend Analysis for Time Series Imagery in R) Efficiently estimate shape parameters of periodic time series imagery with which a statistical seasonal trend analysis (STA) is subsequently performed. STA output can be exported in conventional raster formats. Methods to visualize STA output are also implemented as well as the calculation of additional basic statistics. STA is based on (R. Eastman, F. Sangermano, B. Ghimire, H. Zhu, H. Chen, N. Neeti, Y. Cai, E. Machado and S. Crema, 2009) . Package: r-cran-staat1cho Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-forcats, r-cran-readr, r-cran-rlang, r-cran-shiny Suggests: r-cran-bslib, r-cran-dt, r-cran-ggplot2, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-staat1cho_0.1.0-1.ca2404.1_all.deb Size: 64908 MD5sum: f85a62600b340466754ab531c14a5335 SHA1: 091b465b8cdafd5f11bdcdcedaa84fb6b472fe4e SHA256: dfc17a5e974389950d96e1822a5004ffb360424985472f122532eb8105f6bd8c SHA512: d9afe126b923257dc994645e9cda73ee9f0d862451633fcedf530f484a2a55049b208c6d02dc1a9f0850d3cf2f1b34763946601c3ddeab4ce220dbd0a3cacc9c Homepage: https://cran.r-project.org/package=staat1cho Description: CRAN Package 'staat1cho' (Study Indicators Based on Dutch Higher Education Data (1CHO)) Calculates enrolment, graduation, dropout, and programme-switch indicators from the Dutch higher education registration data (1CHO) supplied by DUO. Includes an interactive 'Shiny' dashboard for exploring results. Package: r-cran-stabiliser Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-dplyr, r-cran-bigstep, r-cran-rsample, r-cran-tibble, r-cran-purrr, r-cran-tidyr, r-cran-stringr, r-cran-ggplot2, r-cran-broom, r-cran-caret, r-cran-ncvreg, r-cran-hmisc, r-cran-expss, r-cran-lme4, r-cran-matrixstats, r-cran-recipes, r-cran-lmertest, r-cran-furrr, r-cran-future Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-markdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-stabiliser_1.0.7-1.ca2404.1_all.deb Size: 177362 MD5sum: 83cd288af1e5dfa0180d08420c48ac1d SHA1: 61c76d9dfe29c840db77029098358b702b7bc707 SHA256: 72edab7f903487cd17cef824adf0854495fa3e3fd6a832d538d08d4a65489c76 SHA512: 3baad1992f481e394676807dc9cd285ec3f8ba7d6a1e2012179fc10a83e8354e8e2a399a91eb23009ef0bde253a1d7073cc4a5e4832b3593b656918bccdc5415 Homepage: https://cran.r-project.org/package=stabiliser Description: CRAN Package 'stabiliser' (Stabilising Variable Selection) A stable approach to variable selection through stability selection and the use of a permutation-based objective stability threshold. Lima et al (2021) , Meinshausen and Buhlmann (2010) . Package: r-cran-stability Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggfortify, r-cran-lme4, r-cran-magrittr, r-cran-matrixstats, r-cran-reshape2, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-stability_0.6.0-1.ca2404.1_all.deb Size: 125336 MD5sum: a612bbd9aaa3913eed8ff191b35e6b50 SHA1: ca38dc9079983548c526f4d4b59eb6b8b90e7f3e SHA256: b1eb9fd95c609c21b4b0b6ebbdb4469d2750aa2cd739b779980c6a06228f9a5f SHA512: 5cd60fe80aca3ca2b1c53c27b96405ede7a91ee72a9ee5b2c1e12b04d76f22afa9639a981100b9e6914ef310dca7deb45e09caa7f30675a466be7815b3a0bff8 Homepage: https://cran.r-project.org/package=stability Description: CRAN Package 'stability' (Stability Analysis of Genotype by Environment Interaction (GEI)) Provides functionalities for performing stability analysis of genotype by environment interaction (GEI) to identify superior and stable genotypes across diverse environments. It implements Eberhart and Russell’s ANOVA method (1966)(), Finlay and Wilkinson’s Joint Linear Regression method (1963) (), Wricke’s Ecovalence (1962, 1964), Shukla’s stability variance parameter (1972) (), Kang’s simultaneous selection for high yield and stability (1991) (), Additive Main Effects and Multiplicative Interaction (AMMI) method and Genotype plus Genotypes by Environment (GGE) Interaction methods. Package: r-cran-stabilityapp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-gridextra, r-cran-patchwork, r-cran-stability, r-cran-shiny, r-cran-shinybs, r-cran-shinydashboardplus Suggests: r-cran-shinytest2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stabilityapp_0.1.0-1.ca2404.1_all.deb Size: 984248 MD5sum: ab9283d3cc93c9fb7e9c5d75b42b1d94 SHA1: d6ccac8b30c7f0cfa533143ba34df22e0edb7ffe SHA256: d9751294732b5a069c480a42ad9a49d1e82d2ed3f08e0812fa7bce982d81b4a3 SHA512: ba689df17d11d93f1e8c1171d7be3c36ad2b000a2c3b50b80a0069c3d28ec3ac61646b535256c81f5bc2d512a9bb63f47cfd506ba9d81008a05a9c6239b8a54b Homepage: https://cran.r-project.org/package=StabilityApp Description: CRAN Package 'StabilityApp' (Stability Analysis App for GEI in Multi-Environment Trials) Provides tools for Genotype by Environment Interaction (GEI) analysis, using statistical models and visualizations to assess genotype performance across environments. It helps researchers explore interaction effects, stability, and adaptability in multi-environment trials, identifying the best-performing genotypes in different conditions. Which Win Where! Package: r-cran-stabilizedregression Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-r6, r-cran-glmnet, r-cran-corpcor, r-cran-ggplot2, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-stabilizedregression_1.1-1.ca2404.1_all.deb Size: 133434 MD5sum: bda341096d4e2341ff170c58f0d30809 SHA1: 62b3d19777b229917487993458776cd5abe3146f SHA256: 5c8086234b79557f2eaa3d18a212a2abba9a60e0b5cd25df0371aee059e177e1 SHA512: 6e60565deb00d8b4dc96055a0ddbd1c4c5a79e2ccbfb806b7d0ddc1adf2868cd83daa833984f40b4a122855c86fa5e535c3b12cddea3587f88b844cdb3bb37ba Homepage: https://cran.r-project.org/package=StabilizedRegression Description: CRAN Package 'StabilizedRegression' (Stabilizing Regression and Variable Selection) Contains an implementation of 'StabilizedRegression', a regression framework for heterogeneous data introduced in Pfister et al. (2021) . The procedure uses averaging to estimate a regression of a set of predictors X on a response variable Y by enforcing stability with respect to a given environment variable. The resulting regression leads to a variable selection procedure which allows to distinguish between stable and unstable predictors. The package further implements a visualization technique which illustrates the trade-off between stability and predictiveness of individual predictors. Package: r-cran-stabilo Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-pracma Filename: pool/dists/noble/main/r-cran-stabilo_0.1.1-1.ca2404.1_all.deb Size: 45402 MD5sum: f116334ff5d449acc562fa66afc55121 SHA1: 8fe3092bd3158c89a98d243d5324b9146a1d0460 SHA256: 4db0c726c022c71244b63929428f6c62fdb97237927a4563153c0d103cd30c9d SHA512: 53187e4c4db32af0c278c93291e3e200d77c837bbbd8f9cd457ee9ab07128850b3d53e67a1e5cdf7446df114de87a60479cbe67f490cda22e18e06679c9da251 Homepage: https://cran.r-project.org/package=stabilo Description: CRAN Package 'stabilo' (Stabilometric Signal Quantification) Functions for stabilometric signal quantification. The input is a data frame containing the x, y coordinates of the center-of-pressure displacement. Jose Magalhaes de Oliveira (2017) "Statokinesigram normalization method"; T E Prieto, J B Myklebust, R G Hoffmann, E G Lovett, B M Myklebust (1996) "Measures of postural steadiness: Differences between healthy young and elderly adults"; L F Oliveira et al (1996) "Calculation of area of stabilometric signals using principal component analisys". Package: r-cran-stabledist Architecture: all Version: 0.7-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-matrix, r-cran-fbasics, r-cran-fmstable, r-cran-runit, r-cran-rmpfr, r-cran-libstable4u Filename: pool/dists/noble/main/r-cran-stabledist_0.7-2-1.ca2404.1_all.deb Size: 77114 MD5sum: d50602539566649c9a7d902f29864743 SHA1: 95cd099372306fbfafb3e4005ed98351d25c6d04 SHA256: a5aeb541b6648c89834ef5858854332b80617de7b3b300d4f0688569cdbb7574 SHA512: 09c44d6a0f0d90706d2e61c86c291f21e7951c6ed4fb34ef36d469b4ab950c8209f9c11bce2a5480af1568c6511069b626993bacc20e022b5dad06366c1a4c8d Homepage: https://cran.r-project.org/package=stabledist Description: CRAN Package 'stabledist' (Stable Distribution Functions) Density, Probability and Quantile functions, and random number generation for (skew) stable distributions, using the parametrizations of Nolan. Package: r-cran-stableestim Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-fbasics, r-cran-mass, r-cran-stabledist, r-cran-rdpack Suggests: r-cran-testthat, r-cran-xtable Filename: pool/dists/noble/main/r-cran-stableestim_2.4-1.ca2404.1_all.deb Size: 424864 MD5sum: 6780d1c1fe4eeb729d6457dcf9bdbb06 SHA1: 6f4f7113c2aff8d94ab8552effe9fed0c603bbbc SHA256: ea08d2a479b6779235f3713248da55d4febd18e13546f0ef9b3b9abddb4f0f93 SHA512: 5c0e071d246c588f821b93e59b1b1089945d7264d58e4716f88b89b5ed21233a91ef8f0b4ca1d9241d25f06fe849a82524e71f7809c353a02a44f6eb3980802d Homepage: https://cran.r-project.org/package=StableEstim Description: CRAN Package 'StableEstim' (Estimate the Four Parameters of Stable Laws using DifferentMethods) Estimate the four parameters of stable laws using maximum likelihood method, generalised method of moments with finite and continuum number of points, iterative Koutrouvelis regression and Kogon-McCulloch method. The asymptotic properties of the estimators (covariance matrix, confidence intervals) are also provided. Package: r-cran-stablegr Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcmcse, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-stablegr_1.2-1.ca2404.1_all.deb Size: 61782 MD5sum: 292662c1eb13cfea79babb70c64f24cd SHA1: 5d6762d99197461f8ff2a8a5088547f6db355ffe SHA256: 456d019d36be7a1c8ea606231e54caaf4d23772a4c25b562ed93d3d6cd17a49c SHA512: 8dff763cbdcf1fde4cb700c8a1e3659df9cdee1a9938bb712859a97e67380f7c4318c1dc540ee10642b8d6af20f3ea1741726b736959e7a3432550a04c4a48f1 Homepage: https://cran.r-project.org/package=stableGR Description: CRAN Package 'stableGR' (A Stable Gelman-Rubin Diagnostic for Markov Chain Monte Carlo) Practitioners of Bayesian statistics often use Markov chain Monte Carlo (MCMC) samplers to sample from a posterior distribution. This package determines whether the MCMC sample is large enough to yield reliable estimates of the target distribution. In particular, this calculates a Gelman-Rubin convergence diagnostic using stable and consistent estimators of Monte Carlo variance. Additionally, this uses the connection between an MCMC sample's effective sample size and the Gelman-Rubin diagnostic to produce a threshold for terminating MCMC simulation. Finally, this informs the user whether enough samples have been collected and (if necessary) estimates the number of samples needed for a desired level of accuracy. The theory underlying these methods can be found in "Revisiting the Gelman-Rubin Diagnostic" by Vats and Knudson (2018) . Package: r-cran-stablelearner Architecture: all Version: 0.1-7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 535 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-e1071, r-cran-partykit, r-cran-party, r-cran-randomforest, r-cran-ranger Suggests: r-cran-formula, r-cran-nnet, r-cran-rpart, r-cran-evtree, r-cran-rchallenge, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stablelearner_0.1-7-1.ca2404.1_all.deb Size: 393462 MD5sum: 551fc912e853d3b30f50c43a3e68179d SHA1: 06b91b7108923bdec47311aea9c550a6176143b9 SHA256: 6e2d029c817689c7560b4a2ec1f7bf9da136cc4b6b210706d768dfe08e43ab11 SHA512: c9c71301ab156432080c6815114d2588ab94d234ca270fe995ff9022182823e057d4dca187464db9ec54f5ddd2dd1ff7ab34300670b7708eddcbc547c7287e40 Homepage: https://cran.r-project.org/package=stablelearner Description: CRAN Package 'stablelearner' (Stability Assessment of Statistical Learning Methods) Graphical and computational methods that can be used to assess the stability of results from supervised statistical learning. Package: r-cran-stablepopulation Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readxl, r-cran-openxlsx Filename: pool/dists/noble/main/r-cran-stablepopulation_1.0.3-1.ca2404.1_all.deb Size: 27940 MD5sum: d249437177af8725c363342ba1070f8f SHA1: b6912a5cfecd964e48b93d77cebb528a177aa17b SHA256: 7bb8b362bcbd364b86abd6179b85f9750afed55b5f228576bb33a0a0adf492be SHA512: 7b7a73490b59a7642e05efd9419e7845f44778ea921f4d735b3cd4f0886b59b12a665d8b2b72077bb0495aef516f6263dad07577b8b1931293db6897f8d2bdae Homepage: https://cran.r-project.org/package=StablePopulation Description: CRAN Package 'StablePopulation' (Calculates Alpha for a Stable Population) Provides tools to calculate the alpha parameter of the Weibull distribution, given beta and the age-specific fertility of a species, so that the population remains stable and stationary. 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Package: r-cran-stablespec Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggm, r-cran-matrixcalc, r-cran-sem, r-cran-nsga2r, r-bioc-graph, r-bioc-rgraphviz, r-cran-polycor, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-stablespec_0.3.0-1.ca2404.1_all.deb Size: 147338 MD5sum: d57fa9a92a90e6ec6d2335f3d5924589 SHA1: 182833bf226eee85b29f936737583831c1ce0848 SHA256: 22c87d73d089c858c5730a0e78dc4f70461e3793187fcc523817cacca83aa2e0 SHA512: d6b3fb1e73e19afb3cf84b74882b470ef86127648aae90bfeac36fd5cfb0d1f814f2640798aeaf2a79872398eee69fd52abb5e5e3d48812d0e28e84852bb705b Homepage: https://cran.r-project.org/package=stablespec Description: CRAN Package 'stablespec' (Stable Specification Search in Structural Equation Models) An exploratory and heuristic approach for specification search in Structural Equation Modeling. The basic idea is to subsample the original data and then search for optimal models on each subset. Optimality is defined through two objectives: model fit and parsimony. As these objectives are conflicting, we apply a multi-objective optimization methods, specifically NSGA-II, to obtain optimal models for the whole range of model complexities. From these optimal models, we consider only the relevant model specifications (structures), i.e., those that are both stable (occur frequently) and parsimonious and use those to infer a causal model. Package: r-cran-stabm Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 549 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-matrix Suggests: r-cran-cowplot, r-cran-ggdendro, r-cran-ggplot2, r-cran-igraph, r-cran-knitr, r-cran-mlbench, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stabm_1.2.2-1.ca2404.1_all.deb Size: 478180 MD5sum: 2d35c65961b4ad74ac3b00b09e53b910 SHA1: f175aa6dab6d20b26a55699d30b8e735eccdb1e1 SHA256: 0f3df40cba502a51f9cb9451c9defad07f19c061bd7f686b49f030b70e646eee SHA512: e42817ca6c95154ecfeaac518a0194e893f52c6b0df3d8aabc824a8d436b99bc8e9bb133845ed6b9502eb38d01151e23f1008fa5442b2b49c199d92dc7bc9f02 Homepage: https://cran.r-project.org/package=stabm Description: CRAN Package 'stabm' (Stability Measures for Feature Selection) An implementation of many measures for the assessment of the stability of feature selection. Both simple measures and measures which take into account the similarities between features are available, see Bommert (2020) . Package: r-cran-stabs Architecture: all Version: 0.7-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-glmnet, r-cran-lars, r-cran-mboost, r-cran-gamboostlss, r-cran-th.data, r-cran-hdi, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stabs_0.7-1-1.ca2404.1_all.deb Size: 144898 MD5sum: 95b97c80265c9b0aa3b810e898104b82 SHA1: 4bd4e656c338161109974bb5817ea6ce7192bd42 SHA256: 6def80979d408d13335710e4969e44625d5debb711ebc0b0db4740788b9eb6fd SHA512: 487bda631dd632d7cabdfa863f66787de12636d3d185d5bc0b3abc1a792e0519354738810a24ef08b06a8986933b31c1601aa76f0ce89b247806e9b4de8c0574 Homepage: https://cran.r-project.org/package=stabs Description: CRAN Package 'stabs' (Stability Selection with Error Control) Resampling procedures to assess the stability of selected variables with additional finite sample error control for high-dimensional variable selection procedures such as Lasso or boosting. Both, standard stability selection (Meinshausen & Buhlmann, 2010, ) and complementary pairs stability selection with improved error bounds (Shah & Samworth, 2013, ) are implemented. The package can be combined with arbitrary user specified variable selection approaches. Package: r-cran-staccuracy Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-autogam, r-cran-testthat Filename: pool/dists/noble/main/r-cran-staccuracy_0.2.2-1.ca2404.1_all.deb Size: 67640 MD5sum: ac625d38f646060f76e90728890e673d SHA1: 8a9a2a575c2b1bfd9c9f91945889f99730f05098 SHA256: c5199d4b1db6cf2fff0d481108f7e4009e6bb2c8e514c17e2458a0652fa759a9 SHA512: cdf3cf9cd1f8cb366a9326ea0ce44750daaea05cdc379671661fe4c9b9f1d43371dc70d1abb0205a57aa7f124c162240c7654404f8db3219de4dde1e36478d5e Homepage: https://cran.r-project.org/package=staccuracy Description: CRAN Package 'staccuracy' (Standardized Accuracy and Other Model Performance Metrics) Standardized accuracy (staccuracy) is a framework for expressing accuracy scores such that 50% represents a reference level of performance and 100% is a perfect prediction. The 'staccuracy' package provides tools for creating staccuracy functions as well as some recommended staccuracy measures. It also provides functions for some classic performance metrics such as mean absolute error (MAE), root mean squared error (RMSE), and area under the receiver operating characteristic curve (AUCROC), as well as their winsorized versions when applicable. Package: r-cran-stackgbm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proc, r-cran-progress, r-cran-rlang Suggests: r-cran-knitr, r-cran-lightgbm, r-cran-msaenet, r-cran-rmarkdown, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-stackgbm_0.1.0-1.ca2404.1_all.deb Size: 378436 MD5sum: 1af9cf4d05b4cf665629bfa64107d1b5 SHA1: 4cedaf3c89a73ef162888fc95b2565222e4bce2e SHA256: 5856b4dc92132db328449ae2dad8065a3167c04a7fe497ee8bc93914ea0bf652 SHA512: 5ca9606a1d733b254cbe2da05d67edcec8e0a8ce54a88ac3498be9ad590ace9a10117c051aa645300ebc76ba759a7c7ed3ac173213bc29ad95fd634729d7714d Homepage: https://cran.r-project.org/package=stackgbm Description: CRAN Package 'stackgbm' (Stacked Gradient Boosting Machines) A minimalist implementation of model stacking by Wolpert (1992) for boosted tree models. A classic, two-layer stacking model is implemented, where the first layer generates features using gradient boosting trees, and the second layer employs a logistic regression model that uses these features as inputs. Utilities for training the base models and parameters tuning are provided, allowing users to experiment with different ensemble configurations easily. It aims to provide a simple and efficient way to combine multiple gradient boosting models to improve predictive model performance and robustness. Package: r-cran-stackimpute Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2104 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sandwich, r-cran-zoo, r-cran-mice, r-cran-dplyr, r-cran-mass, r-cran-magrittr, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stackimpute_0.1.0-1.ca2404.1_all.deb Size: 2056832 MD5sum: c27c54c52dad7bb74834dad6d025ab12 SHA1: a6a23be04befd0ead146c20bff9cb4f122593ec9 SHA256: 8d7c462686ed555a7566f70776e7bfdea1712b777badfb294513c5afa3579d37 SHA512: 14ae03b602ab29e34483fb5ecd37fdc90901d5d347d145fe450cacf43ef6a9c816e8b77205a02a64731d8cf576000b19fa56eb512740b6c80f2c15de3a5102e6 Homepage: https://cran.r-project.org/package=StackImpute Description: CRAN Package 'StackImpute' (Tools for Analysis of Stacked Multiple Imputations) Provides methods for inference using stacked multiple imputations augmented with weights. The vignette provides example R code for implementation in general multiple imputation settings. For additional details about the estimation algorithm, we refer the reader to Beesley, Lauren J and Taylor, Jeremy M G (2020) “A stacked approach for chained equations multiple imputation incorporating the substantive model” , and Beesley, Lauren J and Taylor, Jeremy M G (2021) “Accounting for not-at-random missingness through imputation stacking” . Package: r-cran-stacking Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret Filename: pool/dists/noble/main/r-cran-stacking_0.2.1-1.ca2404.1_all.deb Size: 56518 MD5sum: cbf782772eb64e987548c2a8c1c6c78d SHA1: 2479ef0d711d14e4bc73d02693d0175d470128cd SHA256: 52d5513ce0d70dfc2c46ca4119e998684facd04bda33b72f7369c789975cbbcc SHA512: 5ea80c95508765f18b2c5f490f8c1cf1f6625dc25b5cc51065d470425e12e0790eb21b54f465ae1ebeda21287e209ab2b73542b790962205aec2337ce6b5f375 Homepage: https://cran.r-project.org/package=stacking Description: CRAN Package 'stacking' (Building Predictive Models with Stacking) Building predictive models with stacking which is a type of ensemble learning. Learners can be specified from those implemented in 'caret'. For more information of the package, see Nukui and Onogi (2023) . Package: r-cran-stacks Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1999 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-butcher, r-cran-cli, r-cran-dplyr, r-cran-foreach, r-cran-furrr, r-cran-future, r-cran-generics, r-cran-ggplot2, r-cran-glmnet, r-cran-glue, r-cran-parsnip, r-cran-purrr, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-tibble, r-cran-tidyr, r-cran-tune, r-cran-vctrs, r-cran-workflows Suggests: r-cran-covr, r-cran-h2o, r-cran-kernlab, r-cran-kknn, r-cran-knitr, r-cran-modeldata, r-cran-nnet, r-cran-ranger, r-cran-rmarkdown, r-cran-testthat, r-cran-workflowsets, r-cran-yardstick Filename: pool/dists/noble/main/r-cran-stacks_1.1.1-1.ca2404.1_all.deb Size: 1694748 MD5sum: a36019e5e96c29689180b159180051f0 SHA1: 17fc5feaeb9553235becc03851e2f74590b40b73 SHA256: a9d2f8cd60492b1fff43bc335c3de5f09966e148c9f1d07e1c2c3eddf0d66e69 SHA512: e8f225aaa64dae1cfcb9e9d794e7009c4d37c9fe8bc333356595131e980829f6321ad21013ee1082720b31f135420143faf7905169950d336afc52e78c6a7144 Homepage: https://cran.r-project.org/package=stacks Description: CRAN Package 'stacks' (Tidy Model Stacking) Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. 'stacks' implements a grammar for 'tidymodels'-aligned model stacking. Package: r-cran-stacomir Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2360 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stacomirtools, r-cran-magrittr, r-cran-intervals, r-cran-rcolorbrewer, r-cran-stringr, r-cran-rpostgres, r-cran-ggplot2, r-cran-reshape2, r-cran-lattice, r-cran-hmisc, r-cran-lubridate, r-cran-dplyr, r-cran-xtable, r-cran-mgcv, r-cran-rlang, r-cran-pool, r-cran-dbi, r-cran-withr, r-cran-scales Suggests: r-cran-testthat, r-cran-viridis, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stacomir_0.6.1-1.ca2404.1_all.deb Size: 1703384 MD5sum: 1c4ed5de8e3f2d1132cb032a9810db98 SHA1: b36eaea6e6301751fc4ff1f2f93e13073e740c1f SHA256: 49678ea3b545f2910c0af72dbd0dba4b5fa5e8b6e8b85bec16d348d595619831 SHA512: 9cea8af5bd8dca34f64121d7833d84ce198256d69ebb4a0963bb4e6f3b36d495f7a9334d3d8597a7e83de2f3d804b695f9467f6702c7c3d62a67d4dae0b4e2d5 Homepage: https://cran.r-project.org/package=stacomiR Description: CRAN Package 'stacomiR' (Fish Migration Monitoring) Graphical outputs and treatment for a database of fish pass monitoring. It is a part of the 'STACOMI' open source project developed in France by the French Office for Biodiversity institute to centralize data obtained by fish pass monitoring. This version is available in French and English. See for more information on 'STACOMI'. 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It is used by the package 'stacomiR' for connections to the database. Development versions of 'stacomiR' are available in R-forge. Package: r-cran-stacr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstac, r-cran-tibble, r-cran-cli Suggests: r-cran-gdalcubes, r-cran-jsonlite, r-cran-leaflet, r-cran-sf, r-cran-testthat, r-cran-withr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stacr_0.1.0-1.ca2404.1_all.deb Size: 59198 MD5sum: 1d64d7fb7fd6120187207e3cb8ddc268 SHA1: d1901d64b26aec89bf6b5a92514fd9b79c9a2233 SHA256: 54d71499bd81cbea0bb015b1d7244e0f4814c19f276526daeed44dc4c0fd16fa SHA512: a7ee258106b97efabccb0554ac5420af21cb9abf8869d4d351199fb783eba6f530eca82d468aaf4e3dc00e5463bc20f667a1136d8b825fda935b007a7f1e38a8 Homepage: https://cran.r-project.org/package=stacr Description: CRAN Package 'stacr' (Tidy 'STAC' Workflows for R) Wraps the 'rstac' package with a pipe-friendly, tidy API. All results return 'tibbles' instead of nested lists. Ships with a catalog registry of known 'STAC' endpoints including Planetary Computer, Earth Search, and 'USGS', while supporting any 'STAC' API URL. Package: r-cran-stagedtrees Architecture: all Version: 2.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-bnlearn, r-cran-covr, r-cran-clue, r-cran-igraph Filename: pool/dists/noble/main/r-cran-stagedtrees_2.3.0-1.ca2404.1_all.deb Size: 773156 MD5sum: b5e000449bdd964a71e6361eff90594d SHA1: 69fbe42d3d195689805cd2b6c7d227585c2fd97d SHA256: adcaf9a6c48fa6ae29e45d8f6ecea6031d20c67b94b7937d9da0ad73be52e9ad SHA512: d3f81ca471efb211e64493d512c5a2504af80831b9700c3105016912574fb43e25aa555e3dcaab2c5d3168ffaac6752654d1ec21fa25163309c391b6ad61e14f Homepage: https://cran.r-project.org/package=stagedtrees Description: CRAN Package 'stagedtrees' (Staged Event Trees) Creates and fits staged event tree probability models, which are probabilistic graphical models capable of representing asymmetric conditional independence statements for categorical variables. Includes functions to create, plot and fit staged event trees from data, as well as many efficient structure learning algorithms. References: Carli F, Leonelli M, Riccomagno E, Varando G (2022). . Collazo R. A., Görgen C. and Smith J. Q. (2018, ISBN:9781498729604). Görgen C., Bigatti A., Riccomagno E. and Smith J. Q. (2018) . Thwaites P. A., Smith, J. Q. (2017) . Barclay L. M., Hutton J. L. and Smith J. Q. (2013) . Smith J. Q. and Anderson P. E. (2008) . Package: r-cran-stagepop Architecture: all Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 674 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-desolve, r-cran-pbsddesolve Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-stagepop_1.1-2-1.ca2404.1_all.deb Size: 587118 MD5sum: 8865809d7d8a96b88c6b6ea063f61c3b SHA1: e04dca79afb6e9f49dfebc2397074030b33f6bfe SHA256: b5a91322b2fcccc09c9453f93af3ff58fa32d4dfd0113f3dd54896827e46b2b6 SHA512: 1ea54ca43867d259920e8955b9f86ac9d00f25b3b17d0439cbf0dbada6259352fca81eee0814b1f7b9de4251cf479d4b7f893e937be38bb0bbfe492e17e2d8b0 Homepage: https://cran.r-project.org/package=stagePop Description: CRAN Package 'stagePop' (Modelling the Population Dynamics of a Stage-Structured Speciesin Continuous Time) Provides facilities to implement and run population models of stage-structured species... Package: r-cran-staggr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1275 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-staggr_0.2.0-1.ca2404.1_all.deb Size: 737684 MD5sum: bff892b71e45dc6546fce46dadc9d76f SHA1: 8f43db749a9062cd1b266601b9a2470e4433179e SHA256: a77e6ed2c5bef84776ea9970294d825a546d978df904f382028c7ff30de64731 SHA512: d28659411b162d8327b5bf2f87b9f567ca93dea9fbd79347e4cad149e4e29736b68fc70291586285f6f9a937b8dd1f924e789b3a50f20c42f7bd295bc03e620c Homepage: https://cran.r-project.org/package=staggR Description: CRAN Package 'staggR' (Fit Difference-in-Differences Models with StaggeredInterventions) Fits linear difference-in-differences models in scenarios where intervention roll-outs are staggered over time. The package implements a version of an approach proposed by Sun and Abraham (2021) to estimate cohort- and time-since-treatment specific difference-in-differences parameters, and it provides convenience functions both for specifying the model and for flexibly aggregating coefficients to answer a variety of research questions. Package: r-cran-stagsynth Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3960 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog Suggests: r-cran-ggplot2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stagsynth_0.1.0-1.ca2404.1_all.deb Size: 1034184 MD5sum: 26204d6283a382eda8dced3ed455d9a0 SHA1: 6bbc57eccf7995931e334b133a8ad0abc05b0f18 SHA256: cd04184fd8e72a59aa5066a72cbdf3f9ee1459979967df66aceabfaef01c8b33 SHA512: 44b0664bbbe9f105d7c31ce67f7d98b0639ab03597da23182d09becbf85baa77f17e9d88c1ec7cd793da712788cb08d046b00b8c5b53e71ae4c63201de34766d Homepage: https://cran.r-project.org/package=stagsynth Description: CRAN Package 'stagsynth' (Staggered Synthetic Control Estimation and Inference) Implements the Staggered Synthetic Control (SSC) method for estimating treatment effects in panel data with staggered adoption, as proposed by Cao, Lu, and Wu (2020) . Constructs synthetic control weights via constrained quadratic programming, estimates heterogeneous treatment effects and event-time average treatment effects on the treated (ATT), and provides placebo-in-time confidence intervals and p-values. Package: r-cran-stakeholderanalysis Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-stakeholderanalysis_1.2-1.ca2404.1_all.deb Size: 94312 MD5sum: d2e1417d11e71430af40c2f8bea6e7b4 SHA1: 7a2611ec844a92e0962d840b982e8cd20c5aa615 SHA256: 94013db4497a3093bace1cdb6c037deb36d8615ef47716d0334657b298b05f35 SHA512: c2469edc8839423699ecaa47281085c79a06252a8ec00ac60fbde7f92c4af702157b76255cb9ef4a42d195e1afbcc040d06cb23a5cad98ecbbf3af1d1e53c285 Homepage: https://cran.r-project.org/package=StakeholderAnalysis Description: CRAN Package 'StakeholderAnalysis' (Measuring Stakeholder Influence) Proposes an original instrument for measuring stakeholder influence on the development of an infrastructure project that is carried through by a municipality, drawing on stakeholder classifications (Mitchell, Agle, & Wood, 1997) and input-output modelling (Hester & Adams, 2013). Mitchell R., Agle B.R., & Wood D.J. Hester, P.T., & Adams, K.M. (2013) . Package: r-cran-stampp Architecture: all Version: 1.6.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pegas, r-cran-doparallel, r-cran-foreach, r-cran-adegenet Filename: pool/dists/noble/main/r-cran-stampp_1.6.3-1.ca2404.1_all.deb Size: 131994 MD5sum: ac677bdd0833fdf1a2da8b3fbce96e20 SHA1: a1e569eb5985a758d3117248d7d4a38f744c400a SHA256: 252392549a95d736f4eb2b7b0d146cb1931f75605c4a548c637ff5cb65e7c736 SHA512: be83a8197b054ef5a630174d0ca12f48d5a49a454a8168275d098842fccb5987327468993f94bd9208e865944ad16db3f3991cfc3bf8c5ddd139479bbae2b8cb Homepage: https://cran.r-project.org/package=StAMPP Description: CRAN Package 'StAMPP' (Statistical Analysis of Mixed Ploidy Populations) Allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise Fixation Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship matrixes following Yang et al. (2010) in mixed and single ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects allowing integration with other packages such adegenet. Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. for the appropriate citation and user manual. Thank you in advance. Package: r-cran-stampr Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 619 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-spdep, r-cran-dplyr, r-cran-rlang, r-cran-lwgeom, r-cran-geosphere Filename: pool/dists/noble/main/r-cran-stampr_0.3.1-1.ca2404.1_all.deb Size: 594784 MD5sum: a109493a0a811cff519c84159dce5923 SHA1: 207ec038964854d4ed9be3e3af5a756c565972a3 SHA256: 4663e5acaca9734be119332857f23ab9787b8121df92047dc8950475bd879de6 SHA512: ac681608953e64217fd7603017743f21942278b60956ad49aca1206c58e4d5279f4590dc88e56056fbb5975ee92ebaf9599be2c6afa6c9a094628253f13439a9 Homepage: https://cran.r-project.org/package=stampr Description: CRAN Package 'stampr' (Spatial Temporal Analysis of Moving Polygons) Perform spatial temporal analysis of moving polygons; a longstanding analysis problem in Geographic Information Systems. Facilitates directional analysis, distance analysis, and some other simple functionality for examining spatial-temporal patterns of moving polygons. Package: r-cran-stand Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-stand_2.0-1.ca2404.1_all.deb Size: 209020 MD5sum: 2dbdc94e24e83b0f89125ca58e8e6ee2 SHA1: 8cb6befae5849267ecd46a8134845d26bc00819f SHA256: 27afb4890bebc561c5620473d4ad82e3ba9a29db985df8c364e328616c0278ac SHA512: ea9edb3171a4e65bd45aa65f50f0fe284ebe1042f3aca91109af0ab724924f22c6990bbb918a0068efee39d1761c14fd0f294eee0ac43378b79ca457c2cdb77e Homepage: https://cran.r-project.org/package=STAND Description: CRAN Package 'STAND' (Statistical Analysis of Non-Detects) Provides functions for the analysis of occupational and environmental data with non-detects. Maximum likelihood (ML) methods for censored log-normal data and non-parametric methods based on the product limit estimate (PLE) for left censored data are used to calculate all of the statistics recommended by the American Industrial Hygiene Association (AIHA) for the complete data case. Functions for the analysis of complete samples using exact methods are also provided for the lognormal model. Revised from 2007-11-05 'survfit~1'. Package: r-cran-standardize Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 940 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-mass, r-cran-stringr Suggests: r-cran-afex, r-cran-emmeans, r-cran-knitr, r-cran-lmertest, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-standardize_0.2.2-1.ca2404.1_all.deb Size: 348950 MD5sum: 1bec4f0ec33f79ebc460546f073fc513 SHA1: fdcb75abd148fea61a8de8a89d4dacae406c00ca SHA256: c224b8bf46ecd9ceaa4f53b2779b973ccb8c0bd5dddad9f58a8294eb9f314a73 SHA512: 85c6fec8e547fd0fdcea7a8e0b33290c796d20514e83db862ccad2786215d85d9db6bc8b5d4e336244bf241a806c6f9f4ac46181fe840eec714bf90f3bbb5b8c Homepage: https://cran.r-project.org/package=standardize Description: CRAN Package 'standardize' (Tools for Standardizing Variables for Regression in R) Tools which allow regression variables to be placed on similar scales, offering computational benefits as well as easing interpretation of regression output. Package: r-cran-standardlastprofile Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7614 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lifecycle Suggests: r-cran-covr, r-cran-httr2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-standardlastprofile_1.1.0-1.ca2404.1_all.deb Size: 5988794 MD5sum: 6a3166f7a8abb8d1c8289f504035250b SHA1: f56a4cfcd9bee1f846b9b2695be5005f47968327 SHA256: 10b4ef14fa0a5393d5848ece7de574e4dc719781582ef20faa7796dd83fa0778 SHA512: c5c5823d524244dd896381b4de443aa9f770e1fd54c528628cea3cdf7593c70b8d0047a9cb6a0e8af78cc574fff7523e2f8c9535f708ab9d789e296e74f978a6 Homepage: https://cran.r-project.org/package=standardlastprofile Description: CRAN Package 'standardlastprofile' (BDEW Standard Load Profiles for Electricity) Provides representative standard load profiles (SLPs) for electricity published by the German Association of Energy and Water Industries (BDEW Bundesverband der Energie- und Wasserwirtschaft e.V.) in a tidy format. Covers the 1999 profiles — households (H0), commerce (G0–G6), and agriculture (L0–L2) — and the updated 2025 profiles (H25, G25, L25, P25, S25), which additionally represent households with photovoltaic systems and battery storage. Also provides an interface for generating a standard load profile over a user-defined date range. The 1999 data and methodology are described in VDEW (1999), "Repräsentative VDEW-Lastprofile", . The generation algorithm is described in VDEW (2000), "Anwendung der Repräsentativen VDEW-Lastprofile step-by-step", . The 2025 profiles are described in BDEW (2025), "Standardlastprofile Strom", . 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Package: r-cran-standby Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 900 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-shiny Suggests: r-cran-rmarkdown, r-cran-kableextra, r-cran-knitr, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-standby_0.2.0-1.ca2404.1_all.deb Size: 210946 MD5sum: 28eef87329911a94d14484a41ebf44a9 SHA1: cd42500fc85aa521e33a4bee35a2eded3a39ace0 SHA256: ef2f17bceaed8403c1a60d5c8664f6048c784ae220297189349783cca21d2ff9 SHA512: 93cf4a06be944bc5b912f6e68cd813062e94adc4aa413e8183f741c4af3caee7b5c119f9734cd6a5c32e12a9e2c26b04555b8cdab810da28d6ecb83ccf3f4f2c Homepage: https://cran.r-project.org/package=standby Description: CRAN Package 'standby' (Alerts, Notifications and Loading Screen in 'Shiny') Easily create alerts, notifications, modals, info tips and loading screens in 'Shiny'. 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Package: r-cran-standrecon Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-standrecon_0.1.0-1.ca2404.1_all.deb Size: 28146 MD5sum: 166d2b3873460a97acaea3b366e5dab9 SHA1: 81e9bd245c3c77cd88b6206f346fb0f8bcbfe87e SHA256: d773a08a9576a767a0e9ec3df61373a55951e4f9e30e2c8840c96488d9dd0975 SHA512: 6906dcde1e0e8206756f959ed3ee672744269c5bab3b38380c363921a4d118a43f3403efbc4ea3fe58658dcea4f73f3fbba5e99b8888eb17951bbda54760119e Homepage: https://cran.r-project.org/package=standrecon Description: CRAN Package 'standrecon' (Reconstruct Historical Forest Stand Conditions) Reconstructs forest stand basal area and stem density at user-specified reference years using tree-level inventory data. 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Package: r-cran-staninside Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-fs, r-cran-rappdirs Suggests: r-cran-knitr, r-cran-covr, r-cran-spelling, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-staninside_0.0.4-1.ca2404.1_all.deb Size: 31164 MD5sum: 6a5892f29dd3f19f0df7e7f8b294d89f SHA1: 71515ca7aeeeb6843ba0392b3c16e4828717d112 SHA256: 00b0285aef43591ed5d311600803daa4bd19ace67b514377008efecc4fb4be11 SHA512: 625b849bc37cd6ee8789ec0086dcc6577803f68fa779d1015a1284a7ae7b0abc8e63e39f51b1a3c2f8f71bd1625547a3044bcb1341bc3db264bc12cdd87eaab0 Homepage: https://cran.r-project.org/package=staninside Description: CRAN Package 'staninside' (Facilitating the Use of 'Stan' Within Packages) Infrastructure and functions that can be used for integrating 'Stan' (Carpenter et al. 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Package: r-cran-stanza Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlp, r-cran-checkmate, r-cran-reticulate Filename: pool/dists/noble/main/r-cran-stanza_1.0-3-1.ca2404.1_all.deb Size: 54352 MD5sum: 16964a0be8734812ff9d2145ca7f0c36 SHA1: e4ed535fb841484fcec0342ef35c596232af8283 SHA256: 06e1abf1111f63635d6f054a09c0c14acbb543b4f45470837e071e00e1bd386c SHA512: 8ca49a7c348ecb56d1ab4127e84c733eadc3c57bc2d41caa023d1340d88b4c4ca395e07dc57634b511cdee70702e5707d3b9bfeae313a88d80144b632979b40a Homepage: https://cran.r-project.org/package=stanza Description: CRAN Package 'stanza' ('Stanza' - A 'R' NLP Package for Many Human Languages) An interface to the 'Python' package 'stanza' . 'stanza' is a 'Python' 'NLP' library for many human languages. It contains support for running various accurate natural language processing tools on 60+ languages. Package: r-cran-stapler Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-rnifti Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-stapler_0.8.0-1.ca2404.1_all.deb Size: 109720 MD5sum: 09d53677027661c64e628139f8c26184 SHA1: 1f9aed57eb249d89a6742f63f8be4a45ff056a1b SHA256: b11e581d947c42bf035bdf42f289dd7fbdc5708b0f7265c9adff90d38d0d3cc2 SHA512: 8a24fc380d4f5691014a5ce8ffb266c09826cf13972d7f86ddb9bc96ac0fd9a2826cd97658c883e6202cd32c49fdbb25c4e17aa34b86abb4807436d8247df651 Homepage: https://cran.r-project.org/package=stapler Description: CRAN Package 'stapler' (Simultaneous Truth and Performance Level Estimation) An implementation of Simultaneous Truth and Performance Level Estimation (STAPLE) . This method is used when there are multiple raters for an object, typically an image, and this method fuses these ratings into one rating. It uses an expectation-maximization method to estimate this rating and the individual specificity/sensitivity for each rater. 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Package: r-cran-starburst Architecture: all Version: 0.3.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-globals, r-cran-paws.compute, r-cran-paws.storage, r-cran-paws.management, r-cran-paws.security.identity, r-cran-qs2, r-cran-uuid, r-cran-renv, r-cran-jsonlite, r-cran-crayon, r-cran-digest, r-cran-base64enc, r-cran-processx Suggests: r-cran-future.apply, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-mockery Filename: pool/dists/noble/main/r-cran-starburst_0.3.8-1.ca2404.1_all.deb Size: 2280922 MD5sum: 397e64143ced38210640bdf1523a3995 SHA1: 86e5489d41bf6dd0b8d3472e695f0d44600f4f38 SHA256: b42e26346e80799199d52ff28d2ca2069a012e9a222f505a6627d1d57c34d3a9 SHA512: eff390ad0a91c82f7f613e32c681499e36a75e2d147774b18d5467d5f93d94bd86d28781daf4fd91dd68be161b67ef51d4dad57426332fd8e3aaf588ac0aee0c Homepage: https://cran.r-project.org/package=starburst Description: CRAN Package 'starburst' (Seamless AWS Cloud Bursting for Parallel R Workloads) A 'future' backend that enables seamless execution of parallel R workloads on 'Amazon Web Services' ('AWS', ), including 'EC2' and 'Fargate'. 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Package: r-cran-stardom Architecture: all Version: 1.1.32-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4829 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-pracma, r-cran-zoo, r-cran-tibble, r-cran-multiway, r-cran-ggally, r-cran-doparallel, r-cran-drc, r-cran-foreach, r-cran-data.table, r-cran-matrixstats, r-cran-mba, r-cran-cdom, r-cran-r.matlab, r-cran-readr, r-cran-gtools, r-cran-viridislite, r-cran-purrr, r-cran-rlang Suggests: r-cran-plotly, r-cran-xlsx, r-cran-knitr, r-cran-kableextra, r-cran-askpass, r-cran-httr, r-cran-rmarkdown, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-stardom_1.1.32-1.ca2404.1_all.deb Size: 3240722 MD5sum: 1e016fa8f39fd0682a3de4043af8e88d SHA1: f0ce6885d75a9e504184866182968477d054a2b6 SHA256: db1f2fb26001f02b67f4e4ec90be08561e248a7da3348d7bce15d70dcd2e268b SHA512: f4e56036bc45a9d32a1d43373c48fabea3205089dd82b0f0266af2d75e9ebcf355a94e3ca997b3bcab2aaa6ee9cd7b74b0ded5a0b8930e542adb50a0eb35026e Homepage: https://cran.r-project.org/package=staRdom Description: CRAN Package 'staRdom' (PARAFAC Analysis of EEMs from DOM) 'This is a user-friendly way to run a parallel factor (PARAFAC) analysis (Harshman, 1971) on excitation emission matrix (EEM) data from dissolved organic matter (DOM) samples (Murphy et al., 2013) . The analysis includes profound methods for model validation. Some additional functions allow the calculation of absorbance slope parameters and create beautiful plots.' Package: r-cran-stargazer Architecture: all Version: 5.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 683 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-stargazer_5.2.3-1.ca2404.1_all.deb Size: 612354 MD5sum: 9b9665bfc319e6ea326b83aa9cb6881d SHA1: 5e5f3cec168aaec4bd22dddcd2f5d777ad962039 SHA256: 7fd672f790bb7b4a00501f50f49a31aa4d311cf64458e7b4d42c6c2bb2e7f87a SHA512: 39b5b4b9107249e7193f2fef5c4447848b54216ec841152b01703c4b4ef94ba1598ee3e1b2432b88d72e634cfcdab9a67cb5774cf5c8c50ec80cb1bedca8f09a Homepage: https://cran.r-project.org/package=stargazer Description: CRAN Package 'stargazer' (Well-Formatted Regression and Summary Statistics Tables) Produces LaTeX code, HTML/CSS code and ASCII text for well-formatted tables that hold regression analysis results from several models side-by-side, as well as summary statistics. Package: r-cran-starling Architecture: all Version: 0.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-lubridate, r-cran-janitor, r-cran-stringr, r-cran-tidyr, r-cran-reclin2, r-cran-datawizard, r-cran-digest, r-cran-rlang, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-gtsummary, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-starling_0.6.5-1.ca2404.1_all.deb Size: 148154 MD5sum: f97f98b85219db75eaa5ce1a87e7bd72 SHA1: 5390bd05c9e507f07b2ef09914fc1ce9ebe3893f SHA256: d5512ceb344c670769c5b551625c30a347a935bda069f10285c11bc99a3827ec SHA512: d298e756e93970135b095601c7506b820ebe0c139e44fd455b28d7c18014b47a754b53c8d3e63603e6410bc67332a759b6ae7e4a8c87a1d1055e29da1e9e6e38 Homepage: https://cran.r-project.org/package=starling Description: CRAN Package 'starling' (Link Infectious Disease Cases to Vaccination and HospitalizationRecords) Facilitates probabilistic record linkage between infectious disease surveillance datasets (notifiable disease registers, outbreak line-lists), vaccination registries, and hospitalization records using methods based on Fellegi and Sunter (1969) and Sayers et al. (2016) . The package provides core functions for data preparation, linkage, and analysis: clean_the_nest() standardizes variable names and formats across heterogeneous datasets; murmuration() performs machine learning-based record linkage using blocking variables and similarity metrics; molting() deidentifies datasets for secure sharing; homing() re-identifies previously deidentified datasets; plumage() identifies and categorizes comorbidities; and preening() creates analysis-ready variables including age categories and temporal groupings. Designed for epidemiological research linking acute and post-acute disease outcomes to vaccination status and healthcare utilization. Supports multiple linkage scenarios including case-to-vaccination, case-to-hospitalization, and event-based vaccination status determination (e.g., outbreak attendees, flight passengers, exposure site visitors). Package: r-cran-starm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-starm_0.1.0-1.ca2404.1_all.deb Size: 209510 MD5sum: 1e6b9ef331ab0a30d3ea5ae0740e5b6c SHA1: 9598985ed57a0dcd4d0367813c46cf07066e44cc SHA256: 035f9edca8d489dfc32bcb74d03ffd7c823dfe8b2081770b5a05d57a62b2992e SHA512: 35480d38b21318b795bf1ca465d702bd39c3bdd45832c99b1fced4e794f49af7ccd2bce7a56606e181fe5537ef9fa61c07c2a30f582790dca23b37f86179ba44 Homepage: https://cran.r-project.org/package=starm Description: CRAN Package 'starm' (Spatio-Temporal Autologistic Regression Model) Estimates the coefficients of the two-time centered autologistic regression model based on Gegout-Petit A., Guerin-Dubrana L., Li S. "A new centered spatio-temporal autologistic regression model. Application to local spread of plant diseases." 2019. , using a grid of binary variables to estimate the spread of a disease on the grid over the years. 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Package: r-cran-stars Architecture: all Version: 0.7-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6849 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-sf, r-cran-classint, r-cran-rlang, r-cran-units Suggests: r-cran-cairo, r-cran-cftime, r-cran-openstreetmap, r-cran-rnetcdf, r-cran-clue, r-cran-covr, r-cran-cubble, r-cran-cubelyr, r-cran-digest, r-cran-dplyr, r-cran-exactextractr, r-cran-fnn, r-cran-future.apply, r-cran-ggforce, r-cran-ggplot2, r-cran-ggthemes, r-cran-gstat, r-cran-httr, r-cran-jsonlite, r-cran-knitr, r-cran-lwgeom, r-cran-maps, r-cran-mapdata, r-cran-ncdfcf, r-cran-ncdfgeom, r-cran-ncmeta, r-cran-pbapply, r-cran-plm, r-cran-randomforest, r-cran-raster, r-cran-rmarkdown, r-cran-sp, r-cran-spacetime, r-cran-spatstat, r-cran-spatstat.geom, r-cran-terra, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-tsibble, r-cran-viridis, r-cran-xts, r-cran-zoo Filename: pool/dists/noble/main/r-cran-stars_0.7-2-1.ca2404.1_all.deb Size: 4391336 MD5sum: d06ef93999353b5cff9723a295a89c60 SHA1: 41c5aac80b1a290cb256a9b7af314dfa68b23629 SHA256: da74c27678ab79eed16c49e89f3b40b3ca7f4ee9c3cf98150baaeffde38776c8 SHA512: 7dba32c114407c875c5143cc9578583a831631337524a54d1569933d6073f761f9c4c0dbbdb651f3f3a4b2ba7c66f48ac7398f2b692e7c2b5f5a6ea38a894963 Homepage: https://cran.r-project.org/package=stars Description: CRAN Package 'stars' (Spatiotemporal Arrays, Raster and Vector Data Cubes) Reading, manipulating, writing and plotting spatiotemporal arrays (raster and vector data cubes) in 'R', using 'GDAL' bindings provided by 'sf', and 'NetCDF' bindings by 'ncmeta' and 'RNetCDF'. Package: r-cran-starschemar Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1310 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-generics, r-cran-purrr, r-cran-rlang, r-cran-snakecase, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-pander, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-starschemar_1.2.5-1.ca2404.1_all.deb Size: 865348 MD5sum: 5d6ee405a28b12bc4c11fbc36dc0054b SHA1: c23874c690c87ea5617aad7836031e7ac263b171 SHA256: 4b0988b452cd8a452835fb341bc90500e54eed8d4bb907f7762b7ab5fd2df1b4 SHA512: f0a9dd1c81490a1f3cf5bcfb3ca72e62a569e9ef5b7293913dd830b89228ae00aead2926425984411edf8b6b5f685780573ba84cd1e209ced68bfbd257399c2f Homepage: https://cran.r-project.org/package=starschemar Description: CRAN Package 'starschemar' (Obtaining Stars from Flat Tables) Data in multidimensional systems is obtained from operational systems and is transformed to adapt it to the new structure. 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Such setups include target-decoy competition (TDC) in computational mass spectrometry and the knockoff construction in linear regression. 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They are based on various variable influence methods (i.e., relative variable influence (RVI) and knowledge informed RVI (i.e., KIRVI, and KIRVI2)) that adopted similar ideas as AVI, KIAVI and KIAVI2 in the 'steprf' package, and also based on predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) . Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). . Package: r-cran-stepgwr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qpdf, r-cran-numbers, r-cran-mass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stepgwr_0.1.0-1.ca2404.1_all.deb Size: 27478 MD5sum: 2cdc5bc92dc3a400352f1e225a3cc9eb SHA1: b7d90612e244ef95e371a72606df713c89b815e6 SHA256: 8c424556fe8fa4e37bc54b139ba8c01aa3620b81ceb98fc090aeee4578f3cd9b SHA512: c3825261f2ff6650462c71f122837f1add32effe3c6f0ee7c47faf368feb8fdd046025ecb5750a1a2d803d8df32de5dc027410e219f4908fd035ed2021b8dafb Homepage: https://cran.r-project.org/package=StepGWR Description: CRAN Package 'StepGWR' (A Hybrid Spatial Model for Prediction and Capturing SpatialVariation in the Data) It is a hybrid spatial model that combines the variable selection capabilities of stepwise regression methods with the predictive power of the Geographically Weighted Regression(GWR) model.The developed hybrid model follows a two-step approach where the stepwise variable selection method is applied first to identify the subset of predictors that have the most significant impact on the response variable, and then a GWR model is fitted using those selected variables for spatial prediction at test or unknown locations. For method details,see Leung, Y., Mei, C. L. and Zhang, W. X. (2000)..This hybrid spatial model aims to improve the accuracy and interpretability of GWR predictions by selecting a subset of relevant variables through a stepwise selection process.This approach is particularly useful for modeling spatially varying relationships and improving the accuracy of spatial predictions. Package: r-cran-stepjglm Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsq Filename: pool/dists/noble/main/r-cran-stepjglm_0.0.1-1.ca2404.1_all.deb Size: 65078 MD5sum: 17d657c02878d71206f38d009dc797df SHA1: 8d07c4527bdefabf9bb2f625b5fcdd51850392ab SHA256: fc22c4f92b50befcf999c206911165d234ed4f235b8ca6779a0700a1ca3db56b SHA512: c38240b3cd90726cc2ab5ebbdbf99cb359778494bbbe46e63b157cdab00e3e6bf013ca5e3ee94e8ff6c94ebc8b1b94b43532cf587a9d14d69e62525b9dc07f99 Homepage: https://cran.r-project.org/package=stepjglm Description: CRAN Package 'stepjglm' (Variable Selection for Joint Modeling of Mean and Dispersion) A Package for selecting variables for the joint modeling of mean and dispersion (including models for mixture experiments) based on hypothesis testing and the quality of model's fit. In each iteration of the selection process, a criterion for checking the goodness of fit is used as a filter for choosing the terms that will be evaluated by a hypothesis test. Pinto & Pereira (2021) . Package: r-cran-stepmetrics Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4401 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-physicalactivity Suggests: r-cran-testthat, r-cran-rsqlite, r-cran-spelling, r-cran-withr Filename: pool/dists/noble/main/r-cran-stepmetrics_1.0.3-1.ca2404.1_all.deb Size: 1715884 MD5sum: e5f57a756d2e42c2c03b136d2d17620c SHA1: 4c2106c136388390a46e0ddef7930569059f9796 SHA256: 7db0bfb64aaaa5e64724f8b7a94b6530191e11431f48d06d88debed595a29879 SHA512: 4eadec0706aa9de5d47799bf8e2d7d342749e4ec3c70495d9372b474c82e4a52441287b1ac95dfc3984ebbc8fc88f5c982415895f8df98865cf068f4899b8128 Homepage: https://cran.r-project.org/package=stepmetrics Description: CRAN Package 'stepmetrics' (Calculate Step and Cadence Metrics from Wearable Data) Provides functions to calculate step- and cadence-based metrics from timestamped accelerometer and wearable device data. Supports CSV and AGD files from 'ActiGraph' devices, CSV files from 'Fitbit' devices, and step counts derived with R package 'GGIR' , with automatic handling of epoch lengths from 1 to 60 seconds. Metrics include total steps, cadence peaks, minutes and steps in predefined cadence bands, and time and steps in moderate-to-vigorous physical activity (MVPA). Methods and thresholds are informed by the literature, e.g., Tudor-Locke and Rowe (2012) , Barreira et al. (2012) , and Tudor-Locke et al. (2018) . The package record is also available on Zenodo (2023) . Package: r-cran-stepmixr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-reticulate Filename: pool/dists/noble/main/r-cran-stepmixr_0.1.3-1.ca2404.1_all.deb Size: 65816 MD5sum: 7721da73d666b6b6d375fb81af48ae93 SHA1: 074b32713e937a35c3e50db690f2417aa7d92dc9 SHA256: bb5bd3ac685261bdc9507b29b035b96876532876de7d40eb71c80437a50e42f0 SHA512: 6befbbfd5aa34452fec6182c2011ca7ab121a5c880c5e08926bd24c84f39dd5b4efd753ceec53f33e2195ecfab72e9478b47f35d84a9599d58cba6a9180c9b92 Homepage: https://cran.r-project.org/package=stepmixr Description: CRAN Package 'stepmixr' (Interface to 'Python' Package 'StepMix') This is an interface for the 'Python' package 'StepMix'. It is a 'Python' package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. 'StepMix' handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods based on pseudolikelihood theory. Additional features include support for covariates and distal outcomes, various simulation utilities, and non-parametric bootstrapping, which allows inference in semi-supervised and unsupervised settings. Software paper available at . Package: r-cran-steppedpower Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-plotly, r-cran-rfast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-swcrtdesign, r-cran-testthat, r-cran-pwr Filename: pool/dists/noble/main/r-cran-steppedpower_0.3.5-1.ca2404.1_all.deb Size: 1090078 MD5sum: 0ef37a79957f30b7aacec96007e77437 SHA1: 80433fed97f4a10cc7b753936210cd1c1f532835 SHA256: 7524cfb49ed4ba01e11341ac850eadfc01b8deba26f545c38690f4ca33db26a4 SHA512: fd6d2b15a71f393d04a1e9f52bba7a7f42b2b14aac1f365fcc796a962a5d36ff96c03b88d2c465cfb51ac33d4b51ba679b9f4f87e26de23550da842ad1e68556 Homepage: https://cran.r-project.org/package=SteppedPower Description: CRAN Package 'SteppedPower' (Power Calculation for Stepped Wedge Designs) Tools for power and sample size calculation as well as design diagnostics for longitudinal mixed model settings, with a focus on stepped wedge designs. All calculations are oracle estimates i.e. assume random effect variances to be known (or guessed) in advance. The method is introduced in Hussey and Hughes (2007) , extensions are discussed in Li et al. (2020) . Package: r-cran-steppedwedge Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-forcats, r-cran-stringr, r-cran-magrittr, r-cran-ggplot2, r-cran-lme4, r-cran-geepack, r-cran-performance Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-steppedwedge_1.0.0-1.ca2404.1_all.deb Size: 515750 MD5sum: 13dcbf930ae4b63366e3ce8669cc462b SHA1: f5a1ca8c732f02cf2e71a9a9f83fb3eb1e621a64 SHA256: ddd3f1f0b39bd560c7ede91a726c4353ea3c04ae7c2f71807b57f32dd340cdf7 SHA512: 34720e961c8edda397eba9da1dfca3a660a9639e38d4ddcffe292f322b6e48a4a278484c8b5c2cb68862bdcb714cd44dbeee589baf694280292bb5b9b99b8d3b Homepage: https://cran.r-project.org/package=steppedwedge Description: CRAN Package 'steppedwedge' (Analyze Data from Stepped Wedge Cluster Randomized Trials) Provide various functions and tools to help fit models for estimating treatment effects in stepped wedge cluster randomized trials. Implements methods described in Kenny, Voldal, Xia, and Heagerty (2022) "Analysis of stepped wedge cluster randomized trials in the presence of a time-varying treatment effect", . Package: r-cran-steppenal Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mvtnorm, r-cran-proc, r-cran-dfoptim, r-cran-caret Filename: pool/dists/noble/main/r-cran-steppenal_0.2-1.ca2404.1_all.deb Size: 80202 MD5sum: 1813704bdd73b6a22573d30e4ab2d80e SHA1: a612897d635120e1dbe9393381b7b8e5499087d4 SHA256: 012bcace8d5d54dd59eafb3e7d4372a85a4c59de426b21ade20cac6dffeaa037 SHA512: fe03a742a759730ae1560857acb3b2cb4beb43162c3357608ba5f4b69f42ef099365a63acb8a1d3692de343fa6ed119e1de8cb27dd46251c179aaf68d5e2ee91 Homepage: https://cran.r-project.org/package=stepPenal Description: CRAN Package 'stepPenal' (Stepwise Forward Variable Selection in Penalized Regression) Model Selection Based on Combined Penalties. This package implements a stepwise forward variable selection algorithm based on a penalized likelihood criterion that combines the L0 with L2 or L1 norms. Package: r-cran-stepreg Architecture: all Version: 1.6.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4793 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggrepel, r-cran-mass, r-cran-survival, r-cran-flextable, r-cran-proc, r-cran-survauc Suggests: r-cran-knitr, r-cran-testthat, r-bioc-biocstyle, r-cran-kableextra, r-cran-cowplot, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stepreg_1.6.5-1.ca2404.1_all.deb Size: 3011954 MD5sum: f8f5a29fd449a7d749fefd5ab7250bbd SHA1: 71d2a7c81b4946d285bd8f98812c0054beb83fc2 SHA256: 7c85d625b7be80ca94511bf7bea8cec845f4d19a50f20cfc1e9f868e5027ec46 SHA512: 8f635a6b039f29e940f326dccaa88aad3e32259e0053383d20db428859941ed283ff75cddd65ae043ac20bbe3333422647c00c34983f6f641f3f9953876a6ebb Homepage: https://cran.r-project.org/package=StepReg Description: CRAN Package 'StepReg' (Stepwise Regression Analysis) Stepwise regression is a statistical technique used for model selection. This package streamlines stepwise regression analysis by supporting multiple regression types(linear, Cox, logistic, Poisson, Gamma, and negative binomial), incorporating popular selection strategies(forward, backward, bidirectional, and subset), and offering essential metrics. It enables users to apply multiple selection strategies and metrics in a single function call, visualize variable selection processes, and export results in various formats. StepReg offers a data-splitting option to address potential issues with invalid statistical inference and a randomized forward selection option to avoid overfitting. We validated StepReg's accuracy using public datasets within the SAS software environment. For an interactive web interface, users can install the companion 'StepRegShiny' package. Package: r-cran-stepregshiny Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1724 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-stepreg, r-cran-shiny, r-cran-dt, r-cran-shinythemes, r-cran-shinycssloaders, r-cran-dplyr, r-cran-ggplot2, r-cran-stringr, r-cran-flextable, r-cran-cowplot, r-cran-ggcorrplot, r-cran-tidyr, r-cran-summarytools, r-cran-rmarkdown Suggests: r-cran-knitr, r-cran-testthat, r-bioc-biocstyle, r-cran-survival Filename: pool/dists/noble/main/r-cran-stepregshiny_1.6.1-1.ca2404.1_all.deb Size: 800696 MD5sum: 2bbe731a34364c92022c4cfe4406c716 SHA1: 2fdad0b56acd331adae39f1ec8ae79e8fa765946 SHA256: 195429c3227a52433619a5b434539fb0c260bda620a2999e57d216a4e2435de5 SHA512: bb495217366521fdb715e0b19654a99b02825a9a7c43defba28303d6ceabdc75b826402ef031ff4d2e450bb717d97e8509f7a5a39b801171412d4f3064582ab1 Homepage: https://cran.r-project.org/package=StepRegShiny Description: CRAN Package 'StepRegShiny' (Graphical User Interface for 'StepReg') A web-based 'shiny' interface for the 'StepReg' package enables stepwise regression analysis across linear, generalized linear (including logistic, Poisson, Gamma, and negative binomial), and Cox models. It supports forward, backward, bidirectional, and best-subset selection under a range of criteria. The package also supports stepwise regression to multivariate settings, allowing multiple dependent variables to be modeled simultaneously. Users can explore and combine multiple selection strategies and criteria to optimize model selection. For enhanced robustness, the package offers optional randomized forward selection to reduce overfitting, and a data-splitting workflow for more reliable post-selection inference. Additional features include logging and visualization of the selection process, as well as the ability to export results in common formats. Package: r-cran-steprf Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-spm, r-cran-randomforest, r-cran-spm2, r-cran-psy Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-lattice, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-steprf_1.0.2-1.ca2404.1_all.deb Size: 51332 MD5sum: e8603838a249903fa3d0509c6eb62b4b SHA1: 897ed0e7c38f4772212405365b8147fde2eb66b9 SHA256: e4dade43c921dacbce575528a4f0f096bd98854c5eb4a68c4a069b25b4741b05 SHA512: 4cdb45b00aae9e10be22bd45d9d1ca00175f2db94030895d64968f6abc05367064c0e3ce8c5145043213d96bb2f7fefd502a2aed628f8741f8a01d4e5f6934cd Homepage: https://cran.r-project.org/package=steprf Description: CRAN Package 'steprf' (Stepwise Predictive Variable Selection for Random Forest) An introduction to several novel predictive variable selection methods for random forest. They are based on various variable importance methods (i.e., averaged variable importance (AVI), and knowledge informed AVI (i.e., KIAVI, and KIAVI2)) and predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) . Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). . Package: r-cran-stepssurvey Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1351 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-bslib, r-cran-dplyr, r-cran-dt, r-cran-flextable, r-cran-ggplot2, r-cran-glue, r-cran-haven, r-cran-janitor, r-cran-patchwork, r-cran-purrr, r-cran-readr, r-cran-readxl, r-cran-rmarkdown, r-cran-shiny, r-cran-survey Suggests: r-cran-knitr, r-cran-remotes, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-stepssurvey_0.1.0-1.ca2404.1_all.deb Size: 679604 MD5sum: 0e6e20f8342c08d0ff39f12889d3c67c SHA1: 722d74297e9a5431732778479883a0cb4ba84ede SHA256: 5d1ed57bfed9a88402c4a41a4a0c5fb4a65b155145c93b6f4112548f8204d937 SHA512: 3149e36cf0370610627f1f435e5ebd2542571ccc3d2724cf9b21cbbdf60a3b039a711ccc8aef7b81f02f56f11915ba310c683668c966699ea954c12d05c4abb0 Homepage: https://cran.r-project.org/package=stepssurvey Description: CRAN Package 'stepssurvey' (Analyse WHO STEPS Survey Data) Provides a complete analysis pipeline for the WHO STEPwise Approach to NCD Risk Factor Surveillance (STEPS) as described in Riley et al. (2016) . Imports raw survey data ('CSV', 'Excel', 'Stata', 'SPSS'), applies WHO-standard cleaning and recoding, sets up complex survey designs, computes all standard NCD indicators (tobacco, alcohol, diet, physical activity, anthropometry, blood pressure, biochemical), and generates publication-ready tables, visualisations, and 'Word'/'HTML' reports (fact sheet, data book, country report). Package: r-cran-stevedata Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4456 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stevedata_1.8.0-1.ca2404.1_all.deb Size: 4489878 MD5sum: fd59360c52fe69e6c41df06bdf964f0a SHA1: 18f10387234f3eef4067cf3c81556383d315dcb3 SHA256: 81238c77f161dad5115e1be14234cdd20de3468892374b94a5e2627b27838143 SHA512: f59237ba6fde5054a021ee3a7be3dfe7b2b7dde546a047ff4a02b6e74867b2e81db222a5a2c12832199e14767a473ab01e7ab5e383c4a68466af49bb88a5db61 Homepage: https://cran.r-project.org/package=stevedata Description: CRAN Package 'stevedata' (Steve's Toy Data for Teaching About a Variety of Methodological,Social, and Political Topics) This is a collection of various kinds of data with broad uses for teaching. My students, and academics like me who teach the same topics I teach, should find this useful if their teaching workflow is also built around the R programming language. The applications are multiple but mostly cluster on topics of statistical methodology, international relations, and political economy. Package: r-cran-stevedore Architecture: all Version: 0.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2026 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-crayon, r-cran-curl, r-cran-jsonlite, r-cran-yaml Suggests: r-cran-knitr, r-cran-openssl, r-cran-redux, r-cran-reticulate, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-stevedore_0.9.6-1.ca2404.1_all.deb Size: 1032560 MD5sum: e8e03279539338f58d3fb669dc5ce9bf SHA1: eee93d23fbe85e76542331445b571dbbc556b20c SHA256: 5feb7f4ea1148bd1679293fce1ef57198815674bda3cf53ade168d642378dbd1 SHA512: 54060b262ff436bfc382e2d48bcadb9d254f5240dabe5c100caddbbc8ab435befa53f05b5bfd612f4782118e7f857461d8b8532443cf184ecd2b48be1407fc8d Homepage: https://cran.r-project.org/package=stevedore Description: CRAN Package 'stevedore' (Docker Client) Work with containers over the Docker API. Rather than using system calls to interact with a docker client, using the API directly means that we can receive richer information from docker. The interface in the package is automatically generated using the 'OpenAPI' (a.k.a., 'swagger') specification, and all return values are checked in order to make them type stable. Package: r-cran-stevemisc Architecture: all Version: 1.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1083 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr, r-cran-labelled, r-cran-arm, r-cran-purrr, r-cran-tibble, r-cran-dplyr, r-cran-lme4, r-cran-rlang, r-cran-forcats, r-cran-stringr, r-cran-httr, r-cran-rmarkdown, r-cran-tidyr Suggests: r-cran-knitr, r-cran-dbi, r-cran-rsqlite, r-cran-dbplyr Filename: pool/dists/noble/main/r-cran-stevemisc_1.9.0-1.ca2404.1_all.deb Size: 946208 MD5sum: dcfd3a304bfd9c256e18487f8988752c SHA1: 99a5afebd8c27703bbca5a1aab36b6be41d2bce4 SHA256: 813334a5b98831ae4bb8c09face0e501e115bc3da7cdb206faa089a327b95447 SHA512: 8b6e3fdad3320befe945834a1b1e7993825abf1cfc3910c686790ba1841de609b328d084d219206009a8492a5bdc3f6fea06a07e0225f62e644a240a473c3c47 Homepage: https://cran.r-project.org/package=stevemisc Description: CRAN Package 'stevemisc' (Steve's Miscellaneous Functions) These are miscellaneous functions that I find useful for my research and teaching. The contents include themes for plots, functions for simulating quantities of interest from regression models, functions for simulating various forms of fake data for instructional/research purposes, and many more. All told, the functions provided here are broadly useful for data organization, data presentation, data recoding, and data simulation. 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I believe these themes and theme elements are aesthetically pleasing, both for pedagogical instruction and for the presentation of applied statistical research to a wide audience. These themes imply routine use of easily obtained/free fonts, simple forms of which are included in this package. 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The framework suggests the need to investigate for the presence and nature of any space-time dependencies in the data. It proposes a workflow that creates and refines an initial space-time GAM and includes tools to create and evaluate multiple model forms. The workflow sequence is to: i) Prepare the data by lengthening it to have a single location and time variables for each observation. ii) Create all possible space and/or time models in which each predictor is specified in different ways in smooths. iii) Evaluate each model via their AIC value and pick the best one. iv) Create the final model. v) Calculate the varying coefficient estimates to quantify how the relationships between the target and predictor variables vary over space, time or space-time. vi) Create maps, time series plots etc. The number of knots used in each smooth can be specified directly or iteratively increased. This is illustrated with a climate point dataset of the dry rain forest in South America. This builds on work in Comber et al (2024) and Comber et al (2004) . 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This family of models encompasses many models proposed in the actuarial and demographic literature including the Lee-Carter (1992) and the Cairns-Blake-Dowd (2006) models. It includes functions for fitting mortality models, analysing their goodness-of-fit and performing mortality projections and simulations. 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Package: r-cran-stochlab Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6855 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-logr, r-cran-magrittr, r-cran-msm, r-cran-pracma, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-spelling, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-stochlab_1.1.2-1.ca2404.1_all.deb Size: 6878094 MD5sum: e4667efb7f129c0b308e6213e074306b SHA1: 966eb6e0469fc4c07c16da0dbcf0827a7334c5c7 SHA256: 508ec5edba752f65b878f89d17605b8bf78bc0882f1965031a51d3c759240e43 SHA512: 480bea17850289eec84220b7805de4c86fe3acec1f254631e4796df61a217decb36ba314b56beb66afcfe6be545740ca399e7088f32169345b2eac57510f7112 Homepage: https://cran.r-project.org/package=stochLAB Description: CRAN Package 'stochLAB' (Stochastic Collision Risk Model) Collision Risk Models for avian fauna (seabird and migratory birds) at offshore wind farms. The base deterministic model is derived from Band (2012) . This was further expanded on by Masden (2015) and code used here is heavily derived from this work with input from Dr A. Cook at the British Trust for Ornithology. These collision risk models are useful for marine ornithologists who are working in the offshore wind industry, particularly in UK waters. However, many of the species included in the stochastic collision risk models can also be found in the North Atlantic in the United States and Canada, and could be applied there. Package: r-cran-stockanalyst Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-stockanalyst_1.0.1-1.ca2404.1_all.deb Size: 185626 MD5sum: 819d80775937980e2afe49e62e3bd443 SHA1: a95a410d4b0230b1262cfd3485e8f108d6ca2311 SHA256: 7867607d2527bdb708d839bbf022b7227bbf93e7a79972b40472ae27e7778a16 SHA512: a9fc2efefc8827f28eb2630fca6e541d3a294092a2f69a33a0e5f4de84248c33f6778ed5ac8444a3cf0e989f9c987653eb6184f677ea0f132cb2dd40631adc59 Homepage: https://cran.r-project.org/package=stockAnalyst Description: CRAN Package 'stockAnalyst' (Equity Valuation using Methods of Fundamental Analysis) Methods of Fundamental Analysis for Valuation of Equity included here serve as a quick reference for undergraduate courses on Stock Valuation and Chartered Financial Analyst Levels 1 and 2 Readings on Equity Valuation. Jerald E. Pinto (“Equity Asset Valuation (4th Edition)”, 2020, ISBN: 9781119628194). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 4, pp. 445-491)", 2019, ISBN: 9781119593577). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level II Volumes 1-6. (Vol. 4, pp. 197-447)", 2019, ISBN: 9781119593614). 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The package uses maximum likelihood estimation to find the best-fitting distribution for a given stock. It also offers a function to fit several distributions to one or more assets and compare the distribution with the Akaike Information Criterion (AIC) and then pick the best distribution. References are as follows: Siew et al. (2008) and Benth et al. (2008) . Package: r-cran-stodom Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-ggplot2, r-cran-pracma, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-stodom_0.0.1-1.ca2404.1_all.deb Size: 49378 MD5sum: 61b65a853adb1e1292b47fd3f89170fe SHA1: 1cf0b04a8c41445a87f6d24097ac16508256fdca SHA256: 84f19fa50bdf2b6452e02d406eb15f8fc954af08bf446d12b7c1b995efeae255 SHA512: 9f5ad1274ee592e9389cd6126cf2f400654cf69393544dd5180594034be2974776341c1748a2a2be87ee95ddfa9a38515bd4fa08e38c18aebf610088b15d2041 Homepage: https://cran.r-project.org/package=stodom Description: CRAN Package 'stodom' (Estimating Consistent Tests for Stochastic Dominance) Stochastic dominance tests help ranking different distributions. The package implements the consistent test for stochastic dominance by Barrett and Donald (2003) . 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(See Reichert, P. and Schuwirth, N., A generic framework for deriving process stoichiometry in enviromental models, Environmental Modelling and Software 25, 1241-1251, 2010 for more details.) 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This package is designed to aid in data discovery, filtering, pairing water samples with organism samples, and merging data tables to assist users in preparing data for analyses. For additional examples see "Additional Examples" and the readme file at . Package: r-cran-stokes Architecture: all Version: 1.2-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1350 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-permutations, r-cran-partitions, r-cran-disordr, r-cran-spray Suggests: r-cran-knitr, r-cran-deriv, r-cran-testthat, r-cran-markdown, r-cran-rmarkdown, r-cran-quadform, r-cran-magrittr, r-cran-covr Filename: pool/dists/noble/main/r-cran-stokes_1.2-3-1.ca2404.1_all.deb Size: 331416 MD5sum: 0f5d190ea17c8a63534c3c4a51eb8fff SHA1: 499d9f6dce52c858a84021000dd95b9beacebd35 SHA256: 9b2e58d3ef020e5fb5da4e677312a075dc012c8b4511e395c827729bab17b4d4 SHA512: 0a6cee83be327a0c2e868335f8db3b8945ec8b6f100eb88918c95135fd212b2231a3934b4681f954f1cbbdb1a4a3ab79d9d0ebcb2fbcc9a5ff40f77dc9639fc4 Homepage: https://cran.r-project.org/package=stokes Description: CRAN Package 'stokes' (The Exterior Calculus) Provides functionality for working with tensors, alternating forms, wedge products, Stokes's theorem, and related concepts from the exterior calculus. Uses 'disordR' discipline (Hankin, 2022, ). The canonical reference would be M. Spivak (1965, ISBN:0-8053-9021-9) "Calculus on Manifolds". To cite the package in publications please use Hankin (2022) . Package: r-cran-stopdetection Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-geodist, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stopdetection_0.1.2-1.ca2404.1_all.deb Size: 285398 MD5sum: e161898bee40f9395246b29061ba4ba5 SHA1: 0d6c97a2fe4e79b01363849537ebc762da599df6 SHA256: d1a6cc9e40605cc317634ddc0307bc945625de0554d050902facdf345067d877 SHA512: 052f6d7d4252daf4b87fdc45c92044b0415cfb91aa0c19dc99a388774c71e5d588842d6151b95c99bf9081b90a804303170319b1aeb6e2748990c7d3a8edad35 Homepage: https://cran.r-project.org/package=stopdetection Description: CRAN Package 'stopdetection' (Stop Detection in Timestamped Trajectory Data usingSpatiotemporal Clustering) Trajectory data formed by human or animal movement is often marked by periods of movement interspersed with periods of standing still. It is often of interest to researchers to separate geolocation trajectories of latitude/longitude points by clustering consecutive locations to produce a model of this behavior. This package implements the Stay Point detection algorithm originally described in Ye (2009) that uses time and distance thresholds to characterize spatial regions as 'stops'. This package also implements the concept of merging described in Montoliu (2013) as stay point region estimation, which allows for clustering of temporally adjacent stops for which distance between the midpoints is less than the provided threshold. GPS-like data from various sources can be used, but the temporal thresholds must be considered with respect to the sampling interval, and the spatial thresholds must be considered with respect to the measurement error. Package: r-cran-stopes Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-cvtools, r-cran-glmnet, r-cran-changepoint Filename: pool/dists/noble/main/r-cran-stopes_0.2-1.ca2404.1_all.deb Size: 44484 MD5sum: dfa747b7483bcdfa5235548ab956698c SHA1: 690f7cc9270eae04f1c214d5b1382eb2607f3fbd SHA256: dfeea628879fe8b8539565951d5d5d7bcfbc034cf02a2baf9fe2ede02291ecf8 SHA512: e804f3a2626aafb77edcbe2deb7ed528a5a62d8fbe586bb1a25ca4bdc2356039ebf67b39b3efee6e0a47e0c6054c023b51b2f39a983c8345df43876fb5c1b332 Homepage: https://cran.r-project.org/package=STOPES Description: CRAN Package 'STOPES' (Selection Threshold Optimized Empirically via Splitting) Implements variable selection procedures for low to moderate size generalized linear regressions models. It includes the STOPES functions for linear regression (Capanu M, Giurcanu M, Begg C, Gonen M, Optimized variable selection via repeated data splitting, Statistics in Medicine, 2020, 19(6):2167-2184) as well as subsampling based optimization methods for generalized linear regression models (Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models). Package: r-cran-stopmotion Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 889 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magick, r-cran-checkmate Suggests: r-cran-testthat, r-cran-withr, r-cran-knitr, r-cran-quarto Filename: pool/dists/noble/main/r-cran-stopmotion_0.1.0-1.ca2404.1_all.deb Size: 573012 MD5sum: ee97fefa64e25310bec6ee4cc498c9ed SHA1: d33d081c9c4d1ba5627974c5607f3cdebe303139 SHA256: fe917213a4b0973d23dc55637f333492aa63e6707510e0a05397b5a934d0fc5c SHA512: 655446aa5cfe8b4d8601abb11205c19c038380114ce18c85dba14ad07389b46c54fb0138f3e4b62b62eeafb4624368b717cfa2cff3a426009a0322ac56936ff8 Homepage: https://cran.r-project.org/package=stopmotion Description: CRAN Package 'stopmotion' (Build Stop Motion Animations from Image Sequences) A pipeline-friendly toolkit for assembling stop motion animations from sequences of still images. Provides functions to read image directories, restructure frame sequences (duplicate, splice, arrange), apply per-frame pixel transformations (rotate, wiggle, flip, flop, blur, scale, crop, trim, border, background), and export the result as a GIF. All transformation functions accept a 'frames' argument to target any subset of frames, bridging the gap between 'magick' functions that operate on an entire image stack and fine-grained stop motion editing. Image processing is performed via 'ImageMagick Studio LLC' (2024) . Package: r-cran-stopp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1807 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernsmooth, r-cran-mass, r-cran-fields, r-cran-optimx, r-cran-plot3d, r-cran-sparr, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-spatstat.linnet, r-cran-spatstat.random, r-cran-splancs, r-cran-spatstat.model, r-cran-spatstat.utils, r-cran-stlnpp, r-cran-stpp, r-cran-mgcv, r-cran-spatstat.univar Filename: pool/dists/noble/main/r-cran-stopp_1.0.0-1.ca2404.1_all.deb Size: 1781408 MD5sum: 5f657689433f4b79c5b0481a29e5c8d8 SHA1: 53eb0140411efc82ffd1be0bf1f6193e7ee88039 SHA256: 127d32349d8607afdb336aa5420ee51329d9bb5190f90a01fc841413fb362fc3 SHA512: 9e834b8293deff44278d325208d59be5ddf57c1bcdbcafeaf5d605848f341da5b16dc2707734af81835dd8be423a70a6212f711113df61a3d00488721bbaba04 Homepage: https://cran.r-project.org/package=stopp Description: CRAN Package 'stopp' (Spatio-Temporal Point Pattern Methods, Model Fitting,Diagnostics, Simulation, Local Tests) Toolbox for different kinds of spatio-temporal analyses to be performed on observed point patterns, following the growing stream of literature on point process theory. This R package implements functions to perform different kinds of analyses on point processes, proposed in the papers (Siino, Adelfio, and Mateu 2018; Siino et al. 2018; Adelfio et al. 2020; D’Angelo, Adelfio, and Mateu 2021; D’Angelo, Adelfio, and Mateu 2022; D’Angelo, Adelfio, and Mateu 2023). The main topics include modeling, statistical inference, and simulation issues on spatio-temporal point processes on Euclidean space and linear networks. Version 1.0.0 has been updated for accompanying the journal publication D Angelo and Adelfio 2025 . Package: r-cran-stoppingrule Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pracma, r-cran-matrixstats Filename: pool/dists/noble/main/r-cran-stoppingrule_0.6-1.ca2404.1_all.deb Size: 167412 MD5sum: 184c545835638a1da787399e1e5d879b SHA1: c5623fe9e4b1e10daf735323eb30535a1e8faf28 SHA256: b257f17ca30a275c3530bacc34a0b986b46a6dadf866ed82c48ef99af2d956ea SHA512: 23f62009591306ed252bc25498876f06e5fe068a2650a82f11a4722531e019acd7a5fcb3109a3d5dcf28e6d05d304b8a24f7f3344ca3573d196cdf07902e7e98 Homepage: https://cran.r-project.org/package=stoppingrule Description: CRAN Package 'stoppingrule' (Create and Evaluate Stopping Rules for Safety Monitoring) Provides functions for creating, displaying, and evaluating stopping rules for safety monitoring in clinical studies. Package: r-cran-stops Architecture: all Version: 1.9-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 951 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-smacofx, r-cran-acepack, r-cran-clue, r-cran-cmaes, r-cran-cordillera, r-cran-dfoptim, r-cran-energy, r-cran-minerva, r-cran-nloptr, r-cran-pomp, r-cran-pso, r-cran-registry, r-cran-scagnostics, r-cran-smacof, r-cran-tgp, r-cran-vegan Suggests: r-cran-r.rsp, r-cran-diceoptim, r-cran-dicekriging Filename: pool/dists/noble/main/r-cran-stops_1.9-1-1.ca2404.1_all.deb Size: 704232 MD5sum: b0dda5b3b0812a9198ad0d3c9777df2f SHA1: 83970ff15ad34d08b727cfee0d592be424ea8f7a SHA256: 4a42a8ec2e3bbe9dd41cc78011961f57d61571e217db6f4317a295425d18813a SHA512: a6197c9772439d7815e9f31aac69d4264ea80ba5b9a7a115dc097c52d1806e52379728a7b6553005da5911b98d48c00a9e93f54a399c8e78937f3a061cca485d Homepage: https://cran.r-project.org/package=stops Description: CRAN Package 'stops' (Structure Optimized Proximity Scaling) Methods that use flexible variants of multidimensional scaling (MDS) which incorporate parametric nonlinear distance transformations and trade-off the goodness-of-fit fit with structure considerations to find optimal hyperparameters, also known as structure optimized proximity scaling (STOPS) (Rusch, Mair & Hornik, 2023,). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different 1-way MDS models with ratio, interval, ordinal optimal scaling in a STOPS framework. These cover essentially the functionality of the package smacofx, including Torgerson (classical) scaling with power transformations of dissimilarities, SMACOF MDS with powers of dissimilarities, Sammon mapping with powers of dissimilarities, elastic scaling with powers of dissimilarities, spherical SMACOF with powers of dissimilarities, (ALSCAL) s-stress MDS with powers of dissimilarities, r-stress MDS, MDS with powers of dissimilarities and configuration distances, elastic scaling powers of dissimilarities and configuration distances, Sammon mapping powers of dissimilarities and configuration distances, power stress MDS (POST-MDS), approximate power stress, Box-Cox MDS, local MDS, Isomap, curvilinear component analysis (CLCA), curvilinear distance analysis (CLDA) and sparsified (power) multidimensional scaling and (power) multidimensional distance analysis (experimental models from smacofx influenced by CLCA). All of these models can also be fit by optimizing over hyperparameters based on goodness-of-fit fit only (i.e., no structure considerations). The package further contains functions for optimization, specifically the adaptive Luus-Jaakola algorithm and a wrapper for Bayesian optimization with treed Gaussian process with jumps to linear models, and functions for various c-structuredness indices. Hyperparameter optimization can be done with a number of techniques but we recommend either Bayesian optimization or particle swarm. For using "Kriging", users need to install a version of the archived 'DiceOptim' R package. Package: r-cran-stopwords Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-isocodes Suggests: r-cran-covr, r-cran-quanteda, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stopwords_2.3-1.ca2404.1_all.deb Size: 226068 MD5sum: 471f2cba64a6f5429ecb2079ba6e0e1d SHA1: 62d4eabe53986989148a9092c87be75e495196b9 SHA256: 25e7e73d724c8f06688c849696436148635caa2d52d7a83cf0dcceb08d94c3c1 SHA512: cb871afe48833649073e3a0ec040a4f171005b28175b9ee505528d719964789f12148beda6a07c94050b473f1b987180bd7963f68a47f975acee2ddb3751e8d9 Homepage: https://cran.r-project.org/package=stopwords Description: CRAN Package 'stopwords' (Multilingual Stopword Lists) Provides multiple sources of stopwords, for use in text analysis and natural language processing. Package: r-cran-storm Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-permute, r-cran-rjson Filename: pool/dists/noble/main/r-cran-storm_1.2-1.ca2404.1_all.deb Size: 138538 MD5sum: 95331cba4a47e3488382705fe4f0436c SHA1: aa1b5b72cc8fd350793fb5d40b41f63d83313eac SHA256: a251af19d0c94317143243ed5c820bd6fee8d0f3cda21904a149008e24e4686e SHA512: d6d194c0d0ded83eb234f198704c94ccc4b5cc7c2157b6504e9751cfaa32b3c1afb657c7b2452d13582b18347a3532200ffeb7980a6e9ffcb92aa73de6a6831a Homepage: https://cran.r-project.org/package=Storm Description: CRAN Package 'Storm' (Write Storm Bolts in R using the Storm Multi-Language Protocol) Storm is a distributed real-time computation system. Similar to how Hadoop provides a set of general primitives for doing batch processing, Storm provides a set of general primitives for doing real-time computation. Storm includes a "Multi-Language" (or "Multilang") Protocol to allow implementation of Bolts and Spouts in languages other than Java. This R extension provides implementations of utility functions to allow an application developer to focus on application-specific functionality rather than Storm/R communications plumbing. Package: r-cran-stortingscrape Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 524 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rvest, r-cran-httr2, r-cran-stringr Suggests: r-cran-magick, r-cran-rmarkdown, r-cran-knitr, r-cran-pscl Filename: pool/dists/noble/main/r-cran-stortingscrape_0.4.1-1.ca2404.1_all.deb Size: 411742 MD5sum: 7c8782f3a43a516b11b225a58b339076 SHA1: 556fc056208a7199f0043458db67302a3ec3990a SHA256: a9fc900870ae43ef7b2933f28e296714c10edc4a22907de444c0eab3e722ade7 SHA512: 2b4278212a20ec15b26de7919a3ce4d640889ad073d494c42c12e89e720f0eb22866c283fff045eb1d7d4ea644e73af2424d04b311095bc10c4d392dabebe6b1 Homepage: https://cran.r-project.org/package=stortingscrape Description: CRAN Package 'stortingscrape' (Access Data from the Norwegian Parliament API) Functions for retrieving general and specific data from the Norwegian Parliament, through the Norwegian Parliament API at . Package: r-cran-story Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-crayon, r-cran-dplyr, r-cran-fansi, r-cran-httr, r-cran-lifecycle, r-cran-purrr, r-cran-r6, r-cran-rappdirs, r-cran-readr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyjson, r-cran-tidyr Suggests: r-cran-covr, r-cran-curl, r-cran-isa2, r-cran-jsonlite, r-cran-knitr, r-cran-progress, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-story_0.2.2-1.ca2404.1_all.deb Size: 1082908 MD5sum: b8c19a6ccb8d7c4497c42df1ee73be92 SHA1: 893414f355468703635b49dacfd583425acc0a43 SHA256: 1968f4fa2d2565f849b61fc77d61986e31b28650624b81932518c90a89ddc901 SHA512: f5a29c22cf3c8a256411e283c3d5ec7e1b44c615bb3b6ee9ebd10e6e4842abdbfdbcafc3415f90394c244a0b1a439eea16874033223cd183ad42904208e2384c Homepage: https://cran.r-project.org/package=stoRy Description: CRAN Package 'stoRy' (Download, Explore, and Analyze Literary Theme Ontology Data) Download, explore, and analyze Literary Theme Ontology themes and thematically annotated story data. To learn more about the project visit and . Package: r-cran-storywranglr Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-tibble, r-cran-urltools Filename: pool/dists/noble/main/r-cran-storywranglr_0.2.0-1.ca2404.1_all.deb Size: 54116 MD5sum: dc9e05ece5eb130505d9d25989717ed8 SHA1: b597cccbb16165100d0781c105ea6eeca122cb85 SHA256: 3c809f6affac685043023074ffb01f9719ddedae514dc433f7a395a5f4c1bce1 SHA512: 21f3ef7adacfcfd7a98fa6f076b81f878086e68d14361e61021b0a5196e810585e5ec2e449e87c286791c1c34bba7dcfaf897d9b9c1ba4ff0a56ca36ce7e4951 Homepage: https://cran.r-project.org/package=storywranglr Description: CRAN Package 'storywranglr' (Explore Twitter Trends with the 'Storywrangler' API) An interface to explore trends in Twitter data using the 'Storywrangler' Application Programming Interface (API), which can be found here: . Package: r-cran-stpga Architecture: all Version: 5.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-algdesign, r-cran-scales, r-cran-scatterplot3d, r-cran-emoa Suggests: r-cran-r.rsp, r-cran-emmreml, r-cran-quadprog, r-cran-usingr, r-cran-glmnet, r-cran-leaps, r-cran-matrix Filename: pool/dists/noble/main/r-cran-stpga_5.2.1-1.ca2404.1_all.deb Size: 536116 MD5sum: c3401627af8db108fead0475b70b4edb SHA1: 08d8d1b732d81247c7d1a69bc4284ed96027a026 SHA256: f586ab43d8b271f59dad317ba0034a93297d0d8744bfdc4ce6ea1129bef51291 SHA512: 74fe4f442b794a316e4c1308832c82622014d4ce72946d2a36ce44487337814152d6d2ed4f6066eeb125e35ac186727ddef37c845ee00dc07f30239467f83c05 Homepage: https://cran.r-project.org/package=STPGA Description: CRAN Package 'STPGA' (Selection of Training Populations by Genetic Algorithm) Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems. Package: r-cran-stplanr Architecture: all Version: 1.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2964 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-data.table, r-cran-dplyr, r-cran-geosphere, r-cran-httr, r-cran-jsonlite, r-cran-lwgeom, r-cran-magrittr, r-cran-nabor, r-cran-od, r-cran-pbapply, r-cran-rcpp, r-cran-rlang, r-cran-sf, r-cran-sfheaders Suggests: r-cran-cyclestreets, r-cran-dodgr, r-cran-geodist, r-cran-igraph, r-cran-knitr, r-cran-leaflet, r-cran-mapsapi, r-cran-opentripplanner, r-cran-osrm, r-cran-pct, r-cran-rmarkdown, r-cran-testthat, r-cran-tmap Filename: pool/dists/noble/main/r-cran-stplanr_1.2.3-1.ca2404.1_all.deb Size: 2129972 MD5sum: 01432b99e3875e91fdf99216a15bec33 SHA1: f006b911e910321251198728ee53e47a25f2e80b SHA256: c118aed71a6792957eacadc7d3c73239251dedfca3fa3bcbf4adf60999500e1d SHA512: ab6268f65d3ed27c06abfa74982fef96d66478c306304f4e014ef1215f0cb96b81e03eb99d007a382982a48b589c226e3447858bf5a4b67efff1617347052bbe Homepage: https://cran.r-project.org/package=stplanr Description: CRAN Package 'stplanr' (Sustainable Transport Planning) Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. The package was originally developed to support the 'Propensity to Cycle Tool', a publicly available strategic cycle network planning tool (Lovelace et al. 2017) , but has since been extended to support public transport routing and accessibility analysis (Moreno-Monroy et al. 2017) and routing with locally hosted routing engines such as 'OSRM' (Lowans et al. 2023) . The main functions are for creating and manipulating geographic "desire lines" from origin-destination (OD) data (building on the 'od' package); calculating routes on the transport network locally and via interfaces to routing services such as (Desjardins et al. 2021) ; and calculating route segment attributes such as bearing. The package implements the 'travel flow aggregration' method described in Morgan and Lovelace (2020) and the 'OD jittering' method described in Lovelace et al. (2022) . Further information on the package's aim and scope can be found in the vignettes and in a paper in the R Journal (Lovelace and Ellison 2018) , and in a paper outlining the landscape of open source software for geographic methods in transport planning (Lovelace, 2021) . 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The resulting point cloud may exhibit inherent interactions between both signatures. The simulation integrates microsimulation (Holm, E., (2017)) and agent-based models (Bonabeau, E., (2002)), beginning with the configuration of movement characteristics for the specified agents (referred to as 'walkers') and their interactions within the simulation environment. These interactions (Quaglietta, L. and Porto, M., (2019)) result in specific spatiotemporal patterns that can be visualized, analyzed, and used for various analytical purposes. Given the growing scarcity of detailed spatiotemporal data across many domains, this package provides an alternative data source for applications in social and life sciences. 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The implemented class called "Strategy" allows users to access several methods to analyze performance figures, plots and backtest the strategies. Furthermore, custom strategies can be added, a generic template is available. The custom strategies require a certain input and output so they can be called from the Strategy-constructor. 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Package: r-cran-stratifiedmedicine Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1133 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-partykit, r-cran-ranger, r-cran-survival, r-cran-glmnet, r-cran-ggplot2, r-cran-ggparty, r-cran-mvtnorm, r-cran-coin, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-bart, r-cran-proc, r-cran-survrm2, r-cran-th.data, r-cran-sandwich, r-cran-rpart, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stratifiedmedicine_1.0.7-1.ca2404.1_all.deb Size: 795274 MD5sum: 346773517b36f08f76213898e98b514a SHA1: 545fd1a154b6cf9a6e121d99746489ada8fecd40 SHA256: b19aab12c3015986661670bda4e33eebcb67f25892244b2e95e467b524bcc1a7 SHA512: 198f6fdf32b2e53abf18587cd77cb17cca432ce41fb7b94cb16803b843c2cbf0649982d10fda4c14fe762d6101fb7c6201bb9b28708e90119bd042a3a12b68f4 Homepage: https://cran.r-project.org/package=StratifiedMedicine Description: CRAN Package 'StratifiedMedicine' (Stratified Medicine) A toolkit for stratified medicine, subgroup identification, and precision medicine. Current tools include (1) filtering models (reduce covariate space), (2) patient-level estimate models (counterfactual patient-level quantities, such as the conditional average treatment effect), (3) subgroup identification models (find subsets of patients with similar treatment effects), and (4) treatment effect estimation and inference (for the overall population and discovered subgroups). These tools can be customized and are directly used in PRISM (patient response identifiers for stratified medicine; Jemielita and Mehrotra 2019 ). Package: r-cran-stratifiedrf Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-c50, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-stratifiedrf_0.2.2-1.ca2404.1_all.deb Size: 49146 MD5sum: daa50eb6b176444295031323e7b9233a SHA1: 559d38412b42a10479030135d1da3e6f593a8682 SHA256: 333453a25a74bcbba6e19f81ce19bdd3e8a5fcc70199ac552bb68a571e1b1e45 SHA512: f62215d949205663ef9785661a25eafe0baf0d181407c3e8d275bf49350c83b4ddc3f56672f20d1ad7f8f047db5eba48263b36e3ccbdd63fa5d63f25cf48d40b Homepage: https://cran.r-project.org/package=StratifiedRF Description: CRAN Package 'StratifiedRF' (Builds Trees by Sampling Variables in Groups) Random Forest-like tree ensemble that works with groups of predictor variables. When building a tree, a number of variables is taken randomly from each group separately, thus ensuring that it considers variables from each group for the splits. Useful when rows contain information about different things (e.g. user information and product information) and it's not sensible to make a prediction with information from only one group of variables, or when there are far more variables from one group than the other and it's desired to have groups appear evenly on trees. Trees are grown using the C5.0 algorithm rather than the usual CART algorithm. Supports parallelization (multithreaded), missing values in predictors, and categorical variables (without doing One-Hot encoding in the processing). Can also be used to create a regular (non-stratified) Random Forest-like model, but made up of C5.0 trees and with some additional control options. As it's built with C5.0 trees, it works only for classification (not for regression). Package: r-cran-stratifiedyh Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-stratifiedyh_0.1.0-1.ca2404.1_all.deb Size: 24446 MD5sum: 93a611de985c8732fcdef5e69c601ba2 SHA1: b31162de25d726e13855a3d25d4c976cac28a992 SHA256: 400e9057d5fff8c235551be3a5e6115fcd461a963516c607fb0dadb556328a4f SHA512: 51efd0d0b0aaf1253a11d531bb42be32a3cc66f21b7dc7bc5c01a7944ab3500c2d4537f5f72e360a5349b4b042c7f44540b92246ed41ae754deaf85beea7c85f Homepage: https://cran.r-project.org/package=stratifiedyh Description: CRAN Package 'stratifiedyh' (Stratified Sampling and Labeling of Data in R) Provides functions for stratified sampling and assigning custom labels to data, ensuring randomness within groups. The package supports various sampling methods such as stratified, cluster, and systematic sampling. It allows users to apply transformations and customize the sampling process. This package can be useful for statistical analysis and data preparation tasks. Package: r-cran-stratigrapher Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1253 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml, r-cran-dplyr, r-cran-stringr, r-cran-shiny, r-cran-diagram, r-cran-reshape Suggests: r-cran-astrochron, r-cran-rfoc, r-cran-plyr Filename: pool/dists/noble/main/r-cran-stratigrapher_1.3.1-1.ca2404.1_all.deb Size: 1219446 MD5sum: 70c220813155c818be0b00bff5034ecb SHA1: f9f0d2e79433c84dfcb2eaf91ab7ad62c4fd49cc SHA256: 2b6ca823b66b7ac537c18b9658544b004dd1009adfb6bcf0cfe844c03cc27934 SHA512: 5741b3f9eba24c88b4d8ec7acd4791114ca34d6ce64a84298ddbe0bf3076b9e970240ff9ffacede15af1aef78e4fdd1f3c05aed0a1b9830996465e2c37e40576 Homepage: https://cran.r-project.org/package=StratigrapheR Description: CRAN Package 'StratigrapheR' (Integrated Stratigraphy) Includes bases for litholog generation: graphical functions based on R base graphics, interval management functions and svg importation functions among others. Also include stereographic projection functions, and other functions made to deal with large datasets while keeping options to get into the details of the data. When using for publication please cite Sebastien Wouters, Anne-Christine Da Silva, Frederic Boulvain and Xavier Devleeschouwer, 2021. The R Journal 13:2, 153-178. The palaeomagnetism functions are based on: Tauxe, L., 2010. Essentials of Paleomagnetism. University of California Press. ; Allmendinger, R. W., Cardozo, N. C., and Fisher, D., 2013, Structural Geology Algorithms: Vectors & Tensors: Cambridge, England, Cambridge University Press, 289 pp.; Cardozo, N., and Allmendinger, R. W., 2013, Spherical projections with OSXStereonet: Computers & Geosciences, v. 51, no. 0, p. 193 - 205, . Package: r-cran-stratpal Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1215 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-admtools, r-cran-paleots Suggests: r-cran-ape, r-cran-fossilsim, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-stratpal_0.7.1-1.ca2404.1_all.deb Size: 724808 MD5sum: b388e295cbbd2c792637405dfe798862 SHA1: ddc1c3ed366d386405f56d7f91429a0096446a6f SHA256: c241a93eefc3489b1635bb6bda322157e4cdc36ef15bb0f563af18a06a6f9412 SHA512: 9074e0f2b39b091ed6a6b6ab051dc15c47c89161d373f29c3892874620b94dfc41a7925b2a3fb09c4bf133e70d5b9a0c058579132fa2c7f6fd646b09a526c360 Homepage: https://cran.r-project.org/package=StratPal Description: CRAN Package 'StratPal' (Stratigraphic Paleobiology Modeling Pipelines) The fossil record is a joint expression of ecological, taphonomic, evolutionary, and stratigraphic processes (Holland and Patzkowsky, 2012, ISBN:978-0226649382). This package allowing to simulate biological processes in the time domain (e.g., trait evolution, fossil abundance, phylogenetic trees), and examine how their expression in the rock record (stratigraphic domain) is influenced based on age-depth models, ecological niche models, and taphonomic effects. Functions simulating common processes used in modeling trait evolution, biostratigraphy or event type data such as first/last occurrences are provided and can be used standalone or as part of a pipeline. The package comes with example data sets and tutorials in several vignettes, which can be used as a template to set up one's own simulation. Package: r-cran-stratsel Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-memisc, r-cran-formula, r-cran-mnormt, r-cran-pbivnorm Filename: pool/dists/noble/main/r-cran-stratsel_1.4-1.ca2404.1_all.deb Size: 148156 MD5sum: 4727d4c2dd689963baeb6996f32e424f SHA1: 62afa54f12e0e1b5255f91edf905ccdb41779350 SHA256: dc72d4a081f0a3ec11270ac5f5114fc6424cb70e0db7f4b85918f74a35b56454 SHA512: 05d77b082fcdb9197df0ec077a614b0bd57dba1f8209f8fd657f20e774144d5f8e34445f1448dff677f3f0db64b56d476d738b7db0f4b677df75899248bd5ccb Homepage: https://cran.r-project.org/package=StratSel Description: CRAN Package 'StratSel' (Strategic Selection Estimator) Provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of its class as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets. 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Package: r-cran-straweib Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-straweib_1.1-1.ca2404.1_all.deb Size: 77282 MD5sum: 2e1d23250dd1ccfac88a1e0e9e6a6300 SHA1: d55893f047b893e1cb6c5c57216276aa3c0c21c1 SHA256: adb1d3e5f23b4f710fb860af27b82ead353fad6bdcac41c8a28f6cef60309847 SHA512: d6d9075e485d4016ba7801e07660e2894d9f100795905d5b4a93145b75140e32c60a997e436d922680093564c2d37742861fb5b2b7e34ca6b180f26ebfd14efe Homepage: https://cran.r-project.org/package=straweib Description: CRAN Package 'straweib' (Stratified Weibull Regression Model) The main function is icweib(), which fits a stratified Weibull proportional hazards model for left censored, right censored, interval censored, and non-censored survival data. 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However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. This package implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019) for detecting anomalies in high-dimensional data that addresses these limitations of 'HDoutliers' algorithm. We define an anomaly as an observation that deviates markedly from the majority with a large distance gap. An approach based on extreme value theory is used for the anomalous threshold calculation. Package: r-cran-streak Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ckmeans.1d.dp, r-cran-matrix, r-cran-seurat, r-cran-speck, r-cran-vam Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-streak_1.0.0-1.ca2404.1_all.deb Size: 3148820 MD5sum: ed8b625f04593e1f9aa7bbf107372b05 SHA1: 20dc63473d47cab89a7bdfc7070c6c7b708bfa16 SHA256: e2b17aa516ef4e03a379c2a40e7b46b872d13b8a29d340626f22fe04fb34db90 SHA512: 78a1e3ff178623d0685ae115486a1221ca3088c9534e6f0312c98258eb72508865005b9acd4c17230512725f42422fe008867a8ecfd0a57daffdb882e6d25cd9 Homepage: https://cran.r-project.org/package=STREAK Description: CRAN Package 'STREAK' (Receptor Abundance Estimation using Feature Selection and GeneSet Scoring) Performs receptor abundance estimation for single cell RNA-sequencing data using a supervised feature selection mechanism and a thresholded gene set scoring procedure. Seurat's normalization method is described in: Hao et al., (2021) , Stuart et al., (2019) , Butler et al., (2018) and Satija et al., (2015) . Method for reduced rank reconstruction and rank-k selection is detailed in: Javaid et al., (2022) . Gene set scoring procedure is described in: Frost et al., (2020) . Clustering method is outlined in: Song et al., (2020) and Wang et al., (2011) . 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A detailed introduction to the package can be found in the publication: "Non-perennial stream networks as directed acyclic graphs: The R-package streamDAG" (Aho et al., 2023) , and in the introductory package vignette. 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Poudyal, N., and Spanos, A. (2022) . Spanos, A. (1994) . Package: r-cran-strength Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-strength_0.1.0-1.ca2404.1_all.deb Size: 54840 MD5sum: 0f3e6100c958d9b57927d4f9c7215acf SHA1: 5aacdadec869f80a11df6c040cabc48c86b4783a SHA256: e5eb1f927b4db6b4865a817557360096e4ee5a536821ee3d2fe88ce9e9872e4d SHA512: 3e9b61508ce1ff93f6a757bb8579d51686b33a66aeebf0370fc765d8b121cd282578fedac04dc475719684337e4d7f23be04c9d3d6c005fbdfd70f27a52a7505 Homepage: https://cran.r-project.org/package=strength Description: CRAN Package 'strength' (Operations Designed for Tidy Strength Data) Mappings for estimated one rep max from commonly used formulas. Convenience functions for turning mass/rep/set data into useful derived quantities. 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Package: r-cran-stringx Architecture: all Version: 0.2.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 356 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi Suggests: r-cran-realtest Filename: pool/dists/noble/main/r-cran-stringx_0.2.9-1.ca2404.1_all.deb Size: 224838 MD5sum: 951e8d751e82855bc05212df6613fb87 SHA1: 39c1d8079e4c8c1c542c6aeffe52f60b9940cb96 SHA256: b7440f66133c21fffe705031df63896fed85d06a644734871a6b29cbc928ebd9 SHA512: d1b10e8ab699933b3669389bb284ae31af379dcb7f3946afa590377224710154c3d1e1b788173e85ba91b7adc3849aefdcf109dd3138814e917ef6da24dd18c4 Homepage: https://cran.r-project.org/package=stringx Description: CRAN Package 'stringx' (Replacements for Base String Functions Powered by 'stringi') English is the native language for only 5% of the World population. Also, only 17% of us can understand this text. Moreover, the Latin alphabet is the main one for merely 36% of the total. The early computer era, now a very long time ago, was dominated by the US. Due to the proliferation of the internet, smartphones, social media, and other technologies and communication platforms, this is no longer the case. This package replaces base R string functions (such as grep(), tolower(), sprintf(), and strptime()) with ones that fully support the Unicode standards related to natural language and date-time processing. It also fixes some long-standing inconsistencies, and introduces some new, useful features. Thanks to 'ICU' (International Components for Unicode) and 'stringi', they are fast, reliable, and portable across different platforms. Package: r-cran-strip Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlist Suggests: r-cran-caret, r-cran-e1071, r-cran-knitr, r-cran-randomforest, r-cran-testthat Filename: pool/dists/noble/main/r-cran-strip_1.0.0-1.ca2404.1_all.deb Size: 30276 MD5sum: eb2a1c5aaa0aae6635b88409d6dc0a07 SHA1: 89a3dbb5fef33fe8ec27b8db5d6c09294ac50795 SHA256: b7bd5daf02b0e008f8e45310171b4f03a5c806c9a1509d38c9b262806e0de0ef SHA512: 1e5302c8e72ff6ec86e7637a3dfb4d763d63f7f7016471c8ebf209ed8e5013889456fd7fd187da614fe5b020b0d5fa988be73535a36f5eef1b41221655dae1e1 Homepage: https://cran.r-project.org/package=strip Description: CRAN Package 'strip' (Lighten your R Model Outputs) The strip function deletes components of R model outputs that are useless for specific purposes, such as predict[ing], print[ing], summary[izing], etc. Package: r-cran-stripless Architecture: all Version: 1.0-3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1354 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice Suggests: r-cran-knitr, r-cran-faraway, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-stripless_1.0-3-1.ca2404.1_all.deb Size: 633360 MD5sum: 4c65ef245b9aa7e904351f0bc2e0ec57 SHA1: a8f009753ebef004f606f2ce992b34f67af87532 SHA256: e9a2ced132138022ce8034620f3e38daa2ac991ea28bccdc9a62ef0f3c1d0087 SHA512: ba68a2c559b597b1a2384e602d2fdf5288fd09d71c6fe713921efc8d5318db4ba0c9833b3fa8d0aa888747ef2b03056bfd99587aa1142b76e2693eebc79dd3df Homepage: https://cran.r-project.org/package=stripless Description: CRAN Package 'stripless' (Structured Trellis Displays Without Strips for Lattice Graphics) For making Trellis-type conditioning plots without strip labels. This is useful for displaying the structure of results from factorial designs and other studies when many conditioning variables would clutter the display with layers of redundant strip labels. Settings of the variables are encoded by layout and spacing in the trellis array and decoded by a separate legend. The functionality is implemented by a single S3 generic strucplot() function that is a wrapper for the Lattice package's xyplot() function. This allows access to all Lattice graphics capabilities in the usual way. Package: r-cran-strmps Architecture: all Version: 0.6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 871 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-biostrings, r-bioc-shortread, r-bioc-pwalign, r-bioc-iranges, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-stringr, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-strmps_0.6.8-1.ca2404.1_all.deb Size: 531332 MD5sum: ab353b27b3a4a5eef58521bdbcb927dd SHA1: f1e46cac02a1c1da37604ae27ea1df56d7099cb9 SHA256: 2f47a5464ffaf7fbb484aad221ec08ac1e1190d56b7623aebaf96bb6c0616bf3 SHA512: e8710576e73162a818811761fe9522d71707aa24281768c06a92bcec803201626962c869eca87c25e66d9120b216dc517923453c045199673963130fd658733a Homepage: https://cran.r-project.org/package=STRMPS Description: CRAN Package 'STRMPS' (Analysis of Short Tandem Repeat (STR) Massively ParallelSequencing (MPS) Data) Loading, identifying, aggregating, manipulating, and analysing short tandem repeat regions of massively parallel sequencing data in forensic genetics. 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The package is mainly collected for personal use, but any use beyond that is encouraged. This package has migrated functions from 'agdamsbo/daDoctoR', and new functions has been added. Version follows months and year. See NEWS/Changelog for release notes. This package includes sampled data from the TALOS trial (Kraglund et al (2018) ). The win_prob() function is based on work by Zou et al (2022) . The age_calc() function is based on work by Becker (2020) . Package: r-cran-stroupglmm Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aod, r-cran-broom.mixed, r-cran-car, r-cran-dplyr, r-cran-emmeans, r-cran-ggplot2, r-cran-lattice, r-cran-lmertest, r-cran-magrittr, r-cran-mass, r-cran-mutoss, r-cran-nlme, r-cran-parameters, r-cran-phia, r-cran-scatterplot3d, r-cran-survey Filename: pool/dists/noble/main/r-cran-stroupglmm_0.3.0-1.ca2404.1_all.deb Size: 140360 MD5sum: 68e2ddd7b19fde20e6bc58085c8111ca SHA1: 754c48df871b2fccef5ff78e63eb49f0f92092c6 SHA256: bcdb8a687500bb8f9df5e4cc7647302f73a22cedb023d5e94392cc5e9fac88cd SHA512: 71b4e280260a0b7d6ce0c96479f75f3d2464d7ebb8cda1d5d7fba9e39af806729fd792794922cbf0567f8fcfa2ef65af6b59202f0a694cf2261d5608584f6abf Homepage: https://cran.r-project.org/package=StroupGLMM Description: CRAN Package 'StroupGLMM' (R Codes and Datasets for Generalized Linear Mixed Models: ModernConcepts, Methods and Applications by Walter W. Stroup) R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models Modern Concepts, Methods and Applications, CRC Press. Package: r-cran-strs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringi Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-strs_0.1.0-1.ca2404.1_all.deb Size: 85060 MD5sum: 816031aabdd159e26d60f1c0341b3807 SHA1: 0569786364705044a0259f3795a26ae29738466e SHA256: 2fde3a30b98c9a8b14cce8f242bb42b07a55497b994dc2a63ddc4c500122300d SHA512: 80807b02170a9ab8f0457396f205ca9ed52be926be04dc69cf54333d1aaabd4b9ed7a5e5ee9f34321755d2815cc7a4659e030f5c9234ca01953119dd1cd4f83b Homepage: https://cran.r-project.org/package=strs Description: CRAN Package 'strs' ('Python' Style String Functions) A comprehensive set of string manipulation functions based on those found in 'Python' without relying on 'reticulate'. It provides functions that intend to (1) make it easier for users familiar with 'Python' to work with strings, (2) reduce the complexity often associated with string operations, (3) and enable users to write more readable and maintainable code that manipulates strings. Package: r-cran-structfdr Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-ape, r-cran-cluster, r-cran-dirmult, r-cran-matrixstats Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-reshape Filename: pool/dists/noble/main/r-cran-structfdr_1.4-1.ca2404.1_all.deb Size: 476152 MD5sum: 555e2f03cd27c4db27357d3fa290a378 SHA1: d69a0628a1c65c1eee5a77edfda46a82aa027f9d SHA256: 97578cee7cd4ba02d7dd68aa7f43b8473814d6cabdf7c504250c3bbee797cbd0 SHA512: 036e0248459cd5291c5afe58564cc4308ff52c09664fdddf2f1e3e2de2530bfe0b798a001676afd30546b15c7d29f7e927fe0a444e6975a3cd7d944a5e4c563c Homepage: https://cran.r-project.org/package=StructFDR Description: CRAN Package 'StructFDR' (False Discovery Control Procedure Integrating the PriorStructure Information) Perform more powerful false discovery control (FDR) for microbiome data, taking into account the prior phylogenetic relationship among bacteria species. 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Package: r-cran-structree Architecture: all Version: 1.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv, r-cran-lme4, r-cran-penalized Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-structree_1.1.7-1.ca2404.1_all.deb Size: 328730 MD5sum: 7b81baaf7f46c611cd2b030a7b92707d SHA1: f4455a10898c4982880d34fc7f80d6c3e9d07c36 SHA256: 122251659d1a40e724720805e3204598ca27b4695cc1881d59eeaad5f4f8f1e9 SHA512: a5bd61d408ae99abf9d087cb99eefb183eee3a4e0c884f54a8f2d3d528aabbb31483048da2687b0434d93321d91cc7ca94f7de1fe867c36453995bcae2ed5aaf Homepage: https://cran.r-project.org/package=structree Description: CRAN Package 'structree' (Tree-Structured Clustering) Tree-structured modelling of categorical predictors (Tutz and Berger (2018), ) or measurement units (Berger and Tutz (2018), ). Package: r-cran-structssi Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 445 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-multtest, r-cran-igraph, r-cran-jsonlite, r-cran-ggplot2, r-bioc-phyloseq, r-cran-reshape2 Suggests: r-cran-ape, r-cran-testthat Filename: pool/dists/noble/main/r-cran-structssi_1.2.1-1.ca2404.1_all.deb Size: 293806 MD5sum: 5e55f096b14f53ec40e3a5704544ebac SHA1: 0874f687932130852325b4b04e0200819b4910b9 SHA256: 67a5496979d8faf6d8506faa5f5a61666d47298da76ba1760bc2a08a7f5d9ea0 SHA512: 4e6c14b803cb782978b520a945ed29a191da1657e23225b08ad6c676a7639bbb062e411689f1f1600e4e5a8de67c8a454c96603ec04fe9ed83163f5a65f14df9 Homepage: https://cran.r-project.org/package=structSSI Description: CRAN Package 'structSSI' (Multiple Testing for Hypotheses with Hierarchical or GroupStructure) Performs multiple testing corrections that take specific structure of hypotheses into account, as described in Sankaran & Holmes (2014) . Package: r-cran-structuraldecompose Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-segmented, r-cran-strucchange Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-structuraldecompose_0.1.1-1.ca2404.1_all.deb Size: 111708 MD5sum: 13bc624fe9d8030e5fbf6ac1d256a133 SHA1: 18cd9f9ef184d040eaac36d8ab8cce11c3d53a6c SHA256: b2983954ca39c6af5687035fa2beb771f2092ea886dd9411f36f6a2d42b7dc23 SHA512: 3f9445745c1b400a701b65fb917aa1fd58cf36ef8416b291c11ff1f2d3dbfb7204e8654276deffc5b042043235f7d5421ae1ecadbb5dfd6f4694733111157618 Homepage: https://cran.r-project.org/package=StructuralDecompose Description: CRAN Package 'StructuralDecompose' (Decomposes a Level Shifted Time Series) Explains the behavior of a time series by decomposing it into its trend, seasonality and residuals. It is built to perform very well in the presence of significant level shifts. It is designed to play well with any breakpoint algorithm and any smoothing algorithm. Currently defaults to 'lowess' for smoothing and 'strucchange' for breakpoint identification. The package is useful in areas such as trend analysis, time series decomposition, breakpoint identification and anomaly detection. Package: r-cran-structuremc Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-matrixcalc Filename: pool/dists/noble/main/r-cran-structuremc_1.0-1.ca2404.1_all.deb Size: 17188 MD5sum: 45ac13cead6733ca6d6e855dda332a4c SHA1: 7055d357dca7484347301c5d90689e3761579f8c SHA256: b3525ba2761fcc5eab8fa1343484690ca4f836ac12c82c99518ddf6edadaa46e SHA512: 28e8fd41757f8e75d284cebcdcf7ff9ea7a68fe16c4fc5c44a15d21ea5825e067dac8bacc725bad74f898d4c0deb28968182956ba7e2fe51b5aacfa8e6e41dc6 Homepage: https://cran.r-project.org/package=StructureMC Description: CRAN Package 'StructureMC' (Structured Matrix Completion) Provides an efficient method to recover the missing block of an approximately low-rank matrix. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design [Cai T, Cai TT, Zhang A (2016) ]. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. The main function in our package, smc.FUN(), is for recovery of the missing block A22 of an approximately low-rank matrix A given the other blocks A11, A12, A21. Package: r-cran-strvalidator Architecture: all Version: 2.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3738 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-gwidgets2, r-cran-gwidgets2tcltk, r-cran-gridextra, r-cran-gtable, r-cran-plyr, r-cran-scales, r-cran-data.table, r-cran-dt, r-cran-dplyr, r-cran-plotly, r-cran-mass Suggests: r-cran-resourceselection, r-cran-testthat Filename: pool/dists/noble/main/r-cran-strvalidator_2.4.2-1.ca2404.1_all.deb Size: 2231950 MD5sum: 9da318ea59cf4af4cce4aa857ed1aa3a SHA1: 5b6cfd55013cae303b18069db98bd8cc28042868 SHA256: 0454922df5e3efc4dea78a4da3218df9949d551a70745e496926f33bb2c9a5f2 SHA512: e609736169c6db7e7aec50d09f52742907ec63bed018c707c3e27bae4a6c0859a4a62b3434e33e9aacd1c58c7f6c6882d317eed96bebdb79f0fc6a809e27e3a7 Homepage: https://cran.r-project.org/package=strvalidator Description: CRAN Package 'strvalidator' (Process Control and Validation of Forensic STR Kits) An open source platform for validation and process control. Tools to analyze data from internal validation of forensic short tandem repeat (STR) kits are provided. The tools are developed to provide the necessary data to conform with guidelines for internal validation issued by the European Network of Forensic Science Institutes (ENFSI) DNA Working Group, and the Scientific Working Group on DNA Analysis Methods (SWGDAM). A front-end graphical user interface is provided. More information about each function can be found in the respective help documentation. Package: r-cran-stsd Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-stsd_0.2.0-1.ca2404.1_all.deb Size: 215248 MD5sum: a5da64f4b1f46a44f3ceaa9eb2146201 SHA1: 33b287504eaafce191e4cbeceeb8005900df5707 SHA256: 197f9b5a645001c647e1b2600eeea4ae6ead060595fdeb1486b73d6cdb13aaaf SHA512: ca7949bcfd897edf4a87044e11c20f78e6026b41181ccf7f9fabcd44dbde8767165e554e72f143210cd3eebe74fd8eada76ba3c5472fd52457674f3329f5507e Homepage: https://cran.r-project.org/package=sTSD Description: CRAN Package 'sTSD' (Simulate Time Series Diagnostics) These are tools that allow users to do time series diagnostics, primarily tests of unit root, by way of simulation. While there is nothing necessarily wrong with the received wisdom of critical values generated decades ago, simulation provides its own perks. Not only is simulation broadly informative as to what these various test statistics do and what are their plausible values, simulation provides more flexibility for assessing unit root by way of different thresholds or different hypothesized distributions. Package: r-cran-stt.api Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-jsonlite Suggests: r-cran-tinytest, r-cran-whisper Filename: pool/dists/noble/main/r-cran-stt.api_0.2.1-1.ca2404.1_all.deb Size: 40976 MD5sum: b582289e52a0eb9eafab45b873678a4c SHA1: 39258e7ef63740988952b7271bb66f9cceb7d143 SHA256: dd1e7dc77e8584dda21443ac681f6714b5c56f4b73a7deb45c9ad435245d44b2 SHA512: b2f13f9b3711fd2e95adae1f18bda740cd710c1bc7e7092923f701c89861d9112a0ab8c25cfbd70cde29085aae99f717f65925e06abaf51b7c520d05af816455 Homepage: https://cran.r-project.org/package=stt.api Description: CRAN Package 'stt.api' ('OpenAI' Compatible Speech-to-Text API Client) A minimal-dependency R client for 'OpenAI'-compatible speech-to-text APIs (see ) with optional local fallbacks. Supports 'OpenAI', local servers, and the 'whisper' package for local transcription. Package: r-cran-stuart Architecture: all Version: 0.10.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-lavaan, r-cran-mplusautomation, r-cran-sn Filename: pool/dists/noble/main/r-cran-stuart_0.10.2-1.ca2404.1_all.deb Size: 372552 MD5sum: 47df558d6232820b2d542cd31116d8ca SHA1: 72812f399ae763550b7be15c720d19251e8b8a7b SHA256: 4211974684beb085db71ea9b03dcf692ad01333fa634c6fe22ad651971ce6ddd SHA512: 1f30d4b2706cd5bb269b4b522a16655de939e6b30b718496ee987180e218466993b926aff90e36ff43ff49f25d8d7e900e7b189ac9cd5d3bba0b6421f25affbc Homepage: https://cran.r-project.org/package=stuart Description: CRAN Package 'stuart' (Subtests Using Algorithmic Rummaging Techniques) Construct subtests from a pool of items by using ant-colony-optimization, genetic algorithms, brute force, or random sampling. Schultze (2017) . Package: r-cran-studentlife Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-readr, r-cran-tidyr, r-cran-dplyr, r-cran-jsonlite, r-cran-tibble, r-cran-r.utils, r-cran-skimr, r-cran-visdat, r-cran-ggplot2, r-cran-crayon Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-studentlife_1.1.0-1.ca2404.1_all.deb Size: 153958 MD5sum: 64c166dd8026d1eedfe0a8e910903ec6 SHA1: c796434ed78bfb3389c414d8f47d41e3f4d776c1 SHA256: 75218806853f91d5b17c2215cfb77159e61e3027c0e1bc86699227a0dfd50a7f SHA512: f0d7b2bc5c5b40f3f2c209a9e4ec5d2119e3820227cced79e556a1beb6a431c7a8ca906d69bd36d1fb3ef8727d164af2f8ca35475bd5d087f8766da4c0cbaf8b Homepage: https://cran.r-project.org/package=studentlife Description: CRAN Package 'studentlife' (Tidy Handling and Navigation of the Student-Life Dataset) Download, navigate and analyse the Student-Life dataset. The Student-Life dataset contains passive and automatic sensing data from the phones of a class of 48 Dartmouth college students. It was collected over a 10 week term. Additionally, the dataset contains ecological momentary assessment results along with pre-study and post-study mental health surveys. The intended use is to assess mental health, academic performance and behavioral trends. The raw dataset and additional information is available at . 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Package: r-cran-submitr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2334 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-readr, r-cran-yaml Suggests: r-cran-containr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-toolero, r-cran-withr Filename: pool/dists/noble/main/r-cran-submitr_0.1.0-1.ca2404.1_all.deb Size: 1915658 MD5sum: 4bc10fa87955b9bc469dafbd0badfdff SHA1: fe004da33330580870a2194d95932168be62ee78 SHA256: 0509e5a45deb882fae2c26832a692522db5269be964cdf5a4ecbc74bc076616c SHA512: 56d70265747f33b75c1a28b763e2e892bd68bb3712c02c12b6bad3fb379fd5e06b3e18c0faf2c92ee313cc51ae88329d40eaf5409a921f6526a3304b07560c04 Homepage: https://cran.r-project.org/package=submitr Description: CRAN Package 'submitr' (Scaffold and Submit Computational Jobs to HTC Schedulers) Provides scaffolding tools to help researchers prepare and submit computational jobs to high-throughput computing (HTC) schedulers. Generates the files required to run containerized R analyses on 'HTCondor', including submit files and executable scripts, and wraps the system commands needed to stage files, submit jobs, monitor status, and retrieve results from a CHTC submit node. Provides 'htc_config()' for managing connection details and SSH connection reuse guidance. Works naturally alongside 'containr' for container image management and 'toolero' for dataset splitting and project scaffolding. Package: r-cran-subniche Architecture: all Version: 1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-polyclip, r-cran-wordcloud Suggests: r-cran-adegraphics, r-cran-ape, r-cran-circstats, r-cran-deldir, r-cran-lattice, r-cran-mass, r-cran-pixmap, r-cran-spdep, r-cran-splancs, r-cran-waveslim Filename: pool/dists/noble/main/r-cran-subniche_1.6-1.ca2404.1_all.deb Size: 187032 MD5sum: b9b189820486978456938fce42926936 SHA1: a84f3248239dcc3584575e46394c4e71452e2323 SHA256: fde235b5851d1a9dda1b47b75d8b6048c94b2505de1512a02a98b90f60d4fda0 SHA512: d5e067db020107fcf06e78bf40f5bda9069cea23f9680285790ce536c5880d723908317731d2af6a970ba553aeba2a16eb1fdb7b3da3df668824332f365a116f Homepage: https://cran.r-project.org/package=subniche Description: CRAN Package 'subniche' (Within Outlying Mean Indexes: Refining the 'OMI' Analysis) Complementary indexes calculation to the Outlying Mean Index analysis to explore niche shift of a community and biological constraint within an Euclidean space, with graphical displays. For details see Karasiewicz 'et al.' (2017) . Package: r-cran-subpathwaylnce Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3271 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph, r-bioc-rbgl, r-cran-biasedurn, r-bioc-graph Suggests: r-cran-xml Filename: pool/dists/noble/main/r-cran-subpathwaylnce_1.0-1.ca2404.1_all.deb Size: 3111326 MD5sum: f18b557a9b76741614ed45cd29ad4b4f SHA1: 0b2f0a7d67c3d6d77d1d94f42e8a3714c7176a3e SHA256: 885ead4b0a9cbe1025c08c870385036a75644d29242efd5764af91f2a7b88d15 SHA512: fde376093dad2b74e5cc4eaaa8d81d6457d413eba2e4a5eec842b67d78de5b618b13d785ebfa49b90beeb0000c872d8a2c1f825f8ff80ba681f37564ff1263bc Homepage: https://cran.r-project.org/package=SubpathwayLNCE Description: CRAN Package 'SubpathwayLNCE' (Identify Signal Subpathways Competitively Regulated by LncRNAsBased on ceRNA Theory) Identify dysfunctional subpathways competitively regulated by lncRNAs through integrating lncRNA-mRNA expression profile and pathway topologies. Package: r-cran-subscore Architecture: all Version: 3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ctt, r-cran-irtoys, r-cran-sirt, r-cran-ltm, r-cran-cocor, r-cran-boot Filename: pool/dists/noble/main/r-cran-subscore_3.3-1.ca2404.1_all.deb Size: 110552 MD5sum: 33120f0517eff6fb4b62cbb0f3c6c0a9 SHA1: 46877f2424982af7bfc88e61c34f0f906f311b84 SHA256: 1d6e0fc23766e55ae4caf4fcd7127563a5b2f4d90dca7926d8f5cab3b6167d28 SHA512: f870028b3b5e47199ecdc8c0282c107f19a69168cfe19b79ff349cd6994be2c518c49aeba7a35f70cb16a4296eb897fb59d1943ea30772ae84caef6eca61d19c Homepage: https://cran.r-project.org/package=subscore Description: CRAN Package 'subscore' (Computing Subscores in Classical Test Theory and Item ResponseTheory) Functions for computing test subscores using different methods in both classical test theory (CTT) and item response theory (IRT). This package enables three types of subscoring methods within the framework of CTT and IRT, including (1) Wainer's augmentation method (Wainer et. al., 2001) , (2) Haberman's subscoring methods (Haberman, 2008) , and (3) Yen's objective performance index (OPI; Yen, 1987) . It also includes functions to compute Proportional Reduction of Mean Squared Errors (PRMSEs) in Haberman's methods which are used to examine whether test subscores are of added value. In addition, the package includes a function to assess the local independence assumption of IRT with Yen's Q3 statistic (Yen, 1984 ; Yen, 1993 ). Package: r-cran-subscreen Architecture: all Version: 4.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-data.table, r-cran-ggplot2, r-cran-ggrepel, r-cran-rlang, r-cran-stringr, r-cran-shiny, r-cran-dt, r-cran-shinyjs, r-cran-bsplus, r-cran-colourpicker, r-cran-dplyr, r-cran-ranger, r-cran-shinywidgets Suggests: r-cran-survival, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-subscreen_4.0.1-1.ca2404.1_all.deb Size: 1933602 MD5sum: b233bd335008451702deb33bac7aad9f SHA1: 7129719013be61bc5d37ce0aa7f4903f5946da51 SHA256: b7f885af139cb1962a65c53a6a49a0de22684f6a019af6bdfe894df1ed4692f6 SHA512: 22775169d2e2178d6c27dfa42e37130a09b24ba2c733076a4faad6e1acaf8f3184cdaf9dc0f82e8591318bcf5271dd0ca14d71012391e9880aa77060cd548034 Homepage: https://cran.r-project.org/package=subscreen Description: CRAN Package 'subscreen' (Systematic Screening of Study Data for Subgroup Effects) Identifying outcome relevant subgroups has now become as simple as possible! The formerly lengthy and tedious search for the needle in a haystack will be replaced by a single, comprehensive and coherent presentation. The central result of a subgroup screening is a diagram in which each single dot stands for a subgroup. The diagram may show thousands of them. The position of the dot in the diagram is determined by the sample size of the subgroup and the statistical measure of the treatment effect in that subgroup. The sample size is shown on the horizontal axis while the treatment effect is displayed on the vertical axis. Furthermore, the diagram shows the line of no effect and the overall study results. For small subgroups, which are found on the left side of the plot, larger random deviations from the mean study effect are expected, while for larger subgroups only small deviations from the study mean can be expected to be chance findings. So for a study with no conspicuous subgroup effects, the dots in the figure are expected to form a kind of funnel. 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Package: r-cran-subvis Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-bioc-biostrings Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-subvis_2.0.2-1.ca2404.1_all.deb Size: 50090 MD5sum: 9d021529e780f56591d8319e2ecaaf15 SHA1: 73558dcfa3e7e287ca1149cef876e51dce5878fd SHA256: 48b2ddce1f53f9db8bd89c7e0d19c8fc23e81c7a2522ae1d47ace9f7d6856dbd SHA512: 7841f7f07c45987909e1ed7a5a0fea5f135e878d24943e214a11caaebb1daeac5ace0d6c80bc0cfd20451b2135f4765fa5a7d37286105f9c80bcc694a3cbc7eb Homepage: https://cran.r-project.org/package=SubVis Description: CRAN Package 'SubVis' (Visual Exploration of Protein Alignments Resulting from MultipleSubstitution Matrices) Substitution matrices are important parameters in protein alignment algorithms. 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Package: r-cran-sudachir Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-reticulate, r-cran-tibble, r-cran-tidyselect Suggests: r-cran-rstudioapi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-sudachir_0.1.0-1.ca2404.1_all.deb Size: 79262 MD5sum: 5ba1668bd705854bf37286f85dd68245 SHA1: 8ee68f84f3991fd5a58c9a893503040f7ff87a10 SHA256: 3b41485c73575c7df96bb4ab2a5a7914353ee5573e7e883782e8aad6e5906a34 SHA512: 52bc5cdecf8096358a150ea0352c0d2ff20867b5ca5212bc57ee8f48c89e4f63ec9508f6aa40f9e97a2d010be2d491b530b56a811d11c5aaee55381cfffcbd76 Homepage: https://cran.r-project.org/package=sudachir Description: CRAN Package 'sudachir' (R Interface to 'Sudachi') Interface to 'Sudachi' , a Japanese morphological analyzer. This is a port of what is available in Python. 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Sudden losses, defined as the opposite of sudden gains can also be identified. Two different datasets can be created, one including all sudden gains/losses and one including one selected sudden gain/loss for each case. It can extract scores around sudden gains/losses. It can plot the average change around sudden gains/losses and trajectories of individual cases. 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Here, Suns-Voc curves can be constructed with outdoor time-series I-V curves [1,2,3] of full-size photovoltaic (PV) modules instead of having to be measured in the lab. Time series of four different power loss modes can be calculated based on obtained Isc-Voc curves. This material is based upon work supported by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0008172. Jennifer L. Braid is supported by the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy administered by the Oak Ridge Institute for Science and Education (ORISE) for the DOE. ORISE is managed by Oak Ridge Associated Universities (ORAU) under DOE contract number DE-SC0014664. [1] Wang, M. et al, 2018. . [2] Walters et al, 2018 . [3] Guo, S. et al, 2016. . 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Provides comprehensive database operations including Create, Read, Update, Delete ('CRUD') functionality through both REST API endpoints and direct 'PostgreSQL' database connections. Simplifies authentication, data management, and schema operations for 'Supabase' projects. 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The main function, superb(), return a plot. It can also be used to obtain a dataframe with the statistics and their precision intervals so that other plotting environments (e.g., Excel) can be used. See Cousineau and colleagues (2021) or Cousineau (2017) for a review as well as Cousineau (2005) , Morey (2008) , Baguley (2012) , Cousineau & Laurencelle (2016) , Cousineau & O'Brien (2014) , Calderini & Harding for specific references. The documentation is available at . 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Package contains functions for generating robust biclusters with respect to the initialization parameters for a given bicluster solution contained in a bicluster set in data, the procedure is also known as ensemble biclustering. The set of biclusters is evaluated based on the similarity of its elements (the overlap), and afterwards the hierarchical tree is constructed to obtain cut-off points for the classes of robust biclusters. The result is a number of robust (or super) biclusters with none or low overlap. Package: r-cran-supercell Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3765 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-rann, r-cran-corpcor, r-cran-weights, r-cran-hmisc, r-cran-matrix, r-cran-matrixstats, r-cran-plyr, r-cran-irlba, r-cran-patchwork, r-cran-ggplot2, r-cran-umap, r-cran-entropy, r-cran-rtsne, r-cran-dbscan, r-cran-scales, r-cran-plotfunctions, r-cran-proxy, r-cran-rlang Suggests: r-bioc-singlecellexperiment, r-bioc-summarizedexperiment, r-cran-cowplot, r-bioc-scater, r-cran-seurat, r-cran-knitr, r-cran-rmarkdown, r-cran-remotes, r-bioc-bluster, r-cran-weightedcluster, r-cran-testthat Filename: pool/dists/noble/main/r-cran-supercell_1.1-1.ca2404.1_all.deb Size: 3472990 MD5sum: 6732e9c69e57b3cb85baa8b879710c54 SHA1: 07fc83f3a5662610c312051485d8f4d7818a379c SHA256: e535d3e729717bd14ec8284447009431bad65f2c313b32881dcf5e6b88b32945 SHA512: e8e6b95a6bf84d172ee6c32dd3755427745a6c15f2beea5208e2697281440be3e18d3397903dd40491f3ef1da73cd1bfdffdb6a7503713af001deff3d33f171d Homepage: https://cran.r-project.org/package=SuperCell Description: CRAN Package 'SuperCell' (Simplification of scRNA-Seq Data by Merging Together SimilarCells) Aggregates large single-cell data into metacell dataset by merging together gene expression of very similar cells. 'SuperCell' uses 'velocyto.R' for RNA velocity and 'WeightedCluster' for weighted clustering on metacells. We also recommend installing 'scater' Bioconductor package . 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Package: r-cran-superdiag Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2379 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda Filename: pool/dists/noble/main/r-cran-superdiag_2.0-1.ca2404.1_all.deb Size: 2325784 MD5sum: fff944bef000a23e3b75ee900804d6ba SHA1: c5335f69815d4b29d226a74fedfb50e4a6aa2cb9 SHA256: a887182c3ac745a48f30eef6cc6a4bb12f1bd56cae1920f6caf807634aeef36e SHA512: fed279bc000783b66e13aa8d2202a56834ace06c9f64ac6c0dfff8477c2640c5bfded51368d87b1b541f40358e9bc5a6092629592196348e26b50f42a648ab77 Homepage: https://cran.r-project.org/package=superdiag Description: CRAN Package 'superdiag' (A Comprehensive Test Suite for Testing Markov ChainNonconvergence) The 'superdiag' package provides a comprehensive test suite for testing Markov Chain nonconvergence. 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Package: r-cran-superlearner Architecture: all Version: 2.0-40-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnls, r-cran-gam, r-cran-cvauc Suggests: r-cran-arm, r-cran-bartmachine, r-cran-biglasso, r-cran-bigmemory, r-cran-caret, r-cran-class, r-cran-devtools, r-cran-e1071, r-cran-earth, r-cran-gbm, r-bioc-genefilter, r-cran-ggplot2, r-cran-glmnet, r-cran-ipred, r-cran-kernelknn, r-cran-kernlab, r-cran-knitr, r-cran-lattice, r-cran-logicreg, r-cran-mass, r-cran-mlbench, r-cran-nloptr, r-cran-nnet, r-cran-party, r-cran-polspline, r-cran-prettydoc, r-cran-quadprog, r-cran-randomforest, r-cran-ranger, r-cran-rhpcblasctl, r-cran-rocr, r-cran-rmarkdown, r-cran-rpart, r-cran-sis, r-cran-speedglm, r-cran-spls, r-bioc-sva, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-superlearner_2.0-40-1.ca2404.1_all.deb Size: 570846 MD5sum: cb717d57e88c71c4d61cdf388ea6bd7d SHA1: 631aef3402a4ef70b48d95ed4e37eafbf7fa3b83 SHA256: 37b8fdc75c71edf2283d7c0d83622e87b6214ea933a625bc558202917d428394 SHA512: 9bd7bc6aff00b4e9624b7337c9817045fff40652347b9d483925e553fe00dab26513170a0688d20dd4b0cd30ac6062d554cc8add2b09d249c7a0cc7774902bb1 Homepage: https://cran.r-project.org/package=SuperLearner Description: CRAN Package 'SuperLearner' (Super Learner Prediction) Implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner. 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Laqueur, H. S., Shev, A. B., Kagawa, R. M. C. (2021) . 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'Superpc' is especially useful for high-dimensional data when the number of features p dominates the number of samples n (p >> n paradigm), as generated, for instance, by high-throughput technologies. 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Calculates the observed power and average observed effect size for all main effects and interactions in the ANOVA, and all simple comparisons between conditions. Includes functions for analytic power calculations and additional helper functions that compute effect sizes for ANOVA designs, observed error rates in the simulations, and functions to plot power curves. Please see Lakens, D., & Caldwell, A. R. (2021). "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs". . 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Implements density and cumulative compound Poisson discrete distribution functions (Kremer et al. (2021) ), as well as functions to calculate infectious disease outbreak statistics given epidemiological parameters on individual-level transmission; including the probability of an outbreak becoming an epidemic/extinct (Kucharski et al. (2020) ), or the cluster size statistics, e.g. what proportion of cases cause X\% of transmission (Lloyd-Smith et al. (2005) ). 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The package fits convex combinations of parametric, semiparametric, and machine learning survival learners by minimizing cross-validated risk using inverse probability of censoring weighting (IPCW). It provides tools for automated hyperparameter grid search, high-dimensional variable screening, and evaluation of prediction performance using metrics such as the Brier score, Uno's C-index, and time-dependent area under the curve (AUC). Additional utilities support model interpretation for survival ensembles, including Shapley additive explanations (SHAP), and estimation of covariate-adjusted restricted mean survival time (RMST) contrasts. The methodology is related to treatment-specific survival curve estimation using machine learning described by Westling et al. (2024) , and the unified ensemble framework described in Lyu et al. (2026) . Package: r-cran-supervisedprim Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-prim Suggests: r-cran-kernlab, r-cran-testthat Filename: pool/dists/noble/main/r-cran-supervisedprim_2.0.0-1.ca2404.1_all.deb Size: 19156 MD5sum: 5a1f35bb7e7bf8ef5b4f73584feaa833 SHA1: 614c5ac5787454797549d1f99b0defe54543aea4 SHA256: 7e0d595fb1da580f59ff135498788b417c769a57efeff9b030659afb7dd03d84 SHA512: d4d768e89213e29dda6d821d2f1b8a7ffd5500ecf34afaf4f8a863b891d8106d8f8f2bbd6cf24e1417ea370ad2545067ad715a4bde22475d01e8231761136a2b Homepage: https://cran.r-project.org/package=supervisedPRIM Description: CRAN Package 'supervisedPRIM' (Supervised Classification Learning and Prediction using PatientRule Induction Method (PRIM)) The Patient Rule Induction Method (PRIM) is typically used for "bump hunting" data mining to identify regions with abnormally high concentrations of data with large or small values. This package extends this methodology so that it can be applied to binary classification problems and used for prediction. Package: r-cran-supmz Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-supmz_0.2.0-1.ca2404.1_all.deb Size: 25102 MD5sum: 0a22733f84817f767a32a7b1dc4b7bed SHA1: 0519ff778244cd74d8f884bfbd14f0554bdb9c09 SHA256: 3584c16ce92e4fc0adc36f28ecc870456dbe1c1b20b9a508056cdb59682272db SHA512: 3bd89beb06296ef5421696ced95a46873a599397c6abc04c37c5e26d92163d82396b01cd4c6be277e1e5036eb173334d67e9656127acf7e47174067b75d6ea49 Homepage: https://cran.r-project.org/package=SupMZ Description: CRAN Package 'SupMZ' (Detecting Structural Change with Heteroskedasticity) Calculates the sup MZ value to detect the unknown structural break points under Heteroskedasticity as given in Ahmed et al. (2017) (). 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Facilitates open, reproducible research workflows: scientists re-analyzing published datasets can work with them as easily as if they were stored on their own computer, and others can track their analysis workflow painlessly. The main function suppdata() returns a (temporary) location on the user's computer where the file is stored, making it simple to use suppdata() with standard functions like read.csv(). 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The first is to convert an orthogonal main-effect design into questions, the second is to create a dataset suitable for analysis, and the third is to calculate count-based scores. For details, see Aizaki and Fogarty (2019) . 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Case 3 BWS is a question-based survey method to elicit people's preferences for attribute levels. Case 3 BWS constructs various combinations of attribute levels (profiles) and then asks respondents to select the best and worst profiles in each choice set. A main function creates a dataset for the analysis from the choice sets and the responses to the questions. For details on Case 3 BWS, refer to Louviere et al. (2015) . Package: r-cran-support.bws Architecture: all Version: 0.4-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-doe.base, r-cran-crossdes, r-cran-survival, r-cran-mlogit, r-cran-gmnl, r-cran-apollo Filename: pool/dists/noble/main/r-cran-support.bws_0.4-6-1.ca2404.1_all.deb Size: 127456 MD5sum: f5466d9209f9d0aa3410e0a16c6a4516 SHA1: 3862d0b18206bfd66a09249ec0abef387b4500a4 SHA256: 0788117a4bfe2cff09a8a601cf8528a61818bfe1cb1b48808148f5f2086c178a SHA512: 98065547fdc63df01f46b31775a847ad4d5be2ca6bb47c4c9d867ca38a63c8e39d30e93e45ec97283983412c6293e30980aec6b9bbb1ce72c426a77586b95649 Homepage: https://cran.r-project.org/package=support.BWS Description: CRAN Package 'support.BWS' (Tools for Case 1 Best-Worst Scaling) Provides basic functions that support an implementation of object case (Case 1) best-worst scaling: a function for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; two functions for creating a data set suitable for analysis; a function for calculating count-based scores; a function for calculating shares of preference; and a function for generating artificial responses to questions. See Louviere et al. (2015) for details on best-worst scaling, and Aizaki and Fogarty (2023) for the package. Package: r-cran-support.ces Architecture: all Version: 0.7-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-doe.base, r-cran-mass, r-cran-simex Suggests: r-cran-survival, r-cran-mded Filename: pool/dists/noble/main/r-cran-support.ces_0.7-0-1.ca2404.1_all.deb Size: 131298 MD5sum: 896850e2955c7c8aa0745461bf73f3c4 SHA1: fb6ffede03969ac5e5109e359eff2a9c8e16d0d0 SHA256: 493c85f7637d92064c5243cc3eef5dc59f086d8e090fc7e0680d081cbd0e8858 SHA512: 7af54949e6f04d985a0d9ba87532178c5f01efddab92a37d982e1756d45ea23ba28b95b04538609f26fc002d606138d5b76960316532b2e5581fcdcbffe2849b Homepage: https://cran.r-project.org/package=support.CEs Description: CRAN Package 'support.CEs' (Basic Functions for Supporting an Implementation of ChoiceExperiments) Provides basic functions that support an implementation of (discrete) choice experiments (CEs). CEs is a question-based survey method measuring people's preferences for goods/services and their characteristics. Refer to Louviere et al. (2000) for details on CEs, and Aizaki (2012) for the package. 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The only theme for these functions is that they tend towards simple, short, and narrowly-scoped. These functions are built for tasks that often recur but are not large enough in scope to warrant an ecosystem of interdependent functions. Package: r-cran-sur Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-learnr Filename: pool/dists/noble/main/r-cran-sur_1.0.4-1.ca2404.1_all.deb Size: 191286 MD5sum: a03f5438a2d8661d0d1bdfba9b878bc1 SHA1: fd1d51597d75af8d6f0aa88638b04e11dc036621 SHA256: acb7885a79eab82accde4e330610d05a4a12efef28c6415aea926c83251410ec SHA512: 11fdc3d0e8947f085657c8c3fcc04e6b310e84fefc9e55245455ad95f933aeb3a9a443a13f22ab2db9063178af0231477f60b77f33d709e1658e278f31c9bcb7 Homepage: https://cran.r-project.org/package=sur Description: CRAN Package 'sur' (Companion to "Statistics Using R: An Integrative Approach") Access to the datasets and many of the functions used in "Statistics Using R: An Integrative Approach". These datasets include a subset of the National Education Longitudinal Study, the Framingham Heart Study, as well as several simulated datasets used in the examples throughout the textbook. The functions included in the package reproduce some of the functionality of 'Stata' that is not directly available in 'R'. The package also contains a tutorial on basic data frame management, including how to handle missing data. Package: r-cran-sure Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-goftest, r-cran-gridextra Suggests: r-cran-mass, r-cran-ordinal, r-cran-rms, r-cran-testthat, r-cran-vgam Filename: pool/dists/noble/main/r-cran-sure_0.2.0-1.ca2404.1_all.deb Size: 141346 MD5sum: e9eae1e34e3c7e03b82e2906f5b7f88e SHA1: b624a2109ecd674f3e4ac317731e0e51c9894ced SHA256: 0e9e195f2b0a9e248879f78830f8bb0c1c568309c0cf3b8393aa443d031c3f13 SHA512: 7fe323ca0802ed8fa7715b22bc1abe6494eb2f4a512c9d05fd7b701bea7b767605d08bdc0f97fe8c2feb27214c6790913486ef11aa2b94d961862f717d9d61b3 Homepage: https://cran.r-project.org/package=sure Description: CRAN Package 'sure' (Surrogate Residuals for Ordinal and General Regression Models) An implementation of the surrogate approach to residuals and diagnostics for ordinal and general regression models; for details, see Liu and Zhang (2017) . These residuals can be used to construct standard residual plots for model diagnostics (e.g., residual-vs-fitted value plots, residual-vs-covariate plots, Q-Q plots, etc.). The package also provides an 'autoplot' function for producing standard diagnostic plots using 'ggplot2' graphics. The package currently supports cumulative link models from packages 'MASS', 'ordinal', 'rms', and 'VGAM'. Support for binary regression models using the standard 'glm' function is also available. Package: r-cran-surf.vs Architecture: all Version: 1.1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-survival, r-cran-dplyr Suggests: r-cran-foreach, r-cran-doparallel, r-cran-knitr Filename: pool/dists/noble/main/r-cran-surf.vs_1.1.0.1-1.ca2404.1_all.deb Size: 96290 MD5sum: c87d84fc4dfc8c42f22a1aaece6b9981 SHA1: 0ae648aaabb53d453f55721a04b3034c59b00f3e SHA256: 068ba67b8c7cf66f178bb2aea73518c932e80f8e69a69744e75c6017b0359739 SHA512: 53793cb1eea88e33fd28c550d539fc2f255028e46bbfd04bf7cedb520f7dc2121b35b30a106a6faa42d2484d0253dc0315306464353ae447715e04e8c9226240 Homepage: https://cran.r-project.org/package=SuRF.vs Description: CRAN Package 'SuRF.vs' (Subsampling Ranking Forward Selection (SuRF)) Performs variable selection based on subsampling, ranking forward selection. Details of the method are published in Lihui Liu, Hong Gu, Johan Van Limbergen, Toby Kenney (2020) SuRF: A new method for sparse variable selection, with application in microbiome data analysis Statistics in Medicine 40 897-919 . Xo is the matrix of predictor variables. y is the response variable. Currently only binary responses using logistic regression are supported. X is a matrix of additional predictors which should be scaled to have sum 1 prior to analysis. fold is the number of folds for cross-validation. Alpha is the parameter for the elastic net method used in the subsampling procedure: the default value of 1 corresponds to LASSO. prop is the proportion of variables to remove in the each subsample. weights indicates whether observations should be weighted by class size. When the class sizes are unbalanced, weighting observations can improve results. B is the number of subsamples to use for ranking the variables. C is the number of permutations to use for estimating the critical value of the null distribution. If the 'doParallel' package is installed, the function can be run in parallel by setting ncores to the number of threads to use. If the default value of 1 is used, or if the 'doParallel' package is not installed, the function does not run in parallel. display.progress indicates whether the function should display messages indicating its progress. family is a family variable for the glm() fitting. Note that the 'glmnet' package does not permit the use of nonstandard link functions, so will always use the default link function. However, the glm() fitting will use the specified link. The default is binomial with logistic regression, because this is a common use case. pval is the p-value for inclusion of a variable in the model. Under the null case, the number of false positives will be geometrically distributed with this as probability of success, so if this parameter is set to p, the expected number of false positives should be p/(1-p). Package: r-cran-surface.analytics Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-effectsize, r-cran-factominer, r-cran-factoextra, r-cran-randomforest, r-cran-multcompview Suggests: r-cran-testthat, r-cran-scales Filename: pool/dists/noble/main/r-cran-surface.analytics_0.1.0-1.ca2404.1_all.deb Size: 41870 MD5sum: 4b10efa15d9a324c65c01daa77bd659a SHA1: 836a33bafa462dec0ad05f84c0209f11af45baf0 SHA256: 3162843b0e65ae3c54f9dbb1add7bcb7129c0c562cbe69dbf617ed16ec06ffe3 SHA512: 10a885c682f865d56e94c92d86ff0da4156bd120c2400f584cca3d5ff8de904755ab4d6d09baf4551fd8b18f885ee316f5a88ac7c7db15bb2140f29f8f0be9ec Homepage: https://cran.r-project.org/package=suRface.analytics Description: CRAN Package 'suRface.analytics' (Statistical Analysis and Visualization of Surface-EngineeredMaterial Properties) A collection of functions for statistical and multivariate analysis of surface-related data, with a focus on antimicrobial activity and omniphobicity. Designed to support materials scientists and researchers in exploring structure–function relationships in surface-engineered materials through reproducible and interpretable workflows. For more details, see Li et al. (2021) , and Kwon et al. (2020) . 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The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak. For more information see Ingram and Mahler (2013) . Package: r-cran-surfacetortoise Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra, r-cran-gstat, r-cran-sf Suggests: r-cran-roxygen2, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surfacetortoise_2.0.1-1.ca2404.1_all.deb Size: 40860 MD5sum: edb25ea25ee7efbe231026d563d8ba48 SHA1: 6cd06827e96ff858e9f794a981fac0238cbedaf8 SHA256: ab11ac15d2b1ae52afd09ee08330c4910656cbd7d4e8898210badeb1c0978a26 SHA512: 06cf86dfb7928f5717d9b085162ba3e40daba865fe6ee28f7780028d21590684670d26a278249c273162d7788b1e4ddab7912fd36f22bc624c3b6c191485ca5b Homepage: https://cran.r-project.org/package=SurfaceTortoise Description: CRAN Package 'SurfaceTortoise' (Find Optimal Sampling Locations Based on Spatial Covariate(s)) Create sampling designs using the surface reconstruction algorithm. Original method by: Olsson, D. 2002. A method to optimize soil sampling from ancillary data. Poster presenterad at: NJF seminar no. 336, Implementation of Precision Farming in Practical Agriculture, 10-12 June 2002, Skara, Sweden. Package: r-cran-surfrough Architecture: all Version: 0.0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4465 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-terra Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-surfrough_0.0.1.0-1.ca2404.1_all.deb Size: 4072642 MD5sum: 80b45309616b0119146ac665291f32ec SHA1: 8939356224043051ca1743238eaf1eaabebe6251 SHA256: 72e812a0eb9bc2de1cb5bc99a3a3027174da262ad6326fc2bf0b5c3b08307d41 SHA512: e64da4bf5ee4aacccf374367ff4dac4728e820d0644754ed08809053baf29d886329051f417b0f04c509237b1b29e50950adb82ba5f936f5da3b8efdbe3fbffa Homepage: https://cran.r-project.org/package=SurfRough Description: CRAN Package 'SurfRough' (Calculate Surface/Image Texture Indexes) Methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) ). It provides various functions for the computation of surface texture indices (e.g., omnidirectional roughness and roughness anisotropy), including the ones based on the robust MAD estimator. The kernels included in the software permit also to calculate the surface/image texture indices directly from the input surface (i.e., without de-trending) using increments of order 2. It also provides the new radial roughness index (RRI), representing the improvement of the popular topographic roughness index (TRI). The framework can be easily extended with ad-hoc surface/image texture indices. Package: r-cran-surprisalanalysis Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2561 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matlib, r-cran-shiny, r-cran-ggplot2, r-cran-shinythemes, r-cran-shinyjs, r-cran-shinycssloaders, r-cran-patchwork, r-cran-dt Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-pheatmap, r-cran-peakram, r-cran-data.table, r-cran-biocmanager, r-bioc-clusterprofiler, r-bioc-annotationdbi, r-bioc-org.hs.eg.db, r-bioc-org.mm.eg.db, r-cran-httpuv Filename: pool/dists/noble/main/r-cran-surprisalanalysis_3.0.1-1.ca2404.1_all.deb Size: 2343830 MD5sum: b779a629f293b5a2a92fe3dfb39d4468 SHA1: 0f43eab743ff26b54126a0833df22568cdd592ff SHA256: 8f64f5173a5416b828150d13a36aab85dfed3c4225d1c3f4bc182b9906c3547b SHA512: a78b8df94ebbd8cfdf40771c43ea9a5f48dcbb33f4bb3a0cb772d3eef1ddba1ab39105b8fec152eded1cb3f163d508ec7efad58b12970dddaed270586f971fab Homepage: https://cran.r-project.org/package=SurprisalAnalysis Description: CRAN Package 'SurprisalAnalysis' (Information Theoretic Analysis of Gene Expression Data) Implements Surprisal analysis for gene expression data such as RNA-seq or microarray experiments. Surprisal analysis is an information-theoretic method that decomposes gene expression data into a baseline state and constraint-associated deviations, capturing coordinated gene expression patterns under different biological conditions. References: Kravchenko-Balasha N. et al. (2014) . Zadran S. et al. (2014) . Su Y. et al. (2019) . Bogaert K. A. et al. (2018) . Package: r-cran-surreal Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1124 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-png Suggests: r-cran-bmp, r-cran-bslib, r-cran-jpeg, r-cran-rsvg, r-cran-shiny, r-cran-tiff Filename: pool/dists/noble/main/r-cran-surreal_0.0.2-1.ca2404.1_all.deb Size: 1054394 MD5sum: 06023900104ef988af5c90895820341d SHA1: fcffbd136ae0c7d1daf5a5d2c79463469c526db0 SHA256: 0b73dbac2fbff7346ac75d8727ca15c05edacf7a44c4a2b4485411328aa5069c SHA512: c08f85c4dfd6a8bf254d1e41c9736b64a87d8a94699d816854bbc0a60c92b25a4ec3ebc57c40aed536a637160eb741fc0b73abc7e888f40783b64e69cf60c8e1 Homepage: https://cran.r-project.org/package=surreal Description: CRAN Package 'surreal' (Create Datasets with Hidden Images in Residual Plots) Implements the "Residual (Sur)Realism" algorithm described by Stefanski (2007) to generate datasets that reveal hidden images or messages in their residual plots. It offers both predefined datasets and tools to embed custom text or images into residual structures. Allowing users to create intriguing visual demonstrations for teaching model diagnostics. Package: r-cran-surrogate Architecture: all Version: 3.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2389 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-lattice, r-cran-latticeextra, r-cran-survival, r-cran-nlme, r-cran-lme4, r-cran-logistf, r-cran-rms, r-cran-ks, r-cran-extradistr, r-cran-pbapply, r-cran-flexsurv, r-cran-rvinecopulib, r-cran-maxlik, r-cran-purrr, r-cran-mbess, r-cran-tidyr, r-cran-dplyr, r-cran-tibble, r-cran-lifecycle Suggests: r-cran-copula, r-cran-fnn, r-cran-mgcv, r-cran-testthat, r-cran-vdiffr, r-cran-fitdistrplus, r-cran-kdecopula, r-cran-cubature, r-cran-mvtnorm, r-cran-withr, r-cran-stringr, r-cran-ggplot2, r-cran-sn, r-cran-pracma Filename: pool/dists/noble/main/r-cran-surrogate_3.4.1-1.ca2404.1_all.deb Size: 2227752 MD5sum: df7695f6e30b9554e86785dadb538b3b SHA1: 8ec66a384d879040e51e068c7a2c8590371f833c SHA256: 5c75d7dfef19bb51b92167c21729ad15fdd3cb11a84d8c3378c7f9b75224098e SHA512: 1c0b2ef055ce46b35a27500b926a9fe11d96ec10f1e0fc58c57c3e50a441ad3c6dd4ae3b515ee1d3b7dc57206aade0c80a27b194ddf10b0da90d12dc70d3b443 Homepage: https://cran.r-project.org/package=Surrogate Description: CRAN Package 'Surrogate' (Evaluation of Surrogate Endpoints in Clinical Trials) In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration (Grant Agreement no 602552), the Special Research Fund (BOF) of Hasselt University (BOF-number: BOF2OCPO3), GlaxoSmithKline Biologicals, Baekeland Mandaat (HBC.2022.0145), and Johnson & Johnson Innovative Medicine. Package: r-cran-surrogateoutcome Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-surrogateoutcome_1.2-1.ca2404.1_all.deb Size: 174646 MD5sum: 0838376e54b3ffc68597ef525bd8c5de SHA1: 88d8c04b009a60fce931eda43251501dde2d5225 SHA256: 2c48064a59d862c1c200e9e184c6536b752500333e0d8277f0814af97897c635 SHA512: f16a3e4ac7ed2d91c89fab798bf4abe008f6a04ff363cc80481fddb6286525b3ac732a790e5256cd1696067a58de4882314a82fe2d0858dc40f5e05d4d630018 Homepage: https://cran.r-project.org/package=SurrogateOutcome Description: CRAN Package 'SurrogateOutcome' (Estimation of the Proportion of Treatment Effect Explained bySurrogate Outcome Information) Estimates the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in Parast, et al (2020) "Assessing the Value of a Censored Surrogate Outcome" and Wang et al (2025) "Model-free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival" . A tutorial for this package can be found at . Package: r-cran-surrogateparadoxtest Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-monotonicitytest Filename: pool/dists/noble/main/r-cran-surrogateparadoxtest_2.0-1.ca2404.1_all.deb Size: 50808 MD5sum: ccc5d18935d3faeafdc65f85c885ce73 SHA1: 800985fb21f28b6b99bc356b66949dd257067aa1 SHA256: be44aa83674c817043cedb1d6d764f9a9136814327d8f9e94cd953c1c0f800fa SHA512: 907be0fd9af2a3a9e4ff71fa5fbe9312427dd1630f77cd86f7260f6e193bf13adfb9354076e562ee9e48234fb7c13a48e85ce97f23ec50b8bd4963bf23727548 Homepage: https://cran.r-project.org/package=SurrogateParadoxTest Description: CRAN Package 'SurrogateParadoxTest' (Empirical Testing of Surrogate Paradox Assumptions) Provides functions to nonparametrically assess assumptions necessary to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of regression functions, and non-negative residual treatment effects. More details are available in Hsiao et al 2025 (under review). A tutorial for this package can be found at . Package: r-cran-surrogaterank Architecture: all Version: 2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-pbmcapply Filename: pool/dists/noble/main/r-cran-surrogaterank_2.2-1.ca2404.1_all.deb Size: 472142 MD5sum: 0ab0883bc1441864cc75b6d0efd4e5d3 SHA1: e26212f42b127040dcf2996c7ed444455a4b6c16 SHA256: 36bda945ec7ca7ba7aec40b71075514b4762c1e5eaaf4d1f48f7e56415a2869e SHA512: d9e8f478e17941da77070adb5afbe43457b063d85ceb95d73d752c5a62809856a2c3added86f9cd297e2ba3b109968a466c9c6f5cb1c7f160638e84edf1ff641 Homepage: https://cran.r-project.org/package=SurrogateRank Description: CRAN Package 'SurrogateRank' (Rank-Based Test to Evaluate a Surrogate Marker) Uses a novel rank-based nonparametric approach to evaluate a surrogate marker in a small sample size setting. Details are described in Parast et al (2024) and Hughes A et al (2025) . A tutorial for this package can be found at and a Shiny App implementing the package can be found at . Package: r-cran-surrogatersq Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1264 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-passo, r-cran-progress, r-cran-scales Suggests: r-cran-r.rsp, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-surrogatersq_0.2.1-1.ca2404.1_all.deb Size: 1220444 MD5sum: 57c9af8bb07265b24a0aeccb82bee473 SHA1: fc1dc36e932fda9a4a08b9d412f7c281fa85fd73 SHA256: 0272f0327e29625cead9269a4f8eba0992a8cb415d9ce1032ec313157967ca78 SHA512: 1ffb8f1e73de9dca2f296f3d4889723d0e3f9081be483b0ddb92869bff69d838100e7d2b0271fa971ed235c1d7537b49e7fd72d1ad8aa4d1f61ed2da69e86e35 Homepage: https://cran.r-project.org/package=SurrogateRsq Description: CRAN Package 'SurrogateRsq' (Goodness-of-Fit Analysis for Categorical Data using theSurrogate R-Squared) To assess and compare the models' goodness of fit, R-squared is one of the most popular measures. For categorical data analysis, however, no universally adopted R-squared measure can resemble the ordinary least square (OLS) R-squared for linear models with continuous data. This package implement the surrogate R-squared measure for categorical data analysis, which is proposed in the study of Dungang Liu, Xiaorui Zhu, Brandon Greenwell, and Zewei Lin (2022) . It can generate a point or interval measure of the surrogate R-squared. It can also provide a ranking measure of the percentage contribution of each variable to the overall surrogate R-squared. This ranking assessment allows one to check the importance of each variable in terms of their explained variance. This package can be jointly used with other existing R packages for variable selection and model diagnostics in the model-building process. Package: r-cran-surrogateseq Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-surrogateseq_1.1-1.ca2404.1_all.deb Size: 130358 MD5sum: 39785f6c6fd617e812b2dabafef21ec3 SHA1: 0f241533b6b70c92119f3eb5337caf9c9851efe7 SHA256: 430e5235f91d2a766aa1092ce5d29aeb325127798353be0ba399aa69cacf7f65 SHA512: 0075bb15636109d20a1c8fdf29db6db7053f8e8a21e3cc2edfc0b21db5077c26f85df9fcb10e485d742ddb09a359c23307308c5e1103a88a9cf25b8fca163d4c Homepage: https://cran.r-project.org/package=SurrogateSeq Description: CRAN Package 'SurrogateSeq' (Group Sequential Testing of a Treatment Effect Using a SurrogateMarker) Provides functions to implement group sequential procedures that allow for early stopping to declare efficacy using a surrogate marker and the possibility of futility stopping. More details are available in: Parast, L. and Bartroff, J (2024) . A tutorial for this package can be found at . A Shiny App implementing the methods can be found at . Package: r-cran-surrogatetest Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-surrogatetest_1.3-1.ca2404.1_all.deb Size: 159470 MD5sum: 807a9db43bc3309d06af8bfbc82d13f5 SHA1: c7b157a78e78897881c1f8a46bf7b04b5fc856f3 SHA256: d0c9b0037700ced99c0b8b98278ffc10eb661fff051db4d7f6dec67e020cc127 SHA512: 788d946f6066b55bbde95b19d33d8fbc6b602c50140668943881816d4892db0626a8a5820c7eee316735ff15acd8e2c776707afab1334c1d375b75f7446bfa03 Homepage: https://cran.r-project.org/package=SurrogateTest Description: CRAN Package 'SurrogateTest' (Early Testing for a Treatment Effect using Surrogate MarkerInformation) Provides functions to test for a treatment effect in terms of the difference in survival between a treatment group and a control group using surrogate marker information obtained at some early time point in a time-to-event outcome setting. Nonparametric kernel estimation is used to estimate the test statistic and perturbation resampling is used for variance estimation. More details will be available in the future in: Parast L, Cai T, Tian L (2019) ``Using a Surrogate Marker for Early Testing of a Treatment Effect" Biometrics, 75(4):1253-1263. . Package: r-cran-surrosurv Architecture: all Version: 1.1.27-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 767 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-eha, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-msm, r-cran-mvmeta, r-cran-optimx, r-cran-parfm, r-cran-survival Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surrosurv_1.1.27-1.ca2404.1_all.deb Size: 713728 MD5sum: 575500a03ecdf58d8d5f0526ec81288d SHA1: ede43b5c0840607a19de3b40557e400e5db509f3 SHA256: cc399a413a4c9d1723b5eac50173426fb5519ed45b6b5c1c75eac1504a15ba29 SHA512: 323dc1531c04eda58d002fc34eddacd5332a4ab448600bf6e546f1a1ebe11b7a14fab79904c63d99c66b01faaf074016a10c47f2cf5b38fafbb3c4b9fbb38093 Homepage: https://cran.r-project.org/package=surrosurv Description: CRAN Package 'surrosurv' (Evaluation of Failure Time Surrogate Endpoints in IndividualPatient Data Meta-Analyses) Provides functions for the evaluation of surrogate endpoints when both the surrogate and the true endpoint are failure time variables. The approaches implemented are: (1) the two-step approach (Burzykowski et al, 2001) with a copula model (Clayton, Plackett, Hougaard) at the first step and either a linear regression of log-hazard ratios at the second step (either adjusted or not for measurement error); (2) mixed proportional hazard models estimated via mixed Poisson GLM (Rotolo et al, 2017 ). Package: r-cran-surrosurvroc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-surrosurvroc_0.1.0-1.ca2404.1_all.deb Size: 35672 MD5sum: 017644000fc5d925beef573783e6eaa4 SHA1: 8ca398016b4d83f70cdfe959ebd89caa96f74cfe SHA256: 17d4ebd071444beae9e862a8139abc17ed3ef428fb6e3b5ced0ff6b58ad21f06 SHA512: 631077e00c8c3beb29bef83537130edf00a18dd4fe686fed4cc663ce19dfb23198b9c98daf2f5bf69e48cfc9a81d99898ead17619b78b5158ba2280eb4414743 Homepage: https://cran.r-project.org/package=surrosurvROC Description: CRAN Package 'surrosurvROC' (Surrogate Survival ROC) Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints. Package: r-cran-surtex Architecture: all Version: 0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-surtex_0.9-1.ca2404.1_all.deb Size: 15992 MD5sum: eee4e1109ee4d45885453a6ce2875871 SHA1: 2364e9f6a6f365a1a32e460eebb78c0bf093e678 SHA256: 0af01fd81dd8511a0851ecce7245efdcd98abd1e99df19a683fde55dac541a36 SHA512: 21965916910fb4c0180f57b2076fd588d551374903cea3f84a158cf5b459cb3482f37c6d62b0a2dc24e986ae1a1d4942d75fd718a0051a6deef428599597c9e2 Homepage: https://cran.r-project.org/package=suRtex Description: CRAN Package 'suRtex' (LaTeX descriptive statistic reporting for survey data) suRtex was designed for easy descriptive statistic reporting of categorical survey data (e.g., Likert scales) in LaTeX. suRtex takes a matrix or data frame and produces the LaTeX code necessary for a sideways table creation. Mean, median, standard deviation, and sample size are optional. Package: r-cran-surv2samplecomp Architecture: all Version: 1.0-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-flexsurv, r-cran-plotrix, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-surv2samplecomp_1.0-5-1.ca2404.1_all.deb Size: 60084 MD5sum: 569c652a1e6a8e69bbf4b2488f95ee41 SHA1: 8e19ed17eff6cae3edc745080f86e01b6763710b SHA256: f159086dbf35a49434105bc59731f852599b4d9def288654cbad72799fbf9de8 SHA512: 4189e36bff0149e156fa02837646104ac0f04e2f61df406c7ecbd5fee78bbb5c97eff01990104174982f2917f9fbf6e63d53033d43b6cffa2dabed09803066c1 Homepage: https://cran.r-project.org/package=surv2sampleComp Description: CRAN Package 'surv2sampleComp' (Inference for Model-Free Between-Group Parameters for CensoredSurvival Data) Performs inference of several model-free group contrast measures, which include difference/ratio of cumulative incidence rates at given time points, quantiles, and restricted mean survival times (RMST). Two kinds of covariate adjustment procedures (i.e., regression and augmentation) for inference of the metrics based on RMST are also included. 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It includes semiparametric, parametric, tree-based, ensemble, boosting, kernel, and deep-learning survival learners, together with benchmarking, scoring, calibration, and model-agnostic interpretation utilities. Representative methodological anchors include Cox (1972) , Royston and Parmar (2002) , Ishwaran et al. (2008) , Jaeger et al. (2019) , Harrell et al. (1982) , Graf et al. (1999) , Friedman (2001) , Apley and Zhu (2020) , and Lundberg and Lee (2017) , and other related methods for survival modeling, prediction, and interpretation. 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In total there are six methods provided, the first three methods are traditional residual-based outlier detection methods, the second three are the concordance-based. Package developed during the work on the two following publications: Pinto J., Carvalho A. and Vinga S. (2015) ; Pinto J.D., Carvalho A.M., Vinga S. (2015) . 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Returns internally validated concordance index, time-dependent area under the curve, Brier score, calibration slope, and statistical testing of non-linear ensemble outperforming the baseline Cox model. In this, it helps researchers to quantify the gain of using a more complex survival model, or justify its redundancy. Equally, it shows the performance value of the non-linear and interaction terms, and may highlight the need of further feature transformation. Further details can be found in Shamsutdinova, Stamate, Roberts, & Stahl (2022) "Combining Cox Model and Tree-Based Algorithms to Boost Performance and Preserve Interpretability for Health Outcomes" , where the method is described as Ensemble 1. 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Asymptotic mean and variance of different proportional hazards test statistics using different ties methods given two survival curves and censoring distributions. Score test and Wald test for regression analysis of grouped survival data. Calculation of survival curves for events defined by the response variable in a mixed effects model crossing a threshold with or without confirmation. 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A key feature of this package is that it incorporates survey data structure directly in a novel test of significance via a sequence of simple linear regression models on the canonical variates. See reference - Cruz-Cano, Cohen, and Mead-Morse (2024) "Canonical Correlation Analysis of Survey data: the SurveyCC R package" The R Journal under review. Package: r-cran-surveycore Architecture: all Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5966 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-s7, r-cran-rlang, r-cran-tidyselect, r-cran-cli, r-cran-tibble, r-cran-dplyr, r-cran-marginaleffects, r-cran-pbivnorm Suggests: r-cran-testthat, r-cran-withr, r-cran-survey, r-cran-survival, r-cran-srvyr, r-cran-haven, r-cran-lifecycle, r-cran-broom, r-cran-polycor, r-cran-jtools, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-surveycore_0.8.3-1.ca2404.1_all.deb Size: 5720230 MD5sum: cc9629f57c02733ef10f48c15998eeff SHA1: 3694280d06c34c89ca4e9fe6e3f06375a8fb8f39 SHA256: eb5268c14347c72b7d574a106f792fb9104b823af00254a85448bca8432f82c5 SHA512: 443346fcd1ca5511c685f75b089004ec3b4abfbd3cb5286f59e8b7fc4d4e5e4356cf313362600e6b33d9de90f0584ab2b3e6ff5a47ea384fd9583ea2945ec005 Homepage: https://cran.r-project.org/package=surveycore Description: CRAN Package 'surveycore' (Core Survey Analysis Infrastructure) Provides 'S7'-based infrastructure for survey analysis. Supports Taylor series, replicate weight, and two-phase designs following the methods in 'Lumley' (2004) . Includes design-based estimators such as means, frequencies, and regression models, with weighted 'polychoric' and 'polyserial' correlation following 'Mannan' (2025) . A metadata system automatically preserves 'haven'-style variable labels, value labels, and question-preface attributes through all operations. Uses a 'tidyselect' interface for design specification. Package: r-cran-surveycv Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survey, r-cran-magrittr Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-islr, r-cran-knitr, r-cran-rmarkdown, r-cran-rpms, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveycv_0.2.0-1.ca2404.1_all.deb Size: 740684 MD5sum: fe94bfa63976b47f40ca238bcb619b03 SHA1: 3ce0f442fd1063e46ad83267472f40902f5b44e5 SHA256: 29208932620425d7507e7886e0485d19f1a377beab4388d3f1e1bc14cf9f1162 SHA512: a0ce99c09faf70b3e588c7da99c3bb4e4a2182331befe08cd2e1cd9d989f6030901f8961b3f9da523b5c483427e498e3c545fcedf5a720237f4513cb10e1094a Homepage: https://cran.r-project.org/package=surveyCV Description: CRAN Package 'surveyCV' (Cross Validation Based on Survey Design) Functions to generate K-fold cross validation (CV) folds and CV test error estimates that take into account how a survey dataset's sampling design was constructed (SRS, clustering, stratification, and/or unequal sampling weights). You can input linear and logistic regression models, along with data and a type of survey design in order to get an output that can help you determine which model best fits the data using K-fold cross validation. Our paper on "K-Fold Cross-Validation for Complex Sample Surveys" by Wieczorek, Guerin, and McMahon (2022) explains why differing how we take folds based on survey design is useful. Package: r-cran-surveydata Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 886 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-magrittr, r-cran-purrr, r-cran-ggplot2, r-cran-scales, r-cran-tidyr, r-cran-dt, r-cran-assertthat Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-covr, r-cran-rprojroot, r-cran-spelling Filename: pool/dists/noble/main/r-cran-surveydata_0.2.8-1.ca2404.1_all.deb Size: 372142 MD5sum: 2d2ff1d64a6b78c6fd4d51cd8c6b0fdf SHA1: 4a6bcf7ba77347f635b1dbbaea8bb6242b3b93da SHA256: 3f7a4eb33ac10acdea8f8b5af9f69b203ddc10cb7e7438249ec27bd1fcba19a0 SHA512: 4628ad376cc4b09dd342f36af92490b5fbd15fe6bcb3d6e966502a90a30f19419c5c766b4d2be86a151eba26e2b610561ab0be117f67bdaa951f7e959bc66593 Homepage: https://cran.r-project.org/package=surveydata Description: CRAN Package 'surveydata' (Tools to Work with Survey Data) Data obtained from surveys contains information not only about the survey responses, but also the survey metadata, e.g. the original survey questions and the answer options. The 'surveydata' package makes it easy to keep track of this metadata, and to easily extract columns with specific questions. Package: r-cran-surveydefense Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-flextable Suggests: r-cran-officer Filename: pool/dists/noble/main/r-cran-surveydefense_0.2.0-1.ca2404.1_all.deb Size: 42468 MD5sum: 10869f90b6db9795a5178935f74fc417 SHA1: 7946c810afecd495b7f9e33325d42c8348d03973 SHA256: 9074b0eecd6d83d04dab26e3aed698d5c5a375b9c5e05970a38f66c808bce5e8 SHA512: b885e3f16d475b95d8fa30fb99dbf051269af472fd238658987c8c1d6d32a31d156ba6c396d5e69e073417dd29ffaa60fbcef964234fa57fe8eb09de9a8d40e4 Homepage: https://cran.r-project.org/package=SurveyDefense Description: CRAN Package 'SurveyDefense' (Survey Defense Tool) This tool is designed to analyze up to 5 Fraud Detection Questions integrated into a survey, focusing on potential fraudulent participants to clean the survey dataset from potential fraud. Fraud Detection Questions and further information available at . Package: r-cran-surveydown Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dotenv, r-cran-fs, r-cran-htmltools, r-cran-jsonlite, r-cran-markdown, r-cran-miniui, r-cran-pool, r-cran-quarto, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rstudioapi, r-cran-rvest, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-xml2, r-cran-yaml Suggests: r-cran-glue, r-cran-knitr, r-cran-leaflet, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveydown_1.0.1-1.ca2404.1_all.deb Size: 896328 MD5sum: 3336b72ea4c971a0f9f85eebd412ccbe SHA1: a9d604cc0b479895a4d55ad7f51edd996cbdf27d SHA256: 179ebcdeb07c7dbaae4028cfc75fdbf277cca4db7842c61ca7f627fdbf61fcb6 SHA512: 9c7d25affb43716eba6899663d5edc01d5c65898cc68728c40f75fc122cd88c098d70b3752449dae8c18a508aba4bb9ba8b3fcb2486d53fe9045a0bc144422d1 Homepage: https://cran.r-project.org/package=surveydown Description: CRAN Package 'surveydown' (Markdown-Based Programmable Surveys Using 'Quarto' and 'shiny') Generate programmable surveys using markdown and R code chunks. Surveys are composed of two files: a survey.qmd 'Quarto' file defining the survey content (pages, questions, etc), and an app.R file defining a 'shiny' app with global settings (libraries, database configuration, etc.) and server configuration options (e.g., conditional skipping / display, etc.). Survey data collected from respondents is stored in a 'PostgreSQL' database. Features include controls for conditional skip logic (skip to a page based on an answer to a question), conditional display logic (display a question based on an answer to a question), a customizable progress bar, and a wide variety of question types, including multiple choice (single choice and multiple choices), select, text, numeric, multiple choice buttons, text area, and dates. Because the surveys render into a 'shiny' app, designers can also leverage the reactive capabilities of 'shiny' to create dynamic and interactive surveys. Package: r-cran-surveyexplorer Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1107 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggupset, r-cran-gt, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveyexplorer_0.2.0-1.ca2404.1_all.deb Size: 572908 MD5sum: 98ca0b07a50b29b80b0356bbd0665325 SHA1: 33884b98513ec4ba739531d18dc0deeac1ba190a SHA256: 52964f19b018beb1fe6f2fa9737957175f1456123cd7dc35826391136c87bc17 SHA512: 227ce793210a8bf0efd8a4730e2a935e4a060dad9455e866b6e1766c0f6e85a1508b04638fc109dec31970e31b2229ff00a78dfb6ad360b09a0be143f93745cc Homepage: https://cran.r-project.org/package=surveyexplorer Description: CRAN Package 'surveyexplorer' (Quickly Explore Complex Survey Data) Visualize and tabulate single-choice, multiple-choice, matrix-style questions from survey data. Includes ability to group cross-tabulations, frequency distributions, and plots by categorical variables and to integrate survey weights. Ideal for quickly uncovering descriptive patterns in survey data. Package: r-cran-surveynnet Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-nnet, r-cran-practools, r-cran-survey, r-cran-survival Filename: pool/dists/noble/main/r-cran-surveynnet_1.0.0-1.ca2404.1_all.deb Size: 134012 MD5sum: 94dacc8076166a5704066324a102b8d3 SHA1: c0f9393061bb25a4055b9efc5fac84e946da8e2e SHA256: 8c7e00b7c743b2c0730542688bc2a488c99382ffb23b387da65fa94d7144bb40 SHA512: ed535e65be424021bb4374234078bd8967413ae7e669cdf5600ca267e370a211af08054894fa0bb9a96641797f0c866c627547e7d1ebc3d4da66831a57cecb6a Homepage: https://cran.r-project.org/package=surveynnet Description: CRAN Package 'surveynnet' (Neural Network for Complex Survey Data) The goal of 'surveynnet' is to extend the functionality of 'nnet', which already supports survey weights, by enabling it to handle clustered and stratified data. It achieves this by incorporating design effects through the use of effective sample sizes as outlined by Chen and Rust (2017), , and performed by 'deffCR' in the package 'PracTools' (Valliant, Dever, and Kreuter (2018), ). Package: r-cran-surveyprev Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5835 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survey, r-cran-ggplot2, r-cran-rdhs, r-cran-summer, r-cran-dplyr, r-cran-labelled, r-cran-sjlabelled, r-cran-naniar, r-cran-raster, r-cran-sp, r-cran-spdep, r-cran-stringr, r-cran-tidyverse, r-cran-data.table, r-cran-sf, r-cran-matrixstats Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-kableextra, r-cran-geodata, r-cran-patchwork, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-surveyprev_1.0.0-1.ca2404.1_all.deb Size: 4969868 MD5sum: 880052bf4c2c15031944f21feb9ec859 SHA1: 3de439fbda63a179d66e80380bd3e763187c6142 SHA256: e13a4464539bfb7bc5e08092928257f4f0e14f6eef931af9be9bcd98953b7a2a SHA512: a389ac5cb11b60bd373cc6aca0506367e114a427e6a6f49a31b63220a4e410e5eed29c19e9bd9cf2916caab295216f9edc883d74e628cc81bd772f72630c7ffd Homepage: https://cran.r-project.org/package=surveyPrev Description: CRAN Package 'surveyPrev' (Mapping the Prevalence of Binary Indicators using Survey Data inSmall Areas) Provides a pipeline to perform small area estimation and prevalence mapping of binary indicators using health and demographic survey data, described in Fuglstad et al. (2022) and Wakefield et al. (2020) . Package: r-cran-surveysearch Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveysearch_0.1.0-1.ca2404.1_all.deb Size: 23776 MD5sum: f533f62303d5113eb0adcb3480245da6 SHA1: 86b0fc05f05a0c0b5a34c4470993468528504e8e SHA256: d0d14f60c689dfe3a672db492a741a8e56f3da3b6bfa60ab3926d580d7b3a197 SHA512: d1125eb6e2d7a11fad3d5dc38fe6a36e89361e9a01f537fe0888cd12df83a72b298f5d220d1f2724fe3fbce5e7e2af05412f5cafd60cbbd877e6915e01ca20ac Homepage: https://cran.r-project.org/package=surveysearch Description: CRAN Package 'surveysearch' (Search and Examine Variables Across Survey Datasets) Search for variables across multiple survey datasets, examine variable properties (labels, values, missingness), and explore variable context within datasets. Useful for navigating complex survey data with many variables and understanding variable relationships and metadata. Package: r-cran-surveysimr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-moments Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveysimr_0.1.0-1.ca2404.1_all.deb Size: 38862 MD5sum: 908c23c17c9548eb233279a92990343e SHA1: f26d09c07328c1241f656d29ed0b5f6bc0816dd0 SHA256: 4f33a08952a2d11b0ae36cda5d2dc464f060b3f5ed5d762cf7e24eba559fdaf9 SHA512: 99f9227e097c1fcf07dca1d1097aa97e61677009f2b4fee02e457f712f93f9d8669a028f9913ddc9cec0e78d9000837cab7eec249c6ade7d302850537a05e6fa Homepage: https://cran.r-project.org/package=surveySimR Description: CRAN Package 'surveySimR' (Estimation of Population Total under Complex Sampling Design) Sample surveys use scientific methods to draw inferences about population parameters by observing a representative part of the population, called sample. The SRSWOR (Simple Random Sampling Without Replacement) is one of the most widely used probability sampling designs, wherein every unit has an equal chance of being selected and units are not repeated.This function draws multiple SRSWOR samples from a finite population and estimates the population parameter i.e. total of HT, Ratio, and Regression estimators. Repeated simulations (e.g., 500 times) are used to assess and compare estimators using metrics such as percent relative bias (%RB), percent relative root means square error (%RRMSE).For details on sampling methodology, see, Cochran (1977) "Sampling Techniques" . Package: r-cran-surveystat Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-surveystat_1.0.3-1.ca2404.1_all.deb Size: 70104 MD5sum: 40c3eebf245470c40fa7795a07af1a17 SHA1: 9fac67daf41b779b582fa4794898633126adcfc5 SHA256: b257766ad5cc60917634d36f9e964ad7cc4bdeae3897614628be72f129370e73 SHA512: 6627e526c1d2a9b80e93a3f43b78872f508c30d637fef373356e4543240f86db766eca1f21378aeee9ea8f78641dd0b9a328b8f20a206f211477aa374b9a833d Homepage: https://cran.r-project.org/package=SurveyStat Description: CRAN Package 'SurveyStat' (Survey Data Cleaning, Weighting and Analysis) Provides utilities for cleaning survey data, computing weights, and performing descriptive statistical analysis. Methods follow Lohr (2019, ISBN:978-0367272454) "Sampling: Design and Analysis" and Lumley (2010) . Package: r-cran-surveytable Architecture: all Version: 0.9.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5235 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-magrittr, r-cran-glue, r-cran-survey, r-cran-huxtable Suggests: r-cran-gt, r-cran-kableextra, r-cran-openxlsx2, r-cran-mschart, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-surveytable_0.9.10-1.ca2404.1_all.deb Size: 694526 MD5sum: 47f5b18b0245d5ce53584a3c9a5cef96 SHA1: 61977ee10644f90eb99158158732e3276b234683 SHA256: 84b05d45e343a3001d8236e8dd661b8c5ddb9fd7d95f8f924e30019c86fa6cd0 SHA512: 2bc93bc4c5981461bd1bba46e5c3279420fb16664318b7fa0ea75454f5106c44d02ad5ad0819f778c51150ea8185adafb08492e08ba05fb6dd98104d98ca41a9 Homepage: https://cran.r-project.org/package=surveytable Description: CRAN Package 'surveytable' (Streamlining Complex Survey Estimation and ReliabilityAssessment in R) Short and understandable commands that generate tabulated, formatted, and rounded survey estimates. Mostly a wrapper for the 'survey' package (Lumley (2004) ) that identifies low-precision estimates using the National Center for Health Statistics (NCHS) presentation standards (Parker et al. (2017) , Parker et al. (2023) ). Package: r-cran-surveytidy Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 549 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-haven, r-cran-rlang, r-cran-s7, r-cran-surveycore, r-cran-tidyr, r-cran-tidyselect, r-cran-vctrs, r-cran-withr Suggests: r-cran-covr, r-cran-mockery, r-cran-pkgdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-surveytidy_0.6.0-1.ca2404.1_all.deb Size: 468588 MD5sum: 6c6851091bafd0a576e83ea9a2da2fb6 SHA1: e368973c9ba78f932c7f2b48e892637868346bec SHA256: e5b61487126af45edc9a33afd5f102fbc5eb05e06d8bc93517eb93c89839c777 SHA512: 75154d3fc80d6ceb239c4c9bbfbe029e49fc479952c5645953284769a81f8714e1da460758cdca9f1d1a3165097e10f4b0fb35f03af79f6c842c4983994d987a Homepage: https://cran.r-project.org/package=surveytidy Description: CRAN Package 'surveytidy' (Tidy 'dplyr'/'tidyr' Verbs for Survey Design Objects) Provides 'dplyr' and 'tidyr' verbs, survey-aware recoding helpers, and row-wise statistics for survey design objects created with the 'surveycore' package. filter() uses domain estimation to preserve variance estimation validity; other verbs preserve design variables and metadata automatically. Also supports survey_collection objects for applying the same operation across a list of surveys. Package: r-cran-survgme Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-ncvreg, r-cran-glmnet, r-cran-survival, r-cran-ahaz, r-cran-ggally, r-cran-network, r-cran-sna, r-cran-scales Filename: pool/dists/noble/main/r-cran-survgme_0.1.0-1.ca2404.1_all.deb Size: 71938 MD5sum: 109fbda3ca500b79cda955e8afd5e8c0 SHA1: 034fab1f4d3b5735e170339172c661de5dcb9c11 SHA256: 20a206242267a142538c770159484a1d22ea7eb86ae708963fdba524a573a434 SHA512: 56ac95b45ef8d978ac43e1fa3652055a4e5f89cefd6404e7d6188dc943f587ff350d9ba2ae73e272dfa94011b38ddc276c0624abf4709d11c99bf155f97e820e Homepage: https://cran.r-project.org/package=SurvGME Description: CRAN Package 'SurvGME' (Analysis of Survival Data under Graphical and Measurement ErrorModels) The estimation method proposed by Chen and Yi (2021) is extended to the analysis of survival data, accommodating commonly used survival models while accounting for measurement error and network structures among covariates. Package: r-cran-survhe Architecture: all Version: 2.0.51-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexsurv, r-cran-dplyr, r-cran-ggplot2, r-cran-rms, r-cran-xlsx, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rstan, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survhe_2.0.51-1.ca2404.1_all.deb Size: 299524 MD5sum: 31b4c707f89a1a0b06b4445102dfef51 SHA1: 7d923e167a9d8bc1f03a46f9dc5f0d5b32de5024 SHA256: 5a76f58a789f58101a715268001e3a9d1fb0b54776b1b6280d9ae443e81525d1 SHA512: fd015e7706824d6e621b06eedb05b660bda7b352eae31dbc192067e2d0e1568b4f3db749d586111855f059836494cdd5eb325a6befa69b521dd97c69a832a194 Homepage: https://cran.r-project.org/package=survHE Description: CRAN Package 'survHE' (Survival Analysis in Health Economic Evaluation) Contains a suite of functions for survival analysis in health economics. These can be used to run survival models under a frequentist (based on maximum likelihood) or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian Monte Carlo). To run the Bayesian models, the user needs to install additional modules (packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed from using 'install.packages("survHEhmc", repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org"))' and 'install.packages("survHEinla", repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org"))' respectively. 'survHEinla' is based on the package INLA, which is available for download at . The user can specify a set of parametric models using a common notation and select the preferred mode of inference. The results can also be post-processed to produce probabilistic sensitivity analysis and can be used to export the output to an Excel file (e.g. for a Markov model, as often done by modellers and practitioners). . Package: r-cran-survhidim Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-rdpack, r-cran-readr, r-cran-tidyverse, r-cran-glmnet, r-cran-factoextra, r-cran-igraph, r-cran-useful Filename: pool/dists/noble/main/r-cran-survhidim_0.1.1-1.ca2404.1_all.deb Size: 445040 MD5sum: 9b9e9fc7e5a2dc49785c86d6127dfacb SHA1: ef17927ae0cea022be2300fe7d43c1ed669ee454 SHA256: f1baa7b6d2cdcddc5ac0f15993ac6475539346d2c74d14032e4aa10f1db185e0 SHA512: 01facc08fc2336781c351fdb80d4f515f1344bed60fd2f58fe0cae8105c3e60066f70720820221c2e656cf03b8917991041994ca15ab6676cd4d987909f29c45 Homepage: https://cran.r-project.org/package=SurvHiDim Description: CRAN Package 'SurvHiDim' (High Dimensional Survival Data Analysis) High dimensional time to events data analysis with variable selection technique. Currently support LASSO, clustering and Bonferroni's correction. Package: r-cran-survimchd Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjags, r-cran-r2jags, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-survimchd_0.1.2-1.ca2404.1_all.deb Size: 406114 MD5sum: a7c321b768b4fc89d20981b098947941 SHA1: d5f1a80b624c64921f2dfc39caebb0407603e900 SHA256: d9f7450cd7f406c96c0efa2de59a821d3fc992da0ca597a023aabfee3c103525 SHA512: 0baf44611a861c6ba86ebd9f41eeaaf4489f4fe79fac00e032d8d7edc615a2deff2305778f6a18492374f9609e5ef3732167ef7bb92b181970d91bbef78d61ff Homepage: https://cran.r-project.org/package=SurviMChd Description: CRAN Package 'SurviMChd' (High Dimensional Survival Data Analysis with Markov Chain MonteCarlo) High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). Currently supports frailty data analysis. Allows for Weibull and Exponential distribution. Includes function for interval censored data. Package: r-cran-survimpute Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-vgam, r-cran-mass Suggests: r-cran-mitools Filename: pool/dists/noble/main/r-cran-survimpute_0.1.0-1.ca2404.1_all.deb Size: 65810 MD5sum: 783bacf78a9e66c9978e296c44b4ced0 SHA1: 6db0dd0f7774569f1c98c6b6a56b41bf10a4d385 SHA256: b72e186134aa4efaea9d277e44c8ef16533b79991df4518f986c7ebc7da418d9 SHA512: 2a42ddea4e343528853b8a9e22e8771e7e160898a5afb87cc52008df6507f73ab02c36bf737f8c574fa9d9e73c0510c0c7c665ae2dbcb0450a5642d25d8d918a Homepage: https://cran.r-project.org/package=SurvImpute Description: CRAN Package 'SurvImpute' (Multiple Imputation for Missing Covariates in Time-to-Event Data) Generates multiple imputed datasets from a substantive model compatible fully conditional specification model for time-to-event data. Our method assumes that the censoring process also depends on the covariates with missing values. Details will be available in an upcoming publication. Package: r-cran-survinger Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-purrr, r-cran-rlang, r-cran-tibble Suggests: r-cran-mass, r-cran-matrix, r-cran-nloptr, r-cran-survey, r-cran-tidyr, r-cran-vctrs, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-survinger_0.1.1-1.ca2404.1_all.deb Size: 590966 MD5sum: ef40c1f008ca654c343f9c308a38b0f2 SHA1: 218a42559aa2d31a092e5cad9c1c89b3d8e16556 SHA256: 058994d13e40ef0a2d239e8e00436b7f14eab8afa5a9f0b384069d694356f240 SHA512: cc5ff6f6c11e8925d4b67cd5cdc244006832423034ef262728f3f14e9db2699ef1ead3175b0f6b5c9051e8400adf0a6d8681bbecf04981610152ed7466e9d2e3 Homepage: https://cran.r-project.org/package=survinger Description: CRAN Package 'survinger' (Design-Adjusted Inference for Pathogen Lineage Surveillance) Provides tools for optimizing sequencing resource allocation and estimating pathogen lineage prevalence under real-world genomic surveillance conditions. Implements constrained allocation optimization for limited sequencing capacity across multiple regions and sample sources. Includes Horvitz-Thompson and post-stratified estimators that account for unequal sequencing rates, delay-adjusted nowcasting for right-censored reporting data, and combined design-weighted delay-corrected inference with uncertainty propagation. Package: r-cran-surviv Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-survival, r-cran-rootsolve Filename: pool/dists/noble/main/r-cran-surviv_0.1.3-1.ca2404.1_all.deb Size: 234078 MD5sum: 5c6192dc106db1a7ddb56c0033642542 SHA1: 7b393a83acfac656e220e474266350a0193c9866 SHA256: 8009754d7c5413c479a39b0cb12ceb4f51790f2458c8ef86b67ce35a4519f5d9 SHA512: fc5a2bf8989ed2c869c18b371ff112a595cb5f52a09ebf0920e88e450539d72aa6b16a0dc247efa4dc67d3a343e38faec710f4ad466daf4da8c3a17620d5ab98 Homepage: https://cran.r-project.org/package=surviv Description: CRAN Package 'surviv' (Flexible Instrumental Variable Methods for Survival Analysis) Instrumental variable analysis methods for causal inference on survival data based on the Cox model allowing for various treatment and effect types, including orthogonality method-of-moments instrumental variable estimation for the Cox model, two-stage residual inclusion Cox estimation with frailty, sequential trial emulation, sequential Cox analyses, and sequential two-stage residual inclusion Cox analyses. Methodological background includes MacKenzie et al. (2014) , Martinez-Camblor et al. (2019) , Martinez-Camblor et al. (2019) , Gran et al. (2010) , and Keogh et al. (2023) . Package: r-cran-survival666 Architecture: all Version: 0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2053 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-survminer, r-cran-ggplot2 Suggests: r-cran-tidyverse, r-cran-magrittr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survival666_0.5-1.ca2404.1_all.deb Size: 2045392 MD5sum: ced36b329d3883ae324e219e98b1096d SHA1: fbb37970ada3a10dfa74e827da3c8cd3cd9db0a5 SHA256: 86c32972aba176313699f355fe7762528fe5a1f8e6390e1231fb6adbf0bc467f SHA512: 345048efbe11d9c43df3ac6ad7ffa6d2ddfa58be67efaeccd173505622d088ede4f6f276babb4d11185650b65cd50916152b292a277e5ca3f28755aa843a83c9 Homepage: https://cran.r-project.org/package=survival666 Description: CRAN Package 'survival666' (Eliminate the Influence of Co-Expression Genes on Target Genes) Functions can be used for batch survival analysis, but not only for it. Most importantly, it can verify any P-value calculated according to the gene expression level and eliminate the influence of co-expression genes. Package: r-cran-survivalanalysis Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rlang, r-cran-dplyr, r-cran-forcats, r-cran-magrittr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-gridextra, r-cran-ggplot2, r-cran-scales, r-cran-survminer, r-cran-cowplot, r-cran-tidytidbits Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse Filename: pool/dists/noble/main/r-cran-survivalanalysis_0.4.0-1.ca2404.1_all.deb Size: 392794 MD5sum: 8133908e818182fe306342765a00cab6 SHA1: 286e79c68f783c50e1d0adfb85af806b3b3eca6b SHA256: ac1e113d26710189d9ef9aa44ec6cebf9d93ef667f98249b8509d1b986def01d SHA512: b8b137cdadac1cf95e6fd53f6c0bb54e50e9eaba670d7680e108f76e4be683f359501dff4734d1f1b0a35be93b8fe7a8ad5188f102a50acb07500e130d9a84bc Homepage: https://cran.r-project.org/package=survivalAnalysis Description: CRAN Package 'survivalAnalysis' (High-Level Interface for Survival Analysis and Associated Plots) A high-level interface to perform survival analysis, including Kaplan-Meier analysis and log-rank tests and Cox regression. Aims at providing a clear and elegant syntax, support for use in a pipeline, structured output and plotting. Builds upon the 'survminer' package for Kaplan-Meier plots and provides a customizable implementation for forest plots. Kaplan & Meier (1958) Cox (1972) Journal of the Royal Statistical Society. Series B (Methodological), Vol. 34, No. 2 (1972), pp. 187-220 (34 pages) Peto & Peto (1972) . Package: r-cran-survivalmpl Architecture: all Version: 0.2-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mass Filename: pool/dists/noble/main/r-cran-survivalmpl_0.2-4-1.ca2404.1_all.deb Size: 143890 MD5sum: 78c4531fb4dca5b0207bbcdc2730cc8a SHA1: a803b0e76cd8dbc59934808c6819234c17b4c000 SHA256: b73c5582bde0460f9313358e517afe26340f0e963b369ae2785a8bf616a7ac0e SHA512: 62709feaf952f8beacfab9903e518797ae9222a9d4eefc1860691efdd1b0833ea160ae495dcc1f3a8b3db1718cd469a8df92cf076370d34f1703dae86249dfa8 Homepage: https://cran.r-project.org/package=survivalMPL Description: CRAN Package 'survivalMPL' (Penalised Maximum Likelihood for Survival Analysis Models) Estimate the regression coefficients and the baseline hazard of proportional hazard Cox models with left, right or interval censored survival data using maximum penalised likelihood. A 'non-parametric' smooth estimate of the baseline hazard function is provided. Package: r-cran-survivalmpldc Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 649 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-splines2, r-cran-survival, r-cran-matrixcalc Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-survivalmpldc_0.1.1-1.ca2404.1_all.deb Size: 429016 MD5sum: fefb8d0332435bed61ba2c67d23225c0 SHA1: e67e50f9785d6b77a9a761a7160b5c9593c3f27b SHA256: 27a2a150fed60da23a0729d8d36ab3400ee6cd1cb1236a1a572a277acee94cf0 SHA512: a96f17a71beab45590c7088c1b6de4dc021baa23d7751dd248d03a589de42d2bee5a5781a1e7c22e1ce5dd21fa25ad5744b6e33a1228615ec2130ab6ff5e0281 Homepage: https://cran.r-project.org/package=survivalMPLdc Description: CRAN Package 'survivalMPLdc' (Penalised Likelihood for Survival Analysis with DependentCensoring) Fitting Cox proportional hazard model under dependent right censoring using copula and maximum penalised likelihood methods. Package: r-cran-survivalplann Architecture: all Version: 0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-nnet, r-cran-risca Filename: pool/dists/noble/main/r-cran-survivalplann_0.4-1.ca2404.1_all.deb Size: 267630 MD5sum: 107988a52571e9f4c52f75165f6c8374 SHA1: 95bf8f4541be1072e1644bc968eadf13edaaa2fc SHA256: 6924cb70a6565ca829c0929ce689d5b3ae5ed10fcdc061be0e94180642d06743 SHA512: 9e572d3da9595e621c2b44254dd3286b8ee4efc4797a024acd99fba0be980cad8e38cc1eb436fcd14cf63ed2765bd3a4eeb725e3e36238c93ce2ba4e5f82db06 Homepage: https://cran.r-project.org/package=survivalPLANN Description: CRAN Package 'survivalPLANN' (Neural Networks to Predict Survival) Several functions and S3 methods to predict survival by using neural networks. We implemented Partial Logistic Artificial Neural Networks (PLANN) as proposed by Biganzoli et al. (1998) . Package: r-cran-survivalsl Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-date, r-cran-mass, r-cran-glmnet, r-cran-caret, r-cran-flexsurv, r-cran-randomforestsrc, r-cran-hdnom, r-cran-survivalplann, r-cran-dplyr, r-cran-rpart Filename: pool/dists/noble/main/r-cran-survivalsl_1.0-1.ca2404.1_all.deb Size: 389210 MD5sum: cdf7e207a723d56ffc1352e9340c1b7e SHA1: 747fa76ccaa72c280c6dcbb0f17fc2029b8c3684 SHA256: a127cdb4016b23d14786a51537a5575f7f4e8faffefd60c18d20c3eefdb91bc9 SHA512: 96cc12aa1579394732791401a585e76c63b22e0c663d09e06c19edaa145a74fc68a61eb1c7a0b5774e7c9454b728b8c3178268773c37e6b6c9534cd9c6577d98 Homepage: https://cran.r-project.org/package=survivalSL Description: CRAN Package 'survivalSL' (Super Learner for Survival Prediction from Censored Data) Several functions and S3 methods to construct a super learner in the presence of censored times-to-event and to evaluate its prognostic capacities. Package: r-cran-survivalsurrogate Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-glue, r-cran-mlr3, r-cran-purrr, r-cran-sparsem, r-cran-rbeta2009, r-cran-data.table, r-cran-rpart Suggests: r-cran-mlr3learners Filename: pool/dists/noble/main/r-cran-survivalsurrogate_1.1-1.ca2404.1_all.deb Size: 173492 MD5sum: c68f14894c3fea475444695998ad7c01 SHA1: 4302f9647246c970b604c6d71b6ac6ab15329da0 SHA256: a0158a7ac5d67ba6e89a962b4aaa78c1a8722e340488f9bfe86015854b7fac68 SHA512: e883883c27f6392ab90b6ff07f2fd5870434e34ea1b4e7626173bba8a3f873dc7e0ce128ec0cbef82487116ad6eac618f403c7acf86c7b39f6ff16290ff08755 Homepage: https://cran.r-project.org/package=survivalsurrogate Description: CRAN Package 'survivalsurrogate' (Evaluate a Longitudinal Surrogate with a Censored Outcome) Provides influence function-based methods to evaluate a longitudinal surrogate marker in a censored time-to-event outcome setting, with plug-in and targeted maximum likelihood estimation options. Details are described in: Agniel D and Parast L (2025). "Robust Evaluation of Longitudinal Surrogate Markers with Censored Data." Journal of the Royal Statistical Society: Series B . A tutorial for this package can be found at and a Shiny App implementing the package can be found at . Package: r-cran-survivalsvm Architecture: all Version: 0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-pracma, r-cran-kernlab, r-cran-matrix, r-cran-hmisc Suggests: r-cran-testthat, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-survivalsvm_0.0.6-1.ca2404.1_all.deb Size: 295430 MD5sum: 1ff2af79247345dea3335520db71e699 SHA1: 3108ee8d46736e2c6e2ae3c1e4acc17303c72596 SHA256: 888ea6e37283aa3dbcfa64f366d7942cd9970f0cdd8c412ea01c7865184f801b SHA512: 2c686ebe3286523502de5fb758c72a384cf580a9a3be9159b2d0355ebce6ad56c00f10378c00ee0f1a5f218f2753b15880da59ec047661214654dd3d4987420a Homepage: https://cran.r-project.org/package=survivalsvm Description: CRAN Package 'survivalsvm' (Survival Support Vector Analysis) Performs support vectors analysis for data sets with survival outcome. Three approaches are available in the package: The regression approach takes censoring into account when formulating the inequality constraints of the support vector problem. In the ranking approach, the inequality constraints set the objective to maximize the concordance index for comparable pairs of observations. The hybrid approach combines the regression and ranking constraints in the same model. Package: r-cran-survivaltests Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-weibullness, r-cran-ggplot2 Suggests: r-cran-survival, r-cran-arules Filename: pool/dists/noble/main/r-cran-survivaltests_1.0-1.ca2404.1_all.deb Size: 51948 MD5sum: 9d0e16fd09e5a28bd665aef2d9aec276 SHA1: ccdd613184553c58873707cd39a8b41d1e44778c SHA256: 4c8ac0311b7e4f35ceab875d1c3707ec38ee92efa0a01b2015448654eeb31a38 SHA512: f400263f44d6e255db835be9875442300cc76eed6a16e4bd0e36962986bee72008f7413431514149480abe2c7c8bd2000ce4d6f2e3c2cab7a074e9ca08f75b59 Homepage: https://cran.r-project.org/package=SurvivalTests Description: CRAN Package 'SurvivalTests' (Survival Tests for One-Way Layout) Performs survival analysis for one-way layout. The package includes the generalized test for survival ANOVA (Tsui and Weerahandi (1989) and (Weerahandi, 2004; ISBN:978-0471470175)). It also performs pairwise comparisons and graphical approaches. Moreover, it assesses the weibullness of data in each group via test. The package computes mean and confidence interval under Weibull distribution. Package: r-cran-survivalvignettes Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1058 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-survey, r-cran-geepack, r-cran-mstate, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-survivalvignettes_0.1.6-1.ca2404.1_all.deb Size: 552686 MD5sum: eb3e175cb0371742481417eb88a8aeba SHA1: af1999f15c57928ac35db11fb20b6f1950ea9c71 SHA256: 60afc9209b4cd71bb479ef0a171939c0374445ad208e0426e60c68c001b581d8 SHA512: 65741520c2b74af7fa79bb7e7d6f44c6623e1a54c34976bb2f174d2b25992fa3721ec26459b10f5825771cbf73b918b6e9a3e30b4ea18ae67c8af5941d6f95b4 Homepage: https://cran.r-project.org/package=survivalVignettes Description: CRAN Package 'survivalVignettes' (Survival Analysis Vignettes and Optional Datasets) Vignettes for the 'survival' package. Split from the 'survival' package since the vignettes were getting large. Also, since 'survival' is a recommended package it cannot make use of other packages outside of base+recommended (e.g. 'rmarkdown'). Package: r-cran-survivor Architecture: all Version: 2.3.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidyr, r-cran-ggplot2, r-cran-stringr, r-cran-magrittr, r-cran-glue, r-cran-shiny, r-cran-purrr, r-cran-dplyr, r-cran-crayon, r-cran-readr, r-cran-shinycssloaders, r-cran-lubridate, r-cran-dt, r-cran-shinyjs Suggests: r-cran-forcats, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survivor_2.3.10-1.ca2404.1_all.deb Size: 3325214 MD5sum: 0d8f8b27a963d486f67048e6c73f2a03 SHA1: f682bb9b363e10407c3e70f41afe69e955dc2ab4 SHA256: bcbb003159bf3d501a92ac4ce79737b8cb941ed6f465321d40b5ce9a1e5a30ee SHA512: 9e209091fa9e22251b2f42c43626cb7dd2c98ea38bbae09d0c009a01e10d9e65d44bdebf1b71b010be854d3bf5254720cd47b234058ee25e189fd2a7bd8a44f6 Homepage: https://cran.r-project.org/package=survivoR Description: CRAN Package 'survivoR' (Data from all Seasons of Survivor (US) TV Series in Tidy Format) Datasets detailing the results, castaways, and events of each season of Survivor for the US, Australia, South Africa, New Zealand, and the UK. This includes details on the cast, voting history, immunity and reward challenges, jury votes, boot order, advantage details, and episode ratings. Use this for analysis of trends and statistics of the game. Package: r-cran-survlab Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-survival, r-cran-truncnorm Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survlab_0.1.0-1.ca2404.1_all.deb Size: 1092478 MD5sum: 91a7b3ace02056fefa693009529370ea SHA1: 19ca5aadfa9d45e373200ddfc4ec61c8929d14da SHA256: 52549d2317ff10ad5fc730e89193701377b094af96fd2aeb999575faf95500ab SHA512: e432d0da783d3dcd001d57cc868e672ab2891ac715c4130d8861bb5fd9110fbca66d3c68dee0b8b088b2c607c608c1759638dedf664bd3a24ec2d90dad7341a1 Homepage: https://cran.r-project.org/package=survlab Description: CRAN Package 'survlab' (Survival Model-Based Imputation for Laboratory Non-Detect Data) Implements survival-model-based imputation for censored laboratory measurements, including Tobit-type models with several distribution options. Suitable for data with values below detection or quantification limits, the package identifies the best-fitting distribution and produces realistic imputations that respect the censoring thresholds. Package: r-cran-survlong Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-survlong_1.5-1.ca2404.1_all.deb Size: 109252 MD5sum: 605c49172470d73f16460dccc24341fa SHA1: 272ad06a23ffeadae85fa2cb94cdd1019a46368b SHA256: 9682889e5c8de19f4d86fb6b26b6aabcf81a185cf986ccdb781a62f4214d8f6f SHA512: 97a0e10f1b3bbc2e5ade254405c19caf762f75c3051282a1bb640527a09bba43fcc244b4297bf7077844085c3dd83970f781203f0f424d935c41b0695b3b6ab0 Homepage: https://cran.r-project.org/package=SurvLong Description: CRAN Package 'SurvLong' (Analysis of Proportional Hazards Model with Sparse LongitudinalCovariates) Provides kernel weighting methods for estimation of proportional hazards models with intermittently observed longitudinal covariates. Cao H., Churpek M. M., Zeng D., and Fine J. P. (2015) . Package: r-cran-survma Architecture: all Version: 1.6.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-maxlik, r-cran-pec, r-cran-quadprog Filename: pool/dists/noble/main/r-cran-survma_1.6.8-1.ca2404.1_all.deb Size: 80756 MD5sum: ad03f59c8d9e462790974dedc246d0f1 SHA1: 645474867b1aa53a0c9e07e1c0f71a4451288b5c SHA256: f8f9e126efb555663aa2e88531b3596a4353dc3fd44d2ede5502bef1d087533f SHA512: 8f00f28e52d0a813547d4aca2921a06dac3bfb95bdd664cf7cd51a6369aeba5ca2b09b7af57378161e48ce8af772e039ae8fba89e714083a206504cd22fbb559 Homepage: https://cran.r-project.org/package=SurvMA Description: CRAN Package 'SurvMA' (Model Averaging Prediction of Personalized SurvivalProbabilities) Provide model averaging-based approaches that can be used to predict personalized survival probabilities. The key underlying idea is to approximate the conditional survival function using a weighted average of multiple candidate models. Two scenarios of candidate models are allowed: (Scenario 1) partial linear Cox model and (Scenario 2) time-varying coefficient Cox model. A reference of the underlying methods is Li and Wang (2023) . Package: r-cran-survmetrics Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survminer, r-cran-survival, r-cran-mass, r-cran-pec Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-caret, r-cran-ggplot2, r-cran-ggpubr, r-cran-randomforestsrc, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survmetrics_0.5.1-1.ca2404.1_all.deb Size: 99064 MD5sum: 7f389c7e0aaa4c08f86950cb91937d46 SHA1: 84c2fb60bb00774e13bf952e90b3bd1a66bdabba SHA256: 13d1d4a707b819ea277095216f98711181a70d1d800a0a5b3df65ef423a74ac7 SHA512: 2cbeeb1a6f2459f8bef71104ffbdf9b4bcffcd31ea892fc1a4561b0986be4b3c8df009799383e43473d786adbab9a0514e057abbc4c4c3202a91aa29b6178001 Homepage: https://cran.r-project.org/package=SurvMetrics Description: CRAN Package 'SurvMetrics' (Predictive Evaluation Metrics in Survival Analysis) An implementation of popular evaluation metrics that are commonly used in survival prediction including Concordance Index, Brier Score, Integrated Brier Score, Integrated Square Error, Integrated Absolute Error and Mean Absolute Error. For a detailed information, see (Ishwaran H, Kogalur UB, Blackstone EH and Lauer MS (2008) ) , (Moradian H, Larocque D and Bellavance F (2017) ), (Hanpu Zhou, Hong Wang, Sizheng Wang and Yi Zou (2023) ) for different evaluation metrics. Package: r-cran-survmi Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-zoo Filename: pool/dists/noble/main/r-cran-survmi_0.1.0-1.ca2404.1_all.deb Size: 97262 MD5sum: ccf576ca7d33b0511ff4c7100039b7b9 SHA1: 2c2aa0f2dc89c3fa792ff7c063d7a755891ce231 SHA256: 69d8db53fb163d833f57c12849af192c72e5171aac039ccfe05e3c412d091530 SHA512: f0fc70f0909d0ed78dc24acf411cba931652358e17bf404ce1c60b237ecfca1ac7852a62866bf9727e54ec604afdb854d55b81fd15822e236fa70d74001ccfc9 Homepage: https://cran.r-project.org/package=SurvMI Description: CRAN Package 'SurvMI' (Multiple Imputation Method in Survival Analysis) In clinical trials, endpoints are sometimes evaluated with uncertainty. Adjudication is commonly adopted to ensure the study integrity. We propose to use multiple imputation (MI) introduced by Robin (1987) to incorporate these uncertainties if reasonable event probabilities were provided. The method has been applied to Cox Proportional Hazard (PH) model, Kaplan-Meier (KM) estimation and Log-rank test in this package. Moreover, weighted estimations discussed in Cook (2004) were also implemented with weights calculated from event probabilities. In conclusion, this package can handle time-to-event analysis if events presented with uncertainty by different methods. Package: r-cran-survminer Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6080 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpubr, r-cran-gridextra, r-cran-magrittr, r-cran-maxstat, r-cran-scales, r-cran-survival, r-cran-broom, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-tibble, r-cran-rlang, r-cran-ggtext Suggests: r-cran-knitr, r-cran-flexsurv, r-cran-cmprsk, r-cran-markdown, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-survminer_0.5.2-1.ca2404.1_all.deb Size: 2125796 MD5sum: f6cc745c302cf7df3abce4c4b70a2c26 SHA1: e9d8708741de5314991759b3baca530984401c7e SHA256: 0a12c74b84efdf226b1930cd405e0f1cb979468ac4b636b699bee749cae14a98 SHA512: 864208290c35efe495325be6deb20b1dc0140a6295092c5ad76ad4db74f6b3ad24b805b369705f74e43c452a2414b379818f4d55bd3bc5f461cb8943c100d3fd Homepage: https://cran.r-project.org/package=survminer Description: CRAN Package 'survminer' (Drawing Survival Curves using 'ggplot2') Contains the function 'ggsurvplot()' for drawing easily beautiful and 'ready-to-publish' survival curves with the 'number at risk' table and 'censoring count plot'. 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Package: r-cran-survmixer Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-survmixer_1.3-1.ca2404.1_all.deb Size: 47184 MD5sum: da48f9c24486c9bcebd5874e536d897d SHA1: a041839a89d63a988d42592f8cda2ecb89635d1c SHA256: d1b8039c879bd88cc6e04872eb143e8b11a929e22f46287bfcf1930ade1d56e3 SHA512: 8d1b61889fdfb431a2e37f7d6ec844f44d06aef0dec55297342d392b6722960e92b4ee72ea90bb37ef27d022900577b4eed2c0069d4eef9c2bcf20e740149861 Homepage: https://cran.r-project.org/package=survmixer Description: CRAN Package 'survmixer' (Design of Clinical Trials with Survival Endpoints Based onBinary Responses) Sample size and effect size calculations for survival endpoints based on mixture survival-by-response model. The methods implemented can be found in Bofill, Shen & Gómez (2021) . 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(2010) proposed a Bayesian approach using complicated hierarchical models. Besides, frequentist approaches have been alternative standard methods for the statistical analyses of network meta-analysis, and the methodology has been well established. We proposed an easy-to-implement method for the network meta-analysis based on the frequentist framework in Noma and Maruo (2025) . This package involves some convenient functions to implement the simple synthesis method. Package: r-cran-survobj Architecture: all Version: 3.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 968 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survobj_3.1.1-1.ca2404.1_all.deb Size: 620998 MD5sum: 141695bbe71fb3edca349ae0a064306e SHA1: 3cf1fcba3282ea354f0f8eeb5a599e83196811f2 SHA256: 4ca0b339ffe6834554d71a05325ea25834f9b9109745723dea5e1e7207e0983d SHA512: f36c9f6405a5b0cc5e38d1e7742ff08d04d55813c576fa6aa508477a25a95a5c9caf6e798f0be96f9ef912438e6b5788f75cef70222874e7e1eb2295bfb75cbd Homepage: https://cran.r-project.org/package=survobj Description: CRAN Package 'survobj' (Objects to Simulate Survival Times) Generate objects that simulate survival times. Random values for the distributions are generated using the method described by Bender (2003) and Leemis (1987) in Operations Research, 35(6), 892–894. 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Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions. Hubeaux, S. and Rufibach, K. (2014) . Package: r-cran-survregvb Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayestestr, r-cran-invgamma Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-survival Filename: pool/dists/noble/main/r-cran-survregvb_0.0.2-1.ca2404.1_all.deb Size: 139442 MD5sum: d0086f550ebb1894e1a5eefa3c6bb609 SHA1: 2ff169bd813564309d298660e3be61d3e62d4248 SHA256: 806af6c890bbf14aeb1bed5b99f9f46805602a926c40df883b1d7561fc2f9f4e SHA512: c9e94fdcc2bab504add140b3d623ed46d622877e25bd0c40010caa8dac0686a248da8fe87d2dbe6375dd20e465399b990066fd1a9dca833d84d6ae7ed0947901 Homepage: https://cran.r-project.org/package=survregVB Description: CRAN Package 'survregVB' (Variational Bayesian Analysis of Survival Data) Implements Bayesian inference in accelerated failure time (AFT) models for right-censored survival times assuming a log-logistic distribution. Details of the variational Bayes algorithms, with and without shared frailty, are described in Xian et al. (2024) and Xian et al. (2024) , respectively. Package: r-cran-survrm2 Architecture: all Version: 1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-survrm2_1.0-4-1.ca2404.1_all.deb Size: 178914 MD5sum: 3d6095eba8aa505a3f78d857f670b91d SHA1: f91799bbbfe8ee6758d75dd9a78728eef083c541 SHA256: 495a3642c881a5854065ae7ffd598070e0a4a7113c5e4890e9b252ed68669497 SHA512: 68667356d7ede3bf30816df9434df973c6c8d726f0ac8659b7f2ddd6d9c39d73a52a1f67b502ae70402253fe1e49ae407049d768c8f4eb50364eb138dc122cca Homepage: https://cran.r-project.org/package=survRM2 Description: CRAN Package 'survRM2' (Comparing Restricted Mean Survival Time) Performs two-sample comparisons using the restricted mean survival time (RMST) as a summary measure of the survival time distribution. Three kinds of between-group contrast metrics (i.e., the difference in RMST, the ratio of RMST and the ratio of the restricted mean time lost (RMTL)) are computed. It performs an ANCOVA-type covariate adjustment as well as unadjusted analyses for those measures. Package: r-cran-survrm2adapt Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-survrm2adapt_1.1.0-1.ca2404.1_all.deb Size: 100044 MD5sum: 1e63a9cba020f2e7f7d39ea4b37f8630 SHA1: f19f9e9978e4a45bab0d4a895197001a875e053a SHA256: 1c4711226e36741996bc6c252a8bcfb4ad10bb462869d75408b2974717e8e6d5 SHA512: 37ac07390831ac0461785d39d9adf2c6723f01551c986c81f9fdbaccca96110751c065b2b340fa3c006d0ae90ed7ecbdf9169f46338fb59782845539a8d92310 Homepage: https://cran.r-project.org/package=survRM2adapt Description: CRAN Package 'survRM2adapt' (Flexible and Coherent Test/Estimation Procedure Based onRestricted Mean Survival Times) Estimates the restricted mean survival time (RMST) with the time window [0, tau], where tau is adaptively selected from the procedure, proposed by Horiguchi et al. (2018) . It also estimates the RMST with the time window [tau1, tau2], where tau1 is adaptively selected from the procedure, proposed by Horiguchi et al. (2023) . Package: r-cran-survrm2perm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-survrm2perm_0.1.0-1.ca2404.1_all.deb Size: 52030 MD5sum: b48d6bebf87e064fc92f742564a19749 SHA1: 9bda7912d0e4ee9c53aa48a83c16e11c98026756 SHA256: bc99d9d1faadd91442f720031baec3f36974096ca2b517813b1be9123a17a6c8 SHA512: 596f1a603a44eb472ec370117b1961f515ba5e3552667c116dafbd2f63b110af562b1874f14f87d1a7c0e117d0deba9bd2b590cb2fe3bdf7b27e3926a6b6e554 Homepage: https://cran.r-project.org/package=survRM2perm Description: CRAN Package 'survRM2perm' (Permutation Test for Comparing Restricted Mean Survival Time) Performs the permutation test using difference in the restricted mean survival time (RMST) between groups as a summary measure of the survival time distribution. When the sample size is less than 50 per group, it has been shown that there is non-negligible inflation of the type I error rate in the commonly used asymptotic test for the RMST comparison. Generally, permutation tests can be useful in such a situation. However, when we apply the permutation test for the RMST comparison, particularly in small sample situations, there are some cases where the survival function in either group cannot be defined due to censoring in the permutation process. Horiguchi and Uno (2020) have examined six workable solutions to handle this numerical issue. It performs permutation tests with implementation of the six methods outlined in the paper when the numerical issue arises during the permutation process. The result of the asymptotic test is also provided for a reference. Package: r-cran-survsakk Architecture: all Version: 1.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 836 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-survsakk_1.3.3-1.ca2404.1_all.deb Size: 524968 MD5sum: 017d380523261341f2ef01abbe476815 SHA1: a7f05588c7d1dedfdebd39050f8763b6af0c7b15 SHA256: b0149c0a78bd24706f7382b817ab4121fb92a0074f52de0c28286745f6450bf4 SHA512: 6e838b5ac6652e72c817272cbd8b6c0515d91635b05aed4ab183ab5b845e9ccfc6b559ed3cefc032c984ab40d43355cb95bce80f8cd651fd430101770d1414de Homepage: https://cran.r-project.org/package=survSAKK Description: CRAN Package 'survSAKK' (Create Publication Ready Kaplan-Meier Plots) Incorporate various statistics and layout customization options to enhance the efficiency and adaptability of the Kaplan-Meier plots. Package: r-cran-survsens Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-interp, r-cran-metr, r-cran-reshape2, r-cran-survival Filename: pool/dists/noble/main/r-cran-survsens_1.1.0-1.ca2404.1_all.deb Size: 125554 MD5sum: 6772311aaa83aff176ff07325e907d1d SHA1: f8a33b6663c15217a3d2c149194118eeca4a8318 SHA256: 2058aa1c3725b4cd86a10ad8c212434a5bdcf31f69007aa10654be3f6fa86f4f SHA512: e11edb508af3fa984f31a43d96d3ab91e2b925835085ada96ec083c54122786c7c021804eedd4521a797d614828240cc129df96306d83f6bde66021d7e4cfe0e Homepage: https://cran.r-project.org/package=survSens Description: CRAN Package 'survSens' (Sensitivity Analysis with Time-to-Event Outcomes) Performs a dual-parameter sensitivity analysis of treatment effect to unmeasured confounding in observational studies with either survival or competing risks outcomes. Huang, R., Xu, R. and Dulai, P.S.(2020) . Package: r-cran-survsim Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-eha, r-cran-statmod Filename: pool/dists/noble/main/r-cran-survsim_1.1.8-1.ca2404.1_all.deb Size: 125984 MD5sum: b67a21db79495078c7c44f4f68f8b00c SHA1: 2ca9456afa39b424c5396e9893dd169a921eb652 SHA256: af0e8b856bc6d7c0fa3cd7453a01a3b04b1fc7fc86fc808ec566aaf7a1efaa7c SHA512: 91cc24cec621d08b10da7a37c34bb3bfafdd52d64c28754b15c6c44ac9c5ebf5f946bd01bdb16e6a006c8a269884abda552347f49ed6aa09637000089a7ad720 Homepage: https://cran.r-project.org/package=survsim Description: CRAN Package 'survsim' (Simulation of Simple and Complex Survival Data) Simulation of simple and complex survival data including recurrent and multiple events and competing risks. See Moriña D, Navarro A. (2014) and Moriña D, Navarro A. (2017) . Package: r-cran-survsparse Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-mass, r-cran-nloptr, r-cran-nleqslv, r-cran-tibble, r-cran-foreach, r-cran-gaussquad, r-cran-purrr Filename: pool/dists/noble/main/r-cran-survsparse_0.1-1.ca2404.1_all.deb Size: 64214 MD5sum: 887ddac905244514271e170c3fe0afde SHA1: 4f3fbbc939eaf1fead9092ab8c8fe9fd2c214b8f SHA256: edb24be8b7d536d857c6e004a1cb8be738bd4cfc2f8db0d3eec2be60c9de56ce SHA512: 1e96cdb76f67ca068bd0a898f93af1867b3a3e97db2f38f906ed3d404fc18f7db98947c7b5c3007d8a36994cefd48f12356bd71c93e1138161d15a3fcb14553b Homepage: https://cran.r-project.org/package=SurvSparse Description: CRAN Package 'SurvSparse' (Survival Analysis with Sparse Longitudinal Covariates) Survival analysis with sparse longitudinal covariates under right censoring scheme. Different hazards models are involved. Please cite the manuscripts corresponding to this package: Sun, Z. et al. (2022) , Sun, Z. and Cao, H. (2023) and Sun, D. et al. (2023) . Package: r-cran-survspearman Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-survspearman_1.0.1-1.ca2404.1_all.deb Size: 315476 MD5sum: a28535a50e6759c4b5037aed95665fd0 SHA1: ba258e2e9e288b08453ffce61a6feb79945f20f8 SHA256: 89786d4f4a790fa81e60c7b30014c7f33ceddd389d3ce112b2495a73b9f0d5a4 SHA512: e03b8caeeff92edc000f3a94b41b2f00a6fcd2e70d7820d9589e83feef7bb65cb8d7c44b6fd366856f997357029d3884ce8ee27d31f4e8f05967df73b23ad36f Homepage: https://cran.r-project.org/package=survSpearman Description: CRAN Package 'survSpearman' (Nonparametric Spearman's Correlation for Survival Data) Nonparametric estimation of Spearman's rank correlation with bivariate survival (right-censored) data as described in Eden, S.K., Li, C., Shepherd B.E. 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Package: r-cran-survtrunc Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-survtrunc_0.2.0-1.ca2404.1_all.deb Size: 76782 MD5sum: 58c665814ca947c7c2160d403c291c45 SHA1: 288a1a0e411b162bdad34df41f9660dafa63eeea SHA256: 1ba5b137a86a07a0b889520a728a10d738611d489829cd6d36167302dc9eaad4 SHA512: 4469bf2d3d8f553728c6160653bd0204853fc21e15a3bcccd58955466553dc07771952b074bf5e6d7051aae799dac169a12789ab52772df28a433bdc690ba334 Homepage: https://cran.r-project.org/package=SurvTrunc Description: CRAN Package 'SurvTrunc' (Analysis of Doubly Truncated Data) Package performs Cox regression and survival distribution function estimation when the survival times are subject to double truncation. In case that the survival and truncation times are quasi-independent, the estimation procedure for each method involves inverse probability weighting, where the weights correspond to the inverse of the selection probabilities and are estimated using the survival times and truncation times only. A test for checking this independence assumption is also included in this package. The functions available in this package for Cox regression, survival distribution function estimation, and testing independence under double truncation are based on the following methods, respectively: Rennert and Xie (2018) , Shen (2010) , Martin and Betensky (2005) . When the survival times are dependent on at least one of the truncation times, an EM algorithm is employed to obtain point estimates for the regression coefficients. The standard errors are calculated using the bootstrap method. See Rennert and Xie (2022) . Both the independent and dependent cases assume no censoring is present in the data. Please contact Lior Rennert for questions regarding function coxDT and Yidan Shi for questions regarding function coxDTdep. Package: r-cran-susenas Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1295 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl Filename: pool/dists/noble/main/r-cran-susenas_0.1.0-1.ca2404.1_all.deb Size: 1245902 MD5sum: e8dda19fc87d75abfb38d98ee4188ab0 SHA1: 93c0e74c0384042044c3ea8d05e4257571de6c36 SHA256: 95f6a64f68e74335880fd5657bff53b704947919e025557b7fbdd216778c9288 SHA512: 8bbf737cd0a81bc071aedc163e606b386c1027e06e2ab2acfd935849beab0acfb276b0f43be76139064fa8a451153a3b37b760c13f34c6ba6b4379d052bf880a Homepage: https://cran.r-project.org/package=SUSENAS Description: CRAN Package 'SUSENAS' (National Socio-Economic Survey Data Collection Indonesia) Survey to collect data about the social and economic conditions of Indonesian society. 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Package: r-cran-susier Architecture: all Version: 0.14.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2707 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-matrixstats, r-cran-mixsqp, r-cran-reshape, r-cran-crayon, r-cran-ggplot2 Suggests: r-cran-curl, r-cran-testthat, r-cran-microbenchmark, r-cran-knitr, r-cran-rmarkdown, r-cran-rfast, r-cran-cowplot Filename: pool/dists/noble/main/r-cran-susier_0.14.2-1.ca2404.1_all.deb Size: 2025790 MD5sum: 05fb58ce272f06f05732d46f9b585b47 SHA1: 2779703e84e6bc7653b20116326e6bc492045c20 SHA256: 586303b533357ca3d36ec2e3598e1860c828fc2e99deb7d4eee276f7ea599887 SHA512: dd20dc77b08d5bc69a0bcde87f66f9ab8904d4915035bdc4b98bae2e243d89f14ece5122424228d9253e0cbcecfc96df323bb6b584d75549e0811b243821498a Homepage: https://cran.r-project.org/package=susieR Description: CRAN Package 'susieR' (Sum of Single Effects Linear Regression) Implements methods for variable selection in linear regression based on the "Sum of Single Effects" (SuSiE) model, as described in Wang et al (2020) and Zou et al (2021) . These methods provide simple summaries, called "Credible Sets", for accurately quantifying uncertainty in which variables should be selected. The methods are motivated by genetic fine-mapping applications, and are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse. The fitting algorithm, a Bayesian analogue of stepwise selection methods called "Iterative Bayesian Stepwise Selection" (IBSS), is simple and fast, allowing the SuSiE model be fit to large data sets (thousands of samples and hundreds of thousands of variables). 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Package: r-cran-susy Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-gtools Filename: pool/dists/noble/main/r-cran-susy_0.1.0-1.ca2404.1_all.deb Size: 67244 MD5sum: 8a9b5e362204dc4f5d7379a071a6d008 SHA1: eee9800304196e07d1fae8df8d5ac75c5c23625f SHA256: e9178fc15ce1a7e5895d25ae90f9d312c32db27d19ad8b006e7dae254ee57665 SHA512: 09ff90a621d4670ddba23f9c2a4138b27921acadd7b25d8d81b132e063dd4c317b2ace8db1459cf20223dea8f3131c3ace570bd1267c43b2b908c2ff365f0d92 Homepage: https://cran.r-project.org/package=SUSY Description: CRAN Package 'SUSY' (Surrogate Synchrony) Computes synchrony as windowed cross-correlation based on two-dimensional time series in a text file you can upload. 'SUSY' works as described in Tschacher & Meier (2020) . 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To overcome this issue, this study offers Spearman Variational Mode Decomposition (SVMD), a method that uses the Spearman correlation coefficient to calculate the ideal mode number. Unlike the Pearson correlation coefficient, which only returns a perfect value when X and Y are linearly connected, the Spearman correlation can be calculated without knowing the probability distributions of X and Y. The Spearman correlation coefficient, also called Spearman's rank correlation coefficient, is a subset of a wider correlation coefficient. As VMD decomposes a signal, the Spearman correlation coefficient between the reconstructed and original sequences rises as the mode number K increases. Once the signal has been fully decomposed, subsequent increases in K cause the correlation to gradually level off. When the correlation reaches a specific level, VMD is said to have adequately decomposed the signal. Numerous experiments revealed that a threshold of 0.997 produces the best denoising effect, so the threshold is set at 0.997. This package has been developed using concept of Yang et al. (2021). 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As described in Capanu (2015) , the package contains functions to create the nomogram, validate it using bootstrap, as well as produce the calibration plots. Package: r-cran-svyroc Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-svyvarsel Filename: pool/dists/noble/main/r-cran-svyroc_1.1.0-1.ca2404.1_all.deb Size: 425776 MD5sum: 545b2cad51e26a3a8f92a33ae7788fdc SHA1: ffbc1669d7e50bcb30ef5d78eef9caf9b82ba7d5 SHA256: 617b7c75fdf8adbb7d5b0bc596fa206560467b20288b0d9f0366904b50c29aad SHA512: 431b0b01972cfba2b26f4eececd2be69f95b144a86a940a6d141a206323f141d918c909520c48e2e363b64debfaacb9eb4ade719604744dd420aeb52152c5500 Homepage: https://cran.r-project.org/package=svyROC Description: CRAN Package 'svyROC' (Estimation of the ROC Curve and the AUC for Complex Survey Data) Estimate the receiver operating characteristic (ROC) curve, area under the curve (AUC) and optimal cut-off points for individual classification taking into account complex sampling designs when working with complex survey data. Methods implemented in this package are described in: A. Iparragirre, I. Barrio, I. Arostegui (2024) ; A. Iparragirre, I. Barrio, J. Aramendi, I. Arostegui (2022) ; A. Iparragirre, I. Barrio (2024) . Package: r-cran-svyvarsel Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 741 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survey, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-svyvarsel_1.0.1-1.ca2404.1_all.deb Size: 716094 MD5sum: 820a6b9924d88a1248e3a371120a4029 SHA1: b1abdf7bb9931ca1838a1c69b251728d17df787a SHA256: b56db17e3388718002d89964d385ccacb7ba3c0c31800d922b360fcf4c5b84e5 SHA512: 64c33de48482e479670aea2c08fd6be7f6b2966fbde675e0ecc0770009489e23bb16540667bb7a82bf43beaec5d43745ef792a97fe098dce4ef71a64025a46cc Homepage: https://cran.r-project.org/package=svyVarSel Description: CRAN Package 'svyVarSel' (Variable Selection for Complex Survey Data) Fit design-based linear and logistic elastic nets with complex survey data considering the sampling design when defining training and test sets using replicate weights. Methods implemented in this package are described in: A. Iparragirre, T. Lumley, I. Barrio, I. Arostegui (2024) . Package: r-cran-svyvgam Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vgam, r-cran-survey Suggests: r-cran-pscl, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-svyvgam_1.3-1.ca2404.1_all.deb Size: 76030 MD5sum: 623721629aef6c5be0836b9d2a884d89 SHA1: 34ae7a52ac00627e2bec91cab36260e3dae57025 SHA256: 551e5974a968f436e0f19add315f8e80b24eb52b1ba54308962a8a8530ce8d0f SHA512: 3f30af8653877c36302e326e861b4f0cf8c8fb33e33196407b0040305f5134d1ce4a0d58bfc9b0c8c15c24e0e05590f28d59177a438e8dff97038f3eb6efc9d6 Homepage: https://cran.r-project.org/package=svyVGAM Description: CRAN Package 'svyVGAM' (Design-Based Inference in Vector Generalised Linear Models) Provides inference based on the survey package for the wide range of parametric models in the 'VGAM' package. Package: r-cran-svyweight Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-gdata Suggests: r-cran-dplyr, r-cran-srvyr, r-cran-testthat, r-cran-mice Filename: pool/dists/noble/main/r-cran-svyweight_0.1.1-1.ca2404.1_all.deb Size: 99902 MD5sum: a13a7fb5f26eaf841509e827ea8a773d SHA1: d08c12aea2cfce2bee58b6aea4154778b0dc57ee SHA256: 3012313cb0a088dd4835af074af42cdd0c1bcd944fd3416d74427cadabec4030 SHA512: db423d50a1e3a9e79f08b189f0cd98114d452ac106fb8de014f932f2829bbb094fcf6d4220d396761ae14785be8a0d114a833a1f1d9f7db132c6e66ddf18a6bb Homepage: https://cran.r-project.org/package=svyweight Description: CRAN Package 'svyweight' (Quick and Flexible Survey Weighting) Quickly and flexibly calculates weights for survey data, in order to correct for survey non-response or other sampling issues. Uses rake weighting, a common technique also know as rim weighting or iterative proportional fitting. This technique allows for weighting on multiple variables, even when the interlocked distribution of the two variables is not known. Interacts with Thomas Lumley's 'survey' package, as described in Lumley, Thomas (2011, ISBN:978-1-118-21093-2). Adds additional functionality, more adaptable syntax, and error-checking to the base weighting functionality in 'survey.' 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Variables are scored based on the AUC and the t-statistics. Variables then enter a competition and the semi-finalist variables will be evaluated in a final round of LDA classification. The algorithm then outputs a list of variable selected. Qiao, Sun and Fan (2017) . 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Package: r-cran-swaprinc Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-broom.mixed, r-cran-dplyr, r-cran-gifi, r-cran-lme4, r-cran-magrittr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-swaprinc_1.0.1-1.ca2404.1_all.deb Size: 43434 MD5sum: df4422107f08851e4b05acb8f70311cf SHA1: 14fd94828112e3cdc6db621d7317f7dd939d0f9d SHA256: 799cd774286b817c1448de6763c422fe27a74c60e68e3706445094910d89e568 SHA512: d02527856d8d4eb015b9e4bb88cf51ff68fa642165bd5c43e6cee1de8ad71fa5e6029abbf2f0c6a80a0b04c6033447c4affb1fdfac2911fdd3b32f5bd9e520da Homepage: https://cran.r-project.org/package=swaprinc Description: CRAN Package 'swaprinc' (Swap Principal Components into Regression Models) Obtaining accurate and stable estimates of regression coefficients can be challenging when the suggested statistical model has issues related to multicollinearity, convergence, or overfitting. One solution is to use principal component analysis (PCA) results in the regression, as discussed in Chan and Park (2005) . The swaprinc() package streamlines comparisons between a raw regression model with the full set of raw independent variables and a principal component regression model where principal components are estimated on a subset of the independent variables, then swapped into the regression model in place of those variables. The swaprinc() function compares one raw regression model to one principal component regression model, while the compswap() function compares one raw regression model to many principal component regression models. Package functions include parameters to center, scale, and undo centering and scaling, as described by Harvey and Hansen (2022) . Additionally, the package supports using Gifi methods to extract principal components from categorical variables, as outlined by Rossiter (2021) . 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The swarm space concept, the metrics and data sets included are described in: Papadopoulou Marina, Furtbauer Ines, O'Bryan Lisa R., Garnier Simon, Georgopoulou Dimitra G., Bracken Anna M., Christensen Charlotte and King Andrew J. (2023) . Package: r-cran-swash Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5645 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate, r-cran-sf, r-cran-spdep, r-cran-zoo, r-cran-strucchange Filename: pool/dists/noble/main/r-cran-swash_2.0.0-1.ca2404.1_all.deb Size: 5575034 MD5sum: b61e363643817e310f4c7d8d3bfc1a34 SHA1: c102c69efc1b2bad41d90462eef7b26d27bcac93 SHA256: 9014a1f748f785fd80a8e6f9b45f3c6de347e1b5eef1846f15eaa5c9e0d7459d SHA512: 25b1b8efc9a28c900623df003c50434fc2a10c14a1c94fd19be571361cefc7d2399cefe84e91720b20f6217a213a6ad6aed6e3718980578c171aef5576b9c9a3 Homepage: https://cran.r-project.org/package=swash Description: CRAN Package 'swash' (Health Geography Toolbox for Model-Based Analysis of InfectionsPanel Data) Within epidemic outbreaks, infections grow and decline differently between regions, and the velocity of spatial spread differs between countries. 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Implements benefit-cost ratio (BCR), net present value (NPV), internal rate of return (IRR) via the bisection method of Brent (1973, ISBN:9780130223715), modified BCR, marginal rate of return using the CIMMYT (1988, ISBN:9686127127) method, payback period, soil loss economic valuation via the Universal Soil Loss Equation of Wischmeier and Smith (1978, ISBN:0160016258), groundwater recharge valuation, employment generation ratio, sensitivity analysis, switching value analysis, and Monte Carlo simulation. Six datasets are included: state-wise BCR benchmarks from NABARD (2019) watershed evaluations, USLE erodibility parameters for Indian soil orders from NBSS and LUP, rainfall erosivity for twenty Indian districts from IMD data, SWC unit cost norms from PMKSY-WDC (GoI 2015), and two hypothetical datasets for illustration. Methods follow Gittinger (1982, ISBN:9780801825439) and Squire and van der Tak (1975, ISBN:9780801816697). 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Package: r-cran-sweep Architecture: all Version: 0.2.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3933 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-forecast, r-cran-rlang, r-cran-tibble, r-cran-timetk Suggests: r-cran-fracdiff, r-cran-ggplot2, r-cran-knitr, r-cran-lubridate, r-cran-purrr, r-cran-readr, r-cran-rmarkdown, r-cran-scales, r-cran-stringr, r-cran-testthat, r-cran-tidyr, r-cran-tidyquant, r-cran-zoo Filename: pool/dists/noble/main/r-cran-sweep_0.2.7-1.ca2404.1_all.deb Size: 2787756 MD5sum: 87a24fd75dede581be895316f83371f6 SHA1: 9ebe60c6dd7eb1c327719f2a06a2ee9dc900f9dd SHA256: 0fb24a4cdd65687a6244dd353106db335be3a1b98154da1607c0a27abc3af650 SHA512: 23cecba2be31f67188488bf845e01899ddfd3e0d353bf339a254b30155aa7bd344843bfdf26fe481e0b4c6783c5f5fe3b30d8a3d4ac139347dac52b74daa9f53 Homepage: https://cran.r-project.org/package=sweep Description: CRAN Package 'sweep' (Tidy Tools for Forecasting) Tidies up the forecasting modeling and prediction work flow, extends the 'broom' package with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions for various forecasting models, and enables converting 'forecast' objects to "tidy" data frames with 'sw_sweep'. Package: r-cran-sweepdiscovery Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1705 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-randomforest Filename: pool/dists/noble/main/r-cran-sweepdiscovery_0.1.1-1.ca2404.1_all.deb Size: 1683686 MD5sum: 0c0839ed5fbb0811b11d9830981b4a45 SHA1: 2391d58fa7a67d15160d86db6e952da3c2f6afb3 SHA256: 2d69201c6123b2229785d5ee92190a1db26c638ee32ac30bd3a096cc7647adb7 SHA512: c07b372a98032bfbcbc01353c46ec975ed43c6b739db70e407d96c8be7f85e579529cfe1a7d18b3253058eadf320d56b8d32cb7bc9524e98e5e3493c7f0cd8ac Homepage: https://cran.r-project.org/package=SweepDiscovery Description: CRAN Package 'SweepDiscovery' (Selective Sweep Discovery Tool) Selective sweep is a biological phenomenon in which genetic variation between neighboring beneficial mutant alleles is swept away due to the effect of genetic hitchhiking. 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Package: r-cran-sweidnumbr Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-stringr, r-cran-checkmate Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-sweidnumbr_1.5.0-1.ca2404.1_all.deb Size: 130306 MD5sum: eb546374d2f6bb1746521c9042395d71 SHA1: 4db903dc6ceaf4ecc4b0c727f9566a3802fe40f2 SHA256: ac573bc97c2fa7972c25695325d419747168f003c68fdfad2f913275be8e74e5 SHA512: 8835b99698eb55159150af451dda8e9e1ebcbc03c175d7a70e676762d9444005e890f3da9f840f0e159a382b31c911badb9f3f5997d5c6c8e55d1a74be0e1b68 Homepage: https://cran.r-project.org/package=sweidnumbr Description: CRAN Package 'sweidnumbr' (Handling of Swedish Identity Numbers) Structural handling of identity numbers used in the Swedish administration such as personal identity numbers ('personnummer') and organizational identity numbers ('organisationsnummer'). 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Package: r-cran-swgee Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gee, r-cran-geepack, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-swgee_1.4-1.ca2404.1_all.deb Size: 58844 MD5sum: 4c0a91cf3941488818a17378059e5620 SHA1: 3490dd47482c3cf999ad35571c853446bc7a166d SHA256: 72df68bfaa286bddcc66a4ecbb860798343969364534aefd1ed97936cd5198cd SHA512: 402e812a0b168fd604048424f6bed9f85af00ad3ee3d570ed6327f28e158fe5091873deff4ff75dd9beafe29e3f8c29cbdfb101ec08e33cba5c5ebca1e89e960 Homepage: https://cran.r-project.org/package=swgee Description: CRAN Package 'swgee' (Simulation Extrapolation Inverse Probability WeightedGeneralized Estimating Equations) Simulation extrapolation and inverse probability weighted generalized estimating equations method for longitudinal data with missing observations and measurement error in covariates. References: Yi, G. Y. 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Package: r-cran-swissparl Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-jsonlite, r-cran-magrittr, r-cran-purrr, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-crayon, r-cran-httr, r-cran-ggplot2, r-cran-glue Filename: pool/dists/noble/main/r-cran-swissparl_0.3.0-1.ca2404.1_all.deb Size: 105368 MD5sum: 85d987f922bcc3295131fb70259a8e88 SHA1: 91978bf7408c34594445379b1d53e5999befd6d0 SHA256: 6b46cac897cc9cfc5ed0e66935766ee7cf3770a27447629701a71b7abeaf3330 SHA512: 0d5efef3cf317a2340d68db451d202e3fdfc393692d8f2d0736e7884abb965d22b0d4522b6cf5b48eac61e87cf2d04c51a98a67b4e899bee41227d686143f4de Homepage: https://cran.r-project.org/package=swissparl Description: CRAN Package 'swissparl' (Interface to Swiss Parliament Web Services and the'OpenParlData' API) Provides machine-readable access to parliamentary data of the Swiss Federal Assembly via the 'OData' interface () and the 'OpenParlData' REST API (), which also offers harmonized data for selected cantonal and municipal parliaments. 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These variables are often correlated, and while PCA-based indices can address the issue of multicollinearity, they typically do not account for survey weights, which can lead to inaccurate rankings of survey units such as households, districts, or states. To resolve this, the current package facilitates the development of a principal component analysis-based composite index by incorporating survey weights for each sample observation. This ensures the generation of a survey-weighted principal component-based normalized composite index. Additionally, the package provides a normalized principal component-based composite index and ranks the sample observations based on the values of the composite indices. For method details see, Skinner, C. J., Holmes, D. J. and Smith, T. M. F. (1986) , Singh, D., Basak, P., Kumar, R. and Ahmad, T. (2023) . 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Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the package, please consider subscribing to the koRpus-dev mailing list (). 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Package: r-cran-sym.arma Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-sym.arma_1.0-1.ca2404.1_all.deb Size: 138410 MD5sum: 83da318abd4a4a3e90dabc13367ee0ad SHA1: 75f768152b5bade9f6f9f6537b4d7c43b1ac3005 SHA256: 9c9f3504c050acd946e02e556da0e1f1f719178f91dc4c9e730f9f44f4782502 SHA512: a728ec1c3b3c03c01d159c73aff3146d3cf1bb22f94126e452c9e23e23e78f4bff930987c170cb0f0fa28a36b0572396816009b886c6e1d773451f66c58682da Homepage: https://cran.r-project.org/package=sym.arma Description: CRAN Package 'sym.arma' (Autoregressive and Moving Average Symmetric Models) Functions for fitting the Autoregressive and Moving Average Symmetric Model for univariate time series introduced by Maior and Cysneiros (2018), . Fitting method: conditional maximum likelihood estimation. 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(Milligan, G.W., Cooper, M.C. (1985) , Breiman, L. (1996), , Hubert, L., Arabie, P. (1985), , Ichino, M., & Yaguchi, H. (1994), , Rand, W.M. (1971) , Breckenridge, J.N. (2000) , Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) , Dudek, A. (2007), ). 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Available functions to download SYNOP messages from Ogimet if needed. The decoding logic follows the specifications defined in the World Meteorological Organization (2019) "Manual on Codes, Volume I.1 (WMO-No. 306)". 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Package: r-cran-syntaxr Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-covr, r-cran-haven, r-cran-testthat Filename: pool/dists/noble/main/r-cran-syntaxr_0.8.0-1.ca2404.1_all.deb Size: 24280 MD5sum: a9fe018cb136734ffcffa3695f96cd8b SHA1: 6329a814cc0a9e55a023c17f63b09073fc9b5ea0 SHA256: fc43e898c108474a3d0591e8e7b0064d87d6278fdaa637d07cf636972d4e99b1 SHA512: f808540176ed4fb10616cd1c4b018c23af4b51504f528b78b788fb7e4552563a25558b07714d4ae3d33ed94ccca2c917a82c714eb6e51fd883f6637cb9ea31cb Homepage: https://cran.r-project.org/package=syntaxr Description: CRAN Package 'syntaxr' (An 'SPSS' Syntax Generator for Multi-Variable Manipulation) A set of functions for generating 'SPSS' syntax files from the R environment. Package: r-cran-syntenyplotter Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2862 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-syntenyplotter_1.0.0-1.ca2404.1_all.deb Size: 2336804 MD5sum: 29477993db9cbba8d05c29a583dcb4b1 SHA1: c8f4566ec575ff7acab04eef7a4665e2db9a1f22 SHA256: 3e9ac2050f2528a778f12c8b35d744e24c45bcc282b50d25604db55d1262590f SHA512: c346c0323020f9e40b820dcba18a96e0296472074b958fb7167efb5152b60c30c9e7a09942dd70e9671870dbacc9a1d7c92b7de2131b16edc378972760e933d5 Homepage: https://cran.r-project.org/package=syntenyPlotteR Description: CRAN Package 'syntenyPlotteR' (Genome Synteny Visualization) Draw syntenic relationships between genome assemblies. There are 3 functions which take a tab delimited file containing alignment data for syntenic blocks between genomes to produce either a linear alignment plot, an evolution highway style plot, or a painted ideogram representing syntenic relationships. There is also a function to convert alignment data in the DESCHRAMBLER/inferCAR format to the required data structure. Package: r-cran-synth Architecture: all Version: 1.1-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernlab, r-cran-optimx, r-cran-rgenoud Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-synth_1.1-10-1.ca2404.1_all.deb Size: 130474 MD5sum: c83bfef47941f028ec7ee59c3bbb1828 SHA1: 0eab2194749612f48b8a921710d1ee63f1cf65e7 SHA256: c878edd92bc6759b512071886228d5f28613dd331e55039a98d8b735176cb35a SHA512: 77631ffb069a1737a14e1047a28b0b60b684f343921cf7b2291cf8fd1e243090b3358855011a4052bc389b6a21641f3890cddcfc7b42cfbd9d7092fd74dea30e Homepage: https://cran.r-project.org/package=Synth Description: CRAN Package 'Synth' (Synthetic Control Group Method for Comparative Case Studies) Implements the synthetic control group method for comparative case studies as described in Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010, 2011, 2014). The synthetic control method allows for effect estimation in settings where a single unit (a state, country, firm, etc.) is exposed to an event or intervention. It provides a data-driven procedure to construct synthetic control units based on a weighted combination of comparison units that approximates the characteristics of the unit that is exposed to the intervention. A combination of comparison units often provides a better comparison for the unit exposed to the intervention than any comparison unit alone. Package: r-cran-synthesis Architecture: all Version: 1.2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-zoo, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-devtools Filename: pool/dists/noble/main/r-cran-synthesis_1.2.5-1.ca2404.1_all.deb Size: 1742062 MD5sum: 6ecb96efc719090c01d1fd3b526ede82 SHA1: a0d348a3583d21591b4acd2afcbde5fb47fbc1cb SHA256: d6e4c6dbd48579a2e94d206ecea54761aeb6fea14c6402d3efb91760c9fe03b0 SHA512: efb5a1bfd2c48923325561ccd171a4dd2927ce6f75ee9acd6dd507bd88d23944d8dd14be3909d8eaa4aa5130e5844079419cc4372d7a4edce4b6c6631578ac7c Homepage: https://cran.r-project.org/package=synthesis Description: CRAN Package 'synthesis' (Generate Synthetic Data from Statistical Models) Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. Applications to testing methods can be found in Jiang, Z., Sharma, A., & Johnson, F. (2019) and Jiang, Z., Sharma, A., & Johnson, F. (2020) associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) . Package: r-cran-synthesisr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 851 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-stringdist, r-cran-stringr, r-cran-tibble, r-cran-unglue, r-cran-vroom Suggests: r-cran-cli, r-cran-devtools, r-cran-knitr, r-cran-readr, r-cran-rmarkdown, r-cran-testthat, r-cran-usethis Filename: pool/dists/noble/main/r-cran-synthesisr_0.4.1-1.ca2404.1_all.deb Size: 353198 MD5sum: 90327c0549d19e9770b31c48adfc1fe9 SHA1: c14e5e368f445106bfdbc2d3e118555e32154363 SHA256: 6a15b14931dd6bdc996e1281556da87641d38cff4227bf46985e700d74612843 SHA512: d45644a755ed0c4573b57e154d55c5c099612a8d5701de676755b25c113a12b3e36484622a5211757c7012655142a0eea02c5e769791633ed069f84f01352c83 Homepage: https://cran.r-project.org/package=synthesisr Description: CRAN Package 'synthesisr' (Import, Assemble, and Deduplicate Bibliographic Datasets) A critical first step in systematic literature reviews and mining of academic texts is to identify relevant texts from a range of sources, particularly databases such as 'Web of Science' or 'Scopus'. These databases often export in different formats or with different metadata tags. 'synthesisr' expands on the tools outlined by Westgate (2019) to import bibliographic data from a range of formats (such as 'bibtex', 'ris', or 'ciw') in a standard way, and allows merging and deduplication of the resulting dataset. Package: r-cran-synthesizer Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-simplermarkdown Filename: pool/dists/noble/main/r-cran-synthesizer_0.6.0-1.ca2404.1_all.deb Size: 124560 MD5sum: 6f005953bfbdb5522a0c7bed2dc6d1bb SHA1: 81c7d6367431514bb5cf1415ea9b5b49204163ee SHA256: 69a23a6c51daeef59f6e6c33105ecf4a2fe0c15e45d16674ba596e8d2173f9be SHA512: 1c5e1792e16f2466fbd3cff7444113b36cc9b74167be40494a6304b5408b837ae6d83ebe5033312c960e49f615cdc0324febb5b5d193799ac1606d65898ac462 Homepage: https://cran.r-project.org/package=synthesizer Description: CRAN Package 'synthesizer' (Fast, Robust, and High-Quality Synthetic Data Generation with aTuneable Privacy-Utility Trade-Off) Synthesize numeric, categorical, mixed and time series data. Data circumstances including mixed (or zero-inflated) distributions and missing data patterns are reproduced in the synthetic data. A single parameter allows balancing between high-quality synthetic data that represents correlations of the original data and lower quality but more privacy safe synthetic data without correlations. Tuning can be done per variable or for the whole dataset. Package: r-cran-synthetic Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4976 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr, r-cran-rlang, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-actuar Filename: pool/dists/noble/main/r-cran-synthetic_1.1.1-1.ca2404.1_all.deb Size: 4274048 MD5sum: 53863a1d27f0cdcd049349036d191857 SHA1: 39d23627547bc8f7c8776fabef89093999de4646 SHA256: a90bb7a6898ff1c9805ea771a365e2db3e54c38f735dd65fa375f0a644b027b7 SHA512: 8547050359a2b357f88d9f092096a50a41d229603aca8a8c0c2aabe488bb059d4bd0d6fa0c48cc0d5766721040ce938b69300508dd66642a9459933fb5d9e33b Homepage: https://cran.r-project.org/package=SynthETIC Description: CRAN Package 'SynthETIC' (Synthetic Experience Tracking Insurance Claims) Creation of an individual claims simulator which generates various features of non-life insurance claims. An initial set of test parameters, designed to mirror the experience of an Auto Liability portfolio, were set up and applied by default to generate a realistic test data set of individual claims (see vignette). The simulated data set then allows practitioners to back-test the validity of various reserving models and to prove and/or disprove certain actuarial assumptions made in claims modelling. The distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim simulator with feature control" . Package: r-cran-syntheticdata Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-syntheticdata_0.1.0-1.ca2404.1_all.deb Size: 186532 MD5sum: dde4c511bd2d93ad34c86491725820d2 SHA1: 7e6284b5577f153ba876c26292433c6309349ded SHA256: 89123f5a59785715e9c303a4bcc4b44f4675aa2e26f30a52b069aa60c793ae4c SHA512: f86bd8176d8b3bb18c51585f1f1148ec3a3164d3436776c385aa4506f72906d465826f0ef2a98d08bf3f3531bb0a73e8b3b903e1cd1c1d572614ac398f231099 Homepage: https://cran.r-project.org/package=syntheticdata Description: CRAN Package 'syntheticdata' (Synthetic Clinical Data Generation and Privacy-PreservingValidation) Generates synthetic clinical datasets that preserve statistical properties while reducing re-identification risk. Implements Gaussian copula simulation, bootstrap with noise injection, and Laplace noise perturbation, with built-in utility and privacy validation metrics. Useful for privacy-aware data sharing in multi-site clinical research. Validates synthetic data quality via distributional similarity (Kolmogorov-Smirnov), discriminative accuracy (real-vs-synthetic classifier), and nearest-neighbor privacy ratio. Methods described in Jordon et al. (2022) and Snoke et al. (2018) . Package: r-cran-synthpop Architecture: all Version: 1.9-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2099 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-mass, r-cran-nnet, r-cran-ggplot2, r-cran-rpart, r-cran-party, r-cran-foreign, r-cran-plyr, r-cran-proto, r-cran-polspline, r-cran-randomforest, r-cran-ranger, r-cran-classint, r-cran-mipfp, r-cran-survival, r-cran-stringr, r-cran-rmutil, r-cran-broman, r-cran-forcats Filename: pool/dists/noble/main/r-cran-synthpop_1.9-2-1.ca2404.1_all.deb Size: 1819130 MD5sum: 6d715f37f4dc352c51a828aec7dfbb35 SHA1: 9cf20ec07220ab002005b63079712fcfd75b3c7d SHA256: ae1e06c7edec57bf00cb5855b8a3cb412319a076301bdfba007c34f47f104baa SHA512: 2816d58d6283d84261a49ed94b58157f6ec5de6e8db0c04765ad7046be933e81f8e10ad417b2375cf29b2614a74d4b703d1701eb911b7aa019cb5e4eb645ccdb Homepage: https://cran.r-project.org/package=synthpop Description: CRAN Package 'synthpop' (Generating Synthetic Versions of Sensitive Microdata forStatistical Disclosure Control) A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data. For a description of the implemented method see Nowok, Raab and Dibben (2016) . Functions to assess identity and attribute disclosure for the original and for the synthetic data are included in the package, and their use is illustrated in a vignette on disclosure (Practical Privacy Metrics for Synthetic Data). Package: r-cran-synthreturn Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-corpcor, r-cran-data.table, r-cran-quadprog, r-cran-mirai Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-synthreturn_1.0.0-1.ca2404.1_all.deb Size: 306502 MD5sum: 704f04d74ec8281243757650bb97caa1 SHA1: 6dd1d10c949b1f93f75426e5d9f233ca5db7eda9 SHA256: a6dc62fbe7ecd380e68ede14af8596a03c65fa5f6acfd594f623929680bf1254 SHA512: 3f180c62dada698609ce0558475e8041ab3d8187dbea825422ff8462a8241d7103fd3ab5c2b54588f2e9d744afa41f7fde703fd22b45f1079882f8d8c430ae37 Homepage: https://cran.r-project.org/package=synthReturn Description: CRAN Package 'synthReturn' (Synthetic Matching Method for Returns) Implements the revised Synthetic Matching Algorithm of Kreitmeir, Lane, and Raschky (2025) , building on the original approach of Acemoglu, Johnson, Kermani, Kwak, and Mitton (2016) , to estimate the cumulative treatment effect of an event on treated firms’ stock returns. Package: r-cran-synthtools Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-rdpack Suggests: r-cran-synthpop, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-synthtools_1.0.1-1.ca2404.1_all.deb Size: 101406 MD5sum: efc52e451d32c9b036889a5f84f4f1ee SHA1: f3085f62184e5358788bdd2a2f191585380ebcdc SHA256: 19eb7a9e11eeb77946a36c42439e8c1eb4af756c2852c6993db29eba76051dff SHA512: 79702a60377537fbe881e130b651a50f423fd1f9da4ac2c0008e9fd7089688c313fb930fa20889649198bedb6fd641dc7952c19b3956c8560c4851930c551148 Homepage: https://cran.r-project.org/package=SynthTools Description: CRAN Package 'SynthTools' (Tools and Tests for Experiments with Partially Synthetic DataSets) A set of functions to support experimentation in the utility of partially synthetic data sets. All functions compare an observed data set to one or a set of partially synthetic data sets derived from the observed data to (1) check that data sets have identical attributes, (2) calculate overall and specific variable perturbation rates, (3) check for potential logical inconsistencies, and (4) calculate confidence intervals and standard errors of desired variables in multiple imputed data sets. Confidence interval and standard error formulas have options for either synthetic data sets or multiple imputed data sets. For more information on the formulas and methods used, see Reiter & Raghunathan (2007) . Package: r-cran-syrup Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bench, r-cran-callr, r-cran-dplyr, r-cran-ps, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-vctrs, r-cran-withr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-syrup_0.1.4-1.ca2404.1_all.deb Size: 110348 MD5sum: 7b706f5d4d9db17516cf23f72d22b5b9 SHA1: 18328ecc07119b119d09611e97dbcc9d6421b59e SHA256: a7d9c8e219bfebdd6bef8448f701e0f5aa952f270a543bbea17ef936c11809e0 SHA512: 681251f59d59feb1e31dcec98d35c5c05e717f07e74b63ce3c0ade415cedad4a4c799e0d5f8e860354583787cb9cad2ef301b6b52a5c6c800e620fe110bca907 Homepage: https://cran.r-project.org/package=syrup Description: CRAN Package 'syrup' (Measure Memory and CPU Usage for Parallel R Code) Measures memory and CPU usage of R code by regularly taking snapshots of calls to the system command 'ps'. The package provides an entry point (albeit coarse) to profile usage of system resources by R code run in parallel. Package: r-cran-sysagnps Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-expm, r-cran-ggplot2, r-cran-ggpubr, r-cran-magrittr, r-cran-patchwork, r-cran-purrr, r-cran-rio, r-cran-tibble, r-cran-tidyr, r-cran-rlang, r-cran-rcolorbrewer, r-cran-forcats Filename: pool/dists/noble/main/r-cran-sysagnps_1.0.0-1.ca2404.1_all.deb Size: 431436 MD5sum: 42f3839dee7bbc11f4c744616003ce6d SHA1: 76b1eb842ac11ec4b1d47ce910976ff4dfe671f6 SHA256: 6b945cae51c1b0b388e41acd1f676b25cc1df435532ff75e95a288e17573d47d SHA512: d39693cf0becafcaea978b39ed3ef4d9a9fe1307d458556584e8539ce401342102113e3df405a90bb399ebba6c3076c7b319968dd75b9d69194d8f70f4a75155 Homepage: https://cran.r-project.org/package=sysAgNPs Description: CRAN Package 'sysAgNPs' (Systematic Quantification of AgNPs to Unleash their Potentialfor Applicability) There is variation across AgNPs due to differences in characterization techniques and testing metrics employed in studies. To address this problem, we have developed a systematic evaluation framework called 'sysAgNPs'. Within this framework, Distribution Entropy (DE) is utilized to measure the uncertainty of feature categories of AgNPs, Proclivity Entropy (PE) assesses the preference of these categories, and Combination Entropy (CE) quantifies the uncertainty of feature combinations of AgNPs. Additionally, a Markov chain model is employed to examine the relationships among the sub-features of AgNPs and to determine a Transition Score (TS) scoring standard that is based on steady-state probabilities. The 'sysAgNPs' framework provides metrics for evaluating AgNPs, which helps to unravel their complexity and facilitates effective comparisons among different AgNPs, thereby advancing the scientific research and application of these AgNPs. Package: r-cran-syscselection Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-syscselection_1.0.2-1.ca2404.1_all.deb Size: 81018 MD5sum: 70a1ff5f4df56ee436b60f807b98aa3b SHA1: cd6613cbbcd8eeac98a54b43afd4cb71a90c2e5a SHA256: 0337167f32d0e66f9137280fad261c2e9b08737fe2ce4c6260d31a3217a21fc3 SHA512: 4d47024a4b64d7d55ff15fadab5b64268f0bf8ed97880ea8f20865768bb9fc08e5792d236834f4ca001873a3218bf942fcacc068b687d889fca8f6b57f24344a Homepage: https://cran.r-project.org/package=SyScSelection Description: CRAN Package 'SyScSelection' (Systematic Scenario Selection for Stress Testing) Quasi-Monte-Carlo algorithm for systematic generation of shock scenarios from an arbitrary multivariate elliptical distribution. The algorithm selects a systematic mesh of arbitrary fineness that approximately evenly covers an isoprobability ellipsoid in d dimensions (Flood, Mark D. & Korenko, George G. (2013) ). This package is the 'R' analogy to the 'Matlab' code published by Flood & Korenko in above-mentioned paper. 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Package: r-cran-tables Architecture: all Version: 0.9.33-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1101 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-cran-htmltools Suggests: r-cran-magrittr, r-cran-kableextra, r-cran-hmisc, r-cran-bookdown, r-cran-rmarkdown, r-cran-pkgdown, r-cran-formatters, r-cran-tinytable Filename: pool/dists/noble/main/r-cran-tables_0.9.33-1.ca2404.1_all.deb Size: 849310 MD5sum: 44e01afd6c7edef6bf9dee72ff1ad84d SHA1: a63d2f501fec7b0109a04f35e07e70c3fdf75efa SHA256: 73ec3e128d7ec664d9499461faf576975727c8a934915c6c97811a2f62dbd6ba SHA512: ceab3dd15f1a71bbc2dc7cc7b454945e85127f293af468c09e4fa531cafc7186e0f17c9a5d3afe6850d86ecd41471f4274bbd7842b5cff403fe0a7c8bcafb951 Homepage: https://cran.r-project.org/package=tables Description: CRAN Package 'tables' (Formula-Driven Table Generation) Computes and displays complex tables of summary statistics. 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Package: r-cran-tableschema.r Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-config, r-cran-future, r-cran-httr, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-lubridate, r-cran-purrr, r-cran-r6, r-cran-rcurl, r-cran-rlist, r-cran-stringr, r-cran-urltools Suggests: r-cran-covr, r-cran-foreach, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tableschema.r_1.1.2-1.ca2404.1_all.deb Size: 601122 MD5sum: 39075cbfc25d81801e05fc1b9c9c761f SHA1: 57e71622b19de52e9ab90ed30ab462b02dbc5b85 SHA256: 274a2476b8bc720e0c8aac29324480f62bb289e0d9ac8684ec8bfe93c32278d2 SHA512: b1ca20b92696b09eaa574b69b435932aee2ec7b17412df1461a3988ef0ccc7bddb6df560c474436b752aaf02c014e91fbcd48e0b18b9748c69bf5b5ba23840ea Homepage: https://cran.r-project.org/package=tableschema.r Description: CRAN Package 'tableschema.r' (Table Schema 'Frictionless Data') Allows to work with 'Table Schema' (). 'Table Schema' is well suited for use cases around handling and validating tabular data in text formats such as 'csv', but its utility extends well beyond this core usage, towards a range of applications where data benefits from a portable schema format. The 'tableschema.r' package can load and validate any table schema descriptor, allow the creation and modification of descriptors, expose methods for reading and streaming data that conforms to a 'Table Schema' via the 'Tabular Data Resource' abstraction. Package: r-cran-tablesgg Architecture: all Version: 0.9-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1236 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tables Suggests: r-cran-ggtext, r-cran-gridtext, r-cran-quadprog, r-cran-xtable, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tablesgg_0.9-1-1.ca2404.1_all.deb Size: 1046258 MD5sum: 21e93828026ca4f5891c03d187914f45 SHA1: 0ccc97eca5176368dbc3536591f5b7aad0f5b445 SHA256: 9be38f987d3dc951a18e12c366b9ad6d37dd845d7b2d3ff6333ea617c2c7282b SHA512: 619202cb46af6be53f4ebf4737422f347f43b04167bee053ddd43b3d306e1215ef3ca49f8db49f2a7975801b45cbf37a4e17ac33c4169e42ae5f21f4b9b87004 Homepage: https://cran.r-project.org/package=tablesgg Description: CRAN Package 'tablesgg' (Presentation-Quality Tables, Displayed Using 'ggplot2') Presentation-quality tables are displayed as plots on an R graphics device. Although there are other packages that format tables for display, this package is unique in combining two features: (a) It is aware of the logical structure of the table being presented, and makes use of that for automatic layout and styling of the table. This avoids the need for most manual adjustments to achieve an attractive result. (b) It displays tables using 'ggplot2' graphics. Therefore a table can be presented anywhere a graph could be, with no more effort. External software such as LaTeX or HTML or their viewers is not required. The package provides a full set of tools to control the style and appearance of tables, including titles, footnotes and reference marks, horizontal and vertical rules, and spacing of rows and columns. Methods are included to display matrices; data frames; tables created by R's ftable(), table(), and xtabs() functions; and tables created by the 'tables' and 'xtable' packages. Methods can be added to display other table-like objects. A vignette is included that illustrates usage and options available in the package. Package: r-cran-tablespan Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gt, r-cran-openxlsx, r-cran-rlang, r-cran-scales, r-cran-tibble Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tablespan_0.3.2-1.ca2404.1_all.deb Size: 1938516 MD5sum: dd16591bb75f89dd514b4fb32a1292c1 SHA1: ef8af3aa5bd73588a235713d64fb3db5dd528f95 SHA256: 3ee84dc83ea66556c18b6ccc63e5b5bc1f9e05e993036fdc092f7b8537adfd97 SHA512: 1ea8798344f3c7d82c1ff713d76993de4f6ca3621f63abfa6099003c20e4bab0a1cdf19f35665c50c867ab8131a895547403aba40b4be0174159e67db1ae13da Homepage: https://cran.r-project.org/package=tablespan Description: CRAN Package 'tablespan' (Create Satisficing 'Excel', 'HTML', 'LaTeX', and 'RTF' Tablesusing a Simple Formula) Create "good enough" tables with a single formula. 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Package: r-cran-tablet Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1939 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-yamlet, r-cran-rlang, r-cran-tidyr, r-cran-kableextra, r-cran-spork, r-cran-magrittr, r-cran-fs, r-cran-reactable Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-boot, r-cran-testthat, r-cran-shiny, r-cran-shinyfiles, r-cran-haven, r-cran-yaml, r-cran-sortable, r-cran-latexpdf, r-cran-tinytex, r-cran-csv, r-cran-xtable, r-cran-shinyace, r-cran-r.utils Filename: pool/dists/noble/main/r-cran-tablet_0.7.1-1.ca2404.1_all.deb Size: 840316 MD5sum: 83b28576d5daac93c2c066cca67560ad SHA1: 48450c83cd91e6086b3cfe8bba5d94d4affa85d0 SHA256: 4f2e83dc298365bbc3ab1a14adae8855c09cc3d5712a8315175ce51185345eef SHA512: 76edfd39848e75251f935fdb9c02d63bfba9e2f3bf8532ae9c0c8bd5e925efa23289ad8b8a449b7c5a25cfc78ee038276181e4b652c38b573cc395bff9635095 Homepage: https://cran.r-project.org/package=tablet Description: CRAN Package 'tablet' (Tabulate Descriptive Statistics in Multiple Formats) Creates a table of descriptive statistics for factor and numeric columns in a data frame. Displays these by groups, if any. Highly customizable, with support for 'html' and 'pdf' provided by 'kableExtra'. Respects original column order, column labels, and factor level order. See ?tablet.data.frame and vignettes. Package: r-cran-tabletolongform Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tabletolongform_1.3.2-1.ca2404.1_all.deb Size: 86222 MD5sum: cb12d6e52b2e3c9d1a962d20a8dc91f4 SHA1: 0e04c8727382b1cf33eca4b30edcd7beb5435250 SHA256: 0a2a78fd64a76d11455b09b5a83333e3b85f5bd5a5cbf7e68f4d4d95bf749725 SHA512: 9eab8c1fd953a0cbbe4fc2ceccfc12e06e6f7a2933b552e9794ad0fa64089dd6d46419faef8cdaca05ee3cd6b49270bf79e110a2cf2f720dace553d942a3c6e7 Homepage: https://cran.r-project.org/package=TableToLongForm Description: CRAN Package 'TableToLongForm' (Automatically Convert Hierarchical for-Human Tables toMachine-Readable LongForm Dataframes) A wrapper to a set of algorithms designed to recognise positional cues present in hierarchical for-human Tables (which would normally be interpreted visually by the human brain) to decompose, then reconstruct the data into machine-readable LongForm Dataframes. Package: r-cran-tablexlsx Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 866 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-openxlsx, r-cran-cli Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tablexlsx_1.1.0-1.ca2404.1_all.deb Size: 668046 MD5sum: f2608531d412fd3ecd05bf88c30f6def SHA1: 2d2834d089ed4fdeb191c300d97a216a487678e5 SHA256: eb07f92508c53ae2217914be41fcc37800c01ae15248eed97692bce212bbe37c SHA512: 9700f83b9a894c0e2e71cec1c00b303ccfcd3c7156bd69443c14ae44e4dfbe0bbb1c797dfa80a90466537e085bd1539146cc33df240c8e6adc98cb24e13909dc Homepage: https://cran.r-project.org/package=tablexlsx Description: CRAN Package 'tablexlsx' (Export Data Frames to Excel Workbook) Collection of functions that allow to export data frames to excel workbook. Package: r-cran-tabnet Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4457 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-coro, r-cran-data.tree, r-cran-dials, r-cran-dplyr, r-cran-ggplot2, r-cran-hardhat, r-cran-magrittr, r-cran-matrix, r-cran-parsnip, r-cran-progress, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-torch, r-cran-tune, r-cran-vctrs, r-cran-withr, r-cran-zeallot Suggests: r-cran-cli, r-cran-knitr, r-cran-modeldata, r-cran-patchwork, r-cran-quarto, r-cran-recipes, r-cran-rmarkdown, r-cran-rsample, r-cran-spelling, r-cran-testthat, r-cran-tidymodels, r-cran-tidyverse, r-cran-vip, r-cran-visdat, r-cran-workflows, r-cran-xgboost, r-cran-yardstick Filename: pool/dists/noble/main/r-cran-tabnet_0.8.0-1.ca2404.1_all.deb Size: 4014122 MD5sum: a09e36b265831eb9095f5a5415f93987 SHA1: aa72c15ba6c0144d29d1797a5cd00350da86ad8c SHA256: 9142eae61d290fa0f251b84ee9cc9bf9ceb8ac25ab482b3ad42864c8f1456ca3 SHA512: 455e8ee3d73dc73daff5bb8c0c5e782ffdd2e6cb8118f23e2749b154849c7d979e09cc580785eb3c6aa2c751a69a06b8455a5b4e9a26b546be9dc5804f39b698 Homepage: https://cran.r-project.org/package=tabnet Description: CRAN Package 'tabnet' (Fit 'TabNet' Models for Classification and Regression) Implements the 'TabNet' model by Sercan O. Arik et al. (2019) with 'Coherent Hierarchical Multi-label Classification Networks' by Giunchiglia et al. and provides a consistent interface for fitting and creating predictions. It's also fully compatible with the 'tidymodels' ecosystem. Package: r-cran-taboolar Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-taboolar_0.1.0-1.ca2404.1_all.deb Size: 22942 MD5sum: 349bb78fa45e5c3e72d3ab0648b9e9f8 SHA1: 775527fcf2fe5e9d8e0bf4e49515ac60fd5cffec SHA256: 6d711cb76c42e50df951be705e40e3dc1e6a4137f10072c3c330bc52de3a0c92 SHA512: a057508e3a585eab0879b5b8d5e334838149b254ef83ce74bfa8becb3e24e04ffed8b4c16751c2f769e75664858d7101f0edfc6484393f6c5e9d998c5c5c089f Homepage: https://cran.r-project.org/package=taboolaR Description: CRAN Package 'taboolaR' (Get Data from 'Taboola' via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from 'Taboola' using the 'Windsor.ai' API . Package: r-cran-tabpfn Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-hardhat, r-cran-purrr, r-cran-reticulate, r-cran-rlang, r-cran-tibble Suggests: r-cran-covr, r-cran-modeldata, r-cran-recipes, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-tabpfn_0.2.0-1.ca2404.1_all.deb Size: 251950 MD5sum: 18e3ef75d8925896fa5ee10d5f3e2291 SHA1: c5a8fd6dc83e3071fba510ec8b8e9a2e4ccbc913 SHA256: 7c2f1c9ef69855d16664976d47e43d41bfff54be3ebcbfc2e6638d4fabcfc489 SHA512: 4f2e2d624f9fece8d8970add101b10fde5f47174ae77b72e60b0b50b5804ee9b385b8429771bca7aa6d52893d463fa711d1cb0f6675844d2488b0724e7045f27 Homepage: https://cran.r-project.org/package=tabpfn Description: CRAN Package 'tabpfn' (Prior-Data Fitted Network Foundational Model for Tabular Data) Provides a consistent API for classification and regression models based on the 'TabPFN' model of Hollmann et al. (2025), "Accurate predictions on small data with a tabular foundation model," Nature, 637(8045) . The calculations are served via 'Python' to train and predict the model. Package: r-cran-tabr Architecture: all Version: 0.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1333 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crayon, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-fansi, r-cran-gridextra, r-cran-htmltools, r-cran-kableextra, r-cran-knitr, r-cran-png, r-cran-rmarkdown, r-cran-testthat, r-cran-tuner Filename: pool/dists/noble/main/r-cran-tabr_0.5.5-1.ca2404.1_all.deb Size: 859894 MD5sum: 992dc1daea9103812a2926b5fa422771 SHA1: 19da4a640df318e6d5c99a59f5d770138137bb94 SHA256: 3d92682cf13a912e8ce1f0419754b8fdf8b61c80433eb4aef0ee64d3bc9dc755 SHA512: 138d40ef21dcc7d18cca57070d0cbd8cca39614a466cffa76639822d2870e6ecfbcd9ab7c8dc97ff5c62ea0279bb2c21c76b074a461a2a0f4044587931c1280e Homepage: https://cran.r-project.org/package=tabr Description: CRAN Package 'tabr' (Music Notation Syntax, Manipulation, Analysis and Transcriptionin R) Provides a music notation syntax and a collection of music programming functions for generating, manipulating, organizing, and analyzing musical information in R. Music syntax can be entered directly in character strings, for example to quickly transcribe short pieces of music. The package contains functions for directly performing various mathematical, logical and organizational operations and musical transformations on special object classes that facilitate working with music data and notation. The same music data can be organized in tidy data frames for a familiar and powerful approach to the analysis of large amounts of structured music data. Functions are available for mapping seamlessly between these formats and their representations of musical information. The package also provides an API to 'LilyPond' () for transcribing musical representations in R into tablature ("tabs") and sheet music. 'LilyPond' is open source music engraving software for generating high quality sheet music based on markup syntax. The package generates 'LilyPond' files from R code and can pass them to the 'LilyPond' command line interface to be rendered into sheet music PDF files or inserted into R markdown documents. The package offers nominal MIDI file output support in conjunction with rendering sheet music. The package can read MIDI files and attempts to structure the MIDI data to integrate as best as possible with the data structures and functionality found throughout the package. Package: r-cran-tabs Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2168 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-dplyr, r-cran-htmltools, r-cran-htmlwidgets, r-cran-httr, r-cran-jsonlite, r-cran-geojsonio, r-cran-leaflet, r-cran-leaftime, r-cran-mapedit, r-cran-qs2, r-cran-rlang, r-cran-sf, r-cran-stringi, r-cran-terra, r-cran-gpkg, r-cran-rsqlite Filename: pool/dists/noble/main/r-cran-tabs_0.2.0-1.ca2404.1_all.deb Size: 2067434 MD5sum: bda5bcfe780a96e2a9192efa3112db90 SHA1: ce2e6ac1d148ef6023482995dff2ef5323cdcc70 SHA256: d9ca1d891f9681dc1b9a71a40c34ae2076fc6ea3c662b0548f1524cc48685d8c SHA512: 5bf12700ab69c4fe94b8bf6c847e436aea38d9d110611c59a1fe7fb96f3ea7273967b4481f1dc7dc627403df15bab34bfb8f835aabbc2b36dd2ee5cfe52ec8e9 Homepage: https://cran.r-project.org/package=tabs Description: CRAN Package 'tabs' (Temporal Altitudinal Biogeographic Shifts) A standardized workflow to reconstruct spatial configurations of altitude-bounded biogeographic systems over time. For example, 'tabs' can model how island archipelagos expand or contract with changing sea levels or how alpine biomes shift in response to tree line movements. It provides functionality to account for various geophysical processes such as crustal deformation and other tectonic changes, allowing for a more accurate representation of biogeographic system dynamics. For more information see De Groeve et al. (2025) . Package: r-cran-tabshiftr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1521 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-rlang, r-cran-tibble, r-cran-dplyr, r-cran-tidyr, r-cran-magrittr, r-cran-tidyselect, r-cran-testthat, r-cran-crayon, r-cran-purrr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown, r-cran-readr, r-cran-covr Filename: pool/dists/noble/main/r-cran-tabshiftr_0.4.1-1.ca2404.1_all.deb Size: 621516 MD5sum: e79ac4a1d017e21639c108875d22549b SHA1: c2ac26ecb86f484e4d52714a2312064f34e210a0 SHA256: fa251e5f2592c0a93c41f6d40d2514ba226ff5e2987b501480d6d4c39c29973e SHA512: fef346b9890904710cf3741a01c83f375e79680fad65f69bf0732dce013518e944995534888b423db1b5665f6882cbc28c6b2aef7edbc69037e243dc2446424c Homepage: https://cran.r-project.org/package=tabshiftr Description: CRAN Package 'tabshiftr' (Reshape Disorganised Messy Data) Helps the user to build and register schema descriptions of disorganised (messy) tables. Disorganised tables are tables that are not in a topologically coherent form, where packages such as 'tidyr' could be used for reshaping. The schema description documents the arrangement of input tables and is used to reshape them into a standardised (tidy) output format. Package: r-cran-tabstats Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 432 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-tibble, r-cran-vctrs Suggests: r-cran-broom, r-cran-rstatix, r-cran-glue, r-cran-tidyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-fansi, r-cran-crayon, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-tabstats_0.1.0-1.ca2404.1_all.deb Size: 355334 MD5sum: b598743cd9326691c5e71a512ef2a6c1 SHA1: 7c79360fbe4c0d134777c4d0923d5e6f28302512 SHA256: b2dee2444808b8d9b77767f7a3b87afd558cc974d74b00c97e82e963e6be7af9 SHA512: 774891e5dcff9c8b3378ab6510d4584bb5401c0c7964e62eaf62ad2c4f7ef0d9f015567f74a48f16654195cac5e9df03b8be175c8a6617ab323f2d4033a21297 Homepage: https://cran.r-project.org/package=tabstats Description: CRAN Package 'tabstats' (A Lightweight Toolkit for Displaying Customizable Tables) A lightweight toolkit that provides functions for printing tables from input data in the R console or terminal with customizable formatting. Supported outputs include American Psychological Association (APA)-style tables (American Psychological Association, 2020, ISBN:9781433832178), correlation matrices, contingency tables, and two-column summary tables. 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Missing functionality in existing packages is included here to allow extraction from raster data with 'simple features' and 'Spatial' types and to make extraction consistent and straightforward. Extract cell numbers from raster data and return the cells as a data frame rather than as lists of matrices or vectors. The functions here allow spatial data to be used without special handling for the format currently in use. 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Package: r-cran-tabxplor Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1269 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-stringr, r-cran-crayon, r-cran-forcats, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-vctrs, r-cran-cli, r-cran-tidyselect, r-cran-stringi, r-cran-pillar, r-cran-kableextra, r-cran-desctools, r-cran-data.table Suggests: r-cran-fansi, r-cran-htmltools, r-cran-jmvcore, r-cran-knitr, r-cran-openxlsx, r-cran-r6, r-cran-ggpubr, r-cran-ggplot2, r-cran-cowplot, r-cran-gtable, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-testthat, r-cran-labelled Filename: pool/dists/noble/main/r-cran-tabxplor_1.3.1-1.ca2404.1_all.deb Size: 752288 MD5sum: 70a0e3ba723b698bbc5dd68b8fdfd400 SHA1: de13e48d202c83ec817fbf7c1b19f830ea5091e3 SHA256: f574bb8d7f07dd79c57c91068f715db37b9a364fea4857213881b5157ff45289 SHA512: ac88baa335f735584767470daae6ae06c19cd43e73e9218a4dd1c8da71586712645e405fba5a69f87dc67c2aea24453fec50b24e8dadff9afe5c5fde9b554687 Homepage: https://cran.r-project.org/package=tabxplor Description: CRAN Package 'tabxplor' (User-Friendly Tables with Color Helpers for Data Exploration) Make it easy to deal with multiple cross-tables in data exploration, by creating them, manipulating them, and adding color helpers to highlight important informations (differences from totals, comparisons between lines or columns, contributions to variance, confidence intervals, odds ratios, etc.). 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Package: r-cran-tacmagic Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3035 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-r.matlab Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-tacmagic_0.3.1-1.ca2404.1_all.deb Size: 523262 MD5sum: dc1296afe24c03d1039f9ce4dfeaa545 SHA1: 4d319251af6df461bff75926640efcce6092b83e SHA256: 207bc7fa5334d99a6e760d87bf95704867f3c1d0c19232ea459306264bad2a84 SHA512: 230d8260bd7244a77a5962a634ddc4bd8a352b9724d04097ba47aa630bec30008d340a3617c7ce42388cd199f38a0ec448e7c9de63ac2bbea400352653c9484b Homepage: https://cran.r-project.org/package=tacmagic Description: CRAN Package 'tacmagic' (Positron Emission Tomography Time-Activity Curve Analysis) To facilitate the analysis of positron emission tomography (PET) time activity curve (TAC) data, and to encourage open science and replicability, this package supports data loading and analysis of multiple TAC file formats. Functions are available to analyze loaded TAC data for individual participants or in batches. Major functionality includes weighted TAC merging by region of interest (ROI), calculating models including standardized uptake value ratio (SUVR) and distribution volume ratio (DVR, Logan et al. 1996 ), basic plotting functions and calculation of cut-off values (Aizenstein et al. 2008 ). Please see the walkthrough vignette for a detailed overview of 'tacmagic' functions. 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This framework mobilized a study of the relationship between the moments describing the shape of the distributions: the skewness and the kurtosis (SKR). The SKR allows the identification of commonalities in the shape of trait distributions across contrasting communities. Derived from the SKR, we developed mathematical parameters that summarise the complex pattern of distributions by assessing (i) the R², (ii) the Y-intercept, (iii) the slope, (iv) the functional stability of community (TADstab), and, (v) the distance from specific distribution families (i.e., the distance from the skew-uniform family a limit to the highest degree of evenness: TADeve). 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Each script starts by reading files from a previous step and ends with writing out files for the next step. Convenience functions are provided to version control the required data and software, run analyses, clean residues from previous runs, manage files, manipulate tables, and produce figures. With a focus on stability and reproducible analyses, the TAF package comes with no dependencies. TAF forms a base layer for the 'icesTAF' package and other scientific applications. Package: r-cran-tagr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tagr_1.0.1-1.ca2404.1_all.deb Size: 31404 MD5sum: 1cd527940fdc1c9330b5771936d006f0 SHA1: ac6ebd47f532a69bf43ba4b739bed899b3d88615 SHA256: 45ca921f6ba92618d36d1e881024253e9f697364b51b92163b1739750b45b041 SHA512: e1f70cf8723c065f00ea85dee2636c4846f690e4806335e84cc66bee0f153ec2f826a3148b9fd1bf1ea7dbb6b8790891e9b3734c9bbb24dc628cc992801ccc25 Homepage: https://cran.r-project.org/package=tagr Description: CRAN Package 'tagr' (Tagging and Organizing Objects in R) Provides functions for attaching tags to R objects, searching for objects based on tags, and removing tags from objects. It also includes a function for removing all tags from an object, as well as a function for deleting all objects with a specific tag from the R environment. The package is useful for organizing and managing large collections of objects in R. Package: r-cran-tagtools Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1373 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circstats, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-latex2exp, r-cran-lubridate, r-cran-matlab, r-cran-ncdf4, r-cran-plotly, r-cran-pracma, r-cran-readr, r-cran-signal, r-cran-stringr, r-cran-zoo, r-cran-zoom Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tagtools_0.2.0-1.ca2404.1_all.deb Size: 1280230 MD5sum: cc7ccd614f99dfd4698769b53890dc03 SHA1: ef8365e6188f716be3fd450708f5bf3bed510023 SHA256: c74171ca3fe26561cb17de220fd1ecfea7b5b89a8ca62d2ccc79f948ae70ccca SHA512: d4d96365d4a68cfcce6c071c4e8004a89c40064af3588b0c172d57c6cf4f5c7731a04925ea958556f71f5dd59bdd534c4cf03d59012c091cac7c66f929dd4940 Homepage: https://cran.r-project.org/package=tagtools Description: CRAN Package 'tagtools' (Work with Data from High-Resolution Biologging Tags) High-resolution movement-sensor tags typically include accelerometers to measure body posture and sudden movements or changes in speed, magnetometers to measure direction of travel, and pressure sensors to measure dive depth in aquatic or marine animals. The sensors in these tags usually sample many times per second. Some tags include sensors for speed, turning rate (gyroscopes), and sound. This package provides software tools to facilitate calibration, processing, and analysis of such data. Tools are provided for: data import/export; calibration (from raw data to calibrated data in scientific units); visualization (for example, multi-panel time-series plots); data processing (such as event detection, calculation of derived metrics like jerk and dynamic acceleration, dive detection, and dive parameter calculation); and statistical analysis (for example, track reconstruction, a rotation test, and Mahalanobis distance analysis). Package: r-cran-tai Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tai_0.2.2-1.ca2404.1_all.deb Size: 54532 MD5sum: 58835c8c93a6f696000b3d939c284346 SHA1: 357f8bfbccae8c2b9e5b069dc89d80e0443b002b SHA256: 6813d7cf190a255f1502d1b1ef209ec16e7eabb16a6514ead692128deb29957f SHA512: 04bccd98ce7c344259a7185ed7d44881ad36345c415f48754008e38b51ac3bbee1f5eed9d8dbf8420a6f25589311698d83c28f3a0bdd373ff4c1719fa8c7742f Homepage: https://cran.r-project.org/package=tAI Description: CRAN Package 'tAI' (The tRNA Adaptation Index) Functions and example files to calculate the tRNA adaptation index, a measure of the level of co-adaptation between the set of tRNA genes and the codon usage bias of protein-coding genes in a given genome. The methodology is described in dos Reis, Wernisch and Savva (2003) , and dos Reis, Savva and Wernisch (2004) . Package: r-cran-tailclassifier Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-cowplot, r-cran-scales Filename: pool/dists/noble/main/r-cran-tailclassifier_0.1.2-1.ca2404.1_all.deb Size: 39156 MD5sum: 9203359d6932de75522327ec2d4adfe8 SHA1: cd74727475322a6eb91444839ac9f84e825a3553 SHA256: a6a95d12eb1e5934119953344d1c7470c6c33b780f301493bdbd64404f33af5d SHA512: bc5e84be7eeb65f58531caf5c0935e9d19015cb52758a05e8ce804ac5ffd432a2ac413220bff423f0cf1e6ae67b28ff3a0e72a6c491909ac20bb3b6e6180e91a Homepage: https://cran.r-project.org/package=TailClassifier Description: CRAN Package 'TailClassifier' (Tail Classifier) The function TailClassifier() suggests one of the following types of tail for your discrete data: 1) Power decaying tail; 2) Sub-exponential decaying tail; and 3) Near-exponential decaying tail. The function also provides an estimate of the parameter for the classified-distribution as a reference. Package: r-cran-tailid Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ismev, r-cran-scales Filename: pool/dists/noble/main/r-cran-tailid_1.0.0-1.ca2404.1_all.deb Size: 32694 MD5sum: 368756c2abb659d7505e247d29c2eb6f SHA1: 37652d4684dffc8c83603800162d44a2bcafd2c0 SHA256: 57ee98d47270d7ffd87160087ede13090cffaafc4311c7c00bdfe5164ac205fd SHA512: 4e27860a3e125bebbebba4beda11c0e050ce2e961634574d1bd31afa378c81f50d4a60bf7d295ffd181917e4eabcc21b5b2c9252faccdb002e4ad0c78716fc25 Homepage: https://cran.r-project.org/package=TailID Description: CRAN Package 'TailID' (Detect Sensitive Points in the Tail) The goal of 'TailID' is to detect sensitive points in the tail of a dataset using techniques from Extreme Value Theory (EVT). It utilizes the Generalized Pareto Distribution (GPD) for assessing tail behavior and detecting inconsistent points with the Identical Distribution hypothesis of the tail. For more details see Manau (2025). Package: r-cran-tailloss Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Filename: pool/dists/noble/main/r-cran-tailloss_1.0-1.ca2404.1_all.deb Size: 212658 MD5sum: ef81975754050e77b4f82dd8cce8c2a4 SHA1: 382969f201b2621f124fe7b01ae732f59e83574d SHA256: 9b6b954763684358cad703df6312a5858f68eda14eabfb1e8b6fed330fc4fb47 SHA512: 843a2ec76dac56948e0a0bba1ffd0c11092124a7cd790aba2e4ac45cff14860a8aa75446322be8d53efd6448dbf4da59be9b4728bf3828fb36ed496b57f9f189 Homepage: https://cran.r-project.org/package=tailloss Description: CRAN Package 'tailloss' (Estimate the Probability in the Upper Tail of the Aggregate LossDistribution) Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound. Package: r-cran-tailor Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 522 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-hardhat, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-betacal, r-cran-dials, r-cran-mgcv, r-cran-modeldata, r-cran-probably, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tailor_0.1.0-1.ca2404.1_all.deb Size: 483940 MD5sum: 4b26577144179fe8871b086157ade29c SHA1: 67c2210c89225fd04e5b0fcfc77dc237ea9b2e71 SHA256: b1a8b349d2ebc587703a275d9632711d8ef1ecc61455e449374a9fbb988f5972 SHA512: dfe1e4a3c991c2c54226a08e7216b0b679a637b6c88b21513191b72fc26d7dcbc5f58078d00f4d7c8e9089706a5bff4f6e8882137d87cc305a48f956b2beb664 Homepage: https://cran.r-project.org/package=tailor Description: CRAN Package 'tailor' (Iterative Steps for Postprocessing Model Predictions) Postprocessors refine predictions outputted from machine learning models to improve predictive performance or better satisfy distributional limitations. This package introduces 'tailor' objects, which compose iterative adjustments to model predictions. A number of pre-written adjustments are provided with the package, such as calibration. See Lichtenstein, Fischhoff, and Phillips (1977) . Other methods and utilities to compose new adjustments are also included. Tailors are tightly integrated with the 'tidymodels' framework. Package: r-cran-tailplots Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-resample Suggests: r-cran-actuar Filename: pool/dists/noble/main/r-cran-tailplots_0.1.0-1.ca2404.1_all.deb Size: 60910 MD5sum: f2ec2c5b585e4502c861c816b0c750c3 SHA1: d184609e0ba421c56bede9d60857f5c669b78b5b SHA256: ab3de8e48a65b178be0b0a37c50eeb902cd96a255bc2acb591216b8d3a832e06 SHA512: 97a8c3e18dc997e23d8d8e56805ac57a8e055073e0172e1e11489a8277baa8875a98b9e72a0c7915abef7f0b813b2c257043e177982d8704d0eccf293484b127 Homepage: https://cran.r-project.org/package=tailplots Description: CRAN Package 'tailplots' (Estimators and Plots for Gamma and Pareto Tail Detection) Estimators for two functionals used to detect Gamma or Pareto distributions, as well as distributions exhibiting similar tail behavior, as introduced by Iwashita and Klar (2023) and Klar (2024) . One of these functionals, g, originally proposed by Asmussen and Lehtomaa (2017) , distinguishes between log-convex and log-concave tail behavior. The package also includes methods for visualizing these estimators and their associated confidence intervals across various threshold values. Package: r-cran-tailrank Architecture: all Version: 3.2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-oompabase, r-bioc-biobase, r-cran-oompadata Suggests: r-cran-xtable Filename: pool/dists/noble/main/r-cran-tailrank_3.2.4-1.ca2404.1_all.deb Size: 211164 MD5sum: 45374391f5a9a91debed397b53f21438 SHA1: 355fff1a493899ada9f0e3905af77f0bbbb7ac76 SHA256: bfc6dfa1aa745fa93d73bce807b9ab893f8f6ca12d34bb29cd743a05ffc4dc70 SHA512: 459f6e6b81a199436874ce6ab335711c01fc3c8c13e0dba6b1fb657100c75c4931a39ff4d4819203096d83a783254fc672cb0114c11d0c2b4bc0086868ec9e74 Homepage: https://cran.r-project.org/package=TailRank Description: CRAN Package 'TailRank' (The Tail-Rank Statistic) Implements the tail-rank statistic for selecting biomarkers from a microarray data set, an efficient nonparametric test focused on the distributional tails. See . Package: r-cran-tailtransform Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-sensitivitymw, r-cran-sensitivitymult Filename: pool/dists/noble/main/r-cran-tailtransform_2.0.0-1.ca2404.1_all.deb Size: 60094 MD5sum: 87b9f9ecfc95635f887d8bfc50a7f099 SHA1: ce17de08697a8362293ccefc8a7a86a21f5333b7 SHA256: ba748bde781e00e9daeb8942adc7385c17217cf73e0b08ddc7aaacf86b23fb8f SHA512: 7f547286b9fbee93c0da7da60ca1b6db1799fb883c0142b9d2c35471b1d6782b8739a926acea5d4632748868f769675658bd3f5f31ed5a034d9d13b9c7b85271 Homepage: https://cran.r-project.org/package=tailTransform Description: CRAN Package 'tailTransform' (Symmetric Transformation of Tails for Plotting Differences) When plotting treated-minus-control differences, after-minus-before changes, or difference-in-differences, the ttrans() function symmetrically transforms the positive and negative tails to aid plotting. The package includes an observational study with three control groups and an unaffected outcome; see Rosenbaum (2022) . Package: r-cran-taipan Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1609 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyverse, r-cran-ggplot2, r-cran-purrr, r-cran-shinydashboard Filename: pool/dists/noble/main/r-cran-taipan_0.1.2-1.ca2404.1_all.deb Size: 1234874 MD5sum: 7770d680b6bc4e24d1ee429a9058e187 SHA1: 6c01f54df98937d000fb242775afbba90c37ab69 SHA256: 6237db48cc08fd790dcd17860b334f534e2d30d1e09f5637ba7f4b19bcb3ae0a SHA512: bafb0a1cf74f4da3523bf2d9ab84d17d78b00ac871a5aab4e393f7cac013d85411724c9f9457f89effe89124bd499f11c0a0e7972cff0edd8f261adf10238565 Homepage: https://cran.r-project.org/package=taipan Description: CRAN Package 'taipan' (Tool for Annotating Images in Preparation for Analysis) A tool to help create shiny apps for selecting and annotating elements of images. Users must supply images, questions, and answer choices. The user interface is a dynamic shiny app, that displays the images and questions and answer choices. The data generated can be saved to a file that can be used for subsequent analysis. The original purpose was to annotate still images from tennis video for face recognition and emotion detection purposes. Package: r-cran-takos Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-devemf, r-cran-segmented, r-cran-sfsmisc, r-cran-smoother, r-cran-desolve, r-cran-pracma, r-cran-data.table, r-cran-broom, r-cran-colorramps, r-cran-minpack.lm, r-cran-baseline Filename: pool/dists/noble/main/r-cran-takos_0.2.0-1.ca2404.1_all.deb Size: 140122 MD5sum: fc55fa1811ca2a3837bb544a96fffce1 SHA1: ea218747f06acf3bf74209ad037458fd70b47bf9 SHA256: f0e2a288ba7aecc7adae7021fcbba0c25bde3223a9a7ef85f1ec24a754c2c1a6 SHA512: 65c87d0b8adf5c746581a59c4cb5802184451cb240bde39be498f203c4ca03ac4d1339111ec3b39836a21480290a1ba3971345958efc3969e097f2297eca9b1d Homepage: https://cran.r-project.org/package=takos Description: CRAN Package 'takos' (Analysis of Differential Calorimetry Scans) It includes functions for applying methodologies utilized for single-process kinetic analysis of solid-state processes were recently summarized and described in the Recommendation of ICTAC Kinetic Committee. These methods work with the basic kinetic equation. The Methodologies included refers to Avrami, Friedman, Kissinger, Ozawa, OFM, Mo, Starink, isoconversional methodology (Vyazovkin) according to ICATAC Kinetics Committee recommendations as reported in Vyazovkin S, Chrissafis K, Di Lorenzo ML, et al. ICTAC Kinetics Committee recommendations for collecting experimental thermal analysis data for kinetic computations. Thermochim Acta. 2014;590:1-23. . Package: r-cran-talkr Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-knitr, r-cran-stringr, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-pkgdown, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-talkr_0.1.3-1.ca2404.1_all.deb Size: 251742 MD5sum: 5331a458279b00c3c0e9217d1bc0ac02 SHA1: 5e1af4bf2af668f1530e3be79c8a188939417f68 SHA256: 0e04a1fcbcb39f63cce2b9124b24f87369a564589e64dc9cf07f8503cf434279 SHA512: 7bfadf78cf47ede0d7826f7a0448ca6b5f85a0de03e36c422f1b1f2a91e0db0ddd3f87d54ae1364b141a7f627fcf46f7de15d51783165fc58a359cbe273eb726 Homepage: https://cran.r-project.org/package=talkr Description: CRAN Package 'talkr' (Plotting Conversation Data) Visualisation, analysis and quality control of conversational data. Rapid and visual insights into the nature, timing and quality of time-aligned annotations in conversational corpora. For more details, see Dingemanse et al., (2022) . 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In this simplified implementation of the method, north-west white contours represent illuminated topography and south-east black contours represent shaded topography. See Tanaka (1950) . 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In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream features (such as gene expression) to predict the residuals of the first stage. In our manuscript (Aben et al., 2016, ), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable. Package: r-cran-tangledfeatures Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 338 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-correlation, r-cran-data.table, r-cran-dplyr, r-cran-fastdummies, r-cran-ggplot2, r-cran-igraph, r-cran-janitor, r-cran-matrix, r-cran-purrr, r-cran-ranger Suggests: r-cran-knitr, r-cran-r.rsp, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tangledfeatures_0.1.1-1.ca2404.1_all.deb Size: 237432 MD5sum: 4baa778000576e833abec8cdad4c62c1 SHA1: 970b00329108b8e4b0aa0b4f3baa10b167123947 SHA256: e3bedb6eba79d7a425f24db2ad3250d8629edcbf172041a99ecc6d71c74c363f SHA512: a1f63013baaef85658d6bfe555674c6002260e6f945a90f9efc91435d87044515921b6bb7c7826bf51cea34a2e2dd3e944bff7b95f5748bc5098013f50a62f47 Homepage: https://cran.r-project.org/package=TangledFeatures Description: CRAN Package 'TangledFeatures' (Feature Selection in Highly Correlated Spaces) Feature selection algorithm that extracts features in highly correlated spaces. The extracted features are meant to be fed into simple explainable models such as linear or logistic regressions. The package is useful in the field of explainable modelling as a way to understand variable behavior. Package: r-cran-tangles Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1651 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-sf, r-cran-digest Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tangles_2.0.1-1.ca2404.1_all.deb Size: 1538634 MD5sum: 5d762a52ee0f0fe366cdd2cdd9ee4b89 SHA1: ff78e6f1cd44767f861eb888aa242dbb8ce54370 SHA256: a02e1412d545033a58ad837588e701d8a8c9064ad877f559dbd80a53aa143a36 SHA512: f63d5776abc66d153ad87b920fb5fa45784f16bca4cbacedee809d8973acc07396fea82da62d36f3b062fc12be147afa9bc78181715606751f936f50e987aab6 Homepage: https://cran.r-project.org/package=tangles Description: CRAN Package 'tangles' (Anonymisation of Spatial Point Patterns and Grids) Methods for anonymisation of spatial datasets while preserving spatial structure and relationships. Original coordinates or raster geometries are transformed using randomized or predefined vertical shifts, horizontal shifts, and rotations. Compatible with point-based data in 'matrix', 'data.frame', or 'sf' formats, as well as 'terra' raster objects. Supports reversible anonymisation workflows, hash-based validation, shapefile export, and consistent tangling across related datasets using stored transformation sequences. Approach informed by the De-Identification Decision Making Framework (CM O’Keefe, S Otorepec, M Elliot, E Mackey, and K O’Hara 2017) . Package: r-cran-tangpoemr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jiebar Filename: pool/dists/noble/main/r-cran-tangpoemr_0.1.0-1.ca2404.1_all.deb Size: 20754 MD5sum: 6238ed409d662c7a60a43df0cfedb714 SHA1: d85da994d972aaa555a2228ad8adbf2a171a439a SHA256: 822bfdec6fb06ac0e329d2e720253db8c05a95733dcdba0f66bed6c0f5103b06 SHA512: d7abe8d061328ecf77f7dc1e75c22ce065ed66d8492f08f6f2eccba350fdcb142723d70a84e364df7c61eb893fb159b6fa78ad555a8399e1388f03a2418791c0 Homepage: https://cran.r-project.org/package=TangPoemR Description: CRAN Package 'TangPoemR' (Write Chinese Tang Poems) Write Chinese Tang Poems automatically. Package: r-cran-tangram.pipe Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Suggests: r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tangram.pipe_1.1.2-1.ca2404.1_all.deb Size: 186822 MD5sum: 129dd0283c83ef8d29d851a46f0cc54f SHA1: 2d9eab508c17e5ddb8384219e6a5bff9c601fc3c SHA256: 3b1b524f2825529acb04ef54b69ac1daaea149371c8cfa1b05408992f2b53a23 SHA512: cdd3b89030fb7ff1ea326ff76fda5bbd9b0b216d40a65362bf55760d37b4b52455ef88bbd74a73a6fd48987bd2e67673a026eb7c8014320ba467f8eb2c615092 Homepage: https://cran.r-project.org/package=tangram.pipe Description: CRAN Package 'tangram.pipe' (Row-by-Row Table Building) Builds tables with customizable rows. Users can specify the type of data to use for each row, as well as how to handle missing data and the types of comparison tests to run on the table columns. Package: r-cran-tangram Architecture: all Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 927 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-magrittr, r-cran-knitr, r-cran-stringi, r-cran-stringr, r-cran-base64enc, r-cran-digest, r-cran-htmltools Suggests: r-cran-testthat, r-cran-rms, r-cran-rmarkdown, r-cran-hmisc, r-cran-sandwich, r-cran-dplyr, r-cran-matching, r-cran-epitools Filename: pool/dists/noble/main/r-cran-tangram_0.8.3-1.ca2404.1_all.deb Size: 801440 MD5sum: 1110dd101f9fa77e4e7134e9d299dcb7 SHA1: 8d5b82ce761c4b09ddf7ac1b29b9edbf5c4ab56e SHA256: d6b596648dbb227769cdad7532487dbacd0961a828dda476eb8b6cd9304530ea SHA512: a99aa1330b3c4f7b596a7d437bd973f2083db576cefff05d74dd840b0146d7b2e8bac67ad2c82fb78623554915b55dddbb54b7f6c255041ac1fbf64c5c3aa4dc Homepage: https://cran.r-project.org/package=tangram Description: CRAN Package 'tangram' (The Grammar of Tables) Provides an extensible formula system to quickly and easily create production quality tables. The processing steps are a formula parser, statistical content generation from data as defined by formula, followed by rendering into a table. Each step of the processing is separate and user definable thus creating a set of composable building blocks for highly customizable table generation. A user is not limited by any of the choices of the package creator other than the formula grammar. For example, one could chose to add a different S3 rendering function and output a format not provided in the default package, or possibly one would rather have Gini coefficients for their statistical content in a resulting table. Routines to achieve New England Journal of Medicine style, Lancet style and Hmisc::summaryM() statistics are provided. The package contains rendering for HTML5, Rmarkdown and an indexing format for use in tracing and tracking are provided. Package: r-cran-taper Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-taper_0.5.3-1.ca2404.1_all.deb Size: 341946 MD5sum: b6ab7fb695e6f329d8e1be5bb1ef0fd6 SHA1: 609d87cd48234ff377219e2f98af007bac03cd6e SHA256: 88acbf5dfa1dd1f84d960e85ecd1d996d78794960739a294892976d369f6b3d2 SHA512: e0382679b026be816b5deb8e86dd8e754b7033b6b4d813e4e65b9af6a69b1817ef58e3cd8e5611d4464fcd276c1b6c360e0c900575515d26f517f71e7021a3d6 Homepage: https://cran.r-project.org/package=TapeR Description: CRAN Package 'TapeR' (Flexible Tree Taper Curves Based on Semiparametric Mixed Models) Implementation of functions for fitting taper curves (a semiparametric linear mixed effects taper model) to diameter measurements along stems. Further functions are provided to estimate the uncertainty around the predicted curves, to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. For cases where tree heights are not measured, methods for estimating additional variance in volume predictions resulting from uncertainties in tree height models (tariffs) are provided. The example data include the taper curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees and a subset of section-wise diameter measurements of these trees. The functions implemented here are detailed in Kublin, E., Breidenbach, J., Kaendler, G. (2013) . Package: r-cran-tapkee Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-scatterplot3d, r-cran-rgl, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-tapkee_1.2-1.ca2404.1_all.deb Size: 224582 MD5sum: 89e2d4866606462a8b441bbc3cd31fe5 SHA1: 4ed99fdd72414a4040a914898bc87009de6a6de1 SHA256: 015db124e2beec41c51d9e0656a0bf1497932e787a1b100529864a2a8f2f17f4 SHA512: dddf0ce22bfae38809e9fdbd837b5d219b86f6c855530a20b27d68b280f9427c9c7ee6cda0ec49cc96fafe894cc501086eb60349e9921e8b32d4bf514e3034c9 Homepage: https://cran.r-project.org/package=tapkee Description: CRAN Package 'tapkee' (Wrapper for 'tapkee' Dimension Reduction Library) Wrapper for using 'tapkee' command line utility, it allows to run it from inside R and catch the results for further analysis and plotting. 'Tapkee' is a program for fast dimension reduction, see 'package?tapkee' and for installation and other details. Package: r-cran-taplock Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 658 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-glue, r-cran-httr2, r-cran-jose, r-cran-lubridate, r-cran-promises, r-cran-purrr, r-cran-stringr, r-cran-shiny, r-cran-tower Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-taplock_0.2.0-1.ca2404.1_all.deb Size: 487852 MD5sum: 25c6b136bff83ee228599b5257a6ab06 SHA1: 7355897681a4ba05d012fa0783a06016915a9cd9 SHA256: 9a37d71e13998d4800b37c493f27bea78fe6fdb7ef49763a6093824249f068f5 SHA512: 513d570ff301559721c8eb1aa5647c0685e3708798e16011fee148aa1107d6e8fe78400935ee917033ccb85f41f5b35a19ca6cf471cb3446f1e0be24a70abca8 Homepage: https://cran.r-project.org/package=tapLock Description: CRAN Package 'tapLock' (Seamless Single Sign-on for 'shiny') Swift and seamless Single Sign-On (SSO) integration. Designed for effortless compatibility with popular Single Sign-On providers like Google and Microsoft, it streamlines authentication, enhancing both user experience and application security. Elevate your 'shiny' applications for a simplified, unified, and secure authentication process. Package: r-cran-tapnet Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-bipartite, r-cran-mpsem, r-cran-phytools, r-cran-vegan Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-tapnet_0.6-1.ca2404.1_all.deb Size: 281726 MD5sum: 4037fd7e07cdfa4874f75312324933d8 SHA1: e4a3982f0b026657d2c5c391910b485691081aed SHA256: f9425c857c65f5a1496a2aaae03d9cbd23248cf610278955ec8d9694a858c86a SHA512: 6fbc1e827a7867b9f67e8d10bedf32a4bbba42c1144aeb449d2ed16e01b6dead65e75df5a9cc89cf469935628df758db002867159ddbfec058164b1737a6fa73 Homepage: https://cran.r-project.org/package=tapnet Description: CRAN Package 'tapnet' (Trait Matching and Abundance for Predicting Bipartite Networks) Functions to produce, fit and predict from bipartite networks with abundance, trait and phylogenetic information. Its methods are described in detail in Benadi, G., Dormann, C.F., Fruend, J., Stephan, R. & Vazquez, D.P. (2021) Quantitative prediction of interactions in bipartite networks based on traits, abundances, and phylogeny. The American Naturalist, in press. Package: r-cran-tar Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-tar_1.0-1.ca2404.1_all.deb Size: 157366 MD5sum: 45d9cb2db9dbf649e92c9275ee83bc77 SHA1: 2167ce62d8d5465e4c68086505b350ade00fff27 SHA256: 9edeca361bb5aab584b56964467da116d0588811e37086aa9f258bd44051c2b4 SHA512: 454a05b07cfc4c89d316b9957c27acfd0381768db006788bb8815724dcfc52500ce77490b38bae4c1dee446559b608f78b038fa14c70945b5e186759e0c848f1 Homepage: https://cran.r-project.org/package=TAR Description: CRAN Package 'TAR' (Bayesian Modeling of Autoregressive Threshold Time Series Models) Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data . Package: r-cran-tarchetypes Architecture: all Version: 0.14.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1020 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fs, r-cran-rlang, r-cran-secretbase, r-cran-targets, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs, r-cran-withr Suggests: r-cran-curl, r-cran-knitr, r-cran-nanoparquet, r-cran-parsermd, r-cran-quarto, r-cran-rmarkdown, r-cran-testthat, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-tarchetypes_0.14.1-1.ca2404.1_all.deb Size: 947394 MD5sum: 102d9397c6cecc6b9ab0a75d17788386 SHA1: 3cb88a8d5f44ef332d388df51cb4f2b923f27065 SHA256: 1d267d9e5f77e757810af102b8e2c78ed58300556cf22a99dc2f7625c65e0b4f SHA512: 83174f1e6249b02a957e346eba4e700c11e2c66842b407a7a0d570fb2794b9f31784b635df85bdbc62eff56e374bf7121f647340bf46f3c7214ea7c6b0ff1c84 Homepage: https://cran.r-project.org/package=tarchetypes Description: CRAN Package 'tarchetypes' (Archetypes for Targets) Function-oriented Make-like declarative pipelines for Statistics and data science are supported in the 'targets' R package. As an extension to 'targets', the 'tarchetypes' package provides convenient user-side functions to make 'targets' easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the 'targets' R package. by Will Landau (2018) . Package: r-cran-tarchives Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-fs, r-cran-rlang, r-cran-targets, r-cran-usethis, r-cran-withr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tarchives_0.1.1-1.ca2404.1_all.deb Size: 64062 MD5sum: 1060ff43c54b58abc1a4a0ecc572ee56 SHA1: 1716a72afaa2e155fa130907783de3193304bd9c SHA256: c0b64583520d13b963e11848d6400356b693a8451df60a8e58446d665f3c3af9 SHA512: a0a8b614adb127ae97c4b7344b55d3657efbc3a66dce4701de2abad89fee7711621c1c13416e18b59f00342573f574475e23874a5f671649cadac1b1943d4772 Homepage: https://cran.r-project.org/package=tarchives Description: CRAN Package 'tarchives' (Make Your 'targets' Pipelines into a Package) Runs 'targets' pipeline in '/inst/tarchives' and stores the results in the R user directory. This means that the user does not have to run the process repeatedly, and the developer has the flexibility to update the data as versions are updated. Package: r-cran-targets Architecture: all Version: 1.12.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2922 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64url, r-cran-callr, r-cran-cli, r-cran-codetools, r-cran-data.table, r-cran-igraph, r-cran-knitr, r-cran-prettyunits, r-cran-ps, r-cran-r6, r-cran-rlang, r-cran-secretbase, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs, r-cran-yaml Suggests: r-cran-autometric, r-cran-bslib, r-cran-clustermq, r-cran-crew, r-cran-curl, r-cran-dt, r-cran-dplyr, r-cran-fst, r-cran-future, r-cran-future.batchtools, r-cran-future.callr, r-cran-gargle, r-cran-googlecloudstorager, r-cran-gt, r-cran-keras, r-cran-markdown, r-cran-nanonext, r-cran-rmarkdown, r-cran-parallelly, r-cran-paws.common, r-cran-paws.storage, r-cran-pkgload, r-cran-processx, r-cran-qs2, r-cran-reprex, r-cran-rstudioapi, r-cran-r.utils, r-cran-shiny, r-cran-shinybusy, r-cran-shinywidgets, r-cran-tarchetypes, r-cran-testthat, r-cran-torch, r-cran-usethis, r-cran-visnetwork Filename: pool/dists/noble/main/r-cran-targets_1.12.0-1.ca2404.1_all.deb Size: 2362068 MD5sum: a4b8a32148974604dce3b8948d58d8d3 SHA1: f52c102f647504f6d19df6bf6ec93aaa7f16bf57 SHA256: a27ecb96b09f03c1f651fd7f4a57579d4be3d29fe525417fdd0b37f8bc0b9e26 SHA512: be94563173ab18b32f60a53808b4f222cd7a6cbda520699d03f9b841054a1328b6f13cf5c783b0557423c6736a9bc6343a085103a9d2466dfcdb7db30732d49f Homepage: https://cran.r-project.org/package=targets Description: CRAN Package 'targets' (Dynamic Function-Oriented 'Make'-Like Declarative Pipelines) Pipeline tools coordinate the pieces of computationally demanding analysis projects. The 'targets' package is a 'Make'-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, ). Package: r-cran-tariff Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tariff_1.0.5-1.ca2404.1_all.deb Size: 55648 MD5sum: 3bfd1c2aadfcf85d1866476eeba4f31d SHA1: 47bad47194b6fa62877102e1910281b533f83ac6 SHA256: c08aae679671ae866786cb0e9bb6742b78b49b00be2001042a1a0e28807e0b2e SHA512: 08ea254b3e1715de0e9fb2ee237fbe6e239d3c54dac94b60168612bbdb9ffcfd20adfff4c2ff9440d16e6e5b530736b036bfcc7d96650dbaae4967c3af7b3d63 Homepage: https://cran.r-project.org/package=Tariff Description: CRAN Package 'Tariff' (Replicate Tariff Method for Verbal Autopsy) Implement the Tariff algorithm for coding cause-of-death from verbal autopsies. The Tariff method was originally proposed in James et al (2011) and later refined as Tariff 2.0 in Serina, et al. (2015) . Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist between the this implementation and the implementation available from IHME. Package: r-cran-tashiny Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-igraph, r-cran-tm, r-cran-snowballc, r-cran-dplyr, r-cran-wordcloud2 Filename: pool/dists/noble/main/r-cran-tashiny_0.1.0-1.ca2404.1_all.deb Size: 71202 MD5sum: 1ebf24c282377490c79a6dd4960a93ab SHA1: 43e5b0af4d01d86b9fae14f119df0c23a13d097f SHA256: f0c855db93d6ae4598d55b765e7b6315a41b97557aadd14ea0b406a42bf6b499 SHA512: 37a1b4c17f768a4fe32c66f13ed8fe2b9f7a38b75645301ce40fbed789d0aa8b1e03dc7d2c99418e524a3bfbacb9b2a3f983733e9a56974002072875cb446ffd Homepage: https://cran.r-project.org/package=TAShiny Description: CRAN Package 'TAShiny' ('Text Analyzer Shiny') Interactive shiny application for working with textmining and text analytics. Various visualizations are provided. Package: r-cran-tashu Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate, r-cran-dplyr, r-cran-randomforest, r-cran-plyr, r-cran-reshape2, r-cran-rcolorbrewer, r-cran-drat Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tashu_0.1.1-1.ca2404.1_all.deb Size: 83068 MD5sum: b565da59d668b63562e64e796344f868 SHA1: 56ab9778d5c6b1466d65b1d4250460642250782d SHA256: 5ef3bb43c85ee7e19fa68f23b6f4035c42c83ed343e477b668674b4b38af0e54 SHA512: 4d96684e13e72d46ba3665efe48eeb2b58f24438cc78ba4da5f93c46bd7b017d3687ac7eea50ec1f3fd1aca6a9127645347d18c9b2b394b0b662d79bfed76838 Homepage: https://cran.r-project.org/package=tashu Description: CRAN Package 'tashu' (Analysis and Prediction of Bicycle Rental Amount) Provides functions for analyzing citizens' bicycle usage pattern and predicting rental amount on specific conditions. Functions on this package interacts with data on 'tashudata' package, a 'drat' repository. 'tashudata' package contains rental/return history on public bicycle system('Tashu'), weather for 3 years and bicycle station information. To install this data package, see the instructions at . top10_stations(), top10_paths() function visualizes image showing the most used top 10 stations and paths. daily_bike_rental() and monthly_bike_rental() shows daily, monthly amount of bicycle rental. create_train_dataset(), create_test_dataset() is data processing function for prediction. Bicycle rental history from 2013 to 2014 is used to create training dataset and that on 2015 is for test dataset. Users can make random-forest prediction model by using create_train_model() and predict amount of bicycle rental in 2015 by using predict_bike_rental(). Package: r-cran-taskqueue Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-settings, r-cran-stringr, r-cran-rpostgres, r-cran-ggplot2, r-cran-whisker, r-cran-rlang, r-cran-shiny, r-cran-dbi, r-cran-ssh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-taskqueue_0.2.0-1.ca2404.1_all.deb Size: 173838 MD5sum: 903794f9ecf309946dd47937216a6091 SHA1: ded7d63db29d3b086ff3e3b24ab64001ce08f776 SHA256: f0d8c86485b4237f540618b7eb922e163decb0c6c2d194fae9aae69c2e4008dd SHA512: 68c74f3e4838e98a644e317e717699df1f8a129af958a3f541aeb9b2ad99183716e4bc84db3ad4ba5884ec530a5331a209748c571114fbb6405af77d8719aa16 Homepage: https://cran.r-project.org/package=taskqueue Description: CRAN Package 'taskqueue' (Task Queue for Parallel Computing Based on PostgreSQL) Implements a task queue system for asynchronous parallel computing using 'PostgreSQL' as a backend. 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This package interacts with the 'Tautulli' API of any specified server to get said data into R. The 'Tautulli' API documentation is available at . 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Supported taxonomic authorities include the Integrated Taxonomic Information System ('ITIS'), National Center for Biotechnology Information ('NCBI'), Global Biodiversity Information Facility ('GBIF'), Catalogue of Life ('COL'), and Open Tree Taxonomy ('OTT'). Name and identifier resolution using 'LMDB' can be hundreds of times faster than either relational databases or internet-based queries. Precise data provenance information for data derived from naming providers is also included. 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This method assumes that the effects of sequencing depth vary for each taxon on their mean and also incorporates a rational link of zero probability and taxon dispersion as a function of sequencing depth. Ziyue Wang, Dillon Lloyd, Shanshan Zhao, Alison Motsinger-Reif (2023) . 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Classical correspondence analysis (CA) is a statistical method to analyse 2-dimensional tables of positive numbers and is typically applied to contingency tables (Benzecri, J.-P. (1973). L'Analyse des Donnees. Volume II. L'Analyse des Correspondances. Paris, France: Dunod). Classical CA is based on the Euclidean distance. Taxicab CA is like classical CA but is based on the Taxicab or Manhattan distance. For some tables, Taxicab CA gives more informative results than classical CA. Package: r-cran-taxize Architecture: all Version: 0.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1750 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-crul, r-cran-xml2, r-cran-jsonlite, r-cran-ape, r-cran-zoo, r-cran-data.table, r-cran-tibble, r-cran-rredlist, r-cran-rotl, r-cran-ritis, r-cran-worrms, r-cran-natserv, r-cran-wikitaxa, r-cran-r6, r-cran-crayon, r-cran-cli, r-cran-phangorn, r-cran-lifecycle, r-cran-curl, r-cran-stringi Suggests: r-cran-testthat, r-cran-vegan, r-cran-vcr Filename: pool/dists/noble/main/r-cran-taxize_0.10.1-1.ca2404.1_all.deb Size: 1548732 MD5sum: 130515e21a9975293c7aa959163f94c3 SHA1: d170989555d8a6ffee2f45194e3c6498151b9596 SHA256: 36796fb03f5944c8d52b59b42b0e0341d7db4aa16e91adc8861d98d6f428524f SHA512: 10abbe3f40d3f17bba48cca3823fde867eb0b464c28e201b674bcf12a707140d20aa8d50bb37bb46d64bf66e4a763e93eb117b84e8adb9977ed2defe59c442b3 Homepage: https://cran.r-project.org/package=taxize Description: CRAN Package 'taxize' (Taxonomic Information from Around the Web) Interacts with a suite of web application programming interfaces (API) for taxonomic tasks, such as getting database specific taxonomic identifiers, verifying species names, getting taxonomic hierarchies, fetching downstream and upstream taxonomic names, getting taxonomic synonyms, converting scientific to common names and vice versa, and more. Some of the services supported include 'NCBI E-utilities' (), 'Encyclopedia of Life' (), 'Global Biodiversity Information Facility' (), and many more. Links to the API documentation for other supported services are available in the documentation for their respective functions in this package. Package: r-cran-taxizedb Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-dbi, r-cran-rsqlite, r-cran-dplyr, r-cran-tibble, r-cran-rlang, r-cran-readr, r-cran-dbplyr, r-cran-magrittr, r-cran-hoardr, r-cran-vroom Suggests: r-cran-taxize, r-cran-testthat Filename: pool/dists/noble/main/r-cran-taxizedb_0.3.2-1.ca2404.1_all.deb Size: 142964 MD5sum: 26a239c55d4f8c5178a8c51334122ea7 SHA1: 3207ab961d2dbeff7312ecbe51ffde274488059f SHA256: ac341660812b548dd5348a1e20e68ba7c01e8342c334cf8f08342d686fe29d49 SHA512: d1088bf6db98319bc934e9cf337f2fd071cf5c6a31e5fc9dfe9b0b36068b8aa96bb8f0fcce27d08d436faa73d3cf555d7c59702eefb5e64ec4c9280ab24c8d3d Homepage: https://cran.r-project.org/package=taxizedb Description: CRAN Package 'taxizedb' (Offline Access to Taxonomic Databases) Download taxonomic databases, convert them into 'SQLite' format, and query them locally for fast, reliable, and reproducible access to taxonomic data. Package: r-cran-taxlist Architecture: all Version: 0.3.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2371 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-biblio, r-cran-foreign, r-cran-stringdist, r-cran-stringi, r-cran-stringr Suggests: r-cran-ape, r-cran-knitr, r-cran-rmarkdown, r-cran-taxa, r-cran-testthat Filename: pool/dists/noble/main/r-cran-taxlist_0.3.5-1.ca2404.1_all.deb Size: 1722032 MD5sum: cbaf637ddb7a7870ce59a5f258030b4d SHA1: 452239136988e97523f369b4ce5b8ab2bc153089 SHA256: ba2a3244781fa94061f180ec1ee0e9a8e84e3f717f70e3cbf4b601d8e373ab4b SHA512: a8ac0170977049f84fe722c5701516579bfd07daee24bacb930f4410c7cadbcab2f59e8e45a5d8825f06d81534f6901626763d4ea100e903ab9b6a6dddeffa2e Homepage: https://cran.r-project.org/package=taxlist Description: CRAN Package 'taxlist' (Handling Taxonomic Lists) Handling taxonomic lists through objects of class 'taxlist'. This package provides functions to import species lists from 'Turboveg' () and the possibility to create backups from resulting R-objects. Also quick displays are implemented as summary-methods. Package: r-cran-taxnames Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-taxlist Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-taxnames_0.1.0-1.ca2404.1_all.deb Size: 27714 MD5sum: ee1578edcaf0bd4a09ca7bdf505e36e0 SHA1: e84a88bbb6be32aa089a492cd7c3e744a7bdd152 SHA256: f006dd58980be5ea6c14e0afb8488142f0db2216a84bae7cb1bc737796e23f9c SHA512: 4c38ec2780315f6e368c9a7283189d5f2cbfc1244a9f357efd97f1ad1d8bf59eef15eccc0c417a4b97b62559108112a1fcbdccbdf370d2d1a2836afe440e8b4e Homepage: https://cran.r-project.org/package=taxnames Description: CRAN Package 'taxnames' (Formatting Taxonomic Names in Markdown) A collection of functions used to format taxonomic names in Markdown documents. Those functions work with data structured according to Alvarez and Luebert (2018) . 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Given any two taxon names, retrieves their full lineages, identifies the most recent common ancestor (MRCA), and computes a dissimilarity index based on lineage depth. Outputs native dist objects, enabling direct integration with the R statistical ecosystem for hierarchical clustering, principal coordinate analysis (PCoA), and multivariate ecological analyses. Supports individual distance queries, pairwise distance matrices, clade filtering, and lineage utilities. Package: r-cran-taxonbridge Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 597 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-dplyr, r-cran-vroom, r-cran-ggplot2, r-cran-rje, r-cran-withr, r-cran-stringr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-taxonbridge_1.2.2-1.ca2404.1_all.deb Size: 479868 MD5sum: b11bc123d1dff747d41c0ef51abeec82 SHA1: 37ee092054c1ea6b791901107f704f85f7ceb274 SHA256: dfc51cc548cae4878b6d36da0e49d9171f0c53a5d51f0cd446b6d92e02af2a80 SHA512: 62bcca61606387bf80acb19cabe095efc51b5589a20cf56617f6035a7e96e2047d3d99a09524bb1558491e5c3a0ceaf82be481802e3bfaf7a5c17ba3ed49ed41 Homepage: https://cran.r-project.org/package=taxonbridge Description: CRAN Package 'taxonbridge' (Create Custom Taxonomies Based on the NCBI Taxonomy and GBIFBackbone Taxonomy) The NCBI taxonomy is a popular resource for taxonomic studies but it only contains data on species with sequence data whereas the GBIF has a more extensive coverage of extinct species. Taxonbridge is useful for the creation and analysis of custom taxonomies based on the NCBI taxonomy and GBIF backbone taxonomy. Package: r-cran-taxotools Architecture: all Version: 0.0.148-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-taxize, r-cran-wikitaxa, r-cran-plyr, r-cran-sqldf, r-cran-stringr, r-cran-stringdist, r-cran-rmarkdown, r-cran-stringi Filename: pool/dists/noble/main/r-cran-taxotools_0.0.148-1.ca2404.1_all.deb Size: 202288 MD5sum: 4e0f6553f301e451f3183a0fbd5d45bf SHA1: 4002038a53a87c016e5c3be4343852cd1278c9ea SHA256: c72b301b1bc5ca7719b4f2f4e63607cac34d08bdfda7bbcb5b62c1e40bc7667b SHA512: a59eca55aa3b74a348edef8bfce1403fd90eb14ce86710d6d8ce4a05fa3dbccfd45567f81e9eefc85ad0c12b53d93decf407ceddbf721568b3b0ca382fe3bb6d Homepage: https://cran.r-project.org/package=taxotools Description: CRAN Package 'taxotools' (Taxonomic List Processing) Taxonomic lists matching and merging, casting and melting scientific names, managing taxonomic lists from Global Biodiversity Information Facility 'GBIF' or Integrated Taxonomic Information System 'ITIS', harvesting names from Wikipedia and fuzzy matching. 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Package: r-cran-taylorrussell Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm, r-cran-shiny, r-cran-shinywidgets Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-taylorrussell_1.2.1-1.ca2404.1_all.deb Size: 38622 MD5sum: 33a8048ef6a0c6a331b90c6acf21b331 SHA1: 8fc3e14b6e1d19dc28a9663f1fb479451e221dbb SHA256: 7490afed1834e36d6334d129863549179143d675738e6520baed99d36597ae5e SHA512: f4059c19d681ea5580f8ea0f9e1d0ad27e60dad34e069fbf8519b1ee747af3944aa9b380214cd4376a3fb9348e407c210f332639e2efaab0e42115dae5ee5c77 Homepage: https://cran.r-project.org/package=TaylorRussell Description: CRAN Package 'TaylorRussell' (A Taylor-Russell Function for Multiple Predictors) The Taylor Russell model is a widely used method for assessing test validity in personnel selection tasks. The three functions in this package extend this model in a number of notable ways. TR() estimates test validity for a single selection test via the original Taylor Russell model. It extends this model by allowing users greater flexibility in argument choice. For example, users can specify any three of the four parameters (base rate, selection ratio, criterion validity, and positive predictive value) of the Taylor Russell model and estimate the remaining parameter (see the help file for examples). The TaylorRussell() function generalizes the original Taylor Russell model to allow for multiple selection tests (predictors). To our knowledge, this is the first generalization of the Taylor Russell model to allow for three or more selection tests (it is also the first to correctly handle models with two selection tests). TRDemo() is a 'shiny' program for illustrating the underlying logic of the Taylor Russell model. Taylor, HC and Russell, JT (1939) "The relationship of validity coefficients to the practical effectiveness of tests in selection: Discussion and tables" . Package: r-cran-taylorswift Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-taylorswift_0.1.0-1.ca2404.1_all.deb Size: 301456 MD5sum: 6120b7fbf875a071be00a0043186fd77 SHA1: 21202d811a746a07f1984672740e76376ed4478b SHA256: 008449f31f233dd2be23cc599637e610da8704a356f603078cf724f4a697bdd2 SHA512: d95b23ebacce732ec746883a5d4258165e03dc37161fc66ce275086961f7bc2fc47c8dc59e74f884cf350caaa8cc80af80696dc06d938aa86b5b76e58f9d43bb Homepage: https://cran.r-project.org/package=tayloRswift Description: CRAN Package 'tayloRswift' (Color Palettes Generated by Taylor Swift Albums) For when your colors absolutely should not be excluded from the narrative. 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The package is designed to assist users in visualizing and interpreting experimental data through a user-friendly interface. Each application is launched via a simple function, and users can upload data in 'Excel' format for analysis. For more information, refer to Singh, R.K. and Chaudhary, B.D. (1977, ISBN:9788176633079). Package: r-cran-tbd Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-tbd_0.1.0-1.ca2404.1_all.deb Size: 164526 MD5sum: f46a9b88ee1c9e7cad36c45a5353303b SHA1: 3e160734a8040a6ff60e416576e6dc377041d102 SHA256: 666079db54aa66658f08ec1a3f4129e83e53499c2515114587a70c639356afb1 SHA512: 39bfec62588a5f578ab75931805bbf87c9ab25afd77fc8bcec670f14399da1ddf63a22b34c0a722571d27e1915905a2415bb5789791420291727b54619258968 Homepage: https://cran.r-project.org/package=tbd Description: CRAN Package 'tbd' (Estimation of Causal Effects with Outcomes Truncated by Death) Estimation of the survivor average causal effect under outcomes truncated by death, which requires the existence of a substitution variable. It can be applied to both experimental and observational data. Package: r-cran-tbea Architecture: all Version: 1.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 910 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-rfit, r-cran-boot, r-cran-coda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-kendall Filename: pool/dists/noble/main/r-cran-tbea_1.7.0-1.ca2404.1_all.deb Size: 485706 MD5sum: d624a6bf37930bde984444cfa425f7ec SHA1: 8c035241b6f2694fb996c50f0ab284c7cb36bf27 SHA256: 9bf001f5c67322eff9a6467ce13ebc5eef9d14a9408a1624d654ac519e8e06b6 SHA512: a8ffa9f841d45509502c5eba33ae877331b26f66419eb1ba8e1575ddd698a1416cf7c2c17ef54b0282ed5bbb483475803998be17520bc983ed496196a596cec5 Homepage: https://cran.r-project.org/package=tbea Description: CRAN Package 'tbea' (Pre- And Post-Processing in Bayesian Evolutionary Analyses) Functions are provided for prior specification in divergence time estimation using fossils as well as other kinds of data. It provides tools for interacting with the input and output of Bayesian platforms in evolutionary biology such as 'BEAST2', 'MrBayes', 'RevBayes', or 'MCMCTree'. It Implements a simple measure similarity between probability density functions for comparing prior and posterior Bayesian densities, as well as code for calculating the combination of distributions using conflation of Hill (2008). Functions for estimating the origination time in collections of distributions using the x-intercept (e.g., Draper and Smith, 1998) and stratigraphic intervals (Marshall 2010) are also available. Hill, T. 2008. "Conflations of probability distributions". Transactions of the American Mathematical Society, 363:3351-3372. , Draper, N. R. and Smith, H. 1998. "Applied Regression Analysis". 1--706. Wiley Interscience, New York. , Marshall, C. R. 2010. "Using confidence intervals to quantify the uncertainty in the end-points of stratigraphic ranges". Quantitative Methods in Paleobiology, 291--316. . 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The TBF methodology has been well developed and implemented for the generalised linear model [Held et al. (2015) ] and for the Cox model [Held et al. (2016) ]. 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The TCA framework further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects; particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a three-dimensional tensor of cell-type-specific methylation levels for each individual (i.e. methylation sites by individuals by cell types) and it allows to detect cell-type-specific statistical relations (associations) with phenotypes. For more details see Rahmani et al. (2019) . Package: r-cran-tcftt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tcftt_0.1.0-1.ca2404.1_all.deb Size: 48770 MD5sum: eb5d9f29d776ebf533c36b7e1b8cd334 SHA1: 131e7f3f923f789fd36f5e3295e9458525aa5ac1 SHA256: cf0f5581ad06a04b0da9350d576f1ae0524b144c41b56c795a15feae93b05613 SHA512: a03c186c344bec5ca1e9158de48f95da3e808545dfeadf525bacfa183daf11416177c20c6e69fc8bd046e1556bd27a313ae8cd2613c1949e2471d69437faaa12 Homepage: https://cran.r-project.org/package=tcftt Description: CRAN Package 'tcftt' (Two-Sample Tests for Skewed Data) The classical two-sample t-test works well for the normally distributed data or data with large sample size. The tcfu() and tt() tests implemented in this package provide better type-I-error control with more accurate power when testing the equality of two-sample means for skewed populations having unequal variances. These tests are especially useful when the sample sizes are moderate. The tcfu() uses the Cornish-Fisher expansion to achieve a better approximation to the true percentiles. The tt() provides transformations of the Welch's t-statistic so that the sampling distribution become more symmetric. For more technical details, please refer to Zhang (2019) . Package: r-cran-tcgaretriever Architecture: all Version: 1.10.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-reshape2, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-tcgaretriever_1.10.3-1.ca2404.1_all.deb Size: 596912 MD5sum: 588af1df8da22c84bfce2530344c6aa0 SHA1: 6efc6420218563ffd935ba265e1c432706ac2423 SHA256: b1e62f0ce445ea45b82c5792730ad3eab01dc7701e0786f30592b4035606a3ba SHA512: f6076f6235f3a5492e86d8d1e7f015ee3f60463d692dcfa25374b2b6f64330646729928f8f38eba8815fe69903223912ae3712011f5cc0f08f6d3e6ce1cefdd2 Homepage: https://cran.r-project.org/package=TCGAretriever Description: CRAN Package 'TCGAretriever' (Retrieve Genomic and Clinical Data from CBioPortal IncludingTCGA Data) The Cancer Genome Atlas (TCGA) is a program aimed at improving our understanding of Cancer Biology. 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Method is detailed in: Hejblum, Skinner & Thiebaut (2015) . Package: r-cran-tciapathfinder Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-oro.dicom, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tciapathfinder_1.0.6-1.ca2404.1_all.deb Size: 86474 MD5sum: 724e76b962e805d24118e8cfb3309277 SHA1: b3f4e03cc59fdf743675889cbb58efba53b117fe SHA256: ea03f7d713d0601ed49cbb4f4877ef0b2fdd7f9db1ab2378d45c02d1051843e9 SHA512: 80a97313a10390fc368d995ab34823945778126e8c21cc22b8e8e30b4e874ee0d3cd64f7855566a0d5a6c38d91974e65afaec6da2278bd44c7131e49a6dc029e Homepage: https://cran.r-project.org/package=TCIApathfinder Description: CRAN Package 'TCIApathfinder' (Client for the Cancer Imaging Archive REST API) A wrapper for The Cancer Imaging Archive's REST API. 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Package: r-cran-tcl Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-erm, r-cran-psychotools, r-cran-ltm, r-cran-numderiv, r-cran-mass, r-cran-matrix, r-cran-lattice, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tcl_1.0.1-1.ca2404.1_all.deb Size: 279434 MD5sum: 7df6b79de21f2e79572b5b3d51788e1b SHA1: 2cbc45631be112b711441ab59c462fac056bd38e SHA256: 2216d2ee3b4978708048e0941dde139f3a1085d7d9337b2b10ca9752cdb28a9a SHA512: a5a64a5aa1bc35e969ecbc694d34c01a32d0a81e7877aa29e230355c12b5988f42078eee0ccecb8f20924357241b8b916e674ab4a516d57f3cd54a30460327e0 Homepage: https://cran.r-project.org/package=tcl Description: CRAN Package 'tcl' (Testing in Conditional Likelihood Context) An implementation of hypothesis testing in an extended Rasch modeling framework, including sample size planning procedures and power computations. Provides 4 statistical tests, i.e., gradient test (GR), likelihood ratio test (LR), Rao score or Lagrange multiplier test (RS), and Wald test, for testing a number of hypotheses referring to the Rasch model (RM), linear logistic test model (LLTM), rating scale model (RSM), and partial credit model (PCM). Three types of functions for power and sample size computations are provided. Firstly, functions to compute the sample size given a user-specified (predetermined) deviation from the hypothesis to be tested, the level alpha, and the power of the test. Secondly, functions to evaluate the power of the tests given a user-specified (predetermined) deviation from the hypothesis to be tested, the level alpha of the test, and the sample size. Thirdly, functions to evaluate the so-called post hoc power of the tests. This is the power of the tests given the observed deviation of the data from the hypothesis to be tested and a user-specified level alpha of the test. Power and sample size computations are based on a Monte Carlo simulation approach. It is computationally very efficient. The variance of the random error in computing power and sample size arising from the simulation approach is analytically derived by using the delta method. Additionally, functions to compute the power of the tests as a function of an effect measure interpreted as explained variance are provided. Draxler, C., & Alexandrowicz, R. W. (2015), . Package: r-cran-tcltk2 Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5273 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tcltk2_1.6.1-1.ca2404.1_all.deb Size: 1070342 MD5sum: 41a952c952dc6ab6fa9dad2fe088f69b SHA1: c231246a739eb6c33865988606148e00e540beb6 SHA256: 7b1f9083b61fb06e6fd02f2b6babfe6e7fccadae781d6edf7d231fa57eade30b SHA512: 44ef7e0b94cb4019601a0ef7671a93946fa0bbaa3b761411084fa61041decd32b960735ade2b9665e009af8cc4866c2e3c721ecf582830887a8fc0d157eea9fd Homepage: https://cran.r-project.org/package=tcltk2 Description: CRAN Package 'tcltk2' (Tcl/Tk Additions) A series of additional Tcl commands and Tk widgets to supplement the tcltk package. Package: r-cran-tcomp Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 561 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mcomp, r-cran-forecast Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-dplyr, r-cran-xtable Filename: pool/dists/noble/main/r-cran-tcomp_1.0.1-1.ca2404.1_all.deb Size: 483064 MD5sum: 6d65600f8f84ca1dc810c309db386421 SHA1: a542aa21b3030684fc33e4e8af9157dbcfc996db SHA256: 2f86b54bdb4385a363b41d4a6f3656f690d1b5beb6dfc1c080294fb093e442a3 SHA512: 3593272695bf97002a1e848726cc031cc70fc06719591aef6f77cf57a1cc2315eb34ffa085c126eb5bee13c9ae45bd8f63e8823c5371e69426fe12c4127e6143 Homepage: https://cran.r-project.org/package=Tcomp Description: CRAN Package 'Tcomp' (Data from the 2010 Tourism Forecasting Competition) The 1311 time series from the tourism forecasting competition conducted in 2010 and described in Athanasopoulos et al. (2011) . 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The original tcplFit() function in the tcpl R package performed basic concentration-response curvefitting to 3 models. With tcplfit2, the core tcpl concentration-response functionality has been expanded to process diverse high-throughput screen (HTS) data generated at the US Environmental Protection Agency, including targeted ToxCast, high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). tcplfit2 can be used independently to support analysis for diverse chemical screening efforts. 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Package: r-cran-tdaunif Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-tdaunif_0.2.0-1.ca2404.1_all.deb Size: 346598 MD5sum: 8e64733644d55e7550b47784805cd597 SHA1: 017ff1c370fd1e1ccb36d97f1df5acd6169841b8 SHA256: 62f4c4cb7e2cdb42dd16a9a3d488ac641f7429532addadae14dcfd0e512c81a5 SHA512: 3e2d4add33c6bf0fb2157aad8118822e12b2174c6c87983c838e6dbdc6ff80d63931e12a84fbd2a9af2b0bbab3d30d8c83f4beaffd802a9276ff7248e70dbc74 Homepage: https://cran.r-project.org/package=tdaunif Description: CRAN Package 'tdaunif' (Uniform Manifold Samplers for Topological Data Analysis) Uniform random samples from simple manifolds, sometimes with noise, are commonly used to test topological data analytic (TDA) tools. 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It uses units to ensure that parameters are properly specified and transformed before calculations. It allows separate lower and upper surface conductances to heat and water vapour, so sensible and latent heat loss are calculated for each surface separately as in Foster and Smith (1986) . It's straightforward to model leaf temperature over environmental gradients such as light, air temperature, humidity, and wind. It can also model leaf temperature over trait gradients such as leaf size or stomatal conductance. Other references are Monteith and Unsworth (2013, ISBN:9780123869104), Nobel (2009, ISBN:9780123741431), and Okajima et al. (2012) . 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Package: r-cran-telemetr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1673 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-lubridate, r-cran-zoo, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rerddap, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-telemetr_1.0-1.ca2404.1_all.deb Size: 1493522 MD5sum: 2d908a650bdfdd6d60ba6eb467ff75ee SHA1: 03439bff55aff78a02a7745719c84371df515bd2 SHA256: 5ca919760be7ba0b9a49516277cc034b411b0cadab04db6e6d7e4b9b05c537b9 SHA512: c884b1a4e17ca9490cc8afedefed41bab9563bfbf7e7dab4ffe4311b0eb12494aa4758c93370406f90c6121ecdd2b317497e1d09038345f72a3e80cc3e15c7be Homepage: https://cran.r-project.org/package=telemetR Description: CRAN Package 'telemetR' (Filter and Analyze Generalised Telemetry Data from Organisms) Analyze telemetry datasets generalized to allow any technology. The filtering steps check for false positives caused by reflected transmissions from surfaces and false pings from other noise generating equipment. The filters are based on JSATS filtering algorithms found in package 'filteRjsats' but have been generalized to allow the user to define many of the filtering variables. Additionally, this package contains scripts used to help identify an optimal maximum blanking period as defined in Capello et al (2015) . The functions were written according to their manuscript description, but have not been reviewed by the authors for accuracy. It is included here as is, without warranty. Package: r-cran-telescope Architecture: all Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2036 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-bayesm, r-cran-dirichletreg, r-cran-extradistr, r-cran-mcmcpack, r-cran-mvtnorm Suggests: r-cran-invgamma, r-cran-klar, r-cran-knitr, r-cran-mclust, r-cran-polca, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-telescope_0.2-2-1.ca2404.1_all.deb Size: 1246910 MD5sum: 79e8940185f11f456e510f667c6899b1 SHA1: c6d52c4c5faf0a31c00d394aff77174a16b5f695 SHA256: bf1ec8bb7d94b9a4a1b81426f5895aa671641a071b9026f31ea81b3d7d5397c0 SHA512: f3240087bca9c5d4462876e0a9bd08b4e6b666a321c6f4fb4d2471e522d48b620092d73a752923fee8685aa243cca298e1fd8792ad2537992fbbeac2fd43a806 Homepage: https://cran.r-project.org/package=telescope Description: CRAN Package 'telescope' (Bayesian Mixtures with an Unknown Number of Components) Fits Bayesian finite mixtures with an unknown number of components using the telescoping sampler and different component distributions. For more details see Frühwirth-Schnatter et al. (2021) , Malsiner-Walli et al. (in press) and Malsiner-Walli et al. (2026) . Package: r-cran-telp Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tcltk2, r-cran-tm, r-cran-wordcloud, r-cran-rcolorbrewer, r-cran-arules, r-cran-arulesviz, r-cran-ggplot2, r-cran-gridextra Filename: pool/dists/noble/main/r-cran-telp_1.0.3-1.ca2404.1_all.deb Size: 89118 MD5sum: 5475f53cff53c6e0da0ecb3809dd9d53 SHA1: a18076e43fe9dff0467c3431ac74b4e447d1ee3e SHA256: dabba380c1dfafaab0cc23e5ebb1e503b5d645057515f9814eddbc73ae22c57b SHA512: 5c20afe41bae1f488549929d6c64b75a2f25d7488bcbe1955e1c0b98a8b25e82039a737aebf5bdc6b925c64415708dd62ad8eda9327dafee450b2e0306b70170 Homepage: https://cran.r-project.org/package=TELP Description: CRAN Package 'TELP' (Social Representation Theory Application: The Free Evocation ofWords Technique) Using The Free Evocation of Words Technique method with some functions, this package will make a social representation and other analysis. 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Package: r-cran-telraamstats Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2618 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-config, r-cran-dplyr, r-cran-ggplot2, r-cran-httr, r-cran-jsonlite, r-cran-lubridate, r-cran-paletteer, r-cran-purrr, r-cran-reshape2, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-yaml Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-telraamstats_1.1.2-1.ca2404.1_all.deb Size: 2332022 MD5sum: 52b42775c9c0bba1cdf2ee69aefbbc03 SHA1: be31a5b4299f99576f7e31649b0f4b2547d3f08a SHA256: a5a9f731d7bfc0f4f296093def6c8299bc828a839235ad05ea19b26748276683 SHA512: ef715d1fec43e7dfa7d2e3713482d56e24d9656c7f331cb6d09926450fada7be2682d653261b2d6c2321c5d3ad1f279f5841779cf0716039e8e21ba728c9d7e0 Homepage: https://cran.r-project.org/package=telraamStats Description: CRAN Package 'telraamStats' (Retrieval and Visualization of Mobility Data from 'Telraam'Sensors) Streamline the processing of 'Telraam' data, sourced from open data mobility sensors. 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Package: r-cran-tempcont Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlme Filename: pool/dists/noble/main/r-cran-tempcont_0.1.0-1.ca2404.1_all.deb Size: 49034 MD5sum: 2cb3976f2759129548da594f5e9f24fb SHA1: 6d90831bb03e93cd59f882ecfa268c97c2d90b72 SHA256: d21bd349c32a40c155623694234d43ec8a1aafb80fd05d51530eae375a0deb64 SHA512: fe978db7fca83dc555e4c4ca5bf18e02f3d4047a6296be20b509ff98d3f84e108332c86197766127e2dfbcfc2fe10e8d064bbb9c45c885245a11cb67e535a300 Homepage: https://cran.r-project.org/package=TempCont Description: CRAN Package 'TempCont' (Temporal Contributions on Trends using Mixed Models) Method to estimate the effect of the trend in predictor variables on the observed trend of the response variable using mixed models with temporal autocorrelation. See Fernández-Martínez et al. (2017 and 2019) . 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Package: r-cran-temper Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-purrr, r-cran-imputets, r-cran-lubridate, r-cran-ggplot2, r-cran-scales Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-temper_1.1.0-1.ca2404.1_all.deb Size: 116310 MD5sum: 5408824c1d6d67b1821659fbbda7e072 SHA1: abcb286f9363eb146320fe77ee5b747c2da626a1 SHA256: a050614e9c7b820d7014dacf287c9909d9768ff8f0b69702df24b77974e8ec2d SHA512: 421d3ba1d197c2e4a6681478e2e2b5806659d6699beaa021520eb09a4f0156dc4d464ccace7accd9f195a2ca1bbef3200860a09b74e618884e05fb6c306ab58f Homepage: https://cran.r-project.org/package=temper Description: CRAN Package 'temper' (Temporal Encoder-Masked Probabilistic Ensemble Regressor) Implements a probabilistic ensemble time-series forecaster that combines an auto-encoder with a neural decision forest whose split variables are learned through a differentiable feature-mask layer. 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Package: r-cran-temperatureresponse Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-rootsolve, r-cran-minpack.lm, r-cran-aiccmodavg, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-temperatureresponse_0.2-1.ca2404.1_all.deb Size: 70372 MD5sum: 1cac1723d3e2e674ff1c8b99dbde6a0e SHA1: ea24d32e24e3d2e24ad74b150dcede02106f9bfa SHA256: 6f32346ee9cd560091cd6f2ed694c0d35df1ae684e9d80472da9cdd546ad38b2 SHA512: 494dfb42cbf32fca1a48316e4000e93b20b88892ddb37a350bebc86c45e463fb3a7f67f6d9d48324905260aa6ee782968836954b8353df0e9297e84afe838e93 Homepage: https://cran.r-project.org/package=temperatureresponse Description: CRAN Package 'temperatureresponse' (Temperature Response) Fits temperature response models to rate measurements taken at different temperatures. Etienne Low-Decarie,Tobias G. 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Package: r-cran-templateicar Architecture: all Version: 0.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 532 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-fmritools, r-cran-fmriscrub, r-cran-foreach, r-cran-ica, r-cran-matrix, r-cran-matrixstats, r-cran-pesel, r-cran-squarem Suggests: r-cran-ciftitools, r-cran-excursions, r-cran-rnifti, r-cran-oro.nifti, r-cran-gifti, r-cran-covr, r-cran-doparallel, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-templateicar_0.10.0-1.ca2404.1_all.deb Size: 480832 MD5sum: eb1ae260504e127e1955a018e8355fd5 SHA1: 2c90acfcfcac77dc4321d2ced3fc0cad6d703d16 SHA256: 5ceb1f959bf2984618e3678a7e95f96235c65abf5eb73d5c59c27ad22ec6921c SHA512: eaed6a95af1dd63c4c49883e033f21cffedae36de6018b6177b753e182ca98ce960c3d264fbba840cececd961438ad3c68fff2df818c61e8669feb3fe91c0145 Homepage: https://cran.r-project.org/package=templateICAr Description: CRAN Package 'templateICAr' (Estimate Brain Networks and Connectivity with ICA and EmpiricalPriors) Implements the template ICA (independent components analysis) model proposed in Mejia et al. (2020) and the spatial template ICA model proposed in proposed in Mejia et al. (2022) . Both models estimate subject-level brain as deviations from known population-level networks, which are estimated using standard ICA algorithms. Both models employ an expectation-maximization algorithm for estimation of the latent brain networks and unknown model parameters. Includes direct support for 'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats. Package: r-cran-templates Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-magrittr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-templates_0.4.0-1.ca2404.1_all.deb Size: 29482 MD5sum: 69001c8139bc26a6f722c20774867b1a SHA1: 7d7fd7fe301d69f44b722fe8114e85f2a5f845af SHA256: 693ec2b6e56327d85ae2cb721e6d0565ebabc8d853be37d843af9ca1e6915abc SHA512: ec94952e1276211ac94cdc5f8e4cc0832f389d8ca8aaa1d1681620c44960f4a26826b0f2049c29a4609b11f3fa1859cacea8578282616e089735dfbfa8c715e8 Homepage: https://cran.r-project.org/package=templates Description: CRAN Package 'templates' (A System for Working with Templates) Provides tools to work with template code and text in R. It aims to provide a simple substitution mechanism for R-expressions inside these templates. 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Package: r-cran-tempodisco Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 939 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rwiener Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tempodisco_2.1.0-1.ca2404.1_all.deb Size: 516934 MD5sum: 4c98f34eb50bfcf3eca171eb37829b0e SHA1: bb36abbb155610056c3b4920bca3479fb145247e SHA256: 6a73af0c4f7a0decba44ea87195dddec3d6371aca01e9dcf58972ff4a4c2a75e SHA512: 7097b9e3fe49b23d9a553be7be7e862650eaf8a388bc7845667957b6971d40b7262b151025a0af15f11005c0664e0e53f3bd614ef8d9cdb9628e7999f2c2882d Homepage: https://cran.r-project.org/package=tempodisco Description: CRAN Package 'tempodisco' (Temporal Discounting Models) Tools for working with temporal discounting data, designed for behavioural researchers to simplify data cleaning/scoring and model fitting. The package implements widely used methods such as computing indifference points from adjusting amount task (Frye et al., 2016, ), testing for non-systematic discounting per the criteria of Johnson & Bickel (2008, ), scoring questionnaires according to the methods of Kirby et al. (1999, ) and Wileyto et al (2004, ), Bayesian model selection using a range of discount functions (Franck et al., 2015, ), drift diffusion models of discounting (Peters & D'Esposito, 2020, ), and model-agnostic measures of discounting such as area under the curve (Myerson et al., 2001, ) and ED50 (Yoon & Higgins, 2008, ). Package: r-cran-temporal Architecture: all Version: 0.3.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 657 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-expint, r-cran-numderiv, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-temporal_0.3.0.2-1.ca2404.1_all.deb Size: 593504 MD5sum: f7921f9d0d6225a5da1e997a02a562eb SHA1: 777a892512698195d16a1059ee83192aedb0b3f8 SHA256: 449d5b81bc687f3f3238b637ed5d26c29fa3ff94efcc9d035330a8813f0ac796 SHA512: 465f78a54ee5dbcb7d8216dd82e57fd7b59a899b30dfba9634413b7df600f8d52b282e33841f1cd56ac5c33b83bc14697318874c3a43a3b108e57a32a9abd979 Homepage: https://cran.r-project.org/package=Temporal Description: CRAN Package 'Temporal' (Parametric Time to Event Analysis) Performs maximum likelihood based estimation and inference on time to event data, possibly subject to non-informative right censoring. FitParaSurv() provides maximum likelihood estimates of model parameters and distributional characteristics, including the mean, median, variance, and restricted mean. CompParaSurv() compares the mean, median, and restricted mean survival experiences of two treatment groups. Candidate distributions include the exponential, gamma, generalized gamma, log-normal, and Weibull. Package: r-cran-temporalforest Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wgcna, r-cran-dynamictreecut, r-cran-flashclust, r-cran-glmertree, r-cran-partykit Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-igraph, r-cran-matrix, r-cran-mass Filename: pool/dists/noble/main/r-cran-temporalforest_0.1.4-1.ca2404.1_all.deb Size: 69182 MD5sum: 8e12792ec56e6b4344070f3f38e27a81 SHA1: 5fdcf86d3a1fbc2e98e52e4edf664dc2d7d89231 SHA256: 7ec12ae5b05b406fef42b8213de8ffeffbcac9a161cc20ffced45a6c2288f810 SHA512: efbe06ce3675ce40378d13fb82c2e266130e5d11cd028086bdb42829d587dde3dae3f4759895d091bb440ed2e66c379c3dba421115fbb56c9b4fc8b5fafa76b3 Homepage: https://cran.r-project.org/package=TemporalForest Description: CRAN Package 'TemporalForest' (Network-Guided Temporal Forests for Feature Selection inHigh-Dimensional Longitudinal Data) Implements the Temporal Forest algorithm for feature selection in high-dimensional longitudinal data. The method combines time-aware network construction via weighted gene co-expression network analysis (WGCNA), module-based feature screening, and stability selection using tree-based models. This package provides tools for reproducible longitudinal analysis, closely following the methodology described in Shao, Moore, and Ramirez (2025) . Package: r-cran-temporalgssa Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-temporalgssa_1.0.1-1.ca2404.1_all.deb Size: 25406 MD5sum: 78397a5e696fefb8a00b407955f0cd40 SHA1: e93825dcffa2d56ae3f0781779469f89dfda4fa7 SHA256: 53b77b6d8502d46dfe32f227816ad36953544fd7dedc23e58f60aa7e310ba6a8 SHA512: 24f4b1d31868fd9ab522dcae0a9e88a3a6f3c30590afe6c2d91020286295c4196c4c4c86d0c4e1b8bc7413aacb4a23d6a8c206e8673eb816731214b0d702d395 Homepage: https://cran.r-project.org/package=TemporalGSSA Description: CRAN Package 'TemporalGSSA' (Outputs Temporal Profile of Molecules from Stochastic SimulationAlgorithm Generated Datasets) The data that is generated from independent and consecutive 'GillespieSSA' runs for a generic biochemical network is formatted as rows and constitutes an observation. The first column of each row is the computed timestep for each run. Subsequent columns are used for the number of molecules of each participating molecular species or "metabolite" of a generic biochemical network. In this way 'TemporalGSSA', is a wrapper for the R-package 'GillespieSSA'. The number of observations must be at least 30. This will generate data that is statistically significant. 'TemporalGSSA', transforms this raw data into a simulation time-dependent and metabolite-specific trial. Each such trial is defined as a set of linear models (n >= 30) between a timestep and number of molecules for a metabolite. Each linear model is characterized by coefficients such as the slope, arbitrary constant, etc. The user must enter an integer from 1-4. These specify the statistical modality utilized to compute a representative timestep (mean, median, random, all). These arguments are mandatory and will be checked. Whilst, the numeric indicator "0" indicates suitability, "1" prompts the user to revise and re-enter their data. An optional logical argument controls the output to the console with the default being "TRUE" (curtailed) whilst "FALSE" (verbose). The coefficients of each linear model are averaged (mean slope, mean constant) and are incorporated into a metabolite-specific linear regression model as the dependent variable. The independent variable is the representative timestep chosen previously. The generated data is the imputed molecule number for an in silico experiment with (n >=30) observations. These steps can be replicated with multiple set of observations. The generated "technical replicates" can be statistically evaluated (mean, standard deviation) and will constitute simulation time-dependent molecules for each metabolite. For SSA-generated datasets with varying simulation times 'TemporalGSSA' will generate a simulation time-dependent trajectory for each metabolite of the biochemical network under study. The relevant publication with the mathematical derivation of the algorithm is (2022, Journal of Bioinformatics and Computational Biology) . The algorithm has been deployed in the following publications (2021, Heliyon) and (2016, Journal of Theoretical Biology) . Package: r-cran-tempr Architecture: all Version: 0.10.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tempr_0.10.1.1-1.ca2404.1_all.deb Size: 253848 MD5sum: cfc66456cc0c2f98cfbc79119529b746 SHA1: ea6e6807266bd7abbd313221f38246974a0c4c1f SHA256: fbe5871fdff01a26919d498c61b60440306d562a4474885dba11b3fa017778f2 SHA512: 9bebb3d2b86b2403e1718a9039bdc9090b379535f9ffec71fbe288106bd6f3a042363d7434118fbc7189297cca44807428ba1ca720d8f01f54bc2672d19ce555 Homepage: https://cran.r-project.org/package=tempR Description: CRAN Package 'tempR' (Temporal Sensory Data Analysis) Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) , Castura, Baker, and Ross (2016) , and Pineau et al. (2009) . Package: r-cran-tempstable Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1063 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-copula, r-cran-doparallel, r-cran-foreach, r-cran-gsl, r-cran-hypergeo, r-cran-moments, r-cran-numderiv, r-cran-stabledist, r-cran-stableestim, r-cran-rootsolve, r-cran-vgam Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-v8 Filename: pool/dists/noble/main/r-cran-tempstable_0.2.2-1.ca2404.1_all.deb Size: 876152 MD5sum: 2b76eb3514df6a9a8a782d378ad3260c SHA1: 287551ea8d20dbbb928d9ee91b22299ad42402e1 SHA256: 2b86bab12fbb014de40b94af443e493d61667da38301cb5dc9dcde0991cc0b10 SHA512: ab081ec97441f35b2127228e2c371c4ba02ff32c995880760c5436475b88bc44c1d5e4530220c43bf80e3be17b9770256c1947a70df1386a9bb541583b549507 Homepage: https://cran.r-project.org/package=TempStable Description: CRAN Package 'TempStable' (A Collection of Methods to Estimate Parameters of DifferentTempered Stable Distributions) A collection of methods to estimate parameters of different tempered stable distributions (TSD). Currently, there are seven different tempered stable distributions to choose from: Tempered stable subordinator distribution, classical TSD, generalized classical TSD, normal TSD, modified TSD, rapid decreasing TSD, and Kim-Rachev TSD. The package also provides functions to compute density and probability functions and tools to run Monte Carlo simulations. This package has already been used for the estimation of tempered stable distributions (Massing (2023) ). The following references form the theoretical background for various functions in this package. References for each function are explicitly listed in its documentation: Bianchi et al. (2010) Bianchi et al. (2011) Carrasco (2017) Feuerverger (1981) Hansen et al. (1996) Hansen (1982) Hofert (2011) Kawai & Masuda (2011) Kim et al. (2008) Kim et al. (2009) Kim et al. (2010) Kuechler & Tappe (2013) Rachev et al. (2011) . Package: r-cran-tempted Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3238 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-np, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-tempted_0.1.1-1.ca2404.1_all.deb Size: 3092902 MD5sum: a47f47420598603cd5c89baa7d01411f SHA1: 2f655ec0dd04597c8efa0783e386ca24d58fe6a9 SHA256: c00864ddaec9bf9a709f07b1f0c13b3d59f93742bb2d34c98b9cebe7572530d4 SHA512: c4a217c3dc248c51d0cc2aad8e5466e2cd9fa3b799190c3d2517aa79a8e464bd2357c14c525b0eeff19f0597458baa99b50c8d9b2cd1ac20e7bfc4d8f63c7473 Homepage: https://cran.r-project.org/package=tempted Description: CRAN Package 'tempted' (Temporal Tensor Decomposition, a Dimensionality Reduction Toolfor Longitudinal Multivariate Data) TEMPoral TEnsor Decomposition (TEMPTED), is a dimension reduction method for multivariate longitudinal data with varying temporal sampling. It formats the data into a temporal tensor and decomposes it into a summation of low-dimensional components, each consisting of a subject loading vector, a feature loading vector, and a continuous temporal loading function. These loadings provide a low-dimensional representation of subjects or samples and can be used to identify features associated with clusters of subjects or samples. TEMPTED provides the flexibility of allowing subjects to have different temporal sampling, so time points do not need to be binned, and missing time points do not need to be imputed. Package: r-cran-tendril Architecture: all Version: 2.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1827 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-plyr, r-cran-reshape2, r-cran-magrittr, r-cran-scales, r-cran-plotly Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-tendril_2.0.4-1.ca2404.1_all.deb Size: 1258646 MD5sum: 0387f4b626ed0f148a3bc312045ca2df SHA1: 0254e25c80d080936b8b0fb5d53163780f2e4169 SHA256: 96898c6464d364736d8e9cedf3bcf31f6f2e1b2a2668d00b4786ed6ca959c3dc SHA512: 94a7ab2befb9266bc94dca68f88ea10450102bcfa022853adc34dd9adfb06da7482e22e343d56767c3483655d9b594fd2a25c58dac59b59cdaae0b9feb49b16f Homepage: https://cran.r-project.org/package=Tendril Description: CRAN Package 'Tendril' (Compute and Display Tendril Plots) Compute the coordinates to produce a tendril plot. In the tendril plot, each tendril (branch) represents a type of events, and the direction of the tendril is dictated by on which treatment arm the event is occurring. If an event is occurring on the first of the two specified treatment arms, the tendril bends in a clockwise direction. If an event is occurring on the second of the treatment arms, the tendril bends in an anti-clockwise direction. Ref: Karpefors, M and Weatherall, J., "The Tendril Plot - a novel visual summary of the incidence, significance and temporal aspects of adverse events in clinical trials" - JAMIA 2018; 25(8): 1069-1073 . Package: r-cran-tenispolar Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tenispolar_0.1.4-1.ca2404.1_all.deb Size: 21790 MD5sum: a648e5366b0447b3e470016a10709081 SHA1: 2f354bd9dac96adce6a84beef780903d3c0ff04d SHA256: 3a623ed1906e2de48e83efa42768fe8b39f52b9a76becf05549b0d748b7dcc42 SHA512: 9ff8cef7d47fac8bf2a93cd89c43d6388b0941b2c708e6c77a21b86e83a6febd9f34cf0a26f7b21351e26b17631c2466860a9977c08fa392a9b7787123aa7255 Homepage: https://cran.r-project.org/package=tenispolaR Description: CRAN Package 'tenispolaR' (Provides ZENIT-POLAR Substitution Cipher Method of Encryption) Implementation of ZENIT-POLAR substitution cipher method of encryption using by default the TENIS-POLAR cipher. This last cipher of encryption became famous through the collection of Brazilian books "Os Karas" by the author Pedro Bandeira. For more details, see "A Cryptographic Dictionary" (GC&CS, 1944). Package: r-cran-tenm Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2698 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-terra, r-cran-sf, r-cran-purrr, r-cran-dplyr, r-cran-stringr, r-cran-rgl, r-cran-future, r-cran-tidyr, r-cran-furrr, r-cran-lubridate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tenm_0.5.1-1.ca2404.1_all.deb Size: 1007562 MD5sum: 4d89a8a442d31838fb840c5d44fd2e23 SHA1: 1ce1b93b9323fb18e9fa5cb4b0f40f33056a8660 SHA256: f2249a09160250f908dd4ac494dbee09bba1816c197bf176e4ea20cd8da26743 SHA512: e898d92a984366fb0315ce4025debef22b29e70eaf0ba2dc32dd9f270b74d7d6dd9e9510374be9d20ef7b668a2c5dff14a1eda6b6c7a33f1ddf757587ef99b85 Homepage: https://cran.r-project.org/package=tenm Description: CRAN Package 'tenm' (Temporal Ecological Niche Models) Implements methods and functions to calibrate time-specific niche models (multi-temporal calibration), letting users execute a strict calibration and selection process of niche models based on ellipsoids, as well as functions to project the potential distribution in the present and in global change scenarios.The 'tenm' package has functions to recover information that may be lost or overlooked while applying a data curation protocol. This curation involves preserving occurrences that may appear spatially redundant (occurring in the same pixel) but originate from different time periods. A novel aspect of this package is that it might reconstruct the fundamental niche more accurately than mono-calibrated approaches. The theoretical background of the package can be found in Peterson et al. (2011). Package: r-cran-tensor Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 45 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tensor_1.5.1-1.ca2404.1_all.deb Size: 15422 MD5sum: e67bc890928a48f7ca0f1cf57ae1bed5 SHA1: 518224f534ed10d556fa15ef8f209e5e94fa5446 SHA256: dcf7845216f46a2ba4873d403d4fa24da4a30fff4a520f2a871d898dd644ad81 SHA512: 2b4fd9c7ec73a9cbc48423ac36bfc22a1f18c1125cdd74f4f838def017396e56399b09a8f13133e59877eeeb565dfe1106fc84cf2696ed6f3993afd641acdccd Homepage: https://cran.r-project.org/package=tensor Description: CRAN Package 'tensor' (Tensor Product of Arrays) The tensor product of two arrays is notionally an outer product of the arrays collapsed in specific extents by summing along the appropriate diagonals. Package: r-cran-tensorbf Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tensor Filename: pool/dists/noble/main/r-cran-tensorbf_1.0.2-1.ca2404.1_all.deb Size: 95776 MD5sum: 41eac2287f5bfc50ffda9a910a3daaef SHA1: 04e31a4c16ef520b082190f9afa77cad39369d31 SHA256: fd673b4b9fd2695d1d8acfbc513f8cef99744dd2d73071865c588597a7756f7d SHA512: 10b461b1ac3ec356ee99aeec56151b4d168e56d6a5bc3d5ce43b934c3ebd459c707b4493ebc006929144f2c2bec879c10cfcd0ee9a9d0412e00ebf6a84ef31b2 Homepage: https://cran.r-project.org/package=tensorBF Description: CRAN Package 'tensorBF' (Bayesian Tensor Factorization) Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling. Package: r-cran-tensorcomplete Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-tensorregress, r-cran-mass Filename: pool/dists/noble/main/r-cran-tensorcomplete_0.2.0-1.ca2404.1_all.deb Size: 87962 MD5sum: 29319f892f453139f808049f36e8fefe SHA1: 82c3e80392a53e33a16cee6c76df171215135ac6 SHA256: 0ec814bc85fde9f071a669241280707c413b5c9feacdbd5fe085e49bee216b26 SHA512: a87c40dcf20b75a16b2208f2e661c3eccf15b2a359972bb9b3e35533e718f9b81bf86a296ef7d14723dbc7fa1696e74ceaef51499e3b410b8ceeaea21c3d11d3 Homepage: https://cran.r-project.org/package=TensorComplete Description: CRAN Package 'TensorComplete' (Tensor Noise Reduction and Completion Methods) Efficient algorithms for tensor noise reduction and completion. This package includes a suite of parametric and nonparametric tools for estimating tensor signals from noisy, possibly incomplete observations. The methods allow a broad range of data types, including continuous, binary, and ordinal-valued tensor entries. The algorithms employ the alternating optimization. The detailed algorithm description can be found in the following three references. Package: r-cran-tensorflow Architecture: all Version: 2.20.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-config, r-cran-processx, r-cran-reticulate, r-cran-tfruns, r-cran-yaml, r-cran-tfautograph, r-cran-rstudioapi, r-cran-lifecycle Suggests: r-cran-testthat, r-cran-keras3, r-cran-pillar, r-cran-withr, r-cran-callr Filename: pool/dists/noble/main/r-cran-tensorflow_2.20.0-1.ca2404.1_all.deb Size: 214576 MD5sum: d0a9afec15520ceb80ae7a9e5147a2de SHA1: 8261bd3120889e387333e3f5a8191284f762491f SHA256: ebc773ba6048e8a0a8c5b0baa0fdd3311387d1e7f800122f357a3544377bfd1d SHA512: c954d05d6b75236dbe0b7381f4a8964fc15ceafbf8c6a2066950c98e0969a9ea3e4be9b1d2aa12c6f6d8730f7041e30f1018b961c2996b6ddf5e6a8f39fc27d6 Homepage: https://cran.r-project.org/package=tensorflow Description: CRAN Package 'tensorflow' (R Interface to 'TensorFlow') Interface to 'TensorFlow' , an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, server, or mobile device with a single 'API'. 'TensorFlow' was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well. Package: r-cran-tensorpreave Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1030 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rtensor, r-cran-mass, r-cran-pracma Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tensorpreave_1.1.0-1.ca2404.1_all.deb Size: 965232 MD5sum: 1a4549d0aa45af2bbed7e99bceb5a47f SHA1: 0eca82dac3ae3597044da87530fffdcd587ec9ef SHA256: 66435601a3fcda7ab8d62e96df58a2832777e59aa50edad685225d51adf42690 SHA512: 092af8f76c4104d70557821dacd838691a0cee64cca2891eb344017a6c3d5da2ffe8d3ec7e601e6e377a77f9030208d8e934145fb5d501d8016fb1f95c6bf910 Homepage: https://cran.r-project.org/package=TensorPreAve Description: CRAN Package 'TensorPreAve' (Rank and Factor Loadings Estimation in Time Series Tensor FactorModels) A set of functions to estimate rank and factor loadings of time series tensor factor models. A tensor is a multidimensional array. To analyze high-dimensional tensor time series, factor model is a major dimension reduction tool. 'TensorPreAve' provides functions to estimate the rank of core tensors and factor loading spaces of tensor time series. More specifically, a pre-averaging method that accumulates information from tensor fibres is used to estimate the factor loading spaces. The estimated directions corresponding to the strongest factors are then used for projecting the data for a potentially improved re-estimation of the factor loading spaces themselves. A new rank estimation method is also implemented to utilizes correlation information from the projected data. See Chen and Lam (2023) for more details. Package: r-cran-tensorregress Architecture: all Version: 5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-mass Filename: pool/dists/noble/main/r-cran-tensorregress_5.1-1.ca2404.1_all.deb Size: 539914 MD5sum: c94a2b117265e4445db772de3e5dcc4d SHA1: 1482a28b33a4a4d1ea277cdab5a073063c7e6f22 SHA256: 351d6871ea6e454d65e98cbcee447d82415616303a88ae8f1e91d6d0f23907da SHA512: 833bcba55718319ffe1b54bd624ce4587f21133004c82281df75ad6617c0f19e7a09efb95d339dc5d68543b99774a45982bf26423cc4f32dceedbbe0507feff5 Homepage: https://cran.r-project.org/package=tensorregress Description: CRAN Package 'tensorregress' (Supervised Tensor Decomposition with Side Information) Implement the alternating algorithm for supervised tensor decomposition with interactive side information. Details can be found in the publication Hu, Jiaxin, Chanwoo Lee, and Miaoyan Wang. "Generalized Tensor Decomposition with features on multiple modes." Journal of Computational and Graphical Statistics, Vol. 31, No. 1, 204-218, 2022 . Package: r-cran-tensortest2d Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3373 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-abind, r-cran-glmnet, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tensortest2d_1.1.2-1.ca2404.1_all.deb Size: 3417608 MD5sum: 9d3126a65f3b93d9ca797773ae0654ca SHA1: bbb9864414c44ed004a4b7410c7bbb61a4dd4dc5 SHA256: eef08bf52c5690892445415dd8ac7b7aa0aa3bc03e9f4faef74d6dba2c82aa07 SHA512: 9469fb72fc9a2913c7c9cb6a3e2ba8ea86be391c2ba2a18859f70516768fcc9bec9237d297ac2efc29eca5234d16a8db19095453e5022749262bb083d00cfaf2 Homepage: https://cran.r-project.org/package=TensorTest2D Description: CRAN Package 'TensorTest2D' (Fitting Second-Order Tensor Data) An implementation of fitting generalized linear models on second-order tensor type data. The functions within this package mainly focus on parameter estimation, including parameter coefficients and standard deviation. 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The tools in this package rely on using any discrete transformation (e.g. Fast Fourier Transform (FFT)). Standard tools included are the Eigenvalue decomposition of a tensor, the QR decomposition and LU decomposition. Other functionality includes the inverse of a tensor and the transpose of a symmetric tensor. Functionality in the package is outlined in Kernfeld, E., Kilmer, M., and Aeron, S. (2015) . Package: r-cran-tensorts Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tensor, r-cran-rtensor, r-cran-expm, r-cran-mass, r-cran-abind, r-cran-matrix, r-cran-pracma Filename: pool/dists/noble/main/r-cran-tensorts_1.0.2-1.ca2404.1_all.deb Size: 225978 MD5sum: 7176f31b300c2dfcda63bdb9f307fdd7 SHA1: 85020c8798f80e5ca4b29809e5f21afa230cbe89 SHA256: 52475de544107253c0dfb3377c592099ab25ebcdcd2b65b91082f11bdf9797ea SHA512: ccc44388bcc623c9bfd0707423717b70d00bd6378a70271a6f13baa2e86336d60899f027ebb0658975fe8f4cec31a86b350af197ec23024299575877223e7412 Homepage: https://cran.r-project.org/package=tensorTS Description: CRAN Package 'tensorTS' (Factor and Autoregressive Models for Tensor Time Series) Factor and autoregressive models for matrix and tensor valued time series. We provide functions for estimation, simulation and prediction. The models are discussed in Li et al (2021) , Chen et al (2020) , Chen et al (2020) , and Xiao et al (2020) . 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For estimation, maximum likelihood and Bayesian equivariant estimation procedures are implemented. For testing, a likelihood ratio testing procedure is available. This package also contains additional functions for manipulating and decomposing tensor data sets. This work was partially supported by NSF grant DMS-1505136. Details of the methods are described in Gerard and Hoff (2015) and Gerard and Hoff (2016) . 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Package: r-cran-tern.mmrm Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1299 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tern, r-cran-checkmate, r-cran-cowplot, r-cran-dplyr, r-cran-emmeans, r-cran-formatters, r-cran-generics, r-cran-ggplot2, r-cran-lifecycle, r-cran-magrittr, r-cran-mmrm, r-cran-parallelly, r-cran-rlang, r-cran-rtables, r-cran-tidyr Suggests: r-cran-broom, r-cran-knitr, r-cran-maditr, r-cran-matrix, r-cran-rmarkdown, r-cran-testthat, r-cran-tmb, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-tern.mmrm_0.3.3-1.ca2404.1_all.deb Size: 515514 MD5sum: c5591a3d4e959d034f1823ed52f05643 SHA1: e7bb849e64eb22b3469878c419fa35d6539f3007 SHA256: 9923fced94eb98aacfca4393119a09149e1d007748f6aa149e2c617cb7a0be70 SHA512: 249ee8322fc18d02cc857834e564424727d23ce83f33fc9412634d8620d6ac5e5b73754d23669aca412388012f791463d5f0456e89c3259edbab01f7c8993600 Homepage: https://cran.r-project.org/package=tern.mmrm Description: CRAN Package 'tern.mmrm' (Tables and Graphs for Mixed Models for Repeated Measures (MMRM)) Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see for example Cnaan, Laird and Slasor (1997) . This package provides an interface for fitting MMRM within the 'tern' framework by Zhu et al. (2023) and tabulate results easily using 'rtables' by Becker et al. (2023). It builds on 'mmrm' by Sabanés Bové et al. (2023) for the actual MMRM computations. Package: r-cran-tern.rbmi Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1158 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rbmi, r-cran-tern, r-cran-broom, r-cran-checkmate, r-cran-formatters, r-cran-lifecycle, r-cran-magrittr, r-cran-rtables Suggests: r-cran-bh, r-cran-dplyr, r-cran-knitr, r-cran-matrix, r-cran-rcppeigen, r-cran-rmarkdown, r-cran-rstan, r-cran-testthat, r-cran-tidyr, r-cran-v8 Filename: pool/dists/noble/main/r-cran-tern.rbmi_0.1.6-1.ca2404.1_all.deb Size: 363750 MD5sum: 011ec14d2f9e65b05f50dea06e68f78b SHA1: ba2e99087c1d9ec82ce3b7b6a49a0108f619d308 SHA256: 09f6be22b08417a27d41f672c7a2bf57704c3028d23f4ab1638c9861aec49719 SHA512: 487f7b00a4647bc647552c94a5d35dd960b5f5fa5d22e07cb9a644313ee777a4caf59e019281e87f76f04407412e9bf0d13117f2404417a3156fb81c7152d96c Homepage: https://cran.r-project.org/package=tern.rbmi Description: CRAN Package 'tern.rbmi' (Create Interface for 'RBMI' and 'tern') 'RBMI' implements standard and reference based multiple imputation methods for continuous longitudinal endpoints (Gower-Page et al. (2022) ). This package provides an interface for 'RBMI' uses the 'tern' framework by Zhu et al. (2023) and tabulate results easily using 'rtables' by Becker et al. (2023). Package: r-cran-tern Architecture: all Version: 0.9.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9753 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rtables, r-cran-broom, r-cran-car, r-cran-checkmate, r-cran-cowplot, r-cran-dplyr, r-cran-emmeans, r-cran-forcats, r-cran-formatters, r-cran-ggplot2, r-cran-gridextra, r-cran-gtable, r-cran-labeling, r-cran-lifecycle, r-cran-magrittr, r-cran-mass, r-cran-nestcolor, r-cran-rdpack, r-cran-rlang, r-cran-scales, r-cran-survival, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-lattice, r-cran-lubridate, r-cran-rmarkdown, r-cran-sass, r-cran-stringr, r-cran-svglite, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-tern_0.9.10-1.ca2404.1_all.deb Size: 4519940 MD5sum: d91a758a49b2e9cca9f1d82833eb1377 SHA1: f25ca0f68f96baf14d7b55f62214faf6aaa0b416 SHA256: 1d407b6e70b6b0936c8b0b2f5df255038548a089e2ca076bd5c3ef2b06dbb0e3 SHA512: eb7e81c8a59c3296ec1413b2122b376eb5ed5291b472eea4a293ef18b507aa2e5f67d82e2ed4e6a035eb2479c35530cd68bb21517ec68cd7e3b949578c3ce9ec Homepage: https://cran.r-project.org/package=tern Description: CRAN Package 'tern' (Create Common TLGs Used in Clinical Trials) Table, Listings, and Graphs (TLG) library for common outputs used in clinical trials. Package: r-cran-ternary Architecture: all Version: 2.3.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6455 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-plottools, r-cran-shiny, r-cran-sp, r-cran-treedist Suggests: r-cran-colourpicker, r-cran-knitr, r-cran-readxl, r-cran-rmarkdown, r-cran-shinyjs, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-ternary_2.3.6-1.ca2404.1_all.deb Size: 2852502 MD5sum: 604f36f09cf32e60fa1a4136237e170a SHA1: d6929efc957388413d797f84077bbc8390eb2ede SHA256: 77d6434b6a6e1258c44e51ea11aa00051030c4a876c941edc1062cc9f8f12ab9 SHA512: 4f20c0dccf9bd91f629360ba88385edf2329b5a09c2de3979ba697ef18c78c2ec26207820a0015aa676739f860e780955ceb79c72397604be7c7741578b49a4f Homepage: https://cran.r-project.org/package=Ternary Description: CRAN Package 'Ternary' (Create Ternary and Holdridge Plots) Plots ternary diagrams (simplex plots / Gibbs triangles) and Holdridge life zone plots using the standard graphics functions. 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The package streamlines the analytical workflow by detecting variable types and applying appropriate statistical tests (Welch t-test, Wilcoxon rank-sum, Welch ANOVA, Kruskal-Wallis, Chi-squared, or Fisher's exact test). Results are formatted as 'tibble' objects and can be exported to 'Word' or 'Excel' using the 'officer', 'flextable', and 'writexl' packages. Optional pairwise post-hoc testing for three-group comparisons (Games-Howell and Dunn's test) is available via the 'rstatix' package. Example data are derived from the landmark adjuvant colon cancer trial described in Moertel et al. (1990) . 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Supports scenario-based planning with budget-constrained optimization, optional impassable areas, packaged parity fixtures, and comparative before-and-after connectivity metrics. The package exposes structural, movement-oriented, and species-oriented strategies in a reproducible workflow aligned with a companion GIS plugin while avoiding a desktop GIS dependency. Package: r-cran-tesiprov Architecture: all Version: 0.9.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 725 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-future.apply, r-cran-digest, r-cran-nloptr, r-cran-ggplot2, r-cran-gridextra, r-cran-pracma Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-evd, r-cran-brms Filename: pool/dists/noble/main/r-cran-tesiprov_0.9.6-1.ca2404.1_all.deb Size: 510332 MD5sum: 67ec6eb136bc6acc27d35dd20c146896 SHA1: d4d63cd6220ccf747005baff9412fed2817c6de5 SHA256: 0050f0f7eca09902ca7a9f41300b41cb8ce832dca03762674279a00dccc1004b SHA512: 6b09ae582187c55c3fa6dd974e173ab7dee5948583e3c609c394969a1573b618d2cc3762a882480ad1ce701a4394d89f339f422f6121db26f42b1e7dbe3ae658 Homepage: https://cran.r-project.org/package=TesiproV Description: CRAN Package 'TesiproV' (Reliability Analysis Methods for Structural Engineering) Calculate the failure probability of civil engineering problems with Level I up to Level III Methods. Have fun and enjoy. References: Spaethe (1991, ISBN:3-211-82348-4) "Die Sicherheit tragender Baukonstruktionen", AU,BECK (2001) "Estimation of small failure probabilities in high dimensions by subset simulation." , Breitung (1989) "Asymptotic approximations for probability integrals." . Package: r-cran-tesouror Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6889 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-janitor, r-cran-stringi, r-cran-stringr, r-cran-tibble Suggests: r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-tesouror_0.2.2-1.ca2404.1_all.deb Size: 4814194 MD5sum: c31b857228b8ad2dec39be1f65b5f5b4 SHA1: bfd9d5bafe328d02fe57c41580d77017dc79f74b SHA256: 3315531e7835bb8113693d68f6defde8d04369ba89b01b35bba4e75db2f18ebd SHA512: 442f1477b13effdc794879b5a6f4df50339275fad3d397a2f536b28adbe06fef9fe54612507933e579017a386a37bb48d489c3585f659b89390af294739727ae Homepage: https://cran.r-project.org/package=tesouror Description: CRAN Package 'tesouror' (Access Brazilian National Treasury Open Data APIs) Provides a unified interface to access open data from the Brazilian National Treasury ('Tesouro Nacional') and related government APIs. Covers six data sources: 'SICONFI' for fiscal reports ('RREO', 'RGF', 'DCA', 'MSC') and entity information; 'CUSTOS' for federal government cost data; 'SADIPEM' for public debt and credit operations; 'Transferencias Constitucionais' for constitutional transfers to states and municipalities; 'SIORG' for federal organizational structure; and 'SIOPE' ('FNDE'/'MEC') for education spending data. Features automatic pagination, in-memory caching, retry logic, and tidy output. Package: r-cran-tesselle Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dimensio, r-cran-folio, r-cran-isopleuros, r-cran-kairos, r-cran-khroma, r-cran-nexus, r-cran-tabula Filename: pool/dists/noble/main/r-cran-tesselle_1.6.0-1.ca2404.1_all.deb Size: 101304 MD5sum: 795baf0e028c0624288c825032f3518f SHA1: d5f00100e1e07d11ae9661dc36158cfd27677190 SHA256: 2001139b9ea93f8c9e1e2a383643b16969d7045990abbdcee2e12a24bdd4acab SHA512: 1dbe2db528ff659915cbb74cb82b32ffa7fca504946b3b8a2c5656e24dbe6fee10f8b85c0a1e6f8eafb5433069e4a690528176dbb8655ac62fd88df41eee9064 Homepage: https://cran.r-project.org/package=tesselle Description: CRAN Package 'tesselle' (Easily Install and Load 'tesselle' Packages) Easy install and load key packages from the 'tesselle' suite in a single step. The 'tesselle' suite is a collection of packages for research and teaching in archaeology. These packages focus on quantitative analysis methods developed for archaeology. The 'tesselle' packages are designed to work seamlessly together and to complement general-purpose and other specialized statistical packages. These packages can be used to explore and analyze common data types in archaeology: count data, compositional data and chronological data. Learn more about 'tesselle' at . Package: r-cran-test.assessr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3484 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-checkmate, r-cran-covr, r-cran-dplyr, r-cran-fs, r-cran-jsonlite, r-cran-pkgload, r-cran-remotes, r-cran-rlang, r-cran-rmarkdown, r-cran-runit, r-cran-stringr, r-cran-testthat, r-cran-tinytest, r-cran-tidyr, r-cran-withr Suggests: r-cran-devtools, r-cran-dt, r-cran-here, r-cran-kableextra, r-cran-knitr, r-cran-r6, r-cran-s7, r-cran-roxygen2, r-cran-tidyselect, r-cran-mockery Filename: pool/dists/noble/main/r-cran-test.assessr_2.1.0-1.ca2404.1_all.deb Size: 1686656 MD5sum: ffaaeb046853278dacc6b2524b95b2bd SHA1: cafe99351ee26db28ed93c491bcd37c74d019d18 SHA256: 2ef32937bbb54c82083870156584dec5f1771db8366fe5c0d5410f266ec554f6 SHA512: 28db4921d0638f39669efa6af1a4184c7b22b031c4246526fc9a04487b8c41fdc466bcdc8e8af83d20ab5b8a2ac8f468bfb81ba415d8db1aec1e2f9b5560e368 Homepage: https://cran.r-project.org/package=test.assessr Description: CRAN Package 'test.assessr' (Assessing Package Test Reliability and Quality) A reliable and validated tool that calculates unit test coverage for R packages with standard testing frameworks and non-standard testing frameworks. Package: r-cran-test2norm Architecture: all Version: 0.3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mfp2 Filename: pool/dists/noble/main/r-cran-test2norm_0.3.0.1-1.ca2404.1_all.deb Size: 59062 MD5sum: 4bf733b0513f05527ee9234c7a64acbf SHA1: 32d60caa05104b9d88b212bc27a00da0b29dece8 SHA256: 852e81ae5fc984e8fd1ca8d99571392e8e5fa6e2d9d41eccff303c748dc95c6b SHA512: d9a62168e1db04f4c3a07c04dd280b2a24429583fa497bd2a16dbbacc94d0494fa025fef060492b170485c80e056fcc170070a19bd4b7a3dbbfae15e3b96018d Homepage: https://cran.r-project.org/package=test2norm Description: CRAN Package 'test2norm' (Normative Standards for Cognitive Tests) Package test2norm contains functions to generate formulas for normative standards applied to cognitive tests. It takes raw test scores (e.g., number of correct responses) and converts them to scaled scores and demographically adjusted scores, using methods described in Heaton et al. (2003) & Heaton et al. (2009, ISBN:9780199702800). The scaled scores are calculated as quantiles of the raw test scores, scaled to have the mean of 10 and standard deviation of 3, such that higher values always correspond to better performance on the test. The demographically adjusted scores are calculated from the residuals of a model that regresses scaled scores on demographic predictors (e.g., age). The norming procedure makes use of the mfp2() function from the 'mfp2' package to explore nonlinear associations between cognition and demographic variables. Package: r-cran-testanaapp Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 673 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-mirt, r-cran-shinydashboard, r-cran-estcrm, r-cran-shiny, r-cran-dt, r-cran-golem, r-cran-officedown, r-cran-stringr, r-cran-dplyr, r-cran-brucer, r-cran-officer, r-cran-openxlsx, r-cran-rmarkdown, r-cran-plotrix, r-cran-cowplot, r-cran-flextable, r-cran-shinycssloaders, r-cran-tidyr, r-cran-difr, r-cran-lordif, r-cran-latticeextra, r-cran-semplot Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-testanaapp_1.1.2-1.ca2404.1_all.deb Size: 566838 MD5sum: bf9f9aa1c334eaa972859bb710f67267 SHA1: 36d62c1273ed2f65d8623a5a8b5b7eebe815bb23 SHA256: 3e456fd36a07294d102b2c6cf97584e78f2f6061f452da49695c63f6fab38ab3 SHA512: b1b7dc8fe260c088855ab63edf6dce120b23de1ad8579fec48215a8a91bb05e167d79e595dee94ebaa64179ba3caf34fcbca356658a8f78a1ff4960d1b3070dd Homepage: https://cran.r-project.org/package=TestAnaAPP Description: CRAN Package 'TestAnaAPP' (A 'shiny' App for Test Analysis and Visualization) This application provides exploratory and confirmatory factor analysis, classical test theory, unidimensional and multidimensional item response theory, and continuous item response model analysis, through the 'shiny' interactive interface. In addition, it offers rich functionalities for visualizing and downloading results. Users can download figures, tables, and analysis reports via the interactive interface. Package: r-cran-testarguments Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 540 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-ggplot2, r-cran-reshape2, r-cran-plyr, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/noble/main/r-cran-testarguments_0.0.1-1.ca2404.1_all.deb Size: 371962 MD5sum: 68843ada2246579ac3a6d05a661be225 SHA1: 2b602497a9f523dbe1b789c693dea0412dae3e5f SHA256: aa0dd321db32a73b2685fea4b721a12b364e0f261da5f0db0e892fd69d7d1823 SHA512: 73d25b03b48f2dbb49e7a6cd5ee0bd7d6eb2da5b55469573ecb0543835862d253a7d1094036c474bbf085df88a8bde3ab398c2967f8f7e61efdc37b9c09d42a3 Homepage: https://cran.r-project.org/package=testarguments Description: CRAN Package 'testarguments' (Test (Multiple) Arguments of a User-Defined Prediction Algorithm) Finding the best values for user-specified arguments of a prediction algorithm can be difficult, particularly if there is an interaction between argument levels. This package automates the testing of any user-defined prediction algorithm over an arbitrary number of arguments. It includes functions for testing the algorithm over the given arguments with respect to an arbitrary number of user-defined diagnostics, visualising the results of these tests, and finding the optimal argument combinations with respect to each diagnostic. Package: r-cran-testassay Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-testassay_0.1.1-1.ca2404.1_all.deb Size: 56336 MD5sum: a1cbc156698bce6e445b96bd69a70da3 SHA1: 11afd314a527a5565abe97350c29c13449a03f11 SHA256: 740186cab0340571c779ca7a3497c02e3c8851cb4a33bfe215d4a3677e400cc8 SHA512: faa25938c4336212d60e013757aed768ebeb7e32656a84885020f3404df00f82c7ae0ce640d1bf18dce5f81faa5c5f6049915750ea912bf8130026ccfac06e70 Homepage: https://cran.r-project.org/package=testassay Description: CRAN Package 'testassay' (A Hypothesis Testing Framework for Validating an Assay forPrecision) A common way of validating a biological assay for is through a procedure, where m levels of an analyte are measured with n replicates at each level, and if all m estimates of the coefficient of variation (CV) are less than some prespecified level, then the assay is declared validated for precision within the range of the m analyte levels. Two limitations of this procedure are: there is no clear statistical statement of precision upon passing, and it is unclear how to modify the procedure for assays with constant standard deviation. We provide tools to convert such a procedure into a set of m hypothesis tests. This reframing motivates the m:n:q procedure, which upon completion delivers a 100q% upper confidence limit on the CV. Additionally, for a post-validation assay output of y, the method gives an ``effective standard deviation interval'' of log(y) plus or minus r, which is a 68% confidence interval on log(mu), where mu is the expected value of the assay output for that sample. Further, the m:n:q procedure can be straightforwardly applied to constant standard deviation assays. We illustrate these tools by applying them to a growth inhibition assay. This is an implementation of the methods described in Fay, Sachs, and Miura (2018) . Package: r-cran-testcomparer Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-testcomparer_1.1.1-1.ca2404.1_all.deb Size: 132028 MD5sum: 1b2e6ac2257ba20dcfc8c1114a1586d4 SHA1: fb3ec8959afc3f51f9571cbcc644fb6fbfd6d2c6 SHA256: 0a8f1f2824d68cfc9f4c238c1226d3f5e8c19cb750aec53171c3a6e120995001 SHA512: b8ec3af1fcb85e9a9a5ba114f70216da5fa63ee25eaf87c184daf4e623b3efe0506efe2b2da5029eb61b6dd969bed381bdda00085cf0f251439f57f6d25956d0 Homepage: https://cran.r-project.org/package=testCompareR Description: CRAN Package 'testCompareR' (Comparing Two Diagnostic Tests with Dichotomous Results usingPaired Data) Provides a method for comparing the results of two binary diagnostic tests using paired data. Users can rapidly perform descriptive and inferential statistics in a single function call. Options permit users to select which parameters they are interested in comparing and methods for correction for multiple comparisons. Confidence intervals are calculated using the methods with the best coverage. Hypothesis tests use the methods with the best asymptotic performance. A summary of the methods is available in Roldán-Nofuentes (2020) . This package is targeted at clinical researchers who want to rapidly and effectively compare results from binary diagnostic tests. Package: r-cran-testcorr Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-scales, r-cran-reshape2, r-cran-forcats, r-cran-knitr, r-cran-xts, r-cran-zoo Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-testcorr_0.4.0-1.ca2404.1_all.deb Size: 796266 MD5sum: 904d5be9286a1aaa48222c0911ec32cb SHA1: 351e1fbb618d9c912e9343264d4e3cd2a1d4d871 SHA256: 8705e2ad82f24a2107d224b0e652285019d9751ea28303d761a22bf5111bfab6 SHA512: 5bcc2c65aaab622612743d9dea751b9a07443167f9690c54f11b2627700023e8bf15352bd355c8863e9a12b03f55e74b5ee2168a1adea35c05203f84e9088cd6 Homepage: https://cran.r-project.org/package=testcorr Description: CRAN Package 'testcorr' (Testing Zero Correlation) Computes the test statistics for examining the significance of autocorrelation in univariate time series, cross-correlation in bivariate time series, Pearson correlations in multivariate series and test statistics for i.i.d. property of univariate series given in Dalla, Giraitis and Phillips (2022), , . 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An extension of the 'testthat' unit testing framework with a family of functions and reporting tools for checking and validating data frames. Package: r-cran-testdataimputation Architecture: all Version: 2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 99 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mice, r-cran-amelia Filename: pool/dists/noble/main/r-cran-testdataimputation_2.3-1.ca2404.1_all.deb Size: 65704 MD5sum: 3d66d8d2b8932ed09f0499fc3ab0695f SHA1: c490111fed246205e008b536f445d856216cd7ff SHA256: e6d928ffe7b68d7008160656d6d65f7bb7d9e79b0a03e7589a5060f9269a9e20 SHA512: 86a250a78a7cb10db3cb752eec7143f177212d28197112337a5cb2f6ae29ba4fd312d78d0665f5277be914c952bdff29869aa9e84dd1ff575ae3becb73c37e51 Homepage: https://cran.r-project.org/package=TestDataImputation Description: CRAN Package 'TestDataImputation' (Missing Item Responses Imputation for Test and Assessment Data) Functions for imputing missing item responses for dichotomous and polytomous test and assessment data. This package enables missing imputation methods that are suitable for test and assessment data, including: listwise (LW) deletion (see De Ayala et al. 2001 ), treating as incorrect (IN, see Lord, 1974 ; Mislevy & Wu, 1996 ; Pohl et al., 2014 ), person mean imputation (PM), item mean imputation (IM), two-way (TW) and response function (RF) imputation, (see Sijtsma & van der Ark, 2003 ), logistic regression (LR) imputation, predictive mean matching (PMM), and expectation–maximization (EM) imputation (see Finch, 2008 ). Package: r-cran-testdimorph Architecture: all Version: 0.5.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corrplot, r-cran-dplyr, r-cran-ggplot2, r-cran-morpho, r-cran-multcompview, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-testdimorph_0.5.8-1.ca2404.1_all.deb Size: 320300 MD5sum: 36ab14ed79454626c7010e6e4ef74f9d SHA1: 60bc2422442879b176d7ea21b634ffc36360601b SHA256: 02548956a2816ebd6824f1b35422e858f8c42d5f814e44a4fadcd908c3c0041b SHA512: 28b41151547c230d193d05868aca2e700157730ea19172575e14a6e50a193070d5422374df1301d56e5574f8b1d97ef4d614f8f89e7342a5256ad1ee402b4759 Homepage: https://cran.r-project.org/package=TestDimorph Description: CRAN Package 'TestDimorph' (Analysis of the Interpopulation Difference in Degree of SexualDimorphism Using Summary Statistics) Offers a solution for the unavailability of raw data in most anthropological studies by facilitating the calculations of several sexual dimorphism related analyses using the published summary statistics of metric data (mean, standard deviation and sex specific sample size) as illustrated by the works of Relethford, J. H., & Hodges, D. C. (1985) , Greene, D. L. (1989) and Konigsberg, L. W. (1991) . Package: r-cran-testdriver Architecture: all Version: 0.5.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-testdriver_0.5.3-1.ca2404.1_all.deb Size: 228294 MD5sum: 407bde329ef7a6db53e0047b2c8bfe48 SHA1: e8418b22a7850b8505e647c169ffb082cf696dc0 SHA256: 00548fa73d1f8adfb5434f62887268ef49dbae84afb62ec20b662c133d2e1f4b SHA512: ddf24296e993da593672affd2bba280c90a9cb14b5f170244da3da5bf9cd91cf0a7c71ccbff27169a43fbf0bb841fd920fd5d0c5fb3261fd5cd9a9797df1b983 Homepage: https://cran.r-project.org/package=testDriveR Description: CRAN Package 'testDriveR' (Teaching Data for Statistics and Data Science) Provides data sets for teaching statistics and data science courses. It includes a sample of data from John Edmund Kerrich's famous coinflip experiment. These are data that I used for statistics. The package also contains an R Markdown template with the required formatting for assignments in my former courses. Package: r-cran-testequavar Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 68 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-testequavar_0.1.5-1.ca2404.1_all.deb Size: 39080 MD5sum: 5e6fa2190b9331ca345b34db62d08ba3 SHA1: a856ec24e9b41dc6b448fa0f207bc705dcbf9057 SHA256: 9a5d9203c3d5168913d89421728f2aa60fffa86c64e916814257095e48909f9f SHA512: d439491a3b873f7a313b00e4a6bd33e1cd235901e6a699af74358ef7092d9c6705b939bb94bf42866f4975b755e8e0a2d951d81c8f2549ae01c0490cc3b8af08 Homepage: https://cran.r-project.org/package=testequavar Description: CRAN Package 'testequavar' (Bootstrap Tests for Equality of 2, 3, or 4 Population Variances) Tests the hypothesis that variances are homogeneous or not using bootstrap. The procedure uses a variance-based statistic, and is derived from a normal-theory test. The test equivalently expressed the hypothesis as a function of the log contrasts of the population variances. A box-type acceptance region is constructed to test the hypothesis. See Cahoy (2010) \doi{10.1016/j.csda.2010.04.012}. 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Package: r-cran-text2speech Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-aws.signature, r-cran-cli, r-cran-dplyr, r-cran-googleauthr, r-cran-googlelanguager, r-cran-knitr, r-cran-magrittr, r-cran-conrad, r-cran-tidyr, r-cran-tuner, r-cran-withr Suggests: r-cran-aws.polly, r-cran-covr, r-cran-patrick, r-cran-rmarkdown, r-cran-stringi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-text2speech_1.0.0-1.ca2404.1_all.deb Size: 342814 MD5sum: adeaf7432cb6fef520312c480740a012 SHA1: 7d52381818610f233ffe68828851e43cf2b696d7 SHA256: 1750e880d6743488f529e06137fb7b7b88e2a2b6c5a0c94ccf8c421180d03fd5 SHA512: 09b16f1f72f445e72f3cc4ee625ef47b440d987108311e9ed0661cbe1d877d809cf8d3f174b0c0879f7c0efa5cd87b158eb65a99dcd85051341398f46e5c357c Homepage: https://cran.r-project.org/package=text2speech Description: CRAN Package 'text2speech' (Text to Speech Conversion) Converts text into speech using various text-to-speech (TTS) engines and provides an unified interface for accessing their functionality. 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Package: r-cran-textdata Architecture: all Version: 0.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fs, r-cran-rappdirs, r-cran-readr, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-textdata_0.4.5-1.ca2404.1_all.deb Size: 500882 MD5sum: 6b3d27281ed1d9ea56d111b7f1fc085a SHA1: 3f7e45a69c7bae2631bc51a7111e8fd1bc8be292 SHA256: fa6b12471baf21f92116cf1871cc844b80be2ab4e9543a164be21e3427d3bdb8 SHA512: 80e8460a76cf2067d50f439bdbe979899f3d3b6ce92d648678575aa200a79a8eb222cff792002a770832b3a8d3523f3310fd225f361fd42a5d2a6a95b8f0f76c Homepage: https://cran.r-project.org/package=textdata Description: CRAN Package 'textdata' (Download and Load Various Text Datasets) Provides a framework to download, parse, and store text datasets on the disk and load them when needed. 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Package: r-cran-texter Architecture: all Version: 0.1.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-plyr, r-cran-ggplot2, r-cran-magrittr, r-cran-stringr, r-cran-purrr, r-cran-stopwords, r-cran-textdata, r-cran-tidytext, r-cran-tidyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-texter_0.1.9-1.ca2404.1_all.deb Size: 155396 MD5sum: 7f7fbc3c1c665a54d5fca21179679303 SHA1: 8b8f24edfa3dd387d0beb21dbde2a3309429e930 SHA256: 8179757d45bad56f60786cd8a9855ad9647106f406280558335b350ba4b4ce98 SHA512: ab45a44494e8abeb4234fb0d1681d4af0f6dbe25cb4b1beedf9765e2741ec40041c10859aa4ad053721524cfce7019a1dd02b9ddb4ae3e6da332b2d2de16d08a Homepage: https://cran.r-project.org/package=texter Description: CRAN Package 'texter' (An Easy Text and Sentiment Analysis Library) Implement text and sentiment analysis with 'texter'. 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Package: r-cran-textometry Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-textometry_0.1.7-1.ca2404.1_all.deb Size: 50712 MD5sum: bac302711d20da2ed877a4938ac48099 SHA1: bad40e2d6e190c4ae59349c2e17ea86cd077da90 SHA256: 028d8e2ad698ffa5e5fc562fb0f8d7823b91269ac361f008d0a0aae793ffcd7f SHA512: 9ebec08709f30d27b4a7b183c2a2d0116a31536b5741af7ce4b4ee952c631e030744a12a9dd74cf3f3ae3e6e7dc9a771db9b61c7b8d81512e960c0cd748c7b9d Homepage: https://cran.r-project.org/package=textometry Description: CRAN Package 'textometry' (Textual Data Analysis Package Used by the TXM Software) Statistical exploration of textual corpora using several methods from French 'Textometrie' (new name of 'Lexicometrie') and French 'Data Analysis' schools. 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Package: r-cran-textpress Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-matrix, r-cran-rvest, r-cran-stringi, r-cran-stringr, r-cran-xml2, r-cran-pbapply, r-cran-jsonlite, r-cran-lubridate Suggests: r-cran-snowballc, r-cran-dt, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-textpress_1.1.1-1.ca2404.1_all.deb Size: 128230 MD5sum: a0b2c619844eb9dd000522d49d55d697 SHA1: d0bf618e307cf6a35f3a723d76f657f597367b15 SHA256: eaa5de16f755500067a36402284807a5ed7065834ba0af984fe817ff60ad0b3c SHA512: d1f85e43ba00cd662d7d228d6fc233358494307533899d5799f5c8206239e662797e939b12029a020b13b221f36d983ee3b1510fcf7d411d6b4572e34ac671a5 Homepage: https://cran.r-project.org/package=textpress Description: CRAN Package 'textpress' (A Lightweight and Versatile NLP Toolkit) A toolkit for web scraping, modular NLP pipelines, and text preparation for large language models. 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The p-value calculator for the omnibus version of these tests are also included. For reference, please see Hong Zhang and Zheyang Wu. "TFisher Tests: Optimal and Adaptive Thresholding for Combining p-Values", submitted. 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Package: r-cran-tforge Architecture: all Version: 0.1.17-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-purrr, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tforge_0.1.17-1.ca2404.1_all.deb Size: 310120 MD5sum: a0c8d0b38246fffea8b3dd6da342e65a SHA1: d26dc283be26c43e0dd97c23254f859662f2d9ee SHA256: 0d361d166c92e46c62ed46aaecb84946cfc787b8bce658e89f74a7565cfb7491 SHA512: 0944df02a913059df1b8f6c7eaf12649913dff65d4ec7c893b8fe651923d58c3c774ad5e4d83926493661670bb90a55325e020990ff2a61f74cd513625630f8f Homepage: https://cran.r-project.org/package=TFORGE Description: CRAN Package 'TFORGE' (Tests for Geophysical Eigenvalues) The eigenvalues of observed symmetric matrices are often of intense scientific interest. 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Package: r-cran-tfplot Architecture: all Version: 2021.6-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tframe Suggests: r-cran-googlevis Filename: pool/dists/noble/main/r-cran-tfplot_2021.6-1-1.ca2404.1_all.deb Size: 252670 MD5sum: e1ad61ca59dc7c1c96935c0e6f0b051d SHA1: 5ff226016be94c73a1f824b6344dcd55cb65fe1c SHA256: e432ea83736e866f50f4255b7a388003f3c23b74fd77579a4f5bba429b3aed34 SHA512: c3254eea3e245ecbe1b3b04608939ab1ac238f8cf0cb6a08cb614b11a40f73a3da4303f7dc024d3e41d57dda5703e3c81dfb6ef07cdb52ff314f364d525cc01f Homepage: https://cran.r-project.org/package=tfplot Description: CRAN Package 'tfplot' (Time Frame User Utilities) Utilities for simple manipulation and quick plotting of time series data. These utilities use the 'tframe' package which provides a programming kernel for time series. Extensions to 'tframe' provided in 'tframePlus' can also be used. 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Package: r-cran-tframe Architecture: all Version: 2015.12-1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tframe_2015.12-1.1-1.ca2404.1_all.deb Size: 200804 MD5sum: ed1b588b75261024fb5f6f67b3b01d48 SHA1: ba0f924fc48282452901863ad66bbad27ed67fe5 SHA256: c475f48bc4113a2768b5c6dff484e34a052c8b8ada7b67b99a34581ffd02fdd4 SHA512: 41ff13a55ad6efd4e272e26ad24e20ea7f1fc3de68b5ecea7457468cc000995ff1b3f7191950a5f849863938a01521e9aa67ec50281d64c90db9315452de0149 Homepage: https://cran.r-project.org/package=tframe Description: CRAN Package 'tframe' (Time Frame Coding Kernel) A kernel of functions for programming time series methods in a way that is relatively independently of the representation of time. Also provides plotting, time windowing, and some other utility functions which are specifically intended for time series. 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Package: r-cran-tgs Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 518 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rjson, r-cran-bnstruct, r-cran-ggm, r-cran-foreach, r-cran-doparallel, r-bioc-minet Suggests: r-cran-r.rsp, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tgs_1.0.1-1.ca2404.1_all.deb Size: 184446 MD5sum: c5bf99c4370622ea42ec76d4d97c6969 SHA1: 8ee0436fe21ecf2b1dbcf49934b89b95f8a91809 SHA256: dc9ff20e4cc2bb404c278f8a0331682afb43e0409dc3cf82f362fd921eb8335c SHA512: 22e603dc55aca956bcb188c2e0ed94a51ef09681e7afc76b76ac7e6ff778eb9c164e396f6594b9b216f3d0468e675ff7d1951012eb12556d4f5fe7e5601bf03f Homepage: https://cran.r-project.org/package=TGS Description: CRAN Package 'TGS' (Rapid Reconstruction of Time-Varying Gene Regulatory Networks) Rapid advancements in high-throughput gene sequencing technologies have resulted in genome-scale time-series datasets. Uncovering the underlying temporal sequence of gene regulatory events in the form of time-varying gene regulatory networks demands accurate and computationally efficient algorithms. Such an algorithm is 'TGS'. It is proposed in Saptarshi Pyne, Alok Ranjan Kumar, and Ashish Anand. Rapid reconstruction of time-varying gene regulatory networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(1):278{291, Jan-Feb 2020. The TGS algorithm is shown to consume only 29 minutes for a microarray dataset with 4028 genes. This package provides an implementation of the TGS algorithm and its variants. Package: r-cran-tgst Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-knitr Suggests: r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tgst_1.0-1.ca2404.1_all.deb Size: 118318 MD5sum: f36e9b9873f5460646349dd1e627fc98 SHA1: 1aab819202d9d5b0c872d9fb5806803aeb507039 SHA256: 074fbceb1c2f92542efe7dd028ea7adc94f058c30a990d8a532849130e7081ac SHA512: b43c0c65b3863cb3d82a4caf04659b481c742b59f79e167d84a491ce70ee9091641c0b1ddb07fb2b81f9afe402c1d93833500f126aa0a197469c87aab6991ae2 Homepage: https://cran.r-project.org/package=TGST Description: CRAN Package 'TGST' (Targeted Gold Standard Testing) Functions for implementing the targeted gold standard (GS) testing. You provide the true disease or treatment failure status and the risk score, tell 'TGST' the availability of GS tests and which method to use, and it returns the optimal tripartite rules. Please refer to Liu et al. (2013) for more details. 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Metodiev, Perrot-Dockès, Ouadah, Irons, Latouche, & Raftery (2024). Bayesian Analysis. . 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(2021) , 'feasts' O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) , 'tsfeatures' Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020) , 'tsfresh' Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018) , 'TSFEL' Barandas, M., et al. (2020) , and 'Kats' Facebook Infrastructure Data Science (2021) . 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A number of wrapper functions are provided for plotting and summarising outputs from these simulations. This package is presented in van Steenderen, C.J.M., Sutton, G.F., Owen, C.A., Martin, G.D., and Coetzee, J.A. Sample size assessments for thermal physiology studies: An R package and R Shiny application. 2023. Physiological Entomology. . The GUI version of this package is available on the R Shiny online server at: , or it is accessible via GitHub at . We would like to thank Grant Duffy (University of Otago, Dundedin, New Zealand) for granting us permission to use the source code for the Test of Total Equivalency function. 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This binary status is determined by right-censored times to event and it is unknown for those subjects censored before t. Here we provide three methods (unknown status exclusion, imputation of censored times and using time-dependent ROC curves) to evaluate the diagnostic ability of binary and continuous tests in this context. Two references for the methods used here are Skaltsa et al. (2010) and Heagerty et al. (2000) . 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The analysis should preferably cover an observation period of at least 19 years. For shorter periods low frequency constituents are not taken into account, in accordance with the Rayleigh-Criterion. The main objective of this package is to synthesize or predict a tidal time series. 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Package: r-cran-tidyaudit Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-glue, r-cran-rlang Suggests: r-cran-covr, r-cran-jsonlite, r-cran-knitr, r-cran-pkgdown, r-cran-rmarkdown, r-cran-spelling, r-cran-stringi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidyaudit_0.2.1-1.ca2404.1_all.deb Size: 335364 MD5sum: e3694c62dd6dc071bb91e66c8e262031 SHA1: 9303c2be91400c5d7b435425a7fd2ea3b5c771b5 SHA256: f48d9be3110301fe388259b521ca864ca0db3ba6641c96f33d68b4402765a7ba SHA512: a2da216d4a3527a6d4fa5a4fc2ddd423ddbb48b05052f2f13df583fef2b81f67108519f7b196781713f25d9908f66245794652c9c933a55f559a2d87049824cb Homepage: https://cran.r-project.org/package=tidyaudit Description: CRAN Package 'tidyaudit' (Pipeline Audit Trails and Data Diagnostics for 'tidyverse'Workflows) Provides pipeline audit trails and data diagnostics for 'tidyverse' workflows. The audit trail system captures lightweight metadata snapshots at each step of a pipeline, building a structured record without storing the data itself. Operation-aware taps enrich snapshots with join match rates and filter drop statistics. Trails can be serialized to 'JSON' or 'RDS' and exported as self-contained 'HTML' visualizations. Also includes diagnostic functions for interactive data analysis including frequency tables, string quality auditing, and data comparison. Package: r-cran-tidybayes Architecture: all Version: 3.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3259 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggdist, r-cran-dplyr, r-cran-tidyr, r-cran-ggplot2, r-cran-coda, r-cran-rlang, r-cran-arrayhelpers, r-cran-tidyselect, r-cran-tibble, r-cran-magrittr, r-cran-posterior, r-cran-withr, r-cran-cli, r-cran-vctrs Suggests: r-cran-knitr, r-cran-testthat, r-cran-purrr, r-cran-forcats, r-cran-vdiffr, r-cran-svglite, r-cran-rstan, r-cran-rstantools, r-cran-runjags, r-cran-rjags, r-cran-jagsui, r-cran-rstanarm, r-cran-emmeans, r-cran-broom, r-cran-mcmcglmm, r-cran-bayesplot, r-cran-modelr, r-cran-brms, r-cran-cowplot, r-cran-covr, r-cran-rmarkdown, r-cran-ggrepel, r-cran-bindrcpp, r-cran-rcolorbrewer, r-cran-gganimate, r-cran-gifski, r-cran-png, r-cran-ragg, r-cran-pkgdown, r-cran-distributional, r-cran-transformr Filename: pool/dists/noble/main/r-cran-tidybayes_3.0.7-1.ca2404.1_all.deb Size: 1660576 MD5sum: 3938f460cedcb4213a0127ed93acfed8 SHA1: a51b1fdab76c7d0a2ba64f3014fadbe29abf1828 SHA256: 0fe640f3e29779b726ca2e5437469ff5b95347d77bc841d82cc7beebd9417aa0 SHA512: cea59538f490a3048b15674eb6cb13829b6ad128617242bbc48af1ae33853c1f11b98d609cc3cd97819d193b7c5e5db1c91c4d5e138cb43b91bcfa2141fc4cac Homepage: https://cran.r-project.org/package=tidybayes Description: CRAN Package 'tidybayes' (Tidy Data and 'Geoms' for Bayesian Models) Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models ('JAGS', 'Stan', 'rstanarm', 'brms', 'MCMCglmm', 'coda', ...) in a tidy data format. Functions are provided to help extract tidy data frames of draws from Bayesian models and that generate point summaries and intervals in a tidy format. In addition, 'ggplot2' 'geoms' and 'stats' are provided for common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus densities), and fit curves with multiple, arbitrary uncertainty bands. Package: r-cran-tidybde Architecture: all Version: 0.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-readr, r-cran-scales, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-lifecycle, r-cran-quarto, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidybde_0.6.1-1.ca2404.1_all.deb Size: 280778 MD5sum: d4405848b2856218d944bacec472a764 SHA1: 96690eb0a299ae3c50ff89b2aabd8dcc5a3de8a9 SHA256: 10159c2cc11e9aee63c2f0f4b675c44d5c0732af91d9e070c62bc1913b475b63 SHA512: b4409845845b936e45a6470ead3b0f275a84f9da8570a5fda4208dc038c0361ff84ed7ee134aeeb78459a32acefaa0b156fd47333507db0e38e065f3aab299e1 Homepage: https://cran.r-project.org/package=tidyBdE Description: CRAN Package 'tidyBdE' (Retrieve Data from 'Banco de España') Tools for retrieving time series data from 'Banco de España' ('BdE') as 'tibble' objects. 'Banco de España' is the national central bank and, within the framework of the Single Supervisory Mechanism ('SSM'), the supervisor of the Spanish banking system alongside the European Central Bank. This package is not sponsored, endorsed or administered by 'Banco de España'. Package: r-cran-tidybins Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-stringr, r-cran-tidyselect, r-cran-purrr, r-cran-janitor, r-cran-tibble, r-cran-rlang, r-cran-lubridate, r-cran-scales, r-cran-ggplot2, r-cran-rlist, r-cran-oner, r-cran-strex, r-cran-clusterr, r-cran-framecleaner, r-cran-xgboost, r-cran-badger, r-cran-autostats Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-arulescba, r-cran-embed, r-cran-woebinning, r-cran-recipes Filename: pool/dists/noble/main/r-cran-tidybins_0.1.2-1.ca2404.1_all.deb Size: 71284 MD5sum: 1aaf9f072a09ed87087f3edfbfb39f3e SHA1: 63e35a02bd20214fc5c3bca89b021b225c94f116 SHA256: c6b472a4bb5347c251e7ad6542fda2806547e532fe89018719f3725181ca4920 SHA512: 6474e5832b447dcff394215d013f013e707ead1fbcb87c528b94a4be1eada027629381ad32b8411594168645e68856628cdebd09413557a0065007e25dbf2e3b Homepage: https://cran.r-project.org/package=tidybins Description: CRAN Package 'tidybins' (Make Tidy Bins) Multiple ways to bin numeric columns with a tidy output. Wraps a variety of existing binning methods into one function, and includes a new method for binning by equal value, which is useful for sales data. Provides a function to automatically summarize the properties of the binned columns. Package: r-cran-tidyboot Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-modelr, r-cran-purrr, r-cran-rlang, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-tidyboot_0.1.3-1.ca2404.1_all.deb Size: 28512 MD5sum: dab01336ad336ad96a4533050650852e SHA1: 120300a65a00190e0ff4fc06db44d9ba34ad3f7a SHA256: 2fd2919a9e1802995b7512b57f0293a65fe3a661b8c56359a9e461d3105c91c4 SHA512: 74cc97ca65472b6548cbfc11eacb28168471b6c2a5699ca221eb2b996e86149b21975d12b9765eb6df416c7139fc1c75840827cf8ae1e6bb651324ad111f201c Homepage: https://cran.r-project.org/package=tidyboot Description: CRAN Package 'tidyboot' (Tidyverse-Compatible Bootstrapping) Compute arbitrary non-parametric bootstrap statistics on data in tidy data frames. Package: r-cran-tidycat Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1351 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-stringr, r-cran-forcats Suggests: r-cran-broom, r-cran-ggplot2, r-cran-ggforce, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-tidycat_0.1.2-1.ca2404.1_all.deb Size: 476376 MD5sum: 72e8197be40cd8fd56e41633235f167e SHA1: 0bc36f75853f6548ae85d7d08377eeb8b87f8b81 SHA256: 07e50c5e275701b5dc4985fa37ac69e46d3d67a0c147a548e5e7f272eaceda0d SHA512: dcf3913283d128e9a319289f75e4fd04a80ce8f9ad5c39bfe80605159c929e3f2072ff83208b071a5b4a75a84e8bba7da13e25cbd63de6ffdb2b5329d929a529 Homepage: https://cran.r-project.org/package=tidycat Description: CRAN Package 'tidycat' (Expand Tidy Output for Categorical Parameter Estimates) Create additional rows and columns on broom::tidy() output to allow for easier control on categorical parameter estimates. Package: r-cran-tidycdisc Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3718 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-broom.helpers, r-cran-cicerone, r-cran-cli, r-cran-config, r-cran-dplyr, r-cran-dt, r-cran-fs, r-cran-ggally, r-cran-ggcorrplot, r-cran-ggplot2, r-cran-ggsurvfit, r-cran-glue, r-cran-golem, r-cran-gt, r-cran-gtsummary, r-cran-haven, r-cran-ideafilter, r-cran-pkgload, r-cran-plotly, r-cran-purrr, r-cran-readxl, r-cran-rlang, r-cran-rmarkdown, r-cran-shiny, r-cran-shinyjs, r-cran-shinywidgets, r-cran-sjlabelled, r-cran-stringdist, r-cran-stringr, r-cran-survival, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect, r-cran-timevis, r-cran-tippy, r-cran-vroom Suggests: r-cran-devtools, r-cran-knitr, r-cran-shinyalert, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidycdisc_0.2.2-1.ca2404.1_all.deb Size: 2605528 MD5sum: 3421e58886864c4cfb121901f39c9c89 SHA1: ee4f5ac8a8e9fe75370a118e164c721fde93d757 SHA256: ea25dd05be424d728b29affc97e28a0dd51114b019b26e4bdf52d53cae620cf0 SHA512: e9677e5215e999b13046a30af2b5aef28c92ab3638ed9a6ff647f0a42bc2db11b70bff0f421ec24ac5e1f17c5b97044df24c3064d394ffe232f5f6ae55cca98f Homepage: https://cran.r-project.org/package=tidyCDISC Description: CRAN Package 'tidyCDISC' (Quick Table Generation & Exploratory Analyses on ADaM-IshDatasets) Provides users a quick exploratory dive into common visualizations without writing a single line of code given the users data follows the Analysis Data Model (ADaM) standards put forth by the Clinical Data Interchange Standards Consortium (CDISC) . Prominent modules/ features of the application are the Table Generator, Population Explorer, and the Individual Explorer. The Table Generator allows users to drag and drop variables and desired statistics (frequencies, means, ANOVA, t-test, and other summary statistics) into bins that automagically create stunning tables with validated information. The Population Explorer offers various plots to visualize general trends in the population from various vantage points. Plot modules currently include scatter plot, spaghetti plot, box plot, histogram, means plot, and bar plot. Each plot type allows the user to plot uploaded variables against one another, and dissect the population by filtering out certain subjects. Last, the Individual Explorer establishes a cohesive patient narrative, allowing the user to interact with patient metrics (params) by visit or plotting important patient events on a timeline. All modules allow for concise filtering & downloading bulk outputs into html or pdf formats to save for later. Package: r-cran-tidycensus Architecture: all Version: 1.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3684 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr, r-cran-sf, r-cran-dplyr, r-cran-tigris, r-cran-stringr, r-cran-jsonlite, r-cran-purrr, r-cran-rvest, r-cran-tidyr, r-cran-rappdirs, r-cran-readr, r-cran-xml2, r-cran-units, r-cran-rlang, r-cran-crayon, r-cran-tidyselect Suggests: r-cran-ggplot2, r-cran-survey, r-cran-srvyr, r-cran-terra, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-tidycensus_1.8.0-1.ca2404.1_all.deb Size: 3564006 MD5sum: c512fd9804092bce2218f8ce31dd6f8b SHA1: 8891e5e66a30cb99d469a9e2cba95c3810170b8a SHA256: 021ec4dc560ee40b0a69be4d3fc6d46708aa242612a1ebdde18b2b4d8027d1ca SHA512: de70d47ce4bdffa8c46719567aec24bb42d44b9c2e66f9f97d20c4b8fbbb9de76c279c023b44a182030995cec07e1691216426cc56c4a259f6df8a0f484265f9 Homepage: https://cran.r-project.org/package=tidycensus Description: CRAN Package 'tidycensus' (Load US Census Boundary and Attribute Data as 'tidyverse' and'sf'-Ready Data Frames) An integrated R interface to several United States Census Bureau APIs () and the US Census Bureau's geographic boundary files. Allows R users to return Census and ACS data as tidyverse-ready data frames, and optionally returns a list-column with feature geometry for mapping and spatial analysis. Package: r-cran-tidycensuskr Architecture: all Version: 0.2.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7406 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-sf, r-cran-dplyr, r-cran-tidyr, r-cran-kosis, r-cran-rlang Suggests: r-cran-knitr, r-cran-withr, r-cran-purrr, r-cran-rmarkdown, r-cran-janitor, r-cran-geofacet, r-cran-ggplot2, r-cran-biscale, r-cran-cowplot, r-cran-tmap, r-cran-geodata, r-cran-testthat, r-cran-ggspatial, r-cran-spdep Filename: pool/dists/noble/main/r-cran-tidycensuskr_0.2.8-1.ca2404.1_all.deb Size: 6661234 MD5sum: f15ee9890643f6859615551125bee910 SHA1: de10356c652117f7714acc1f692eed213af4d7b9 SHA256: 21acce003dcd6a018bea8f9cd9eb4e1794941d277f9eaa5eb5f36b38c5d3bf7b SHA512: 8e1cc0f3e0b7f1dd36fe6bb57373afc44afe50c8dc669394279fc99cdb9db58a3484369df4f279ada5c5fb8aaafc0ece3553d171f881fb1779d52ffa91d722e6 Homepage: https://cran.r-project.org/package=tidycensuskr Description: CRAN Package 'tidycensuskr' (Easy Access for South Korea Census Data and Boundaries) Census and administrative data in South Korea are a basic source of quantitative and mixed-methods research for social and urban scientists. This package provides a 'sf' (Pebesma et al., 2024 ) based standardized workflow based on direct open API access to the major census and administrative data sources and pre-generated files in South Korea. Package: r-cran-tidychangepoint Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1080 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-changepoint, r-cran-changepointga, r-cran-cli, r-cran-dplyr, r-cran-ga, r-cran-generics, r-cran-ggplot2, r-cran-lifecycle, r-cran-lubridate, r-cran-memoise, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-segmented, r-cran-stringr, r-cran-strucchange, r-cran-tibble, r-cran-tidyr, r-cran-tsibble, r-cran-vctrs, r-cran-wbs, r-cran-xts, r-cran-zoo Suggests: r-cran-bench, r-cran-broom, r-cran-knitr, r-cran-here, r-cran-multitaper, r-cran-patchwork, r-cran-readr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidychangepoint_1.0.5-1.ca2404.1_all.deb Size: 669608 MD5sum: 2a88011a02aef63e0390678af4f3bac0 SHA1: 716dae4cc4e65320b305a107df83c27e0b7f73b4 SHA256: 02ea801cdfef5e319cfa7a0389c4334af41b21d4488eeefe2bf0abdf0be0e832 SHA512: 3e31e3ae095a7185a680c565107737213748b20e62dc5b25170e1926da7a06f1a0cf7db587c872803e3487b8d70ba3f6731f379f07c89016434995d5ce365f83 Homepage: https://cran.r-project.org/package=tidychangepoint Description: CRAN Package 'tidychangepoint' (A Tidy Framework for Changepoint Detection Analysis) Changepoint detection algorithms for R are widespread but have different interfaces and reporting conventions. This makes the comparative analysis of results difficult. We solve this problem by providing a tidy, unified interface for several different changepoint detection algorithms. We also provide consistent numerical and graphical reporting leveraging the 'broom' and 'ggplot2' packages. Package: r-cran-tidycharts Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1782 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-knitr, r-cran-magick, r-cran-rsvg, r-cran-rlang, r-cran-testthat, r-cran-htmlwidgets, r-cran-lubridate, r-cran-stringr Suggests: r-cran-rmarkdown, r-cran-palmerpenguins, r-cran-tidyverse, r-cran-dplyr, r-cran-covr Filename: pool/dists/noble/main/r-cran-tidycharts_0.1.3-1.ca2404.1_all.deb Size: 467602 MD5sum: 3efbd44927d366cbd4aa9796b41077b8 SHA1: 430d3cff11ce36d415a789780ebd6e13573e34cd SHA256: 1d5d32442acd189d9c0bcfe8443e8b33dbe556b0fc9f7379e50d559ece014216 SHA512: 7dd1a3cbbfd21f1c4e0ec627f6f5276b3f36605496bf778b4686fd4c70d99d378b570d059c6790093c2fc5f6ec591e06abbff88a0d8ef513572a1c08477951b0 Homepage: https://cran.r-project.org/package=tidycharts Description: CRAN Package 'tidycharts' (Generate Tidy Charts Inspired by 'IBCS') There is a wide range of R packages created for data visualization, but still, there was no simple and easily accessible way to create clean and transparent charts - up to now. The 'tidycharts' package enables the user to generate charts compliant with International Business Communication Standards ('IBCS'). It means unified bar widths, colors, chart sizes, etc. Creating homogeneous reports has never been that easy! Additionally, users can apply semantic notation to indicate different data scenarios (plan, budget, forecast). What's more, it is possible to customize the charts by creating a personal color pallet with the possibility of switching to default options after the experiments. We wanted the package to be helpful in writing reports, so we also made joining charts in a one, clear image possible. All charts are generated in SVG format and can be shown in the 'RStudio' viewer pane or exported to HTML output of 'knitr'/'markdown'. Package: r-cran-tidyclust Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2055 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-lifecycle, r-cran-dials, r-cran-dplyr, r-cran-flexclust, r-cran-generics, r-cran-glue, r-cran-hardhat, r-cran-mclust, r-cran-modelenv, r-cran-parsnip, r-cran-philentropy, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-rsample, r-cran-tibble, r-cran-tidyr, r-cran-tune, r-cran-vctrs Suggests: r-cran-butcher, r-cran-cluster, r-cran-clusterr, r-cran-clustmixtype, r-cran-covr, r-cran-dbscan, r-cran-future, r-cran-future.apply, r-cran-klar, r-cran-knitr, r-cran-lpcm, r-cran-meanshiftr, r-cran-mirai, r-cran-modeldata, r-cran-rcpphungarian, r-cran-recipes, r-cran-rmarkdown, r-cran-testthat, r-cran-withr, r-cran-workflows Filename: pool/dists/noble/main/r-cran-tidyclust_0.3.0-1.ca2404.1_all.deb Size: 570088 MD5sum: b2bd2d194ac6595536851f519ac76e50 SHA1: ffe1de085a768e4e0806ac06b62b339041aaf540 SHA256: 604fc73a1ac7e29206a3b59ce80da5f6a5286eb75ce1b6c664030ae6795ed3af SHA512: 5b138087619ae6402baf52dcf938243a9aa0e71e4c0f1787fc72c2693c930f259d119325a26bf68149ef8e052dd061e6569c1b640a5f52081403f9a76434f3be Homepage: https://cran.r-project.org/package=tidyclust Description: CRAN Package 'tidyclust' (A Common API to Clustering) A common interface to specifying clustering models, in the same style as 'parsnip'. Creates unified interface across different functions and computational engines. 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Package: r-cran-tidygeocoder Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 546 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-httr, r-cran-jsonlite, r-cran-progress Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-ggrepel, r-cran-maps, r-cran-testthat, r-cran-spelling Filename: pool/dists/noble/main/r-cran-tidygeocoder_1.0.6-1.ca2404.1_all.deb Size: 406930 MD5sum: 936b06198d5eba3eb4b798c7087ba38a SHA1: dcd06368c141d38d82a15412a6cd5c9682356393 SHA256: ad5b7740f0a0eb78aee5bdfc7a0bdeb17ea1339da7350bce22855958b95ee29f SHA512: c32b8ff99607535813d69c04816fa132ab2012ef2614143ecb7db121d8e53c120bc2a50d3920738b1e7d22562ca3d59bde6ea5b68b84390ea0ec1ca8918757e5 Homepage: https://cran.r-project.org/package=tidygeocoder Description: CRAN Package 'tidygeocoder' (Geocoding Made Easy) An intuitive interface for getting data from geocoding services. Package: r-cran-tidygeorss Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xml2, r-cran-httr, r-cran-anytime, r-cran-dplyr, r-cran-tidyrss, r-cran-jsonlite, r-cran-strex, r-cran-stringr, r-cran-magrittr, r-cran-purrr, r-cran-sf, r-cran-rlang Suggests: r-cran-httptest, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidygeorss_0.0.1-1.ca2404.1_all.deb Size: 43792 MD5sum: de5a94e36faf95a456b8a63f6c3caaeb SHA1: 378a98671f70fd7d4e191c937d91903c04424ba9 SHA256: c66212724db8ac61366cf950df917d1a9078c04ab5498b763b2e0ad3558540a8 SHA512: dfe5631f401f1655fd741251b4e7145de3553fefc5bfdfdf4c52a882adaf79616fcb9ef5afadf8248be22473bdc2b0af7af5852b9db3528267de848327fd5f80 Homepage: https://cran.r-project.org/package=tidygeoRSS Description: CRAN Package 'tidygeoRSS' (Tidy GeoRSS) In order to easily integrate geoRSS data into analysis, 'tidygeoRSS' parses 'geo' feeds and returns tidy simple features data frames. Package: r-cran-tidyheatmap Architecture: all Version: 1.13.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-cran-rlang, r-cran-purrr, r-cran-tibble, r-bioc-complexheatmap, r-cran-viridis, r-cran-circlize, r-cran-rcolorbrewer, r-cran-lifecycle, r-cran-dendextend, r-cran-patchwork Suggests: r-cran-spelling, r-cran-testthat, r-cran-vdiffr, r-cran-biocmanager, r-cran-knitr, r-cran-rmarkdown, r-cran-qpdf, r-cran-covr, r-cran-roxygen2, r-cran-forcats, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-tidyheatmap_1.13.1-1.ca2404.1_all.deb Size: 3976064 MD5sum: aa88380aec91a6d3a400ac92081754a8 SHA1: bf995eee8d8ec8dfd5f4349aa26d8f8a8213b58b SHA256: c585383015401c0c899937a3b5645f08fc127cc9ab004e4c4cf0b9564fe356d3 SHA512: e71d055fa58ee6d91c04ecc59afdb05bdad2f4414263d4fd1c2cfa60e1d344558ef3ff63a08326335c0ace12e50b711b1f0570fa9ebbbcdda70c7e23dc70b3e1 Homepage: https://cran.r-project.org/package=tidyHeatmap Description: CRAN Package 'tidyHeatmap' (A Tidy Implementation of Heatmap) This is a tidy implementation for heatmap. At the moment it is based on the (great) package 'ComplexHeatmap'. The goal of this package is to interface a tidy data frame with this powerful tool. Some of the advantages are: Row and/or columns colour annotations are easy to integrate just specifying one parameter (column names). Custom grouping of rows is easy to specify providing a grouped tbl. For example: df %>% group_by(...). Labels size adjusted by row and column total number. Default use of Brewer and Viridis palettes. Package: r-cran-tidyheatmaps Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 844 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-pheatmap, r-cran-rlang, r-cran-tidyr, r-cran-tibble, r-cran-rcolorbrewer Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tidyheatmaps_0.2.1-1.ca2404.1_all.deb Size: 503818 MD5sum: 7e19d7879ee30c04dd944c047354e33c SHA1: 69390d7cda565f5fed9fce3f3d8a77af74903370 SHA256: 99751d2acf2c016e6ec8624358871a03a529cf687c7cfc1d8e2cfabd4b0ac38a SHA512: 2e71c3b087e2ffbceabbc07bb2e8b62e179eceaacb61a7bdd8670e41e4c1e8a363e0213cd781680a437ac897274dfb16e978c2c482b5bb0b567fa7ffa649ba87 Homepage: https://cran.r-project.org/package=tidyheatmaps Description: CRAN Package 'tidyheatmaps' (Heatmaps from Tidy Data) The goal of 'tidyheatmaps' is to simplify the generation of publication-ready heatmaps from tidy data. 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Package: r-cran-tidyhte Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1853 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-lifecycle, r-cran-magrittr, r-cran-progress, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-superlearner, r-cran-tibble Suggests: r-cran-covr, r-cran-devtools, r-cran-estimatr, r-cran-ggplot2, r-cran-glmnet, r-cran-knitr, r-cran-mockr, r-cran-nprobust, r-cran-palmerpenguins, r-cran-quadprog, r-cran-quickblock, r-cran-rmarkdown, r-cran-testthat, r-cran-vimp, r-cran-weightedroc Filename: pool/dists/noble/main/r-cran-tidyhte_1.0.4-1.ca2404.1_all.deb Size: 1197684 MD5sum: 2b25eb1f9b40c7ead3236315aaa817e4 SHA1: 28cfcf763e1651b08efe8330e835a5cfa21426b6 SHA256: 96a35955611b62dd5863edc80f1e0c1d3be3208d376fdcd7cddc5df3f730d72a SHA512: 74e5086eace7bcaf2a6378ec5cca9ccd9a970027f338507e7505893ec92f299bb90a3ab2905ba2ec9f1c3278a40c35ddefdd57d9b48acff3525d979966e1a3e1 Homepage: https://cran.r-project.org/package=tidyhte Description: CRAN Package 'tidyhte' (Tidy Estimation of Heterogeneous Treatment Effects) Estimates heterogeneous treatment effects using tidy semantics on experimental or observational data. Methods are based on the doubly-robust learner of Kennedy (2023) . You provide a simple recipe for what machine learning algorithms to use in estimating the nuisance functions and 'tidyhte' will take care of cross-validation, estimation, model selection, diagnostics and construction of relevant quantities of interest about the variability of treatment effects. Package: r-cran-tidyhydat Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3881 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-crayon, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-httr2, r-cran-lubridate, r-cran-rappdirs, r-cran-readr, r-cran-rlang, r-cran-rsqlite, r-cran-tidyr Suggests: r-cran-ggplot2, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/noble/main/r-cran-tidyhydat_1.0.0-1.ca2404.1_all.deb Size: 2103222 MD5sum: 22c95a38c8b1ca5e4303fe26701250eb SHA1: 4196d0f6a7f1a95a9f3e2a2c501bf109ef3b3dea SHA256: 45c409dd9eca35afe20f0507a0516605e9282d998c123af5cd15bbabe16b11c1 SHA512: 6574145783eb08d52d10a3c54a06fb84b9945c7f00b11d6aecd334944c66162eb790eeb5ddbb7945158c11c9f605d69e2e3489538eecb3d6f1a9eae6a98042f1 Homepage: https://cran.r-project.org/package=tidyhydat Description: CRAN Package 'tidyhydat' (Extract and Tidy Canadian 'Hydrometric' Data) Provides functions to access historical and real-time national 'hydrometric' data from Water Survey of Canada data sources and then applies tidy data principles. 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Encodes time structure, enforces within-between decomposition, provides spacing-aware lags, and integrates diagnostics and visualization. Use ild_prepare(), ild_center(), ild_lag(), and related functions for a unified pipeline from raw EMA/diary data to interpretable models. 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'tidyindex' contains modules for transforming variables, aggregating variables across time, reducing data dimension through weighting, and fitting distributions. A manuscript describing the methodology can be found at . 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Package: r-cran-tidyklips Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 490 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-stringr, r-cran-readxl, r-cran-haven, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidyklips_0.3.0-1.ca2404.1_all.deb Size: 144094 MD5sum: cd3757d1c5a058b255e12e6ccbea502c SHA1: 8f74d95ff8aa0b118c53d6efe235a7a1871dc585 SHA256: b3b384372f57384f7a9e458767d760f604977041441f31259e63de37acfae29b SHA512: f9115b6c235178e7cdbfffddafb977017a6a67577f5cdf1d244dd8f24668f68761ec9cc5bc146364f09e8d8d6cffc53a464f2da3ce730e7b27f257f425fa3d8c Homepage: https://cran.r-project.org/package=tidyklips Description: CRAN Package 'tidyklips' (Load Korea Labor & Income Panel Study (KLIPS) Data as DataFrames) Loading the Korea Labor Institute's KLIPS (Korea Labor & Income Panel Study) panel data and returning data frames. Users must download 26 years of panel data from the Korea Labor Institute website and save it in a folder in an appropriate path. Afterwards, users can easily convert the data into a data frame using this package. Package: r-cran-tidylaslog Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-arrow, r-cran-jsonlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidylaslog_0.1.2-1.ca2404.1_all.deb Size: 236604 MD5sum: c1ebc6f013ccb7622962056444175018 SHA1: ecbd25c503f455827733acb5a25cb85b51e12290 SHA256: dda8499eb9fcaeaf914fc1e34235044bf02710c9a31cc7ac1f575c72b5e124a2 SHA512: 02c152ffdd651f987a03941fe42761503526e0d56d4967e2773df873ad88acbeb832e10beb62def4697fe2284412839f73bf45f58a2d8e5ec3a2f35898efae44 Homepage: https://cran.r-project.org/package=tidylaslog Description: CRAN Package 'tidylaslog' (Tidy Import, Indexing, and Export of LAS Well Log Data) Provides tools for reading, parsing, indexing, and exporting LAS (Log ASCII Standard) well log files into tidy, analysis-ready tabular formats. The package separates LAS header information and log data into structured components, builds a searchable index across collections of LAS files, and enables reproducible subsetting of wells based on metadata or curve availability. Output tables can be written to CSV or Parquet formats to support large-scale statistical, machine learning, and earth science workflows. The tidy data structure follows Wickham (2014) . The LAS file structure follows the Canadian Well Logging Society LAS standard . Package: r-cran-tidylearn Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2855 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-tibble, r-cran-tidyr, r-cran-purrr, r-cran-rlang, r-cran-magrittr, r-cran-e1071, r-cran-gbm, r-cran-glmnet, r-cran-nnet, r-cran-randomforest, r-cran-rpart, r-cran-rsample, r-cran-rocr, r-cran-yardstick, r-cran-cluster, r-cran-dbscan, r-cran-mass, r-cran-smacof Suggests: r-cran-arules, r-cran-arulesviz, r-cran-bigrquery, r-cran-car, r-cran-dbi, r-cran-dt, r-cran-ggally, r-cran-ggforce, r-cran-gridextra, r-cran-gt, r-cran-jsonlite, r-cran-keras, r-cran-knitr, r-cran-lmtest, r-cran-moments, r-cran-nanoparquet, r-cran-neuralnettools, r-cran-paws.storage, r-cran-readr, r-cran-readxl, r-cran-rmariadb, r-cran-rmarkdown, r-cran-rpostgres, r-cran-rpart.plot, r-cran-rsqlite, r-cran-scales, r-cran-shiny, r-cran-shinydashboard, r-cran-tensorflow, r-cran-testthat, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-tidylearn_0.3.1-1.ca2404.1_all.deb Size: 2075770 MD5sum: cddfdd59e02f45625958f308e9a67239 SHA1: 413b3cf8166f74b07900efd18a4a38f0c8e2624a SHA256: 34d670abcb907085f75fd9e92949166e00f8a1d1ccdfffc5cd7a45d1072b3377 SHA512: e526dde984fa24141d155e991ad59cc06f8e1975e52d7c85e899ab9cfc8ee0b4b0a1642dff964363bd3ad406d31dc87b04cd5d463c22f689526794e0de2a6d25 Homepage: https://cran.r-project.org/package=tidylearn Description: CRAN Package 'tidylearn' (A Unified Tidy Interface to R's Machine Learning Ecosystem) Provides a unified tidyverse-compatible interface to R's machine learning ecosystem - from data ingestion to model publishing. The tl_read() family reads data from files ('CSV', 'Excel', 'Parquet', 'JSON'), databases ('SQLite', 'PostgreSQL', 'MySQL', 'BigQuery'), and cloud sources ('S3', 'GitHub', 'Kaggle'). The tl_model() function wraps established implementations from 'glmnet', 'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster', 'dbscan', and others with consistent function signatures and tidy tibble output. Results flow into unified 'ggplot2'-based visualization and optional formatted 'gt' tables via the tl_table() family. The underlying algorithms are unchanged; 'tidylearn' simply makes them easier to use together. Access raw model objects via the $fit slot for package-specific functionality. Methods include random forests Breiman (2001) , LASSO regression Tibshirani (1996) , elastic net Zou and Hastie (2005) , support vector machines Cortes and Vapnik (1995) , and gradient boosting Friedman (2001) . Package: r-cran-tidyllm Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1762 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-s7, r-cran-base64enc, r-cran-glue, r-cran-jsonlite, r-cran-curl, r-cran-httr2, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-pdftools, r-cran-tibble, r-cran-cli, r-cran-png, r-cran-lifecycle Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-httptest2, r-cran-httpuv, r-cran-ellmer Filename: pool/dists/noble/main/r-cran-tidyllm_0.5.0-1.ca2404.1_all.deb Size: 1452318 MD5sum: 54bbbe18b479c0ff028c2b1125b2f574 SHA1: 435878b6306c00a735748adada5143bfb9fc9abe SHA256: b2276b4234e0df34599775b8fec0f5b938d93befd731b5d467afe40d33200f20 SHA512: fab564b4cb958d7540b63172e86acc67868da005692a8489a3787ffa8df25be0c98af9fbaebf367f537ff5c92cea4d8328de28cc99784a35924d42fe086c8c5d Homepage: https://cran.r-project.org/package=tidyllm Description: CRAN Package 'tidyllm' (Tidy Integration of Large Language Models) A tidy interface for integrating large language model (LLM) APIs such as 'Claude', 'OpenAI', 'Gemini', 'Mistral', and local models via 'Ollama' into R workflows. The package supports text, image, audio, video, and document interactions; a unified media interface for attaching inline files or uploading to provider file stores; batch request APIs for cost-efficient large-scale processing; and a pipeline-oriented interface for seamless integration into data workflows. Web services are available at , , , and . Package: r-cran-tidylo Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Suggests: r-cran-covr, r-cran-ggplot2, r-cran-janeaustenr, r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tidytext Filename: pool/dists/noble/main/r-cran-tidylo_0.2.0-1.ca2404.1_all.deb Size: 207422 MD5sum: def47a45c0175997bc7b1bd82a26fe83 SHA1: 322554a92cf8ea36ac165cc89f1a1455470fd127 SHA256: 4e48d7a0dac87dd9da79cd5e10c8ec70bef5e09ce553f926d46110658e50c42e SHA512: e70fee7901f8b8f119243a4dd6bc03e65058f6b82d45e9afa8d1c97b794d1c7e9f515453d60df683a74f864376e94f202e5fe1cf30c8eec71be64d4c2221ed47 Homepage: https://cran.r-project.org/package=tidylo Description: CRAN Package 'tidylo' (Weighted Tidy Log Odds Ratio) How can we measure how the usage or frequency of some feature, such as words, differs across some group or set, such as documents? One option is to use the log odds ratio, but the log odds ratio alone does not account for sampling variability; we haven't counted every feature the same number of times so how do we know which differences are meaningful? Enter the weighted log odds, which 'tidylo' provides an implementation for, using tidy data principles. In particular, here we use the method outlined in Monroe, Colaresi, and Quinn (2008) to weight the log odds ratio by a prior. By default, the prior is estimated from the data itself, an empirical Bayes approach, but an uninformative prior is also available. Package: r-cran-tidylog Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-glue, r-cran-clisymbols, r-cran-rlang Suggests: r-cran-testthat, r-cran-units, r-cran-nycflights13, r-cran-covr, r-cran-forcats, r-cran-knitr, r-cran-rmarkdown, r-cran-bench Filename: pool/dists/noble/main/r-cran-tidylog_1.1.0-1.ca2404.1_all.deb Size: 147352 MD5sum: 3eefc59ba5fa1aa4905c46151fcfe635 SHA1: ce9d8f8d96c3e0d6fbcfd6b54c4041d74ea40e41 SHA256: 2fdc61926def4c2eb2b30f133d5f879486c6e4c79b2efacfa02cbfd6f0f39960 SHA512: 60264fae17a266962ceecc0de19fc588fae3531a3901622acdd4a0cd64115345cf67578658798e09bda79ac5217196cac9b20fc103897730d85e0bcd67d13aec Homepage: https://cran.r-project.org/package=tidylog Description: CRAN Package 'tidylog' (Logging for 'dplyr' and 'tidyr' Functions) Provides feedback about 'dplyr' and 'tidyr' operations. Package: r-cran-tidylpa Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 963 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidysem, r-cran-dplyr, r-cran-ggplot2, r-cran-mclust, r-cran-mplusautomation, r-cran-tibble, r-cran-rlang Suggests: r-cran-knitr, r-cran-lme4, r-cran-missforest, r-cran-pillar, r-cran-rmarkdown, r-cran-testthat, r-cran-openmx, r-cran-mix Filename: pool/dists/noble/main/r-cran-tidylpa_2.0.2-1.ca2404.1_all.deb Size: 729000 MD5sum: 2b65d347694681ce30ff1c8e62ed843e SHA1: 8d0154d00205d1c81dbd565c8120f4739d3382c8 SHA256: 3965b8f3b08f91b4ad8fc910339d26c01f02ad7b943366d6947e96db3524c593 SHA512: 5bd048a3f55189c5826ac6b10793b1c90b62ca5a832eb90169c78979aadb193050ea4e1edffbaec66f370f8f633f595db2f407ed64f88011d5cf58c4a1f19404 Homepage: https://cran.r-project.org/package=tidyLPA Description: CRAN Package 'tidyLPA' (Easily Carry Out Latent Profile Analysis (LPA) Using Open-Sourceor Commercial Software) Easily carry out latent profile analysis ("LPA"), determine the correct number of classes based on best practices, and tabulate and plot the results. Provides functionality to estimate commonly-specified models with free means, variances, and covariances for each profile. Follows a tidy approach, in that output is in the form of a data frame that can subsequently be computed on. Models can be estimated using the free open source 'R' packages 'Mclust' and 'OpenMx', or using the commercial program 'MPlus', via the 'MplusAutomation' package. Package: r-cran-tidymc Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-dplyr, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-hms, r-cran-kableextra, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidymc_1.0.1-1.ca2404.1_all.deb Size: 189340 MD5sum: 18bd727601680266025e5a9638f88fc6 SHA1: aecf461d71586d13114e310b984e380a62dd430c SHA256: 4c61ccd57b9cc1d72a67544a9b87b63e42613fd0e4a559942bb241bfd99a1641 SHA512: 96db6897aed50e5356f2269f025362ce9f9cdc039fbd477e46472e8384591160ee897fdccdd383160c6a4f320adfcab14c85f945f447eddd84fde2748808a22d Homepage: https://cran.r-project.org/package=tidyMC Description: CRAN Package 'tidyMC' (Monte Carlo Simulations Made Easy and Tidy) Framework to run Monte Carlo simulations over a parameter grid. 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Package: r-cran-tidymodels Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-broom, r-cran-cli, r-cran-conflicted, r-cran-dials, r-cran-dplyr, r-cran-ggplot2, r-cran-hardhat, r-cran-infer, r-cran-modeldata, r-cran-parsnip, r-cran-purrr, r-cran-recipes, r-cran-rlang, r-cran-rsample, r-cran-rstudioapi, r-cran-tailor, r-cran-tidyr, r-cran-tune, r-cran-workflows, r-cran-workflowsets, r-cran-yardstick Suggests: r-cran-covr, r-cran-glue, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-tidymodels_1.5.0-1.ca2404.1_all.deb Size: 84554 MD5sum: bced9abb24cae61f18732bde57357e66 SHA1: 1a01980fbc768027356ac6ecaa7da147237f33c5 SHA256: 502aaad422c919bc4705f0e057a5b4f111db9c475108096b32a3021a004a3724 SHA512: 6603dc599f9b5c9f547b84af8b8e71947620929ee7938a59aa1eff80f44984d1e8e0462a26371f0498254681afa4dc03a01c3d83ba7cc4890a3835ca9774f285 Homepage: https://cran.r-project.org/package=tidymodels Description: CRAN Package 'tidymodels' (Easily Install and Load the 'Tidymodels' Packages) The tidy modeling "verse" is a collection of packages for modeling and statistical analysis that share the underlying design philosophy, grammar, and data structures of the tidyverse. Package: r-cran-tidymodlr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-dplyr, r-cran-tidyr, r-cran-tm, r-cran-corrr, r-cran-factominer Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tidymodlr_1.0.0-1.ca2404.1_all.deb Size: 203936 MD5sum: 79183d407a86c65744579365bbd1f8e6 SHA1: 4ee52b13ba194fb6171d48bcaa8d5995456777c4 SHA256: 840eb4277c4879ffcdafa05709bbb7fc9a75c1b556c941547cb00afa6700ecec SHA512: 4ce72e2e5247dec1387212d7f49f89ddf345c73d47b669095cbfa31d32fde2f4b0f3dc2ca93e02e91c6ed1b7b759f8c6e92e32538cb9e063aab73602dcebd88a Homepage: https://cran.r-project.org/package=tidymodlr Description: CRAN Package 'tidymodlr' (An R6 Class to Perform Analysis on Long Tidy Data) Transforms long data into a matrix form to allow for ease of input into modelling packages for regression, principal components, imputation or machine learning. 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Package: r-cran-tidymultiqc Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2788 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-assertthat, r-cran-dplyr, r-cran-jsonlite, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-tidyr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-histdat Filename: pool/dists/noble/main/r-cran-tidymultiqc_1.0.3-1.ca2404.1_all.deb Size: 358678 MD5sum: 76fc3e73f0359b77418270b1fc6892e2 SHA1: b9e8053f1848d3875faf1c5bc66f3b82c607b995 SHA256: ef25ef0362b5713376bd818678fe125b6aa66997e3cbf0b26e8ef1f0f38828ae SHA512: 9264021749fc504b905bb7ff859dc9378e658c84c483db5311688788d63ecd2fd2233768779d5c84f0861950daffde1facfc9f12613eab709092cfe42fce70d8 Homepage: https://cran.r-project.org/package=TidyMultiqc Description: CRAN Package 'TidyMultiqc' (Converts 'MultiQC' Reports into Tidy Data Frames) Provides the means to convert 'multiqc_data.json' files, produced by the wonderful 'MultiQC' tool, into tidy data frames for downstream analysis in R. This analysis might involve cohort analysis, quality control visualisation, change-point detection, statistical process control, clustering, or any other type of quality analysis. 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Explore the contents of a 'NetCDF' source (file or URL) presented as variables organized by grid with a database-like interface. The hyper_filter() interactive function translates the filter value or index expressions to array-slicing form. No data is read until explicitly requested, as a data frame or list of arrays via hyper_tibble() or hyper_array(). 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Provides base-R re-implementations of common 'purrr' functional helpers, tools to convert plain data frames into 'CIRCE' concept set expressions, SQL generators for resolving concept sets against an OMOP vocabulary schema without requiring 'CirceR'. 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To answer this, a model can be created were the performance statistic is the resampling statistics (e.g. accuracy or RMSE). These values are explained by the model types. In doing this, we can get parameter estimates for each model's affect on performance and make statistical (and practical) comparisons between models. The methods included here are similar to Benavoli et al (2017) . 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'tidyprompt' introduces the concept of prompt wraps, which are building blocks that you can use to quickly turn a simple prompt into a complex one. Prompt wraps do not just modify the prompt text, but also add extraction and validation functions that will be applied to the response of the LLM. This ensures that the user gets the desired output. 'tidyprompt' can add various features to prompts and their evaluation by LLMs, such as structured output, automatic feedback, retries, reasoning modes, autonomous R function calling, and R code generation and evaluation. It is designed to be compatible with any LLM provider that offers chat completion. 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The 'tidyquant' package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' and 'PerformanceAnalytics' package functions and returns the objects in the tidy 'tibble' format. The main advantage is being able to use quantitative functions with the 'tidyverse' functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See the 'tidyquant' website for more information, documentation and examples. 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Package: r-cran-tidysummary Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-car, r-cran-cli, r-cran-dplyr, r-cran-fbasics, r-cran-glue, r-cran-qqplotr, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-tidyplots, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tidysummary_0.1.0-1.ca2404.1_all.deb Size: 133650 MD5sum: 1ecc7f65b7baf58dd33ea983c8156441 SHA1: 930b5c249962a6fcc4f5ca3322bef639390d4715 SHA256: 9d09c71ca3969d47fbbe8a641c4639edf622676ccd0b4cc6f8559d21b9a702de SHA512: f8b7fcf595cc5e268cefd4d94bc6bacf6490dd2128be324352353a89b069f34141462809b855c666fe3110d49604c34176bb6e63295d0e33562608cbd7d300fd Homepage: https://cran.r-project.org/package=tidysummary Description: CRAN Package 'tidysummary' (An Elegant Approach to Summarizing Clinical Data) Streamlines the analysis of clinical data by automatically selecting appropriate statistical descriptions and inference methods based on variable types. 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Much of the infrastructure needed for text mining with tidy data frames already exists in packages like 'dplyr', 'broom', 'tidyr', and 'ggplot2'. In this package, we provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages. 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Package: r-cran-tidytitanic Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tidytitanic_0.0.1-1.ca2404.1_all.deb Size: 389700 MD5sum: 29a9dd8e384c38a6615bf0cfdc78abeb SHA1: 2c1c40cc9ef11f9f17ddc856f75f869cea089a26 SHA256: cc0bef65ad6de577eb8a90ad9208e827840306df78cee4f9e356707cf407ac31 SHA512: 51a5d7d94b3162c343e5362d52685e306e84eae9b79242abdc08352ba614d607d022a458d9b0269988f6be5f0356ba688af52449e24975b4f0e2cf88332cb0bf Homepage: https://cran.r-project.org/package=tidytitanic Description: CRAN Package 'tidytitanic' (Dataframes Based on Titanic Passengers and Crew) A version of the Titanic survival data tailored for people analytics demonstrations and practice. While another package, 'titanic', reproduces the Kaggle competition files with minimal preprocessing, 'tidytitanic' combines the train and test datasets into the single dataset, 'passengers', for exploration and summary across all passengers. It also extracts personal identifiers—such as first names, last names, and titles from the raw 'name' field, enabling demographic analysis. The 'passengers' data does not cover the crew, but this package also provides the more bare-bones, crew-containing datasets 'tidy_titanic' and 'flat_titanic' based on the 'Titanic' data set from 'datasets' for further exploration. This human-centered data package is designed to support exploratory data analysis, feature engineering, and pedagogical use cases. 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(2019) . These functional metrics are extremely useful in characterizing the distribution of traits in a plant community. It also provides tidyverse-friendly wrappers to seamlessly calculate advanced functional diversity indices (e.g., FDis, Rao's Q) using 'fundiversity' (Grenie et al. 2023 ) and functional rarity indices using 'funrar' (Grenie et al. 2017 ). Evaluating these community-weighted moments and diversity metrics allows researchers to evaluate shifts in optimal phenotypes and understand ecological filtering with exactness. 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The method uses fine balance, optimal subset matching (Rosenbaum, 2012 ) and two-criteria matching (Zhang et al 2023 ). The main function is tighten(). The suggested 'rrelaxiv' package for solving minimum cost flow problems: (i) derives from Bertsekas and Tseng (1988) , (ii) is not available on CRAN due to its academic license, (iii) may be downloaded from GitHub at , (iv) is not essential to use the package. 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Package: r-cran-tikatuwq Architecture: all Version: 0.8.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-readr, r-cran-tibble, r-cran-rlang, r-cran-ggplot2, r-cran-tidyr, r-cran-lubridate, r-cran-stringr, r-cran-glue, r-cran-scales, r-cran-broom, r-cran-purrr Suggests: r-cran-testthat, r-cran-spelling, r-cran-rmarkdown, r-cran-knitr, r-cran-pkgdown, r-cran-leaflet, r-cran-withr Filename: pool/dists/noble/main/r-cran-tikatuwq_0.8.2-1.ca2404.1_all.deb Size: 314778 MD5sum: 456a5b8aa72fbec5585bbda2d174f71a SHA1: d384cd8eb2387ebcb67e73326b79fd45047b3c54 SHA256: 45aca1d295129b1292d8b52f45cb62f7736b591f316b70238f48d50fd76a51e9 SHA512: b4ab85261d5374a8758630f783514306ad9578f837a3e1729588359003a0e8e5d81530c86a21e91d850a7734394086c12a602eaa83a7226ddff0720ea0fd61d5 Homepage: https://cran.r-project.org/package=tikatuwq Description: CRAN Package 'tikatuwq' (Water Quality Assessment and Environmental Compliance in Brazil) Tools to import, clean, validate, and analyze freshwater quality data in Brazil. Implements water quality indices including the Water Quality Index (WQI/IQA), the Trophic State Index (TSI/IET) after Carlson (1977) and Lamparelli (2004) , and the National Sanitation Foundation Water Quality Index (NSF WQI) . The package also checks compliance with Brazilian standard CONAMA Resolution 357/2005 and generates reproducible reports for routine monitoring workflows. The example dataset (`wq_demo`) is now a real subset from monitoring data (BURANHEM river, 2020-2024, 4 points, 20 rows, 14 columns including extra `rio`, `lat`, `lon`). All core examples and vignettes use this realistic sample, improving reproducibility and documentation value for users. Package: r-cran-tiktokadsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-tiktokadsr_0.1.0-1.ca2404.1_all.deb Size: 22738 MD5sum: c7fe2dd5f8e055cf3aacd5d3089f8304 SHA1: ec012ebc4df08376d860327b87561622d1c60379 SHA256: 61c26f56bd378c0d3bbc53d83aa9c11580dc9ba326c21385722ae1af82a2fed5 SHA512: 47d6e77ea7cc27d4e018141fa1dd42b03d61541d4d87989f0faa3fe18ec77506cd4f218dd1f41a791953d5990df6f1b0c3a5c3258a3b52a6440bd5c270e9a1db Homepage: https://cran.r-project.org/package=tiktokadsR Description: CRAN Package 'tiktokadsR' (Access to TikTok Ads via the 'Windsor.ai' API) Collect marketing data from TikTok Ads using the 'Windsor.ai' API . 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Package: r-cran-tilemaps Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1749 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-clue, r-cran-ggplot2, r-cran-igraph, r-cran-sf, r-cran-smoothr Suggests: r-cran-dplyr, r-cran-knitr, r-cran-lwgeom, r-cran-rmarkdown, r-cran-spdata Filename: pool/dists/noble/main/r-cran-tilemaps_0.2.2-1.ca2404.1_all.deb Size: 1441786 MD5sum: 7b83ddb8b33839bfcd6d74557aea1219 SHA1: e8bb6bc350fb97f09913b72dcf00664731bcd203 SHA256: 57948ade2d3bbe3829519830ecfc0cb23620b9b45a852ed17fa24ea149978202 SHA512: a404786f9caa71da9d63f8450b3ddfdb86784c335e1e9b2c3ea4fa14d7548863bed8e3eefda32806c6176128dbb8904ef74cf3d3539946b2c1fe85b92ddccd8b Homepage: https://cran.r-project.org/package=tilemaps Description: CRAN Package 'tilemaps' (Generate Tile Maps) Implements an algorithm for generating maps, known as tile maps, in which each region is represented by a single tile of the same shape and size. 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Package: r-cran-tilting Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-tilting_1.1.1-1.ca2404.1_all.deb Size: 34404 MD5sum: 31f07b22144762863e87f4b28e64dae2 SHA1: 497ada5f5649b9c060b87e75a8bf178015cf4d9f SHA256: 3baa9f7353a395893aa9c4d6cb0e917d1bb97bddcba43521d4b9d533a0334e31 SHA512: 85bb394e6887ef9848e568d23bd3ed9d54e3f85ad3fd05f4bf575b8874e24254312be3fddba925a5d495b76e8448c6760a57512b57abc475f39b615d4d7ca20b Homepage: https://cran.r-project.org/package=tilting Description: CRAN Package 'tilting' (Variable Selection via Tilted Correlation Screening Algorithm) Implements an algorithm for variable selection in high-dimensional linear regression using the "tilted correlation", a new way of measuring the contribution of each variable to the response which takes into account high correlations among the variables in a data-driven way. 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One function supports iterative workflows by predicting and showing the total time required, while another reports the time taken for individual steps within a process. 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Beyond these standards it provides the "Financial Center" concept which allows to handle data records collected in different time zones and mix them up to have always the proper time stamps with respect to your personal financial center, or alternatively to the GMT reference time. It can thus also handle time stamps from historical data records from the same time zone, even if the financial centers changed day light saving times at different calendar dates. Package: r-cran-timedelay Architecture: all Version: 1.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-timedelay_1.0.11-1.ca2404.1_all.deb Size: 122388 MD5sum: 046bf0868cba5c73c3a9d17219f17952 SHA1: b70edff56fa892d8ed1e862854208a20d0713b49 SHA256: 27757ca1405aaff1aab66be2a641b1098889a3d889e42708779ecd5efd275049 SHA512: 88b3637e59db3b243ea21ace936bb2d03ca45b0880a216397256ccf6bdb519cf988f6a81e078415f76c59db93b662ddb52ccdf15a62850bc17fd133367178502 Homepage: https://cran.r-project.org/package=timedelay Description: CRAN Package 'timedelay' (Time Delay Estimation for Stochastic Time Series ofGravitationally Lensed Quasars) We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement. A new functionality is added in version 1.0.9 for estimating the time delay between doubly-lensed light curves observed in two bands. See also Tak et al. (2017) , Tak et al. (2018) , Hu and Tak (2020) . Package: r-cran-timedepfrail Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-timedepfrail_0.1.0-1.ca2404.1_all.deb Size: 260156 MD5sum: dd766b2b60a608e7170183d8c19f8f20 SHA1: 8c271bea9ef0cd25b75da89a3c4578610fb8befd SHA256: 758ad32ed29bc0ad68e17def210a674787b32b6f5702a7a7fea615f9bc955694 SHA512: e2b13d393642c54f232cb7d357ae4246f7aeae55da318172f67cc0f35f42dc75b457dea41d015fbe8d75ad51cc84478330d315e13a7cf53a74d3ae2045274b68 Homepage: https://cran.r-project.org/package=TimeDepFrail Description: CRAN Package 'TimeDepFrail' (Time Dependent Shared Frailty Cox Model) Fits time-dependent shared frailty Cox model (specifically the adapted Paik et al.'s Model) based on the paper "Centre-Effect on Survival After Bone Marrow Transplantation: Application of Time-Dependent Frailty Models", by C.M. Wintrebert, H. Putter, A.H. Zwinderman and J.C. van Houwelingen (2004) . Package: r-cran-timedeppar Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-timedeppar_1.0.3-1.ca2404.1_all.deb Size: 187276 MD5sum: 6c5c87b05ceea3b0e27212a16b7b119e SHA1: 0bf730e9c678a4b15279caaede0caa917e698931 SHA256: 668f8dc206f81bdd63ad30fc1469c59cb9d1968396d72f03d1e9178bccd7ae8f SHA512: f054b123d6b07c99878d754ec4dd6dd512f63447b540a401b300fadd3e2b890beded5e73727106d2f252a8374985cfac85c64ed3b6e23c5c7a845f6111e5d6ab Homepage: https://cran.r-project.org/package=timedeppar Description: CRAN Package 'timedeppar' (Infer Constant and Stochastic, Time-Dependent Model Parameters) Infer constant and stochastic, time-dependent parameters to consider intrinsic stochasticity of a dynamic model and/or to analyze model structure modifications that could reduce model deficits. The concept is based on inferring time-dependent parameters as stochastic processes in the form of Ornstein-Uhlenbeck processes jointly with inferring constant model parameters and parameters of the Ornstein-Uhlenbeck processes. The package also contains functions to sample from and calculate densities of Ornstein-Uhlenbeck processes. References: Tomassini, L., Reichert, P., Kuensch, H.-R. Buser, C., Knutti, R. and Borsuk, M.E. (2009), A smoothing algorithm for estimating stochastic, continuous-time model parameters and its application to a simple climate model, Journal of the Royal Statistical Society: Series C (Applied Statistics) 58, 679-704, Reichert, P., and Mieleitner, J. (2009), Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters. Water Resources Research, 45, W10402, Reichert, P., Ammann, L. and Fenicia, F. (2021), Potential and challenges of investigating intrinsic uncertainty of hydrological models with time-dependent, stochastic parameters. Water Resources Research 57(8), e2020WR028311, Reichert, P. (2022), timedeppar: An R package for inferring stochastic, time-dependent model parameters, in preparation. 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Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed. See Blanche et al. (2013) and references therein for the details of the methods implemented in the package. 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The 'timeseriesdb' package was designed to manage a large catalog of time series from official statistics which are typically published on a monthly, quarterly or yearly basis. Thus timeseriesdb is optimized to handle updates caused by data revision as well as elaborate, multi-lingual meta information. 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It performs a non-parametric kernel smoothing (using a common bandwidth) of all underlying components required for the computation of a correlation coefficient (i.e., x, y, x^2, y^2, xy). An automatic selection procedure for the bandwidth parameter is implemented. Alternative kernels can be used (Epanechnikov, box and normal). Both Pearson and Spearman correlation coefficients can be estimated and change in correlation over time can be tested. Package: r-cran-timevis Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1054 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-magrittr, r-cran-rmarkdown, r-cran-shiny Suggests: r-cran-lubridate, r-cran-testthat, r-cran-shinydisconnect Filename: pool/dists/noble/main/r-cran-timevis_2.1.0-1.ca2404.1_all.deb Size: 535304 MD5sum: 89ca7bc28c720458d52a5284c9ac8cea SHA1: f7e44fbde64ae2c1ff2609ed5faa78c693c6e65a SHA256: 2b941bc660214ac8f94acfab125f790b0f647775005d4e10cff82c978172ed29 SHA512: 38c86aa6aadaa88bfbeaa907bff307edc79a8f4886eb52278a515f5cc01901a28709b6eca2993e4642c4685987357fba0a90c55ee22a5dd2ef47572faeee04ce Homepage: https://cran.r-project.org/package=timevis Description: CRAN Package 'timevis' (Create Interactive Timeline Visualizations in R) Create rich and fully interactive timeline visualizations. 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Package: r-cran-timevizpro Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-countrycode, r-cran-sparkline, r-cran-ggplot2, r-cran-ggiraph, r-cran-dplyr Suggests: r-cran-openxlsx, r-cran-htmltools, r-cran-htmlwidgets, r-cran-dt Filename: pool/dists/noble/main/r-cran-timevizpro_1.0.1-1.ca2404.1_all.deb Size: 39168 MD5sum: 35616674b2c979c01707cd6af35ba4bf SHA1: 3be149aabfbcd46f45bcaf499f3d031ab8663446 SHA256: d009120f5ef0834c5c2ffe250ab7a203d776061932a2c807b9f8d5bd6633e2dd SHA512: 0727e430969f15bead36c3290a1be1a4bd156fe133a47c12fadc3238f5421b4fd1b7cf226582e39b2b7ea13ec0feadd9112735aed517667632d99c19748774e9 Homepage: https://cran.r-project.org/package=TimeVizPro Description: CRAN Package 'TimeVizPro' (Dynamic Data Explorer: Visualize and Forecast with 'TimeVizPro') Unleash the power of time-series data visualization with ease using our package. Designed with simplicity in mind, it offers three key features through the 'shiny' package output. The first tab shows time- series charts with forecasts, allowing users to visualize trends and changes effortlessly. The second one displays Averages per country presented in tables with accompanying sparklines, providing a quick and attractive overview of the data. The last tab presents A customizable world map colored based on user-defined variables for any chosen number of countries, offering an advanced visual approach to understanding geographical data distributions. This package operates with just a few simple arguments, enabling users to conduct sophisticated analyses without the need for complex programming skills. Transform your time-series data analysis experience with our user-friendly tool. Package: r-cran-timevtree Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival Filename: pool/dists/noble/main/r-cran-timevtree_0.3.1-1.ca2404.1_all.deb Size: 125320 MD5sum: ce53410f7268b0f631162ece0713f511 SHA1: f21e8834b68b10dc01d1949c9b6c57c4db23e311 SHA256: d60b002fce5f8bdaf15d9672b990545f93106545433ef747828614a3b8bc201d SHA512: c7c96466c561a1e70a4cd6091b31dccfcf5d770ebabb1db44e9f368a4e602565c06052a50532069006fc6ec40dc52178601f7ebb7f57496ae4e09bb2922f5770 Homepage: https://cran.r-project.org/package=TimeVTree Description: CRAN Package 'TimeVTree' (Survival Analysis of Time Varying Coefficients Using aTree-Based Approach) Estimates time varying regression effects under Cox type models in survival data using classification and regression tree. The codes in this package were originally written in S-Plus for the paper "Survival Analysis with Time-Varying Regression Effects Using a Tree-Based Approach," by Xu, R. and Adak, S. (2002) , Biometrics, 58: 305-315. Development of this package was supported by NIH grants AG053983 and AG057707, and by the UCSD Altman Translational Research Institute, NIH grant UL1TR001442. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The example data are from the Honolulu Heart Program/Honolulu Asia Aging Study (HHP/HAAS). 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Harrison, Qing He, and Hsin-Hsiung Huang (2022) . TIP is a Bayesian prior that uses pairwise distance and similarity information to cluster vectors, matrices, or tensors. Package: r-cran-tipa Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-optimx Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tipa_1.0.8-1.ca2404.1_all.deb Size: 27760 MD5sum: dfef8675462c748b98b6a90a7613bd5b SHA1: b274888a3fad841eb36f3fdf00992e3e5ae7f07e SHA256: 9401376b57db75276694a0e7f59e71413ff4e1226a6bf271fbbc74db4111f111 SHA512: d1309401e31621ad1386ee022a736c9dc8768f42f849a068efe6f4ac0848683364303ebc8c1a0c6016707c353a6578e172ab4d58035a3805bf400ef04231b170 Homepage: https://cran.r-project.org/package=tipa Description: CRAN Package 'tipa' (Tau-Independent Phase Analysis for Circadian Time-Course Data) Accurately estimates phase shifts by accounting for period changes and for the point in the circadian cycle at which the stimulus occurs. See Tackenberg et al. (2018) . 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Package: r-cran-tipmap Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2435 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-ggplot2, r-cran-rbest, r-cran-assertthat, r-cran-furrr, r-cran-future Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-tibble, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tipmap_0.5.2-1.ca2404.1_all.deb Size: 741600 MD5sum: fba7a7316dc40671d3ef5b29d3476436 SHA1: 4c6fa064b51ff75b0aece50519278c285bd2148e SHA256: 1dab5ab047ce15f2fe2038b7069dc08b40893d1d1528266b17ba1cde836f384d SHA512: d66c6e0978e636fbafc01cb9a668a2d4f690cc453936b3857db56a5e31b7a39ba102b2a662bf12626f6caa94ac9c089ba3ab3ad5c0c33d4843c0da8e0398c06c Homepage: https://cran.r-project.org/package=tipmap Description: CRAN Package 'tipmap' (Tipping Point Analysis for Bayesian Dynamic Borrowing) Tipping point analysis for clinical trials that employ Bayesian dynamic borrowing via robust meta-analytic predictive (MAP) priors. 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Package: r-cran-tippy Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools, r-cran-shiny, r-cran-jsonlite Filename: pool/dists/noble/main/r-cran-tippy_0.1.0-1.ca2404.1_all.deb Size: 45600 MD5sum: d7a4fe48f3a23ed8a433b14826d6b3b4 SHA1: d76de492662ff5f935cda78bffdd8c4daf20063b SHA256: bc6ddfd9ec9555bffac7310a1aba841caf2441d0f93b6b29ec14b8a36fe8b685 SHA512: 4016bccf5d5191f590740b3579503cda2a938928fcceef4793ac2f74e42666fcddfecf272a55fcc2d54cfc2d91443785580f713795b2e62fe6de263d0ab6d458 Homepage: https://cran.r-project.org/package=tippy Description: CRAN Package 'tippy' (Add Tooltips to 'R markdown' Documents or 'Shiny' Apps) 'Htmlwidget' of 'Tippyjs' to add tooltips to 'Shiny' apps and 'R markdown' documents. Package: r-cran-tipr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-sensemakr, r-cran-tibble Suggests: r-cran-broom, r-cran-dplyr, r-cran-mass, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tipr_1.0.2-1.ca2404.1_all.deb Size: 307714 MD5sum: fb93b11a8e3ca600b7e6fdd556b1a50c SHA1: b90f41a7770ac5e62254b5d3fb82fe7a0f5f45e7 SHA256: 1f63f6253b58e82d6af7df06db7eec866ce30f6e4b0afd9a3dc05e03e131fa6a SHA512: 46487db943f38e2487db3cd90039b1298047b69a02ab9c9d396774f95bbbc890b23550ff3b708fc44661aeb438b1d760d9b6bf510e55ac67960faa06e9f2925d Homepage: https://cran.r-project.org/package=tipr Description: CRAN Package 'tipr' (Tipping Point Analyses) The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. We focus on three key quantities: the observed bound of the confidence interval closest to the null, the relationship between an unmeasured confounder and the outcome, for example a plausible residual effect size for an unmeasured continuous or binary confounder, and the relationship between an unmeasured confounder and the exposure, for example a realistic mean difference or prevalence difference for this hypothetical confounder between exposure groups. Building on the methods put forth by Cornfield et al. (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983), Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli & Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele (2016), we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance. Package: r-cran-tipse Architecture: all Version: 2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-ggplot2, r-cran-survival, r-cran-dplyr, r-cran-knitr, r-cran-purrr, r-cran-rmarkdown Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tipse_2.0-1.ca2404.1_all.deb Size: 335522 MD5sum: bc53a007d295f9f377dd3c26c9c220fd SHA1: 9039482e6616345aa62382f8f418646badb3be4e SHA256: 367654217250624555e6cf7a4ab93fa542b4fbb7cd0a9d9ca14a3a712af54640 SHA512: 07cd350a2103d8d75ad008fcf2e0e1d84713d098b2e7d0e24856e30b56911c944dcc9c2e44745bef045cf9b18d5c6d5673df35c3eb49435027d96664dbee6476 Homepage: https://cran.r-project.org/package=tipse Description: CRAN Package 'tipse' (Tipping Point Analysis for Survival Endpoints) Implements tipping point sensitivity analysis for time-to-event endpoints under different missing data scenarios, as described in Oodally et al. (2025) . Supports both model-based and model-free imputation, multiple imputation workflows, plausibility assessment and visualizations. Enables robust assessment for regulatory and exploratory analyses. Package: r-cran-tirt Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3016 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-gtools Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tirt_0.3.1-1.ca2404.1_all.deb Size: 2930924 MD5sum: d4d326d87080c547d682fa4ae3b81c55 SHA1: 74ba9cbeb7de7e6cc6c0c5e8e8d4e230e6af845a SHA256: fc24e263302aec735f47eaca3002e4f8650554ee9f53aa24dd213a4ade9d878d SHA512: 014896cab73fedd927b5eb3d3ff4c78df7679e404b6c9713bb12124d79b22cbfbcbe390d426f8d59a35d8f750117a33da1f51ed4a8ecb702ede71aeda8354213 Homepage: https://cran.r-project.org/package=tirt Description: CRAN Package 'tirt' (Testlet Item Response Theory) Implementation of Testlet and Item Response Theory. A light-version yet comprehensive and streamlined framework for psychometric analysis using unidimensional and multidimensional Item Response Theory (IRT; Baker & Kim (2004) ) and Testlet Response Theory (TRT; Wainer et al., (2007) ). Designed for researchers, this package supports the estimation of item and person parameters for a wide variety of models, including binary (i.e., Rasch, 2-Parameter Logistic, 3-Parameter Logistic) and polytomous (Partial Credit Model, Generalized Partial Credit Model, Graded Response Model) formats. It also supports the estimation of Testlet models (Rasch Testlet, 2-Parameter Logistic Testlet, 3-Parameter Logistic Testlet, Bifactor, Partial Credit Model Testlet, Graded Response), allowing users to account for local item dependence in bundled items. A key feature is the specialized support for combination use and joint estimation of item response model and testlet response model in one calibration. Beyond standard estimation via Marginal Maximum Likelihood with Expectation-Maximization (EM) or Joint Maximum Likelihood, the package also offers Bayesian estimation using priors with maximum a posteriori (MAP) method for unidimensional item response theory models. It also provides functions for scale linking and equating (Mean-Mean, Mean-Sigma, Stocking-Lord) to ensure comparability across mixed-format test forms. It also facilitates fixed-parameter calibration, enabling users to estimate person abilities with known item parameters or vice versa, which is essential for pre-equating studies and item bank maintenance. Comprehensive data simulation functions are included to generate synthetic datasets with complex structures, including mixed-model blocks and specific testlet effects, aiding in methodological research and study design validation. Researchers can try multiple simulation situations. Package: r-cran-tissot Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2379 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-gdalraster Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-tissot_0.2.0-1.ca2404.1_all.deb Size: 2097734 MD5sum: 03fefc79d16db6f218fc219ef057d0b5 SHA1: 22cf21f5291b2d916f92fdcb9e5fba1e4dc409f3 SHA256: 970bff2fda9ab82e1b5c0bada88a9a4fae89cc927e74e094c50a8b9d7e1ad462 SHA512: 57b2855c66a781bb11a71698c921e2fffb49808b61a5f96b14abbfd1f7e6e46b9020fd1de03c4958eb417b78d94e8040f09a90ac4d6cde49ca9d837d7928e61b Homepage: https://cran.r-project.org/package=tissot Description: CRAN Package 'tissot' (Tissot Indicatrix for Map Projection Distortion) Compute and visualize the 'Tissot Indicatrix' for map projections. The indicatrix characterizes projection distortion by computing scale factors, angular deformation, areal distortion, and convergence at arbitrary points. Based on the calculations shared by Bill Huber on . Uses 'GDAL' for coordinate transformation. Developed using the method published in Snyder, JP (1987) . Package: r-cran-titan2 Architecture: all Version: 2.4.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2521 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-cowplot, r-cran-ggridges, r-cran-purrr, r-cran-tibble, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-titan2_2.4.4-1.ca2404.1_all.deb Size: 2225824 MD5sum: ca52b727271d552466386d9100595645 SHA1: 260fd083bb7b284e437e87b5b4dad333a0e36303 SHA256: 3f327418f62dd331fb83685edb2d5386d8c3e6d2a87dee05828e328549a832a4 SHA512: 06f3d4bccbd601593cb6265350531468b68ccae8631d3a6828e20b85c3056a122a7a5475207c6e5555ac17620ee601c608616e57b5c63d0800690a89128407ea Homepage: https://cran.r-project.org/package=TITAN2 Description: CRAN Package 'TITAN2' (Threshold Indicator Taxa Analysis) Uses indicator species scores across binary partitions of a sample set to detect congruence in taxon-specific changes of abundance and occurrence frequency along an environmental gradient as evidence of an ecological community threshold. Relevant references include Baker and King (2010) , King and Baker (2010) , and Baker and King (2013) . Package: r-cran-titanic Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-titanic_0.1.0-1.ca2404.1_all.deb Size: 84492 MD5sum: 0751d023d6849c03b31cf49ddfce788a SHA1: 4a66097cdea5831786279abef45f695d7837d2cc SHA256: f9773271e42bd41d62d41959275345f8165e58ede293963cc9b1401fffc2fc59 SHA512: f506ed8571908984a7e5e05a5d67b4e64ac3ca411d254d1513ffbcd9b3645b64f946d8fc82b16939f443e2464a6641e5aef2011a2ab01758b8bd1c144fe42b0c Homepage: https://cran.r-project.org/package=titanic Description: CRAN Package 'titanic' (Titanic Passenger Survival Data Set) This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner "Titanic", summarized according to economic status (class), sex, age and survival. Whereas the base R Titanic data found by calling data("Titanic") is an array resulting from cross-tabulating 2201 observations, these data sets are the individual non-aggregated observations and formatted in a machine learning context with a training sample, a testing sample, and two additional data sets that can be used for deeper machine learning analysis. These data sets are also the data sets downloaded from the Kaggle competition and thus lowers the barrier to entry for users new to R or machine learing. Package: r-cran-titegboin Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-titegboin_0.4.0-1.ca2404.1_all.deb Size: 59326 MD5sum: 90b4fe70d8e2905ed291d0023d50ff2b SHA1: a684f1c725ff8d87468c048ffb37f83ecba48925 SHA256: ca04d4bc84af4a491752a9d40af60fe8a27d06c7d153f889e92555916ed15c2e SHA512: a078b936fd77f7f42a1bb160028aecd577b1dcaa738100ab34bd5575daf113f85d21f0960242e46498ed167faac0a9d564a9d50eecc5a839dd1490c95ea86160 Homepage: https://cran.r-project.org/package=TITEgBOIN Description: CRAN Package 'TITEgBOIN' (Time-to-Event Dose-Finding Design for Multiple Toxicity Grades) In some phase I trials, the design goal is to find the dose associated with a certain target toxicity rate or the dose with a certain weighted sum of rates of various toxicity grades. 'TITEgBOIN' provides the set up and calculations needed to run a dose-finding trial using bayesian optimal interval (BOIN) (Yuan et al. (2016) ), generalized bayesian optimal interval (gBOIN) (Mu et al. (2019) ), time-to-event bayesian optimal interval (TITEBOIN) (Lin et al. (2020) ) and time-to-event generalized bayesian optimal interval (TITEgBOIN) (Takeda et al. (2022) ) designs. 'TITEgBOIN' can conduct tasks: run simulations and get operating characteristics; determine the dose for the next cohort; select maximum tolerated dose (MTD). These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose limiting toxicity (DLT) rates or target normalized equivalent toxicity score (ETS) rates to account for discrete toxicity score, and incorporate safety and/or stopping rules. Package: r-cran-titeir Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-iso Filename: pool/dists/noble/main/r-cran-titeir_0.1.0-1.ca2404.1_all.deb Size: 27200 MD5sum: cb8355f35e9c414867797881647fc5af SHA1: f4a0b6a1ea0ca94bc2cafdbcc84c3536737ecd7d SHA256: 2d287ee68e0e4c56513f14715845d25ca9d14e58285c816af50bf9b906e51af4 SHA512: 0ff4315e88767a55f0673a0a46e1d6c7848c49408fda63fd3c8a6b89ee1eae59765720ab45f2f6f4efcab9eeb6717226f564dcd8722ff0ca28b65e87f345ab91 Homepage: https://cran.r-project.org/package=titeIR Description: CRAN Package 'titeIR' (Isotonic Designs for Phase 1 Trials with Late-Onset Toxicities) Functions to design phase 1 trials using an isotonic regression based design incorporating time-to-event information. Simulation and design functions are available, which incorporate information about followup and DLTs, and apply isotonic regression to devise estimates of DLT probability. Package: r-cran-titrationcurves Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 567 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-titrationcurves_0.1.0-1.ca2404.1_all.deb Size: 461698 MD5sum: 60d08a052a935b95de95777219d7b8bb SHA1: 8bc6d5f125cb83943c2663bbe6d63bbb7d347b43 SHA256: 11a6e82c820cb42695683b2e717b5eb2c4c693fbbc78690c3cfb328e7828b474 SHA512: 7d5cb7f101a1da966200349b56065de68faf56321aedadd9966017f5bea0336e61bce5507999adaf55f083948bccfbf36fbee7ac5cf9de3da30443a540a20b40 Homepage: https://cran.r-project.org/package=titrationCurves Description: CRAN Package 'titrationCurves' (Acid/Base, Complexation, Redox, and Precipitation TitrationCurves) A collection of functions to plot acid/base titration curves (pH vs. volume of titrant), complexation titration curves (pMetal vs. volume of EDTA), redox titration curves (potential vs.volume of titrant), and precipitation titration curves (either pAnalyte or pTitrant vs. volume of titrant). Options include the titration of mixtures, the ability to overlay two or more titration curves, and the ability to show equivalence points. Package: r-cran-tivy Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1780 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-lubridate, r-cran-stringr, r-cran-stringi, r-cran-ggplot2, r-cran-leaflet, r-cran-rcolorbrewer, r-cran-rlang, r-cran-httr, r-cran-rvest, r-cran-jsonlite, r-cran-pdftools, r-cran-future, r-cran-future.apply, r-cran-patchwork, r-cran-scales Suggests: r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tivy_0.1.1-1.ca2404.1_all.deb Size: 1625224 MD5sum: abfc6bf70118b66fb79d90a4f8a5b89c SHA1: ead76c1165b7e654dc957fda5326cf073e833476 SHA256: a7fdb4014715662280a7a66fec340b04432ff646f533b75cd363696a9665fa05 SHA512: 019d4405882b13ac68c3e50b4524b1644a78c928547f1773d0830d96392e176ad3a69768cebb3d201ea57ffb8b127a845c6cfc51e431e42aafbba38d8f34b07b Homepage: https://cran.r-project.org/package=Tivy Description: CRAN Package 'Tivy' (Toolkit for Investigation and Visualization of Young Anchovies) Specialized toolkit for processing biological and fisheries data from Peru's anchovy (Engraulis ringens) fishery. Provides functions to analyze fishing logbooks, calculate biological indicators (length-weight relationships, juvenile percentages), generate spatial fishing indicators, and visualize regulatory measures from Peru's Ministry of Production. Features automated data processing from multiple file formats, coordinate validation, spatial analysis of fishing zones, and tools for analyzing fishing closure announcements and regulatory compliance. Includes built-in datasets of Peruvian coastal coordinates and parallel lines for analyzing fishing activities within regulatory zones. Package: r-cran-tkcat Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4011 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-redamor, r-cran-dbi, r-cran-visnetwork, r-cran-dplyr, r-cran-clickhousehttp, r-cran-rlang, r-cran-tidyselect, r-cran-askpass, r-cran-shiny, r-cran-shinydashboard, r-cran-dt, r-cran-htmltools, r-cran-readr, r-cran-jsonlite, r-cran-jsonvalidate, r-cran-markdown, r-cran-promises, r-cran-future, r-cran-xml2, r-cran-matrix, r-cran-uuid, r-cran-crayon, r-cran-roxygen2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-rclickhouse, r-cran-stringr, r-cran-data.tree, r-cran-bed Filename: pool/dists/noble/main/r-cran-tkcat_1.2.1-1.ca2404.1_all.deb Size: 1551726 MD5sum: 85e9b39861eaa943710e4e7be1a44ad6 SHA1: f33caa9809e4ee667432236748dfef51bd0a4526 SHA256: 0bddfb49b0d2fdd8a5efbe3908fcddf80082768e79d3e2b197d7643e93825f56 SHA512: aa10ef858656588adf003f5ba1e003b354e9a07920546559c538f09fc367042745e26f66972d0c16d8306a5d12129ae3bf1da167150ebffbd109499d652bf0e0 Homepage: https://cran.r-project.org/package=TKCat Description: CRAN Package 'TKCat' (Tailored Knowledge Catalog) Facilitate the management of data from knowledge resources that are frequently used alone or together in research environments. In 'TKCat', knowledge resources are manipulated as modeled database (MDB) objects. These objects provide access to the data tables along with a general description of the resource and a detailed data model documenting the tables, their fields and their relationships. These MDBs are then gathered in catalogs that can be easily explored and shared. Finally, 'TKCat' provides tools to easily subset, filter and combine MDBs and create new catalogs suited for specific needs. Package: r-cran-tkimgr Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 744 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tkrplotr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tkimgr_0.0.5-1.ca2404.1_all.deb Size: 78648 MD5sum: 5e2b0903062f7c14fef7dd5ba6f054c4 SHA1: 2e4c5be8734916ee1d6fb831621982622e0e044d SHA256: 1f91b7529c8a15d0383cac07bdd4b3f8f95c6d7d69516fbc5bc4a7dbd55e90f8 SHA512: dd6a1f1e1e7acad5567221a905fd6402eb71bb28abaeda89b68404feb3e75dc459e98287a98abff1e4e2df28a14cd139d55ca4b2ef20e24fc31199d5178ca477 Homepage: https://cran.r-project.org/package=tkImgR Description: CRAN Package 'tkImgR' (Simple Image Viewer for R Using the 'tcltk' Package) A 'Tcl/Tk' Graphical User Interface (GUI) to display images than can be zoomed and panned using the mouse and keyboard shortcuts. 'tkImgR' read and write different image formats (PPM/PGM, PNG and GIF) using the standard 'Tcl/Tk' distribution (>=8.6), but other formats (JPEG, TIFF, CR2) can be handled using the 'tkImg' package for 'Tcl/Tk'. Package: r-cran-tkrplotr Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tkrplotr_0.1.7-1.ca2404.1_all.deb Size: 65642 MD5sum: c019641ef905214e658f73e09eabfa6e SHA1: 095cb97607048865d1adf84f4ff083ca7a9577c5 SHA256: 683311c9a8cd873b0018817a73f198ed9aea0c07fb4383a41c637c21d0bd798d SHA512: 8768f663e8b1b93ca19ce8859e667b6f044f04e571c277f513e04dbb7a1bca6f62a0a80f4120b115271a246a3452a208e6a7c25d98b209582230cb3103215495 Homepage: https://cran.r-project.org/package=tkRplotR Description: CRAN Package 'tkRplotR' (Display Resizable Plots) Display a plot in a Tk canvas. Package: r-cran-tlaginterim Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-r.utils Filename: pool/dists/noble/main/r-cran-tlaginterim_1.1-1.ca2404.1_all.deb Size: 101538 MD5sum: 08551eb54a7dc11915d49ea95815617d SHA1: 4fe55e71863eff4ef28da6dd472a966e29c5442d SHA256: f9204a5582fbe9e377b07288f66718f3eae1633423f8ce6cc7f28cc975338b6c SHA512: 685cbabc75921a0e6439147ca93a3b4d218d424a328ace8d3291fb80f9cee241e64d97c10ef053cc4fb57f0247dac78dc5e504f891436123d5f149972a8b1995 Homepage: https://cran.r-project.org/package=tLagInterim Description: CRAN Package 'tLagInterim' (Interim Monitoring of Clinical Trials with Time-Lagged Outcome) Implements inverse and augmented inverse probability weighted estimators for common treatment effect parameters at an interim analysis with time-lagged outcome that may not be available for all enrolled subjects. Produces estimators, standard errors, and information that can be used to compute stopping boundaries using software that assumes that the estimators/test statistics have independent increments. Tsiatis, A. A. and Davidian, M., (2022) . 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Tsiatis AA, Davidian M, Holloway ST (2023) . 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Package: r-cran-tm.plugin.factiva Architecture: all Version: 1.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-nlp, r-cran-tm, r-cran-xml2, r-cran-rvest Filename: pool/dists/noble/main/r-cran-tm.plugin.factiva_1.8.1-1.ca2404.1_all.deb Size: 64552 MD5sum: da03e0173f1738bda7af4bda9a228e98 SHA1: 47ee44ee6e3e05b9c560f9d67b0a485c36068e38 SHA256: da2389055aaa9119acd4b7325e9f5712fc52d0b67acf9c29d7ac42444fca3b2b SHA512: 6782d071fd1419f02f7b145d08c8e5af081aba46ee9b87b8e6e0c2c2b6453f7d10a728e2c452b3f47024e0d2e40584d7f875e339458d99bb95387339f21d86e4 Homepage: https://cran.r-project.org/package=tm.plugin.factiva Description: CRAN Package 'tm.plugin.factiva' (Import Articles from 'Factiva' Using the 'tm' Text MiningFramework) Provides a 'tm' Source to create corpora from articles exported from the Dow Jones 'Factiva' content provider as XML or HTML files. 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Package: r-cran-tm.plugin.korpus Architecture: all Version: 0.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2077 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-korpus, r-cran-sylly, r-cran-tm, r-cran-nlp Suggests: r-cran-korpus.lang.en, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tm.plugin.korpus_0.4-2-1.ca2404.1_all.deb Size: 974962 MD5sum: 4ddc3521381a3ce90b27ff0db83220b9 SHA1: e2722870d1f2a90dec4b3da4a45905f1ca679b11 SHA256: be65d8cc3ed489715478b1c4732ff9709572aac095355f50a25352a6c269ce90 SHA512: f3ed985e2bbb33e61c09f2c26c2b76bf1e684104bc630c22b8e7c0a949462c014fbcd5ab95946dee45ee658afad1c328fd295f720191e4ed009ac81767c1a5ba Homepage: https://cran.r-project.org/package=tm.plugin.koRpus Description: CRAN Package 'tm.plugin.koRpus' (Full Corpus Support for the 'koRpus' Package) Enhances 'koRpus' text object classes and methods to also support large corpora. 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Package: r-cran-tm1r Architecture: all Version: 1.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-httr Filename: pool/dists/noble/main/r-cran-tm1r_1.1.8-1.ca2404.1_all.deb Size: 118606 MD5sum: 5736b0659c9d5cea6c614967de8d7817 SHA1: cf35e40f779a7bbb1ec3e5de2dfe4d4de6d8099e SHA256: d841aa021c5009ec3391b92e488d94be74ba99365ed9a898b93f5ceff43e5522 SHA512: 4de05c92ae83b5441d1d3147c8dbcc4feb554110996f2148e4419ebb09d53d0990ab07dc1c788b677164f74c99df5e07f93534394ad11401da2e14cb96fc244e Homepage: https://cran.r-project.org/package=tm1r Description: CRAN Package 'tm1r' (The Integration Between 'IBM COGNOS TM1' and R) Useful functions to connect to 'TM1' instance from R via REST API. With the functions in the package, data can be imported from 'TM1' via mdx view or native view, data can be sent to 'TM1', processes and chores can be executed, and cube and dimension metadata information can be taken. Package: r-cran-tmap.cartogram Architecture: all Version: 0.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmap, r-cran-sf, r-cran-cartogram Suggests: r-cran-knitr, r-cran-transformr, r-cran-gifski Filename: pool/dists/noble/main/r-cran-tmap.cartogram_0.2-1-1.ca2404.1_all.deb Size: 80130 MD5sum: 9b1ac1d6aed68696e158d0b6bcd68511 SHA1: 64ef185e04c598b729ca0ebea4514daffc6c1ef2 SHA256: 3fda00979c1ec17365ee9eb5fb54d09fa32ff6fe4c96f7f4238163a658774388 SHA512: 5a60995fbe18fd0425d5146aa75c34e1a07ce2b7b154d160a0a2780d3cfee0608e0ddd2d76922692e8c40bec82345700e6ca690a2839fc894197a34e138a36ee Homepage: https://cran.r-project.org/package=tmap.cartogram Description: CRAN Package 'tmap.cartogram' (Extension to 'tmap' for Creating Cartograms) Provides new layer functions to 'tmap' for creating various types of cartograms. A cartogram is a type of thematic map in which geographic areas are resized or distorted based on a quantitative variable, such as population. The goal is to make the area sizes proportional to the selected variable while preserving geographic positions as much as possible. Package: r-cran-tmap.glyphs Architecture: all Version: 0.1-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmap, r-cran-data.table Filename: pool/dists/noble/main/r-cran-tmap.glyphs_0.1-1-1.ca2404.1_all.deb Size: 83848 MD5sum: 59b11fe803a2b8a000a17206a1454706 SHA1: a9798645c3aca16b9bacc35a61c2249ada4e3a7c SHA256: 67a8eae4eefaa8ada31e446abc8783ca48a23ece8201e93d8641534a8e2cdd28 SHA512: d4da9bb0c6b660fe91e274d69702a5dc7d851ad1392ff48f99de74aae31ceb639f726cfaacf832e693a50f8e28789be9a3f9b02b56f30c2d52a916863cfcdaf6 Homepage: https://cran.r-project.org/package=tmap.glyphs Description: CRAN Package 'tmap.glyphs' (Extension to 'tmap' for Creating Glyphs) Provides new layer functions to 'tmap' for drawing glyphs. A glyph is a small chart (e.g., donut chart) shown at specific map locations to visualize multivariate or time-series data. The functions work with the syntax of 'tmap' and allow flexible control over size, layout, and appearance. Package: r-cran-tmap.mapgl Architecture: all Version: 0.2-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmap, r-cran-mapgl, r-cran-terra, r-cran-rlang, r-cran-cli, r-cran-stars, r-cran-data.table, r-cran-colorspace, r-cran-htmltools, r-cran-htmlwidgets, r-cran-sf, r-cran-tmaptools, r-cran-units Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-tmap.mapgl_0.2-1-1.ca2404.1_all.deb Size: 380722 MD5sum: 85c4e3ee4d6e1fa6ee5da9335a53f8f8 SHA1: 5f52e05deee5790e117ae6f24198e6dc99b54c0a SHA256: cb93b45b9976925b5af93863bb7f6a09f55b01134208f791b0843ee2a64bef3a SHA512: 85ab1dda54854b50bc6e662c0bcca2df7337ee5d399f0ac41a926fc162d485c8a45353964bf5376f272d89b13eb6ab26d2e41997ff04198f9ebcbdc75ba2ea49 Homepage: https://cran.r-project.org/package=tmap.mapgl Description: CRAN Package 'tmap.mapgl' (Extensions to 'tmap' with Two New Modes: 'mapbox' and 'maplibre') The 'tmap' package provides two plotting modes for static and interactive thematic maps. This package extends 'tmap' with two additional modes based on 'Mapbox GL JS' and 'MapLibre GL JS'. These modes feature interactive vector tiles, globe views, and other modern web-mapping capabilities, while maintaining a consistent 'tmap' interface across all plotting modes. Package: r-cran-tmap.networks Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tmap, r-cran-sf, r-cran-sfnetworks, r-cran-lwgeom, r-cran-data.table, r-cran-igraph Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-tmap.networks_0.2-1.ca2404.1_all.deb Size: 102300 MD5sum: e765588d33f29d3f44a0b201a8c35ef6 SHA1: 32792c4aca0e993833d4ca52d4b69f3a8bd743d3 SHA256: 0e7a627e53b931affa4df38f009f23ff49d958342edc7be962103791629fe7dc SHA512: 69fa939725056cf10547eeecb587caa998f751cd54bc47a6dc6c0218f546f0254594a4bfb26727a0bf7805b5cc9f315c22aee6915c4bbea53b1805c388b86007 Homepage: https://cran.r-project.org/package=tmap.networks Description: CRAN Package 'tmap.networks' (Extension to 'tmap' for Creating Network Visualizations) Provides functions for visualizing networks with 'tmap'. It supports 'sfnetworks' objects natively but is not limited to them. Useful for adding network layers such as edges and nodes to 'tmap' maps. More features may be added in future versions. Package: r-cran-tmap Architecture: all Version: 4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4301 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-classint, r-cran-cli, r-cran-cols4all, r-cran-data.table, r-cran-htmltools, r-cran-htmlwidgets, r-cran-base64enc, r-cran-leafem, r-cran-leafgl, r-cran-leaflegend, r-cran-leaflet, r-cran-leafsync, r-cran-maptiles, r-cran-rlang, r-cran-sf, r-cran-stars, r-cran-s2, r-cran-tmaptools, r-cran-units, r-cran-servr Suggests: r-cran-av, r-cran-transformr, r-cran-colorspace, r-cran-ggplot2, r-cran-gifski, r-cran-knitr, r-cran-shiny, r-cran-terra, r-cran-testthat, r-cran-widgetframe, r-cran-lobstr, r-cran-rsvg, r-cran-rstudioapi Filename: pool/dists/noble/main/r-cran-tmap_4.3-1.ca2404.1_all.deb Size: 4201060 MD5sum: c90ebb19d27b6c46dcd7bc39ae7a02e3 SHA1: e0bf0e4f51d71881272215a1a5da3dcab5f2c3d4 SHA256: f67fe33a461ad8012d60a7c3450c6f21855cc7214cd9c013cb0620bc28f814b3 SHA512: a320aa67f840eb4950f00b1983fe423c6630b93b8058dab898bbce00d91cd687d3407791a70c66cd6d46fe6badd8a1c4675453453505f059a8e70b57d62b13fc Homepage: https://cran.r-project.org/package=tmap Description: CRAN Package 'tmap' (Thematic Maps) Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps. Package: r-cran-tmaptools Architecture: all Version: 3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-lwgeom, r-cran-stars, r-cran-units, r-cran-xml Suggests: r-cran-tmap, r-cran-cols4all, r-cran-rmapshaper, r-cran-osmdata, r-cran-openstreetmap, r-cran-raster Filename: pool/dists/noble/main/r-cran-tmaptools_3.3-1.ca2404.1_all.deb Size: 125328 MD5sum: cd8fe7ea20d30c1e3cd403d4154ac9b0 SHA1: e6b290dd0fb60d83eb1562246e98bf3d0eeb8778 SHA256: ac04a2764f593b28fe7ac833806a9b7a8e90df4bdec667dac6443a89c0cb31fb SHA512: 96a40da7b0a0b2a186ebf4775cf6e5a7f460b034b5cacfa6f6b6735ffff507b4b222a23b1af1ac07d13486366c77aa3c535c1c7980da5d9aae8e06085f04ad20 Homepage: https://cran.r-project.org/package=tmaptools Description: CRAN Package 'tmaptools' (Thematic Map Tools) Set of tools for reading and processing spatial data. The aim is to supply the workflow to create thematic maps. This package also facilitates 'tmap', the package for visualizing thematic maps. Package: r-cran-tmapverse Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tmap, r-cran-tmap.glyphs, r-cran-tmap.networks, r-cran-tmap.cartogram, r-cran-tmap.mapgl, r-cran-sf, r-cran-stars, r-cran-terra, r-cran-cols4all, r-cran-cli, r-cran-crayon Filename: pool/dists/noble/main/r-cran-tmapverse_0.1.0-1.ca2404.1_all.deb Size: 20182 MD5sum: d1cf562e0e9440b1164d93a5bbf14843 SHA1: 86f31a83c0cb566ecb1bb0b96b02db6996f0ad63 SHA256: 8550ed6e1d94d6c7a98899127a298ea28ef9e92c3b8eb699ef786d46633eda58 SHA512: 01a1974c081f84a34399f469bb83d677333bf11da4da60cb3ee7366ed9d5284725d3deee33129a91df2f86be9c3bca5f291c8714f8bf600222fd1f8015b1c760 Homepage: https://cran.r-project.org/package=tmapverse Description: CRAN Package 'tmapverse' (Meta-Package for Thematic Mapping with 'tmap') Attaches a set of packages commonly used for spatial plotting with 'tmap'. It includes 'tmap' and its extensions ('tmap.glyphs', 'tmap.networks', 'tmap.cartogram', 'tmap.mapgl'), as well as supporting spatial data packages ('sf', 'stars', 'terra') and 'cols4all' for exploring color palettes. The collection is designed for thematic mapping workflows and does not include the full set of packages from the R-spatial ecosystem. Package: r-cran-tmcalculator Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4489 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-seqinr, r-bioc-bsgenome, r-bioc-biostrings, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-genomeinfodb, r-bioc-gviz, r-cran-dplyr, r-bioc-ggbio, r-cran-ggplot2, r-bioc-karyoploter, r-cran-viridis, r-cran-rlang, r-cran-plotly Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools, r-cran-roxygen2, r-bioc-bsgenome.hsapiens.ucsc.hg38 Filename: pool/dists/noble/main/r-cran-tmcalculator_1.0.4-1.ca2404.1_all.deb Size: 1437062 MD5sum: 894b0fca1e881e3041336e02c23766a7 SHA1: 15612a28600607a200c4103ee30f3903b121d6ce SHA256: 5efebcc27902ed1d99790535a9327761b16cc6cc87233379ac8c5d771a1ad204 SHA512: ac0212db41d3dbd0655c936611f6eef9a62bc45bd520272cdb9b60a9535fb9d72777efd40cd3328fe0ff631648ab2cd20e48e42861e4eea89e61af068a3dc4db Homepage: https://cran.r-project.org/package=TmCalculator Description: CRAN Package 'TmCalculator' (A Calculator for Melting Temperature of Nucleic Acid Sequences) A comprehensive R package for calculating melting temperatures of nucleic acid sequences. Implements three calculation methods: 1. Wallace rule (Thein & Wallace, 1986) 2. Empirical formulas based on GC content (Marmur, 1962; Schildkraut, 2010; Wetmur, 1991; Untergasser, 2012; von Ahsen, 2001) 3. Nearest neighbor thermodynamics (Breslauer, 1986; Sugimoto, 1996; Allawi, 1998; SantaLucia, 2004; Freier, 1986; Xia, 1998; Chen, 2012; Bommarito, 2000; Turner, 2010; Sugimoto, 1995; Allawi, 1997; Santalucia, 2005) Includes corrections for: - Salt ions (SantaLucia, 1996, 1998; Owczarzy, 2004, 2008) - Chemical compounds (dimethyl sulfoxide, formamide) Supports both direct sequence input and FASTA file input. Package: r-cran-tmdb Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite, r-cran-stringi Filename: pool/dists/noble/main/r-cran-tmdb_1.1-1.ca2404.1_all.deb Size: 313160 MD5sum: 64f1406d15cfc1f6ee575c7ee48d236a SHA1: d0a3a0b4e02ef362da23617f3bfa6bfd520d2756 SHA256: 2b4ac3ca9d4b44cd921fe657521c1b14755822b64e114a4d4cb031946d367427 SHA512: d4d219d942a417c1fd17143d2d3be0e2454564f07b0cd8384e63dac3ebc2c5c30be962e82ac83d45a33b909e34a6fadc470f14b26ac5a3feb1d5a70aad9c67f9 Homepage: https://cran.r-project.org/package=TMDb Description: CRAN Package 'TMDb' (Access to TMDb API) Provides an R-interface to the TMDb API (see TMDb API on ). The Movie Database (TMDb) is a popular user editable database for movies and TV shows (see ). 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These methods may be summarized in the following references: Yoshida, et al. (2022) , Barnhill et al. (2023) , Barnhill and Yoshida (2023) , Aliatimis et al. (2023) , Yoshida et al. (2022) , and Yoshida et al. (2019) . 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This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments. 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Package includes tests for enrichment based on ranked lists of features, functions for visualisation and multivariate functional analysis. See Zyla et al (2019) . 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The basic data format requirement for 'toolStability' is a data frame with 3 columns including numeric trait values, genotype,and environmental labels. Output format of each function is the dataframe with chosen stability index for each genotype. Function "table_stability" offers the summary table of all stability indices in this package. This R package toolStability is part of the main publication: Wang, Casadebaig and Chen (2023) . Analysis pipeline for main publication can be found on github: . Sample dataset in this package is derived from another publication: Casadebaig P, Zheng B, Chapman S et al. (2016) . For detailed documentation of dataset, please see on Zenodo . Indices used in this package are from: Döring TF, Reckling M (2018) . Eberhart SA, Russell WA (1966) . Eskridge KM (1990) . Finlay KW, Wilkinson GN (1963) . Hanson WD (1970) Genotypic stability. . Lin CS, Binns MR (1988). Nassar R, Hühn M (1987). Pinthus MJ (1973) . Römer T (1917). Shukla GK (1972). Wricke G (1962). 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Volume calculation of dental materials. Principal component analysis of thickness maps with associated morphometric map variations. Package: r-cran-topcc Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-topcc_1.2-1.ca2404.1_all.deb Size: 56326 MD5sum: 6dd70117559045cccad8256816b5013e SHA1: 6b6ceafb1836c13e0d724da8b28dbb594c4bcc73 SHA256: bc4b94dd733702144ee1bcd7b10ef2968dc5922f894370752a6aeef7fa20f155 SHA512: dadcbd037a8231b3ea53bf6bec51f864287edb40e821b87cc63fe051c5a4d5158ef54806d23c4c6516f2b4215ed8bed49664e0a2cbe079fe7a146c5242e14d7c Homepage: https://cran.r-project.org/package=topcc Description: CRAN Package 'topcc' (Topological Correlation Coefficient) Topological correlation coefficient is used to identify dependencies between Time-Dependent Objects and is applicable to objects such as time series, chaotic systems, and dynamic networks. Package: r-cran-topchef Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyr, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-magrittr, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-topchef_0.2.0-1.ca2404.1_all.deb Size: 193978 MD5sum: 231ac60a634f05e6ad8486bad0ac18c7 SHA1: 0303c7b459843a679bbd8b74e75d5ab519a5e0af SHA256: 9087d9957c4047472f5b3cfbea5c525838a036f71a767d88897108879995f58a SHA512: f1e42c90649052fe6b4e97f215803ca1e694103133f0fa914c8f5275173e81b31ff6721fffdd70b41b2c5d419c15d14834f0c5a5e5e88a8e36caf983cfb7f001 Homepage: https://cran.r-project.org/package=topChef Description: CRAN Package 'topChef' (Top Chef Data) Several datasets which describe the chef contestants in Top Chef, the challenges that they compete in, and the results of those challenges. This data is useful for practicing data wrangling, graphing, and analyzing how each season of Top Chef played out. Package: r-cran-topdom Architecture: all Version: 0.10.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4545 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrixstats, r-cran-ggplot2, r-cran-reshape2, r-cran-tibble Suggests: r-cran-diffobj Filename: pool/dists/noble/main/r-cran-topdom_0.10.1-1.ca2404.1_all.deb Size: 4573068 MD5sum: 2c8b4a05241ced3a76943f09701734e5 SHA1: 3b01b488b751fe97032011f1efca8464f9518c94 SHA256: f9b1badf78849037bb6889b319e3daa80e1f50588ecdf4102d1fd8b754c3f70c SHA512: a8c8f1b08e53c621fb91b745b4d03e611cc06f3d2ed19ad70a456deb8ae4def0cd7d1979814b737603fd7747598fa5d2113d2717c74884dd84ba4440185fa457 Homepage: https://cran.r-project.org/package=TopDom Description: CRAN Package 'TopDom' (An Efficient and Deterministic Method for IdentifyingTopological Domains in Genomes) The 'TopDom' method identifies topological domains in genomes from Hi-C sequence data (Shin et al., 2016 ). The authors published an implementation of their method as an R script (two different versions; also available in this package). This package originates from those original 'TopDom' R scripts and provides help pages adopted from the original 'TopDom' PDF documentation. It also provides a small number of bug fixes to the original code. Package: r-cran-topdowntimeratio Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-geodist, r-cran-lubridate, r-cran-magrittr Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-topdowntimeratio_0.1.0-1.ca2404.1_all.deb Size: 71300 MD5sum: 0f90dce1659c02093f9e46c11c3a4d45 SHA1: 5aa35a11478d2e2039c89ede958633c293510374 SHA256: bc7af135cf36964fda10181b779ca16a14ccf6bc9db89ca09d80e776147a6830 SHA512: 1e2173ac34a5b4af0e44b81523e295a064d8f4f8fd4a2ef1c387ad5b3251eee57bdde32205425295fb21ba3ad144548d047818aa4386282b35fd3cb967682652 Homepage: https://cran.r-project.org/package=topdowntimeratio Description: CRAN Package 'topdowntimeratio' (Top-Down Time Ratio Segmentation for Coordinate Trajectories) Data collected on movement behavior is often in the form of time- stamped latitude/longitude coordinates sampled from the underlying movement behavior. These data can be compressed into a set of segments via the Top- Down Time Ratio Segmentation method described in Meratnia and de By (2004) which, with some loss of information, can both reduce the size of the data as well as provide corrective smoothing mechanisms to help reduce the impact of measurement error. This is an improvement on the well-known Douglas-Peucker algorithm for segmentation that operates not on the basis of perpendicular distances. Top-Down Time Ratio segmentation allows for disparate sampling time intervals by calculating the distance between locations and segments with respect to time. Provided a trajectory with timestamps, tdtr() returns a set of straight- line segments that can represent the full trajectory. McCool, Lugtig, and Schouten (2022) describe this method as implemented here in more detail. Package: r-cran-topicdoc Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-slam, r-cran-topicmodels Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stm, r-cran-testthat Filename: pool/dists/noble/main/r-cran-topicdoc_0.1.1-1.ca2404.1_all.deb Size: 121030 MD5sum: cbd0b29b422c50ed9ea0f513e8e4e90e SHA1: 797ccf0d13a71c0a87aa11e4a7310e2e07e10780 SHA256: 1226834d2cad06e85140eb22a2788713cbd206b6a80bf6d8242e2d8ca4be8293 SHA512: 6c969c4334af6dd04260612b9f881ea0ca282b3de0568919713e8aace93cdb1608fdf2d8cb35323097809d23cd133d674443ac1c5deb004477a1611a5b6a060d Homepage: https://cran.r-project.org/package=topicdoc Description: CRAN Package 'topicdoc' (Topic-Specific Diagnostics for LDA and CTM Topic Models) Calculates topic-specific diagnostics (e.g. mean token length, exclusivity) for Latent Dirichlet Allocation and Correlated Topic Models fit using the 'topicmodels' package. For more details, see Chapter 12 in Airoldi et al. (2014, ISBN:9781466504080), pp 262-272 Mimno et al. (2011, ISBN:9781937284114), and Bischof et al. (2014) . Package: r-cran-topiclabels Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-httr, r-cran-progress, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-topiclabels_0.4.0-1.ca2404.1_all.deb Size: 179058 MD5sum: 293afd73f891897bfe20c1fef5d2505d SHA1: 5d0d68f0b39a7bbfaa042189104261f2c76dd175 SHA256: 3b76f74cb00d8294b70108835ad7653cdafcaf11ccc3a0da5a427eaab315da24 SHA512: e97ea0debb44658813fd50baa9b9c047417c8b49375ee58b6a3e69d46675f36c7b3a38d49e137764f25a26db94ea3d6d140af1e4569409d5fad2aceeb3b9db93 Homepage: https://cran.r-project.org/package=topiclabels Description: CRAN Package 'topiclabels' (Automated Topic Labeling with Language Models) Leveraging (large) language models for automatic topic labeling. The main function converts a list of top terms into a label for each topic. Hence, it is complementary to any topic modeling package that produces a list of top terms for each topic. While human judgement is indispensable for topic validation (i.e., inspecting top terms and most representative documents), automatic topic labeling can be a valuable tool for researchers in various scenarios. Package: r-cran-topicmodels.etm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-torch Suggests: r-cran-udpipe, r-cran-word2vec, r-cran-uwot, r-cran-tinytest, r-cran-textplot, r-cran-ggrepel Filename: pool/dists/noble/main/r-cran-topicmodels.etm_0.1.1-1.ca2404.1_all.deb Size: 4112184 MD5sum: 93d0bd636fea5376698547dd599a64ab SHA1: cc98c1baf7d26e99d6d07a258dec4c3bfeca6d01 SHA256: 9c6357a3326d097ba1b70ca69c17ccefc80a7d10909d7d6d805e6bc015ddcfb4 SHA512: c44d0d920d9c65879a2150a79a47f5d70df678dffd4cce70d656c2b58dd6783af5d45e5f2f1497b83514dca451377595253bfe6f46e0abbd9493c1329b4cedbd Homepage: https://cran.r-project.org/package=topicmodels.etm Description: CRAN Package 'topicmodels.etm' (Topic Modelling in Embedding Spaces) Find topics in texts which are semantically embedded using techniques like word2vec or Glove. This topic modelling technique models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic. The techniques are explained in detail in the paper 'Topic Modeling in Embedding Spaces' by Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei (2019), available at . Package: r-cran-topics Architecture: all Version: 0.70-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4991 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-textminer, r-cran-quanteda, r-cran-ggplot2, r-cran-dplyr, r-cran-ggwordcloud, r-cran-tibble, r-cran-readr, r-cran-stopwords, r-cran-matrix, r-cran-stringr, r-cran-stringi, r-cran-rlang, r-cran-tidyr, r-cran-purrr, r-cran-data.table, r-cran-ggforce, r-cran-patchwork Suggests: r-cran-text, r-cran-rjava, r-cran-mallet, r-cran-glmnet, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-vdiffr, r-cran-httr, r-cran-arrow Filename: pool/dists/noble/main/r-cran-topics_0.70-1.ca2404.1_all.deb Size: 3048226 MD5sum: b6b119931a637466927e056762dad0e5 SHA1: ac1f5985ebd5932c43cae062179e6d59b3163662 SHA256: a8237d64ee473bef0c5c908d19937b4e2e70162a7d29a7e40cd2e7735eb660d1 SHA512: 47c80ef6c3b12d9ef4e8ba9712251b858ce490c73f56500adbfd58119755bdcdba3367ebf5d622ffc399321e1047913760ac2a8019647d2b09ef3813dd8ccf4e Homepage: https://cran.r-project.org/package=topics Description: CRAN Package 'topics' (Creating and Significance Testing Language Features forVisualisation) Implements differential language analysis with statistical tests and offers various language visualization techniques for n-grams and topics. It also supports the 'text' package. For more information, visit and . Package: r-cran-topicscore Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rspectra, r-cran-combinat, r-cran-quadprog, r-cran-matrix, r-cran-slam Filename: pool/dists/noble/main/r-cran-topicscore_0.0.1-1.ca2404.1_all.deb Size: 701366 MD5sum: 081dc9df425c06e33b4233c7438e44e3 SHA1: e1289ec0f21843cd1a773cb768aa0cab35081b4e SHA256: ddfbf32ff917878b0144f244ed6f30ff2089e5ca01898b360d3a951e8ddb48ab SHA512: db43aeca1c6ec318365f84f273bc25a2385e8a43f79e22590ce29d562c09b2b16eb90eee0cefe94881c9774f999561f11bd2d18f92275fc2712bf74023ff7957 Homepage: https://cran.r-project.org/package=TopicScore Description: CRAN Package 'TopicScore' (The Topic SCORE Algorithm to Fit Topic Models) Provides implementation of the "Topic SCORE" algorithm that is proposed by Tracy Ke and Minzhe Wang. The singular value decomposition step is optimized through the usage of svds() function in 'RSpectra' package, on a 'dgRMatrix' sparse matrix. Also provides a column-wise error measure in the word-topic matrix A, and an algorithm for recovering the topic-document matrix W given A and D based on quadratic programming. The details about the techniques are explained in the paper "A new SVD approach to optimal topic estimation" by Tracy Ke and Minzhe Wang (2017) . Package: r-cran-topictestlet Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-topicmodels, r-cran-tm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-topictestlet_0.1.0-1.ca2404.1_all.deb Size: 85100 MD5sum: 829ee7651ac2714406c31ab0b0807198 SHA1: eb08f9f0bd09946f8773840feba8f6a94c9e8b08 SHA256: c354577dfbd5b8b10a6badbfb2986d9efb74d0f32c72ede80f70650c6a721d56 SHA512: 47f426a2cee57cfb4e78bf740c84c843b95f51280633c24ddd5e3c9aa9dbed5c627b03204f4fb4f8363e408138c58b82edf8fc57aead133f92dfe40c0da94696 Homepage: https://cran.r-project.org/package=TopicTestlet Description: CRAN Package 'TopicTestlet' (A Topic Testlet Model for Calibrating Testlet ConstructedResponses) Implements the Topic Testlet Model (TTM) as described by Xiong et al. (2025) . The package integrates Latent Dirichlet Allocation (LDA) with the Partial Credit Model to account for local item dependence in testlets using latent topics from student textual responses. Package: r-cran-topksignal Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-foreach, r-cran-doparallel, r-cran-nloptr, r-cran-matrix, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-topksignal_1.0-1.ca2404.1_all.deb Size: 139404 MD5sum: d0e0ab394cad71a745e5572a6217de13 SHA1: 153b364c28273587dccfa50ac78f90de60676311 SHA256: c1ba98da1252def0f99400d39f1c672f3242a80d101ff027a407a2caf8543d77 SHA512: e7242f360c6fdb833eb8e21c7313af28af19f670667080412ca0ceaf3a5da6a71fecedd526e5742322c4a02ede6c93ffab74016ed229e3d98acbe0944960aa0f Homepage: https://cran.r-project.org/package=TopKSignal Description: CRAN Package 'TopKSignal' (A Convex Optimization Tool for Signal Reconstruction fromMultiple Ranked Lists) A mathematical optimization procedure in combination with statistical bootstrap for the estimation of the latent signals (sometimes called scores) informing the global consensus ranking (often named aggregation ranking). To solve mid/large-scale problems, users should install the 'gurobi' optimiser (available from ). Package: r-cran-topodistance Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1009 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gdistance, r-cran-plotly, r-cran-raster, r-cran-rcolorbrewer, r-cran-scales, r-cran-sp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-topodistance_1.0.2-1.ca2404.1_all.deb Size: 886986 MD5sum: 192aa70244a0ab6b3c90f3a2ede04b8d SHA1: 7ba3e562cc199628b345aaacd8a85e49486c40fa SHA256: 4cb40eeef9eceeb68e70da3f819168a745b673e98e72a6e18b3469d9d5169a1a SHA512: 3ba44a4b13154997b25d3a4d9ee18b226ac979f0d4ca6f4ccb1d03e15e522dbd4227c8a86f07bf0ced5803c65f7c4c2637f3eee5fed1bdaf3dfc06c4ba470a8d Homepage: https://cran.r-project.org/package=topoDistance Description: CRAN Package 'topoDistance' (Calculating Topographic Paths and Distances) A toolkit for calculating topographic distances and identifying and plotting topographic paths. Topographic distances can be calculated along shortest topographic paths (Wang (2009) ), weighted topographic paths (Zhan et al. (1993) ), and topographic least cost paths (Wang and Summers (2010) ). Functions can map topographic paths on colored or hill shade maps and plot topographic cross sections (elevation profiles) for the paths. Package: r-cran-topologygsa Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-graph, r-cran-grbase, r-bioc-qpgraph, r-cran-fields, r-cran-igraph Suggests: r-bioc-rgraphviz Filename: pool/dists/noble/main/r-cran-topologygsa_1.5.0-1.ca2404.1_all.deb Size: 74780 MD5sum: fcbbb7d00ef053a0c573e0b0e81101c3 SHA1: 728ecfc623a068ad6d67bf2805c92dbbc9a945c6 SHA256: 0d8f9a909365deef2f127049331eb6768e2b7dd8fe4e3adf1eb9f1e3278b297d SHA512: 395d9e09eaaaf3a66745d2471d9815b97e129e9c465c385298fa1cdc37518040e47c09dd77487a436917e03263d2c1190ce2fef53d581fbbd7793c47788cd1bf Homepage: https://cran.r-project.org/package=topologyGSA Description: CRAN Package 'topologyGSA' (Gene Set Analysis Exploiting Pathway Topology) Using Gaussian graphical models we propose a novel approach to perform pathway analysis using gene expression. Given the structure of a graph (a pathway) we introduce two statistical tests to compare the mean and the concentration matrices between two groups. Specifically, these tests can be performed on the graph and on its connected components (cliques). The package is based on the method described in Massa M.S., Chiogna M., Romualdi C. (2010) . Package: r-cran-topologyr Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3494 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-topologyr_0.1.2-1.ca2404.1_all.deb Size: 1116868 MD5sum: 371811051b1f41d07d2b842662dc26c7 SHA1: b6b5fc6f9a5683e450915b6a46d6b02b5f1aa854 SHA256: 8e9d03dd2eb2f95d6348e3200584f5351c277ad4a6c80637fc76c77ed04cd219 SHA512: f124623edfd9115924e1a91cb6cd22b7528f96cd7cba403725264cac672a699126e94f52153d61a1b6b21b42c171a44a4a02ed13068a9c0d4719219e346d9ce7 Homepage: https://cran.r-project.org/package=topologyR Description: CRAN Package 'topologyR' (Topological Connectivity Analysis for Numeric Data) Description: Implementation of topological data analysis methods based on graph-theoretic approaches for discovering topological structures in data. The core algorithm constructs topological spaces from graphs following Nada et al. (2018) "New types of topological structures via graphs". Package: r-cran-topolow Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2213 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future, r-cran-lifecycle, r-cran-ggplot2, r-cran-dplyr, r-cran-data.table, r-cran-reshape2, r-cran-filelock, r-cran-lhs, r-cran-rlang Suggests: r-cran-coda, r-cran-rtsne, r-cran-ape, r-cran-racmacs, r-cran-vegan, r-cran-umap, r-cran-igraph, r-cran-rgl, r-cran-scales, r-cran-ggrepel, r-cran-plotly, r-cran-gridextra, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-topolow_2.0.1-1.ca2404.1_all.deb Size: 1801074 MD5sum: 2a674166cb86996d4b66a3c52d51873b SHA1: 9680b7e0c8b8107aab04c43019228904e275fe9a SHA256: 06c97304b47fd17f89514e3d65165bc109e56a4d3eb45f7905bad80c81e5e80d SHA512: 12676c6052db6aa1adc5d3a2576f723b2bf5a4dae7d5eafe8305776a8ce008d7bfc29c86b5dbc5c6c38ff72572cad955f295a58f32ae14453abd7cc66aed0865 Homepage: https://cran.r-project.org/package=topolow Description: CRAN Package 'topolow' (Force-Directed Euclidean Embedding of Dissimilarity Data) A robust implementation of Topolow algorithm. It embeds objects into a low-dimensional Euclidean space from a matrix of pairwise dissimilarities, even when the data do not satisfy metric or Euclidean axioms. The package is particularly well-suited for sparse, incomplete, and censored (thresholded) datasets such as antigenic relationships. The core is a physics-inspired, gradient-free optimization framework that models objects as particles in a physical system, where observed dissimilarities define spring rest lengths and unobserved pairs exert repulsive forces. The package also provides functions specific to antigenic mapping to transform cross-reactivity and binding affinity measurements into accurate spatial representations in a phenotype space. Key features include: * Robust Embedding from Sparse Data: Effectively creates complete and consistent maps (in optimal dimensions) even with high proportions of missing data (e.g., >95%). * Physics-Inspired Optimization: Models objects (e.g., antigens, landmarks) as particles connected by springs (for measured dissimilarities) and subject to repulsive forces (for missing dissimilarities), and simulates the physical system using laws of mechanics, reducing the need for complex gradient computations. * Automatic Dimensionality Detection: Employs a likelihood-based approach to determine the optimal number of dimensions for the embedding/map, avoiding distortions common in methods with fixed low dimensions. * Noise and Bias Reduction: Naturally mitigates experimental noise and bias through its network-based, error-dampening mechanism. * Antigenic Velocity Calculation (for antigenic data): Introduces and quantifies "antigenic velocity," a vector that describes the rate and direction of antigenic drift for each pathogen isolate. This can help identify cluster transitions and potential lineage replacements. * Broad Applicability: Analyzes data from various objects that their dissimilarity may be of interest, ranging from complex biological measurements such as continuous and relational phenotypes, antibody-antigen interactions, and protein folding to abstract concepts, such as customer perception of different brands. Methods are described in the context of bioinformatics applications in Arhami and Rohani (2025a) , and mathematical proofs and Euclidean embedding details are in Arhami and Rohani (2025b) . 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A regular expression filters data and in a second step a map is created displaying all locations in the filtered data set. The functions make data and plots available for further analysis—either within R or in a chosen directory. Users can select regions within countries, provide coordinates to define regions, or specify a region within the package to restrict the data selection to that region or compare regions with the remainder of countries. This package relies on the R packages 'geodata' for map data and 'ggplot2' for plotting purposes. For more information on the study of toponyms, see Wichmann & Chevallier (2025) . 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Prospective users should test these optimizers with their data, since performance depends on the specific problem being solved. The packages includes the following optimizers: (a) 'adabelief' by Zhuang et al (2020), ; (b) 'adabound' by Luo et al.(2019), ; (c) 'adahessian' by Yao et al.(2021) ; (d) 'adamw' by Loshchilov & Hutter (2019), ; (e) 'madgrad' by Defazio and Jelassi (2021), ; (f) 'nadam' by Dozat (2019), ; (g) 'qhadam' by Ma and Yarats(2019), ; (h) 'radam' by Liu et al. (2019), ; (i) 'swats' by Shekar and Sochee (2018), ; (j) 'yogi' by Zaheer et al.(2019), . 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Package: r-cran-tower Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-purrr, r-cran-stringr, r-cran-curl, r-cran-jsonlite, r-cran-rlang Suggests: r-cran-testthat, r-cran-shiny Filename: pool/dists/noble/main/r-cran-tower_0.2.0-1.ca2404.1_all.deb Size: 59364 MD5sum: 97d01fad0e088b53415b65621df22994 SHA1: 9b1c052a375f12c65e30ecfdc766c0cdf10a4c45 SHA256: 3a864126cdfec070ff58a867d43b6712af7e2afba219089f4f20ba2e17b1e9c8 SHA512: 05520c3b6ecc94887788b824b80e434a7074cd22ef19c1e846c6aab16439713269417d047cc23f885850cc6199cb8019905de04ea5a52c0420f54a89c0ab57c5 Homepage: https://cran.r-project.org/package=tower Description: CRAN Package 'tower' (Easy Middle Ware Library for 'shiny') The best way to implement middle ware for 'shiny' Applications. 'tower' is designed to make implementing behavior on top of 'shiny' easy with a layering model for incoming HTTP requests and server sessions. 'tower' is a very minimal package with little overhead, it is mainly meant for other package developers to implement new behavior. Package: r-cran-toweranna Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-regtools, r-cran-rmarkdown, r-cran-fnn, r-cran-pdist Filename: pool/dists/noble/main/r-cran-toweranna_0.1.0-1.ca2404.1_all.deb Size: 70772 MD5sum: 7ede02d71eb379d6e4c51e7b3b578b4f SHA1: 29ab64d98bf7f5eea8dae1794a009126ddcba8d5 SHA256: 04df1a0f811bd6f1c1b65ae3d8ec2a0d44ddd31e78ee304fbfb1d82d938b6619 SHA512: 895d95485cbaa875c3e383527382364f5b6d41633aeecd33fd9db5cfc7f745fe99a820f1a8fc77e3a4bd5c3b69c70c2d2f4d8e642df4c5c5b0a2a50ce960574c Homepage: https://cran.r-project.org/package=toweranNA Description: CRAN Package 'toweranNA' (A Method for Handling Missing Values in Prediction Applications) Non-imputational method for handling missing values in a prediction context, meaning that not only are there missing values in the training dataset, but also some values may be missing in future cases to be predicted. Based on the notion of regression averaging (Matloff (2017, ISBN: 9781498710916)). Package: r-cran-toxcrit Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-toxcrit_1.1-1.ca2404.1_all.deb Size: 14308 MD5sum: 3e4237bd409de7e16d2c9ab818982eb3 SHA1: 7e768997ab51318902463ba0785bf2c9da33605f SHA256: bd6165dede3ad0cbc4479e79d6ff05331dec515bff2c482e78ad25a1fc196e12 SHA512: 36d491ffe4389b236d18dd8fa9677358232d79b391c1312db15b0ff7c75adfb3203d8ba5d2d71bb0b0691bea4c5bce85c5c8b0c5d3622d5c6eeefa4d2b87db17 Homepage: https://cran.r-project.org/package=ToxCrit Description: CRAN Package 'ToxCrit' (Calculates Safety Stopping Boundaries for a Single-Arm Trialusing Bayes) Computation of stopping boundaries for a single-arm trial using a Bayesian criterion. For each m<=n (n=total patient number of the trial) the smallest number of observed toxicities is calculated leading to the termination of the trial/accrual according to the specified criteria. The probabilities of stopping the trial/accrual at and up until (resp.) the m-th patient (m<=n) is also calculated. This design is more conservative than the frequentist approach (using Clopper Pearson CIs) which might be preferred as it concerns safety. See also Aamot et al. (2010) "Continuous monitoring of toxicity in clinical Trials - simulating the risk of stopping prematurely" . Package: r-cran-toxdrc Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-outliers, r-cran-purrr, r-cran-rlang, r-cran-drc Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-toxdrc_1.0.1-1.ca2404.1_all.deb Size: 95138 MD5sum: 7ce7d283e74cfa8f617ec2a6a01f4cd9 SHA1: 4960b03de14f5ed04ceb7aa1ce1762bb06b868da SHA256: c32a79697e97258f88baab44df441d084d55a59e5a4d9f2e23207eca23837697 SHA512: 224fcfd72309e620baf0adf3c8288bc68335ef03b1a2780c14939b89a6a1489f67db705209296aac95bc3a8053d595baee5e3ba5060ff383aa9d41d6bb31e7c6 Homepage: https://cran.r-project.org/package=toxdrc Description: CRAN Package 'toxdrc' (Pipeline for Dose-Response Curve Analysis) Provides a variety of tools for assessing dose response curves, with an emphasis on toxicity test data. The main feature of this package are modular functions which can be combined through the namesake pipeline, 'runtoxdrc', to automate the analysis for large and complex datasets. This includes optional data preprocessing steps, like outlier detection, solvent effects, blank correction, averaging technical replicates, and much more. Additionally, this pipeline is adaptable to any long form dataset, and does not require specific column or group naming to work. Package: r-cran-toxeval Architecture: all Version: 1.4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3433 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-dt, r-cran-leaflet, r-cran-ggplot2, r-cran-magrittr, r-cran-shiny, r-cran-shinydashboard, r-cran-rcolorbrewer, r-cran-readxl, r-cran-shinyace, r-cran-shinycssloaders Suggests: r-cran-rmarkdown, r-cran-testthat, r-cran-knitr, r-cran-here, r-cran-tcpl, r-cran-openxlsx, r-cran-covr Filename: pool/dists/noble/main/r-cran-toxeval_1.4.2-1.ca2404.1_all.deb Size: 3128066 MD5sum: 95837f90ee20c8e03420ad945e5b3405 SHA1: 398feea16f529d104c38e471c53d2fdc1e08cf0b SHA256: 13ff9b8e93b18c9e2185a08f66536c59c140ee8ba8a1dfb51f3cb7f531ae8f3b SHA512: 33f1eb7399566552872441c7d68abc74294cf9a5f439dfc7e78c1f27786521a45ff0f2b433801b79e5304025d9f712d151ac1ca93f85eee47e36b9f2d6df61a4 Homepage: https://cran.r-project.org/package=toxEval Description: CRAN Package 'toxEval' (Exploring Biological Relevance of Environmental ChemistryObservations) Data analysis package for estimating potential biological effects from chemical concentrations in environmental samples. Included are a set of functions to analyze, visualize, and organize measured concentration data as it relates to user-selected chemical-biological interaction benchmark data such as water quality criteria. The intent of these analyses is to develop a better understanding of the potential biological relevance of environmental chemistry data. Results can be used to prioritize which chemicals at which sites may be of greatest concern. These methods are meant to be used as a screening technique to predict potential for biological influence from chemicals that ultimately need to be validated with direct biological assays. A description of the analysis can be found in Blackwell (2017) . Package: r-cran-toxpir Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13292 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-s4vectors, r-cran-rlang, r-bioc-biocgenerics, r-cran-tidyr, r-cran-ggplot2 Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-covr, r-cran-dbi Filename: pool/dists/noble/main/r-cran-toxpir_1.3.2-1.ca2404.1_all.deb Size: 7020520 MD5sum: 8d8af474cf43e257bd3f19f0e401021a SHA1: 2abc7dc079a6e5b66d3565ce0f0b74984febd28f SHA256: 81ec769449e18702b6929ab44483946de948cf334d58b47beb3b156d83841f09 SHA512: 7802a597337120ab8dcff826c692e8d08d72adf13b13f66f948550f4a05adba365af27d7d7bcff19f45443fee49dc3aa588bcaaeb9fe8df234da142cba06b89c Homepage: https://cran.r-project.org/package=toxpiR Description: CRAN Package 'toxpiR' (Create ToxPi Prioritization Models) Enables users to build 'ToxPi' prioritization models and provides functionality within the grid framework for plotting ToxPi graphs. 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Package: r-cran-tpac Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-mass, r-cran-tpacdata Filename: pool/dists/noble/main/r-cran-tpac_0.3.0-1.ca2404.1_all.deb Size: 309230 MD5sum: ac3eff14c5f0f3cb8c9b25ad06134396 SHA1: 6773907460e0cbb756a630d573dcd018ed7b8a71 SHA256: 09c8a1157d9e2a1a1a355b6475cf4344165463f0590d33b42047fc63c89c64e1 SHA512: 89627b6695adc3ccdec4418972b93a9ecaabc4228bfaede8615f8e260c6e745eed99d6a9b90e5d6a0f7a24f4b658c0fd7bd29e34c18fc8def8b814220c39aca0 Homepage: https://cran.r-project.org/package=TPAC Description: CRAN Package 'TPAC' (Tissue-Adjusted Pathway Analysis of Cancer (TPAC)) Contains logic for single sample gene set testing of cancer transcriptomic data with adjustment for normal tissue-specificity. Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" . 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Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" . Package: r-cran-tpauc Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proc Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tpauc_2.1.1-1.ca2404.1_all.deb Size: 65184 MD5sum: 7f9526f39375a4c3a1d65da80be54617 SHA1: 1a873d94ff73733199a92ddf6933e7c07675c94f SHA256: ff0db6069bb2e2bd7585dddcd8b7ab684d985d7e8f49d4c2709e3771a0fea927 SHA512: f0132954c09edd2d3221d29e5ede04d2d9713a96b4f9dc1d9c44e25c2ce6365bef689cd2f8a8beac6b2e52a7cbd2f33105e0082f6b8c1785c1b5789dcdcb79cd Homepage: https://cran.r-project.org/package=tpAUC Description: CRAN Package 'tpAUC' (Estimation and Inference of Two-Way pAUC, pAUC and pODC) Tools for estimating and inferring two-way partial area under receiver operating characteristic curves (two-way pAUC), partial area under receiver operating characteristic curves (pAUC), and partial area under ordinal dominance curves (pODC). Methods includes Mann-Whitney statistic and Jackknife, etc. Package: r-cran-tpc Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 961 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcalg, r-bioc-graph Suggests: r-bioc-rgraphviz, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tpc_1.0-1.ca2404.1_all.deb Size: 920758 MD5sum: abd33c625eb4d56561f5aa2e03ddd7c0 SHA1: 546c5ec709dac2d1f42ef1a84df36e335b3fb6b8 SHA256: f36dd29b4bb6cc9bc82a9b07a340273cb1ec618670c0c0567f08186881006a66 SHA512: 18edffa572d0fdb3e8b3e32abbaaf7c15ca77e36ac2b174b4df88be005499b37b77618470251c13e472446b6fbc10c36ac20e746ecdca53c54a2a22ce7b49ec0 Homepage: https://cran.r-project.org/package=tpc Description: CRAN Package 'tpc' (Tiered PC Algorithm) Constraint-based causal discovery using the PC algorithm while accounting for a partial node ordering, for example a partial temporal ordering when the data were collected in different waves of a cohort study. Andrews RM, Foraita R, Didelez V, Witte J (2021) provide a guide how to use tpc to analyse cohort data. Package: r-cran-tpcselect Architecture: all Version: 0.8.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corpcor, r-cran-psych, r-cran-mass, r-cran-kernsmooth Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tpcselect_0.8.3-1.ca2404.1_all.deb Size: 43850 MD5sum: eca24026702f612848f3e88ad6b0ecd8 SHA1: 903921e512b0cd84d7e2d4192bfacac11ccd8494 SHA256: 865b0d221ba56277b825881622ab1e8a7b60daeb42a8030e995f95d88f8e20ba SHA512: 55db300d00cbc1a248440bdd9306db0ed5dc938cf250347f85bd246c340a62ec8b4846ce837836effb28713191dfd9a62d082201388b84306c99018b5afec97c Homepage: https://cran.r-project.org/package=TPCselect Description: CRAN Package 'TPCselect' (Variable Selection via Threshold Partial Correlation) A threshold partial correlation approach to selecting important variables in linear models of L. and others (2017) at , and in partial linear models of L. and others (2018) at . This package also extends the PC-simple algorithm of B. and others (2010) at to partial linear models. Package: r-cran-tpd Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1578 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ks, r-cran-gridextra, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tpd_1.1.0-1.ca2404.1_all.deb Size: 935154 MD5sum: adefd2e306cc43aabddbc17a69d03a4f SHA1: b8d442b59375e2010a33f39f94f80c61ed979d08 SHA256: 7efea175042969ebd4761552ae37f087d58b7000eea819453aefee49395ebbc6 SHA512: 4000fbd7fb740db3cc15de6114ef4c17c22c6d71af89009902888d62f21c6b82b5181fb69217c60c87dd7499c09afe5569261ad81e8783cbee88764757db54a6 Homepage: https://cran.r-project.org/package=TPD Description: CRAN Package 'TPD' (Methods for Measuring Functional Diversity Based on TraitProbability Density) Tools to calculate trait probability density functions (TPD) at any scale (e.g. populations, species, communities). 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(2016, ISBN:978-92-870-4367-2) "An Advanced Guide to Trade Policy Analysis" and functions to report regression summaries with clustered robust standard errors. 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Package: r-cran-trajmsm Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-flexmix, r-cran-ggplot2, r-cran-survival, r-cran-sandwich Filename: pool/dists/noble/main/r-cran-trajmsm_0.1.5-1.ca2404.1_all.deb Size: 131862 MD5sum: 815be2f4526df271276f2a80274bada9 SHA1: d8e3bd1bf1bb88cf5c2c8584e58837fefbb32ab7 SHA256: 70f61e5d1f6d5613124207ea7fec04c6c11f5bbd50a941e97e2bec1991359099 SHA512: 961b2814eb44c0b9ce7fbb0be5e6875d43b639ca709e01f8763255a1deba25395c8d995904f1722ccdaa38f60da771d84e4e8938cce13c91d6ea7d025ec56131 Homepage: https://cran.r-project.org/package=trajmsm Description: CRAN Package 'trajmsm' (Marginal Structural Models with Latent Class Growth Analysis ofTreatment Trajectories) Implements marginal structural models combined with a latent class growth analysis framework for assessing the causal effect of treatment trajectories. Based on the approach described in "Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories" Diop, A., Sirois, C., Guertin, J.R., Schnitzer, M.E., Candas, B., Cossette, B., Poirier, P., Brophy, J., Mésidor, M., Blais, C. and Hamel, D., (2023) . Package: r-cran-trajr Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1429 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-signal, r-cran-plotrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-readr, r-cran-sp, r-cran-mass, r-cran-covr Filename: pool/dists/noble/main/r-cran-trajr_1.5.1-1.ca2404.1_all.deb Size: 694090 MD5sum: 06f7170bd7235e1492641b8afe703fcc SHA1: 05622258858543621f9d0db91367a429c40ae416 SHA256: 7cafa36dce15dd78d218ed926a3f7de2c72f4811a0023fae4e4e6bb1653c46dd SHA512: 6a3222f989590c2181f5c7a8ed22eff6523db161c277a7b311bac850cf9f1a61e7871b03a468ac4eb6fbf663e924bb4a5d51be869aca0a12ce2245cca9466925 Homepage: https://cran.r-project.org/package=trajr Description: CRAN Package 'trajr' (Animal Trajectory Analysis) A toolbox to assist with statistical analysis of animal trajectories. It provides simple access to algorithms for calculating and assessing a variety of characteristics such as speed and acceleration, as well as multiple measures of straightness or tortuosity. Some support is provided for 3-dimensional trajectories. McLean & Skowron Volponi (2018) . Package: r-cran-traktok Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-askpass, r-cran-cli, r-cran-cookiemonster, r-cran-curl, r-cran-dplyr, r-cran-glue, r-cran-httr2, r-cran-jsonlite, r-cran-lobstr, r-cran-openssl, r-cran-purrr, r-cran-rlang, r-cran-rvest, r-cran-tibble Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-traktok_0.1.1-1.ca2404.1_all.deb Size: 285488 MD5sum: 03b707eaf1b2d1d93318312f0e6800b8 SHA1: ae15b6d9a828f6dcf0a835832afb939a08e7522a SHA256: 5a449df25a9133ecb7bd9bd7344a160f7b483a2c473f1554c8ad6abf7cf5b99f SHA512: cd78c6c15138194e4e3aab46f69d718f6bccb2a7489f026fb1b0514ae9ea9e9c5027e8ea36b971c8554e007e6f893a782f0492e05c287f7eded27c440e70b4a6 Homepage: https://cran.r-project.org/package=traktok Description: CRAN Package 'traktok' (Collecting 'TikTok' Data) Getting 'TikTok' data () through the official and unofficial APIs—in other words, you can track 'TikTok'. Package: r-cran-tram Architecture: all Version: 1.4-2-1.ca2404.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3256 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-mlt, r-cran-mvtnorm, r-cran-formula, r-cran-multcomp, r-cran-variables, r-cran-basefun, r-cran-sandwich, r-cran-survival, r-cran-matrix Suggests: r-cran-mass, r-cran-th.data, r-cran-trtf, r-cran-mlbench, r-cran-knitr, r-cran-quantreg, r-cran-colorspace, r-cran-atr, r-cran-lme4, r-cran-reformulas, r-cran-merderiv, r-cran-sparsegrid, r-cran-alabama, r-cran-numderiv, r-cran-gridextra, r-cran-lattice, r-cran-latticeextra, r-cran-hsaur3, r-cran-ordinalcont, r-cran-coxme, r-cran-mlt.docreg, r-cran-ordinal, r-cran-coin, r-cran-asht, r-cran-gamlss, r-cran-randomforestsrc, r-cran-tramme, r-cran-glmmtmb, r-cran-geepack, r-cran-ranger, r-cran-eha, r-cran-flexsurv, r-cran-frailtyem, r-cran-frailtypack, r-cran-gamlss.cens, r-cran-icenreg, r-cran-mpr, r-cran-rms, r-cran-rstpm2, r-cran-timereg, r-cran-stat2data, r-cran-cotram, r-cran-latex2exp, r-cran-tramvs, r-cran-aer, r-cran-konpsurv, r-cran-gamlss.data, r-cran-xtable, r-cran-english, r-cran-bibtex, r-cran-expm, r-cran-mgcv, r-cran-pracma Filename: pool/dists/noble/main/r-cran-tram_1.4-2-1.ca2404.2_all.deb Size: 2230128 MD5sum: 635b918ef5afcccf390ec054a5cbad6a SHA1: d831720b587203118772ce11d3d0832a840b51d0 SHA256: 230dabd2f9ac791d8299412d64845536a8a50c293aec21ecd943960652eab295 SHA512: 7b1353df1dfba322ab95d53b6c63ff62c750ecb9e742325631a0d0882399decff98715eebc84130758157952a801d5869ee9e2d67dcc9b0e08844771c2d84da9 Homepage: https://cran.r-project.org/package=tram Description: CRAN Package 'tram' (Transformation Models) Formula-based user-interfaces to specific transformation models implemented in package 'mlt' (, ). Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, ). The underlying theory is described in Hothorn et al. (2018) . An extension to transformation models for clustered data is provided (Barbanti and Hothorn, 2022, ) and a tutorial explains applications in survival analysis (Siegfried et al., 2025, ). Multivariate conditional transformation models (Klein et al, 2022, ) and shift-scale transformation models (Siegfried et al, 2023, ) can be fitted as well. The package contains an implementation of a doubly robust score test, described in Kook et al. (2024, ). Package: r-cran-tramicp Architecture: all Version: 0.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tram, r-cran-mlt, r-cran-coin, r-cran-multcomp, r-cran-survival, r-cran-variables, r-cran-basefun, r-cran-mass, r-cran-cotram, r-cran-dhsic, r-cran-ranger, r-cran-sandwich Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tramicp_0.1-0-1.ca2404.1_all.deb Size: 121178 MD5sum: ba6db4f9082db65711bd0e12a1c134f3 SHA1: 8fe1f0fffc44b7852e6d4c330659b1c20cadf289 SHA256: cf98d6fcc6d96cae74ef00582b8c5c2094c949a20357a514f6a93b3c41e549b3 SHA512: f056199c5dcec9ec634c95b5d35bc6a0bf7bd3d583617f6eb10dc5bb2502c128b09dea851ca62b91c81f6b9322a401145598f90d4546c587bffa8dacb7a77f93 Homepage: https://cran.r-project.org/package=tramicp Description: CRAN Package 'tramicp' (Model-Based Causal Feature Selection for General Response Types) Extends invariant causal prediction (Peters et al., 2016, ) to generalized linear and transformation models (Hothorn et al., 2018, ). The methodology is described in Kook et al. (2023, ). Package: r-cran-tramnet Architecture: all Version: 0.0-991-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1553 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tram, r-cran-mlt, r-cran-basefun, r-cran-sandwich, r-cran-paramhelpers, r-cran-lhs, r-cran-mlr, r-cran-smoof Suggests: r-cran-penalized, r-cran-th.data, r-cran-survival, r-cran-knitr, r-cran-mlbench, r-cran-colorspace, r-cran-mvtnorm, r-cran-glmnet, r-cran-trtf, r-cran-matrix, r-cran-lattice, r-cran-kableextra, r-cran-coin, r-cran-tbm, r-cran-dicekriging Filename: pool/dists/noble/main/r-cran-tramnet_0.0-991-1.ca2404.1_all.deb Size: 879648 MD5sum: d93c7a5a5373d84acfc9933300375df2 SHA1: a522e507e50ac74d096377eb5e394786b0b940e7 SHA256: ae477ecc35c739a53dd06b4531e8cf983ed2e06d845877c49ec499d0f71b1411 SHA512: 478651ac7a519fbd7aac9839bf957c42cd8f7765082e9f9bc2097d933825e195656eb0e7fcca7404a1c3761218650b9afd54551d822e15ee7e10d58d72edeb2c Homepage: https://cran.r-project.org/package=tramnet Description: CRAN Package 'tramnet' (Penalized Transformation Models) Partially penalized versions of specific transformation models implemented in package 'mlt'. Available models include a fully parametric version of the Cox model, other parametric survival models (Weibull, etc.), models for binary and ordered categorical variables, normal and transformed-normal (Box-Cox type) linear models, and continuous outcome logistic regression. Hyperparameter tuning is facilitated through model-based optimization functionalities from package 'mlrMBO'. The accompanying vignette describes the methodology used in 'tramnet' in detail. Transformation models and model-based optimization are described in Hothorn et al. (2019) and Bischl et al. (2016) , respectively. Package: r-cran-trampoline Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coro, r-cran-fastmap, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-bench, r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-trampoline_0.1.1-1.ca2404.1_all.deb Size: 45524 MD5sum: f3f70aec1944123c81115601c518afa1 SHA1: 75dffdfc234fb779e7f6a7c7f331f7bbb243270c SHA256: 26e6e58a9a4d5ccec16bf936b80e52cbf516caca145cdb80d0403369c8a80d02 SHA512: 61bf7814e0f440cb02b72d05a46ca7c23c4c899cf89b43c888b98466a779b0f0d76e6587ad89d71bfeb2a0581fd513e15070d3afbf0fcf4c4b53182945282c2c Homepage: https://cran.r-project.org/package=trampoline Description: CRAN Package 'trampoline' (Make Functions that Can Recurse Infinitely) Implements a trampoline algorithm for R that let's users write recursive functions that get around R's stack call limitations, enabling theoretically infinite recursion. The algorithm is based around generator function as implemented in the 'coro' package, and is based almost completely on the 'trampoline' module from Python . Package: r-cran-trampr Architecture: all Version: 1.0-10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 659 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-trampr_1.0-10-1.ca2404.1_all.deb Size: 488302 MD5sum: dedcf1ac61bdb405642554e49a6858d6 SHA1: 1027124baf73b7efdf805d5e8b70494f36525648 SHA256: 0f189f74dc5a01fac37033460c539777593d1c964a9f873e9a7a783f1598587b SHA512: 98078460268f78fca21c450343d961ab31a7c60f9a2360cab61d41ba5a34ee6f6f61cfccece8d8e3566401db992d2c9b30938296ed8245cf7267cbb60ee1436e Homepage: https://cran.r-project.org/package=TRAMPR Description: CRAN Package 'TRAMPR' ('TRFLP' Analysis and Matching Package for R) Matching terminal restriction fragment length polymorphism ('TRFLP') profiles between unknown samples and a database of known samples. 'TRAMPR' facilitates analysis of many unknown profiles at once, and provides tools for working directly with electrophoresis output through to generating summaries suitable for community analyses with R's rich set of statistical functions. 'TRAMPR' also resolves the issues of multiple 'TRFLP' profiles within a species, and shared 'TRFLP' profiles across species. Package: r-cran-tramvs Architecture: all Version: 0.0-9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tram, r-cran-variables, r-cran-cotram, r-cran-future, r-cran-future.apply, r-cran-mvtnorm Suggests: r-cran-abess, r-cran-tramnet, r-cran-colorspace, r-cran-knitr, r-cran-mlt, r-cran-th.data, r-cran-survival, r-cran-ordinal, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tramvs_0.0-9-1.ca2404.1_all.deb Size: 229926 MD5sum: 3fa847ae0cc3e6ed936ce38fde6ada2b SHA1: 39428e6baab66bb718d1c61c5df71683c91ea3f7 SHA256: 616ccac262246b6e8ba8dfa48a918a1d6bf423101469abec4a6bd643a88fa91b SHA512: 3317bac5146f8ec74bd9d014a1455e934ce1e011116b1c1381bbad3d8731692a91048347ff231b5c8432ef265c14ced1977fcc862aaf1d385af45475b5367157 Homepage: https://cran.r-project.org/package=tramvs Description: CRAN Package 'tramvs' (Optimal Subset Selection for Transformation Models) Greedy optimal subset selection for transformation models (Hothorn et al., 2018, ) based on the abess algorithm (Zhu et al., 2020, ). Applicable to models from packages 'tram' and 'cotram'. Application to shift-scale transformation models are described in Siegfried et al. (2024, ). Package: r-cran-transda Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-transda_1.0.2-1.ca2404.1_all.deb Size: 70262 MD5sum: 5017a4ed42ac1ac905af1f7e8499a1e9 SHA1: 638f6e091712bfd8dcc16cf8b4511c3ae3107cf1 SHA256: b3cf00932b1ed16da440353ba0d13b734b42045c6290d9ca82aa357d04303056 SHA512: 388ad2309fcd8c618cfede6f00cd1f7bef01f31cf2b056b8af8afd42a1b5176ba2f86c62fab1cc9bafd985d94e5d6f63d91ed4bad7c01ced2d971eaa7ed1f4e8 Homepage: https://cran.r-project.org/package=transDA Description: CRAN Package 'transDA' (Transformation Discriminant Analysis) Performs transformation discrimination analysis and non-transformation discrimination analysis. It also includes functions for Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Mixture Discriminant Analysis. In the context of mixture discriminant analysis, it offers options for both common covariance matrix (common sigma) and individual covariance matrices (uncommon sigma) for the mixture components. Package: r-cran-transforemotion Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3974 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-googledrive, r-cran-lsafun, r-cran-matrix, r-cran-pbapply, r-cran-remotes, r-cran-reticulate, r-cran-textdata, r-cran-jsonlite, r-cran-ggplot2, r-cran-reshape2, r-cran-httr Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-rstudioapi, r-cran-testthat Filename: pool/dists/noble/main/r-cran-transforemotion_0.1.7-1.ca2404.1_all.deb Size: 3871074 MD5sum: 5ed4e18952fb84374cb92a74b88455f3 SHA1: 8a973834ec11bdd53547bb3109c011091bf1c770 SHA256: 0596e08f5822eaf6eab704c8fa4fc0169ce228051c48190b6d808a61e0db36b3 SHA512: 7a1657c3125e5b67a86d4ecb3f8795a1a6599d6645feb9085e3ec7d145dca3d9862d23444f5f2ce080112c87da1648c6b38dd384a4eb3cb6d184e9f33c8692d1 Homepage: https://cran.r-project.org/package=transforEmotion Description: CRAN Package 'transforEmotion' (Sentiment Analysis for Text, Image and Video using TransformerModels) Implements sentiment analysis using huggingface transformer zero-shot classification model pipelines for text and image data. The default text pipeline is Cross-Encoder's DistilRoBERTa and default image/video pipeline is Open AI's CLIP . All other zero-shot classification model pipelines can be implemented using their model name from . Package: r-cran-transform.hazards Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-timereg, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-transform.hazards_0.1.1-1.ca2404.1_all.deb Size: 64380 MD5sum: 6ecf3b3edb79742ad2aaee1dafc45f56 SHA1: cb86c7534ae82b43b335e8787c847c80ead0b10e SHA256: 790941fc48fb450dc1be169b3d3fc8f7bf9816a1e75ef76f1982a3dd5816010c SHA512: f04e040030aa530a952f5830737660513ca315926ca73f88c48c4be781e9d727220c3144c6c8cc987c99bd059af5fefdcd7d7d6b7b8393fc4e32d5f2c81a2327 Homepage: https://cran.r-project.org/package=transform.hazards Description: CRAN Package 'transform.hazards' (Transforms Cumulative Hazards to Parameter Specified by ODESystem) Targets parameters that solve Ordinary Differential Equations (ODEs) driven by a vector of cumulative hazard functions. The package provides a method for estimating these parameters using an estimator defined by a corresponding Stochastic Differential Equation (SDE) system driven by cumulative hazard estimates. By providing cumulative hazard estimates as input, the package gives estimates of the parameter as output, along with pointwise (co)variances derived from an asymptotic expression. Examples of parameters that can be targeted in this way include the survival function, the restricted mean survival function, cumulative incidence functions, among others; see Ryalen, Stensrud, and Røysland (2018) , and further applications in Stensrud, Røysland, and Ryalen (2019) and Ryalen et al. (2021) . Package: r-cran-transform Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-transform_1.0-1.ca2404.1_all.deb Size: 105272 MD5sum: 9ec5b7f67ab8a0e89e47dac6c03493d3 SHA1: 233895f8a8dabfccf7496fdeabb77e368350b9de SHA256: a71a7e44dd28ba71cd358aaf4963f11e5652e1b9b8f01cb83ce5035b4cfa2c1c SHA512: 785687c446cb154f958375b9a228ac09ff182b2ac8a7eb4412f884d3cb6ce8d6e63a6a44a0a56cf524cac6c8b226dad4ea36a92e9a6dc3edc70a2b9e8e5e0ae1 Homepage: https://cran.r-project.org/package=Transform Description: CRAN Package 'Transform' (Statistical Transformations) Performs various statistical transformations; Box-Cox and Log (Box and Cox, 1964) , Glog (Durbin et al., 2002) , Neglog (Whittaker et al., 2005) , Reciprocal (Tukey, 1957), Log Shift (Feng et al., 2016) , Bickel-Docksum (Bickel and Doksum, 1981) , Yeo-Johnson (Yeo and Johnson, 2000) , Square Root (Medina et al., 2019), Manly (Manly, 1976) , Modulus (John and Draper, 1980) , Dual (Yang, 2006) , Gpower (Kelmansky et al., 2013) . It also performs graphical approaches, assesses the success of the transformation via tests and plots. Package: r-cran-transformer Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-attention Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-transformer_0.2.0-1.ca2404.1_all.deb Size: 21854 MD5sum: 94165a7e68209ae6a2ffb27b50c557bf SHA1: 76bb2ca3fd20328b98f63317cf6ede8bfe1773af SHA256: 93f1d4eee1733019f2e83f1c60c63e38e245e16d8e3accb94a06a902dc9606a4 SHA512: 86835ca1b79cb5b2b4747ff7b797a579008878b3a801ec8e34be320684e8750937ab8443a98ccd2a373cc55148ecf8d49473eed28c479b9b313d81be97f7701e Homepage: https://cran.r-project.org/package=transformer Description: CRAN Package 'transformer' (Implementation of Transformer Deep Neural Network with Vignettes) Transformer is a Deep Neural Network Architecture based i.a. on the Attention mechanism (Vaswani et al. (2017) ). Package: r-cran-transformerforecasting Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-keras, r-cran-tensorflow, r-cran-magrittr, r-cran-reticulate Suggests: r-cran-dplyr, r-cran-knitr, r-cran-lubridate, r-cran-readr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-transformerforecasting_0.1.0-1.ca2404.1_all.deb Size: 70442 MD5sum: 0cd5128b9b1196396407fd98bde97518 SHA1: 2c893abc4034394473be06b10d54df7f2de5c5cd SHA256: 2cf54dcdbf83c8fbc4584a07138cf830e5f3d1b16c73d27a2ab5c5762a5dbb71 SHA512: 133dadc4704d2830da32e210d54d2df3bb0045f746b9aa6c0a38f9cf8f7fc2b895f776403edfc2871e900e25bee433d8d7a6ca00c8deb96970cab6e50f5d0153 Homepage: https://cran.r-project.org/package=transformerForecasting Description: CRAN Package 'transformerForecasting' (Transformer Deep Learning Model for Time Series Forecasting) Time series forecasting faces challenges due to the non-stationarity, nonlinearity, and chaotic nature of the data. Traditional deep learning models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) process data sequentially but are inefficient for long sequences. To overcome the limitations of these models, we proposed a transformer-based deep learning architecture utilizing an attention mechanism for parallel processing, enhancing prediction accuracy and efficiency. This paper presents user-friendly code for the implementation of the proposed transformer-based deep learning architecture utilizing an attention mechanism for parallel processing. References: Nayak et al. (2024) and Nayak et al. (2024) . Package: r-cran-transformmos Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 46 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-transformmos_0.1.0-1.ca2404.1_all.deb Size: 15786 MD5sum: 303f79fe91272210c9c4acb3b7278129 SHA1: ee5bd03981b02446af70339d841e4ea899bb3276 SHA256: 50d6d95186b1f4d49e7169bab1b8f3e437a980752c6cbbcea540162a860c70d2 SHA512: 1f228fb3dfdbf44f8cf950260491414cd0b3bea247c6b3910275c216ead53fc3ae116cdc0d30cb421d92577d5af3917956bef074bc44ef9dbcf369220a34242e Homepage: https://cran.r-project.org/package=transformmos Description: CRAN Package 'transformmos' (Transform MOS Values to be Robust for using Rank BasedStatistics) Implementation of the transformation of the Mean Opinion Scores (MOS) to be used before applying the rank based statistical techniques. The method and its necessity is described in: Babak Naderi, Sebastian Möller (2020) . 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The package provides functions for single and multiple source transfer learning, source detection to identify positive and negative transfer sources, factor decomposition using Maximum Likelihood Estimation (MLE), and information criteria ('IC1' and 'IC2') for rank selection. The methods are particularly useful for high-dimensional data analysis where auxiliary information from related source datasets can improve estimation efficiency in the target domain. Package: r-cran-transgraph Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-glasso, r-cran-clime, r-cran-heteroggm, r-cran-dcov, r-cran-huge, r-cran-evaluationmeasures Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-transgraph_1.1.0-1.ca2404.1_all.deb Size: 99358 MD5sum: af4b4924655d3beea724e65ac8a32e0c SHA1: 890ffb0c15de55afbb2789f75daa4a5801ab6b75 SHA256: 059273ff3702f578317b49c2d73edecf44e8678805623481a3871368c9a072f2 SHA512: 95cdae1c9c15f5542f3011882513470189761c0277ca26fdc887db397ad6498a4eb5f681f62ca7c9fff07ba5e26e6c9eb89d3650683e51e6b53a2c2b3bbb3177 Homepage: https://cran.r-project.org/package=TransGraph Description: CRAN Package 'TransGraph' (Transfer Graph Learning) Transfer learning, aiming to use auxiliary domains to help improve learning of the target domain of interest when multiple heterogeneous datasets are available, has been a hot topic in statistical machine learning. The recent transfer learning methods with statistical guarantees mainly focus on the overall parameter transfer for supervised models in the ideal case with the informative auxiliary domains with overall similarity. In contrast, transfer learning for unsupervised graph learning is in its infancy and largely follows the idea of overall parameter transfer as for supervised learning. In this package, the transfer learning for several complex graphical models is implemented, including Tensor Gaussian graphical models, non-Gaussian directed acyclic graph (DAG), and Gaussian graphical mixture models. Notably, this package promotes local transfer at node-level and subgroup-level in DAG structural learning and Gaussian graphical mixture models, respectively, which are more flexible and robust than the existing overall parameter transfer. As by-products, transfer learning for undirected graphical model (precision matrix) via D-trace loss, transfer learning for mean vector estimation, and single non-Gaussian learning via topological layer method are also included in this package. Moreover, the aggregation of auxiliary information is an important issue in transfer learning, and this package provides multiple user-friendly aggregation methods, including sample weighting, similarity weighting, and most informative selection. (Note: the transfer for tensor GGM has been temporarily removed in the current version as its dependent R package Tlasso has been archived. The historical version TransGraph_1.0.0.tar.gz can be downloaded at ) Reference: Ren, M., Zhen Y., and Wang J. (2024) "Transfer learning for tensor graphical models". Ren, M., He X., and Wang J. (2023) "Structural transfer learning of non-Gaussian DAG". Zhao, R., He X., and Wang J. (2022) "Learning linear non-Gaussian directed acyclic graph with diverging number of nodes". 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The main function TransHDM() integrates large-scale source data to improve the detection power of potential mediators in small-sample target studies. It addresses data heterogeneity via transfer regularization and debiased estimation while controlling the false discovery rate. The package also includes utilities for data generation (gen_simData_homo(), gen_simData_hetero()), baseline methods such as lasso() and dblasso(), sure independence screening via SIS(), and model diagnostics through source_detection(). The methodology is described in Pan et al. (2025) . 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Provide translations and seamlessly retrieve them at runtime. Package: r-cran-transmem Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cmna, r-cran-ggformula, r-cran-ggplot2, r-cran-plot3d Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-transmem_0.1.1-1.ca2404.1_all.deb Size: 122728 MD5sum: ca6772c9a47ae5d7e12de6eeabcdfffd SHA1: fa09a027b81ada5f778545124c102fa049cc1c9f SHA256: 91dbe65437993981a1e34e0629d4af0974a9a32e3fd3a450669d9b270777113a SHA512: 0603052daadba6c8adf0bd6493f2bddfdbf00e562e8aa7b8b518f8dccc8a55b853a4238ca0caad68247098a9f59bde520198b1db139260eeb4a0d741a5ecdf93 Homepage: https://cran.r-project.org/package=transmem Description: CRAN Package 'transmem' (Treatment of Membrane-Transport Data) Treatment and visualization of membrane (selective) transport data. Transport profiles involving up to three species are produced as publication-ready plots and several membrane performance parameters (e.g. separation factors as defined in Koros et al. (1996) and non-linear regression parameters for the equations described in Rodriguez de San Miguel et al. (2014) ) can be obtained. Many widely used experimental setups (e.g. membrane physical aging) can be easily studied through the package's graphical representations. 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These functions include donor and recipient risk indices as used by NHS Blood & Transplant, OPTN/UNOS and Eurotransplant, tools for quantifying HLA mismatches, functions for calculating estimated glomerular filtration rate (eGFR), a function to calculate the APRI (AST to platelet ratio) score used in initial screening of suitability to receive a transplant from a hepatitis C seropositive donor and some biochemical unit converter functions. All functions are designed to work with either US or international units. References for the equations are provided in the vignettes and function documentation. 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Package: r-cran-transportr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-generics, r-cran-ife, r-cran-mlr3superlearner, r-cran-origami, r-cran-r6 Suggests: r-cran-ranger, r-cran-testthat Filename: pool/dists/noble/main/r-cran-transportr_0.1.0-1.ca2404.1_all.deb Size: 164516 MD5sum: b86a8946a6beb8b432b8292e121030fd SHA1: 070bb4145119ad53c73da59df26c80db64218f87 SHA256: e99e0d674e7ab9251e9e2008ce77732e8cd463a36e7330d521233bf30760eda1 SHA512: 376e828a6d7bd274f19af66023a69adfa619c7c8c59b031c87bcb67aa39323f0ea0175b6b968888b649d34ddda7a5099f72e787ae5ed81c365ee7bcd5e2eb87b Homepage: https://cran.r-project.org/package=transportr Description: CRAN Package 'transportr' (Transporting Intervention Effects from One Population to Another) Doubly-robust, non-parametric estimators for the transported average treatment effect from Rudolph, Williams, Stuart, and Diaz (2023) and the intent-to-treatment average treatment effect from Rudolph and van der Laan (2017) . Estimators are fit using cross-fitting and nuisance parameters are estimated using the Super Learner algorithm. Package: r-cran-transpror Architecture: all Version: 1.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1727 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-dplyr, r-cran-rlang, r-cran-tibble, r-bioc-sva, r-bioc-deseq2, r-bioc-edger, r-bioc-limma, r-cran-ggplot2, r-cran-ggvenndiagram, r-cran-ggdensity, r-cran-ggpubr, r-bioc-ggtree, r-cran-ggraph, r-cran-tidygraph, r-cran-tidyr, r-cran-stringr, r-cran-geomtextpath, r-cran-ggnewscale, r-cran-hmisc, r-cran-circlize, r-cran-spiralize, r-bioc-complexheatmap Suggests: r-cran-prettydoc, r-cran-knitr, r-bioc-ggtreeextra, r-cran-rmarkdown, r-cran-systemfonts Filename: pool/dists/noble/main/r-cran-transpror_1.0.7-1.ca2404.1_all.deb Size: 1117610 MD5sum: e5be6f3a6a18d214b4f51bc58fd40b4a SHA1: 3c7548f15af367e77c4b3317410adfac40486478 SHA256: 6e26d3d3bd135437feb11d60d59d4f4fb36d401c86880958036cedfbababa9bf SHA512: ce0a76b85b68425efeda412cf270b574fe37080cf9218ed7b814cbe0859461cad0146cc9409cde980149b6e563987d1a4eb488e8c189bebfad401efcaae548a5 Homepage: https://cran.r-project.org/package=TransProR Description: CRAN Package 'TransProR' (Analysis and Visualization of Multi-Omics Data) A tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. It enables researchers to identify key biological processes, disease biomarkers, and gene regulatory mechanisms. 'TransProR' is aimed at researchers and bioinformaticians working with RNA-Seq data, providing an intuitive framework for in-depth analysis and visualization of transcriptomic datasets. The package includes comprehensive documentation and usage examples to guide users through the entire analysis pipeline. The differential expression analysis methods incorporated in the package include 'limma' (Ritchie et al., 2015, ; Smyth, 2005, ), 'edgeR' (Robinson et al., 2010, ), 'DESeq2' (Love et al., 2014, ), and Wilcoxon tests (Li et al., 2022, ), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications. 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Package: r-cran-transx Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rlang Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-cli, r-cran-testthat, r-cran-knitr, r-cran-desctools, r-cran-outliers, r-cran-rmarkdown, r-cran-mfilter, r-cran-covr Filename: pool/dists/noble/main/r-cran-transx_0.0.1-1.ca2404.1_all.deb Size: 198600 MD5sum: 04e80a7ab86080b563fcc36c4623f613 SHA1: df75aea58b81b9919d10dcda8f5e8f330d3c3010 SHA256: 17084eebd1372278c2cc29f93c5b10794ca8c33d1ff69043fd003b63ffbc4433 SHA512: d829bd3575141ea15e196b786e2112a9430f4b61c0a60dcfa972551e22fdabedae0f521d364684ab35f88c0d20dc0f4215fec6ce6dcb55ec839f4fecc3955d59 Homepage: https://cran.r-project.org/package=transx Description: CRAN Package 'transx' (Transform Univariate Time Series) Univariate time series operations that follow an opinionated design. 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Package: r-cran-tredesigns Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-tredesigns_1.0.1-1.ca2404.1_all.deb Size: 42116 MD5sum: 516ef526cc14610d85e51003ceb5fd11 SHA1: fdbe43c33d6ddff68736bbf7cef3fdefb19f2f92 SHA256: 22ea8f13e07e6a3d946d1be60ecde2924b712b534a1e2c6d3e8b24ba151e7979 SHA512: d281417477966ee356f501a7393208027ba8bab418fde3a5830e27b9d5444e768bc04a260427ac343a96a618a425be0ae0df8989bd6602260855355b09d02edd Homepage: https://cran.r-project.org/package=TREDesigns Description: CRAN Package 'TREDesigns' (Ternary Residual Effect Designs) There are some experimental scenarios where each experimental unit receives a sequence of treatments across multiple periods, and treatment effects persist beyond the period of application. It focuses on the construction and calculation of the parametric value of the residual effect designs balanced for carryover effects, also referred to as crossover designs, change-over designs, or repeated measurements designs (Aggarwal and Jha, 2010). The primary objective of the package is to generate a new class of Balanced Ternary Residual Effect Designs (BTREDs), balanced for carryover effects tailored explicitly for situations where the number of periods is less than or equal to the number of treatments. In addition, the package provides four new classes of Partially Balanced Ternary Residual Effect Designs (PBTREDs), constructed using incomplete block designs, initial sequences, and rectangular association scheme. In addition, one extra function is included to help study the parametric properties of a given residual effect design. Package: r-cran-tree3d Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2325 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rayvertex Filename: pool/dists/noble/main/r-cran-tree3d_0.1.2-1.ca2404.1_all.deb Size: 388294 MD5sum: 794e868ce691dd6a5d0f33637123673a SHA1: c4744503be530c2eab858f14c2451748a6410c60 SHA256: a196cb0b889494c012bcfb06f2ee6091d89f20cf1cee04e189550769d346436b SHA512: 15e4f6ce0752934e87640e0acdc21ecbb8f9a90a42381f0266b37e43a3dda37ed532857a1bf7561ea7984fc19a56ffa8710d45e6c7465381b0106a0ebe2fb5ab Homepage: https://cran.r-project.org/package=tree3d Description: CRAN Package 'tree3d' (3D Tree Models) Provides customizable 3D tree models (as 'OBJ' files) for use in data visualization. Includes both planar and solid tree models, various crown types (columnar, oval, palm, pyramidal, rounded, spreading, vase, weeping), and options to change the diameter, height, and color of the tree's crown and trunk. Package: r-cran-treebalance Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-memoise, r-cran-gmp Filename: pool/dists/noble/main/r-cran-treebalance_1.2.0-1.ca2404.1_all.deb Size: 280186 MD5sum: dd7d570323de8459e3ad08609b8d877d SHA1: d41442933d6d0a94079971bbf8145a1287f0f06b SHA256: c546b442878acf8b587e5fc64e0665e78f4b3243b8c69d8a7adbca5307f984ac SHA512: 85f87e04982eed320c864652b293429629d17a063ac6195917066cab17a1a3aec817e2d98330b974274085405cb463db3e1bc7ecc1ab845d0c5e2800f7d4ce6f Homepage: https://cran.r-project.org/package=treebalance Description: CRAN Package 'treebalance' (Computation of Tree (Im)Balance Indices) The aim of the 'R' package 'treebalance' is to provide functions for the computation of a large variety of (im)balance indices for rooted trees. The package accompanies the book ''Tree balance indices: a comprehensive survey'' by M. Fischer, L. Herbst, S. Kersting, L. Kuehn and K. Wicke (2023) , , which gives a precise definition for the terms 'balance index' and 'imbalance index' (Chapter 4) and provides an overview of the terminology in this manual (Chapter 2). For further information on (im)balance indices, see also Fischer et al. (2021) . Considering both established and new (im)balance indices, 'treebalance' provides (among others) functions for calculating the following 18 established indices and index families: the average leaf depth, the B1 and B2 index, the Colijn-Plazzotta rank, the normal, corrected, quadratic and equal weights Colless index, the family of Colless-like indices, the family of I-based indices, the Rogers J index, the Furnas rank, the rooted quartet index, the s-shape statistic, the Sackin index, the symmetry nodes index, the total cophenetic index and the variance of leaf depths. Additionally, we include 9 tree shape statistics that satisfy the definition of an (im)balance index but have not been thoroughly analyzed in terms of tree balance in the literature yet. These are: the total internal path length, the total path length, the average vertex depth, the maximum width, the modified maximum difference in widths, the maximum depth, the maximum width over maximum depth, the stairs1 and the stairs2 index. As input, most functions of 'treebalance' require a rooted (phylogenetic) tree in 'phylo' format (as introduced in 'ape' 1.9 in November 2006). 'phylo' is used to store (phylogenetic) trees with no vertices of out-degree one. For further information on the format we kindly refer the reader to E. Paradis (2012) . Package: r-cran-treebase Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4225 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-xml, r-cran-rcurl, r-cran-httr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-treebase_0.1.5-1.ca2404.1_all.deb Size: 4262372 MD5sum: f436107591a71a5bbd8a842ca6881be1 SHA1: 1a82b1516d48eff4eb5ee67454774d861709e298 SHA256: 2e177b1186834f1371182858e122072364750bf54b14f8a5512b19449d454ad6 SHA512: 9786ca61542adfb1a4cc305a9db9a481a4980d07c64ee0c6300feacb86ab8c09802fe0a2a68d53c916f0bc004a496308e1e0492250168197b3a929891a8cc18a Homepage: https://cran.r-project.org/package=treebase Description: CRAN Package 'treebase' (Discovery, Access and Manipulation of 'TreeBASE' Phylogenies) Interface to the API for 'TreeBASE' from 'R.' 'TreeBASE' is a repository of user-submitted phylogenetic trees (of species, population, or genes) and the data used to create them. Package: r-cran-treeclust Architecture: all Version: 1.1-7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 104 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rpart, r-cran-cluster Filename: pool/dists/noble/main/r-cran-treeclust_1.1-7.1-1.ca2404.1_all.deb Size: 76906 MD5sum: 0621494e69c94799e1213a0323b3c340 SHA1: 706161ff654c82ee61c06141a89c8d4c9120af49 SHA256: 85ec38f2dad7b977af5e4c86a127fa8bfafca644d161dca5d40b4204d711eef2 SHA512: 60fae979e72289714f403a074ff4f07dba5361ee871266652496a4b3495d77ecb94c80d7073c3a876897706c1b1d93d4c7ac4ec295cc4b72ddc8a0b2b20deca4 Homepage: https://cran.r-project.org/package=treeClust Description: CRAN Package 'treeClust' (Cluster Distances Through Trees) Create a measure of inter-point dissimilarity useful for clustering mixed data, and, optionally, perform the clustering. Package: r-cran-treeda Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1488 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparselda, r-cran-matrix, r-cran-mvtnorm, r-cran-reshape2, r-cran-gtable, r-bioc-phyloseq, r-cran-ggplot2, r-cran-ape Suggests: r-cran-adaptivegpca, r-cran-knitr, r-cran-testthat, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-treeda_0.0.5-1.ca2404.1_all.deb Size: 610030 MD5sum: bc381f150376ec89d3e7565c5832a1be SHA1: 05a7c3c0d8c75ea210ade73e2f06cdbc75818021 SHA256: 6e1b2e50ebf706ec3e67952dfd281a903f15c16ab3794512126057da78b69a0d SHA512: bbb84904fb6a73f6ce61b70eb347aa377ba26fadbe3d3246f2e0ac2e8776a2c99154d2f32ce5fdcdec1fb62d60b29d7a3dd1669354a0439cb8481083a808a050 Homepage: https://cran.r-project.org/package=treeDA Description: CRAN Package 'treeDA' (Tree-Based Discriminant Analysis) Performs sparse discriminant analysis on a combination of node and leaf predictors when the predictor variables are structured according to a tree, as described in Fukuyama et al. (2017) . Package: r-cran-treedata.table Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-lazyeval, r-cran-geiger, r-cran-data.table Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-utf8 Filename: pool/dists/noble/main/r-cran-treedata.table_0.1.1-1.ca2404.1_all.deb Size: 193404 MD5sum: c0eee10e33b457936170be3f75c86ff7 SHA1: e4a9e78b85b5d0e750088dbd1ab3d142ee4faaf9 SHA256: 5d74dea2b75b5108a4d5f80fc3d42b74e6e452902fc57e7c97efd7be92835b6a SHA512: 7f895a509a94ba134d263e672c17fcc8ee62483db97a0c9ba4d16b6e944411a5af0f923906a5cb4f4932ec61131d0505739c4cf0e9e5cec79be3391bec171af9 Homepage: https://cran.r-project.org/package=treedata.table Description: CRAN Package 'treedata.table' (Manipulation of Matched Phylogenies and Data using 'data.table') An implementation that combines trait data and a phylogenetic tree (or trees) into a single object of class 'treedata.table'. The resulting object can be easily manipulated to simultaneously change the trait- and tree-level sampling. Currently implemented functions allow users to use a 'data.table' syntax when performing operations on the trait dataset within the 'treedata.table' object. For more details see Roman-Palacios et al. (2021) . Package: r-cran-treedater Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-limsolve Suggests: r-cran-lubridate, r-cran-ggplot2, r-cran-foreach, r-cran-iterators, r-cran-mgcv, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-treedater_1.0.2-1.ca2404.1_all.deb Size: 290496 MD5sum: 4299519d03a90fee8c9b527a0e12bdd7 SHA1: 1e7784cba84a1fcb5e54a4d7ab0b7f5e21455f20 SHA256: 0dd5efb877ab988ebe093be97c7f3fe36988fc2f35db6138993074ca4d301af5 SHA512: 9f17165ac9901d17da77be60a908de5470d7ebaa15477dc2b21009e0aade1c463da14535e809dfbfb1e6ad98117c6c42655b8108ae62cc2a16b73cfcbade2a5c Homepage: https://cran.r-project.org/package=treedater Description: CRAN Package 'treedater' (Fast Molecular Clock Dating of Phylogenetic Trees with RateVariation) Functions for estimating times of common ancestry and molecular clock rates of evolution using a variety of evolutionary models, parametric and nonparametric bootstrap confidence intervals, methods for detecting outlier lineages, root-to-tip regression, and a statistical test for selecting molecular clock models. For more details see Volz and Frost (2017) . Package: r-cran-treedbalance Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rgl, r-cran-r.matlab Filename: pool/dists/noble/main/r-cran-treedbalance_1.2.0-1.ca2404.1_all.deb Size: 244578 MD5sum: 85a7397d9097d704d1771c78894b1ea4 SHA1: 0b1164806c82098de6f493146b09fd6ac762656e SHA256: 30007c0b1512ac7b9c43116855243f94ad68e548117c010c4c6d82104541b151 SHA512: a5d43531a6177d402ef93996a3be4b61481dd7f8379a7ff61bb49c502de1c5610b11cbbb69e5bf1f65f39dde5375cca14e7b75a0fbc5d22e2a2a70df70c3e1dc Homepage: https://cran.r-project.org/package=treeDbalance Description: CRAN Package 'treeDbalance' (Computation of 3D Tree Imbalance) The main goal of the R package 'treeDbalance' is to provide functions for the computation of several measurements of 3D node imbalance and their respective 3D tree imbalance indices, as well as to introduce the new 'phylo3D' format for rooted 3D tree objects. Moreover, it encompasses an example dataset of 3D models of 63 beans in 'phylo3D' format. Please note that this R package was developed alongside the project described in the manuscript 'Measuring 3D tree imbalance of plant models using graph-theoretical approaches' by M. Fischer, S. Kersting, and L. Kühn (2023) , which provides precise mathematical definitions of the measurements. Furthermore, the package contains several helpful functions, for example, some auxiliary functions for computing the ancestors, descendants, and depths of the nodes, which ensures that the computations can be done in linear time, or functions that convert existing formats of 3D tree models of other software into the 'phylo3D' format. Moreover, it comprises functions to extract the graph-theoretical topology without vertices of in- and out-degree 1 of rooted 3D trees as well as to adapt node enumerations to the common 'phylo' format. Most functions of 'treeDbalance' require as input a rooted tree in the 'phylo3D' format, an extended 'phylo' format (as introduced in the R package 'ape' 1.9 in November 2006). Such a 'phylo3D' object must have at least two new attributes next to those required by the 'phylo' format: 'node.coord', the coordinates of the nodes, as well as 'edge.weight', the literal weight or volume of the edges. Optional attributes are 'edge.diam', the diameter of the edges, and 'edge.length', the length of the edges. For visualization purposes one can also specify 'edge.type', which ranges from normal cylinder to bud to leaf, as well as 'edge.color' to change the color of the edge depiction. This project was supported by the joint research project DIG-IT! funded by the European Social Fund (ESF), reference: ESF/14-BM-A55-0017/19, and the Ministry of Education, Science and Culture of Mecklenburg-Western Pomerania, Germany, as well as by the project ArtIGROW, which is a part of the WIR!-Alliance 'ArtIFARM – Artificial Intelligence in Farming' funded by the German Federal Ministry of Education and Research (FKZ: 03WIR4805). Package: r-cran-treedep Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-treedep_0.1.3-1.ca2404.1_all.deb Size: 281988 MD5sum: 019c6c1178d69864df43958187df80cc SHA1: 63ca7ac122d45ea359385f3703716b2ee91adc44 SHA256: b1604789a99a34108024d5ce4557a99df675a9a28104134c79d74505d1220d8e SHA512: 42355bbeb8b2d08a881b48abd8052f0fde9a2d61f06c61d3e4fe6b5c284fb07c8556944af940dd0bc3ed4462b2782a7bdd60be9dde0637a4ee100360968dce3d Homepage: https://cran.r-project.org/package=TreeDep Description: CRAN Package 'TreeDep' (Air Pollution Removal by Dry Deposition on Trees) The model estimates air pollution removal by dry deposition on trees. It also estimates or uses hourly values for aerodynamic resistance, boundary layer resistance, canopy resistance, stomatal resistance, cuticular resistance, mesophyll resistance, soil resistance, friction velocity and deposition velocity. It also allows plotting graphical results for a specific time period. The pollutants are nitrogen dioxide, ozone, sulphur dioxide, carbon monoxide and particulate matter. Baldocchi D (1994) . Farquhar GD, von Caemmerer S, Berry JA (1980) Planta 149: 78-90. Hirabayashi S, Kroll CN, Nowak DJ (2015) i-Tree Eco Dry Deposition Model. Nowak DJ, Crane DE, Stevens JC (2006) . US EPA (1999) PCRAMMET User's Guide. EPA-454/B-96-001. Weiss A, Norman JM (1985) Agricultural and Forest Meteorology 34: 205—213. Package: r-cran-treediagram Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 86 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ape, r-cran-cowplot, r-cran-tree, r-cran-stringr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-treediagram_0.1.1-1.ca2404.1_all.deb Size: 51878 MD5sum: 0bf82d29b1995c08ce78e8eccbd80af5 SHA1: 0ea611080ba2d5db8f26fa602b9b65acab686257 SHA256: 68846bd0c5b999406fd9f6e8fe57b4d7311e715e310c715eff46cc67a431aa32 SHA512: 2cf7b51abd53c88ac7b6e59dbcf466a46dda6255079694b7e47964c93b973ed9bf1c3b8681cc698957338c6babfcc2645203f36b660cf5a45e734bded7965c40 Homepage: https://cran.r-project.org/package=TreeDiagram Description: CRAN Package 'TreeDiagram' (Tree Diagram) Visualizing cuts for either axis-align or non axis-align tree methods (e.g. decision tree, random tessellation process). Package: r-cran-treediff Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-adjclust, r-bioc-biocgenerics, r-bioc-csaw, r-cran-data.table, r-cran-dplyr, r-bioc-interactionset, r-bioc-limma, r-bioc-summarizedexperiment, r-cran-reshape2, r-cran-testthat, r-cran-rlang, r-bioc-hicdoc, r-cran-purrr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-treediff_0.2.2-1.ca2404.1_all.deb Size: 94018 MD5sum: 70d87b06cd3b386ef99ff6996643cf08 SHA1: 39352ee2a1ab0e9dfad1b6d61e920c7d1b97696a SHA256: 2ffa2f4c5bf83ffdd76d7182bc0a4ee0b19504f9e7e1d15b8d190d1eafaa5224 SHA512: 2afac9161c78633631b86b353de49eef9c22bc9db3d7c6ff21cd02e2e648d7197c232a74a3c0a0128fa15823910b14e6242c4431be89b02b7adddd1ab0472cfe Homepage: https://cran.r-project.org/package=treediff Description: CRAN Package 'treediff' (Testing Differences Between Families of Trees) Perform test to detect differences in structure between families of trees. The method is based on cophenetic distances and aggregated Student's tests. 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'treeheatr' utilizes the customizable 'ggparty' package for drawing decision trees. 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Tree-based Scan Statistics are an exploratory method to identify event clusters across the space of a hierarchical tree. 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The null hypothesis assumes all group means are equal, while the alternative assumes the control mean is less than or equal to each treatment mean with at least one strict inequality. Inputs are a list of numeric vectors (groups) and a significance level; outputs include the test statistic, critical value, and decision. Methods described in "Testing Against Tree Ordered Alternatives in One-way ANOVA" . Package: r-cran-treeplotarea Architecture: all Version: 3.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1206 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fritools, r-cran-sf Suggests: r-cran-checkmate, r-cran-pkgload, r-cran-plotrix, r-cran-rmarkdown, r-cran-rprojroot, r-cran-runit, r-cran-testthat, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-treeplotarea_3.1.0-1.ca2404.1_all.deb Size: 1060880 MD5sum: d743368c77644a3106db41b7eb6888d8 SHA1: d5a1521e107a6439dfd26bdb27119ec556330f06 SHA256: 9aad2cb7c8a6150e8a3c194b602b020677c06a6974376f4c19cd2d5f7a726e9b SHA512: 643e44fa15a2b6999cc84cfcc85576153d70bbb884772ba70a01bd38a7461fa592aafefc3eab989511c6661d6b812937eab866d25631a7eec6840751fca97f86 Homepage: https://cran.r-project.org/package=treePlotArea Description: CRAN Package 'treePlotArea' (Correction Factors for Tree Plot Areas Intersected by StandBoundaries) The German national forest inventory uses angle count sampling, a sampling method first published as `Bitterlich, W.: Die Winkelzählmessung. Allgemeine Forst- und Holzwirtschaftliche Zeitung, 58. Jahrg., Folge 11/12 vom Juni 1947` and extended by Grosenbaugh () as probability proportional to size sampling. When plots are located near stand boundaries, their sizes and hence their probabilities need to be corrected. Package: r-cran-treeringshape Architecture: all Version: 3.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1165 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-sf Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-waldo Filename: pool/dists/noble/main/r-cran-treeringshape_3.0.5-1.ca2404.1_all.deb Size: 909224 MD5sum: 7a70001f65e05dec8f67a271099918a3 SHA1: 042223d6e666e1984c8a7ea6fe70cc27b0f09b5d SHA256: 14422ea4fd973f60ae2c5e79270dfe7665f50e4e92e26ec7ab6f52f5538ee173 SHA512: 6476a32ff7ed3e5c5d2c478dd49fcb28c62fcb2a6bdb70b31d5f5f9c9b8db0dc32865c42a4d35798d5bc9358faf6bf87808ddd107f44b6191efd33caf0bc3fb8 Homepage: https://cran.r-project.org/package=TreeRingShape Description: CRAN Package 'TreeRingShape' (Recording Tree-Ring Shapes of Tree Disks with Manual Digitizingand Interpolating Model) Record all tree-ring Shapefile of tree disk with GIS soft 'Qgis' and interpolating model from high resolution tree disk image. Package: r-cran-treesim Architecture: all Version: 2.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ape, r-cran-geiger Filename: pool/dists/noble/main/r-cran-treesim_2.4-1.ca2404.1_all.deb Size: 172880 MD5sum: a614a59c74f7035db3655ca828af91b8 SHA1: 6855bc68d3a70188c6e4454b72c2c7bc7d34800e SHA256: d5f7b247cfff9a2042aa7cbf65e7bc39fbe02b2e0dd47213b35f58cef77280aa SHA512: 24d40503abb773af57a92f81805db1904a290d867551adf2173e8ac56093a0055a7df9cd2f74e1df2e01c70d4c4166869606669b3af482d1257d485e793f42e0 Homepage: https://cran.r-project.org/package=TreeSim Description: CRAN Package 'TreeSim' (Simulating Phylogenetic Trees) Simulation methods for phylogenetic trees where (i) all tips are sampled at one time point or (ii) tips are sampled sequentially through time. (i) For sampling at one time point, simulations are performed under a constant rate birth-death process, conditioned on having a fixed number of final tips (sim.bd.taxa()), or a fixed age (sim.bd.age()), or a fixed age and number of tips (sim.bd.taxa.age()). When conditioning on the number of final tips, the method allows for shifts in rates and mass extinction events during the birth-death process (sim.rateshift.taxa()). The function sim.bd.age() (and sim.rateshift.taxa() without extinction) allow the speciation rate to change in a density-dependent way. The LTT plots of the simulations can be displayed using LTT.plot(), LTT.plot.gen() and LTT.average.root(). TreeSim further samples trees with n final tips from a set of trees generated by the common sampling algorithm stopping when a fixed number m>>n of tips is first reached (sim.gsa.taxa()). This latter method is appropriate for m-tip trees generated under a big class of models (details in the sim.gsa.taxa() man page). For incomplete phylogeny, the missing speciation events can be added through simulations (corsim()). (ii) sim.rateshifts.taxa() is generalized to sim.bdsky.stt() for serially sampled trees, where the trees are conditioned on either the number of sampled tips or the age. Furthermore, for a multitype-branching process with sequential sampling, trees on a fixed number of tips can be simulated using sim.bdtypes.stt.taxa(). This function further allows to simulate under epidemiological models with an exposed class. The function sim.genespeciestree() simulates coalescent gene trees within birth-death species trees, and sim.genetree() simulates coalescent gene trees. Package: r-cran-treesimgm Architecture: all Version: 2.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-treesim, r-cran-ape Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-treesimgm_2.5-1.ca2404.1_all.deb Size: 154798 MD5sum: b85fc55f8f7c9b8a482374fc750c3b1a SHA1: b8c238a1380f6ce29b02016f35bd5dcaffc513b7 SHA256: 5a203aea2015ce38f1570cf75e83145fbd5ac3a98e3208f0a63b165da55c9f17 SHA512: 8be8fcebdfe1963fc65a813e1f7c413fc2e85be593545e20a0742c0a329b86cfeb0c36e925886a00b317abf9e9612bbcb8379886292b044b963245d881d72f5e Homepage: https://cran.r-project.org/package=TreeSimGM Description: CRAN Package 'TreeSimGM' (Simulating Phylogenetic Trees under General Bellman Harris andLineage Shift Model) Provides a flexible simulation tool for phylogenetic trees under a general model for speciation and extinction. Trees with a user-specified number of extant tips, or a user-specified stem age are simulated. It is possible to assume any probability distribution for the waiting time until speciation and extinction. Furthermore, the waiting times to speciation / extinction may be scaled in different parts of the tree, meaning we can simulate trees with clade-dependent diversification processes. At a speciation event, one species splits into two. We allow for two different modes at these splits: (i) symmetric, where for every speciation event new waiting times until speciation and extinction are drawn for both daughter lineages; and (ii) asymmetric, where a speciation event results in one species with new waiting times, and another that carries the extinction time and age of its ancestor. The symmetric mode can be seen as an vicariant or allopatric process where divided populations suffer equal evolutionary forces while the asymmetric mode could be seen as a peripatric speciation where a mother lineage continues to exist. Reference: O. Hagen and T. Stadler (2017). TreeSimGM: Simulating phylogenetic trees under general Bellman Harris models with lineage-specific shifts of speciation and extinction in R. Methods in Ecology and Evolution. . 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Package: r-cran-tremendousr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-crayon, r-cran-crul, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-clipr, r-cran-vcr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tremendousr_1.0.0-1.ca2404.1_all.deb Size: 188172 MD5sum: afc387cac4bdcfb76f6b1c7becf39221 SHA1: 1e03a76f106547ad15a66dcd17549aa466dbde69 SHA256: c79903da43a56080482e0a529b0d238893687a8a3df66e23ca7700f4d97813d7 SHA512: 27c5fccdfc5a3763a517f5872bcd3b6bb5a8e0a009ed928c34d99503e398c539e4275fed46486d8a3fcc0c2072d70e967d725f7b2c5f7f8e9d9ccbfea5772aeb Homepage: https://cran.r-project.org/package=tremendousr Description: CRAN Package 'tremendousr' (Easily Send Rewards and Incentives with 'Tremendous' from R) A slightly-opinionated R interface for the 'Tremendous' API (). In addition to supporting GET and POST requests, 'tremendousr' has, dare I say, tremendously intuitive functions for sending digital rewards and incentives directly from R. Package: r-cran-trenchr Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-msm, r-cran-rdpack, r-cran-zoo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-trenchr_1.2.1-1.ca2404.1_all.deb Size: 2645264 MD5sum: 8a88bdd9688d1687e40cca1288caac3a SHA1: 158ec0ad8d74f66bbd4e4b3729d36cbea16d058b SHA256: e9b648e62d0abff18d7187d346c1ac11850a771176e5e40d274ca2b41c58e810 SHA512: 66283381261b8d440acfa576b98643e9242c5cc10aa909d93c87e3e607389b3617c1a39b342e61b7251b4f1103d2f0a1d6e3b1816bd4e3f40af248f30182f01d Homepage: https://cran.r-project.org/package=TrenchR Description: CRAN Package 'TrenchR' (Tools for Microclimate and Biophysical Ecology) Tools for translating environmental change into organismal response. Microclimate models to vertically scale weather station data to organismal heights. The biophysical modeling tools include both general models for heat flows and specific models to predict body temperatures for a variety of ectothermic taxa. Additional functions model and temporally partition air and soil temperatures and solar radiation. Utility functions estimate the organismal and environmental parameters needed for biophysical ecology. 'TrenchR' focuses on relatively simple and modular functions so users can create transparent and flexible biophysical models. Many functions are derived from Gates (1980) and Campbell and Norman (1988) . Package: r-cran-trendchange Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-trendchange_1.2-1.ca2404.1_all.deb Size: 30158 MD5sum: 2939ac48a247c23a97137e0bdf0cc50a SHA1: 12749249c9916e507d666f057b900cde3efb378a SHA256: 4853a472b305cafb6addfa1476d5dbec5a57687225b444c8956798d012b273ad SHA512: 6d09d4bc69a05b24aa75feb74686122f9d235485124f510b42ecceefa5af745d63376cf44ea4d7de65c0a979512fceea191239714dfc837bb7710ebc44424f3d Homepage: https://cran.r-project.org/package=trendchange Description: CRAN Package 'trendchange' (Innovative Trend Analysis and Time-Series Change Point Analysis) Innovative Trend Analysis is a graphical method to examine the trends in time series data. Sequential Mann-Kendall test uses the intersection of prograde and retrograde series to indicate the possible change point in time series data. Distribution free cumulative sum charts indicate location and significance of the change point in time series. Zekai, S. (2011). . Grayson, R. B. et al. (1996). Hydrological Recipes: Estimation Techniques in Australian Hydrology. Cooperative Research Centre for Catchment Hydrology, Australia, p. 125. Sneyers, S. (1990). On the statistical analysis of series of observations. Technical note no 5 143, WMO No 725 415. Secretariat of the World Meteorological Organization, Geneva, 192 pp. Package: r-cran-trendeval Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-trending, r-cran-yardstick, r-cran-rsample, r-cran-tibble Suggests: r-cran-testthat, r-cran-dplyr, r-cran-outbreaks Filename: pool/dists/noble/main/r-cran-trendeval_0.1.1-1.ca2404.1_all.deb Size: 101610 MD5sum: b7ee399d2f203eab2b6061f511b64521 SHA1: 75b2614dc0e0703901845843c34ae831e4523384 SHA256: 555ca2d4b1179e18713e8cbfd6a3a57a2a1935d27b8fdf26f9252e0ba6513791 SHA512: 9af7d7cb582849eccbb9019e40ed152927a7ed2f9633c599c4d0bf2d78b2ecdb4087d120a95cd79a59a24368a4af95752a6b2151d1063dbda54727d7364f76d1 Homepage: https://cran.r-project.org/package=trendeval Description: CRAN Package 'trendeval' (Evaluate Trending Models) Provides a coherent interface for evaluating models fit with the trending package. This package is part of the RECON () toolkit for outbreak analysis. Package: r-cran-trending Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-citools, r-cran-mass, r-cran-tibble, r-cran-vctrs, r-cran-pillar Suggests: r-cran-knitr, r-cran-brms, r-cran-bh, r-cran-rcppeigen, r-cran-rmarkdown, r-cran-dplyr, r-cran-outbreaks, r-cran-testthat, r-cran-ggplot2, r-cran-patchwork, r-cran-covr Filename: pool/dists/noble/main/r-cran-trending_0.1.0-1.ca2404.1_all.deb Size: 190362 MD5sum: 55b000cf7e057cbf4fc1c56a7e0c3d7d SHA1: bd3e5d9b0e5cdc2690ef08b6772527047c99479a SHA256: 6084ea9b871d8a9abede76778bef13342b6ecbad4f493589822062f31fce3d8d SHA512: 1ca294e2507e40ebf24a399eea525a9e1db5f38a8401ba42a370728a03b02c747d3620417f11993c9b5771edc03a13ac4696886d778b4ebf4fcc76f9f2a8c315 Homepage: https://cran.r-project.org/package=trending Description: CRAN Package 'trending' (Model Temporal Trends) Provides a coherent interface to multiple modelling tools for fitting trends along with a standardised approach for generating confidence and prediction intervals. Package: r-cran-trendintrend Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-proc, r-cran-rms, r-cran-nleqslv, r-cran-pracma Filename: pool/dists/noble/main/r-cran-trendintrend_1.1.3-1.ca2404.1_all.deb Size: 63538 MD5sum: eb9b74e35c9527d8d2bb56ef38bb31cf SHA1: 6e0c64b9771b652cd99920af397e9f679720cec6 SHA256: 82dc49e4b4ca9589a80850d722a307a644542b19d01dd06bd9775f8e7bd847db SHA512: 528d9d90667e2d53d16ecb3763baf0e0b2d19c45ac3d099c44f6c40221c72dd64d7effbd6c23bde8f0aa462c9803917ca3a9f461b6a10fd4f257a8191f754699 Homepage: https://cran.r-project.org/package=TrendInTrend Description: CRAN Package 'TrendInTrend' (Odds Ratio Estimation and Power Calculation for the Trend inTrend Model) Estimation of causal odds ratio and power calculation given trends in exposure prevalence and outcome frequencies of stratified data. Package: r-cran-trendlsw Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 302 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-wavethresh, r-cran-locits Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-trendlsw_1.0.6-1.ca2404.1_all.deb Size: 262152 MD5sum: 00c92a97a103a8a55fa32318f31d547c SHA1: 94f728c067f08e710924c191ec8e89e45e09b2da SHA256: 7a2a24d76075b10876230adafd799f1af382b822b4c3f824babff3199588e600 SHA512: c5b4cd98f518fc5f2da8d7262af3745a2980725e231eb04c08507c68867334de100a9712e5ba97fe6fabf32bb28a05aaaf97b399238e3a3972735187dfe86ac6 Homepage: https://cran.r-project.org/package=TrendLSW Description: CRAN Package 'TrendLSW' (Wavelet Methods for Analysing Locally Stationary Time Series) Fitting models for, and simulation of, trend locally stationary wavelet (TLSW) time series models, which take account of time-varying trend and dependence structure in a univariate time series. The TLSW model, and its estimation, is described in McGonigle, Killick and Nunes (2022a) , (2022b) . Further information regarding the use of the package, along with detailed examples, can be found in McGonigle, Killick and Nunes (2025) . New users will likely want to start with the TLSW function. Package: r-cran-trendsegmentr Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-trendsegmentr_1.3.2-1.ca2404.1_all.deb Size: 68890 MD5sum: 4d64b740f02c26ee1ada8e7fce29ad15 SHA1: d9d8bc58b98c3367df27788750185e1299518fd7 SHA256: d49bd52d11c53b294df03605fcce909f7b48f391d0173c373342d2627bebc21f SHA512: dc52c54fd92491848572ebcc2c983c610545ca2d02857cf81452d455b6a78f83d8a79918f06b8fff6e117869f4c39469e9eeda23e0965e267cf57850ba72984a Homepage: https://cran.r-project.org/package=trendsegmentR Description: CRAN Package 'trendsegmentR' (Linear Trend Segmentation) Performs the detection of linear trend changes for univariate time series by implementing the bottom-up unbalanced wavelet transformation proposed by H. Maeng and P. Fryzlewicz (2023). The estimated number and locations of the change-points are returned with the piecewise-linear estimator for signal. Package: r-cran-trendseries Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2587 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dlm, r-cran-glue, r-cran-hpfilter, r-cran-lubridate, r-cran-mfilter, r-cran-rcpproll, r-cran-tibble, r-cran-tsbox Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat, r-cran-tidyr, r-cran-xts Filename: pool/dists/noble/main/r-cran-trendseries_1.2.0-1.ca2404.1_all.deb Size: 1907834 MD5sum: 2b59df216e6fc677de50dfc2dee1cc06 SHA1: 281107cbb5920bf4137d558ff21ec659fe91ed04 SHA256: 1d77b5fa12f4616baa90ec2026f95ae68bdb1da284db9239f0b392129a9dbd4f SHA512: b25bbc1f3f601a184b5fe03d06b2017acbbb056e56d5e3d60dfcd6c6e4c1b1e6c276974f71813dcecf6c30c857a70ad527a0db190618010631f14d6b96503e08 Homepage: https://cran.r-project.org/package=trendseries Description: CRAN Package 'trendseries' (Extract Trends from Time Series) Extract trends from monthly and quarterly economic time series. Provides two main functions: augment_trends() for pipe-friendly 'tibble' workflows and extract_trends() for direct time series analysis. Includes established econometric filters such as Hodrick-Prescott (HP), Baxter-King, Christiano-Fitzgerald, and Hamilton, alongside moving averages and smoothing methods. Smart defaults are tuned for common economic frequencies following Ravn and Uhlig (2002) . Package: r-cran-trendslr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-changepoint, r-cran-forecast, r-cran-plyr, r-cran-rssa, r-cran-tseries, r-cran-zoo, r-cran-imputets Filename: pool/dists/noble/main/r-cran-trendslr_1.0-1.ca2404.1_all.deb Size: 176946 MD5sum: f5dad02bffc365a3390fbafd3db9dfe2 SHA1: b126f022b20885c8801d2af4be4b1cf9779dd3a4 SHA256: b6e899705ff0a7fb249e9c9e9ffa8d71a25f35d003c3088b3e4dc4888d71b7d8 SHA512: f3f4e5c3c79834c244248d23d5563b7e8747150a1606e60912ac82b83184ed7cc87c3fdf1a16b2f942d96790eb9ce5d4f0a8e8e1791710a2936a4087082024f7 Homepage: https://cran.r-project.org/package=TrendSLR Description: CRAN Package 'TrendSLR' (Estimating Trend, Velocity and Acceleration from Sea LevelRecords) Analysis of annual average ocean water level time series, providing improved estimates of trend (mean sea level) and associated real-time velocities and accelerations. Improved trend estimates are based on singular spectrum analysis methods. Various gap-filling options are included to accommodate incomplete time series records. The package also includes a range of diagnostic tools to inspect the components comprising the original time series which enables expert interpretation and selection of likely trend components. A wide range of screen and plot to file options are available in the package. Package: r-cran-trendtestr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-emmeans, r-cran-e1071, r-cran-forecast, r-cran-mass, r-cran-multcomp, r-cran-tidyselect, r-cran-tidyr, r-cran-tseries, r-cran-car, r-cran-fsa, r-cran-ggpubr, r-cran-rlang, r-cran-pscl, r-cran-mgcv Suggests: r-cran-testthat, r-cran-mockr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-trendtestr_1.0.1-1.ca2404.1_all.deb Size: 254706 MD5sum: 9e8e5fdf2537cf2593f193037d1415b4 SHA1: 2ac4bdd21b6d41d6755e7d9903af7c2548c6cc79 SHA256: 63bf6fff9262fef45199cc86633e56e814f035e3638125663effb1d8a49e8d83 SHA512: f34621c249aed8b5a11b038223134b34052672d0535b5530db2be2c085557c69dd7688dce74744988c35917abc2d21ce13c5d81715f7c9843c78b136d1117870 Homepage: https://cran.r-project.org/package=trendtestR Description: CRAN Package 'trendtestR' (Exploratory Trend Analysis and Visualization for Time-Series andGrouped Data) Provides a set of exploratory data analysis (EDA) tools for visualizing trends, diagnosing data types for beginner-friendly workflows, and automatically routing to suitable statistical tests or trend exploration models. Includes unified plotting functions for trend lines, grouped boxplots, and comparative scatterplots; automated statistical testing (e.g., t-test, Wilcoxon, ANOVA, Kruskal-Wallis, Tukey, Dunn) with optional effect size calculation; and model-based trend analysis using generalized additive models (GAM) for count data, generalized linear models (GLM) for continuous data, and zero-inflated models (ZIP/ZINB) for count data with potential zero-inflation. Also supports time-window continuity checks, cross-year handling in compare_monthly_cases(), and ARIMA-ready preparation with stationarity diagnostics, ensuring consistent parameter styles for reproducible research and user-friendly workflows.Methods are based on R Core Team (2024) , Wood, S.N.(2017, ISBN:978-1498728331), Hyndman RJ, Khandakar Y (2008) , Simon Jackman (2024) , Achim Zeileis, Christian Kleiber, Simon Jackman (2008) . Package: r-cran-trendtm Architecture: all Version: 2.0.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-softimpute, r-cran-capushe, r-cran-fda Filename: pool/dists/noble/main/r-cran-trendtm_2.0.21-1.ca2404.1_all.deb Size: 51126 MD5sum: 9e0b372727945d009cd3c27d0719d069 SHA1: 5b34f05d1bbd04b6017db92185388f4d573c02f8 SHA256: d950b126b1442ce746e206ac71cc996729bb8ec8b4e1777425dcd71a4b90fb95 SHA512: fdb857afef7b0671ed5c037d8db9005027ea55d98de25e089edf5be25c992c026ebbe3942036f2333b16ef29267dc24466984227ed72718a0952198cb9ece97d Homepage: https://cran.r-project.org/package=TrendTM Description: CRAN Package 'TrendTM' (Trend of High-Dimensional Time Series Matrix Estimation) Matrix factorization for multivariate time series with both low rank and temporal structures. The procedure is the one proposed by Alquier, P. and Marie, N. "Matrix factorization for multivariate time series analysis." Electronic Journal of Statistics, 13(2), 4346-4366 (2019). Package: r-cran-trendtwosub Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-usethis Filename: pool/dists/noble/main/r-cran-trendtwosub_0.0.2-1.ca2404.1_all.deb Size: 77432 MD5sum: 66c59f9ab98cc16d3d29f12b0ce1e0e6 SHA1: 27647ce3f1c3164ca04eb2bf7ac109562898a891 SHA256: 58be22eced8c075498e4957d5877c12d2325aeeb4553a374cb16bcd376edd8c5 SHA512: 5c83db78c6d9cb7285a797542b8ac9de509a5e180953c400bd6b9746fffde749a4ba3db49c03a3987853dbc47dae8d6b74ab63a3a0133dede8cae9dc6ad6cc03 Homepage: https://cran.r-project.org/package=Trendtwosub Description: CRAN Package 'Trendtwosub' (Two Sample Order Free Trend Nonparametric Inference) Non-parametric trend comparison of two independent samples with sequential subsamples. For more details, please refer to Wang, Stapleton, and Chen (2018) . Package: r-cran-trendyy Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gtrendsr, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-crayon, r-cran-stringr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-trendyy_0.1.1-1.ca2404.1_all.deb Size: 27166 MD5sum: acb66364657d7264a1d0521640b673fb SHA1: c16950e9e3664a9360a6e46c52e4adc42e670878 SHA256: d65690cb1d7ca60ef32f3240760cf008f535a3e4cb6d7d53ae6b163159b51633 SHA512: 8781bc1bcf35898ec6722eb1a8689dd5f17fbace6eb3a50829c37fc3bacf7d0de7edcb58a7eb3219394fa917689b539340e84ed15aedcdc32161161d22e2c834 Homepage: https://cran.r-project.org/package=trendyy Description: CRAN Package 'trendyy' (A Tidy Wrapper Around 'gtrendsR') Access Google Trends information. This package provides a tidy wrapper to the 'gtrendsR' package. Use four spaces when indenting paragraphs within the Description. Package: r-cran-tres Architecture: all Version: 1.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4278 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-manifoldoptim, r-cran-mass, r-cran-pracma, r-cran-rtensor Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tres_1.1.5-1.ca2404.1_all.deb Size: 4291634 MD5sum: c0ef2861805daed4a6153982a245abf7 SHA1: 718a7a5642240bfb430b2fa057ffd24d61ed4daf SHA256: a1a92d50d1b62c974c2579e54b0c497b4a2567e56caab42b9a13bca7b35a3627 SHA512: e704408854f154f4d32069fd672e088244127f36317e8fc6d2df338975d2e1ad3dc9e8148b497a51fbce70100aa4f2c9df77683067e9fcc30333988fb46a5f39 Homepage: https://cran.r-project.org/package=TRES Description: CRAN Package 'TRES' (Tensor Regression with Envelope Structure) Provides three estimators for tensor response regression (TRR) and tensor predictor regression (TPR) models with tensor envelope structure. The three types of estimation approaches are generic and can be applied to any envelope estimation problems. The full Grassmannian (FG) optimization is often associated with likelihood-based estimation but requires heavy computation and good initialization; the one-directional optimization approaches (1D and ECD algorithms) are faster, stable and does not require carefully chosen initial values; the SIMPLS-type is motivated by the partial least squares regression and is computationally the least expensive. For details of TRR, see Li L, Zhang X (2017) . For details of TPR, see Zhang X, Li L (2017) . For details of 1D algorithm, see Cook RD, Zhang X (2016) . For details of ECD algorithm, see Cook RD, Zhang X (2018) . For more details of the package, see Zeng J, Wang W, Zhang X (2021) . Package: r-cran-trexselector Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2005 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-tlars, r-cran-doparallel, r-cran-foreach, r-cran-dorng, r-cran-glmnet, r-cran-boot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-patchwork, r-cran-wgcna, r-cran-fastcluster, r-cran-testthat Filename: pool/dists/noble/main/r-cran-trexselector_1.0.0-1.ca2404.1_all.deb Size: 861552 MD5sum: 5707966e8616d00fa28a52be174aae9f SHA1: f1e63724133a713f3b3999a60a79552d33ce2d78 SHA256: 6b562af4257c29aa0e9fc4b29a378431b91639cb08282829fdd27970a349a18a SHA512: 0d0d6275a5401609ccac80fd35342c1f9a1fdca8712f84c9e3ed21342a98179fe9947401d523f28ab2986cf6d7e801973a0a0e5731c6bae1f3a0915c82c1eb63 Homepage: https://cran.r-project.org/package=TRexSelector Description: CRAN Package 'TRexSelector' (T-Rex Selector: High-Dimensional Variable Selection & FDRControl) Performs fast variable selection in high-dimensional settings while controlling the false discovery rate (FDR) at a user-defined target level. The package is based on the paper Machkour, Muma, and Palomar (2022) . Package: r-cran-tri.hierarchical.ibds Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 106 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tri.hierarchical.ibds_1.0.0-1.ca2404.1_all.deb Size: 75934 MD5sum: 03df42227674d81e3652fa0b08654a8b SHA1: 99306b922989c767ec34226c5d3651d0cc6640c9 SHA256: 2f145c50d4deec5e1baeea0eaa3aa68bec20ea55692500202f7f5acee58f79b3 SHA512: 9d448814aed6eda61394186b02e1d38e7ef12fd0cc20bb75f1f93929c16c2b09348b2f9dc0984e752b02e593815cd852a9c49ca27c8766797298fa51c3ddf0fb Homepage: https://cran.r-project.org/package=Tri.Hierarchical.IBDs Description: CRAN Package 'Tri.Hierarchical.IBDs' (Tri-Hierarchical IBDs (Tri- Hierarchical Incomplete BlockDesigns)) Tri-hierarchical incomplete block design is defined as an arrangement of v treatments each replicated r times in a three system of blocks if, each block of the first system contains m_1 blocks of second system and each block of the second system contains m_2 blocks of the third system. Ignoring the first and second system of blocks, it leaves an incomplete block design with b_3 blocks of size k_3i units; ignoring first and third system of blocks, it leaves an incomplete block design with b_2 blocks each of size k_2i units and ignoring the second and third system of blocks, it leaves an incomplete block design with b_1 blocks each of size k_1 units. For dealing with experimental circumstances where there are three nested sources of variation, a tri-hierarchical incomplete block design can be adopted. Tri - hierarchical incomplete block designs can find application potential in obtaining mating-environmental designs for breeding trials. To know more about nested block designs one can refer Preece (1967) . This package includes series1(), series2(), series3() and series4() functions. This package generates tri-hierarchical designs with six component designs under certain parameter restrictions. Package: r-cran-triact Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17950 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-r6, r-cran-data.table, r-cran-checkmate, r-cran-lubridate Suggests: r-cran-signal, r-cran-tibble, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-triact_0.3.1-1.ca2404.1_all.deb Size: 2351220 MD5sum: 25252b3904bb0b152fb67e56d3916328 SHA1: d15bf88cd273a9dcfe07c04690f59b671713d003 SHA256: 5c8e8c1d7c6f91cc8171bc121069e91d889593b3015c9023e6e9ab16f1406f83 SHA512: 57cf2b24acbcbd55f6e898021c0126fd2ac1ccba202ade590ab0976bbe2b58c6c3879b508cd4f706fcc536373895c702d2973d1071904de10ede998f11f948a6 Homepage: https://cran.r-project.org/package=triact Description: CRAN Package 'triact' (Analyzing the Lying Behavior of Cows from Accelerometer Data) Assists in analyzing the lying behavior of cows from raw data recorded with a triaxial accelerometer attached to the hind leg of a cow. Allows the determination of common measures for lying behavior including total lying duration, the number of lying bouts, and the mean duration of lying bouts. Further capabilities are the description of lying laterality and the calculation of proxies for the level of physical activity of the cow. Reference: Simmler M., Brouwers S. P. (2024) . Package: r-cran-triadsim Architecture: all Version: 0.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1627 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-snpstats, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-triadsim_0.3.1-1.ca2404.1_all.deb Size: 687242 MD5sum: 45c74846ffb7e7370939d0ed75cc780e SHA1: ee7d7c738eaf491de51dc87d1882deed74daf222 SHA256: e216bb0ae6c60bf638a1e5469c359c601759c49c227b0b0cbc64eb3f687d2f34 SHA512: 52b8ba76d66371e9cf5888d5cdfbbdf1ac4e340e885b73a04278de885191007712ef6a6e26b69dae8bd396ff91fb19528638ab6d2d10714bfb64cc297e056110 Homepage: https://cran.r-project.org/package=TriadSim Description: CRAN Package 'TriadSim' (Simulating Triad Genomewide Genotypes) Simulate genotypes for case-parent triads, case-control, and quantitative trait samples with realistic linkage diequilibrium structure and allele frequency distribution. For studies of epistasis one can simulate models that involve specific SNPs at specific sets of loci, which we will refer to as "pathways". TriadSim generates genotype data by resampling triad genotypes from existing data. The details of the method is described in the manuscript under preparation "Simulating Autosomal Genotypes with Realistic Linkage Disequilibrium and a Spiked in Genetic Effect" Shi, M., Umbach, D.M., Wise A.S., Weinberg, C.R. Package: r-cran-trialsimulator Architecture: all Version: 1.17.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4755 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-base64enc, r-cran-copula, r-cran-dplyr, r-cran-emmeans, r-cran-ggplot2, r-cran-gmcplite, r-cran-htmltools, r-cran-mvtnorm, r-cran-r6, r-cran-rlang, r-cran-rpact, r-cran-rstudioapi, r-cran-survival Suggests: r-cran-data.table, r-cran-dosefinding, r-cran-graphicalmcp, r-cran-kableextra, r-cran-knitr, r-cran-mirai, r-cran-pweall, r-cran-pwexp, r-cran-rmarkdown, r-cran-simdata, r-cran-survminer, r-cran-testthat Filename: pool/dists/noble/main/r-cran-trialsimulator_1.17.1-1.ca2404.1_all.deb Size: 2855384 MD5sum: 65545f5999290e59efc26b4c96141e09 SHA1: 58235937469bd6c4e9274283331063c3675d7b3c SHA256: 6987e2dc95a05d9b68c1ff8df8c96b2647b8c174248fcd81324a6540689588c6 SHA512: ab774af3316247d261b460e1f26cf8bbe5454271cd6aa814d88045e661c70a2ba4ed2ed9883bbbb29ac9e6ada6cfbffe9406400b2a7dedcfed091e96500c4ecd Homepage: https://cran.r-project.org/package=TrialSimulator Description: CRAN Package 'TrialSimulator' (Clinical Trial Simulator) Simulate phase II and/or phase III clinical trials. It supports various types of endpoints and adaptive strategies. Tools for carrying out graphical testing procedure and combination test under group sequential design are also provided. Package: r-cran-triangle Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-rmpfr Filename: pool/dists/noble/main/r-cran-triangle_1.1.0-1.ca2404.1_all.deb Size: 130780 MD5sum: 7e5ed6f53d5f8d8d346196596ac8483b SHA1: 5f02093a08f1c89c212cce572ab27698aee86480 SHA256: 6fa9277fe0d8416763db9b65ec37be5b67e4c7fd8da5c5cd6f07fc8755f623ef SHA512: 0272a3f2103e894af095eb807067574b45bac979bc3685031f9903aef9acd839a9fcee3dd17086e09b727ae04be09d53054a8e4b5c613908dd265cc12133eb15 Homepage: https://cran.r-project.org/package=triangle Description: CRAN Package 'triangle' (Distribution Functions and Parameter Estimates for the TriangleDistribution) Provides the "r, q, p, and d" distribution functions for the triangle distribution. Also includes maximum likelihood estimation of parameters. Package: r-cran-triangulation Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 62 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-triangulation_0.5.0-1.ca2404.1_all.deb Size: 32786 MD5sum: 0dfb63deb5ed07745f9fd3a1bfa7b16d SHA1: 30f497d3dab1d3ab4da403cd6c4dc34b531e4450 SHA256: eb76f0f6deb86c9c7af08fbe4ec58e32c55e25a5c670a596b576be5d9acf1d51 SHA512: 122b30a8fb881321481e6127930ccdce4222d95107c5167f137fb3c9f22ab8703422d5d3c05d1f56cdc20fff3f1dda140315b32eeb2a8adc9160e5bca9d314d5 Homepage: https://cran.r-project.org/package=triangulation Description: CRAN Package 'triangulation' (Determine Position of Observer) Measuring angles between points in a landscape is much easier than measuring distances. When the location of three points is known the position of the observer can be determined based solely on the angles between these points as seen by the observer. This task (known as triangulation) however requires onerous calculations - these calculations are automated by this package. Package: r-cran-tricolore Architecture: all Version: 1.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2605 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-ggtern, r-cran-rlang, r-cran-shiny, r-cran-assertthat Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sf, r-cran-leaflet, r-cran-httpuv, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-tricolore_1.2.6-1.ca2404.1_all.deb Size: 1300512 MD5sum: 9d4d8d95e5eb2be6c05bc0410b765dc4 SHA1: 3460a8271f65b69a6b406417183f191a1eafd8cd SHA256: 9d1f00e34d2347de3822d25c57ba6879b6c02d1106d118fa6b92d7d4d7e153da SHA512: 05d86dbc84f740089af0ad2a36c6ee6257a039e74b0908c4b1071bf8baed7ca9adeda0462ab4aa98155ebb5344f875df0f8c974704602fe0693f9b6bf354891b Homepage: https://cran.r-project.org/package=tricolore Description: CRAN Package 'tricolore' (A Flexible Color Scale for Ternary Compositions) Compositional data consisting of three-parts can be color mapped with a ternary color scale. Such a scale is provided by the tricolore packages with options for discrete and continuous colors, mean-centering and scaling. See Jonas Schöley (2021) "The centered ternary balance scheme. A technique to visualize surfaces of unbalanced three-part compositions" , Jonas Schöley, Frans Willekens (2017) "Visualizing compositional data on the Lexis surface" , and Ilya Kashnitsky, Jonas Schöley (2018) "Regional population structures at a glance" . Package: r-cran-triggerstrategy Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ga, r-cran-ldbounds, r-cran-mvtnorm, r-cran-nleqslv Filename: pool/dists/noble/main/r-cran-triggerstrategy_1.2.0-1.ca2404.1_all.deb Size: 96310 MD5sum: 0203cd24aad47d54b22b3bdde8592175 SHA1: f4a182f2f410aea13146e46db1b0a6df1944b11c SHA256: 69428abb194926713998df250c0c491103d10f33e50b8937699d74e8715f1f01 SHA512: d5767986c7e22a6646039be673422647e070098a80582d04eb34a1142286b9e4de743d6d2e5050a89733c1c7a7b88a99ae786117d3a8553560f79e543017e40a Homepage: https://cran.r-project.org/package=triggerstrategy Description: CRAN Package 'triggerstrategy' (Trigger Strategy in Clinical Trials) The trigger strategy is a general framework for a multistage statistical design with multiple hypotheses, allowing an adaptive selection of interim analyses. The selection of interim stages can be associated with some prespecified endpoints which serve as the trigger. This selection allows us to refine the critical boundaries in hypotheses testing procedures, and potentially increase the statistical power. This package includes several trial designs using the trigger strategy. See Gou, J. (2023), "Trigger strategy in repeated tests on multiple hypotheses", Statistics in Biopharmaceutical Research, 15(1), 133-140, and Gou, J. (2022), "Sample size optimization and initial allocation of the significance levels in group sequential trials with multiple endpoints", Biometrical Journal, 64(2), 301-311. Package: r-cran-trigon Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4390 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-caret, r-cran-data.table, r-cran-dplyr, r-cran-dt, r-cran-factoextra, r-cran-ggcorrplot, r-cran-ggplot2, r-cran-ggpubr, r-cran-ggridges, r-cran-markdown, r-cran-patchwork, r-cran-randomforest, r-cran-rcolorbrewer, r-cran-readxl, r-cran-sessioninfo, r-cran-shiny, r-cran-shinydashboardplus, r-cran-shinydashboard, r-cran-shinywidgets, r-cran-simpleboot, r-cran-writexl Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-trigon_0.3.3-1.ca2404.1_all.deb Size: 1669592 MD5sum: cf043552efc3536144881a5412719226 SHA1: 093dd0936e99d2b81c6b0858f822275ebd0355bc SHA256: 6586dbbf4082b4f029f72a6b17d1ea96d69f354690e5d1e92df5a9a689a37422 SHA512: 1da59d57e03f37b5d6df7c3f7c19dffbc3cf7c04e2f4979c8d725ce22c352bc044012924d98498a0ac90e0b1aadcca474c42342f5282bcf3555b486b2045d2ba Homepage: https://cran.r-project.org/package=tRigon Description: CRAN Package 'tRigon' (Toolbox for Integrative Pathomics Analysis) Processing and analysis of pathomics, omics and other medical datasets. 'tRigon' serves as a toolbox for descriptive and statistical analysis, correlations, plotting and many other methods for exploratory analysis of high-dimensional datasets. 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Trig points (aka triangulation stations) are fixed survey points used to improve the accuracy of map making in Great Britain during the 20th Century. Trig points are typically located on hilltops so still serve as a useful navigational aid for walkers and hikers today. Package: r-cran-trilliem Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 241 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-trilliem_0.1.1-1.ca2404.1_all.deb Size: 130028 MD5sum: e52ddfac02052f527f029653bc2efe50 SHA1: 1adfb4a60f2d0f83a19cbcdc83126629b4e8d608 SHA256: 137bd60d97477193dab89a2ddc333bf360599720a9ab41f01729c7b982e29abf SHA512: 6270544cf8dbd1c2280bc81ab9c3768a23a554a56d511d6d7d87153d0c157ec3a8b63763f9209fd35cb23ba6abc9eab01435c5b4eaa978ce807cf91f49118199 Homepage: https://cran.r-project.org/package=TriLLIEM Description: CRAN Package 'TriLLIEM' (Log-Linear Modelling of Triad Genotype Data) Triad Log-Linear modelling of Imprinting Environmental interactions, and Maternal effects (TriLLIEM). 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Package: r-cran-trimetstops Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-trimetstops_0.1.0-1.ca2404.1_all.deb Size: 179294 MD5sum: 141dbb90454826d1d9255b83c19fd2a0 SHA1: d4885d02f1b4e6493515109fa5cf0e25919d6c5b SHA256: e211cc283d94d85f739c230611cb827235c9743dbab9145dde2922a5f0267b33 SHA512: 54ad69ca68ab40f8178568d69c987c6757d9dfd2f36dc3bf83e68cd642dc64dca90fe9abb64f10843884a96aa1ffa9eeea85dee2ff437734fb11d1e9ef670c10 Homepage: https://cran.r-project.org/package=trimetStops Description: CRAN Package 'trimetStops' (Information on all of the TriMet Stops in the Portland MetroArea) Information on all of the TriMet stops in the Portland Metro Area. It includes information such as the longitude, latitude, cross street, and direction of the stop. TriMet has catalogued these stops, 6880 in total. Package: r-cran-trimmer Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-crayon, r-cran-cli, r-cran-pryr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-trimmer_0.8.1-1.ca2404.1_all.deb Size: 71484 MD5sum: 2ec3f6b7e1d67c6e9e4a4b1c5acfbb0b SHA1: e7e23bf53c21e5dc63c84eaf559649bc32871bf7 SHA256: 49545241c50cd8f656b9f9bf8562a6da31dbe8ed65fbc9b44c1783b425402e72 SHA512: 8bc0ac6565f22b63ff02d2c025772ccf49386c8e3a8b17fa68571f5eaa2bea8ef674355985043e1f851e9047b3259c062cec062e76c6643d1e8b1e08ac86ca72 Homepage: https://cran.r-project.org/package=trimmer Description: CRAN Package 'trimmer' (Trim an Object) A lightweight toolkit to reduce the size of a list object. The object is minimized by recursively removing elements from the object one-by-one. 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Package: r-cran-trinroc Architecture: all Version: 0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-rgl, r-cran-gridextra Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-reshape Filename: pool/dists/noble/main/r-cran-trinroc_0.7-1.ca2404.1_all.deb Size: 306320 MD5sum: 5c7acd01b93e59bbb1c299b56614bac4 SHA1: 1d45740665df41d616cdfb6cf5cea83f02e7916a SHA256: aded170e04a58e40a55519a61e38fb27b6f416ad5a1897c100770f967889d971 SHA512: da83564e7f9c38897428493ae082babc16d7fe429270b066cc21b808170573d75575bf2b7442ac1c30fb0f16f8ecc27b39731e7f7743c933d1d2a12ab7bcda6e Homepage: https://cran.r-project.org/package=trinROC Description: CRAN Package 'trinROC' (Statistical Tests for Assessing Trinormal ROC Data) Several statistical test functions as well as a function for exploratory data analysis to investigate classifiers allocating individuals to one of three disjoint and ordered classes. In a single classifier assessment the discriminatory power is compared to classification by chance. In a comparison of two classifiers the null hypothesis corresponds to equal discriminatory power of the two classifiers. See also "ROC Analysis for Classification and Prediction in Practice" by Nakas, Bantis and Gatsonis (2023), ISBN 9781482233704. Package: r-cran-trip Architecture: all Version: 1.10.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3796 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-geodist, r-cran-mass, r-cran-raster, r-cran-reproj, r-cran-sp, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-glue, r-cran-viridis, r-cran-traipse, r-cran-crsmeta, r-cran-dplyr, r-cran-rlang Suggests: r-cran-adehabitatlt, r-cran-knitr, r-cran-testthat, r-cran-covr, r-cran-rmarkdown, r-cran-lubridate, r-cran-maps, r-cran-spelling, r-cran-lattice Filename: pool/dists/noble/main/r-cran-trip_1.10.0-1.ca2404.1_all.deb Size: 1708032 MD5sum: 9d47990940ec8c50a7ef97af0f886775 SHA1: c96dab66dfeb1604ab28361ae58c02e65ceec3e9 SHA256: 406c00d22273ff755b831bbdcc6b7f69ae2b07cb0a40a662d60e58157746b8ce SHA512: 2dffc0b8562d22f35dc137786a016003036cae3510872b75628bce00c59e8706c77aa6997649cd77e2e74819f95c0fe3789e7c896b0e402b8681255708c166a6 Homepage: https://cran.r-project.org/package=trip Description: CRAN Package 'trip' (Tracking Data) Access and manipulate spatial tracking data, with straightforward coercion from and to other formats. Filter for speed and create time spent maps from tracking data. There are coercion methods to convert between 'trip' and 'ltraj' from 'adehabitatLT', and between 'trip' and 'psp' and 'ppp' from 'spatstat'. Trip objects can be created from raw or grouped data frames, and from types in the 'sp', sf', 'amt', 'trackeR', 'mousetrap', and other packages, Sumner, MD (2011) . Package: r-cran-tripestimation Architecture: all Version: 0.0-46-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lattice, r-cran-mgcv, r-cran-reproj, r-cran-sp, r-cran-zoo Filename: pool/dists/noble/main/r-cran-tripestimation_0.0-46-1.ca2404.1_all.deb Size: 171126 MD5sum: cdb739cc2e1ab837ff0ed22e295e868d SHA1: c737e89cf023ff6fbaa823188f5254e66dee6798 SHA256: e8f382d5b480d44854562add04f966899560feebdf59e9528aeb0133a3290214 SHA512: 9a3437f3c34415c1c0c0051844bdaf503cac14a4d3b02c10f141d0c32082f5907ef46b619d734efaf222bdc0f7b1fb3800984c72d0111828e7bc692830208b80 Homepage: https://cran.r-project.org/package=tripEstimation Description: CRAN Package 'tripEstimation' (Metropolis Sampler and Supporting Functions for EstimatingAnimal Movement from Archival Tags and Satellite Fixes) Data handling and estimation functions for animal movement estimation from archival or satellite tags. 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Package: r-cran-tripler Architecture: all Version: 1.5.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 949 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-plyr Suggests: r-cran-lme4, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-tripler_1.5.5-1.ca2404.1_all.deb Size: 593060 MD5sum: 71ea26d027bcec615bb0253d57f07ad2 SHA1: 09fc86d32e8f4bbf5c4d212bf5174ba73b75abde SHA256: efe9c418506157fb1245c32e2830bca4cdd40acb39f08f43d905db26107dd2d0 SHA512: e9250b63d4b61b9f398d5f5bf9f565284fb8d8480734853033d32dfa49ace82708f760b4a4075e7d6037f526174fa80ea2ec59687d62bc46dd72cf8936265c72 Homepage: https://cran.r-project.org/package=TripleR Description: CRAN Package 'TripleR' (Social Relation Model (SRM) Analyses for Single or MultipleGroups) Social Relation Model (SRM) analyses for single or multiple round-robin groups are performed. These analyses are either based on one manifest variable, one latent construct measured by two manifest variables, two manifest variables and their bivariate relations, or two latent constructs each measured by two manifest variables. Within-group t-tests for variance components and covariances are provided for single groups. For multiple groups two types of significance tests are provided: between-groups t-tests (as in SOREMO) and enhanced standard errors based on Lashley and Bond (1997) . Handling for missing values is provided. Package: r-cran-triplesmatch Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcbalance, r-cran-rlemon, r-cran-mass, r-cran-optmatch, r-cran-rlang Suggests: r-cran-testthat, r-cran-sensitivityfull, r-cran-informedsen, r-cran-matrix Filename: pool/dists/noble/main/r-cran-triplesmatch_1.1.0-1.ca2404.1_all.deb Size: 110390 MD5sum: 6c569b6e550e53bc0b67a543e706d3da SHA1: eb27b46f57ee041e3540cff1f5977956a7ce3de4 SHA256: f36b3423a434aaaf600bfbb42dbaecd54f2d78effec9c6f133fa3b38c50defd8 SHA512: 0763ab47798c44a60bb4687aa129593139abf4b81c2d08082938cfa74752e5eae0b530e36396a167397c3009323385fc15138b225698bb822624eaa9d9c580d6 Homepage: https://cran.r-project.org/package=triplesmatch Description: CRAN Package 'triplesmatch' (Match Triples Consisting of Two Controls and a Treated Unit orVice Versa) Attain excellent covariate balance by matching two treated units and one control unit or vice versa within strata. Using such triples, as opposed to also allowing pairs of treated and control units, allows easier interpretation of the two possible weights of observations and better insensitivity to unmeasured bias in the test statistic. Using triples instead of matching in a fixed 1:2 or 2:1 ratio allows for the match to be feasible in more situations. The 'rrelaxiv' package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from 'GitHub' at . The 'Gurobi' commercial optimization software is required to use the two functions [infsentrip()] and [triplesIP()]. These functions are not essential to the main purpose of this package. A free academic license can be obtained at . The 'gurobi' R package can then be installed following the instructions at . Package: r-cran-triplot Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dalex, r-cran-glmnet, r-cran-ggdendro, r-cran-patchwork Suggests: r-cran-testthat, r-cran-knitr, r-cran-randomforest, r-cran-mlbench, r-cran-ranger, r-cran-gbm, r-cran-covr Filename: pool/dists/noble/main/r-cran-triplot_1.3.0-1.ca2404.1_all.deb Size: 161326 MD5sum: db9f8a3f5a7380610673bc061e4376a3 SHA1: 7d41956414c63ac378ebb74f1ee3eb9aa7914230 SHA256: 675e6e617e0ba5afada93fb492d1596f6e16b8d330b00aa4e194ed379efdcb7d SHA512: 9bbd0de62e3cd1e6b13ab72c9789087970667c85105658c9f67e153911bcc0431f6b8b011122cc4763632ce22dbca35086ddc427078b05942d8d1cdfce0807e1 Homepage: https://cran.r-project.org/package=triplot Description: CRAN Package 'triplot' (Explaining Correlated Features in Machine Learning Models) Tools for exploring effects of correlated features in predictive models. The predict_triplot() function delivers instance-level explanations that calculate the importance of the groups of explanatory variables. The model_triplot() function delivers data-level explanations. The generic plot function visualises in a concise way importance of hierarchical groups of predictors. All of the the tools are model agnostic, therefore works for any predictive machine learning models. Find more details in Biecek (2018) . Package: r-cran-tripsanddipr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tripsanddipr_0.1.0-1.ca2404.1_all.deb Size: 17328 MD5sum: eac056cd4686af650641ac77909dfa61 SHA1: ff5c0f80da41300e1f1ca6a5facc1b4f6a46dc7a SHA256: 50aa97104bf902f114a54b36e75246d9956ddca72f3db0e456ff00b6918cc3dd SHA512: 0b5e5719c93c945c0dbfdd80fff18514a5e5bb9b3b3f8d7f8122aa00a75a09638ac168dfa9a378c52879438fcd6a1fc292dcf73c39fa4b1a2fe851534d361e36 Homepage: https://cran.r-project.org/package=tripsAndDipR Description: CRAN Package 'tripsAndDipR' (Identification of 2n and 3n Samples from Amplicon SequencingData) Uses read counts for biallelic single nucleotide polymorphisms (SNPs) to compare the likelihoods for the observed read counts given that a sample is either diploid or triploid. It allows parameters to be specified to account for sequencing error rates and allelic bias. For details of the algorithm, please see Delomas (2019) . Package: r-cran-triversity Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-data.tree Filename: pool/dists/noble/main/r-cran-triversity_1.0-1.ca2404.1_all.deb Size: 65970 MD5sum: a18f6e6efd0eda837f8434fac6872ac3 SHA1: d9d614a9999d114157f24ee79a05260f3d63cf4d SHA256: f19725ae5d9dcd146ef7c34ca3bb9a2de317fad0f6b6f29c9e02578324ffffca SHA512: bd87fac304878fe76a406ceb54efb7ec6a2046a90cdba1d432e40d12886402d6e6aa6be186331d1537257a01494881ff1b2385dc9e3625cae8bd768c239e8936 Homepage: https://cran.r-project.org/package=triversity Description: CRAN Package 'triversity' (Diversity Measures on Tripartite Graphs) Computing diversity measures on tripartite graphs. This package first implements a parametrized family of such diversity measures which apply on probability distributions. Sometimes called "True Diversity", this family contains famous measures such as the richness, the Shannon entropy, the Herfindahl-Hirschman index, and the Berger-Parker index. Second, the package allows to apply these measures on probability distributions resulting from random walks between the levels of tripartite graphs. By defining an initial distribution at a given level of the graph and a path to follow between the three levels, the probability of the walker's position within the final level is then computed, thus providing a particular instance of diversity to measure. Package: r-cran-trmf Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-limsolve, r-cran-generics Suggests: r-cran-magrittr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-trmf_0.2.1-1.ca2404.1_all.deb Size: 132020 MD5sum: f6de4128ede4100c243441a0bead20c8 SHA1: d1d8591cb9720bc831240f19ba38b4534e2368b2 SHA256: f558de443518ec305f0cbe42c7ab198b8826078b1fa7a2495500ecd950729530 SHA512: 869f444d3b9d56dcfa4a68d8a15cf1a6b1da36cdd8081d2959183ea6dd8b27ba01c9c316f50cc9dcc3c60c1bcf510f9ab291ebe6ee2a7c91d5f63f12a3981390 Homepage: https://cran.r-project.org/package=TRMF Description: CRAN Package 'TRMF' (Temporally Regularized Matrix Factorization) Functions to estimate temporally regularized matrix factorizations (TRMF) for forecasting and imputing values in short but high-dimensional time series. 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This package provides functions for pulling country-year troop deployment and basing data. Subsequent versions will hopefully include cross-national data on deploying countries. Package: r-cran-tropalgebra Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tropalgebra_0.1.1-1.ca2404.1_all.deb Size: 44798 MD5sum: 09d3639da90464f0b6e59e57253a4f41 SHA1: 2c4613f3a674995090790d9b4223abd231e1a4a9 SHA256: e1b6ec7a2787cbf5068904bf75a677fda00958092a03ab9ef481298de0e9528a SHA512: 9c65ad4566178f8282fdf646f0e1ec22e243c0e42cdb1010bd17318c22731f75f1d77380b2e97c06da0014ec9fb99dbad202afafeae53b2686f841ee7c457fd8 Homepage: https://cran.r-project.org/package=tropAlgebra Description: CRAN Package 'tropAlgebra' (Tropical Algebraic Functions) It includes functions like tropical addition, tropical multiplication for vectors and matrices. In tropical algebra, the tropical sum of two numbers is their minimum and the tropical product of two numbers is their ordinary sum. For more information see also I. Simon (1988) Recognizable sets with multiplicities in the tropical semi ring: Volume 324 Lecture Notes I Computer Science, pages 107-120 . Package: r-cran-tropfishr Architecture: all Version: 1.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2251 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-msm, r-cran-reshape2, r-cran-mass, r-cran-gensa, r-cran-ga, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-plyr Filename: pool/dists/noble/main/r-cran-tropfishr_1.6.6-1.ca2404.1_all.deb Size: 1680186 MD5sum: a5c743a120ca42ed444409f33611415f SHA1: d21ad5af6cc940d888d688808d6b11d5897c72f9 SHA256: 817fa457117ba71f9f3d60d7744ea43cf20712acd584cadc5bee0bcf4ea1c471 SHA512: fedb024d72bbba9f71750fbb8d2ceca6c7d9c0efe955fe3b3525dab539474a1023dcbfac01c8c68986e8b829dd2af98ec93e4b174c6421ae1419f5c2221caf86 Homepage: https://cran.r-project.org/package=TropFishR Description: CRAN Package 'TropFishR' (Tropical Fisheries Analysis) A compilation of fish stock assessment methods for the analysis of length-frequency data in the context of data-poor fisheries. 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Package: r-cran-trotter Architecture: all Version: 0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-trotter_0.6-1.ca2404.1_all.deb Size: 161118 MD5sum: e6e59e5a5e90a5fa4eec91efdd626016 SHA1: 496d79ea03db2fe5c36f203496fcf989793c3439 SHA256: 2d5741ef9acd8afdf7f22771fd80219b282f72bd8c603e7d65bde2dcbed12866 SHA512: c92788dcd7322e1788b616f166f85d179e4b180088f0fd5c8843e43a606cc9f004bc15f85faa0321ccc3207fdf47441158c819eeb4eb5655c95e11d4f8a569e0 Homepage: https://cran.r-project.org/package=trotter Description: CRAN Package 'trotter' (Pseudo-Vectors Containing All Permutations, Combinations andSubsets of Objects Taken from a Vector) Class definitions and constructors for pseudo-vectors containing all permutations, combinations and subsets of objects taken from a vector. Simplifies working with structures commonly encountered in combinatorics. 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Package: r-cran-truelies Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hdrcde Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-mass, r-cran-purrr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-truelies_0.2.0-1.ca2404.1_all.deb Size: 72576 MD5sum: 7ca94f209153920cbad6ec97b4b5b801 SHA1: e4f2c773032728b0b1f6dffa7ceaf3cc31dccfde SHA256: 8faeb97a2cacfd033f2f71c4b3426dd6a70be0fbe71de1af8be55a5685976709 SHA512: 8f1b15df9b213ef001f7738282716055dad998c6b3bfda320ad9a0842fb5580a4155c9cfa161255b971a5990e16601c2dbf88348a0f2fd74c6f2097016d008c4 Homepage: https://cran.r-project.org/package=truelies Description: CRAN Package 'truelies' (Bayesian Methods to Estimate the Proportion of Liars in CoinFlip Experiments) Implements Bayesian methods, described in Hugh-Jones (2019) , for estimating the proportion of liars in coin flip-style experiments, where subjects report a random outcome and are paid for reporting a "good" outcome. 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Package: r-cran-truewap Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-ttr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-truewap_0.1.0-1.ca2404.1_all.deb Size: 323160 MD5sum: af4f5b5dc9fa6f31d36b3fcca6fc0886 SHA1: 3ce5b5768dd4453d3eac2f4a3c5743299ccf594b SHA256: 4dba04d7766e48a2dae33780ded1970c3ec562cf8c7e37035819aa1d3c8b1318 SHA512: af7ee9b67a924471547d193b423346bb1f30b0103fe01eea6d8eed141599acb12c36653c766b18408fec188547232f3981b983c6806ec8c9beb4daaf720295ee Homepage: https://cran.r-project.org/package=TrueWAP Description: CRAN Package 'TrueWAP' (True Range-Weighted Average Price ('TrueWAP')) This groundbreaking technical indicator directly integrates volatility into price averaging by weighting median range-bound prices using the True Range. 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Package: r-cran-truh Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rfast, r-cran-cluster, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-fpc Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-truh_1.0.0-1.ca2404.1_all.deb Size: 56432 MD5sum: 8ddaf8d61f9c32512d93fa502c8d5610 SHA1: 4aef2da6e3b59bc368c67d1ec4d20a8f0d46c81f SHA256: 76b1c4ec64d230b12aee156eeef6fc52f5fcd634502c0acdc468a544acdc097b SHA512: 988ba045d052b1d3285f5a180b34758b38b44fd298bdc9b90472a9c12200825237776612e9b9015cc8a9b6b3709099e5c3a9d8ce1c8371f352474e0c672264ee Homepage: https://cran.r-project.org/package=truh Description: CRAN Package 'truh' (Two-Sample Nonparametric Testing Under Heterogeneity) Implements the TRUH test statistic for two sample testing under heterogeneity. 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Package: r-cran-trumpetplots Architecture: all Version: 0.0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr Filename: pool/dists/noble/main/r-cran-trumpetplots_0.0.1.1-1.ca2404.1_all.deb Size: 191832 MD5sum: 835b403288fe9bc809decb8f121a4c99 SHA1: 4750e0ad46e3e86d85b0f7181088e990c1fcb587 SHA256: 361a71950a8fc14ebaa304e79afe50712b1d97546179902504c01cb53428f946 SHA512: 8e7b77423ca4df3f0898cb70c9ba568e3bb54769c926eb3d737af95f6bf823c19794798e26e0dfbac1b4c7e77a519f2a91042648cbba133a1aa085771cf1ba4f Homepage: https://cran.r-project.org/package=TrumpetPlots Description: CRAN Package 'TrumpetPlots' (Visualization of Genetic Association Studies) Visualizes the relationship between allele frequency and effect size in genetic association studies. The input is a data frame containing association results. The output is a plot with the effect size of risk variants in the Y axis, and the allele frequency spectrum in the X axis. Corte et al (2023) . Package: r-cran-truncaipw Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-survival, r-cran-survpen Filename: pool/dists/noble/main/r-cran-truncaipw_1.0.1-1.ca2404.1_all.deb Size: 112306 MD5sum: cec02ad691e03d2c11a6ff1915433e24 SHA1: fb1a0a20867a91441d35ce4281b05134cc6de80b SHA256: d6b2bc26985eeeee7664953372aa1afce736e8e63a669e7b2b45111ae4efa21d SHA512: 79ee7d66da76fd52242104d80e3646712effe3c96be028aa0e51b0ff41ea0ba5d58dd3523495aac1ae83518594df07f20e4c9edfb89b42de9ab9ab13512ff357 Homepage: https://cran.r-project.org/package=truncAIPW Description: CRAN Package 'truncAIPW' (Doubly Robust Estimation under Covariate-Induced Dependent LeftTruncation) Doubly robust estimation for the mean of an arbitrarily transformed survival time under covariate-induced dependent left truncation and noninformative right censoring. 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Package: r-cran-truncatedpcqm Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 92 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-truncatedpcqm_0.1.2-1.ca2404.1_all.deb Size: 62300 MD5sum: 9b750136eb02fe960f6061b39cf1954c SHA1: a16299d4531ee172d6f01af5f1fc37d602102a4f SHA256: b55f87b78334d3bd172ed1477440ea2cdba14d23b8af99cc1be6221f6019315c SHA512: 448a3deb93168eaedb89862f30460f4e4dee82cb6c4a587d34302e5aa84d51d18c792558bca7e0b479d839b1fb4d16a9ddf6964c687be475c7054302547607a4 Homepage: https://cran.r-project.org/package=TruncatedPCQM Description: CRAN Package 'TruncatedPCQM' (Density Estimation for Point-Centered Quarter Method withTruncated Sampling) Implements a systematic methodology for estimating population density from point-centered quarter method (PCQM) surveys when distance measurements are truncated by a maximum search radius (right-censored). 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Package: r-cran-tsallisqexp Architecture: all Version: 0.9-5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tsallisqexp_0.9-5-1.ca2404.1_all.deb Size: 305968 MD5sum: 243bcf7795306146b97b5436abd12155 SHA1: a258d89e2743aa12fdca401447357467b0f9a3df SHA256: 1bfa41b1b89460ec61ed4f7332af9a1754c4e5b810dffeb26b7b5fb35ddadfe7 SHA512: 8d1d29d8757e827577c9dd382404363ab98606e9635aefe80b82d0a3ce8010175bf01d6ebebb9025cbd0f6ccddac46647084d27d2516239adcedfea362d88cdd Homepage: https://cran.r-project.org/package=tsallisqexp Description: CRAN Package 'tsallisqexp' (Tsallis q-Exp Distribution) Tsallis distribution also known as the q-exponential family distribution. Provide distribution d, p, q, r functions, fitting and testing functions. Project initiated by Paul Higbie and based on Cosma Shalizi's code. Package: r-cran-tsann Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 52 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-forecast, r-cran-gtools Filename: pool/dists/noble/main/r-cran-tsann_0.1.0-1.ca2404.1_all.deb Size: 19938 MD5sum: 555bf6ea7fbc3da101c10850de432a5f SHA1: 51564e1b978286b75472473a0349caae20d8b3b8 SHA256: d2b4cdcd4cdc7e84c513d147e8b04b04ff6f82c49082858cf2ba66089492e02e SHA512: 440928d5cea7f053aab099764451d9a325c31dc854cb881505aa7e8c4bfb87d25a1de9e31a4bf97118002936252793d17bca94b81546cd035ffdcf6f48b7ac20 Homepage: https://cran.r-project.org/package=TSANN Description: CRAN Package 'TSANN' (Time Series Artificial Neural Network) The best ANN structure for time series data analysis is a demanding need in the present era. This package will find the best-fitted ANN model based on forecasting accuracy. 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Package: r-cran-tsapp Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vars, r-cran-fftwtools, r-cran-hdm Filename: pool/dists/noble/main/r-cran-tsapp_1.0.4-1.ca2404.1_all.deb Size: 686794 MD5sum: efc11ed31369e6529ab4123fd294f0a5 SHA1: e93c40c39558f83d47c60c6932adde3f1160dc59 SHA256: abbbf1e55bca0832fab66356e3e396fcdc6dd909e6e569d2d723b215d42b518d SHA512: 908e20cb322b8d7794c13ed08fdc973b504f453d4c8ca1fb24961710e8b63f1e12bef14cb5d612ea418fa701188b784e6b9d6b976777b82b84c7fb088a81b7a6 Homepage: https://cran.r-project.org/package=tsapp Description: CRAN Package 'tsapp' (Time Series, Analysis and Application) Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. 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Based on Guo and Buehlmann (2022) "Two Stage Curvature Identification with Machine Learning: Causal Inference with Possibly Invalid Instrumental Variables" . The vignette is available in Carl, Emmenegger, Bühlmann and Guo (2025) "TSCI: Two Stage Curvature Identification for Causal Inference with Invalid Instruments in R" . Package: r-cran-tsclust Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-pdc, r-cran-cluster, r-cran-locpol, r-cran-kernsmooth, r-cran-dtw, r-cran-longitudinaldata, r-cran-forecast Filename: pool/dists/noble/main/r-cran-tsclust_1.3.2-1.ca2404.1_all.deb Size: 443346 MD5sum: d60a58c873be74351db4531e43093d4c SHA1: 6fc9c5d3d934eb47687d0cc6a630d1c4dda9024b SHA256: fcb976a4d490c72c2310a030f744322667e6c642380aeb2a93a031143d3bd588 SHA512: 3fb5ceb14adaf614bd4e8bf9bee96d5d2cf579dd50671e601c1a622d01ee4de46253e20ff4852a4c73dabb2ea8ae93e1d65de71c94711b552a60721726d75d23 Homepage: https://cran.r-project.org/package=TSclust Description: CRAN Package 'TSclust' (Time Series Clustering Utilities) A set of measures of dissimilarity between time series to perform time series clustering. 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Package: r-cran-tscopula Architecture: all Version: 0.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2625 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-zoo, r-cran-xts, r-cran-fkf, r-cran-ltsa, r-cran-rvinecopulib, r-cran-arfima, r-cran-matrix, r-cran-polynom, r-cran-kdensity Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tscopula_0.3.9-1.ca2404.1_all.deb Size: 1859466 MD5sum: 08a75381b1d5cdc6da5d008bc2b8cd2f SHA1: d3a3600fa2ecab2621e5ae5aeebffc6f12cb5b9a SHA256: d8375c5b1a1b7ecc7e5a6383bede6d30e8d27754a14b5bd8e4bb5c7f22963c7d SHA512: b137236b414f3c1323254d8d874e47429d65ac87f5b340b4a48944f9a12e00096ab38a96f71ffe7cb0242796d4dc8bafbf446cfe136933b92b30dba43be6960b Homepage: https://cran.r-project.org/package=tscopula Description: CRAN Package 'tscopula' (Time Series Copula Models) Functions for the analysis of time series using copula models. The package is based on methodology described in the following references. McNeil, A.J. (2021) , Bladt, M., & McNeil, A.J. (2021) , Bladt, M., & McNeil, A.J. (2022) . Package: r-cran-tscount Architecture: all Version: 1.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1696 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ltsa Suggests: r-cran-matrix, r-cran-xtable, r-cran-gamlss.data, r-cran-surveillance Filename: pool/dists/noble/main/r-cran-tscount_1.4.3-1.ca2404.1_all.deb Size: 1467792 MD5sum: af79549ecc37c46c999adbe9fba0480e SHA1: 9e2fafda4ee5de2227d3f8d35e2b61ed85df05e6 SHA256: 5b18410ca38e2c8bde2e14819e2e0e6666a1ee43489b3ae8030a92b95ba6f2a9 SHA512: 91fdd1a7d79444b26d854b0c3139c2c2454d1504a151edc5c0bd15a616575bdb5098466813a7c0f71f2536fb714688c285b4810a58b0e7ed4ca2f1dd1252d7cf Homepage: https://cran.r-project.org/package=tscount Description: CRAN Package 'tscount' (Analysis of Count Time Series) Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial. Package: r-cran-tscs Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1548 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tseries, r-cran-rgl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-tscs_0.1.1-1.ca2404.1_all.deb Size: 1382170 MD5sum: 81ff8a59653f5f29aa927519404d1685 SHA1: d849661918bf88496b5a2dcf8c36cf09744cde46 SHA256: 96acd09c533a5f783154532234c3a781df1ae0fcf856c0ac10b664dc69b08994 SHA512: f6530ccc411ed2c8f83b397d629e07572356cde7ec195c6f236fd38f55847060ea21b5b25e9a7d68599b09cdbd8761192ec7f5cea86e2e80c8247bf4ec3ed74a Homepage: https://cran.r-project.org/package=TSCS Description: CRAN Package 'TSCS' (Time Series Cointegrated System) A set of functions to implement Time Series Cointegrated System (TSCS) spatial interpolation and relevant data visualization. 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This is mainly a set of wrapper and helper functions as well as some extensions for the packages 'tsibble', 'fable', and 'fabletools'. Package: r-cran-tsdataleaks Architecture: all Version: 2.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-slider, r-cran-purrr, r-cran-cowplot, r-cran-plyr, r-cran-viridis Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tsdataleaks_2.1.1-1.ca2404.1_all.deb Size: 228138 MD5sum: 63a0dfacedfd8d4e1fb130b15c109adf SHA1: dd613e53775ede2dfb16c0b146cbc1e25a97b6b0 SHA256: 7250eb628cc161d1ead3df94eb3685965166da49fb5977d62d1fba0f556dd044 SHA512: bae7481d76296cc8d96e3f69cada4254b2f3ff07f7df9b88bf60b7464809c805683ebcaab9ca5a7ede90dba3b8c6fe09f343a2ab4b9df22a075da2ea72834240 Homepage: https://cran.r-project.org/package=tsdataleaks Description: CRAN Package 'tsdataleaks' (Exploit Data Leakages in Time Series Forecasting Competitions) Forecasting competitions are of increasing importance as a mean to learn best practices and gain knowledge. Data leakage is one of the most common issues that can often be found in competitions. Data leaks can happen when the training data contains information about the test data. For example: randomly chosen blocks of time series are concatenated to form a new time series, scale-shifts, repeating patterns in time series, white noise is added in the original time series to form a new time series, etc. 'tsdataleaks' package can be used to detect data leakages in a collection of time series. Package: r-cran-tsdb Architecture: all Version: 1.1-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-datetimeutils, r-cran-fastmatch, r-cran-zoo Suggests: r-cran-data.table, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-tsdb_1.1-0-1.ca2404.1_all.deb Size: 62852 MD5sum: e6ec26ae27779d68dcf6130b396676da SHA1: 1bb4b3a7a6b0cc1103220b4ff7aea49bc875421d SHA256: 27eed9499c6bfa969dd5ecb24a46fed85bed0cd2926c9d713ed24fa28629e5fb SHA512: 942a42af403d09bc2200748036d83395fb236b0d114eca094a3bc4dc2f6aaf2fdc0574e7bfab877cb31b0bf63e7fee9840e75b0f46263be1311621767797db76 Homepage: https://cran.r-project.org/package=tsdb Description: CRAN Package 'tsdb' (Terribly-Simple Data Base for Time Series) A terribly-simple data base for numeric time series, written purely in R, so no external database-software is needed. Series are stored in plain-text files (the most-portable and enduring file type) in CSV format. Timestamps are encoded using R's native numeric representation for 'Date'/'POSIXct', which makes them fast to parse, but keeps them accessible with other software. The package provides tools for saving and updating series in this standardised format, for retrieving and joining data, for summarising files and directories, and for coercing series from and to other data types (such as 'zoo' series). Package: r-cran-tsdecomp Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tsdecomp_0.2-1.ca2404.1_all.deb Size: 203818 MD5sum: f8a3e45ce3a9d96ff58b791dc37e6100 SHA1: cd11e8bb67ca817d3dadb03dbc0f8621160a4637 SHA256: c253de557b009d858a31eaefe40653542efba91290b92b2199104becfa631345 SHA512: ea0f421971569d2f553a1284d77f6faa577ce555532bb5da6cb6560b05225adb2197e8e8846a9250ec8ae90fbe67ac55c9d4d52a3b9cfc41bdd4e2b9ab66772d Homepage: https://cran.r-project.org/package=tsdecomp Description: CRAN Package 'tsdecomp' (Decomposition of Time Series Data) ARIMA-model-based decomposition of quarterly and monthly time series data. The methodology is developed and described, among others, in Burman (1980) and Hillmer and Tiao (1982) . Package: r-cran-tsdeeplearning Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensorflow, r-cran-keras, r-cran-reticulate, r-cran-tsutils, r-bioc-biocgenerics, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-tsdeeplearning_1.0.1-1.ca2404.1_all.deb Size: 37790 MD5sum: a153ccd461d460673f66de336d7d4e61 SHA1: b0876166191a4d5521268353412bb0ac0fcdd04b SHA256: 0a68f4a7097e1c80cb32cc21c01eb27cc3b03d2315ce5335b6622b83074723ac SHA512: d4f053c843ab996414ab410cda889a952d9a4d7987e4e9aded72f6fe56d6fb69c2ea664e9c8ebdae8e4c06462051cb3c77c465c02de018e0adc5362ac0aba1ca Homepage: https://cran.r-project.org/package=TSdeeplearning Description: CRAN Package 'TSdeeplearning' (Deep Learning Model for Time Series Forecasting) Provides deep learning models for time series forecasting using Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). These models capture temporal dependencies and address vanishing gradient issues in sequential data. The package enables efficient forecasting for univariate time series. For methodological details see Jaiswal and co-authors (2022). . 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The Phase II design is based on Zhong (2012) . Package: r-cran-tsdisagg2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-tsdisagg2_0.1.0-1.ca2404.1_all.deb Size: 341606 MD5sum: a3ca4ef98359c9358afe3693e4cb0ad2 SHA1: 842e193ebb8c9676663e86e1ef74b1756e36701a SHA256: 320801e13c430334adefbe4376fffe33cb85ca71e96f23f7a26d8332b56fdbb6 SHA512: cb166749d70fe7d6b4b0395f46a6bc50763c47052a08689372ebf5db5d61cf077b3abc9f0398683f5abe61f92039d082bb55d11c62652008957b2c17be3a1cf1 Homepage: https://cran.r-project.org/package=tsdisagg2 Description: CRAN Package 'tsdisagg2' (Time Series Disaggregation) Disaggregates low frequency time series data to higher frequency series. Implements the following methods for temporal disaggregation: Boot, Feibes and Lisman (1967) , Chow and Lin (1971) , Fernandez (1981) and Litterman (1983) . Package: r-cran-tsdisaggregation Architecture: all Version: 2.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-zoo, r-cran-lars, r-cran-matrix, r-cran-withr Filename: pool/dists/noble/main/r-cran-tsdisaggregation_2.0.0-1.ca2404.1_all.deb Size: 64856 MD5sum: 3f72d84c827f872001da15f7d65e48e2 SHA1: 181252d03054190e7fd0b32deb43b41302c190dc SHA256: 5c48d1b04d346cfb3f7c9ba939e16e1764a2c447c7f6c45cdc4a6ba76e2f96d8 SHA512: 5376577b50c922f37ed625d0baa2b864be1200bb2cee7f13021a2e1240bac21b21e97e8df28b4a58a99b98eaea093243da871cbd623c8af5f7dde0f6d8d0112a Homepage: https://cran.r-project.org/package=TSdisaggregation Description: CRAN Package 'TSdisaggregation' (High-Dimensional Temporal Disaggregation) First - Generates (potentially high-dimensional) high-frequency and low-frequency series for simulation studies in temporal disaggregation; Second - a toolkit utilizing temporal disaggregation and benchmarking techniques with a low-dimensional matrix of indicator series previously proposed in Dagum and Cholette (2006, ISBN:978-0-387-35439-2) ; and Third - novel techniques proposed by Mosley, Gibberd and Eckley (2021) for disaggregating low-frequency series in the presence of high-dimensional indicator matrices. Package: r-cran-tsdt Architecture: all Version: 1.0.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mlbench, r-cran-hash, r-cran-party, r-cran-rpart, r-cran-survival, r-cran-survrm2, r-cran-modeltools Filename: pool/dists/noble/main/r-cran-tsdt_1.0.8-1.ca2404.1_all.deb Size: 456290 MD5sum: 25206d72272559cc21157fba6c1fc836 SHA1: 312f064ed93407e61039dfa524dd3c213f8bd935 SHA256: 90334cb1c20236ce1c06af530ae1d8cc8ad5aa39223d5ad0fd877ea6167bb7af SHA512: 31c1b11f71bd0d9c529648986194e20f574a5ceb35868e57cfe1b7ff8f5f8c387f034c331840b27b6b19ff2fa997cb94cef847ee88977cb2c3bae5d9dfd4ea65 Homepage: https://cran.r-project.org/package=TSDT Description: CRAN Package 'TSDT' (Treatment-Specific Subgroup Detection Tool) Implements a method for identifying subgroups with superior response relative to the overall sample. Package: r-cran-tse Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 98 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tse_0.1.0-1.ca2404.1_all.deb Size: 62240 MD5sum: 38c08c4e4edac02405b587114251b175 SHA1: e7894f94f7bfa218a536f57d5785db9b29abfd7c SHA256: 182907ce5191aebfea4ff707048182cb36a80372fc77130804c975b82fc9f628 SHA512: 335cdeaeebd942f561d6551ad4e0ba8b9df09b67dc1f24a6d1a41692db761e62be7974a313f614c2be369bc6d7211e6a5aaab12a01fcad01e408d751003d0aec Homepage: https://cran.r-project.org/package=TSE Description: CRAN Package 'TSE' (Total Survey Error) Calculates total survey error (TSE) for one or more surveys, using common scale-dependent and/or scale-independent metrics. On TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) . Package: r-cran-tseal Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4393 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory, r-cran-caret, r-cran-checkmate, r-cran-magrittr, r-cran-mass, r-cran-parallelly, r-cran-statcomp, r-cran-waveslim, r-cran-wdm Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tseal_0.1.5-1.ca2404.1_all.deb Size: 4255086 MD5sum: ac75768f249f054f8e708723b787aca4 SHA1: 3d14439733f8d9fc1e1daee49673673a42f8123f SHA256: 9b9cc5e254da38ca5596dec8215d63cc48501123bdc7be3322de209d55922376 SHA512: af91967c7943d69d0f5949ce15639e1a7fb99be172ae5a6f870ff917d064ec565f446b5a140cc6449f95d36dd53de4a2e4bbf72e66f98064c27b9d1f96e7812d Homepage: https://cran.r-project.org/package=TSEAL Description: CRAN Package 'TSEAL' (Time Series Analysis Library) The library allows to perform a multivariate time series classification based on the use of Discrete Wavelet Transform for feature extraction, a step wise discriminant to select the most relevant features and finally, the use of a linear or quadratic discriminant for classification. Note that all these steps can be done separately which allows to implement new steps. Velasco, I., Sipols, A., de Blas, C. S., Pastor, L., & Bayona, S. (2023) . Percival, D. B., & Walden, A. T. (2000,ISBN:0521640687). Maharaj, E. A., & Alonso, A. M. (2014) . Package: r-cran-tseffects Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mpoly, r-cran-car, r-cran-ggplot2, r-cran-sandwich Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tseffects_0.2.1-1.ca2404.1_all.deb Size: 380306 MD5sum: 77bac0c08b364774eb4745933d94e6c9 SHA1: f6863a23c8031fb1d39ebe0f8fbeb80014d11ecb SHA256: ead500573c42e339ed2676ece9c3799148c8c83bd5d2d33c0caf8510cfa1bcde SHA512: 1e56547772668de28b2851580995d34e1524975e2061a09c6f004bf113117da5c1821f65ea988b83f159bdb9eeffd0fa33252f38978b0421db0de76a31b7d2f2 Homepage: https://cran.r-project.org/package=tseffects Description: CRAN Package 'tseffects' (Dynamic Inferences from Time Series (with Interactions)) Autoregressive distributed lag (A[R]DL) models (and their reparameterized equivalent, the Generalized Error-Correction Model [GECM]) are the workhorse models in uncovering dynamic inferences. ADL models are simple to estimate; this is what makes them attractive. Once these models are estimated, what is less clear is how to uncover a rich set of dynamic inferences from these models. We provide tools for recovering those inferences. These tools apply to traditional time-series quantities of interest: especially instantaneous effects for any period and cumulative effects for any period (including the long-run effect). They also allow for a variety of shock histories to be applied to the independent variable (beyond just a one-time, one-unit increase) as well as the recovery of inferences in levels for shocks applies to (in)dependent variables in differences (what we call the Generalized Dynamic Response Function). These effects are also available for the general conditional dynamic model advocated by Warner, Vande Kamp, and Jordan (2026 ). We also provide the actual formulae for these effects. Package: r-cran-tseind Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tseind_0.1.0-1.ca2404.1_all.deb Size: 57992 MD5sum: cfa440db1ac82bad0bc00d65a84e0022 SHA1: 3fd63785f0d936a9e7c77c444e29d03cee8d7247 SHA256: a44605b62ca411c0f3766868cdfe0f17462ad05d6adc855dc97930fa84a4e170 SHA512: ab37c14a34401ea4b7c2cd68a00bd47d9809a0512f7f37df04e5fa576bd4ab3753114be603305c9ba3540f5c76bef4cdeee455a6fc3df0da30e26911ad1b0d3b Homepage: https://cran.r-project.org/package=TSEind Description: CRAN Package 'TSEind' (Total Survey Error (Independent Samples)) Calculates total survey error (TSE) for one or more surveys, using both scale-dependent and scale-independent metrics. Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTIND and its documentation), properly input column names (see functions documentation), and run your functions. For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) ; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc. Package: r-cran-tsensembler Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 631 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-rcpproll, r-cran-ranger, r-cran-glmnet, r-cran-earth, r-cran-kernlab, r-cran-cubist, r-cran-gbm, r-cran-pls, r-cran-monmlp, r-cran-doparallel, r-cran-foreach, r-cran-xgboost, r-cran-softimpute Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tsensembler_0.1.0-1.ca2404.1_all.deb Size: 502178 MD5sum: fd10f8667c537a09e4bf916af1f31caa SHA1: eb6790a344242d1fe825ce22bb0fd8830126da8e SHA256: 38745445d0a9da2f56942afe516d0d617aee108734f83faa0623e66b0719fce4 SHA512: d3c860db72e10cdcfd4aab09645d2b914787444023b46c76ce597d1acf8bc41aaa2c99e5b2715d31570e72fc64d2b13e8f6c35f5625019d4a49982f3cb443e9e Homepage: https://cran.r-project.org/package=tsensembler Description: CRAN Package 'tsensembler' (Dynamic Ensembles for Time Series Forecasting) A framework for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches. The predictive methods receive a data matrix as input, representing an embedded time series, and return a predictive ensemble model. The ensemble use generic functions 'predict()' and 'forecast()' to forecast future values of the time series. Moreover, an ensemble can be updated using methods, such as 'update_weights()' or 'update_base_models()'. A complete description of the methods can be found in: Cerqueira, V., Torgo, L., Pinto, F., and Soares, C. "Arbitrated Ensemble for Time Series Forecasting." to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017; and Cerqueira, V., Torgo, L., and Soares, C.: "Arbitrated Ensemble for Solar Radiation Forecasting." International Work-Conference on Artificial Neural Networks. Springer, 2017 . Package: r-cran-tsentiment Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-reshape2, r-cran-wordcloud, r-cran-ggplot2, r-cran-httr, r-cran-syuzhet, r-cran-tidytext, r-cran-dplyr, r-cran-tibble, r-cran-stringi Filename: pool/dists/noble/main/r-cran-tsentiment_1.0.5-1.ca2404.1_all.deb Size: 42312 MD5sum: 2d87737c56b64580175de17a4d0fac9a SHA1: e3c19b7c9274565aa3928b0a9dc22e75fd8b3dfe SHA256: a4c18fde127a7e8555293d2894de6027b7c6ee4a457d396d3f12804f4c3d5189 SHA512: 84f6bb31393f018fd8837affd79d26c4cc977a8fff51e9aa24b553560ec5d3af5440c0eca05a17280790ee05c3c7973eb0d6ec0482a5eaab9d948496bd82b2ea Homepage: https://cran.r-project.org/package=tsentiment Description: CRAN Package 'tsentiment' (Fetching Tweet Data for Sentiment Analysis) Which uses Twitter APIs for the necessary data in sentiment analysis, acts as a middleware with the approved Twitter Application. A special access key is given to users who subscribe to the application with their Twitter account. With this special access key, the user defined keyword for sentiment analysis can be searched in twitter recent searches and results can be obtained( more information ). In addition, a service named tsentiment-services has been developed to provide all these operations ( for more information ). After the successful results obtained and in line with the permissions given by the user, the results of the analysis of the word cloud and bar graph saved in the user folder directory can be seen. In each analysis performed, the previous analysis visual result is deleted and this is the basic information you need to know as a practice rule. 'tsentiment' package provides a free service that acts as a middleware for easy data extraction from Twitter, and in return, the user rate limit is reduced by 30 requests from the total limit and the remaining requests are used. These 30 requests are reserved for use in application analytics. For information about endpoints, you can refer to the limit information in the "GET search/tweets" row in the Endpoints column in the list at . Package: r-cran-tseriesmma Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tseriesmma_0.1.1-1.ca2404.1_all.deb Size: 16220 MD5sum: d20575a51e3352097788f6f9231637f8 SHA1: 383d526bf10bfebcf0070fc68115b852a9558e24 SHA256: 41fdc8e2e2725f29257318d0592db7d034bd50208aaa4cd3619a6e6b89c4b0c3 SHA512: ef40d3fefc2f0fa3cdef8e939976e97038da415de7ce547b297ff8ca92f6b0fef74e34f5f633615f366a037ec497ea1b6c0922f4bcdfbba5d4c559d9cf7d81a5 Homepage: https://cran.r-project.org/package=TSeriesMMA Description: CRAN Package 'TSeriesMMA' (Multiscale Multifractal Analysis of Time Series Data) Multiscale multifractal analysis (MMA) (Gierałtowski et al., 2012) is a time series analysis method, designed to describe scaling properties of fluctuations within the signal analyzed. The main result of this procedure is the so called Hurst surface h(q,s) , which is a dependence of the local Hurst exponent h (fluctuation scaling exponent) on the multifractal parameter q and the scale of observation s (data window width). Package: r-cran-tsetools Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-quantmod, r-cran-quadprog, r-cran-httr, r-cran-rjson Filename: pool/dists/noble/main/r-cran-tsetools_1.0.0-1.ca2404.1_all.deb Size: 83000 MD5sum: 0da51df83b3f0a32fbc5f8b6260e2673 SHA1: 0b0d0259963980bd2faff5fa708520b03441946a SHA256: 9ee75d6c6ab779e8a28b56c39a1e8114bf32eda7ee7c0ece749944afaf0a748e SHA512: f0c8b48f759a41cce6ec98659aa168afa056a9abade3d3a26109f36f477b6c173d9f12b4aa604889c1fdb4a97b8012647a2d573d753ba39b0c41d5e38ce11ffa Homepage: https://cran.r-project.org/package=TSEtools Description: CRAN Package 'TSEtools' (Manage Data from the Finance Markets) A set of tools designed to perform descriptive data analysis on assets, manage asset portfolios and capital allocation, and download, organize, and maintain data from the "Tehran Stock Exchange" and "NOBITEX" platforms. Package: r-cran-tsewgt Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 96 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tsewgt_0.1.0-1.ca2404.1_all.deb Size: 60432 MD5sum: 0900080313664db82babba768ad587cc SHA1: af2c32ea8acfa1beed5bc6e21403010425741767 SHA256: ffb93a781bb066ac6723562461ecfe487d53ca9802271c8267bb780ba6834060 SHA512: ac3caf58d98e03ecaa7bf76eff0981578176eeb43deef22f77432b50d685ccef4cbd4ec57a3327ae17735c0666f515a25aed1775cd4f6f48f3674e848c5927c9 Homepage: https://cran.r-project.org/package=TSEwgt Description: CRAN Package 'TSEwgt' (Total Survey Error Under Multiple, Different Weighting Schemes) Calculates total survey error (TSE) for a survey under multiple, different weighting schemes, using both scale-dependent and scale-independent metrics. Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTWGT and its documentation), properly input column names (see functions documentation), and run your functions. For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) ; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc. Package: r-cran-tsf Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fracdiff, r-cran-forecast Filename: pool/dists/noble/main/r-cran-tsf_0.1.1-1.ca2404.1_all.deb Size: 21842 MD5sum: 584f36c265a6be2d2150ece775dcb2b2 SHA1: b171f93bf61fe23985c164cd9f8d993d69f39044 SHA256: 445c00fd64455e4d4440aa964fcbacde3375a19a82753f93b4e08a5f4ee5694e SHA512: f3462a4334c5ff4dfd7842e2ff3efbc901f431bd1b9d4778f0b1f49020c7a3ee531269473be91a754e7fc43ccb3529f1cd21b3f1eb64d81cb601661bc9a8b4bb Homepage: https://cran.r-project.org/package=TSF Description: CRAN Package 'TSF' (Two Stage Forecasting (TSF) for Long Memory Time Series inPresence of Structural Break) Forecasting of long memory time series in presence of structural break by using TSF algorithm by Papailias and Dias (2015) . Package: r-cran-tsfeatures Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-fracdiff, r-cran-forecast, r-cran-purrr, r-cran-rcpproll, r-cran-tibble, r-cran-tseries, r-cran-urca, r-cran-future, r-cran-furrr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-mcomp, r-cran-ggally Filename: pool/dists/noble/main/r-cran-tsfeatures_1.1.1-1.ca2404.1_all.deb Size: 242622 MD5sum: 28ef78224099a2b06a7b94390d95d76a SHA1: 78ba1fa194515e13fa4b85d0821748538d3f8a8a SHA256: 7c267c7547aafae08141464965b5ff6303a3c502d4ccd8bef1e5b67a5804aed1 SHA512: 6f28befaf3452566130ec6e1386871162776c334496913d9af67c3c65ebee71f0951083a8f1c4da4cdc27cd90610fbc26a4d864bd801e0232c60fe8daa97d334 Homepage: https://cran.r-project.org/package=tsfeatures Description: CRAN Package 'tsfeatures' (Time Series Feature Extraction) Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) , Kang, Hyndman and Smith-Miles (2017) and from Fulcher, Little and Jones (2013) . Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions. Package: r-cran-tsfngm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 47 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tsfngm_0.1.0-1.ca2404.1_all.deb Size: 16106 MD5sum: 409b9123b4886d3824e1d41b678e9b46 SHA1: bfb1c1e5303009d73553c81695dd0222cbdbda83 SHA256: e56407357265836bc4eb011ec992e022f4efd2ce53cdd7f2d7624373e16d97cd SHA512: 29a95768b2f494fc3492bc671f8f50cdcecb72890f182ab29082097b10d9248601584085372ecf466a3166a42707b51983ffbbe6c4b494b4347ab357367609dc Homepage: https://cran.r-project.org/package=tsfngm Description: CRAN Package 'tsfngm' (Time Series Forecasting using Nonlinear Growth Models) Nonlinear growth models are extremely useful in gaining insight into the underlying mechanism. These models are generally 'mechanistic,' with parameters that have biological meaning. This package allows you to fit and forecast time series data using nonlinear growth models. Package: r-cran-tsforecast Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lubridate, r-cran-forecast, r-cran-tseries, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-tsforecast_1.3.0-1.ca2404.1_all.deb Size: 291370 MD5sum: b9617c2c0ea33f6ad77175b457fad76b SHA1: 76072679c6dcaca9042ff6ec0abe447e2016de7d SHA256: 0f8280ab9857f3148df2f167ed12ef661af82be6872ee90802d99ebda9b3645f SHA512: f59161c64abab394f0099a524a76f45fa4c0299f33750aa52fd3d9f358d5338cd7bdefc3fc1ab74ba7a862ff8703bfaeb5d7fd6bb4d332253c8db9f5d69a557d Homepage: https://cran.r-project.org/package=tsforecast Description: CRAN Package 'tsforecast' (Time Series Forecasting Functions) Fundamental time series forecasting models such as autoregressive integrated moving average (ARIMA), exponential smoothing, and simple moving average are included. For ARIMA models, the output follows the traditional parameterisation by Box and Jenkins (1970, ISBN: 0816210942, 9780816210947). Furthermore, there are functions for detailed time series exploration and decomposition, respectively. All data and result visualisations are generated by 'ggplot2' instead of conventional R graphical output. For more details regarding the theoretical background of the models see Hyndman, R.J. and Athanasopoulos, G. (2021) . Package: r-cran-tsg Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 779 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-haven, r-cran-openxlsx, r-cran-fs, r-cran-forcats, r-cran-purrr, r-cran-tidyr, r-cran-glue, r-cran-jsonlite, r-cran-yaml, r-cran-dplyr, r-cran-stringr, r-cran-rlang, r-cran-gt, r-cran-lifecycle, r-cran-officer, r-cran-flextable, r-cran-qpdf Suggests: r-cran-testthat, r-cran-cli, r-cran-devtools, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-webshot2, r-cran-withr Filename: pool/dists/noble/main/r-cran-tsg_0.1.4-1.ca2404.1_all.deb Size: 383762 MD5sum: a51e4fe089b2308bd73dd1f5234e354a SHA1: acdce370fff98e4488ace48c88e256ace94a0aa1 SHA256: d57ee68fb77238b6c29ac9e54386b4f98c24f977d51edcbc62527a766aea4b19 SHA512: 51d5937c8abbded4a68fbbf7e79887d1a68b275a919d67e6061c3f92efab2e0bdb4b107947cabb80aeff10d06f47137099159b82c17ea973ffa3d6a6e2822307 Homepage: https://cran.r-project.org/package=tsg Description: CRAN Package 'tsg' (Generate Publication-Ready Statistical Tables) A collection of functions for generating frequency tables and cross-tabulations of categorical variables. The resulting tables can be exported to various formats (Excel, PDF, HTML, etc.) with extensive formatting and layout customization options. Package: r-cran-tsgc Architecture: all Version: 0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1208 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kfas, r-cran-xts, r-cran-ggplot2, r-cran-ggthemes, r-cran-zoo, r-cran-magrittr, r-cran-scales, r-cran-dplyr, r-cran-tidyr Suggests: r-cran-ggfortify, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-ggforce, r-cran-gridextra, r-cran-latex2exp, r-cran-here, r-cran-timetk, r-cran-testthat, r-cran-purrr, r-cran-kableextra Filename: pool/dists/noble/main/r-cran-tsgc_0.0-1.ca2404.1_all.deb Size: 836382 MD5sum: 8f69d4ac66c2497f7f0491fa1b870ae2 SHA1: 7e2539bdaae0f81513679b37758f6a3767fd0ba2 SHA256: 0abfbfa303588f0a103ad945fcaf612eb4b88c6d36845fb268f247007489bee7 SHA512: 12cdf23a43520f4c6070ccadacbe822599b3677afedc8d1a134d1970be59767cf164d46cf90f1f2e79d8d5a85e24ecb6bed8f93c26d8409396048ce7ee2fe80e Homepage: https://cran.r-project.org/package=tsgc Description: CRAN Package 'tsgc' (Time Series Methods Based on Growth Curves) The 'tsgc' package provides comprehensive tools for the analysis and forecasting of epidemic trajectories. It is designed to model the progression of an epidemic over time while accounting for the various uncertainties inherent in real-time data. Underpinned by a dynamic Gompertz model, the package adopts a state space approach, using the Kalman filter for flexible and robust estimation of the non-linear growth pattern commonly observed in epidemic data. The reinitialization feature enhances the model’s ability to adapt to the emergence of new waves. The forecasts generated by the package are of value to public health officials and researchers who need to understand and predict the course of an epidemic to inform decision-making. Beyond its application in public health, the package is also a useful resource for researchers and practitioners in fields where the trajectories of interest resemble those of epidemics, such as innovation diffusion. The package includes functionalities for data preprocessing, model fitting, and forecast visualization, as well as tools for evaluating forecast accuracy. The core methodologies implemented in 'tsgc' are based on well-established statistical techniques as described in Harvey and Kattuman (2020) , Harvey and Kattuman (2021) , and Ashby, Harvey, Kattuman, and Thamotheram (2024) . Package: r-cran-tsgs Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-bioc-edger, r-cran-fastmatch, r-cran-genalg, r-cran-kernlab, r-cran-e1071 Filename: pool/dists/noble/main/r-cran-tsgs_1.0-1.ca2404.1_all.deb Size: 62100 MD5sum: f83eb5b3ac3d48d90fc7a7c7c5b83aa2 SHA1: c21211ff3efb0de5d60bf817db90527113c0b46b SHA256: a7b82732067597cf8998cac82821802a732b239d89de8d54cb234124f4b544d3 SHA512: 785edd43aa7673de96913738dcc971958d3e15b4ea00580d1545810b161af02ee4da1aa94c2c0e122d7a14d201cdfe00889b295c0de79479900e5f30a4066f9f Homepage: https://cran.r-project.org/package=TSGS Description: CRAN Package 'TSGS' (Trait Specific Gene Selection using SVM and GA) Obtaining relevant set of trait specific genes from gene expression data is important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package returns the trait specific gene set from the high dimensional RNA-seq count data by applying combination of two conventional machine learning algorithms, support vector machine (SVM) and genetic algorithm (GA). GA is used to control and optimize the subset of genes sent to the SVM for classification and evaluation. Genetic algorithm uses repeated learning steps and cross validation over number of possible solution and selects the best. The algorithm selects the set of genes based on a fitness function that is obtained via support vector machines. Using SVM as the classifier performance and the genetic algorithm for feature selection, a set of trait specific gene set is obtained. Package: r-cran-tsgsis Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 60 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glmnet, r-cran-mass Filename: pool/dists/noble/main/r-cran-tsgsis_0.1-1.ca2404.1_all.deb Size: 30170 MD5sum: 5fcdc0c6dd3ab71776a4c1d150bda0fa SHA1: 1f60ec392b2d3d2f52b60034e9a68f9a820ffea7 SHA256: e22dd88e8b1cabfda4801269bc918d80a7a2c2652ca0d979b21ce936f4d48cae SHA512: 78fa45ca64a789c18fb1149c6c1e5fb988e4a5317aceca0fb14a4fe7165ffb13c55120030f0e86084811ba6ba61789613516db6ba4b7d394d6a23dca0a055e1c Homepage: https://cran.r-project.org/package=TSGSIS Description: CRAN Package 'TSGSIS' (Two Stage-Grouped Sure Independence Screening) To provide a high dimensional grouped variable selection approach for detection of whole-genome SNP effects and SNP-SNP interactions, as described in Fang et al. (2017, under review). Package: r-cran-tsibble Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1385 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-anytime, r-cran-dplyr, r-cran-generics, r-cran-lifecycle, r-cran-lubridate, r-cran-rlang, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs Suggests: r-cran-ggplot2, r-cran-hms, r-cran-knitr, r-cran-nanotime, r-cran-nycflights13, r-cran-rmarkdown, r-cran-scales, r-cran-spelling, r-cran-testthat, r-cran-tidyr, r-cran-timedate Filename: pool/dists/noble/main/r-cran-tsibble_1.2.0-1.ca2404.1_all.deb Size: 991660 MD5sum: ba742afcb08b2d16db98c60aacf1fe29 SHA1: 35944c3b0f640e376f9789f6cde6ba6808b9cb6e SHA256: 217d47307d5f3492f25aaf19eefe5d6488cc3ff638b7147f4cf6dbd469ab20f7 SHA512: 8ed857a9644cf7537256c915776d60956517168938f72fa5d2abfeead7124b8d6e460f075c2942c38d5d18b5121500df0e7da68ad6cd84b9940ddea7a026c27f Homepage: https://cran.r-project.org/package=tsibble Description: CRAN Package 'tsibble' (Tidy Temporal Data Frames and Tools) Provides a 'tbl_ts' class (the 'tsibble') for temporal data in an data- and model-oriented format. The 'tsibble' provides tools to easily manipulate and analyse temporal data, such as filling in time gaps and aggregating over calendar periods. Package: r-cran-tsibbledata Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2477 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tsibble, r-cran-vctrs, r-cran-rappdirs Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-tsibbledata_0.4.1-1.ca2404.1_all.deb Size: 2105264 MD5sum: c9ab01b9a21e0a87128d7f4fe3df4d16 SHA1: 47dbf5ac6b94b8b7f51517077667b08c51c9bf6f SHA256: 679f7cc003e7bc412500f193b634d35becb948b6ed84bcc3b53d3e855c5a067e SHA512: 02a9f744d149b0783a8beb48053f54305bf63276fe1877dce6412ee38f37835a06d362a320cddf5711108265bae64efe09d41633b97ce2772cf2ddad8ca40792 Homepage: https://cran.r-project.org/package=tsibbledata Description: CRAN Package 'tsibbledata' (Diverse Datasets for 'tsibble') Provides diverse datasets in the 'tsibble' data structure. These datasets are useful for learning and demonstrating how tidy temporal data can tidied, visualised, and forecasted. 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A shiny module is provided to visually explore periodic/aperiodic temporal patterns. Package: r-cran-tsintermittent Architecture: all Version: 1.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mapa Filename: pool/dists/noble/main/r-cran-tsintermittent_1.10-1.ca2404.1_all.deb Size: 111736 MD5sum: 90cdfb509885dea0b46ad8187c1f5143 SHA1: 08803c30144dc736b686efb662fd6df83c7b6ad8 SHA256: 98d2ebbba0bc0b1897c04621abd3ac46fe566c2cc9382d2fb6ada2738b383ed6 SHA512: 4edb3f449b63d119246df7c25d27a3f093c836565826f6acfe68807421f0474b61cf4e5d4b60eaf312674fefca6437d3d46f31b383abf6ebebf40a2ce6b48853 Homepage: https://cran.r-project.org/package=tsintermittent Description: CRAN Package 'tsintermittent' (Intermittent Time Series Forecasting) Time series methods for intermittent demand forecasting. Includes Croston's method and its variants (Moving Average, SBA), and the TSB method. 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Package: r-cran-tsir Architecture: all Version: 0.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-kernlab, r-cran-reshape2 Filename: pool/dists/noble/main/r-cran-tsir_0.4.3-1.ca2404.1_all.deb Size: 341478 MD5sum: 5638cad429a8dffcdfef6effbb2c3aaa SHA1: 0736c14e99873d79d9055f61c1ad93feea822043 SHA256: e91b7fe133a0c5108499b0d4d089266a75a19ceeb37b5863f6ffbb81dafa032b SHA512: d68e47688881063ba0645979d198c5099e7c0ad6f8899f12edeae3ca0af2b539abe80c63f2e0537a6c573a6bf5bbce0a5184b78268c4e7169a0df518c11f31fb Homepage: https://cran.r-project.org/package=tsiR Description: CRAN Package 'tsiR' (An Implementation of the TSIR Model) An implementation of the time-series Susceptible-Infected-Recovered (TSIR) model using a number of different fitting options for infectious disease time series data. The manuscript based on this package can be found here . The method implemented here is described by Finkenstadt and Grenfell (2000) . Package: r-cran-tslstm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 55 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-keras, r-cran-tensorflow, r-cran-tsutils Filename: pool/dists/noble/main/r-cran-tslstm_0.1.0-1.ca2404.1_all.deb Size: 23986 MD5sum: 31228ec10de9381353aec2d63992e849 SHA1: b29f09b3208a9e9a78c996a99a726c2c479f7664 SHA256: e85bdc49c4625d5be92b6538c06d48c87ed3864e317cbc324838dec88af8ba84 SHA512: 5c99d2b14c7ed8872587792c87ceee3beb46ca6a2093bf03ebf31dd626ec60c6b060a86a627c22ccdd39cfd4f9847e1aa61a0d4fbf83556ace15d9fee1a7394d Homepage: https://cran.r-project.org/package=TSLSTM Description: CRAN Package 'TSLSTM' (Long Short Term Memory (LSTM) Model for Time Series Forecasting) The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Min-Max transformation has been used for data preparation. Here, we have used one LSTM layer as a simple LSTM model and a Dense layer is used as the output layer. Then, compile the model using the loss function, optimizer and metrics. This package is based on Keras and TensorFlow modules and the algorithm of Paul and Garai (2021) . Package: r-cran-tslstmplus Architecture: all Version: 1.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-keras, r-cran-tensorflow, r-cran-abind Filename: pool/dists/noble/main/r-cran-tslstmplus_1.0.6-1.ca2404.1_all.deb Size: 55558 MD5sum: 04e2e75cdcc77af27b18bfad48a0f384 SHA1: 7047cacb9f3aac8a9a7d68948bb4e261ae41c0db SHA256: 7add41430a2e0182eb00eb87daad25848f3a1d7f0ff394c09b3346a9c6de141b SHA512: d24872be062f3b134c409a96965621928b874fdfa92fb44ff0e0e79ed0bfd3285319c78c3c370325194676290f48a7eb67258ebe53f88528ac000ab49f7b555b Homepage: https://cran.r-project.org/package=TSLSTMplus Description: CRAN Package 'TSLSTMplus' (Long-Short Term Memory for Time-Series Forecasting, Enhanced) The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Customizable configurations for the model are allowed, improving the capabilities and usability of this model compared to other packages. This package is based on 'keras' and 'tensorflow' modules and the algorithm of Paul and Garai (2021) . Package: r-cran-tslstmx Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tensorflow, r-cran-allmetrics, r-cran-keras, r-cran-reticulate Filename: pool/dists/noble/main/r-cran-tslstmx_0.1.0-1.ca2404.1_all.deb Size: 68252 MD5sum: bd218aa52548980c18d9d33cb5719e62 SHA1: 7ec2efa362341c02f2407980b36875fcbda8a2cc SHA256: 8452e1ed241156b0bccdcfa90e8914438197646b83541b1671c36169193598c4 SHA512: 4e381adc555361391ec1e5a3b8c19784ed11a3a7e37595b848d9b3ec042bd76e887790c27e70f4a9c9967375a83ddc499efa0dde6df6adeca27d01d38b5dffe5 Homepage: https://cran.r-project.org/package=tsLSTMx Description: CRAN Package 'tsLSTMx' (Predict Time Series Using LSTM Model Including ExogenousVariable to Denote Zero Values) It is a versatile tool for predicting time series data using Long Short-Term Memory (LSTM) models. 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Package: r-cran-tsmcp Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ncvreg, r-cran-lars Suggests: r-cran-mass Filename: pool/dists/noble/main/r-cran-tsmcp_1.1-1.ca2404.1_all.deb Size: 42876 MD5sum: eabf0b236313d057d7083ada36ecf9a8 SHA1: 38ce25e42a39d4437987d1d9bc74c40c49619797 SHA256: b8a5d91ba4300005485c7480829239c4127ec0ea1218c7b678d4c6772a406b78 SHA512: 52eeea6aec600f4653dcd793b429c56d1f28aa9dd3878e5346397b48462067f8dcdb1d68ab5d7c227db119b0c662fa9d6b91888c655df48cbc31b5ed982caa84 Homepage: https://cran.r-project.org/package=TSMCP Description: CRAN Package 'TSMCP' (Fast Two Stage Multiple Change Point Detection) A novel and fast two stage method for simultaneous multiple change point detection and variable selection for piecewise stationary autoregressive (PSAR) processes and linear regression model. 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This work was supported by grant R01HD68395 from the National Institute of Health. Package: r-cran-tsne Architecture: all Version: 0.2-0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tsne_0.2-0-1.ca2404.1_all.deb Size: 22400 MD5sum: 2f3a78a1d626ff5226f2014c3aa39953 SHA1: 4edca2687f5ecc94c372e8f5f539c4b89d7acacc SHA256: fd3a7671f855d0582188082ce6cc6117ceab96ed80f44a6646700cec5a8fb83d SHA512: ea4b82a4f7e8fa49597e57b9d50835f43aa310f65f3fcfd00a8203106226a06cb08a939ec2df79a7f5a13d2e20651f1cea1fc24641ebde6017b5dcd614e5e8e9 Homepage: https://cran.r-project.org/package=tsne Description: CRAN Package 'tsne' (T-Distributed Stochastic Neighbor Embedding for R (t-SNE)) A "pure R" implementation of the t-SNE algorithm. 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The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used. 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TSPredIT (Time Series Prediction with Integrated Tuning) is a framework that provides a seamless integration of data preprocessing, decomposition, model training, hyperparameter optimization, and evaluation. Unlike other frameworks, TSPredIT emphasizes the co-optimization of both preprocessing and modeling steps, improving predictive performance. It supports a variety of statistical and machine learning models, filtering techniques, outlier detection, data augmentation, and ensemble strategies. More information is available in Salles et al. . Package: r-cran-tsqca Architecture: all Version: 1.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 656 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qca Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tsqca_1.3.2-1.ca2404.1_all.deb Size: 313078 MD5sum: 1f603bb2ae87dc9c068eb775baeac555 SHA1: 64bdd0976720d52aaf4b3d80903875b361ba961e SHA256: 63de5b1d348aeebb0d8ab537b14029d042d9d47de1a30b84683b1e4893907803 SHA512: 9e3571b44ff398f54f420c9b138ee211b026f0721e4320a630cf79976282e62d6457d1fe9f33d61b8e31333702982ab72737f8664d06440f552c598780497814 Homepage: https://cran.r-project.org/package=TSQCA Description: CRAN Package 'TSQCA' (Threshold Sweep Extensions for Qualitative Comparative Analysis) Provides threshold sweep methods for Qualitative Comparative Analysis (QCA). Implements Condition Threshold Sweep-Single (CTS-S), Condition Threshold Sweep-Multiple (CTS-M), Outcome Threshold Sweep (OTS), and Dual Threshold Sweep (DTS) for systematic exploration of threshold calibration effects on crisp-set QCA results. These methods extend traditional robustness approaches by treating threshold variation as an exploratory tool for discovering causal structures. Also provides Fiss (2011) core/peripheral condition classification via compute_fiss_core() and generate_fiss_chart(), enabling four-symbol configuration charts that distinguish core conditions (present in both parsimonious and intermediate solutions) from peripheral conditions (intermediate only). Built on top of the 'QCA' package by Dusa (2019) , with function arguments following 'QCA' conventions. Based on set-theoretic methods by Ragin (2008) and established robustness protocols by Rubinson et al. (2019) . 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A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). Paper: Albrecht, Marco, et al. (2017). 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Package: r-cran-ttscreening Architecture: all Version: 1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrixstats, r-bioc-sva, r-bioc-limma, r-cran-corpcor, r-cran-simsalapar, r-cran-mass, r-cran-brglm2 Suggests: r-cran-mvtnorm Filename: pool/dists/noble/main/r-cran-ttscreening_1.8-1.ca2404.1_all.deb Size: 69594 MD5sum: 5295868c34ad7e24d0e610d8654b71d3 SHA1: 48a14745243e890500100cde82e612df45119889 SHA256: 29d2e16da75271d513556498248c7c67f31f894ebb2935d3a48cd9bc196baeac SHA512: 5a5a4f48c8704217650558ee9c184b089d666d8aadc9af437784d1706e1e60842edeb338e8fc1123df17f8f96492bc6dee3c9cb23ac3239e6820b24cc944dfc8 Homepage: https://cran.r-project.org/package=ttScreening Description: CRAN Package 'ttScreening' (Genome-Wide DNA Methylation Sites Screening by Use of Trainingand Testing Samples) A screening process utilizing training and testing samples to filter out uninformative DNA methylation sites. 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Package: r-cran-tuglab Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-geometry, r-cran-plotly, r-cran-rcdd, r-cran-stringr, r-cran-volesti Filename: pool/dists/noble/main/r-cran-tuglab_0.0.1-1.ca2404.1_all.deb Size: 476480 MD5sum: 72d2d5a0512bc3d7c3c6ebea2a642dc9 SHA1: 1a17a44ab815ce6b73d10ba19eb97d916631ed74 SHA256: 6bbc82a43b0046f7bd5c56123efac00d2ed4f35b21312d62f766e0e37974ac28 SHA512: eacbb2ec509d202e0f3eb6aa6d42263db4e25d0aecad413c778aef58e517226d6edbb89034ee53eb548dabf0472eb6494fb0e45f46f48adbe7831315e2e2c580 Homepage: https://cran.r-project.org/package=TUGLab Description: CRAN Package 'TUGLab' (A Laboratory for TU Games) Cooperative game theory models decision-making situations in which a group of agents, called players, may achieve certain benefits by cooperating to reach an optimal outcome. 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Package: r-cran-tukeyc Architecture: all Version: 1.3-44-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emmeans, r-cran-xtable Suggests: r-cran-pbkrtest, r-cran-lme4 Filename: pool/dists/noble/main/r-cran-tukeyc_1.3-44-1.ca2404.1_all.deb Size: 151504 MD5sum: 6308c3957d6863222e896a5aa42760ac SHA1: 83497e6bd26a3a111853811f74c0b60758ae762a SHA256: f1b3ff6d66259412c8f1275d069c9c9cc4deee180f38e4ebd2003ca10c3f7d92 SHA512: eeb999a11d7edd9483f3fb418f0c86a288e719286fdc74fa7d62237301e289e25318b5f879a300a995b173ad1bc8484d041b2e14bb7023f1ab985ff522513865 Homepage: https://cran.r-project.org/package=TukeyC Description: CRAN Package 'TukeyC' (Conventional Tukey Test) Provides tools to perform multiple comparison analyses, based on the well-known Tukey's "Honestly Significant Difference" (HSD) test. 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Package: r-cran-tushare Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-tidyverse, r-cran-forecast, r-cran-data.table Filename: pool/dists/noble/main/r-cran-tushare_0.1.4-1.ca2404.1_all.deb Size: 23700 MD5sum: cd5f71e1fef4189a6fbf4a3676c737bf SHA1: 6fae872d475bab50cccec595fdd97a9764f5a051 SHA256: 76b71cd0b14d9137fdb640ade220c49bd5c030f5219bd2e2dcb7a78c98a65432 SHA512: 6e4a27384447e1ea55ec0845a5a642b03b1b3e4ba5a71d64ae3871762d3543af61217981d365a8b04342a403f7343f31adf65e66719707619ac189feba0955ab Homepage: https://cran.r-project.org/package=Tushare Description: CRAN Package 'Tushare' (Interface to 'Tushare Pro' API) Helps the R users to get data from 'Tushare Pro'. 'Tushare Pro' is a platform as well as a community with a lot of staffs working in financial area. 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Package: r-cran-tuvalues Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-roi, r-cran-roi.plugin.glpk Filename: pool/dists/noble/main/r-cran-tuvalues_1.1.1-1.ca2404.1_all.deb Size: 110136 MD5sum: da58d45941a2268d1ed181e1621ee30f SHA1: 5aa41c4cc77055d6253dc1d3d16edad338601c01 SHA256: c0ddb979ee9cb9dd3c5b6206142cdebf0dfc3ce94138f8db6d4b663333568fe4 SHA512: 7f2a5b4b500ecb852bafa3b30c7346699523826bb2e91a09c89cd8e1a4e30297de2675474d13a056af05a19c5de36b5374b48f069315c73e5d8bc89cbd530b8d Homepage: https://cran.r-project.org/package=TUvalues Description: CRAN Package 'TUvalues' (Tools for Calculating Allocations in Game Theory using Exact andApproximated Methods) The main objective of cooperative Transferable-Utility games (TU-games) is to allocate a good among the agents involved. The package implements major solution concepts including the Shapley value, Banzhaf value, and egalitarian rules, alongside their extensions for structured games: the Owen value and Banzhaf-Owen value for games with a priori unions, and the Myerson value for communication games on networks. To address the inherent exponential computational complexity of exact evaluation, the package offers both exact algorithms and linear approximation methods based on sampling, enabling the analysis of large-scale games. Additionally, it supports core set-based solutions, allowing computation of the vertices and the centroid of the core. Package: r-cran-tv Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lubridate, r-cran-dplyr, r-cran-magrittr, r-cran-rlang Suggests: r-cran-knitr, r-cran-tibble, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tv_2.0.2-1.ca2404.1_all.deb Size: 45746 MD5sum: c164792003eb68343a2f80e79b0e8662 SHA1: 63cefd7d64237d77aa374e8e18e17563e6aec490 SHA256: b2f41b63de6688ea61cb18aee48cbbf9249e03ef56dc85d214fe029c9fb9fff5 SHA512: fc4f3f8bfb68983923fccb8ca932054f465e766360c9587e5c36f008e2aee6aafa63ee4e8cc72c02b854d9c2c112fec47e6827b479b0be8620177b66800a1420 Homepage: https://cran.r-project.org/package=tv Description: CRAN Package 'tv' (Tools for Creating Time-Varying Datasets) Create a time-varying dataset using features, exposure, and look back specifications. Package: r-cran-tvcure Architecture: all Version: 0.6.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 598 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cubicbsplines, r-cran-rfast, r-cran-mass, r-cran-matrix, r-cran-mgcv Filename: pool/dists/noble/main/r-cran-tvcure_0.6.6-1.ca2404.1_all.deb Size: 532138 MD5sum: 83cae1b13fc3fa4fd9480a39879b4455 SHA1: 06f47cd1b383efb5ca49e2418d80bd80e1e28d9d SHA256: 1e3f4d54f4f452965a5f65074ac7cc551e75d6e3e7c6ed39c6da0bc073ad3e3f SHA512: d1619d7acc84c55587f145287b4e2f2a0beed28c7005f1aff395d4bb5a7824627baeeaeb17a3fe059c73c72b072af39182d40f70a9d4e2e323fe31b2353bd4e8 Homepage: https://cran.r-project.org/package=tvcure Description: CRAN Package 'tvcure' (Additive Cure Survival Model with Time-Varying Covariates) Fit of a double additive cure survival model with time-varying covariates. The additive terms in the long- and short-term survival submodels, modelling the cure probability and the event timing for susceptible units, are estimated using Laplace P-splines. For more details, see Lambert and Kreyenfeld (2025) . Package: r-cran-tvem Architecture: all Version: 1.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mgcv Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tvem_1.4.1-1.ca2404.1_all.deb Size: 123684 MD5sum: 71845f2a925594a47b6bea82c18d0c28 SHA1: cfb290c9d61a690f08dbed0cece90f7bbdaedbd2 SHA256: 8595805370524e7d91143731fcee528b183f593bb849a4917cc873b95b89a7d9 SHA512: b376707f942d6efad1864eaef85778b2a8ba454f45f1f113252b1f2155c2f886cbd06faf251862ef24cb1aeae932c9fa8d01d39022427fa01793e29b182d8445 Homepage: https://cran.r-project.org/package=tvem Description: CRAN Package 'tvem' (Time-Varying Effect Models) Fits time-varying effect models (TVEM). These are a kind of application of varying-coefficient models in the context of longitudinal data, allowing the strength of linear, logistic, or Poisson regression relationships to change over time. These models are described further in Tan, Shiyko, Li, Li & Dierker (2012) . We thank Kaylee Litson, Patricia Berglund, Yajnaseni Chakraborti, and Hanjoo Kim for their valuable help with testing the package and the documentation. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. Package: r-cran-tvgarch Architecture: all Version: 2.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-garchx, r-cran-zoo, r-cran-numderiv Filename: pool/dists/noble/main/r-cran-tvgarch_2.4.3-1.ca2404.1_all.deb Size: 246898 MD5sum: 10e773000bc61041ee60db6f4eae3de7 SHA1: 5589b36c1fc8e6a605ae4efa1d1d3a84210ee662 SHA256: d25a555a7b8b4b88357aff66bd9108403309c7cbe29f39ad0a1d0f7f720fefcd SHA512: c5bedad2184d59da211111855eda57fec8559c0f812ba9a812f2b270d2c07611b933edd068d1668c7fce72619d31df12ef026d48d8f3c176f95a01a817d5542d Homepage: https://cran.r-project.org/package=tvgarch Description: CRAN Package 'tvgarch' (Time Varying GARCH Modelling) Simulation, estimation and inference for univariate and multivariate TV(s)-GARCH(p,q,r)-X models, where s indicates the number and shape of the transition functions, p is the ARCH order, q is the GARCH order, r is the asymmetry order, and 'X' indicates that covariates can be included; see Campos-Martins and Sucarrat (2024) . In the multivariate case, variances are estimated equation by equation and dynamic conditional correlations are allowed. The TV long-term component of the variance as in the multiplicative TV-GARCH model of Amado and Terasvirta (2013) introduces non-stationarity whereas the GARCH-X short-term component describes conditional heteroscedasticity. Maximisation by parts leads to consistent and asymptotically normal estimates. Package: r-cran-tvgeom Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggthemes, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-knitr, r-cran-rmarkdown, r-cran-magrittr, r-cran-gridextra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tvgeom_1.0.1-1.ca2404.1_all.deb Size: 85032 MD5sum: d4d4fb4609abbaab1db40f8c01788b6b SHA1: 9eaa8018a43e3322fa396c583c0ea8c2f9178e81 SHA256: 9869cf998272c265b6a080a254e3aadca835cba14abbe6825747e835f08d378c SHA512: b1844227e7520658437e619622e1078ae6a207a1df013872f299f4b22ba7a3e7c82d7d99d74aecf6fd09e827a9bc5ae0d7e5e3fa2f87ccdd6336da79c7ae623b Homepage: https://cran.r-project.org/package=tvgeom Description: CRAN Package 'tvgeom' (The Time-Varying (Right-Truncated) Geometric Distribution) Probability mass (d), distribution (p), quantile (q), and random number generating (r and rt) functions for the time-varying right-truncated geometric (tvgeom) distribution. Also provided are functions to calculate the first and second central moments of the distribution. The tvgeom distribution is similar to the geometric distribution, but the probability of success is allowed to vary at each time step, and there are a limited number of trials. This distribution is essentially a Markov chain, and it is useful for modeling Markov chain systems with a set number of time steps. Package: r-cran-tvm Architecture: all Version: 0.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-scales Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tvm_0.5.2-1.ca2404.1_all.deb Size: 80668 MD5sum: c071faa443564b9ae02526eea1675ecf SHA1: d45f3b73004b764385a480415549c77046f16c93 SHA256: 572fb82201943d9b9c5a33a8b0257c97e05033e9b6aaf5070b6d04c44e3e52a3 SHA512: a6d92618710378259bdaca4080dfa1ab8638f472a8232b24c1c33b6c9463feb5693a4b193344706fb7f2f950c040f326187bdc51150b47b1134ff81dd687b37a Homepage: https://cran.r-project.org/package=tvm Description: CRAN Package 'tvm' (Time Value of Money Functions) Functions for managing cashflows and interest rate curves. Package: r-cran-tvmcomp Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/noble/main/r-cran-tvmcomp_1.0.2-1.ca2404.1_all.deb Size: 126022 MD5sum: d6157d8f03bf6eee0b1be92be716c217 SHA1: 2841a58a1f0437cfa0734fb19cedbdae4e57fc0d SHA256: bca11d5e217d6ef7aa3f5d67baa0ceb18bf56fdb964278d10a650bf6f672cc30 SHA512: 397a4477f8f6c7d466f06eeb583862c0947b7edf5485027c25bd0f4bf069ac01513975bec3aa4ae7a6779e1bd8a10fe233ac247c94b91f1a7b0922de7d6bee9b Homepage: https://cran.r-project.org/package=tvmComp Description: CRAN Package 'tvmComp' (Discounting and Compounding Calculations for Various Scenarios) Functions for compounding and discounting calculations included here serve as a complete reference for various scenarios of time value of money. Raymond M. Brooks (“Financial Management,” 2018, ISBN: 9780134730417). Sheridan Titman, Arthur J. Keown, John D. Martin (“Financial Management: Principles and Applications,” 2017, ISBN: 9780134417219). Jonathan Berk, Peter DeMarzo, David Stangeland, Andras Marosi (“Fundamentals of Corporate Finance,” 2019, ISBN: 9780134735313). S. A. Hummelbrunner, Kelly Halliday, Ali R. Hassanlou (“Contemporary Business Mathematics with Canadian Applications,” 2020, ISBN: 9780135285015). Package: r-cran-tvmediation Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1016 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-locpol Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-tvmediation_1.1.1-1.ca2404.1_all.deb Size: 673736 MD5sum: 9f89191fb5b2dc9ec8bf2eba8f7f2997 SHA1: 126a8dc2d1870d9fd7564fac49e8c16ac909caac SHA256: ee857a78b3a6963ec6b56854dde9cb608056a5e94826362ba937a8447e538c89 SHA512: 5844056042c827f55126027ac1f50c90afd4a25f624db6834331874e021ad886232462f57e79e6eaa1332a11537b3a1f154564e565ba47c9a11900bbc9a406f4 Homepage: https://cran.r-project.org/package=tvmediation Description: CRAN Package 'tvmediation' (Time Varying Mediation Analysis) Provides functions for estimating mediation effects that vary over time as described in Cai X, Coffman DL, Piper ME, Li R. Estimation and inference for the mediation effect in a time-varying mediation model. BMC Med Res Methodol. 2022;22(1):1-12. 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Package: r-cran-twilio Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-purrr, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-lubridate Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-twilio_0.1.0-1.ca2404.1_all.deb Size: 26190 MD5sum: 97baf85968b61b0b35cf54f4a656e868 SHA1: 550eab6c1273446ffd32dab26c3456329470f765 SHA256: e1196750f0a310ee7218f2501f6246ce089dc4f0ceaa1a7c4a67ef68349ec6ff SHA512: 9ad2151b97e14e2b7c40e1c496b4a4e62ce043efebba1ab7e018e30e70eeb3fc9f1d1fdb589783a4b2333f5803e8e3144e07f20f2c944e66a7ef7ee2e1cdc08e Homepage: https://cran.r-project.org/package=twilio Description: CRAN Package 'twilio' (An Interface to the Twilio API for R) The Twilio web service provides an API for computer programs to interact with telephony. 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Collect, pre-process and analyze the contents of tweets using LDA and structural topic models (STM). Comes with visualizing capabilities like tweet and hashtag maps and built-in support for 'LDAvis'. 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Package: r-cran-twitteradsr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-curl Filename: pool/dists/noble/main/r-cran-twitteradsr_0.1.0-1.ca2404.1_all.deb Size: 23028 MD5sum: 427299a0722027078b14285459cad646 SHA1: 335cbf75b8de07cfc122e586000d9413fe257359 SHA256: 4da5bea0a2e3e1325b64319d79f85160df343ab2774eb6700c6516503e2c6cc4 SHA512: 42a36c9ab5b3f8d9b28dd7566cb219954ebaa4fd52ff5ff97db0308bab4e8af81e1a3ad6b95d9a3cebc36c1921e486d6992c3723251740529a99258813b96eaf Homepage: https://cran.r-project.org/package=twitteradsR Description: CRAN Package 'twitteradsR' (Get Twitter Ads Data via the 'Windsor.ai' API) Collect your data on digital marketing campaigns from Twitter Ads using the 'Windsor.ai' API . Package: r-cran-twitterautomatedtrading Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-dplyr, r-cran-jsonlite, r-cran-lubridate, r-cran-plyr, r-cran-purrr, r-cran-tibble, r-cran-twitter, r-cran-naptime, r-cran-tidytext, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-twitterautomatedtrading_0.1.0-1.ca2404.1_all.deb Size: 123008 MD5sum: 72a6f0872b81d48f865a94f3ae22e97e SHA1: 32628ace5df605400c06e193c90d05bcf284f26d SHA256: 54cf37854a3fe22787020ea265761b5db9f56600c56938c024b59079b332a65e SHA512: a9e0944b1978ae4e6ff5f62a22c9855f8593804d925410711157bbf347560ede0f82d04fc0fb8badf9322a5d4b63d2570ecb80022868780e8f63284bf5a78bdb Homepage: https://cran.r-project.org/package=TwitterAutomatedTrading Description: CRAN Package 'TwitterAutomatedTrading' (Automated Trading Using Tweets) Provides an integration to the 'metatrader 5'. The functionalities carry out automated trading using sentiment indexes computed from 'twitter' and/or 'stockwits'. The sentiment indexes are based on the ph.d. dissertation "Essays on Economic Forecasting Models" (Godeiro,2018) The integration between the 'R' and the 'metatrader 5' allows sending buy/sell orders to the brokerage. Package: r-cran-twitterwidget Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets Filename: pool/dists/noble/main/r-cran-twitterwidget_0.1.1-1.ca2404.1_all.deb Size: 40082 MD5sum: 20f5e46228488a63bdf2408a9847a937 SHA1: 951cb3175fe9a936cc5fc2efd4d2990c0efa485e SHA256: c8e79eb55905a5c995e80a94e0140b70d67b91604d2c54c3e1929a25312ac2ee SHA512: 8a613c267b5658ca0281ea46fe7390d41524476731f0bbde66110cf14043561ac17b0c2c59a8d1ff359fffef54dd73668cad4705374dc47acfa10aee5a11b6b3 Homepage: https://cran.r-project.org/package=twitterwidget Description: CRAN Package 'twitterwidget' (Render a Twitter Status in R Markdown Pages) Include the Twitter status widgets in HTML pages created using R markdown. The package uses the Twitter javascript APIs to embed in your document Twitter cards associated to specific statuses. The main targets are regular HTML pages or dashboards. Package: r-cran-twl Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2331 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-mcmcpack, r-cran-corrplot, r-cran-rfast Filename: pool/dists/noble/main/r-cran-twl_1.0-1.ca2404.1_all.deb Size: 1975644 MD5sum: 41726bde3926a7578e0080363532525a SHA1: 12478c46706bf14d8c916086d6825d8ec42909b2 SHA256: b20dcfef085f8c94f45f3ca7b0573b8d3f3f0c70e84afd1deba1bdfbd54742e8 SHA512: 1594ff5dce8fe2efa41103626bc76e6be536c52ed3f462e7888778fd699f77dcdb94779deef7d926df2163ff3d93130d1580cef414be0a890eb824af608d5250 Homepage: https://cran.r-project.org/package=twl Description: CRAN Package 'twl' (Two-Way Latent Structure Clustering Model) Implementation of a Bayesian two-way latent structure model for integrative genomic clustering. The model clusters samples in relation to distinct data sources, with each subject-dataset receiving a latent cluster label, though cluster labels have across-dataset meaning because of the model formulation. A common scaling across data sources is unneeded, and inference is obtained by a Gibbs Sampler. The model can fit multivariate Gaussian distributed clusters or a heavier-tailed modification of a Gaussian density. Uniquely among integrative clustering models, the formulation makes no nestedness assumptions of samples across data sources -- the user can still fit the model if a study subject only has information from one data source. The package provides a variety of post-processing functions for model examination including ones for quantifying observed alignment of clusterings across genomic data sources. Run time is optimized so that analyses of datasets on the order of thousands of features on fewer than 5 datasets and hundreds of subjects can converge in 1 or 2 days on a single CPU. See "Swanson DM, Lien T, Bergholtz H, Sorlie T, Frigessi A, Investigating Coordinated Architectures Across Clusters in Integrative Studies: a Bayesian Two-Way Latent Structure Model, 2018, , Cold Spring Harbor Laboratory" at for model details. Package: r-cran-twn Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2691 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-crayon, r-cran-rlang, r-cran-stringr Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-tidyr, r-cran-readr Filename: pool/dists/noble/main/r-cran-twn_0.2.6-1.ca2404.1_all.deb Size: 2470128 MD5sum: b94645ec24600bd8366702705c128ccb SHA1: e342a6d525d1afaeb1fd319099793fe233ad157e SHA256: c6bb9344a62c526758fd73c7d92f101f16acf62e2c2a57422547ee5671303591 SHA512: 2fa31eaee7fea44a893dff1784a0ed02aa443c80999f719c07361f82be5c329e3fe811ebcd998ed4121d01fccd116832b864f53a0e560a1b0e5a279351bde490 Homepage: https://cran.r-project.org/package=twn Description: CRAN Package 'twn' (Taxa Waterbeheer Nederland voor R) The TWN-list (Taxa Waterbeheer Nederland) is the Dutch standard for naming taxons in Dutch Watermanagement. This package makes it easier to use the TWN-list for ecological analyses. It consists of two parts. First it makes the TWN-list itself available in R. Second, it has a few functions that make it easy to perform some basic and often recurring tasks for checking and consulting taxonomic data from the TWN-list. Package: r-cran-twoarmsurvsim Architecture: all Version: 0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-blockrand, r-cran-dplyr, r-cran-survival, r-cran-simsurv Filename: pool/dists/noble/main/r-cran-twoarmsurvsim_0.2-1.ca2404.1_all.deb Size: 140852 MD5sum: 96546fb3be045b39450fc644bcc3582e SHA1: add811ef6a813b9e92957677e4b9c0f17c8b1edb SHA256: c4c1a1464692ba2ad84671147102e33ac73e38effffe602952bb28f5581fbc23 SHA512: 22c1b66156ba1722bd03754da63fe6ca47ba7fe613fae111e6b831e0806f5c4d647e81c92d881d83ca5c258fd4a43434b39a068f3e50a25f6b68fb0b1fd0f359 Homepage: https://cran.r-project.org/package=TwoArmSurvSim Description: CRAN Package 'TwoArmSurvSim' (Simulate Survival Data for Randomized Clinical Trials) A system to simulate clinical trials with time to event endpoints. Event simulation is based on Cox models allowing for covariates in addition to the treatment or group factor. 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Package: r-cran-twocoprimary Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 801 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-pbivnorm, r-cran-fpcompare Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tidyr, r-cran-readr Filename: pool/dists/noble/main/r-cran-twocoprimary_1.0.0-1.ca2404.1_all.deb Size: 294846 MD5sum: 78625264085394058024b6d45135e1ca SHA1: 703f73fd10c58400550224121ddb953d2a619c90 SHA256: 5d6c46e02dd02d91a7da356ebd0e4212ffb7789123c33be039f06d005b6d666b SHA512: bc1181680e4b74a78ceebd5d817053b0a1fca77c5fed607c4272ead4ac05e98543f27c07201e48b33a9633ae4215939d17e58fb3d4fad9bbc9d469ec38d62361 Homepage: https://cran.r-project.org/package=twoCoprimary Description: CRAN Package 'twoCoprimary' (Sample Size and Power Calculation for Two Co-Primary Endpoints) Comprehensive functions to calculate sample size and power for clinical trials with two co-primary endpoints. The package supports five endpoint combinations: two continuous endpoints (Sozu et al. 2011 ), two binary endpoints using asymptotic methods (Sozu et al. 2010 ) and exact methods (Homma and Yoshida 2025 ), mixed continuous and binary endpoints (Sozu et al. 2012 ), and mixed count and continuous endpoints (Homma and Yoshida 2024 ). All methods appropriately account for correlation between endpoints and provide both sample size and power calculation capabilities. Package: r-cran-twodcdapsa Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rlang Filename: pool/dists/noble/main/r-cran-twodcdapsa_0.1.1-1.ca2404.1_all.deb Size: 45356 MD5sum: 74eaa11a68726bf1142c7c0a1815121c SHA1: 2076029752755fae7b03d1691fdeaae7a1a9ad4d SHA256: cec63e3a72e545e0d57202ed2501fc2b1e853bd2148703833c019faa093a260b SHA512: 43871a618067e99509fda08eb4fb459d9f6c0dc9d936700d715f731e563f4883e637aed482fe602f1438e95aac0e74e50350b1dd6c56e8247e46102a28fc603a Homepage: https://cran.r-project.org/package=TwoDcDAPSA Description: CRAN Package 'TwoDcDAPSA' (Calculate TwoDcDAPSA: PROs-Joint Contrast (PJC) andSwollen–Tender Joints Contrast (STC) Scores and Quartiles) Provides a calculator for the two-dimensional clinical Disease Activity index for Psoriatic Arthritis (TwoDcDAPSA), a principal component-derived measure that complements the conventional clinical DAPSA score. TwoDcDAPSA captures residual variation in patient-reported outcomes (pain and patient global assessment) and joint counts (swollen and tender) after adjusting for standardized cDAPSA using natural spline coefficients derived from published models. Residuals are standardized and combined with fixed principal component loadings to yield two continuous component scores: the PROs-Joint Contrast (PJC) and the Swollen–Tender joints Contrast (STC), along with quartile-based groupings (including optional combined quartile groupings). The package applies pre-specified coefficients, residual standardization, and loadings to new datasets but does not estimate spline models or principal components itself. Package: r-cran-twodiref Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-qgam, r-cran-mgcv, r-cran-sp, r-cran-ggplot2, r-cran-scales, r-cran-shiny Suggests: r-cran-refreg Filename: pool/dists/noble/main/r-cran-twodiref_0.1.0-1.ca2404.1_all.deb Size: 79972 MD5sum: 996d02d5201a41f5ae702ec8cbe8d1cc SHA1: 0c1acd65acd7f76119623d8df9e03ad97dd29a02 SHA256: 2d1c29bf47b03e33220bdaf48e7b85924d52679a6cab775c56360c6fb9970692 SHA512: 853b8d28b7b9cda01f717d11d7636c3be220c3e3af5b31c5c0258ee92c064c2caac5ea3c765314632aad6e7c1c89f8ad8756bf860964b8ca924993621074322e Homepage: https://cran.r-project.org/package=TwoDiRef Description: CRAN Package 'TwoDiRef' (Robust Estimation of Conditional 2D Reference Regions) Provides tools for constructing conditional two-dimensional reference regions in continuous data, particularly suited for clinical, biological, or epidemiological studies requiring robust multivariate assessment. 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Package: r-cran-twomodeclusteringga Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ga, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-twomodeclusteringga_1.0.0-1.ca2404.1_all.deb Size: 87244 MD5sum: 8dacb72719043abb861cdf48b3fc633e SHA1: 6fcaed39dc9be7df05ff046ee0aa213d74eecd4e SHA256: 80bfb8d997946566d46c73713aefd33c48f08e0d292723f529dde8dc165aaed7 SHA512: c0aaecc8d97ddbf7f78bbab7a7fcca72892148afc4b775a7513eb7d3aff99cfc701256a70819e107c198caf131ebb8368d2a78e06e71fad65fcd1473e4d56f24 Homepage: https://cran.r-project.org/package=twomodeclusteringGA Description: CRAN Package 'twomodeclusteringGA' (Genetic Algorithm Based Two-Mode Clustering) Implements two-mode clustering (biclustering) using genetic algorithms. The method was first introduced in Hageman et al. (2008) . The package provides tools for fitting, visualization, and validation of two-mode cluster structures in data matrices. Package: r-cran-twopartm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 903 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-mass Filename: pool/dists/noble/main/r-cran-twopartm_0.1.0-1.ca2404.1_all.deb Size: 814928 MD5sum: e7248821dd2a48a4412752169b0c2ef3 SHA1: de303c5c88296148976e2750877c920ab75aa860 SHA256: ff418486f1572abe56468865440ce0713275792f740fd3890bb01e150745bf4f SHA512: 74ab0bdd50d04e5c204f2e3e2b8c4a6711ead937515df6e847e3f77cc7a05efe157960353aef7ced84462158de7f55c5416d33a0c6115eb818385b3458d56455 Homepage: https://cran.r-project.org/package=twopartm Description: CRAN Package 'twopartm' (Two-Part Model with Marginal Effects) Fit two-part regression models for zero-inflated data. The models and their components are represented using S4 classes and methods. Average Marginal effects and predictive margins with standard errors and confidence intervals can be calculated from two-part model objects. Belotti, F., Deb, P., Manning, W. G., & Norton, E. C. (2015) . Package: r-cran-twopexp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-twopexp_0.1.0-1.ca2404.1_all.deb Size: 39682 MD5sum: ac45247310aa373175c217f8f23e7cf6 SHA1: 6be3e70ad475b5329a28a67051202bdc4c0cd952 SHA256: 640e7d3192ff324285c725ff7c0036a2bcd8be6f0cf75d2af0e8d92fb17136c2 SHA512: 943b50212f234b3a7d1ea489f7ae52a37ccd51a99d3891814e01875970118d050720603a4788863427527274bb876a9b06704be4a97378d8b9a2de07c2571d31 Homepage: https://cran.r-project.org/package=twopexp Description: CRAN Package 'twopexp' (The Two Parameter Exponential Distribution) Density, distribution function, quantile function, and random generation function, maximum likelihood estimation (MLE), penalized maximum likelihood estimation (PMLE), the quartiles method estimation (QM), and median rank estimation (MEDRANK) for the two-parameter exponential distribution. MLE and PMLE are based on Mengjie Zheng (2013). QM is based on Entisar Elgmati and Nadia Gregni (2016). MEDRANK is based on Matthew Reid (2022). 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Version 1.1.1 includes improved efficiency factor classification with tolerance control, updated plot visualizations, and improved clarity of the results. The conceptual framework and the term two-phase were introduced by McIntyre (1955) ). Package: r-cran-tworegression Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4838 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-gridextra, r-cran-lubridate, r-cran-magrittr, r-cran-pautilities, r-cran-proc, r-cran-rcpproll, r-cran-rlang, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-tworegression_1.1.1-1.ca2404.1_all.deb Size: 873654 MD5sum: ee7115abdd971143d99b9c70024c8539 SHA1: bcd83278e96f55aa04ffed31690b8ea949ba9757 SHA256: 6fe51260cb8e34bdb5019ede6519f5ab97f780ce368e164748ba163d630cd394 SHA512: e258e571cb09be3bc698822d12abe75c7caec96cb406d10ef68f13ad6009ad965ffbd8d5e443cccea7639307f93cd148bfae48064a36762cbe48caae46711312 Homepage: https://cran.r-project.org/package=TwoRegression Description: CRAN Package 'TwoRegression' (Develop and Apply Two-Regression Algorithms) Facilitates development and application of two-regression algorithms for research-grade wearable devices. 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Clément de Chaisemartin, Xavier D'Haultfœuille (2020) . Package: r-cran-twowaytests Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-onewaytests, r-cran-ggplot2, r-cran-nortest, r-cran-car, r-cran-wesanderson, r-cran-mass Suggests: r-cran-wrs2 Filename: pool/dists/noble/main/r-cran-twowaytests_1.5-1.ca2404.1_all.deb Size: 137564 MD5sum: 3e8a73b44d33abc0fa3648129a3e895f SHA1: 9d301be813451c894144be227465a7250ae13d7a SHA256: f89afd51cdde250f5efb273d1ecf1357bce89de6a3fe8529139fc6d19060fcab SHA512: 1baf29ad1dfecbb12b55d277ef15c2882f0850f160690c1d1cd0808124a81eb0c61052a81bec037f613498a1e7389a5c556e5b08a0a958dbccb9290ce411f8c7 Homepage: https://cran.r-project.org/package=twowaytests Description: CRAN Package 'twowaytests' (Two-Way Tests in Independent Groups Designs) Performs two-way tests in independent groups designs. 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The package provides tools to compute measures of effect (odds ratio, risk ratio, and risk difference), calculate impact numbers and attributable fractions, and perform hypothesis testing. Statistical analysis methods are oriented towards epidemiological investigation of relationships between exposures and outcomes. Package: r-cran-tww Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-tww_0.1.0-1.ca2404.1_all.deb Size: 22258 MD5sum: 750c87e122520751556faefe8a4cdad0 SHA1: 498bc02977b50c758ab183a2d2b61676a54d73ce SHA256: ced44534db1c7a1749c8c0b1058b241c812e268f1c3ba668f3f88106d0842c36 SHA512: e9ca4f9de3e2344d19c84177683413c92f1f17e6209f6762ac0058e1455f2b02e571f099f829281d7e9fe038ad44ba36c987f275ff544eb211b08cc5d6d6afae Homepage: https://cran.r-project.org/package=TWW Description: CRAN Package 'TWW' (Growth Models) A model for the growth of self-limiting populations using three, four, or five parameter functions, which have wide applications in a variety of fields. The dependent variable in a dynamical modeling could be the population size at time x, where x is the independent variable. In the analysis of quantitative polymerase chain reaction (qPCR), the dependent variable would be the fluorescence intensity and the independent variable the cycle number. This package then would calculate the TWW cycle threshold. Package: r-cran-txeffectssurvival Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-txeffectssurvival_1.0.2-1.ca2404.1_all.deb Size: 85538 MD5sum: 293aa48e9ba26a166da4068d188e83ca SHA1: c21cc10169c4930f0e5e29b926dbb93d96b13757 SHA256: 3083d1e686091e85061a7c2d7821ceebae39a98a3b1089e58bb9c6702a66a6b6 SHA512: 93aab75a786d40b5436e4f1b7d746da073dccf838ca2ca7b7addd6d895311650c7b18d9ff993244f025df76ce98868cf0eb7acaf43368de2432ab29de1c2259a Homepage: https://cran.r-project.org/package=TxEffectsSurvival Description: CRAN Package 'TxEffectsSurvival' (Treatment Effect Inference for Terminal and Non-Terminal Eventsunder Competing Risks) Provides several confidence interval and testing procedures, based on either semiparametric (using event-specific win ratios) or nonparametric measures, including the ratio of integrated cumulative hazard (RICH) and the ratio of integrated transformed cumulative hazard (RITCH), for treatment effect inference with terminal and non-terminal events under competing risks. The semiparametric results were developed in Yang et al. (2022 ), and the nonparametric results were developed in Yang (2025 ). For comparison, results for the win ratio (Finkelstein and Schoenfeld 1999 ), Pocock et al. 2012 , and Bebu and Lachin 2016 ) are included. The package also supports univariate survival analysis with a single event. In this package, effect size estimates and confidence intervals are obtained for each event type, and several testing procedures are implemented for the global null hypothesis of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazards assumptions, under which the event-specific win ratios converge to hazard ratios, and a test of equal hazard ratios, are provided. For summarizing the treatment effect across all events, confidence intervals for linear combinations of the event-specific win ratios, RICH, or RITCH are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al. (2022 ) and Yang (2025 ). 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It creates either intermediate flight control files for the DJI-Litchi supported series or ready to upload control files for the pixhawk-based flight controller. Additionally it contains some useful tools for digitizing and data manipulation. 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'UBayFS' is an ensemble feature selection technique embedded in a Bayesian statistical framework. The method combines data and user knowledge, where the first is extracted via data-driven ensemble feature selection. The user can control the feature selection by assigning prior weights to features and penalizing specific feature combinations. 'UBayFS' can be used for common feature selection as well as block feature selection. Package: r-cran-ubcrm Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 107 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-ubcrm_1.0.3-1.ca2404.1_all.deb Size: 74796 MD5sum: 959c7255be67b4bc2221245c62816fbf SHA1: ba941522e2a6cbb9b1074aae27aa977a217f7b57 SHA256: 5f77be734eb21b4fa379b20c78a841900b32a070db63a48cca8d52abe59de7b1 SHA512: 0bbb1ce3f6ec689b10db6b2cf2e71f47b47f52d2959bd89b4cb46258690efe7d36d07a63eb14e3ca792117d05a59d6311918e32f46ec920fd6fcbd97e53e3f88 Homepage: https://cran.r-project.org/package=UBCRM Description: CRAN Package 'UBCRM' (Simulate and Conduct Dose-Escalation Phase I Studies) Two Phase I designs are implemented in the package: the classical 3+3 and the Continual Reassessment Method (). Simulations tools are also available to estimate the operating characteristics of the methods with several user-dependent options. 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Package: r-cran-ucimlrepo Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ucimlrepo_0.0.2-1.ca2404.1_all.deb Size: 660318 MD5sum: 382bd52a349b615d6d16c847525031c0 SHA1: 33013df7c97bf677fa44653414be8d2a4ff305bf SHA256: 518bb5f1f0002d8f3433aeffb6465c7ca1a86c1ea4434ccf6cea590851029001 SHA512: 76b4e673b05b9d520caa70d9de0663dd1e0f6db940fcf69788a6ab1c81d7f06fcd049469070c53baa9edb3dd98231be5fcae1c10e10f1be4d72e60dfc1058cea Homepage: https://cran.r-project.org/package=ucimlrepo Description: CRAN Package 'ucimlrepo' (Explore UCI ML Repository Datasets) Find and import datasets from the University of California Irvine Machine Learning (UCI ML) Repository into R. Supports working with data from UCI ML repository inside of R scripts, notebooks, and 'Quarto'/'RMarkdown' documents. 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The package contains implementations of nonparametric statistical tests for sample homogeneity, group separation, clustering, and classification of multivariate data. The methods have high statistical power and are tailored for data in which the dimension L is much larger than sample size n. See Gabriela B. Cybis, Marcio Valk and Sílvia RC Lopes (2018) , Marcio Valk and Gabriela B. Cybis (2020) , Debora Z. Bello, Marcio Valk and Gabriela B. Cybis (2021) . 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Package: r-cran-ucscxenashiny Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8549 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-ezcox, r-cran-forcats, r-cran-ggplot2, r-cran-ggpubr, r-cran-httr, r-cran-magrittr, r-cran-ppcor, r-cran-psych, r-cran-purrr, r-cran-rlang, r-cran-shiny, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-ucscxenatools Suggests: r-cran-covr, r-cran-cowplot, r-cran-dt, r-cran-funkyheatmap, r-cran-furrr, r-cran-future, r-cran-ggrepel, r-cran-ggstatsplot, r-cran-glue, r-cran-knitr, r-cran-pacman, r-cran-plotly, r-cran-plyr, r-cran-rcolorbrewer, r-cran-readr, r-cran-rmarkdown, r-cran-rtsne, r-cran-scales, r-cran-survival, r-cran-survminer, r-cran-testthat, r-cran-umap Filename: pool/dists/noble/main/r-cran-ucscxenashiny_2.2.1-1.ca2404.1_all.deb Size: 3812978 MD5sum: 16509432854de486be264943dc5a7d6a SHA1: 7ae9e6b5344eb4a83114b32f4ea6919d0165e29b SHA256: 2227f8ac689ab16b57c434b4f8749da0137d863c91f70ea25e7be75903fd70de SHA512: 5b41b93ec4c07becd82ca1f79597a8ddfcebfbb50da1ae3ea178721f50de013fb621ce08b9096dfc1aa365fdb21bbc0379ac8a261feab804aa81a868cad932f5 Homepage: https://cran.r-project.org/package=UCSCXenaShiny Description: CRAN Package 'UCSCXenaShiny' (Interactive Analysis of UCSC Xena Data) Provides functions and a Shiny application for downloading, analyzing and visualizing datasets from UCSC Xena (), which is a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. 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Package: r-cran-ucsfindustrydocs Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 95 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-arrow, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-magrittr, r-cran-dplyr, r-cran-r6, r-cran-stringr Suggests: r-cran-mockery, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ucsfindustrydocs_0.1.0-1.ca2404.1_all.deb Size: 65494 MD5sum: 9edc9fc00976a748505373baab1c2e6b SHA1: 5ec65accd8b9a166dd8121aa4c813bf1949cfa47 SHA256: de1532ee09181d7023ffc0b06007dace8f783090a0e27425367506f88c87a05e SHA512: 35b2cdb27ac4a11c311182f5ae5aab0b3c32376a97c3591043596bfcf6f35193f5ca6c9e3140f1f323cf676d884e237ccbe79865acd68c6e65b49b6c1a48714c Homepage: https://cran.r-project.org/package=ucsfindustrydocs Description: CRAN Package 'ucsfindustrydocs' (UCSF Industry Documents Library API Wrapper) Serves as a R wrapper for the University of California San Francisco's [Industry Documents Digital Library] API. 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Package: r-cran-udderquarterinfectiondata Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-udderquarterinfectiondata_1.0.0-1.ca2404.1_all.deb Size: 39538 MD5sum: 02ad4ed9e9628b9d61996566a361b36b SHA1: 2306e829bc4ba9ffaf40129fe9048696e25f328b SHA256: 61fdf24d270642d622148f5c0d9060ec34ff17fdf8aad407fce803a5bd8597b7 SHA512: ca071adc7b56358cf0909c68b5915a25f39fb767516ac0cf03525474067bab6a040bb8b0a70c36a5a221372a870f13f587353b3b95998d94e75b005a59922afe Homepage: https://cran.r-project.org/package=UdderQuarterInfectionData Description: CRAN Package 'UdderQuarterInfectionData' (Udder Quarter Infection Data) The udder quarter infection data set contains infection times of individual cow udder quarters with Corynebacterium bovis (Laevens et al. 1997 ). Obviously, the four udder quarters are clustered within a cow, and udder quarters are sampled only approximately monthly, generating interval-censored data. The data set contains both covariates that change within a cow (e.g., front and rear udder quarters) and covariates that change between cows (e.g., parity [the number of previous calvings]). The correlation between udder infection times within a cow also is of interest, because this is a measure of the infectivity of the agent causing the disease. Various models have been applied to address the problem of interdependence for right-censored event times. These models, as applied to this data set, can be found back in the publications found in the reference list. 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This package has been developed to compute Uniform Error Index from ten different loss function like Error Square, Square of Square Error, Quasi Likelihood Error, LogR-Square, Absolute Error, Absolute Square Error etc. The weights are determined using Principal Component Analysis (PCA) algorithm of Yeasin and Paul (2024) . 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For the latter the estimation of the nonparametric scale function is carried out by means of a data-driven smoothing approach. Model quality, in terms of forecasting VaR and ES, can be assessed by means of various backtesting methods such as the traffic light test for VaR and a newly developed traffic light test for ES. The approaches implemented in this package are described in e.g. Feng Y., Beran J., Letmathe S. and Ghosh S. (2020) as well as Letmathe S., Feng Y. and Uhde A. (2021) . 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Therefore, distinct functionalities are being 'consciously uncoupled' into different packages. This package contains the general-purpose tools and utilities (see the 'behaviorchange' package, the 'rosetta' package, and the soon-to-be-released 'scd' package for other functionality), and is the most direct 'successor' of the original 'userfriendlyscience' package. For example, this package contains a number of basic functions to create higher level plots, such as diamond plots, to easily plot sampling distributions, to generate confidence intervals, to plan study sample sizes for confidence intervals, and to do some basic operations such as (dis)attenuate effect size estimates. Package: r-cran-ugarima Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lamw Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-ugarima_0.1.0-1.ca2404.1_all.deb Size: 20456 MD5sum: 68a1e4e6d873bd00981080f64c00bba5 SHA1: 9aae4dd30841de2634eec011f98eb63fd93c517f SHA256: 753602adbf1a61af61c08ccbd0efe538a57ae6ce74ae3f14b00036121d567bf3 SHA512: 2d8792482efb3cb735aae28fa0026276e42678d7186c1c4d7a08cdfd2a173c2787d1bd8b65f6ca9da4549449ec043f7ffefc66889c681109525bce306d9029fe Homepage: https://cran.r-project.org/package=UGarima Description: CRAN Package 'UGarima' (The Unit-Garima Distribution) Density, distribution function, quantile function, and random generating function of the Unit-Garima distribution based on Ayuyuen, S., & Bodhisuwan, W. (2024). 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Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2015) , Mika Meitz, Daniel Preve, Pentti Saikkonen (2023) , Savi Virolainen (2022) . 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Package: r-cran-ukbtools Architecture: all Version: 0.11.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3747 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-ggplot2, r-cran-xml, r-cran-magrittr, r-cran-tibble, r-cran-tidyr, r-cran-scales, r-cran-stringr, r-cran-foreach, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ukbtools_0.11.3-1.ca2404.1_all.deb Size: 2118692 MD5sum: 137b4bdf3316e99f67d0ddd25f9d346b SHA1: 9c9969b2ce9d2d1c005ca734b5b3d7e0bd190a48 SHA256: d33f7e17d1a5ab4bd5631f2fb50b4e643fa91171989234070c26d8800284b955 SHA512: 5021643624ebf527c73348d761df6e388369fc72b19c5f511ceda05633c2b420e4c6681b0e1d7f705095917f6adbe9289ba03acbc7f17ec0680b696ba533e8d4 Homepage: https://cran.r-project.org/package=ukbtools Description: CRAN Package 'ukbtools' (Manipulate and Explore UK Biobank Data) A set of tools to create a UK Biobank dataset from a UKB fileset (.tab, .r, .html), visualize primary demographic data for a sample subset, query ICD diagnoses, retrieve genetic metadata, read and write standard file formats for genetic analyses. Package: r-cran-ukc19 Architecture: all Version: 0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4472 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-ukc19_0.0.3-1.ca2404.1_all.deb Size: 3350452 MD5sum: 7a04d5acf809b60573bd7178d372d5a6 SHA1: af6bf808a80e144d251bc52adf0f54da5a63d8ca SHA256: 9e521dd0e552ec00f1dc6ad03ce3277108fba53933266631d987eb3df22951f2 SHA512: 553161d4f9ca61a7f212c5f040266dfe2ad76c7d4aace0ff20c4335f451f625153c6c9399a6d66156266800377641548454d1a09c692459873f54141f0c4b0d1 Homepage: https://cran.r-project.org/package=ukc19 Description: CRAN Package 'ukc19' (Datasets from the UK COVID-19 Outbreak) Provides easy access to a curated selection of pre-processed data sets relevant to the COVID-19 outbreak in the UK for teaching and demonstration purposes. Package: r-cran-ukfe Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1059 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-sf Filename: pool/dists/noble/main/r-cran-ukfe_2.0.1-1.ca2404.1_all.deb Size: 1013392 MD5sum: 0481deb6b2324429716458fd4b487cfa SHA1: ce28ad17141c42f8acce4130154b749d644910d2 SHA256: 8ebc0f2a193f59cb7bad2e498cc7ab060a9421a3be70eae4ba2928902cefdc47 SHA512: 60ce779728aa16bc5799bcb5d40d65a3b71bbc3b1a81d9fdfd08f88ed6ae0a29058c03000b4cb3895a446ff40fe3d149addfd2a8e9ba952ce815e012ef507d6a Homepage: https://cran.r-project.org/package=UKFE Description: CRAN Package 'UKFE' (UK Flood Estimation) Functions to implement the methods of the Flood Estimation Handbook (FEH), associated updates and the revitalised flood hydrograph model (ReFH). Currently the package uses NRFA peak flow dataset version 14. Aside from FEH functionality, further hydrological functions are available. Most of the methods implemented in this package are described in one or more of the following: "Flood Estimation Handbook", Centre for Ecology & Hydrology (1999, ISBN:0 948540 94 X). "Flood Estimation Handbook Supplementary Report No. 1", Kjeldsen (2007, ISBN:0 903741 15 7). "Regional Frequency Analysis - an approach based on L-moments", Hosking & Wallis (1997, ISBN: 978 0 521 01940 8). "Making better use of local data in flood frequency estimation", Environment Agency (2017, ISBN: 978 1 84911 387 8). "Sampling uncertainty of UK design flood estimation" , Hammond (2021, ). "The FEH 2025 statistical method update", UK Centre for Ecology and Hydrology (2025). "Low flow estimation in the United Kingdom", Institute of Hydrology (1992, ISBN 0 948540 45 1). Data from the UK National River Flow Archive (, terms and conditions: ). Package: r-cran-ukgasapi Architecture: all Version: 0.21-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 75 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-xml Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ukgasapi_0.21-1.ca2404.1_all.deb Size: 27962 MD5sum: 7f0fa9c55072762169a6c9768603bf62 SHA1: 91fd30bf0c311baa79d18974f0bbbc93e4e77011 SHA256: 38d888d11c973c79ed223c12052cd7fdd5e206def81f5b0582d9076185e6579b SHA512: 9d4711aba088214b420e9d8bf35530e57fe12f1b9c05ba0504e26b86c5fc4ae1976d4837765345575f03eee816e9e937982e39a19536a12767d84ceeb0760f63 Homepage: https://cran.r-project.org/package=ukgasapi Description: CRAN Package 'ukgasapi' (API for UK Energy Market Information) Allows users to access live UK energy market information via various APIs. 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Package: r-cran-ukhousing Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-sf, r-cran-testthat Filename: pool/dists/noble/main/r-cran-ukhousing_0.1.0-1.ca2404.1_all.deb Size: 140026 MD5sum: 57c3f4349a92ad9a90038d4649b6cc85 SHA1: a46af18cedc3c3dd2bcc26e4c507218e8489bfdd SHA256: 91e22479c08e7f7f0d4b7a1fb9a10c06dd1594111f0ee9ef34141eca9d8fd2f0 SHA512: 0b9b209f55a27c973a626e3bb391e14577bf245d25d962550d4456744ba9b5c893195defd7d92f6b1d38ce044608b5e429a2309f77b94d195e6ca34b1dc96db4 Homepage: https://cran.r-project.org/package=ukhousing Description: CRAN Package 'ukhousing' (Access UK Housing Data from Land Registry, EPC, and Planning) Fetch UK housing data from official sources. Access the UK House Price Index and Price Paid Data from 'HM Land Registry' , domestic and non-domestic Energy Performance Certificates from the 'MHCLG' Open Data service , and planning application, brownfield land, and local plan data from 'planning.data.gov.uk' . Data covers all 441 UK local authorities from 1995 to the present. Functions accept flexible filters (postcode, local authority, property type, date range) and return tidy data frames. Downloaded data is cached locally for subsequent calls. Package: r-cran-ukhsadatr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-ukhsadatr_0.1.1-1.ca2404.1_all.deb Size: 25636 MD5sum: ad90c9494f95160d9678e3a2e7604050 SHA1: 9bb64015a8a4d3e06575e698a3a876ec7e80650d SHA256: 8e9264ee6303480f80eabe3b1fbebe15491bdbe41a944f397e33b31cce84ff70 SHA512: b44dd0ce9139a3ca36827b7abf4a99a0c1c092c3f7267ff98e491a605d23465a424a7d365406323dee8d3bfc4e072b6845b682fe1f0930e418e381b30fec0132 Homepage: https://cran.r-project.org/package=ukhsadatR Description: CRAN Package 'ukhsadatR' (R Interface to Access UKHSA Dashboard Data) Programmatic interface to access data from the UK Health Security Agency (UKHSA) Data Dashboard API. The package was originally based on the 'ukcovid19' package by Pouria Hadjibagheri and has been substantially rewritten and extended. For more information on the API, see . Package: r-cran-uklr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3181 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-curl, r-cran-httr, r-cran-jsonlite, r-cran-tibble Suggests: r-cran-markdown, r-cran-covr, r-cran-spelling, r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-uklr_1.0.2-1.ca2404.1_all.deb Size: 1453982 MD5sum: bbf3c5d7316973c0de9ed6382fa5854b SHA1: dd3854db676c33c3445f9b327b530704fe9bed58 SHA256: bdd230feef03e66341f1cc5ce52a28693bd5732d89505db641951331f9b3b463 SHA512: 78e5870bcecefa24e7727abb4405eae6b296e2fc8fb7c4fe485c3b0d644a7da8b450f92ad4bb1a87833115e0267bc9c4596394440b89501d7e9a8ded7d0b3cb4 Homepage: https://cran.r-project.org/package=uklr Description: CRAN Package 'uklr' (Client to United Kingdom Land Registry) Access data from Land Registry Open Data through 'SPARQL' queries. 'uklr' supports the house price index, transaction and price paid data. Package: r-cran-ukpolice Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-tibble, r-cran-purrr, r-cran-httr, r-cran-snakecase Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-dplyr, r-cran-leaflet, r-cran-htmltools, r-cran-scales, r-cran-sf, r-cran-sp Filename: pool/dists/noble/main/r-cran-ukpolice_0.2.2-1.ca2404.1_all.deb Size: 258414 MD5sum: c813f45d71e447c23e75de627e141af9 SHA1: 11df670f6cb5b9d5c4f64c2b2f8591eb426f6db5 SHA256: 313d3e2b0e2308ef98848ca0f77defe01eea9b57f42ae8b969e2d333b41f4b7b SHA512: cc484413360e14a68a8e69fb79750a6fde4e90b14e5c3017e8feff27a12c2db9f7cb797232bec97cabf7f84df03a2b4e09d41f5ca5dc18d2cac85f1dedcb78e3 Homepage: https://cran.r-project.org/package=ukpolice Description: CRAN Package 'ukpolice' (Download Data on UK Police and Crime) Downloads data from the 'UK Police' public data API, the full docs of which are available at . 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Package: r-cran-ulex Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 911 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-readr, r-cran-purrr, r-cran-tidytext, r-cran-stringr, r-cran-stringi, r-cran-ngram, r-cran-hunspell, r-cran-stringdist, r-cran-tm, r-cran-raster, r-cran-sf, r-cran-quanteda, r-cran-geodist, r-cran-spacyr Filename: pool/dists/noble/main/r-cran-ulex_0.1.1-1.ca2404.1_all.deb Size: 890852 MD5sum: ab8d47b5c34cc699c682540185ef7392 SHA1: a6ee69f9d746165050f3d261f7a62cdd6d0e901a SHA256: 761549622a33877872c1129e763af2e8c47be191f77b50c63eac945fa892b9f1 SHA512: be0ff55c8cae2386fb766fb772ec386c1b7c448c32599f0a645860a4c7f46dd92c918d4b1200c75dad4173748f75578f67bbe533d08c7a59f7651bb50ffc5d09 Homepage: https://cran.r-project.org/package=ulex Description: CRAN Package 'ulex' (Unique Location Extractor) Extracts coordinates of an event location from text based on dictionaries of landmarks, roads, and areas. 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Package: r-cran-ulrb Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2233 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-rlang, r-cran-tidyr, r-cran-clustersim, r-cran-gridextra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-vegan Filename: pool/dists/noble/main/r-cran-ulrb_0.1.8-1.ca2404.1_all.deb Size: 1504512 MD5sum: 099df4435a2eeb8e090922854204ba12 SHA1: 6aa2de27d319ffa6aff5f6f616a31024bec57594 SHA256: c16b82328dba01da257038eedfac9e1e3d7c5cf45c485c99b187a388635a5c75 SHA512: 3b4db60e764501184b08cd7c61d42507b91997715289fe1a116e34e339d2368f3dabb339ea5f9c05ef9522b4e9e592f2ced50f1d00b11037e3a586dd55f3058b Homepage: https://cran.r-project.org/package=ulrb Description: CRAN Package 'ulrb' (Unsupervised Learning Based Definition of Microbial RareBiosphere) A tool to define the rare biosphere. 'ulrb' solves the problem of the definition of rarity by replacing arbitrary thresholds with an unsupervised machine learning algorithm (partitioning around medoids, or k-medoids). This algorithm works for any type of microbiome data, provided there is an abundance table. This method also works for non-microbiome data. Package: r-cran-ultimixt Architecture: all Version: 2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-coda, r-cran-gtools Filename: pool/dists/noble/main/r-cran-ultimixt_2.1-1.ca2404.1_all.deb Size: 157408 MD5sum: f139133a6ae0b591c9284b0fa8a0275f SHA1: 71fe1137123ef783bba0b2874641a1e7cd142a3b SHA256: c990396a35b9e7332a76a5f64a51c556fa6d26d6daca6b9c61be7e92dc76c825 SHA512: 9862b2e507c3edb245aa1a671ec8c397e1e65f760bc00ecc2e7058655ae232177db132e2657b739f4cdccc986802fa7d6f46c410f851bc3e3ad629c3c92b2108 Homepage: https://cran.r-project.org/package=Ultimixt Description: CRAN Package 'Ultimixt' (Bayesian Analysis of Location-Scale Mixture Models using aWeakly Informative Prior) A generic reference Bayesian analysis of unidimensional mixture distributions obtained by a location-scale parameterisation of the model is implemented. 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Package: r-cran-ultrapolarplot Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-tibble, r-cran-rjson, r-cran-ggplot2, r-cran-cairo, r-cran-purrr, r-cran-plyr, r-cran-tidyr, r-cran-readr, r-cran-stringr, r-cran-ggsignif, r-cran-ggpubr, r-cran-car, r-cran-rstatix, r-cran-vegan Filename: pool/dists/noble/main/r-cran-ultrapolarplot_0.2.1-1.ca2404.1_all.deb Size: 159396 MD5sum: 56cd7dbc54ab4060b57ce384c8655206 SHA1: a0113297f89b195b91c9a012dae3476c97cdddf5 SHA256: fd839c4253037d7571e52a3f1dd4deebce0ba936023d76c29835c4d5355915d8 SHA512: e9908ad4dbf4488fb00594780b2fb8075dab0471642848fa58e2adfad195efca8ee41d8e7938dfd377e808f4afc2780b9508da260c3d3a7d788249b945b8935c Homepage: https://cran.r-project.org/package=ultrapolaRplot Description: CRAN Package 'ultrapolaRplot' (Plotting Ultrasound Tongue Traces) Plots traced ultrasound tongue imaging data according to a polar coordinate system. There is currently support for plotting means and standard deviations of each category's trace; Smoothing Splines Analysis of Variance (SSANOVA) could be implemented as well. The origin of the polar coordinates may be defined manually or automatically determined based on different algorithms. Points for each category can be split into two groups (anterior and posterior) at the point of maximum curvature of each trace. User can specify rays to intersect various parts of the tongue; intersections along these rays serve as input for a pairwise t-test to measure significant contrasts between segments. Currently 'ultrapolaRplot' supports ultrasound tongue imaging trace data from 'UltraTrace' (). 'UltraTrace' is capable of importing data from Articulate Instruments AAA. 'read_textgrid.R' is required for opening TextGrids to determine category and alignment information of ultrasound traces. Package: r-cran-umbridge Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-umbridge_1.0-1.ca2404.1_all.deb Size: 39650 MD5sum: bcf1dc8d0352ee0a6bae128ad23026d1 SHA1: dad36f27b3fda01d9d9de105f813d77dfaddf2de SHA256: 9c94af86067638b497d860c59a64c00cf03341f52292f37857968c0b41383a9e SHA512: bf2d125806e97978ba359ab0f23f9a0e2a3bb0e0949616d9ce10a223cc1d16c8ac917d76160fa806fdc39a8ba9303b9f4e795b4e5d23577c85520ba49ae31237 Homepage: https://cran.r-project.org/package=umbridge Description: CRAN Package 'umbridge' (Integration for the UM-Bridge Protocol) A convenient wrapper for the UM-Bridge protocol. UM-Bridge is a protocol designed for coupling uncertainty quantification (or statistical / optimization) software to numerical models. 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The methodology follows the workflow described in Leefmann et al. (2019) . The package supports the inspection, filtering and visualization of molecular formula data and includes utilities for calculating common molecular parameters (e.g., double bond equivalents, DBE). A graphical user interface is available via the 'shiny'-based 'ume' application. 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Package: r-cran-umoments Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-umoments_1.0.1-1.ca2404.1_all.deb Size: 280336 MD5sum: 97a8e18346886d3926ea15e25bc772e5 SHA1: 04f9d70248bc78e83e36961b79dd9730b93cc726 SHA256: df13a3f09b73cc68145f4ce8a1aacf040801131409991c8e70e42e6f86f2a379 SHA512: d92e478a9b66622c181a1785e17c1bd0708f951e311d30ce8af67627924b98ebc78c3bb2832bf1fdddc35bc2a595f641dc5928fe8fb2c186ee2df32d238bf1aa Homepage: https://cran.r-project.org/package=Umoments Description: CRAN Package 'Umoments' (Unbiased Central Moment Estimates) Calculates one-sample unbiased central moment estimates and two-sample pooled estimates up to 6th order, including estimates of powers and products of central moments. 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Package: r-cran-umpire Architecture: all Version: 2.0.11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 834 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mc2d, r-cran-bimodalindex Suggests: r-cran-mclust, r-cran-survival, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-umpire_2.0.11-1.ca2404.1_all.deb Size: 561186 MD5sum: aad94e0bedbbd17445598847fc92ae46 SHA1: 816c75628d501066b22af92e8021c7daa4a8e394 SHA256: 46ba19ee76e416885a524cf35957a68bed52dace96452025c2465ce9564c0ade SHA512: c6fa7943253cc03b0093f726e82b47e4e8c90934ae350a8d6c9736399019250bb21b8a78379817cb5fc391e5b26ad11527b219f205182c5318ca118c0468086e Homepage: https://cran.r-project.org/package=Umpire Description: CRAN Package 'Umpire' (Simulating Realistic Gene Expression and Clinical Data) The Ultimate Microrray Prediction, Reality and Inference Engine (UMPIRE) is a package to facilitate the simulation of realistic microarray data sets with links to associated outcomes. See Zhang and Coombes (2012) . Version 2.0 adds the ability to simulate realistic mixed-typed clinical data. Package: r-cran-umweltapir Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-tidyr Suggests: r-cran-testthat, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-umweltapir_0.1.0-1.ca2404.1_all.deb Size: 27294 MD5sum: 84d51ace266d780933c7af3b6fa000ac SHA1: 5fcd07944d814513ebb4c8c6bf6f7e7a36f2f7eb SHA256: e6716322ed7aa7910baa56217523c54de64e9350c9ef84bba763a03ea2148c5b SHA512: 283cab34d7b9e7237db8f27f8ecddf525b409fd3e2d47a3d4b19c4e5451f06d134b06334dd27ac8d7d37e3b9843fc368e33415aeb494d49e4c18804b6ea4942f Homepage: https://cran.r-project.org/package=umweltapir Description: CRAN Package 'umweltapir' (Access Umwelt.Info API) Provides an R-based access to the datasets including their resources from the portal . The package allows for an easy integration of those datasets into your R-based workflows. The functionality of the package mirrors the web-based access as provided at . You can use the same queries and get the same datasets by accessing our API. 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See '?umx' for help, and umx_open_CRAN_page("umx") for NEWS. Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R. Twin Research and Human Genetics, 22, 27-41. . Package: r-cran-unalr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3069 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-d3r, r-cran-data.tree, r-cran-dplyr, r-cran-dt, r-cran-dygraphs, r-cran-echarts4r, r-cran-fmsb, r-cran-forcats, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggspatial, r-cran-gridsvg, r-cran-gt, r-cran-gtextras, r-cran-highcharter, r-cran-htmltools, r-cran-htmlwidgets, r-cran-jsonlite, r-cran-leaflet, r-cran-leaflet.extras, r-cran-lifecycle, r-cran-magrittr, r-cran-maps, r-cran-plotly, r-cran-png, r-cran-rlang, r-cran-scales, r-cran-sf, r-cran-sp, r-cran-stringr, r-cran-sunburstr, r-cran-tidyr, r-cran-treemap, r-cran-webshot, r-cran-xml, r-cran-xts, r-cran-zoo Suggests: r-cran-testthat, r-cran-cowplot, r-cran-ggthemes, r-cran-magick, r-cran-pals, r-cran-readxl, r-cran-rsvg, r-cran-tibble, r-cran-viridis Filename: pool/dists/noble/main/r-cran-unalr_1.0.1-1.ca2404.1_all.deb Size: 3005622 MD5sum: 3b0c1c5844d739f3fb48722a33a18b81 SHA1: c145f15b9fcef6d8d372c6c8659d718d6aecc2a8 SHA256: a56ab86ed535630b1c57dc7fe6391c1f2fc55328cc4aad5d02ccc973a13f26a0 SHA512: 57f53900201b9bd3132ccc6d05bb8554ab2a0512a485f1651c4dfa3bccb31b23af426a24189179c022addb8d020ecd941409d12545dbd9eb935419955c9b6042 Homepage: https://cran.r-project.org/package=UnalR Description: CRAN Package 'UnalR' (Una implementación de funciones de uso interno) Una herramienta rápida y consistente para la disposición de microdatos y la visualización de las cifras y estadísticas oficiales de la Universidad Nacional de Colombia . Contiene una biblioteca de funciones gráficas, tanto estáticas como interactivas, que ofrece numerosos tipos de gráficos con una sintaxis altamente configurable y simple. Entre estos encontramos la visualización de tablas HTML, series, gráficos de barras y circulares, mapas, etc. Todo lo anterior apoyado en bibliotecas de JavaScript. English: A fast and consistent tool for the arrangement of microdata and the visualization of official figures and statistics from the National University of Colombia . It includes a library of graphical functions, both static and interactive, offering numerous types of charts with a highly configurable and simple syntax. Among these, we find the visualization of HTML tables, series, bar and pie charts, maps, etc. It provides the capability to transition from the interactive to the dynamic world and from one library to another without changing function or syntax. 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This package includes the ANOVA-based method, the cumulative uncertainty-based method, and the balanced decomposition method. Yongdai Kim et al. (2019) is a related paper which is accessible via the URL below. 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Package: r-cran-universalcvi Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-mclust Filename: pool/dists/noble/main/r-cran-universalcvi_1.4.0-1.ca2404.1_all.deb Size: 641158 MD5sum: 717350a439839d186a2aac5c87493e75 SHA1: 6af20f72c375e73cff0ff8d4eaf79a6d6231f6e5 SHA256: 09ab1e1274e3e1af262cdee6ae1a8ea9fa14465da05fd22510a9022740546ee8 SHA512: 3ef467465b50af38ddda82a158da224f66619dd7faf1fb1c24acd8b6bbe6788b733ad702598b44827031a7ac981a7ae8786c65c511d0d5e7ee63f0c80482aa07 Homepage: https://cran.r-project.org/package=UniversalCVI Description: CRAN Package 'UniversalCVI' (Hard and Soft Cluster Validity Indices) Algorithms for checking the accuracy of a clustering result with known classes, computing cluster validity indices, and generating plots for comparing them. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). The details of the indices in this package can be found in: J. C. Bezdek, M. Moshtaghi, T. Runkler, C. Leckie (2016) , T. Calinski, J. Harabasz (1974) , C. H. Chou, M. C. Su, E. Lai (2004) , D. L. Davies, D. W. Bouldin (1979) , J. C. Dunn (1973) , F. Haouas, Z. Ben Dhiaf, A. Hammouda, B. Solaiman (2017) , M. Kim, R. S. Ramakrishna (2005) , S. H. Kwon (1998) , S. H. Kwon, J. Kim, S. H. Son (2021) , G. W. Miligan (1980) , M. K. Pakhira, S. Bandyopadhyay, U. Maulik (2004) , M. Popescu, J. C. Bezdek, T. C. Havens, J. M. Keller (2013) , S. Saitta, B. Raphael, I. Smith (2007) , A. Starczewski (2017) , Y. Tang, F. Sun, Z. Sun (2005) , N. Wiroonsri (2024) , N. Wiroonsri, O. Preedasawakul (2023) , C. H. Wu, C. S. Ouyang, L. W. Chen, L. W. Lu (2015) , X. Xie, G. Beni (1991) and P.J. Rousseeuw (1987) and L. Kaufman and P.J. Rousseeuw(2009) and C. Alok. (2010). Package: r-cran-universals Architecture: all Version: 0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 120 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-nlist, r-cran-testthat Filename: pool/dists/noble/main/r-cran-universals_0.0.5-1.ca2404.1_all.deb Size: 73396 MD5sum: 9f09670b8c82e9cfc701399227732611 SHA1: 55481085ca33eee83165f47c02b6e8f6d9e7c015 SHA256: a947ae38eb0cdacc4679c4091d414b544bc667c30075c96658157de467f25a83 SHA512: a88996d081d66b072da189bf1f8256bdad33c98aa7dadd83ef6967f676082e4c4a53685d1b918255fcfdfef6da9fb9eead97081053826fe977516dcd20254b26 Homepage: https://cran.r-project.org/package=universals Description: CRAN Package 'universals' (S3 Generics for Bayesian Analyses) Provides S3 generic methods and some default implementations for Bayesian analyses that generate Markov Chain Monte Carlo (MCMC) samples. The purpose of 'universals' is to reduce package dependencies and conflicts. 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Package: r-cran-unvs.med Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-snowfall Suggests: r-cran-fixest, r-cran-lme4, r-cran-mass, r-cran-ordinal, r-cran-pglm Filename: pool/dists/noble/main/r-cran-unvs.med_1.1.0-1.ca2404.1_all.deb Size: 129036 MD5sum: ffaab422c2aa8bad02db5a027935ecf1 SHA1: b54ea441520b9f68da4b06953216280d6d6e7299 SHA256: 04514b3cd09bbe70ae872c993ca90904305ff8aeee7de36aa32b3e8a884ff594 SHA512: bee5c6eca0f09aa2fa1bfb885868211c48793425d0697a138eaa7043a8280dab817170f7a92ebb9dd08cbb1ef62bae4edc38b36bf6193463f5b12edfbd03b0b5 Homepage: https://cran.r-project.org/package=unvs.med Description: CRAN Package 'unvs.med' (A Universal Approach for Causal Mediation Analysis) This program realizes a universal estimation approach that accommodates multi-category variables and effect scales, making up for the deficiencies of the existing approaches when dealing with non-binary exposures and complex models. The estimation via bootstrapping can simultaneously provide results of causal mediation on risk difference (RD), odds ratio (OR) and risk ratio (RR) scales with tests of the effects' difference. The estimation is also applicable to many other settings, e.g., moderated mediation, inconsistent covariates, panel data, etc. The high flexibility and compatibility make it possible to apply for any type of model, greatly meeting the needs of current empirical researches. Package: r-cran-uotm Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 65 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-boot, r-cran-forecast, r-cran-ggplot2, r-cran-hash Filename: pool/dists/noble/main/r-cran-uotm_0.1.6-1.ca2404.1_all.deb Size: 34332 MD5sum: d4623c879ce7313802e13627f460911e SHA1: 2811314137d9eaafc101895d95e098636e10955c SHA256: 62217030e367671fc8e1555b6bdb4399bee8e8677e5da1a5366af27a885450e2 SHA512: 7e918a1e17e1e9c340f9a56a07ff1f0ae3c9ef58e24b375bfb2c4db777ab7cbb85a90944df306153d65af94cabff08cf00c7d43f4c6c78f21bb0c8157d08e7af Homepage: https://cran.r-project.org/package=uotm Description: CRAN Package 'uotm' (Uncertainty of Time Series Model Selection Methods) We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance not pay attention to the accuracy of prediction, but focus on model selection uncertainty and providing more information of the model selection results. And to estimate the model measures, we propose an simplify and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance focuses on model selection uncertainty and providing more information of the model selection results. To estimate the model uncertainty variance, we propose an simplified and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. Please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403. for more information. 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The plots visualise changes in indices and markets, showing how the changes for sectors or for individual components contribute to the overall change. Data can be classified by up to three levels of grouping variables in a layered, hierarchical plot. Each level can be ordered in several ways including by baseline, by percentage change, and by absolute change. The vignettes give examples. 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The package uses 'renv' to install; immediately replenishing your 'renv' package cache. 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Finally the package provides a dataframe mimicking a pig farming system subsected to disturbances simulated according to Le et al.(2022) . 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It is prepared to use photometry and spatial positions, but it can take into account other types of data. The method is able to take into account arbitrary error models, and it is unsupervised, data-driven, physical-model-free and relies on as few assumptions as possible. The approach followed for membership assessment is based on an iterative process, dimensionality reduction, a clustering algorithm and a kernel density estimation. 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This is the first package that comprehensively addresses UD's needs. Recent applied UD tutorial: Oron et al., 2022 . Recent methodological overview: Oron and Flournoy, 2024 . 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These contributions were derived from variation partitioning (VP) and hierarchical partitioning (HP), applying the algorithm of "Lai et al. (2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution, 13: 782-788 ". 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These contributions were derived from variance partitioning analysis (VPA) and hierarchical partitioning (HP), applying the algorithm of Lai J., Zou Y., Zhang J., Peres-Neto P. (2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution, 13: 782-788 . 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Package: r-cran-us.census.geoheader Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble Suggests: r-cran-r.rsp Filename: pool/dists/noble/main/r-cran-us.census.geoheader_1.0.2-1.ca2404.1_all.deb Size: 265622 MD5sum: 6047e1c2127eb5e8aa2cc83e12ba5e92 SHA1: f4f8b1185bb413aa82ef5b43390a53f939281221 SHA256: 82aa4aaf4ef4dab0571c06d2a37f167d551a4037ace0b17b9f5a2537744fcc67 SHA512: 6565c54ef6b224534fb4d8d43bb66fc6540e89b4b0bc7c26040c775674f61a09a194f4f014a462f9cd52b0b63138e537f4ff8168ff24c9b1a61ded218234d5ca Homepage: https://cran.r-project.org/package=us.census.geoheader Description: CRAN Package 'us.census.geoheader' (US 2010 Census SF2 Geographic Header Summary Levels 010-050) A simple interface to the Geographic Header information from the "2010 US Census Summary File 2". 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The new data is compiled from a number of sources, primarily from the United States Census Bureau or the relevant federal agency. Modern tidy tibbles provide richer state-level data including identifiers, geography, capitals, demographics, and socioeconomic statistics. Convenience vectors parallel the base 'datasets' state objects but extend coverage to all 51 jurisdictions: the 50 states and the District of Columbia. Package: r-cran-usaboundaries Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 327 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-covr, r-cran-dplyr, r-cran-knitr, r-cran-leaflet, r-cran-pak, r-cran-rmarkdown, r-cran-sf, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-usaboundaries_0.5.1-1.ca2404.1_all.deb Size: 237400 MD5sum: f0cb6383b81d5939b60f53e4b559bb12 SHA1: 0750d9bf050a5bf2a831ada3c2d07b6b05d732ea SHA256: 3cc0620157393a4974cda863887a16e9369ebdf8922ee70cb4c8e55822ad15e5 SHA512: e583dbd2704be1983df0135f48cd899caa64891bc7cf13c19efe60337209cea93e431b44661c709a7bb0fc9e7f838bce068405d1792ba33fef30851fbfd33082 Homepage: https://cran.r-project.org/package=USAboundaries Description: CRAN Package 'USAboundaries' (Historical and Contemporary Boundaries of the United States ofAmerica) The boundaries for geographical units in the United States of America contained in this package include state, county, congressional district, and zip code tabulation area. Contemporary boundaries are provided by the U.S. Census Bureau (public domain). Historical boundaries for the years from 1629 to 2000 are provided form the Newberry Library's Atlas of Historical County Boundaries (licensed CC BY-NC-SA). Additional data is provided in the USAboundariesData package; this package provides an interface to access that data. 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Package: r-cran-uscoauditlog Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-stringr, r-cran-openxlsx, r-cran-readxl Filename: pool/dists/noble/main/r-cran-uscoauditlog_1.0.3-1.ca2404.1_all.deb Size: 40984 MD5sum: cfc2a4e0f972f3c110eb26747fca3a40 SHA1: 52766c761064484e07759eaeb54c7eaeab31df3b SHA256: 383ddcde5b614309b8119efb003c84fe8ad54194694fbd3471bc362455a477cb SHA512: 8b3ee9475406af32fb3d9b211844688fe1a59386bae9b0be0aefe7f23205e10af600b5bff5062447cd10b2ae944b96b7f5c2bbe7174fa1d66d658f4fad49cc12 Homepage: https://cran.r-project.org/package=uscoauditlog Description: CRAN Package 'uscoauditlog' (United States Copyright Office Product Management Division SRAudit Data Dataset Cleaning Algorithms) Intended to be used by the United States Copyright Office Product Management Division Business Analysts. Include algorithms for the United States Copyright Office Product Management Division SR Audit Data dataset. The algorithm takes in the SR Audit Data excel file and reformat the spreadsheet such that the values and variables fit the format of the online database. Support functions in this package include clean_str(), which cleans instances of variable AUDIT_LOG; clean_data_to_excel(), which cleans and output the reorganized SR Audit Data dataset in excel format; clean_data_to_dataframe(), which cleans and stores the reorganized SR Audit Data data set to a data frame; format_from_excel(), which reads in the outputted excel file from the clean_data_to_excel() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys. format_from_dataframe(), which reads in the outputted data frame from the clean_data_to_dataframe() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys; support_function(), which takes in the dictionary outputted either from the format_from_dataframe() or format_from_excel() function and returns the data as a formatted data frame according to the original U.S. Copyright Office SR Audit Data online database. The main function of this package is clean_format_all(), which takes in an excel file and returns the formatted data into a new excel and text file according to the format from the U.S. Copyright Office SR Audit Data online database. 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Optional parameters allow users to specify congressional sessions, and the maximum number of speeches to retrieve. Data is parsed, cleaned, and returned in a structured dataframe for analysis. 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Also provide relevant information and metadata for each of the input variables needed for sending the data inquiry. 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You must sign up for an API token from the mentioned website in order for this package to work. 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Leveraging the data collected in the Child Health and Mortality Prevention Surveillance (CHAMPS;) project, the package stores misclassification matrix estimates of three CCVA algorithms (EAVA, InSilicoVA, and InterVA) and two age groups (neonates aged 0-27 days, and children aged 1-59 months) across countries (specific estimates for Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa, and a combined estimate for all other countries), enabling global calibration. These estimates are obtained using the framework proposed in Pramanik et al. (2025;) and are analyzed in Pramanik et al. (2026;). Given VA-only data for an age group, CCVA algorithm, and country, the package utilizes the corresponding misclassification matrix estimate in the modular VA-Calibration framework (Pramanik et al.,2025;) and produces calibrated estimates of CSMFs. The package also supports ensemble calibration to accommodate multiple algorithms. More generally, this allows calibration of population-level prevalence derived from single-class predictions of discrete classifiers. For this, users need to provide fixed or uncertainty-quantified misclassification matrices. This work is supported by the Eunice Kennedy Shriver National Institute of Child Health K99 NIH Pathway to Independence Award (1K99HD114884-01A1), the Bill and Melinda Gates Foundation (INV-034842), and the Johns Hopkins Data Science and AI Institute. Package: r-cran-vaccinationimpact Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vaccinationimpact_0.1.0-1.ca2404.1_all.deb Size: 38846 MD5sum: 162f634e22e1c7d65cceec872d432bbc SHA1: 65c03048c3dd194cf804292d4bbc21d0376e3869 SHA256: 77f3ea4023d709a6893ddf1fac2a5510702eb64b6974e837f11476413e9ee902 SHA512: a720e1b428ca6aa7990145603de9eaf8f3734b3414ff0608459cac9902458e373e7d9c4f444f1e6a4190d473ac3778bc5506e577b584f2c9106b15c0f9073cd5 Homepage: https://cran.r-project.org/package=vaccinationimpact Description: CRAN Package 'vaccinationimpact' (Impact Study of Vaccination Campaigns) Tools to estimate the impact of vaccination campaigns at population level (number of events averted, number of avertable events, number needed to vaccinate). Inspired by the methodology proposed by Foppa et al. (2015) and Machado et al. (2019) for influenza vaccination impact. Package: r-cran-vaccine Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 813 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-fdrtool, r-cran-ggplot2, r-cran-ggpubr, r-cran-rlang, r-cran-rsolnp, r-cran-superlearner, r-cran-survival, r-cran-survml, r-cran-truncnorm, r-cran-e1071, r-cran-gam, r-cran-ranger Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vaccine_1.3.1-1.ca2404.1_all.deb Size: 751788 MD5sum: 011cf0d75063099e44c90eb8aa69e297 SHA1: b4920eade6dcd5f2e18270d99f5ebf0368df1a76 SHA256: 3222729953ca2fd4e00ba1c843f467ebb18cd73448cf48fbc3d22927e65ff9a5 SHA512: 2353488892b2d61c5c3e8b043c7d65e5deae3d07e1a9eacb2d619e0a795d2529fc52176237e07ec75a62d7698baa5f860748bec5107bf76b22ffe82b6cdab20f Homepage: https://cran.r-project.org/package=vaccine Description: CRAN Package 'vaccine' (Statistical Tools for Immune Correlates Analysis of VaccineClinical Trial Data) Various semiparametric and nonparametric statistical tools for immune correlates analysis of vaccine clinical trial data. This includes calculation of summary statistics and estimation of risk, vaccine efficacy, controlled effects (controlled risk and controlled vaccine efficacy), and mediation effects (natural direct effect, natural indirect effect, proportion mediated). See Gilbert P, Fong Y, Kenny A, and Carone, M (2022) and Fay MP and Follmann DA (2023) . Package: r-cran-vaccineff Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1619 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-ggplot2, r-cran-linelist, r-cran-matchit, r-cran-rlang, r-cran-scales, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vaccineff_1.0.1-1.ca2404.1_all.deb Size: 1189246 MD5sum: 8df515045f47dca2879361d449867abe SHA1: 8216cc9ae0142ed7b17fd025f29a6fcdaf8c8720 SHA256: ace765129de2d551a4c3fe5df140e5ea20c4716df62c2269e21e57fdb81000c1 SHA512: b13fafa8d4e16a0eab06fa1f268e925faf9ec043588a322ddde920bc7054c916b61bc9959c9b1724cc794ee07384a4fdb15bd885da5d8ca672c1406d6c856573 Homepage: https://cran.r-project.org/package=vaccineff Description: CRAN Package 'vaccineff' (Estimate Vaccine Effectiveness Based on Different Study Designs) Provides tools for estimating vaccine effectiveness and related metrics. The 'vaccineff_data' class manages key features for preparing, visualizing, and organizing cohort data, as well as estimating vaccine effectiveness. The results and model performance are assessed using the 'vaccineff' class. Package: r-cran-vachette Architecture: all Version: 0.40.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1185 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-magrittr, r-cran-prospectr, r-cran-hmisc, r-cran-purrr, r-cran-minpack.lm, r-cran-progress, r-cran-rlang, r-cran-photobiology Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mrgsolve, r-cran-tidyvpc, r-cran-testthat, r-cran-learnr Filename: pool/dists/noble/main/r-cran-vachette_0.40.1-1.ca2404.1_all.deb Size: 477102 MD5sum: de17dd35ccae5c7de0f06135c8f1016c SHA1: 2fa6b864ab1244aa95874b7b7b63edb0bf6eae4a SHA256: b2d7bf1c5c537f1b0ed9bf0a7ad9219447aea6ca717ca4a964853d26e169a81c SHA512: e9b7ff038da138a281573d610e116f99a17b0e7ad721088b0008d6329349c1fe239a15bf9dffb783a2e5e93d4dabbf444c4192d272a91025dfd5ce2bd426f5c7 Homepage: https://cran.r-project.org/package=vachette Description: CRAN Package 'vachette' (A Method for Visualization of Pharmacometric Models) A method to visualize pharmacometric analyses which are impacted by covariate effects. Variability-aligned covariate harmonized-effects and time-transformation equivalent ('vachette') facilitates intuitive overlays of data and model predictions, allowing for comprehensive comparison without dilution effects. 'vachette' improves upon previous methods Lommerse et al. (2021) , enabling its application to all pharmacometric models and enhancing Visual Predictive Checks (VPC) by integrating data into cohesive plots that can highlight model misspecification. Package: r-cran-vacuum Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-knitr, r-cran-tidyr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vacuum_0.1.0-1.ca2404.1_all.deb Size: 79546 MD5sum: ae0dd5310b1fe75e72a196b5c3075242 SHA1: b71367205783324b123be6347b61f6d74f408a5c SHA256: 131e0e89e52d6625a428349c416100fd38b73bd03516bb6de4059fdc2f43f7da SHA512: d060c00eeb0050aa886dd2b9b51589994d8d1c57f75e52ab3c5afb770b9b6ccd862afb47d9c25b2a5a20b4233552e51dff0483614a463c78a889708f457649df Homepage: https://cran.r-project.org/package=vacuum Description: CRAN Package 'vacuum' (Tukey's Vacuum Cleaner) An implementation of three procedures developed by John Tukey: FUNOP (FUll NOrmal Plot), FUNOR-FUNOM (FUll NOrmal Rejection-FUll NOrmal Modification), and vacuum cleaner. Combined, they provide a way to identify, treat, and analyze outliers in two-way (i.e., contingency) tables, as described in his landmark paper "The Future of Data Analysis", Tukey, John W. (1962) . Package: r-cran-vader Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tm Suggests: r-cran-spelling Filename: pool/dists/noble/main/r-cran-vader_0.2.1-1.ca2404.1_all.deb Size: 145264 MD5sum: eeaa0719607e9ef42328803bbf433ebb SHA1: 83b315bcaca3fbb4013d77de0faa0b1423683970 SHA256: 34657a5a3842ce269c624107cc896fce28f8281d99030fdcafd2a18fe0f5f7c6 SHA512: c533b2c2dc86f54fc2fcfa475b02d5a0bb11b3aa9d2ac339f3c8bee60fba29a7a9bd83cff16057f085810c84a8a5d55ac11ad7b6535b34ad6c7785b0467352e9 Homepage: https://cran.r-project.org/package=vader Description: CRAN Package 'vader' (Valence Aware Dictionary and sEntiment Reasoner (VADER)) A lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains. Hutto & Gilbert (2014) . Package: r-cran-vaersndvax Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-vaersvax, r-cran-data.table, r-cran-dplyr, r-cran-rpivottable Filename: pool/dists/noble/main/r-cran-vaersndvax_1.0.4-1.ca2404.1_all.deb Size: 588174 MD5sum: be42519d95292bf72806618620c1778c SHA1: 7d205c081acce3c1ef6b06413aeec3db61cd29f7 SHA256: eed22cd43ed4eb8c66cef254824e9ac8a68a73bbd9f698c7f18c14873e1327ae SHA512: 69d18faacaca304687d12c56bca4a8828d870576122e2ab5bfdda94f48b2151f7ffb081a8ccc96255e3e7835b33729f20a536f5c1c438dd4d182497c97b597a5 Homepage: https://cran.r-project.org/package=vaersNDvax Description: CRAN Package 'vaersNDvax' (Non-Domestic Vaccine Adverse Event Reporting System (VAERS)Vaccine Data for Present) Non-Domestic VAERS vaccine data for 01/01/2016 - 06/14/2016. If you want to explore the full VAERS data for 1990 - Present (data, symptoms, and vaccines), then check out the 'vaersND' package from the URL below. The URL and BugReports below correspond to the 'vaersND' package, of which 'vaersNDvax' is a small subset (2016 only). 'vaersND' is not hosted on CRAN due to the large size of the data set. To install the Suggested 'vaers' and 'vaersND' packages, use the following R code: 'devtools::install_git("https://gitlab.com/iembry/vaers.git", build_vignettes = TRUE)' and 'devtools::install_git("https://gitlab.com/iembry/vaersND.git", build_vignettes = TRUE)'. "VAERS is a national vaccine safety surveillance program co-sponsored by the US Centers for Disease Control and Prevention (CDC) and the US Food and Drug Administration (FDA). VAERS is a post-marketing safety surveillance program, collecting information about adverse events (possible side effects) that occur after the administration of vaccines licensed for use in the United States." For more information about the data, visit . For information about vaccination/immunization hazards, visit . Package: r-cran-vaersvax Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-vaersndvax, r-cran-dplyr, r-cran-data.table, r-cran-pivottabler, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vaersvax_1.0.5-1.ca2404.1_all.deb Size: 148358 MD5sum: 8e39cd5cb6709d6cd4473959c76cc02b SHA1: 201c2a00721af579064bbf28f27a30562f4a88e5 SHA256: 7673e6b00bf2a301a59daec0ffbe856bca69a8e59b9fdc13b6f44d133f206430 SHA512: a3770a41aae4354fa906b70a597654bb52d1c279ef65b79d34f9cd52e9c5e20b6625e38ea1a23f9ce50d3a395f72a4be213042dce68711b28edfccb67e96f7d1 Homepage: https://cran.r-project.org/package=vaersvax Description: CRAN Package 'vaersvax' (US Vaccine Adverse Event Reporting System (VAERS) Vaccine Datafor Present) US VAERS vaccine data for 01/01/2018 - 06/14/2018. If you want to explore the full VAERS data for 1990 - Present (data, symptoms, and vaccines), then check out the 'vaers' package from the URL below. The URL and BugReports below correspond to the 'vaers' package, of which 'vaersvax' is a small subset (2018 only). 'vaers' is not hosted on CRAN due to the large size of the data set. To install the Suggested 'vaers' and 'vaersND' packages, use the following R code: 'devtools::install_git("", build_vignettes = TRUE)' and 'devtools::install_git("", build_vignettes = TRUE)'. "The Vaccine Adverse Event Reporting System (VAERS) is a national early warning system to detect possible safety problems in U.S.-licensed vaccines. VAERS is co-managed by the Centers for Disease Control and Prevention (CDC) and the U.S. Food and Drug Administration (FDA)." For more information about the data, visit . For information about vaccination/immunization hazards, visit . Package: r-cran-vagalumer Architecture: all Version: 0.1.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-jsonlite, r-cran-httr, r-cran-stringr, r-cran-dplyr, r-cran-purrr, r-cran-magrittr Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-tm Filename: pool/dists/noble/main/r-cran-vagalumer_0.1.6-1.ca2404.1_all.deb Size: 30780 MD5sum: f01804c98d73778b62061f920ab907ad SHA1: 2c69dd1deb368bdcb2454449a1aebc6ce892bb1f SHA256: 70746039e850e5966ad9de6d01a12db40e646a96e75455a0ce9d04c97d8095af SHA512: 9bafba740014a7f4ffef3d517516b733af4b2d833ef353b413607f878c15e57a083de9c8560faf6c8cee6a65845f07ec73b8ebf23336d783c789d166fcc5100d Homepage: https://cran.r-project.org/package=vagalumeR Description: CRAN Package 'vagalumeR' (Access to the 'Vagalume' API) Provides access to the 'Vagalume' API . The data extracted is basically lyrics of songs and information about artists/bands. 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In brief, the VA framework provides a fully or at least closed to fully tractable lower bound approximation to the marginal likelihood of a GAM when it is parameterized as a mixed model (using penalized splines, say). In doing so, the VA framework aims offers both the stability and natural inference tools available in the mixed model approach to GAMs, while achieving computation times comparable to that of using the penalized likelihood approach to GAMs. See Hui et al. (2018) . Package: r-cran-valaddin Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lazyeval Suggests: r-cran-magrittr, r-cran-testthat, r-cran-stringr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-valaddin_1.0.2-1.ca2404.1_all.deb Size: 185174 MD5sum: f30e8e978ce74ccaa06f8278fd93824a SHA1: ce0861006db3687987033d59423b7efccae47199 SHA256: d1fab98d69574edcfacffb535bd5e62cfcb8a72236924c4ac5dcd5c86e727056 SHA512: 1a52fa739ab38af9ff0c0138315b3fdcb7487d080da83dd2688d1bcf0465b66a9b98851d36e92c5276e2659eee8f0747c93225d85e2de1de359ebfadea516568 Homepage: https://cran.r-project.org/package=valaddin Description: CRAN Package 'valaddin' (Functional Input Validation) A set of basic tools to transform functions into functions with input validation checks, in a manner suitable for both programmatic and interactive use. 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The package supports batch-oriented data processing for large datasets, standardized data transformation workflows, and visualization utilities for sports science research and performance monitoring. It is designed to facilitate reproducible analysis across multiple sports with comprehensive documentation and error handling. Package: r-cran-valdr Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 377 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-base64enc, r-cran-keyring, r-cran-jsonlite, r-cran-dplyr, r-cran-readr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-valdr_3.0.0-1.ca2404.1_all.deb Size: 237848 MD5sum: 3deac6c3cefb3313e71b029224dc35e4 SHA1: a03e2dfbdffd4bdeaf81f2809259405b87e44828 SHA256: ff3614de269cdf7123612f4fdab7ab8b85eef632c916fabf39d4108839017589 SHA512: 015faead9af3c8bbe368f897d767f2240aa1eb30bb335d30b380a7b5d1a7edb6e315c0fd0aa5db824f02802313cf9fc9716be6548eeab53aebee90a13f2b1774 Homepage: https://cran.r-project.org/package=valdr Description: CRAN Package 'valdr' (Access and Analyse 'VALD' Data via Our External 'APIs') Provides helper functions and wrappers to simplify authentication, data retrieval, and result processing from the 'VALD' 'APIs'. Designed to streamline integration for analysts and researchers working with 'VALD's external 'APIs'. For further documentation on integrating with 'VALD' 'APIs', see: . For a step-by-step guide to using this package, see: . 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Includes interactive modules for metric exploration, radar charts, longitudinal comparisons, quadrant plots, and athlete reports. Package: r-cran-valection Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-testthat Filename: pool/dists/noble/main/r-cran-valection_1.0.0-1.ca2404.1_all.deb Size: 130216 MD5sum: 5adab1defdd33c51b7dc53f3ea52c1a8 SHA1: d5c75d1f9464e35280eaac717e4dc4dfb8de45fd SHA256: f0ddf81c85620c672f0d19d83daccd8db99bbaf1c094de7ca32f6a11ea837aab SHA512: 572b48fc03a0655127a9b75d95a0993bd558ba6c960a29eb45d404b2fb15b08e39cb8c720e1571cddc359bea2e09460ddbde9b6914262b7ac5359ee93b5a7f9a Homepage: https://cran.r-project.org/package=valection Description: CRAN Package 'valection' (Sampler for Verification Studies) A binding for the 'valection' program which offers various ways to sample the outputs of competing algorithms or parameterizations, and fairly assess their performance against each other. The 'valection' C library is required to use this package and can be downloaded from: . Cooper CI, et al; Valection: Design Optimization for Validation and Verification Studies; Biorxiv 2018; . Package: r-cran-valentine Architecture: all Version: 2025.2.14-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-ellmer, r-cran-glue, r-cran-rlang Filename: pool/dists/noble/main/r-cran-valentine_2025.2.14-1.ca2404.1_all.deb Size: 539828 MD5sum: d688a3d9f092aa6cf3d9182828a6b07d SHA1: 5c9a03edac647cea80718bb8d37bcf54f9f939c8 SHA256: 280469cb22ff331b9598b20e9460b7c3882f25cb147c418a75804d0555de0fa1 SHA512: ce45c08941ce5bee7ba924a131e6dbbd23379604e12bb0663154d7524aedb8665f0ac1aeccfd1d82d60f582d1b43c56e3902b094b5e40a3cff4270d989c9787d Homepage: https://cran.r-project.org/package=valentine Description: CRAN Package 'valentine' (Spread the Love for R Packages with Poetry) Uses large language models to create poems about R packages. Currently contains the roses() function to make "roses are red, ..." style poems and the prompt() function to only assemble the prompt without submitting it to the model. Package: r-cran-valerie Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9112 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-bioc-genomicalignments, r-bioc-genomicranges, r-bioc-iranges, r-bioc-rsamtools, r-cran-plyr, r-cran-ggplot2, r-cran-pheatmap, r-cran-ggplotify, r-cran-ggpubr, r-cran-scales Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-valerie_1.1.0-1.ca2404.1_all.deb Size: 4448490 MD5sum: 191de99d4b247514fd5af417b4593b27 SHA1: e5414c6e798bfccff316cf089eb14bfd165eeb96 SHA256: ea5861d3feea7e7916c0171db3fa623495dc9a45b0b35574e17a4145568d644d SHA512: 462a059804294eb21cbc3d20d137d87f5a0ef1acb8a5be97f1ae89bce58d6a42737ccf9eb6924c8061a8826d5418e78f78c232eb516df1810259d30e0bef4197 Homepage: https://cran.r-project.org/package=VALERIE Description: CRAN Package 'VALERIE' (Visualising Splicing at Single-Cell Resolution) Alternative splicing produces a variety of different protein products from a given gene. 'VALERIE' enables visualisation of alternative splicing events from high-throughput single-cell RNA-sequencing experiments. 'VALERIE' computes percent spliced-in (PSI) values for user-specified genomic coordinates corresponding to alternative splicing events. PSI is the proportion of sequencing reads supporting the included exon/intron as defined by Shiozawa (2018) . PSI are inferred from sequencing reads data based on specialised infrastructures for representing and computing annotated genomic ranges by Lawrence (2013) . Computed PSI for each single cell are subsequently presented in the form of a heatmap implemented using the 'pheatmap' package by Kolde (2010) . Board overview of the mean PSI difference and associated p-values across different user-defined groups of single cells are presented in the form of a line graph using the 'ggplot2' package by Wickham (2007) . Package: r-cran-valet Architecture: all Version: 0.9.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magrittr, r-cran-httr, r-cran-dplyr, r-cran-readr, r-cran-purrr, r-cran-jsonlite Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-valet_0.9.1-1.ca2404.1_all.deb Size: 26274 MD5sum: c880a6c6d94424fba946cb7b5fff648a SHA1: b756b10c2a704b20d2e1d0c3750e5b3069fee7c5 SHA256: cd6ef20fbe7df75eae5f772cc812f1463f82b1d7582052781c89aa862b968884 SHA512: 085df7d43b5b9a48e5c33d7046644420fe6281d677c8aa2a6e7cc23b337a4e3ac8aba836228d512755d20eeecc71448f99d3565e9a07717530657c06eb9efd1f Homepage: https://cran.r-project.org/package=valet Description: CRAN Package 'valet' (Provide R Client to the Bank of Canada's Valet API) The Bank of Canada updated their Valet API , and no R client currently exists. This provides access to all of Valet's endpoints and serves responses in wide format easy for researchers to handle but also provides tools to access API responses as a list. 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This package enables the computation of routes, trips, isochrones and travel distances matrices (travel time and kilometer distance). Package: r-cran-valhallr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-purrr, r-cran-jsonlite, r-cran-dplyr, r-cran-magrittr, r-cran-tibble, r-cran-tidyr, r-cran-sf, r-cran-leaflet, r-cran-ggplot2, r-cran-htmltools, r-cran-stringr, r-cran-ggspatial, r-cran-geojsonio, r-cran-rlang, r-cran-cairo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-valhallr_0.1.0-1.ca2404.1_all.deb Size: 320776 MD5sum: 18ad762ceca8c303f7960ae029037ebe SHA1: bbf93cbe57e8035170815a0cdfc81b9bb60c6aca SHA256: c61c9209b965f3e5731b9ba2768abc59e503c9e2a8f4290c0d050c227ac1a83e SHA512: 8404be507d95439d1ce8a010eeb72c6ae62e76490097094fe54cc8815be447791c43a1efeed5cea3100a7719a9992876f47f97170f1973579947ea98072a557e Homepage: https://cran.r-project.org/package=valhallr Description: CRAN Package 'valhallr' (A Tidy Interface to the 'Valhalla' Routing Engine) An interface to the 'Valhalla' routing engine’s application programming interfaces (APIs) for turn-by-turn routing, isochrones, and origin-destination analyses. Also includes several user-friendly functions for plotting outputs, and strives to follow "tidy" design principles. Please note that this package requires access to a running instance of 'Valhalla', which is open source and can be downloaded from . Package: r-cran-validann Architecture: all Version: 1.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-moments Suggests: r-cran-nnet, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-validann_1.2.1-1.ca2404.1_all.deb Size: 105308 MD5sum: 999ff6eabebf8e0c1ea83331a4308d27 SHA1: 938e830034b5457022e2418583918b1284449b53 SHA256: 5864516213c260d18d9cc1b559030ca1eb73ebfeb6d913ac40c070fd384231ac SHA512: f9185a791f9bd7b4a85b58fd6966788fb1f841a9c8ad195f1f5d253e80e5e485e9d0c3d37fd46a0e22b9c957c1ce4e73c6150fd1ee02ec7d3ed5bfe75321a5b0 Homepage: https://cran.r-project.org/package=validann Description: CRAN Package 'validann' (Validation Tools for Artificial Neural Networks) Methods and tools for analysing and validating the outputs and modelled functions of artificial neural networks (ANNs) in terms of predictive, replicative and structural validity. 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This helps ensure that the topic modeling results are accurate and useful for research purposes. See Ying and others (2022) . For more information, please visit . 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Functions for finding redundancies and problematic rules are provided, given a set a rules formulated with 'validate'. 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Methods are detailed in: Hivert B, Agniel D, Thiebaut R & Hejblum BP (2022). "Post-clustering difference testing: valid inference and practical considerations", . Package: r-cran-validmind Architecture: all Version: 2.12.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-reticulate, r-cran-dplyr, r-cran-plotly, r-cran-htmltools, r-cran-rmarkdown, r-cran-dt, r-cran-base64enc Filename: pool/dists/noble/main/r-cran-validmind_2.12.5-1.ca2404.1_all.deb Size: 43634 MD5sum: be2e46d28af9eaf57f136612f84ad9e6 SHA1: 3bd089f6e8c2e9aa28802fdd0a32120586c1915d SHA256: b84a383f88eb38cd522af69ab77a32240da44fb0c403604d920f747ba5e8a93c SHA512: 08694aa3788c75de3cdc68c446c2eba93051cf4f3328f13c27e67521c72f966b419d72f551e74b3e7532f42a084a2670dc1f765d064620a02ace2f590cea2ec8 Homepage: https://cran.r-project.org/package=validmind Description: CRAN Package 'validmind' (Interface to the 'ValidMind' Platform) Deploy, execute, and analyze the results of models hosted on the 'ValidMind' Platform . This package interfaces with the 'Python' Library API in order to allow advanced diagnostics and insight into trained models all from an 'R' environment. Package: r-cran-valmetrics Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2045 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-roxygen2, r-cran-knitr, r-cran-markdown Filename: pool/dists/noble/main/r-cran-valmetrics_1.0.0-1.ca2404.1_all.deb Size: 1316170 MD5sum: 25191b9db4b2d4fd6d986d0f873c973f SHA1: 9cfea935128050240a106667c9c863b24191bf9e SHA256: dc69bbb1c6a9445584913d24ed5889450a7718ef47acdedd07bce5fca1d917c3 SHA512: 6fb713d812b1ccd295ec70c41b74021cddf8c19bd6cd0f731f36a435406978b564206f072cea87cea7a89daf463b794a68dbf953fcd5c0101a943a01de7f70b2 Homepage: https://cran.r-project.org/package=valmetrics Description: CRAN Package 'valmetrics' (Metrics and Plots for Model Evaluation) Functions for metrics and plots for model evaluation. Based on vectors of observed and predicted values. Method: Kristin Piikki, Johanna Wetterlind, Mats Soderstrom and Bo Stenberg (2021). . Package: r-cran-valottery Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-valottery_0.0.1-1.ca2404.1_all.deb Size: 228908 MD5sum: 357d9523899051e4a7ef40dfb122a962 SHA1: 905c085e329b0d0c0bc39ab41886a8fa39cff6af SHA256: d779fe2f926ffd2de02e44db801384627c26abb59472367677116348bf0f306e SHA512: a51caf6acddba174c0cb046d8ccfcdc28c27a4b6ed369c9e64c44ac054035e384b8fbae9d18caee5c741a2e335543dfbe105e88b28067bf3ec74f0dbc5773830 Homepage: https://cran.r-project.org/package=valottery Description: CRAN Package 'valottery' (Results from the Virginia Lottery Draw Games) Historical results for the state of Virginia lottery draw games. Data were downloaded from https://www.valottery.com/. Package: r-cran-valueeq5d Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 600 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-testthat Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/noble/main/r-cran-valueeq5d_0.7.2-1.ca2404.1_all.deb Size: 488112 MD5sum: f192ca26ba8409fa2d64b3945cebfa3d SHA1: a35d39ad0efa8c936c1ae613a29722ee942ee5f3 SHA256: 593cabe694a26fb079d5dead4bf5a357e7d940de839c826fd9a15bcc4e9e5079 SHA512: 93e03a8b66c485f86362540ad4fd6309dc88b6e8f72d3ee44cfaf968650b53da3fd5cf7a679fefe4ddc00429b7ca830e349c5851e73d815eb7635ae8ef13f3d5 Homepage: https://cran.r-project.org/package=valueEQ5D Description: CRAN Package 'valueEQ5D' (Scoring EQ-5d Descriptive System) EQ-5D is a standard instrument () that measures the quality of life often used in clinical and economic evaluations of health care technologies. Both adult versions of EQ-5D (EQ-5D-3L and EQ-5D-5L) contain a descriptive system and visual analog scale. The descriptive system measures the patient's health in 5 dimensions: the 5L versions has 5 levels and 3L version has 3 levels. The descriptive system scores are usually converted to index values using country specific values sets (that incorporates the country preferences). This package allows the calculation of both descriptive system scores to the index value scores. The value sets for EQ-5D-3L are from the references mentioned in the website The value sets for EQ-5D-3L for a total of 31 countries are used for the valuation (see the user guide for a complete list of references). The value sets for EQ-5D-5L are obtained from references mentioned in the and other sources. The value sets for EQ-5D-5L for a total of 17 countries are used for the valuation (see the user guide for a complete list of references). The package can also be used to map 5L scores to 3L index values for 10 countries: Denmark, France, Germany, Japan, Netherlands, Spain, Thailand, UK, USA, and Zimbabwe. The value set and method for mapping are obtained from Van Hout et al (2012) . 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Each instrument and value set characterizes and values health differently. Identical health states can yield different utility values when valued using different value sets. The 'valueSetCompare' package facilitates comparisons of HRQoL value sets, enabling both theoretical and empirical comparisons. For empirical comparisons, it employs a novel simulation-based method by Jiang et al. (2022) , allowing users to investigate the responsiveness of HRQoL instruments across the entire health spectrum using cross-sectional data with external health anchors. 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Implements Bland-Altman analysis for assessing agreement between measurement methods (Bland & Altman (1986) ), Passing-Bablok regression for non-parametric method comparison (Passing & Bablok (1983) ), and Deming regression accounting for measurement error in both variables (Linnet (1993) ). Also includes tools for setting quality goals based on biological variation (Fraser & Petersen (1993) ) and calculating Six Sigma metrics, precision experiments with variance component analysis, precision profiles for functional sensitivity estimation (Kroll & Emancipator (1993) ). Commonly used in clinical laboratory method validation. Provides publication-ready plots and comprehensive statistical summaries. 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Package: r-cran-var.spec Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-var.spec_1.0-1.ca2404.1_all.deb Size: 122944 MD5sum: f4237bbc1bc3fc0201cfdace4023dee3 SHA1: 9169b1e31811a1e255da08db80d086bb55020f92 SHA256: 266e6ab0583442d72261559fb102f3083a3cdda754db3158e46d9f2c73edd278 SHA512: 3243539df500300f05399b49c4a34e948b3901966fa8004d1040c2b03af21fa4f773fd848dce5455d496b9e32fd05ef7d6287cb10d9a89b1362354886c608ec2 Homepage: https://cran.r-project.org/package=VAR.spec Description: CRAN Package 'VAR.spec' (Allows Specifying a Bivariate VAR (Vector Autoregression) withDesired Spectral Characteristics) The spectral characteristics of a bivariate series (Marginal Spectra, Coherency- and Phase-Spectrum) determine whether there is a strong presence of short-, medium-, or long-term fluctuations (components of certain frequencies in the spectral representation of the series) in each one of them. 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This way, one may e.g. specify a VAR inducing a strong presence of long-term fluctuations in series 1 and in series 2, which are weakly correlated, but lagged by a number of time units to each other, while short-term fluctuations in series 1 and in series 2, are strongly present only in one of the two series, while they are strongly correlated to each other between the two series. Simulation from such models allows studying the behavior of data-analysis tools, such as estimation of the spectra, under different circumstances, as e.g. peaks in the spectra, generating bias, induced by leakage. Package: r-cran-varbin Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rpart Filename: pool/dists/noble/main/r-cran-varbin_0.2.1-1.ca2404.1_all.deb Size: 49414 MD5sum: d0a1359a9b75e4a94e6eb5c367543b4f SHA1: d7a02e447f1df3b5e46871c3af67d46a39541931 SHA256: 6873ab19954f89d1563868292ba7d72894246e9659e5c11e769cb9066a60b9ba SHA512: b0715d68f8e9f025642bc6903d522185fc94ea2c7fb693b9e19ee3756c910653baac073cc5a2e8fcc9cc12ea534bad304c22371063efd5e5a27312e3cfc7d09d Homepage: https://cran.r-project.org/package=varbin Description: CRAN Package 'varbin' (Optimal Binning of Continuous and Categorical Variables) Tool for easy and efficient discretization of continuous and categorical data. The package calculates the most optimal binning of a given explanatory variable with respect to a user-specified target variable. 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Package: r-cran-varcheck Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 766 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-patchwork Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-varcheck_0.1.0-1.ca2404.1_all.deb Size: 494520 MD5sum: b6f0bb75dc58c5ab69f46255cbadbc28 SHA1: 4173a392374fd1c5d798495ab0ca252d2226e4f2 SHA256: 3631b2e5778fc3da082a4ecc786d11cc64b75ecf620f01c18ec16455c8d70417 SHA512: d78aed8e3edb5382e94494c0e98e23845544e048c45a4678aeb2116bde9fa98020855fd933efc467f2e10308d559a5ccfde98db0a1b23a9adb0331b13cb6983e Homepage: https://cran.r-project.org/package=VARcheck Description: CRAN Package 'VARcheck' (Visual Diagnostic Checks for Vector Autoregressive Models) Provides model-agnostic visual diagnostics for vector autoregressive (VAR) models. Given empirical data, model predictions, residuals, and optionally simulated data, the package assembles a multi-panel diagnostic grid: empirical vs. predicted time series, residual inspection, residuals vs. predictions scatter, and posterior predictive style checks via simulated trajectories. Output is a 'patchwork' object composed of 'ggplot2' plots, allowing further customisation via standard 'ggplot2' theme calls. Follows the approach described in Haslbeck et al. (2026) . Package: r-cran-varclust Architecture: all Version: 0.9.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcppeigen, r-cran-foreach, r-cran-doparallel, r-cran-dorng, r-cran-pesel Suggests: r-cran-knitr, r-cran-mclust, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-varclust_0.9.4-1.ca2404.1_all.deb Size: 155788 MD5sum: 5039ae319eec48815306b0170bcc59af SHA1: 77af46f1f363368187150a22406af1a1429076c1 SHA256: d06f8e0328e0af744a1d4c3678c9206d4c585f0a0ac8b8c6678e968fd2b05ef7 SHA512: 02d07c51057c2a1a95c5818f0f73be3535819167958a29a5c21fd4c7106a6a9e8cdf13a2e63aec2931e708ec3052f6bca897dd72b4153372dc5fcde066af16a0 Homepage: https://cran.r-project.org/package=varclust Description: CRAN Package 'varclust' (Variables Clustering) Performs clustering of quantitative variables, assuming that clusters lie in low-dimensional subspaces. Segmentation of variables, number of clusters and their dimensions are selected based on BIC. Candidate models are identified based on many runs of K-means algorithm with different random initializations of cluster centers. Package: r-cran-varcpdetectonline Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4286 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-corpcor, r-cran-matrix, r-cran-glmnet, r-cran-doparallel Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-varcpdetectonline_0.2.0-1.ca2404.1_all.deb Size: 4352734 MD5sum: bae48e42295bf13ae70fb169eecb241b SHA1: 1f59f31279981914eece71025fe71f2ec3b8f3f1 SHA256: 55494a2cff88257b71f550e5306ead2d2f9b7df54761f112306c1dae2ada47da SHA512: b53f5fa2fafd0cd4ae5bf97a99f6fbea32ea4ecc04b70491325068b0759197754afd8c7d9754eb654377cc5d86685c085111cf4276bbf369f976588181fb2806 Homepage: https://cran.r-project.org/package=VARcpDetectOnline Description: CRAN Package 'VARcpDetectOnline' (Sequential Change Point Detection for High-Dimensional VARModels) Implements the algorithm introduced in Tian, Y., and Safikhani, A. (2024) , "Sequential Change Point Detection in High-dimensional Vector Auto-regressive Models". This package provides tools for detecting change points in the transition matrices of VAR models, effectively identifying shifts in temporal and cross-correlations within high-dimensional time series data. Package: r-cran-vardiag Architecture: all Version: 0.2-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-vardiag_0.2-2-1.ca2404.1_all.deb Size: 319548 MD5sum: 939f7c7acc2ea1e636d26df74091b05b SHA1: 0014306e9f3ad77def6fb263a5ad34e383f1aa62 SHA256: ae715c54f342b897be9224f19fc171988b017cd3325496db03435a9c272ad73e SHA512: 2d3477f886d0bad3a82ade8b6f84be158f054e1ae4cb1e86a029e3ca4fe93003b2da067e699d00c10ceebd903a204f4f326f19daaa2b5e68f6ec15c90bd94e05 Homepage: https://cran.r-project.org/package=vardiag Description: CRAN Package 'vardiag' (Variogram Diagnostics) Interactive variogram diagnostics. Package: r-cran-vardpoor Architecture: all Version: 0.21.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-data.table, r-cran-mass, r-cran-stringr, r-cran-surveyplanning, r-cran-laeken Filename: pool/dists/noble/main/r-cran-vardpoor_0.21.0-1.ca2404.1_all.deb Size: 586126 MD5sum: bb4e1374c97de5e0595b89d7240cf9a1 SHA1: c160c27abd556c436c51abedf6223db21cfb983f SHA256: b206e143a766a7c78264f2920b405030ca007b8a4a9c3da970be3d04f00a4f9a SHA512: 206226fd62d8e3f02651d023678ca23d4b777a0e7f73f53077b2e0f6d229c704d892fd06a963844861dab800bb44fd6f4546f56fc4add72bc1f1ac38ed2dd023 Homepage: https://cran.r-project.org/package=vardpoor Description: CRAN Package 'vardpoor' (Variance Estimation for Sample Surveys by the Ultimate ClusterMethod) Generation of domain variables, linearization of several non-linear population statistics (the ratio of two totals, weighted income percentile, relative median income ratio, at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, Sample Survey Methods And Theory, vol. I: Methods and Applications; vol. II: Theory. 1953, New York: John Wiley and Sons), variance estimation for longitudinal, cross-sectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y. G., 2015, ). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect. 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Also computed are the corresponding probability density function and cumulative distribution function. See Chan, Nadarajah and Afuecheta (2015) for more details. Package: r-cran-varest Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 84 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sam, r-cran-caret, r-cran-lm.beta, r-cran-glmnet Filename: pool/dists/noble/main/r-cran-varest_0.1.0-1.ca2404.1_all.deb Size: 55686 MD5sum: 4bde9d0e9e1fbe1f4aef106773c2f1b1 SHA1: 03399978cc913469b71c205013948019952d720e SHA256: ac0a42831b0121256e4e6f950df5bedb8e332bd72fe72e3d6b5e20085aa5cf09 SHA512: ff02fcbbe65d3cc30fabed6c26b05933fb37e06af57597661c5bc37fa11b5d96ee5eb86a52138b21d05efae002cbf2959ccae301fe2cd0914dc3872303851e78 Homepage: https://cran.r-project.org/package=varEst Description: CRAN Package 'varEst' (Variance Estimation) Error variance estimation in ultrahigh dimensional datasets with four different methods, viz. 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This package contains some functions to help user (especially data explorers) to make more sense of their variables and take the most out of variables and hardware resources. These functions are written and crafted since 2014 with years of experience in statistical data analysis on high-dimensional data, and for each of them there was a need. Functions in this package are supposed to be efficient and easy to use, hence they will be frequently updated to make them more convenient. 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The package also has functions for genotypic and phenotypic covariance, correlation and path analysis. Dataset has been added to facilitate example. For more information refer Singh, R.K. and Chaudhary, B.D. (1977, ISBN:81766330709788176633079). Package: r-cran-variables Architecture: all Version: 1.1-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-variables_1.1-2-1.ca2404.1_all.deb Size: 43302 MD5sum: 6fce5387ec1a028cbc2d328ad9cc6f50 SHA1: 97febafbf7ea5c41a7a92e6e2b2ed339eb82c5c1 SHA256: bb7178bf98509900f7110d330a4b0724b56267b3f946ce1d894bede240aa3479 SHA512: 31f45dae19db1542cf3420099b59c31330b70217cccb0980c1139360053eef13a666989ebe62ff62a8e01d19a7d093bf047de597839f0d7d5f75a7476dff9c57 Homepage: https://cran.r-project.org/package=variables Description: CRAN Package 'variables' (Variable Descriptions) Abstract descriptions of (yet) unobserved variables. Package: r-cran-variablescreening Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 102 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gee, r-cran-expm, r-cran-mass, r-cran-energy Filename: pool/dists/noble/main/r-cran-variablescreening_0.2.1-1.ca2404.1_all.deb Size: 72964 MD5sum: 4fa82ef52747cbbf2dfb591ac54ceeee SHA1: 9cb17c4211d6430622b877ddfcbdf8bbd742d6c8 SHA256: 36ea55fd977ab5e7e211c087e96f04a9332cb23247cd5a624af1dfe3a5b08299 SHA512: 974ac812579fff04f359ff4a0f4e5210fd439e2cc4192f4d4afb18639f306d139a84c7540ff83d40e961af3911a6947a4ea9fb85bee51fe472db833eb54953c5 Homepage: https://cran.r-project.org/package=VariableScreening Description: CRAN Package 'VariableScreening' (High-Dimensional Screening for Semiparametric LongitudinalRegression) Implements variable screening techniques for ultra-high dimensional regression settings. Techniques for independent (iid) data, varying-coefficient models, and longitudinal data are implemented. The package currently contains three screen functions: screenIID(), screenLD() and screenVCM(), and six methods for simulating dataset: simulateDCSIS(), simulateLD, simulateMVSIS(), simulateMVSISNY(), simulateSIRS() and simulateVCM(). The package is based on the work of Li-Ping ZHU, Lexin LI, Runze LI, and Li-Xing ZHU (2011) , Runze LI, Wei ZHONG, & Liping ZHU (2012) , Jingyuan LIU, Runze LI, & Rongling WU (2014) Hengjian CUI, Runze LI, & Wei ZHONG (2015) , and Wanghuan CHU, Runze LI and Matthew REIMHERR (2016) . 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Given a set of candidate predictors, it evaluates candidate models and returns model-level summaries (BIC and MPP) and predictor-level posterior inclusion probabilities (PIP). For more details see Xu, S., Ferreira, M. A., & Tegge, A. N. (2025) . Package: r-cran-varian Architecture: all Version: 0.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rstan, r-cran-ggplot2, r-cran-mass, r-cran-formula, r-cran-gridextra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-varian_0.2.2-1.ca2404.1_all.deb Size: 85510 MD5sum: 0bb7cf9695c3a8211b8a65e5397155b8 SHA1: 67bdd0643d10a3eda01535f2dc75217340707a0b SHA256: ff1e840855b942d2faa507af02b917114a89e4035dd08d62e228b827529524ca SHA512: 863ca42053fd53803de57793a0963bc5f047b6fc1f315dee8da4b214e1cb932c8083e2f51d88bbfa3416ccba8a12592596ff2ab3b1a36f5c7df8373006d431d2 Homepage: https://cran.r-project.org/package=varian Description: CRAN Package 'varian' (Variability Analysis in R) Uses a Bayesian model to estimate the variability in a repeated measure outcome and use that as an outcome or a predictor in a second stage model. Package: r-cran-variancegamma Architecture: all Version: 0.4-2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-distributionutils, r-cran-generalizedhyperbolic Suggests: r-cran-runit Filename: pool/dists/noble/main/r-cran-variancegamma_0.4-2-1.ca2404.1_all.deb Size: 145292 MD5sum: bd71cfe2d43b95ee8906be30a01705c9 SHA1: a644a0d12bd2daaa597ed6ccdabc8d0591994141 SHA256: 95d2c5cd73cac60510ad47c5cbd4399bd37a9b9b896888c5a79fe5f1e87d7fd4 SHA512: 0fc5b9a565a61324a65ba4acc2d5a5732a19ae962c76115f0f439bac4408bbf2b8492d2a038cebd87282076ca4cb0b92e6fde77e6af2ba8757c425439ad19539 Homepage: https://cran.r-project.org/package=VarianceGamma Description: CRAN Package 'VarianceGamma' (The Variance Gamma Distribution) Provides functions for the variance gamma distribution. Density, distribution and quantile functions. Functions for random number generation and fitting of the variance gamma to data. Also, functions for computing moments of the variance gamma distribution of any order about any location. In addition, there are functions for checking the validity of parameters and to interchange different sets of parameterizations for the variance gamma distribution. Package: r-cran-variantspark Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1530 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sparklyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-variantspark_0.1.1-1.ca2404.1_all.deb Size: 1418656 MD5sum: 182f20a054ecae95e6a97b4fe4ff5d50 SHA1: 0e4e5822895cd94c97c1cae73cb5185b9d2b891d SHA256: 187de8f2171ca095055257a57458245897765bd52db3752844b3f5a96490e7e5 SHA512: 66d4f63bfccd6891b8a58e1c45e81feba1858fc4cc355ec70ec9772f7ddd89fe4e2f5aaa7416436cb7cd734f00b8abbdbec3275e49330c1f0547ceae99700450 Homepage: https://cran.r-project.org/package=variantspark Description: CRAN Package 'variantspark' (A 'Sparklyr' Extension for 'VariantSpark') This is a 'sparklyr' extension integrating 'VariantSpark' and R. 'VariantSpark' is a framework based on 'scala' and 'spark' to analyze genome datasets, see . It was tested on datasets with 3000 samples each one containing 80 million features in either unsupervised clustering approaches and supervised applications, like classification and regression. The genome datasets are usually writing in VCF, a specific text file format used in bioinformatics for storing gene sequence variations. So, 'VariantSpark' is a great tool for genome research, because it is able to read VCF files, run analyses and return the output in a 'spark' data frame. Package: r-cran-variationaldcm Architecture: all Version: 2.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-variationaldcm_2.0.1-1.ca2404.1_all.deb Size: 138976 MD5sum: ca7720c0c7beeb59b28ee2d1bfa47d7b SHA1: 33d3a8f164bcf70bd294f9863a0db1417cf3afe8 SHA256: 557a503f249c6a737055af4414df11607d092781e88ffe05d8ad4e4faa75a6f2 SHA512: c7753ad8caafd431cebd450b69d54ff3def92f7670fd9f1820785cebcd7eb5a3dcf87caee203ab3e31fe6cbc1437fd439c21c868cc7250465afcd4e9d3978052 Homepage: https://cran.r-project.org/package=variationalDCM Description: CRAN Package 'variationalDCM' (Variational Bayesian Estimation for Diagnostic ClassificationModels) Enables computationally efficient parameters-estimation by variational Bayesian methods for various diagnostic classification models (DCMs). DCMs are a class of discrete latent variable models for classifying respondents into latent classes that typically represent distinct combinations of skills they possess. Recently, to meet the growing need of large-scale diagnostic measurement in the field of educational, psychological, and psychiatric measurements, variational Bayesian inference has been developed as a computationally efficient alternative to the Markov chain Monte Carlo methods, e.g., Yamaguchi and Okada (2020a) , Yamaguchi and Okada (2020b) , Yamaguchi (2020) , Oka and Okada (2023) , and Yamaguchi and Martinez (2023) . 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Includes a function (varImp) that computes the VIMP for arbitrary measures from the 'measures' package. For calculating the VIMP regarding the measures accuracy and AUC two extra functions exist (varImpACC and varImpAUC). 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Package: r-cran-vbv Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-vbv_0.6.2-1.ca2404.1_all.deb Size: 28122 MD5sum: 8503b3b7782e6ab8d379de5d687830f0 SHA1: 4969bbb8e38d0013491207c15d08f5e55500a9f4 SHA256: f11e54f54d5efb529a8d6c0771023f858a99404482746510975fcf6d085f0aa7 SHA512: 32ee52ed1cbd3408801cce11344f22eeb8ea518e99fa610425e134c0205078dad9d1df0585001dfca85bb9346b6320db199c1feeb88420022ead00bd84d3aaec Homepage: https://cran.r-project.org/package=VBV Description: CRAN Package 'VBV' (The Generalized Berlin Method for Time Series Decomposition) Time series decomposition for univariate time series using the "Verallgemeinerte Berliner Verfahren" (Generalized Berlin Method) as described in 'Kontinuierliche Messgrößen und Stichprobenstrategien in Raum und Zeit mit Anwendungen in den Natur-, Umwelt-, Wirtschafts- und Finanzwissenschaften', by Hebbel and Steuer, Springer Berlin Heidelberg, 2022 , or 'Decomposition of Time Series using the Generalised Berlin Method (VBV)' by Hebbel and Steuer, in Jan Beran, Yuanhua Feng, Hartmut Hebbel (Eds.): Empirical Economic and Financial Research - Theory, Methods and Practice, Festschrift in Honour of Prof. Siegfried Heiler. Series: Advanced Studies in Theoretical and Applied Econometrics. Springer 2014, p. 9-40. Package: r-cran-vccp Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vinecopula, r-cran-mosum, r-cran-mvtnorm Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vccp_0.1.1-1.ca2404.1_all.deb Size: 139660 MD5sum: 7a0822cb0a229668dcaab6fb7eb9927c SHA1: c1f30591185ac68b6dd9b3f098d3e234b1bbb8b7 SHA256: 38dfb39ae4a5e53948f4613b7f89d98df56afbb6b53e22169b69b35e17d0a2b7 SHA512: 2389ab3c71d852d98811a03f8f42a58159c489127a13077e67c7676e01c3c0b04a4f6ce7c363ceed1aad52ece25d519614d51c462d8b02daab7b10f295fd6bd4 Homepage: https://cran.r-project.org/package=vccp Description: CRAN Package 'vccp' (Vine Copula Change Point Detection in Multivariate Time Series) Implements the Vine Copula Change Point (VCCP) methodology for the estimation of the number and location of multiple change points in the vine copula structure of multivariate time series. The method uses vine copulas, various state-of-the-art segmentation methods to identify multiple change points, and a likelihood ratio test or the stationary bootstrap for inference. The vine copulas allow for various forms of dependence between time series including tail, symmetric and asymmetric dependence. The functions have been extensively tested on simulated multivariate time series data and fMRI data. For details on the VCCP methodology, please see Xiong & Cribben (2021). Package: r-cran-vcd2df Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 61 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-quarto Filename: pool/dists/noble/main/r-cran-vcd2df_1.0.1-1.ca2404.1_all.deb Size: 17292 MD5sum: d9a8ad9012981eb9a4f154fdf9c19646 SHA1: e7559c94ce9609b57e421da115b48be94f7b5d10 SHA256: 9b426f818ff186ba26ba12ba1d2a894b70fa5b415d7467e4295e0957989cbee7 SHA512: 1616f0e1760c62036becf12056ba71a6d569e229d8330de4048d24269a7fc74ab9ad8990ebdc87c5b5ed7c4f1e9b2b3ae88670d4f9ef759f0a57aa98b27a0bfe Homepage: https://cran.r-project.org/package=vcd2df Description: CRAN Package 'vcd2df' (Value Change Dump to Data Frame) Provides the 'vcd2df' function, which loads a IEEE 1364-1995/2001 VCD (.vcd) file, specified as a parameter of type string containing exactly a file path, and returns an R dataframe containing values over time. A VCD file captures the register values at discrete timepoints from a simulated trace of execution of a hardware design in Verilog or VHDL. The returned dataframe contains a row for each register, by name, and a column for each time point, specified VCD-style using octothorpe-prefixed multiples of the timescale as strings. The only non-trivial implementation details are that (1) VCD 'x' and 'z' non-numerical values are encoded as negative value -1 (as otherwise all bit values are positive) and (2) registers with repeated names in distinct modules are ignored, rather than duplicated, as we anticipate these registers to have the same values. Read more in arXiv preprint: 'vcd2df' -- Leveraging Data Science Insights for Hardware Security Research . Package: r-cran-vcd Architecture: all Version: 1.4-13-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1507 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-colorspace, r-cran-lmtest Suggests: r-cran-kernsmooth, r-cran-mvtnorm, r-cran-kernlab, r-cran-hsaur3, r-cran-coin Filename: pool/dists/noble/main/r-cran-vcd_1.4-13-1.ca2404.1_all.deb Size: 1281532 MD5sum: 4eef43e247cae2b44375c5b1df34d1d2 SHA1: 44b9ad227e2ed6aee95b71e00faae546cac9797a SHA256: d7ec3ea0572205bc753e0fdd05b139a2613793bf7fca2b8f11147a057c77fae9 SHA512: 2c30be2b860a2b26bd1f47cf5df5afc0fbcc6df9e6fe2ede978d615b0b49e46ff07dbecfd78a9ccadfd9280580a2f6287f67bea9f7071bfe5440a3b0ff95b79c Homepage: https://cran.r-project.org/package=vcd Description: CRAN Package 'vcd' (Visualizing Categorical Data) Visualization techniques, data sets, summary and inference procedures aimed particularly at categorical data. Special emphasis is given to highly extensible grid graphics. The package was package was originally inspired by the book "Visualizing Categorical Data" by Michael Friendly and is now the main support package for a new book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer (2015). Package: r-cran-vcdextra Architecture: all Version: 0.9.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4199 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-vcd, r-cran-gnm, r-cran-mass, r-cran-dplyr, r-cran-ca, r-cran-igraph, r-cran-colorspace, r-cran-gt, r-cran-scales, r-cran-knitr, r-cran-htmlwidgets, r-cran-webshot2 Suggests: r-cran-gmodels, r-cran-fahrmeir, r-cran-effects, r-cran-vgam, r-cran-plyr, r-cran-lmtest, r-cran-nnet, r-cran-ggplot2, r-cran-sleuth2, r-cran-car, r-cran-lattice, r-cran-aer, r-cran-coin, r-cran-hmisc, r-cran-rmarkdown, r-cran-seriation, r-cran-testthat, r-cran-tibble, r-cran-glue, r-cran-here, r-cran-purrr, r-cran-readxl, r-cran-stringr, r-cran-tidyr, r-cran-rgl Filename: pool/dists/noble/main/r-cran-vcdextra_0.9.3-1.ca2404.1_all.deb Size: 2998264 MD5sum: 1755ff1ba8081b3e0a3ba72b449ea579 SHA1: b7e87f69e645a698f3cd7ebcc775505515f700b2 SHA256: 2d3a1d3b6b5f6f0135ae30f26fb712622cd2e5d78fd6679a290814d894af7a9d SHA512: 7a2bf647ced23033d2b08aa6562e2211686532d3e16b2a6825b947a647f868ccb7bcd1bc5f2384a46e866d0bf42bc24addad676846eff3cbb726395d19760ddc Homepage: https://cran.r-project.org/package=vcdExtra Description: CRAN Package 'vcdExtra' ('vcd' Extensions and Additions) Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data and the 'gnm' package for Generalized Nonlinear Models. In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and adds a 3D version in 'mosaic3d'. Additionally, methods are provided for comparing and visualizing lists of 'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer. Package: r-cran-vcfheader Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1157 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-vcfheader_0.1.0-1.ca2404.1_all.deb Size: 922358 MD5sum: b8fb20809af55eedca1ba5f437e47271 SHA1: b718161be932988b4482b0cd5a352489df49d8bb SHA256: b312d667c06bcd81852a1c656778731092ed782b3db55cd009fbd97a7fbae0cd SHA512: 3c7da0bda8011f790e1e135a807e7c368f67476f5772ca900e164d2711601fe8e0a0a024109f7f08b709ccc986a76f63c81414815fd11d2f7afca8eaadadd8e8 Homepage: https://cran.r-project.org/package=vcfheader Description: CRAN Package 'vcfheader' (Fast Genetic Variant Call Format File Header Intelligence andAudit) Streams and parses variant call format file headers without reading full files. Provides structured metadata, validation, inference, and HTML reporting. 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'VChart', more than just a cross-platform charting library, but also an expressive data storyteller. 'VChart' examples and documentation are available here: . Package: r-cran-vcmeta Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mathjaxr, r-cran-rdpack, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-vcmeta_1.6.0-1.ca2404.1_all.deb Size: 654244 MD5sum: 2e3cd3e736136412e59a819733be0cf7 SHA1: cf485b1214a6e86e355dce3070f0e90da761ccb4 SHA256: e2ee14b6e353f47c5f5cf426816cc238b2bb4aa7cbfb1037a836e50ce1ef4ea7 SHA512: ec19390e28d67268b7cc641b1b5ed8602317d6aecd1c48dd7a17dd95643b2f137859d38ef410c4363842d10db189232d54059a653d1994fda561083170d05eed Homepage: https://cran.r-project.org/package=vcmeta Description: CRAN Package 'vcmeta' (Varying Coefficient Meta-Analysis) Implements functions for varying coefficient meta-analysis methods. 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A port of the Ruby gem of the same name (). Works by recording real 'HTTP' requests/responses on disk in 'cassettes', and then replaying matching responses on subsequent requests. 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Sources include IMGT from MP Lefranc (2009) and Vgenerepertoire from publication DN Olivieri (2014) . Package: r-cran-vdpo Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-sop Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vdpo_0.1.0-1.ca2404.1_all.deb Size: 291158 MD5sum: 85aca80832dfe42d59a7b6565caa63d9 SHA1: b506ae91def464bb50d8c48c43061d63e55ae158 SHA256: a18d6950c0d7f7bb4c660fcb46c86cb99127ea4c4adfb744f751e00590737b72 SHA512: 5cf9288ef15ed1030f62895ec71fcfadd3230cbdd190de2e53d3775c62bd44e233378357c14f1db4f5781097d655a9c06d8c3fc8c43f15f244daf311ba3448c2 Homepage: https://cran.r-project.org/package=VDPO Description: CRAN Package 'VDPO' (Working with and Analyzing Functional Data of Varying Lengths) Comprehensive set of tools for analyzing and manipulating functional data with non-uniform lengths. This package addresses two common scenarios in functional data analysis: Variable Domain Data, where the observation domain differs across samples, and Partially Observed Data, where observations are incomplete over the domain of interest. 'VDPO' enhances the flexibility and applicability of functional data analysis in 'R'. See Amaro et al. (2024) . Package: r-cran-vdsm Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-plyr, r-cran-dplyr, r-cran-viridis, r-cran-gridextra, r-cran-knitr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-vdsm_0.1.1-1.ca2404.1_all.deb Size: 84554 MD5sum: b64e795b1fc690a212d3df0a267ccbbe SHA1: 1a64617914fe6bed797ecebdab98782ad26b848a SHA256: 1cd52bc59d82e999c64a268c3a7914f5067a5d9be2d7516f489b71b7384598d9 SHA512: 899df08dffac0174e92502ea1bd1f7612989f563fb59f7b745b730e53f355d44dff06fd22e7d4c3cb69163173202f4f45d3b94d8832988bf7959c34a885f2f1e Homepage: https://cran.r-project.org/package=VDSM Description: CRAN Package 'VDSM' (Visualization of Distribution of Selected Model) Although model selection is ubiquitous in scientific discovery, the stability and uncertainty of the selected model is often hard to evaluate. How to characterize the random behavior of the model selection procedure is the key to understand and quantify the model selection uncertainty. This R package offers several graphical tools to visualize the distribution of the selected model. For example, Gplot(), Hplot(), VDSM_scatterplot() and VDSM_heatmap(). To the best of our knowledge, this is the first attempt to visualize such a distribution. About what distribution of selected model is and how it work please see Qin,Y.and Wang,L. (2021) "Visualization of Model Selection Uncertainty" . 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The package includes tools for visualizing initial confounder imbalances, estimating treatment assignment probabilities using various methods, defining the common support region, performing matching across multiple groups, and evaluating matching quality. For more details, see Lopez and Gutman (2017) . Package: r-cran-vecsets Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 58 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma Filename: pool/dists/noble/main/r-cran-vecsets_1.4-1.ca2404.1_all.deb Size: 29436 MD5sum: 6c9334b511a36a78dcd42b6644cd78a1 SHA1: a47bb6261b8aa7672cfae1f93f4b028083a86efd SHA256: ebd49ad0f39aa3aaea150139146bfd46fced6b1df28b2c252d25af18b8507a66 SHA512: 4dce1398b5942162902f3dacc836740e380799cd95c7456be6445954bf25589ec06938dd5349bbd30988acfb6f38a832024b951295900af92790fb79c0c7b912 Homepage: https://cran.r-project.org/package=vecsets Description: CRAN Package 'vecsets' (Like Set Tools in 'Base' Package but Keeps Duplicate Elements) The 'base' tools union() intersect(), etc., follow the algebraic definition that each element of a set must be unique. Since it's often helpful to compare all elements of two vectors, this toolset treats every element as unique for counting purposes. 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Package: r-cran-vectorcoder Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 85 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-readxl, r-cran-tidyverse, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vectorcoder_0.2.0-1.ca2404.1_all.deb Size: 38016 MD5sum: 21899b34c47e5e8660324169a35fd203 SHA1: 5e759dc40962fef6f0086fd5d1e29a96134ad467 SHA256: b8ca436ee9775b06d5259a577537a508f3736f42df526eb116c3b76595d729ff SHA512: 1438b8d5fbf01999eddb1efa0c5fced178ea57dc632f8d9815edcab9ce98f7605ba4333cc8027668c6e5bb2e7d476167af170f68e2afb242a2c5b6048992556e Homepage: https://cran.r-project.org/package=VectorCodeR Description: CRAN Package 'VectorCodeR' (Easily Analyze Your Gait Patterns Using Vector Coding Technique) Facilitate the analysis of inter-limb and intra-limb coordination in human movement. It provides functions for calculating the phase angle between two segments, enabling researchers and practitioners to quantify the coordination patterns within and between limbs during various motor tasks. Needham, R., Naemi, R., & Chockalingam, N. (2014) . Needham, R., Naemi, R., & Chockalingam, N. (2015) . Tepavac, D., & Field-Fote, E. C. (2001) . Park, J.H., Lee, H., Cho, Js. et al. (2021) . 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Includes functions for parametric curves, scalar and vector fields, gradients, divergences, curls, line and surface integrals, and dynamic 2D/3D graphical analysis to support teaching and learning. The implemented methods follow standard treatments in vector calculus and multivariable analysis as presented in Marsden and Tromba (2011) , Stewart (2015) , Thomas, Weir and Hass (2018) , Larson and Edwards (2016) , Apostol (1969) , Spivak (1971) , Schey (2005) , Colley (2019) , Lizarazo Osorio (2020) , Sievert (2020) , and Borowko (2013) . 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Supports dense and hybrid search, optional HNSW (Hierarchical Navigable Small World) approximate nearest-neighbor indexing, faceted filters with ACL (Access Control List) metadata, command-line tools, and a local dashboard built with 'shiny'. The HNSW method is described by Malkov and Yashunin (2018) . 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Some examples of semantically related classes include time across different granularities (e.g. daily, monthly, annual) and probability distributions (e.g. Normal, Uniform, Poisson). These groups of vector types typically share common statistical operations which vary in results with the attributes of each vector. The 'vecvec' data structure facilitates efficient storage and computation across multiple vectors within the same object. 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Package: r-cran-vegetablessrilanka Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-tsibble, r-cran-dplyr, r-cran-naniar, r-cran-visdat, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vegetablessrilanka_1.1.0-1.ca2404.1_all.deb Size: 156390 MD5sum: a2dc0a4c273cfb3e13cc6253f560548a SHA1: 12e9641dee920f2e6d89d617e325aa9235d2778a SHA256: 8fdfb01d1637369b82d967fde8b4eba1ae1866cb57d49adb8da69136cd8fa7bc SHA512: 5ee84637eba7d4c67fe15126db6bcc3b1b168b805ecbb65d4ec66e7aa92d7c7113497ce19b858cdadb103a2a35839faa9d65b14c941b2f991af54540e571e35a Homepage: https://cran.r-project.org/package=vegetablesSriLanka Description: CRAN Package 'vegetablesSriLanka' (Daily Vegetable Prices of Sri Lanka) Provides retail and wholesale vegetable price data from two major market hubs in Sri Lanka, Dambulla and Pettah. Includes tools for analyzing, visualizing, and comparing vegetable prices across markets. Package: r-cran-vegindexcalc Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-vegindexcalc_0.1.0-1.ca2404.1_all.deb Size: 21444 MD5sum: 5e08d01774f2af184df666577c2825f1 SHA1: 833794e546870869c7863b9324d2ff5701dfb49b SHA256: 66b37cfe27da642a313c60a17f0829b01a6fb23b1a455a2858d0783a3d72881a SHA512: 5219bcdbb1fb32b5b0b52aa4224ea1eefd90f22fcfff80f56b6cc8f99ca6258e7691faf8ca155164349e0725e301d3ec9d44de3bbfd7a0186fa579d6706e5545 Homepage: https://cran.r-project.org/package=vegIndexCalc Description: CRAN Package 'vegIndexCalc' (Vegetation Indices (VIs) Calculation for Remote Sensing Analysis) It provides a comprehensive toolkit for calculating a suite of common vegetation indices (VIs) derived from remote sensing imagery. VIs are essential tools used to quantify vegetation characteristics, such as biomass, leaf area index (LAI) and photosynthetic activity, which are essential parameters in various ecological, agricultural, and environmental studies. Applications of this package include biomass estimation, crop monitoring, forest management, land use and land cover change analysis and climate change studies. For method details see, Deb,D.,Deb,S.,Chakraborty,D.,Singh,J.P.,Singh,A.K.,Dutta,P.and Choudhury,A.(2020). Utilizing this R package, users can effectively extract and analyze critical information from remote sensing imagery, enhancing their comprehension of vegetation dynamics and their importance in global ecosystems. The package includes the function vegetation_indices(). 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There is a great need for spectral index catalogues and computing tools as a result of the quick rise of suggested spectral indices. Unfortunately, the majority of these resources lack a standard Application Programming Interface, are out-of-date, closed-source, or are not linked to a catalogue. We now introduce 'VegSpecIndex', a standardised list of spectral indices for studies of the earth system. A thorough inventory of spectral indices is offered by 'VegSpecIndex' and is connected to an R library. For every spectral index, 'VegSpecIndex' provides a comprehensive collection of information, such as names, formulae, and source references. The user community may add more items to the catalogue, which will keep 'VegSpecIndex' up to date and allow for further scientific uses. Additionally, the R library makes it possible to apply the catalogue to actual data, which makes it easier to employ remote sensing resources effectively across a variety of Earth system domains. 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Build, render, and statistically analyze Venn / 'UpSet' diagrams from 'CSV' / 'TSV' / 'GMT' / 'GMX' inputs. Provides the same 44 SVG models, intersection / 'Jaccard' / hypergeometric statistics, and PDF report layout as the web tool, with byte-equivalent 'TSV' exports (parity-tested against the published Python package). Integrates with 'ggplot2', 'tidygraph', and 'broom'. 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The data are 100 replicates from a multiple imputation through chained equations as described in Van Buuren and Groothuis-Oudshoorn (2011) . With the replicates the user can examine four human rights violations that occurred in the Colombian conflict accounting for the impact of missing fields and fully missing observations. 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The original data set may be downloaded from . This data set is for a real-time quantitative polymerase chain reaction (PCR) experiment that comprises the raw fluorescence data of 24,576 amplification curves. This data set comprises 59 genes of interest and 5 reference genes. Each gene was assessed on 366 neuroblastoma complementary DNA (cDNA) samples and on 18 standard dilution series samples (10-fold 5-point dilution series x 3 replicates + no template controls (NTC) x 3 replicates). 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'versions' fits in the narrow gap between the 'devtools' install_version() function and the 'checkpoint' package. devtools::install_version() installs a stated package version from source files stored on the CRAN archives. However CRAN does not store binary versions of packages so Windows users need to have RTools installed and Windows and OSX users get longer installation times. 'checkpoint' uses the Revolution Analytics MRAN server to install packages (from source or binary) as they were available on a given date. It also provides a helpful interface to detect the packages in use in a directory and install all of those packages for a given date. 'checkpoint' doesn't provide install.packages-like functionality however, and that's what 'versions' aims to do, by querying MRAN. As MRAN only goes back to 2014-09-17, 'versions' can't install packages archived before this date. 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It also includes functions for deriving climate parameters from measured daily weather data, and for simulating rainfall. Models implemented include MUSLE (Williams, 1975) and APLE (Vadas et al., 2009 ). 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Wallau. et al. (2016) . The purpose of the method is to detect horizontal transfers of transposable elements, by contrasting the divergence of transposable element sequences with that of regular genes. 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These Vietnamese encodings (TCVN3, VISCII, VPS) are not natively supported in R and lead to printing of wrong characters and garbled text (mojibake). This package fixes that problem and provides readable output with the correct Unicode characters (with or without diacritics). 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Package: r-cran-vigicaen Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1617 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-cli, r-cran-dplyr, r-cran-data.table, r-cran-ggplot2, r-cran-glue, r-cran-gridextra, r-cran-lifecycle, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-tidyr Suggests: r-cran-here, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tzdb, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-vigicaen_1.0.0-1.ca2404.1_all.deb Size: 1091080 MD5sum: 169e013750ba6eaf94f113cd5439ab55 SHA1: a44972e97cd62ed9800a2de4f413f26f1babe3e9 SHA256: 599195e89bde57f80f9b79b10318f53f3b5acb4da01ba1a72e8648d8fe587d1a SHA512: 0c9b5e73963c6bf754af834040dc67e21bf467c4987f71072b8f20908a9bead4daafa9b90305d96699a372402a3123dd4f81fdc95d3af46417fd2dd832581196 Homepage: https://cran.r-project.org/package=vigicaen Description: CRAN Package 'vigicaen' ('VigiBase' Pharmacovigilance Database Toolbox) Perform the analysis of the World Health Organization (WHO) Pharmacovigilance database 'VigiBase' (Extract Case Level version), e.g., load data, perform data management, disproportionality analysis, and descriptive statistics. 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For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020). Package: r-cran-vimpclust Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1929 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pcamixdata, r-cran-ggplot2, r-cran-polychrome, r-cran-mclust, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vimpclust_0.1.0-1.ca2404.1_all.deb Size: 1494422 MD5sum: e25e82554c80e2b0a9ae7dbca8b94db0 SHA1: 80b55394986658cbc35c7d12511a9d409702f12b SHA256: d6bcd03ae6a4f5942935cbf6bd165aee35ba0f4603cf61f09679bc57c2a23805 SHA512: 0f7ab9b821922926eb78777b57cc5c3241e52e54d2a8dc6eadd16d4b72d1242c385da6b7d6390c130f830b06ac95bc7ff2ce389ed6294ccd3cbc5c3a51cc40b9 Homepage: https://cran.r-project.org/package=vimpclust Description: CRAN Package 'vimpclust' (Variable Importance in Clustering) An implementation of methods related to sparse clustering and variable importance in clustering. The package currently allows to perform sparse k-means clustering with a group penalty, so that it automatically selects groups of numerical features. It also allows to perform sparse clustering and variable selection on mixed data (categorical and numerical features), by preprocessing each categorical feature as a group of numerical features. Several methods for visualizing and exploring the results are also provided. M. Chavent, J. Lacaille, A. Mourer and M. Olteanu (2020). 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Aside from some standard model- specific variable importance measures, this package also provides model- agnostic approaches that can be applied to any supervised learning algorithm. These include 1) an efficient permutation-based variable importance measure, 2) variable importance based on Shapley values (Strumbelj and Kononenko, 2014) , and 3) the variance-based approach described in Greenwell et al. (2018) . A variance-based method for quantifying the relative strength of interaction effects is also included (see the previous reference for details). Package: r-cran-vipor Architecture: all Version: 0.4.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4679 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-beeswarm, r-cran-lattice, r-cran-ggplot2, r-cran-beanplot, r-cran-vioplot, r-cran-ggbeeswarm Filename: pool/dists/noble/main/r-cran-vipor_0.4.7-1.ca2404.1_all.deb Size: 4489944 MD5sum: 9e6663dd347fdd713fbb825b36d4a469 SHA1: ec6adf29e7789a7cb4f616f666b5c9c80658e434 SHA256: 46a7db79654a601301071515014c8f69536bd22d8ad0efd2b5f544f3c8039e02 SHA512: f142fcbe23d7d4bdb32eb6c29e6b3be04b1a77747e27f04bed9cb654ac369b81b82b660cb008355fd44c2de0f66e1624f796e27af74818371ef3140ab3130e52 Homepage: https://cran.r-project.org/package=vipor Description: CRAN Package 'vipor' (Plot Categorical Data Using Quasirandom Noise and DensityEstimates) Generate a violin point plot, a combination of a violin/histogram plot and a scatter plot by offsetting points within a category based on their density using quasirandom noise. Package: r-cran-viprodesign Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 94 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biostrings, r-bioc-decipher, r-cran-cluster, r-cran-pathviewr, r-cran-dbscan, r-cran-ape Suggests: r-cran-optparse Filename: pool/dists/noble/main/r-cran-viprodesign_0.1.0-1.ca2404.1_all.deb Size: 42198 MD5sum: fc7d56917622906ab21d17c60e3264d9 SHA1: cf8e1062e31d7d8625ce1890b69062831a4cd704 SHA256: b9e8df5cefe2bc9e584b4010fd2c1ed39afe303e06f3f1601265aa4affccda22 SHA512: 81f364ae676805566714e649a3aad7c9a85192dc30a08ab7f772835ae795e59ea909dc3b5f9cc22ebdc72bf108f6ec940b4e58d512b811668918c4d6d6a5371e Homepage: https://cran.r-project.org/package=VIProDesign Description: CRAN Package 'VIProDesign' (A Comprehensive Tool for Protein Design) Provides tools for designing virus protein panels through sequence clustering and protein sequence analysis. The package includes functionality for filtering sequences, removing redundancy, identifying outliers, clustering sequences, and calculating entropy to evaluate clustering quality. A publication describing these methods is in preparation and will be added once available. Package: r-cran-viraldomain Architecture: all Version: 0.0.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-applicable, r-cran-dplyr, r-cran-earth, r-cran-kknn, r-cran-magrittr, r-cran-parsnip, r-cran-ranger, r-cran-recipes, r-cran-tidyselect, r-cran-workflows Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-viraldomain_0.0.7-1.ca2404.1_all.deb Size: 31218 MD5sum: b05f9e94b22f9d9d8bb329a4d8be1792 SHA1: 3790c205eedccf981c78a4b2d5057971a55e50b8 SHA256: bc5a5fc1c827db7ad0400732b80e8efcc93708064d72dd8e2f04e4810bfde865 SHA512: bf2cbf1eb7336822ee87dbf4f4e6b52c65195d856f8b23bcba95892185f12198bc26f3779797e6b411bf3a403361c4fc0f10520ee19ff0d664e31a8db9ed20cd Homepage: https://cran.r-project.org/package=viraldomain Description: CRAN Package 'viraldomain' (Applicability Domain Methods of Viral Load and CD4 Lymphocytes) Provides methods for assessing the applicability domain of models that predict viral load and CD4 (Cluster of Differentiation 4) lymphocyte counts. 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Package: r-cran-viralx Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dalex, r-cran-dalextra, r-cran-parsnip, r-cran-recipes, r-cran-workflows Suggests: r-cran-cubist, r-cran-dplyr, r-cran-earth, r-cran-kknn, r-cran-rsample, r-cran-rules, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-viralx_1.3.1-1.ca2404.1_all.deb Size: 71036 MD5sum: 4a9e81c0531bcb91aa7f86744197755b SHA1: 60f363370fcc9a2ff13f1be1d36333b357c59942 SHA256: fe57e881ccaa7136d3b9982c0c793dde34acc953547d8044f9adb01011eef897 SHA512: ed6788f58f8172c7391324c796048164288663d6855c56e0a95b43a21ae8a74dbc252db49eb0cdf2f60fce94049de6ef804246d794ae21c35a77c934b2369a08 Homepage: https://cran.r-project.org/package=viralx Description: CRAN Package 'viralx' (Explainers for Regression Models in HIV Research) A dedicated viral-explainer model tool designed to empower researchers in the field of HIV research, particularly in viral load and CD4 (Cluster of Differentiation 4) lymphocytes regression modeling. Drawing inspiration from the 'tidymodels' framework for rigorous model building of Max Kuhn and Hadley Wickham (2020) , and the 'DALEXtra' tool for explainability by Przemyslaw Biecek (2020) . It aims to facilitate interpretable and reproducible research in biostatistics and computational biology for the benefit of understanding HIV dynamics. 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The color maps are also perceptually-uniform, both in regular form and also when converted to black-and-white for printing. This is the 'lite' version of the 'viridis' package that also contains 'ggplot2' bindings for discrete and continuous color and fill scales and can be found at . Package: r-cran-viroreportr Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-epiestim, r-cran-ggplot2, r-cran-incidence, r-cran-lubridate, r-cran-projections, r-cran-purrr, r-cran-rlang, r-cran-tibble, r-cran-tidyr, r-cran-glue, r-cran-mgcv, r-cran-kableextra, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-viroreportr_1.0.4-1.ca2404.1_all.deb Size: 173518 MD5sum: 7349066e2505f4b52ff28e78e0b0f558 SHA1: 2e65e1c3fb9538d4b58ba8a59f9f07031d664522 SHA256: b2330038ec5e2ba3ecb9b4a46e8d423f708290bb2cf3bcefcf0ca221ad073e54 SHA512: f4d8912e7e7288a70ab6fc563b8a0d0ca040cfdb6bc6fed35d6682ff14b98c69d523172fa890976fa703d3ff7244252949c7782d56e5936c1e62f40141b21dbf Homepage: https://cran.r-project.org/package=ViroReportR Description: CRAN Package 'ViroReportR' (Respiratory Viral Infection Forecast Reporting) Tools for reporting and forecasting viral respiratory infections, using case surveillance data. Report generation tools for short-term forecasts, and validation metrics for an arbitrary number of customizable respiratory viruses. Estimation of the effective reproduction number is based on the 'EpiEstim' framework described in work by 'Cori' and colleagues. (2013) . 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It also provides the means to simulate quasi-realistic pollen-data conditions by applying simulated accumulation rates and given depth intervals between consecutive samples. 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Fertility histories are simulated for every individual and their offspring, producing a multi-generation virtual population. Package: r-cran-virtualspecies Architecture: all Version: 1.6.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-terra, r-cran-ade4, r-cran-rnaturalearth, r-cran-raster, r-cran-viridis Filename: pool/dists/noble/main/r-cran-virtualspecies_1.6.1-1.ca2404.1_all.deb Size: 247576 MD5sum: 807225a99e0e6c8c3c24bfc67a2e9dee SHA1: 1a36688f1cee332a812db398dc8b748cbc2237d0 SHA256: 7d4e283ea22bf8b8fb8351ba68b313a394b247f012d8fc3112abf6c342f351b7 SHA512: 25aec0164e61dc477b06e46a24ece39d348eaa5c711569f183e67e157af604189aff76f4304e1ae0c567b6edc00593bed84ead28de45ea33707eaf3236e36cdc Homepage: https://cran.r-project.org/package=virtualspecies Description: CRAN Package 'virtualspecies' (Generation of Virtual Species Distributions) Provides a framework for generating virtual species distributions, a procedure increasingly used in ecology to improve species distribution models. This package integrates the existing methodological approaches with the objective of generating virtual species distributions with increased ecological realism. Package: r-cran-virtuoso Architecture: all Version: 0.1.8-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-odbc, r-cran-processx, r-cran-dbi, r-cran-ini, r-cran-rappdirs, r-cran-curl, r-cran-fs, r-cran-digest, r-cran-ps Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-nycflights13, r-cran-testthat, r-cran-covr, r-cran-jsonld, r-cran-dplyr, r-cran-spelling Filename: pool/dists/noble/main/r-cran-virtuoso_0.1.8-1.ca2404.1_all.deb Size: 161410 MD5sum: 2abbf7090225a07fb4e50fb717dcd461 SHA1: eddd66e0ad562717332c57813e8469b82fab0bfd SHA256: ae2ba6e2caa6ab9eb7c3965aa1f49a2875dfd032c22b843705878ca6a56da18a SHA512: db9def1a59d41f869361accca1474be3150e05b4fdebcd1fde420fbeac2150c8cbfb04168520a93f05fe9fee68bdc361f34c1bc90cfbe39694d0b7461b79e451 Homepage: https://cran.r-project.org/package=virtuoso Description: CRAN Package 'virtuoso' (Interface to 'Virtuoso' using 'ODBC') Provides users with a simple and convenient mechanism to manage and query a 'Virtuoso' database using the 'DBI' (Data-Base Interface) compatible 'ODBC' (Open Database Connectivity) interface. 'Virtuoso' is a high-performance "universal server," which can act as both a relational database, supporting standard Structured Query Language ('SQL') queries, while also supporting data following the Resource Description Framework ('RDF') model for Linked Data. 'RDF' data can be queried using 'SPARQL' ('SPARQL' Protocol and 'RDF' Query Language) queries, a graph-based query that supports semantic reasoning. This allows users to leverage the performance of local or remote 'Virtuoso' servers using popular 'R' packages such as 'DBI' and 'dplyr', while also providing a high-performance solution for working with large 'RDF' 'triplestores' from 'R.' The package also provides helper routines to install, launch, and manage a 'Virtuoso' server locally on 'Mac', 'Windows' and 'Linux' platforms using the standard interactive installers from the 'R' command-line. By automatically handling these setup steps, the package can make using 'Virtuoso' considerably faster and easier for a most users to deploy in a local environment. Managing the bulk import of triples from common serializations with a single intuitive command is another key feature of this package. Bulk import performance can be tens to hundreds of times faster than the comparable imports using existing 'R' tools, including 'rdflib' and 'redland' packages. Package: r-cran-virusparies Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 587 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chromote, r-cran-cowplot, r-cran-dplyr, r-cran-ggplot2, r-cran-gt, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-virusparies_1.1.0-1.ca2404.1_all.deb Size: 448662 MD5sum: 5c91f8f4148f0ac3e6c3ffec9a49cfc7 SHA1: 818723155395b24085f0f269017731cd6d2ebf72 SHA256: d0a4093ce26df311661797ffd3c34d5d3e7da9d51efa17078da5474e7d52daf1 SHA512: 1f9988adebd9bf371cbea9ec2b7f9b56f36a5dd96504f70c01579443840960c2837a3bd8a1973f869f1f786a8023e136181659806106b250843c5846ff7ffd07 Homepage: https://cran.r-project.org/package=Virusparies Description: CRAN Package 'Virusparies' (Visualize and Process Output from 'VirusHunterGatherer') A collection of tools for downstream analysis of 'VirusHunterGatherer' output. 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Package: r-cran-virustotal Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 896 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr, r-cran-dplyr, r-cran-base64enc, r-cran-jsonlite, r-cran-checkmate, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-lintr, r-cran-httptest, r-cran-covr, r-cran-pkgdown, r-cran-spelling Filename: pool/dists/noble/main/r-cran-virustotal_0.6.0-1.ca2404.1_all.deb Size: 425836 MD5sum: c79d42a5940210c59e3c1708920af4f3 SHA1: 3d2df7a5420a136bc5ae7b8da3578827d3d75bc3 SHA256: 381bb32ffad2056cd24546c7d8dcc44cdfc78f0f028543b4691c47fcf4736e47 SHA512: 9452821e0c62d2a92149093d2b8112392bc9c0db797e761119ba4e4651e401785d1f9fcbc2685b51132c17a54f3f13e46998c5365026bb87e3530da0afce4f93 Homepage: https://cran.r-project.org/package=virustotal Description: CRAN Package 'virustotal' (R Client for the 'VirusTotal' API) Provides a comprehensive R interface to the 'VirusTotal' API v3.0 , a Google service that analyzes files and URLs for viruses, worms, trojans and other malware. Features include file/URL scanning, domain categorization, passive DNS information, IP reputation analysis, IoC relationships, sandbox analysis, and comment/voting systems. Implements rate limiting, error handling, and response validation for robust security analysis workflows. Package: r-cran-visa Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-ggpmisc, r-cran-magrittr, r-cran-matrix, r-cran-plot3d, r-cran-plotly, r-cran-reshape2, r-cran-rcolorbrewer Suggests: r-cran-devtools, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-stringi Filename: pool/dists/noble/main/r-cran-visa_1.0.0-1.ca2404.1_all.deb Size: 366276 MD5sum: 4cd8292e0e3265fd7819e79974ef820d SHA1: cbcc42a4ea7a94494ded9538c039e90052c9bbc1 SHA256: 29aeec92188a0c2a49dd0845294c7a7933a87f88fb086639c9185e37a3f01123 SHA512: 09e02c4232feddf6e076cdb7b2536e7ef72a176359b9b5be9f2fc6b5abd698ebd42a4ef4edc197d10282a2f20fb6dc023ff025f9ff03a05ade694969ca274b6d Homepage: https://cran.r-project.org/package=visa Description: CRAN Package 'visa' (Vegetation Imaging Spectroscopy Analyzer) Provides easy-to-use tools for data analysis and visualization for hyperspectral remote sensing (also known as imaging spectroscopy), with a particular focus on vegetation hyperspectral data analysis. It consists of a set of functions, ranging from the organization of hyperspectral data in the proper data structure for spectral feature selection, calculation of vegetation index, multivariate analysis, as well as to the visualization of spectra and results of analysis in the 'ggplot2' style. Package: r-cran-visachartr Architecture: all Version: 4.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6611 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-htmlwidgets Suggests: r-cran-dplyr, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble Filename: pool/dists/noble/main/r-cran-visachartr_4.0.1-1.ca2404.1_all.deb Size: 1569848 MD5sum: 7bc96fc5a711af707034df7bf5eb6b7e SHA1: 7f71fa658893346f7cb006df817f5db1184676cd SHA256: 619add40a56cb5a10390e571cc6e59be386e6f121689090192c0abab1173b0e6 SHA512: 452bd77f227d4cd209ac2025de8bd656d916f190f8d5ecba76200cdc914c605da0a0bacc8e713d29384f06ecd748efea38a427ca5d5b94c420ce3e6c74c3bd53 Homepage: https://cran.r-project.org/package=visachartR Description: CRAN Package 'visachartR' (Wrapper for 'Visa Chart Components') Provides a set of wrapper functions for 'Visa Chart Components'. 'Visa Chart Components' is an accessibility focused, framework agnostic set of data experience design systems components for the web. Package: r-cran-visae Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-shiny, r-cran-dplyr, r-cran-ggplot2, r-cran-shinyjs, r-cran-ca, r-cran-tidyr, r-cran-ggrepel, r-cran-rlang, r-cran-dt Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-visae_0.2.1-1.ca2404.1_all.deb Size: 451330 MD5sum: 5a938dca410e0d3fff33b6927e88e2cb SHA1: 0708165caa3b7911d6d96b40de5909a628cd7de3 SHA256: 4033498c4a21ca156b8cd8dd8416dd7d1de0144fec353ea6cea98063a1da56eb SHA512: 9fa25478813300e7179736842f926e059c00d7a0fb5f4c58c7bb4165a8bb9d7577de638f9f6b358a2bbfc5b5b66dfcb81377081c4a4bbf206cc6bef4b211f62a Homepage: https://cran.r-project.org/package=visae Description: CRAN Package 'visae' (Visualization of Adverse Events) Implementation of 'shiny' app to visualize adverse events based on the Common Terminology Criteria for Adverse Events (CTCAE) using stacked correspondence analysis as described in Diniz et. al (2021). Package: r-cran-visaotr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 79 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rglpk, r-cran-e1071, r-cran-kernlab, r-cran-matrix, r-cran-mboost, r-cran-randomforest, r-cran-xgboost Filename: pool/dists/noble/main/r-cran-visaotr_0.1.0-1.ca2404.1_all.deb Size: 45420 MD5sum: a59a732f39257f7236f788d96a622e4a SHA1: 4a504e0b6293bcc718fcbd8a68d55dbb1fbc4fe2 SHA256: 82e8cb55e1a12c77dadd8abb76b43ada35a0ffcfc90a86414ece1b58f67eebc8 SHA512: 8502e30bbba73ffc5b7a4b1ea7e0511bce462f08886a8cc0c32f6e64ddc8bb6a0c52c43f2343231d29ded1d511133edbe798c25fedf384327253e0857f258647 Homepage: https://cran.r-project.org/package=visaOTR Description: CRAN Package 'visaOTR' (Valid Improved Sparsity A-Learning for Optimal TreatmentDecision) Valid Improved Sparsity A-Learning (VISA) provides a new method for selecting important variables involved in optimal treatment regime from a multiply robust perspective. The VISA estimator achieves its success by borrowing the strengths of both model averaging (ARM, Yuhong Yang, 2001) and variable selection (PAL, Chengchun Shi, Ailin Fan, Rui Song and Wenbin Lu, 2018) . The package is an implementation of Zishu Zhan and Jingxiao Zhang. (2022+). Package: r-cran-viscollin Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-car, r-cran-corrgram, r-cran-corrplot, r-cran-dplyr, r-cran-lmtest, r-cran-knitr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-viscollin_0.1.2-1.ca2404.1_all.deb Size: 190064 MD5sum: 537f087455031fc80cc43a0a2bd191f0 SHA1: 5dc5788086de7a6b5cfbc3443adfcb497450731b SHA256: 271279a257ee75786fb78450e1f8793eb2045b4a033b373a1edf41e14a41f3dd SHA512: 5e04b8cd42d022c5abe147798f0f97077b5e71c122b1e965e01edd5f7d9aa3f5d29ecbf4360d9321f577c89c46d32d24362e1c6713c8ada7df05eeabb773bd26 Homepage: https://cran.r-project.org/package=VisCollin Description: CRAN Package 'VisCollin' (Visualizing Collinearity Diagnostics) Provides methods to calculate diagnostics for multicollinearity among predictors in a linear or generalized linear model. It also provides methods to visualize those diagnostics following Friendly & Kwan (2009), "Where’s Waldo: Visualizing Collinearity Diagnostics", . These include better tabular presentation of collinearity diagnostics that highlight the important numbers, a semi-graphic tableplot of the diagnostics to make warning and danger levels more salient, and a "collinearity biplot" of the smallest dimensions of predictor space, where collinearity is most apparent. Package: r-cran-viscomp Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-circlize, r-cran-dplyr, r-cran-ggextra, r-cran-ggnewscale, r-cran-ggplot2, r-cran-hmisc, r-cran-mass, r-cran-netmeta, r-cran-plyr, r-cran-qgraph, r-cran-reshape2, r-cran-tibble, r-cran-tidyr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-viscomp_1.0.0-1.ca2404.1_all.deb Size: 460948 MD5sum: 55ea3590a363fecf8855cc8b504e37ec SHA1: fae6ffec363fe60485d303692feb0f0234ec4d7f SHA256: 412e2be9f6a68c7f328eafe8d42e44ddcb4b48e2c9c0448c8d5a144652bf0f80 SHA512: b6aea4bb25682a57afff4d1ce82e3971eaa2bef1cf31764e2a73a342a64c056548e61302f1dc08a52af66b31f44de94c6de46b5fa22056ff1f56397e3d6b1209 Homepage: https://cran.r-project.org/package=viscomp Description: CRAN Package 'viscomp' (Visualize Multi-Component Interventions in Network Meta-Analysis) A set of functions providing several visualization tools for exploring the behavior of the components in a network meta-analysis of multi-component (complex) interventions: - components descriptive analysis - heat plot of the two-by-two component combinations - leaving one component combination out scatter plot - violin plot for specific component combinations' effects - density plot for components' effects - waterfall plot for the interventions' effects that differ by a certain component combination - network graph of components - rank heat plot of components for multiple outcomes. The implemented tools are described by Seitidis et al. (2023) . Package: r-cran-viscov Architecture: all Version: 1.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-bayesm, r-cran-clustergeneration, r-cran-scatterplot3d, r-cran-kernsmooth, r-cran-trialr Filename: pool/dists/noble/main/r-cran-viscov_1.6.0-1.ca2404.1_all.deb Size: 86692 MD5sum: 1561d891c6cef1c3bc5498f03671cb20 SHA1: 4bfd79ec47c17d60c9996ba0c081c0540cb7ce72 SHA256: 1cc569da5cf6837b6c3ae0d90e8ca8b18b68179e35e33b1b4adeaf7a6d37dd62 SHA512: 686d68b3696b00df25f7b481185d2cfc53f4a45664894cb819c337cc011f5c7753ea803db59f3eab507f5e01297908edc1f72d99e3c71ff53426d9452535f90e Homepage: https://cran.r-project.org/package=VisCov Description: CRAN Package 'VisCov' (Visualizing of Distributions of Covariance Matrices) Visualizing of distributions of covariance matrices. The package implements the methodology described in Tokuda, T., Goodrich, B., Van Mechelen, I., Gelman, A., & Tuerlinckx, F. (2012) . Package: r-cran-visdat Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1569 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-tidyr, r-cran-dplyr, r-cran-purrr, r-cran-readr, r-cran-magrittr, r-cran-tibble, r-cran-glue, r-cran-forcats, r-cran-cli, r-cran-scales Suggests: r-cran-testthat, r-cran-plotly, r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr, r-cran-spelling, r-cran-covr, r-cran-stringr Filename: pool/dists/noble/main/r-cran-visdat_0.6.0-1.ca2404.1_all.deb Size: 1034300 MD5sum: f671cb9166525aa423df5c7b8e255ef0 SHA1: 8d1b2ee0c4fb92add2db64cfc5be3846576c8588 SHA256: 3d10ea6d9a973f33d46526cd8c348e5f70351b64cd04ff811bbc48c828f69b3e SHA512: dff951aa8d3eeea557ed47ba90da0e0c074c727b68accf80d85cdef562b04dd1764d08d75c55b7f59b5470c0dfc31215de40a006104916dd0e4ce513d64377fd Homepage: https://cran.r-project.org/package=visdat Description: CRAN Package 'visdat' (Preliminary Visualisation of Data) Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using 'ggplot2'. Package: r-cran-vise Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 935 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-scales, r-cran-cowplot, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-plotly Filename: pool/dists/noble/main/r-cran-vise_0.1.3-1.ca2404.1_all.deb Size: 604376 MD5sum: e20f6043dc83ec87aa5bbc6f77462906 SHA1: 755c153a48f17c62e5dc34c76b637bfef441776b SHA256: 189f4719bee580c0229c481f5255f7bc8ebccc872a68385d0a8b5db1a11f28e8 SHA512: 0187d163f5d7f4deb41ddfb2eac4f25118448e595843b44b03b3279494a9f15777ea6a032ecd4ab555a53d0d21e5713f9629650526aacb635270a6fbc673adb5 Homepage: https://cran.r-project.org/package=ViSe Description: CRAN Package 'ViSe' (Visualizing Sensitivity) Designed to help the user to determine the sensitivity of an proposed causal effect to unconsidered common causes. Users can create visualizations of sensitivity, effect sizes, and determine which pattern of effects would support a causal claim for between group differences. Number needed to treat formula from Kraemer H.C. & Kupfer D.J. (2006) . Package: r-cran-visielse Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1157 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-chron, r-cran-matrix, r-cran-colorspace, r-cran-stringr, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-visielse_1.2.2-1.ca2404.1_all.deb Size: 669654 MD5sum: 08c7c70f283464c23af4d636fb81fedd SHA1: cabb18bad8ab6a498cab2fc531c5035ff6031f1c SHA256: d325aead6df54284ce11abcab122e319fb6aa7b98ffa2172b3a4c1b01dc3bfcd SHA512: c7322a455c888eab0f0d9b08b0edfd46cd030dc5ae88f83c9c4a17e4f236c90be07090f7b295f91b9532fb7de9a50f4c42a47c560274b9d540abf861d2de6adc Homepage: https://cran.r-project.org/package=ViSiElse Description: CRAN Package 'ViSiElse' (A Visual Tool for Behavior Analysis over Time) A graphical R package designed to visualize behavioral observations over time. Based on raw time data extracted from video recorded sessions of experimental observations, ViSiElse grants a global overview of a process by combining the visualization of multiple actions timestamps for all participants in a single graph. Individuals and/or group behavior can easily be assessed. Supplementary features allow users to further inspect their data by adding summary statistics (mean, standard deviation, quantile or statistical test) and/or time constraints to assess the accuracy of the realized actions. Package: r-cran-visitorcounts Architecture: all Version: 2.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1123 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rssa, r-cran-ggplot2, r-cran-zoo, r-cran-cowplot Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-visitorcounts_2.0.3-1.ca2404.1_all.deb Size: 821278 MD5sum: d0e58bf4364b8ea6088d55b8dac93781 SHA1: 9b84d58dbbb748354b76e228bfff438a4a5c6d9b SHA256: e5abc24a1eff58a473fed5ca823b2c402f3780653bfc1159726404b24475d173 SHA512: 2837e1c11cd468d41bfe2df165064c7a3826920cdb851548bec166ce26e8a7e58ee9325f634219480f08fbd04c72d21e43a643ef25c209f34d247a2d3d9f6648 Homepage: https://cran.r-project.org/package=VisitorCounts Description: CRAN Package 'VisitorCounts' (Modeling and Forecasting Visitor Counts Using Social Media) Performs modeling and forecasting of park visitor counts using social media data and (partial) on-site visitor counts. Specifically, the model is built based on an automatic decomposition of the trend and seasonal components of the social media-based park visitor counts, from which short-term forecasts of the visitor counts and percent changes in the visitor counts can be made. A reference for the underlying model that 'VisitorCounts' uses can be found at Russell Goebel, Austin Schmaltz, Beth Ann Brackett, Spencer A. Wood, Kimihiro Noguchi (2023) . Package: r-cran-vismeteor Architecture: all Version: 3.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1618 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-testthat, r-cran-httptest2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-vismeteor_3.0.1-1.ca2404.1_all.deb Size: 1132556 MD5sum: baf4384ee28f6250bf1374561500b60a SHA1: 32fb82c4b1f44f62c4071f5f7f8f25e11ce55f21 SHA256: 86d1bdc3f71e8af65a0351b936205f26c6fce88a3ae350e98f2aaf3cd2169f62 SHA512: 93ce6fcb0895e05b110a497ab0fb5143fc4c152ec0691cdd8ee2054d961eeff31ceb2608f54ccf31f06be2a02ac8af7bf5a4937fca7e061fcba9bb73c6e46a86 Homepage: https://cran.r-project.org/package=vismeteor Description: CRAN Package 'vismeteor' (Analysis of Visual Meteor Data) Provides a suite of analytical functionalities to process and analyze visual meteor observations from the Visual Meteor Database of the International Meteor Organization . Package: r-cran-vismi Architecture: all Version: 0.9.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2106 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-ggally, r-cran-ggplot2, r-cran-ggtext, r-cran-gridextra, r-cran-ggridges, r-cran-mixgb, r-cran-patchwork, r-cran-plotly, r-cran-purrr, r-cran-rlang, r-cran-scales, r-cran-tidyr, r-cran-trelliscopejs Filename: pool/dists/noble/main/r-cran-vismi_0.9.5-1.ca2404.1_all.deb Size: 1920024 MD5sum: 6698c5fdf32480e6349e339ffc3aea10 SHA1: fce1209b483b39aaeb73198dd2e713555c9235a7 SHA256: cd176ea336dd26cde9da467a6c293b53dd38e788ef3b4538cc415b676b6ee47a SHA512: 63e10e19f70e5a316281b5936c0b9ebbae7dfd336ed713fa1a580d719510894826232d31e25d25321180ea64bdcd1ac55fa709a20f1c7fec0cbc2c7912b3f7b4 Homepage: https://cran.r-project.org/package=vismi Description: CRAN Package 'vismi' (Visual Diagnostics for Multiple Imputation) A comprehensive suite of static and interactive visual diagnostics for assessing the quality of multiply-imputed data obtained from packages such as 'mixgb' and 'mice'. The package supports inspection of distributional characteristics, diagnostics based on masking observed values and comparing them with re-imputed values, and convergence diagnostics. Package: r-cran-visnetwork Architecture: all Version: 2.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10701 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-htmlwidgets, r-cran-htmltools, r-cran-jsonlite, r-cran-magrittr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-webshot, r-cran-igraph, r-cran-rpart, r-cran-shiny, r-cran-shinywidgets, r-cran-colourpicker, r-cran-sparkline, r-cran-ggraph, r-cran-tidygraph, r-cran-flashclust Filename: pool/dists/noble/main/r-cran-visnetwork_2.1.4-1.ca2404.1_all.deb Size: 3766098 MD5sum: 8555c490345ef9bfe9c106b7f1b3e5db SHA1: 9f6be8762183fe802058a939eacea80436fd6e75 SHA256: 5da84773719b58eab4b2ad997d0bb1fcc568f19d88fcedeb7ff6c6b48a6d1138 SHA512: 61503eec8e0be1353e5178baf2ceae064ab2f4f2ae8baeab065fb398780ec7d8977ac9edefb007d8fd5c2e9f1fb003a8e080a6389c3e9628d6f78bb44b5849c6 Homepage: https://cran.r-project.org/package=visNetwork Description: CRAN Package 'visNetwork' (Network Visualization using 'vis.js' Library) Provides an R interface to the 'vis.js' JavaScript charting library. It allows an interactive visualization of networks. Package: r-cran-visomopresults Architecture: all Version: 1.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4819 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-brand.yml, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-glue, r-cran-omopgenerics, r-cran-purrr, r-cran-rlang, r-cran-stringr, r-cran-systemfonts, r-cran-tidyr Suggests: r-cran-bslib, r-cran-cohortcharacteristics, r-cran-covr, r-cran-dt, r-cran-flextable, r-cran-ggalluvial, r-cran-ggplot2, r-cran-ggsankeyfier, r-cran-gt, r-cran-here, r-cran-htmltools, r-cran-incidenceprevalence, r-cran-knitr, r-cran-lifecycle, r-cran-officer, r-cran-palmerpenguins, r-cran-patientprofiles, r-cran-plotly, r-cran-reactable, r-cran-rmarkdown, r-cran-shiny, r-cran-shinycssloaders, r-cran-shinywidgets, r-cran-sortable, r-cran-testthat, r-cran-tinytable, r-cran-yaml Filename: pool/dists/noble/main/r-cran-visomopresults_1.5.0-1.ca2404.1_all.deb Size: 2452938 MD5sum: 1bf064d32f75d07dda024d23973d5ac3 SHA1: 3c263b5d8919988c370a86554433c487821fc57b SHA256: 9b1a7a8edb8c488530a1b2b717fd5cbd09ca1f6d700c5f436ac1918c0be85fc4 SHA512: 08896d9c590bf3884855e36dfba78b2e3f536b806440ccf07363bcaa2656e12a9ad22a83777fab620450300cd19fc4a8e8d51f09a74625c6091feaddde433cab Homepage: https://cran.r-project.org/package=visOmopResults Description: CRAN Package 'visOmopResults' (Graphs and Tables for OMOP Results) Provides methods to transform omop_result objects into formatted tables and figures, facilitating the visualisation of study results working with the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Package: r-cran-visor Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-sfheaders Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-visor_0.1.1-1.ca2404.1_all.deb Size: 87318 MD5sum: 0ce2d5841e62b59deac2694001153790 SHA1: f93ec86dfc39a8a020018cab933ef034e32dd50b SHA256: 5aa79dd5dfa657052c470dd01b33a1776d8e3fe7751d8bf1b53bfc0f6e7fbbf9 SHA512: 1bcf5b25687f4b4e28f0e7f7f702a22c227f0b9491f17aa76fb583093589bb996f9ad4d345ee4273aae62b0dd20f915548c529a8a47c165cd9e57f2de3cb3484 Homepage: https://cran.r-project.org/package=visor Description: CRAN Package 'visor' (Geospatial Tools for Visibility Analysis) Provides tools for visibility analysis in geospatial data. It offers functionality to perform isovist calculations, using arbitrary geometries as both viewpoints and occluders. Package: r-cran-vispedigree Architecture: all Version: 0.7.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2204 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-igraph, r-cran-nadiv Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-vispedigree_0.7.1-1.ca2404.1_all.deb Size: 1244164 MD5sum: 241a27ea105aa95950663119626e0088 SHA1: a14533a371fa9ed495779682d4a091c31e112f54 SHA256: f53f86acf0c881ad946078fb1b50f5ee619f642ce855ae2692c5c927499660e8 SHA512: 12498218b92a9d46c273b575b12d9c3b782bf52bfba556cd39bb87cebe93b391c39495ce898dd59af4d5cc704faa5c16683fefbc74dd302bfa5f7d59eae3fca2 Homepage: https://cran.r-project.org/package=visPedigree Description: CRAN Package 'visPedigree' (Tidying and Visualizing Animal Pedigrees) Built on graph theory and the high-performance 'data.table' framework, this package provides a comprehensive suite of tools for tidying, pruning, and visualizing animal pedigrees. By modeling pedigrees as directed acyclic graphs using 'igraph', it ensures robust loop detection, efficient generation assignment, and sophisticated hierarchical layouts. Key features include standardizing pedigree formats, flexible ancestry tracing, and generating legible vector-based PDF graphs. A unique compaction algorithm enables the visualization of massive pedigrees (e.g., in aquaculture selective breeding population) by grouping full-sib families, maintaining structural clarity without overcrowding. Package: r-cran-visr Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2086 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-broom, r-cran-cowplot, r-cran-dplyr, r-cran-dt, r-cran-forcats, r-cran-ggplot2, r-cran-gridextra, r-cran-gt, r-cran-gtable, r-cran-kableextra, r-cran-knitr, r-cran-lifecycle, r-cran-rlang, r-cran-survival, r-cran-tibble, r-cran-tidycmprsk, r-cran-tidyr Suggests: r-cran-data.table, r-cran-ggpubr, r-cran-learnr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-visr_0.4.1-1.ca2404.1_all.deb Size: 1233408 MD5sum: a33f85e3d6e4c011e33f947c8e0d47c5 SHA1: 0aa87bf8c3ce1f809cc833eb3ebb90d4bce286c5 SHA256: c9624cfae9dc18439926b52e07eddb41d41c166b66cb6bc74c5ca3dd07eb7157 SHA512: 3f8b26d00eaf763660f2c0c870d7bf5a8eb40c3f939398bf3a911d35c572491d2f5cdba1f94e9ff57bb90bc6e57b89e033a7a9db63d3df08b0afbe987941fed7 Homepage: https://cran.r-project.org/package=visR Description: CRAN Package 'visR' (Clinical Graphs and Tables Adhering to Graphical Principles) To enable fit-for-purpose, reusable clinical and medical research focused visualizations and tables with sensible defaults and based on graphical principles as described in: "Vandemeulebroecke et al. (2018)" , "Vandemeulebroecke et al. (2019)" , and "Morris et al. (2019)" . Package: r-cran-visreg Architecture: all Version: 2.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Suggests: r-cran-ggplot2, r-cran-glmmtmb, r-cran-knitr, r-cran-lme4, r-cran-mass, r-cran-matrix, r-cran-rgl, r-cran-rmarkdown, r-cran-survival, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-visreg_2.8.0-1.ca2404.1_all.deb Size: 284530 MD5sum: 601f31b88c234fcd83fca8bf4f598ce2 SHA1: fc5ecab2de09a5019b2b697d53e2fd8afd9d8592 SHA256: db425871f779a56a556e554f292afc9a571192a0e8f4c6a19d77c53b77e8954c SHA512: 23a18da6dcb6b2399a403ea92b5061ff9843ab58619b035746ebd4189f2a1ec157e5fb12abfaca55dd7547d2925404ee971ee19b0dd1e9582ca5960a99f027ef Homepage: https://cran.r-project.org/package=visreg Description: CRAN Package 'visreg' (Visualization of Regression Models) Provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R ('lm', 'glm', 'coxph', 'rlm', 'gam', 'locfit', 'lmer', 'randomForest', etc.) Package: r-cran-visstatistics Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4809 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cairo, r-cran-multcompview, r-cran-nortest, r-cran-vcd Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-visstatistics_0.2.0-1.ca2404.1_all.deb Size: 3284338 MD5sum: 76b398b788840a6cbbe530b5edf3f923 SHA1: e1aea670c94be9e6447b0398f3e64734c1a55173 SHA256: 5b099bedf8f1498afa8b1ce90203485b4b7a0bc101a98ba5dd29139596f19880 SHA512: de8e9b4d3f73126d9471440f9066643a160aac65343e50a28f4fff7b305378020a3e086f7031c9f25c0be6d2d2696b4564229b7c4029249c60a27279546d645a Homepage: https://cran.r-project.org/package=visStatistics Description: CRAN Package 'visStatistics' (Automated Selection and Visualisation of Statistical HypothesisTests) The right test, visualised. 'visStatistics' automatically selects and visualises statistical hypothesis tests comparing two vectors, based on their class, distribution, and sample size. Visual outputs, including box plots, bar charts, regression lines with confidence bands, mosaic plots, residual plots, and Q-Q plots, are annotated with relevant test statistics, assumption checks, and post-hoc analyses where applicable. The algorithmic workflow shifts attention from ad-hoc test selection to visual diagnostic assessment and statistical interpretation. It is particularly suited for server-side R applications, where end users interact solely through a web interface to select data groups and receive a complete visual statistical analysis automatically. The same automation makes it useful in time-constrained contexts such as statistical consulting, where it reduces effort spent on test selection and leaves more room for interpretation. The implemented tests cover the most frequently applied inferential methods in biomedical research (Hayat et al. (2017) ). The test selection algorithm proceeds as follows: Input vectors of class numeric or integer are considered numerical; those of class factor are considered categorical; those of class ordered are considered ordinal. Assumptions of residual normality and homogeneity of variances are considered met if the corresponding test yields a p-value greater than the significance level alpha = 1 - conf.level. (1) When the response is numerical and the predictor is categorical, a test comparing central tendencies is selected. If every group contains more than 50 observations, the sampling distribution of the group means is assumed approximately normal by the central limit theorem (Lumley et al. (2002) ); otherwise, residual normality is assessed using shapiro.test() applied to the standardised residuals of lm(). If normality is not met, wilcox.test() is used when the predictor has two levels and kruskal.test() followed by pairwise.wilcox.test() otherwise. If normality is met, levene.test() assesses variance homogeneity. For two-level predictors, Student's t.test(var.equal = TRUE) is applied if variances are homogeneous and Welch's t.test() otherwise. For predictors with more than two levels, aov() followed by TukeyHSD() is applied if variances are homogeneous, and oneway.test() followed by games.howell() otherwise. (2) When both vectors are numerical, lm() is fitted by default (correlation = FALSE). If correlation = TRUE, Spearman rank correlation is performed. (3) When the response is ordinal, it is converted to numeric ranks and the non-parametric path from (1) is followed (Wilcoxon or Kruskal-Wallis). When both variables are ordinal and correlation = TRUE, Kendall's tau_b is used instead. (4) When both vectors are categorical, Cochran's rule (Cochran (1954) ) is applied to test independence either by chisq.test() or fisher.test(). Package: r-cran-vistime Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5848 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-assertthat, r-cran-plotly, r-cran-ggplot2, r-cran-ggrepel, r-cran-rcolorbrewer Suggests: r-cran-prettydoc, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr, r-cran-highcharter Filename: pool/dists/noble/main/r-cran-vistime_1.3.0-1.ca2404.1_all.deb Size: 1547742 MD5sum: 611b719a1619bdc2a8caf9c98b86df9f SHA1: 7f6f5261029287de5e5eb345400893cbf5aab5e7 SHA256: 6d80edf30aac9be07972fd9a5bec02ed9a636331561b7cede0000b997f5e30a0 SHA512: a21feafb5c76e8f7f808af64b9debc123f71c47be8f0ce24e2a67cd1255508940f6670fa047fcc2b46d4d92dd08af415c49b481618cc0cf1455336e8bb2d9f9d Homepage: https://cran.r-project.org/package=vistime Description: CRAN Package 'vistime' (Pretty Timelines in R) A library for creating time based charts, like Gantt or timelines. Possible outputs include 'ggplot2' diagrams, 'plotly.js' graphs, 'Highcharts.js' widgets and data.frames. Results can be used in the 'RStudio' viewer pane, in 'RMarkdown' documents or in Shiny apps. In the interactive outputs created by vistime() and hc_vistime(), you can interact with the plot using mouse hover or zoom. Package: r-cran-vistree Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-partykit, r-cran-rpart, r-cran-colorspace Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-vistree_0.8.1-1.ca2404.1_all.deb Size: 77000 MD5sum: 0e5e20bfd8e1f62cffd4ae8a26b86a41 SHA1: 5d416420c453f088bb0885cabda0fccbb4a9eda3 SHA256: 1275b9395b231e1048bf3b1f464f43952a26c521049a1cfa607915478ac342db SHA512: 47650a196a7d17fab6809eab8c1bd16313c18acb94ce7fd2807f88786aa87ac977a86da2bf53c86373c92a4895f178f9bddf640fe9ef0ec6f2e37e5c554cfd2d Homepage: https://cran.r-project.org/package=visTree Description: CRAN Package 'visTree' (Visualization of Subgroups for Decision Trees) Provides a visualization for characterizing subgroups defined by a decision tree structure. The visualization simplifies the ability to interpret individual pathways to subgroups; each sub-plot describes the distribution of observations within individual terminal nodes and percentile ranges for the associated inner nodes. Package: r-cran-vistributions Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-magrittr Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-vdiffr, r-cran-xplorerr Filename: pool/dists/noble/main/r-cran-vistributions_0.2.0-1.ca2404.1_all.deb Size: 388418 MD5sum: e3096bd329d9e97b48d43d130f1f312d SHA1: da49df54c733f42f0803df46c04f323927286632 SHA256: 8d24b784358de3680fa3d6446122b3327e391de3a17ffa2bc601545cf264b582 SHA512: 27cd820eccfa827f701a1b4a1244b4fe3ddeb171fe07780f4e3dd006166d2cce9706b2f7a8e43e95b6bbcf077104411bc7caff35bc690c843aa8a0dfba45ef5b Homepage: https://cran.r-project.org/package=vistributions Description: CRAN Package 'vistributions' (Visualize Probability Distributions) Visualize and compute percentiles/probabilities of normal, t, f, chi square and binomial distributions. Package: r-cran-visualdom Architecture: all Version: 0.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-waveslim, r-cran-wavemulcor, r-cran-plot3d Filename: pool/dists/noble/main/r-cran-visualdom_0.8.0-1.ca2404.1_all.deb Size: 97778 MD5sum: a2948a937624b06ccbbe8a1f4aaf7d53 SHA1: afbec47cc1ed9cdad4025b7d342ddf7c2897386a SHA256: eb540c4763e82f588073be3dd4063c5334cfdfa4f24a49d22595ad1a26c095a0 SHA512: dd3d1d3c85e3516ae3a9efb11896ca596314e667083bcd011345cd176c109664ade2e9bc71d9ddbe50bcbddd6bb3b20f12632bba42b613e7f97d9b440f47ae23 Homepage: https://cran.r-project.org/package=VisualDom Description: CRAN Package 'VisualDom' (Visualize Dominant Variables in Wavelet Multiple Correlation) Estimates and plots as a heat map the correlation coefficients obtained via the wavelet local multiple correlation 'WLMC' (Fernández-Macho 2018) and the 'dominant' variable/s, i.e., the variable/s that maximizes the multiple correlation through time and scale (Polanco-Martínez et al. 2020, Polanco-Martínez 2022). We improve the graphical outputs of WLMC proposing a didactic and useful way to visualize the 'dominant' variable(s) for a set of time series. The WLMC was designed for financial time series, but other kinds of data (e.g., climatic, ecological, etc.) can be used. The functions contained in 'VisualDom' are highly flexible since these contains several parameters to personalize the time series under analysis and the heat maps. In addition, we have also included two data sets (named 'rdata_climate' and 'rdata_Lorenz') to exemplify the use of the functions contained in 'VisualDom'. Methods derived from Fernández-Macho (2018) , Polanco-Martínez et al. (2020) and Polanco-Martínez (2023, in press). 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It provides a framework for development and use of innovative methods for visualization, statistical analysis, and clinical interpretation of visual-field loss and its change over time. It is intended to be a tool for collaborative research. The package is described in Marin-Franch and Swanson (2013) and is part of the Open Perimetry Initiative (OPI) [Turpin, Artes, and McKendrick (2012) ]. 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Visualize is able to provide lower tail, bounded, upper tail, and two tail calculations. Supports strict and equal to inequalities. Also provided on the graph is the mean and variance of the distribution. 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To evaluate and visualize the operating characteristics of Simon's two-stage design. 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Provides helpers for building chain-ladder triangles (cumulative, decumulative, run-off, development factors with optional weighting), constructing exposure columns from policy start/end dates, parsing Czech birth numbers ('rodné číslo') into dates, generating smooth RGB color palettes for charts, and loading multi-sheet 'xlsx'/'xlsb' files into a list of data frames. The chain-ladder helpers follow the standard methodology of Mack (1993) . 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Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2021) when referring to this package in publications. 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This package offers an accessible and easy-to-use interface, including an interactive Shiny app, that simplifies the processing, extraction, analysis, and reporting of voice recording data in the behavioral and social sciences. The package includes batch processing capabilities to read and analyze multiple voice files in parallel, automates the extraction of key vocal features for further analysis, and automatically generates APA formatted reports for typical between-group comparisons in experimental social science research. A more extensive methodological introduction that inspired the development of the 'voiceR' package is provided in Hildebrand et al. 2020 . 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The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919). John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979). 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Most analyses and algorithms provided in the package are based on the concept of space exploration and are described in Lecigne et al. (2018, ). 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The NMF procedure decomposes a matrix X into a product C * D. Given conditions such that the matrix C is non-negative and has sufficiently spread columns, then volume minimization of a matrix D delivers a correct and unique, up to a scale and permutation, solution (C, D). This package provides both an implementation of volume-regularized NMF and "anchor-free" NMF, whereby the standard NMF problem is reformulated in the covariance domain. This algorithm was applied in Vladimir B. Seplyarskiy Ruslan A. Soldatov, et al. "Population sequencing data reveal a compendium of mutational processes in the human germ line". Science, 12 Aug 2021. . This package interacts with data available through the 'simulatedNMF' package, which is available in a 'drat' repository. To access this data package, see the instructions at . The size of the 'simulatedNMF' package is approximately 8 MB. 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Reference: Li, Y., Yang, H., Yu, H., Huang, H., Shen, Y*. (2023) "Penalized estimating equations for generalized linear models with multiple imputation", . Li, Y., Yang, H., Yu, H., Huang, H., Shen, Y*. (2023) "Penalized weighted least-squares estimate for variable selection on correlated multiply imputed data", . 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Using a bagging strategy in combination of a parametric method or inflection point search method for cut-off threshold determination. This package can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates. Luo H, Zhao Q, et al (2020) for more details. 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Package: r-cran-wal Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 581 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-freesurferformats, r-cran-imager, r-cran-jpeg, r-cran-png, r-cran-spacesxyz Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wal_0.1.1-1.ca2404.1_all.deb Size: 444702 MD5sum: 79b76e4f687171ad807fa3b844282ecd SHA1: 303d10cbc78c83ec008ba793a5dc149ed4f2b907 SHA256: e36e10be0e99dc9bff00fa257f42afa9d5eb6434b360b6b3182249a6e0150b58 SHA512: 30bd4dcf650aab71bc7ed5b9081c8ada588d641ae9eccf548894cf87ab3f517289a1b331918aa1c87df89728eb76359f789ce292f9e7e6b5781084f5cfcf6e81 Homepage: https://cran.r-project.org/package=wal Description: CRAN Package 'wal' (Read and Write 'wal' Bitmap Image Files and Other 'Quake' Assets) Read 'Quake' assets including bitmap images and textures in 'wal' file format. 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Package: r-cran-walkboutr Architecture: all Version: 0.6.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-dplyr, r-cran-geosphere, r-cran-ggforce, r-cran-ggplot2, r-cran-lubridate, r-cran-lwgeom, r-cran-magrittr, r-cran-measurements, r-cran-sf, r-cran-sp, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-walkboutr_0.6.0-1.ca2404.1_all.deb Size: 531850 MD5sum: 71d67849fe28e763ffe9c10dccad30ab SHA1: aed8e30c91a84b54303bd05ce71964dfe6ecfd21 SHA256: fb32301e052d37e4c220369b66039b71212d029e505817bc533162f8aa152e22 SHA512: b180184117bef55f0515add9fcf786553c90bae2dc837023ff7f3529220f79e2502c2098fdcbe5f76f26b2de7c9ab1ed5a806e99192d16f01c27aad3695f6093 Homepage: https://cran.r-project.org/package=walkboutr Description: CRAN Package 'walkboutr' (Generate Walk Bouts from GPS and Accelerometry Data) Process GPS and accelerometry data to generate walk bouts. A walk bout is a period of activity with accelerometer movement matching the patterns of walking with corresponding GPS measurements that confirm travel. The inputs of the 'walkboutr' package are individual-level accelerometry and GPS data. The outputs of the model are walk bouts with corresponding times, duration, and summary statistics on the sample population, which collapse all personally identifying information. These bouts can be used to measure walking both as an outcome of a change to the built environment or as a predictor of health outcomes such as a cardioprotective behavior. Kang B, Moudon AV, Hurvitz PM, Saelens BE (2017) . Package: r-cran-walkscore Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 48 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-walkscore_0.1.2-1.ca2404.1_all.deb Size: 17016 MD5sum: e7e6563de52b8471e3c428e6eed459f9 SHA1: 0ed01d7a24200398d00ffdc0923f358cac05b4f6 SHA256: c1142c6d240e023e12d2a8881d154904f0ec63f9af238996f241baa7fcccc4dc SHA512: 153ed3213910b7eccd3e873db50e09322947d425bfb1bef13a762efbfef3e1f15b55d8521bd021e220cef0f474a67d668682ad656abd6d4bfeaf955e8450cdc6 Homepage: https://cran.r-project.org/package=walkscore Description: CRAN Package 'walkscore' (A Tidy Interface to the 'Walk Score' API) Easily collect walk scores, bike scores, and transit scores (where available) from the 'Walk Score' API , a proprietary API that assigns locations a walkability score between 0 and 100. Package: r-cran-walkscoreapi Architecture: all Version: 1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 93 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-walkscoreapi_1.2-1.ca2404.1_all.deb Size: 63626 MD5sum: a87995a08b26a8e6d121fb870183a1ea SHA1: 267e492d5b208f7d21c0bf3368d6b13e27a6a156 SHA256: d5e94a5681ec0110ba2ddbed01b9976637b835b79e96087bd4c26b5a85ead376 SHA512: b8e5f731dc20f1c768700174abd0ac37b2291aac1925c77dc987f8ab50ed8481f79f13f43f1bd8a163af480f247fc9d29c17848ccf7d0ed45107b2ccd9004a11 Homepage: https://cran.r-project.org/package=walkscoreAPI Description: CRAN Package 'walkscoreAPI' (Walk Score and Transit Score API) A collection of functions to perform the Application Programming Interface (API) calls associated with the Walk Score website (www.walkscore.com) within the R environment. These functions can be used to query the Walk Score and Transit Score database for a wide variety of information using R scripts. This package includes the simple Walk Score and Transit Score API calls, which return the scores associated with an input location, as well as calls which return some data used to calculate the scores. These functions are especially useful for mass data collection and gathering Walk Score and Transit Score values for large lists of locations. Package: r-cran-wallace Architecture: all Version: 2.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2803 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-dt, r-cran-ecospat, r-cran-enmeval, r-cran-geodata, r-cran-htmltools, r-cran-knitcitations, r-cran-leafem, r-cran-magrittr, r-cran-markdown, r-cran-rcolorbrewer, r-cran-rjava, r-cran-rlang, r-cran-rmarkdown, r-cran-sf, r-cran-shinyalert, r-cran-shinyjs, r-cran-shinywidgets, r-cran-spocc, r-cran-spthin, r-cran-zip Suggests: r-cran-ade4, r-cran-bien, r-cran-dismo, r-cran-glue, r-cran-jsonlite, r-cran-knitr, r-cran-mapview, r-cran-maxnet, r-cran-predicts, r-cran-rangemodelmetadata, r-cran-raster, r-cran-rgbif, r-cran-sp, r-cran-terra, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wallace_2.2.1-1.ca2404.1_all.deb Size: 1847182 MD5sum: ba5b4b6e50c98c0cb7218644d3b9365b SHA1: 380efba412baff74378779f4c97c04fd8c92a627 SHA256: 2fe2accdb13479ceb98c4c011c3cf3fa2b8bc7a892cea063f2d82a287c25fe70 SHA512: d57a3cbfb9533620e95c713e3567b6c048fe4df9a6b0bb89e51a8d1dea3dda84b454a443114ddaa9c0122ba90a21e9167bc972574e1f856912a1131e615d35c8 Homepage: https://cran.r-project.org/package=wallace Description: CRAN Package 'wallace' (A Modular Platform for Reproducible Modeling of Species Nichesand Distributions) The 'shiny' application Wallace is a modular platform for reproducible modeling of species niches and distributions. Wallace guides users through a complete analysis, from the acquisition of species occurrence and environmental data to visualizing model predictions on an interactive map, thus bundling complex workflows into a single, streamlined interface. An extensive vignette, which guides users through most package functionality can be found on the package's GitHub Pages website: . Package: r-cran-wallomicsdata Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3503 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-wallomicsdata_1.0-1.ca2404.1_all.deb Size: 3488660 MD5sum: bcb5ef7cb36dc9152db4f0b36ebad5aa SHA1: 496fd7487ab69f99a4eb5bcea6d0acad8ebb3564 SHA256: 2560f58545fa57e18d7525fc5d121d035111d19e9c3dcdf85dc12d3af745d853 SHA512: 79176bd367a4ff46447096e03aeb1929d449aead110c1bddaaa9d531e3aa9b3c2bcb4d6c5d5c43d0464277f67db7cd8ae376f6e3372634c3a48bc24510889cb5 Homepage: https://cran.r-project.org/package=WallomicsData Description: CRAN Package 'WallomicsData' (Datasets for Multi-Omics Integration in a Plant Abiotic StressContext) Datasets from the WallOmics project. Contains phenomics, metabolomics, proteomics and transcriptomics data collected from two organs of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. Exploratory and integrative analyses of these data are presented in Durufle et al (2020) and Durufle et al (2020) . Package: r-cran-wally Architecture: all Version: 1.0.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-prodlim, r-cran-riskregression, r-cran-data.table Suggests: r-cran-testthat, r-cran-survival Filename: pool/dists/noble/main/r-cran-wally_1.0.10-1.ca2404.1_all.deb Size: 114608 MD5sum: d5604df30069bfc4427103dd6f6599cb SHA1: 03b70283005d8327a1eae1663c14f8fb6dc5be89 SHA256: eea7151d1c09bcdf91d0f61260fe85f4ac94d60ecc359569c0843b9aec3f9e53 SHA512: f991950219eac043f80ef1f2d2420a2b1194d12dd8ac5c4f7731dae6a7aeb76c8d0de2b7eb6379e1889622e1e13d9059362baa9a7f1b32e5fec6fff01455c279 Homepage: https://cran.r-project.org/package=wally Description: CRAN Package 'wally' (The Wally Calibration Plot for Risk Prediction Models) A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events. Package: r-cran-walmartapi Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-purrr, r-cran-httr, r-cran-tibble, r-cran-magrittr, r-cran-stringr, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-walmartapi_0.1.5-1.ca2404.1_all.deb Size: 44534 MD5sum: f556e31f03f1525b6c6ab253b7962bc6 SHA1: 244bf33a6f17a09b631e788c34b49a94f3094942 SHA256: 03fd1b235247bc2899c2e7398863100ebbe4f78b45f7f7f8f32c6a6a3d9360f0 SHA512: 1aec64f6dacfc597bbbd0d13ce7d2c28523794ee71dca8590fb9f2881b3b068241279447d4d7a4ca7284c92d31d9239b53ea958b9d3d83e7f39c3931a5ad3e5b Homepage: https://cran.r-project.org/package=walmartAPI Description: CRAN Package 'walmartAPI' (Walmart Open API Wrapper) Provides API access to the Walmart Open API , that contains data about stores, Value of the day and products which includes names, sale prices, shipping rates and taxonomies. 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Package: r-cran-wals Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-mass, r-cran-rdpack Suggests: r-cran-aer, r-cran-bayesvarsel, r-cran-bms, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wals_0.2.6-1.ca2404.1_all.deb Size: 347916 MD5sum: 6d0bc45c2c1f96139837d249be68ada7 SHA1: 63702e7fbd82c3c4461a77ed50b590d5e9e156c7 SHA256: 454b73831e6d639d6d2c8674debd21bee56df57a263bab06f5d326e1a1f76f26 SHA512: 5dfeb1183265122a7a501013591fd77aec0e2a70cad3d7ad2ac3d9dda7c19c23171411b17d8ee72585c34a1f2d4706c55c350e0cb67de8610e4927bfb27ed1c5 Homepage: https://cran.r-project.org/package=WALS Description: CRAN Package 'WALS' (Weighted-Average Least Squares Model Averaging) Implements Weighted-Average Least Squares model averaging for negative binomial regression models of Huynh (2024) , generalized linear models of De Luca, Magnus, Peracchi (2018) and linear regression models of Magnus, Powell, Pruefer (2010) , see also Magnus, De Luca (2016) . Weighted-Average Least Squares for the linear regression model is based on the original 'MATLAB' code by Magnus and De Luca , see also Kumar, Magnus (2013) and De Luca, Magnus (2011) . Package: r-cran-wamasim Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-wamasim_1.0.0-1.ca2404.1_all.deb Size: 69976 MD5sum: d864f5691fb9df893908b6b29e539b59 SHA1: 32db49c8acf6f5bc9b4572f56bc0ebafd595d1f6 SHA256: 067fc2f23493c242c5858fc98510cecc8cd5d717de2b577c7a6f825c3411ca29 SHA512: 5c00de5aad9fe043e6b8e144d4b555dfa153bb05b9576a0a3810fd272e1a4000d2dcf7fc2e095f802327a53abd91e56334f7be1668361d4009abd890d24bd1ee Homepage: https://cran.r-project.org/package=WaMaSim Description: CRAN Package 'WaMaSim' (Simulate Rehabilitation Strategies for Water DistributionSystems) The outcome of various rehabilitation strategies for water distribution systems can be modeled with the Water Management Simulator (WaMaSim). Pipe breaks and the corresponding damage and rehabilitation costs are simulated. It is mainly intended to be used as educational tool for the Water Infrastructure Experimental and Computer Laboratory at ETH Zurich, Switzerland. Package: r-cran-wand Architecture: all Version: 0.5.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-wand_0.5.0-1.ca2404.1_all.deb Size: 190334 MD5sum: e2c6d291fc8ac419aecc386385fda637 SHA1: c576d090d93844fc59b5f2a2f0603269c2acdaac SHA256: c36545425aa6478fb864059e7f4cfe9b28ac3d73f5e47d8f37f1f3921e77e9b6 SHA512: d98671795df8a5e7ec2785331d2d12d4cfd6759d04682064dda96249032c7b8f7a4b05df77ab7c0eacc21857ad85bb2f11dd05938c228dae9cd8316a26dd963c Homepage: https://cran.r-project.org/package=wand Description: CRAN Package 'wand' (Retrieve 'Magic' Attributes from Files and Directories) 'MIME' types are shorthand descriptors for file contents and can be determined from "magic" bytes in file headers, file contents or intuited from file extensions. 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Package: r-cran-wanova Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 72 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-car, r-cran-suppdists Suggests: r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wanova_0.4.0-1.ca2404.1_all.deb Size: 40466 MD5sum: 9fa2c10fbb9902f3a35e30d5a5ac9ef8 SHA1: 8ec5b7d851af7ffed2db734a7c7359714845373d SHA256: 4c7ccca0bf007a81bcfb5d4322ed486544e15ee3244e1ac8b08e478b381f20e8 SHA512: 3b24f8afa949cc93a2f3ab71329631476aa19d615c979c2cf6379f8b524d707f732d8d56e9170be27e6f8744554e3f7a7c5ed438f4cdf633ee534d68bce0eea8 Homepage: https://cran.r-project.org/package=WAnova Description: CRAN Package 'WAnova' (Welch's Anova from Summary Statistics) Provides the functions to perform a Welch's one-way Anova with fixed effects based on summary statistics (sample size, means, standard deviation) and the Games-Howell post hoc test for multiple comparisons and provides the effect size estimator adjusted omega squared. In addition sample size estimation can be computed based on Levy's method, and a Monte Carlo simulation is included to bootstrap residual normality and homoscedasticity Welch, B. L. (1951) Kirk, R. E. (1996) Carroll, R. M., & Nordholm, L. A. (1975) Albers, C., & Lakens, D. (2018) Games, P. A., & Howell, J. F. (1976) Levy, K. J. (1978a) Show-Li, J., & Gwowen, S. (2014) . Package: r-cran-waou Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4413 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glmnet, r-cran-dplyr, r-cran-stringr, r-cran-glue, r-cran-mice, r-cran-nonprobsvy, r-cran-survey, r-cran-ggplot2, r-cran-purrr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-waou_0.1.0-1.ca2404.1_all.deb Size: 4460516 MD5sum: fdaf46be14b6663524efb0f74a0edcb2 SHA1: 9752a196b3ef487f6ca5e974de18352a6a2047b8 SHA256: 6d5793a7926385043eb2cd79d5a686ebc330ac0aad80caece9f7be532421bd62 SHA512: 1724f17d05fdd57324352fb9524c9770d56eb468e585676637e62ea767bb1c11c0f790c2f5fe063e4b4c74026960ae3b6cb0a23508c97ca9ee03e1ff16dc6fd5 Homepage: https://cran.r-project.org/package=waou Description: CRAN Package 'waou' (Weighting All of Us) Utilities for using a probability sample to reweight prevalence estimates calculated from the All of Us research program. Weighted estimates will still not be representative of the general U.S. population. However, they will provide an early indication for how unweighted estimates may be biased by the sampling bias in the All of Us sample. 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The area under command is differentially owned by different individual farmers. The Outlet runs for free of cost to irrigate the area under command 24X7. So, flow time of the outlet has to be divided based on an area owned by an individual farmer and the location of his land or farm. This roster is known as 'warabandi' and its generation in agriculture practices is a very tedious task. Calculations of time in microseconds are more error-prone, especially whenever it is performed by hands. That division of flow time for an individual farmer can be calculated by 'warabandi'. However, it generates a full publishable report for an outlet and all the farmers who have farms subjected to be irrigated. It reduces error risk and makes a more reproducible roster. For more details about warabandi system you can found elsewhere in Bandaragoda DJ(1995) . 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These citywide surveys have been collecting data allowing water and sanitation service levels across the entire city to be characterised, while also allowing more detailed data to be collected in areas of the city of particular interest. These surveys are intended to generate useful information for others working in the water and sanitation sector. Current release version includes datasets collected from a survey conducted in Dhaka, Bangladesh in March 2017. This survey in Dhaka is one of a series of surveys to be conducted by WSUP in various cities in which they operate including Accra, Ghana; Nakuru, Kenya; Antananarivo, Madagascar; Maputo, Mozambique; and, Lusaka, Zambia. This package will be updated once the surveys in other cities are completed and datasets have been made available. 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Package: r-cran-waspasr Architecture: all Version: 0.1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-waspasr_0.1.5-1.ca2404.1_all.deb Size: 111256 MD5sum: 7570ba66cb4de6a2696b036629c03f23 SHA1: 9fde0b67355fc0463ab5b9a9f33bc9b07d2d67f7 SHA256: 2066dae30ee5f3fa5e7467f90f17df7b31332594948e6fb4360b6a507a789421 SHA512: 8e28afe30754bd3bb344c2f2ca89dee84f1b1d684de7ef910a11b1e58eb7b7a7de9067768bb499909edba6bf0f6ef3bd05eece85b1775e8dd4cbdb0d5561f7d4 Homepage: https://cran.r-project.org/package=waspasR Description: CRAN Package 'waspasR' (Tool Kit to Implement a W.A.S.P.A.S. Based Multi-CriteriaDecision Analysis Solution) Provides a set of functions to implement decision-making systems based on the W.A.S.P.A.S. method (Weighted Aggregated Sum Product Assessment), Chakraborty and Zavadskas (2012) . So this package offers functions that analyze and validate the raw data, which must be entered in a determined format; extract specific vectors and matrices from this raw database; normalize the input data; calculate rankings by intermediate methods; apply the lambda parameter for the main method; and a function that does everything at once. The package has an example database called choppers, with which the user can see how the input data should be organized so that everything works as recommended by the decision methods based on multiple criteria that this package solves. Basically, the data are composed of a set of alternatives, which will be ranked, a set of choice criteria, a matrix of values for each Alternative-Criterion relationship, a vector of weights associated with the criteria, since certain criteria are considered more important than others, as well as a vector that defines each criterion as cost or benefit, this determines the calculation formula, as there are those criteria that we want the highest possible value (e.g. durability) and others that we want the lowest possible value (e.g. price). 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For more details see Piernicke et al. (2025) . 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Johansen et al. (2019) . Package: r-cran-wateryeartype Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-wateryeartype_1.0.1-1.ca2404.1_all.deb Size: 15792 MD5sum: 3c14484959c7d25e7574718ea39ad858 SHA1: 532a2179227a5376a068fad1fe60d8fd6566ab27 SHA256: add34475febc2d20463c3149857066ef2b47e8af32f3f6f3df188a5ae4ba1392 SHA512: 25b42739761938eb4a69d170cbc5b489d22628b84d0ecc33b2f3a410b3f31e0d8a764fccfbb5849ca452289aba3f7bd9115be2857134c804dce07f6da9943a1c Homepage: https://cran.r-project.org/package=waterYearType Description: CRAN Package 'waterYearType' (Sacramento and San Joaquin Valley Water Year Types) Provides Water Year Hydrologic Classification Indices based on measured unimpaired runoff (in million acre-feet). Data is provided by California Department of Water Resources and subject to revision. Package: r-cran-wats Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2191 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-colorspace, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-rcolorbrewer, r-cran-rlang, r-cran-testit, r-cran-tibble, r-cran-zoo Suggests: r-cran-boot, r-cran-covr, r-cran-devtools, r-cran-knitr, r-cran-scales, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wats_1.0.1-1.ca2404.1_all.deb Size: 1612106 MD5sum: cab3c777559a2e54b8e8472f5f425cd1 SHA1: 375e9dd99801310ee722849e6b04b2e153e4d386 SHA256: 653d514e4a77f1348f6803160e12e0af8317dd7c38795f59a97da26439196bae SHA512: ec5e3c20b623442bdcf439a4095aa24a85f9c36add4bd2842207d0ee2c76fc346466849c2d72dce12a27073bf015176e53dab317bc1432a4b6e0fd8938f77cd8 Homepage: https://cran.r-project.org/package=Wats Description: CRAN Package 'Wats' (Wrap Around Time Series Graphics) Wrap-around Time Series (WATS) plots for interrupted time series designs with seasonal patterns. 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Package: r-cran-waved Architecture: all Version: 1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-waved_1.3-1.ca2404.1_all.deb Size: 138728 MD5sum: 0fff0270f38dfdbddcc29d4f64d966ff SHA1: 58bb3764fb8815bc628b390067d96efccaf874a9 SHA256: 203aaabb4bdbb822898a968155331991112e0fb0c25b5aecf6f88c637016bfcd SHA512: 2a815d65a3d592a560822026ce2e60c626c3bd8841d9cfe81e681aa5f782cb830343c480848430b4321c277503df9ff0785ea2ad25d9e347d71dfa3f34151dea Homepage: https://cran.r-project.org/package=waved Description: CRAN Package 'waved' (Wavelet Deconvolution) Makes available code necessary to reproduce figures and tables in papers on the WaveD method for wavelet deconvolution of noisy signals as presented in The WaveD Transform in R, Journal of Statistical Software Volume 21, No. 3, 2007. Package: r-cran-waveletann Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavelets, r-cran-fracdiff, r-cran-forecast, r-cran-metrics Filename: pool/dists/noble/main/r-cran-waveletann_0.1.2-1.ca2404.1_all.deb Size: 19582 MD5sum: 668290bc40b4fa9376125839ba6993fe SHA1: 5f862a416fc8be32fa7c6d34d144557ab9be7865 SHA256: af0c6b51eaa1a1c3ec8bab7eff4c46b38a80fd50ae0002240d4c6b44d32639c1 SHA512: 64a7badb53a1d0916702ce14eeabe1a606b5f1bb01baa57fb70157f2f224f8aea89b4e9bf2054e84b587152555bacd389eadc9f7b4f2fd010acb77b3a9a35abe Homepage: https://cran.r-project.org/package=WaveletANN Description: CRAN Package 'WaveletANN' (Wavelet ANN Model) The wavelet and ANN technique have been combined to reduce the effect of data noise. This wavelet-ANN conjunction model is able to forecast time series data with better accuracy than the traditional time series model. This package fits hybrid Wavelet ANN model for time series forecasting using algorithm by Anjoy and Paul (2017) . Package: r-cran-waveletarima Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavelets, r-cran-fracdiff, r-cran-forecast Filename: pool/dists/noble/main/r-cran-waveletarima_0.1.2-1.ca2404.1_all.deb Size: 19752 MD5sum: 6a4f62e8e19ec2231dd14790ede4d58c SHA1: 3a61654c78985233c3603bff5bb40b2d6e0d57a9 SHA256: 8af4f8c41e68de32eb627aff8189e1ce0738798be7b631d88ce208179de9480f SHA512: 7785802ed3b00cdf16c0e68ea507c10c66f2b70b759d25fe2f6b4fea9f9a0c42bde02b4c82a7ed34fa337f4eb2722ff9c68692b6ed71d3bb87169eb4e394dc26 Homepage: https://cran.r-project.org/package=WaveletArima Description: CRAN Package 'WaveletArima' (Wavelet-ARIMA Model for Time Series Forecasting) Noise in the time-series data significantly affects the accuracy of the ARIMA model. 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Package: r-cran-waveletets Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 50 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-metrics, r-cran-tseries, r-cran-wavelets, r-cran-forecast, r-cran-caretforecast Filename: pool/dists/noble/main/r-cran-waveletets_0.1.0-1.ca2404.1_all.deb Size: 19202 MD5sum: d3445de919b7c03a0e9272a29ccef62a SHA1: d482295b7387da61a162352935754a40d5bf055b SHA256: 30d9380710ce6a0ca153e71e2d3d0e0fa4f3c20b7442a893c205d6a2fd9fb494 SHA512: 117260af92c8725d9bf87bd38254fab5aeb03fa0ee7e42c08dc40ddf82bd517ed97fe1b622ab3dc1efdb787551c4a31312eb5adcdaa52708a3d62b8063b5538a Homepage: https://cran.r-project.org/package=WaveletETS Description: CRAN Package 'WaveletETS' (Wavelet Based Error Trend Seasonality Model) ETS stands for Error, Trend, and Seasonality, and it is a popular time series forecasting method. Wavelet decomposition can be used for denoising, compression, and feature extraction of signals. By removing the high-frequency components, wavelet decomposition can remove noise from the data while preserving important features. A hybrid Wavelet ETS (Error Trend-Seasonality) model has been developed for time series forecasting using algorithm of Anjoy and Paul (2017) . Package: r-cran-waveletgarch Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavelets, r-cran-fints, r-cran-forecast, r-cran-rugarch, r-cran-fracdiff Filename: pool/dists/noble/main/r-cran-waveletgarch_0.1.1-1.ca2404.1_all.deb Size: 37596 MD5sum: 4c7987f37c2e443b1790d2a18a66fa20 SHA1: 24d41a63a5b53f88bb4dbae2f28c354a3ed50122 SHA256: 5655b6b9f2b01cab6dcfe76dfd8393c3e316e9a77063d6265297f830621ce5fd SHA512: 88c0fdc6bafc53e716261bb3a6ccf4994b1229ba74d115b95ad876998781736d7628a374474902ca70001cd656c977b780f3eae049cbe8af3cffe7bbd446e03a Homepage: https://cran.r-project.org/package=WaveletGARCH Description: CRAN Package 'WaveletGARCH' (Fit the Wavelet-GARCH Model to Volatile Time Series Data) Fits the combination of Wavelet-GARCH model for time series forecasting using algorithm by Paul (2015) . Package: r-cran-waveletgbm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 54 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-caretforecast, r-cran-metrics, r-cran-tseries, r-cran-wavelets, r-cran-gbm Filename: pool/dists/noble/main/r-cran-waveletgbm_0.1.0-1.ca2404.1_all.deb Size: 22974 MD5sum: 781b4f05e6fa239da0d7864b6334cb21 SHA1: 60eea04e89a58a0c9d0f4f8eba27df1e682a18db SHA256: b3bfc2107f11264a82945e7f4ab8b5a3eec31600d8c5cdce8d8fbb19deddb921 SHA512: 4bfddd14c0a042afa1b80c8624a940b6d522cdea7e39976ffae696e9590c0a4e0a20688fe2a8c61c96841017339f981c7eb1284492d912165f7c02004ac9972e Homepage: https://cran.r-project.org/package=WaveletGBM Description: CRAN Package 'WaveletGBM' (Wavelet Based Gradient Boosting Method) Wavelet decomposition method is very useful for modelling noisy time series data. Wavelet decomposition using 'haar' algorithm has been implemented to developed hybrid Wavelet GBM (Gradient Boosting Method) model for time series forecasting using algorithm by Anjoy and Paul (2017) . Package: r-cran-waveletknn Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-caretforecast, r-cran-metrics, r-cran-tseries, r-cran-wavelets Filename: pool/dists/noble/main/r-cran-waveletknn_0.1.0-1.ca2404.1_all.deb Size: 22720 MD5sum: 9bfca8c9980b3792c16bd8a998e77fd3 SHA1: be933910bdf846920bf36ebae23173c2365255ff SHA256: 61fb7043d852591229c966043d429887be9748e3ce10435362408a8e7b0e78f6 SHA512: 87d68825e8b44b8617780d1445f2ded0ec3be0a6cc1999544bf124900fdcac8df47e2cdf6ebb2980fefd3e4c6a1112105483f876f77f2bac122cb2367416ac80 Homepage: https://cran.r-project.org/package=WaveletKNN Description: CRAN Package 'WaveletKNN' (Wavelet Based K-Nearest Neighbor Model) The employment of the Wavelet decomposition technique proves to be highly advantageous in the modelling of noisy time series data. Wavelet decomposition technique using the "haar" algorithm has been incorporated to formulate a hybrid Wavelet KNN (K-Nearest Neighbour) model for time series forecasting, as proposed by Anjoy and Paul (2017) . Package: r-cran-waveletlstm Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 51 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-caretforecast, r-cran-tseries, r-cran-wavelets, r-cran-tslstm Filename: pool/dists/noble/main/r-cran-waveletlstm_0.1.0-1.ca2404.1_all.deb Size: 20810 MD5sum: fa9b328dcbcc14ad35d6c723dfce7ed8 SHA1: ffeadfbd7cb188aa09590630f113ebafe326df7d SHA256: f5db1e12af8976a0806eae2cec4602de7acb3fdc896711cad68d68d3f5bca9cc SHA512: 6641feed880e8355859fa6cb86d05f72c342c13307e690f5437f376d2771a71f3af280be85a3785505eb35d6f65779da42b2779dc9aab2b3c76bca3e8c581d2c Homepage: https://cran.r-project.org/package=WaveletLSTM Description: CRAN Package 'WaveletLSTM' (Wavelet Based LSTM Model) A wavelet-based LSTM model is a type of neural network architecture that uses wavelet technique to pre-process the input data before passing it through a Long Short-Term Memory (LSTM) network. The wavelet-based LSTM model is a powerful approach that combines the benefits of wavelet analysis and LSTM networks to improve the accuracy of predictions in various applications. This package has been developed using the algorithm of Anjoy and Paul (2017) and Paul and Garai (2021) . Package: r-cran-waveletml Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 80 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavelets, r-cran-tseries, r-cran-forecast, r-cran-fgarch, r-cran-atsa, r-cran-fints, r-cran-lsts, r-cran-earth, r-cran-caret, r-cran-neuralnet, r-cran-e1071, r-cran-pso Filename: pool/dists/noble/main/r-cran-waveletml_0.1.0-1.ca2404.1_all.deb Size: 50246 MD5sum: bd20ce997f0bbaf54e40b8755abb8397 SHA1: 414f17ddefcef8e1f8174ccf95a7d47f692d71c6 SHA256: 3d151b5af9b71f5fd201a6f4012beb6d048c4d6df02cd9b3c77aacc5508ff8cd SHA512: c7e8f83ef2e0711093e4a347cc3da7473c3ad84b1715b74f8d15e858132210b9c76301a75805c07252f3933b8582604ef763cbe5136677ff822eb0d19e541b0e Homepage: https://cran.r-project.org/package=WaveletML Description: CRAN Package 'WaveletML' (Wavelet Decomposition Based Hybrid Machine Learning Models) Wavelet decomposes a series into multiple sub series called detailed and smooth components which helps to capture volatility at multi resolution level by various models. Two hybrid Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression have been used) have been developed in combination with stochastic models, feature selection, and optimization algorithms for prediction of the data. The algorithms have been developed following Paul and Garai (2021) . Package: r-cran-waveletmlbestfl Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-waveletml, r-cran-ceemdanml, r-cran-describedf Filename: pool/dists/noble/main/r-cran-waveletmlbestfl_0.1.0-1.ca2404.1_all.deb Size: 33488 MD5sum: cf3c5f549e66571d6f59324246b2cd9e SHA1: f3e5d6590912ac6f441e1df554e36ffa9381650c SHA256: 1c60368fed0eaa866f375d0f3c887c9069f8abb57ef6c4ee0223008934d620b7 SHA512: 9f1c34af451da2fa995948666887eb81eb1ff227e2c3cb6a86fbe81c774c43bcdee6dda2240d361563e1f16c317e39e82ecc3bbe2373f4f47e712ae33585fe86 Homepage: https://cran.r-project.org/package=WaveletMLbestFL Description: CRAN Package 'WaveletMLbestFL' (The Best Wavelet Filter-Level for Prepared Wavelet-Based Models) Four filters have been chosen namely 'haar', 'c6', 'la8', and 'bl14' (Kindly refer to 'wavelets' in 'CRAN' repository for more supported filters). Levels of decomposition are 2, 3, 4, etc. up to maximum decomposition level which is ceiling value of logarithm of length of the series base 2. For each combination two models are run separately. Results are stored in 'input'. First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as 'MIN' and other values are denoted as 'NA'. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as 'MAX' and other values are denoted as 'NA'. 'output' contains the similar number of rows (which is 8) and columns (which is number filter-level combinations) as of 'input'. Values in 'output' are corresponding 'NA', 'MIN' or 'MAX'. Finally, the column containing minimum number of 'NA' values is denoted as the best ('FL'). In special case, if two columns having equal 'NA', it has been checked among these two columns which one is having least 'NA' in first five rows and has been inferred as the best. 'FL_metrics_values' are the corresponding metrics values. 'WARIGAANbest' is the data frame (dimension: 1*8) containing different metrics of the best filter-level combination. More details can be found in Garai and others (2023) . Package: r-cran-waveletrf Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavelets, r-cran-fracdiff, r-cran-forecast, r-cran-randomforest, r-cran-tsutils Filename: pool/dists/noble/main/r-cran-waveletrf_0.1.0-1.ca2404.1_all.deb Size: 22364 MD5sum: 90dde4f27ef5a2160b98f90effd2f3e7 SHA1: 8a8ec064739c7d37762abcaaee0180f7f336101f SHA256: efac0855a93e6491f39a16d6c5d2b856c061213ed32931d184c02a2c6e40a8a6 SHA512: 5da7bed3a5d683aadd50632f6c91c58c31dfb1c7d2d09635b68947ec0a0e5662bb4bbc75f60ea438916bfc4c9bb7d6b51ef0679276de27eba9d605b7e60c54f5 Homepage: https://cran.r-project.org/package=WaveletRF Description: CRAN Package 'WaveletRF' (Wavelet-RF Hybrid Model for Time Series Forecasting) The Wavelet Decomposition followed by Random Forest Regression (RF) models have been applied for time series forecasting. The maximum overlap discrete wavelet transform (MODWT) algorithm was chosen as it works for any length of the series. The series is first divided into training and testing sets. In each of the wavelet decomposed series, the supervised machine learning approach namely random forest was employed to train the model. This package also provides accuracy metrics in the form of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Ding et al. (2021) . Package: r-cran-waveletsvr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 53 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-wavelets, r-cran-fracdiff, r-cran-forecast, r-cran-e1071, r-cran-tsutils Filename: pool/dists/noble/main/r-cran-waveletsvr_0.1.0-1.ca2404.1_all.deb Size: 22166 MD5sum: 705246a4f89ccad1d7ff0ce74e00a875 SHA1: 87ee76a3612c20473a963087002f735a9f1428ac SHA256: 2b66976bce46b75b1566a70dd890fc1bc1c7a3a5712d3027b326494012a4fde6 SHA512: 10f67d9e37b6dce949704c44b2cec079f78c8e79832a705b98d936a42ef2155abb4e620954a861845a7ead91574afabc9575c0cc68d06aca0b07e4d527b9df7e Homepage: https://cran.r-project.org/package=WaveletSVR Description: CRAN Package 'WaveletSVR' (Wavelet-SVR Hybrid Model for Time Series Forecasting) The main aim of this package is to combine the advantage of wavelet and support vector machine models for time series forecasting. This package also gives the accuracy measurements in terms of RMSE and MAPE. This package fits the hybrid Wavelet SVR model for time series forecasting The main aim of this package is to combine the advantage of wavelet and Support Vector Regression (SVR) models for time series forecasting. This package also gives the accuracy measurements in terms of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Raimundo and Okamoto (2018) . Package: r-cran-wavemulcor Architecture: all Version: 3.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2647 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-waveslim, r-cran-plot3d, r-cran-rcolorbrewer Suggests: r-cran-covr, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wavemulcor_3.1.2-1.ca2404.1_all.deb Size: 1686872 MD5sum: 32f030c7ce8cb7b5ae984c36752fb699 SHA1: 30f1833b4a084ae9f2674b9c26f573803f4abe0e SHA256: 491fe4254885ca589b13d314a6fecaa662ee4f14d59b5e50eb8be16f44ba9e3b SHA512: f48e74d26505c89757e0a0102cea5786ac31f6294db669476c2b1ed2d5a8cc958f52d68d2a82814947abd7c11f8e85c14bb96f53581d93f2efa75a601f8d43a2 Homepage: https://cran.r-project.org/package=wavemulcor Description: CRAN Package 'wavemulcor' (Wavelet Routines for Global and Local Multiple Regression andCorrelation) Wavelet routines that calculate single sets of wavelet multiple regressions and correlations, and cross-regressions and cross-correlations from a multivariate time series. Dynamic versions of the routines allow the wavelet local multiple (cross-)regressions and (cross-)correlations to evolve over time. Package: r-cran-waver Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 63 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-geosphere Suggests: r-cran-lwgeom, r-cran-sp, r-cran-testthat Filename: pool/dists/noble/main/r-cran-waver_0.3.0-1.ca2404.1_all.deb Size: 33910 MD5sum: 35e8d23393486ab7ca4e382ddc2333ae SHA1: 2586ec77abd6272cf02b1037daedcde0cbcc5dff SHA256: 75edd6595d7511a3b66a7c69f148953900e82b97751853d3ab16a69ab07a4145 SHA512: 42cb3bd3dc2a5a99cab6c93afffc3a8743343fa501556615e70e09b6823600008b2a928565edd89ca9b325d8145064713ff349d034b586515fd3f66a06a9a1c2 Homepage: https://cran.r-project.org/package=waver Description: CRAN Package 'waver' (Calculate Fetch and Wave Energy) Functions for calculating the fetch (length of open water distance along given directions) and estimating wave energy from wind and wave monitoring data. Package: r-cran-waverider Architecture: all Version: 0.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1192 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-desctools, r-cran-hmisc, r-cran-matrix, r-cran-colorednoise, r-cran-dosnow, r-cran-foreach, r-cran-matrixstats, r-cran-reshape2, r-cran-truncnorm, r-cran-astrochron, r-cran-rcolorbrewer, r-cran-colorramps, r-cran-viridis, r-cran-magick, r-cran-rlist, r-cran-trapezoid, r-cran-fancova, r-cran-decomposer, r-cran-scico Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-waverider_0.5.1-1.ca2404.1_all.deb Size: 1146734 MD5sum: 977bb136ccbcb7dae5c445ff285828dd SHA1: 0755d2743353ae686f051d6b2db7524ebe29c16b SHA256: a3529474d3f78818186b125f04778db1149c279ab1bb78722b1a9216bf95a8d3 SHA512: 55f4e4710934fafd959ef4ca4ab836238d87ff4a90d75915cfa6ab61d9e4acdae646e214dfba0c4f3c03bdfbee03b3883ff1db4a1ac935388385ece962b003e1 Homepage: https://cran.r-project.org/package=WaverideR Description: CRAN Package 'WaverideR' (Extracting Signals from Wavelet Spectra) Tools for extracting and analyzing cyclic signals from time series. Package: r-cran-waverr Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-kimisc Filename: pool/dists/noble/main/r-cran-waverr_1.0-1.ca2404.1_all.deb Size: 18900 MD5sum: 37b2c05599122ecd154f823e656a1184 SHA1: 3ae4e8b08f6ce691774e3bd38a235e4a0f4d4040 SHA256: c0eb06fd69960a88800e2e850f1d552306a6d5126b2aed5183b08fc7f7315280 SHA512: 1181720ad9d91391b2921798bd5bdd022543df92781abd52aa8217466c6136a5f29a42b63c52fab6d03ac4410dc87e6be3007fb9c477a08a2cbc8c17397ab33e Homepage: https://cran.r-project.org/package=WaverR Description: CRAN Package 'WaverR' (Data Estimation using Weighted Averages of Multiple Regressions) For multivariate datasets, this function enables the estimation of missing data using the Weighted AVERage of all possible Regressions using the data available. Package: r-cran-waves Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4016 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-magrittr, r-cran-pls, r-cran-prospectr, r-cran-randomforest, r-cran-readr, r-cran-rlang, r-cran-scales, r-cran-spectacles, r-cran-stringr, r-cran-tibble, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-waves_0.2.6-1.ca2404.1_all.deb Size: 3744136 MD5sum: d33757e4c8934212713fbabd864bc072 SHA1: fca0c41987879344f20cb60599f85a18a4124518 SHA256: 101d18212d852bfbc7dd85e72a286f74319368beb6a524e5371c8699565fe50f SHA512: 9f35e94449f8239ef9ad4818515bd85dd370a46f4565e84e8cb7a1bf4860854668a993bba3a74671b8d1ab0a5e00db1e1446ed4d01f0218a8c50449aa8f0c19a Homepage: https://cran.r-project.org/package=waves Description: CRAN Package 'waves' (Vis-NIR Spectral Analysis Wrapper) Originally designed application in the context of resource-limited plant research and breeding programs, 'waves' provides an open-source solution to spectral data processing and model development by bringing useful packages together into a streamlined pipeline. This package is wrapper for functions related to the analysis of point visible and near-infrared reflectance measurements. It includes visualization, filtering, aggregation, preprocessing, cross-validation set formation, model training, and prediction functions to enable open-source association of spectral and reference data. This package is documented in a peer-reviewed manuscript in the Plant Phenome Journal . Specialized cross-validation schemes are described in detail in Jarquín et al. (2017) . Example data is from Ikeogu et al. (2017) . Package: r-cran-wavest Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 71 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-forecast, r-cran-neuralnet, r-cran-tsutils, r-cran-wavelets Suggests: r-cran-devtools, r-cran-roxygen2, r-cran-usethis Filename: pool/dists/noble/main/r-cran-wavest_0.1.0-1.ca2404.1_all.deb Size: 41764 MD5sum: 2e1bddf7cd1dfe922f444c7a49810a6d SHA1: aef25f014b604453ed23e73f81bdb38ca22b2261 SHA256: b6ef13dd7a9321e22183c68862dddcaf04423dc3bad7ad643b2b7698187e40af SHA512: 9cd316a0dae4b0925561a6b696ab51bed166342baa2711ae38c9eba0635e9054ab0d071259c2fbbad31d96658adb319b4b953ac451b0b77be329571bb125b7b3 Homepage: https://cran.r-project.org/package=WaveST Description: CRAN Package 'WaveST' (Wavelet-Based Spatial Time Series Models) An integrated wavelet-based spatial time series modelling framework designed to enhance predictive accuracy under noisy and nonstationary conditions by jointly exploiting multi-resolution (wavelet) information and spatial dependence. The package implements WaveSARIMA() (Wavelet Based Spatial AutoRegressive Integrated Moving Average model using regression features with forecast::auto.arima()) and WaveSNN() (Wavelet Based Spatial Neural Network model using neuralnet with hyperparameter search). Both functions support spatial transformation via a user-supplied spatial matrix, lag feature construction, MODWT-based wavelet sub-series feature generation, time-ordered train/test splitting, and performance evaluation (Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), and Mean Absolute Percentage Error (MAPE)), returning fitted models and actual vs predicted values for train and test sets. The package has been developed using the algorithm of Paul et al. (2023) . Package: r-cran-wavscalogram Architecture: all Version: 1.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-abind, r-cran-fields Filename: pool/dists/noble/main/r-cran-wavscalogram_1.1.3-1.ca2404.1_all.deb Size: 132052 MD5sum: 2c013f16a7fe63ef7aff2abe0f3626fb SHA1: b852d55184867814ab36709e3e824e3b1b6c453c SHA256: a1de8004215195b89da81a43b69490998225e3645391834256a8f5e59d64a44a SHA512: 03a33ff607c16c811f24989dbad3ee5eff45df31e15cb4f7e569d467731c38bbf35c956f2c2f1376d98d8cf38444a256f69cf317fa8f2f8141d74d26f928fb73 Homepage: https://cran.r-project.org/package=wavScalogram Description: CRAN Package 'wavScalogram' (Wavelet Scalogram Tools for Time Series Analysis) Provides scalogram based wavelet tools for time series analysis: wavelet power spectrum, scalogram, windowed scalogram, windowed scalogram difference (see Bolos et al. (2017) ), scale index and windowed scale index (Benitez et al. (2010) ). Package: r-cran-waydown Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2324 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numderiv, r-cran-matrix Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-desolve, r-cran-dplyr, r-cran-colorramps, r-cran-ggplot2, r-cran-gridextra, r-cran-latticeextra, r-cran-bindrcpp Filename: pool/dists/noble/main/r-cran-waydown_1.1.0-1.ca2404.1_all.deb Size: 2029128 MD5sum: 019970745fa868304044abe28a982478 SHA1: 6557bd6b6dd9abd1edcac40a623d9e4a3e64ab36 SHA256: 9a0b9e4fbbd893bb35b6322a1dcc57e2fa67f652e4e1ef560a5db21af4e2125d SHA512: fc70124031239e16b8c21b71b888e8e22119efe9ad8fca44725dcd884a7e79988533d39c80cef147539b3d68ed5f4e10fa24b84129d3b77141f84c6034132c31 Homepage: https://cran.r-project.org/package=waydown Description: CRAN Package 'waydown' (Computation of Approximate Potentials for Weakly Non-GradientFields) Computation of approximate potentials for both gradient and non gradient fields. It is known from physics that only gradient fields, also known as conservative, have a well defined potential function. Here we present an algorithm, based on the classical Helmholtz decomposition, to obtain an approximate potential function for non gradient fields. More information in Rodríguez-Sánchez (2020) . Package: r-cran-wayfindr Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2261 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-igraph, r-bioc-keggrest, r-cran-desctools, r-bioc-rgraphviz Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-polychrome, r-bioc-annotationdbi, r-bioc-org.hs.eg.db Filename: pool/dists/noble/main/r-cran-wayfindr_0.7.0-1.ca2404.1_all.deb Size: 1741934 MD5sum: ffe9e13f65a79a4d36324155c4d72e82 SHA1: d75c15aa7cf1e4508ce36d48dc4e5a69febfa0a4 SHA256: 2c03d6aac6bca83c0ecf46adb94e5ec5465664ab91c2933c80ab6fe2baaf06db SHA512: 58f050de5c45aa8176a58462f2c101085d67a32a9cda68be585f00f1928afd3e61e2f1366287b8bfcebfcca22d023e3b63bbb3d29d65492b0a29d199a735dac5 Homepage: https://cran.r-project.org/package=WayFindR Description: CRAN Package 'WayFindR' (Computing Graph Structures on WikiPathways) Converts pathways from 'WikiPathways' GPML format or 'KEGG' KGML format into 'igraph' objects. Includes tools to find all cycles in the resulting graphs and determine which ones involve negative feedback (inhibition). 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Methods are provided for assessing models fit to spatial data, including approaches for measuring the spatial structure of model errors, assessing model predictions at multiple spatial scales, and evaluating where predictions can be made safely. Methods are particularly useful for models fit using the 'tidymodels' framework. Methods include Moran's I ('Moran' (1950) ), Geary's C ('Geary' (1954) ), Getis-Ord's G ('Ord' and 'Getis' (1995) ), agreement coefficients from 'Ji' and Gallo (2006) (), agreement metrics from 'Willmott' (1981) () and 'Willmott' 'et' 'al'. (2012) (), an implementation of the area of applicability methodology from 'Meyer' and 'Pebesma' (2021) (), and an implementation of multi-scale assessment as described in 'Riemann' 'et' 'al'. (2010) (). 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Cherasia et al., (2022) . Amaratunga et al., (2009) . 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The WCM algorithm attributed to Pervot et al.(1993) . The authors are grateful to SAC, ISRO, Ahmedabad for providing financial support to Dr. Prashant K Srivastava to conduct this research work. 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Package: r-cran-wconf Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-caret Filename: pool/dists/noble/main/r-cran-wconf_1.2.0-1.ca2404.1_all.deb Size: 66648 MD5sum: 5ab744edd7735e855bed52f6e7fa1624 SHA1: 47c07f31af50f0bda5011309c4f6e277d094d532 SHA256: 0a804492374f466d0f1e2697e832ac23822fc224d2386312fc3d69a759b79146 SHA512: aee0462606382c52d2a25885e485785f024000fff87cd7730bf9607cb06fc831ba7aa0d4ef9ff5e3674867fea07db6796d38ab09f1c94d5743c1b9492fb8a101 Homepage: https://cran.r-project.org/package=wconf Description: CRAN Package 'wconf' (Weighted Confusion Matrix) Allows users to create weighted confusion matrices and accuracy metrics that help with the model selection process for classification problems, where distance from the correct category is important. The package includes several weighting schemes which can be parameterized, as well as custom configuration options. Furthermore, users can decide whether they wish to positively or negatively affect the accuracy score as a result of applying weights to the confusion matrix. Functions are included to calculate accuracy metrics for imbalanced data. Finally, 'wconf' integrates well with the 'caret' package, but it can also work standalone when provided data in matrix form. References: Kuhn, M. (2008) "Building Perspective Models in R Using the caret Package" Monahov, A. (2021) "Model Evaluation with Weighted Threshold Optimization (and the mewto R package)" Monahov, A. (2024) "Improved Accuracy Metrics for Classification with Imbalanced Data and Where Distance from the Truth Matters, with the wconf R Package" Starovoitov, V., Golub, Y. (2020). New Function for Estimating Imbalanced Data Classification Results. Pattern Recognition and Image Analysis, 295–302 Van de Velden, M., Iodice D'Enza, A., Markos, A., Cavicchia, C. (2023) "A general framework for implementing distances for categorical variables" . 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Package: r-cran-wcvpmatch Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-cli, r-cran-dplyr, r-cran-fozziejoin, r-cran-lifecycle, r-cran-magrittr, r-cran-memoise, r-cran-purrr, r-cran-stringdist, r-cran-stringr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-rlang, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wcvpmatch_0.0.1-1.ca2404.1_all.deb Size: 224698 MD5sum: a820b51c42f4e51ec7ade2bc6c181f68 SHA1: e97ee2706ee5f2f95955293fc887907506719036 SHA256: f00cfb642d271fa992de8b122b8cb9d289ef81a9503477ff167b448ef74147c2 SHA512: a071442c262f6358fd20581a946941e53dc0be20fa083aabdf778b4785d2a4c0bfa7f4dc8e68f0ba6d0f9bce2c1bb049debad3f81efd613dfa135519a2d988c3 Homepage: https://cran.r-project.org/package=wcvpmatch Description: CRAN Package 'wcvpmatch' (Taxonomic Name Reconciliation Against the 'WCVP' Backbone) Standardizes and reconciles scientific plant names against a World Checklist of Vascular Plants ('WCVP')-style taxonomic backbone. The package parses names into taxonomic components and applies staged exact and fuzzy matching for binomial and trinomial inputs, including infraspecific rank-aware checks. It also returns accepted-name context and row-level matching flags to support reproducible, auditable preprocessing for downstream biodiversity, spatial, and trait analyses. A user-supplied backbone can be passed through 'target_df'; when the optional companion package 'wcvpdata' is installed, its default checklist can also be used. Package: r-cran-wdata Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1000 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bayesmeta, r-cran-evmix, r-cran-kscorrect, r-cran-progress, r-cran-rcpp, r-cran-rdpack Suggests: r-cran-roxygen2, r-cran-testthat, r-cran-vdiffr Filename: pool/dists/noble/main/r-cran-wdata_0.1.1-1.ca2404.1_all.deb Size: 870080 MD5sum: 3bb6f9b11f9502eb77d2ec8bfabefd94 SHA1: f225201ec335b9d368af3db9e042ac9c91980c44 SHA256: eefa60bf360e22cc23b4632edda360298c5cba8e24dc7371c547f829cdb3f8f5 SHA512: e68f1aa3c83d88f0e3966d5579c48547577a19ab9bc583300c343b09125083d0e8473007c08108e39b9f4dbcf9f87ba294c7e734fabd3b65204ac0e74f45d9d6 Homepage: https://cran.r-project.org/package=WData Description: CRAN Package 'WData' (Statistical Inference for Weighted Data) Analyzes and models data subject to sampling biases. Provides functions to estimate the density and cumulative distribution functions from biased samples of continuous distributions. Includes the estimators proposed by Bhattacharyya et al. (1988) and Jones (1991) for density, and by Cox (2005, ISBN:052184939X) and Bose and Dutta (2022) for distribution, with different bandwidth selectors. Also includes a real length-biased dataset on shrub width from Muttlak (1988) . Package: r-cran-wdi2 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 111 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-httr2, r-cran-purrr, r-cran-tibble, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wdi2_0.1.0-1.ca2404.1_all.deb Size: 46848 MD5sum: 435e9eaf79f7dddcf9c2c9fcabff7baf SHA1: acb8091f373bbb0c4cdc3b2ad5e5ba597b4867bd SHA256: 28213e550e7694d23c77e842743aa9ed5e6df431d8da3c76d599a8ec4170ec4f SHA512: fe8192c37d817aa875d46cb6fa2f6ce9a3705e205fe6ce672a93f4d902cc74f714fba7442666fb5b10d5a0582ebd43ee3261023332f09f475ef7c7072657092d Homepage: https://cran.r-project.org/package=wdi2 Description: CRAN Package 'wdi2' (Download World Development Indicators from the World BankIndicators API) A modern, flexible interface for accessing the World Bank’s World Development Indicators (WDI) API . Similar to the existing 'WDI' package, 'wdi2' allows users to download, process, and analyze indicator data for multiple countries and years. However, 'wdi2' differs by relying on 'httr2' for multi-page request and error handling, providing support for downloading multiple indicators with a single function call, using progress bars to keep users informed about the data processing, and returning the processed data in a tidy data format in line with Wickham (2014) . Package: r-cran-wdi Architecture: all Version: 2.7.10-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 853 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-altdoc, r-cran-curl, r-cran-testthat, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-wdi_2.7.10-1.ca2404.1_all.deb Size: 806842 MD5sum: f4442cb036804edacf8a667d68a37bdd SHA1: f015185ad6b07d7e65565f044a915fe474bc166d SHA256: 0040b61212d6123dc2629b46831fea7d1e2d183436718773ad57b6d414bd7026 SHA512: 4e1ca37a00116d63a66e235a3cda9b1bd4a60abaaeb12ba115b2613fa53048ed4c9b89c2f9a713dedc515e4302bd475bc7958473d0214f4ef72ce29429815e2d Homepage: https://cran.r-project.org/package=WDI Description: CRAN Package 'WDI' (World Development Indicators and Other World Bank Data) Search and download data from over 40 databases hosted by the World Bank, including the World Development Indicators ('WDI'), International Debt Statistics, Doing Business, Human Capital Index, and Sub-national Poverty indicators. Package: r-cran-wdief Architecture: all Version: 1.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1753 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-terra Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-wdief_1.0.4-1.ca2404.1_all.deb Size: 1656112 MD5sum: 7ddb1d1359e6f3d6e90d8b49472491de SHA1: 5358086a3b2cdf0067859e523e0d02684cb810aa SHA256: 2de18865ab331f534fb02131d517a33d135d7cac33e9b98ccb800acca9e58820 SHA512: d568ccaf143c3c8ef01c7c28102198765bfeca466449582b3537f8d335b49783c4b41636ef2f5ec7e30ac45b18c98e570dc12d1504bda5bbaff124ea4f01d7fc Homepage: https://cran.r-project.org/package=wdiEF Description: CRAN Package 'wdiEF' (Calculation of the Water Deficit Index (WDI) and the EvaporativeFraction (EF) on Rasters) Provides functions to calculate the Water Deficit Index (WDI) and the Evaporative Fraction (EF) using geospatial raster data such as fractional vegetation cover (FVC) and surface-air temperature difference (TS-TA). The package automates regression-based edge fitting and produces continuous spatial maps of surface moisture and evaporative dynamics. Package: r-cran-wdiexplorer Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7097 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-tidyr, r-cran-tidyselect, r-cran-tibble, r-cran-tsibble, r-cran-rlang, r-cran-wdi, r-cran-cluster, r-cran-fabletools, r-cran-feasts, r-cran-forcats, r-cran-ggplot2, r-cran-ggiraph, r-cran-ggtext, r-cran-ggdist, r-cran-scales, r-cran-patchwork, r-cran-ggnewscale Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-naniar, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wdiexplorer_0.1.2-1.ca2404.1_all.deb Size: 2586184 MD5sum: 7781e1a5c1289d0d089be22852bef1db SHA1: 435ff192447c83013b3d81b39e8eaa7b364ae4e1 SHA256: b9567648758a4568a9907c0dc01defbd01753724ca3c47ad920df9d42b3b434c SHA512: 159226ae3a86634f75a87b7a4ac196884f1f8328a1a3f185835ce5cdac2dfd2b30b00c8d72b734ba40ff2e414dbff0df90e8bc781a9a4228a0ad3c2d5c34243f Homepage: https://cran.r-project.org/package=wdiexplorer Description: CRAN Package 'wdiexplorer' (Explore World Development Indicators Data) Provides a workflow for exploring World Development Indicators (WDI) country-level panel data. It downloads WDI data using the 'WDI' package and computes diagnostic indices that capture the temporal behaviour of the data by incorporating the grouping structure of the data. The set of diagnostic indices implemented includes variation features, trend and shape features, and sequential temporal features. This method is described in Akinfenwa, Cahill, and Hurley (2025) "'wdiexplorer': An R package Designed for Exploratory Analysis of World Development Indicators (WDI) Data" . We adapt the clustering diagnostics and visualisation methodology described in Rousseeuw (1987) and selected time series features from Hyndman and Athanasopoulos (2021) "Forecasting: Principles and Practice" . Package: r-cran-wdman Architecture: all Version: 0.2.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-binman, r-cran-assertthat, r-cran-processx, r-cran-yaml, r-cran-semver Suggests: r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wdman_0.2.6-1.ca2404.1_all.deb Size: 82074 MD5sum: e3eee55afef4d763ed13f7d2fd83dcb2 SHA1: 58b6e6b3ff3ffb0bba2b0c7a64c445456e08c771 SHA256: 41e62e7554b2e08fe9232548ddd35fed8b3cb7d5dd8e05c01601c3ca0586cf0d SHA512: 1a9bfe1b98b206f4337e9312bb05f9935e4e4268f0cac9dcec118801157efe431c2c4a7e3bba973c09b7af852b1d5169a1480abc8cdbb42df4f10eb24abd26b8 Homepage: https://cran.r-project.org/package=wdman Description: CRAN Package 'wdman' ('Webdriver'/'Selenium' Binary Manager) There are a number of binary files associated with the 'Webdriver'/'Selenium' project. This package provides functions to download these binaries and to manage processes involving them. Package: r-cran-wdnr.gis Architecture: all Version: 0.1.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-arcpullr, r-cran-sf, r-cran-dplyr, r-cran-rlang Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wdnr.gis_0.1.7-1.ca2404.1_all.deb Size: 214096 MD5sum: f9c520d8fcb1a0d95fb64807cfbcb227 SHA1: 89157d90e20e85eee6eef1f1b7e28a88aa5dc116 SHA256: 8699ba3df6de7b8f23915005e9a9d6901514d11a7c334e05774265840a155b86 SHA512: eaeab0958bb056d388bfed2e59b88f07e1c99c73668c87201b6f7e540008033625a5ba8d135425e22e8e652b3e122e851afda1d46a2596020528b44888516892 Homepage: https://cran.r-project.org/package=wdnr.gis Description: CRAN Package 'wdnr.gis' (Pull Spatial Layers from 'WDNR ArcGIS REST API') Functions for finding and pulling data from the 'Wisconsin Department of Natural Resources ArcGIS REST APIs' and . Package: r-cran-wdpar Architecture: all Version: 1.3.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-assertthat, r-cran-progress, r-cran-curl, r-cran-httr, r-cran-countrycode, r-cran-chromote, r-cran-xml2, r-cran-cli, r-cran-lwgeom, r-cran-tibble, r-cran-pingr, r-cran-rappdirs, r-cran-withr, r-cran-archive Suggests: r-cran-testthat, r-cran-knitr, r-cran-roxygen2, r-cran-rmarkdown, r-cran-ggmap, r-cran-ggplot2, r-cran-dplyr, r-cran-ps Filename: pool/dists/noble/main/r-cran-wdpar_1.3.9-1.ca2404.1_all.deb Size: 204248 MD5sum: 4ef45b2c6172b6a0b9204dc59d7d5007 SHA1: 1e5f52c723c6fdd18e3d24446fc184d9ab1b8b5b SHA256: 43d8539797214c49f08300a5818562527667124c94c83bb65fcd817eb0eb65f2 SHA512: f13ebe137bbaa2929322302df2e15e102d14b9933ad480f07d73e6242a9ee9c27e81af9d9bf05415b95fbd10f03477ab1192409da7c3227639f1a3413f961ea9 Homepage: https://cran.r-project.org/package=wdpar Description: CRAN Package 'wdpar' (Interface to the World Database on Protected Areas) Fetch and clean data from the World Database on Protected Areas (WDPA) and the World Database on Other Effective Area-Based Conservation Measures (WDOECM). Data is obtained from Protected Planet . To augment data cleaning procedures, users can install the 'prepr' R package (available at ). For more information on this package, see Hanson (2022) . Package: r-cran-wdsmatch Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 97 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wdsmatch_0.1.1-1.ca2404.1_all.deb Size: 63392 MD5sum: b12fa5ea9fd89b57387c0d0af999ae9e SHA1: 107e6622416b0208c97fda5801133dfc8cec2656 SHA256: 4ebb3f688ba70ae6c1fb8866f2d6bf3e118c30767faa161bac01960b002a9799 SHA512: 3850e78f7b3c28848c10969123615b4a610de535dc5d83a57999323c27eeb31cd78fa8f3bdd35d46895f4538cfa32d0701d3de97ebdafd0e0bc16ef23763a3df Homepage: https://cran.r-project.org/package=wdsmatch Description: CRAN Package 'wdsmatch' (Weighted Double Score Matching for Survey-Weighted CausalInference) Implements weighted double score matching (WDSM) for estimating population-level causal effects from complex survey data. Combines propensity scores and prognostic scores with survey design weights for matching, survey-weighted imputation within match sets, and Hajek normalization to target the population average treatment effect (PATE) and the population average treatment effect on the treated (PATT). Supports both retrospective (treatment-dependent) and prospective (treatment-independent) sampling designs. Achieves double robustness: consistent estimation when either the propensity score or prognostic score model is correctly specified. Provides polynomial sieve bias correction and linearization-based multinomial bootstrap variance estimation that preserves the survey-weighted matching structure without re-matching. Methods are described in Zeng, Tong, Tong, Lu, Mukherjee, and Li (2026, under review) "Where to weight? Estimating population causal effects with weighted double score matching in complex surveys". Package: r-cran-weaana Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1518 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-settings, r-cran-reshape2, r-cran-lubridate, r-cran-magrittr, r-cran-rlang, r-cran-dplyr, r-cran-tibble Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-weaana_0.3.0-1.ca2404.1_all.deb Size: 1060694 MD5sum: ec78c659e7b329706bb5e1aedccba6d3 SHA1: aeca801ffc7565b1ad621baebf76b7ed31d1e537 SHA256: 87204f1841fa44f0399f9b3ef853c46f6b0655dfb606020e26889c23b0e4b8f7 SHA512: 4cf9e4c2f8f2cfd6357a6409f0c8c8840cd06d15d059016096727d9950654dc1965a1d9bfb775b781b1404abc37378d4915fb9faffaadfd3bfad28e335c95ab3 Homepage: https://cran.r-project.org/package=weaana Description: CRAN Package 'weaana' (Analysis the Weather Data) Functions are collected to analyse weather data for agriculture purposes including to read weather records in multiple formats, calculate extreme climate index. Demonstration data are included the SILO daily climate data (licensed under CC BY 4.0, ). Package: r-cran-weakarma Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-compquadform, r-cran-mass, r-cran-matrixstats, r-cran-vars Suggests: r-cran-timeseries, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-renv Filename: pool/dists/noble/main/r-cran-weakarma_1.0.3-1.ca2404.1_all.deb Size: 275004 MD5sum: f5531f9a0224f2d6aacd522d9fcb6ceb SHA1: 419ff544822908f461479c6a0722e9fde0360787 SHA256: fcb4df70182fbc72a7671cecf67cc1ceeb28ba48a0efe90da74ed6fa7c51e7d1 SHA512: 07a7a6d76aaa174a505057633d63c4c1cbeee838ee375c18b82d6cbe338a3139bb728862582e6a436439f45c3fb3905de75eeb86648a9d41473b12edd0bddcce Homepage: https://cran.r-project.org/package=weakARMA Description: CRAN Package 'weakARMA' (Tools for the Analysis of Weak ARMA Models) Numerous time series admit autoregressive moving average (ARMA) representations, in which the errors are uncorrelated but not necessarily independent. These models are called weak ARMA by opposition to the standard ARMA models, also called strong ARMA models, in which the error terms are supposed to be independent and identically distributed (iid). This package allows the study of nonlinear time series models through weak ARMA representations. It determines identification, estimation and validation for ARMA models and for AR and MA models in particular. Functions can also be used in the strong case. This package also works on white noises by omitting arguments 'p', 'q', 'ar' and 'ma'. See Francq, C. and Zakoïan, J. (1998) and Boubacar Maïnassara, Y. and Saussereau, B. (2018) for more details. Package: r-cran-wearables Architecture: all Version: 0.11.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2434 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-ggplot2, r-cran-kernlab, r-cran-lubridate, r-cran-magrittr, r-cran-padr, r-cran-r.utils, r-cran-rhrv, r-cran-rlang, r-cran-signal, r-cran-sparklyr, r-cran-varian, r-cran-waveslim, r-cran-xts, r-cran-jsonlite Suggests: r-cran-testthat, r-cran-futile.logger Filename: pool/dists/noble/main/r-cran-wearables_0.11.3-1.ca2404.1_all.deb Size: 1925888 MD5sum: 24496c616836afa5476bf9b637812b19 SHA1: c73a5d7f6a8acb0837b05cff45f3a788efe70420 SHA256: acecc2b8f8a999d0a6285b354a5539681e35b161fa1a7089563728852322412c SHA512: 2eb0732987cd136ef4fb6ba73259efe423dca26c5eeb5051e7759e365023e3e756b137a201829d4cdd090053d1ecf594502238aa37418a4485c8c954416d0a00 Homepage: https://cran.r-project.org/package=wearables Description: CRAN Package 'wearables' (Tools to Read and Convert Wearables Data) Package to read Empatica E4, Embrace Plus, and Nowatch data, perform several transformations, perform signal processing and analyses, including batch analyses. Package: r-cran-weatherindices Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-weatherindices_0.1.0-1.ca2404.1_all.deb Size: 43150 MD5sum: fb7b9eaa418d6d21eb54cc676e495423 SHA1: 285c5c92d4fdaa5346453d5bca8483d7ec02a8e4 SHA256: 64597913e5ed701135e3ef8cdde448bafbd17b9cebc8e5461b66508c3f0b5e87 SHA512: 3445603b99354e3e4909af08361f22c8be8fa4d8f342fdb1120264fbcb00bdcde632964eeed7720346027fe7af2cc5df151557bcfb391b0a5cc74398ccbaa91e Homepage: https://cran.r-project.org/package=weatherindices Description: CRAN Package 'weatherindices' (Calculate Weather Indices) Weather indices represent the overall weekly effect of a weather variable on crop yield throughout the cropping season. This package contains functions that can convert the weekly weather data into yearly weighted Weather indices with weights being the correlation coefficient between weekly weather data over the years and crop yield over the years. This can be done for an individual weather variable and for two weather variables at a time as the interaction effect. This method was first devised by Jain, RC, Agrawal R, and Jha, MP (1980), "Effect of climatic variables on rice yield and its forecast",MAUSAM, 31(4), 591–596, . Later, the method have been used by various researchers and the latest can found in Gupta, AK, Sarkar, KA, Dhakre, DS, & Bhattacharya, D (2022), "Weather Based Potato Yield Modelling using Statistical and Machine Learning Technique",Environment and Ecology, 40(3B), 1444–1449,. Package: r-cran-weatherjoin Architecture: all Version: 0.2.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-jsonlite Suggests: r-cran-nasapower, r-cran-digest, r-cran-fst, r-cran-anytime, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/noble/main/r-cran-weatherjoin_0.2.3-1.ca2404.1_all.deb Size: 134216 MD5sum: 11f8a63f527eaaaadbe4f89e77df9d47 SHA1: e3ec59317de42fd820063ecfe79ce99bda8bab79 SHA256: dca6213bda14f02ed33fe42ecb7815e71186cf6440076c9ab6ff6d4d8257bddc SHA512: 50e1c9beb8049739ddf6852eb0b598c6d375bd7e14c3b551462bea56644cdfba1ee9e89e406ea4f434ef594942a58871584773cb108d0548ad886d757445909f Homepage: https://cran.r-project.org/package=weatherjoin Description: CRAN Package 'weatherjoin' (Join Gridded Weather Data to Event Tables) High-level tools to attach gridded weather data from the NASA POWER Project to event-based datasets. The package plans efficient spatio-temporal API calls via the 'nasapower' R package, caches downloaded segments locally, and joins weather variables back to the input table using exact or rolling joins. This package is not affiliated with or endorsed by NASA. Package: r-cran-weathermetrics Architecture: all Version: 1.2.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-weathermetrics_1.2.2-1.ca2404.1_all.deb Size: 101608 MD5sum: a128300d0b573b94326260c6de273f2c SHA1: 1c94918e8b8c32bc47f6db857e00a507e320d130 SHA256: a967fbd6ab6cc8952466c9363030f70a565c3a79c8cca74f516f99b9ea9783f2 SHA512: 8ec2876a9092496f705a9e223965026d920b3f8d560315e4746e0cfbbf4fb1d1843b825f5d13d10c497e9276941e91fd80e9608b86403810552615b0b4a6ed91 Homepage: https://cran.r-project.org/package=weathermetrics Description: CRAN Package 'weathermetrics' (Functions to Convert Between Weather Metrics) Functions to convert between weather metrics, including conversions for metrics of temperature, air moisture, wind speed, and precipitation. This package also includes functions to calculate the heat index from air temperature and air moisture. Package: r-cran-weatheroz Architecture: all Version: 3.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1752 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-apsimx, r-cran-clock, r-cran-crayon, r-cran-curl, r-cran-crul, r-cran-data.table, r-cran-foreign, r-cran-jsonlite, r-cran-knitr, r-cran-lubridate, r-cran-magick, r-cran-sf, r-cran-stars, r-cran-terra, r-cran-xml2 Suggests: r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-ggthemes, r-cran-gridextra, r-cran-mailr, r-cran-mapproj, r-cran-maps, r-cran-rmarkdown, r-cran-roxyglobals, r-cran-spelling, r-cran-testthat, r-cran-usethis, r-cran-vcr, r-cran-vdiffr, r-cran-withr Filename: pool/dists/noble/main/r-cran-weatheroz_3.0.0-1.ca2404.1_all.deb Size: 820420 MD5sum: 6a14b58050d20c7c46fdb996357a5ba3 SHA1: ca8f6a839a66686c0a2d5e57cc142f6b4f83ebf0 SHA256: 51b5421b607968f6a69b76bd048754b1cbff79c8f9b728497afa3afabc9ed90b SHA512: 06c5127bf7d67d637191507e46575f125a271a0edae0cd3fc06bac309d2799a6a634719f62506383a5c718296ffdb72edf48d8003889c6d446db649e5dff8659 Homepage: https://cran.r-project.org/package=weatherOz Description: CRAN Package 'weatherOz' (An API Client for Australian Weather and Climate Data Resources) Provides automated downloading, parsing and formatting of weather data for Australia through API endpoints provided by the Department of Primary Industries and Regional Development (DPIRD) of Western Australia and by the Science and Technology Division of the Queensland Government's Department of Environment and Science (DES). As well as the Bureau of Meteorology (BOM) of the Australian government precis and coastal forecasts, and downloading and importing radar and satellite imagery files. DPIRD weather data are accessed through public APIs provided by DPIRD, , providing access to weather station data from the DPIRD weather station network. Australia-wide weather data are based on data from the Australian Bureau of Meteorology (BOM) data and accessed through SILO (Scientific Information for Land Owners) Jeffrey et al. (2001) . DPIRD data are made available under a Creative Commons Attribution 3.0 Licence (CC BY 3.0 AU) license . SILO data are released under a Creative Commons Attribution 4.0 International licence (CC BY 4.0) . BOM data are (c) Australian Government Bureau of Meteorology and released under a Creative Commons (CC) Attribution 3.0 licence or Public Access Licence (PAL) as appropriate, see for further details. Package: r-cran-weathersentiment Architecture: all Version: 1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 64 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tidyverse, r-cran-wordcloud, r-cran-sentimentr, r-cran-tidytext, r-cran-ggplot2, r-cran-stringr, r-cran-data.table, r-cran-rcolorbrewer, r-cran-tidyr Suggests: r-cran-dplyr, r-cran-syuzhet Filename: pool/dists/noble/main/r-cran-weathersentiment_1.0-1.ca2404.1_all.deb Size: 34348 MD5sum: 927294de01246eb6bd263639909a0ef7 SHA1: 42849ec443341d2eea7f4f157a3058806645f908 SHA256: 88f7f960a05e4bd5e5ffb6f1aaeafd53163b6b96b331c52d74763d2ce22062cb SHA512: 2f3a9f41789957b7e51a289b4179d53944848fb99ccc4c37118d4ee0d27e83053ad40c791675c01767f9d93a4f0ee37f3eadf3502cb668e93b97ace2bec63e08 Homepage: https://cran.r-project.org/package=WeatherSentiment Description: CRAN Package 'WeatherSentiment' (Comprehensive Analysis of Tweet Sentiments and Weather Data) A comprehensive suite of functions for processing, analyzing, and visualizing textual data from tweets is offered. Users can clean tweets, analyze their sentiments, visualize data, and examine the correlation between sentiments and environmental data such as weather conditions. Main features include text processing, sentiment analysis, data visualization, correlation analysis, and synthetic data generation. Text processing involves cleaning and preparing tweets by removing textual noise and irrelevant words. Sentiment analysis extracts and accurately analyzes sentiments from tweet texts using advanced algorithms. Data visualization creates various charts like word clouds and sentiment polarity graphs for visual representation of data. Correlation analysis examines and calculates the correlation between tweet sentiments and environmental variables such as weather conditions. Additionally, random tweets can be generated for testing and evaluating the performance of analyses, empowering users to effectively analyze and interpret 'Twitter' data for research and commercial purposes. Package: r-cran-weatherstats Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-weatherstats_0.1-1.ca2404.1_all.deb Size: 148796 MD5sum: 67aa073752ff4bcca15c8cd5f91d6393 SHA1: 902958ec930d4d45777d6c7449129f0b15c6ee85 SHA256: e1a250768adf25b01f4c8c8b4dc64e45323054f2ca8f77af5431ca04c9c45814 SHA512: 4cbea7076d92753236bb1a24edb5dccc020258fef70574f4ee2e4f29f46af71325745a6f4672833071261458be4acfc761fcce4a47f73dd79327bb5bc1dc4c21 Homepage: https://cran.r-project.org/package=weatherStats Description: CRAN Package 'weatherStats' (Airport Weather Station Statistics) Download daily weather data recorded at airport weather stations using the National Centers for Environmental Information (NCEI) API . Package: r-cran-weathr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 100 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-httr2, r-cran-janitor, r-cran-jsonlite, r-cran-lubridate, r-cran-lutz, r-cran-magrittr, r-cran-purrr, r-cran-sf, r-cran-tibble Suggests: r-cran-gt, r-cran-snakecase, r-cran-tmap Filename: pool/dists/noble/main/r-cran-weathr_0.1.0-1.ca2404.1_all.deb Size: 67004 MD5sum: cb173eccddd7eb6ccf097ca48b25e864 SHA1: bdcfb1717fe1a00bcb49456e176b868854d4c799 SHA256: 45d43eac40d7f9da720af44e6c62ddaaba221323ad70d6b3a08db0ab4ae77507 SHA512: 25ab1e8f906b1ea27de42904c182e5d1bfa45bfbd5456c56dd9af9b5072b47028f97d1090bb41badc32b52ebcfc9bd0c2c4bf6ccfc733a138d8e81a0bf64f0ab Homepage: https://cran.r-project.org/package=weathR Description: CRAN Package 'weathR' (Interact with the U.S. National Weather Service API) Enables interaction with the National Weather Service application programming web-interface for fetching of real-time and forecast meteorological data. 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Package: r-cran-webchem Architecture: all Version: 1.3.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 446 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml2, r-cran-httr, r-cran-rvest, r-cran-jsonlite, r-cran-stringr, r-cran-dplyr, r-cran-purrr, r-cran-data.tree, r-cran-tibble, r-cran-base64enc, r-cran-rlang Suggests: r-cran-testthat, r-cran-rcdk, r-cran-covr, r-cran-robotstxt, r-cran-knitr, r-cran-rmarkdown, r-cran-plot.matrix, r-cran-usethis, r-cran-vcr Filename: pool/dists/noble/main/r-cran-webchem_1.3.1-1.ca2404.1_all.deb Size: 361112 MD5sum: a5f2c8e9db9a4feb87f6197a83dc05d3 SHA1: a610bea8b4b1fd4ce9ecb0974851ee7de69e71e4 SHA256: 4e8ae5019ea07eab76cff9b6803f9d8651493efbc6f646bed35a427e732daec8 SHA512: ef16c6364fed9740ed039197b3ecad26ffb8772c47987ff0345677b7aece7cb4d7d727c0d974c4f8d606ca17cdac07c83b94292c374ad41eba29f56751bcb215 Homepage: https://cran.r-project.org/package=webchem Description: CRAN Package 'webchem' (Chemical Information from the Web) Chemical information from around the web. This package interacts with a suite of web services for chemical information. Sources include: Alan Wood's Compendium of Pesticide Common Names, Chemical Identifier Resolver, ChEBI, Chemical Translation Service, ChemSpider, ETOX, Flavornet, NIST Chemistry WebBook, OPSIN, PubChem, SRS, Wikidata. 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Package: r-cran-webdeveloper Architecture: all Version: 1.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httpuv, r-cran-html5, r-cran-future, r-cran-promises, r-cran-readr, r-cran-stringi Filename: pool/dists/noble/main/r-cran-webdeveloper_1.0.5-1.ca2404.1_all.deb Size: 52210 MD5sum: 9e3a1653d84415d8834207454af3df74 SHA1: c15357b27a3faec401257c9fa190f4477b648ac0 SHA256: 1106f9604ca46de4ec92164a069bb39cf3ee2584bc948fe1c666e44a755fe2bb SHA512: 4df96532e8f3a936a7b0a997b6b01054b95506fcc84402947d556dc524962c3a52c048e2c28f8dfed61c57ade5ded806cb87ce2786e9cc7bf3496652106696f3 Homepage: https://cran.r-project.org/package=webdeveloper Description: CRAN Package 'webdeveloper' (Functions for Web Development) Organizational framework for web development in R including functions to serve static and dynamic content via HTTP methods, includes the html5 package to create HTML pages, and offers other utility functions for common tasks related to web development. 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It also serves as the engine for conducting power analysis online at . 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The functions cover data preprocessing steps, enriching web tracking data with external information and methods for the analysis of digital behavior as used in several academic papers (e.g., Clemm von Hohenberg et al., 2023 ; Stier et al., 2022 ). 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Package: r-cran-wec Architecture: all Version: 0.4-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr Filename: pool/dists/noble/main/r-cran-wec_0.4-1-1.ca2404.1_all.deb Size: 118500 MD5sum: ad92eea4e0df154fe377dfe2af5b42e2 SHA1: f2b7f38af56eb9c977b72fac42b661cd9cfaf3a0 SHA256: 7c928ae0299195d6bde15c4d6b9e980d8de52747d16733588bd8dabf63677df9 SHA512: d126836e318ced323b2ae96a3180dd89f932a870a447c0e355e7e7a74b279109bb82ffa133cffabe148a987e2b311a10cb62f325c51ac23e6f93e54770c44e95 Homepage: https://cran.r-project.org/package=wec Description: CRAN Package 'wec' (Weighted Effect Coding) Provides functions to create factor variables with contrasts based on weighted effect coding, and their interactions. In weighted effect coding the estimates from a first order regression model show the deviations per group from the sample mean. This is especially useful when a researcher has no directional hypotheses and uses a sample from a population in which the number of observation per group is different. 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These Datasets are manually filled and have several formatting and compatibility issues. Weed aims to resolve these with its functions. Package: r-cran-wefnexus Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wefnexus_1.0.0-1.ca2404.1_all.deb Size: 339070 MD5sum: 339be048594b64db368bfe91dfd76913 SHA1: cbabdd2ec4f76266fe4d8448c9ca2c8859a83c1c SHA256: a0d72c383f9072fe5078596dc59b864f61322025d6a3f93e2ac0eaa7b41d1f8f SHA512: 04b7edb321381253d1c2ce949b0747620d797158e8f3af3c148d0f21e8aeba5a39cb78a5bf9decf541526a5c1c61216fe490277764b1d8442ff4cef3ba3cefa7 Homepage: https://cran.r-project.org/package=wefnexus Description: CRAN Package 'wefnexus' (Water-Energy-Food-Nutrient-Carbon Nexus Analysis for AgronomicSystems) Provides functions for analysing Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus interactions in agricultural production systems. Includes functions for computing water use efficiency (WUE), water productivity (WP), and water footprint (WF) including green, blue, and grey components following the methodology of Hoekstra et al. (2011, ISBN:9781849712798). Includes energy budgeting tools for energy use efficiency (EUE), energy return on investment (EROI), net energy (NE), and energy productivity (EP). Computes nutrient use efficiency (NUE) metrics including agronomic efficiency (AE), physiological efficiency (PE), recovery efficiency (RE), and partial factor productivity (PFP) as defined by Dobermann (2007) and Congreves et al. (2021) . Estimates carbon footprint (CF), greenhouse gas (GHG) emissions, soil organic carbon (SOC) stocks, and global warming potential (GWP) using Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) default values (CH4 = 27, N2O = 273) as reported in Forster et al. (2021) . Computes composite Water-Energy-Food-Nutrient-Carbon (WEFNC) nexus indices, trade-off correlation matrices, and generates radar and heatmap visualizations for comparing agricultural treatments. Supports conservation agriculture (CA), irrigated and rain-fed systems, and arid and semi-arid production environments. Methods follow Lal (2004) for carbon emissions from farm operations, and Hoover et al. (2023) for water use efficiency indicators. Package: r-cran-wege Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 83 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-sp, r-cran-raster Filename: pool/dists/noble/main/r-cran-wege_0.1.0-1.ca2404.1_all.deb Size: 52040 MD5sum: b27d02b86d843835484313814b5ff007 SHA1: d3eb6863fe0456a30c2b9c2e19254a163e0d2694 SHA256: a766b479be1f47ded70a0a886aac5df6b885e877f79f553797eda1569fc3917f SHA512: 44c7923126e79e6750e0f65d2a8f9c544a178757987c688e22d46db4c99b2ea1396ed685b4098ae0973b74c251a3ce91bd430ca95363a80a20d75f6a7fdbc661 Homepage: https://cran.r-project.org/package=WEGE Description: CRAN Package 'WEGE' (A Metric to Rank Locations for Biodiversity Conservation) Calculates the WEGE (Weighted Endemism including Global Endangerment index) index for a particular area. Additionally it also calculates rasters of KBA's (Key Biodiversity Area) criteria (A1a, A1b, A1e, and B1), Weighted endemism (WE), the EDGE (Evolutionarily Distinct and Globally Endangered) score, Evolutionary Distinctiveness (ED) and Extinction risk (ER). Farooq, H., Azevedo, J., Belluardo F., Nanvonamuquitxo, C., Bennett, D., Moat, J., Soares, A., Faurby, S. & Antonelli, A. (2020) . Package: r-cran-wehoop Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1803 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-cli, r-cran-data.table, r-cran-dplyr, r-cran-glue, r-cran-janitor, r-cran-jsonlite, r-cran-lubridate, r-cran-magrittr, r-cran-purrr, r-cran-progressr, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-rvest, r-cran-stringr, r-cran-tidyr, r-cran-usethis Suggests: r-cran-crayon, r-cran-curl, r-cran-dbi, r-cran-ggplot2, r-cran-ggrepel, r-cran-httr, r-cran-knitr, r-cran-lifecycle, r-cran-rmarkdown, r-cran-rsqlite, r-cran-stringi, r-cran-testthat, r-cran-tibble, r-cran-tictoc, r-cran-xml2 Filename: pool/dists/noble/main/r-cran-wehoop_2.1.0-1.ca2404.1_all.deb Size: 1685430 MD5sum: b1cab669492b89e56bcb50779a58d7a9 SHA1: 199cdc9bb6da1d6a1f00c8c8d99f5f07616c51fc SHA256: 959a89ab9256d7d38db23bf7578330e694cb95470518263c91a3944707994e84 SHA512: 3d60b82f120c9569965a0fe9c403276e9c4d76a7176595ed8856a53d2811a5f6ec170bb84bd3e20b1a9a14f24ecbcbafcf94118a0fa7ab9832391ff4bf06412f Homepage: https://cran.r-project.org/package=wehoop Description: CRAN Package 'wehoop' (Access Women's Basketball Play by Play Data) A utility for working with women's basketball data. A scraping and aggregating interface for the WNBA Stats API and ESPN's women's college basketball and WNBA statistics. It provides users with the capability to access the game play-by-plays, box scores, standings and results to analyze the data for themselves. Package: r-cran-weibullfit Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-glue, r-cran-xtable, r-cran-sqldf, r-cran-r.oo, r-cran-fadist, r-cran-mixdist, r-cran-optimx, r-cran-ksamples, r-cran-e1071, r-cran-r.methodss3 Filename: pool/dists/noble/main/r-cran-weibullfit_0.1.0-1.ca2404.1_all.deb Size: 201138 MD5sum: 51a4d50754d483d109f9f01a1c608b11 SHA1: 961cc46c2bfd82aae285c2094c73cc1933da5434 SHA256: 238c11b6b786e805fe7038e48f6146e44fa323555d69e8de0c70a422e90c8dc0 SHA512: e6239a7ee837328e5c13264376ba0306f8c14b319e61b8e8c8bd5ce5d71f7041d2ec36b2c7818749e76395d60e8080abd055eef48b5b81892e672ce573e0e0e6 Homepage: https://cran.r-project.org/package=WeibullFit Description: CRAN Package 'WeibullFit' (Fits and Plots a Dataset to the Weibull Probability DistributionFunction) Provides a single function to fit data of an input data frame into one of the selected Weibull functions (w2, w3 and it's truncated versions), calculating the scale, location and shape parameters accordingly. The resulting plots and files are saved into the 'folder' parameter provided by the user. References: a) John C. Nash, Ravi Varadhan (2011). "Unifying Optimization Algorithms to Aid Software System Users: optimx for R" . Package: r-cran-weibullmodiamr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-weibullmodiamr_0.1.0-1.ca2404.1_all.deb Size: 35366 MD5sum: 8805f6d385180ed2d7a04d13aed11294 SHA1: 6f11d6e47224fcb2d7c40ad6f760ee00228bfe1a SHA256: b3e1f117f5aa2a3cd4ba02f27106002406f6d6f964b5e83cf4133203283b9e73 SHA512: ff7d231a7827282bfa2a3fcef8a98845a4916e5a5ef4b9ced214b88654cb72c795616e2375fe832a4f9805a3f04149550df5b15737da1583bf723f6467565abe Homepage: https://cran.r-project.org/package=WeibullModiAMR Description: CRAN Package 'WeibullModiAMR' (Fit Modified Weibull-Type Distributions) Provides maximum likelihood estimation methods for eight modified Weibull-type distributions. It returns parameter estimates, log-likelihood, AIC, and BIC, and also supports model fitting, validation, and comparison across different distributional forms. These methods can be applied to reliability, survival, and lifetime data analysis, making the package useful for researchers and practitioners in statistics, engineering, and medicine. The following distributions are included: Rangoli2023, Peng2014, Lai2003, Xie1996, Sarhan2009, Rangoli2025, Mustafa2012, and Alwasel2009. Package: r-cran-weibullness Architecture: all Version: 1.24.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4396 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-bsgof Filename: pool/dists/noble/main/r-cran-weibullness_1.24.1-1.ca2404.1_all.deb Size: 3549448 MD5sum: 18aa91a9daacaafb37dfcd58cc3fd9ee SHA1: 9be924f290520e29651bb75a45944735910b5d00 SHA256: 6c4eef22c78a9f4746501c0650980ae965854f635529e528a6d3a88947f772cc SHA512: b3582ef83114abd58c1f0f2a038278298a3382283d1d2f47435b812f91522aef918e9a3d40f35e5c1a8b55b996490e5840185001e00e19ce07cafba40750ce89 Homepage: https://cran.r-project.org/package=weibullness Description: CRAN Package 'weibullness' (Goodness-of-Fit Test for Weibull Distribution (Weibullness)) Conducts a goodness-of-fit test for the Weibull distribution (referred to as the weibullness test) and furnishes parameter estimations for both the two-parameter and three-parameter Weibull distributions. Notably, the threshold parameter is derived through correlation from the Weibull plot. Additionally, this package conducts goodness-of-fit assessments for the exponential, Gumbel, and inverse Weibull distributions, accompanied by parameter estimations. For more details, see Park (2017) , Park (2018) , and Park (2023) . This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528). Package: r-cran-weibullr.alt Architecture: all Version: 0.7.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-weibullr Filename: pool/dists/noble/main/r-cran-weibullr.alt_0.7.2-1.ca2404.1_all.deb Size: 107302 MD5sum: 3cebc85ea96823b5fe74de93e487d2bc SHA1: d962b3715fc0a348c18b74d44960def40b986a1d SHA256: 59647ac1cf7a2f83c8ab033575315dae6a2d4f4180905620cdb4aa4f9c8e9a6b SHA512: 498cfd0537764f0cb8314c36ad76e505244381460de148c50d314eb47ab1aee3ea4bc0f64a53da7039bd828f57013c4393a42f7295c4f19f86329555097a26ed Homepage: https://cran.r-project.org/package=WeibullR.ALT Description: CRAN Package 'WeibullR.ALT' (Accelerated Life Testing Using 'WeibullR') Graphical data analysis of accelerated life tests. Methods derived from Wayne Nelson (1990, ISBN: 9780471522775), William Q. Meeker and Lois A. Escobar (1998, ISBN: 1-471-14328-6). Package: r-cran-weibullr.learnr Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3291 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-learnr, r-cran-reliagrowr, r-cran-weibullr, r-cran-weibullr.alt Suggests: r-cran-weibullr.plotly, r-cran-weibullr.shiny Filename: pool/dists/noble/main/r-cran-weibullr.learnr_0.2.1-1.ca2404.1_all.deb Size: 2033264 MD5sum: a4c71d965a4bbdcb3f16ac6e463e72ad SHA1: 5e3ddb9bb357b2f916f4b4b2e87f315759b4e079 SHA256: 78a793d7aaee53129e7b78f4e51f48cec3c903b7c65ed488a97bb93501895696 SHA512: e87db780096cff4a4f3141e145b6ce41e9298efe5c7068fcc2748eabbab6f84c32d5831be2a17ffbacf5c9e4369437fd145343b04c98cc6a4ace3aafbf795ab5 Homepage: https://cran.r-project.org/package=WeibullR.learnr Description: CRAN Package 'WeibullR.learnr' (An Interactive Introduction to Life Data Analysis) An interactive introduction to Life Data Analysis that depends on 'WeibullR' by David Silkworth and Jurgen Symynck (2022) , a R package for Weibull Analysis, and 'learnr' by Garrick Aden-Buie et al. 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The package can generate complete and connected spatial partitions that respect complex boundaries, heterogeneous point weights, and optional resistance or terrain effects. The methods extend weighted Voronoi tessellations to constrained domains and graph-based cost-distance surfaces. For background see Aurenhammer (1991) and van Etten (2017) . 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Available methods include those that rely on parametric modeling, optimization, and machine learning. Also allows for assessment of weights and checking of covariate balance by interfacing directly with the 'cobalt' package. Methods for estimating weighted regression models that take into account uncertainty in the estimation of the weights via M-estimation or bootstrapping are available. See the vignette "Installing Supporting Packages" for instructions on how to install any optional package 'WeightIt' uses, including those that may not be on CRAN. 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To improve construct validity, this method re-computes scores by utilizing the item discrimination index in conjunction with a condition established upon person ability and item difficulty. 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See Jacqmin-Gadda, Rouanet, Mba, Philipps, Dartigues (2020) for details . Package: r-cran-weightr Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2, r-cran-scales Suggests: r-cran-shiny Filename: pool/dists/noble/main/r-cran-weightr_2.0.2-1.ca2404.1_all.deb Size: 184186 MD5sum: 5e40d63545fd3a5d0356ee6c92d0d40f SHA1: 82d0f30640dc5a67a768660553d70508c910de51 SHA256: 06bd3285375933108a6adc802e4c156d0e1b8cb0f5a09e21ddc07bfe9ed1b207 SHA512: 3cce165d38f777802ea1ec5aa3a71dff9f2f1ee37a2581b4c02698bf1691e9d901bb5ef58b4eca7299f2d90d7044daccd5d65242d9dd7cfda8075a6e0cf743f6 Homepage: https://cran.r-project.org/package=weightr Description: CRAN Package 'weightr' (Estimating Weight-Function Models for Publication Bias) Estimates the Vevea and Hedges (1995) weight-function model. By specifying arguments, users can also estimate the modified model described in Vevea and Woods (2005), which may be more practical with small datasets. Users can also specify moderators to estimate a linear model. The package functionality allows users to easily extract the results of these analyses as R objects for other uses. In addition, the package includes a function to launch both models as a Shiny application. Although the Shiny application is also available online, this function allows users to launch it locally if they choose. 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All packages needed to run the examples are also loaded. Package: r-cran-weirs Architecture: all Version: 0.26-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-weirs_0.26-1.ca2404.1_all.deb Size: 172226 MD5sum: ebd11b5781445e94df9d9ba24ec5e1f9 SHA1: 13423ad7e744880014b018ba9fa9d4c44e8e9be9 SHA256: fa997de411e668598cf534e357f60a25bb235c0f79aa69d0d2f2d59515ced0fd SHA512: 71ebc41075bf717edaedd9d59db32dfa0c32dd499a874df64188a71242190d6af7b20809dc83f8121fff1662def12c8f4659540a3b11978977cd7f703c4463c2 Homepage: https://cran.r-project.org/package=weirs Description: CRAN Package 'weirs' (A Hydraulics Package to Compute Open-Channel Flow over Weirs) Provides computational support for flow over weirs, such as sharp-crested, broad-crested, and embankments. Initially, the package supports broad- and sharp-crested weirs. Package: r-cran-welchadf Architecture: all Version: 0.3.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-welchadf_0.3.2-1.ca2404.1_all.deb Size: 369444 MD5sum: 39d6baaa4a04767dd31b3a7394c9887d SHA1: 03400fc0c21e53e8f746afc477620614662a3a13 SHA256: 28db072255dc670e339da26169f193951dc141f74e45e6e9ba086102ad775604 SHA512: 41ce5a1c979850f54e4fc05a8fdd2c27a85fb23d550e8eda0bd204587a2accc826ea95e598c559e3595cc6b52f257ee0c12c01b29870d46b6494c65fb4249469 Homepage: https://cran.r-project.org/package=welchADF Description: CRAN Package 'welchADF' (Welch-James Statistic for Robust Hypothesis Testing underHeterocedasticity and Non-Normality) Implementation of Johansen's general formulation of Welch-James's statistic with Approximate Degrees of Freedom, which makes it suitable for testing any linear hypothesis concerning cell means in univariate and multivariate mixed model designs when the data pose non-normality and non-homogeneous variance. Some improvements, namely trimmed means and Winsorized variances, and bootstrapping for calculating an empirical critical value, have been added to the classical formulation. The code departs from a previous SAS implementation by L.M. Lix and H.J. Keselman, available at and published in Keselman, H.J., Wilcox, R.R., and Lix, L.M. (2003) . Package: r-cran-welo Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xts, r-cran-rdpack, r-cran-boot, r-cran-rio, r-cran-ggplot2, r-cran-reshape2 Suggests: r-cran-knitr Filename: pool/dists/noble/main/r-cran-welo_0.1.4-1.ca2404.1_all.deb Size: 262256 MD5sum: 8ea4033c776014380e36d6738c16e723 SHA1: 3351bac6d3435db60e7d3669860dde60b3e0ce82 SHA256: 0daf0c86b8b7fbbf3d1a664bd483b03c83ea590dd84fe1a435dcb922c57a5960 SHA512: 83554f48a8f637d37568e7c2689eea4fadccfd18e7f439168c7e40f6fbb3d7e6d69944dcffe92a78b7f69ad305945a5d95821ac802351fc37742ebf793c17ed8 Homepage: https://cran.r-project.org/package=welo Description: CRAN Package 'welo' (Weighted and Standard Elo Rates) Estimates the standard and weighted Elo (WElo, Angelini et al., 2022 ) rates. The current version provides Elo and WElo rates for tennis, according to different systems of weights (games or sets) and scale factors (constant, proportional to the number of matches, with more weight on Grand Slam matches or matches played on a specific surface). Moreover, the package gives the possibility of estimating the (bootstrap) standard errors for the rates. Finally, the package includes betting functions that automatically select the matches on which place a bet. Package: r-cran-wemix Architecture: all Version: 4.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 744 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-numderiv, r-cran-matrix, r-cran-minqa, r-cran-matrixstats Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr, r-cran-tidyr, r-cran-edsurvey, r-cran-glmmtmb Filename: pool/dists/noble/main/r-cran-wemix_4.0.3-1.ca2404.1_all.deb Size: 592658 MD5sum: 50d9a769119fb96917508d2154a03750 SHA1: ba67195d3a0f07352ee09ae9a0df8665ac5ac3b3 SHA256: 6f13ff84adabedf2d44919091095d382c42b79dcec6907860f62ce2794c1b225 SHA512: ba58a9148bc7055f3f3863801e68cf6d06487b411796273f54dc5f69f9897f8220976a21df6c02ff41a9331566f075b1bb7746ab5e19d8aadaab181ba4839998 Homepage: https://cran.r-project.org/package=WeMix Description: CRAN Package 'WeMix' (Weighted Mixed-Effects Models Using Multilevel Pseudo MaximumLikelihood Estimation) Run mixed-effects models that include weights at every level. The WeMix package fits a weighted mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. Both linear and logit models are supported. Models may have up to three levels. Random effects are estimated using the PIRLS algorithm from 'lme4pureR' (Walker and Bates (2013) ). 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Environmental Sampling (ES) of infectious diseases involves collecting samples from various sources (such as sewage, water, air, soil, or surfaces) to monitor the presence of pathogens in the environment. Analysis of ES data often requires the calculation of Real-Time Quantitative PCR (qPCR) variables, normalizing ES observations, and analyzing sampling site characteristics. To help reduce the complexity of these analyses we have implemented tools that assist with establishing standardized ES data formats, absolute and relative quantification of qPCR data, estimation of qPCR amplification efficiency, and collating open-source spatial data for sampling sites. Package: r-cran-wesanderson Architecture: all Version: 0.3.7-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 56 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-wesanderson_0.3.7-1.ca2404.1_all.deb Size: 20004 MD5sum: 68f81a1b0dc6cc90879ec34bf6223768 SHA1: 1ae70600887f468f08fabe3833cb85b2634f8f9b SHA256: b1896c1a857c520a450117599b24d7329dd40d97a7f89039f7c1e45c4a775dca SHA512: 1bfaa175d94b6b9d095422991b532d012dca693f2981c3d6e81d35da2365cecf9dbaa5fee52c88994de60be0f0afdd50763f967f3d21aa66920039cf545c085a Homepage: https://cran.r-project.org/package=wesanderson Description: CRAN Package 'wesanderson' (A Wes Anderson Palette Generator) Palettes generated mostly from 'Wes Anderson' movies. Package: r-cran-westerlund Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scales, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-westerlund_0.1.3-1.ca2404.1_all.deb Size: 244510 MD5sum: f646797886f8459064cd9f5a065a1a6f SHA1: c9f0f1eff5ba8219ec16782262019a9aec0fe2c1 SHA256: 185503805bfe52fd8f9076912aee964c87ca94750b0053408f155cd50fc5c365 SHA512: de4bb5ea52b7e02d2def3881413ffa97a54930abb7d909e517875cad8e5812b4914508c064a053b6f3d85eeed1af9ed77c2435d95a40c4984cea3fd6f777680c Homepage: https://cran.r-project.org/package=Westerlund Description: CRAN Package 'Westerlund' (Panel Cointegration Tests Based on Westerlund (2007)) Implements a functional approximation of the four panel cointegration tests developed by Westerlund (2007) . 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Package: r-cran-wevid Architecture: all Version: 0.7.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-proc, r-cran-reshape2, r-cran-zoo Filename: pool/dists/noble/main/r-cran-wevid_0.7.0-1.ca2404.1_all.deb Size: 258760 MD5sum: 8d91ca4134de7006b9934cda2357723f SHA1: f5b96e1dfd0b0c75fd61c7b1ba22f0b4d82a470a SHA256: 0e832c5014a54361475e73407cf19fde0b7ff59674ed47c6fc06a2051ff5fadd SHA512: fa48980f05027bccdf967ad3e4ae90b5407aa05f9b791d16a51c9e463bfe554b4341f97aaa997ae7a1b64f82170a4b1dd93ae70d7aa4da755f99a3f06dc9a746 Homepage: https://cran.r-project.org/package=wevid Description: CRAN Package 'wevid' (Weight of Evidence for Quantifying Performance of a BinaryClassifier) The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test. This package can be used with any test dataset on which you have computed prior probabilities of case status, posterior probabilities of case status, and you have the observed case-control status. In comparison with the C-statistic (area under ROC curve), the expected weight of evidence (expected information for discrimination) has several advantages as a summary measure of predictive performance. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted. Package: r-cran-wex Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fkf, r-cran-kfas Filename: pool/dists/noble/main/r-cran-wex_0.1.1-1.ca2404.1_all.deb Size: 66906 MD5sum: c7a112ee3b074a515fca28d11a26f810 SHA1: c39662911861c71626a4a8031cd6512e135e336b SHA256: 3c7a13c543eced66cf34d3d8009c95fa358bacce0117b453da15a2c66a0eef70 SHA512: fd76f817e9547a25a6fe2f61413e501b2d9050ad72d3a258099383a1809c4c1b4b00717cd6761c4c3b152e9386d6909adf3caaf3364fa7cc2bc40f7d398a938e Homepage: https://cran.r-project.org/package=wex Description: CRAN Package 'wex' (Exact Observation Weights for the Kalman Filter and Smoother) Computes exact observation weights for the Kalman filter and smoother, following Koopman and Harvey (2003) . The package provides tools for analyzing linear Gaussian state-space models, allowing users to quantify the contribution of individual observations to filtered and smoothed state estimates. These weights can be used for interpretation, decomposition, and diagnostic analysis in time series models, including applications such as dynamic factor models. See the README for examples. 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To cite the package in publications, use Hankin 2022 . Package: r-cran-wf Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-fs, r-cran-gh, r-cran-jsonlite, r-cran-rlang, r-cran-yaml Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-wf_0.0.1-1.ca2404.1_all.deb Size: 76818 MD5sum: b3b57ca8dbbb13683b2a682c678c6d71 SHA1: 43c90006445ba25a168e04dc270285d3ea68d317 SHA256: 7ee0f2add3d237293ca0931566091dcb2f9c8ffc773e1c1b9ae91520c5defd95 SHA512: ed3b1eb845289cf4eebb9eb26d6de5c1f6a756efd99f5152a19a8274e9b47c133e064052a81539b24cfeebdc772725af05ab10533fad6f6603f13bea27716abc Homepage: https://cran.r-project.org/package=wf Description: CRAN Package 'wf' (Artificial Intelligence Workflow Tools) Manage skills for large language model coding agents. Supports installing skills from 'GitHub' or local directories, tracking versions in a lock file, and keeping installations current. Installations can be scoped to a single project or shared globally across projects. Package: r-cran-wfg Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 57 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-igraph Filename: pool/dists/noble/main/r-cran-wfg_0.1-1.ca2404.1_all.deb Size: 27632 MD5sum: 8dc1db03daab0679e4f4ddb3456c276b SHA1: 68fa28e65bea6a0ba0929c3c966bbecc6220e97f SHA256: 83294626cab163b42474c4da287e5a81b9f684d140ab9475e9bf11a052799f0d SHA512: f2eaee47f5ce00f053de890d96bd8c87a6c068ce57ea11323d7a8ecf778d421b2763e62bc019a9e8f7637ed6004c3788fcb70d237cff422a189140300058c6dc Homepage: https://cran.r-project.org/package=wfg Description: CRAN Package 'wfg' (Weighted Fast Greedy Algorithm) Implementation of Weighted Fast Greedy algorithm for community detection in networks with mixed types of attributes. Package: r-cran-wfindr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1008 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr Filename: pool/dists/noble/main/r-cran-wfindr_0.1.0-1.ca2404.1_all.deb Size: 996292 MD5sum: d996490c40da4ac04db8e70d4f5b9d70 SHA1: 60c3ea2c96d37051bd20217beacecb90211db0a8 SHA256: 7a2e72a156a37901924d7184c15ab88860e817f2f8cf18c6fc96c29f22ec7b77 SHA512: b2c40d014e29375da5ad3cd02bc0331b4bb50e9bb06878899e02e86df246c4cd7c4415a11d19c48201c3ff5e1fc5f0009533e58cddc4e142fd37437e0803cec4 Homepage: https://cran.r-project.org/package=wfindr Description: CRAN Package 'wfindr' (Crossword, Scrabble and Anagram Solver) Provides a large English words list and tools to find words by patterns. In particular, anagram finder and scrabble word finder. 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Package: r-cran-wgaim Architecture: all Version: 2.0-6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1610 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-qtl, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-prettydoc Filename: pool/dists/noble/main/r-cran-wgaim_2.0-6-1.ca2404.1_all.deb Size: 625812 MD5sum: d89ab9c0e59a86f6ab2da66f622e71ce SHA1: f6c3c61555a771ac545e55ff7b6d825a6bddfc9a SHA256: 40977162a71dee1af90f9d0c31b7534b92e009f27e0a6342ffd33ed60eb5ef44 SHA512: 48ba9782a51f8426b79edff387f5b80a7a4ac23c9cab0887167e57170302057a4c251b670f9ca87e2e1074e66519faa9ac11e672d4556aa03aae6b547d3d6855 Homepage: https://cran.r-project.org/package=wgaim Description: CRAN Package 'wgaim' (Whole Genome Average Interval Mapping for QTL Detection andEstimation using ASReml-R) A computationally efficient whole genome approach to detecting and estimating significant QTL in linkage maps using the flexible linear mixed modelling functionality of ASReml-R. Package: r-cran-wgeesel Architecture: all Version: 1.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass, r-cran-geepack, r-cran-bindata, r-cran-poisnor, r-cran-crtgeedr Filename: pool/dists/noble/main/r-cran-wgeesel_1.5-1.ca2404.1_all.deb Size: 159596 MD5sum: 61942c296b4d6017c62df50bacf62dbc SHA1: bc44a3b765042fc458237aa74f91037d80722e13 SHA256: 763aaa5ade26b14a840961f54966789d4d066c2f3e43c73deb43f693faf745dc SHA512: 7146af23ac2af20cd6f614a992102758abe2e36186b0c65e5a8eb29109cc39ed7c3b9b5e6f1cbde4942d773209dc9f055e4305809364891779171d04a7b65303 Homepage: https://cran.r-project.org/package=wgeesel Description: CRAN Package 'wgeesel' (Weighted Generalized Estimating Equations and Model Selection) Weighted generalized estimating equations (WGEE) is an extension of generalized linear models to longitudinal clustered data by incorporating the correlation within-cluster when data is missing at random (MAR). 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Package: r-cran-wget Architecture: all Version: 0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 44 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-yulab.utils Filename: pool/dists/noble/main/r-cran-wget_0.0.4-1.ca2404.1_all.deb Size: 13930 MD5sum: 0e19a1fbbcd4a7c803131cc63ea80526 SHA1: ff872ab3118d8a444e26b36a0c701b4cc5f8496b SHA256: 677d827ed8f05509b922a183d965f97d8b621d28178a8c68026809d5db11f8d3 SHA512: 15140a036f1d97ec58ff6f0eb3f3cacf355ff38c45ffb9c2e08b70d1c44f6b10c8124bb1417d6015c3bf05fa3d56a57c792debb74bb1bccdc7a0a511aba9fe25 Homepage: https://cran.r-project.org/package=wget Description: CRAN Package 'wget' (Setting Download Method to 'wget') Provides function, wget_set(), to change the method (default to 'wget -c') using in download.file(). Using 'wget -c' allowing continued downloading, which is especially useful for slow internet connection and for downloading large files. User can run wget_unset() to restore previous setting. Package: r-cran-wgscan Architecture: all Version: 0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-skat, r-cran-matrix, r-cran-mass, r-cran-seqminer, r-cran-data.table Filename: pool/dists/noble/main/r-cran-wgscan_0.1-1.ca2404.1_all.deb Size: 111844 MD5sum: d0c4ba03931d1b2f9c90abfea966934d SHA1: 33553e4955deb63f141d028f3c6cd3608441002e SHA256: f94c17aa6508571e58147d4c305713f36eb7c9de1e3ec8d9d88e77420d95bf5c SHA512: 2cc5a55fbba0f0c81c6899af52550cf0a357a7097f4bc294f4897d4c8e07837e295e2b93d7864dba2cd5c1d0bb2f6ceba9bfe0f682fd9fe25d25e2a8c905745d Homepage: https://cran.r-project.org/package=WGScan Description: CRAN Package 'WGScan' (A Genome-Wide Scan Statistic Framework for Whole-Genome SequenceData Analysis) Functions for the analysis of whole-genome sequencing studies to simultaneously detect the existence, and estimate the locations of association signals at genome-wide scale. The functions allow genome-wide association scan, candidate region scan and single window test. Package: r-cran-wgteff Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wgteff_0.1.2-1.ca2404.1_all.deb Size: 30190 MD5sum: 04883df70cb75fce4166947f90741e1e SHA1: d68da155625953f40452603166b4c8975b4ff422 SHA256: 44956753247e64f62b5adde98e216554dc6ba5244eaed2aa9263039407c24fae SHA512: 19afe3bf332015b154f50daeee768bab6945c3eb0a6c7bd2f9ef4c15383277afa47c8b99be25bfac8c5cee335d6a8879ce9f71eb3691118ba42eca901e8ee06e Homepage: https://cran.r-project.org/package=WgtEff Description: CRAN Package 'WgtEff' (Functions for Weighting Effects) Functions for determining the effect of data weights on the variance of survey data: users will load a data set which has a weights column, and the package will calculate the design effect (DEFF), weighting loss, root design effect (DEFT), effective sample size (ESS), and/or weighted margin of error. Package: r-cran-wh Architecture: all Version: 1.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wh_1.1.2-1.ca2404.1_all.deb Size: 283868 MD5sum: 37a3864ecabe3fb9e101b3357a486efd SHA1: 33652fe3bfe1b9b63b6e8a512e110dbb0db9c149 SHA256: 8bc1e08ad9b24f05cf936f15b298a09ea69c5452acfa06cbe780437b7a169755 SHA512: d32b94915e81d1f03155bdb69097c3e6c7de28a1031981dcfce8cb67dfb3190ca51ba13f9b7c15e339869c9b2a2cec71f98a7dd418de47bcfec088759f1e8fad Homepage: https://cran.r-project.org/package=WH Description: CRAN Package 'WH' (Enhanced Implementation of Whittaker-Henderson Smoothing) An enhanced implementation of Whittaker-Henderson smoothing for the gradation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. 'WH' is based on the methods described in Biessy (2023) . Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, automatically selects the smoothing parameter(s) and extrapolates beyond the range of data. Package: r-cran-whalestrike Architecture: all Version: 0.6.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1096 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bslib, r-cran-desolve, r-cran-shiny Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-whalestrike_0.6.2-1.ca2404.1_all.deb Size: 712388 MD5sum: 8f238e1de837faaeea705c032e112452 SHA1: 32df413281d67bfb20f595b9b8272d3e3495dbe1 SHA256: 58304242ff06188c550964f2d0679687aa659664069bb18db2fa13deaaae4aee SHA512: f870de6c86c0fdf6332eccd3b1b1b59c6f3442ba0ec88caaeb89a5433f6dc60801bf465d1139fe8e23b98bf2ee755ad0c8174ef0e41ebf34a20a036da7c10765 Homepage: https://cran.r-project.org/package=whalestrike Description: CRAN Package 'whalestrike' (Simulate Whale Ship Strikes) Provides tools for simulating the biophysical effects of vessel-strikes on whales. The aim is to support the evaluation of marine policies limiting ship speeds through regions in which whales reside. This is important because ship strikes are a major source of lethality for several whale species, including the critically endangered North Atlantic right whale. In this analysis, whales are modelled with a four-layer system comprising skin, blubber, sub-layer (muscle or organ) and bone. Reasonable values for the material properties of these layers, along with other factors such as whale surface area and mass, are provided for a variety of whale species. Similarly, key values are provided for several ship types. The collision is modelled according to Newtonian dynamics, with stresses and strains within the whale layers being simulated over time. The simulation results are analyzed in the context of whale-strike data, to develop a Lethality Index for the whale in the modelled collision. For the underlying science, see Kelley and other "Assessing the Lethality of Ship Strikes on Whales Using Simple Biophysical Models." (2021) . For more on the R code, see Kelley "`whalestrike`: An R package for simulating ship strikes on whales" (2024) . Package: r-cran-whapi Architecture: all Version: 0.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-cli, r-cran-tibble, r-cran-purrr, r-cran-stringr, r-cran-stringi, r-cran-dplyr, r-cran-readr, r-cran-lubridate, r-cran-jsonlite, r-cran-mime, r-cran-openssl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-pkgdown Filename: pool/dists/noble/main/r-cran-whapi_0.0.2-1.ca2404.1_all.deb Size: 245452 MD5sum: 05a3cc85b68284c4768e53f56d9c1d0e SHA1: 16bef81b0c1f61e203bcf4eb5567880310e78167 SHA256: da72053eef338c9466b87f5d3fd3ecc25862aca48e019902db6355ffadd163b6 SHA512: a1974e9a2aac74ab16052fe5355126ce0102043b569e5f7b0ad9a5289b0420a1022847021d0122ac61296e7870d7e50a25470d8368e1a3c578dcc9aac5449b5b Homepage: https://cran.r-project.org/package=whapi Description: CRAN Package 'whapi' (R Client for 'whapi.cloud') Provides an 'R' interface to the 'Whapi' 'API' , enabling sending and receiving 'WhatsApp' messages directly from 'R'. Functions include sending text, images, documents, stickers, geographic locations, and interactive messages (buttons and lists). Also includes 'webhook' parsing utilities and channel health checks. Package: r-cran-whatif Architecture: all Version: 1.5-11-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolve, r-cran-pbmcapply Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-whatif_1.5-11-1.ca2404.1_all.deb Size: 82182 MD5sum: 4e87eef6d7b311a50b8e465ed5bdae49 SHA1: 340ff881ed86b3f699796222b961a4bb5dda6217 SHA256: 9b36de21e6a75853ecfd4d7911c2d648973bcb8388c6798465c6c5d50ead1087 SHA512: 268aab442daff3dc4244386973a94c0f015bc0a1dfb3b3d5d510284db060ea600996025447be7d43aacd41ff3f485c87fbfd3a9cf9d70d6f6b0d7cd45fd30a60 Homepage: https://cran.r-project.org/package=WhatIf Description: CRAN Package 'WhatIf' (Software for Evaluating Counterfactuals) Inferences about counterfactuals are essential for prediction, answering what if questions, and estimating causal effects. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based largely on speculation hidden in convenient modeling assumptions that few would be willing to defend. Unfortunately, standard statistical approaches assume the veracity of the model rather than revealing the degree of model-dependence, which makes this problem hard to detect. 'WhatIf' offers easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models. If an analysis fails the tests offered here, then we know that substantive inferences will be sensitive to at least some modeling choices that are not based on empirical evidence, no matter what method of inference one chooses to use. 'WhatIf' implements the methods for evaluating counterfactuals discussed in Gary King and Langche Zeng, 2006, "The Dangers of Extreme Counterfactuals," Political Analysis 14 (2) ; and Gary King and Langche Zeng, 2007, "When Can History Be Our Guide? The Pitfalls of Counterfactual Inference," International Studies Quarterly 51 (March) . Package: r-cran-whatifbandit Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 633 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-bandit, r-cran-data.table, r-cran-dplyr, r-cran-furrr, r-cran-ggplot2, r-cran-lubridate, r-cran-purrr, r-cran-randomizr, r-cran-rlang, r-cran-tibble, r-cran-tidyr Suggests: r-cran-future, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-whatifbandit_0.3.0-1.ca2404.1_all.deb Size: 474248 MD5sum: 5a1168decf16573a5fdb14624510b2f0 SHA1: 4883b4c480356f1690c0d0df9c5b69a0ec615399 SHA256: 17895758aa1583c8be457464f2ccad8240675d95328236e355bf074595fe378e SHA512: 3304b45e99fa89f0b01a82096f6547c8b28d777a5169cd3fbe74d3cc7695b8538f2660de5a773d363ef2afb41b66ac3764172f6dad8ce70efd6b6dbb32ef6f55 Homepage: https://cran.r-project.org/package=whatifbandit Description: CRAN Package 'whatifbandit' (Analyzing Randomized Experiments as Multi-Arm Bandits) Simulates the results of completed randomized controlled trials, as if they had been conducted as adaptive Multi-Arm Bandit (MAB) trials instead. 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The parser works on chats exported from Android or iOS phones and on Linux, macOS and Windows. The parser has multiple options for extracting smileys and emojis from the messages, extracting URLs and domains from the messages, extracting names and types of sent media files from the messages, extracting timestamps from messages, extracting and anonymizing author names from messages. Can be used to create anonymized versions of data. Package: r-cran-whatthreewords Architecture: all Version: 0.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 82 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-whatthreewords_0.1.3-1.ca2404.1_all.deb Size: 27076 MD5sum: 91159a82996fa9295fa48c6bcc7a6828 SHA1: a84ec8b33924f579a45c6f3ae8e081bade8980e4 SHA256: a06abb10f732c5b0a6e742f4351644639d640d05f24ebc1ff214937db155d475 SHA512: e42a352d186eb27ff7be29e62c085e503fa226ecdc72b73a9b624c39ebb56e11e15c7382e8f5b963462a02403005cfca95480655f53fa2badf4bf2126cb5e9ba Homepage: https://cran.r-project.org/package=whatthreewords Description: CRAN Package 'whatthreewords' (Work with the 'what3words' API for Easy Location Referencing) Use the 'what3words' API to return three words which uniquely identify every 3m x 3m square on Earth. 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'whirl' enables execution of scripts in batch, while simultaneously creating logs for the execution of each script, and providing an overview summary log of the entire batch execution. Package: r-cran-whisker Architecture: all Version: 0.4.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-markdown Filename: pool/dists/noble/main/r-cran-whisker_0.4.1-1.ca2404.1_all.deb Size: 63006 MD5sum: d4b04023243f45e35ccd4b31eff53931 SHA1: c09135cd8c6d8b9135cc6e12ff3feaf85c531950 SHA256: ebcf8e3e7511078c4eb101a35f199aadf696707263a2984552ffd782663dce35 SHA512: f9c6883db1c7d5c05619bef3a0a1dc07ce8963290bba3b0ae44660e4a81a81933f5d86489dc443bd72cf6f643ffcc8fe24dee9802e13bce7e1bc762f0fc77d58 Homepage: https://cran.r-project.org/package=whisker Description: CRAN Package 'whisker' ({{mustache}} for R, Logicless Templating) Implements 'Mustache' logicless templating. Package: r-cran-whisper Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1660 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-torch, r-cran-av, r-cran-jsonlite, r-cran-hfhub, r-cran-safetensors Suggests: r-cran-tinytest Filename: pool/dists/noble/main/r-cran-whisper_0.3.0-1.ca2404.1_all.deb Size: 579148 MD5sum: 1ed2cb7a41cd23378117cf5423aacb0b SHA1: ba3e35654e355f6ef2bd9ed1c38b1670c333d645 SHA256: a114705617679c8c708be7a99a677b34f4ea39f76dd3d71fe27f525e032a36c6 SHA512: feb712bfdcfb9b363aac1d5ca78873a74e1153cb77161055d779fcf80ea477510ba43764ebbe6f9546046d0ad5b50fd4d432c82d0266d86db97433cac061abb8 Homepage: https://cran.r-project.org/package=whisper Description: CRAN Package 'whisper' (Native R 'torch' Implementation of 'OpenAI' 'Whisper') Speech-to-text transcription using a native R 'torch' implementation of 'OpenAI' 'Whisper' model . Supports multiple model sizes from tiny (39M parameters) to large-v3 (1.5B parameters) with integrated download from 'HuggingFace' via the 'hfhub' package. Provides automatic speech recognition with optional language detection and translation to English. Audio preprocessing, mel spectrogram computation, and transformer-based encoder-decoder inference are all implemented in R using the 'torch' package. Package: r-cran-whitebox Architecture: all Version: 2.4.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3635 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-terra, r-cran-sf, r-cran-raster Filename: pool/dists/noble/main/r-cran-whitebox_2.4.3-1.ca2404.1_all.deb Size: 2734790 MD5sum: 6f069f6e138c8d7cff603e810f9074ef SHA1: eeb409d700f0ba4931431082e81410d388e4723d SHA256: fc0a412923b6fb9c044a4116fa973f6681ffbf095761a9dcdb3047fb401ffbbf SHA512: 1071ade7b96dd00ef565a8919ee82217d00d934cfe11ed7cd25a8fd9bf9d00557a430d4946c8f09e0b5cd27f82e34437f073140eec9a4f80333372dfa451461d Homepage: https://cran.r-project.org/package=whitebox Description: CRAN Package 'whitebox' ('WhiteboxTools' R Frontend) An R frontend for the 'WhiteboxTools' library, which is an advanced geospatial data analysis platform developed by Prof. John Lindsay at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. 'WhiteboxTools' can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. 'WhiteboxTools' also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. Suggested citation: Lindsay (2016) . Package: r-cran-whitechapelr Architecture: all Version: 0.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 73 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-plyr, r-cran-igraph Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/noble/main/r-cran-whitechapelr_0.3.0-1.ca2404.1_all.deb Size: 37558 MD5sum: d643ee467f6e9a20610d209c8fe815a7 SHA1: 7f2e2ae86b4ff5edcda0d1b9ee6e0d246686fad3 SHA256: d3e9a0d4ecd07911b6bfff395d472475bc7f80c84aeb85ee5d29491ef4d4bbcf SHA512: 409f1461df6d90215e2dc7848ca6b75607d73c65e201258441cafdf8328690a980317c3759d725e81af2d162e1dea29bf693682e5c652d50d36ffb20d10a53bd Homepage: https://cran.r-project.org/package=whitechapelR Description: CRAN Package 'whitechapelR' (Advanced Policing Techniques for the Board Game "Letters fromWhitechapel") Provides a set of functions to make tracking the hidden movements of the 'Jack' player easier. 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Package: r-cran-whitening Architecture: all Version: 1.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 560 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-corpcor Filename: pool/dists/noble/main/r-cran-whitening_1.4.0-1.ca2404.1_all.deb Size: 527162 MD5sum: e60e4531a4c5ca66c03b7ef70b95b60d SHA1: d49aa3952ff057148f61d7c6de376928e1c70bd5 SHA256: cf2c2dc75ff2528fb655e56a8533d43d6ebd97ea0f4572f17d954b2f10478cd6 SHA512: 17b0ecba886b1ac76d803c06983b2c38bc4700d1bcd4ad2ae2e5009d950aac41852541574556cbab6969e521189167e6249d3307c5304848e0c940239ea5ca2b Homepage: https://cran.r-project.org/package=whitening Description: CRAN Package 'whitening' (Whitening and High-Dimensional Canonical Correlation Analysis) Implements the whitening methods (ZCA, PCA, Cholesky, ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018) "Optimal whitening and decorrelation", , as well as the whitening approach to canonical correlation analysis allowing negative canonical correlations described in Jendoubi and Strimmer (2019) "A whitening approach to probabilistic canonical correlation analysis for omics data integration", . The package also offers functions to simulate random orthogonal matrices, compute (correlation) loadings and explained variation. It also contains four example data sets (extended UCI wine data, TCGA LUSC data, nutrimouse data, extended pitprops data). 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(2023) . Package: r-cran-winr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rdpack, r-cran-tidyr, r-cran-dplyr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-winr_1.0.0-1.ca2404.1_all.deb Size: 87590 MD5sum: 28f8170b882f460997b907541d958788 SHA1: 23937595afefb4ba88367577e3fe04fab20aa981 SHA256: 43c22199f42d6bb8a9d9d66dcab7a66ba980095a9e162b6c96f7d645c5d059ba SHA512: 88906e8141a7a09bfe8763fec0623cbbe40e7048a9d5345aab1ceb2ede71ae151f5ff33c99e0e387641453c56b2e85ad0e43ad3216cb623e2ebc412803728910 Homepage: https://cran.r-project.org/package=winr Description: CRAN Package 'winr' (Randomization-Based Covariance Adjustment of Win Statistics) A multi-visit clinical trial may collect participant responses on an ordinal scale and may utilize a stratified design, such as randomization within centers, to assess treatment efficacy across multiple visits. Baseline characteristics may be strongly associated with the outcome, and adjustment for them can improve power. The win ratio (ignores ties) and the win odds (accounts for ties) can be useful when analyzing these types of data from randomized controlled trials. This package provides straightforward functions for adjustment of the win ratio and win odds for stratification and baseline covariates, facilitating the comparison of test and control treatments in multi-visit clinical trials. For additional information concerning the methodologies and applied examples within this package, please refer to the following publications: 1. Weideman, A.M.K., Kowalewski, E.K., & Koch, G.G. (2024). “Randomization-based covariance adjustment of win ratios and win odds for randomized multi-visit studies with ordinal outcomes.” Journal of Statistical Research, 58(1), 33–48. . 2. Kowalewski, E.K., Weideman, A.M.K., & Koch, G.G. (2023). “SAS macro for randomization-based methods for covariance and stratified adjustment of win ratios and win odds for ordinal outcomes.” SESUG 2023 Proceedings, Paper 139-2023. Package: r-cran-wins Architecture: all Version: 1.5.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-ggplot2, r-cran-ggpubr, r-cran-reshape2, r-cran-survival, r-cran-stringr, r-cran-viridis Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wins_1.5.1-1.ca2404.1_all.deb Size: 440802 MD5sum: 45841d02e2aff685ac7138ba67d9576d SHA1: 81b322cea8f4762cc0133540bb084fe13ac73f58 SHA256: ac4db9472a705b22145c0253d7b72f7bffc1669204f6cfe8d4a127929efcaa89 SHA512: 238c9e6c2765d19eca3866a936a4900efef86ed2fa7e336abd383cb1feacb6d53db3bfe3cc2a16292ec6a33d81d700feeb261a8868dfd0c2e66d4c80f6cf3d13 Homepage: https://cran.r-project.org/package=WINS Description: CRAN Package 'WINS' (The R WINS Package) Calculate the win statistics (win ratio, net benefit and win odds) for prioritized multiple endpoints, plot the win statistics and win proportions over study time if at least one time-to-event endpoint is analyzed, and simulate datasets with dependent endpoints. The package can handle any type of outcomes (continuous, ordinal, binary, time-to-event) and allow users to perform stratified analysis, inverse probability of censoring weighting (IPCW) and inverse probability of treatment weighting (IPTW) analysis. 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These methods are used to calculate and compare treatment effects on ordered composite endpoints. The package handles event times, event indicators, and treatment arm indicators and supports calculations on observed and resampled data. Detailed explanations of each method and usage examples are provided in "Use of win time for ordered composite endpoints in clinical trials," by Troendle et al. (2024). For more information, see the package documentation or the vignette titled "Introduction to wintime." Package: r-cran-wipf Architecture: all Version: 0.1.0-4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-wipf_0.1.0-4-1.ca2404.1_all.deb Size: 86506 MD5sum: c6bd9655e3686ba03e3f9ba03ab26caa SHA1: 67bc96d2f2ec3179c96fc309e9a8c78e4fa98543 SHA256: c6c62a5caef78fb53e8dbd9ba60e30ac55ad92af7bd18f506364ea7f0f5ca22e SHA512: b08bc3e0e432e732e2c38f703af4025e08cb8eab205b1996cca953bfa26038b02a4249a75e8f6edb14be680e160b8e5f1dd62b8a54f9c25d0ec9be3673a7d5ed Homepage: https://cran.r-project.org/package=WIPF Description: CRAN Package 'WIPF' (Weighted Iterative Proportional Fitting) Implementation of the weighted iterative proportional fitting (WIPF) procedure for updating/adjusting a N-dimensional array given a weight structure and some target marginals. Acknowledgements: The author wish to thank Conselleria de Educación, Cultura, Universidades y Empleo (grant CIAICO/2023/031), Ministerio de Ciencia, Innovación y Universidades (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el cálculo de productos de vida') for supporting this research. Package: r-cran-wiqid Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 840 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-hdinterval, r-cran-mcmcoutput, r-cran-truncnorm, r-cran-mass, r-cran-coda, r-cran-plotrix Suggests: r-cran-secr, r-cran-shiny, r-cran-rjags Filename: pool/dists/noble/main/r-cran-wiqid_0.3.3-1.ca2404.1_all.deb Size: 646818 MD5sum: a8e829f879ff9d154a9137d1ba3e7894 SHA1: 79116a60178af974d743e8d8868211d973303cad SHA256: 22556b5549355f7ba8482b9cc3aac85499caf034fa3de3450a54a35b459cd58d SHA512: ec280d4f4990ca73ca428492ccec0d5ac6c9381d810f02dca8d14471de6f3482caa5c2ce2e7546fc63dc89917582bdb40d4e981353c28b87a0676f60e8cef588 Homepage: https://cran.r-project.org/package=wiqid Description: CRAN Package 'wiqid' (Quick and Dirty Estimates for Wildlife Populations) Provides simple, fast functions for maximum likelihood and Bayesian estimates of wildlife population parameters, suitable for use with simulated data or bootstraps. 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Package: r-cran-wordnet Architecture: all Version: 0.1-18-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rjava Filename: pool/dists/noble/main/r-cran-wordnet_0.1-18-1.ca2404.1_all.deb Size: 126290 MD5sum: 13e4aa144bbd21cfdb5f09fa8a30c180 SHA1: 5ad3d5f2e4c34f2e9562d19c952d9089b10eaf2f SHA256: 0454bda9bc0c4e11c11c91ef4c69f8186fbc7ec031718daa66da34559698aad1 SHA512: c274cb60d1e10a5a73e71ffdb881ea0af7d3da2e9216826291be7f59498e02f6cae03a47f6068e5434dcad5c0df8c20b55048f3627794baa074729d0a9faeb0b Homepage: https://cran.r-project.org/package=wordnet Description: CRAN Package 'wordnet' (WordNet Interface) An interface to WordNet using the Jawbone Java API to WordNet. WordNet () is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations. Please note that WordNet(R) is a registered tradename. Princeton University makes WordNet available to research and commercial users free of charge provided the terms of their license () are followed, and proper reference is made to the project using an appropriate citation (). The WordNet database files need to be made available separately, either via package 'wordnetDicts' from , installing system packages where available, or direct download from . Package: r-cran-wordofmouth Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lambertw Filename: pool/dists/noble/main/r-cran-wordofmouth_1.2.0-1.ca2404.1_all.deb Size: 161696 MD5sum: 82cfa388626f42804e4ecd1f18679ecb SHA1: 977b7aa7ada842cd1eb6a91900dfbce671176c45 SHA256: 344b6c135e8b4477af35f14d7003ba757588cc8e40189755e09ea30922f9bfc4 SHA512: e94e4d9f94e53ce3a71eb305a6376192a7ea001ec373ce0082378d270a72ae6809ccf9c45b374308db3bc9261a4b0ebc10fd51aefdc8309a3a435dfb94c07d20 Homepage: https://cran.r-project.org/package=WordOfMouth Description: CRAN Package 'WordOfMouth' (Estimates Economic Variables for Word-of-Mouth-Campaigns) Methods for estimating profit, profit-maximizing price, demand and consumer surplus of Word-of-Mouth-campaigns on mean-field networks. 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Package: r-cran-wordpiece Architecture: all Version: 2.1.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dlr, r-cran-fastmatch, r-cran-memoise, r-cran-piecemaker, r-cran-rlang, r-cran-stringi, r-cran-wordpiece.data Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wordpiece_2.1.3-1.ca2404.1_all.deb Size: 50940 MD5sum: 938f2954cebc5a0cc6bc921d40863963 SHA1: 94da03f40d8099d85148707d6d5ec7aa31bebd6e SHA256: c5d07e2a2558cba517f704e969d8392ad3830470abacfd4624e14d30328b8cbf SHA512: 8a68fe93616110b21ef0bcd0f02a150281df71857eb6b51e3b55cc11b99040db073c84a7b0e4ba2b2f5a32d9623174dc45685277f7606e08f19976207dbda80e Homepage: https://cran.r-project.org/package=wordpiece Description: CRAN Package 'wordpiece' (R Implementation of Wordpiece Tokenization) Apply 'Wordpiece' () tokenization to input text, given an appropriate vocabulary. The 'BERT' () tokenization conventions are used by default. Package: r-cran-wordpools Architecture: all Version: 1.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr Filename: pool/dists/noble/main/r-cran-wordpools_1.2.0-1.ca2404.1_all.deb Size: 178474 MD5sum: 90c4b7cf8613d62e8de5a4e5a9f88678 SHA1: bdf1fa8f2ce26c2767ad41772346f246ca3d7769 SHA256: cde23f321ed4418c697c2b3aed5d137b272d686e5541933bcf39876c9fe1e627 SHA512: 586d03744022416fc34bff1897ab7a489d8a6ea740a2f8ca6cce277902aca86cc0d22a3135c870c6951b86311b13806d2e57b36d1ecebd61e61ecf7332ad76db Homepage: https://cran.r-project.org/package=WordPools Description: CRAN Package 'WordPools' (Word Pools Used in Studies of Learning and Memory) Collects several classical word pools used most often to provide lists of words in psychological studies of learning and memory. 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It provides data cleaning, data sampling, extracting tokens from text, model generation, model evaluation and word prediction. For information on how n-gram models work we referred to: "Speech and Language Processing" . For optimizing R code and using R6 classes we referred to "Advanced R" . For writing R extensions we referred to "R Packages", . Package: r-cran-wordpuzzler Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 88 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-purrr, r-cran-scales, r-cran-stringr Filename: pool/dists/noble/main/r-cran-wordpuzzler_0.1.2-1.ca2404.1_all.deb Size: 39328 MD5sum: e3d2033887ce3e341227ed71345af970 SHA1: fede5be0d08175518da11acd7faa00f2415e6589 SHA256: b666eba9ee158ec4a161409d5bdf8c1ba22f33b6697b80ff45022442c3979409 SHA512: 50f13baf30e3b491c679f3330bb4533eb3b744aa0aafa96931fa63551121a64bcfa67e0c6f39c6443d741b236712253924e49cae56bc087e814331e15fc93ca9 Homepage: https://cran.r-project.org/package=wordPuzzleR Description: CRAN Package 'wordPuzzleR' (Word Puzzle Game) The word puzzle game requires you to find out the letters in a word within a limited number of guesses. In each round, if your guess hit any letters in the word, they reveal themselves. 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Package: r-cran-words Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 684 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-words_1.0.1-1.ca2404.1_all.deb Size: 660750 MD5sum: 490706526c92806adc3e7b633f4fcc6d SHA1: 68db4658b440c2809de3a69c76eb0c4e84d74b20 SHA256: 3bcef57e2c57a159d3acd2b7a9d5b23380dee29406fc0b69c099f8c86f7b9758 SHA512: 84fd4b6e0079deee2a3b7193d241686d03a3751e13f4b9fc35243e965a2cdb403436e00349c2b6e9f7da21b3b1e7317862fe48bf3990536db952306a74eacd32 Homepage: https://cran.r-project.org/package=words Description: CRAN Package 'words' (List of English Words from the Scrabble Dictionary) List of english scrabble words as listed in the OTCWL2014 . Words are collated from the 'Word Game Dictionary' . Package: r-cran-wordsalad Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 89 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-tibble, r-cran-text2vec, r-cran-word2vec, r-cran-fasttextr Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-wordsalad_0.2.0-1.ca2404.1_all.deb Size: 53852 MD5sum: ea9cf2b46e02b951110a57f4ecefcd72 SHA1: 96825301fb37668d6f14f251ef758554760403a7 SHA256: d1836db55714858f83e1714a05f65daeff7e0b38654cfbc05dd115c7e4bd39d9 SHA512: 7a3ae73260d160bfcf3faac64d6561c6458a094f11df4c95666ea98dcd2a9ca2045808efc8ca9d197561285e9782570262ed7adbfd2a21f423bca2bbfa0a0567 Homepage: https://cran.r-project.org/package=wordsalad Description: CRAN Package 'wordsalad' (Provide Tools to Extract and Analyze Word Vectors) Provides access to various word embedding methods (GloVe, fasttext and word2vec) to extract word vectors using a unified framework to increase reproducibility and correctness. 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The goal of 'workflows' is to streamline this process by bundling the model alongside the preprocessor, all within the same object. Package: r-cran-workflowsets Architecture: all Version: 1.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3153 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-dplyr, r-cran-generics, r-cran-ggplot2, r-cran-hardhat, r-cran-lifecycle, r-cran-parsnip, r-cran-pillar, r-cran-prettyunits, r-cran-purrr, r-cran-rlang, r-cran-rsample, r-cran-tibble, r-cran-tidyr, r-cran-tune, r-cran-vctrs, r-cran-withr, r-cran-workflows Suggests: r-cran-covr, r-cran-dials, r-cran-finetune, r-cran-kknn, r-cran-knitr, r-cran-modeldata, r-cran-recipes, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyclust, r-cran-yardstick Filename: pool/dists/noble/main/r-cran-workflowsets_1.1.1-1.ca2404.1_all.deb Size: 1857678 MD5sum: 7abb59b4509d4d9c05154877e0761108 SHA1: 7798bb757a4473cfa723e748b1b08447760623cb SHA256: 985ae6f19c3af2f2db88b86ff13547b479912e696967d5121463f0421e213568 SHA512: 7741ada32f4dd2d83bf7f3227e9048fcd7824be807a0104415c1012a99b493fd3bcdfd29db0a223f9b64e51b9b1633c611a757257891f4c994aa66b439b41244 Homepage: https://cran.r-project.org/package=workflowsets Description: CRAN Package 'workflowsets' (Create a Collection of 'tidymodels' Workflows) A workflow is a combination of a model and preprocessors (e.g, a formula, recipe, etc.) (Kuhn and Silge (2021) ). In order to try different combinations of these, an object can be created that contains many workflows. There are functions to create workflows en masse as well as training them and visualizing the results. Package: r-cran-workloopr Architecture: all Version: 1.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3797 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pracma, r-cran-signal Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-purrr, r-cran-tidyr Filename: pool/dists/noble/main/r-cran-workloopr_1.1.4-1.ca2404.1_all.deb Size: 773616 MD5sum: 1faeadce75d5c5cb0f369114dc272410 SHA1: f151fefb7faeb874200fe811f24f591026f8adf8 SHA256: 74b1f7590750a579c6a3c5858e02f755e91d0d4dcc5b7efb5ca29b7d277d2639 SHA512: 1197baeb0b3a077b11c3ed30c9507a967ba4fbeb76eb6b375006456ac5dcad1e2da430139f6d3ab6faf8d2c48985b2e979066d500fe399cf07ac9edd97548ee2 Homepage: https://cran.r-project.org/package=workloopR Description: CRAN Package 'workloopR' (Analysis of Work Loops and Other Data from Muscle PhysiologyExperiments) Functions for the import, transformation, and analysis of data from muscle physiology experiments. The work loop technique is used to evaluate the mechanical work and power output of muscle. Josephson (1985) modernized the technique for application in comparative biomechanics. Although our initial motivation was to provide functions to analyze work loop experiment data, as we developed the package we incorporated the ability to analyze data from experiments that are often complementary to work loops. There are currently three supported experiment types: work loops, simple twitches, and tetanus trials. Data can be imported directly from .ddf files or via an object constructor function. Through either method, data can then be cleaned or transformed via methods typically used in studies of muscle physiology. Data can then be analyzed to determine the timing and magnitude of force development and relaxation (for isometric trials) or the magnitude of work, net power, and instantaneous power among other things (for work loops). Although we do not provide plotting functions, all resultant objects are designed to be friendly to visualization via either base-R plotting or 'tidyverse' functions. This package has been peer-reviewed by rOpenSci (v. 1.1.0). 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Using open and interoperable formats makes it possible to exchange bundled data from 'R' to other languages such as 'Python' or 'Julia'. Multiple formats are supported 'Parquet', 'JSON', 'yaml', spatial data and raster data are supported. Package: r-cran-worldbank Architecture: all Version: 0.9.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-httr2 Suggests: r-cran-ggplot2, r-cran-scales, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-worldbank_0.9.0-1.ca2404.1_all.deb Size: 405130 MD5sum: 68383d161f96daa5423ea20f0cf0dc94 SHA1: 64802a9041423badef34db75bb1a79b7ad4f69a0 SHA256: 9ff76e2776a447f16291fe73a4ef1e20352c686963158ab07cc05cbe8d1b5357 SHA512: b04de4d017948ffb900f3f9e7b8f46ab67920cc2eb825918e863dbb14aff04b6550e6ff32213348efbff6ff889f77b6af5ff64132527cde07adb3e3ec1c890a9 Homepage: https://cran.r-project.org/package=worldbank Description: CRAN Package 'worldbank' (Client for the 'World Bank' APIs) Download and search data from the 'World Bank' APIs, including the 'Indicators' API, the 'Poverty and Inequality Platform (PIP)' API, the 'Finances One' API, and the 'Projects' API. 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Package: r-cran-worldmet Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-carrier, r-cran-cli, r-cran-dplyr, r-cran-lifecycle, r-cran-mirai, r-cran-purrr, r-cran-readr, r-cran-rlang, r-cran-sf, r-cran-tibble, r-cran-tidyr Suggests: r-cran-arrow, r-cran-knitr, r-cran-leaflet, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-worldmet_1.1.0-1.ca2404.1_all.deb Size: 363500 MD5sum: 344f74eace01a9c485348a922c64943b SHA1: 73da42afb727d9557fb4dbfe7c968383594581ba SHA256: b68882d6951353f02efbaf6ac633015aa8bf2fbea1d088589bd624312061edf5 SHA512: d68c79a571c85231d617b04143f1c29e1164eb9937414ad41ffb2d35f51e598d8bca19d6a796c385655cdd0a0907a9d673a415607668b7bc61e0d18f3598738b Homepage: https://cran.r-project.org/package=worldmet Description: CRAN Package 'worldmet' (Import Surface Meteorological Data from NOAA) Functions to import data from more than 30,000 surface meteorological sites around the world managed by the National Oceanic and Atmospheric Administration (NOAA) Global Historical Climate Network (GHCN) and Integrated Surface Database (ISD). 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Package: r-cran-wotply Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-network, r-cran-ggally, r-cran-sna Suggests: r-cran-testthat, r-cran-knitr Filename: pool/dists/noble/main/r-cran-wotply_0.1.0-1.ca2404.1_all.deb Size: 127508 MD5sum: c9c16a11de394972f3d0937da36e69b7 SHA1: 448052379cbfdb337584f871c56801f77e121d44 SHA256: 2ec7a6eafa2c6f3eee16aa010aa4c2ae2d524b8c2e50d50e1da3cc618016a9af SHA512: d0288a4688df2a2e3ffe9b63b75da6040a8246517440803eb927a8715fcb9e844946a40501cb2556f181b40670e2e9ca68edfadffc1349036fcbc896e8ae345d Homepage: https://cran.r-project.org/package=WOTPLY Description: CRAN Package 'WOTPLY' (Plot Connectivity Between Cells from Different Time Points) It shows the connections between selected clusters from the latest time point and the clusters from all the previous time points. The transition matrices between time point t and t+1 are obtained from Waddington-OT analysis . 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The algorithm arises from the problem of zeroing elements of a matrix. This interpolation method is useful for showing specific d-D frames in the tour, as opposed to d-D planes, as done by the geodesic interpolation. It is useful for projection pursuit indexes which are not s invariant. See more details in Buj, Cook, Asimov and Hurley (2005) and Batsaikhan, Cook and Laa (2023) . 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There are three main types of functions in 'wpa': (i) Standard functions create a 'ggplot' visual or a summary table based on a specific Viva Insights metric; (2) Report Generation functions generate HTML reports on a specific analysis area, e.g. Collaboration; (3) Other miscellaneous functions cover more specific applications (e.g. Subject Line text mining) of Viva Insights data. This package adheres to 'tidyverse' principles and works well with the pipe syntax. 'wpa' is built with the beginner-to-intermediate R users in mind, and is optimised for simplicity. 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While primarily designed to process outputs from the 'COLONY' (Jones & Wang (2010) ) pedigree reconstruction software, it can also accommodate data from other sources. By linking reconstructed pedigrees with genetic sample metadata, 'wpeR' produces spatial and temporal visualizations as well as tabular summaries that support interpretation of family structures and dynamics. The main goal of the package is to provide a solution for the analysis of complex wild pedigree data and to help the user to gain insights into genetic relationships within wild animal populations. 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Wavelet quantile correlation is used to capture the dependency between two time series across quantiles and different frequencies. This method is useful in identifying potential hedges and safe-haven instruments for investment purposes. See Kumar and Padakandla(2022) for further details. Package: r-cran-wql Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3483 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-reshape2, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wql_1.0.3-1.ca2404.1_all.deb Size: 2031412 MD5sum: 9e2009ea69294c8260791aa4f617580c SHA1: 3151a21108aaa7e6f1789360853f8a5bcc288245 SHA256: 665cdb9e1a818c0948a3361addfb133294ab0191aab61768641ed8860ed3f371 SHA512: 5c0bd62d2a097aef9bf27f91d81ff0ed0034c9301d10dd967b93f60db1ca5afbee799f9efdfdf0c69d1c230775d25fad16adabb7b3c5ad800f256c4d93d6481c Homepage: https://cran.r-project.org/package=wql Description: CRAN Package 'wql' (Exploring Water Quality Monitoring Data) Functions to assist in the processing and exploration of data from environmental monitoring programs. 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Package: r-cran-wqm Architecture: all Version: 0.1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1720 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mbc, r-cran-waveletcomp, r-cran-matrixstats, r-cran-ggplot2 Suggests: r-cran-tidyr, r-cran-dplyr, r-cran-scales, r-cran-data.table, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-wqm_0.1.4-1.ca2404.1_all.deb Size: 1219786 MD5sum: 8159d34d6bc9c0376e1d5e2ff64f23b6 SHA1: 3ba629eeada489cbb75c8a7669b43c7054c33369 SHA256: 3fbc8cf3c212ef814245f293d79d9e25e95262e0e7e5bcfe0a06dc5052c633e2 SHA512: a9b157877190878c5166da7f07681f05194b685c05517edf46a500bf1241c54e39edce8ee4c765f5fe1608e6bc8392be4f4fa80e43411f8ed72fe5ee5b88277a Homepage: https://cran.r-project.org/package=WQM Description: CRAN Package 'WQM' (Wavelet-Based Quantile Mapping for Postprocessing NumericalWeather Predictions) The wavelet-based quantile mapping (WQM) technique is designed to correct biases in spatio-temporal precipitation forecasts across multiple time scales. 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Package: r-cran-wqs Architecture: all Version: 0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rsolnp, r-cran-glm2 Filename: pool/dists/noble/main/r-cran-wqs_0.0.1-1.ca2404.1_all.deb Size: 107956 MD5sum: ca12a580c6c91ace3fd260b289d7ffa4 SHA1: e473b01252c8f8d6c4b85abed7868e1467afa916 SHA256: 396b1bbea4c9fb11df18f0f987efc8c10394742f19e0e0177fb20d30097cdfd8 SHA512: eb89cdcf7ffd1d5798412530d22ed5c39613395c59d276bc508f185391b3822cfc61d7d7858c3e89134972a1257af242d9a598b1cd826f9b83d65af4b01c1627 Homepage: https://cran.r-project.org/package=wqs Description: CRAN Package 'wqs' (Weighted Quantile Sum Regression) Fits weighted quantile sum regression models, calculates weighted quantile sum index and estimated component weights. 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Weighted quantile sum regression is a statistical technique to evaluate the effect of complex exposure mixtures on an outcome (Carrico et al. 2015 ). The model features a statistical power and Type I error (i.e., false positive) rate trade-off, as there is a machine learning step to determine the weights that optimize the linear model fit. This package provides an alternative method based on a permutation test that should reliably allow for both high power and low false positive rate when utilizing WQS regression (Day et al. 2022 ). Package: r-cran-wqtrends Architecture: all Version: 1.5.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4549 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-mgcv, r-cran-mixmeta, r-cran-plotly, r-cran-purrr, r-cran-tibble, r-cran-tidyr, r-cran-viridislite Suggests: r-cran-testthat, r-cran-covr, r-cran-english, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wqtrends_1.5.2-1.ca2404.1_all.deb Size: 1448526 MD5sum: 99baf403751d24acf84917891707ff60 SHA1: baf16781242be7e075ca7f00051048cb7955e595 SHA256: a8ba74944f5c59b6f23cf7e9723af1accc18afd240ae9cf97de7c2d4d4fa1f36 SHA512: 394acdbc9c87930c08145bbb1fb66aa4fe8ef96dedafadda41522db1b3bf56184bb47ec9b7209d1efdec1a32d205663c66ec8a85995d2f1f1fbbdb6770e33ddd Homepage: https://cran.r-project.org/package=wqtrends Description: CRAN Package 'wqtrends' (Assess Water Quality Trends with Generalized Additive Models) Assess Water Quality Trends for Long-Term Monitoring Data in Estuaries using Generalized Additive Models following Wood (2017) and Error Propagation with Mixed-Effects Meta-Analysis following Sera et al. (2019) . Methods are available for model fitting, assessment of fit, annual and seasonal trend tests, and visualization of results. 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The function status() counts rows that have missing values in grouping columns (returned by na() ), have non-unique combinations of grouping columns (returned by dup() ), and that are not locally sorted (returned by unsorted() ). Functions enumerate() and itemize() give sorted unique combinations of columns, with or without occurrence counts, respectively. Function ignore() drops columns in x that are present in y, and informative() drops columns in x that are entirely NA; constant() returns values that are constant, given a key. Data that have defined unique combinations of grouping values behave more predictably during merge operations. 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Functions were mainly written for my own daily work or teaching, but may be of use to others as well. Package: r-cran-wrappr Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-wrappr_0.1.0-1.ca2404.1_all.deb Size: 82104 MD5sum: 8620b8269c93fb8f83e85bde75c2fad0 SHA1: 4c25fc966374a2c47d5b103e692dc7180da924bc SHA256: 475c1148600886ed06d97efd0c6a6a24aca417d73106230ed94156c72f9268b2 SHA512: e1aa1e3a41805b0487261453be82c0a1475059b738cef57d8525e5e02f9d769571903577c3afd5b45cfb8140aed04d4c3336042d40651dd2ac6f7d79ae5547a6 Homepage: https://cran.r-project.org/package=wrappr Description: CRAN Package 'wrappr' (A Collection of Helper and Wrapper Functions) Helper functions to easily add functionality to functions. The package can assign functions to have an lazy evaluation allowing you to save and update the arguments before and after each function call. You can set a temporary working directory within functions and wrap console messages around other functions. Package: r-cran-wrapr Architecture: all Version: 2.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1184 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-tinytest Filename: pool/dists/noble/main/r-cran-wrapr_2.1.0-1.ca2404.1_all.deb Size: 676086 MD5sum: b02a2dc47e301c669090ba378edbc642 SHA1: 50f3419e9c1c24fe29c1e693806e89762ea7b25e SHA256: 4301313eb4c8b573fd0a617e2267141c9d5e25a39a8083c326b9c7ed2bdaebb9 SHA512: 1b3341b2e4000bbde3cadc146f996349743a8a6ae86d57e93f4a098f0e0eb7a0f86c7d1cd00fe304682f228ea30c79661ae527b1e7718c788b043723f8098783 Homepage: https://cran.r-project.org/package=wrapr Description: CRAN Package 'wrapr' (Wrap R Tools for Debugging and Parametric Programming) Tools for writing and debugging R code. Provides: '%.>%' dot-pipe (an 'S3' configurable pipe), unpack/to (R style multiple assignment/return), 'build_frame()'/'draw_frame()' ('data.frame' example tools), 'qc()' (quoting concatenate), ':=' (named map builder), 'let()' (converts non-standard evaluation interfaces to parametric standard evaluation interfaces, inspired by 'gtools::strmacro()' and 'base::bquote()'), and more. Package: r-cran-wrds Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-dplyr, r-cran-keyring, r-cran-rlang, r-cran-rpostgres, r-cran-tidylog Suggests: r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-wrds_0.1.1-1.ca2404.1_all.deb Size: 87404 MD5sum: 97bf67c169819e8038b805318622ba65 SHA1: bff568f4aa16a011970dfdc40712c272f633e2a9 SHA256: 0c08a70351fd87e3bef16786c71ef6518dbaafb962444d8ce049fa0309e77bb9 SHA512: fab113244639ca4a6f3b753ba5468a0e9e7ca898fa6ac2ae323ecd000ffe5797d0f80f2367e87963c876f678a9579e005ccecab1b692b9baf6fbb4fb0e757ba8 Homepage: https://cran.r-project.org/package=wrds Description: CRAN Package 'wrds' (Access 'Wharton Research Data Services' ('WRDS')) Provides simple functions for accessing data from 'Wharton Research Data Services' ('WRDS'), a widely used financial database in academic research. Includes credential management via the system keyring, database tools, and functions for downloading generic tables, 'Compustat' fundamentals, and linking tables. Package: r-cran-wrensbookshelf Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 76 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-wrensbookshelf_0.1.0-1.ca2404.1_all.deb Size: 44246 MD5sum: f97be78f0d061665f3e28b3503669d2d SHA1: 488616c607e50f11506e1ecaf23ba97220d89e2a SHA256: 189583526dd8efae5e2f9ecc43db9ec2f9ca7fc4c81314da890b0c91b08785b5 SHA512: 079d2990d350c5ae6c3b0cbe0ca31d54ab2f76b983f88431bcce7aa4b9455f84d382d7d63291989c51cec1c7f01f20c7fdb106565d8a8590d89ce63fa1704f5c Homepage: https://cran.r-project.org/package=WrensBookshelf Description: CRAN Package 'WrensBookshelf' (A Collection of Palettes and Some Functions to Help Use Them) A collection of color palettes that were extracted from various books on my sons(Wren) bookshelf. Also included are a number of functions and wrappers to utilize them, as well as to subset the palettes to desired number/specific colors. Package: r-cran-wrestimates Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 91 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wrestimates_0.1.0-1.ca2404.1_all.deb Size: 34750 MD5sum: 93dd44da6a6962221c39626312463e7d SHA1: 91312af195a58b475587ad4dc26402f4c390a1c9 SHA256: f9f8a6a46256a004721505fcc003cc112710998918da23dae3d744f96b0d7a3e SHA512: 961b9223361f59f881dfe180f10a20ef9cd70b86aaf923dce51b8453d820832daa5cc8aa3daffb910be671ad14948c32d40c13d6fa0188643f84b3d497750ad5 Homepage: https://cran.r-project.org/package=WRestimates Description: CRAN Package 'WRestimates' (Sample Size, Power and CI for the Win Ratio) Calculates non-parametric estimates of the sample size, power and confidence intervals for the win-ratio. For more detail on the theory behind the methodologies implemented see Yu, R. X. and Ganju, J. (2022) . Package: r-cran-wrgraph Architecture: all Version: 1.3.15-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1554 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-rcolorbrewer, r-cran-wrmisc Suggests: r-cran-dplyr, r-cran-factoextra, r-cran-factominer, r-cran-ggpubr, r-cran-knitr, r-bioc-limma, r-cran-rmarkdown, r-cran-sm Filename: pool/dists/noble/main/r-cran-wrgraph_1.3.15-1.ca2404.1_all.deb Size: 1145624 MD5sum: a551423e2d9c3a257fc8c0f7f656f163 SHA1: 5606120b927f1f9423915e2dd92a7688db503f25 SHA256: 2d49a8da778861872d1760696eb27b6fa14d44751f778adc6e0dfae4d71f07cd SHA512: 331c2503cb5fbeb18cffd9eec584250ddf1b8f77c6bfa74c49f09ba3994aae267c04ba1e4f59cb0d5cdbdd55bf1667e101e364b941c94d7b51cb5dd392a2eb8e Homepage: https://cran.r-project.org/package=wrGraph Description: CRAN Package 'wrGraph' (Graphics in the Context of Analyzing High-Throughput Data) Additional options for making graphics in the context of analyzing high-throughput data are available here. This includes automatic segmenting of the current device (eg window) to accommodate multiple new plots, automatic checking for optimal location of legends in plots, small histograms to insert as legends, histograms re-transforming axis labels to linear when plotting log2-transformed data, a violin-plot function for a wide variety of input-formats, principal components analysis (PCA) with bag-plots to highlight and compare the center areas for groups of samples, generic MA-plots (differential- versus average-value plots) , staggered count plots and generation of mouse-over interactive html pages. Package: r-cran-wrictools Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1396 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rcurl, r-cran-readr, r-cran-rlang, r-cran-tidyr, r-cran-readxl Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/noble/main/r-cran-wrictools_1.0.1-1.ca2404.1_all.deb Size: 543806 MD5sum: 12781de1228770d2f2734f2870397dcd SHA1: 41fa353fec622fef46aecd334e83ba8423751d8c SHA256: 404ce4fb927e8028ab41b90ab2073d208e2b6f3680778000875e5c7218ac980a SHA512: 7cc8af83fa994ed171aa1c60c42768bdf168f355664de6dfc801fcec01b6b05a53b2ec03f886037a3e1a4f66e707ca9947f7ff41727d8ad9b8f3128e9f11a5e0 Homepage: https://cran.r-project.org/package=wrictools Description: CRAN Package 'wrictools' (Analyze Whole Room Indirect Calorimetry (WRIC) Data) Provides functions, tutorials, and examples to preprocess, analyze, and visualize data from whole room indirect calorimeters (WRIC) by Maastricht Instruments, using the 'OmniCal' software. Some functions may also work with WRICs from other manufacturers, though full functionality has only been validated for Maastricht Instruments devices. Package: r-cran-wrightmap Architecture: all Version: 1.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 603 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcolorbrewer Filename: pool/dists/noble/main/r-cran-wrightmap_1.4-1.ca2404.1_all.deb Size: 240490 MD5sum: 6b32c8cc5ae14b954a929b79d2a9777d SHA1: 27f49939c5e4ac8dc2871e97a18b72ffe159112d SHA256: dac427e37fff3ca117b05c6174559d0fe88a84fa451eb531b84b0de24026f721 SHA512: c6e194905681b80d0ba8da0fc9f93b363734cf5fff28edb3431ee5fdca0970ab836bbfe1573468d99cf175f22fcf2d68468b7f495155a4041bf3d7f9534c9214 Homepage: https://cran.r-project.org/package=WrightMap Description: CRAN Package 'WrightMap' (IRT Item-Person Map with 'ConQuest' Integration) A powerful yet simple graphical tool available in the field of psychometrics is the Wright Map (also known as item maps or item-person maps), which presents the location of both respondents and items on the same scale. Wright Maps are commonly used to present the results of dichotomous or polytomous item response models. The 'WrightMap' package provides functions to create these plots from item parameters and person estimates stored as R objects. Although the package can be used in conjunction with any software used to estimate the IRT model (e.g. 'TAM', 'mirt', 'eRm' or 'IRToys' in 'R', or 'Stata', 'Mplus', etc.), 'WrightMap' features special integration with 'ConQuest' to facilitate reading and plotting its output directly.The 'wrightMap' function creates Wright Maps based on person estimates and item parameters produced by an item response analysis. The 'CQmodel' function reads output files created using 'ConQuest' software and creates a set of data frames for easy data manipulation, bundled in a 'CQmodel' object. The 'wrightMap' function can take a 'CQmodel' object as input or it can be used to create Wright Maps directly from data frames of person and item parameters. Package: r-cran-write.snns Architecture: all Version: 0.0-4.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 42 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-write.snns_0.0-4.2-1.ca2404.1_all.deb Size: 11562 MD5sum: f3ecffd495f1f2e7eb14246d94d8953f SHA1: 4a6c492a6ecf57de9c01afb06e01dfafc16036d3 SHA256: 6ce8730ba5a74d889fd59b0cd1b50b6dffd08026cb85450e93396fbf65b6ec1f SHA512: c2ae55d7ffeccfddf4a80c8df7be0100a9a4764fbc6738286d3cfa4c7c12d9642e7fd825b3d3d2d1af9fa63263ed05cd88ab7f878ec7636381d83dd9aeb07d77 Homepage: https://cran.r-project.org/package=write.snns Description: CRAN Package 'write.snns' (Function for exporting data to SNNS pattern files) Function for writing a SNNS pattern file from a data.frame or matrix. Package: r-cran-writealizer Architecture: all Version: 1.7.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1684 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-cli, r-cran-digest, r-cran-dplyr, r-cran-glue, r-cran-magrittr, r-cran-rlang, r-cran-tidyselect Suggests: r-cran-caretensemble, r-cran-cubist, r-cran-curl, r-cran-earth, r-cran-gbm, r-cran-glmnet, r-cran-kernlab, r-cran-knitr, r-cran-pls, r-cran-randomforest, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-writealizer_1.7.3-1.ca2404.1_all.deb Size: 453398 MD5sum: 6f5d68036d0378ca347ccbd98320f4a9 SHA1: bac2f43c6c1b3b4fe4af838fb8b649131c743a80 SHA256: 82970f8acacb0aad4ac9036b478bac2aa3a83172a7ae14a18fe9e56685c9f4f5 SHA512: 9b79da2b7ea9f1536f6afbd71e7a7fe50da2425e7067119f4019fdc801689047b027580f1c9b51d0e36e59f621eb8e65a95a68714984412ec6428f4260e4ce7c Homepage: https://cran.r-project.org/package=writeAlizer Description: CRAN Package 'writeAlizer' (Generate Predicted Writing Quality Scores) Imports variables from 'ReaderBench' (Dascalu et al., 2018), 'Coh-Metrix' (McNamara et al., 2014), and/or 'GAMET' (Crossley et al., 2019) output files; downloads predictive scoring models described in Mercer & Cannon (2022) and Mercer et al.(2021); and generates predicted writing quality and curriculum-based measurement (McMaster & Espin, 2007) scores. Package: r-cran-writer Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 404 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dbi, r-cran-dplyr, r-cran-dbplyr, r-cran-glue, r-cran-cli, r-cran-rlang Suggests: r-cran-rsqlite, r-cran-testthat Filename: pool/dists/noble/main/r-cran-writer_0.1.0-1.ca2404.1_all.deb Size: 345618 MD5sum: 59740000402860dd70ae5cd8e3a6a6f6 SHA1: 2e1372077d8e14ea95a087f4d20678379e7f64aa SHA256: bb0f24418f464a7b97af7813312acd193ea660ace980fe1670d29b81439c5b2d SHA512: f841318696ff8745e2b82c39d619c0d024f0015799df5d252c66794c1209621f5b3e40fd184a60c0b226dc162d3bde61c2912a48aabc4332f0bfe4bb2d14206a Homepage: https://cran.r-project.org/package=writer Description: CRAN Package 'writer' (Write from Multiple Sources to a Database Table) Provides unified syntax to write data from lazy 'dplyr' 'tbl' or 'dplyr' 'sql' query or a dataframe to a database table with modes such as create, append, insert, update, upsert, patch, delete, overwrite, overwrite_schema. Package: r-cran-writexls Architecture: all Version: 6.8.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3822 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-writexls_6.8.0-1.ca2404.1_all.deb Size: 562234 MD5sum: 25dc2076481da8ffc758001b55ad1a32 SHA1: e3283acaf9c934a3bdeaab4256c12244d3673c61 SHA256: b82fae25bcea94f4893a90c81d2c8c41cf9067c6c99544039db41f9b7825c1cf SHA512: 405fe429acba221aa310f06fff8f1326a9b402f27027e167c5fbdb26467ecd8b5ba072079677cc8a811b6a5b3bb6f983da0ccb96a17b9af892b513ce4b48044a Homepage: https://cran.r-project.org/package=WriteXLS Description: CRAN Package 'WriteXLS' (Cross-Platform Perl Based R Function to Create Excel 2003 (XLS)and Excel 2007 (XLSX) Files) Cross-platform Perl based R function to create Excel 2003 (XLS) and Excel 2007 (XLSX) files from one or more data frames. Each data frame will be written to a separate named worksheet in the Excel spreadsheet. The worksheet name will be the name of the data frame it contains or can be specified by the user. Package: r-cran-wrmisc Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2539 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Suggests: r-cran-bbmisc, r-cran-boot, r-cran-coin, r-cran-data.table, r-cran-data.tree, r-cran-fdrtool, r-cran-flexclust, r-cran-knitr, r-bioc-limma, r-cran-markdown, r-cran-mixdist, r-cran-nbclust, r-bioc-preprocesscore, r-bioc-qvalue, r-cran-rcpp, r-cran-rcolorbrewer, r-cran-readxl, r-cran-rmarkdown, r-cran-som, r-cran-stringi, r-cran-vgam, r-bioc-vsn, r-cran-wrgraph Filename: pool/dists/noble/main/r-cran-wrmisc_2.0.2-1.ca2404.1_all.deb Size: 1982612 MD5sum: 16eb79523d05eec27e4a0bb2fa77c22f SHA1: 825c45a39c792bfd3d3f42bce806a9c399163464 SHA256: 9dedb2195f523e926a5e57d74149949b550fc971e6d0ec4b2bfbf342cd13a39b SHA512: 634291545c0149347573a74e328caab9b130b8dc7540f7d30419b851429d31070066cf288a2938a2547a22de59d7232c23947a44ff4d65692def77aabbc4613e Homepage: https://cran.r-project.org/package=wrMisc Description: CRAN Package 'wrMisc' (Analyze Experimental High-Throughput (Omics) Data) The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples. Many times large experimental datasets need some additional filtering, adequate functions are provided. Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too. Batch reading (or writing) of sets of files and combining data to arrays is supported, too. Package: r-cran-wrproteo Architecture: all Version: 2.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7073 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-knitr, r-bioc-limma, r-cran-wrmisc Suggests: r-cran-data.table, r-cran-fdrtool, r-cran-kableextra, r-cran-mass, r-cran-rcolorbrewer, r-cran-readxl, r-bioc-rots, r-cran-rmarkdown, r-cran-r.utils, r-cran-sm, r-cran-wrgraph Filename: pool/dists/noble/main/r-cran-wrproteo_2.0.2-1.ca2404.1_all.deb Size: 5239012 MD5sum: c6c7a19b9e92754539a7bb6e55bb625c SHA1: 6c947513a97006b48a01316055f0ab98bb5e229d SHA256: c092e163165f68619462aebdcca54319d64d6fa0d3f8fc3b23144f32b00072a0 SHA512: 04e813bccecb1f71f5fed0b9cfe34d115e28da16424e893a183d82c3ca9ef4462c9865921037b51b00dad7a6a11b6e6c3d58eb364ecd01dc8dcf974f4032c47b Homepage: https://cran.r-project.org/package=wrProteo Description: CRAN Package 'wrProteo' (Proteomics Data Analysis Functions) Data analysis of proteomics experiments by mass spectrometry is supported by this collection of functions mostly dedicated to the analysis of (bottom-up) quantitative (XIC) data. Fasta-formatted proteomes (eg from UniProt Consortium ) can be read with automatic parsing and multiple annotation types (like species origin, abbreviated gene names, etc) extracted. Initial results from multiple software for protein (and peptide) quantitation can be imported (to a common format): MaxQuant (Tyanova et al 2016 ), Dia-NN (Demichev et al 2020 ), Fragpipe (da Veiga et al 2020 ), ionbot (Degroeve et al 2021 ), MassChroq (Valot et al 2011 ), OpenMS (Strauss et al 2021 ), ProteomeDiscoverer (Orsburn 2021 ), Proline (Bouyssie et al 2020 ), AlphaPept (preprint Strauss et al ) and Wombat-P (Bouyssie et al 2023 . Meta-data provided by initial analysis software and/or in sdrf format can be integrated to the analysis. Quantitative proteomics measurements frequently contain multiple NA values, due to physical absence of given peptides in some samples, limitations in sensitivity or other reasons. Help is provided to inspect the data graphically to investigate the nature of NA-values via their respective replicate measurements and to help/confirm the choice of NA-replacement algorithms. Meta-data in sdrf-format (Perez-Riverol et al 2020 ) or similar tabular formats can be imported and included. Missing values can be inspected and imputed based on the concept of NA-neighbours or other methods. Dedicated filtering and statistical testing using the framework of package 'limma' can be run, enhanced by multiple rounds of NA-replacements to provide robustness towards rare stochastic events. Multi-species samples, as frequently used in benchmark-tests (eg Navarro et al 2016 , Ramus et al 2016 ), can be run with special options considering such sub-groups during normalization and testing. Subsequently, ROC curves (Hand and Till 2001 ) can be constructed to compare multiple analysis approaches. As detailed example the data-set from Ramus et al 2016 ) quantified by MaxQuant, ProteomeDiscoverer, and Proline is provided with a detailed analysis of heterologous spike-in proteins. 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It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures. 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Package: r-cran-wwntests Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sde, r-cran-ftsa, r-cran-rainbow, r-cran-mass, r-cran-fda Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-compquadform, r-cran-tensora Filename: pool/dists/noble/main/r-cran-wwntests_1.1.0-1.ca2404.1_all.deb Size: 148874 MD5sum: e334b031800e13e5f16f24878593c5b0 SHA1: 23f3676d0c861ed01fec3c7dad6064b5e874e258 SHA256: abd803401e1be7c786d632bfd00dfc29a8262de7c881dcf288a568aaf48628fe SHA512: 932e7f78759c4d4549cbb5d434bd549ff9b1ab0b5be01d76d3fb021c35b910c9d0acd9bd69e9b06aa9df67bf97aadee1a9e530b213b6c1c6c5862de829da6d2f Homepage: https://cran.r-project.org/package=wwntests Description: CRAN Package 'wwntests' (Hypothesis Tests for Functional Time Series) Provides a collection of white noise hypothesis tests for functional time series and related visualizations. These include tests based on the norms of autocovariance operators that are built under both strong and weak white noise assumptions. Additionally, tests based on the spectral density operator and on principal component dimensional reduction are included, which are built under strong white noise assumptions. Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models. These methods are described in Kokoszka et al. (2017) , Characiejus and Rice (2019) , Gabrys and Kokoszka (2007) , and Kim et al. (2023) respectively. Package: r-cran-wxgenr Architecture: all Version: 1.4.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2627 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-lubridate, r-cran-msm, r-cran-sm, r-cran-dplyr, r-cran-plyr, r-cran-foreach, r-cran-doparallel, r-cran-magrittr, r-cran-dorng, r-cran-mc2d, r-cran-qmap, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-reshape2, r-cran-ggpubr, r-cran-moments, r-cran-seas, r-cran-padr, r-cran-hydrogof, r-cran-specsverification, r-cran-ggridges, r-cran-tigris, r-cran-terra, r-cran-stringr, r-cran-mapview, r-cran-xts, r-cran-sf, r-cran-lfstat, r-cran-feddata, r-cran-ggplot2 Filename: pool/dists/noble/main/r-cran-wxgenr_1.4.5-1.ca2404.1_all.deb Size: 2062952 MD5sum: 078fd3baf2ded1963e4426e18a9c8372 SHA1: 709ea55665e61de1e70fe25723ac9fb93e28c0bb SHA256: a5d6a244def4c2ea6182adf1754f380504794b7b1128c847ec7cc1915d649d16 SHA512: 19375fb917d5ba0204caded12e9066a8a5c937d4a95a58a54dfae93ece9609e14cd856881c15951988c3a28291a2ea4bb9af65d3a396bcd22a72d2f1529c2b01 Homepage: https://cran.r-project.org/package=wxgenR Description: CRAN Package 'wxgenR' (A Stochastic Weather Generator with Seasonality) A weather generator to simulate precipitation and temperature for regions with seasonality. Users input training data containing precipitation, temperature, and seasonality (up to 26 seasons). Including weather season as a training variable allows users to explore the effects of potential changes in season duration as well as average start- and end-time dates due to phenomena like climate change. Data for training should be a single time series but can originate from station data, basin averages, grid cells, etc. Bearup, L., Gangopadhyay, S., & Mikkelson, K. (2021). "Hydroclimate Analysis Lower Santa Cruz River Basin Study (Technical Memorandum No ENV-2020-056)." Bureau of Reclamation. Gangopadhyay, S., Bearup, L. A., Verdin, A., Pruitt, T., Halper, E., & Shamir, E. (2019, December 1). "A collaborative stochastic weather generator for climate impacts assessment in the Lower Santa Cruz River Basin, Arizona." Fall Meeting 2019, American Geophysical Union. . Package: r-cran-wyz.code.metatesting Architecture: all Version: 1.1.22-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-tidyr, r-cran-wyz.code.offensiveprogramming, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wyz.code.metatesting_1.1.22-1.ca2404.1_all.deb Size: 222908 MD5sum: e0035cf5d21c10f630a7388966c94a92 SHA1: f03b70fa932dca6e52cff8597193a4ed8a443a61 SHA256: 1e2721f6a1ee1d5238bea22ed9e74ddcc8889be6fdd9dc0bc7daf99fb06616cc SHA512: 0de98c0a101aa1a236dc38b9c945601dd42c2261a95286ae6ca29382987a865db9502b662ffc06102cc97173c56fb22351d5c746d8d57d99eb99a69ff8d46884 Homepage: https://cran.r-project.org/package=wyz.code.metaTesting Description: CRAN Package 'wyz.code.metaTesting' (Wizardry Code Meta Testing) Meta testing is the ability to test a function without having to provide its parameter values. Those values will be generated, based on semantic naming of parameters, as introduced by package 'wyz.code.offensiveProgramming'. Value generation logic can be completed with your own data types and generation schemes. This to meet your most specific requirements and to answer to a wide variety of usages, from general use case to very specific ones. While using meta testing, it becomes easier to generate stress test campaigns, non-regression test campaigns and robustness test campaigns, as generated tests can be saved and reused from session to session. Main benefits of using 'wyz.code.metaTesting' is ability to discover valid and invalid function parameter combinations, ability to infer valid parameter values, and to provide smart summaries that allows you to focus on dysfunctional cases. Package: r-cran-wyz.code.offensiveprogramming Architecture: all Version: 1.1.24-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-tidyr, r-cran-stringr, r-cran-r6, r-cran-crayon Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wyz.code.offensiveprogramming_1.1.24-1.ca2404.1_all.deb Size: 296874 MD5sum: 3d0b415247239319f76f94942b37e6f4 SHA1: ad0342ecc153a1d15f66ed7f36fede61175a978d SHA256: 890ee0f40e04a20a6e6ccbecffb93d6345c43d28bb0f536a130d2aa8cf07fe82 SHA512: c9a8a4249b8efdc5ab5a21284fb37f303c92efe8ad3fafb16d3ab27e96852a54686550ddc02778c32c6a2f1a2a83c7e1659b97582a8926b8bb875ee2e8136d6d Homepage: https://cran.r-project.org/package=wyz.code.offensiveProgramming Description: CRAN Package 'wyz.code.offensiveProgramming' (Wizardry Code Offensive Programming) Allows to turn standard R code into offensive programming code. Provides code instrumentation to ease this change and tools to assist and accelerate code production and tuning while using offensive programming code technics. Should improve code robustness and quality. Function calls can be easily verified on-demand or in batch mode to assess parameter types and length conformities. Should improve coders productivity as offensive programming reduces the code size due to reduced number of controls all along the call chain. Should speed up processing as many checks will be reduced to one single check. 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It populates documentation sections, either automatically or by considering your input. Input code could be standard R code or offensive programming code. Documentation content completeness depends on the type of code you use. With offensive programming code, expect generated documentation to be fully completed, from a format and content point of view. With some standard R code, you will have to activate post processing to fill-in any section that requires complements. Produced manual page validity is automatically tested against R documentation compliance rules. Documentation language proficiency, wording style, and phrasal adjustments remains your job. Package: r-cran-wyz.code.testthat Architecture: all Version: 1.1.20-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-tidyr, r-cran-wyz.code.offensiveprogramming, r-cran-r6 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-wyz.code.testthat_1.1.20-1.ca2404.1_all.deb Size: 143106 MD5sum: bf5ab55b82e3ac9a01f76955ff0280a8 SHA1: 38e1c9d995fa1796938f412a9c5814af00bf4824 SHA256: 6b45cbdc849748cc7721d6ad19c30549e2ae52662ca68681f14f976b5a624c7e SHA512: 680ba526c7e16af2a353b794d552738c4cd2a6313307e9314c152ff5785aa9957a6da10f4f876d6d806a1a5877f9208be6eb7ca5e87e896622c2c908b4452f23 Homepage: https://cran.r-project.org/package=wyz.code.testthat Description: CRAN Package 'wyz.code.testthat' (Wizardry Code Offensive Programming Test Generation) Allows to generate automatically 'testthat' code files from offensive programming test cases. Generated test files are complete and ready to run. Using 'wyz.code.testthat' you will earn a lot of time, reduce the number of errors in test case production, be able to test immediately generated files without any need to view or modify them, and enter a zero time latency between code implementation and industrial testing. As with 'testthat', you may complete provided test cases according to your needs to push testing further, but this need is nearly void when using 'wyz.code.offensiveProgramming'. 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The Bayesian model is described in "Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones" (Bierlich et al., 2021, ). 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Package: r-cran-xega Architecture: all Version: 0.9.0.23-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-parallelly, r-cran-filelock, r-cran-xegaselectgene, r-cran-xegabnf, r-cran-xegaderivationtrees, r-cran-xegagagene, r-cran-xegagpgene, r-cran-xegagegene, r-cran-xegadfgene, r-cran-xegapermgene, r-cran-xegapopulation Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xega_0.9.0.23-1.ca2404.1_all.deb Size: 207212 MD5sum: 77d19d6193a6786f19a19708032a742e SHA1: 8b604fdff764235a6d2058af9762a82f584d1e93 SHA256: 844fdc69733b964e6e88b303ab3a9fb59916f665acf869b7cdfa43497c897134 SHA512: a2bcc1b2720e1f02d10d99efd8dbb4ca7c2c582e422b7ca127859ebd5c84d93c4141b4adf4d425037dbec62162136f300021701b68ef41a689731f9db9c6e76e Homepage: https://cran.r-project.org/package=xega Description: CRAN Package 'xega' (Extended Evolutionary and Genetic Algorithms) Implementation of a scalable, highly configurable, and e(x)tended architecture for (e)volutionary and (g)enetic (a)lgorithms. Multiple representations (binary, real-coded, permutation, and derivation-tree), a rich collection of genetic operators, as well as an extended processing pipeline are provided for genetic algorithms (Goldberg, D. E. (1989, ISBN:0-201-15767-5)), differential evolution (Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) ), simulated annealing (Aarts, E., and Korst, J. (1989, ISBN:0-471-92146-7)), grammar-based genetic programming (Geyer-Schulz (1997, ISBN:978-3-7908-0830-X)), grammatical evolution (Ryan, C., O'Neill, M., and Collins, J. J. (2018) ), and grammatical differential evolution (O'Neill, M. and Brabazon, A. (2006) in Arabinia, H. (2006, ISBN:978-193-241596-3). All algorithms reuse basic adaptive mechanisms for performance optimization. For 'xega''s architecture, see Geyer-Schulz, A. (2025) . Sequential or parallel execution (on multi-core machines, local clusters, and high-performance computing environments) is available for all algorithms. See . Package: r-cran-xegabnf Architecture: all Version: 1.0.0.5-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegabnf_1.0.0.5-1.ca2404.1_all.deb Size: 120886 MD5sum: 37fbd3f261cca7ad85c2504ba3830bc9 SHA1: 3584270e1dbb34093cbe588343f35ac624805694 SHA256: b280c4e1944f5004ea0f093880d91075d3cc0ebc932f6e1a16349401d3cd1bab SHA512: 70db44519bd7bab242e14ac48b0355f87cb9e4555ae9be3130421fe25e2ec363395c1ad003a141ddbc4ddd7b415f0c9b7c032e0448ec6995f0b497e12ab9c7c4 Homepage: https://cran.r-project.org/package=xegaBNF Description: CRAN Package 'xegaBNF' (Compile a Backus-Naur Form Specification into an R GrammarObject) Translates a BNF (Backus-Naur Form) specification of a context-free language into an R grammar object which consists of the start symbol, the symbol table, the production table, and a short production table. The short production table is non-recursive. The grammar object contains the file name from which it was generated (without a path). In addition, it provides functions to determine the type of a symbol (isTerminal() and isNonterminal()) and functions to access the production table (rules() and derives()). For the BNF specification, see Backus, John et al. (1962) "Revised Report on the Algorithmic Language ALGOL 60". (ALGOL60 standards page , html-edition ) A preprocessor for macros which expand to standard BNF is included. The grammar compiler is an extension of the APL2 implementation in Geyer-Schulz, Andreas (1997, ISBN:978-3-7908-0830-X). 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These operations are necessary for the initialization and for decoders of a random population of programs, as well as for implementing crossover and mutation operators. Depth-bounds are guaranteed by switching to a grammar without recursive production rules. For executing the examples, the package 'BNF' is needed. The basic tree operations for generating, extracting, and inserting derivation trees as well as the conditions for guaranteeing complete derivation trees have been presented in Geyer-Schulz (1997, ISBN:978-3-7908-0830-X). The use of random integer vectors for the generation of derivation trees has been introduced in Ryan, C., Collins, J. J., and O'Neill, M. (1998) for grammatical evolution. Package: r-cran-xegadfgene Architecture: all Version: 1.0.0.9-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-xegaselectgene Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegadfgene_1.0.0.9-1.ca2404.1_all.deb Size: 108648 MD5sum: 73e91e347668023503fa8602c7c451a0 SHA1: 6fefba39d908a2d15714ba20166f0c829d95ffd8 SHA256: bebd3edc812e0dbdb93c94cf2f11430512fb5e66cf27736b1aec5f39e93f0c6c SHA512: 21a2c8f27a82618f16ec627949837ed9307f4fc64090937b0dd95715bb9fb4756d41d6395e6a3342132d44ea1a42fe5befd7d407dd8728385a212bc88755bd15 Homepage: https://cran.r-project.org/package=xegaDfGene Description: CRAN Package 'xegaDfGene' (Gene Operations for Real-Coded Genes) Representation-dependent gene-level operations for genetic and evolutionary algorithms with real-coded genes used in the R-package 'xega' are collected in this package. The common feature of the gene operations is that all of them are useful for derivation-free optimization algorithms. At the moment the package implements initialization, mutation, crossover, and replication operations for differential evolution as described in Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) . In addition, several (more recent) methods for determining the scale factor are provided. For 'xega''s architecture, see Geyer-Schulz, A. (2025) . Package: r-cran-xegagagene Architecture: all Version: 1.0.0.6-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-xegaselectgene Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegagagene_1.0.0.6-1.ca2404.1_all.deb Size: 181014 MD5sum: 9ea1c754e932962180cebd875095a13a SHA1: 8bbad701815a4e3881c1fa564a7f48137d9a66b5 SHA256: 5c435ffad213f88df3571a10dbc0c4d0c80a8b3f915e300bcac3943aa1ed2080 SHA512: 1ceb6f784d1208c49ff7d78fff78489b03ae56c63c2761049d6411e6d7ca81ae3a092202d9b2c72c58b9a08db17d734fee09b32f5ca50f99cd231ef64b7ea99f Homepage: https://cran.r-project.org/package=xegaGaGene Description: CRAN Package 'xegaGaGene' (Binary Gene Operations for Genetic Algorithms) Representation-dependent gene level operations of a genetic algorithm with binary coded genes for the R-package 'xega' : Initialization of random binary genes, several gene maps for binary genes, several mutation operators, several crossover operators with 1 and 2 kids, replication pipelines for 1 and 2 kids, and, last but not least, function factories for configuration. See Goldberg, D. E. (1989, ISBN:0-201-15767-5). For crossover operators, see Syswerda, G. (1989, ISBN:1-55860-066-3), Spears, W. and De Jong, K. (1991, ISBN:1-55860-208-9). For mutation operators, see Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7). For 'xega''s architecture, see Geyer-Schulz, A. (2025) . Package: r-cran-xegagegene Architecture: all Version: 1.0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-numbers, r-cran-xegaselectgene, r-cran-xegabnf, r-cran-xegaderivationtrees Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegagegene_1.0.0.3-1.ca2404.1_all.deb Size: 81048 MD5sum: 364266b416b0512c3d932da72d19378d SHA1: 14fee5f20cc04de7358aff33dc27d6770e721e81 SHA256: ce833309e47ef03511273174599685d24d561fe51c747caf2cae9a5a89a1b313 SHA512: edce1ac3200aef02101454d9a3f292551e5d1cbe6d8e25d6a86920d35bc402fa681f9d8c8c3b22fd15e8f4cfe5eec67adfd3d6a7e3bf8ec302e558cc14cd2a44 Homepage: https://cran.r-project.org/package=xegaGeGene Description: CRAN Package 'xegaGeGene' (Grammatical Evolution) Grammatical evolution (see O'Neil, M. and Ryan, C. (2003,ISBN:1-4020-7444-1)) uses decoders to convert linear (binary or integer genes) into programs. In addition, automatic determination of codon precision with a limited rule choice bias is provided. For a recent survey of grammatical evolution, see Ryan, C., O'Neill, M., and Collins, J. J. (2018) . Package: r-cran-xegagpgene Architecture: all Version: 1.0.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xegabnf, r-cran-xegaderivationtrees, r-cran-xegaselectgene Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegagpgene_1.0.0.3-1.ca2404.1_all.deb Size: 77000 MD5sum: aed74a39c81ee5c968f24bd1c465e6e6 SHA1: 69c5a7358d60b5d8e4ba6dc1616ac91993f5dd60 SHA256: dbdb64d9d4bc8799ad593531aecda1c443eb64e598f908dbe883a1eca51ef82b SHA512: 41e9f27d41b735b8c747b956d8e0e16906b15ba3ee1d278af6ceaf56c50c1217ed6ce5c9fcc3073540905dfeaeb009c3df1cdd7495a27bec25038f31e6f8f1f0 Homepage: https://cran.r-project.org/package=xegaGpGene Description: CRAN Package 'xegaGpGene' (Genetic Operations for Grammar-Based Genetic Programming) An implementation of the representation-dependent gene level operations of grammar-based genetic programming with genes which are derivation trees of a context-free grammar: Initialization of a gene with a complete random derivation tree, decoding of a derivation tree. Crossover is implemented by exchanging subtrees. Depth-bounds for the minimal and the maximal depth of the roots of the subtrees exchanged by crossover can be set. Mutation is implemented by replacing a subtree by a random subtree. The depth of the random subtree and the insertion node are configurable. For details, see Geyer-Schulz (1997, ISBN:978-3-7908-0830-X). Package: r-cran-xegapermgene Architecture: all Version: 1.0.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-xegaselectgene Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegapermgene_1.0.0.1-1.ca2404.1_all.deb Size: 85788 MD5sum: 4a4d56993506712437f2a4902bb9b85e SHA1: 6cf8b9be50b5459ee7f0f7e1b5e30a7b0c13996e SHA256: 70fda706e61c830ea316a37bde80e26435f158e28d5da080cae37be204eac866 SHA512: 6a8f98a440090fc7437212495a9f2103f8145ee9423bd1a947cd2b36760bcb7d814e29aa41d44f650239e90fa29cc1cb393cfadc0a2a67a9f9d40dbd3ffaf810 Homepage: https://cran.r-project.org/package=xegaPermGene Description: CRAN Package 'xegaPermGene' (Operations on Permutation Genes) An implementation of representation-dependent gene level operations for genetic algorithms with genes representing permutations: Initialization of genes, mutation, and crossover. The crossover operation provided is position-based crossover (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8). For mutation, several variants are included: Order-based mutation (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8), randomized Lin-Kernighan heuristics (Croes, G. A. (1958) and Lin, S. and Kernighan. B. W. (1973) ), and randomized greedy operators. A random mix operator for mutation selects a mutation variant randomly. Package: r-cran-xegapopulation Architecture: all Version: 1.0.0.12-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-future.apply, r-cran-xegagagene, r-cran-xegaselectgene Suggests: r-cran-testthat, r-cran-future, r-cran-parallelly Filename: pool/dists/noble/main/r-cran-xegapopulation_1.0.0.12-1.ca2404.1_all.deb Size: 223980 MD5sum: 3175fa82b36f1ec7fa74d5bbafacd5c1 SHA1: 405573cf2b1fbabe50e4e839c33d7a7263bca7a7 SHA256: e19f793c64e8d47fafe665b66d17fdf67011aedff3b1e963d096bb9877118dc6 SHA512: 8954322d820a9e90de6f3e7256831222231b73eaf23ad872498a25331371aa970d491006d17dc69d7d61557c4d18700df6b7c5b076a3ab5263e7069e229ec001 Homepage: https://cran.r-project.org/package=xegaPopulation Description: CRAN Package 'xegaPopulation' (Genetic Population Level Functions) This collection of gene representation-independent functions implements the population layer of extended evolutionary and genetic algorithms and its support for the R-package 'xega' . The population layer consists of functions for initializing, logging, observing, evaluating a population of genes, as well as of computing the next population. For parallel evaluation of a population of genes 4 execution models - named Sequential, MultiCore, FutureApply, and Cluster - are provided. They are implemented by configuring the lapply() function. The execution model FutureApply can be externally configured as recommended by Bengtsson (2021) . Configurable acceptance rules and cooling schedules (see Kirkpatrick, S., Gelatt, C. D. J, and Vecchi, M. P. (1983) , and Aarts, E., and Korst, J. (1989, ISBN:0-471-92146-7) offer simulated annealing or greedy randomized approximate search procedure elements. Adaptive crossover and mutation rates depending on population statistics generalize the approach of Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7). For 'xega''s architecture, see Geyer-Schulz, A. (2025) . Package: r-cran-xegaselectgene Architecture: all Version: 1.0.0.4-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-xegaselectgene_1.0.0.4-1.ca2404.1_all.deb Size: 253614 MD5sum: a64df6f6c2a3559769cf16ca24117f20 SHA1: 5fa54bd0b4d7aba9f362d5525fe00df0e8069bd4 SHA256: 96c134d39aa9272f13bd6ad82f55bc13c5095ff49b1728dfd81c59ba15772e9a SHA512: 8d4157172bf0a27748945c828bfc7135b6536e24e1301c36d1b1f28bd45081da1b53b1cadca5e69f44c991735da39f0c589a90759258811a653d5c01ddbb4129 Homepage: https://cran.r-project.org/package=xegaSelectGene Description: CRAN Package 'xegaSelectGene' (Selection of Genes and Gene Representation Independent Functions) This collection of gene representation-independent mechanisms for evolutionary and genetic algorithms for the R-package xega contains four groups of functions: First, functions for selecting a gene in a population of genes according to its fitness value and for adaptive scaling of the fitness values as well as for performance optimization and measurement offer several variants for implementing the survival of the fittest. Second, evaluation functions for deterministic functions avoid recomputation. Evaluation of stochastic functions incrementally improve the estimation of the mean and variance of fitness values at almost no additional cost. Evaluation functions for gene repair handle error-correcting decoders. Third, timing and counting functions for profiling the algorithm pipeline are provided to assess bottlenecks in the algorithms. Fourth, a small collection of problem environments for function optimization, combinatorial optimization, and grammar-based genetic programming and grammatical evolution is provided for tutorial examples. For xega's architecture, see Geyer-Schulz, A. (2025) . The methods in the package are described by the following references: Baker, James E. (1987, ISBN:978-08058-0158-8), De Jong, Kenneth A. (1975) , Geyer-Schulz, Andreas (1997, ISBN:978-3-7908-0830-X), Grefenstette, John J. (1987, ISBN:978-08058-0158-8), Grefenstette, John J. and Baker, James E. (1989, ISBN:1-55860-066-3), Holland, John (1975, ISBN:0-472-08460-7), Lau, H. T. (1986) , Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) , Reynolds, J. C. (1993) , Schaffer, J. David (1989, ISBN:1-55860-066-3), Wenstop, Fred (1980) , Whitley, Darrell (1989, ISBN:1-55860-066-3), Wickham, Hadley (2019, ISBN:978-815384571). 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Each distribution includes the traditional functions as well as an additional function called the family function, which can be used to estimate parameters using Generalized Additive Models for Location, Scale and Shape, GAMLSS by Rigby & Stasinopoulos (2005) . Package: r-cran-zerotradeflow Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2489 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-tidyverse, r-cran-rlang, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-cli Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-zerotradeflow_0.1.0-1.ca2404.1_all.deb Size: 2512110 MD5sum: 612d46e3bf9854aaade1f61819a2c9ea SHA1: 18d70f0bef42a18a4821f570f240d42271f08d8d SHA256: e8b9977e17a6870013763bb466e26dcc554a86976b97d57a1c6dcf5e5e116952 SHA512: 2b70ea81a86f0aa7e52a272945ae7c35f05b590415bf87dbbd3422ac956bd837aa52fec06e9f12dcf4bfb032b624e9ef5cbae9d666da3088907f97a7509b82c9 Homepage: https://cran.r-project.org/package=zerotradeflow Description: CRAN Package 'zerotradeflow' (An Implementation for the Gravitational Models of Trade) A system for creating the bilateral trade flow between a country pair equal to zero. You provide the data, tell get_zerotradeflow() which variables are of interest and it expands the base by creating the bilateral zero trade flow. The bases on the flow of trade between countries only report positive trade (greater than zero), however, for some analyzes of gravitacional models, data on zero flow is also necessary. Some examples for Gravity Model: Figueiredo and Loures (2016) and Yotov, Piermartini, Monteiro and Larch . Package: r-cran-zetadiv Architecture: all Version: 1.3.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-scam, r-cran-car, r-cran-mgcv, r-cran-vegan, r-cran-geodist, r-cran-nnls, r-cran-glm2 Filename: pool/dists/noble/main/r-cran-zetadiv_1.3.0-1.ca2404.1_all.deb Size: 413268 MD5sum: 47fde7e44c8c8b81724db6bf8d95c53f SHA1: a798dd0abf29d26700d5d3fd4bf32b44865e87d7 SHA256: e921059709ad0fc0c758d06758d78e69121c9c7ca95d4c8ce5264cb84a8ecc12 SHA512: 4191a2f25690af0e0a55d7ea8ef287b14aee043fbd17061ba55b9356b5d978f033050d16488328ab749b7b293d1a10ab22f8a55e0e03a9c1e641566d35e6b3a3 Homepage: https://cran.r-project.org/package=zetadiv Description: CRAN Package 'zetadiv' (Functions to Compute Compositional Turnover Using Zeta Diversity) Functions to compute compositional turnover using zeta-diversity, the number of species shared by multiple assemblages. The package includes functions to compute zeta-diversity for a specific number of assemblages and to compute zeta-diversity for a range of numbers of assemblages. It also includes functions to explain how zeta-diversity varies with distance and with differences in environmental variables between assemblages, using generalised linear models, linear models with negative constraints, generalised additive models,shape constrained additive models, and I-splines. Package: r-cran-zetasuite Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4809 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-rtsne, r-cran-e1071, r-cran-ggplot2, r-cran-reshape2, r-cran-gridextra, r-cran-mixtools, r-cran-shinyjs, r-cran-shinydashboard, r-cran-shiny, r-cran-plotly, r-cran-dt Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-zetasuite_1.0.3-1.ca2404.1_all.deb Size: 4492072 MD5sum: c197e87fec64a1a07e9f0e1b985823e7 SHA1: fc882fcdd33a9f1d1538d0387ac8117bd78e6142 SHA256: 3a4ac9ae2b0c8342e743afe258ab1dedd10a73b96c86ff95530e78c73bc56101 SHA512: 3cb340034305c113b990943cbb3863ed75dc2d07f5f5f3370abc19ff8893f37dba621fbda033d52864e96a06808bc63750cb564771282855e3f375f735b7b752 Homepage: https://cran.r-project.org/package=ZetaSuite Description: CRAN Package 'ZetaSuite' (Analyze High-Dimensional High-Throughput Dataset and QualityControl Single-Cell RNA-Seq) The advent of genomic technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., monitoring multiple changes in gene expression in genome-wide siRNA screens across many different cell types (E Robert McDonald 3rd (2017) and Tsherniak A (2017) ) or single cell transcriptomics under different experimental conditions. We found that simple computational methods based on a single statistical criterion is no longer adequate for analyzing such multi-dimensional data. We herein introduce 'ZetaSuite', a statistical package initially designed to score hits from two-dimensional RNAi screens.We also illustrate a unique utility of 'ZetaSuite' in analyzing single cell transcriptomics to differentiate rare cells from damaged ones (Vento-Tormo R (2018) ). In 'ZetaSuite', we have the following steps: QC of input datasets, normalization using Z-transformation, Zeta score calculation and hits selection based on defined Screen Strength. Package: r-cran-zfit Architecture: all Version: 0.4.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-dplyr, r-cran-estimatr, r-cran-mass, r-cran-pls, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-zfit_0.4.0-1.ca2404.1_all.deb Size: 285850 MD5sum: f0e4291dd8c17eda33fb91bcdb280b35 SHA1: a23ffa40c33662d36f819d7b463cac6665bdc5c4 SHA256: d851e900f362e723f5b3d2d71059378f004ea3f378d0d3413d9ebe44aa2b4355 SHA512: 1e43280d1d3d2781511f5c7500853ded289897b4342e8a907c6ef7c96c7023ddc7526903dc0de1aaab3851575f1f62181139f95321c90ad1c44700d11c18d1b9 Homepage: https://cran.r-project.org/package=zfit Description: CRAN Package 'zfit' (Fit Models in a Pipe) Improve the usage of model fitting functions within a piped work flow. Package: r-cran-zibr Architecture: all Version: 1.0.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod Suggests: r-cran-betareg, r-cran-dplyr, r-cran-lme4, r-cran-nlme, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/noble/main/r-cran-zibr_1.0.3-1.ca2404.1_all.deb Size: 362524 MD5sum: baa5c766c8f6023d123522ddd1bd709c SHA1: a8b5caacf73ff8e0dde29b71a74abb13928a7bd1 SHA256: 9bc53ecdbd0807ffda0fd0dc59de81f64839c8af9d5d745ba1faad87f5008f44 SHA512: bbbad7abcc09265dc1e87a15b37756764518ba056ad8d1ae44c9d786fb8869f0231bbb5224dc98fe60d4077615959216eee2a7cc3d1c25d102e146dcf272cb95 Homepage: https://cran.r-project.org/package=ZIBR Description: CRAN Package 'ZIBR' (A Zero-Inflated Beta Random Effect Model) A two-part zero-inflated Beta regression model with random effects (ZIBR) for testing the association between microbial abundance and clinical covariates for longitudinal microbiome data. Eric Z. Chen and Hongzhe Li (2016) . Package: r-cran-zidw Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dwreg, r-cran-actuar, r-cran-maxlik, r-cran-count, r-cran-gtools, r-cran-matrixcalc, r-cran-discreteweibull, r-cran-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-tibble Filename: pool/dists/noble/main/r-cran-zidw_0.1.0-1.ca2404.1_all.deb Size: 137228 MD5sum: a73062f4415279306b3466abb2d17d86 SHA1: de7dc09955ddc9d04c2d4989068e8742e874ad5a SHA256: 649c7504c734afbed3fa2598e9b9efd0eac2865ac512884e15b929dba50e782b SHA512: b98e9a3bff84fd8ff9b69041968f45b20b3359654d98c575402c1777fe5f0d386ecb699151f8a330ca6612c3bedbb6a414a1987b1b9186b2601b01fc6735b151 Homepage: https://cran.r-project.org/package=ZIDW Description: CRAN Package 'ZIDW' (Zero-Inflated Discrete Weibull Models) Parameter estimation for zero-inflated discrete Weibull (ZIDW) regression models, the univariate setting, distribution functions, functions to generate randomized quantile residuals a pseudo R2, and plotting of rootograms. For more details, see Kalktawi (2017) , Taconeli and Rodrigues de Lara (2022) , and Yeh and Young (2025) . Package: r-cran-zihinar1 Architecture: all Version: 0.2.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-rstan, r-cran-vgam, r-cran-actuar, r-cran-matrixstats, r-cran-coda, r-cran-ggplot2, r-cran-gridextra, r-cran-knitr, r-cran-hmmpa Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-zihinar1_0.2.1-1.ca2404.1_all.deb Size: 73620 MD5sum: 5ebcc9da4f0d08233c8ed91073638a73 SHA1: 2e4787f1294fa3280bc546361c989315372b3874 SHA256: 3ad7afcf1a8ba2acdf6755df3b6efdf110bf1df63c4b0d954f3771d859d39c60 SHA512: 7499fc48c560a2c0710362703d592172661c21d9440d0f3939063822a12e39776bc55df76d6edbc88ea7ac383ebea979a23bcab1cf0bfc7ceee68625a0a89091 Homepage: https://cran.r-project.org/package=ZIHINAR1 Description: CRAN Package 'ZIHINAR1' (Zero-Inflated and Hurdle INAR(1) Models) Provides tools for estimating Zero-Inflated INAR(1) (ZI-INAR(1)) and Hurdle INAR(1) (H-INAR(1)) models using 'Stan'. It allows users to simulate time series data for these models, estimate parameters, and evaluate model fit using various criteria. Functions include model estimation, simulation, and likelihood-based metrics. Package: r-cran-zillowr Architecture: all Version: 1.0.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rcurl, r-cran-xml Suggests: r-cran-covr, r-cran-dt, r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-zillowr_1.0.0-1.ca2404.1_all.deb Size: 63894 MD5sum: 070ff4fbff435835e0bf52ba75f8dc4a SHA1: cc449b17efa73ad54ab19d7503936d0d80460eb6 SHA256: 9fa97a6c1d289b264ffb246b444d356966851b56ea030f5f68406a21465c7a84 SHA512: 4fb4b1d6e89d2d0706971f4c979fac2f118fe163376cf445c39de94221ff478ceb476eace7f85775a9bf6e2bc8e97c2afb82509144373b1cca85ccd8e2ee92b0 Homepage: https://cran.r-project.org/package=ZillowR Description: CRAN Package 'ZillowR' (R Interface to Zillow Real Estate and Mortgage Data API) Zillow, an online real estate company, provides real estate and mortgage data for the United States through a REST API. 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Package: r-cran-zim4rv Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 710 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-pscl, r-cran-compquadform, r-cran-skat, r-cran-rnomni Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-zim4rv_0.1.1-1.ca2404.1_all.deb Size: 261672 MD5sum: d9d9afc30b4b512aa28c98c06556a67d SHA1: 354559f1588c48125b5eb18f95959a281d34e8d7 SHA256: c05a37d40033cfb29b8aa27da947f45b4d72b34e3fedfe3890ff55d0268d0630 SHA512: b8e9b2087f75991691ac7ee24ecb7e52ae4c42ee27b25ece0dbc2fece1f735bb518256240fa2ef2a5e17632691f347a9a65c6ec3318cb66cc35ba3765e42f57d Homepage: https://cran.r-project.org/package=ZIM4rv Description: CRAN Package 'ZIM4rv' (Gene‐based Association Tests of Zero‐inflated Count Phenotypefor Rare Variants) Gene‐based association tests to model count data with excessive zeros and rare variants using zero-inflated Poisson/zero-inflated negative Binomial regression framework. This method was originally described by Fan, Sun, and Li in Genetic Epidemiology 46(1):73-86 . Package: r-cran-zim Architecture: all Version: 1.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-mass Suggests: r-cran-pscl, r-cran-tsa Filename: pool/dists/noble/main/r-cran-zim_1.1.0-1.ca2404.1_all.deb Size: 147246 MD5sum: 05950af07788f69a193f9afa4eeeda90 SHA1: 75672bca121df72c2bd0bd84ae82b16bb290d226 SHA256: a78104022b707165028b0f49b9b07ed488392e38baf654b567f549460322f4e9 SHA512: cc6fc7b89012208230f1b9ff04fb8e052cfea59d919e76d6909c6c52ae4028a013d38d545ffbc8184170a53ebc2364b1b8fe2dc574790a5d6d5cd7f1fd0b92a2 Homepage: https://cran.r-project.org/package=ZIM Description: CRAN Package 'ZIM' (Zero-Inflated Models (ZIM) for Count Time Series with ExcessZeros) Analyze count time series with excess zeros. Two types of statistical models are supported: Markov regression by Yang et al. (2013) and state-space models by Yang et al. (2015) . They are also known as observation-driven and parameter-driven models respectively in the time series literature. The functions used for Markov regression or observation-driven models can also be used to fit ordinary regression models with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) assumption. Besides, the package contains some miscellaneous functions to compute density, distribution, quantile, and generate random numbers from ZIP and ZINB distributions. Package: r-cran-zinar1 Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 78 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-gamlss.dist, r-cran-vgam, r-cran-mass, r-cran-statmod, r-cran-gtools, r-cran-scales Suggests: r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/noble/main/r-cran-zinar1_0.1.0-1.ca2404.1_all.deb Size: 44102 MD5sum: 17beb950f0f3c987f815b9d1f146df69 SHA1: b048bdade54c153886d7d6e8052305b5ef01f006 SHA256: 257e24ab5400bf478f585ca6341d57e828b94bcc5d7eca72975253b37340dde1 SHA512: 667cfe5c318bb9d03075b7deba023fd235d64a6fdd662bad76f15fe29d8c35c761943dbdeffd4e550198ac2330da344e59b0e779538f1216b7e7173952e08a43 Homepage: https://cran.r-project.org/package=ZINAR1 Description: CRAN Package 'ZINAR1' (Simulates ZINAR(1) Model and Estimates Its Parameters UnderFrequentist Approach) Generates Realizations of First-Order Integer Valued Autoregressive Processes with Zero-Inflated Innovations (ZINAR(1)) and Estimates its Parameters as described in Garay et al. (2021) . Package: r-cran-zinarp Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 74 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-progress Filename: pool/dists/noble/main/r-cran-zinarp_0.1.0-1.ca2404.1_all.deb Size: 40172 MD5sum: 2238f3d96c121531bee00f8517896d7b SHA1: b532c4b81c918e9ca2c808f5c774f2eb66881d8e SHA256: 98fc1fbcb2085a3e59aadf574ba06c318e5c85c91fbb12d430a5170b7dca6b93 SHA512: fe77967ad33dcfcedadae8df5331121fcc797f468af1d4c378c3ea8792c54fbfbbce9ecbbe9cb6b1cfdfc9747c2ba1aa6ee92305d149cc3e32f5250e8a50d4bb Homepage: https://cran.r-project.org/package=ZINARp Description: CRAN Package 'ZINARp' (Simulate INAR/ZINAR(p) Models and Estimate Its Parameters) Simulation, exploratory data analysis and Bayesian analysis of the p-order Integer-valued Autoregressive (INAR(p)) and Zero-inflated p-order Integer-valued Autoregressive (ZINAR(p)) processes, as described in Garay et al. (2020) . Package: r-cran-zipangu Architecture: all Version: 0.3.3-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-dplyr, r-cran-lifecycle, r-cran-lubridate, r-cran-magrittr, r-cran-memoise, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tibble, r-cran-arabic2kansuji Suggests: r-cran-curl, r-cran-testthat, r-cran-scales Filename: pool/dists/noble/main/r-cran-zipangu_0.3.3-1.ca2404.1_all.deb Size: 248748 MD5sum: 22461d4d2ec80b8811baa1d8e584ff11 SHA1: 368db2b1489c51dabcd987ef301a8e46a90b62e5 SHA256: 2ade9f0288d9eb6ba33ddb39d619d0e2141e83cfa2b1fe2db9ec8a9264d00364 SHA512: caa6d1a43aa92c21daf002f2c9611c1c09ddf0ae0c1486961b0d1936d1b807bc6ce1a6a1f7c695d174583109611a7dafa4864a8fb6b35fb417c958fb20b95d4b Homepage: https://cran.r-project.org/package=zipangu Description: CRAN Package 'zipangu' (Japanese Utility Functions and Data) Some data treated by the Japanese R user require unique operations and processing. These are caused by address, Kanji, and traditional year representations. 'zipangu' transforms specific to Japan into something more general one. 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Search ZIP codes by multiple geographies, including state, county, city & across time zones. Also included are functions for relating ZIP codes to Census data, geocoding & distance calculations. Package: r-cran-zipfa Architecture: all Version: 0.8.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 105 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-optimx, r-cran-trustoptim Filename: pool/dists/noble/main/r-cran-zipfa_0.8.1-1.ca2404.1_all.deb Size: 72090 MD5sum: 9df8880af3976c6353288e104832698f SHA1: f4807632d153f31a9e6f0f5e8fd0d4de81e0f05d SHA256: 65775ab5957e7d30567001fc69ba103f9f4b7f0437c2c9d9073722a6943f4051 SHA512: 020452b4d22dc85c3de2d61684aa8803831b3ef50ec18638c78d460b4a8cd7a64b48ccc29b502e914c5f6fdee56642d826ae5e9a4387541abb3be8704b3d5979 Homepage: https://cran.r-project.org/package=ZIPFA Description: CRAN Package 'ZIPFA' (Zero Inflated Poisson Factor Analysis) Estimation methods for zero-inflated Poisson factor analysis (ZIPFA) on sparse data. It provides estimates of coefficients in a new type of zero-inflated regression. It provides a cross-validation method to determine the potential rank of the data in the ZIPFA and conducts zero-inflated Poisson factor analysis based on the determined rank. Package: r-cran-zipfextr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-vgam, r-cran-tolerance, r-cran-copula Suggests: r-cran-testthat Filename: pool/dists/noble/main/r-cran-zipfextr_1.0.2-1.ca2404.1_all.deb Size: 170428 MD5sum: d906287aed92cac8c0049fc54f5e8b70 SHA1: 80f80f1e1c14737aa91bc18d9f95427fe622aa43 SHA256: 2439ae1d2299531c7c91f680abad4b6ca6191f0bdd6ccd9372a749d326f424ec SHA512: 82290272d621408eee8af901b29294df066de22ff64f85d2bbc895b40ebc118e708d4e994a72feec9de016a7814d00a5cc564997ba32dd4e7f5aba837ae95282 Homepage: https://cran.r-project.org/package=zipfextR Description: CRAN Package 'zipfextR' (Zipf Extended Distributions) Implementation of four extensions of the Zipf distribution: the Marshall-Olkin Extended Zipf (MOEZipf) Pérez-Casany, M., & Casellas, A. (2013) , the Zipf-Poisson Extreme (Zipf-PE), the Zipf-Poisson Stopped Sum (Zipf-PSS) and the Zipf-Polylog distributions. In log-log scale, the two first extensions allow for top-concavity and top-convexity while the third one only allows for top-concavity. All the extensions maintain the linearity associated with the Zipf model in the tail. Package: r-cran-zipfr Architecture: all Version: 0.6-70-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2667 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-zipfr_0.6-70-1.ca2404.1_all.deb Size: 2477978 MD5sum: cb65c1dcad91653c2ead666486f9b53f SHA1: ca538e000f4beef14369d019ca0aef22a0e21093 SHA256: 80765276db0336f3902c6b479f3bee2181fac4fdeefa5acf1f7e93670cfdda7f SHA512: 8cf6e98c7647a8e83e7af9033c74a44233d3b0d3e2b6131addf226b99529facb3e9f6e2ce13382414a1485d5603062ccd2b6cb4c2b000788ed2fc38fc590cc59 Homepage: https://cran.r-project.org/package=zipfR Description: CRAN Package 'zipfR' (Statistical Models for Word Frequency Distributions) Statistical models and utilities for the analysis of word frequency distributions. The utilities include functions for loading, manipulating and visualizing word frequency data and vocabulary growth curves. 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Package: r-cran-zipg Architecture: all Version: 1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-pscl, r-cran-mass Filename: pool/dists/noble/main/r-cran-zipg_1.1-1.ca2404.1_all.deb Size: 309382 MD5sum: d62a2dc8b19389b6e6086b111e09bc2d SHA1: 4938fcf0ee4b8c3fb3af997c415328cd2fb63b37 SHA256: b7e22d734dea94fdeaa8a06fc37a6db9766aaf394643f84e21f39cfb2af4ef1c SHA512: f5119dcf33897d5ab12b255d75da0d4dc43897c9b5f3503e28dc9d1ca2148612c5ae1aa3b131afa5b4928d64270ab1c3fc05cb7043344f6a20569316af3b6374 Homepage: https://cran.r-project.org/package=ZIPG Description: CRAN Package 'ZIPG' (Zero-Inflated Poisson-Gamma Regression) We provide a flexible Zero-inflated Poisson-Gamma Model (ZIPG) by connecting both the mean abundance and the variability to different covariates, and build valid statistical inference procedures for both parameter estimation and hypothesis testing. These functions can be used to analyze microbiome count data with zero-inflation and overdispersion. The model is discussed in Jiang et al (2023) . 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These include accessing ZIP Code to ZIP Code Tabulation Area (ZCTA) crosswalks, retrieving demographic data for ZCTAs, and tabulating demographic data for three-digit ZCTAs. Package: r-cran-zipr Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 67 Depends: r-base-core (>= 4.4.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/noble/main/r-cran-zipr_0.1.1-1.ca2404.1_all.deb Size: 20160 MD5sum: dfad663d8df58abc2a2297ba2381323f SHA1: 26a16b3375fb966285c6e90fa6b790fba3f340dc SHA256: 2dd8653e539d365f0409e303d09ed6a3880c4e7b7189d35c10c0654d24bf8c2f SHA512: f9de401634738e7bc5672eeac42dcf018a5685442a5759a7508c853892b9770a47d02c0ef6f5b3ab82e28e94209dd4b7ee58cafc8fa0f000ec32194cb88e4bf9 Homepage: https://cran.r-project.org/package=zipR Description: CRAN Package 'zipR' (Pythonic Zip() for R) Implements Python-style zip for R. Is a more flexible version of cbind. Package: r-cran-ziprop Architecture: all Version: 0.1.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-rgenoud, r-cran-purrr, r-cran-data.table Suggests: r-cran-markdown, r-cran-knitr, r-cran-ggplot2, r-cran-ggrepel, r-cran-ggthemes, r-cran-kableextra, r-cran-stringr Filename: pool/dists/noble/main/r-cran-ziprop_0.1.1-1.ca2404.1_all.deb Size: 202898 MD5sum: ae99bdbd8d67de6642f3e0d23fe8ec31 SHA1: 39c0766dce0b36bf6ee5b51801f4982399cdad41 SHA256: 04bc4ddfc5acb1fe65263a57e224a7669527337746414938c59be8fdd8b3e0ec SHA512: dce546731e1f830ee47cdabcce54805b33c4e97dd6e56578cbe089764b05fa9c9fbebc720d7b78b4924ab8b01da7e79c294ebee1d34007e9f47ea81a5e78d2cd Homepage: https://cran.r-project.org/package=ZIprop Description: CRAN Package 'ZIprop' (Permutations Tests and Performance Indicator for Zero-InflatedProportions Response) Permutations tests to identify factor correlated to zero-inflated proportions response. Provide a performance indicator based on Spearman correlation to quantify the part of correlation explained by the selected set of factors. See details for the method at the following preprint e.g.: . Package: r-cran-zipsae Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 59 Depends: r-base-core (>= 4.4.0), r-api-4.0 Filename: pool/dists/noble/main/r-cran-zipsae_1.0.2-1.ca2404.1_all.deb Size: 25240 MD5sum: cccb294ec1b1b7b3ee742fd848cee85f SHA1: 36bd18c34ac8d253ed05adec74f3689318886688 SHA256: bfc946d9f38aaafab0435a4bb4b8d49e29f0e9035106dfd80f4425abbfc5ab20 SHA512: fa3d22f0c55feca0205a3f99e03d5e5f30493b92052b31d06ab6f3329d3592c5dafa5a49c710635d3362b0902136a733929c8b11e9a85fa8eec21795b7e4f7f1 Homepage: https://cran.r-project.org/package=zipsae Description: CRAN Package 'zipsae' (Small Area Estimation with Zero-Inflated Model) This function produces empirical best linier unbiased predictions (EBLUPs) for Zero-Inflated data and its Relative Standard Error. Small Area Estimation with Zero-Inflated Model (SAE-ZIP) is a model developed for Zero-Inflated data that can lead us to overdispersion situation. 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Package: r-cran-zlavian Architecture: all Version: 0.2.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-lme4, r-cran-performance, r-cran-doparallel Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/noble/main/r-cran-zlavian_0.2.0-1.ca2404.1_all.deb Size: 133284 MD5sum: c5c1684b2d798d1de30e16b22122f509 SHA1: a507eb1e34f8772218f4201e66d9620adf11eee3 SHA256: aa7795e6ff81cb4260cf403ef20e3e5b5a26feb54c3621539c0976202442fc83 SHA512: ec9ceadce8523a5f9e6ac8517b63f193da9818674ec64aeae699b65747ffca81a5bd0d0752f75faf988504a629d311bd62f2913193d4f6cbc286a4234bead457 Homepage: https://cran.r-project.org/package=ZLAvian Description: CRAN Package 'ZLAvian' (Zipf's Law of Abbreviation in Animal Vocalisations) Assesses evidence for Zipf's Law of Abbreviation in animal vocalisation using IDs, note class and note duration. The package also provides a web plot function for visualisation. 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Package: r-cran-zoltr Architecture: all Version: 1.0.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-data.table, r-cran-httr, r-cran-jsonlite, r-cran-readr, r-cran-base64url, r-cran-dplyr, r-cran-mmwrweek, r-cran-rlang, r-cran-magrittr, r-cran-lubridate Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-webmockr, r-cran-mockery Filename: pool/dists/noble/main/r-cran-zoltr_1.0.2-1.ca2404.1_all.deb Size: 146726 MD5sum: aef58cf0b10cfdda20601d0cbdb107ff SHA1: 85224c6da32192a5574297843b4c12ce211b6e33 SHA256: abd96738f1605936668bd5638fafb5fc43f709f062ab3f948e2d074ddb0fd049 SHA512: 68e2ef58b3ca2f6140b6b7a6fdbba8b52c2a8bb8933e2b873940a4989a0e872e662d22bc34f36429e0c2f04d9f1dd0ab0d76b9983eed031d2116ec23f559b612 Homepage: https://cran.r-project.org/package=zoltr Description: CRAN Package 'zoltr' (Interface to the 'Zoltar' Forecast Repository API) 'Zoltar' is a website that provides a repository of model forecast results in a standardized format and a central location. It supports storing, retrieving, comparing, and analyzing time series forecasts for prediction challenges of interest to the modeling community. This package provides functions for working with the 'Zoltar' API, including connecting and authenticating, getting meta information (projects, models, and forecasts, and truth), and uploading, downloading, and deleting forecast and truth data. Package: r-cran-zonationr Architecture: all Version: 1.0.1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4033 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggspatial, r-cran-rlang, r-cran-terra, r-cran-tidyr, r-cran-viridis Suggests: r-cran-knitr, r-cran-mockery, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/noble/main/r-cran-zonationr_1.0.1-1.ca2404.1_all.deb Size: 2610128 MD5sum: af01ff61db373cf06dd6f0e04ffbe768 SHA1: d5d7601181026afe85255c249f233c351e1149a6 SHA256: 9bbf4a8effeb97360a1b8783a6386433ad57754f8bf33c8ab133b6896192c4ed SHA512: 820aef3d19a7d7a43cf4a4a34763ce1d4a9961596bbc7879f5ab5ce825315106a4bcb7c3be489c9a0d1c5cd31ee20dbff1218d26593165369eff0a321c481011 Homepage: https://cran.r-project.org/package=ZonationR Description: CRAN Package 'ZonationR' (Interface to 'Zonation' for Reproducible PrioritizationWorkflows) An interface to 'Zonation' software, enabling users to run spatial conservation prioritization workflows in 'R'. It streamlines input preparation, execution, and post-processing, while supporting reproducibility and lowering the entry barrier for learning, teaching, and research in conservation planning. The methods implemented in 'Zonation' are described in Moilanen et al. (2022) . Package: r-cran-zonebuilder Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6075 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-sf, r-cran-rcolorbrewer Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tmap, r-cran-tmaptools, r-cran-dplyr, r-cran-lwgeom, r-cran-leaflet, r-cran-covr, r-cran-bookdown Filename: pool/dists/noble/main/r-cran-zonebuilder_0.1.0-1.ca2404.1_all.deb Size: 4675350 MD5sum: 4bdc0e92820430296badfabf9d6aae36 SHA1: 1ecdbe2e13d761c9bced38f5297656571916cc60 SHA256: 6d21dfb5ebee0def3c185536bed3ec3fef50040fd785ad8bfc952d885e063474 SHA512: dfcf1e674339822dd7be0f3f169815aa03101cd9c4fed6c42567ee79546556b04a4e4b00eb45b19d3ffdc025901635d5eb21342578d7198b3129f98992105f88 Homepage: https://cran.r-project.org/package=zonebuilder Description: CRAN Package 'zonebuilder' (Create and Explore Geographic Zoning Systems) Functions, documentation and example data to help divide geographic space into discrete polygons (zones). The package supports new zoning systems that are documented in the accompanying paper, "ClockBoard: A zoning system for urban analysis", by Lovelace et al. (2022) . The functions are motivated by research into the merits of different zoning systems (Openshaw, 1977) . A flexible ClockBoard zoning system is provided, which breaks-up space by concentric rings and radial lines emanating from a central point. By default, the diameter of the rings grow according to the triangular number sequence (Ross & Knott, 2019) with the first 4 doughnuts (or annuli) measuring 1, 3, 6, and 10 km wide. These annuli are subdivided into equal segments (12 by default), creating the visual impression of a dartboard. Zones are labelled according to distance to the centre and angular distance from North, creating a simple geographic zoning and labelling system useful for visualising geographic phenomena with a clearly demarcated central location such as cities. 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Package: r-cran-ztype Architecture: all Version: 0.1.0-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 49 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-magrittr, r-cran-rvest, r-cran-stringr, r-cran-dplyr, r-cran-ggplot2, r-cran-lubridate, r-cran-assertthat Filename: pool/dists/noble/main/r-cran-ztype_0.1.0-1.ca2404.1_all.deb Size: 20234 MD5sum: d9610622819b0df22f77411b780bfcc5 SHA1: 9a08eb2605eb5bf5ef39642514942377ac008b82 SHA256: bf66d800cbe8f6ae5585a1f41ab14b8ac536330476cb17a27faa862071f36eef SHA512: e64c38ee9f00130f1bdf65dfbf2be0acbe21869de07a13ba7e035d034797c38b95432700f92b1dc6a884769d2267d67bda7f0f398a54491b7584d3a40716073a Homepage: https://cran.r-project.org/package=ztype Description: CRAN Package 'ztype' (Run a Ztype Game Loaded with R Functions) How fast can you type R functions on your keyboard? Find out by running a 'zty.pe' game: export R functions as instructions to type to destroy opponents vessels. Package: r-cran-zyp Architecture: all Version: 0.11-1-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 69 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-kendall Filename: pool/dists/noble/main/r-cran-zyp_0.11-1-1.ca2404.1_all.deb Size: 39822 MD5sum: 76be797bf0da0a5ee7f738d2ce75e404 SHA1: 7b354558b870b3992c2b9c12624bdeb5fa56cf3c SHA256: 2a94a0ea48767161e5c2af28566fa6b78a285d870a3903cd5e57f122ebf608a2 SHA512: c1eb05595a57059323c3df056ac36847075d65c100d20dba60b8e57c1310a59e2c7c9f3cdb7ed56641663d63bbe07aed828e5876589ecaccb1676bef83552177 Homepage: https://cran.r-project.org/package=zyp Description: CRAN Package 'zyp' (Zhang + Yue-Pilon Trends Package) An efficient implementation of the slope method described by Sen (1968) plus implementation of prewhitening approaches to determining trends in climate data described by Zhang, Vincent, Hogg, and Niitsoo (2000) and Yue, Pilon, Phinney, and Cavadias (2002) . Package: r-cran-zzlite Architecture: all Version: 0.1.2-1.ca2404.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: r-base-core (>= 4.4.0), r-api-4.0, r-cran-httr, r-cran-jsonlite Suggests: r-cran-httptest, r-cran-roxygen2, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/noble/main/r-cran-zzlite_0.1.2-1.ca2404.1_all.deb Size: 50062 MD5sum: 331755e82fbefe712f1531e9ae1862cd SHA1: cf24721de2fef163d414c710d501c7a8d8925730 SHA256: c21cafc34e54c9f2db97c1db0b6258f8938f9a1b9340c8d327fb8259a1b649f0 SHA512: 894f686017f7f42b76d302aa25815072e05714ad2fb0f65b9872c44baf0dab8bdcfee461563443ed51d9ed152c0a1350451ca498552e91b443a284a6c774709b Homepage: https://cran.r-project.org/package=zzlite Description: CRAN Package 'zzlite' (Lite Wrapper for the 'Zamzar File Conversion' API) A minor collection of HTTP wrappers for the 'Zamzar File Conversion' API. The wrappers makes it easy to utilize the API and thus convert between more than 100 different file formats (ranging from audio files, images, movie formats, etc., etc.) through an R session. For specifics regarding the API, please see .